Datasets:
File size: 3,547 Bytes
89f63de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # GraspNet-1Billion example scene (scene_0000, RealSense, all 256 views)
One complete scene of [GraspNet-1Billion](https://graspnet.net/) (Fang et al., CVPR 2020) with everything needed to
reconstruct it: the 9 object meshes, every per-view object pose, the camera trajectory, and the RGB-D images with
segmentation labels. It is included as the reference for rendering multi-object scenes in the GraspNet style.
**Licence: this folder is CC BY-NC-SA 4.0** (GraspNet team, MVIG, SJTU: free for non-commercial use, redistribution
under the same conditions, https://graspnet.net/about), separate from the CC-BY / CC0 objects in `objects/`. Cite:
```
@inproceedings{fang2020graspnet, title={GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping},
author={Fang, Hao-Shu and Wang, Chenxi and Gou, Minghao and Lu, Cewu}, booktitle={CVPR}, year={2020}}
```
## Files
```
scene_0000_realsense.zip
scene_0000/object_id_list.txt the 9 GraspNet object ids in this scene (14 0 5 15 20 48 66 70 46)
scene_0000/rs_wrt_kn.npy RealSense pose with respect to the Kinect (not needed here)
scene_0000/realsense/camK.npy 3x3 intrinsics (also inside every meta file)
scene_0000/realsense/camera_poses.npy (256,4,4) pose of camera v with respect to camera 0 (view 0)
scene_0000/realsense/cam0_wrt_table.npy (4,4) pose of camera 0 with respect to the table frame (z up)
scene_0000/realsense/rgb/VVVV.png 1280x720 colour
scene_0000/realsense/depth/VVVV.png 16-bit depth in millimetres (divide by factor_depth = 1000)
scene_0000/realsense/label/VVVV.png per-pixel GraspNet object id + 1, 0 = background
scene_0000/realsense/meta/VVVV.mat poses (3,4,N) object->camera transforms in metres, cls_indexes (N,) = object id + 1,
intrinsic_matrix (3,3), factor_depth
scene_0000/realsense/annotations/VVVV.xml the same poses as position + quaternion in the camera frame (graspnetAPI's XML form)
models_scene_0000.zip
models/OID/textured.obj + .mtl + texture image, nontextured.ply, nontextured_simplified.ply (metres, object frame)
reconstruct_scene.py standalone reconstruction and check (numpy, scipy, Pillow, trimesh)
```
Views are indexed 0..255 along the recorded camera trajectory; object poses are given per view in that view's camera
frame, so the same scene can be reconstructed from any view. `camera_poses.npy` and `cam0_wrt_table.npy` put all views
in one table frame: `cam_v_wrt_table = cam0_wrt_table @ camera_poses[v]`.
## Reconstruct
```
unzip scene_0000_realsense.zip; unzip models_scene_0000.zip
python reconstruct_scene.py --scene scene_0000 --models models --view 0 --out out # camera frame of view 0
python reconstruct_scene.py --scene scene_0000 --models models --view 137 --table-frame --out out
```
The script prints each object's pose, writes `out/scene_VVVV.glb` (the textured meshes at their poses; add `--no-texture`
for a small file) and `out/depth_VVVV.ply` (the back-projected depth of the object pixels, coloured), and reports the
distance between those depth points and the reconstructed surfaces as a check (a few millimetres for a correct pose
convention). The pose logic follows graspnetAPI's `loadSceneModel` / `generate_scene_model` as used in GraspFormer.
Not included: the grasp labels (`grasp_label/`, per object) and the per-scene collision labels (`collision_label/`,
184 MB for this scene); both are in the original GraspNet-1Billion release.
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