Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

TL;DR

This paper introduces a 3D to 2D label transfer method for street scene annotation, significantly reducing manual effort and improving accuracy in generating semantic and instance labels for large-scale datasets.

Contribution

It presents a novel approach that lifts semantic labeling from 2D images into 3D space and transfers annotations back, enhancing efficiency and accuracy in street scene annotation.

Findings

3D information improves annotation efficiency and accuracy

Generated 400k semantic and instance annotations for a new dataset

Outperforms state-of-the-art label transfer baselines

Abstract

Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.