The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, state-of-the-art methods require ground-truth dense point sets as the supervision, which makes them limited to be trained under synthetic paired training data and not suitable to be under real-scanned sparse data. However, it is expensive and tedious to obtain large numbers of paired sparse-dense point sets as supervision from real-scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate the self-attention module with the graph convolution network (GCN) to capture context information inside and among local regions simultaneously. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms as a joint loss to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performances to state-of-the-art supervised methods.
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes t…
Point cloud upsampling focuses on generating a dense, uniform and proximity-to-surface point set. Most previous approaches accomplish these objectives by carefully designing a single-stage network, which makes it still challenging to generate a high-fidelity point distribution. Instead, upsampling point cloud in a coarse-to-fine manner is a decent solution. However, existing coarse-to-fine upsampling methods require extra training strategies, which are complicated and time-consuming during the training. In this paper, we propose a simple yet effective cascaded refinement network, consisting of…
In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. We accordingly first propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-t…
SPAC-Net: Rethinking Point Cloud Completion with Structural Prior
School of Digital Media Technology, Hangzhou Dianzi University（杭州电子科技大学数字媒体技术学院）；School of Informatics, Xiamen University（厦门大学信息学院）；School of Information, Renmin University of China（中国人民大学信息学院）
CORIS Institute, Oregon State University（俄勒冈州立大学CORIS研究所）；Nuro, Inc.（Nuro公司）
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers to efficiently handle 3D point cloud data. Unlike traditional cost volumes that require exhaustively computing all the cost values on a high-dimensional grid, our point-based formulation discretizes the cost volume onto input 3D points, and a Point…
T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image
School of Data Science, Fudan University（复旦大学数据科学学院）；Youtu Lab, Tencent（腾讯优图实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；Google（谷歌公司）
MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction
The Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Lightspeed Studios（光速工作室）
Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sensor to sensor domain adaptation. In this work, we address the problem of lidar upsampling. Learning on lidar point clouds is rather a challenging task due to their irregular and sparse structure. Here we propose a method for lidar point cloud upsampling which can reconstruct fine-grained lidar scan patterns. The key idea is to utilize edge-aware dense convolutions for both feature extraction and feature expansion. Add…
PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models
Telecom Guizhou Branch（贵州电信支局）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；College of Engineering, Huaqiao University（华侨大学工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology（哈尔滨工业大学深圳研究生院生物计算研究中心）；Key Laboratory of Network Oriented Intelligent Computation（网络导向智能计算重点实验室）
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling
TU Wien, Center for Geometry and Computational Design（维也纳技术大学，几何与计算设计中心）；University of Innsbruck, Department of Design, i.sd | Structure and Design（因斯布鲁克大学，设计系，i.sd | 结构与设计）
National University of Singapore（新加坡国立大学）；JD Intelligent Cities Research（京东智能城市研究院）
The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we present an approach for inferring the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we d…
Yahoo Labs（雅虎研究院）；National Institutes of Health（美国国立卫生研究院）；Georgia State University（佐治亚州立大学）；Siemens Medical Solutions Inc（西门子医疗解决方案公司）
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivity with desirably low false positive (FP) rate is critical for a CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. Given various spurious imagery noises which cause observation uncertainties, this remains a very challenging task. In this paper, we propose a novel, two-tiered coarse-to-fine (CTF) classification cascade framework to tackle this problem. We first obtain classi…
National University of Singapore（新加坡国立大学）；Xidian University（西安电子科技大学）；JD Intelligent City Research & JD Urban Computing Business Unit（京东智能城市研究院与京东城市计算事业部）
Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between …
Coarse-to-fine crack cue for robust crack detection
National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan,China（多媒体软件国家工程研究中心，武汉大学，武汉，中国）；Institute of Artificial Intelligence, School of Computer Science, Wuhan University,Wuhan,China（人工智能学院，计算机科学学院，武汉大学，武汉，中国）；Université Gustave Eiffel, CNRS, Laboratoire d'Informatique Gaspard-Monge (LIGM), Marne-la-Vall, France（Gustave Eiffel 大学，CNRS，Gaspard-Monge 信息学实验室（LIGM），法国 Marne-la-Vall）
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
School of Integrated Technology, Yonsei University（延世大学整合技术学院）；BK21 Graduate Program in Intelligent Semiconductor Technology, Yonsei University（延世大学智能半导体技术研究生项目）；Department of Artificial Intelligence, Chung-Ang University（成均馆大学人工智能系）
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing
Department of Computer Science University of Central Florida Orlando, FL, USA（计算机科学系 佛罗里达中央大学 或兰多市）；Department of Radiology Northwestern University Chicago, IL, USA（放射学系 北western大学 芝加哥市）
Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation
Dyson School of Design Engineering, Imperial College London（帝国理工学院伦敦设计工程学院）；TUM School of Engineering and Design, Technical University of Munich（慕尼黑技术大学工程与设计学院）；Faculty of Mechanical Engineering, OTH Regensburg（雷根斯堡机械工程学院）；NVIDIA（NVIDIA公司）
VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
Department of Automation, Southeast University（东南大学自动化部门）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Key Laboratory of Transportation Meteorology, China Meteorological Administration（中国气象局交通运输气象重点实验室）
Frequency-aware Feature Fusion for Dense Image Prediction
Beijing Institute of Technology（北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；RIKEN AIP（理化学研究所人工智能项目）；RCAST, The University of Tokyo（东京大学先进科学技术研究中心）；Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）
