Add README and WDS manifest pinned to e64095946e6e
Browse files- README.md +136 -0
- manifest.json +1238 -0
README.md
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| 1 |
+
---
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| 2 |
+
pretty_name: Infinigen Trajectory Videos 2K
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| 3 |
+
task_categories:
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- object-detection
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- depth-estimation
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tags:
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- 3d-object-detection
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- oriented-bounding-box
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- video
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- synthetic
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- infinigen
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- camera-pose
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size_categories:
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- 100K<n<1M
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---
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# Infinigen Trajectory Videos 2K
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+
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+
2,000 synthetic indoor videos rendered from [Infinigen](https://github.com/princeton-vl/infinigen) scenes, with per-frame
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RGB, metric depth, camera intrinsics and poses, instance masks and per-frame 3D oriented boxes. Each video is a slow,
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wall-avoiding camera path between two real camera poses of one scene. The data is in the native scene-JSON format of
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SpatialEncoder v2 Small (`spatial_encoder_v2_small`), plus WebDataset shards that its HF/WDS training loader can stream.
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+
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| | train | val | total |
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|---|---|---|---|
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| Videos | 1,822 | 178 | 2,000 |
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| Frames | 179,400 | 17,580 | 196,980 |
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| Object tracks | | | 39,303 (73 categories) |
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| WDS shards / samples | 74 / 3,609 | 8 / 353 | 81.5 GB |
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- 1,003 layouts of about 1,000 Infinigen scenes, 1 or 2 videos per layout; the split follows the layout's split.
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- 45–120 frames per video (multiples of 15, mean 98.5), 15 fps, 1024 × 1024.
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- Camera: 0.3 m/s, 20°/s, path length 0.8–2 m, total turn ≤ 90°; about 2 cm and 0.5° of motion per frame,
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similar to ScanNet (0.9 cm / 0.7° median).
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- Horizontal field of view fixed per video, 48–82° across videos (median 67°); pinhole, no distortion.
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## Layout
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```
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README.md
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manifest.json WDS shard manifest (schema spatialencoder-wds-native-v1, pinned URLs)
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catalog.json training catalog with the "Infinigen" source ($INFINIGEN_ROOT paths)
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dataset/Infinigen.json index: [["train-json/<video>.json", num_frames, "train"|"val"], ...]
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Infinigen/train-json/<video>.json one scene JSON per video (all splits; the index gives the split)
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videos/<video>.tar per-video package, extracts to <video>/...
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<video>/render/rgb_frames/NNNNNN.jpg RGB
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<video>/render/rgb.mp4 the same frames as a video
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<video>/depth/depth_mm_uint16/NNNNNN.png depth, uint16 millimetres, 0 = invalid
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<video>/depth/depth_preview.mp4
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<video>/instance/instance_id_png/NNNNNN.png instance ids
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| 51 |
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<video>/annotations/ renderer output: video_annotation.json, per_frame/*.json, categories, source
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| 52 |
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<video>/trajectory.json planned camera path
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wds/Infinigen/infinigen-<split>-NNNNNN.tar ~1 GB WebDataset shards (rgb + depth + metadata)
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metadata/registry.jsonl every scene, layout and Part 3 camera used (for de-duplicating later batches)
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metadata/selections/ layout selection lists
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metadata/trajectory_plans.tar per-layout trajectory plans
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metadata/stats.json counts and the category histogram
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| 58 |
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metadata/videos_sha256.json size and sha256 of every videos/*.tar
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```
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## Conventions
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| 62 |
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| 63 |
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Scene JSON `frames[frame_id]`:
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| 64 |
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| Field | Meaning |
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|---|---|
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| `rgb_path`, `depth_path`, `instance_path` | relative to `videos/` (after extracting the tars) |
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| `rgb_intrinsic`, `depth_intrinsic` | 3 × 3 K in pixels, pixel centres at integer coordinates (cx = cy = 511.5) |
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| `camera_pose` | 4 × 4 camera-to-world, metres, OpenCV camera axes (+X right, +Y down, +Z forward), world +Z up |
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| `original_width`, `original_height` | 1024, 1024 |
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| `time_s` | frame time at 15 fps |
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Scene JSON `objects[object_id][frame_id]` (only frames where the object is visible):
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| Field | Meaning |
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|---|---|
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| `camera_center` | box centre in the camera frame, metres |
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| `camera_scale` | full side lengths, metres, in the order of `camera_R`'s columns |
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| `camera_R` | box-local → camera rotation; local Y is the box axis closest to world vertical, pointing down |
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| `camera_box_2d_rend` | **2D prompt / 2D target used by the loader**: envelope of the projected cuboid, clipped to the image `[-0.5, W-0.5] × [-0.5, H-0.5]` (same convention as ScanNet val-json and ADT) |
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| `camera_box_2d_visible` | box of the visible instance mask |
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| `camera_box_2d_proj` | envelope of the projected cuboid, not clipped |
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| `category_name`, `visible_pixels` | query text and visible pixel count |
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Object-level fields: `3d_category` (query text, Part 3 shared vocabulary with `_` → space), `part3_category`,
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`instance_id`, `is_added` (object added by Infinigen layout augmentation). Doors and windows are not exported as objects.
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Depth is planar z in millimetres (`default_scale` 1000).
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## Using it with spatial_encoder_v2_small
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The training code builds an `ObjectDetectionDataset` only for names in `DATASET_IDS`
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(`sam3/train/data/object_detection_dataset.py`), so register the source first, e.g.
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`DATASET_IDS.setdefault("Infinigen", max(DATASET_IDS.values()) + 1)` in every training process.
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**Native files.**
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```bash
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huggingface-cli download vveicao/infinigen-traj-2k --repo-type dataset --local-dir $INFINIGEN_ROOT \
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--include "videos/*" "Infinigen/*" "dataset/*" "catalog.json"
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for f in $INFINIGEN_ROOT/videos/*.tar; do tar -xf "$f" -C $INFINIGEN_ROOT/videos && rm "$f"; done
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export INFINIGEN_ROOT
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# then train with ++trainer.data.train.dataset.catalog_path=$INFINIGEN_ROOT/catalog.json
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```
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**WebDataset streaming.** `manifest.json` follows the same schema as the `xxxspatialencoderwds*/data_*` releases, so
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`training/prepare_hf_wds.py` can read this repository as one more manifest repo. One sample is a block of 64 clip
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starts plus 14 look-ahead frames of one video (the native loader's 15-frame windows); shards mix videos. To add the
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source to a lock, include `vveicao/infinigen-traj-2k` in the repos passed to `prepare_lock`, add `"Infinigen"` to
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`EXPECTED_SOURCES` (`sam3/train/data/wds_manifest.py`) and to `wds.train_sources` / `wds.val_sources`, and set its
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weight in `wds.train_weights`.
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## How it was made
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- Scenes: Infinigen indoor `.blend` files (Part 1), with camera poses, categories and splits from the matching Part 3
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release; every `.blend` was checked against the Part 3 sha256 (2,000 / 2,000).
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- Paths: Dijkstra on a 0.1 m floor grid, at least 0.2 m from walls and objects, then straightened and smoothed;
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height, yaw and pitch interpolated linearly, roll 0; the first and last frames equal the Part 3 camera poses.
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Views keep median depth ≥ 0.8 m and at least 2 trainable objects in ≥ 90 % of frames.
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- Rendering: Blender 5.0.1 Cycles, 64 samples + OIDN denoising for RGB; a separate geometry pass for depth and instance ids.
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- Categories: the Part 3 name of the same scene object (73 categories here); objects Part 3 does not know (mostly
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architectural pillars) keep the renderer's name.
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## Quality checks
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- Depth reprojection between adjacent frames (K, poses, depth): ≥ 98.45 % of overlapping pixels agree within 1 %
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(median 99.9 %).
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- The spatial_encoder_v2_small training loader reads boxes that match the JSON (< 1e-6 m, 0.02°) on sampled clips.
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- WDS shards decode with the repository's own `TarCursor` and `SpatialWDSDecoder`; sample counts match the manifest.
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## Differences from real indoor scans
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Compared with ScanNet val88: objects are farther from the camera (median 2.68 m vs 2.12 m), cameras are slightly
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higher (median 1.62 m vs 1.47 m above the floor), the field of view is wider (median 67° vs 58°), and the category mix
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differs (chair 3 % of tracks vs 30 %). In a fine-tuning test, a model trained only on this data shifted its distance
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prior and placed ScanNet objects 15–25 % farther; mixing it with real data is recommended.
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manifest.json
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| 1 |
+
{
|
| 2 |
+
"schema": "spatialencoder-wds-native-v1",
|
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