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arXiv · 2111.15143

HEAT: Holistic Edge Attention Transformer for Structured Reconstruction

Jiacheng Chen·Yiming Qian·Yasutaka Furukawa
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Abstract

This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge classification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points; 2) fuses image feature to each edge candidate by deformable attention; 3) employs two weight-sharing Transformer decoders to learn holistic structural patterns over the graph edge candidates; and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor floorplan planar graph reconstruction. Extensive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction.github.io.

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Jiacheng Chen, Yiming Qian, Yasutaka Furukawa. 2021-11-30. HEAT: Holistic Edge Attention Transformer for Structured Reconstruction. https://arxiv.org/abs/2111.15143

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