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The surgical usage of Mixed Reality (MR) has received growing attention inareas such as surgical navigation systems, skill assessment, and robot-assistedsurgeries. For such applications, pose estimation for hand and surgicalinstruments from an egocentric perspective is a fundamental task and has beenstudied extensively in the computer vision field in recent years. However, thedevelopment of this field has been impeded by a lack of datasets, especially inthe surgical field, where bloody gloves and reflective metallic tools make ithard to obtain 3D pose annotations for hands and objects using conventionalmethods. To address this issue, we propose POV-Surgery, a large-scale,synthetic, egocentric dataset focusing on pose estimation for hands withdifferent surgical gloves and three orthopedic surgical instruments, namelyscalpel, friem, and diskplacer. Our dataset consists of 53 sequences and 88,329frames, featuring high-resolution RGB-D video streams with activityannotations, accurate 3D and 2D annotations for hand-object pose, and 2Dhand-object segmentation masks. We fine-tune the current SOTA methods onPOV-Surgery and further show the generalizability when applying to real-lifecases with surgical gloves and tools by extensive evaluations. The code and thedataset are publicly available at batfacewayne.github.io/POV_Surgery_io/.

POV-Surgery: A Dataset for Egocentric Hand and Tool Pose Estimation During Surgical Activities