Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908941108117504 |
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| author | Qian, Shiyao Ren, Yuan Bai, Dongfeng Liu, Bingbing |
| author_facet | Qian, Shiyao Ren, Yuan Bai, Dongfeng Liu, Bingbing |
| contents | Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances.
We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05070 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation Qian, Shiyao Ren, Yuan Bai, Dongfeng Liu, Bingbing Artificial Intelligence Computer Vision and Pattern Recognition Robotics I.2.10; I.3.7; I.2.6 Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances. We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models. |
| title | Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Robotics I.2.10; I.3.7; I.2.6 |
| url | https://arxiv.org/abs/2604.05070 |