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.

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 57	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 116	52.71 127	85.04 131	88.63 37	66.08 53	86.77 2	92.75 21	72.05 1	91.46 66	83.35 6	93.53 1	92.23 29
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
MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 26	92.77 2	86.30 79	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
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
DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 178	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 61	93.25 2	94.80 1

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 14	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 26	92.48 3	88.94 27	57.50 201	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 80	85.46 62	49.56 198	90.99 20	86.66 73	70.58 14	80.07 17	95.30 1	56.18 15	90.97 77	82.57 10	86.22 32	93.28 10

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 85	91.60 13	86.47 75	73.13 5	79.89 18	93.10 15	49.88 51	92.98 31	84.09 5	84.75 47	93.08 14

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 18	92.34 5	89.99 14	57.71 195	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21
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
HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 58	89.93 29	87.55 59	66.04 56	79.46 19	93.00 19	53.10 27	91.76 59	80.40 20	89.56 7	92.68 22

CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 15	89.29 40	91.54 3	59.19 161	71.82 67	90.05 84	59.72 6	96.04 7	78.37 28	88.40 12	93.75 5

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 63	88.70 49	87.92 50	55.55 229	81.21 12	93.69 9	56.51 14	94.27 21	78.36 29	85.70 36	91.51 49

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 56	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 175	92.50 42	82.75 8	86.25 31	91.57 46

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 72	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 180	92.28 49	82.73 9	85.71 35	91.57 46

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 24	91.38 15	88.17 46	67.71 36	73.81 44	92.75 21	46.88 73	93.28 28	78.79 26	84.07 53	91.50 50

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 79	55.87 43	87.58 67	86.76 70	61.48 121	80.26 16	93.10 15	46.53 78	92.41 45	79.97 21	88.77 9	92.08 33
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
ETH3D-3000-0.1		78.73 18	78.71 18	78.78 52	85.58 56	52.40 135	88.42 53	89.03 24	60.01 142	76.06 29	92.80 20	48.34 57	92.88 33	81.66 15	86.48 29	91.04 62

EPNet		78.36 23	78.49 19	77.97 78	85.49 59	52.04 142	89.36 37	84.07 135	73.22 4	77.03 25	91.72 43	49.32 55	90.17 100	73.46 67	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 25	78.26 20	78.48 61	81.33 158	56.31 38	81.59 217	86.41 76	69.61 21	81.72 11	88.16 118	55.09 19	88.04 160	74.12 60	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 80	89.76 33	87.77 54	55.91 224	78.56 20	92.49 25	48.20 59	92.65 40	79.49 22	83.04 57	90.39 77

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 85	52.68 128	86.70 88	89.02 25	59.13 167	75.37 31	92.49 25	49.06 56	93.20 29	80.67 19	87.08 21	90.71 70

9.14			78.19 22		85.67 53		88.32 54	88.84 31	59.89 144	74.58 39	92.62 24	46.80 74	92.66 39	81.40 17	85.62 37

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 114	55.24 55	91.57 14	84.82 116	69.12 23	76.67 26	92.02 36	44.82 102	90.23 98	80.83 18	80.09 84	92.08 33

alignmvs		78.08 28	77.98 25	78.39 66	83.53 97	53.22 114	89.77 32	85.45 92	66.11 51	76.59 28	91.99 38	54.07 24	89.05 121	77.34 39	77.00 108	92.89 18

CS-MVS		78.19 26	77.97 26	78.82 47	83.52 98	53.08 119	89.10 42	86.30 79	68.01 32	73.57 46	91.26 52	47.28 69	92.35 47	78.21 30	84.51 50	91.05 61

VNet		77.99 30	77.92 27	78.19 71	87.43 35	50.12 188	90.93 21	91.41 4	67.48 39	75.12 32	90.15 82	46.77 75	91.00 75	73.52 66	78.46 98	93.44 7

canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 81	54.22 84	87.71 60	84.57 123	67.70 37	77.70 22	92.11 35	50.90 42	89.95 103	78.18 33	77.54 103	93.20 12

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 23	55.12 60	89.95 28	85.98 86	68.31 27	71.33 73	92.75 21	45.52 92	90.37 91	71.15 78	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		77.82 31	77.59 30	78.49 60	85.25 68	50.27 185	90.02 26	90.57 9	56.58 218	74.26 41	91.60 46	54.26 21	92.16 52	75.87 45	79.91 88	93.05 15

WTY-MVS		77.47 36	77.52 31	77.30 93	88.33 26	46.25 259	88.46 52	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 107	75.42 122	93.28 10

Regformer-1		77.80 32	77.44 32	78.88 44	87.78 32	52.44 134	87.60 62	90.08 12	68.86 24	72.49 62	91.79 40	47.69 66	94.90 16	73.57 65	77.05 105	89.31 98

SF-MVS		77.64 33	77.42 33	78.32 69	83.75 95	52.47 133	86.63 90	87.80 51	58.78 175	74.63 36	92.38 27	47.75 64	91.35 68	78.18 33	86.85 24	91.15 58

test_prior3		77.59 34	77.33 34	78.39 66	86.35 44	54.91 69	89.04 43	85.45 92	61.88 112	73.55 47	91.46 50	48.01 62	89.70 109	74.73 55	85.46 38	90.55 72

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 92	74.63 36	90.83 63	41.38 144	94.40 19	75.42 51	79.90 89	94.72 2

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 66	50.69 169	88.57 51	88.59 41	58.14 184	73.60 45	93.31 12	43.14 125	93.79 25	73.81 62	88.53 11	92.37 27

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 43	64.83 70	73.52 49	88.09 119	48.07 60	92.19 51	62.24 137	84.53 49	91.53 48

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 176	54.66 78	87.06 79	85.88 87	72.11 8	71.57 70	88.63 112	50.89 44	90.35 92	76.00 44	79.11 93	91.63 43

Regformer-2		77.15 40	76.82 39	78.14 72	87.78 32	51.84 148	87.60 62	89.12 21	67.23 40	71.93 66	91.79 40	46.03 84	93.53 27	72.85 71	77.05 105	89.05 107

ETV-MVS		77.17 39	76.74 40	78.48 61	81.80 140	54.55 80	86.13 99	85.33 98	68.20 29	73.10 52	90.52 71	45.23 95	90.66 84	79.37 23	80.95 72	90.22 81

xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 64	56.34 36	89.31 38	72.84 298	61.55 117	74.63 36	92.38 27	47.75 64	91.35 68	78.18 33	86.85 24	91.15 58

PVSNet_Blended		76.53 51	76.54 41	76.50 111	85.91 48	51.83 149	88.89 46	84.24 132	67.82 34	69.09 84	89.33 99	46.70 76	88.13 156	75.43 49	81.48 71	89.55 94

testtj		76.96 43	76.48 43	78.40 65	89.89 9	53.67 95	88.72 48	86.15 83	54.56 242	74.86 34	92.31 30	44.38 107	91.97 57	75.19 53	82.24 63	89.54 95

jason		77.01 42	76.45 44	78.69 55	79.69 181	54.74 72	90.56 24	83.99 138	68.26 28	74.10 42	90.91 60	42.14 133	89.99 102	79.30 24	79.12 92	91.36 54
jason: jason.
train_agg		76.91 44	76.40 45	78.45 63	85.68 51	55.42 49	87.59 65	84.00 136	57.84 192	72.99 53	90.98 56	44.99 97	88.58 138	78.19 31	85.32 41	91.34 56

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 161	55.31 53	89.76 33	86.91 66	62.94 96	71.65 68	91.56 47	42.33 129	92.56 41	77.14 40	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 169	55.02 64	86.39 93	86.71 71	66.96 43	67.91 93	89.97 86	48.03 61	91.41 67	75.60 48	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		76.68 50	76.24 48	78.00 76	85.64 54	54.92 67	87.55 68	83.61 145	57.99 188	72.53 60	91.05 53	45.36 93	88.10 158	77.76 37	84.68 48	90.99 65

baseline		76.86 47	76.24 48	78.71 54	80.47 175	54.20 87	83.90 158	84.88 115	71.38 11	71.51 71	89.15 102	50.51 45	90.55 88	75.71 46	78.65 96	91.39 52

PAPM		76.76 48	76.07 50	78.81 48	80.20 177	59.11 7	86.86 86	86.23 81	68.60 25	70.18 81	88.84 107	51.57 36	87.16 181	65.48 114	86.68 26	90.15 84

APD-MVS		76.15 56	75.68 51	77.54 87	88.52 23	53.44 105	87.26 76	85.03 111	53.79 246	74.91 33	91.68 45	43.80 112	90.31 94	74.36 58	81.82 67	88.87 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		76.43 52	75.66 52	78.73 53	81.92 138	54.67 77	84.06 153	85.35 97	61.10 126	72.99 53	91.50 48	40.25 153	91.00 75	76.84 41	86.98 22	90.51 76

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 76	89.14 41	89.11 22	62.95 95	70.54 79	92.33 29	41.05 145	94.95 15	57.90 179	86.55 28	91.00 64
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
MVS		76.91 44	75.48 54	81.23 16	84.56 77	55.21 57	80.23 240	91.64 2	58.65 177	65.37 119	91.48 49	45.72 90	95.05 14	72.11 75	89.52 8	93.44 7

Regformer-3		76.02 59	75.47 55	77.70 83	85.49 59	51.47 157	85.12 127	90.19 11	68.52 26	69.36 82	90.66 65	46.45 79	94.81 17	70.25 83	73.16 139	86.81 152

CLD-MVS		75.60 65	75.39 56	76.24 116	80.69 170	52.40 135	90.69 22	86.20 82	74.40 3	65.01 125	88.93 104	42.05 135	90.58 87	76.57 42	73.96 133	85.73 170
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 66	56.47 33	88.15 55	84.97 112	65.15 68	66.06 111	89.88 87	43.79 113	92.16 52	75.03 54	80.03 87	89.64 93

CDPH-MVS		76.05 58	75.19 58	78.62 58	86.51 43	54.98 66	87.32 71	84.59 122	58.62 178	70.75 77	90.85 62	43.10 127	90.63 86	70.50 81	84.51 50	90.24 80

EIA-MVS		75.92 60	75.18 59	78.13 73	85.14 69	51.60 153	87.17 77	85.32 99	64.69 71	68.56 87	90.53 70	45.79 89	91.58 62	67.21 99	82.18 65	91.20 57

MP-MVS-pluss		75.54 66	75.03 60	77.04 99	81.37 157	52.65 130	84.34 145	84.46 124	61.16 124	69.14 83	91.76 42	39.98 159	88.99 127	78.19 31	84.89 46	89.48 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		75.82 64	75.02 61	78.23 70	83.88 93	53.80 91	86.91 85	86.05 85	59.71 146	67.85 94	90.55 69	42.23 131	91.02 74	72.66 73	85.29 42	89.87 89

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 84	53.33 111	91.13 19	85.88 87	65.33 64	72.37 63	89.34 97	32.52 239	92.76 37	77.90 36	75.96 116	92.22 31

MVS_Test		75.85 61	74.93 63	78.62 58	84.08 87	55.20 58	83.99 156	85.17 106	68.07 30	73.38 50	82.76 183	50.44 46	89.00 125	65.90 110	80.61 77	91.64 42

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 63	56.90 25	85.75 108	82.45 164	56.79 213	74.48 40	91.81 39	43.72 116	90.75 82	74.61 57	78.65 96	92.91 17
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
test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 189	71.19 75	89.20 100	42.03 136	92.77 35	69.41 85	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 189	71.19 75	89.20 100	42.03 136	92.77 35	69.41 85	75.07 127	92.01 36

diffmvs		75.11 72	74.65 67	76.46 113	78.52 205	53.35 109	83.28 178	79.94 200	70.51 15	71.64 69	88.72 108	46.02 85	86.08 214	77.52 38	75.75 120	89.96 87

Regformer-4		75.06 73	74.59 68	76.47 112	85.49 59	50.33 181	85.12 127	88.61 39	66.42 45	68.48 88	90.66 65	44.15 108	92.68 38	69.24 87	73.16 139	86.39 159

baseline2		75.15 71	74.54 69	76.98 103	81.67 143	51.74 151	83.84 159	91.94 1	69.97 18	58.98 195	86.02 146	59.73 5	91.73 60	68.37 92	70.40 165	87.48 137

MP-MVS		74.99 74	74.33 70	76.95 104	82.89 120	53.05 121	85.63 112	83.50 147	57.86 191	67.25 98	90.24 77	43.38 122	88.85 133	76.03 43	82.23 64	88.96 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PAPR		75.20 70	74.13 71	78.41 64	88.31 27	55.10 62	84.31 146	85.66 90	63.76 83	67.55 95	90.73 64	43.48 121	89.40 115	66.36 106	77.03 107	90.73 69

ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 56	84.52 78	55.59 45	88.92 45	89.21 20	68.06 31	53.13 264	90.22 78	49.71 52	87.62 172	72.12 74	70.82 162	92.82 19

CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 110	62.84 2	85.73 110	85.39 95	69.79 19	64.87 127	83.49 173	41.52 143	93.69 26	70.55 80	81.82 67	92.12 32

GST-MVS		74.87 75	73.90 74	77.77 81	83.30 104	53.45 104	85.75 108	85.29 101	59.22 160	66.50 106	89.85 88	40.94 146	90.76 81	70.94 79	83.35 56	89.10 106

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 23	82.86 187	87.96 49	72.59 6	67.29 97	87.79 124	51.61 35	91.52 64	54.75 201	72.63 147	92.29 28

#test#		74.86 76	73.78 76	78.10 74	84.30 81	53.68 93	86.95 82	84.36 126	59.00 171	65.78 114	90.56 67	40.70 150	90.90 78	71.48 76	80.88 73	89.71 90

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 138	57.42 20	85.21 121	76.71 265	60.68 135	73.32 51	89.34 97	47.30 68	91.63 61	68.28 93	79.72 90	91.42 51

PVSNet_BlendedMVS		73.42 92	73.30 78	73.76 169	85.91 48	51.83 149	86.18 98	84.24 132	65.40 61	69.09 84	80.86 209	46.70 76	88.13 156	75.43 49	65.92 195	81.33 243

CANet_DTU		73.71 89	73.14 79	75.40 133	82.61 130	50.05 189	84.67 140	79.36 215	69.72 20	75.39 30	90.03 85	29.41 263	85.93 220	67.99 95	79.11 93	90.22 81

HY-MVS	67.03 5	73.90 84	73.14 79	76.18 119	84.70 76	47.36 243	75.56 270	86.36 78	66.27 48	70.66 78	83.91 166	51.05 40	89.31 116	67.10 100	72.61 148	91.88 39

HFP-MVS		74.37 78	73.13 81	78.10 74	84.30 81	53.68 93	85.58 113	84.36 126	56.82 211	65.78 114	90.56 67	40.70 150	90.90 78	69.18 88	80.88 73	89.71 90

zzz-MVS		74.15 82	73.11 82	77.27 95	81.54 150	53.57 97	84.02 155	81.31 180	59.41 153	68.39 89	90.96 58	36.07 207	89.01 123	73.80 63	82.45 61	89.23 100

ACMMPR		73.76 87	72.61 83	77.24 98	83.92 91	52.96 124	85.58 113	84.29 128	56.82 211	65.12 121	90.45 72	37.24 190	90.18 99	69.18 88	80.84 75	88.58 118

EI-MVSNet-Vis-set		73.19 95	72.60 84	74.99 142	82.56 131	49.80 194	82.55 194	89.00 26	66.17 50	65.89 113	88.98 103	43.83 111	92.29 48	65.38 121	69.01 173	82.87 221

region2R		73.75 88	72.55 85	77.33 92	83.90 92	52.98 123	85.54 116	84.09 134	56.83 210	65.10 122	90.45 72	37.34 188	90.24 97	68.89 90	80.83 76	88.77 114

3Dnovator	64.70 6	74.46 77	72.48 86	80.41 20	82.84 122	55.40 52	83.08 181	88.61 39	67.61 38	59.85 179	88.66 109	34.57 220	93.97 22	58.42 170	88.70 10	91.85 40

PVSNet_Blended_VisFu		73.40 93	72.44 87	76.30 114	81.32 159	54.70 75	85.81 104	78.82 225	63.70 84	64.53 131	85.38 154	47.11 72	87.38 178	67.75 96	77.55 102	86.81 152

TESTMET0.1,1		72.86 98	72.33 88	74.46 149	81.98 137	50.77 167	85.13 124	85.47 91	66.09 52	67.30 96	83.69 171	37.27 189	83.57 251	65.06 122	78.97 95	89.05 107

MVSTER		73.25 94	72.33 88	76.01 124	85.54 58	53.76 92	83.52 164	87.16 62	67.06 42	63.88 144	81.66 201	52.77 28	90.44 89	64.66 124	64.69 200	83.84 203

CostFormer		73.89 85	72.30 90	78.66 57	82.36 134	56.58 29	75.56 270	85.30 100	66.06 54	70.50 80	76.88 249	57.02 12	89.06 119	68.27 94	68.74 175	90.33 79

MSLP-MVS++		74.21 80	72.25 91	80.11 27	81.45 155	56.47 33	86.32 95	79.65 208	58.19 183	66.36 107	92.29 31	36.11 206	90.66 84	67.39 97	82.49 60	93.18 13

thisisatest0515		73.64 90	72.20 92	77.97 78	81.63 144	53.01 122	86.69 89	88.81 32	62.53 101	64.06 138	85.65 150	52.15 33	92.50 42	58.43 168	69.84 168	88.39 122

MVSFormer		73.53 91	72.19 93	77.57 86	83.02 114	55.24 55	81.63 214	81.44 178	50.28 269	76.67 26	90.91 60	44.82 102	86.11 208	60.83 148	80.09 84	91.36 54

test_part1		73.80 86	72.13 94	78.79 51	85.92 47	58.26 10	90.60 23	86.85 68	63.98 78	63.95 141	81.54 203	52.08 34	92.24 50	64.93 123	59.32 239	85.87 168

VDDNet		74.37 78	72.13 94	81.09 17	79.58 182	56.52 32	90.02 26	86.70 72	52.61 255	71.23 74	87.20 131	31.75 249	93.96 23	74.30 59	75.77 119	92.79 20

baseline1		72.51 105	72.12 96	73.69 171	85.05 70	44.46 276	83.51 168	86.13 84	71.61 9	64.64 129	87.97 122	55.00 20	89.48 114	59.07 163	56.05 273	87.13 144

API-MVS		74.17 81	72.07 97	80.49 19	90.02 8	58.55 9	87.30 73	84.27 129	57.51 200	65.77 116	87.77 126	41.61 142	95.97 8	51.71 221	82.63 59	86.94 145

PMMVS		72.98 96	72.05 98	75.78 128	83.57 96	48.60 218	84.08 151	82.85 160	61.62 116	68.24 91	90.33 76	28.35 267	87.78 168	72.71 72	76.69 110	90.95 66

IB-MVS	68.87 2	74.01 83	72.03 99	79.94 29	83.04 113	55.50 47	90.24 25	88.65 35	67.14 41	61.38 167	81.74 200	53.21 26	94.28 20	60.45 156	62.41 222	90.03 86
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
EI-MVSNet-UG-set		72.37 106	71.73 100	74.29 155	81.60 146	49.29 204	81.85 209	88.64 36	65.29 66	65.05 123	88.29 115	43.18 123	91.83 58	63.74 128	67.97 180	81.75 231

XVS		72.92 97	71.62 101	76.81 106	83.41 99	52.48 131	84.88 136	83.20 153	58.03 185	63.91 142	89.63 92	35.50 213	89.78 106	65.50 112	80.50 79	88.16 123

nrg030		72.27 111	71.56 102	74.42 151	75.93 242	50.60 171	86.97 81	83.21 152	62.75 98	67.15 99	84.38 161	50.07 48	86.66 195	71.19 77	62.37 223	85.99 163

HPM-MVS		72.60 102	71.50 103	75.89 126	82.02 136	51.42 159	80.70 234	83.05 155	56.12 223	64.03 139	89.53 93	37.55 182	88.37 146	70.48 82	80.04 86	87.88 130
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 104	71.46 104	76.00 125	82.93 119	52.32 139	86.93 84	82.48 163	55.15 233	63.65 147	90.44 75	35.03 217	88.53 142	68.69 91	77.83 101	87.15 143

HQP-MVS		72.34 107	71.44 105	75.03 140	79.02 191	51.56 154	88.00 56	83.68 142	65.45 58	64.48 132	85.13 155	37.35 186	88.62 136	66.70 102	73.12 141	84.91 184

VPNet		72.07 112	71.42 106	74.04 160	78.64 202	47.17 247	89.91 31	87.97 48	72.56 7	64.66 128	85.04 157	41.83 140	88.33 149	61.17 145	60.97 229	86.62 154

MS-PatchMatch		72.34 107	71.26 107	75.61 129	82.38 133	55.55 46	88.00 56	89.95 15	65.38 62	56.51 240	80.74 211	32.28 242	92.89 32	57.95 178	88.10 13	78.39 276

MTAPA		72.73 100	71.22 108	77.27 95	81.54 150	53.57 97	67.06 314	81.31 180	59.41 153	68.39 89	90.96 58	36.07 207	89.01 123	73.80 63	82.45 61	89.23 100

PGM-MVS		72.60 102	71.20 109	76.80 108	82.95 117	52.82 126	83.07 182	82.14 165	56.51 219	63.18 151	89.81 89	35.68 212	89.76 108	67.30 98	80.19 83	87.83 131

RRT_test8_iter05		72.74 99	71.20 109	77.36 91	87.25 38	53.51 99	88.68 50	89.53 16	65.20 67	61.32 168	81.27 205	45.89 86	92.48 44	65.99 108	55.65 279	86.10 162

Fast-Effi-MVS+		72.73 100	71.15 111	77.48 88	82.75 125	54.76 71	86.77 87	80.64 190	63.05 94	65.93 112	84.01 164	44.42 106	89.03 122	56.45 192	76.36 115	88.64 116

mvs_anonymous		72.29 109	70.74 112	76.94 105	82.85 121	54.72 74	78.43 257	81.54 176	63.77 82	61.69 166	79.32 219	51.11 39	85.31 227	62.15 139	75.79 118	90.79 68

VPA-MVSNet		71.12 121	70.66 113	72.49 194	78.75 197	44.43 278	87.64 61	90.02 13	63.97 79	65.02 124	81.58 202	42.14 133	87.42 177	63.42 130	63.38 211	85.63 174

3Dnovator+	62.71 7	72.29 109	70.50 114	77.65 85	83.40 102	51.29 163	87.32 71	86.40 77	59.01 170	58.49 208	88.32 114	32.40 240	91.27 70	57.04 187	82.15 66	90.38 78

MVP-Stereo		70.97 124	70.44 115	72.59 191	76.03 241	51.36 160	85.02 133	86.99 65	60.31 139	56.53 239	78.92 224	40.11 157	90.00 101	60.00 161	90.01 4	76.41 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mPP-MVS		71.79 116	70.38 116	76.04 123	82.65 129	52.06 141	84.45 142	81.78 173	55.59 228	62.05 164	89.68 91	33.48 231	88.28 153	65.45 117	78.24 100	87.77 133

DP-MVS Recon		71.99 113	70.31 117	77.01 101	90.65 6	53.44 105	89.37 36	82.97 158	56.33 221	63.56 149	89.47 94	34.02 224	92.15 54	54.05 204	72.41 149	85.43 177

xiu_mvs_v1_base_debu		71.60 117	70.29 118	75.55 130	77.26 224	53.15 115	85.34 117	79.37 212	55.83 225	72.54 57	90.19 79	22.38 307	86.66 195	73.28 68	76.39 112	86.85 149

xiu_mvs_v1_base		71.60 117	70.29 118	75.55 130	77.26 224	53.15 115	85.34 117	79.37 212	55.83 225	72.54 57	90.19 79	22.38 307	86.66 195	73.28 68	76.39 112	86.85 149

xiu_mvs_v1_base_debi		71.60 117	70.29 118	75.55 130	77.26 224	53.15 115	85.34 117	79.37 212	55.83 225	72.54 57	90.19 79	22.38 307	86.66 195	73.28 68	76.39 112	86.85 149

FIs		70.00 141	70.24 121	69.30 249	77.93 215	38.55 314	83.99 156	87.72 56	66.86 44	57.66 221	84.17 163	52.28 31	85.31 227	52.72 218	68.80 174	84.02 194

sss		70.49 132	70.13 122	71.58 218	81.59 147	39.02 312	80.78 233	84.71 120	59.34 156	66.61 103	88.09 119	37.17 191	85.52 223	61.82 142	71.02 160	90.20 83

EPP-MVSNet		71.14 120	70.07 123	74.33 154	79.18 188	46.52 253	83.81 160	86.49 74	56.32 222	57.95 214	84.90 159	54.23 22	89.14 118	58.14 174	69.65 170	87.33 140

PAPM_NR		71.80 115	69.98 124	77.26 97	81.54 150	53.34 110	78.60 256	85.25 104	53.46 248	60.53 176	88.66 109	45.69 91	89.24 117	56.49 189	79.62 91	89.19 103

HQP_MVS		70.96 125	69.91 125	74.12 158	77.95 213	49.57 196	85.76 106	82.59 161	63.60 87	62.15 161	83.28 177	36.04 209	88.30 151	65.46 115	72.34 150	84.49 187

tpmrst		71.04 123	69.77 126	74.86 143	83.19 107	55.86 44	75.64 269	78.73 228	67.88 33	64.99 126	73.73 275	49.96 50	79.56 285	65.92 109	67.85 182	89.14 105

SR-MVS		70.92 126	69.73 127	74.50 148	83.38 103	50.48 175	84.27 147	79.35 216	48.96 278	66.57 105	90.45 72	33.65 230	87.11 182	66.42 104	74.56 130	85.91 166

OPM-MVS		70.75 129	69.58 128	74.26 156	75.55 247	51.34 161	86.05 101	83.29 151	61.94 111	62.95 154	85.77 149	34.15 223	88.44 144	65.44 118	71.07 159	82.99 218

CDS-MVSNet		70.48 133	69.43 129	73.64 173	77.56 219	48.83 215	83.51 168	77.45 251	63.27 91	62.33 160	85.54 153	43.85 110	83.29 255	57.38 186	74.00 132	88.79 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		71.11 122	69.41 130	76.22 117	79.32 185	50.49 174	80.23 240	85.14 109	59.44 152	58.93 197	88.89 106	33.83 229	89.60 113	61.49 143	77.42 104	88.57 119

1112_ss		70.05 139	69.37 131	72.10 201	80.77 168	42.78 292	85.12 127	76.75 264	59.69 147	61.19 170	92.12 33	47.48 67	83.84 246	53.04 210	68.21 177	89.66 92

Vis-MVSNet		70.61 131	69.34 132	74.42 151	80.95 165	48.49 223	86.03 102	77.51 250	58.74 176	65.55 118	87.78 125	34.37 221	85.95 219	52.53 219	80.61 77	88.80 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
QAPM		71.88 114	69.33 133	79.52 32	82.20 135	54.30 83	86.30 96	88.77 33	56.61 217	59.72 181	87.48 129	33.90 227	95.36 11	47.48 245	81.49 70	88.90 110

ACMMP		70.81 128	69.29 134	75.39 134	81.52 154	51.92 146	83.43 171	83.03 156	56.67 216	58.80 202	88.91 105	31.92 247	88.58 138	65.89 111	73.39 138	85.67 171
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
XXY-MVS		70.18 135	69.28 135	72.89 187	77.64 217	42.88 291	85.06 130	87.50 60	62.58 100	62.66 158	82.34 193	43.64 118	89.83 105	58.42 170	63.70 207	85.96 165

ab-mvs		70.65 130	69.11 136	75.29 136	80.87 166	46.23 260	73.48 283	85.24 105	59.99 143	66.65 101	80.94 208	43.13 126	88.69 134	63.58 129	68.07 178	90.95 66

test-LLR		69.65 149	69.01 137	71.60 216	78.67 199	48.17 231	85.13 124	79.72 205	59.18 163	63.13 152	82.58 188	36.91 195	80.24 276	60.56 152	75.17 124	86.39 159

miper_enhance_ethall		69.77 145	68.90 138	72.38 197	78.93 194	49.91 192	83.29 177	78.85 223	64.90 69	59.37 188	79.46 217	52.77 28	85.16 233	63.78 127	58.72 243	82.08 226

EI-MVSNet		69.70 148	68.70 139	72.68 189	75.00 253	48.90 212	79.54 248	87.16 62	61.05 127	63.88 144	83.74 169	45.87 87	90.44 89	57.42 185	64.68 201	78.70 269

thisisatest0530		70.47 134	68.56 140	76.20 118	79.78 180	51.52 156	83.49 170	88.58 42	57.62 198	58.60 204	82.79 182	51.03 41	91.48 65	52.84 212	62.36 224	85.59 175

BH-w/o		70.02 140	68.51 141	74.56 147	82.77 123	50.39 178	86.60 91	78.14 239	59.77 145	59.65 182	85.57 152	39.27 164	87.30 179	49.86 230	74.94 129	85.99 163

tpm2		70.82 127	68.44 142	77.98 77	80.78 167	56.11 40	74.21 280	81.28 183	60.24 140	68.04 92	75.27 267	52.26 32	88.50 143	55.82 195	68.03 179	89.33 97

PCF-MVS	61.03 10	70.10 137	68.40 143	75.22 139	77.15 228	51.99 143	79.30 252	82.12 166	56.47 220	61.88 165	86.48 144	43.98 109	87.24 180	55.37 196	72.79 146	86.43 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test1172		69.64 150	68.38 144	73.41 179	82.77 123	48.84 214	82.79 189	78.34 237	47.02 288	65.27 120	90.07 83	31.17 253	86.09 212	64.51 125	73.49 137	85.31 178

UniMVSNet_NR-MVSNet		68.82 161	68.29 145	70.40 236	75.71 245	42.59 294	84.23 148	86.78 69	66.31 47	58.51 205	82.45 190	51.57 36	84.64 242	53.11 208	55.96 274	83.96 200

APD-MVS_3200maxsize		69.62 151	68.23 146	73.80 168	81.58 148	48.22 230	81.91 207	79.50 211	48.21 280	64.24 137	89.75 90	31.91 248	87.55 174	63.08 131	73.85 135	85.64 173

TAMVS		69.51 153	68.16 147	73.56 176	76.30 236	48.71 217	82.57 192	77.17 256	62.10 107	61.32 168	84.23 162	41.90 138	83.46 253	54.80 200	73.09 143	88.50 121

BH-RMVSNet		70.08 138	68.01 148	76.27 115	84.21 85	51.22 165	87.29 74	79.33 218	58.96 173	63.63 148	86.77 138	33.29 233	90.30 96	44.63 260	73.96 133	87.30 142

FC-MVSNet-test		67.49 188	67.91 149	66.21 279	76.06 239	33.06 331	80.82 232	87.18 61	64.44 73	54.81 248	82.87 180	50.40 47	82.60 257	48.05 242	66.55 188	82.98 219

MVS_111021_LR		69.07 155	67.91 149	72.54 192	77.27 223	49.56 198	79.77 245	73.96 289	59.33 158	60.73 173	87.82 123	30.19 260	81.53 263	69.94 84	72.19 152	86.53 155

Anonymous202405211		70.11 136	67.88 151	76.79 109	87.20 39	47.24 246	89.49 35	77.38 253	54.88 238	66.14 109	86.84 137	20.93 316	91.54 63	56.45 192	71.62 155	91.59 44

114514_t		69.87 144	67.88 151	75.85 127	88.38 25	52.35 138	86.94 83	83.68 142	53.70 247	55.68 246	85.60 151	30.07 261	91.20 71	55.84 194	71.02 160	83.99 196

TR-MVS		69.71 146	67.85 153	75.27 137	82.94 118	48.48 224	87.40 70	80.86 187	57.15 206	64.61 130	87.08 134	32.67 238	89.64 112	46.38 252	71.55 157	87.68 135

PVSNet	62.49 8	69.27 154	67.81 154	73.64 173	84.41 80	51.85 147	84.63 141	77.80 244	66.42 45	59.80 180	84.95 158	22.14 311	80.44 274	55.03 197	75.11 126	88.62 117

cl-mvsnet2		68.85 159	67.69 155	72.35 198	78.07 212	49.98 191	82.45 197	78.48 234	62.50 102	58.46 209	77.95 230	49.99 49	85.17 232	62.55 134	58.72 243	81.90 228

v2v482		69.55 152	67.64 156	75.26 138	72.32 282	53.83 90	84.93 135	81.94 168	65.37 63	60.80 172	79.25 220	41.62 141	88.98 128	63.03 132	59.51 236	82.98 219

miper_ehance_all_eth		68.70 168	67.58 157	72.08 202	76.91 230	49.48 201	82.47 196	78.45 235	62.68 99	58.28 213	77.88 232	50.90 42	85.01 237	61.91 140	58.72 243	81.75 231

HyFIR lowres test		69.94 143	67.58 157	77.04 99	77.11 229	57.29 21	81.49 222	79.11 221	58.27 182	58.86 200	80.41 212	42.33 129	86.96 187	61.91 140	68.68 176	86.87 147

mvs-test1		69.04 156	67.57 159	73.44 178	75.17 248	51.68 152	86.57 92	74.48 283	62.15 105	62.07 163	85.79 148	30.59 257	87.48 175	65.40 119	65.94 194	81.18 247

IS-MVSNet		68.80 163	67.55 160	72.54 192	78.50 206	43.43 286	81.03 228	79.35 216	59.12 168	57.27 231	86.71 139	46.05 83	87.70 170	44.32 261	75.60 121	86.49 156

OpenMVS	61.00 11	69.99 142	67.55 160	77.30 93	78.37 209	54.07 89	84.36 144	85.76 89	57.22 205	56.71 236	87.67 127	30.79 256	92.83 34	43.04 266	84.06 54	85.01 182

tpm		68.36 170	67.48 162	70.97 228	79.93 179	51.34 161	76.58 266	78.75 227	67.73 35	63.54 150	74.86 269	48.33 58	72.36 328	53.93 205	63.71 206	89.21 102

FMVSNet3		68.84 160	67.40 163	73.19 182	85.05 70	48.53 221	85.71 111	85.36 96	60.90 131	57.58 223	79.15 222	42.16 132	86.77 191	47.25 247	63.40 208	84.27 191

test-mter		68.36 170	67.29 164	71.60 216	78.67 199	48.17 231	85.13 124	79.72 205	53.38 249	63.13 152	82.58 188	27.23 278	80.24 276	60.56 152	75.17 124	86.39 159

Anonymous20240529		69.71 146	67.28 165	77.00 102	83.78 94	50.36 179	88.87 47	85.10 110	47.22 285	64.03 139	83.37 175	27.93 272	92.10 55	57.78 181	67.44 183	88.53 120

thres200		68.71 166	67.27 166	73.02 183	84.73 75	46.76 250	85.03 132	87.73 55	62.34 104	59.87 178	83.45 174	43.15 124	88.32 150	31.25 312	67.91 181	83.98 198

PS-MVSNAJss		68.78 165	67.17 167	73.62 175	73.01 272	48.33 229	84.95 134	84.81 117	59.30 159	58.91 199	79.84 215	37.77 175	88.86 132	62.83 133	63.12 217	83.67 205

UGNet		68.71 166	67.11 168	73.50 177	80.55 174	47.61 240	84.08 151	78.51 233	59.45 151	65.68 117	82.73 186	23.78 299	85.08 236	52.80 213	76.40 111	87.80 132
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
SR-MVS-dyc-post		68.27 174	66.87 169	72.48 195	80.96 162	48.14 233	81.54 218	76.98 259	46.42 293	62.75 156	89.42 95	31.17 253	86.09 212	60.52 154	72.06 153	83.19 214

v1144		68.81 162	66.82 170	74.80 145	72.34 281	53.46 101	84.68 139	81.77 174	64.25 75	60.28 177	77.91 231	40.23 154	88.95 129	60.37 157	59.52 235	81.97 227

UniMVSNet (Re)		67.71 183	66.80 171	70.45 234	74.44 258	42.93 290	82.42 198	84.90 114	63.69 85	59.63 183	80.99 207	47.18 70	85.23 231	51.17 224	56.75 265	83.19 214

1121		68.79 164	66.77 172	74.82 144	83.08 111	53.46 101	80.23 240	71.53 308	45.47 300	66.31 108	87.19 132	34.02 224	85.13 234	52.78 214	80.36 81	85.87 168

WR-MVS		67.58 185	66.76 173	70.04 243	75.92 243	45.06 274	86.23 97	85.28 102	64.31 74	58.50 207	81.00 206	44.80 104	82.00 262	49.21 235	55.57 280	83.06 217

EPNet_dtu		66.25 213	66.71 174	64.87 289	78.66 201	34.12 326	82.80 188	75.51 275	61.75 114	64.47 135	86.90 136	37.06 192	72.46 327	43.65 264	69.63 171	88.02 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GA-MVS		69.04 156	66.70 175	76.06 122	75.11 250	52.36 137	83.12 180	80.23 196	63.32 90	60.65 174	79.22 221	30.98 255	88.37 146	61.25 144	66.41 189	87.46 138

RE-MVS-def			66.66 176		80.96 162	48.14 233	81.54 218	76.98 259	46.42 293	62.75 156	89.42 95	29.28 265		60.52 154	72.06 153	83.19 214

cl_fuxian		67.97 178	66.66 176	71.91 213	76.20 238	49.31 203	82.13 203	78.00 242	61.99 109	57.64 222	76.94 246	49.41 53	84.93 238	60.62 151	57.01 263	81.49 235

BH-untuned		68.28 173	66.40 178	73.91 163	81.62 145	50.01 190	85.56 115	77.39 252	57.63 197	57.47 228	83.69 171	36.36 204	87.08 183	44.81 259	73.08 144	84.65 186

AUN-MVS		68.20 176	66.35 179	73.76 169	76.37 233	47.45 242	79.52 250	79.52 210	60.98 129	62.34 159	86.02 146	36.59 203	86.94 188	62.32 136	53.47 294	86.89 146

v148		68.24 175	66.35 179	73.88 164	71.76 285	51.47 157	84.23 148	81.90 172	63.69 85	58.94 196	76.44 254	43.72 116	87.78 168	60.63 150	55.86 276	82.39 224

tttt0517		68.33 172	66.29 181	74.46 149	78.08 211	49.06 206	80.88 231	89.08 23	54.40 244	54.75 250	80.77 210	51.31 38	90.33 93	49.35 234	58.01 254	83.99 196

HPM-MVS_fast		67.86 180	66.28 182	72.61 190	80.67 171	48.34 228	81.18 226	75.95 273	50.81 268	59.55 186	88.05 121	27.86 273	85.98 216	58.83 165	73.58 136	83.51 206

UA-Net		67.32 194	66.23 183	70.59 232	78.85 195	41.23 305	73.60 282	75.45 277	61.54 119	66.61 103	84.53 160	38.73 168	86.57 200	42.48 271	74.24 131	83.98 198

Test_1112_low_res		67.18 197	66.23 183	70.02 244	78.75 197	41.02 306	83.43 171	73.69 291	57.29 204	58.45 210	82.39 192	45.30 94	80.88 269	50.50 226	66.26 193	88.16 123

abl_6		68.03 177	66.15 185	73.66 172	78.54 204	48.48 224	79.77 245	78.04 240	47.39 284	63.70 146	88.25 116	28.21 268	89.06 119	60.17 160	71.25 158	83.45 207

tfpn200view9		67.57 186	66.13 186	71.89 214	84.05 88	45.07 271	83.40 173	87.71 57	60.79 132	57.79 218	82.76 183	43.53 119	87.80 165	28.80 318	66.36 190	82.78 222

thres400		67.40 193	66.13 186	71.19 224	84.05 88	45.07 271	83.40 173	87.71 57	60.79 132	57.79 218	82.76 183	43.53 119	87.80 165	28.80 318	66.36 190	80.71 253

cascas		69.01 158	66.13 186	77.66 84	79.36 183	55.41 51	86.99 80	83.75 141	56.69 215	58.92 198	81.35 204	24.31 297	92.10 55	53.23 207	70.61 163	85.46 176

NR-MVSNet		67.25 195	65.99 189	71.04 227	73.27 270	43.91 282	85.32 120	84.75 119	66.05 55	53.65 262	82.11 196	45.05 96	85.97 218	47.55 244	56.18 271	83.24 212

cl-mvsnet_		67.43 190	65.93 190	71.95 210	76.33 234	48.02 236	82.58 191	79.12 220	61.30 123	56.72 235	76.92 247	46.12 81	86.44 202	57.98 176	56.31 268	81.38 242

cl-mvsnet1		67.43 190	65.93 190	71.94 211	76.33 234	48.01 237	82.57 192	79.11 221	61.31 122	56.73 234	76.92 247	46.09 82	86.43 203	57.98 176	56.31 268	81.39 241

CPTT-MVS		67.15 198	65.84 192	71.07 226	80.96 162	50.32 182	81.94 206	74.10 286	46.18 296	57.91 215	87.64 128	29.57 262	81.31 265	64.10 126	70.18 167	81.56 234

FMVSNet2		67.57 186	65.79 193	72.90 185	82.71 126	47.97 238	85.15 123	84.93 113	58.55 179	56.71 236	78.26 229	36.72 200	86.67 194	46.15 254	62.94 219	84.07 193

v144192		67.86 180	65.76 194	74.16 157	71.68 286	53.09 118	84.14 150	80.83 188	62.85 97	59.21 192	77.28 241	39.30 163	88.00 161	58.67 167	57.88 258	81.40 240

v1192		67.96 179	65.74 195	74.63 146	71.79 284	53.43 107	84.06 153	80.99 186	63.19 93	59.56 185	77.46 238	37.50 185	88.65 135	58.20 173	58.93 242	81.79 230

DU-MVS		66.84 206	65.74 195	70.16 239	73.27 270	42.59 294	81.50 220	82.92 159	63.53 89	58.51 205	82.11 196	40.75 147	84.64 242	53.11 208	55.96 274	83.24 212

Vis-MVSNet (Re-imp)		65.52 219	65.63 197	65.17 287	77.49 220	30.54 338	75.49 273	77.73 246	59.34 156	52.26 271	86.69 140	49.38 54	80.53 273	37.07 284	75.28 123	84.42 189

TranMVSNet+NR-MVSNet		66.94 204	65.61 198	70.93 229	73.45 267	43.38 287	83.02 184	84.25 130	65.31 65	58.33 212	81.90 199	39.92 160	85.52 223	49.43 233	54.89 283	83.89 202

V42		67.66 184	65.60 199	73.86 165	70.69 297	53.63 96	81.50 220	78.61 231	63.85 81	59.49 187	77.49 237	37.98 172	87.65 171	62.33 135	58.43 247	80.29 258

AdaColmap		67.86 180	65.48 200	75.00 141	88.15 29	54.99 65	86.10 100	76.63 267	49.30 275	57.80 217	86.65 141	29.39 264	88.94 131	45.10 258	70.21 166	81.06 248

GBi-Net		67.09 199	65.47 201	71.96 207	82.71 126	46.36 255	83.52 164	83.31 148	58.55 179	57.58 223	76.23 258	36.72 200	86.20 204	47.25 247	63.40 208	83.32 209

test1		67.09 199	65.47 201	71.96 207	82.71 126	46.36 255	83.52 164	83.31 148	58.55 179	57.58 223	76.23 258	36.72 200	86.20 204	47.25 247	63.40 208	83.32 209

EPMVS		68.45 169	65.44 203	77.47 89	84.91 73	56.17 39	71.89 299	81.91 171	61.72 115	60.85 171	72.49 289	36.21 205	87.06 184	47.32 246	71.62 155	89.17 104

thres100view900		66.87 205	65.42 204	71.24 222	83.29 105	43.15 288	81.67 213	87.78 52	59.04 169	55.92 244	82.18 195	43.73 114	87.80 165	28.80 318	66.36 190	82.78 222

IterMVS-LS		66.63 207	65.36 205	70.42 235	75.10 251	48.90 212	81.45 223	76.69 266	61.05 127	55.71 245	77.10 244	45.86 88	83.65 250	57.44 184	57.88 258	78.70 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
eth_miper_zixun_eth		66.98 203	65.28 206	72.06 203	75.61 246	50.40 177	81.00 229	76.97 262	62.00 108	56.99 233	76.97 245	44.84 101	85.58 222	58.75 166	54.42 286	80.21 259

v1921920		67.45 189	65.23 207	74.10 159	71.51 289	52.90 125	83.75 162	80.44 193	62.48 103	59.12 194	77.13 242	36.98 193	87.90 162	57.53 183	58.14 252	81.49 235

thres600view7		66.46 210	65.12 208	70.47 233	83.41 99	43.80 284	82.15 202	87.78 52	59.37 155	56.02 243	82.21 194	43.73 114	86.90 189	26.51 329	64.94 197	80.71 253

OMC-MVS		65.97 217	65.06 209	68.71 258	72.97 273	42.58 296	78.61 255	75.35 278	54.72 239	59.31 190	86.25 145	33.30 232	77.88 299	57.99 175	67.05 185	85.66 172

v8		67.25 195	64.99 210	74.04 160	72.89 275	53.31 112	82.37 199	80.11 198	61.54 119	54.29 255	76.02 263	42.89 128	88.41 145	58.43 168	56.36 266	80.39 257

Effi-MVS+-dtu		66.24 214	64.96 211	70.08 241	75.17 248	49.64 195	82.01 204	74.48 283	62.15 105	57.83 216	76.08 262	30.59 257	83.79 247	65.40 119	60.93 230	76.81 291

v1240		66.99 202	64.68 212	73.93 162	71.38 292	52.66 129	83.39 175	79.98 199	61.97 110	58.44 211	77.11 243	35.25 215	87.81 164	56.46 191	58.15 250	81.33 243

LPG-MVS_test		66.44 211	64.58 213	72.02 204	74.42 259	48.60 218	83.07 182	80.64 190	54.69 240	53.75 260	83.83 167	25.73 288	86.98 185	60.33 158	64.71 198	80.48 255

gg-mvs-nofinetune		67.43 190	64.53 214	76.13 120	85.95 46	47.79 239	64.38 318	88.28 45	39.34 323	66.62 102	41.27 344	58.69 11	89.00 125	49.64 232	86.62 27	91.59 44

ACMP	61.11 9	66.24 214	64.33 215	72.00 206	74.89 255	49.12 205	83.18 179	79.83 203	55.41 231	52.29 269	82.68 187	25.83 286	86.10 210	60.89 147	63.94 205	80.78 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Baseline_NR-MVSNet		65.49 221	64.27 216	69.13 250	74.37 261	41.65 301	83.39 175	78.85 223	59.56 149	59.62 184	76.88 249	40.75 147	87.44 176	49.99 228	55.05 281	78.28 278

v10		66.61 208	64.20 217	73.83 167	72.59 278	53.37 108	81.88 208	79.91 202	61.11 125	54.09 257	75.60 265	40.06 158	88.26 154	56.47 190	56.10 272	79.86 264

Fast-Effi-MVS+-dtu		66.53 209	64.10 218	73.84 166	72.41 280	52.30 140	84.73 138	75.66 274	59.51 150	56.34 241	79.11 223	28.11 270	85.85 221	57.74 182	63.29 212	83.35 208

RRT_MVS		65.43 222	64.01 219	69.68 246	81.54 150	50.15 186	82.31 200	76.78 263	55.25 232	60.64 175	82.00 198	25.18 292	79.00 286	60.96 146	51.45 300	79.89 263

Anonymous20231211		66.08 216	63.67 220	73.31 180	83.07 112	48.75 216	86.01 103	84.67 121	45.27 301	56.54 238	76.67 252	28.06 271	88.95 129	52.78 214	59.95 232	82.23 225

PatchmatchNet		67.07 201	63.63 221	77.40 90	83.10 108	58.03 11	72.11 297	77.77 245	58.85 174	59.37 188	70.83 302	37.84 174	84.93 238	42.96 268	69.83 169	89.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
bset_n11_16_dypcd		65.51 220	63.21 222	72.41 196	68.84 306	50.15 186	81.25 224	72.40 300	59.17 165	59.20 193	78.66 226	25.69 290	85.27 229	66.80 101	56.88 264	81.80 229

tpm cat1		66.28 212	62.78 223	76.77 110	81.40 156	57.14 22	70.03 306	77.19 255	53.00 252	58.76 203	70.73 305	46.17 80	86.73 193	43.27 265	64.46 202	86.44 157

pm-mvs1		64.12 226	62.56 224	68.78 256	71.68 286	38.87 313	82.89 186	81.57 175	55.54 230	53.89 259	77.82 233	37.73 178	86.74 192	48.46 240	53.49 293	80.72 252

test0.0.03 1		62.54 239	62.44 225	62.86 299	72.28 283	29.51 340	82.93 185	78.78 226	59.18 163	53.07 265	82.41 191	36.91 195	77.39 303	37.45 280	58.96 241	81.66 233

miper_lstm_enhance		63.91 227	62.30 226	68.75 257	75.06 252	46.78 249	69.02 310	81.14 184	59.68 148	52.76 266	72.39 292	40.71 149	77.99 297	56.81 188	53.09 295	81.48 237

X-MVStestdata		65.85 218	62.20 227	76.81 106	83.41 99	52.48 131	84.88 136	83.20 153	58.03 185	63.91 142	4.82 359	35.50 213	89.78 106	65.50 112	80.50 79	88.16 123

FMVSNet1		64.57 223	62.11 228	71.96 207	77.32 222	46.36 255	83.52 164	83.31 148	52.43 257	54.42 253	76.23 258	27.80 274	86.20 204	42.59 270	61.34 228	83.32 209

ACMM	58.35 12	64.35 225	62.01 229	71.38 220	74.21 262	48.51 222	82.25 201	79.66 207	47.61 282	54.54 252	80.11 213	25.26 291	86.00 215	51.26 222	63.16 215	79.64 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		63.77 230	61.67 230	70.08 241	72.68 277	51.24 164	80.44 236	75.51 275	60.51 137	51.41 274	73.70 278	32.08 244	78.91 287	54.30 203	54.35 287	80.08 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		64.41 224	61.58 231	72.90 185	82.40 132	54.09 88	72.53 289	76.59 268	60.39 138	55.68 246	70.39 306	35.18 216	76.90 307	39.34 276	61.71 226	87.73 134

test_djsdf		63.84 228	61.56 232	70.70 231	68.78 307	44.69 275	81.63 214	81.44 178	50.28 269	52.27 270	76.26 257	26.72 281	86.11 208	60.83 148	55.84 277	81.29 246

MDTV_nov1_ep13			61.56 232		81.68 142	55.12 60	72.41 291	78.18 238	59.19 161	58.85 201	69.29 310	34.69 219	86.16 207	36.76 288	62.96 218

D2MVS		63.49 231	61.39 234	69.77 245	69.29 304	48.93 211	78.89 254	77.71 247	60.64 136	49.70 283	72.10 297	27.08 279	83.48 252	54.48 202	62.65 220	76.90 290

pmmvs5		62.80 238	61.18 235	67.66 266	69.53 303	42.37 299	82.65 190	75.19 279	54.30 245	52.03 272	78.51 228	31.64 250	80.67 270	48.60 238	58.15 250	79.95 262

CL-MVSNet_2432*1600		62.98 235	61.14 236	68.50 262	65.86 321	42.96 289	84.37 143	82.98 157	60.98 129	53.95 258	72.70 288	40.43 152	83.71 249	41.10 272	47.93 307	78.83 268

pmmvs4		63.34 232	61.07 237	70.16 239	70.14 299	50.53 173	79.97 244	71.41 310	55.08 234	54.12 256	78.58 227	32.79 237	82.09 261	50.33 227	57.22 262	77.86 282

jajsoiax		63.21 233	60.84 238	70.32 237	68.33 312	44.45 277	81.23 225	81.05 185	53.37 250	50.96 279	77.81 234	17.49 329	85.49 225	59.31 162	58.05 253	81.02 249

TransMVSNet (Re)		62.82 237	60.76 239	69.02 251	73.98 264	41.61 302	86.36 94	79.30 219	56.90 208	52.53 267	76.44 254	41.85 139	87.60 173	38.83 277	40.61 329	77.86 282

mvs_tets		62.96 236	60.55 240	70.19 238	68.22 315	44.24 281	80.90 230	80.74 189	52.99 253	50.82 281	77.56 235	16.74 332	85.44 226	59.04 164	57.94 255	80.89 250

UniMVSNet_ETH3D		62.51 240	60.49 241	68.57 261	68.30 313	40.88 308	73.89 281	79.93 201	51.81 263	54.77 249	79.61 216	24.80 295	81.10 266	49.93 229	61.35 227	83.73 204

CVMVSNet		60.85 251	60.44 242	62.07 300	75.00 253	32.73 333	79.54 248	73.49 294	36.98 331	56.28 242	83.74 169	29.28 265	69.53 335	46.48 251	63.23 213	83.94 201

MIMVSNet		63.12 234	60.29 243	71.61 215	75.92 243	46.65 251	65.15 315	81.94 168	59.14 166	54.65 251	69.47 309	25.74 287	80.63 271	41.03 273	69.56 172	87.55 136

TAPA-MVS	56.12 14	61.82 247	60.18 244	66.71 275	78.48 207	37.97 317	75.19 275	76.41 270	46.82 289	57.04 232	86.52 143	27.67 276	77.03 305	26.50 330	67.02 186	85.14 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SCA		63.84 228	60.01 245	75.32 135	78.58 203	57.92 12	61.61 326	77.53 249	56.71 214	57.75 220	70.77 303	31.97 245	79.91 282	48.80 236	56.36 266	88.13 126

EG-PatchMatch MVS		62.40 244	59.59 246	70.81 230	73.29 269	49.05 207	85.81 104	84.78 118	51.85 262	44.19 305	73.48 281	15.52 337	89.85 104	40.16 274	67.24 184	73.54 317

XVG-OURS-SEG-HR		62.02 245	59.54 247	69.46 248	65.30 324	45.88 262	65.06 316	73.57 293	46.45 292	57.42 229	83.35 176	26.95 280	78.09 293	53.77 206	64.03 203	84.42 189

tpmvs		62.45 243	59.42 248	71.53 219	83.93 90	54.32 82	70.03 306	77.61 248	51.91 260	53.48 263	68.29 313	37.91 173	86.66 195	33.36 302	58.27 248	73.62 316

XVG-OURS		61.88 246	59.34 249	69.49 247	65.37 323	46.27 258	64.80 317	73.49 294	47.04 287	57.41 230	82.85 181	25.15 293	78.18 291	53.00 211	64.98 196	84.01 195

v7n		62.50 241	59.27 250	72.20 200	67.25 318	49.83 193	77.87 259	80.12 197	52.50 256	48.80 287	73.07 283	32.10 243	87.90 162	46.83 250	54.92 282	78.86 267

tfpnnormal		61.47 248	59.09 251	68.62 260	76.29 237	41.69 300	81.14 227	85.16 107	54.48 243	51.32 275	73.63 279	32.32 241	86.89 190	21.78 339	55.71 278	77.29 288

CR-MVSNet		62.47 242	59.04 252	72.77 188	73.97 265	56.57 30	60.52 329	71.72 304	60.04 141	57.49 226	65.86 320	38.94 165	80.31 275	42.86 269	59.93 233	81.42 238

PLC	52.38 18	60.89 250	58.97 253	66.68 277	81.77 141	45.70 266	78.96 253	74.04 288	43.66 313	47.63 293	83.19 179	23.52 302	77.78 302	37.47 279	60.46 231	76.55 297
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IterMVS-SCA-FT		59.12 260	58.81 254	60.08 311	70.68 298	45.07 271	80.42 237	74.25 285	43.54 314	50.02 282	73.73 275	31.97 245	56.74 344	51.06 225	53.60 292	78.42 275

CNLPA		60.59 252	58.44 255	67.05 272	79.21 187	47.26 245	79.75 247	64.34 328	42.46 319	51.90 273	83.94 165	27.79 275	75.41 312	37.12 282	59.49 237	78.47 273

WR-MVS_H		58.91 265	58.04 256	61.54 305	69.07 305	33.83 328	76.91 263	81.99 167	51.40 265	48.17 288	74.67 270	40.23 154	74.15 316	31.78 309	48.10 305	76.64 295

anonymousdsp		60.46 253	57.65 257	68.88 252	63.63 332	45.09 270	72.93 287	78.63 230	46.52 291	51.12 276	72.80 287	21.46 314	83.07 256	57.79 180	53.97 288	78.47 273

Anonymous20231206		59.08 262	57.59 258	63.55 294	68.77 308	32.14 336	80.26 239	79.78 204	50.00 272	49.39 284	72.39 292	26.64 282	78.36 290	33.12 305	57.94 255	80.14 260

CP-MVSNet		58.54 271	57.57 259	61.46 306	68.50 310	33.96 327	76.90 264	78.60 232	51.67 264	47.83 291	76.60 253	34.99 218	72.79 325	35.45 291	47.58 308	77.64 286

PVSNet_0	57.04 13	61.19 249	57.24 260	73.02 183	77.45 221	50.31 183	79.43 251	77.36 254	63.96 80	47.51 296	72.45 291	25.03 294	83.78 248	52.76 217	19.22 350	84.96 183

pmmvs6		59.64 255	57.15 261	67.09 270	66.01 319	36.86 321	80.50 235	78.64 229	45.05 303	49.05 286	73.94 274	27.28 277	86.10 210	43.96 263	49.94 303	78.31 277

PEN-MVS		58.35 272	57.15 261	61.94 302	67.55 317	34.39 325	77.01 262	78.35 236	51.87 261	47.72 292	76.73 251	33.91 226	73.75 320	34.03 300	47.17 312	77.68 284

PS-CasMVS		58.12 273	57.03 263	61.37 307	68.24 314	33.80 329	76.73 265	78.01 241	51.20 266	47.54 295	76.20 261	32.85 235	72.76 326	35.17 295	47.37 310	77.55 287

LCM-MVSNet-Re		58.82 266	56.54 264	65.68 281	79.31 186	29.09 343	61.39 328	45.79 346	60.73 134	37.65 329	72.47 290	31.42 251	81.08 267	49.66 231	70.41 164	86.87 147

FMVSNet5		58.61 268	56.45 265	65.10 288	77.20 227	39.74 310	74.77 276	77.12 257	50.27 271	43.28 311	67.71 315	26.15 285	76.90 307	36.78 287	54.78 284	78.65 271

KD-MVS_2432*1600		59.04 263	56.44 266	66.86 273	79.07 189	45.87 263	72.13 295	80.42 194	55.03 235	48.15 289	71.01 300	36.73 198	78.05 295	35.21 293	30.18 345	76.67 292

miper_refine_blended		59.04 263	56.44 266	66.86 273	79.07 189	45.87 263	72.13 295	80.42 194	55.03 235	48.15 289	71.01 300	36.73 198	78.05 295	35.21 293	30.18 345	76.67 292

CHOSEN 280x420		57.53 275	56.38 268	60.97 309	74.01 263	48.10 235	46.30 342	54.31 340	48.18 281	50.88 280	77.43 239	38.37 171	59.16 343	54.83 198	63.14 216	75.66 302

DP-MVS		59.24 258	56.12 269	68.63 259	88.24 28	50.35 180	82.51 195	64.43 327	41.10 321	46.70 299	78.77 225	24.75 296	88.57 141	22.26 337	56.29 270	66.96 335

OpenMVS_ROB	53.19 17	59.20 259	56.00 270	68.83 254	71.13 294	44.30 279	83.64 163	75.02 280	46.42 293	46.48 301	73.03 284	18.69 324	88.14 155	27.74 325	61.80 225	74.05 313

ACMH	53.70 16	59.78 254	55.94 271	71.28 221	76.59 232	48.35 227	80.15 243	76.11 271	49.74 273	41.91 316	73.45 282	16.50 334	90.31 94	31.42 310	57.63 260	75.17 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		57.03 276	55.73 272	60.95 310	65.94 320	32.57 334	75.71 268	77.09 258	51.16 267	46.65 300	76.34 256	32.84 236	73.22 324	30.94 313	44.87 320	77.06 289

ACMH+	54.58 15	58.55 270	55.24 273	68.50 262	74.68 257	45.80 265	80.27 238	70.21 315	47.15 286	42.77 313	75.48 266	16.73 333	85.98 216	35.10 297	54.78 284	73.72 315

MVS_0304		56.72 277	55.17 274	61.37 307	70.71 295	36.80 322	75.74 267	68.75 319	44.11 311	52.53 267	68.20 314	15.05 338	74.53 315	42.98 267	58.44 246	72.79 322

UnsupCasMVSNet_eth		57.56 274	55.15 275	64.79 290	64.57 329	33.12 330	73.17 286	83.87 140	58.98 172	41.75 317	70.03 307	22.54 306	79.92 280	46.12 255	35.31 335	81.32 245

MSDG		59.44 256	55.14 276	72.32 199	74.69 256	50.71 168	74.39 279	73.58 292	44.44 307	43.40 310	77.52 236	19.45 320	90.87 80	31.31 311	57.49 261	75.38 304

our_test_3		59.11 261	55.08 277	71.18 225	71.42 290	53.29 113	81.96 205	74.52 282	48.32 279	42.08 314	69.28 311	28.14 269	82.15 259	34.35 299	45.68 319	78.11 281

ppachtmachnet_test		58.56 269	54.34 278	71.24 222	71.42 290	54.74 72	81.84 210	72.27 301	49.02 277	45.86 304	68.99 312	26.27 283	83.30 254	30.12 314	43.23 324	75.69 301

Patchmatch-RL test		58.72 267	54.32 279	71.92 212	63.91 331	44.25 280	61.73 325	55.19 338	57.38 203	49.31 285	54.24 341	37.60 181	80.89 268	62.19 138	47.28 311	90.63 71

RPMNet		59.29 257	54.25 280	74.42 151	73.97 265	56.57 30	60.52 329	76.98 259	35.72 334	57.49 226	58.87 337	37.73 178	85.26 230	27.01 328	59.93 233	81.42 238

test20.03		55.22 288	54.07 281	58.68 315	63.14 334	25.00 347	77.69 260	74.78 281	52.64 254	43.43 309	72.39 292	26.21 284	74.76 314	29.31 316	47.05 314	76.28 299

LS3D		56.40 282	53.82 282	64.12 291	81.12 160	45.69 267	73.42 284	66.14 323	35.30 338	43.24 312	79.88 214	22.18 310	79.62 284	19.10 345	64.00 204	67.05 334

PatchMatch-RL		56.66 278	53.75 283	65.37 286	77.91 216	45.28 269	69.78 308	60.38 333	41.35 320	47.57 294	73.73 275	16.83 331	76.91 306	36.99 285	59.21 240	73.92 314

F-COLMAP		55.96 286	53.65 284	62.87 298	72.76 276	42.77 293	74.70 278	70.37 314	40.03 322	41.11 320	79.36 218	17.77 328	73.70 321	32.80 306	53.96 289	72.15 323

test_0402		56.45 281	53.03 285	66.69 276	76.78 231	50.31 183	81.76 211	69.61 317	42.79 317	43.88 306	72.13 295	22.82 305	86.46 201	16.57 347	50.94 301	63.31 341

PatchT		56.60 279	52.97 286	67.48 267	72.94 274	46.16 261	57.30 334	73.78 290	38.77 325	54.37 254	57.26 340	37.52 183	78.06 294	32.02 307	52.79 296	78.23 280

Patchmtry		56.56 280	52.95 287	67.42 268	72.53 279	50.59 172	59.05 331	71.72 304	37.86 329	46.92 297	65.86 320	38.94 165	80.06 279	36.94 286	46.72 316	71.60 326

XVG-ACMP-BASELINE		56.03 284	52.85 288	65.58 282	61.91 337	40.95 307	63.36 319	72.43 299	45.20 302	46.02 302	74.09 272	9.20 347	78.12 292	45.13 257	58.27 248	77.66 285

pmmvs-eth3d		55.97 285	52.78 289	65.54 283	61.02 339	46.44 254	75.36 274	67.72 321	49.61 274	43.65 308	67.58 316	21.63 313	77.04 304	44.11 262	44.33 321	73.15 321

CMPMVS	40.41 21	55.34 287	52.64 290	63.46 295	60.88 340	43.84 283	61.58 327	71.06 311	30.43 341	36.33 330	74.63 271	24.14 298	75.44 311	48.05 242	66.62 187	71.12 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		54.25 292	52.57 291	59.29 313	62.76 335	21.65 352	72.21 294	70.47 313	53.25 251	41.94 315	77.33 240	14.28 339	77.95 298	29.18 317	51.72 299	78.28 278

ADS-MVSNet		56.17 283	51.95 292	68.84 253	80.60 172	53.07 120	55.03 336	70.02 316	44.72 304	51.00 277	61.19 330	22.83 303	78.88 288	28.54 321	53.63 290	74.57 310

ADS-MVSNet2		55.21 289	51.44 293	66.51 278	80.60 172	49.56 198	55.03 336	65.44 324	44.72 304	51.00 277	61.19 330	22.83 303	75.41 312	28.54 321	53.63 290	74.57 310

USDC		54.36 291	51.23 294	63.76 293	64.29 330	37.71 318	62.84 324	73.48 296	56.85 209	35.47 333	71.94 298	9.23 346	78.43 289	38.43 278	48.57 304	75.13 307

EU-MVSNet		52.63 299	50.72 295	58.37 316	62.69 336	28.13 345	72.60 288	75.97 272	30.94 340	40.76 322	72.11 296	20.16 318	70.80 331	35.11 296	46.11 317	76.19 300

UnsupCasMVSNet_bld		53.86 294	50.53 296	63.84 292	63.52 333	34.75 324	71.38 300	81.92 170	46.53 290	38.95 326	57.93 338	20.55 317	80.20 278	39.91 275	34.09 341	76.57 296

SixPastTwentyTwo		54.37 290	50.10 297	67.21 269	70.70 296	41.46 303	74.73 277	64.69 326	47.56 283	39.12 325	69.49 308	18.49 326	84.69 241	31.87 308	34.20 340	75.48 303

YYNet1		53.82 295	49.96 298	65.41 285	70.09 301	48.95 209	72.30 292	71.66 306	44.25 309	31.89 342	63.07 327	23.73 300	73.95 318	33.26 303	39.40 331	73.34 318

MDA-MVSNet_test_wron		53.82 295	49.95 299	65.43 284	70.13 300	49.05 207	72.30 292	71.65 307	44.23 310	31.85 343	63.13 326	23.68 301	74.01 317	33.25 304	39.35 332	73.23 320

LTVRE_ROB	45.45 19	52.73 298	49.74 300	61.69 304	69.78 302	34.99 323	44.52 343	67.60 322	43.11 316	43.79 307	74.03 273	18.54 325	81.45 264	28.39 323	57.94 255	68.62 332
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
K. test v3		54.04 293	49.42 301	67.92 265	68.55 309	42.57 297	75.51 272	63.07 330	52.07 258	39.21 324	64.59 324	19.34 321	82.21 258	37.11 283	25.31 347	78.97 266

OurMVSNet-221017-0		52.39 300	48.73 302	63.35 296	65.21 325	38.42 315	68.54 312	64.95 325	38.19 326	39.57 323	71.43 299	13.23 341	79.92 280	37.16 281	40.32 330	71.72 325

Patchmatch-test		53.33 297	48.17 303	68.81 255	73.31 268	42.38 298	42.98 345	58.23 335	32.53 339	38.79 327	70.77 303	39.66 161	73.51 322	25.18 332	52.06 298	90.55 72

COLMAP_ROB	43.60 20	50.90 303	48.05 304	59.47 312	67.81 316	40.57 309	71.25 301	62.72 332	36.49 333	36.19 331	73.51 280	13.48 340	73.92 319	20.71 341	50.26 302	63.92 340
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 304	47.81 305	57.96 317	61.53 338	27.80 346	67.40 313	74.06 287	43.25 315	33.31 341	65.38 323	16.03 335	71.34 329	21.80 338	47.55 309	74.75 308

JIA-IIPM		52.33 301	47.77 306	66.03 280	71.20 293	46.92 248	40.00 349	76.48 269	37.10 330	46.73 298	37.02 346	32.96 234	77.88 299	35.97 289	52.45 297	73.29 319

MDA-MVSNet-bldmvs		51.56 302	47.75 307	63.00 297	71.60 288	47.32 244	69.70 309	72.12 302	43.81 312	27.65 347	63.38 325	21.97 312	75.96 309	27.30 327	32.19 342	65.70 338

DIV-MVS_2432*1600		49.24 305	46.85 308	56.44 319	54.32 345	22.87 349	57.39 333	73.36 297	44.36 308	37.98 328	59.30 336	18.97 323	71.17 330	33.48 301	42.44 325	75.26 305

new-patchmatchnet		48.21 307	46.55 309	53.18 323	57.73 343	18.19 356	70.24 304	71.02 312	45.70 297	33.70 337	60.23 332	18.00 327	69.86 334	27.97 324	34.35 338	71.49 328

MVS-HIRNet		49.01 306	44.71 310	61.92 303	76.06 239	46.61 252	63.23 321	54.90 339	24.77 345	33.56 338	36.60 347	21.28 315	75.88 310	29.49 315	62.54 221	63.26 342

AllTest		47.32 309	44.66 311	55.32 321	65.08 326	37.50 319	62.96 323	54.25 341	35.45 336	33.42 339	72.82 285	9.98 344	59.33 341	24.13 334	43.84 322	69.13 330

TinyColmap		48.15 308	44.49 312	59.13 314	65.73 322	38.04 316	63.34 320	62.86 331	38.78 324	29.48 345	67.23 318	6.46 352	73.30 323	24.59 333	41.90 327	66.04 336

RPSCF		45.77 311	44.13 313	50.68 325	57.67 344	29.66 339	54.92 338	45.25 348	26.69 344	45.92 303	75.92 264	17.43 330	45.70 352	27.44 326	45.95 318	76.67 292

PM-MVS		46.92 310	43.76 314	56.41 320	52.18 347	32.26 335	63.21 322	38.18 351	37.99 328	40.78 321	66.20 319	5.09 355	65.42 338	48.19 241	41.99 326	71.54 327

pmmvs3		45.53 312	41.55 315	57.44 318	48.97 350	39.68 311	70.06 305	57.66 336	28.32 343	34.06 336	57.29 339	8.50 348	66.85 337	34.86 298	34.26 339	65.80 337

N_pmnet		41.25 313	39.77 316	45.66 329	68.50 310	0.82 364	72.51 290	0.38 364	35.61 335	35.26 334	61.51 329	20.07 319	67.74 336	23.51 336	40.63 328	68.42 333

TDRefinement		40.91 314	38.37 317	48.55 327	50.45 348	33.03 332	58.98 332	50.97 344	28.50 342	29.89 344	67.39 317	6.21 354	54.51 345	17.67 346	35.25 336	58.11 343

DSMNet-mixed		38.35 315	35.36 318	47.33 328	48.11 351	14.91 358	37.87 350	36.60 353	19.18 349	34.37 335	59.56 335	15.53 336	53.01 347	20.14 343	46.89 315	74.07 312

FPMVS		35.40 316	33.67 319	40.57 331	46.34 352	28.74 344	41.05 347	57.05 337	20.37 348	22.27 349	53.38 342	6.87 350	44.94 353	8.62 351	47.11 313	48.01 347

LF4IMVS		33.04 319	32.55 320	34.52 335	40.96 353	22.03 351	44.45 344	35.62 354	20.42 347	28.12 346	62.35 328	5.03 356	31.88 357	21.61 340	34.42 337	49.63 346

new_pmnet		33.56 318	31.89 321	38.59 332	49.01 349	20.42 353	51.01 339	37.92 352	20.58 346	23.45 348	46.79 343	6.66 351	49.28 350	20.00 344	31.57 344	46.09 348

ANet_high		34.39 317	29.59 322	48.78 326	30.34 358	22.28 350	55.53 335	63.79 329	38.11 327	15.47 351	36.56 348	6.94 349	59.98 340	13.93 349	5.64 358	64.08 339

cdsmvs_eth3d_5k		18.33 326	24.44 323	0.00 345	0.00 365	0.00 366	0.00 357	89.40 17	0.00 360	0.00 363	92.02 36	38.55 169	0.00 361	0.00 361	0.00 359	0.00 359

Gipuma		27.47 321	24.26 324	37.12 334	60.55 341	29.17 342	11.68 355	60.00 334	14.18 352	10.52 355	15.12 356	2.20 360	63.01 339	8.39 352	35.65 334	19.18 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 320	23.85 325	40.71 330	27.46 360	18.93 355	30.82 352	46.19 345	12.76 353	16.40 350	34.70 350	1.90 361	48.69 351	20.25 342	24.22 348	54.51 344

PMMVS2		26.71 322	22.98 326	37.87 333	36.89 355	8.51 362	42.51 346	29.32 358	19.09 350	13.01 352	37.54 345	2.23 359	53.11 346	14.54 348	11.71 351	51.99 345

PMVS	19.57 22	25.07 323	22.43 327	32.99 336	23.12 361	22.98 348	40.98 348	35.19 355	15.99 351	11.95 354	35.87 349	1.47 363	49.29 349	5.41 356	31.90 343	26.70 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		19.16 324	18.40 328	21.44 338	36.19 356	13.63 359	47.59 340	30.89 356	10.73 354	5.91 358	16.59 354	3.66 358	39.77 354	5.95 355	8.14 353	10.92 353

EMVS		18.42 325	17.66 329	20.71 339	34.13 357	12.64 360	46.94 341	29.94 357	10.46 356	5.58 359	14.93 357	4.23 357	38.83 355	5.24 357	7.51 355	10.67 354

MVE	16.60 23	17.34 327	13.39 330	29.16 337	28.43 359	19.72 354	13.73 354	23.63 359	7.23 357	7.96 356	21.41 352	0.80 364	36.08 356	6.97 353	10.39 352	31.69 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 328	10.68 331	5.73 342	2.49 363	4.21 363	10.48 356	18.04 360	0.34 359	12.59 353	20.49 353	11.39 342	7.03 360	13.84 350	6.46 357	5.95 355

ab-mvs-re		7.68 330	10.24 332	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	92.12 33	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

wuyk23d		9.11 329	8.77 333	10.15 341	40.18 354	16.76 357	20.28 353	1.01 363	2.58 358	2.66 360	0.98 360	0.23 365	12.49 359	4.08 358	6.90 356	1.19 356

testmvs		6.14 331	8.18 334	0.01 343	0.01 364	0.00 366	73.40 285	0.00 365	0.00 360	0.02 361	0.15 361	0.00 366	0.00 361	0.02 359	0.00 359	0.02 357

test123		6.01 332	8.01 335	0.01 343	0.00 365	0.01 365	71.93 298	0.00 365	0.00 360	0.02 361	0.11 362	0.00 366	0.00 361	0.02 359	0.00 359	0.02 357

pcd_1.5k_mvsjas		3.15 333	4.20 336	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	37.77 175	0.00 361	0.00 361	0.00 359	0.00 359

uanet_test		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 365	0.00 366	0.00 357	0.00 365	0.00 360	0.00 363	0.00 363	0.00 366	0.00 361	0.00 361	0.00 359	0.00 359

ZD-MVS					89.55 11	53.46 101		84.38 125	57.02 207	73.97 43	91.03 54	44.57 105	91.17 72	75.41 52	81.78 69

IU-MVS					89.48 14	57.49 17		91.38 5	66.22 49	88.26 1				82.83 7	87.60 16	92.44 25

OPU-MVS				81.71 12	92.05 3	55.97 42	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

test_241102_TWO								88.76 34	57.50 201	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

test_241102_ONE					89.48 14	56.89 26		88.94 27	57.53 199	84.61 3	93.29 13	58.81 9	96.45 1

save fliter					85.35 64	56.34 36	89.31 38	81.46 177	61.55 117

test_0728_THIRD									58.00 187	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

test_0728_SECOND				82.20 8	89.50 12	57.73 13	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

test0726					89.40 17	57.45 18	92.32 7	88.63 37	57.71 195	83.14 7	93.96 7	55.17 17

GSMVS																88.13 126

test_part2					89.33 19	55.48 48				82.27 8

sam_mvs1												38.86 167				88.13 126

sam_mvs												35.99 211

ambc				62.06 301	53.98 346	29.38 341	35.08 351	79.65 208		41.37 318	59.96 333	6.27 353	82.15 259	35.34 292	38.22 333	74.65 309

MTGPA								81.31 180

test_post1							70.84 303				14.72 358	34.33 222	83.86 245	48.80 236

test_post											16.22 355	37.52 183	84.72 240

patchmatchnet-post											59.74 334	38.41 170	79.91 282

GG-mvs-BLEND				77.77 81	86.68 42	50.61 170	68.67 311	88.45 44		68.73 86	87.45 130	59.15 8	90.67 83	54.83 198	87.67 15	92.03 35

MTMP							87.27 75	15.34 361

gm-plane-assit					83.24 106	54.21 85			70.91 12		88.23 117		95.25 12	66.37 105

test9_res														78.72 27	85.44 40	91.39 52

TEST9					85.68 51	55.42 49	87.59 65	84.00 136	57.72 194	72.99 53	90.98 56	44.87 100	88.58 138

test_8					85.72 50	55.31 53	87.60 62	83.88 139	57.84 192	72.84 56	90.99 55	44.99 97	88.34 148

agg_prior2														75.65 47	85.11 44	91.01 63

agg_prior					85.64 54	54.92 67		83.61 145		72.53 60			88.10 158

TestCases				55.32 321	65.08 326	37.50 319		54.25 341	35.45 336	33.42 339	72.82 285	9.98 344	59.33 341	24.13 334	43.84 322	69.13 330

test_prior4						56.39 35	87.15 78

test_prior2							89.04 43		61.88 112	73.55 47	91.46 50	48.01 62		74.73 55	85.46 38

test_prior				78.39 66	86.35 44	54.91 69		85.45 92					89.70 109			90.55 72

旧先验2							81.73 212		45.53 299	74.66 35			70.48 333	58.31 172

新几何2							81.61 216

新几何1				73.30 181	83.10 108	53.48 100		71.43 309	45.55 298	66.14 109	87.17 133	33.88 228	80.54 272	48.50 239	80.33 82	85.88 167

旧先验1					81.57 149	47.48 241		71.83 303			88.66 109	36.94 194			78.34 99	88.67 115

无先验							85.19 122	78.00 242	49.08 276				85.13 234	52.78 214		87.45 139

原ACMM2							83.77 161

原ACMM1				76.13 120	84.89 74	54.59 79		85.26 103	51.98 259	66.70 100	87.07 135	40.15 156	89.70 109	51.23 223	85.06 45	84.10 192

test222					79.36 183	50.97 166	77.99 258	67.84 320	42.54 318	62.84 155	86.53 142	30.26 259			76.91 109	85.23 179

testdata2													77.81 301	45.64 256

segment_acmp												44.97 99

testdata				67.08 271	77.59 218	45.46 268		69.20 318	44.47 306	71.50 72	88.34 113	31.21 252	70.76 332	52.20 220	75.88 117	85.03 181

testdata1							77.55 261		64.14 76

test12				79.24 36	86.89 40	56.08 41		85.16 107		72.27 65		47.15 71	91.10 73		85.93 33	90.54 75

plane_prior7					77.95 213	48.46 226

plane_prior6					78.42 208	49.39 202						36.04 209

plane_prior5								82.59 161					88.30 151	65.46 115	72.34 150	84.49 187

plane_prior4											83.28 177

plane_prior3						48.95 209			64.01 77	62.15 161

plane_prior2							85.76 106		63.60 87

plane_prior1					78.31 210

plane_prior						49.57 196	87.43 69		64.57 72						72.84 145

n2								0.00 365

nn								0.00 365

door-mid								41.31 350

lessismore_v0				67.98 264	64.76 328	41.25 304		45.75 347		36.03 332	65.63 322	19.29 322	84.11 244	35.67 290	21.24 349	78.59 272

LGP-MVS_train				72.02 204	74.42 259	48.60 218		80.64 190	54.69 240	53.75 260	83.83 167	25.73 288	86.98 185	60.33 158	64.71 198	80.48 255

test11								84.25 130

door								43.27 349

HQP5-MVS						51.56 154

HQP-NCC					79.02 191		88.00 56		65.45 58	64.48 132

ACMP_Plane					79.02 191		88.00 56		65.45 58	64.48 132

BP-MVS														66.70 102

HQP4-MVS										64.47 135			88.61 137			84.91 184

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 186

NP-MVS					78.76 196	50.43 176					85.12 156

MDTV_nov1_ep13_2view						43.62 285	71.13 302		54.95 237	59.29 191		36.76 197		46.33 253		87.32 141

ACMMP++_ref															63.20 214

ACMMP++															59.38 238

Test By Simon												39.38 162

ITE_SJBPF				51.84 324	58.03 342	31.94 337		53.57 343	36.67 332	41.32 319	75.23 268	11.17 343	51.57 348	25.81 331	48.04 306	72.02 324

DeepMVS_CX				13.10 340	21.34 362	8.99 361		10.02 362	10.59 355	7.53 357	30.55 351	1.82 362	14.55 358	6.83 354	7.52 354	15.75 352

High-resolution multi-view benchmark