High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MM		80.20 8	80.28 10	79.99 2	82.19 90	60.01 49	86.19 21	83.93 60	73.19 1	77.08 45	91.21 20	57.23 37	90.73 10	83.35 1	88.12 37	89.22 8

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 13	89.23 25	81.51 2	88.44 30	88.09 45
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MGCNet		78.45 21	78.28 22	78.98 29	80.73 115	57.91 90	84.68 41	81.64 131	68.35 2	75.77 51	90.38 34	53.98 79	90.26 13	81.30 3	87.68 45	88.77 17

MP-MVS-pluss		78.35 23	78.46 20	78.03 45	84.96 56	59.52 58	82.93 70	85.39 33	62.15 80	76.41 49	91.51 11	52.47 108	86.78 76	80.66 4	89.64 19	87.80 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 101	78.34 180	55.37 139	77.30 186	73.95 311	61.40 95	79.46 23	90.14 41	57.07 38	81.15 229	80.00 5	79.31 152	88.51 29

SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 24	61.95 19	86.33 17	85.75 27	62.49 70	82.20 19	92.28 1	56.53 42	89.70 20	79.85 6	91.48 1	88.19 40
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 30

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 45

ACMMP_NAP		78.77 18	78.78 17	78.74 34	85.44 46	61.04 31	83.84 60	85.16 37	62.88 61	78.10 34	91.26 19	52.51 106	88.39 35	79.34 9	90.52 13	86.78 98

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 53

No_MVS				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 53

fmvsm_s_conf0.5_n_4		72.04 127	71.85 116	72.58 190	73.74 320	52.49 202	76.69 209	72.42 329	56.42 220	75.32 55	87.04 112	52.13 115	78.01 307	79.29 12	73.65 253	87.26 81

IU-MVS					87.77 4	59.15 68		85.53 32	53.93 283	84.64 3				79.07 13	90.87 5	88.37 32

HPM-MVS++		79.88 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 16	83.70 78	65.37 13	78.78 29	90.64 24	58.63 29	87.24 60	79.00 14	90.37 14	85.26 174

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 21	60.02 48	88.12 3	86.42 15	62.94 59	82.40 16	92.12 2	59.64 23	89.76 19	78.70 15	88.32 34	86.79 97
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_3		73.55 89	74.39 68	71.03 246	74.09 316	51.86 217	77.77 170	75.60 275	61.18 101	78.67 30	88.98 63	55.88 60	77.73 316	78.69 16	78.68 173	83.50 240

CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 17	84.37 45	85.03 42	66.96 5	77.58 39	90.06 45	59.47 25	89.13 27	78.67 17	89.73 16	87.03 88

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 73	87.85 5	85.03 42	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 162
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 64

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 78	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 38

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 62

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 141	75.33 278	52.89 189	78.24 147	77.32 240	61.65 90	78.13 33	88.90 66	52.82 102	81.54 219	78.46 22	78.67 174	87.60 65

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 81	62.18 16	87.60 9	85.83 25	66.69 9	78.03 36	90.98 21	54.26 74	90.06 14	78.42 23	89.02 26	87.69 60
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		73.27 96	73.66 85	72.09 204	73.82 317	52.72 195	77.45 180	74.28 304	56.61 215	77.10 44	88.16 77	56.17 48	77.09 330	78.27 24	81.13 117	86.48 111

test_fmvsmconf0.1_n		72.81 106	72.33 110	74.24 128	69.89 394	55.81 126	78.22 153	75.40 282	54.17 279	75.00 63	88.03 84	53.82 84	80.23 256	78.08 25	78.34 183	86.69 101

test_fmvsmconf0.01_n		72.17 123	71.50 122	74.16 131	67.96 423	55.58 134	78.06 160	74.67 297	54.19 278	74.54 75	88.23 75	50.35 144	80.24 255	78.07 26	77.46 197	86.65 105

test_fmvsmconf_n		73.01 102	72.59 105	74.27 126	71.28 371	55.88 125	78.21 154	75.56 277	54.31 277	74.86 68	87.80 88	54.72 70	80.23 256	78.07 26	78.48 179	86.70 100

fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 120	74.61 298	52.86 191	78.10 159	77.06 244	57.14 198	78.24 32	88.79 71	52.83 101	82.26 205	77.79 28	81.30 115	88.32 33

9.14			78.75 18		83.10 78		84.15 54	88.26 1	59.90 137	78.57 31	90.36 35	57.51 36	86.86 74	77.39 29	89.52 21

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 254	80.97 157	65.13 15	75.77 51	90.88 22	48.63 167	86.66 79	77.23 30	88.17 36	84.81 190

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 84	63.89 39	73.60 95	90.60 25	54.85 69	86.72 77	77.20 31	88.06 39	85.74 148
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_6		72.59 113	72.87 101	71.73 215	75.14 285	51.96 215	76.28 218	77.12 243	57.63 192	73.85 91	86.91 115	51.54 126	77.87 312	77.18 32	80.18 134	85.37 168

SF-MVS		78.82 16	79.22 15	77.60 52	82.88 83	57.83 91	84.99 37	88.13 2	61.86 88	79.16 26	90.75 23	57.96 30	87.09 69	77.08 33	90.18 15	87.87 52

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 23	60.95 105	83.65 12	90.57 27		89.91 16	77.02 34	89.43 22	88.10 43

MED-MVS		80.40 6	80.84 6	79.07 25	85.30 50	59.25 64	86.84 11	85.86 23	63.31 48	83.65 12	91.48 12	64.70 10	89.91 16	77.02 34	89.43 22	88.06 48

ME-MVS		80.04 10	80.36 9	79.08 24	86.63 23	59.25 64	85.62 32	86.73 12	63.10 56	82.27 18	90.57 27	61.90 16	89.88 18	77.02 34	89.43 22	88.10 43

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 31	84.96 56	61.41 26	84.03 56	83.82 73	59.34 153	79.37 25	89.76 54	59.84 20	87.62 57	76.69 37	86.74 58	87.68 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_5		72.69 110	72.80 102	72.37 200	74.11 315	53.21 180	78.12 156	73.31 318	53.98 282	76.81 46	88.05 81	53.38 92	77.37 325	76.64 38	80.78 119	86.53 109

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 47	75.08 61	90.47 33	53.96 81	88.68 32	76.48 39	89.63 20	87.16 85

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 51	83.27 15	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 95
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_3		73.23 97	73.13 97	73.55 166	74.40 305	55.13 142	78.97 130	74.96 294	56.64 209	74.76 72	88.75 72	55.02 66	78.77 297	76.33 41	78.31 184	86.74 99

fmvsm_l_conf0.5_n		70.99 146	70.82 139	71.48 224	71.45 364	54.40 151	77.18 192	70.46 348	48.67 367	75.17 58	86.86 116	53.77 86	76.86 338	76.33 41	77.51 196	83.17 252

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 50	59.08 72	86.84 11	86.01 20	63.31 48	82.37 17	91.48 12	60.88 18	89.61 21	76.25 43	86.13 65	88.06 48

fmvsm_s_conf0.5_n_11		73.16 98	73.35 92	72.58 190	75.48 273	52.41 206	78.84 132	76.85 248	58.64 166	73.58 96	87.25 107	54.09 78	79.47 268	76.19 44	79.27 153	85.86 138

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 64	61.88 21	84.55 43	83.87 66	60.37 123	79.89 22	89.38 58	54.97 67	85.58 114	76.12 45	84.94 71	86.33 120
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
test_fmvsmvis_n_1920		70.84 148	70.38 148	72.22 203	71.16 372	55.39 138	75.86 231	72.21 332	49.03 362	73.28 104	86.17 147	51.83 121	77.29 327	75.80 46	78.05 187	83.98 217

fmvsm_s_conf0.5_n		69.58 184	68.84 179	71.79 213	72.31 350	52.90 187	77.90 162	62.43 422	49.97 350	72.85 120	85.90 158	52.21 112	76.49 347	75.75 47	70.26 314	85.97 133

fmvsm_s_conf0.5_n_2		69.82 174	69.27 170	71.46 225	72.00 354	51.08 224	73.30 288	67.79 371	55.06 258	75.24 57	87.51 91	44.02 231	77.00 334	75.67 48	72.86 271	86.31 125

lecture		77.75 28	77.84 28	77.50 54	82.75 85	57.62 94	85.92 25	86.20 18	60.53 116	78.99 28	91.45 14	51.51 127	87.78 52	75.65 49	87.55 46	87.10 87

fmvsm_s_conf0.1_n_2		69.64 182	69.01 176	71.52 223	71.66 359	51.04 225	73.39 287	67.14 377	55.02 262	75.11 59	87.64 90	42.94 243	77.01 333	75.55 50	72.63 277	86.52 110

fmvsm_s_conf0.1_n		69.41 192	68.60 185	71.83 210	71.07 373	52.88 190	77.85 166	62.44 421	49.58 355	72.97 115	86.22 144	51.68 124	76.48 348	75.53 51	70.10 317	86.14 128

fmvsm_l_conf0.5_n_a		70.50 157	70.27 150	71.18 240	71.30 370	54.09 156	76.89 203	69.87 352	47.90 382	74.37 78	86.49 136	53.07 100	76.69 344	75.41 52	77.11 205	82.76 259

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 39	62.57 68	73.09 113	89.97 50	50.90 138	87.48 58	75.30 53	86.85 56	87.33 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test9_res														75.28 54	88.31 35	83.81 225

train_agg		76.27 47	76.15 44	76.64 70	85.58 44	61.59 24	81.62 91	81.26 146	55.86 230	74.93 64	88.81 68	53.70 88	84.68 138	75.24 55	88.33 33	83.65 236

fmvsm_s_conf0.5_n_a		69.54 186	68.74 182	71.93 207	72.47 344	53.82 161	78.25 146	62.26 424	49.78 352	73.12 112	86.21 145	52.66 104	76.79 340	75.02 56	68.88 340	85.18 175

test_fmvsm_n_1920		71.73 133	71.14 133	73.50 167	72.52 342	56.53 112	75.60 235	76.16 263	48.11 378	77.22 41	85.56 168	53.10 98	77.43 322	74.86 57	77.14 204	86.55 108

fmvsm_s_conf0.1_n_a		69.32 193	68.44 191	71.96 205	70.91 375	53.78 162	78.12 156	62.30 423	49.35 358	73.20 106	86.55 135	51.99 117	76.79 340	74.83 58	68.68 345	85.32 170

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 36	63.56 43	74.29 80	90.03 47	52.56 105	88.53 34	74.79 59	88.34 32	86.63 106

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 71	61.62 23	84.17 53	86.85 6	63.23 53	73.84 92	90.25 40	57.68 33	89.96 15	74.62 60	89.03 25	87.89 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PC_three_1452									55.09 253	84.46 4	89.84 52	66.68 5	89.41 23	74.24 61	91.38 2	88.42 30

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 34	62.86 62	80.17 21	90.03 47	61.76 17	88.95 29	74.21 62	88.67 29	88.12 42

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 51	66.73 8	74.67 74	89.38 58	55.30 63	89.18 26	74.19 63	87.34 49	86.38 113

ZD-MVS					86.64 21	60.38 45		82.70 116	57.95 183	78.10 34	90.06 45	56.12 51	88.84 31	74.05 64	87.00 54

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 53	62.82 63	73.96 85	90.50 31	53.20 96	88.35 36	74.02 65	87.05 50	86.13 129

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 54	62.82 63	73.55 97	90.56 29	49.80 150	88.24 38	74.02 65	87.03 51	86.32 122

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 56	62.81 65	73.30 102	90.58 26	49.90 147	88.21 39	73.78 67	87.03 51	86.29 126

MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 85	83.22 66	86.93 5	56.91 205	74.91 66	88.19 76	59.15 27	87.68 56	73.67 68	87.45 48	86.57 107

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 87	62.44 72	72.68 123	90.50 31	48.18 172	87.34 59	73.59 69	85.71 67	84.76 193

APD-MVS		78.02 26	78.04 26	77.98 46	86.44 28	60.81 38	85.52 33	84.36 52	60.61 114	79.05 27	90.30 38	55.54 62	88.32 37	73.48 70	87.03 51	84.83 189
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 38

agg_prior2														73.09 72	87.93 43	84.33 203

BridgeMVS		76.58 42	76.55 41	76.68 67	81.73 96	52.90 187	80.94 99	85.70 29	61.12 103	74.90 67	87.17 110	56.46 43	88.14 41	72.87 73	88.03 41	89.00 10

casdiffmvs_mvg		76.14 50	76.30 43	75.66 88	76.46 257	51.83 218	79.67 121	85.08 39	65.02 19	75.84 50	88.58 74	59.42 26	85.08 126	72.75 74	83.93 83	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet		76.46 44	75.93 48	78.06 43	81.29 105	57.53 96	82.35 80	83.31 95	67.78 3	70.09 157	86.34 141	54.92 68	88.90 30	72.68 75	84.55 74	87.76 58

reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 92	60.22 130	77.85 37	91.42 16	50.67 139	87.69 54	72.46 76	84.53 75	85.46 160

our_new_method		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 92	60.22 130	77.85 37	91.42 16	50.67 139	87.69 54	72.46 76	84.53 75	85.46 160

PGM-MVS		76.77 41	76.06 46	78.88 32	86.14 36	62.73 9	82.55 78	83.74 75	61.71 89	72.45 129	90.34 37	48.48 170	88.13 42	72.32 78	86.85 56	85.78 142

test_prior2							81.75 89		60.37 123	75.01 62	89.06 61	56.22 47		72.19 79	88.96 27

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 68	80.84 113	53.06 184	78.62 138	85.13 38	59.65 143	71.53 142	87.47 93	56.92 39	88.17 40	72.18 80	86.63 61	88.80 14

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 50	63.04 57	69.80 167	89.74 55	45.43 212	87.16 66	72.01 81	82.87 97	85.14 176
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
reproduce_model		76.43 45	76.08 45	77.49 55	83.47 75	60.09 47	84.60 42	82.90 112	59.65 143	77.31 40	91.43 15	49.62 152	87.24 60	71.99 82	83.75 87	85.14 176

EC-MVSNet		75.84 54	75.87 50	75.74 86	78.86 159	52.65 196	83.73 61	86.08 19	63.47 45	72.77 122	87.25 107	53.13 97	87.93 47	71.97 83	85.57 69	86.66 104

fmvsm_s_conf0.5_n_7		69.54 186	69.67 161	69.15 285	73.47 325	51.41 222	70.35 345	73.34 317	57.05 201	68.41 189	85.83 161	49.86 148	72.84 368	71.86 84	76.83 209	83.19 248

CS-MVS		76.25 49	75.98 47	77.06 61	80.15 129	55.63 131	84.51 44	83.90 63	63.24 52	73.30 102	87.27 102	55.06 65	86.30 94	71.78 85	84.58 73	89.25 7

mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 121	62.90 60	71.77 136	90.26 39	46.61 198	86.55 85	71.71 86	85.66 68	84.97 185

SR-MVS		76.13 51	75.70 52	77.40 58	85.87 41	61.20 29	85.52 33	82.19 122	59.99 136	75.10 60	90.35 36	47.66 179	86.52 86	71.64 87	82.99 92	84.47 202

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 131	90.01 49	47.95 174	88.01 45	71.55 88	86.74 58	86.37 115

X-MVStestdata		70.21 164	67.28 223	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 131	6.49 498	47.95 174	88.01 45	71.55 88	86.74 58	86.37 115

BP-MVS1		73.41 92	72.25 111	76.88 62	76.68 250	53.70 163	79.15 128	81.07 153	60.66 113	71.81 135	87.39 97	40.93 275	87.24 60	71.23 90	81.29 116	89.71 2

dcpmvs_2		74.55 70	75.23 58	72.48 195	82.34 88	53.34 176	77.87 164	81.46 135	57.80 188	75.49 53	86.81 118	62.22 14	77.75 315	71.09 91	82.02 106	86.34 117

diffmvs_AUTHOR		71.02 144	70.87 138	71.45 227	69.89 394	48.97 283	73.16 295	78.33 220	57.79 189	72.11 133	85.26 176	51.84 120	77.89 311	71.00 92	78.47 181	87.49 69

PHI-MVS		75.87 53	75.36 55	77.41 56	80.62 120	55.91 124	84.28 50	85.78 26	56.08 228	73.41 98	86.58 132	50.94 137	88.54 33	70.79 93	89.71 17	87.79 57

diffmvs		70.69 153	70.43 146	71.46 225	69.45 401	48.95 284	72.93 298	78.46 213	57.27 196	71.69 137	83.97 206	51.48 128	77.92 310	70.70 94	77.95 189	87.53 68
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
h-mvs33		72.71 109	71.49 123	76.40 73	81.99 93	59.58 57	76.92 202	76.74 254	60.40 120	74.81 69	85.95 156	45.54 208	85.76 110	70.41 95	70.61 305	83.86 224

hse-mvs2		71.04 143	69.86 157	74.60 114	79.58 138	57.12 107	73.96 273	75.25 285	60.40 120	74.81 69	81.95 260	45.54 208	82.90 184	70.41 95	66.83 360	83.77 229

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 68	82.20 89	58.24 86	83.67 62	83.29 96	58.41 171	73.71 93	90.14 41	45.62 205	85.99 104	69.64 97	82.85 98	85.78 142

baseline		74.61 68	74.70 64	74.34 123	75.70 266	49.99 258	77.54 176	84.63 48	62.73 67	73.98 84	87.79 89	57.67 34	83.82 155	69.49 98	82.74 100	89.20 9

OPM-MVS		74.73 65	74.25 71	76.19 77	80.81 114	59.01 76	82.60 77	83.64 81	63.74 41	72.52 126	87.49 92	47.18 189	85.88 107	69.47 99	80.78 119	83.66 235
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
casdiffmvs		74.80 63	74.89 63	74.53 118	75.59 271	50.37 248	78.17 155	85.06 41	62.80 66	74.40 77	87.86 86	57.88 31	83.61 159	69.46 100	82.79 99	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 57	56.32 222	74.05 83	88.98 63	53.34 93	87.92 48	69.23 101	88.42 31	87.59 66

CPTT-MVS		72.78 107	72.08 114	74.87 104	84.88 61	61.41 26	84.15 54	77.86 226	55.27 248	67.51 220	88.08 80	41.93 254	81.85 212	69.04 102	80.01 135	81.35 293

balanced_ft_v1		72.98 103	72.55 106	74.27 126	79.52 141	50.64 238	77.78 169	83.29 96	56.76 206	67.88 207	85.95 156	49.42 156	85.29 124	68.64 103	83.76 86	86.87 93

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 46	61.98 87	73.06 114	88.88 67	53.72 87	89.06 28	68.27 104	88.04 40	87.42 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		74.57 69	73.90 79	76.58 71	83.49 73	59.87 54	84.29 48	81.36 139	58.07 177	73.14 109	90.07 43	44.74 222	85.84 108	68.20 105	81.76 110	84.03 214

RE-MVS-def			73.71 84		83.49 73	59.87 54	84.29 48	81.36 139	58.07 177	73.14 109	90.07 43	43.06 241		68.20 105	81.76 110	84.03 214

E6new		74.10 76	74.09 73	74.15 133	77.14 228	50.74 233	78.24 147	83.85 71	62.34 74	73.95 86	87.27 102	55.98 55	82.95 178	68.17 107	79.85 138	88.77 17

E6		74.10 76	74.09 73	74.15 133	77.14 228	50.74 233	78.24 147	83.85 71	62.34 74	73.95 86	87.27 102	55.98 55	82.95 178	68.17 107	79.85 138	88.77 17

E5new		74.10 76	74.09 73	74.15 133	77.14 228	50.74 233	78.24 147	83.86 69	62.34 74	73.95 86	87.27 102	55.97 57	82.95 178	68.16 109	79.86 136	88.77 17

E5		74.10 76	74.09 73	74.15 133	77.14 228	50.74 233	78.24 147	83.86 69	62.34 74	73.95 86	87.27 102	55.97 57	82.95 178	68.16 109	79.86 136	88.77 17

HQP_MVS		74.31 72	73.73 83	76.06 78	81.41 102	56.31 113	84.22 51	84.01 58	64.52 27	69.27 176	86.10 149	45.26 216	87.21 64	68.16 109	80.58 125	84.65 194

plane_prior5								84.01 58					87.21 64	68.16 109	80.58 125	84.65 194

NormalMVS		76.26 48	75.74 51	77.83 50	82.75 85	59.89 52	84.36 46	83.21 100	64.69 22	74.21 81	87.40 95	49.48 153	86.17 97	68.04 113	87.55 46	87.42 72

SymmetryMVS		75.28 59	74.60 65	77.30 59	83.85 70	59.89 52	84.36 46	75.51 279	64.69 22	74.21 81	87.40 95	49.48 153	86.17 97	68.04 113	83.88 84	85.85 139

E4		73.91 82	73.83 82	74.15 133	77.13 232	50.47 245	77.15 193	83.79 74	62.21 79	73.61 94	87.19 109	56.08 53	83.03 171	67.91 115	79.35 150	88.94 12

CSCG		76.92 37	76.75 35	77.41 56	83.96 69	59.60 56	82.95 69	86.50 14	60.78 110	75.27 56	84.83 179	60.76 19	86.56 82	67.86 116	87.87 44	86.06 131

SPE-MVS-test		75.62 57	75.31 57	76.56 72	80.63 119	55.13 142	83.88 59	85.22 35	62.05 84	71.49 143	86.03 152	53.83 83	86.36 92	67.74 117	86.91 55	88.19 40

LPG-MVS_test		72.74 108	71.74 119	75.76 84	80.22 124	57.51 97	82.55 78	83.40 89	61.32 96	66.67 238	87.33 100	39.15 295	86.59 80	67.70 118	77.30 202	83.19 248

LGP-MVS_train				75.76 84	80.22 124	57.51 97		83.40 89	61.32 96	66.67 238	87.33 100	39.15 295	86.59 80	67.70 118	77.30 202	83.19 248

HPM-MVS_fast		74.30 73	73.46 89	76.80 64	84.45 65	59.04 75	83.65 63	81.05 154	60.15 132	70.43 153	89.84 52	41.09 274	85.59 113	67.61 120	82.90 96	85.77 145

MVS_111021_HR		74.02 80	73.46 89	75.69 87	83.01 81	60.63 40	77.29 187	78.40 218	61.18 101	70.58 152	85.97 155	54.18 76	84.00 152	67.52 121	82.98 94	82.45 270

E2		73.72 86	73.60 86	74.06 138	77.16 226	50.40 246	76.97 198	83.74 75	61.64 91	73.36 99	86.75 122	56.14 49	82.99 173	67.50 122	79.18 160	88.80 14

E3		73.72 86	73.60 86	74.06 138	77.16 226	50.40 246	76.97 198	83.74 75	61.64 91	73.36 99	86.76 119	56.13 50	82.99 173	67.50 122	79.18 160	88.80 14

ETV-MVS		74.46 71	73.84 81	76.33 75	79.27 147	55.24 141	79.22 127	85.00 44	64.97 21	72.65 124	79.46 312	53.65 91	87.87 49	67.45 124	82.91 95	85.89 137

DELS-MVS		74.76 64	74.46 67	75.65 89	77.84 199	52.25 207	75.59 236	84.17 55	63.76 40	73.15 108	82.79 229	59.58 24	86.80 75	67.24 125	86.04 66	87.89 50
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
viewcassd2359sk11		73.56 88	73.41 91	74.00 142	77.13 232	50.35 249	76.86 205	83.69 79	61.23 100	73.14 109	86.38 140	56.09 52	82.96 176	67.15 126	79.01 165	88.70 23

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 60	82.00 92	58.77 81	81.80 88	78.57 207	58.58 168	74.32 79	84.51 194	55.94 59	87.22 63	67.11 127	84.48 78	85.52 156

BP-MVS														67.04 128

HQP-MVS		73.45 90	72.80 102	75.40 93	80.66 116	54.94 144	82.31 82	83.90 63	62.10 81	67.85 208	85.54 171	45.46 210	86.93 72	67.04 128	80.35 130	84.32 204

viewmacassd2359aftdt		73.15 99	73.16 96	73.11 179	75.15 284	49.31 274	77.53 178	83.21 100	60.42 119	73.20 106	87.34 99	53.82 84	81.05 234	67.02 130	80.79 118	88.96 11

GDP-MVS		72.64 111	71.28 130	76.70 65	77.72 203	54.22 155	79.57 124	84.45 49	55.30 247	71.38 144	86.97 114	39.94 281	87.00 71	67.02 130	79.20 157	88.89 13

ACMP	63.53 6	72.30 120	71.20 132	75.59 92	80.28 122	57.54 95	82.74 74	82.84 115	60.58 115	65.24 270	86.18 146	39.25 293	86.03 103	66.95 132	76.79 210	83.22 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
E3new		73.41 92	73.22 94	73.95 145	77.06 237	50.31 250	76.78 208	83.66 80	60.90 106	72.93 117	86.02 153	55.99 54	82.95 178	66.89 133	78.77 170	88.61 25

viewmanbaseed2359cas		72.92 105	72.89 100	73.00 181	75.16 282	49.25 277	77.25 190	83.11 108	59.52 150	72.93 117	86.63 128	54.11 77	80.98 235	66.63 134	80.67 122	88.76 22

EI-MVSNet-Vis-set		72.42 118	71.59 120	74.91 102	78.47 173	54.02 157	77.05 196	79.33 185	65.03 18	71.68 138	79.35 315	52.75 103	84.89 133	66.46 135	74.23 243	85.83 141

DPM-MVS		75.47 58	75.00 60	76.88 62	81.38 104	59.16 67	79.94 114	85.71 28	56.59 216	72.46 127	86.76 119	56.89 40	87.86 50	66.36 136	88.91 28	83.64 237

patch_mono-2		69.85 173	71.09 134	66.16 334	79.11 154	54.80 148	71.97 316	74.31 302	53.50 293	70.90 148	84.17 199	57.63 35	63.31 429	66.17 137	82.02 106	80.38 318

MVSFormer		71.50 137	70.38 148	74.88 103	78.76 162	57.15 105	82.79 72	78.48 211	51.26 332	69.49 170	83.22 224	43.99 232	83.24 167	66.06 138	79.37 147	84.23 208

test_djsdf		69.45 191	67.74 206	74.58 115	74.57 301	54.92 146	82.79 72	78.48 211	51.26 332	65.41 263	83.49 220	38.37 306	83.24 167	66.06 138	69.25 335	85.56 155

sasdasda		74.67 66	74.98 61	73.71 156	78.94 157	50.56 242	80.23 107	83.87 66	60.30 127	77.15 42	86.56 133	59.65 21	82.00 209	66.01 140	82.12 103	88.58 27

canonicalmvs		74.67 66	74.98 61	73.71 156	78.94 157	50.56 242	80.23 107	83.87 66	60.30 127	77.15 42	86.56 133	59.65 21	82.00 209	66.01 140	82.12 103	88.58 27

MVS_Test		72.45 116	72.46 108	72.42 199	74.88 287	48.50 292	76.28 218	83.14 106	59.40 151	72.46 127	84.68 184	55.66 61	81.12 230	65.98 142	79.66 143	87.63 63

alignmvs		73.86 83	73.99 77	73.45 170	78.20 184	50.50 244	78.57 140	82.43 119	59.40 151	76.57 47	86.71 125	56.42 45	81.23 228	65.84 143	81.79 109	88.62 24

nrg030		72.96 104	73.01 98	72.84 185	75.41 276	50.24 251	80.02 112	82.89 114	58.36 173	74.44 76	86.73 123	58.90 28	80.83 241	65.84 143	74.46 239	87.44 71

MVS_111021_LR		69.50 189	68.78 181	71.65 220	78.38 176	59.33 61	74.82 256	70.11 350	58.08 176	67.83 213	84.68 184	41.96 252	76.34 351	65.62 145	77.54 194	79.30 342

EI-MVSNet-UG-set		71.92 128	71.06 135	74.52 119	77.98 195	53.56 168	76.62 210	79.16 186	64.40 29	71.18 145	78.95 320	52.19 113	84.66 140	65.47 146	73.57 256	85.32 170

viewdifsd2359ckpt07		71.90 129	71.97 115	71.69 218	74.81 291	48.08 298	75.30 241	80.49 165	60.00 135	71.63 139	86.33 142	56.34 46	79.25 273	65.40 147	77.41 198	87.76 58

PS-MVSNAJss		72.24 121	71.21 131	75.31 95	78.50 171	55.93 123	81.63 90	82.12 123	56.24 225	70.02 161	85.68 167	47.05 191	84.34 144	65.27 148	74.41 242	85.67 151

MSLP-MVS++		73.77 84	73.47 88	74.66 110	83.02 80	59.29 63	82.30 85	81.88 126	59.34 153	71.59 140	86.83 117	45.94 203	83.65 158	65.09 149	85.22 70	81.06 303

v2v482		70.50 157	69.45 166	73.66 159	72.62 339	50.03 257	77.58 173	80.51 164	59.90 137	69.52 169	82.14 256	47.53 182	84.88 135	65.07 150	70.17 315	86.09 130

RRT-MVS		71.46 138	70.70 142	73.74 154	77.76 202	49.30 275	76.60 211	80.45 166	61.25 99	68.17 195	84.78 181	44.64 224	84.90 132	64.79 151	77.88 190	87.03 88

jason		69.65 181	68.39 193	73.43 172	78.27 183	56.88 109	77.12 194	73.71 314	46.53 402	69.34 175	83.22 224	43.37 236	79.18 275	64.77 152	79.20 157	84.23 208
jason: jason.
anonymousdsp		67.00 253	64.82 273	73.57 165	70.09 390	56.13 118	76.35 216	77.35 238	48.43 373	64.99 278	80.84 286	33.01 368	80.34 251	64.66 153	67.64 353	84.23 208

lupinMVS		69.57 185	68.28 198	73.44 171	78.76 162	57.15 105	76.57 212	73.29 320	46.19 405	69.49 170	82.18 252	43.99 232	79.23 274	64.66 153	79.37 147	83.93 219

CLD-MVS		73.33 94	72.68 104	75.29 97	78.82 161	53.33 177	78.23 152	84.79 47	61.30 98	70.41 154	81.04 278	52.41 109	87.12 67	64.61 155	82.49 102	85.41 166
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		68.65 210	67.35 221	72.56 192	68.93 411	50.18 253	72.90 300	79.47 182	56.92 204	69.45 172	80.26 294	46.29 201	82.99 173	64.07 156	67.82 351	84.53 199

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 87	59.92 51	85.83 27	86.32 17	66.92 7	67.80 214	89.24 60	42.03 251	89.38 24	64.07 156	86.50 62	89.69 3

v1144		70.42 159	69.31 168	73.76 151	73.22 327	50.64 238	77.83 167	81.43 136	58.58 168	69.40 173	81.16 275	47.53 182	85.29 124	64.01 158	70.64 303	85.34 169

Effi-MVS+		73.31 95	72.54 107	75.62 90	77.87 197	53.64 165	79.62 123	79.61 179	61.63 93	72.02 134	82.61 234	56.44 44	85.97 105	63.99 159	79.07 163	87.25 82

MGCFI-Net		72.45 116	73.34 93	69.81 271	77.77 201	43.21 356	75.84 233	81.18 150	59.59 148	75.45 54	86.64 126	57.74 32	77.94 308	63.92 160	81.90 108	88.30 34

SDMVSNet		68.03 227	68.10 203	67.84 301	77.13 232	48.72 288	65.32 397	79.10 187	58.02 179	65.08 273	82.55 240	47.83 176	73.40 365	63.92 160	73.92 247	81.41 288

KinetiMVS		71.26 141	70.16 153	74.57 116	74.59 299	52.77 194	75.91 230	81.20 149	60.72 112	69.10 182	85.71 166	41.67 262	83.53 161	63.91 162	78.62 176	87.42 72

xiu_mvs_v1_base_debu		68.58 212	67.28 223	72.48 195	78.19 185	57.19 102	75.28 242	75.09 290	51.61 321	70.04 158	81.41 272	32.79 371	79.02 288	63.81 163	77.31 199	81.22 296

xiu_mvs_v1_base		68.58 212	67.28 223	72.48 195	78.19 185	57.19 102	75.28 242	75.09 290	51.61 321	70.04 158	81.41 272	32.79 371	79.02 288	63.81 163	77.31 199	81.22 296

xiu_mvs_v1_base_debi		68.58 212	67.28 223	72.48 195	78.19 185	57.19 102	75.28 242	75.09 290	51.61 321	70.04 158	81.41 272	32.79 371	79.02 288	63.81 163	77.31 199	81.22 296

viewdifsd2359ckpt09		73.42 91	72.45 109	76.30 76	77.25 224	53.27 178	80.36 106	82.48 118	57.96 182	72.24 130	85.73 165	53.22 94	86.27 95	63.79 166	79.06 164	89.36 6

Elysia		70.19 166	68.29 196	75.88 81	74.15 312	54.33 153	78.26 144	83.21 100	55.04 259	67.28 223	83.59 215	30.16 398	86.11 99	63.67 167	79.26 154	87.20 83

StellarMVS		70.19 166	68.29 196	75.88 81	74.15 312	54.33 153	78.26 144	83.21 100	55.04 259	67.28 223	83.59 215	30.16 398	86.11 99	63.67 167	79.26 154	87.20 83

v8		70.33 162	69.28 169	73.49 168	73.15 329	50.22 252	78.62 138	80.78 160	60.79 109	66.45 242	82.11 258	49.35 157	84.98 129	63.58 169	68.71 343	85.28 172

jajsoiax		68.25 221	66.45 241	73.66 159	75.62 269	55.49 136	80.82 101	78.51 210	52.33 311	64.33 286	84.11 201	28.28 419	81.81 214	63.48 170	70.62 304	83.67 233

mvs_tets		68.18 224	66.36 247	73.63 162	75.61 270	55.35 140	80.77 102	78.56 208	52.48 310	64.27 288	84.10 202	27.45 428	81.84 213	63.45 171	70.56 306	83.69 232

viewdifsd2359ckpt11		69.13 198	68.38 194	71.38 232	71.57 361	48.61 289	73.22 293	73.18 321	57.65 190	70.67 150	84.73 182	50.03 145	79.80 260	63.25 172	71.10 299	85.74 148

viewmsd2359difaftdt		69.13 198	68.38 194	71.38 232	71.57 361	48.61 289	73.22 293	73.18 321	57.65 190	70.67 150	84.73 182	50.03 145	79.80 260	63.25 172	71.10 299	85.74 148

AstraMVS		67.86 233	66.83 234	70.93 248	73.50 324	49.34 273	73.28 291	74.01 309	55.45 244	68.10 202	83.28 222	38.93 298	79.14 280	63.22 174	71.74 290	84.30 206

LuminaMVS		68.24 222	66.82 235	72.51 194	73.46 326	53.60 167	76.23 220	78.88 194	52.78 302	68.08 203	80.13 296	32.70 376	81.41 221	63.16 175	75.97 221	82.53 266

viewdifsd2359ckpt13		72.40 119	71.79 118	74.22 129	75.63 268	51.77 219	78.67 136	83.13 107	57.08 199	71.59 140	85.36 175	53.10 98	82.64 196	63.07 176	78.51 178	88.24 37

v144192		69.71 177	68.51 186	73.33 175	73.10 330	50.13 254	77.54 176	80.64 161	56.65 208	68.57 187	80.55 288	46.87 196	84.96 131	62.98 177	69.66 328	84.89 188

v1192		69.97 171	68.68 183	73.85 146	73.19 328	50.94 227	77.68 172	81.36 139	57.51 194	68.95 183	80.85 285	45.28 215	85.33 123	62.97 178	70.37 309	85.27 173

v10		70.21 164	69.02 174	73.81 148	73.51 323	50.92 229	78.74 134	81.39 137	60.05 134	66.39 243	81.83 263	47.58 181	85.41 122	62.80 179	68.86 342	85.09 180

OMC-MVS		71.40 140	70.60 143	73.78 149	76.60 253	53.15 181	79.74 120	79.78 175	58.37 172	68.75 184	86.45 138	45.43 212	80.60 245	62.58 180	77.73 191	87.58 67

XVG-OURS-SEG-HR		68.81 206	67.47 216	72.82 187	74.40 305	56.87 110	70.59 340	79.04 190	54.77 267	66.99 230	86.01 154	39.57 287	78.21 304	62.54 181	73.33 263	83.37 242

EPNet		73.09 101	72.16 112	75.90 80	75.95 263	56.28 115	83.05 67	72.39 330	66.53 10	65.27 266	87.00 113	50.40 142	85.47 119	62.48 182	86.32 64	85.94 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		69.47 190	68.17 200	73.36 174	73.06 331	50.10 255	77.39 181	80.56 162	56.58 217	68.59 185	80.37 290	44.72 223	84.98 129	62.47 183	69.82 323	85.00 182

c3_l		68.33 219	67.56 210	70.62 255	70.87 376	46.21 319	74.47 264	78.80 197	56.22 226	66.19 246	78.53 328	51.88 118	81.40 222	62.08 184	69.04 338	84.25 207

AUN-MVS		68.45 218	66.41 245	74.57 116	79.53 140	57.08 108	73.93 276	75.23 286	54.44 275	66.69 236	81.85 262	37.10 324	82.89 185	62.07 185	66.84 359	83.75 230

XVG-OURS		68.76 209	67.37 219	72.90 184	74.32 308	57.22 100	70.09 349	78.81 196	55.24 249	67.79 215	85.81 164	36.54 330	78.28 303	62.04 186	75.74 225	83.19 248

v1240		69.24 196	67.91 205	73.25 178	73.02 333	49.82 259	77.21 191	80.54 163	56.43 219	68.34 192	80.51 289	43.33 237	84.99 127	62.03 187	69.77 326	84.95 186

ET-MVSNet_ETH3D		67.96 230	65.72 259	74.68 109	76.67 251	55.62 133	75.11 247	74.74 295	52.91 300	60.03 347	80.12 297	33.68 360	82.64 196	61.86 188	76.34 214	85.78 142

VDD-MVS		72.50 114	72.09 113	73.75 153	81.58 98	49.69 267	77.76 171	77.63 231	63.21 54	73.21 105	89.02 62	42.14 250	83.32 165	61.72 189	82.50 101	88.25 36

casdiffseed414692147		73.73 85	73.22 94	75.28 98	76.76 248	52.16 209	80.05 111	83.01 109	63.38 46	73.35 101	87.11 111	53.22 94	84.14 146	61.71 190	80.38 129	89.55 5

PS-MVSNAJ		70.51 156	69.70 160	72.93 183	81.52 99	55.79 127	74.92 254	79.00 191	55.04 259	69.88 165	78.66 323	47.05 191	82.19 206	61.61 191	79.58 144	80.83 307

xiu_mvs_v2_base		70.52 155	69.75 158	72.84 185	81.21 108	55.63 131	75.11 247	78.92 193	54.92 264	69.96 164	79.68 307	47.00 195	82.09 208	61.60 192	79.37 147	80.81 308

cl22		67.47 241	66.45 241	70.54 257	69.85 396	46.49 315	73.85 279	77.35 238	55.07 256	65.51 261	77.92 337	47.64 180	81.10 231	61.58 193	69.32 332	84.01 216

viewmambaseed2359dif		68.91 203	68.18 199	71.11 243	70.21 386	48.05 301	72.28 311	75.90 269	51.96 316	70.93 147	84.47 195	51.37 129	78.59 299	61.55 194	74.97 235	86.68 102

miper_ehance_all_eth		68.03 227	67.24 227	70.40 259	70.54 380	46.21 319	73.98 272	78.68 201	55.07 256	66.05 250	77.80 343	52.16 114	81.31 225	61.53 195	69.32 332	83.67 233

MG-MVS		73.96 81	73.89 80	74.16 131	85.65 43	49.69 267	81.59 93	81.29 145	61.45 94	71.05 146	88.11 78	51.77 122	87.73 53	61.05 196	83.09 90	85.05 181

guyue		68.10 226	67.23 229	70.71 254	73.67 322	49.27 276	73.65 283	76.04 268	55.62 240	67.84 212	82.26 250	41.24 272	78.91 295	61.01 197	73.72 251	83.94 218

miper_enhance_ethall		67.11 250	66.09 254	70.17 263	69.21 405	45.98 321	72.85 301	78.41 217	51.38 329	65.65 259	75.98 378	51.17 133	81.25 226	60.82 198	69.32 332	83.29 245

ACMM	61.98 7	70.80 152	69.73 159	74.02 140	80.59 121	58.59 83	82.68 75	82.02 125	55.46 243	67.18 227	84.39 197	38.51 304	83.17 169	60.65 199	76.10 220	80.30 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		69.64 182	67.53 213	75.95 79	76.10 261	62.29 15	80.20 110	76.06 267	59.83 142	65.26 269	77.09 355	41.56 265	84.02 151	60.60 200	71.09 301	81.53 286

PVSNet_Blended_VisFu		71.45 139	70.39 147	74.65 111	82.01 91	58.82 80	79.93 115	80.35 169	55.09 253	65.82 258	82.16 255	49.17 161	82.64 196	60.34 201	78.62 176	82.50 269

MVSTER		67.16 249	65.58 262	71.88 209	70.37 385	49.70 265	70.25 347	78.45 214	51.52 324	69.16 180	80.37 290	38.45 305	82.50 200	60.19 202	71.46 294	83.44 241

EIA-MVS		71.78 131	70.60 143	75.30 96	79.85 133	53.54 169	77.27 189	83.26 99	57.92 184	66.49 240	79.39 313	52.07 116	86.69 78	60.05 203	79.14 162	85.66 152

v148		68.24 222	67.19 230	71.40 231	70.43 383	47.77 304	75.76 234	77.03 245	58.91 159	67.36 221	80.10 298	48.60 169	81.89 211	60.01 204	66.52 363	84.53 199

test_vis1_n_1920		58.86 362	59.06 351	58.25 409	63.76 449	43.14 358	67.49 377	66.36 384	40.22 451	65.89 255	71.95 415	31.04 390	59.75 443	59.94 205	64.90 373	71.85 434

CANet_DTU		68.18 224	67.71 209	69.59 274	74.83 290	46.24 318	78.66 137	76.85 248	59.60 145	63.45 297	82.09 259	35.25 340	77.41 323	59.88 206	78.76 171	85.14 176

IterMVS-LS		69.22 197	68.48 187	71.43 230	74.44 304	49.40 271	76.23 220	77.55 232	59.60 145	65.85 257	81.59 270	51.28 131	81.58 218	59.87 207	69.90 322	83.30 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 195	68.44 191	71.73 215	74.47 302	49.39 272	75.20 245	78.45 214	59.60 145	69.16 180	76.51 368	51.29 130	82.50 200	59.86 208	71.45 295	83.30 243

3Dnovator	64.47 5	72.49 115	71.39 126	75.79 83	77.70 204	58.99 77	80.66 104	83.15 105	62.24 78	65.46 262	86.59 131	42.38 249	85.52 115	59.59 209	84.72 72	82.85 258

eth_miper_zixun_eth		67.63 238	66.28 251	71.67 219	71.60 360	48.33 294	73.68 282	77.88 225	55.80 234	65.91 253	78.62 326	47.35 188	82.88 186	59.45 210	66.25 364	83.81 225

DIV-MVS_self_test		67.18 247	66.26 252	69.94 266	70.20 387	45.74 323	73.29 290	76.83 250	55.10 251	65.27 266	79.58 308	47.38 187	80.53 247	59.43 211	69.22 336	83.54 238

cl____		67.18 247	66.26 252	69.94 266	70.20 387	45.74 323	73.30 288	76.83 250	55.10 251	65.27 266	79.57 309	47.39 186	80.53 247	59.41 212	69.22 336	83.53 239

mvsmamba		68.47 216	66.56 238	74.21 130	79.60 137	52.95 185	74.94 253	75.48 280	52.09 315	60.10 345	83.27 223	36.54 330	84.70 137	59.32 213	77.69 192	84.99 184

icg_test_0407_2		66.41 266	66.75 236	65.37 352	77.06 237	49.73 261	63.79 413	78.60 203	52.70 303	66.19 246	82.58 235	45.17 218	63.65 428	59.20 214	75.46 230	82.74 260

IMVS_0407		68.90 204	67.93 204	71.82 211	77.06 237	49.73 261	74.40 267	78.60 203	52.70 303	66.19 246	82.58 235	45.17 218	83.00 172	59.20 214	75.46 230	82.74 260

IMVS_0404		64.63 289	64.22 277	65.88 342	77.06 237	49.73 261	64.40 406	78.60 203	52.70 303	53.16 431	82.58 235	34.82 345	65.16 422	59.20 214	75.46 230	82.74 260

IMVS_0403		69.09 200	68.14 201	71.95 206	77.06 237	49.73 261	74.51 262	78.60 203	52.70 303	66.69 236	82.58 235	46.43 199	83.38 164	59.20 214	75.46 230	82.74 260

VortexMVS		66.41 266	65.50 263	69.16 284	73.75 318	48.14 296	73.41 286	78.28 221	53.73 289	64.98 279	78.33 329	40.62 277	79.07 283	58.88 218	67.50 354	80.26 324

SSM_0407		70.41 160	68.96 177	74.75 106	78.65 166	53.46 171	77.28 188	80.00 173	53.88 284	68.14 197	84.61 188	43.21 238	86.26 96	58.80 219	76.11 217	84.54 196

SSM_0404		70.84 148	69.41 167	75.12 100	79.20 149	53.86 159	77.89 163	80.00 173	53.88 284	69.40 173	84.61 188	43.21 238	86.56 82	58.80 219	77.68 193	84.95 186

reproduce_monomvs		62.56 316	61.20 325	66.62 325	70.62 379	44.30 341	70.13 348	73.13 324	54.78 266	61.13 337	76.37 371	25.63 444	75.63 355	58.75 221	60.29 423	79.93 330

旧先验2							76.08 224		45.32 413	76.55 48			65.56 420	58.75 221

VDDNet		71.81 130	71.33 128	73.26 177	82.80 84	47.60 307	78.74 134	75.27 284	59.59 148	72.94 116	89.40 57	41.51 267	83.91 153	58.75 221	82.99 92	88.26 35

mmtdpeth		60.40 349	59.12 349	64.27 363	69.59 398	48.99 281	70.67 339	70.06 351	54.96 263	62.78 308	73.26 406	27.00 433	67.66 403	58.44 224	45.29 469	76.16 385

114514_t		70.83 150	69.56 162	74.64 112	86.21 32	54.63 149	82.34 81	81.81 128	48.22 376	63.01 306	85.83 161	40.92 276	87.10 68	57.91 225	79.79 140	82.18 275

Vis-MVSNet		72.18 122	71.37 127	74.61 113	81.29 105	55.41 137	80.90 100	78.28 221	60.73 111	69.23 179	88.09 79	44.36 228	82.65 195	57.68 226	81.75 112	85.77 145
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_cas_vis1_n_1920		56.91 380	56.71 373	57.51 418	59.13 472	45.40 329	63.58 414	61.29 429	36.24 460	67.14 228	71.85 416	29.89 402	56.69 459	57.65 227	63.58 388	70.46 448

PAPM_NR		72.63 112	71.80 117	75.13 99	81.72 97	53.42 175	79.91 116	83.28 98	59.14 155	66.31 245	85.90 158	51.86 119	86.06 101	57.45 228	80.62 123	85.91 136

LFMVS		71.78 131	71.59 120	72.32 201	83.40 76	46.38 316	79.75 119	71.08 339	64.18 34	72.80 121	88.64 73	42.58 246	83.72 156	57.41 229	84.49 77	86.86 94

v7n		69.01 202	67.36 220	73.98 143	72.51 343	52.65 196	78.54 142	81.30 144	60.26 129	62.67 312	81.62 267	43.61 234	84.49 141	57.01 230	68.70 344	84.79 191

GeoE		71.01 145	70.15 154	73.60 164	79.57 139	52.17 208	78.93 131	78.12 223	58.02 179	67.76 217	83.87 207	52.36 110	82.72 193	56.90 231	75.79 224	85.92 135

FA-MVS(test-final)		69.82 174	68.48 187	73.84 147	78.44 174	50.04 256	75.58 238	78.99 192	58.16 175	67.59 218	82.14 256	42.66 244	85.63 111	56.60 232	76.19 216	85.84 140

mamba_0408		67.78 235	65.42 264	74.85 105	78.65 166	53.46 171	50.83 470	79.09 188	53.75 287	68.14 197	83.83 208	41.79 260	86.56 82	56.58 233	76.11 217	84.54 196

SSM_04072		64.98 285	65.42 264	63.68 367	78.65 166	53.46 171	50.83 470	79.09 188	53.75 287	68.14 197	83.83 208	41.79 260	53.03 473	56.58 233	76.11 217	84.54 196

mvs_anonymous		68.03 227	67.51 214	69.59 274	72.08 352	44.57 339	71.99 315	75.23 286	51.67 319	67.06 229	82.57 239	54.68 71	77.94 308	56.56 235	75.71 226	86.26 127

Patchmatch-RL test		58.16 371	55.49 387	66.15 335	67.92 424	48.89 285	60.66 434	51.07 465	47.86 385	59.36 357	62.71 467	34.02 355	72.27 374	56.41 236	59.40 426	77.30 370

miper_lstm_enhance		62.03 329	60.88 330	65.49 349	66.71 433	46.25 317	56.29 454	75.70 273	50.68 340	61.27 335	75.48 385	40.21 280	68.03 401	56.31 237	65.25 371	82.18 275

thisisatest0530		67.92 231	65.78 258	74.33 124	76.29 258	51.03 226	76.89 203	74.25 305	53.67 291	65.59 260	81.76 265	35.15 341	85.50 117	55.94 238	72.47 278	86.47 112

EPP-MVSNet		72.16 125	71.31 129	74.71 107	78.68 165	49.70 265	82.10 86	81.65 130	60.40 120	65.94 252	85.84 160	51.74 123	86.37 91	55.93 239	79.55 146	88.07 47

PVSNet_BlendedMVS		68.56 215	67.72 207	71.07 245	77.03 243	50.57 240	74.50 263	81.52 132	53.66 292	64.22 291	79.72 306	49.13 162	82.87 187	55.82 240	73.92 247	79.77 337

PVSNet_Blended		68.59 211	67.72 207	71.19 239	77.03 243	50.57 240	72.51 307	81.52 132	51.91 317	64.22 291	77.77 346	49.13 162	82.87 187	55.82 240	79.58 144	80.14 327

PAPR		71.72 134	70.82 139	74.41 122	81.20 109	51.17 223	79.55 125	83.33 94	55.81 233	66.93 232	84.61 188	50.95 136	86.06 101	55.79 242	79.20 157	86.00 132

tttt0517		67.83 234	65.66 260	74.33 124	76.69 249	50.82 231	77.86 165	73.99 310	54.54 273	64.64 283	82.53 243	35.06 342	85.50 117	55.71 243	69.91 321	86.67 103

IterMVS-SCA-FT		62.49 317	61.52 317	65.40 351	71.99 355	50.80 232	71.15 330	69.63 355	45.71 411	60.61 341	77.93 336	37.45 316	65.99 418	55.67 244	63.50 389	79.42 340

tt0805		67.77 236	67.24 227	69.34 279	74.87 288	40.08 391	77.36 182	81.37 138	55.31 246	66.33 244	84.65 186	37.35 318	82.55 199	55.65 245	72.28 283	85.39 167

XVG-ACMP-BASELINE		64.36 294	62.23 309	70.74 252	72.35 348	52.45 204	70.80 338	78.45 214	53.84 286	59.87 350	81.10 277	16.24 469	79.32 272	55.64 246	71.76 289	80.47 314

Anonymous20231211		69.28 194	68.47 189	71.73 215	80.28 122	47.18 311	79.98 113	82.37 120	54.61 270	67.24 225	84.01 204	39.43 288	82.41 203	55.45 247	72.83 272	85.62 154

GA-MVS		65.53 276	63.70 285	71.02 247	70.87 376	48.10 297	70.48 342	74.40 300	56.69 207	64.70 282	76.77 360	33.66 361	81.10 231	55.42 248	70.32 312	83.87 223

test_yl		69.69 178	69.13 171	71.36 234	78.37 178	45.74 323	74.71 258	80.20 170	57.91 185	70.01 162	83.83 208	42.44 247	82.87 187	54.97 249	79.72 141	85.48 158

DCV-MVSNet		69.69 178	69.13 171	71.36 234	78.37 178	45.74 323	74.71 258	80.20 170	57.91 185	70.01 162	83.83 208	42.44 247	82.87 187	54.97 249	79.72 141	85.48 158

1314		64.61 290	63.21 297	68.80 288	71.87 357	47.46 308	73.95 274	78.39 219	42.88 436	59.97 348	76.60 367	38.11 311	79.39 271	54.84 251	72.32 281	79.55 338

Fast-Effi-MVS+-dtu		67.37 242	65.33 268	73.48 169	72.94 334	57.78 93	77.47 179	76.88 247	57.60 193	61.97 324	76.85 359	39.31 291	80.49 250	54.72 252	70.28 313	82.17 277

UniMVSNet_NR-MVSNet		71.11 142	71.00 136	71.44 228	79.20 149	44.13 342	76.02 228	82.60 117	66.48 11	68.20 193	84.60 191	56.82 41	82.82 191	54.62 253	70.43 307	87.36 79

DU-MVS		70.01 169	69.53 163	71.44 228	78.05 192	44.13 342	75.01 250	81.51 134	64.37 30	68.20 193	84.52 192	49.12 164	82.82 191	54.62 253	70.43 307	87.37 77

FIs		70.82 151	71.43 124	68.98 286	78.33 181	38.14 412	76.96 200	83.59 83	61.02 104	67.33 222	86.73 123	55.07 64	81.64 215	54.61 255	79.22 156	87.14 86

VPA-MVSNet		69.02 201	69.47 165	67.69 305	77.42 218	41.00 385	74.04 271	79.68 177	60.06 133	69.26 178	84.81 180	51.06 135	77.58 320	54.44 256	74.43 241	84.48 201

MonoMVSNet		64.15 297	63.31 295	66.69 321	70.51 381	44.12 344	74.47 264	74.21 306	57.81 187	63.03 304	76.62 364	38.33 307	77.31 326	54.22 257	60.59 422	78.64 351

Anonymous20240529		69.91 172	69.02 174	72.56 192	80.19 127	47.65 305	77.56 175	80.99 156	55.45 244	69.88 165	86.76 119	39.24 294	82.18 207	54.04 258	77.10 206	87.85 53

UniMVSNet (Re)		70.63 154	70.20 151	71.89 208	78.55 170	45.29 330	75.94 229	82.92 111	63.68 42	68.16 196	83.59 215	53.89 82	83.49 163	53.97 259	71.12 298	86.89 92

D2MVS		62.30 324	60.29 339	68.34 296	66.46 436	48.42 293	65.70 389	73.42 316	47.71 386	58.16 374	75.02 389	30.51 393	77.71 317	53.96 260	71.68 292	78.90 349

原ACMM1				74.69 108	85.39 48	59.40 59		83.42 88	51.47 328	70.27 156	86.61 130	48.61 168	86.51 87	53.85 261	87.96 42	78.16 356

无先验							79.66 122	74.30 303	48.40 374				80.78 243	53.62 262		79.03 347

UA-Net		73.13 100	72.93 99	73.76 151	83.58 72	51.66 220	78.75 133	77.66 230	67.75 4	72.61 125	89.42 56	49.82 149	83.29 166	53.61 263	83.14 89	86.32 122

VNet		69.68 180	70.19 152	68.16 299	79.73 135	41.63 378	70.53 341	77.38 237	60.37 123	70.69 149	86.63 128	51.08 134	77.09 330	53.61 263	81.69 114	85.75 147

Fast-Effi-MVS+		70.28 163	69.12 173	73.73 155	78.50 171	51.50 221	75.01 250	79.46 183	56.16 227	68.59 185	79.55 310	53.97 80	84.05 148	53.34 265	77.53 195	85.65 153

testdata				64.66 358	81.52 99	52.93 186		65.29 393	46.09 406	73.88 90	87.46 94	38.08 312	66.26 416	53.31 266	78.48 179	74.78 404

thisisatest0515		65.83 272	63.50 290	72.82 187	73.75 318	49.50 270	71.32 325	73.12 325	49.39 357	63.82 293	76.50 370	34.95 344	84.84 136	53.20 267	75.49 229	84.13 213

MVS		67.37 242	66.33 248	70.51 258	75.46 274	50.94 227	73.95 274	81.85 127	41.57 443	62.54 316	78.57 327	47.98 173	85.47 119	52.97 268	82.05 105	75.14 396

IterMVS		62.79 314	61.27 322	67.35 312	69.37 402	52.04 213	71.17 328	68.24 369	52.63 309	59.82 351	76.91 358	37.32 319	72.36 371	52.80 269	63.19 393	77.66 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		69.80 176	70.58 145	67.46 309	77.61 213	34.73 445	76.05 226	83.19 104	60.84 108	65.88 256	86.46 137	54.52 73	80.76 244	52.52 270	78.12 186	86.91 91

TranMVSNet+NR-MVSNet		70.36 161	70.10 156	71.17 241	78.64 169	42.97 363	76.53 213	81.16 152	66.95 6	68.53 188	85.42 173	51.61 125	83.07 170	52.32 271	69.70 327	87.46 70

Baseline_NR-MVSNet		67.05 251	67.56 210	65.50 348	75.65 267	37.70 418	75.42 239	74.65 298	59.90 137	68.14 197	83.15 227	49.12 164	77.20 328	52.23 272	69.78 324	81.60 283

UniMVSNet_ETH3D		67.60 239	67.07 232	69.18 283	77.39 219	42.29 369	74.18 270	75.59 276	60.37 123	66.77 234	86.06 151	37.64 314	78.93 293	52.16 273	73.49 258	86.32 122

ECVR-MVS		67.72 237	67.51 214	68.35 295	79.46 142	36.29 435	74.79 257	66.93 379	58.72 162	67.19 226	88.05 81	36.10 332	81.38 223	52.07 274	84.25 79	87.39 75

test1111		67.21 244	67.14 231	67.42 310	79.24 148	34.76 444	73.89 278	65.65 389	58.71 164	66.96 231	87.95 85	36.09 333	80.53 247	52.03 275	83.79 85	86.97 90

test2506		65.33 280	64.61 274	67.50 306	79.46 142	34.19 450	74.43 266	51.92 461	58.72 162	66.75 235	88.05 81	25.99 441	80.92 239	51.94 276	84.25 79	87.39 75

API-MVS		72.17 123	71.41 125	74.45 121	81.95 94	57.22 100	84.03 56	80.38 168	59.89 141	68.40 190	82.33 247	49.64 151	87.83 51	51.87 277	84.16 82	78.30 354

PCF-MVS	61.88 8	70.95 147	69.49 164	75.35 94	77.63 208	55.71 128	76.04 227	81.81 128	50.30 345	69.66 168	85.40 174	52.51 106	84.89 133	51.82 278	80.24 132	85.45 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 126	70.73 141	76.40 73	86.57 25	57.99 89	81.15 98	82.96 110	57.03 202	66.78 233	85.56 168	44.50 226	88.11 43	51.77 279	80.23 133	83.10 253

UGNet		68.81 206	67.39 218	73.06 180	78.33 181	54.47 150	79.77 118	75.40 282	60.45 118	63.22 299	84.40 196	32.71 375	80.91 240	51.71 280	80.56 127	83.81 225
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
MAR-MVS		71.51 136	70.15 154	75.60 91	81.84 95	59.39 60	81.38 95	82.90 112	54.90 265	68.08 203	78.70 321	47.73 177	85.51 116	51.68 281	84.17 81	81.88 281
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
VPNet		67.52 240	68.11 202	65.74 344	79.18 151	36.80 427	72.17 313	72.83 326	62.04 85	67.79 215	85.83 161	48.88 166	76.60 346	51.30 282	72.97 270	83.81 225

test_fmvs1_n		51.37 418	50.35 421	54.42 433	52.85 479	37.71 417	61.16 431	51.93 460	28.15 472	63.81 294	69.73 439	13.72 473	53.95 470	51.16 283	60.65 420	71.59 437

test_fmvs1		51.32 420	50.48 420	53.81 435	53.57 477	37.51 419	60.63 435	51.16 463	28.02 474	63.62 295	69.23 443	16.41 468	53.93 471	51.01 284	60.70 419	69.99 452

QAPM		70.05 168	68.81 180	73.78 149	76.54 255	53.43 174	83.23 65	83.48 85	52.89 301	65.90 254	86.29 143	41.55 266	86.49 88	51.01 284	78.40 182	81.42 287

NR-MVSNet		69.54 186	68.85 178	71.59 222	78.05 192	43.81 347	74.20 269	80.86 159	65.18 14	62.76 310	84.52 192	52.35 111	83.59 160	50.96 286	70.78 302	87.37 77

IB-MVS	56.42 12	65.40 279	62.73 303	73.40 173	74.89 286	52.78 193	73.09 297	75.13 289	55.69 236	58.48 370	73.73 401	32.86 370	86.32 93	50.63 287	70.11 316	81.10 301
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
PM-MVS		52.33 413	50.19 422	58.75 406	62.10 458	45.14 331	65.75 388	40.38 487	43.60 427	53.52 427	72.65 407	9.16 487	65.87 419	50.41 288	54.18 448	65.24 464

cascas		65.98 270	63.42 292	73.64 161	77.26 223	52.58 199	72.26 312	77.21 241	48.56 369	61.21 336	74.60 393	32.57 382	85.82 109	50.38 289	76.75 211	82.52 268

IS-MVSNet		71.57 135	71.00 136	73.27 176	78.86 159	45.63 327	80.22 109	78.69 200	64.14 37	66.46 241	87.36 98	49.30 158	85.60 112	50.26 290	83.71 88	88.59 26

WR-MVS		68.47 216	68.47 189	68.44 294	80.20 126	39.84 394	73.75 281	76.07 266	64.68 24	68.11 201	83.63 214	50.39 143	79.14 280	49.78 291	69.66 328	86.34 117

CVMVSNet		59.63 357	59.14 348	61.08 391	74.47 302	38.84 405	75.20 245	68.74 365	31.15 468	58.24 372	76.51 368	32.39 384	68.58 397	49.77 292	65.84 367	75.81 388

CostFormer		64.04 299	62.51 304	68.61 291	71.88 356	45.77 322	71.30 326	70.60 347	47.55 389	64.31 287	76.61 366	41.63 263	79.62 265	49.74 293	69.00 339	80.42 316

新几何1				70.76 251	85.66 42	61.13 30		66.43 383	44.68 417	70.29 155	86.64 126	41.29 269	75.23 357	49.72 294	81.75 112	75.93 387

test-LLR		58.15 372	58.13 362	58.22 410	68.57 414	44.80 333	65.46 394	57.92 441	50.08 348	55.44 402	69.82 437	32.62 379	57.44 455	49.66 295	73.62 254	72.41 427

test-mter		56.42 385	55.82 383	58.22 410	68.57 414	44.80 333	65.46 394	57.92 441	39.94 454	55.44 402	69.82 437	21.92 455	57.44 455	49.66 295	73.62 254	72.41 427

Anonymous202405211		66.84 256	65.99 255	69.40 278	80.19 127	42.21 371	71.11 331	71.31 338	58.80 161	67.90 205	86.39 139	29.83 403	79.65 263	49.60 297	78.78 169	86.33 120

test_fmvs2		48.69 427	47.49 432	52.29 447	48.63 486	33.06 459	57.76 447	48.05 475	25.71 478	59.76 353	69.60 441	11.57 480	52.23 477	49.45 298	56.86 436	71.58 438

tpmrst		58.24 370	58.70 355	56.84 419	66.97 430	34.32 448	69.57 359	61.14 430	47.17 396	58.58 369	71.60 417	41.28 270	60.41 439	49.20 299	62.84 396	75.78 389

test_vis1_n		49.89 425	48.69 427	53.50 438	53.97 476	37.38 420	61.53 425	47.33 477	28.54 471	59.62 355	67.10 455	13.52 474	52.27 476	49.07 300	57.52 433	70.84 446

pm-mvs1		65.24 281	64.97 272	66.04 338	72.38 347	39.40 401	72.62 304	75.63 274	55.53 241	62.35 323	83.18 226	47.45 184	76.47 349	49.06 301	66.54 362	82.24 274

gm-plane-assit					71.40 368	41.72 377			48.85 366		73.31 404		82.48 202	48.90 302

CMPMVS	42.80 21	57.81 375	55.97 381	63.32 370	60.98 466	47.38 309	64.66 404	69.50 358	32.06 466	46.83 458	77.80 343	29.50 406	71.36 379	48.68 303	73.75 250	71.21 443
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 260	66.42 244	67.37 311	76.17 260	41.73 375	70.41 344	76.14 265	53.99 281	65.98 251	83.51 219	49.48 153	76.24 352	48.60 304	73.46 260	84.14 212

OurMVSNet-221017-0		61.37 341	58.63 356	69.61 273	72.05 353	48.06 299	73.93 276	72.51 328	47.23 395	54.74 413	80.92 282	21.49 459	81.24 227	48.57 305	56.22 440	79.53 339

OpenMVS	61.03 9	68.85 205	67.56 210	72.70 189	74.26 310	53.99 158	81.21 97	81.34 143	52.70 303	62.75 311	85.55 170	38.86 299	84.14 146	48.41 306	83.01 91	79.97 329

testing91		64.46 292	63.80 283	66.47 327	78.43 175	40.06 392	67.63 374	69.59 356	59.06 156	63.18 301	78.05 333	34.05 353	76.99 335	48.30 307	75.87 223	82.37 272

testing99		64.05 298	63.29 296	66.34 329	78.17 188	39.76 396	67.33 379	68.00 370	58.60 167	63.03 304	78.10 332	32.57 382	76.94 337	48.22 308	75.58 227	82.34 273

baseline2		63.42 304	61.26 323	69.89 270	72.55 341	47.62 306	71.54 322	68.38 367	50.11 347	54.82 412	75.55 383	43.06 241	80.96 236	48.13 309	67.16 358	81.11 300

TESTMET0.1,1		55.28 395	54.90 391	56.42 421	66.56 434	43.67 349	65.46 394	56.27 451	39.18 456	53.83 422	67.44 451	24.21 450	55.46 466	48.04 310	73.11 268	70.13 451

test_fmvs3		44.30 435	42.55 438	49.55 453	42.83 491	27.15 483	53.03 462	44.93 481	22.03 486	53.69 425	64.94 462	4.21 495	49.63 479	47.47 311	49.82 461	71.88 433

usedtu_blend_shiyan5		62.63 315	60.77 333	68.20 297	68.53 416	44.64 336	73.47 285	77.00 246	51.91 317	57.10 385	69.95 433	38.83 300	79.61 266	47.44 312	62.67 397	80.37 319

blend_shiyan4		61.38 340	59.10 350	68.20 297	68.94 410	44.64 336	70.81 337	76.52 256	51.63 320	57.56 381	69.94 436	28.30 418	79.61 266	47.44 312	60.78 418	80.36 322

K. test v3		60.47 348	57.11 366	70.56 256	73.74 320	48.22 295	75.10 249	62.55 419	58.27 174	53.62 426	76.31 372	27.81 424	81.59 217	47.42 314	39.18 477	81.88 281

pmmvs6		63.69 302	62.82 302	66.27 332	70.63 378	39.27 402	73.13 296	75.47 281	52.69 308	59.75 354	82.30 248	39.71 286	77.03 332	47.40 315	64.35 380	82.53 266

blended_shiyan8		62.46 319	60.71 334	67.71 303	69.15 407	43.43 351	70.83 335	76.52 256	51.49 326	57.67 378	71.36 421	39.38 289	79.07 283	47.37 316	62.67 397	80.62 312

blended_shiyan6		62.46 319	60.71 334	67.71 303	69.14 408	43.42 352	70.82 336	76.52 256	51.50 325	57.64 379	71.37 420	39.38 289	79.08 282	47.36 317	62.67 397	80.65 311

sd_testset		64.46 292	64.45 275	64.51 360	77.13 232	42.25 370	62.67 420	72.11 333	58.02 179	65.08 273	82.55 240	41.22 273	69.88 391	47.32 318	73.92 247	81.41 288

baseline1		63.81 301	63.87 282	63.62 368	76.29 258	36.36 430	71.78 320	67.29 375	56.05 229	64.23 290	82.95 228	47.11 190	74.41 361	47.30 319	61.85 410	80.10 328

gbinet_0.2-2-1-0.02		62.43 321	60.41 337	68.49 292	68.91 412	43.71 348	71.73 321	75.89 270	52.10 314	58.33 371	69.67 440	36.86 328	80.59 246	47.18 320	63.05 395	81.16 299

GBi-Net		67.21 244	66.55 239	69.19 280	77.63 208	43.33 353	77.31 183	77.83 227	56.62 212	65.04 275	82.70 230	41.85 257	80.33 252	47.18 320	72.76 273	83.92 220

test1		67.21 244	66.55 239	69.19 280	77.63 208	43.33 353	77.31 183	77.83 227	56.62 212	65.04 275	82.70 230	41.85 257	80.33 252	47.18 320	72.76 273	83.92 220

FMVSNet3		66.32 268	65.61 261	68.46 293	76.48 256	42.34 368	74.98 252	77.15 242	55.83 232	65.04 275	81.16 275	39.91 282	80.14 259	47.18 320	72.76 273	82.90 257

wanda-best-256-512		62.00 331	60.17 340	67.49 307	68.53 416	43.07 360	69.65 353	76.38 260	51.26 332	57.10 385	69.95 433	38.83 300	79.04 286	47.14 324	62.67 397	80.37 319

FE-blended-shiyan7		62.00 331	60.17 340	67.49 307	68.53 416	43.07 360	69.65 353	76.38 260	51.26 332	57.10 385	69.95 433	38.83 300	79.04 286	47.14 324	62.67 397	80.37 319

FMVSNet2		66.93 254	66.31 250	68.79 289	77.63 208	42.98 362	76.11 223	77.47 233	56.62 212	65.22 272	82.17 254	41.85 257	80.18 258	47.05 326	72.72 276	83.20 247

testdata2													72.18 376	46.95 327

BH-RMVSNet		68.81 206	67.42 217	72.97 182	80.11 130	52.53 200	74.26 268	76.29 262	58.48 170	68.38 191	84.20 198	42.59 245	83.83 154	46.53 328	75.91 222	82.56 264

AdaColmap		69.99 170	68.66 184	73.97 144	84.94 58	57.83 91	82.63 76	78.71 199	56.28 224	64.34 285	84.14 200	41.57 264	87.06 70	46.45 329	78.88 166	77.02 375

EG-PatchMatch MVS		64.71 287	62.87 300	70.22 260	77.68 205	53.48 170	77.99 161	78.82 195	53.37 294	56.03 398	77.41 351	24.75 449	84.04 149	46.37 330	73.42 262	73.14 416

usedtu_dtu_shiyan1		64.34 295	63.57 287	66.66 322	72.44 345	40.74 388	69.60 356	76.80 252	53.21 296	61.73 329	77.92 337	41.92 255	77.68 318	46.23 331	72.25 284	81.57 284

FE-MVSNET3		64.34 295	63.57 287	66.66 322	72.44 345	40.74 388	69.60 356	76.80 252	53.21 296	61.73 329	77.92 337	41.92 255	77.68 318	46.23 331	72.25 284	81.57 284

1112_ss		64.00 300	63.36 293	65.93 340	79.28 146	42.58 367	71.35 324	72.36 331	46.41 403	60.55 342	77.89 341	46.27 202	73.28 366	46.18 333	69.97 319	81.92 280

FMVSNet1		66.70 259	65.87 256	69.19 280	77.49 216	43.33 353	77.31 183	77.83 227	56.45 218	64.60 284	82.70 230	38.08 312	80.33 252	46.08 334	72.31 282	83.92 220

HyFIR lowres test		65.67 274	63.01 299	73.67 158	79.97 132	55.65 130	69.07 363	75.52 278	42.68 437	63.53 296	77.95 335	40.43 279	81.64 215	46.01 335	71.91 288	83.73 231

lessismore_v0				69.91 268	71.42 367	47.80 302		50.90 466		50.39 446	75.56 382	27.43 429	81.33 224	45.91 336	34.10 483	80.59 313

CHOSEN 1792x2688		65.08 284	62.84 301	71.82 211	81.49 101	56.26 116	66.32 385	74.20 307	40.53 449	63.16 302	78.65 324	41.30 268	77.80 314	45.80 337	74.09 244	81.40 290

LCM-MVSNet-Re		61.88 334	61.35 320	63.46 369	74.58 300	31.48 466	61.42 427	58.14 440	58.71 164	53.02 433	79.55 310	43.07 240	76.80 339	45.69 338	77.96 188	82.11 278

ambc				65.13 356	63.72 451	37.07 424	47.66 477	78.78 198		54.37 419	71.42 418	11.24 482	80.94 237	45.64 339	53.85 451	77.38 369

MS-PatchMatch		62.42 322	61.46 318	65.31 354	75.21 280	52.10 210	72.05 314	74.05 308	46.41 403	57.42 384	74.36 394	34.35 351	77.57 321	45.62 340	73.67 252	66.26 462

ACMH+	57.40 11	66.12 269	64.06 278	72.30 202	77.79 200	52.83 192	80.39 105	78.03 224	57.30 195	57.47 382	82.55 240	27.68 426	84.17 145	45.54 341	69.78 324	79.90 331

testing11		62.81 313	61.90 313	65.54 346	78.38 176	40.76 387	67.59 376	66.78 381	55.48 242	60.13 344	77.11 354	31.67 389	76.79 340	45.53 342	74.45 240	79.06 345

CR-MVSNet		59.91 352	57.90 363	65.96 339	69.96 392	52.07 211	65.31 398	63.15 415	42.48 438	59.36 357	74.84 390	35.83 335	70.75 384	45.50 343	64.65 376	75.06 397

0.4-1-1-0.1		59.29 360	56.70 374	67.07 314	69.35 403	43.16 357	66.59 381	70.87 344	48.59 368	55.11 408	62.25 468	28.22 420	78.92 294	45.49 344	63.79 384	79.14 343

CDS-MVSNet		66.80 257	65.37 266	71.10 244	78.98 156	53.13 183	73.27 292	71.07 340	52.15 313	64.72 281	80.23 295	43.56 235	77.10 329	45.48 345	78.88 166	83.05 254
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		66.49 264	66.41 245	66.72 318	77.67 206	36.33 432	76.83 207	79.52 181	62.45 71	62.54 316	83.47 221	46.32 200	78.37 301	45.47 346	63.43 390	85.45 162

BH-untuned		68.27 220	67.29 222	71.21 238	79.74 134	53.22 179	76.06 225	77.46 235	57.19 197	66.10 249	81.61 268	45.37 214	83.50 162	45.42 347	76.68 212	76.91 379

PS-CasMVS		66.42 265	66.32 249	66.70 320	77.60 214	36.30 434	76.94 201	79.61 179	62.36 73	62.43 321	83.66 213	45.69 204	78.37 301	45.35 348	63.26 392	85.42 165

XXY-MVS		60.68 343	61.67 315	57.70 417	70.43 383	38.45 409	64.19 409	66.47 382	48.05 380	63.22 299	80.86 284	49.28 159	60.47 438	45.25 349	67.28 357	74.19 411

0.3-1-1-0.015		58.40 366	55.56 385	66.91 316	68.08 422	43.09 359	65.25 400	70.96 343	47.89 384	53.10 432	59.82 471	26.48 436	78.79 296	45.07 350	63.43 390	78.84 350

HY-MVS	56.14 13	64.55 291	63.89 280	66.55 326	74.73 294	41.02 382	69.96 350	74.43 299	49.29 359	61.66 331	80.92 282	47.43 185	76.68 345	44.91 351	71.69 291	81.94 279

0.4-1-1-0.2		58.31 369	55.53 386	66.64 324	67.46 427	42.78 366	64.38 407	70.97 342	47.65 387	53.38 430	59.02 472	28.39 417	78.72 298	44.86 352	63.63 386	78.42 353

FE-MVSNET2		62.01 330	60.88 330	65.42 350	68.74 413	38.43 410	72.92 299	77.39 236	54.74 269	55.40 404	76.71 361	35.46 338	76.72 343	44.25 353	62.31 406	81.10 301

PEN-MVS		66.60 261	66.45 241	67.04 315	77.11 236	36.56 429	77.03 197	80.42 167	62.95 58	62.51 318	84.03 203	46.69 197	79.07 283	44.22 354	63.08 394	85.51 157

test_post1							68.67 365				3.64 499	32.39 384	69.49 392	44.17 355

SCA		60.49 347	58.38 358	66.80 317	74.14 314	48.06 299	63.35 416	63.23 414	49.13 361	59.33 360	72.10 412	37.45 316	74.27 362	44.17 355	62.57 403	78.05 358

PMMVS		53.96 402	53.26 408	56.04 422	62.60 456	50.92 229	61.17 430	56.09 452	32.81 465	53.51 428	66.84 456	34.04 354	59.93 442	44.14 357	68.18 348	57.27 474

MVP-Stereo		65.41 278	63.80 283	70.22 260	77.62 212	55.53 135	76.30 217	78.53 209	50.59 343	56.47 394	78.65 324	39.84 284	82.68 194	44.10 358	72.12 287	72.44 426
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FE-MVS		65.91 271	63.33 294	73.63 162	77.36 220	51.95 216	72.62 304	75.81 271	53.70 290	65.31 264	78.96 319	28.81 413	86.39 90	43.93 359	73.48 259	82.55 265

CNLPA		65.43 277	64.02 279	69.68 272	78.73 164	58.07 88	77.82 168	70.71 346	51.49 326	61.57 333	83.58 218	38.23 310	70.82 383	43.90 360	70.10 317	80.16 326

pmmvs4		61.48 339	59.39 346	67.76 302	71.57 361	53.86 159	71.42 323	65.34 392	44.20 422	59.46 356	77.92 337	35.90 334	74.71 359	43.87 361	64.87 374	74.71 406

mvs5depth		55.64 392	53.81 403	61.11 390	59.39 471	40.98 386	65.89 387	68.28 368	50.21 346	58.11 375	75.42 386	17.03 465	67.63 405	43.79 362	46.21 466	74.73 405

Test_1112_low_res		62.32 323	61.77 314	64.00 365	79.08 155	39.53 400	68.17 370	70.17 349	43.25 431	59.03 362	79.90 300	44.08 229	71.24 381	43.79 362	68.42 346	81.25 295

sc_t1		59.76 354	57.84 364	65.54 346	74.87 288	42.95 364	69.61 355	64.16 405	48.90 364	58.68 365	77.12 353	28.19 421	72.35 372	43.75 364	55.28 443	81.31 294

TransMVSNet (Re)		64.72 286	64.33 276	65.87 343	75.22 279	38.56 407	74.66 260	75.08 293	58.90 160	61.79 327	82.63 233	51.18 132	78.07 306	43.63 365	55.87 441	80.99 305

pmmvs-eth3d		58.81 363	56.31 379	66.30 331	67.61 425	52.42 205	72.30 310	64.76 397	43.55 428	54.94 411	74.19 396	28.95 410	72.60 369	43.31 366	57.21 435	73.88 414

SixPastTwentyTwo		61.65 336	58.80 354	70.20 262	75.80 264	47.22 310	75.59 236	69.68 354	54.61 270	54.11 420	79.26 316	27.07 432	82.96 176	43.27 367	49.79 462	80.41 317

BH-w/o		66.85 255	65.83 257	69.90 269	79.29 144	52.46 203	74.66 260	76.65 255	54.51 274	64.85 280	78.12 331	45.59 207	82.95 178	43.26 368	75.54 228	74.27 410

TR-MVS		66.59 263	65.07 271	71.17 241	79.18 151	49.63 269	73.48 284	75.20 288	52.95 299	67.90 205	80.33 293	39.81 285	83.68 157	43.20 369	73.56 257	80.20 325

EU-MVSNet		55.61 393	54.41 396	59.19 403	65.41 442	33.42 455	72.44 308	71.91 335	28.81 470	51.27 438	73.87 400	24.76 448	69.08 394	43.04 370	58.20 431	75.06 397

PatchmatchNet		59.84 353	58.24 359	64.65 359	73.05 332	46.70 314	69.42 360	62.18 425	47.55 389	58.88 363	71.96 414	34.49 349	69.16 393	42.99 371	63.60 387	78.07 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		67.02 252	66.92 233	67.33 313	77.95 196	37.75 416	77.57 174	82.11 124	62.03 86	62.65 313	82.48 244	50.57 141	79.46 269	42.91 372	64.01 381	84.79 191

ACMH	55.70 15	65.20 282	63.57 287	70.07 264	78.07 191	52.01 214	79.48 126	79.69 176	55.75 235	56.59 391	80.98 280	27.12 431	80.94 237	42.90 373	71.58 293	77.25 373
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240521		55.30 394	54.41 396	57.96 414	60.92 468	41.73 375	71.09 332	71.06 341	41.18 444	48.65 452	73.31 404	16.93 466	59.25 445	42.54 374	64.01 381	72.90 418

WTY-MVS		59.75 355	60.39 338	57.85 415	72.32 349	37.83 415	61.05 432	64.18 403	45.95 410	61.91 325	79.11 318	47.01 194	60.88 437	42.50 375	69.49 331	74.83 402

TAMVS		66.78 258	65.27 269	71.33 237	79.16 153	53.67 164	73.84 280	69.59 356	52.32 312	65.28 265	81.72 266	44.49 227	77.40 324	42.32 376	78.66 175	82.92 255

LTVRE_ROB	55.42 16	63.15 310	61.23 324	68.92 287	76.57 254	47.80 302	59.92 436	76.39 259	54.35 276	58.67 366	82.46 245	29.44 407	81.49 220	42.12 377	71.14 297	77.46 367
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
WBMVS		60.54 346	60.61 336	60.34 394	78.00 194	35.95 437	64.55 405	64.89 395	49.63 353	63.39 298	78.70 321	33.85 358	67.65 404	42.10 378	70.35 311	77.43 368

sss		56.17 388	56.57 375	54.96 428	66.93 431	36.32 433	57.94 445	61.69 427	41.67 441	58.64 367	75.32 388	38.72 303	56.25 462	42.04 379	66.19 365	72.31 430

UnsupCasMVSNet_eth		53.16 412	52.47 409	55.23 427	59.45 470	33.39 456	59.43 439	69.13 362	45.98 407	50.35 447	72.32 409	29.30 408	58.26 452	42.02 380	44.30 470	74.05 412

tpm2		62.07 327	60.10 342	67.99 300	72.79 336	43.86 346	71.05 333	66.85 380	43.14 433	62.77 309	75.39 387	38.32 308	80.80 242	41.69 381	68.88 340	79.32 341

PLC	56.13 14	65.09 283	63.21 297	70.72 253	81.04 111	54.87 147	78.57 140	77.47 233	48.51 371	55.71 399	81.89 261	33.71 359	79.71 262	41.66 382	70.37 309	77.58 366
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 402	53.69 405	54.79 430	66.12 439	31.96 464	62.34 423	49.05 469	44.42 421	55.54 400	71.33 422	30.22 397	56.70 458	41.65 383	62.54 404	75.71 390

DTE-MVSNet		65.58 275	65.34 267	66.31 330	76.06 262	34.79 442	76.43 215	79.38 184	62.55 69	61.66 331	83.83 208	45.60 206	79.15 279	41.64 384	60.88 416	85.00 182

tt0320-xc		58.33 368	56.41 378	64.08 364	75.79 265	41.34 379	68.30 369	62.72 418	47.90 382	56.29 395	74.16 398	28.53 414	71.04 382	41.50 385	52.50 454	79.88 332

PAPM		67.92 231	66.69 237	71.63 221	78.09 190	49.02 280	77.09 195	81.24 148	51.04 337	60.91 339	83.98 205	47.71 178	84.99 127	40.81 386	79.32 151	80.90 306

tpm		57.34 377	58.16 360	54.86 429	71.80 358	34.77 443	67.47 378	56.04 453	48.20 377	60.10 345	76.92 357	37.17 322	53.41 472	40.76 387	65.01 372	76.40 383

KD-MVS_self_test		55.22 396	53.89 402	59.21 402	57.80 475	27.47 480	57.75 448	74.32 301	47.38 391	50.90 441	70.00 432	28.45 416	70.30 389	40.44 388	57.92 432	79.87 333

F-COLMAP		63.05 312	60.87 332	69.58 276	76.99 245	53.63 166	78.12 156	76.16 263	47.97 381	52.41 435	81.61 268	27.87 423	78.11 305	40.07 389	66.66 361	77.00 376

Patchmtry		57.16 378	56.47 376	59.23 401	69.17 406	34.58 446	62.98 418	63.15 415	44.53 418	56.83 389	74.84 390	35.83 335	68.71 396	40.03 390	60.91 415	74.39 409

pmmvs5		56.47 384	55.68 384	58.86 405	61.41 462	36.71 428	66.37 384	62.75 417	40.38 450	53.70 423	76.62 364	34.56 347	67.05 409	40.02 391	65.27 370	72.83 419

testing3-2		62.06 328	62.36 307	61.17 389	79.29 144	30.31 470	64.09 412	63.49 411	63.50 44	62.84 307	82.22 251	32.35 386	69.02 395	40.01 392	73.43 261	84.17 211

tt0320		58.59 364	56.81 372	63.92 366	75.46 274	41.32 380	68.63 366	64.06 406	47.05 397	56.19 396	74.19 396	30.34 395	71.36 379	39.92 393	55.45 442	79.09 344

EPNet_dtu		61.90 333	61.97 312	61.68 382	72.89 335	39.78 395	75.85 232	65.62 390	55.09 253	54.56 416	79.36 314	37.59 315	67.02 410	39.80 394	76.95 207	78.25 355
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CL-MVSNet_self_test		61.53 337	60.94 329	63.30 371	68.95 409	36.93 426	67.60 375	72.80 327	55.67 237	59.95 349	76.63 363	45.01 221	72.22 375	39.74 395	62.09 409	80.74 310

SSC-MVS3.2		60.57 345	61.39 319	58.12 413	74.29 309	32.63 460	59.52 437	65.53 391	59.90 137	62.45 319	79.75 305	41.96 252	63.90 427	39.47 396	69.65 330	77.84 363

test_vis1_rt		41.35 443	39.45 444	47.03 456	46.65 490	37.86 414	47.76 475	38.65 488	23.10 482	44.21 468	51.22 482	11.20 483	44.08 486	39.27 397	53.02 452	59.14 469

Vis-MVSNet (Re-imp)		63.69 302	63.88 281	63.14 373	74.75 293	31.04 468	71.16 329	63.64 410	56.32 222	59.80 352	84.99 177	44.51 225	75.46 356	39.12 398	80.62 123	82.92 255

PVSNet	50.76 19	58.40 366	57.39 365	61.42 385	75.53 272	44.04 345	61.43 426	63.45 412	47.04 398	56.91 388	73.61 402	27.00 433	64.76 423	39.12 398	72.40 279	75.47 393

UBG		59.62 358	59.53 345	59.89 395	78.12 189	35.92 438	64.11 411	60.81 432	49.45 356	61.34 334	75.55 383	33.05 366	67.39 408	38.68 400	74.62 238	76.35 384

MDTV_nov1_ep13_2view						25.89 486	61.22 429		40.10 452	51.10 439		32.97 369		38.49 401		78.61 352

our_test_3		56.49 383	54.42 395	62.68 377	69.51 399	45.48 328	66.08 386	61.49 428	44.11 425	50.73 444	69.60 441	33.05 366	68.15 398	38.38 402	56.86 436	74.40 408

tpm cat1		59.25 361	56.95 369	66.15 335	72.19 351	46.96 312	68.09 371	65.76 388	40.03 453	57.81 377	70.56 426	38.32 308	74.51 360	38.26 403	61.50 413	77.00 376

USDC		56.35 386	54.24 399	62.69 376	64.74 445	40.31 390	65.05 401	73.83 312	43.93 426	47.58 454	77.71 347	15.36 472	75.05 358	38.19 404	61.81 411	72.70 420

MSDG		61.81 335	59.23 347	69.55 277	72.64 338	52.63 198	70.45 343	75.81 271	51.38 329	53.70 423	76.11 373	29.52 405	81.08 233	37.70 405	65.79 368	74.93 401

MDTV_nov1_ep13			57.00 368		72.73 337	38.26 411	65.02 402	64.73 398	44.74 416	55.46 401	72.48 408	32.61 381	70.47 385	37.47 406	67.75 352

SD_0403		63.07 311	63.49 291	61.82 381	75.16 282	31.14 467	71.89 319	73.47 315	53.34 295	58.22 373	81.81 264	45.17 218	73.86 364	37.43 407	74.87 237	80.45 315

gg-mvs-nofinetune		57.86 374	56.43 377	62.18 379	72.62 339	35.35 440	66.57 382	56.33 450	50.65 341	57.64 379	57.10 476	30.65 392	76.36 350	37.38 408	78.88 166	74.82 403

dmvs_re		56.77 381	56.83 371	56.61 420	69.23 404	41.02 382	58.37 442	64.18 403	50.59 343	57.45 383	71.42 418	35.54 337	58.94 448	37.23 409	67.45 355	69.87 453

RPSCF		55.80 391	54.22 400	60.53 393	65.13 444	42.91 365	64.30 408	57.62 443	36.84 459	58.05 376	82.28 249	28.01 422	56.24 463	37.14 410	58.61 430	82.44 271

testing222		62.29 325	61.31 321	65.25 355	77.87 197	38.53 408	68.34 368	66.31 385	56.37 221	63.15 303	77.58 349	28.47 415	76.18 354	37.04 411	76.65 213	81.05 304

PatchT		53.17 411	53.44 407	52.33 446	68.29 421	25.34 488	58.21 443	54.41 456	44.46 420	54.56 416	69.05 444	33.32 364	60.94 436	36.93 412	61.76 412	70.73 447

YYNet1		50.73 421	48.96 423	56.03 423	61.10 464	41.78 374	51.94 465	56.44 448	40.94 447	44.84 464	67.80 448	30.08 400	55.08 468	36.77 413	50.71 458	71.22 442

TAPA-MVS	59.36 10	66.60 261	65.20 270	70.81 250	76.63 252	48.75 286	76.52 214	80.04 172	50.64 342	65.24 270	84.93 178	39.15 295	78.54 300	36.77 413	76.88 208	85.14 176
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 422	48.95 424	56.00 424	61.17 463	41.84 373	51.90 466	56.45 447	40.96 446	44.79 465	67.84 447	30.04 401	55.07 469	36.71 415	50.69 459	71.11 445

ppachtmachnet_test		58.06 373	55.38 388	66.10 337	69.51 399	48.99 281	68.01 372	66.13 387	44.50 419	54.05 421	70.74 425	32.09 387	72.34 373	36.68 416	56.71 439	76.99 378

tpmvs		58.47 365	56.95 369	63.03 375	70.20 387	41.21 381	67.90 373	67.23 376	49.62 354	54.73 414	70.84 424	34.14 352	76.24 352	36.64 417	61.29 414	71.64 436

CHOSEN 280x420		47.83 429	46.36 433	52.24 448	67.37 428	49.78 260	38.91 488	43.11 485	35.00 462	43.27 470	63.30 466	28.95 410	49.19 480	36.53 418	60.80 417	57.76 473

PatchMatch-RL		56.25 387	54.55 394	61.32 388	77.06 237	56.07 120	65.57 391	54.10 458	44.13 424	53.49 429	71.27 423	25.20 446	66.78 411	36.52 419	63.66 385	61.12 466

RPMNet		61.53 337	58.42 357	70.86 249	69.96 392	52.07 211	65.31 398	81.36 139	43.20 432	59.36 357	70.15 431	35.37 339	85.47 119	36.42 420	64.65 376	75.06 397

ITE_SJBPF				62.09 380	66.16 438	44.55 340		64.32 401	47.36 392	55.31 405	80.34 292	19.27 461	62.68 432	36.29 421	62.39 405	79.04 346

myMVS_eth3d28		60.66 344	61.04 327	59.51 397	77.32 221	31.58 465	63.11 417	63.87 407	59.00 157	60.90 340	78.26 330	32.69 377	66.15 417	36.10 422	78.13 185	80.81 308

JIA-IIPM		51.56 417	47.68 431	63.21 372	64.61 446	50.73 237	47.71 476	58.77 438	42.90 435	48.46 453	51.72 480	24.97 447	70.24 390	36.06 423	53.89 450	68.64 459

KD-MVS_2432*1600		53.45 406	51.50 415	59.30 399	62.82 453	37.14 422	55.33 455	71.79 336	47.34 393	55.09 409	70.52 427	21.91 456	70.45 386	35.72 424	42.97 472	70.31 449

miper_refine_blended		53.45 406	51.50 415	59.30 399	62.82 453	37.14 422	55.33 455	71.79 336	47.34 393	55.09 409	70.52 427	21.91 456	70.45 386	35.72 424	42.97 472	70.31 449

OpenMVS_ROB	52.78 18	60.03 351	58.14 361	65.69 345	70.47 382	44.82 332	75.33 240	70.86 345	45.04 414	56.06 397	76.00 375	26.89 435	79.65 263	35.36 426	67.29 356	72.60 421

GG-mvs-BLEND				62.34 378	71.36 369	37.04 425	69.20 362	57.33 446		54.73 414	65.48 461	30.37 394	77.82 313	34.82 427	74.93 236	72.17 431

UnsupCasMVSNet_bld		50.07 424	48.87 425	53.66 436	60.97 467	33.67 454	57.62 449	64.56 400	39.47 455	47.38 455	64.02 465	27.47 427	59.32 444	34.69 428	43.68 471	67.98 461

MDA-MVSNet-bldmvs		53.87 404	50.81 417	63.05 374	66.25 437	48.58 291	56.93 452	63.82 408	48.09 379	41.22 472	70.48 429	30.34 395	68.00 402	34.24 429	45.92 468	72.57 422

dp		51.89 416	51.60 414	52.77 443	68.44 420	32.45 462	62.36 422	54.57 455	44.16 423	49.31 451	67.91 446	28.87 412	56.61 460	33.89 430	54.89 445	69.24 458

AllTest		57.08 379	54.65 392	64.39 361	71.44 365	49.03 278	69.92 351	67.30 373	45.97 408	47.16 456	79.77 303	17.47 463	67.56 406	33.65 431	59.16 427	76.57 381

TestCases				64.39 361	71.44 365	49.03 278		67.30 373	45.97 408	47.16 456	79.77 303	17.47 463	67.56 406	33.65 431	59.16 427	76.57 381

test_vis3_rt		32.09 454	30.20 459	37.76 470	35.36 501	27.48 479	40.60 487	28.29 497	16.69 491	32.52 485	40.53 490	1.96 501	37.40 493	33.64 433	42.21 474	48.39 480

UWE-MVS		60.18 350	59.78 343	61.39 387	77.67 206	33.92 453	69.04 364	63.82 408	48.56 369	64.27 288	77.64 348	27.20 430	70.40 388	33.56 434	76.24 215	79.83 334

FMVSNet5		55.86 390	54.93 390	58.66 407	71.05 374	36.35 431	64.18 410	62.48 420	46.76 401	50.66 445	74.73 392	25.80 442	64.04 425	33.11 435	65.57 369	75.59 391

mvsany_test1		39.38 445	38.16 448	43.02 463	49.05 484	34.28 449	44.16 484	25.94 498	22.74 484	46.57 460	62.21 469	23.85 451	41.16 491	33.01 436	35.91 480	53.63 477

DP-MVS		65.68 273	63.66 286	71.75 214	84.93 59	56.87 110	80.74 103	73.16 323	53.06 298	59.09 361	82.35 246	36.79 329	85.94 106	32.82 437	69.96 320	72.45 425

PVSNet_0	43.31 20	47.46 431	45.64 434	52.92 442	67.60 426	44.65 335	54.06 460	54.64 454	41.59 442	46.15 461	58.75 473	30.99 391	58.66 449	32.18 438	24.81 488	55.46 476

ETVMVS		59.51 359	58.81 352	61.58 384	77.46 217	34.87 441	64.94 403	59.35 435	54.06 280	61.08 338	76.67 362	29.54 404	71.87 377	32.16 439	74.07 245	78.01 362

WB-MVSnew		59.66 356	59.69 344	59.56 396	75.19 281	35.78 439	69.34 361	64.28 402	46.88 399	61.76 328	75.79 379	40.61 278	65.20 421	32.16 439	71.21 296	77.70 364

TinyColmap		54.14 401	51.72 413	61.40 386	66.84 432	41.97 372	66.52 383	68.51 366	44.81 415	42.69 471	75.77 380	11.66 479	72.94 367	31.96 441	56.77 438	69.27 457

MIMVSNet		57.35 376	57.07 367	58.22 410	74.21 311	37.18 421	62.46 421	60.88 431	48.88 365	55.29 406	75.99 377	31.68 388	62.04 434	31.87 442	72.35 280	75.43 394

thres100view900		63.28 307	62.41 306	65.89 341	77.31 222	38.66 406	72.65 302	69.11 363	57.07 200	62.45 319	81.03 279	37.01 326	79.17 276	31.84 443	73.25 265	79.83 334

tfpn200view9		63.18 309	62.18 310	66.21 333	76.85 246	39.62 398	71.96 317	69.44 359	56.63 210	62.61 314	79.83 301	37.18 320	79.17 276	31.84 443	73.25 265	79.83 334

thres400		63.31 305	62.18 310	66.72 318	76.85 246	39.62 398	71.96 317	69.44 359	56.63 210	62.61 314	79.83 301	37.18 320	79.17 276	31.84 443	73.25 265	81.36 291

pmmvs3		44.92 434	41.95 441	53.86 434	52.58 481	43.55 350	62.11 424	46.90 479	26.05 477	40.63 473	60.19 470	11.08 484	57.91 453	31.83 446	46.15 467	60.11 467

LF4IMVS		42.95 437	42.26 439	45.04 458	48.30 487	32.50 461	54.80 457	48.49 471	28.03 473	40.51 474	70.16 430	9.24 486	43.89 487	31.63 447	49.18 464	58.72 470

COLMAP_ROB	52.97 17	61.27 342	58.81 352	68.64 290	74.63 297	52.51 201	78.42 143	73.30 319	49.92 351	50.96 440	81.51 271	23.06 452	79.40 270	31.63 447	65.85 366	74.01 413
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 430	47.73 430	47.06 455	58.81 473	9.37 503	48.78 474	59.21 436	43.28 430	44.22 467	68.66 445	25.67 443	57.20 457	31.57 449	49.35 463	74.62 407

thres600view7		63.30 306	62.27 308	66.41 328	77.18 225	38.87 404	72.35 309	69.11 363	56.98 203	62.37 322	80.96 281	37.01 326	79.00 291	31.43 450	73.05 269	81.36 291

thres200		62.20 326	61.16 326	65.34 353	75.38 277	39.99 393	69.60 356	69.29 361	55.64 239	61.87 326	76.99 356	37.07 325	78.96 292	31.28 451	73.28 264	77.06 374

LCM-MVSNet		40.30 444	35.88 450	53.57 437	42.24 492	29.15 473	45.21 482	60.53 433	22.23 485	28.02 487	50.98 483	3.72 497	61.78 435	31.22 452	38.76 478	69.78 454

test_f		31.86 455	31.05 456	34.28 472	32.33 503	21.86 493	32.34 490	30.46 495	16.02 492	39.78 478	55.45 477	4.80 493	32.36 497	30.61 453	37.66 479	48.64 479

test0.0.03 1		53.32 410	53.59 406	52.50 445	62.81 455	29.45 472	59.51 438	54.11 457	50.08 348	54.40 418	74.31 395	32.62 379	55.92 464	30.50 454	63.95 383	72.15 432

FE-MVSNET		55.16 398	53.75 404	59.41 398	65.29 443	33.20 457	67.21 380	66.21 386	48.39 375	49.56 450	73.53 403	29.03 409	72.51 370	30.38 455	54.10 449	72.52 423

Anonymous20231206		55.10 399	55.30 389	54.48 431	69.81 397	33.94 452	62.91 419	62.13 426	41.08 445	55.18 407	75.65 381	32.75 374	56.59 461	30.32 456	67.86 350	72.91 417

tfpnnormal		62.47 318	61.63 316	64.99 357	74.81 291	39.01 403	71.22 327	73.72 313	55.22 250	60.21 343	80.09 299	41.26 271	76.98 336	30.02 457	68.09 349	78.97 348

test20.03		53.87 404	54.02 401	53.41 439	61.47 461	28.11 477	61.30 428	59.21 436	51.34 331	52.09 436	77.43 350	33.29 365	58.55 450	29.76 458	60.27 424	73.58 415

LS3D		64.71 287	62.50 305	71.34 236	79.72 136	55.71 128	79.82 117	74.72 296	48.50 372	56.62 390	84.62 187	33.59 362	82.34 204	29.65 459	75.23 234	75.97 386

mvsany_test3		32.62 453	30.57 458	38.77 469	36.16 500	24.20 490	38.10 489	20.63 502	19.14 488	40.36 476	57.43 475	5.06 492	36.63 494	29.59 460	28.66 485	55.49 475

testgi		51.90 415	52.37 410	50.51 452	60.39 469	23.55 491	58.42 441	58.15 439	49.03 362	51.83 437	79.21 317	22.39 453	55.59 465	29.24 461	62.64 402	72.40 429

MIMVSNet1		55.17 397	54.31 398	57.77 416	70.03 391	32.01 463	65.68 390	64.81 396	49.19 360	46.75 459	76.00 375	25.53 445	64.04 425	28.65 462	62.13 408	77.26 372

TDRefinement		53.44 408	50.72 419	61.60 383	64.31 448	46.96 312	70.89 334	65.27 394	41.78 439	44.61 466	77.98 334	11.52 481	66.36 415	28.57 463	51.59 456	71.49 439

usedtu_dtu_shiyan2		53.34 409	50.78 418	61.00 392	61.86 460	39.63 397	68.47 367	64.58 399	42.94 434	45.22 463	67.61 450	19.25 462	66.71 412	28.08 464	59.05 429	76.66 380

WAC-MVS						27.31 481								27.77 465

myMVS_eth3d		54.86 400	54.61 393	55.61 425	74.69 295	27.31 481	65.52 392	57.49 444	50.97 338	56.52 392	72.18 410	21.87 458	68.09 399	27.70 466	64.59 378	71.44 440

ttmdpeth		45.56 432	42.95 437	53.39 440	52.33 482	29.15 473	57.77 446	48.20 474	31.81 467	49.86 449	77.21 352	8.69 488	59.16 446	27.31 467	33.40 484	71.84 435

ADS-MVSNet2		51.33 419	48.76 426	59.07 404	66.02 440	44.60 338	50.90 468	59.76 434	36.90 457	50.74 442	66.18 459	26.38 437	63.11 430	27.17 468	54.76 446	69.50 455

ADS-MVSNet		48.48 428	47.77 429	50.63 451	66.02 440	29.92 471	50.90 468	50.87 467	36.90 457	50.74 442	66.18 459	26.38 437	52.47 475	27.17 468	54.76 446	69.50 455

Patchmatch-test		49.08 426	48.28 428	51.50 450	64.40 447	30.85 469	45.68 480	48.46 472	35.60 461	46.10 462	72.10 412	34.47 350	46.37 484	27.08 470	60.65 420	77.27 371

MVS-HIRNet		45.52 433	44.48 435	48.65 454	68.49 419	34.05 451	59.41 440	44.50 482	27.03 475	37.96 482	50.47 484	26.16 440	64.10 424	26.74 471	59.52 425	47.82 483

test_0402		63.25 308	61.01 328	69.96 265	80.00 131	54.37 152	76.86 205	72.02 334	54.58 272	58.71 364	80.79 287	35.00 343	84.36 143	26.41 472	64.71 375	71.15 444

N_pmnet		39.35 446	40.28 443	36.54 471	63.76 449	1.62 508	49.37 473	0.76 507	34.62 463	43.61 469	66.38 458	26.25 439	42.57 488	26.02 473	51.77 455	65.44 463

testing3		56.54 382	55.92 382	58.41 408	77.52 215	27.93 478	69.72 352	56.36 449	54.75 268	58.63 368	77.80 343	20.88 460	71.75 378	25.31 474	62.25 407	75.53 392

Syy-MVS		56.00 389	56.23 380	55.32 426	74.69 295	26.44 484	65.52 392	57.49 444	50.97 338	56.52 392	72.18 410	39.89 283	68.09 399	24.20 475	64.59 378	71.44 440

MVStest1		42.65 438	39.29 445	52.71 444	47.26 489	34.58 446	54.41 459	50.84 468	23.35 480	39.31 480	74.08 399	12.57 476	55.09 467	23.32 476	28.47 486	68.47 460

DSMNet-mixed		39.30 447	38.72 446	41.03 466	51.22 483	19.66 495	45.53 481	31.35 494	15.83 493	39.80 477	67.42 453	22.19 454	45.13 485	22.43 477	52.69 453	58.31 471

dmvs_testset		50.16 423	51.90 412	44.94 460	66.49 435	11.78 500	61.01 433	51.50 462	51.17 336	50.30 448	67.44 451	39.28 292	60.29 440	22.38 478	57.49 434	62.76 465

ANet_high		41.38 442	37.47 449	53.11 441	39.73 497	24.45 489	56.94 451	69.69 353	47.65 387	26.04 489	52.32 479	12.44 477	62.38 433	21.80 479	10.61 498	72.49 424

new_pmnet		34.13 452	34.29 453	33.64 473	52.63 480	18.23 497	44.43 483	33.90 493	22.81 483	30.89 486	53.18 478	10.48 485	35.72 495	20.77 480	39.51 476	46.98 484

UWE-MVS-28		52.25 414	52.35 411	51.93 449	66.99 429	22.79 492	63.48 415	48.31 473	46.78 400	52.73 434	76.11 373	27.78 425	57.82 454	20.58 481	68.41 347	75.17 395

APD_test1		37.39 448	34.94 451	44.72 461	48.88 485	33.19 458	52.95 463	44.00 484	19.49 487	27.28 488	58.59 474	3.18 499	52.84 474	18.92 482	41.17 475	48.14 482

EGC-MVSNET		42.47 439	38.48 447	54.46 432	74.33 307	48.73 287	70.33 346	51.10 464	0.03 501	0.18 502	67.78 449	13.28 475	66.49 414	18.91 483	50.36 460	48.15 481

PMMVS2		27.40 459	25.91 462	31.87 476	39.46 498	6.57 505	31.17 491	28.52 496	23.96 479	20.45 493	48.94 487	4.20 496	37.94 492	16.51 484	19.97 491	51.09 478

test_method		19.68 463	18.10 466	24.41 480	13.68 505	3.11 507	12.06 496	42.37 486	2.00 499	11.97 497	36.38 491	5.77 491	29.35 499	15.06 485	23.65 489	40.76 488

Gipuma		34.77 450	31.91 455	43.33 462	62.05 459	37.87 413	20.39 493	67.03 378	23.23 481	18.41 494	25.84 494	4.24 494	62.73 431	14.71 486	51.32 457	29.38 492
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 440	41.11 442	45.39 457	58.03 474	41.01 384	49.50 472	53.81 459	30.07 469	33.71 484	64.03 463	11.69 478	52.08 478	14.01 487	55.11 444	43.09 485

testf1		31.46 456	28.89 460	39.16 467	41.99 494	28.78 475	46.45 478	37.56 489	14.28 494	21.10 490	48.96 485	1.48 503	47.11 482	13.63 488	34.56 481	41.60 486

APD_test2		31.46 456	28.89 460	39.16 467	41.99 494	28.78 475	46.45 478	37.56 489	14.28 494	21.10 490	48.96 485	1.48 503	47.11 482	13.63 488	34.56 481	41.60 486

tmp_tt		9.43 466	11.14 469	4.30 483	2.38 506	4.40 506	13.62 495	16.08 504	0.39 500	15.89 495	13.06 497	15.80 471	5.54 502	12.63 490	10.46 499	2.95 497

dongtai		34.52 451	34.94 451	33.26 474	61.06 465	16.00 499	52.79 464	23.78 500	40.71 448	39.33 479	48.65 488	16.91 467	48.34 481	12.18 491	19.05 492	35.44 491

WB-MVS		43.26 436	43.41 436	42.83 464	63.32 452	10.32 502	58.17 444	45.20 480	45.42 412	40.44 475	67.26 454	34.01 356	58.98 447	11.96 492	24.88 487	59.20 468

SSC-MVS		41.96 441	41.99 440	41.90 465	62.46 457	9.28 504	57.41 450	44.32 483	43.38 429	38.30 481	66.45 457	32.67 378	58.42 451	10.98 493	21.91 490	57.99 472

MVE	17.77 23	21.41 462	17.77 467	32.34 475	34.34 502	25.44 487	16.11 494	24.11 499	11.19 496	13.22 496	31.92 492	1.58 502	30.95 498	10.47 494	17.03 494	40.62 489
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 460	22.73 464	26.90 477	42.02 493	20.67 494	42.66 485	35.70 491	17.43 489	10.28 499	25.05 495	6.42 490	42.39 489	10.28 495	14.71 495	17.63 494

PMVS	28.69 22	36.22 449	33.29 454	45.02 459	36.82 499	35.98 436	54.68 458	48.74 470	26.31 476	21.02 492	51.61 481	2.88 500	60.10 441	9.99 496	47.58 465	38.99 490
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 461	21.84 465	26.36 478	40.20 496	19.53 496	41.95 486	34.64 492	17.09 490	9.73 500	22.83 496	7.29 489	42.22 490	9.18 497	13.66 496	17.32 495

DeepMVS_CX				12.03 482	17.97 504	10.91 501		10.60 505	7.46 497	11.07 498	28.36 493	3.28 498	11.29 501	8.01 498	9.74 500	13.89 496

kuosan		29.62 458	30.82 457	26.02 479	52.99 478	16.22 498	51.09 467	22.71 501	33.91 464	33.99 483	40.85 489	15.89 470	33.11 496	7.59 499	18.37 493	28.72 493

wuyk23d		13.32 465	12.52 468	15.71 481	47.54 488	26.27 485	31.06 492	1.98 506	4.93 498	5.18 501	1.94 501	0.45 505	18.54 500	6.81 500	12.83 497	2.33 498

testmvs		4.52 469	6.03 472	0.01 485	0.01 507	0.00 510	53.86 461	0.00 508	0.01 502	0.04 503	0.27 502	0.00 507	0.00 503	0.04 501	0.00 501	0.03 500

test123		4.73 468	6.30 471	0.02 484	0.01 507	0.01 509	56.36 453	0.00 508	0.01 502	0.04 503	0.21 503	0.01 506	0.00 503	0.03 502	0.00 501	0.04 499

mmdepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

monomultidepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

test_blank		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

uanet_test		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

DCPMVS		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

cdsmvs_eth3d_5k		17.50 464	23.34 463	0.00 486	0.00 509	0.00 510	0.00 497	78.63 202	0.00 504	0.00 505	82.18 252	49.25 160	0.00 503	0.00 503	0.00 501	0.00 501

pcd_1.5k_mvsjas		3.92 470	5.23 473	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	47.05 191	0.00 503	0.00 503	0.00 501	0.00 501

sosnet-low-res		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

sosnet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

uncertanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

Regformer		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

ab-mvs-re		6.49 467	8.65 470	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	77.89 341	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

uanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 510	0.00 497	0.00 508	0.00 504	0.00 505	0.00 504	0.00 507	0.00 503	0.00 503	0.00 501	0.00 501

TestfortrainingZip				78.05 44	84.66 62	58.22 87	86.84 11	85.98 22	63.31 48	79.39 24	88.94 65	62.01 15	89.61 21		86.45 63	86.34 117

FOURS1					86.12 37	60.82 37	88.18 1	83.61 82	60.87 107	81.50 20

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 12

eth-test2					0.00 509

eth-test					0.00 509

test_241102_ONE					87.77 4	58.90 78		86.78 10	64.20 33	85.97 1	91.34 18	66.87 3	90.78 7

save fliter					86.17 34	61.30 28	83.98 58	79.66 178	59.00 157

test0726					87.75 7	59.07 73	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 358

test_part2					87.58 9	60.47 42				83.42 14

sam_mvs1												34.74 346				78.05 358

sam_mvs												33.43 363

MTGPA								80.97 157

test_post											3.55 500	33.90 357	66.52 413

patchmatchnet-post											64.03 463	34.50 348	74.27 362

MTMP							86.03 23	17.08 503

TEST9					85.58 44	61.59 24	81.62 91	81.26 146	55.65 238	74.93 64	88.81 68	53.70 88	84.68 138

test_8					85.40 47	60.96 34	81.54 94	81.18 150	55.86 230	74.81 69	88.80 70	53.70 88	84.45 142

agg_prior					85.04 54	59.96 50		81.04 155		74.68 73			84.04 149

test_prior4						62.51 14	82.08 87

test_prior				76.69 66	84.20 66	57.27 99		84.88 45					86.43 89			86.38 113

新几何2							76.12 222

旧先验1					83.04 79	53.15 181		67.52 372			87.85 87	44.08 229			80.76 121	78.03 361

原ACMM2							79.02 129

test222					83.14 77	58.68 82	72.57 306	63.45 412	41.78 439	67.56 219	86.12 148	37.13 323			78.73 172	74.98 400

segment_acmp												54.23 75

testdata1							72.65 302		60.50 117

test12				77.76 51	84.52 63	58.41 84		83.36 91		72.93 117		54.61 72	88.05 44		88.12 37	86.81 96

plane_prior7					81.41 102	55.96 122

plane_prior6					81.20 109	56.24 117						45.26 216

plane_prior4											86.10 149

plane_prior3						56.09 119			63.92 38	69.27 176

plane_prior2							84.22 51		64.52 27

plane_prior1					81.27 107

plane_prior						56.31 113	83.58 64		63.19 55						80.48 128

n2								0.00 508

nn								0.00 508

door-mid								47.19 478

test11								83.47 86

door								47.60 476

HQP5-MVS						54.94 144

HQP-NCC					80.66 116		82.31 82		62.10 81	67.85 208

ACMP_Plane					80.66 116		82.31 82		62.10 81	67.85 208

HQP4-MVS										67.85 208			86.93 72			84.32 204

HQP3-MVS								83.90 63							80.35 130

HQP2-MVS												45.46 210

NP-MVS					80.98 112	56.05 121					85.54 171

ACMMP++_ref															74.07 245

ACMMP++															72.16 286

Test By Simon												48.33 171

High-resolution multi-view benchmark