Learning-based scene representations such as neural radiance fields or light
field networks, that rely on fitting a scene model to image observations, commonly
encounter challenges in the presence of inconsistencies within the images caused by
occlusions, inaccurately estimated camera parameters or effects like lens flare. To
address this challenge, we introduce RANdom RAy Consensus (RANRAC), an efficient
approach to eliminate the effect of inconsistent data, thereby taking inspiration from
classical RANSAC based outlier detection for model fitting. In contrast to the
down-weighting of the effect of outliers based on robust loss formulations, our approach
reliably detects and excludes inconsistent perspectives, resulting in clean images
without floating artifacts. For this purpose, we formulate a fuzzy adaption of the
RANSAC paradigm, enabling its application to large scale models. We interpret the
minimal number of samples to determine the model parameters as a tunable
hyperparameter, investigate the generation of hypotheses with data-driven models, and analyse
the validation of hypotheses in noisy environments. We demonstrate the
compatibility and potential of our solution for both photo-realistic robust multi-view
reconstruction from real-world images based on neural radiance fields and for single-shot
reconstruction based on light-field networks. In particular, the results indicate significant
improvements compared to state-of-the-art robust methods for novel-view synthesis on both
synthetic and captured scenes with various inconsistencies including occlusions, noisy
camera pose estimates, and unfocused perspectives. The results further indicate
significant improvements for single-shot reconstruction from occluded images.
