Monocular Depth Estimation on Make3D

12개 결과 · ⬇ CSV · JSON

Abs Rel

Rank	Model	Abs Rel	RMSE	Sq Rel	Paper	Code	Year
1	SPIDepth	0.299	6.672	1.931	SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation	Lavreniuk/SPIdepth	2024
2	Monodepth2	0.322	7.417	3.589	Digging Into Self-Supervised Monocular Depth Estimation	nianticlabs/monodepth2 · FangGet/tf-monodepth2 · minghanz/DepthC3D · +12	2018
3	SharinGAN	0.377	8.388	4.9	SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation	koutilya40192/SharinGAN	2020
4	GCNDepth	0.424	6.757	3.075	GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network	arminmasoumian/gcndepth	2021
5	AnyNet [88]	–	0.232	–	A Survey on Deep Learning Techniques for Stereo-based Depth Estimation		2020
6	HighResNet [32]	–	0.474	–	A Survey on Deep Learning Techniques for Stereo-based Depth Estimation		2020
7	SPIDepth	0.299	6.672	1.931	SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation	Lavreniuk/SPIdepth	2024
8	Monodepth2	0.322	7.417	3.589	Digging Into Self-Supervised Monocular Depth Estimation	nianticlabs/monodepth2 · FangGet/tf-monodepth2 · minghanz/DepthC3D · +12	2018
9	SharinGAN	0.377	8.388	4.9	SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation	koutilya40192/SharinGAN	2020
10	GCNDepth	0.424	6.757	3.075	GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network	arminmasoumian/gcndepth	2021
11	AnyNet [88]	–	0.232	–	A Survey on Deep Learning Techniques for Stereo-based Depth Estimation		2020
12	HighResNet [32]	–	0.474	–	A Survey on Deep Learning Techniques for Stereo-based Depth Estimation		2020