3D Object Perception via Multi-Modal Fusion
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Solution Overview
Problem
Current methods for three-dimensional (3D) object perception in complex environments, such as those involving multiple objects of the same class with different geometric characteristics, face challenges in accurately distinguishing and detecting these objects.
Innovation Solution
A method that utilizes a multi-modal decoding model to generate decoding outputs by extracting correlations from target image features, point cloud features, and detection guide information, allowing for the detection of 3D bounding boxes corresponding to target objects in a 3D space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-modal object detection methods are used, then the detection process is simple, but the accuracy of distinguishing objects with same class but different geometric characteristics deteriorates
Solution Approach 1:
The patent combines multiple modalities (image data, point cloud data, and language descriptions) into a unified detection framework. The multi-modal fusion architecture integrates visual features from images, geometric features from point clouds, and semantic constraints from language inputs to achieve accurate distinction between objects of the same class with different geometric characteristics.
Solution Approach 2:
The patent transitions from traditional 2D image-based detection to 3D space-aware detection by incorporating point cloud data and generating 3D bounding boxes. This dimensional enhancement allows the system to capture geometric characteristics in three-dimensional space, enabling differentiation of objects based on spatial dimensions beyond what 2D images provide.
2Measurement precision
If multi-modal data integration is implemented, then object distinction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary processing of each modality independently before fusion: image encoding extracts visual features, point cloud encoding extracts geometric features, and language encoding extracts semantic constraints. These pre-processed features are then fed into the detection model, reducing the computational burden during the fusion and detection phases.
Solution Approach 2:
The patent introduces a multi-modal fusion module as an intermediary that bridges different data modalities. This fusion module processes and integrates features from images, point clouds, and language descriptions in a structured manner, managing computational complexity by providing a dedicated interface for multi-modal interaction rather than direct complex interactions between all modalities.
3Measurement precision
If 3D bounding box detection is performed, then geometric characteristic recognition improves, but detection time increases
Solution Approach 1:
The patent performs preliminary encoding of point cloud data into compact geometric features before the detection stage. This pre-processing step extracts essential 3D geometric characteristics in advance, reducing the computational workload during 3D bounding box generation and enabling faster detection while maintaining geometric recognition accuracy.
Solution Approach 2:
The patent transforms raw point cloud data into encoded geometric features through a point cloud encoding model, changing the parameter representation from raw coordinate data to compressed feature vectors. This parameter transformation reduces data dimensionality and complexity, enabling more efficient 3D bounding box detection while preserving geometric characteristic information.
Data Source
AI summary
A method for (3D) object detection includes: receiving an input image with respect to a 3D space, an input point cloud with respect to the 3D space, and an input language with respect to a target object in the 3D space; using an encoding model to generate candidate image features of partial areas of the input image, a point cloud feature of the input point cloud, and a linguistic feature of the input language; selecting a target image feature corresponding to the linguistic feature from among the candidate image features based on similarity scores of similarities between the candidate image features and the linguistic feature; generating a decoding output by executing a multi-modal decoding model based on the target image feature and the point cloud feature; and detecting a 3D bounding box corresponding to the target object by executing an object detection model based on the decoding output.