Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss

• URL: http://arxiv.org/abs/2206.10360v1
• Date: Tue, 21 Jun 2022 13:10:14 GMT
• Title: Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss
• Authors: Yikang Ding, Zhenyang Li, Dihe Huang, Zhiheng Li, Kai Zhang
• Abstract summary: We propose a new method to enhance the performance of existing networks inspired by contrastive learning and feature matching. Our method achieves state-of-the-art performance and significant improvement over baseline network.
• Score: 10.847120224170698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based multi-view stereo (MVS) methods have made impressive progress and surpassed traditional methods in recent years. However, their accuracy and completeness are still struggling. In this paper, we propose a new method to enhance the performance of existing networks inspired by contrastive learning and feature matching. First, we propose a Contrast Matching Loss (CML), which treats the correct matching points in depth-dimension as positive sample and other points as negative samples, and computes the contrastive loss based on the similarity of features. We further propose a Weighted Focal Loss (WFL) for better classification capability, which weakens the contribution of low-confidence pixels in unimportant areas to the loss according to predicted confidence. Extensive experiments performed on DTU, Tanks and Temples and BlendedMVS datasets show our method achieves state-of-the-art performance and significant improvement over baseline network.

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