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	53.93 212	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 49	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

testtj		78.47 18	78.43 18	78.61 29	86.82 13	60.67 43	86.07 16	85.38 27	62.12 67	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 68	90.58 19	49.90 98	88.21 32	73.78 39	87.03 48	86.29 75

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 66	90.56 20	49.80 100	88.24 31	74.02 37	87.03 48	86.32 72

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 57	90.50 22	53.20 68	88.35 28	74.02 37	87.05 46	86.13 77

#test#		77.83 27	77.41 30	79.10 17	86.71 16	62.81 10	85.69 25	84.32 44	61.61 77	73.96 57	90.50 22	53.20 68	88.35 28	73.68 40	87.05 46	86.13 77

MCST-MVS		77.48 32	77.45 29	77.54 47	86.67 18	58.36 81	83.22 57	86.93 8	56.91 155	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	57.95 142	78.10 24	90.06 38	56.12 37	88.84 23	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 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 93	70.73 102	76.40 65	86.57 22	57.99 86	81.15 92	82.96 79	57.03 152	66.78 162	85.56 112	44.50 167	88.11 34	51.77 199	80.23 110	83.10 182

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 65	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 89	90.26 32	46.61 144	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 30	73.48 44	87.03 48	84.83 124
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 24	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	58.56 132	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 86	90.01 43	47.95 121	88.01 37	71.55 54	86.74 54	86.37 67

X-MVStestdata		70.21 119	67.28 160	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 24	72.17 86	6.49 359	47.95 121	88.01 37	71.55 54	86.74 54	86.37 67

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		70.83 106	69.56 116	74.64 99	86.21 30	54.63 138	82.34 73	81.81 98	48.22 267	63.01 220	85.83 108	40.92 206	87.10 55	57.91 154	79.79 112	82.18 196

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 46	79.95 140	59.00 123	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	59.00 123

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 73	73.06 75	88.88 61	53.72 62	89.06 19	68.27 70	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 75	72.45 85	90.34 29	48.48 117	88.13 33	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	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

MTAPA		76.90 38	76.42 40	78.35 34	86.08 36	63.57 2	74.92 200	80.97 125	65.13 18	75.77 34	90.88 13	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

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 54	90.03 41	52.56 71	88.53 26	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 63	72.68 79	90.50 22	48.18 119	87.34 48	73.59 42	85.71 60	84.76 129

SR-MVS		76.13 45	75.70 46	77.40 51	85.87 40	61.20 33	85.52 26	82.19 90	59.99 106	75.10 38	90.35 27	47.66 125	86.52 75	71.64 53	82.99 75	84.47 135

新几何1				70.76 188	85.66 41	61.13 35		66.43 290	44.68 299	70.29 100	86.64 86	41.29 202	75.23 268	49.72 212	81.75 93	75.93 277

MG-MVS		73.96 70	73.89 65	74.16 110	85.65 42	49.69 205	81.59 86	81.29 115	61.45 78	71.05 95	88.11 67	51.77 82	87.73 43	61.05 136	83.09 73	85.05 118

1121		68.53 156	67.16 166	72.63 149	85.64 43	61.14 34	73.95 214	66.46 289	44.61 300	70.28 101	86.68 85	41.42 200	80.78 202	53.62 184	81.79 89	75.97 275

TEST9					85.58 44	61.59 27	81.62 84	81.26 116	55.65 186	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 84	81.26 116	55.86 178	74.93 42	88.81 62	53.70 63	84.68 118	75.24 29	88.33 31	83.65 166

test1172		75.36 53	74.81 55	77.02 55	85.47 46	60.79 42	83.94 49	81.63 102	59.52 117	74.66 50	90.18 34	44.74 163	85.84 91	70.63 60	82.52 84	84.42 136

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 27	79.34 5	90.52 9	86.78 58

test_8					85.40 48	60.96 37	81.54 87	81.18 119	55.86 178	74.81 45	88.80 64	53.70 63	84.45 123

原ACMM1				74.69 95	85.39 49	59.40 59		83.42 67	51.47 236	70.27 102	86.61 88	48.61 115	86.51 76	53.85 183	87.96 39	78.16 252

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 50	59.14 65	81.31 90	84.02 49	56.32 168	74.05 55	88.98 59	53.34 67	87.92 39	69.23 67	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	69.80 113	89.74 49	45.43 156	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 76	85.04 52	59.96 51	81.44 88	81.04 122	56.14 174	74.68 47	88.90 60	53.91 59	84.04 130	75.01 30	87.92 41	83.16 181

agg_prior					85.04 52	59.96 51		81.04 122		74.68 47			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 74	89.97 44	50.90 94	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 66	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	59.34 120	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		69.99 123	68.66 132	73.97 113	84.94 57	57.83 88	82.63 68	78.71 161	56.28 170	64.34 207	84.14 133	41.57 195	87.06 58	46.45 235	78.88 128	77.02 267

DP-MVS		65.68 203	63.66 213	71.75 162	84.93 58	56.87 107	80.74 96	73.16 244	53.06 218	59.09 261	82.35 167	36.79 245	85.94 89	32.82 320	69.96 229	72.45 312

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 93	84.88 60	61.41 29	84.15 41	77.86 181	55.27 191	67.51 154	88.08 69	41.93 189	81.85 177	69.04 69	80.01 111	81.35 210

test12				77.76 43	84.52 61	58.41 80		83.36 70		72.93 77		54.61 50	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 95	79.89 14	89.38 53	54.97 45	85.58 97	76.12 26	84.94 63	86.33 70
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 66	73.46 73	76.80 58	84.45 63	59.04 69	83.65 52	81.05 121	60.15 103	70.43 98	89.84 47	41.09 205	85.59 96	67.61 79	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 81	84.88 36	60.37 95	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 76	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 62	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	73.76 118	83.58 68	51.66 176	78.75 122	77.66 185	67.75 4	72.61 81	89.42 51	49.82 99	83.29 146	53.61 186	83.14 72	86.32 72

SR-MVS-dyc-post		74.57 62	73.90 64	76.58 63	83.49 69	59.87 54	84.29 35	81.36 109	58.07 140	73.14 71	90.07 36	44.74 163	85.84 91	68.20 71	81.76 91	84.03 146

RE-MVS-def			73.71 68		83.49 69	59.87 54	84.29 35	81.36 109	58.07 140	73.14 71	90.07 36	43.06 179		68.20 71	81.76 91	84.03 146

LFMVS		71.78 96	71.59 87	72.32 156	83.40 71	46.38 240	79.75 112	71.08 256	64.18 33	72.80 78	88.64 65	42.58 182	83.72 138	57.41 157	84.49 67	86.86 53

test222					83.14 72	58.68 76	72.57 235	63.45 306	41.78 320	67.56 153	86.12 99	37.13 240			78.73 133	74.98 288

9.14			78.75 15		83.10 73		84.15 41	88.26 2	59.90 107	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 95	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	53.15 154		67.52 281			87.85 71	44.08 170			80.76 98	78.03 257

MSLP-MVS++		73.77 73	73.47 72	74.66 97	83.02 76	59.29 62	82.30 77	81.88 95	59.34 120	71.59 92	86.83 79	45.94 148	83.65 140	65.09 102	85.22 62	81.06 217

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 21	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 79	83.01 77	60.63 44	77.29 152	78.40 176	61.18 82	70.58 97	85.97 104	54.18 55	84.00 134	67.52 80	82.98 77	82.45 193

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 56	77.08 20	90.18 11	87.87 21

VDDNet		71.81 95	71.33 94	73.26 138	82.80 80	47.60 231	78.74 123	75.27 218	59.59 116	72.94 76	89.40 52	41.51 199	83.91 135	58.75 152	82.99 75	88.26 8

abl_6		74.34 64	73.50 70	76.86 57	82.43 81	60.16 49	83.48 54	81.86 96	58.81 127	73.95 59	89.86 46	41.87 190	86.62 70	67.98 75	81.23 96	83.80 159

3Dnovator+	66.72 4	75.84 49	74.57 57	79.66 6	82.40 82	59.92 53	85.83 20	86.32 18	66.92 8	67.80 150	89.24 55	42.03 187	89.38 14	64.07 108	86.50 57	89.69 1

APD-MVS_3200maxsize		74.96 54	74.39 60	76.67 61	82.20 83	58.24 83	83.67 51	83.29 73	58.41 134	73.71 63	90.14 35	45.62 150	85.99 86	69.64 63	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	58.33 137	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	55.09 196	65.82 183	82.16 174	49.17 107	82.64 165	60.34 141	78.62 135	82.50 192

TSAR-MVS + GP.		74.90 55	74.15 62	77.17 53	82.00 86	58.77 75	81.80 80	78.57 165	58.58 130	74.32 53	84.51 129	55.94 38	87.22 50	67.11 83	84.48 68	85.52 100

hse-mvs3		72.71 82	71.49 89	76.40 65	81.99 87	59.58 57	76.92 161	76.74 201	60.40 93	74.81 45	85.95 105	45.54 153	85.76 94	70.41 61	70.61 217	83.86 154

API-MVS		72.17 91	71.41 91	74.45 105	81.95 88	57.22 98	84.03 44	80.38 135	59.89 110	68.40 133	82.33 168	49.64 101	87.83 42	51.87 197	84.16 70	78.30 250

MAR-MVS		71.51 100	70.15 111	75.60 82	81.84 89	59.39 60	81.38 89	82.90 82	54.90 202	68.08 142	78.70 241	47.73 123	85.51 100	51.68 201	84.17 69	81.88 202
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 89	81.72 90	53.42 151	79.91 109	83.28 74	59.14 122	66.31 173	85.90 106	51.86 81	86.06 83	57.45 156	80.62 99	85.91 83

test_part1		74.74 58	74.42 59	75.70 78	81.69 91	51.26 178	83.98 46	87.05 7	65.31 16	73.10 73	86.20 97	53.94 58	88.06 35	65.32 99	73.17 187	87.77 26

VDD-MVS		72.50 84	72.09 83	73.75 120	81.58 92	49.69 205	77.76 140	77.63 186	63.21 46	73.21 69	89.02 58	42.14 186	83.32 145	61.72 131	82.50 85	88.25 9

PS-MVSNAJ		70.51 112	69.70 115	72.93 142	81.52 93	55.79 122	74.92 200	79.00 155	55.04 200	69.88 111	78.66 242	47.05 137	82.19 172	61.61 132	79.58 116	80.83 220

testdata				64.66 264	81.52 93	52.93 157		65.29 296	46.09 289	73.88 61	87.46 74	38.08 230	66.26 308	53.31 189	78.48 136	74.78 292

CHOSEN 1792x2688		65.08 214	62.84 222	71.82 161	81.49 95	56.26 113	66.32 288	74.20 235	40.53 329	63.16 219	78.65 243	41.30 201	77.80 247	45.80 241	74.09 170	81.40 207

HQP_MVS		74.31 65	73.73 67	76.06 69	81.41 96	56.31 110	84.22 38	84.01 50	64.52 26	69.27 121	86.10 100	45.26 160	87.21 51	68.16 73	80.58 101	84.65 130

plane_prior7					81.41 96	55.96 119

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 98	59.16 63	79.94 107	85.71 24	56.59 163	72.46 83	86.76 80	56.89 29	87.86 41	66.36 88	88.91 26	83.64 167

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	70.09 103	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	55.41 131	80.90 93	78.28 178	60.73 88	69.23 124	88.09 68	44.36 169	82.65 164	57.68 155	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		70.52 111	69.75 113	72.84 144	81.21 102	55.63 126	75.11 194	78.92 156	54.92 201	69.96 110	79.68 227	47.00 141	82.09 174	61.60 133	79.37 119	80.81 221

plane_prior6					81.20 103	56.24 114						45.26 160

PAPR		71.72 98	70.82 101	74.41 106	81.20 103	51.17 179	79.55 116	83.33 71	55.81 181	66.93 161	84.61 125	50.95 92	86.06 83	55.79 166	79.20 124	86.00 81

PLC	56.13 14	65.09 213	63.21 218	70.72 190	81.04 105	54.87 137	78.57 127	77.47 188	48.51 263	55.71 284	81.89 178	33.71 266	79.71 215	41.66 278	70.37 221	77.58 259
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 106	56.05 118					85.54 114

OPM-MVS		74.73 59	74.25 61	76.19 68	80.81 107	59.01 70	82.60 69	83.64 61	63.74 40	72.52 82	87.49 73	47.18 135	85.88 90	69.47 65	80.78 97	83.66 165

HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 145

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 145

HQP-MVS		73.45 74	72.80 78	75.40 84	80.66 108	54.94 134	82.31 74	83.90 54	62.10 68	67.85 145	85.54 114	45.46 154	86.93 60	67.04 84	80.35 107	84.32 138

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 111	55.91 121	84.28 37	85.78 22	56.08 176	73.41 67	86.58 90	50.94 93	88.54 25	70.79 58	89.71 17	87.79 25

ACMM	61.98 7	70.80 108	69.73 114	74.02 111	80.59 112	58.59 78	82.68 67	82.02 94	55.46 189	67.18 158	84.39 131	38.51 223	83.17 149	60.65 138	76.10 158	80.30 227
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	80.75 205	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	80.75 205	55.73 40	86.80 63	71.44 56	80.38 105	87.50 36

Anonymous20231211		69.28 138	68.47 135	71.73 163	80.28 115	47.18 235	79.98 106	82.37 88	54.61 204	67.24 157	84.01 137	39.43 214	82.41 170	55.45 170	72.83 191	85.62 98

ACMP	63.53 6	72.30 88	71.20 97	75.59 83	80.28 115	57.54 92	82.74 65	82.84 84	60.58 90	65.24 194	86.18 98	39.25 216	86.03 85	66.95 86	76.79 154	83.22 176
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 74	80.22 117	57.51 94	82.55 70	83.40 68	61.32 79	66.67 165	87.33 76	39.15 218	86.59 71	67.70 77	77.30 147	83.19 178

LGP-MVS_train				75.76 74	80.22 117	57.51 94		83.40 68	61.32 79	66.67 165	87.33 76	39.15 218	86.59 71	67.70 77	77.30 147	83.19 178

WR-MVS		68.47 157	68.47 135	68.44 225	80.20 119	39.84 295	73.75 221	76.07 207	64.68 23	68.11 141	83.63 145	50.39 97	79.14 229	49.78 209	69.66 237	86.34 69

Anonymous20240529		69.91 125	69.02 126	72.56 150	80.19 120	47.65 229	77.56 144	80.99 124	55.45 190	69.88 111	86.76 80	39.24 217	82.18 173	54.04 180	77.10 149	87.85 23

Anonymous202405211		66.84 189	65.99 188	69.40 213	80.19 120	42.21 280	71.11 257	71.31 255	58.80 128	67.90 143	86.39 94	29.83 301	79.65 216	49.60 215	78.78 131	86.33 70

BH-RMVSNet		68.81 145	67.42 154	72.97 141	80.11 122	52.53 163	74.26 209	76.29 204	58.48 133	68.38 134	84.20 132	42.59 181	83.83 136	46.53 234	75.91 159	82.56 189

test_0402		63.25 230	61.01 243	69.96 201	80.00 123	54.37 140	76.86 163	72.02 251	54.58 206	58.71 264	80.79 204	35.00 254	84.36 124	26.41 346	64.71 281	71.15 324

HyFIR lowres test		65.67 204	63.01 220	73.67 123	79.97 124	55.65 125	69.07 277	75.52 215	42.68 318	63.53 215	77.95 250	40.43 207	81.64 181	46.01 239	71.91 204	83.73 161

EIA-MVS		71.78 96	70.60 103	75.30 87	79.85 125	53.54 148	77.27 153	83.26 75	57.92 143	66.49 168	79.39 233	52.07 79	86.69 67	60.05 143	79.14 126	85.66 95

Regformer-3		73.89 71	73.28 75	75.71 77	79.75 126	55.48 130	78.54 129	79.93 141	66.58 11	73.62 64	80.30 213	54.87 47	84.54 121	69.09 68	76.84 152	87.10 49

Regformer-4		74.25 67	73.48 71	76.57 64	79.75 126	56.54 109	78.54 129	81.49 106	66.93 7	73.90 60	80.30 213	53.84 61	85.98 87	69.76 62	76.84 152	87.17 46

BH-untuned		68.27 160	67.29 159	71.21 178	79.74 128	53.22 153	76.06 178	77.46 190	57.19 150	66.10 175	81.61 184	45.37 158	83.50 143	45.42 249	76.68 156	76.91 271

VNet		69.68 130	70.19 110	68.16 227	79.73 129	41.63 288	70.53 264	77.38 191	60.37 95	70.69 96	86.63 87	51.08 90	77.09 256	53.61 186	81.69 95	85.75 92

LS3D		64.71 216	62.50 226	71.34 176	79.72 130	55.71 123	79.82 110	74.72 228	48.50 264	56.62 279	84.62 124	33.59 269	82.34 171	29.65 338	75.23 164	75.97 275

AUN-MVS		68.45 158	66.41 178	74.57 102	79.53 131	57.08 105	73.93 217	75.23 219	54.44 209	66.69 164	81.85 179	37.10 241	82.89 155	62.07 127	66.84 266	83.75 160

BH-w/o		66.85 188	65.83 190	69.90 205	79.29 132	52.46 166	74.66 205	76.65 202	54.51 208	64.85 202	78.12 248	45.59 152	82.95 154	43.26 264	75.54 162	74.27 297

1112_ss		64.00 222	63.36 217	65.93 252	79.28 133	42.58 277	71.35 250	72.36 250	46.41 286	60.55 246	77.89 254	46.27 147	73.28 275	46.18 237	69.97 228	81.92 201

ETV-MVS		74.46 63	73.84 66	76.33 67	79.27 134	55.24 133	79.22 120	85.00 35	64.97 22	72.65 80	79.46 232	53.65 66	87.87 40	67.45 81	82.91 78	85.89 84

UniMVSNet_NR-MVSNet		71.11 104	71.00 99	71.44 170	79.20 135	44.13 264	76.02 181	82.60 86	66.48 13	68.20 136	84.60 126	56.82 30	82.82 161	54.62 176	70.43 219	87.36 43

VPNet		67.52 174	68.11 142	65.74 255	79.18 136	36.80 320	72.17 241	72.83 246	62.04 71	67.79 151	85.83 108	48.88 112	76.60 261	51.30 202	72.97 190	83.81 155

TR-MVS		66.59 196	65.07 201	71.17 181	79.18 136	49.63 207	73.48 223	75.20 221	52.95 219	67.90 143	80.33 212	39.81 211	83.68 139	43.20 265	73.56 178	80.20 228

TAMVS		66.78 191	65.27 199	71.33 177	79.16 138	53.67 144	73.84 220	69.59 269	52.32 227	65.28 189	81.72 182	44.49 168	77.40 253	42.32 272	78.66 134	82.92 184

Test_1112_low_res		62.32 238	61.77 233	64.00 268	79.08 139	39.53 299	68.17 279	70.17 263	43.25 313	59.03 262	79.90 220	44.08 170	71.24 284	43.79 260	68.42 254	81.25 211

CS-MVS		74.18 68	73.60 69	75.92 71	78.99 140	52.53 163	80.61 98	85.93 20	61.17 83	71.15 94	79.34 236	54.52 51	88.86 22	66.07 89	84.67 65	86.38 63

CDS-MVSNet		66.80 190	65.37 196	71.10 183	78.98 141	53.13 156	73.27 226	71.07 257	52.15 228	64.72 203	80.23 216	43.56 175	77.10 255	45.48 247	78.88 128	83.05 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 60	74.98 52	73.71 122	78.94 142	50.56 191	80.23 102	83.87 56	60.30 101	77.15 29	86.56 91	59.65 14	82.00 175	66.01 92	82.12 87	88.58 6

IS-MVSNet		71.57 99	71.00 99	73.27 137	78.86 143	45.63 253	80.22 103	78.69 162	64.14 36	66.46 169	87.36 75	49.30 104	85.60 95	50.26 208	83.71 71	88.59 5

CLD-MVS		73.33 75	72.68 79	75.29 88	78.82 144	53.33 152	78.23 133	84.79 39	61.30 81	70.41 99	81.04 195	52.41 75	87.12 54	64.61 107	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 92	78.76 145	57.15 103	82.79 63	78.48 169	51.26 240	69.49 116	83.22 151	43.99 172	83.24 147	66.06 90	79.37 119	84.23 141

lupinMVS		69.57 133	68.28 139	73.44 132	78.76 145	57.15 103	76.57 166	73.29 243	46.19 288	69.49 116	82.18 171	43.99 172	79.23 223	64.66 105	79.37 119	83.93 149

CNLPA		65.43 208	64.02 206	69.68 207	78.73 147	58.07 85	77.82 139	70.71 260	51.49 235	61.57 241	83.58 147	38.23 228	70.82 285	43.90 258	70.10 227	80.16 229

EPP-MVSNet		72.16 92	71.31 95	74.71 94	78.68 148	49.70 203	82.10 78	81.65 101	60.40 93	65.94 178	85.84 107	51.74 83	86.37 80	55.93 163	79.55 118	88.07 17

TranMVSNet+NR-MVSNet		70.36 116	70.10 112	71.17 181	78.64 149	42.97 275	76.53 167	81.16 120	66.95 6	68.53 132	85.42 116	51.61 84	83.07 150	52.32 194	69.70 236	87.46 38

UniMVSNet (Re)		70.63 110	70.20 109	71.89 159	78.55 150	45.29 255	75.94 182	82.92 80	63.68 41	68.16 139	83.59 146	53.89 60	83.49 144	53.97 181	71.12 212	86.89 52

Fast-Effi-MVS+		70.28 118	69.12 125	73.73 121	78.50 151	51.50 177	75.01 197	79.46 150	56.16 173	68.59 129	79.55 230	53.97 56	84.05 129	53.34 188	77.53 143	85.65 96

PS-MVSNAJss		72.24 89	71.21 96	75.31 86	78.50 151	55.93 120	81.63 83	82.12 92	56.24 171	70.02 107	85.68 111	47.05 137	84.34 125	65.27 100	74.41 168	85.67 94

EI-MVSNet-Vis-set		72.42 87	71.59 87	74.91 91	78.47 153	54.02 141	77.05 157	79.33 152	65.03 21	71.68 91	79.35 235	52.75 70	84.89 114	66.46 87	74.23 169	85.83 86

MVS_111021_LR		69.50 135	68.78 130	71.65 166	78.38 154	59.33 61	74.82 202	70.11 264	58.08 139	67.83 149	84.68 122	41.96 188	76.34 264	65.62 97	77.54 142	79.30 244

test_yl		69.69 128	69.13 123	71.36 174	78.37 155	45.74 249	74.71 203	80.20 137	57.91 144	70.01 108	83.83 140	42.44 183	82.87 157	54.97 172	79.72 113	85.48 102

DCV-MVSNet		69.69 128	69.13 123	71.36 174	78.37 155	45.74 249	74.71 203	80.20 137	57.91 144	70.01 108	83.83 140	42.44 183	82.87 157	54.97 172	79.72 113	85.48 102

FIs		70.82 107	71.43 90	68.98 218	78.33 157	38.14 309	76.96 159	83.59 62	61.02 84	67.33 156	86.73 82	55.07 44	81.64 181	54.61 178	79.22 123	87.14 48

UGNet		68.81 145	67.39 155	73.06 140	78.33 157	54.47 139	79.77 111	75.40 217	60.45 92	63.22 217	84.40 130	32.71 281	80.91 199	51.71 200	80.56 103	83.81 155
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		69.65 131	68.39 138	73.43 133	78.27 159	56.88 106	77.12 155	73.71 240	46.53 285	69.34 120	83.22 151	43.37 176	79.18 224	64.77 104	79.20 124	84.23 141
jason: jason.
alignmvs		73.86 72	73.99 63	73.45 131	78.20 160	50.50 192	78.57 127	82.43 87	59.40 118	76.57 30	86.71 84	56.42 34	81.23 191	65.84 94	81.79 89	88.62 4

xiu_mvs_v1_base_debu		68.58 152	67.28 160	72.48 152	78.19 161	57.19 100	75.28 189	75.09 223	51.61 231	70.04 104	81.41 188	32.79 277	79.02 231	63.81 112	77.31 144	81.22 212

xiu_mvs_v1_base		68.58 152	67.28 160	72.48 152	78.19 161	57.19 100	75.28 189	75.09 223	51.61 231	70.04 104	81.41 188	32.79 277	79.02 231	63.81 112	77.31 144	81.22 212

xiu_mvs_v1_base_debi		68.58 152	67.28 160	72.48 152	78.19 161	57.19 100	75.28 189	75.09 223	51.61 231	70.04 104	81.41 188	32.79 277	79.02 231	63.81 112	77.31 144	81.22 212

PAPM		67.92 169	66.69 171	71.63 167	78.09 164	49.02 213	77.09 156	81.24 118	51.04 242	60.91 244	83.98 138	47.71 124	84.99 109	40.81 281	79.32 122	80.90 219

ACMH	55.70 15	65.20 212	63.57 214	70.07 200	78.07 165	52.01 174	79.48 119	79.69 143	55.75 183	56.59 280	80.98 197	27.12 319	80.94 197	42.90 269	71.58 208	77.25 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 122	69.53 117	71.44 170	78.05 166	44.13 264	75.01 197	81.51 105	64.37 29	68.20 136	84.52 127	49.12 110	82.82 161	54.62 176	70.43 219	87.37 41

NR-MVSNet		69.54 134	68.85 128	71.59 168	78.05 166	43.81 268	74.20 210	80.86 128	65.18 17	62.76 222	84.52 127	52.35 76	83.59 142	50.96 204	70.78 214	87.37 41

EI-MVSNet-UG-set		71.92 94	71.06 98	74.52 104	77.98 168	53.56 147	76.62 165	79.16 153	64.40 28	71.18 93	78.95 240	52.19 77	84.66 120	65.47 98	73.57 177	85.32 110

WR-MVS_H		67.02 185	66.92 168	67.33 235	77.95 169	37.75 312	77.57 143	82.11 93	62.03 72	62.65 225	82.48 165	50.57 95	79.46 219	42.91 268	64.01 285	84.79 127

Effi-MVS+		73.31 76	72.54 80	75.62 81	77.87 170	53.64 145	79.62 115	79.61 146	61.63 76	72.02 88	82.61 161	56.44 33	85.97 88	63.99 111	79.07 127	87.25 45

DELS-MVS		74.76 57	74.46 58	75.65 80	77.84 171	52.25 169	75.59 185	84.17 47	63.76 39	73.15 70	82.79 156	59.58 16	86.80 63	67.24 82	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	66.12 200	64.06 205	72.30 157	77.79 172	52.83 158	80.39 101	78.03 179	57.30 148	57.47 275	82.55 163	27.68 315	84.17 127	45.54 245	69.78 233	79.90 234

3Dnovator	64.47 5	72.49 85	71.39 92	75.79 73	77.70 173	58.99 71	80.66 97	83.15 77	62.24 65	65.46 187	86.59 89	42.38 185	85.52 99	59.59 148	84.72 64	82.85 187

EG-PatchMatch MVS		64.71 216	62.87 221	70.22 196	77.68 174	53.48 149	77.99 136	78.82 157	53.37 216	56.03 283	77.41 261	24.75 333	84.04 130	46.37 236	73.42 181	73.14 304

CP-MVSNet		66.49 197	66.41 178	66.72 238	77.67 175	36.33 325	76.83 164	79.52 148	62.45 62	62.54 228	83.47 150	46.32 145	78.37 238	45.47 248	63.43 291	85.45 104

GBi-Net		67.21 178	66.55 172	69.19 214	77.63 176	43.33 271	77.31 149	77.83 182	56.62 160	65.04 198	82.70 157	41.85 191	80.33 210	47.18 229	72.76 193	83.92 150

test1		67.21 178	66.55 172	69.19 214	77.63 176	43.33 271	77.31 149	77.83 182	56.62 160	65.04 198	82.70 157	41.85 191	80.33 210	47.18 229	72.76 193	83.92 150

FMVSNet2		66.93 187	66.31 183	68.79 221	77.63 176	42.98 274	76.11 176	77.47 188	56.62 160	65.22 196	82.17 173	41.85 191	80.18 213	47.05 232	72.72 196	83.20 177

PCF-MVS	61.88 8	70.95 105	69.49 118	75.35 85	77.63 176	55.71 123	76.04 180	81.81 98	50.30 248	69.66 114	85.40 117	52.51 72	84.89 114	51.82 198	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		65.41 209	63.80 210	70.22 196	77.62 180	55.53 128	76.30 172	78.53 167	50.59 247	56.47 281	78.65 243	39.84 210	82.68 163	44.10 257	72.12 203	72.44 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 126	70.58 105	67.46 232	77.61 181	34.73 332	76.05 179	83.19 76	60.84 85	65.88 181	86.46 92	54.52 51	80.76 204	52.52 193	78.12 138	86.91 51

PS-CasMVS		66.42 198	66.32 182	66.70 240	77.60 182	36.30 327	76.94 160	79.61 146	62.36 64	62.43 232	83.66 144	45.69 149	78.37 238	45.35 250	63.26 292	85.42 107

FMVSNet1		66.70 192	65.87 189	69.19 214	77.49 183	43.33 271	77.31 149	77.83 182	56.45 165	64.60 206	82.70 157	38.08 230	80.33 210	46.08 238	72.31 201	83.92 150

VPA-MVSNet		69.02 142	69.47 119	67.69 231	77.42 184	41.00 292	74.04 212	79.68 144	60.06 104	69.26 123	84.81 121	51.06 91	77.58 250	54.44 179	74.43 167	84.48 134

UniMVSNet_ETH3D		67.60 173	67.07 167	69.18 217	77.39 185	42.29 279	74.18 211	75.59 214	60.37 95	66.77 163	86.06 102	37.64 232	78.93 236	52.16 196	73.49 179	86.32 72

thres100view900		63.28 229	62.41 227	65.89 253	77.31 186	38.66 305	72.65 232	69.11 275	57.07 151	62.45 231	81.03 196	37.01 243	79.17 225	31.84 324	73.25 184	79.83 236

cascas		65.98 201	63.42 216	73.64 126	77.26 187	52.58 162	72.26 240	77.21 194	48.56 262	61.21 243	74.60 293	32.57 285	85.82 93	50.38 207	76.75 155	82.52 191

thres600view7		63.30 228	62.27 228	66.41 242	77.18 188	38.87 303	72.35 238	69.11 275	56.98 153	62.37 233	80.96 198	37.01 243	79.00 234	31.43 331	73.05 189	81.36 208

PEN-MVS		66.60 194	66.45 174	67.04 236	77.11 189	36.56 322	77.03 158	80.42 134	62.95 49	62.51 230	84.03 136	46.69 143	79.07 230	44.22 253	63.08 294	85.51 101

PatchMatch-RL		56.25 280	54.55 285	61.32 287	77.06 190	56.07 117	65.57 293	54.10 341	44.13 307	53.49 311	71.27 312	25.20 330	66.78 305	36.52 306	63.66 288	61.12 340

PVSNet_BlendedMVS		68.56 155	67.72 145	71.07 184	77.03 191	50.57 189	74.50 207	81.52 103	53.66 215	64.22 212	79.72 226	49.13 108	82.87 157	55.82 164	73.92 172	79.77 239

PVSNet_Blended		68.59 151	67.72 145	71.19 179	77.03 191	50.57 189	72.51 236	81.52 103	51.91 229	64.22 212	77.77 258	49.13 108	82.87 157	55.82 164	79.58 116	80.14 230

F-COLMAP		63.05 233	60.87 246	69.58 211	76.99 193	53.63 146	78.12 135	76.16 205	47.97 271	52.41 314	81.61 184	27.87 313	78.11 242	40.07 284	66.66 267	77.00 268

tfpn200view9		63.18 231	62.18 230	66.21 246	76.85 194	39.62 297	71.96 244	69.44 271	56.63 158	62.61 226	79.83 222	37.18 237	79.17 225	31.84 324	73.25 184	79.83 236

thres400		63.31 227	62.18 230	66.72 238	76.85 194	39.62 297	71.96 244	69.44 271	56.63 158	62.61 226	79.83 222	37.18 237	79.17 225	31.84 324	73.25 184	81.36 208

tttt0517		67.83 171	65.66 193	74.33 108	76.69 196	50.82 185	77.86 137	73.99 237	54.54 207	64.64 205	82.53 164	35.06 253	85.50 101	55.71 167	69.91 230	86.67 59

ET-MVSNet_ETH3D		67.96 168	65.72 192	74.68 96	76.67 197	55.62 127	75.11 194	74.74 227	52.91 220	60.03 249	80.12 217	33.68 267	82.64 165	61.86 130	76.34 157	85.78 87

TAPA-MVS	59.36 10	66.60 194	65.20 200	70.81 187	76.63 198	48.75 217	76.52 168	80.04 139	50.64 246	65.24 194	84.93 119	39.15 218	78.54 237	36.77 300	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	73.78 116	76.60 199	53.15 154	79.74 113	79.78 142	58.37 135	68.75 128	86.45 93	45.43 156	80.60 205	62.58 122	77.73 141	87.58 34

LTVRE_ROB	55.42 16	63.15 232	61.23 241	68.92 219	76.57 200	47.80 226	59.92 319	76.39 203	54.35 210	58.67 265	82.46 166	29.44 304	81.49 185	42.12 274	71.14 211	77.46 260
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		70.05 121	68.81 129	73.78 116	76.54 201	53.43 150	83.23 56	83.48 64	52.89 221	65.90 180	86.29 96	41.55 198	86.49 77	51.01 203	78.40 137	81.42 206

FMVSNet3		66.32 199	65.61 194	68.46 224	76.48 202	42.34 278	74.98 199	77.15 195	55.83 180	65.04 198	81.16 192	39.91 209	80.14 214	47.18 229	72.76 193	82.90 186

thisisatest0530		67.92 169	65.78 191	74.33 108	76.29 203	51.03 180	76.89 162	74.25 234	53.67 214	65.59 185	81.76 181	35.15 252	85.50 101	55.94 162	72.47 197	86.47 62

baseline1		63.81 223	63.87 209	63.62 269	76.29 203	36.36 323	71.78 246	67.29 284	56.05 177	64.23 211	82.95 155	47.11 136	74.41 272	47.30 228	61.85 302	80.10 231

ab-mvs		66.65 193	66.42 177	67.37 233	76.17 205	41.73 285	70.41 267	76.14 206	53.99 211	65.98 177	83.51 148	49.48 102	76.24 265	48.60 221	73.46 180	84.14 144

Effi-MVS+-dtu		69.64 132	67.53 151	75.95 70	76.10 206	62.29 18	80.20 104	76.06 208	59.83 111	65.26 193	77.09 262	41.56 196	84.02 133	60.60 139	71.09 213	81.53 205

mvs-test1		70.44 114	68.19 140	77.18 52	76.10 206	63.22 6	80.59 99	76.06 208	59.83 111	66.32 172	79.87 221	41.56 196	85.53 98	60.60 139	72.77 192	82.80 188

DTE-MVSNet		65.58 205	65.34 197	66.31 243	76.06 208	34.79 330	76.43 169	79.38 151	62.55 60	61.66 239	83.83 140	45.60 151	79.15 228	41.64 280	60.88 308	85.00 119

EPNet		73.09 78	72.16 82	75.90 72	75.95 209	56.28 112	83.05 58	72.39 249	66.53 12	65.27 190	87.00 78	50.40 96	85.47 103	62.48 124	86.32 58	85.94 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		68.77 148	66.71 170	74.95 90	75.93 210	58.55 79	80.50 100	75.84 210	56.09 175	68.17 138	83.74 143	28.50 309	82.98 152	65.67 96	65.91 272	83.33 172

RRT_test8_iter05		68.17 165	66.86 169	72.07 158	75.81 211	46.33 241	76.41 170	81.81 98	56.43 166	66.52 167	81.30 191	31.90 289	84.25 126	63.77 115	67.83 259	85.64 97

SixPastTwentyTwo		61.65 247	58.80 255	70.20 198	75.80 212	47.22 234	75.59 185	69.68 267	54.61 204	54.11 303	79.26 237	27.07 320	82.96 153	43.27 263	49.79 338	80.41 226

baseline		74.61 61	74.70 56	74.34 107	75.70 213	49.99 200	77.54 145	84.63 40	62.73 58	73.98 56	87.79 72	57.67 27	83.82 137	69.49 64	82.74 83	89.20 3

Baseline_NR-MVSNet		67.05 184	67.56 148	65.50 257	75.65 214	37.70 313	75.42 187	74.65 229	59.90 107	68.14 140	83.15 154	49.12 110	77.20 254	52.23 195	69.78 233	81.60 204

jajsoiax		68.25 161	66.45 174	73.66 124	75.62 215	55.49 129	80.82 94	78.51 168	52.33 226	64.33 208	84.11 134	28.28 311	81.81 179	63.48 117	70.62 216	83.67 163

mvs_tets		68.18 163	66.36 180	73.63 127	75.61 216	55.35 132	80.77 95	78.56 166	52.48 225	64.27 210	84.10 135	27.45 317	81.84 178	63.45 118	70.56 218	83.69 162

casdiffmvs		74.80 56	74.89 54	74.53 103	75.59 217	50.37 193	78.17 134	85.06 32	62.80 57	74.40 52	87.86 70	57.88 24	83.61 141	69.46 66	82.79 82	89.59 2

PVSNet	50.76 19	58.40 265	57.39 265	61.42 285	75.53 218	44.04 266	61.43 312	63.45 306	47.04 283	56.91 277	73.61 300	27.00 321	64.76 312	39.12 289	72.40 198	75.47 282

MVS		67.37 176	66.33 181	70.51 194	75.46 219	50.94 181	73.95 214	81.85 97	41.57 324	62.54 228	78.57 246	47.98 120	85.47 103	52.97 191	82.05 88	75.14 284

nrg030		72.96 79	73.01 76	72.84 144	75.41 220	50.24 194	80.02 105	82.89 83	58.36 136	74.44 51	86.73 82	58.90 19	80.83 200	65.84 94	74.46 166	87.44 39

thres200		62.20 240	61.16 242	65.34 260	75.38 221	39.99 294	69.60 272	69.29 273	55.64 187	61.87 237	76.99 263	37.07 242	78.96 235	31.28 332	73.28 183	77.06 266

TransMVSNet (Re)		64.72 215	64.33 204	65.87 254	75.22 222	38.56 306	74.66 205	75.08 226	58.90 126	61.79 238	82.63 160	51.18 88	78.07 243	43.63 261	55.87 324	80.99 218

MS-PatchMatch		62.42 237	61.46 237	65.31 261	75.21 223	52.10 170	72.05 242	74.05 236	46.41 286	57.42 276	74.36 294	34.35 261	77.57 251	45.62 244	73.67 174	66.26 337

IB-MVS	56.42 12	65.40 210	62.73 224	73.40 134	74.89 224	52.78 159	73.09 228	75.13 222	55.69 184	58.48 269	73.73 299	32.86 276	86.32 81	50.63 205	70.11 226	81.10 216
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	72.42 155	74.88 225	48.50 219	76.28 173	83.14 78	59.40 118	72.46 83	84.68 122	55.66 41	81.12 192	65.98 93	79.66 115	87.63 31

CANet_DTU		68.18 163	67.71 147	69.59 209	74.83 226	46.24 243	78.66 125	76.85 198	59.60 113	63.45 216	82.09 177	35.25 251	77.41 252	59.88 145	78.76 132	85.14 114

tfpnnormal		62.47 236	61.63 235	64.99 263	74.81 227	39.01 302	71.22 253	73.72 239	55.22 193	60.21 247	80.09 219	41.26 204	76.98 258	30.02 336	68.09 256	78.97 247

Vis-MVSNet (Re-imp)		63.69 224	63.88 208	63.14 274	74.75 228	31.04 345	71.16 255	63.64 305	56.32 168	59.80 254	84.99 118	44.51 166	75.46 267	39.12 289	80.62 99	82.92 184

HY-MVS	56.14 13	64.55 219	63.89 207	66.55 241	74.73 229	41.02 290	69.96 270	74.43 230	49.29 256	61.66 239	80.92 199	47.43 131	76.68 260	44.91 252	71.69 206	81.94 200

COLMAP_ROB	52.97 17	61.27 252	58.81 254	68.64 222	74.63 230	52.51 165	78.42 132	73.30 242	49.92 253	50.96 319	81.51 187	23.06 335	79.40 220	31.63 328	65.85 273	74.01 300
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 245	61.35 238	63.46 270	74.58 231	31.48 344	61.42 313	58.14 326	58.71 129	53.02 313	79.55 230	43.07 178	76.80 259	45.69 242	77.96 139	82.11 199

test_djsdf		69.45 137	67.74 144	74.58 101	74.57 232	54.92 136	82.79 63	78.48 169	51.26 240	65.41 188	83.49 149	38.37 225	83.24 147	66.06 90	69.25 243	85.56 99

EI-MVSNet		69.27 139	68.44 137	71.73 163	74.47 233	49.39 210	75.20 192	78.45 172	59.60 113	69.16 125	76.51 272	51.29 86	82.50 167	59.86 147	71.45 210	83.30 173

CVMVSNet		59.63 260	59.14 253	61.08 288	74.47 233	38.84 304	75.20 192	68.74 277	31.15 344	58.24 270	76.51 272	32.39 286	68.58 297	49.77 210	65.84 274	75.81 278

IterMVS-LS		69.22 141	68.48 134	71.43 172	74.44 235	49.40 209	76.23 174	77.55 187	59.60 113	65.85 182	81.59 186	51.28 87	81.58 184	59.87 146	69.90 231	83.30 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 145	67.47 153	72.82 146	74.40 236	56.87 107	70.59 263	79.04 154	54.77 203	66.99 160	86.01 103	39.57 213	78.21 241	62.54 123	73.33 182	83.37 171

XVG-OURS		68.76 149	67.37 156	72.90 143	74.32 237	57.22 98	70.09 269	78.81 158	55.24 192	67.79 151	85.81 110	36.54 246	78.28 240	62.04 128	75.74 160	83.19 178

OpenMVS	61.03 9	68.85 144	67.56 148	72.70 148	74.26 238	53.99 142	81.21 91	81.34 113	52.70 222	62.75 223	85.55 113	38.86 221	84.14 128	48.41 223	83.01 74	79.97 233

MIMVSNet		57.35 272	57.07 268	58.22 299	74.21 239	37.18 314	62.46 308	60.88 319	48.88 260	55.29 290	75.99 280	31.68 290	62.04 320	31.87 323	72.35 199	75.43 283

SCA		60.49 254	58.38 259	66.80 237	74.14 240	48.06 224	63.35 305	63.23 308	49.13 258	59.33 260	72.10 306	37.45 234	74.27 273	44.17 254	62.57 297	78.05 254

thisisatest0515		65.83 202	63.50 215	72.82 146	73.75 241	49.50 208	71.32 251	73.12 245	49.39 255	63.82 214	76.50 274	34.95 255	84.84 117	53.20 190	75.49 163	84.13 145

K. test v3		60.47 255	57.11 267	70.56 192	73.74 242	48.22 222	75.10 196	62.55 312	58.27 138	53.62 308	76.31 276	27.81 314	81.59 183	47.42 226	39.18 349	81.88 202

v10		70.21 119	69.02 126	73.81 115	73.51 243	50.92 183	78.74 123	81.39 108	60.05 105	66.39 171	81.83 180	47.58 127	85.41 106	62.80 121	68.86 250	85.09 117

v1144		70.42 115	69.31 121	73.76 118	73.22 244	50.64 188	77.83 138	81.43 107	58.58 130	69.40 119	81.16 192	47.53 128	85.29 108	64.01 110	70.64 215	85.34 109

v1192		69.97 124	68.68 131	73.85 114	73.19 245	50.94 181	77.68 141	81.36 109	57.51 147	68.95 127	80.85 202	45.28 159	85.33 107	62.97 120	70.37 221	85.27 112

v8		70.33 117	69.28 122	73.49 129	73.15 246	50.22 195	78.62 126	80.78 129	60.79 86	66.45 170	82.11 176	49.35 103	84.98 111	63.58 116	68.71 251	85.28 111

v144192		69.71 127	68.51 133	73.33 136	73.10 247	50.13 197	77.54 145	80.64 130	56.65 157	68.57 131	80.55 207	46.87 142	84.96 113	62.98 119	69.66 237	84.89 123

v1921920		69.47 136	68.17 141	73.36 135	73.06 248	50.10 198	77.39 148	80.56 131	56.58 164	68.59 129	80.37 209	44.72 165	84.98 111	62.47 125	69.82 232	85.00 119

PatchmatchNet		59.84 258	58.24 260	64.65 265	73.05 249	46.70 238	69.42 274	62.18 314	47.55 275	58.88 263	71.96 308	34.49 259	69.16 294	42.99 267	63.60 289	78.07 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 140	67.91 143	73.25 139	73.02 250	49.82 201	77.21 154	80.54 132	56.43 166	68.34 135	80.51 208	43.33 177	84.99 109	62.03 129	69.77 235	84.95 122

Fast-Effi-MVS+-dtu		67.37 176	65.33 198	73.48 130	72.94 251	57.78 90	77.47 147	76.88 197	57.60 146	61.97 235	76.85 266	39.31 215	80.49 208	54.72 175	70.28 224	82.17 198

EPNet_dtu		61.90 243	61.97 232	61.68 283	72.89 252	39.78 296	75.85 183	65.62 293	55.09 196	54.56 299	79.36 234	37.59 233	67.02 304	39.80 287	76.95 150	78.25 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 241	60.10 249	67.99 228	72.79 253	43.86 267	71.05 259	66.85 287	43.14 315	62.77 221	75.39 287	38.32 226	80.80 201	41.69 277	68.88 249	79.32 243

MDTV_nov1_ep13			57.00 269		72.73 254	38.26 308	65.02 300	64.73 300	44.74 298	55.46 286	72.48 304	32.61 284	70.47 287	37.47 296	67.75 261

MSDG		61.81 246	59.23 252	69.55 212	72.64 255	52.63 161	70.45 266	75.81 211	51.38 237	53.70 306	76.11 277	29.52 302	81.08 195	37.70 295	65.79 275	74.93 289

gg-mvs-nofinetune		57.86 270	56.43 274	62.18 280	72.62 256	35.35 329	66.57 285	56.33 333	50.65 245	57.64 274	57.10 345	30.65 293	76.36 263	37.38 297	78.88 128	74.82 291

v2v482		70.50 113	69.45 120	73.66 124	72.62 256	50.03 199	77.58 142	80.51 133	59.90 107	69.52 115	82.14 175	47.53 128	84.88 116	65.07 103	70.17 225	86.09 79

baseline2		63.42 226	61.26 240	69.89 206	72.55 258	47.62 230	71.54 247	68.38 279	50.11 249	54.82 295	75.55 285	43.06 179	80.96 196	48.13 224	67.16 265	81.11 215

v7n		69.01 143	67.36 157	73.98 112	72.51 259	52.65 160	78.54 129	81.30 114	60.26 102	62.67 224	81.62 183	43.61 174	84.49 122	57.01 158	68.70 252	84.79 127

pm-mvs1		65.24 211	64.97 202	66.04 250	72.38 260	39.40 300	72.62 234	75.63 213	55.53 188	62.35 234	83.18 153	47.45 130	76.47 262	49.06 218	66.54 268	82.24 195

XVG-ACMP-BASELINE		64.36 220	62.23 229	70.74 189	72.35 261	52.45 167	70.80 262	78.45 172	53.84 213	59.87 252	81.10 194	16.24 347	79.32 222	55.64 169	71.76 205	80.47 224

WTY-MVS		59.75 259	60.39 247	57.85 303	72.32 262	37.83 311	61.05 317	64.18 303	45.95 293	61.91 236	79.11 239	47.01 140	60.88 323	42.50 271	69.49 239	74.83 290

tpm cat1		59.25 261	56.95 270	66.15 247	72.19 263	46.96 236	68.09 280	65.76 292	40.03 332	57.81 273	70.56 316	38.32 226	74.51 271	38.26 293	61.50 305	77.00 268

mvs_anonymous		68.03 166	67.51 152	69.59 209	72.08 264	44.57 262	71.99 243	75.23 219	51.67 230	67.06 159	82.57 162	54.68 49	77.94 244	56.56 159	75.71 161	86.26 76

OurMVSNet-221017-0		61.37 251	58.63 257	69.61 208	72.05 265	48.06 224	73.93 217	72.51 248	47.23 281	54.74 296	80.92 199	21.49 342	81.24 190	48.57 222	56.22 323	79.53 241

IterMVS-SCA-FT		62.49 235	61.52 236	65.40 259	71.99 266	50.80 186	71.15 256	69.63 268	45.71 294	60.61 245	77.93 251	37.45 234	65.99 309	55.67 168	63.50 290	79.42 242

DWT-MVSNet_test		61.90 243	59.93 250	67.83 229	71.98 267	46.09 246	71.03 260	69.71 265	50.09 250	58.51 268	70.62 315	30.21 298	77.63 249	49.28 216	67.91 257	79.78 238

CostFormer		64.04 221	62.51 225	68.61 223	71.88 268	45.77 248	71.30 252	70.60 261	47.55 275	64.31 209	76.61 270	41.63 194	79.62 218	49.74 211	69.00 248	80.42 225

1314		64.61 218	63.21 218	68.80 220	71.87 269	47.46 232	73.95 214	78.39 177	42.88 317	59.97 250	76.60 271	38.11 229	79.39 221	54.84 174	72.32 200	79.55 240

tpm		57.34 273	58.16 261	54.86 313	71.80 270	34.77 331	67.47 284	56.04 336	48.20 268	60.10 248	76.92 264	37.17 239	53.41 347	40.76 282	65.01 279	76.40 274

eth_miper_zixun_eth		67.63 172	66.28 184	71.67 165	71.60 271	48.33 221	73.68 222	77.88 180	55.80 182	65.91 179	78.62 245	47.35 134	82.88 156	59.45 149	66.25 270	83.81 155

pmmvs4		61.48 250	59.39 251	67.76 230	71.57 272	53.86 143	71.42 248	65.34 295	44.20 305	59.46 256	77.92 252	35.90 247	74.71 270	43.87 259	64.87 280	74.71 293

AllTest		57.08 275	54.65 284	64.39 266	71.44 273	49.03 211	69.92 271	67.30 282	45.97 291	47.16 332	79.77 224	17.47 344	67.56 301	33.65 317	59.16 314	76.57 272

TestCases				64.39 266	71.44 273	49.03 211		67.30 282	45.97 291	47.16 332	79.77 224	17.47 344	67.56 301	33.65 317	59.16 314	76.57 272

lessismore_v0				69.91 204	71.42 275	47.80 226		50.90 344		50.39 325	75.56 284	27.43 318	81.33 187	45.91 240	34.10 351	80.59 223

gm-plane-assit					71.40 276	41.72 287			48.85 261		73.31 301		82.48 169	48.90 219

GG-mvs-BLEND				62.34 279	71.36 277	37.04 318	69.20 276	57.33 330		54.73 297	65.48 337	30.37 295	77.82 246	34.82 313	74.93 165	72.17 318

FMVSNet5		55.86 282	54.93 282	58.66 298	71.05 278	36.35 324	64.18 304	62.48 313	46.76 284	50.66 324	74.73 292	25.80 327	64.04 314	33.11 319	65.57 276	75.59 281

cl_fuxian		68.33 159	67.56 148	70.62 191	70.87 279	46.21 244	74.47 208	78.80 159	56.22 172	66.19 174	78.53 247	51.88 80	81.40 186	62.08 126	69.04 246	84.25 140

GA-MVS		65.53 207	63.70 211	71.02 185	70.87 279	48.10 223	70.48 265	74.40 231	56.69 156	64.70 204	76.77 267	33.66 268	81.10 193	55.42 171	70.32 223	83.87 153

bset_n11_16_dypcd		65.57 206	63.69 212	71.19 179	70.84 281	51.79 175	71.37 249	70.48 262	53.33 217	65.19 197	76.41 275	31.46 291	81.76 180	65.12 101	69.04 246	80.01 232

pmmvs6		63.69 224	62.82 223	66.27 245	70.63 282	39.27 301	73.13 227	75.47 216	52.69 223	59.75 255	82.30 169	39.71 212	77.03 257	47.40 227	64.35 284	82.53 190

miper_ehance_all_eth		68.03 166	67.24 164	70.40 195	70.54 283	46.21 244	73.98 213	78.68 163	55.07 198	66.05 176	77.80 256	52.16 78	81.31 188	61.53 135	69.32 240	83.67 163

OpenMVS_ROB	52.78 18	60.03 256	58.14 262	65.69 256	70.47 284	44.82 257	75.33 188	70.86 259	45.04 296	56.06 282	76.00 278	26.89 322	79.65 216	35.36 312	67.29 263	72.60 309

v148		68.24 162	67.19 165	71.40 173	70.43 285	47.77 228	75.76 184	77.03 196	58.91 125	67.36 155	80.10 218	48.60 116	81.89 176	60.01 144	66.52 269	84.53 132

XXY-MVS		60.68 253	61.67 234	57.70 305	70.43 285	38.45 307	64.19 303	66.47 288	48.05 270	63.22 217	80.86 201	49.28 105	60.47 324	45.25 251	67.28 264	74.19 298

MVSTER		67.16 182	65.58 195	71.88 160	70.37 287	49.70 203	70.25 268	78.45 172	51.52 234	69.16 125	80.37 209	38.45 224	82.50 167	60.19 142	71.46 209	83.44 170

cl-mvsnet_		67.18 180	66.26 185	69.94 202	70.20 288	45.74 249	73.30 224	76.83 199	55.10 194	65.27 190	79.57 229	47.39 132	80.53 206	59.41 151	69.22 244	83.53 169

cl-mvsnet1		67.18 180	66.26 185	69.94 202	70.20 288	45.74 249	73.29 225	76.83 199	55.10 194	65.27 190	79.58 228	47.38 133	80.53 206	59.43 150	69.22 244	83.54 168

tpmvs		58.47 264	56.95 270	63.03 276	70.20 288	41.21 289	67.90 282	67.23 285	49.62 254	54.73 297	70.84 313	34.14 262	76.24 265	36.64 304	61.29 306	71.64 320

anonymousdsp		67.00 186	64.82 203	73.57 128	70.09 291	56.13 115	76.35 171	77.35 192	48.43 265	64.99 201	80.84 203	33.01 274	80.34 209	64.66 105	67.64 262	84.23 141

MVS_0304		58.51 263	57.36 266	61.96 282	70.04 292	41.83 283	69.40 275	65.46 294	50.73 243	53.30 312	74.06 297	22.65 336	70.18 292	42.16 273	68.44 253	73.86 302

MIMVSNet1		55.17 288	54.31 289	57.77 304	70.03 293	32.01 342	65.68 292	64.81 298	49.19 257	46.75 335	76.00 278	25.53 329	64.04 314	28.65 340	62.13 300	77.26 264

CR-MVSNet		59.91 257	57.90 264	65.96 251	69.96 294	52.07 171	65.31 297	63.15 309	42.48 319	59.36 257	74.84 290	35.83 248	70.75 286	45.50 246	64.65 282	75.06 285

RPMNet		61.53 248	58.42 258	70.86 186	69.96 294	52.07 171	65.31 297	81.36 109	43.20 314	59.36 257	70.15 321	35.37 250	85.47 103	36.42 307	64.65 282	75.06 285

cl-mvsnet2		67.47 175	66.45 174	70.54 193	69.85 296	46.49 239	73.85 219	77.35 192	55.07 198	65.51 186	77.92 252	47.64 126	81.10 193	61.58 134	69.32 240	84.01 148

Anonymous20231206		55.10 289	55.30 281	54.48 315	69.81 297	33.94 336	62.91 307	62.13 315	41.08 326	55.18 291	75.65 283	32.75 280	56.59 339	30.32 335	67.86 258	72.91 305

our_test_3		56.49 276	54.42 286	62.68 278	69.51 298	45.48 254	66.08 289	61.49 317	44.11 308	50.73 323	69.60 325	33.05 273	68.15 298	38.38 292	56.86 320	74.40 295

ppachtmachnet_test		58.06 269	55.38 280	66.10 249	69.51 298	48.99 214	68.01 281	66.13 291	44.50 302	54.05 304	70.74 314	32.09 288	72.34 279	36.68 303	56.71 322	76.99 270

diffmvs		70.69 109	70.43 106	71.46 169	69.45 300	48.95 215	72.93 229	78.46 171	57.27 149	71.69 90	83.97 139	51.48 85	77.92 245	70.70 59	77.95 140	87.53 35

IterMVS		62.79 234	61.27 239	67.35 234	69.37 301	52.04 173	71.17 254	68.24 280	52.63 224	59.82 253	76.91 265	37.32 236	72.36 278	52.80 192	63.19 293	77.66 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 183	66.09 187	70.17 199	69.21 302	45.98 247	72.85 231	78.41 175	51.38 237	65.65 184	75.98 281	51.17 89	81.25 189	60.82 137	69.32 240	83.29 175

Patchmtry		57.16 274	56.47 273	59.23 292	69.17 303	34.58 333	62.98 306	63.15 309	44.53 301	56.83 278	74.84 290	35.83 248	68.71 296	40.03 285	60.91 307	74.39 296

CL-MVSNet_2432*1600		61.53 248	60.94 244	63.30 272	68.95 304	36.93 319	67.60 283	72.80 247	55.67 185	59.95 251	76.63 268	45.01 162	72.22 281	39.74 288	62.09 301	80.74 222

V42		68.65 150	67.35 158	72.56 150	68.93 305	50.18 196	72.90 230	79.47 149	56.92 154	69.45 118	80.26 215	46.29 146	82.99 151	64.07 108	67.82 260	84.53 132

test-LLR		58.15 268	58.13 263	58.22 299	68.57 306	44.80 258	65.46 294	57.92 327	50.08 251	55.44 287	69.82 323	32.62 282	57.44 334	49.66 213	73.62 175	72.41 314

test-mter		56.42 278	55.82 277	58.22 299	68.57 306	44.80 258	65.46 294	57.92 327	39.94 333	55.44 287	69.82 323	21.92 339	57.44 334	49.66 213	73.62 175	72.41 314

MVS-HIRNet		45.52 314	44.48 317	48.65 329	68.49 308	34.05 335	59.41 322	44.50 353	27.03 348	37.96 349	50.47 351	26.16 326	64.10 313	26.74 345	59.52 312	47.82 348

dp		51.89 303	51.60 301	52.77 322	68.44 309	32.45 341	62.36 309	54.57 338	44.16 306	49.31 327	67.91 328	28.87 308	56.61 338	33.89 316	54.89 326	69.24 334

PatchT		53.17 299	53.44 296	52.33 324	68.29 310	25.34 355	58.21 324	54.41 339	44.46 303	54.56 299	69.05 326	33.32 271	60.94 322	36.93 299	61.76 304	70.73 326

Patchmatch-RL test		58.16 267	55.49 279	66.15 247	67.92 311	48.89 216	60.66 318	51.07 343	47.86 272	59.36 257	62.71 342	34.02 264	72.27 280	56.41 160	59.40 313	77.30 262

pmmvs-eth3d		58.81 262	56.31 275	66.30 244	67.61 312	52.42 168	72.30 239	64.76 299	43.55 311	54.94 294	74.19 296	28.95 306	72.60 277	43.31 262	57.21 319	73.88 301

PVSNet_0	43.31 20	47.46 313	45.64 316	52.92 321	67.60 313	44.65 260	54.06 336	54.64 337	41.59 323	46.15 336	58.75 344	30.99 292	58.66 330	32.18 321	24.81 352	55.46 346

CHOSEN 280x420		47.83 311	46.36 315	52.24 325	67.37 314	49.78 202	38.91 353	43.11 354	35.00 340	43.27 343	63.30 341	28.95 306	49.19 350	36.53 305	60.80 309	57.76 344

tpmrst		58.24 266	58.70 256	56.84 306	66.97 315	34.32 334	69.57 273	61.14 318	47.17 282	58.58 267	71.60 309	41.28 203	60.41 325	49.20 217	62.84 295	75.78 279

sss		56.17 281	56.57 272	54.96 312	66.93 316	36.32 326	57.94 325	61.69 316	41.67 322	58.64 266	75.32 288	38.72 222	56.25 340	42.04 275	66.19 271	72.31 317

TinyColmap		54.14 290	51.72 300	61.40 286	66.84 317	41.97 281	66.52 286	68.51 278	44.81 297	42.69 344	75.77 282	11.66 352	72.94 276	31.96 322	56.77 321	69.27 333

miper_lstm_enhance		62.03 242	60.88 245	65.49 258	66.71 318	46.25 242	56.29 331	75.70 212	50.68 244	61.27 242	75.48 286	40.21 208	68.03 299	56.31 161	65.25 278	82.18 196

TESTMET0.1,1		55.28 286	54.90 283	56.42 307	66.56 319	43.67 269	65.46 294	56.27 334	39.18 335	53.83 305	67.44 331	24.21 334	55.46 344	48.04 225	73.11 188	70.13 329

D2MVS		62.30 239	60.29 248	68.34 226	66.46 320	48.42 220	65.70 291	73.42 241	47.71 273	58.16 271	75.02 289	30.51 294	77.71 248	53.96 182	71.68 207	78.90 248

MDA-MVSNet-bldmvs		53.87 293	50.81 304	63.05 275	66.25 321	48.58 218	56.93 329	63.82 304	48.09 269	41.22 345	70.48 319	30.34 296	68.00 300	34.24 315	45.92 343	72.57 310

ITE_SJBPF				62.09 281	66.16 322	44.55 263		64.32 302	47.36 278	55.31 289	80.34 211	19.27 343	62.68 318	36.29 308	62.39 299	79.04 245

EPMVS		53.96 291	53.69 294	54.79 314	66.12 323	31.96 343	62.34 310	49.05 346	44.42 304	55.54 285	71.33 311	30.22 297	56.70 337	41.65 279	62.54 298	75.71 280

ADS-MVSNet2		51.33 305	48.76 310	59.07 295	66.02 324	44.60 261	50.90 340	59.76 321	36.90 336	50.74 321	66.18 335	26.38 323	63.11 316	27.17 342	54.76 327	69.50 331

ADS-MVSNet		48.48 310	47.77 312	50.63 327	66.02 324	29.92 347	50.90 340	50.87 345	36.90 336	50.74 321	66.18 335	26.38 323	52.47 348	27.17 342	54.76 327	69.50 331

EU-MVSNet		55.61 284	54.41 287	59.19 294	65.41 326	33.42 338	72.44 237	71.91 252	28.81 346	51.27 317	73.87 298	24.76 332	69.08 295	43.04 266	58.20 317	75.06 285

RPSCF		55.80 283	54.22 291	60.53 289	65.13 327	42.91 276	64.30 302	57.62 329	36.84 338	58.05 272	82.28 170	28.01 312	56.24 341	37.14 298	58.61 316	82.44 194

USDC		56.35 279	54.24 290	62.69 277	64.74 328	40.31 293	65.05 299	73.83 238	43.93 309	47.58 330	77.71 259	15.36 349	75.05 269	38.19 294	61.81 303	72.70 308

JIA-IIPM		51.56 304	47.68 314	63.21 273	64.61 329	50.73 187	47.71 345	58.77 324	42.90 316	48.46 329	51.72 348	24.97 331	70.24 291	36.06 309	53.89 330	68.64 335

Patchmatch-test		49.08 309	48.28 311	51.50 326	64.40 330	30.85 346	45.68 347	48.46 349	35.60 339	46.10 337	72.10 306	34.47 260	46.37 351	27.08 344	60.65 310	77.27 263

TDRefinement		53.44 297	50.72 305	61.60 284	64.31 331	46.96 236	70.89 261	65.27 297	41.78 320	44.61 340	77.98 249	11.52 353	66.36 307	28.57 341	51.59 334	71.49 321

N_pmnet		39.35 320	40.28 321	36.54 336	63.76 332	1.62 366	49.37 343	0.76 366	34.62 341	43.61 342	66.38 334	26.25 325	42.57 354	26.02 347	51.77 333	65.44 338

ambc				65.13 262	63.72 333	37.07 317	47.66 346	78.78 160		54.37 302	71.42 310	11.24 354	80.94 197	45.64 243	53.85 331	77.38 261

KD-MVS_2432*1600		53.45 295	51.50 302	59.30 290	62.82 334	37.14 315	55.33 332	71.79 253	47.34 279	55.09 292	70.52 317	21.91 340	70.45 288	35.72 310	42.97 346	70.31 327

miper_refine_blended		53.45 295	51.50 302	59.30 290	62.82 334	37.14 315	55.33 332	71.79 253	47.34 279	55.09 292	70.52 317	21.91 340	70.45 288	35.72 310	42.97 346	70.31 327

test0.0.03 1		53.32 298	53.59 295	52.50 323	62.81 336	29.45 348	59.51 320	54.11 340	50.08 251	54.40 301	74.31 295	32.62 282	55.92 342	30.50 334	63.95 287	72.15 319

PMMVS		53.96 291	53.26 297	56.04 308	62.60 337	50.92 183	61.17 316	56.09 335	32.81 342	53.51 310	66.84 333	34.04 263	59.93 327	44.14 256	68.18 255	57.27 345

PM-MVS		52.33 301	50.19 306	58.75 297	62.10 338	45.14 256	65.75 290	40.38 355	43.60 310	53.52 309	72.65 303	9.16 358	65.87 310	50.41 206	54.18 329	65.24 339

Gipuma		34.77 323	31.91 327	43.33 334	62.05 339	37.87 310	20.39 356	67.03 286	23.23 352	18.41 356	25.84 355	4.24 361	62.73 317	14.71 352	51.32 335	29.38 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 293	54.02 292	53.41 319	61.47 340	28.11 350	61.30 314	59.21 322	51.34 239	52.09 315	77.43 260	33.29 272	58.55 331	29.76 337	60.27 311	73.58 303

pmmvs5		56.47 277	55.68 278	58.86 296	61.41 341	36.71 321	66.37 287	62.75 311	40.38 330	53.70 306	76.62 269	34.56 257	67.05 303	40.02 286	65.27 277	72.83 307

MDA-MVSNet_test_wron		50.71 307	48.95 308	56.00 310	61.17 342	41.84 282	51.90 339	56.45 331	40.96 327	44.79 339	67.84 329	30.04 300	55.07 346	36.71 302	50.69 337	71.11 325

YYNet1		50.73 306	48.96 307	56.03 309	61.10 343	41.78 284	51.94 338	56.44 332	40.94 328	44.84 338	67.80 330	30.08 299	55.08 345	36.77 300	50.71 336	71.22 322

CMPMVS	42.80 21	57.81 271	55.97 276	63.32 271	60.98 344	47.38 233	64.66 301	69.50 270	32.06 343	46.83 334	77.80 256	29.50 303	71.36 283	48.68 220	73.75 173	71.21 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 308	48.87 309	53.66 317	60.97 345	33.67 337	57.62 327	64.56 301	39.47 334	47.38 331	64.02 340	27.47 316	59.32 328	34.69 314	43.68 345	67.98 336

Anonymous20240521		55.30 285	54.41 287	57.96 302	60.92 346	41.73 285	71.09 258	71.06 258	41.18 325	48.65 328	73.31 301	16.93 346	59.25 329	42.54 270	64.01 285	72.90 306

testgi		51.90 302	52.37 299	50.51 328	60.39 347	23.55 357	58.42 323	58.15 325	49.03 259	51.83 316	79.21 238	22.39 337	55.59 343	29.24 339	62.64 296	72.40 316

UnsupCasMVSNet_eth		53.16 300	52.47 298	55.23 311	59.45 348	33.39 339	59.43 321	69.13 274	45.98 290	50.35 326	72.32 305	29.30 305	58.26 332	42.02 276	44.30 344	74.05 299

new-patchmatchnet		47.56 312	47.73 313	47.06 330	58.81 349	9.37 363	48.78 344	59.21 322	43.28 312	44.22 341	68.66 327	25.67 328	57.20 336	31.57 330	49.35 339	74.62 294

FPMVS		42.18 317	41.11 320	45.39 331	58.03 350	41.01 291	49.50 342	53.81 342	30.07 345	33.71 350	64.03 338	11.69 351	52.08 349	14.01 353	55.11 325	43.09 350

DIV-MVS_2432*1600		55.22 287	53.89 293	59.21 293	57.80 351	27.47 351	57.75 326	74.32 232	47.38 277	50.90 320	70.00 322	28.45 310	70.30 290	40.44 283	57.92 318	79.87 235

new_pmnet		34.13 324	34.29 325	33.64 337	52.63 352	18.23 361	44.43 350	33.90 358	22.81 353	30.89 351	53.18 346	10.48 356	35.72 358	20.77 350	39.51 348	46.98 349

pmmvs3		44.92 315	41.95 319	53.86 316	52.58 353	43.55 270	62.11 311	46.90 352	26.05 350	40.63 346	60.19 343	11.08 355	57.91 333	31.83 327	46.15 342	60.11 341

DSMNet-mixed		39.30 321	38.72 322	41.03 335	51.22 354	19.66 359	45.53 348	31.35 359	15.83 357	39.80 348	67.42 332	22.19 338	45.13 352	22.43 348	52.69 332	58.31 343

LF4IMVS		42.95 316	42.26 318	45.04 332	48.30 355	32.50 340	54.80 334	48.49 348	28.03 347	40.51 347	70.16 320	9.24 357	43.89 353	31.63 328	49.18 340	58.72 342

wuyk23d		13.32 330	12.52 333	15.71 342	47.54 356	26.27 352	31.06 355	1.98 365	4.93 360	5.18 362	1.94 362	0.45 367	18.54 360	6.81 360	12.83 357	2.33 358

LCM-MVSNet		40.30 319	35.88 324	53.57 318	42.24 357	29.15 349	45.21 349	60.53 320	22.23 354	28.02 352	50.98 350	3.72 363	61.78 321	31.22 333	38.76 350	69.78 330

E-PMN		23.77 326	22.73 330	26.90 340	42.02 358	20.67 358	42.66 351	35.70 356	17.43 355	10.28 360	25.05 356	6.42 360	42.39 355	10.28 356	14.71 355	17.63 354

EMVS		22.97 327	21.84 331	26.36 341	40.20 359	19.53 360	41.95 352	34.64 357	17.09 356	9.73 361	22.83 357	7.29 359	42.22 356	9.18 358	13.66 356	17.32 355

ANet_high		41.38 318	37.47 323	53.11 320	39.73 360	24.45 356	56.94 328	69.69 266	47.65 274	26.04 353	52.32 347	12.44 350	62.38 319	21.80 349	10.61 358	72.49 311

PMMVS2		27.40 325	25.91 328	31.87 339	39.46 361	6.57 364	31.17 354	28.52 360	23.96 351	20.45 355	48.94 352	4.20 362	37.94 357	16.51 351	19.97 353	51.09 347

PMVS	28.69 22	36.22 322	33.29 326	45.02 333	36.82 362	35.98 328	54.68 335	48.74 347	26.31 349	21.02 354	51.61 349	2.88 365	60.10 326	9.99 357	47.58 341	38.99 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 328	17.77 332	32.34 338	34.34 363	25.44 354	16.11 357	24.11 361	11.19 358	13.22 358	31.92 353	1.58 366	30.95 359	10.47 355	17.03 354	40.62 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 343	17.97 364	10.91 362		10.60 364	7.46 359	11.07 359	28.36 354	3.28 364	11.29 361	8.01 359	9.74 360	13.89 356

tmp_tt		9.43 331	11.14 334	4.30 344	2.38 365	4.40 365	13.62 358	16.08 363	0.39 361	15.89 357	13.06 358	15.80 348	5.54 362	12.63 354	10.46 359	2.95 357

testmvs		4.52 334	6.03 337	0.01 346	0.01 366	0.00 368	53.86 337	0.00 367	0.01 362	0.04 363	0.27 363	0.00 369	0.00 363	0.04 361	0.00 361	0.03 360

test123		4.73 333	6.30 336	0.02 345	0.01 366	0.01 367	56.36 330	0.00 367	0.01 362	0.04 363	0.21 364	0.01 368	0.00 363	0.03 362	0.00 361	0.04 359

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

cdsmvs_eth3d_5k		17.50 329	23.34 329	0.00 347	0.00 368	0.00 368	0.00 359	78.63 164	0.00 364	0.00 365	82.18 171	49.25 106	0.00 363	0.00 363	0.00 361	0.00 361

pcd_1.5k_mvsjas		3.92 335	5.23 338	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	47.05 137	0.00 363	0.00 363	0.00 361	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

ab-mvs-re		6.49 332	8.65 335	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	77.89 254	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 368	0.00 359	0.00 367	0.00 364	0.00 365	0.00 365	0.00 369	0.00 363	0.00 363	0.00 361	0.00 361

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																78.05 254

sam_mvs1												34.74 256				78.05 254

sam_mvs												33.43 270

MTGPA								80.97 125

test_post1							68.67 278				3.64 360	32.39 286	69.49 293	44.17 254

test_post											3.55 361	33.90 265	66.52 306

patchmatchnet-post											64.03 338	34.50 258	74.27 273

MTMP							86.03 17	17.08 362

test9_res														75.28 28	88.31 33	83.81 155

agg_prior2														73.09 46	87.93 40	84.33 137

test_prior4						62.51 17	82.08 79

test_prior2							81.75 81		60.37 95	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

旧先验2							76.08 177		45.32 295	76.55 31			65.56 311	58.75 152

新几何2							76.12 175

无先验							79.66 114	74.30 233	48.40 266				80.78 202	53.62 184		79.03 246

原ACMM2							79.02 121

testdata2													72.18 282	46.95 233

segment_acmp												54.23 54

testdata1							72.65 232		60.50 91

plane_prior5								84.01 50					87.21 51	68.16 73	80.58 101	84.65 130

plane_prior4											86.10 100

plane_prior3						56.09 116			63.92 37	69.27 121

plane_prior2							84.22 38		64.52 26

plane_prior						56.31 110	83.58 53		63.19 47						80.48 104

n2								0.00 367

nn								0.00 367

door-mid								47.19 351

test11								83.47 65

door								47.60 350

HQP5-MVS						54.94 134

BP-MVS														67.04 84

HQP4-MVS										67.85 145			86.93 60			84.32 138

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 154

MDTV_nov1_ep13_2view						25.89 353	61.22 315		40.10 331	51.10 318		32.97 275		38.49 291		78.61 249

ACMMP++_ref															74.07 171

ACMMP++															72.16 202

Test By Simon												48.33 118

High-resolution multi-view benchmark