3D Occupancy Prediction With Forward-Backward View Transformation
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Solution Overview
Problem
Existing 3D occupancy prediction methods for autonomous vehicles are inefficient in terms of time, quality, and computing resources, hindering effective motion planning and obstacle avoidance.
Innovation Solution
A neural network-based 3D occupancy prediction model that combines forward projection and backward projection neural networks to generate accurate voxel representations, incorporating depth estimation and semantic segmentation, enabling precise occupancy classification and motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D occupancy prediction methods are used, then computational resources are consumed, but prediction accuracy and efficiency deteriorate
Solution Approach 1:
The patent divides the 3D space into discrete voxels and processes occupancy prediction for each voxel independently through neural network transformations. This segmentation allows parallel processing of spatial regions, improving both accuracy through detailed voxel-level analysis and efficiency through distributed computation across multiple processing units.
Solution Approach 2:
The patent transforms 2D image data into 3D voxel space through forward-backward view transformations and depth estimation. By adding the depth dimension and creating a 3D representation from 2D inputs, the system achieves more accurate occupancy prediction while maintaining computational efficiency through structured dimensional transformation.
2Measurement precision
If computational resources are increased for 3D occupancy prediction, then prediction quality improves, but time consumption increases
Solution Approach 1:
The patent performs depth estimation and generates 3D voxel representations as preliminary steps before final occupancy classification. By pre-processing the spatial transformation and organizing data into voxel structures in advance, the system reduces the computational burden during the actual occupancy prediction phase, thereby decreasing prediction time while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional geometric and physics-based occupancy calculation methods with neural network-based forward-backward view transformations. This substitution leverages learned patterns from training data to directly predict occupancy states, significantly reducing computation time compared to exhaustive geometric reasoning while preserving or improving accuracy.
3Measurement precision
If complex neural network transformations are applied, then occupancy prediction accuracy improves, but computing resource consumption increases
Solution Approach 1:
The patent designs the neural network transformations to serve multiple functions simultaneously: forward view transformation extracts spatial features, backward view transformation refines occupancy predictions, and depth estimation provides geometric context. This multi-functionality allows a single integrated system to achieve high voxel representation accuracy without requiring separate specialized modules, thereby reducing overall computational energy consumption.
Data Source
AI summary
Apparatuses, systems, and techniques of using one or more machine learning processes (e.g., neural network(s)) to predict occupancy using an image input. In at least one embodiment, image data is processed using a neural network to predict occupancy in a 3D voxel space. In at least one embodiment, image data is processed using a neural network to detect objects in a 3D space.