Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving

TL;DR

This paper introduces a novel image-guided outdoor LiDAR point cloud quality assessment method for autonomous driving, utilizing a two-component approach with generation and regression algorithms to evaluate perception quality.

Contribution

It presents the first outdoor environment-specific LiDAR quality assessment algorithm that combines image guidance with transformer-based regression for real-time evaluation.

Findings

Achieves a PLCC of 0.86 on nuScenes dataset.

Achieves a PLCC of 0.97 on Waymo dataset.

Provides consistent perception quality indices.

Abstract

LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR quality assessment methods, leading to uncertainty in detection performance. Additionally, existing point cloud quality assessment algorithms are predominantly designed for indoor environments or single-object scenarios. In this paper, we introduce a novel image-guided point cloud quality assessment algorithm for outdoor autonomous driving environments, named the Image-Guided Outdoor Point Cloud Quality Assessment (IGO-PQA) algorithm. Our proposed algorithm comprises two main components. The first component is the IGO-PQA generation algorithm, which leverages point cloud data, corresponding RGB surrounding view images, and agent objects' ground truth annotations to generate an overall quality score for a single-frame LiDAR-based point cloud. The second component is a transformer-based IGO-PQA regression algorithm for no-reference outdoor point cloud quality assessment. This regression algorithm allows for the direct prediction of IGO-PQA scores in an online manner, without requiring image data and object ground truth annotations. We evaluate our proposed algorithm using the nuScenes and Waymo open datasets. The IGO-PQA generation algorithm provides consistent and reasonable perception quality indices. Furthermore, our proposed IGO-PQA regression algorithm achieves a Pearson Linear Correlation Coefficient (PLCC) of 0.86 on the nuScenes dataset and 0.97 on the Waymo dataset.