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 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 64	65.37 13	78.78 22	90.64 18	58.63 25	87.24 52	79.00 12	90.37 14	85.26 128

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 36	66.96 5	77.58 27	90.06 36	59.47 21	89.13 22	78.67 14	89.73 16	87.03 57

test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 41

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 20

IU-MVS					87.77 4	59.15 60		85.53 26	53.93 227	84.64 3				79.07 11	90.87 5	88.37 16

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 117
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 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

test_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

test_part2					87.58 9	60.47 42				83.42 12

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 20

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 44	66.73 8	74.67 55	89.38 49	55.30 46	89.18 21	74.19 46	87.34 44	86.38 76

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 60	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 14

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 31

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 31

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 49	62.81 57	73.30 71	90.58 20	49.90 111	88.21 34	73.78 50	87.03 46	86.29 87

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 47	62.82 55	73.55 69	90.56 21	49.80 113	88.24 33	74.02 48	87.03 46	86.32 84

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 46	62.82 55	73.96 64	90.50 23	53.20 72	88.35 31	74.02 48	87.05 45	86.13 90

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 164	74.91 48	88.19 62	59.15 23	87.68 48	73.67 51	87.45 43	86.57 73

ZD-MVS					86.64 21	60.38 43		82.70 89	57.95 149	78.10 24	90.06 36	56.12 42	88.84 26	74.05 47	87.00 49

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 19	89.76 16	78.70 13	88.32 31	86.79 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 22
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 91	70.73 103	76.40 60	86.57 24	57.99 79	81.15 87	82.96 84	57.03 161	66.78 177	85.56 126	44.50 182	88.11 38	51.77 218	80.23 114	83.10 197

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 68	63.89 37	73.60 68	90.60 19	54.85 51	86.72 66	77.20 25	88.06 36	85.74 107
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 37	75.93 41	78.34 36	86.47 26	63.50 3	85.74 25	82.28 93	62.90 52	71.77 98	90.26 31	46.61 158	86.55 72	71.71 65	85.66 61	84.97 138

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 45	60.61 91	79.05 21	90.30 30	55.54 45	88.32 32	73.48 53	87.03 46	84.83 141
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 43	90.47 25	53.96 61	88.68 27	76.48 28	89.63 20	87.16 55

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 54	64.55 23	72.17 95	90.01 40	47.95 133	88.01 40	71.55 67	86.74 53	86.37 78

X-MVStestdata		70.21 123	67.28 172	79.00 23	86.32 29	62.62 11	85.83 22	83.92 54	64.55 23	72.17 95	6.49 413	47.95 133	88.01 40	71.55 67	86.74 53	86.37 78

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 22	63.71 12	89.23 20	81.51 2	88.44 27	88.09 25
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
114514_t		70.83 110	69.56 122	74.64 91	86.21 31	54.63 136	82.34 70	81.81 100	48.22 296	63.01 246	85.83 122	40.92 220	87.10 58	57.91 166	79.79 116	82.18 214

save fliter					86.17 33	61.30 28	83.98 47	79.66 145	59.00 126

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 32	84.85 40	61.98 74	73.06 81	88.88 55	53.72 66	89.06 23	68.27 80	88.04 37	87.42 47
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 35	76.06 39	78.88 28	86.14 35	62.73 9	82.55 67	83.74 63	61.71 76	72.45 94	90.34 29	48.48 129	88.13 37	72.32 59	86.85 51	85.78 101

FOURS1					86.12 36	60.82 37	88.18 1	83.61 66	60.87 86	81.50 16

MTAPA		76.90 34	76.42 36	78.35 35	86.08 37	63.57 2	74.92 209	80.97 127	65.13 15	75.77 36	90.88 16	48.63 126	86.66 68	77.23 24	88.17 33	84.81 142

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 61	90.03 38	52.56 78	88.53 29	74.79 42	88.34 29	86.63 72

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 71	62.44 64	72.68 88	90.50 23	48.18 131	87.34 51	73.59 52	85.71 60	84.76 145

SR-MVS		76.13 43	75.70 44	77.40 48	85.87 40	61.20 29	85.52 27	82.19 94	59.99 108	75.10 42	90.35 28	47.66 138	86.52 73	71.64 66	82.99 80	84.47 151

新几何1				70.76 196	85.66 41	61.13 30		66.43 306	44.68 332	70.29 111	86.64 92	41.29 215	75.23 286	49.72 233	81.75 100	75.93 303

MG-MVS		73.96 65	73.89 63	74.16 106	85.65 42	49.69 217	81.59 82	81.29 117	61.45 78	71.05 105	88.11 63	51.77 93	87.73 47	61.05 146	83.09 78	85.05 134

TEST9					85.58 43	61.59 24	81.62 80	81.26 118	55.65 193	74.93 46	88.81 56	53.70 67	84.68 118

train_agg		76.27 40	76.15 38	76.64 57	85.58 43	61.59 24	81.62 80	81.26 118	55.86 185	74.93 46	88.81 56	53.70 67	84.68 118	75.24 38	88.33 30	83.65 182

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 49	85.16 31	62.88 53	78.10 24	91.26 13	52.51 79	88.39 30	79.34 8	90.52 13	86.78 66

test_8					85.40 46	60.96 34	81.54 83	81.18 121	55.86 185	74.81 51	88.80 58	53.70 67	84.45 122

原ACMM1				74.69 87	85.39 47	59.40 54		83.42 72	51.47 253	70.27 112	86.61 95	48.61 127	86.51 74	53.85 200	87.96 39	78.16 275

CDPH-MVS		76.31 39	75.67 45	78.22 37	85.35 48	59.14 62	81.31 85	84.02 50	56.32 177	74.05 62	88.98 54	53.34 71	87.92 43	69.23 78	88.42 28	87.59 42

ACMMP		76.02 44	75.33 48	78.07 38	85.20 49	61.91 20	85.49 29	84.44 43	63.04 49	69.80 123	89.74 46	45.43 171	87.16 56	72.01 62	82.87 85	85.14 130
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_prior					85.04 50	59.96 47		81.04 125		74.68 54			84.04 128

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 33	62.57 60	73.09 80	89.97 41	50.90 106	87.48 50	75.30 36	86.85 51	87.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 53	82.93 59	85.39 27	62.15 67	76.41 34	91.51 11	52.47 81	86.78 65	80.66 4	89.64 19	87.80 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 45	83.82 62	59.34 122	79.37 19	89.76 45	59.84 16	87.62 49	76.69 27	86.74 53	87.68 38
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 127	68.66 140	73.97 110	84.94 54	57.83 81	82.63 65	78.71 163	56.28 179	64.34 225	84.14 151	41.57 210	87.06 60	46.45 261	78.88 132	77.02 293

DP-MVS		65.68 216	63.66 227	71.75 169	84.93 55	56.87 99	80.74 92	73.16 254	53.06 234	59.09 294	82.35 188	36.79 264	85.94 87	32.82 356	69.96 255	72.45 339

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 44	83.03 57	85.33 28	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 24

CPTT-MVS		72.78 76	72.08 81	74.87 85	84.88 57	61.41 26	84.15 43	77.86 184	55.27 200	67.51 166	88.08 65	41.93 205	81.85 177	69.04 79	80.01 115	81.35 230

test12				77.76 43	84.52 58	58.41 75		83.36 75		72.93 84		54.61 54	88.05 39		88.12 34	86.81 64

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 34	83.87 59	60.37 98	79.89 18	89.38 49	54.97 49	85.58 95	76.12 31	84.94 64	86.33 82
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 63	73.46 68	76.80 52	84.45 60	59.04 66	83.65 52	81.05 124	60.15 105	70.43 109	89.84 43	41.09 219	85.59 94	67.61 89	82.90 84	85.77 104

test_prior				76.69 53	84.20 61	57.27 88		84.88 39					86.43 76			86.38 76

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 89	75.27 40	84.83 135	60.76 15	86.56 71	67.86 85	87.87 41	86.06 92

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 66	90.25 32	57.68 29	89.96 15	74.62 43	89.03 22	87.89 28
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 71	72.93 72	73.76 116	83.58 64	51.66 187	78.75 116	77.66 188	67.75 4	72.61 90	89.42 47	49.82 112	83.29 143	53.61 202	83.14 77	86.32 84

SR-MVS-dyc-post		74.57 59	73.90 62	76.58 58	83.49 65	59.87 49	84.29 37	81.36 111	58.07 144	73.14 77	90.07 34	44.74 178	85.84 89	68.20 81	81.76 98	84.03 161

RE-MVS-def			73.71 66		83.49 65	59.87 49	84.29 37	81.36 111	58.07 144	73.14 77	90.07 34	43.06 194		68.20 81	81.76 98	84.03 161

LFMVS		71.78 94	71.59 84	72.32 159	83.40 67	46.38 255	79.75 106	71.08 268	64.18 32	72.80 86	88.64 59	42.58 198	83.72 135	57.41 170	84.49 68	86.86 62

test222					83.14 68	58.68 73	72.57 249	63.45 328	41.78 353	67.56 165	86.12 111	37.13 259			78.73 137	74.98 315

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 109	78.57 23	90.36 27	57.51 32	86.86 63	77.39 23	89.52 21

旧先验1					83.04 70	53.15 158		67.52 296			87.85 71	44.08 185			80.76 104	78.03 280

MSLP-MVS++		73.77 67	73.47 67	74.66 89	83.02 71	59.29 58	82.30 74	81.88 98	59.34 122	71.59 101	86.83 85	45.94 162	83.65 137	65.09 110	85.22 63	81.06 237

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 15	54.26 56	90.06 14	78.42 19	89.02 23	87.69 37
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 64	73.46 68	75.69 71	83.01 72	60.63 40	77.29 154	78.40 178	61.18 83	70.58 108	85.97 117	54.18 58	84.00 131	67.52 90	82.98 82	82.45 209

SF-MVS		78.82 13	79.22 12	77.60 44	82.88 74	57.83 81	84.99 31	88.13 2	61.86 75	79.16 20	90.75 17	57.96 26	87.09 59	77.08 26	90.18 15	87.87 30

VDDNet		71.81 93	71.33 92	73.26 141	82.80 75	47.60 246	78.74 117	75.27 224	59.59 118	72.94 83	89.40 48	41.51 213	83.91 132	58.75 164	82.99 80	88.26 18

3Dnovator+	66.72 4	75.84 46	74.57 56	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 160	89.24 51	42.03 203	89.38 19	64.07 117	86.50 57	89.69 2

dcpmvs_2		74.55 60	75.23 50	72.48 154	82.34 77	53.34 155	77.87 136	81.46 107	57.80 154	75.49 38	86.81 86	62.22 13	77.75 253	71.09 69	82.02 94	86.34 80

APD-MVS_3200maxsize		74.96 51	74.39 58	76.67 55	82.20 78	58.24 77	83.67 51	83.29 78	58.41 138	73.71 67	90.14 33	45.62 164	85.99 85	69.64 74	82.85 86	85.78 101

MM		80.20 7	80.28 8	79.99 2	82.19 79	60.01 46	86.19 17	83.93 53	73.19 1	77.08 31	91.21 14	57.23 33	90.73 10	83.35 1	88.12 34	89.22 5

PVSNet_Blended_VisFu		71.45 102	70.39 109	74.65 90	82.01 80	58.82 71	79.93 102	80.35 138	55.09 205	65.82 199	82.16 195	49.17 120	82.64 163	60.34 151	78.62 139	82.50 208

TSAR-MVS + GP.		74.90 52	74.15 60	77.17 49	82.00 81	58.77 72	81.80 77	78.57 167	58.58 135	74.32 60	84.51 146	55.94 43	87.22 53	67.11 93	84.48 69	85.52 113

h-mvs33		72.71 78	71.49 87	76.40 60	81.99 82	59.58 52	76.92 164	76.74 204	60.40 95	74.81 51	85.95 118	45.54 167	85.76 91	70.41 72	70.61 240	83.86 170

API-MVS		72.17 88	71.41 89	74.45 98	81.95 83	57.22 89	84.03 45	80.38 137	59.89 112	68.40 143	82.33 189	49.64 114	87.83 46	51.87 216	84.16 73	78.30 273

MAR-MVS		71.51 99	70.15 115	75.60 75	81.84 84	59.39 55	81.38 84	82.90 86	54.90 212	68.08 152	78.70 259	47.73 136	85.51 97	51.68 220	84.17 72	81.88 220
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
balanced_conf03		76.58 36	76.55 35	76.68 54	81.73 85	52.90 164	80.94 88	85.70 23	61.12 84	74.90 49	87.17 82	56.46 38	88.14 36	72.87 56	88.03 38	89.00 7

PAPM_NR		72.63 79	71.80 82	75.13 82	81.72 86	53.42 154	79.91 103	83.28 79	59.14 124	66.31 188	85.90 119	51.86 91	86.06 82	57.45 169	80.62 105	85.91 97

VDD-MVS		72.50 80	72.09 80	73.75 118	81.58 87	49.69 217	77.76 141	77.63 189	63.21 47	73.21 74	89.02 53	42.14 202	83.32 142	61.72 141	82.50 89	88.25 19

PS-MVSNAJ		70.51 116	69.70 121	72.93 145	81.52 88	55.79 116	74.92 209	79.00 156	55.04 210	69.88 121	78.66 261	47.05 151	82.19 171	61.61 142	79.58 120	80.83 241

testdata				64.66 283	81.52 88	52.93 163		65.29 314	46.09 321	73.88 65	87.46 76	38.08 248	66.26 336	53.31 205	78.48 140	74.78 319

CHOSEN 1792x2688		65.08 227	62.84 239	71.82 167	81.49 90	56.26 105	66.32 309	74.20 245	40.53 363	63.16 242	78.65 262	41.30 214	77.80 252	45.80 267	74.09 189	81.40 227

HQP_MVS		74.31 62	73.73 65	76.06 64	81.41 91	56.31 102	84.22 40	84.01 51	64.52 25	69.27 131	86.10 112	45.26 175	87.21 54	68.16 83	80.58 107	84.65 146

plane_prior7					81.41 91	55.96 111

DPM-MVS		75.47 50	75.00 51	76.88 51	81.38 93	59.16 59	79.94 101	85.71 22	56.59 172	72.46 92	86.76 87	56.89 35	87.86 45	66.36 98	88.91 25	83.64 183

CANet		76.46 38	75.93 41	78.06 39	81.29 94	57.53 85	82.35 69	83.31 77	67.78 3	70.09 113	86.34 105	54.92 50	88.90 25	72.68 58	84.55 67	87.76 36

Vis-MVSNet		72.18 87	71.37 91	74.61 92	81.29 94	55.41 126	80.90 89	78.28 180	60.73 90	69.23 134	88.09 64	44.36 184	82.65 162	57.68 167	81.75 100	85.77 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 96

xiu_mvs_v2_base		70.52 115	69.75 119	72.84 147	81.21 97	55.63 120	75.11 202	78.92 158	54.92 211	69.96 120	79.68 244	47.00 155	82.09 173	61.60 143	79.37 123	80.81 242

plane_prior6					81.20 98	56.24 106						45.26 175

PAPR		71.72 97	70.82 101	74.41 99	81.20 98	51.17 189	79.55 111	83.33 76	55.81 188	66.93 176	84.61 142	50.95 104	86.06 82	55.79 181	79.20 128	86.00 93

PLC	56.13 14	65.09 226	63.21 235	70.72 198	81.04 100	54.87 134	78.57 122	77.47 191	48.51 292	55.71 322	81.89 201	33.71 292	79.71 219	41.66 307	70.37 244	77.58 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 101	56.05 110					85.54 129

MVSMamba_PlusPlus		75.75 48	75.44 46	76.67 55	80.84 102	53.06 161	78.62 120	85.13 32	59.65 114	71.53 102	87.47 75	56.92 34	88.17 35	72.18 61	86.63 56	88.80 8

OPM-MVS		74.73 55	74.25 59	76.19 63	80.81 103	59.01 67	82.60 66	83.64 65	63.74 39	72.52 91	87.49 74	47.18 149	85.88 88	69.47 76	80.78 103	83.66 181
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 104	57.91 80	84.68 33	81.64 103	68.35 2	75.77 36	90.38 26	53.98 59	90.26 13	81.30 3	87.68 42	88.77 9

HQP-NCC					80.66 105		82.31 71		62.10 68	67.85 155

ACMP_Plane					80.66 105		82.31 71		62.10 68	67.85 155

HQP-MVS		73.45 68	72.80 73	75.40 77	80.66 105	54.94 131	82.31 71	83.90 56	62.10 68	67.85 155	85.54 129	45.46 169	86.93 61	67.04 94	80.35 111	84.32 153

CS-MVS-test		75.62 49	75.31 49	76.56 59	80.63 108	55.13 130	83.88 48	85.22 29	62.05 71	71.49 103	86.03 115	53.83 63	86.36 79	67.74 86	86.91 50	88.19 22

PHI-MVS		75.87 45	75.36 47	77.41 46	80.62 109	55.91 113	84.28 39	85.78 20	56.08 183	73.41 70	86.58 97	50.94 105	88.54 28	70.79 70	89.71 17	87.79 35

ACMM	61.98 7	70.80 112	69.73 120	74.02 108	80.59 110	58.59 74	82.68 64	82.02 97	55.46 197	67.18 171	84.39 148	38.51 240	83.17 146	60.65 149	76.10 172	80.30 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		69.28 148	68.47 145	71.73 170	80.28 111	47.18 250	79.98 100	82.37 92	54.61 215	67.24 169	84.01 155	39.43 230	82.41 169	55.45 186	72.83 213	85.62 111

ACMP	63.53 6	72.30 85	71.20 95	75.59 76	80.28 111	57.54 84	82.74 63	82.84 88	60.58 92	65.24 211	86.18 109	39.25 233	86.03 84	66.95 96	76.79 165	83.22 191
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 77	71.74 83	75.76 68	80.22 113	57.51 86	82.55 67	83.40 73	61.32 79	66.67 181	87.33 78	39.15 235	86.59 69	67.70 87	77.30 158	83.19 193

LGP-MVS_train				75.76 68	80.22 113	57.51 86		83.40 73	61.32 79	66.67 181	87.33 78	39.15 235	86.59 69	67.70 87	77.30 158	83.19 193

WR-MVS		68.47 165	68.47 145	68.44 235	80.20 115	39.84 317	73.75 234	76.07 211	64.68 22	68.11 151	83.63 163	50.39 109	79.14 233	49.78 230	69.66 263	86.34 80

Anonymous20240529		69.91 129	69.02 132	72.56 152	80.19 116	47.65 244	77.56 145	80.99 126	55.45 198	69.88 121	86.76 87	39.24 234	82.18 172	54.04 197	77.10 162	87.85 31

Anonymous202405211		66.84 201	65.99 200	69.40 222	80.19 116	42.21 298	71.11 271	71.31 267	58.80 129	67.90 153	86.39 104	29.83 330	79.65 220	49.60 236	78.78 135	86.33 82

CS-MVS		76.25 41	75.98 40	77.06 50	80.15 118	55.63 120	84.51 35	83.90 56	63.24 45	73.30 71	87.27 80	55.06 48	86.30 81	71.78 64	84.58 66	89.25 4

BH-RMVSNet		68.81 155	67.42 166	72.97 144	80.11 119	52.53 173	74.26 221	76.29 207	58.48 137	68.38 144	84.20 149	42.59 197	83.83 133	46.53 260	75.91 173	82.56 204

test_0402		63.25 247	61.01 262	69.96 209	80.00 120	54.37 140	76.86 167	72.02 263	54.58 217	58.71 297	80.79 226	35.00 277	84.36 123	26.41 389	64.71 307	71.15 358

HyFIR lowres test		65.67 217	63.01 237	73.67 123	79.97 121	55.65 119	69.07 292	75.52 219	42.68 351	63.53 236	77.95 271	40.43 222	81.64 180	46.01 265	71.91 226	83.73 177

EIA-MVS		71.78 94	70.60 105	75.30 80	79.85 122	53.54 151	77.27 155	83.26 80	57.92 150	66.49 183	79.39 251	52.07 88	86.69 67	60.05 153	79.14 130	85.66 109

BH-untuned		68.27 169	67.29 171	71.21 186	79.74 123	53.22 157	76.06 182	77.46 193	57.19 159	66.10 190	81.61 207	45.37 173	83.50 140	45.42 276	76.68 167	76.91 297

VNet		69.68 136	70.19 114	68.16 238	79.73 124	41.63 305	70.53 277	77.38 194	60.37 98	70.69 107	86.63 94	51.08 102	77.09 264	53.61 202	81.69 102	85.75 106

LS3D		64.71 229	62.50 243	71.34 184	79.72 125	55.71 117	79.82 104	74.72 235	48.50 293	56.62 315	84.62 141	33.59 295	82.34 170	29.65 377	75.23 181	75.97 302

mvsmamba		68.47 165	66.56 183	74.21 105	79.60 126	52.95 162	74.94 208	75.48 220	52.09 245	60.10 278	83.27 169	36.54 265	84.70 117	59.32 163	77.69 150	84.99 137

hse-mvs2		71.04 105	69.86 118	74.60 93	79.58 127	57.12 96	73.96 226	75.25 225	60.40 95	74.81 51	81.95 200	45.54 167	82.90 151	70.41 72	66.83 292	83.77 175

GeoE		71.01 106	70.15 115	73.60 129	79.57 128	52.17 179	78.93 115	78.12 181	58.02 146	67.76 163	83.87 158	52.36 83	82.72 160	56.90 172	75.79 175	85.92 96

AUN-MVS		68.45 167	66.41 190	74.57 95	79.53 129	57.08 97	73.93 229	75.23 226	54.44 220	66.69 180	81.85 202	37.10 260	82.89 152	62.07 137	66.84 291	83.75 176

test2506		65.33 223	64.61 216	67.50 243	79.46 130	34.19 371	74.43 220	51.92 377	58.72 130	66.75 179	88.05 66	25.99 359	80.92 199	51.94 215	84.25 70	87.39 48

ECVR-MVS		67.72 182	67.51 163	68.35 236	79.46 130	36.29 356	74.79 212	66.93 302	58.72 130	67.19 170	88.05 66	36.10 267	81.38 186	52.07 213	84.25 70	87.39 48

BH-w/o		66.85 200	65.83 202	69.90 213	79.29 132	52.46 175	74.66 215	76.65 205	54.51 219	64.85 220	78.12 267	45.59 166	82.95 150	43.26 293	75.54 179	74.27 325

1112_ss		64.00 239	63.36 231	65.93 269	79.28 133	42.58 294	71.35 264	72.36 260	46.41 318	60.55 275	77.89 275	46.27 161	73.28 294	46.18 263	69.97 254	81.92 219

ETV-MVS		74.46 61	73.84 64	76.33 62	79.27 134	55.24 129	79.22 113	85.00 38	64.97 21	72.65 89	79.46 249	53.65 70	87.87 44	67.45 91	82.91 83	85.89 98

test1111		67.21 189	67.14 179	67.42 245	79.24 135	34.76 365	73.89 231	65.65 311	58.71 132	66.96 175	87.95 69	36.09 268	80.53 206	52.03 214	83.79 75	86.97 59

UniMVSNet_NR-MVSNet		71.11 104	71.00 99	71.44 178	79.20 136	44.13 278	76.02 185	82.60 90	66.48 11	68.20 146	84.60 143	56.82 36	82.82 158	54.62 192	70.43 242	87.36 52

VPNet		67.52 185	68.11 152	65.74 272	79.18 137	36.80 348	72.17 255	72.83 256	62.04 72	67.79 161	85.83 122	48.88 125	76.60 276	51.30 221	72.97 212	83.81 171

TR-MVS		66.59 208	65.07 213	71.17 189	79.18 137	49.63 219	73.48 236	75.20 228	52.95 235	67.90 153	80.33 232	39.81 227	83.68 136	43.20 294	73.56 200	80.20 250

TAMVS		66.78 203	65.27 211	71.33 185	79.16 139	53.67 147	73.84 233	69.59 281	52.32 243	65.28 206	81.72 205	44.49 183	77.40 259	42.32 301	78.66 138	82.92 199

patch_mono-2		69.85 130	71.09 97	66.16 263	79.11 140	54.80 135	71.97 258	74.31 241	53.50 232	70.90 106	84.17 150	57.63 31	63.31 345	66.17 99	82.02 94	80.38 248

Test_1112_low_res		62.32 256	61.77 251	64.00 288	79.08 141	39.53 322	68.17 296	70.17 275	43.25 346	59.03 295	79.90 238	44.08 185	71.24 305	43.79 288	68.42 280	81.25 231

CDS-MVSNet		66.80 202	65.37 208	71.10 191	78.98 142	53.13 160	73.27 239	71.07 269	52.15 244	64.72 221	80.23 234	43.56 190	77.10 263	45.48 274	78.88 132	83.05 198
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sasdasda		74.67 56	74.98 52	73.71 121	78.94 143	50.56 201	80.23 95	83.87 59	60.30 102	77.15 29	86.56 98	59.65 17	82.00 174	66.01 102	82.12 91	88.58 12

canonicalmvs		74.67 56	74.98 52	73.71 121	78.94 143	50.56 201	80.23 95	83.87 59	60.30 102	77.15 29	86.56 98	59.65 17	82.00 174	66.01 102	82.12 91	88.58 12

EC-MVSNet		75.84 46	75.87 43	75.74 70	78.86 145	52.65 169	83.73 50	86.08 17	63.47 42	72.77 87	87.25 81	53.13 73	87.93 42	71.97 63	85.57 62	86.66 70

IS-MVSNet		71.57 98	71.00 99	73.27 140	78.86 145	45.63 266	80.22 97	78.69 164	64.14 35	66.46 184	87.36 77	49.30 117	85.60 93	50.26 229	83.71 76	88.59 11

CLD-MVS		73.33 69	72.68 74	75.29 81	78.82 147	53.33 156	78.23 127	84.79 41	61.30 81	70.41 110	81.04 217	52.41 82	87.12 57	64.61 116	82.49 90	85.41 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 100	70.38 110	74.88 84	78.76 148	57.15 94	82.79 61	78.48 171	51.26 257	69.49 126	83.22 170	43.99 187	83.24 144	66.06 100	79.37 123	84.23 156

lupinMVS		69.57 140	68.28 150	73.44 135	78.76 148	57.15 94	76.57 171	73.29 253	46.19 320	69.49 126	82.18 192	43.99 187	79.23 227	64.66 114	79.37 123	83.93 165

CNLPA		65.43 220	64.02 220	69.68 216	78.73 150	58.07 78	77.82 140	70.71 272	51.49 252	61.57 268	83.58 165	38.23 246	70.82 306	43.90 286	70.10 252	80.16 251

EPP-MVSNet		72.16 90	71.31 93	74.71 86	78.68 151	49.70 215	82.10 75	81.65 102	60.40 95	65.94 193	85.84 121	51.74 94	86.37 78	55.93 178	79.55 122	88.07 27

TranMVSNet+NR-MVSNet		70.36 120	70.10 117	71.17 189	78.64 152	42.97 292	76.53 172	81.16 123	66.95 6	68.53 142	85.42 131	51.61 96	83.07 147	52.32 210	69.70 262	87.46 45

UniMVSNet (Re)		70.63 114	70.20 113	71.89 164	78.55 153	45.29 269	75.94 186	82.92 85	63.68 40	68.16 149	83.59 164	53.89 62	83.49 141	53.97 198	71.12 235	86.89 61

Fast-Effi-MVS+		70.28 122	69.12 131	73.73 120	78.50 154	51.50 188	75.01 205	79.46 150	56.16 182	68.59 139	79.55 247	53.97 60	84.05 127	53.34 204	77.53 152	85.65 110

PS-MVSNAJss		72.24 86	71.21 94	75.31 79	78.50 154	55.93 112	81.63 79	82.12 95	56.24 180	70.02 117	85.68 125	47.05 151	84.34 124	65.27 109	74.41 187	85.67 108

EI-MVSNet-Vis-set		72.42 84	71.59 84	74.91 83	78.47 156	54.02 142	77.05 160	79.33 152	65.03 18	71.68 100	79.35 253	52.75 76	84.89 113	66.46 97	74.23 188	85.83 100

FA-MVS(test-final)		69.82 131	68.48 143	73.84 112	78.44 157	50.04 210	75.58 194	78.99 157	58.16 142	67.59 164	82.14 196	42.66 196	85.63 92	56.60 173	76.19 171	85.84 99

testing91		64.46 233	63.80 224	66.47 256	78.43 158	40.06 315	67.63 300	69.59 281	59.06 125	63.18 241	78.05 269	34.05 286	76.99 266	48.30 246	75.87 174	82.37 211

testing11		62.81 251	61.90 250	65.54 274	78.38 159	40.76 312	67.59 302	66.78 304	55.48 196	60.13 277	77.11 287	31.67 319	76.79 271	45.53 272	74.45 185	79.06 266

MVS_111021_LR		69.50 143	68.78 137	71.65 173	78.38 159	59.33 56	74.82 211	70.11 276	58.08 143	67.83 159	84.68 138	41.96 204	76.34 281	65.62 107	77.54 151	79.30 265

test_yl		69.69 134	69.13 129	71.36 182	78.37 161	45.74 262	74.71 213	80.20 139	57.91 151	70.01 118	83.83 159	42.44 199	82.87 154	54.97 188	79.72 117	85.48 115

DCV-MVSNet		69.69 134	69.13 129	71.36 182	78.37 161	45.74 262	74.71 213	80.20 139	57.91 151	70.01 118	83.83 159	42.44 199	82.87 154	54.97 188	79.72 117	85.48 115

FIs		70.82 111	71.43 88	68.98 228	78.33 163	38.14 333	76.96 162	83.59 67	61.02 85	67.33 168	86.73 89	55.07 47	81.64 180	54.61 194	79.22 127	87.14 56

UGNet		68.81 155	67.39 167	73.06 143	78.33 163	54.47 137	79.77 105	75.40 222	60.45 94	63.22 239	84.40 147	32.71 308	80.91 200	51.71 219	80.56 109	83.81 171
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		69.65 137	68.39 149	73.43 136	78.27 165	56.88 98	77.12 158	73.71 250	46.53 317	69.34 130	83.22 170	43.37 191	79.18 228	64.77 113	79.20 128	84.23 156
jason: jason.
alignmvs		73.86 66	73.99 61	73.45 134	78.20 166	50.50 203	78.57 122	82.43 91	59.40 120	76.57 32	86.71 91	56.42 40	81.23 191	65.84 105	81.79 97	88.62 10

xiu_mvs_v1_base_debu		68.58 161	67.28 172	72.48 154	78.19 167	57.19 91	75.28 197	75.09 230	51.61 248	70.04 114	81.41 211	32.79 304	79.02 235	63.81 123	77.31 155	81.22 232

xiu_mvs_v1_base		68.58 161	67.28 172	72.48 154	78.19 167	57.19 91	75.28 197	75.09 230	51.61 248	70.04 114	81.41 211	32.79 304	79.02 235	63.81 123	77.31 155	81.22 232

xiu_mvs_v1_base_debi		68.58 161	67.28 172	72.48 154	78.19 167	57.19 91	75.28 197	75.09 230	51.61 248	70.04 114	81.41 211	32.79 304	79.02 235	63.81 123	77.31 155	81.22 232

testing99		64.05 237	63.29 234	66.34 258	78.17 170	39.76 319	67.33 305	68.00 295	58.60 134	63.03 244	78.10 268	32.57 313	76.94 268	48.22 247	75.58 178	82.34 212

UBG		59.62 282	59.53 272	59.89 313	78.12 171	35.92 359	64.11 332	60.81 347	49.45 279	61.34 269	75.55 313	33.05 299	67.39 329	38.68 321	74.62 183	76.35 301

PAPM		67.92 178	66.69 182	71.63 174	78.09 172	49.02 226	77.09 159	81.24 120	51.04 260	60.91 273	83.98 156	47.71 137	84.99 107	40.81 310	79.32 126	80.90 240

ACMH	55.70 15	65.20 225	63.57 228	70.07 208	78.07 173	52.01 184	79.48 112	79.69 143	55.75 190	56.59 316	80.98 219	27.12 351	80.94 197	42.90 298	71.58 230	77.25 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 126	69.53 123	71.44 178	78.05 174	44.13 278	75.01 205	81.51 106	64.37 28	68.20 146	84.52 144	49.12 123	82.82 158	54.62 192	70.43 242	87.37 50

NR-MVSNet		69.54 141	68.85 134	71.59 175	78.05 174	43.81 283	74.20 222	80.86 129	65.18 14	62.76 248	84.52 144	52.35 84	83.59 139	50.96 225	70.78 237	87.37 50

WBMVS		60.54 272	60.61 266	60.34 312	78.00 176	35.95 358	64.55 328	64.89 316	49.63 276	63.39 238	78.70 259	33.85 291	67.65 325	42.10 303	70.35 246	77.43 286

EI-MVSNet-UG-set		71.92 92	71.06 98	74.52 97	77.98 177	53.56 150	76.62 169	79.16 153	64.40 27	71.18 104	78.95 258	52.19 86	84.66 120	65.47 108	73.57 199	85.32 124

WR-MVS_H		67.02 197	66.92 181	67.33 248	77.95 178	37.75 337	77.57 144	82.11 96	62.03 73	62.65 251	82.48 186	50.57 107	79.46 223	42.91 297	64.01 313	84.79 143

testing222		62.29 258	61.31 257	65.25 280	77.87 179	38.53 330	68.34 295	66.31 308	56.37 176	63.15 243	77.58 283	28.47 340	76.18 284	37.04 331	76.65 168	81.05 238

Effi-MVS+		73.31 70	72.54 76	75.62 74	77.87 179	53.64 148	79.62 110	79.61 146	61.63 77	72.02 97	82.61 180	56.44 39	85.97 86	63.99 120	79.07 131	87.25 54

DELS-MVS		74.76 54	74.46 57	75.65 73	77.84 181	52.25 178	75.59 192	84.17 48	63.76 38	73.15 76	82.79 175	59.58 20	86.80 64	67.24 92	86.04 59	87.89 28
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	66.12 212	64.06 219	72.30 160	77.79 182	52.83 167	80.39 94	78.03 182	57.30 157	57.47 309	82.55 182	27.68 346	84.17 125	45.54 271	69.78 259	79.90 255

MGCFI-Net		72.45 82	73.34 70	69.81 215	77.77 183	43.21 289	75.84 189	81.18 121	59.59 118	75.45 39	86.64 92	57.74 28	77.94 248	63.92 121	81.90 96	88.30 17

RRT-MVS		71.46 101	70.70 104	73.74 119	77.76 184	49.30 223	76.60 170	80.45 135	61.25 82	68.17 148	84.78 137	44.64 180	84.90 112	64.79 112	77.88 148	87.03 57

3Dnovator	64.47 5	72.49 81	71.39 90	75.79 67	77.70 185	58.99 68	80.66 93	83.15 82	62.24 66	65.46 203	86.59 96	42.38 201	85.52 96	59.59 159	84.72 65	82.85 202

EG-PatchMatch MVS		64.71 229	62.87 238	70.22 204	77.68 186	53.48 152	77.99 134	78.82 159	53.37 233	56.03 321	77.41 285	24.75 366	84.04 128	46.37 262	73.42 204	73.14 331

UWE-MVS		60.18 275	59.78 270	61.39 307	77.67 187	33.92 374	69.04 293	63.82 325	48.56 290	64.27 228	77.64 282	27.20 350	70.40 311	33.56 353	76.24 170	79.83 257

CP-MVSNet		66.49 209	66.41 190	66.72 251	77.67 187	36.33 353	76.83 168	79.52 148	62.45 63	62.54 254	83.47 168	46.32 159	78.37 242	45.47 275	63.43 320	85.45 117

GBi-Net		67.21 189	66.55 184	69.19 224	77.63 189	43.33 286	77.31 151	77.83 185	56.62 169	65.04 216	82.70 176	41.85 206	80.33 211	47.18 255	72.76 214	83.92 166

test1		67.21 189	66.55 184	69.19 224	77.63 189	43.33 286	77.31 151	77.83 185	56.62 169	65.04 216	82.70 176	41.85 206	80.33 211	47.18 255	72.76 214	83.92 166

FMVSNet2		66.93 199	66.31 195	68.79 231	77.63 189	42.98 291	76.11 180	77.47 191	56.62 169	65.22 213	82.17 194	41.85 206	80.18 217	47.05 258	72.72 217	83.20 192

PCF-MVS	61.88 8	70.95 108	69.49 124	75.35 78	77.63 189	55.71 117	76.04 184	81.81 100	50.30 268	69.66 124	85.40 132	52.51 79	84.89 113	51.82 217	80.24 113	85.45 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 221	63.80 224	70.22 204	77.62 193	55.53 124	76.30 176	78.53 169	50.59 266	56.47 319	78.65 262	39.84 226	82.68 161	44.10 284	72.12 225	72.44 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 132	70.58 107	67.46 244	77.61 194	34.73 366	76.05 183	83.19 81	60.84 87	65.88 197	86.46 102	54.52 55	80.76 204	52.52 209	78.12 144	86.91 60

PS-CasMVS		66.42 210	66.32 194	66.70 253	77.60 195	36.30 355	76.94 163	79.61 146	62.36 65	62.43 258	83.66 162	45.69 163	78.37 242	45.35 277	63.26 321	85.42 120

testing3		56.54 302	55.92 304	58.41 324	77.52 196	27.93 394	69.72 286	56.36 364	54.75 214	58.63 300	77.80 277	20.88 377	71.75 303	25.31 391	62.25 329	75.53 308

FMVSNet1		66.70 204	65.87 201	69.19 224	77.49 197	43.33 286	77.31 151	77.83 185	56.45 174	64.60 224	82.70 176	38.08 248	80.33 211	46.08 264	72.31 222	83.92 166

ETVMVS		59.51 283	58.81 277	61.58 304	77.46 198	34.87 362	64.94 326	59.35 350	54.06 225	61.08 272	76.67 294	29.54 331	71.87 302	32.16 358	74.07 190	78.01 281

VPA-MVSNet		69.02 152	69.47 125	67.69 242	77.42 199	41.00 310	74.04 224	79.68 144	60.06 106	69.26 133	84.81 136	51.06 103	77.58 255	54.44 195	74.43 186	84.48 150

UniMVSNet_ETH3D		67.60 184	67.07 180	69.18 227	77.39 200	42.29 296	74.18 223	75.59 217	60.37 98	66.77 178	86.06 114	37.64 250	78.93 240	52.16 212	73.49 201	86.32 84

FE-MVS		65.91 214	63.33 232	73.63 127	77.36 201	51.95 185	72.62 247	75.81 213	53.70 229	65.31 205	78.96 257	28.81 339	86.39 77	43.93 285	73.48 202	82.55 205

thres100view900		63.28 246	62.41 244	65.89 270	77.31 202	38.66 328	72.65 245	69.11 288	57.07 160	62.45 257	81.03 218	37.01 262	79.17 229	31.84 362	73.25 207	79.83 257

cascas		65.98 213	63.42 230	73.64 126	77.26 203	52.58 172	72.26 254	77.21 197	48.56 290	61.21 271	74.60 323	32.57 313	85.82 90	50.38 228	76.75 166	82.52 207

thres600view7		63.30 245	62.27 245	66.41 257	77.18 204	38.87 326	72.35 252	69.11 288	56.98 162	62.37 259	80.96 220	37.01 262	79.00 238	31.43 369	73.05 211	81.36 228

SDMVSNet		68.03 174	68.10 153	67.84 240	77.13 205	48.72 232	65.32 321	79.10 154	58.02 146	65.08 214	82.55 182	47.83 135	73.40 293	63.92 121	73.92 192	81.41 225

sd_testset		64.46 233	64.45 217	64.51 285	77.13 205	42.25 297	62.67 337	72.11 262	58.02 146	65.08 214	82.55 182	41.22 218	69.88 314	47.32 253	73.92 192	81.41 225

PEN-MVS		66.60 206	66.45 186	67.04 249	77.11 207	36.56 350	77.03 161	80.42 136	62.95 50	62.51 256	84.03 154	46.69 157	79.07 234	44.22 280	63.08 323	85.51 114

PatchMatch-RL		56.25 307	54.55 314	61.32 308	77.06 208	56.07 109	65.57 315	54.10 374	44.13 339	53.49 350	71.27 347	25.20 363	66.78 332	36.52 339	63.66 316	61.12 381

PVSNet_BlendedMVS		68.56 164	67.72 156	71.07 192	77.03 209	50.57 199	74.50 217	81.52 104	53.66 231	64.22 231	79.72 243	49.13 121	82.87 154	55.82 179	73.92 192	79.77 260

PVSNet_Blended		68.59 160	67.72 156	71.19 187	77.03 209	50.57 199	72.51 250	81.52 104	51.91 246	64.22 231	77.77 280	49.13 121	82.87 154	55.82 179	79.58 120	80.14 252

F-COLMAP		63.05 250	60.87 265	69.58 220	76.99 211	53.63 149	78.12 131	76.16 208	47.97 301	52.41 352	81.61 207	27.87 344	78.11 246	40.07 313	66.66 293	77.00 294

tfpn200view9		63.18 248	62.18 247	66.21 262	76.85 212	39.62 320	71.96 259	69.44 284	56.63 167	62.61 252	79.83 239	37.18 256	79.17 229	31.84 362	73.25 207	79.83 257

thres400		63.31 244	62.18 247	66.72 251	76.85 212	39.62 320	71.96 259	69.44 284	56.63 167	62.61 252	79.83 239	37.18 256	79.17 229	31.84 362	73.25 207	81.36 228

tttt0517		67.83 180	65.66 205	74.33 101	76.69 214	50.82 195	77.86 137	73.99 247	54.54 218	64.64 223	82.53 185	35.06 276	85.50 98	55.71 182	69.91 256	86.67 69

ET-MVSNet_ETH3D		67.96 177	65.72 204	74.68 88	76.67 215	55.62 122	75.11 202	74.74 234	52.91 236	60.03 280	80.12 235	33.68 293	82.64 163	61.86 140	76.34 169	85.78 101

TAPA-MVS	59.36 10	66.60 206	65.20 212	70.81 195	76.63 216	48.75 230	76.52 173	80.04 141	50.64 265	65.24 211	84.93 134	39.15 235	78.54 241	36.77 333	76.88 164	85.14 130
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 103	70.60 105	73.78 114	76.60 217	53.15 158	79.74 107	79.78 142	58.37 139	68.75 138	86.45 103	45.43 171	80.60 205	62.58 132	77.73 149	87.58 43

LTVRE_ROB	55.42 16	63.15 249	61.23 260	68.92 229	76.57 218	47.80 241	59.92 353	76.39 206	54.35 221	58.67 298	82.46 187	29.44 334	81.49 184	42.12 302	71.14 234	77.46 285
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
QAPM		70.05 125	68.81 136	73.78 114	76.54 219	53.43 153	83.23 54	83.48 69	52.89 237	65.90 195	86.29 106	41.55 212	86.49 75	51.01 223	78.40 142	81.42 224

FMVSNet3		66.32 211	65.61 206	68.46 234	76.48 220	42.34 295	74.98 207	77.15 198	55.83 187	65.04 216	81.16 214	39.91 224	80.14 218	47.18 255	72.76 214	82.90 201

casdiffmvs_mvg		76.14 42	76.30 37	75.66 72	76.46 221	51.83 186	79.67 108	85.08 33	65.02 19	75.84 35	88.58 60	59.42 22	85.08 106	72.75 57	83.93 74	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
thisisatest0530		67.92 178	65.78 203	74.33 101	76.29 222	51.03 190	76.89 165	74.25 243	53.67 230	65.59 201	81.76 204	35.15 275	85.50 98	55.94 177	72.47 218	86.47 75

baseline1		63.81 240	63.87 223	63.62 289	76.29 222	36.36 351	71.78 261	67.29 299	56.05 184	64.23 230	82.95 174	47.11 150	74.41 290	47.30 254	61.85 332	80.10 253

ab-mvs		66.65 205	66.42 189	67.37 246	76.17 224	41.73 302	70.41 280	76.14 210	53.99 226	65.98 192	83.51 166	49.48 115	76.24 282	48.60 243	73.46 203	84.14 159

Effi-MVS+-dtu		69.64 138	67.53 162	75.95 65	76.10 225	62.29 15	80.20 98	76.06 212	59.83 113	65.26 210	77.09 288	41.56 211	84.02 130	60.60 150	71.09 236	81.53 223

DTE-MVSNet		65.58 218	65.34 209	66.31 259	76.06 226	34.79 363	76.43 174	79.38 151	62.55 61	61.66 266	83.83 159	45.60 165	79.15 232	41.64 309	60.88 338	85.00 135

EPNet		73.09 72	72.16 79	75.90 66	75.95 227	56.28 104	83.05 56	72.39 259	66.53 10	65.27 207	87.00 83	50.40 108	85.47 100	62.48 134	86.32 58	85.94 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		61.65 265	58.80 279	70.20 206	75.80 228	47.22 249	75.59 192	69.68 279	54.61 215	54.11 341	79.26 254	27.07 352	82.96 149	43.27 292	49.79 378	80.41 247

baseline		74.61 58	74.70 55	74.34 100	75.70 229	49.99 212	77.54 146	84.63 42	62.73 59	73.98 63	87.79 73	57.67 30	83.82 134	69.49 75	82.74 88	89.20 6

Baseline_NR-MVSNet		67.05 196	67.56 159	65.50 275	75.65 230	37.70 339	75.42 195	74.65 237	59.90 109	68.14 150	83.15 173	49.12 123	77.20 262	52.23 211	69.78 259	81.60 222

jajsoiax		68.25 170	66.45 186	73.66 124	75.62 231	55.49 125	80.82 90	78.51 170	52.33 242	64.33 226	84.11 152	28.28 342	81.81 179	63.48 127	70.62 239	83.67 179

mvs_tets		68.18 172	66.36 192	73.63 127	75.61 232	55.35 128	80.77 91	78.56 168	52.48 241	64.27 228	84.10 153	27.45 348	81.84 178	63.45 128	70.56 241	83.69 178

casdiffmvs		74.80 53	74.89 54	74.53 96	75.59 233	50.37 204	78.17 130	85.06 35	62.80 58	74.40 58	87.86 70	57.88 27	83.61 138	69.46 77	82.79 87	89.59 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet	50.76 19	58.40 288	57.39 290	61.42 305	75.53 234	44.04 281	61.43 343	63.45 328	47.04 314	56.91 313	73.61 330	27.00 353	64.76 341	39.12 319	72.40 219	75.47 309

MVS		67.37 187	66.33 193	70.51 202	75.46 235	50.94 191	73.95 227	81.85 99	41.57 357	62.54 254	78.57 265	47.98 132	85.47 100	52.97 207	82.05 93	75.14 311

nrg030		72.96 74	73.01 71	72.84 147	75.41 236	50.24 205	80.02 99	82.89 87	58.36 140	74.44 57	86.73 89	58.90 24	80.83 201	65.84 105	74.46 184	87.44 46

thres200		62.20 259	61.16 261	65.34 278	75.38 237	39.99 316	69.60 287	69.29 286	55.64 194	61.87 263	76.99 289	37.07 261	78.96 239	31.28 370	73.28 206	77.06 292

TransMVSNet (Re)		64.72 228	64.33 218	65.87 271	75.22 238	38.56 329	74.66 215	75.08 233	58.90 128	61.79 264	82.63 179	51.18 100	78.07 247	43.63 290	55.87 360	80.99 239

MS-PatchMatch		62.42 255	61.46 255	65.31 279	75.21 239	52.10 180	72.05 256	74.05 246	46.41 318	57.42 311	74.36 324	34.35 284	77.57 256	45.62 270	73.67 196	66.26 377

WB-MVSnew		59.66 280	59.69 271	59.56 314	75.19 240	35.78 360	69.34 290	64.28 322	46.88 315	61.76 265	75.79 309	40.61 221	65.20 340	32.16 358	71.21 233	77.70 282

IB-MVS	56.42 12	65.40 222	62.73 241	73.40 137	74.89 241	52.78 168	73.09 241	75.13 229	55.69 191	58.48 302	73.73 329	32.86 303	86.32 80	50.63 226	70.11 251	81.10 236
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 82	72.46 77	72.42 158	74.88 242	48.50 234	76.28 177	83.14 83	59.40 120	72.46 92	84.68 138	55.66 44	81.12 192	65.98 104	79.66 119	87.63 40

tt0805		67.77 181	67.24 176	69.34 223	74.87 243	40.08 314	77.36 150	81.37 110	55.31 199	66.33 187	84.65 140	37.35 254	82.55 165	55.65 184	72.28 223	85.39 122

CANet_DTU		68.18 172	67.71 158	69.59 218	74.83 244	46.24 257	78.66 119	76.85 201	59.60 115	63.45 237	82.09 199	35.25 274	77.41 258	59.88 156	78.76 136	85.14 130

tfpnnormal		62.47 254	61.63 253	64.99 282	74.81 245	39.01 325	71.22 267	73.72 249	55.22 202	60.21 276	80.09 237	41.26 217	76.98 267	30.02 375	68.09 282	78.97 269

Vis-MVSNet (Re-imp)		63.69 241	63.88 222	63.14 294	74.75 246	31.04 385	71.16 269	63.64 327	56.32 177	59.80 285	84.99 133	44.51 181	75.46 285	39.12 319	80.62 105	82.92 199

HY-MVS	56.14 13	64.55 232	63.89 221	66.55 255	74.73 247	41.02 307	69.96 284	74.43 238	49.29 282	61.66 266	80.92 221	47.43 145	76.68 275	44.91 279	71.69 228	81.94 218

Syy-MVS		56.00 309	56.23 302	55.32 341	74.69 248	26.44 400	65.52 316	57.49 359	50.97 261	56.52 317	72.18 336	39.89 225	68.09 321	24.20 392	64.59 310	71.44 354

myMVS_eth3d		54.86 319	54.61 313	55.61 340	74.69 248	27.31 397	65.52 316	57.49 359	50.97 261	56.52 317	72.18 336	21.87 375	68.09 321	27.70 383	64.59 310	71.44 354

COLMAP_ROB	52.97 17	61.27 270	58.81 277	68.64 232	74.63 250	52.51 174	78.42 125	73.30 252	49.92 274	50.96 357	81.51 210	23.06 369	79.40 224	31.63 366	65.85 298	74.01 328
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 263	61.35 256	63.46 290	74.58 251	31.48 384	61.42 344	58.14 355	58.71 132	53.02 351	79.55 247	43.07 193	76.80 270	45.69 268	77.96 146	82.11 217

test_djsdf		69.45 145	67.74 155	74.58 94	74.57 252	54.92 133	82.79 61	78.48 171	51.26 257	65.41 204	83.49 167	38.37 242	83.24 144	66.06 100	69.25 269	85.56 112

EI-MVSNet		69.27 149	68.44 147	71.73 170	74.47 253	49.39 222	75.20 200	78.45 174	59.60 115	69.16 135	76.51 300	51.29 98	82.50 166	59.86 158	71.45 232	83.30 188

CVMVSNet		59.63 281	59.14 275	61.08 310	74.47 253	38.84 327	75.20 200	68.74 290	31.15 383	58.24 303	76.51 300	32.39 315	68.58 319	49.77 231	65.84 299	75.81 304

IterMVS-LS		69.22 151	68.48 143	71.43 180	74.44 255	49.40 221	76.23 178	77.55 190	59.60 115	65.85 198	81.59 209	51.28 99	81.58 183	59.87 157	69.90 257	83.30 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 155	67.47 165	72.82 149	74.40 256	56.87 99	70.59 276	79.04 155	54.77 213	66.99 174	86.01 116	39.57 229	78.21 245	62.54 133	73.33 205	83.37 187

EGC-MVSNET		42.47 356	38.48 364	54.46 347	74.33 257	48.73 231	70.33 281	51.10 380	0.03 416	0.18 417	67.78 368	13.28 391	66.49 334	18.91 399	50.36 376	48.15 396

XVG-OURS		68.76 158	67.37 168	72.90 146	74.32 258	57.22 89	70.09 283	78.81 160	55.24 201	67.79 161	85.81 124	36.54 265	78.28 244	62.04 138	75.74 176	83.19 193

OpenMVS	61.03 9	68.85 154	67.56 159	72.70 151	74.26 259	53.99 143	81.21 86	81.34 115	52.70 238	62.75 249	85.55 128	38.86 238	84.14 126	48.41 245	83.01 79	79.97 254

MIMVSNet		57.35 295	57.07 292	58.22 326	74.21 260	37.18 342	62.46 338	60.88 346	48.88 287	55.29 328	75.99 307	31.68 318	62.04 350	31.87 361	72.35 220	75.43 310

SCA		60.49 273	58.38 283	66.80 250	74.14 261	48.06 239	63.35 334	63.23 330	49.13 284	59.33 293	72.10 338	37.45 252	74.27 291	44.17 281	62.57 326	78.05 277

thisisatest0515		65.83 215	63.50 229	72.82 149	73.75 262	49.50 220	71.32 265	73.12 255	49.39 280	63.82 233	76.50 302	34.95 278	84.84 116	53.20 206	75.49 180	84.13 160

K. test v3		60.47 274	57.11 291	70.56 200	73.74 263	48.22 237	75.10 204	62.55 334	58.27 141	53.62 347	76.31 303	27.81 345	81.59 182	47.42 251	39.18 392	81.88 220

v10		70.21 123	69.02 132	73.81 113	73.51 264	50.92 193	78.74 117	81.39 109	60.05 107	66.39 186	81.83 203	47.58 140	85.41 103	62.80 131	68.86 276	85.09 133

v1144		70.42 119	69.31 127	73.76 116	73.22 265	50.64 198	77.83 139	81.43 108	58.58 135	69.40 129	81.16 214	47.53 142	85.29 105	64.01 119	70.64 238	85.34 123

v1192		69.97 128	68.68 139	73.85 111	73.19 266	50.94 191	77.68 142	81.36 111	57.51 156	68.95 137	80.85 224	45.28 174	85.33 104	62.97 130	70.37 244	85.27 127

v8		70.33 121	69.28 128	73.49 132	73.15 267	50.22 206	78.62 120	80.78 130	60.79 88	66.45 185	82.11 198	49.35 116	84.98 109	63.58 126	68.71 277	85.28 126

v144192		69.71 133	68.51 142	73.33 139	73.10 268	50.13 208	77.54 146	80.64 131	56.65 166	68.57 141	80.55 227	46.87 156	84.96 111	62.98 129	69.66 263	84.89 140

v1921920		69.47 144	68.17 151	73.36 138	73.06 269	50.10 209	77.39 149	80.56 132	56.58 173	68.59 139	80.37 229	44.72 179	84.98 109	62.47 135	69.82 258	85.00 135

PatchmatchNet		59.84 278	58.24 284	64.65 284	73.05 270	46.70 253	69.42 289	62.18 340	47.55 306	58.88 296	71.96 340	34.49 282	69.16 316	42.99 296	63.60 317	78.07 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 150	67.91 154	73.25 142	73.02 271	49.82 213	77.21 156	80.54 133	56.43 175	68.34 145	80.51 228	43.33 192	84.99 107	62.03 139	69.77 261	84.95 139

Fast-Effi-MVS+-dtu		67.37 187	65.33 210	73.48 133	72.94 272	57.78 83	77.47 148	76.88 200	57.60 155	61.97 261	76.85 292	39.31 231	80.49 209	54.72 191	70.28 248	82.17 216

EPNet_dtu		61.90 262	61.97 249	61.68 302	72.89 273	39.78 318	75.85 188	65.62 312	55.09 205	54.56 337	79.36 252	37.59 251	67.02 331	39.80 316	76.95 163	78.25 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 260	60.10 269	67.99 239	72.79 274	43.86 282	71.05 273	66.85 303	43.14 348	62.77 247	75.39 317	38.32 244	80.80 202	41.69 306	68.88 274	79.32 264

MDTV_nov1_ep13			57.00 293		72.73 275	38.26 332	65.02 325	64.73 319	44.74 331	55.46 324	72.48 334	32.61 312	70.47 308	37.47 327	67.75 285

MSDG		61.81 264	59.23 274	69.55 221	72.64 276	52.63 171	70.45 279	75.81 213	51.38 254	53.70 344	76.11 304	29.52 332	81.08 195	37.70 326	65.79 300	74.93 316

gg-mvs-nofinetune		57.86 293	56.43 300	62.18 300	72.62 277	35.35 361	66.57 306	56.33 365	50.65 264	57.64 308	57.10 391	30.65 322	76.36 280	37.38 328	78.88 132	74.82 318

v2v482		70.50 117	69.45 126	73.66 124	72.62 277	50.03 211	77.58 143	80.51 134	59.90 109	69.52 125	82.14 196	47.53 142	84.88 115	65.07 111	70.17 250	86.09 91

baseline2		63.42 243	61.26 259	69.89 214	72.55 279	47.62 245	71.54 262	68.38 292	50.11 270	54.82 333	75.55 313	43.06 194	80.96 196	48.13 248	67.16 290	81.11 235

test_fmvsm_n_1920		71.73 96	71.14 96	73.50 131	72.52 280	56.53 101	75.60 191	76.16 208	48.11 298	77.22 28	85.56 126	53.10 74	77.43 257	74.86 40	77.14 160	86.55 74

v7n		69.01 153	67.36 169	73.98 109	72.51 281	52.65 169	78.54 124	81.30 116	60.26 104	62.67 250	81.62 206	43.61 189	84.49 121	57.01 171	68.70 278	84.79 143

fmvsm_s_conf0.5_n_a		69.54 141	68.74 138	71.93 163	72.47 282	53.82 145	78.25 126	62.26 339	49.78 275	73.12 79	86.21 108	52.66 77	76.79 271	75.02 39	68.88 274	85.18 129

mamv4		56.85 300	58.00 288	53.43 354	72.46 283	54.47 137	57.56 366	54.74 369	38.81 371	57.42 311	79.45 250	47.57 141	38.70 406	60.88 147	53.07 368	67.11 376

pm-mvs1		65.24 224	64.97 214	66.04 267	72.38 284	39.40 323	72.62 247	75.63 216	55.53 195	62.35 260	83.18 172	47.45 144	76.47 279	49.06 240	66.54 294	82.24 213

XVG-ACMP-BASELINE		64.36 235	62.23 246	70.74 197	72.35 285	52.45 176	70.80 275	78.45 174	53.84 228	59.87 283	81.10 216	16.24 385	79.32 226	55.64 185	71.76 227	80.47 245

WTY-MVS		59.75 279	60.39 267	57.85 330	72.32 286	37.83 336	61.05 349	64.18 323	45.95 325	61.91 262	79.11 256	47.01 154	60.88 353	42.50 300	69.49 265	74.83 317

fmvsm_s_conf0.5_n		69.58 139	68.84 135	71.79 168	72.31 287	52.90 164	77.90 135	62.43 337	49.97 273	72.85 85	85.90 119	52.21 85	76.49 277	75.75 33	70.26 249	85.97 94

tpm cat1		59.25 284	56.95 294	66.15 264	72.19 288	46.96 251	68.09 297	65.76 310	40.03 367	57.81 307	70.56 350	38.32 244	74.51 289	38.26 324	61.50 335	77.00 294

mvs_anonymous		68.03 174	67.51 163	69.59 218	72.08 289	44.57 276	71.99 257	75.23 226	51.67 247	67.06 173	82.57 181	54.68 53	77.94 248	56.56 174	75.71 177	86.26 88

OurMVSNet-221017-0		61.37 269	58.63 281	69.61 217	72.05 290	48.06 239	73.93 229	72.51 258	47.23 312	54.74 334	80.92 221	21.49 376	81.24 190	48.57 244	56.22 359	79.53 262

IterMVS-SCA-FT		62.49 253	61.52 254	65.40 277	71.99 291	50.80 196	71.15 270	69.63 280	45.71 326	60.61 274	77.93 272	37.45 252	65.99 337	55.67 183	63.50 319	79.42 263

CostFormer		64.04 238	62.51 242	68.61 233	71.88 292	45.77 261	71.30 266	70.60 273	47.55 306	64.31 227	76.61 298	41.63 209	79.62 222	49.74 232	69.00 273	80.42 246

1314		64.61 231	63.21 235	68.80 230	71.87 293	47.46 247	73.95 227	78.39 179	42.88 350	59.97 281	76.60 299	38.11 247	79.39 225	54.84 190	72.32 221	79.55 261

tpm		57.34 296	58.16 285	54.86 344	71.80 294	34.77 364	67.47 304	56.04 368	48.20 297	60.10 278	76.92 290	37.17 258	53.41 387	40.76 311	65.01 304	76.40 300

eth_miper_zixun_eth		67.63 183	66.28 196	71.67 172	71.60 295	48.33 236	73.68 235	77.88 183	55.80 189	65.91 194	78.62 264	47.35 148	82.88 153	59.45 160	66.25 296	83.81 171

pmmvs4		61.48 268	59.39 273	67.76 241	71.57 296	53.86 144	71.42 263	65.34 313	44.20 337	59.46 289	77.92 273	35.90 269	74.71 288	43.87 287	64.87 306	74.71 321

fmvsm_l_conf0.5_n		70.99 107	70.82 101	71.48 176	71.45 297	54.40 139	77.18 157	70.46 274	48.67 289	75.17 41	86.86 84	53.77 65	76.86 269	76.33 30	77.51 153	83.17 196

AllTest		57.08 298	54.65 312	64.39 286	71.44 298	49.03 224	69.92 285	67.30 297	45.97 323	47.16 372	79.77 241	17.47 379	67.56 327	33.65 350	59.16 347	76.57 298

TestCases				64.39 286	71.44 298	49.03 224		67.30 297	45.97 323	47.16 372	79.77 241	17.47 379	67.56 327	33.65 350	59.16 347	76.57 298

lessismore_v0				69.91 212	71.42 300	47.80 241		50.90 382		50.39 363	75.56 312	27.43 349	81.33 187	45.91 266	34.10 398	80.59 244

gm-plane-assit					71.40 301	41.72 304			48.85 288		73.31 331		82.48 168	48.90 241

GG-mvs-BLEND				62.34 299	71.36 302	37.04 346	69.20 291	57.33 361		54.73 335	65.48 379	30.37 324	77.82 251	34.82 346	74.93 182	72.17 345

fmvsm_l_conf0.5_n_a		70.50 117	70.27 112	71.18 188	71.30 303	54.09 141	76.89 165	69.87 277	47.90 302	74.37 59	86.49 101	53.07 75	76.69 274	75.41 35	77.11 161	82.76 203

test_fmvsmconf_n		73.01 73	72.59 75	74.27 103	71.28 304	55.88 114	78.21 129	75.56 218	54.31 222	74.86 50	87.80 72	54.72 52	80.23 215	78.07 21	78.48 140	86.70 67

test_fmvsmvis_n_1920		70.84 109	70.38 110	72.22 161	71.16 305	55.39 127	75.86 187	72.21 261	49.03 285	73.28 73	86.17 110	51.83 92	77.29 261	75.80 32	78.05 145	83.98 164

fmvsm_s_conf0.1_n		69.41 146	68.60 141	71.83 166	71.07 306	52.88 166	77.85 138	62.44 336	49.58 278	72.97 82	86.22 107	51.68 95	76.48 278	75.53 34	70.10 252	86.14 89

FMVSNet5		55.86 310	54.93 310	58.66 323	71.05 307	36.35 352	64.18 331	62.48 335	46.76 316	50.66 362	74.73 322	25.80 360	64.04 343	33.11 354	65.57 301	75.59 307

fmvsm_s_conf0.1_n_a		69.32 147	68.44 147	71.96 162	70.91 308	53.78 146	78.12 131	62.30 338	49.35 281	73.20 75	86.55 100	51.99 89	76.79 271	74.83 41	68.68 279	85.32 124

c3_l		68.33 168	67.56 159	70.62 199	70.87 309	46.21 258	74.47 218	78.80 161	56.22 181	66.19 189	78.53 266	51.88 90	81.40 185	62.08 136	69.04 272	84.25 155

GA-MVS		65.53 219	63.70 226	71.02 193	70.87 309	48.10 238	70.48 278	74.40 239	56.69 165	64.70 222	76.77 293	33.66 294	81.10 193	55.42 187	70.32 247	83.87 169

pmmvs6		63.69 241	62.82 240	66.27 261	70.63 311	39.27 324	73.13 240	75.47 221	52.69 239	59.75 287	82.30 190	39.71 228	77.03 265	47.40 252	64.35 312	82.53 206

miper_ehance_all_eth		68.03 174	67.24 176	70.40 203	70.54 312	46.21 258	73.98 225	78.68 165	55.07 208	66.05 191	77.80 277	52.16 87	81.31 188	61.53 145	69.32 266	83.67 179

MonoMVSNet		64.15 236	63.31 233	66.69 254	70.51 313	44.12 280	74.47 218	74.21 244	57.81 153	63.03 244	76.62 296	38.33 243	77.31 260	54.22 196	60.59 343	78.64 271

OpenMVS_ROB	52.78 18	60.03 276	58.14 286	65.69 273	70.47 314	44.82 271	75.33 196	70.86 271	45.04 329	56.06 320	76.00 305	26.89 354	79.65 220	35.36 345	67.29 288	72.60 336

v148		68.24 171	67.19 178	71.40 181	70.43 315	47.77 243	75.76 190	77.03 199	58.91 127	67.36 167	80.10 236	48.60 128	81.89 176	60.01 154	66.52 295	84.53 148

XXY-MVS		60.68 271	61.67 252	57.70 332	70.43 315	38.45 331	64.19 330	66.47 305	48.05 300	63.22 239	80.86 223	49.28 118	60.47 354	45.25 278	67.28 289	74.19 326

MVSTER		67.16 194	65.58 207	71.88 165	70.37 317	49.70 215	70.25 282	78.45 174	51.52 251	69.16 135	80.37 229	38.45 241	82.50 166	60.19 152	71.46 231	83.44 186

cl____		67.18 192	66.26 197	69.94 210	70.20 318	45.74 262	73.30 237	76.83 202	55.10 203	65.27 207	79.57 246	47.39 146	80.53 206	59.41 162	69.22 270	83.53 185

DIV-MVS_self_test		67.18 192	66.26 197	69.94 210	70.20 318	45.74 262	73.29 238	76.83 202	55.10 203	65.27 207	79.58 245	47.38 147	80.53 206	59.43 161	69.22 270	83.54 184

tpmvs		58.47 287	56.95 294	63.03 296	70.20 318	41.21 306	67.90 299	67.23 300	49.62 277	54.73 335	70.84 348	34.14 285	76.24 282	36.64 337	61.29 336	71.64 350

anonymousdsp		67.00 198	64.82 215	73.57 130	70.09 321	56.13 107	76.35 175	77.35 195	48.43 294	64.99 219	80.84 225	33.01 301	80.34 210	64.66 114	67.64 286	84.23 156

MIMVSNet1		55.17 317	54.31 318	57.77 331	70.03 322	32.01 382	65.68 314	64.81 317	49.19 283	46.75 375	76.00 305	25.53 362	64.04 343	28.65 380	62.13 330	77.26 290

CR-MVSNet		59.91 277	57.90 289	65.96 268	69.96 323	52.07 181	65.31 322	63.15 331	42.48 352	59.36 290	74.84 320	35.83 270	70.75 307	45.50 273	64.65 308	75.06 312

RPMNet		61.53 266	58.42 282	70.86 194	69.96 323	52.07 181	65.31 322	81.36 111	43.20 347	59.36 290	70.15 355	35.37 273	85.47 100	36.42 340	64.65 308	75.06 312

test_fmvsmconf0.1_n		72.81 75	72.33 78	74.24 104	69.89 325	55.81 115	78.22 128	75.40 222	54.17 224	75.00 45	88.03 68	53.82 64	80.23 215	78.08 20	78.34 143	86.69 68

cl22		67.47 186	66.45 186	70.54 201	69.85 326	46.49 254	73.85 232	77.35 195	55.07 208	65.51 202	77.92 273	47.64 139	81.10 193	61.58 144	69.32 266	84.01 163

Anonymous20231206		55.10 318	55.30 309	54.48 346	69.81 327	33.94 373	62.91 336	62.13 341	41.08 359	55.18 329	75.65 311	32.75 307	56.59 376	30.32 374	67.86 283	72.91 332

our_test_3		56.49 303	54.42 315	62.68 298	69.51 328	45.48 267	66.08 310	61.49 343	44.11 340	50.73 361	69.60 360	33.05 299	68.15 320	38.38 323	56.86 355	74.40 323

ppachtmachnet_test		58.06 292	55.38 308	66.10 266	69.51 328	48.99 227	68.01 298	66.13 309	44.50 334	54.05 342	70.74 349	32.09 317	72.34 298	36.68 336	56.71 358	76.99 296

diffmvs		70.69 113	70.43 108	71.46 177	69.45 330	48.95 228	72.93 242	78.46 173	57.27 158	71.69 99	83.97 157	51.48 97	77.92 250	70.70 71	77.95 147	87.53 44
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS		62.79 252	61.27 258	67.35 247	69.37 331	52.04 183	71.17 268	68.24 294	52.63 240	59.82 284	76.91 291	37.32 255	72.36 297	52.80 208	63.19 322	77.66 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		56.77 301	56.83 296	56.61 335	69.23 332	41.02 307	58.37 358	64.18 323	50.59 266	57.45 310	71.42 344	35.54 272	58.94 364	37.23 329	67.45 287	69.87 367

miper_enhance_ethall		67.11 195	66.09 199	70.17 207	69.21 333	45.98 260	72.85 244	78.41 177	51.38 254	65.65 200	75.98 308	51.17 101	81.25 189	60.82 148	69.32 266	83.29 190

Patchmtry		57.16 297	56.47 299	59.23 317	69.17 334	34.58 367	62.98 335	63.15 331	44.53 333	56.83 314	74.84 320	35.83 270	68.71 318	40.03 314	60.91 337	74.39 324

CL-MVSNet_self_test		61.53 266	60.94 263	63.30 292	68.95 335	36.93 347	67.60 301	72.80 257	55.67 192	59.95 282	76.63 295	45.01 177	72.22 300	39.74 317	62.09 331	80.74 243

V42		68.65 159	67.35 170	72.56 152	68.93 336	50.18 207	72.90 243	79.47 149	56.92 163	69.45 128	80.26 233	46.29 160	82.99 148	64.07 117	67.82 284	84.53 148

test-LLR		58.15 291	58.13 287	58.22 326	68.57 337	44.80 272	65.46 318	57.92 356	50.08 271	55.44 325	69.82 357	32.62 310	57.44 370	49.66 234	73.62 197	72.41 341

test-mter		56.42 305	55.82 305	58.22 326	68.57 337	44.80 272	65.46 318	57.92 356	39.94 368	55.44 325	69.82 357	21.92 372	57.44 370	49.66 234	73.62 197	72.41 341

MVS-HIRNet		45.52 350	44.48 352	48.65 369	68.49 339	34.05 372	59.41 356	44.50 397	27.03 390	37.96 397	50.47 399	26.16 358	64.10 342	26.74 388	59.52 345	47.82 398

dp		51.89 333	51.60 332	52.77 359	68.44 340	32.45 381	62.36 339	54.57 371	44.16 338	49.31 367	67.91 365	28.87 338	56.61 375	33.89 349	54.89 362	69.24 372

PatchT		53.17 329	53.44 326	52.33 362	68.29 341	25.34 404	58.21 359	54.41 372	44.46 335	54.56 337	69.05 363	33.32 297	60.94 352	36.93 332	61.76 334	70.73 361

test_fmvsmconf0.01_n		72.17 88	71.50 86	74.16 106	67.96 342	55.58 123	78.06 133	74.67 236	54.19 223	74.54 56	88.23 61	50.35 110	80.24 214	78.07 21	77.46 154	86.65 71

Patchmatch-RL test		58.16 290	55.49 307	66.15 264	67.92 343	48.89 229	60.66 351	51.07 381	47.86 303	59.36 290	62.71 385	34.02 288	72.27 299	56.41 175	59.40 346	77.30 288

pmmvs-eth3d		58.81 286	56.31 301	66.30 260	67.61 344	52.42 177	72.30 253	64.76 318	43.55 343	54.94 332	74.19 326	28.95 336	72.60 296	43.31 291	57.21 354	73.88 329

PVSNet_0	43.31 20	47.46 348	45.64 351	52.92 358	67.60 345	44.65 274	54.06 377	54.64 370	41.59 356	46.15 377	58.75 388	30.99 321	58.66 365	32.18 357	24.81 403	55.46 391

CHOSEN 280x420		47.83 346	46.36 350	52.24 364	67.37 346	49.78 214	38.91 403	43.11 400	35.00 377	43.27 385	63.30 384	28.95 336	49.19 394	36.53 338	60.80 339	57.76 388

tpmrst		58.24 289	58.70 280	56.84 334	66.97 347	34.32 369	69.57 288	61.14 345	47.17 313	58.58 301	71.60 343	41.28 216	60.41 355	49.20 238	62.84 324	75.78 305

sss		56.17 308	56.57 298	54.96 343	66.93 348	36.32 354	57.94 361	61.69 342	41.67 355	58.64 299	75.32 318	38.72 239	56.25 377	42.04 304	66.19 297	72.31 344

TinyColmap		54.14 320	51.72 331	61.40 306	66.84 349	41.97 299	66.52 307	68.51 291	44.81 330	42.69 386	75.77 310	11.66 395	72.94 295	31.96 360	56.77 357	69.27 371

miper_lstm_enhance		62.03 261	60.88 264	65.49 276	66.71 350	46.25 256	56.29 371	75.70 215	50.68 263	61.27 270	75.48 315	40.21 223	68.03 323	56.31 176	65.25 303	82.18 214

TESTMET0.1,1		55.28 315	54.90 311	56.42 336	66.56 351	43.67 284	65.46 318	56.27 366	39.18 370	53.83 343	67.44 369	24.21 367	55.46 381	48.04 249	73.11 210	70.13 365

dmvs_testset		50.16 340	51.90 330	44.94 375	66.49 352	11.78 415	61.01 350	51.50 378	51.17 259	50.30 365	67.44 369	39.28 232	60.29 356	22.38 395	57.49 353	62.76 380

D2MVS		62.30 257	60.29 268	68.34 237	66.46 353	48.42 235	65.70 313	73.42 251	47.71 304	58.16 304	75.02 319	30.51 323	77.71 254	53.96 199	71.68 229	78.90 270

MDA-MVSNet-bldmvs		53.87 323	50.81 335	63.05 295	66.25 354	48.58 233	56.93 369	63.82 325	48.09 299	41.22 387	70.48 353	30.34 325	68.00 324	34.24 348	45.92 384	72.57 337

ITE_SJBPF				62.09 301	66.16 355	44.55 277		64.32 321	47.36 309	55.31 327	80.34 231	19.27 378	62.68 348	36.29 341	62.39 328	79.04 267

EPMVS		53.96 321	53.69 324	54.79 345	66.12 356	31.96 383	62.34 340	49.05 385	44.42 336	55.54 323	71.33 346	30.22 326	56.70 373	41.65 308	62.54 327	75.71 306

ADS-MVSNet2		51.33 336	48.76 343	59.07 320	66.02 357	44.60 275	50.90 385	59.76 349	36.90 372	50.74 359	66.18 377	26.38 355	63.11 346	27.17 385	54.76 363	69.50 369

ADS-MVSNet		48.48 345	47.77 346	50.63 366	66.02 357	29.92 387	50.90 385	50.87 383	36.90 372	50.74 359	66.18 377	26.38 355	52.47 389	27.17 385	54.76 363	69.50 369

EU-MVSNet		55.61 313	54.41 316	59.19 319	65.41 359	33.42 376	72.44 251	71.91 264	28.81 385	51.27 355	73.87 328	24.76 365	69.08 317	43.04 295	58.20 350	75.06 312

RPSCF		55.80 311	54.22 320	60.53 311	65.13 360	42.91 293	64.30 329	57.62 358	36.84 374	58.05 306	82.28 191	28.01 343	56.24 378	37.14 330	58.61 349	82.44 210

USDC		56.35 306	54.24 319	62.69 297	64.74 361	40.31 313	65.05 324	73.83 248	43.93 341	47.58 370	77.71 281	15.36 388	75.05 287	38.19 325	61.81 333	72.70 335

JIA-IIPM		51.56 334	47.68 348	63.21 293	64.61 362	50.73 197	47.71 391	58.77 353	42.90 349	48.46 369	51.72 395	24.97 364	70.24 313	36.06 342	53.89 366	68.64 373

Patchmatch-test		49.08 343	48.28 345	51.50 365	64.40 363	30.85 386	45.68 395	48.46 388	35.60 376	46.10 378	72.10 338	34.47 283	46.37 398	27.08 387	60.65 341	77.27 289

TDRefinement		53.44 327	50.72 336	61.60 303	64.31 364	46.96 251	70.89 274	65.27 315	41.78 353	44.61 381	77.98 270	11.52 397	66.36 335	28.57 381	51.59 372	71.49 353

test_vis1_n_1920		58.86 285	59.06 276	58.25 325	63.76 365	43.14 290	67.49 303	66.36 307	40.22 365	65.89 196	71.95 341	31.04 320	59.75 359	59.94 155	64.90 305	71.85 348

N_pmnet		39.35 363	40.28 360	36.54 386	63.76 365	1.62 423	49.37 388	0.76 422	34.62 378	43.61 384	66.38 376	26.25 357	42.57 402	26.02 390	51.77 371	65.44 378

ambc				65.13 281	63.72 367	37.07 345	47.66 392	78.78 162		54.37 340	71.42 344	11.24 398	80.94 197	45.64 269	53.85 367	77.38 287

WB-MVS		43.26 353	43.41 353	42.83 379	63.32 368	10.32 417	58.17 360	45.20 395	45.42 327	40.44 390	67.26 372	34.01 289	58.98 363	11.96 408	24.88 402	59.20 383

KD-MVS_2432*1600		53.45 325	51.50 333	59.30 315	62.82 369	37.14 343	55.33 372	71.79 265	47.34 310	55.09 330	70.52 351	21.91 373	70.45 309	35.72 343	42.97 387	70.31 363

miper_refine_blended		53.45 325	51.50 333	59.30 315	62.82 369	37.14 343	55.33 372	71.79 265	47.34 310	55.09 330	70.52 351	21.91 373	70.45 309	35.72 343	42.97 387	70.31 363

test0.0.03 1		53.32 328	53.59 325	52.50 361	62.81 371	29.45 388	59.51 354	54.11 373	50.08 271	54.40 339	74.31 325	32.62 310	55.92 379	30.50 373	63.95 315	72.15 346

PMMVS		53.96 321	53.26 327	56.04 337	62.60 372	50.92 193	61.17 347	56.09 367	32.81 380	53.51 349	66.84 374	34.04 287	59.93 358	44.14 283	68.18 281	57.27 389

SSC-MVS		41.96 358	41.99 357	41.90 380	62.46 373	9.28 419	57.41 367	44.32 398	43.38 344	38.30 396	66.45 375	32.67 309	58.42 367	10.98 409	21.91 405	57.99 387

PM-MVS		52.33 331	50.19 339	58.75 322	62.10 374	45.14 270	65.75 312	40.38 402	43.60 342	53.52 348	72.65 333	9.16 403	65.87 338	50.41 227	54.18 365	65.24 379

Gipuma		34.77 367	31.91 372	43.33 377	62.05 375	37.87 334	20.39 408	67.03 301	23.23 396	18.41 409	25.84 409	4.24 410	62.73 347	14.71 402	51.32 373	29.38 407
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 323	54.02 321	53.41 355	61.47 376	28.11 393	61.30 345	59.21 351	51.34 256	52.09 353	77.43 284	33.29 298	58.55 366	29.76 376	60.27 344	73.58 330

pmmvs5		56.47 304	55.68 306	58.86 321	61.41 377	36.71 349	66.37 308	62.75 333	40.38 364	53.70 344	76.62 296	34.56 280	67.05 330	40.02 315	65.27 302	72.83 334

MDA-MVSNet_test_wron		50.71 339	48.95 341	56.00 339	61.17 378	41.84 300	51.90 383	56.45 362	40.96 360	44.79 380	67.84 366	30.04 328	55.07 384	36.71 335	50.69 375	71.11 359

YYNet1		50.73 338	48.96 340	56.03 338	61.10 379	41.78 301	51.94 382	56.44 363	40.94 361	44.84 379	67.80 367	30.08 327	55.08 383	36.77 333	50.71 374	71.22 356

dongtai		34.52 368	34.94 368	33.26 389	61.06 380	16.00 414	52.79 381	23.78 415	40.71 362	39.33 394	48.65 403	16.91 383	48.34 395	12.18 407	19.05 407	35.44 406

CMPMVS	42.80 21	57.81 294	55.97 303	63.32 291	60.98 381	47.38 248	64.66 327	69.50 283	32.06 381	46.83 374	77.80 277	29.50 333	71.36 304	48.68 242	73.75 195	71.21 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 341	48.87 342	53.66 351	60.97 382	33.67 375	57.62 365	64.56 320	39.47 369	47.38 371	64.02 383	27.47 347	59.32 360	34.69 347	43.68 386	67.98 375

Anonymous20240521		55.30 314	54.41 316	57.96 329	60.92 383	41.73 302	71.09 272	71.06 270	41.18 358	48.65 368	73.31 331	16.93 382	59.25 361	42.54 299	64.01 313	72.90 333

testgi		51.90 332	52.37 329	50.51 367	60.39 384	23.55 407	58.42 357	58.15 354	49.03 285	51.83 354	79.21 255	22.39 370	55.59 380	29.24 379	62.64 325	72.40 343

UnsupCasMVSNet_eth		53.16 330	52.47 328	55.23 342	59.45 385	33.39 377	59.43 355	69.13 287	45.98 322	50.35 364	72.32 335	29.30 335	58.26 368	42.02 305	44.30 385	74.05 327

mvs5depth		55.64 312	53.81 323	61.11 309	59.39 386	40.98 311	65.89 311	68.28 293	50.21 269	58.11 305	75.42 316	17.03 381	67.63 326	43.79 288	46.21 382	74.73 320

test_cas_vis1_n_1920		56.91 299	56.71 297	57.51 333	59.13 387	45.40 268	63.58 333	61.29 344	36.24 375	67.14 172	71.85 342	29.89 329	56.69 374	57.65 168	63.58 318	70.46 362

new-patchmatchnet		47.56 347	47.73 347	47.06 370	58.81 388	9.37 418	48.78 389	59.21 351	43.28 345	44.22 382	68.66 364	25.67 361	57.20 372	31.57 368	49.35 379	74.62 322

FPMVS		42.18 357	41.11 359	45.39 372	58.03 389	41.01 309	49.50 387	53.81 375	30.07 384	33.71 399	64.03 381	11.69 394	52.08 392	14.01 403	55.11 361	43.09 400

KD-MVS_self_test		55.22 316	53.89 322	59.21 318	57.80 390	27.47 396	57.75 364	74.32 240	47.38 308	50.90 358	70.00 356	28.45 341	70.30 312	40.44 312	57.92 351	79.87 256

test_vis1_n		49.89 342	48.69 344	53.50 353	53.97 391	37.38 341	61.53 342	47.33 392	28.54 386	59.62 288	67.10 373	13.52 390	52.27 390	49.07 239	57.52 352	70.84 360

test_fmvs1		51.32 337	50.48 337	53.81 350	53.57 392	37.51 340	60.63 352	51.16 379	28.02 389	63.62 235	69.23 362	16.41 384	53.93 386	51.01 223	60.70 340	69.99 366

kuosan		29.62 375	30.82 374	26.02 394	52.99 393	16.22 413	51.09 384	22.71 416	33.91 379	33.99 398	40.85 404	15.89 386	33.11 411	7.59 415	18.37 408	28.72 408

test_fmvs1_n		51.37 335	50.35 338	54.42 348	52.85 394	37.71 338	61.16 348	51.93 376	28.15 387	63.81 234	69.73 359	13.72 389	53.95 385	51.16 222	60.65 341	71.59 351

new_pmnet		34.13 369	34.29 370	33.64 388	52.63 395	18.23 412	44.43 398	33.90 408	22.81 398	30.89 401	53.18 393	10.48 401	35.72 410	20.77 397	39.51 391	46.98 399

pmmvs3		44.92 351	41.95 358	53.86 349	52.58 396	43.55 285	62.11 341	46.90 394	26.05 392	40.63 388	60.19 387	11.08 400	57.91 369	31.83 365	46.15 383	60.11 382

m2depth		45.56 349	42.95 354	53.39 356	52.33 397	29.15 389	57.77 362	48.20 389	31.81 382	49.86 366	77.21 286	8.69 404	59.16 362	27.31 384	33.40 399	71.84 349

DSMNet-mixed		39.30 364	38.72 363	41.03 381	51.22 398	19.66 410	45.53 396	31.35 409	15.83 408	39.80 392	67.42 371	22.19 371	45.13 399	22.43 394	52.69 370	58.31 386

mvsany_test1		39.38 362	38.16 365	43.02 378	49.05 399	34.28 370	44.16 399	25.94 413	22.74 399	46.57 376	62.21 386	23.85 368	41.16 405	33.01 355	35.91 395	53.63 392

APD_test1		37.39 365	34.94 368	44.72 376	48.88 400	33.19 378	52.95 380	44.00 399	19.49 402	27.28 403	58.59 389	3.18 415	52.84 388	18.92 398	41.17 390	48.14 397

test_fmvs2		48.69 344	47.49 349	52.29 363	48.63 401	33.06 379	57.76 363	48.05 390	25.71 393	59.76 286	69.60 360	11.57 396	52.23 391	49.45 237	56.86 355	71.58 352

LF4IMVS		42.95 354	42.26 356	45.04 373	48.30 402	32.50 380	54.80 374	48.49 387	28.03 388	40.51 389	70.16 354	9.24 402	43.89 401	31.63 366	49.18 380	58.72 385

wuyk23d		13.32 382	12.52 385	15.71 396	47.54 403	26.27 401	31.06 407	1.98 421	4.93 413	5.18 416	1.94 416	0.45 421	18.54 415	6.81 416	12.83 412	2.33 413

MVStest1		42.65 355	39.29 362	52.71 360	47.26 404	34.58 367	54.41 376	50.84 384	23.35 395	39.31 395	74.08 327	12.57 392	55.09 382	23.32 393	28.47 401	68.47 374

test_vis1_rt		41.35 360	39.45 361	47.03 371	46.65 405	37.86 335	47.76 390	38.65 403	23.10 397	44.21 383	51.22 397	11.20 399	44.08 400	39.27 318	53.02 369	59.14 384

test_fmvs3		44.30 352	42.55 355	49.55 368	42.83 406	27.15 399	53.03 379	44.93 396	22.03 401	53.69 346	64.94 380	4.21 411	49.63 393	47.47 250	49.82 377	71.88 347

LCM-MVSNet		40.30 361	35.88 367	53.57 352	42.24 407	29.15 389	45.21 397	60.53 348	22.23 400	28.02 402	50.98 398	3.72 413	61.78 351	31.22 371	38.76 393	69.78 368

E-PMN		23.77 377	22.73 381	26.90 392	42.02 408	20.67 409	42.66 400	35.70 406	17.43 404	10.28 414	25.05 410	6.42 406	42.39 403	10.28 411	14.71 410	17.63 409

testf1		31.46 373	28.89 377	39.16 382	41.99 409	28.78 391	46.45 393	37.56 404	14.28 409	21.10 405	48.96 400	1.48 419	47.11 396	13.63 404	34.56 396	41.60 401

APD_test2		31.46 373	28.89 377	39.16 382	41.99 409	28.78 391	46.45 393	37.56 404	14.28 409	21.10 405	48.96 400	1.48 419	47.11 396	13.63 404	34.56 396	41.60 401

EMVS		22.97 378	21.84 382	26.36 393	40.20 411	19.53 411	41.95 401	34.64 407	17.09 405	9.73 415	22.83 411	7.29 405	42.22 404	9.18 413	13.66 411	17.32 410

ANet_high		41.38 359	37.47 366	53.11 357	39.73 412	24.45 405	56.94 368	69.69 278	47.65 305	26.04 404	52.32 394	12.44 393	62.38 349	21.80 396	10.61 413	72.49 338

PMMVS2		27.40 376	25.91 379	31.87 391	39.46 413	6.57 420	31.17 406	28.52 411	23.96 394	20.45 408	48.94 402	4.20 412	37.94 407	16.51 400	19.97 406	51.09 393

PMVS	28.69 22	36.22 366	33.29 371	45.02 374	36.82 414	35.98 357	54.68 375	48.74 386	26.31 391	21.02 407	51.61 396	2.88 416	60.10 357	9.99 412	47.58 381	38.99 405
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvsany_test3		32.62 370	30.57 375	38.77 384	36.16 415	24.20 406	38.10 404	20.63 417	19.14 403	40.36 391	57.43 390	5.06 408	36.63 409	29.59 378	28.66 400	55.49 390

test_vis3_rt		32.09 371	30.20 376	37.76 385	35.36 416	27.48 395	40.60 402	28.29 412	16.69 406	32.52 400	40.53 405	1.96 417	37.40 408	33.64 352	42.21 389	48.39 395

MVE	17.77 23	21.41 379	17.77 384	32.34 390	34.34 417	25.44 403	16.11 409	24.11 414	11.19 411	13.22 411	31.92 407	1.58 418	30.95 413	10.47 410	17.03 409	40.62 404
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		31.86 372	31.05 373	34.28 387	32.33 418	21.86 408	32.34 405	30.46 410	16.02 407	39.78 393	55.45 392	4.80 409	32.36 412	30.61 372	37.66 394	48.64 394

DeepMVS_CX				12.03 397	17.97 419	10.91 416		10.60 420	7.46 412	11.07 413	28.36 408	3.28 414	11.29 416	8.01 414	9.74 415	13.89 411

test_method		19.68 380	18.10 383	24.41 395	13.68 420	3.11 422	12.06 411	42.37 401	2.00 414	11.97 412	36.38 406	5.77 407	29.35 414	15.06 401	23.65 404	40.76 403

tmp_tt		9.43 383	11.14 386	4.30 398	2.38 421	4.40 421	13.62 410	16.08 419	0.39 415	15.89 410	13.06 412	15.80 387	5.54 417	12.63 406	10.46 414	2.95 412

testmvs		4.52 386	6.03 389	0.01 400	0.01 422	0.00 425	53.86 378	0.00 423	0.01 417	0.04 418	0.27 417	0.00 423	0.00 418	0.04 417	0.00 416	0.03 415

test123		4.73 385	6.30 388	0.02 399	0.01 422	0.01 424	56.36 370	0.00 423	0.01 417	0.04 418	0.21 418	0.01 422	0.00 418	0.03 418	0.00 416	0.04 414

test_blank		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

eth-test2					0.00 424

eth-test					0.00 424

uanet_test		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

DCPMVS		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

cdsmvs_eth3d_5k		17.50 381	23.34 380	0.00 401	0.00 424	0.00 425	0.00 412	78.63 166	0.00 419	0.00 420	82.18 192	49.25 119	0.00 418	0.00 419	0.00 416	0.00 416

pcd_1.5k_mvsjas		3.92 387	5.23 390	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	47.05 151	0.00 418	0.00 419	0.00 416	0.00 416

sosnet-low-res		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

sosnet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

uncertanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

Regformer		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

ab-mvs-re		6.49 384	8.65 387	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	77.89 275	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

uanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 425	0.00 412	0.00 423	0.00 419	0.00 420	0.00 419	0.00 423	0.00 418	0.00 419	0.00 416	0.00 416

WAC-MVS						27.31 397								27.77 382

PC_three_1452									55.09 205	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 14

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 39

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 25

GSMVS																78.05 277

sam_mvs1												34.74 279				78.05 277

sam_mvs												33.43 296

MTGPA								80.97 127

test_post1							68.67 294				3.64 414	32.39 315	69.49 315	44.17 281

test_post											3.55 415	33.90 290	66.52 333

patchmatchnet-post											64.03 381	34.50 281	74.27 291

MTMP							86.03 19	17.08 418

test9_res														75.28 37	88.31 32	83.81 171

agg_prior2														73.09 55	87.93 40	84.33 152

test_prior4						62.51 14	82.08 76

test_prior2							81.75 78		60.37 98	75.01 44	89.06 52	56.22 41		72.19 60	88.96 24

旧先验2							76.08 181		45.32 328	76.55 33			65.56 339	58.75 164

新几何2							76.12 179

无先验							79.66 109	74.30 242	48.40 295				80.78 203	53.62 201		79.03 268

原ACMM2							79.02 114

testdata2													72.18 301	46.95 259

segment_acmp												54.23 57

testdata1							72.65 245		60.50 93

plane_prior5								84.01 51					87.21 54	68.16 83	80.58 107	84.65 146

plane_prior4											86.10 112

plane_prior3						56.09 108			63.92 36	69.27 131

plane_prior2							84.22 40		64.52 25

plane_prior						56.31 102	83.58 53		63.19 48						80.48 110

n2								0.00 423

nn								0.00 423

door-mid								47.19 393

test11								83.47 70

door								47.60 391

HQP5-MVS						54.94 131

BP-MVS														67.04 94

HQP4-MVS										67.85 155			86.93 61			84.32 153

HQP3-MVS								83.90 56							80.35 111

HQP2-MVS												45.46 169

MDTV_nov1_ep13_2view						25.89 402	61.22 346		40.10 366	51.10 356		32.97 302		38.49 322		78.61 272

ACMMP++_ref															74.07 190

ACMMP++															72.16 224

Test By Simon												48.33 130

High-resolution multi-view benchmark