{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discrete-time-convolution-for-fast-event","title":"Discrete Time Convolution for Fast Event-Based Stereo","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Kaixuan Zhang","Kaiwei Che","JianGuo Zhang","Jie Cheng","Ziyang Zhang","Qinghai Guo","Luziwei Leng"],"abstract":"    Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes of pixel intensity, giving it a series of advantages over traditional frame-based camera, such as high dynamical range, high temporal resolution and low power consumption. However, extracting information from highly asynchronous event data is a challenging task. Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics. Adopting channel-wise parameterization, temporal dynamics of the model is synchronized on the same feature map and diverges across different ones, enabling it to embed data in a variety of temporal scales. Abstracted from CTC, we further develop discrete time convolution (DTC) which accelerates the process with lower computational cost. We apply these methods to event-based multi-view stereo matching where they surpass state-of-the-art methods on benchmark criteria of the MVSEC dataset. Spatially sparse event data often leads to inaccurate estimation of edges and local contours. To address this problem, we propose a dual-path architecture in which the feature map is complemented by underlying edge information from original events extracted with spatially-adaptive denormalization. We demonstrate the superiority of our model in terms of speed (up to 110 FPS), accuracy and robustness, showing a great potential for real-time fast depth estimation. Finally, we perform experiments on the recent DSEC dataset to demonstrate the general usage of our model.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discrete-time-convolution-for-fast-event","repo_url":"https://github.com/Huawei-BIC/Discrete_Time_Convolution_for_Fast_Event_Based_Stereo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}