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.

CHOSEN 1792x2688		76.24 58	74.03 78	82.88 1	83.09 118	62.84 2	85.73 113	85.39 98	69.79 19	64.87 138	83.49 187	41.52 153	93.69 30	70.55 91	81.82 73	92.12 36

MG-MVS		78.42 24	76.99 40	82.73 2	93.17 1	64.46 1	89.93 29	88.51 45	64.83 75	73.52 52	88.09 127	48.07 64	92.19 53	62.24 148	84.53 54	91.53 53

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 7	59.33 6	92.10 9	90.95 9	65.68 63	80.67 18	94.42 3	59.41 10	95.89 12	86.74 3	89.75 7	92.94 18

LFMVS		78.52 22	77.14 38	82.67 4	89.58 15	58.90 8	91.27 18	88.05 51	63.22 101	74.63 40	90.83 68	41.38 154	94.40 23	75.42 58	79.90 97	94.72 2

DVP-MVS++		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 137	88.88 30	58.00 199	83.60 6	93.39 13	67.21 2	96.39 4	81.64 19	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 6	80.12 188	59.50 5	92.24 8	90.72 10	69.37 22	83.22 8	94.47 2	63.81 5	93.18 34	74.02 71	93.25 2	94.80 1

CSCG		80.41 14	79.72 14	82.49 6	89.12 27	57.67 15	89.29 41	91.54 3	59.19 174	71.82 75	90.05 91	59.72 9	96.04 10	78.37 36	88.40 14	93.75 7

SED-MVS		81.92 7	81.75 8	82.44 8	89.48 19	56.89 27	92.48 3	88.94 28	57.50 214	84.61 4	94.09 4	58.81 12	96.37 6	82.28 14	87.60 18	94.06 3

DVP-MVS		81.30 9	81.00 12	82.20 9	89.40 22	57.45 19	92.34 5	89.99 16	57.71 208	81.91 12	93.64 11	55.17 21	96.44 2	81.68 16	87.13 21	92.72 24
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				82.20 9	89.50 17	57.73 13	92.34 5	88.88 30					96.39 4	81.68 16	87.13 21	92.47 27

MCST-MVS		83.01 2	83.30 3	82.15 11	92.84 2	57.58 16	93.77 1	91.10 6	75.95 3	77.10 28	93.09 20	54.15 28	95.57 13	85.80 4	85.87 38	93.31 11

DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 46	56.89 27	92.77 2	86.30 82	77.83 1	77.88 25	92.13 35	60.24 6	94.78 22	78.97 31	89.61 8	93.69 8
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
OPU-MVS				81.71 13	92.05 3	55.97 46	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

PS-MVSNAJ		80.06 15	79.52 16	81.68 14	85.58 62	60.97 3	91.69 11	87.02 67	70.62 13	80.75 17	93.22 17	37.77 185	92.50 46	82.75 11	86.25 35	91.57 51

MSC_two_6792asdad				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

No_MVS				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 78	60.73 4	91.65 12	86.86 70	70.30 17	80.77 16	93.07 21	37.63 190	92.28 51	82.73 12	85.71 39	91.57 51

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 11	57.70 14	91.71 10	88.87 32	70.31 16	77.64 27	93.87 8	52.58 35	93.91 28	84.17 6	87.92 16	92.39 29

MVS		76.91 47	75.48 58	81.23 19	84.56 84	55.21 62	80.23 252	91.64 2	58.65 189	65.37 130	91.48 54	45.72 94	95.05 17	72.11 85	89.52 10	93.44 9

VDDNet		74.37 82	72.13 101	81.09 20	79.58 194	56.52 34	90.02 26	86.70 75	52.61 269	71.23 82	87.20 141	31.75 262	93.96 27	74.30 69	75.77 130	92.79 23

NCCC		79.57 18	79.23 18	80.59 21	89.50 17	56.99 25	91.38 15	88.17 49	67.71 39	73.81 48	92.75 24	46.88 78	93.28 32	78.79 34	84.07 57	91.50 55

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 14	56.54 33	84.83 143	83.68 148	67.85 36	69.36 91	90.24 82	60.20 7	92.10 57	84.14 7	80.40 87	92.82 21

API-MVS		74.17 85	72.07 104	80.49 22	90.02 12	58.55 9	87.30 76	84.27 135	57.51 213	65.77 127	87.77 133	41.61 151	95.97 11	51.71 237	82.63 63	86.94 156

3Dnovator	64.70 6	74.46 81	72.48 91	80.41 24	82.84 130	55.40 57	83.08 194	88.61 41	67.61 41	59.85 192	88.66 116	34.57 233	93.97 26	58.42 184	88.70 12	91.85 45

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 26	55.08 68	88.70 49	87.92 53	55.55 243	81.21 15	93.69 10	56.51 17	94.27 25	78.36 37	85.70 40	91.51 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		76.76 52	75.60 57	80.21 26	90.87 8	54.68 81	89.14 42	89.11 23	62.95 104	70.54 88	92.33 32	41.05 155	94.95 18	57.90 193	86.55 32	91.00 69
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
SD-MVS		76.18 59	74.85 69	80.18 27	85.39 69	56.90 26	85.75 111	82.45 173	56.79 227	74.48 44	91.81 43	43.72 122	90.75 90	74.61 65	78.65 106	92.91 19
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
Effi-MVS+		75.24 72	73.61 81	80.16 28	81.92 146	57.42 21	85.21 124	76.71 278	60.68 146	73.32 55	89.34 104	47.30 74	91.63 64	68.28 104	79.72 98	91.42 56

SMA-MVS		79.10 20	78.76 20	80.12 29	84.42 86	55.87 48	87.58 69	86.76 73	61.48 131	80.26 19	93.10 18	46.53 83	92.41 48	79.97 27	88.77 11	92.08 37
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
MSLP-MVS++		74.21 84	72.25 97	80.11 30	81.45 162	56.47 35	86.32 98	79.65 218	58.19 195	66.36 116	92.29 34	36.11 216	90.66 92	67.39 108	82.49 65	93.18 15

CANet		80.90 10	81.17 11	80.09 31	87.62 41	54.21 90	91.60 13	86.47 78	73.13 6	79.89 21	93.10 18	49.88 55	92.98 35	84.09 8	84.75 51	93.08 16

IB-MVS	68.87 2	74.01 87	72.03 106	79.94 32	83.04 121	55.50 52	90.24 25	88.65 37	67.14 44	61.38 181	81.74 213	53.21 31	94.28 24	60.45 168	62.41 237	90.03 94
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
HPM-MVS++		80.50 13	80.71 13	79.88 33	87.34 43	55.20 63	89.93 29	87.55 62	66.04 61	79.46 22	93.00 22	53.10 32	91.76 62	80.40 26	89.56 9	92.68 25

xxxxxxxxxxxxxcwj		77.31 40	76.54 45	79.61 34	85.35 70	56.34 38	89.31 39	72.84 314	61.55 127	74.63 40	92.38 30	47.75 68	91.35 70	78.18 40	86.85 26	91.15 64

QAPM		71.88 120	69.33 143	79.52 35	82.20 143	54.30 88	86.30 99	88.77 35	56.61 231	59.72 194	87.48 136	33.90 240	95.36 14	47.48 262	81.49 76	88.90 120

VDD-MVS		76.08 61	74.97 67	79.44 36	84.27 91	53.33 117	91.13 19	85.88 89	65.33 70	72.37 69	89.34 104	32.52 252	92.76 41	77.90 43	75.96 127	92.22 35

MVS_111021_HR		76.39 57	75.38 61	79.42 37	85.33 72	56.47 35	88.15 55	84.97 116	65.15 73	66.06 121	89.88 94	43.79 119	92.16 54	75.03 61	80.03 95	89.64 102

SteuartSystems-ACMMP		77.08 44	76.33 50	79.34 38	80.98 169	55.31 58	89.76 33	86.91 69	62.94 105	71.65 76	91.56 52	42.33 138	92.56 45	77.14 47	83.69 59	90.15 90
Skip Steuart: Steuart Systems R&D Blog.
test12				79.24 39	86.89 45	56.08 43		85.16 111		72.27 71		47.15 76	91.10 78		85.93 37	90.54 81

APDe-MVS		78.44 23	78.20 24	79.19 40	88.56 28	54.55 85	89.76 33	87.77 57	55.91 238	78.56 24	92.49 28	48.20 63	92.65 44	79.49 28	83.04 61	90.39 83

lupinMVS		78.38 25	78.11 27	79.19 40	83.02 122	55.24 60	91.57 14	84.82 120	69.12 23	76.67 30	92.02 39	44.82 108	90.23 106	80.83 24	80.09 92	92.08 37

DeepC-MVS_fast	67.50 3	78.00 31	77.63 31	79.13 42	88.52 29	55.12 65	89.95 28	85.98 88	68.31 28	71.33 81	92.75 24	45.52 97	90.37 99	71.15 88	85.14 47	91.91 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		78.17 29	77.86 30	79.12 43	84.30 88	54.22 89	87.71 62	84.57 129	67.70 40	77.70 26	92.11 38	50.90 46	89.95 112	78.18 40	77.54 114	93.20 14

PHI-MVS		77.49 37	77.00 39	78.95 44	85.33 72	50.69 175	88.57 51	88.59 43	58.14 196	73.60 49	93.31 15	43.14 131	93.79 29	73.81 72	88.53 13	92.37 30

test_yl		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 202	71.19 83	89.20 107	42.03 145	92.77 39	69.41 96	75.07 138	92.01 40

DCV-MVSNet		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 202	71.19 83	89.20 107	42.03 145	92.77 39	69.41 96	75.07 138	92.01 40

Regformer-1		77.80 34	77.44 34	78.88 47	87.78 39	52.44 139	87.60 64	90.08 14	68.86 24	72.49 67	91.79 44	47.69 70	94.90 20	73.57 75	77.05 116	89.31 108

casdiffmvs		77.36 39	76.85 41	78.88 47	80.40 185	54.66 83	87.06 82	85.88 89	72.11 8	71.57 78	88.63 120	50.89 48	90.35 100	76.00 51	79.11 103	91.63 48

ETH3D cwj APD-0.16		78.36 26	78.19 25	78.86 49	84.21 92	52.68 133	86.70 91	89.02 26	59.13 179	75.37 35	92.49 28	49.06 60	93.20 33	80.67 25	87.08 23	90.71 76

PAPM		76.76 52	76.07 54	78.81 50	80.20 186	59.11 7	86.86 89	86.23 83	68.60 26	70.18 90	88.84 114	51.57 40	87.16 197	65.48 123	86.68 30	90.15 90

MSP-MVS		82.30 6	83.47 1	78.80 51	82.99 124	52.71 132	85.04 134	88.63 39	66.08 58	86.77 3	92.75 24	72.05 1	91.46 68	83.35 9	93.53 1	92.23 33
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
DeepC-MVS	67.15 4	76.90 49	76.27 51	78.80 51	80.70 178	55.02 69	86.39 96	86.71 74	66.96 46	67.91 103	89.97 93	48.03 65	91.41 69	75.60 55	84.14 56	89.96 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_part1		73.80 91	72.13 101	78.79 53	85.92 53	58.26 10	90.60 23	86.85 71	63.98 86	63.95 154	81.54 216	52.08 39	92.24 52	64.93 133	59.32 256	85.87 181

ETH3D-3000-0.1		78.73 21	78.71 21	78.78 54	85.58 62	52.40 140	88.42 53	89.03 25	60.01 154	76.06 33	92.80 23	48.34 61	92.88 37	81.66 18	86.48 33	91.04 67

ACMMP_NAP		76.43 56	75.66 56	78.73 55	81.92 146	54.67 82	84.06 164	85.35 100	61.10 136	72.99 57	91.50 53	40.25 163	91.00 80	76.84 48	86.98 24	90.51 82

baseline		76.86 50	76.24 52	78.71 56	80.47 184	54.20 92	83.90 169	84.88 119	71.38 11	71.51 79	89.15 109	50.51 49	90.55 96	75.71 53	78.65 106	91.39 57

jason		77.01 45	76.45 48	78.69 57	79.69 193	54.74 77	90.56 24	83.99 144	68.26 29	74.10 46	90.91 65	42.14 142	89.99 111	79.30 30	79.12 102	91.36 59
jason: jason.
ET-MVSNet_ETH3D		75.23 73	74.08 77	78.67 58	84.52 85	55.59 50	88.92 45	89.21 21	68.06 34	53.13 278	90.22 84	49.71 56	87.62 188	72.12 84	70.82 172	92.82 21

CostFormer		73.89 90	72.30 96	78.66 59	82.36 142	56.58 30	75.56 285	85.30 103	66.06 59	70.50 89	76.88 265	57.02 15	89.06 131	68.27 105	68.74 187	90.33 85

patch_mono-2		80.84 11	81.59 9	78.62 60	90.34 10	53.77 97	88.08 56	88.36 47	76.17 2	79.40 23	91.09 57	55.43 20	90.09 109	85.01 5	80.40 87	91.99 42

MVS_Test		75.85 65	74.93 68	78.62 60	84.08 94	55.20 63	83.99 167	85.17 110	68.07 33	73.38 54	82.76 197	50.44 50	89.00 137	65.90 119	80.61 83	91.64 47

CDPH-MVS		76.05 62	75.19 63	78.62 60	86.51 48	54.98 71	87.32 74	84.59 128	58.62 190	70.75 86	90.85 67	43.10 133	90.63 94	70.50 92	84.51 55	90.24 86

TSAR-MVS + GP.		77.82 33	77.59 32	78.49 63	85.25 74	50.27 192	90.02 26	90.57 11	56.58 232	74.26 45	91.60 51	54.26 26	92.16 54	75.87 52	79.91 96	93.05 17

ETV-MVS		77.17 42	76.74 43	78.48 64	81.80 148	54.55 85	86.13 102	85.33 101	68.20 30	73.10 56	90.52 76	45.23 100	90.66 92	79.37 29	80.95 78	90.22 87

TSAR-MVS + MP.		78.31 28	78.26 23	78.48 64	81.33 165	56.31 40	81.59 229	86.41 79	69.61 21	81.72 14	88.16 126	55.09 23	88.04 174	74.12 70	86.31 34	91.09 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
train_agg		76.91 47	76.40 49	78.45 66	85.68 57	55.42 54	87.59 67	84.00 142	57.84 205	72.99 57	90.98 61	44.99 103	88.58 151	78.19 38	85.32 45	91.34 61

PAPR		75.20 74	74.13 76	78.41 67	88.31 33	55.10 67	84.31 156	85.66 93	63.76 91	67.55 105	90.73 69	43.48 127	89.40 124	66.36 116	77.03 118	90.73 75

testtj		76.96 46	76.48 47	78.40 68	89.89 13	53.67 101	88.72 48	86.15 85	54.56 255	74.86 38	92.31 33	44.38 113	91.97 60	75.19 60	82.24 68	89.54 104

alignmvs		78.08 30	77.98 28	78.39 69	83.53 105	53.22 120	89.77 32	85.45 95	66.11 56	76.59 32	91.99 41	54.07 29	89.05 133	77.34 46	77.00 119	92.89 20

test_prior3		77.59 36	77.33 36	78.39 69	86.35 49	54.91 74	89.04 43	85.45 95	61.88 122	73.55 50	91.46 55	48.01 66	89.70 118	74.73 63	85.46 42	90.55 78

test_prior				78.39 69	86.35 49	54.91 74		85.45 95					89.70 118			90.55 78

SF-MVS		77.64 35	77.42 35	78.32 72	83.75 102	52.47 138	86.63 93	87.80 54	58.78 187	74.63 40	92.38 30	47.75 68	91.35 70	78.18 40	86.85 26	91.15 64

ZNCC-MVS		75.82 68	75.02 66	78.23 73	83.88 100	53.80 96	86.91 88	86.05 87	59.71 159	67.85 104	90.55 74	42.23 140	91.02 79	72.66 83	85.29 46	89.87 98

VNet		77.99 32	77.92 29	78.19 74	87.43 42	50.12 193	90.93 21	91.41 4	67.48 42	75.12 36	90.15 88	46.77 80	91.00 80	73.52 76	78.46 108	93.44 9

Regformer-2		77.15 43	76.82 42	78.14 75	87.78 39	51.84 153	87.60 64	89.12 22	67.23 43	71.93 74	91.79 44	46.03 89	93.53 31	72.85 81	77.05 116	89.05 117

EIA-MVS		75.92 64	75.18 64	78.13 76	85.14 75	51.60 159	87.17 80	85.32 102	64.69 76	68.56 97	90.53 75	45.79 93	91.58 65	67.21 110	82.18 70	91.20 63

HFP-MVS		74.37 82	73.13 85	78.10 77	84.30 88	53.68 99	85.58 116	84.36 132	56.82 225	65.78 125	90.56 72	40.70 160	90.90 84	69.18 99	80.88 79	89.71 99

#test#		74.86 80	73.78 80	78.10 77	84.30 88	53.68 99	86.95 85	84.36 132	59.00 183	65.78 125	90.56 72	40.70 160	90.90 84	71.48 86	80.88 79	89.71 99

agg_prior1		76.68 54	76.24 52	78.00 79	85.64 60	54.92 72	87.55 70	83.61 152	57.99 201	72.53 65	91.05 58	45.36 98	88.10 171	77.76 44	84.68 52	90.99 70

tpm2		70.82 137	68.44 152	77.98 80	80.78 176	56.11 42	74.21 295	81.28 193	60.24 152	68.04 102	75.27 283	52.26 37	88.50 156	55.82 211	68.03 191	89.33 107

thisisatest0515		73.64 95	72.20 99	77.97 81	81.63 152	53.01 127	86.69 92	88.81 34	62.53 111	64.06 150	85.65 161	52.15 38	92.50 46	58.43 182	69.84 180	88.39 133

EPNet		78.36 26	78.49 22	77.97 81	85.49 65	52.04 147	89.36 38	84.07 141	73.22 5	77.03 29	91.72 47	49.32 59	90.17 108	73.46 77	82.77 62	91.69 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 83	85.46 68	49.56 204	90.99 20	86.66 76	70.58 14	80.07 20	95.30 1	56.18 18	90.97 83	82.57 13	86.22 36	93.28 12

GST-MVS		74.87 79	73.90 79	77.77 84	83.30 111	53.45 110	85.75 111	85.29 104	59.22 173	66.50 115	89.85 95	40.94 156	90.76 89	70.94 90	83.35 60	89.10 116

GG-mvs-BLEND				77.77 84	86.68 47	50.61 176	68.67 327	88.45 46		68.73 96	87.45 137	59.15 11	90.67 91	54.83 214	87.67 17	92.03 39

Regformer-3		76.02 63	75.47 59	77.70 86	85.49 65	51.47 163	85.12 130	90.19 13	68.52 27	69.36 91	90.66 70	46.45 84	94.81 21	70.25 94	73.16 150	86.81 164

cascas		69.01 169	66.13 197	77.66 87	79.36 196	55.41 56	86.99 83	83.75 147	56.69 229	58.92 210	81.35 218	24.31 309	92.10 57	53.23 223	70.61 173	85.46 189

3Dnovator+	62.71 7	72.29 115	70.50 122	77.65 88	83.40 109	51.29 169	87.32 74	86.40 80	59.01 182	58.49 220	88.32 122	32.40 253	91.27 72	57.04 202	82.15 71	90.38 84

MVSFormer		73.53 96	72.19 100	77.57 89	83.02 122	55.24 60	81.63 226	81.44 188	50.28 285	76.67 30	90.91 65	44.82 108	86.11 226	60.83 160	80.09 92	91.36 59

APD-MVS		76.15 60	75.68 55	77.54 90	88.52 29	53.44 111	87.26 79	85.03 115	53.79 260	74.91 37	91.68 49	43.80 118	90.31 102	74.36 67	81.82 73	88.87 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Fast-Effi-MVS+		72.73 106	71.15 117	77.48 91	82.75 133	54.76 76	86.77 90	80.64 200	63.05 103	65.93 123	84.01 178	44.42 112	89.03 134	56.45 208	76.36 126	88.64 127

EPMVS		68.45 180	65.44 214	77.47 92	84.91 79	56.17 41	71.89 315	81.91 181	61.72 125	60.85 185	72.49 305	36.21 215	87.06 200	47.32 263	71.62 165	89.17 114

PatchmatchNet		67.07 212	63.63 232	77.40 93	83.10 116	58.03 11	72.11 313	77.77 258	58.85 186	59.37 201	70.83 318	37.84 184	84.93 255	42.96 285	69.83 181	89.26 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
region2R		73.75 93	72.55 90	77.33 94	83.90 99	52.98 128	85.54 119	84.09 140	56.83 224	65.10 133	90.45 77	37.34 198	90.24 105	68.89 101	80.83 82	88.77 125

iter_conf05		73.51 97	72.24 98	77.33 94	87.93 38	55.97 46	87.90 61	70.81 328	68.72 25	64.04 151	84.36 174	47.54 72	90.87 86	71.11 89	67.75 195	85.13 194

WTY-MVS		77.47 38	77.52 33	77.30 96	88.33 32	46.25 271	88.46 52	90.32 12	71.40 10	72.32 70	91.72 47	53.44 30	92.37 49	66.28 117	75.42 133	93.28 12

OpenMVS	61.00 11	69.99 152	67.55 171	77.30 96	78.37 222	54.07 94	84.36 154	85.76 92	57.22 218	56.71 248	87.67 134	30.79 269	92.83 38	43.04 283	84.06 58	85.01 196

zzz-MVS		74.15 86	73.11 86	77.27 98	81.54 158	53.57 103	84.02 166	81.31 190	59.41 166	68.39 99	90.96 63	36.07 217	89.01 135	73.80 73	82.45 66	89.23 110

MTAPA		72.73 106	71.22 115	77.27 98	81.54 158	53.57 103	67.06 330	81.31 190	59.41 166	68.39 99	90.96 63	36.07 217	89.01 135	73.80 73	82.45 66	89.23 110

PAPM_NR		71.80 122	69.98 133	77.26 100	81.54 158	53.34 116	78.60 270	85.25 107	53.46 262	60.53 189	88.66 116	45.69 95	89.24 126	56.49 205	79.62 101	89.19 113

ACMMPR		73.76 92	72.61 88	77.24 101	83.92 98	52.96 129	85.58 116	84.29 134	56.82 225	65.12 132	90.45 77	37.24 200	90.18 107	69.18 99	80.84 81	88.58 129

h-mvs33		73.95 88	72.89 87	77.15 102	80.17 187	50.37 185	84.68 148	83.33 155	68.08 31	71.97 72	88.65 119	42.50 136	91.15 76	78.82 32	57.78 277	89.91 97

CS-MVS-test		77.20 41	77.25 37	77.05 103	84.60 83	49.04 215	89.42 36	85.83 91	65.90 62	72.85 60	91.98 42	45.10 101	91.27 72	75.02 62	84.56 53	90.84 73

MP-MVS-pluss		75.54 70	75.03 65	77.04 104	81.37 164	52.65 135	84.34 155	84.46 130	61.16 134	69.14 93	91.76 46	39.98 169	88.99 139	78.19 38	84.89 50	89.48 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HyFIR lowres test		69.94 154	67.58 168	77.04 104	77.11 242	57.29 22	81.49 234	79.11 232	58.27 194	58.86 212	80.41 226	42.33 138	86.96 203	61.91 152	68.68 188	86.87 158

DP-MVS Recon		71.99 119	70.31 126	77.01 106	90.65 9	53.44 111	89.37 37	82.97 166	56.33 235	63.56 162	89.47 101	34.02 237	92.15 56	54.05 220	72.41 159	85.43 190

Anonymous20240529		69.71 157	67.28 176	77.00 107	83.78 101	50.36 186	88.87 47	85.10 114	47.22 302	64.03 152	83.37 189	27.93 286	92.10 57	57.78 196	67.44 196	88.53 131

CS-MVS		76.77 51	76.70 44	76.99 108	83.55 104	48.75 224	88.60 50	85.18 109	66.38 51	72.47 68	91.62 50	45.53 96	90.99 82	74.48 66	82.51 64	91.23 62

baseline2		75.15 75	74.54 74	76.98 109	81.67 151	51.74 156	83.84 170	91.94 1	69.97 18	58.98 207	86.02 156	59.73 8	91.73 63	68.37 103	70.40 177	87.48 148

MP-MVS		74.99 78	74.33 75	76.95 110	82.89 128	53.05 126	85.63 115	83.50 154	57.86 204	67.25 107	90.24 82	43.38 128	88.85 145	76.03 50	82.23 69	88.96 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 115	70.74 119	76.94 111	82.85 129	54.72 79	78.43 271	81.54 186	63.77 90	61.69 180	79.32 234	51.11 43	85.31 245	62.15 150	75.79 129	90.79 74

iter_conf_final		71.46 127	69.68 137	76.81 112	86.03 51	53.49 105	84.73 145	74.37 299	60.27 151	66.28 118	84.36 174	35.14 228	90.87 86	65.41 128	70.51 175	86.05 175

XVS		72.92 103	71.62 108	76.81 112	83.41 106	52.48 136	84.88 141	83.20 161	58.03 197	63.91 155	89.63 99	35.50 223	89.78 115	65.50 121	80.50 85	88.16 134

X-MVStestdata		65.85 230	62.20 238	76.81 112	83.41 106	52.48 136	84.88 141	83.20 161	58.03 197	63.91 155	4.82 378	35.50 223	89.78 115	65.50 121	80.50 85	88.16 134

PGM-MVS		72.60 108	71.20 116	76.80 115	82.95 125	52.82 131	83.07 195	82.14 175	56.51 233	63.18 164	89.81 96	35.68 222	89.76 117	67.30 109	80.19 91	87.83 142

Anonymous202405211		70.11 146	67.88 161	76.79 116	87.20 44	47.24 257	89.49 35	77.38 266	54.88 251	66.14 119	86.84 147	20.93 331	91.54 66	56.45 208	71.62 165	91.59 49

tpm cat1		66.28 224	62.78 234	76.77 117	81.40 163	57.14 24	70.03 322	77.19 268	53.00 266	58.76 215	70.73 321	46.17 85	86.73 210	43.27 282	64.46 215	86.44 170

PVSNet_Blended		76.53 55	76.54 45	76.50 118	85.91 54	51.83 154	88.89 46	84.24 138	67.82 37	69.09 94	89.33 106	46.70 81	88.13 169	75.43 56	81.48 77	89.55 103

Regformer-4		75.06 77	74.59 73	76.47 119	85.49 65	50.33 188	85.12 130	88.61 41	66.42 49	68.48 98	90.66 70	44.15 114	92.68 42	69.24 98	73.16 150	86.39 172

diffmvs		75.11 76	74.65 72	76.46 120	78.52 218	53.35 115	83.28 190	79.94 210	70.51 15	71.64 77	88.72 115	46.02 90	86.08 232	77.52 45	75.75 131	89.96 95

PVSNet_Blended_VisFu		73.40 99	72.44 92	76.30 121	81.32 166	54.70 80	85.81 107	78.82 236	63.70 92	64.53 143	85.38 165	47.11 77	87.38 194	67.75 107	77.55 113	86.81 164

BH-RMVSNet		70.08 148	68.01 158	76.27 122	84.21 92	51.22 171	87.29 77	79.33 229	58.96 185	63.63 161	86.77 148	33.29 246	90.30 104	44.63 277	73.96 144	87.30 153

CLD-MVS		75.60 69	75.39 60	76.24 123	80.69 179	52.40 140	90.69 22	86.20 84	74.40 4	65.01 136	88.93 111	42.05 144	90.58 95	76.57 49	73.96 144	85.73 183
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		69.96 153	67.88 161	76.22 124	81.11 168	51.71 157	84.15 160	76.74 277	59.83 157	60.91 184	84.38 172	41.56 152	88.10 171	51.67 238	70.57 174	88.84 122

1314		71.11 131	69.41 140	76.22 124	79.32 198	50.49 180	80.23 252	85.14 113	59.44 165	58.93 209	88.89 113	33.83 242	89.60 122	61.49 155	77.42 115	88.57 130

thisisatest0530		70.47 144	68.56 150	76.20 126	79.78 192	51.52 162	83.49 181	88.58 44	57.62 211	58.60 216	82.79 196	51.03 45	91.48 67	52.84 228	62.36 239	85.59 188

HY-MVS	67.03 5	73.90 89	73.14 83	76.18 127	84.70 82	47.36 253	75.56 285	86.36 81	66.27 53	70.66 87	83.91 180	51.05 44	89.31 125	67.10 111	72.61 158	91.88 44

gg-mvs-nofinetune		67.43 201	64.53 226	76.13 128	85.95 52	47.79 248	64.38 334	88.28 48	39.34 340	66.62 111	41.27 362	58.69 14	89.00 137	49.64 249	86.62 31	91.59 49

原ACMM1				76.13 128	84.89 80	54.59 84		85.26 106	51.98 274	66.70 109	87.07 145	40.15 166	89.70 118	51.23 240	85.06 49	84.10 206

GA-MVS		69.04 167	66.70 186	76.06 130	75.11 264	52.36 142	83.12 193	80.23 206	63.32 99	60.65 188	79.22 237	30.98 268	88.37 159	61.25 156	66.41 202	87.46 149

mPP-MVS		71.79 123	70.38 125	76.04 131	82.65 137	52.06 146	84.45 152	81.78 183	55.59 242	62.05 178	89.68 98	33.48 244	88.28 166	65.45 126	78.24 111	87.77 144

MVSTER		73.25 100	72.33 94	76.01 132	85.54 64	53.76 98	83.52 175	87.16 65	67.06 45	63.88 157	81.66 214	52.77 33	90.44 97	64.66 134	64.69 213	83.84 217

CP-MVS		72.59 110	71.46 111	76.00 133	82.93 127	52.32 144	86.93 87	82.48 172	55.15 246	63.65 160	90.44 80	35.03 230	88.53 155	68.69 102	77.83 112	87.15 154

HPM-MVS		72.60 108	71.50 110	75.89 134	82.02 144	51.42 165	80.70 246	83.05 163	56.12 237	64.03 152	89.53 100	37.55 192	88.37 159	70.48 93	80.04 94	87.88 141
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		69.87 155	67.88 161	75.85 135	88.38 31	52.35 143	86.94 86	83.68 148	53.70 261	55.68 258	85.60 162	30.07 274	91.20 74	55.84 210	71.02 170	83.99 210

PMMVS		72.98 102	72.05 105	75.78 136	83.57 103	48.60 227	84.08 162	82.85 168	61.62 126	68.24 101	90.33 81	28.35 281	87.78 183	72.71 82	76.69 121	90.95 71

DROMVSNet		75.30 71	75.20 62	75.62 137	80.98 169	49.00 216	87.43 71	84.68 126	63.49 98	70.97 85	90.15 88	42.86 135	91.14 77	74.33 68	81.90 72	86.71 166

MS-PatchMatch		72.34 113	71.26 114	75.61 138	82.38 141	55.55 51	88.00 57	89.95 17	65.38 68	56.51 252	80.74 224	32.28 255	92.89 36	57.95 192	88.10 15	78.39 293

xiu_mvs_v1_base_debu		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 239	72.54 62	90.19 85	22.38 321	86.66 212	73.28 78	76.39 123	86.85 160

xiu_mvs_v1_base		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 239	72.54 62	90.19 85	22.38 321	86.66 212	73.28 78	76.39 123	86.85 160

xiu_mvs_v1_base_debi		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 239	72.54 62	90.19 85	22.38 321	86.66 212	73.28 78	76.39 123	86.85 160

CANet_DTU		73.71 94	73.14 83	75.40 142	82.61 138	50.05 194	84.67 150	79.36 226	69.72 20	75.39 34	90.03 92	29.41 277	85.93 238	67.99 106	79.11 103	90.22 87

ACMMP		70.81 138	69.29 144	75.39 143	81.52 161	51.92 151	83.43 182	83.03 164	56.67 230	58.80 214	88.91 112	31.92 260	88.58 151	65.89 120	73.39 149	85.67 184
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
SCA		63.84 239	60.01 258	75.32 144	78.58 216	57.92 12	61.61 342	77.53 262	56.71 228	57.75 232	70.77 319	31.97 258	79.91 299	48.80 253	56.36 283	88.13 137

ab-mvs		70.65 140	69.11 146	75.29 145	80.87 175	46.23 272	73.48 299	85.24 108	59.99 155	66.65 110	80.94 221	43.13 132	88.69 147	63.58 139	68.07 190	90.95 71

TR-MVS		69.71 157	67.85 164	75.27 146	82.94 126	48.48 233	87.40 73	80.86 197	57.15 220	64.61 142	87.08 144	32.67 251	89.64 121	46.38 269	71.55 167	87.68 146

v2v482		69.55 163	67.64 167	75.26 147	72.32 299	53.83 95	84.93 140	81.94 178	65.37 69	60.80 186	79.25 236	41.62 150	88.98 140	63.03 143	59.51 253	82.98 233

PCF-MVS	61.03 10	70.10 147	68.40 153	75.22 148	77.15 241	51.99 148	79.30 266	82.12 176	56.47 234	61.88 179	86.48 154	43.98 115	87.24 196	55.37 212	72.79 157	86.43 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		72.34 113	71.44 112	75.03 149	79.02 204	51.56 160	88.00 57	83.68 148	65.45 64	64.48 144	85.13 166	37.35 196	88.62 149	66.70 112	73.12 152	84.91 198

AdaColmap		67.86 191	65.48 211	75.00 150	88.15 35	54.99 70	86.10 103	76.63 280	49.30 291	57.80 229	86.65 151	29.39 278	88.94 143	45.10 275	70.21 178	81.06 265

EI-MVSNet-Vis-set		73.19 101	72.60 89	74.99 151	82.56 139	49.80 200	82.55 207	89.00 27	66.17 55	65.89 124	88.98 110	43.83 117	92.29 50	65.38 131	69.01 185	82.87 235

tpmrst		71.04 133	69.77 135	74.86 152	83.19 115	55.86 49	75.64 284	78.73 240	67.88 35	64.99 137	73.73 291	49.96 54	79.56 302	65.92 118	67.85 194	89.14 115

1121		68.79 175	66.77 183	74.82 153	83.08 119	53.46 107	80.23 252	71.53 323	45.47 317	66.31 117	87.19 142	34.02 237	85.13 251	52.78 230	80.36 89	85.87 181

v1144		68.81 173	66.82 181	74.80 154	72.34 298	53.46 107	84.68 148	81.77 184	64.25 80	60.28 190	77.91 246	40.23 164	88.95 141	60.37 169	59.52 252	81.97 243

v1192		67.96 190	65.74 206	74.63 155	71.79 301	53.43 113	84.06 164	80.99 196	63.19 102	59.56 198	77.46 253	37.50 195	88.65 148	58.20 187	58.93 259	81.79 245

BH-w/o		70.02 150	68.51 151	74.56 156	82.77 131	50.39 184	86.60 94	78.14 251	59.77 158	59.65 195	85.57 163	39.27 174	87.30 195	49.86 247	74.94 140	85.99 176

SR-MVS		70.92 136	69.73 136	74.50 157	83.38 110	50.48 181	84.27 157	79.35 227	48.96 294	66.57 114	90.45 77	33.65 243	87.11 198	66.42 114	74.56 141	85.91 179

tttt0517		68.33 183	66.29 192	74.46 158	78.08 224	49.06 212	80.88 243	89.08 24	54.40 257	54.75 263	80.77 223	51.31 42	90.33 101	49.35 251	58.01 271	83.99 210

TESTMET0.1,1		72.86 105	72.33 94	74.46 158	81.98 145	50.77 173	85.13 127	85.47 94	66.09 57	67.30 106	83.69 185	37.27 199	83.57 268	65.06 132	78.97 105	89.05 117

nrg030		72.27 117	71.56 109	74.42 160	75.93 256	50.60 177	86.97 84	83.21 160	62.75 107	67.15 108	84.38 172	50.07 52	86.66 212	71.19 87	62.37 238	85.99 176

RPMNet		59.29 271	54.25 294	74.42 160	73.97 281	56.57 31	60.52 345	76.98 272	35.72 352	57.49 238	58.87 354	37.73 188	85.26 247	27.01 346	59.93 249	81.42 255

Vis-MVSNet		70.61 141	69.34 142	74.42 160	80.95 174	48.49 232	86.03 105	77.51 263	58.74 188	65.55 129	87.78 132	34.37 234	85.95 237	52.53 235	80.61 83	88.80 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		71.14 129	70.07 132	74.33 163	79.18 201	46.52 265	83.81 171	86.49 77	56.32 236	57.95 226	84.90 170	54.23 27	89.14 130	58.14 188	69.65 182	87.33 151

test2506		72.91 104	72.43 93	74.32 164	80.12 188	44.18 296	83.19 191	84.77 123	64.02 83	65.97 122	87.43 138	47.67 71	88.72 146	59.08 175	79.66 99	90.08 92

EI-MVSNet-UG-set		72.37 112	71.73 107	74.29 165	81.60 154	49.29 210	81.85 221	88.64 38	65.29 72	65.05 134	88.29 123	43.18 129	91.83 61	63.74 138	67.97 192	81.75 246

ECVR-MVS		71.81 121	71.00 118	74.26 166	80.12 188	43.49 302	84.69 147	82.16 174	64.02 83	64.64 140	87.43 138	35.04 229	89.21 127	61.24 157	79.66 99	90.08 92

OPM-MVS		70.75 139	69.58 138	74.26 166	75.55 261	51.34 167	86.05 104	83.29 159	61.94 121	62.95 168	85.77 160	34.15 236	88.44 157	65.44 127	71.07 169	82.99 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		67.86 191	65.76 205	74.16 168	71.68 303	53.09 124	84.14 161	80.83 198	62.85 106	59.21 205	77.28 256	39.30 173	88.00 175	58.67 180	57.88 275	81.40 257

HQP_MVS		70.96 135	69.91 134	74.12 169	77.95 226	49.57 202	85.76 109	82.59 170	63.60 95	62.15 175	83.28 191	36.04 219	88.30 164	65.46 124	72.34 160	84.49 201

v1921920		67.45 200	65.23 218	74.10 170	71.51 306	52.90 130	83.75 173	80.44 203	62.48 113	59.12 206	77.13 257	36.98 203	87.90 177	57.53 198	58.14 269	81.49 251

v8		67.25 206	64.99 221	74.04 171	72.89 292	53.31 118	82.37 212	80.11 208	61.54 129	54.29 269	76.02 279	42.89 134	88.41 158	58.43 182	56.36 283	80.39 274

VPNet		72.07 118	71.42 113	74.04 171	78.64 215	47.17 258	89.91 31	87.97 52	72.56 7	64.66 139	85.04 168	41.83 149	88.33 162	61.17 158	60.97 244	86.62 167

v1240		66.99 213	64.68 224	73.93 173	71.38 310	52.66 134	83.39 186	79.98 209	61.97 120	58.44 223	77.11 258	35.25 225	87.81 179	56.46 207	58.15 267	81.33 260

BH-untuned		68.28 184	66.40 189	73.91 174	81.62 153	50.01 195	85.56 118	77.39 265	57.63 210	57.47 240	83.69 185	36.36 214	87.08 199	44.81 276	73.08 155	84.65 200

v148		68.24 186	66.35 190	73.88 175	71.76 302	51.47 163	84.23 158	81.90 182	63.69 93	58.94 208	76.44 270	43.72 122	87.78 183	60.63 162	55.86 293	82.39 240

V42		67.66 195	65.60 210	73.86 176	70.69 315	53.63 102	81.50 232	78.61 243	63.85 89	59.49 200	77.49 252	37.98 182	87.65 186	62.33 146	58.43 264	80.29 275

Fast-Effi-MVS+-dtu		66.53 221	64.10 230	73.84 177	72.41 297	52.30 145	84.73 145	75.66 288	59.51 163	56.34 253	79.11 239	28.11 284	85.85 239	57.74 197	63.29 226	83.35 222

v10		66.61 220	64.20 229	73.83 178	72.59 295	53.37 114	81.88 220	79.91 212	61.11 135	54.09 271	75.60 281	40.06 168	88.26 167	56.47 206	56.10 289	79.86 280

APD-MVS_3200maxsize		69.62 162	68.23 156	73.80 179	81.58 156	48.22 239	81.91 219	79.50 221	48.21 296	64.24 149	89.75 97	31.91 261	87.55 190	63.08 142	73.85 146	85.64 186

AUN-MVS		68.20 187	66.35 190	73.76 180	76.37 246	47.45 251	79.52 263	79.52 220	60.98 139	62.34 173	86.02 156	36.59 213	86.94 204	62.32 147	53.47 311	86.89 157

PVSNet_BlendedMVS		73.42 98	73.30 82	73.76 180	85.91 54	51.83 154	86.18 101	84.24 138	65.40 67	69.09 94	80.86 222	46.70 81	88.13 169	75.43 56	65.92 208	81.33 260

hse-mvs2		71.44 128	70.68 120	73.73 182	76.34 247	47.44 252	79.45 264	79.47 222	68.08 31	71.97 72	86.01 158	42.50 136	86.93 205	78.82 32	53.46 312	86.83 163

baseline1		72.51 111	72.12 103	73.69 183	85.05 76	44.46 290	83.51 179	86.13 86	71.61 9	64.64 140	87.97 130	55.00 24	89.48 123	59.07 176	56.05 290	87.13 155

abl_6		68.03 188	66.15 196	73.66 184	78.54 217	48.48 233	79.77 257	78.04 252	47.39 301	63.70 159	88.25 124	28.21 282	89.06 131	60.17 172	71.25 168	83.45 221

CDS-MVSNet		70.48 143	69.43 139	73.64 185	77.56 232	48.83 223	83.51 179	77.45 264	63.27 100	62.33 174	85.54 164	43.85 116	83.29 272	57.38 201	74.00 143	88.79 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet	62.49 8	69.27 165	67.81 165	73.64 185	84.41 87	51.85 152	84.63 151	77.80 256	66.42 49	59.80 193	84.95 169	22.14 326	80.44 291	55.03 213	75.11 137	88.62 128

PS-MVSNAJss		68.78 176	67.17 178	73.62 187	73.01 289	48.33 238	84.95 139	84.81 121	59.30 172	58.91 211	79.84 229	37.77 185	88.86 144	62.83 144	63.12 232	83.67 219

TAMVS		69.51 164	68.16 157	73.56 188	76.30 250	48.71 226	82.57 205	77.17 269	62.10 117	61.32 182	84.23 176	41.90 147	83.46 270	54.80 216	73.09 154	88.50 132

UGNet		68.71 177	67.11 179	73.50 189	80.55 183	47.61 249	84.08 162	78.51 245	59.45 164	65.68 128	82.73 200	23.78 312	85.08 253	52.80 229	76.40 122	87.80 143
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-test1		69.04 167	67.57 170	73.44 190	75.17 262	51.68 158	86.57 95	74.48 297	62.15 115	62.07 177	85.79 159	30.59 270	87.48 191	65.40 129	65.94 207	81.18 264

test1172		69.64 161	68.38 154	73.41 191	82.77 131	48.84 222	82.79 201	78.34 249	47.02 305	65.27 131	90.07 90	31.17 266	86.09 230	64.51 135	73.49 148	85.31 191

Anonymous20231211		66.08 228	63.67 231	73.31 192	83.07 120	48.75 224	86.01 106	84.67 127	45.27 318	56.54 250	76.67 268	28.06 285	88.95 141	52.78 230	59.95 248	82.23 241

新几何1				73.30 193	83.10 116	53.48 106		71.43 324	45.55 315	66.14 119	87.17 143	33.88 241	80.54 289	48.50 256	80.33 90	85.88 180

FMVSNet3		68.84 171	67.40 174	73.19 194	85.05 76	48.53 230	85.71 114	85.36 99	60.90 141	57.58 235	79.15 238	42.16 141	86.77 208	47.25 264	63.40 222	84.27 205

mvsmamba		66.93 216	64.88 223	73.09 195	75.06 266	47.26 255	83.36 188	69.21 334	62.64 109	55.68 258	81.43 217	29.72 275	89.20 128	63.35 141	63.50 221	82.79 236

thres200		68.71 177	67.27 177	73.02 196	84.73 81	46.76 261	85.03 135	87.73 58	62.34 114	59.87 191	83.45 188	43.15 130	88.32 163	31.25 330	67.91 193	83.98 212

PVSNet_0	57.04 13	61.19 262	57.24 273	73.02 196	77.45 234	50.31 190	79.43 265	77.36 267	63.96 88	47.51 312	72.45 307	25.03 306	83.78 265	52.76 233	19.22 370	84.96 197

test1111		71.06 132	70.42 124	72.97 198	79.48 195	41.49 320	84.82 144	82.74 169	64.20 81	62.98 167	87.43 138	35.20 226	87.92 176	58.54 181	78.42 109	89.49 105

dp		64.41 235	61.58 242	72.90 199	82.40 140	54.09 93	72.53 305	76.59 281	60.39 149	55.68 258	70.39 322	35.18 227	76.90 324	39.34 293	61.71 241	87.73 145

FMVSNet2		67.57 197	65.79 204	72.90 199	82.71 134	47.97 247	85.15 126	84.93 117	58.55 191	56.71 248	78.26 244	36.72 210	86.67 211	46.15 271	62.94 234	84.07 207

XXY-MVS		70.18 145	69.28 145	72.89 201	77.64 230	42.88 308	85.06 133	87.50 63	62.58 110	62.66 172	82.34 207	43.64 124	89.83 114	58.42 184	63.70 220	85.96 178

bld_raw_conf005		65.62 231	63.24 233	72.77 202	74.32 277	46.61 263	82.74 202	66.71 340	60.71 145	54.67 264	80.73 225	24.12 311	89.16 129	61.92 151	63.22 228	82.54 239

CR-MVSNet		62.47 255	59.04 265	72.77 202	73.97 281	56.57 31	60.52 345	71.72 319	60.04 153	57.49 238	65.86 336	38.94 175	80.31 292	42.86 286	59.93 249	81.42 255

EI-MVSNet		69.70 159	68.70 149	72.68 204	75.00 268	48.90 220	79.54 261	87.16 65	61.05 137	63.88 157	83.74 183	45.87 91	90.44 97	57.42 200	64.68 214	78.70 286

HPM-MVS_fast		67.86 191	66.28 193	72.61 205	80.67 180	48.34 237	81.18 238	75.95 287	50.81 283	59.55 199	88.05 129	27.86 287	85.98 234	58.83 178	73.58 147	83.51 220

MVP-Stereo		70.97 134	70.44 123	72.59 206	76.03 255	51.36 166	85.02 136	86.99 68	60.31 150	56.53 251	78.92 240	40.11 167	90.00 110	60.00 173	90.01 6	76.41 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		69.07 166	67.91 159	72.54 207	77.27 236	49.56 204	79.77 257	73.96 304	59.33 171	60.73 187	87.82 131	30.19 273	81.53 280	69.94 95	72.19 162	86.53 168

IS-MVSNet		68.80 174	67.55 171	72.54 207	78.50 219	43.43 303	81.03 240	79.35 227	59.12 180	57.27 243	86.71 149	46.05 88	87.70 185	44.32 278	75.60 132	86.49 169

VPA-MVSNet		71.12 130	70.66 121	72.49 209	78.75 210	44.43 292	87.64 63	90.02 15	63.97 87	65.02 135	81.58 215	42.14 142	87.42 193	63.42 140	63.38 225	85.63 187

SR-MVS-dyc-post		68.27 185	66.87 180	72.48 210	80.96 171	48.14 242	81.54 230	76.98 272	46.42 310	62.75 170	89.42 102	31.17 266	86.09 230	60.52 166	72.06 163	83.19 228

miper_enhance_ethall		69.77 156	68.90 148	72.38 211	78.93 207	49.91 197	83.29 189	78.85 234	64.90 74	59.37 201	79.46 232	52.77 33	85.16 250	63.78 137	58.72 260	82.08 242

cl22		68.85 170	67.69 166	72.35 212	78.07 225	49.98 196	82.45 210	78.48 246	62.50 112	58.46 221	77.95 245	49.99 53	85.17 249	62.55 145	58.72 260	81.90 244

MSDG		59.44 270	55.14 290	72.32 213	74.69 271	50.71 174	74.39 294	73.58 307	44.44 324	43.40 327	77.52 251	19.45 335	90.87 86	31.31 329	57.49 279	75.38 321

v7n		62.50 254	59.27 263	72.20 214	67.25 335	49.83 199	77.87 273	80.12 207	52.50 270	48.80 303	73.07 299	32.10 256	87.90 177	46.83 267	54.92 299	78.86 284

1112_ss		70.05 149	69.37 141	72.10 215	80.77 177	42.78 309	85.12 130	76.75 276	59.69 160	61.19 183	92.12 36	47.48 73	83.84 263	53.04 226	68.21 189	89.66 101

miper_ehance_all_eth		68.70 179	67.58 168	72.08 216	76.91 243	49.48 207	82.47 209	78.45 247	62.68 108	58.28 225	77.88 247	50.90 46	85.01 254	61.91 152	58.72 260	81.75 246

eth_miper_zixun_eth		66.98 214	65.28 217	72.06 217	75.61 260	50.40 183	81.00 241	76.97 275	62.00 118	56.99 245	76.97 261	44.84 107	85.58 240	58.75 179	54.42 303	80.21 276

LPG-MVS_test		66.44 223	64.58 225	72.02 218	74.42 274	48.60 227	83.07 195	80.64 200	54.69 253	53.75 274	83.83 181	25.73 302	86.98 201	60.33 170	64.71 211	80.48 272

LGP-MVS_train				72.02 218	74.42 274	48.60 227		80.64 200	54.69 253	53.75 274	83.83 181	25.73 302	86.98 201	60.33 170	64.71 211	80.48 272

ACMP	61.11 9	66.24 226	64.33 227	72.00 220	74.89 270	49.12 211	83.18 192	79.83 213	55.41 245	52.29 284	82.68 201	25.83 300	86.10 228	60.89 159	63.94 218	80.78 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GBi-Net		67.09 210	65.47 212	71.96 221	82.71 134	46.36 267	83.52 175	83.31 156	58.55 191	57.58 235	76.23 274	36.72 210	86.20 222	47.25 264	63.40 222	83.32 223

test1		67.09 210	65.47 212	71.96 221	82.71 134	46.36 267	83.52 175	83.31 156	58.55 191	57.58 235	76.23 274	36.72 210	86.20 222	47.25 264	63.40 222	83.32 223

FMVSNet1		64.57 234	62.11 239	71.96 221	77.32 235	46.36 267	83.52 175	83.31 156	52.43 272	54.42 267	76.23 274	27.80 288	86.20 222	42.59 287	61.34 243	83.32 223

cl____		67.43 201	65.93 201	71.95 224	76.33 248	48.02 245	82.58 204	79.12 231	61.30 133	56.72 247	76.92 263	46.12 86	86.44 220	57.98 190	56.31 285	81.38 259

DIV-MVS_self_test		67.43 201	65.93 201	71.94 225	76.33 248	48.01 246	82.57 205	79.11 232	61.31 132	56.73 246	76.92 263	46.09 87	86.43 221	57.98 190	56.31 285	81.39 258

Patchmatch-RL test		58.72 281	54.32 293	71.92 226	63.91 350	44.25 294	61.73 341	55.19 357	57.38 216	49.31 301	54.24 358	37.60 191	80.89 285	62.19 149	47.28 329	90.63 77

c3_l		67.97 189	66.66 187	71.91 227	76.20 252	49.31 209	82.13 215	78.00 254	61.99 119	57.64 234	76.94 262	49.41 57	84.93 255	60.62 163	57.01 281	81.49 251

tfpn200view9		67.57 197	66.13 197	71.89 228	84.05 95	45.07 285	83.40 184	87.71 60	60.79 142	57.79 230	82.76 197	43.53 125	87.80 180	28.80 336	66.36 203	82.78 237

RRT_MVS		63.68 242	61.01 250	71.70 229	73.48 283	45.98 274	81.19 237	76.08 285	54.33 258	52.84 280	79.27 235	22.21 324	87.65 186	54.13 219	55.54 297	81.46 254

MIMVSNet		63.12 247	60.29 256	71.61 230	75.92 257	46.65 262	65.15 331	81.94 178	59.14 178	54.65 265	69.47 325	25.74 301	80.63 288	41.03 290	69.56 184	87.55 147

test-LLR		69.65 160	69.01 147	71.60 231	78.67 212	48.17 240	85.13 127	79.72 215	59.18 176	63.13 165	82.58 202	36.91 205	80.24 293	60.56 164	75.17 135	86.39 172

test-mter		68.36 181	67.29 175	71.60 231	78.67 212	48.17 240	85.13 127	79.72 215	53.38 263	63.13 165	82.58 202	27.23 292	80.24 293	60.56 164	75.17 135	86.39 172

sss		70.49 142	70.13 131	71.58 233	81.59 155	39.02 331	80.78 245	84.71 125	59.34 169	66.61 112	88.09 127	37.17 201	85.52 241	61.82 154	71.02 170	90.20 89

tpmvs		62.45 256	59.42 261	71.53 234	83.93 97	54.32 87	70.03 322	77.61 261	51.91 275	53.48 277	68.29 329	37.91 183	86.66 212	33.36 320	58.27 265	73.62 333

ACMM	58.35 12	64.35 236	62.01 240	71.38 235	74.21 278	48.51 231	82.25 213	79.66 217	47.61 299	54.54 266	80.11 227	25.26 304	86.00 233	51.26 239	63.16 230	79.64 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	53.70 16	59.78 267	55.94 285	71.28 236	76.59 245	48.35 236	80.15 255	76.11 284	49.74 289	41.91 333	73.45 298	16.50 350	90.31 102	31.42 328	57.63 278	75.17 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ppachtmachnet_test		58.56 283	54.34 292	71.24 237	71.42 308	54.74 77	81.84 222	72.27 316	49.02 293	45.86 321	68.99 328	26.27 297	83.30 271	30.12 332	43.23 342	75.69 318

thres100view900		66.87 217	65.42 215	71.24 237	83.29 112	43.15 305	81.67 225	87.78 55	59.04 181	55.92 256	82.18 209	43.73 120	87.80 180	28.80 336	66.36 203	82.78 237

thres400		67.40 204	66.13 197	71.19 239	84.05 95	45.07 285	83.40 184	87.71 60	60.79 142	57.79 230	82.76 197	43.53 125	87.80 180	28.80 336	66.36 203	80.71 270

our_test_3		59.11 275	55.08 291	71.18 240	71.42 308	53.29 119	81.96 217	74.52 296	48.32 295	42.08 331	69.28 327	28.14 283	82.15 276	34.35 317	45.68 337	78.11 298

CPTT-MVS		67.15 209	65.84 203	71.07 241	80.96 171	50.32 189	81.94 218	74.10 301	46.18 313	57.91 227	87.64 135	29.57 276	81.31 282	64.10 136	70.18 179	81.56 250

NR-MVSNet		67.25 206	65.99 200	71.04 242	73.27 287	43.91 297	85.32 123	84.75 124	66.05 60	53.65 276	82.11 210	45.05 102	85.97 236	47.55 261	56.18 288	83.24 226

tpm		68.36 181	67.48 173	70.97 243	79.93 191	51.34 167	76.58 281	78.75 239	67.73 38	63.54 163	74.86 285	48.33 62	72.36 345	53.93 221	63.71 219	89.21 112

TranMVSNet+NR-MVSNet		66.94 215	65.61 209	70.93 244	73.45 284	43.38 304	83.02 197	84.25 136	65.31 71	58.33 224	81.90 212	39.92 170	85.52 241	49.43 250	54.89 300	83.89 216

EG-PatchMatch MVS		62.40 257	59.59 259	70.81 245	73.29 286	49.05 213	85.81 107	84.78 122	51.85 277	44.19 322	73.48 297	15.52 353	89.85 113	40.16 291	67.24 197	73.54 334

test_djsdf		63.84 239	61.56 243	70.70 246	68.78 324	44.69 289	81.63 226	81.44 188	50.28 285	52.27 285	76.26 273	26.72 295	86.11 226	60.83 160	55.84 294	81.29 263

UA-Net		67.32 205	66.23 194	70.59 247	78.85 208	41.23 323	73.60 297	75.45 291	61.54 129	66.61 112	84.53 171	38.73 178	86.57 217	42.48 288	74.24 142	83.98 212

test_low_dy_conf_001		63.62 243	61.32 246	70.53 248	71.46 307	43.56 301	79.77 257	77.78 257	52.44 271	50.15 297	79.69 230	22.46 320	86.45 219	57.90 193	60.21 247	81.57 249

thres600view7		66.46 222	65.12 219	70.47 249	83.41 106	43.80 299	82.15 214	87.78 55	59.37 168	56.02 255	82.21 208	43.73 120	86.90 206	26.51 347	64.94 210	80.71 270

UniMVSNet (Re)		67.71 194	66.80 182	70.45 250	74.44 273	42.93 307	82.42 211	84.90 118	63.69 93	59.63 196	80.99 220	47.18 75	85.23 248	51.17 241	56.75 282	83.19 228

IterMVS-LS		66.63 219	65.36 216	70.42 251	75.10 265	48.90 220	81.45 235	76.69 279	61.05 137	55.71 257	77.10 259	45.86 92	83.65 267	57.44 199	57.88 275	78.70 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		68.82 172	68.29 155	70.40 252	75.71 259	42.59 311	84.23 158	86.78 72	66.31 52	58.51 217	82.45 204	51.57 40	84.64 259	53.11 224	55.96 291	83.96 214

jajsoiax		63.21 246	60.84 251	70.32 253	68.33 329	44.45 291	81.23 236	81.05 195	53.37 264	50.96 294	77.81 249	17.49 344	85.49 243	59.31 174	58.05 270	81.02 266

mvs_tets		62.96 249	60.55 253	70.19 254	68.22 332	44.24 295	80.90 242	80.74 199	52.99 267	50.82 296	77.56 250	16.74 348	85.44 244	59.04 177	57.94 272	80.89 267

pmmvs4		63.34 245	61.07 249	70.16 255	70.14 317	50.53 179	79.97 256	71.41 325	55.08 247	54.12 270	78.58 242	32.79 250	82.09 278	50.33 244	57.22 280	77.86 299

DU-MVS		66.84 218	65.74 206	70.16 255	73.27 287	42.59 311	81.50 232	82.92 167	63.53 97	58.51 217	82.11 210	40.75 157	84.64 259	53.11 224	55.96 291	83.24 226

Effi-MVS+-dtu		66.24 226	64.96 222	70.08 257	75.17 262	49.64 201	82.01 216	74.48 297	62.15 115	57.83 228	76.08 278	30.59 270	83.79 264	65.40 129	60.93 245	76.81 308

IterMVS		63.77 241	61.67 241	70.08 257	72.68 294	51.24 170	80.44 248	75.51 289	60.51 148	51.41 289	73.70 294	32.08 257	78.91 304	54.30 218	54.35 304	80.08 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		67.58 196	66.76 184	70.04 259	75.92 257	45.06 288	86.23 100	85.28 105	64.31 79	58.50 219	81.00 219	44.80 110	82.00 279	49.21 252	55.57 296	83.06 231

Test_1112_low_res		67.18 208	66.23 194	70.02 260	78.75 210	41.02 324	83.43 182	73.69 306	57.29 217	58.45 222	82.39 206	45.30 99	80.88 286	50.50 243	66.26 206	88.16 134

D2MVS		63.49 244	61.39 245	69.77 261	69.29 322	48.93 219	78.89 268	77.71 260	60.64 147	49.70 299	72.10 313	27.08 293	83.48 269	54.48 217	62.65 235	76.90 307

XVG-OURS		61.88 259	59.34 262	69.49 262	65.37 341	46.27 270	64.80 333	73.49 309	47.04 304	57.41 242	82.85 195	25.15 305	78.18 308	53.00 227	64.98 209	84.01 209

XVG-OURS-SEG-HR		62.02 258	59.54 260	69.46 263	65.30 342	45.88 275	65.06 332	73.57 308	46.45 309	57.42 241	83.35 190	26.95 294	78.09 310	53.77 222	64.03 216	84.42 203

FIs		70.00 151	70.24 130	69.30 264	77.93 228	38.55 333	83.99 167	87.72 59	66.86 47	57.66 233	84.17 177	52.28 36	85.31 245	52.72 234	68.80 186	84.02 208

Baseline_NR-MVSNet		65.49 233	64.27 228	69.13 265	74.37 276	41.65 318	83.39 186	78.85 234	59.56 162	59.62 197	76.88 265	40.75 157	87.44 192	49.99 245	55.05 298	78.28 295

TransMVSNet (Re)		62.82 250	60.76 252	69.02 266	73.98 280	41.61 319	86.36 97	79.30 230	56.90 222	52.53 282	76.44 270	41.85 148	87.60 189	38.83 294	40.61 347	77.86 299

anonymousdsp		60.46 266	57.65 270	68.88 267	63.63 351	45.09 284	72.93 303	78.63 242	46.52 308	51.12 291	72.80 303	21.46 329	83.07 273	57.79 195	53.97 305	78.47 290

ADS-MVSNet		56.17 297	51.95 306	68.84 268	80.60 181	53.07 125	55.03 353	70.02 332	44.72 321	51.00 292	61.19 347	22.83 316	78.88 305	28.54 339	53.63 307	74.57 327

OpenMVS_ROB	53.19 17	59.20 273	56.00 284	68.83 269	71.13 312	44.30 293	83.64 174	75.02 294	46.42 310	46.48 318	73.03 300	18.69 339	88.14 168	27.74 343	61.80 240	74.05 330

Patchmatch-test		53.33 311	48.17 318	68.81 270	73.31 285	42.38 315	42.98 362	58.23 354	32.53 357	38.79 344	70.77 319	39.66 171	73.51 339	25.18 350	52.06 316	90.55 78

pm-mvs1		64.12 237	62.56 235	68.78 271	71.68 303	38.87 332	82.89 199	81.57 185	55.54 244	53.89 273	77.82 248	37.73 188	86.74 209	48.46 257	53.49 310	80.72 269

miper_lstm_enhance		63.91 238	62.30 237	68.75 272	75.06 266	46.78 260	69.02 326	81.14 194	59.68 161	52.76 281	72.39 308	40.71 159	77.99 314	56.81 204	53.09 313	81.48 253

OMC-MVS		65.97 229	65.06 220	68.71 273	72.97 290	42.58 313	78.61 269	75.35 292	54.72 252	59.31 203	86.25 155	33.30 245	77.88 316	57.99 189	67.05 198	85.66 185

DP-MVS		59.24 272	56.12 283	68.63 274	88.24 34	50.35 187	82.51 208	64.43 345	41.10 338	46.70 316	78.77 241	24.75 308	88.57 154	22.26 355	56.29 287	66.96 353

tfpnnormal		61.47 261	59.09 264	68.62 275	76.29 251	41.69 317	81.14 239	85.16 111	54.48 256	51.32 290	73.63 295	32.32 254	86.89 207	21.78 357	55.71 295	77.29 305

UniMVSNet_ETH3D		62.51 253	60.49 254	68.57 276	68.30 330	40.88 326	73.89 296	79.93 211	51.81 278	54.77 262	79.61 231	24.80 307	81.10 283	49.93 246	61.35 242	83.73 218

CL-MVSNet_self_test		62.98 248	61.14 248	68.50 277	65.86 339	42.96 306	84.37 153	82.98 165	60.98 139	53.95 272	72.70 304	40.43 162	83.71 266	41.10 289	47.93 325	78.83 285

ACMH+	54.58 15	58.55 284	55.24 287	68.50 277	74.68 272	45.80 278	80.27 250	70.21 331	47.15 303	42.77 330	75.48 282	16.73 349	85.98 234	35.10 315	54.78 301	73.72 332

lessismore_v0				67.98 279	64.76 347	41.25 322		45.75 366		36.03 350	65.63 338	19.29 337	84.11 261	35.67 307	21.24 369	78.59 289

bld_raw_dy_0_64		59.75 268	57.01 277	67.96 280	66.73 336	45.30 282	77.59 275	59.97 353	50.49 284	47.15 313	77.03 260	17.45 345	79.06 303	56.92 203	59.76 251	79.51 282

K. test v3		54.04 307	49.42 315	67.92 281	68.55 326	42.57 314	75.51 287	63.07 348	52.07 273	39.21 341	64.59 340	19.34 336	82.21 275	37.11 300	25.31 366	78.97 283

pmmvs5		62.80 251	61.18 247	67.66 282	69.53 321	42.37 316	82.65 203	75.19 293	54.30 259	52.03 287	78.51 243	31.64 263	80.67 287	48.60 255	58.15 267	79.95 279

PatchT		56.60 293	52.97 300	67.48 283	72.94 291	46.16 273	57.30 351	73.78 305	38.77 342	54.37 268	57.26 357	37.52 193	78.06 311	32.02 325	52.79 314	78.23 297

Patchmtry		56.56 294	52.95 301	67.42 284	72.53 296	50.59 178	59.05 348	71.72 319	37.86 346	46.92 314	65.86 336	38.94 175	80.06 296	36.94 303	46.72 334	71.60 344

SixPastTwentyTwo		54.37 304	50.10 311	67.21 285	70.70 314	41.46 321	74.73 292	64.69 344	47.56 300	39.12 342	69.49 324	18.49 341	84.69 258	31.87 326	34.20 359	75.48 320

pmmvs6		59.64 269	57.15 274	67.09 286	66.01 337	36.86 340	80.50 247	78.64 241	45.05 320	49.05 302	73.94 290	27.28 291	86.10 228	43.96 280	49.94 320	78.31 294

testdata				67.08 287	77.59 231	45.46 281		69.20 335	44.47 323	71.50 80	88.34 121	31.21 265	70.76 350	52.20 236	75.88 128	85.03 195

CNLPA		60.59 265	58.44 268	67.05 288	79.21 200	47.26 255	79.75 260	64.34 346	42.46 336	51.90 288	83.94 179	27.79 289	75.41 329	37.12 299	59.49 254	78.47 290

KD-MVS_2432*1600		59.04 277	56.44 280	66.86 289	79.07 202	45.87 276	72.13 311	80.42 204	55.03 248	48.15 305	71.01 316	36.73 208	78.05 312	35.21 311	30.18 364	76.67 309

miper_refine_blended		59.04 277	56.44 280	66.86 289	79.07 202	45.87 276	72.13 311	80.42 204	55.03 248	48.15 305	71.01 316	36.73 208	78.05 312	35.21 311	30.18 364	76.67 309

TAPA-MVS	56.12 14	61.82 260	60.18 257	66.71 291	78.48 220	37.97 336	75.19 290	76.41 283	46.82 306	57.04 244	86.52 153	27.67 290	77.03 322	26.50 348	67.02 199	85.14 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_0402		56.45 295	53.03 299	66.69 292	76.78 244	50.31 190	81.76 223	69.61 333	42.79 334	43.88 323	72.13 311	22.82 318	86.46 218	16.57 366	50.94 318	63.31 359

PLC	52.38 18	60.89 263	58.97 266	66.68 293	81.77 149	45.70 279	78.96 267	74.04 303	43.66 330	47.63 309	83.19 193	23.52 315	77.78 319	37.47 296	60.46 246	76.55 314
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 303	51.44 307	66.51 294	80.60 181	49.56 204	55.03 353	65.44 342	44.72 321	51.00 292	61.19 347	22.83 316	75.41 329	28.54 339	53.63 307	74.57 327

FC-MVSNet-test		67.49 199	67.91 159	66.21 295	76.06 253	33.06 351	80.82 244	87.18 64	64.44 78	54.81 261	82.87 194	50.40 51	82.60 274	48.05 259	66.55 201	82.98 233

JIA-IIPM		52.33 315	47.77 321	66.03 296	71.20 311	46.92 259	40.00 366	76.48 282	37.10 347	46.73 315	37.02 364	32.96 247	77.88 316	35.97 306	52.45 315	73.29 336

LCM-MVSNet-Re		58.82 280	56.54 278	65.68 297	79.31 199	29.09 363	61.39 344	45.79 365	60.73 144	37.65 346	72.47 306	31.42 264	81.08 284	49.66 248	70.41 176	86.87 158

XVG-ACMP-BASELINE		56.03 298	52.85 302	65.58 298	61.91 356	40.95 325	63.36 335	72.43 315	45.20 319	46.02 319	74.09 288	9.20 364	78.12 309	45.13 274	58.27 265	77.66 302

pmmvs-eth3d		55.97 299	52.78 303	65.54 299	61.02 358	46.44 266	75.36 289	67.72 338	49.61 290	43.65 325	67.58 332	21.63 328	77.04 321	44.11 279	44.33 339	73.15 338

MDA-MVSNet_test_wron		53.82 309	49.95 313	65.43 300	70.13 318	49.05 213	72.30 308	71.65 322	44.23 327	31.85 361	63.13 343	23.68 314	74.01 334	33.25 322	39.35 350	73.23 337

YYNet1		53.82 309	49.96 312	65.41 301	70.09 319	48.95 217	72.30 308	71.66 321	44.25 326	31.89 360	63.07 344	23.73 313	73.95 335	33.26 321	39.40 349	73.34 335

PatchMatch-RL		56.66 292	53.75 297	65.37 302	77.91 229	45.28 283	69.78 324	60.38 351	41.35 337	47.57 310	73.73 291	16.83 347	76.91 323	36.99 302	59.21 257	73.92 331

Vis-MVSNet (Re-imp)		65.52 232	65.63 208	65.17 303	77.49 233	30.54 358	75.49 288	77.73 259	59.34 169	52.26 286	86.69 150	49.38 58	80.53 290	37.07 301	75.28 134	84.42 203

FMVSNet5		58.61 282	56.45 279	65.10 304	77.20 240	39.74 328	74.77 291	77.12 270	50.27 287	43.28 328	67.71 331	26.15 299	76.90 324	36.78 304	54.78 301	78.65 288

EPNet_dtu		66.25 225	66.71 185	64.87 305	78.66 214	34.12 346	82.80 200	75.51 289	61.75 124	64.47 147	86.90 146	37.06 202	72.46 344	43.65 281	69.63 183	88.02 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		57.56 288	55.15 289	64.79 306	64.57 348	33.12 350	73.17 302	83.87 146	58.98 184	41.75 334	70.03 323	22.54 319	79.92 297	46.12 272	35.31 353	81.32 262

LS3D		56.40 296	53.82 296	64.12 307	81.12 167	45.69 280	73.42 300	66.14 341	35.30 356	43.24 329	79.88 228	22.18 325	79.62 301	19.10 363	64.00 217	67.05 352

UnsupCasMVSNet_bld		53.86 308	50.53 310	63.84 308	63.52 352	34.75 344	71.38 316	81.92 180	46.53 307	38.95 343	57.93 355	20.55 332	80.20 295	39.91 292	34.09 360	76.57 313

USDC		54.36 305	51.23 308	63.76 309	64.29 349	37.71 337	62.84 340	73.48 311	56.85 223	35.47 351	71.94 314	9.23 363	78.43 306	38.43 295	48.57 322	75.13 324

Anonymous20231206		59.08 276	57.59 271	63.55 310	68.77 325	32.14 356	80.26 251	79.78 214	50.00 288	49.39 300	72.39 308	26.64 296	78.36 307	33.12 323	57.94 272	80.14 277

CMPMVS	40.41 21	55.34 301	52.64 304	63.46 311	60.88 359	43.84 298	61.58 343	71.06 326	30.43 359	36.33 348	74.63 287	24.14 310	75.44 328	48.05 259	66.62 200	71.12 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-0		52.39 314	48.73 316	63.35 312	65.21 343	38.42 334	68.54 328	64.95 343	38.19 343	39.57 340	71.43 315	13.23 357	79.92 297	37.16 298	40.32 348	71.72 343

MDA-MVSNet-bldmvs		51.56 317	47.75 322	63.00 313	71.60 305	47.32 254	69.70 325	72.12 317	43.81 329	27.65 365	63.38 342	21.97 327	75.96 326	27.30 345	32.19 361	65.70 356

F-COLMAP		55.96 300	53.65 298	62.87 314	72.76 293	42.77 310	74.70 293	70.37 330	40.03 339	41.11 337	79.36 233	17.77 343	73.70 338	32.80 324	53.96 306	72.15 340

test0.0.03 1		62.54 252	62.44 236	62.86 315	72.28 300	29.51 360	82.93 198	78.78 237	59.18 176	53.07 279	82.41 205	36.91 205	77.39 320	37.45 297	58.96 258	81.66 248

CVMVSNet		60.85 264	60.44 255	62.07 316	75.00 268	32.73 353	79.54 261	73.49 309	36.98 348	56.28 254	83.74 183	29.28 279	69.53 353	46.48 268	63.23 227	83.94 215

ambc				62.06 317	53.98 366	29.38 361	35.08 368	79.65 218		41.37 335	59.96 350	6.27 371	82.15 276	35.34 310	38.22 351	74.65 326

PEN-MVS		58.35 286	57.15 274	61.94 318	67.55 334	34.39 345	77.01 277	78.35 248	51.87 276	47.72 308	76.73 267	33.91 239	73.75 337	34.03 318	47.17 330	77.68 301

MVS-HIRNet		49.01 321	44.71 325	61.92 319	76.06 253	46.61 263	63.23 337	54.90 358	24.77 363	33.56 356	36.60 366	21.28 330	75.88 327	29.49 333	62.54 236	63.26 360

LTVRE_ROB	45.45 19	52.73 312	49.74 314	61.69 320	69.78 320	34.99 343	44.52 360	67.60 339	43.11 333	43.79 324	74.03 289	18.54 340	81.45 281	28.39 341	57.94 272	68.62 350
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
WR-MVS_H		58.91 279	58.04 269	61.54 321	69.07 323	33.83 348	76.91 278	81.99 177	51.40 280	48.17 304	74.67 286	40.23 164	74.15 333	31.78 327	48.10 323	76.64 312

CP-MVSNet		58.54 285	57.57 272	61.46 322	68.50 327	33.96 347	76.90 279	78.60 244	51.67 279	47.83 307	76.60 269	34.99 231	72.79 342	35.45 308	47.58 326	77.64 303

MVS_0304		56.72 291	55.17 288	61.37 323	70.71 313	36.80 341	75.74 282	68.75 336	44.11 328	52.53 282	68.20 330	15.05 354	74.53 332	42.98 284	58.44 263	72.79 339

PS-CasMVS		58.12 287	57.03 276	61.37 323	68.24 331	33.80 349	76.73 280	78.01 253	51.20 281	47.54 311	76.20 277	32.85 248	72.76 343	35.17 313	47.37 328	77.55 304

Anonymous20240521		51.65 316	48.42 317	61.34 325	56.43 364	39.65 330	73.57 298	73.47 312	36.64 350	36.59 347	63.98 341	10.75 360	72.25 346	35.35 309	49.01 321	72.11 341

CHOSEN 280x420		57.53 289	56.38 282	60.97 326	74.01 279	48.10 244	46.30 359	54.31 359	48.18 297	50.88 295	77.43 254	38.37 181	59.16 362	54.83 214	63.14 231	75.66 319

DTE-MVSNet		57.03 290	55.73 286	60.95 327	65.94 338	32.57 354	75.71 283	77.09 271	51.16 282	46.65 317	76.34 272	32.84 249	73.22 341	30.94 331	44.87 338	77.06 306

IterMVS-SCA-FT		59.12 274	58.81 267	60.08 328	70.68 316	45.07 285	80.42 249	74.25 300	43.54 331	50.02 298	73.73 291	31.97 258	56.74 363	51.06 242	53.60 309	78.42 292

COLMAP_ROB	43.60 20	50.90 318	48.05 319	59.47 329	67.81 333	40.57 327	71.25 317	62.72 350	36.49 351	36.19 349	73.51 296	13.48 356	73.92 336	20.71 359	50.26 319	63.92 358
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		54.25 306	52.57 305	59.29 330	62.76 354	21.65 372	72.21 310	70.47 329	53.25 265	41.94 332	77.33 255	14.28 355	77.95 315	29.18 335	51.72 317	78.28 295

TinyColmap		48.15 323	44.49 327	59.13 331	65.73 340	38.04 335	63.34 336	62.86 349	38.78 341	29.48 363	67.23 334	6.46 370	73.30 340	24.59 351	41.90 345	66.04 354

test20.03		55.22 302	54.07 295	58.68 332	63.14 353	25.00 367	77.69 274	74.78 295	52.64 268	43.43 326	72.39 308	26.21 298	74.76 331	29.31 334	47.05 332	76.28 316

EU-MVSNet		52.63 313	50.72 309	58.37 333	62.69 355	28.13 365	72.60 304	75.97 286	30.94 358	40.76 339	72.11 312	20.16 333	70.80 349	35.11 314	46.11 335	76.19 317

MIMVSNet1		50.35 319	47.81 320	57.96 334	61.53 357	27.80 366	67.40 329	74.06 302	43.25 332	33.31 359	65.38 339	16.03 351	71.34 347	21.80 356	47.55 327	74.75 325

pmmvs3		45.53 327	41.55 330	57.44 335	48.97 370	39.68 329	70.06 321	57.66 355	28.32 361	34.06 354	57.29 356	8.50 365	66.85 355	34.86 316	34.26 358	65.80 355

KD-MVS_self_test		49.24 320	46.85 323	56.44 336	54.32 365	22.87 369	57.39 350	73.36 313	44.36 325	37.98 345	59.30 353	18.97 338	71.17 348	33.48 319	42.44 343	75.26 322

PM-MVS		46.92 325	43.76 329	56.41 337	52.18 367	32.26 355	63.21 338	38.18 371	37.99 345	40.78 338	66.20 335	5.09 373	65.42 356	48.19 258	41.99 344	71.54 345

AllTest		47.32 324	44.66 326	55.32 338	65.08 344	37.50 338	62.96 339	54.25 360	35.45 354	33.42 357	72.82 301	9.98 361	59.33 360	24.13 352	43.84 340	69.13 348

TestCases				55.32 338	65.08 344	37.50 338		54.25 360	35.45 354	33.42 357	72.82 301	9.98 361	59.33 360	24.13 352	43.84 340	69.13 348

new-patchmatchnet		48.21 322	46.55 324	53.18 340	57.73 362	18.19 376	70.24 320	71.02 327	45.70 314	33.70 355	60.23 349	18.00 342	69.86 352	27.97 342	34.35 357	71.49 346

ITE_SJBPF				51.84 341	58.03 361	31.94 357		53.57 362	36.67 349	41.32 336	75.23 284	11.17 359	51.57 367	25.81 349	48.04 324	72.02 342

RPSCF		45.77 326	44.13 328	50.68 342	57.67 363	29.66 359	54.92 355	45.25 367	26.69 362	45.92 320	75.92 280	17.43 346	45.70 371	27.44 344	45.95 336	76.67 309

ANet_high		34.39 332	29.59 338	48.78 343	30.34 378	22.28 370	55.53 352	63.79 347	38.11 344	15.47 369	36.56 367	6.94 367	59.98 359	13.93 368	5.64 378	64.08 357

TDRefinement		40.91 329	38.37 332	48.55 344	50.45 368	33.03 352	58.98 349	50.97 363	28.50 360	29.89 362	67.39 333	6.21 372	54.51 364	17.67 365	35.25 354	58.11 361

DSMNet-mixed		38.35 330	35.36 333	47.33 345	48.11 371	14.91 378	37.87 367	36.60 373	19.18 367	34.37 353	59.56 352	15.53 352	53.01 366	20.14 361	46.89 333	74.07 329

N_pmnet		41.25 328	39.77 331	45.66 346	68.50 327	0.82 385	72.51 306	0.38 385	35.61 353	35.26 352	61.51 346	20.07 334	67.74 354	23.51 354	40.63 346	68.42 351

EGC-MVSNET		33.75 333	30.42 337	43.75 347	64.94 346	36.21 342	60.47 347	40.70 370	0.02 379	0.10 380	53.79 359	7.39 366	60.26 358	11.09 370	35.23 355	34.79 368

LCM-MVSNet		28.07 336	23.85 341	40.71 348	27.46 381	18.93 375	30.82 369	46.19 364	12.76 371	16.40 368	34.70 369	1.90 380	48.69 370	20.25 360	24.22 367	54.51 363

FPMVS		35.40 331	33.67 334	40.57 349	46.34 372	28.74 364	41.05 364	57.05 356	20.37 366	22.27 367	53.38 360	6.87 368	44.94 372	8.62 371	47.11 331	48.01 366

new_pmnet		33.56 334	31.89 336	38.59 350	49.01 369	20.42 373	51.01 356	37.92 372	20.58 364	23.45 366	46.79 361	6.66 369	49.28 369	20.00 362	31.57 363	46.09 367

PMMVS2		26.71 338	22.98 342	37.87 351	36.89 375	8.51 382	42.51 363	29.32 379	19.09 368	13.01 371	37.54 363	2.23 378	53.11 365	14.54 367	11.71 371	51.99 364

Gipuma		27.47 337	24.26 340	37.12 352	60.55 360	29.17 362	11.68 373	60.00 352	14.18 370	10.52 374	15.12 375	2.20 379	63.01 357	8.39 372	35.65 352	19.18 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		33.04 335	32.55 335	34.52 353	40.96 373	22.03 371	44.45 361	35.62 374	20.42 365	28.12 364	62.35 345	5.03 374	31.88 377	21.61 358	34.42 356	49.63 365

test_method		24.09 340	21.07 344	33.16 354	27.67 380	8.35 383	26.63 370	35.11 376	3.40 376	14.35 370	36.98 365	3.46 377	35.31 376	19.08 364	22.95 368	55.81 362

PMVS	19.57 22	25.07 339	22.43 343	32.99 355	23.12 382	22.98 368	40.98 365	35.19 375	15.99 369	11.95 373	35.87 368	1.47 382	49.29 368	5.41 376	31.90 362	26.70 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	16.60 23	17.34 344	13.39 347	29.16 356	28.43 379	19.72 374	13.73 372	23.63 380	7.23 375	7.96 375	21.41 371	0.80 383	36.08 375	6.97 373	10.39 372	31.69 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		19.16 341	18.40 345	21.44 357	36.19 376	13.63 379	47.59 357	30.89 377	10.73 372	5.91 377	16.59 373	3.66 376	39.77 373	5.95 375	8.14 373	10.92 373

EMVS		18.42 342	17.66 346	20.71 358	34.13 377	12.64 380	46.94 358	29.94 378	10.46 374	5.58 378	14.93 376	4.23 375	38.83 374	5.24 377	7.51 375	10.67 374

DeepMVS_CX				13.10 359	21.34 383	8.99 381		10.02 383	10.59 373	7.53 376	30.55 370	1.82 381	14.55 378	6.83 374	7.52 374	15.75 372

wuyk23d		9.11 346	8.77 350	10.15 360	40.18 374	16.76 377	20.28 371	1.01 384	2.58 377	2.66 379	0.98 379	0.23 384	12.49 379	4.08 378	6.90 376	1.19 376

tmp_tt		9.44 345	10.68 348	5.73 361	2.49 384	4.21 384	10.48 374	18.04 381	0.34 378	12.59 372	20.49 372	11.39 358	7.03 380	13.84 369	6.46 377	5.95 375

testmvs		6.14 348	8.18 351	0.01 362	0.01 385	0.00 387	73.40 301	0.00 386	0.00 380	0.02 381	0.15 380	0.00 385	0.00 381	0.02 379	0.00 379	0.02 377

test123		6.01 349	8.01 352	0.01 362	0.00 386	0.01 386	71.93 314	0.00 386	0.00 380	0.02 381	0.11 381	0.00 385	0.00 381	0.02 379	0.00 379	0.02 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

cdsmvs_eth3d_5k		18.33 343	24.44 339	0.00 364	0.00 386	0.00 387	0.00 375	89.40 18	0.00 380	0.00 383	92.02 39	38.55 179	0.00 381	0.00 381	0.00 379	0.00 379

pcd_1.5k_mvsjas		3.15 350	4.20 353	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	37.77 185	0.00 381	0.00 381	0.00 379	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

ab-mvs-re		7.68 347	10.24 349	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	92.12 36	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 386	0.00 387	0.00 375	0.00 386	0.00 380	0.00 383	0.00 382	0.00 385	0.00 381	0.00 381	0.00 379	0.00 379

FOURS1					83.24 113	49.90 198	84.98 137	78.76 238	47.71 298	73.42 53

PC_three_1452									66.58 48	87.27 2	93.70 9	66.82 4	94.95 18	89.74 2	91.98 4	93.98 5

test_one_0601					89.39 24	57.29 22		88.09 50	57.21 219	82.06 11	93.39 13	54.94 25

eth-test2					0.00 386

eth-test					0.00 386

ZD-MVS					89.55 16	53.46 107		84.38 131	57.02 221	73.97 47	91.03 59	44.57 111	91.17 75	75.41 59	81.78 75

RE-MVS-def			66.66 187		80.96 171	48.14 242	81.54 230	76.98 272	46.42 310	62.75 170	89.42 102	29.28 279		60.52 166	72.06 163	83.19 228

IU-MVS					89.48 19	57.49 17		91.38 5	66.22 54	88.26 1				82.83 10	87.60 18	92.44 28

test_241102_TWO								88.76 36	57.50 214	83.60 6	94.09 4	56.14 19	96.37 6	82.28 14	87.43 20	92.55 26

test_241102_ONE					89.48 19	56.89 27		88.94 28	57.53 212	84.61 4	93.29 16	58.81 12	96.45 1

9.14			78.19 25		85.67 59		88.32 54	88.84 33	59.89 156	74.58 43	92.62 27	46.80 79	92.66 43	81.40 23	85.62 41

save fliter					85.35 70	56.34 38	89.31 39	81.46 187	61.55 127

test_0728_THIRD									58.00 199	81.91 12	93.64 11	56.54 16	96.44 2	81.64 19	86.86 25	92.23 33

test0726					89.40 22	57.45 19	92.32 7	88.63 39	57.71 208	83.14 9	93.96 7	55.17 21

GSMVS																88.13 137

test_part2					89.33 25	55.48 53				82.27 10

sam_mvs1												38.86 177				88.13 137

sam_mvs												35.99 221

MTGPA								81.31 190

test_post1							70.84 319				14.72 377	34.33 235	83.86 262	48.80 253

test_post											16.22 374	37.52 193	84.72 257

patchmatchnet-post											59.74 351	38.41 180	79.91 299

MTMP							87.27 78	15.34 382

gm-plane-assit					83.24 113	54.21 90			70.91 12		88.23 125		95.25 15	66.37 115

test9_res														78.72 35	85.44 44	91.39 57

TEST9					85.68 57	55.42 54	87.59 67	84.00 142	57.72 207	72.99 57	90.98 61	44.87 106	88.58 151

test_8					85.72 56	55.31 58	87.60 64	83.88 145	57.84 205	72.84 61	90.99 60	44.99 103	88.34 161

agg_prior2														75.65 54	85.11 48	91.01 68

agg_prior					85.64 60	54.92 72		83.61 152		72.53 65			88.10 171

test_prior4						56.39 37	87.15 81

test_prior2							89.04 43		61.88 122	73.55 50	91.46 55	48.01 66		74.73 63	85.46 42

旧先验2							81.73 224		45.53 316	74.66 39			70.48 351	58.31 186

新几何2							81.61 228

旧先验1					81.57 157	47.48 250		71.83 318			88.66 116	36.94 204			78.34 110	88.67 126

无先验							85.19 125	78.00 254	49.08 292				85.13 251	52.78 230		87.45 150

原ACMM2							83.77 172

test222					79.36 196	50.97 172	77.99 272	67.84 337	42.54 335	62.84 169	86.53 152	30.26 272			76.91 120	85.23 192

testdata2													77.81 318	45.64 273

segment_acmp												44.97 105

testdata1							77.55 276		64.14 82

plane_prior7					77.95 226	48.46 235

plane_prior6					78.42 221	49.39 208						36.04 219

plane_prior5								82.59 170					88.30 164	65.46 124	72.34 160	84.49 201

plane_prior4											83.28 191

plane_prior3						48.95 217			64.01 85	62.15 175

plane_prior2							85.76 109		63.60 95

plane_prior1					78.31 223

plane_prior						49.57 202	87.43 71		64.57 77						72.84 156

n2								0.00 386

nn								0.00 386

door-mid								41.31 369

test11								84.25 136

door								43.27 368

HQP5-MVS						51.56 160

HQP-NCC					79.02 204		88.00 57		65.45 64	64.48 144

ACMP_Plane					79.02 204		88.00 57		65.45 64	64.48 144

BP-MVS														66.70 112

HQP4-MVS										64.47 147			88.61 150			84.91 198

HQP3-MVS								83.68 148							73.12 152

HQP2-MVS												37.35 196

NP-MVS					78.76 209	50.43 182					85.12 167

MDTV_nov1_ep13_2view						43.62 300	71.13 318		54.95 250	59.29 204		36.76 207		46.33 270		87.32 152

MDTV_nov1_ep13			61.56 243		81.68 150	55.12 65	72.41 307	78.18 250	59.19 174	58.85 213	69.29 326	34.69 232	86.16 225	36.76 305	62.96 233

ACMMP++_ref															63.20 229

ACMMP++															59.38 255

Test By Simon												39.38 172

High-resolution multi-view benchmark