1. [Publications](/index.php/publications) 2. PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image # PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image ![](/sites/default/files/styles/wide/public/publications/planercnn.jpg?itok=crVEw2hS) **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. ## Authors Chen Liu (Washington University in St. Louis) Kihwan Kim (NVIDIA) Jinwei Gu (SenseTime) Yasutaka Furukawa (Simon Fraser University) [Jan Kautz](/index.php/person/jan-kautz) ## Publication Date Sunday, June 16, 2019 ## Published in [IEEE CVPR 2019 (Oral)](http://cvpr2019.thecvf.com/) ## Research Area [Computer Vision](/index.php/research-area/computer-vision) [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) ## External Links [Paper (arxiv)](https://arxiv.org/pdf/1812.04072.pdf) [Video (Youtube)](https://www.youtube.com/watch?v=d9XfMvVXGwM) [Chen Liu's homepage](http://art-programmer.github.io/index.html) [code (github)](https://github.com/NVlabs/planercnn) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .