Explorerโ€บComputer Visionโ€บComputer Vision
Research PaperResearchia:202610.08006

Tetris3D: 3D Scene Generation With Objects That Fit Together

Jaeyeong Kim

Abstract

We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene. Existing methods often generate objects independently or couple them implicitly, providing limited guidance for ensuring fine-grained spatial compatibility between neighboring objects that interact with one another. To address this, we explicitly condition the generation of each object on the geometry of surrounding objects and the...

Submitted: October 8, 2026Subjects: Computer Vision; Computer Vision

Description / Details

We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene. Existing methods often generate objects independently or couple them implicitly, providing limited guidance for ensuring fine-grained spatial compatibility between neighboring objects that interact with one another. To address this, we explicitly condition the generation of each object on the geometry of surrounding objects and their physical relationships, guiding its shape and pose to remain geometrically and physically plausible within the scene. Moreover, we introduce ComOb, a physics simulation-based dataset of 1.2M scenes featuring physical interactions across diverse object categories, with per-object meshes and pairwise physical relation annotations. Comprehensive experiments on synthetic and realworld scenes show that Tetris3D recovers coherent object shapes and poses even when interacting regions are occluded, and achieves state-of-the-art performance in both generation quality and physical stability.


Source: arXiv:2610.10539v1 - http://arxiv.org/abs/2610.10539v1 PDF: https://arxiv.org/pdf/2610.10539v1 Original Link: http://arxiv.org/abs/2610.10539v1

Please sign in to join the discussion.

No comments yet. Be the first to share your thoughts!

Access Paper
View Source PDF
Submission Info
Date:
Oct 8, 2026
Topic:
Computer Vision
Area:
Computer Vision
Comments:
0
Bookmark