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.

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 64	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 64	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

PC_three_1452									55.09 224	84.46 4	89.84 47	66.68 5	89.41 18	74.24 53	91.38 2	88.42 16

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 46	67.01 1	90.33 12	73.16 63	91.15 4	88.23 22

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 73	87.82 7	86.78 10	64.18 33	85.97 1	91.84 8	66.87 3	90.83 5	78.63 19	90.87 5	88.23 22

IU-MVS					87.77 4	59.15 64		85.53 27	53.93 253	84.64 3				79.07 12	90.87 5	88.37 18

test_241102_TWO								86.73 12	64.18 33	84.26 5	91.84 8	65.19 6	90.83 5	78.63 19	90.70 7	87.65 41

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 9	90.65 8	87.85 33

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 68	87.85 5	85.03 37	64.26 30	83.82 8	92.00 3	64.82 8	90.75 8	78.66 17	90.61 11	85.45 132
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				79.19 16	87.82 3	59.11 67	87.85 5	87.15 3					90.84 3	78.66 17	90.61 11	87.62 43

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 54	85.16 32	62.88 55	78.10 27	91.26 17	52.51 83	88.39 30	79.34 8	90.52 13	86.78 74

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 23	90.64 22	58.63 25	87.24 55	79.00 13	90.37 14	85.26 144

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 78	57.83 85	84.99 32	88.13 2	61.86 77	79.16 20	90.75 21	57.96 26	87.09 64	77.08 30	90.18 15	87.87 32

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 32	90.06 40	59.47 21	89.13 22	78.67 16	89.73 16	87.03 65

PHI-MVS		75.87 49	75.36 51	77.41 50	80.62 114	55.91 118	84.28 44	85.78 21	56.08 199	73.41 83	86.58 112	50.94 112	88.54 28	70.79 84	89.71 17	87.79 37

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 46	83.27 13	91.83 10	64.96 7	90.47 11	76.41 34	89.67 18	86.84 71
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 57	82.93 64	85.39 28	62.15 69	76.41 41	91.51 11	52.47 85	86.78 71	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 45	75.08 53	90.47 29	53.96 63	88.68 27	76.48 33	89.63 20	87.16 62

9.14			78.75 15		83.10 73		84.15 48	88.26 1	59.90 118	78.57 25	90.36 31	57.51 32	86.86 69	77.39 26	89.52 21

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 47	86.85 6	63.23 48	73.84 79	90.25 36	57.68 29	89.96 15	74.62 52	89.03 22	87.89 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 29	90.98 19	54.26 58	90.06 14	78.42 22	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
test_prior2							81.75 83		60.37 105	75.01 54	89.06 56	56.22 41		72.19 71	88.96 24

DPM-MVS		75.47 54	75.00 55	76.88 56	81.38 98	59.16 63	79.94 106	85.71 23	56.59 187	72.46 105	86.76 102	56.89 35	87.86 45	66.36 114	88.91 25	83.64 203

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 62	85.33 29	62.86 56	80.17 17	90.03 42	61.76 14	88.95 24	74.21 54	88.67 26	88.12 26

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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
CDPH-MVS		76.31 43	75.67 49	78.22 37	85.35 48	59.14 66	81.31 90	84.02 52	56.32 193	74.05 74	88.98 58	53.34 74	87.92 43	69.23 92	88.42 28	87.59 45

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 42	74.29 72	90.03 42	52.56 82	88.53 29	74.79 51	88.34 29	86.63 81

train_agg		76.27 44	76.15 41	76.64 64	85.58 43	61.59 24	81.62 85	81.26 125	55.86 201	74.93 56	88.81 62	53.70 69	84.68 127	75.24 47	88.33 30	83.65 202

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 53	82.40 14	92.12 2	59.64 19	89.76 16	78.70 14	88.32 31	86.79 73
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test9_res														75.28 46	88.31 32	83.81 191

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 226	80.97 136	65.13 15	75.77 43	90.88 20	48.63 138	86.66 74	77.23 27	88.17 33	84.81 159

MM		80.20 7	80.28 8	79.99 2	82.19 84	60.01 49	86.19 17	83.93 55	73.19 1	77.08 37	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

test12				77.76 45	84.52 58	58.41 79		83.36 77		72.93 97		54.61 56	88.05 39		88.12 34	86.81 72

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 38	73.60 81	90.60 23	54.85 53	86.72 72	77.20 28	88.06 36	85.74 120
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 76	73.06 94	88.88 61	53.72 68	89.06 23	68.27 94	88.04 37	87.42 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		76.58 39	76.55 38	76.68 61	81.73 90	52.90 176	80.94 93	85.70 24	61.12 88	74.90 59	87.17 94	56.46 38	88.14 36	72.87 65	88.03 38	89.00 8

原ACMM1				74.69 96	85.39 47	59.40 58		83.42 74	51.47 281	70.27 127	86.61 110	48.61 139	86.51 80	53.85 225	87.96 39	78.16 304

agg_prior2														73.09 64	87.93 40	84.33 169

CSCG		76.92 34	76.75 32	77.41 50	83.96 64	59.60 55	82.95 63	86.50 13	60.78 93	75.27 48	84.83 152	60.76 15	86.56 77	67.86 100	87.87 41	86.06 104

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 109	57.91 84	84.68 36	81.64 110	68.35 2	75.77 43	90.38 30	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

lecture		77.75 25	77.84 25	77.50 48	82.75 80	57.62 88	85.92 21	86.20 17	60.53 99	78.99 22	91.45 12	51.51 103	87.78 47	75.65 41	87.55 43	87.10 64

MCST-MVS		77.48 29	77.45 28	77.54 47	86.67 20	58.36 80	83.22 60	86.93 5	56.91 178	74.91 58	88.19 69	59.15 23	87.68 51	73.67 60	87.45 44	86.57 82

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 66	89.38 53	55.30 47	89.18 21	74.19 55	87.34 45	86.38 87

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 57	73.96 76	90.50 27	53.20 75	88.35 31	74.02 57	87.05 46	86.13 102

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 59	73.30 84	90.58 24	49.90 120	88.21 34	73.78 59	87.03 47	86.29 99

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 57	73.55 82	90.56 25	49.80 123	88.24 33	74.02 57	87.03 47	86.32 95

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 97	79.05 21	90.30 34	55.54 46	88.32 32	73.48 62	87.03 47	84.83 158
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZD-MVS					86.64 21	60.38 45		82.70 96	57.95 161	78.10 27	90.06 40	56.12 42	88.84 26	74.05 56	87.00 50

SPE-MVS-test		75.62 53	75.31 53	76.56 66	80.63 113	55.13 135	83.88 53	85.22 30	62.05 73	71.49 117	86.03 130	53.83 65	86.36 85	67.74 101	86.91 51	88.19 24

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 72	83.74 65	61.71 78	72.45 107	90.34 33	48.48 141	88.13 37	72.32 70	86.85 52	85.78 114

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 65	85.08 34	62.57 62	73.09 93	89.97 45	50.90 113	87.48 53	75.30 45	86.85 52	87.33 57
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 50	83.82 64	59.34 133	79.37 19	89.76 49	59.84 16	87.62 52	76.69 31	86.74 54	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 24	72.17 108	90.01 44	47.95 145	88.01 40	71.55 80	86.74 54	86.37 89

X-MVStestdata		70.21 137	67.28 191	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 24	72.17 108	6.49 445	47.95 145	88.01 40	71.55 80	86.74 54	86.37 89

MVSMamba_PlusPlus		75.75 52	75.44 50	76.67 62	80.84 107	53.06 173	78.62 128	85.13 33	59.65 124	71.53 116	87.47 85	56.92 34	88.17 35	72.18 72	86.63 57	88.80 10

3Dnovator+	66.72 4	75.84 50	74.57 61	79.66 9	82.40 81	59.92 51	85.83 23	86.32 16	66.92 7	67.80 180	89.24 55	42.03 217	89.38 19	64.07 133	86.50 58	89.69 3

EPNet		73.09 81	72.16 90	75.90 73	75.95 236	56.28 109	83.05 61	72.39 281	66.53 10	65.27 229	87.00 96	50.40 117	85.47 109	62.48 155	86.32 59	85.94 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		74.76 59	74.46 62	75.65 82	77.84 187	52.25 193	75.59 209	84.17 50	63.76 39	73.15 89	82.79 195	59.58 20	86.80 70	67.24 107	86.04 60	87.89 30
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
CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 66	72.68 101	90.50 27	48.18 143	87.34 54	73.59 61	85.71 61	84.76 162

mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 100	62.90 54	71.77 112	90.26 35	46.61 170	86.55 78	71.71 78	85.66 62	84.97 155

EC-MVSNet		75.84 50	75.87 47	75.74 79	78.86 151	52.65 183	83.73 55	86.08 18	63.47 44	72.77 100	87.25 93	53.13 76	87.93 42	71.97 75	85.57 63	86.66 79

MSLP-MVS++		73.77 73	73.47 74	74.66 98	83.02 75	59.29 62	82.30 79	81.88 105	59.34 133	71.59 115	86.83 100	45.94 174	83.65 146	65.09 126	85.22 64	81.06 261

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 105	79.89 18	89.38 53	54.97 51	85.58 104	76.12 37	84.94 65	86.33 93
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
3Dnovator	64.47 5	72.49 93	71.39 102	75.79 76	77.70 192	58.99 72	80.66 98	83.15 88	62.24 68	65.46 225	86.59 111	42.38 215	85.52 105	59.59 181	84.72 66	82.85 224

CS-MVS		76.25 45	75.98 44	77.06 55	80.15 123	55.63 125	84.51 39	83.90 58	63.24 47	73.30 84	87.27 92	55.06 49	86.30 87	71.78 77	84.58 67	89.25 5

CANet		76.46 41	75.93 45	78.06 39	81.29 99	57.53 90	82.35 74	83.31 81	67.78 3	70.09 128	86.34 120	54.92 52	88.90 25	72.68 67	84.55 68	87.76 38

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 112	77.85 30	91.42 14	50.67 114	87.69 49	72.46 68	84.53 69	85.46 130

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 112	77.85 30	91.42 14	50.67 114	87.69 49	72.46 68	84.53 69	85.46 130

LFMVS		71.78 107	71.59 96	72.32 174	83.40 71	46.38 279	79.75 111	71.08 290	64.18 33	72.80 99	88.64 66	42.58 212	83.72 144	57.41 195	84.49 71	86.86 70

TSAR-MVS + GP.		74.90 57	74.15 67	77.17 54	82.00 86	58.77 76	81.80 82	78.57 177	58.58 147	74.32 71	84.51 163	55.94 43	87.22 58	67.11 108	84.48 72	85.52 126

test2506		65.33 246	64.61 239	67.50 266	79.46 135	34.19 399	74.43 237	51.92 408	58.72 142	66.75 201	88.05 73	25.99 390	80.92 211	51.94 240	84.25 73	87.39 52

ECVR-MVS		67.72 204	67.51 182	68.35 259	79.46 135	36.29 384	74.79 229	66.93 327	58.72 142	67.19 192	88.05 73	36.10 288	81.38 198	52.07 238	84.25 73	87.39 52

MAR-MVS		71.51 112	70.15 129	75.60 84	81.84 89	59.39 59	81.38 89	82.90 92	54.90 236	68.08 170	78.70 283	47.73 148	85.51 106	51.68 245	84.17 75	81.88 243
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
API-MVS		72.17 100	71.41 101	74.45 108	81.95 88	57.22 94	84.03 50	80.38 146	59.89 122	68.40 160	82.33 209	49.64 124	87.83 46	51.87 241	84.16 76	78.30 302

casdiffmvs_mvg		76.14 46	76.30 40	75.66 81	76.46 230	51.83 203	79.67 113	85.08 34	65.02 19	75.84 42	88.58 67	59.42 22	85.08 115	72.75 66	83.93 77	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SymmetryMVS		75.28 55	74.60 60	77.30 53	83.85 65	59.89 52	84.36 41	75.51 235	64.69 22	74.21 73	87.40 87	49.48 126	86.17 88	68.04 99	83.88 78	85.85 111

test1111		67.21 211	67.14 199	67.42 268	79.24 141	34.76 393	73.89 248	65.65 336	58.71 144	66.96 197	87.95 77	36.09 289	80.53 218	52.03 239	83.79 79	86.97 67

reproduce_model		76.43 42	76.08 42	77.49 49	83.47 70	60.09 47	84.60 37	82.90 92	59.65 124	77.31 33	91.43 13	49.62 125	87.24 55	71.99 74	83.75 80	85.14 146

IS-MVSNet		71.57 111	71.00 112	73.27 152	78.86 151	45.63 290	80.22 102	78.69 174	64.14 36	66.46 206	87.36 89	49.30 129	85.60 102	50.26 254	83.71 81	88.59 13

UA-Net		73.13 80	72.93 80	73.76 127	83.58 67	51.66 204	78.75 124	77.66 199	67.75 4	72.61 103	89.42 51	49.82 122	83.29 153	53.61 227	83.14 82	86.32 95

MG-MVS		73.96 71	73.89 70	74.16 116	85.65 42	49.69 237	81.59 87	81.29 124	61.45 81	71.05 120	88.11 70	51.77 98	87.73 48	61.05 167	83.09 83	85.05 151

OpenMVS	61.03 9	68.85 173	67.56 178	72.70 163	74.26 276	53.99 152	81.21 91	81.34 122	52.70 266	62.75 274	85.55 145	38.86 259	84.14 135	48.41 270	83.01 84	79.97 280

SR-MVS		76.13 47	75.70 48	77.40 52	85.87 40	61.20 29	85.52 28	82.19 101	59.99 117	75.10 52	90.35 32	47.66 150	86.52 79	71.64 79	82.99 85	84.47 168

VDDNet		71.81 106	71.33 104	73.26 153	82.80 79	47.60 270	78.74 125	75.27 240	59.59 129	72.94 96	89.40 52	41.51 229	83.91 141	58.75 187	82.99 85	88.26 20

MVS_111021_HR		74.02 70	73.46 75	75.69 80	83.01 76	60.63 40	77.29 167	78.40 188	61.18 86	70.58 123	85.97 132	54.18 60	84.00 140	67.52 105	82.98 87	82.45 232

ETV-MVS		74.46 66	73.84 71	76.33 69	79.27 140	55.24 134	79.22 119	85.00 39	64.97 21	72.65 102	79.46 273	53.65 72	87.87 44	67.45 106	82.91 88	85.89 110

HPM-MVS_fast		74.30 68	73.46 75	76.80 58	84.45 60	59.04 70	83.65 57	81.05 133	60.15 114	70.43 124	89.84 47	41.09 236	85.59 103	67.61 104	82.90 89	85.77 117

ACMMP		76.02 48	75.33 52	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 51	69.80 138	89.74 50	45.43 183	87.16 61	72.01 73	82.87 90	85.14 146
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
APD-MVS_3200maxsize		74.96 56	74.39 63	76.67 62	82.20 83	58.24 81	83.67 56	83.29 82	58.41 150	73.71 80	90.14 37	45.62 176	85.99 94	69.64 88	82.85 91	85.78 114

casdiffmvs		74.80 58	74.89 58	74.53 106	75.59 243	50.37 224	78.17 141	85.06 36	62.80 60	74.40 69	87.86 78	57.88 27	83.61 147	69.46 91	82.79 92	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		74.61 63	74.70 59	74.34 110	75.70 239	49.99 232	77.54 159	84.63 43	62.73 61	73.98 75	87.79 81	57.67 30	83.82 143	69.49 89	82.74 93	89.20 7

VDD-MVS		72.50 92	72.09 91	73.75 129	81.58 92	49.69 237	77.76 154	77.63 200	63.21 49	73.21 87	89.02 57	42.14 216	83.32 152	61.72 162	82.50 94	88.25 21

CLD-MVS		73.33 77	72.68 84	75.29 90	78.82 153	53.33 167	78.23 138	84.79 42	61.30 84	70.41 125	81.04 239	52.41 86	87.12 62	64.61 132	82.49 95	85.41 136
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sasdasda		74.67 61	74.98 56	73.71 132	78.94 149	50.56 221	80.23 100	83.87 61	60.30 109	77.15 35	86.56 113	59.65 17	82.00 184	66.01 118	82.12 96	88.58 14

canonicalmvs		74.67 61	74.98 56	73.71 132	78.94 149	50.56 221	80.23 100	83.87 61	60.30 109	77.15 35	86.56 113	59.65 17	82.00 184	66.01 118	82.12 96	88.58 14

MVS		67.37 209	66.33 215	70.51 223	75.46 245	50.94 211	73.95 244	81.85 106	41.57 389	62.54 279	78.57 289	47.98 144	85.47 109	52.97 232	82.05 98	75.14 343

patch_mono-2		69.85 146	71.09 110	66.16 287	79.11 146	54.80 141	71.97 280	74.31 258	53.50 259	70.90 121	84.17 167	57.63 31	63.31 376	66.17 115	82.02 99	80.38 273

dcpmvs_2		74.55 65	75.23 54	72.48 168	82.34 82	53.34 166	77.87 148	81.46 114	57.80 166	75.49 45	86.81 101	62.22 13	77.75 271	71.09 83	82.02 99	86.34 91

MGCFI-Net		72.45 94	73.34 77	69.81 236	77.77 189	43.21 314	75.84 206	81.18 129	59.59 129	75.45 46	86.64 107	57.74 28	77.94 265	63.92 137	81.90 101	88.30 19

alignmvs		73.86 72	73.99 68	73.45 146	78.20 172	50.50 223	78.57 130	82.43 98	59.40 131	76.57 39	86.71 106	56.42 40	81.23 203	65.84 121	81.79 102	88.62 12

SR-MVS-dyc-post		74.57 64	73.90 69	76.58 65	83.49 68	59.87 53	84.29 42	81.36 118	58.07 156	73.14 90	90.07 38	44.74 190	85.84 98	68.20 95	81.76 103	84.03 180

RE-MVS-def			73.71 73		83.49 68	59.87 53	84.29 42	81.36 118	58.07 156	73.14 90	90.07 38	43.06 207		68.20 95	81.76 103	84.03 180

新几何1				70.76 216	85.66 41	61.13 30		66.43 331	44.68 364	70.29 126	86.64 107	41.29 231	75.23 309	49.72 258	81.75 105	75.93 334

Vis-MVSNet		72.18 99	71.37 103	74.61 101	81.29 99	55.41 131	80.90 94	78.28 190	60.73 94	69.23 149	88.09 71	44.36 196	82.65 172	57.68 192	81.75 105	85.77 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 153	70.19 127	68.16 261	79.73 129	41.63 331	70.53 300	77.38 205	60.37 105	70.69 122	86.63 109	51.08 109	77.09 284	53.61 227	81.69 107	85.75 119

BP-MVS1		73.41 76	72.25 89	76.88 56	76.68 223	53.70 156	79.15 120	81.07 132	60.66 96	71.81 111	87.39 88	40.93 237	87.24 55	71.23 82	81.29 108	89.71 2

fmvsm_s_conf0.5_n_5		72.69 88	72.80 82	72.37 173	74.11 281	53.21 169	78.12 142	73.31 271	53.98 252	76.81 38	88.05 73	53.38 73	77.37 279	76.64 32	80.78 109	86.53 84

OPM-MVS		74.73 60	74.25 66	76.19 70	80.81 108	59.01 71	82.60 71	83.64 67	63.74 40	72.52 104	87.49 84	47.18 161	85.88 97	69.47 90	80.78 109	83.66 201
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
旧先验1					83.04 74	53.15 170		67.52 320			87.85 79	44.08 197			80.76 111	78.03 309

PAPM_NR		72.63 90	71.80 94	75.13 91	81.72 91	53.42 165	79.91 108	83.28 83	59.14 135	66.31 210	85.90 134	51.86 96	86.06 91	57.45 194	80.62 112	85.91 109

Vis-MVSNet (Re-imp)		63.69 264	63.88 245	63.14 322	74.75 260	31.04 415	71.16 291	63.64 356	56.32 193	59.80 313	84.99 150	44.51 193	75.46 308	39.12 349	80.62 112	82.92 221

HQP_MVS		74.31 67	73.73 72	76.06 71	81.41 96	56.31 107	84.22 45	84.01 53	64.52 26	69.27 146	86.10 127	45.26 187	87.21 59	68.16 97	80.58 114	84.65 163

plane_prior5								84.01 53					87.21 59	68.16 97	80.58 114	84.65 163

UGNet		68.81 174	67.39 186	73.06 155	78.33 169	54.47 143	79.77 110	75.40 238	60.45 101	63.22 262	84.40 164	32.71 329	80.91 212	51.71 244	80.56 116	83.81 191
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
plane_prior						56.31 107	83.58 58		63.19 50						80.48 117

HQP3-MVS								83.90 58							80.35 118

HQP-MVS		73.45 75	72.80 82	75.40 86	80.66 110	54.94 137	82.31 76	83.90 58	62.10 70	67.85 174	85.54 146	45.46 181	86.93 67	67.04 109	80.35 118	84.32 170

PCF-MVS	61.88 8	70.95 122	69.49 139	75.35 87	77.63 196	55.71 122	76.04 200	81.81 107	50.30 296	69.66 139	85.40 149	52.51 83	84.89 122	51.82 242	80.24 120	85.45 132
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 103	70.73 116	76.40 67	86.57 24	57.99 83	81.15 92	82.96 90	57.03 175	66.78 199	85.56 143	44.50 194	88.11 38	51.77 243	80.23 121	83.10 219

fmvsm_s_conf0.5_n_6		72.59 91	72.87 81	71.73 185	75.14 253	51.96 200	76.28 191	77.12 211	57.63 167	73.85 78	86.91 98	51.54 102	77.87 268	77.18 29	80.18 122	85.37 138

CPTT-MVS		72.78 85	72.08 92	74.87 94	84.88 57	61.41 26	84.15 48	77.86 195	55.27 219	67.51 186	88.08 72	41.93 220	81.85 187	69.04 93	80.01 123	81.35 253

114514_t		70.83 124	69.56 137	74.64 100	86.21 31	54.63 142	82.34 75	81.81 107	48.22 325	63.01 269	85.83 137	40.92 238	87.10 63	57.91 191	79.79 124	82.18 237

test_yl		69.69 151	69.13 145	71.36 200	78.37 167	45.74 286	74.71 230	80.20 148	57.91 163	70.01 133	83.83 176	42.44 213	82.87 164	54.97 213	79.72 125	85.48 128

DCV-MVSNet		69.69 151	69.13 145	71.36 200	78.37 167	45.74 286	74.71 230	80.20 148	57.91 163	70.01 133	83.83 176	42.44 213	82.87 164	54.97 213	79.72 125	85.48 128

MVS_Test		72.45 94	72.46 87	72.42 172	74.88 255	48.50 257	76.28 191	83.14 89	59.40 131	72.46 105	84.68 155	55.66 45	81.12 204	65.98 120	79.66 127	87.63 42

PS-MVSNAJ		70.51 130	69.70 135	72.93 157	81.52 93	55.79 121	74.92 226	79.00 165	55.04 230	69.88 136	78.66 285	47.05 163	82.19 181	61.61 163	79.58 128	80.83 265

PVSNet_Blended		68.59 179	67.72 175	71.19 205	77.03 217	50.57 219	72.51 272	81.52 111	51.91 274	64.22 254	77.77 306	49.13 133	82.87 164	55.82 204	79.58 128	80.14 278

EPP-MVSNet		72.16 102	71.31 105	74.71 95	78.68 157	49.70 235	82.10 80	81.65 109	60.40 102	65.94 215	85.84 136	51.74 99	86.37 84	55.93 203	79.55 130	88.07 29

xiu_mvs_v2_base		70.52 129	69.75 133	72.84 159	81.21 102	55.63 125	75.11 219	78.92 167	54.92 235	69.96 135	79.68 268	47.00 167	82.09 183	61.60 164	79.37 131	80.81 266

MVSFormer		71.50 113	70.38 123	74.88 93	78.76 154	57.15 99	82.79 66	78.48 181	51.26 285	69.49 141	83.22 190	43.99 200	83.24 154	66.06 116	79.37 131	84.23 174

lupinMVS		69.57 158	68.28 169	73.44 147	78.76 154	57.15 99	76.57 185	73.29 273	46.19 352	69.49 141	82.18 214	43.99 200	79.23 239	64.66 130	79.37 131	83.93 185

PAPM		67.92 199	66.69 204	71.63 190	78.09 178	49.02 248	77.09 172	81.24 127	51.04 288	60.91 300	83.98 173	47.71 149	84.99 116	40.81 337	79.32 134	80.90 264

Elysia		70.19 139	68.29 167	75.88 74	74.15 278	54.33 147	78.26 134	83.21 85	55.04 230	67.28 189	83.59 181	30.16 352	86.11 89	63.67 143	79.26 135	87.20 60

StellarMVS		70.19 139	68.29 167	75.88 74	74.15 278	54.33 147	78.26 134	83.21 85	55.04 230	67.28 189	83.59 181	30.16 352	86.11 89	63.67 143	79.26 135	87.20 60

FIs		70.82 125	71.43 100	68.98 251	78.33 169	38.14 361	76.96 175	83.59 69	61.02 89	67.33 188	86.73 104	55.07 48	81.64 190	54.61 219	79.22 137	87.14 63

GDP-MVS		72.64 89	71.28 106	76.70 59	77.72 191	54.22 149	79.57 116	84.45 44	55.30 218	71.38 118	86.97 97	39.94 243	87.00 66	67.02 111	79.20 138	88.89 9

jason		69.65 154	68.39 166	73.43 148	78.27 171	56.88 103	77.12 171	73.71 268	46.53 349	69.34 145	83.22 190	43.37 204	79.18 240	64.77 129	79.20 138	84.23 174
jason: jason.
PAPR		71.72 110	70.82 114	74.41 109	81.20 103	51.17 207	79.55 117	83.33 80	55.81 204	66.93 198	84.61 159	50.95 111	86.06 91	55.79 206	79.20 138	86.00 105

EIA-MVS		71.78 107	70.60 118	75.30 89	79.85 127	53.54 162	77.27 168	83.26 84	57.92 162	66.49 205	79.39 275	52.07 93	86.69 73	60.05 175	79.14 141	85.66 122

Effi-MVS+		73.31 78	72.54 86	75.62 83	77.87 185	53.64 158	79.62 115	79.61 155	61.63 80	72.02 110	82.61 200	56.44 39	85.97 95	63.99 136	79.07 142	87.25 59

gg-mvs-nofinetune		57.86 324	56.43 330	62.18 328	72.62 304	35.35 389	66.57 334	56.33 396	50.65 292	57.64 337	57.10 423	30.65 346	76.36 302	37.38 358	78.88 143	74.82 350

CDS-MVSNet		66.80 224	65.37 231	71.10 209	78.98 148	53.13 172	73.27 261	71.07 291	52.15 272	64.72 244	80.23 256	43.56 203	77.10 283	45.48 299	78.88 143	83.05 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
AdaColmap		69.99 143	68.66 157	73.97 121	84.94 54	57.83 85	82.63 70	78.71 173	56.28 195	64.34 248	84.14 168	41.57 226	87.06 65	46.45 286	78.88 143	77.02 323

Anonymous202405211		66.84 223	65.99 222	69.40 243	80.19 121	42.21 324	71.11 293	71.31 289	58.80 141	67.90 172	86.39 119	29.83 357	79.65 232	49.60 261	78.78 146	86.33 93

CANet_DTU		68.18 192	67.71 177	69.59 239	74.83 258	46.24 281	78.66 127	76.85 214	59.60 126	63.45 260	82.09 221	35.25 295	77.41 277	59.88 178	78.76 147	85.14 146

test222					83.14 72	58.68 77	72.57 271	63.45 358	41.78 385	67.56 185	86.12 126	37.13 280			78.73 148	74.98 347

fmvsm_s_conf0.5_n_3		73.55 74	74.39 63	71.03 211	74.09 282	51.86 202	77.77 153	75.60 231	61.18 86	78.67 24	88.98 58	55.88 44	77.73 272	78.69 15	78.68 149	83.50 206

fmvsm_s_conf0.5_n_8		74.30 68	74.39 63	74.01 119	75.33 249	52.89 178	78.24 137	77.32 208	61.65 79	78.13 26	88.90 60	52.82 79	81.54 194	78.46 21	78.67 150	87.60 44

TAMVS		66.78 225	65.27 234	71.33 203	79.16 145	53.67 157	73.84 250	69.59 304	52.32 271	65.28 228	81.72 227	44.49 195	77.40 278	42.32 327	78.66 151	82.92 221

KinetiMVS		71.26 117	70.16 128	74.57 104	74.59 265	52.77 182	75.91 203	81.20 128	60.72 95	69.10 152	85.71 141	41.67 224	83.53 149	63.91 139	78.62 152	87.42 50

PVSNet_Blended_VisFu		71.45 115	70.39 122	74.65 99	82.01 85	58.82 75	79.93 107	80.35 147	55.09 224	65.82 221	82.16 217	49.17 132	82.64 173	60.34 173	78.62 152	82.50 231

test_fmvsmconf_n		73.01 82	72.59 85	74.27 113	71.28 333	55.88 119	78.21 140	75.56 233	54.31 247	74.86 60	87.80 80	54.72 54	80.23 227	78.07 24	78.48 154	86.70 76

testdata				64.66 308	81.52 93	52.93 175		65.29 340	46.09 353	73.88 77	87.46 86	38.08 269	66.26 365	53.31 230	78.48 154	74.78 351

QAPM		70.05 141	68.81 153	73.78 125	76.54 228	53.43 164	83.23 59	83.48 71	52.89 264	65.90 217	86.29 121	41.55 228	86.49 81	51.01 248	78.40 156	81.42 247

test_fmvsmconf0.1_n		72.81 84	72.33 88	74.24 114	69.89 355	55.81 120	78.22 139	75.40 238	54.17 249	75.00 55	88.03 76	53.82 66	80.23 227	78.08 23	78.34 157	86.69 77

fmvsm_l_conf0.5_n_3		73.23 79	73.13 78	73.55 142	74.40 271	55.13 135	78.97 122	74.96 250	56.64 181	74.76 64	88.75 65	55.02 50	78.77 256	76.33 35	78.31 158	86.74 75

myMVS_eth3d28		60.66 297	61.04 288	59.51 344	77.32 209	31.58 413	63.11 365	63.87 353	59.00 137	60.90 301	78.26 292	32.69 331	66.15 366	36.10 372	78.13 159	80.81 266

FC-MVSNet-test		69.80 149	70.58 120	67.46 267	77.61 201	34.73 394	76.05 199	83.19 87	60.84 91	65.88 219	86.46 117	54.52 57	80.76 216	52.52 234	78.12 160	86.91 68

test_fmvsmvis_n_1920		70.84 123	70.38 123	72.22 176	71.16 334	55.39 132	75.86 204	72.21 283	49.03 313	73.28 86	86.17 125	51.83 97	77.29 281	75.80 38	78.05 161	83.98 183

LCM-MVSNet-Re		61.88 288	61.35 281	63.46 318	74.58 266	31.48 414	61.42 375	58.14 386	58.71 144	53.02 382	79.55 271	43.07 206	76.80 292	45.69 293	77.96 162	82.11 240

diffmvs		70.69 127	70.43 121	71.46 194	69.45 361	48.95 251	72.93 264	78.46 183	57.27 171	71.69 113	83.97 174	51.48 104	77.92 267	70.70 85	77.95 163	87.53 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
RRT-MVS		71.46 114	70.70 117	73.74 130	77.76 190	49.30 244	76.60 184	80.45 144	61.25 85	68.17 165	84.78 154	44.64 192	84.90 121	64.79 128	77.88 164	87.03 65

OMC-MVS		71.40 116	70.60 118	73.78 125	76.60 226	53.15 170	79.74 112	79.78 151	58.37 151	68.75 154	86.45 118	45.43 183	80.60 217	62.58 153	77.73 165	87.58 46

mvsmamba		68.47 184	66.56 205	74.21 115	79.60 131	52.95 174	74.94 225	75.48 236	52.09 273	60.10 306	83.27 189	36.54 286	84.70 126	59.32 185	77.69 166	84.99 154

MVS_111021_LR		69.50 162	68.78 154	71.65 189	78.38 165	59.33 60	74.82 228	70.11 298	58.08 155	67.83 179	84.68 155	41.96 218	76.34 303	65.62 123	77.54 167	79.30 293

Fast-Effi-MVS+		70.28 136	69.12 147	73.73 131	78.50 160	51.50 205	75.01 222	79.46 159	56.16 198	68.59 155	79.55 271	53.97 62	84.05 136	53.34 229	77.53 168	85.65 123

fmvsm_l_conf0.5_n		70.99 121	70.82 114	71.48 193	71.45 326	54.40 145	77.18 170	70.46 296	48.67 318	75.17 50	86.86 99	53.77 67	76.86 291	76.33 35	77.51 169	83.17 218

test_fmvsmconf0.01_n		72.17 100	71.50 98	74.16 116	67.96 373	55.58 128	78.06 145	74.67 253	54.19 248	74.54 67	88.23 68	50.35 119	80.24 226	78.07 24	77.46 170	86.65 80

xiu_mvs_v1_base_debu		68.58 180	67.28 191	72.48 168	78.19 173	57.19 96	75.28 214	75.09 246	51.61 276	70.04 129	81.41 233	32.79 325	79.02 249	63.81 140	77.31 171	81.22 256

xiu_mvs_v1_base		68.58 180	67.28 191	72.48 168	78.19 173	57.19 96	75.28 214	75.09 246	51.61 276	70.04 129	81.41 233	32.79 325	79.02 249	63.81 140	77.31 171	81.22 256

xiu_mvs_v1_base_debi		68.58 180	67.28 191	72.48 168	78.19 173	57.19 96	75.28 214	75.09 246	51.61 276	70.04 129	81.41 233	32.79 325	79.02 249	63.81 140	77.31 171	81.22 256

LPG-MVS_test		72.74 86	71.74 95	75.76 77	80.22 118	57.51 91	82.55 72	83.40 75	61.32 82	66.67 203	87.33 90	39.15 255	86.59 75	67.70 102	77.30 174	83.19 214

LGP-MVS_train				75.76 77	80.22 118	57.51 91		83.40 75	61.32 82	66.67 203	87.33 90	39.15 255	86.59 75	67.70 102	77.30 174	83.19 214

test_fmvsm_n_1920		71.73 109	71.14 109	73.50 143	72.52 307	56.53 106	75.60 208	76.16 221	48.11 327	77.22 34	85.56 143	53.10 77	77.43 276	74.86 49	77.14 176	86.55 83

fmvsm_l_conf0.5_n_a		70.50 131	70.27 125	71.18 206	71.30 332	54.09 150	76.89 178	69.87 300	47.90 331	74.37 70	86.49 116	53.07 78	76.69 296	75.41 44	77.11 177	82.76 225

Anonymous20240529		69.91 145	69.02 148	72.56 165	80.19 121	47.65 268	77.56 158	80.99 135	55.45 215	69.88 136	86.76 102	39.24 254	82.18 182	54.04 222	77.10 178	87.85 33

EPNet_dtu		61.90 287	61.97 273	61.68 330	72.89 300	39.78 346	75.85 205	65.62 337	55.09 224	54.56 368	79.36 276	37.59 272	67.02 360	39.80 345	76.95 179	78.25 303
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	59.36 10	66.60 228	65.20 235	70.81 215	76.63 225	48.75 253	76.52 187	80.04 150	50.64 293	65.24 233	84.93 151	39.15 255	78.54 257	36.77 363	76.88 180	85.14 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_s_conf0.5_n_7		69.54 159	69.67 136	69.15 250	73.47 290	51.41 206	70.35 304	73.34 270	57.05 174	68.41 159	85.83 137	49.86 121	72.84 319	71.86 76	76.83 181	83.19 214

ACMP	63.53 6	72.30 97	71.20 108	75.59 85	80.28 116	57.54 89	82.74 68	82.84 95	60.58 98	65.24 233	86.18 124	39.25 253	86.03 93	66.95 112	76.79 182	83.22 212
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		65.98 236	63.42 253	73.64 137	77.26 211	52.58 186	72.26 276	77.21 209	48.56 319	61.21 297	74.60 352	32.57 336	85.82 99	50.38 253	76.75 183	82.52 230

BH-untuned		68.27 188	67.29 190	71.21 204	79.74 128	53.22 168	76.06 198	77.46 204	57.19 172	66.10 212	81.61 229	45.37 185	83.50 150	45.42 301	76.68 184	76.91 327

testing222		62.29 282	61.31 282	65.25 305	77.87 185	38.53 358	68.34 322	66.31 333	56.37 192	63.15 266	77.58 309	28.47 368	76.18 306	37.04 361	76.65 185	81.05 262

ET-MVSNet_ETH3D		67.96 198	65.72 226	74.68 97	76.67 224	55.62 127	75.11 219	74.74 251	52.91 263	60.03 308	80.12 258	33.68 314	82.64 173	61.86 161	76.34 186	85.78 114

UWE-MVS		60.18 303	59.78 297	61.39 335	77.67 194	33.92 402	69.04 319	63.82 354	48.56 319	64.27 251	77.64 308	27.20 380	70.40 338	33.56 384	76.24 187	79.83 285

FA-MVS(test-final)		69.82 147	68.48 160	73.84 123	78.44 163	50.04 230	75.58 211	78.99 166	58.16 154	67.59 184	82.14 218	42.66 210	85.63 101	56.60 198	76.19 188	85.84 112

ACMM	61.98 7	70.80 126	69.73 134	74.02 118	80.59 115	58.59 78	82.68 69	82.02 104	55.46 214	67.18 193	84.39 165	38.51 261	83.17 156	60.65 171	76.10 189	80.30 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		68.24 190	66.82 203	72.51 167	73.46 291	53.60 160	76.23 193	78.88 168	52.78 265	68.08 170	80.13 257	32.70 330	81.41 196	63.16 149	75.97 190	82.53 228

BH-RMVSNet		68.81 174	67.42 185	72.97 156	80.11 124	52.53 187	74.26 238	76.29 220	58.48 149	68.38 161	84.20 166	42.59 211	83.83 142	46.53 285	75.91 191	82.56 226

testing91		64.46 256	63.80 247	66.47 280	78.43 164	40.06 343	67.63 328	69.59 304	59.06 136	63.18 264	78.05 295	34.05 307	76.99 288	48.30 271	75.87 192	82.37 234

GeoE		71.01 120	70.15 129	73.60 140	79.57 133	52.17 194	78.93 123	78.12 192	58.02 158	67.76 183	83.87 175	52.36 87	82.72 170	56.90 197	75.79 193	85.92 108

XVG-OURS		68.76 177	67.37 187	72.90 158	74.32 274	57.22 94	70.09 308	78.81 170	55.24 220	67.79 181	85.81 140	36.54 286	78.28 260	62.04 159	75.74 194	83.19 214

mvs_anonymous		68.03 195	67.51 182	69.59 239	72.08 316	44.57 300	71.99 279	75.23 242	51.67 275	67.06 195	82.57 201	54.68 55	77.94 265	56.56 199	75.71 195	86.26 100

testing99		64.05 260	63.29 257	66.34 282	78.17 176	39.76 347	67.33 333	68.00 318	58.60 146	63.03 267	78.10 294	32.57 336	76.94 290	48.22 272	75.58 196	82.34 235

BH-w/o		66.85 222	65.83 224	69.90 234	79.29 137	52.46 190	74.66 232	76.65 218	54.51 244	64.85 243	78.12 293	45.59 178	82.95 160	43.26 319	75.54 197	74.27 357

thisisatest0515		65.83 238	63.50 252	72.82 161	73.75 283	49.50 240	71.32 287	73.12 276	49.39 308	63.82 256	76.50 329	34.95 299	84.84 125	53.20 231	75.49 198	84.13 179

LS3D		64.71 252	62.50 266	71.34 202	79.72 130	55.71 122	79.82 109	74.72 252	48.50 322	56.62 344	84.62 158	33.59 316	82.34 180	29.65 408	75.23 199	75.97 333

GG-mvs-BLEND				62.34 327	71.36 331	37.04 374	69.20 317	57.33 392		54.73 366	65.48 411	30.37 348	77.82 269	34.82 377	74.93 200	72.17 377

UBG		59.62 311	59.53 299	59.89 342	78.12 177	35.92 387	64.11 360	60.81 378	49.45 307	61.34 295	75.55 342	33.05 320	67.39 358	38.68 351	74.62 201	76.35 331

nrg030		72.96 83	73.01 79	72.84 159	75.41 247	50.24 225	80.02 104	82.89 94	58.36 152	74.44 68	86.73 104	58.90 24	80.83 213	65.84 121	74.46 202	87.44 49

testing11		62.81 274	61.90 274	65.54 298	78.38 165	40.76 340	67.59 330	66.78 329	55.48 213	60.13 305	77.11 314	31.67 343	76.79 293	45.53 297	74.45 203	79.06 295

VPA-MVSNet		69.02 171	69.47 140	67.69 265	77.42 206	41.00 338	74.04 241	79.68 153	60.06 115	69.26 148	84.81 153	51.06 110	77.58 274	54.44 220	74.43 204	84.48 167

PS-MVSNAJss		72.24 98	71.21 107	75.31 88	78.50 160	55.93 117	81.63 84	82.12 102	56.24 196	70.02 132	85.68 142	47.05 163	84.34 133	65.27 125	74.41 205	85.67 121

EI-MVSNet-Vis-set		72.42 96	71.59 96	74.91 92	78.47 162	54.02 151	77.05 173	79.33 161	65.03 18	71.68 114	79.35 277	52.75 80	84.89 122	66.46 113	74.23 206	85.83 113

CHOSEN 1792x2688		65.08 250	62.84 262	71.82 182	81.49 95	56.26 110	66.32 337	74.20 262	40.53 395	63.16 265	78.65 286	41.30 230	77.80 270	45.80 292	74.09 207	81.40 250

ETVMVS		59.51 312	58.81 305	61.58 332	77.46 205	34.87 390	64.94 354	59.35 381	54.06 250	61.08 299	76.67 321	29.54 358	71.87 327	32.16 389	74.07 208	78.01 310

ACMMP++_ref															74.07 208

SDMVSNet		68.03 195	68.10 172	67.84 263	77.13 213	48.72 255	65.32 349	79.10 163	58.02 158	65.08 236	82.55 202	47.83 147	73.40 316	63.92 137	73.92 210	81.41 248

sd_testset		64.46 256	64.45 240	64.51 310	77.13 213	42.25 323	62.67 368	72.11 284	58.02 158	65.08 236	82.55 202	41.22 235	69.88 341	47.32 278	73.92 210	81.41 248

PVSNet_BlendedMVS		68.56 183	67.72 175	71.07 210	77.03 217	50.57 219	74.50 234	81.52 111	53.66 258	64.22 254	79.72 267	49.13 133	82.87 164	55.82 204	73.92 210	79.77 288

CMPMVS	42.80 21	57.81 325	55.97 334	63.32 319	60.98 413	47.38 272	64.66 355	69.50 306	32.06 413	46.83 406	77.80 303	29.50 360	71.36 329	48.68 267	73.75 213	71.21 389
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
guyue		68.10 194	67.23 197	70.71 219	73.67 287	49.27 245	73.65 253	76.04 226	55.62 211	67.84 178	82.26 212	41.24 234	78.91 255	61.01 168	73.72 214	83.94 184

MS-PatchMatch		62.42 279	61.46 279	65.31 304	75.21 251	52.10 195	72.05 278	74.05 263	46.41 350	57.42 340	74.36 353	34.35 305	77.57 275	45.62 295	73.67 215	66.26 409

fmvsm_s_conf0.5_n_4		72.04 104	71.85 93	72.58 164	73.74 285	52.49 189	76.69 182	72.42 280	56.42 191	75.32 47	87.04 95	52.13 92	78.01 264	79.29 11	73.65 216	87.26 58

test-LLR		58.15 322	58.13 315	58.22 356	68.57 368	44.80 296	65.46 346	57.92 387	50.08 299	55.44 356	69.82 389	32.62 333	57.44 402	49.66 259	73.62 217	72.41 373

test-mter		56.42 336	55.82 336	58.22 356	68.57 368	44.80 296	65.46 346	57.92 387	39.94 400	55.44 356	69.82 389	21.92 404	57.44 402	49.66 259	73.62 217	72.41 373

EI-MVSNet-UG-set		71.92 105	71.06 111	74.52 107	77.98 183	53.56 161	76.62 183	79.16 162	64.40 28	71.18 119	78.95 282	52.19 90	84.66 129	65.47 124	73.57 219	85.32 140

TR-MVS		66.59 230	65.07 236	71.17 207	79.18 143	49.63 239	73.48 254	75.20 244	52.95 262	67.90 172	80.33 254	39.81 247	83.68 145	43.20 320	73.56 220	80.20 276

UniMVSNet_ETH3D		67.60 206	67.07 200	69.18 248	77.39 207	42.29 322	74.18 240	75.59 232	60.37 105	66.77 200	86.06 129	37.64 271	78.93 254	52.16 237	73.49 221	86.32 95

FE-MVS		65.91 237	63.33 255	73.63 138	77.36 208	51.95 201	72.62 269	75.81 227	53.70 256	65.31 227	78.96 281	28.81 366	86.39 83	43.93 310	73.48 222	82.55 227

ab-mvs		66.65 227	66.42 211	67.37 269	76.17 233	41.73 328	70.41 303	76.14 223	53.99 251	65.98 214	83.51 185	49.48 126	76.24 304	48.60 268	73.46 223	84.14 178

testing3-2		62.06 285	62.36 268	61.17 337	79.29 137	30.31 417	64.09 361	63.49 357	63.50 43	62.84 270	82.22 213	32.35 340	69.02 345	40.01 343	73.43 224	84.17 177

EG-PatchMatch MVS		64.71 252	62.87 261	70.22 225	77.68 193	53.48 163	77.99 146	78.82 169	53.37 260	56.03 352	77.41 311	24.75 398	84.04 137	46.37 287	73.42 225	73.14 363

XVG-OURS-SEG-HR		68.81 174	67.47 184	72.82 161	74.40 271	56.87 104	70.59 299	79.04 164	54.77 238	66.99 196	86.01 131	39.57 249	78.21 261	62.54 154	73.33 226	83.37 208

thres200		62.20 283	61.16 287	65.34 303	75.38 248	39.99 344	69.60 313	69.29 309	55.64 210	61.87 289	76.99 316	37.07 282	78.96 253	31.28 401	73.28 227	77.06 322

thres100view900		63.28 269	62.41 267	65.89 294	77.31 210	38.66 356	72.65 267	69.11 311	57.07 173	62.45 282	81.03 240	37.01 283	79.17 241	31.84 393	73.25 228	79.83 285

tfpn200view9		63.18 271	62.18 271	66.21 286	76.85 220	39.62 348	71.96 281	69.44 307	56.63 182	62.61 277	79.83 262	37.18 277	79.17 241	31.84 393	73.25 228	79.83 285

thres400		63.31 267	62.18 271	66.72 274	76.85 220	39.62 348	71.96 281	69.44 307	56.63 182	62.61 277	79.83 262	37.18 277	79.17 241	31.84 393	73.25 228	81.36 251

TESTMET0.1,1		55.28 346	54.90 342	56.42 367	66.56 383	43.67 309	65.46 346	56.27 397	39.18 402	53.83 374	67.44 401	24.21 399	55.46 413	48.04 274	73.11 231	70.13 397

thres600view7		63.30 268	62.27 269	66.41 281	77.18 212	38.87 354	72.35 274	69.11 311	56.98 176	62.37 285	80.96 242	37.01 283	79.00 252	31.43 400	73.05 232	81.36 251

VPNet		67.52 207	68.11 171	65.74 296	79.18 143	36.80 376	72.17 277	72.83 277	62.04 74	67.79 181	85.83 137	48.88 137	76.60 298	51.30 246	72.97 233	83.81 191

fmvsm_s_conf0.5_n_2		69.82 147	69.27 144	71.46 194	72.00 318	51.08 208	73.30 257	67.79 319	55.06 229	75.24 49	87.51 83	44.02 199	77.00 287	75.67 40	72.86 234	86.31 98

Anonymous20231211		69.28 167	68.47 162	71.73 185	80.28 116	47.18 274	79.98 105	82.37 99	54.61 240	67.24 191	84.01 172	39.43 250	82.41 179	55.45 211	72.83 235	85.62 124

GBi-Net		67.21 211	66.55 206	69.19 245	77.63 196	43.33 311	77.31 164	77.83 196	56.62 184	65.04 238	82.70 196	41.85 221	80.33 223	47.18 280	72.76 236	83.92 186

test1		67.21 211	66.55 206	69.19 245	77.63 196	43.33 311	77.31 164	77.83 196	56.62 184	65.04 238	82.70 196	41.85 221	80.33 223	47.18 280	72.76 236	83.92 186

FMVSNet3		66.32 234	65.61 228	68.46 257	76.48 229	42.34 321	74.98 224	77.15 210	55.83 203	65.04 238	81.16 236	39.91 244	80.14 230	47.18 280	72.76 236	82.90 223

FMVSNet2		66.93 221	66.31 217	68.79 254	77.63 196	42.98 316	76.11 196	77.47 202	56.62 184	65.22 235	82.17 216	41.85 221	80.18 229	47.05 283	72.72 239	83.20 213

fmvsm_s_conf0.1_n_2		69.64 155	69.01 150	71.52 192	71.66 323	51.04 209	73.39 256	67.14 325	55.02 233	75.11 51	87.64 82	42.94 209	77.01 286	75.55 42	72.63 240	86.52 85

thisisatest0530		67.92 199	65.78 225	74.33 111	76.29 231	51.03 210	76.89 178	74.25 260	53.67 257	65.59 223	81.76 226	35.15 296	85.50 107	55.94 202	72.47 241	86.47 86

PVSNet	50.76 19	58.40 318	57.39 319	61.42 333	75.53 244	44.04 306	61.43 374	63.45 358	47.04 345	56.91 342	73.61 361	27.00 383	64.76 371	39.12 349	72.40 242	75.47 340

MIMVSNet		57.35 326	57.07 321	58.22 356	74.21 277	37.18 370	62.46 369	60.88 377	48.88 316	55.29 359	75.99 336	31.68 342	62.04 381	31.87 392	72.35 243	75.43 341

1314		64.61 254	63.21 258	68.80 253	71.87 321	47.46 271	73.95 244	78.39 189	42.88 382	59.97 309	76.60 326	38.11 268	79.39 237	54.84 215	72.32 244	79.55 289

FMVSNet1		66.70 226	65.87 223	69.19 245	77.49 204	43.33 311	77.31 164	77.83 196	56.45 189	64.60 247	82.70 196	38.08 269	80.33 223	46.08 289	72.31 245	83.92 186

tt0805		67.77 203	67.24 195	69.34 244	74.87 256	40.08 342	77.36 163	81.37 117	55.31 217	66.33 209	84.65 157	37.35 275	82.55 175	55.65 209	72.28 246	85.39 137

ACMMP++															72.16 247

MVP-Stereo		65.41 244	63.80 247	70.22 225	77.62 200	55.53 129	76.30 190	78.53 179	50.59 294	56.47 348	78.65 286	39.84 246	82.68 171	44.10 309	72.12 248	72.44 372
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HyFIR lowres test		65.67 240	63.01 260	73.67 134	79.97 126	55.65 124	69.07 318	75.52 234	42.68 383	63.53 259	77.95 297	40.43 241	81.64 190	46.01 290	71.91 249	83.73 197

XVG-ACMP-BASELINE		64.36 258	62.23 270	70.74 217	72.35 312	52.45 191	70.80 297	78.45 184	53.84 254	59.87 311	81.10 238	16.24 417	79.32 238	55.64 210	71.76 250	80.47 270

AstraMVS		67.86 201	66.83 202	70.93 213	73.50 289	49.34 243	73.28 260	74.01 264	55.45 215	68.10 169	83.28 188	38.93 258	79.14 245	63.22 148	71.74 251	84.30 172

HY-MVS	56.14 13	64.55 255	63.89 244	66.55 279	74.73 261	41.02 335	69.96 309	74.43 255	49.29 310	61.66 292	80.92 243	47.43 157	76.68 297	44.91 304	71.69 252	81.94 241

D2MVS		62.30 281	60.29 295	68.34 260	66.46 385	48.42 258	65.70 341	73.42 269	47.71 334	58.16 333	75.02 348	30.51 347	77.71 273	53.96 224	71.68 253	78.90 299

ACMH	55.70 15	65.20 248	63.57 251	70.07 229	78.07 179	52.01 199	79.48 118	79.69 152	55.75 206	56.59 345	80.98 241	27.12 381	80.94 209	42.90 324	71.58 254	77.25 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 216	65.58 229	71.88 180	70.37 347	49.70 235	70.25 306	78.45 184	51.52 279	69.16 150	80.37 251	38.45 262	82.50 176	60.19 174	71.46 255	83.44 207

EI-MVSNet		69.27 168	68.44 164	71.73 185	74.47 268	49.39 242	75.20 217	78.45 184	59.60 126	69.16 150	76.51 327	51.29 105	82.50 176	59.86 180	71.45 256	83.30 209

WB-MVSnew		59.66 309	59.69 298	59.56 343	75.19 252	35.78 388	69.34 316	64.28 348	46.88 346	61.76 291	75.79 338	40.61 240	65.20 370	32.16 389	71.21 257	77.70 312

LTVRE_ROB	55.42 16	63.15 272	61.23 285	68.92 252	76.57 227	47.80 265	59.92 384	76.39 219	54.35 246	58.67 327	82.46 207	29.44 361	81.49 195	42.12 328	71.14 258	77.46 315
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UniMVSNet (Re)		70.63 128	70.20 126	71.89 179	78.55 159	45.29 293	75.94 202	82.92 91	63.68 41	68.16 166	83.59 181	53.89 64	83.49 151	53.97 223	71.12 259	86.89 69

Effi-MVS+-dtu		69.64 155	67.53 181	75.95 72	76.10 234	62.29 15	80.20 103	76.06 225	59.83 123	65.26 232	77.09 315	41.56 227	84.02 139	60.60 172	71.09 260	81.53 246

NR-MVSNet		69.54 159	68.85 151	71.59 191	78.05 180	43.81 308	74.20 239	80.86 138	65.18 14	62.76 273	84.52 161	52.35 88	83.59 148	50.96 250	70.78 261	87.37 54

v1144		70.42 133	69.31 142	73.76 127	73.22 292	50.64 218	77.83 151	81.43 115	58.58 147	69.40 144	81.16 236	47.53 154	85.29 114	64.01 135	70.64 262	85.34 139

jajsoiax		68.25 189	66.45 208	73.66 135	75.62 241	55.49 130	80.82 95	78.51 180	52.33 270	64.33 249	84.11 169	28.28 370	81.81 189	63.48 146	70.62 263	83.67 199

h-mvs33		72.71 87	71.49 99	76.40 67	81.99 87	59.58 56	76.92 177	76.74 217	60.40 102	74.81 61	85.95 133	45.54 179	85.76 100	70.41 86	70.61 264	83.86 190

mvs_tets		68.18 192	66.36 214	73.63 138	75.61 242	55.35 133	80.77 96	78.56 178	52.48 269	64.27 251	84.10 170	27.45 378	81.84 188	63.45 147	70.56 265	83.69 198

UniMVSNet_NR-MVSNet		71.11 118	71.00 112	71.44 196	79.20 142	44.13 303	76.02 201	82.60 97	66.48 11	68.20 163	84.60 160	56.82 36	82.82 168	54.62 217	70.43 266	87.36 56

DU-MVS		70.01 142	69.53 138	71.44 196	78.05 180	44.13 303	75.01 222	81.51 113	64.37 29	68.20 163	84.52 161	49.12 135	82.82 168	54.62 217	70.43 266	87.37 54

v1192		69.97 144	68.68 156	73.85 122	73.19 293	50.94 211	77.68 155	81.36 118	57.51 169	68.95 153	80.85 246	45.28 186	85.33 113	62.97 151	70.37 268	85.27 143

PLC	56.13 14	65.09 249	63.21 258	70.72 218	81.04 105	54.87 140	78.57 130	77.47 202	48.51 321	55.71 353	81.89 223	33.71 313	79.71 231	41.66 333	70.37 268	77.58 314
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WBMVS		60.54 299	60.61 293	60.34 341	78.00 182	35.95 386	64.55 356	64.89 342	49.63 304	63.39 261	78.70 283	33.85 312	67.65 354	42.10 329	70.35 270	77.43 316

GA-MVS		65.53 242	63.70 249	71.02 212	70.87 338	48.10 262	70.48 301	74.40 256	56.69 179	64.70 245	76.77 320	33.66 315	81.10 205	55.42 212	70.32 271	83.87 189

Fast-Effi-MVS+-dtu		67.37 209	65.33 233	73.48 145	72.94 299	57.78 87	77.47 161	76.88 213	57.60 168	61.97 287	76.85 319	39.31 251	80.49 221	54.72 216	70.28 272	82.17 239

fmvsm_s_conf0.5_n		69.58 157	68.84 152	71.79 183	72.31 314	52.90 176	77.90 147	62.43 368	49.97 301	72.85 98	85.90 134	52.21 89	76.49 299	75.75 39	70.26 273	85.97 106

v2v482		70.50 131	69.45 141	73.66 135	72.62 304	50.03 231	77.58 156	80.51 143	59.90 118	69.52 140	82.14 218	47.53 154	84.88 124	65.07 127	70.17 274	86.09 103

IB-MVS	56.42 12	65.40 245	62.73 264	73.40 149	74.89 254	52.78 181	73.09 263	75.13 245	55.69 207	58.48 331	73.73 360	32.86 324	86.32 86	50.63 251	70.11 275	81.10 260
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
fmvsm_s_conf0.1_n		69.41 165	68.60 158	71.83 181	71.07 335	52.88 179	77.85 150	62.44 367	49.58 306	72.97 95	86.22 122	51.68 100	76.48 300	75.53 43	70.10 276	86.14 101

CNLPA		65.43 243	64.02 243	69.68 237	78.73 156	58.07 82	77.82 152	70.71 294	51.49 280	61.57 294	83.58 184	38.23 267	70.82 333	43.90 311	70.10 276	80.16 277

1112_ss		64.00 262	63.36 254	65.93 293	79.28 139	42.58 320	71.35 286	72.36 282	46.41 350	60.55 303	77.89 301	46.27 173	73.28 317	46.18 288	69.97 278	81.92 242

DP-MVS		65.68 239	63.66 250	71.75 184	84.93 55	56.87 104	80.74 97	73.16 274	53.06 261	59.09 322	82.35 208	36.79 285	85.94 96	32.82 387	69.96 279	72.45 371

tttt0517		67.83 202	65.66 227	74.33 111	76.69 222	50.82 215	77.86 149	73.99 265	54.54 243	64.64 246	82.53 205	35.06 297	85.50 107	55.71 207	69.91 280	86.67 78

IterMVS-LS		69.22 170	68.48 160	71.43 198	74.44 270	49.40 241	76.23 193	77.55 201	59.60 126	65.85 220	81.59 231	51.28 106	81.58 193	59.87 179	69.90 281	83.30 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		69.47 163	68.17 170	73.36 150	73.06 296	50.10 229	77.39 162	80.56 141	56.58 188	68.59 155	80.37 251	44.72 191	84.98 118	62.47 156	69.82 282	85.00 152

Baseline_NR-MVSNet		67.05 218	67.56 178	65.50 300	75.65 240	37.70 367	75.42 212	74.65 254	59.90 118	68.14 167	83.15 193	49.12 135	77.20 282	52.23 236	69.78 283	81.60 245

ACMH+	57.40 11	66.12 235	64.06 242	72.30 175	77.79 188	52.83 180	80.39 99	78.03 193	57.30 170	57.47 338	82.55 202	27.68 376	84.17 134	45.54 296	69.78 283	79.90 282

v1240		69.24 169	67.91 173	73.25 154	73.02 298	49.82 233	77.21 169	80.54 142	56.43 190	68.34 162	80.51 250	43.33 205	84.99 116	62.03 160	69.77 285	84.95 156

TranMVSNet+NR-MVSNet		70.36 134	70.10 131	71.17 207	78.64 158	42.97 317	76.53 186	81.16 131	66.95 6	68.53 158	85.42 148	51.61 101	83.07 157	52.32 235	69.70 286	87.46 48

v144192		69.71 150	68.51 159	73.33 151	73.10 295	50.13 228	77.54 159	80.64 140	56.65 180	68.57 157	80.55 249	46.87 168	84.96 120	62.98 150	69.66 287	84.89 157

WR-MVS		68.47 184	68.47 162	68.44 258	80.20 120	39.84 345	73.75 251	76.07 224	64.68 23	68.11 168	83.63 180	50.39 118	79.14 245	49.78 255	69.66 287	86.34 91

SSC-MVS3.2		60.57 298	61.39 280	58.12 359	74.29 275	32.63 408	59.52 385	65.53 338	59.90 118	62.45 282	79.75 266	41.96 218	63.90 375	39.47 347	69.65 289	77.84 311

WTY-MVS		59.75 308	60.39 294	57.85 361	72.32 313	37.83 364	61.05 380	64.18 349	45.95 357	61.91 288	79.11 280	47.01 166	60.88 384	42.50 326	69.49 290	74.83 349

cl22		67.47 208	66.45 208	70.54 222	69.85 356	46.49 278	73.85 249	77.35 206	55.07 227	65.51 224	77.92 299	47.64 151	81.10 205	61.58 165	69.32 291	84.01 182

miper_ehance_all_eth		68.03 195	67.24 195	70.40 224	70.54 342	46.21 282	73.98 242	78.68 175	55.07 227	66.05 213	77.80 303	52.16 91	81.31 200	61.53 166	69.32 291	83.67 199

miper_enhance_ethall		67.11 217	66.09 221	70.17 228	69.21 364	45.98 284	72.85 266	78.41 187	51.38 282	65.65 222	75.98 337	51.17 108	81.25 201	60.82 170	69.32 291	83.29 211

test_djsdf		69.45 164	67.74 174	74.58 103	74.57 267	54.92 139	82.79 66	78.48 181	51.26 285	65.41 226	83.49 186	38.37 263	83.24 154	66.06 116	69.25 294	85.56 125

cl____		67.18 214	66.26 219	69.94 231	70.20 348	45.74 286	73.30 257	76.83 215	55.10 222	65.27 229	79.57 270	47.39 158	80.53 218	59.41 184	69.22 295	83.53 205

DIV-MVS_self_test		67.18 214	66.26 219	69.94 231	70.20 348	45.74 286	73.29 259	76.83 215	55.10 222	65.27 229	79.58 269	47.38 159	80.53 218	59.43 183	69.22 295	83.54 204

c3_l		68.33 187	67.56 178	70.62 220	70.87 338	46.21 282	74.47 235	78.80 171	56.22 197	66.19 211	78.53 290	51.88 95	81.40 197	62.08 157	69.04 297	84.25 173

CostFormer		64.04 261	62.51 265	68.61 256	71.88 320	45.77 285	71.30 288	70.60 295	47.55 336	64.31 250	76.61 325	41.63 225	79.62 234	49.74 257	69.00 298	80.42 271

fmvsm_s_conf0.5_n_a		69.54 159	68.74 155	71.93 178	72.47 309	53.82 154	78.25 136	62.26 370	49.78 303	73.12 92	86.21 123	52.66 81	76.79 293	75.02 48	68.88 299	85.18 145

tpm2		62.07 284	60.10 296	67.99 262	72.79 301	43.86 307	71.05 295	66.85 328	43.14 380	62.77 272	75.39 346	38.32 265	80.80 214	41.69 332	68.88 299	79.32 292

v10		70.21 137	69.02 148	73.81 124	73.51 288	50.92 213	78.74 125	81.39 116	60.05 116	66.39 208	81.83 225	47.58 152	85.41 112	62.80 152	68.86 301	85.09 150

v8		70.33 135	69.28 143	73.49 144	73.15 294	50.22 226	78.62 128	80.78 139	60.79 92	66.45 207	82.11 220	49.35 128	84.98 118	63.58 145	68.71 302	85.28 142

v7n		69.01 172	67.36 188	73.98 120	72.51 308	52.65 183	78.54 132	81.30 123	60.26 111	62.67 275	81.62 228	43.61 202	84.49 130	57.01 196	68.70 303	84.79 160

fmvsm_s_conf0.1_n_a		69.32 166	68.44 164	71.96 177	70.91 337	53.78 155	78.12 142	62.30 369	49.35 309	73.20 88	86.55 115	51.99 94	76.79 293	74.83 50	68.68 304	85.32 140

Test_1112_low_res		62.32 280	61.77 275	64.00 315	79.08 147	39.53 350	68.17 324	70.17 297	43.25 378	59.03 323	79.90 261	44.08 197	71.24 331	43.79 313	68.42 305	81.25 255

UWE-MVS-28		52.25 363	52.35 361	51.93 396	66.99 378	22.79 439	63.48 363	48.31 420	46.78 347	52.73 383	76.11 332	27.78 375	57.82 401	20.58 429	68.41 306	75.17 342

PMMVS		53.96 352	53.26 358	56.04 368	62.60 404	50.92 213	61.17 378	56.09 398	32.81 412	53.51 380	66.84 406	34.04 308	59.93 389	44.14 308	68.18 307	57.27 421

tfpnnormal		62.47 278	61.63 277	64.99 307	74.81 259	39.01 353	71.22 289	73.72 267	55.22 221	60.21 304	80.09 260	41.26 233	76.98 289	30.02 406	68.09 308	78.97 298

Anonymous20231206		55.10 349	55.30 340	54.48 377	69.81 357	33.94 401	62.91 367	62.13 372	41.08 391	55.18 360	75.65 340	32.75 328	56.59 408	30.32 405	67.86 309	72.91 364

V42		68.65 178	67.35 189	72.56 165	68.93 367	50.18 227	72.90 265	79.47 158	56.92 177	69.45 143	80.26 255	46.29 172	82.99 158	64.07 133	67.82 310	84.53 165

MDTV_nov1_ep13			57.00 322		72.73 302	38.26 360	65.02 353	64.73 345	44.74 363	55.46 355	72.48 366	32.61 335	70.47 335	37.47 357	67.75 311

anonymousdsp		67.00 220	64.82 238	73.57 141	70.09 351	56.13 112	76.35 189	77.35 206	48.43 323	64.99 241	80.84 247	33.01 322	80.34 222	64.66 130	67.64 312	84.23 174

VortexMVS		66.41 233	65.50 230	69.16 249	73.75 283	48.14 261	73.41 255	78.28 190	53.73 255	64.98 242	78.33 291	40.62 239	79.07 247	58.88 186	67.50 313	80.26 275

dmvs_re		56.77 332	56.83 325	56.61 366	69.23 363	41.02 335	58.37 390	64.18 349	50.59 294	57.45 339	71.42 376	35.54 293	58.94 395	37.23 359	67.45 314	69.87 399

OpenMVS_ROB	52.78 18	60.03 304	58.14 314	65.69 297	70.47 344	44.82 295	75.33 213	70.86 293	45.04 361	56.06 351	76.00 334	26.89 385	79.65 232	35.36 376	67.29 315	72.60 368

XXY-MVS		60.68 296	61.67 276	57.70 363	70.43 345	38.45 359	64.19 358	66.47 330	48.05 329	63.22 262	80.86 245	49.28 130	60.47 385	45.25 303	67.28 316	74.19 358

baseline2		63.42 266	61.26 284	69.89 235	72.55 306	47.62 269	71.54 284	68.38 315	50.11 298	54.82 364	75.55 342	43.06 207	80.96 208	48.13 273	67.16 317	81.11 259

AUN-MVS		68.45 186	66.41 212	74.57 104	79.53 134	57.08 102	73.93 246	75.23 242	54.44 245	66.69 202	81.85 224	37.10 281	82.89 162	62.07 158	66.84 318	83.75 196

hse-mvs2		71.04 119	69.86 132	74.60 102	79.58 132	57.12 101	73.96 243	75.25 241	60.40 102	74.81 61	81.95 222	45.54 179	82.90 161	70.41 86	66.83 319	83.77 195

F-COLMAP		63.05 273	60.87 292	69.58 241	76.99 219	53.63 159	78.12 142	76.16 221	47.97 330	52.41 384	81.61 229	27.87 373	78.11 262	40.07 340	66.66 320	77.00 324

pm-mvs1		65.24 247	64.97 237	66.04 291	72.38 311	39.40 351	72.62 269	75.63 230	55.53 212	62.35 286	83.18 192	47.45 156	76.47 301	49.06 265	66.54 321	82.24 236

v148		68.24 190	67.19 198	71.40 199	70.43 345	47.77 267	75.76 207	77.03 212	58.91 139	67.36 187	80.10 259	48.60 140	81.89 186	60.01 176	66.52 322	84.53 165

eth_miper_zixun_eth		67.63 205	66.28 218	71.67 188	71.60 324	48.33 259	73.68 252	77.88 194	55.80 205	65.91 216	78.62 288	47.35 160	82.88 163	59.45 182	66.25 323	83.81 191

sss		56.17 339	56.57 328	54.96 374	66.93 380	36.32 382	57.94 393	61.69 373	41.67 387	58.64 328	75.32 347	38.72 260	56.25 409	42.04 330	66.19 324	72.31 376

COLMAP_ROB	52.97 17	61.27 295	58.81 305	68.64 255	74.63 264	52.51 188	78.42 133	73.30 272	49.92 302	50.96 389	81.51 232	23.06 401	79.40 236	31.63 397	65.85 325	74.01 360
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		59.63 310	59.14 302	61.08 339	74.47 268	38.84 355	75.20 217	68.74 313	31.15 415	58.24 332	76.51 327	32.39 338	68.58 347	49.77 256	65.84 326	75.81 335

MSDG		61.81 289	59.23 301	69.55 242	72.64 303	52.63 185	70.45 302	75.81 227	51.38 282	53.70 375	76.11 332	29.52 359	81.08 207	37.70 356	65.79 327	74.93 348

FMVSNet5		55.86 341	54.93 341	58.66 353	71.05 336	36.35 380	64.18 359	62.48 366	46.76 348	50.66 394	74.73 351	25.80 391	64.04 373	33.11 385	65.57 328	75.59 338

pmmvs5		56.47 335	55.68 337	58.86 351	61.41 409	36.71 377	66.37 336	62.75 363	40.38 396	53.70 375	76.62 323	34.56 301	67.05 359	40.02 342	65.27 329	72.83 366

miper_lstm_enhance		62.03 286	60.88 291	65.49 301	66.71 382	46.25 280	56.29 403	75.70 229	50.68 291	61.27 296	75.48 344	40.21 242	68.03 351	56.31 201	65.25 330	82.18 237

tpm		57.34 327	58.16 313	54.86 375	71.80 322	34.77 392	67.47 332	56.04 399	48.20 326	60.10 306	76.92 317	37.17 279	53.41 419	40.76 338	65.01 331	76.40 330

test_vis1_n_1920		58.86 314	59.06 304	58.25 355	63.76 397	43.14 315	67.49 331	66.36 332	40.22 397	65.89 218	71.95 373	31.04 344	59.75 390	59.94 177	64.90 332	71.85 380

pmmvs4		61.48 293	59.39 300	67.76 264	71.57 325	53.86 153	71.42 285	65.34 339	44.20 369	59.46 317	77.92 299	35.90 290	74.71 311	43.87 312	64.87 333	74.71 353

test_0402		63.25 270	61.01 289	69.96 230	80.00 125	54.37 146	76.86 180	72.02 285	54.58 242	58.71 325	80.79 248	35.00 298	84.36 132	26.41 420	64.71 334	71.15 390

CR-MVSNet		59.91 305	57.90 317	65.96 292	69.96 353	52.07 196	65.31 350	63.15 361	42.48 384	59.36 318	74.84 349	35.83 291	70.75 334	45.50 298	64.65 335	75.06 344

RPMNet		61.53 291	58.42 310	70.86 214	69.96 353	52.07 196	65.31 350	81.36 118	43.20 379	59.36 318	70.15 387	35.37 294	85.47 109	36.42 370	64.65 335	75.06 344

Syy-MVS		56.00 340	56.23 333	55.32 372	74.69 262	26.44 431	65.52 344	57.49 390	50.97 289	56.52 346	72.18 368	39.89 245	68.09 349	24.20 423	64.59 337	71.44 386

myMVS_eth3d		54.86 350	54.61 344	55.61 371	74.69 262	27.31 428	65.52 344	57.49 390	50.97 289	56.52 346	72.18 368	21.87 407	68.09 349	27.70 414	64.59 337	71.44 386

pmmvs6		63.69 264	62.82 263	66.27 285	70.63 340	39.27 352	73.13 262	75.47 237	52.69 267	59.75 315	82.30 210	39.71 248	77.03 285	47.40 277	64.35 339	82.53 228

Anonymous20240521		55.30 345	54.41 347	57.96 360	60.92 415	41.73 328	71.09 294	71.06 292	41.18 390	48.65 400	73.31 362	16.93 414	59.25 392	42.54 325	64.01 340	72.90 365

WR-MVS_H		67.02 219	66.92 201	67.33 271	77.95 184	37.75 365	77.57 157	82.11 103	62.03 75	62.65 276	82.48 206	50.57 116	79.46 235	42.91 323	64.01 340	84.79 160

test0.0.03 1		53.32 359	53.59 356	52.50 392	62.81 403	29.45 419	59.51 386	54.11 404	50.08 299	54.40 370	74.31 354	32.62 333	55.92 411	30.50 404	63.95 342	72.15 378

PatchMatch-RL		56.25 338	54.55 345	61.32 336	77.06 216	56.07 114	65.57 343	54.10 405	44.13 371	53.49 381	71.27 379	25.20 395	66.78 361	36.52 369	63.66 343	61.12 413

PatchmatchNet		59.84 306	58.24 312	64.65 309	73.05 297	46.70 277	69.42 315	62.18 371	47.55 336	58.88 324	71.96 372	34.49 303	69.16 343	42.99 322	63.60 344	78.07 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_cas_vis1_n_1920		56.91 330	56.71 327	57.51 364	59.13 419	45.40 292	63.58 362	61.29 375	36.24 407	67.14 194	71.85 374	29.89 356	56.69 406	57.65 193	63.58 345	70.46 394

IterMVS-SCA-FT		62.49 277	61.52 278	65.40 302	71.99 319	50.80 216	71.15 292	69.63 303	45.71 358	60.61 302	77.93 298	37.45 273	65.99 367	55.67 208	63.50 346	79.42 291

CP-MVSNet		66.49 231	66.41 212	66.72 274	77.67 194	36.33 381	76.83 181	79.52 157	62.45 65	62.54 279	83.47 187	46.32 171	78.37 258	45.47 300	63.43 347	85.45 132

PS-CasMVS		66.42 232	66.32 216	66.70 276	77.60 202	36.30 383	76.94 176	79.61 155	62.36 67	62.43 284	83.66 179	45.69 175	78.37 258	45.35 302	63.26 348	85.42 135

IterMVS		62.79 275	61.27 283	67.35 270	69.37 362	52.04 198	71.17 290	68.24 317	52.63 268	59.82 312	76.91 318	37.32 276	72.36 321	52.80 233	63.19 349	77.66 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		66.60 228	66.45 208	67.04 272	77.11 215	36.56 378	77.03 174	80.42 145	62.95 52	62.51 281	84.03 171	46.69 169	79.07 247	44.22 305	63.08 350	85.51 127

tpmrst		58.24 320	58.70 308	56.84 365	66.97 379	34.32 397	69.57 314	61.14 376	47.17 343	58.58 330	71.60 375	41.28 232	60.41 386	49.20 263	62.84 351	75.78 336

testgi		51.90 364	52.37 360	50.51 399	60.39 416	23.55 438	58.42 389	58.15 385	49.03 313	51.83 386	79.21 279	22.39 402	55.59 412	29.24 410	62.64 352	72.40 375

SCA		60.49 300	58.38 311	66.80 273	74.14 280	48.06 263	63.35 364	63.23 360	49.13 312	59.33 321	72.10 370	37.45 273	74.27 314	44.17 306	62.57 353	78.05 306

EPMVS		53.96 352	53.69 355	54.79 376	66.12 388	31.96 412	62.34 371	49.05 416	44.42 368	55.54 354	71.33 378	30.22 351	56.70 405	41.65 334	62.54 354	75.71 337

ITE_SJBPF				62.09 329	66.16 387	44.55 301		64.32 347	47.36 339	55.31 358	80.34 253	19.27 410	62.68 379	36.29 371	62.39 355	79.04 296

testing3		56.54 333	55.92 335	58.41 354	77.52 203	27.93 425	69.72 311	56.36 395	54.75 239	58.63 329	77.80 303	20.88 409	71.75 328	25.31 422	62.25 356	75.53 339

MIMVSNet1		55.17 348	54.31 349	57.77 362	70.03 352	32.01 411	65.68 342	64.81 343	49.19 311	46.75 407	76.00 334	25.53 394	64.04 373	28.65 411	62.13 357	77.26 320

CL-MVSNet_self_test		61.53 291	60.94 290	63.30 320	68.95 366	36.93 375	67.60 329	72.80 278	55.67 208	59.95 310	76.63 322	45.01 189	72.22 325	39.74 346	62.09 358	80.74 268

baseline1		63.81 263	63.87 246	63.62 317	76.29 231	36.36 379	71.78 283	67.29 323	56.05 200	64.23 253	82.95 194	47.11 162	74.41 313	47.30 279	61.85 359	80.10 279

USDC		56.35 337	54.24 350	62.69 325	64.74 393	40.31 341	65.05 352	73.83 266	43.93 373	47.58 402	77.71 307	15.36 420	75.05 310	38.19 355	61.81 360	72.70 367

PatchT		53.17 360	53.44 357	52.33 393	68.29 372	25.34 435	58.21 391	54.41 403	44.46 367	54.56 368	69.05 395	33.32 318	60.94 383	36.93 362	61.76 361	70.73 393

tpm cat1		59.25 313	56.95 323	66.15 288	72.19 315	46.96 275	68.09 325	65.76 335	40.03 399	57.81 336	70.56 382	38.32 265	74.51 312	38.26 354	61.50 362	77.00 324

tpmvs		58.47 317	56.95 323	63.03 324	70.20 348	41.21 334	67.90 327	67.23 324	49.62 305	54.73 366	70.84 380	34.14 306	76.24 304	36.64 367	61.29 363	71.64 382

Patchmtry		57.16 328	56.47 329	59.23 347	69.17 365	34.58 395	62.98 366	63.15 361	44.53 365	56.83 343	74.84 349	35.83 291	68.71 346	40.03 341	60.91 364	74.39 356

DTE-MVSNet		65.58 241	65.34 232	66.31 283	76.06 235	34.79 391	76.43 188	79.38 160	62.55 63	61.66 292	83.83 176	45.60 177	79.15 244	41.64 335	60.88 365	85.00 152

CHOSEN 280x420		47.83 378	46.36 382	52.24 395	67.37 377	49.78 234	38.91 435	43.11 432	35.00 409	43.27 417	63.30 416	28.95 363	49.19 426	36.53 368	60.80 366	57.76 420

test_fmvs1		51.32 369	50.48 369	53.81 381	53.57 424	37.51 368	60.63 383	51.16 410	28.02 421	63.62 258	69.23 394	16.41 416	53.93 418	51.01 248	60.70 367	69.99 398

test_fmvs1_n		51.37 367	50.35 370	54.42 379	52.85 426	37.71 366	61.16 379	51.93 407	28.15 419	63.81 257	69.73 391	13.72 421	53.95 417	51.16 247	60.65 368	71.59 383

Patchmatch-test		49.08 375	48.28 377	51.50 397	64.40 395	30.85 416	45.68 427	48.46 419	35.60 408	46.10 410	72.10 370	34.47 304	46.37 430	27.08 418	60.65 368	77.27 319

MonoMVSNet		64.15 259	63.31 256	66.69 277	70.51 343	44.12 305	74.47 235	74.21 261	57.81 165	63.03 267	76.62 323	38.33 264	77.31 280	54.22 221	60.59 370	78.64 300

reproduce_monomvs		62.56 276	61.20 286	66.62 278	70.62 341	44.30 302	70.13 307	73.13 275	54.78 237	61.13 298	76.37 330	25.63 393	75.63 307	58.75 187	60.29 371	79.93 281

test20.03		53.87 354	54.02 352	53.41 386	61.47 408	28.11 424	61.30 376	59.21 382	51.34 284	52.09 385	77.43 310	33.29 319	58.55 397	29.76 407	60.27 372	73.58 362

MVS-HIRNet		45.52 382	44.48 384	48.65 401	68.49 370	34.05 400	59.41 388	44.50 429	27.03 422	37.96 429	50.47 431	26.16 389	64.10 372	26.74 419	59.52 373	47.82 430

Patchmatch-RL test		58.16 321	55.49 338	66.15 288	67.92 374	48.89 252	60.66 382	51.07 412	47.86 333	59.36 318	62.71 417	34.02 309	72.27 324	56.41 200	59.40 374	77.30 318

AllTest		57.08 329	54.65 343	64.39 311	71.44 327	49.03 246	69.92 310	67.30 321	45.97 355	47.16 404	79.77 264	17.47 411	67.56 356	33.65 381	59.16 375	76.57 328

TestCases				64.39 311	71.44 327	49.03 246		67.30 321	45.97 355	47.16 404	79.77 264	17.47 411	67.56 356	33.65 381	59.16 375	76.57 328

RPSCF		55.80 342	54.22 351	60.53 340	65.13 392	42.91 319	64.30 357	57.62 389	36.84 406	58.05 335	82.28 211	28.01 372	56.24 410	37.14 360	58.61 377	82.44 233

EU-MVSNet		55.61 344	54.41 347	59.19 349	65.41 391	33.42 404	72.44 273	71.91 286	28.81 417	51.27 387	73.87 359	24.76 397	69.08 344	43.04 321	58.20 378	75.06 344

KD-MVS_self_test		55.22 347	53.89 353	59.21 348	57.80 422	27.47 427	57.75 396	74.32 257	47.38 338	50.90 390	70.00 388	28.45 369	70.30 339	40.44 339	57.92 379	79.87 284

test_vis1_n		49.89 374	48.69 376	53.50 384	53.97 423	37.38 369	61.53 373	47.33 424	28.54 418	59.62 316	67.10 405	13.52 422	52.27 422	49.07 264	57.52 380	70.84 392

dmvs_testset		50.16 372	51.90 362	44.94 407	66.49 384	11.78 447	61.01 381	51.50 409	51.17 287	50.30 397	67.44 401	39.28 252	60.29 387	22.38 426	57.49 381	62.76 412

pmmvs-eth3d		58.81 315	56.31 332	66.30 284	67.61 375	52.42 192	72.30 275	64.76 344	43.55 375	54.94 363	74.19 355	28.95 363	72.60 320	43.31 317	57.21 382	73.88 361

test_fmvs2		48.69 376	47.49 381	52.29 394	48.63 433	33.06 407	57.76 395	48.05 422	25.71 425	59.76 314	69.60 392	11.57 428	52.23 423	49.45 262	56.86 383	71.58 384

our_test_3		56.49 334	54.42 346	62.68 326	69.51 359	45.48 291	66.08 338	61.49 374	44.11 372	50.73 393	69.60 392	33.05 320	68.15 348	38.38 353	56.86 383	74.40 355

TinyColmap		54.14 351	51.72 363	61.40 334	66.84 381	41.97 325	66.52 335	68.51 314	44.81 362	42.69 418	75.77 339	11.66 427	72.94 318	31.96 391	56.77 385	69.27 403

ppachtmachnet_test		58.06 323	55.38 339	66.10 290	69.51 359	48.99 249	68.01 326	66.13 334	44.50 366	54.05 373	70.74 381	32.09 341	72.34 323	36.68 366	56.71 386	76.99 326

OurMVSNet-221017-0		61.37 294	58.63 309	69.61 238	72.05 317	48.06 263	73.93 246	72.51 279	47.23 342	54.74 365	80.92 243	21.49 408	81.24 202	48.57 269	56.22 387	79.53 290

TransMVSNet (Re)		64.72 251	64.33 241	65.87 295	75.22 250	38.56 357	74.66 232	75.08 249	58.90 140	61.79 290	82.63 199	51.18 107	78.07 263	43.63 316	55.87 388	80.99 263

tt0320		58.59 316	56.81 326	63.92 316	75.46 245	41.32 333	68.63 321	64.06 352	47.05 344	56.19 350	74.19 355	30.34 349	71.36 329	39.92 344	55.45 389	79.09 294

sc_t1		59.76 307	57.84 318	65.54 298	74.87 256	42.95 318	69.61 312	64.16 351	48.90 315	58.68 326	77.12 313	28.19 371	72.35 322	43.75 315	55.28 390	81.31 254

FPMVS		42.18 389	41.11 391	45.39 404	58.03 421	41.01 337	49.50 419	53.81 406	30.07 416	33.71 431	64.03 413	11.69 426	52.08 424	14.01 435	55.11 391	43.09 432

dp		51.89 365	51.60 364	52.77 390	68.44 371	32.45 410	62.36 370	54.57 402	44.16 370	49.31 399	67.91 397	28.87 365	56.61 407	33.89 380	54.89 392	69.24 404

ADS-MVSNet2		51.33 368	48.76 375	59.07 350	66.02 389	44.60 299	50.90 417	59.76 380	36.90 404	50.74 391	66.18 409	26.38 386	63.11 377	27.17 416	54.76 393	69.50 401

ADS-MVSNet		48.48 377	47.77 378	50.63 398	66.02 389	29.92 418	50.90 417	50.87 414	36.90 404	50.74 391	66.18 409	26.38 386	52.47 421	27.17 416	54.76 393	69.50 401

PM-MVS		52.33 362	50.19 371	58.75 352	62.10 406	45.14 294	65.75 340	40.38 434	43.60 374	53.52 379	72.65 365	9.16 435	65.87 368	50.41 252	54.18 395	65.24 411

JIA-IIPM		51.56 366	47.68 380	63.21 321	64.61 394	50.73 217	47.71 423	58.77 384	42.90 381	48.46 401	51.72 427	24.97 396	70.24 340	36.06 373	53.89 396	68.64 405

ambc				65.13 306	63.72 399	37.07 373	47.66 424	78.78 172		54.37 371	71.42 376	11.24 430	80.94 209	45.64 294	53.85 397	77.38 317

mamv4		56.85 331	58.00 316	53.43 385	72.46 310	54.47 143	57.56 398	54.74 400	38.81 403	57.42 340	79.45 274	47.57 153	38.70 438	60.88 169	53.07 398	67.11 408

test_vis1_rt		41.35 392	39.45 393	47.03 403	46.65 437	37.86 363	47.76 422	38.65 435	23.10 429	44.21 415	51.22 429	11.20 431	44.08 432	39.27 348	53.02 399	59.14 416

DSMNet-mixed		39.30 396	38.72 395	41.03 413	51.22 430	19.66 442	45.53 428	31.35 441	15.83 440	39.80 424	67.42 403	22.19 403	45.13 431	22.43 425	52.69 400	58.31 418

tt0320-xc		58.33 319	56.41 331	64.08 314	75.79 238	41.34 332	68.30 323	62.72 364	47.90 331	56.29 349	74.16 357	28.53 367	71.04 332	41.50 336	52.50 401	79.88 283

N_pmnet		39.35 395	40.28 392	36.54 418	63.76 397	1.62 455	49.37 420	0.76 454	34.62 410	43.61 416	66.38 408	26.25 388	42.57 434	26.02 421	51.77 402	65.44 410

TDRefinement		53.44 358	50.72 368	61.60 331	64.31 396	46.96 275	70.89 296	65.27 341	41.78 385	44.61 413	77.98 296	11.52 429	66.36 364	28.57 412	51.59 403	71.49 385

Gipuma		34.77 399	31.91 404	43.33 409	62.05 407	37.87 362	20.39 440	67.03 326	23.23 428	18.41 441	25.84 441	4.24 442	62.73 378	14.71 434	51.32 404	29.38 439
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		50.73 370	48.96 372	56.03 369	61.10 411	41.78 327	51.94 414	56.44 394	40.94 393	44.84 411	67.80 399	30.08 354	55.08 415	36.77 363	50.71 405	71.22 388

MDA-MVSNet_test_wron		50.71 371	48.95 373	56.00 370	61.17 410	41.84 326	51.90 415	56.45 393	40.96 392	44.79 412	67.84 398	30.04 355	55.07 416	36.71 365	50.69 406	71.11 391

EGC-MVSNET		42.47 388	38.48 396	54.46 378	74.33 273	48.73 254	70.33 305	51.10 411	0.03 448	0.18 449	67.78 400	13.28 423	66.49 363	18.91 431	50.36 407	48.15 428

test_fmvs3		44.30 384	42.55 387	49.55 400	42.83 438	27.15 430	53.03 411	44.93 428	22.03 433	53.69 377	64.94 412	4.21 443	49.63 425	47.47 275	49.82 408	71.88 379

SixPastTwentyTwo		61.65 290	58.80 307	70.20 227	75.80 237	47.22 273	75.59 209	69.68 302	54.61 240	54.11 372	79.26 278	27.07 382	82.96 159	43.27 318	49.79 409	80.41 272

new-patchmatchnet		47.56 379	47.73 379	47.06 402	58.81 420	9.37 450	48.78 421	59.21 382	43.28 377	44.22 414	68.66 396	25.67 392	57.20 404	31.57 399	49.35 410	74.62 354

LF4IMVS		42.95 386	42.26 388	45.04 405	48.30 434	32.50 409	54.80 406	48.49 418	28.03 420	40.51 421	70.16 386	9.24 434	43.89 433	31.63 397	49.18 411	58.72 417

PMVS	28.69 22	36.22 398	33.29 403	45.02 406	36.82 446	35.98 385	54.68 407	48.74 417	26.31 423	21.02 439	51.61 428	2.88 448	60.10 388	9.99 444	47.58 412	38.99 437
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvs5depth		55.64 343	53.81 354	61.11 338	59.39 418	40.98 339	65.89 339	68.28 316	50.21 297	58.11 334	75.42 345	17.03 413	67.63 355	43.79 313	46.21 413	74.73 352

pmmvs3		44.92 383	41.95 390	53.86 380	52.58 428	43.55 310	62.11 372	46.90 426	26.05 424	40.63 420	60.19 419	11.08 432	57.91 400	31.83 396	46.15 414	60.11 414

MDA-MVSNet-bldmvs		53.87 354	50.81 367	63.05 323	66.25 386	48.58 256	56.93 401	63.82 354	48.09 328	41.22 419	70.48 385	30.34 349	68.00 352	34.24 379	45.92 415	72.57 369

mmtdpeth		60.40 302	59.12 303	64.27 313	69.59 358	48.99 249	70.67 298	70.06 299	54.96 234	62.78 271	73.26 364	27.00 383	67.66 353	58.44 190	45.29 416	76.16 332

UnsupCasMVSNet_eth		53.16 361	52.47 359	55.23 373	59.45 417	33.39 405	59.43 387	69.13 310	45.98 354	50.35 396	72.32 367	29.30 362	58.26 399	42.02 331	44.30 417	74.05 359

UnsupCasMVSNet_bld		50.07 373	48.87 374	53.66 382	60.97 414	33.67 403	57.62 397	64.56 346	39.47 401	47.38 403	64.02 415	27.47 377	59.32 391	34.69 378	43.68 418	67.98 407

KD-MVS_2432*1600		53.45 356	51.50 365	59.30 345	62.82 401	37.14 371	55.33 404	71.79 287	47.34 340	55.09 361	70.52 383	21.91 405	70.45 336	35.72 374	42.97 419	70.31 395

miper_refine_blended		53.45 356	51.50 365	59.30 345	62.82 401	37.14 371	55.33 404	71.79 287	47.34 340	55.09 361	70.52 383	21.91 405	70.45 336	35.72 374	42.97 419	70.31 395

test_vis3_rt		32.09 403	30.20 408	37.76 417	35.36 448	27.48 426	40.60 434	28.29 444	16.69 438	32.52 432	40.53 437	1.96 449	37.40 440	33.64 383	42.21 421	48.39 427

APD_test1		37.39 397	34.94 400	44.72 408	48.88 432	33.19 406	52.95 412	44.00 431	19.49 434	27.28 435	58.59 421	3.18 447	52.84 420	18.92 430	41.17 422	48.14 429

new_pmnet		34.13 401	34.29 402	33.64 420	52.63 427	18.23 444	44.43 430	33.90 440	22.81 430	30.89 433	53.18 425	10.48 433	35.72 442	20.77 428	39.51 423	46.98 431

K. test v3		60.47 301	57.11 320	70.56 221	73.74 285	48.22 260	75.10 221	62.55 365	58.27 153	53.62 378	76.31 331	27.81 374	81.59 192	47.42 276	39.18 424	81.88 243

LCM-MVSNet		40.30 393	35.88 399	53.57 383	42.24 439	29.15 420	45.21 429	60.53 379	22.23 432	28.02 434	50.98 430	3.72 445	61.78 382	31.22 402	38.76 425	69.78 400

test_f		31.86 404	31.05 405	34.28 419	32.33 450	21.86 440	32.34 437	30.46 442	16.02 439	39.78 425	55.45 424	4.80 441	32.36 444	30.61 403	37.66 426	48.64 426

mvsany_test1		39.38 394	38.16 397	43.02 410	49.05 431	34.28 398	44.16 431	25.94 445	22.74 431	46.57 408	62.21 418	23.85 400	41.16 437	33.01 386	35.91 427	53.63 424

testf1		31.46 405	28.89 409	39.16 414	41.99 441	28.78 422	46.45 425	37.56 436	14.28 441	21.10 437	48.96 432	1.48 451	47.11 428	13.63 436	34.56 428	41.60 433

APD_test2		31.46 405	28.89 409	39.16 414	41.99 441	28.78 422	46.45 425	37.56 436	14.28 441	21.10 437	48.96 432	1.48 451	47.11 428	13.63 436	34.56 428	41.60 433

lessismore_v0				69.91 233	71.42 329	47.80 265		50.90 413		50.39 395	75.56 341	27.43 379	81.33 199	45.91 291	34.10 430	80.59 269

ttmdpeth		45.56 381	42.95 386	53.39 387	52.33 429	29.15 420	57.77 394	48.20 421	31.81 414	49.86 398	77.21 312	8.69 436	59.16 393	27.31 415	33.40 431	71.84 381

mvsany_test3		32.62 402	30.57 407	38.77 416	36.16 447	24.20 437	38.10 436	20.63 449	19.14 435	40.36 423	57.43 422	5.06 440	36.63 441	29.59 409	28.66 432	55.49 422

MVStest1		42.65 387	39.29 394	52.71 391	47.26 436	34.58 395	54.41 408	50.84 415	23.35 427	39.31 427	74.08 358	12.57 424	55.09 414	23.32 424	28.47 433	68.47 406

WB-MVS		43.26 385	43.41 385	42.83 411	63.32 400	10.32 449	58.17 392	45.20 427	45.42 359	40.44 422	67.26 404	34.01 310	58.98 394	11.96 440	24.88 434	59.20 415

PVSNet_0	43.31 20	47.46 380	45.64 383	52.92 389	67.60 376	44.65 298	54.06 409	54.64 401	41.59 388	46.15 409	58.75 420	30.99 345	58.66 396	32.18 388	24.81 435	55.46 423

test_method		19.68 412	18.10 415	24.41 427	13.68 452	3.11 454	12.06 443	42.37 433	2.00 446	11.97 444	36.38 438	5.77 439	29.35 446	15.06 433	23.65 436	40.76 435

SSC-MVS		41.96 390	41.99 389	41.90 412	62.46 405	9.28 451	57.41 399	44.32 430	43.38 376	38.30 428	66.45 407	32.67 332	58.42 398	10.98 441	21.91 437	57.99 419

PMMVS2		27.40 408	25.91 411	31.87 423	39.46 445	6.57 452	31.17 438	28.52 443	23.96 426	20.45 440	48.94 434	4.20 444	37.94 439	16.51 432	19.97 438	51.09 425

dongtai		34.52 400	34.94 400	33.26 421	61.06 412	16.00 446	52.79 413	23.78 447	40.71 394	39.33 426	48.65 435	16.91 415	48.34 427	12.18 439	19.05 439	35.44 438

kuosan		29.62 407	30.82 406	26.02 426	52.99 425	16.22 445	51.09 416	22.71 448	33.91 411	33.99 430	40.85 436	15.89 418	33.11 443	7.59 447	18.37 440	28.72 440

MVE	17.77 23	21.41 411	17.77 416	32.34 422	34.34 449	25.44 434	16.11 441	24.11 446	11.19 443	13.22 443	31.92 439	1.58 450	30.95 445	10.47 442	17.03 441	40.62 436
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 409	22.73 413	26.90 424	42.02 440	20.67 441	42.66 432	35.70 438	17.43 436	10.28 446	25.05 442	6.42 438	42.39 435	10.28 443	14.71 442	17.63 441

EMVS		22.97 410	21.84 414	26.36 425	40.20 443	19.53 443	41.95 433	34.64 439	17.09 437	9.73 447	22.83 443	7.29 437	42.22 436	9.18 445	13.66 443	17.32 442

wuyk23d		13.32 414	12.52 417	15.71 428	47.54 435	26.27 432	31.06 439	1.98 453	4.93 445	5.18 448	1.94 448	0.45 453	18.54 447	6.81 448	12.83 444	2.33 445

ANet_high		41.38 391	37.47 398	53.11 388	39.73 444	24.45 436	56.94 400	69.69 301	47.65 335	26.04 436	52.32 426	12.44 425	62.38 380	21.80 427	10.61 445	72.49 370

tmp_tt		9.43 415	11.14 418	4.30 430	2.38 453	4.40 453	13.62 442	16.08 451	0.39 447	15.89 442	13.06 444	15.80 419	5.54 449	12.63 438	10.46 446	2.95 444

DeepMVS_CX				12.03 429	17.97 451	10.91 448		10.60 452	7.46 444	11.07 445	28.36 440	3.28 446	11.29 448	8.01 446	9.74 447	13.89 443

mmdepth		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

monomultidepth		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

test_blank		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

uanet_test		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

DCPMVS		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

cdsmvs_eth3d_5k		17.50 413	23.34 412	0.00 433	0.00 456	0.00 457	0.00 444	78.63 176	0.00 451	0.00 452	82.18 214	49.25 131	0.00 450	0.00 451	0.00 448	0.00 448

pcd_1.5k_mvsjas		3.92 419	5.23 422	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	47.05 163	0.00 450	0.00 451	0.00 448	0.00 448

sosnet-low-res		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

sosnet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

uncertanet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

Regformer		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

testmvs		4.52 418	6.03 421	0.01 432	0.01 454	0.00 457	53.86 410	0.00 455	0.01 449	0.04 450	0.27 449	0.00 455	0.00 450	0.04 449	0.00 448	0.03 447

test123		4.73 417	6.30 420	0.02 431	0.01 454	0.01 456	56.36 402	0.00 455	0.01 449	0.04 450	0.21 450	0.01 454	0.00 450	0.03 450	0.00 448	0.04 446

ab-mvs-re		6.49 416	8.65 419	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	77.89 301	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

uanet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 457	0.00 444	0.00 455	0.00 451	0.00 452	0.00 451	0.00 455	0.00 450	0.00 451	0.00 448	0.00 448

WAC-MVS						27.31 428								27.77 413

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 90	81.50 16

test_one_0601					87.58 9	59.30 61		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 456

eth-test					0.00 456

test_241102_ONE					87.77 4	58.90 73		86.78 10	64.20 32	85.97 1	91.34 16	66.87 3	90.78 7

save fliter					86.17 33	61.30 28	83.98 52	79.66 154	59.00 137

test0726					87.75 7	59.07 68	87.86 4	86.83 8	64.26 30	84.19 7	91.92 5	64.82 8

GSMVS																78.05 306

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 300				78.05 306

sam_mvs												33.43 317

MTGPA								80.97 136

test_post1							68.67 320				3.64 446	32.39 338	69.49 342	44.17 306

test_post											3.55 447	33.90 311	66.52 362

patchmatchnet-post											64.03 413	34.50 302	74.27 314

MTMP							86.03 19	17.08 450

gm-plane-assit					71.40 330	41.72 330			48.85 317		73.31 362		82.48 178	48.90 266

TEST9					85.58 43	61.59 24	81.62 85	81.26 125	55.65 209	74.93 56	88.81 62	53.70 69	84.68 127

test_8					85.40 46	60.96 34	81.54 88	81.18 129	55.86 201	74.81 61	88.80 64	53.70 69	84.45 131

agg_prior					85.04 50	59.96 50		81.04 134		74.68 65			84.04 137

test_prior4						62.51 14	82.08 81

test_prior				76.69 60	84.20 61	57.27 93		84.88 40					86.43 82			86.38 87

旧先验2							76.08 197		45.32 360	76.55 40			65.56 369	58.75 187

新几何2							76.12 195

无先验							79.66 114	74.30 259	48.40 324				80.78 215	53.62 226		79.03 297

原ACMM2							79.02 121

testdata2													72.18 326	46.95 284

segment_acmp												54.23 59

testdata1							72.65 267		60.50 100

plane_prior7					81.41 96	55.96 116

plane_prior6					81.20 103	56.24 111						45.26 187

plane_prior4											86.10 127

plane_prior3						56.09 113			63.92 37	69.27 146

plane_prior2							84.22 45		64.52 26

plane_prior1					81.27 101

n2								0.00 455

nn								0.00 455

door-mid								47.19 425

test11								83.47 72

door								47.60 423

HQP5-MVS						54.94 137

HQP-NCC					80.66 110		82.31 76		62.10 70	67.85 174

ACMP_Plane					80.66 110		82.31 76		62.10 70	67.85 174

BP-MVS														67.04 109

HQP4-MVS										67.85 174			86.93 67			84.32 170

HQP2-MVS												45.46 181

NP-MVS					80.98 106	56.05 115					85.54 146

MDTV_nov1_ep13_2view						25.89 433	61.22 377		40.10 398	51.10 388		32.97 323		38.49 352		78.61 301

Test By Simon												48.33 142

High-resolution multi-view benchmark