WaveNeRF：基于小波的泛化神经辐射场
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
AI总结：
WaveNeRF将小波频率分解融入MVS与NeRF，通过小波域多视图立体、混合神经渲染器和频率引导采样，在仅三张输入图像下实现无需逐场景优化的高质量可泛化新视角合成。
摘要：
神经辐射场（NeRF）通过隐式场景表示在新视角合成中展现出卓越性能。然而，由于每个新场景都需要密集采样的图像，其通常面临可扩展性差的问题。若干研究尝试通过将多视图立体（MVS）技术融入NeRF来缓解该问题，但它们对新场景仍需要繁琐的微调过程。值得注意的是，若缺少该微调过程，渲染质量会严重下降，且误差主要出现在高频特征附近。基于这一观察，我们设计了WaveNeRF，它将小波频率分解融入MVS和NeRF，从而在无需任何逐场景优化的情况下实现可泛化且高质量的合成。为了在生成三维特征体时保留高频信息，WaveNeRF通过将离散小波变换融入经典级联MVS，在小波域中构建多视图立体，从而显式解耦高频信息。借助这一点，解耦的频率特征可以通过一种新颖的混合神经渲染器注入经典NeRF，以生成逼真的高频细节，并且可以设计一种直观的频率引导采样策略来抑制高频区域周围的伪影。在三个广泛研究的基准上的大量实验表明，当仅给定三张图像作为输入时，WaveNeRF实现了优越的可泛化辐射场建模。
英文摘要：
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation. However, it usually suffers from poor scalability as requiring densely sampled images for each new scene. Several studies have attempted to mitigate this problem by integrating Multi-View Stereo (MVS) technique into NeRF while they still entail a cumbersome fine-tuning process for new scenes. Notably, the rendering quality will drop severely without this fine-tuning process and the errors mainly appear around the high-frequency features. In the light of this observation, we design WaveNeRF, which integrates wavelet frequency decomposition into MVS and NeRF to achieve generalizable yet high-quality synthesis without any per-scene optimization. To preserve high-frequency information when generating 3D feature volumes, WaveNeRF builds Multi-View Stereo in the Wavelet domain by integrating the discrete wavelet transform into the classical cascade MVS, which disentangles high-frequency information explicitly. With that, disentangled frequency features can be injected into classic NeRF via a novel hybrid neural renderer to yield faithful high-frequency details, and an intuitive frequency-guided sampling strategy can be designed to suppress artifacts around high-frequency regions. Extensive experiments over three widely studied benchmarks show that WaveNeRF achieves superior generalizable radiance field modeling when only given three images as input.
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