In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more challenging. In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios. Furthermore, we construct a new multi-frame point cloud interpolation dataset called NL-Drive for large nonlinear motions in autonomous driving scenes to better demonstrate the superiority of our method. Ultimately, NeuralPCI achieves state-of-the-art performance on both DHB (Dynamic Human Bodies) and NL-Drive datasets. Beyond the interpolation task, our method can be naturally extended to point cloud extrapolation, morphing, and auto-labeling, which indicates its substantial potential in other domains. Codes are available at https://github.com/ispc-lab/NeuralPCI.

翻译：近年来，计算机视觉的插值任务受到显著关注。尽管视频插值取得了巨大进步，但点云插值仍未被充分探索。与此同时，真实场景中大量存在的非线性大运动使得点云插值任务更具挑战性。针对这些问题，我们提出NeuralPCI：一种端到端的4D时空神经场用于三维点云插值，该方法隐式融合多帧信息以处理室内外场景中的非线性大运动。此外，我们构建了一个名为NL-Drive的多帧点云插值数据集，专门针对自动驾驶场景中的大非线性运动，以更好地展示我们方法的优越性。最终，NeuralPCI在DHB（动态人体）和NL-Drive两个数据集上均达到了最先进的性能。除插值任务外，我们的方法可自然扩展至点云外推、形变和自动标注，这表明其在其他领域具有巨大潜力。代码开源于https://github.com/ispc-lab/NeuralPCI。