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Current datasets lack data with ground-truth depth information in dynamic scenarios; therefore, we present DRENDS (Depth in Robotic Endoscopy with Dynamic Scenarios)\n                    <jats:sup>1<\/jats:sup>\n                    , a novel dataset comprising sequences of high-resolution stereo images captured during the robotic laparoscopic manipulation of a human phantom and\n                    <jats:italic>ex vivo<\/jats:italic>\n                    porcine tissue, along with ground-truth point clouds for each frame and calibration data. The data were collected under three illumination conditions and across different anatomies involving tissue manipulation and non-rigid deformations. Our code for rectifying stereo images, handling camera-perspective occlusions, and obtaining depth maps per frame is open source for reproducibility and easy adaptation. Finally, we also conduct baseline evaluations using state-of-the-art depth estimation models to establish benchmark performance on our dataset. The results and data highlight the challenges and potential of metric temporally consistent depth estimation in robotic surgery, encouraging further advancements in tissue deformation prediction for medical applications. We publicly release DRENDS\n                    <jats:sup>1<\/jats:sup>\n                    to foster innovation and collaboration in this critical field.\n                  <\/jats:p>","DOI":"10.1038\/s41597-026-07775-2","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T08:53:05Z","timestamp":1786351985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dataset for Depth in Robotic Endoscopy with Dynamic Scenarios (DRENDS)"],"prefix":"10.1038","volume":"13","author":[{"given":"Gerardo","family":"Loza-Galindo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattia","family":"Magro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Calm\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Ruffaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominic","family":"Jones","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"De Momi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Valdastri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"7775_CR1","doi-asserted-by":"publisher","unstructured":"Loza, G. et al. DRENDS: a dataset for depth in robotic endoscopy with dynamic scenarios. Zenodo, version 1.0, https:\/\/doi.org\/10.5281\/zenodo.17598453 (2025).","DOI":"10.5281\/zenodo.17598453"},{"issue":"5","key":"7775_CR2","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1016\/j.surg.2021.10.047","volume":"171","author":"TJ Loftus","year":"2022","unstructured":"Loftus, T. J. et al. Executive summary of the artificial intelligence in surgery series. Surgery 171(5), 1435\u20131439, https:\/\/doi.org\/10.1016\/j.surg.2021.10.047 (2022).","journal-title":"Surgery"},{"issue":"1","key":"7775_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-022-00733-3","volume":"5","author":"S Ali","year":"2022","unstructured":"Ali, S. 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