arXiv Computer Vision
Tetris3D: 3D Scene Generation With Objects That Fit Together
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arXiv AI
Sep 24
MessyKitchens: Contact-rich object-level 3D scene reconstruction
MessyKitchens introduces a new dataset of cluttered real-world kitchen scenes with detailed 3D object shapes, poses, and accurate contact information. The authors extend the SAM 3D single-object reconstruction method with a Multi-Object Decoder (MOD) to jointly reconstruct entire scenes, achieving better registration accuracy and reduced inter-object penetration compared to prior work. The dataset, benchmark, code, and pretrained models will be publicly released on the project website.
arXiv Computer Vision
Sep 7
WorldSculpt: Generating Compositional Worlds from Grounded Videos
WorldSculpt presents a method for generating compositional 3D representations of cluttered scenes with hundreds of objects by adapting a single-object 3D generative prior to multi-view observations. The approach, built on Pixal3D with a multi-view conditioning pathway, can generalize to highly occluded scenes without scene-level training. The authors also introduce the UE-MeshyScene benchmark and demonstrate that their method outperforms prior approaches across various evaluation settings, including converting existing 3DGS worlds into compositional mesh scenes.
arXiv AI
Jun 2
Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
arXiv:2602. 08058v3 Announce Type: replace-cross Abstract: In the presence of occlusions and measurement noise, geometrically accurate scene reconstructions -- which fit the sensor data -- can still be physically incorrect.
arXiv AI
Jul 2
Interact3D: Compositional 3D Generation of Interactive Objects
arXiv:2603. 16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets.
arXiv Computer Vision
Oct 1
SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images
arXiv:2609.39590v1 Announce Type: new Abstract: Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-condit...
arXiv Computer Vision
5d ago
DistScene: Object-to-Scene Distillation for 3D Scene Generation
DistScene is a framework for generating 3D scenes from a single image by jointly modeling the environment and individual objects. It introduces Scene-Frame Generation to produce separate environment and object components in a shared coordinate frame, and Object-Centric Refinement to fine‑tune each object with scene context. The method also employs Object-to-Scene Distillation to transfer pretrained object-generation priors to scene generation using automatically composed synthetic scenes, achieving improved spatial coherence on indoor and outdoor benchmarks.