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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".