LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference

• URL: http://arxiv.org/abs/2307.12217v2
• Date: Wed, 9 Aug 2023 10:34:43 GMT
• Title: LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference
• Authors: Cong Wang, Yu-Ping Wang, Dinesh Manocha
• Abstract summary: We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately. Compared to MINE, our approach has an LPIPS reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%.
• Score: 66.45326873274908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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