arXiv Computer Vision
Trainspotting: Learning Object-Centric 3D Reconstruction from Found Photos
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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
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One View Is Enough: In-the-Wild Monocular Pretraining for Novel View Generation
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arXiv Computer Vision
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Building Rome from a Single Image
arXiv:2610.08790v1 Announce Type: new Abstract: Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretra...
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Building Rome from a Single Image
Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretrained 3D object generators encode a strong shape p...
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SAM 3D: 3Dfy Anything in Images
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arXiv Computer Vision
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OCH3R: Object-Centric Holistic 3D Reconstruction
arXiv:2605.13018v2 Announce Type: replace Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that firs...