A Prediction-as-Perception Framework for 3D Object Detection

2026-03-12

AI Summary

This work addresses the accuracy and efficiency bottlenecks in 3D object detection for highly dynamic scenes by introducing a novel Prediction-as-Perception (PAP) framework. Inspired by the human brain’s predictive-perceptual synergy, PAP is the first to incorporate a biologically inspired prediction-perception closed loop into 3D detection. Leveraging sequential input frames, the framework employs a prediction module to forecast the future states of both the ego-vehicle and surrounding traffic participants, which are then used as queries to guide the perception module, enabling iterative interaction between the two. Implemented within the end-to-end UniAD architecture, the proposed method achieves a 10% improvement in object tracking accuracy and a 15% increase in inference speed on the nuScenes dataset, while simultaneously reducing computational overhead.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• 3D object detection
• perception accuracy
• dynamic traffic scenes
• moving objects
• computational efficiency

Innovation

• Prediction-as-Perception
• 3D object detection
• perception-prediction loop
• temporal modeling
• biomimetic architecture