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.

MSP-MVS		90.38 4	91.87 1	85.88 85	92.83 83	64.03 189	93.06 105	94.33 51	82.19 21	93.65 3	96.15 30	85.89 1	97.19 84	91.02 20	97.75 1	96.43 26
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
DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 157	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 4	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 37	93.96 69	94.37 49	72.48 165	92.07 6	96.85 12	83.82 2	99.15 2	91.53 16	97.42 4	97.55 4

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 34	68.23 75	95.24 32	94.49 41	82.43 19	88.90 24	96.35 24	71.89 34	98.63 24	88.76 36	96.40 6	96.06 36

SMA-MVS		88.14 16	88.29 19	87.67 28	93.21 75	68.72 62	93.85 79	94.03 60	74.18 127	91.74 9	96.67 16	65.61 69	98.42 32	89.24 30	96.08 7	95.88 43
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
ETH3 D test6400		90.27 6	90.44 8	89.75 6	96.82 9	74.33 7	95.89 16	94.80 29	77.13 88	89.13 22	97.38 2	74.49 18	98.48 28	92.32 11	95.98 8	96.46 25

DELS-MVS		90.05 7	90.09 10	89.94 4	93.14 78	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 69	74.11 20	98.91 17	87.26 51	95.94 9	97.03 10
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
MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 44	96.26 27	72.84 27	99.38 1	92.64 6	95.93 10	97.08 9

CNVR-MVS		90.32 5	90.89 6	88.61 19	96.76 10	70.65 26	96.47 12	94.83 26	84.83 9	89.07 23	96.80 15	70.86 35	99.06 15	92.64 6	95.71 11	96.12 35

PHI-MVS		86.83 40	86.85 41	86.78 54	93.47 68	65.55 147	95.39 29	95.10 18	71.77 192	85.69 47	96.52 18	62.07 109	98.77 21	86.06 61	95.60 12	96.03 38

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 81	58.16 292	96.72 7	94.41 45	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 13	97.32 6

SED-MVS		89.94 8	90.36 9	88.70 16	96.45 14	69.38 47	96.89 4	94.44 43	71.65 195	92.11 4	97.21 5	76.79 9	99.11 6	92.34 8	95.36 14	97.62 2

IU-MVS					96.46 13	69.91 37		95.18 14	80.75 37	95.28 1				92.34 8	95.36 14	96.47 24

test_241102_TWO								94.41 45	71.65 195	92.07 6	97.21 5	74.58 17	99.11 6	92.34 8	95.36 14	96.59 15

test_0728_THIRD									72.48 165	90.55 16	96.93 10	76.24 11	99.08 11	91.53 16	94.99 17	96.43 26

test9_res														89.41 26	94.96 18	95.29 62

ACMMP_NAP		86.05 50	85.80 53	86.80 53	91.58 121	67.53 93	91.79 160	93.49 81	74.93 118	84.61 57	95.30 49	59.42 136	97.92 45	86.13 59	94.92 19	94.94 81

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 22	67.56 91	94.17 55	94.15 57	68.77 246	90.74 14	97.27 3	76.09 12	98.49 27	90.58 23	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 17	69.99 33	96.64 8	94.52 39	71.92 181	90.55 16	96.93 10	73.77 22	99.08 11	91.91 14	94.90 21	96.29 30
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
test_0728_SECOND				88.70 16	96.45 14	70.43 29	96.64 8	94.37 49					99.15 2	91.91 14	94.90 21	96.51 20

train_agg		87.21 31	87.42 31	86.60 60	94.18 46	67.28 100	94.16 56	93.51 78	71.87 186	85.52 48	95.33 47	68.19 41	97.27 82	89.09 31	94.90 21	95.25 69

DeepC-MVS_fast	79.48 2	87.95 20	88.00 20	87.79 27	95.86 29	68.32 70	95.74 20	94.11 59	83.82 12	83.49 69	96.19 29	64.53 81	98.44 30	83.42 81	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 64	91.73 117	68.04 79	90.36 216	93.55 77	82.89 14	91.29 12	92.89 120	72.27 31	96.03 133	87.99 40	94.77 25	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_prior3		87.38 27	87.70 24	86.42 69	94.71 39	67.35 98	95.10 37	93.10 100	75.40 109	85.25 54	95.61 40	67.94 45	96.84 107	87.47 46	94.77 25	95.05 76

test_prior2							95.10 37		75.40 109	85.25 54	95.61 40	67.94 45		87.47 46	94.77 25

agg_prior2														86.41 57	94.75 30	95.33 58

ETH3D cwj APD-0.16		87.06 34	87.18 35	86.71 56	91.99 108	67.48 96	92.97 110	94.21 54	71.48 206	85.72 45	96.32 26	68.13 43	98.00 42	89.06 32	94.70 31	94.65 92

MVS		84.66 70	82.86 94	90.06 2	90.93 135	74.56 6	87.91 266	95.54 9	68.55 248	72.35 185	94.71 74	59.78 132	98.90 18	81.29 102	94.69 32	96.74 12

xxxxxxxxxxxxxcwj		87.14 32	87.19 34	86.99 47	93.84 56	67.89 83	95.05 39	84.72 316	78.19 70	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

SF-MVS		87.03 35	87.09 36	86.84 50	92.70 89	67.45 97	93.64 87	93.76 67	70.78 219	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

NCCC		89.07 14	89.46 14	87.91 24	96.60 12	69.05 54	96.38 13	94.64 36	84.42 10	86.74 34	96.20 28	66.56 60	98.76 22	89.03 34	94.56 33	95.92 41

ETH3D-3000-0.1		87.61 24	87.89 21	86.75 55	93.58 64	67.21 102	94.31 53	94.14 58	72.92 156	87.13 30	96.62 17	67.81 49	97.94 43	90.13 24	94.42 36	95.09 74

3Dnovator	73.91 6	82.69 107	80.82 121	88.31 22	89.57 159	71.26 20	92.60 127	94.39 48	78.84 63	67.89 238	92.48 130	48.42 249	98.52 26	68.80 200	94.40 37	95.15 71

agg_prior1		87.02 36	87.26 33	86.28 76	94.16 50	66.97 111	94.08 63	93.31 88	71.85 188	84.49 59	95.39 45	68.91 38	96.75 111	88.84 35	94.32 38	95.13 72

CDPH-MVS		85.71 56	85.46 57	86.46 67	94.75 38	67.19 103	93.89 77	92.83 109	70.90 215	83.09 72	95.28 50	63.62 93	97.36 73	80.63 105	94.18 39	94.84 85

MG-MVS		87.11 33	86.27 44	89.62 8	97.79 1	76.27 4	94.96 44	94.49 41	78.74 66	83.87 68	92.94 117	64.34 83	96.94 103	75.19 140	94.09 40	95.66 47

9.14			87.63 25		93.86 55		94.41 51	94.18 55	72.76 159	86.21 38	96.51 19	66.64 58	97.88 49	90.08 25	94.04 41

原ACMM1				84.42 136	93.21 75	64.27 185		93.40 87	65.39 271	79.51 107	92.50 127	58.11 149	96.69 113	65.27 235	93.96 42	92.32 167

MSLP-MVS++		86.27 46	85.91 51	87.35 38	92.01 106	68.97 57	95.04 41	92.70 112	79.04 61	81.50 83	96.50 20	58.98 143	96.78 109	83.49 80	93.93 43	96.29 30

CANet		89.61 11	89.99 11	88.46 20	94.39 44	69.71 43	96.53 11	93.78 64	86.89 4	89.68 19	95.78 34	65.94 64	99.10 9	92.99 4	93.91 44	96.58 17

MP-MVS-pluss		85.24 61	85.13 60	85.56 99	91.42 126	65.59 146	91.54 172	92.51 122	74.56 121	80.62 94	95.64 39	59.15 140	97.00 95	86.94 54	93.80 45	94.07 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVP-Stereo		77.12 201	76.23 194	79.79 251	81.72 290	66.34 127	89.29 243	90.88 189	70.56 223	62.01 292	82.88 249	49.34 240	94.13 207	65.55 232	93.80 45	78.88 344
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GG-mvs-BLEND				86.53 65	91.91 112	69.67 45	75.02 341	94.75 31		78.67 121	90.85 154	77.91 7	94.56 190	72.25 165	93.74 47	95.36 57

ZNCC-MVS		85.33 60	85.08 61	86.06 80	93.09 80	65.65 144	93.89 77	93.41 86	73.75 138	79.94 101	94.68 75	60.61 122	98.03 41	82.63 88	93.72 48	94.52 98

CSCG		86.87 38	86.26 45	88.72 15	95.05 35	70.79 25	93.83 83	95.33 11	68.48 250	77.63 128	94.35 86	73.04 25	98.45 29	84.92 70	93.71 49	96.92 11

test12				87.09 44	94.60 41	68.86 58		92.91 106		82.67 76		65.44 70	97.55 64		93.69 50	94.84 85

PAPM		85.89 53	85.46 57	87.18 41	88.20 196	72.42 14	92.41 134	92.77 110	82.11 22	80.34 97	93.07 114	68.27 40	95.02 172	78.39 123	93.59 51	94.09 115

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 62	90.42 143	66.38 125	96.09 15	93.87 62	77.73 79	84.01 67	95.66 38	63.39 97	97.94 43	87.40 48	93.55 52	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		87.54 25	87.84 22	86.65 59	96.07 25	66.30 128	94.84 46	93.78 64	69.35 236	88.39 25	96.34 25	67.74 50	97.66 58	90.62 22	93.44 53	96.01 39

CS-MVS-test		86.14 49	87.01 37	83.52 158	92.63 91	59.36 278	95.49 26	91.92 142	80.09 42	85.46 50	95.53 43	61.82 112	95.77 141	86.77 56	93.37 54	95.41 53

PS-MVSNAJ		88.14 16	87.61 26	89.71 7	92.06 103	76.72 1	95.75 19	93.26 90	83.86 11	89.55 20	96.06 31	53.55 204	97.89 48	91.10 18	93.31 55	94.54 96

MAR-MVS		84.18 80	83.43 80	86.44 68	96.25 23	65.93 137	94.28 54	94.27 53	74.41 122	79.16 112	95.61 40	53.99 199	98.88 20	69.62 190	93.26 56	94.50 100
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
gg-mvs-nofinetune		77.18 200	74.31 218	85.80 90	91.42 126	68.36 69	71.78 343	94.72 32	49.61 347	77.12 135	45.92 364	77.41 8	93.98 219	67.62 208	93.16 57	95.05 76

testtj		86.62 43	86.66 42	86.50 66	96.95 8	65.70 142	94.41 51	93.45 82	67.74 252	86.19 39	96.39 23	64.38 82	97.91 46	87.33 49	93.14 58	95.90 42

ZD-MVS					96.63 11	65.50 149		93.50 80	70.74 220	85.26 53	95.19 58	64.92 77	97.29 78	87.51 45	93.01 59

APD-MVS		85.93 52	85.99 49	85.76 92	95.98 28	65.21 154	93.59 90	92.58 120	66.54 263	86.17 40	95.88 33	63.83 89	97.00 95	86.39 58	92.94 60	95.06 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
新几何1				84.73 125	92.32 96	64.28 184		91.46 165	59.56 315	79.77 104	92.90 119	56.95 164	96.57 116	63.40 244	92.91 61	93.34 138

DeepC-MVS	77.85 3	85.52 57	85.24 59	86.37 72	88.80 179	66.64 119	92.15 140	93.68 72	81.07 34	76.91 138	93.64 103	62.59 105	98.44 30	85.50 64	92.84 62	94.03 119
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1121		81.25 127	80.05 132	84.87 122	92.30 97	64.31 182	87.91 266	91.39 167	59.44 316	79.94 101	92.91 118	57.09 157	97.01 93	66.63 215	92.81 63	93.29 141

xiu_mvs_v2_base		87.92 21	87.38 32	89.55 12	91.41 128	76.43 3	95.74 20	93.12 99	83.53 13	89.55 20	95.95 32	53.45 208	97.68 54	91.07 19	92.62 64	94.54 96

MP-MVS		85.02 63	84.97 63	85.17 113	92.60 92	64.27 185	93.24 100	92.27 127	73.13 150	79.63 106	94.43 80	61.90 110	97.17 85	85.00 68	92.56 65	94.06 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		84.73 68	84.47 67	85.50 100	91.89 113	65.16 156	91.55 171	92.23 128	75.32 111	80.53 95	95.21 56	56.06 176	97.16 86	84.86 71	92.55 66	94.18 108

MTAPA		83.91 85	83.38 84	85.50 100	91.89 113	65.16 156	81.75 309	92.23 128	75.32 111	80.53 95	95.21 56	56.06 176	97.16 86	84.86 71	92.55 66	94.18 108

GST-MVS		84.63 71	84.29 71	85.66 97	92.82 85	65.27 152	93.04 107	93.13 98	73.20 148	78.89 114	94.18 93	59.41 137	97.85 50	81.45 98	92.48 68	93.86 127

HFP-MVS		84.73 68	84.40 70	85.72 93	93.75 60	65.01 162	93.50 94	93.19 94	72.19 175	79.22 110	94.93 66	59.04 141	97.67 55	81.55 96	92.21 69	94.49 101

#test#		84.98 65	84.74 66	85.72 93	93.75 60	65.01 162	94.09 62	93.19 94	73.55 144	79.22 110	94.93 66	59.04 141	97.67 55	82.66 86	92.21 69	94.49 101

ACMMPR		84.37 73	84.06 72	85.28 109	93.56 65	64.37 180	93.50 94	93.15 97	72.19 175	78.85 119	94.86 70	56.69 168	97.45 67	81.55 96	92.20 71	94.02 120

MS-PatchMatch		77.90 192	76.50 190	82.12 196	85.99 234	69.95 36	91.75 165	92.70 112	73.97 132	62.58 289	84.44 236	41.11 288	95.78 139	63.76 243	92.17 72	80.62 331

region2R		84.36 74	84.03 73	85.36 107	93.54 66	64.31 182	93.43 97	92.95 105	72.16 178	78.86 118	94.84 71	56.97 163	97.53 65	81.38 100	92.11 73	94.24 106

CS-MVS		85.80 54	86.65 43	83.27 166	92.00 107	58.92 283	95.31 30	91.86 146	79.97 43	84.82 56	95.40 44	62.26 107	95.51 160	86.11 60	92.08 74	95.37 56

patch_mono-2		89.71 10	90.99 5	85.85 88	96.04 26	63.70 197	95.04 41	95.19 13	86.74 5	91.53 11	95.15 59	73.86 21	97.58 63	93.38 3	92.00 75	96.28 32

dcpmvs_2		87.37 28	87.55 28	86.85 49	95.04 36	68.20 76	90.36 216	90.66 195	79.37 52	81.20 86	93.67 102	74.73 15	96.55 118	90.88 21	92.00 75	95.82 44

旧先验1					91.94 109	60.74 256		91.50 163			94.36 82	65.23 71			91.84 77	94.55 94

MVSFormer		83.75 89	82.88 93	86.37 72	89.24 169	71.18 21	89.07 250	90.69 192	65.80 268	87.13 30	94.34 87	64.99 74	92.67 257	72.83 157	91.80 78	95.27 66

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 169	71.18 21	96.57 10	92.90 107	82.70 18	87.13 30	95.27 52	64.99 74	95.80 138	89.34 28	91.80 78	95.93 40

EPNet		87.84 22	88.38 17	86.23 77	93.30 71	66.05 132	95.26 31	94.84 25	87.09 3	88.06 26	94.53 77	66.79 57	97.34 75	83.89 78	91.68 80	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	73.60 7	82.10 116	80.60 126	86.60 60	90.89 137	66.80 116	95.20 33	93.44 84	74.05 129	67.42 244	92.49 129	49.46 239	97.65 59	70.80 179	91.68 80	95.33 58

XVS		83.87 86	83.47 78	85.05 114	93.22 73	63.78 192	92.92 114	92.66 115	73.99 130	78.18 123	94.31 89	55.25 182	97.41 70	79.16 114	91.58 82	93.95 122

X-MVStestdata		76.86 202	74.13 222	85.05 114	93.22 73	63.78 192	92.92 114	92.66 115	73.99 130	78.18 123	10.19 376	55.25 182	97.41 70	79.16 114	91.58 82	93.95 122

SD-MVS		87.49 26	87.49 29	87.50 35	93.60 63	68.82 60	93.90 76	92.63 118	76.86 91	87.90 27	95.76 35	66.17 61	97.63 60	89.06 32	91.48 84	96.05 37
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
DROMVSNet		84.53 72	85.04 62	83.01 170	89.34 164	61.37 243	94.42 50	91.09 181	77.91 76	83.24 70	94.20 92	58.37 146	95.40 161	85.35 66	91.41 85	92.27 172

PGM-MVS		83.25 96	82.70 97	84.92 119	92.81 87	64.07 188	90.44 212	92.20 133	71.28 208	77.23 134	94.43 80	55.17 186	97.31 77	79.33 113	91.38 86	93.37 137

PVSNet_Blended		86.73 42	86.86 40	86.31 75	93.76 58	67.53 93	96.33 14	93.61 74	82.34 20	81.00 91	93.08 112	63.19 100	97.29 78	87.08 52	91.38 86	94.13 113

HPM-MVS		83.25 96	82.95 91	84.17 143	92.25 99	62.88 218	90.91 197	91.86 146	70.30 225	77.12 135	93.96 98	56.75 166	96.28 122	82.04 92	91.34 88	93.34 138
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EIA-MVS		84.84 67	84.88 64	84.69 128	91.30 129	62.36 225	93.85 79	92.04 137	79.45 50	79.33 109	94.28 90	62.42 106	96.35 121	80.05 108	91.25 89	95.38 55

MVS_111021_HR		86.19 48	85.80 53	87.37 37	93.17 77	69.79 40	93.99 68	93.76 67	79.08 60	78.88 117	93.99 97	62.25 108	98.15 38	85.93 62	91.15 90	94.15 112

test222					89.77 155	61.60 239	89.55 237	89.42 240	56.83 329	77.28 133	92.43 131	52.76 211			91.14 91	93.09 148

jason		86.40 44	86.17 47	87.11 43	86.16 232	70.54 28	95.71 23	92.19 134	82.00 24	84.58 58	94.34 87	61.86 111	95.53 159	87.76 42	90.89 92	95.27 66
jason: jason.
mPP-MVS		82.96 102	82.44 102	84.52 133	92.83 83	62.92 216	92.76 117	91.85 148	71.52 203	75.61 150	94.24 91	53.48 207	96.99 98	78.97 117	90.73 93	93.64 133

CP-MVS		83.71 90	83.40 83	84.65 129	93.14 78	63.84 190	94.59 48	92.28 126	71.03 213	77.41 131	94.92 68	55.21 185	96.19 124	81.32 101	90.70 94	93.91 124

OpenMVS	70.45 11	78.54 180	75.92 198	86.41 71	85.93 238	71.68 17	92.74 118	92.51 122	66.49 264	64.56 269	91.96 139	43.88 280	98.10 39	54.61 284	90.65 95	89.44 211

PAPM_NR		82.97 101	81.84 108	86.37 72	94.10 52	66.76 117	87.66 270	92.84 108	69.96 229	74.07 163	93.57 105	63.10 102	97.50 66	70.66 182	90.58 96	94.85 82

testdata				81.34 213	89.02 173	57.72 296		89.84 226	58.65 320	85.32 52	94.09 94	57.03 159	93.28 235	69.34 193	90.56 97	93.03 150

Vis-MVSNet		80.92 135	79.98 136	83.74 151	88.48 184	61.80 234	93.44 96	88.26 282	73.96 133	77.73 126	91.76 142	49.94 235	94.76 179	65.84 227	90.37 98	94.65 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		84.98 65	83.45 79	89.57 11	89.94 152	75.14 5	92.07 146	92.32 125	81.87 25	75.68 147	88.27 189	60.18 125	98.60 25	80.46 107	90.27 99	94.96 80

ETV-MVS		86.01 51	86.11 48	85.70 96	90.21 148	67.02 110	93.43 97	91.92 142	81.21 32	84.13 65	94.07 96	60.93 119	95.63 149	89.28 29	89.81 100	94.46 103

QAPM		79.95 151	77.39 180	87.64 29	89.63 158	71.41 19	93.30 99	93.70 71	65.34 273	67.39 246	91.75 143	47.83 255	98.96 16	57.71 275	89.81 100	92.54 162

CANet_DTU		84.09 82	83.52 76	85.81 89	90.30 146	66.82 114	91.87 156	89.01 259	85.27 7	86.09 41	93.74 101	47.71 257	96.98 99	77.90 127	89.78 102	93.65 132

API-MVS		82.28 111	80.53 127	87.54 34	96.13 24	70.59 27	93.63 88	91.04 186	65.72 270	75.45 152	92.83 123	56.11 175	98.89 19	64.10 240	89.75 103	93.15 146

test2506		83.29 94	82.92 92	84.37 138	88.39 189	63.18 209	92.01 149	91.35 169	77.66 81	78.49 122	91.42 146	64.58 80	95.09 170	73.19 153	89.23 104	94.85 82

ECVR-MVS		81.29 126	80.38 130	84.01 147	88.39 189	61.96 232	92.56 132	86.79 297	77.66 81	76.63 139	91.42 146	46.34 266	95.24 168	74.36 150	89.23 104	94.85 82

MVS_Test		84.16 81	83.20 86	87.05 45	91.56 122	69.82 39	89.99 230	92.05 136	77.77 78	82.84 73	86.57 210	63.93 88	96.09 128	74.91 146	89.18 106	95.25 69

PAPR		85.15 62	84.47 67	87.18 41	96.02 27	68.29 71	91.85 158	93.00 104	76.59 96	79.03 113	95.00 62	61.59 113	97.61 62	78.16 124	89.00 107	95.63 48

TSAR-MVS + GP.		87.96 19	88.37 18	86.70 58	93.51 67	65.32 151	95.15 35	93.84 63	78.17 72	85.93 43	94.80 72	75.80 13	98.21 35	89.38 27	88.78 108	96.59 15

SR-MVS		82.81 103	82.58 99	83.50 161	93.35 69	61.16 246	92.23 139	91.28 173	64.48 276	81.27 85	95.28 50	53.71 203	95.86 137	82.87 84	88.77 109	93.49 136

test1111		80.84 136	80.02 133	83.33 164	87.87 204	60.76 254	92.62 126	86.86 296	77.86 77	75.73 146	91.39 148	46.35 265	94.70 185	72.79 159	88.68 110	94.52 98

Regformer-1		87.24 30	87.60 27	86.15 79	95.14 32	65.83 140	93.95 72	95.12 16	82.11 22	84.25 61	95.73 36	67.88 48	98.35 33	85.60 63	88.64 111	94.26 105

Regformer-2		87.00 37	87.43 30	85.71 95	95.14 32	64.73 168	93.95 72	94.95 23	81.69 27	84.03 66	95.73 36	67.35 52	98.19 37	85.40 65	88.64 111	94.20 107

HPM-MVS_fast		80.25 144	79.55 144	82.33 186	91.55 123	59.95 268	91.32 185	89.16 250	65.23 274	74.71 156	93.07 114	47.81 256	95.74 142	74.87 148	88.23 113	91.31 189

PVSNet_Blended_VisFu		83.97 84	83.50 77	85.39 106	90.02 150	66.59 122	93.77 84	91.73 152	77.43 87	77.08 137	89.81 173	63.77 91	96.97 100	79.67 110	88.21 114	92.60 160

test1172		81.90 119	81.83 109	82.13 195	93.23 72	57.52 301	91.61 170	90.98 188	64.32 278	80.20 99	95.00 62	51.26 224	95.61 151	81.73 95	88.13 115	93.26 142

Vis-MVSNet (Re-imp)		79.24 163	79.57 141	78.24 274	88.46 185	52.29 331	90.41 214	89.12 253	74.24 126	69.13 217	91.91 140	65.77 67	90.09 307	59.00 272	88.09 116	92.33 166

APD-MVS_3200maxsize		81.64 122	81.32 114	82.59 180	92.36 95	58.74 285	91.39 179	91.01 187	63.35 284	79.72 105	94.62 76	51.82 217	96.14 126	79.71 109	87.93 117	92.89 155

Effi-MVS+		83.82 87	82.76 95	86.99 47	89.56 160	69.40 46	91.35 183	86.12 305	72.59 161	83.22 71	92.81 124	59.60 134	96.01 135	81.76 94	87.80 118	95.56 50

1314		80.70 137	78.95 154	85.94 84	87.77 206	67.56 91	87.91 266	92.55 121	72.17 177	67.44 243	93.09 111	50.27 232	97.04 92	71.68 174	87.64 119	93.23 144

PMMVS		81.98 118	82.04 106	81.78 203	89.76 156	56.17 311	91.13 193	90.69 192	77.96 74	80.09 100	93.57 105	46.33 267	94.99 173	81.41 99	87.46 120	94.17 110

casdiffmvs		85.37 59	84.87 65	86.84 50	88.25 194	69.07 53	93.04 107	91.76 151	81.27 31	80.84 93	92.07 138	64.23 84	96.06 131	84.98 69	87.43 121	95.39 54

UGNet		79.87 152	78.68 155	83.45 163	89.96 151	61.51 240	92.13 141	90.79 190	76.83 93	78.85 119	86.33 213	38.16 303	96.17 125	67.93 205	87.17 122	92.67 158
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
MVS_111021_LR		82.02 117	81.52 112	83.51 160	88.42 187	62.88 218	89.77 234	88.93 261	76.78 94	75.55 151	93.10 110	50.31 231	95.38 163	83.82 79	87.02 123	92.26 173

xiu_mvs_v1_base_debu		82.16 113	81.12 116	85.26 110	86.42 226	68.72 62	92.59 129	90.44 201	73.12 151	84.20 62	94.36 82	38.04 305	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base		82.16 113	81.12 116	85.26 110	86.42 226	68.72 62	92.59 129	90.44 201	73.12 151	84.20 62	94.36 82	38.04 305	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base_debi		82.16 113	81.12 116	85.26 110	86.42 226	68.72 62	92.59 129	90.44 201	73.12 151	84.20 62	94.36 82	38.04 305	95.73 143	84.12 75	86.81 124	91.33 185

SR-MVS-dyc-post		81.06 132	80.70 123	82.15 193	92.02 104	58.56 287	90.90 198	90.45 198	62.76 290	78.89 114	94.46 78	51.26 224	95.61 151	78.77 120	86.77 127	92.28 169

RE-MVS-def			80.48 128		92.02 104	58.56 287	90.90 198	90.45 198	62.76 290	78.89 114	94.46 78	49.30 241		78.77 120	86.77 127	92.28 169

baseline		85.01 64	84.44 69	86.71 56	88.33 191	68.73 61	90.24 221	91.82 150	81.05 35	81.18 87	92.50 127	63.69 92	96.08 130	84.45 73	86.71 129	95.32 60

TESTMET0.1,1		82.41 109	81.98 107	83.72 154	88.08 197	63.74 194	92.70 121	93.77 66	79.30 53	77.61 129	87.57 201	58.19 148	94.08 210	73.91 152	86.68 130	93.33 140

IS-MVSNet		80.14 146	79.41 146	82.33 186	87.91 202	60.08 267	91.97 153	88.27 280	72.90 157	71.44 195	91.73 144	61.44 114	93.66 229	62.47 253	86.53 131	93.24 143

CPTT-MVS		79.59 157	79.16 152	80.89 229	91.54 124	59.80 270	92.10 143	88.54 275	60.42 308	72.96 171	93.28 109	48.27 250	92.80 251	78.89 119	86.50 132	90.06 202

BH-w/o		80.49 141	79.30 149	84.05 146	90.83 139	64.36 181	93.60 89	89.42 240	74.35 124	69.09 218	90.15 168	55.23 184	95.61 151	64.61 237	86.43 133	92.17 174

PVSNet	73.49 8	80.05 148	78.63 156	84.31 140	90.92 136	64.97 164	92.47 133	91.05 185	79.18 56	72.43 183	90.51 160	37.05 317	94.06 212	68.06 203	86.00 134	93.90 126

mvs_anonymous		81.36 125	79.99 135	85.46 102	90.39 145	68.40 68	86.88 280	90.61 197	74.41 122	70.31 206	84.67 233	63.79 90	92.32 272	73.13 154	85.70 135	95.67 46

DP-MVS Recon		82.73 104	81.65 111	85.98 82	97.31 4	67.06 107	95.15 35	91.99 139	69.08 243	76.50 142	93.89 99	54.48 194	98.20 36	70.76 180	85.66 136	92.69 157

BH-RMVSNet		79.46 161	77.65 172	84.89 120	91.68 119	65.66 143	93.55 91	88.09 284	72.93 155	73.37 168	91.12 151	46.20 269	96.12 127	56.28 280	85.61 137	92.91 154

abl_6		79.82 153	79.20 151	81.70 207	89.85 153	58.34 289	88.47 259	90.07 217	62.56 293	77.71 127	93.08 112	47.65 258	96.78 109	77.94 126	85.45 138	89.99 204

diffmvs		84.28 76	83.83 74	85.61 98	87.40 212	68.02 80	90.88 200	89.24 245	80.54 39	81.64 82	92.52 126	59.83 131	94.52 194	87.32 50	85.11 139	94.29 104

Fast-Effi-MVS+		81.14 129	80.01 134	84.51 134	90.24 147	65.86 138	94.12 61	89.15 251	73.81 137	75.37 153	88.26 190	57.26 155	94.53 193	66.97 214	84.92 140	93.15 146

LFMVS		84.34 75	82.73 96	89.18 13	94.76 37	73.25 10	94.99 43	91.89 145	71.90 183	82.16 78	93.49 107	47.98 254	97.05 90	82.55 89	84.82 141	97.25 7

BH-untuned		78.68 176	77.08 182	83.48 162	89.84 154	63.74 194	92.70 121	88.59 273	71.57 201	66.83 252	88.65 182	51.75 219	95.39 162	59.03 271	84.77 142	91.32 188

test-LLR		80.10 147	79.56 142	81.72 205	86.93 221	61.17 244	92.70 121	91.54 160	71.51 204	75.62 148	86.94 207	53.83 200	92.38 268	72.21 166	84.76 143	91.60 179

test-mter		79.96 150	79.38 148	81.72 205	86.93 221	61.17 244	92.70 121	91.54 160	73.85 135	75.62 148	86.94 207	49.84 237	92.38 268	72.21 166	84.76 143	91.60 179

Regformer-3		85.80 54	85.92 50	85.46 102	94.17 48	65.09 161	92.95 112	95.11 17	81.13 33	81.68 81	95.04 60	65.82 66	98.32 34	83.02 83	84.36 145	92.97 152

Regformer-4		85.45 58	85.69 55	84.73 125	94.17 48	63.23 206	92.95 112	94.83 26	80.66 38	81.29 84	95.04 60	65.12 72	98.08 40	82.74 85	84.36 145	92.88 156

canonicalmvs		86.85 39	86.25 46	88.66 18	91.80 116	71.92 15	93.54 92	91.71 154	80.26 41	87.55 28	95.25 54	63.59 95	96.93 105	88.18 39	84.34 147	97.11 8

alignmvs		87.28 29	86.97 38	88.24 23	91.30 129	71.14 23	95.61 24	93.56 76	79.30 53	87.07 33	95.25 54	68.43 39	96.93 105	87.87 41	84.33 148	96.65 13

VNet		86.20 47	85.65 56	87.84 26	93.92 54	69.99 33	95.73 22	95.94 6	78.43 68	86.00 42	93.07 114	58.22 147	97.00 95	85.22 67	84.33 148	96.52 19

UA-Net		80.02 149	79.65 140	81.11 220	89.33 166	57.72 296	86.33 283	89.00 260	77.44 86	81.01 90	89.15 178	59.33 138	95.90 136	61.01 260	84.28 150	89.73 207

LCM-MVSNet-Re		72.93 252	71.84 250	76.18 297	88.49 183	48.02 349	80.07 325	70.17 361	73.96 133	52.25 331	80.09 293	49.98 234	88.24 319	67.35 209	84.23 151	92.28 169

ACMMP		81.49 123	80.67 124	83.93 148	91.71 118	62.90 217	92.13 141	92.22 132	71.79 191	71.68 193	93.49 107	50.32 230	96.96 101	78.47 122	84.22 152	91.93 176
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
114514_t		79.17 164	77.67 171	83.68 155	95.32 31	65.53 148	92.85 116	91.60 159	63.49 283	67.92 236	90.63 158	46.65 262	95.72 147	67.01 213	83.54 153	89.79 205

EPMVS		78.49 181	75.98 197	86.02 81	91.21 131	69.68 44	80.23 322	91.20 174	75.25 113	72.48 181	78.11 306	54.65 190	93.69 228	57.66 276	83.04 154	94.69 88

AdaColmap		78.94 168	77.00 185	84.76 124	96.34 19	65.86 138	92.66 125	87.97 287	62.18 296	70.56 200	92.37 133	43.53 281	97.35 74	64.50 238	82.86 155	91.05 193

CDS-MVSNet		81.43 124	80.74 122	83.52 158	86.26 230	64.45 174	92.09 144	90.65 196	75.83 103	73.95 165	89.81 173	63.97 87	92.91 247	71.27 176	82.82 156	93.20 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 280x420		77.35 198	76.95 186	78.55 269	87.07 218	62.68 222	69.71 349	82.95 332	68.80 245	71.48 194	87.27 206	66.03 63	84.00 343	76.47 134	82.81 157	88.95 212

PCF-MVS	73.15 9	79.29 162	77.63 173	84.29 141	86.06 233	65.96 136	87.03 276	91.10 180	69.86 231	69.79 214	90.64 156	57.54 154	96.59 114	64.37 239	82.29 158	90.32 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WTY-MVS		86.32 45	85.81 52	87.85 25	92.82 85	69.37 49	95.20 33	95.25 12	82.71 17	81.91 79	94.73 73	67.93 47	97.63 60	79.55 111	82.25 159	96.54 18

HY-MVS	76.49 5	84.28 76	83.36 85	87.02 46	92.22 100	67.74 87	84.65 289	94.50 40	79.15 57	82.23 77	87.93 196	66.88 56	96.94 103	80.53 106	82.20 160	96.39 28

VDD-MVS		83.06 99	81.81 110	86.81 52	90.86 138	67.70 88	95.40 28	91.50 163	75.46 106	81.78 80	92.34 134	40.09 291	97.13 88	86.85 55	82.04 161	95.60 49

TAMVS		80.37 142	79.45 145	83.13 169	85.14 247	63.37 203	91.23 188	90.76 191	74.81 120	72.65 176	88.49 183	60.63 121	92.95 242	69.41 192	81.95 162	93.08 149

test_yl		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 240	81.09 88	92.88 121	57.00 161	97.44 68	81.11 103	81.76 163	96.23 33

DCV-MVSNet		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 240	81.09 88	92.88 121	57.00 161	97.44 68	81.11 103	81.76 163	96.23 33

thisisatest0515		83.41 92	82.49 101	86.16 78	89.46 163	68.26 73	93.54 92	94.70 33	74.31 125	75.75 145	90.92 152	72.62 29	96.52 119	69.64 188	81.50 165	93.71 130

baseline2		83.68 91	83.42 82	84.48 135	87.37 213	66.00 134	90.06 225	95.93 7	79.71 48	69.08 219	90.39 163	77.92 6	96.28 122	78.91 118	81.38 166	91.16 191

PatchmatchNet		77.46 196	74.63 211	85.96 83	89.55 161	70.35 30	79.97 326	89.55 236	72.23 174	70.94 197	76.91 317	57.03 159	92.79 252	54.27 286	81.17 167	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VDDNet		80.50 140	78.26 161	87.21 40	86.19 231	69.79 40	94.48 49	91.31 170	60.42 308	79.34 108	90.91 153	38.48 301	96.56 117	82.16 90	81.05 168	95.27 66

EPNet_dtu		78.80 172	79.26 150	77.43 283	88.06 198	49.71 344	91.96 154	91.95 141	77.67 80	76.56 141	91.28 150	58.51 145	90.20 305	56.37 279	80.95 169	92.39 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		82.71 106	82.38 103	83.73 153	89.25 168	59.58 273	92.24 138	94.89 24	77.96 74	79.86 103	92.38 132	56.70 167	97.05 90	77.26 130	80.86 170	94.55 94

GeoE		78.90 169	77.43 176	83.29 165	88.95 175	62.02 230	92.31 135	86.23 303	70.24 226	71.34 196	89.27 176	54.43 195	94.04 215	63.31 245	80.81 171	93.81 129

TR-MVS		78.77 174	77.37 181	82.95 171	90.49 142	60.88 250	93.67 86	90.07 217	70.08 228	74.51 157	91.37 149	45.69 271	95.70 148	60.12 266	80.32 172	92.29 168

TAPA-MVS	70.22 12	74.94 234	73.53 230	79.17 262	90.40 144	52.07 332	89.19 248	89.61 235	62.69 292	70.07 208	92.67 125	48.89 248	94.32 198	38.26 347	79.97 173	91.12 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
cascas		78.18 185	75.77 200	85.41 105	87.14 217	69.11 52	92.96 111	91.15 178	66.71 262	70.47 201	86.07 216	37.49 311	96.48 120	70.15 185	79.80 174	90.65 196

HyFIR lowres test		81.03 133	79.56 142	85.43 104	87.81 205	68.11 78	90.18 222	90.01 222	70.65 221	72.95 172	86.06 217	63.61 94	94.50 195	75.01 144	79.75 175	93.67 131

LS3D		69.17 277	66.40 280	77.50 281	91.92 111	56.12 312	85.12 286	80.37 341	46.96 353	56.50 319	87.51 202	37.25 312	93.71 227	32.52 362	79.40 176	82.68 313

EI-MVSNet-Vis-set		83.77 88	83.67 75	84.06 145	92.79 88	63.56 202	91.76 163	94.81 28	79.65 49	77.87 125	94.09 94	63.35 98	97.90 47	79.35 112	79.36 177	90.74 195

CVMVSNet		74.04 242	74.27 219	73.33 314	85.33 243	43.94 360	89.53 239	88.39 276	54.33 336	70.37 204	90.13 169	49.17 244	84.05 341	61.83 257	79.36 177	91.99 175

EPP-MVSNet		81.79 121	81.52 112	82.61 179	88.77 180	60.21 265	93.02 109	93.66 73	68.52 249	72.90 173	90.39 163	72.19 32	94.96 174	74.93 145	79.29 179	92.67 158

CLD-MVS		82.73 104	82.35 104	83.86 149	87.90 203	67.65 90	95.45 27	92.18 135	85.06 8	72.58 178	92.27 135	52.46 214	95.78 139	84.18 74	79.06 180	88.16 227
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP3-MVS								91.70 155							78.90 181

HQP-MVS		81.14 129	80.64 125	82.64 178	87.54 208	63.66 200	94.06 64	91.70 155	79.80 45	74.18 159	90.30 165	51.63 221	95.61 151	77.63 128	78.90 181	88.63 217

plane_prior						62.42 223	93.85 79		79.38 51						78.80 183

thres200		79.66 155	78.33 159	83.66 157	92.54 93	65.82 141	93.06 105	96.31 3	74.90 119	73.30 169	88.66 181	59.67 133	95.61 151	47.84 311	78.67 184	89.56 210

ET-MVSNet_ETH3D		84.01 83	83.15 89	86.58 62	90.78 140	70.89 24	94.74 47	94.62 37	81.44 30	58.19 309	93.64 103	73.64 24	92.35 271	82.66 86	78.66 185	96.50 23

HQP_MVS		80.34 143	79.75 139	82.12 196	86.94 219	62.42 223	93.13 103	91.31 170	78.81 64	72.53 179	89.14 179	50.66 228	95.55 157	76.74 131	78.53 186	88.39 223

plane_prior5								91.31 170					95.55 157	76.74 131	78.53 186	88.39 223

EI-MVSNet-UG-set		83.14 98	82.96 90	83.67 156	92.28 98	63.19 208	91.38 181	94.68 34	79.22 55	76.60 140	93.75 100	62.64 104	97.76 53	78.07 125	78.01 188	90.05 203

OMC-MVS		78.67 178	77.91 169	80.95 227	85.76 239	57.40 304	88.49 258	88.67 270	73.85 135	72.43 183	92.10 137	49.29 242	94.55 191	72.73 160	77.89 189	90.91 194

1112_ss		80.56 139	79.83 138	82.77 174	88.65 181	60.78 252	92.29 136	88.36 277	72.58 162	72.46 182	94.95 64	65.09 73	93.42 234	66.38 221	77.71 190	94.10 114

OPM-MVS		79.00 166	78.09 163	81.73 204	83.52 273	63.83 191	91.64 169	90.30 208	76.36 99	71.97 188	89.93 172	46.30 268	95.17 169	75.10 141	77.70 191	86.19 261
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		72.06 260	69.98 261	78.28 272	89.51 162	55.70 315	83.49 296	83.39 330	61.24 304	63.72 279	82.76 250	34.77 324	93.03 239	53.37 291	77.59 192	86.12 265

thres100view900		78.37 182	77.01 184	82.46 181	91.89 113	63.21 207	91.19 192	96.33 1	72.28 173	70.45 203	87.89 197	60.31 123	95.32 164	45.16 321	77.58 193	88.83 213

tfpn200view9		78.79 173	77.43 176	82.88 172	92.21 101	64.49 171	92.05 147	96.28 4	73.48 145	71.75 191	88.26 190	60.07 128	95.32 164	45.16 321	77.58 193	88.83 213

thres400		78.68 176	77.43 176	82.43 182	92.21 101	64.49 171	92.05 147	96.28 4	73.48 145	71.75 191	88.26 190	60.07 128	95.32 164	45.16 321	77.58 193	87.48 233

mvs-test1		78.74 175	77.95 167	81.14 218	83.22 275	57.13 306	93.96 69	87.78 288	75.42 107	72.68 175	90.80 155	45.08 275	94.54 192	75.08 142	77.49 196	91.74 178

CostFormer		82.33 110	81.15 115	85.86 87	89.01 174	68.46 67	82.39 307	93.01 102	75.59 104	80.25 98	81.57 267	72.03 33	94.96 174	79.06 116	77.48 197	94.16 111

tpm2		79.80 154	77.95 167	85.34 108	88.28 192	68.26 73	81.56 312	91.42 166	70.11 227	77.59 130	80.50 285	67.40 51	94.26 203	67.34 210	77.35 198	93.51 135

Test_1112_low_res		79.56 158	78.60 157	82.43 182	88.24 195	60.39 262	92.09 144	87.99 286	72.10 179	71.84 189	87.42 203	64.62 79	93.04 238	65.80 228	77.30 199	93.85 128

tpmrst		80.57 138	79.14 153	84.84 123	90.10 149	68.28 72	81.70 310	89.72 233	77.63 83	75.96 144	79.54 298	64.94 76	92.71 254	75.43 138	77.28 200	93.55 134

Anonymous202405211		77.96 189	75.33 205	85.87 86	93.73 62	64.52 170	94.85 45	85.36 311	62.52 294	76.11 143	90.18 167	29.43 342	97.29 78	68.51 201	77.24 201	95.81 45

GA-MVS		78.33 184	76.23 194	84.65 129	83.65 271	66.30 128	91.44 173	90.14 215	76.01 101	70.32 205	84.02 239	42.50 284	94.72 182	70.98 177	77.00 202	92.94 153

thisisatest0530		81.15 128	80.07 131	84.39 137	88.26 193	65.63 145	91.40 177	94.62 37	71.27 209	70.93 198	89.18 177	72.47 30	96.04 132	65.62 230	76.89 203	91.49 181

thres600view7		78.00 187	76.66 189	82.03 201	91.93 110	63.69 198	91.30 186	96.33 1	72.43 168	70.46 202	87.89 197	60.31 123	94.92 177	42.64 333	76.64 204	87.48 233

PLC	68.80 14	75.23 230	73.68 229	79.86 249	92.93 82	58.68 286	90.64 209	88.30 278	60.90 305	64.43 273	90.53 159	42.38 285	94.57 188	56.52 278	76.54 205	86.33 256
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MIMVSNet		71.64 262	68.44 271	81.23 215	81.97 289	64.44 175	73.05 342	88.80 265	69.67 233	64.59 267	74.79 329	32.79 329	87.82 323	53.99 287	76.35 206	91.42 183

MVS-HIRNet		60.25 321	55.55 327	74.35 307	84.37 261	56.57 310	71.64 344	74.11 354	34.44 364	45.54 353	42.24 367	31.11 338	89.81 308	40.36 341	76.10 207	76.67 352

CNLPA		74.31 240	72.30 246	80.32 234	91.49 125	61.66 238	90.85 202	80.72 339	56.67 330	63.85 278	90.64 156	46.75 261	90.84 295	53.79 288	75.99 208	88.47 222

ab-mvs		80.18 145	78.31 160	85.80 90	88.44 186	65.49 150	83.00 304	92.67 114	71.82 190	77.36 132	85.01 227	54.50 191	96.59 114	76.35 135	75.63 209	95.32 60

iter_conf05		83.27 95	82.70 97	84.98 117	93.32 70	71.84 16	94.16 56	81.76 335	82.74 16	73.83 166	88.40 186	72.77 28	94.61 186	82.10 91	75.21 210	88.48 220

FIs		79.47 160	79.41 146	79.67 253	85.95 235	59.40 275	91.68 167	93.94 61	78.06 73	68.96 222	88.28 188	66.61 59	91.77 283	66.20 224	74.99 211	87.82 229

CMPMVS	48.56 21	66.77 297	64.41 296	73.84 311	70.65 356	50.31 341	77.79 336	85.73 309	45.54 356	44.76 354	82.14 258	35.40 322	90.14 306	63.18 247	74.54 212	81.07 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS		74.25 241	72.46 245	79.63 254	78.45 328	57.59 300	80.33 320	87.39 291	63.86 281	68.76 226	89.62 175	40.50 290	91.72 284	69.00 197	74.25 213	89.58 208

tpm cat1		75.30 229	72.21 247	84.58 132	88.52 182	67.77 86	78.16 335	88.02 285	61.88 301	68.45 231	76.37 321	60.65 120	94.03 217	53.77 289	74.11 214	91.93 176

XVG-OURS-SEG-HR		74.70 236	73.08 235	79.57 256	78.25 329	57.33 305	80.49 318	87.32 292	63.22 286	68.76 226	90.12 171	44.89 277	91.59 286	70.55 183	74.09 215	89.79 205

FC-MVSNet-test		77.99 188	78.08 164	77.70 278	84.89 252	55.51 316	90.27 219	93.75 70	76.87 90	66.80 253	87.59 200	65.71 68	90.23 304	62.89 250	73.94 216	87.37 236

PVSNet_BlendedMVS		83.38 93	83.43 80	83.22 167	93.76 58	67.53 93	94.06 64	93.61 74	79.13 58	81.00 91	85.14 226	63.19 100	97.29 78	87.08 52	73.91 217	84.83 287

tttt0517		79.50 159	78.53 158	82.41 185	87.22 215	61.43 242	89.75 235	94.76 30	69.29 238	67.91 237	88.06 195	72.92 26	95.63 149	62.91 249	73.90 218	90.16 201

MDTV_nov1_ep13			72.61 242		89.06 172	68.48 66	80.33 320	90.11 216	71.84 189	71.81 190	75.92 325	53.01 210	93.92 222	48.04 308	73.38 219

SCA		75.82 221	72.76 239	85.01 116	86.63 223	70.08 32	81.06 316	89.19 248	71.60 200	70.01 209	77.09 315	45.53 272	90.25 300	60.43 263	73.27 220	94.68 89

CR-MVSNet		73.79 246	70.82 258	82.70 176	83.15 277	67.96 81	70.25 346	84.00 324	73.67 142	69.97 211	72.41 336	57.82 151	89.48 311	52.99 292	73.13 221	90.64 197

RPMNet		70.42 269	65.68 285	84.63 131	83.15 277	67.96 81	70.25 346	90.45 198	46.83 355	69.97 211	65.10 353	56.48 172	95.30 167	35.79 352	73.13 221	90.64 197

Fast-Effi-MVS+-dtu		75.04 232	73.37 232	80.07 242	80.86 296	59.52 274	91.20 191	85.38 310	71.90 183	65.20 262	84.84 231	41.46 287	92.97 240	66.50 220	72.96 223	87.73 230

mvsmamba		76.85 204	75.71 202	80.25 238	83.07 279	59.16 280	91.44 173	80.64 340	76.84 92	67.95 235	86.33 213	46.17 270	94.24 204	76.06 136	72.92 224	87.36 237

LPG-MVS_test		75.82 221	74.58 213	79.56 257	84.31 262	59.37 276	90.44 212	89.73 231	69.49 234	64.86 264	88.42 184	38.65 299	94.30 199	72.56 162	72.76 225	85.01 285

LGP-MVS_train				79.56 257	84.31 262	59.37 276		89.73 231	69.49 234	64.86 264	88.42 184	38.65 299	94.30 199	72.56 162	72.76 225	85.01 285

EG-PatchMatch MVS		68.55 283	65.41 288	77.96 277	78.69 325	62.93 214	89.86 232	89.17 249	60.55 307	50.27 339	77.73 309	22.60 356	94.06 212	47.18 314	72.65 227	76.88 351

bld_raw_conf005		75.32 228	73.81 228	79.87 248	82.38 284	58.28 291	90.86 201	78.04 345	75.22 114	66.60 255	85.65 222	38.81 296	94.20 205	74.18 151	72.54 228	87.14 242

EI-MVSNet		78.97 167	78.22 162	81.25 214	85.33 243	62.73 221	89.53 239	93.21 91	72.39 170	72.14 186	90.13 169	60.99 117	94.72 182	67.73 207	72.49 229	86.29 257

MVSTER		82.47 108	82.05 105	83.74 151	92.68 90	69.01 55	91.90 155	93.21 91	79.83 44	72.14 186	85.71 221	74.72 16	94.72 182	75.72 137	72.49 229	87.50 232

Anonymous20240529		76.84 205	74.15 221	84.88 121	91.02 133	64.95 165	93.84 82	91.09 181	53.57 337	73.00 170	87.42 203	35.91 321	97.32 76	69.14 196	72.41 231	92.36 165

D2MVS		73.80 245	72.02 249	79.15 264	79.15 318	62.97 212	88.58 257	90.07 217	72.94 154	59.22 303	78.30 303	42.31 286	92.70 256	65.59 231	72.00 232	81.79 322

PS-MVSNAJss		77.26 199	76.31 193	80.13 241	80.64 300	59.16 280	90.63 211	91.06 184	72.80 158	68.58 229	84.57 235	53.55 204	93.96 220	72.97 155	71.96 233	87.27 241

Effi-MVS+-dtu		76.14 212	75.28 206	78.72 268	83.22 275	55.17 318	89.87 231	87.78 288	75.42 107	67.98 234	81.43 269	45.08 275	92.52 264	75.08 142	71.63 234	88.48 220

ACMMP++_ref															71.63 234

ACMM	69.62 13	74.34 239	72.73 240	79.17 262	84.25 264	57.87 294	90.36 216	89.93 223	63.17 287	65.64 259	86.04 218	37.79 309	94.10 208	65.89 226	71.52 236	85.55 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	71.68 10	75.58 226	74.23 220	79.62 255	84.97 251	59.64 271	90.80 204	89.07 256	70.39 224	62.95 285	87.30 205	38.28 302	93.87 224	72.89 156	71.45 237	85.36 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dp		75.01 233	72.09 248	83.76 150	89.28 167	66.22 131	79.96 327	89.75 228	71.16 210	67.80 240	77.19 314	51.81 218	92.54 263	50.39 297	71.44 238	92.51 163

tpm		78.58 179	77.03 183	83.22 167	85.94 237	64.56 169	83.21 302	91.14 179	78.31 69	73.67 167	79.68 296	64.01 86	92.09 277	66.07 225	71.26 239	93.03 150

DP-MVS		69.90 273	66.48 279	80.14 240	95.36 30	62.93 214	89.56 236	76.11 348	50.27 346	57.69 315	85.23 225	39.68 292	95.73 143	33.35 357	71.05 240	81.78 323

test_low_dy_conf_001		74.54 237	73.33 233	78.16 275	80.91 295	55.04 319	89.24 245	89.06 257	69.33 237	64.21 275	85.60 223	39.35 293	92.96 241	71.28 175	71.03 241	86.49 254

UniMVSNet_ETH3D		72.74 256	70.53 259	79.36 259	78.62 327	56.64 309	85.01 287	89.20 247	63.77 282	64.84 266	84.44 236	34.05 326	91.86 281	63.94 241	70.89 242	89.57 209

MVS_0304		68.99 281	67.23 278	74.28 309	80.36 303	52.54 329	87.01 278	86.36 299	59.89 314	66.22 256	73.56 332	24.25 351	88.03 321	57.34 277	70.11 243	82.27 318

jajsoiax		73.05 250	71.51 254	77.67 279	77.46 334	54.83 320	88.81 253	90.04 221	69.13 242	62.85 287	83.51 244	31.16 337	92.75 253	70.83 178	69.80 244	85.43 280

ACMMP++															69.72 245

mvs_tets		72.71 257	71.11 255	77.52 280	77.41 335	54.52 322	88.45 260	89.76 227	68.76 247	62.70 288	83.26 247	29.49 341	92.71 254	70.51 184	69.62 246	85.34 282

tpmvs		72.88 254	69.76 266	82.22 191	90.98 134	67.05 108	78.22 334	88.30 278	63.10 288	64.35 274	74.98 328	55.09 187	94.27 201	43.25 327	69.57 247	85.34 282

GBi-Net		75.65 223	73.83 226	81.10 221	88.85 176	65.11 158	90.01 227	90.32 204	70.84 216	67.04 249	80.25 290	48.03 251	91.54 288	59.80 268	69.34 248	86.64 249

test1		75.65 223	73.83 226	81.10 221	88.85 176	65.11 158	90.01 227	90.32 204	70.84 216	67.04 249	80.25 290	48.03 251	91.54 288	59.80 268	69.34 248	86.64 249

FMVSNet3		77.73 193	76.04 196	82.80 173	91.20 132	68.99 56	91.87 156	91.99 139	73.35 147	67.04 249	83.19 248	56.62 169	92.14 274	59.80 268	69.34 248	87.28 240

MSDG		69.54 275	65.73 284	80.96 226	85.11 249	63.71 196	84.19 291	83.28 331	56.95 327	54.50 322	84.03 238	31.50 335	96.03 133	42.87 331	69.13 251	83.14 306

JIA-IIPM		66.06 300	62.45 307	76.88 292	81.42 293	54.45 323	57.49 364	88.67 270	49.36 348	63.86 277	46.86 363	56.06 176	90.25 300	49.53 301	68.83 252	85.95 269

OpenMVS_ROB	61.12 18	66.39 298	62.92 304	76.80 293	76.51 338	57.77 295	89.22 246	83.41 329	55.48 334	53.86 326	77.84 308	26.28 350	93.95 221	34.90 354	68.76 253	78.68 346

FMVSNet2		76.07 213	74.01 224	82.26 190	88.85 176	67.66 89	91.33 184	91.61 158	70.84 216	65.98 257	82.25 256	48.03 251	92.00 279	58.46 273	68.73 254	87.10 243

test_djsdf		73.76 247	72.56 243	77.39 284	77.00 337	53.93 324	89.07 250	90.69 192	65.80 268	63.92 276	82.03 259	43.14 283	92.67 257	72.83 157	68.53 255	85.57 277

F-COLMAP		70.66 266	68.44 271	77.32 285	86.37 229	55.91 313	88.00 264	86.32 300	56.94 328	57.28 317	88.07 194	33.58 327	92.49 265	51.02 295	68.37 256	83.55 296

XVG-ACMP-BASELINE		68.04 288	65.53 287	75.56 299	74.06 347	52.37 330	78.43 331	85.88 307	62.03 298	58.91 307	81.21 277	20.38 360	91.15 294	60.69 262	68.18 257	83.16 305

LTVRE_ROB	59.60 19	66.27 299	63.54 300	74.45 306	84.00 267	51.55 334	67.08 356	83.53 327	58.78 319	54.94 321	80.31 288	34.54 325	93.23 236	40.64 340	68.03 258	78.58 347
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
XXY-MVS		77.94 190	76.44 191	82.43 182	82.60 282	64.44 175	92.01 149	91.83 149	73.59 143	70.00 210	85.82 219	54.43 195	94.76 179	69.63 189	68.02 259	88.10 228

ADS-MVSNet2		66.90 296	63.44 301	77.26 287	88.06 198	60.70 257	68.01 353	75.56 351	57.57 322	64.48 270	69.87 345	38.68 297	84.10 340	40.87 338	67.89 260	86.97 244

ADS-MVSNet		68.54 284	64.38 297	81.03 225	88.06 198	66.90 113	68.01 353	84.02 323	57.57 322	64.48 270	69.87 345	38.68 297	89.21 313	40.87 338	67.89 260	86.97 244

test0.0.03 1		72.76 255	72.71 241	72.88 318	80.25 306	47.99 350	91.22 189	89.45 238	71.51 204	62.51 290	87.66 199	53.83 200	85.06 338	50.16 298	67.84 262	85.58 276

anonymousdsp		71.14 265	69.37 267	76.45 294	72.95 349	54.71 321	84.19 291	88.88 262	61.92 300	62.15 291	79.77 295	38.14 304	91.44 293	68.90 199	67.45 263	83.21 304

VPA-MVSNet		79.03 165	78.00 165	82.11 199	85.95 235	64.48 173	93.22 102	94.66 35	75.05 117	74.04 164	84.95 229	52.17 216	93.52 231	74.90 147	67.04 264	88.32 225

nrg030		80.93 134	79.86 137	84.13 144	83.69 270	68.83 59	93.23 101	91.20 174	75.55 105	75.06 154	88.22 193	63.04 103	94.74 181	81.88 93	66.88 265	88.82 215

FMVSNet1		72.71 257	69.91 264	81.10 221	83.60 272	65.11 158	90.01 227	90.32 204	63.92 280	63.56 280	80.25 290	36.35 320	91.54 288	54.46 285	66.75 266	86.64 249

PatchT		69.11 278	65.37 289	80.32 234	82.07 288	63.68 199	67.96 355	87.62 290	50.86 344	69.37 215	65.18 352	57.09 157	88.53 317	41.59 336	66.60 267	88.74 216

IB-MVS	77.80 4	82.18 112	80.46 129	87.35 38	89.14 171	70.28 31	95.59 25	95.17 15	78.85 62	70.19 207	85.82 219	70.66 36	97.67 55	72.19 168	66.52 268	94.09 115
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
RRT_MVS		74.44 238	72.97 237	78.84 267	82.36 285	57.66 298	89.83 233	88.79 267	70.61 222	64.58 268	84.89 230	39.24 294	92.65 260	70.11 186	66.34 269	86.21 260

pmmvs5		73.35 248	71.52 253	78.86 266	78.64 326	60.61 260	91.08 194	86.90 295	67.69 253	63.32 282	83.64 242	44.33 279	90.53 297	62.04 255	66.02 270	85.46 279

pmmvs4		73.92 244	71.81 251	80.25 238	79.17 317	65.24 153	87.43 273	87.26 294	67.64 256	63.46 281	83.91 241	48.96 247	91.53 291	62.94 248	65.49 271	83.96 292

cl22		77.94 190	76.78 187	81.42 211	87.57 207	64.93 166	90.67 207	88.86 264	72.45 167	67.63 242	82.68 252	64.07 85	92.91 247	71.79 170	65.30 272	86.44 255

miper_ehance_all_eth		77.60 194	76.44 191	81.09 224	85.70 240	64.41 178	90.65 208	88.64 272	72.31 171	67.37 247	82.52 253	64.77 78	92.64 261	70.67 181	65.30 272	86.24 259

miper_enhance_ethall		78.86 170	77.97 166	81.54 209	88.00 201	65.17 155	91.41 175	89.15 251	75.19 115	68.79 225	83.98 240	67.17 53	92.82 249	72.73 160	65.30 272	86.62 253

v1144		76.73 208	74.88 208	82.27 188	80.23 307	66.60 121	91.68 167	90.21 214	73.69 140	69.06 220	81.89 260	52.73 212	94.40 197	69.21 195	65.23 275	85.80 272

DSMNet-mixed		56.78 325	54.44 328	63.79 342	63.21 365	29.44 373	64.43 358	64.10 368	42.12 361	51.32 335	71.60 341	31.76 334	75.04 362	36.23 349	65.20 276	86.87 247

v1192		75.98 218	73.92 225	82.15 193	79.73 309	66.24 130	91.22 189	89.75 228	72.67 160	68.49 230	81.42 270	49.86 236	94.27 201	67.08 212	65.02 277	85.95 269

v2v482		77.42 197	75.65 203	82.73 175	80.38 302	67.13 106	91.85 158	90.23 212	75.09 116	69.37 215	83.39 246	53.79 202	94.44 196	71.77 171	65.00 278	86.63 252

V42		76.46 210	74.55 214	82.19 192	79.14 319	67.82 85	90.26 220	89.42 240	73.75 138	68.63 228	81.89 260	51.31 223	94.09 209	71.69 173	64.84 279	84.66 288

ACMH	63.93 17	68.62 282	64.81 290	80.03 243	85.22 245	63.25 205	87.72 269	84.66 318	60.83 306	51.57 334	79.43 299	27.29 347	94.96 174	41.76 334	64.84 279	81.88 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		81.84 120	81.03 120	84.28 142	91.60 120	66.62 120	91.08 194	91.66 157	81.87 25	74.86 155	91.67 145	69.98 37	94.92 177	71.76 172	64.75 281	91.29 190

v1240		75.21 231	72.98 236	81.88 202	79.20 316	66.00 134	90.75 206	89.11 254	71.63 199	67.41 245	81.22 275	47.36 259	93.87 224	65.46 233	64.72 282	85.77 273

IterMVS-LS		76.49 209	75.18 207	80.43 233	84.49 258	62.74 220	90.64 209	88.80 265	72.40 169	65.16 263	81.72 263	60.98 118	92.27 273	67.74 206	64.65 283	86.29 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		75.63 225	73.49 231	82.06 200	79.38 314	66.35 126	91.07 196	89.48 237	71.98 180	67.99 233	81.22 275	49.16 245	93.90 223	66.56 217	64.56 284	85.92 271

v144192		76.05 216	74.03 223	82.12 196	79.50 313	66.55 123	91.39 179	89.71 234	72.30 172	68.17 232	81.33 272	51.75 219	94.03 217	67.94 204	64.19 285	85.77 273

Anonymous20231211		73.08 249	70.39 260	81.13 219	90.62 141	63.33 204	91.40 177	90.06 220	51.84 341	64.46 272	80.67 283	36.49 319	94.07 211	63.83 242	64.17 286	85.98 268

Patchmatch-test		65.86 301	60.94 313	80.62 231	83.75 269	58.83 284	58.91 363	75.26 353	44.50 359	50.95 338	77.09 315	58.81 144	87.90 322	35.13 353	64.03 287	95.12 73

USDC		67.43 295	64.51 294	76.19 296	77.94 332	55.29 317	78.38 332	85.00 314	73.17 149	48.36 345	80.37 287	21.23 358	92.48 266	52.15 293	64.02 288	80.81 329

VPNet		78.82 171	77.53 175	82.70 176	84.52 257	66.44 124	93.93 74	92.23 128	80.46 40	72.60 177	88.38 187	49.18 243	93.13 237	72.47 164	63.97 289	88.55 219

Anonymous20231206		67.53 293	65.78 283	72.79 319	74.95 343	47.59 352	88.23 262	87.32 292	61.75 303	58.07 311	77.29 312	37.79 309	87.29 330	42.91 329	63.71 290	83.48 299

WR-MVS		76.76 207	75.74 201	79.82 250	84.60 255	62.27 228	92.60 127	92.51 122	76.06 100	67.87 239	85.34 224	56.76 165	90.24 303	62.20 254	63.69 291	86.94 246

h-mvs33		83.01 100	82.56 100	84.35 139	89.34 164	62.02 230	92.72 119	93.76 67	81.45 28	82.73 74	92.25 136	60.11 126	97.13 88	87.69 43	62.96 292	93.91 124

test_part1		79.63 156	77.86 170	84.93 118	92.50 94	71.43 18	94.15 59	91.08 183	72.51 164	70.66 199	84.98 228	59.84 130	95.07 171	72.07 169	62.94 293	88.30 226

c3_l		76.83 206	75.47 204	80.93 228	85.02 250	64.18 187	90.39 215	88.11 283	71.66 194	66.65 254	81.64 265	63.58 96	92.56 262	69.31 194	62.86 294	86.04 266

UniMVSNet_NR-MVSNet		78.15 186	77.55 174	79.98 244	84.46 259	60.26 263	92.25 137	93.20 93	77.50 85	68.88 223	86.61 209	66.10 62	92.13 275	66.38 221	62.55 295	87.54 231

DU-MVS		76.86 202	75.84 199	79.91 246	82.96 280	60.26 263	91.26 187	91.54 160	76.46 98	68.88 223	86.35 211	56.16 173	92.13 275	66.38 221	62.55 295	87.35 238

UniMVSNet (Re)		77.58 195	76.78 187	79.98 244	84.11 265	60.80 251	91.76 163	93.17 96	76.56 97	69.93 213	84.78 232	63.32 99	92.36 270	64.89 236	62.51 297	86.78 248

v8		75.35 227	73.26 234	81.61 208	80.67 299	66.82 114	89.54 238	89.27 244	71.65 195	63.30 283	80.30 289	54.99 188	94.06 212	67.33 211	62.33 298	83.94 293

cl____		76.07 213	74.67 209	80.28 236	85.15 246	61.76 236	90.12 223	88.73 268	71.16 210	65.43 260	81.57 267	61.15 115	92.95 242	66.54 218	62.17 299	86.13 264

v10		74.77 235	72.54 244	81.46 210	80.33 305	66.71 118	89.15 249	89.08 255	70.94 214	63.08 284	79.86 294	52.52 213	94.04 215	65.70 229	62.17 299	83.64 295

DIV-MVS_self_test		76.07 213	74.67 209	80.28 236	85.14 247	61.75 237	90.12 223	88.73 268	71.16 210	65.42 261	81.60 266	61.15 115	92.94 246	66.54 218	62.16 301	86.14 262

IterMVS-SCA-FT		71.55 263	69.97 262	76.32 295	81.48 291	60.67 258	87.64 271	85.99 306	66.17 266	59.50 301	78.88 300	45.53 272	83.65 345	62.58 252	61.93 302	84.63 290

IterMVS		72.65 259	70.83 257	78.09 276	82.17 286	62.96 213	87.64 271	86.28 301	71.56 202	60.44 297	78.85 301	45.42 274	86.66 332	63.30 246	61.83 303	84.65 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 288	65.66 286	75.18 302	84.43 260	57.89 293	83.54 295	86.26 302	61.83 302	53.64 327	73.30 333	37.15 315	85.08 337	48.99 303	61.77 304	82.56 315

v7n		71.31 264	68.65 269	79.28 260	76.40 339	60.77 253	86.71 281	89.45 238	64.17 279	58.77 308	78.24 304	44.59 278	93.54 230	57.76 274	61.75 305	83.52 298

v148		76.19 211	74.47 216	81.36 212	80.05 308	64.44 175	91.75 165	90.23 212	73.68 141	67.13 248	80.84 280	55.92 179	93.86 226	68.95 198	61.73 306	85.76 275

tfpnnormal		70.10 270	67.36 276	78.32 271	83.45 274	60.97 249	88.85 252	92.77 110	64.85 275	60.83 296	78.53 302	43.52 282	93.48 232	31.73 363	61.70 307	80.52 332

ACMH+	65.35 16	67.65 291	64.55 293	76.96 291	84.59 256	57.10 307	88.08 263	80.79 338	58.59 321	53.00 328	81.09 279	26.63 349	92.95 242	46.51 315	61.69 308	80.82 328

ITE_SJBPF				70.43 331	74.44 345	47.06 354		77.32 346	60.16 311	54.04 325	83.53 243	23.30 355	84.01 342	43.07 328	61.58 309	80.21 337

NR-MVSNet		76.05 216	74.59 212	80.44 232	82.96 280	62.18 229	90.83 203	91.73 152	77.12 89	60.96 295	86.35 211	59.28 139	91.80 282	60.74 261	61.34 310	87.35 238

test_0402		64.54 306	61.09 312	74.92 303	84.10 266	60.75 255	87.95 265	79.71 343	52.03 340	52.41 330	77.20 313	32.21 333	91.64 285	23.14 366	61.03 311	72.36 358

Baseline_NR-MVSNet		73.99 243	72.83 238	77.48 282	80.78 297	59.29 279	91.79 160	84.55 319	68.85 244	68.99 221	80.70 281	56.16 173	92.04 278	62.67 251	60.98 312	81.11 325

TranMVSNet+NR-MVSNet		75.86 220	74.52 215	79.89 247	82.44 283	60.64 259	91.37 182	91.37 168	76.63 95	67.65 241	86.21 215	52.37 215	91.55 287	61.84 256	60.81 313	87.48 233

testgi		64.48 307	62.87 305	69.31 333	71.24 352	40.62 364	85.49 285	79.92 342	65.36 272	54.18 324	83.49 245	23.74 354	84.55 339	41.60 335	60.79 314	82.77 308

eth_miper_zixun_eth		75.96 219	74.40 217	80.66 230	84.66 254	63.02 211	89.28 244	88.27 280	71.88 185	65.73 258	81.65 264	59.45 135	92.81 250	68.13 202	60.53 315	86.14 262

COLMAP_ROB	57.96 20	62.98 314	59.65 316	72.98 317	81.44 292	53.00 328	83.75 294	75.53 352	48.34 351	48.81 344	81.40 271	24.14 352	90.30 299	32.95 359	60.52 316	75.65 354
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		78.37 182	77.43 176	81.17 216	86.60 224	57.45 303	89.46 241	91.16 176	74.11 128	74.40 158	90.49 161	55.52 181	94.57 188	74.73 149	60.43 317	91.48 182

hse-mvs2		81.12 131	81.11 119	81.16 217	86.52 225	57.48 302	89.40 242	91.16 176	81.45 28	82.73 74	90.49 161	60.11 126	94.58 187	87.69 43	60.41 318	91.41 184

RPSCF		64.24 308	61.98 310	71.01 330	76.10 341	45.00 357	75.83 339	75.94 349	46.94 354	58.96 306	84.59 234	31.40 336	82.00 357	47.76 312	60.33 319	86.04 266

miper_lstm_enhance		73.05 250	71.73 252	77.03 288	83.80 268	58.32 290	81.76 308	88.88 262	69.80 232	61.01 294	78.23 305	57.19 156	87.51 328	65.34 234	59.53 320	85.27 284

CP-MVSNet		70.50 268	69.91 264	72.26 323	80.71 298	51.00 338	87.23 275	90.30 208	67.84 251	59.64 300	82.69 251	50.23 233	82.30 355	51.28 294	59.28 321	83.46 300

PS-CasMVS		69.86 274	69.13 268	72.07 326	80.35 304	50.57 340	87.02 277	89.75 228	67.27 258	59.19 304	82.28 255	46.58 263	82.24 356	50.69 296	59.02 322	83.39 302

pm-mvs1		72.89 253	71.09 256	78.26 273	79.10 320	57.62 299	90.80 204	89.30 243	67.66 254	62.91 286	81.78 262	49.11 246	92.95 242	60.29 265	58.89 323	84.22 291

Anonymous20240521		62.09 315	59.08 318	71.10 329	67.19 361	48.72 348	83.91 293	85.23 312	50.38 345	47.84 346	71.22 344	20.74 359	85.51 336	46.47 316	58.75 324	79.06 343

WR-MVS_H		70.59 267	69.94 263	72.53 320	81.03 294	51.43 335	87.35 274	92.03 138	67.38 257	60.23 298	80.70 281	55.84 180	83.45 347	46.33 317	58.58 325	82.72 310

PEN-MVS		69.46 276	68.56 270	72.17 325	79.27 315	49.71 344	86.90 279	89.24 245	67.24 261	59.08 305	82.51 254	47.23 260	83.54 346	48.42 306	57.12 326	83.25 303

EU-MVSNet		64.01 309	63.01 303	67.02 340	74.40 346	38.86 369	83.27 300	86.19 304	45.11 357	54.27 323	81.15 278	36.91 318	80.01 360	48.79 305	57.02 327	82.19 320

AllTest		61.66 316	58.06 320	72.46 321	79.57 310	51.42 336	80.17 323	68.61 363	51.25 342	45.88 349	81.23 273	19.86 361	86.58 333	38.98 344	57.01 328	79.39 340

TestCases				72.46 321	79.57 310	51.42 336		68.61 363	51.25 342	45.88 349	81.23 273	19.86 361	86.58 333	38.98 344	57.01 328	79.39 340

Patchmtry		67.53 293	63.93 298	78.34 270	82.12 287	64.38 179	68.72 350	84.00 324	48.23 352	59.24 302	72.41 336	57.82 151	89.27 312	46.10 318	56.68 330	81.36 324

our_test_3		68.29 286	64.69 292	79.11 265	78.92 321	64.85 167	88.40 261	85.06 313	60.32 310	52.68 329	76.12 323	40.81 289	89.80 310	44.25 326	55.65 331	82.67 314

FPMVS		45.64 331	43.10 334	53.23 349	51.42 373	36.46 370	64.97 357	71.91 358	29.13 366	27.53 366	61.55 358	9.83 371	65.01 370	16.00 370	55.58 332	58.22 365

DTE-MVSNet		68.46 285	67.33 277	71.87 328	77.94 332	49.00 347	86.16 284	88.58 274	66.36 265	58.19 309	82.21 257	46.36 264	83.87 344	44.97 324	55.17 333	82.73 309

MIMVSNet1		60.16 322	57.33 323	68.67 334	69.71 358	44.13 359	78.92 329	84.21 320	55.05 335	44.63 355	71.85 340	23.91 353	81.54 359	32.63 361	55.03 334	80.35 333

pmmvs6		67.57 292	64.76 291	76.00 298	72.82 351	53.37 326	88.71 254	86.78 298	53.19 338	57.58 316	78.03 307	35.33 323	92.41 267	55.56 282	54.88 335	82.21 319

TinyColmap		60.32 320	56.42 326	72.00 327	78.78 323	53.18 327	78.36 333	75.64 350	52.30 339	41.59 360	75.82 326	14.76 366	88.35 318	35.84 350	54.71 336	74.46 355

test20.03		63.83 310	62.65 306	67.38 339	70.58 357	39.94 365	86.57 282	84.17 321	63.29 285	51.86 332	77.30 311	37.09 316	82.47 353	38.87 346	54.13 337	79.73 338

OurMVSNet-221017-0		64.68 305	62.17 309	72.21 324	76.08 342	47.35 353	80.67 317	81.02 337	56.19 331	51.60 333	79.66 297	27.05 348	88.56 316	53.60 290	53.63 338	80.71 330

Patchmatch-RL test		68.17 287	64.49 295	79.19 261	71.22 353	53.93 324	70.07 348	71.54 360	69.22 239	56.79 318	62.89 355	56.58 170	88.61 314	69.53 191	52.61 339	95.03 79

ppachtmachnet_test		67.72 290	63.70 299	79.77 252	78.92 321	66.04 133	88.68 255	82.90 333	60.11 312	55.45 320	75.96 324	39.19 295	90.55 296	39.53 342	52.55 340	82.71 311

LF4IMVS		54.01 328	52.12 329	59.69 343	62.41 367	39.91 367	68.59 351	68.28 365	42.96 360	44.55 356	75.18 327	14.09 368	68.39 366	41.36 337	51.68 341	70.78 359

N_pmnet		50.55 329	49.11 332	54.88 347	77.17 336	4.02 383	84.36 290	2.00 382	48.59 349	45.86 351	68.82 347	32.22 332	82.80 352	31.58 364	51.38 342	77.81 349

pmmvs-eth3d		65.53 303	62.32 308	75.19 301	69.39 359	59.59 272	82.80 305	83.43 328	62.52 294	51.30 336	72.49 334	32.86 328	87.16 331	55.32 283	50.73 343	78.83 345

CL-MVSNet_self_test		69.92 272	68.09 274	75.41 300	73.25 348	55.90 314	90.05 226	89.90 224	69.96 229	61.96 293	76.54 318	51.05 226	87.64 325	49.51 302	50.59 344	82.70 312

PM-MVS		59.40 323	56.59 324	67.84 335	63.63 364	41.86 361	76.76 337	63.22 369	59.01 318	51.07 337	72.27 339	11.72 369	83.25 349	61.34 258	50.28 345	78.39 348

MDA-MVSNet_test_wron		63.78 311	60.16 314	74.64 304	78.15 330	60.41 261	83.49 296	84.03 322	56.17 333	39.17 362	71.59 342	37.22 313	83.24 350	42.87 331	48.73 346	80.26 335

YYNet1		63.76 312	60.14 315	74.62 305	78.06 331	60.19 266	83.46 298	83.99 326	56.18 332	39.25 361	71.56 343	37.18 314	83.34 348	42.90 330	48.70 347	80.32 334

KD-MVS_self_test		60.87 319	58.60 319	67.68 337	66.13 362	39.93 366	75.63 340	84.70 317	57.32 325	49.57 342	68.45 348	29.55 340	82.87 351	48.09 307	47.94 348	80.25 336

SixPastTwentyTwo		64.92 304	61.78 311	74.34 308	78.74 324	49.76 343	83.42 299	79.51 344	62.86 289	50.27 339	77.35 310	30.92 339	90.49 298	45.89 319	47.06 349	82.78 307

new_pmnet		49.31 330	46.44 333	57.93 344	62.84 366	40.74 363	68.47 352	62.96 370	36.48 363	35.09 363	57.81 359	14.97 365	72.18 364	32.86 360	46.44 350	60.88 364

EGC-MVSNET		42.35 332	38.09 335	55.11 346	74.57 344	46.62 355	71.63 345	55.77 372	0.04 377	0.24 378	62.70 356	14.24 367	74.91 363	17.59 369	46.06 351	43.80 366

TransMVSNet (Re)		70.07 271	67.66 275	77.31 286	80.62 301	59.13 282	91.78 162	84.94 315	65.97 267	60.08 299	80.44 286	50.78 227	91.87 280	48.84 304	45.46 352	80.94 327

ambc				69.61 332	61.38 368	41.35 362	49.07 367	85.86 308		50.18 341	66.40 350	10.16 370	88.14 320	45.73 320	44.20 353	79.32 342

TDRefinement		55.28 327	51.58 330	66.39 341	59.53 369	46.15 356	76.23 338	72.80 356	44.60 358	42.49 359	76.28 322	15.29 364	82.39 354	33.20 358	43.75 354	70.62 360

Gipuma		34.91 337	31.44 340	45.30 352	70.99 354	39.64 368	19.85 372	72.56 357	20.10 370	16.16 372	21.47 373	5.08 377	71.16 365	13.07 371	43.70 355	25.08 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		61.54 318	57.70 322	73.05 316	79.53 312	57.00 308	83.08 303	81.23 336	57.57 322	34.91 364	72.45 335	32.79 329	86.26 335	35.81 351	41.95 356	75.89 353

new-patchmatchnet		59.30 324	56.48 325	67.79 336	65.86 363	44.19 358	82.47 306	81.77 334	59.94 313	43.65 358	66.20 351	27.67 346	81.68 358	39.34 343	41.40 357	77.50 350

UnsupCasMVSNet_eth		65.79 302	63.10 302	73.88 310	70.71 355	50.29 342	81.09 315	89.88 225	72.58 162	49.25 343	74.77 330	32.57 331	87.43 329	55.96 281	41.04 358	83.90 294

pmmvs3		55.51 326	51.50 331	67.53 338	57.90 370	50.93 339	80.37 319	73.66 355	40.63 362	44.15 357	64.75 354	16.30 363	78.97 361	44.77 325	40.98 359	72.69 356

UnsupCasMVSNet_bld		61.60 317	57.71 321	73.29 315	68.73 360	51.64 333	78.61 330	89.05 258	57.20 326	46.11 348	61.96 357	28.70 344	88.60 315	50.08 299	38.90 360	79.63 339

PMVS	26.43 22	31.84 338	28.16 341	42.89 353	25.87 381	27.58 374	50.92 366	49.78 375	21.37 369	14.17 373	40.81 369	2.01 380	66.62 367	9.61 373	38.88 361	34.49 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
K. test v3		63.09 313	59.61 317	73.53 313	76.26 340	49.38 346	83.27 300	77.15 347	64.35 277	47.77 347	72.32 338	28.73 343	87.79 324	49.93 300	36.69 362	83.41 301

KD-MVS_2432*1600		69.03 279	66.37 281	77.01 289	85.56 241	61.06 247	81.44 313	90.25 210	67.27 258	58.00 312	76.53 319	54.49 192	87.63 326	48.04 308	35.77 363	82.34 316

miper_refine_blended		69.03 279	66.37 281	77.01 289	85.56 241	61.06 247	81.44 313	90.25 210	67.27 258	58.00 312	76.53 319	54.49 192	87.63 326	48.04 308	35.77 363	82.34 316

LCM-MVSNet		40.54 333	35.79 336	54.76 348	36.92 378	30.81 372	51.41 365	69.02 362	22.07 368	24.63 367	45.37 365	4.56 378	65.81 368	33.67 356	34.50 365	67.67 361

test_method		38.59 335	35.16 338	48.89 351	54.33 371	21.35 378	45.32 368	53.71 373	7.41 374	28.74 365	51.62 361	8.70 373	52.87 372	33.73 355	32.89 366	72.47 357

lessismore_v0				73.72 312	72.93 350	47.83 351		61.72 371		45.86 351	73.76 331	28.63 345	89.81 308	47.75 313	31.37 367	83.53 297

PVSNet_0	68.08 15	71.81 261	68.32 273	82.27 188	84.68 253	62.31 227	88.68 255	90.31 207	75.84 102	57.93 314	80.65 284	37.85 308	94.19 206	69.94 187	29.05 368	90.31 200

DeepMVS_CX				34.71 355	51.45 372	24.73 377		28.48 381	31.46 365	17.49 371	52.75 360	5.80 376	42.60 376	18.18 368	19.42 369	36.81 368

PMMVS2		37.93 336	33.61 339	50.92 350	46.31 375	24.76 376	60.55 362	50.05 374	28.94 367	20.93 368	47.59 362	4.41 379	65.13 369	25.14 365	18.55 370	62.87 363

MVE	24.84 23	24.35 340	19.77 346	38.09 354	34.56 380	26.92 375	26.57 370	38.87 378	11.73 373	11.37 374	27.44 370	1.37 381	50.42 373	11.41 372	14.60 371	36.93 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 339	24.00 343	26.45 356	43.74 376	18.44 380	60.86 360	39.66 376	15.11 371	9.53 375	22.10 372	6.52 375	46.94 374	8.31 374	10.14 372	13.98 372

EMVS		23.76 341	23.20 345	25.46 357	41.52 377	16.90 381	60.56 361	38.79 379	14.62 372	8.99 376	20.24 375	7.35 374	45.82 375	7.25 375	9.46 373	13.64 373

tmp_tt		22.26 342	23.75 344	17.80 358	5.23 382	12.06 382	35.26 369	39.48 377	2.82 376	18.94 369	44.20 366	22.23 357	24.64 377	36.30 348	9.31 374	16.69 371

ANet_high		40.27 334	35.20 337	55.47 345	34.74 379	34.47 371	63.84 359	71.56 359	48.42 350	18.80 370	41.08 368	9.52 372	64.45 371	20.18 367	8.66 375	67.49 362

wuyk23d		11.30 344	10.95 347	12.33 359	48.05 374	19.89 379	25.89 371	1.92 383	3.58 375	3.12 377	1.37 377	0.64 382	15.77 378	6.23 376	7.77 376	1.35 374

testmvs		7.23 346	9.62 349	0.06 361	0.04 383	0.02 385	84.98 288	0.02 384	0.03 378	0.18 379	1.21 378	0.01 384	0.02 379	0.14 377	0.01 377	0.13 376

test_blank		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

cdsmvs_eth3d_5k		19.86 343	26.47 342	0.00 362	0.00 385	0.00 386	0.00 373	93.45 82	0.00 380	0.00 381	95.27 52	49.56 238	0.00 381	0.00 379	0.00 378	0.00 377

pcd_1.5k_mvsjas		4.46 348	5.95 351	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	53.55 204	0.00 381	0.00 379	0.00 378	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

test123		6.92 347	9.21 350	0.08 360	0.03 384	0.05 384	81.65 311	0.01 385	0.02 379	0.14 380	0.85 379	0.03 383	0.02 379	0.12 378	0.00 378	0.16 375

ab-mvs-re		7.91 345	10.55 348	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	94.95 64	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 378	0.00 377

FOURS1					93.95 53	61.77 235	93.96 69	91.92 142	62.14 297	86.57 35

test_one_0601					96.32 20	69.74 42		94.18 55	71.42 207	90.67 15	96.85 12	74.45 19

eth-test2					0.00 385

eth-test					0.00 385

test_241102_ONE					96.45 14	69.38 47		94.44 43	71.65 195	92.11 4	97.05 8	76.79 9	99.11 6

save fliter					93.84 56	67.89 83	95.05 39	92.66 115	78.19 70

test0726					96.40 17	69.99 33	96.76 6	94.33 51	71.92 181	91.89 8	97.11 7	73.77 22

GSMVS																94.68 89

test_part2					96.29 21	68.16 77				90.78 13

sam_mvs1												57.85 150				94.68 89

sam_mvs												54.91 189

MTGPA								92.23 128

test_post1							78.95 328				20.70 374	53.05 209	91.50 292	60.43 263

test_post											23.01 371	56.49 171	92.67 257

patchmatchnet-post											67.62 349	57.62 153	90.25 300

MTMP							93.77 84	32.52 380

gm-plane-assit					88.42 187	67.04 109			78.62 67		91.83 141		97.37 72	76.57 133

TEST9					94.18 46	67.28 100	94.16 56	93.51 78	71.75 193	85.52 48	95.33 47	68.01 44	97.27 82

test_8					94.19 45	67.19 103	94.15 59	93.42 85	71.87 186	85.38 51	95.35 46	68.19 41	96.95 102

agg_prior					94.16 50	66.97 111		93.31 88		84.49 59			96.75 111

test_prior4						67.18 105	93.92 75

test_prior				86.42 69	94.71 39	67.35 98		93.10 100					96.84 107			95.05 76

旧先验2							92.00 152		59.37 317	87.54 29			93.47 233	75.39 139

新几何2							91.41 175

无先验							92.71 120	92.61 119	62.03 298				97.01 93	66.63 215		93.97 121

原ACMM2							92.01 149

testdata2													96.09 128	61.26 259

segment_acmp												65.94 64

testdata1							89.21 247		77.55 84

plane_prior7					86.94 219	61.51 240

plane_prior6					87.23 214	62.32 226						50.66 228

plane_prior4											89.14 179

plane_prior3						61.95 233			79.09 59	72.53 179

plane_prior2							93.13 103		78.81 64

plane_prior1					87.15 216

n2								0.00 386

nn								0.00 386

door-mid								66.01 367

test11								93.01 102

door								66.57 366

HQP5-MVS						63.66 200

HQP-NCC					87.54 208		94.06 64		79.80 45	74.18 159

ACMP_Plane					87.54 208		94.06 64		79.80 45	74.18 159

BP-MVS														77.63 128

HQP4-MVS										74.18 159			95.61 151			88.63 217

HQP2-MVS												51.63 221

NP-MVS					87.41 211	63.04 210					90.30 165

MDTV_nov1_ep13_2view						59.90 269	80.13 324		67.65 255	72.79 174		54.33 197		59.83 267		92.58 161

Test By Simon												54.21 198

High-resolution multi-view benchmark