Video inpainting fills in corrupted video content with plausible replacements. While recent advances in endoscopic video inpainting have shown potential for enhancing the quality of endoscopic videos, they mainly repair 2D visual information without effectively preserving crucial 3D spatial details for clinical reference. Depth-aware inpainting methods attempt to preserve these details by incorporating depth information. Still, in endoscopic contexts, they face challenges including reliance on pre-acquired depth maps, less effective fusion designs, and ignorance of the fidelity of 3D spatial details. To address them, we introduce a novel Depth-aware Endoscopic Video Inpainting (DAEVI) framework. It features a Spatial-Temporal Guided Depth Estimation module for direct depth estimation from visual features, a Bi-Modal Paired Channel Fusion module for effective channel-by-channel fusion of visual and depth information, and a Depth Enhanced Discriminator to assess the fidelity of the RGB-D sequence comprised of the inpainted frames and estimated depth images. Experimental evaluations on established benchmarks demonstrate our framework's superiority, achieving a 2% improvement in PSNR and a 6% reduction in MSE compared to state-of-the-art methods. Qualitative analyses further validate its enhanced ability to inpaint fine details, highlighting the benefits of integrating depth information into endoscopic inpainting.
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…
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）
Robotic-assisted surgery allows surgeons to conduct precise surgical operations with stereo vision and flexible motor control. However, the lack of 3D spatial perception limits situational awareness during procedures and hinders mastering surgical skills in the narrow abdominal space. Depth estimation, as a representative perception task, is typically defined as an image reconstruction problem. In this work, we show that depth estimation can be reformed from a 3D surface perspective. We propose a loss function for depth estimation that integrates the surface-aware constraints, leading to a fas…
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）
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）
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）
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）
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
The Chinese University of Hong Kong（香港中文大学）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院惠康/工程与物理科学研究委员会介入与外科科学中心）；National University of Singapore（新加坡国立大学）
The Chinese University of Hong Kong(Shenzhen)（香港中文大学（深圳））；Dalian University of Technology（大连理工大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；Texas A&M University（得克萨斯农工大学）
We present a deep reinforcement learning method of progressive view inpainting for 3D point scene completion under volume guidance, achieving high-quality scene reconstruction from only a single depth image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D depth map inpainting, and multi-view selection for completion. Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing informati…
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL（伦敦大学学院威康/EPSRC介入与外科科学中心）；National Center for Tumor Diseases (NCT) Dresden（德累斯顿国家肿瘤疾病中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）
In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom images or biological tissue samples covering a relatively small region of the endoscope field-of-view. We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT). Two {\it ex vivo} small porcine full torso cadav…
Endoscopic Depth Estimation Based on Deep Learning: A Survey
Beijing Information Science and Technology University（北京信息科技大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；The Third Hospital of Xiamen（厦门市第三医院）；Fujian Institute of Geriatrics（福建省老年医学研究所）；Fujian Medical University Union Hospital（福建医科大学附属协和医院）；Beijing Tiromu Medical Technology Co., Ltd.（北京提罗木医疗科技有限公司）；National University of Singapore（新加坡国立大学）；University of Southern Queensland（南昆士兰大学）
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）
Nagoya University（名古屋大学）；Kyoto Second Red Cross Hospital（京都第二红十字医院）；Sapporo-Minami-Sanjo Hospital（札幌南三条医院）；Sapporo-Kosei General Hospital（札幌厚生综合医院）；Keiwakai Nishioka Hospital（惠和会西冈医院）；National Institute of Informatics（国立信息学研究所）
We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale edge loss. We employ a two-step estimation process including Lambertian surface translation from unpaired data and depth estimation. The texture and specular reflection on the surface of an organ reduce the accuracy of depth estimations. We apply Lambertian surface translation to an endoscopic image to remove these texture and reflections. Then, we estimate the depth by using a fully convolutional network (FCN). Duri…
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…
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve compelling results. A drawback of these techniques is the use of hard depth layering, making them unable to model intricate appearance details such as thin hair-like structures. We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views. In addition, we propose a novel depth-aware training stra…
Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-driven Surface Normal-aware Tracking and Mapping
The Chinese University of Hong Kong (CUHK)（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
Real-Time Spatio-Temporal Reconstruction of Dynamic Endoscopic Scenes with 4D Gaussian Splatting
University of Liverpool, Liverpool, UK（利物浦大学）；Xi'an Jiaotong-Liverpool University, Suzhou, China（西安交通大学利物浦大学）；University of Cambridge, Cambridge, UK（剑桥大学）
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）
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
In this work we propose a novel approach to remove undesired objects from RGB-D sequences captured with freely moving cameras, which enables static 3D reconstruction. Our method jointly uses existing information from multiple frames as well as generates new one via inpainting techniques. We use balanced rules to select source frames; local homography based image warping method for alignment and Markov random field (MRF) based approach for combining existing information. For the left holes, we employ exemplar based multi-view inpainting method to deal with the color image and coherently use it …
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
School of Advanced Technology, Xi'an Jiaotong Liverpool University（西安交通大学利物浦大学先进技术学院）；School of AI and Advanced Computing, Xi'an Jiaotong Liverpool University（西安交通大学利物浦大学人工智能与高级计算学院）；School of Physical Sciences, University of Liverpool（利物浦大学物理科学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Heidelberg University Hospital（海德堡大学医院）；Mannheim University of Applied Sciences（曼海姆应用科学大学）
To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. It is desirable to view such manually measured points together with the endoscopic image for further assistance. Therefore, hand-eye calibration is required that links both coordinate systems and is a prerequisite to project the points onto the image plane. A complementary approach to this is to use a vision-based endoscopic stereo-setup to detect and triangulate points of interest, to obtain the 3D coordinates. In this paper, we aim to c…
Intuitive Inc.（直觉外科公司）；Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (HHI)（弗劳恩霍夫海因里希·赫兹通信研究所）；University of Western Ontario（西安大略大学）；Digital Surgery Ltd.（数字外科有限公司）；Norwegian University of Science and Technology（挪威科技大学）；Rediminds Inc.（Rediminds公司）；Harbin Institute of Technology（哈尔滨工业大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Heidelberg University（海德堡大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) UCL（伦敦大学学院惠康/EPSRC介入与外科科学中心）；University of Bern（伯尔尼大学）
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform dense depth estimation using 7 training datasets and 2 test sets of structured light data captured using porcine cadavers. These were provided by a team at Intuitive Surgical. 10 teams participated in the challenge day. This paper contains 3 additional methods which were submitted after the challenge finished as well as a supplemental section from these teams on issues they found with the dataset.
The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of exten…
