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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 52	99.27 1	99.54 1

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 47	91.91 15	84.21 155	96.51 8	57.84 287	88.93 80	92.84 93	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 117	98.57 15	98.80 6

PEN-MVS		90.03 45	91.88 16	84.48 147	96.57 6	58.88 279	88.95 78	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 120	98.72 9	98.97 3

PS-CasMVS		90.06 43	91.92 13	84.47 148	96.56 7	58.83 282	89.04 77	92.74 96	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 115	98.74 6	99.00 2

CP-MVSNet		89.27 61	90.91 43	84.37 149	96.34 9	58.61 284	88.66 87	92.06 110	90.78 6	95.67 7	95.17 39	81.80 110	95.54 41	79.00 118	98.69 10	98.95 4

LS3D		90.60 33	90.34 50	91.38 27	89.03 183	84.23 46	93.58 6	94.68 19	90.65 7	90.33 91	93.95 94	84.50 72	95.37 53	80.87 97	95.50 144	94.53 77

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 35	94.17 96	86.07 43	98.48 18	97.22 17

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 66	86.15 22	93.37 10	95.10 14	90.28 9	92.11 60	95.03 42	89.75 21	94.93 69	79.95 107	98.27 27	95.04 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		89.91 50	91.31 31	85.71 127	96.32 10	62.39 240	89.54 69	93.31 70	90.21 10	95.57 9	95.66 28	81.42 114	95.90 15	80.94 96	98.80 3	98.84 5

test1172		92.40 7	92.41 7	92.37 6	94.68 44	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 80	88.83 24	95.51 44	87.11 30	97.54 72	92.54 156

abl_6		93.02 4	93.16 4	92.60 4	94.73 43	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 36	91.86 3	95.09 64	87.54 18	98.02 40	93.71 111

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 139	81.66 63	91.25 38	94.13 36	88.89 13	88.83 125	94.26 74	77.55 150	95.86 21	84.88 55	95.87 132	95.24 57

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 65	85.72 32	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 139	91.10 1	97.53 73	96.58 29
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
UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 153	97.00 2	64.33 218	89.67 64	88.38 197	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 192	97.65 62	97.34 14

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	88.83 24	95.51 44	87.16 28	97.60 66	92.73 146

RE-MVS-def			92.61 5		94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	90.64 11		87.16 28	97.60 66	92.73 146

test_0402		88.65 70	89.58 59	85.88 123	92.55 92	72.22 152	84.01 156	89.44 182	88.63 18	94.38 18	95.77 24	86.38 60	93.59 121	79.84 108	95.21 153	91.82 187

PMVS	80.48 6	90.08 41	90.66 47	88.34 82	96.71 3	92.97 1	90.31 50	89.57 180	88.51 19	90.11 93	95.12 41	90.98 7	88.92 249	77.55 139	97.07 86	83.13 314
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 55	93.52 7	92.79 94	88.22 20	88.53 130	97.64 2	83.45 83	94.55 85	86.02 46	98.60 13	96.67 26

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 41	94.02 86	90.15 17	95.67 33	86.82 32	97.34 79	92.19 175

DP-MVS		88.60 71	89.01 66	87.36 94	91.30 137	77.50 100	87.55 99	92.97 88	87.95 22	89.62 110	92.87 120	84.56 71	93.89 107	77.65 137	96.62 100	90.70 211

ACMH+	77.89 11	90.73 30	91.50 23	88.44 79	93.00 81	76.26 119	89.65 65	95.55 7	87.72 23	93.89 27	94.94 44	91.62 4	93.44 129	78.35 124	98.76 4	95.61 47

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 80	85.17 35	92.47 26	95.05 15	87.65 24	93.21 42	94.39 69	90.09 18	95.08 65	86.67 33	97.60 66	94.18 90

Anonymous20231211		88.40 72	89.62 58	84.73 143	90.46 161	65.27 209	88.86 81	93.02 86	87.15 25	93.05 43	97.10 6	82.28 98	92.02 173	76.70 147	97.99 42	96.88 23

gg-mvs-nofinetune		68.96 306	69.11 302	68.52 325	76.12 345	45.32 358	83.59 171	55.88 369	86.68 26	64.62 356	97.01 7	30.36 370	83.97 307	44.78 353	82.94 328	76.26 348

test_one_0601					93.85 62	73.27 134		94.11 37	86.57 27	93.47 40	94.64 56	88.42 27

v7n		90.13 40	90.96 41	87.65 91	91.95 112	71.06 165	89.99 56	93.05 82	86.53 28	94.29 19	96.27 17	82.69 90	94.08 100	86.25 40	97.63 63	97.82 8

VDDNet		84.35 136	85.39 120	81.25 212	95.13 33	59.32 272	85.42 135	81.11 275	86.41 29	87.41 148	96.21 19	73.61 191	90.61 216	66.33 241	96.85 92	93.81 108

IS-MVSNet		86.66 93	86.82 97	86.17 118	92.05 110	66.87 198	91.21 39	88.64 193	86.30 30	89.60 113	92.59 128	69.22 225	94.91 70	73.89 175	97.89 51	96.72 24

Anonymous20240529		86.20 102	87.13 88	83.42 174	90.19 165	64.55 216	84.55 146	90.71 147	85.85 31	89.94 100	95.24 38	82.13 100	90.40 220	69.19 220	96.40 110	95.31 54

DVP-MVS++.		90.07 42	91.09 35	87.00 97	91.55 130	72.64 140	96.19 2	94.10 38	85.33 32	93.49 38	94.64 56	81.12 118	95.88 16	87.41 21	95.94 128	92.48 158

test_0728_THIRD									85.33 32	93.75 31	94.65 53	87.44 45	95.78 27	87.41 21	98.21 30	92.98 137

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 31	92.99 13	94.23 26	85.21 34	92.51 54	95.13 40	90.65 10	95.34 54	88.06 9	98.15 35	95.95 40

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	94.03 85	86.57 56	95.80 24	87.35 23	97.62 64	94.20 88

X-MVStestdata		85.04 121	82.70 165	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	16.05 370	86.57 56	95.80 24	87.35 23	97.62 64	94.20 88

TranMVSNet+NR-MVSNet		87.86 79	88.76 73	85.18 135	94.02 57	64.13 219	84.38 151	91.29 132	84.88 37	92.06 62	93.84 97	86.45 58	93.73 113	73.22 183	98.66 11	97.69 9

DPE-MVS		90.53 35	91.08 36	88.88 66	93.38 72	78.65 87	89.15 76	94.05 40	84.68 38	93.90 25	94.11 83	88.13 36	96.30 3	84.51 60	97.81 54	91.70 190
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Gipuma		84.44 134	86.33 100	78.78 250	84.20 271	73.57 131	89.55 67	90.44 154	84.24 39	84.38 207	94.89 45	76.35 168	80.40 323	76.14 153	96.80 96	82.36 322
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 18	88.94 79	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 167

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 18	92.02 30	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 167

K. test v3		85.14 117	84.73 130	86.37 108	91.13 145	69.63 175	85.45 134	76.68 301	84.06 42	92.44 56	96.99 8	62.03 261	94.65 77	80.58 102	93.24 204	94.83 69

ANet_high		83.17 165	85.68 115	75.65 290	81.24 299	45.26 359	79.94 244	92.91 89	83.83 43	91.33 75	96.88 10	80.25 129	85.92 288	68.89 223	95.89 131	95.76 42

SED-MVS		90.46 37	91.64 19	86.93 98	94.18 49	72.65 138	90.47 48	93.69 54	83.77 44	94.11 23	94.27 71	90.28 15	95.84 22	86.03 44	97.92 48	92.29 169

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 71	89.27 22	95.84 22	86.03 44	97.82 53	92.04 178

test_241102_ONE					94.18 49	72.65 138		93.69 54	83.62 46	94.11 23	93.78 101	90.28 15	95.50 47

DVP-MVS		90.06 43	91.32 30	86.29 111	94.16 52	72.56 144	90.54 45	91.01 140	83.61 47	93.75 31	94.65 53	89.76 19	95.78 27	86.42 34	97.97 45	90.55 217
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					94.16 52	72.56 144	90.63 44	93.90 46	83.61 47	93.75 31	94.49 60	89.76 19

pmmvs6		86.52 95	88.06 78	81.90 202	92.22 104	62.28 243	84.66 144	89.15 186	83.54 49	89.85 102	97.32 4	88.08 38	86.80 275	70.43 209	97.30 81	96.62 27

testtj		89.51 57	89.48 60	89.59 58	92.26 101	80.80 68	90.14 53	93.54 61	83.37 50	90.57 88	92.55 131	84.99 68	96.15 5	81.26 91	96.61 101	91.83 186

Regformer-2		86.74 92	86.08 106	88.73 71	84.18 272	79.20 81	83.52 172	89.33 184	83.33 51	89.92 101	85.07 281	83.23 86	93.16 139	83.39 69	92.72 219	93.83 103

APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 82	78.04 91	92.84 16	94.14 35	83.33 51	93.90 25	95.73 25	88.77 26	96.41 1	87.60 16	97.98 44	92.98 137

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 79	94.00 87	88.26 32	95.71 31	87.28 26	98.39 21	92.55 155

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 99	83.09 54	91.54 70	94.25 75	87.67 43	95.51 44	87.21 27	98.11 36	93.12 132

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 118	92.86 86	67.02 196	82.55 201	91.56 123	83.08 55	90.92 81	91.82 151	78.25 143	93.99 102	74.16 171	98.35 22	97.49 13

LFMVS		80.15 212	80.56 196	78.89 248	89.19 181	55.93 301	85.22 137	73.78 321	82.96 56	84.28 213	92.72 125	57.38 291	90.07 234	63.80 258	95.75 138	90.68 212

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 42	93.51 8	94.85 16	82.88 57	91.77 68	93.94 95	90.55 13	95.73 30	88.50 7	98.23 29	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-4		86.41 96	85.71 114	88.52 75	84.27 268	77.57 99	84.07 154	88.00 205	82.82 58	89.84 103	85.48 269	82.06 102	92.77 152	83.83 67	91.04 246	95.22 60

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 60	80.97 67	91.49 37	93.48 63	82.82 58	92.60 53	93.97 88	88.19 33	96.29 4	87.61 15	98.20 32	94.39 83
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 15	92.20 27	93.03 85	82.59 60	88.52 131	94.37 70	86.74 53	95.41 51	86.32 37	98.21 30	93.19 130
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_part1		87.15 87	87.82 80	85.15 136	88.88 187	63.04 230	87.98 93	94.85 16	82.52 61	93.61 37	95.73 25	67.51 233	95.71 31	80.48 104	98.83 2	96.69 25

ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 26	93.35 11	94.16 31	82.52 61	92.39 57	94.14 81	89.15 23	95.62 34	87.35 23	98.24 28	94.56 74
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
LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 41	81.69 60	90.00 54	94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 41	81.69 60		94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

Regformer-1		86.00 104	85.50 118	87.49 92	84.18 272	76.90 111	83.52 172	87.94 207	82.18 65	89.19 119	85.07 281	82.28 98	91.89 177	82.40 81	92.72 219	93.69 112

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 43	92.58 22	93.29 73	81.99 66	91.47 71	93.96 91	88.35 30	95.56 37	87.74 11	97.74 58	92.85 141

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 28	92.58 22	93.25 75	81.99 66	91.40 73	94.17 79	87.51 44	95.87 18	87.74 11	97.76 56	93.99 96

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 27	92.63 21	93.30 72	81.91 68	90.88 84	94.21 76	87.75 41	95.87 18	87.60 16	97.71 60	93.83 103

ACMH	76.49 14	89.34 60	91.14 34	83.96 162	92.50 94	70.36 170	89.55 67	93.84 50	81.89 69	94.70 13	95.44 33	90.69 9	88.31 259	83.33 71	98.30 26	93.20 129
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		86.80 91	86.99 92	86.21 116	93.24 76	67.02 196	83.16 186	92.21 106	81.73 70	90.92 81	91.97 145	77.20 153	93.99 102	74.16 171	98.35 22	97.61 10

SixPastTwentyTwo		87.20 86	87.45 85	86.45 107	92.52 93	69.19 182	87.84 97	88.05 203	81.66 71	94.64 14	96.53 14	65.94 242	94.75 74	83.02 75	96.83 94	95.41 50

ITE_SJBPF				90.11 50	90.72 155	84.97 37		90.30 161	81.56 72	90.02 96	91.20 166	82.40 94	90.81 209	73.58 180	94.66 173	94.56 74

EPP-MVSNet		85.47 112	85.04 125	86.77 102	91.52 133	69.37 176	91.63 36	87.98 206	81.51 73	87.05 156	91.83 150	66.18 241	95.29 55	70.75 204	96.89 90	95.64 45

SF-MVS		90.27 39	90.80 45	88.68 74	92.86 86	77.09 108	91.19 40	95.74 5	81.38 74	92.28 58	93.80 98	86.89 51	94.64 78	85.52 48	97.51 74	94.30 86

WR-MVS		83.56 156	84.40 142	81.06 217	93.43 71	54.88 310	78.67 265	85.02 250	81.24 75	90.74 85	91.56 158	72.85 203	91.08 199	68.00 231	98.04 37	97.23 16

Anonymous202405211		80.51 201	81.19 190	78.49 256	88.48 193	57.26 293	76.63 291	82.49 266	81.21 76	84.30 212	92.24 142	67.99 231	86.24 284	62.22 268	95.13 156	91.98 183

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 78	80.37 70	91.91 34	93.11 79	81.10 77	95.32 10	97.24 5	72.94 202	94.85 72	85.07 52	97.78 55	97.26 15

NR-MVSNet		86.00 104	86.22 103	85.34 133	93.24 76	64.56 215	82.21 214	90.46 153	80.99 78	88.42 133	91.97 145	77.56 149	93.85 108	72.46 193	98.65 12	97.61 10

GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 56	91.75 35	93.74 52	80.98 79	91.38 74	93.80 98	87.20 48	95.80 24	87.10 31	97.69 61	93.93 99

DROMVSNet		88.01 76	88.32 76	87.09 96	89.28 178	72.03 154	90.31 50	96.31 3	80.88 80	85.12 193	89.67 206	84.47 74	95.46 48	82.56 79	96.26 117	93.77 109

APD-MVS		89.54 56	89.63 57	89.26 63	92.57 91	81.34 65	90.19 52	93.08 81	80.87 81	91.13 77	93.19 109	86.22 61	95.97 12	82.23 83	97.18 84	90.45 219
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-3		85.06 120	84.67 135	86.22 114	84.27 268	73.43 132	84.07 154	85.26 243	80.77 82	88.62 129	85.48 269	80.56 126	90.39 221	81.99 85	91.04 246	94.85 67

EI-MVSNet-Vis-set		85.12 118	84.53 138	86.88 99	84.01 275	72.76 137	83.91 161	85.18 245	80.44 83	88.75 126	85.49 268	80.08 130	91.92 175	82.02 84	90.85 255	95.97 38

UniMVSNet (Re)		86.87 88	86.98 93	86.55 105	93.11 79	68.48 186	83.80 165	92.87 90	80.37 84	89.61 112	91.81 152	77.72 147	94.18 94	75.00 167	98.53 16	96.99 22

CSCG		86.26 99	86.47 99	85.60 129	90.87 151	74.26 128	87.98 93	91.85 116	80.35 85	89.54 116	88.01 229	79.09 136	92.13 167	75.51 160	95.06 160	90.41 220

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 29	90.00 54	93.90 46	80.32 86	91.74 69	94.41 66	88.17 34	95.98 11	86.37 36	97.99 42	93.96 98

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 100	83.87 278	72.52 146	83.82 163	85.15 246	80.27 87	88.75 126	85.45 272	79.95 132	91.90 176	81.92 87	90.80 256	96.13 33

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 24	85.91 126	93.60 60	80.16 88	89.13 121	93.44 106	83.82 78	90.98 201	83.86 66	95.30 152	93.60 118

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 53	92.18 28	94.22 27	80.14 89	91.29 76	93.97 88	87.93 40	95.87 18	88.65 4	97.96 47	94.12 93

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 25	86.84 111	93.91 45	80.07 90	86.75 162	93.26 108	93.64 2	90.93 203	84.60 59	90.75 257	93.97 97

VDD-MVS		84.23 142	84.58 137	83.20 178	91.17 144	65.16 211	83.25 182	84.97 253	79.79 91	87.18 150	94.27 71	74.77 180	90.89 206	69.24 217	96.54 104	93.55 122

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 14	92.09 29	92.30 105	79.74 92	87.50 147	92.38 134	81.42 114	93.28 134	83.07 73	97.24 82	91.67 191

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 45	89.49 71	93.98 42	79.68 93	92.09 61	93.89 96	83.80 79	93.10 143	82.67 78	98.04 37	93.64 116

TransMVSNet (Re)		84.02 147	85.74 113	78.85 249	91.00 148	55.20 309	82.29 210	87.26 213	79.65 94	88.38 135	95.52 32	83.00 87	86.88 273	67.97 232	96.60 102	94.45 81

AllTest		87.97 78	87.40 87	89.68 54	91.59 124	83.40 49	89.50 70	95.44 9	79.47 95	88.00 140	93.03 112	82.66 91	91.47 185	70.81 201	96.14 121	94.16 91

TestCases				89.68 54	91.59 124	83.40 49		95.44 9	79.47 95	88.00 140	93.03 112	82.66 91	91.47 185	70.81 201	96.14 121	94.16 91

HQP_MVS		87.75 83	87.43 86	88.70 73	93.45 69	76.42 117	89.45 72	93.61 58	79.44 97	86.55 167	92.95 117	74.84 177	95.22 59	80.78 99	95.83 133	94.46 79

plane_prior2							89.45 72		79.44 97

RPSCF		88.00 77	86.93 94	91.22 31	90.08 167	89.30 5	89.68 63	91.11 137	79.26 99	89.68 107	94.81 51	82.44 93	87.74 263	76.54 149	88.74 279	96.61 28

ACMM	79.39 9	90.65 31	90.99 40	89.63 56	95.03 35	83.53 48	89.62 66	93.35 66	79.20 100	93.83 28	93.60 105	90.81 8	92.96 146	85.02 54	98.45 19	92.41 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		83.55 157	83.10 161	84.90 139	89.34 177	83.87 47	84.54 148	88.77 190	79.09 101	83.54 224	88.66 222	74.87 176	81.73 317	66.84 238	92.29 224	89.11 240

Baseline_NR-MVSNet		84.00 148	85.90 109	78.29 261	91.47 135	53.44 318	82.29 210	87.00 225	79.06 102	89.55 114	95.72 27	77.20 153	86.14 286	72.30 194	98.51 17	95.28 55

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 46	80.98 66	89.16 75	94.05 40	79.03 103	92.87 46	93.74 102	90.60 12	95.21 61	82.87 76	98.76 4	94.87 64
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SD-MVS		88.96 67	89.88 53	86.22 114	91.63 123	77.07 109	89.82 60	93.77 51	78.90 104	92.88 45	92.29 139	86.11 62	90.22 225	86.24 41	97.24 82	91.36 198
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
Vis-MVSNet		86.86 89	86.58 98	87.72 89	92.09 108	77.43 103	87.35 102	92.09 109	78.87 105	84.27 214	94.05 84	78.35 142	93.65 115	80.54 103	91.58 240	92.08 177
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		89.80 51	89.97 52	89.27 62	94.76 40	79.86 74	86.76 115	92.78 95	78.78 106	92.51 54	93.64 104	88.13 36	93.84 110	84.83 57	97.55 69	94.10 94
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
NCCC		87.36 84	86.87 95	88.83 67	92.32 100	78.84 85	86.58 119	91.09 138	78.77 107	84.85 199	90.89 178	80.85 121	95.29 55	81.14 93	95.32 149	92.34 165

ETV-MVS		84.31 137	83.91 151	85.52 130	88.58 191	70.40 169	84.50 150	93.37 64	78.76 108	84.07 216	78.72 340	80.39 127	95.13 63	73.82 177	92.98 212	91.04 200

Effi-MVS+		83.90 151	84.01 148	83.57 172	87.22 219	65.61 208	86.55 120	92.40 102	78.64 109	81.34 256	84.18 293	83.65 81	92.93 148	74.22 170	87.87 289	92.17 176

FMVSNet1		84.55 131	85.45 119	81.85 204	90.27 164	61.05 254	86.83 112	88.27 200	78.57 110	89.66 109	95.64 29	75.43 171	90.68 213	69.09 221	95.33 148	93.82 105

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 23	92.75 17	93.90 46	78.43 111	89.16 120	92.25 141	72.03 214	96.36 2	88.21 8	90.93 252	92.98 137
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
API-MVS		82.28 174	82.61 169	81.30 211	86.29 240	69.79 171	88.71 85	87.67 209	78.42 112	82.15 242	84.15 294	77.98 144	91.59 183	65.39 249	92.75 216	82.51 321

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 29	89.70 61	91.27 133	78.20 113	86.69 165	92.28 140	80.36 128	95.06 66	86.17 42	96.49 106	90.22 222

AdaColmap		83.66 154	83.69 153	83.57 172	90.05 170	72.26 151	86.29 125	90.00 172	78.19 114	81.65 251	87.16 245	83.40 84	94.24 90	61.69 274	94.76 172	84.21 296

PAPM_NR		83.23 163	83.19 159	83.33 175	90.90 150	65.98 205	88.19 91	90.78 146	78.13 115	80.87 260	87.92 233	73.49 195	92.42 158	70.07 211	88.40 280	91.60 193

plane_prior3						76.85 112			77.79 116	86.55 167

ACMMP_NAP		90.65 31	91.07 38	89.42 60	95.93 16	79.54 78	89.95 57	93.68 56	77.65 117	91.97 65	94.89 45	88.38 28	95.45 49	89.27 3	97.87 52	93.27 126

MSDG		80.06 214	79.99 210	80.25 230	83.91 277	68.04 190	77.51 281	89.19 185	77.65 117	81.94 244	83.45 300	76.37 167	86.31 283	63.31 263	86.59 299	86.41 273

CS-MVS-test		85.00 123	85.28 121	84.17 158	87.84 205	66.12 204	87.30 103	95.67 6	77.63 119	80.02 273	85.85 265	81.34 116	95.41 51	78.18 129	93.71 194	90.99 201

MIMVSNet1		83.63 155	84.59 136	80.74 222	94.06 56	62.77 234	82.72 195	84.53 256	77.57 120	90.34 90	95.92 23	76.88 165	85.83 290	61.88 272	97.42 77	93.62 117

FC-MVSNet-test		85.93 107	87.05 91	82.58 193	92.25 102	56.44 299	85.75 130	93.09 80	77.33 121	91.94 66	94.65 53	74.78 179	93.41 131	75.11 166	98.58 14	97.88 7

CNVR-MVS		87.81 82	87.68 82	88.21 84	92.87 84	77.30 106	85.25 136	91.23 134	77.31 122	87.07 155	91.47 160	82.94 88	94.71 75	84.67 58	96.27 116	92.62 153

CANet		83.79 152	82.85 163	86.63 103	86.17 244	72.21 153	83.76 166	91.43 127	77.24 123	74.39 316	87.45 240	75.36 172	95.42 50	77.03 146	92.83 215	92.25 173

UGNet		82.78 167	81.64 182	86.21 116	86.20 243	76.24 120	86.86 110	85.68 237	77.07 124	73.76 319	92.82 121	69.64 222	91.82 180	69.04 222	93.69 195	90.56 216
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
#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 43	90.65 43	93.29 73	77.00 125	91.47 71	93.96 91	88.35 30	95.56 37	84.88 55	97.74 58	92.85 141

tfpnnormal		81.79 184	82.95 162	78.31 259	88.93 186	55.40 305	80.83 235	82.85 264	76.81 126	85.90 183	94.14 81	74.58 183	86.51 280	66.82 239	95.68 141	93.01 136

v8		86.22 101	86.83 96	84.36 151	87.82 206	62.35 242	86.42 121	91.33 131	76.78 127	92.73 51	94.48 61	73.41 196	93.72 114	83.10 72	95.41 145	97.01 21

LCM-MVSNet-Re		83.48 158	85.06 124	78.75 251	85.94 247	55.75 304	80.05 242	94.27 23	76.47 128	96.09 5	94.54 58	83.31 85	89.75 240	59.95 286	94.89 167	90.75 209

VPA-MVSNet		83.47 159	84.73 130	79.69 240	90.29 163	57.52 291	81.30 228	88.69 192	76.29 129	87.58 146	94.44 62	80.60 125	87.20 268	66.60 240	96.82 95	94.34 85

MVS_0304		78.17 229	77.23 237	80.99 220	84.13 274	69.07 185	81.39 225	80.81 278	76.28 130	67.53 344	89.11 215	62.87 259	86.77 276	60.90 282	92.01 233	87.13 267

EPNet		80.37 205	78.41 225	86.23 113	76.75 338	73.28 133	87.18 106	77.45 296	76.24 131	68.14 339	88.93 218	65.41 244	93.85 108	69.47 215	96.12 123	91.55 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETH3D-3000-0.1		88.85 69	88.96 69	88.52 75	91.94 114	77.27 107	88.71 85	95.26 13	76.08 132	90.66 87	92.69 126	84.48 73	93.83 111	83.38 70	97.48 76	94.47 78

EI-MVSNet		82.61 169	82.42 173	83.20 178	83.25 282	63.66 222	83.50 175	85.07 247	76.06 133	86.55 167	85.10 278	73.41 196	90.25 222	78.15 132	90.67 259	95.68 44

IterMVS-LS		84.73 127	84.98 126	83.96 162	87.35 216	63.66 222	83.25 182	89.88 174	76.06 133	89.62 110	92.37 137	73.40 198	92.52 157	78.16 130	94.77 171	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OMC-MVS		88.19 74	87.52 84	90.19 49	91.94 114	81.68 62	87.49 101	93.17 78	76.02 135	88.64 128	91.22 164	84.24 76	93.37 132	77.97 135	97.03 87	95.52 48

test_yl		78.71 224	78.51 223	79.32 245	84.32 266	58.84 280	78.38 267	85.33 241	75.99 136	82.49 235	86.57 251	58.01 285	90.02 236	62.74 265	92.73 217	89.10 241

DCV-MVSNet		78.71 224	78.51 223	79.32 245	84.32 266	58.84 280	78.38 267	85.33 241	75.99 136	82.49 235	86.57 251	58.01 285	90.02 236	62.74 265	92.73 217	89.10 241

MSLP-MVS++		85.00 123	86.03 107	81.90 202	91.84 119	71.56 163	86.75 116	93.02 86	75.95 138	87.12 151	89.39 208	77.98 144	89.40 245	77.46 140	94.78 169	84.75 291

plane_prior						76.42 117	87.15 107		75.94 139						95.03 161

FIs		85.35 114	86.27 102	82.60 192	91.86 117	57.31 292	85.10 138	93.05 82	75.83 140	91.02 80	93.97 88	73.57 192	92.91 150	73.97 174	98.02 40	97.58 12

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 73	88.13 92	94.51 21	75.79 141	92.94 44	94.96 43	88.36 29	95.01 67	90.70 2	98.40 20	95.09 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
thres100view900		75.45 257	75.05 257	76.66 283	87.27 217	51.88 331	81.07 231	73.26 325	75.68 142	83.25 227	86.37 254	45.54 335	88.80 250	51.98 329	90.99 248	89.31 236

3Dnovator	80.37 7	84.80 126	84.71 133	85.06 138	86.36 237	74.71 125	88.77 84	90.00 172	75.65 143	84.96 195	93.17 110	74.06 186	91.19 195	78.28 126	91.09 244	89.29 238

pm-mvs1		83.69 153	84.95 127	79.91 235	90.04 171	59.66 269	82.43 205	87.44 210	75.52 144	87.85 142	95.26 37	81.25 117	85.65 292	68.74 225	96.04 124	94.42 82

test_prior3		86.31 98	86.31 101	86.32 109	90.59 158	71.99 155	83.37 178	92.85 91	75.43 145	84.58 203	91.57 156	81.92 108	94.17 96	79.54 112	96.97 88	92.80 143

test_prior2							83.37 178		75.43 145	84.58 203	91.57 156	81.92 108		79.54 112	96.97 88

v10		86.54 94	87.10 89	84.84 140	88.16 201	63.28 227	86.64 118	92.20 107	75.42 147	92.81 50	94.50 59	74.05 187	94.06 101	83.88 65	96.28 114	97.17 18

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 79	90.98 41	93.24 76	75.37 148	92.84 48	95.28 35	85.58 66	96.09 7	87.92 10	97.76 56	93.88 101
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
thres600view7		75.97 254	75.35 256	77.85 270	87.01 227	51.84 332	80.45 238	73.26 325	75.20 149	83.10 230	86.31 257	45.54 335	89.05 246	55.03 314	92.24 226	92.66 151

9.14			89.29 62		91.84 119		88.80 83	95.32 11	75.14 150	91.07 78	92.89 119	87.27 46	93.78 112	83.69 68	97.55 69

wuyk23d		75.13 260	79.30 214	62.63 338	75.56 347	75.18 124	80.89 233	73.10 327	75.06 151	94.76 12	95.32 34	87.73 42	52.85 367	34.16 366	97.11 85	59.85 363

RPMNet		78.88 220	78.28 226	80.68 225	79.58 318	62.64 236	82.58 199	94.16 31	74.80 152	75.72 306	92.59 128	48.69 320	95.56 37	73.48 181	82.91 329	83.85 301

TSAR-MVS + GP.		83.95 149	82.69 166	87.72 89	89.27 179	81.45 64	83.72 167	81.58 274	74.73 153	85.66 185	86.06 260	72.56 208	92.69 154	75.44 162	95.21 153	89.01 246

casdiffmvs		85.21 115	85.85 110	83.31 176	86.17 244	62.77 234	83.03 188	93.93 44	74.69 154	88.21 138	92.68 127	82.29 97	91.89 177	77.87 136	93.75 193	95.27 56

Effi-MVS+-dtu		85.82 108	83.38 155	93.14 3	87.13 221	91.15 2	87.70 98	88.42 195	74.57 155	83.56 223	85.65 266	78.49 140	94.21 92	72.04 195	92.88 214	94.05 95

mvs-test1		84.55 131	82.12 175	91.84 20	87.13 221	89.54 4	85.05 139	88.42 195	74.57 155	80.60 262	82.98 304	78.49 140	93.98 104	72.04 195	89.77 266	92.00 180

baseline		85.20 116	85.93 108	83.02 181	86.30 239	62.37 241	84.55 146	93.96 43	74.48 157	87.12 151	92.03 144	82.30 96	91.94 174	78.39 122	94.21 182	94.74 70

VNet		79.31 216	80.27 201	76.44 284	87.92 204	53.95 314	75.58 303	84.35 257	74.39 158	82.23 240	90.72 183	72.84 204	84.39 303	60.38 285	93.98 188	90.97 203

BH-RMVSNet		80.53 200	80.22 204	81.49 210	87.19 220	66.21 203	77.79 276	86.23 230	74.21 159	83.69 219	88.50 223	73.25 200	90.75 210	63.18 264	87.90 288	87.52 262

nrg030		87.85 80	88.49 74	85.91 121	90.07 169	69.73 173	87.86 96	94.20 28	74.04 160	92.70 52	94.66 52	85.88 65	91.50 184	79.72 109	97.32 80	96.50 30

Vis-MVSNet (Re-imp)		77.82 234	77.79 231	77.92 267	88.82 188	51.29 336	83.28 180	71.97 334	74.04 160	82.23 240	89.78 204	57.38 291	89.41 244	57.22 299	95.41 145	93.05 134

testdata1							79.62 248		73.95 162

Patchmtry		76.56 249	77.46 233	73.83 299	79.37 323	46.60 356	82.41 206	76.90 298	73.81 163	85.56 188	92.38 134	48.07 322	83.98 306	63.36 262	95.31 151	90.92 205

tttt0517		81.07 191	79.58 212	85.52 130	88.99 185	66.45 202	87.03 109	75.51 309	73.76 164	88.32 137	90.20 196	37.96 360	94.16 99	79.36 116	95.13 156	95.93 41

PatchT		70.52 295	72.76 278	63.79 337	79.38 322	33.53 371	77.63 278	65.37 356	73.61 165	71.77 327	92.79 124	44.38 347	75.65 337	64.53 257	85.37 309	82.18 324

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 61	93.64 67	79.07 82	88.54 88	94.20 28	73.53 166	89.71 106	94.82 48	85.09 67	95.77 29	84.17 63	98.03 39	93.26 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VPNet		80.25 208	81.68 181	75.94 289	92.46 95	47.98 350	76.70 290	81.67 273	73.45 167	84.87 198	92.82 121	74.66 182	86.51 280	61.66 275	96.85 92	93.33 123

canonicalmvs		85.50 111	86.14 105	83.58 171	87.97 202	67.13 194	87.55 99	94.32 22	73.44 168	88.47 132	87.54 238	86.45 58	91.06 200	75.76 159	93.76 191	92.54 156

MVS_111021_HR		84.63 128	84.34 144	85.49 132	90.18 166	75.86 121	79.23 258	87.13 217	73.35 169	85.56 188	89.34 209	83.60 82	90.50 218	76.64 148	94.05 187	90.09 227

tfpn200view9		74.86 265	74.23 264	76.74 282	86.24 241	52.12 328	79.24 256	73.87 319	73.34 170	81.82 247	84.60 290	46.02 329	88.80 250	51.98 329	90.99 248	89.31 236

thres400		75.14 259	74.23 264	77.86 269	86.24 241	52.12 328	79.24 256	73.87 319	73.34 170	81.82 247	84.60 290	46.02 329	88.80 250	51.98 329	90.99 248	92.66 151

HQP-NCC					91.19 141		84.77 140		73.30 172	80.55 265

ACMP_Plane					91.19 141		84.77 140		73.30 172	80.55 265

HQP-MVS		84.61 129	84.06 147	86.27 112	91.19 141	70.66 167	84.77 140	92.68 97	73.30 172	80.55 265	90.17 199	72.10 210	94.61 80	77.30 143	94.47 177	93.56 120

alignmvs		83.94 150	83.98 149	83.80 164	87.80 207	67.88 191	84.54 148	91.42 129	73.27 175	88.41 134	87.96 230	72.33 209	90.83 208	76.02 156	94.11 185	92.69 150

F-COLMAP		84.97 125	83.42 154	89.63 56	92.39 96	83.40 49	88.83 82	91.92 115	73.19 176	80.18 272	89.15 214	77.04 157	93.28 134	65.82 247	92.28 225	92.21 174

MDA-MVSNet-bldmvs		77.47 237	76.90 241	79.16 247	79.03 326	64.59 213	66.58 344	75.67 307	73.15 177	88.86 123	88.99 217	66.94 236	81.23 319	64.71 253	88.22 286	91.64 192

PHI-MVS		86.38 97	85.81 111	88.08 85	88.44 195	77.34 104	89.35 74	93.05 82	73.15 177	84.76 200	87.70 235	78.87 138	94.18 94	80.67 101	96.29 113	92.73 146

Fast-Effi-MVS+-dtu		82.54 171	81.41 186	85.90 122	85.60 249	76.53 116	83.07 187	89.62 179	73.02 179	79.11 281	83.51 298	80.74 123	90.24 224	68.76 224	89.29 270	90.94 204

v148		82.31 173	82.48 172	81.81 207	85.59 250	59.66 269	81.47 224	86.02 233	72.85 180	88.05 139	90.65 187	70.73 220	90.91 205	75.15 165	91.79 235	94.87 64

BH-untuned		80.96 193	80.99 192	80.84 221	88.55 192	68.23 187	80.33 240	88.46 194	72.79 181	86.55 167	86.76 250	74.72 181	91.77 181	61.79 273	88.99 274	82.52 320

MVS_111021_LR		84.28 139	83.76 152	85.83 125	89.23 180	83.07 52	80.99 232	83.56 260	72.71 182	86.07 178	89.07 216	81.75 111	86.19 285	77.11 145	93.36 200	88.24 251

EG-PatchMatch MVS		84.08 145	84.11 146	83.98 161	92.22 104	72.61 143	82.20 216	87.02 222	72.63 183	88.86 123	91.02 172	78.52 139	91.11 198	73.41 182	91.09 244	88.21 252

ECVR-MVS11		78.53 226	78.85 217	77.56 272	92.22 104	47.49 352	82.61 197	69.24 346	72.43 184	85.28 191	94.20 77	51.91 310	90.07 234	65.36 250	96.45 108	95.11 61

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 152	83.93 276	69.66 174	76.28 297	81.09 276	72.43 184	86.47 173	90.19 197	60.46 267	93.15 141	77.45 141	86.39 302	90.22 222

GBi-Net		82.02 179	82.07 176	81.85 204	86.38 234	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 201	93.82 105

test1		82.02 179	82.07 176	81.85 204	86.38 234	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 201	93.82 105

FMVSNet2		81.31 188	81.61 183	80.41 228	86.38 234	58.75 283	83.93 160	86.58 228	72.43 184	87.65 144	92.98 114	63.78 251	90.22 225	66.86 236	93.92 189	92.27 171

GeoE		85.45 113	85.81 111	84.37 149	90.08 167	67.07 195	85.86 129	91.39 130	72.33 189	87.59 145	90.25 195	84.85 69	92.37 161	78.00 133	91.94 234	93.66 113

RRT_MVS		83.25 162	81.08 191	89.74 53	80.55 312	79.32 80	86.41 122	86.69 226	72.33 189	87.00 157	91.08 169	44.98 344	95.55 40	84.47 61	96.24 118	94.36 84

ECVR-MVS		78.44 227	78.63 221	77.88 268	91.85 118	48.95 346	83.68 169	69.91 344	72.30 191	84.26 215	94.20 77	51.89 311	89.82 238	63.58 259	96.02 125	94.87 64

v2v482		84.09 144	84.24 145	83.62 170	87.13 221	61.40 248	82.71 196	89.71 176	72.19 192	89.55 114	91.41 161	70.70 221	93.20 136	81.02 94	93.76 191	96.25 31

ETH3D cwj APD-0.16		87.83 81	87.62 83	88.47 77	91.21 140	78.20 89	87.26 104	94.54 20	72.05 193	88.89 122	92.31 138	83.86 77	94.24 90	81.59 90	96.87 91	92.97 140

DP-MVS Recon		84.05 146	83.22 157	86.52 106	91.73 122	75.27 123	83.23 184	92.40 102	72.04 194	82.04 243	88.33 225	77.91 146	93.95 105	66.17 242	95.12 158	90.34 221

MG-MVS		80.32 207	80.94 193	78.47 257	88.18 199	52.62 325	82.29 210	85.01 251	72.01 195	79.24 280	92.54 132	69.36 224	93.36 133	70.65 206	89.19 273	89.45 232

FPMVS		72.29 285	72.00 285	73.14 302	88.63 190	85.00 36	74.65 312	67.39 348	71.94 196	77.80 291	87.66 236	50.48 317	75.83 336	49.95 335	79.51 341	58.58 365

MVSFormer		82.23 175	81.57 185	84.19 157	85.54 251	69.26 179	91.98 31	90.08 170	71.54 197	76.23 300	85.07 281	58.69 282	94.27 87	86.26 38	88.77 277	89.03 244

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 97	82.98 54	91.98 31	90.08 170	71.54 197	94.28 21	96.54 13	81.57 112	94.27 87	86.26 38	96.49 106	97.09 19

h-mvs33		84.25 140	82.76 164	88.72 72	91.82 121	82.60 57	84.00 157	84.98 252	71.27 199	86.70 163	90.55 189	63.04 257	93.92 106	78.26 127	94.20 183	89.63 229

hse-mvs2		83.47 159	81.81 180	88.47 77	91.03 147	82.27 58	82.61 197	83.69 258	71.27 199	86.70 163	86.05 261	63.04 257	92.41 159	78.26 127	93.62 198	90.71 210

TinyColmap		81.25 189	82.34 174	77.99 266	85.33 253	60.68 261	82.32 209	88.33 198	71.26 201	86.97 158	92.22 143	77.10 156	86.98 272	62.37 267	95.17 155	86.31 275

CS-MVS		82.02 179	82.63 168	80.19 232	84.80 258	57.56 290	82.39 207	94.72 18	71.24 202	80.22 271	84.89 285	75.85 169	94.56 84	76.08 154	93.49 199	88.46 250

ZD-MVS					92.22 104	80.48 69		91.85 116	71.22 203	90.38 89	92.98 114	86.06 63	96.11 6	81.99 85	96.75 97

MVS_Test		82.47 172	83.22 157	80.22 231	82.62 289	57.75 289	82.54 202	91.96 114	71.16 204	82.89 232	92.52 133	77.41 151	90.50 218	80.04 106	87.84 290	92.40 162

DELS-MVS		81.44 187	81.25 187	82.03 200	84.27 268	62.87 233	76.47 295	92.49 101	70.97 205	81.64 252	83.83 295	75.03 174	92.70 153	74.29 169	92.22 228	90.51 218
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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 70	93.75 63	77.44 101	86.31 123	95.27 12	70.80 206	92.28 58	93.80 98	86.89 51	94.64 78	85.52 48	97.51 74	94.30 86

save fliter					93.75 63	77.44 101	86.31 123	89.72 175	70.80 206

PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 74	77.96 94	87.94 95	91.97 113	70.73 208	94.19 22	96.67 11	76.94 159	94.57 82	83.07 73	96.28 114	96.15 32

DeepC-MVS_fast	80.27 8	86.23 100	85.65 116	87.96 88	91.30 137	76.92 110	87.19 105	91.99 112	70.56 209	84.96 195	90.69 184	80.01 131	95.14 62	78.37 123	95.78 137	91.82 187
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		82.19 176	81.23 189	85.10 137	87.95 203	69.17 183	83.22 185	93.33 67	70.42 210	78.58 284	79.77 337	77.29 152	94.20 93	71.51 198	88.96 275	91.93 184

test20.03		73.75 273	74.59 261	71.22 312	81.11 301	51.12 338	70.15 332	72.10 333	70.42 210	80.28 270	91.50 159	64.21 248	74.72 340	46.96 349	94.58 175	87.82 261

JIA-IIPM		69.41 304	66.64 316	77.70 271	73.19 359	71.24 164	75.67 301	65.56 355	70.42 210	65.18 351	92.97 116	33.64 367	83.06 310	53.52 321	69.61 364	78.79 345

v1144		84.54 133	84.72 132	84.00 160	87.67 210	62.55 238	82.97 190	90.93 143	70.32 213	89.80 104	90.99 173	73.50 193	93.48 127	81.69 89	94.65 174	95.97 38

DeepPCF-MVS	81.24 5	87.28 85	86.21 104	90.49 43	91.48 134	84.90 38	83.41 177	92.38 104	70.25 214	89.35 118	90.68 185	82.85 89	94.57 82	79.55 111	95.95 127	92.00 180

KD-MVS_self_test		81.93 183	83.14 160	78.30 260	84.75 259	52.75 322	80.37 239	89.42 183	70.24 215	90.26 92	93.39 107	74.55 184	86.77 276	68.61 227	96.64 99	95.38 51

thres200		72.34 284	71.55 290	74.70 296	83.48 279	51.60 333	75.02 308	73.71 322	70.14 216	78.56 285	80.57 328	46.20 327	88.20 260	46.99 348	89.29 270	84.32 295

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 68	84.79 40	89.89 59	90.63 150	70.00 217	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 126	95.52 48

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 173	86.67 16	90.51 47	90.20 167	69.87 218	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 112	97.09 19

PM-MVS		80.20 210	79.00 216	83.78 166	88.17 200	86.66 17	81.31 226	66.81 354	69.64 219	88.33 136	90.19 197	64.58 245	83.63 309	71.99 197	90.03 264	81.06 340

V42		83.47 159	83.37 156	83.75 167	83.16 284	63.33 226	81.31 226	90.23 166	69.51 220	90.91 83	90.81 181	74.16 185	92.29 165	80.06 105	90.22 263	95.62 46

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 72	84.72 41	89.70 61	90.29 164	69.27 221	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 130	95.34 52

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 81	88.78 189	79.72 75	87.15 107	93.50 62	69.17 222	85.80 184	89.56 207	80.76 122	92.13 167	73.21 188	95.51 143	93.25 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet_DTU		77.81 235	77.05 238	80.09 234	81.37 298	59.90 267	83.26 181	88.29 199	69.16 223	67.83 342	83.72 296	60.93 264	89.47 241	69.22 219	89.70 267	90.88 206

v1192		84.57 130	84.69 134	84.21 155	87.75 208	62.88 232	83.02 189	91.43 127	69.08 224	89.98 99	90.89 178	72.70 206	93.62 120	82.41 80	94.97 164	96.13 33

FMVSNet3		78.80 222	78.55 222	79.57 242	82.89 288	56.89 297	81.76 218	85.77 236	69.04 225	86.00 179	90.44 191	51.75 312	90.09 233	65.95 243	93.34 201	91.72 189

ab-mvs		79.67 215	80.56 196	76.99 276	88.48 193	56.93 295	84.70 143	86.06 232	68.95 226	80.78 261	93.08 111	75.30 173	84.62 301	56.78 300	90.90 253	89.43 234

thisisatest0530		79.07 218	77.33 236	84.26 154	87.13 221	64.58 214	83.66 170	75.95 304	68.86 227	85.22 192	87.36 242	38.10 358	93.57 124	75.47 161	94.28 181	94.62 71

Anonymous20240521		80.18 211	81.25 187	76.95 277	83.15 285	60.84 259	82.46 204	85.99 234	68.76 228	86.78 160	93.73 103	59.13 279	77.44 330	73.71 178	97.55 69	92.56 154

GA-MVS		75.83 255	74.61 259	79.48 244	81.87 292	59.25 273	73.42 319	82.88 263	68.68 229	79.75 274	81.80 318	50.62 316	89.46 242	66.85 237	85.64 307	89.72 228

c3_l		81.64 185	81.59 184	81.79 208	80.86 305	59.15 276	78.61 266	90.18 168	68.36 230	87.20 149	87.11 247	69.39 223	91.62 182	78.16 130	94.43 179	94.60 73

CLD-MVS		83.18 164	82.64 167	84.79 141	89.05 182	67.82 192	77.93 273	92.52 100	68.33 231	85.07 194	81.54 321	82.06 102	92.96 146	69.35 216	97.91 50	93.57 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CL-MVSNet_self_test		76.81 245	77.38 235	75.12 293	86.90 229	51.34 334	73.20 321	80.63 280	68.30 232	81.80 249	88.40 224	66.92 237	80.90 320	55.35 311	94.90 166	93.12 132

PLC	73.85 16	82.09 178	80.31 200	87.45 93	90.86 152	80.29 71	85.88 128	90.65 149	68.17 233	76.32 299	86.33 255	73.12 201	92.61 156	61.40 278	90.02 265	89.44 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+		81.04 192	80.57 195	82.46 197	87.50 214	63.22 228	78.37 269	89.63 178	68.01 234	81.87 245	82.08 316	82.31 95	92.65 155	67.10 235	88.30 285	91.51 196

LF4IMVS		82.75 168	81.93 179	85.19 134	82.08 290	80.15 72	85.53 133	88.76 191	68.01 234	85.58 187	87.75 234	71.80 215	86.85 274	74.02 173	93.87 190	88.58 249

QAPM		82.59 170	82.59 170	82.58 193	86.44 232	66.69 200	89.94 58	90.36 157	67.97 236	84.94 197	92.58 130	72.71 205	92.18 166	70.63 207	87.73 291	88.85 247

v1921920		84.23 142	84.37 143	83.79 165	87.64 212	61.71 246	82.91 192	91.20 135	67.94 237	90.06 94	90.34 192	72.04 213	93.59 121	82.32 82	94.91 165	96.07 35

v1240		84.30 138	84.51 139	83.65 169	87.65 211	61.26 251	82.85 193	91.54 124	67.94 237	90.68 86	90.65 187	71.71 216	93.64 116	82.84 77	94.78 169	96.07 35

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 65	95.72 22	76.74 113	92.49 25	91.19 136	67.85 239	86.63 166	94.84 47	79.58 134	95.96 13	87.62 14	94.50 176	94.56 74
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v144192		84.24 141	84.41 141	83.71 168	87.59 213	61.57 247	82.95 191	91.03 139	67.82 240	89.80 104	90.49 190	73.28 199	93.51 126	81.88 88	94.89 167	96.04 37

RRT_test8_iter05		78.08 231	77.52 232	79.75 238	80.84 306	52.54 326	80.61 236	88.96 188	67.77 241	84.62 202	89.29 210	33.89 365	92.10 170	77.59 138	94.15 184	94.62 71

DIV-MVS_self_test		80.43 202	80.23 202	81.02 218	79.99 315	59.25 273	77.07 286	87.02 222	67.38 242	86.19 174	89.22 211	63.09 255	90.16 227	76.32 150	95.80 135	93.66 113

cl____		80.42 203	80.23 202	81.02 218	79.99 315	59.25 273	77.07 286	87.02 222	67.37 243	86.18 176	89.21 212	63.08 256	90.16 227	76.31 151	95.80 135	93.65 115

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 190	81.41 297	60.98 257	77.81 275	90.14 169	67.31 244	86.95 159	87.24 244	64.26 247	92.31 163	75.23 164	91.61 238	94.85 67

EMVS		61.10 329	60.81 330	61.99 340	65.96 371	55.86 302	53.10 362	58.97 366	67.06 245	56.89 368	63.33 364	40.98 353	67.03 355	54.79 315	86.18 304	63.08 360

OpenMVS	76.72 13	81.98 182	82.00 178	81.93 201	84.42 264	68.22 188	88.50 89	89.48 181	66.92 246	81.80 249	91.86 147	72.59 207	90.16 227	71.19 200	91.25 243	87.40 264

testgi		72.36 283	74.61 259	65.59 332	80.56 311	42.82 365	68.29 337	73.35 324	66.87 247	81.84 246	89.93 202	72.08 212	66.92 356	46.05 351	92.54 221	87.01 269

E-PMN		61.59 326	61.62 328	61.49 342	66.81 370	55.40 305	53.77 361	60.34 364	66.80 248	58.90 365	65.50 363	40.48 355	66.12 359	55.72 306	86.25 303	62.95 361

diffmvs		80.40 204	80.48 199	80.17 233	79.02 327	60.04 265	77.54 280	90.28 165	66.65 249	82.40 237	87.33 243	73.50 193	87.35 267	77.98 134	89.62 268	93.13 131

EPNet_dtu		72.87 280	71.33 292	77.49 274	77.72 332	60.55 262	82.35 208	75.79 305	66.49 250	58.39 367	81.06 324	53.68 307	85.98 287	53.55 320	92.97 213	85.95 278
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
agg_prior1		85.72 109	85.20 123	87.28 95	91.58 127	77.69 97	83.69 168	90.30 161	66.29 251	84.32 209	91.07 171	82.13 100	93.18 137	81.02 94	96.36 111	90.98 202

bset_n11_16_dypcd		79.19 217	77.97 229	82.86 188	85.81 248	66.85 199	75.02 308	79.31 286	66.07 252	83.50 225	83.37 303	55.04 305	92.10 170	78.63 121	94.99 163	89.63 229

baseline1		73.26 275	73.54 270	72.43 308	84.92 256	47.79 351	79.89 245	74.00 317	65.93 253	78.81 283	86.28 258	56.36 296	81.63 318	56.63 301	79.04 346	87.87 260

CDPH-MVS		86.17 103	85.54 117	88.05 87	92.25 102	75.45 122	83.85 162	92.01 111	65.91 254	86.19 174	91.75 154	83.77 80	94.98 68	77.43 142	96.71 98	93.73 110

cl22		78.97 219	78.21 227	81.24 214	77.74 331	59.01 277	77.46 283	87.13 217	65.79 255	84.32 209	85.10 278	58.96 281	90.88 207	75.36 163	92.03 230	93.84 102

train_agg		85.98 106	85.28 121	88.07 86	92.34 98	79.70 76	83.94 158	90.32 158	65.79 255	84.49 205	90.97 174	81.93 106	93.63 117	81.21 92	96.54 104	90.88 206

test_8					92.09 108	78.87 84	83.82 163	90.31 160	65.79 255	84.36 208	90.96 176	81.93 106	93.44 129

miper_ehance_all_eth		80.34 206	80.04 209	81.24 214	79.82 317	58.95 278	77.66 277	89.66 177	65.75 258	85.99 182	85.11 277	68.29 230	91.42 189	76.03 155	92.03 230	93.33 123

BH-w/o		76.57 248	76.07 249	78.10 264	86.88 230	65.92 206	77.63 278	86.33 229	65.69 259	80.89 259	79.95 334	68.97 228	90.74 211	53.01 325	85.25 311	77.62 346

MAR-MVS		80.24 209	78.74 220	84.73 143	86.87 231	78.18 90	85.75 130	87.81 208	65.67 260	77.84 289	78.50 341	73.79 190	90.53 217	61.59 277	90.87 254	85.49 284
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
ETH3 D test6400		85.09 119	84.87 128	85.75 126	90.80 153	69.34 177	85.90 127	93.31 70	65.43 261	86.11 177	89.95 201	80.92 120	94.86 71	75.90 157	95.57 142	93.05 134

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 184	88.31 196	71.73 158	79.53 249	87.17 214	65.43 261	79.59 275	82.73 311	76.94 159	90.14 230	73.22 183	88.33 281	86.90 270

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 184	88.31 196	71.73 158	79.53 249	87.17 214	65.43 261	79.59 275	82.73 311	76.94 159	90.14 230	73.22 183	88.33 281	86.90 270

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 184	88.31 196	71.73 158	79.53 249	87.17 214	65.43 261	79.59 275	82.73 311	76.94 159	90.14 230	73.22 183	88.33 281	86.90 270

TEST9					92.34 98	79.70 76	83.94 158	90.32 158	65.41 265	84.49 205	90.97 174	82.03 104	93.63 117

TR-MVS		76.77 246	75.79 250	79.72 239	86.10 246	65.79 207	77.14 284	83.02 262	65.20 266	81.40 254	82.10 315	66.30 239	90.73 212	55.57 308	85.27 310	82.65 316

tpmvs		70.16 297	69.56 301	71.96 310	74.71 355	48.13 348	79.63 247	75.45 310	65.02 267	70.26 333	81.88 317	45.34 340	85.68 291	58.34 293	75.39 354	82.08 325

IterMVS		76.91 243	76.34 246	78.64 253	80.91 303	64.03 220	76.30 296	79.03 289	64.88 268	83.11 229	89.16 213	59.90 273	84.46 302	68.61 227	85.15 312	87.42 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AUN-MVS		81.18 190	78.78 218	88.39 80	90.93 149	82.14 59	82.51 203	83.67 259	64.69 269	80.29 268	85.91 264	51.07 314	92.38 160	76.29 152	93.63 197	90.65 214

PatchMatch-RL		74.48 268	73.22 273	78.27 262	87.70 209	85.26 34	75.92 300	70.09 342	64.34 270	76.09 302	81.25 323	65.87 243	78.07 329	53.86 319	83.82 323	71.48 354

miper_lstm_enhance		76.45 251	76.10 248	77.51 273	76.72 339	60.97 258	64.69 347	85.04 249	63.98 271	83.20 228	88.22 226	56.67 294	78.79 328	73.22 183	93.12 207	92.78 145

FMVSNet5		72.10 286	71.69 287	73.32 300	81.57 295	53.02 321	76.77 289	78.37 292	63.31 272	76.37 297	91.85 148	36.68 362	78.98 326	47.87 345	92.45 222	87.95 257

IB-MVS	62.13 19	71.64 289	68.97 303	79.66 241	80.80 308	62.26 244	73.94 315	76.90 298	63.27 273	68.63 338	76.79 349	33.83 366	91.84 179	59.28 290	87.26 294	84.88 289
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
new-patchmatchnet		70.10 298	73.37 272	60.29 345	81.23 300	16.95 375	59.54 355	74.62 312	62.93 274	80.97 257	87.93 232	62.83 260	71.90 343	55.24 312	95.01 162	92.00 180

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 145	91.58 127	73.24 135	84.21 152	91.67 122	62.86 275	80.94 258	87.16 245	67.27 235	92.87 151	69.82 213	88.94 276	87.99 256

原ACMM1				84.60 146	92.81 89	74.01 129		91.50 125	62.59 276	82.73 234	90.67 186	76.53 166	94.25 89	69.24 217	95.69 140	85.55 282

PAPR		78.84 221	78.10 228	81.07 216	85.17 254	60.22 264	82.21 214	90.57 152	62.51 277	75.32 311	84.61 289	74.99 175	92.30 164	59.48 289	88.04 287	90.68 212

Patchmatch-test		65.91 318	67.38 310	61.48 343	75.51 348	43.21 364	68.84 335	63.79 358	62.48 278	72.80 323	83.42 301	44.89 345	59.52 365	48.27 344	86.45 300	81.70 328

OpenMVS_ROB	70.19 17	77.77 236	77.46 233	78.71 252	84.39 265	61.15 252	81.18 230	82.52 265	62.45 279	83.34 226	87.37 241	66.20 240	88.66 255	64.69 254	85.02 313	86.32 274

test-LLR		67.21 310	66.74 314	68.63 323	76.45 342	55.21 307	67.89 338	67.14 351	62.43 280	65.08 352	72.39 356	43.41 348	69.37 346	61.00 279	84.89 316	81.31 333

test0.0.03 1		64.66 321	64.36 322	65.57 333	75.03 353	46.89 355	64.69 347	61.58 363	62.43 280	71.18 331	77.54 344	43.41 348	68.47 351	40.75 360	82.65 331	81.35 332

MCST-MVS		84.36 135	83.93 150	85.63 128	91.59 124	71.58 162	83.52 172	92.13 108	61.82 282	83.96 217	89.75 205	79.93 133	93.46 128	78.33 125	94.34 180	91.87 185

SCA		73.32 274	72.57 281	75.58 291	81.62 294	55.86 302	78.89 261	71.37 339	61.73 283	74.93 314	83.42 301	60.46 267	87.01 269	58.11 296	82.63 333	83.88 298

TAMVS		78.08 231	76.36 245	83.23 177	90.62 157	72.87 136	79.08 259	80.01 284	61.72 284	81.35 255	86.92 249	63.96 250	88.78 253	50.61 333	93.01 211	88.04 255

PVSNet_BlendedMVS		78.80 222	77.84 230	81.65 209	84.43 262	63.41 224	79.49 252	90.44 154	61.70 285	75.43 309	87.07 248	69.11 226	91.44 187	60.68 283	92.24 226	90.11 226

mvs_anonymous		78.13 230	78.76 219	76.23 288	79.24 324	50.31 343	78.69 264	84.82 254	61.60 286	83.09 231	92.82 121	73.89 189	87.01 269	68.33 230	86.41 301	91.37 197

PS-MVSNAJ		77.04 242	76.53 244	78.56 254	87.09 226	61.40 248	75.26 306	87.13 217	61.25 287	74.38 317	77.22 348	76.94 159	90.94 202	64.63 255	84.83 318	83.35 309

xiu_mvs_v2_base		77.19 240	76.75 242	78.52 255	87.01 227	61.30 250	75.55 304	87.12 220	61.24 288	74.45 315	78.79 339	77.20 153	90.93 203	64.62 256	84.80 319	83.32 310

KD-MVS_2432*1600		66.87 312	65.81 317	70.04 313	67.50 368	47.49 352	62.56 351	79.16 287	61.21 289	77.98 287	80.61 326	25.29 376	82.48 313	53.02 323	84.92 314	80.16 343

miper_refine_blended		66.87 312	65.81 317	70.04 313	67.50 368	47.49 352	62.56 351	79.16 287	61.21 289	77.98 287	80.61 326	25.29 376	82.48 313	53.02 323	84.92 314	80.16 343

CDS-MVSNet		77.32 239	75.40 254	83.06 180	89.00 184	72.48 147	77.90 274	82.17 269	60.81 291	78.94 282	83.49 299	59.30 277	88.76 254	54.64 317	92.37 223	87.93 258
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		77.09 241	75.70 252	81.25 212	75.27 351	61.08 253	77.49 282	85.07 247	60.78 292	86.55 167	88.68 221	43.14 350	90.25 222	73.69 179	90.67 259	92.42 160

XXY-MVS		74.44 270	76.19 247	69.21 319	84.61 260	52.43 327	71.70 326	77.18 297	60.73 293	80.60 262	90.96 176	75.44 170	69.35 348	56.13 304	88.33 281	85.86 280

ET-MVSNet_ETH3D		75.28 258	72.77 277	82.81 189	83.03 287	68.11 189	77.09 285	76.51 302	60.67 294	77.60 293	80.52 329	38.04 359	91.15 197	70.78 203	90.68 258	89.17 239

MVP-Stereo		75.81 256	73.51 271	82.71 190	89.35 176	73.62 130	80.06 241	85.20 244	60.30 295	73.96 318	87.94 231	57.89 289	89.45 243	52.02 328	74.87 355	85.06 288
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DPM-MVS		80.10 213	79.18 215	82.88 187	90.71 156	69.74 172	78.87 262	90.84 144	60.29 296	75.64 308	85.92 263	67.28 234	93.11 142	71.24 199	91.79 235	85.77 281

MIMVSNet		71.09 292	71.59 288	69.57 318	87.23 218	50.07 344	78.91 260	71.83 335	60.20 297	71.26 329	91.76 153	55.08 304	76.09 334	41.06 359	87.02 297	82.54 319

testdata				79.54 243	92.87 84	72.34 149		80.14 283	59.91 298	85.47 190	91.75 154	67.96 232	85.24 294	68.57 229	92.18 229	81.06 340

UnsupCasMVSNet_eth		71.63 290	72.30 284	69.62 317	76.47 341	52.70 324	70.03 333	80.97 277	59.18 299	79.36 278	88.21 227	60.50 266	69.12 349	58.33 294	77.62 350	87.04 268

PC_three_1452									58.96 300	90.06 94	91.33 162	80.66 124	93.03 145	75.78 158	95.94 128	92.48 158

our_test_3		71.85 287	71.59 288	72.62 306	80.71 309	53.78 315	69.72 334	71.71 338	58.80 301	78.03 286	80.51 330	56.61 295	78.84 327	62.20 269	86.04 305	85.23 285

MDA-MVSNet_test_wron		70.05 300	70.44 295	68.88 321	73.84 356	53.47 317	58.93 359	67.28 349	58.43 302	87.09 154	85.40 273	59.80 275	67.25 354	59.66 288	83.54 324	85.92 279

YYNet1		70.06 299	70.44 295	68.90 320	73.76 357	53.42 319	58.99 358	67.20 350	58.42 303	87.10 153	85.39 274	59.82 274	67.32 353	59.79 287	83.50 325	85.96 277

ppachtmachnet_test		74.73 267	74.00 266	76.90 279	80.71 309	56.89 297	71.53 327	78.42 291	58.24 304	79.32 279	82.92 308	57.91 288	84.26 304	65.60 248	91.36 242	89.56 231

无先验							82.81 194	85.62 238	58.09 305				91.41 190	67.95 233		84.48 292

miper_enhance_ethall		77.83 233	76.93 240	80.51 226	76.15 344	58.01 286	75.47 305	88.82 189	58.05 306	83.59 222	80.69 325	64.41 246	91.20 194	73.16 189	92.03 230	92.33 166

thisisatest0515		73.00 279	70.52 294	80.46 227	81.45 296	59.90 267	73.16 322	74.31 316	57.86 307	76.08 303	77.78 343	37.60 361	92.12 169	65.00 251	91.45 241	89.35 235

Patchmatch-RL test		74.48 268	73.68 268	76.89 280	84.83 257	66.54 201	72.29 324	69.16 347	57.70 308	86.76 161	86.33 255	45.79 334	82.59 312	69.63 214	90.65 261	81.54 331

PatchmatchNet		69.71 303	68.83 304	72.33 309	77.66 333	53.60 316	79.29 254	69.99 343	57.66 309	72.53 324	82.93 307	46.45 326	80.08 325	60.91 281	72.09 358	83.31 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
D2MVS		76.84 244	75.67 253	80.34 229	80.48 313	62.16 245	73.50 318	84.80 255	57.61 310	82.24 239	87.54 238	51.31 313	87.65 264	70.40 210	93.19 206	91.23 199

baseline2		69.77 302	66.89 312	78.41 258	79.51 320	58.09 285	76.23 298	69.57 345	57.50 311	64.82 355	77.45 346	46.02 329	88.44 256	53.08 322	77.83 348	88.70 248

PVSNet_Blended		76.49 250	75.40 254	79.76 237	84.43 262	63.41 224	75.14 307	90.44 154	57.36 312	75.43 309	78.30 342	69.11 226	91.44 187	60.68 283	87.70 292	84.42 294

PCF-MVS	74.62 15	82.15 177	80.92 194	85.84 124	89.43 175	72.30 150	80.53 237	91.82 118	57.36 312	87.81 143	89.92 203	77.67 148	93.63 117	58.69 291	95.08 159	91.58 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IU-MVS					94.18 49	72.64 140		90.82 145	56.98 314	89.67 108				85.78 47	97.92 48	93.28 125

旧先验2							81.73 219		56.88 315	86.54 172			84.90 298	72.81 190

HY-MVS	64.64 18	73.03 278	72.47 283	74.71 295	83.36 281	54.19 312	82.14 217	81.96 270	56.76 316	69.57 336	86.21 259	60.03 271	84.83 300	49.58 338	82.65 331	85.11 287

cascas		76.29 253	74.81 258	80.72 224	84.47 261	62.94 231	73.89 316	87.34 211	55.94 317	75.16 313	76.53 351	63.97 249	91.16 196	65.00 251	90.97 251	88.06 254

pmmvs-eth3d		78.42 228	77.04 239	82.57 195	87.44 215	74.41 127	80.86 234	79.67 285	55.68 318	84.69 201	90.31 194	60.91 265	85.42 293	62.20 269	91.59 239	87.88 259

新几何1				82.95 183	93.96 58	78.56 88		80.24 281	55.45 319	83.93 218	91.08 169	71.19 219	88.33 258	65.84 246	93.07 208	81.95 327

1121		80.86 194	79.81 211	84.02 159	93.93 59	78.70 86	81.64 221	80.18 282	55.43 320	83.67 220	91.15 167	71.29 218	91.41 190	67.95 233	93.06 209	81.96 326

N_pmnet		70.20 296	68.80 305	74.38 297	80.91 303	84.81 39	59.12 357	76.45 303	55.06 321	75.31 312	82.36 314	55.74 299	54.82 366	47.02 347	87.24 295	83.52 305

tpm		67.95 308	68.08 309	67.55 327	78.74 329	43.53 363	75.60 302	67.10 353	54.92 322	72.23 325	88.10 228	42.87 351	75.97 335	52.21 327	80.95 340	83.15 313

114514_t		83.10 166	82.54 171	84.77 142	92.90 83	69.10 184	86.65 117	90.62 151	54.66 323	81.46 253	90.81 181	76.98 158	94.38 86	72.62 191	96.18 119	90.82 208

1112_ss		74.82 266	73.74 267	78.04 265	89.57 174	60.04 265	76.49 294	87.09 221	54.31 324	73.66 320	79.80 335	60.25 270	86.76 278	58.37 292	84.15 322	87.32 265

UnsupCasMVSNet_bld		69.21 305	69.68 300	67.82 326	79.42 321	51.15 337	67.82 341	75.79 305	54.15 325	77.47 294	85.36 276	59.26 278	70.64 345	48.46 342	79.35 343	81.66 329

DWT-MVSNet_test		66.43 315	64.37 321	72.63 305	74.86 354	50.86 340	76.52 293	72.74 328	54.06 326	65.50 349	68.30 362	32.13 368	84.84 299	61.63 276	73.59 356	82.19 323

EPMVS		62.47 322	62.63 326	62.01 339	70.63 367	38.74 367	74.76 310	52.86 371	53.91 327	67.71 343	80.01 333	39.40 356	66.60 357	55.54 309	68.81 365	80.68 342

WTY-MVS		67.91 309	68.35 307	66.58 330	80.82 307	48.12 349	65.96 345	72.60 329	53.67 328	71.20 330	81.68 320	58.97 280	69.06 350	48.57 341	81.67 334	82.55 318

PAPM		71.77 288	70.06 299	76.92 278	86.39 233	53.97 313	76.62 292	86.62 227	53.44 329	63.97 357	84.73 288	57.79 290	92.34 162	39.65 361	81.33 337	84.45 293

PMMVS2		55.64 335	59.27 335	44.74 351	64.30 373	12.32 376	40.60 364	49.79 373	53.19 330	65.06 354	84.81 286	53.60 308	49.76 368	32.68 368	89.41 269	72.15 353

tpmrst		66.28 317	66.69 315	65.05 335	72.82 363	39.33 366	78.20 270	70.69 341	53.16 331	67.88 341	80.36 331	48.18 321	74.75 339	58.13 295	70.79 360	81.08 338

pmmvs4		74.92 264	72.98 276	80.73 223	84.95 255	71.71 161	76.23 298	77.59 295	52.83 332	77.73 292	86.38 253	56.35 297	84.97 297	57.72 298	87.05 296	85.51 283

test222					93.31 74	76.54 114	79.38 253	77.79 294	52.59 333	82.36 238	90.84 180	66.83 238			91.69 237	81.25 335

Anonymous20231206		71.38 291	71.88 286	69.88 315	86.31 238	54.37 311	70.39 331	74.62 312	52.57 334	76.73 295	88.76 219	59.94 272	72.06 342	44.35 354	93.23 205	83.23 312

MS-PatchMatch		70.93 293	70.22 297	73.06 303	81.85 293	62.50 239	73.82 317	77.90 293	52.44 335	75.92 304	81.27 322	55.67 300	81.75 316	55.37 310	77.70 349	74.94 350

gm-plane-assit					75.42 350	44.97 361			52.17 336		72.36 358		87.90 261	54.10 318

MDTV_nov1_ep13			68.29 308		78.03 330	43.87 362	74.12 314	72.22 332	52.17 336	67.02 345	85.54 267	45.36 339	80.85 321	55.73 305	84.42 321

USDC		76.63 247	76.73 243	76.34 286	83.46 280	57.20 294	80.02 243	88.04 204	52.14 338	83.65 221	91.25 163	63.24 254	86.65 279	54.66 316	94.11 185	85.17 286

sss		66.92 311	67.26 311	65.90 331	77.23 334	51.10 339	64.79 346	71.72 337	52.12 339	70.13 334	80.18 332	57.96 287	65.36 361	50.21 334	81.01 339	81.25 335

CostFormer		69.98 301	68.68 306	73.87 298	77.14 335	50.72 341	79.26 255	74.51 314	51.94 340	70.97 332	84.75 287	45.16 343	87.49 265	55.16 313	79.23 344	83.40 308

1314		73.22 276	72.56 282	75.20 292	80.41 314	57.84 287	81.64 221	85.36 240	51.68 341	73.10 322	76.65 350	61.45 263	85.19 295	63.54 260	79.21 345	82.59 317

jason		77.42 238	75.75 251	82.43 198	87.10 225	69.27 178	77.99 272	81.94 271	51.47 342	77.84 289	85.07 281	60.32 269	89.00 247	70.74 205	89.27 272	89.03 244
jason: jason.
dp		60.70 331	60.29 333	61.92 341	72.04 365	38.67 368	70.83 328	64.08 357	51.28 343	60.75 360	77.28 347	36.59 363	71.58 344	47.41 346	62.34 367	75.52 349

PVSNet	58.17 21	66.41 316	65.63 319	68.75 322	81.96 291	49.88 345	62.19 353	72.51 331	51.03 344	68.04 340	75.34 354	50.84 315	74.77 338	45.82 352	82.96 327	81.60 330

test-mter		65.00 320	63.79 323	68.63 323	76.45 342	55.21 307	67.89 338	67.14 351	50.98 345	65.08 352	72.39 356	28.27 373	69.37 346	61.00 279	84.89 316	81.31 333

CMPMVS	59.41 20	75.12 261	73.57 269	79.77 236	75.84 346	67.22 193	81.21 229	82.18 268	50.78 346	76.50 296	87.66 236	55.20 303	82.99 311	62.17 271	90.64 262	89.09 243
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Test_1112_low_res		73.90 272	73.08 274	76.35 285	90.35 162	55.95 300	73.40 320	86.17 231	50.70 347	73.14 321	85.94 262	58.31 284	85.90 289	56.51 302	83.22 326	87.20 266

lupinMVS		76.37 252	74.46 262	82.09 199	85.54 251	69.26 179	76.79 288	80.77 279	50.68 348	76.23 300	82.82 309	58.69 282	88.94 248	69.85 212	88.77 277	88.07 253

CR-MVSNet		74.00 271	73.04 275	76.85 281	79.58 318	62.64 236	82.58 199	76.90 298	50.50 349	75.72 306	92.38 134	48.07 322	84.07 305	68.72 226	82.91 329	83.85 301

pmmvs5		70.73 294	70.07 298	72.72 304	77.03 337	52.73 323	74.14 313	75.65 308	50.36 350	72.17 326	85.37 275	55.42 302	80.67 322	52.86 326	87.59 293	84.77 290

ADS-MVSNet2		65.87 319	63.64 324	72.55 307	73.16 360	56.92 296	67.10 342	74.81 311	49.74 351	66.04 347	82.97 305	46.71 324	77.26 331	42.29 356	69.96 362	83.46 306

ADS-MVSNet		61.90 324	62.19 327	61.03 344	73.16 360	36.42 369	67.10 342	61.75 361	49.74 351	66.04 347	82.97 305	46.71 324	63.21 363	42.29 356	69.96 362	83.46 306

tpm2		68.45 307	66.83 313	73.30 301	78.93 328	48.50 347	79.76 246	71.76 336	47.50 353	69.92 335	83.60 297	42.07 352	88.40 257	48.44 343	79.51 341	83.01 315

HyFIR lowres test		75.12 261	72.66 279	82.50 196	91.44 136	65.19 210	72.47 323	87.31 212	46.79 354	80.29 268	84.30 292	52.70 309	92.10 170	51.88 332	86.73 298	90.22 222

MVS-HIRNet		61.16 328	62.92 325	55.87 348	79.09 325	35.34 370	71.83 325	57.98 368	46.56 355	59.05 364	91.14 168	49.95 318	76.43 333	38.74 362	71.92 359	55.84 366

MDTV_nov1_ep13_2view						27.60 374	70.76 329		46.47 356	61.27 359		45.20 341		49.18 339		83.75 303

MVS		73.21 277	72.59 280	75.06 294	80.97 302	60.81 260	81.64 221	85.92 235	46.03 357	71.68 328	77.54 344	68.47 229	89.77 239	55.70 307	85.39 308	74.60 351

TESTMET0.1,1		61.29 327	60.32 332	64.19 336	72.06 364	51.30 335	67.89 338	62.09 359	45.27 358	60.65 361	69.01 359	27.93 374	64.74 362	56.31 303	81.65 336	76.53 347

tpm cat1		66.76 314	65.21 320	71.42 311	77.09 336	50.62 342	78.01 271	73.68 323	44.89 359	68.64 337	79.00 338	45.51 337	82.42 315	49.91 336	70.15 361	81.23 337

PVSNet_0	51.08 22	56.10 333	54.97 338	59.48 346	75.12 352	53.28 320	55.16 360	61.89 360	44.30 360	59.16 363	62.48 365	54.22 306	65.91 360	35.40 365	47.01 368	59.25 364

CHOSEN 280x420		59.08 332	56.52 337	66.76 329	76.51 340	64.39 217	49.62 363	59.00 365	43.86 361	55.66 369	68.41 361	35.55 364	68.21 352	43.25 355	76.78 353	67.69 359

new_pmnet		55.69 334	57.66 336	49.76 350	75.47 349	30.59 372	59.56 354	51.45 372	43.62 362	62.49 358	75.48 353	40.96 354	49.15 369	37.39 364	72.52 357	69.55 357

CHOSEN 1792x2688		72.45 282	70.56 293	78.13 263	90.02 172	63.08 229	68.72 336	83.16 261	42.99 363	75.92 304	85.46 271	57.22 293	85.18 296	49.87 337	81.67 334	86.14 276

MVE	40.22 23	51.82 336	50.47 339	55.87 348	62.66 374	51.91 330	31.61 366	39.28 375	40.65 364	50.76 370	74.98 355	56.24 298	44.67 370	33.94 367	64.11 366	71.04 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		62.47 322	60.02 334	69.80 316	71.58 366	64.00 221	70.52 330	58.44 367	39.77 365	66.05 346	75.84 352	27.10 375	72.28 341	46.15 350	84.77 320	73.11 352

EU-MVSNet		75.12 261	74.43 263	77.18 275	83.11 286	59.48 271	85.71 132	82.43 267	39.76 366	85.64 186	88.76 219	44.71 346	87.88 262	73.86 176	85.88 306	84.16 297

CVMVSNet		72.62 281	71.41 291	76.28 287	83.25 282	60.34 263	83.50 175	79.02 290	37.77 367	76.33 298	85.10 278	49.60 319	87.41 266	70.54 208	77.54 351	81.08 338

PMMVS		61.65 325	60.38 331	65.47 334	65.40 372	69.26 179	63.97 349	61.73 362	36.80 368	60.11 362	68.43 360	59.42 276	66.35 358	48.97 340	78.57 347	60.81 362

DSMNet-mixed		60.98 330	61.61 329	59.09 347	72.88 362	45.05 360	74.70 311	46.61 374	26.20 369	65.34 350	90.32 193	55.46 301	63.12 364	41.72 358	81.30 338	69.09 358

DeepMVS_CX				24.13 353	32.95 375	29.49 373		21.63 378	12.07 370	37.95 371	45.07 368	30.84 369	19.21 372	17.94 371	33.06 371	23.69 368

test_method		30.46 337	29.60 340	33.06 352	17.99 376	3.84 378	13.62 367	73.92 318	2.79 371	18.29 373	53.41 367	28.53 372	43.25 371	22.56 369	35.27 370	52.11 367

tmp_tt		20.25 339	24.50 342	7.49 354	4.47 377	8.70 377	34.17 365	25.16 377	1.00 372	32.43 372	18.49 369	39.37 357	9.21 373	21.64 370	43.75 369	4.57 369

test123		6.27 342	8.08 345	0.84 355	1.11 379	0.57 379	62.90 350	0.82 379	0.54 373	1.07 375	2.75 374	1.26 378	0.30 374	1.04 372	1.26 373	1.66 370

testmvs		5.91 343	7.65 346	0.72 356	1.20 378	0.37 380	59.14 356	0.67 380	0.49 374	1.11 374	2.76 373	0.94 379	0.24 375	1.02 373	1.47 372	1.55 371

test_blank		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet_test		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

cdsmvs_eth3d_5k		20.81 338	27.75 341	0.00 357	0.00 380	0.00 381	0.00 368	85.44 239	0.00 375	0.00 376	82.82 309	81.46 113	0.00 376	0.00 374	0.00 374	0.00 372

pcd_1.5k_mvsjas		6.41 341	8.55 344	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	76.94 159	0.00 376	0.00 374	0.00 374	0.00 372

sosnet-low-res		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

sosnet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uncertanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

Regformer		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

ab-mvs-re		6.65 340	8.87 343	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	79.80 335	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

MSC_two_6792asdad				88.81 68	91.55 130	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 162

No_MVS				88.81 68	91.55 130	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 162

eth-test2					0.00 380

eth-test					0.00 380

OPU-MVS				88.27 83	91.89 116	77.83 95	90.47 48				91.22 164	81.12 118	94.68 76	74.48 168	95.35 147	92.29 169

test_0728_SECOND				86.79 101	94.25 48	72.45 148	90.54 45	94.10 38					95.88 16	86.42 34	97.97 45	92.02 179

GSMVS																83.88 298

test_part2					93.86 61	77.77 96				92.84 48

sam_mvs1												46.11 328				83.88 298

sam_mvs												45.92 333

ambc				82.98 182	90.55 160	64.86 212	88.20 90	89.15 186		89.40 117	93.96 91	71.67 217	91.38 192	78.83 119	96.55 103	92.71 149

MTGPA								91.81 119

test_post1							78.85 263				3.13 371	45.19 342	80.13 324	58.11 296

test_post											3.10 372	45.43 338	77.22 332

patchmatchnet-post											81.71 319	45.93 332	87.01 269

GG-mvs-BLEND				67.16 328	73.36 358	46.54 357	84.15 153	55.04 370		58.64 366	61.95 366	29.93 371	83.87 308	38.71 363	76.92 352	71.07 355

MTMP							90.66 42	33.14 376

test9_res														80.83 98	96.45 108	90.57 215

agg_prior2														79.68 110	96.16 120	90.22 222

agg_prior					91.58 127	77.69 97		90.30 161		84.32 209			93.18 137

test_prior4						78.97 83	84.59 145

test_prior				86.32 109	90.59 158	71.99 155		92.85 91					94.17 96			92.80 143

新几何2							81.72 220

旧先验1					91.97 111	71.77 157		81.78 272			91.84 149	73.92 188			93.65 196	83.61 304

原ACMM2							82.26 213

testdata2													86.43 282	63.52 261

segment_acmp												81.94 105

test12				86.57 104	90.74 154	72.63 142		90.69 148		82.76 233		79.20 135	94.80 73		95.32 149	92.27 171

plane_prior7					93.45 69	77.31 105

plane_prior6					92.61 90	76.54 114						74.84 177

plane_prior5								93.61 58					95.22 59	80.78 99	95.83 133	94.46 79

plane_prior4											92.95 117

plane_prior1					92.83 88

n2								0.00 381

nn								0.00 381

door-mid								74.45 315

lessismore_v0				85.95 120	91.10 146	70.99 166		70.91 340		91.79 67	94.42 65	61.76 262	92.93 148	79.52 114	93.03 210	93.93 99

test11								91.46 126

door								72.57 330

HQP5-MVS						70.66 167

BP-MVS														77.30 143

HQP4-MVS										80.56 264			94.61 80			93.56 120

HQP3-MVS								92.68 97							94.47 177

HQP2-MVS												72.10 210

NP-MVS					91.95 112	74.55 126					90.17 199

ACMMP++_ref															95.74 139

ACMMP++															97.35 78

Test By Simon												79.09 136

High-resolution multi-view benchmark