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.

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	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 25

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 260	84.64 3				79.07 13	90.87 5	88.37 21

PC_three_1452									55.09 231	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 19

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	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 19

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 44

test0726					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 140
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_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 30

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 30
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
test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

test_part2					87.58 9	60.47 42				83.42 12

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 36	89.67 18	86.84 76
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 78
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 27
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
FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	81.50 16

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 56	88.67 26	88.12 29

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 108	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 100
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
fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 171	73.95 280	61.40 83	79.46 19	90.14 37	57.07 34	81.15 210	80.00 5	79.31 139	88.51 18

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 137	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 43
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 32	90.18 15	87.87 35

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 48	88.32 32	73.48 64	87.03 48	84.83 167
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 108	87.78 47	75.65 43	87.55 43	87.10 69

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 152

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 224	74.09 295	51.86 209	77.77 155	75.60 244	61.18 88	78.67 25	88.98 59	55.88 46	77.73 284	78.69 16	78.68 154	83.50 218

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 121	78.57 26	90.36 31	57.51 32	86.86 69	77.39 28	89.52 21

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 124	75.33 259	52.89 184	78.24 138	77.32 220	61.65 80	78.13 27	88.90 61	52.82 83	81.54 200	78.46 22	78.67 155	87.60 47

ZD-MVS					86.64 21	60.38 45		82.70 99	57.95 165	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 87	88.39 30	79.34 9	90.52 13	86.78 79

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 60	90.06 14	78.42 23	89.02 23	87.69 42
Skip Steuart: Steuart Systems R&D Blog.
reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 120	87.69 49	72.46 70	84.53 70	85.46 138

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 120	87.69 49	72.46 70	84.53 70	85.46 138

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 70

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 95	59.65 127	77.31 34	91.43 13	49.62 132	87.24 55	71.99 76	83.75 81	85.14 154

test_fmvsm_n_1920		71.73 114	71.14 114	73.50 148	72.52 321	56.53 107	75.60 215	76.16 233	48.11 344	77.22 35	85.56 148	53.10 81	77.43 288	74.86 51	77.14 183	86.55 89

sasdasda		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 190	66.01 124	82.12 97	88.58 16

canonicalmvs		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 190	66.01 124	82.12 97	88.58 16

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 184	73.82 296	52.72 189	77.45 165	74.28 273	56.61 193	77.10 38	88.16 71	56.17 43	77.09 296	78.27 24	81.13 110	86.48 92

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 39	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

fmvsm_s_conf0.5_n_5		72.69 93	72.80 87	72.37 180	74.11 294	53.21 175	78.12 143	73.31 287	53.98 259	76.81 40	88.05 75	53.38 77	77.37 291	76.64 34	80.78 112	86.53 90

alignmvs		73.86 74	73.99 70	73.45 151	78.20 178	50.50 230	78.57 131	82.43 101	59.40 135	76.57 41	86.71 110	56.42 41	81.23 209	65.84 127	81.79 103	88.62 14

旧先验2							76.08 204		45.32 377	76.55 42			65.56 383	58.75 201

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 43	91.51 11	52.47 89	86.78 71	80.66 4	89.64 19	87.80 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 240	51.83 210	79.67 114	85.08 34	65.02 19	75.84 44	88.58 68	59.42 22	85.08 119	72.75 68	83.93 78	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
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 113	68.35 2	75.77 45	90.38 30	53.98 64	90.26 13	81.30 3	87.68 42	88.77 12

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 233	80.97 139	65.13 15	75.77 45	90.88 20	48.63 146	86.66 74	77.23 29	88.17 33	84.81 168

dcpmvs_2		74.55 67	75.23 55	72.48 175	82.34 83	53.34 172	77.87 150	81.46 117	57.80 170	75.49 47	86.81 105	62.22 13	77.75 283	71.09 85	82.02 100	86.34 98

MGCFI-Net		72.45 99	73.34 80	69.81 249	77.77 195	43.21 330	75.84 213	81.18 132	59.59 132	75.45 48	86.64 111	57.74 28	77.94 276	63.92 143	81.90 102	88.30 22

fmvsm_s_conf0.5_n_4		72.04 109	71.85 98	72.58 171	73.74 299	52.49 196	76.69 189	72.42 297	56.42 198	75.32 49	87.04 99	52.13 96	78.01 275	79.29 12	73.65 232	87.26 63

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 50	84.83 158	60.76 15	86.56 77	67.86 104	87.87 41	86.06 111

fmvsm_s_conf0.5_n_2		69.82 155	69.27 151	71.46 204	72.00 332	51.08 215	73.30 266	67.79 336	55.06 236	75.24 51	87.51 85	44.02 211	77.00 300	75.67 42	72.86 250	86.31 105

fmvsm_l_conf0.5_n		70.99 127	70.82 120	71.48 203	71.45 341	54.40 147	77.18 177	70.46 313	48.67 335	75.17 52	86.86 103	53.77 71	76.86 304	76.33 37	77.51 176	83.17 230

fmvsm_s_conf0.1_n_2		69.64 163	69.01 157	71.52 202	71.66 337	51.04 216	73.39 265	67.14 342	55.02 240	75.11 53	87.64 84	42.94 223	77.01 299	75.55 44	72.63 256	86.52 91

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 104	59.99 120	75.10 54	90.35 32	47.66 158	86.52 81	71.64 81	82.99 86	84.47 180

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 55	90.47 29	53.96 66	88.68 27	76.48 35	89.63 20	87.16 67

test_prior2							81.75 84		60.37 108	75.01 56	89.06 57	56.22 42		72.19 73	88.96 24

test_fmvsmconf0.1_n		72.81 89	72.33 93	74.24 119	69.89 371	55.81 121	78.22 140	75.40 251	54.17 256	75.00 57	88.03 78	53.82 69	80.23 236	78.08 25	78.34 163	86.69 82

TEST9					85.58 43	61.59 24	81.62 86	81.26 128	55.65 216	74.93 58	88.81 63	53.70 73	84.68 131

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 128	55.86 208	74.93 58	88.81 63	53.70 73	84.68 131	75.24 49	88.33 30	83.65 214

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 184	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 88

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 182	80.94 94	85.70 24	61.12 90	74.90 61	87.17 98	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

test_fmvsmconf_n		73.01 86	72.59 90	74.27 118	71.28 348	55.88 120	78.21 141	75.56 246	54.31 254	74.86 62	87.80 82	54.72 56	80.23 236	78.07 26	78.48 159	86.70 81

h-mvs33		72.71 92	71.49 104	76.40 68	81.99 88	59.58 57	76.92 184	76.74 229	60.40 105	74.81 63	85.95 138	45.54 188	85.76 104	70.41 89	70.61 281	83.86 202

hse-mvs2		71.04 124	69.86 138	74.60 107	79.58 133	57.12 102	73.96 252	75.25 254	60.40 105	74.81 63	81.95 238	45.54 188	82.90 167	70.41 89	66.83 336	83.77 207

test_8					85.40 46	60.96 34	81.54 89	81.18 132	55.86 208	74.81 63	88.80 65	53.70 73	84.45 135

fmvsm_l_conf0.5_n_3		73.23 82	73.13 82	73.55 147	74.40 284	55.13 137	78.97 123	74.96 263	56.64 187	74.76 66	88.75 66	55.02 52	78.77 266	76.33 37	78.31 164	86.74 80

agg_prior					85.04 50	59.96 50		81.04 137		74.68 67			84.04 141

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 68	89.38 54	55.30 49	89.18 21	74.19 57	87.34 46	86.38 94

test_fmvsmconf0.01_n		72.17 105	71.50 103	74.16 121	67.96 390	55.58 129	78.06 146	74.67 266	54.19 255	74.54 69	88.23 69	50.35 125	80.24 235	78.07 26	77.46 177	86.65 86

nrg030		72.96 87	73.01 83	72.84 166	75.41 257	50.24 232	80.02 105	82.89 97	58.36 156	74.44 70	86.73 108	58.90 24	80.83 222	65.84 127	74.46 218	87.44 53

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 253	50.37 231	78.17 142	85.06 36	62.80 61	74.40 71	87.86 80	57.88 27	83.61 151	69.46 94	82.79 93	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
fmvsm_l_conf0.5_n_a		70.50 138	70.27 131	71.18 218	71.30 347	54.09 152	76.89 185	69.87 317	47.90 348	74.37 72	86.49 121	53.07 82	76.69 309	75.41 46	77.11 184	82.76 237

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 188	58.58 151	74.32 73	84.51 172	55.94 45	87.22 58	67.11 112	84.48 73	85.52 134

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 74	90.03 43	52.56 86	88.53 29	74.79 53	88.34 29	86.63 87

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 75	87.40 89	49.48 133	86.17 91	68.04 102	87.55 43	87.42 54

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 248	64.69 22	74.21 75	87.40 89	49.48 133	86.17 91	68.04 102	83.88 79	85.85 118

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 200	74.05 77	88.98 59	53.34 78	87.92 43	69.23 95	88.42 28	87.59 48

baseline		74.61 65	74.70 61	74.34 115	75.70 249	49.99 239	77.54 161	84.63 43	62.73 62	73.98 78	87.79 83	57.67 30	83.82 147	69.49 92	82.74 94	89.20 7

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 79	90.50 27	53.20 79	88.35 31	74.02 59	87.05 47	86.13 109

testdata				64.66 323	81.52 94	52.93 181		65.29 357	46.09 370	73.88 80	87.46 88	38.08 285	66.26 379	53.31 246	78.48 159	74.78 368

fmvsm_s_conf0.5_n_6		72.59 96	72.87 86	71.73 195	75.14 266	51.96 207	76.28 198	77.12 223	57.63 173	73.85 81	86.91 102	51.54 107	77.87 280	77.18 31	80.18 126	85.37 146

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 82	90.25 36	57.68 29	89.96 15	74.62 54	89.03 22	87.89 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 154	73.71 83	90.14 37	45.62 185	85.99 98	69.64 91	82.85 92	85.78 121

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 84	90.60 23	54.85 55	86.72 72	77.20 30	88.06 36	85.74 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 85	90.56 25	49.80 130	88.24 33	74.02 59	87.03 48	86.32 102

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 206	73.41 86	86.58 117	50.94 118	88.54 28	70.79 87	89.71 17	87.79 40

CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 87	87.27 96	55.06 51	86.30 89	71.78 79	84.58 68	89.25 5

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 87	90.58 24	49.90 127	88.21 34	73.78 61	87.03 48	86.29 106

test_fmvsmvis_n_1920		70.84 129	70.38 129	72.22 183	71.16 349	55.39 133	75.86 211	72.21 300	49.03 330	73.28 89	86.17 130	51.83 102	77.29 293	75.80 40	78.05 167	83.98 195

VDD-MVS		72.50 97	72.09 96	73.75 134	81.58 93	49.69 248	77.76 156	77.63 212	63.21 50	73.21 90	89.02 58	42.14 230	83.32 157	61.72 169	82.50 95	88.25 24

viewmacassd2359aftdt		73.15 83	73.16 81	73.11 160	75.15 265	49.31 255	77.53 163	83.21 85	60.42 104	73.20 91	87.34 93	53.82 69	81.05 215	67.02 115	80.79 111	88.96 9

fmvsm_s_conf0.1_n_a		69.32 174	68.44 172	71.96 185	70.91 352	53.78 158	78.12 143	62.30 386	49.35 326	73.20 91	86.55 120	51.99 98	76.79 306	74.83 52	68.68 321	85.32 148

DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 193	52.25 200	75.59 216	84.17 50	63.76 40	73.15 93	82.79 207	59.58 20	86.80 70	67.24 111	86.04 61	87.89 33
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
SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 121	58.07 160	73.14 94	90.07 39	44.74 202	85.84 102	68.20 98	81.76 104	84.03 192

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 121	58.07 160	73.14 94	90.07 39	43.06 221		68.20 98	81.76 104	84.03 192

fmvsm_s_conf0.5_n_a		69.54 167	68.74 163	71.93 187	72.47 323	53.82 157	78.25 137	62.26 387	49.78 320	73.12 96	86.21 128	52.66 85	76.79 306	75.02 50	68.88 316	85.18 153

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 97	89.97 46	50.90 119	87.48 53	75.30 47	86.85 53	87.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 98	88.88 62	53.72 72	89.06 23	68.27 97	88.04 37	87.42 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n		69.41 173	68.60 166	71.83 190	71.07 350	52.88 185	77.85 152	62.44 384	49.58 323	72.97 99	86.22 127	51.68 105	76.48 313	75.53 45	70.10 293	86.14 108

VDDNet		71.81 111	71.33 109	73.26 158	82.80 79	47.60 286	78.74 126	75.27 253	59.59 132	72.94 100	89.40 53	41.51 245	83.91 145	58.75 201	82.99 86	88.26 23

viewmanbaseed2359cas		72.92 88	72.89 85	73.00 162	75.16 263	49.25 258	77.25 175	83.11 92	59.52 134	72.93 101	86.63 113	54.11 63	80.98 216	66.63 118	80.67 115	88.76 13

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 101		54.61 58	88.05 39		88.12 34	86.81 77

fmvsm_s_conf0.5_n		69.58 165	68.84 160	71.79 193	72.31 328	52.90 182	77.90 148	62.43 385	49.97 318	72.85 103	85.90 139	52.21 93	76.49 312	75.75 41	70.26 290	85.97 113

LFMVS		71.78 112	71.59 101	72.32 181	83.40 71	46.38 295	79.75 112	71.08 307	64.18 34	72.80 104	88.64 67	42.58 226	83.72 148	57.41 209	84.49 72	86.86 75

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 190	83.73 56	86.08 18	63.47 45	72.77 105	87.25 97	53.13 80	87.93 42	71.97 77	85.57 64	86.66 85

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 106	90.50 27	48.18 151	87.34 54	73.59 63	85.71 62	84.76 171

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 107	79.46 290	53.65 76	87.87 44	67.45 110	82.91 89	85.89 117

UA-Net		73.13 84	72.93 84	73.76 132	83.58 67	51.66 211	78.75 125	77.66 211	67.75 4	72.61 108	89.42 52	49.82 129	83.29 158	53.61 243	83.14 83	86.32 102

OPM-MVS		74.73 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 109	87.49 86	47.18 169	85.88 101	69.47 93	80.78 112	83.66 213
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 194	72.46 110	86.76 106	56.89 36	87.86 45	66.36 120	88.91 25	83.64 215

MVS_Test		72.45 99	72.46 92	72.42 179	74.88 268	48.50 272	76.28 198	83.14 91	59.40 135	72.46 110	84.68 162	55.66 47	81.12 211	65.98 126	79.66 131	87.63 45

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 112	90.34 33	48.48 149	88.13 37	72.32 72	86.85 53	85.78 121

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 113	90.01 45	47.95 153	88.01 40	71.55 82	86.74 55	86.37 96

X-MVStestdata		70.21 145	67.28 203	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 113	6.49 462	47.95 153	88.01 40	71.55 82	86.74 55	86.37 96

diffmvs_AUTHOR		71.02 125	70.87 119	71.45 206	69.89 371	48.97 264	73.16 272	78.33 201	57.79 171	72.11 115	85.26 155	51.84 101	77.89 279	71.00 86	78.47 161	87.49 51

Effi-MVS+		73.31 80	72.54 91	75.62 84	77.87 191	53.64 161	79.62 116	79.61 160	61.63 81	72.02 116	82.61 212	56.44 40	85.97 99	63.99 142	79.07 147	87.25 64

BP-MVS1		73.41 78	72.25 94	76.88 57	76.68 233	53.70 159	79.15 121	81.07 135	60.66 98	71.81 117	87.39 91	40.93 253	87.24 55	71.23 84	81.29 109	89.71 2

mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 103	62.90 55	71.77 118	90.26 35	46.61 178	86.55 80	71.71 80	85.66 63	84.97 163

diffmvs		70.69 134	70.43 127	71.46 204	69.45 378	48.95 265	72.93 275	78.46 194	57.27 177	71.69 119	83.97 184	51.48 109	77.92 278	70.70 88	77.95 169	87.53 50
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-Vis-set		72.42 101	71.59 101	74.91 95	78.47 167	54.02 153	77.05 180	79.33 166	65.03 18	71.68 120	79.35 294	52.75 84	84.89 126	66.46 119	74.23 222	85.83 120

MSLP-MVS++		73.77 75	73.47 77	74.66 103	83.02 75	59.29 63	82.30 80	81.88 108	59.34 137	71.59 121	86.83 104	45.94 183	83.65 150	65.09 132	85.22 65	81.06 277

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 129	85.13 33	59.65 127	71.53 122	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 11

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 123	86.03 135	53.83 68	86.36 87	67.74 105	86.91 52	88.19 27

GDP-MVS		72.64 94	71.28 111	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 225	71.38 124	86.97 101	39.94 259	87.00 66	67.02 115	79.20 143	88.89 10

EI-MVSNet-UG-set		71.92 110	71.06 116	74.52 112	77.98 189	53.56 164	76.62 190	79.16 167	64.40 29	71.18 125	78.95 299	52.19 94	84.66 133	65.47 130	73.57 235	85.32 148

MG-MVS		73.96 73	73.89 72	74.16 121	85.65 42	49.69 248	81.59 88	81.29 127	61.45 82	71.05 126	88.11 72	51.77 103	87.73 48	61.05 175	83.09 84	85.05 159

viewmambaseed2359dif		68.91 183	68.18 179	71.11 221	70.21 363	48.05 280	72.28 287	75.90 239	51.96 290	70.93 127	84.47 173	51.37 110	78.59 267	61.55 173	74.97 214	86.68 83

patch_mono-2		69.85 154	71.09 115	66.16 300	79.11 148	54.80 143	71.97 292	74.31 271	53.50 270	70.90 128	84.17 177	57.63 31	63.31 392	66.17 121	82.02 100	80.38 290

VNet		69.68 161	70.19 133	68.16 274	79.73 130	41.63 347	70.53 313	77.38 217	60.37 108	70.69 129	86.63 113	51.08 115	77.09 296	53.61 243	81.69 108	85.75 126

viewmsd2359difaftdt		69.13 179	68.38 175	71.38 211	71.57 339	48.61 270	73.22 271	73.18 290	57.65 172	70.67 130	84.73 161	50.03 126	79.80 240	63.25 154	71.10 276	85.74 127

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 172	78.40 199	61.18 88	70.58 131	85.97 137	54.18 62	84.00 144	67.52 109	82.98 88	82.45 248

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 136	60.15 117	70.43 132	89.84 48	41.09 252	85.59 107	67.61 108	82.90 90	85.77 124

CLD-MVS		73.33 79	72.68 89	75.29 91	78.82 155	53.33 173	78.23 139	84.79 42	61.30 86	70.41 133	81.04 256	52.41 90	87.12 62	64.61 138	82.49 96	85.41 144
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
新几何1				70.76 229	85.66 41	61.13 30		66.43 348	44.68 381	70.29 134	86.64 111	41.29 247	75.23 322	49.72 274	81.75 106	75.93 351

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 298	70.27 135	86.61 115	48.61 147	86.51 82	53.85 241	87.96 39	78.16 321

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 136	86.34 125	54.92 54	88.90 25	72.68 69	84.55 69	87.76 41

xiu_mvs_v1_base_debu		68.58 192	67.28 203	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 293	70.04 137	81.41 250	32.79 342	79.02 259	63.81 146	77.31 178	81.22 272

xiu_mvs_v1_base		68.58 192	67.28 203	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 293	70.04 137	81.41 250	32.79 342	79.02 259	63.81 146	77.31 178	81.22 272

xiu_mvs_v1_base_debi		68.58 192	67.28 203	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 293	70.04 137	81.41 250	32.79 342	79.02 259	63.81 146	77.31 178	81.22 272

PS-MVSNAJss		72.24 103	71.21 112	75.31 89	78.50 165	55.93 118	81.63 85	82.12 105	56.24 203	70.02 140	85.68 147	47.05 171	84.34 137	65.27 131	74.41 221	85.67 129

test_yl		69.69 159	69.13 152	71.36 212	78.37 172	45.74 302	74.71 237	80.20 151	57.91 167	70.01 141	83.83 186	42.44 227	82.87 170	54.97 229	79.72 129	85.48 136

DCV-MVSNet		69.69 159	69.13 152	71.36 212	78.37 172	45.74 302	74.71 237	80.20 151	57.91 167	70.01 141	83.83 186	42.44 227	82.87 170	54.97 229	79.72 129	85.48 136

xiu_mvs_v2_base		70.52 136	69.75 139	72.84 166	81.21 103	55.63 126	75.11 226	78.92 174	54.92 242	69.96 143	79.68 285	47.00 175	82.09 189	61.60 171	79.37 135	80.81 282

Anonymous20240529		69.91 153	69.02 155	72.56 172	80.19 122	47.65 284	77.56 160	80.99 138	55.45 222	69.88 144	86.76 106	39.24 270	82.18 188	54.04 238	77.10 185	87.85 36

PS-MVSNAJ		70.51 137	69.70 141	72.93 164	81.52 94	55.79 122	74.92 233	79.00 172	55.04 237	69.88 144	78.66 302	47.05 171	82.19 187	61.61 170	79.58 132	80.83 281

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 146	89.74 51	45.43 192	87.16 61	72.01 75	82.87 91	85.14 154
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
PCF-MVS	61.88 8	70.95 128	69.49 145	75.35 88	77.63 202	55.71 123	76.04 207	81.81 110	50.30 313	69.66 147	85.40 154	52.51 87	84.89 126	51.82 258	80.24 124	85.45 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v2v482		70.50 138	69.45 147	73.66 140	72.62 318	50.03 238	77.58 158	80.51 146	59.90 121	69.52 148	82.14 234	47.53 162	84.88 128	65.07 133	70.17 291	86.09 110

MVSFormer		71.50 118	70.38 129	74.88 96	78.76 156	57.15 100	82.79 67	78.48 192	51.26 302	69.49 149	83.22 202	43.99 212	83.24 159	66.06 122	79.37 135	84.23 186

lupinMVS		69.57 166	68.28 178	73.44 152	78.76 156	57.15 100	76.57 192	73.29 289	46.19 369	69.49 149	82.18 230	43.99 212	79.23 249	64.66 136	79.37 135	83.93 197

V42		68.65 190	67.35 201	72.56 172	68.93 384	50.18 234	72.90 276	79.47 163	56.92 183	69.45 151	80.26 272	46.29 181	82.99 164	64.07 139	67.82 327	84.53 177

SSM_0404		70.84 129	69.41 148	75.12 93	79.20 143	53.86 155	77.89 149	80.00 154	53.88 261	69.40 152	84.61 166	43.21 218	86.56 77	58.80 199	77.68 173	84.95 164

v1144		70.42 140	69.31 149	73.76 132	73.22 306	50.64 225	77.83 153	81.43 118	58.58 151	69.40 152	81.16 253	47.53 162	85.29 118	64.01 141	70.64 279	85.34 147

jason		69.65 162	68.39 174	73.43 153	78.27 177	56.88 104	77.12 178	73.71 283	46.53 366	69.34 154	83.22 202	43.37 216	79.18 250	64.77 135	79.20 143	84.23 186
jason: jason.
HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 155	86.10 132	45.26 196	87.21 59	68.16 100	80.58 118	84.65 172

plane_prior3						56.09 114			63.92 38	69.27 155

VPA-MVSNet		69.02 181	69.47 146	67.69 278	77.42 212	41.00 354	74.04 250	79.68 158	60.06 118	69.26 157	84.81 159	51.06 116	77.58 286	54.44 236	74.43 220	84.48 179

Vis-MVSNet		72.18 104	71.37 108	74.61 106	81.29 100	55.41 132	80.90 95	78.28 202	60.73 96	69.23 158	88.09 73	44.36 208	82.65 178	57.68 206	81.75 106	85.77 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet		69.27 176	68.44 172	71.73 195	74.47 281	49.39 253	75.20 224	78.45 195	59.60 129	69.16 159	76.51 344	51.29 111	82.50 182	59.86 188	71.45 272	83.30 221

MVSTER		67.16 229	65.58 242	71.88 189	70.37 362	49.70 246	70.25 319	78.45 195	51.52 296	69.16 159	80.37 268	38.45 278	82.50 182	60.19 182	71.46 271	83.44 219

KinetiMVS		71.26 122	70.16 134	74.57 109	74.59 278	52.77 188	75.91 210	81.20 131	60.72 97	69.10 161	85.71 146	41.67 240	83.53 153	63.91 145	78.62 157	87.42 54

v1192		69.97 152	68.68 164	73.85 127	73.19 307	50.94 218	77.68 157	81.36 121	57.51 175	68.95 162	80.85 263	45.28 195	85.33 117	62.97 158	70.37 285	85.27 151

OMC-MVS		71.40 121	70.60 124	73.78 130	76.60 236	53.15 176	79.74 113	79.78 156	58.37 155	68.75 163	86.45 123	45.43 192	80.60 226	62.58 160	77.73 171	87.58 49

Fast-Effi-MVS+		70.28 144	69.12 154	73.73 136	78.50 165	51.50 212	75.01 229	79.46 164	56.16 205	68.59 164	79.55 288	53.97 65	84.05 140	53.34 245	77.53 175	85.65 131

v1921920		69.47 171	68.17 180	73.36 155	73.06 310	50.10 236	77.39 166	80.56 144	56.58 195	68.59 164	80.37 268	44.72 203	84.98 122	62.47 163	69.82 299	85.00 160

v144192		69.71 158	68.51 167	73.33 156	73.10 309	50.13 235	77.54 161	80.64 143	56.65 186	68.57 166	80.55 266	46.87 176	84.96 124	62.98 157	69.66 304	84.89 166

TranMVSNet+NR-MVSNet		70.36 142	70.10 137	71.17 219	78.64 163	42.97 333	76.53 193	81.16 134	66.95 6	68.53 167	85.42 153	51.61 106	83.07 162	52.32 251	69.70 303	87.46 52

fmvsm_s_conf0.5_n_7		69.54 167	69.67 142	69.15 263	73.47 304	51.41 213	70.35 317	73.34 286	57.05 180	68.41 168	85.83 142	49.86 128	72.84 333	71.86 78	76.83 188	83.19 226

API-MVS		72.17 105	71.41 106	74.45 113	81.95 89	57.22 95	84.03 51	80.38 149	59.89 125	68.40 169	82.33 225	49.64 131	87.83 46	51.87 257	84.16 77	78.30 319

BH-RMVSNet		68.81 186	67.42 197	72.97 163	80.11 125	52.53 194	74.26 247	76.29 232	58.48 153	68.38 170	84.20 176	42.59 225	83.83 146	46.53 301	75.91 201	82.56 242

v1240		69.24 177	67.91 185	73.25 159	73.02 312	49.82 240	77.21 176	80.54 145	56.43 197	68.34 171	80.51 267	43.33 217	84.99 120	62.03 167	69.77 302	84.95 164

UniMVSNet_NR-MVSNet		71.11 123	71.00 117	71.44 207	79.20 143	44.13 319	76.02 208	82.60 100	66.48 11	68.20 172	84.60 169	56.82 37	82.82 174	54.62 233	70.43 283	87.36 61

DU-MVS		70.01 150	69.53 144	71.44 207	78.05 186	44.13 319	75.01 229	81.51 116	64.37 30	68.20 172	84.52 170	49.12 143	82.82 174	54.62 233	70.43 283	87.37 59

RRT-MVS		71.46 119	70.70 123	73.74 135	77.76 196	49.30 256	76.60 191	80.45 147	61.25 87	68.17 174	84.78 160	44.64 204	84.90 125	64.79 134	77.88 170	87.03 70

UniMVSNet (Re)		70.63 135	70.20 132	71.89 188	78.55 164	45.29 309	75.94 209	82.92 94	63.68 42	68.16 175	83.59 193	53.89 67	83.49 155	53.97 239	71.12 275	86.89 74

mamba_0408		67.78 215	65.42 244	74.85 98	78.65 160	53.46 167	50.83 434	79.09 169	53.75 264	68.14 176	83.83 186	41.79 238	86.56 77	56.58 213	76.11 196	84.54 174

SSM_04072		64.98 265	65.42 244	63.68 332	78.65 160	53.46 167	50.83 434	79.09 169	53.75 264	68.14 176	83.83 186	41.79 238	53.03 436	56.58 213	76.11 196	84.54 174

SSM_0407		70.41 141	68.96 158	74.75 99	78.65 160	53.46 167	77.28 173	80.00 154	53.88 261	68.14 176	84.61 166	43.21 218	86.26 90	58.80 199	76.11 196	84.54 174

Baseline_NR-MVSNet		67.05 231	67.56 190	65.50 314	75.65 250	37.70 383	75.42 219	74.65 267	59.90 121	68.14 176	83.15 205	49.12 143	77.20 294	52.23 252	69.78 300	81.60 261

WR-MVS		68.47 196	68.47 170	68.44 271	80.20 121	39.84 361	73.75 260	76.07 236	64.68 24	68.11 180	83.63 192	50.39 124	79.14 255	49.78 271	69.66 304	86.34 98

AstraMVS		67.86 213	66.83 214	70.93 226	73.50 303	49.34 254	73.28 269	74.01 278	55.45 222	68.10 181	83.28 200	38.93 274	79.14 255	63.22 155	71.74 267	84.30 184

LuminaMVS		68.24 202	66.82 215	72.51 174	73.46 305	53.60 163	76.23 200	78.88 175	52.78 277	68.08 182	80.13 274	32.70 347	81.41 202	63.16 156	75.97 200	82.53 244

MAR-MVS		71.51 117	70.15 135	75.60 85	81.84 90	59.39 60	81.38 90	82.90 95	54.90 243	68.08 182	78.70 300	47.73 156	85.51 110	51.68 261	84.17 76	81.88 259
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
Anonymous202405211		66.84 236	65.99 235	69.40 256	80.19 122	42.21 340	71.11 306	71.31 306	58.80 145	67.90 184	86.39 124	29.83 374	79.65 242	49.60 277	78.78 151	86.33 100

TR-MVS		66.59 243	65.07 251	71.17 219	79.18 145	49.63 250	73.48 263	75.20 257	52.95 274	67.90 184	80.33 271	39.81 263	83.68 149	43.20 336	73.56 236	80.20 293

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 186

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 186

HQP4-MVS										67.85 186			86.93 67			84.32 182

HQP-MVS		73.45 77	72.80 87	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 186	85.54 151	45.46 190	86.93 67	67.04 113	80.35 122	84.32 182

guyue		68.10 206	67.23 209	70.71 232	73.67 301	49.27 257	73.65 262	76.04 238	55.62 218	67.84 190	82.26 228	41.24 250	78.91 265	61.01 176	73.72 230	83.94 196

MVS_111021_LR		69.50 170	68.78 162	71.65 199	78.38 170	59.33 61	74.82 235	70.11 315	58.08 159	67.83 191	84.68 162	41.96 232	76.34 316	65.62 129	77.54 174	79.30 310

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 192	89.24 56	42.03 231	89.38 19	64.07 139	86.50 59	89.69 3

VPNet		67.52 220	68.11 182	65.74 310	79.18 145	36.80 392	72.17 289	72.83 294	62.04 75	67.79 193	85.83 142	48.88 145	76.60 311	51.30 262	72.97 249	83.81 203

XVG-OURS		68.76 189	67.37 199	72.90 165	74.32 287	57.22 95	70.09 321	78.81 177	55.24 227	67.79 193	85.81 145	36.54 302	78.28 271	62.04 166	75.74 204	83.19 226

GeoE		71.01 126	70.15 135	73.60 145	79.57 134	52.17 201	78.93 124	78.12 204	58.02 162	67.76 195	83.87 185	52.36 91	82.72 176	56.90 211	75.79 203	85.92 115

FA-MVS(test-final)		69.82 155	68.48 168	73.84 128	78.44 168	50.04 237	75.58 218	78.99 173	58.16 158	67.59 196	82.14 234	42.66 224	85.63 105	56.60 212	76.19 195	85.84 119

test222					83.14 72	58.68 78	72.57 282	63.45 375	41.78 402	67.56 197	86.12 131	37.13 296			78.73 153	74.98 364

CPTT-MVS		72.78 90	72.08 97	74.87 97	84.88 57	61.41 26	84.15 49	77.86 207	55.27 226	67.51 198	88.08 74	41.93 234	81.85 193	69.04 96	80.01 127	81.35 269

v148		68.24 202	67.19 210	71.40 210	70.43 360	47.77 283	75.76 214	77.03 224	58.91 143	67.36 199	80.10 276	48.60 148	81.89 192	60.01 184	66.52 339	84.53 177

FIs		70.82 132	71.43 105	68.98 264	78.33 175	38.14 377	76.96 182	83.59 69	61.02 91	67.33 200	86.73 108	55.07 50	81.64 196	54.61 235	79.22 142	87.14 68

Elysia		70.19 147	68.29 176	75.88 75	74.15 291	54.33 149	78.26 135	83.21 85	55.04 237	67.28 201	83.59 193	30.16 369	86.11 93	63.67 149	79.26 140	87.20 65

StellarMVS		70.19 147	68.29 176	75.88 75	74.15 291	54.33 149	78.26 135	83.21 85	55.04 237	67.28 201	83.59 193	30.16 369	86.11 93	63.67 149	79.26 140	87.20 65

Anonymous20231211		69.28 175	68.47 170	71.73 195	80.28 117	47.18 290	79.98 106	82.37 102	54.61 247	67.24 203	84.01 182	39.43 266	82.41 185	55.45 227	72.83 251	85.62 132

ECVR-MVS		67.72 217	67.51 194	68.35 272	79.46 136	36.29 400	74.79 236	66.93 344	58.72 146	67.19 204	88.05 75	36.10 304	81.38 204	52.07 254	84.25 74	87.39 57

ACMM	61.98 7	70.80 133	69.73 140	74.02 123	80.59 116	58.59 79	82.68 70	82.02 107	55.46 221	67.18 205	84.39 175	38.51 277	83.17 161	60.65 179	76.10 199	80.30 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_cas_vis1_n_1920		56.91 347	56.71 344	57.51 381	59.13 436	45.40 308	63.58 377	61.29 392	36.24 424	67.14 206	71.85 391	29.89 373	56.69 422	57.65 207	63.58 362	70.46 411

mvs_anonymous		68.03 207	67.51 194	69.59 252	72.08 330	44.57 316	71.99 291	75.23 255	51.67 292	67.06 207	82.57 217	54.68 57	77.94 276	56.56 215	75.71 205	86.26 107

XVG-OURS-SEG-HR		68.81 186	67.47 196	72.82 168	74.40 284	56.87 105	70.59 312	79.04 171	54.77 245	66.99 208	86.01 136	39.57 265	78.21 272	62.54 161	73.33 242	83.37 220

test1111		67.21 224	67.14 211	67.42 281	79.24 142	34.76 409	73.89 257	65.65 353	58.71 148	66.96 209	87.95 79	36.09 305	80.53 227	52.03 255	83.79 80	86.97 72

PAPR		71.72 115	70.82 120	74.41 114	81.20 104	51.17 214	79.55 118	83.33 80	55.81 211	66.93 210	84.61 166	50.95 117	86.06 95	55.79 222	79.20 143	86.00 112

DP-MVS Recon		72.15 108	70.73 122	76.40 68	86.57 24	57.99 84	81.15 93	82.96 93	57.03 181	66.78 211	85.56 148	44.50 206	88.11 38	51.77 259	80.23 125	83.10 231

UniMVSNet_ETH3D		67.60 219	67.07 212	69.18 261	77.39 213	42.29 338	74.18 249	75.59 245	60.37 108	66.77 212	86.06 134	37.64 287	78.93 264	52.16 253	73.49 237	86.32 102

test2506		65.33 260	64.61 254	67.50 279	79.46 136	34.19 415	74.43 245	51.92 425	58.72 146	66.75 213	88.05 75	25.99 407	80.92 220	51.94 256	84.25 74	87.39 57

IMVS_0403		69.09 180	68.14 181	71.95 186	77.06 222	49.73 242	74.51 241	78.60 184	52.70 278	66.69 214	82.58 213	46.43 179	83.38 156	59.20 194	75.46 209	82.74 238

AUN-MVS		68.45 198	66.41 225	74.57 109	79.53 135	57.08 103	73.93 255	75.23 255	54.44 252	66.69 214	81.85 240	37.10 297	82.89 168	62.07 165	66.84 335	83.75 208

LPG-MVS_test		72.74 91	71.74 100	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 216	87.33 94	39.15 271	86.59 75	67.70 106	77.30 181	83.19 226

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 216	87.33 94	39.15 271	86.59 75	67.70 106	77.30 181	83.19 226

EIA-MVS		71.78 112	70.60 124	75.30 90	79.85 128	53.54 165	77.27 174	83.26 84	57.92 166	66.49 218	79.39 292	52.07 97	86.69 73	60.05 183	79.14 146	85.66 130

IS-MVSNet		71.57 116	71.00 117	73.27 157	78.86 153	45.63 306	80.22 103	78.69 181	64.14 37	66.46 219	87.36 92	49.30 137	85.60 106	50.26 270	83.71 82	88.59 15

v8		70.33 143	69.28 150	73.49 149	73.15 308	50.22 233	78.62 129	80.78 142	60.79 94	66.45 220	82.11 236	49.35 136	84.98 122	63.58 151	68.71 319	85.28 150

v10		70.21 145	69.02 155	73.81 129	73.51 302	50.92 220	78.74 126	81.39 119	60.05 119	66.39 221	81.83 241	47.58 160	85.41 116	62.80 159	68.86 318	85.09 158

tt0805		67.77 216	67.24 207	69.34 257	74.87 269	40.08 358	77.36 167	81.37 120	55.31 224	66.33 222	84.65 164	37.35 291	82.55 181	55.65 225	72.28 262	85.39 145

PAPM_NR		72.63 95	71.80 99	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 139	66.31 223	85.90 139	51.86 100	86.06 95	57.45 208	80.62 116	85.91 116

icg_test_0407_2		66.41 246	66.75 216	65.37 317	77.06 222	49.73 242	63.79 376	78.60 184	52.70 278	66.19 224	82.58 213	45.17 198	63.65 391	59.20 194	75.46 209	82.74 238

IMVS_0407		68.90 184	67.93 184	71.82 191	77.06 222	49.73 242	74.40 246	78.60 184	52.70 278	66.19 224	82.58 213	45.17 198	83.00 163	59.20 194	75.46 209	82.74 238

c3_l		68.33 199	67.56 190	70.62 233	70.87 353	46.21 298	74.47 243	78.80 178	56.22 204	66.19 224	78.53 307	51.88 99	81.40 203	62.08 164	69.04 314	84.25 185

BH-untuned		68.27 200	67.29 202	71.21 216	79.74 129	53.22 174	76.06 205	77.46 216	57.19 178	66.10 227	81.61 246	45.37 194	83.50 154	45.42 317	76.68 191	76.91 344

miper_ehance_all_eth		68.03 207	67.24 207	70.40 237	70.54 357	46.21 298	73.98 251	78.68 182	55.07 234	66.05 228	77.80 320	52.16 95	81.31 206	61.53 174	69.32 308	83.67 211

ab-mvs		66.65 240	66.42 224	67.37 282	76.17 243	41.73 344	70.41 316	76.14 235	53.99 258	65.98 229	83.51 197	49.48 133	76.24 317	48.60 284	73.46 239	84.14 190

EPP-MVSNet		72.16 107	71.31 110	74.71 100	78.68 159	49.70 246	82.10 81	81.65 112	60.40 105	65.94 230	85.84 141	51.74 104	86.37 86	55.93 219	79.55 134	88.07 32

eth_miper_zixun_eth		67.63 218	66.28 231	71.67 198	71.60 338	48.33 274	73.68 261	77.88 206	55.80 212	65.91 231	78.62 305	47.35 168	82.88 169	59.45 190	66.25 340	83.81 203

QAPM		70.05 149	68.81 161	73.78 130	76.54 238	53.43 170	83.23 60	83.48 71	52.89 276	65.90 232	86.29 126	41.55 244	86.49 83	51.01 264	78.40 162	81.42 263

test_vis1_n_1920		58.86 331	59.06 321	58.25 372	63.76 414	43.14 331	67.49 344	66.36 349	40.22 414	65.89 233	71.95 390	31.04 361	59.75 406	59.94 185	64.90 349	71.85 397

FC-MVSNet-test		69.80 157	70.58 126	67.46 280	77.61 207	34.73 410	76.05 206	83.19 89	60.84 93	65.88 234	86.46 122	54.52 59	80.76 225	52.52 250	78.12 166	86.91 73

IterMVS-LS		69.22 178	68.48 168	71.43 209	74.44 283	49.40 252	76.23 200	77.55 213	59.60 129	65.85 235	81.59 248	51.28 112	81.58 199	59.87 187	69.90 298	83.30 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_Blended_VisFu		71.45 120	70.39 128	74.65 104	82.01 86	58.82 76	79.93 108	80.35 150	55.09 231	65.82 236	82.16 233	49.17 140	82.64 179	60.34 181	78.62 157	82.50 247

miper_enhance_ethall		67.11 230	66.09 234	70.17 241	69.21 381	45.98 300	72.85 277	78.41 198	51.38 299	65.65 237	75.98 354	51.17 114	81.25 207	60.82 178	69.32 308	83.29 223

thisisatest0530		67.92 211	65.78 238	74.33 116	76.29 241	51.03 217	76.89 185	74.25 274	53.67 268	65.59 238	81.76 243	35.15 312	85.50 111	55.94 218	72.47 257	86.47 93

cl22		67.47 221	66.45 221	70.54 235	69.85 373	46.49 294	73.85 258	77.35 218	55.07 234	65.51 239	77.92 316	47.64 159	81.10 212	61.58 172	69.32 308	84.01 194

3Dnovator	64.47 5	72.49 98	71.39 107	75.79 77	77.70 198	58.99 73	80.66 99	83.15 90	62.24 69	65.46 240	86.59 116	42.38 229	85.52 109	59.59 189	84.72 67	82.85 236

test_djsdf		69.45 172	67.74 186	74.58 108	74.57 280	54.92 141	82.79 67	78.48 192	51.26 302	65.41 241	83.49 198	38.37 279	83.24 159	66.06 122	69.25 311	85.56 133

FE-MVS		65.91 251	63.33 272	73.63 143	77.36 214	51.95 208	72.62 280	75.81 240	53.70 267	65.31 242	78.96 298	28.81 383	86.39 85	43.93 326	73.48 238	82.55 243

TAMVS		66.78 238	65.27 249	71.33 215	79.16 147	53.67 160	73.84 259	69.59 321	52.32 287	65.28 243	81.72 244	44.49 207	77.40 290	42.32 343	78.66 156	82.92 233

cl____		67.18 227	66.26 232	69.94 244	70.20 364	45.74 302	73.30 266	76.83 227	55.10 229	65.27 244	79.57 287	47.39 166	80.53 227	59.41 192	69.22 312	83.53 217

DIV-MVS_self_test		67.18 227	66.26 232	69.94 244	70.20 364	45.74 302	73.29 268	76.83 227	55.10 229	65.27 244	79.58 286	47.38 167	80.53 227	59.43 191	69.22 312	83.54 216

EPNet		73.09 85	72.16 95	75.90 74	75.95 246	56.28 110	83.05 62	72.39 298	66.53 10	65.27 244	87.00 100	50.40 123	85.47 113	62.48 162	86.32 60	85.94 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		69.64 163	67.53 193	75.95 73	76.10 244	62.29 15	80.20 104	76.06 237	59.83 126	65.26 247	77.09 332	41.56 243	84.02 143	60.60 180	71.09 277	81.53 262

ACMP	63.53 6	72.30 102	71.20 113	75.59 86	80.28 117	57.54 90	82.74 69	82.84 98	60.58 100	65.24 248	86.18 129	39.25 269	86.03 97	66.95 117	76.79 189	83.22 224
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	59.36 10	66.60 241	65.20 250	70.81 228	76.63 235	48.75 267	76.52 194	80.04 153	50.64 310	65.24 248	84.93 157	39.15 271	78.54 268	36.77 380	76.88 187	85.14 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		66.93 234	66.31 230	68.79 267	77.63 202	42.98 332	76.11 203	77.47 214	56.62 190	65.22 250	82.17 232	41.85 235	80.18 238	47.05 299	72.72 255	83.20 225

SDMVSNet		68.03 207	68.10 183	67.84 276	77.13 219	48.72 269	65.32 362	79.10 168	58.02 162	65.08 251	82.55 218	47.83 155	73.40 330	63.92 143	73.92 226	81.41 264

sd_testset		64.46 272	64.45 255	64.51 325	77.13 219	42.25 339	62.67 383	72.11 301	58.02 162	65.08 251	82.55 218	41.22 251	69.88 355	47.32 294	73.92 226	81.41 264

GBi-Net		67.21 224	66.55 219	69.19 258	77.63 202	43.33 327	77.31 168	77.83 208	56.62 190	65.04 253	82.70 208	41.85 235	80.33 232	47.18 296	72.76 252	83.92 198

test1		67.21 224	66.55 219	69.19 258	77.63 202	43.33 327	77.31 168	77.83 208	56.62 190	65.04 253	82.70 208	41.85 235	80.33 232	47.18 296	72.76 252	83.92 198

FMVSNet3		66.32 248	65.61 241	68.46 270	76.48 239	42.34 337	74.98 231	77.15 222	55.83 210	65.04 253	81.16 253	39.91 260	80.14 239	47.18 296	72.76 252	82.90 235

anonymousdsp		67.00 233	64.82 253	73.57 146	70.09 367	56.13 113	76.35 196	77.35 218	48.43 340	64.99 256	80.84 264	33.01 339	80.34 231	64.66 136	67.64 329	84.23 186

VortexMVS		66.41 246	65.50 243	69.16 262	73.75 297	48.14 276	73.41 264	78.28 202	53.73 266	64.98 257	78.33 308	40.62 255	79.07 257	58.88 198	67.50 330	80.26 292

BH-w/o		66.85 235	65.83 237	69.90 247	79.29 138	52.46 197	74.66 239	76.65 230	54.51 251	64.85 258	78.12 310	45.59 187	82.95 166	43.26 335	75.54 207	74.27 374

CDS-MVSNet		66.80 237	65.37 246	71.10 222	78.98 150	53.13 178	73.27 270	71.07 308	52.15 288	64.72 259	80.23 273	43.56 215	77.10 295	45.48 315	78.88 148	83.05 232
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GA-MVS		65.53 256	63.70 265	71.02 225	70.87 353	48.10 277	70.48 314	74.40 269	56.69 185	64.70 260	76.77 337	33.66 332	81.10 212	55.42 228	70.32 288	83.87 201

tttt0517		67.83 214	65.66 240	74.33 116	76.69 232	50.82 222	77.86 151	73.99 279	54.54 250	64.64 261	82.53 221	35.06 313	85.50 111	55.71 223	69.91 297	86.67 84

FMVSNet1		66.70 239	65.87 236	69.19 258	77.49 210	43.33 327	77.31 168	77.83 208	56.45 196	64.60 262	82.70 208	38.08 285	80.33 232	46.08 305	72.31 261	83.92 198

AdaColmap		69.99 151	68.66 165	73.97 126	84.94 54	57.83 86	82.63 71	78.71 180	56.28 202	64.34 263	84.14 178	41.57 242	87.06 65	46.45 302	78.88 148	77.02 340

jajsoiax		68.25 201	66.45 221	73.66 140	75.62 251	55.49 131	80.82 96	78.51 191	52.33 286	64.33 264	84.11 179	28.28 387	81.81 195	63.48 152	70.62 280	83.67 211

CostFormer		64.04 277	62.51 282	68.61 269	71.88 334	45.77 301	71.30 301	70.60 312	47.55 353	64.31 265	76.61 342	41.63 241	79.62 244	49.74 273	69.00 315	80.42 288

UWE-MVS		60.18 320	59.78 314	61.39 352	77.67 200	33.92 418	69.04 332	63.82 371	48.56 336	64.27 266	77.64 325	27.20 397	70.40 352	33.56 401	76.24 194	79.83 302

mvs_tets		68.18 204	66.36 227	73.63 143	75.61 252	55.35 135	80.77 97	78.56 189	52.48 285	64.27 266	84.10 180	27.45 395	81.84 194	63.45 153	70.56 282	83.69 210

baseline1		63.81 279	63.87 262	63.62 333	76.29 241	36.36 395	71.78 296	67.29 340	56.05 207	64.23 268	82.95 206	47.11 170	74.41 326	47.30 295	61.85 376	80.10 296

PVSNet_BlendedMVS		68.56 195	67.72 187	71.07 223	77.03 227	50.57 226	74.50 242	81.52 114	53.66 269	64.22 269	79.72 284	49.13 141	82.87 170	55.82 220	73.92 226	79.77 305

PVSNet_Blended		68.59 191	67.72 187	71.19 217	77.03 227	50.57 226	72.51 283	81.52 114	51.91 291	64.22 269	77.77 323	49.13 141	82.87 170	55.82 220	79.58 132	80.14 295

thisisatest0515		65.83 252	63.50 268	72.82 168	73.75 297	49.50 251	71.32 300	73.12 293	49.39 325	63.82 271	76.50 346	34.95 315	84.84 129	53.20 247	75.49 208	84.13 191

test_fmvs1_n		51.37 384	50.35 387	54.42 396	52.85 443	37.71 382	61.16 394	51.93 424	28.15 436	63.81 272	69.73 408	13.72 438	53.95 433	51.16 263	60.65 385	71.59 400

test_fmvs1		51.32 386	50.48 386	53.81 398	53.57 441	37.51 384	60.63 398	51.16 427	28.02 438	63.62 273	69.23 411	16.41 433	53.93 434	51.01 264	60.70 384	69.99 415

HyFIR lowres test		65.67 254	63.01 277	73.67 139	79.97 127	55.65 125	69.07 331	75.52 247	42.68 400	63.53 274	77.95 314	40.43 257	81.64 196	46.01 306	71.91 265	83.73 209

CANet_DTU		68.18 204	67.71 189	69.59 252	74.83 271	46.24 297	78.66 128	76.85 226	59.60 129	63.45 275	82.09 237	35.25 311	77.41 289	59.88 186	78.76 152	85.14 154

WBMVS		60.54 316	60.61 310	60.34 358	78.00 188	35.95 402	64.55 369	64.89 359	49.63 321	63.39 276	78.70 300	33.85 329	67.65 368	42.10 345	70.35 287	77.43 333

UGNet		68.81 186	67.39 198	73.06 161	78.33 175	54.47 145	79.77 111	75.40 251	60.45 103	63.22 277	84.40 174	32.71 346	80.91 221	51.71 260	80.56 120	83.81 203
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
XXY-MVS		60.68 313	61.67 293	57.70 380	70.43 360	38.45 375	64.19 372	66.47 347	48.05 346	63.22 277	80.86 262	49.28 138	60.47 401	45.25 319	67.28 333	74.19 375

testing91		64.46 272	63.80 263	66.47 293	78.43 169	40.06 359	67.63 341	69.59 321	59.06 140	63.18 279	78.05 312	34.05 324	76.99 301	48.30 287	75.87 202	82.37 250

CHOSEN 1792x2688		65.08 264	62.84 279	71.82 191	81.49 96	56.26 111	66.32 350	74.20 276	40.53 412	63.16 280	78.65 303	41.30 246	77.80 282	45.80 308	74.09 223	81.40 266

testing222		62.29 299	61.31 299	65.25 320	77.87 191	38.53 374	68.34 335	66.31 350	56.37 199	63.15 281	77.58 326	28.47 385	76.18 319	37.04 378	76.65 192	81.05 278

testing99		64.05 276	63.29 274	66.34 295	78.17 182	39.76 363	67.33 346	68.00 335	58.60 150	63.03 282	78.10 311	32.57 353	76.94 303	48.22 288	75.58 206	82.34 251

MonoMVSNet		64.15 275	63.31 273	66.69 290	70.51 358	44.12 321	74.47 243	74.21 275	57.81 169	63.03 282	76.62 340	38.33 280	77.31 292	54.22 237	60.59 387	78.64 317

114514_t		70.83 131	69.56 143	74.64 105	86.21 31	54.63 144	82.34 76	81.81 110	48.22 342	63.01 284	85.83 142	40.92 254	87.10 63	57.91 205	79.79 128	82.18 253

testing3-2		62.06 302	62.36 285	61.17 354	79.29 138	30.31 434	64.09 375	63.49 374	63.50 44	62.84 285	82.22 229	32.35 357	69.02 359	40.01 359	73.43 240	84.17 189

mmtdpeth		60.40 319	59.12 320	64.27 328	69.59 375	48.99 262	70.67 311	70.06 316	54.96 241	62.78 286	73.26 381	27.00 400	67.66 367	58.44 204	45.29 433	76.16 349

tpm2		62.07 301	60.10 313	67.99 275	72.79 315	43.86 323	71.05 308	66.85 345	43.14 397	62.77 287	75.39 363	38.32 281	80.80 223	41.69 348	68.88 316	79.32 309

NR-MVSNet		69.54 167	68.85 159	71.59 201	78.05 186	43.81 324	74.20 248	80.86 141	65.18 14	62.76 288	84.52 170	52.35 92	83.59 152	50.96 266	70.78 278	87.37 59

OpenMVS	61.03 9	68.85 185	67.56 190	72.70 170	74.26 289	53.99 154	81.21 92	81.34 125	52.70 278	62.75 289	85.55 150	38.86 275	84.14 139	48.41 286	83.01 85	79.97 297

v7n		69.01 182	67.36 200	73.98 125	72.51 322	52.65 190	78.54 133	81.30 126	60.26 114	62.67 290	81.62 245	43.61 214	84.49 134	57.01 210	68.70 320	84.79 169

WR-MVS_H		67.02 232	66.92 213	67.33 284	77.95 190	37.75 381	77.57 159	82.11 106	62.03 76	62.65 291	82.48 222	50.57 122	79.46 245	42.91 339	64.01 357	84.79 169

tfpn200view9		63.18 287	62.18 288	66.21 299	76.85 230	39.62 364	71.96 293	69.44 324	56.63 188	62.61 292	79.83 279	37.18 293	79.17 251	31.84 410	73.25 244	79.83 302

thres400		63.31 283	62.18 288	66.72 287	76.85 230	39.62 364	71.96 293	69.44 324	56.63 188	62.61 292	79.83 279	37.18 293	79.17 251	31.84 410	73.25 244	81.36 267

MVS		67.37 222	66.33 228	70.51 236	75.46 255	50.94 218	73.95 253	81.85 109	41.57 406	62.54 294	78.57 306	47.98 152	85.47 113	52.97 248	82.05 99	75.14 360

CP-MVSNet		66.49 244	66.41 225	66.72 287	77.67 200	36.33 397	76.83 188	79.52 162	62.45 66	62.54 294	83.47 199	46.32 180	78.37 269	45.47 316	63.43 364	85.45 140

PEN-MVS		66.60 241	66.45 221	67.04 285	77.11 221	36.56 394	77.03 181	80.42 148	62.95 53	62.51 296	84.03 181	46.69 177	79.07 257	44.22 321	63.08 367	85.51 135

SSC-MVS3.2		60.57 315	61.39 297	58.12 376	74.29 288	32.63 424	59.52 400	65.53 355	59.90 121	62.45 297	79.75 283	41.96 232	63.90 390	39.47 363	69.65 306	77.84 328

thres100view900		63.28 285	62.41 284	65.89 307	77.31 216	38.66 372	72.65 278	69.11 328	57.07 179	62.45 297	81.03 257	37.01 299	79.17 251	31.84 410	73.25 244	79.83 302

PS-CasMVS		66.42 245	66.32 229	66.70 289	77.60 208	36.30 399	76.94 183	79.61 160	62.36 68	62.43 299	83.66 191	45.69 184	78.37 269	45.35 318	63.26 365	85.42 143

thres600view7		63.30 284	62.27 286	66.41 294	77.18 218	38.87 370	72.35 285	69.11 328	56.98 182	62.37 300	80.96 259	37.01 299	79.00 262	31.43 417	73.05 248	81.36 267

pm-mvs1		65.24 261	64.97 252	66.04 304	72.38 325	39.40 367	72.62 280	75.63 243	55.53 219	62.35 301	83.18 204	47.45 164	76.47 314	49.06 281	66.54 338	82.24 252

Fast-Effi-MVS+-dtu		67.37 222	65.33 248	73.48 150	72.94 313	57.78 88	77.47 164	76.88 225	57.60 174	61.97 302	76.85 336	39.31 267	80.49 230	54.72 232	70.28 289	82.17 255

WTY-MVS		59.75 325	60.39 311	57.85 378	72.32 327	37.83 380	61.05 395	64.18 366	45.95 374	61.91 303	79.11 297	47.01 174	60.88 400	42.50 342	69.49 307	74.83 366

thres200		62.20 300	61.16 304	65.34 318	75.38 258	39.99 360	69.60 326	69.29 326	55.64 217	61.87 304	76.99 333	37.07 298	78.96 263	31.28 418	73.28 243	77.06 339

TransMVSNet (Re)		64.72 266	64.33 256	65.87 309	75.22 260	38.56 373	74.66 239	75.08 262	58.90 144	61.79 305	82.63 211	51.18 113	78.07 274	43.63 332	55.87 405	80.99 279

WB-MVSnew		59.66 326	59.69 315	59.56 360	75.19 262	35.78 404	69.34 329	64.28 365	46.88 363	61.76 306	75.79 355	40.61 256	65.20 384	32.16 406	71.21 273	77.70 329

DTE-MVSNet		65.58 255	65.34 247	66.31 296	76.06 245	34.79 407	76.43 195	79.38 165	62.55 64	61.66 307	83.83 186	45.60 186	79.15 254	41.64 351	60.88 382	85.00 160

HY-MVS	56.14 13	64.55 271	63.89 260	66.55 292	74.73 274	41.02 351	69.96 322	74.43 268	49.29 327	61.66 307	80.92 260	47.43 165	76.68 310	44.91 320	71.69 268	81.94 257

CNLPA		65.43 257	64.02 259	69.68 250	78.73 158	58.07 83	77.82 154	70.71 311	51.49 297	61.57 309	83.58 196	38.23 283	70.82 347	43.90 327	70.10 293	80.16 294

UBG		59.62 328	59.53 316	59.89 359	78.12 183	35.92 403	64.11 374	60.81 395	49.45 324	61.34 310	75.55 359	33.05 337	67.39 372	38.68 367	74.62 217	76.35 348

miper_lstm_enhance		62.03 303	60.88 308	65.49 315	66.71 399	46.25 296	56.29 418	75.70 242	50.68 308	61.27 311	75.48 361	40.21 258	68.03 365	56.31 217	65.25 347	82.18 253

cascas		65.98 250	63.42 270	73.64 142	77.26 217	52.58 193	72.26 288	77.21 221	48.56 336	61.21 312	74.60 369	32.57 353	85.82 103	50.38 269	76.75 190	82.52 246

reproduce_monomvs		62.56 293	61.20 303	66.62 291	70.62 356	44.30 318	70.13 320	73.13 292	54.78 244	61.13 313	76.37 347	25.63 410	75.63 320	58.75 201	60.29 388	79.93 298

ETVMVS		59.51 329	58.81 322	61.58 349	77.46 211	34.87 406	64.94 367	59.35 398	54.06 257	61.08 314	76.67 338	29.54 375	71.87 341	32.16 406	74.07 224	78.01 327

PAPM		67.92 211	66.69 217	71.63 200	78.09 184	49.02 261	77.09 179	81.24 130	51.04 305	60.91 315	83.98 183	47.71 157	84.99 120	40.81 353	79.32 138	80.90 280

myMVS_eth3d28		60.66 314	61.04 305	59.51 361	77.32 215	31.58 429	63.11 380	63.87 370	59.00 141	60.90 316	78.26 309	32.69 348	66.15 380	36.10 389	78.13 165	80.81 282

IterMVS-SCA-FT		62.49 294	61.52 295	65.40 316	71.99 333	50.80 223	71.15 305	69.63 320	45.71 375	60.61 317	77.93 315	37.45 289	65.99 381	55.67 224	63.50 363	79.42 308

1112_ss		64.00 278	63.36 271	65.93 306	79.28 140	42.58 336	71.35 299	72.36 299	46.41 367	60.55 318	77.89 318	46.27 182	73.28 331	46.18 304	69.97 295	81.92 258

tfpnnormal		62.47 295	61.63 294	64.99 322	74.81 272	39.01 369	71.22 302	73.72 282	55.22 228	60.21 319	80.09 277	41.26 249	76.98 302	30.02 423	68.09 325	78.97 315

testing11		62.81 291	61.90 291	65.54 312	78.38 170	40.76 356	67.59 343	66.78 346	55.48 220	60.13 320	77.11 331	31.67 360	76.79 306	45.53 313	74.45 219	79.06 312

mvsmamba		68.47 196	66.56 218	74.21 120	79.60 132	52.95 180	74.94 232	75.48 249	52.09 289	60.10 321	83.27 201	36.54 302	84.70 130	59.32 193	77.69 172	84.99 162

tpm		57.34 344	58.16 330	54.86 392	71.80 336	34.77 408	67.47 345	56.04 416	48.20 343	60.10 321	76.92 334	37.17 295	53.41 435	40.76 354	65.01 348	76.40 347

ET-MVSNet_ETH3D		67.96 210	65.72 239	74.68 102	76.67 234	55.62 128	75.11 226	74.74 264	52.91 275	60.03 323	80.12 275	33.68 331	82.64 179	61.86 168	76.34 193	85.78 121

1314		64.61 270	63.21 275	68.80 266	71.87 335	47.46 287	73.95 253	78.39 200	42.88 399	59.97 324	76.60 343	38.11 284	79.39 247	54.84 231	72.32 260	79.55 306

CL-MVSNet_self_test		61.53 308	60.94 307	63.30 336	68.95 383	36.93 391	67.60 342	72.80 295	55.67 215	59.95 325	76.63 339	45.01 201	72.22 339	39.74 362	62.09 375	80.74 284

XVG-ACMP-BASELINE		64.36 274	62.23 287	70.74 230	72.35 326	52.45 198	70.80 310	78.45 195	53.84 263	59.87 326	81.10 255	16.24 434	79.32 248	55.64 226	71.76 266	80.47 286

IterMVS		62.79 292	61.27 300	67.35 283	69.37 379	52.04 205	71.17 303	68.24 334	52.63 284	59.82 327	76.91 335	37.32 292	72.36 335	52.80 249	63.19 366	77.66 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 280	63.88 261	63.14 338	74.75 273	31.04 432	71.16 304	63.64 373	56.32 200	59.80 328	84.99 156	44.51 205	75.46 321	39.12 365	80.62 116	82.92 233

test_fmvs2		48.69 393	47.49 398	52.29 411	48.63 450	33.06 423	57.76 410	48.05 439	25.71 442	59.76 329	69.60 409	11.57 445	52.23 440	49.45 278	56.86 400	71.58 401

pmmvs6		63.69 280	62.82 280	66.27 298	70.63 355	39.27 368	73.13 273	75.47 250	52.69 283	59.75 330	82.30 226	39.71 264	77.03 298	47.40 293	64.35 356	82.53 244

test_vis1_n		49.89 391	48.69 393	53.50 401	53.97 440	37.38 385	61.53 388	47.33 441	28.54 435	59.62 331	67.10 422	13.52 439	52.27 439	49.07 280	57.52 397	70.84 409

pmmvs4		61.48 310	59.39 317	67.76 277	71.57 339	53.86 155	71.42 298	65.34 356	44.20 386	59.46 332	77.92 316	35.90 306	74.71 324	43.87 328	64.87 350	74.71 370

Patchmatch-RL test		58.16 338	55.49 355	66.15 301	67.92 391	48.89 266	60.66 397	51.07 429	47.86 350	59.36 333	62.71 434	34.02 326	72.27 338	56.41 216	59.40 391	77.30 335

CR-MVSNet		59.91 322	57.90 334	65.96 305	69.96 369	52.07 203	65.31 363	63.15 378	42.48 401	59.36 333	74.84 366	35.83 307	70.75 348	45.50 314	64.65 352	75.06 361

RPMNet		61.53 308	58.42 327	70.86 227	69.96 369	52.07 203	65.31 363	81.36 121	43.20 396	59.36 333	70.15 404	35.37 310	85.47 113	36.42 387	64.65 352	75.06 361

SCA		60.49 317	58.38 328	66.80 286	74.14 293	48.06 278	63.35 379	63.23 377	49.13 329	59.33 336	72.10 387	37.45 289	74.27 327	44.17 322	62.57 370	78.05 323

DP-MVS		65.68 253	63.66 266	71.75 194	84.93 55	56.87 105	80.74 98	73.16 291	53.06 273	59.09 337	82.35 224	36.79 301	85.94 100	32.82 404	69.96 296	72.45 388

Test_1112_low_res		62.32 297	61.77 292	64.00 330	79.08 149	39.53 366	68.17 337	70.17 314	43.25 395	59.03 338	79.90 278	44.08 209	71.24 345	43.79 329	68.42 322	81.25 271

PatchmatchNet		59.84 323	58.24 329	64.65 324	73.05 311	46.70 293	69.42 328	62.18 388	47.55 353	58.88 339	71.96 389	34.49 320	69.16 357	42.99 338	63.60 361	78.07 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_0402		63.25 286	61.01 306	69.96 243	80.00 126	54.37 148	76.86 187	72.02 302	54.58 249	58.71 340	80.79 265	35.00 314	84.36 136	26.41 437	64.71 351	71.15 407

sc_t1		59.76 324	57.84 335	65.54 312	74.87 269	42.95 334	69.61 325	64.16 368	48.90 332	58.68 341	77.12 330	28.19 388	72.35 336	43.75 331	55.28 407	81.31 270

LTVRE_ROB	55.42 16	63.15 288	61.23 302	68.92 265	76.57 237	47.80 281	59.92 399	76.39 231	54.35 253	58.67 342	82.46 223	29.44 378	81.49 201	42.12 344	71.14 274	77.46 332
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
sss		56.17 356	56.57 345	54.96 391	66.93 397	36.32 398	57.94 408	61.69 390	41.67 404	58.64 343	75.32 364	38.72 276	56.25 425	42.04 346	66.19 341	72.31 393

testing3		56.54 350	55.92 352	58.41 371	77.52 209	27.93 442	69.72 324	56.36 412	54.75 246	58.63 344	77.80 320	20.88 426	71.75 342	25.31 439	62.25 373	75.53 356

tpmrst		58.24 337	58.70 325	56.84 382	66.97 396	34.32 413	69.57 327	61.14 393	47.17 360	58.58 345	71.60 392	41.28 248	60.41 402	49.20 279	62.84 368	75.78 353

IB-MVS	56.42 12	65.40 259	62.73 281	73.40 154	74.89 267	52.78 187	73.09 274	75.13 258	55.69 214	58.48 346	73.73 377	32.86 341	86.32 88	50.63 267	70.11 292	81.10 276
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
CVMVSNet		59.63 327	59.14 319	61.08 356	74.47 281	38.84 371	75.20 224	68.74 330	31.15 432	58.24 347	76.51 344	32.39 355	68.58 361	49.77 272	65.84 343	75.81 352

SD_0403		63.07 289	63.49 269	61.82 346	75.16 263	31.14 431	71.89 295	73.47 284	53.34 272	58.22 348	81.81 242	45.17 198	73.86 329	37.43 374	74.87 216	80.45 287

D2MVS		62.30 298	60.29 312	68.34 273	66.46 402	48.42 273	65.70 354	73.42 285	47.71 351	58.16 349	75.02 365	30.51 364	77.71 285	53.96 240	71.68 269	78.90 316

mvs5depth		55.64 360	53.81 371	61.11 355	59.39 435	40.98 355	65.89 352	68.28 333	50.21 314	58.11 350	75.42 362	17.03 430	67.63 369	43.79 329	46.21 430	74.73 369

RPSCF		55.80 359	54.22 368	60.53 357	65.13 409	42.91 335	64.30 371	57.62 406	36.84 423	58.05 351	82.28 227	28.01 389	56.24 426	37.14 377	58.61 394	82.44 249

tpm cat1		59.25 330	56.95 340	66.15 301	72.19 329	46.96 291	68.09 338	65.76 352	40.03 416	57.81 352	70.56 399	38.32 281	74.51 325	38.26 370	61.50 379	77.00 341

gg-mvs-nofinetune		57.86 341	56.43 347	62.18 344	72.62 318	35.35 405	66.57 347	56.33 413	50.65 309	57.64 353	57.10 440	30.65 363	76.36 315	37.38 375	78.88 148	74.82 367

ACMH+	57.40 11	66.12 249	64.06 258	72.30 182	77.79 194	52.83 186	80.39 100	78.03 205	57.30 176	57.47 354	82.55 218	27.68 393	84.17 138	45.54 312	69.78 300	79.90 299

dmvs_re		56.77 349	56.83 342	56.61 383	69.23 380	41.02 351	58.37 405	64.18 366	50.59 311	57.45 355	71.42 393	35.54 309	58.94 411	37.23 376	67.45 331	69.87 416

MS-PatchMatch		62.42 296	61.46 296	65.31 319	75.21 261	52.10 202	72.05 290	74.05 277	46.41 367	57.42 356	74.36 370	34.35 322	77.57 287	45.62 311	73.67 231	66.26 426

mamv4		56.85 348	58.00 333	53.43 402	72.46 324	54.47 145	57.56 413	54.74 417	38.81 420	57.42 356	79.45 291	47.57 161	38.70 455	60.88 177	53.07 415	67.11 425

PVSNet	50.76 19	58.40 335	57.39 336	61.42 350	75.53 254	44.04 322	61.43 389	63.45 375	47.04 362	56.91 358	73.61 378	27.00 400	64.76 386	39.12 365	72.40 258	75.47 357

Patchmtry		57.16 345	56.47 346	59.23 364	69.17 382	34.58 411	62.98 381	63.15 378	44.53 382	56.83 359	74.84 366	35.83 307	68.71 360	40.03 357	60.91 381	74.39 373

LS3D		64.71 267	62.50 283	71.34 214	79.72 131	55.71 123	79.82 110	74.72 265	48.50 339	56.62 360	84.62 165	33.59 333	82.34 186	29.65 425	75.23 213	75.97 350

ACMH	55.70 15	65.20 262	63.57 267	70.07 242	78.07 185	52.01 206	79.48 119	79.69 157	55.75 213	56.59 361	80.98 258	27.12 398	80.94 218	42.90 340	71.58 270	77.25 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Syy-MVS		56.00 357	56.23 350	55.32 389	74.69 275	26.44 448	65.52 357	57.49 407	50.97 306	56.52 362	72.18 385	39.89 261	68.09 363	24.20 440	64.59 354	71.44 403

myMVS_eth3d		54.86 367	54.61 361	55.61 388	74.69 275	27.31 445	65.52 357	57.49 407	50.97 306	56.52 362	72.18 385	21.87 424	68.09 363	27.70 431	64.59 354	71.44 403

MVP-Stereo		65.41 258	63.80 263	70.22 238	77.62 206	55.53 130	76.30 197	78.53 190	50.59 311	56.47 364	78.65 303	39.84 262	82.68 177	44.10 325	72.12 264	72.44 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tt0320-xc		58.33 336	56.41 348	64.08 329	75.79 248	41.34 348	68.30 336	62.72 381	47.90 348	56.29 365	74.16 374	28.53 384	71.04 346	41.50 352	52.50 418	79.88 300

tt0320		58.59 333	56.81 343	63.92 331	75.46 255	41.32 349	68.63 334	64.06 369	47.05 361	56.19 366	74.19 372	30.34 366	71.36 343	39.92 360	55.45 406	79.09 311

OpenMVS_ROB	52.78 18	60.03 321	58.14 331	65.69 311	70.47 359	44.82 311	75.33 220	70.86 310	45.04 378	56.06 367	76.00 351	26.89 402	79.65 242	35.36 393	67.29 332	72.60 385

EG-PatchMatch MVS		64.71 267	62.87 278	70.22 238	77.68 199	53.48 166	77.99 147	78.82 176	53.37 271	56.03 368	77.41 328	24.75 415	84.04 141	46.37 303	73.42 241	73.14 380

PLC	56.13 14	65.09 263	63.21 275	70.72 231	81.04 106	54.87 142	78.57 131	77.47 214	48.51 338	55.71 369	81.89 239	33.71 330	79.71 241	41.66 349	70.37 285	77.58 331
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 369	53.69 372	54.79 393	66.12 405	31.96 428	62.34 386	49.05 433	44.42 385	55.54 370	71.33 395	30.22 368	56.70 421	41.65 350	62.54 371	75.71 354

MDTV_nov1_ep13			57.00 339		72.73 316	38.26 376	65.02 366	64.73 362	44.74 380	55.46 371	72.48 383	32.61 352	70.47 349	37.47 373	67.75 328

test-LLR		58.15 339	58.13 332	58.22 373	68.57 385	44.80 312	65.46 359	57.92 404	50.08 316	55.44 372	69.82 406	32.62 350	57.44 418	49.66 275	73.62 233	72.41 390

test-mter		56.42 353	55.82 353	58.22 373	68.57 385	44.80 312	65.46 359	57.92 404	39.94 417	55.44 372	69.82 406	21.92 421	57.44 418	49.66 275	73.62 233	72.41 390

ITE_SJBPF				62.09 345	66.16 404	44.55 317		64.32 364	47.36 356	55.31 374	80.34 270	19.27 427	62.68 395	36.29 388	62.39 372	79.04 313

MIMVSNet		57.35 343	57.07 338	58.22 373	74.21 290	37.18 386	62.46 384	60.88 394	48.88 333	55.29 375	75.99 353	31.68 359	62.04 397	31.87 409	72.35 259	75.43 358

Anonymous20231206		55.10 366	55.30 357	54.48 394	69.81 374	33.94 417	62.91 382	62.13 389	41.08 408	55.18 376	75.65 357	32.75 345	56.59 424	30.32 422	67.86 326	72.91 381

KD-MVS_2432*1600		53.45 373	51.50 382	59.30 362	62.82 418	37.14 387	55.33 419	71.79 304	47.34 357	55.09 377	70.52 400	21.91 422	70.45 350	35.72 391	42.97 436	70.31 412

miper_refine_blended		53.45 373	51.50 382	59.30 362	62.82 418	37.14 387	55.33 419	71.79 304	47.34 357	55.09 377	70.52 400	21.91 422	70.45 350	35.72 391	42.97 436	70.31 412

pmmvs-eth3d		58.81 332	56.31 349	66.30 297	67.61 392	52.42 199	72.30 286	64.76 361	43.55 392	54.94 379	74.19 372	28.95 380	72.60 334	43.31 333	57.21 399	73.88 378

baseline2		63.42 282	61.26 301	69.89 248	72.55 320	47.62 285	71.54 297	68.38 332	50.11 315	54.82 380	75.55 359	43.06 221	80.96 217	48.13 289	67.16 334	81.11 275

OurMVSNet-221017-0		61.37 311	58.63 326	69.61 251	72.05 331	48.06 278	73.93 255	72.51 296	47.23 359	54.74 381	80.92 260	21.49 425	81.24 208	48.57 285	56.22 404	79.53 307

GG-mvs-BLEND				62.34 343	71.36 346	37.04 390	69.20 330	57.33 409		54.73 382	65.48 428	30.37 365	77.82 281	34.82 394	74.93 215	72.17 394

tpmvs		58.47 334	56.95 340	63.03 340	70.20 364	41.21 350	67.90 340	67.23 341	49.62 322	54.73 382	70.84 397	34.14 323	76.24 317	36.64 384	61.29 380	71.64 399

EPNet_dtu		61.90 304	61.97 290	61.68 347	72.89 314	39.78 362	75.85 212	65.62 354	55.09 231	54.56 384	79.36 293	37.59 288	67.02 374	39.80 361	76.95 186	78.25 320
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		53.17 377	53.44 374	52.33 410	68.29 389	25.34 452	58.21 406	54.41 420	44.46 384	54.56 384	69.05 412	33.32 335	60.94 399	36.93 379	61.76 378	70.73 410

test0.0.03 1		53.32 376	53.59 373	52.50 409	62.81 420	29.45 436	59.51 401	54.11 421	50.08 316	54.40 386	74.31 371	32.62 350	55.92 427	30.50 421	63.95 359	72.15 395

ambc				65.13 321	63.72 416	37.07 389	47.66 441	78.78 179		54.37 387	71.42 393	11.24 447	80.94 218	45.64 310	53.85 414	77.38 334

SixPastTwentyTwo		61.65 307	58.80 324	70.20 240	75.80 247	47.22 289	75.59 216	69.68 319	54.61 247	54.11 388	79.26 295	27.07 399	82.96 165	43.27 334	49.79 426	80.41 289

ppachtmachnet_test		58.06 340	55.38 356	66.10 303	69.51 376	48.99 262	68.01 339	66.13 351	44.50 383	54.05 389	70.74 398	32.09 358	72.34 337	36.68 383	56.71 403	76.99 343

TESTMET0.1,1		55.28 363	54.90 359	56.42 384	66.56 400	43.67 325	65.46 359	56.27 414	39.18 419	53.83 390	67.44 418	24.21 416	55.46 429	48.04 290	73.11 247	70.13 414

pmmvs5		56.47 352	55.68 354	58.86 368	61.41 426	36.71 393	66.37 349	62.75 380	40.38 413	53.70 391	76.62 340	34.56 318	67.05 373	40.02 358	65.27 346	72.83 383

MSDG		61.81 306	59.23 318	69.55 255	72.64 317	52.63 192	70.45 315	75.81 240	51.38 299	53.70 391	76.11 349	29.52 376	81.08 214	37.70 372	65.79 344	74.93 365

test_fmvs3		44.30 401	42.55 404	49.55 417	42.83 455	27.15 447	53.03 426	44.93 445	22.03 450	53.69 393	64.94 429	4.21 460	49.63 442	47.47 291	49.82 425	71.88 396

K. test v3		60.47 318	57.11 337	70.56 234	73.74 299	48.22 275	75.10 228	62.55 382	58.27 157	53.62 394	76.31 348	27.81 391	81.59 198	47.42 292	39.18 441	81.88 259

PM-MVS		52.33 379	50.19 388	58.75 369	62.10 423	45.14 310	65.75 353	40.38 451	43.60 391	53.52 395	72.65 382	9.16 452	65.87 382	50.41 268	54.18 412	65.24 428

PMMVS		53.96 369	53.26 375	56.04 385	62.60 421	50.92 220	61.17 393	56.09 415	32.81 429	53.51 396	66.84 423	34.04 325	59.93 405	44.14 324	68.18 324	57.27 438

PatchMatch-RL		56.25 355	54.55 362	61.32 353	77.06 222	56.07 115	65.57 356	54.10 422	44.13 388	53.49 397	71.27 396	25.20 412	66.78 375	36.52 386	63.66 360	61.12 430

IMVS_0404		64.63 269	64.22 257	65.88 308	77.06 222	49.73 242	64.40 370	78.60 184	52.70 278	53.16 398	82.58 213	34.82 316	65.16 385	59.20 194	75.46 209	82.74 238

LCM-MVSNet-Re		61.88 305	61.35 298	63.46 334	74.58 279	31.48 430	61.42 390	58.14 403	58.71 148	53.02 399	79.55 288	43.07 220	76.80 305	45.69 309	77.96 168	82.11 256

UWE-MVS-28		52.25 380	52.35 378	51.93 413	66.99 395	22.79 456	63.48 378	48.31 437	46.78 364	52.73 400	76.11 349	27.78 392	57.82 417	20.58 446	68.41 323	75.17 359

F-COLMAP		63.05 290	60.87 309	69.58 254	76.99 229	53.63 162	78.12 143	76.16 233	47.97 347	52.41 401	81.61 246	27.87 390	78.11 273	40.07 356	66.66 337	77.00 341

test20.03		53.87 371	54.02 369	53.41 403	61.47 425	28.11 441	61.30 391	59.21 399	51.34 301	52.09 402	77.43 327	33.29 336	58.55 413	29.76 424	60.27 389	73.58 379

testgi		51.90 381	52.37 377	50.51 416	60.39 433	23.55 455	58.42 404	58.15 402	49.03 330	51.83 403	79.21 296	22.39 419	55.59 428	29.24 427	62.64 369	72.40 392

EU-MVSNet		55.61 361	54.41 364	59.19 366	65.41 408	33.42 420	72.44 284	71.91 303	28.81 434	51.27 404	73.87 376	24.76 414	69.08 358	43.04 337	58.20 395	75.06 361

MDTV_nov1_ep13_2view						25.89 450	61.22 392		40.10 415	51.10 405		32.97 340		38.49 368		78.61 318

COLMAP_ROB	52.97 17	61.27 312	58.81 322	68.64 268	74.63 277	52.51 195	78.42 134	73.30 288	49.92 319	50.96 406	81.51 249	23.06 418	79.40 246	31.63 414	65.85 342	74.01 377
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
KD-MVS_self_test		55.22 364	53.89 370	59.21 365	57.80 439	27.47 444	57.75 411	74.32 270	47.38 355	50.90 407	70.00 405	28.45 386	70.30 353	40.44 355	57.92 396	79.87 301

ADS-MVSNet2		51.33 385	48.76 392	59.07 367	66.02 406	44.60 315	50.90 432	59.76 397	36.90 421	50.74 408	66.18 426	26.38 403	63.11 393	27.17 433	54.76 410	69.50 418

ADS-MVSNet		48.48 394	47.77 395	50.63 415	66.02 406	29.92 435	50.90 432	50.87 431	36.90 421	50.74 408	66.18 426	26.38 403	52.47 438	27.17 433	54.76 410	69.50 418

our_test_3		56.49 351	54.42 363	62.68 342	69.51 376	45.48 307	66.08 351	61.49 391	44.11 389	50.73 410	69.60 409	33.05 337	68.15 362	38.38 369	56.86 400	74.40 372

FMVSNet5		55.86 358	54.93 358	58.66 370	71.05 351	36.35 396	64.18 373	62.48 383	46.76 365	50.66 411	74.73 368	25.80 408	64.04 388	33.11 402	65.57 345	75.59 355

lessismore_v0				69.91 246	71.42 344	47.80 281		50.90 430		50.39 412	75.56 358	27.43 396	81.33 205	45.91 307	34.10 447	80.59 285

UnsupCasMVSNet_eth		53.16 378	52.47 376	55.23 390	59.45 434	33.39 421	59.43 402	69.13 327	45.98 371	50.35 413	72.32 384	29.30 379	58.26 415	42.02 347	44.30 434	74.05 376

dmvs_testset		50.16 389	51.90 379	44.94 424	66.49 401	11.78 464	61.01 396	51.50 426	51.17 304	50.30 414	67.44 418	39.28 268	60.29 403	22.38 443	57.49 398	62.76 429

ttmdpeth		45.56 398	42.95 403	53.39 404	52.33 446	29.15 437	57.77 409	48.20 438	31.81 431	49.86 415	77.21 329	8.69 453	59.16 409	27.31 432	33.40 448	71.84 398

dp		51.89 382	51.60 381	52.77 407	68.44 388	32.45 426	62.36 385	54.57 419	44.16 387	49.31 416	67.91 414	28.87 382	56.61 423	33.89 397	54.89 409	69.24 421

Anonymous20240521		55.30 362	54.41 364	57.96 377	60.92 432	41.73 344	71.09 307	71.06 309	41.18 407	48.65 417	73.31 379	16.93 431	59.25 408	42.54 341	64.01 357	72.90 382

JIA-IIPM		51.56 383	47.68 397	63.21 337	64.61 411	50.73 224	47.71 440	58.77 401	42.90 398	48.46 418	51.72 444	24.97 413	70.24 354	36.06 390	53.89 413	68.64 422

USDC		56.35 354	54.24 367	62.69 341	64.74 410	40.31 357	65.05 365	73.83 281	43.93 390	47.58 419	77.71 324	15.36 437	75.05 323	38.19 371	61.81 377	72.70 384

UnsupCasMVSNet_bld		50.07 390	48.87 391	53.66 399	60.97 431	33.67 419	57.62 412	64.56 363	39.47 418	47.38 420	64.02 432	27.47 394	59.32 407	34.69 395	43.68 435	67.98 424

AllTest		57.08 346	54.65 360	64.39 326	71.44 342	49.03 259	69.92 323	67.30 338	45.97 372	47.16 421	79.77 281	17.47 428	67.56 370	33.65 398	59.16 392	76.57 345

TestCases				64.39 326	71.44 342	49.03 259		67.30 338	45.97 372	47.16 421	79.77 281	17.47 428	67.56 370	33.65 398	59.16 392	76.57 345

CMPMVS	42.80 21	57.81 342	55.97 351	63.32 335	60.98 430	47.38 288	64.66 368	69.50 323	32.06 430	46.83 423	77.80 320	29.50 377	71.36 343	48.68 283	73.75 229	71.21 406
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		55.17 365	54.31 366	57.77 379	70.03 368	32.01 427	65.68 355	64.81 360	49.19 328	46.75 424	76.00 351	25.53 411	64.04 388	28.65 428	62.13 374	77.26 337

mvsany_test1		39.38 411	38.16 414	43.02 427	49.05 448	34.28 414	44.16 448	25.94 462	22.74 448	46.57 425	62.21 435	23.85 417	41.16 454	33.01 403	35.91 444	53.63 441

PVSNet_0	43.31 20	47.46 397	45.64 400	52.92 406	67.60 393	44.65 314	54.06 424	54.64 418	41.59 405	46.15 426	58.75 437	30.99 362	58.66 412	32.18 405	24.81 452	55.46 440

Patchmatch-test		49.08 392	48.28 394	51.50 414	64.40 412	30.85 433	45.68 444	48.46 436	35.60 425	46.10 427	72.10 387	34.47 321	46.37 447	27.08 435	60.65 385	77.27 336

YYNet1		50.73 387	48.96 389	56.03 386	61.10 428	41.78 343	51.94 429	56.44 411	40.94 410	44.84 428	67.80 416	30.08 371	55.08 431	36.77 380	50.71 422	71.22 405

MDA-MVSNet_test_wron		50.71 388	48.95 390	56.00 387	61.17 427	41.84 342	51.90 430	56.45 410	40.96 409	44.79 429	67.84 415	30.04 372	55.07 432	36.71 382	50.69 423	71.11 408

TDRefinement		53.44 375	50.72 385	61.60 348	64.31 413	46.96 291	70.89 309	65.27 358	41.78 402	44.61 430	77.98 313	11.52 446	66.36 378	28.57 429	51.59 420	71.49 402

new-patchmatchnet		47.56 396	47.73 396	47.06 419	58.81 437	9.37 467	48.78 438	59.21 399	43.28 394	44.22 431	68.66 413	25.67 409	57.20 420	31.57 416	49.35 427	74.62 371

test_vis1_rt		41.35 409	39.45 410	47.03 420	46.65 454	37.86 379	47.76 439	38.65 452	23.10 446	44.21 432	51.22 446	11.20 448	44.08 449	39.27 364	53.02 416	59.14 433

N_pmnet		39.35 412	40.28 409	36.54 435	63.76 414	1.62 472	49.37 437	0.76 471	34.62 427	43.61 433	66.38 425	26.25 405	42.57 451	26.02 438	51.77 419	65.44 427

CHOSEN 280x420		47.83 395	46.36 399	52.24 412	67.37 394	49.78 241	38.91 452	43.11 449	35.00 426	43.27 434	63.30 433	28.95 380	49.19 443	36.53 385	60.80 383	57.76 437

TinyColmap		54.14 368	51.72 380	61.40 351	66.84 398	41.97 341	66.52 348	68.51 331	44.81 379	42.69 435	75.77 356	11.66 444	72.94 332	31.96 408	56.77 402	69.27 420

MDA-MVSNet-bldmvs		53.87 371	50.81 384	63.05 339	66.25 403	48.58 271	56.93 416	63.82 371	48.09 345	41.22 436	70.48 402	30.34 366	68.00 366	34.24 396	45.92 432	72.57 386

pmmvs3		44.92 400	41.95 407	53.86 397	52.58 445	43.55 326	62.11 387	46.90 443	26.05 441	40.63 437	60.19 436	11.08 449	57.91 416	31.83 413	46.15 431	60.11 431

LF4IMVS		42.95 403	42.26 405	45.04 422	48.30 451	32.50 425	54.80 421	48.49 435	28.03 437	40.51 438	70.16 403	9.24 451	43.89 450	31.63 414	49.18 428	58.72 434

WB-MVS		43.26 402	43.41 402	42.83 428	63.32 417	10.32 466	58.17 407	45.20 444	45.42 376	40.44 439	67.26 421	34.01 327	58.98 410	11.96 457	24.88 451	59.20 432

mvsany_test3		32.62 419	30.57 424	38.77 433	36.16 464	24.20 454	38.10 453	20.63 466	19.14 452	40.36 440	57.43 439	5.06 457	36.63 458	29.59 426	28.66 449	55.49 439

DSMNet-mixed		39.30 413	38.72 412	41.03 430	51.22 447	19.66 459	45.53 445	31.35 458	15.83 457	39.80 441	67.42 420	22.19 420	45.13 448	22.43 442	52.69 417	58.31 435

test_f		31.86 421	31.05 422	34.28 436	32.33 467	21.86 457	32.34 454	30.46 459	16.02 456	39.78 442	55.45 441	4.80 458	32.36 461	30.61 420	37.66 443	48.64 443

dongtai		34.52 417	34.94 417	33.26 438	61.06 429	16.00 463	52.79 428	23.78 464	40.71 411	39.33 443	48.65 452	16.91 432	48.34 444	12.18 456	19.05 456	35.44 455

MVStest1		42.65 404	39.29 411	52.71 408	47.26 453	34.58 411	54.41 423	50.84 432	23.35 444	39.31 444	74.08 375	12.57 441	55.09 430	23.32 441	28.47 450	68.47 423

SSC-MVS		41.96 407	41.99 406	41.90 429	62.46 422	9.28 468	57.41 414	44.32 447	43.38 393	38.30 445	66.45 424	32.67 349	58.42 414	10.98 458	21.91 454	57.99 436

MVS-HIRNet		45.52 399	44.48 401	48.65 418	68.49 387	34.05 416	59.41 403	44.50 446	27.03 439	37.96 446	50.47 448	26.16 406	64.10 387	26.74 436	59.52 390	47.82 447

kuosan		29.62 424	30.82 423	26.02 443	52.99 442	16.22 462	51.09 431	22.71 465	33.91 428	33.99 447	40.85 453	15.89 435	33.11 460	7.59 464	18.37 457	28.72 457

FPMVS		42.18 406	41.11 408	45.39 421	58.03 438	41.01 353	49.50 436	53.81 423	30.07 433	33.71 448	64.03 430	11.69 443	52.08 441	14.01 452	55.11 408	43.09 449

test_vis3_rt		32.09 420	30.20 425	37.76 434	35.36 465	27.48 443	40.60 451	28.29 461	16.69 455	32.52 449	40.53 454	1.96 466	37.40 457	33.64 400	42.21 438	48.39 444

new_pmnet		34.13 418	34.29 419	33.64 437	52.63 444	18.23 461	44.43 447	33.90 457	22.81 447	30.89 450	53.18 442	10.48 450	35.72 459	20.77 445	39.51 440	46.98 448

LCM-MVSNet		40.30 410	35.88 416	53.57 400	42.24 456	29.15 437	45.21 446	60.53 396	22.23 449	28.02 451	50.98 447	3.72 462	61.78 398	31.22 419	38.76 442	69.78 417

APD_test1		37.39 414	34.94 417	44.72 425	48.88 449	33.19 422	52.95 427	44.00 448	19.49 451	27.28 452	58.59 438	3.18 464	52.84 437	18.92 447	41.17 439	48.14 446

ANet_high		41.38 408	37.47 415	53.11 405	39.73 461	24.45 453	56.94 415	69.69 318	47.65 352	26.04 453	52.32 443	12.44 442	62.38 396	21.80 444	10.61 462	72.49 387

testf1		31.46 422	28.89 426	39.16 431	41.99 458	28.78 439	46.45 442	37.56 453	14.28 458	21.10 454	48.96 449	1.48 468	47.11 445	13.63 453	34.56 445	41.60 450

APD_test2		31.46 422	28.89 426	39.16 431	41.99 458	28.78 439	46.45 442	37.56 453	14.28 458	21.10 454	48.96 449	1.48 468	47.11 445	13.63 453	34.56 445	41.60 450

PMVS	28.69 22	36.22 415	33.29 420	45.02 423	36.82 463	35.98 401	54.68 422	48.74 434	26.31 440	21.02 456	51.61 445	2.88 465	60.10 404	9.99 461	47.58 429	38.99 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		27.40 425	25.91 428	31.87 440	39.46 462	6.57 469	31.17 455	28.52 460	23.96 443	20.45 457	48.94 451	4.20 461	37.94 456	16.51 449	19.97 455	51.09 442

Gipuma		34.77 416	31.91 421	43.33 426	62.05 424	37.87 378	20.39 457	67.03 343	23.23 445	18.41 458	25.84 458	4.24 459	62.73 394	14.71 451	51.32 421	29.38 456
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		9.43 432	11.14 435	4.30 447	2.38 470	4.40 470	13.62 459	16.08 468	0.39 464	15.89 459	13.06 461	15.80 436	5.54 466	12.63 455	10.46 463	2.95 461

MVE	17.77 23	21.41 428	17.77 433	32.34 439	34.34 466	25.44 451	16.11 458	24.11 463	11.19 460	13.22 460	31.92 456	1.58 467	30.95 462	10.47 459	17.03 458	40.62 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 429	18.10 432	24.41 444	13.68 469	3.11 471	12.06 460	42.37 450	2.00 463	11.97 461	36.38 455	5.77 456	29.35 463	15.06 450	23.65 453	40.76 452

DeepMVS_CX				12.03 446	17.97 468	10.91 465		10.60 469	7.46 461	11.07 462	28.36 457	3.28 463	11.29 465	8.01 463	9.74 464	13.89 460

E-PMN		23.77 426	22.73 430	26.90 441	42.02 457	20.67 458	42.66 449	35.70 455	17.43 453	10.28 463	25.05 459	6.42 455	42.39 452	10.28 460	14.71 459	17.63 458

EMVS		22.97 427	21.84 431	26.36 442	40.20 460	19.53 460	41.95 450	34.64 456	17.09 454	9.73 464	22.83 460	7.29 454	42.22 453	9.18 462	13.66 460	17.32 459

wuyk23d		13.32 431	12.52 434	15.71 445	47.54 452	26.27 449	31.06 456	1.98 470	4.93 462	5.18 465	1.94 465	0.45 470	18.54 464	6.81 465	12.83 461	2.33 462

EGC-MVSNET		42.47 405	38.48 413	54.46 395	74.33 286	48.73 268	70.33 318	51.10 428	0.03 465	0.18 466	67.78 417	13.28 440	66.49 377	18.91 448	50.36 424	48.15 445

testmvs		4.52 435	6.03 438	0.01 449	0.01 471	0.00 474	53.86 425	0.00 472	0.01 466	0.04 467	0.27 466	0.00 472	0.00 467	0.04 466	0.00 465	0.03 464

test123		4.73 434	6.30 437	0.02 448	0.01 471	0.01 473	56.36 417	0.00 472	0.01 466	0.04 467	0.21 467	0.01 471	0.00 467	0.03 467	0.00 465	0.04 463

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

cdsmvs_eth3d_5k		17.50 430	23.34 429	0.00 450	0.00 473	0.00 474	0.00 461	78.63 183	0.00 468	0.00 469	82.18 230	49.25 139	0.00 467	0.00 468	0.00 465	0.00 465

pcd_1.5k_mvsjas		3.92 436	5.23 439	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	47.05 171	0.00 467	0.00 468	0.00 465	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

ab-mvs-re		6.49 433	8.65 436	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	77.89 318	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 474	0.00 461	0.00 472	0.00 468	0.00 469	0.00 468	0.00 472	0.00 467	0.00 468	0.00 465	0.00 465

WAC-MVS						27.31 445								27.77 430

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 36

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 36

eth-test2					0.00 473

eth-test					0.00 473

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 65	91.15 4	88.23 25

save fliter					86.17 33	61.30 28	83.98 53	79.66 159	59.00 141

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 46

GSMVS																78.05 323

sam_mvs1												34.74 317				78.05 323

sam_mvs												33.43 334

MTGPA								80.97 139

test_post1							68.67 333				3.64 463	32.39 355	69.49 356	44.17 322

test_post											3.55 464	33.90 328	66.52 376

patchmatchnet-post											64.03 430	34.50 319	74.27 327

MTMP							86.03 19	17.08 467

gm-plane-assit					71.40 345	41.72 346			48.85 334		73.31 379		82.48 184	48.90 282

test9_res														75.28 48	88.31 32	83.81 203

agg_prior2														73.09 66	87.93 40	84.33 181

test_prior4						62.51 14	82.08 82

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 84			86.38 94

新几何2							76.12 202

旧先验1					83.04 74	53.15 176		67.52 337			87.85 81	44.08 209			80.76 114	78.03 326

无先验							79.66 115	74.30 272	48.40 341				80.78 224	53.62 242		79.03 314

原ACMM2							79.02 122

testdata2													72.18 340	46.95 300

segment_acmp												54.23 61

testdata1							72.65 278		60.50 102

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 196

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 118	84.65 172

plane_prior4											86.10 132

plane_prior2							84.22 46		64.52 27

plane_prior1					81.27 102

plane_prior						56.31 108	83.58 59		63.19 51						80.48 121

n2								0.00 472

nn								0.00 472

door-mid								47.19 442

test11								83.47 72

door								47.60 440

HQP5-MVS						54.94 139

BP-MVS														67.04 113

HQP3-MVS								83.90 58							80.35 122

HQP2-MVS												45.46 190

NP-MVS					80.98 107	56.05 116					85.54 151

ACMMP++_ref															74.07 224

ACMMP++															72.16 263

Test By Simon												48.33 150

High-resolution multi-view benchmark