PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
This work addresses robust plane extraction for applications in Robotics, Augmented Reality, and Virtual Reality, representing an incremental advancement.
The paper tackles the problem of detecting and reconstructing piecewise planar surfaces from a single RGB image, achieving significant improvements over existing state-of-the-art methods in plane detection, segmentation, and reconstruction metrics.
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.