{"doi":"10.1364/josaa.466286","title":"FlatNet3D: intensity and absolute depth from single-shot lensless capture","abstract":"Lensless cameras are ultra-thin imaging systems that replace the lens with a thin passive optical mask and computation. Passive mask-based lensless cameras encode depth information in their measurements for a certain depth range. Early works have shown that this encoded depth can be used to perform 3D reconstruction of close-range scenes. However, these approaches for 3D reconstructions are typically optimization based and require strong hand-crafted priors and hundreds of iterations to reconstruct. Moreover, the reconstructions suffer from low resolution, noise, and artifacts. In this work, we propose FlatNet3D-a feed-forward deep network that can estimate both depth and intensity from a single lensless capture. FlatNet3D is an end-to-end trainable deep network that directly reconstructs depth and intensity from a lensless measurement using an efficient physics-based 3D mapping stage and a fully convolutional network. Our algorithm is fast and produces high-quality results, which we validate using both simulated and real scenes captured using PhlatCam.","journal":"Journal of the Optical Society of America A","year":2022,"id":266046,"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":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9509,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":925658,"name":"Sanjana Prabhu","orcid":"0000-0002-9145-9698","position":1,"is_corresponding":false},{"id":925659,"name":"Salman S. Khan","orcid":"0000-0001-5481-8199","position":2,"is_corresponding":false},{"id":926204,"name":"D Tony Fredrick","orcid":null,"position":3,"is_corresponding":false},{"id":254312,"name":"Vivek Boominathan","orcid":"0000-0003-4875-3135","position":4,"is_corresponding":false},{"id":254314,"name":"Ashok Veeraraghavan","orcid":"0000-0001-5043-7460","position":5,"is_corresponding":false},{"id":288073,"name":"Kaushik Mitra","orcid":"0000-0001-6747-9050","position":6,"is_corresponding":false},{"id":926203,"name":"Dhruvjyoti Bagadthey","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T00:26:54.081997Z","pmid":"36215563","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":[]}