3D Pose Estimation Using 2D Convolution and Recurrent Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning-based 3D perception technologies face challenges in accurately detecting the pose of objects with reflective symmetry, particularly in real-world scenarios where objects are partially occluded or arbitrarily rotated, due to the computational expense of 3D convolutions and the need for high-resolution processing.
Innovation Solution
A novel encoder architecture that processes 3D data using 2D convolutions within a 2D CNN, followed by a convolutional recurrent neural network (CRNN), allowing for efficient extraction of high-quality features and symmetry detection without relying on expensive 3D convolutions, and a method for estimating 2D symmetry planes to improve 3D pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D convolutions are used for processing 3D perception data, then measurement precision of 3D pose estimation is improved, but productivity deteriorates due to computational expense
Solution Approach 1:
The patent replaces the mechanical 3D convolution operation with a combination of 2D convolutions and recurrent neural networks. Specifically, it uses 2D convolutions to process individual image planes and a recurrent network to integrate information across the depth dimension, thereby substituting the computationally intensive 3D convolution mechanism with a more efficient alternative that achieves comparable pose estimation accuracy
Solution Approach 2:
The patent segments the 3D processing task into separate 2D processing stages. Instead of applying a single 3D convolution across the entire volume, it divides the processing into sequential 2D convolution operations on individual planes followed by recurrent integration, breaking down the complex 3D operation into manageable 2D segments that are computationally less expensive
2Measurement precision
If high-resolution processing is applied to detect symmetry in partially occluded objects, then measurement precision is improved, but use of energy deteriorates
Solution Approach 1:
The patent changes the processing parameters by using 2D convolutions with smaller computational footprints compared to 3D convolutions. By processing each 2D plane independently and then integrating results through recurrent connections, it achieves high-resolution symmetry detection with reduced energy consumption per computational operation
Solution Approach 2:
The patent applies partial action by processing only the necessary 2D planes and using recurrent networks to infer missing information rather than exhaustively processing the entire 3D volume at high resolution. This allows symmetry detection in partially occluded objects while avoiding the energy cost of complete high-resolution 3D processing
3Manufacturing precision
If 3D convolutions are used for feature extraction, then manufacturing precision of feature representation is improved, but device complexity worsens
Solution Approach 1:
The patent substitutes the complex 3D convolution mechanism with a structured combination of 2D convolutions and recurrent neural networks. This replacement maintains feature extraction quality by preserving the hierarchical processing capability while using simpler, well-understood 2D convolution operations that are less complex to implement and train
4Productivity
If 2D convolutions with CRNN are used instead of 3D convolutions, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent replaces 3D convolutions with 2D convolutions combined with recurrent neural networks, substituting a single complex operation with a sequential processing system that maintains accuracy through iterative refinement across time steps while improving processing efficiency
Solution Approach 2:
The patent ensures continuity of useful action by using recurrent neural networks that continuously integrate information across sequential 2D processing stages. This continuous integration maintains the accuracy that would otherwise require full 3D convolutions while allowing the system to process data more efficiently through sequential rather than simultaneous computation
Data Source
AI summary
A computer-implemented method of estimating a 3D object pose, the method comprising: receiving 3D data comprising a full or partial view of a 3D object, the 3D object exhibiting reflective symmetry about an unknown 2D symmetry plane; applying symmetry detection to the 3D data, and thereby calculating, in 3D space, an estimated 2D symmetry plane for the 3D object; and applying 3D pose detection to the 3D data based on the estimated 2D symmetry plane, thereby computing a 3D pose estimate of the 3D object that is informed by the reflective symmetry of the 3D object.