DetTrack: Detect Target from Local Region for 3D Single Object Tracking in Point Clouds
Bibliographic record
Abstract
D single object tracking (3D SOT) is an essential task in computer vision, widely applied in autonomous driving and robotics. Existing Siamese-based and motion-centric methods often require additional parameters and specialized architectures, resulting in high computational demands and failing to fully utilize advancements in 3D object detection. In this paper, we believe that if the search region is sufficiently localized, 3D SOT can be reformulated as a sequential detection problem within these regions. Based on this insight, we propose a novel 3D SOT framework, DetTrack, which comprises two core components: a search region estimator to predict the potential region including tracking target, and a modular 3D detector switcher to integrate pretrained 3D detectors for object localization. Our framework simplifies the tracking pipeline, eliminates the need for specialized 3D SOT models, and significantly reduces computational costs. Furthermore, the 3D detector switcher enables DetTrack to seamlessly integrate various state-of-the-art 3D detectors, making the framework highly adaptable and future-proof. Experimental results demonstrate that our method achieves performance comparable to state-of-the-art 3D SOT methods on multiple datasets while enabling real-time inference speed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".