模仿人类预测与感知结合，提升3D目标检测精度与速度
A Prediction-as-Perception Framework for 3D Object Detection
- 用预测结果作为感知查询，实现前后帧信息闭环反馈
- 在nuScenes上使跟踪准确率提升10%，推理速度加快15%
- 适合自动驾驶中需高效精准感知的场景
人类通过预测与感知结合来观察世界。面对快速移动的鸟或昆虫时，我们通过预测其下一位置并聚焦于此，才能清晰感知。受此启发，本文提出预测即感知（PAP）框架，将预测-感知架构融入3D目标感知任务，提升模型感知精度。PAP包含预测与感知两个模块，主要以连续帧信息为输入。首先，预测模块基于当前帧的感知结果，预测自车及周围交通参与者未来可能的位置；这些预测位置作为查询传递给下一帧的感知模块。感知结果再迭代反馈至预测模块。在nuScenes数据集上，使用端到端模型UniAD进行评估，结果表明PAP结构使UniAD的目标跟踪准确率提升10%，推理速度提高15%。这说明该生物启发设计显著提升了感知模型的效率与准确性，同时降低了计算资源消耗。
原文摘要 · Abstract (English)
Humans combine prediction and perception to observe the world. When faced with rapidly moving birds or insects, we can only perceive them clearly by predicting their next position and focusing our gaze there. Inspired by this, this paper proposes the Prediction-As-Perception (PAP) framework, integrating a prediction-perception architecture into 3D object perception tasks to enhance the model's perceptual accuracy. The PAP framework consists of two main modules: prediction and perception, primarily utilizing continuous frame information as input. Firstly, the prediction module forecasts the potential future positions of ego vehicles and surrounding traffic participants based on the perception results of the current frame. These predicted positions are then passed as queries to the perception module of the subsequent frame. The perceived results are iteratively fed back into the prediction module. We evaluated the PAP structure using the end-to-end model UniAD on the nuScenes dataset. The results demonstrate that the PAP structure improves UniAD's target tracking accuracy by 10% and increases the inference speed by 15%. This indicates that such a biomimetic design significantly enhances the efficiency and accuracy of perception models while reducing computational resource consumption.
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