A real-time reconstructed 3D environment augmented with virtual objects rendered with correct occlusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it