# 3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

## TL;DR

This paper introduces 3D BAT, a web-based semi-automatic annotation toolbox that streamlines the creation of 2D and 3D labels for road objects using multi-view and point cloud data, enhancing efficiency and usability.

## Contribution

The paper presents a novel open-source 3D annotation toolbox that supports semi-automatic labeling, multi-view visualization, and automatic projection of 3D annotations into images, improving annotation speed and accuracy.

## Key findings

- Faster annotation process compared to existing tools
- Supports semi-automatic track labeling with interpolation
- Provides multi-view visualization for better annotation accuracy

## Abstract

In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT). Our open source, web-based 3D BAT incorporates several smart features to improve usability and efficiency. For instance, this annotation toolbox supports semi-automatic labeling of tracks using interpolation, which is vital for downstream tasks like tracking, motion planning and motion prediction. Moreover, annotations for all camera images are automatically obtained by projecting annotations from 3D space into the image domain. In addition to the raw image and point cloud feeds, a Masterview consisting of the top view (bird's-eye-view), side view and front views is made available to observe objects of interest from different perspectives. Comparisons of our method with other publicly available annotation tools reveal that 3D annotations can be obtained faster and more efficiently by using our toolbox.