Multi-View 3D Object Detection for Occluded Scene Perception
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Solution Overview
Problem
Conventional 3D object detection techniques often lack comprehensive spatial context and struggle with detecting objects that are obscured or have ambiguous appearances from certain orientations, particularly in single-view approaches like LiDAR point cloud projections to 2D views.
Innovation Solution
The method employs multi-2D-viewpoint 3D object detection by obtaining and performing cross-attention between multiple 2D views from different sensors or viewpoints, such as LiDAR and cameras, to generate cross-attended features and centerness heatmaps, enhancing detection accuracy and handling occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single-view approach is used for 3D object detection, then computational efficiency is improved, but detection accuracy for obscured objects deteriorates
Solution Approach 1:
The patent transitions from single-view 2D detection to multi-view 3D detection by projecting LiDAR point clouds from 3D space onto multiple 2D planes (front, side, top views). This dimensional transformation enables comprehensive spatial context while maintaining computational feasibility through efficient projection operations.
Solution Approach 2:
The patent segments the 3D detection problem into multiple independent 2D view processing tasks. Each view (front, side, top) is processed separately to extract features, then these segmented results are integrated through cross-attention mechanisms to form comprehensive 3D object detections.
2Device complexity
If single-view approach is used for 3D object detection, then device complexity is reduced, but spatial context completeness deteriorates
Solution Approach 1:
The patent creates a multi-functional detection system where a single LiDAR sensor serves multiple purposes by generating point clouds that are projected onto multiple 2D planes. This universal approach allows one sensor to provide comprehensive spatial context across front, side, and top views simultaneously.
Solution Approach 2:
The patent introduces cross-attention mechanisms as intermediaries that integrate information from multiple 2D views. These attention mechanisms act as mediators that combine spatial context from different perspectives while filtering redundant information, achieving comprehensive context without proportionally increasing system complexity.
3Measurement precision
If multiple 2D views are used for 3D object detection, then detection accuracy for obscured objects is improved, but computational load increases
Solution Approach 1:
The patent performs preliminary projection of 3D point clouds onto multiple 2D planes before detection. By pre-processing the spatial data into standardized 2D views with consistent coordinate systems, the system avoids computationally expensive operations during the actual detection phase, improving overall efficiency.
Solution Approach 2:
The patent merges features from multiple 2D views through cross-attention mechanisms that efficiently integrate information. Instead of processing each view independently to completion, the system combines intermediate features from front, side, and top views, achieving accurate 3D detection with reduced redundant computation.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for performing 3D object detection. Such techniques may include obtaining one or more inputs associated with one or more two-dimensional (2D) views of a scene; selecting a set of 2D views of the scene from a plurality of 2D views of the scene based on the one or more inputs, the set of 2D views comprising a first 2D view of the scene and a second 2D view of the scene; and performing three-dimensional (3D) object detection in the scene based on the set of 2D views.