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Transforming Omnidirectional RGB-LiDAR data into 3D Gaussian Splatting

Semin Bae, Hansol Lim, Jongseong Brad Choi

TL;DR

This work provides a deterministic workflow for creating simulation-grade digital twins from standard archived sensor logs and successfully repurposes a considerable fraction of discarded data into usable SfM geometry.

Abstract

The demand for large-scale digital twins is rapidly growing in robotics and autonomous driving. However, constructing these environments with 3D Gaussian Splatting (3DGS) usually requires expensive, purpose-built data collection. Meanwhile, deployed platforms routinely collect extensive omnidirectional RGB and LiDAR logs, but a significant portion of these sensor data is directly discarded or strictly underutilized due to transmission constraints and the lack of scalable reuse pipeline. In this paper, we present an omnidirectional RGB-LiDAR reuse pipeline that transforms these archived logs into robust initialization assets for 3DGS. Direct conversion of such raw logs introduces practical bottlenecks: inherent non-linear distortion leads to unreliable Structure-from-Motion (SfM) tracking, and dense, unorganized LiDAR clouds cause computational overhead during 3DGS optimization. To overcome these challenges, our pipeline strategically integrates an ERP-to-cubemap conversion module for deterministic spatial anchoring, alongside PRISM-a color stratified downsampling strategy. By bridging these multi-modal inputs via Fast Point Feature Histograms (FPFH) based global registration and Iterative Closest Point (ICP), our pipeline successfully repurposes a considerable fraction of discarded data into usable SfM geometry. Furthermore, our LiDAR-reinforced initialization consistently enhances the final 3DGS rendering fidelity in structurally complex scenes compared to vision-only baselines. Ultimately, this work provides a deterministic workflow for creating simulation-grade digital twins from standard archived sensor logs.

Transforming Omnidirectional RGB-LiDAR data into 3D Gaussian Splatting

TL;DR

This work provides a deterministic workflow for creating simulation-grade digital twins from standard archived sensor logs and successfully repurposes a considerable fraction of discarded data into usable SfM geometry.

Abstract

The demand for large-scale digital twins is rapidly growing in robotics and autonomous driving. However, constructing these environments with 3D Gaussian Splatting (3DGS) usually requires expensive, purpose-built data collection. Meanwhile, deployed platforms routinely collect extensive omnidirectional RGB and LiDAR logs, but a significant portion of these sensor data is directly discarded or strictly underutilized due to transmission constraints and the lack of scalable reuse pipeline. In this paper, we present an omnidirectional RGB-LiDAR reuse pipeline that transforms these archived logs into robust initialization assets for 3DGS. Direct conversion of such raw logs introduces practical bottlenecks: inherent non-linear distortion leads to unreliable Structure-from-Motion (SfM) tracking, and dense, unorganized LiDAR clouds cause computational overhead during 3DGS optimization. To overcome these challenges, our pipeline strategically integrates an ERP-to-cubemap conversion module for deterministic spatial anchoring, alongside PRISM-a color stratified downsampling strategy. By bridging these multi-modal inputs via Fast Point Feature Histograms (FPFH) based global registration and Iterative Closest Point (ICP), our pipeline successfully repurposes a considerable fraction of discarded data into usable SfM geometry. Furthermore, our LiDAR-reinforced initialization consistently enhances the final 3DGS rendering fidelity in structurally complex scenes compared to vision-only baselines. Ultimately, this work provides a deterministic workflow for creating simulation-grade digital twins from standard archived sensor logs.
Paper Structure (11 sections, 5 equations, 3 figures, 6 tables)

This paper contains 11 sections, 5 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. LITERATURE REVIEW
  3. Methodology
  4. Experimental Setup and Implementation
  5. Results and Discussion
  6. Pipeline Reuse and Throughput
  7. PRISM Sweep and Alignment Behavior
  8. 3DGS Quantitative Comparison
  9. Discussion
  10. Limitations and Future Work
  11. Conclusion

Figures (3)

  • Figure 1: Example of ERP-to-cubemap conversion used in our pipeline. One omnidirectional panorama is projected into six rectilinear cubemap faces for robust feature matching in SfM.
  • Figure 2: Overview of the proposed omnidirectional RGB-LiDAR reuse pipeline. ERP images are converted to cubemaps for robust SfM anchoring, LiDAR odometry maps are colorized and PRISM-downsampled, and ICP-based registration fuses both modalities into a final 3DGS-ready asset.
  • Figure 3: Qualitative comparison across the three sequences. Columns show Vanilla, Ours ($n=50$), and GT.