Joint Pose-Shape Estimation From Incomplete LiDAR Point Clouds
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Solution Overview
Problem
Existing methods for pose estimation and shape completion in autonomous vehicles are inefficient and prone to errors due to sequential processing and reliance on known 3D shapes and canonical orientations.
Innovation Solution
A neural network system that jointly estimates the pose and shape of objects from incomplete LIDAR data using a shared encoder and separate pose and shape decoders, trained with loss functions to minimize errors in shape completion and pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential processing (pose estimation followed by shape completion) is used, then the processing pipeline is simple, but errors propagate and runtime efficiency is poor
Solution Approach 1:
The patent merges pose estimation and shape completion into a single joint neural network that processes LIDAR data simultaneously for both tasks. The shared encoder extracts features once, and separate decoders generate both pose and shape outputs, eliminating sequential processing and error propagation while improving runtime efficiency.
Solution Approach 2:
The shared encoder in the neural network serves multiple functions by extracting features that are simultaneously used for both pose estimation and shape completion. This multi-functional design eliminates redundant processing and improves overall system efficiency while maintaining accuracy.
2Measurement precision
If known 3D shape and canonical orientation are assumed, then pose estimation can be performed, but the method fails for unknown objects or inaccurate canonical orientations
Solution Approach 1:
The patent extracts and removes the dependency on known 3D shapes and canonical orientations from the pose estimation process. The neural network learns to estimate pose directly from incomplete LIDAR data without requiring pre-defined object models, enabling the system to handle unknown objects and varying orientations effectively.
Solution Approach 2:
The patent changes the fundamental parameters of the estimation approach by transitioning from model-based methods (requiring known shapes and orientations) to a learning-based method that directly predicts pose and shape from raw sensor data, significantly improving adaptability to unknown objects.
3Ease of operation
If incomplete LIDAR data is used, then real-world sensing constraints are satisfied, but full 3D shape and pose cannot be accurately estimated
Solution Approach 1:
The neural network performs preliminary feature extraction from incomplete LIDAR data through the shared encoder, creating a rich feature representation that contains sufficient information for both pose and shape estimation. This preliminary processing enables accurate estimation despite incomplete input data.
Solution Approach 2:
The shared encoder acts as an intermediary that transforms incomplete LIDAR data into a comprehensive feature representation, bridging the gap between partial sensor input and complete pose-shape estimation. This intermediate feature space preserves critical information needed for accurate reconstruction.
Data Source
AI summary
Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.