Multi-Modal 3D Pose Estimation with Pseudo-Labeled Point Clouds
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately estimating the 3-D pose of objects using neural networks due to the high cost and potential errors in manually labeling large volumes of LIDAR point clouds, which are necessary for training, and the inefficiency of existing training methods.
Innovation Solution
A method is described that automatically generates pseudo labels for unlabeled point clouds based on corresponding labeled 2-D images, allowing a multi-task training system to improve the neural network's performance in 3-D pose estimation by leveraging richer training signals from related auxiliary tasks, such as pointwise classification of the point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually labeled LIDAR point clouds are used for training, then pose estimation accuracy can be improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent uses 2D image labels as a copy or proxy for the difficult-to-obtain 3D point cloud labels. By projecting 2D keypoint locations onto the 3D point cloud space, the system generates pseudo-labels that approximate the ground truth without requiring manual 3D annotation, thus reducing time and cost while maintaining reasonable accuracy
Solution Approach 2:
The patent introduces an intermediary approach where 2D image data serves as a mediator to generate 3D pose labels. The system first detects keypoints in 2D images, then uses camera extrinsics and projection geometry to transfer these labels to the 3D point cloud domain, creating a bridge between easily obtainable 2D data and required 3D labels
2Measurement precision
If manually labeled LIDAR point clouds are used for training, then pose estimation accuracy can be improved, but the cost increases due to high labor requirements
Solution Approach 1:
The system creates a copy of the labeling process by using 2D image annotations to generate 3D point cloud labels automatically. This copying approach eliminates the need for expensive manual 3D labeling while preserving the essential pose information needed for training
Solution Approach 2:
The system enables self-service labeling where the training data generation process becomes autonomous. By automatically projecting 2D keypoints to 3D space and generating pseudo-labels without human intervention, the system makes the training data preparation process self-sufficient and cost-effective
3Productivity
If existing training methods are used, then implementation simplicity is maintained, but training efficiency and pose estimation accuracy are insufficient
Solution Approach 1:
The patent implements a multi-task training system where a single neural network performs multiple functions: 2D keypoint detection, 3D pose estimation, and pseudo-label generation. This multi-functional approach improves training efficiency by leveraging shared features across tasks while maintaining manageable system complexity through unified architecture
Solution Approach 2:
The system transitions from 2D image space to 3D point cloud space by applying camera extrinsics and projection geometry. This dimensional transformation enables the network to learn from 2D data while producing 3D pose estimates, effectively adding a spatial dimension to the training process without requiring complex 3D annotation infrastructure
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.