In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model, being able to parse all its structural compositions and design steps. In this paper, we propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset. We have carefully annotated the 50K and 45K scans of both the datasets with appropriate topological relations (e.g., next, mate, previous) between the geometrical primitives (i.e., boundaries, junctions, loops, faces) of their BRep data structures. The proposed solution decomposes the Scan-to-CAD problem in Scan-to-BRep ensuring the right step towards feature-based modeling, and therefore, leveraging other existing BRep-to-CAD modeling methods. Our proposed Scan-to-BRep neural network learns to detect BRep boundaries and junctions by minimizing focal-loss and non-maximal suppression (NMS) during training time. Experimental results show that our BRepDetNet with NMS-Loss achieves impressive results.
We introduce a bottom-up model for simultaneously finding many boundary elements in an image, including contours, corners and junctions. The model explains boundary shape in each small patch using a 'generalized M-junction' comprising M angles and a freely-moving vertex. Images are analyzed using non-convex optimization to cooperatively find M+2 junction values at every location, with spatial consistency being enforced by a novel regularizer that reduces curvature while preserving corners and junctions. The resulting 'field of junctions' is simultaneously a contour detector, corner/junction detector, and boundary-aware smoothing of regional appearance. Notably, its unified analysis of contours, corners, junctions and uniform regions allows it to succeed at high noise levels, where other methods for segmentation and boundary detection fail.
In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe (see Fig. 1) contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geometry and object shapes. To this end, we have built a very large new dataset of over 5,000 images with wireframes thoroughly labelled by humans. We have proposed two convolutional neural networks that are suitable for extracting junctions and lines with large spatial support, respectively. The networks trained on our dataset have achieved significantly better performance than state-of-the-art methods for junction detection and line segment detection, respectively. We have conducted extensive experiments to evaluate quantitatively and qualitatively the wireframes obtained by our method, and have convincingly shown that effectively and efficiently parsing wireframes for images of man-made environments is a feasible goal within reach. Such wireframes could benefit many important visual tasks such as feature correspondence, 3D reconstruction, vision-based mapping, localization, and navigation. The data and source code are available at https://github.com/huangkuns/wireframe.