Pseudo-Stereo++: Cycled Generative Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
Bibliographic record
Abstract
Recently, the feature-level generation has demonstrated the effectiveness of pseudo-stereo synthesis in Monocular 3D Detection (M3D). In this paper, we aim to further bridge the gap between the stereo and the monocular 3D object detectors in autonomous driving through direct image-level pseudo-stereo generation. We propose a novel Cycled Generative Pseudo-Stereo (CGPS) architecture to generate the right-view image from the left-view for constructing a pseudo-stereo pair to stereo 3D object detectors while maintaining the natural of M3D with the left-view image as the only input. Moreover, we use a triplet consistency loss to focus on the detected objects in the pseudo-stereo generation. Besides, we demonstrate that the proposed CGPS is an ad-hoc module to adapt top stereo 3D object detectors into monocular 3D object detectors. The proposed framework with CGPS achieves 74.80%, 55.28%, and 46.70% 3DAP for easy, moderate, and hard difficulty levels in monocular 3D detection on the KITTI benchmark with comparable performance to the stereo 3D object detectors but using a monocular image as the only input. Till the submission, the proposed M3D framework ranks 1<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> with dramatic improvements against the existing monocular 3D detectors on the KITTI benchmark.
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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 | 0.002 |
| 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".