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 …

While the ready availability of 3D scan data has influenced research throughout computer
vision, less attention has focused on 4D data; that is 3D scans of moving non-rigid objects …

We contribute a large scale database for 3D object recognition, named ObjectNet3D, that
consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D …

Many algorithms for the computation of correspondences between deformable shapes rely
on some variant of nearest neighbor matching in a descriptor space. Such are, for example …

In this work, we present a novel method for capturing human body shape from a single
scaled silhouette. We combine deep correlated features capturing different 2D views, and …

We represent human body shape estimation from binary silhouettes or shaded images as a
regression problem, and describe a novel method to tackle it using CNNs. Utilizing a …

We propose a novel mixed-integer programming (MIP) formulation for generating precise
sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected …

In this work we propose to combine the advantages of learning-based and combinatorial
formalisms for 3D shape matching. While learning-based methods lead to state-of-the-art …

We consider the problem of finding a continuous and non-rigid matching between a 2D
contour and a 3D mesh. While such problems can be solved to global optimality by finding a …

We present a scalable combinatorial algorithm for globally optimizing over the space of
geometrically consistent mappings between 3D shapes. We use the mathematically elegant …