3D Object Detection Multi-Resolution Feature Recovery
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Solution Overview
Problem
Conventional 3D object detection methods suffer from information loss due to down-sampling processes in convolutional neural networks, which reduces the accuracy and effectiveness of object detection.
Innovation Solution
The proposed system and method incorporate multi-resolution feature recovery using panoptic segmentation information, where discarded features from earlier stages of the 3D object detection network are retrieved and processed to augment the later stages, thereby minimizing information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If down-sampling processes are applied in convolutional neural networks for 3D object detection, then computational efficiency is improved, but information loss occurs reducing detection accuracy
Solution Approach 1:
The patent applies the discarding and recovering principle by capturing discarded multi-resolution features from early stages of the 3D object detection network, storing them in a feature buffer, and recovering them to augment later stages. This resolves the contradiction by preserving information that would otherwise be lost during down-sampling, while still allowing the down-sampling process to proceed for computational efficiency.
Solution Approach 2:
The patent implements nesting by embedding multiple levels of feature maps at different resolutions within the detection network. Early-stage high-resolution features are nested within the network architecture and made available to later stages through the feature buffer, allowing fine-grained information to be preserved and utilized alongside down-sampled features.
2Measurement precision
If multi-resolution features are recovered and integrated, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a feature buffer as an intermediary component that mediates between early and late stages of the detection network. This buffer stores and manages multi-resolution features, providing a structured interface for feature retrieval and integration. The intermediary simplifies the complexity by centralizing feature management rather than requiring complex point-to-point connections between network stages.
Solution Approach 2:
The patent segments the feature processing into distinct functional modules: feature extraction in early stages, feature storage in the buffer, feature retrieval based on panoptic segmentation information, and feature integration in later stages. This modular segmentation reduces system complexity by making each component's function explicit and manageable.
3Loss of information
If panoptic segmentation information is utilized for feature retrieval, then feature relevance is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by generating panoptic segmentation information and identifying relevant feature locations in advance, before the actual feature retrieval and integration process. This pre-processing of segmentation data enables faster and more targeted feature retrieval from the buffer, reducing the time penalty associated with using segmentation information.
Data Source
AI summary
A system and method for 3D object detection using multi-resolution features recovery using panoptic segmentation information. Panoptic segmentation predictions from a panoptic segmentation network and intermediate feature maps from one or more early layers of an object detection network are received. Feature vectors are retrieved from the intermediate feature maps using the panoptic segmentation predictions. The retrieved feature vectors are combined with feature maps from one or more late layers of the object detection network for generating object detection predictions.