Run the LiDAR-Intersection Fusion Demo#

  • Time to Complete: 30-45 minutes (plus first-time PointPillars model build)

Disclaimer: demo-only, not for production use. This demo has known limitations that make it unsuitable as a benchmark or production reference:

  • PointPillars model quality is limited: the LiDAR branch does not reliably detect all vehicles/cyclists in every frame - expect missed and inconsistent detections, not a complete/accurate 3-D detection record.

  • The recorded clip is very short: playback is a single ~25-second, 251-frame sequence (LIDAR_START_INDEX-LIDAR_STOP_INDEX, looped), not a representative long-running capture.

  • LiDAR/camera synchronization is a recorded-playback artifact: see LiDAR/camera stream synchronization below - it does not reflect how real, independently-clocked sensors behave.

This guide walks through running the LiDAR-Intersection fusion demo, a separate, opt-in Scenescape demo that fuses a recorded LiDAR point-cloud stream with a recorded camera image sequence of the same real-world intersection. It is fully independent from the default apriltag/queuing demo: all of its data, scene configuration, and pipeline assets live under sample_data/lidar_intersection/ and it is started with its own dedicated make demo-lidar target, so it never affects the standard make demo deployment.

Note: The scene itself is seeded using Scenescape’s existing scene import feature - no Manager DB-fixture changes needed. Default vehicle/cyclist Asset3D objects, 2D/3D UI source labels (lidar vs camera), and source field forwarding through scene_common and Analytics live in the main source tree on this branch (not as demo-only patches).

What this demo adds#

Asset

Purpose

sample_data/lidar_intersection/docker-compose.lidar-override.yml

Opt-in Compose override adding the lidar-scene-init, lidar-data-init, lidar-model-init, and lidar-stream services

sample_data/lidar_intersection/lidar_publisher.py

Runs the LiDAR (PointPillars) and camera (person-vehicle-bike) GStreamer pipelines and publishes detections over MQTT

sample_data/lidar_intersection/convert_pcd_to_bin.py

Converts the manually-downloaded dataset’s .pcd LiDAR frames to the .bin format lidar_publisher.py/DLStreamer expect - see Prerequisites

sample_data/lidar_intersection/reencode_jpegs.py

Re-encodes the dataset’s .jpg camera frames at a lower JPEG quality (same resolution) so decode/detect/preview keep up with CAM_FRAME_RATE

sample_data/lidar_intersection/

Scene config, map image, scene-import ZIP, and the PointPillars model installer, all scoped to this demo (the recorded LiDAR/camera frames themselves are NOT part of the repo - see Prerequisites)

Architecture#

        flowchart LR
    subgraph init["One-shot init (run once per volume)"]
        DataInit["lidar-data-init\n.pcd -> .bin, copy .jpg"]
        ModelInit["lidar-model-init\nbuild PointPillars"]
        SceneInit["lidar-scene-init\nimport scene via REST API"]
    end
    subgraph vols["Shared Docker volumes"]
        SampleVol[("vol-sample-data\nvelodyne_bin/, images/")]
        ModelVol[("vol-models\npointpillars_ov_config.json + IR")]
    end
    DataInit --> SampleVol
    ModelInit --> ModelVol

    subgraph stream["lidar-stream (one gst-launch-1.0 process)"]
        direction LR
        LidarBranch["multifilesrc(.bin) -> g3dlidarparse\n-> g3dinference(PointPillars, GPU)\n-> gvametaconvert -> gvametapublish"]
        CamBranch["multifilesrc(.jpg) -> jpegdec -> videoconvert\n-> gvafpsthrottle -> gvadetect(person-vehicle-bike, CPU)\n-> gvametaconvert -> gvametapublish"]
        Pub["lidar_publisher.py\n(reads both FIFOs, builds MQTT messages)"]
        LidarBranch -->|FIFO| Pub
        CamBranch -->|FIFO| Pub
    end
    SampleVol -.-> LidarBranch
    SampleVol -.-> CamBranch
    ModelVol -.-> LidarBranch

    Pub -->|"scenescape/data/camera/intersection-lidar1\n(3-D bbox_3d)"| MQTT((MQTT broker))
    Pub -->|"scenescape/data/camera/intersection-cam1\n(2-D bounding_box_px)"| MQTT
    SceneInit -.->|imports scene/sensors once| Manager["Manager (web)"]
    MQTT --> Controller["Scene Controller\n(fuses LiDAR + camera per-sensor detections)"]
    Controller -->|"scenescape/regulated/scene/{scene_uid}"| MQTT
    MQTT --> Manager

lidar-data-init/lidar-model-init/lidar-scene-init are one-shot containers (restart: "no") that populate the two shared volumes and seed the scene once; lidar-stream waits for all three to finish successfully, then is the only long-running service (docker compose logs -f lidar-stream in the steps below watches it).

PointPillars model initialization#

lidar-model-init runs model_installer/install-pointpillars, which turns a pinned upstream commit into everything g3dinference needs at runtime, all written into vol-models:

  1. Get the source: reuses a sibling openvino_contrib checkout if one is already present (OPENVINO_CONTRIB_DIR/POINTPILLARS_ROOT), otherwise does a --filter=blob:none --sparse git clone of openvinotoolkit/openvino_contrib restricted to modules/3d/pointPillars, checked out at a pinned commit (OPENVINO_CONTRIB_REF, defaults to the commit that introduced the module) - not a moving target, so re-runs are reproducible.

  2. Copy the pretrained IR model: copies the four pointpillars_ov_*.{xml,bin} files from that checkout’s pretrained/ directory into vol-models/public/pointpillars/FP16/.

  3. Build the OpenVINO extension: compiles libov_pointpillars_extensions.so (a custom OpenVINO op needed for PointPillars’ voxelization/scatter layers) via the checkout’s own ov_extensions/build.sh, using a Python with openvino installed (auto-detected) and cmake/g++ (installed via apt-get if missing) - then copies the built .so alongside the IR files.

  4. Write the runtime config: generates pointpillars_ov_config.json (voxel size/range, max points/voxels, and paths to the IR files + extension library) - this is the file LIDAR_MODEL_CONFIG points g3dinference at.

Steps 2-4 are skip-if-already-done (checks for existing files before copying/building), so re-running lidar-model-init on a volume that already has the model is fast; only the first run on a fresh vol-models actually clones/builds anything (the several-minutes-first-run cost mentioned in Enable the demo).

Prerequisites#

  • Complete Get Scenescape and Preparation (get the source and build the container images) at least once.

  • Download the recorded LiDAR/camera dataset manually - it is not committed to this repo because it’s too large (hundreds of MB of .pcd point clouds and .jpg images):

  1. Download the V2X-Seq-SPD-Example .zip archive.

    📌 Source: Official sample dataset provided by Tsinghua University (AIR-THU).

    ⚠️ Manual Download Required: Google Drive’s virus scan prompt for large files makes scripted downloads (wget/curl) fail. Please download this file manually via your browser and move it to your Scenescape directory.

  2. Extract it so the result is sample_data/lidar_intersection/V2X-Seq-SPD-Example/infrastructure-side/, containing (at least) an image/ directory of .jpg frames, a velodyne/ directory of .pcd frames, and a data_info.json file (this is the path docker-compose.lidar-override.yml’s lidar-data-init service expects by default; override it with the LIDAR_RAW_DATASET_DIR environment variable if you’d rather extract it elsewhere):

    unzip V2X-Seq-SPD-Example.zip -d sample_data/lidar_intersection
ls sample_data/lidar_intersection/V2X-Seq-SPD-Example/infrastructure-side

  3. This is a one-time step per checkout - the extracted folder is git-ignored (sample_data/lidar_intersection/V2X-Seq-SPD-Example/ in .gitignore) and lidar-data-init re-converts it into the shared Docker volume on every make demo-lidar.

This dataset is the infrastructure-side subset of the V2X-Seq-SPD dataset from the DAIR-V2X-Seq project (Tsinghua University, Apache-2.0) - see that repository for the full dataset, license terms, and citation details.

  • An Intel GPU is used by default for the LiDAR (PointPillars) inference branch, and requires the host to have /dev/dri and the Intel GPU driver installed. The camera branch defaults to CPU (a lighter model that runs fine without a GPU). Install/verify drivers with the DL Streamer prerequisites script (./DLS_install_prerequisites.sh) or see the DL Streamer system requirements and install guide for details (NPU is not used by this demo, but the same script covers it).

    Performance note: running the LiDAR branch without a GPU (LIDAR_DEVICE=CPU, see Using GPU acceleration) works on any target, but PointPillars 3-D inference on CPU is significantly slower than on GPU and can noticeably drop below the LIDAR_FRAME_RATE target (choppy playback, growing detection latency). Prefer GPU whenever available.

Enable the demo#

Run the dedicated demo-lidar target instead of make demo (a basic demo only - no ReID or tracker):

export SUPASS=<password>
make demo-lidar

This starts four extra containers, on top of the normal demo services:

Service

Role

lidar-scene-init

One-shot: seeds the “Lidar Intersection” scene, camera, and sensor via the Scene Import REST API (idempotent - skips if the scene already exists)

lidar-data-init

One-shot: converts the manually-downloaded raw dataset’s .pcd LiDAR frames to .bin (via convert_pcd_to_bin.py) and re-encodes its .jpg camera frames at a lower JPEG quality (via reencode_jpegs.py) into the shared sample-data volume - only mounts the image//velodyne/ subdirectories, see Prerequisites

lidar-model-init

One-shot: builds and installs the PointPillars OpenVINO model + GStreamer inference extension into the shared models volume (first run only; can take several minutes)

lidar-stream

Long-running: runs both GStreamer pipelines and publishes fused-ready detections over MQTT

Check the one-shot containers completed successfully, and that lidar-stream is running:

docker compose ps lidar-scene-init lidar-data-init lidar-model-init lidar-stream
docker compose logs -f lidar-stream

You should see log lines like:

[lidar-publisher] lidar_sensor=intersection-lidar1 cam_sensor=intersection-cam1 ...
[camera-publisher] Connected to broker.scenescape.intel.com:1883
[lidar-publisher] frames=100 fps=10.0 objects={'vehicle': 2, 'cyclist': 1}

lidar-data-init (docker compose logs lidar-data-init) converts the dataset and exits:

Converting 251 frames from /src/velodyne -> /dst/lidar_intersection/velodyne_bin
  50/251 done
  100/251 done
  150/251 done
  200/251 done
  250/251 done
  251/251 done
Conversion complete.
Re-encoding 251 frames from /src/image -> /dst/lidar_intersection/images at quality=50
  50/251 done
  100/251 done
  150/251 done
  200/251 done
  250/251 done
  251/251 done
Re-encoding complete.
lidar_intersection data ready

lidar-model-init (docker compose logs lidar-model-init) builds/installs the PointPillars model. On the first run (several minutes: clones openvino_contrib and compiles the OpenVINO extension):

[install-pointpillars] MODELS_PATH=/home/pipeline-server/models
[install-pointpillars] Sparse-cloning openvino_contrib into /tmp/pointpillars-cache/openvino_contrib at ref d131b42505ee77e064638e0b38e6a84b52b779d6...
[install-pointpillars] Using sparse-cloned source: /tmp/pointpillars-cache/openvino_contrib/modules/3d/pointPillars
[install-pointpillars] Copying pointpillars_ov_nn.bin
[install-pointpillars] Copying pointpillars_ov_nn.xml
[install-pointpillars] Copying pointpillars_ov_pillar_layer.xml
[install-pointpillars] Copying pointpillars_ov_postproc.xml
[install-pointpillars] Building PointPillars OpenVINO extension...
[install-pointpillars] Extension built: /tmp/pointpillars-cache/openvino_contrib/modules/3d/pointPillars/ov_extensions/build/libov_pointpillars_extensions.so
[install-pointpillars] Copying extension to /home/pipeline-server/models/public/pointpillars/FP16/libov_pointpillars_extensions.so
[install-pointpillars] Config written: /home/pipeline-server/models/public/pointpillars/FP16/pointpillars_ov_config.json
[install-pointpillars] Done. Set LIDAR_MODEL_CONFIG=/home/pipeline-server/models/public/pointpillars/FP16/pointpillars_ov_config.json

On later runs (model already present on vol-models), it skips the clone/build and finishes in seconds:

[install-pointpillars] MODELS_PATH=/home/pipeline-server/models
[install-pointpillars] Using sparse-cloned source: /tmp/pointpillars-cache/openvino_contrib/modules/3d/pointPillars
[install-pointpillars] Already present: pointpillars_ov_nn.bin
[install-pointpillars] Already present: pointpillars_ov_nn.xml
[install-pointpillars] Already present: pointpillars_ov_pillar_layer.xml
[install-pointpillars] Already present: pointpillars_ov_postproc.xml
[install-pointpillars] Extension already built: /tmp/pointpillars-cache/openvino_contrib/modules/3d/pointPillars/ov_extensions/build/libov_pointpillars_extensions.so
[install-pointpillars] Config written: /home/pipeline-server/models/public/pointpillars/FP16/pointpillars_ov_config.json
[install-pointpillars] Done. Set LIDAR_MODEL_CONFIG=/home/pipeline-server/models/public/pointpillars/FP16/pointpillars_ov_config.json

Verify demo is working#

Scene is seeded automatically#

lidar-scene-init waits for the Manager (web) to become healthy, then imports sample_data/lidar_intersection/LidarIntersection-scene-import.zip via the Scene Import REST API (POST /api/v1/import-scene/) - no manual step, and no Manager source or DB-fixture changes are needed. It checks GET /api/v1/scenes first and skips the import if a scene named “Lidar Intersection” already exists, so it’s safe to leave enabled across restarts; after a full make demo-close (which wipes volumes) it re-imports cleanly on the next make demo-lidar.

docker compose logs lidar-scene-init

On the first run you should see:

lidar-scene-init: importing 'Lidar Intersection' scene...
lidar-scene-init: done

On later runs (scene already imported), it exits early instead:

lidar-scene-init: 'Lidar Intersection' scene already exists, skipping import

After it runs, a new Lidar Intersection scene appears with the intersection-cam1 camera and intersection-lidar1 LiDAR sensor already positioned and calibrated.

Manual re-import: if you ever need to force a re-import (e.g. after editing LidarIntersection-scene-import.zip), delete the existing “Lidar Intersection” scene from the UI (or via the API) and re-run docker compose up -d lidar-scene-init, or import the same ZIP manually via Scenes -> + Import Scene in the UI, or curl -F zipFile=@sample_data/lidar_intersection/LidarIntersection-scene-import.zip against /api/v1/import-scene/ (see Importing the scene for the full token/curl pattern).

What to expect in the UI#

Open the Lidar Intersection scene in the UI:

  • 3D view (default): tracked vehicles and cyclists appear as marks moving through the intersection. Marks are labeled/styled by detection source (lidar vs camera - see the debug UI changes in Source labels and default assets), so you can tell which sensor(s) are currently contributing to a given tracked object; a vehicle or cyclist seen by both sensors is fused into a single tracked mark rather than appearing twice.

  • Point cloud projection (3D UI): open the intersection-lidar1 camera panel and enable project point cloud (or use project all sensors under Cameras). The live LiDAR frame is requested over MQTT (getpointcloud → scenescape/pointcloud/camera/{id}) and rendered as a colored THREE.Points cloud in scene coordinates, using the same sensor pose as detections. Adjust point size and point cloud opacity as needed; pause point cloud freezes the last frame.

  • 2D view: switching to intersection-cam1 shows the replayed camera frames with 2-D detection boxes overlaid. intersection-lidar1 has no camera picture and always shows offline/no-preview in this view - that is expected for a LiDAR sensor (see Troubleshooting).

Verify fusion via MQTT#

Inspect the raw per-sensor detections directly (the broker is not published to the host by default, so sniff traffic from inside the container):

docker compose exec broker mosquitto_sub -h localhost -p 1883 \
  --cafile /mosquitto/secrets/certs/scenescape-ca.pem --insecure -v \
  -t 'scenescape/data/camera/intersection-lidar1' \
  -t 'scenescape/data/camera/intersection-cam1'

Each message includes a "source" sensor id and category-grouped objects; over time you should see the same real-world vehicle tracked by both sensors and fused into a single tracked object in the scene.

Stopping the demo#

make demo-close

This stops and removes all demo services and volumes, including the LiDAR demo containers - the same command used for any other demo, no separate teardown step is needed.

Using GPU acceleration#

The LiDAR branch defaults to LIDAR_DEVICE=GPU, with the lidar-stream service’s devices/group_add/device_cgroup_rules already enabled to pass through /dev/dri. Make sure the host has an Intel GPU driver installed first (see Prerequisites). The camera branch defaults to CAM_DEVICE=CPU; set CAM_DEVICE=GPU too if you want the camera detector to also use the GPU.

Why GPU is the default, not just an optional speed-up: PointPillars is a voxel-based 3-D CNN, noticeably heavier than the 2-D person-vehicle-bike detector the camera branch uses, and it runs in the same gst-launch-1.0 process/host that also has to keep decoding and detecting camera frames. On CPU, PointPillars inference routinely can’t keep up with the default LIDAR_FRAME_RATE=10. The pace gate keeps lag bounded, so the symptom is not runaway lag but both branches’ fps= values sitting below LIDAR_FRAME_RATE. Prefer GPU; fall back to CPU only when none is available, and expect choppier playback.

Falling back to CPU (e.g. no GPU available): in sample_data/lidar_intersection/docker-compose.lidar-override.yml, comment out the devices, group_add, and device_cgroup_rules entries under the lidar-stream service, and set LIDAR_DEVICE=CPU under the same service’s environment section. CPU inference works but is noticeably slower (see the performance note in Prerequisites).

Configuration reference#

The lidar-stream service reads its configuration from environment variables (see the commented examples in sample_data/lidar_intersection/docker-compose.lidar-override.yml):

Variable

Default

Description

LIDAR_SENSOR_ID

intersection-lidar1

Sensor id used for the MQTT topic and payload

LIDAR_DEVICE

GPU

OpenVINO device for PointPillars inference (CPU fallback is much slower)

LIDAR_SCORE_THRESHOLD

0.70

Minimum detection confidence to publish

LIDAR_FRAME_RATE

10

Target playback frame rate

LIDAR_LOOP

true

Loop the recorded frame sequence

CAM_SENSOR_ID

intersection-cam1

Sensor id used for the MQTT topic and payload

CAM_DEVICE

CPU

OpenVINO device for the camera detector

CAM_SCORE_THRESHOLD

0.8

Minimum detection confidence to publish

CAM_DETECTION_LABELS

vehicle,cyclist

Comma-separated category allow-list

LIDAR_CAM_LAG_TOLERANCE

2

Max frames one branch may run ahead of the other before it is paced back (keeps lag bounded)

LIDAR_VIZ_MAX_POINTS

40000

Max points returned per getpointcloud request for 3D UI visualization

lidar-data-init (the dataset conversion step) has its own variable, set as a docker compose/Makefile-level environment variable rather than inside the compose file itself:

Variable

Default

Description

LIDAR_RAW_DATASET_DIR

./sample_data/lidar_intersection/V2X-Seq-SPD-Example

Host path to the extracted raw dataset (must contain an infrastructure-side/ directory) - see Prerequisites

JPEG_QUALITY

50

JPEG re-encode quality (1-95) applied to camera frames by reencode_jpegs.py - lower is faster to decode/detect but blockier

Source labels and default assets#

The following live in the main source tree (not as detachable patches):

  • Default vehicle/cyclist Asset3D objects, and UI styling that labels tracked marks by detection source (lidar vs camera - see What to expect in the UI).

  • Pass-through of the source field through scene_common and Analytics so it reaches the regulated/UI-facing output end-to-end.

make build-core-lidar is an alias of make build-core.

Rotation/orientation handling#

LiDAR gives real 3-D orientation (rotation on each detection) directly from PointPillars, used as-is. The Controller’s existing (unpatched) camera-only heading inference (inferRotationFromVelocity(), gated by has_detection_rotation being false and the class’s rotation_from_velocity flag) still applies to intersection-cam1’s 2-D detections: when enabled, heading is inferred from the Kalman-estimated velocity direction with hysteresis (SPEED_THRESHOLD_ON/OFF) to avoid flapping at low speed.

The vehicle/cyclist default assets seeded by patch 0001 (above) set rotation_from_velocity=true so this existing feature is active out of the box for this demo’s camera-sourced tracks. No LiDAR-specific rotation fix is applied - PointPillars’ own front/back heading ambiguity (a known limitation of oriented-bbox 3-D detectors) is not corrected here. If you reset the objects library or add these classes another way, re-enable it per class from the Manager UI’s asset config (or manager_asset3d.rotation_from_velocity directly) to keep this behavior.

LiDAR/camera stream synchronization (recorded-playback only)#

lidar_publisher.py replays two independent pre-recorded file sequences (the .bin LiDAR frames and the .jpg camera frames) as two branches of one gst-launch-1.0 process, each paced by its own multifilesrc/ gvafpsthrottle. Because PointPillars can take several seconds to load and compile on first use while the camera branch starts producing frames almost immediately, the camera branch would otherwise race ahead of the LiDAR branch by a growing number of file-index positions before LiDAR ever publishes its first detection.

To keep the two recorded sequences aligned, the script:

  • Holds the camera branch back until LiDAR’s own first frame is processed (_lidar_ready in lidar_publisher.py), logged as [lidar-publisher] first LiDAR frame processed - releasing camera stream.

  • Then keeps both branches paced to each other with a bidirectional back-pressure gate: whichever branch gets more than LIDAR_CAM_LAG_TOLERANCE frames ahead of the other stops draining its own FIFO, which fills the pipe and back-pressures that branch’s GStreamer chain so it physically slows to the sibling’s rate. This bounds the drift in either direction (camera-ahead or LiDAR-ahead), so the coupled pair effectively plays back at the rate of whichever branch is momentarily slower.

  • Logs a running cam=<count> (lag=<n>) value alongside every 100th LiDAR frame in docker compose logs lidar-stream, so you can see at a glance whether the two streams are keeping pace with each other.

This is purely a recorded-playback artifact and does not apply to real sensors. A real LiDAR unit and a real camera each publish their own hardware/NTP-timestamped detections continuously and independently as they capture live data - there is no shared “file index”/multifilesrc counter to keep aligned, and no GPU-model-load-vs-camera-startup race to reconcile, since a live LiDAR sensor is already running and producing detections continuously well before (and after) any given camera comes online. The Scene Controller’s per-sensor Hungarian association/fusion already handles sensors that start, stop, or report at different rates generically - this script-level startup-flush/pace-gate logic exists only to make a pre-recorded demo behave sensibly, not because live multi-sensor fusion needs it. With the pace gate, lag stays bounded to roughly ±LIDAR_CAM_LAG_TOLERANCE once the demo is running; if you see it stuck at that bound while both branches’ fps= values sit below LIDAR_FRAME_RATE, that means one branch genuinely can’t keep up and is holding the other back (see Using GPU acceleration) - raise the LiDAR to GPU or lower the target frame rate rather than letting the two drift apart.

Troubleshooting#

  • lidar-scene-init fails to authenticate: confirm SUPASS matches the password used for the running deployment (it must be the same value passed to make demo-lidar); check docker compose logs lidar-scene-init.

  • lidar-model-init fails or times out: it compiles a GStreamer extension from openvino_contrib source on first run and needs outbound network access (respects HTTPS_PROXY/https_proxy); check docker compose logs lidar-model-init.

  • lidar-data-init fails (No such file or directory for image/velodyne, or pip install errors): confirm you completed the manual dataset download in Prerequisites and extracted it to sample_data/lidar_intersection/V2X-Seq-SPD-Example/infrastructure-side/ (or set LIDAR_RAW_DATASET_DIR to wherever you extracted it) - only its image/ and velodyne/ subdirectories are actually mounted/used; pip install failures usually mean no outbound network access (respects HTTPS_PROXY/https_proxy, same as lidar-model-init). Check docker compose logs lidar-data-init.

  • No detections published: confirm lidar-data-init completed successfully (docker compose logs lidar-data-init) - the pipelines need the converted .bin/.jpg frames in the shared sample-data volume.

  • Scene appears empty (or missing) after make demo-close + a fresh make demo-lidar: check docker compose logs lidar-scene-init - it depends on web being healthy first, so a slow Manager startup can briefly delay the import; the container exits after one attempt and is not retried automatically. Re-trigger it with docker compose up -d lidar-scene-init if needed.

  • A LiDAR-sourced vehicle/cyclist’s heading occasionally flips ~180 degrees (front/back) even while moving in a straight line: this is a known PointPillars/oriented-bbox-detector limitation (see Rotation/orientation handling) and is not corrected in this demo - it only affects LiDAR-sourced tracks, since camera-sourced tracks infer heading from velocity instead of a raw detector yaw. If a camera-sourced track’s heading looks frozen or jumpy instead, confirm rotation_from_velocity is still true for that class in the Manager’s asset config (Django manager_asset3d table).

  • intersection-cam1 shows “offline”/no picture in the UI: the Manager UI only marks a camera online once it gets a reply to its “getimage” request; lidar_publisher.py answers this for intersection-cam1 by reading the current camera frame’s .jpg file directly off disk and publishing it as-is (no re-encoding). If it still shows offline, check docker compose logs lidar-stream for frame-read errors, and confirm lidar-data-init completed (the preview needs the same extracted .jpg frames as detection). intersection-lidar1 has no camera picture and will always show offline/no-preview - that’s expected for a LiDAR sensor.

  • cam=... (lag=...) grows past ±LIDAR_CAM_LAG_TOLERANCE in docker compose logs lidar-stream: the bidirectional pace gate normally holds lag within roughly ±LIDAR_CAM_LAG_TOLERANCE (default 2). A brief overshoot right after startup is fine (FIFO/read-ahead buffering), but a lag that keeps growing means the gate is not engaging - check that neither branch has crashed ([camera-publisher]/[lidar-publisher] FATAL/Done lines). If lag instead sits pinned at the tolerance while both fps= values are below LIDAR_FRAME_RATE, the streams are aligned but one branch can’t keep up - see LiDAR/camera stream synchronization and Using GPU acceleration (usually the LiDAR branch running on CPU); move LiDAR to GPU or lower LIDAR_FRAME_RATE.