The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

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 …

Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are
often corrupted with significant amount of noise and outliers. Traditional methods for point …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required
to be consolidated. In this paper, we present the first deep learning based {em edge-aware} …

Point set is a flexible and lightweight representation widely used for 3D deep learning.
However, their discrete nature prevents them from representing continuous and fine …

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for
point clouds. Gradients for point locations and normals are carefully designed to handle …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

We present an adaptive deep representation of volumetric fields of 3D shapes and an
efficient approach to learn this deep representation for high-quality 3D shape reconstruction …

Points acquired by laser scanners are not intrinsically equipped with normals, which are
essential to surface reconstruction and point set rendering using surfels. Normal estimation …