Multi-camera setups find widespread use across various applications, such as
autonomous driving, as they greatly expand sensing capabilities. Despite the
fast development of Neural radiance field (NeRF) techniques and their wide
applications in both indoor and outdoor scenes, applying NeRF to multi-camera
systems remains very challenging. This is primarily due to the inherent
under-calibration issues in multi-camera setup, including inconsistent imaging
effects stemming from separately calibrated image signal processing units in
diverse cameras, and system errors arising from mechanical vibrations during
driving that affect relative camera poses. In this paper, we present UC-NeRF, a
novel method tailored for novel view synthesis in under-calibrated multi-view
camera systems. Firstly, we propose a layer-based color correction to rectify
the color inconsistency in different image regions. Second, we propose virtual
warping to generate more viewpoint-diverse but color-consistent virtual views
for color correction and 3D recovery. Finally, a spatiotemporally constrained
pose refinement is designed for more robust and accurate pose calibration in
multi-camera systems. Our method not only achieves state-of-the-art performance
of novel view synthesis in multi-camera setups, but also effectively
facilitates depth estimation in large-scale outdoor scenes with the synthesized
novel views.

UC-NeRF 是一种专门针对欠校准多视角摄像系统的新视角合成方法，通过图层级颜色校正、虚拟畸变、时空约束的位姿细化等技术实现了在多摄像头环境下的优秀新视角合成和大规模室外场景的深度估计。