This paper tackles the problem of novel view synthesis (NVS) from 2D images without known camera poses and intrinsics. Among various NVS techniques, Neural Radiance Field (NeRF) has recently gained popularity due to its remarkable synthesis quality. Existing NeRF-based approaches assume that the camera parameters associated with each input image are either directly accessible at training, or can be accurately estimated with conventional techniques based on correspondences, such as Structure-from-Motion. In this work, we propose an end-to-end framework, termed NeRF--, for training NeRF models given only RGB images, without pre-computed camera parameters. Specifically, we show that the camera parameters, including both intrinsics and extrinsics, can be automatically discovered via joint optimisation during the training of the NeRF model. On the standard LLFF benchmark, our model achieves comparable novel view synthesis results compared to the baseline trained with COLMAP pre-computed camera parameters. We also conduct extensive analyses to understand the model behaviour under different camera trajectories, and show that in scenarios where COLMAP fails, our model still produces robust results.

本文提出NeRF--用于学习相机内参和位姿，在各种相机运动情况下实现了准确的相机参数估计，并且在合成数据集BLEFF上实现了可比较的新视角渲染质量，为2D图像生成3D场景的任务提供了新思路。

无相机已知参数的神经辐射场（Neural Radiance Fields）--NeRF--