In this paper, we propose a channel-wise interaction based binary convolutional neural
network learning method (CI-BCNN) for efficient inference. Conventional methods apply …

We present a data-driven approach for mesh denoising. Our key idea is to formulate the
denoising process with cascaded non-linear regression functions and learn them from a set …

We propose a robust normal estimation method for both point clouds and meshes using a
low rank matrix approximation algorithm. First, we compute a local isotropic structure for …

Mesh denoising is a classical, yet not well-solved problem in digital geometry processing.
The challenge arises from noise removal with the minimal disturbance of surface intrinsic …

Point cloud is the primary source from 3D scanners and depth cameras. It usually contains
more raw geometric features, as well as higher levels of noise than the reconstructed mesh …

Point set filtering, which aims at reconstructing noise-free point sets from their corresponding
noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of …

This study proposes a deep-learning framework for mesh denoising from a single noisy
input, where two graph convolutional networks are trained jointly to filter vertex positions and …

This paper presents a simple and effective two-stage mesh denoising algorithm, where in
the first stage, face normal filtering is done by using bilateral normal filtering in a robust …

This paper presents a two-stage mesh denoising algorithm. Unlike other traditional
averaging approaches, our approach uses an element-based normal voting tensor to …

Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove
surface noise while preserving surface intrinsic signals as accurately as possible. While …