{"doi":"10.36227/techrxiv.21312369.v1","title":"Image Depth Estimation Using Stereo Vision","abstract":"<jats:p>In modern research, the most studied step of stereo vision algorithms is\nthe process of pixel correspondence, better known as stereo matching. As\nof now, researchers are attempting to integrate optimization techniques\nwith stereo matching to improve stereo system performance. This paper\nseeks to provide an in-depth explanation of the stereo vision process in\ngeneral and find value in the application of optimization techniques to\nstereo-matching algorithms. To analyze and implement a sound stereo\nvision algorithm as well as an optimized matching algorithm, scholarly\nsources involving stereo vision and optimization techniques were\nstudied. After implementing a standard stereo-matching algorithm and an\nalgorithm that involves a famous optimization technique known as Dynamic\nProgramming, I found that there was a significant increase in both\naccuracy and efficiency in the depth estimates provided by the\nalgorithm.</jats:p>","journal":"INDIGO (University of Illinois at Chicago)","year":null,"id":15507,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":118156,"name":"Mrinall Umasudhan","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21071399","pmcid":null,"openalex_id":"https://openalex.org/W4306249900","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.09635521,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.36227/techrxiv.21312369.v1","host_type":""},{"url":"https://doi.org/10.36227/techrxiv.21312369.v1","host_type":""},{"url":"https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.21312369.v1","host_type":"publisher"},{"url":"https://figshare.com/articles/preprint/Image_Depth_Estimation_Using_Stereo_Vision/21312369","host_type":"repository"}],"fields_of_study":["Advanced Vision and Imaging"],"mesh_terms":[],"keywords":["Computer stereo vision","Stereopsis","Computer vision","Artificial intelligence","Stereo cameras","Matching (statistics)","Computer science","Process (computing)","Stereo camera","Pixel","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Industry, innovation and infrastructure"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-01T17:58:44.892246Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}