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The core of Multi-view Stereo(MVS) is the matching process among referenceand source pixels. Cost aggregation plays a significant role in this process,while previous methods focus on handling it via CNNs. This may inherit thenatural limitation of CNNs that fail to discriminate repetitive or incorrectmatches due to limited local receptive fields. To handle the issue, we aim toinvolve Transformer into cost aggregation. However, another problem may occurdue to the quadratically growing computational complexity caused byTransformer, resulting in memory overflow and inference latency. In this paper,we overcome these limits with an efficient Transformer-based cost aggregationnetwork, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) isproposed to aggregate long-range features on cost volume via self-attentionmechanisms along the depth and spatial dimensions. Furthermore, ResidualRegression Transformer(RRT) is proposed to enhance spatial attention. Theproposed method is a universal plug-in to improve learning-based MVS methods.

CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo