{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:19:13Z","timestamp":1773771553842,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"the Ministry of Trade, Industry &amp; Energy (MOTIE, Korea)","doi-asserted-by":"publisher","award":["20023305"],"award-info":[{"award-number":["20023305"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"the Ministry of Trade, Industry &amp; Energy (MOTIE, Korea)","doi-asserted-by":"publisher","award":["23ZD1130"],"award-info":[{"award-number":["23ZD1130"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003696","name":"the Electronics and Telecommunications Research Institute (ETRI)","doi-asserted-by":"publisher","award":["20023305"],"award-info":[{"award-number":["20023305"]}],"id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003696","name":"the Electronics and Telecommunications Research Institute (ETRI)","doi-asserted-by":"publisher","award":["23ZD1130"],"award-info":[{"award-number":["23ZD1130"]}],"id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher\u2013student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models.<\/jats:p>","DOI":"10.3390\/s23249866","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9866","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8219-405X","authenticated-orcid":false,"given":"Eunjin","family":"Son","sequence":"first","affiliation":[{"name":"Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0771-149X","authenticated-orcid":false,"given":"Jiho","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6483-5263","authenticated-orcid":false,"given":"Jimin","family":"Song","sequence":"additional","affiliation":[{"name":"Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0487-0241","authenticated-orcid":false,"given":"Yongsik","family":"Jin","sequence":"additional","affiliation":[{"name":"IT Convergence Research Section, Electronics and Telecommunications Research Institute (ETRI), Daegu 42995, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9312-6299","authenticated-orcid":false,"given":"Sang Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gochoo, M., Otgonbold, M.E., Ganbold, E., Hsieh, J.W., Chang, M.C., Chen, P.Y., Dorj, B., Al Jassmi, H., Batnasan, G., and Alnajjar, F. 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