Holistically-Attracted Wireframe Parsing

Abstract

This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset [14] and the YorkUrban dataset [8]. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN [40], it improves the challenging mean structural average precision (msAP) by a large margin (2.8% absolute improvements), and achieves 29.5 FPS on single GPU (89% relative improvement). A systematic ablation study is performed to further justify the proposed method. * Corresponding author (a) Image (b) Learned Lines (c) HAWP (score>0.9) (d) Junction Proposals (e) Enumerated Lines (f) L-CNN (score>0.9)

PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds

“…LCNN (Zhou et al, 2019a) is an end-to-end trainable system that directly outputs a vectorised wireframe. The current state-of-the-art appears to be the improved LCNN of Xue et al (2020), which uses a holistic "attraction field map" to characterise line segments. Zou et al (2018) estimate the 3D layout of an indoor scene from a single perspective or panoramic image with an encoder-decoder architecture, while Zhou et al (2019b) obtain a compact 3D wireframe representation from a single image by exploiting global structural regularities.…”

“…Similarly to our PC2WF, Polyfit works best with objects made of straight edges and planar surfaces. The concurrent work Wang et al (2020) proposed a deep neural network that is trained to identify a collection of parametric edges.…”