Active Observers in a 3D World: The 3D Same-Different Task
Bibliographic record
Abstract
Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how to look. Here, we are investigating active, visual observation in a 3D world. To focus, we ask subjects to decide if two 3D objects are the same or different, with no constraints on how they view those objects. Such 3D unconstrained, active observation seems under-studied. While many studies explore human performance, usually, they use line drawings portrayed in 2D, and no active observer is involved. The ability to compare two objects seems a core visual capability, one we use many times a day. It would also be essential for any robotic vision system whose role it is to be a real assistant at home, manufacturing or medical setting. To explore the 3D 'same-different task', we designed a novel experimental environment and created a set of twelve 3D printed objects with known complexity. The subject is allowed to move around freely in a 4m x 3m controlled environment, outfitted with eye gaze tracker and observed by head trackers. In this environment, two objects are presented at a time at a fixed 3D locations but with a varying 3D pose. We track precise 6D head motion, gaze and record a video of all actions, synchronized at microsecond resolution. Additionally, the subject is interviewed about how the task was approached. Our results show that at least six strategies for solving this task are employed, not always independently. We found that the strategy used is dependent on three variables: object complexity, object orientation, and initial viewpoint. Furthermore, we show that performance improves over time as subjects refine their strategies throughout the study. Since no external feedback is given, an internal feedback mechanism must exist that refines strategies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".