217 citations · 307 across the 36 of their papers we have counts for
8 papers · 1 filter
CAD-NeRF: Learning NeRFs from Uncalibrated Few-view Images by CAD Model Retrieval
Xin Wen, Xuening Zhu, Renjiao Yi +3
Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of n…
LLM-enhanced Scene Graph Learning for Household Rearrangement
Wenhao Li, Zhiyuan Yu, Qijin She +5
The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places. It depends both on common-sense knowledge on the objective…
Generic Objects as Pose Probes for Few-shot View Synthesis
Zhirui Gao, Renjiao Yi, Chenyang Zhu +3
Radiance fields including NeRFs and 3D Gaussians demonstrate great potential in high-fidelity rendering and scene reconstruction, while they require a substantial number of posed i…
Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadrics
Zhirui Gao, Renjiao Yi, Yuhang Huang +3
Low-level 3D representations, such as point clouds, meshes, NeRFs and 3D Gaussians, are commonly used for modeling 3D objects and scenes. However, cognitive studies indicate that h…
Relighting Scenes with Object Insertions in Neural Radiance Fields
Xuening Zhu, Renjiao Yi, Xin Wen +2
The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR). Previous methods focused on inserting virtual objects using CAD m…
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems
Hang Zhao, Kexiong Yu, Yuhang Huang +3
Combinatorial Optimization (CO) problems are fundamentally important in numerous real-world applications across diverse industries, characterized by entailing enormous solution spa…
F2M-Reg: Unsupervised RGB-D Point Cloud Registration with Frame-to-Model Optimization
Zhinan Yu, Zheng Qin, Yijie Tang +4
This work studies the problem of unsupervised RGB-D point cloud registration, which aims at training a robust registration model without ground-truth pose supervision. Existing met…
Learning Dual-arm Object Rearrangement for Cartesian Robots
Shishun Zhang, Qijin She, Wenhao Li +4
This work focuses on the dual-arm object rearrangement problem abstracted from a realistic industrial scenario of Cartesian robots. The goal of this problem is to transfer all the…