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Performing single image holistic understanding and 3D reconstruction is acentral task in computer vision. This paper presents an integrated system thatperforms dense scene labeling, object detection, instance segmentation, depthestimation, 3D shape reconstruction, and 3D layout estimation for indoor andoutdoor scenes from a single RGB image. We name our system panoptic 3D parsing(Panoptic3D) in which panoptic segmentation ("stuff" segmentation and "things"detection/segmentation) with 3D reconstruction is performed. We design astage-wise system, Panoptic3D (stage-wise), where a complete set of annotationsis absent. Additionally, we present an end-to-end pipeline, Panoptic3D(end-to-end), trained on a synthetic dataset with a full set of annotations. Weshow results on both indoor (3D-FRONT) and outdoor (COCO and Cityscapes)scenes. Our proposed panoptic 3D parsing framework points to a promisingdirection in computer vision. Panoptic3D can be applied to a variety ofapplications, including autonomous driving, mapping, robotics, design, computergraphics, robotics, human-computer interaction, and augmented reality.

Towards Panoptic 3D Parsing for Single Image in the Wild