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 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 77	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 172

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 87

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 63

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 37

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 281	84.64 3				79.07 13	90.87 5	88.37 31

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 160
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 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

test_part2					87.58 9	60.47 42				83.42 15

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 37

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 62	89.18 25	74.19 63	87.34 50	86.38 112

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 94
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 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 29

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 100	90.58 24	49.90 145	88.21 38	73.78 67	87.03 52	86.29 124

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 96	90.56 29	49.80 148	88.24 37	74.02 65	87.03 52	86.32 120

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 52	62.82 60	73.96 84	90.50 31	53.20 94	88.35 35	74.02 65	87.05 51	86.13 127

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 203	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 106

ZD-MVS					86.64 21	60.38 45		82.70 114	57.95 181	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 96
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 42

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 39
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		72.15 125	70.73 140	76.40 72	86.57 25	57.99 88	81.15 98	82.96 108	57.03 200	66.78 231	85.56 166	44.50 224	88.11 42	51.77 278	80.23 131	83.10 251

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 83	63.89 39	73.60 94	90.60 23	54.85 68	86.72 76	77.20 31	88.06 40	85.74 146
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 119	62.90 57	71.77 134	90.26 39	46.61 196	86.55 84	71.71 86	85.66 67	84.97 183

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 112	79.05 26	90.30 38	55.54 61	88.32 36	73.48 70	87.03 52	84.83 187
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 60	90.47 33	53.96 80	88.68 31	76.48 39	89.63 20	87.16 84

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	90.01 49	47.95 172	88.01 44	71.55 88	86.74 59	86.37 114

X-MVStestdata		70.21 163	67.28 222	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	6.49 493	47.95 172	88.01 44	71.55 88	86.74 59	86.37 114

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 24	81.51 2	88.44 31	88.09 45
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		70.83 149	69.56 161	74.64 110	86.21 32	54.63 148	82.34 81	81.81 126	48.22 372	63.01 304	85.83 159	40.92 274	87.10 67	57.91 224	79.79 138	82.18 273

save fliter					86.17 34	61.30 28	83.98 58	79.66 176	59.00 155

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 45	61.98 84	73.06 112	88.88 66	53.72 86	89.06 27	68.27 104	88.04 41	87.42 71
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 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 74	61.71 86	72.45 127	90.34 37	48.48 168	88.13 41	72.32 78	86.85 57	85.78 140

FOURS1					86.12 37	60.82 37	88.18 1	83.61 81	60.87 105	81.50 20

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 253	80.97 155	65.13 15	75.77 50	90.88 20	48.63 165	86.66 78	77.23 30	88.17 37	84.81 188

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 35	63.56 43	74.29 79	90.03 47	52.56 103	88.53 33	74.79 59	88.34 33	86.63 105

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 86	62.44 69	72.68 121	90.50 31	48.18 170	87.34 58	73.59 69	85.71 66	84.76 191

SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 120	59.99 134	75.10 59	90.35 36	47.66 177	86.52 85	71.64 87	82.99 91	84.47 200

新几何1				70.76 249	85.66 42	61.13 30		66.43 378	44.68 412	70.29 153	86.64 124	41.29 267	75.23 352	49.72 293	81.75 111	75.93 382

MG-MVS		73.96 81	73.89 80	74.16 129	85.65 43	49.69 265	81.59 93	81.29 143	61.45 91	71.05 144	88.11 77	51.77 120	87.73 52	61.05 195	83.09 89	85.05 179

TEST9					85.58 44	61.59 24	81.62 91	81.26 144	55.65 236	74.93 63	88.81 67	53.70 87	84.68 137

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 144	55.86 228	74.93 63	88.81 67	53.70 87	84.68 137	75.24 55	88.33 34	83.65 234

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 104	88.39 34	79.34 9	90.52 13	86.78 97

test_8					85.40 47	60.96 34	81.54 94	81.18 148	55.86 228	74.81 68	88.80 69	53.70 87	84.45 141

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 87	51.47 325	70.27 154	86.61 128	48.61 166	86.51 86	53.85 260	87.96 43	78.16 351

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 220	74.05 82	88.98 63	53.34 92	87.92 47	69.23 101	88.42 32	87.59 65

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 42

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 42

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 48

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 165	89.74 55	45.43 210	87.16 65	72.01 81	82.87 96	85.14 174
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 54	59.96 50		81.04 153		74.68 72			84.04 147

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 111	89.97 50	50.90 136	87.48 57	75.30 53	86.85 57	87.33 79
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 77	76.41 48	91.51 11	52.47 106	86.78 75	80.66 4	89.64 19	87.80 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 72	59.34 151	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 169	68.66 183	73.97 142	84.94 58	57.83 90	82.63 76	78.71 197	56.28 222	64.34 283	84.14 198	41.57 262	87.06 69	46.45 327	78.88 164	77.02 370

DP-MVS		65.68 272	63.66 285	71.75 212	84.93 59	56.87 109	80.74 103	73.16 320	53.06 296	59.09 359	82.35 244	36.79 326	85.94 105	32.82 433	69.96 318	72.45 420

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 41

CPTT-MVS		72.78 106	72.08 113	74.87 102	84.88 61	61.41 26	84.15 54	77.86 224	55.27 246	67.51 218	88.08 79	41.93 252	81.85 210	69.04 102	80.01 133	81.35 291

test12				77.76 50	84.52 62	58.41 83		83.36 90		72.93 115		54.61 71	88.05 43		88.12 38	86.81 95

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 121	79.89 22	89.38 58	54.97 66	85.58 113	76.12 45	84.94 70	86.33 118
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 73	73.46 89	76.80 63	84.45 64	59.04 74	83.65 63	81.05 152	60.15 130	70.43 151	89.84 52	41.09 272	85.59 112	67.61 120	82.90 95	85.77 143

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 112

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 158

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 158

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 108	75.27 55	84.83 177	60.76 18	86.56 81	67.86 116	87.87 45	86.06 129

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 276	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 113	83.88 83	85.85 137

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 91	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 49
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 99	72.93 98	73.76 149	83.58 71	51.66 218	78.75 132	77.66 228	67.75 4	72.61 123	89.42 56	49.82 147	83.29 164	53.61 262	83.14 88	86.32 120

SR-MVS-dyc-post		74.57 69	73.90 79	76.58 70	83.49 72	59.87 54	84.29 48	81.36 137	58.07 175	73.14 107	90.07 43	44.74 220	85.84 107	68.20 105	81.76 109	84.03 212

RE-MVS-def			73.71 84		83.49 72	59.87 54	84.29 48	81.36 137	58.07 175	73.14 107	90.07 43	43.06 239		68.20 105	81.76 109	84.03 212

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 110	59.65 141	77.31 39	91.43 13	49.62 150	87.24 59	71.99 82	83.75 86	85.14 174

LFMVS		71.78 130	71.59 119	72.32 199	83.40 75	46.38 314	79.75 118	71.08 336	64.18 34	72.80 119	88.64 72	42.58 244	83.72 154	57.41 228	84.49 76	86.86 93

test222					83.14 76	58.68 81	72.57 305	63.45 407	41.78 434	67.56 217	86.12 146	37.13 321			78.73 170	74.98 395

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 135	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

旧先验1					83.04 78	53.15 180		67.52 367			87.85 86	44.08 227			80.76 120	78.03 356

MSLP-MVS++		73.77 84	73.47 88	74.66 108	83.02 79	59.29 63	82.30 85	81.88 124	59.34 151	71.59 138	86.83 115	45.94 201	83.65 156	65.09 149	85.22 69	81.06 300

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 73	90.06 14	78.42 23	89.02 27	87.69 59
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 80	73.46 89	75.69 86	83.01 80	60.63 40	77.29 186	78.40 216	61.18 98	70.58 150	85.97 153	54.18 75	84.00 150	67.52 121	82.98 93	82.45 268

SF-MVS		78.82 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 85	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 51

VDDNet		71.81 129	71.33 127	73.26 175	82.80 83	47.60 305	78.74 133	75.27 281	59.59 146	72.94 114	89.40 57	41.51 265	83.91 151	58.75 220	82.99 91	88.26 34

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 99	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 113	87.55 47	87.42 71

lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 114	78.99 27	91.45 12	51.51 125	87.78 51	75.65 49	87.55 47	87.10 86

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 212	89.24 60	42.03 249	89.38 23	64.07 156	86.50 63	89.69 3

dcpmvs_2		74.55 70	75.23 58	72.48 193	82.34 87	53.34 175	77.87 163	81.46 133	57.80 186	75.49 52	86.81 116	62.22 15	77.75 310	71.09 91	82.02 105	86.34 116

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 95	58.41 169	73.71 92	90.14 41	45.62 203	85.99 103	69.64 97	82.85 97	85.78 140

MM		80.20 8	80.28 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

PVSNet_Blended_VisFu		71.45 138	70.39 146	74.65 109	82.01 90	58.82 79	79.93 114	80.35 167	55.09 251	65.82 256	82.16 253	49.17 159	82.64 194	60.34 200	78.62 174	82.50 267

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 205	58.58 166	74.32 78	84.51 192	55.94 58	87.22 62	67.11 127	84.48 77	85.52 154

h-mvs33		72.71 108	71.49 122	76.40 72	81.99 92	59.58 57	76.92 201	76.74 252	60.40 118	74.81 68	85.95 154	45.54 206	85.76 109	70.41 95	70.61 303	83.86 222

API-MVS		72.17 122	71.41 124	74.45 119	81.95 93	57.22 99	84.03 56	80.38 166	59.89 139	68.40 188	82.33 245	49.64 149	87.83 50	51.87 276	84.16 81	78.30 349

MAR-MVS		71.51 135	70.15 153	75.60 90	81.84 94	59.39 60	81.38 95	82.90 110	54.90 263	68.08 201	78.70 319	47.73 175	85.51 115	51.68 280	84.17 80	81.88 279
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 42	76.55 41	76.68 66	81.73 95	52.90 186	80.94 99	85.70 28	61.12 100	74.90 66	87.17 109	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

PAPM_NR		72.63 111	71.80 116	75.13 97	81.72 96	53.42 174	79.91 115	83.28 97	59.14 153	66.31 243	85.90 156	51.86 117	86.06 100	57.45 227	80.62 122	85.91 134

VDD-MVS		72.50 113	72.09 112	73.75 151	81.58 97	49.69 265	77.76 170	77.63 229	63.21 50	73.21 103	89.02 62	42.14 248	83.32 163	61.72 189	82.50 100	88.25 35

PS-MVSNAJ		70.51 155	69.70 159	72.93 181	81.52 98	55.79 126	74.92 253	79.00 189	55.04 257	69.88 163	78.66 321	47.05 189	82.19 204	61.61 190	79.58 142	80.83 304

testdata				64.66 353	81.52 98	52.93 185		65.29 388	46.09 401	73.88 89	87.46 93	38.08 310	66.26 411	53.31 265	78.48 177	74.78 399

CHOSEN 1792x2688		65.08 283	62.84 300	71.82 209	81.49 100	56.26 115	66.32 382	74.20 304	40.53 444	63.16 300	78.65 322	41.30 266	77.80 309	45.80 335	74.09 242	81.40 288

HQP_MVS		74.31 72	73.73 83	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 174	86.10 147	45.26 214	87.21 63	68.16 109	80.58 124	84.65 192

plane_prior7					81.41 101	55.96 121

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 214	72.46 125	86.76 117	56.89 39	87.86 49	66.36 136	88.91 29	83.64 235

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 94	67.78 3	70.09 155	86.34 139	54.92 67	88.90 29	72.68 75	84.55 73	87.76 57

Vis-MVSNet		72.18 121	71.37 126	74.61 111	81.29 104	55.41 136	80.90 100	78.28 219	60.73 109	69.23 177	88.09 78	44.36 226	82.65 193	57.68 225	81.75 111	85.77 143
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 106

xiu_mvs_v2_base		70.52 154	69.75 157	72.84 183	81.21 107	55.63 130	75.11 246	78.92 191	54.92 262	69.96 162	79.68 305	47.00 193	82.09 206	61.60 191	79.37 145	80.81 305

plane_prior6					81.20 108	56.24 116						45.26 214

PAPR		71.72 133	70.82 138	74.41 120	81.20 108	51.17 221	79.55 124	83.33 93	55.81 231	66.93 230	84.61 186	50.95 134	86.06 100	55.79 241	79.20 155	86.00 130

PLC	56.13 14	65.09 282	63.21 296	70.72 251	81.04 110	54.87 146	78.57 139	77.47 231	48.51 367	55.71 396	81.89 259	33.71 356	79.71 259	41.66 378	70.37 307	77.58 361
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 111	56.05 120					85.54 169

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 183	78.62 137	85.13 37	59.65 141	71.53 140	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

OPM-MVS		74.73 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 80	63.74 41	72.52 124	87.49 91	47.18 187	85.88 106	69.47 99	80.78 118	83.66 233
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 129	68.35 2	75.77 50	90.38 34	53.98 78	90.26 13	81.30 3	87.68 46	88.77 16

HQP-NCC					80.66 115		82.31 82		62.10 78	67.85 206

ACMP_Plane					80.66 115		82.31 82		62.10 78	67.85 206

HQP-MVS		73.45 89	72.80 101	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 78	67.85 206	85.54 169	45.46 208	86.93 71	67.04 128	80.35 128	84.32 202

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 81	71.49 141	86.03 150	53.83 82	86.36 91	67.74 117	86.91 56	88.19 39

PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 226	73.41 97	86.58 130	50.94 135	88.54 32	70.79 93	89.71 17	87.79 56

ACMM	61.98 7	70.80 151	69.73 158	74.02 138	80.59 120	58.59 82	82.68 75	82.02 123	55.46 241	67.18 225	84.39 195	38.51 302	83.17 167	60.65 198	76.10 218	80.30 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		69.28 193	68.47 188	71.73 213	80.28 121	47.18 309	79.98 112	82.37 118	54.61 268	67.24 223	84.01 202	39.43 286	82.41 201	55.45 246	72.83 270	85.62 152

ACMP	63.53 6	72.30 119	71.20 131	75.59 91	80.28 121	57.54 94	82.74 74	82.84 113	60.58 113	65.24 268	86.18 144	39.25 291	86.03 102	66.95 132	76.79 208	83.22 244
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 107	71.74 118	75.76 83	80.22 123	57.51 96	82.55 78	83.40 88	61.32 93	66.67 236	87.33 99	39.15 293	86.59 79	67.70 118	77.30 200	83.19 246

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 88	61.32 93	66.67 236	87.33 99	39.15 293	86.59 79	67.70 118	77.30 200	83.19 246

WR-MVS		68.47 215	68.47 188	68.44 291	80.20 125	39.84 389	73.75 280	76.07 264	64.68 24	68.11 199	83.63 212	50.39 141	79.14 277	49.78 290	69.66 326	86.34 116

Anonymous20240529		69.91 171	69.02 173	72.56 190	80.19 126	47.65 303	77.56 174	80.99 154	55.45 242	69.88 163	86.76 117	39.24 292	82.18 205	54.04 257	77.10 204	87.85 52

Anonymous202405211		66.84 255	65.99 254	69.40 276	80.19 126	42.21 366	71.11 329	71.31 335	58.80 159	67.90 203	86.39 137	29.83 400	79.65 260	49.60 296	78.78 167	86.33 118

CS-MVS		76.25 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 100	87.27 101	55.06 64	86.30 93	71.78 85	84.58 72	89.25 6

BH-RMVSNet		68.81 205	67.42 216	72.97 180	80.11 129	52.53 199	74.26 267	76.29 260	58.48 168	68.38 189	84.20 196	42.59 243	83.83 152	46.53 326	75.91 220	82.56 262

test_0402		63.25 307	61.01 327	69.96 263	80.00 130	54.37 151	76.86 204	72.02 331	54.58 270	58.71 362	80.79 285	35.00 340	84.36 142	26.41 468	64.71 373	71.15 439

HyFIR lowres test		65.67 273	63.01 298	73.67 156	79.97 131	55.65 129	69.07 361	75.52 275	42.68 432	63.53 294	77.95 333	40.43 277	81.64 213	46.01 333	71.91 286	83.73 229

EIA-MVS		71.78 130	70.60 142	75.30 95	79.85 132	53.54 168	77.27 188	83.26 98	57.92 182	66.49 238	79.39 311	52.07 114	86.69 77	60.05 202	79.14 160	85.66 150

BH-untuned		68.27 219	67.29 221	71.21 236	79.74 133	53.22 178	76.06 224	77.46 233	57.19 195	66.10 247	81.61 266	45.37 212	83.50 160	45.42 344	76.68 210	76.91 374

VNet		69.68 179	70.19 151	68.16 296	79.73 134	41.63 373	70.53 339	77.38 235	60.37 121	70.69 147	86.63 126	51.08 132	77.09 325	53.61 262	81.69 113	85.75 145

LS3D		64.71 286	62.50 304	71.34 234	79.72 135	55.71 127	79.82 116	74.72 293	48.50 368	56.62 387	84.62 185	33.59 359	82.34 202	29.65 455	75.23 232	75.97 381

mvsmamba		68.47 215	66.56 237	74.21 128	79.60 136	52.95 184	74.94 252	75.48 277	52.09 312	60.10 343	83.27 221	36.54 327	84.70 136	59.32 212	77.69 190	84.99 182

hse-mvs2		71.04 142	69.86 156	74.60 112	79.58 137	57.12 106	73.96 272	75.25 282	60.40 118	74.81 68	81.95 258	45.54 206	82.90 182	70.41 95	66.83 358	83.77 227

GeoE		71.01 144	70.15 153	73.60 162	79.57 138	52.17 207	78.93 130	78.12 221	58.02 177	67.76 215	83.87 205	52.36 108	82.72 191	56.90 230	75.79 222	85.92 133

AUN-MVS		68.45 217	66.41 244	74.57 114	79.53 139	57.08 107	73.93 275	75.23 283	54.44 273	66.69 234	81.85 260	37.10 322	82.89 183	62.07 185	66.84 357	83.75 228

balanced_ft_v1		72.98 102	72.55 105	74.27 124	79.52 140	50.64 236	77.78 168	83.29 95	56.76 204	67.88 205	85.95 154	49.42 154	85.29 123	68.64 103	83.76 85	86.87 92

test2506		65.33 279	64.61 273	67.50 303	79.46 141	34.19 445	74.43 265	51.92 456	58.72 160	66.75 233	88.05 80	25.99 436	80.92 237	51.94 275	84.25 78	87.39 74

ECVR-MVS		67.72 236	67.51 213	68.35 292	79.46 141	36.29 430	74.79 256	66.93 374	58.72 160	67.19 224	88.05 80	36.10 329	81.38 221	52.07 273	84.25 78	87.39 74

testing3-2		62.06 326	62.36 306	61.17 384	79.29 143	30.31 465	64.09 408	63.49 406	63.50 44	62.84 305	82.22 249	32.35 383	69.02 390	40.01 388	73.43 259	84.17 209

BH-w/o		66.85 254	65.83 256	69.90 267	79.29 143	52.46 202	74.66 259	76.65 253	54.51 272	64.85 278	78.12 329	45.59 205	82.95 176	43.26 364	75.54 226	74.27 405

1112_ss		64.00 299	63.36 292	65.93 335	79.28 145	42.58 362	71.35 322	72.36 328	46.41 398	60.55 340	77.89 339	46.27 200	73.28 361	46.18 331	69.97 317	81.92 278

ETV-MVS		74.46 71	73.84 81	76.33 74	79.27 146	55.24 140	79.22 126	85.00 43	64.97 21	72.65 122	79.46 310	53.65 90	87.87 48	67.45 124	82.91 94	85.89 135

test1111		67.21 243	67.14 230	67.42 307	79.24 147	34.76 439	73.89 277	65.65 384	58.71 162	66.96 229	87.95 84	36.09 330	80.53 244	52.03 274	83.79 84	86.97 89

SSM_0404		70.84 147	69.41 166	75.12 98	79.20 148	53.86 158	77.89 162	80.00 171	53.88 282	69.40 171	84.61 186	43.21 236	86.56 81	58.80 218	77.68 191	84.95 184

UniMVSNet_NR-MVSNet		71.11 141	71.00 135	71.44 226	79.20 148	44.13 340	76.02 227	82.60 115	66.48 11	68.20 191	84.60 189	56.82 40	82.82 189	54.62 252	70.43 305	87.36 78

VPNet		67.52 239	68.11 201	65.74 339	79.18 150	36.80 422	72.17 312	72.83 323	62.04 82	67.79 213	85.83 159	48.88 164	76.60 341	51.30 281	72.97 268	83.81 223

TR-MVS		66.59 262	65.07 270	71.17 239	79.18 150	49.63 267	73.48 283	75.20 285	52.95 297	67.90 203	80.33 291	39.81 283	83.68 155	43.20 365	73.56 255	80.20 322

TAMVS		66.78 257	65.27 268	71.33 235	79.16 152	53.67 163	73.84 279	69.59 351	52.32 310	65.28 263	81.72 264	44.49 225	77.40 319	42.32 372	78.66 173	82.92 253

patch_mono-2		69.85 172	71.09 133	66.16 329	79.11 153	54.80 147	71.97 315	74.31 299	53.50 291	70.90 146	84.17 197	57.63 34	63.31 424	66.17 137	82.02 105	80.38 315

Test_1112_low_res		62.32 321	61.77 313	64.00 360	79.08 154	39.53 395	68.17 368	70.17 344	43.25 426	59.03 360	79.90 298	44.08 227	71.24 376	43.79 358	68.42 344	81.25 293

CDS-MVSNet		66.80 256	65.37 265	71.10 242	78.98 155	53.13 182	73.27 291	71.07 337	52.15 311	64.72 279	80.23 293	43.56 233	77.10 324	45.48 342	78.88 164	83.05 252
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sasdasda		74.67 66	74.98 61	73.71 154	78.94 156	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 207	66.01 140	82.12 102	88.58 26

canonicalmvs		74.67 66	74.98 61	73.71 154	78.94 156	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 207	66.01 140	82.12 102	88.58 26

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 158	52.65 195	83.73 61	86.08 19	63.47 45	72.77 120	87.25 106	53.13 95	87.93 46	71.97 83	85.57 68	86.66 103

IS-MVSNet		71.57 134	71.00 135	73.27 174	78.86 158	45.63 325	80.22 109	78.69 198	64.14 37	66.46 239	87.36 97	49.30 156	85.60 111	50.26 289	83.71 87	88.59 25

CLD-MVS		73.33 93	72.68 103	75.29 96	78.82 160	53.33 176	78.23 151	84.79 46	61.30 95	70.41 152	81.04 276	52.41 107	87.12 66	64.61 155	82.49 101	85.41 164
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 136	70.38 147	74.88 101	78.76 161	57.15 104	82.79 72	78.48 209	51.26 329	69.49 168	83.22 222	43.99 230	83.24 165	66.06 138	79.37 145	84.23 206

lupinMVS		69.57 184	68.28 197	73.44 169	78.76 161	57.15 104	76.57 211	73.29 317	46.19 400	69.49 168	82.18 250	43.99 230	79.23 271	64.66 153	79.37 145	83.93 217

CNLPA		65.43 276	64.02 278	69.68 270	78.73 163	58.07 87	77.82 167	70.71 341	51.49 323	61.57 331	83.58 216	38.23 308	70.82 378	43.90 356	70.10 315	80.16 323

EPP-MVSNet		72.16 124	71.31 128	74.71 105	78.68 164	49.70 263	82.10 86	81.65 128	60.40 118	65.94 250	85.84 158	51.74 121	86.37 90	55.93 238	79.55 144	88.07 47

mamba_0408		67.78 234	65.42 263	74.85 103	78.65 165	53.46 170	50.83 466	79.09 186	53.75 285	68.14 195	83.83 206	41.79 258	86.56 81	56.58 232	76.11 215	84.54 194

SSM_04072		64.98 284	65.42 263	63.68 362	78.65 165	53.46 170	50.83 466	79.09 186	53.75 285	68.14 195	83.83 206	41.79 258	53.03 468	56.58 232	76.11 215	84.54 194

SSM_0407		70.41 159	68.96 176	74.75 104	78.65 165	53.46 170	77.28 187	80.00 171	53.88 282	68.14 195	84.61 186	43.21 236	86.26 95	58.80 218	76.11 215	84.54 194

TranMVSNet+NR-MVSNet		70.36 160	70.10 155	71.17 239	78.64 168	42.97 358	76.53 212	81.16 150	66.95 6	68.53 186	85.42 171	51.61 123	83.07 168	52.32 270	69.70 325	87.46 69

UniMVSNet (Re)		70.63 153	70.20 150	71.89 206	78.55 169	45.29 328	75.94 228	82.92 109	63.68 42	68.16 194	83.59 213	53.89 81	83.49 161	53.97 258	71.12 296	86.89 91

Fast-Effi-MVS+		70.28 162	69.12 172	73.73 153	78.50 170	51.50 219	75.01 249	79.46 181	56.16 225	68.59 183	79.55 308	53.97 79	84.05 146	53.34 264	77.53 193	85.65 151

PS-MVSNAJss		72.24 120	71.21 130	75.31 94	78.50 170	55.93 122	81.63 90	82.12 121	56.24 223	70.02 159	85.68 165	47.05 189	84.34 143	65.27 148	74.41 240	85.67 149

EI-MVSNet-Vis-set		72.42 117	71.59 119	74.91 100	78.47 172	54.02 156	77.05 195	79.33 183	65.03 18	71.68 136	79.35 313	52.75 101	84.89 132	66.46 135	74.23 241	85.83 139

FA-MVS(test-final)		69.82 173	68.48 186	73.84 145	78.44 173	50.04 254	75.58 237	78.99 190	58.16 173	67.59 216	82.14 254	42.66 242	85.63 110	56.60 231	76.19 214	85.84 138

testing91		64.46 291	63.80 282	66.47 322	78.43 174	40.06 387	67.63 372	69.59 351	59.06 154	63.18 299	78.05 331	34.05 350	76.99 330	48.30 306	75.87 221	82.37 270

testing11		62.81 312	61.90 312	65.54 341	78.38 175	40.76 382	67.59 374	66.78 376	55.48 240	60.13 342	77.11 352	31.67 386	76.79 335	45.53 340	74.45 238	79.06 341

MVS_111021_LR		69.50 188	68.78 180	71.65 218	78.38 175	59.33 61	74.82 255	70.11 345	58.08 174	67.83 211	84.68 182	41.96 250	76.34 346	65.62 145	77.54 192	79.30 339

test_yl		69.69 177	69.13 170	71.36 232	78.37 177	45.74 321	74.71 257	80.20 168	57.91 183	70.01 160	83.83 206	42.44 245	82.87 185	54.97 248	79.72 139	85.48 156

DCV-MVSNet		69.69 177	69.13 170	71.36 232	78.37 177	45.74 321	74.71 257	80.20 168	57.91 183	70.01 160	83.83 206	42.44 245	82.87 185	54.97 248	79.72 139	85.48 156

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 179	55.37 138	77.30 185	73.95 308	61.40 92	79.46 23	90.14 41	57.07 37	81.15 227	80.00 5	79.31 150	88.51 28

FIs		70.82 150	71.43 123	68.98 284	78.33 180	38.14 407	76.96 199	83.59 82	61.02 101	67.33 220	86.73 121	55.07 63	81.64 213	54.61 254	79.22 154	87.14 85

UGNet		68.81 205	67.39 217	73.06 178	78.33 180	54.47 149	79.77 117	75.40 279	60.45 116	63.22 297	84.40 194	32.71 372	80.91 238	51.71 279	80.56 126	83.81 223
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		69.65 180	68.39 192	73.43 170	78.27 182	56.88 108	77.12 193	73.71 311	46.53 397	69.34 173	83.22 222	43.37 234	79.18 272	64.77 152	79.20 155	84.23 206
jason: jason.
alignmvs		73.86 83	73.99 77	73.45 168	78.20 183	50.50 242	78.57 139	82.43 117	59.40 149	76.57 46	86.71 123	56.42 44	81.23 226	65.84 143	81.79 108	88.62 23

xiu_mvs_v1_base_debu		68.58 211	67.28 222	72.48 193	78.19 184	57.19 101	75.28 241	75.09 287	51.61 318	70.04 156	81.41 270	32.79 368	79.02 285	63.81 163	77.31 197	81.22 294

xiu_mvs_v1_base		68.58 211	67.28 222	72.48 193	78.19 184	57.19 101	75.28 241	75.09 287	51.61 318	70.04 156	81.41 270	32.79 368	79.02 285	63.81 163	77.31 197	81.22 294

xiu_mvs_v1_base_debi		68.58 211	67.28 222	72.48 193	78.19 184	57.19 101	75.28 241	75.09 287	51.61 318	70.04 156	81.41 270	32.79 368	79.02 285	63.81 163	77.31 197	81.22 294

testing99		64.05 297	63.29 295	66.34 324	78.17 187	39.76 391	67.33 377	68.00 365	58.60 165	63.03 302	78.10 330	32.57 379	76.94 332	48.22 307	75.58 225	82.34 271

UBG		59.62 356	59.53 343	59.89 390	78.12 188	35.92 433	64.11 407	60.81 427	49.45 353	61.34 332	75.55 381	33.05 363	67.39 403	38.68 396	74.62 236	76.35 379

PAPM		67.92 230	66.69 236	71.63 219	78.09 189	49.02 278	77.09 194	81.24 146	51.04 334	60.91 337	83.98 203	47.71 176	84.99 126	40.81 382	79.32 149	80.90 303

ACMH	55.70 15	65.20 281	63.57 286	70.07 262	78.07 190	52.01 212	79.48 125	79.69 174	55.75 233	56.59 388	80.98 278	27.12 427	80.94 235	42.90 369	71.58 291	77.25 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 168	69.53 162	71.44 226	78.05 191	44.13 340	75.01 249	81.51 132	64.37 30	68.20 191	84.52 190	49.12 162	82.82 189	54.62 252	70.43 305	87.37 76

NR-MVSNet		69.54 185	68.85 177	71.59 220	78.05 191	43.81 345	74.20 268	80.86 157	65.18 14	62.76 308	84.52 190	52.35 109	83.59 158	50.96 285	70.78 300	87.37 76

WBMVS		60.54 344	60.61 335	60.34 389	78.00 193	35.95 432	64.55 401	64.89 390	49.63 350	63.39 296	78.70 319	33.85 355	67.65 399	42.10 374	70.35 309	77.43 363

EI-MVSNet-UG-set		71.92 127	71.06 134	74.52 117	77.98 194	53.56 167	76.62 209	79.16 184	64.40 29	71.18 143	78.95 318	52.19 111	84.66 139	65.47 146	73.57 254	85.32 168

WR-MVS_H		67.02 251	66.92 232	67.33 310	77.95 195	37.75 411	77.57 173	82.11 122	62.03 83	62.65 311	82.48 242	50.57 139	79.46 266	42.91 368	64.01 379	84.79 189

testing222		62.29 323	61.31 320	65.25 350	77.87 196	38.53 403	68.34 366	66.31 380	56.37 219	63.15 301	77.58 347	28.47 412	76.18 349	37.04 407	76.65 211	81.05 301

Effi-MVS+		73.31 94	72.54 106	75.62 89	77.87 196	53.64 164	79.62 122	79.61 177	61.63 90	72.02 132	82.61 232	56.44 43	85.97 104	63.99 159	79.07 161	87.25 81

DELS-MVS		74.76 64	74.46 67	75.65 88	77.84 198	52.25 206	75.59 235	84.17 54	63.76 40	73.15 106	82.79 227	59.58 23	86.80 74	67.24 125	86.04 65	87.89 49
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	66.12 268	64.06 277	72.30 200	77.79 199	52.83 191	80.39 105	78.03 222	57.30 193	57.47 379	82.55 238	27.68 422	84.17 144	45.54 339	69.78 322	79.90 328

MGCFI-Net		72.45 115	73.34 93	69.81 269	77.77 200	43.21 353	75.84 232	81.18 148	59.59 146	75.45 53	86.64 124	57.74 31	77.94 303	63.92 160	81.90 107	88.30 33

RRT-MVS		71.46 137	70.70 141	73.74 152	77.76 201	49.30 273	76.60 210	80.45 164	61.25 96	68.17 193	84.78 179	44.64 222	84.90 131	64.79 151	77.88 188	87.03 87

GDP-MVS		72.64 110	71.28 129	76.70 64	77.72 202	54.22 154	79.57 123	84.45 48	55.30 245	71.38 142	86.97 112	39.94 279	87.00 70	67.02 130	79.20 155	88.89 12

3Dnovator	64.47 5	72.49 114	71.39 125	75.79 82	77.70 203	58.99 76	80.66 104	83.15 104	62.24 75	65.46 260	86.59 129	42.38 247	85.52 114	59.59 208	84.72 71	82.85 256

EG-PatchMatch MVS		64.71 286	62.87 299	70.22 258	77.68 204	53.48 169	77.99 160	78.82 193	53.37 292	56.03 395	77.41 349	24.75 444	84.04 147	46.37 328	73.42 260	73.14 411

UWE-MVS		60.18 348	59.78 341	61.39 382	77.67 205	33.92 448	69.04 362	63.82 403	48.56 365	64.27 286	77.64 346	27.20 426	70.40 383	33.56 430	76.24 213	79.83 331

CP-MVSNet		66.49 263	66.41 244	66.72 313	77.67 205	36.33 427	76.83 206	79.52 179	62.45 68	62.54 314	83.47 219	46.32 198	78.37 296	45.47 343	63.43 387	85.45 160

GBi-Net		67.21 243	66.55 238	69.19 278	77.63 207	43.33 350	77.31 182	77.83 225	56.62 210	65.04 273	82.70 228	41.85 255	80.33 249	47.18 319	72.76 271	83.92 218

test1		67.21 243	66.55 238	69.19 278	77.63 207	43.33 350	77.31 182	77.83 225	56.62 210	65.04 273	82.70 228	41.85 255	80.33 249	47.18 319	72.76 271	83.92 218

FMVSNet2		66.93 253	66.31 249	68.79 287	77.63 207	42.98 357	76.11 222	77.47 231	56.62 210	65.22 270	82.17 252	41.85 255	80.18 255	47.05 324	72.72 274	83.20 245

PCF-MVS	61.88 8	70.95 146	69.49 163	75.35 93	77.63 207	55.71 127	76.04 226	81.81 126	50.30 342	69.66 166	85.40 172	52.51 104	84.89 132	51.82 277	80.24 130	85.45 160
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 277	63.80 282	70.22 258	77.62 211	55.53 134	76.30 216	78.53 207	50.59 340	56.47 391	78.65 322	39.84 282	82.68 192	44.10 354	72.12 285	72.44 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 175	70.58 144	67.46 306	77.61 212	34.73 440	76.05 225	83.19 103	60.84 106	65.88 254	86.46 135	54.52 72	80.76 242	52.52 269	78.12 184	86.91 90

PS-CasMVS		66.42 264	66.32 248	66.70 315	77.60 213	36.30 429	76.94 200	79.61 177	62.36 70	62.43 319	83.66 211	45.69 202	78.37 296	45.35 345	63.26 388	85.42 163

testing3		56.54 378	55.92 379	58.41 403	77.52 214	27.93 473	69.72 350	56.36 444	54.75 266	58.63 366	77.80 341	20.88 455	71.75 373	25.31 470	62.25 402	75.53 387

FMVSNet1		66.70 258	65.87 255	69.19 278	77.49 215	43.33 350	77.31 182	77.83 225	56.45 216	64.60 282	82.70 228	38.08 310	80.33 249	46.08 332	72.31 280	83.92 218

ETVMVS		59.51 357	58.81 350	61.58 379	77.46 216	34.87 436	64.94 399	59.35 430	54.06 278	61.08 336	76.67 360	29.54 401	71.87 372	32.16 435	74.07 243	78.01 357

VPA-MVSNet		69.02 200	69.47 164	67.69 302	77.42 217	41.00 380	74.04 270	79.68 175	60.06 131	69.26 176	84.81 178	51.06 133	77.58 315	54.44 255	74.43 239	84.48 199

UniMVSNet_ETH3D		67.60 238	67.07 231	69.18 281	77.39 218	42.29 364	74.18 269	75.59 273	60.37 121	66.77 232	86.06 149	37.64 312	78.93 290	52.16 272	73.49 256	86.32 120

FE-MVS		65.91 270	63.33 293	73.63 160	77.36 219	51.95 214	72.62 303	75.81 268	53.70 288	65.31 262	78.96 317	28.81 410	86.39 89	43.93 355	73.48 257	82.55 263

myMVS_eth3d28		60.66 342	61.04 326	59.51 392	77.32 220	31.58 460	63.11 413	63.87 402	59.00 155	60.90 338	78.26 328	32.69 374	66.15 412	36.10 418	78.13 183	80.81 305

thres100view900		63.28 306	62.41 305	65.89 336	77.31 221	38.66 401	72.65 301	69.11 358	57.07 198	62.45 317	81.03 277	37.01 324	79.17 273	31.84 439	73.25 263	79.83 331

cascas		65.98 269	63.42 291	73.64 159	77.26 222	52.58 198	72.26 311	77.21 239	48.56 365	61.21 334	74.60 391	32.57 379	85.82 108	50.38 288	76.75 209	82.52 266

viewdifsd2359ckpt09		73.42 90	72.45 108	76.30 75	77.25 223	53.27 177	80.36 106	82.48 116	57.96 180	72.24 128	85.73 163	53.22 93	86.27 94	63.79 166	79.06 162	89.36 5

thres600view7		63.30 305	62.27 307	66.41 323	77.18 224	38.87 399	72.35 308	69.11 358	56.98 201	62.37 320	80.96 279	37.01 324	79.00 288	31.43 446	73.05 267	81.36 289

E2		73.72 85	73.60 86	74.06 136	77.16 225	50.40 244	76.97 197	83.74 74	61.64 88	73.36 98	86.75 120	56.14 48	82.99 171	67.50 122	79.18 158	88.80 13

E3		73.72 85	73.60 86	74.06 136	77.16 225	50.40 244	76.97 197	83.74 74	61.64 88	73.36 98	86.76 117	56.13 49	82.99 171	67.50 122	79.18 158	88.80 13

E5new		74.10 76	74.09 73	74.15 131	77.14 227	50.74 231	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 176	68.16 109	79.86 134	88.77 16

E6new		74.10 76	74.09 73	74.15 131	77.14 227	50.74 231	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 176	68.17 107	79.85 136	88.77 16

E6		74.10 76	74.09 73	74.15 131	77.14 227	50.74 231	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 176	68.17 107	79.85 136	88.77 16

E5		74.10 76	74.09 73	74.15 131	77.14 227	50.74 231	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 176	68.16 109	79.86 134	88.77 16

E4		73.91 82	73.83 82	74.15 131	77.13 231	50.47 243	77.15 192	83.79 73	62.21 76	73.61 93	87.19 108	56.08 52	83.03 169	67.91 115	79.35 148	88.94 11

viewcassd2359sk11		73.56 87	73.41 91	74.00 140	77.13 231	50.35 247	76.86 204	83.69 78	61.23 97	73.14 107	86.38 138	56.09 51	82.96 174	67.15 126	79.01 163	88.70 22

SDMVSNet		68.03 226	68.10 202	67.84 298	77.13 231	48.72 286	65.32 394	79.10 185	58.02 177	65.08 271	82.55 238	47.83 174	73.40 360	63.92 160	73.92 245	81.41 286

sd_testset		64.46 291	64.45 274	64.51 355	77.13 231	42.25 365	62.67 416	72.11 330	58.02 177	65.08 271	82.55 238	41.22 271	69.88 386	47.32 317	73.92 245	81.41 286

PEN-MVS		66.60 260	66.45 240	67.04 311	77.11 235	36.56 424	77.03 196	80.42 165	62.95 55	62.51 316	84.03 201	46.69 195	79.07 280	44.22 350	63.08 390	85.51 155

E3new		73.41 91	73.22 94	73.95 143	77.06 236	50.31 248	76.78 207	83.66 79	60.90 104	72.93 115	86.02 151	55.99 53	82.95 176	66.89 133	78.77 168	88.61 24

icg_test_0407_2		66.41 265	66.75 235	65.37 347	77.06 236	49.73 259	63.79 409	78.60 201	52.70 301	66.19 244	82.58 233	45.17 216	63.65 423	59.20 213	75.46 228	82.74 258

IMVS_0407		68.90 203	67.93 203	71.82 209	77.06 236	49.73 259	74.40 266	78.60 201	52.70 301	66.19 244	82.58 233	45.17 216	83.00 170	59.20 213	75.46 228	82.74 258

IMVS_0404		64.63 288	64.22 276	65.88 337	77.06 236	49.73 259	64.40 402	78.60 201	52.70 301	53.16 427	82.58 233	34.82 342	65.16 417	59.20 213	75.46 228	82.74 258

IMVS_0403		69.09 199	68.14 200	71.95 204	77.06 236	49.73 259	74.51 261	78.60 201	52.70 301	66.69 234	82.58 233	46.43 197	83.38 162	59.20 213	75.46 228	82.74 258

PatchMatch-RL		56.25 383	54.55 390	61.32 383	77.06 236	56.07 119	65.57 388	54.10 453	44.13 419	53.49 425	71.27 421	25.20 441	66.78 406	36.52 415	63.66 382	61.12 461

PVSNet_BlendedMVS		68.56 214	67.72 206	71.07 243	77.03 242	50.57 238	74.50 262	81.52 130	53.66 290	64.22 289	79.72 304	49.13 160	82.87 185	55.82 239	73.92 245	79.77 334

PVSNet_Blended		68.59 210	67.72 206	71.19 237	77.03 242	50.57 238	72.51 306	81.52 130	51.91 314	64.22 289	77.77 344	49.13 160	82.87 185	55.82 239	79.58 142	80.14 324

F-COLMAP		63.05 311	60.87 331	69.58 274	76.99 244	53.63 165	78.12 155	76.16 261	47.97 377	52.41 430	81.61 266	27.87 419	78.11 300	40.07 385	66.66 359	77.00 371

tfpn200view9		63.18 308	62.18 309	66.21 328	76.85 245	39.62 393	71.96 316	69.44 354	56.63 208	62.61 312	79.83 299	37.18 318	79.17 273	31.84 439	73.25 263	79.83 331

thres400		63.31 304	62.18 309	66.72 313	76.85 245	39.62 393	71.96 316	69.44 354	56.63 208	62.61 312	79.83 299	37.18 318	79.17 273	31.84 439	73.25 263	81.36 289

tttt0517		67.83 233	65.66 259	74.33 122	76.69 247	50.82 229	77.86 164	73.99 307	54.54 271	64.64 281	82.53 241	35.06 339	85.50 116	55.71 242	69.91 319	86.67 102

BP-MVS1		73.41 91	72.25 110	76.88 61	76.68 248	53.70 162	79.15 127	81.07 151	60.66 111	71.81 133	87.39 96	40.93 273	87.24 59	71.23 90	81.29 115	89.71 2

ET-MVSNet_ETH3D		67.96 229	65.72 258	74.68 107	76.67 249	55.62 132	75.11 246	74.74 292	52.91 298	60.03 345	80.12 295	33.68 357	82.64 194	61.86 188	76.34 212	85.78 140

TAPA-MVS	59.36 10	66.60 260	65.20 269	70.81 248	76.63 250	48.75 284	76.52 213	80.04 170	50.64 339	65.24 268	84.93 176	39.15 293	78.54 295	36.77 409	76.88 206	85.14 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 139	70.60 142	73.78 147	76.60 251	53.15 180	79.74 119	79.78 173	58.37 170	68.75 182	86.45 136	45.43 210	80.60 243	62.58 180	77.73 189	87.58 66

LTVRE_ROB	55.42 16	63.15 309	61.23 323	68.92 285	76.57 252	47.80 300	59.92 432	76.39 257	54.35 274	58.67 364	82.46 243	29.44 404	81.49 218	42.12 373	71.14 295	77.46 362
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		70.05 167	68.81 179	73.78 147	76.54 253	53.43 173	83.23 65	83.48 84	52.89 299	65.90 252	86.29 141	41.55 264	86.49 87	51.01 283	78.40 180	81.42 285

FMVSNet3		66.32 267	65.61 260	68.46 290	76.48 254	42.34 363	74.98 251	77.15 240	55.83 230	65.04 273	81.16 273	39.91 280	80.14 256	47.18 319	72.76 271	82.90 255

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 255	51.83 216	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 125	72.75 74	83.93 82	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		67.92 230	65.78 257	74.33 122	76.29 256	51.03 224	76.89 202	74.25 302	53.67 289	65.59 258	81.76 263	35.15 338	85.50 116	55.94 237	72.47 276	86.47 111

baseline1		63.81 300	63.87 281	63.62 363	76.29 256	36.36 425	71.78 319	67.29 370	56.05 227	64.23 288	82.95 226	47.11 188	74.41 356	47.30 318	61.85 405	80.10 325

ab-mvs		66.65 259	66.42 243	67.37 308	76.17 258	41.73 370	70.41 342	76.14 263	53.99 279	65.98 249	83.51 217	49.48 151	76.24 347	48.60 303	73.46 258	84.14 210

Effi-MVS+-dtu		69.64 181	67.53 212	75.95 78	76.10 259	62.29 15	80.20 110	76.06 265	59.83 140	65.26 267	77.09 353	41.56 263	84.02 149	60.60 199	71.09 299	81.53 284

DTE-MVSNet		65.58 274	65.34 266	66.31 325	76.06 260	34.79 437	76.43 214	79.38 182	62.55 66	61.66 329	83.83 206	45.60 204	79.15 276	41.64 380	60.88 411	85.00 180

EPNet		73.09 100	72.16 111	75.90 79	75.95 261	56.28 114	83.05 67	72.39 327	66.53 10	65.27 264	87.00 111	50.40 140	85.47 118	62.48 182	86.32 64	85.94 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		61.65 334	58.80 352	70.20 260	75.80 262	47.22 308	75.59 235	69.68 349	54.61 268	54.11 416	79.26 314	27.07 428	82.96 174	43.27 363	49.79 457	80.41 314

tt0320-xc		58.33 364	56.41 375	64.08 359	75.79 263	41.34 374	68.30 367	62.72 413	47.90 378	56.29 392	74.16 396	28.53 411	71.04 377	41.50 381	52.50 449	79.88 329

baseline		74.61 68	74.70 64	74.34 121	75.70 264	49.99 256	77.54 175	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 153	69.49 98	82.74 99	89.20 8

Baseline_NR-MVSNet		67.05 250	67.56 209	65.50 343	75.65 265	37.70 413	75.42 238	74.65 295	59.90 135	68.14 195	83.15 225	49.12 162	77.20 323	52.23 271	69.78 322	81.60 281

viewdifsd2359ckpt13		72.40 118	71.79 117	74.22 127	75.63 266	51.77 217	78.67 135	83.13 106	57.08 197	71.59 138	85.36 173	53.10 96	82.64 194	63.07 176	78.51 176	88.24 36

jajsoiax		68.25 220	66.45 240	73.66 157	75.62 267	55.49 135	80.82 101	78.51 208	52.33 309	64.33 284	84.11 199	28.28 416	81.81 212	63.48 170	70.62 302	83.67 231

mvs_tets		68.18 223	66.36 246	73.63 160	75.61 268	55.35 139	80.77 102	78.56 206	52.48 308	64.27 286	84.10 200	27.45 424	81.84 211	63.45 171	70.56 304	83.69 230

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 269	50.37 246	78.17 154	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 157	69.46 100	82.79 98	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet	50.76 19	58.40 363	57.39 363	61.42 380	75.53 270	44.04 343	61.43 422	63.45 407	47.04 393	56.91 385	73.61 400	27.00 429	64.76 418	39.12 394	72.40 277	75.47 388

fmvsm_s_conf0.5_n_11		73.16 97	73.35 92	72.58 188	75.48 271	52.41 205	78.84 131	76.85 246	58.64 164	73.58 95	87.25 106	54.09 77	79.47 265	76.19 44	79.27 151	85.86 136

tt0320		58.59 361	56.81 370	63.92 361	75.46 272	41.32 375	68.63 364	64.06 401	47.05 392	56.19 393	74.19 394	30.34 392	71.36 374	39.92 389	55.45 437	79.09 340

MVS		67.37 241	66.33 247	70.51 256	75.46 272	50.94 225	73.95 273	81.85 125	41.57 438	62.54 314	78.57 325	47.98 171	85.47 118	52.97 267	82.05 104	75.14 391

nrg030		72.96 103	73.01 97	72.84 183	75.41 274	50.24 249	80.02 111	82.89 112	58.36 171	74.44 75	86.73 121	58.90 27	80.83 239	65.84 143	74.46 237	87.44 70

thres200		62.20 324	61.16 325	65.34 348	75.38 275	39.99 388	69.60 354	69.29 356	55.64 237	61.87 324	76.99 354	37.07 323	78.96 289	31.28 447	73.28 262	77.06 369

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 139	75.33 276	52.89 188	78.24 146	77.32 238	61.65 87	78.13 32	88.90 65	52.82 100	81.54 217	78.46 22	78.67 172	87.60 64

TransMVSNet (Re)		64.72 285	64.33 275	65.87 338	75.22 277	38.56 402	74.66 259	75.08 290	58.90 158	61.79 325	82.63 231	51.18 130	78.07 301	43.63 361	55.87 436	80.99 302

MS-PatchMatch		62.42 320	61.46 317	65.31 349	75.21 278	52.10 208	72.05 313	74.05 305	46.41 398	57.42 381	74.36 392	34.35 348	77.57 316	45.62 338	73.67 250	66.26 457

WB-MVSnew		59.66 354	59.69 342	59.56 391	75.19 279	35.78 434	69.34 359	64.28 397	46.88 394	61.76 326	75.79 377	40.61 276	65.20 416	32.16 435	71.21 294	77.70 359

viewmanbaseed2359cas		72.92 104	72.89 99	73.00 179	75.16 280	49.25 275	77.25 189	83.11 107	59.52 148	72.93 115	86.63 126	54.11 76	80.98 233	66.63 134	80.67 121	88.76 21

SD_0403		63.07 310	63.49 290	61.82 376	75.16 280	31.14 462	71.89 318	73.47 312	53.34 293	58.22 370	81.81 262	45.17 216	73.86 359	37.43 403	74.87 235	80.45 312

viewmacassd2359aftdt		73.15 98	73.16 95	73.11 177	75.15 282	49.31 272	77.53 177	83.21 99	60.42 117	73.20 104	87.34 98	53.82 83	81.05 232	67.02 130	80.79 117	88.96 10

fmvsm_s_conf0.5_n_6		72.59 112	72.87 100	71.73 213	75.14 283	51.96 213	76.28 217	77.12 241	57.63 190	73.85 90	86.91 113	51.54 124	77.87 307	77.18 32	80.18 132	85.37 166

IB-MVS	56.42 12	65.40 278	62.73 302	73.40 171	74.89 284	52.78 192	73.09 296	75.13 286	55.69 234	58.48 368	73.73 399	32.86 367	86.32 92	50.63 286	70.11 314	81.10 298
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		72.45 115	72.46 107	72.42 197	74.88 285	48.50 290	76.28 217	83.14 105	59.40 149	72.46 125	84.68 182	55.66 60	81.12 228	65.98 142	79.66 141	87.63 62

sc_t1		59.76 352	57.84 362	65.54 341	74.87 286	42.95 359	69.61 353	64.16 400	48.90 361	58.68 363	77.12 351	28.19 417	72.35 367	43.75 360	55.28 438	81.31 292

tt0805		67.77 235	67.24 226	69.34 277	74.87 286	40.08 386	77.36 181	81.37 136	55.31 244	66.33 242	84.65 184	37.35 316	82.55 197	55.65 244	72.28 281	85.39 165

CANet_DTU		68.18 223	67.71 208	69.59 272	74.83 288	46.24 316	78.66 136	76.85 246	59.60 143	63.45 295	82.09 257	35.25 337	77.41 318	59.88 205	78.76 169	85.14 174

viewdifsd2359ckpt07		71.90 128	71.97 114	71.69 216	74.81 289	48.08 296	75.30 240	80.49 163	60.00 133	71.63 137	86.33 140	56.34 45	79.25 270	65.40 147	77.41 196	87.76 57

tfpnnormal		62.47 317	61.63 315	64.99 352	74.81 289	39.01 398	71.22 325	73.72 310	55.22 248	60.21 341	80.09 297	41.26 269	76.98 331	30.02 453	68.09 347	78.97 344

Vis-MVSNet (Re-imp)		63.69 301	63.88 280	63.14 368	74.75 291	31.04 463	71.16 327	63.64 405	56.32 220	59.80 350	84.99 175	44.51 223	75.46 351	39.12 394	80.62 122	82.92 253

HY-MVS	56.14 13	64.55 290	63.89 279	66.55 321	74.73 292	41.02 377	69.96 348	74.43 296	49.29 356	61.66 329	80.92 280	47.43 183	76.68 340	44.91 347	71.69 289	81.94 277

Syy-MVS		56.00 385	56.23 377	55.32 421	74.69 293	26.44 479	65.52 389	57.49 439	50.97 335	56.52 389	72.18 408	39.89 281	68.09 394	24.20 471	64.59 376	71.44 435

myMVS_eth3d		54.86 396	54.61 389	55.61 420	74.69 293	27.31 476	65.52 389	57.49 439	50.97 335	56.52 389	72.18 408	21.87 453	68.09 394	27.70 462	64.59 376	71.44 435

COLMAP_ROB	52.97 17	61.27 340	58.81 350	68.64 288	74.63 295	52.51 200	78.42 142	73.30 316	49.92 348	50.96 435	81.51 269	23.06 447	79.40 267	31.63 443	65.85 364	74.01 408
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 118	74.61 296	52.86 190	78.10 158	77.06 242	57.14 196	78.24 31	88.79 70	52.83 99	82.26 203	77.79 28	81.30 114	88.32 32

KinetiMVS		71.26 140	70.16 152	74.57 114	74.59 297	52.77 193	75.91 229	81.20 147	60.72 110	69.10 180	85.71 164	41.67 260	83.53 159	63.91 162	78.62 174	87.42 71

LCM-MVSNet-Re		61.88 332	61.35 319	63.46 364	74.58 298	31.48 461	61.42 423	58.14 435	58.71 162	53.02 428	79.55 308	43.07 238	76.80 334	45.69 336	77.96 186	82.11 276

test_djsdf		69.45 190	67.74 205	74.58 113	74.57 299	54.92 145	82.79 72	78.48 209	51.26 329	65.41 261	83.49 218	38.37 304	83.24 165	66.06 138	69.25 333	85.56 153

EI-MVSNet		69.27 194	68.44 190	71.73 213	74.47 300	49.39 270	75.20 244	78.45 212	59.60 143	69.16 178	76.51 366	51.29 128	82.50 198	59.86 207	71.45 293	83.30 241

CVMVSNet		59.63 355	59.14 346	61.08 386	74.47 300	38.84 400	75.20 244	68.74 360	31.15 463	58.24 369	76.51 366	32.39 381	68.58 392	49.77 291	65.84 365	75.81 383

IterMVS-LS		69.22 196	68.48 186	71.43 228	74.44 302	49.40 269	76.23 219	77.55 230	59.60 143	65.85 255	81.59 268	51.28 129	81.58 216	59.87 206	69.90 320	83.30 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_3		73.23 96	73.13 96	73.55 164	74.40 303	55.13 141	78.97 129	74.96 291	56.64 207	74.76 71	88.75 71	55.02 65	78.77 292	76.33 41	78.31 182	86.74 98

XVG-OURS-SEG-HR		68.81 205	67.47 215	72.82 185	74.40 303	56.87 109	70.59 338	79.04 188	54.77 265	66.99 228	86.01 152	39.57 285	78.21 299	62.54 181	73.33 261	83.37 240

EGC-MVSNET		42.47 435	38.48 443	54.46 427	74.33 305	48.73 285	70.33 344	51.10 459	0.03 496	0.18 497	67.78 446	13.28 470	66.49 409	18.91 479	50.36 455	48.15 476

XVG-OURS		68.76 208	67.37 218	72.90 182	74.32 306	57.22 99	70.09 347	78.81 194	55.24 247	67.79 213	85.81 162	36.54 327	78.28 298	62.04 186	75.74 223	83.19 246

SSC-MVS3.2		60.57 343	61.39 318	58.12 408	74.29 307	32.63 455	59.52 433	65.53 386	59.90 135	62.45 317	79.75 303	41.96 250	63.90 422	39.47 392	69.65 328	77.84 358

OpenMVS	61.03 9	68.85 204	67.56 209	72.70 187	74.26 308	53.99 157	81.21 97	81.34 141	52.70 301	62.75 309	85.55 168	38.86 297	84.14 145	48.41 305	83.01 90	79.97 326

MIMVSNet		57.35 372	57.07 365	58.22 405	74.21 309	37.18 416	62.46 417	60.88 426	48.88 362	55.29 403	75.99 375	31.68 385	62.04 429	31.87 438	72.35 278	75.43 389

Elysia		70.19 165	68.29 195	75.88 80	74.15 310	54.33 152	78.26 143	83.21 99	55.04 257	67.28 221	83.59 213	30.16 395	86.11 98	63.67 167	79.26 152	87.20 82

StellarMVS		70.19 165	68.29 195	75.88 80	74.15 310	54.33 152	78.26 143	83.21 99	55.04 257	67.28 221	83.59 213	30.16 395	86.11 98	63.67 167	79.26 152	87.20 82

SCA		60.49 345	58.38 356	66.80 312	74.14 312	48.06 297	63.35 412	63.23 409	49.13 358	59.33 358	72.10 410	37.45 314	74.27 357	44.17 351	62.57 398	78.05 353

fmvsm_s_conf0.5_n_5		72.69 109	72.80 101	72.37 198	74.11 313	53.21 179	78.12 155	73.31 315	53.98 280	76.81 45	88.05 80	53.38 91	77.37 320	76.64 38	80.78 118	86.53 108

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 244	74.09 314	51.86 215	77.77 169	75.60 272	61.18 98	78.67 29	88.98 63	55.88 59	77.73 311	78.69 16	78.68 171	83.50 238

fmvsm_l_conf0.5_n_9		73.27 95	73.66 85	72.09 202	73.82 315	52.72 194	77.45 179	74.28 301	56.61 213	77.10 43	88.16 76	56.17 47	77.09 325	78.27 24	81.13 116	86.48 110

VortexMVS		66.41 265	65.50 262	69.16 282	73.75 316	48.14 294	73.41 285	78.28 219	53.73 287	64.98 277	78.33 327	40.62 275	79.07 280	58.88 217	67.50 352	80.26 321

thisisatest0515		65.83 271	63.50 289	72.82 185	73.75 316	49.50 268	71.32 323	73.12 322	49.39 354	63.82 291	76.50 368	34.95 341	84.84 135	53.20 266	75.49 227	84.13 211

fmvsm_s_conf0.5_n_4		72.04 126	71.85 115	72.58 188	73.74 318	52.49 201	76.69 208	72.42 326	56.42 218	75.32 54	87.04 110	52.13 113	78.01 302	79.29 12	73.65 251	87.26 80

K. test v3		60.47 346	57.11 364	70.56 254	73.74 318	48.22 293	75.10 248	62.55 414	58.27 172	53.62 422	76.31 370	27.81 420	81.59 215	47.42 313	39.18 472	81.88 279

guyue		68.10 225	67.23 228	70.71 252	73.67 320	49.27 274	73.65 282	76.04 266	55.62 238	67.84 210	82.26 248	41.24 270	78.91 291	61.01 196	73.72 249	83.94 216

v10		70.21 163	69.02 173	73.81 146	73.51 321	50.92 227	78.74 133	81.39 135	60.05 132	66.39 241	81.83 261	47.58 179	85.41 121	62.80 179	68.86 340	85.09 178

AstraMVS		67.86 232	66.83 233	70.93 246	73.50 322	49.34 271	73.28 290	74.01 306	55.45 242	68.10 200	83.28 220	38.93 296	79.14 277	63.22 174	71.74 288	84.30 204

fmvsm_s_conf0.5_n_7		69.54 185	69.67 160	69.15 283	73.47 323	51.41 220	70.35 343	73.34 314	57.05 199	68.41 187	85.83 159	49.86 146	72.84 363	71.86 84	76.83 207	83.19 246

LuminaMVS		68.24 221	66.82 234	72.51 192	73.46 324	53.60 166	76.23 219	78.88 192	52.78 300	68.08 201	80.13 294	32.70 373	81.41 219	63.16 175	75.97 219	82.53 264

v1144		70.42 158	69.31 167	73.76 149	73.22 325	50.64 236	77.83 166	81.43 134	58.58 166	69.40 171	81.16 273	47.53 180	85.29 123	64.01 158	70.64 301	85.34 167

v1192		69.97 170	68.68 182	73.85 144	73.19 326	50.94 225	77.68 171	81.36 137	57.51 192	68.95 181	80.85 283	45.28 213	85.33 122	62.97 178	70.37 307	85.27 171

v8		70.33 161	69.28 168	73.49 166	73.15 327	50.22 250	78.62 137	80.78 158	60.79 107	66.45 240	82.11 256	49.35 155	84.98 128	63.58 169	68.71 341	85.28 170

v144192		69.71 176	68.51 185	73.33 173	73.10 328	50.13 252	77.54 175	80.64 159	56.65 206	68.57 185	80.55 286	46.87 194	84.96 130	62.98 177	69.66 326	84.89 186

v1921920		69.47 189	68.17 199	73.36 172	73.06 329	50.10 253	77.39 180	80.56 160	56.58 215	68.59 183	80.37 288	44.72 221	84.98 128	62.47 183	69.82 321	85.00 180

PatchmatchNet		59.84 351	58.24 357	64.65 354	73.05 330	46.70 312	69.42 358	62.18 420	47.55 384	58.88 361	71.96 412	34.49 346	69.16 388	42.99 367	63.60 384	78.07 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 195	67.91 204	73.25 176	73.02 331	49.82 257	77.21 190	80.54 161	56.43 217	68.34 190	80.51 287	43.33 235	84.99 126	62.03 187	69.77 324	84.95 184

Fast-Effi-MVS+-dtu		67.37 241	65.33 267	73.48 167	72.94 332	57.78 92	77.47 178	76.88 245	57.60 191	61.97 322	76.85 357	39.31 289	80.49 247	54.72 251	70.28 311	82.17 275

EPNet_dtu		61.90 331	61.97 311	61.68 377	72.89 333	39.78 390	75.85 231	65.62 385	55.09 251	54.56 412	79.36 312	37.59 313	67.02 405	39.80 390	76.95 205	78.25 350
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 325	60.10 340	67.99 297	72.79 334	43.86 344	71.05 331	66.85 375	43.14 428	62.77 307	75.39 385	38.32 306	80.80 240	41.69 377	68.88 338	79.32 338

MDTV_nov1_ep13			57.00 366		72.73 335	38.26 406	65.02 398	64.73 393	44.74 411	55.46 398	72.48 406	32.61 378	70.47 380	37.47 402	67.75 350

MSDG		61.81 333	59.23 345	69.55 275	72.64 336	52.63 197	70.45 341	75.81 268	51.38 326	53.70 419	76.11 371	29.52 402	81.08 231	37.70 401	65.79 366	74.93 396

gg-mvs-nofinetune		57.86 370	56.43 374	62.18 374	72.62 337	35.35 435	66.57 379	56.33 445	50.65 338	57.64 376	57.10 471	30.65 389	76.36 345	37.38 404	78.88 164	74.82 398

v2v482		70.50 156	69.45 165	73.66 157	72.62 337	50.03 255	77.58 172	80.51 162	59.90 135	69.52 167	82.14 254	47.53 180	84.88 134	65.07 150	70.17 313	86.09 128

baseline2		63.42 303	61.26 322	69.89 268	72.55 339	47.62 304	71.54 320	68.38 362	50.11 344	54.82 408	75.55 381	43.06 239	80.96 234	48.13 308	67.16 356	81.11 297

test_fmvsm_n_1920		71.73 132	71.14 132	73.50 165	72.52 340	56.53 111	75.60 234	76.16 261	48.11 374	77.22 40	85.56 166	53.10 96	77.43 317	74.86 57	77.14 202	86.55 107

v7n		69.01 201	67.36 219	73.98 141	72.51 341	52.65 195	78.54 141	81.30 142	60.26 127	62.67 310	81.62 265	43.61 232	84.49 140	57.01 229	68.70 342	84.79 189

fmvsm_s_conf0.5_n_a		69.54 185	68.74 181	71.93 205	72.47 342	53.82 160	78.25 145	62.26 419	49.78 349	73.12 110	86.21 143	52.66 102	76.79 335	75.02 56	68.88 338	85.18 173

usedtu_dtu_shiyan1		64.34 294	63.57 286	66.66 317	72.44 343	40.74 383	69.60 354	76.80 250	53.21 294	61.73 327	77.92 335	41.92 253	77.68 313	46.23 329	72.25 282	81.57 282

FE-MVSNET3		64.34 294	63.57 286	66.66 317	72.44 343	40.74 383	69.60 354	76.80 250	53.21 294	61.73 327	77.92 335	41.92 253	77.68 313	46.23 329	72.25 282	81.57 282

pm-mvs1		65.24 280	64.97 271	66.04 333	72.38 345	39.40 396	72.62 303	75.63 271	55.53 239	62.35 321	83.18 224	47.45 182	76.47 344	49.06 300	66.54 360	82.24 272

XVG-ACMP-BASELINE		64.36 293	62.23 308	70.74 250	72.35 346	52.45 203	70.80 336	78.45 212	53.84 284	59.87 348	81.10 275	16.24 464	79.32 269	55.64 245	71.76 287	80.47 311

WTY-MVS		59.75 353	60.39 336	57.85 410	72.32 347	37.83 410	61.05 428	64.18 398	45.95 405	61.91 323	79.11 316	47.01 192	60.88 432	42.50 371	69.49 329	74.83 397

fmvsm_s_conf0.5_n		69.58 183	68.84 178	71.79 211	72.31 348	52.90 186	77.90 161	62.43 417	49.97 347	72.85 118	85.90 156	52.21 110	76.49 342	75.75 47	70.26 312	85.97 131

tpm cat1		59.25 358	56.95 367	66.15 330	72.19 349	46.96 310	68.09 369	65.76 383	40.03 448	57.81 374	70.56 424	38.32 306	74.51 355	38.26 399	61.50 408	77.00 371

mvs_anonymous		68.03 226	67.51 213	69.59 272	72.08 350	44.57 337	71.99 314	75.23 283	51.67 316	67.06 227	82.57 237	54.68 70	77.94 303	56.56 234	75.71 224	86.26 125

OurMVSNet-221017-0		61.37 339	58.63 354	69.61 271	72.05 351	48.06 297	73.93 275	72.51 325	47.23 390	54.74 409	80.92 280	21.49 454	81.24 225	48.57 304	56.22 435	79.53 336

fmvsm_s_conf0.5_n_2		69.82 173	69.27 169	71.46 223	72.00 352	51.08 222	73.30 287	67.79 366	55.06 256	75.24 56	87.51 90	44.02 229	77.00 329	75.67 48	72.86 269	86.31 123

IterMVS-SCA-FT		62.49 316	61.52 316	65.40 346	71.99 353	50.80 230	71.15 328	69.63 350	45.71 406	60.61 339	77.93 334	37.45 314	65.99 413	55.67 243	63.50 386	79.42 337

CostFormer		64.04 298	62.51 303	68.61 289	71.88 354	45.77 320	71.30 324	70.60 342	47.55 384	64.31 285	76.61 364	41.63 261	79.62 262	49.74 292	69.00 337	80.42 313

1314		64.61 289	63.21 296	68.80 286	71.87 355	47.46 306	73.95 273	78.39 217	42.88 431	59.97 346	76.60 365	38.11 309	79.39 268	54.84 250	72.32 279	79.55 335

tpm		57.34 373	58.16 358	54.86 424	71.80 356	34.77 438	67.47 376	56.04 448	48.20 373	60.10 343	76.92 355	37.17 320	53.41 467	40.76 383	65.01 370	76.40 378

fmvsm_s_conf0.1_n_2		69.64 181	69.01 175	71.52 221	71.66 357	51.04 223	73.39 286	67.14 372	55.02 260	75.11 58	87.64 89	42.94 241	77.01 328	75.55 50	72.63 275	86.52 109

eth_miper_zixun_eth		67.63 237	66.28 250	71.67 217	71.60 358	48.33 292	73.68 281	77.88 223	55.80 232	65.91 251	78.62 324	47.35 186	82.88 184	59.45 209	66.25 362	83.81 223

viewdifsd2359ckpt11		69.13 197	68.38 193	71.38 230	71.57 359	48.61 287	73.22 292	73.18 318	57.65 188	70.67 148	84.73 180	50.03 143	79.80 257	63.25 172	71.10 297	85.74 146

viewmsd2359difaftdt		69.13 197	68.38 193	71.38 230	71.57 359	48.61 287	73.22 292	73.18 318	57.65 188	70.67 148	84.73 180	50.03 143	79.80 257	63.25 172	71.10 297	85.74 146

pmmvs4		61.48 337	59.39 344	67.76 299	71.57 359	53.86 158	71.42 321	65.34 387	44.20 417	59.46 354	77.92 335	35.90 331	74.71 354	43.87 357	64.87 372	74.71 401

fmvsm_l_conf0.5_n		70.99 145	70.82 138	71.48 222	71.45 362	54.40 150	77.18 191	70.46 343	48.67 364	75.17 57	86.86 114	53.77 85	76.86 333	76.33 41	77.51 194	83.17 250

AllTest		57.08 375	54.65 388	64.39 356	71.44 363	49.03 276	69.92 349	67.30 368	45.97 403	47.16 451	79.77 301	17.47 458	67.56 401	33.65 427	59.16 422	76.57 376

TestCases				64.39 356	71.44 363	49.03 276		67.30 368	45.97 403	47.16 451	79.77 301	17.47 458	67.56 401	33.65 427	59.16 422	76.57 376

lessismore_v0				69.91 266	71.42 365	47.80 300		50.90 461		50.39 441	75.56 380	27.43 425	81.33 222	45.91 334	34.10 478	80.59 310

gm-plane-assit					71.40 366	41.72 372			48.85 363		73.31 402		82.48 200	48.90 301

GG-mvs-BLEND				62.34 373	71.36 367	37.04 420	69.20 360	57.33 441		54.73 410	65.48 458	30.37 391	77.82 308	34.82 423	74.93 234	72.17 426

fmvsm_l_conf0.5_n_a		70.50 156	70.27 149	71.18 238	71.30 368	54.09 155	76.89 202	69.87 347	47.90 378	74.37 77	86.49 134	53.07 98	76.69 339	75.41 52	77.11 203	82.76 257

test_fmvsmconf_n		73.01 101	72.59 104	74.27 124	71.28 369	55.88 124	78.21 153	75.56 274	54.31 275	74.86 67	87.80 87	54.72 69	80.23 253	78.07 26	78.48 177	86.70 99

test_fmvsmvis_n_1920		70.84 147	70.38 147	72.22 201	71.16 370	55.39 137	75.86 230	72.21 329	49.03 359	73.28 102	86.17 145	51.83 119	77.29 322	75.80 46	78.05 185	83.98 215

fmvsm_s_conf0.1_n		69.41 191	68.60 184	71.83 208	71.07 371	52.88 189	77.85 165	62.44 416	49.58 352	72.97 113	86.22 142	51.68 122	76.48 343	75.53 51	70.10 315	86.14 126

FMVSNet5		55.86 386	54.93 386	58.66 402	71.05 372	36.35 426	64.18 406	62.48 415	46.76 396	50.66 440	74.73 390	25.80 437	64.04 420	33.11 431	65.57 367	75.59 386

fmvsm_s_conf0.1_n_a		69.32 192	68.44 190	71.96 203	70.91 373	53.78 161	78.12 155	62.30 418	49.35 355	73.20 104	86.55 133	51.99 115	76.79 335	74.83 58	68.68 343	85.32 168

c3_l		68.33 218	67.56 209	70.62 253	70.87 374	46.21 317	74.47 263	78.80 195	56.22 224	66.19 244	78.53 326	51.88 116	81.40 220	62.08 184	69.04 336	84.25 205

GA-MVS		65.53 275	63.70 284	71.02 245	70.87 374	48.10 295	70.48 340	74.40 297	56.69 205	64.70 280	76.77 358	33.66 358	81.10 229	55.42 247	70.32 310	83.87 221

pmmvs6		63.69 301	62.82 301	66.27 327	70.63 376	39.27 397	73.13 295	75.47 278	52.69 306	59.75 352	82.30 246	39.71 284	77.03 327	47.40 314	64.35 378	82.53 264

reproduce_monomvs		62.56 315	61.20 324	66.62 320	70.62 377	44.30 339	70.13 346	73.13 321	54.78 264	61.13 335	76.37 369	25.63 439	75.63 350	58.75 220	60.29 418	79.93 327

miper_ehance_all_eth		68.03 226	67.24 226	70.40 257	70.54 378	46.21 317	73.98 271	78.68 199	55.07 254	66.05 248	77.80 341	52.16 112	81.31 223	61.53 194	69.32 330	83.67 231

MonoMVSNet		64.15 296	63.31 294	66.69 316	70.51 379	44.12 342	74.47 263	74.21 303	57.81 185	63.03 302	76.62 362	38.33 305	77.31 321	54.22 256	60.59 417	78.64 346

OpenMVS_ROB	52.78 18	60.03 349	58.14 359	65.69 340	70.47 380	44.82 330	75.33 239	70.86 340	45.04 409	56.06 394	76.00 373	26.89 431	79.65 260	35.36 422	67.29 354	72.60 416

v148		68.24 221	67.19 229	71.40 229	70.43 381	47.77 302	75.76 233	77.03 243	58.91 157	67.36 219	80.10 296	48.60 167	81.89 209	60.01 203	66.52 361	84.53 197

XXY-MVS		60.68 341	61.67 314	57.70 412	70.43 381	38.45 404	64.19 405	66.47 377	48.05 376	63.22 297	80.86 282	49.28 157	60.47 433	45.25 346	67.28 355	74.19 406

MVSTER		67.16 248	65.58 261	71.88 207	70.37 383	49.70 263	70.25 345	78.45 212	51.52 321	69.16 178	80.37 288	38.45 303	82.50 198	60.19 201	71.46 292	83.44 239

viewmambaseed2359dif		68.91 202	68.18 198	71.11 241	70.21 384	48.05 299	72.28 310	75.90 267	51.96 313	70.93 145	84.47 193	51.37 127	78.59 294	61.55 193	74.97 233	86.68 101

cl____		67.18 246	66.26 251	69.94 264	70.20 385	45.74 321	73.30 287	76.83 248	55.10 249	65.27 264	79.57 307	47.39 184	80.53 244	59.41 211	69.22 334	83.53 237

DIV-MVS_self_test		67.18 246	66.26 251	69.94 264	70.20 385	45.74 321	73.29 289	76.83 248	55.10 249	65.27 264	79.58 306	47.38 185	80.53 244	59.43 210	69.22 334	83.54 236

tpmvs		58.47 362	56.95 367	63.03 370	70.20 385	41.21 376	67.90 371	67.23 371	49.62 351	54.73 410	70.84 422	34.14 349	76.24 347	36.64 413	61.29 409	71.64 431

anonymousdsp		67.00 252	64.82 272	73.57 163	70.09 388	56.13 117	76.35 215	77.35 236	48.43 369	64.99 276	80.84 284	33.01 365	80.34 248	64.66 153	67.64 351	84.23 206

MIMVSNet1		55.17 393	54.31 394	57.77 411	70.03 389	32.01 458	65.68 387	64.81 391	49.19 357	46.75 454	76.00 373	25.53 440	64.04 420	28.65 458	62.13 403	77.26 367

CR-MVSNet		59.91 350	57.90 361	65.96 334	69.96 390	52.07 209	65.31 395	63.15 410	42.48 433	59.36 355	74.84 388	35.83 332	70.75 379	45.50 341	64.65 374	75.06 392

RPMNet		61.53 335	58.42 355	70.86 247	69.96 390	52.07 209	65.31 395	81.36 137	43.20 427	59.36 355	70.15 429	35.37 336	85.47 118	36.42 416	64.65 374	75.06 392

diffmvs_AUTHOR		71.02 143	70.87 137	71.45 225	69.89 392	48.97 281	73.16 294	78.33 218	57.79 187	72.11 131	85.26 174	51.84 118	77.89 306	71.00 92	78.47 179	87.49 68

test_fmvsmconf0.1_n		72.81 105	72.33 109	74.24 126	69.89 392	55.81 125	78.22 152	75.40 279	54.17 277	75.00 62	88.03 83	53.82 83	80.23 253	78.08 25	78.34 181	86.69 100

cl22		67.47 240	66.45 240	70.54 255	69.85 394	46.49 313	73.85 278	77.35 236	55.07 254	65.51 259	77.92 335	47.64 178	81.10 229	61.58 192	69.32 330	84.01 214

Anonymous20231206		55.10 395	55.30 385	54.48 426	69.81 395	33.94 447	62.91 415	62.13 421	41.08 440	55.18 404	75.65 379	32.75 371	56.59 456	30.32 452	67.86 348	72.91 412

mmtdpeth		60.40 347	59.12 347	64.27 358	69.59 396	48.99 279	70.67 337	70.06 346	54.96 261	62.78 306	73.26 404	27.00 429	67.66 398	58.44 223	45.29 464	76.16 380

our_test_3		56.49 379	54.42 391	62.68 372	69.51 397	45.48 326	66.08 383	61.49 423	44.11 420	50.73 439	69.60 438	33.05 363	68.15 393	38.38 398	56.86 431	74.40 403

ppachtmachnet_test		58.06 369	55.38 384	66.10 332	69.51 397	48.99 279	68.01 370	66.13 382	44.50 414	54.05 417	70.74 423	32.09 384	72.34 368	36.68 412	56.71 434	76.99 373

diffmvs		70.69 152	70.43 145	71.46 223	69.45 399	48.95 282	72.93 297	78.46 211	57.27 194	71.69 135	83.97 204	51.48 126	77.92 305	70.70 94	77.95 187	87.53 67
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		62.79 313	61.27 321	67.35 309	69.37 400	52.04 211	71.17 326	68.24 364	52.63 307	59.82 349	76.91 356	37.32 317	72.36 366	52.80 268	63.19 389	77.66 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		56.77 377	56.83 369	56.61 415	69.23 401	41.02 377	58.37 438	64.18 398	50.59 340	57.45 380	71.42 416	35.54 334	58.94 443	37.23 405	67.45 353	69.87 448

miper_enhance_ethall		67.11 249	66.09 253	70.17 261	69.21 402	45.98 319	72.85 300	78.41 215	51.38 326	65.65 257	75.98 376	51.17 131	81.25 224	60.82 197	69.32 330	83.29 243

Patchmtry		57.16 374	56.47 373	59.23 396	69.17 403	34.58 441	62.98 414	63.15 410	44.53 413	56.83 386	74.84 388	35.83 332	68.71 391	40.03 386	60.91 410	74.39 404

blended_shiyan8		62.46 318	60.71 333	67.71 300	69.15 404	43.43 348	70.83 333	76.52 254	51.49 323	57.67 375	71.36 419	39.38 287	79.07 280	47.37 315	62.67 392	80.62 309

blended_shiyan6		62.46 318	60.71 333	67.71 300	69.14 405	43.42 349	70.82 334	76.52 254	51.50 322	57.64 376	71.37 418	39.38 287	79.08 279	47.36 316	62.67 392	80.65 308

CL-MVSNet_self_test		61.53 335	60.94 328	63.30 366	68.95 406	36.93 421	67.60 373	72.80 324	55.67 235	59.95 347	76.63 361	45.01 219	72.22 370	39.74 391	62.09 404	80.74 307

blend_shiyan4		61.38 338	59.10 348	68.20 294	68.94 407	44.64 334	70.81 335	76.52 254	51.63 317	57.56 378	69.94 434	28.30 415	79.61 263	47.44 311	60.78 413	80.36 319

V42		68.65 209	67.35 220	72.56 190	68.93 408	50.18 251	72.90 299	79.47 180	56.92 202	69.45 170	80.26 292	46.29 199	82.99 171	64.07 156	67.82 349	84.53 197

FE-MVSNET2		62.01 328	60.88 329	65.42 345	68.74 409	38.43 405	72.92 298	77.39 234	54.74 267	55.40 401	76.71 359	35.46 335	76.72 338	44.25 349	62.31 401	81.10 298

test-LLR		58.15 368	58.13 360	58.22 405	68.57 410	44.80 331	65.46 391	57.92 436	50.08 345	55.44 399	69.82 435	32.62 376	57.44 450	49.66 294	73.62 252	72.41 422

test-mter		56.42 381	55.82 380	58.22 405	68.57 410	44.80 331	65.46 391	57.92 436	39.94 449	55.44 399	69.82 435	21.92 450	57.44 450	49.66 294	73.62 252	72.41 422

wanda-best-256-512		62.00 329	60.17 338	67.49 304	68.53 412	43.07 355	69.65 351	76.38 258	51.26 329	57.10 382	69.95 431	38.83 298	79.04 283	47.14 322	62.67 392	80.37 316

FE-blended-shiyan7		62.00 329	60.17 338	67.49 304	68.53 412	43.07 355	69.65 351	76.38 258	51.26 329	57.10 382	69.95 431	38.83 298	79.04 283	47.14 322	62.67 392	80.37 316

usedtu_blend_shiyan5		62.63 314	60.77 332	68.20 294	68.53 412	44.64 334	73.47 284	77.00 244	51.91 314	57.10 382	69.95 431	38.83 298	79.61 263	47.44 311	62.67 392	80.37 316

MVS-HIRNet		45.52 429	44.48 431	48.65 449	68.49 415	34.05 446	59.41 436	44.50 477	27.03 470	37.96 477	50.47 479	26.16 435	64.10 419	26.74 467	59.52 420	47.82 478

dp		51.89 412	51.60 410	52.77 438	68.44 416	32.45 457	62.36 418	54.57 450	44.16 418	49.31 446	67.91 443	28.87 409	56.61 455	33.89 426	54.89 440	69.24 453

PatchT		53.17 407	53.44 403	52.33 441	68.29 417	25.34 483	58.21 439	54.41 451	44.46 415	54.56 412	69.05 441	33.32 361	60.94 431	36.93 408	61.76 407	70.73 442

test_fmvsmconf0.01_n		72.17 122	71.50 121	74.16 129	67.96 418	55.58 133	78.06 159	74.67 294	54.19 276	74.54 74	88.23 74	50.35 142	80.24 252	78.07 26	77.46 195	86.65 104

Patchmatch-RL test		58.16 367	55.49 383	66.15 330	67.92 419	48.89 283	60.66 430	51.07 460	47.86 380	59.36 355	62.71 464	34.02 352	72.27 369	56.41 235	59.40 421	77.30 365

pmmvs-eth3d		58.81 360	56.31 376	66.30 326	67.61 420	52.42 204	72.30 309	64.76 392	43.55 423	54.94 407	74.19 394	28.95 407	72.60 364	43.31 362	57.21 430	73.88 409

PVSNet_0	43.31 20	47.46 427	45.64 430	52.92 437	67.60 421	44.65 333	54.06 456	54.64 449	41.59 437	46.15 456	58.75 468	30.99 388	58.66 444	32.18 434	24.81 483	55.46 471

0.4-1-1-0.2		58.31 365	55.53 382	66.64 319	67.46 422	42.78 361	64.38 403	70.97 339	47.65 382	53.38 426	59.02 467	28.39 414	78.72 293	44.86 348	63.63 383	78.42 348

CHOSEN 280x420		47.83 425	46.36 429	52.24 443	67.37 423	49.78 258	38.91 484	43.11 480	35.00 457	43.27 465	63.30 463	28.95 407	49.19 475	36.53 414	60.80 412	57.76 468

UWE-MVS-28		52.25 410	52.35 407	51.93 444	66.99 424	22.79 487	63.48 411	48.31 468	46.78 395	52.73 429	76.11 371	27.78 421	57.82 449	20.58 477	68.41 345	75.17 390

tpmrst		58.24 366	58.70 353	56.84 414	66.97 425	34.32 443	69.57 357	61.14 425	47.17 391	58.58 367	71.60 415	41.28 268	60.41 434	49.20 298	62.84 391	75.78 384

sss		56.17 384	56.57 372	54.96 423	66.93 426	36.32 428	57.94 441	61.69 422	41.67 436	58.64 365	75.32 386	38.72 301	56.25 457	42.04 375	66.19 363	72.31 425

TinyColmap		54.14 397	51.72 409	61.40 381	66.84 427	41.97 367	66.52 380	68.51 361	44.81 410	42.69 466	75.77 378	11.66 474	72.94 362	31.96 437	56.77 433	69.27 452

miper_lstm_enhance		62.03 327	60.88 329	65.49 344	66.71 428	46.25 315	56.29 450	75.70 270	50.68 337	61.27 333	75.48 383	40.21 278	68.03 396	56.31 236	65.25 369	82.18 273

TESTMET0.1,1		55.28 391	54.90 387	56.42 416	66.56 429	43.67 346	65.46 391	56.27 446	39.18 451	53.83 418	67.44 448	24.21 445	55.46 461	48.04 309	73.11 266	70.13 446

dmvs_testset		50.16 419	51.90 408	44.94 455	66.49 430	11.78 495	61.01 429	51.50 457	51.17 333	50.30 443	67.44 448	39.28 290	60.29 435	22.38 474	57.49 429	62.76 460

D2MVS		62.30 322	60.29 337	68.34 293	66.46 431	48.42 291	65.70 386	73.42 313	47.71 381	58.16 371	75.02 387	30.51 390	77.71 312	53.96 259	71.68 290	78.90 345

MDA-MVSNet-bldmvs		53.87 400	50.81 413	63.05 369	66.25 432	48.58 289	56.93 448	63.82 403	48.09 375	41.22 467	70.48 427	30.34 392	68.00 397	34.24 425	45.92 463	72.57 417

ITE_SJBPF				62.09 375	66.16 433	44.55 338		64.32 396	47.36 387	55.31 402	80.34 290	19.27 456	62.68 427	36.29 417	62.39 400	79.04 342

EPMVS		53.96 398	53.69 401	54.79 425	66.12 434	31.96 459	62.34 419	49.05 464	44.42 416	55.54 397	71.33 420	30.22 394	56.70 453	41.65 379	62.54 399	75.71 385

ADS-MVSNet2		51.33 415	48.76 422	59.07 399	66.02 435	44.60 336	50.90 464	59.76 429	36.90 452	50.74 437	66.18 456	26.38 432	63.11 425	27.17 464	54.76 441	69.50 450

ADS-MVSNet		48.48 424	47.77 425	50.63 446	66.02 435	29.92 466	50.90 464	50.87 462	36.90 452	50.74 437	66.18 456	26.38 432	52.47 470	27.17 464	54.76 441	69.50 450

EU-MVSNet		55.61 389	54.41 392	59.19 398	65.41 437	33.42 450	72.44 307	71.91 332	28.81 465	51.27 433	73.87 398	24.76 443	69.08 389	43.04 366	58.20 426	75.06 392

FE-MVSNET		55.16 394	53.75 400	59.41 393	65.29 438	33.20 452	67.21 378	66.21 381	48.39 371	49.56 445	73.53 401	29.03 406	72.51 365	30.38 451	54.10 444	72.52 418

RPSCF		55.80 387	54.22 396	60.53 388	65.13 439	42.91 360	64.30 404	57.62 438	36.84 454	58.05 373	82.28 247	28.01 418	56.24 458	37.14 406	58.61 425	82.44 269

USDC		56.35 382	54.24 395	62.69 371	64.74 440	40.31 385	65.05 397	73.83 309	43.93 421	47.58 449	77.71 345	15.36 467	75.05 353	38.19 400	61.81 406	72.70 415

JIA-IIPM		51.56 413	47.68 427	63.21 367	64.61 441	50.73 235	47.71 472	58.77 433	42.90 430	48.46 448	51.72 475	24.97 442	70.24 385	36.06 419	53.89 445	68.64 454

Patchmatch-test		49.08 422	48.28 424	51.50 445	64.40 442	30.85 464	45.68 476	48.46 467	35.60 456	46.10 457	72.10 410	34.47 347	46.37 479	27.08 466	60.65 415	77.27 366

TDRefinement		53.44 404	50.72 415	61.60 378	64.31 443	46.96 310	70.89 332	65.27 389	41.78 434	44.61 461	77.98 332	11.52 476	66.36 410	28.57 459	51.59 451	71.49 434

test_vis1_n_1920		58.86 359	59.06 349	58.25 404	63.76 444	43.14 354	67.49 375	66.36 379	40.22 446	65.89 253	71.95 413	31.04 387	59.75 438	59.94 204	64.90 371	71.85 429

N_pmnet		39.35 442	40.28 439	36.54 466	63.76 444	1.62 503	49.37 469	0.76 502	34.62 458	43.61 464	66.38 455	26.25 434	42.57 483	26.02 469	51.77 450	65.44 458

ambc				65.13 351	63.72 446	37.07 419	47.66 473	78.78 196		54.37 415	71.42 416	11.24 477	80.94 235	45.64 337	53.85 446	77.38 364

WB-MVS		43.26 432	43.41 432	42.83 459	63.32 447	10.32 497	58.17 440	45.20 475	45.42 407	40.44 470	67.26 451	34.01 353	58.98 442	11.96 488	24.88 482	59.20 463

KD-MVS_2432*1600		53.45 402	51.50 411	59.30 394	62.82 448	37.14 417	55.33 451	71.79 333	47.34 388	55.09 405	70.52 425	21.91 451	70.45 381	35.72 420	42.97 467	70.31 444

miper_refine_blended		53.45 402	51.50 411	59.30 394	62.82 448	37.14 417	55.33 451	71.79 333	47.34 388	55.09 405	70.52 425	21.91 451	70.45 381	35.72 420	42.97 467	70.31 444

test0.0.03 1		53.32 406	53.59 402	52.50 440	62.81 450	29.45 467	59.51 434	54.11 452	50.08 345	54.40 414	74.31 393	32.62 376	55.92 459	30.50 450	63.95 381	72.15 427

PMMVS		53.96 398	53.26 404	56.04 417	62.60 451	50.92 227	61.17 426	56.09 447	32.81 460	53.51 424	66.84 453	34.04 351	59.93 437	44.14 353	68.18 346	57.27 469

SSC-MVS		41.96 437	41.99 436	41.90 460	62.46 452	9.28 499	57.41 446	44.32 478	43.38 424	38.30 476	66.45 454	32.67 375	58.42 446	10.98 489	21.91 485	57.99 467

PM-MVS		52.33 409	50.19 418	58.75 401	62.10 453	45.14 329	65.75 385	40.38 482	43.60 422	53.52 423	72.65 405	9.16 482	65.87 414	50.41 287	54.18 443	65.24 459

Gipuma		34.77 446	31.91 451	43.33 457	62.05 454	37.87 408	20.39 489	67.03 373	23.23 476	18.41 489	25.84 489	4.24 489	62.73 426	14.71 482	51.32 452	29.38 487
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
usedtu_dtu_shiyan2		53.34 405	50.78 414	61.00 387	61.86 455	39.63 392	68.47 365	64.58 394	42.94 429	45.22 458	67.61 447	19.25 457	66.71 407	28.08 460	59.05 424	76.66 375

test20.03		53.87 400	54.02 397	53.41 434	61.47 456	28.11 472	61.30 424	59.21 431	51.34 328	52.09 431	77.43 348	33.29 362	58.55 445	29.76 454	60.27 419	73.58 410

pmmvs5		56.47 380	55.68 381	58.86 400	61.41 457	36.71 423	66.37 381	62.75 412	40.38 445	53.70 419	76.62 362	34.56 344	67.05 404	40.02 387	65.27 368	72.83 414

MDA-MVSNet_test_wron		50.71 418	48.95 420	56.00 419	61.17 458	41.84 368	51.90 462	56.45 442	40.96 441	44.79 460	67.84 444	30.04 398	55.07 464	36.71 411	50.69 454	71.11 440

YYNet1		50.73 417	48.96 419	56.03 418	61.10 459	41.78 369	51.94 461	56.44 443	40.94 442	44.84 459	67.80 445	30.08 397	55.08 463	36.77 409	50.71 453	71.22 437

dongtai		34.52 447	34.94 447	33.26 469	61.06 460	16.00 494	52.79 460	23.78 495	40.71 443	39.33 474	48.65 483	16.91 462	48.34 476	12.18 487	19.05 487	35.44 486

CMPMVS	42.80 21	57.81 371	55.97 378	63.32 365	60.98 461	47.38 307	64.66 400	69.50 353	32.06 461	46.83 453	77.80 341	29.50 403	71.36 374	48.68 302	73.75 248	71.21 438
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 420	48.87 421	53.66 431	60.97 462	33.67 449	57.62 445	64.56 395	39.47 450	47.38 450	64.02 462	27.47 423	59.32 439	34.69 424	43.68 466	67.98 456

Anonymous20240521		55.30 390	54.41 392	57.96 409	60.92 463	41.73 370	71.09 330	71.06 338	41.18 439	48.65 447	73.31 402	16.93 461	59.25 440	42.54 370	64.01 379	72.90 413

testgi		51.90 411	52.37 406	50.51 447	60.39 464	23.55 486	58.42 437	58.15 434	49.03 359	51.83 432	79.21 315	22.39 448	55.59 460	29.24 457	62.64 397	72.40 424

UnsupCasMVSNet_eth		53.16 408	52.47 405	55.23 422	59.45 465	33.39 451	59.43 435	69.13 357	45.98 402	50.35 442	72.32 407	29.30 405	58.26 447	42.02 376	44.30 465	74.05 407

mvs5depth		55.64 388	53.81 399	61.11 385	59.39 466	40.98 381	65.89 384	68.28 363	50.21 343	58.11 372	75.42 384	17.03 460	67.63 400	43.79 358	46.21 461	74.73 400

test_cas_vis1_n_1920		56.91 376	56.71 371	57.51 413	59.13 467	45.40 327	63.58 410	61.29 424	36.24 455	67.14 226	71.85 414	29.89 399	56.69 454	57.65 226	63.58 385	70.46 443

new-patchmatchnet		47.56 426	47.73 426	47.06 450	58.81 468	9.37 498	48.78 470	59.21 431	43.28 425	44.22 462	68.66 442	25.67 438	57.20 452	31.57 445	49.35 458	74.62 402

FPMVS		42.18 436	41.11 438	45.39 452	58.03 469	41.01 379	49.50 468	53.81 454	30.07 464	33.71 479	64.03 460	11.69 473	52.08 473	14.01 483	55.11 439	43.09 480

KD-MVS_self_test		55.22 392	53.89 398	59.21 397	57.80 470	27.47 475	57.75 444	74.32 298	47.38 386	50.90 436	70.00 430	28.45 413	70.30 384	40.44 384	57.92 427	79.87 330

test_vis1_n		49.89 421	48.69 423	53.50 433	53.97 471	37.38 415	61.53 421	47.33 472	28.54 466	59.62 353	67.10 452	13.52 469	52.27 471	49.07 299	57.52 428	70.84 441

test_fmvs1		51.32 416	50.48 416	53.81 430	53.57 472	37.51 414	60.63 431	51.16 458	28.02 469	63.62 293	69.23 440	16.41 463	53.93 466	51.01 283	60.70 414	69.99 447

kuosan		29.62 454	30.82 453	26.02 474	52.99 473	16.22 493	51.09 463	22.71 496	33.91 459	33.99 478	40.85 484	15.89 465	33.11 491	7.59 495	18.37 488	28.72 488

test_fmvs1_n		51.37 414	50.35 417	54.42 428	52.85 474	37.71 412	61.16 427	51.93 455	28.15 467	63.81 292	69.73 437	13.72 468	53.95 465	51.16 282	60.65 415	71.59 432

new_pmnet		34.13 448	34.29 449	33.64 468	52.63 475	18.23 492	44.43 479	33.90 488	22.81 478	30.89 481	53.18 473	10.48 480	35.72 490	20.77 476	39.51 471	46.98 479

pmmvs3		44.92 430	41.95 437	53.86 429	52.58 476	43.55 347	62.11 420	46.90 474	26.05 472	40.63 468	60.19 466	11.08 479	57.91 448	31.83 442	46.15 462	60.11 462

ttmdpeth		45.56 428	42.95 433	53.39 435	52.33 477	29.15 468	57.77 442	48.20 469	31.81 462	49.86 444	77.21 350	8.69 483	59.16 441	27.31 463	33.40 479	71.84 430

DSMNet-mixed		39.30 443	38.72 442	41.03 461	51.22 478	19.66 490	45.53 477	31.35 489	15.83 488	39.80 472	67.42 450	22.19 449	45.13 480	22.43 473	52.69 448	58.31 466

mvsany_test1		39.38 441	38.16 444	43.02 458	49.05 479	34.28 444	44.16 480	25.94 493	22.74 479	46.57 455	62.21 465	23.85 446	41.16 486	33.01 432	35.91 475	53.63 472

APD_test1		37.39 444	34.94 447	44.72 456	48.88 480	33.19 453	52.95 459	44.00 479	19.49 482	27.28 483	58.59 469	3.18 494	52.84 469	18.92 478	41.17 470	48.14 477

test_fmvs2		48.69 423	47.49 428	52.29 442	48.63 481	33.06 454	57.76 443	48.05 470	25.71 473	59.76 351	69.60 438	11.57 475	52.23 472	49.45 297	56.86 431	71.58 433

LF4IMVS		42.95 433	42.26 435	45.04 453	48.30 482	32.50 456	54.80 453	48.49 466	28.03 468	40.51 469	70.16 428	9.24 481	43.89 482	31.63 443	49.18 459	58.72 465

wuyk23d		13.32 461	12.52 464	15.71 476	47.54 483	26.27 480	31.06 488	1.98 501	4.93 493	5.18 496	1.94 496	0.45 500	18.54 495	6.81 496	12.83 492	2.33 493

MVStest1		42.65 434	39.29 441	52.71 439	47.26 484	34.58 441	54.41 455	50.84 463	23.35 475	39.31 475	74.08 397	12.57 471	55.09 462	23.32 472	28.47 481	68.47 455

test_vis1_rt		41.35 439	39.45 440	47.03 451	46.65 485	37.86 409	47.76 471	38.65 483	23.10 477	44.21 463	51.22 477	11.20 478	44.08 481	39.27 393	53.02 447	59.14 464

test_fmvs3		44.30 431	42.55 434	49.55 448	42.83 486	27.15 478	53.03 458	44.93 476	22.03 481	53.69 421	64.94 459	4.21 490	49.63 474	47.47 310	49.82 456	71.88 428

LCM-MVSNet		40.30 440	35.88 446	53.57 432	42.24 487	29.15 468	45.21 478	60.53 428	22.23 480	28.02 482	50.98 478	3.72 492	61.78 430	31.22 448	38.76 473	69.78 449

E-PMN		23.77 456	22.73 460	26.90 472	42.02 488	20.67 489	42.66 481	35.70 486	17.43 484	10.28 494	25.05 490	6.42 485	42.39 484	10.28 491	14.71 490	17.63 489

testf1		31.46 452	28.89 456	39.16 462	41.99 489	28.78 470	46.45 474	37.56 484	14.28 489	21.10 485	48.96 480	1.48 498	47.11 477	13.63 484	34.56 476	41.60 481

APD_test2		31.46 452	28.89 456	39.16 462	41.99 489	28.78 470	46.45 474	37.56 484	14.28 489	21.10 485	48.96 480	1.48 498	47.11 477	13.63 484	34.56 476	41.60 481

EMVS		22.97 457	21.84 461	26.36 473	40.20 491	19.53 491	41.95 482	34.64 487	17.09 485	9.73 495	22.83 491	7.29 484	42.22 485	9.18 493	13.66 491	17.32 490

ANet_high		41.38 438	37.47 445	53.11 436	39.73 492	24.45 484	56.94 447	69.69 348	47.65 382	26.04 484	52.32 474	12.44 472	62.38 428	21.80 475	10.61 493	72.49 419

PMMVS2		27.40 455	25.91 458	31.87 471	39.46 493	6.57 500	31.17 487	28.52 491	23.96 474	20.45 488	48.94 482	4.20 491	37.94 487	16.51 480	19.97 486	51.09 473

PMVS	28.69 22	36.22 445	33.29 450	45.02 454	36.82 494	35.98 431	54.68 454	48.74 465	26.31 471	21.02 487	51.61 476	2.88 495	60.10 436	9.99 492	47.58 460	38.99 485
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvsany_test3		32.62 449	30.57 454	38.77 464	36.16 495	24.20 485	38.10 485	20.63 497	19.14 483	40.36 471	57.43 470	5.06 487	36.63 489	29.59 456	28.66 480	55.49 470

test_vis3_rt		32.09 450	30.20 455	37.76 465	35.36 496	27.48 474	40.60 483	28.29 492	16.69 486	32.52 480	40.53 485	1.96 496	37.40 488	33.64 429	42.21 469	48.39 475

MVE	17.77 23	21.41 458	17.77 463	32.34 470	34.34 497	25.44 482	16.11 490	24.11 494	11.19 491	13.22 491	31.92 487	1.58 497	30.95 493	10.47 490	17.03 489	40.62 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		31.86 451	31.05 452	34.28 467	32.33 498	21.86 488	32.34 486	30.46 490	16.02 487	39.78 473	55.45 472	4.80 488	32.36 492	30.61 449	37.66 474	48.64 474

DeepMVS_CX				12.03 477	17.97 499	10.91 496		10.60 500	7.46 492	11.07 493	28.36 488	3.28 493	11.29 496	8.01 494	9.74 495	13.89 491

test_method		19.68 459	18.10 462	24.41 475	13.68 500	3.11 502	12.06 492	42.37 481	2.00 494	11.97 492	36.38 486	5.77 486	29.35 494	15.06 481	23.65 484	40.76 483

tmp_tt		9.43 462	11.14 465	4.30 478	2.38 501	4.40 501	13.62 491	16.08 499	0.39 495	15.89 490	13.06 492	15.80 466	5.54 497	12.63 486	10.46 494	2.95 492

testmvs		4.52 465	6.03 468	0.01 480	0.01 502	0.00 505	53.86 457	0.00 503	0.01 497	0.04 498	0.27 497	0.00 502	0.00 498	0.04 497	0.00 496	0.03 495

test123		4.73 464	6.30 467	0.02 479	0.01 502	0.01 504	56.36 449	0.00 503	0.01 497	0.04 498	0.21 498	0.01 501	0.00 498	0.03 498	0.00 496	0.04 494

mmdepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

monomultidepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

test_blank		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

eth-test2					0.00 504

eth-test					0.00 504

uanet_test		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

DCPMVS		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

cdsmvs_eth3d_5k		17.50 460	23.34 459	0.00 481	0.00 504	0.00 505	0.00 493	78.63 200	0.00 499	0.00 500	82.18 250	49.25 158	0.00 498	0.00 499	0.00 496	0.00 496

pcd_1.5k_mvsjas		3.92 466	5.23 469	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	47.05 189	0.00 498	0.00 499	0.00 496	0.00 496

sosnet-low-res		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

sosnet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

uncertanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

Regformer		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

ab-mvs-re		6.49 463	8.65 466	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	77.89 339	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

uanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 505	0.00 493	0.00 503	0.00 499	0.00 500	0.00 499	0.00 502	0.00 498	0.00 499	0.00 496	0.00 496

TestfortrainingZip							86.84 11

WAC-MVS						27.31 476								27.77 461

PC_three_1452									55.09 251	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 29

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 61

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 45

GSMVS																78.05 353

sam_mvs1												34.74 343				78.05 353

sam_mvs												33.43 360

MTGPA								80.97 155

test_post1							68.67 363				3.64 494	32.39 381	69.49 387	44.17 351

test_post											3.55 495	33.90 354	66.52 408

patchmatchnet-post											64.03 460	34.50 345	74.27 357

MTMP							86.03 23	17.08 498

test9_res														75.28 54	88.31 36	83.81 223

agg_prior2														73.09 72	87.93 44	84.33 201

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 121	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

旧先验2							76.08 223		45.32 408	76.55 47			65.56 415	58.75 220

新几何2							76.12 221

无先验							79.66 121	74.30 300	48.40 370				80.78 241	53.62 261		79.03 343

原ACMM2							79.02 128

testdata2													72.18 371	46.95 325

segment_acmp												54.23 74

testdata1							72.65 301		60.50 115

plane_prior5								84.01 57					87.21 63	68.16 109	80.58 124	84.65 192

plane_prior4											86.10 147

plane_prior3						56.09 118			63.92 38	69.27 174

plane_prior2							84.22 51		64.52 27

plane_prior						56.31 112	83.58 64		63.19 51						80.48 127

n2								0.00 503

nn								0.00 503

door-mid								47.19 473

test11								83.47 85

door								47.60 471

HQP5-MVS						54.94 143

BP-MVS														67.04 128

HQP4-MVS										67.85 206			86.93 71			84.32 202

HQP3-MVS								83.90 62							80.35 128

HQP2-MVS												45.46 208

MDTV_nov1_ep13_2view						25.89 481	61.22 425		40.10 447	51.10 434		32.97 366		38.49 397		78.61 347

ACMMP++_ref															74.07 243

ACMMP++															72.16 284

Test By Simon												48.33 169

High-resolution multi-view benchmark