{"url":"/method/bi3d","slug":"bi3d","name":"Bi3D","full_name":"Bi3D","full_name_withheld":false,"description_markdown":"**Bi3D** is a stereo depth estimation framework that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth *D*, as existing stereo methods do, it classifies them as being closer or farther than *D*. It takes the stereo pair and a disparity $d\\_{i}$ and produces a confidence map, which can be thresholded to yield the binary segmentation. To estimate depth on $N + 1$ quantization levels we run this network $N$ times and maximize the probability in Equation 8 (see paper). To estimate continuous depth, whether full or selective, we run the [SegNet](https://paperswithcode.com/method/segnet) block of Bi3DNet for each disparity level and work directly on the confidence volume.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Bi3D: Stereo Depth Estimation via Binary Classifications","paper":"/paper/bi3d-stereo-depth-estimation-via-binary","first_author":"Abhishek Badki","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/bi3d-stereo-depth-estimation-via-binary"},"source":{"url":"https://arxiv.org/abs/2005.07274v2","title":"Bi3D: Stereo Depth Estimation via Binary Classifications","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Stereo Depth Estimation Models","url":"/methods/category/stereo-depth-estimation-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/bi3d-bi-domain-active-learning-for-cross","title":"Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection","date":"2023-03-10","arxiv_id":"2303.05886","n_code_links":1,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":0}},{"paper":"/paper/bi3d-stereo-depth-estimation-via-binary","title":"Bi3D: Stereo Depth Estimation via Binary Classifications","date":"2020-05-14","arxiv_id":"2005.07274","n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/3d-object-detection","name":"3D Object Detection","papers":1},{"task":"/task/active-learning","name":"Active Learning","papers":1},{"task":"/task/autonomous-navigation","name":"Autonomous Navigation","papers":1},{"task":"/task/depth-estimation","name":"Depth Estimation","papers":1},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/quantization","name":"Quantization","papers":1},{"task":"/task/stereo-depth-estimation","name":"Stereo Depth Estimation","papers":1},{"task":"/task/unsupervised-domain-adaptation","name":"Unsupervised Domain Adaptation","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/bi3d"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}