面向自动驾驶场景的神经辐射场综述

doi:10.11996/JG.j.2095-302X.2023061091

图学学报 ›› 2023, Vol. 44 ›› Issue (6): 1091-1103.DOI: 10.11996/JG.j.2095-302X.2023061091

面向自动驾驶场景的神经辐射场综述

成欢¹(), 王硕², 李孟², 秦伦明¹(), 赵芳³

1.上海电力大学电子与信息工程学院，上海 200050
2.中国科学院上海微系统与信息技术研究所，上海 200050
3.神鳍科技(上海)有限公司，上海 200050

收稿日期:2023-06-27 接受日期:2023-09-08 出版日期:2023-12-31 发布日期:2023-12-17
通讯作者: 秦伦明(1983-)，男，副教授，博士。主要研究方向为计算机视觉与图像分割等。E-mail：lunming.qin@shiep.edu.cn
作者简介:
成欢(1999-)，女，硕士研究生。主要研究方向为计算机视觉与计算机图形学。E-mail：chenghuan0116@gmail.com
基金资助:
国家重点研发计划项目(2021YFF1200700)

A review of neural radiance field for autonomous driving scene

CHENG Huan¹(), WANG Shuo², LI Meng², QIN Lun-ming¹(), ZHAO Fang³

1. School of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200050, China
2. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
3. DiFint Technology (Shanghai) Co., Shanghai 200050, China

Received:2023-06-27 Accepted:2023-09-08 Online:2023-12-31 Published:2023-12-17
Contact: QIN Lun-ming (1983-), associate professor, Ph.D. His main research interests cover computer vision and image segmentation, etc. E-mail：lunming.qin@shiep.edu.cn
About author:
CHENG Huan (1999-), master student. Her main research interests cover computer vision and computer graphics.
E-mail：chenghuan0116@gmail.com
Supported by:
National Key Research and Development Program(2021YFF1200700)

摘要/Abstract

摘要：

神经辐射场(NeRF)是一种可用于重建真实的视觉效果以及合成新颖视角的关键技术，其主要是通过摄像机捕获的二维图像数据来渲染合成三维场景，将已知视角推理到未知视角下使得用户可从不同视角观看合成视图，增强人机交互感。作为一种新颖视角合成的三维重建方法，神经辐射场技术在机器人、自动驾驶、虚拟现实和数字孪生等领域具有重要的研究与应用价值。通过NeRF技术与自动驾驶场景相结合，可实现对复杂驾驶场景的高质量重建，并模拟恶劣情况下的不同场景，从而丰富自动驾驶的训练数据，以较低成本提高自动驾驶系统的准确性和安全性，并验证自动驾驶算法的有效性。鉴于目前NeRF在自动驾驶场景有重要的应用前景以及现有的相关综述较少，首先，从传统的显式三维场景表征方法出发，引入场景的隐式表征方法——NeRF，并介绍了NeRF技术的原理；其次，对于NeRF与自动驾驶场景相结合所面临的挑战进行了探讨和分析，其中包括稀疏视角重建、大尺度场景重建、运动场景、训练加速以及合成自动驾驶场景的问题；最后，对NeRF技术进行总结，以及展望其未来发展方向。

关键词: 神经辐射场, 视图合成, 自动驾驶, 三维重建, 场景重建

Abstract:

The neural radiance field (NeRF) is a crucial technology for reconstructing realistic visual effects and synthesizing novel views of scenes. It primarily renders synthetic 3D scenes based on 2D image data captured by cameras, inferring known views to unknown views, so that users can observe synthetic views from different viewpoints to enhance human-computer interaction. As a method for novel viewpoint synthesis and 3D reconstruction, NeRF exhibited significant research and application value in the fields of robotics, autonomous driving, virtual reality, and digital twins. Its integration with autonomous driving scenarios allowed for high-quality reconstruction of complex driving scenes and the simulation of different scenes under adverse conditions. This could enrich training data for autonomous driving systems, enhance their accuracy and safety at minimal costs, and validate the effectiveness of autonomous driving algorithms. Given NeRF's current important application prospect in autonomous driving scenes and its limited coverage in existing reviews, firstly, the traditional explicit 3D scene representation method was employed to introduce the implicit representation method of scenes, namely NeRF, along with the introduction of the principle of NeRF technology. Secondly, the discussion analysis were conducted regarding the challenges encountered when combining NeRF and autonomous driving scenes, including the problems of sparse view reconstruction, large-scale scene reconstruction, motion scenes, training acceleration, and the synthesis of autonomous driving scenes. Finally, insights were provided into the future development directions of NeRF technology.

Key words: neural radiance field, view synthesis, autonomous driving, 3D reconstruction, scene reconstruction

中图分类号:

TP391

成欢, 王硕, 李孟, 秦伦明, 赵芳. 面向自动驾驶场景的神经辐射场综述[J]. 图学学报, 2023, 44(6): 1091-1103.

CHENG Huan, WANG Shuo, LI Meng, QIN Lun-ming, ZHAO Fang. A review of neural radiance field for autonomous driving scene[J]. Journal of Graphics, 2023, 44(6): 1091-1103.

图/表 12

图1 不同的场景表征方法((a)体素[6]；(b)点云[7]；(c)网格[8]；(d)占用预测网络[9])

Fig. 1 Scene representation methods based ((a) Voxel[6]; (b) Point[7]; (c) Mesh[8]; (d) Occupancy networks[9])

图2 神经辐射场技术的算法流程[1] ((a)输入；(b) 3D点采样；(c)体积渲染；(d)优化)

Fig. 2 Pipeline of NeRF[1] ((a) Input; (b) 3D point sampling; (c) Volume rendering; (d) Optimize)

表1 NeRF及其扩展研究在驾驶场景的性能比较

Table 1 Performance comparison of NeRF and its extensions in driving scene

Method	PSNR (dB)	SSIM	LPIPS	Datasets
NeRF^[1]	18.56	0.557	0.554	KITTI
NSG^[17]	21.53	0.673	0.254	KITTI
pixelNeRF^[18]	20.1	0.761	0.175	KITTI
SUDS^[19]	22.77	0.797	0.171	KITTI
MARS^[20]	24.23	0.845	0.160	KITTI
Urban-NeRF^[21]	21.49	0.661	0.491	NuScenes
Mip-NeRF^[22]	18.22	0.655	0.421	NuScenes

表2 NeRF及其扩展研究的性能比较

Table 2 Performance comparison of NeRF and its extended reseraches

Method	Enocode	PSNR (dB)	SSIM	LPIPS	Train time	Iteration (K)
NeRF^[3]	位置编码	31.01	0.947	0.081	>12 h	300
pixelNeRF^[18]	位置编码	-	-	-	>12 h	400
Mip-NeRF^[22]	集成位置编码	33.09	0.961	0.043	≈6 h	612
GRF^[23]	位置编码	27.07	0.924	0.090	-	-
Point-NeRF^[24]	位置编码	33.00	0.978	0.055	≈7 h	200
Instant NGP^[25]	哈希编码	33.18	-	-	≈5 m	256
Plenoxels^[26]	位置编码	31.71	0.958	0.050	≈11 m	10
DVGO^[27]	位置编码	31.95	0.957	0.053	≈15 m	20
PlenOctree^[28]	位置编码	31.71	0.958	0.053	>12 h	-

图3 不同场景采集的相机轨迹((a)前向场景；(b)环绕场景；(c)驾驶场景)

Fig. 3 Camera trajectories in different scenes ((a) Forward-facing scene; (b) Surrounding scene; (c) Driving scene)

图4 自动驾驶场景不同视角下的重建((a)固定视角；(b)稀疏视角)

Fig. 4 Reconstruction of the autonomous driving scene in different viewpoints ((a) Fixed viewpoint; (b) Sparse viewpoint)

图5 Point-NeRF的算法流程[24]

Fig. 5 Pipeline of Point-NeRF[24]

图6 不同光照情况下的重建效果[23]

Fig. 6 Reconstruction performances under different illuminations[23]

图7 NeRF及其扩展研究在运动场景重建效果((a)输入图像；(b) NeRF；(c) NeRF+Time；(d) NSG；(e) SUDS)

Fig. 7 Performance of NeRF and its extensions in reconstructing motion scene ((a) Input image; (b) NeRF; (c) NeRF+Time; (d) NSG; (e) SUDS)

图8 NSG[17]对自动驾驶场景的重建((a)输入场景；(b)场景前景；(c)场景背景；(d)场景重建)

Fig. 8 Reconstruction of autonomous driving scenarios by NSG[17] ((a) Input scene (b) Scene foreground; (c) Scene background; (d) Scene reconstruction)

图9 多分辨率哈希编码[25] ((a)体素顶点的哈希值；(b)查找；(c)线性插值；(d)串联；(e)神经网络)

Fig. 9 Multiresolution Hash encoding[25] ((a) Hashing of voxel vertices; (b) Lookup; (c) Linear interpolation; (d) Concatenation; (e) Neural network)

图10 Hex Plane的算法流程[54]

Fig. 10 Pipeline of Hex Plane[54]

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面向自动驾驶场景的神经辐射场综述

A review of neural radiance field for autonomous driving scene

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