| # 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. |
| |
