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3D Object Visibility Prediction in Autonomous Driving

arXiv 2403.03681 ↗First posted 6 Mar 2024cs.RO cs.CVcited by 0

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Abstract (arXiv)

With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth. The prevailing framework for multi-sensor autonomous driving encompasses sensor installation, perception, path planning, decision-making, and motion control. At the perception phase, a common approach involves utilizing neural networks to infer 3D bounding box (Bbox) attributes from raw sensor data, including classification, size, and orientation. In this paper, we present a novel attribute and its corresponding algorithm: 3D object visibility. By incorporating multi-task learning, the introduction of this attribute, visibility, negligibly affects the model's effectiveness and efficiency. Our proposal of this attribute and its computational strategy aims to expand the capabilities for downstream tasks, thereby enhancing the safety and reliability of real-time autonomous driving in real-world scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 6 Mar 2024.

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18 of 23 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  9. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  10. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  11. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  12. 2016MultiNet: Real-time Joint Semantic Reasoning for Autonomous Drivingcited by 90
6 more
  1. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  2. 2019What You See is What You Get: Exploiting Visibility for 3D Object Detectioncited by 69
  3. 2021Immortal Tracker: Tracklet Never Diescited by 42
  4. 2023DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Cloudscited by 31
  5. 2022Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Drivingcited by 30
  6. 2021A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understandingcited by 17

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