LiDAR Surface Normal Estimation With Spherical Point Patches
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Solution Overview
Problem
Existing neural network models for estimating surface normals are computationally expensive and require dedicated GPUs, making them unsuitable for low power and thermally constrained applications like autonomous drones and underwater vehicles, and lack formal verification for safety-critical applications.
Innovation Solution
A neural network system that uses a parallelizable k-nearest neighbor sorting algorithm to transform Euclidean point cloud data into spherical coordinates, allowing for efficient estimation of surface normals with a significantly reduced number of parameters, eliminating the need for convolutional layers and enabling deployment on low power devices without GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural network models with 1M to 15M parameters are used for surface normal estimation, then measurement precision is improved, but device complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the point cloud data into local patches of k-nearest neighbor points around each query point. This segmentation allows the neural network to process smaller, localized regions rather than entire point clouds, reducing the computational burden and parameter requirements while maintaining estimation accuracy through focused local analysis.
Solution Approach 2:
The patent transforms the 3D Euclidean point cloud data into a 2D spherical coordinate system centered at each query point. This dimensional transformation projects the local 3D neighborhood onto a 2D spherical surface, reducing the complexity of the input space and enabling more efficient processing with fewer network parameters while preserving the geometric relationships needed for accurate surface normal estimation.
2Measurement precision
If large neural network models are deployed for real-time processing, then measurement precision is improved, but speed of operation decreases due to computational expense
Solution Approach 1:
By segmenting the processing task into independent local patches around each query point, the patent enables parallel processing of multiple points simultaneously. Each patch can be processed independently, allowing for efficient batch computation and real-time performance even with moderate network sizes, as the segmentation eliminates sequential dependencies.
Solution Approach 2:
The patent uses a fixed, limited number of k-nearest neighbor points for each query point rather than processing all available points. This partial action approach provides sufficient local geometric information for accurate surface normal estimation while dramatically reducing the computational workload compared to using the entire point cloud, enabling real-time processing speeds.
3Measurement precision
If dedicated GPUs are used to operate neural network models, then measurement precision is improved, but use of energy and cooling requirements increase
Solution Approach 1:
The patent replaces expensive, power-hungry dedicated GPUs with simpler, lower-power processing units. By reducing the neural network size through point cloud segmentation and spherical coordinate transformation, the system can achieve the same measurement precision using inexpensive processors with significantly lower power consumption and thermal output, suitable for resource-constrained autonomous platforms.
4Reliability
If formal verification techniques are applied to large neural networks, then reliability is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the neural network into smaller functional components corresponding to the processing of local point patches. This modular segmentation makes the network more amenable to formal verification techniques, as each smaller module can be verified independently with simpler proofs, reducing the overall complexity of verification while improving reliability through systematic validation of critical components.
Data Source
AI summary
Described is a system for training a neural network for estimating surface normals for use in operating an autonomous platform. The system uses a parallelizable k-nearest neighbor sorting algorithm to provide a patch of points, sampled from the point cloud data, as input to the neural network model. The points are transformed from Euclidean coordinates in a Euclidean space to spherical coordinates. A polar angle of a surface normal of the point cloud data is estimated in the spherical coordinates. The trained neural network model is utilized on the autonomous platform, and the estimate of the polar angle of the surface normal is used to guide operation of the autonomous platform within the environment.