Kaixin Yao1,2* Longwen Zhang1,2* Xinhao Yan1,2 Yan Zeng1,2 Qixuan Zhang1,2†
Wei Yang3 Lan Xu1† Jiayuan Gu1† Jingyi Yu1†
1ShanghaiTech University 2Deemos Technology 3Huazhong University of Science and Technology
*Equal contribution †Project Leader
Abstract
Recovering high-quality 3D scenes from a single RGB image is a challenging task in computer graphics. Current methods often struggle with domain-specific limitations or low-quality object generation. To address these, we propose CAST (Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel method for 3D scene reconstruction and recovery. CAST starts by extracting object-level 2D segmentation and relative depth information from the input image, followed by using a GPT-based model to analyze inter-object spatial relationships. This enables the understanding of how objects relate to each other within the scene, ensuring more coherent reconstruction. CAST then employs an occlusion-aware large-scale 3D generation model to independently generate each object's full geometry, using MAE and point cloud conditioning to mitigate the effects of occlusions and partial object information, ensuring accurate alignment with the source image's geometry and texture. To align each object with the scene, the alignment generation model computes the necessary transformations, allowing the generated meshes to be accurately placed and integrated into the scene's point cloud. Finally, CAST incorporates a physics-aware correction step that leverages a fine-grained relation graph to generate a constraint graph. This graph guides the optimization of object poses, ensuring physical consistency and spatial coherence. By utilizing Signed Distance Fields (SDF), the model effectively addresses issues such as occlusions, object penetration, and floating objects, ensuring that the generated scene accurately reflects real-world physical interactions. Experimental results demonstrate that CAST significantly improves the quality of single-image 3D scene reconstruction, offering enhanced realism and accuracy in scene recovery tasks. CAST has practical applications in virtual content creation, such as immersive game environments and film production, where real-world setups can be seamlessly integrated into virtual landscapes. Additionally, CAST can be leveraged in robotics, enabling efficient real-to-simulation workflows and providing realistic, scalable simulation environments for robotic systems.
Video
Overview
The input RGB image is processed through scene analysis to extract key information, followed by pose-aware generation to create initial 3D models. Physical constraint refinement ensures realistic interactions and spatial relationships, yielding a high-quality, mesh-based 3D scene.
Results
Bringing the vibrant diversity of the real world into the virtual realm, this collection reimagines open-vocabulary scenes as immersive digital environments, capturing the richness and depth of each unique setting. For each scene, the images display as follows: the top-left shows the input image, the top-center displays the rendered geometry, and the right presents the rendered image with realistic textures.
CAST enables realistic physics-based animations, immersive game environments, and efficient real-to-simulation transitions, driving innovation across various fields.
Citation
@misc{yao2025castcomponentaligned3dscene,
title={CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image},
author={Kaixin Yao and Longwen Zhang and Xinhao Yan and Yan Zeng and Qixuan Zhang and Lan Xu and Wei Yang and Jiayuan Gu and Jingyi Yu},
year={2025},
eprint={2502.12894},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.12894},
}
Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of database.