Geometry-Aware Instance Segmentation with Disparity Maps
arXiv:2006.07802
Abstract
Most previous works of outdoor instance segmentation for images only use color information. We explore a novel direction of sensor fusion to exploit stereo cameras. Geometric information from disparities helps separate overlapping objects of the same or different classes. Moreover, geometric information penalizes region proposals with unlikely 3D shapes thus suppressing false positive detections. Mask regression is based on 2D, 2.5D, and 3D ROI using the pseudo-lidar and image-based representations. These mask predictions are fused by a mask scoring process. However, public datasets only adopt stereo systems with shorter baseline and focal legnth, which limit measuring ranges of stereo cameras. We collect and utilize High-Quality Driving Stereo (HQDS) dataset, using much longer baseline and focal length with higher resolution. Our performance attains state of the art. Please refer to our project page. The full paper is available here.
CVPR 2020 Workshop of Scalability in Autonomous Driving (WSAD). Please refer to WSAD site for details; fix typos
References in corpus (5)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Semantic Instance Segmentation with a Discriminative Loss Function
- HDNET: Exploiting HD Maps for 3D Object Detection
- Semantic Instance Segmentation via Deep Metric Learning
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving