Summary of Image-guided Outdoor Lidar Perception Quality Assessment For Autonomous Driving, by Ce Zhang et al.
Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving
by Ce Zhang, Azim Eskandarian
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors propose an innovative LiDAR-based point cloud quality assessment algorithm for autonomous driving environments, called Image-Guided Outdoor Point Cloud Quality Assessment (IGO-PQA). The IGO-PQA algorithm consists of two components: a generation algorithm that generates a quality score based on point cloud data, surrounding images, and agent objects’ annotations; and a transformer-based regression algorithm that predicts the quality score without requiring image data or ground truth annotations. The authors evaluate their proposed algorithm using nuScenes and Waymo open datasets, achieving consistent and reasonable perception quality indices with a Pearson Linear Correlation Coefficient (PLCC) of 0.86 on nuScenes and 0.97 on Waymo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to measure the quality of LiDAR point clouds used in self-driving cars. This is important because current methods aren’t very good at assessing the quality of these point clouds, especially in outdoor environments with many objects. The authors create an algorithm that uses images and object labels to rate the quality of a single-frame point cloud. They also develop a separate algorithm that can predict the quality score without needing image data or object labels. The algorithm is tested on two big datasets for autonomous driving, nuScenes and Waymo, and it does a great job of predicting the quality scores. |
Keywords
» Artificial intelligence » Regression » Transformer
