Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such regular 3D voxel grids or collections images. This, however, renders unnecessarily voluminous and causes issues. In this paper, we design a novel neural network that directly consumes point clouds, which well respects the permutation invariance points in input. Our network, named PointNet, provides unified architecture for applications ranging from object classification,...

Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, design does not capture local structures induced the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability complex scenes. In work, we introduce hierarchical neural network that applies recursively nested partitioning of input set. By exploiting distances, our able learn features with increasing contextual scales. With further...

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models objects. ShapeNet contains from multitude semantic categories and organizes them under the WordNet taxonomy. It is collection datasets providing many annotations for each model such as consistent rigid alignments, parts bilateral symmetry planes, physical sizes, keywords, well other planned annotations. Annotations are made available through public web-based interface to enable data...

In this work, we study 3D object detection from RGBD data in both indoor and outdoor scenes. While previous methods focus on images or voxels, often obscuring natural patterns invariances of data, directly operate raw point clouds by popping up RGB-D scans. However, a key challenge approach is how to efficiently localize objects large-scale scenes (region proposal). Instead solely relying proposals, our method leverages mature 2D detectors advanced deep learning for localization, achieving...

We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on clouds without any intermediate representation. The convolution weights KPConv are located in Euclidean space by kernel points, and applied to the input points close them. Its capacity use number gives more flexibility than fixed grid convolutions. Furthermore, these locations continuous can be learned network. Therefore, extended deformable convolutions learn adapt local geometry. Thanks...

Generation of 3D data by deep neural networks has been attracting increasing attention in the research community. The majority extant works resort to regular representations such as volumetric grids or collections images; however, these obscure natural invariance shapes under geometric transformations, and also suffer from a number other issues. In this paper we address problem reconstruction single image, generating straight-forward form output - point cloud coordinates. Along with arises...

We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects minimal amount of work must be performed to transform one distribution into other by moving "distribution mass" around. This is special case transportation problem from linear optimization, for efficient algorithms are available. The EMD also allows partial matching. When used compare have same overall mass, true metric, and has easy-to-compute lower bounds. In this paper focus...

3D shape models are becoming widely available and easier to capture, making information crucial for progress in object classification. Current state-of-theart methods rely on CNNs address this problem. Recently, we witness two types of being developed: based upon volumetric representations versus multi-view representations. Empirical results from these exhibit a large gap, indicating that existing CNN architectures approaches unable fully exploit the power In paper, aim improve both...

Abstract We propose a novel point signature based on the properties of heat diffusion process shape. Our signature, called Heat Kernel Signature (or HKS), is obtained by restricting well‐known kernel to temporal domain. Remarkably we show that under certain mild assumptions, HKS captures all information contained in kernel, and characterizes shape up isometry. This means restriction domain, one hand, makes much more concise easily commensurable, while other it preserves about intrinsic...

The following problem is discussed: given n points in the plane (the sites) and an arbitrary query point q , find site that closest to . This can be solved by constructing Voronoi diagram of griven sites then locating inone its regions. Two algorithms are given, one constructs O ( log ) time, another inserts a new sit on O(n) time. Both based use dual, or Delaunay triangulation, simple enough practical value. simplicity both attributed separation geometrical topological aspects two but...

Large repositories of 3D shapes provide valuable input for data-driven analysis and modeling tools. They are especially powerful once annotated with semantic information such as salient regions functional parts. We propose a novel active learning method capable enriching massive geometric datasets accurate region annotations. Given shape collection user-specified label our goal is to correctly demarcate the corresponding minimal manual work. Our framework achieves this by cycling between...

Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in detectors, they often convert point clouds regular grids (i.e., voxel or bird's eye view images), rely on images propose boxes. Few works have attempted directly detect objects clouds. this work, we return first principles construct a pipeline for cloud data and as generic possible. However, due the sparse nature of - samples from manifolds space face major challenge when...

Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence structure among tasks. Knowing this has notable values; it is concept underlying transfer learning and provides principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision related solve many in one system without piling up complexity. We proposes...

The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object image retrieval applications. These allow representing images as collections “visual words” treat them using text search approaches following the “bag features” paradigm. In this article, we explore analogous 3D world applied to problem nonrigid shape large databases. Using multiscale diffusion heat kernels “geometric words,” construct compact informative descriptors by...

We present a novel representation of maps between pairs shapes that allows for efficient inference and manipulation. Key to our approach is generalization the notion map puts in correspondence real-valued functions rather than points on shapes. By choosing multi-scale basis function space each shape, such as eigenfunctions its Laplace-Beltrami operator, we obtain very compact, yet fully suitable global inference. Perhaps more remarkably, most natural constraints map, descriptor preservation,...

Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with annotations, and a lack powerful features. Inspired by the growing availability 3D models, we propose framework to address both combining render-based image synthesis CNNs (Convolutional Neural Networks). We believe that models have potential generating large number high variation, which can be well exploited deep CNN learning capacity....

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections single-view 2D photographs has been a long-standing challenge. Existing GANs are either compute intensive or make approximations that not 3D-consistent; the former limits quality resolution generated latter adversely affects multi-view consistency shape quality. In this work, we improve computational efficiency image without overly relying on these approximations. We introduce an...

In this work, we propose to use attributes and parts for recognizing human actions in still images. We define action as the verbs that describe properties of actions, while are objects poselets closely related actions. jointly model by learning a set sparse bases shown carry much semantic meaning. Then, an image can be reconstructed from coefficients with respect learned bases. This dual sparsity provides theoretical guarantee our feature reconstruction approach. On PASCAL dataset new...

Article Free Access Share on Optimally combining sampling techniques for Monte Carlo rendering Authors: Eric Veach Computer Science Department, Robotics Laboratory, Stanford University, CA CAView Profile , Leonidas J. Guibas Authors Info & Claims SIGGRAPH '95: Proceedings of the 22nd annual conference graphics and interactive techniquesSeptember 1995 Pages 419–428https://doi.org/10.1145/218380.218498Online:15 September 1995Publication History 366citation2,665DownloadsMetricsTotal...

The goal of this paper is to estimate the 6D pose and dimensions unseen object instances in an RGB-D image. Contrary "instance-level'' estimation tasks, our problem assumes that no exact CAD models are available during either training or testing time. To handle different a given category, we introduce Normalized Object Coordinate Space (NOCS)-a shared canonical representation for all possible within category. Our region-based neural network then trained directly infer correspondence from...