Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Point cloud is a widely used 3D data form, which can be produced by depth sensors, such
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …

Convolutional Neural Networks (CNNs) have become the method of choice for learning
problems involving 2D planar images. However, a number of problems of recent interest …

We present a convolutional network that is equivariant to rigid body motions. The model
uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and …

We present a new deep learning architecture (called Kd-network) that is designed for 3D
model recognition tasks and works with unstructured point clouds. The new architecture …

We study 3D shape modeling from a single image and make contributions to it in three
aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with …

We address the problem of 3D rotation equivariance in convolutional neural networks. 3D
rotations have been a challenging nuisance in 3D classification tasks requiring higher …

Exploring contextual information in the local region is important for shape understanding
and analysis. Existing studies often employ hand-crafted or explicit ways to encode …

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative
power of deep learning models with softmax loss for the classification of 3D data, while …

We tackle the problem of point cloud recognition. Unlike previous approaches where a point
cloud is either converted into a volume/image or represented independently in a …