TL;DRHole-free close-up neural point rendering
We extend PAPR for robust close-up neural point rendering and significantly reduce holes and artifacts while preserving fine details.

@inproceedings{peng2024papr,
  title={PAPR in Motion: Seamless Point-level 3D Scene Interpolation},
  author={Shichong Peng and Yanshu Zhang and Ke Li},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

TL;DRSeamless point-level 4D motion interpolation
We introduce the novel problem of point-level 3D scene interpolation. Given observations of a scene at two distinct states from multiple views, the goal is to synthesize a smooth point-level interpolation between them, without any intermediate supervision. Our method, PAPR in Motion, builds upon Proximity Attention Point Rendering (PAPR) technique, and generates seamless interpolations of both the scene geometry and appearance.

@inproceedings{zhang2023papr,
  title={PAPR: Proximity Attention Point Rendering},
  author={Yanshu Zhang and Shichong Peng and Alireza Moazeni and Ke Li},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}

TL;DRReconstruct and render point clouds using attention
PAPR is a point-based surface representation that uses proximity attention to interpolate between nearby points to rays for rendering high-quality images, enabling non-volume-preserving geometry deformation by directly adjusting point positions, and, unlike 3D Gaussian Splatting, it avoids creating holes while preserving texture details after deformation.