Deep Scene Flow Learning: From 2D Images to 3D Point Clouds
Bibliographic record
Abstract
Scene flow describes the 3D motion in a scene. It can be modeled as a single task or as a composite of the auxiliary tasks of depth, camera motion, and optical flow estimation. Deep learning's emergence in recent years has broadened the horizons for new methodologies in estimating these tasks, either as separate tasks or as joint tasks to reconstruct the scene flow. The sequence of images that are either synthesized or captured by a camera is used as input for these methods, which face the challenge of dealing with various situations in images to provide the most accurate motion, such as image quality. Nowadays, images have been superseded by point clouds, which provide 3D information, thereby expediting and enhancing the estimated motion. In this paper, we dig deeply into scene flow estimation in the deep learning era. We provide a comprehensive overview of the important topics regarding both image-based and point-cloud-based methods. In addition, we cover the methodologies for each category, highlighting the network architecture. Furthermore, we provide a comparison between these methods in terms of performance and efficiency. Finally, we conclude this survey with insights and discussions on the open issues and future research directions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".