PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

• URL: http://arxiv.org/abs/2107.13108v1
• Date: Tue, 27 Jul 2021 23:55:40 GMT
• Title: PlaneTR: Structure-Guided Transformers for 3D Plane Recovery
• Authors: Bin Tan and Nan Xue and Song Bai and Tianfu Wu and Gui-Song Xia
• Abstract summary: PlaneTR simultaneously detects and reconstructs planes from a single image. PlaneTR achieves a state-of-the-art performance on the ScanNet and NYUv2 datasets.
• Score: 56.23402171871664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image. Different from previous methods, PlaneTR jointly leverages the context information and the geometric structures in a sequence-to-sequence way to holistically detect plane instances in one forward pass. Specifically, we represent the geometric structures as line segments and conduct the network with three main components: (i) context and line segments encoders, (ii) a structure-guided plane decoder, (iii) a pixel-wise plane embedding decoder. Given an image and its detected line segments, PlaneTR generates the context and line segment sequences via two specially designed encoders and then feeds them into a Transformers-based decoder to directly predict a sequence of plane instances by simultaneously considering the context and global structure cues. Finally, the pixel-wise embeddings are computed to assign each pixel to one predicted plane instance which is nearest to it in embedding space. Comprehensive experiments demonstrate that PlaneTR achieves a state-of-the-art performance on the ScanNet and NYUv2 datasets.

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