InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization

• URL: http://arxiv.org/abs/2607.16862v1
• Date: Sat, 18 Jul 2026 15:55:36 GMT
• Title: InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization
• Abstract summary: We present InLiER, a learning-free pipeline based on an intermediate tokenization step.<n>InLiER achieves state-of-the-art performance on the HeLiPR dataset and in real-world field experiments.
• Score: 17.209765684998427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs with different fields of view, resolutions, and scanning patterns, existing descriptors degrade because they are tightly coupled to sensor-specific characteristics. We present InLiER, a learning-free pipeline based on an intermediate tokenization step. Height-sliced keypoints from structural elements receive mixed-radix token IDs encoding height, radial distance, local shape, and azimuth from local 3D geometry, in a compact sub-2KB representation. The same vocabulary is reorganized across three retrieval stages: height-ceiling histogram intersection for fast rotation-invariant shortlisting, binary bitmask alignment for yaw estimation and reranking, and token-guided geometric verification for 6-DoF pose estimation. InLiER achieves state-of-the-art performance on the HeLiPR dataset and in real-world field experiments, among modern handcrafted methods and outperforms the learning-based baseline on most cross-sensor configurations.

Related papers

• Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark [2.714583452862024]
  This study introduces an incidence-aware sampling strategy for individual LiDAR scans.<n>The method maps points to a geometry-normalized manifold space for voxel-based selection.<n>Experiments with Point Transformer and PointNeXt show improved resolution-averaged segmentation performance.
  arXiv  Detail & Related papers  (2026-08-07T20:42:43Z)
• SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry [69.89196162649091]
  SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry) is a novel approach for estimating 3D geometry from extended monocular video sequences.<n>Our approach generates affine-invariant 3D point maps with shared parameters across entire sequences, enabling consistent scale-invariant representations.
  arXiv  Detail & Related papers  (2026-06-19T10:23:25Z)
• DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited [48.627686484608525]
  We find that standard functional map implementations solve k independent linear systems serially, which is a computational bottleneck at higher spectral resolution.<n>We propose a vectorized reformulation that solves all systems in a single kernel call, achieving up to a 33x speedup.<n>We present an open-source, DeepShapeMatchingKit, that incorporates these improvements and standardizes training, evaluation, and data pipelines for common deep shape matching methods.
  arXiv  Detail & Related papers  (2026-04-11T23:15:07Z)
• Towards Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups [41.033896572767496]
  We present BUFFER-X, a training-free registration framework that achieves zero-shot generalization.<n>Our approach employs hierarchical multi-scale matching to extract correspondences across local, middle, and global receptive fields.<n>For efficiency-critical applications, we introduce BUFFER-X-Lite, which reduces total time by 43%.
  arXiv  Detail & Related papers  (2026-01-06T06:51:24Z)
• CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition [6.450462817607078]
  We propose a novel matching approach that learns rich-spatialtemporal representations of iris features.<n>Our approach first splits each iris image along one rotation, generating a sequence of sub-images that serve as input to a 3D-CNN.<n>This design yields a robust and generalizable solution for iris authentication.
  arXiv  Detail & Related papers  (2025-11-26T06:32:22Z)
• Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing [55.291219073365546]
  Open-Vocabulary Remote Sensing Image (OVRSIS) is an emerging task that adapts Open-Vocabulary (OVS) to the remote sensing (RS) domain.<n>textbfRSKT-Seg is a novel open-vocabulary segmentation framework tailored for remote sensing.<n> RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2x faster inference through efficient aggregation.
  arXiv  Detail & Related papers  (2025-09-15T15:24:49Z)
• Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding [87.68271178167373]
  We present a universal 3D tokenizer designed for scale-invariant representation learning with a frozen CLIP backbone.<n>S4Token is a tokenization pipeline that produces semantically-informed tokens regardless of scene scale.
  arXiv  Detail & Related papers  (2025-05-24T18:26:30Z)
• LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes [2.822816116516042]
  Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scale 3D semantic reconstruction through implicit representations from posed LiDAR measurements alone.
  arXiv  Detail & Related papers  (2023-11-04T03:55:38Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• DiSCO: Differentiable Scan Context with Orientation [13.797651328615347]
  We propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO) It simultaneously finds the scan at a similar place and estimates their relative orientation. DiSCO is validated on three datasets with long-term outdoor conditions.
  arXiv  Detail & Related papers  (2020-10-21T12:38:21Z)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)

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.