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OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Z. Song, B. Yang
Advances in Neural Information Processing Systems (NeurIPS), 2022
arXiv /
Video/
Code
We introduce the first unsupervised 3D object segmentation method on point clouds.
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Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
Y. Yang, B. Yang
Advances in Neural Information Processing Systems (NeurIPS), 2022
arXiv /
Project Page/
Code
We systematically investigate the effectiveness of existing unsupervised models on challenging real-world images.
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GRF: Learning a General Radiance Field for 3D Representation and Rendering
A. Trevithick, B. Yang
IEEE International Conference on Computer Vision (ICCV), 2021
arXiv /
Code
We introduce a simple implicit neural function to represent complex 3D geometries purely from 2D images.
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Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
Q. Hu, B. Yang*, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021 (IF=16.39)
arXiv/
IEEE Xplore/
Code
(* indicates corresponding author)
The journal version of our RandLA-Net. More experiments and analysis are included.
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Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, N. Trigoni
Advances in Neural Information Processing Systems (NeurIPS), 2019 (Spotlight, 200/6743)
arXiv /
ScanNet Benchmark /
Reddit Discussion /
News:
(新智元,
图像算法,
AI科技评论,
将门创投,
CVer,
泡泡机器人)/
Video/
Code
We propose a simple and efficient neural architecture for accurate 3D instance segmentation on point clouds.
It achieves the SOTA performance on ScanNet and S3DIS (June 2019).
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