Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with arbitrary topology. Since a continuous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, large datasets such as ShapeNet are required to train such models. In this paper, we present a new mid-level patch-based surface representation. At the level of patches, objects across different categories share similarities, which leads to more generalizable models. We then introduce a novel method to learn this patch-based representation in a canonical space, such that it is as object-agnostic as possible. We show that our representation trained on one category of objects from ShapeNet can also well represent detailed shapes from any other category. In addition, it can be trained using much fewer shapes, compared to existing approaches. We show several applications of our new representation, including shape interpolation and partial point cloud completion. Due to explicit control over positions, orientations and scales of patches, our representation is also more controllable compared to object-level representations, which enables us to deform encoded shapes non-rigidly.
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of California, Irvine（加利福尼亚大学尔湾分校）；ETH Zurich（苏黎世联邦理工学院）；Tongji University（同济大学）；Chinese University of Hong Kong（香港中文大学）
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
The University of Sydney（悉尼大学）；SenseTime Computer Vision Research Group（商汤科技计算机视觉研究组）；SenseTime Research（商汤科技研究院）；Dalian University of Technology（大连理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
South China University of Technology（南方科技大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；Peking University（北京大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室）；Harbin Institute of Technology（哈尔滨工业大学）
VGD: Visual Geometry Gaussian Splatting for Feed-Forward Surround-view Driving Reconstruction
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸媒体技术重点实验室）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）
nnMIL: A generalizable multiple instance learning framework for computational pathology
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA（放射肿瘤科，斯坦福大学医学院，斯坦福，CA，USA）；Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA, USA（斯坦福大学人本人工智能研究所，斯坦福，CA，USA）
Unlocking Compositional Generalization in Continual Few-Shot Learning
Faculty of Information Technology, University of Science, Vietnam National University（信息科技学院，科学大学，越南国家大学）；Department of Computer Science, University of Warwick（计算机科学系，沃里克大学）
GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations
SEAI Lab, Department of Geography and the Environment, The University of Texas at Austin（地理与环境系SEAI实验室，德克萨斯大学奥斯汀分校）；Google LLC, Mountain View, CA, USA（谷歌公司，山景城，加利福尼亚州，美国）；SIT Lab, School of Computing and Information Science, The University of Maine（计算与信息科学系SIT实验室，缅因大学）；Urban Information Lab, School of Architecture, The University of Texas at Austin（城市信息实验室，建筑系，德克萨斯大学奥斯汀分校）
