{"paper":{"title":"SegICP-DSR: Dense Semantic Scene Reconstruction and Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Abraham Schneider, Connor Lawson, David M.S. Johnson, Gian-Luca Mariottini, Jay M. Wong, Justin Rooney, Mitchell Hebert, Rahul Chipalkatty, Rebecca Russell, Syler Wagner, Vincent Kee","submitted_at":"2017-11-06T23:13:41Z","abstract_excerpt":"To enable autonomous robotic manipulation in unstructured environments, we present SegICP-DSR, a real- time, dense, semantic scene reconstruction and pose estimation algorithm that achieves mm-level pose accuracy and standard deviation (7.9 mm, {\\sigma}=7.6 mm and 1.7 deg, {\\sigma}=0.7 deg) and suc- cessfully identified the object pose in 97% of test cases. This represents a 29% increase in accuracy, and a 14% increase in success rate compared to SegICP in cluttered, unstruc- tured environments. The performance increase of SegICP-DSR arises from (1) improved deep semantic segmentation under ad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02216","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}