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arXiv · 1812.04072

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

Chen Liu·Kihwan Kim·Jinwei Gu·Yasutaka Furukawa·Jan Kautz
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Abstract

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces 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 jointly refines all the 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, which would have an immediate impact on a wide range of applications including Robotics, Augmented Reality, and Virtual Reality.

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Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, Jan Kautz. 2018-12-10. PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image. https://arxiv.org/abs/1812.04072

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