KinectScenes: Robust Real-Time RGB-D Fusion for Animal Behavioural Monitoring
Bibliographic record
Abstract
Animal Behavioural Monitoring requires near real-time and robust scene information. State of art methods to construct a complete RGB-D video of a scene are typically slow, or done offline and often both computationally and monetarily expensive. In this paper, we present a near real-time method called KinectScenes that fuses N RGB-D point clouds acquired from statically positioned commodity hardware to reconstruct large & complete dynamic 3D scenes. Specifically, we introduce a pipeline that fuses RGB-D sensor data to generate a spatio-temporal coherent 3D free viewpoint video with near 360° purview -- only limited by the number and placement of the sensors. KinectScenes differentiates from existing state of art in that 1. It is near real-time: 7~15 fps, and 2. It remains unperturbed to minute motion of the static sensors. In addition, we contribute a new dataset of RGB-D scenes to the research community as to enable rapid progress for behavioral tracking research. Applications include precise human and animal behavioral tracking, inexpensive VR scene generation, object recognition and interaction. Early results demonstrate that KinectScenes is both efficient in near real-time and can densely and richly reconstruct a given dynamic scene using 3 sensors.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".