PC2WF: 3D WIREFRAME RECONSTRUCTION FROM RAW POINT CLOUDS

Abstract

We introduce PC2WF, the first end-to-end trainable deep network architecture to convert a 3D point cloud into a wireframe model. The network takes as input an unordered set of 3D points sampled from the surface of some object, and outputs a wireframe of that object, i.e., a sparse set of corner points linked by line segments. Recovering the wireframe is a challenging task, where the numbers of both vertices and edges are different for every instance, and a-priori unknown. Our architecture gradually builds up the model: It starts by encoding the points into feature vectors. Based on those features, it identifies a pool of candidate vertices, then prunes those candidates to a final set of corner vertices and refines their locations. Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe. All steps are trainable, and errors can be backpropagated through the entire sequence. We validate the proposed model on a publicly available synthetic dataset, for which the ground truth wireframes are accessible, as well as on a new real-world dataset. Our model produces wireframe abstractions of good quality and outperforms several baselines.

1. INTRODUCTION

Many practical 3D sensing systems, like stereo cameras or laser scanners, produce unstructured 3D point clouds. That choice of output format is really just a "smallest common denominator", the least committal representation that can be reliably generated with low-level signal processing. Most users would prefer a more efficient and more intuitive representation that describes the scanned object's geometry as a compact collection of geometric primitives, together with their topological relations. Specifically, many man-made objects are (approximately) polyhedral and can be described by corners, straight edges and/or planar surfaces. Roughly speaking there are two ways to abstract a point cloud into a polyhedral model: either find the planar surfaces and intersect them to find the edges and corners, e.g., Schnabel et al. ( 2007 A wireframe is a graph representation of an object's shape, where vertices correspond to corner points (with high Gaussian curvature) that are linked by edges (with high principal curvature). Wireframes are a good match for polyhedral structures like mechanical parts, furniture or building interiors. In particular, since wireframes focus on the edge structure, they are best suited for piece-wise smooth objects with few pronounced crease edges (whereas they are less suitable for smooth objects without defined edges or for very rough ones with edges everywhere). We emphasise that wireframes are not only a "compression technique" to save storage. Their biggest advantage in many applications is that they are easy to manipulate and edit, automatically or interactively in CAD software, because they make the salient contours and their connectivity explicit. Reconstructed wireframes can drive and help to create 3D CAD models for manufacturing parts, metrology, quality inspection, as well as visualisation, animation, and rendering. Inferring the wireframe from a noisy point cloud is a challenging task. We can think of the process as a sequence of steps: find the corners, localise them accurately (as they are not contained in the point cloud), and link them with the appropriate edges. However, these steps are intricately correlated. For example, corner detection should "know" about the subsequent edge detection: curvature is affected by noise (as any user of an interest point detector can testify), so to qualify as a corner



); Fang et al. (2018); Coudron et al. (2018); or directly find the salient corner and/or edges, e.g., Jung et al. (2016); Hackel et al. (2016).

