{"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/seasondepth-cross-season-monocular-depth","title":"SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments","arxiv_id":"2011.04408","date":"2020-11-09","proceeding":null,"authors":["Hanjiang Hu","Baoquan Yang","Zhijian Qiao","Shiqi Liu","Jiacheng Zhu","Zuxin Liu","Wenhao Ding","Ding Zhao","Hesheng Wang"],"abstract":"Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem. Although monocular depth prediction has been well studied recently, few works focus on the robustness of learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark, SeasonDepth, is introduced to benchmark the depth estimation performance under different environments. We investigate several state-of-the-art representative open-source supervised and self-supervised depth prediction methods using newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset and cross-dataset evaluation with current autonomous driving datasets, the performance and robustness against the influence of multiple environments are analyzed qualitatively and quantitatively. We show that long-term monocular depth prediction is still challenging and believe our work can boost further research on the long-term robustness and generalization for outdoor visual perception. The dataset is available on https://seasondepth.github.io, and the benchmark toolkit is available on https://github.com/ SeasonDepth/SeasonDepth.","url_abs":"https://arxiv.org/abs/2011.04408v7","url_pdf":"https://arxiv.org/pdf/2011.04408v7.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":"seasondepth-cross-season-monocular-depth","repo_url":"https://github.com/SeasonDepth/SeasonDepth","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"visual-localization","task_name":"Visual Localization"}],"methods":[],"datasets_introduced":[{"slug":"seasondepth","name":"SeasonDepth","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2011.04408","atlas_url":"https://app.syntology.ai/?focus=2011.04408","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}