# Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

arXiv: https://arxiv.org/abs/2105.00373 · HTML: https://mlsanity.com/paper/2105.00373/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 49 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 30 of 31)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](https://mlsanity.com/paper/1907.10471/) (2019)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [Multi-Task Multi-Sensor Fusion for 3D Object Detection](https://mlsanity.com/paper/2012.12397/) (2020)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers](https://mlsanity.com/paper/1901.03006/) (2019)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration](https://mlsanity.com/paper/1905.04153/) (2019)
- [Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach](https://mlsanity.com/paper/1809.05845/) (2018)
- [Active Perception using Light Curtains for Autonomous Driving](https://mlsanity.com/paper/2008.02191/) (2020)
- [Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design](https://mlsanity.com/paper/2104.06615/) (2021)
- [A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition](https://mlsanity.com/paper/2012.05018/) (2020)

## Built on by (most cited first, 22 of 22)

- [GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving](https://mlsanity.com/paper/2111.08575/) (2021)
- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [Is Your LiDAR Placement Optimized for 3D Scene Understanding?](https://mlsanity.com/paper/2403.17009/) (2024)
- [Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models](https://mlsanity.com/paper/2505.10892/) (2025)
- [LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving](https://mlsanity.com/paper/2501.04005/) (2025)
- [Optimizing the Placement of Roadside LiDARs for Autonomous Driving](https://mlsanity.com/paper/2310.07247/) (2023)
- [Online Bandit Learning with Offline Preference Data for Improved RLHF](https://mlsanity.com/paper/2406.09574/) (2024)
- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection](https://mlsanity.com/paper/2510.24688/) (2025)
- [TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis](https://mlsanity.com/paper/2211.14456/) (2022)
- [Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations](https://mlsanity.com/paper/2309.13150/) (2023)
- [e-COP : Episodic Constrained Optimization of Policies](https://mlsanity.com/paper/2406.09563/) (2024)
- [Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors](https://mlsanity.com/paper/2402.00467/) (2024)
- [UniDrive: Towards Universal Driving Perception Across Camera Configurations](https://mlsanity.com/paper/2410.13864/) (2024)
- [An Investigation of Beam Density on LiDAR Object Detection Performance](https://mlsanity.com/paper/2503.15087/) (2025)
- [Task-Driven Implicit Representations for Automated Design of LiDAR Systems](https://mlsanity.com/paper/2505.22344/) (2025)
- [Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving](https://mlsanity.com/paper/2506.23523/) (2025)
- [High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception](https://mlsanity.com/paper/2509.02904/) (2025)
- [Enhancing Certifiable Semantic Robustness via Robust Pruning of Deep Neural Networks](https://mlsanity.com/paper/2510.00083/) (2025)
- [Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics](https://mlsanity.com/paper/2606.27554/) (2026)
- [ALFRED: Requirement-driven development of an open-source mobile manipulator for long-term plant monitoring](https://mlsanity.com/paper/2610.01477/) (2026)

## Abstract (arXiv)

The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on developing new deep learning algorithms or model architectures, we study the problem from the physical design perspective, i.e., how different placements of multiple LiDARs influence the learning-based perception. To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects. We also present a new data collection, detection model training and evaluation framework in the realistic CARLA simulator to evaluate disparate multi-LiDAR configurations. Using several prevalent placements inspired by the designs of self-driving companies, we show the correlation between our surrogate metric and object detection performance of different representative algorithms on KITTI through extensive experiments, validating the effectiveness of our LiDAR placement evaluation approach. Our results show that sensor placement is non-negligible in 3D point cloud-based object detection, which will contribute up to 10% performance discrepancy in terms of average precision in challenging 3D object detection settings. We believe that this is one of the first studies to quantitatively investigate the influence of LiDAR placement on perception performance. The code is available on https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection.