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 25	91.50 1	63.30 123	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 70	68.08 93	97.05 1	96.93 1

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 43	81.06 3	87.20 28	90.28 69	79.20 14	85.58 48	78.04 27	94.08 56	83.55 135

DTE-MVSNet		80.35 53	82.89 38	72.74 151	89.84 8	37.34 317	77.16 112	81.81 104	80.45 4	90.92 3	92.95 7	74.57 51	86.12 30	63.65 131	94.68 33	94.76 6

PEN-MVS		80.46 51	82.91 37	73.11 139	89.83 9	39.02 302	77.06 115	82.61 95	80.04 5	90.60 6	92.85 9	74.93 47	85.21 57	63.15 139	95.15 18	95.09 2

PS-CasMVS		80.41 52	82.86 39	73.07 140	89.93 7	39.21 299	77.15 113	81.28 114	79.74 6	90.87 4	92.73 11	75.03 45	84.93 63	63.83 130	95.19 16	95.07 3

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 109	74.39 36	87.18 11	88.18 7	78.98 7	86.11 39	91.47 29	79.70 12	85.76 42	66.91 108	95.46 11	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVSNet		79.48 60	81.65 50	72.98 143	89.66 13	39.06 301	76.76 117	80.46 133	78.91 8	90.32 7	91.70 24	68.49 97	84.89 64	63.40 136	95.12 19	95.01 4

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 67	74.51 51	96.15 2	92.88 7

WR-MVS_H		80.22 56	82.17 45	74.39 117	89.46 15	42.69 275	78.24 100	82.24 98	78.21 10	89.57 9	92.10 18	68.05 102	85.59 47	66.04 111	95.62 9	94.88 5

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 54	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 45	79.20 14	85.58 48	78.11 25	94.46 38	84.89 92

RE-MVS-def			85.50 4		86.19 54	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 45	81.38 7		78.11 25	94.46 38	84.89 92

abl_6		84.92 3	85.70 3	82.57 17	86.72 48	79.27 8	87.56 7	86.08 28	77.48 13	88.12 14	91.53 27	81.18 8	84.31 76	78.12 24	94.47 37	84.15 123

LS3D		80.99 46	80.85 55	81.41 30	78.37 168	71.37 55	87.45 8	85.87 32	77.48 13	81.98 91	89.95 77	69.14 90	85.26 54	66.15 109	91.24 104	87.61 51

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 42	77.42 15	86.15 37	90.24 70	81.69 5	85.94 33	77.77 29	93.58 67	83.09 151

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 132	72.03 49	84.38 37	86.23 26	77.28 16	80.65 110	90.18 73	59.80 176	87.58 4	73.06 60	91.34 102	89.01 33

UA-Net		81.56 37	82.28 44	79.40 54	88.91 30	69.16 78	84.67 35	80.01 142	75.34 17	79.80 119	94.91 2	69.79 87	80.25 149	72.63 63	94.46 38	88.78 41

test_0402		78.17 76	79.48 66	74.24 120	83.50 97	59.15 159	72.52 160	74.60 208	75.34 17	88.69 13	91.81 22	75.06 44	82.37 109	65.10 117	88.68 160	81.20 188

test_one_0601					85.84 66	61.45 134		85.63 33	75.27 19	85.62 47	90.38 63	76.72 29

DP-MVS		78.44 73	79.29 67	75.90 101	81.86 126	65.33 105	79.05 89	84.63 60	74.83 20	80.41 113	86.27 154	71.68 71	83.45 90	62.45 143	92.40 85	78.92 227

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 112	73.53 44	85.50 28	87.45 14	74.11 21	86.45 34	90.52 53	80.02 11	84.48 71	77.73 30	94.34 49	85.93 73

PMVS	70.70 6	81.70 36	83.15 34	77.36 83	90.35 6	82.82 2	82.15 57	79.22 151	74.08 22	87.16 30	91.97 19	84.80 2	76.97 205	64.98 119	93.61 65	72.28 285
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++.		81.24 39	82.74 40	76.76 88	83.14 102	60.90 141	91.64 1	85.49 35	74.03 23	84.93 56	90.38 63	66.82 113	85.90 37	77.43 33	90.78 120	83.49 137

test_0728_THIRD									74.03 23	85.83 42	90.41 58	75.58 39	85.69 44	77.43 33	94.74 31	84.31 119

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 36	91.70 24	78.23 20	85.20 58	79.45 13	94.91 25	88.15 45

DPE-MVS		82.00 34	83.02 36	78.95 62	85.36 70	67.25 89	82.91 52	84.98 48	73.52 26	85.43 50	90.03 74	76.37 31	86.97 11	74.56 50	94.02 59	82.62 167
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 77	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 121	81.53 3	92.15 89	88.91 37
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
ACMH+	66.64 10	81.20 40	82.48 43	77.35 84	81.16 135	62.39 127	80.51 67	87.80 8	73.02 28	87.57 21	91.08 36	80.28 10	82.44 107	64.82 120	96.10 4	87.21 56

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	90.39 61	73.86 56	86.31 20	78.84 20	94.03 57	84.64 102

X-MVStestdata		76.81 84	74.79 103	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	9.95 370	73.86 56	86.31 20	78.84 20	94.03 57	84.64 102

test_241102_ONE					86.12 58	61.06 137		84.72 55	72.64 31	87.38 25	89.47 83	77.48 24	85.74 43

SED-MVS		81.78 35	83.48 27	76.67 89	86.12 58	61.06 137	83.62 44	84.72 55	72.61 32	87.38 25	89.70 80	77.48 24	85.89 39	75.29 45	94.39 43	83.08 152

test_241102_TWO								84.80 51	72.61 32	84.93 56	89.70 80	77.73 23	85.89 39	75.29 45	94.22 55	83.25 147

DVP-MVS		81.15 42	83.12 35	75.24 110	86.16 56	60.78 143	83.77 42	80.58 131	72.48 34	85.83 42	90.41 58	78.57 18	85.69 44	75.86 42	94.39 43	79.24 223
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					86.16 56	60.78 143	83.81 41	85.10 46	72.48 34	85.27 53	89.96 76	78.57 18

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 92	72.41 36	85.11 55	90.85 41	76.65 30	84.89 64	79.30 17	94.63 34	82.35 174

UniMVSNet_ETH3D		76.74 85	79.02 68	69.92 189	89.27 20	43.81 263	74.47 147	71.70 228	72.33 37	85.50 49	93.65 3	77.98 22	76.88 208	54.60 205	91.64 94	89.08 31

testtj		81.19 41	81.70 49	79.67 51	83.95 92	69.77 70	83.58 47	84.63 60	72.13 38	82.85 84	88.36 110	75.00 46	86.79 12	71.99 72	92.84 76	82.44 172

zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 55	80.63 128	72.08 39	84.93 56	90.79 42	74.65 49	84.42 73	80.98 4	94.75 29	80.82 199

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 32	80.63 128	72.08 39	84.93 56	90.79 42	74.65 49	84.42 73	80.98 4	94.75 29	80.82 199

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 79	66.72 95	86.54 21	85.11 45	72.00 41	86.65 33	91.75 23	78.20 21	87.04 9	77.93 28	94.32 50	83.47 140

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 65	71.96 42	84.70 62	90.56 50	77.12 26	86.18 27	79.24 18	95.36 13	82.49 171

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 83	71.31 43	81.26 102	90.96 38	74.57 51	84.69 68	78.41 22	94.78 28	82.74 165
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
gg-mvs-nofinetune		55.75 290	56.75 290	52.72 315	62.87 326	28.04 360	68.92 209	41.36 364	71.09 44	50.80 350	92.63 12	20.74 368	66.86 296	29.97 349	72.41 318	63.25 340

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 31	71.03 45	85.85 41	90.58 49	78.77 17	85.78 41	79.37 16	95.17 17	84.62 104
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
SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 73	71.00 59	85.53 27	84.78 52	70.91 46	85.64 44	90.41 58	75.55 40	87.69 3	79.75 8	95.08 20	85.36 82
Skip Steuart: Steuart Systems R&D Blog.
v7n		79.37 62	80.41 58	76.28 96	78.67 167	55.81 177	79.22 87	82.51 97	70.72 47	87.54 22	92.44 14	68.00 104	81.34 122	72.84 61	91.72 91	91.69 10

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 39	74.40 35	85.24 29	87.21 16	70.69 48	85.14 54	90.42 57	78.99 16	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 28	87.01 15	84.27 69	70.23 49	84.47 65	90.43 55	76.79 27	85.94 33	79.58 11	94.23 53	82.82 161

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 74	70.23 49	84.49 64	90.67 48	75.15 43	86.37 19	79.58 11	94.26 51	84.18 122

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 73	70.19 51	83.86 73	90.72 47	75.20 42	86.27 22	79.41 15	94.25 52	83.95 126

IS-MVSNet		75.10 100	75.42 101	74.15 122	79.23 154	48.05 227	79.43 82	78.04 172	70.09 52	79.17 126	88.02 119	53.04 223	83.60 85	58.05 176	93.76 64	90.79 17

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 43	70.41 65	82.04 59	86.35 20	69.77 53	87.75 16	91.13 34	81.83 3	86.20 25	77.13 37	95.96 5	86.08 69

LGP-MVS_train				80.90 38	87.00 43	70.41 65		86.35 20	69.77 53	87.75 16	91.13 34	81.83 3	86.20 25	77.13 37	95.96 5	86.08 69

APD-MVS		81.13 43	81.73 48	79.36 55	84.47 84	70.53 64	83.85 40	83.70 81	69.43 55	83.67 75	88.96 99	75.89 36	86.41 17	72.62 64	92.95 74	81.14 190
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		75.54 94	77.19 85	70.59 174	77.67 181	45.70 253	74.73 143	80.19 138	68.80 56	82.95 81	92.91 8	66.26 120	76.76 211	58.41 174	92.77 79	89.30 26

CPTT-MVS		81.51 38	81.76 47	80.76 40	89.20 24	78.75 12	86.48 22	82.03 101	68.80 56	80.92 107	88.52 105	72.00 68	82.39 108	74.80 47	93.04 73	81.14 190

VDDNet		71.60 153	73.13 133	67.02 229	86.29 52	41.11 285	69.97 195	66.50 267	68.72 58	74.74 191	91.70 24	59.90 173	75.81 218	48.58 249	91.72 91	84.15 123

TranMVSNet+NR-MVSNet		76.13 88	77.66 81	71.56 165	84.61 82	42.57 277	70.98 185	78.29 169	68.67 59	83.04 79	89.26 87	72.99 63	80.75 140	55.58 198	95.47 10	91.35 11

GST-MVS		82.79 27	83.27 32	81.34 32	88.99 28	73.29 45	85.94 26	85.13 44	68.58 60	84.14 70	90.21 72	73.37 61	86.41 17	79.09 19	93.98 60	84.30 121

Regformer-2		75.32 96	74.47 107	77.88 76	74.22 229	66.65 96	72.77 158	77.54 178	68.47 61	80.44 112	72.08 311	70.60 80	80.97 134	70.08 80	84.02 225	86.01 72

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 59	85.40 39	67.96 62	84.91 60	90.88 39	75.59 38	86.57 15	78.16 23	94.71 32	83.82 127

Regformer-4		74.64 108	73.67 121	77.55 78	74.74 218	64.49 114	72.91 156	75.42 199	67.45 63	80.24 116	72.07 313	68.98 92	80.19 153	70.29 78	80.91 257	87.98 46

ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 26	73.28 46	86.32 24	85.97 30	67.39 64	84.02 71	90.39 61	74.73 48	86.46 16	80.73 7	94.43 42	84.60 107

Anonymous20240529		72.56 144	73.79 118	68.86 205	76.89 191	45.21 255	68.80 214	77.25 184	67.16 65	76.89 159	90.44 54	65.95 123	74.19 240	50.75 231	90.00 135	87.18 58

XVG-OURS		79.51 59	79.82 62	78.58 67	86.11 61	74.96 33	76.33 123	84.95 50	66.89 66	82.75 85	88.99 98	66.82 113	78.37 183	74.80 47	90.76 123	82.40 173

ITE_SJBPF				80.35 44	76.94 188	73.60 42		80.48 132	66.87 67	83.64 76	86.18 157	70.25 83	79.90 156	61.12 152	88.95 158	87.56 52

Regformer-1		74.28 110	73.63 123	76.21 98	74.22 229	64.12 117	72.77 158	75.46 198	66.86 68	79.27 124	72.08 311	69.29 89	78.74 171	68.73 87	84.02 225	85.77 80

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 50	69.44 73	82.30 56	86.08 28	66.80 69	86.70 32	89.99 75	81.64 6	85.95 32	74.35 52	96.11 3	85.81 75
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 68	86.46 51	74.79 34	77.15 113	85.39 40	66.73 70	80.39 114	88.85 101	74.43 54	78.33 185	74.73 49	85.79 197	82.35 174

test_part1		76.97 83	78.21 77	73.25 136	77.87 176	45.76 251	78.27 99	87.26 15	66.69 71	85.31 52	91.43 31	55.95 213	84.24 78	65.71 113	95.43 12	89.75 22

UniMVSNet_NR-MVSNet		74.90 105	75.65 97	72.64 154	83.04 107	45.79 249	69.26 205	78.81 157	66.66 72	81.74 96	86.88 130	63.26 141	81.07 130	56.21 190	94.98 21	91.05 13

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 61	87.01 42	72.91 47	80.23 76	85.56 34	66.56 73	85.64 44	89.57 82	69.12 91	80.55 143	72.51 65	93.37 69	83.48 139

ACMM	69.25 9	82.11 33	83.31 30	78.49 68	88.17 40	73.96 38	83.11 51	84.52 64	66.40 74	87.45 23	89.16 93	81.02 9	80.52 144	74.27 53	95.73 7	80.98 195
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PAPM_NR		73.91 113	74.16 112	73.16 138	81.90 125	53.50 192	81.28 63	81.40 111	66.17 75	73.30 212	83.31 199	59.96 172	83.10 98	58.45 173	81.66 251	82.87 159

K. test v3		73.67 117	73.61 124	73.87 125	79.78 145	55.62 180	74.69 145	62.04 295	66.16 76	84.76 61	93.23 5	49.47 242	80.97 134	65.66 114	86.67 190	85.02 91

NCCC		78.25 75	78.04 79	78.89 63	85.61 67	69.45 72	79.80 81	80.99 123	65.77 77	75.55 181	86.25 156	67.42 107	85.42 50	70.10 79	90.88 118	81.81 183

OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 47	69.39 74	79.41 84	84.00 79	65.64 78	85.54 48	89.28 86	76.32 33	83.47 89	74.03 54	93.57 68	84.35 118
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AdaColmap		74.22 111	74.56 105	73.20 137	81.95 124	60.97 139	79.43 82	80.90 124	65.57 79	72.54 221	81.76 215	70.98 78	85.26 54	47.88 255	90.00 135	73.37 273

HQP_MVS		78.77 67	78.78 71	78.72 64	85.18 71	65.18 107	82.74 53	85.49 35	65.45 80	78.23 136	89.11 94	60.83 167	86.15 28	71.09 74	90.94 112	84.82 96

plane_prior2							82.74 53		65.45 80

CNLPA		73.44 121	73.03 136	74.66 112	78.27 169	75.29 31	75.99 127	78.49 165	65.39 82	75.67 179	83.22 203	61.23 163	66.77 299	53.70 215	85.33 204	81.92 182

AllTest		77.66 77	77.43 82	78.35 71	79.19 156	70.81 60	78.60 93	88.64 3	65.37 83	80.09 117	88.17 114	70.33 81	78.43 180	55.60 195	90.90 116	85.81 75

TestCases				78.35 71	79.19 156	70.81 60		88.64 3	65.37 83	80.09 117	88.17 114	70.33 81	78.43 180	55.60 195	90.90 116	85.81 75

SF-MVS		80.72 48	81.80 46	77.48 80	82.03 123	64.40 115	83.41 49	88.46 6	65.28 85	84.29 67	89.18 90	73.73 59	83.22 94	76.01 39	93.77 62	84.81 98

DU-MVS		74.91 104	75.57 99	72.93 145	83.50 97	45.79 249	69.47 202	80.14 140	65.22 86	81.74 96	87.08 124	61.82 155	81.07 130	56.21 190	94.98 21	91.93 8

LFMVS		67.06 209	67.89 200	64.56 246	78.02 173	38.25 308	70.81 189	59.60 301	65.18 87	71.06 243	86.56 147	43.85 272	75.22 225	46.35 266	89.63 143	80.21 213

EPP-MVSNet		73.86 114	73.38 127	75.31 107	78.19 170	53.35 194	80.45 68	77.32 182	65.11 88	76.47 172	86.80 132	49.47 242	83.77 82	53.89 213	92.72 82	88.81 40

WR-MVS		71.20 155	72.48 142	67.36 224	84.98 75	35.70 327	64.43 275	68.66 258	65.05 89	81.49 99	86.43 151	57.57 200	76.48 213	50.36 235	93.32 71	89.90 21

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 46	64.94 90	81.05 104	88.38 109	57.10 204	87.10 8	79.75 8	83.87 227	84.31 119
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
ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 80	83.62 44	84.98 48	64.77 91	83.97 72	91.02 37	75.53 41	85.93 36	82.00 2	94.36 47	83.35 145

Regformer-3		72.86 139	72.28 145	74.62 113	74.74 218	60.18 149	72.91 156	71.76 227	64.74 92	78.42 134	72.07 313	67.00 110	76.28 215	67.97 97	80.91 257	87.39 53

HPM-MVS++		79.89 57	79.80 63	80.18 45	89.02 27	78.44 13	83.49 48	80.18 139	64.71 93	78.11 139	88.39 108	65.46 128	83.14 96	77.64 32	91.20 105	78.94 226

#test#		82.40 30	82.71 41	81.48 27	89.25 21	75.69 28	84.47 36	84.27 69	64.45 94	84.47 65	90.43 55	76.79 27	85.94 33	76.01 39	94.23 53	82.82 161

SD-MVS		80.28 55	81.55 52	76.47 94	83.57 96	67.83 86	83.39 50	85.35 41	64.42 95	86.14 38	87.07 126	74.02 55	80.97 134	77.70 31	92.32 87	80.62 206
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
NR-MVSNet		73.62 119	74.05 113	72.33 160	83.50 97	43.71 264	65.65 260	77.32 182	64.32 96	75.59 180	87.08 124	62.45 148	81.34 122	54.90 201	95.63 8	91.93 8

Gipuma		69.55 174	72.83 139	59.70 291	63.63 324	53.97 188	80.08 78	75.93 193	64.24 97	73.49 208	88.93 100	57.89 198	62.46 316	59.75 166	91.55 98	62.67 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SixPastTwentyTwo		75.77 90	76.34 91	74.06 123	81.69 129	54.84 182	76.47 118	75.49 197	64.10 98	87.73 18	92.24 17	50.45 238	81.30 124	67.41 102	91.46 99	86.04 71

EI-MVSNet-Vis-set		72.78 140	71.87 149	75.54 105	74.77 217	59.02 160	72.24 162	71.56 231	63.92 99	78.59 130	71.59 319	66.22 121	78.60 173	67.58 100	80.32 265	89.00 34

CNVR-MVS		78.49 71	78.59 74	78.16 73	85.86 65	67.40 88	78.12 103	81.50 108	63.92 99	77.51 148	86.56 147	68.43 99	84.82 66	73.83 55	91.61 96	82.26 177

plane_prior3						65.67 103			63.82 101	78.23 136

UniMVSNet (Re)		75.00 102	75.48 100	73.56 130	83.14 102	47.92 229	70.41 193	81.04 122	63.67 102	79.54 121	86.37 152	62.83 143	81.82 116	57.10 181	95.25 15	90.94 15

ANet_high		67.08 208	69.94 171	58.51 299	57.55 353	27.09 362	58.43 316	76.80 187	63.56 103	82.40 89	91.93 20	59.82 175	64.98 307	50.10 237	88.86 159	83.46 141

SMA-MVS		82.12 32	82.68 42	80.43 42	88.90 31	69.52 71	85.12 30	84.76 53	63.53 104	84.23 69	91.47 29	72.02 67	87.16 7	79.74 10	94.36 47	84.61 105
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
EI-MVSNet-UG-set		72.63 143	71.68 153	75.47 106	74.67 221	58.64 165	72.02 165	71.50 232	63.53 104	78.58 132	71.39 322	65.98 122	78.53 175	67.30 106	80.18 267	89.23 28

pmmvs6		71.82 151	73.66 122	66.31 236	75.94 202	42.01 280	66.99 242	72.53 222	63.45 106	76.43 173	92.78 10	72.95 64	69.69 277	51.41 226	90.46 126	87.22 55

DROMVSNet		77.08 82	77.39 83	76.14 99	76.86 192	56.87 172	80.32 73	87.52 13	63.45 106	74.66 195	84.52 181	69.87 86	84.94 62	69.76 84	89.59 145	86.60 65

ACMH	63.62 14	77.50 79	80.11 60	69.68 190	79.61 147	56.28 174	78.81 90	83.62 82	63.41 108	87.14 31	90.23 71	76.11 34	73.32 243	67.58 100	94.44 41	79.44 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211		66.02 215	66.89 213	63.43 259	74.22 229	38.14 309	59.00 313	66.13 268	63.33 109	69.76 256	85.95 167	51.88 228	70.50 272	44.23 277	87.52 172	81.64 185

CANet		73.00 134	71.84 150	76.48 93	75.82 203	61.28 135	74.81 139	80.37 136	63.17 110	62.43 304	80.50 228	61.10 165	85.16 60	64.00 126	84.34 221	83.01 156

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 32	68.44 82	81.57 62	86.33 22	63.17 110	85.38 51	91.26 33	76.33 32	84.67 69	83.30 1	94.96 23	86.17 68
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Vis-MVSNet		74.85 107	74.56 105	75.72 103	81.63 130	64.64 111	76.35 121	79.06 153	62.85 112	73.33 211	88.41 107	62.54 147	79.59 160	63.94 129	82.92 235	82.94 157
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		72.10 149	72.28 145	71.58 164	74.21 232	50.33 207	74.72 144	82.73 93	62.62 113	70.77 245	76.83 272	69.96 85	80.97 134	60.20 158	78.43 284	83.45 142

OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 139	70.71 63	80.91 65	84.76 53	62.54 114	81.77 94	86.65 143	71.46 73	83.53 88	67.95 98	92.44 84	89.60 23

API-MVS		70.97 158	71.51 158	69.37 192	75.20 209	55.94 176	80.99 64	76.84 186	62.48 115	71.24 240	77.51 268	61.51 159	80.96 138	52.04 221	85.76 198	71.22 294

CSCG		74.12 112	74.39 108	73.33 133	79.35 151	61.66 133	77.45 108	81.98 102	62.47 116	79.06 127	80.19 233	61.83 154	78.79 170	59.83 165	87.35 177	79.54 220

ETV-MVS		72.72 141	72.16 148	74.38 118	76.90 190	55.95 175	73.34 154	84.67 57	62.04 117	72.19 227	70.81 323	65.90 124	85.24 56	58.64 171	84.96 212	81.95 181

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 65	80.48 140	64.16 116	80.24 75	82.06 100	61.89 118	88.77 12	93.32 4	57.15 202	82.60 106	70.08 80	92.80 78	89.25 27

plane_prior						65.18 107	80.06 79		61.88 119						89.91 139

UGNet		70.20 165	69.05 181	73.65 127	76.24 197	63.64 119	75.87 128	72.53 222	61.48 120	60.93 314	86.14 160	52.37 227	77.12 204	50.67 232	85.21 206	80.17 214
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
CS-MVS-test		73.63 118	73.74 120	73.30 135	75.80 205	51.70 199	77.02 116	86.83 19	61.29 121	68.47 267	79.23 246	65.42 129	85.14 61	64.04 125	85.55 199	83.07 154

VDD-MVS		70.81 159	71.44 159	68.91 204	79.07 161	46.51 244	67.82 228	70.83 247	61.23 122	74.07 203	88.69 102	59.86 174	75.62 221	51.11 228	90.28 128	84.61 105

FMVSNet1		71.06 156	72.48 142	66.81 230	77.65 182	40.68 290	71.96 167	73.03 214	61.14 123	79.45 123	90.36 66	60.44 169	75.20 226	50.20 236	88.05 164	84.54 109

TransMVSNet (Re)		69.62 172	71.63 154	63.57 256	76.51 194	35.93 325	65.75 259	71.29 238	61.05 124	75.02 186	89.90 78	65.88 125	70.41 275	49.79 238	89.48 147	84.38 117

EPNet		69.10 183	67.32 207	74.46 114	68.33 286	61.27 136	77.56 106	63.57 284	60.95 125	56.62 330	82.75 204	51.53 232	81.24 125	54.36 210	90.20 129	80.88 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		67.47 205	67.48 206	67.46 223	70.70 266	54.69 184	66.90 245	78.17 170	60.88 126	70.41 248	74.76 286	61.22 164	73.18 244	47.38 258	76.87 292	74.49 265

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 87	80.67 138	64.57 112	80.26 74	87.60 12	60.74 127	82.47 88	88.03 118	71.73 70	81.81 117	73.12 59	93.61 65	85.09 87

TSAR-MVS + GP.		73.08 129	71.60 156	77.54 79	78.99 163	70.73 62	74.96 136	69.38 254	60.73 128	74.39 199	78.44 257	57.72 199	82.78 102	60.16 160	89.60 144	79.11 225

MSLP-MVS++		74.48 109	75.78 96	70.59 174	84.66 80	62.40 126	78.65 92	84.24 72	60.55 129	77.71 146	81.98 211	63.12 142	77.64 199	62.95 140	88.14 162	71.73 290

Baseline_NR-MVSNet		70.62 161	73.19 131	62.92 267	76.97 187	34.44 335	68.84 210	70.88 246	60.25 130	79.50 122	90.53 51	61.82 155	69.11 281	54.67 204	95.27 14	85.22 83

v8		75.07 101	75.64 98	73.35 132	73.42 239	47.46 236	75.20 134	81.45 110	60.05 131	85.64 44	89.26 87	58.08 194	81.80 118	69.71 85	87.97 167	90.79 17

9.14			80.22 59		80.68 137		80.35 72	87.69 11	59.90 132	83.00 80	88.20 113	74.57 51	81.75 119	73.75 56	93.78 61

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 49	70.03 69	82.06 58	87.00 17	59.89 133	80.91 108	90.53 51	72.19 65	88.56 1	73.67 57	94.52 36	85.92 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
F-COLMAP		75.29 97	73.99 114	79.18 56	81.73 128	71.90 50	81.86 61	82.98 89	59.86 134	72.27 224	84.00 188	64.56 137	83.07 99	51.48 225	87.19 183	82.56 170

MVS_0304		62.51 252	62.27 250	63.25 260	69.39 279	48.47 221	64.05 279	62.48 288	59.69 135	54.10 342	81.04 222	45.71 259	66.31 302	41.38 292	82.58 239	74.96 262

RPSCF		75.76 91	74.37 109	79.93 46	74.81 216	77.53 20	77.53 107	79.30 150	59.44 136	78.88 128	89.80 79	71.26 76	73.09 245	57.45 178	80.89 259	89.17 30

HQP-NCC					82.37 116		77.32 109		59.08 137	71.58 232

ACMP_Plane					82.37 116		77.32 109		59.08 137	71.58 232

HQP-MVS		75.24 98	75.01 102	75.94 100	82.37 116	58.80 162	77.32 109	84.12 75	59.08 137	71.58 232	85.96 166	58.09 192	85.30 53	67.38 104	89.16 152	83.73 133

v10		75.69 93	76.20 93	74.16 121	74.44 227	48.69 218	75.84 129	82.93 91	59.02 140	85.92 40	89.17 92	58.56 187	82.74 103	70.73 76	89.14 155	91.05 13

test_prior3		76.71 86	77.19 85	75.27 108	82.15 121	59.85 152	75.57 130	84.33 67	58.92 141	76.53 170	86.78 134	67.83 105	83.39 91	69.81 82	92.76 80	82.58 168

test_prior2							75.57 130		58.92 141	76.53 170	86.78 134	67.83 105		69.81 82	92.76 80

ZD-MVS					83.91 93	69.36 75		81.09 120	58.91 143	82.73 86	89.11 94	75.77 37	86.63 13	72.73 62	92.93 75

MG-MVS		70.47 163	71.34 160	67.85 218	79.26 153	40.42 294	74.67 146	75.15 204	58.41 144	68.74 266	88.14 117	56.08 212	83.69 83	59.90 164	81.71 250	79.43 222

EI-MVSNet		69.61 173	69.01 183	71.41 168	73.94 234	49.90 211	71.31 180	71.32 236	58.22 145	75.40 184	70.44 325	58.16 190	75.85 216	62.51 141	79.81 271	88.48 43

IterMVS-LS		73.01 133	73.12 134	72.66 153	73.79 236	49.90 211	71.63 174	78.44 166	58.22 145	80.51 111	86.63 144	58.15 191	79.62 158	62.51 141	88.20 161	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-RMVSNet		68.69 189	68.20 197	70.14 183	76.40 195	53.90 190	64.62 272	73.48 213	58.01 147	73.91 205	81.78 213	59.09 182	78.22 187	48.59 248	77.96 289	78.31 233

test_yl		65.11 222	65.09 228	65.18 242	70.59 267	40.86 287	63.22 290	72.79 217	57.91 148	68.88 263	79.07 252	42.85 279	74.89 230	45.50 271	84.97 209	79.81 216

DCV-MVSNet		65.11 222	65.09 228	65.18 242	70.59 267	40.86 287	63.22 290	72.79 217	57.91 148	68.88 263	79.07 252	42.85 279	74.89 230	45.50 271	84.97 209	79.81 216

DP-MVS Recon		73.57 120	72.69 140	76.23 97	82.85 111	63.39 121	74.32 148	82.96 90	57.75 150	70.35 249	81.98 211	64.34 138	84.41 75	49.69 239	89.95 137	80.89 197

Effi-MVS+-dtu		75.43 95	72.28 145	84.91 2	77.05 184	83.58 1	78.47 95	77.70 176	57.68 151	74.89 188	78.13 262	64.80 134	84.26 77	56.46 187	85.32 205	86.88 61

mvs-test1		73.81 115	70.69 167	83.18 5	77.05 184	81.39 3	75.39 132	77.70 176	57.68 151	71.19 242	74.72 288	64.80 134	83.66 84	56.46 187	81.19 255	84.50 114

MVS_111021_HR		72.98 136	72.97 138	72.99 142	80.82 136	65.47 104	68.81 212	72.77 219	57.67 153	75.76 178	82.38 208	71.01 77	77.17 203	61.38 148	86.15 193	76.32 251

3Dnovator	65.95 11	71.50 154	71.22 161	72.34 159	73.16 243	63.09 124	78.37 96	78.32 167	57.67 153	72.22 226	84.61 179	54.77 215	78.47 177	60.82 155	81.07 256	75.45 257

FC-MVSNet-test		73.32 125	74.78 104	68.93 203	79.21 155	36.57 319	71.82 173	79.54 149	57.63 155	82.57 87	90.38 63	59.38 180	78.99 166	57.91 177	94.56 35	91.23 12

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 82	80.09 143	66.16 100	79.08 88	86.13 27	57.55 156	80.93 106	87.76 121	71.98 69	82.73 104	72.11 71	92.83 77	83.25 147

FPMVS		59.43 275	60.07 266	57.51 302	77.62 183	71.52 53	62.33 294	50.92 338	57.40 157	69.40 257	80.00 235	39.14 301	61.92 319	37.47 317	66.36 343	39.09 366

testdata1							68.34 223		57.24 158

MIMVSNet1		66.57 212	69.23 179	58.59 298	81.26 134	37.73 314	64.06 278	57.62 307	57.02 159	78.40 135	90.75 44	62.65 144	58.10 329	41.77 290	89.58 146	79.95 215

RRT_MVS		73.80 116	71.19 162	81.60 24	71.04 262	70.33 67	78.78 91	74.91 205	56.96 160	77.83 143	85.56 171	32.82 325	87.39 5	71.16 73	91.68 93	87.07 60

MVS_111021_LR		72.10 149	71.82 151	72.95 144	79.53 149	73.90 40	70.45 192	66.64 266	56.87 161	76.81 163	81.76 215	68.78 93	71.76 263	61.81 144	83.74 229	73.18 275

LCM-MVSNet-Re		69.10 183	71.57 157	61.70 275	70.37 272	34.30 337	61.45 298	79.62 144	56.81 162	89.59 8	88.16 116	68.44 98	72.94 246	42.30 285	87.33 178	77.85 243

BH-untuned		69.39 177	69.46 175	69.18 196	77.96 175	56.88 171	68.47 222	77.53 179	56.77 163	77.79 144	79.63 240	60.30 171	80.20 152	46.04 268	80.65 262	70.47 299

DeepC-MVS_fast	69.89 7	77.17 81	76.33 92	79.70 50	83.90 94	67.94 84	80.06 79	83.75 80	56.73 164	74.88 189	85.32 173	65.54 126	87.79 2	65.61 115	91.14 108	83.35 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	71.07 5	78.48 72	77.14 87	82.52 19	84.39 88	77.04 25	76.35 121	84.05 77	56.66 165	80.27 115	85.31 174	68.56 96	87.03 10	67.39 103	91.26 103	83.50 136

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 70	87.00 43	67.23 90	79.24 85	88.61 5	56.65 166	84.29 67	89.18 90	73.73 59	83.22 94	76.01 39	93.77 62	84.81 98

save fliter					87.00 43	67.23 90	79.24 85	77.94 174	56.65 166

VPA-MVSNet		68.71 188	70.37 169	63.72 254	76.13 199	38.06 311	64.10 277	71.48 233	56.60 168	74.10 202	88.31 111	64.78 136	69.72 276	47.69 257	90.15 132	83.37 144

GeoE		73.14 127	73.77 119	71.26 169	78.09 172	52.64 197	74.32 148	79.56 148	56.32 169	76.35 175	83.36 198	70.76 79	77.96 193	63.32 137	81.84 246	83.18 150

FIs		72.56 144	73.80 117	68.84 206	78.74 166	37.74 313	71.02 184	79.83 143	56.12 170	80.88 109	89.45 84	58.18 189	78.28 186	56.63 183	93.36 70	90.51 19

tfpnnormal		66.48 213	67.93 199	62.16 273	73.40 240	36.65 318	63.45 285	64.99 275	55.97 171	72.82 217	87.80 120	57.06 205	69.10 282	48.31 252	87.54 171	80.72 205

baseline		73.10 128	73.96 115	70.51 176	71.46 260	46.39 247	72.08 164	84.40 66	55.95 172	76.62 166	86.46 150	67.20 108	78.03 192	64.22 124	87.27 181	87.11 59

wuyk23d		61.97 255	66.25 215	49.12 326	58.19 352	60.77 145	66.32 251	52.97 333	55.93 173	90.62 5	86.91 129	73.07 62	35.98 366	20.63 368	91.63 95	50.62 357

Fast-Effi-MVS+-dtu		70.00 167	68.74 188	73.77 126	73.47 238	64.53 113	71.36 178	78.14 171	55.81 174	68.84 265	74.71 289	65.36 130	75.75 219	52.00 222	79.00 278	81.03 192

casdiffmvs		73.06 131	73.84 116	70.72 172	71.32 261	46.71 243	70.93 186	84.26 71	55.62 175	77.46 149	87.10 123	67.09 109	77.81 195	63.95 127	86.83 187	87.64 50

pm-mvs1		68.40 191	69.85 173	64.04 252	73.10 247	39.94 296	64.61 273	70.50 248	55.52 176	73.97 204	89.33 85	63.91 140	68.38 285	49.68 240	88.02 165	83.81 129

v2v482		72.55 146	72.58 141	72.43 157	72.92 251	46.72 242	71.41 177	79.13 152	55.27 177	81.17 103	85.25 175	55.41 214	81.13 127	67.25 107	85.46 200	89.43 25

thres100view900		61.17 262	61.09 258	61.39 279	72.14 256	35.01 331	65.42 264	56.99 314	55.23 178	70.71 246	79.90 236	32.07 332	72.09 257	35.61 329	81.73 247	77.08 248

TAPA-MVS	65.27 12	75.16 99	74.29 111	77.77 77	74.86 215	68.08 83	77.89 104	84.04 78	55.15 179	76.19 177	83.39 194	66.91 111	80.11 154	60.04 163	90.14 133	85.13 86
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EG-PatchMatch MVS		70.70 160	70.88 164	70.16 182	82.64 115	58.80 162	71.48 175	73.64 212	54.98 180	76.55 168	81.77 214	61.10 165	78.94 167	54.87 202	80.84 260	72.74 280

GBi-Net		68.30 193	68.79 185	66.81 230	73.14 244	40.68 290	71.96 167	73.03 214	54.81 181	74.72 192	90.36 66	48.63 251	75.20 226	47.12 259	85.37 201	84.54 109

test1		68.30 193	68.79 185	66.81 230	73.14 244	40.68 290	71.96 167	73.03 214	54.81 181	74.72 192	90.36 66	48.63 251	75.20 226	47.12 259	85.37 201	84.54 109

FMVSNet2		67.48 204	68.21 196	65.29 241	73.14 244	38.94 303	68.81 212	71.21 242	54.81 181	76.73 165	86.48 149	48.63 251	74.60 234	47.98 254	86.11 195	82.35 174

v148		69.38 178	69.39 176	69.36 193	69.14 282	44.56 258	68.83 211	72.70 220	54.79 184	78.59 130	84.12 186	54.69 216	76.74 212	59.40 168	82.20 241	86.79 62

thres600view7		61.82 257	61.38 257	63.12 262	71.81 258	34.93 332	64.64 271	56.99 314	54.78 185	70.33 250	79.74 238	32.07 332	72.42 255	38.61 307	83.46 231	82.02 179

tttt0517		69.46 175	67.79 202	74.46 114	75.34 207	52.72 196	75.05 135	63.27 286	54.69 186	78.87 129	84.37 183	26.63 354	81.15 126	63.95 127	87.93 168	89.51 24

RPMNet		65.77 218	65.08 230	67.84 219	66.37 301	48.24 224	70.93 186	86.27 23	54.66 187	61.35 308	86.77 136	33.29 322	85.67 46	55.93 192	70.17 330	69.62 308

VNet		64.01 239	65.15 225	60.57 286	73.28 242	35.61 328	57.60 320	67.08 264	54.61 188	66.76 280	83.37 196	56.28 210	66.87 295	42.19 286	85.20 207	79.23 224

PLC	62.01 16	71.79 152	70.28 170	76.33 95	80.31 142	68.63 81	78.18 102	81.24 115	54.57 189	67.09 279	80.63 226	59.44 177	81.74 120	46.91 262	84.17 222	78.63 228
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
nrg030		74.87 106	75.99 95	71.52 166	74.90 214	49.88 214	74.10 151	82.58 96	54.55 190	83.50 77	89.21 89	71.51 72	75.74 220	61.24 149	92.34 86	88.94 36

agg_prior1		75.89 89	76.41 90	74.31 119	84.44 86	66.02 101	76.12 125	78.62 163	54.40 191	76.95 156	86.85 131	66.44 119	80.34 146	72.45 67	91.42 100	76.57 250

canonicalmvs		72.29 148	73.38 127	69.04 198	74.23 228	47.37 237	73.93 152	83.18 86	54.36 192	76.61 167	81.64 217	72.03 66	75.34 224	57.12 180	87.28 180	84.40 116

h-mvs33		73.08 129	71.61 155	77.48 80	83.89 95	72.89 48	70.47 191	71.12 243	54.28 193	77.89 140	83.41 193	49.04 245	80.98 133	63.62 132	90.77 122	78.58 230

hse-mvs2		72.32 147	70.66 168	77.31 85	83.10 106	71.77 51	69.19 207	71.45 234	54.28 193	77.89 140	78.26 259	49.04 245	79.23 162	63.62 132	89.13 156	80.92 196

ECVR-MVS623		61.23 261	60.85 261	62.38 271	78.80 164	27.88 361	67.33 237	37.42 369	54.23 195	67.55 275	88.68 103	17.87 373	74.39 237	46.33 267	89.41 149	84.86 94

ECVR-MVS		64.82 226	65.22 221	63.60 255	78.80 164	31.14 351	66.97 243	56.47 317	54.23 195	69.94 253	88.68 103	37.23 311	74.81 232	45.28 273	89.41 149	84.86 94

CDPH-MVS		77.33 80	77.06 88	78.14 74	84.21 89	63.98 118	76.07 126	83.45 84	54.20 197	77.68 147	87.18 122	69.98 84	85.37 51	68.01 95	92.72 82	85.08 89

VPNet		65.58 219	67.56 203	59.65 292	79.72 146	30.17 354	60.27 307	62.14 290	54.19 198	71.24 240	86.63 144	58.80 185	67.62 290	44.17 278	90.87 119	81.18 189

PHI-MVS		74.92 103	74.36 110	76.61 90	76.40 195	62.32 128	80.38 70	83.15 87	54.16 199	73.23 213	80.75 224	62.19 152	83.86 81	68.02 94	90.92 115	83.65 134

ECVR-MVS11		64.62 228	65.19 222	62.93 266	79.01 162	29.91 355	65.45 263	54.41 325	54.09 200	71.47 239	88.48 106	37.02 312	74.29 239	46.83 264	89.94 138	84.58 108

Patchmtry		60.91 263	63.01 245	54.62 309	66.10 307	26.27 365	67.47 232	56.40 318	54.05 201	72.04 228	86.66 141	33.19 323	60.17 323	43.69 279	87.45 175	77.42 244

train_agg		76.38 87	76.55 89	75.86 102	85.47 68	69.32 76	76.42 119	78.69 160	54.00 202	76.97 154	86.74 137	66.60 116	81.10 128	72.50 66	91.56 97	77.15 246

test_8					85.09 74	67.89 85	76.26 124	78.66 162	54.00 202	76.89 159	86.72 139	66.60 116	80.89 139

DELS-MVS		68.83 185	68.31 191	70.38 177	70.55 271	48.31 222	63.78 282	82.13 99	54.00 202	68.96 262	75.17 284	58.95 184	80.06 155	58.55 172	82.74 237	82.76 163
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
alignmvs		70.54 162	71.00 163	69.15 197	73.50 237	48.04 228	69.85 198	79.62 144	53.94 205	76.54 169	82.00 210	59.00 183	74.68 233	57.32 179	87.21 182	84.72 100

v1144		73.29 126	73.39 126	73.01 141	74.12 233	48.11 226	72.01 166	81.08 121	53.83 206	81.77 94	84.68 178	58.07 195	81.91 115	68.10 92	86.86 186	88.99 35

TEST9					85.47 68	69.32 76	76.42 119	78.69 160	53.73 207	76.97 154	86.74 137	66.84 112	81.10 128

Vis-MVSNet (Re-imp)		62.74 250	63.21 243	61.34 280	72.19 255	31.56 348	67.31 238	53.87 326	53.60 208	69.88 254	83.37 196	40.52 294	70.98 268	41.40 291	86.78 189	81.48 187

PS-MVSNAJss		77.54 78	77.35 84	78.13 75	84.88 76	66.37 98	78.55 94	79.59 147	53.48 209	86.29 35	92.43 15	62.39 149	80.25 149	67.90 99	90.61 124	87.77 48

MDA-MVSNet-bldmvs		62.34 254	61.73 251	64.16 248	61.64 332	49.90 211	48.11 344	57.24 313	53.31 210	80.95 105	79.39 243	49.00 247	61.55 320	45.92 269	80.05 268	81.03 192

CS-MVS		69.29 179	69.70 174	68.07 216	70.59 267	42.36 279	69.70 200	84.56 63	53.13 211	67.96 270	76.74 273	59.41 178	83.56 86	60.33 157	84.84 214	78.28 235

TinyColmap		67.98 197	69.28 177	64.08 250	67.98 290	46.82 241	70.04 194	75.26 202	53.05 212	77.36 150	86.79 133	59.39 179	72.59 253	45.64 270	88.01 166	72.83 278

tfpn200view9		60.35 269	59.97 267	61.51 277	70.78 264	35.35 329	63.27 288	57.47 308	53.00 213	68.31 268	77.09 270	32.45 329	72.09 257	35.61 329	81.73 247	77.08 248

thres400		60.77 266	59.97 267	63.15 261	70.78 264	35.35 329	63.27 288	57.47 308	53.00 213	68.31 268	77.09 270	32.45 329	72.09 257	35.61 329	81.73 247	82.02 179

ETH3 D test6400		75.73 92	76.00 94	74.92 111	81.75 127	56.93 170	78.31 97	84.60 62	52.83 215	77.15 151	85.14 176	68.59 95	84.03 79	65.44 116	90.20 129	83.82 127

v1192		73.40 123	73.42 125	73.32 134	74.65 224	48.67 219	72.21 163	81.73 105	52.76 216	81.85 92	84.56 180	57.12 203	82.24 113	68.58 88	87.33 178	89.06 32

MVS_Test		69.84 170	70.71 166	67.24 225	67.49 295	43.25 271	69.87 197	81.22 117	52.69 217	71.57 235	86.68 140	62.09 153	74.51 235	66.05 110	78.74 280	83.96 125

EIA-MVS		68.59 190	67.16 209	72.90 146	75.18 210	55.64 179	69.39 203	81.29 113	52.44 218	64.53 289	70.69 324	60.33 170	82.30 111	54.27 211	76.31 295	80.75 203

MVSFormer		69.93 169	69.03 182	72.63 155	74.93 212	59.19 156	83.98 38	75.72 195	52.27 219	63.53 300	76.74 273	43.19 276	80.56 141	72.28 68	78.67 282	78.14 238

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 131	71.24 57	83.98 38	75.72 195	52.27 219	87.37 27	92.25 16	68.04 103	80.56 141	72.28 68	91.15 107	90.32 20

CLD-MVS		72.88 138	72.36 144	74.43 116	77.03 186	54.30 186	68.77 215	83.43 85	52.12 221	76.79 164	74.44 292	69.54 88	83.91 80	55.88 193	93.25 72	85.09 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		53.35 302	56.47 292	43.99 342	64.19 320	17.46 373	59.15 311	43.10 355	52.11 222	54.74 338	86.95 128	29.97 349	49.98 339	43.62 280	74.40 310	64.53 339

CANet_DTU		64.04 238	63.83 235	64.66 245	68.39 283	42.97 273	73.45 153	74.50 209	52.05 223	54.78 337	75.44 283	43.99 271	70.42 274	53.49 217	78.41 285	80.59 207

mvs_tets		78.93 65	78.67 73	79.72 49	84.81 78	73.93 39	80.65 66	76.50 189	51.98 224	87.40 24	91.86 21	76.09 35	78.53 175	68.58 88	90.20 129	86.69 64

v1240		73.06 131	73.14 132	72.84 148	74.74 218	47.27 239	71.88 172	81.11 118	51.80 225	82.28 90	84.21 185	56.22 211	82.34 110	68.82 86	87.17 184	88.91 37

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 29	67.52 87	86.61 20	81.38 112	51.71 226	77.15 151	91.42 32	65.49 127	87.20 6	79.44 14	87.17 184	84.51 113
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1921920		72.96 137	72.98 137	72.89 147	74.67 221	47.58 234	71.92 170	80.69 127	51.70 227	81.69 98	83.89 189	56.58 209	82.25 112	68.34 90	87.36 176	88.82 39

v144192		72.99 135	73.06 135	72.77 149	74.58 225	47.48 235	71.90 171	80.44 134	51.57 228	81.46 100	84.11 187	58.04 196	82.12 114	67.98 96	87.47 174	88.70 42

FMVSNet3		65.00 225	65.16 223	64.52 247	69.47 278	37.56 316	66.63 248	70.38 249	51.55 229	74.72 192	83.27 200	37.89 309	74.44 236	47.12 259	85.37 201	81.57 186

c3_l		69.82 171	69.89 172	69.61 191	66.24 304	43.48 267	68.12 225	79.61 146	51.43 230	77.72 145	80.18 234	54.61 218	78.15 191	63.62 132	87.50 173	87.20 57

V42		71.06 156	70.83 165	71.72 163	67.25 296	47.14 240	65.94 254	80.35 137	51.35 231	83.40 78	83.23 201	59.25 181	78.80 169	65.91 112	80.81 261	89.23 28

jajsoiax		78.51 70	78.16 78	79.59 52	84.65 81	73.83 41	80.42 69	76.12 191	51.33 232	87.19 29	91.51 28	73.79 58	78.44 179	68.27 91	90.13 134	86.49 66

GA-MVS		62.91 247	61.66 252	66.66 234	67.09 298	44.49 259	61.18 302	69.36 255	51.33 232	69.33 258	74.47 291	36.83 313	74.94 229	50.60 233	74.72 307	80.57 208

CL-MVSNet_self_test		62.44 253	63.40 240	59.55 293	72.34 254	32.38 344	56.39 322	64.84 276	51.21 234	67.46 276	81.01 223	50.75 236	63.51 314	38.47 309	88.12 163	82.75 164

PM-MVS		64.49 231	63.61 238	67.14 228	76.68 193	75.15 32	68.49 221	42.85 356	51.17 235	77.85 142	80.51 227	45.76 258	66.31 302	52.83 220	76.35 294	59.96 349

原ACMM1				73.90 124	85.90 62	65.15 109		81.67 106	50.97 236	74.25 200	86.16 159	61.60 157	83.54 87	56.75 182	91.08 110	73.00 276

JIA-IIPM		54.03 299	51.62 311	61.25 281	59.14 347	55.21 181	59.10 312	47.72 348	50.85 237	50.31 354	85.81 169	20.10 370	63.97 310	36.16 327	55.41 365	64.55 338

KD-MVS_self_test		66.38 214	67.51 204	62.97 265	61.76 331	34.39 336	58.11 318	75.30 201	50.84 238	77.12 153	85.42 172	56.84 207	69.44 278	51.07 229	91.16 106	85.08 89

eth_miper_zixun_eth		69.42 176	68.73 189	71.50 167	67.99 289	46.42 245	67.58 230	78.81 157	50.72 239	78.13 138	80.34 230	50.15 240	80.34 146	60.18 159	84.65 215	87.74 49

Fast-Effi-MVS+		68.81 186	68.30 192	70.35 178	74.66 223	48.61 220	66.06 253	78.32 167	50.62 240	71.48 238	75.54 280	68.75 94	79.59 160	50.55 234	78.73 281	82.86 160

anonymousdsp		78.60 68	77.80 80	81.00 37	78.01 174	74.34 37	80.09 77	76.12 191	50.51 241	89.19 10	90.88 39	71.45 74	77.78 197	73.38 58	90.60 125	90.90 16

thres200		57.55 285	57.02 287	59.17 294	67.89 292	34.93 332	58.91 315	57.25 312	50.24 242	64.01 294	71.46 321	32.49 328	71.39 265	31.31 343	79.57 275	71.19 296

thisisatest0530		67.05 210	65.16 223	72.73 152	73.10 247	50.55 206	71.26 182	63.91 282	50.22 243	74.46 198	80.75 224	26.81 353	80.25 149	59.43 167	86.50 191	87.37 54

test20.03		55.74 291	57.51 285	50.42 319	59.89 344	32.09 346	50.63 338	49.01 344	50.11 244	65.07 288	83.23 201	45.61 261	48.11 342	30.22 347	83.82 228	71.07 297

BH-w/o		64.81 227	64.29 233	66.36 235	76.08 201	54.71 183	65.61 261	75.23 203	50.10 245	71.05 244	71.86 318	54.33 219	79.02 165	38.20 311	76.14 296	65.36 331

cl____		68.26 196	68.26 193	68.29 212	64.98 316	43.67 265	65.89 255	74.67 206	50.04 246	76.86 161	82.42 207	48.74 249	75.38 222	60.92 154	89.81 140	85.80 79

DIV-MVS_self_test		68.27 195	68.26 193	68.29 212	64.98 316	43.67 265	65.89 255	74.67 206	50.04 246	76.86 161	82.43 206	48.74 249	75.38 222	60.94 153	89.81 140	85.81 75

RRT_test8_iter05		65.80 217	65.13 226	67.80 221	67.02 299	40.85 289	67.13 240	75.33 200	49.73 248	72.69 218	81.32 218	24.45 364	77.37 202	61.69 147	86.82 188	85.18 85

EPNet_dtu		58.93 278	58.52 277	60.16 290	67.91 291	47.70 233	69.97 195	58.02 305	49.73 248	47.28 358	73.02 307	38.14 305	62.34 317	36.57 323	85.99 196	70.43 300
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		69.18 182	69.26 178	68.94 202	71.61 259	52.58 198	80.37 71	78.79 159	49.63 250	73.51 207	85.14 176	53.66 221	79.12 164	55.11 200	75.54 300	75.11 261

PAPR		69.20 181	68.66 190	70.82 171	75.15 211	47.77 231	75.31 133	81.11 118	49.62 251	66.33 281	79.27 245	61.53 158	82.96 100	48.12 253	81.50 253	81.74 184

TR-MVS		64.59 229	63.54 239	67.73 222	75.75 206	50.83 205	63.39 286	70.29 250	49.33 252	71.55 236	74.55 290	50.94 235	78.46 178	40.43 297	75.69 298	73.89 270

cl22		67.14 207	66.51 214	69.03 199	63.20 325	43.46 268	66.88 246	76.25 190	49.22 253	74.48 197	77.88 264	45.49 262	77.40 201	60.64 156	84.59 217	86.24 67

AUN-MVS		70.22 164	67.88 201	77.22 86	82.96 110	71.61 52	69.08 208	71.39 235	49.17 254	71.70 230	78.07 263	37.62 310	79.21 163	61.81 144	89.15 154	80.82 199

miper_ehance_all_eth		68.36 192	68.16 198	68.98 200	65.14 315	43.34 269	67.07 241	78.92 156	49.11 255	76.21 176	77.72 265	53.48 222	77.92 194	61.16 151	84.59 217	85.68 81

ab-mvs		64.11 237	65.13 226	61.05 282	71.99 257	38.03 312	67.59 229	68.79 257	49.08 256	65.32 286	86.26 155	58.02 197	66.85 297	39.33 301	79.79 273	78.27 236

OpenMVS	62.51 15	68.76 187	68.75 187	68.78 207	70.56 270	53.91 189	78.29 98	77.35 181	48.85 257	70.22 251	83.52 192	52.65 226	76.93 206	55.31 199	81.99 243	75.49 256

MAR-MVS		67.72 201	66.16 216	72.40 158	74.45 226	64.99 110	74.87 137	77.50 180	48.67 258	65.78 285	68.58 343	57.01 206	77.79 196	46.68 265	81.92 244	74.42 266
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
bset_n11_16_dypcd		66.91 211	65.84 219	70.12 184	72.95 250	53.54 191	63.64 283	68.65 259	48.54 259	72.54 221	74.28 295	40.58 293	78.54 174	63.52 135	87.82 169	78.29 234

PVSNet_Blended_VisFu		70.04 166	68.88 184	73.53 131	82.71 113	63.62 120	74.81 139	81.95 103	48.53 260	67.16 278	79.18 249	51.42 233	78.38 182	54.39 209	79.72 274	78.60 229

diffmvs		67.42 206	67.50 205	67.20 226	62.26 329	45.21 255	64.87 269	77.04 185	48.21 261	71.74 229	79.70 239	58.40 188	71.17 267	64.99 118	80.27 266	85.22 83

IterMVS-SCA-FT		67.68 202	66.07 217	72.49 156	73.34 241	58.20 167	63.80 281	65.55 272	48.10 262	76.91 158	82.64 205	45.20 263	78.84 168	61.20 150	77.89 290	80.44 210

xiu_mvs_v1_base_debu		67.87 198	67.07 210	70.26 179	79.13 158	61.90 130	67.34 234	71.25 239	47.98 263	67.70 272	74.19 298	61.31 160	72.62 250	56.51 184	78.26 286	76.27 252

xiu_mvs_v1_base		67.87 198	67.07 210	70.26 179	79.13 158	61.90 130	67.34 234	71.25 239	47.98 263	67.70 272	74.19 298	61.31 160	72.62 250	56.51 184	78.26 286	76.27 252

xiu_mvs_v1_base_debi		67.87 198	67.07 210	70.26 179	79.13 158	61.90 130	67.34 234	71.25 239	47.98 263	67.70 272	74.19 298	61.31 160	72.62 250	56.51 184	78.26 286	76.27 252

testdata				64.13 249	85.87 64	63.34 122		61.80 296	47.83 266	76.42 174	86.60 146	48.83 248	62.31 318	54.46 208	81.26 254	66.74 326

DPM-MVS		69.98 168	69.22 180	72.26 161	82.69 114	58.82 161	70.53 190	81.23 116	47.79 267	64.16 293	80.21 231	51.32 234	83.12 97	60.14 161	84.95 213	74.83 263

无先验							74.82 138	70.94 245	47.75 268				76.85 209	54.47 206		72.09 287

IB-MVS	49.67 18	59.69 273	56.96 288	67.90 217	68.19 287	50.30 208	61.42 299	65.18 274	47.57 269	55.83 334	67.15 350	23.77 365	79.60 159	43.56 281	79.97 269	73.79 271
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
tpmvs		55.84 289	55.45 299	57.01 303	60.33 340	33.20 342	65.89 255	59.29 303	47.52 270	56.04 332	73.60 301	31.05 341	68.06 287	40.64 296	64.64 346	69.77 306

PatchMatch-RL		58.68 280	57.72 283	61.57 276	76.21 198	73.59 43	61.83 296	49.00 345	47.30 271	61.08 310	68.97 338	50.16 239	59.01 326	36.06 328	68.84 337	52.10 356

Anonymous20240521		63.55 240	66.07 217	55.99 306	66.18 306	44.04 262	68.77 215	68.80 256	46.99 272	72.57 219	85.84 168	39.87 297	50.22 338	53.40 219	92.23 88	73.71 272

PC_three_1452									46.98 273	81.83 93	86.28 153	66.55 118	84.47 72	63.31 138	90.78 120	83.49 137

EMVS		44.61 330	44.45 334	45.10 339	48.91 373	43.00 272	37.92 361	41.10 366	46.75 274	38.00 370	48.43 367	26.42 355	46.27 344	37.11 320	75.38 303	46.03 362

IterMVS		63.12 245	62.48 249	65.02 244	66.34 303	52.86 195	63.81 280	62.25 289	46.57 275	71.51 237	80.40 229	44.60 268	66.82 298	51.38 227	75.47 301	75.38 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
E-PMN		45.17 326	45.36 329	44.60 340	50.07 371	42.75 274	38.66 360	42.29 360	46.39 276	39.55 368	51.15 365	26.00 357	45.37 349	37.68 314	76.41 293	45.69 363

baseline1		57.82 284	58.36 280	56.19 305	69.17 281	30.76 353	62.94 292	55.21 321	46.04 277	63.83 296	78.47 256	41.20 287	63.68 312	39.44 300	68.99 336	74.13 267

MCST-MVS		73.42 122	73.34 129	73.63 129	81.28 133	59.17 158	74.80 141	83.13 88	45.50 278	72.84 216	83.78 191	65.15 131	80.99 132	64.54 121	89.09 157	80.73 204

PVSNet_BlendedMVS		65.38 220	64.30 232	68.61 208	69.81 275	49.36 215	65.60 262	78.96 154	45.50 278	59.98 317	78.61 255	51.82 229	78.20 188	44.30 275	84.11 223	78.27 236

IU-MVS					86.12 58	60.90 141		80.38 135	45.49 280	81.31 101				75.64 44	94.39 43	84.65 101

testgi		54.00 301	56.86 289	45.45 336	58.20 351	25.81 366	49.05 340	49.50 343	45.43 281	67.84 271	81.17 221	51.81 231	43.20 358	29.30 352	79.41 276	67.34 321

PCF-MVS	63.80 13	72.70 142	71.69 152	75.72 103	78.10 171	60.01 151	73.04 155	81.50 108	45.34 282	79.66 120	84.35 184	65.15 131	82.65 105	48.70 247	89.38 151	84.50 114
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAMVS		65.31 221	63.75 236	69.97 188	82.23 120	59.76 154	66.78 247	63.37 285	45.20 283	69.79 255	79.37 244	47.42 257	72.17 256	34.48 333	85.15 208	77.99 242

旧先验2							71.17 183		45.11 284	78.54 133			61.28 321	59.19 169

PS-MVSNAJ		64.27 236	63.73 237	65.90 239	77.82 178	51.42 202	63.33 287	72.33 224	45.09 285	61.60 306	68.04 344	62.39 149	73.95 241	49.07 243	73.87 313	72.34 283

xiu_mvs_v2_base		64.43 233	63.96 234	65.85 240	77.72 180	51.32 203	63.63 284	72.31 225	45.06 286	61.70 305	69.66 333	62.56 145	73.93 242	49.06 244	73.91 312	72.31 284

LF4IMVS		67.50 203	67.31 208	68.08 215	58.86 348	61.93 129	71.43 176	75.90 194	44.67 287	72.42 223	80.20 232	57.16 201	70.44 273	58.99 170	86.12 194	71.88 288

CDS-MVSNet		64.33 235	62.66 248	69.35 194	80.44 141	58.28 166	65.26 265	65.66 270	44.36 288	67.30 277	75.54 280	43.27 275	71.77 262	37.68 314	84.44 220	78.01 241
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
miper_lstm_enhance		61.97 255	61.63 254	62.98 264	60.04 341	45.74 252	47.53 346	70.95 244	44.04 289	73.06 214	78.84 254	39.72 298	60.33 322	55.82 194	84.64 216	82.88 158

新几何1				69.99 187	88.37 36	71.34 56		62.08 292	43.85 290	74.99 187	86.11 162	52.85 225	70.57 271	50.99 230	83.23 234	68.05 317

1121		69.23 180	68.26 193	72.12 162	88.36 37	71.40 54	68.59 217	62.06 293	43.80 291	74.75 190	86.18 157	52.92 224	76.85 209	54.47 206	83.27 233	68.12 316

114514_t		73.40 123	73.33 130	73.64 128	84.15 91	57.11 169	78.20 101	80.02 141	43.76 292	72.55 220	86.07 164	64.00 139	83.35 93	60.14 161	91.03 111	80.45 209

OpenMVS_ROB	54.93 17	63.23 244	63.28 241	63.07 263	69.81 275	45.34 254	68.52 220	67.14 263	43.74 293	70.61 247	79.22 247	47.90 255	72.66 249	48.75 246	73.84 314	71.21 295

FMVSNet5		55.08 295	55.54 298	53.71 310	65.80 308	33.50 341	56.22 324	52.50 335	43.72 294	61.06 311	83.38 195	25.46 360	54.87 332	30.11 348	81.64 252	72.75 279

MVP-Stereo		61.56 259	59.22 271	68.58 209	79.28 152	60.44 147	69.20 206	71.57 230	43.58 295	56.42 331	78.37 258	39.57 300	76.46 214	34.86 332	60.16 354	68.86 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs_anonymous		65.08 224	65.49 220	63.83 253	63.79 322	37.60 315	66.52 250	69.82 252	43.44 296	73.46 209	86.08 163	58.79 186	71.75 264	51.90 223	75.63 299	82.15 178

test-LLR		50.43 314	50.69 319	49.64 322	60.76 337	41.87 281	53.18 333	45.48 352	43.41 297	49.41 355	60.47 358	29.22 351	44.73 352	42.09 287	72.14 319	62.33 345

test0.0.03 1		47.72 321	48.31 323	45.93 334	55.53 362	29.39 356	46.40 350	41.21 365	43.41 297	55.81 335	67.65 345	29.22 351	43.77 357	25.73 361	69.87 332	64.62 337

SCA		58.57 281	58.04 281	60.17 289	70.17 273	41.07 286	65.19 266	53.38 331	43.34 299	61.00 313	73.48 302	45.20 263	69.38 279	40.34 298	70.31 329	70.05 303

ET-MVSNet_ETH3D		63.32 242	60.69 263	71.20 170	70.15 274	55.66 178	65.02 268	64.32 281	43.28 300	68.99 261	72.05 317	25.46 360	78.19 190	54.16 212	82.80 236	79.74 219

miper_enhance_ethall		65.86 216	65.05 231	68.28 214	61.62 333	42.62 276	64.74 270	77.97 173	42.52 301	73.42 210	72.79 308	49.66 241	77.68 198	58.12 175	84.59 217	84.54 109

cascas		64.59 229	62.77 247	70.05 186	75.27 208	50.02 210	61.79 297	71.61 229	42.46 302	63.68 298	68.89 340	49.33 244	80.35 145	47.82 256	84.05 224	79.78 218

PVSNet_Blended		62.90 248	61.64 253	66.69 233	69.81 275	49.36 215	61.23 301	78.96 154	42.04 303	59.98 317	68.86 341	51.82 229	78.20 188	44.30 275	77.77 291	72.52 281

MVSTER		63.29 243	61.60 255	68.36 210	59.77 345	46.21 248	60.62 304	71.32 236	41.83 304	75.40 184	79.12 250	30.25 346	75.85 216	56.30 189	79.81 271	83.03 155

MIMVSNet		54.39 297	56.12 295	49.20 324	72.57 252	30.91 352	59.98 308	48.43 347	41.66 305	55.94 333	83.86 190	41.19 288	50.42 337	26.05 358	75.38 303	66.27 327

KD-MVS_2432*1600		52.05 310	51.58 312	53.44 311	52.11 369	31.20 349	44.88 353	64.83 277	41.53 306	64.37 290	70.03 330	15.61 377	64.20 308	36.25 324	74.61 308	64.93 335

miper_refine_blended		52.05 310	51.58 312	53.44 311	52.11 369	31.20 349	44.88 353	64.83 277	41.53 306	64.37 290	70.03 330	15.61 377	64.20 308	36.25 324	74.61 308	64.93 335

new-patchmatchnet		52.89 304	55.76 297	44.26 341	59.94 343	6.31 376	37.36 363	50.76 340	41.10 308	64.28 292	79.82 237	44.77 266	48.43 341	36.24 326	87.61 170	78.03 240

test222					87.30 41	69.15 79	67.85 227	59.59 302	41.06 309	73.05 215	85.72 170	48.03 254			80.65 262	66.92 322

Patchmatch-RL test		59.95 271	59.12 272	62.44 270	72.46 253	54.61 185	59.63 310	47.51 349	41.05 310	74.58 196	74.30 294	31.06 340	65.31 304	51.61 224	79.85 270	67.39 319

thisisatest0515		60.48 268	57.86 282	68.34 211	67.25 296	46.42 245	60.58 305	62.14 290	40.82 311	63.58 299	69.12 336	26.28 356	78.34 184	48.83 245	82.13 242	80.26 212

ppachtmachnet_test		60.26 270	59.61 270	62.20 272	67.70 293	44.33 260	58.18 317	60.96 298	40.75 312	65.80 284	72.57 309	41.23 286	63.92 311	46.87 263	82.42 240	78.33 232

PAPM		61.79 258	60.37 265	66.05 237	76.09 200	41.87 281	69.30 204	76.79 188	40.64 313	53.80 343	79.62 241	44.38 269	82.92 101	29.64 351	73.11 316	73.36 274

our_test_3		56.46 287	56.51 291	56.30 304	67.70 293	39.66 298	55.36 328	52.34 336	40.57 314	63.85 295	69.91 332	40.04 296	58.22 328	43.49 282	75.29 305	71.03 298

PatchmatchNet		54.60 296	54.27 302	55.59 307	65.17 314	39.08 300	66.92 244	51.80 337	39.89 315	58.39 323	73.12 306	31.69 334	58.33 327	43.01 283	58.38 360	69.38 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
D2MVS		62.58 251	61.05 259	67.20 226	63.85 321	47.92 229	56.29 323	69.58 253	39.32 316	70.07 252	78.19 260	34.93 318	72.68 248	53.44 218	83.74 229	81.00 194

Patchmatch-test		47.93 320	49.96 321	41.84 345	57.42 354	24.26 368	48.75 341	41.49 363	39.30 317	56.79 329	73.48 302	30.48 345	33.87 367	29.29 353	72.61 317	67.39 319

HY-MVS	49.31 19	57.96 283	57.59 284	59.10 295	66.85 300	36.17 322	65.13 267	65.39 273	39.24 318	54.69 339	78.14 261	44.28 270	67.18 294	33.75 337	70.79 326	73.95 269

baseline2		55.57 293	52.74 307	64.05 251	65.26 311	44.11 261	62.38 293	54.43 324	39.03 319	51.21 348	67.35 348	33.66 321	72.45 254	37.14 319	64.22 348	75.60 255

XXY-MVS		55.19 294	57.40 286	48.56 328	64.45 319	34.84 334	51.54 337	53.59 328	38.99 320	63.79 297	79.43 242	56.59 208	45.57 346	36.92 321	71.29 323	65.25 332

pmmvs-eth3d		64.41 234	63.27 242	67.82 220	75.81 204	60.18 149	69.49 201	62.05 294	38.81 321	74.13 201	82.23 209	43.76 273	68.65 283	42.53 284	80.63 264	74.63 264

DWT-MVSNet_test		53.04 303	51.12 316	58.77 297	61.23 334	38.67 304	62.16 295	57.74 306	38.24 322	51.76 347	59.07 360	21.36 367	67.40 292	44.80 274	63.76 349	70.25 302

MDA-MVSNet_test_wron		52.57 306	53.49 305	49.81 321	54.24 366	36.47 320	40.48 358	46.58 350	38.13 323	75.47 183	73.32 304	41.05 291	43.85 356	40.98 294	71.20 324	69.10 314

YYNet1		52.58 305	53.50 304	49.85 320	54.15 367	36.45 321	40.53 357	46.55 351	38.09 324	75.52 182	73.31 305	41.08 290	43.88 355	41.10 293	71.14 325	69.21 312

1112_ss		59.48 274	58.99 274	60.96 284	77.84 177	42.39 278	61.42 299	68.45 260	37.96 325	59.93 320	67.46 346	45.11 265	65.07 306	40.89 295	71.81 321	75.41 258

UnsupCasMVSNet_eth		52.26 308	53.29 306	49.16 325	55.08 363	33.67 340	50.03 339	58.79 304	37.67 326	63.43 302	74.75 287	41.82 284	45.83 345	38.59 308	59.42 356	67.98 318

tpm		50.60 313	52.42 310	45.14 338	65.18 313	26.29 364	60.30 306	43.50 354	37.41 327	57.01 327	79.09 251	30.20 348	42.32 359	32.77 340	66.36 343	66.81 325

gm-plane-assit					62.51 327	33.91 339			37.25 328		62.71 355		72.74 247	38.70 305

CostFormer		57.35 286	56.14 294	60.97 283	63.76 323	38.43 305	67.50 231	60.22 299	37.14 329	59.12 322	76.34 276	32.78 326	71.99 260	39.12 303	69.27 335	72.47 282

pmmvs4		60.78 265	59.04 273	66.00 238	73.06 249	57.67 168	64.53 274	60.22 299	36.91 330	65.96 282	77.27 269	39.66 299	68.54 284	38.87 304	74.89 306	71.80 289

PVSNet	43.83 21	51.56 312	51.17 315	52.73 314	68.34 285	38.27 307	48.22 343	53.56 329	36.41 331	54.29 340	64.94 353	34.60 319	54.20 335	30.34 346	69.87 332	65.71 330

tpmrst		50.15 315	51.38 314	46.45 333	56.05 358	24.77 367	64.40 276	49.98 341	36.14 332	53.32 344	69.59 334	35.16 317	48.69 340	39.24 302	58.51 359	65.89 328

MS-PatchMatch		55.59 292	54.89 300	57.68 301	69.18 280	49.05 217	61.00 303	62.93 287	35.98 333	58.36 324	68.93 339	36.71 314	66.59 300	37.62 316	63.30 350	57.39 352

MDTV_nov1_ep13			54.05 303		65.54 310	29.30 357	59.00 313	55.22 320	35.96 334	52.44 345	75.98 277	30.77 343	59.62 324	38.21 310	73.33 315

USDC		62.80 249	63.10 244	61.89 274	65.19 312	43.30 270	67.42 233	74.20 210	35.80 335	72.25 225	84.48 182	45.67 260	71.95 261	37.95 313	84.97 209	70.42 301

jason		64.47 232	62.84 246	69.34 195	76.91 189	59.20 155	67.15 239	65.67 269	35.29 336	65.16 287	76.74 273	44.67 267	70.68 269	54.74 203	79.28 277	78.14 238
jason: jason.
Anonymous20231206		54.13 298	55.82 296	49.04 327	70.89 263	35.96 324	51.73 336	50.87 339	34.86 337	62.49 303	79.22 247	42.52 282	44.29 354	27.95 356	81.88 245	66.88 323

dp		44.09 331	44.88 332	41.72 347	58.53 350	23.18 369	54.70 330	42.38 359	34.80 338	44.25 365	65.61 352	24.48 363	44.80 351	29.77 350	49.42 367	57.18 353

Test_1112_low_res		58.78 279	58.69 276	59.04 296	79.41 150	38.13 310	57.62 319	66.98 265	34.74 339	59.62 321	77.56 267	42.92 278	63.65 313	38.66 306	70.73 327	75.35 260

EPMVS		45.74 324	46.53 327	43.39 343	54.14 368	22.33 370	55.02 329	35.00 372	34.69 340	51.09 349	70.20 329	25.92 358	42.04 361	37.19 318	55.50 364	65.78 329

lupinMVS		63.36 241	61.49 256	68.97 201	74.93 212	59.19 156	65.80 258	64.52 280	34.68 341	63.53 300	74.25 296	43.19 276	70.62 270	53.88 214	78.67 282	77.10 247

UnsupCasMVSNet_bld		50.01 316	51.03 318	46.95 329	58.61 349	32.64 343	48.31 342	53.27 332	34.27 342	60.47 315	71.53 320	41.40 285	47.07 343	30.68 345	60.78 353	61.13 347

CMPMVS	48.73 20	61.54 260	60.89 260	63.52 257	61.08 336	51.55 201	68.07 226	68.00 262	33.88 343	65.87 283	81.25 220	37.91 308	67.71 288	49.32 242	82.60 238	71.31 293
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WTY-MVS		49.39 317	50.31 320	46.62 332	61.22 335	32.00 347	46.61 349	49.77 342	33.87 344	54.12 341	69.55 335	41.96 283	45.40 348	31.28 344	64.42 347	62.47 344

N_pmnet		52.06 309	51.11 317	54.92 308	59.64 346	71.03 58	37.42 362	61.62 297	33.68 345	57.12 326	72.10 310	37.94 307	31.03 368	29.13 355	71.35 322	62.70 342

HyFIR lowres test		63.01 246	60.47 264	70.61 173	83.04 107	54.10 187	59.93 309	72.24 226	33.67 346	69.00 260	75.63 279	38.69 303	76.93 206	36.60 322	75.45 302	80.81 202

tpm2		56.12 288	54.64 301	60.55 287	66.24 304	36.01 323	68.14 224	56.77 316	33.60 347	58.25 325	75.52 282	30.25 346	74.33 238	33.27 338	69.76 334	71.32 292

1314		59.83 272	58.86 275	62.74 268	65.71 309	44.78 257	68.59 217	72.63 221	33.54 348	61.05 312	67.29 349	43.62 274	71.26 266	49.49 241	67.84 341	72.19 286

CR-MVSNet		58.96 277	58.49 278	60.36 288	66.37 301	48.24 224	70.93 186	56.40 318	32.87 349	61.35 308	86.66 141	33.19 323	63.22 315	48.50 250	70.17 330	69.62 308

MVS		60.62 267	59.97 267	62.58 269	68.13 288	47.28 238	68.59 217	73.96 211	32.19 350	59.94 319	68.86 341	50.48 237	77.64 199	41.85 289	75.74 297	62.83 341

tpm cat1		54.02 300	52.63 308	58.19 300	64.85 318	39.86 297	66.26 252	57.28 311	32.16 351	56.90 328	70.39 327	32.75 327	65.30 305	34.29 334	58.79 357	69.41 310

pmmvs5		52.49 307	52.58 309	52.21 317	54.99 364	32.38 344	55.45 327	53.84 327	32.15 352	55.49 336	74.81 285	38.08 306	57.37 330	34.02 335	74.40 310	66.88 323

PMMVS2		37.74 335	40.87 336	28.36 352	42.41 375	5.35 377	24.61 365	27.75 374	32.15 352	47.85 357	70.27 328	35.85 316	29.51 369	19.08 369	67.85 340	50.22 358

sss		47.59 322	48.32 322	45.40 337	56.73 357	33.96 338	45.17 352	48.51 346	32.11 354	52.37 346	65.79 351	40.39 295	41.91 362	31.85 341	61.97 351	60.35 348

test-mter		48.56 319	48.20 324	49.64 322	60.76 337	41.87 281	53.18 333	45.48 352	31.91 355	49.41 355	60.47 358	18.34 371	44.73 352	42.09 287	72.14 319	62.33 345

MDTV_nov1_ep13_2view						18.41 372	53.74 332		31.57 356	44.89 362		29.90 350		32.93 339		71.48 291

ADS-MVSNet2		48.76 318	47.25 326	53.29 313	55.90 360	40.54 293	47.34 347	54.99 323	31.41 357	50.48 351	72.06 315	31.23 337	54.26 334	25.93 359	55.93 362	65.07 333

ADS-MVSNet		44.62 329	45.58 328	41.73 346	55.90 360	20.83 371	47.34 347	39.94 367	31.41 357	50.48 351	72.06 315	31.23 337	39.31 364	25.93 359	55.93 362	65.07 333

PVSNet_0	36.71 22	41.12 334	40.78 337	42.14 344	59.97 342	40.13 295	40.97 356	42.24 361	30.81 359	44.86 363	49.41 366	40.70 292	45.12 350	23.15 365	34.96 369	41.16 365

MVS-HIRNet		45.53 325	47.29 325	40.24 348	62.29 328	26.82 363	56.02 325	37.41 370	29.74 360	43.69 367	81.27 219	33.96 320	55.48 331	24.46 364	56.79 361	38.43 367

CHOSEN 1792x2688		58.09 282	56.30 293	63.45 258	79.95 144	50.93 204	54.07 331	65.59 271	28.56 361	61.53 307	74.33 293	41.09 289	66.52 301	33.91 336	67.69 342	72.92 277

TESTMET0.1,1		45.17 326	44.93 331	45.89 335	56.02 359	38.31 306	53.18 333	41.94 362	27.85 362	44.86 363	56.47 361	17.93 372	41.50 363	38.08 312	68.06 339	57.85 351

CHOSEN 280x420		41.62 333	39.89 338	46.80 331	61.81 330	51.59 200	33.56 364	35.74 371	27.48 363	37.64 371	53.53 362	23.24 366	42.09 360	27.39 357	58.64 358	46.72 361

EU-MVSNet		60.82 264	60.80 262	60.86 285	68.37 284	41.16 284	72.27 161	68.27 261	26.96 364	69.08 259	75.71 278	32.09 331	67.44 291	55.59 197	78.90 279	73.97 268

CVMVSNet		59.21 276	58.44 279	61.51 277	73.94 234	47.76 232	71.31 180	64.56 279	26.91 365	60.34 316	70.44 325	36.24 315	67.65 289	53.57 216	68.66 338	69.12 313

new_pmnet		37.55 336	39.80 339	30.79 351	56.83 355	16.46 374	39.35 359	30.65 373	25.59 366	45.26 361	61.60 357	24.54 362	28.02 370	21.60 366	52.80 366	47.90 360

MVE	27.91 23	36.69 337	35.64 340	39.84 349	43.37 374	35.85 326	19.49 366	24.61 376	24.68 367	39.05 369	62.63 356	38.67 304	27.10 371	21.04 367	47.25 368	56.56 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 323	45.09 330	51.55 318	56.76 356	48.25 223	55.78 326	39.53 368	24.13 368	50.35 353	63.40 354	15.90 376	51.08 336	29.29 353	70.69 328	55.33 355

PMMVS		44.69 328	43.95 335	46.92 330	50.05 372	53.47 193	48.08 345	42.40 358	22.36 369	44.01 366	53.05 363	42.60 281	45.49 347	31.69 342	61.36 352	41.79 364

DSMNet-mixed		43.18 332	44.66 333	38.75 350	54.75 365	28.88 359	57.06 321	27.42 375	13.47 370	47.27 359	77.67 266	38.83 302	39.29 365	25.32 363	60.12 355	48.08 359

DeepMVS_CX				11.83 354	15.51 376	13.86 375		11.25 379	5.76 371	20.85 373	26.46 369	17.06 375	9.22 373	9.69 372	13.82 372	12.42 369

test_method		19.26 338	19.12 342	19.71 353	9.09 377	1.91 379	7.79 368	53.44 330	1.42 372	10.27 374	35.80 368	17.42 374	25.11 372	12.44 370	24.38 371	32.10 368

tmp_tt		11.98 340	14.73 343	3.72 355	2.28 378	4.62 378	19.44 367	14.50 378	0.47 373	21.55 372	9.58 371	25.78 359	4.57 374	11.61 371	27.37 370	1.96 370

test123		4.43 343	5.78 346	0.39 357	0.97 379	0.28 380	46.33 351	0.45 380	0.31 374	0.62 375	1.50 374	0.61 380	0.11 376	0.56 373	0.63 373	0.77 372

testmvs		4.06 344	5.28 347	0.41 356	0.64 380	0.16 381	42.54 355	0.31 381	0.26 375	0.50 376	1.40 375	0.77 379	0.17 375	0.56 373	0.55 374	0.90 371

test_blank		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet_test		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

cdsmvs_eth3d_5k		17.71 339	23.62 341	0.00 358	0.00 381	0.00 382	0.00 369	70.17 251	0.00 376	0.00 377	74.25 296	68.16 101	0.00 377	0.00 375	0.00 375	0.00 373

pcd_1.5k_mvsjas		5.20 342	6.93 345	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	62.39 149	0.00 377	0.00 375	0.00 375	0.00 373

sosnet-low-res		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

sosnet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uncertanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

Regformer		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

ab-mvs-re		5.62 341	7.50 344	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	67.46 346	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

MSC_two_6792asdad				79.02 59	83.14 102	67.03 92		80.75 125					86.24 23	77.27 35	94.85 26	83.78 130

No_MVS				79.02 59	83.14 102	67.03 92		80.75 125					86.24 23	77.27 35	94.85 26	83.78 130

eth-test2					0.00 381

eth-test					0.00 381

OPU-MVS				78.65 66	83.44 100	66.85 94	83.62 44				86.12 161	66.82 113	86.01 31	61.72 146	89.79 142	83.08 152

test_0728_SECOND				76.57 91	86.20 53	60.57 146	83.77 42	85.49 35					85.90 37	75.86 42	94.39 43	83.25 147

GSMVS																70.05 303

test_part2					85.90 62	66.44 97				84.61 63

sam_mvs1												31.41 335				70.05 303

sam_mvs												31.21 339

ambc				70.10 185	77.74 179	50.21 209	74.28 150	77.93 175		79.26 125	88.29 112	54.11 220	79.77 157	64.43 122	91.10 109	80.30 211

MTGPA								80.63 128

test_post1							66.63 248				2.08 372	30.66 344	59.33 325	40.34 298

test_post											1.99 373	30.91 342	54.76 333

patchmatchnet-post											68.99 337	31.32 336	69.38 279

GG-mvs-BLEND				52.24 316	60.64 339	29.21 358	69.73 199	42.41 357		45.47 360	52.33 364	20.43 369	68.16 286	25.52 362	65.42 345	59.36 350

MTMP							84.83 33	19.26 377

test9_res														72.12 70	91.37 101	77.40 245

agg_prior2														70.70 77	90.93 114	78.55 231

agg_prior					84.44 86	66.02 101		78.62 163		76.95 156			80.34 146

test_prior4						70.14 68	77.57 105

test_prior				75.27 108	82.15 121	59.85 152		84.33 67					83.39 91			82.58 168

新几何2							71.33 179

旧先验1					84.55 83	60.36 148		63.69 283			87.05 127	54.65 217			83.34 232	69.66 307

原ACMM2							74.78 142

testdata2													67.30 293	48.34 251

segment_acmp												68.30 100

test12				76.51 92	82.28 119	60.94 140		81.64 107		73.60 206		64.88 133	85.19 59		90.42 127	83.38 143

plane_prior7					85.18 71	66.21 99

plane_prior6					84.18 90	65.31 106						60.83 167

plane_prior5								85.49 35					86.15 28	71.09 74	90.94 112	84.82 96

plane_prior4											89.11 94

plane_prior1					84.46 85

n2								0.00 382

nn								0.00 382

door-mid								55.02 322

lessismore_v0				72.75 150	79.60 148	56.83 173		57.37 310		83.80 74	89.01 97	47.45 256	78.74 171	64.39 123	86.49 192	82.69 166

test11								82.71 94

door								52.91 334

HQP5-MVS						58.80 162

BP-MVS														67.38 104

HQP4-MVS										71.59 231			85.31 52			83.74 132

HQP3-MVS								84.12 75							89.16 152

HQP2-MVS												58.09 192

NP-MVS					83.34 101	63.07 125					85.97 165

ACMMP++_ref															89.47 148

ACMMP++															91.96 90

Test By Simon												62.56 145

High-resolution multi-view benchmark