Vehicle NeRF Scene Modeling With Lidar-Guided Geometry and Color
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Solution Overview
Problem
Existing vehicle sensors struggle to accurately model large outdoor scenes with sparse data, particularly when the vehicle is stationary, leading to sensor artifacts and illumination effects.
Innovation Solution
A neural radiance field approach using geometric and color NeRF MLPs, combined with an occupancy grid map, is trained jointly with lidar and camera data to model scene geometry and colors, employing importance sampling and loss functions to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle sensors are used to model outdoor scenes, then the system is simple to implement, but the scene representation becomes sparse and inaccurate
Solution Approach 1:
The scene modeling is segmented into two distinct neural network components: a geometric NeRF MLP for spatial structure and a color NeRF MLP for visual appearance. This segmentation allows each network to specialize in one aspect, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from traditional 2D image processing to 3D volumetric scene representation using neural radiance fields. By modeling scenes in three-dimensional space with continuous density and color functions, the system achieves more accurate and comprehensive scene understanding beyond flat image planes.
2Measurement precision
If joint training of geometric and color NeRF MLPs is performed, then scene modeling accuracy improves, but training time and computational resources increase
Solution Approach 1:
The training process is divided into sequential phases: first training the geometric NeRF MLP on lidar data to establish accurate spatial structure, then training the color NeRF MLP on camera data to add visual appearance. This preliminary action approach allows each network to converge more efficiently before joint fine-tuning, reducing overall training time compared to simultaneous training.
Solution Approach 2:
The system dynamically adjusts training focus between geometric and color components based on data availability and scene characteristics. During joint training, the optimization process adaptively balances gradients from both modalities, allowing the model to prioritize learning from the most informative data sources at each training step.
3Measurement precision
If lidar and camera data are used for supervision, then geometric accuracy improves, but sensor artifacts and illumination effects negatively impact color accuracy
Solution Approach 1:
The patent extracts and separates geometric information from color information by using distinct neural networks for each purpose. The geometric NeRF MLP is supervised solely by lidar data to achieve accurate spatial modeling, while the color NeRF MLP is supervised by camera data to capture visual appearance, effectively isolating the harmful effects of sensor artifacts and illumination from the geometric modeling process.
Solution Approach 2:
The geometric NeRF MLP acts as an intermediary that provides accurate spatial structure to guide the color NeRF MLP. By first establishing a clean geometric framework from lidar data, the system creates a stable foundation that helps the color network focus on appearance variations without being misled by sensor artifacts or lighting changes.
4Measurement precision
If importance sampling is employed during training, then rendering accuracy improves, but computational complexity increases
Solution Approach 1:
The system dynamically changes sampling parameters during training by adjusting the importance sampling distribution based on learned scene properties. As training progresses, the sampling strategy adapts to focus computational resources on regions of higher importance, improving rendering accuracy while managing computational complexity through adaptive parameter adjustment.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to jointly train a geometric NeRF multilayer perceptron (MLP) and a color NeRF MLP to model a scene using an occupancy grid map, camera data of the scene from a camera, and lidar data of the scene from a lidar; supervise the geometric NeRF MLP with the lidar data during the joint training; and supervise the color NeRF MLP with the camera data during the joint training. The geometric NeRF MLP is a neural radiance field modeling a geometry of the scene, and the color NeRF MLP is a neural radiance field modeling colors of the scene.