RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
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
Augmented reality devices require multiple sensors to perform various tasks\nsuch as localization and tracking. Currently, popular cameras are mostly\nframe-based (e.g. RGB and Depth) which impose a high data bandwidth and power\nusage. With the necessity for low power and more responsive augmented reality\nsystems, using solely frame-based sensors imposes limits to the various\nalgorithms that needs high frequency data from the environement. As such,\nevent-based sensors have become increasingly popular due to their low power,\nbandwidth and latency, as well as their very high frequency data acquisition\ncapabilities. In this paper, we propose, for the first time, to use an\nevent-based camera to increase the speed of 3D object tracking in 6 degrees of\nfreedom. This application requires handling very high object speed to convey\ncompelling AR experiences. To this end, we propose a new system which combines\na recent RGB-D sensor (Kinect Azure) with an event camera (DAVIS346). We\ndevelop a deep learning approach, which combines an existing RGB-D network\nalong with a novel event-based network in a cascade fashion, and demonstrate\nthat our approach significantly improves the robustness of a state-of-the-art\nframe-based 6-DOF object tracker using our RGB-D-E pipeline.\n
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it