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.

LCM-MVSNet		86.90 1	88.67 1	81.57 20	91.50 1	63.30 105	84.80 26	87.77 7	86.18 1	96.26 2	96.06 1	90.32 1	84.49 49	68.08 82	97.05 3	96.93 1

zzz-MVS		83.01 21	83.63 20	81.13 29	91.16 2	78.16 12	82.72 40	80.63 110	72.08 25	84.93 56	90.79 46	74.65 35	84.42 51	80.98 4	94.75 28	80.82 181

MTAPA		83.19 17	83.87 16	81.13 29	91.16 2	78.16 12	84.87 24	80.63 110	72.08 25	84.93 56	90.79 46	74.65 35	84.42 51	80.98 4	94.75 28	80.82 181

mPP-MVS		84.01 9	84.39 9	82.88 5	90.65 4	81.38 5	87.08 9	82.79 67	72.41 24	85.11 55	90.85 45	76.65 20	84.89 43	79.30 15	94.63 32	82.35 152

MP-MVS		83.19 17	83.54 21	82.14 18	90.54 5	79.00 9	86.42 18	83.59 56	71.31 29	81.26 97	90.96 42	74.57 37	84.69 47	78.41 20	94.78 27	82.74 144
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 30	83.15 27	77.36 70	90.35 6	82.82 3	82.15 42	79.22 134	74.08 16	87.16 27	91.97 19	84.80 2	76.97 183	64.98 116	93.61 50	72.28 260
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 41	82.86 31	73.07 129	89.93 7	39.21 272	77.15 93	81.28 93	79.74 4	90.87 6	92.73 11	75.03 33	84.93 42	63.83 123	95.19 17	95.07 3

DTE-MVSNet		80.35 42	82.89 30	72.74 140	89.84 8	37.34 290	77.16 92	81.81 81	80.45 2	90.92 5	92.95 7	74.57 37	86.12 24	63.65 124	94.68 31	94.76 6

PEN-MVS		80.46 40	82.91 29	73.11 128	89.83 9	39.02 275	77.06 95	82.61 70	80.04 3	90.60 8	92.85 9	74.93 34	85.21 38	63.15 126	95.15 19	95.09 2

region2R		83.54 13	83.86 17	82.58 13	89.82 10	77.53 16	87.06 10	84.23 45	70.19 37	83.86 70	90.72 50	75.20 30	86.27 17	79.41 13	94.25 43	83.95 118

ACMMPR		83.62 11	83.93 15	82.69 10	89.78 11	77.51 18	87.01 11	84.19 46	70.23 35	84.49 64	90.67 51	75.15 31	86.37 14	79.58 9	94.26 42	84.18 113

HSP-MVS		79.69 46	79.17 55	81.27 28	89.70 12	77.46 19	87.16 8	80.58 113	64.94 74	81.05 102	88.38 104	57.10 200	87.10 6	79.75 7	83.87 200	79.24 204

CP-MVSNet		79.48 49	81.65 39	72.98 133	89.66 13	39.06 274	76.76 97	80.46 115	78.91 6	90.32 9	91.70 26	68.49 77	84.89 43	63.40 125	95.12 20	95.01 4

PGM-MVS		83.07 19	83.25 26	82.54 15	89.57 14	77.21 20	82.04 44	85.40 23	67.96 47	84.91 58	90.88 43	75.59 27	86.57 12	78.16 21	94.71 30	83.82 119

WR-MVS_H		80.22 44	82.17 36	74.39 97	89.46 15	42.69 249	78.24 80	82.24 74	78.21 8	89.57 11	92.10 18	68.05 82	85.59 31	66.04 108	95.62 11	94.88 5

XVS		83.51 14	83.73 18	82.85 7	89.43 16	77.61 14	86.80 13	84.66 35	72.71 22	82.87 78	90.39 63	73.86 41	86.31 15	78.84 18	94.03 46	84.64 100

X-MVStestdata		76.81 72	74.79 99	82.85 7	89.43 16	77.61 14	86.80 13	84.66 35	72.71 22	82.87 78	9.95 360	73.86 41	86.31 15	78.84 18	94.03 46	84.64 100

CP-MVS		84.12 7	84.55 8	82.80 9	89.42 18	79.74 7	88.19 3	84.43 39	71.96 28	84.70 61	90.56 53	77.12 17	86.18 20	79.24 16	95.36 14	82.49 150

ACMMP_Plus		82.33 26	83.28 25	79.46 46	89.28 19	69.09 69	83.62 33	84.98 27	64.77 75	83.97 69	91.02 39	75.53 29	85.93 28	82.00 2	94.36 38	83.35 134

HFP-MVS		83.39 16	84.03 14	81.48 22	89.25 20	75.69 24	87.01 11	84.27 42	70.23 35	84.47 65	90.43 58	76.79 18	85.94 26	79.58 9	94.23 44	82.82 141

#test#		82.40 25	82.71 32	81.48 22	89.25 20	75.69 24	84.47 28	84.27 42	64.45 78	84.47 65	90.43 58	76.79 18	85.94 26	76.01 31	94.23 44	82.82 141

ACMMP		84.22 5	84.84 6	82.35 17	89.23 22	76.66 22	87.65 4	85.89 18	71.03 31	85.85 44	90.58 52	78.77 13	85.78 29	79.37 14	95.17 18	84.62 102
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
CPTT-MVS		81.51 32	81.76 37	80.76 34	89.20 23	78.75 10	86.48 17	82.03 77	68.80 42	80.92 105	88.52 100	72.00 53	82.39 79	74.80 32	93.04 56	81.14 174

HPM-MVS++		79.89 45	79.80 50	80.18 38	89.02 24	78.44 11	83.49 34	80.18 123	64.71 77	78.11 137	88.39 103	65.46 105	83.14 69	77.64 26	91.20 84	78.94 207

TSAR-MVS + MP.		79.05 52	78.81 56	79.74 42	88.94 25	67.52 76	86.61 15	81.38 92	51.71 214	77.15 144	91.42 34	65.49 104	87.20 5	79.44 12	87.17 155	84.51 107

UA-Net		81.56 31	82.28 35	79.40 47	88.91 26	69.16 67	84.67 27	80.01 126	75.34 12	79.80 117	94.91 2	69.79 68	80.25 135	72.63 45	94.46 36	88.78 52

MP-MVS-pluss		82.54 24	83.46 22	79.76 41	88.88 27	68.44 71	81.57 47	86.33 14	63.17 96	85.38 52	91.26 35	76.33 22	84.67 48	83.30 1	94.96 24	86.17 78
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		81.91 29	82.54 33	80.03 39	88.66 28	69.52 61	85.12 23	84.76 33	63.53 91	84.23 68	90.99 41	72.02 52	86.87 10	79.21 17	94.36 38	83.68 123

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 29	81.19 6	88.84 2	90.72 1	78.27 7	87.95 17	92.53 13	79.37 11	84.79 46	74.51 36	96.15 4	92.88 9

HPM-MVS_fast		84.59 4	85.10 4	83.06 4	88.60 30	75.83 23	86.27 19	86.89 11	73.69 17	86.17 39	91.70 26	78.23 15	85.20 39	79.45 11	94.91 26	88.15 62

新几何1				69.99 177	88.37 31	71.34 47		62.08 258	43.85 278	74.99 174	86.11 152	52.85 217	70.57 247	50.99 203	83.23 207	68.05 298

1121		69.23 176	68.26 192	72.12 155	88.36 32	71.40 45	68.59 209	62.06 259	43.80 279	74.75 177	86.18 148	52.92 216	76.85 186	54.47 184	83.27 206	68.12 297

HPM-MVS		84.12 7	84.63 7	82.60 12	88.21 33	74.40 31	85.24 22	87.21 9	70.69 34	85.14 53	90.42 61	78.99 12	86.62 11	80.83 6	94.93 25	86.79 74
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMM	69.25 9	82.11 27	83.31 24	78.49 58	88.17 34	73.96 34	83.11 36	84.52 38	66.40 57	87.45 23	89.16 87	81.02 7	80.52 131	74.27 38	95.73 9	80.98 178
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 35	69.15 68	67.85 218	59.59 270	41.06 294	73.05 197	85.72 159	48.03 239			80.65 244	66.92 304

XVG-ACMP-BASELINE		80.54 39	81.06 42	78.98 52	87.01 36	72.91 41	80.23 59	85.56 20	66.56 56	85.64 45	89.57 78	69.12 72	80.55 130	72.51 47	93.37 52	83.48 127

LPG-MVS_test		83.47 15	84.33 10	80.90 32	87.00 37	70.41 56	82.04 44	86.35 12	69.77 39	87.75 18	91.13 36	81.83 3	86.20 18	77.13 27	95.96 7	86.08 79

LGP-MVS_train				80.90 32	87.00 37	70.41 56		86.35 12	69.77 39	87.75 18	91.13 36	81.83 3	86.20 18	77.13 27	95.96 7	86.08 79

OPM-MVS		80.99 37	81.63 40	79.07 51	86.86 39	69.39 64	79.41 68	84.00 51	65.64 62	85.54 49	89.28 81	76.32 23	83.47 64	74.03 39	93.57 51	84.35 112

abl_6		84.92 3	85.70 3	82.57 14	86.72 40	79.27 8	87.56 5	86.08 16	77.48 9	88.12 16	91.53 31	81.18 6	84.31 54	78.12 22	94.47 35	84.15 114

DeepC-MVS	72.44 4	81.00 36	80.83 44	81.50 21	86.70 41	70.03 60	82.06 43	87.00 10	59.89 124	80.91 106	90.53 54	72.19 48	88.56 1	73.67 41	94.52 34	85.92 84
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	69.50 8	82.64 23	83.38 23	80.40 36	86.50 42	69.44 63	82.30 41	86.08 16	66.80 53	86.70 32	89.99 73	81.64 5	85.95 25	74.35 37	96.11 5	85.81 85
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 47	79.99 48	78.49 58	86.46 43	74.79 30	77.15 93	85.39 24	66.73 54	80.39 112	88.85 97	74.43 39	78.33 170	74.73 34	85.79 167	82.35 152

VDDNet		71.60 154	73.13 127	67.02 210	86.29 44	41.11 258	69.97 191	66.50 241	68.72 44	74.74 178	91.70 26	59.90 152	75.81 195	48.58 222	91.72 71	84.15 114

XVG-OURS		79.51 48	79.82 49	78.58 57	86.11 45	74.96 29	76.33 105	84.95 29	66.89 50	82.75 80	88.99 93	66.82 92	78.37 169	74.80 32	90.76 98	82.40 151

test_part2					85.90 46	66.44 81				84.61 62

v1.0		34.83 337	46.44 321	0.00 354	85.90 46	0.00 369	0.00 360	84.94 30	73.27 20	84.61 62	89.25 84	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

原ACMM1				73.90 103	85.90 46	65.15 92		81.67 83	50.97 229	74.25 184	86.16 150	61.60 134	83.54 62	56.75 162	91.08 88	73.00 251

testdata				64.13 229	85.87 49	63.34 104		61.80 262	47.83 251	76.42 162	86.60 137	48.83 235	62.31 299	54.46 186	81.26 236	66.74 308

agg_prior3		76.32 75	76.33 82	76.28 79	85.86 50	70.13 59	76.50 99	78.26 155	53.41 200	75.78 164	86.49 140	66.58 95	81.57 91	72.50 48	91.56 75	77.15 224

CNVR-MVS		78.49 61	78.59 60	78.16 62	85.86 50	67.40 77	78.12 83	81.50 85	63.92 85	77.51 142	86.56 138	68.43 79	84.82 45	73.83 40	91.61 74	82.26 155

NCCC		78.25 64	78.04 64	78.89 54	85.61 52	69.45 62	79.80 64	80.99 106	65.77 61	75.55 168	86.25 147	67.42 87	85.42 32	70.10 66	90.88 96	81.81 164

TEST9					85.47 53	69.32 65	76.42 101	78.69 145	53.73 196	76.97 145	86.74 128	66.84 91	81.10 112

train_agg		76.38 74	76.55 77	75.86 85	85.47 53	69.32 65	76.42 101	78.69 145	54.00 191	76.97 145	86.74 128	66.60 93	81.10 112	72.50 48	91.56 75	77.15 224

ESAPD		82.00 28	83.02 28	78.95 53	85.36 55	67.25 78	82.91 37	84.98 27	73.52 18	85.43 51	90.03 72	76.37 21	86.97 9	74.56 35	94.02 48	82.62 146

HQP_MVS		78.77 57	78.78 58	78.72 55	85.18 56	65.18 90	82.74 38	85.49 21	65.45 64	78.23 135	89.11 89	60.83 145	86.15 21	71.09 55	90.94 90	84.82 97

plane_prior7					85.18 56	66.21 83

SteuartSystems-ACMMP		83.07 19	83.64 19	81.35 25	85.14 58	71.00 50	85.53 20	84.78 32	70.91 32	85.64 45	90.41 62	75.55 28	87.69 3	79.75 7	95.08 21	85.36 90
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 59	67.89 74	76.26 106	78.66 147	54.00 191	76.89 150	86.72 130	66.60 93	80.89 124

WR-MVS		71.20 156	72.48 143	67.36 207	84.98 60	35.70 301	64.43 260	68.66 233	65.05 73	81.49 95	86.43 142	57.57 193	76.48 190	50.36 209	93.32 54	89.90 34

PS-MVSNAJss		77.54 67	77.35 69	78.13 64	84.88 61	66.37 82	78.55 74	79.59 131	53.48 198	86.29 38	92.43 15	62.39 126	80.25 135	67.90 90	90.61 99	87.77 65

LTVRE_ROB	75.46 1	84.22 5	84.98 5	81.94 19	84.82 62	75.40 26	91.60 1	87.80 5	73.52 18	88.90 13	93.06 6	71.39 58	81.53 92	81.53 3	92.15 69	88.91 48
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
mvs_tets		78.93 55	78.67 59	79.72 43	84.81 63	73.93 35	80.65 52	76.50 175	51.98 212	87.40 24	91.86 22	76.09 25	78.53 160	68.58 77	90.20 105	86.69 76

APDe-MVS		82.88 22	84.14 12	79.08 50	84.80 64	66.72 79	86.54 16	85.11 26	72.00 27	86.65 33	91.75 25	78.20 16	87.04 7	77.93 23	94.32 41	83.47 128

MSLP-MVS++		74.48 108	75.78 89	70.59 166	84.66 65	62.40 108	78.65 72	84.24 44	60.55 120	77.71 140	81.98 206	63.12 119	77.64 179	62.95 127	88.14 135	71.73 265

jajsoiax		78.51 60	78.16 63	79.59 45	84.65 66	73.83 37	80.42 55	76.12 177	51.33 219	87.19 26	91.51 32	73.79 43	78.44 164	68.27 80	90.13 109	86.49 77

pcd1.5k->3k		35.00 336	36.93 337	29.21 347	84.62 67	0.00 369	0.00 360	78.90 142	0.00 364	0.00 366	0.00 366	78.26 14	0.00 366	0.00 363	90.55 101	87.62 66

TranMVSNet+NR-MVSNet		76.13 77	77.66 67	71.56 158	84.61 68	42.57 250	70.98 182	78.29 154	68.67 45	83.04 76	89.26 82	72.99 46	80.75 127	55.58 177	95.47 12	91.35 14

旧先验1					84.55 69	60.36 122		63.69 252			87.05 118	54.65 211			83.34 205	69.66 287

APD-MVS		81.13 34	81.73 38	79.36 48	84.47 70	70.53 55	83.85 32	83.70 53	69.43 41	83.67 72	88.96 95	75.89 26	86.41 13	72.62 46	92.95 57	81.14 174
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 71

agg_prior1		75.89 79	76.41 80	74.31 98	84.44 72	66.02 84	76.12 109	78.62 148	54.40 185	76.95 147	86.85 122	66.44 97	80.34 133	72.45 50	91.42 79	76.57 229

agg_prior					84.44 72	66.02 84		78.62 148		76.95 147			80.34 133

DeepPCF-MVS	71.07 5	78.48 62	77.14 72	82.52 16	84.39 74	77.04 21	76.35 103	84.05 49	56.66 155	80.27 113	85.31 161	68.56 76	87.03 8	67.39 94	91.26 82	83.50 126

CDPH-MVS		77.33 69	77.06 73	78.14 63	84.21 75	63.98 99	76.07 110	83.45 59	54.20 187	77.68 141	87.18 114	69.98 66	85.37 33	68.01 85	92.72 63	85.08 94

plane_prior6					84.18 76	65.31 89						60.83 145

114514_t		73.40 121	73.33 124	73.64 111	84.15 77	57.11 142	78.20 81	80.02 125	43.76 280	72.55 205	86.07 154	64.00 115	83.35 68	60.14 141	91.03 89	80.45 189

DeepC-MVS_fast	69.89 7	77.17 70	76.33 82	79.70 44	83.90 78	67.94 73	80.06 62	83.75 52	56.73 154	74.88 176	85.32 160	65.54 103	87.79 2	65.61 112	91.14 86	83.35 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		80.28 43	81.55 41	76.47 75	83.57 79	67.83 75	83.39 35	85.35 25	64.42 81	86.14 40	87.07 117	74.02 40	80.97 117	77.70 25	92.32 68	80.62 186

DU-MVS		74.91 100	75.57 93	72.93 135	83.50 80	45.79 232	69.47 198	80.14 124	65.22 70	81.74 89	87.08 115	61.82 132	81.07 114	56.21 170	94.98 22	91.93 10

NR-MVSNet		73.62 116	74.05 109	72.33 151	83.50 80	43.71 243	65.65 247	77.32 168	64.32 82	75.59 167	87.08 115	62.45 125	81.34 102	54.90 180	95.63 10	91.93 10

test_0402		78.17 65	79.48 53	74.24 99	83.50 80	59.15 133	72.52 150	74.60 191	75.34 12	88.69 15	91.81 23	75.06 32	82.37 80	65.10 114	88.68 128	81.20 171

NP-MVS					83.34 83	63.07 107					85.97 155

UniMVSNet (Re)		75.00 97	75.48 94	73.56 113	83.14 84	47.92 204	70.41 188	81.04 104	63.67 88	79.54 119	86.37 144	62.83 120	81.82 88	57.10 161	95.25 16	90.94 25

UniMVSNet_NR-MVSNet		74.90 101	75.65 90	72.64 142	83.04 85	45.79 232	69.26 200	78.81 143	66.66 55	81.74 89	86.88 121	63.26 118	81.07 114	56.21 170	94.98 22	91.05 20

HyFIR lowres test		63.01 227	60.47 245	70.61 165	83.04 85	54.10 158	59.93 296	72.24 208	33.67 332	69.00 241	75.63 268	38.69 281	76.93 184	36.60 302	75.45 284	80.81 183

COLMAP_ROB	72.78 3	83.75 10	84.11 13	82.68 11	82.97 87	74.39 32	87.18 7	88.18 4	78.98 5	86.11 41	91.47 33	79.70 10	85.76 30	66.91 99	95.46 13	87.89 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DP-MVS Recon		73.57 118	72.69 141	76.23 81	82.85 88	63.39 103	74.32 138	82.96 65	57.75 138	70.35 232	81.98 206	64.34 114	84.41 53	49.69 213	89.95 113	80.89 179

APD-MVS_3200maxsize		83.57 12	84.33 10	81.31 26	82.83 89	73.53 40	85.50 21	87.45 8	74.11 15	86.45 35	90.52 56	80.02 9	84.48 50	77.73 24	94.34 40	85.93 83

PVSNet_Blended_VisFu		70.04 166	68.88 183	73.53 115	82.71 90	63.62 102	74.81 130	81.95 80	48.53 244	67.16 261	79.18 239	51.42 226	78.38 168	54.39 187	79.72 256	78.60 210

EG-PatchMatch MVS		70.70 161	70.88 164	70.16 173	82.64 91	58.80 135	71.48 169	73.64 195	54.98 174	76.55 156	81.77 209	61.10 142	78.94 151	54.87 181	80.84 242	72.74 255

HQP-NCC					82.37 92		77.32 89		59.08 127	71.58 213

ACMP_Plane					82.37 92		77.32 89		59.08 127	71.58 213

HQP-MVS		75.24 92	75.01 98	75.94 83	82.37 92	58.80 135	77.32 89	84.12 47	59.08 127	71.58 213	85.96 156	58.09 180	85.30 35	67.38 95	89.16 121	83.73 122

test12				76.51 73	82.28 95	60.94 119		81.64 84		73.60 190		64.88 109	85.19 40		90.42 103	83.38 131

TAMVS		65.31 207	63.75 216	69.97 178	82.23 96	59.76 128	66.78 232	63.37 254	45.20 270	69.79 236	79.37 235	47.42 242	72.17 232	34.48 312	85.15 186	77.99 220

test_prior3		76.71 73	77.19 70	75.27 91	82.15 97	59.85 126	75.57 117	84.33 40	58.92 131	76.53 158	86.78 125	67.83 85	83.39 66	69.81 69	92.76 60	82.58 147

test_prior				75.27 91	82.15 97	59.85 126		84.33 40					83.39 66			82.58 147

AdaColmap		74.22 110	74.56 101	73.20 126	81.95 99	60.97 118	79.43 66	80.90 107	65.57 63	72.54 206	81.76 210	70.98 61	85.26 36	47.88 228	90.00 111	73.37 248

PAPM_NR		73.91 112	74.16 108	73.16 127	81.90 100	53.50 162	81.28 48	81.40 91	66.17 58	73.30 195	83.31 188	59.96 151	83.10 70	58.45 152	81.66 226	82.87 139

DP-MVS		78.44 63	79.29 54	75.90 84	81.86 101	65.33 88	79.05 70	84.63 37	74.83 14	80.41 111	86.27 145	71.68 54	83.45 65	62.45 130	92.40 66	78.92 208

F-COLMAP		75.29 91	73.99 110	79.18 49	81.73 102	71.90 43	81.86 46	82.98 64	59.86 125	72.27 208	84.00 179	64.56 113	83.07 71	51.48 199	87.19 154	82.56 149

SixPastTwentyTwo		75.77 81	76.34 81	74.06 102	81.69 103	54.84 152	76.47 100	75.49 183	64.10 84	87.73 20	92.24 17	50.45 229	81.30 104	67.41 93	91.46 78	86.04 81

Vis-MVSNet		74.85 104	74.56 101	75.72 86	81.63 104	64.64 94	76.35 103	79.06 139	62.85 98	73.33 194	88.41 102	62.54 124	79.59 145	63.94 122	82.92 208	82.94 138
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 56	78.27 62	80.70 35	81.42 105	71.24 48	83.98 30	75.72 181	52.27 207	87.37 25	92.25 16	68.04 83	80.56 128	72.28 51	91.15 85	90.32 32

3Dnovator+	73.19 2	81.08 35	80.48 45	82.87 6	81.41 106	72.03 42	84.38 29	86.23 15	77.28 11	80.65 108	90.18 70	59.80 155	87.58 4	73.06 43	91.34 81	89.01 43

MCST-MVS		73.42 120	73.34 123	73.63 112	81.28 107	59.17 132	74.80 132	83.13 63	45.50 266	72.84 199	83.78 182	65.15 107	80.99 116	64.54 118	89.09 124	80.73 184

MIMVSNet1		66.57 202	69.23 177	58.59 279	81.26 108	37.73 287	64.06 263	57.62 277	57.02 150	78.40 132	90.75 48	62.65 121	58.10 310	41.77 272	89.58 117	79.95 198

ACMH+	66.64 10	81.20 33	82.48 34	77.35 71	81.16 109	62.39 109	80.51 53	87.80 5	73.02 21	87.57 21	91.08 38	80.28 8	82.44 78	64.82 117	96.10 6	87.21 71

MVS_111021_HR		72.98 130	72.97 135	72.99 132	80.82 110	65.47 87	68.81 205	72.77 201	57.67 141	75.76 165	82.38 201	71.01 60	77.17 181	61.38 134	86.15 163	76.32 230

OMC-MVS		79.41 50	78.79 57	81.28 27	80.62 111	70.71 54	80.91 50	84.76 33	62.54 101	81.77 87	86.65 134	71.46 56	83.53 63	67.95 89	92.44 65	89.60 35

OurMVSNet-221017-0		78.57 59	78.53 61	78.67 56	80.48 112	64.16 97	80.24 58	82.06 76	61.89 105	88.77 14	93.32 4	57.15 198	82.60 77	70.08 67	92.80 58	89.25 38

CDS-MVSNet		64.33 218	62.66 231	69.35 182	80.44 113	58.28 139	65.26 253	65.66 244	44.36 276	67.30 260	75.54 269	43.27 257	71.77 239	37.68 295	84.44 193	78.01 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 153	70.28 169	76.33 78	80.31 114	68.63 70	78.18 82	81.24 95	54.57 183	67.09 262	80.63 218	59.44 158	81.74 90	46.91 235	84.17 195	78.63 209
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		58.09 270	56.30 280	63.45 237	79.95 115	50.93 173	54.07 317	65.59 245	28.56 350	61.53 287	74.33 284	41.09 270	66.52 284	33.91 316	67.69 324	72.92 252

K. test v3		73.67 115	73.61 117	73.87 104	79.78 116	55.62 148	74.69 136	62.04 261	66.16 59	84.76 60	93.23 5	49.47 231	80.97 117	65.66 110	86.67 160	85.02 95

VPNet		65.58 205	67.56 197	59.65 274	79.72 117	30.17 338	60.27 294	62.14 257	54.19 188	71.24 220	86.63 135	58.80 167	67.62 273	44.17 248	90.87 97	81.18 172

ACMH	63.62 14	77.50 68	80.11 47	69.68 179	79.61 118	56.28 145	78.81 71	83.62 55	63.41 94	87.14 28	90.23 69	76.11 24	73.32 216	67.58 91	94.44 37	79.44 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 139	79.60 119	56.83 144		57.37 280		83.80 71	89.01 92	47.45 241	78.74 156	64.39 120	86.49 162	82.69 145

MVS_111021_LR		72.10 149	71.82 153	72.95 134	79.53 120	73.90 36	70.45 187	66.64 240	56.87 151	76.81 152	81.76 210	68.78 74	71.76 240	61.81 131	83.74 202	73.18 250

Test_1112_low_res		58.78 263	58.69 264	59.04 277	79.41 121	38.13 283	57.62 306	66.98 239	34.74 324	59.62 304	77.56 249	42.92 260	63.65 293	38.66 287	70.73 307	75.35 238

CSCG		74.12 111	74.39 104	73.33 122	79.35 122	61.66 115	77.45 88	81.98 78	62.47 103	79.06 125	80.19 223	61.83 131	78.79 155	59.83 145	87.35 149	79.54 201

MVP-Stereo		61.56 241	59.22 252	68.58 198	79.28 123	60.44 121	69.20 201	71.57 211	43.58 283	56.42 317	78.37 245	39.57 278	76.46 191	34.86 311	60.16 338	68.86 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 164	71.34 161	67.85 202	79.26 124	40.42 266	74.67 137	75.15 189	58.41 133	68.74 245	88.14 110	56.08 207	83.69 59	59.90 143	81.71 225	79.43 203

IS-MVSNet		75.10 94	75.42 95	74.15 101	79.23 125	48.05 202	79.43 66	78.04 160	70.09 38	79.17 124	88.02 111	53.04 215	83.60 61	58.05 154	93.76 49	90.79 28

FC-MVSNet-test		73.32 123	74.78 100	68.93 190	79.21 126	36.57 292	71.82 165	79.54 132	57.63 143	82.57 81	90.38 64	59.38 160	78.99 150	57.91 155	94.56 33	91.23 16

AllTest		77.66 66	77.43 68	78.35 60	79.19 127	70.81 51	78.60 73	88.64 2	65.37 67	80.09 115	88.17 107	70.33 63	78.43 165	55.60 174	90.90 94	85.81 85

TestCases				78.35 60	79.19 127	70.81 51		88.64 2	65.37 67	80.09 115	88.17 107	70.33 63	78.43 165	55.60 174	90.90 94	85.81 85

xiu_mvs_v1_base_debu		67.87 193	67.07 201	70.26 170	79.13 129	61.90 112	67.34 224	71.25 218	47.98 248	67.70 250	74.19 288	61.31 137	72.62 227	56.51 164	78.26 268	76.27 231

xiu_mvs_v1_base		67.87 193	67.07 201	70.26 170	79.13 129	61.90 112	67.34 224	71.25 218	47.98 248	67.70 250	74.19 288	61.31 137	72.62 227	56.51 164	78.26 268	76.27 231

xiu_mvs_v1_base_debi		67.87 193	67.07 201	70.26 170	79.13 129	61.90 112	67.34 224	71.25 218	47.98 248	67.70 250	74.19 288	61.31 137	72.62 227	56.51 164	78.26 268	76.27 231

VDD-MVS		70.81 160	71.44 160	68.91 192	79.07 132	46.51 230	67.82 219	70.83 224	61.23 110	74.07 187	88.69 98	59.86 153	75.62 198	51.11 202	90.28 104	84.61 103

TSAR-MVS + GP.		73.08 125	71.60 157	77.54 69	78.99 133	70.73 53	74.96 127	69.38 230	60.73 117	74.39 183	78.44 244	57.72 192	82.78 74	60.16 140	89.60 116	79.11 206

wuykxyi23d		75.33 89	76.75 76	71.04 162	78.83 134	85.01 1	71.78 166	61.00 264	53.47 199	96.33 1	93.38 3	73.07 44	68.04 270	65.65 111	97.28 2	60.07 331

FIs		72.56 144	73.80 111	68.84 195	78.74 135	37.74 286	71.02 181	79.83 128	56.12 157	80.88 107	89.45 79	58.18 177	78.28 171	56.63 163	93.36 53	90.51 31

v7n		79.37 51	80.41 46	76.28 79	78.67 136	55.81 147	79.22 69	82.51 73	70.72 33	87.54 22	92.44 14	68.00 84	81.34 102	72.84 44	91.72 71	91.69 12

LS3D		80.99 37	80.85 43	81.41 24	78.37 137	71.37 46	87.45 6	85.87 19	77.48 9	81.98 85	89.95 74	69.14 71	85.26 36	66.15 106	91.24 83	87.61 67

CNLPA		73.44 119	73.03 133	74.66 93	78.27 138	75.29 27	75.99 111	78.49 150	65.39 66	75.67 166	83.22 193	61.23 140	66.77 282	53.70 191	85.33 181	81.92 163

EPP-MVSNet		73.86 113	73.38 121	75.31 90	78.19 139	53.35 165	80.45 54	77.32 168	65.11 72	76.47 160	86.80 123	49.47 231	83.77 58	53.89 189	92.72 63	88.81 51

PCF-MVS	63.80 13	72.70 139	71.69 154	75.72 86	78.10 140	60.01 125	73.04 143	81.50 85	45.34 269	79.66 118	84.35 175	65.15 107	82.65 76	48.70 220	89.38 120	84.50 108
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LFMVS		67.06 201	67.89 196	64.56 226	78.02 141	38.25 281	70.81 185	59.60 269	65.18 71	71.06 223	86.56 138	43.85 254	75.22 201	46.35 239	89.63 115	80.21 192

anonymousdsp		78.60 58	77.80 66	81.00 31	78.01 142	74.34 33	80.09 60	76.12 177	50.51 232	89.19 12	90.88 43	71.45 57	77.78 178	73.38 42	90.60 100	90.90 26

Anonymous20240521		74.99 98	76.21 85	71.33 161	77.99 143	44.41 241	75.24 124	77.16 171	65.86 60	84.89 59	91.96 20	60.23 149	79.31 147	59.86 144	92.75 62	90.27 33

BH-untuned		69.39 174	69.46 173	69.18 185	77.96 144	56.88 143	68.47 214	77.53 165	56.77 153	77.79 139	79.63 231	60.30 148	80.20 137	46.04 240	80.65 244	70.47 275

1112_ss		59.48 256	58.99 255	60.96 264	77.84 145	42.39 251	61.42 287	68.45 234	37.96 309	59.93 303	67.46 332	45.11 247	65.07 288	40.89 276	71.81 301	75.41 236

PS-MVSNAJ		64.27 219	63.73 217	65.90 220	77.82 146	51.42 171	63.33 269	72.33 206	45.09 272	61.60 286	68.04 330	62.39 126	73.95 213	49.07 217	73.87 293	72.34 258

ambc				70.10 175	77.74 147	50.21 177	74.28 139	77.93 161		79.26 123	88.29 106	54.11 213	79.77 142	64.43 119	91.10 87	80.30 191

xiu_mvs_v2_base		64.43 216	63.96 214	65.85 221	77.72 148	51.32 172	63.63 266	72.31 207	45.06 273	61.70 285	69.66 320	62.56 122	73.93 214	49.06 218	73.91 292	72.31 259

casdiffmvs1		72.89 135	72.85 136	73.04 130	77.69 149	53.36 164	80.89 51	80.76 108	44.66 275	72.86 198	88.56 99	66.45 96	80.91 122	61.58 132	82.17 214	84.84 96

Anonymous20231211		75.54 86	77.19 70	70.59 166	77.67 150	45.70 234	74.73 134	80.19 122	68.80 42	82.95 77	92.91 8	66.26 98	76.76 188	58.41 153	92.77 59	89.30 37

FMVSNet1		71.06 157	72.48 143	66.81 211	77.65 151	40.68 261	71.96 159	73.03 197	61.14 112	79.45 121	90.36 66	60.44 147	75.20 202	50.20 210	88.05 136	84.54 104

FPMVS		59.43 257	60.07 247	57.51 285	77.62 152	71.52 44	62.33 274	50.92 319	57.40 144	69.40 238	80.00 224	39.14 279	61.92 300	37.47 298	66.36 326	39.09 355

MVS_0304		74.55 107	73.47 118	77.80 66	77.41 153	63.88 100	75.75 116	83.67 54	63.55 90	66.12 265	82.16 204	60.20 150	86.15 21	65.37 113	86.98 157	83.38 131

Effi-MVS+-dtu		75.43 87	72.28 147	84.91 2	77.05 154	83.58 2	78.47 77	77.70 162	57.68 139	74.89 175	78.13 247	64.80 110	84.26 55	56.46 167	85.32 182	86.88 73

mvs-test1		73.81 114	70.69 167	83.18 3	77.05 154	81.39 4	75.39 122	77.70 162	57.68 139	71.19 222	74.72 279	64.80 110	83.66 60	56.46 167	81.19 237	84.50 108

CLD-MVS		72.88 136	72.36 145	74.43 96	77.03 156	54.30 157	68.77 208	83.43 60	52.12 209	76.79 153	74.44 283	69.54 69	83.91 56	55.88 173	93.25 55	85.09 93
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Baseline_NR-MVSNet		70.62 162	73.19 125	62.92 242	76.97 157	34.44 311	68.84 203	70.88 223	60.25 121	79.50 120	90.53 54	61.82 132	69.11 255	54.67 183	95.27 15	85.22 91

ITE_SJBPF				80.35 37	76.94 158	73.60 38		80.48 114	66.87 51	83.64 73	86.18 148	70.25 65	79.90 141	61.12 137	88.95 125	87.56 68

jason		64.47 215	62.84 229	69.34 183	76.91 159	59.20 129	67.15 228	65.67 243	35.29 321	65.16 271	76.74 255	44.67 249	70.68 245	54.74 182	79.28 259	78.14 216
jason: jason.
Anonymous20240529		72.56 144	73.79 112	68.86 194	76.89 160	45.21 237	68.80 207	77.25 170	67.16 49	76.89 150	90.44 57	65.95 101	74.19 212	50.75 205	90.00 111	87.18 72

PM-MVS		64.49 214	63.61 218	67.14 209	76.68 161	75.15 28	68.49 213	42.85 344	51.17 222	77.85 138	80.51 219	45.76 243	66.31 285	52.83 194	76.35 277	59.96 333

TransMVSNet (Re)		69.62 170	71.63 156	63.57 235	76.51 162	35.93 299	65.75 246	71.29 217	61.05 113	75.02 173	89.90 75	65.88 102	70.41 251	49.79 212	89.48 118	84.38 111

BH-RMVSNet		68.69 188	68.20 194	70.14 174	76.40 163	53.90 161	64.62 257	73.48 196	58.01 136	73.91 189	81.78 208	59.09 164	78.22 173	48.59 221	77.96 271	78.31 213

PHI-MVS		74.92 99	74.36 106	76.61 72	76.40 163	62.32 110	80.38 56	83.15 62	54.16 189	73.23 196	80.75 217	62.19 129	83.86 57	68.02 84	90.92 93	83.65 124

UGNet		70.20 165	69.05 178	73.65 110	76.24 165	63.64 101	75.87 114	72.53 204	61.48 109	60.93 295	86.14 151	52.37 219	77.12 182	50.67 206	85.21 184	80.17 197
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
PatchMatch-RL		58.68 264	57.72 269	61.57 256	76.21 166	73.59 39	61.83 282	49.00 326	47.30 256	61.08 290	68.97 324	50.16 230	59.01 307	36.06 306	68.84 318	52.10 344

VPA-MVSNet		68.71 187	70.37 168	63.72 234	76.13 167	38.06 284	64.10 262	71.48 214	56.60 156	74.10 186	88.31 105	64.78 112	69.72 252	47.69 230	90.15 107	83.37 133

PAPM		61.79 240	60.37 246	66.05 218	76.09 168	41.87 253	69.30 199	76.79 174	40.64 298	53.80 330	79.62 232	44.38 251	82.92 73	29.64 332	73.11 296	73.36 249

BH-w/o		64.81 211	64.29 213	66.36 216	76.08 169	54.71 153	65.61 248	75.23 188	50.10 235	71.05 224	71.86 309	54.33 212	79.02 149	38.20 292	76.14 278	65.36 313

pmmvs6		71.82 152	73.66 115	66.31 217	75.94 170	42.01 252	66.99 229	72.53 204	63.45 93	76.43 161	92.78 10	72.95 47	69.69 253	51.41 200	90.46 102	87.22 70

CANet		73.00 128	71.84 151	76.48 74	75.82 171	61.28 116	74.81 130	80.37 117	63.17 96	62.43 284	80.50 220	61.10 142	85.16 41	64.00 121	84.34 194	83.01 137

pmmvs-eth3d		64.41 217	63.27 221	67.82 203	75.81 172	60.18 123	69.49 197	62.05 260	38.81 304	74.13 185	82.23 203	43.76 255	68.65 265	42.53 265	80.63 246	74.63 241

TR-MVS		64.59 212	63.54 219	67.73 204	75.75 173	50.83 174	63.39 268	70.29 227	49.33 239	71.55 217	74.55 281	50.94 227	78.46 163	40.43 279	75.69 280	73.89 246

cascas		64.59 212	62.77 230	70.05 176	75.27 174	50.02 178	61.79 283	71.61 210	42.46 288	63.68 279	68.89 326	49.33 233	80.35 132	47.82 229	84.05 197	79.78 200

API-MVS		70.97 159	71.51 159	69.37 180	75.20 175	55.94 146	80.99 49	76.84 172	62.48 102	71.24 220	77.51 250	61.51 136	80.96 121	52.04 195	85.76 168	71.22 269

PAPR		69.20 177	68.66 189	70.82 164	75.15 176	47.77 207	75.31 123	81.11 97	49.62 238	66.33 264	79.27 236	61.53 135	82.96 72	48.12 226	81.50 228	81.74 165

casdiffmvs		72.24 148	71.83 152	73.47 117	75.01 177	54.46 156	79.73 65	82.60 71	45.66 263	70.90 225	87.73 113	63.41 117	82.32 82	65.09 115	76.36 276	83.64 125

MVSFormer		69.93 168	69.03 180	72.63 143	74.93 178	59.19 130	83.98 30	75.72 181	52.27 207	63.53 280	76.74 255	43.19 258	80.56 128	72.28 51	78.67 264	78.14 216

lupinMVS		63.36 223	61.49 239	68.97 188	74.93 178	59.19 130	65.80 245	64.52 251	34.68 326	63.53 280	74.25 286	43.19 258	70.62 246	53.88 190	78.67 264	77.10 226

nrg030		74.87 103	75.99 88	71.52 159	74.90 180	49.88 182	74.10 140	82.58 72	54.55 184	83.50 74	89.21 85	71.51 55	75.74 197	61.24 135	92.34 67	88.94 47

TAPA-MVS	65.27 12	75.16 93	74.29 107	77.77 67	74.86 181	68.08 72	77.89 84	84.04 50	55.15 170	76.19 163	83.39 184	66.91 90	80.11 139	60.04 142	90.14 108	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 82	74.37 105	79.93 40	74.81 182	77.53 16	77.53 87	79.30 133	59.44 126	78.88 126	89.80 76	71.26 59	73.09 218	57.45 157	80.89 241	89.17 41

EI-MVSNet-Vis-set		72.78 138	71.87 150	75.54 88	74.77 183	59.02 134	72.24 152	71.56 212	63.92 85	78.59 127	71.59 310	66.22 99	78.60 158	67.58 91	80.32 247	89.00 44

Regformer-3		72.86 137	72.28 147	74.62 94	74.74 184	60.18 123	72.91 144	71.76 209	64.74 76	78.42 131	72.07 303	67.00 89	76.28 192	67.97 88	80.91 239	87.39 69

Regformer-4		74.64 105	73.67 114	77.55 68	74.74 184	64.49 96	72.91 144	75.42 186	67.45 48	80.24 114	72.07 303	68.98 73	80.19 138	70.29 63	80.91 239	87.98 63

v1240		73.06 126	73.14 126	72.84 137	74.74 184	47.27 221	71.88 164	81.11 97	51.80 213	82.28 83	84.21 176	56.22 206	82.34 81	68.82 75	87.17 155	88.91 48

v1921920		72.96 131	72.98 134	72.89 136	74.67 187	47.58 212	71.92 162	80.69 109	51.70 215	81.69 91	83.89 180	56.58 204	82.25 83	68.34 79	87.36 148	88.82 50

EI-MVSNet-UG-set		72.63 140	71.68 155	75.47 89	74.67 187	58.64 138	72.02 157	71.50 213	63.53 91	78.58 129	71.39 313	65.98 100	78.53 160	67.30 97	80.18 248	89.23 39

Fast-Effi-MVS+		68.81 185	68.30 191	70.35 169	74.66 189	48.61 192	66.06 242	78.32 152	50.62 231	71.48 219	75.54 269	68.75 75	79.59 145	50.55 208	78.73 263	82.86 140

v1192		73.40 121	73.42 119	73.32 123	74.65 190	48.67 191	72.21 153	81.73 82	52.76 205	81.85 86	84.56 173	57.12 199	82.24 84	68.58 77	87.33 150	89.06 42

v13		76.23 76	77.02 74	73.86 106	74.61 191	48.80 187	76.91 96	81.10 100	62.66 99	87.02 29	91.01 40	59.76 156	81.41 97	71.29 54	88.78 127	91.38 13

v144192		72.99 129	73.06 132	72.77 138	74.58 192	47.48 213	71.90 163	80.44 116	51.57 216	81.46 96	84.11 178	58.04 184	82.12 86	67.98 87	87.47 144	88.70 53

v12		76.03 78	76.79 75	73.76 108	74.45 193	48.60 193	76.59 98	81.11 97	62.22 104	86.79 31	90.74 49	59.51 157	81.40 99	71.01 57	88.67 129	91.29 15

MAR-MVS		67.72 196	66.16 206	72.40 149	74.45 193	64.99 93	74.87 128	77.50 166	48.67 243	65.78 269	68.58 329	57.01 202	77.79 177	46.68 238	81.92 218	74.42 243
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
v10		75.69 84	76.20 86	74.16 100	74.44 195	48.69 189	75.84 115	82.93 66	59.02 130	85.92 43	89.17 86	58.56 172	82.74 75	70.73 59	89.14 123	91.05 20

v7		73.59 117	73.69 113	73.28 124	74.42 196	48.68 190	72.74 149	81.98 78	54.76 180	82.07 84	85.05 166	58.53 173	82.22 85	67.99 86	85.66 171	88.95 46

v11		75.76 82	76.51 79	73.48 116	74.28 197	47.81 206	76.16 108	81.28 93	61.56 108	86.39 36	90.38 64	59.32 161	81.41 97	70.85 58	88.41 132	91.23 16

V9		75.82 80	76.53 78	73.66 109	74.28 197	48.37 194	76.26 106	81.10 100	61.73 107	86.59 34	90.43 58	59.16 163	81.42 96	70.71 60	88.56 130	91.21 18

canonicalmvs		72.29 147	73.38 121	69.04 187	74.23 199	47.37 218	73.93 141	83.18 61	54.36 186	76.61 155	81.64 212	72.03 51	75.34 200	57.12 160	87.28 152	84.40 110

Anonymous202405211		66.02 204	66.89 204	63.43 238	74.22 200	38.14 282	59.00 300	66.13 242	63.33 95	69.76 237	85.95 157	51.88 221	70.50 248	44.23 247	87.52 143	81.64 167

Regformer-1		74.28 109	73.63 116	76.21 82	74.22 200	64.12 98	72.77 147	75.46 185	66.86 52	79.27 122	72.08 300	69.29 70	78.74 156	68.73 76	84.02 198	85.77 88

Regformer-2		75.32 90	74.47 103	77.88 65	74.22 200	66.65 80	72.77 147	77.54 164	68.47 46	80.44 110	72.08 300	70.60 62	80.97 117	70.08 67	84.02 198	86.01 82

Effi-MVS+		72.10 149	72.28 147	71.58 157	74.21 203	50.33 175	74.72 135	82.73 68	62.62 100	70.77 226	76.83 254	69.96 67	80.97 117	60.20 139	78.43 266	83.45 130

v1144		73.29 124	73.39 120	73.01 131	74.12 204	48.11 200	72.01 158	81.08 102	53.83 195	81.77 87	84.68 171	58.07 183	81.91 87	68.10 81	86.86 158	88.99 45

V14		75.58 85	76.26 84	73.55 114	74.10 205	48.13 199	75.91 112	81.07 103	61.19 111	86.34 37	90.11 71	58.80 167	81.40 99	70.40 62	88.43 131	91.12 19

v748		76.93 71	77.95 65	73.87 104	73.94 206	52.44 168	75.90 113	79.98 127	65.34 69	86.97 30	91.77 24	67.40 88	78.40 167	70.23 64	90.01 110	90.76 30

EI-MVSNet		69.61 171	69.01 181	71.41 160	73.94 206	49.90 179	71.31 177	71.32 215	58.22 134	75.40 171	70.44 314	58.16 178	75.85 193	62.51 128	79.81 253	88.48 60

CVMVSNet		59.21 260	58.44 267	61.51 257	73.94 206	47.76 208	71.31 177	64.56 250	26.91 354	60.34 297	70.44 314	36.24 291	67.65 272	53.57 192	68.66 320	69.12 293

v15		75.37 88	76.01 87	73.44 118	73.91 209	47.87 205	75.55 119	81.04 104	60.76 116	86.11 41	89.76 77	58.53 173	81.40 99	70.11 65	88.32 133	91.04 22

IterMVS-LS		73.01 127	73.12 128	72.66 141	73.79 210	49.90 179	71.63 168	78.44 151	58.22 134	80.51 109	86.63 135	58.15 179	79.62 143	62.51 128	88.20 134	88.48 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v17		75.03 96	75.59 92	73.36 119	73.56 211	47.66 210	75.48 120	81.45 88	60.58 118	85.55 48	89.02 91	58.36 175	81.47 93	69.69 72	86.59 161	90.96 23

v16		74.89 102	75.41 96	73.35 120	73.54 212	47.62 211	75.47 121	81.45 88	60.58 118	85.46 50	88.97 94	58.27 176	81.47 93	69.66 73	85.25 183	90.95 24

alignmvs		70.54 163	71.00 163	69.15 186	73.50 213	48.04 203	69.85 194	79.62 129	53.94 194	76.54 157	82.00 205	59.00 165	74.68 207	57.32 158	87.21 153	84.72 99

Fast-Effi-MVS+-dtu		70.00 167	68.74 188	73.77 107	73.47 214	64.53 95	71.36 175	78.14 158	55.81 160	68.84 244	74.71 280	65.36 106	75.75 196	52.00 196	79.00 260	81.03 176

v8		75.07 95	75.64 91	73.35 120	73.42 215	47.46 215	75.20 125	81.45 88	60.05 122	85.64 45	89.26 82	58.08 182	81.80 89	69.71 71	87.97 139	90.79 28

v6		72.93 132	73.08 129	72.48 145	73.42 215	47.47 214	72.17 154	80.25 121	55.63 162	81.65 92	85.04 167	57.95 188	81.28 105	66.56 103	85.01 188	88.70 53

v1neww		72.93 132	73.07 130	72.48 145	73.41 217	47.46 215	72.17 154	80.26 119	55.63 162	81.63 93	85.07 164	57.97 186	81.28 105	66.55 104	84.98 189	88.70 53

v7new		72.93 132	73.07 130	72.48 145	73.41 217	47.46 215	72.17 154	80.26 119	55.63 162	81.63 93	85.07 164	57.97 186	81.28 105	66.55 104	84.98 189	88.70 53

tfpnnormal		66.48 203	67.93 195	62.16 253	73.40 219	36.65 291	63.45 267	64.99 249	55.97 158	72.82 200	87.80 112	57.06 201	69.10 256	48.31 225	87.54 142	80.72 185

semantic-postprocess				72.49 144	73.34 220	58.20 140		65.55 246	48.10 247	76.91 149	82.64 196	42.25 264	78.84 153	61.20 136	77.89 272	80.44 190

v18		74.60 106	75.06 97	73.22 125	73.29 221	47.36 219	75.02 126	81.47 87	60.01 123	85.13 54	88.44 101	57.93 189	81.47 93	69.26 74	85.02 187	90.84 27

VNet		64.01 222	65.15 209	60.57 266	73.28 222	35.61 302	57.60 307	67.08 238	54.61 182	66.76 263	83.37 186	56.28 205	66.87 278	42.19 267	85.20 185	79.23 205

3Dnovator	65.95 11	71.50 155	71.22 162	72.34 150	73.16 223	63.09 106	78.37 78	78.32 152	57.67 141	72.22 210	84.61 172	54.77 209	78.47 162	60.82 138	81.07 238	75.45 235

GBi-Net		68.30 190	68.79 185	66.81 211	73.14 224	40.68 261	71.96 159	73.03 197	54.81 175	74.72 179	90.36 66	48.63 236	75.20 202	47.12 232	85.37 178	84.54 104

test1		68.30 190	68.79 185	66.81 211	73.14 224	40.68 261	71.96 159	73.03 197	54.81 175	74.72 179	90.36 66	48.63 236	75.20 202	47.12 232	85.37 178	84.54 104

FMVSNet2		67.48 198	68.21 193	65.29 222	73.14 224	38.94 276	68.81 205	71.21 221	54.81 175	76.73 154	86.48 141	48.63 236	74.60 208	47.98 227	86.11 165	82.35 152

v1		72.60 141	72.73 138	72.19 152	73.12 227	47.01 224	71.48 169	79.10 138	55.01 171	81.24 98	84.92 170	57.46 194	80.90 123	66.59 100	85.67 169	88.68 57

v1141		72.59 143	72.73 138	72.19 152	73.10 228	47.00 225	71.48 169	79.11 136	55.01 171	81.23 99	84.94 169	57.45 195	80.89 124	66.58 101	85.65 172	88.68 57

pm-mvs1		68.40 189	69.85 172	64.04 231	73.10 228	39.94 268	64.61 258	70.50 225	55.52 165	73.97 188	89.33 80	63.91 116	68.38 267	49.68 214	88.02 137	83.81 120

divwei89l23v2f112		72.60 141	72.73 138	72.19 152	73.10 228	47.00 225	71.48 169	79.11 136	55.01 171	81.23 99	84.95 168	57.45 195	80.89 124	66.58 101	85.67 169	88.68 57

pmmvs4		60.78 248	59.04 254	66.00 219	73.06 231	57.67 141	64.53 259	60.22 267	36.91 314	65.96 266	77.27 251	39.66 277	68.54 266	38.87 284	74.89 288	71.80 264

v52		78.96 53	79.79 51	76.46 76	73.03 232	54.90 150	78.48 75	83.48 57	64.43 79	91.19 4	91.54 29	72.08 49	81.11 110	76.45 29	87.47 144	93.38 7

V4		78.96 53	79.79 51	76.46 76	73.02 233	54.90 150	78.48 75	83.47 58	64.43 79	91.20 3	91.54 29	72.08 49	81.11 110	76.45 29	87.46 146	93.38 7

view600		62.88 230	62.90 225	62.82 243	72.97 234	33.66 317	66.10 238	55.01 294	57.05 146	72.66 201	82.56 197	31.60 313	72.78 221	42.64 261	85.55 173	82.02 157

view800		62.88 230	62.90 225	62.82 243	72.97 234	33.66 317	66.10 238	55.01 294	57.05 146	72.66 201	82.56 197	31.60 313	72.78 221	42.64 261	85.55 173	82.02 157

conf0.05thres1000		62.88 230	62.90 225	62.82 243	72.97 234	33.66 317	66.10 238	55.01 294	57.05 146	72.66 201	82.56 197	31.60 313	72.78 221	42.64 261	85.55 173	82.02 157

tfpn		62.88 230	62.90 225	62.82 243	72.97 234	33.66 317	66.10 238	55.01 294	57.05 146	72.66 201	82.56 197	31.60 313	72.78 221	42.64 261	85.55 173	82.02 157

v2v482		72.55 146	72.58 142	72.43 148	72.92 238	46.72 229	71.41 174	79.13 135	55.27 166	81.17 101	85.25 162	55.41 208	81.13 109	67.25 98	85.46 177	89.43 36

MIMVSNet		54.39 287	56.12 282	49.20 311	72.57 239	30.91 337	59.98 295	48.43 328	41.66 292	55.94 319	83.86 181	41.19 269	50.42 319	26.05 339	75.38 285	66.27 309

Patchmatch-RL test		59.95 253	59.12 253	62.44 250	72.46 240	54.61 155	59.63 297	47.51 331	41.05 295	74.58 182	74.30 285	31.06 322	65.31 286	51.61 198	79.85 252	67.39 301

Vis-MVSNet (Re-imp)		62.74 235	63.21 222	61.34 260	72.19 241	31.56 336	67.31 227	53.87 301	53.60 197	69.88 235	83.37 186	40.52 274	70.98 244	41.40 273	86.78 159	81.48 169

tfpn111		61.91 238	61.65 235	62.68 248	72.14 242	35.01 305	65.42 250	56.99 284	55.23 167	70.71 227	79.90 225	32.07 308	72.85 220	38.80 285	83.61 203	80.18 193

conf200view11		61.42 243	61.09 241	62.43 251	72.14 242	35.01 305	65.42 250	56.99 284	55.23 167	70.71 227	79.90 225	32.07 308	72.09 233	35.61 307	81.73 221	80.18 193

thres100view900		61.17 245	61.09 241	61.39 259	72.14 242	35.01 305	65.42 250	56.99 284	55.23 167	70.71 227	79.90 225	32.07 308	72.09 233	35.61 307	81.73 221	77.08 227

testmv		52.91 296	54.31 291	48.71 315	72.13 245	36.18 295	50.26 325	47.78 329	44.15 277	64.61 273	79.78 229	38.18 283	50.20 321	21.96 350	69.93 312	59.75 334

ab-mvs		64.11 220	65.13 210	61.05 262	71.99 246	38.03 285	67.59 220	68.79 232	49.08 240	65.32 270	86.26 146	58.02 185	66.85 280	39.33 281	79.79 255	78.27 214

thres600view7		61.82 239	61.38 240	63.12 240	71.81 247	34.93 308	64.64 256	56.99 284	54.78 179	70.33 233	79.74 230	32.07 308	72.42 231	38.61 288	83.46 204	82.02 157

QAPM		69.18 178	69.26 176	68.94 189	71.61 248	52.58 167	80.37 57	78.79 144	49.63 237	73.51 191	85.14 163	53.66 214	79.12 148	55.11 179	75.54 282	75.11 239

Anonymous20231206		54.13 288	55.82 283	49.04 314	70.89 249	35.96 298	51.73 322	50.87 320	34.86 322	62.49 283	79.22 237	42.52 263	44.29 339	27.95 337	81.88 220	66.88 305

testing_2		72.01 151	72.36 145	70.95 163	70.79 250	48.70 188	72.81 146	78.09 159	48.79 242	84.46 67	89.15 88	57.90 190	78.55 159	61.55 133	87.74 140	85.61 89

tfpn200view9		60.35 251	59.97 248	61.51 257	70.78 251	35.35 303	63.27 270	57.47 278	53.00 203	68.31 246	77.09 252	32.45 305	72.09 233	35.61 307	81.73 221	77.08 227

thres400		60.77 249	59.97 248	63.15 239	70.78 251	35.35 303	63.27 270	57.47 278	53.00 203	68.31 246	77.09 252	32.45 305	72.09 233	35.61 307	81.73 221	82.02 157

MSDG		67.47 199	67.48 198	67.46 206	70.70 253	54.69 154	66.90 231	78.17 157	60.88 115	70.41 231	74.76 277	61.22 141	73.18 217	47.38 231	76.87 274	74.49 242

0601test		65.11 208	65.09 211	65.18 223	70.59 254	40.86 260	63.22 272	72.79 200	57.91 137	68.88 243	79.07 242	42.85 261	74.89 206	45.50 243	84.97 191	79.81 199

OpenMVS	62.51 15	68.76 186	68.75 187	68.78 196	70.56 255	53.91 160	78.29 79	77.35 167	48.85 241	70.22 234	83.52 183	52.65 218	76.93 184	55.31 178	81.99 217	75.49 234

DELS-MVS		68.83 184	68.31 190	70.38 168	70.55 256	48.31 195	63.78 265	82.13 75	54.00 191	68.96 242	75.17 275	58.95 166	80.06 140	58.55 150	82.74 209	82.76 143
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
LCM-MVSNet-Re		69.10 179	71.57 158	61.70 255	70.37 257	34.30 312	61.45 286	79.62 129	56.81 152	89.59 10	88.16 109	68.44 78	72.94 219	42.30 266	87.33 150	77.85 221

Patchmatch-test1		57.81 272	58.04 268	57.13 286	70.17 258	41.07 259	65.19 254	53.38 305	43.34 287	61.00 293	71.94 307	45.20 246	62.69 296	41.81 271	70.31 309	67.63 300

PVSNet_BlendedMVS		65.38 206	64.30 212	68.61 197	69.81 259	49.36 183	65.60 249	78.96 140	45.50 266	59.98 300	78.61 243	51.82 222	78.20 174	44.30 245	84.11 196	78.27 214

PVSNet_Blended		62.90 229	61.64 236	66.69 214	69.81 259	49.36 183	61.23 289	78.96 140	42.04 290	59.98 300	68.86 327	51.82 222	78.20 174	44.30 245	77.77 273	72.52 256

OpenMVS_ROB	54.93 17	63.23 225	63.28 220	63.07 241	69.81 259	45.34 235	68.52 212	67.14 237	43.74 281	70.61 230	79.22 237	47.90 240	72.66 226	48.75 219	73.84 294	71.21 270

DI_MVS_plusplus_test		69.01 182	69.04 179	68.93 190	69.54 262	46.74 228	70.14 189	75.49 183	46.64 258	78.30 133	83.18 194	58.80 167	78.86 152	57.14 159	82.15 215	81.18 172

Test4		69.04 181	68.95 182	69.32 184	69.52 263	48.10 201	70.69 186	78.25 156	45.90 262	80.99 103	82.24 202	51.91 220	78.11 176	58.46 151	82.58 211	81.74 165

FMVSNet3		65.00 210	65.16 208	64.52 227	69.47 264	37.56 289	66.63 233	70.38 226	51.55 217	74.72 179	83.27 190	37.89 288	74.44 210	47.12 232	85.37 178	81.57 168

test_normal		68.88 183	68.88 183	68.88 193	69.43 265	47.03 223	69.85 194	74.83 190	46.06 261	78.30 133	83.29 189	58.76 171	78.23 172	57.51 156	81.90 219	81.36 170

MS-PatchMatch		55.59 283	54.89 289	57.68 283	69.18 266	49.05 186	61.00 291	62.93 255	35.98 317	58.36 308	68.93 325	36.71 290	66.59 283	37.62 297	63.30 332	57.39 337

v148		69.38 175	69.39 174	69.36 181	69.14 267	44.56 239	68.83 204	72.70 202	54.79 178	78.59 127	84.12 177	54.69 210	76.74 189	59.40 147	82.20 213	86.79 74

conf0.01		59.26 258	58.88 256	60.40 268	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	80.18 193

conf0.002		59.26 258	58.88 256	60.40 268	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	80.18 193

thresconf0.02		58.38 265	58.88 256	56.91 288	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	69.70 283

tfpn_n400		58.38 265	58.88 256	56.91 288	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	69.70 283

tfpnconf		58.38 265	58.88 256	56.91 288	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	69.70 283

tfpnview11		58.38 265	58.88 256	56.91 288	68.66 268	31.96 330	62.04 276	51.95 311	50.99 223	67.57 253	75.91 261	28.59 338	69.07 257	42.77 255	81.40 229	69.70 283

test1235678		48.41 311	49.60 311	44.83 328	68.52 274	33.81 315	46.33 337	45.89 336	38.72 305	58.46 306	72.08 300	29.85 333	47.82 326	19.67 354	66.91 325	52.88 342

CANet_DTU		64.04 221	63.83 215	64.66 225	68.39 275	42.97 247	73.45 142	74.50 192	52.05 211	54.78 324	75.44 274	43.99 253	70.42 250	53.49 193	78.41 267	80.59 187

EU-MVSNet		60.82 247	60.80 244	60.86 265	68.37 276	41.16 257	72.27 151	68.27 235	26.96 353	69.08 240	75.71 267	32.09 307	67.44 274	55.59 176	78.90 261	73.97 244

PVSNet	43.83 21	51.56 303	51.17 304	52.73 301	68.34 277	38.27 280	48.22 330	53.56 304	36.41 315	54.29 328	64.94 338	34.60 295	54.20 316	30.34 327	69.87 313	65.71 312

EPNet		69.10 179	67.32 199	74.46 95	68.33 278	61.27 117	77.56 86	63.57 253	60.95 114	56.62 316	82.75 195	51.53 225	81.24 108	54.36 188	90.20 105	80.88 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	49.67 18	59.69 255	56.96 275	67.90 201	68.19 279	50.30 176	61.42 287	65.18 248	47.57 254	55.83 320	67.15 335	23.77 353	79.60 144	43.56 251	79.97 251	73.79 247
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		60.62 250	59.97 248	62.58 249	68.13 280	47.28 220	68.59 209	73.96 194	32.19 337	59.94 302	68.86 327	50.48 228	77.64 179	41.85 270	75.74 279	62.83 323

tfpn1000		58.28 269	58.86 262	56.53 292	68.05 281	32.26 327	62.58 273	51.67 318	51.25 221	67.38 259	75.95 260	27.24 345	68.83 263	43.51 252	82.11 216	68.49 296

TinyColmap		67.98 192	69.28 175	64.08 230	67.98 282	46.82 227	70.04 190	75.26 187	53.05 202	77.36 143	86.79 124	59.39 159	72.59 230	45.64 242	88.01 138	72.83 253

EPNet_dtu		58.93 262	58.52 265	60.16 272	67.91 283	47.70 209	69.97 191	58.02 274	49.73 236	47.28 345	73.02 297	38.14 284	62.34 298	36.57 303	85.99 166	70.43 276
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 274	57.02 274	59.17 275	67.89 284	34.93 308	58.91 302	57.25 282	50.24 233	64.01 276	71.46 312	32.49 304	71.39 242	31.31 323	79.57 257	71.19 271

our_test_3		56.46 277	56.51 278	56.30 293	67.70 285	39.66 270	55.36 314	52.34 310	40.57 299	63.85 277	69.91 319	40.04 276	58.22 309	43.49 253	75.29 287	71.03 274

ppachtmachnet_test		60.26 252	59.61 251	62.20 252	67.70 285	44.33 242	58.18 305	60.96 265	40.75 296	65.80 268	72.57 298	41.23 268	63.92 292	46.87 236	82.42 212	78.33 212

MVS_Test		69.84 169	70.71 166	67.24 208	67.49 287	43.25 245	69.87 193	81.22 96	52.69 206	71.57 216	86.68 131	62.09 130	74.51 209	66.05 107	78.74 262	83.96 117

V42		71.06 157	70.83 165	71.72 156	67.25 288	47.14 222	65.94 243	80.35 118	51.35 218	83.40 75	83.23 191	59.25 162	78.80 154	65.91 109	80.81 243	89.23 39

tfpn_ndepth		56.91 276	57.30 273	55.71 294	67.22 289	33.26 322	61.72 284	53.98 300	48.49 245	64.16 275	71.94 307	27.65 344	68.71 264	40.49 278	80.08 249	65.17 315

GA-MVS		62.91 228	61.66 234	66.66 215	67.09 290	44.49 240	61.18 290	69.36 231	51.33 219	69.33 239	74.47 282	36.83 289	74.94 205	50.60 207	74.72 289	80.57 188

HY-MVS	49.31 19	57.96 271	57.59 270	59.10 276	66.85 291	36.17 296	65.13 255	65.39 247	39.24 302	54.69 326	78.14 246	44.28 252	67.18 277	33.75 317	70.79 306	73.95 245

tpmp4_e23		57.57 273	55.46 287	63.93 232	66.48 292	41.56 256	71.68 167	60.65 266	35.64 320	55.35 323	76.25 258	29.53 334	75.41 199	34.40 313	69.12 317	74.83 240

CR-MVSNet		58.96 261	58.49 266	60.36 270	66.37 293	48.24 197	70.93 183	56.40 289	32.87 336	61.35 288	86.66 132	33.19 300	63.22 294	48.50 223	70.17 310	69.62 288

RPMNet		61.25 244	61.55 238	60.36 270	66.37 293	48.24 197	70.93 183	54.45 299	54.66 181	61.35 288	86.77 127	33.29 299	63.22 294	55.93 172	70.17 310	69.62 288

IterMVS		63.12 226	62.48 232	65.02 224	66.34 295	52.86 166	63.81 264	62.25 256	46.57 259	71.51 218	80.40 221	44.60 250	66.82 281	51.38 201	75.47 283	75.38 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm2		56.12 278	54.64 290	60.55 267	66.24 296	36.01 297	68.14 216	56.77 288	33.60 334	58.25 309	75.52 271	30.25 328	74.33 211	33.27 318	69.76 315	71.32 267

Patchmtry		60.91 246	63.01 224	54.62 298	66.10 297	26.27 349	67.47 222	56.40 289	54.05 190	72.04 211	86.66 132	33.19 300	60.17 304	43.69 249	87.45 147	77.42 222

FMVSNet5		55.08 285	55.54 286	53.71 299	65.80 298	33.50 321	56.22 309	52.50 309	43.72 282	61.06 291	83.38 185	25.46 350	54.87 313	30.11 329	81.64 227	72.75 254

1314		59.83 254	58.86 262	62.74 247	65.71 299	44.78 238	68.59 209	72.63 203	33.54 335	61.05 292	67.29 334	43.62 256	71.26 243	49.49 215	67.84 323	72.19 261

MDTV_nov1_ep13			54.05 293		65.54 300	29.30 340	59.00 300	55.22 291	35.96 318	52.44 332	75.98 259	30.77 325	59.62 305	38.21 291	73.33 295

USDC		62.80 234	63.10 223	61.89 254	65.19 301	43.30 244	67.42 223	74.20 193	35.80 319	72.25 209	84.48 174	45.67 244	71.95 238	37.95 294	84.97 191	70.42 277

tpm		50.60 304	52.42 300	45.14 326	65.18 302	26.29 348	60.30 293	43.50 341	37.41 311	57.01 312	79.09 241	30.20 330	42.32 345	32.77 320	66.36 326	66.81 307

PatchmatchNet		54.60 286	54.27 292	55.59 295	65.17 303	39.08 273	66.92 230	51.80 317	39.89 300	58.39 307	73.12 296	31.69 312	58.33 308	43.01 254	58.38 347	69.38 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		54.02 290	52.63 298	58.19 281	64.85 304	39.86 269	66.26 237	57.28 281	32.16 338	56.90 314	70.39 316	32.75 303	65.30 287	34.29 314	58.79 343	69.41 290

XXY-MVS		55.19 284	57.40 272	48.56 316	64.45 305	34.84 310	51.54 323	53.59 303	38.99 303	63.79 278	79.43 233	56.59 203	45.57 330	36.92 301	71.29 303	65.25 314

PatchT		53.35 293	56.47 279	43.99 331	64.19 306	17.46 358	59.15 298	43.10 342	52.11 210	54.74 325	86.95 119	29.97 331	49.98 322	43.62 250	74.40 290	64.53 320

mvs_anonymous		65.08 209	65.49 207	63.83 233	63.79 307	37.60 288	66.52 235	69.82 229	43.44 284	73.46 193	86.08 153	58.79 170	71.75 241	51.90 197	75.63 281	82.15 156

CostFormer		57.35 275	56.14 281	60.97 263	63.76 308	38.43 278	67.50 221	60.22 267	37.14 313	59.12 305	76.34 257	32.78 302	71.99 237	39.12 283	69.27 316	72.47 257

diffmvs		69.55 172	70.18 170	67.66 205	63.63 309	45.24 236	71.26 179	76.21 176	55.79 161	67.89 248	86.41 143	61.00 144	73.76 215	68.03 83	81.40 229	83.98 116

Gipuma		69.55 172	72.83 137	59.70 273	63.63 309	53.97 159	80.08 61	75.93 179	64.24 83	73.49 192	88.93 96	57.89 191	62.46 297	59.75 146	91.55 77	62.67 325
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		55.75 281	56.75 277	52.72 302	62.87 311	28.04 344	68.92 202	41.36 352	71.09 30	50.80 336	92.63 12	20.74 356	66.86 279	29.97 330	72.41 298	63.25 321

no-one		56.11 279	55.62 285	57.60 284	62.68 312	49.23 185	39.12 350	58.99 272	33.72 330	60.98 294	80.90 216	36.07 292	60.36 303	30.68 325	97.40 1	63.22 322

testus		45.03 322	46.49 320	40.65 338	62.53 313	25.24 351	42.54 342	46.23 335	31.16 347	57.69 310	62.90 341	34.60 295	42.33 344	17.72 356	63.01 333	54.37 341

gm-plane-assit					62.51 314	33.91 314			37.25 312		62.71 342		72.74 225	38.70 286

MVS-HIRNet		45.53 317	47.29 317	40.24 339	62.29 315	26.82 347	56.02 310	37.41 358	29.74 349	43.69 356	81.27 213	33.96 297	55.48 312	24.46 345	56.79 348	38.43 356

PatchFormer-LS_test		53.94 292	52.64 297	57.85 282	61.87 316	39.59 271	61.60 285	57.63 276	40.65 297	54.52 327	58.64 349	29.07 337	64.18 290	46.78 237	62.98 334	69.78 281

CHOSEN 280x420		41.62 328	39.89 335	46.80 319	61.81 317	51.59 169	33.56 356	35.74 359	27.48 352	37.64 361	53.53 352	23.24 354	42.09 346	27.39 338	58.64 344	46.72 349

MDA-MVSNet-bldmvs		62.34 236	61.73 233	64.16 228	61.64 318	49.90 179	48.11 331	57.24 283	53.31 201	80.95 104	79.39 234	49.00 234	61.55 301	45.92 241	80.05 250	81.03 176

DWT-MVSNet_test		53.04 294	51.12 305	58.77 278	61.23 319	38.67 277	62.16 275	57.74 275	38.24 306	51.76 334	59.07 348	21.36 355	67.40 275	44.80 244	63.76 331	70.25 278

WTY-MVS		49.39 308	50.31 309	46.62 320	61.22 320	32.00 329	46.61 335	49.77 323	33.87 329	54.12 329	69.55 322	41.96 265	45.40 332	31.28 324	64.42 330	62.47 326

CMPMVS	48.73 20	61.54 242	60.89 243	63.52 236	61.08 321	51.55 170	68.07 217	68.00 236	33.88 328	65.87 267	81.25 214	37.91 287	67.71 271	49.32 216	82.60 210	71.31 268
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 305	50.69 308	49.64 309	60.76 322	41.87 253	53.18 319	45.48 339	43.41 285	49.41 341	60.47 346	29.22 335	44.73 336	42.09 268	72.14 299	62.33 327

test-mter		48.56 310	48.20 315	49.64 309	60.76 322	41.87 253	53.18 319	45.48 339	31.91 343	49.41 341	60.47 346	18.34 359	44.73 336	42.09 268	72.14 299	62.33 327

GG-mvs-BLEND				52.24 303	60.64 324	29.21 341	69.73 196	42.41 345		45.47 348	52.33 354	20.43 357	68.16 268	25.52 343	65.42 328	59.36 335

tpmvs		55.84 280	55.45 288	57.01 287	60.33 325	33.20 323	65.89 244	59.29 271	47.52 255	56.04 318	73.60 291	31.05 323	68.06 269	40.64 277	64.64 329	69.77 282

PVSNet_0	36.71 22	41.12 329	40.78 332	42.14 333	59.97 326	40.13 267	40.97 344	42.24 349	30.81 348	44.86 351	49.41 357	40.70 273	45.12 334	23.15 348	34.96 357	41.16 354

new-patchmatchnet		52.89 297	55.76 284	44.26 330	59.94 327	6.31 364	37.36 354	50.76 321	41.10 293	64.28 274	79.82 228	44.77 248	48.43 324	36.24 304	87.61 141	78.03 218

test20.03		55.74 282	57.51 271	50.42 306	59.89 328	32.09 328	50.63 324	49.01 325	50.11 234	65.07 272	83.23 191	45.61 245	48.11 325	30.22 328	83.82 201	71.07 273

MVSTER		63.29 224	61.60 237	68.36 199	59.77 329	46.21 231	60.62 292	71.32 215	41.83 291	75.40 171	79.12 240	30.25 328	75.85 193	56.30 169	79.81 253	83.03 136

N_pmnet		52.06 302	51.11 306	54.92 297	59.64 330	71.03 49	37.42 353	61.62 263	33.68 331	57.12 311	72.10 299	37.94 286	31.03 358	29.13 336	71.35 302	62.70 324

1111		45.08 321	47.96 316	36.43 343	59.56 331	14.82 360	43.56 340	45.65 337	45.60 264	60.04 298	75.47 272	9.31 366	34.46 355	23.66 346	68.76 319	60.02 332

.test1245		34.47 338	40.38 334	16.73 349	59.56 331	14.82 360	43.56 340	45.65 337	45.60 264	60.04 298	75.47 272	9.31 366	34.46 355	23.66 346	0.55 362	0.90 361

JIA-IIPM		54.03 289	51.62 302	61.25 261	59.14 333	55.21 149	59.10 299	47.72 330	50.85 230	50.31 340	85.81 158	20.10 358	63.97 291	36.16 305	55.41 352	64.55 319

LF4IMVS		67.50 197	67.31 200	68.08 200	58.86 334	61.93 111	71.43 173	75.90 180	44.67 274	72.42 207	80.20 222	57.16 197	70.44 249	58.99 149	86.12 164	71.88 263

test2356		40.85 330	40.47 333	41.98 334	58.78 335	28.65 343	39.45 348	40.98 355	31.95 342	48.47 343	56.63 350	12.54 365	44.41 338	15.84 358	59.58 340	52.88 342

UnsupCasMVSNet_bld		50.01 307	51.03 307	46.95 317	58.61 336	32.64 325	48.31 329	53.27 306	34.27 327	60.47 296	71.53 311	41.40 267	47.07 327	30.68 325	60.78 337	61.13 329

dp		44.09 326	44.88 326	41.72 337	58.53 337	23.18 354	54.70 316	42.38 347	34.80 323	44.25 354	65.61 337	24.48 352	44.80 335	29.77 331	49.42 355	57.18 338

test12356		38.35 331	40.80 331	31.01 345	58.31 338	9.09 363	36.67 355	46.65 332	33.65 333	44.39 353	60.94 345	17.56 361	39.23 353	16.01 357	53.03 353	44.72 352

testgi		54.00 291	56.86 276	45.45 324	58.20 339	25.81 350	49.05 327	49.50 324	45.43 268	67.84 249	81.17 215	51.81 224	43.20 343	29.30 333	79.41 258	67.34 303

PNet_i23d		36.76 334	36.63 338	37.12 342	58.19 340	33.00 324	39.86 347	32.55 361	48.44 246	39.64 357	51.31 355	6.89 368	41.83 349	22.29 349	30.55 358	36.54 357

wuyk23d		61.97 237	66.25 205	49.12 313	58.19 340	60.77 120	66.32 236	52.97 307	55.93 159	90.62 7	86.91 120	73.07 44	35.98 354	20.63 353	91.63 73	50.62 345

ANet_high		67.08 200	69.94 171	58.51 280	57.55 342	27.09 345	58.43 304	76.80 173	63.56 89	82.40 82	91.93 21	59.82 154	64.98 289	50.10 211	88.86 126	83.46 129

Patchmatch-test		47.93 312	49.96 310	41.84 335	57.42 343	24.26 353	48.75 328	41.49 351	39.30 301	56.79 315	73.48 292	30.48 327	33.87 357	29.29 334	72.61 297	67.39 301

new_pmnet		37.55 333	39.80 336	30.79 346	56.83 344	16.46 359	39.35 349	30.65 362	25.59 355	45.26 349	61.60 344	24.54 351	28.02 360	21.60 351	52.80 354	47.90 348

pmmvs3		46.71 315	45.09 324	51.55 305	56.76 345	48.25 196	55.78 311	39.53 357	24.13 357	50.35 339	63.40 340	15.90 363	51.08 318	29.29 334	70.69 308	55.33 340

sss		47.59 314	48.32 313	45.40 325	56.73 346	33.96 313	45.17 339	48.51 327	32.11 341	52.37 333	65.79 336	40.39 275	41.91 348	31.85 321	61.97 335	60.35 330

tpmrst		50.15 306	51.38 303	46.45 321	56.05 347	24.77 352	64.40 261	49.98 322	36.14 316	53.32 331	69.59 321	35.16 294	48.69 323	39.24 282	58.51 346	65.89 310

TESTMET0.1,1		45.17 319	44.93 325	45.89 323	56.02 348	38.31 279	53.18 319	41.94 350	27.85 351	44.86 351	56.47 351	17.93 360	41.50 350	38.08 293	68.06 321	57.85 336

ADS-MVSNet2		48.76 309	47.25 318	53.29 300	55.90 349	40.54 265	47.34 333	54.99 298	31.41 345	50.48 337	72.06 305	31.23 319	54.26 315	25.93 340	55.93 349	65.07 316

ADS-MVSNet		44.62 324	45.58 322	41.73 336	55.90 349	20.83 356	47.34 333	39.94 356	31.41 345	50.48 337	72.06 305	31.23 319	39.31 351	25.93 340	55.93 349	65.07 316

LP		53.02 295	52.27 301	55.27 296	55.76 351	40.55 264	55.64 312	55.07 292	42.46 288	56.95 313	73.21 295	33.67 298	54.18 317	38.41 290	59.29 342	71.08 272

test0.0.03 1		47.72 313	48.31 314	45.93 322	55.53 352	29.39 339	46.40 336	41.21 353	43.41 285	55.81 321	67.65 331	29.22 335	43.77 342	25.73 342	69.87 313	64.62 318

UnsupCasMVSNet_eth		52.26 301	53.29 296	49.16 312	55.08 353	33.67 316	50.03 326	58.79 273	37.67 310	63.43 282	74.75 278	41.82 266	45.83 329	38.59 289	59.42 341	67.98 299

pmmvs5		52.49 300	52.58 299	52.21 304	54.99 354	32.38 326	55.45 313	53.84 302	32.15 339	55.49 322	74.81 276	38.08 285	57.37 311	34.02 315	74.40 290	66.88 305

DSMNet-mixed		43.18 327	44.66 327	38.75 341	54.75 355	28.88 342	57.06 308	27.42 364	13.47 359	47.27 346	77.67 248	38.83 280	39.29 352	25.32 344	60.12 339	48.08 347

MDA-MVSNet_test_wron		52.57 299	53.49 295	49.81 308	54.24 356	36.47 293	40.48 346	46.58 333	38.13 307	75.47 170	73.32 293	41.05 272	43.85 341	40.98 275	71.20 304	69.10 294

YYNet1		52.58 298	53.50 294	49.85 307	54.15 357	36.45 294	40.53 345	46.55 334	38.09 308	75.52 169	73.31 294	41.08 271	43.88 340	41.10 274	71.14 305	69.21 292

EPMVS		45.74 316	46.53 319	43.39 332	54.14 358	22.33 355	55.02 315	35.00 360	34.69 325	51.09 335	70.20 318	25.92 348	42.04 347	37.19 299	55.50 351	65.78 311

testpf		45.32 318	48.47 312	35.88 344	53.56 359	26.84 346	58.86 303	42.95 343	47.78 252	46.18 347	63.70 339	13.73 364	50.29 320	50.81 204	58.61 345	30.51 358

E-PMN		45.17 319	45.36 323	44.60 329	50.07 360	42.75 248	38.66 351	42.29 348	46.39 260	39.55 358	51.15 356	26.00 347	45.37 333	37.68 295	76.41 275	45.69 351

PMMVS		44.69 323	43.95 329	46.92 318	50.05 361	53.47 163	48.08 332	42.40 346	22.36 358	44.01 355	53.05 353	42.60 262	45.49 331	31.69 322	61.36 336	41.79 353

EMVS		44.61 325	44.45 328	45.10 327	48.91 362	43.00 246	37.92 352	41.10 354	46.75 257	38.00 360	48.43 358	26.42 346	46.27 328	37.11 300	75.38 285	46.03 350

MVE	27.91 23	36.69 335	35.64 339	39.84 340	43.37 363	35.85 300	19.49 358	24.61 365	24.68 356	39.05 359	62.63 343	38.67 282	27.10 361	21.04 352	47.25 356	56.56 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 332	40.87 330	28.36 348	42.41 364	5.35 365	24.61 357	27.75 363	32.15 339	47.85 344	70.27 317	35.85 293	29.51 359	19.08 355	67.85 322	50.22 346

DeepMVS_CX				11.83 350	15.51 365	13.86 362		11.25 368	5.76 360	20.85 363	26.46 359	17.06 362	9.22 362	9.69 360	13.82 360	12.42 359

tmp_tt		11.98 340	14.73 341	3.72 351	2.28 366	4.62 366	19.44 359	14.50 367	0.47 361	21.55 362	9.58 361	25.78 349	4.57 363	11.61 359	27.37 359	1.96 360

test123		4.43 343	5.78 344	0.39 353	0.97 367	0.28 367	46.33 337	0.45 369	0.31 362	0.62 364	1.50 364	0.61 370	0.11 365	0.56 361	0.63 361	0.77 363

testmvs		4.06 344	5.28 345	0.41 352	0.64 368	0.16 368	42.54 342	0.31 370	0.26 363	0.50 365	1.40 365	0.77 369	0.17 364	0.56 361	0.55 362	0.90 361

cdsmvs_eth3d_5k		17.71 339	23.62 340	0.00 354	0.00 369	0.00 369	0.00 360	70.17 228	0.00 364	0.00 366	74.25 286	68.16 81	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		5.20 342	6.93 343	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	62.39 126	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		5.62 341	7.50 342	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	67.46 332	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																70.05 279

test_part1				0.00 354		0.00 369	0.00 360	84.94 30				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												31.41 317				70.05 279

sam_mvs												31.21 321

MTGPA								80.63 110

test_post1							66.63 233				2.08 362	30.66 326	59.33 306	40.34 280

test_post											1.99 363	30.91 324	54.76 314

patchmatchnet-post											68.99 323	31.32 318	69.38 254

MTMP							84.83 25	19.26 366

test9_res														72.12 53	91.37 80	77.40 223

agg_prior2														70.70 61	90.93 92	78.55 211

test_prior4						70.14 58	77.57 85

test_prior2							75.57 117		58.92 131	76.53 158	86.78 125	67.83 85		69.81 69	92.76 60

旧先验2							71.17 180		45.11 271	78.54 130			61.28 302	59.19 148

新几何2							71.33 176

无先验							74.82 129	70.94 222	47.75 253				76.85 186	54.47 184		72.09 262

原ACMM2							74.78 133

testdata2													67.30 276	48.34 224

segment_acmp												68.30 80

testdata1							68.34 215		57.24 145

plane_prior5								85.49 21					86.15 21	71.09 55	90.94 90	84.82 97

plane_prior4											89.11 89

plane_prior3						65.67 86			63.82 87	78.23 135

plane_prior2							82.74 38		65.45 64

plane_prior						65.18 90	80.06 62		61.88 106						89.91 114

n2								0.00 371

nn								0.00 371

door-mid								55.02 293

test11								82.71 69

door								52.91 308

HQP5-MVS						58.80 135

BP-MVS														67.38 95

HQP4-MVS										71.59 212			85.31 34			83.74 121

HQP3-MVS								84.12 47							89.16 121

HQP2-MVS												58.09 180

MDTV_nov1_ep13_2view						18.41 357	53.74 318		31.57 344	44.89 350		29.90 332		32.93 319		71.48 266

ACMMP++_ref															89.47 119

ACMMP++															91.96 70

Test By Simon												62.56 122

High-resolution multi-view benchmark