Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete
point cloud observation is a long-standing problem in computer vision and graphics …

Today's manufacturing processes are pushed to their limits to generate products with ever-
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

In this work, we develop intuitive controls for editing the style of 3D objects. Our framework,
Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which …

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …

We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …

Abstract We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed
to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog …

In recent years, neural implicit representations gained popularity in 3D reconstruction due to
their expressiveness and flexibility. However, the implicit nature of neural implicit …

Creation of 3D content by stylization is a promising yet challenging problem in computer
vision and graphics research. In this work, we focus on stylizing photorealistic appearance …

Implicit neural networks have been successfully used for surface reconstruction from point
clouds. However, many of them face scalability issues as they encode the isosurface …