Point2skh: End-to-end Parametric Primitive Inference from Point Clouds with Improved Denoising Transformer

Abstract

Recovering the CAD command sequence from the point cloud is an essential component in CAD reverse engineering. In this paper, we strive to solve this problem from both the perspectives of artificial intelligence and the procedures of procedural CAD models. We propose a CAD reconstruction method based on an end-to-end point-to-sketch network (Point2Skh) that can produce the CAD modeling sequence from the input geometrical point cloud by recovering the inverse sketch-and-extrude process. The point cloud is first segmented into point sets corresponding to the same extrusion. The modeling sequence can then be recovered by combining the network prediction of each point set. The proposed Point2Skh can detect and infer command vectors of sketch curves (line, arc, and circle) and the extrusion operation from the input point cloud of a single extrusion. By directly representing the sketch with its curves and inferring the command parameters, accurate sketch reconstruction is produced, which further leads to precise CAD reconstruction with sharp edges. The produced CAD modeling sequence is human-interpretable and can be readily edited by importing it into CAD tools. Experiments show that the Chamfer Distance (CD) between the predicted results and the ground truth is 0.312, and the primitive type and parameter accuracy are 93.87% and 83.24%, respectively.