Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors
📰 ArXiv cs.AI
Points-to-3D generates 3D structures using point cloud priors and diffusion-based methods
Action Steps
- Utilize point cloud data from sources like LiDAR or VGGT as priors for 3D generation
- Apply diffusion-based methods to generate 3D structures from point cloud priors
- Exploit explicit geometric constraints from point clouds to improve 3D generation accuracy
- Integrate Points-to-3D with existing computer vision pipelines for various applications
Who Needs to Know This
Computer vision engineers and researchers can benefit from this work as it provides a novel approach to 3D generation, while product managers and software engineers can apply this technology to various applications such as robotics, autonomous vehicles, and 3D modeling
Key Insight
💡 Point cloud priors can be effectively used for 3D generation with diffusion-based methods
Share This
💡 Generate 3D structures from point clouds with Points-to-3D!
Key Takeaways
Points-to-3D generates 3D structures using point cloud priors and diffusion-based methods
Full Article
Title: Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors
Abstract:
arXiv:2603.18782v2 Announce Type: replace-cross Abstract: Recent progress in 3D generation has been driven largely by models conditioned on images or text, while readily available 3D priors are still underused. In many real-world scenarios, the visible-region point cloud are easy to obtain from active sensors such as LiDAR or from feed-forward predictors like VGGT, offering explicit geometric constraints that current methods fail to exploit. In this work, we introduce Points-to-3D, a diffusion-b
Abstract:
arXiv:2603.18782v2 Announce Type: replace-cross Abstract: Recent progress in 3D generation has been driven largely by models conditioned on images or text, while readily available 3D priors are still underused. In many real-world scenarios, the visible-region point cloud are easy to obtain from active sensors such as LiDAR or from feed-forward predictors like VGGT, offering explicit geometric constraints that current methods fail to exploit. In this work, we introduce Points-to-3D, a diffusion-b
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