Multi-Modal Sensor Fusion With Coarse-to-Fine Attention Sampling
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Solution Overview
Problem
Current sensor fusion technologies face challenges such as high computational cost, loss of spatial context, over-dependence on light detection and ranging equipment, sub-optimal fusion strategies, and limited multi-sensor data for training deep models, leading to inefficiencies in object detection and environment perception.
Innovation Solution
A coarse-to-fine attention-based method that dynamically samples coarse predictions using radio detection and ranging equipment detections, projects image data onto a point cloud, and refines features using a deformable attention module to achieve robust object detection and segmentation, leveraging both camera and radio detection and ranging equipment inputs in an end-to-end manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current sensor fusion technologies use dense point cloud data and deep learning models, then object detection and environment perception performance is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the point cloud processing into two distinct stages: a coarse stage that processes all points with basic operations, and a fine stage that processes only selected points with complex attention operations. This segmentation reduces computational cost by applying expensive operations only where necessary, while maintaining detection performance through the two-stage refinement process.
Solution Approach 2:
The patent applies partial action by selectively processing only a subset of points in the fine stage rather than all points. The deformable attention module identifies and processes only the most relevant points for object detection, avoiding unnecessary computational operations on all point cloud data while maintaining accurate detection results.
2Use of energy by moving object
If the system uses only radio detection and ranging equipment, then computational cost is reduced, but spatial context and detection accuracy are lost
Solution Approach 1:
The patent merges the advantages of different sensor modalities by combining radio detection and ranging equipment (which provides accurate 3D spatial information) with camera data (which provides rich semantic information). The fusion module integrates features from both sources, allowing the system to achieve both accurate spatial context and semantic understanding while maintaining computational efficiency through the coarse-to-fine processing strategy.
3Measurement precision
If the system processes all point cloud points with high-resolution operations, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the processing pipeline into coarse and fine stages, where the coarse stage performs quick operations on all points to establish a baseline representation, and the fine stage performs high-resolution operations only on selected points. This temporal and computational segmentation maintains detection accuracy on critical regions while significantly reducing overall processing time.
Solution Approach 2:
The patent applies preliminary action by first performing coarse processing on all point cloud points to create an initial representation and identify potential objects of interest. This preliminary stage prepares the data structure and highlights relevant regions before the fine processing stage applies detailed attention operations, thereby avoiding unnecessary high-resolution processing on all points and reducing overall processing time.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for coarse-to-fine attention-based sensor fusion. The method includes obtaining a 3D voxel image space of an environment, the 3D voxel image space comprising first features of the environment extracted from a plurality of images; obtaining a point cloud corresponding to the environment, the point cloud comprising points, wherein the points are labeled with second features; generating a coarse representation of the environment, the coarse representation comprising a projection of the first features onto the points of the point cloud, wherein the projection is based on combining a respective set of the first features within a first radius from a point with a respective set of the second features corresponding to the point; applying a deformable attention module to predict fine sampling locations and extract fine features from the first features; and generating, with an attention module, a fine representation of the environment.