CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes
Bibliographic record
Abstract
Camera-only multi-view 3D object detection in autonomous driving has witnessed encouraging developments in recent years, largely attributed to the revolution of fundamental architectures in modeling bird’s eye view (BEV). Despite the growing overall average performance, we contend that the exploration of more specific and challenging corner cases hasn’t received adequate attention. In this work, we delve into a specific yet critical issue for safe autonomous driving: occlusion. To alleviate this challenge, we draw inspiration from the human amodal perception system, which is proven to have the capacity for mentally reconstructing the complete semantic concept of occluded objects with prior knowledge. More specifically, we introduce auxiliary visual and language prototypes, akin to human prior knowledge, to enhance the diminished object features caused by occlusion. Inspired by Siamese object tracking, we fuse the information from these prototypes with the baseline model through an efficient depth-wise correlation, thereby enhancing the quality of object-related features and guiding the learning of 3D object queries, especially for partially occluded ones. Furthermore, we propose the random pixel drop to mimic occlusion and the multi-modal contrastive loss to align visual features of different occlusion levels to a unified space during training. Our inspiration originates from addressing occlusion, however, we are surprised to find that the proposed framework also enhances robustness in various challenging scenarios that diminish object representation, such as inclement weather conditions. By applying our model to different baselines, i.e., BEVFormer and SparseBEV, we demonstrate consistent improvements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".