NeRO: Neural Road Surface Reconstruction (2405.10554v2)

Abstract: Accurately reconstructing road surfaces is pivotal for various applications especially in autonomous driving. This paper introduces a position encoding Multi-Layer Perceptrons (MLPs) framework to reconstruct road surfaces, with input as world coordinates x and y, and output as height, color, and semantic information. The effectiveness of this method is demonstrated through its compatibility with a variety of road height sources like vehicle camera poses, LiDAR point clouds, and SFM point clouds, robust to the semantic noise of images like sparse labels and noise semantic prediction, and fast training speed, which indicates a promising application for rendering road surfaces with semantics, particularly in applications demanding visualization of road surface, 4D labeling, and semantic groupings.

• The paper introduces a novel MLP-based framework using multi-resolution hash positional encoding to reconstruct detailed road surfaces in terms of height, color, and semantics.
• The paper demonstrates that multi-resolution hash encoding outperforms traditional methods with a PSNR of 29.20 and mIoU of 0.994 while accelerating training.
• The paper shows robust performance in handling incomplete and noisy data, enabling reliable semantic labelling and enhanced mapping for autonomous driving.

Demystifying NeRO: Neural Road Surface Reconstruction

Introduction

NeRO: Neural Road Surface Reconstruction is a deep dive into enhancing the accuracy and efficiency of reconstructing road surfaces in the context of autonomous driving. Specifically, this paper leverages Multi-Layer Perceptrons (MLPs) to achieve detailed reconstructions in terms of height, color, and semantic information starting from simple world coordinates. NeRO stands out by integrating semantic information directly into the reconstruction process, thereby promising significant benefits for visualisation and labelling applications.

Detailed Breakdown of NeRO's Approach

NeRO Network Structure

The core of NeRO revolves around processing a 2D coordinate system (x, y) as input. These coordinates are normalized and encoded using techniques like Positional Encoding and Multi-Resolution Hash Positional Encoding. Here's a closer look at what happens next:

1. Normalization and Encoding: The normalized inputs are fed into encoding functions to calculate vertical z height, RGB colors, and semantic values.
2. Multi-task Processing: Three different MLPs process the encoded coordinates to produce road surface height, RGB values, and semantic outputs respectively.

This unified framework ensures that all three aspects—height, color, and semantics—are meticulously reconstructed.

Encoding Techniques

1. Positional Encoding: This method transforms input coordinates to a higher-dimensional space via sine and cosine functions, enabling the model to learn high-frequency details effectively. It's used primarily to capture intricate details of color information.
2. Multi-Resolution Hash Positional Encoding: This technique takes it up a notch by creating grids at various scales and stores interpolated points in a hash table. This method significantly reduces the network size while maintaining or even enhancing the quality of rendering.

Reconstruction Methods

Z-axis Reconstruction: Uses data from vehicle camera poses, LiDAR, and Structure from Motion (SfM) points to supervise the learning of height values (z).

Color Reconstruction: Involves projecting the 3D coordinates into the pixel coordinate system using camera extrinsic and intrinsic parameters to match the network outputs with the ground truth colors.

Semantic Reconstruction: Like color reconstruction, this uses pixel-level semantic information to train the network to output meaningful semantic labels for the road surfaces.

Key Experiments and Results

Render Quality and Training Efficiency

NeRO was tested on the KITTI Odometry dataset. The results indicated that the Multi-Resolution Hash Positional Encoding consistently outperformed traditional Positional Encoding across various metrics, including PSNR and mIoU. Notably, NeRO:

• Achieved a PSNR of 29.20 and an mIoU of 0.994 in LiDAR datasets using Multi-Resolution Hash Positional Encoding.
• Demonstrated superior training speed and converged faster to global minima in color and semantic losses.

Handling Incomplete and Noisy Data

One fascinating aspect of NeRO is its robustness in reconstructing even when input data is incomplete or noisy:

• Incomplete Road Surfaces: Positional Encoding filled holes efficiently but lacked smoothness, while Multi-Resolution Hash Positional Encoding provided smoother results.
• Sparse Label Data: Even with only 10% of semantic labels, Multi-Resolution Hash Positional Encoding managed to render detailed road surfaces.
• Denoising Capabilities: Positional Encoding showed better noise reduction when up to 50% of semantic labels were noisy, but the Multi-Resolution Hash Positional Encoding began incorporating noise above a 60% noise ratio.

Practical and Theoretical Implications

NeRO's advancements in road surface reconstruction have broad implications:

Practical Applications:

• Autonomous Driving: High-fidelity road reconstructions improve vehicle navigation and safety.
• Urban Planning: Detailed 3D maps enable better infrastructure maintenance and development.

Theoretical Contributions:

• Encoding Techniques: The introduction of Muti-Resolution Hash Positional Encoding can inspire similar applications in other fields requiring high-resolution reconstructions.
• Multi-task Learning: Integrating various outputs (height, color, and semantics) in a single model paves the way for more holistic approaches in neural scene reconstruction.

Future Prospects

NeRO has set a solid foundation, but future developments could address some limitations such as balancing noise reduction and accurately delineating road features. Exploring smaller, specialized networks or occupancy networks could further refine the reconstruction quality.

In summary, NeRO presents an effective end-to-end MLPs-based method for neural road reconstruction, showing not only improved rendering quality but also robustness against sparse and noisy data.