SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation
The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light sensor. However, the depth maps for non-keyframe images rely on robot kinematics that introduce substantial pose errors, limiting the reliably labeled portion of the dataset to 35 keyframes. We present SCARED-C, a corrected version of the SCARED dataset that expands the number of reliable RGB-D pairs from 35 to 17,135. Our pipeline applies COLMAP, a Structure-from-Motion system, to re-estimate camera poses for all frames, followed by a scale recovery step that aligns the resulting reconstructions to metric space using the ground-truth keyframe depth maps. We validate the corrected poses through (1) stereo disparity evaluation and (2) monocular depth estimation experiments. The corrected dataset and code are publicly released to the community.
Code (0)
등록된 구현이 없습니다.
Tasks
Monocular Depth EstimationSimilar Papers 제목 키워드 기반
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limit the parameter search to a low-rank sub…
Depth EstimationSelf-LearningEndo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
Reconstructing 3D scenes from monocular surgical videos can enhance surgeon's perception and therefore plays a vital role in various computer-assisted surgery tasks. However, achieving scale-consistent reconstruction rem…
Camera Pose EstimationDepth EstimationDepth PredictionDynamic Reconstruction+1Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion
Accurate depth and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular depth estimation of…
Camera Pose EstimationDepth EstimationMonocular Depth EstimationPose Estimation+1BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications
Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a p…
3D ReconstructionDepth EstimationMonocular Depth EstimationPose Estimation+1Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of d…
Depth EstimationMotion EstimationSelf-Supervised Learning