Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by reducing blind spots, minimizing the risk of missed or recurrent lesions, and lowering the likelihood of incomplete examinations. However, this task remains challenging due to the presence of texture-less surfaces, complex illumination patterns, deformation, and a lack of in-vivo datasets with reliable ground truth. In this paper, we propose **PRISM** (Pose-Refinement with Intrinsic Shading and edge Maps), a self-supervised learning framework that leverages anatomical and illumination priors to guide geometric learning. Our approach uniquely incorporates edge detection and luminance decoupling for structural guidance. Specifically, edge maps are derived using a learning-based edge detector (e.g., DexiNed or HED) trained to capture thin and high-frequency boundaries, while luminance decoupling is obtained through an intrinsic decomposition module that separates shading and reflectance, enabling the model to exploit shading cues for depth estimation. Experimental results on multiple real and synthetic datasets demonstrate state-of-the-art performance. We further conduct a thorough ablation study on training data selection to establish best practices for pose and depth estimation in colonoscopy. This analysis yields two practical insights: (1) self-supervised training on real-world data outperforms supervised training on realistic phantom data, underscoring the superiority of domain realism over ground truth availability; and (2) video frame rate is an extremely important factor for model performance, where dataset-specific video frame sampling is necessary for generating high quality training data.
The Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）
We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading. Our method only relies on unlabeled monocular endoscopic videos and conventional multi-view stereo algorithms, and requires neither manual interaction nor patient CT in both training and application phases. In a cross-patient study using CT scans as groundtruth, we show that our method is able to produce photo-realistic dense 3D reconstructions with submillimeter mean residual errors from endoscopic videos from unseen pat…
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
The Chinese University of Hong Kong (CUHK)（香港中文大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）；Shenzhen Research Institute, CUHK（香港中文大学深圳研究院）
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images
Huazhong University of Science and Technology（华中科技大学）；Wuhan United Imaging Healthcare Surgical Technology Co., Ltd.（武汉联影外科技术有限公司）；Tongji Hospital（同济医院）；Tongji Medical College（同济医学院）；Britton Chance Center for Biomedical Photonics（布里顿·钱斯生物光子学中心）；Wuhan National Laboratory for Optoelectronics（武汉光电国家实验室）
Institute of Biomedical Engineering, Bogazici University（波加兹奇大学生物医学工程研究所）；Computer Science Department, University of Oxford（牛津大学计算机科学系）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；Cancer Data Science, Dana Farber Cancer Institute（丹娜-法伯癌症研究所癌症数据科学部）；Cancer Program, Broad Institute of Harvard and MIT（哈佛-麻省理工学院布罗德研究所癌症项目）；Institute for Systems and Robotics, University of Coimbra（科英布拉大学系统与机器人研究所）；Faculty of Medicine, Clinical Academic Center of Coimbra, University of Coimbra（科英布拉大学科英布拉临床学术中心医学院）；Department of Biomedical Engineering, Johns Hopkins University（约翰霍普金斯大学生物医学工程系）
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings as well as synthetically generated data. A Panda robotic arm, two commercially available capsule endoscopes, two conventional endoscopes with different camera properties, and two high precision 3D scanners were…
EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes
Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；College of Biomedical Engineering, Fudan University（复旦大学生物医学工程学院）；Shanghai Key laboratory of Medical Image Computing and Computer Assisted Intervention（上海医学影像计算与计算机辅助手术重点实验室）；ENT Institute and Department of Otolaryngology, Eye & ENT Hospital of Fudan University（复旦大学眼耳鼻喉科医院耳鼻喉科及耳鼻喉科研究所）
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
The Chinese University of Hong Kong（香港中文大学）；Hong Kong Center for Logistics Robotics（香港物流机器人中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shanghai Jiao Tong University（上海交通大学）
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Huawei European Research Center（华为欧洲研究中心）；Technische Universität München（慕尼黑工业大学）
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is …
EndoSfM3D: Learning to 3D Reconstruct Any Endoscopic Surgery Scene using Self-supervised Foundation Model
UCL Hawkes Institute（UCL哈维斯研究所）；Department of Medical Physics（医学物理系）；Biomedical Engineering, University College London, UK（生物医学工程，伦敦大学学院，英国）；Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK（信息学、成像与数据科学部门，曼彻斯特大学，曼彻斯特，英国）
Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）；Chubu University（中部大学）
A deep learning-based monocular depth estimation (MDE) technique is proposed for selection of most informative frames (key frames) of an endoscopic video. In most of the cases, ground truth depth maps of polyps are not readily available and that is why the transfer learning approach is adopted in our method. An endoscopic modalities generally capture thousands of frames. In this scenario, it is quite important to discard low-quality and clinically irrelevant frames of an endoscopic video while the most informative frames should be retained for clinical diagnosis. In this view, a key-frame sele…
We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field …
The Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT in the training and application phases. We demonstrate the performance of our method on sinus endosco…
4D Monocular Surgical Reconstruction under Arbitrary Camera Motions
Department of Mechanical and Automation Engineering（机械与自动化工程系）；T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（T Stone机器人研究院，香港中文大学，香港）；Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China（沈阳自动化研究所，中国科学院，沈阳，中国）；State Key Laboratory of Robotics and Intelligent Systems, Shenyang, China（机器人与智能系统国家重点实验室，中国，沈阳）；School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai, China（集成电路学院，上海交通大学，中国，上海）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（自动化与智能感知学院，上海交通大学，中国，上海）；Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China（系统控制与信息处理重点实验室，中国教育部，中国，上海）
Hikvision Research Institute (HRI), Hangzhou Hikvision Digital Technology Co. Ltd（海康威视研究院（HRI），杭州海康威视数字技术有限公司）
This paper addresses the importance of full-image supervision for monocular depth estimation. We propose a semi-supervised architecture, which combines both unsupervised framework of using image consistency and supervised framework of dense depth completion. The latter provides full-image depth as supervision for the former. Ego-motion from navigation system is also embedded into the unsupervised framework as output supervision of an inner temporal transform network, making monocular depth estimation better. In the evaluation, we show that our proposed model outperforms other approaches on dep…
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 depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably…
Renault Software Factory（雷诺软件工厂）；CNRS-I3S, Côte d’Azur University（法国国家科学研究中心I3S实验室，蔚蓝海岸大学）
This paper proposes a self-supervised monocular image-to-depth prediction framework that is trained with an end-to-end photometric loss that handles not only 6-DOF camera motion but also 6-DOF moving object instances. Self-supervision is performed by warping the images across a video sequence using depth and scene motion including object instances. One novelty of the proposed method is the use of the multi-head attention of the transformer network that matches moving objects across time and models their interaction and dynamics. This enables accurate and robust pose estimation for each object …
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)（惠康/英国工程与物理科学研究委员会介入与外科科学中心（WEISS））；University College London（伦敦大学学院）；Politecnico di Milano（米兰理工大学）；Centre for Medical Image Computing（医学影像计算中心）
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in self-supervised depth training based on strong geometric cues, both from stereo cameras and more recently from monocular video sequences. In this paper we investigate how these two approaches (supervised & self-supervised) can be effectively combined, so that a depth model can learn…
A geometry-aware deep network for depth estimation in monocular endoscopy
Northeastern University（东北大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；Beihang University（北京航空航天大学）；General Hospital of Northern Theater Command（北部战区总医院）
