As a part of the perception results of intelligent driving systems, static
object detection (SOD) in 3D space provides crucial cues for driving
environment understanding. With the rapid deployment of deep neural networks
for SOD tasks, the demand for high-quality training samples soars. The
traditional, also reliable, way is manual labeling over the dense LiDAR point
clouds and reference images. Though most public driving datasets adopt this
strategy to provide SOD ground truth (GT), it is still expensive (requires
LiDAR scanners) and low-efficient (time-consuming and unscalable) in practice.
This paper introduces VRSO, a visual-centric approach for static object
annotation. VRSO is distinguished in low cost, high efficiency, and high
quality: (1) It recovers static objects in 3D space with only camera images as
input, and (2) manual labeling is barely involved since GT for SOD tasks is
generated based on an automatic reconstruction and annotation pipeline. (3)
Experiments on the Waymo Open Dataset show that the mean reprojection error
from VRSO annotation is only 2.6 pixels, around four times lower than the Waymo
labeling (10.6 pixels). Source code is available at:
this https URL

该论文介绍了 VRSO，一种用于静态物体注释的基于视觉的方法，它使用相机图像作为输入，可以在 3D 空间中恢复静态物体，并基于自动重建和注释流程生成用于静态对象检测任务的 GT，其平均重投影误差仅为 2.6 像素，约为 Waymo 标注的四分之一（10.6 像素）。