# SegICP-DSR: Dense Semantic Scene Reconstruction and Registration Source: [SegICP-DSR: Dense Semantic Scene Reconstruction and Registration]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:48:18.501Z Authors: Jay M. Wong, Syler Wagner, Connor Lawson, Vincent Kee, Mitchell Hebert, Justin Rooney, Gian-Luca Mariottini, Rebecca Russell, Abraham Schneider, Rahul Chipalkatty, David M.S. Johnson Published: 2017-11-06T23:13:41.000Z ## Abstract 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 adversarial training, (2) precise automated calibration of the camera intrinsic and extrinsic parameters, (3) viewpoint specific ray-casting of the model geometry, and (4) dense semantic ElasticFusion point clouds for registration. We benchmark the performance of SegICP-DSR on thousands of pose-annotated video frames and demonstrate its accuracy and efficacy on two tight tolerance grasping and insertion tasks using a KUKA LBR iiwa robotic arm. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()