[Paper Review] Implicit LOD using points ordering for processing and visualisation in Point Cloud Servers

Rémi Cura, Julien Perret+1 more|arXiv (Cornell University)|Feb 22, 2016

Remote Sensing and LiDAR Applications25 references3 citations

TL;DR

This paper proposes a novel implicit Level of Detail (LOD) scheme for point cloud servers by ordering points within spatial patches to encode geometric LOD without external metadata. Using a midpoint-octree (MidOc) ordering, the method enables efficient, on-the-fly LOD selection for visualization, density correction, and algorithm acceleration, achieving up to 10x faster transfer by leveraging hierarchical point grouping and spatial locality.

ABSTRACT

Lidar datasets now commonly reach Billions of points and are very dense. Using these point cloud becomes challenging, as the high number of points is intractable for most applications and for visualisation.In this work we propose a new paradigm to easily get a portable geometric Level Of Details (LOD) inside a Point Cloud Server.The main idea is to not store the LOD information in an external additional file, but instead to store it implicitly by exploiting the order of the points.The point cloud is divided into groups (patches). These patches are ordered so that their order gradually provides more and more details on the patch. We demonstrate the interest of our method with several classical uses of LOD, such as visualisation of massive point cloud, algorithm acceleration, fast density peak detection and correction.

Motivation & Objective

• Address the challenge of managing massive, high-density point clouds in the Big Data era, where full-resolution data are impractical for most applications.
• Overcome limitations of existing LOD methods that rely on external metadata or complex geometric reconstruction by embedding LOD implicitly in point order.
• Enable efficient, scalable processing and visualization of billion-point datasets through a portable, self-contained LOD representation.
• Support practical use cases such as interactive web visualization, density peak detection, and data reduction with minimal computational overhead.

Proposed method

• Divide a point cloud into spatial patches to enable localized, hierarchical processing and LOD representation.
• Apply a midpoint-octree (MidOc) ordering strategy that selects the point closest to each octree cell center to represent the patch at each LOD level.
• Use the inherent order of points in the server to implicitly encode multiple levels of detail, eliminating the need for external LOD files.
• Leverage Morton (Z-order) or Hilbert curve-based spatial indexing to enable fast, bit-level access to hierarchical point subsets.
• Support partial LOD access by retrieving only the required level of detail based on viewing distance (e.g., for frustum culling in visualization).
• Perform intra-level point ordering to maintain geometric fidelity and coverage even at coarse LODs, ensuring robustness across varying point densities.

Experimental results

Research questions

• RQ1 Can LOD be embedded implicitly in point cloud data structure without external metadata, enabling efficient and portable processing?
• RQ2 How can point ordering within spatial patches be used to generate a reliable, geometrically meaningful hierarchy of detail?
• RQ3 To what extent does implicit LOD via MidOc ordering improve performance in visualization, density correction, and algorithm acceleration?
• RQ4 What are the trade-offs between data transfer speed, computational cost, and LOD quality when using ordered patches in a point cloud server?
• RQ5 Can this method support real-time or near-real-time processing for web-based visualization under bandwidth constraints?

Key findings

• The implicit LOD scheme using MidOc ordering enables up to 10x faster data transfer compared to full-resolution streaming, significantly improving web-based visualization performance.
• The method achieves fast density peak detection by analyzing point counts per patch without reading individual points, enabling sub-second anomaly detection on large datasets.
• Density correction is performed instantly by limiting the number of points per patch to a user-defined maximum, with no data loss and minimal runtime cost.
• The approach supports partial LOD access: only the required level of detail is retrieved, reducing I/O and accelerating rendering, especially at distance.
• The method is robust to variable point density and does not require prior surface reconstruction or complex geometric modeling.
• Despite current I/O bottlenecks (180M points/hour), the approach is scalable and can be accelerated via low-level C implementations and GPU-based octree construction.