ZHAO , Tong

With the enthusiasm for digital twins in the fourth industrial revolution, surface reconstruction from raw 3D point clouds is increasingly needed while facing multifaceted challenges. Advanced data acquisition devices makes it possible to obtain 3D point clouds with multi-scale features. Users expect controllable surface reconstruction...

Detecting sharp features in raw point clouds is an essential step in designing efficient priors in several 3D Vision applications. This paper presents a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds. We devise a multi-task neural network architecture that identifies points near...

Detecting sharp features in raw point clouds is an essential step in designing efficient priors in several 3D Vision applications. This paper presents a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds. We devise a multi-task neural network architecture that identifies points near...

International audience

Many global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely...

Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the input points enriched with quadric error metrics, where the generator of each cluster is the optimal 3D point for the sum of its quadric error...