@inproceedings{
zhang2024learning,
title={Learning Geometry Consistent Neural Radiance Fields from Sparse and Unposed Views},
author={Qi Zhang and Chi Huang and Qian Zhang and Nan Li and Wei Feng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=CqjO5w70cA}
}
The recent progress in novel view synthesis is attributed to the Neural Radiance Field (NeRF), which requires plenty of images with precise camera poses. However, collecting available dense input images with accurate camera poses is a formidable challenge in real-world scenarios. In this paper, we propose Learning Geometry Consistent Neural Radiance Field (GC-NeRF), to tackle this challenge by jointly optimizing a NeRF and camera poses under sparse (as low as 2) and unposed images. First, GC-NeRF establishes the geometric consistencies in the image-level, which produce photometric constraints from inter- and intra-views for updating NeRF and camera poses in a fine-grained manner. Second, we adopt geometry projection with camera extrinsic parameters to present the region-level consistency supervisions, which construct pseudo-pixel labels for capturing critical matching correlations. Moreover, GC-NeRF presents an adaptive high-frequency mapping function to augment the geometry and texture information of the 3D scene. We evaluate the effectiveness of GC-NeRF, which sets a new state-of-the-art in the sparse view jointly optimized regime on multiple challenge real-world datasets.
Learning Geometry Consistent Neural Radiance Fields from Sparse and Unposed Views
Qi Zhang Chi Huang Qian Zhang Nan Li Wei Feng