A real-time reconstructed 3D environment augmented with virtual objects rendered with correct occlusion
Bibliographic record
Abstract
In this work we present a novel framework for the real-time interaction with 3D models in augmented virtual reality. Our framework incorporates view-dependent stereoscopic rendering of the reconstructed environment together with user's hands and a virtual object, and high-precision gesture recognition to manipulate it. Proposed setup consists of a Creative RGB-D camera, Oculus Rift VR head mounted display (HMD), Leap Motion hands and fingers tracker and an AR marker. The system is capable of augmenting the user's hands in relation to their point of view (POV) using the depth sensor mounted on the HMD, and allows manipulation of the environment through the Leap Motion sensor. The AR marker is used to determine the location of the Leap Motion sensor to help with consolidation of transformations between the Oculus and the Leap Motion sensor. Combined with accurate information from the Oculus HMD, the system is able to track the user's head and fingers, with 6-DOF, to provide a spatially accurate augmentation of the user's virtual hands. Such an approach allows us to achieve high level of user immersion since the augmented objects occlude the user's hands properly; something which is not possible with conventional AR. We hypothesize that users of our system will be able to perform better object manipulation tasks in this particular augmented VR setup as compared to virtual reality (VR) where user's hands are not visible, or if visible, always occlude virtual objects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".