3D Object Localization via Fused Image and Depth Data
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Solution Overview
Problem
Conventional 2D object detection algorithms fail to accurately localize 3D objects with significant spatial dimensions and do not fully utilize additional depth data, making them inadequate for robotics and other applications requiring 3D reference points like the center of mass or centroid.
Innovation Solution
A modified convolutional neural network architecture that fuses image and depth data using a YOLO v3-like structure, incorporating feature extraction layers for both modalities, a fusion layer for concatenation and reduction, and an output block for predicting 3D localization data, along with a training process that includes depth-aware data augmentation and transfer learning from 2D object detection datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D object detection algorithms are used, then detection speed and simplicity are maintained, but 3D localization accuracy is insufficient
Solution Approach 1:
The patent transitions from 2D object detection to 3D localization by incorporating depth information as an additional dimension. The model processes both RGB images and depth maps simultaneously, enabling the detection system to output 3D bounding boxes and centroid positions rather than just 2D coordinates. This dimensional extension directly resolves the contradiction by providing accurate 3D localization while maintaining a unified detection framework.
Solution Approach 2:
The patent merges multiple data modalities (RGB images and depth maps) and multiple detection outputs (2D bounding boxes and 3D localization data) into a single unified neural network model. By combining these elements in one integrated architecture rather than using separate processing pipelines, the system achieves 3D localization accuracy without proportionally increasing algorithmic complexity.
2Measurement precision
If depth data is fully utilized for 3D detection, then 3D localization quality improves, but training data requirements increase
Solution Approach 1:
The patent applies transfer learning by pre-training the model on large-scale 2D object detection datasets before fine-tuning on smaller 3D detection datasets. This preliminary training on abundant 2D data allows the model to learn general object recognition features, which are then transferred to the 3D detection task. Consequently, the system achieves high 3D detection quality without requiring proportionally large amounts of annotated 3D training data.
Solution Approach 2:
The patent modifies the detection model's output parameters to include 3D bounding box coordinates and centroid positions in addition to 2D detection results. By changing the parameter space from 2D to 3D while maintaining the same input modalities and leveraging transfer learning, the model achieves improved 3D detection quality without requiring a proportional increase in training data volume.
3Measurement precision
If 3D centroid localization is implemented, then object reference point accuracy improves, but computational requirements increase
Solution Approach 1:
The patent performs preliminary 2D object detection and localization within the neural network before computing the final 3D centroid position. By first identifying the object's 2D bounding box and then extending this to 3D using depth information and predicted dimensions, the model avoids computationally expensive direct 3D optimization. This two-stage approach within the unified network achieves high reference point accuracy while controlling computational energy consumption.
Data Source
Figure 1
AI summary
The invention relates to a computer-implemented method for 3D localization of an object based on image data and depth data indicating distance information for pixels of an image represented by the image data, wherein the method applies a convolutional neural network (1) with a first set (2) of consecutive layers (21), a second set (3) of consecutive layers (31) and a third set (5) of consecutive layers (51), each layer being configured with one or more filters, wherein the convolutional neural network (1) is trained to associate an identification/ classification of an object and corresponding 3D localization data for the identified/classified object to an image data item and a depth data item, comprising the steps of: - Extracting one or more image data features from the image data applying the first set (2) of layers (21) of the convolutional neural network (1); - Extracting one or more depth data features from the depth data applying the second set (3) of layers (31) of the convolutional neural network (1); - Fusing the one or more image data features and the one or more depth data features to obtain at least one fused feature map; - Processing the at least one fused feature map by applying the third set (5) of layers (51) of the convolutional neural network (1) to identify/classify the object and to provide the 3D localization data for the identified/classified object, wherein the 3D localization data includes object reference point data.