mlsanityData to 14 Oct 2026

Rethinking Voxelization and Classification for 3D Object Detection

arXiv 2301.04058 ↗First posted 10 Jan 2023cs.CV cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. In other words, the detecting network must be confident enough about its predictions. In this paper, we present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer that works on fast pillar-based models in the same way a voxelizer works on slow voxel-based models. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects that significantly improves model precision in a negligible time and computing cost. The developed code is publicly available at: https://github.com/YoushaaMurhij/RVCDet.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 25 references that are in the map. First posted on arXiv 10 Jan 2023.

Builds on

21 of 25 references.

References · 21

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  7. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  8. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  9. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  10. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  11. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  12. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
9 more
  1. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  2. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  3. 2022VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Gridscited by 133
  4. 2015Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Imagescited by 111
  5. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 107
  6. 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
  7. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  8. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  9. 2018Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learningcited by 33

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environmentcited by 8
  2. 2025M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environmentscited by 1
  3. 2025RCDINO: Enhancing Radar-Camera 3D Object Detection with DINOv2 Semantic Featurescited by 0