Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of class poses challenges in the leveraging of deep metric learning. Addressing this gap, this paper introduces MetricDepth, a novel method that integrates deep metric learning to enhance the performance of monocular depth estimation. To overcome the inapplicability of the class-based sample identification in previous deep metric learning methods to monocular depth estimation task, we design the differential-based sample identification. This innovative approach identifies feature samples as different sample types by their depth differentials relative to anchor, laying a foundation for feature regularizing in monocular depth estimation models. Building upon this advancement, we then address another critical problem caused by the vast range and the continuity of depth annotations in monocular depth estimation. The extensive and continuous annotations lead to the diverse differentials of negative samples to anchor feature, representing the varied impact of negative samples during feature regularizing. Recognizing the inadequacy of the uniform strategy in previous deep metric learning methods for handling negative samples in monocular depth estimation task, we propose the multi-range strategy. Through further distinction on negative samples according to depth differential ranges and implementation of diverse regularizing, our multi-range strategy facilitates differentiated regularization interactions between anchor feature and its negative samples. Experiments across various datasets and model types demonstrate the effectiveness and versatility of MetricDepth,confirming its potential for performance enhancement in monocular depth estimation task.
Univrses AB（Univrses AB公司）；University of Bologna（博洛尼亚大学）
Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras. In this field, deep learning yielded outstanding results at the cost of needing large amounts of data labeled with precise depth measurements for training. An issue softened by self-supervised approaches leveraging monocular sequences or stereo pairs in place of expensive ground truth depth annotations. This paper enables to further improve monocular depth estimation by integrating into existing self-supervised networks a geometrical prior. Specifically, we prop…
University of São Paulo（圣保罗大学）；Toyota Research Institute（丰田研究院）
This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are considered the standard sensor, which comparatively provides much sparser measurements, especially in areas further away. Rather…
Monocular Depth Guided Occlusion-Aware Disparity Refinement via Semi-supervised Learning in Laparoscopic Images
School of Life Science and Technology, Harbin Institute of Technology（生命科学与技术学院，哈尔滨工业大学）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（机器人与系统国家重点实验室，哈尔滨工业大学）
Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction
Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（日本理化学研究所先进智能项目中心（AIP））
University of Oulu（奥卢大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Tampere University（坦佩雷大学）
In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance. Unlike existing depth completion methods, our approach performs well on extremely sparse and unevenly distributed point clouds, which makes it agnostic to the source of the 3D points. We achieve this by introducing a novel multi-scale 3D point fusion network that is both lightweight and efficient. We demonstrate its versatility on two different depth estimation problems where the 3D points have been acquired with conventional structure-from-motion and LiDAR. In both cases, our network performs…
Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
Industrial Research Organisation, Canberra, ACT Australia；College of Engineering Computing \& Cybernetics, The Australian National University, Canberra, ACT Australia；Faculty of Engineering, The University of Hong Kong, Hong Kong SAR China；Industrial Research Organisation；College of Engineering Computing \& Cybernetics, The Australian National University；Faculty of Engineering, The University of Hong Kong
Monocular 3D scene understanding tasks, such as object size estimation, heading angle estimation and 3D localization, is challenging. Successful modern day methods for 3D scene understanding require the use of a 3D sensor. On the other hand, single image based methods have significantly worse performance. In this work, we aim at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input. Specifically, we perform monocular depth estimation and lift the input image to a point cloud representation, whi…
MonoPartNeRF:Human Reconstruction from Monocular Video via Part-Based Neural Radiance Fields
The School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机科学与信息安全学院）；Metaverse Application Engineering Center（元宇宙应用工程中心）
Rethinking Transparent Object Grasping: Depth Completion with Monocular Depth Estimation and Instance Mask
State Key Laboratory of Robotics and System at Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；Lanzhou University of Technology（兰州理工大学）；Department of Computer Science（计算机科学系）；University of Liverpool（利物浦大学）
Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能 MOE 实验室，上海交通大学人工智能学院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究所，东技术研究所，宁波，中国）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Ningbo, China（宁波空间智能与数字衍生关键实验室，宁波，中国）；University of Science and Technology of China（中国科学技术大学）；CASIA；Tsinghua University（清华大学）
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；School of Biomedical Engineering & Imaging Sciences, King’s college London（伦敦国王学院生物医学工程与影像科学学院）
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
S-Lab, Nanyang Technological University（新加坡南洋理工大学S-Lab）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid Transformer
School of Mechanical Engineering（机械工程学院）；State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process（矿冶过程智能优化制造国家重点实验室）；Hubei Key Laboratory of Modern Manufacturing Quality Engineering（现代制造质量工程湖北省重点实验室）；School of Mechanical Science and Engineering（机械科学与工程学院）；National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）
Enhancing Monocular Height Estimation via Weak Supervision from Imperfect Labels
Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（地球观测数据科学教授职位，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Engineering and Design, Technical University of Munich (TUM)（工程与设计学院，慕尼黑技术大学）
ERGO: Excess-Risk-Guided Optimization for High-Fidelity Monocular 3D Gaussian Splatting
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Peking University（北京大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guilin University of Electronic Technology（桂林电子科技大学）；UiT The Arctic University of Norway（UiT北欧大学）
RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos
The Chinese University of Hong Kong（香港中文大学）；The University of Manchester（曼彻斯特大学）；Centre for Perceptual and Interactive Intelligence (CPII) Limited（感知与互动智能中心有限公司）
V$^2$-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy
The Chinese University of Hong Kong（香港中文大学）；City University of Hong Kong（香港城市大学）；Southern University of Science and Technology（南方科技大学）；Hubei University of Technology（湖北工业大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nantong University（南通大学）
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）
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Xiaomi EV（小米电动车）
Occlusion-Aware Temporally Consistent Amodal Completion for 3D Human-Object Interaction Reconstruction
Elmore Family School of Electrical and Computer Engineering（电气与计算机工程学院）；Purdue University（普渡大学）；Department of Artificial Intelligence（人工智能系）；Korea University（韩国大学）；Honda Research Institute USA（本田美国研究院）；School of Mechanical Engineering（机械工程学院）
