End-to-End LiDAR optimization for 3D point cloud registration

• URL: http://arxiv.org/abs/2602.10492v1
• Date: Wed, 11 Feb 2026 03:51:10 GMT
• Title: End-to-End LiDAR optimization for 3D point cloud registration
• Authors: Siddhant Katyan, Marc-André Gardner, Jean-François Lalonde,
• Abstract summary: LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration.<n>In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters.<n>Our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency.
• Score: 10.662051602024944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR configurations, leading to suboptimal data collection and significant computational overhead for sampling, noise filtering, and parameter tuning. In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters, jointly optimizing LiDAR acquisition and registration hyperparameters. By integrating registration feedback into the sensing loop, our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency. Evaluations in the CARLA simulation demonstrate that our method outperforms fixed-parameter baselines while retaining generalization abilities, highlighting the potential of adaptive LiDAR for autonomous perception and robotic applications.

Related papers

• RLCNet: An end-to-end deep learning framework for simultaneous online calibration of LiDAR, RADAR, and Camera [2.825460805162022]
  This paper presents RLCNet, a novel end-to-end trainable deep learning framework for the simultaneous online calibration of LiDAR, RADAR, and camera sensors.<n>An online calibration framework is introduced that incorporates a weighted moving average and outlier rejection, enabling dynamic adjustment of calibration parameters.<n>An ablation study highlights the significance of architectural choices, while comparisons with existing methods demonstrate the superior accuracy and robustness of the proposed approach.
  arXiv  Detail & Related papers  (2025-12-09T05:38:30Z)
• RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations [4.219537240663029]
  We introduce RS2AD, a novel framework for reconstructing and synthesizing vehicle-mounted LiDAR data from roadside sensor observations.<n>To the best of our knowledge, this is the first approach to reconstruct vehicle-mounted LiDAR data from roadside sensor inputs.
  arXiv  Detail & Related papers  (2025-03-10T09:08:05Z)
• SOLVR: Submap Oriented LiDAR-Visual Re-Localisation [13.434340164323473]
  SOLVR performs place recognition and 6-DoF registration across sensor modalities.<n>We show that SOLVR achieves state-of-the-art performance for LiDAR-Visual place recognition and registration.
  arXiv  Detail & Related papers  (2024-09-16T12:58:03Z)
• Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
  We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning.<n>Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations.<n>This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
  arXiv  Detail & Related papers  (2024-05-08T17:59:53Z)
• Learning Moving-Object Tracking with FMCW LiDAR [53.05551269151209]
  We propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances to improve the tracking quality.
  arXiv  Detail & Related papers  (2022-03-02T09:11:36Z)
• End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization [87.56144220508587]
  We take a new route to learn to optimize the LiDAR beam configuration for a given application. We propose a reinforcement learning-based learning-to-optimize framework to automatically optimize the beam configuration. Our method is especially useful when a low-resolution (low-cost) LiDAR is needed.
  arXiv  Detail & Related papers  (2022-01-11T09:46:31Z)
• LiDAR Point--to--point Correspondences for Rigorous Registration of Kinematic Scanning in Dynamic Networks [0.0]
  We propose a novel trajectory adjustment procedure to improve the registration of LiDAR point clouds. We describe the method for selecting correspondences and how they are inserted into the Dynamic Network as new observation models. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors.
  arXiv  Detail & Related papers  (2022-01-03T11:53:55Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles [16.45799795374353]
  We propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% 20% on the state-of-the-art perception algorithms.
  arXiv  Detail & Related papers  (2021-05-02T01:52:18Z)
• ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation [111.56730703473411]
  Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations. Simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels. ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning.
  arXiv  Detail & Related papers  (2020-09-07T23:46:08Z)
• Tracking Performance of Online Stochastic Learners [57.14673504239551]
  Online algorithms are popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable accuracy. We establish a link between steady-state performance derived under stationarity assumptions and the tracking performance of online learners under random walk models.
  arXiv  Detail & Related papers  (2020-04-04T14:16:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.