mlsanityData to 14 Oct 2026

Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques

arXiv 2202.02521 ↗First posted 5 Feb 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 5 Feb 2022.

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19 of 37 references.

References · 19

  1. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  2. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  3. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  4. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  5. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  6. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  7. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  8. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 314
  9. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  10. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 232
  11. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  12. 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
7 more
  1. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 107
  2. 2018Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detectioncited by 82
  3. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  4. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  5. 2019Sensor Fusion for Joint 3D Object Detection and Semantic Segmentationcited by 48
  6. 2018IPOD: Intensive Point-based Object Detector for Point Cloudcited by 46
  7. 2018BirdNet: a 3D Object Detection Framework from LiDAR informationcited by 33

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