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 60	65.37 15	78.78 21	90.64 17	58.63 20	87.24 49	79.00 7	90.37 10	85.26 113

CNVR-MVS		79.84 8	79.97 8	79.45 7	87.90 2	62.17 20	84.37 34	85.03 33	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 25	53.93 214	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 33	64.26 30	83.82 6	92.00 3	64.82 6	90.75 5	78.66 10	90.61 7	85.45 104
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 42	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 27	62.12 68	78.65 22	90.29 31	55.76 39	89.31 15	73.55 43	87.22 45	85.84 85

region2R		77.67 30	77.18 33	79.15 14	86.76 14	62.95 8	86.29 12	84.16 48	62.81 56	73.30 69	90.58 19	49.90 99	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 46	62.82 54	73.55 67	90.56 20	49.80 101	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 44	62.82 54	73.96 58	90.50 22	53.20 68	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 44	61.61 78	73.96 58	90.50 22	53.20 68	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	56.91 157	74.91 44	88.19 66	59.15 18	87.68 44	73.67 41	87.45 43	86.57 61

ZD-MVS					86.64 19	60.38 47		82.70 85	57.95 144	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 23	62.49 62	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 93	70.73 102	76.40 65	86.57 22	57.99 86	81.15 93	82.96 79	57.03 154	66.78 164	85.56 112	44.50 169	88.11 34	51.77 201	80.23 110	83.10 184

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 23	63.47 5	86.02 18	83.55 63	63.89 38	73.60 66	90.60 18	54.85 48	86.72 66	77.20 18	88.06 37	85.74 93
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 89	62.90 51	71.77 90	90.26 32	46.61 145	86.55 74	71.71 52	85.66 61	84.97 121

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 25	60.81 40	85.52 26	84.36 43	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 19	63.32 43	75.08 39	90.47 25	53.96 57	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	58.56 133	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 52	64.55 24	72.17 87	90.01 43	47.95 122	88.01 37	71.55 54	86.74 54	86.37 66

X-MVStestdata		70.21 121	67.28 162	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 24	72.17 87	6.49 362	47.95 122	88.01 37	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		70.83 108	69.56 118	74.64 99	86.21 30	54.63 139	82.34 73	81.81 98	48.22 269	63.01 222	85.83 108	40.92 208	87.10 55	57.91 155	79.79 112	82.18 198

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 46	79.95 140	59.00 124	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

save fliter					86.17 32	61.30 31	83.98 46	79.66 145	59.00 124

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 38	61.98 74	73.06 76	88.88 61	53.72 62	89.06 19	68.27 72	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 70	83.74 59	61.71 76	72.45 86	90.34 29	48.48 118	88.13 32	72.32 48	86.85 52	85.78 87

zzz-MVS		77.61 31	77.36 31	78.35 34	86.08 36	63.57 2	83.37 55	80.97 125	65.13 18	75.77 34	90.88 13	48.63 114	86.66 68	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	74.92 201	80.97 125	65.13 18	75.77 34	90.88 13	48.63 114	86.66 68	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 29	63.56 42	74.29 55	90.03 41	52.56 71	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 66	62.44 64	72.68 80	90.50 22	48.18 120	87.34 48	73.59 42	85.71 60	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 90	59.99 107	75.10 38	90.35 27	47.66 126	86.52 75	71.64 53	82.99 75	84.47 136

新几何1				70.76 190	85.66 41	61.13 35		66.43 292	44.68 301	70.29 101	86.64 86	41.29 204	75.23 270	49.72 214	81.75 93	75.93 279

MG-MVS		73.96 70	73.89 66	74.16 111	85.65 42	49.69 207	81.59 87	81.29 115	61.45 79	71.05 96	88.11 67	51.77 83	87.73 43	61.05 137	83.09 73	85.05 118

1121		68.53 158	67.16 168	72.63 151	85.64 43	61.14 34	73.95 216	66.46 291	44.61 302	70.28 102	86.68 85	41.42 202	80.78 204	53.62 186	81.79 89	75.97 277

TEST9					85.58 44	61.59 27	81.62 85	81.26 116	55.65 188	74.93 42	88.81 62	53.70 63	84.68 118

train_agg		76.27 44	76.15 41	76.64 62	85.58 44	61.59 27	81.62 85	81.26 116	55.86 180	74.93 42	88.81 62	53.70 63	84.68 118	75.24 29	88.33 31	83.65 168

test1172		75.36 53	74.81 56	77.02 55	85.47 46	60.79 42	83.94 49	81.63 102	59.52 118	74.66 51	90.18 34	44.74 165	85.84 91	70.63 60	82.52 84	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 30	62.88 52	78.10 24	91.26 11	52.51 72	88.39 26	79.34 5	90.52 9	86.78 58

test_8					85.40 48	60.96 37	81.54 88	81.18 119	55.86 180	74.81 45	88.80 64	53.70 63	84.45 123

原ACMM1				74.69 94	85.39 49	59.40 59		83.42 67	51.47 238	70.27 103	86.61 88	48.61 116	86.51 76	53.85 185	87.96 39	78.16 254

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 50	59.14 65	81.31 91	84.02 49	56.32 170	74.05 56	88.98 59	53.34 67	87.92 39	69.23 69	88.42 29	87.59 33

ACMMP		76.02 46	75.33 49	78.07 38	85.20 51	61.91 23	85.49 28	84.44 41	63.04 48	69.80 114	89.74 49	45.43 158	87.16 53	72.01 51	82.87 80	85.14 114
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	81.04 122	56.14 176	74.68 48	88.90 60	53.91 59	84.04 130	75.01 30	87.92 41	83.16 183

agg_prior					85.04 52	59.96 51		81.04 122		74.68 48			84.04 130

HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 54	60.81 40	82.91 62	85.08 31	62.57 59	73.09 75	89.97 44	50.90 95	87.48 47	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 26	62.15 67	76.41 32	91.51 9	52.47 74	86.78 65	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 58	59.34 121	79.37 15	89.76 48	59.84 13	87.62 46	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		69.99 125	68.66 134	73.97 114	84.94 57	57.83 88	82.63 68	78.71 161	56.28 172	64.34 209	84.14 134	41.57 197	87.06 58	46.45 237	78.88 128	77.02 269

DP-MVS		65.68 205	63.66 215	71.75 164	84.93 58	56.87 108	80.74 97	73.16 246	53.06 220	59.09 263	82.35 169	36.79 247	85.94 89	32.82 322	69.96 230	72.45 314

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 28	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 80	72.08 84	74.87 92	84.88 60	61.41 29	84.15 41	77.86 182	55.27 193	67.51 156	88.08 69	41.93 191	81.85 179	69.04 71	80.01 111	81.35 212

test12				77.76 43	84.52 61	58.41 80		83.36 70		72.93 78		54.61 51	88.05 36		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 56	60.37 96	79.89 14	89.38 53	54.97 45	85.58 97	76.12 26	84.94 64	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 73	76.80 58	84.45 63	59.04 69	83.65 52	81.05 121	60.15 104	70.43 99	89.84 47	41.09 207	85.59 96	67.61 81	82.90 79	85.77 90

test_prior3		76.89 39	76.96 34	76.69 59	84.20 64	57.27 96	81.75 82	84.88 36	60.37 96	75.01 40	89.06 56	56.22 35	86.43 78	72.19 49	88.96 24	86.38 63

test_prior				76.69 59	84.20 64	57.27 96		84.88 36					86.43 78			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	84.83 120	60.76 12	86.56 73	67.86 78	87.87 42	86.06 79

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 77	72.93 77	73.76 119	83.58 68	51.66 177	78.75 123	77.66 186	67.75 4	72.61 82	89.42 51	49.82 100	83.29 146	53.61 188	83.14 72	86.32 71

SR-MVS-dyc-post		74.57 63	73.90 65	76.58 63	83.49 69	59.87 54	84.29 35	81.36 109	58.07 141	73.14 72	90.07 36	44.74 165	85.84 91	68.20 73	81.76 91	84.03 147

RE-MVS-def			73.71 69		83.49 69	59.87 54	84.29 35	81.36 109	58.07 141	73.14 72	90.07 36	43.06 181		68.20 73	81.76 91	84.03 147

LFMVS		71.78 96	71.59 87	72.32 158	83.40 71	46.38 242	79.75 112	71.08 258	64.18 33	72.80 79	88.64 65	42.58 184	83.72 138	57.41 158	84.49 67	86.86 53

test222					83.14 72	58.68 76	72.57 237	63.45 308	41.78 322	67.56 155	86.12 99	37.13 242			78.73 133	74.98 290

9.14			78.75 15		83.10 73		84.15 41	88.26 2	59.90 108	78.57 23	90.36 26	57.51 28	86.86 62	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 96	79.14 18	90.35 27	57.76 25	87.00 59	77.16 19	89.90 14	87.97 18

旧先验1					83.04 75	53.15 155		67.52 283			87.85 71	44.08 172			80.76 98	78.03 259

MSLP-MVS++		73.77 73	73.47 72	74.66 97	83.02 76	59.29 62	82.30 77	81.88 95	59.34 121	71.59 93	86.83 79	45.94 149	83.65 140	65.09 103	85.22 62	81.06 219

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 20	66.69 10	78.03 27	90.98 12	54.26 53	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 73	75.69 78	83.01 77	60.63 44	77.29 153	78.40 176	61.18 83	70.58 98	85.97 104	54.18 55	84.00 134	67.52 82	82.98 77	82.45 195

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 75	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

VDDNet		71.81 95	71.33 94	73.26 140	82.80 80	47.60 233	78.74 124	75.27 219	59.59 117	72.94 77	89.40 52	41.51 201	83.91 135	58.75 153	82.99 75	88.26 8

abl_6		74.34 65	73.50 70	76.86 57	82.43 81	60.16 49	83.48 54	81.86 96	58.81 128	73.95 60	89.86 46	41.87 192	86.62 70	67.98 77	81.23 96	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	67.80 151	89.24 55	42.03 189	89.38 14	64.07 109	86.50 57	89.69 1

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 61	82.20 83	58.24 83	83.67 51	83.29 73	58.41 135	73.71 64	90.14 35	45.62 151	85.99 86	69.64 65	82.85 81	85.78 87

ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 44	82.10 84	58.65 77	82.72 66	87.55 5	58.33 138	78.05 26	90.06 38	58.35 21	87.65 45	76.15 25	89.86 15	86.82 55

PVSNet_Blended_VisFu		71.45 102	70.39 107	74.65 98	82.01 85	58.82 74	79.93 108	80.35 136	55.09 198	65.82 185	82.16 176	49.17 108	82.64 167	60.34 142	78.62 135	82.50 194

TSAR-MVS + GP.		74.90 55	74.15 63	77.17 53	82.00 86	58.77 75	81.80 81	78.57 165	58.58 131	74.32 54	84.51 130	55.94 38	87.22 50	67.11 85	84.48 68	85.52 100

hse-mvs3		72.71 82	71.49 89	76.40 65	81.99 87	59.58 57	76.92 162	76.74 202	60.40 93	74.81 45	85.95 105	45.54 154	85.76 94	70.41 61	70.61 218	83.86 155

API-MVS		72.17 91	71.41 91	74.45 106	81.95 88	57.22 98	84.03 44	80.38 135	59.89 111	68.40 134	82.33 170	49.64 102	87.83 42	51.87 199	84.16 70	78.30 252

MAR-MVS		71.51 100	70.15 111	75.60 81	81.84 89	59.39 60	81.38 90	82.90 82	54.90 204	68.08 143	78.70 243	47.73 124	85.51 100	51.68 203	84.17 69	81.88 204
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 83	71.80 85	75.13 88	81.72 90	53.42 152	79.91 109	83.28 74	59.14 123	66.31 175	85.90 106	51.86 82	86.06 83	57.45 157	80.62 99	85.91 83

test_part1		74.74 59	74.42 60	75.70 77	81.69 91	51.26 179	83.98 46	87.05 7	65.31 16	73.10 74	86.20 97	53.94 58	88.06 35	65.32 100	73.17 188	87.77 26

VDD-MVS		72.50 84	72.09 83	73.75 121	81.58 92	49.69 207	77.76 141	77.63 187	63.21 46	73.21 70	89.02 58	42.14 188	83.32 145	61.72 132	82.50 85	88.25 9

PS-MVSNAJ		70.51 114	69.70 117	72.93 144	81.52 93	55.79 123	74.92 201	79.00 155	55.04 202	69.88 112	78.66 244	47.05 138	82.19 174	61.61 133	79.58 116	80.83 222

testdata				64.66 266	81.52 93	52.93 158		65.29 298	46.09 291	73.88 62	87.46 74	38.08 232	66.26 310	53.31 191	78.48 136	74.78 294

CHOSEN 1792x2688		65.08 216	62.84 224	71.82 163	81.49 95	56.26 114	66.32 290	74.20 237	40.53 331	63.16 221	78.65 245	41.30 203	77.80 249	45.80 243	74.09 171	81.40 209

HQP_MVS		74.31 66	73.73 68	76.06 69	81.41 96	56.31 111	84.22 38	84.01 50	64.52 26	69.27 122	86.10 100	45.26 162	87.21 51	68.16 75	80.58 101	84.65 131

plane_prior7					81.41 96	55.96 120

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 98	59.16 63	79.94 107	85.71 24	56.59 165	72.46 84	86.76 80	56.89 29	87.86 41	66.36 90	88.91 26	83.64 169

CANet		76.46 42	75.93 44	78.06 39	81.29 99	57.53 93	82.35 72	83.31 72	67.78 3	70.09 104	86.34 95	54.92 46	88.90 21	72.68 47	84.55 66	87.76 27

Vis-MVSNet		72.18 90	71.37 93	74.61 100	81.29 99	55.41 132	80.90 94	78.28 178	60.73 88	69.23 125	88.09 68	44.36 171	82.65 166	57.68 156	81.75 93	85.77 90
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		70.52 113	69.75 115	72.84 146	81.21 102	55.63 127	75.11 195	78.92 156	54.92 203	69.96 111	79.68 230	47.00 142	82.09 176	61.60 134	79.37 119	80.81 223

plane_prior6					81.20 103	56.24 115						45.26 162

PAPR		71.72 98	70.82 101	74.41 107	81.20 103	51.17 180	79.55 116	83.33 71	55.81 183	66.93 163	84.61 126	50.95 93	86.06 83	55.79 168	79.20 124	86.00 80

PLC	56.13 14	65.09 215	63.21 220	70.72 192	81.04 105	54.87 138	78.57 128	77.47 189	48.51 265	55.71 286	81.89 181	33.71 268	79.71 217	41.66 280	70.37 222	77.58 261
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					85.54 114

OPM-MVS		74.73 60	74.25 62	76.19 68	80.81 107	59.01 70	82.60 69	83.64 61	63.74 40	72.52 83	87.49 73	47.18 136	85.88 90	69.47 67	80.78 97	83.66 167

HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 146

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 146

HQP-MVS		73.45 74	72.80 78	75.40 83	80.66 108	54.94 135	82.31 74	83.90 54	62.10 69	67.85 146	85.54 114	45.46 156	86.93 60	67.04 86	80.35 107	84.32 139

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 111	55.91 122	84.28 37	85.78 21	56.08 178	73.41 68	86.58 90	50.94 94	88.54 24	70.79 58	89.71 17	87.79 25

ACMM	61.98 7	70.80 110	69.73 116	74.02 112	80.59 112	58.59 78	82.68 67	82.02 94	55.46 191	67.18 160	84.39 132	38.51 225	83.17 149	60.65 139	76.10 158	80.30 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-1		75.47 51	74.93 53	77.09 54	80.43 113	57.70 91	79.50 117	82.13 91	67.84 1	75.73 36	80.75 208	56.50 32	86.07 82	71.07 57	80.38 105	87.50 36

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 113	58.32 82	79.50 117	82.92 80	67.84 1	75.94 33	80.75 208	55.73 40	86.80 63	71.44 56	80.38 105	87.50 36

Anonymous20231211		69.28 140	68.47 137	71.73 165	80.28 115	47.18 237	79.98 106	82.37 88	54.61 206	67.24 159	84.01 138	39.43 216	82.41 172	55.45 172	72.83 192	85.62 98

ACMP	63.53 6	72.30 88	71.20 97	75.59 82	80.28 115	57.54 92	82.74 65	82.84 84	60.58 90	65.24 196	86.18 98	39.25 218	86.03 85	66.95 88	76.79 154	83.22 178
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 81	71.74 86	75.76 73	80.22 117	57.51 94	82.55 70	83.40 68	61.32 80	66.67 167	87.33 76	39.15 220	86.59 71	67.70 79	77.30 147	83.19 180

LGP-MVS_train				75.76 73	80.22 117	57.51 94		83.40 68	61.32 80	66.67 167	87.33 76	39.15 220	86.59 71	67.70 79	77.30 147	83.19 180

WR-MVS		68.47 159	68.47 137	68.44 227	80.20 119	39.84 297	73.75 223	76.07 208	64.68 23	68.11 142	83.63 147	50.39 98	79.14 231	49.78 211	69.66 238	86.34 68

Anonymous20240529		69.91 127	69.02 128	72.56 152	80.19 120	47.65 231	77.56 145	80.99 124	55.45 192	69.88 112	86.76 80	39.24 219	82.18 175	54.04 182	77.10 149	87.85 23

Anonymous202405211		66.84 191	65.99 190	69.40 215	80.19 120	42.21 282	71.11 259	71.31 257	58.80 129	67.90 144	86.39 94	29.83 303	79.65 218	49.60 217	78.78 131	86.33 69

BH-RMVSNet		68.81 147	67.42 156	72.97 143	80.11 122	52.53 164	74.26 210	76.29 205	58.48 134	68.38 135	84.20 133	42.59 183	83.83 136	46.53 236	75.91 159	82.56 191

test_0402		63.25 232	61.01 245	69.96 203	80.00 123	54.37 141	76.86 164	72.02 253	54.58 208	58.71 266	80.79 207	35.00 256	84.36 124	26.41 348	64.71 283	71.15 326

HyFIR lowres test		65.67 206	63.01 222	73.67 124	79.97 124	55.65 126	69.07 279	75.52 216	42.68 320	63.53 217	77.95 252	40.43 209	81.64 183	46.01 241	71.91 205	83.73 163

EIA-MVS		71.78 96	70.60 103	75.30 86	79.85 125	53.54 149	77.27 154	83.26 75	57.92 145	66.49 170	79.39 236	52.07 80	86.69 67	60.05 144	79.14 126	85.66 95

Regformer-3		73.89 71	73.28 75	75.71 76	79.75 126	55.48 131	78.54 130	79.93 141	66.58 11	73.62 65	80.30 216	54.87 47	84.54 121	69.09 70	76.84 152	87.10 49

Regformer-4		74.25 68	73.48 71	76.57 64	79.75 126	56.54 110	78.54 130	81.49 106	66.93 7	73.90 61	80.30 216	53.84 61	85.98 87	69.76 64	76.84 152	87.17 46

BH-untuned		68.27 162	67.29 161	71.21 180	79.74 128	53.22 154	76.06 179	77.46 191	57.19 152	66.10 177	81.61 187	45.37 160	83.50 143	45.42 251	76.68 156	76.91 273

VNet		69.68 132	70.19 110	68.16 229	79.73 129	41.63 290	70.53 266	77.38 192	60.37 96	70.69 97	86.63 87	51.08 91	77.09 258	53.61 188	81.69 95	85.75 92

LS3D		64.71 218	62.50 228	71.34 178	79.72 130	55.71 124	79.82 110	74.72 230	48.50 266	56.62 281	84.62 125	33.59 271	82.34 173	29.65 340	75.23 165	75.97 277

hse-mvs2		71.04 105	69.86 114	74.60 101	79.58 131	57.12 105	73.96 215	75.25 220	60.40 93	74.81 45	81.95 180	45.54 154	82.90 155	70.41 61	66.83 268	83.77 161

GeoE		71.01 106	70.15 111	73.60 129	79.57 132	52.17 170	78.93 122	78.12 179	58.02 143	67.76 154	83.87 141	52.36 76	82.72 164	56.90 160	75.79 160	85.92 82

AUN-MVS		68.45 160	66.41 180	74.57 103	79.53 133	57.08 106	73.93 219	75.23 221	54.44 211	66.69 166	81.85 182	37.10 243	82.89 156	62.07 128	66.84 267	83.75 162

BH-w/o		66.85 190	65.83 192	69.90 207	79.29 134	52.46 166	74.66 206	76.65 203	54.51 210	64.85 204	78.12 250	45.59 153	82.95 154	43.26 266	75.54 163	74.27 299

1112_ss		64.00 224	63.36 219	65.93 254	79.28 135	42.58 279	71.35 252	72.36 252	46.41 288	60.55 248	77.89 256	46.27 148	73.28 277	46.18 239	69.97 229	81.92 203

ETV-MVS		74.46 64	73.84 67	76.33 67	79.27 136	55.24 134	79.22 120	85.00 35	64.97 22	72.65 81	79.46 235	53.65 66	87.87 40	67.45 83	82.91 78	85.89 84

UniMVSNet_NR-MVSNet		71.11 104	71.00 99	71.44 172	79.20 137	44.13 266	76.02 182	82.60 86	66.48 13	68.20 137	84.60 127	56.82 30	82.82 162	54.62 178	70.43 220	87.36 43

VPNet		67.52 176	68.11 144	65.74 257	79.18 138	36.80 322	72.17 243	72.83 248	62.04 72	67.79 152	85.83 108	48.88 113	76.60 263	51.30 204	72.97 191	83.81 156

TR-MVS		66.59 198	65.07 203	71.17 183	79.18 138	49.63 209	73.48 225	75.20 223	52.95 221	67.90 144	80.33 215	39.81 213	83.68 139	43.20 267	73.56 179	80.20 230

TAMVS		66.78 193	65.27 201	71.33 179	79.16 140	53.67 145	73.84 222	69.59 271	52.32 229	65.28 191	81.72 185	44.49 170	77.40 255	42.32 274	78.66 134	82.92 186

Test_1112_low_res		62.32 240	61.77 235	64.00 270	79.08 141	39.53 301	68.17 281	70.17 265	43.25 315	59.03 264	79.90 223	44.08 172	71.24 286	43.79 262	68.42 255	81.25 213

CDS-MVSNet		66.80 192	65.37 198	71.10 185	78.98 142	53.13 157	73.27 228	71.07 259	52.15 230	64.72 205	80.23 219	43.56 177	77.10 257	45.48 249	78.88 128	83.05 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 61	74.98 52	73.71 123	78.94 143	50.56 193	80.23 102	83.87 56	60.30 102	77.15 29	86.56 91	59.65 14	82.00 177	66.01 93	82.12 87	88.58 6

IS-MVSNet		71.57 99	71.00 99	73.27 139	78.86 144	45.63 255	80.22 103	78.69 162	64.14 36	66.46 171	87.36 75	49.30 105	85.60 95	50.26 210	83.71 71	88.59 5

CLD-MVS		73.33 75	72.68 79	75.29 87	78.82 145	53.33 153	78.23 134	84.79 39	61.30 82	70.41 100	81.04 198	52.41 75	87.12 54	64.61 108	82.49 86	85.41 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 101	70.38 108	74.88 91	78.76 146	57.15 103	82.79 63	78.48 169	51.26 242	69.49 117	83.22 153	43.99 174	83.24 147	66.06 91	79.37 119	84.23 142

lupinMVS		69.57 135	68.28 141	73.44 134	78.76 146	57.15 103	76.57 167	73.29 245	46.19 290	69.49 117	82.18 173	43.99 174	79.23 225	64.66 106	79.37 119	83.93 150

CNLPA		65.43 210	64.02 208	69.68 209	78.73 148	58.07 85	77.82 140	70.71 262	51.49 237	61.57 243	83.58 149	38.23 230	70.82 287	43.90 260	70.10 228	80.16 231

EPP-MVSNet		72.16 92	71.31 95	74.71 93	78.68 149	49.70 205	82.10 78	81.65 101	60.40 93	65.94 180	85.84 107	51.74 84	86.37 80	55.93 165	79.55 118	88.07 17

TranMVSNet+NR-MVSNet		70.36 118	70.10 113	71.17 183	78.64 150	42.97 277	76.53 168	81.16 120	66.95 6	68.53 133	85.42 116	51.61 85	83.07 150	52.32 196	69.70 237	87.46 38

UniMVSNet (Re)		70.63 112	70.20 109	71.89 161	78.55 151	45.29 257	75.94 183	82.92 80	63.68 41	68.16 140	83.59 148	53.89 60	83.49 144	53.97 183	71.12 213	86.89 52

Fast-Effi-MVS+		70.28 120	69.12 127	73.73 122	78.50 152	51.50 178	75.01 198	79.46 150	56.16 175	68.59 130	79.55 233	53.97 56	84.05 129	53.34 190	77.53 143	85.65 96

PS-MVSNAJss		72.24 89	71.21 96	75.31 85	78.50 152	55.93 121	81.63 84	82.12 92	56.24 173	70.02 108	85.68 111	47.05 138	84.34 125	65.27 101	74.41 169	85.67 94

EI-MVSNet-Vis-set		72.42 87	71.59 87	74.91 90	78.47 154	54.02 142	77.05 158	79.33 152	65.03 21	71.68 92	79.35 238	52.75 70	84.89 114	66.46 89	74.23 170	85.83 86

MVS_111021_LR		69.50 137	68.78 132	71.65 168	78.38 155	59.33 61	74.82 203	70.11 266	58.08 140	67.83 150	84.68 122	41.96 190	76.34 266	65.62 98	77.54 142	79.30 246

test_yl		69.69 130	69.13 125	71.36 176	78.37 156	45.74 251	74.71 204	80.20 137	57.91 146	70.01 109	83.83 142	42.44 185	82.87 158	54.97 174	79.72 113	85.48 102

DCV-MVSNet		69.69 130	69.13 125	71.36 176	78.37 156	45.74 251	74.71 204	80.20 137	57.91 146	70.01 109	83.83 142	42.44 185	82.87 158	54.97 174	79.72 113	85.48 102

FIs		70.82 109	71.43 90	68.98 220	78.33 158	38.14 311	76.96 160	83.59 62	61.02 84	67.33 158	86.73 82	55.07 44	81.64 183	54.61 180	79.22 123	87.14 48

UGNet		68.81 147	67.39 157	73.06 142	78.33 158	54.47 140	79.77 111	75.40 218	60.45 92	63.22 219	84.40 131	32.71 283	80.91 201	51.71 202	80.56 103	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		69.65 133	68.39 140	73.43 135	78.27 160	56.88 107	77.12 156	73.71 242	46.53 287	69.34 121	83.22 153	43.37 178	79.18 226	64.77 105	79.20 124	84.23 142
jason: jason.
alignmvs		73.86 72	73.99 64	73.45 133	78.20 161	50.50 194	78.57 128	82.43 87	59.40 119	76.57 30	86.71 84	56.42 34	81.23 193	65.84 95	81.79 89	88.62 4

xiu_mvs_v1_base_debu		68.58 154	67.28 162	72.48 154	78.19 162	57.19 100	75.28 190	75.09 225	51.61 233	70.04 105	81.41 191	32.79 279	79.02 233	63.81 113	77.31 144	81.22 214

xiu_mvs_v1_base		68.58 154	67.28 162	72.48 154	78.19 162	57.19 100	75.28 190	75.09 225	51.61 233	70.04 105	81.41 191	32.79 279	79.02 233	63.81 113	77.31 144	81.22 214

xiu_mvs_v1_base_debi		68.58 154	67.28 162	72.48 154	78.19 162	57.19 100	75.28 190	75.09 225	51.61 233	70.04 105	81.41 191	32.79 279	79.02 233	63.81 113	77.31 144	81.22 214

PAPM		67.92 171	66.69 173	71.63 169	78.09 165	49.02 215	77.09 157	81.24 118	51.04 244	60.91 246	83.98 139	47.71 125	84.99 109	40.81 283	79.32 122	80.90 221

ACMH	55.70 15	65.20 214	63.57 216	70.07 202	78.07 166	52.01 175	79.48 119	79.69 143	55.75 185	56.59 282	80.98 200	27.12 321	80.94 199	42.90 271	71.58 209	77.25 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 124	69.53 119	71.44 172	78.05 167	44.13 266	75.01 198	81.51 105	64.37 29	68.20 137	84.52 128	49.12 111	82.82 162	54.62 178	70.43 220	87.37 41

NR-MVSNet		69.54 136	68.85 130	71.59 170	78.05 167	43.81 270	74.20 211	80.86 128	65.18 17	62.76 224	84.52 128	52.35 77	83.59 142	50.96 206	70.78 215	87.37 41

EI-MVSNet-UG-set		71.92 94	71.06 98	74.52 105	77.98 169	53.56 148	76.62 166	79.16 153	64.40 28	71.18 95	78.95 242	52.19 78	84.66 120	65.47 99	73.57 178	85.32 110

WR-MVS_H		67.02 187	66.92 170	67.33 237	77.95 170	37.75 314	77.57 144	82.11 93	62.03 73	62.65 227	82.48 167	50.57 96	79.46 221	42.91 270	64.01 287	84.79 128

Effi-MVS+		73.31 76	72.54 80	75.62 80	77.87 171	53.64 146	79.62 115	79.61 146	61.63 77	72.02 89	82.61 163	56.44 33	85.97 88	63.99 112	79.07 127	87.25 45

DELS-MVS		74.76 58	74.46 59	75.65 79	77.84 172	52.25 169	75.59 186	84.17 47	63.76 39	73.15 71	82.79 158	59.58 16	86.80 63	67.24 84	86.04 59	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	66.12 202	64.06 207	72.30 159	77.79 173	52.83 159	80.39 101	78.03 180	57.30 150	57.47 277	82.55 165	27.68 317	84.17 127	45.54 247	69.78 234	79.90 236

3Dnovator	64.47 5	72.49 85	71.39 92	75.79 72	77.70 174	58.99 71	80.66 98	83.15 77	62.24 66	65.46 189	86.59 89	42.38 187	85.52 99	59.59 149	84.72 65	82.85 189

EG-PatchMatch MVS		64.71 218	62.87 223	70.22 198	77.68 175	53.48 150	77.99 137	78.82 157	53.37 218	56.03 285	77.41 263	24.75 335	84.04 130	46.37 238	73.42 182	73.14 306

CP-MVSNet		66.49 199	66.41 180	66.72 240	77.67 176	36.33 327	76.83 165	79.52 148	62.45 63	62.54 230	83.47 152	46.32 146	78.37 240	45.47 250	63.43 293	85.45 104

GBi-Net		67.21 180	66.55 174	69.19 216	77.63 177	43.33 273	77.31 150	77.83 183	56.62 162	65.04 200	82.70 159	41.85 193	80.33 212	47.18 231	72.76 194	83.92 151

test1		67.21 180	66.55 174	69.19 216	77.63 177	43.33 273	77.31 150	77.83 183	56.62 162	65.04 200	82.70 159	41.85 193	80.33 212	47.18 231	72.76 194	83.92 151

FMVSNet2		66.93 189	66.31 185	68.79 223	77.63 177	42.98 276	76.11 177	77.47 189	56.62 162	65.22 198	82.17 175	41.85 193	80.18 215	47.05 234	72.72 197	83.20 179

PCF-MVS	61.88 8	70.95 107	69.49 120	75.35 84	77.63 177	55.71 124	76.04 181	81.81 98	50.30 250	69.66 115	85.40 117	52.51 72	84.89 114	51.82 200	80.24 109	85.45 104
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 211	63.80 212	70.22 198	77.62 181	55.53 129	76.30 173	78.53 167	50.59 249	56.47 283	78.65 245	39.84 212	82.68 165	44.10 259	72.12 204	72.44 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 128	70.58 105	67.46 234	77.61 182	34.73 334	76.05 180	83.19 76	60.84 85	65.88 183	86.46 92	54.52 52	80.76 206	52.52 195	78.12 138	86.91 51

PS-CasMVS		66.42 200	66.32 184	66.70 242	77.60 183	36.30 329	76.94 161	79.61 146	62.36 65	62.43 234	83.66 146	45.69 150	78.37 240	45.35 252	63.26 294	85.42 107

FMVSNet1		66.70 194	65.87 191	69.19 216	77.49 184	43.33 273	77.31 150	77.83 183	56.45 167	64.60 208	82.70 159	38.08 232	80.33 212	46.08 240	72.31 202	83.92 151

VPA-MVSNet		69.02 144	69.47 121	67.69 233	77.42 185	41.00 294	74.04 213	79.68 144	60.06 105	69.26 124	84.81 121	51.06 92	77.58 252	54.44 181	74.43 168	84.48 135

UniMVSNet_ETH3D		67.60 175	67.07 169	69.18 219	77.39 186	42.29 281	74.18 212	75.59 215	60.37 96	66.77 165	86.06 102	37.64 234	78.93 238	52.16 198	73.49 180	86.32 71

thres100view900		63.28 231	62.41 229	65.89 255	77.31 187	38.66 307	72.65 234	69.11 277	57.07 153	62.45 233	81.03 199	37.01 245	79.17 227	31.84 326	73.25 185	79.83 238

cascas		65.98 203	63.42 218	73.64 127	77.26 188	52.58 163	72.26 242	77.21 195	48.56 264	61.21 245	74.60 295	32.57 287	85.82 93	50.38 209	76.75 155	82.52 193

thres600view7		63.30 230	62.27 230	66.41 244	77.18 189	38.87 305	72.35 240	69.11 277	56.98 155	62.37 235	80.96 201	37.01 245	79.00 236	31.43 333	73.05 190	81.36 210

CS-MVS		74.79 57	74.83 55	74.69 94	77.15 190	51.07 181	81.99 80	85.78 21	62.52 61	71.29 94	84.64 124	54.65 50	88.12 33	70.31 63	85.15 63	84.87 124

PEN-MVS		66.60 196	66.45 176	67.04 238	77.11 191	36.56 324	77.03 159	80.42 134	62.95 49	62.51 232	84.03 137	46.69 144	79.07 232	44.22 255	63.08 296	85.51 101

PatchMatch-RL		56.25 282	54.55 287	61.32 289	77.06 192	56.07 118	65.57 295	54.10 343	44.13 309	53.49 313	71.27 314	25.20 332	66.78 307	36.52 308	63.66 290	61.12 342

PVSNet_BlendedMVS		68.56 157	67.72 147	71.07 186	77.03 193	50.57 191	74.50 208	81.52 103	53.66 217	64.22 214	79.72 229	49.13 109	82.87 158	55.82 166	73.92 173	79.77 241

PVSNet_Blended		68.59 153	67.72 147	71.19 181	77.03 193	50.57 191	72.51 238	81.52 103	51.91 231	64.22 214	77.77 260	49.13 109	82.87 158	55.82 166	79.58 116	80.14 232

F-COLMAP		63.05 235	60.87 248	69.58 213	76.99 195	53.63 147	78.12 136	76.16 206	47.97 273	52.41 316	81.61 187	27.87 315	78.11 244	40.07 286	66.66 269	77.00 270

tfpn200view9		63.18 233	62.18 232	66.21 248	76.85 196	39.62 299	71.96 246	69.44 273	56.63 160	62.61 228	79.83 225	37.18 239	79.17 227	31.84 326	73.25 185	79.83 238

thres400		63.31 229	62.18 232	66.72 240	76.85 196	39.62 299	71.96 246	69.44 273	56.63 160	62.61 228	79.83 225	37.18 239	79.17 227	31.84 326	73.25 185	81.36 210

tttt0517		67.83 173	65.66 195	74.33 109	76.69 198	50.82 187	77.86 138	73.99 239	54.54 209	64.64 207	82.53 166	35.06 255	85.50 101	55.71 169	69.91 231	86.67 59

ET-MVSNet_ETH3D		67.96 170	65.72 194	74.68 96	76.67 199	55.62 128	75.11 195	74.74 229	52.91 222	60.03 251	80.12 220	33.68 269	82.64 167	61.86 131	76.34 157	85.78 87

TAPA-MVS	59.36 10	66.60 196	65.20 202	70.81 189	76.63 200	48.75 219	76.52 169	80.04 139	50.64 248	65.24 196	84.93 119	39.15 220	78.54 239	36.77 302	76.88 151	85.14 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 103	70.60 103	73.78 117	76.60 201	53.15 155	79.74 113	79.78 142	58.37 136	68.75 129	86.45 93	45.43 158	80.60 207	62.58 123	77.73 141	87.58 34

LTVRE_ROB	55.42 16	63.15 234	61.23 243	68.92 221	76.57 202	47.80 228	59.92 321	76.39 204	54.35 212	58.67 267	82.46 168	29.44 306	81.49 187	42.12 276	71.14 212	77.46 262
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		70.05 123	68.81 131	73.78 117	76.54 203	53.43 151	83.23 56	83.48 64	52.89 223	65.90 182	86.29 96	41.55 200	86.49 77	51.01 205	78.40 137	81.42 208

FMVSNet3		66.32 201	65.61 196	68.46 226	76.48 204	42.34 280	74.98 200	77.15 196	55.83 182	65.04 200	81.16 195	39.91 211	80.14 216	47.18 231	72.76 194	82.90 188

thisisatest0530		67.92 171	65.78 193	74.33 109	76.29 205	51.03 182	76.89 163	74.25 236	53.67 216	65.59 187	81.76 184	35.15 254	85.50 101	55.94 164	72.47 198	86.47 62

baseline1		63.81 225	63.87 211	63.62 271	76.29 205	36.36 325	71.78 248	67.29 286	56.05 179	64.23 213	82.95 157	47.11 137	74.41 274	47.30 230	61.85 304	80.10 233

ab-mvs		66.65 195	66.42 179	67.37 235	76.17 207	41.73 287	70.41 269	76.14 207	53.99 213	65.98 179	83.51 150	49.48 103	76.24 267	48.60 223	73.46 181	84.14 145

Effi-MVS+-dtu		69.64 134	67.53 153	75.95 70	76.10 208	62.29 18	80.20 104	76.06 209	59.83 112	65.26 195	77.09 264	41.56 198	84.02 133	60.60 140	71.09 214	81.53 207

mvs-test1		70.44 116	68.19 142	77.18 52	76.10 208	63.22 6	80.59 99	76.06 209	59.83 112	66.32 174	79.87 224	41.56 198	85.53 98	60.60 140	72.77 193	82.80 190

DTE-MVSNet		65.58 207	65.34 199	66.31 245	76.06 210	34.79 332	76.43 170	79.38 151	62.55 60	61.66 241	83.83 142	45.60 152	79.15 230	41.64 282	60.88 310	85.00 119

EPNet		73.09 78	72.16 82	75.90 71	75.95 211	56.28 113	83.05 58	72.39 251	66.53 12	65.27 192	87.00 78	50.40 97	85.47 103	62.48 125	86.32 58	85.94 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		68.77 150	66.71 172	74.95 89	75.93 212	58.55 79	80.50 100	75.84 211	56.09 177	68.17 139	83.74 145	28.50 311	82.98 152	65.67 97	65.91 274	83.33 174

RRT_test8_iter05		68.17 167	66.86 171	72.07 160	75.81 213	46.33 243	76.41 171	81.81 98	56.43 168	66.52 169	81.30 194	31.90 291	84.25 126	63.77 116	67.83 260	85.64 97

SixPastTwentyTwo		61.65 249	58.80 257	70.20 200	75.80 214	47.22 236	75.59 186	69.68 269	54.61 206	54.11 305	79.26 239	27.07 322	82.96 153	43.27 265	49.79 340	80.41 228

baseline		74.61 62	74.70 57	74.34 108	75.70 215	49.99 202	77.54 146	84.63 40	62.73 58	73.98 57	87.79 72	57.67 27	83.82 137	69.49 66	82.74 83	89.20 3

Baseline_NR-MVSNet		67.05 186	67.56 150	65.50 259	75.65 216	37.70 315	75.42 188	74.65 231	59.90 108	68.14 141	83.15 156	49.12 111	77.20 256	52.23 197	69.78 234	81.60 206

jajsoiax		68.25 163	66.45 176	73.66 125	75.62 217	55.49 130	80.82 95	78.51 168	52.33 228	64.33 210	84.11 135	28.28 313	81.81 181	63.48 118	70.62 217	83.67 165

mvs_tets		68.18 165	66.36 182	73.63 128	75.61 218	55.35 133	80.77 96	78.56 166	52.48 227	64.27 212	84.10 136	27.45 319	81.84 180	63.45 119	70.56 219	83.69 164

casdiffmvs		74.80 56	74.89 54	74.53 104	75.59 219	50.37 195	78.17 135	85.06 32	62.80 57	74.40 53	87.86 70	57.88 24	83.61 141	69.46 68	82.79 82	89.59 2

PVSNet	50.76 19	58.40 267	57.39 267	61.42 287	75.53 220	44.04 268	61.43 314	63.45 308	47.04 285	56.91 279	73.61 302	27.00 323	64.76 314	39.12 291	72.40 199	75.47 284

MVS		67.37 178	66.33 183	70.51 196	75.46 221	50.94 183	73.95 216	81.85 97	41.57 326	62.54 230	78.57 248	47.98 121	85.47 103	52.97 193	82.05 88	75.14 286

nrg030		72.96 79	73.01 76	72.84 146	75.41 222	50.24 196	80.02 105	82.89 83	58.36 137	74.44 52	86.73 82	58.90 19	80.83 202	65.84 95	74.46 167	87.44 39

thres200		62.20 242	61.16 244	65.34 262	75.38 223	39.99 296	69.60 274	69.29 275	55.64 189	61.87 239	76.99 265	37.07 244	78.96 237	31.28 334	73.28 184	77.06 268

TransMVSNet (Re)		64.72 217	64.33 206	65.87 256	75.22 224	38.56 308	74.66 206	75.08 228	58.90 127	61.79 240	82.63 162	51.18 89	78.07 245	43.63 263	55.87 326	80.99 220

MS-PatchMatch		62.42 239	61.46 239	65.31 263	75.21 225	52.10 171	72.05 244	74.05 238	46.41 288	57.42 278	74.36 296	34.35 263	77.57 253	45.62 246	73.67 175	66.26 339

IB-MVS	56.42 12	65.40 212	62.73 226	73.40 136	74.89 226	52.78 160	73.09 230	75.13 224	55.69 186	58.48 271	73.73 301	32.86 278	86.32 81	50.63 207	70.11 227	81.10 218
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 86	72.46 81	72.42 157	74.88 227	48.50 221	76.28 174	83.14 78	59.40 119	72.46 84	84.68 122	55.66 41	81.12 194	65.98 94	79.66 115	87.63 31

CANet_DTU		68.18 165	67.71 149	69.59 211	74.83 228	46.24 245	78.66 126	76.85 199	59.60 114	63.45 218	82.09 179	35.25 253	77.41 254	59.88 146	78.76 132	85.14 114

tfpnnormal		62.47 238	61.63 237	64.99 265	74.81 229	39.01 304	71.22 255	73.72 241	55.22 195	60.21 249	80.09 222	41.26 206	76.98 260	30.02 338	68.09 257	78.97 249

Vis-MVSNet (Re-imp)		63.69 226	63.88 210	63.14 276	74.75 230	31.04 347	71.16 257	63.64 307	56.32 170	59.80 256	84.99 118	44.51 168	75.46 269	39.12 291	80.62 99	82.92 186

HY-MVS	56.14 13	64.55 221	63.89 209	66.55 243	74.73 231	41.02 292	69.96 272	74.43 232	49.29 258	61.66 241	80.92 202	47.43 132	76.68 262	44.91 254	71.69 207	81.94 202

COLMAP_ROB	52.97 17	61.27 254	58.81 256	68.64 224	74.63 232	52.51 165	78.42 133	73.30 244	49.92 255	50.96 321	81.51 190	23.06 337	79.40 222	31.63 330	65.85 275	74.01 302
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 247	61.35 240	63.46 272	74.58 233	31.48 346	61.42 315	58.14 328	58.71 130	53.02 315	79.55 233	43.07 180	76.80 261	45.69 244	77.96 139	82.11 201

test_djsdf		69.45 139	67.74 146	74.58 102	74.57 234	54.92 137	82.79 63	78.48 169	51.26 242	65.41 190	83.49 151	38.37 227	83.24 147	66.06 91	69.25 244	85.56 99

EI-MVSNet		69.27 141	68.44 139	71.73 165	74.47 235	49.39 212	75.20 193	78.45 172	59.60 114	69.16 126	76.51 274	51.29 87	82.50 169	59.86 148	71.45 211	83.30 175

CVMVSNet		59.63 262	59.14 255	61.08 290	74.47 235	38.84 306	75.20 193	68.74 279	31.15 346	58.24 272	76.51 274	32.39 288	68.58 299	49.77 212	65.84 276	75.81 280

IterMVS-LS		69.22 143	68.48 136	71.43 174	74.44 237	49.40 211	76.23 175	77.55 188	59.60 114	65.85 184	81.59 189	51.28 88	81.58 186	59.87 147	69.90 232	83.30 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 147	67.47 155	72.82 148	74.40 238	56.87 108	70.59 265	79.04 154	54.77 205	66.99 162	86.01 103	39.57 215	78.21 243	62.54 124	73.33 183	83.37 173

XVG-OURS		68.76 151	67.37 158	72.90 145	74.32 239	57.22 98	70.09 271	78.81 158	55.24 194	67.79 152	85.81 110	36.54 248	78.28 242	62.04 129	75.74 161	83.19 180

OpenMVS	61.03 9	68.85 146	67.56 150	72.70 150	74.26 240	53.99 143	81.21 92	81.34 113	52.70 224	62.75 225	85.55 113	38.86 223	84.14 128	48.41 225	83.01 74	79.97 235

MIMVSNet		57.35 274	57.07 270	58.22 301	74.21 241	37.18 316	62.46 310	60.88 321	48.88 262	55.29 292	75.99 282	31.68 292	62.04 322	31.87 325	72.35 200	75.43 285

SCA		60.49 256	58.38 261	66.80 239	74.14 242	48.06 226	63.35 307	63.23 310	49.13 260	59.33 262	72.10 308	37.45 236	74.27 275	44.17 256	62.57 299	78.05 256

thisisatest0515		65.83 204	63.50 217	72.82 148	73.75 243	49.50 210	71.32 253	73.12 247	49.39 257	63.82 216	76.50 276	34.95 257	84.84 117	53.20 192	75.49 164	84.13 146

K. test v3		60.47 257	57.11 269	70.56 194	73.74 244	48.22 224	75.10 197	62.55 314	58.27 139	53.62 310	76.31 278	27.81 316	81.59 185	47.42 228	39.18 351	81.88 204

v10		70.21 121	69.02 128	73.81 116	73.51 245	50.92 185	78.74 124	81.39 108	60.05 106	66.39 173	81.83 183	47.58 128	85.41 106	62.80 122	68.86 251	85.09 117

v1144		70.42 117	69.31 123	73.76 119	73.22 246	50.64 190	77.83 139	81.43 107	58.58 131	69.40 120	81.16 195	47.53 129	85.29 108	64.01 111	70.64 216	85.34 109

v1192		69.97 126	68.68 133	73.85 115	73.19 247	50.94 183	77.68 142	81.36 109	57.51 149	68.95 128	80.85 205	45.28 161	85.33 107	62.97 121	70.37 222	85.27 112

v8		70.33 119	69.28 124	73.49 131	73.15 248	50.22 197	78.62 127	80.78 129	60.79 86	66.45 172	82.11 178	49.35 104	84.98 111	63.58 117	68.71 252	85.28 111

v144192		69.71 129	68.51 135	73.33 138	73.10 249	50.13 199	77.54 146	80.64 130	56.65 159	68.57 132	80.55 210	46.87 143	84.96 113	62.98 120	69.66 238	84.89 123

v1921920		69.47 138	68.17 143	73.36 137	73.06 250	50.10 200	77.39 149	80.56 131	56.58 166	68.59 130	80.37 212	44.72 167	84.98 111	62.47 126	69.82 233	85.00 119

PatchmatchNet		59.84 260	58.24 262	64.65 267	73.05 251	46.70 240	69.42 276	62.18 316	47.55 277	58.88 265	71.96 310	34.49 261	69.16 296	42.99 269	63.60 291	78.07 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 142	67.91 145	73.25 141	73.02 252	49.82 203	77.21 155	80.54 132	56.43 168	68.34 136	80.51 211	43.33 179	84.99 109	62.03 130	69.77 236	84.95 122

Fast-Effi-MVS+-dtu		67.37 178	65.33 200	73.48 132	72.94 253	57.78 90	77.47 148	76.88 198	57.60 148	61.97 237	76.85 268	39.31 217	80.49 210	54.72 177	70.28 225	82.17 200

EPNet_dtu		61.90 245	61.97 234	61.68 285	72.89 254	39.78 298	75.85 184	65.62 295	55.09 198	54.56 301	79.36 237	37.59 235	67.02 306	39.80 289	76.95 150	78.25 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 243	60.10 251	67.99 230	72.79 255	43.86 269	71.05 261	66.85 289	43.14 317	62.77 223	75.39 289	38.32 228	80.80 203	41.69 279	68.88 250	79.32 245

MDTV_nov1_ep13			57.00 271		72.73 256	38.26 310	65.02 302	64.73 302	44.74 300	55.46 288	72.48 306	32.61 286	70.47 289	37.47 298	67.75 262

MSDG		61.81 248	59.23 254	69.55 214	72.64 257	52.63 162	70.45 268	75.81 212	51.38 239	53.70 308	76.11 279	29.52 304	81.08 197	37.70 297	65.79 277	74.93 291

gg-mvs-nofinetune		57.86 272	56.43 276	62.18 282	72.62 258	35.35 331	66.57 287	56.33 335	50.65 247	57.64 276	57.10 347	30.65 295	76.36 265	37.38 299	78.88 128	74.82 293

v2v482		70.50 115	69.45 122	73.66 125	72.62 258	50.03 201	77.58 143	80.51 133	59.90 108	69.52 116	82.14 177	47.53 129	84.88 116	65.07 104	70.17 226	86.09 78

baseline2		63.42 228	61.26 242	69.89 208	72.55 260	47.62 232	71.54 249	68.38 281	50.11 251	54.82 297	75.55 287	43.06 181	80.96 198	48.13 226	67.16 266	81.11 217

v7n		69.01 145	67.36 159	73.98 113	72.51 261	52.65 161	78.54 130	81.30 114	60.26 103	62.67 226	81.62 186	43.61 176	84.49 122	57.01 159	68.70 253	84.79 128

pm-mvs1		65.24 213	64.97 204	66.04 252	72.38 262	39.40 302	72.62 236	75.63 214	55.53 190	62.35 236	83.18 155	47.45 131	76.47 264	49.06 220	66.54 270	82.24 197

XVG-ACMP-BASELINE		64.36 222	62.23 231	70.74 191	72.35 263	52.45 167	70.80 264	78.45 172	53.84 215	59.87 254	81.10 197	16.24 349	79.32 224	55.64 171	71.76 206	80.47 226

WTY-MVS		59.75 261	60.39 249	57.85 305	72.32 264	37.83 313	61.05 319	64.18 305	45.95 295	61.91 238	79.11 241	47.01 141	60.88 325	42.50 273	69.49 240	74.83 292

tpm cat1		59.25 263	56.95 272	66.15 249	72.19 265	46.96 238	68.09 282	65.76 294	40.03 334	57.81 275	70.56 318	38.32 228	74.51 273	38.26 295	61.50 307	77.00 270

mvs_anonymous		68.03 168	67.51 154	69.59 211	72.08 266	44.57 264	71.99 245	75.23 221	51.67 232	67.06 161	82.57 164	54.68 49	77.94 246	56.56 161	75.71 162	86.26 75

OurMVSNet-221017-0		61.37 253	58.63 259	69.61 210	72.05 267	48.06 226	73.93 219	72.51 250	47.23 283	54.74 298	80.92 202	21.49 344	81.24 192	48.57 224	56.22 325	79.53 243

IterMVS-SCA-FT		62.49 237	61.52 238	65.40 261	71.99 268	50.80 188	71.15 258	69.63 270	45.71 296	60.61 247	77.93 253	37.45 236	65.99 311	55.67 170	63.50 292	79.42 244

DWT-MVSNet_test		61.90 245	59.93 252	67.83 231	71.98 269	46.09 248	71.03 262	69.71 267	50.09 252	58.51 270	70.62 317	30.21 300	77.63 251	49.28 218	67.91 258	79.78 240

CostFormer		64.04 223	62.51 227	68.61 225	71.88 270	45.77 250	71.30 254	70.60 263	47.55 277	64.31 211	76.61 272	41.63 196	79.62 220	49.74 213	69.00 249	80.42 227

1314		64.61 220	63.21 220	68.80 222	71.87 271	47.46 234	73.95 216	78.39 177	42.88 319	59.97 252	76.60 273	38.11 231	79.39 223	54.84 176	72.32 201	79.55 242

tpm		57.34 275	58.16 263	54.86 315	71.80 272	34.77 333	67.47 286	56.04 338	48.20 270	60.10 250	76.92 266	37.17 241	53.41 349	40.76 284	65.01 281	76.40 276

eth_miper_zixun_eth		67.63 174	66.28 186	71.67 167	71.60 273	48.33 223	73.68 224	77.88 181	55.80 184	65.91 181	78.62 247	47.35 135	82.88 157	59.45 150	66.25 272	83.81 156

pmmvs4		61.48 252	59.39 253	67.76 232	71.57 274	53.86 144	71.42 250	65.34 297	44.20 307	59.46 258	77.92 254	35.90 249	74.71 272	43.87 261	64.87 282	74.71 295

AllTest		57.08 277	54.65 286	64.39 268	71.44 275	49.03 213	69.92 273	67.30 284	45.97 293	47.16 334	79.77 227	17.47 346	67.56 303	33.65 319	59.16 316	76.57 274

TestCases				64.39 268	71.44 275	49.03 213		67.30 284	45.97 293	47.16 334	79.77 227	17.47 346	67.56 303	33.65 319	59.16 316	76.57 274

lessismore_v0				69.91 206	71.42 277	47.80 228		50.90 346		50.39 327	75.56 286	27.43 320	81.33 189	45.91 242	34.10 353	80.59 225

gm-plane-assit					71.40 278	41.72 289			48.85 263		73.31 303		82.48 171	48.90 221

GG-mvs-BLEND				62.34 281	71.36 279	37.04 320	69.20 278	57.33 332		54.73 299	65.48 339	30.37 297	77.82 248	34.82 315	74.93 166	72.17 320

FMVSNet5		55.86 284	54.93 284	58.66 300	71.05 280	36.35 326	64.18 306	62.48 315	46.76 286	50.66 326	74.73 294	25.80 329	64.04 316	33.11 321	65.57 278	75.59 283

cl_fuxian		68.33 161	67.56 150	70.62 193	70.87 281	46.21 246	74.47 209	78.80 159	56.22 174	66.19 176	78.53 249	51.88 81	81.40 188	62.08 127	69.04 247	84.25 141

GA-MVS		65.53 209	63.70 213	71.02 187	70.87 281	48.10 225	70.48 267	74.40 233	56.69 158	64.70 206	76.77 269	33.66 270	81.10 195	55.42 173	70.32 224	83.87 154

bset_n11_16_dypcd		65.57 208	63.69 214	71.19 181	70.84 283	51.79 176	71.37 251	70.48 264	53.33 219	65.19 199	76.41 277	31.46 293	81.76 182	65.12 102	69.04 247	80.01 234

pmmvs6		63.69 226	62.82 225	66.27 247	70.63 284	39.27 303	73.13 229	75.47 217	52.69 225	59.75 257	82.30 171	39.71 214	77.03 259	47.40 229	64.35 286	82.53 192

miper_ehance_all_eth		68.03 168	67.24 166	70.40 197	70.54 285	46.21 246	73.98 214	78.68 163	55.07 200	66.05 178	77.80 258	52.16 79	81.31 190	61.53 136	69.32 241	83.67 165

OpenMVS_ROB	52.78 18	60.03 258	58.14 264	65.69 258	70.47 286	44.82 259	75.33 189	70.86 261	45.04 298	56.06 284	76.00 280	26.89 324	79.65 218	35.36 314	67.29 264	72.60 311

v148		68.24 164	67.19 167	71.40 175	70.43 287	47.77 230	75.76 185	77.03 197	58.91 126	67.36 157	80.10 221	48.60 117	81.89 178	60.01 145	66.52 271	84.53 133

XXY-MVS		60.68 255	61.67 236	57.70 307	70.43 287	38.45 309	64.19 305	66.47 290	48.05 272	63.22 219	80.86 204	49.28 106	60.47 326	45.25 253	67.28 265	74.19 300

MVSTER		67.16 184	65.58 197	71.88 162	70.37 289	49.70 205	70.25 270	78.45 172	51.52 236	69.16 126	80.37 212	38.45 226	82.50 169	60.19 143	71.46 210	83.44 172

cl-mvsnet____		67.18 182	66.26 187	69.94 204	70.20 290	45.74 251	73.30 226	76.83 200	55.10 196	65.27 192	79.57 232	47.39 133	80.53 208	59.41 152	69.22 245	83.53 171

cl-mvsnet1		67.18 182	66.26 187	69.94 204	70.20 290	45.74 251	73.29 227	76.83 200	55.10 196	65.27 192	79.58 231	47.38 134	80.53 208	59.43 151	69.22 245	83.54 170

tpmvs		58.47 266	56.95 272	63.03 278	70.20 290	41.21 291	67.90 284	67.23 287	49.62 256	54.73 299	70.84 315	34.14 264	76.24 267	36.64 306	61.29 308	71.64 322

anonymousdsp		67.00 188	64.82 205	73.57 130	70.09 293	56.13 116	76.35 172	77.35 193	48.43 267	64.99 203	80.84 206	33.01 276	80.34 211	64.66 106	67.64 263	84.23 142

MVS_0304		58.51 265	57.36 268	61.96 284	70.04 294	41.83 285	69.40 277	65.46 296	50.73 245	53.30 314	74.06 299	22.65 338	70.18 294	42.16 275	68.44 254	73.86 304

MIMVSNet1		55.17 290	54.31 291	57.77 306	70.03 295	32.01 344	65.68 294	64.81 300	49.19 259	46.75 337	76.00 280	25.53 331	64.04 316	28.65 342	62.13 302	77.26 266

CR-MVSNet		59.91 259	57.90 266	65.96 253	69.96 296	52.07 172	65.31 299	63.15 311	42.48 321	59.36 259	74.84 292	35.83 250	70.75 288	45.50 248	64.65 284	75.06 287

RPMNet		61.53 250	58.42 260	70.86 188	69.96 296	52.07 172	65.31 299	81.36 109	43.20 316	59.36 259	70.15 323	35.37 252	85.47 103	36.42 309	64.65 284	75.06 287

cl-mvsnet2		67.47 177	66.45 176	70.54 195	69.85 298	46.49 241	73.85 221	77.35 193	55.07 200	65.51 188	77.92 254	47.64 127	81.10 195	61.58 135	69.32 241	84.01 149

Anonymous20231206		55.10 291	55.30 283	54.48 317	69.81 299	33.94 338	62.91 309	62.13 317	41.08 328	55.18 293	75.65 285	32.75 282	56.59 341	30.32 337	67.86 259	72.91 307

our_test_3		56.49 278	54.42 288	62.68 280	69.51 300	45.48 256	66.08 291	61.49 319	44.11 310	50.73 325	69.60 327	33.05 275	68.15 300	38.38 294	56.86 322	74.40 297

ppachtmachnet_test		58.06 271	55.38 282	66.10 251	69.51 300	48.99 216	68.01 283	66.13 293	44.50 304	54.05 306	70.74 316	32.09 290	72.34 281	36.68 305	56.71 324	76.99 272

diffmvs		70.69 111	70.43 106	71.46 171	69.45 302	48.95 217	72.93 231	78.46 171	57.27 151	71.69 91	83.97 140	51.48 86	77.92 247	70.70 59	77.95 140	87.53 35

IterMVS		62.79 236	61.27 241	67.35 236	69.37 303	52.04 174	71.17 256	68.24 282	52.63 226	59.82 255	76.91 267	37.32 238	72.36 280	52.80 194	63.19 295	77.66 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 185	66.09 189	70.17 201	69.21 304	45.98 249	72.85 233	78.41 175	51.38 239	65.65 186	75.98 283	51.17 90	81.25 191	60.82 138	69.32 241	83.29 177

Patchmtry		57.16 276	56.47 275	59.23 294	69.17 305	34.58 335	62.98 308	63.15 311	44.53 303	56.83 280	74.84 292	35.83 250	68.71 298	40.03 287	60.91 309	74.39 298

CL-MVSNet_2432*1600		61.53 250	60.94 246	63.30 274	68.95 306	36.93 321	67.60 285	72.80 249	55.67 187	59.95 253	76.63 270	45.01 164	72.22 283	39.74 290	62.09 303	80.74 224

V42		68.65 152	67.35 160	72.56 152	68.93 307	50.18 198	72.90 232	79.47 149	56.92 156	69.45 119	80.26 218	46.29 147	82.99 151	64.07 109	67.82 261	84.53 133

test-LLR		58.15 270	58.13 265	58.22 301	68.57 308	44.80 260	65.46 296	57.92 329	50.08 253	55.44 289	69.82 325	32.62 284	57.44 336	49.66 215	73.62 176	72.41 316

test-mter		56.42 280	55.82 279	58.22 301	68.57 308	44.80 260	65.46 296	57.92 329	39.94 335	55.44 289	69.82 325	21.92 341	57.44 336	49.66 215	73.62 176	72.41 316

MVS-HIRNet		45.52 316	44.48 319	48.65 331	68.49 310	34.05 337	59.41 324	44.50 355	27.03 350	37.96 351	50.47 353	26.16 328	64.10 315	26.74 347	59.52 314	47.82 350

dp		51.89 305	51.60 303	52.77 324	68.44 311	32.45 343	62.36 311	54.57 340	44.16 308	49.31 329	67.91 330	28.87 310	56.61 340	33.89 318	54.89 328	69.24 336

PatchT		53.17 301	53.44 298	52.33 326	68.29 312	25.34 357	58.21 326	54.41 341	44.46 305	54.56 301	69.05 328	33.32 273	60.94 324	36.93 301	61.76 306	70.73 328

Patchmatch-RL test		58.16 269	55.49 281	66.15 249	67.92 313	48.89 218	60.66 320	51.07 345	47.86 274	59.36 259	62.71 344	34.02 266	72.27 282	56.41 162	59.40 315	77.30 264

pmmvs-eth3d		58.81 264	56.31 277	66.30 246	67.61 314	52.42 168	72.30 241	64.76 301	43.55 313	54.94 296	74.19 298	28.95 308	72.60 279	43.31 264	57.21 321	73.88 303

PVSNet_0	43.31 20	47.46 315	45.64 318	52.92 323	67.60 315	44.65 262	54.06 338	54.64 339	41.59 325	46.15 338	58.75 346	30.99 294	58.66 332	32.18 323	24.81 354	55.46 348

CHOSEN 280x420		47.83 313	46.36 317	52.24 327	67.37 316	49.78 204	38.91 355	43.11 356	35.00 342	43.27 345	63.30 343	28.95 308	49.19 352	36.53 307	60.80 311	57.76 346

tpmrst		58.24 268	58.70 258	56.84 308	66.97 317	34.32 336	69.57 275	61.14 320	47.17 284	58.58 269	71.60 311	41.28 205	60.41 327	49.20 219	62.84 297	75.78 281

sss		56.17 283	56.57 274	54.96 314	66.93 318	36.32 328	57.94 327	61.69 318	41.67 324	58.64 268	75.32 290	38.72 224	56.25 342	42.04 277	66.19 273	72.31 319

TinyColmap		54.14 292	51.72 302	61.40 288	66.84 319	41.97 283	66.52 288	68.51 280	44.81 299	42.69 346	75.77 284	11.66 354	72.94 278	31.96 324	56.77 323	69.27 335

miper_lstm_enhance		62.03 244	60.88 247	65.49 260	66.71 320	46.25 244	56.29 333	75.70 213	50.68 246	61.27 244	75.48 288	40.21 210	68.03 301	56.31 163	65.25 280	82.18 198

TESTMET0.1,1		55.28 288	54.90 285	56.42 309	66.56 321	43.67 271	65.46 296	56.27 336	39.18 337	53.83 307	67.44 333	24.21 336	55.46 346	48.04 227	73.11 189	70.13 331

D2MVS		62.30 241	60.29 250	68.34 228	66.46 322	48.42 222	65.70 293	73.42 243	47.71 275	58.16 273	75.02 291	30.51 296	77.71 250	53.96 184	71.68 208	78.90 250

MDA-MVSNet-bldmvs		53.87 295	50.81 306	63.05 277	66.25 323	48.58 220	56.93 331	63.82 306	48.09 271	41.22 347	70.48 321	30.34 298	68.00 302	34.24 317	45.92 345	72.57 312

ITE_SJBPF				62.09 283	66.16 324	44.55 265		64.32 304	47.36 280	55.31 291	80.34 214	19.27 345	62.68 320	36.29 310	62.39 301	79.04 247

EPMVS		53.96 293	53.69 296	54.79 316	66.12 325	31.96 345	62.34 312	49.05 348	44.42 306	55.54 287	71.33 313	30.22 299	56.70 339	41.65 281	62.54 300	75.71 282

ADS-MVSNet2		51.33 307	48.76 312	59.07 297	66.02 326	44.60 263	50.90 342	59.76 323	36.90 338	50.74 323	66.18 337	26.38 325	63.11 318	27.17 344	54.76 329	69.50 333

ADS-MVSNet		48.48 312	47.77 314	50.63 329	66.02 326	29.92 349	50.90 342	50.87 347	36.90 338	50.74 323	66.18 337	26.38 325	52.47 350	27.17 344	54.76 329	69.50 333

EU-MVSNet		55.61 286	54.41 289	59.19 296	65.41 328	33.42 340	72.44 239	71.91 254	28.81 348	51.27 319	73.87 300	24.76 334	69.08 297	43.04 268	58.20 319	75.06 287

RPSCF		55.80 285	54.22 293	60.53 291	65.13 329	42.91 278	64.30 304	57.62 331	36.84 340	58.05 274	82.28 172	28.01 314	56.24 343	37.14 300	58.61 318	82.44 196

USDC		56.35 281	54.24 292	62.69 279	64.74 330	40.31 295	65.05 301	73.83 240	43.93 311	47.58 332	77.71 261	15.36 351	75.05 271	38.19 296	61.81 305	72.70 310

JIA-IIPM		51.56 306	47.68 316	63.21 275	64.61 331	50.73 189	47.71 347	58.77 326	42.90 318	48.46 331	51.72 350	24.97 333	70.24 293	36.06 311	53.89 332	68.64 337

Patchmatch-test		49.08 311	48.28 313	51.50 328	64.40 332	30.85 348	45.68 349	48.46 351	35.60 341	46.10 339	72.10 308	34.47 262	46.37 353	27.08 346	60.65 312	77.27 265

TDRefinement		53.44 299	50.72 307	61.60 286	64.31 333	46.96 238	70.89 263	65.27 299	41.78 322	44.61 342	77.98 251	11.52 355	66.36 309	28.57 343	51.59 336	71.49 323

N_pmnet		39.35 322	40.28 323	36.54 338	63.76 334	1.62 369	49.37 345	0.76 369	34.62 343	43.61 344	66.38 336	26.25 327	42.57 356	26.02 349	51.77 335	65.44 340

ambc				65.13 264	63.72 335	37.07 319	47.66 348	78.78 160		54.37 304	71.42 312	11.24 356	80.94 199	45.64 245	53.85 333	77.38 263

KD-MVS_2432*1600		53.45 297	51.50 304	59.30 292	62.82 336	37.14 317	55.33 334	71.79 255	47.34 281	55.09 294	70.52 319	21.91 342	70.45 290	35.72 312	42.97 348	70.31 329

miper_refine_blended		53.45 297	51.50 304	59.30 292	62.82 336	37.14 317	55.33 334	71.79 255	47.34 281	55.09 294	70.52 319	21.91 342	70.45 290	35.72 312	42.97 348	70.31 329

test0.0.03 1		53.32 300	53.59 297	52.50 325	62.81 338	29.45 350	59.51 322	54.11 342	50.08 253	54.40 303	74.31 297	32.62 284	55.92 344	30.50 336	63.95 289	72.15 321

PMMVS		53.96 293	53.26 299	56.04 310	62.60 339	50.92 185	61.17 318	56.09 337	32.81 344	53.51 312	66.84 335	34.04 265	59.93 329	44.14 258	68.18 256	57.27 347

PM-MVS		52.33 303	50.19 308	58.75 299	62.10 340	45.14 258	65.75 292	40.38 358	43.60 312	53.52 311	72.65 305	9.16 360	65.87 312	50.41 208	54.18 331	65.24 341

Gipuma		34.77 325	31.91 329	43.33 336	62.05 341	37.87 312	20.39 358	67.03 288	23.23 354	18.41 358	25.84 358	4.24 364	62.73 319	14.71 355	51.32 337	29.38 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 295	54.02 294	53.41 321	61.47 342	28.11 352	61.30 316	59.21 324	51.34 241	52.09 317	77.43 262	33.29 274	58.55 333	29.76 339	60.27 313	73.58 305

pmmvs5		56.47 279	55.68 280	58.86 298	61.41 343	36.71 323	66.37 289	62.75 313	40.38 332	53.70 308	76.62 271	34.56 259	67.05 305	40.02 288	65.27 279	72.83 309

MDA-MVSNet_test_wron		50.71 309	48.95 310	56.00 312	61.17 344	41.84 284	51.90 341	56.45 333	40.96 329	44.79 341	67.84 331	30.04 302	55.07 348	36.71 304	50.69 339	71.11 327

YYNet1		50.73 308	48.96 309	56.03 311	61.10 345	41.78 286	51.94 340	56.44 334	40.94 330	44.84 340	67.80 332	30.08 301	55.08 347	36.77 302	50.71 338	71.22 324

CMPMVS	42.80 21	57.81 273	55.97 278	63.32 273	60.98 346	47.38 235	64.66 303	69.50 272	32.06 345	46.83 336	77.80 258	29.50 305	71.36 285	48.68 222	73.75 174	71.21 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 310	48.87 311	53.66 319	60.97 347	33.67 339	57.62 329	64.56 303	39.47 336	47.38 333	64.02 342	27.47 318	59.32 330	34.69 316	43.68 347	67.98 338

Anonymous20240521		55.30 287	54.41 289	57.96 304	60.92 348	41.73 287	71.09 260	71.06 260	41.18 327	48.65 330	73.31 303	16.93 348	59.25 331	42.54 272	64.01 287	72.90 308

testgi		51.90 304	52.37 301	50.51 330	60.39 349	23.55 359	58.42 325	58.15 327	49.03 261	51.83 318	79.21 240	22.39 339	55.59 345	29.24 341	62.64 298	72.40 318

UnsupCasMVSNet_eth		53.16 302	52.47 300	55.23 313	59.45 350	33.39 341	59.43 323	69.13 276	45.98 292	50.35 328	72.32 307	29.30 307	58.26 334	42.02 278	44.30 346	74.05 301

new-patchmatchnet		47.56 314	47.73 315	47.06 332	58.81 351	9.37 365	48.78 346	59.21 324	43.28 314	44.22 343	68.66 329	25.67 330	57.20 338	31.57 332	49.35 341	74.62 296

FPMVS		42.18 319	41.11 322	45.39 333	58.03 352	41.01 293	49.50 344	53.81 344	30.07 347	33.71 352	64.03 340	11.69 353	52.08 351	14.01 356	55.11 327	43.09 352

DIV-MVS_2432*1600		55.22 289	53.89 295	59.21 295	57.80 353	27.47 353	57.75 328	74.32 234	47.38 279	50.90 322	70.00 324	28.45 312	70.30 292	40.44 285	57.92 320	79.87 237

new_pmnet		34.13 326	34.29 327	33.64 339	52.63 354	18.23 363	44.43 352	33.90 361	22.81 355	30.89 353	53.18 348	10.48 358	35.72 360	20.77 352	39.51 350	46.98 351

pmmvs3		44.92 317	41.95 321	53.86 318	52.58 355	43.55 272	62.11 313	46.90 354	26.05 352	40.63 348	60.19 345	11.08 357	57.91 335	31.83 329	46.15 344	60.11 343

DSMNet-mixed		39.30 323	38.72 324	41.03 337	51.22 356	19.66 361	45.53 350	31.35 362	15.83 359	39.80 350	67.42 334	22.19 340	45.13 354	22.43 350	52.69 334	58.31 345

LF4IMVS		42.95 318	42.26 320	45.04 334	48.30 357	32.50 342	54.80 336	48.49 350	28.03 349	40.51 349	70.16 322	9.24 359	43.89 355	31.63 330	49.18 342	58.72 344

wuyk23d		13.32 333	12.52 336	15.71 345	47.54 358	26.27 354	31.06 357	1.98 368	4.93 362	5.18 365	1.94 365	0.45 370	18.54 363	6.81 363	12.83 360	2.33 361

LCM-MVSNet		40.30 321	35.88 326	53.57 320	42.24 359	29.15 351	45.21 351	60.53 322	22.23 356	28.02 354	50.98 352	3.72 366	61.78 323	31.22 335	38.76 352	69.78 332

E-PMN		23.77 328	22.73 332	26.90 342	42.02 360	20.67 360	42.66 353	35.70 359	17.43 357	10.28 363	25.05 359	6.42 362	42.39 357	10.28 359	14.71 358	17.63 357

EMVS		22.97 329	21.84 333	26.36 343	40.20 361	19.53 362	41.95 354	34.64 360	17.09 358	9.73 364	22.83 360	7.29 361	42.22 358	9.18 361	13.66 359	17.32 358

ANet_high		41.38 320	37.47 325	53.11 322	39.73 362	24.45 358	56.94 330	69.69 268	47.65 276	26.04 355	52.32 349	12.44 352	62.38 321	21.80 351	10.61 361	72.49 313

PMMVS2		27.40 327	25.91 330	31.87 341	39.46 363	6.57 366	31.17 356	28.52 363	23.96 353	20.45 357	48.94 354	4.20 365	37.94 359	16.51 353	19.97 356	51.09 349

PMVS	28.69 22	36.22 324	33.29 328	45.02 335	36.82 364	35.98 330	54.68 337	48.74 349	26.31 351	21.02 356	51.61 351	2.88 368	60.10 328	9.99 360	47.58 343	38.99 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 330	17.77 335	32.34 340	34.34 365	25.44 356	16.11 359	24.11 364	11.19 360	13.22 360	31.92 356	1.58 369	30.95 361	10.47 358	17.03 357	40.62 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 346	17.97 366	10.91 364		10.60 367	7.46 361	11.07 362	28.36 357	3.28 367	11.29 364	8.01 362	9.74 363	13.89 359

test_method		19.68 331	18.10 334	24.41 344	13.68 367	3.11 368	12.06 361	42.37 357	2.00 363	11.97 361	36.38 355	5.77 363	29.35 362	15.06 354	23.65 355	40.76 353

tmp_tt		9.43 334	11.14 337	4.30 347	2.38 368	4.40 367	13.62 360	16.08 366	0.39 364	15.89 359	13.06 361	15.80 350	5.54 365	12.63 357	10.46 362	2.95 360

testmvs		4.52 337	6.03 340	0.01 349	0.01 369	0.00 371	53.86 339	0.00 370	0.01 365	0.04 366	0.27 366	0.00 372	0.00 366	0.04 364	0.00 364	0.03 363

test123		4.73 336	6.30 339	0.02 348	0.01 369	0.01 370	56.36 332	0.00 370	0.01 365	0.04 366	0.21 367	0.01 371	0.00 366	0.03 365	0.00 364	0.04 362

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

cdsmvs_eth3d_5k		17.50 332	23.34 331	0.00 350	0.00 371	0.00 371	0.00 362	78.63 164	0.00 367	0.00 368	82.18 173	49.25 107	0.00 366	0.00 366	0.00 364	0.00 364

pcd_1.5k_mvsjas		3.92 338	5.23 341	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	47.05 138	0.00 366	0.00 366	0.00 364	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

ab-mvs-re		6.49 335	8.65 338	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	77.89 256	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 371	0.00 362	0.00 370	0.00 367	0.00 368	0.00 368	0.00 372	0.00 366	0.00 366	0.00 364	0.00 364

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																78.05 256

sam_mvs1												34.74 258				78.05 256

sam_mvs												33.43 272

MTGPA								80.97 125

test_post1							68.67 280				3.64 363	32.39 288	69.49 295	44.17 256

test_post											3.55 364	33.90 267	66.52 308

patchmatchnet-post											64.03 340	34.50 260	74.27 275

MTMP							86.03 17	17.08 365

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 79

test_prior2							81.75 82		60.37 96	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

旧先验2							76.08 178		45.32 297	76.55 31			65.56 313	58.75 153

新几何2							76.12 176

无先验							79.66 114	74.30 235	48.40 268				80.78 204	53.62 186		79.03 248

原ACMM2							79.02 121

testdata2													72.18 284	46.95 235

segment_acmp												54.23 54

testdata1							72.65 234		60.50 91

plane_prior5								84.01 50					87.21 51	68.16 75	80.58 101	84.65 131

plane_prior4											86.10 100

plane_prior3						56.09 117			63.92 37	69.27 122

plane_prior2							84.22 38		64.52 26

plane_prior						56.31 111	83.58 53		63.19 47						80.48 104

n2								0.00 370

nn								0.00 370

door-mid								47.19 353

test11								83.47 65

door								47.60 352

HQP5-MVS						54.94 135

BP-MVS														67.04 86

HQP4-MVS										67.85 146			86.93 60			84.32 139

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 156

MDTV_nov1_ep13_2view						25.89 355	61.22 317		40.10 333	51.10 320		32.97 277		38.49 293		78.61 251

ACMMP++_ref															74.07 172

ACMMP++															72.16 203

Test By Simon												48.33 119

High-resolution multi-view benchmark