  We propose a novel method, LoLep, which regresses Locally-Learned planes from
a single RGB image to represent scenes accurately, thus generating better novel
views. Without the depth information, regressing appropriate plane locations is
a challenging problem. To solve this issue, we pre-partition the disparity
space into bins and design a disparity sampler to regress local offsets for
multiple planes in each bin. However, only using such a sampler makes the
network not convergent; we further propose two optimizing strategies that
combine with different disparity distributions of datasets and propose an
occlusion-aware reprojection loss as a simple yet effective geometric
supervision technique. We also introduce a self-attention mechanism to improve
occlusion inference and present a Block-Sampling Self-Attention (BS-SA) module
to address the problem of applying self-attention to large feature maps. We
demonstrate the effectiveness of our approach and generate state-of-the-art
results on different datasets. Compared to MINE, our approach has an LPIPS
reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%. We also evaluate the
performance on real-world images and demonstrate the benefits.
