Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

In recent years, the popularity of depth sensors and 3D scanners has led to a rapid
development of 3D point clouds. Semantic segmentation of point cloud, as a key step in …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We present a simple and general framework for feature learning from point cloud. The key to
the success of CNNs is the convolution operator that is capable of leveraging spatially-local …

Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as
images, videos, and 3D shapes. Whilst some of this data is naturally dense (eg, photos) …

Convolutional networks for single-view object reconstruction have shown impressive
performance and have become a popular subject of research. All existing techniques are …

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 …

Point clouds are an efficient data format for 3D data. However, existing 3D segmentation
methods for point clouds either do not model local dependencies or require added …

We propose a method to create plausible geometric and texture style variations of 3D
objects in the quest to democratize 3D content creation. Given a pair of textured source and …

Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms
in medical imaging are predominantly diverging from computer vision, where voxel grids …