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

TL;DR

POV-Surgery introduces a large-scale synthetic egocentric dataset with detailed annotations for hand and tool pose estimation in surgical scenarios, addressing data scarcity and challenging visual conditions.

Contribution

This paper presents a novel synthetic dataset for egocentric surgical hand and tool pose estimation, enabling improved model training and evaluation in complex surgical environments.

Findings

Fine-tuned SOTA methods on POV-Surgery show promising results.

The dataset enhances generalization to real surgical cases.

Extensive evaluations validate the dataset's utility.

Abstract

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