2.4k citations · 5k across the 23 of their papers we have counts for
23 papers
Semi-Weakly Supervised Object Kinematic Motion Prediction
Gengxin Liu, Qian Sun, Haibin Huang +5
Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological str…
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
Xiaomeng Xu, Yanchao Yang, Kaichun Mo +3
We propose a method that trains a neural radiance field (NeRF) to encode not only the appearance of the scene but also semantic correlations between scene points, regions, or entit…
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
Junbo Zhang, Guofan Fan, Guanghan Wang +3
Learning descriptive 3D features is crucial for understanding 3D scenes with diverse objects and complex structures. However, it is usually unknown whether important geometric attr…
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Yining Hong, Kaichun Mo, Li Yi +4
This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D object…
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
Xueyi Liu, Xiaomeng Xu, Anyi Rao +2
Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specif…
Curriculum DeepSDF
Yueqi Duan, Haidong Zhu, He Wang +3
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this…