Semantic-Anchored Multi-State Retrieval for Robust 3D Perception

Engineering notes

Key topics: autonomous driving, perception, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Robust autonomous driving requires stable 3D perception under long-tail distributions and open-world conditions. Conventional detectors rely on binary decision thresholds, which yield unstable predictions when object confidence lies near the boundary. We reformulate 3D perception as a multi-state semantic retrieval problem. Instead of enforcing hard binary decisions, predictions are partitioned into three states, i.e., acceptance, rejection, and deferment. The deferment state acts as a reasoning buffer, preventing uncertain proposals from premature suppression. Unlike traditional methods with fixed thresholds, we integrate probabilistic rough sets with decision-theoretic rough sets to optimize detection thresholds. A tunable consistency coefficient allows real-time adjustment based on environmental conditions. To resolve uncertainty, we introduce a vision-language model as a semantic retrieval module. The VLM evaluates semantic consistency between ambiguous proposals and category descriptions for buffered proposals, which provides complementary evidence beyond detector confidence. Experiments demonstrate consistent improvements on rare classes with higher retrieval recall and fewer false negatives. The code will be available once the paper is accepted.

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