CVNet: Contour Vibration Network for Building Extraction

Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 1383-1391


The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibration network (CVNet) for automatic building boundary delineation. Different from the previous contour models, the CVNet originally roots in the force and motion principle of contour string. Through the infinitesimal analysis and Newton's second law, we derive the spatial-temporal contour vibration model of object shapes, which is mathematically reduced to second-order differential equation. To concretize the dynamic model, we transform the vibration model into the space of image features, and reparameterize the equation coefficients as the learnable state from feature domain. The contour changes are finally evolved in a progressive mode through the computation of contour vibration equation. Both the polygon contour evolution and the model optimization are modulated to form a close-looping end-to-end network. Comprehensive experiments on three datasets demonstrate the effectiveness and superiority of our CVNet over other baselines and state-of-the-art methods for the polygon-based building extraction. The code is available at

Related Material

@InProceedings{Xu_2022_CVPR, author = {Xu, Ziqiang and Xu, Chunyan and Cui, Zhen and Zheng, Xiangwei and Yang, Jian}, title = {CVNet: Contour Vibration Network for Building Extraction}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {1383-1391} }