Towards Panoptic 3D Parsing for Single Image in the Wild
This addresses the problem of comprehensive scene parsing for applications like autonomous driving and robotics, though it appears incremental as it builds on existing methods for multi-task integration.
The paper tackles holistic 3D scene understanding from a single RGB image by integrating tasks like panoptic segmentation and 3D reconstruction, achieving results on indoor and outdoor datasets such as 3D-FRONT, COCO, and Cityscapes.
Performing single image holistic understanding and 3D reconstruction is a central task in computer vision. This paper presents an integrated system that performs dense scene labeling, object detection, instance segmentation, depth estimation, 3D shape reconstruction, and 3D layout estimation for indoor and outdoor 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 a stage-wise system, Panoptic3D (stage-wise), where a complete set of annotations is absent. Additionally, we present an end-to-end pipeline, Panoptic3D (end-to-end), trained on a synthetic dataset with a full set of annotations. We show results on both indoor (3D-FRONT) and outdoor (COCO and Cityscapes) scenes. Our proposed panoptic 3D parsing framework points to a promising direction in computer vision. Panoptic3D can be applied to a variety of applications, including autonomous driving, mapping, robotics, design, computer graphics, robotics, human-computer interaction, and augmented reality.