A survey on end‐to‐end point cloud learning
Bibliographic record
Abstract
Abstract Point cloud is an important expression form of three‐dimensional (3D) data. It has enjoyed continuous development and attracted increasing attention due to its wide applications in many areas, such as artificial intelligence, deep learning, autonomous driving and tracking. Recently, there is a large number of end‐to‐end point cloud‐based deep learning methods being proposed which are successful in the 3D domain. In order to better use point cloud data for analysis and to explore future research directions, this paper presents a comprehensive review of existing methods and publicly available datasets, with a focus on the methods and research status of using point cloud data as direct input. The background of point cloud is first introduced, including data acquisition methods, basic concepts, and challenges. Following that, the deep learning methods based on point cloud data are investigated and analysed according to classification, detection and tracking, and segmentation. Furthermore, the existing public datasets and evaluation metrics are introduced. Finally, promising research directions are proposed in conjunction with existing methods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".