Scene Prior Filtering for Depth Super-Resolution

• URL: http://arxiv.org/abs/2402.13876v3
• Date: Fri, 18 Oct 2024 05:04:40 GMT
• Title: Scene Prior Filtering for Depth Super-Resolution
• Authors: Zhengxue Wang, Zhiqiang Yan, Ming-Hsuan Yang, Jinshan Pan, Guangwei Gao, Ying Tai, Jian Yang,
• Abstract summary: We introduce a Scene Prior Filtering network, SPFNet, to mitigate texture interference and edge inaccuracy. Our SPFNet has been extensively evaluated on both real and synthetic datasets, achieving state-of-the-art performance.
• Score: 97.30137398361823
• License:
• Abstract: Multi-modal fusion is vital to the success of super-resolution of depth maps. However, commonly used fusion strategies, such as addition and concatenation, fall short of effectively bridging the modal gap. As a result, guided image filtering methods have been introduced to mitigate this issue. Nevertheless, it is observed that their filter kernels usually encounter significant texture interference and edge inaccuracy. To tackle these two challenges, we introduce a Scene Prior Filtering network, SPFNet, which utilizes the priors surface normal and semantic map from large-scale models. Specifically, we design an All-in-one Prior Propagation that computes the similarity between multi-modal scene priors, i.e., RGB, normal, semantic, and depth, to reduce the texture interference. In addition, we present a One-to-one Prior Embedding that continuously embeds each single-modal prior into depth using Mutual Guided Filtering, further alleviating the texture interference while enhancing edges. Our SPFNet has been extensively evaluated on both real and synthetic datasets, achieving state-of-the-art performance.

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