mlsanityData to 7 Oct 2026

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles

arXiv 2501.04213 ↗First posted 8 Jan 2025cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

To enhance perception in autonomous vehicles (AVs), recent efforts are concentrating on 3D object detectors, which deliver more comprehensive predictions than traditional 2D object detectors, at the cost of increased memory footprint and computational resource usage. We present a novel framework called UPAQ, which leverages semi-structured pattern pruning and quantization to improve the efficiency of LiDAR point-cloud and camera-based 3D object detectors on resource-constrained embedded AV platforms. Experimental results on the Jetson Orin Nano embedded platform indicate that UPAQ achieves up to 5.62x and 5.13x model compression rates, up to 1.97x and 1.86x boost in inference speed, and up to 2.07x and 1.87x reduction in energy consumption compared to state-of-the-art model compression frameworks, on the Pointpillar and SMOKE models respectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 8 Jan 2025.

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23 of 40 references.

References · 23

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2015Fast R-CNNcited by 2,930
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  4. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  5. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  6. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
  7. 2019Importance Estimation for Neural Network Pruningcited by 531
  8. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 521
  9. 2020Pruning neural networks without any data by iteratively conserving synaptic flowcited by 475
  10. 2020SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimationcited by 202
  11. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 184
  12. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
11 more
  1. 2019Loss Aware Post-training Quantizationcited by 94
  2. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  3. 2020PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruningcited by 75
  4. 2023Pruning vs Quantization: Which is Better?cited by 45
  5. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
  6. 2024LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detectioncited by 29
  7. 2022Object Detection in Autonomous Vehicles: Status and Open Challengescited by 14
  8. 2019Localization-aware Channel Pruning for Object Detectioncited by 13
  9. 2021LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platformscited by 9
  10. 2022Roadmap for Cybersecurity in Autonomous Vehiclescited by 5
  11. 2023R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruningcited by 4

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