Enhancing Aerial Camera-LiDAR Registration through Combined LiDAR Feature Layers and Graph Neural Networks
Bibliographic record
Abstract
Abstract. Integrating optical images with Light Detection and Ranging (LiDAR) data is an important advance in Photogrammetry, Geomatics and Computer Vision, registering the strengths of both modalities (height and spectral information). Most orthoimages and aerial LiDAR data are georeferenced to a common ground coordinate system; however, a registration gap remains, and achieving high-accuracy registration between these datasets is challenging due to their differing data formats and frames of reference. In this paper, we propose an approach to enhance camera-LiDAR registration through combined LiDAR feature layer generation and Deep Learning. Our method involves creating weighted combinations of feature layers from LiDAR data, leveraging intensity, elevation, and bearing angle attributes. Subsequently, a 2D-2D Graph Neural Network (GNN) pipeline serves as an intermediate step for feature detection and matching, followed by a 2D-3D affine transformation model to register optical images to point clouds. Experimental validation across aerial scenes demonstrates significant improvements in registration accuracy. Notably, in urban building areas, we achieved an RMSE of around 1.1 pixel, marking a reduction of 5 pixels compared to georeferenced baseline values. In rural road scenes, our method yielded a pixel RMSE of 1.3, with a 4-pixel reduction compared to baseline results. Additionally, in water scenes, which tend to be noisy in LiDAR data, we achieved a pixel RMSE of 1.8, representing a slight half-pixel reduction compared to the baseline. Therefore, by using weighted and combined LiDAR feature layer and GNN feature matching, this approach augments the number of key points and matches, directly correlating with the observed registration reduction in pixel RMSE across diverse aerial scene types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".