EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision

Author:

Lei Jiahui1,Deng Congyue2,Schmeckpeper Karl1,Guibas Leonidas2,Daniilidis Kostas1

Affiliation:

1. University of Pennsylvania

2. Stanford University

Funder

NSF

ARL

ARO

ONR

Stanford University

Publisher

IEEE

Reference81 articles.

1. TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation

2. Implicit neural representations with periodic activation functions;sitzmann;Advances in neural information processing systems,2020

3. Box2mask: Weakly supervised 3d semantic instance segmentation using bounding boxes;chibane;European Conference on Computer Vision,0

4. Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

5. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

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2. EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. U3DS3: Unsupervised 3D Semantic Scene Segmentation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

5. Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and Aggregation;IEEE Transactions on Multimedia;2024

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