Tetris3D: 3D Scene Generation With Objects That Fit Together
cs.CV
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: Project page: https://cvlab-kaist.github.io/Tetris3D/
Project page: https://cvlab-kaist.github.io/Tetris3D
License: http://creativecommons.org/licenses/by/4.0/
The gist: We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene.
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 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.
Sources
- MessyKitchens: Contact-rich object-level 3D scene reconstruction
- Qwen3-VL Technical Report
- PhyScene3D: Physically Consistent Interactive 3D Tabletop Scene Generation
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- Classifier-Free Diffusion Guidance
- Axolotl3D: a Unified Framework for Faithful 3D Shape Completion
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- $\phi$-Scene: Physically Grounded Image-to-3D Scene Reconstruction
- SPREAD: Spatial-Physical REasoning via geometry Aware Diffusion
- Flow Matching for Generative Modeling
- Decoupled Weight Decay Regularization
- Point-E: A System for Generating 3D Point Clouds from Complex Prompts
- ShapeR: Robust Conditional 3D Shape Generation from Casual Captures
- DINOv3
- Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
- TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation
- Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
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- Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
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