Q-LiDAR: Efficient and Accurate Training-Free Quantization for Point Cloud 3D Object Detection Models
Published in Conference on Computer Vision and Pattern Recognition (CVPR), 2024
This paper presents novel dynamic and static post-training quantization techniques for 3D object detection models, achieving 35% reduction in inference time while maintaining accuracy through progressive quantization and sensitivity analysis.
Recommended citation: Chi, B., Zheng, H., & Zhang, M. (2024). "Q-LiDAR: Efficient and Accurate Training-Free Quantization for Point Cloud 3D Object Detection Models." Conference on Computer Vision and Pattern Recognition (CVPR). Submitted.
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