3D Opacity Grid From LiDAR for Sparse Scene Forecasting
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Solution Overview
Problem
Existing autonomous driving systems face challenges in efficiently encoding and forecasting 3D scene geometry using LiDAR data due to its sparse and unstructured nature, lacking effective grid representations that can leverage neural networks for scene forecasting.
Innovation Solution
A method to form a 3D opacity grid by mapping LiDAR points to a grid, employing volume densification and a 3D convolutional network to generate a dense and continuous representation, enabling scene forecasting and other applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR data is directly used for scene representation, then measurement precision is improved, but device complexity increases due to sparse and unstructured data nature
Solution Approach 1:
The patent segments the 3D space into a grid of voxels, transforming the unstructured LiDAR points into a structured grid representation. Each voxel can independently represent occupancy or geometric information, converting the sparse point cloud into a more manageable and structured format that neural networks can process effectively.
Solution Approach 2:
The patent introduces an intermediate representation layer (the 3D grid/voxel map) between the raw LiDAR data and the neural network processing stage. This intermediate structure serves as a mediator that organizes the sparse LiDAR points into a dense, structured format, facilitating easier processing while preserving measurement precision.
2Productivity
If grid-based 3D scene representations are used, then productivity is improved through computational efficiency, but loss of information occurs in sparse regions
Solution Approach 1:
The patent applies different processing strategies to different regions of the 3D grid based on their occupancy density. Occupied voxels (containing LiDAR points) are processed to preserve geometric information, while empty voxels are handled differently, allowing the system to maintain computational efficiency in sparse regions without completely losing spatial information.
Solution Approach 2:
The patent changes the representation parameters dynamically - using occupancy probability or density values in the grid cells to represent both occupied and unoccupied spaces. This parameter transformation allows the system to maintain information about spatial distribution and density while achieving computational efficiency through the structured grid format.
3Reliability
If traditional object detection and pose estimation methods are used, then reliability is improved through robustness, but device complexity increases due to heavy reliance on data annotation
Solution Approach 1:
The patent enables the system to generate its own structured 3D representation directly from raw LiDAR data without requiring external annotation or complex preprocessing. The 3D grid is automatically constructed from the point cloud data, allowing the system to serve its own needs for scene understanding without relying on pre-labeled data or complex annotation pipelines.
Solution Approach 2:
The patent replaces traditional mechanical/algorithmic approaches (object detection and pose estimation) with a neural network-based approach that directly processes the 3D grid representation. This substitution eliminates the need for complex annotation-based methods while maintaining robustness through learned features from the structured data representation.
4Ease of operation
If camera-based occupancy networks are used, then ease of operation is improved, but measurement precision deteriorates due to depth ambiguity
Solution Approach 1:
The patent substitutes camera-based operations with LiDAR-based operations, replacing the optical measurement system with an active sensing system. This substitution eliminates depth ambiguity inherent in camera data while maintaining ease of operation through automated point cloud processing and 3D grid generation from the LiDAR sensor data.
Data Source
AI summary
A method of forming a three dimensional (3D) opacity grid is provided. The method may map light detection and ranging (LiDAR) points to a grid. The method may employ a volume densification to the grid to generate a 3D opacity grid representing a surrounding scene.