47 citations · 187 across the 17 of their papers we have counts for
19 papers
InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts
Weipeng Zhong, Peizhou Cao, Yichen Jin +9
The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typic…
MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Bingquan Dai, Li Ray Luo, Qihong Tang +9
Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-spec…
STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Yushi Lan, Yihang Luo, Fangzhou Hong +7
We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-…
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
Xinyu Lian, Zichao Yu, Ruiming Liang +9
Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are eithe…
GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation
Yushi Lan, Shangchen Zhou, Zhaoyang Lyu +5
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper intro…
GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation
Zhaoyang Lyu, Ben Fei, Jinyi Wang +4
Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares. However, due to its irregular structure, me…