3D Voxel Object Detection Refinement Network
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Solution Overview
Problem
Current object detection solutions, particularly in 3D scenarios, suffer from low accuracy due to the loss of positioning information during feature extraction and processing.
Innovation Solution
The method involves obtaining a point cloud of a scene, mapping it to a 3D voxel representation, performing convolution operations to extract feature information, and adjusting initial positioning information based on location and convolution features to improve detection accuracy by fusing location and convolution feature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is mapped to 3D voxel representation for feature extraction, then object detection efficiency is improved, but positioning information is lost
Solution Approach 1:
The patent introduces a refinement network that operates in the original point cloud space (3D spatial coordinates) to compensate for information loss during voxelization. By adding this refinement dimension after the main voxel-based detection pipeline, the system recovers precise positioning information while maintaining the efficiency benefits of voxel representation.
Solution Approach 2:
The refinement network acts as an intermediary component between the voxel-based detection system and the final output. It takes the candidate object regions from the voxel pipeline and refines their positioning by incorporating detailed point cloud features, thus mediating between efficiency-oriented voxel processing and precision-oriented location accuracy.
2Speed
If 3D voxel representation is used for object detection, then processing speed is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent segments the object detection process into two distinct stages: a coarse detection stage using efficient voxel representation for speed, and a refinement stage using detailed point cloud features for accuracy. This segmentation allows each stage to optimize for its specific goal without compromising the other.
Solution Approach 2:
The voxel-based detection performs preliminary action by quickly identifying candidate object regions and their approximate locations. This preliminary detection narrows down the search space, allowing the refinement network to focus computational resources only on relevant regions, thus achieving both speed and accuracy.
3Reliability
If feature extraction is performed on point cloud data, then object detection capability is improved, but positioning precision is reduced
Solution Approach 1:
The refinement network implements feedback by taking the candidate regions from feature extraction and using them to guide further processing. It feeds back detailed point cloud features specifically for the regions of interest, allowing the system to maintain high detection capability while recovering positioning precision through targeted refinement.
Data Source
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Figure 1c~1d
AI summary
An object detection method and apparatus, an electronic device and a storage medium. Said method comprises the following steps: acquiring a point cloud of a scene, the point cloud comprising position information of points (101); mapping the point cloud to a three-dimensional voxel representation, so as to obtain feature information concerning a three-dimensional voxel (102); performing a convolution operation on the feature information concerning the three-dimensional voxel, to obtain a convolution feature set (103); determining, on the basis of the convolution feature set, initial positioning information concerning a candidate object area (104); selecting, from the point cloud, a target point located within the candidate object area (105); and on the basis of position information concerning the target point and target convolution feature information, adjusting the initial positioning information concerning the candidate object area, to obtain positioning information concerning a target object area, the target convolution feature information being a convolution feature in the convolution feature set corresponding to the position of the target point (106). The method above improves the accuracy of object detection.