A review of autonomous driving image synthesis methods: from simulators to new paradigms

doi:10.11996/JG.j.2095-302X.2025050931

Abstract

Abstract:

Image synthesis techniques are crucial for the development of autonomous driving, aiming to provide training and testing data for autonomous driving systems in a cost-effective manner. With the development of computer vision and artificial intelligence (AI) technologies, neural radiance fields (NeRF), 3D Gaussian splatting (3DGS), and generative modeling have attracted much attention in the field of image synthesis. These new paradigms show great potential in autonomous driving scene construction and image data synthesis. Recognizing the importance of these methods for the development of autonomous driving technology, their development history was reviewed and the latest research works were collected, and the methods were re-examined from the practical perspective of the autonomous driving image synthesis problem. The progress of NeRF, 3DGS, generative modeling, and reality-virtual fusion synthesis methods in the field of autonomous driving was introduced, with special focus on NeRF and 3DGS, two reconstruction-based methods. First, some important issues were analyzed for the task of autonomous driving image generation, followed by detailed examination of representative schemes of NeRF and 3DGS in terms of the limited viewpoint problem, large-scale scene problem, dynamics problem, and acceleration problem faced by autonomous driving scenes. Considering the potential benefits of generative models for creating corner cases of autonomous driving, practical issues and existing research works on the use of autonomous driving world models for scenario generation were also presented. Then, the cutting-edge applications of virtual-reality fusion for autonomous driving image synthesis were analyzed, as well as the potential of NeRF and 3DGS combined with AI generative modeling for the task of autonomous driving scenario generation. Finally, current achievements were summarized and future research directions were outlined.

Key words: autonomous driving, image synthesis, neural radiance field, 3D gaussian splatting, generation model

CLC Number:

HUANG Jing, SHI Ruihao, SONG Wenming, GUO Hepan, WEI Huang, WEI Xiaosong, YAO Jian. A review of autonomous driving image synthesis methods: from simulators to new paradigms[J]. Journal of Graphics, 2025, 46(5): 931-949.

Figures/Tables 20

References 96

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方法	辅助先验	PSNR↑	SSIM↑	LPIPS↓	数据集
NeRF^[1]	无	18.56	0.557	0.554	KITTI
S-NeRF^[9]	LiDAR	18.71	0.606	0.352	KITTI
EmerNeRF^[10]	LiDAR、2D语义	25.24	0.801	0.237	KITTI
NSG^[11]	无	21.53	0.673	0.254	KITTI
PixelNeRF^[12]	无	20.10	0.761	0.175	KITTI
SUDS^[13]	LiDAR、2D光流	22.77	0.797	0.171	KITTI
Urban-NeRF^[14]	LiDAR	21.49	0.661	0.491	nuScenes
Mip-NeRF^[15]	无	18.22	0.655	0.421	nuScenes
3DGS^[2]	无	26.08	0.717	0.298	nuScenes
PVG^[16]	无	22.43	0.896	0.114	KITTI
StreetGaussian^[17]	LiDAR、2D语义	25.79	0.844	0.081	KITTI
DrivingGaussian^[18]	无	28.36	0.851	0.256	nuScenes
DrivingGaussian^[18]	LiDAR	28.74	0.865	0.237	nuScenes
HuGS^[19]	2D/3D语义、光流	26.81	0.866	0.059	KITTI
DeSiRe-GS^[20]	LiDAR	28.87	0.901	0.106	KITTI

方法	辅助先验	PSNR↑	SSIM↑	LPIPS↓	数据集
NeRF^[1]	无	18.56	0.557	0.554	KITTI
S-NeRF^[9]	LiDAR	18.71	0.606	0.352	KITTI
EmerNeRF^[10]	LiDAR、2D语义	25.24	0.801	0.237	KITTI
NSG^[11]	无	21.53	0.673	0.254	KITTI
PixelNeRF^[12]	无	20.10	0.761	0.175	KITTI
SUDS^[13]	LiDAR、2D光流	22.77	0.797	0.171	KITTI
Urban-NeRF^[14]	LiDAR	21.49	0.661	0.491	nuScenes
Mip-NeRF^[15]	无	18.22	0.655	0.421	nuScenes
3DGS^[2]	无	26.08	0.717	0.298	nuScenes
PVG^[16]	无	22.43	0.896	0.114	KITTI
StreetGaussian^[17]	LiDAR、2D语义	25.79	0.844	0.081	KITTI
DrivingGaussian^[18]	无	28.36	0.851	0.256	nuScenes
DrivingGaussian^[18]	LiDAR	28.74	0.865	0.237	nuScenes
HuGS^[19]	2D/3D语义、光流	26.81	0.866	0.059	KITTI
DeSiRe-GS^[20]	LiDAR	28.87	0.901	0.106	KITTI

方法	关键技术	注解
DR-Gaussian^[27]	${D}_{\text{dense}}=s\cdot {F}_{\theta }(I)+t$ ${D}_{\text{dense}}=s\cdot {F}_{\theta }(I)+t$	利用尺度系数s和偏移量t将单目深度F_θ(I)对齐到稀疏点D_sparse，ω归一化特征点可靠性权值
DN-Splatter^[28]	$s,t=\mathrm{arg}\underset{s,t}{\mathrm{min}}{\displaystyle \sum _{p\in {\text{D}}_{\text{sparse}}}{‖(s\cdot {D}_{\text{mono}}(p)+t)-{D}_{\text{sparse}}(p)‖}^{2}}$ $s,t=\mathrm{arg}\underset{s,t}{\mathrm{min}}{\displaystyle \sum _{p\in {\text{D}}_{\text{sparse}}}{‖(s\cdot {D}_{\text{mono}}(p)+t)-{D}_{\text{sparse}}(p)‖}^{2}}$	利用单目深度D_mono(p)到稀疏点D_sparse(p)的线性回归求解深度尺度系数s和偏移量t，g_rgb=exp(-▽I)作为绝对尺度可靠性度量
Hierarchy GS^[29]	$D=\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}D+t\left({D}_{\text{sparse}}\right)-t(D)\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}$ $D=\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}D+t\left({D}_{\text{sparse}}\right)-t(D)\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}$	将单目逆深度图D对齐到SfM尺度D_sparse
DNGaussian^[30]	$D*(x)=\frac{D(x)-\text{mean}(D(p))}{\text{std}(D(p))}$	将深度图分割为小块p，然后利用块内深度均值meanD(p)和标准差stdD(p)归一化深度分布函数

方法	关键技术	注解
DR-Gaussian^[27]	${D}_{\text{dense}}=s\cdot {F}_{\theta }(I)+t$ ${D}_{\text{dense}}=s\cdot {F}_{\theta }(I)+t$	利用尺度系数s和偏移量t将单目深度F_θ(I)对齐到稀疏点D_sparse，ω归一化特征点可靠性权值
DN-Splatter^[28]	$s,t=\mathrm{arg}\underset{s,t}{\mathrm{min}}{\displaystyle \sum _{p\in {\text{D}}_{\text{sparse}}}{‖(s\cdot {D}_{\text{mono}}(p)+t)-{D}_{\text{sparse}}(p)‖}^{2}}$ $s,t=\mathrm{arg}\underset{s,t}{\mathrm{min}}{\displaystyle \sum _{p\in {\text{D}}_{\text{sparse}}}{‖(s\cdot {D}_{\text{mono}}(p)+t)-{D}_{\text{sparse}}(p)‖}^{2}}$	利用单目深度D_mono(p)到稀疏点D_sparse(p)的线性回归求解深度尺度系数s和偏移量t，g_rgb=exp(-▽I)作为绝对尺度可靠性度量
Hierarchy GS^[29]	$D=\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}D+t\left({D}_{\text{sparse}}\right)-t(D)\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}$ $D=\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}D+t\left({D}_{\text{sparse}}\right)-t(D)\frac{s\left({D}_{\text{sparse}}\right)}{s(D)}$	将单目逆深度图D对齐到SfM尺度D_sparse
DNGaussian^[30]	$D*(x)=\frac{D(x)-\text{mean}(D(p))}{\text{std}(D(p))}$	将深度图分割为小块p，然后利用块内深度均值meanD(p)和标准差stdD(p)归一化深度分布函数

Scenes	Building			Rubble			Campus			Residence			Sci-Art
Scenes	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓	PSNR↑	SSIM↑	LPIPS↓
Mega-NeRF^[40]	20.92	0.547	0.454	24.06	0.553	0.508	23.42	0.537	0.636	22.08	0.628	0.401	25.60	0.770	0.312
Switch-NeRF^[42]	21.54	0.579	0.397	23.41	0.562	0.478	23.62	0.541	0.616	22.57	0.654	0.352	26.51	0.795	0.271
3DGS^[2]	22.53	0.738	0.214	25.51	0.725	0.316	23.67	0.688	0.347	22.36	0.745	0.247	24.13	0.791	0.262
VastGaussian^[46]	21.80	0.728	0.225	25.20	0.742	0.264	23.82	0.695	0.329	21.01	0.699	0.261	22.64	0.761	0.261
Hierarchy GS^[29]	21.52	0.723	0.297	24.64	0.755	0.284
DoGaussian^[48]	22.73	0.759	0.204	25.78	0.765	0.257	24.01	0.681	0.377	21.94	0.740	0.244	24.42	0.804	0.219
CoSurfGS^[50]	22.40	0.750	0.262	25.39	0.774	0.267	23.63	0.719	0.360	22.31	0.776	0.261	23.29	0.802	0.277