PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image

Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, Jan Kautz; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4450-4459

Abstract


This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar regions from a single RGB image. PlaneRCNN employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks. PlaneRCNN then refines an arbitrary number of segmentation masks with a novel loss enforcing the consistency with a nearby view during training. The paper also presents a new benchmark with more fine-grained plane segmentations in the ground-truth, in which, PlaneRCNN outperforms existing state-of-the-art methods with significant margins in the plane detection, segmentation, and reconstruction metrics. PlaneRCNN makes an important step towards robust plane extraction method, which would have immediate impact on a wide range of applications including Robotics, Augmented Reality, and Virtual Reality.

Related Material


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[bibtex]
@InProceedings{Liu_2019_CVPR,
author = {Liu, Chen and Kim, Kihwan and Gu, Jinwei and Furukawa, Yasutaka and Kautz, Jan},
title = {PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}