PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image
Chen Liu,Kihwan Kim,Jinwei Gu,Yasutaka Furukawa,Jan Kautz +4 more
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TLDR
In this article, a deep neural architecture, PlaneRCNN, is proposed to detect and reconstruct piecewise planar regions from a single RGB image, which employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks.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.read more
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Proceedings ArticleDOI
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Structured3D: A Large Photo-Realistic Dataset for Structured 3D Modeling
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References
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