mlsanityData to 14 Oct 2026

Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection

arXiv 2410.00582 ↗First posted 1 Oct 2024cs.CV cs.ROcited by 1

Not on the map. Why?

Abstract (arXiv)

Efficient point cloud (PC) compression is crucial for streaming applications, such as augmented reality and cooperative perception. Classic PC compression techniques encode all the points in a frame. Tailoring compression towards perception tasks at the receiver side, we ask the question, "Can we remove the ground points during transmission without sacrificing the detection performance?" Our study reveals a strong dependency on the ground from state-of-the-art (SOTA) 3D object detection models, especially on those points below and around the object. In this work, we propose a lightweight obstacle-aware Pillar-based Ground Removal (PGR) algorithm. PGR filters out ground points that do not provide context to object recognition, significantly improving compression ratio without sacrificing the receiver side perception performance. Not using heavy object detection or semantic segmentation models, PGR is light-weight, highly parallelizable, and effective. Our evaluations on KITTI and Waymo Open Dataset show that SOTA detection models work equally well with PGR removing 20-30% of the points, with a speeding of 86 FPS.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

31 of 56 references.

References · 31

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  10. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
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  12. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
19 more
  1. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  2. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  3. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  4. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
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  8. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
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  10. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  11. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  12. 2022COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehiclescited by 95
  13. 2020HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detectioncited by 82
  14. 2019Learned Point Cloud Geometry Compressioncited by 58
  15. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  16. 2021Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensorcited by 29
  17. 2021AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Drivingcited by 26
  18. 2023PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformercited by 21
  19. 2024GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimationcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Geometry vs Structure: Graph-Based Diagnostics for LiDAR Point-Cloud Simulation Fidelitycited by 0