mlsanityData to 7 Oct 2026

Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving

arXiv 2406.17265 ↗First posted 25 Jun 2024cs.CV cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 44 of its 60 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

44 of 60 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025A Novel Multi-layer Task-centric and Data Quality Framework for Autonomous Drivingcited by 2
  2. 2026PRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Drivingcited by 2
  3. 2026Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Drivingcited by 0