  Considering the problem of novel view synthesis (NVS) from only a set of 2D
images, we simplify the training process of Neural Radiance Field (NeRF) on
forward-facing scenes by removing the requirement of known or pre-computed
camera parameters, including both intrinsics and 6DoF poses. To this end, we
propose NeRF$--$, with three contributions: First, we show that the camera
parameters can be jointly optimised as learnable parameters with NeRF training,
through a photometric reconstruction; Second, to benchmark the camera parameter
estimation and the quality of novel view renderings, we introduce a new dataset
of path-traced synthetic scenes, termed as Blender Forward-Facing Dataset
(BLEFF); Third, we conduct extensive analyses to understand the training
behaviours under various camera motions, and show that in most scenarios, the
joint optimisation pipeline can recover accurate camera parameters and achieve
comparable novel view synthesis quality as those trained with COLMAP
pre-computed camera parameters. Our code and data are available at
https://nerfmm.active.vision.
