Search Results for author:
Found 5 papers, 2 papers with code
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention
Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices.
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans.
