Table C: The reproduced scores of 32 monocular depth estimation models under the standard KITTI Eigen split. Blocks from top to bottom: [1st] the baseline MonoDepth2  ${}_{\mathrm{R}{18}}$  [17]; [2nd] methods w/ monocular inputs; [3rd] methods w/ stereo inputs; [4th] methods w/ monocular + stereo inputs. M+S denotes models with both monocular and stereo inputs. The checkpoints for reproduction will be publicly available in our GitHub repository.
Method	Modality	Backbone	Resolution	Pretrain	Abs Rel ↓	Sq Rel ↓	RMSE ↓	log RMSE ↓	δ < 1.25 ↑	δ < 1.252 ↑	δ < 1.253 ↑
MonoDepth2 R18 [17]	Mono	ResNet-18	640 x 192	ImageNet	0.115	0.905	4.864	0.193	0.877	0.959	0.981
MonoDepth2 nopt [17]	Mono	ResNet-18	640 x 192	None	0.132	1.042	5.138	0.210	0.845	0.948	0.977
MonoDepth2 HR [17]	Mono	ResNet-18	1024 x 320	ImageNet	0.106	0.807	4.631	0.193	0.877	0.959	0.980
MaskOcc R18 [48]	Mono	ResNet-18	640 x 192	ImageNet	0.113	0.865	4.788	0.191	0.878	0.960	0.981
DNet R18 [59]	Mono	ResNet-18	640 x 192	ImageNet	0.114	0.867	4.819	0.191	0.877	0.960	0.981
CADepth [60]	Mono	ResNet-50	640 x 192	ImageNet	0.107	0.803	4.592	0.183	0.890	0.963	0.983
HR-Depth [39]	Mono	ResNet-18	640 x 192	Cityscapes	0.109	0.792	4.632	0.185	0.884	0.962	0.983
DIFFNet [67]	Mono	HRNet	640 x 192	ImageNet	0.102	0.753	4.459	0.179	0.897	0.965	0.983
ManyDepth R18 [56]	Mono	ResNet-18	640 x 192	ImageNet	0.118	0.894	4.765	0.192	0.871	0.959	0.982
FSRE-Depth [22]	Mono	ResNet-18	640 x 192	ImageNet	0.105	0.708	4.546	0.182	0.886	0.964	0.983
MonoViT [66]	Mono	MPViT	640 x 192	ImageNet	0.099	0.708	4.373	0.175	0.900	0.967	0.984
MonoViTRH [66]	Mono	MPViT	1024 x 320	ImageNet	0.096	0.713	4.292	0.172	0.908	0.968	0.984
DynaDepth R18 [64]	Mono	ResNet-18	640 x 192	ImageNet	0.112	0.839	4.848	0.192	0.877	0.959	0.981
DynaDepth R50 [64]	Mono	ResNet-50	640 x 192	ImageNet	0.109	0.785	4.645	0.188	0.882	0.961	0.982
RA-Depth [42]	Mono	ResNet-18	640 x 192	ImageNet	0.096	0.632	4.216	0.171	0.903	0.968	0.985
TriDepth R18 [6]	Mono	ResNet-18	640 x 192	ImageNet	0.112	0.775	4.574	0.186	0.878	0.962	0.983
Lite-Mono Tiny [63]	Mono	ResNet-18	640 x 192	ImageNet	0.110	0.841	4.715	0.187	0.880	0.960	0.982
Lite-Mono Small [63]	Mono	ResNet-18	640 x 192	ImageNet	0.110	0.809	4.677	0.186	0.879	0.961	0.982
Lite-Mono Base [63]	Mono	ResNet-18	640 x 192	ImageNet	0.107	0.766	4.560	0.183	0.886	0.963	0.983
Lite-Mono Large [63]	Mono	ResNet-18	640 x 192	ImageNet	0.101	0.732	4.459	0.178	0.897	0.965	0.983
MonoDepth2 R18 [17]	Stereo	ResNet-18	640 x 192	ImageNet	0.110	0.877	4.967	0.209	0.864	0.948	0.975
MonoDepth2 nopt [17]	Stereo	ResNet-18	640 x 192	None	0.130	1.143	5.482	0.232	0.831	0.933	0.968
MonoDepth2 HR [17]	Stereo	ResNet-18	1024 x 320	ImageNet	0.107	0.851	4.776	0.202	0.873	0.953	0.977
DepthHints [57]	Stereo	ResNet-18	640 x 192	ImageNet	0.104	0.791	4.655	0.191	0.878	0.959	0.981
DepthHints nopt+HR [57]	Stereo	ResNet-18	1024 x 320	None	0.118	0.971	5.099	0.211	0.851	0.947	0.976
DepthHints HR [57]	Stereo	ResNet-18	1024 x 320	ImageNet	0.097	0.734	4.454	0.187	0.889	0.961	0.981
MonoDepth2 R18 [17]	M+S	ResNet-18	640 x 192	ImageNet	0.106	0.821	4.752	0.196	0.874	0.957	0.979
MonoDepth2 nopt [17]	M+S	ResNet-18	640 x 192	None	0.127	1.027	5.263	0.220	0.836	0.943	0.974
MonoDepth2 HR [17]	M+S	ResNet-18	1024 x 320	ImageNet	0.106	0.807	4.631	0.193	0.877	0.959	0.980
CADepth [60]	M+S	ResNet-18	640 x 192	ImageNet	0.102	0.788	4.645	0.192	0.881	0.958	0.980
MonoViT [66]	M+S	MPViT	640 x 192	ImageNet	0.098	0.683	4.333	0.174	0.904	0.967	0.984