Summary
WorldSculpt is a computer vision paper (arXiv:2609.05416) by Muyao Niu, Jixuan He, Ruihan Yu, and colleagues that tackles generating compositional 3D representations of heavily cluttered scenes containing hundreds of objects. The goal is to represent a scene as individual object meshes placed in a shared world frame, which is required for downstream applications such as gaming, AR/VR, simulation, and robotics. The task is difficult because objects occlude one another and each view reveals only a fraction of their geometry. Traditional geometry-based methods reconstruct the scene as a single representation, leaving incomplete geometry in occluded regions, while prior compositional methods with generative priors are largely limited to relatively simple scenes. WorldSculpt demonstrates that complex scenes with hundreds of objects can instead be generated compositionally, grounded in video input, overcoming occlusion-induced incompleteness and scaling beyond simple scenes.
Paper Overview
Research Area: Computer Vision (CV)
Authors: Muyao Niu, Jixuan He, Ruihan Yu, Lian Fu, Yonghao Yu, Zheng-Hui Huang, Yifan Zhan, Fengbo Lan, Yongtao Ge, Yinqiang Zheng, Kaipeng Zhang, Zhixiang Wang
Published: 2026-09-04
arXiv: 2609.05416
Abstract
We study the problem of generating a compositional 3D representation of a cluttered scene containing hundreds of objects. The goal is to represent the scene as a collection of individual object meshes placed in a shared world frame, as required by downstream applications such as gaming, AR/VR, simulation, and robotics. This task is challenging in densely cluttered scenes, where objects heavily occlude one another and each view reveals only a fraction of their geometry. Geometry-based approaches typically reconstruct the scene as a single representation and leave incomplete geometry in occluded regions, while existing compositional methods with generative priors are largely limited to relatively simple scenes. We show that complex scenes with hundreds of objects can instead be generated com...
*(Full abstract truncated in the original source post.)*
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*Automatically collected on 2026-09-08.*
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