feat(zed): improve MCAP export batching and defaults

Default ZED MCAP export to neural_plus depth across the CLI and Python wrappers, and add tail-frame handling plus better corrupted-frame diagnostics in zed_svo_to_mcap.

Add mixed hardware/software worker pools to the batch MCAP wrapper, replace tqdm with progress-table on TTYs, keep text event logging and heartbeats for non-TTY runs, and document the NVENC session-limit rationale for mixed mode in the README.

Also refresh Python dependencies for the batch tooling and move the OpenSSL lookup in CMake so the local workspace build remains compatible with the vendored cnats setup.

CMakeLists.txt

@@ -14,6 +14,19 @@ option(
 	ON)
 
 find_package(Threads REQUIRED)
+find_package(OpenSSL REQUIRED)
+if (NOT TARGET OpenSSL::SSL AND DEFINED OPENSSL_SSL_LIBRARY)
+	add_library(OpenSSL::SSL UNKNOWN IMPORTED)
+	set_target_properties(OpenSSL::SSL PROPERTIES
+		IMPORTED_LOCATION "${OPENSSL_SSL_LIBRARY}"
+		INTERFACE_INCLUDE_DIRECTORIES "${OPENSSL_INCLUDE_DIR}")
+endif()
+if (NOT TARGET OpenSSL::Crypto AND DEFINED OPENSSL_CRYPTO_LIBRARY)
+	add_library(OpenSSL::Crypto UNKNOWN IMPORTED)
+	set_target_properties(OpenSSL::Crypto PROPERTIES
+		IMPORTED_LOCATION "${OPENSSL_CRYPTO_LIBRARY}"
+		INTERFACE_INCLUDE_DIRECTORIES "${OPENSSL_INCLUDE_DIR}")
+endif()
 find_package(cppzmq QUIET)
 if (DEFINED CVMMAP_STREAMER_USE_SYSTEM_CNATS)
 	message(FATAL_ERROR
@@ -43,7 +56,6 @@ else()
 		message(FATAL_ERROR
 			"workspace cnats provider requires ${CVMMAP_LOCAL_NATS_STATIC}")
 	endif()
-	find_package(OpenSSL REQUIRED)
 	if (NOT TARGET cnats::nats_static)
 		add_library(cnats::nats_static STATIC IMPORTED GLOBAL)
 		set_target_properties(cnats::nats_static PROPERTIES

README.md

@@ -232,7 +232,7 @@ timestamp,activity,group_path,segment_dir,camera,relative_path
 This workflow depends on the `zed_svo_to_mcap` binary, which is only built when
 the ZED SDK is detected during CMake configure.
 
-Use the wrapper to recurse through a dataset root, run `zed_svo_to_mcap --segment-dir` on every matched multi-camera segment, and show one aggregate tqdm progress bar:
+Use the wrapper to recurse through a dataset root, run `zed_svo_to_mcap --segment-dir` on every matched multi-camera segment, and show interactive table progress on TTYs with durable text logging elsewhere:
 
 ```bash
 uv run python scripts/zed_batch_svo_to_mcap.py \
@@ -266,7 +266,31 @@ uv run python scripts/zed_batch_svo_to_mcap.py \
 The batch MCAP wrapper writes `<segment>/<segment>.mcap` by default, skips existing outputs unless told otherwise, and returns a nonzero exit code if any segment fails.
 The repo includes a minimal pose config at `config/zed_pose_config.toml` so MCAP conversion does not depend on a separate `cv-mmap` checkout.
 In bundled multi-camera mode, `--start-frame` and `--end-frame` mean the first and last emitted synced frame-group indices from the common start timestamp, inclusive.
-When stderr is attached to a TTY, `zed_svo_to_mcap` shows a tqdm-like progress bar. In bundled mode without `--end-frame`, it uses approximate progress over the common synchronized time window so export starts immediately without a full pre-count pass.
+When stderr is attached to a TTY, `zed_batch_svo_to_mcap.py` uses a `progress-table` view by default; otherwise it emits line-oriented start/completion/failure logs plus periodic heartbeat summaries. Use `--progress-ui table` or `--progress-ui text` to override the automatic mode selection.
+
+### Why Mixed Hardware/Software Mode Exists
+
+Bundled MCAP export opens one video encoder per camera stream. A four-camera segment therefore consumes four H.264/H.265 encoder sessions at once.
+
+This matters because NVIDIA's NVENC session limit is separate from raw CUDA utilization. In NVIDIA's Video Codec SDK documentation, non-qualified systems are capped at 8 concurrent encode sessions across all non-qualified GPUs in the system, and NVIDIA's SDK readme still calls out a 5-session GeForce limit in some contexts. In practice, consumer/GeForce hosts often hit NVENC session-init failures before the GPUs look "full" in `nvidia-smi`.
+
+That is why the batch wrapper supports mixed pools such as two NVENC workers plus two software-encoded workers:
+
+```bash
+uv run python scripts/zed_batch_svo_to_mcap.py \
+    <DATASET_ROOT> \
+    --recursive \
+    --overwrite \
+    --hardware-jobs 2 \
+    --hardware-cuda-visible-devices 0,1 \
+    --software-jobs 2 \
+    --software-cuda-visible-devices 0,1 \
+    --depth-mode neural_plus
+```
+
+With bundled four-camera segments, `4` all-hardware jobs would try to open about `16` NVENC sessions, which is why mixed mode is the safe default for high-throughput rebuilds on GeForce-class machines. The software workers still use the GPUs for ZED neural depth; only video encoding moves to CPU.
+
+If you intentionally want to bypass NVIDIA's consumer NVENC session cap, there is an unofficial driver patch at [`keylase/nvidia-patch`](https://github.com/keylase/nvidia-patch). That can make larger all-hardware batches viable, but it is not NVIDIA-supported and should be treated as an explicit ops decision rather than a project requirement.
 
 Use `--probe-existing` to validate existing MCAPs before skipping them. Invalid outputs are treated as missing and requeued:
 

pyproject.toml

@@ -4,7 +4,11 @@ version = "0.0.0"
 requires-python = ">=3.10"
 dependencies = [
     "click>=8.1",
-    "tqdm>=4.66",
+    "numpy>=2.2",
+    "opencv-python-headless>=4.11",
+    "progress-table>=3.2",
+    "protobuf>=5.29",
+    "zstandard>=0.23",
 ]
 
 [tool.uv]

scripts/zed_batch_svo_to_mcap.py

@@ -9,11 +9,12 @@ import os
 import re
 import subprocess
 import sys
+import time
 from dataclasses import dataclass
 from pathlib import Path
 
 import click
-from tqdm import tqdm
+from progress_table import ProgressTable
 
 
 SCRIPT_PATH = Path(__file__).resolve()
@@ -27,11 +28,9 @@ SEGMENT_FILE_PATTERN = re.compile(r".*_zed([0-9]+)\.svo2?$", re.IGNORECASE)
 class BatchConfig:
     zed_bin: Path | None
     probe_existing: bool
-    cuda_visible_devices: str | None
     overwrite: bool
     fail_fast: bool
     codec: str
-    encoder_device: str
     mcap_compression: str
     depth_mode: str
     depth_size: str
@@ -41,6 +40,7 @@ class BatchConfig:
     start_frame: int | None
     end_frame: int | None
     sync_tolerance_ms: float | None
+    progress_ui: str
 
 
 @dataclass(slots=True, frozen=True)
@@ -48,6 +48,14 @@ class ConversionJob:
     segment_dir: Path
     output_path: Path
     camera_labels: tuple[str, ...]
+    display_name: str
+
+
+@dataclass(slots=True, frozen=True)
+class WorkerSlot:
+    label: str
+    encoder_device: str
+    cuda_visible_devices: str | None
 
 
 @dataclass(slots=True, frozen=True)
@@ -84,7 +92,175 @@ class OutputProbeResult:
     reason: str = ""
 
 
+@dataclass(slots=True)
+class ActiveJobState:
+    submission_index: int
+    job: ConversionJob
+    slot: WorkerSlot
+    started_at_monotonic: float
+    row_index: int | None = None
+
+
 _MCAP_READER_MODULE = None
+TABLE_REFRESH_SECONDS = 1.0
+TEXT_HEARTBEAT_SECONDS = 30.0
+
+
+def format_elapsed(seconds: float) -> str:
+    rounded = max(0, int(round(seconds)))
+    minutes, secs = divmod(rounded, 60)
+    hours, minutes = divmod(minutes, 60)
+    if hours > 0:
+        return f"{hours:d}:{minutes:02d}:{secs:02d}"
+    return f"{minutes:02d}:{secs:02d}"
+
+
+class ProgressReporter:
+    heartbeat_interval_seconds: float
+
+    def __init__(self, total_jobs: int) -> None:
+        self.total_jobs = total_jobs
+        self.heartbeat_interval_seconds = TEXT_HEARTBEAT_SECONDS
+
+    def job_started(self, state: ActiveJobState) -> None:
+        return
+
+    def job_finished(self, state: ActiveJobState, result: JobResult) -> None:
+        return
+
+    def heartbeat(
+        self,
+        *,
+        completed_count: int,
+        failed_count: int,
+        active_states: list[ActiveJobState],
+    ) -> None:
+        return
+
+    def close(self) -> None:
+        return
+
+
+class TextProgressReporter(ProgressReporter):
+    def __init__(self, total_jobs: int) -> None:
+        super().__init__(total_jobs)
+        self.heartbeat_interval_seconds = TEXT_HEARTBEAT_SECONDS
+
+    def job_started(self, state: ActiveJobState) -> None:
+        cuda_label = state.slot.cuda_visible_devices or "-"
+        click.echo(
+            (
+                f"started: [{state.submission_index}/{self.total_jobs}] "
+                f"{state.slot.label} encoder={state.slot.encoder_device} cuda={cuda_label} "
+                f"segment={state.job.display_name}"
+            ),
+            err=True,
+        )
+
+    def job_finished(self, state: ActiveJobState, result: JobResult) -> None:
+        elapsed = format_elapsed(time.monotonic() - state.started_at_monotonic)
+        prefix = "completed" if result.status == "converted" else "failed"
+        exit_text = "" if result.status == "converted" else f" exit={result.return_code}"
+        click.echo(
+            (
+                f"{prefix}: [{state.submission_index}/{self.total_jobs}] "
+                f"{state.slot.label} elapsed={elapsed}{exit_text} segment={state.job.display_name}"
+            ),
+            err=True,
+        )
+        if result.status == "failed":
+            for line in failure_excerpt(result):
+                click.echo(f"  {line}", err=True)
+
+    def heartbeat(
+        self,
+        *,
+        completed_count: int,
+        failed_count: int,
+        active_states: list[ActiveJobState],
+    ) -> None:
+        active_count = len(active_states)
+        remaining_count = self.total_jobs - completed_count - failed_count - active_count
+        click.echo(
+            (
+                f"progress: completed={completed_count} failed={failed_count} "
+                f"active={active_count} remaining={remaining_count}"
+            ),
+            err=True,
+        )
+
+
+class TableProgressReporter(ProgressReporter):
+    def __init__(self, total_jobs: int) -> None:
+        super().__init__(total_jobs)
+        self.heartbeat_interval_seconds = TABLE_REFRESH_SECONDS
+        self.table = ProgressTable(
+            "#",
+            "segment",
+            "worker",
+            "encoder",
+            "cuda",
+            "status",
+            "elapsed_s",
+            interactive=2,
+            refresh_rate=10,
+            default_column_alignment="left",
+            default_column_width=12,
+            pbar_show_throughput=False,
+            pbar_show_progress=False,
+            pbar_show_percents=False,
+            pbar_show_eta=False,
+            print_header_every_n_rows=30,
+            file=sys.stderr,
+        )
+        self.table.add_column("#", width=4, alignment="right")
+        self.table.add_column("segment", width=44, alignment="left")
+        self.table.add_column("worker", width=8, alignment="left")
+        self.table.add_column("encoder", width=10, alignment="left")
+        self.table.add_column("cuda", width=6, alignment="left")
+        self.table.add_column("status", width=12, alignment="left")
+        self.table.add_column("elapsed_s", width=10, alignment="right")
+
+    def job_started(self, state: ActiveJobState) -> None:
+        self.table.add_row(
+            state.submission_index,
+            state.job.display_name,
+            state.slot.label,
+            state.slot.encoder_device,
+            state.slot.cuda_visible_devices or "-",
+            "running",
+            format_elapsed(0.0),
+        )
+        state.row_index = self.table.num_rows() - 1
+
+    def job_finished(self, state: ActiveJobState, result: JobResult) -> None:
+        if state.row_index is None:
+            return
+        self.table.update("status", "converted" if result.status == "converted" else f"failed({result.return_code})", row=state.row_index)
+        self.table.update(
+            "elapsed_s",
+            format_elapsed(time.monotonic() - state.started_at_monotonic),
+            row=state.row_index,
+        )
+
+    def heartbeat(
+        self,
+        *,
+        completed_count: int,
+        failed_count: int,
+        active_states: list[ActiveJobState],
+    ) -> None:
+        for state in active_states:
+            if state.row_index is None:
+                continue
+            self.table.update(
+                "elapsed_s",
+                format_elapsed(time.monotonic() - state.started_at_monotonic),
+                row=state.row_index,
+            )
+
+    def close(self) -> None:
+        self.table.close()
 
 
 def locate_binary(override: Path | None) -> Path:
@@ -272,7 +448,41 @@ def output_path_for(segment_dir: Path) -> Path:
     return segment_dir / f"{segment_dir.name}.mcap"
 
 
-def command_for_job(job: ConversionJob, config: BatchConfig) -> list[str]:
+def common_segment_parent(segment_dirs: tuple[Path, ...]) -> Path | None:
+    if len(segment_dirs) <= 1:
+        return None
+    try:
+        return Path(os.path.commonpath([str(path) for path in segment_dirs]))
+    except ValueError:
+        return None
+
+
+def display_name_for_segment(
+    segment_dir: Path,
+    *,
+    source_mode: str,
+    input_root: Path | None,
+    common_parent: Path | None,
+) -> str:
+    if source_mode == "discovery" and input_root is not None:
+        try:
+            return str(segment_dir.relative_to(input_root))
+        except ValueError:
+            pass
+    if common_parent is not None:
+        try:
+            relative = segment_dir.relative_to(common_parent)
+            if str(relative) != ".":
+                return str(relative)
+        except ValueError:
+            pass
+    parent_name = segment_dir.parent.name
+    if parent_name:
+        return str(Path(parent_name) / segment_dir.name)
+    return segment_dir.name
+
+
+def command_for_job(job: ConversionJob, config: BatchConfig, encoder_device: str) -> list[str]:
     if config.zed_bin is None:
         raise RuntimeError("zed_svo_to_mcap binary is not configured")
 
@@ -283,7 +493,7 @@ def command_for_job(job: ConversionJob, config: BatchConfig) -> list[str]:
         "--codec",
         config.codec,
         "--encoder-device",
-        config.encoder_device,
+        encoder_device,
         "--mcap-compression",
         config.mcap_compression,
         "--depth-mode",
@@ -307,12 +517,33 @@ def command_for_job(job: ConversionJob, config: BatchConfig) -> list[str]:
 
 
 def env_for_job(config: BatchConfig) -> dict[str, str]:
+    return env_for_job_with_cuda(None)
+
+
+def env_for_job_with_cuda(assigned_cuda_visible_devices: str | None) -> dict[str, str]:
     env = dict(os.environ)
-    if config.cuda_visible_devices is not None:
+    if assigned_cuda_visible_devices is not None:
-        env["CUDA_VISIBLE_DEVICES"] = config.cuda_visible_devices
+        env["CUDA_VISIBLE_DEVICES"] = assigned_cuda_visible_devices
     return env
 
 
+def parse_cuda_device_pool(raw_value: str | None) -> tuple[str, ...]:
+    if raw_value is None:
+        return ()
+    devices = tuple(device.strip() for device in raw_value.split(",") if device.strip())
+    return devices
+
+
+def choose_progress_reporter(progress_ui: str, total_jobs: int) -> ProgressReporter:
+    if progress_ui == "table":
+        return TableProgressReporter(total_jobs)
+    if progress_ui == "text":
+        return TextProgressReporter(total_jobs)
+    if sys.stderr.isatty():
+        return TableProgressReporter(total_jobs)
+    return TextProgressReporter(total_jobs)
+
+
 def load_mcap_reader():
     global _MCAP_READER_MODULE
     if _MCAP_READER_MODULE is not None:
@@ -368,13 +599,25 @@ def probe_output(output_path: Path, camera_labels: tuple[str, ...]) -> OutputPro
 
 
 def run_conversion(job: ConversionJob, config: BatchConfig) -> JobResult:
-    command = command_for_job(job, config)
+    return run_conversion_on_slot(
+        job,
+        config,
+        WorkerSlot(label="job-1", encoder_device="auto", cuda_visible_devices=None),
+    )
+
+
+def run_conversion_on_slot(
+    job: ConversionJob,
+    config: BatchConfig,
+    slot: WorkerSlot,
+) -> JobResult:
+    command = command_for_job(job, config, slot.encoder_device)
     completed = subprocess.run(
         command,
         check=False,
         capture_output=True,
         text=True,
-        env=env_for_job(config),
+        env=env_for_job_with_cuda(slot.cuda_visible_devices),
     )
     status = "converted" if completed.returncode == 0 else "failed"
     return JobResult(
@@ -388,6 +631,23 @@ def run_conversion(job: ConversionJob, config: BatchConfig) -> JobResult:
     )
 
 
+def split_lines_for_excerpt(text: str, max_lines: int = 8) -> list[str]:
+    lines = [line.rstrip() for line in text.splitlines() if line.strip()]
+    if len(lines) <= max_lines:
+        return lines
+    excerpt = lines[:max_lines]
+    excerpt.append(f"... ({len(lines) - max_lines} more lines)")
+    return excerpt
+
+
+def failure_excerpt(result: JobResult, max_lines: int = 8) -> list[str]:
+    if result.stderr.strip():
+        return split_lines_for_excerpt(result.stderr, max_lines=max_lines)
+    if result.stdout.strip():
+        return split_lines_for_excerpt(result.stdout, max_lines=max_lines)
+    return []
+
+
 def summarize_failures(results: list[JobResult]) -> None:
     failed_results = [result for result in results if result.status == "failed"]
     if not failed_results:
@@ -437,64 +697,216 @@ def report_dry_run_plan(
         click.echo(line, err=True)
 
 
-def run_batch(jobs: list[ConversionJob], config: BatchConfig, jobs_limit: int) -> tuple[list[JobResult], int]:
+def run_batch(
+    jobs: list[ConversionJob],
+    config: BatchConfig,
+    worker_slots: list[WorkerSlot],
+) -> tuple[list[JobResult], int]:
     results: list[JobResult] = []
     aborted_count = 0
     if not jobs:
         return results, aborted_count
+    if not worker_slots:
+        raise click.ClickException("no worker slots configured")
 
-    future_to_job: dict[concurrent.futures.Future[JobResult], ConversionJob] = {}
+    available_slots = list(worker_slots)
+    max_parallel_jobs = len(worker_slots)
+    future_to_job: dict[concurrent.futures.Future[JobResult], ActiveJobState] = {}
     job_iter = iter(jobs)
     stop_submitting = False
+    completed_count = 0
+    failed_count = 0
+    submission_index = 0
+    reporter = choose_progress_reporter(config.progress_ui, len(jobs))
+    last_heartbeat_at = time.monotonic()
 
-    with concurrent.futures.ThreadPoolExecutor(max_workers=jobs_limit) as executor:
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_parallel_jobs) as executor:
-        with tqdm(total=len(jobs), unit="segment", dynamic_ncols=True) as progress:
 
-            def submit_next() -> bool:
+        def submit_next() -> bool:
-                if stop_submitting:
+            nonlocal submission_index
-                    return False
+            if stop_submitting or not available_slots:
-                try:
+                return False
-                    job = next(job_iter)
+            slot = available_slots.pop(0)
-                except StopIteration:
+            try:
-                    return False
+                job = next(job_iter)
-                future = executor.submit(run_conversion, job, config)
+            except StopIteration:
-                future_to_job[future] = job
+                available_slots.insert(0, slot)
-                return True
+                return False
 
-            for _ in range(min(jobs_limit, len(jobs))):
+            submission_index += 1
-                submit_next()
+            state = ActiveJobState(
+                submission_index=submission_index,
+                job=job,
+                slot=slot,
+                started_at_monotonic=time.monotonic(),
+            )
+            reporter.job_started(state)
+            future = executor.submit(run_conversion_on_slot, job, config, slot)
+            future_to_job[future] = state
+            return True
 
-            while future_to_job:
+        for _ in range(min(max_parallel_jobs, len(jobs))):
-                done, _ = concurrent.futures.wait(
+            submit_next()
-                    future_to_job,
+
-                    return_when=concurrent.futures.FIRST_COMPLETED,
+        while future_to_job:
+            done, _ = concurrent.futures.wait(
+                future_to_job,
+                timeout=reporter.heartbeat_interval_seconds,
+                return_when=concurrent.futures.FIRST_COMPLETED,
+            )
+            if not done:
+                reporter.heartbeat(
+                    completed_count=completed_count,
+                    failed_count=failed_count,
+                    active_states=list(future_to_job.values()),
                 )
-                for future in done:
+                last_heartbeat_at = time.monotonic()
-                    job = future_to_job.pop(future)
+                continue
-                    result = future.result()
-                    results.append(result)
-                    progress.update(1)
 
-                    if result.status == "failed":
+            for future in done:
-                        tqdm.write(
+                state = future_to_job.pop(future)
-                            f"failed: {job.segment_dir} (exit {result.return_code})",
+                available_slots.append(state.slot)
-                            file=sys.stderr,
+                result = future.result()
-                        )
+                results.append(result)
-                        if config.fail_fast:
+                reporter.job_finished(state, result)
-                            stop_submitting = True
 
-                    if not stop_submitting:
+                if result.status == "failed":
-                        submit_next()
+                    failed_count += 1
+                    if config.fail_fast:
+                        stop_submitting = True
+                else:
+                    completed_count += 1
 
-                if stop_submitting:
+                if not stop_submitting:
-                    remaining = sum(1 for _ in job_iter)
+                    submit_next()
-                    aborted_count = remaining
+
-                    progress.total = progress.n + len(future_to_job)
+            now = time.monotonic()
-                    progress.refresh()
+            if now - last_heartbeat_at >= reporter.heartbeat_interval_seconds:
+                reporter.heartbeat(
+                    completed_count=completed_count,
+                    failed_count=failed_count,
+                    active_states=list(future_to_job.values()),
+                )
+                last_heartbeat_at = now
+
+        if stop_submitting:
+            aborted_count = sum(1 for _ in job_iter)
+
+    reporter.close()
 
     return results, aborted_count
 
 
+def build_uniform_worker_slots(
+    jobs: int,
+    encoder_device: str,
+    cuda_visible_devices: str | None,
+) -> list[WorkerSlot]:
+    if jobs < 1:
+        raise click.ClickException("--jobs must be at least 1")
+
+    if cuda_visible_devices is None:
+        return [
+            WorkerSlot(
+                label=f"job-{index + 1}",
+                encoder_device=encoder_device,
+                cuda_visible_devices=None,
+            )
+            for index in range(jobs)
+        ]
+
+    device_pool = parse_cuda_device_pool(cuda_visible_devices)
+    if len(device_pool) < jobs:
+        raise click.ClickException(
+            f"--cuda-visible-devices must provide at least {jobs} entries when --jobs={jobs}"
+        )
+    return [
+        WorkerSlot(
+            label=f"job-{index + 1}",
+            encoder_device=encoder_device,
+            cuda_visible_devices=device_pool[index],
+        )
+        for index in range(jobs)
+    ]
+
+
+def parse_required_device_pool(raw_value: str | None, expected_count: int, flag_name: str) -> tuple[str, ...]:
+    if expected_count == 0:
+        if raw_value is None:
+            return ()
+        raise click.ClickException(f"{flag_name} cannot be used when the matching job count is 0")
+
+    device_pool = parse_cuda_device_pool(raw_value)
+    if len(device_pool) != expected_count:
+        raise click.ClickException(
+            f"{flag_name} must provide exactly {expected_count} entries when the matching job count is {expected_count}"
+        )
+    return device_pool
+
+
+def build_worker_slots(
+    *,
+    jobs: int,
+    encoder_device: str,
+    cuda_visible_devices: str | None,
+    hardware_jobs: int,
+    hardware_cuda_visible_devices: str | None,
+    software_jobs: int,
+    software_cuda_visible_devices: str | None,
+) -> list[WorkerSlot]:
+    mixed_mode_requested = any(
+        (
+            hardware_jobs > 0,
+            software_jobs > 0,
+            hardware_cuda_visible_devices is not None,
+            software_cuda_visible_devices is not None,
+        )
+    )
+    if not mixed_mode_requested:
+        return build_uniform_worker_slots(jobs, encoder_device, cuda_visible_devices)
+
+    if jobs != 1:
+        raise click.ClickException("--jobs cannot be combined with mixed worker pool flags")
+    if cuda_visible_devices is not None:
+        raise click.ClickException("--cuda-visible-devices cannot be combined with mixed worker pool flags")
+    if encoder_device != "auto":
+        raise click.ClickException("--encoder-device cannot be combined with mixed worker pool flags")
+
+    total_jobs = hardware_jobs + software_jobs
+    if total_jobs < 1:
+        raise click.ClickException("mixed worker pool flags require at least one hardware or software job")
+
+    hardware_device_pool = parse_required_device_pool(
+        hardware_cuda_visible_devices,
+        hardware_jobs,
+        "--hardware-cuda-visible-devices",
+    )
+    software_device_pool = parse_required_device_pool(
+        software_cuda_visible_devices,
+        software_jobs,
+        "--software-cuda-visible-devices",
+    )
+
+    worker_slots: list[WorkerSlot] = []
+    worker_slots.extend(
+        WorkerSlot(
+            label=f"hw-{index + 1}",
+            encoder_device="nvidia",
+            cuda_visible_devices=device,
+        )
+        for index, device in enumerate(hardware_device_pool)
+    )
+    worker_slots.extend(
+        WorkerSlot(
+            label=f"sw-{index + 1}",
+            encoder_device="software",
+            cuda_visible_devices=device,
+        )
+        for index, device in enumerate(software_device_pool)
+    )
+    return worker_slots
+
+
 @click.command()
 @click.argument(
     "input_dir",
@@ -520,6 +932,28 @@ def run_batch(jobs: list[ConversionJob], config: BatchConfig, jobs_limit: int) -
 )
 @click.option("--recursive/--no-recursive", default=True, show_default=True, help="Recurse when discovering segment directories from INPUT_DIR.")
 @click.option("--jobs", default=1, show_default=True, type=click.IntRange(min=1), help="Parallel conversion jobs.")
+@click.option(
+    "--hardware-jobs",
+    default=0,
+    show_default=True,
+    type=click.IntRange(min=0),
+    help="Mixed mode: number of hardware-encoded workers.",
+)
+@click.option(
+    "--hardware-cuda-visible-devices",
+    help="Mixed mode: comma-separated CUDA_VISIBLE_DEVICES assignments for hardware workers, one entry per worker.",
+)
+@click.option(
+    "--software-jobs",
+    default=0,
+    show_default=True,
+    type=click.IntRange(min=0),
+    help="Mixed mode: number of software-encoded workers.",
+)
+@click.option(
+    "--software-cuda-visible-devices",
+    help="Mixed mode: comma-separated CUDA_VISIBLE_DEVICES assignments for software workers, one entry per worker.",
+)
 @click.option(
     "--zed-bin",
     type=click.Path(path_type=Path, dir_okay=False),
@@ -527,7 +961,7 @@ def run_batch(jobs: list[ConversionJob], config: BatchConfig, jobs_limit: int) -
 )
 @click.option(
     "--cuda-visible-devices",
-    help="Optional CUDA_VISIBLE_DEVICES value exported for each conversion subprocess.",
+    help="Optional CUDA_VISIBLE_DEVICES value. A comma-separated list is distributed across concurrent jobs one GPU per subprocess.",
 )
 @click.option("--overwrite/--skip-existing", default=False, show_default=True, help="Overwrite existing MCAP files.")
 @click.option(
@@ -568,7 +1002,7 @@ def run_batch(jobs: list[ConversionJob], config: BatchConfig, jobs_limit: int) -
 @click.option(
     "--depth-mode",
     type=click.Choice(("neural_light", "neural", "neural_plus")),
-    default="neural",
+    default="neural_plus",
     show_default=True,
 )
 @click.option(
@@ -606,6 +1040,13 @@ def run_batch(jobs: list[ConversionJob], config: BatchConfig, jobs_limit: int) -
     default=None,
     help="Override the maximum timestamp delta used for bundled multi-camera sync.",
 )
+@click.option(
+    "--progress-ui",
+    type=click.Choice(("auto", "table", "text")),
+    default="auto",
+    show_default=True,
+    help="Progress output mode. Auto uses a table on TTY and text logging otherwise.",
+)
 def main(
     input_dir: Path | None,
     segment_dirs: tuple[Path, ...],
@@ -613,6 +1054,10 @@ def main(
     csv_root: Path | None,
     recursive: bool,
     jobs: int,
+    hardware_jobs: int,
+    hardware_cuda_visible_devices: str | None,
+    software_jobs: int,
+    software_cuda_visible_devices: str | None,
     zed_bin: Path | None,
     cuda_visible_devices: str | None,
     overwrite: bool,
@@ -631,6 +1076,7 @@ def main(
     start_frame: int | None,
     end_frame: int | None,
     sync_tolerance_ms: float | None,
+    progress_ui: str,
 ) -> None:
     """Batch-convert multi-camera ZED segments into bundled MCAP files."""
     if report_existing and dry_run:
@@ -638,14 +1084,21 @@ def main(
 
     binary_path = None if report_existing else locate_binary(zed_bin)
     sources = resolve_sources(input_dir, segment_dirs, segments_csv, csv_root, recursive)
+    worker_slots = build_worker_slots(
+        jobs=jobs,
+        encoder_device=encoder_device,
+        cuda_visible_devices=cuda_visible_devices,
+        hardware_jobs=hardware_jobs,
+        hardware_cuda_visible_devices=hardware_cuda_visible_devices,
+        software_jobs=software_jobs,
+        software_cuda_visible_devices=software_cuda_visible_devices,
+    )
     config = BatchConfig(
         zed_bin=binary_path,
         probe_existing=probe_existing or report_existing,
-        cuda_visible_devices=cuda_visible_devices,
         overwrite=overwrite,
         fail_fast=fail_fast,
         codec=codec,
-        encoder_device=encoder_device,
         mcap_compression=mcap_compression,
         depth_mode=depth_mode,
         depth_size=depth_size,
@@ -655,7 +1108,10 @@ def main(
         start_frame=start_frame,
         end_frame=end_frame,
         sync_tolerance_ms=sync_tolerance_ms,
+        progress_ui=progress_ui,
     )
+    input_root = input_dir.expanduser().resolve() if input_dir is not None else None
+    display_parent = common_segment_parent(sources.segment_dirs)
 
     skipped_results: list[JobResult] = []
     failed_results: list[JobResult] = []
@@ -673,8 +1129,19 @@ def main(
             segment_dir=segment_dir,
             output_path=output_path,
             camera_labels=scan.camera_labels,
+            display_name=display_name_for_segment(
+                segment_dir,
+                source_mode=sources.mode,
+                input_root=input_root,
+                common_parent=display_parent,
+            ),
+        )
+        default_encoder_device = worker_slots[0].encoder_device if worker_slots else encoder_device
+        command = (
+            tuple(command_for_job(job, config, default_encoder_device))
+            if config.zed_bin is not None
+            else ()
         )
-        command = tuple(command_for_job(job, config)) if config.zed_bin is not None else ()
         if not scan.is_valid:
             failed_results.append(
                 JobResult(
@@ -762,7 +1229,7 @@ def main(
     click.echo(
         (
             f"source={sources.mode} matched={len(sources.segment_dirs)} pending={len(pending_jobs)} "
-            f"skipped={len(skipped_results)} invalid={len(failed_results)} jobs={jobs} "
+            f"skipped={len(skipped_results)} invalid={len(failed_results)} jobs={len(worker_slots)} "
             f"dry_run={'yes' if dry_run else 'no'}"
         ),
         err=True,
@@ -787,7 +1254,7 @@ def main(
 
     results = list(skipped_results)
     results.extend(failed_results)
-    conversion_results, aborted_count = run_batch(pending_jobs, config, jobs)
+    conversion_results, aborted_count = run_batch(pending_jobs, config, worker_slots)
     results.extend(conversion_results)
 
     converted_count = sum(1 for result in results if result.status == "converted")

scripts/zed_recording_mcap_tool.py

@@ -67,7 +67,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--depth-mode",
         choices=("neural_light", "neural", "neural_plus"),
-        default="neural",
+        default="neural_plus",
         help="Depth mode passed to zed_svo_to_mcap",
     )
     parser.add_argument(

src/tools/zed_svo_to_mcap.cpp

@@ -53,7 +53,7 @@ struct CliOptions {
 	std::string codec{"h265"};
 	std::string encoder_device{"auto"};
 	std::string mcap_compression{"zstd"};
-	std::string depth_mode{"neural"};
+	std::string depth_mode{"neural_plus"};
 	std::string depth_size{"optimal"};
 	bool with_pose{false};
 	std::uint32_t start_frame{0};
@@ -670,9 +670,25 @@ std::expected<void, std::string> read_frame_data(
 	if (grab_status == sl::ERROR_CODE::END_OF_SVOFILE_REACHED) {
 		return std::unexpected("end-of-svo");
 	}
+	if (grab_status == sl::ERROR_CODE::CORRUPTED_FRAME &&
+		stream.last_timestamp_ns != 0 &&
+		stream.current_timestamp_ns >= stream.last_timestamp_ns) {
+		spdlog::warn(
+			"treating unreadable tail frame as end-of-svo for {} current_timestamp_ns={} last_timestamp_ns={}",
+			stream.source.path.string(),
+			stream.current_timestamp_ns,
+			stream.last_timestamp_ns);
+		return std::unexpected("end-of-svo");
+	}
 	if (grab_status != sl::ERROR_CODE::SUCCESS) {
+		const auto svo_position = stream.camera->getSVOPosition();
 		return std::unexpected(
-			"failed to grab frame for " + stream.source.label + ": " + zed_status_string(grab_status));
+			"failed to grab frame for " + stream.source.label +
+			": " + zed_status_string(grab_status) +
+			" svo_position=" + std::to_string(svo_position) +
+			" total_frames=" + std::to_string(stream.total_frames) +
+			" current_timestamp_ns=" + std::to_string(stream.current_timestamp_ns) +
+			" last_timestamp_ns=" + std::to_string(stream.last_timestamp_ns));
 	}
 
 	const auto image_status = stream.camera->retrieveImage(left_frame, sl::VIEW::LEFT_BGR, sl::MEM::CPU);
@@ -1545,6 +1561,14 @@ int run_single_source(
 		if (grab_status == sl::ERROR_CODE::END_OF_SVOFILE_REACHED) {
 			break;
 		}
+		if (grab_status == sl::ERROR_CODE::CORRUPTED_FRAME &&
+			camera.getSVOPosition() >= static_cast<int>(last_frame)) {
+			spdlog::warn(
+				"treating unreadable tail frame as end-of-svo for {} at frame {}",
+				input_path.string(),
+				camera.getSVOPosition());
+			break;
+		}
 		if (grab_status != sl::ERROR_CODE::SUCCESS) {
 			progress.finish(emitted_frames, false);
 			sink->close();
@@ -2049,6 +2073,21 @@ int run_multi_source(
 			stream.source.label);
 	}
 	close_camera_streams(streams);
+	if (!interrupted && emitted_groups == 0) {
+		std::error_code remove_error{};
+		std::filesystem::remove(output_path, remove_error);
+		if (remove_error) {
+			spdlog::warn(
+				"failed to remove empty MCAP output '{}': {}",
+				output_path.string(),
+				remove_error.message());
+		}
+		spdlog::error(
+			"no synced frame groups were found across {} camera(s) for '{}'",
+			sources.size(),
+			output_path.string());
+		return exit_code(ToolExitCode::RuntimeError);
+	}
 
 	if (interrupted) {
 		spdlog::warn(

uv.lock

@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.11'",
+    "python_full_version < '3.11'",
+]
 
 [[package]]
 name = "click"
@@ -29,23 +33,313 @@ version = "0.0.0"
 source = { virtual = "." }
 dependencies = [
     { name = "click" },
-    { name = "tqdm" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "opencv-python-headless" },
+    { name = "progress-table" },
+    { name = "protobuf" },
+    { name = "zstandard" },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "click", specifier = ">=8.1" },
-    { name = "tqdm", specifier = ">=4.66" },
+    { name = "numpy", specifier = ">=2.2" },
+    { name = "opencv-python-headless", specifier = ">=4.11" },
+    { name = "progress-table", specifier = ">=3.2" },
+    { name = "protobuf", specifier = ">=5.29" },
+    { name = "zstandard", specifier = ">=0.23" },
 ]
 
 [[package]]
-name = "tqdm"
+name = "numpy"
-version = "4.67.3"
+version = "2.2.6"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.11'",
+]
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