{"type":"ScholarlyArticle","title":"CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo","authors":[{"name":"Weitao Chen","slug":"weitao-chen","given":"Weitao","family":"Chen","affiliation":"Alibaba Group"},{"name":"Hongbin Xu","slug":"hongbin-xu","given":"Hongbin","family":"Xu","affiliation":"Alibaba Group; South China University of Technology"},{"name":"Zhipeng Zhou","slug":"zhipeng-zhou","given":"Zhipeng","family":"Zhou","affiliation":"Alibaba Group"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"Alibaba Group"},{"name":"Baigui Sun","slug":"baigui-sun","given":"Baigui","family":"Sun","affiliation":"Alibaba Group"},{"name":"Wenxiong Kang","slug":"wenxiong-kang","given":"Wenxiong","family":"Kang","affiliation":"South China University of Technology; Guangdong Artificial Intelligence and Digital Enconomy Laboratory , Pazhou Lab"},{"name":"Xuansong Xie","slug":"xuansong-xie","given":"Xuansong","family":"Xie","affiliation":"Alibaba Group"}],"abstract":"The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handle the issue, we aim to involve Transformer into cost aggregation. However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions. Furthermore, Residual Regression Transformer(RRT) is proposed to enhance spatial attention. The proposed method is a universal plug-in to improve learning-based MVS methods.","doi":"10.24963/ijcai.2023/67","url":"https://ijcai.org/proceedings/2023/67","pdf":"https://ijcai.org/proceedings/2023/0067.pdf","markdown":"https://ijcai.org/proceedings/2023/67.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/67","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":67,"pages":{"first":599,"last":608},"keywords":["Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Aanæs et al., 2016] Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and An- ders Bjorholm Dahl. 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