Auto-Annotation with Expert-Crafted Guidelines: A Study through 3D LiDAR Detection Benchmark

TL;DR

This paper introduces AutoExpert, a benchmark for auto-annotation of 3D LiDAR data using expert-crafted guidelines, leveraging foundation models to improve detection accuracy significantly.

Contribution

It presents a novel benchmark and method for auto-annotation in 3D LiDAR data using foundation models, addressing data-modality and annotation discrepancies.

Findings

Boosts 3D detection mAP to 25.4 from 12.1

Utilizes foundation models for 2D detection and segmentation

Provides a new benchmark with expert-crafted guidelines

Abstract

Data annotation is crucial for developing machine learning solutions. The current paradigm is to hire ordinary human annotators to annotate data instructed by expert-crafted guidelines. As this paradigm is laborious, tedious, and costly, we are motivated to explore auto-annotation with expert-crafted guidelines. To this end, we first develop a supporting benchmark AutoExpert by repurposing the established nuScenes dataset, which has been widely used in autonomous driving research and provides authentic expert-crafted guidelines. The guidelines define 18 object classes using both nuanced language descriptions and a few visual examples, and require annotating objects in LiDAR data with 3D cuboids. Notably, the guidelines do not provide LiDAR visuals to demonstrate how to annotate. Therefore, AutoExpert requires methods to learn on few-shot labeled images and texts to perform 3D detection in LiDAR data. Clearly, the challenges of AutoExpert lie in the data-modality and annotation-task discrepancies. Meanwhile, publicly-available foundation models (FMs) serve as promising tools to tackle these challenges. Hence, we address AutoExpert by leveraging appropriate FMs within a conceptually simple pipeline, which (1) utilizes FMs for 2D object detection and segmentation in RGB images, (2) lifts 2D detections into 3D using known sensor poses, and (3) generates 3D cuboids for the 2D detections. In this pipeline, we progressively refine key components and eventually boost 3D detection mAP to 25.4, significantly higher than 12.1 achieved by prior arts.