  Faithfully reconstructing 3D geometry and generating novel views of scenes
are critical tasks in 3D computer vision. Despite the widespread use of image
augmentations across computer vision applications, their potential remains
underexplored when learning neural rendering methods (NRMs) for 3D scenes. This
paper presents a comprehensive analysis of the use of image augmentations in
NRMs, where we explore different augmentation strategies. We found that
introducing image augmentations during training presents challenges such as
geometric and photometric inconsistencies for learning NRMs from images.
Specifically, geometric inconsistencies arise from alterations in shapes,
positions, and orientations from the augmentations, disrupting spatial cues
necessary for accurate 3D reconstruction. On the other hand, photometric
inconsistencies arise from changes in pixel intensities introduced by the
augmentations, affecting the ability to capture the underlying 3D structures of
the scene. We alleviate these issues by focusing on color manipulations and
introducing learnable appearance embeddings that allow NRMs to explain away
photometric variations. Our experiments demonstrate the benefits of
incorporating augmentations when learning NRMs, including improved photometric
quality and surface reconstruction, as well as enhanced robustness against data
quality issues, such as reduced training data and image degradations.
