Papers › SNAKE: Shape-aware Neural 3D Keypoint Field

SNAKE: Shape-aware Neural 3D Keypoint Field

3 Jun 2022arXiv:2206.01724archive 2025-07-28

Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun, Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang

Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection? Existing methods either seek salient features according to statistics of different orders or learn to predict keypoints that are invariant to transformation. Nevertheless, the idea of incorporating shape reconstruction into 3D keypoint detection is under-explored. We argue that this is restricted by former problem formulations. To this end, a novel unsupervised paradigm named SNAKE is proposed, which is short for shape-aware neural 3D keypoint field. Similar to recent coordinate-based radiance or distance field, our network takes 3D coordinates as inputs and predicts implicit shape indicators and keypoint saliency simultaneously, thus naturally entangling 3D keypoint detection and shape reconstruction. We achieve superior performance on various public benchmarks, including standalone object datasets ModelNet40, KeypointNet, SMPL meshes and scene-level datasets 3DMatch and Redwood. Intrinsic shape awareness brings several advantages as follows. (1) SNAKE generates 3D keypoints consistent with human semantic annotation, even without such supervision. (2) SNAKE outperforms counterparts in terms of repeatability, especially when the input point clouds are down-sampled. (3) the generated keypoints allow accurate geometric registration, notably in a zero-shot setting. Codes are available at https://github.com/zhongcl-thu/SNAKE

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angles2rotation_matrix zhongcl-thu/SNAKE/core/datasets/augmentation.py official repository ran fingerprinted MIT (permissive) · 60f2f4207ae20ab9 · report
atomic_rotate_pytorch_batch zhongcl-thu/SNAKE/core/datasets/augmentation.py official repository ran MIT (permissive) · 61969ba9c876c10d · report
chamfer_distance_naive zhongcl-thu/SNAKE/core/nets/common.py official repository ran fingerprinted MIT (permissive) · 8e6b5059f61d84e6 · report
compute_iou zhongcl-thu/SNAKE/core/nets/common.py official repository ran MIT (permissive) · 0072de07b144b152 · report
get_ray_directions_cam zhongcl-thu/SNAKE/core/nets/geometry.py official repository ran fingerprinted MIT (permissive) · dcf31906f80d979c · report
make_3d_grid zhongcl-thu/SNAKE/core/datasets/train_dataset.py official repository ran MIT (permissive) · 9babaac7293b5582 · report
reflect_vector_on_vector zhongcl-thu/SNAKE/core/nets/geometry.py official repository ran fingerprinted MIT (permissive) · 54dc24b743feff79 · report
accuracy zhongcl-thu/SNAKE/core/utils/common.py official repository unverified MIT (permissive) · 9a8db8d09e7923d3 · report
augment zhongcl-thu/SNAKE/core/datasets/augmentation.py official repository unverified MIT (permissive) · 60c37454e2a095ce · report
chamfer_distance zhongcl-thu/SNAKE/core/nets/common.py official repository unverified MIT (permissive) · f4d8f4c4d0f7da71 · report
load_last_iter zhongcl-thu/SNAKE/core/utils/common.py official repository unverified MIT (permissive) · 97c47b2462e29db3 · report
load_state zhongcl-thu/SNAKE/core/utils/common.py official repository unverified MIT (permissive) · 3777dfeb89e049b9 · report
naive_read_pcd zhongcl-thu/SNAKE/core/datasets/train_dataset.py official repository unverified MIT (permissive) · c994f6f4b55acda6 · report
sample_vertex_from_mesh zhongcl-thu/SNAKE/core/datasets/smpl_model.py official repository unverified MIT (permissive) · 0bd31bdc88752b70 · report

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Keypoint Detection

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