Terrain reconstruction based on descent images for the Chang ’ e III landing area
Bibliographic record
Abstract
A new method that combined image matching and shape from shading for terrain reconstruction was proposed to solve the lack of terrain in the landing area of Chang'e III. First, the reflection equation was established based on the Lommel-Seeliger reflection model. After edge extraction, the gradients of points on the edge were solved. The normal vectors of adjacent points were obtained using the smoothness constraint. Furthermore, the gradients of residual points in the image were determined through evolution. The inadequacy of the reflection equation was eliminated by considering the gradient as the constraint of the reflection equation. The normal vector of each point could be obtained by solving the reflection equation. The terrain without coordinate information was reconstructed by iterating the vector field. After using scaleinvariant feature transform to extract matching points in the descent images, the terrain was converted to a lander centroid coordinate system. Experiments were carried out with MATLAB-simulated images, laboratory images, and descent images of Chang'e III. Results show that the proposed method performs better than the classical SFS algorithm. The new method can provide reference for other deep space exploration activities.
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.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".