Spectrum analysis of motion parallax in a 3D cluttered scene and application to egomotion
Bibliographic record
Abstract
Previous methods for estimating observer motion in a rigid 3D scene assume that image velocities can be measured at isolated points. When the observer is moving through a cluttered 3D scene such as a forest, however, pointwise measurements of image velocity are more challenging to obtain because multiple depths, and hence multiple velocities, are present in most local image regions. We introduce a method for estimating egomotion that avoids pointwise image velocity estimation as a first step. In its place, the direction of motion parallax in local image regions is estimated, using a spectrum-based method, and these directions are then combined to directly estimate 3D observer motion. There are two advantages to this approach. First, the method can be applied to a wide range of 3D cluttered scenes, including those for which pointwise image velocities cannot be measured because only normal velocity information is available. Second, the egomotion estimates can be used as a posterior constraint on estimating pointwise image velocities, since known egomotion parameters constrain the candidate image velocities at each point to a one-dimensional rather than a two-dimensional space.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".