3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis

• URL: http://arxiv.org/abs/2606.24257v2
• Date: Mon, 29 Jun 2026 12:26:21 GMT
• Title: 3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis
• Abstract summary: 3DCarGen is a scalable single-view 3D car generation framework for real-world images.<n>Our approach achieves robust geometric consistency and reconstruction fidelity compared to existing methods.
• Score: 35.43901502343984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality 3D vehicle assets are essential for autonomous driving simulation. Although multi-view diffusion-based paradigms enable controllable single-image reconstruction, they typically produce limited viewpoints and exhibit cross-view geometric inconsistencies, thereby reducing reconstruction fidelity in real-world scenarios. In this work, we introduce 3DCarGen, a scalable single-view 3D car generation framework designed for real-world images by synthesizing an arbitrary number of 3D-consistent multi-view images. Specifically, given a single image as input, we first synthesize a set of images from fixed viewpoints. These images are then fed into a feed-forward reconstruction model, resulting in a coarse 3D representation based on 3D Gaussian Splatting. Conditioned on this explicit 3D prior, our multi-view diffusion model generates 3D-consistent images from arbitrary camera viewpoints. We further extend a fast mesh reconstruction algorithm by incorporating color-normal joint optimization to recover detailed and coherent 3D vehicle models from the synthesized dense views. Extensive experiments on synthetic and real-world datasets demonstrate that our approach achieves robust geometric consistency and reconstruction fidelity compared to existing methods. Project page: https://honglixiao.github.io/3dcargen.github.io/.

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