[Paper] LitS: A novel Neighborhood Descriptor for Point Clouds

Source: arXiv - 2602.04838v1

Overview

The paper presents LitS, a fresh way to describe the local geometry of points in 2‑D and 3‑D point clouds. By turning a point’s surroundings into a piecewise‑constant function on the unit circle, LitS lets algorithms quickly query “how many neighbors lie in this direction?” – a capability that can improve everything from object detection to mesh reconstruction.

Key Contributions

• Novel descriptor: LitS encodes neighbor distribution as a directional histogram on the unit circle, preserving angular information that traditional scalar descriptors (e.g., density, curvature) lose.
• Two variants:
  • Regular LitS: raw counts per angular sector.
  • Cumulative LitS: aggregates counts across sectors, offering smoother, noise‑robust signatures.
• Parameter‑light design: Only two tunable parameters (number of angular bins B and cone aperture α), making it easy to adapt to different sensor resolutions and point‑cloud densities.
• Robustness: Demonstrated resilience to variable point density, Gaussian noise, and outliers—common pain points in real‑world scans.
• Broad applicability: Works for both 2‑D (e.g., LiDAR scans) and 3‑D point clouds (e.g., RGB‑D, photogrammetry), and can be plugged into downstream pipelines such as classification, segmentation, and registration.

Methodology

1. Local reference frame – For each point p, a simple orthonormal basis is built (e.g., using PCA on its k nearest neighbors).
2. Angular binning – The unit circle (or sphere’s azimuthal plane) is divided into B equal angular sectors.
3. Cone counting – For every neighbor q of p, the direction vector (q‑p) is projected onto the reference frame. If the angle between this vector and the sector’s central direction is ≤ α, the neighbor contributes to that sector’s count.
4. Piecewise‑constant function – The resulting counts form LitS(p): a mapping from direction → neighbor count.
5. Regular vs. cumulative –
   • Regular: the raw count per sector.
   • Cumulative: each sector’s value is the sum of counts of all preceding sectors (circularly), smoothing abrupt spikes caused by noise.
6. Descriptor usage – LitS can be compared between points using simple ℓ₁/ℓ₂ distances, fed into classic machine‑learning models (e.g., Random Forests) or concatenated with learned features in deep networks.

The pipeline is lightweight: constructing LitS for a point cloud of N points costs O(N · k) (k = neighbor search size) and requires only a few extra arithmetic operations per neighbor.

Results & Findings

Overall, LitS consistently outperformed classic hand‑crafted descriptors, especially when point density was non‑uniform or when the data contained moderate noise.

Practical Implications

• Robotics & Autonomous Navigation – Fast, direction‑aware neighborhood info can improve obstacle detection and SLAM front‑ends without heavy GPU reliance.
• AR/VR content pipelines – When cleaning or simplifying scanned meshes, LitS helps identify planar vs. edge‑rich regions, guiding adaptive decimation.
• Industrial inspection – Point‑cloud‑based defect detection benefits from LitS’s sensitivity to subtle geometric deviations (e.g., dents, warps).
• Edge‑computing – Because LitS needs only two small parameters and simple arithmetic, it can run on embedded CPUs (e.g., Jetson Nano) in real time.
• Hybrid pipelines – LitS can be concatenated with learned point‑cloud embeddings (PointNet++, KPConv) to inject explicit geometric priors, often boosting downstream accuracy with minimal extra cost.

Limitations & Future Work

• Parameter sensitivity – Selecting the optimal number of bins B and cone aperture α still requires dataset‑specific tuning.
• Scalability to massive clouds – The current implementation performs a naïve k-NN search per point; integrating hierarchical spatial indexes (e.g., Octree, FAISS) would be needed for million‑point scenes.
• Spherical extension – LitS currently projects onto the azimuthal plane; a full spherical version could capture elevation variations more faithfully.
• Dynamic / temporal point clouds – The paper focuses on static data; extending LitS to handle streaming LiDAR frames (e.g., for motion prediction) is an open direction.
• Deep integration – Future work could embed LitS directly into differentiable layers, allowing end‑to‑end training of networks that learn optimal binning or cone shapes.

Bottom line: LitS offers a simple yet powerful way to “look around” a point in a cloud, delivering richer geometric cues than many traditional descriptors while staying lightweight enough for on‑device use. Developers building 3‑D perception stacks now have a new tool that can boost robustness and accuracy without a heavy computational footprint.

Authors

• Jonatan B. Bastos
• Francisco F. Rivera
• Oscar G. Lorenzo
• David L. Vilariño
• José C. Cabaleiro
• Alberto M. Esmorís
• Tomás F. Pena

Paper Information

• arXiv ID: 2602.04838v1
• Categories: cs.CV
• Published: February 4, 2026
• PDF: Download PDF