Hierarchical Grouping Approach for Fast Approximate RGB-D Scene Flow
Bibliographic record
Abstract
A new approach to efficiently compute RGB-Dscene flow is introduced based on matching 3D points fromone frame to the next in a hierarchical fashion. Most stateof-the-art RGB-D scene flow methods are set in a variationalframework and formulated as an energy minimization problem. While these methods are able to provide high accuracy, theyare computationally expensive and not robust under largermotions in the scene. As well, the RGB-D scene flow datasetspresented to date are mostly based on qualitative evaluationof real scenes. The main contributions of this work are topresent a method of efficiently computing approximate sceneflow and provide an RGB-D scene flow dataset with groundtruth flow for quantitative evaluation. Quickly determiningapproximate motions in a scene is tremendously useful forany computer vision tasks that benefit from motion cues suchobstacle avoidance, object recognition, action recognition, etc. The proposed method, named Hierarchical Spectral GroupingScene Flow (HSG-SF), uses a simple coarse-to-fine voxelizationscheme combined with spectral grouping methods to providefast estimates of motion and accommodate for larger motions. Experimental results show that HSG-SF can provide reliablescene flow estimates at significantly faster runtime speed thancurrent methods.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".