Novel view synthesis from raw images provides superior high dynamic range
(HDR) information compared to reconstructions from low dynamic range RGB
images. However, the inherent noise in unprocessed raw images compromises the
accuracy of 3D scene representation. Our study reveals that 3D Gaussian
Splatting (3DGS) is particularly susceptible to this noise, leading to numerous
elongated Gaussian shapes that overfit the noise, thereby significantly
degrading reconstruction quality and reducing inference speed, especially in
scenarios with limited views. To address these issues, we introduce a novel
self-supervised learning framework designed to reconstruct HDR 3DGS from a
limited number of noisy raw images. This framework enhances 3DGS by integrating
a noise extractor and employing a noise-robust reconstruction loss that
leverages a noise distribution prior. Experimental results show that our method
outperforms LDR/HDR 3DGS and previous state-of-the-art (SOTA) self-supervised
and supervised pre-trained models in both reconstruction quality and inference
speed on the RawNeRF dataset across a broad range of training views. Code can
be found in https://lizhihao6.github.io/Raw3DGS.

通过自我监督学习的新框架，我们能够在有限的噪声原始图像数量下，从原始图像中重建高动态范围（HDR）3DGS，克服了噪声的影响，提高了重建质量和推断速度。