论文标题
几何感知实例分割带有差异图
Geometry-Aware Instance Segmentation with Disparity Maps
论文作者
论文摘要
图像的室外实例分割的大多数作品仅使用颜色信息。我们探索传感器融合的新方向以利用立体声摄像机。来自差异的几何信息有助于分离相同或不同类别的重叠对象。此外，几何信息对不太可能3D形状的区域提案进行了惩罚，从而抑制了假阳性检测。蒙版回归基于2D，2.5D和3D ROI，使用伪LIDAR和基于图像的表示。这些面具预测是通过掩盖评分过程融合的。但是，公共数据集仅采用具有较短基线和焦点的立体声系统，从而限制了立体声摄像机的测量范围。我们使用更长的基线和焦距具有更高的分辨率，收集和利用高质量的驾驶立体声（HQDS）数据集。我们的表演达到了最新的状态。请参阅我们的项目页面。完整的纸在这里可用。
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.
