Abstract
Multi-view Stereo(MVS) has been studied for decades as a critical algorithm for 3D reconstruction. Lately, many learning-based methods have improved the reconstruction performance of traditional algorithms, but they pay limited attention to memory consumption and runtime. To address this issue, we propose a novel and effective learning-based MVS framework(LE-MVSNet), based on our exploration of the depth hypothesis and cost volume in this work. Firstly, to decrease the number of depth hypotheses, we establish a more reasonable depth hypothesis space based on its sparse point cloud corresponding to the image set, replacing the previous method of randomly depth hypothesis in evenly divided depth layers within a predefined depth range. Secondly, to reduce memory consumption, we design a lightweight group-wise correlation by compressing the channel of the aggregated cost volumes to one. In addition, for acceleration, we propose SE-UNet, which executes U-Net regularization in the width and height direction, and SE-Net for self-attention in the depth direction. Finally, our method achieves competitive performance on DTU and BlendedMVS dataset with significantly higher efficiency. Compared to MVSNet, our method reduces memory consumption by 52.78\(\%\) and runtime by 88.57\(\%\).
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 62176237 and 61906168 ), Zhejiang Provincial Natural Science Foundation of China (Grant No. LY23F020023), the Hangzhou AI major scientific and technological innovation project (2022AIZD0061) and the “Pioneer” and “Leading Goose” R &D Program of Zhejiang Province (Grant No. 2023C01022).
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Kong, C., Zhang, Z., Mao, J., Chan, S., Sheng, W. (2023). LE-MVSNet: Lightweight Efficient Multi-view Stereo Network. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_40
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