NeVRF：用于长时序列的神经视频辐射场
NeVRF: Neural Video-based Radiance Fields for Long-duration Sequences
AI总结：
本文提出NeVRF表示，融合NeRF与基于图像的渲染，并结合持续学习和定制压缩，实现长时动态场景的高质量自由视点渲染与紧凑存储。
摘要：
将神经辐射场（Neural Radiance Fields，NeRF）应用于长时动态序列一直具有挑战性。现有方法难以在质量与存储大小之间取得平衡，并且在处理拓扑变化和大幅运动等复杂场景变化时遇到困难。为解决这些问题，我们提出了一种新颖的神经视频辐射场（neural video-based radiance fields，NeVRF）表示。NeVRF将神经辐射场与基于图像的渲染相结合，以支持在长时动态内视场景中进行照片级真实感的新视角合成。我们引入了一种新颖的多视角辐射混合方法，直接从多视角视频预测辐射。通过结合持续学习技术，NeVRF能够高效地从序列数据重建帧，而无需重新访问先前帧，从而实现长时自由视点视频。此外，借助定制的压缩方法，NeVRF能够紧凑地表示动态场景，使动态辐射场在真实场景中更加实用。大量实验证明了NeVRF在实现长时序列渲染、序列数据重建和紧凑数据存储方面的有效性。
英文摘要：
Adopting Neural Radiance Fields (NeRF) to long-duration dynamic sequences has been challenging. Existing methods struggle to balance between quality and storage size and encounter difficulties with complex scene changes such as topological changes and large motions. To tackle these issues, we propose a novel neural video-based radiance fields (NeVRF) representation. NeVRF marries neural radiance field with image-based rendering to support photo-realistic novel view synthesis on long-duration dynamic inward-looking scenes. We introduce a novel multi-view radiance blending approach to predict radiance directly from multi-view videos. By incorporating continual learning techniques, NeVRF can efficiently reconstruct frames from sequential data without revisiting previous frames, enabling long-duration free-viewpoint video. Furthermore, with a tailored compression approach, NeVRF can compactly represent dynamic scenes, making dynamic radiance fields more practical in real-world scenarios. Our extensive experiments demonstrate the effectiveness of NeVRF in enabling long-duration sequence rendering, sequential data reconstruction, and compact data storage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
