Bibliographic record
Abstract
In computer vision and graphics as well as in mobile robotics one is often interested in capturing 3D real world scenes. In the former, models are captured for such purposes as photorealistic rendering. In the latter, models are captured to generate navigation maps for robot control. The objective in both fields is similar: How can the modeling of existing scenes be achieved? This thesis investigates alternative ways of capturing the geometry and appearance of an indoor environment by using image-based modeling. Calibrated and uncalibrated methods are investigated and contrasted. The resulting models are validated by utilizing them in two robotics tasks. We first use the model as a navigation map to localize the pose of a robot and to track its motion based on images from a camera fitted on the robot. Second, we generate synthetic images by reprojecting and texturing the captured model given a desired camera pose. The first approach is based on a panoramic image mosaic augmented with depth information and is built using calibrated cameras and range sensors (a trinocular device and a laser range-finder). Several methods for registering camera and range sensors were developed and compared. The model is segmented into planar pieces that can be reprojected in new positions. The second approach uses an uncalibrated camera that samples a scene. By maintaining visual tracking of corresponding feature points, the geometry of the scene is reconstructed using stratified structure from motion. The geometric model is then bundle-adjusted and reprojected into the original images to acquire surface appearance. Surface appearance is represented not using a single traditional texture, but by acquiring a basis that captures the view dependency of the surface.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".