4 citations · 6 across the 3 of their papers we have counts for
5 papers · 1 filter
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds
Zihui Zhang, Weisheng Dai, Hongtao Wen +1
We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into lea…
unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning
Yafei Yang, Zihui Zhang, Bo Yang
We study the challenging problem of unsupervised multi-object segmentation on single images. Existing methods, which rely on image reconstruction objectives to learn objectness or…
GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
Zihui Zhang, Bo Yang, Bing Wang +1
We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural netw…
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering
Yu-Tao Liu, Li Wang, Jie yang +4
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed dis…
NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images
Xiaoxu Meng, Weikai Chen, Bo Yang
Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limit…