Multi-Stage Multi-View Detection With Selective High-Resolution Patches
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Solution Overview
Problem
Current multi-view 3D object detectors require high-resolution input images for optimal detection, which is computationally expensive, or they use downsampled images that degrade detection performance.
Innovation Solution
A two-stage multi-view object detection method that extracts high-resolution patches from input images, determines 3D coordinates, back-projects these onto images, and uses cross-attention to fuse information from multiple patches for improved detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution input images are used for object detection, then detection performance is improved, but computational cost increases
Solution Approach 1:
The patent segments the image processing task into two stages: first processing downsampled low-resolution images for initial object detection, then selectively processing high-resolution patches only for regions where objects are detected. This segmentation allows the system to maintain high detection performance while significantly reducing overall computational cost by avoiding unnecessary high-resolution processing of the entire image.
Solution Approach 2:
The patent applies local quality by processing different regions of the image at different resolutions. High-resolution patches are extracted and processed only for specific regions of interest where objects are detected in the low-resolution representation, while other regions are processed at lower resolution. This maintains high detection accuracy in critical areas while reducing computational burden overall.
2Use of energy by moving object
If downsampled input images are used for object detection, then computational cost is reduced, but detection performance degrades
Solution Approach 1:
The patent divides the detection process into two stages: initial detection using downsampled low-resolution images for computational efficiency, followed by a refinement stage that processes high-resolution patches only for regions containing detected objects. This segmentation enables the system to achieve both low computational cost and high detection performance.
Solution Approach 2:
The patent performs preliminary object detection using downsampled low-resolution images before processing high-resolution patches. This preliminary action identifies object locations efficiently with minimal computational resources, allowing subsequent high-resolution processing to focus only on relevant regions, thus achieving both efficiency and accuracy.
3Measurement precision
If high-resolution patches are extracted and processed, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts and processes high-resolution patches only for specific local regions where objects are detected in the low-resolution representation. This localized approach maintains high detection accuracy for objects of interest while minimizing processing time by avoiding high-resolution processing of the entire image.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the image at high resolution - specifically the patches corresponding to detected object regions - rather than processing the entire image at high resolution. This partial processing achieves sufficient accuracy while reducing overall processing time.
Data Source
AI summary
Systems and techniques are described herein for object detection. For example, a computing device can extract, by an encoder of the computing device, a plurality of features from one or more images of an environment of the computing device. The computing device can determine, based on the plurality of features, a first detection of one or more objects and three-dimensional (3D) coordinates for the one or more objects. The computing device can back-project the 3D coordinates of the one or more objects onto the one or more images. The computing device can determine one or more regions of at least one first image of the one or more images based on the back-projection of the 3D coordinates of the one or more objects. computing device determine, based on the one or more regions of the at least one first image, a second detection of the one or more objects.