Transparent object perception remains a major challenge in computer vision research, as transparency confounds both depth estimation and semantic segmentation. Recent work has explored multi-task learning frameworks to improve robustness, yet negative cross-task interactions often hinder performance. In this work, we introduce Edge-Guided Spatial Attention (EGSA), a fusion mechanism designed to mitigate destructive interactions by incorporating boundary information into the fusion between semantic and geometric features. On both Syn-TODD and ClearPose benchmarks, EGSA consistently improved depth accuracy over the current state of the art method (MODEST), while preserving competitive segmentation performance, with the largest improvements appearing in transparent regions. Besides our fusion design, our second contribution is a multi-modal progressive training strategy, where learning transitions from edges derived from RGB images to edges derived from predicted depth images. This approach allows the system to bootstrap learning from the rich textures contained in RGB images, and then switch to more relevant geometric content in depth maps, while it eliminates the need for ground-truth depth at training time. Together, these contributions highlight edge-guided fusion as a robust approach capable of improving transparent object perception.
ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Shenzhen Loop Area Institute（深圳河套学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；Baidu Inc.（百度公司）；LightSpeed Studios, Tencent America（美国光速工作室，腾讯）；School of Computer Science and Artificial Intelligence, Lanzhou University of Technology（兰州理工大学计算机科学与人工智能学院）
DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院目标认知与应用技术重点实验室）
The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
3D interacting hand reconstruction is essential to facilitate human-machine interaction and human behaviors understanding. Previous works in this field either rely on auxiliary inputs such as depth images or they can only handle a single hand if monocular single RGB images are used. Single-hand methods tend to generate collided hand meshes, when applied to closely interacting hands, since they cannot model the interactions between two hands explicitly. In this paper, we make the first attempt to reconstruct 3D interacting hands from monocular single RGB images. Our method can generate 3D hand …
Nanjing University of Science and Technology（南京理工大学）；Renmin University of China（中国人民大学）；Nreal（Nreal（太若科技））
Monocular 3D object detection is a fundamental but very important task to many applications including autonomous driving, robotic grasping and augmented reality. Existing leading methods tend to estimate the depth of the input image first, and detect the 3D object based on point cloud. This routine suffers from the inherent gap between depth estimation and object detection. Besides, the prediction error accumulation would also affect the performance. In this paper, a novel method named MonoSIM is proposed. The insight behind introducing MonoSIM is that we propose to simulate the feature learni…
Self-supervised learning of depth has been a highly studied topic of research as it alleviates the requirement of having ground truth annotations for predicting depth. Depth is learnt as an intermediate solution to the task of view synthesis, utilising warped photometric consistency. Although it gives good results when trained using stereo data, the predicted depth is still sensitive to noise, illumination changes and specular reflections. Also, occlusion can be tackled better by learning depth from a single camera. We propose ADAA, utilising depth augmentation as depth supervision for learnin…
RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Automotive Studies, Tongji University（同济大学汽车学院）；Momoni AI, Gothenburg, Sweden（Momoni AI（瑞典哥德堡））；College of Energy Engineering, Zhejiang University（浙江大学能源工程学院）
NRGS-SLAM: Monocular Non-Rigid SLAM for Endoscopy via Deformation-Aware 3D Gaussian Splatting
Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系和T Stone机器人研究所，香港中文大学）；School of Automation and Intelligent Sensing, the State Key Laboratory of Avionics Integration and Aviation System of-Systems Synthesis, and Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University（自动化与智能感知学院、航空系统集成与航空系统综合国家重点实验室、上海导航与定位服务重点实验室，上海交通大学）
CMAMBADEPTH: Self-supervised Monocular Depth Estimation with Channel Mamba and Hybrid Attention
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
National Engineering Laboratory for Brain-inspired Intelligence Technology and Application（类脑智能技术与应用国家工程实验室）；University of Science and Technology of China（中国科学技术大学）
Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural networks (CNNs) significantly improve the performance of depth estimation. However, few of them take into account multi-scale structures in complex scenes. In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit mul…
Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Science and Technology, Shijiazhuang Tiedao University（石家庄铁道大学信息科学与技术学院）；D-Robotics（杭州宇树科技有限公司）
University of Trento（特伦托大学）；University of Oxford（牛津大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；Huawei Technologies Ireland（华为技术爱尔兰）
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps. We introduce a new network architecture, named Progressive Fusion Network (PFN), that is specifically designed for binocular stereo depth estimation. This network is based on a multi-scale refinement strategy that combines the information provided by both stereo views. In addition, we pro…
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with expensive LiDAR point clouds, it poses a great challenge for 3D object detection given only a monocular image. While there exist different alternatives for tackling this problem, it is found that they are either equipped with heavy networks to fuse RGB and depth information or empirically ineffective to process millions of pseudo-LiDAR points. With in-depth examination, we realize that these limitations are rooted in …
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）
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）
TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航天科学与工程学院）
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）
Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues
Department of Information Science and Technology, Ocean University of China（信息科学与技术学院，中国海洋大学）；School of Computing and Mathematical Sciences, University of Leicester（计算与数学科学学院，莱斯特大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程学院，密苏里州立大学）
MonoSE(3)-Diffusion: A Monocular SE(3) Diffusion Framework for Robust Camera-to-Robot Pose Estimation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；ANGEL CorpLab and College of Computing and Data Science, Nanyang Technological University（南洋理工大学ANGEL CorpLab和计算与数据科学学院）；College of Computer Science, Nankai University（南开大学计算机科学学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
The Hong Kong University of Science and Technology（香港科技大学）；DJI（大疆创新）
Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocular depth-sensing from high-level learned features, the pixel-level cues are usually omitted due to the deep convolution mechanism. To benefit from both the powerful feature representation in DNN and pixel-level geometric constraints, we reformulate the monocular object depth estimation as a progressive refinement problem and propose a joint semantic and geometric cost volume to model the depth error. Specifically, w…
The Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）
We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map from local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicle orientation. This approach features a deep model that transforms perceived intensities to IGRs, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. Core problems are what IGRs to use and how to learn them more effectively. We ans…
University of Adelaide（阿德莱德大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Monash University（莫纳什大学）
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this paper, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localise, track, and reconstruct multiple objects in an online fashion when an RGB…
National University of Defense Technology（国防科技大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）
Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g. locations and orientations. Although deep learning approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion - a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer fr…
SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）；Basic Algorithm Center, PCG, Tencent（腾讯计算机系统有限公司基础算法中心）
VistaDepth: Improving far-range Depth Estimation with Spectral Modulation and Adaptive Reweighting
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
CASA-SDF: Curriculum-Aware Spatial Adaptation with Curvature-Guided Density for Neural Implicit Surface Reconstruction
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion
Dalian University of Technology（大连理工大学）；Beihang University（北京航空航天大学）；College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）
Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院电子工程系）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）
