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.

HPM-MVS++		79.88 7	80.14 7	79.10 17	88.17 1	64.80 1	86.59 10	83.70 62	65.37 15	78.78 21	90.64 17	58.63 20	87.24 51	79.00 7	90.37 10	85.26 114

CNVR-MVS		79.84 8	79.97 8	79.45 7	87.90 2	62.17 20	84.37 34	85.03 35	66.96 5	77.58 28	90.06 38	59.47 17	89.13 18	78.67 9	89.73 16	87.03 50

test_0728_SECOND				79.19 12	87.82 3	59.11 66	87.85 3	87.15 6					90.84 1	78.66 10	90.61 7	87.62 32

SED-MVS		81.56 1	82.30 1	79.32 9	87.77 4	58.90 72	87.82 5	86.78 12	64.18 33	85.97 1	91.84 6	66.87 2	90.83 2	78.63 12	90.87 3	88.23 10

IU-MVS					87.77 4	59.15 64		85.53 27	53.93 216	84.64 3				79.07 6	90.87 3	88.37 7

test_241102_ONE					87.77 4	58.90 72		86.78 12	64.20 32	85.97 1	91.34 10	66.87 2	90.78 4

DVP-MVS		80.84 3	81.64 2	78.42 33	87.75 7	59.07 67	87.85 3	85.03 35	64.26 30	83.82 6	92.00 3	64.82 6	90.75 5	78.66 10	90.61 7	85.45 105
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
test0726					87.75 7	59.07 67	87.86 2	86.83 10	64.26 30	84.19 5	91.92 5	64.82 6

test_part2					87.58 9	60.47 46				83.42 9

OPU-MVS				79.83 4	87.54 10	60.93 38	87.82 5				89.89 45	67.01 1	90.33 8	73.16 45	91.15 2	88.23 10

NCCC		78.58 15	78.31 19	79.39 8	87.51 11	62.61 16	85.20 29	84.42 44	66.73 9	74.67 50	89.38 53	55.30 43	89.18 17	74.19 35	87.34 44	86.38 63

DPE-MVS		80.56 4	80.98 4	79.29 11	87.27 12	60.56 45	85.71 24	86.42 16	63.28 44	83.27 10	91.83 8	64.96 5	90.47 7	76.41 24	89.67 18	86.84 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
testtj		78.47 18	78.43 18	78.61 29	86.82 13	60.67 43	86.07 16	85.38 29	62.12 67	78.65 22	90.29 31	55.76 39	89.31 15	73.55 43	87.22 45	85.84 86

region2R		77.67 30	77.18 33	79.15 14	86.76 14	62.95 8	86.29 12	84.16 50	62.81 56	73.30 69	90.58 19	49.90 101	88.21 31	73.78 39	87.03 48	86.29 74

ACMMPR		77.71 28	77.23 32	79.16 13	86.75 15	62.93 9	86.29 12	84.24 48	62.82 54	73.55 67	90.56 20	49.80 103	88.24 30	74.02 37	87.03 48	86.32 71

HFP-MVS		78.01 26	77.65 27	79.10 17	86.71 16	62.81 10	86.29 12	84.32 46	62.82 54	73.96 58	90.50 22	53.20 70	88.35 27	74.02 37	87.05 46	86.13 76

#test#		77.83 27	77.41 30	79.10 17	86.71 16	62.81 10	85.69 25	84.32 46	61.61 78	73.96 58	90.50 22	53.20 70	88.35 27	73.68 40	87.05 46	86.13 76

MCST-MVS		77.48 32	77.45 29	77.54 47	86.67 18	58.36 81	83.22 57	86.93 8	56.91 159	74.91 44	88.19 66	59.15 18	87.68 46	73.67 41	87.45 43	86.57 61

ZD-MVS					86.64 19	60.38 47		82.70 87	57.95 146	78.10 24	90.06 38	56.12 37	88.84 22	74.05 36	87.00 51

APDe-MVS		80.16 6	80.59 5	78.86 25	86.64 19	60.02 50	88.12 1	86.42 16	62.94 50	82.40 11	92.12 2	59.64 15	89.76 11	78.70 8	88.32 32	86.79 57

SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 21	61.95 22	86.33 11	85.75 25	62.49 61	82.20 12	92.28 1	56.53 31	89.70 12	79.85 3	91.48 1	88.19 12
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
DP-MVS Recon		72.15 95	70.73 104	76.40 65	86.57 22	57.99 86	81.15 93	82.96 81	57.03 156	66.78 166	85.56 113	44.50 171	88.11 33	51.77 203	80.23 112	83.10 186

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 23	63.47 5	86.02 18	83.55 65	63.89 38	73.60 66	90.60 18	54.85 50	86.72 68	77.20 18	88.06 37	85.74 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 41	75.93 44	78.34 36	86.47 24	63.50 4	85.74 23	82.28 91	62.90 51	71.77 91	90.26 32	46.61 147	86.55 76	71.71 52	85.66 64	84.97 122

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 25	60.81 40	85.52 26	84.36 45	60.61 89	79.05 19	90.30 30	55.54 42	88.32 29	73.48 44	87.03 48	84.83 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 12	78.67 16	79.30 10	86.43 26	62.05 21	86.62 9	86.01 21	63.32 43	75.08 39	90.47 25	53.96 59	88.68 23	76.48 23	89.63 20	87.16 47

ETH3 D test6400		79.14 10	79.32 9	78.61 29	86.34 27	58.11 84	84.65 32	87.66 4	58.56 135	78.87 20	89.54 50	63.67 10	89.57 13	74.60 33	89.98 13	88.14 13

XVS		77.17 35	76.56 39	79.00 20	86.32 28	62.62 14	85.83 20	83.92 54	64.55 24	72.17 87	90.01 43	47.95 124	88.01 36	71.55 54	86.74 54	86.37 66

X-MVStestdata		70.21 123	67.28 164	79.00 20	86.32 28	62.62 14	85.83 20	83.92 54	64.55 24	72.17 87	6.49 364	47.95 124	88.01 36	71.55 54	86.74 54	86.37 66

MSP-MVS		81.06 2	81.40 3	80.02 1	86.21 30	62.73 12	86.09 15	86.83 10	65.51 14	83.81 8	90.51 21	63.71 9	89.23 16	81.51 1	88.44 28	88.09 15
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
114514_t		70.83 110	69.56 120	74.64 99	86.21 30	54.63 139	82.34 74	81.81 100	48.22 271	63.01 224	85.83 109	40.92 210	87.10 57	57.91 157	79.79 114	82.18 200

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 46	79.95 142	59.00 126	79.16 16	90.75 15	57.96 22	87.09 58	77.08 20	90.18 11	87.87 21

save fliter					86.17 32	61.30 31	83.98 46	79.66 147	59.00 126

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 34	60.86 39	84.71 31	84.85 40	61.98 73	73.06 76	88.88 61	53.72 64	89.06 19	68.27 74	88.04 38	87.42 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		76.77 40	76.06 42	78.88 24	86.14 35	62.73 12	82.55 71	83.74 61	61.71 76	72.45 86	90.34 29	48.48 120	88.13 32	72.32 48	86.85 52	85.78 88

zzz-MVS		77.61 31	77.36 31	78.35 34	86.08 36	63.57 2	83.37 55	80.97 127	65.13 18	75.77 34	90.88 13	48.63 116	86.66 70	77.23 16	88.17 34	84.81 126

MTAPA		76.90 38	76.42 40	78.35 34	86.08 36	63.57 2	74.92 203	80.97 127	65.13 18	75.77 34	90.88 13	48.63 116	86.66 70	77.23 16	88.17 34	84.81 126

GST-MVS		78.14 23	77.85 26	78.99 22	86.05 38	61.82 25	85.84 19	85.21 31	63.56 42	74.29 55	90.03 41	52.56 73	88.53 25	74.79 31	88.34 30	86.63 60

CP-MVS		77.12 36	76.68 37	78.43 32	86.05 38	63.18 7	87.55 8	83.45 68	62.44 63	72.68 80	90.50 22	48.18 122	87.34 50	73.59 42	85.71 62	84.76 130

SR-MVS		76.13 45	75.70 46	77.40 51	85.87 40	61.20 33	85.52 26	82.19 92	59.99 108	75.10 38	90.35 27	47.66 128	86.52 77	71.64 53	82.99 77	84.47 136

新几何1				70.76 192	85.66 41	61.13 35		66.43 294	44.68 303	70.29 102	86.64 86	41.29 206	75.23 272	49.72 216	81.75 95	75.93 281

MG-MVS		73.96 71	73.89 67	74.16 111	85.65 42	49.69 208	81.59 87	81.29 117	61.45 79	71.05 97	88.11 67	51.77 85	87.73 45	61.05 139	83.09 75	85.05 119

1121		68.53 160	67.16 170	72.63 153	85.64 43	61.14 34	73.95 218	66.46 293	44.61 304	70.28 103	86.68 85	41.42 204	80.78 206	53.62 188	81.79 91	75.97 279

TEST9					85.58 44	61.59 27	81.62 85	81.26 118	55.65 190	74.93 42	88.81 62	53.70 65	84.68 120

train_agg		76.27 44	76.15 41	76.64 62	85.58 44	61.59 27	81.62 85	81.26 118	55.86 182	74.93 42	88.81 62	53.70 65	84.68 120	75.24 29	88.33 31	83.65 169

test1172		75.36 54	74.81 56	77.02 55	85.47 46	60.79 42	83.94 49	81.63 104	59.52 119	74.66 51	90.18 34	44.74 167	85.84 93	70.63 61	82.52 86	84.42 137

ACMMP_NAP		78.77 14	78.78 14	78.74 27	85.44 47	61.04 36	83.84 50	85.16 32	62.88 52	78.10 24	91.26 11	52.51 74	88.39 26	79.34 5	90.52 9	86.78 58

test_8					85.40 48	60.96 37	81.54 88	81.18 121	55.86 182	74.81 45	88.80 64	53.70 65	84.45 125

原ACMM1				74.69 95	85.39 49	59.40 59		83.42 69	51.47 240	70.27 104	86.61 88	48.61 118	86.51 78	53.85 187	87.96 39	78.16 256

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 50	59.14 65	81.31 91	84.02 51	56.32 172	74.05 56	88.98 59	53.34 69	87.92 40	69.23 69	88.42 29	87.59 33

ACMMP		76.02 46	75.33 50	78.07 38	85.20 51	61.91 23	85.49 28	84.44 43	63.04 48	69.80 116	89.74 49	45.43 160	87.16 55	72.01 51	82.87 82	85.14 115
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
agg_prior1		75.94 47	76.01 43	75.72 75	85.04 52	59.96 51	81.44 89	81.04 124	56.14 178	74.68 48	88.90 60	53.91 61	84.04 132	75.01 30	87.92 41	83.16 185

agg_prior					85.04 52	59.96 51		81.04 124		74.68 48			84.04 132

HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 54	60.81 40	82.91 62	85.08 33	62.57 59	73.09 75	89.97 44	50.90 97	87.48 49	75.30 27	86.85 52	87.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 21	78.46 17	78.03 40	84.96 55	59.52 58	82.93 61	85.39 28	62.15 66	76.41 32	91.51 9	52.47 76	86.78 67	80.66 2	89.64 19	87.80 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 19	78.28 20	78.90 23	84.96 55	61.41 29	84.03 44	83.82 60	59.34 122	79.37 15	89.76 48	59.84 13	87.62 48	76.69 22	86.74 54	87.68 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 127	68.66 136	73.97 114	84.94 57	57.83 88	82.63 69	78.71 163	56.28 174	64.34 211	84.14 134	41.57 199	87.06 60	46.45 239	78.88 130	77.02 271

DP-MVS		65.68 207	63.66 217	71.75 166	84.93 58	56.87 108	80.74 97	73.16 248	53.06 222	59.09 265	82.35 171	36.79 249	85.94 91	32.82 324	69.96 232	72.45 316

DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 15	84.92 59	60.32 48	83.03 59	85.33 30	62.86 53	80.17 13	90.03 41	61.76 11	88.95 20	74.21 34	88.67 27	88.12 14

CPTT-MVS		72.78 82	72.08 86	74.87 93	84.88 60	61.41 29	84.15 41	77.86 184	55.27 195	67.51 158	88.08 69	41.93 193	81.85 181	69.04 72	80.01 113	81.35 214

test12				77.76 43	84.52 61	58.41 80		83.36 72		72.93 78		54.61 52	88.05 35		88.12 36	86.81 56

SD-MVS		77.70 29	77.62 28	77.93 42	84.47 62	61.88 24	84.55 33	83.87 58	60.37 97	79.89 14	89.38 53	54.97 47	85.58 99	76.12 26	84.94 66	86.33 69
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
HPM-MVS_fast		74.30 67	73.46 75	76.80 58	84.45 63	59.04 69	83.65 52	81.05 123	60.15 105	70.43 100	89.84 47	41.09 209	85.59 98	67.61 83	82.90 81	85.77 91

test_prior3		76.89 39	76.96 34	76.69 59	84.20 64	57.27 96	81.75 82	84.88 38	60.37 97	75.01 40	89.06 56	56.22 35	86.43 80	72.19 49	88.96 24	86.38 63

test_prior				76.69 59	84.20 64	57.27 96		84.88 38					86.43 80			86.38 63

CSCG		76.92 37	76.75 36	77.41 49	83.96 66	59.60 56	82.95 60	86.50 15	60.78 87	75.27 37	84.83 121	60.76 12	86.56 75	67.86 80	87.87 42	86.06 80

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 4	83.60 67	61.62 26	84.17 40	86.85 9	63.23 45	73.84 63	90.25 33	57.68 26	89.96 10	74.62 32	89.03 22	87.89 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		73.13 79	72.93 79	73.76 120	83.58 68	51.66 178	78.75 125	77.66 188	67.75 4	72.61 82	89.42 51	49.82 102	83.29 148	53.61 190	83.14 74	86.32 71

SR-MVS-dyc-post		74.57 63	73.90 66	76.58 63	83.49 69	59.87 54	84.29 35	81.36 111	58.07 143	73.14 72	90.07 36	44.74 167	85.84 93	68.20 75	81.76 93	84.03 147

RE-MVS-def			73.71 71		83.49 69	59.87 54	84.29 35	81.36 111	58.07 143	73.14 72	90.07 36	43.06 183		68.20 75	81.76 93	84.03 147

LFMVS		71.78 98	71.59 89	72.32 160	83.40 71	46.38 244	79.75 112	71.08 260	64.18 33	72.80 79	88.64 65	42.58 186	83.72 140	57.41 160	84.49 69	86.86 53

test222					83.14 72	58.68 76	72.57 239	63.45 310	41.78 324	67.56 157	86.12 100	37.13 244			78.73 135	74.98 292

9.14			78.75 15		83.10 73		84.15 41	88.26 2	59.90 109	78.57 23	90.36 26	57.51 28	86.86 64	77.39 15	89.52 21

ETH3D-3000-0.1		78.58 15	78.91 13	77.61 45	83.06 74	57.86 87	84.14 43	88.31 1	60.37 97	79.14 18	90.35 27	57.76 25	87.00 61	77.16 19	89.90 14	87.97 18

旧先验1					83.04 75	53.15 155		67.52 285			87.85 71	44.08 174			80.76 100	78.03 261

MSLP-MVS++		73.77 75	73.47 74	74.66 97	83.02 76	59.29 62	82.30 78	81.88 97	59.34 122	71.59 95	86.83 79	45.94 151	83.65 142	65.09 105	85.22 65	81.06 221

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 23	66.69 10	78.03 27	90.98 12	54.26 54	90.06 9	78.42 14	89.02 23	87.69 28
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 69	73.46 75	75.69 78	83.01 77	60.63 44	77.29 155	78.40 178	61.18 83	70.58 99	85.97 105	54.18 57	84.00 136	67.52 84	82.98 79	82.45 197

SF-MVS		78.82 12	79.22 11	77.60 46	82.88 79	57.83 88	84.99 30	88.13 3	61.86 74	79.16 16	90.75 15	57.96 22	87.09 58	77.08 20	90.18 11	87.87 21

VDDNet		71.81 97	71.33 96	73.26 142	82.80 80	47.60 235	78.74 126	75.27 221	59.59 118	72.94 77	89.40 52	41.51 203	83.91 137	58.75 155	82.99 77	88.26 8

abl_6		74.34 65	73.50 72	76.86 57	82.43 81	60.16 49	83.48 54	81.86 98	58.81 130	73.95 60	89.86 46	41.87 194	86.62 72	67.98 79	81.23 98	83.80 160

3Dnovator+	66.72 4	75.84 49	74.57 58	79.66 6	82.40 82	59.92 53	85.83 20	86.32 18	66.92 8	67.80 153	89.24 55	42.03 191	89.38 14	64.07 111	86.50 57	89.69 1

APD-MVS_3200maxsize		74.96 55	74.39 61	76.67 61	82.20 83	58.24 83	83.67 51	83.29 75	58.41 137	73.71 64	90.14 35	45.62 153	85.99 88	69.64 65	82.85 83	85.78 88

ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 44	82.10 84	58.65 77	82.72 67	87.55 5	58.33 140	78.05 26	90.06 38	58.35 21	87.65 47	76.15 25	89.86 15	86.82 55

PVSNet_Blended_VisFu		71.45 104	70.39 109	74.65 98	82.01 85	58.82 74	79.93 108	80.35 138	55.09 200	65.82 187	82.16 178	49.17 110	82.64 169	60.34 144	78.62 137	82.50 196

TSAR-MVS + GP.		74.90 56	74.15 64	77.17 53	82.00 86	58.77 75	81.80 81	78.57 167	58.58 133	74.32 54	84.51 130	55.94 38	87.22 52	67.11 87	84.48 70	85.52 101

hse-mvs3		72.71 84	71.49 91	76.40 65	81.99 87	59.58 57	76.92 164	76.74 204	60.40 94	74.81 45	85.95 106	45.54 156	85.76 96	70.41 62	70.61 220	83.86 155

API-MVS		72.17 93	71.41 93	74.45 106	81.95 88	57.22 98	84.03 44	80.38 137	59.89 112	68.40 136	82.33 172	49.64 104	87.83 43	51.87 201	84.16 72	78.30 254

MAR-MVS		71.51 102	70.15 113	75.60 81	81.84 89	59.39 60	81.38 90	82.90 84	54.90 206	68.08 145	78.70 245	47.73 126	85.51 102	51.68 205	84.17 71	81.88 206
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
PAPM_NR		72.63 85	71.80 87	75.13 89	81.72 90	53.42 152	79.91 109	83.28 76	59.14 125	66.31 177	85.90 107	51.86 84	86.06 85	57.45 159	80.62 101	85.91 84

test_part1		74.74 59	74.42 60	75.70 77	81.69 91	51.26 180	83.98 46	87.05 7	65.31 16	73.10 74	86.20 98	53.94 60	88.06 34	65.32 102	73.17 190	87.77 26

VDD-MVS		72.50 86	72.09 85	73.75 122	81.58 92	49.69 208	77.76 143	77.63 189	63.21 46	73.21 70	89.02 58	42.14 190	83.32 147	61.72 134	82.50 87	88.25 9

PS-MVSNAJ		70.51 116	69.70 119	72.93 146	81.52 93	55.79 123	74.92 203	79.00 157	55.04 204	69.88 114	78.66 246	47.05 140	82.19 176	61.61 135	79.58 118	80.83 224

testdata				64.66 268	81.52 93	52.93 158		65.29 300	46.09 293	73.88 62	87.46 74	38.08 234	66.26 312	53.31 193	78.48 138	74.78 296

CHOSEN 1792x2688		65.08 218	62.84 226	71.82 165	81.49 95	56.26 114	66.32 292	74.20 239	40.53 333	63.16 223	78.65 247	41.30 205	77.80 251	45.80 245	74.09 173	81.40 211

HQP_MVS		74.31 66	73.73 70	76.06 69	81.41 96	56.31 111	84.22 38	84.01 52	64.52 26	69.27 124	86.10 101	45.26 164	87.21 53	68.16 77	80.58 103	84.65 131

plane_prior7					81.41 96	55.96 120

DPM-MVS		75.47 51	75.00 51	76.88 56	81.38 98	59.16 63	79.94 107	85.71 26	56.59 167	72.46 84	86.76 80	56.89 29	87.86 42	66.36 92	88.91 26	83.64 170

CANet		76.46 42	75.93 44	78.06 39	81.29 99	57.53 93	82.35 73	83.31 74	67.78 3	70.09 106	86.34 96	54.92 48	88.90 21	72.68 47	84.55 68	87.76 27

Vis-MVSNet		72.18 92	71.37 95	74.61 100	81.29 99	55.41 132	80.90 94	78.28 180	60.73 88	69.23 127	88.09 68	44.36 173	82.65 168	57.68 158	81.75 95	85.77 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 101

xiu_mvs_v2_base		70.52 115	69.75 117	72.84 148	81.21 102	55.63 127	75.11 197	78.92 158	54.92 205	69.96 113	79.68 232	47.00 144	82.09 178	61.60 136	79.37 121	80.81 225

plane_prior6					81.20 103	56.24 115						45.26 164

PAPR		71.72 100	70.82 103	74.41 107	81.20 103	51.17 181	79.55 116	83.33 73	55.81 185	66.93 165	84.61 126	50.95 95	86.06 85	55.79 170	79.20 126	86.00 81

PLC	56.13 14	65.09 217	63.21 222	70.72 194	81.04 105	54.87 138	78.57 130	77.47 191	48.51 267	55.71 288	81.89 183	33.71 270	79.71 219	41.66 282	70.37 224	77.58 263
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 106	56.05 119					85.54 115

OPM-MVS		74.73 60	74.25 62	76.19 68	80.81 107	59.01 70	82.60 70	83.64 63	63.74 40	72.52 83	87.49 73	47.18 138	85.88 92	69.47 67	80.78 99	83.66 168

HQP-NCC					80.66 108		82.31 75		62.10 68	67.85 148

ACMP_Plane					80.66 108		82.31 75		62.10 68	67.85 148

HQP-MVS		73.45 76	72.80 80	75.40 83	80.66 108	54.94 135	82.31 75	83.90 56	62.10 68	67.85 148	85.54 115	45.46 158	86.93 62	67.04 88	80.35 109	84.32 139

PHI-MVS		75.87 48	75.36 49	77.41 49	80.62 111	55.91 122	84.28 37	85.78 24	56.08 180	73.41 68	86.58 90	50.94 96	88.54 24	70.79 59	89.71 17	87.79 25

ACMM	61.98 7	70.80 112	69.73 118	74.02 112	80.59 112	58.59 78	82.68 68	82.02 96	55.46 193	67.18 162	84.39 132	38.51 227	83.17 151	60.65 141	76.10 160	80.30 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-1		75.47 51	74.93 54	77.09 54	80.43 113	57.70 91	79.50 117	82.13 93	67.84 1	75.73 36	80.75 210	56.50 32	86.07 84	71.07 58	80.38 107	87.50 36

Regformer-2		75.63 50	74.99 52	77.54 47	80.43 113	58.32 82	79.50 117	82.92 82	67.84 1	75.94 33	80.75 210	55.73 40	86.80 65	71.44 57	80.38 107	87.50 36

Anonymous20231211		69.28 142	68.47 139	71.73 167	80.28 115	47.18 239	79.98 106	82.37 90	54.61 208	67.24 161	84.01 138	39.43 218	82.41 174	55.45 174	72.83 194	85.62 99

ACMP	63.53 6	72.30 90	71.20 99	75.59 82	80.28 115	57.54 92	82.74 66	82.84 86	60.58 90	65.24 198	86.18 99	39.25 220	86.03 87	66.95 90	76.79 156	83.22 180
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 83	71.74 88	75.76 73	80.22 117	57.51 94	82.55 71	83.40 70	61.32 80	66.67 169	87.33 76	39.15 222	86.59 73	67.70 81	77.30 149	83.19 182

LGP-MVS_train				75.76 73	80.22 117	57.51 94		83.40 70	61.32 80	66.67 169	87.33 76	39.15 222	86.59 73	67.70 81	77.30 149	83.19 182

WR-MVS		68.47 161	68.47 139	68.44 229	80.20 119	39.84 299	73.75 225	76.07 210	64.68 23	68.11 144	83.63 148	50.39 100	79.14 233	49.78 213	69.66 240	86.34 68

Anonymous20240529		69.91 129	69.02 130	72.56 154	80.19 120	47.65 233	77.56 147	80.99 126	55.45 194	69.88 114	86.76 80	39.24 221	82.18 177	54.04 184	77.10 151	87.85 23

Anonymous202405211		66.84 193	65.99 192	69.40 217	80.19 120	42.21 284	71.11 261	71.31 259	58.80 131	67.90 146	86.39 94	29.83 305	79.65 220	49.60 219	78.78 133	86.33 69

BH-RMVSNet		68.81 149	67.42 158	72.97 145	80.11 122	52.53 164	74.26 212	76.29 207	58.48 136	68.38 137	84.20 133	42.59 185	83.83 138	46.53 238	75.91 161	82.56 193

test_0402		63.25 234	61.01 247	69.96 205	80.00 123	54.37 141	76.86 166	72.02 255	54.58 210	58.71 268	80.79 209	35.00 258	84.36 126	26.41 350	64.71 285	71.15 328

HyFIR lowres test		65.67 208	63.01 224	73.67 125	79.97 124	55.65 126	69.07 281	75.52 218	42.68 322	63.53 219	77.95 254	40.43 211	81.64 185	46.01 243	71.91 207	83.73 164

EIA-MVS		71.78 98	70.60 105	75.30 86	79.85 125	53.54 149	77.27 156	83.26 77	57.92 147	66.49 172	79.39 238	52.07 82	86.69 69	60.05 146	79.14 128	85.66 96

Regformer-3		73.89 72	73.28 77	75.71 76	79.75 126	55.48 131	78.54 132	79.93 143	66.58 11	73.62 65	80.30 218	54.87 49	84.54 123	69.09 71	76.84 154	87.10 49

Regformer-4		74.25 68	73.48 73	76.57 64	79.75 126	56.54 110	78.54 132	81.49 108	66.93 7	73.90 61	80.30 218	53.84 63	85.98 89	69.76 64	76.84 154	87.17 46

BH-untuned		68.27 164	67.29 163	71.21 182	79.74 128	53.22 154	76.06 181	77.46 193	57.19 154	66.10 179	81.61 189	45.37 162	83.50 145	45.42 253	76.68 158	76.91 275

VNet		69.68 134	70.19 112	68.16 231	79.73 129	41.63 292	70.53 268	77.38 194	60.37 97	70.69 98	86.63 87	51.08 93	77.09 260	53.61 190	81.69 97	85.75 93

LS3D		64.71 220	62.50 230	71.34 180	79.72 130	55.71 124	79.82 110	74.72 232	48.50 268	56.62 283	84.62 125	33.59 273	82.34 175	29.65 342	75.23 167	75.97 279

hse-mvs2		71.04 107	69.86 116	74.60 101	79.58 131	57.12 105	73.96 217	75.25 222	60.40 94	74.81 45	81.95 182	45.54 156	82.90 157	70.41 62	66.83 270	83.77 162

GeoE		71.01 108	70.15 113	73.60 130	79.57 132	52.17 170	78.93 124	78.12 181	58.02 145	67.76 156	83.87 141	52.36 78	82.72 166	56.90 162	75.79 162	85.92 83

AUN-MVS		68.45 162	66.41 182	74.57 103	79.53 133	57.08 106	73.93 221	75.23 223	54.44 213	66.69 168	81.85 184	37.10 245	82.89 158	62.07 130	66.84 269	83.75 163

BH-w/o		66.85 192	65.83 194	69.90 209	79.29 134	52.46 166	74.66 208	76.65 205	54.51 212	64.85 206	78.12 252	45.59 155	82.95 156	43.26 268	75.54 165	74.27 301

1112_ss		64.00 226	63.36 221	65.93 256	79.28 135	42.58 281	71.35 254	72.36 254	46.41 290	60.55 250	77.89 258	46.27 150	73.28 279	46.18 241	69.97 231	81.92 205

ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 136	55.24 134	79.22 120	85.00 37	64.97 22	72.65 81	79.46 237	53.65 68	87.87 41	67.45 85	82.91 80	85.89 85

UniMVSNet_NR-MVSNet		71.11 106	71.00 101	71.44 174	79.20 137	44.13 268	76.02 184	82.60 88	66.48 13	68.20 139	84.60 127	56.82 30	82.82 164	54.62 180	70.43 222	87.36 43

VPNet		67.52 178	68.11 146	65.74 259	79.18 138	36.80 324	72.17 245	72.83 250	62.04 71	67.79 154	85.83 109	48.88 115	76.60 265	51.30 206	72.97 193	83.81 156

TR-MVS		66.59 200	65.07 205	71.17 185	79.18 138	49.63 210	73.48 227	75.20 225	52.95 223	67.90 146	80.33 217	39.81 215	83.68 141	43.20 269	73.56 181	80.20 232

TAMVS		66.78 195	65.27 203	71.33 181	79.16 140	53.67 145	73.84 224	69.59 273	52.32 231	65.28 193	81.72 187	44.49 172	77.40 257	42.32 276	78.66 136	82.92 188

Test_1112_low_res		62.32 242	61.77 237	64.00 272	79.08 141	39.53 303	68.17 283	70.17 267	43.25 317	59.03 266	79.90 225	44.08 174	71.24 288	43.79 264	68.42 257	81.25 215

CDS-MVSNet		66.80 194	65.37 200	71.10 187	78.98 142	53.13 157	73.27 230	71.07 261	52.15 232	64.72 207	80.23 221	43.56 179	77.10 259	45.48 251	78.88 130	83.05 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 61	74.98 53	73.71 124	78.94 143	50.56 193	80.23 102	83.87 58	60.30 103	77.15 29	86.56 91	59.65 14	82.00 179	66.01 95	82.12 89	88.58 6

IS-MVSNet		71.57 101	71.00 101	73.27 141	78.86 144	45.63 257	80.22 103	78.69 164	64.14 36	66.46 173	87.36 75	49.30 107	85.60 97	50.26 212	83.71 73	88.59 5

CLD-MVS		73.33 77	72.68 81	75.29 87	78.82 145	53.33 153	78.23 136	84.79 41	61.30 82	70.41 101	81.04 200	52.41 77	87.12 56	64.61 110	82.49 88	85.41 109
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 103	70.38 110	74.88 92	78.76 146	57.15 103	82.79 63	78.48 171	51.26 244	69.49 119	83.22 154	43.99 176	83.24 149	66.06 93	79.37 121	84.23 142

lupinMVS		69.57 137	68.28 143	73.44 135	78.76 146	57.15 103	76.57 169	73.29 247	46.19 292	69.49 119	82.18 175	43.99 176	79.23 227	64.66 108	79.37 121	83.93 150

CNLPA		65.43 212	64.02 210	69.68 211	78.73 148	58.07 85	77.82 142	70.71 264	51.49 239	61.57 245	83.58 150	38.23 232	70.82 289	43.90 262	70.10 230	80.16 233

EPP-MVSNet		72.16 94	71.31 97	74.71 94	78.68 149	49.70 206	82.10 79	81.65 103	60.40 94	65.94 182	85.84 108	51.74 86	86.37 82	55.93 167	79.55 120	88.07 17

TranMVSNet+NR-MVSNet		70.36 120	70.10 115	71.17 185	78.64 150	42.97 279	76.53 170	81.16 122	66.95 6	68.53 135	85.42 117	51.61 87	83.07 152	52.32 198	69.70 239	87.46 38

UniMVSNet (Re)		70.63 114	70.20 111	71.89 163	78.55 151	45.29 259	75.94 185	82.92 82	63.68 41	68.16 142	83.59 149	53.89 62	83.49 146	53.97 185	71.12 215	86.89 52

Fast-Effi-MVS+		70.28 122	69.12 129	73.73 123	78.50 152	51.50 179	75.01 200	79.46 152	56.16 177	68.59 132	79.55 235	53.97 58	84.05 131	53.34 192	77.53 145	85.65 97

PS-MVSNAJss		72.24 91	71.21 98	75.31 85	78.50 152	55.93 121	81.63 84	82.12 94	56.24 175	70.02 110	85.68 112	47.05 140	84.34 127	65.27 103	74.41 171	85.67 95

EI-MVSNet-Vis-set		72.42 89	71.59 89	74.91 91	78.47 154	54.02 142	77.05 160	79.33 154	65.03 21	71.68 93	79.35 240	52.75 72	84.89 116	66.46 91	74.23 172	85.83 87

MVS_111021_LR		69.50 139	68.78 134	71.65 170	78.38 155	59.33 61	74.82 205	70.11 268	58.08 142	67.83 152	84.68 123	41.96 192	76.34 268	65.62 100	77.54 144	79.30 248

test_yl		69.69 132	69.13 127	71.36 178	78.37 156	45.74 253	74.71 206	80.20 139	57.91 148	70.01 111	83.83 142	42.44 187	82.87 160	54.97 176	79.72 115	85.48 103

DCV-MVSNet		69.69 132	69.13 127	71.36 178	78.37 156	45.74 253	74.71 206	80.20 139	57.91 148	70.01 111	83.83 142	42.44 187	82.87 160	54.97 176	79.72 115	85.48 103

FIs		70.82 111	71.43 92	68.98 222	78.33 158	38.14 313	76.96 162	83.59 64	61.02 84	67.33 160	86.73 82	55.07 46	81.64 185	54.61 182	79.22 125	87.14 48

UGNet		68.81 149	67.39 159	73.06 144	78.33 158	54.47 140	79.77 111	75.40 220	60.45 93	63.22 221	84.40 131	32.71 285	80.91 203	51.71 204	80.56 105	83.81 156
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
jason		69.65 135	68.39 142	73.43 136	78.27 160	56.88 107	77.12 158	73.71 244	46.53 289	69.34 123	83.22 154	43.37 180	79.18 228	64.77 107	79.20 126	84.23 142
jason: jason.
DROMVSNet		75.37 53	75.40 48	75.28 88	78.22 161	51.74 177	82.78 65	85.99 22	61.80 75	72.09 89	86.37 95	54.19 56	88.01 36	71.49 56	85.69 63	86.08 79

alignmvs		73.86 73	73.99 65	73.45 134	78.20 162	50.50 194	78.57 130	82.43 89	59.40 120	76.57 30	86.71 84	56.42 34	81.23 195	65.84 97	81.79 91	88.62 4

xiu_mvs_v1_base_debu		68.58 156	67.28 164	72.48 156	78.19 163	57.19 100	75.28 192	75.09 227	51.61 235	70.04 107	81.41 193	32.79 281	79.02 235	63.81 115	77.31 146	81.22 216

xiu_mvs_v1_base		68.58 156	67.28 164	72.48 156	78.19 163	57.19 100	75.28 192	75.09 227	51.61 235	70.04 107	81.41 193	32.79 281	79.02 235	63.81 115	77.31 146	81.22 216

xiu_mvs_v1_base_debi		68.58 156	67.28 164	72.48 156	78.19 163	57.19 100	75.28 192	75.09 227	51.61 235	70.04 107	81.41 193	32.79 281	79.02 235	63.81 115	77.31 146	81.22 216

PAPM		67.92 173	66.69 175	71.63 171	78.09 166	49.02 216	77.09 159	81.24 120	51.04 246	60.91 248	83.98 139	47.71 127	84.99 111	40.81 285	79.32 124	80.90 223

ACMH	55.70 15	65.20 216	63.57 218	70.07 204	78.07 167	52.01 175	79.48 119	79.69 145	55.75 187	56.59 284	80.98 202	27.12 323	80.94 201	42.90 273	71.58 211	77.25 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 126	69.53 121	71.44 174	78.05 168	44.13 268	75.01 200	81.51 107	64.37 29	68.20 139	84.52 128	49.12 113	82.82 164	54.62 180	70.43 222	87.37 41

NR-MVSNet		69.54 138	68.85 132	71.59 172	78.05 168	43.81 272	74.20 213	80.86 130	65.18 17	62.76 226	84.52 128	52.35 79	83.59 144	50.96 208	70.78 217	87.37 41

EI-MVSNet-UG-set		71.92 96	71.06 100	74.52 105	77.98 170	53.56 148	76.62 168	79.16 155	64.40 28	71.18 96	78.95 244	52.19 80	84.66 122	65.47 101	73.57 180	85.32 111

WR-MVS_H		67.02 189	66.92 172	67.33 239	77.95 171	37.75 316	77.57 146	82.11 95	62.03 72	62.65 229	82.48 169	50.57 98	79.46 223	42.91 272	64.01 289	84.79 128

Effi-MVS+		73.31 78	72.54 82	75.62 80	77.87 172	53.64 146	79.62 115	79.61 148	61.63 77	72.02 90	82.61 164	56.44 33	85.97 90	63.99 114	79.07 129	87.25 45

DELS-MVS		74.76 58	74.46 59	75.65 79	77.84 173	52.25 169	75.59 188	84.17 49	63.76 39	73.15 71	82.79 159	59.58 16	86.80 65	67.24 86	86.04 60	87.89 19
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
ACMH+	57.40 11	66.12 204	64.06 209	72.30 161	77.79 174	52.83 159	80.39 101	78.03 182	57.30 152	57.47 279	82.55 166	27.68 319	84.17 129	45.54 249	69.78 236	79.90 238

3Dnovator	64.47 5	72.49 87	71.39 94	75.79 72	77.70 175	58.99 71	80.66 98	83.15 79	62.24 65	65.46 191	86.59 89	42.38 189	85.52 101	59.59 151	84.72 67	82.85 191

EG-PatchMatch MVS		64.71 220	62.87 225	70.22 200	77.68 176	53.48 150	77.99 139	78.82 159	53.37 220	56.03 287	77.41 265	24.75 337	84.04 132	46.37 240	73.42 184	73.14 308

CP-MVSNet		66.49 201	66.41 182	66.72 242	77.67 177	36.33 329	76.83 167	79.52 150	62.45 62	62.54 232	83.47 153	46.32 148	78.37 242	45.47 252	63.43 295	85.45 105

GBi-Net		67.21 182	66.55 176	69.19 218	77.63 178	43.33 275	77.31 152	77.83 185	56.62 164	65.04 202	82.70 160	41.85 195	80.33 214	47.18 233	72.76 196	83.92 151

test1		67.21 182	66.55 176	69.19 218	77.63 178	43.33 275	77.31 152	77.83 185	56.62 164	65.04 202	82.70 160	41.85 195	80.33 214	47.18 233	72.76 196	83.92 151

FMVSNet2		66.93 191	66.31 187	68.79 225	77.63 178	42.98 278	76.11 179	77.47 191	56.62 164	65.22 200	82.17 177	41.85 195	80.18 217	47.05 236	72.72 199	83.20 181

PCF-MVS	61.88 8	70.95 109	69.49 122	75.35 84	77.63 178	55.71 124	76.04 183	81.81 100	50.30 252	69.66 117	85.40 118	52.51 74	84.89 116	51.82 202	80.24 111	85.45 105
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 213	63.80 214	70.22 200	77.62 182	55.53 129	76.30 175	78.53 169	50.59 251	56.47 285	78.65 247	39.84 214	82.68 167	44.10 261	72.12 206	72.44 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 130	70.58 107	67.46 236	77.61 183	34.73 336	76.05 182	83.19 78	60.84 85	65.88 185	86.46 92	54.52 53	80.76 208	52.52 197	78.12 140	86.91 51

PS-CasMVS		66.42 202	66.32 186	66.70 244	77.60 184	36.30 331	76.94 163	79.61 148	62.36 64	62.43 236	83.66 147	45.69 152	78.37 242	45.35 254	63.26 296	85.42 108

FMVSNet1		66.70 196	65.87 193	69.19 218	77.49 185	43.33 275	77.31 152	77.83 185	56.45 169	64.60 210	82.70 160	38.08 234	80.33 214	46.08 242	72.31 204	83.92 151

VPA-MVSNet		69.02 146	69.47 123	67.69 235	77.42 186	41.00 296	74.04 215	79.68 146	60.06 106	69.26 126	84.81 122	51.06 94	77.58 254	54.44 183	74.43 170	84.48 135

UniMVSNet_ETH3D		67.60 177	67.07 171	69.18 221	77.39 187	42.29 283	74.18 214	75.59 217	60.37 97	66.77 167	86.06 103	37.64 236	78.93 240	52.16 200	73.49 182	86.32 71

CS-MVS-test		73.80 74	73.79 69	73.85 115	77.34 188	49.97 203	79.08 122	86.30 19	59.19 124	70.26 105	82.55 166	55.11 45	87.79 44	68.47 73	85.78 61	83.79 161

thres100view900		63.28 233	62.41 231	65.89 257	77.31 189	38.66 309	72.65 236	69.11 279	57.07 155	62.45 235	81.03 201	37.01 247	79.17 229	31.84 328	73.25 187	79.83 240

cascas		65.98 205	63.42 220	73.64 128	77.26 190	52.58 163	72.26 244	77.21 197	48.56 266	61.21 247	74.60 297	32.57 289	85.82 95	50.38 211	76.75 157	82.52 195

thres600view7		63.30 232	62.27 232	66.41 246	77.18 191	38.87 307	72.35 242	69.11 279	56.98 157	62.37 237	80.96 203	37.01 247	79.00 238	31.43 335	73.05 192	81.36 212

PEN-MVS		66.60 198	66.45 178	67.04 240	77.11 192	36.56 326	77.03 161	80.42 136	62.95 49	62.51 234	84.03 137	46.69 146	79.07 234	44.22 257	63.08 298	85.51 102

PatchMatch-RL		56.25 284	54.55 289	61.32 291	77.06 193	56.07 118	65.57 297	54.10 345	44.13 311	53.49 315	71.27 316	25.20 334	66.78 309	36.52 310	63.66 292	61.12 344

PVSNet_BlendedMVS		68.56 159	67.72 149	71.07 188	77.03 194	50.57 191	74.50 210	81.52 105	53.66 219	64.22 216	79.72 231	49.13 111	82.87 160	55.82 168	73.92 175	79.77 243

PVSNet_Blended		68.59 155	67.72 149	71.19 183	77.03 194	50.57 191	72.51 240	81.52 105	51.91 233	64.22 216	77.77 262	49.13 111	82.87 160	55.82 168	79.58 118	80.14 234

F-COLMAP		63.05 237	60.87 250	69.58 215	76.99 196	53.63 147	78.12 138	76.16 208	47.97 275	52.41 318	81.61 189	27.87 317	78.11 246	40.07 288	66.66 271	77.00 272

tfpn200view9		63.18 235	62.18 234	66.21 250	76.85 197	39.62 301	71.96 248	69.44 275	56.63 162	62.61 230	79.83 227	37.18 241	79.17 229	31.84 328	73.25 187	79.83 240

thres400		63.31 231	62.18 234	66.72 242	76.85 197	39.62 301	71.96 248	69.44 275	56.63 162	62.61 230	79.83 227	37.18 241	79.17 229	31.84 328	73.25 187	81.36 212

tttt0517		67.83 175	65.66 197	74.33 109	76.69 199	50.82 187	77.86 140	73.99 241	54.54 211	64.64 209	82.53 168	35.06 257	85.50 103	55.71 171	69.91 233	86.67 59

ET-MVSNet_ETH3D		67.96 172	65.72 196	74.68 96	76.67 200	55.62 128	75.11 197	74.74 231	52.91 224	60.03 253	80.12 222	33.68 271	82.64 169	61.86 133	76.34 159	85.78 88

TAPA-MVS	59.36 10	66.60 198	65.20 204	70.81 191	76.63 201	48.75 220	76.52 171	80.04 141	50.64 250	65.24 198	84.93 120	39.15 222	78.54 241	36.77 304	76.88 153	85.14 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 105	70.60 105	73.78 118	76.60 202	53.15 155	79.74 113	79.78 144	58.37 138	68.75 131	86.45 93	45.43 160	80.60 209	62.58 125	77.73 143	87.58 34

LTVRE_ROB	55.42 16	63.15 236	61.23 245	68.92 223	76.57 203	47.80 230	59.92 323	76.39 206	54.35 214	58.67 269	82.46 170	29.44 308	81.49 189	42.12 278	71.14 214	77.46 264
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
QAPM		70.05 125	68.81 133	73.78 118	76.54 204	53.43 151	83.23 56	83.48 66	52.89 225	65.90 184	86.29 97	41.55 202	86.49 79	51.01 207	78.40 139	81.42 210

FMVSNet3		66.32 203	65.61 198	68.46 228	76.48 205	42.34 282	74.98 202	77.15 198	55.83 184	65.04 202	81.16 197	39.91 213	80.14 218	47.18 233	72.76 196	82.90 190

CS-MVS		74.01 70	74.24 63	73.32 140	76.47 206	48.51 222	79.19 121	86.17 20	60.56 91	71.62 94	83.71 146	55.16 44	87.94 39	69.21 70	86.11 59	83.51 173

thisisatest0530		67.92 173	65.78 195	74.33 109	76.29 207	51.03 182	76.89 165	74.25 238	53.67 218	65.59 189	81.76 186	35.15 256	85.50 103	55.94 166	72.47 200	86.47 62

baseline1		63.81 227	63.87 213	63.62 273	76.29 207	36.36 327	71.78 250	67.29 288	56.05 181	64.23 215	82.95 158	47.11 139	74.41 276	47.30 232	61.85 306	80.10 235

ab-mvs		66.65 197	66.42 181	67.37 237	76.17 209	41.73 289	70.41 271	76.14 209	53.99 215	65.98 181	83.51 151	49.48 105	76.24 269	48.60 225	73.46 183	84.14 145

Effi-MVS+-dtu		69.64 136	67.53 155	75.95 70	76.10 210	62.29 18	80.20 104	76.06 211	59.83 113	65.26 197	77.09 266	41.56 200	84.02 135	60.60 142	71.09 216	81.53 209

mvs-test1		70.44 118	68.19 144	77.18 52	76.10 210	63.22 6	80.59 99	76.06 211	59.83 113	66.32 176	79.87 226	41.56 200	85.53 100	60.60 142	72.77 195	82.80 192

DTE-MVSNet		65.58 209	65.34 201	66.31 247	76.06 212	34.79 334	76.43 172	79.38 153	62.55 60	61.66 243	83.83 142	45.60 154	79.15 232	41.64 284	60.88 312	85.00 120

EPNet		73.09 80	72.16 84	75.90 71	75.95 213	56.28 113	83.05 58	72.39 253	66.53 12	65.27 194	87.00 78	50.40 99	85.47 105	62.48 127	86.32 58	85.94 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		68.77 152	66.71 174	74.95 90	75.93 214	58.55 79	80.50 100	75.84 213	56.09 179	68.17 141	83.74 145	28.50 313	82.98 154	65.67 99	65.91 276	83.33 176

RRT_test8_iter05		68.17 169	66.86 173	72.07 162	75.81 215	46.33 245	76.41 173	81.81 100	56.43 170	66.52 171	81.30 196	31.90 293	84.25 128	63.77 118	67.83 262	85.64 98

SixPastTwentyTwo		61.65 251	58.80 259	70.20 202	75.80 216	47.22 238	75.59 188	69.68 271	54.61 208	54.11 307	79.26 241	27.07 324	82.96 155	43.27 267	49.79 342	80.41 230

baseline		74.61 62	74.70 57	74.34 108	75.70 217	49.99 202	77.54 148	84.63 42	62.73 58	73.98 57	87.79 72	57.67 27	83.82 139	69.49 66	82.74 85	89.20 3

Baseline_NR-MVSNet		67.05 188	67.56 152	65.50 261	75.65 218	37.70 317	75.42 190	74.65 233	59.90 109	68.14 143	83.15 157	49.12 113	77.20 258	52.23 199	69.78 236	81.60 208

jajsoiax		68.25 165	66.45 178	73.66 126	75.62 219	55.49 130	80.82 95	78.51 170	52.33 230	64.33 212	84.11 135	28.28 315	81.81 183	63.48 120	70.62 219	83.67 166

mvs_tets		68.18 167	66.36 184	73.63 129	75.61 220	55.35 133	80.77 96	78.56 168	52.48 229	64.27 214	84.10 136	27.45 321	81.84 182	63.45 121	70.56 221	83.69 165

casdiffmvs		74.80 57	74.89 55	74.53 104	75.59 221	50.37 195	78.17 137	85.06 34	62.80 57	74.40 53	87.86 70	57.88 24	83.61 143	69.46 68	82.79 84	89.59 2

PVSNet	50.76 19	58.40 269	57.39 269	61.42 289	75.53 222	44.04 270	61.43 316	63.45 310	47.04 287	56.91 281	73.61 304	27.00 325	64.76 316	39.12 293	72.40 201	75.47 286

MVS		67.37 180	66.33 185	70.51 198	75.46 223	50.94 183	73.95 218	81.85 99	41.57 328	62.54 232	78.57 250	47.98 123	85.47 105	52.97 195	82.05 90	75.14 288

nrg030		72.96 81	73.01 78	72.84 148	75.41 224	50.24 196	80.02 105	82.89 85	58.36 139	74.44 52	86.73 82	58.90 19	80.83 204	65.84 97	74.46 169	87.44 39

thres200		62.20 244	61.16 246	65.34 264	75.38 225	39.99 298	69.60 276	69.29 277	55.64 191	61.87 241	76.99 267	37.07 246	78.96 239	31.28 336	73.28 186	77.06 270

TransMVSNet (Re)		64.72 219	64.33 208	65.87 258	75.22 226	38.56 310	74.66 208	75.08 230	58.90 129	61.79 242	82.63 163	51.18 91	78.07 247	43.63 265	55.87 328	80.99 222

MS-PatchMatch		62.42 241	61.46 241	65.31 265	75.21 227	52.10 171	72.05 246	74.05 240	46.41 290	57.42 280	74.36 298	34.35 265	77.57 255	45.62 248	73.67 177	66.26 341

IB-MVS	56.42 12	65.40 214	62.73 228	73.40 137	74.89 228	52.78 160	73.09 232	75.13 226	55.69 188	58.48 273	73.73 303	32.86 280	86.32 83	50.63 209	70.11 229	81.10 220
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
MVS_Test		72.45 88	72.46 83	72.42 159	74.88 229	48.50 223	76.28 176	83.14 80	59.40 120	72.46 84	84.68 123	55.66 41	81.12 196	65.98 96	79.66 117	87.63 31

CANet_DTU		68.18 167	67.71 151	69.59 213	74.83 230	46.24 247	78.66 128	76.85 201	59.60 115	63.45 220	82.09 181	35.25 255	77.41 256	59.88 148	78.76 134	85.14 115

tfpnnormal		62.47 240	61.63 239	64.99 267	74.81 231	39.01 306	71.22 257	73.72 243	55.22 197	60.21 251	80.09 224	41.26 208	76.98 262	30.02 340	68.09 259	78.97 251

Vis-MVSNet (Re-imp)		63.69 228	63.88 212	63.14 278	74.75 232	31.04 349	71.16 259	63.64 309	56.32 172	59.80 258	84.99 119	44.51 170	75.46 271	39.12 293	80.62 101	82.92 188

HY-MVS	56.14 13	64.55 223	63.89 211	66.55 245	74.73 233	41.02 294	69.96 274	74.43 234	49.29 260	61.66 243	80.92 204	47.43 134	76.68 264	44.91 256	71.69 209	81.94 204

COLMAP_ROB	52.97 17	61.27 256	58.81 258	68.64 226	74.63 234	52.51 165	78.42 135	73.30 246	49.92 257	50.96 323	81.51 192	23.06 339	79.40 224	31.63 332	65.85 277	74.01 304
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 249	61.35 242	63.46 274	74.58 235	31.48 348	61.42 317	58.14 330	58.71 132	53.02 317	79.55 235	43.07 182	76.80 263	45.69 246	77.96 141	82.11 203

test_djsdf		69.45 141	67.74 148	74.58 102	74.57 236	54.92 137	82.79 63	78.48 171	51.26 244	65.41 192	83.49 152	38.37 229	83.24 149	66.06 93	69.25 246	85.56 100

EI-MVSNet		69.27 143	68.44 141	71.73 167	74.47 237	49.39 213	75.20 195	78.45 174	59.60 115	69.16 128	76.51 276	51.29 89	82.50 171	59.86 150	71.45 213	83.30 177

CVMVSNet		59.63 264	59.14 257	61.08 292	74.47 237	38.84 308	75.20 195	68.74 281	31.15 348	58.24 274	76.51 276	32.39 290	68.58 301	49.77 214	65.84 278	75.81 282

IterMVS-LS		69.22 145	68.48 138	71.43 176	74.44 239	49.40 212	76.23 177	77.55 190	59.60 115	65.85 186	81.59 191	51.28 90	81.58 188	59.87 149	69.90 234	83.30 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 149	67.47 157	72.82 150	74.40 240	56.87 108	70.59 267	79.04 156	54.77 207	66.99 164	86.01 104	39.57 217	78.21 245	62.54 126	73.33 185	83.37 175

XVG-OURS		68.76 153	67.37 160	72.90 147	74.32 241	57.22 98	70.09 273	78.81 160	55.24 196	67.79 154	85.81 111	36.54 250	78.28 244	62.04 131	75.74 163	83.19 182

OpenMVS	61.03 9	68.85 148	67.56 152	72.70 152	74.26 242	53.99 143	81.21 92	81.34 115	52.70 226	62.75 227	85.55 114	38.86 225	84.14 130	48.41 227	83.01 76	79.97 237

MIMVSNet		57.35 276	57.07 272	58.22 303	74.21 243	37.18 318	62.46 312	60.88 323	48.88 264	55.29 294	75.99 284	31.68 294	62.04 324	31.87 327	72.35 202	75.43 287

SCA		60.49 258	58.38 263	66.80 241	74.14 244	48.06 228	63.35 309	63.23 312	49.13 262	59.33 264	72.10 310	37.45 238	74.27 277	44.17 258	62.57 301	78.05 258

thisisatest0515		65.83 206	63.50 219	72.82 150	73.75 245	49.50 211	71.32 255	73.12 249	49.39 259	63.82 218	76.50 278	34.95 259	84.84 119	53.20 194	75.49 166	84.13 146

K. test v3		60.47 259	57.11 271	70.56 196	73.74 246	48.22 226	75.10 199	62.55 316	58.27 141	53.62 312	76.31 280	27.81 318	81.59 187	47.42 230	39.18 353	81.88 206

v10		70.21 123	69.02 130	73.81 117	73.51 247	50.92 185	78.74 126	81.39 110	60.05 107	66.39 175	81.83 185	47.58 130	85.41 108	62.80 124	68.86 253	85.09 118

v1144		70.42 119	69.31 125	73.76 120	73.22 248	50.64 190	77.83 141	81.43 109	58.58 133	69.40 122	81.16 197	47.53 131	85.29 110	64.01 113	70.64 218	85.34 110

v1192		69.97 128	68.68 135	73.85 115	73.19 249	50.94 183	77.68 144	81.36 111	57.51 151	68.95 130	80.85 207	45.28 163	85.33 109	62.97 123	70.37 224	85.27 113

v8		70.33 121	69.28 126	73.49 132	73.15 250	50.22 197	78.62 129	80.78 131	60.79 86	66.45 174	82.11 180	49.35 106	84.98 113	63.58 119	68.71 254	85.28 112

v144192		69.71 131	68.51 137	73.33 139	73.10 251	50.13 199	77.54 148	80.64 132	56.65 161	68.57 134	80.55 212	46.87 145	84.96 115	62.98 122	69.66 240	84.89 124

v1921920		69.47 140	68.17 145	73.36 138	73.06 252	50.10 200	77.39 151	80.56 133	56.58 168	68.59 132	80.37 214	44.72 169	84.98 113	62.47 128	69.82 235	85.00 120

PatchmatchNet		59.84 262	58.24 264	64.65 269	73.05 253	46.70 242	69.42 278	62.18 318	47.55 279	58.88 267	71.96 312	34.49 263	69.16 298	42.99 271	63.60 293	78.07 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 144	67.91 147	73.25 143	73.02 254	49.82 204	77.21 157	80.54 134	56.43 170	68.34 138	80.51 213	43.33 181	84.99 111	62.03 132	69.77 238	84.95 123

Fast-Effi-MVS+-dtu		67.37 180	65.33 202	73.48 133	72.94 255	57.78 90	77.47 150	76.88 200	57.60 150	61.97 239	76.85 270	39.31 219	80.49 212	54.72 179	70.28 227	82.17 202

EPNet_dtu		61.90 247	61.97 236	61.68 287	72.89 256	39.78 300	75.85 186	65.62 297	55.09 200	54.56 303	79.36 239	37.59 237	67.02 308	39.80 291	76.95 152	78.25 255
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 245	60.10 253	67.99 232	72.79 257	43.86 271	71.05 263	66.85 291	43.14 319	62.77 225	75.39 291	38.32 230	80.80 205	41.69 281	68.88 252	79.32 247

MDTV_nov1_ep13			57.00 273		72.73 258	38.26 312	65.02 304	64.73 304	44.74 302	55.46 290	72.48 308	32.61 288	70.47 291	37.47 300	67.75 264

MSDG		61.81 250	59.23 256	69.55 216	72.64 259	52.63 162	70.45 270	75.81 214	51.38 241	53.70 310	76.11 281	29.52 306	81.08 199	37.70 299	65.79 279	74.93 293

gg-mvs-nofinetune		57.86 274	56.43 278	62.18 284	72.62 260	35.35 333	66.57 289	56.33 337	50.65 249	57.64 278	57.10 349	30.65 297	76.36 267	37.38 301	78.88 130	74.82 295

v2v482		70.50 117	69.45 124	73.66 126	72.62 260	50.03 201	77.58 145	80.51 135	59.90 109	69.52 118	82.14 179	47.53 131	84.88 118	65.07 106	70.17 228	86.09 78

baseline2		63.42 230	61.26 244	69.89 210	72.55 262	47.62 234	71.54 251	68.38 283	50.11 253	54.82 299	75.55 289	43.06 183	80.96 200	48.13 228	67.16 268	81.11 219

v7n		69.01 147	67.36 161	73.98 113	72.51 263	52.65 161	78.54 132	81.30 116	60.26 104	62.67 228	81.62 188	43.61 178	84.49 124	57.01 161	68.70 255	84.79 128

pm-mvs1		65.24 215	64.97 206	66.04 254	72.38 264	39.40 304	72.62 238	75.63 216	55.53 192	62.35 238	83.18 156	47.45 133	76.47 266	49.06 222	66.54 272	82.24 199

XVG-ACMP-BASELINE		64.36 224	62.23 233	70.74 193	72.35 265	52.45 167	70.80 266	78.45 174	53.84 217	59.87 256	81.10 199	16.24 351	79.32 226	55.64 173	71.76 208	80.47 228

WTY-MVS		59.75 263	60.39 251	57.85 307	72.32 266	37.83 315	61.05 321	64.18 307	45.95 297	61.91 240	79.11 243	47.01 143	60.88 327	42.50 275	69.49 242	74.83 294

tpm cat1		59.25 265	56.95 274	66.15 251	72.19 267	46.96 240	68.09 284	65.76 296	40.03 336	57.81 277	70.56 320	38.32 230	74.51 275	38.26 297	61.50 309	77.00 272

mvs_anonymous		68.03 170	67.51 156	69.59 213	72.08 268	44.57 266	71.99 247	75.23 223	51.67 234	67.06 163	82.57 165	54.68 51	77.94 248	56.56 163	75.71 164	86.26 75

OurMVSNet-221017-0		61.37 255	58.63 261	69.61 212	72.05 269	48.06 228	73.93 221	72.51 252	47.23 285	54.74 300	80.92 204	21.49 346	81.24 194	48.57 226	56.22 327	79.53 245

IterMVS-SCA-FT		62.49 239	61.52 240	65.40 263	71.99 270	50.80 188	71.15 260	69.63 272	45.71 298	60.61 249	77.93 255	37.45 238	65.99 313	55.67 172	63.50 294	79.42 246

DWT-MVSNet_test		61.90 247	59.93 254	67.83 233	71.98 271	46.09 250	71.03 264	69.71 269	50.09 254	58.51 272	70.62 319	30.21 302	77.63 253	49.28 220	67.91 260	79.78 242

CostFormer		64.04 225	62.51 229	68.61 227	71.88 272	45.77 252	71.30 256	70.60 265	47.55 279	64.31 213	76.61 274	41.63 198	79.62 222	49.74 215	69.00 251	80.42 229

1314		64.61 222	63.21 222	68.80 224	71.87 273	47.46 236	73.95 218	78.39 179	42.88 321	59.97 254	76.60 275	38.11 233	79.39 225	54.84 178	72.32 203	79.55 244

tpm		57.34 277	58.16 265	54.86 317	71.80 274	34.77 335	67.47 288	56.04 340	48.20 272	60.10 252	76.92 268	37.17 243	53.41 351	40.76 286	65.01 283	76.40 278

eth_miper_zixun_eth		67.63 176	66.28 188	71.67 169	71.60 275	48.33 225	73.68 226	77.88 183	55.80 186	65.91 183	78.62 249	47.35 137	82.88 159	59.45 152	66.25 274	83.81 156

pmmvs4		61.48 254	59.39 255	67.76 234	71.57 276	53.86 144	71.42 252	65.34 299	44.20 309	59.46 260	77.92 256	35.90 251	74.71 274	43.87 263	64.87 284	74.71 297

AllTest		57.08 279	54.65 288	64.39 270	71.44 277	49.03 214	69.92 275	67.30 286	45.97 295	47.16 336	79.77 229	17.47 348	67.56 305	33.65 321	59.16 318	76.57 276

TestCases				64.39 270	71.44 277	49.03 214		67.30 286	45.97 295	47.16 336	79.77 229	17.47 348	67.56 305	33.65 321	59.16 318	76.57 276

lessismore_v0				69.91 208	71.42 279	47.80 230		50.90 348		50.39 329	75.56 288	27.43 322	81.33 191	45.91 244	34.10 355	80.59 227

gm-plane-assit					71.40 280	41.72 291			48.85 265		73.31 305		82.48 173	48.90 223

GG-mvs-BLEND				62.34 283	71.36 281	37.04 322	69.20 280	57.33 334		54.73 301	65.48 341	30.37 299	77.82 250	34.82 317	74.93 168	72.17 322

FMVSNet5		55.86 286	54.93 286	58.66 302	71.05 282	36.35 328	64.18 308	62.48 317	46.76 288	50.66 328	74.73 296	25.80 331	64.04 318	33.11 323	65.57 280	75.59 285

cl_fuxian		68.33 163	67.56 152	70.62 195	70.87 283	46.21 248	74.47 211	78.80 161	56.22 176	66.19 178	78.53 251	51.88 83	81.40 190	62.08 129	69.04 249	84.25 141

GA-MVS		65.53 211	63.70 215	71.02 189	70.87 283	48.10 227	70.48 269	74.40 235	56.69 160	64.70 208	76.77 271	33.66 272	81.10 197	55.42 175	70.32 226	83.87 154

bset_n11_16_dypcd		65.57 210	63.69 216	71.19 183	70.84 285	51.79 176	71.37 253	70.48 266	53.33 221	65.19 201	76.41 279	31.46 295	81.76 184	65.12 104	69.04 249	80.01 236

pmmvs6		63.69 228	62.82 227	66.27 249	70.63 286	39.27 305	73.13 231	75.47 219	52.69 227	59.75 259	82.30 173	39.71 216	77.03 261	47.40 231	64.35 288	82.53 194

miper_ehance_all_eth		68.03 170	67.24 168	70.40 199	70.54 287	46.21 248	73.98 216	78.68 165	55.07 202	66.05 180	77.80 260	52.16 81	81.31 192	61.53 138	69.32 243	83.67 166

OpenMVS_ROB	52.78 18	60.03 260	58.14 266	65.69 260	70.47 288	44.82 261	75.33 191	70.86 263	45.04 300	56.06 286	76.00 282	26.89 326	79.65 220	35.36 316	67.29 266	72.60 313

v148		68.24 166	67.19 169	71.40 177	70.43 289	47.77 232	75.76 187	77.03 199	58.91 128	67.36 159	80.10 223	48.60 119	81.89 180	60.01 147	66.52 273	84.53 133

XXY-MVS		60.68 257	61.67 238	57.70 309	70.43 289	38.45 311	64.19 307	66.47 292	48.05 274	63.22 221	80.86 206	49.28 108	60.47 328	45.25 255	67.28 267	74.19 302

MVSTER		67.16 186	65.58 199	71.88 164	70.37 291	49.70 206	70.25 272	78.45 174	51.52 238	69.16 128	80.37 214	38.45 228	82.50 171	60.19 145	71.46 212	83.44 174

cl-mvsnet____		67.18 184	66.26 189	69.94 206	70.20 292	45.74 253	73.30 228	76.83 202	55.10 198	65.27 194	79.57 234	47.39 135	80.53 210	59.41 154	69.22 247	83.53 172

cl-mvsnet1		67.18 184	66.26 189	69.94 206	70.20 292	45.74 253	73.29 229	76.83 202	55.10 198	65.27 194	79.58 233	47.38 136	80.53 210	59.43 153	69.22 247	83.54 171

tpmvs		58.47 268	56.95 274	63.03 280	70.20 292	41.21 293	67.90 286	67.23 289	49.62 258	54.73 301	70.84 317	34.14 266	76.24 269	36.64 308	61.29 310	71.64 324

anonymousdsp		67.00 190	64.82 207	73.57 131	70.09 295	56.13 116	76.35 174	77.35 195	48.43 269	64.99 205	80.84 208	33.01 278	80.34 213	64.66 108	67.64 265	84.23 142

MVS_0304		58.51 267	57.36 270	61.96 286	70.04 296	41.83 287	69.40 279	65.46 298	50.73 247	53.30 316	74.06 301	22.65 340	70.18 296	42.16 277	68.44 256	73.86 306

MIMVSNet1		55.17 292	54.31 293	57.77 308	70.03 297	32.01 346	65.68 296	64.81 302	49.19 261	46.75 339	76.00 282	25.53 333	64.04 318	28.65 344	62.13 304	77.26 268

CR-MVSNet		59.91 261	57.90 268	65.96 255	69.96 298	52.07 172	65.31 301	63.15 313	42.48 323	59.36 261	74.84 294	35.83 252	70.75 290	45.50 250	64.65 286	75.06 289

RPMNet		61.53 252	58.42 262	70.86 190	69.96 298	52.07 172	65.31 301	81.36 111	43.20 318	59.36 261	70.15 325	35.37 254	85.47 105	36.42 311	64.65 286	75.06 289

cl-mvsnet2		67.47 179	66.45 178	70.54 197	69.85 300	46.49 243	73.85 223	77.35 195	55.07 202	65.51 190	77.92 256	47.64 129	81.10 197	61.58 137	69.32 243	84.01 149

Anonymous20231206		55.10 293	55.30 285	54.48 319	69.81 301	33.94 340	62.91 311	62.13 319	41.08 330	55.18 295	75.65 287	32.75 284	56.59 343	30.32 339	67.86 261	72.91 309

our_test_3		56.49 280	54.42 290	62.68 282	69.51 302	45.48 258	66.08 293	61.49 321	44.11 312	50.73 327	69.60 329	33.05 277	68.15 302	38.38 296	56.86 324	74.40 299

ppachtmachnet_test		58.06 273	55.38 284	66.10 253	69.51 302	48.99 217	68.01 285	66.13 295	44.50 306	54.05 308	70.74 318	32.09 292	72.34 283	36.68 307	56.71 326	76.99 274

diffmvs		70.69 113	70.43 108	71.46 173	69.45 304	48.95 218	72.93 233	78.46 173	57.27 153	71.69 92	83.97 140	51.48 88	77.92 249	70.70 60	77.95 142	87.53 35

IterMVS		62.79 238	61.27 243	67.35 238	69.37 305	52.04 174	71.17 258	68.24 284	52.63 228	59.82 257	76.91 269	37.32 240	72.36 282	52.80 196	63.19 297	77.66 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 187	66.09 191	70.17 203	69.21 306	45.98 251	72.85 235	78.41 177	51.38 241	65.65 188	75.98 285	51.17 92	81.25 193	60.82 140	69.32 243	83.29 179

Patchmtry		57.16 278	56.47 277	59.23 296	69.17 307	34.58 337	62.98 310	63.15 313	44.53 305	56.83 282	74.84 294	35.83 252	68.71 300	40.03 289	60.91 311	74.39 300

CL-MVSNet_2432*1600		61.53 252	60.94 248	63.30 276	68.95 308	36.93 323	67.60 287	72.80 251	55.67 189	59.95 255	76.63 272	45.01 166	72.22 285	39.74 292	62.09 305	80.74 226

V42		68.65 154	67.35 162	72.56 154	68.93 309	50.18 198	72.90 234	79.47 151	56.92 158	69.45 121	80.26 220	46.29 149	82.99 153	64.07 111	67.82 263	84.53 133

test-LLR		58.15 272	58.13 267	58.22 303	68.57 310	44.80 262	65.46 298	57.92 331	50.08 255	55.44 291	69.82 327	32.62 286	57.44 338	49.66 217	73.62 178	72.41 318

test-mter		56.42 282	55.82 281	58.22 303	68.57 310	44.80 262	65.46 298	57.92 331	39.94 337	55.44 291	69.82 327	21.92 343	57.44 338	49.66 217	73.62 178	72.41 318

MVS-HIRNet		45.52 318	44.48 321	48.65 333	68.49 312	34.05 339	59.41 326	44.50 357	27.03 352	37.96 353	50.47 355	26.16 330	64.10 317	26.74 349	59.52 316	47.82 352

dp		51.89 307	51.60 305	52.77 326	68.44 313	32.45 345	62.36 313	54.57 342	44.16 310	49.31 331	67.91 332	28.87 312	56.61 342	33.89 320	54.89 330	69.24 338

PatchT		53.17 303	53.44 300	52.33 328	68.29 314	25.34 359	58.21 328	54.41 343	44.46 307	54.56 303	69.05 330	33.32 275	60.94 326	36.93 303	61.76 308	70.73 330

Patchmatch-RL test		58.16 271	55.49 283	66.15 251	67.92 315	48.89 219	60.66 322	51.07 347	47.86 276	59.36 261	62.71 346	34.02 268	72.27 284	56.41 164	59.40 317	77.30 266

pmmvs-eth3d		58.81 266	56.31 279	66.30 248	67.61 316	52.42 168	72.30 243	64.76 303	43.55 315	54.94 298	74.19 300	28.95 310	72.60 281	43.31 266	57.21 323	73.88 305

PVSNet_0	43.31 20	47.46 317	45.64 320	52.92 325	67.60 317	44.65 264	54.06 340	54.64 341	41.59 327	46.15 340	58.75 348	30.99 296	58.66 334	32.18 325	24.81 356	55.46 350

CHOSEN 280x420		47.83 315	46.36 319	52.24 329	67.37 318	49.78 205	38.91 357	43.11 358	35.00 344	43.27 347	63.30 345	28.95 310	49.19 354	36.53 309	60.80 313	57.76 348

tpmrst		58.24 270	58.70 260	56.84 310	66.97 319	34.32 338	69.57 277	61.14 322	47.17 286	58.58 271	71.60 313	41.28 207	60.41 329	49.20 221	62.84 299	75.78 283

sss		56.17 285	56.57 276	54.96 316	66.93 320	36.32 330	57.94 329	61.69 320	41.67 326	58.64 270	75.32 292	38.72 226	56.25 344	42.04 279	66.19 275	72.31 321

TinyColmap		54.14 294	51.72 304	61.40 290	66.84 321	41.97 285	66.52 290	68.51 282	44.81 301	42.69 348	75.77 286	11.66 356	72.94 280	31.96 326	56.77 325	69.27 337

miper_lstm_enhance		62.03 246	60.88 249	65.49 262	66.71 322	46.25 246	56.29 335	75.70 215	50.68 248	61.27 246	75.48 290	40.21 212	68.03 303	56.31 165	65.25 282	82.18 200

TESTMET0.1,1		55.28 290	54.90 287	56.42 311	66.56 323	43.67 273	65.46 298	56.27 338	39.18 339	53.83 309	67.44 335	24.21 338	55.46 348	48.04 229	73.11 191	70.13 333

D2MVS		62.30 243	60.29 252	68.34 230	66.46 324	48.42 224	65.70 295	73.42 245	47.71 277	58.16 275	75.02 293	30.51 298	77.71 252	53.96 186	71.68 210	78.90 252

MDA-MVSNet-bldmvs		53.87 297	50.81 308	63.05 279	66.25 325	48.58 221	56.93 333	63.82 308	48.09 273	41.22 349	70.48 323	30.34 300	68.00 304	34.24 319	45.92 347	72.57 314

ITE_SJBPF				62.09 285	66.16 326	44.55 267		64.32 306	47.36 282	55.31 293	80.34 216	19.27 347	62.68 322	36.29 312	62.39 303	79.04 249

EPMVS		53.96 295	53.69 298	54.79 318	66.12 327	31.96 347	62.34 314	49.05 350	44.42 308	55.54 289	71.33 315	30.22 301	56.70 341	41.65 283	62.54 302	75.71 284

ADS-MVSNet2		51.33 309	48.76 314	59.07 299	66.02 328	44.60 265	50.90 344	59.76 325	36.90 340	50.74 325	66.18 339	26.38 327	63.11 320	27.17 346	54.76 331	69.50 335

ADS-MVSNet		48.48 314	47.77 316	50.63 331	66.02 328	29.92 351	50.90 344	50.87 349	36.90 340	50.74 325	66.18 339	26.38 327	52.47 352	27.17 346	54.76 331	69.50 335

EU-MVSNet		55.61 288	54.41 291	59.19 298	65.41 330	33.42 342	72.44 241	71.91 256	28.81 350	51.27 321	73.87 302	24.76 336	69.08 299	43.04 270	58.20 321	75.06 289

RPSCF		55.80 287	54.22 295	60.53 293	65.13 331	42.91 280	64.30 306	57.62 333	36.84 342	58.05 276	82.28 174	28.01 316	56.24 345	37.14 302	58.61 320	82.44 198

USDC		56.35 283	54.24 294	62.69 281	64.74 332	40.31 297	65.05 303	73.83 242	43.93 313	47.58 334	77.71 263	15.36 353	75.05 273	38.19 298	61.81 307	72.70 312

JIA-IIPM		51.56 308	47.68 318	63.21 277	64.61 333	50.73 189	47.71 349	58.77 328	42.90 320	48.46 333	51.72 352	24.97 335	70.24 295	36.06 313	53.89 334	68.64 339

Patchmatch-test		49.08 313	48.28 315	51.50 330	64.40 334	30.85 350	45.68 351	48.46 353	35.60 343	46.10 341	72.10 310	34.47 264	46.37 355	27.08 348	60.65 314	77.27 267

TDRefinement		53.44 301	50.72 309	61.60 288	64.31 335	46.96 240	70.89 265	65.27 301	41.78 324	44.61 344	77.98 253	11.52 357	66.36 311	28.57 345	51.59 338	71.49 325

N_pmnet		39.35 324	40.28 325	36.54 340	63.76 336	1.62 371	49.37 347	0.76 371	34.62 345	43.61 346	66.38 338	26.25 329	42.57 358	26.02 351	51.77 337	65.44 342

ambc				65.13 266	63.72 337	37.07 321	47.66 350	78.78 162		54.37 306	71.42 314	11.24 358	80.94 201	45.64 247	53.85 335	77.38 265

KD-MVS_2432*1600		53.45 299	51.50 306	59.30 294	62.82 338	37.14 319	55.33 336	71.79 257	47.34 283	55.09 296	70.52 321	21.91 344	70.45 292	35.72 314	42.97 350	70.31 331

miper_refine_blended		53.45 299	51.50 306	59.30 294	62.82 338	37.14 319	55.33 336	71.79 257	47.34 283	55.09 296	70.52 321	21.91 344	70.45 292	35.72 314	42.97 350	70.31 331

test0.0.03 1		53.32 302	53.59 299	52.50 327	62.81 340	29.45 352	59.51 324	54.11 344	50.08 255	54.40 305	74.31 299	32.62 286	55.92 346	30.50 338	63.95 291	72.15 323

PMMVS		53.96 295	53.26 301	56.04 312	62.60 341	50.92 185	61.17 320	56.09 339	32.81 346	53.51 314	66.84 337	34.04 267	59.93 331	44.14 260	68.18 258	57.27 349

PM-MVS		52.33 305	50.19 310	58.75 301	62.10 342	45.14 260	65.75 294	40.38 360	43.60 314	53.52 313	72.65 307	9.16 362	65.87 314	50.41 210	54.18 333	65.24 343

Gipuma		34.77 327	31.91 331	43.33 338	62.05 343	37.87 314	20.39 360	67.03 290	23.23 356	18.41 360	25.84 360	4.24 366	62.73 321	14.71 357	51.32 339	29.38 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 297	54.02 296	53.41 323	61.47 344	28.11 354	61.30 318	59.21 326	51.34 243	52.09 319	77.43 264	33.29 276	58.55 335	29.76 341	60.27 315	73.58 307

pmmvs5		56.47 281	55.68 282	58.86 300	61.41 345	36.71 325	66.37 291	62.75 315	40.38 334	53.70 310	76.62 273	34.56 261	67.05 307	40.02 290	65.27 281	72.83 311

MDA-MVSNet_test_wron		50.71 311	48.95 312	56.00 314	61.17 346	41.84 286	51.90 343	56.45 335	40.96 331	44.79 343	67.84 333	30.04 304	55.07 350	36.71 306	50.69 341	71.11 329

YYNet1		50.73 310	48.96 311	56.03 313	61.10 347	41.78 288	51.94 342	56.44 336	40.94 332	44.84 342	67.80 334	30.08 303	55.08 349	36.77 304	50.71 340	71.22 326

CMPMVS	42.80 21	57.81 275	55.97 280	63.32 275	60.98 348	47.38 237	64.66 305	69.50 274	32.06 347	46.83 338	77.80 260	29.50 307	71.36 287	48.68 224	73.75 176	71.21 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 312	48.87 313	53.66 321	60.97 349	33.67 341	57.62 331	64.56 305	39.47 338	47.38 335	64.02 344	27.47 320	59.32 332	34.69 318	43.68 349	67.98 340

Anonymous20240521		55.30 289	54.41 291	57.96 306	60.92 350	41.73 289	71.09 262	71.06 262	41.18 329	48.65 332	73.31 305	16.93 350	59.25 333	42.54 274	64.01 289	72.90 310

testgi		51.90 306	52.37 303	50.51 332	60.39 351	23.55 361	58.42 327	58.15 329	49.03 263	51.83 320	79.21 242	22.39 341	55.59 347	29.24 343	62.64 300	72.40 320

UnsupCasMVSNet_eth		53.16 304	52.47 302	55.23 315	59.45 352	33.39 343	59.43 325	69.13 278	45.98 294	50.35 330	72.32 309	29.30 309	58.26 336	42.02 280	44.30 348	74.05 303

new-patchmatchnet		47.56 316	47.73 317	47.06 334	58.81 353	9.37 367	48.78 348	59.21 326	43.28 316	44.22 345	68.66 331	25.67 332	57.20 340	31.57 334	49.35 343	74.62 298

FPMVS		42.18 321	41.11 324	45.39 335	58.03 354	41.01 295	49.50 346	53.81 346	30.07 349	33.71 354	64.03 342	11.69 355	52.08 353	14.01 358	55.11 329	43.09 354

DIV-MVS_2432*1600		55.22 291	53.89 297	59.21 297	57.80 355	27.47 355	57.75 330	74.32 236	47.38 281	50.90 324	70.00 326	28.45 314	70.30 294	40.44 287	57.92 322	79.87 239

new_pmnet		34.13 328	34.29 329	33.64 341	52.63 356	18.23 365	44.43 354	33.90 363	22.81 357	30.89 355	53.18 350	10.48 360	35.72 362	20.77 354	39.51 352	46.98 353

pmmvs3		44.92 319	41.95 323	53.86 320	52.58 357	43.55 274	62.11 315	46.90 356	26.05 354	40.63 350	60.19 347	11.08 359	57.91 337	31.83 331	46.15 346	60.11 345

DSMNet-mixed		39.30 325	38.72 326	41.03 339	51.22 358	19.66 363	45.53 352	31.35 364	15.83 361	39.80 352	67.42 336	22.19 342	45.13 356	22.43 352	52.69 336	58.31 347

LF4IMVS		42.95 320	42.26 322	45.04 336	48.30 359	32.50 344	54.80 338	48.49 352	28.03 351	40.51 351	70.16 324	9.24 361	43.89 357	31.63 332	49.18 344	58.72 346

wuyk23d		13.32 335	12.52 338	15.71 347	47.54 360	26.27 356	31.06 359	1.98 370	4.93 364	5.18 367	1.94 367	0.45 372	18.54 365	6.81 365	12.83 362	2.33 363

LCM-MVSNet		40.30 323	35.88 328	53.57 322	42.24 361	29.15 353	45.21 353	60.53 324	22.23 358	28.02 356	50.98 354	3.72 368	61.78 325	31.22 337	38.76 354	69.78 334

E-PMN		23.77 330	22.73 334	26.90 344	42.02 362	20.67 362	42.66 355	35.70 361	17.43 359	10.28 365	25.05 361	6.42 364	42.39 359	10.28 361	14.71 360	17.63 359

EMVS		22.97 331	21.84 335	26.36 345	40.20 363	19.53 364	41.95 356	34.64 362	17.09 360	9.73 366	22.83 362	7.29 363	42.22 360	9.18 363	13.66 361	17.32 360

ANet_high		41.38 322	37.47 327	53.11 324	39.73 364	24.45 360	56.94 332	69.69 270	47.65 278	26.04 357	52.32 351	12.44 354	62.38 323	21.80 353	10.61 363	72.49 315

PMMVS2		27.40 329	25.91 332	31.87 343	39.46 365	6.57 368	31.17 358	28.52 365	23.96 355	20.45 359	48.94 356	4.20 367	37.94 361	16.51 355	19.97 358	51.09 351

PMVS	28.69 22	36.22 326	33.29 330	45.02 337	36.82 366	35.98 332	54.68 339	48.74 351	26.31 353	21.02 358	51.61 353	2.88 370	60.10 330	9.99 362	47.58 345	38.99 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 332	17.77 337	32.34 342	34.34 367	25.44 358	16.11 361	24.11 366	11.19 362	13.22 362	31.92 358	1.58 371	30.95 363	10.47 360	17.03 359	40.62 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 348	17.97 368	10.91 366		10.60 369	7.46 363	11.07 364	28.36 359	3.28 369	11.29 366	8.01 364	9.74 365	13.89 361

test_method		19.68 333	18.10 336	24.41 346	13.68 369	3.11 370	12.06 363	42.37 359	2.00 365	11.97 363	36.38 357	5.77 365	29.35 364	15.06 356	23.65 357	40.76 355

tmp_tt		9.43 336	11.14 339	4.30 349	2.38 370	4.40 369	13.62 362	16.08 368	0.39 366	15.89 361	13.06 363	15.80 352	5.54 367	12.63 359	10.46 364	2.95 362

testmvs		4.52 339	6.03 342	0.01 351	0.01 371	0.00 373	53.86 341	0.00 372	0.01 367	0.04 368	0.27 368	0.00 374	0.00 368	0.04 366	0.00 366	0.03 365

test123		4.73 338	6.30 341	0.02 350	0.01 371	0.01 372	56.36 334	0.00 372	0.01 367	0.04 368	0.21 369	0.01 373	0.00 368	0.03 367	0.00 366	0.04 364

eth-test2					0.00 373

eth-test					0.00 373

uanet_test		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

cdsmvs_eth3d_5k		17.50 334	23.34 333	0.00 352	0.00 373	0.00 373	0.00 364	78.63 166	0.00 369	0.00 370	82.18 175	49.25 109	0.00 368	0.00 368	0.00 366	0.00 366

pcd_1.5k_mvsjas		3.92 340	5.23 343	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	47.05 140	0.00 368	0.00 368	0.00 366	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

ab-mvs-re		6.49 337	8.65 340	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	77.89 258	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 373	0.00 364	0.00 372	0.00 369	0.00 370	0.00 370	0.00 374	0.00 368	0.00 368	0.00 366	0.00 366

test_241102_TWO								86.73 14	64.18 33	84.26 4	91.84 6	65.19 4	90.83 2	78.63 12	90.70 5	87.65 30

test_0728_THIRD									65.04 20	83.82 6	92.00 3	64.69 8	90.75 5	79.48 4	90.63 6	88.09 15

GSMVS																78.05 258

sam_mvs1												34.74 260				78.05 258

sam_mvs												33.43 274

MTGPA								80.97 127

test_post1							68.67 282				3.64 365	32.39 290	69.49 297	44.17 258

test_post											3.55 366	33.90 269	66.52 310

patchmatchnet-post											64.03 342	34.50 262	74.27 277

MTMP							86.03 17	17.08 367

test9_res														75.28 28	88.31 33	83.81 156

agg_prior2														73.09 46	87.93 40	84.33 138

test_prior4						62.51 17	82.08 80

test_prior2							81.75 82		60.37 97	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

旧先验2							76.08 180		45.32 299	76.55 31			65.56 315	58.75 155

新几何2							76.12 178

无先验							79.66 114	74.30 237	48.40 270				80.78 206	53.62 188		79.03 250

原ACMM2							79.02 123

testdata2													72.18 286	46.95 237

segment_acmp												54.23 55

testdata1							72.65 236		60.50 92

plane_prior5								84.01 52					87.21 53	68.16 77	80.58 103	84.65 131

plane_prior4											86.10 101

plane_prior3						56.09 117			63.92 37	69.27 124

plane_prior2							84.22 38		64.52 26

plane_prior						56.31 111	83.58 53		63.19 47						80.48 106

n2								0.00 372

nn								0.00 372

door-mid								47.19 355

test11								83.47 67

door								47.60 354

HQP5-MVS						54.94 135

BP-MVS														67.04 88

HQP4-MVS										67.85 148			86.93 62			84.32 139

HQP3-MVS								83.90 56							80.35 109

HQP2-MVS												45.46 158

MDTV_nov1_ep13_2view						25.89 357	61.22 319		40.10 335	51.10 322		32.97 279		38.49 295		78.61 253

ACMMP++_ref															74.07 174

ACMMP++															72.16 205

Test By Simon												48.33 121

High-resolution multi-view benchmark