CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings

Zhiwen Fan, Tianlong Chen, Peihao Wang, Zhangyang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10986-10996


Understanding 2D computer-aided design (CAD) drawings plays a crucial role for creating 3D prototypes in architecture, engineering and construction (AEC) industries. The task of automated panoptic symbol spotting, i.e., to spot and parse both countable object instances (windows, doors, tables, etc.) and uncountable stuff (wall, railing, etc.) from CAD drawings, has recently drawn interests from the computer vision community. Unfortunately, the highly irregular ordering and orientations set major roadblocks for this task. Existing methods, based on convolutional neural networks (CNNs) and/or graph neural networks (GNNs), regress instance bounding boxes in the pixel domain and then convert the predictions into symbols. In this paper, we present a novel framework named CADTransformer, that can painlessly modify existing vision transformer (ViT) backbones to tackle the above limitations for the panoptic symbol spotting task. CADTransformer tokenizes directly from the set of graphical primitives in CAD drawings, and correspondingly optimizes line-grained semantic and instance symbol spotting altogether by a pair of prediction heads. The backbone is further enhanced with a few plug-and-play modifications, including a neighborhood aware self-attention, hierarchical feature aggregation, and graphic entity position encoding, to bake in the structure prior while optimizing the efficiency. Besides, a new data augmentation method, termed Random Layer, is proposed by the layer-wise separation and recombination of a CAD drawing. Overall, CADTransformer significantly boosts the previous state-of-the-art from 0.595 to 0.685 in the panoptic quality (PQ) metric, on the recently released FloorPlanCAD dataset. We further demonstrate that our model can spot symbols with irregular shapes and arbitrary orientations. Our codes are available in

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@InProceedings{Fan_2022_CVPR, author = {Fan, Zhiwen and Chen, Tianlong and Wang, Peihao and Wang, Zhangyang}, title = {CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10986-10996} }