Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to unseen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the target domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-ofthe-art performance under the semi-supervised setting. The code and trained models are available at https://github.com/microsoft/S2R-DepthNet.
The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；College of Electronic & Information Engineering, Tongji University（同济大学电子与信息工程学院）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Institute of Intelligent Science and Technology（上海智能科学技术研究院）
Depth-Guided Bundle Sampling for Efficient Generalizable Neural Radiance Field Reconstruction
Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education（媒体音频与视频重点实验室（中国传媒大学），教育部）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）
针对可泛化神经辐射场渲染计算量大、采样冗余的问题，提出深度引导的束采样策略，通过分组采样和自适应分配采样点，在DTU数据集上实现1.27 dB PSNR提升和47% FPS增加，并达到2倍渲染加速。
SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning
Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Shanghai Institute of Intelligent Science and Technology（上海智能科学技术研究院）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）
Monocular and Generalizable Gaussian Talking Head Animation
South China University of Technology（华南理工大学）；Technical University of Munich（慕尼黑工业大学）；Pazhou Laboratory（琶洲实验室）；Guangdong University of Technology（广东工业大学）；The Hong Kong Polytechnic University（香港理工大学）
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）
REArtGS++: Generalizable Articulation Reconstruction with Temporal Geometry Constraint via Planar Gaussian Splatting
Hefei Institutes of Physical Science, Chinese Academy of Sciences（合肥物理科学研究院，中国科学院）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）；RoboticsX, Tencent（腾讯机器人院）；Shanghai Jiao Tong University（上海交通大学）
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
Function Hub, Hong Kong University of Science and Technology（香港科技大学（广州）功能中心）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）
Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；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, Sun Yat-sen University（中山大学第一附属医院）；Centre for Artificial Intelligence and Robotics, Chinese Academy of Sciences, HK, China（中国科学院香港人工智能与机器人中心）；School of Engineering and Imaging Sciences, King’s College London, UK（伦敦国王学院工程与影像科学学院）
ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera
Samsung R&D Institute China-Beijing（三星中国北京研发中心）；CFCS, School of Computer Science, Peking University（计算机科学系，北京大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
SaLon3R: Structure-aware Long-term Generalizable 3D Reconstruction from Unposed Images
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）；University of California, Berkeley（加州大学伯克利分校）；Zhejiang University（浙江大学）
