KITTI-360

We select 10 static scenes with a driving distance of ∼ 50 meters each for evaluating NVS at a 90% drop rate. We select one frame every ∼ 4.0 meters driving distance (corresponding to the overall average distance between frames) to avoid redundancy when the vehicle is slow. We release 50% of the frames for training and retain 50% for evaluation. Our evaluation table ranks all methods according to the peak signal-to-noise ratio (PSNR). We also evaluate structural similarity index (SSIM) and perceptual smilarity (LPIPS). Our evaluation table ranks all methods according to the peak signal-to-noise ratio (PSNR). We also evaluate structural similarity index (SSIM) and perceptual smilarity (LPIPS).

• PSNR: Peak signal-to-noise ratio
• SSIM: Structural similarity index
• LPIPS: Perceptual Similarity using AlexNet

	Method	Setting	Code	PSNR	SSIM	LPIPS	Runtime	Environment
1	DGNerf		code	17.33	0.714	0.397	1 s	1 core @ 2.5 Ghz (C/C++)
2	MVSRegNeRF			17.20	0.702	0.424	2 s	1 core @ 2.5 Ghz (C/C++)
F. Bian, S. Xiong, R. Yi and L. Ma: Multi-view stereo-regulated NeRF for urban scene novel view synthesis. The Visual Computer 2024.
3	NeRF			15.74	0.648	0.590	10 s	1 core @ 2.5 Ghz (C/C++)
B. Mildenhall, P. Srinivasan, M. Tancik, J. Barron, R. Ramamoorthi and R. Ng: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV 2020.