Ground Camera Image and Large-Scale 3-D Image-Based Point Cloud Registration Based on Learning Domain Invariant Feature Descriptors
Bibliographic record
Abstract
Multisource data are captured from different sensors or generated with different generation mechanisms. Ground camera images (images taken from ground-based camera) and rendered images (synthesized by the position information from 3-D image-based point cloud) are different-source geospatial data, called cross-domain images. Particularly, in outdoor environments, the registration relationship between the above cross-domain images is available to establish the spatial relationship between 2-D and 3-D space, which is an indirect solution for virtual-real registration of augmented reality (AR). However, the traditional handcrafted feature descriptors cannot match the above cross-domain images because of the low quality of rendered images and the domain gap between cross-domain images. In this article, inspired by the success achieved by deep learning in computer vision, we first propose an end-to-end network, DIFD-Net, to learn domain invariant feature descriptors (DIFDs) for cross-domain image patches. The DIFDs are used for cross-domain image patch retrieval to the registration of ground camera and rendered images. Second, we construct a domain-kept consistent loss function, which balances the feature descriptors for narrowing the gap in different domains, to optimize DIFD-Net. Specially, the negative samples are generated from positive during training, and the introduced constraint of intermediate feature maps increases extra supervision information to learn feature descriptors. Finally, experiments show the superiority of DIFDs for the retrieval of cross-domain image patches, which achieves state-of-the-art retrieval performance. Additionally, we use DIFDs to match ground camera images and rendered images, and verify the feasibility of the derived AR virtual-real registration in open outdoor environments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".