Three‐dimensional Scan Registration using Curvelet Features in Planetary Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Topographic mapping in planetary environments relies on accurate three‐dimensional (3D) scan registration methods. However, most global registration algorithms relying on features such as fast point feature histograms and Harris‐3D show poor alignment accuracy in these settings due to the poor structure of the Mars‐like terrain, and the variable‐resolution, occluded, sparse range data that are difficult to register without some a priori knowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multiresolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle, and spatial position. Features are detected in the curvelet domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest‐neighbor‐based matching is used to calculate the feature correspondences. Correspondence rejection using random sample consensus identifies inliers, and a locally optimal singular value decomposition‐based estimation of the rigid‐body transformation aligns the laser scans given the reprojected correspondences in the metric space. Experimental results on a publicly available dataset of a planetary analogue indoor facility, as well as simulated and real‐world scans from Neptec Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard, demonstrate improved performance over existing methods in the challenging sparse Mars‐like terrain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it