Objects Check

The Objects check evaluates whether dynamic objects (vehicles, pedestrians, motorcycles, bicycles) in a Cosmos-generated video appear at the positions specified by the RDS-HQ ground truth annotations. It uses semantic segmentation to measure how well detected objects in the generated video correspond to their expected locations from the driving dataset.

How It Works

The check uses a SegFormer semantic segmentation model (the dynamic processor) to analyze dynamic objects — vehicles, pedestrians, motorcycles, and bicycles — in a driving scene. It requires the generated video and an RDS-HQ dataset as inputs. When a world-model video is also supplied, the static processor additionally runs the CWIP (Contrastive World-Image Pre-training) model to score camera-to-world consistency and detect per-patch object presence.

For each frame, the check:

Projects 3D object cuboids from the RDS-HQ ground truth into the camera's image space
Runs the segmentation model on the generated video frame
Measures the overlap between the projected ground truth and the detected segments
Produces a per-object, per-frame correspondence score (0.0 to 1.0)

The check also includes a hallucination detector that identifies false positive objects — segments in the generated video that don't correspond to any ground truth object.

Model Setup

SegFormer Model (Dynamic Processor)

The dynamic processor uses the SegFormer ONNX model for semantic segmentation. The model is bundled with the repository at checks/utils/models/segformer/cityformer_final_1024_dynamic_input_v11_sim.onnx (tracked via Git LFS), so no separate download is required — it is loaded automatically at runtime.

Note: Because the model is stored via Git LFS, make sure Git LFS is installed before cloning (or run git lfs pull in an existing clone) so the .onnx file is materialized rather than left as a pointer.

CWIP Model (Static Processor)

The static processor uses the CWIP (Contrastive World-Image Pre-training) model, which projects a camera frame and a world-model frame into a joint embedding space to score camera-to-world consistency and emit per-patch object-presence and object-type classifications. The weights are bundled with the repository in HuggingFace format (config.json + model.safetensors) at checks/utils/models/cwip/checkpoints/ (tracked via Git LFS), so no separate download is required — they are loaded automatically at runtime. The static processor only runs when a world-model video is provided (--world_video_path on the CLI / world_model_video_url on the REST path).

Note: Because the weights are stored via Git LFS, make sure Git LFS is installed before cloning (or run git lfs pull in an existing clone) so model.safetensors is materialized rather than left as a pointer.

To run the CWIP model on its own — outside the Objects check, e.g. for a standalone camera/world video pair with visualizations — see the CWIP model README.

Data Inputs

Input	Description
RDS-HQ dataset	Directory containing ground truth annotations, camera poses, and intrinsics in WebDataset `.tar` format
Generated video	The Cosmos-generated camera video to evaluate (MP4)

RDS-HQ Dataset Structure

The RDS-HQ dataset directory should have the following layout:

dataset_dir/
├── all_object_info/               # Dynamic object annotations
│   └── {clip_id}.tar
├── pose/                          # Camera poses (30 FPS)
│   └── {clip_id}.tar
├── pinhole_intrinsic/             # Pinhole camera intrinsics
│   └── {clip_id}.tar              #   Contains: pinhole_intrinsic.{camera_name}.npy
├── ftheta_intrinsic/              # Fisheye camera intrinsics (if applicable)
│   └── {clip_id}.tar              #   Contains: ftheta_intrinsic.{camera_name}.npy
├── 3d_traffic_lights/             # Static traffic light cuboids
│   └── {clip_id}.tar
├── 3d_traffic_signs/              # Static traffic sign cuboids
│   └── {clip_id}.tar
├── 3d_lanelines/                  # Lane line polylines
│   └── {clip_id}.tar
├── 3d_road_boundaries/            # Road boundary polylines
│   └── {clip_id}.tar
├── 3d_wait_lines/                 # Wait line polylines
│   └── {clip_id}.tar
└── 3d_crosswalks/                 # Crosswalk surfaces
    └── {clip_id}.tar

Each .tar file uses the WebDataset format.

Clip ID

The clip_id is used to locate the correct .tar files in each RDS-HQ subdirectory (e.g., all_object_info/{clip_id}.tar). It must match the base name of the .tar files in the dataset.

In the standard RDS-HQ format, the clip ID follows the pattern {session_uuid}_{start_timestamp_us} (e.g., 0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000). The session UUID is extracted from the portion before the first underscore, and the start timestamp (in microseconds) is parsed from the portion after it. The timestamp is used to derive the clip's time range.

Usage

CLI

The CLI operates on local filesystem paths only — all inputs (RDS-HQ dataset, video file) must be available on disk.

First, extract the sample RDS-HQ dataset:

unzip checks/sample_data/morning/rds_hq.zip -d checks/sample_data/morning/

Run the objects check directly from the command line (dynamic processor only):

dazel run //checks/objects/single_view:run -- \
  --input_data $(pwd)/checks/sample_data/morning/rds_hq/ \
  --clip_id 01ce78ad-9e9a-4df9-95d1-1d50e41a04ce_764657799000_764677799000 \
  --camera_name camera_front_wide_120fov \
  --video_path $(pwd)/checks/sample_data/morning/01ce78ad-9e9a-4df9-95d1-1d50e41a04ce_764657799000_764677799000_0_Morning.30fps.mp4 \
  --output_dir $(pwd)/output_dir

Note: Pass file and directory arguments as absolute paths (the $(pwd)/ prefix above). dazel run executes the binary from its Bazel runfiles directory rather than your shell's working directory, so relative paths like checks/sample_data/... are resolved against the wrong root and fail with a "does not exist" error. The repo is bind-mounted into the dazel container at the same absolute path, so $(pwd)/... paths resolve correctly inside the container.

To also run the static (CWIP) processor, add --world_video_path pointing at the world-model video for the same clip and camera. The repo bundles a matching sample set — RDS-HQ dataset, camera (augmented) video, and world-model video for clip 0d21d408-… — under checks/sample_data/. First extract its single-clip RDS-HQ dataset:

unzip checks/sample_data/rds_hq_single_clip.zip -d checks/sample_data/rds_hq_single_clip/

Then run both processors:

dazel run //checks/objects/single_view:run -- \
  --input_data $(pwd)/checks/sample_data/rds_hq_single_clip/ \
  --clip_id 0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028189000 \
  --camera_name camera_front_wide_120fov \
  --video_path $(pwd)/checks/sample_data/0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000_rgb.mp4 \
  --world_video_path $(pwd)/checks/sample_data/0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000_0.mp4 \
  --output_dir $(pwd)/output_dir

Note: The --clip_id timestamp (…189000) is the one baked into the RDS-HQ tar filenames, which differs from the timestamp in the video filenames (…200000). Match --clip_id to the RDS-HQ dataset; the video paths are passed explicitly and their filenames need not match the clip_id.

With --world_video_path supplied, the check produces both {clip_id}.dynamic.objects.results.json and {clip_id}.static.objects.results.json; without it, only the dynamic results are produced.

Arguments:

Argument	Required	Default	Description
`--input_data`	Yes	—	Path to the RDS-HQ dataset directory
`--clip_id`	Yes	—	Clip identifier in the format `{session_uuid}_{start_timestamp_us}` (e.g., `0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000`). Used to locate the correct `.tar` files in each RDS-HQ subdirectory and to derive the session UUID and clip time range.
`--camera_name`	Yes	—	Camera stream name (e.g., `camera_front_wide_120fov`)
`--video_path`	Yes	—	Path to the generated video file (MP4)
`--output_dir`	Yes	—	Directory to write results, logs, and visualizations
`--world_video_path`	No	None	Path to the world model video used by the static (CWIP) processor. If omitted, static processing is skipped and only the dynamic results are produced.
`--model_device`	No	`cuda`	Device for model inference: `cuda` or `cpu`
`--trial`	No	None	Process only the first N frames (useful for debugging)
`--target_fps`	No	`30.0`	Target FPS for processing
`--verbose`	No	`INFO`	Logging level: `DEBUG`, `INFO`, `WARNING`, `ERROR`
`--config`	No	`cosmos_evaluator`	Name of the config file to load (without the `.yaml` extension)

Docker Container

Build the Container

dazel run //services/objects/single_view:image_load

This builds the OCI image and loads it into the local Docker daemon as objects-checker:<version>.

Setup Environment Variables

The Objects check uses the StorageProvider framework to download input files (RDS HQ data, augmented video) from URLs provided in the /process request, and to upload output files (result JSONs, visualization videos) back to storage.

Storage is configured through the following environment variables:

Variable	Required	Default	Description
`COSMOS_EVALUATOR_STORAGE_TYPE`	No	`s3`	Storage backend: `s3` or `local`
`COSMOS_EVALUATOR_STORAGE_BUCKET`	Yes (S3)	—	S3 bucket name for storing outputs
`COSMOS_EVALUATOR_STORAGE_REGION`	Yes (S3)	—	AWS region (also reads `AWS_DEFAULT_REGION`)
`COSMOS_EVALUATOR_STORAGE_ACCESS_KEY`	No	—	AWS access key (also reads `AWS_ACCESS_KEY_ID`)
`COSMOS_EVALUATOR_STORAGE_SECRET_KEY`	No	—	AWS secret key (also reads `AWS_SECRET_ACCESS_KEY`)

For convenience, create a ~/.cosmos_evaluator/.env file with your storage configuration so it is automatically loaded at startup:

mkdir -p ~/.cosmos_evaluator
cat > ~/.cosmos_evaluator/.env << 'EOF'
COSMOS_EVALUATOR_STORAGE_TYPE=s3
COSMOS_EVALUATOR_STORAGE_BUCKET=my-bucket
COSMOS_EVALUATOR_STORAGE_REGION=us-west-2
COSMOS_EVALUATOR_STORAGE_ACCESS_KEY=your_access_key
COSMOS_EVALUATOR_STORAGE_SECRET_KEY=your_secret_key
EOF

If you already have AWS credentials in a ~/.aws/.env file, the storage settings will also read from AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION as fallbacks if passed via --env-file below.

The storage type also determines how input URLs in the /process request are resolved. With s3, input URLs can be s3:// URIs, presigned URLs, or any HTTPS URL. With local, input paths are mounted filesystem paths. Output storage follows the same provider — S3 uploads for s3, local file copies for local.

Start the Service

# Running in local mode with sample data mounted at /data and output at /tmp/output
docker run --gpus all --env-file ~/.cosmos_evaluator/.env \
  -e COSMOS_EVALUATOR_STORAGE_TYPE=local \
  -v $(pwd)/checks/sample_data:/data \
  -v $(pwd)/output_dir:/tmp/output \
  -p 8000:8000 objects-checker:1.1.0

Verify that the service is running:

curl http://localhost:8000/health

Test the Service

The /process path auto-derives the clip_id from the RDS-HQ archive, so the archive must contain exactly one clip with its layer folders at the top level. After download, the service unzips rds_hq_url and derives clip_id from the first *.tar it finds; the RDS loader then resolves each layer as <layer>/<clip_id>.tar (e.g. pose/<clip_id>.tar) directly under the extraction root. A multi-clip archive makes the derived clip_id ambiguous, and an archive whose layer folders sit under an extra wrapper directory (e.g. rds_hq/pose/...) will not resolve. The shipped checks/sample_data/morning/rds_hq.zip is both multi-clip and wrapped in an rds_hq/ directory, so it is intended for the CLI path (which takes an explicit --input_data and --clip_id), not this REST example. Note also that world_model_video_url is required by the REST request (unlike the CLI's optional --world_video_path).

The repo ships a ready-made REST fixture — checks/sample_data/rds_hq_single_clip.zip — a flat, single-clip archive (clip 0d21d408-...189000) that pairs with the 0d21d408 world-model (_0.mp4) and augmented (_rgb.mp4) sample videos, all for the camera_front_wide_120fov camera. The example below uses it directly.

curl -X POST http://localhost:8000/process \
  -H "Content-Type: application/json" \
  -d '{
    "camera_name": "camera_front_wide_120fov",
    "rds_hq_url": "/data/rds_hq_single_clip.zip",
    "augmented_video_url": "/data/0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000_rgb.mp4",
    "world_model_video_url": "/data/0d21d408-ceca-4af6-9c4b-4f6f78ee7459_1727936028200000_0.mp4",
    "output_storage_prefix": "/tmp/output"
  }'

Example output (illustrative dynamic visualization, rendered from a different sample clip): 01ce78ad-9e9a-4df9-95d1-1d50e41a04ce_764657799000_764677799000.dynamic.object.mp4

Request fields:

Field	Required	Default	Description
`rds_hq_url`	Yes	—	URL (or mounted filesystem path) to the RDS-HQ dataset archive. The `clip_id` is auto-derived from the archive and is not accepted as a request field.
`augmented_video_url`	Yes	—	URL or mounted filesystem path to the generated video (e.g. `/data/video.mp4`)
`world_model_video_url`	Yes	—	URL or mounted filesystem path to the world-model video used by the static (CWIP) processor. Required on the REST path (the CLI's `--world_video_path` is optional).
`camera_name`	Yes	—	Camera stream name (e.g., `camera_front_wide_120fov`)
`config`	No	None	Full configuration dictionary (replaces the default config entirely). If omitted, the default config from `checks/cosmos_evaluator.yaml` is used. Use the `/config` endpoint to retrieve the defaults as a starting point.
`trial_frames`	No	None	Process only the first N frames (useful for debugging)
`verbose`	No	`INFO`	Logging level: `DEBUG`, `INFO`, `WARNING`, `ERROR`
`output_storage_prefix`	No	`{service_name}/{clip_id}/`	Storage prefix for outputs. For cloud storage this is a key prefix and any leading slash is stripped before being used as a key. For local storage this is a directory path: an absolute path (e.g. `/tmp/output`) anchors uploads at the filesystem root, while a relative path resolves against the service's working directory. The storage backend itself (S3 vs local) and bucket / region are picked up from environment variables; service-specific overrides (e.g. `OBJECTS_*`) take precedence over the global defaults.

Other endpoints:

# Health check
curl http://localhost:8000/health

# Get default configuration
curl http://localhost:8000/config

Configuration

Configuration is managed through checks/cosmos_evaluator.yaml under the av.objects_single_view key. You can modify the defaults or pass a custom config dict via the API.

Importance Filter

Controls which objects are included in the evaluation:

Parameter	Default	Description
`allow_all_static_objects`	`true`	Include all static objects (infrastructure) regardless of other filters
`distance_threshold_m`	`100`	Maximum distance (meters) from ego vehicle. Objects beyond this are excluded.
`relevant_lanes`	`[ego, left, right]`	Only evaluate objects in these lane positions relative to the ego vehicle
`skip_oncoming_objects`	`false`	If true, exclude vehicles traveling in the opposite direction

Overlap Check Methods

Per-class scoring method configuration. Each object class uses one of two methods:

Method	Description	Best For
`ratio`	`overlap_pixels / projected_area_pixels`. Simple area ratio.	Large, well-defined objects (vehicles, infrastructure)
`cluster`	Connected component analysis. Selects the largest connected region overlapping the projected cuboid, avoiding over-scoring from background segments.	Smaller objects that may be near similar-class objects (pedestrians, motorcycles)

Default assignments:

overlap_check:
  vehicle:        { method: ratio }
  pedestrian:     { method: cluster }
  motorcycle:     { method: cluster }
  bicycle:        { method: cluster }
  traffic_light:  { method: ratio }
  traffic_sign:   { method: ratio }
  lane_line:      { method: ratio }
  road_boundary:  { method: ratio }
  wait_line:      { method: ratio }
  crosswalk:      { method: ratio }

Hallucination Detector

Detects false positive objects in the generated video that don't correspond to any ground truth:

Parameter	Default	Description
`enabled`	`true`	Enable/disable hallucination detection
`max_cluster_per_frame`	`100`	Maximum clusters to analyze per frame
`classes.<class>.min_cluster_area`	varies	Minimum pixel area for a cluster to be considered a hallucination. Smaller values catch more detections but may increase false positives.

Default minimum cluster areas:

Class	Min Area (pixels)
vehicle	5000
motorcycle	3000
bicycle	2000
pedestrian	1000
traffic_light	1000
traffic_sign	1000

Visualization

Parameter	Default	Description
`enabled`	`true`	Generate overlay visualization videos showing projected cuboids and segmentation masks

Output Format

The check produces one JSON result file per processor that runs:

{clip_id}.dynamic.objects.results.json — Dynamic processor results (always produced)
{clip_id}.static.objects.results.json — Static (CWIP) processor results (produced when a world-model video is provided: it is optional via --world_video_path on the CLI, and required via world_model_video_url in a /process request. If omitted on the CLI, static processing is skipped)

If visualization is enabled, corresponding video files are also generated (the static video is likewise only produced when the static processor runs):

{clip_id}.dynamic.object.viz.mp4
{clip_id}.static.object.viz.mp4

JSON Structure

{
  "processed_frames": 150,
  "total_video_frames": 300,
  "track_ids": [5, 12, 23, 45, 67],
  "processed_frame_ids": [0, 5, 10, 15, ...],

  "mean_score": 0.723,
  "std_score": 0.234,
  "min_score": 0.001,
  "max_score": 0.998,

  "processor_output_labels": [
    "scored",
    "occluded",
    "distance_threshold_m (100m)",
    "relevant_lanes (['ego', 'left', 'right'])"
  ],

  "tracks": [
    {
      "track_id": 5,
      "object_type": "Car",
      "processor_output": [10, 2, 10]
    },
    {
      "track_id": 1001,
      "object_type": "LaneLine",
      "object_type_index": 19,
      "processor_output": [150, 0, 0]
    }
  ],

  "score_matrix": {
    "format": "sparse",
    "shape": [150, 5],
    "data": [
      {"frame_idx": 0, "track_idx": 0, "score": 0.85},
      {"frame_idx": 0, "track_idx": 1, "score": 0.72}
    ]
  },

  "metadata": {
    "matrix_format": "sparse",
    "version": "1.0"
  }
}

Field Descriptions

Field	Description
`processed_frames`	Number of frames where at least one object was evaluated
`total_video_frames`	Total frames in the input video
`track_ids`	List of unique object track IDs evaluated
`processed_frame_ids`	Frame indices that were processed
`mean_score`	Mean correspondence score across all valid (object, frame) pairs
`std_score`	Standard deviation of scores
`min_score` / `max_score`	Range of scores
`processor_output_labels`	Labels for the `processor_output` array in each track
`tracks[].track_id`	Unique object identifier
`tracks[].object_type`	Object class name
`tracks[].processor_output`	Per-label counts (e.g., [scored, occluded, filtered_by_distance])
`score_matrix`	Sparse matrix of per-frame, per-track scores

Reconstructing the Score Matrix

The score matrix is stored in sparse format for efficiency. To reconstruct it:

import json
import numpy as np

with open("results.json") as f:
    results = json.load(f)

matrix = results["score_matrix"]
scores = np.full(matrix["shape"], np.nan)

for entry in matrix["data"]:
    scores[entry["frame_idx"], entry["track_idx"]] = entry["score"]

# scores[i, j] = correspondence score for frame i, track j
# NaN means the object was not visible/evaluated in that frame

Score Interpretation

Score Range	Meaning
0.8 – 1.0	Strong correspondence. The object appears where expected with good shape alignment.
0.3 – 0.8	Partial correspondence. The object is present but may be shifted, partially occluded, or have shape differences.
0.0 – 0.3	Poor correspondence. The object may be missing, severely misplaced, or incorrectly rendered.

The mean_score across all objects and frames provides an overall quality metric for the generated video.

Troubleshooting

CUDA Out of Memory

The check runs a GPU model. If you encounter OOM errors:

Reduce video resolution before processing
Use --trial N to process fewer frames

CPU Mode

If no GPU is available, use --model_device cpu. Processing will be significantly slower but functionally identical.

Debug Logging

Use --verbose DEBUG for detailed frame-by-frame processing logs, including object projections, overlap calculations, and filtering decisions.

Navigation Menu

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Objects Check

How It Works

Model Setup

SegFormer Model (Dynamic Processor)

CWIP Model (Static Processor)

Data Inputs

RDS-HQ Dataset Structure

Clip ID

Usage

CLI

Docker Container

Build the Container

Setup Environment Variables

Start the Service

Test the Service

Configuration

Importance Filter

Overlap Check Methods

Hallucination Detector

Visualization

Output Format

JSON Structure

Field Descriptions

Reconstructing the Score Matrix

Score Interpretation

Troubleshooting

CUDA Out of Memory

CPU Mode

Debug Logging

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objects.md

File metadata and controls

Objects Check

How It Works

Model Setup

SegFormer Model (Dynamic Processor)

CWIP Model (Static Processor)

Data Inputs

RDS-HQ Dataset Structure

Clip ID

Usage

CLI

Docker Container

Build the Container

Setup Environment Variables

Start the Service

Test the Service

Configuration

Importance Filter

Overlap Check Methods

Hallucination Detector

Visualization

Output Format

JSON Structure

Field Descriptions

Reconstructing the Score Matrix

Score Interpretation

Troubleshooting

CUDA Out of Memory

CPU Mode

Debug Logging