Ray-Patch: An Efficient Decoder for Light Field Transformers

In this paper we propose the Ray-Patch decoder, a novel model to efficiently query transformers to decode implicit representations into target views. Our Ray-Patch decoding reduces the computational footprint up to two orders of magnitude compared to previous models, without losing global attention, and hence maintaining specific task metrics. The key idea of our novel decoder is to split the target image into a set of patches, then querying the transformer for each patch to extract a set of feature vectors, which are finally decoded into the target image using convolutional layers. Our experimental results quantify the effectiveness of our method, specifically the notable boost in rendering speed and equal specific task metrics for different baselines and datasets.