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.

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 16

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

IU-MVS					87.77 4	59.15 60		85.53 25	53.93 225	84.64 3				79.07 11	90.87 5	88.37 13

PC_three_1452									55.09 201	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 11

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 60	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 11

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 35

test0726					87.75 7	59.07 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 34	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 116
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_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 21

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 23	63.71 12	89.23 20	81.51 3	88.44 27	88.09 21
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
test_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

test_part2					87.58 9	60.47 42				83.42 12

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 59
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		80.16 8	80.59 6	78.86 28	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 18	89.76 15	78.70 13	88.32 31	86.79 61
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 34	89.70 16	79.85 5	91.48 1	88.19 18
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
FOURS1					86.12 36	60.82 37	88.18 1	83.61 63	60.87 84	81.50 16

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 44	83.03 57	85.33 27	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 20

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 34	83.87 57	60.37 96	79.89 18	89.38 49	54.97 46	85.58 97	76.12 31	84.94 62	86.33 78
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
TSAR-MVS + MP.		78.44 19	78.28 20	78.90 26	84.96 52	61.41 26	84.03 45	83.82 59	59.34 117	79.37 19	89.76 45	59.84 16	87.62 47	76.69 27	86.74 52	87.68 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		78.82 13	79.22 12	77.60 44	82.88 74	57.83 80	84.99 32	88.13 2	61.86 75	79.16 20	90.75 18	57.96 25	87.09 60	77.08 26	90.18 15	87.87 26

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 43	60.61 89	79.05 21	90.30 30	55.54 41	88.32 32	73.48 53	87.03 45	84.83 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 61	65.37 13	78.78 22	90.64 19	58.63 24	87.24 51	79.00 12	90.37 14	85.26 127

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 106	78.57 23	90.36 27	57.51 30	86.86 64	77.39 23	89.52 21

ZD-MVS					86.64 21	60.38 43		82.70 86	57.95 144	78.10 24	90.06 36	56.12 38	88.84 26	74.05 47	87.00 48

ACMMP_NAP		78.77 15	78.78 14	78.74 29	85.44 45	61.04 31	83.84 49	85.16 30	62.88 53	78.10 24	91.26 13	52.51 75	88.39 30	79.34 8	90.52 13	86.78 62

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 16	54.26 53	90.06 13	78.42 19	89.02 23	87.69 33
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 34	66.96 5	77.58 27	90.06 36	59.47 20	89.13 22	78.67 14	89.73 16	87.03 53

test_fmvsm_n_1920		71.73 92	71.14 92	73.50 130	72.52 277	56.53 101	75.60 191	76.16 204	48.11 293	77.22 28	85.56 123	53.10 70	77.43 255	74.86 40	77.14 155	86.55 70

canonicalmvs		74.67 54	74.98 50	73.71 121	78.94 141	50.56 202	80.23 96	83.87 57	60.30 100	77.15 29	86.56 95	59.65 17	82.00 174	66.01 101	82.12 89	88.58 10

MM		80.20 7	80.28 8	79.99 2	82.19 79	60.01 46	86.19 17	83.93 51	73.19 1	77.08 30	91.21 15	57.23 31	90.73 10	83.35 1	88.12 35	89.22 5

alignmvs		73.86 63	73.99 58	73.45 133	78.20 163	50.50 203	78.57 123	82.43 88	59.40 115	76.57 31	86.71 89	56.42 36	81.23 190	65.84 103	81.79 93	88.62 8

旧先验2							76.08 182		45.32 323	76.55 32			65.56 334	58.75 162

MP-MVS-pluss		78.35 20	78.46 18	78.03 40	84.96 52	59.52 53	82.93 59	85.39 26	62.15 67	76.41 33	91.51 11	52.47 77	86.78 67	80.66 4	89.64 19	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
casdiffmvs_mvg		76.14 41	76.30 36	75.66 71	76.46 217	51.83 186	79.67 109	85.08 31	65.02 19	75.84 34	88.58 60	59.42 21	85.08 108	72.75 56	83.93 72	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
MTAPA		76.90 34	76.42 35	78.35 35	86.08 37	63.57 2	74.92 208	80.97 123	65.13 15	75.77 35	90.88 17	48.63 122	86.66 70	77.23 24	88.17 33	84.81 140

dcpmvs_2		74.55 57	75.23 48	72.48 153	82.34 77	53.34 155	77.87 138	81.46 103	57.80 148	75.49 36	86.81 84	62.22 13	77.75 251	71.09 67	82.02 91	86.34 76

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 87	75.27 37	84.83 133	60.76 15	86.56 73	67.86 84	87.87 41	86.06 89

fmvsm_l_conf0.5_n		70.99 103	70.82 97	71.48 175	71.45 293	54.40 138	77.18 159	70.46 271	48.67 284	75.17 38	86.86 82	53.77 61	76.86 266	76.33 30	77.51 148	83.17 194

SR-MVS		76.13 42	75.70 43	77.40 48	85.87 40	61.20 29	85.52 27	82.19 91	59.99 105	75.10 39	90.35 28	47.66 134	86.52 75	71.64 64	82.99 78	84.47 149

ZNCC-MVS		78.82 13	78.67 17	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 40	90.47 26	53.96 57	88.68 27	76.48 28	89.63 20	87.16 51

test_prior2							81.75 80		60.37 96	75.01 41	89.06 52	56.22 37		72.19 59	88.96 24

test_fmvsmconf0.1_n		72.81 72	72.33 74	74.24 106	69.89 320	55.81 115	78.22 129	75.40 217	54.17 222	75.00 42	88.03 68	53.82 60	80.23 214	78.08 20	78.34 139	86.69 64

TEST9					85.58 43	61.59 24	81.62 82	81.26 115	55.65 189	74.93 43	88.81 56	53.70 63	84.68 118

train_agg		76.27 39	76.15 37	76.64 55	85.58 43	61.59 24	81.62 82	81.26 115	55.86 180	74.93 43	88.81 56	53.70 63	84.68 118	75.24 38	88.33 30	83.65 180

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 159	74.91 45	88.19 62	59.15 22	87.68 46	73.67 51	87.45 42	86.57 69

test_fmvsmconf_n		73.01 70	72.59 71	74.27 105	71.28 300	55.88 114	78.21 130	75.56 214	54.31 220	74.86 46	87.80 72	54.72 49	80.23 214	78.07 21	78.48 136	86.70 63

h-mvs33		72.71 75	71.49 83	76.40 58	81.99 82	59.58 52	76.92 166	76.74 200	60.40 93	74.81 47	85.95 115	45.54 162	85.76 93	70.41 70	70.61 237	83.86 168

hse-mvs2		71.04 101	69.86 113	74.60 95	79.58 124	57.12 96	73.96 224	75.25 220	60.40 93	74.81 47	81.95 199	45.54 162	82.90 151	70.41 70	66.83 289	83.77 173

test_8					85.40 46	60.96 34	81.54 85	81.18 118	55.86 180	74.81 47	88.80 58	53.70 63	84.45 122

agg_prior					85.04 50	59.96 47		81.04 121		74.68 50			84.04 128

NCCC		78.58 17	78.31 19	79.39 12	87.51 12	62.61 13	85.20 31	84.42 42	66.73 8	74.67 51	89.38 49	55.30 42	89.18 21	74.19 46	87.34 43	86.38 72

test_fmvsmconf0.01_n		72.17 84	71.50 82	74.16 107	67.96 337	55.58 123	78.06 135	74.67 232	54.19 221	74.54 52	88.23 61	50.35 106	80.24 213	78.07 21	77.46 149	86.65 67

nrg030		72.96 71	73.01 67	72.84 146	75.41 232	50.24 205	80.02 100	82.89 84	58.36 135	74.44 53	86.73 87	58.90 23	80.83 200	65.84 103	74.46 180	87.44 42

casdiffmvs		74.80 51	74.89 51	74.53 98	75.59 229	50.37 204	78.17 131	85.06 33	62.80 58	74.40 54	87.86 70	57.88 26	83.61 138	69.46 75	82.79 85	89.59 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_l_conf0.5_n_a		70.50 113	70.27 107	71.18 187	71.30 299	54.09 140	76.89 167	69.87 274	47.90 297	74.37 55	86.49 97	53.07 71	76.69 271	75.41 35	77.11 156	82.76 201

TSAR-MVS + GP.		74.90 50	74.15 57	77.17 49	82.00 81	58.77 72	81.80 79	78.57 162	58.58 130	74.32 56	84.51 143	55.94 39	87.22 52	67.11 92	84.48 67	85.52 112

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 29	63.56 41	74.29 57	90.03 38	52.56 74	88.53 29	74.79 42	88.34 29	86.63 68

MVS_0304		78.73 16	78.75 15	78.66 30	80.82 101	57.62 83	85.31 30	81.31 112	70.51 2	74.17 58	91.24 14	54.99 45	89.56 17	82.29 2	88.13 34	88.80 7

CDPH-MVS		76.31 38	75.67 44	78.22 37	85.35 48	59.14 62	81.31 87	84.02 48	56.32 172	74.05 59	88.98 54	53.34 67	87.92 41	69.23 76	88.42 28	87.59 38

baseline		74.61 55	74.70 52	74.34 102	75.70 225	49.99 212	77.54 148	84.63 40	62.73 59	73.98 60	87.79 73	57.67 28	83.82 134	69.49 73	82.74 86	89.20 6

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 44	62.82 55	73.96 61	90.50 24	53.20 68	88.35 31	74.02 48	87.05 44	86.13 87

testdata				64.66 281	81.52 87	52.93 162		65.29 310	46.09 316	73.88 62	87.46 75	38.08 241	66.26 331	53.31 202	78.48 136	74.78 315

DeepC-MVS	69.38 2	78.56 18	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 63	90.25 32	57.68 27	89.96 14	74.62 43	89.03 22	87.89 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS_3200maxsize		74.96 49	74.39 55	76.67 54	82.20 78	58.24 77	83.67 51	83.29 75	58.41 133	73.71 64	90.14 33	45.62 159	85.99 87	69.64 72	82.85 84	85.78 99

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 65	63.89 37	73.60 65	90.60 20	54.85 48	86.72 68	77.20 25	88.06 37	85.74 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 45	62.82 55	73.55 66	90.56 22	49.80 109	88.24 33	74.02 48	87.03 45	86.32 80

PHI-MVS		75.87 44	75.36 45	77.41 46	80.62 107	55.91 113	84.28 39	85.78 20	56.08 178	73.41 67	86.58 94	50.94 101	88.54 28	70.79 68	89.71 17	87.79 31

CS-MVS		76.25 40	75.98 39	77.06 50	80.15 116	55.63 120	84.51 35	83.90 54	63.24 45	73.30 68	87.27 79	55.06 44	86.30 83	71.78 62	84.58 64	89.25 4

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 47	62.81 57	73.30 68	90.58 21	49.90 107	88.21 34	73.78 50	87.03 45	86.29 83

test_fmvsmvis_n_1920		70.84 105	70.38 105	72.22 160	71.16 301	55.39 127	75.86 188	72.21 258	49.03 280	73.28 70	86.17 106	51.83 88	77.29 258	75.80 32	78.05 141	83.98 162

VDD-MVS		72.50 77	72.09 76	73.75 119	81.58 86	49.69 217	77.76 143	77.63 185	63.21 47	73.21 71	89.02 53	42.14 196	83.32 142	61.72 140	82.50 87	88.25 15

fmvsm_s_conf0.1_n_a		69.32 146	68.44 143	71.96 161	70.91 304	53.78 145	78.12 133	62.30 333	49.35 276	73.20 72	86.55 96	51.99 85	76.79 268	74.83 41	68.68 276	85.32 123

DELS-MVS		74.76 52	74.46 54	75.65 72	77.84 177	52.25 178	75.59 192	84.17 46	63.76 38	73.15 73	82.79 174	59.58 19	86.80 66	67.24 91	86.04 57	87.89 24
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
SR-MVS-dyc-post		74.57 56	73.90 59	76.58 56	83.49 65	59.87 49	84.29 37	81.36 107	58.07 139	73.14 74	90.07 34	44.74 173	85.84 91	68.20 79	81.76 94	84.03 159

RE-MVS-def			73.71 63		83.49 65	59.87 49	84.29 37	81.36 107	58.07 139	73.14 74	90.07 34	43.06 188		68.20 79	81.76 94	84.03 159

fmvsm_s_conf0.5_n_a		69.54 138	68.74 134	71.93 162	72.47 279	53.82 144	78.25 127	62.26 334	49.78 272	73.12 76	86.21 104	52.66 73	76.79 268	75.02 39	68.88 271	85.18 128

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 31	62.57 60	73.09 77	89.97 41	50.90 102	87.48 49	75.30 36	86.85 50	87.33 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 33	84.85 38	61.98 74	73.06 78	88.88 55	53.72 62	89.06 23	68.27 78	88.04 38	87.42 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n		69.41 144	68.60 137	71.83 165	71.07 302	52.88 165	77.85 140	62.44 331	49.58 274	72.97 79	86.22 103	51.68 91	76.48 275	75.53 34	70.10 248	86.14 86

VDDNet		71.81 89	71.33 88	73.26 140	82.80 75	47.60 245	78.74 119	75.27 219	59.59 114	72.94 80	89.40 48	41.51 207	83.91 132	58.75 162	82.99 78	88.26 14

test12				77.76 43	84.52 58	58.41 75		83.36 72		72.93 81		54.61 51	88.05 37		88.12 35	86.81 60

fmvsm_s_conf0.5_n		69.58 136	68.84 131	71.79 167	72.31 283	52.90 164	77.90 137	62.43 332	49.97 270	72.85 82	85.90 116	52.21 81	76.49 274	75.75 33	70.26 245	85.97 91

LFMVS		71.78 90	71.59 80	72.32 158	83.40 67	46.38 254	79.75 107	71.08 265	64.18 32	72.80 83	88.64 59	42.58 192	83.72 135	57.41 168	84.49 66	86.86 58

EC-MVSNet		75.84 45	75.87 42	75.74 69	78.86 142	52.65 168	83.73 50	86.08 17	63.47 42	72.77 84	87.25 80	53.13 69	87.93 40	71.97 61	85.57 60	86.66 66

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 68	62.44 64	72.68 85	90.50 24	48.18 127	87.34 50	73.59 52	85.71 58	84.76 143

ETV-MVS		74.46 58	73.84 61	76.33 60	79.27 132	55.24 129	79.22 115	85.00 36	64.97 21	72.65 86	79.46 248	53.65 66	87.87 42	67.45 90	82.91 81	85.89 96

UA-Net		73.13 68	72.93 68	73.76 117	83.58 64	51.66 187	78.75 118	77.66 184	67.75 4	72.61 87	89.42 47	49.82 108	83.29 143	53.61 199	83.14 75	86.32 80

OPM-MVS		74.73 53	74.25 56	76.19 61	80.81 102	59.01 67	82.60 66	83.64 62	63.74 39	72.52 88	87.49 74	47.18 144	85.88 90	69.47 74	80.78 99	83.66 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DPM-MVS		75.47 48	75.00 49	76.88 51	81.38 92	59.16 59	79.94 102	85.71 22	56.59 167	72.46 89	86.76 85	56.89 32	87.86 43	66.36 97	88.91 25	83.64 181

MVS_Test		72.45 79	72.46 73	72.42 157	74.88 238	48.50 233	76.28 178	83.14 80	59.40 115	72.46 89	84.68 135	55.66 40	81.12 191	65.98 102	79.66 115	87.63 36

PGM-MVS		76.77 35	76.06 38	78.88 27	86.14 35	62.73 9	82.55 67	83.74 60	61.71 76	72.45 91	90.34 29	48.48 125	88.13 35	72.32 58	86.85 50	85.78 99

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	90.01 40	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

X-MVStestdata		70.21 119	67.28 171	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	6.49 403	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

Effi-MVS+		73.31 67	72.54 72	75.62 73	77.87 175	53.64 147	79.62 111	79.61 141	61.63 77	72.02 94	82.61 179	56.44 35	85.97 88	63.99 119	79.07 127	87.25 50

mPP-MVS		76.54 36	75.93 40	78.34 36	86.47 26	63.50 3	85.74 25	82.28 90	62.90 52	71.77 95	90.26 31	46.61 153	86.55 74	71.71 63	85.66 59	84.97 136

diffmvs		70.69 109	70.43 103	71.46 176	69.45 325	48.95 227	72.93 240	78.46 168	57.27 153	71.69 96	83.97 154	51.48 93	77.92 248	70.70 69	77.95 143	87.53 40
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-Vis-set		72.42 80	71.59 80	74.91 84	78.47 153	54.02 141	77.05 162	79.33 147	65.03 18	71.68 97	79.35 251	52.75 72	84.89 114	66.46 96	74.23 184	85.83 98

MSLP-MVS++		73.77 64	73.47 64	74.66 91	83.02 71	59.29 58	82.30 74	81.88 95	59.34 117	71.59 98	86.83 83	45.94 157	83.65 137	65.09 110	85.22 61	81.06 236

CS-MVS-test		75.62 47	75.31 47	76.56 57	80.63 106	55.13 130	83.88 48	85.22 28	62.05 71	71.49 99	86.03 111	53.83 59	86.36 81	67.74 85	86.91 49	88.19 18

EI-MVSNet-UG-set		71.92 88	71.06 94	74.52 99	77.98 173	53.56 149	76.62 171	79.16 148	64.40 27	71.18 100	78.95 256	52.19 82	84.66 120	65.47 107	73.57 195	85.32 123

MG-MVS		73.96 62	73.89 60	74.16 107	85.65 42	49.69 217	81.59 84	81.29 114	61.45 78	71.05 101	88.11 63	51.77 89	87.73 45	61.05 146	83.09 76	85.05 133

patch_mono-2		69.85 126	71.09 93	66.16 261	79.11 138	54.80 135	71.97 256	74.31 237	53.50 230	70.90 102	84.17 147	57.63 29	63.31 340	66.17 98	82.02 91	80.38 247

VNet		69.68 133	70.19 109	68.16 237	79.73 122	41.63 302	70.53 275	77.38 190	60.37 96	70.69 103	86.63 91	51.08 98	77.09 261	53.61 199	81.69 98	85.75 104

MVS_111021_HR		74.02 61	73.46 65	75.69 70	83.01 72	60.63 40	77.29 156	78.40 173	61.18 82	70.58 104	85.97 113	54.18 55	84.00 131	67.52 89	82.98 80	82.45 207

HPM-MVS_fast		74.30 60	73.46 65	76.80 52	84.45 60	59.04 66	83.65 52	81.05 120	60.15 102	70.43 105	89.84 43	41.09 213	85.59 96	67.61 88	82.90 82	85.77 102

CLD-MVS		73.33 66	72.68 70	75.29 80	78.82 144	53.33 156	78.23 128	84.79 39	61.30 81	70.41 106	81.04 216	52.41 78	87.12 58	64.61 115	82.49 88	85.41 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
新几何1				70.76 195	85.66 41	61.13 30		66.43 302	44.68 327	70.29 107	86.64 90	41.29 209	75.23 283	49.72 230	81.75 96	75.93 299

原ACMM1				74.69 89	85.39 47	59.40 54		83.42 69	51.47 250	70.27 108	86.61 92	48.61 123	86.51 76	53.85 197	87.96 39	78.16 273

CANet		76.46 37	75.93 40	78.06 39	81.29 93	57.53 85	82.35 69	83.31 74	67.78 3	70.09 109	86.34 101	54.92 47	88.90 25	72.68 57	84.55 65	87.76 32

xiu_mvs_v1_base_debu		68.58 160	67.28 171	72.48 153	78.19 164	57.19 91	75.28 197	75.09 226	51.61 245	70.04 110	81.41 210	32.79 295	79.02 234	63.81 121	77.31 150	81.22 231

xiu_mvs_v1_base		68.58 160	67.28 171	72.48 153	78.19 164	57.19 91	75.28 197	75.09 226	51.61 245	70.04 110	81.41 210	32.79 295	79.02 234	63.81 121	77.31 150	81.22 231

xiu_mvs_v1_base_debi		68.58 160	67.28 171	72.48 153	78.19 164	57.19 91	75.28 197	75.09 226	51.61 245	70.04 110	81.41 210	32.79 295	79.02 234	63.81 121	77.31 150	81.22 231

PS-MVSNAJss		72.24 82	71.21 90	75.31 78	78.50 151	55.93 112	81.63 81	82.12 92	56.24 175	70.02 113	85.68 122	47.05 146	84.34 124	65.27 109	74.41 183	85.67 106

test_yl		69.69 131	69.13 125	71.36 181	78.37 158	45.74 261	74.71 212	80.20 134	57.91 146	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

DCV-MVSNet		69.69 131	69.13 125	71.36 181	78.37 158	45.74 261	74.71 212	80.20 134	57.91 146	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

xiu_mvs_v2_base		70.52 111	69.75 114	72.84 146	81.21 96	55.63 120	75.11 202	78.92 153	54.92 208	69.96 116	79.68 243	47.00 150	82.09 173	61.60 142	79.37 119	80.81 241

Anonymous20240529		69.91 125	69.02 128	72.56 151	80.19 114	47.65 243	77.56 147	80.99 122	55.45 194	69.88 117	86.76 85	39.24 228	82.18 172	54.04 194	77.10 157	87.85 27

PS-MVSNAJ		70.51 112	69.70 116	72.93 144	81.52 87	55.79 116	74.92 208	79.00 151	55.04 206	69.88 117	78.66 258	47.05 146	82.19 171	61.61 141	79.58 116	80.83 240

ACMMP		76.02 43	75.33 46	78.07 38	85.20 49	61.91 20	85.49 29	84.44 41	63.04 49	69.80 119	89.74 46	45.43 166	87.16 55	72.01 60	82.87 83	85.14 129
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
PCF-MVS	61.88 8	70.95 104	69.49 120	75.35 77	77.63 184	55.71 117	76.04 185	81.81 97	50.30 266	69.66 120	85.40 129	52.51 75	84.89 114	51.82 214	80.24 109	85.45 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v2v482		70.50 113	69.45 122	73.66 123	72.62 274	50.03 211	77.58 145	80.51 130	59.90 106	69.52 121	82.14 195	47.53 137	84.88 116	65.07 111	70.17 246	86.09 88

MVSFormer		71.50 96	70.38 105	74.88 85	78.76 145	57.15 94	82.79 61	78.48 166	51.26 254	69.49 122	83.22 168	43.99 181	83.24 144	66.06 99	79.37 119	84.23 154

lupinMVS		69.57 137	68.28 146	73.44 134	78.76 145	57.15 94	76.57 172	73.29 250	46.19 315	69.49 122	82.18 191	43.99 181	79.23 226	64.66 113	79.37 119	83.93 163

V42		68.65 158	67.35 169	72.56 151	68.93 331	50.18 207	72.90 241	79.47 144	56.92 158	69.45 124	80.26 232	46.29 155	82.99 148	64.07 116	67.82 281	84.53 146

v1144		70.42 115	69.31 123	73.76 117	73.22 262	50.64 199	77.83 141	81.43 104	58.58 130	69.40 125	81.16 213	47.53 137	85.29 107	64.01 118	70.64 235	85.34 122

jason		69.65 134	68.39 145	73.43 135	78.27 162	56.88 98	77.12 160	73.71 246	46.53 312	69.34 126	83.22 168	43.37 185	79.18 227	64.77 112	79.20 124	84.23 154
jason: jason.
HQP_MVS		74.31 59	73.73 62	76.06 62	81.41 90	56.31 102	84.22 40	84.01 49	64.52 25	69.27 127	86.10 108	45.26 170	87.21 53	68.16 81	80.58 103	84.65 144

plane_prior3						56.09 108			63.92 36	69.27 127

VPA-MVSNet		69.02 151	69.47 121	67.69 241	77.42 195	41.00 307	74.04 222	79.68 139	60.06 103	69.26 129	84.81 134	51.06 99	77.58 253	54.44 193	74.43 182	84.48 148

Vis-MVSNet		72.18 83	71.37 87	74.61 94	81.29 93	55.41 126	80.90 90	78.28 175	60.73 88	69.23 130	88.09 64	44.36 178	82.65 162	57.68 165	81.75 96	85.77 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet		69.27 148	68.44 143	71.73 169	74.47 250	49.39 222	75.20 200	78.45 169	59.60 111	69.16 131	76.51 295	51.29 94	82.50 166	59.86 157	71.45 229	83.30 186

MVSTER		67.16 192	65.58 205	71.88 164	70.37 312	49.70 215	70.25 280	78.45 169	51.52 248	69.16 131	80.37 228	38.45 235	82.50 166	60.19 151	71.46 228	83.44 184

v1192		69.97 124	68.68 135	73.85 112	73.19 263	50.94 192	77.68 144	81.36 107	57.51 151	68.95 133	80.85 223	45.28 169	85.33 106	62.97 129	70.37 241	85.27 126

OMC-MVS		71.40 98	70.60 100	73.78 115	76.60 213	53.15 159	79.74 108	79.78 137	58.37 134	68.75 134	86.45 99	45.43 166	80.60 204	62.58 131	77.73 144	87.58 39

Fast-Effi-MVS+		70.28 118	69.12 127	73.73 120	78.50 151	51.50 188	75.01 205	79.46 145	56.16 177	68.59 135	79.55 246	53.97 56	84.05 127	53.34 201	77.53 147	85.65 108

v1921920		69.47 141	68.17 147	73.36 137	73.06 266	50.10 209	77.39 151	80.56 128	56.58 168	68.59 135	80.37 228	44.72 174	84.98 111	62.47 134	69.82 255	85.00 134

v144192		69.71 130	68.51 138	73.33 138	73.10 265	50.13 208	77.54 148	80.64 127	56.65 161	68.57 137	80.55 226	46.87 151	84.96 113	62.98 128	69.66 260	84.89 138

TranMVSNet+NR-MVSNet		70.36 116	70.10 112	71.17 188	78.64 149	42.97 289	76.53 173	81.16 119	66.95 6	68.53 138	85.42 128	51.61 92	83.07 147	52.32 207	69.70 259	87.46 41

API-MVS		72.17 84	71.41 85	74.45 100	81.95 83	57.22 89	84.03 45	80.38 132	59.89 109	68.40 139	82.33 188	49.64 110	87.83 44	51.87 213	84.16 71	78.30 271

BH-RMVSNet		68.81 154	67.42 165	72.97 143	80.11 117	52.53 173	74.26 219	76.29 203	58.48 132	68.38 140	84.20 146	42.59 191	83.83 133	46.53 257	75.91 169	82.56 202

v1240		69.24 149	67.91 151	73.25 141	73.02 268	49.82 213	77.21 158	80.54 129	56.43 170	68.34 141	80.51 227	43.33 186	84.99 109	62.03 138	69.77 258	84.95 137

UniMVSNet_NR-MVSNet		71.11 100	71.00 95	71.44 177	79.20 134	44.13 277	76.02 186	82.60 87	66.48 11	68.20 142	84.60 140	56.82 33	82.82 158	54.62 190	70.43 239	87.36 48

DU-MVS		70.01 122	69.53 119	71.44 177	78.05 170	44.13 277	75.01 205	81.51 102	64.37 28	68.20 142	84.52 141	49.12 119	82.82 158	54.62 190	70.43 239	87.37 46

iter_conf05		69.40 145	67.62 156	74.73 87	77.84 177	51.13 190	79.28 114	73.71 246	54.62 212	68.17 144	83.59 161	28.68 333	87.16 55	65.74 105	76.95 158	85.91 94

UniMVSNet (Re)		70.63 110	70.20 108	71.89 163	78.55 150	45.29 268	75.94 187	82.92 82	63.68 40	68.16 145	83.59 161	53.89 58	83.49 141	53.97 195	71.12 232	86.89 57

Baseline_NR-MVSNet		67.05 194	67.56 157	65.50 273	75.65 226	37.70 335	75.42 195	74.65 233	59.90 106	68.14 146	83.15 171	49.12 119	77.20 259	52.23 208	69.78 256	81.60 221

WR-MVS		68.47 164	68.47 141	68.44 234	80.20 113	39.84 313	73.75 232	76.07 207	64.68 22	68.11 147	83.63 160	50.39 105	79.14 232	49.78 227	69.66 260	86.34 76

MAR-MVS		71.51 95	70.15 110	75.60 74	81.84 84	59.39 55	81.38 86	82.90 83	54.90 209	68.08 148	78.70 257	47.73 132	85.51 99	51.68 217	84.17 70	81.88 219
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
iter_conf_final		69.82 127	68.02 150	75.23 81	79.38 129	52.91 163	80.11 99	73.96 243	54.99 207	68.04 149	83.59 161	29.05 328	87.16 55	65.41 108	77.62 145	85.63 109

Anonymous202405211		66.84 199	65.99 198	69.40 221	80.19 114	42.21 295	71.11 269	71.31 264	58.80 124	67.90 150	86.39 100	29.83 322	79.65 219	49.60 233	78.78 131	86.33 78

TR-MVS		66.59 206	65.07 211	71.17 188	79.18 135	49.63 219	73.48 234	75.20 223	52.95 233	67.90 150	80.33 231	39.81 221	83.68 136	43.20 290	73.56 196	80.20 249

HQP-NCC					80.66 103		82.31 71		62.10 68	67.85 152

ACMP_Plane					80.66 103		82.31 71		62.10 68	67.85 152

HQP4-MVS										67.85 152			86.93 62			84.32 151

HQP-MVS		73.45 65	72.80 69	75.40 76	80.66 103	54.94 131	82.31 71	83.90 54	62.10 68	67.85 152	85.54 126	45.46 164	86.93 62	67.04 93	80.35 107	84.32 151

MVS_111021_LR		69.50 140	68.78 133	71.65 172	78.38 156	59.33 56	74.82 210	70.11 273	58.08 138	67.83 156	84.68 135	41.96 198	76.34 278	65.62 106	77.54 146	79.30 264

3Dnovator+	66.72 4	75.84 45	74.57 53	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 157	89.24 51	42.03 197	89.38 19	64.07 116	86.50 55	89.69 2

VPNet		67.52 183	68.11 148	65.74 270	79.18 135	36.80 344	72.17 253	72.83 253	62.04 72	67.79 158	85.83 119	48.88 121	76.60 273	51.30 218	72.97 208	83.81 169

XVG-OURS		68.76 157	67.37 167	72.90 145	74.32 255	57.22 89	70.09 281	78.81 155	55.24 197	67.79 158	85.81 121	36.54 259	78.28 243	62.04 137	75.74 172	83.19 191

GeoE		71.01 102	70.15 110	73.60 128	79.57 125	52.17 179	78.93 117	78.12 177	58.02 141	67.76 160	83.87 155	52.36 79	82.72 160	56.90 170	75.79 171	85.92 93

FA-MVS(test-final)		69.82 127	68.48 139	73.84 113	78.44 154	50.04 210	75.58 194	78.99 152	58.16 137	67.59 161	82.14 195	42.66 190	85.63 94	56.60 171	76.19 167	85.84 97

test222					83.14 68	58.68 73	72.57 247	63.45 323	41.78 348	67.56 162	86.12 107	37.13 253			78.73 133	74.98 311

CPTT-MVS		72.78 73	72.08 77	74.87 86	84.88 57	61.41 26	84.15 43	77.86 180	55.27 196	67.51 163	88.08 65	41.93 199	81.85 176	69.04 77	80.01 111	81.35 229

v148		68.24 169	67.19 177	71.40 180	70.43 310	47.77 242	75.76 190	77.03 195	58.91 122	67.36 164	80.10 235	48.60 124	81.89 175	60.01 153	66.52 292	84.53 146

FIs		70.82 107	71.43 84	68.98 227	78.33 160	38.14 329	76.96 164	83.59 64	61.02 83	67.33 165	86.73 87	55.07 43	81.64 179	54.61 192	79.22 123	87.14 52

Anonymous20231211		69.28 147	68.47 141	71.73 169	80.28 109	47.18 249	79.98 101	82.37 89	54.61 213	67.24 166	84.01 152	39.43 224	82.41 169	55.45 184	72.83 209	85.62 110

ECVR-MVS		67.72 180	67.51 161	68.35 235	79.46 127	36.29 352	74.79 211	66.93 298	58.72 125	67.19 167	88.05 66	36.10 260	81.38 185	52.07 210	84.25 68	87.39 44

ACMM	61.98 7	70.80 108	69.73 115	74.02 109	80.59 108	58.59 74	82.68 64	82.02 94	55.46 193	67.18 168	84.39 145	38.51 234	83.17 146	60.65 148	76.10 168	80.30 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_cas_vis1_n_1920		56.91 295	56.71 292	57.51 328	59.13 380	45.40 267	63.58 328	61.29 340	36.24 368	67.14 169	71.85 334	29.89 321	56.69 368	57.65 166	63.58 315	70.46 356

mvs_anonymous		68.03 172	67.51 161	69.59 217	72.08 285	44.57 275	71.99 255	75.23 221	51.67 244	67.06 170	82.57 180	54.68 50	77.94 247	56.56 172	75.71 173	86.26 84

XVG-OURS-SEG-HR		68.81 154	67.47 164	72.82 148	74.40 253	56.87 99	70.59 274	79.04 150	54.77 210	66.99 171	86.01 112	39.57 223	78.21 244	62.54 132	73.33 201	83.37 185

test1111		67.21 187	67.14 178	67.42 244	79.24 133	34.76 359	73.89 229	65.65 307	58.71 127	66.96 172	87.95 69	36.09 261	80.53 205	52.03 211	83.79 73	86.97 54

mvsmamba		71.15 99	69.54 118	75.99 63	77.61 189	53.46 152	81.95 78	75.11 225	57.73 149	66.95 173	85.96 114	37.14 252	87.56 48	67.94 83	75.49 176	86.97 54

PAPR		71.72 93	70.82 97	74.41 101	81.20 97	51.17 189	79.55 112	83.33 73	55.81 184	66.93 174	84.61 139	50.95 100	86.06 84	55.79 179	79.20 124	86.00 90

DP-MVS Recon		72.15 87	70.73 99	76.40 58	86.57 24	57.99 79	81.15 89	82.96 81	57.03 156	66.78 175	85.56 123	44.50 176	88.11 36	51.77 215	80.23 110	83.10 195

UniMVSNet_ETH3D		67.60 182	67.07 179	69.18 226	77.39 196	42.29 293	74.18 221	75.59 213	60.37 96	66.77 176	86.06 110	37.64 243	78.93 239	52.16 209	73.49 197	86.32 80

test2506		65.33 221	64.61 214	67.50 242	79.46 127	34.19 364	74.43 218	51.92 371	58.72 125	66.75 177	88.05 66	25.99 353	80.92 198	51.94 212	84.25 68	87.39 44

AUN-MVS		68.45 165	66.41 188	74.57 97	79.53 126	57.08 97	73.93 227	75.23 221	54.44 218	66.69 178	81.85 201	37.10 254	82.89 152	62.07 136	66.84 288	83.75 174

LPG-MVS_test		72.74 74	71.74 79	75.76 67	80.22 111	57.51 86	82.55 67	83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

LGP-MVS_train				75.76 67	80.22 111	57.51 86		83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

EIA-MVS		71.78 90	70.60 100	75.30 79	79.85 120	53.54 150	77.27 157	83.26 77	57.92 145	66.49 181	79.39 249	52.07 84	86.69 69	60.05 152	79.14 126	85.66 107

IS-MVSNet		71.57 94	71.00 95	73.27 139	78.86 142	45.63 265	80.22 97	78.69 159	64.14 35	66.46 182	87.36 76	49.30 113	85.60 95	50.26 226	83.71 74	88.59 9

v8		70.33 117	69.28 124	73.49 131	73.15 264	50.22 206	78.62 122	80.78 126	60.79 86	66.45 183	82.11 197	49.35 112	84.98 111	63.58 124	68.71 274	85.28 125

v10		70.21 119	69.02 128	73.81 114	73.51 261	50.92 194	78.74 119	81.39 105	60.05 104	66.39 184	81.83 202	47.58 136	85.41 105	62.80 130	68.86 273	85.09 132

tt0805		67.77 179	67.24 175	69.34 222	74.87 239	40.08 310	77.36 152	81.37 106	55.31 195	66.33 185	84.65 137	37.35 247	82.55 165	55.65 182	72.28 220	85.39 121

PAPM_NR		72.63 76	71.80 78	75.13 83	81.72 85	53.42 154	79.91 104	83.28 76	59.14 119	66.31 186	85.90 116	51.86 87	86.06 84	57.45 167	80.62 101	85.91 94

c3_l		68.33 166	67.56 157	70.62 198	70.87 305	46.21 257	74.47 217	78.80 156	56.22 176	66.19 187	78.53 263	51.88 86	81.40 184	62.08 135	69.04 269	84.25 153

BH-untuned		68.27 167	67.29 170	71.21 185	79.74 121	53.22 158	76.06 183	77.46 189	57.19 154	66.10 188	81.61 206	45.37 168	83.50 140	45.42 273	76.68 163	76.91 294

miper_ehance_all_eth		68.03 172	67.24 175	70.40 202	70.54 308	46.21 257	73.98 223	78.68 160	55.07 204	66.05 189	77.80 274	52.16 83	81.31 187	61.53 145	69.32 263	83.67 177

ab-mvs		66.65 203	66.42 187	67.37 245	76.17 220	41.73 299	70.41 278	76.14 206	53.99 224	65.98 190	83.51 165	49.48 111	76.24 279	48.60 240	73.46 199	84.14 157

EPP-MVSNet		72.16 86	71.31 89	74.71 88	78.68 148	49.70 215	82.10 76	81.65 99	60.40 93	65.94 191	85.84 118	51.74 90	86.37 80	55.93 176	79.55 118	88.07 23

eth_miper_zixun_eth		67.63 181	66.28 194	71.67 171	71.60 291	48.33 235	73.68 233	77.88 179	55.80 185	65.91 192	78.62 261	47.35 143	82.88 153	59.45 159	66.25 293	83.81 169

QAPM		70.05 121	68.81 132	73.78 115	76.54 215	53.43 153	83.23 54	83.48 66	52.89 235	65.90 193	86.29 102	41.55 206	86.49 77	51.01 220	78.40 138	81.42 223

test_vis1_n_1920		58.86 281	59.06 272	58.25 320	63.76 360	43.14 287	67.49 301	66.36 303	40.22 359	65.89 194	71.95 333	31.04 311	59.75 354	59.94 154	64.90 302	71.85 343

FC-MVSNet-test		69.80 129	70.58 102	67.46 243	77.61 189	34.73 360	76.05 184	83.19 78	60.84 85	65.88 195	86.46 98	54.52 52	80.76 203	52.52 206	78.12 140	86.91 56

IterMVS-LS		69.22 150	68.48 139	71.43 179	74.44 252	49.40 221	76.23 179	77.55 186	59.60 111	65.85 196	81.59 208	51.28 95	81.58 182	59.87 156	69.90 254	83.30 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_Blended_VisFu		71.45 97	70.39 104	74.65 92	82.01 80	58.82 71	79.93 103	80.35 133	55.09 201	65.82 197	82.16 194	49.17 116	82.64 163	60.34 150	78.62 135	82.50 206

miper_enhance_ethall		67.11 193	66.09 197	70.17 206	69.21 328	45.98 259	72.85 242	78.41 172	51.38 251	65.65 198	75.98 303	51.17 97	81.25 188	60.82 147	69.32 263	83.29 188

RRT_MVS		69.42 143	67.49 163	75.21 82	78.01 172	52.56 172	82.23 75	78.15 176	55.84 182	65.65 198	85.07 130	30.86 313	86.83 65	61.56 144	70.00 250	86.24 85

thisisatest0530		67.92 176	65.78 201	74.33 103	76.29 218	51.03 191	76.89 167	74.25 239	53.67 228	65.59 200	81.76 203	35.15 268	85.50 100	55.94 175	72.47 214	86.47 71

cl22		67.47 184	66.45 184	70.54 200	69.85 321	46.49 253	73.85 230	77.35 191	55.07 204	65.51 201	77.92 270	47.64 135	81.10 192	61.58 143	69.32 263	84.01 161

3Dnovator	64.47 5	72.49 78	71.39 86	75.79 66	77.70 180	58.99 68	80.66 94	83.15 79	62.24 66	65.46 202	86.59 93	42.38 195	85.52 98	59.59 158	84.72 63	82.85 200

test_djsdf		69.45 142	67.74 152	74.58 96	74.57 249	54.92 133	82.79 61	78.48 166	51.26 254	65.41 203	83.49 166	38.37 236	83.24 144	66.06 99	69.25 266	85.56 111

FE-MVS		65.91 212	63.33 230	73.63 126	77.36 197	51.95 185	72.62 245	75.81 209	53.70 227	65.31 204	78.96 255	28.81 332	86.39 79	43.93 282	73.48 198	82.55 203

TAMVS		66.78 201	65.27 209	71.33 184	79.16 137	53.67 146	73.84 231	69.59 278	52.32 241	65.28 205	81.72 204	44.49 177	77.40 257	42.32 297	78.66 134	82.92 197

cl____		67.18 190	66.26 195	69.94 209	70.20 313	45.74 261	73.30 235	76.83 198	55.10 199	65.27 206	79.57 245	47.39 141	80.53 205	59.41 161	69.22 267	83.53 183

DIV-MVS_self_test		67.18 190	66.26 195	69.94 209	70.20 313	45.74 261	73.29 236	76.83 198	55.10 199	65.27 206	79.58 244	47.38 142	80.53 205	59.43 160	69.22 267	83.54 182

EPNet		73.09 69	72.16 75	75.90 65	75.95 223	56.28 104	83.05 56	72.39 256	66.53 10	65.27 206	87.00 81	50.40 104	85.47 102	62.48 133	86.32 56	85.94 92
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		69.64 135	67.53 160	75.95 64	76.10 221	62.29 15	80.20 98	76.06 208	59.83 110	65.26 209	77.09 284	41.56 205	84.02 130	60.60 149	71.09 233	81.53 222

ACMP	63.53 6	72.30 81	71.20 91	75.59 75	80.28 109	57.54 84	82.74 63	82.84 85	60.58 90	65.24 210	86.18 105	39.25 227	86.03 86	66.95 95	76.79 161	83.22 189
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	59.36 10	66.60 204	65.20 210	70.81 194	76.63 212	48.75 229	76.52 174	80.04 136	50.64 263	65.24 210	84.93 132	39.15 229	78.54 240	36.77 327	76.88 160	85.14 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		66.93 197	66.31 193	68.79 230	77.63 184	42.98 288	76.11 181	77.47 187	56.62 164	65.22 212	82.17 193	41.85 200	80.18 216	47.05 255	72.72 213	83.20 190

SDMVSNet		68.03 172	68.10 149	67.84 239	77.13 201	48.72 231	65.32 318	79.10 149	58.02 141	65.08 213	82.55 181	47.83 131	73.40 291	63.92 120	73.92 188	81.41 224

sd_testset		64.46 232	64.45 215	64.51 283	77.13 201	42.25 294	62.67 332	72.11 259	58.02 141	65.08 213	82.55 181	41.22 212	69.88 312	47.32 250	73.92 188	81.41 224

GBi-Net		67.21 187	66.55 182	69.19 223	77.63 184	43.33 284	77.31 153	77.83 181	56.62 164	65.04 215	82.70 175	41.85 200	80.33 210	47.18 252	72.76 210	83.92 164

test1		67.21 187	66.55 182	69.19 223	77.63 184	43.33 284	77.31 153	77.83 181	56.62 164	65.04 215	82.70 175	41.85 200	80.33 210	47.18 252	72.76 210	83.92 164

FMVSNet3		66.32 209	65.61 204	68.46 233	76.48 216	42.34 292	74.98 207	77.15 194	55.83 183	65.04 215	81.16 213	39.91 218	80.14 217	47.18 252	72.76 210	82.90 199

anonymousdsp		67.00 196	64.82 213	73.57 129	70.09 316	56.13 107	76.35 176	77.35 191	48.43 289	64.99 218	80.84 224	33.01 292	80.34 209	64.66 113	67.64 283	84.23 154

BH-w/o		66.85 198	65.83 200	69.90 212	79.29 130	52.46 175	74.66 214	76.65 201	54.51 217	64.85 219	78.12 264	45.59 161	82.95 150	43.26 289	75.54 175	74.27 320

CDS-MVSNet		66.80 200	65.37 206	71.10 190	78.98 140	53.13 161	73.27 237	71.07 266	52.15 242	64.72 220	80.23 233	43.56 184	77.10 260	45.48 271	78.88 128	83.05 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GA-MVS		65.53 217	63.70 224	71.02 192	70.87 305	48.10 237	70.48 276	74.40 235	56.69 160	64.70 221	76.77 289	33.66 286	81.10 192	55.42 185	70.32 243	83.87 167

tttt0517		67.83 178	65.66 203	74.33 103	76.69 210	50.82 196	77.86 139	73.99 242	54.54 216	64.64 222	82.53 184	35.06 269	85.50 100	55.71 180	69.91 253	86.67 65

FMVSNet1		66.70 202	65.87 199	69.19 223	77.49 193	43.33 284	77.31 153	77.83 181	56.45 169	64.60 223	82.70 175	38.08 241	80.33 210	46.08 261	72.31 219	83.92 164

AdaColmap		69.99 123	68.66 136	73.97 111	84.94 54	57.83 80	82.63 65	78.71 158	56.28 174	64.34 224	84.14 148	41.57 204	87.06 61	46.45 258	78.88 128	77.02 290

jajsoiax		68.25 168	66.45 184	73.66 123	75.62 227	55.49 125	80.82 91	78.51 165	52.33 240	64.33 225	84.11 149	28.28 336	81.81 178	63.48 125	70.62 236	83.67 177

CostFormer		64.04 236	62.51 240	68.61 232	71.88 288	45.77 260	71.30 264	70.60 270	47.55 301	64.31 226	76.61 293	41.63 203	79.62 221	49.74 229	69.00 270	80.42 245

UWE-MVS		60.18 272	59.78 267	61.39 305	77.67 182	33.92 367	69.04 291	63.82 320	48.56 285	64.27 227	77.64 279	27.20 344	70.40 309	33.56 347	76.24 166	79.83 256

mvs_tets		68.18 170	66.36 190	73.63 126	75.61 228	55.35 128	80.77 92	78.56 163	52.48 239	64.27 227	84.10 150	27.45 342	81.84 177	63.45 126	70.56 238	83.69 176

baseline1		63.81 238	63.87 221	63.62 287	76.29 218	36.36 347	71.78 259	67.29 295	56.05 179	64.23 229	82.95 173	47.11 145	74.41 287	47.30 251	61.85 329	80.10 252

PVSNet_BlendedMVS		68.56 163	67.72 153	71.07 191	77.03 205	50.57 200	74.50 216	81.52 100	53.66 229	64.22 230	79.72 242	49.13 117	82.87 154	55.82 177	73.92 188	79.77 259

PVSNet_Blended		68.59 159	67.72 153	71.19 186	77.03 205	50.57 200	72.51 248	81.52 100	51.91 243	64.22 230	77.77 277	49.13 117	82.87 154	55.82 177	79.58 116	80.14 251

thisisatest0515		65.83 213	63.50 227	72.82 148	73.75 259	49.50 220	71.32 263	73.12 252	49.39 275	63.82 232	76.50 297	34.95 271	84.84 117	53.20 203	75.49 176	84.13 158

test_fmvs1_n		51.37 329	50.35 332	54.42 343	52.85 386	37.71 334	61.16 343	51.93 370	28.15 378	63.81 233	69.73 351	13.72 381	53.95 378	51.16 219	60.65 338	71.59 345

test_fmvs1		51.32 331	50.48 331	53.81 345	53.57 385	37.51 336	60.63 347	51.16 373	28.02 380	63.62 234	69.23 354	16.41 377	53.93 379	51.01 220	60.70 337	69.99 360

HyFIR lowres test		65.67 215	63.01 235	73.67 122	79.97 119	55.65 119	69.07 290	75.52 215	42.68 346	63.53 235	77.95 268	40.43 216	81.64 179	46.01 262	71.91 223	83.73 175

CANet_DTU		68.18 170	67.71 155	69.59 217	74.83 240	46.24 256	78.66 121	76.85 197	59.60 111	63.45 236	82.09 198	35.25 267	77.41 256	59.88 155	78.76 132	85.14 129

UGNet		68.81 154	67.39 166	73.06 142	78.33 160	54.47 137	79.77 106	75.40 217	60.45 92	63.22 237	84.40 144	32.71 299	80.91 199	51.71 216	80.56 105	83.81 169
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
XXY-MVS		60.68 269	61.67 250	57.70 327	70.43 310	38.45 327	64.19 326	66.47 301	48.05 295	63.22 237	80.86 222	49.28 114	60.47 349	45.25 275	67.28 286	74.19 321

testing91		64.46 232	63.80 222	66.47 254	78.43 155	40.06 311	67.63 298	69.59 278	59.06 120	63.18 239	78.05 266	34.05 279	76.99 263	48.30 243	75.87 170	82.37 209

CHOSEN 1792x2688		65.08 225	62.84 237	71.82 166	81.49 89	56.26 105	66.32 307	74.20 240	40.53 357	63.16 240	78.65 259	41.30 208	77.80 250	45.80 264	74.09 185	81.40 226

testing222		62.29 256	61.31 255	65.25 278	77.87 175	38.53 326	68.34 293	66.31 304	56.37 171	63.15 241	77.58 280	28.47 334	76.18 281	37.04 325	76.65 164	81.05 237

testing99		64.05 235	63.29 231	66.34 256	78.17 167	39.76 315	67.33 303	68.00 291	58.60 129	63.03 242	78.10 265	32.57 304	76.94 265	48.22 244	75.58 174	82.34 210

114514_t		70.83 106	69.56 117	74.64 93	86.21 31	54.63 136	82.34 70	81.81 97	48.22 291	63.01 243	85.83 119	40.92 214	87.10 59	57.91 164	79.79 112	82.18 212

tpm2		62.07 258	60.10 266	67.99 238	72.79 271	43.86 280	71.05 271	66.85 299	43.14 343	62.77 244	75.39 310	38.32 237	80.80 201	41.69 301	68.88 271	79.32 263

NR-MVSNet		69.54 138	68.85 130	71.59 174	78.05 170	43.81 281	74.20 220	80.86 125	65.18 14	62.76 245	84.52 141	52.35 80	83.59 139	50.96 222	70.78 234	87.37 46

OpenMVS	61.03 9	68.85 153	67.56 157	72.70 150	74.26 256	53.99 142	81.21 88	81.34 111	52.70 236	62.75 246	85.55 125	38.86 232	84.14 126	48.41 242	83.01 77	79.97 253

v7n		69.01 152	67.36 168	73.98 110	72.51 278	52.65 168	78.54 125	81.30 113	60.26 101	62.67 247	81.62 205	43.61 183	84.49 121	57.01 169	68.70 275	84.79 141

WR-MVS_H		67.02 195	66.92 180	67.33 247	77.95 174	37.75 333	77.57 146	82.11 93	62.03 73	62.65 248	82.48 185	50.57 103	79.46 222	42.91 293	64.01 310	84.79 141

tfpn200view9		63.18 246	62.18 245	66.21 260	76.85 208	39.62 316	71.96 257	69.44 281	56.63 162	62.61 249	79.83 238	37.18 249	79.17 228	31.84 356	73.25 203	79.83 256

thres400		63.31 242	62.18 245	66.72 250	76.85 208	39.62 316	71.96 257	69.44 281	56.63 162	62.61 249	79.83 238	37.18 249	79.17 228	31.84 356	73.25 203	81.36 227

MVS		67.37 185	66.33 191	70.51 201	75.46 231	50.94 192	73.95 225	81.85 96	41.57 352	62.54 251	78.57 262	47.98 128	85.47 102	52.97 204	82.05 90	75.14 307

CP-MVSNet		66.49 207	66.41 188	66.72 250	77.67 182	36.33 349	76.83 170	79.52 143	62.45 63	62.54 251	83.47 167	46.32 154	78.37 241	45.47 272	63.43 317	85.45 116

PEN-MVS		66.60 204	66.45 184	67.04 248	77.11 203	36.56 346	77.03 163	80.42 131	62.95 50	62.51 253	84.03 151	46.69 152	79.07 233	44.22 277	63.08 320	85.51 113

thres100view900		63.28 244	62.41 242	65.89 268	77.31 198	38.66 324	72.65 243	69.11 285	57.07 155	62.45 254	81.03 217	37.01 256	79.17 228	31.84 356	73.25 203	79.83 256

PS-CasMVS		66.42 208	66.32 192	66.70 252	77.60 191	36.30 351	76.94 165	79.61 141	62.36 65	62.43 255	83.66 159	45.69 158	78.37 241	45.35 274	63.26 318	85.42 119

thres600view7		63.30 243	62.27 243	66.41 255	77.18 200	38.87 322	72.35 250	69.11 285	56.98 157	62.37 256	80.96 219	37.01 256	79.00 237	31.43 363	73.05 207	81.36 227

pm-mvs1		65.24 222	64.97 212	66.04 265	72.38 280	39.40 319	72.62 245	75.63 212	55.53 191	62.35 257	83.18 170	47.45 139	76.47 276	49.06 237	66.54 291	82.24 211

Fast-Effi-MVS+-dtu		67.37 185	65.33 208	73.48 132	72.94 269	57.78 82	77.47 150	76.88 196	57.60 150	61.97 258	76.85 288	39.31 225	80.49 208	54.72 189	70.28 244	82.17 214

WTY-MVS		59.75 276	60.39 264	57.85 325	72.32 282	37.83 332	61.05 344	64.18 318	45.95 320	61.91 259	79.11 254	47.01 149	60.88 348	42.50 296	69.49 262	74.83 313

thres200		62.20 257	61.16 259	65.34 276	75.38 233	39.99 312	69.60 285	69.29 283	55.64 190	61.87 260	76.99 285	37.07 255	78.96 238	31.28 364	73.28 202	77.06 289

TransMVSNet (Re)		64.72 227	64.33 216	65.87 269	75.22 234	38.56 325	74.66 214	75.08 229	58.90 123	61.79 261	82.63 178	51.18 96	78.07 246	43.63 286	55.87 356	80.99 238

WB-MVSnew		59.66 277	59.69 268	59.56 309	75.19 236	35.78 354	69.34 288	64.28 317	46.88 310	61.76 262	75.79 304	40.61 215	65.20 335	32.16 352	71.21 230	77.70 280

DTE-MVSNet		65.58 216	65.34 207	66.31 257	76.06 222	34.79 357	76.43 175	79.38 146	62.55 61	61.66 263	83.83 156	45.60 160	79.15 231	41.64 304	60.88 335	85.00 134

HY-MVS	56.14 13	64.55 231	63.89 219	66.55 253	74.73 244	41.02 304	69.96 282	74.43 234	49.29 277	61.66 263	80.92 220	47.43 140	76.68 272	44.91 276	71.69 225	81.94 217

CNLPA		65.43 218	64.02 218	69.68 215	78.73 147	58.07 78	77.82 142	70.71 269	51.49 249	61.57 265	83.58 164	38.23 239	70.82 304	43.90 283	70.10 248	80.16 250

miper_lstm_enhance		62.03 259	60.88 262	65.49 274	66.71 345	46.25 255	56.29 364	75.70 211	50.68 261	61.27 266	75.48 309	40.21 217	68.03 321	56.31 174	65.25 300	82.18 212

cascas		65.98 211	63.42 228	73.64 125	77.26 199	52.58 171	72.26 252	77.21 193	48.56 285	61.21 267	74.60 316	32.57 304	85.82 92	50.38 225	76.75 162	82.52 205

ETVMVS		59.51 279	58.81 273	61.58 302	77.46 194	34.87 356	64.94 323	59.35 345	54.06 223	61.08 268	76.67 290	29.54 323	71.87 300	32.16 352	74.07 186	78.01 279

PAPM		67.92 176	66.69 181	71.63 173	78.09 168	49.02 225	77.09 161	81.24 117	51.04 258	60.91 269	83.98 153	47.71 133	84.99 109	40.81 305	79.32 122	80.90 239

IterMVS-SCA-FT		62.49 251	61.52 252	65.40 275	71.99 287	50.80 197	71.15 268	69.63 277	45.71 321	60.61 270	77.93 269	37.45 245	65.99 332	55.67 181	63.50 316	79.42 262

1112_ss		64.00 237	63.36 229	65.93 267	79.28 131	42.58 291	71.35 262	72.36 257	46.41 313	60.55 271	77.89 272	46.27 156	73.28 292	46.18 260	69.97 251	81.92 218

tfpnnormal		62.47 252	61.63 251	64.99 280	74.81 241	39.01 321	71.22 265	73.72 245	55.22 198	60.21 272	80.09 236	41.26 211	76.98 264	30.02 369	68.09 279	78.97 268

bld_raw_dy_0_64		64.87 226	63.22 232	69.83 214	74.79 242	53.32 157	78.15 132	62.02 337	51.20 256	60.17 273	83.12 172	24.15 362	74.20 290	63.08 127	72.33 217	81.96 216

testing11		62.81 249	61.90 248	65.54 272	78.38 156	40.76 308	67.59 300	66.78 300	55.48 192	60.13 274	77.11 283	31.67 310	76.79 268	45.53 269	74.45 181	79.06 265

tpm		57.34 292	58.16 281	54.86 339	71.80 290	34.77 358	67.47 302	56.04 363	48.20 292	60.10 275	76.92 286	37.17 251	53.41 380	40.76 306	65.01 301	76.40 297

ET-MVSNet_ETH3D		67.96 175	65.72 202	74.68 90	76.67 211	55.62 122	75.11 202	74.74 230	52.91 234	60.03 276	80.12 234	33.68 285	82.64 163	61.86 139	76.34 165	85.78 99

1314		64.61 230	63.21 233	68.80 229	71.87 289	47.46 246	73.95 225	78.39 174	42.88 345	59.97 277	76.60 294	38.11 240	79.39 224	54.84 188	72.32 218	79.55 260

CL-MVSNet_self_test		61.53 264	60.94 261	63.30 290	68.95 330	36.93 343	67.60 299	72.80 254	55.67 188	59.95 278	76.63 291	45.01 172	72.22 298	39.74 312	62.09 328	80.74 242

XVG-ACMP-BASELINE		64.36 234	62.23 244	70.74 196	72.35 281	52.45 176	70.80 273	78.45 169	53.84 226	59.87 279	81.10 215	16.24 378	79.32 225	55.64 183	71.76 224	80.47 244

IterMVS		62.79 250	61.27 256	67.35 246	69.37 326	52.04 183	71.17 266	68.24 290	52.63 238	59.82 280	76.91 287	37.32 248	72.36 295	52.80 205	63.19 319	77.66 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 239	63.88 220	63.14 292	74.75 243	31.04 378	71.16 267	63.64 322	56.32 172	59.80 281	84.99 131	44.51 175	75.46 282	39.12 314	80.62 101	82.92 197

test_fmvs2		48.69 338	47.49 343	52.29 355	48.63 392	33.06 372	57.76 357	48.05 382	25.71 384	59.76 282	69.60 352	11.57 387	52.23 384	49.45 234	56.86 351	71.58 346

pmmvs6		63.69 239	62.82 238	66.27 259	70.63 307	39.27 320	73.13 238	75.47 216	52.69 237	59.75 283	82.30 189	39.71 222	77.03 262	47.40 249	64.35 309	82.53 204

test_vis1_n		49.89 336	48.69 338	53.50 348	53.97 384	37.38 337	61.53 337	47.33 384	28.54 377	59.62 284	67.10 365	13.52 382	52.27 383	49.07 236	57.52 348	70.84 354

pmmvs4		61.48 266	59.39 269	67.76 240	71.57 292	53.86 143	71.42 261	65.34 309	44.20 332	59.46 285	77.92 270	35.90 262	74.71 285	43.87 284	64.87 303	74.71 316

Patchmatch-RL test		58.16 286	55.49 302	66.15 262	67.92 338	48.89 228	60.66 346	51.07 375	47.86 298	59.36 286	62.71 377	34.02 281	72.27 297	56.41 173	59.40 342	77.30 285

CR-MVSNet		59.91 274	57.90 284	65.96 266	69.96 318	52.07 181	65.31 319	63.15 326	42.48 347	59.36 286	74.84 313	35.83 263	70.75 305	45.50 270	64.65 305	75.06 308

RPMNet		61.53 264	58.42 278	70.86 193	69.96 318	52.07 181	65.31 319	81.36 107	43.20 342	59.36 286	70.15 347	35.37 266	85.47 102	36.42 334	64.65 305	75.06 308

SCA		60.49 270	58.38 279	66.80 249	74.14 258	48.06 238	63.35 329	63.23 325	49.13 279	59.33 289	72.10 330	37.45 245	74.27 288	44.17 278	62.57 323	78.05 275

DP-MVS		65.68 214	63.66 225	71.75 168	84.93 55	56.87 99	80.74 93	73.16 251	53.06 232	59.09 290	82.35 187	36.79 258	85.94 89	32.82 350	69.96 252	72.45 334

Test_1112_low_res		62.32 254	61.77 249	64.00 286	79.08 139	39.53 318	68.17 294	70.17 272	43.25 341	59.03 291	79.90 237	44.08 179	71.24 303	43.79 285	68.42 277	81.25 230

PatchmatchNet		59.84 275	58.24 280	64.65 282	73.05 267	46.70 252	69.42 287	62.18 335	47.55 301	58.88 292	71.96 332	34.49 275	69.16 314	42.99 292	63.60 314	78.07 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_0402		63.25 245	61.01 260	69.96 208	80.00 118	54.37 139	76.86 169	72.02 260	54.58 215	58.71 293	80.79 225	35.00 270	84.36 123	26.41 382	64.71 304	71.15 352

LTVRE_ROB	55.42 16	63.15 247	61.23 258	68.92 228	76.57 214	47.80 240	59.92 348	76.39 202	54.35 219	58.67 294	82.46 186	29.44 326	81.49 183	42.12 298	71.14 231	77.46 283
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
sss		56.17 303	56.57 293	54.96 338	66.93 343	36.32 350	57.94 356	61.69 338	41.67 350	58.64 295	75.32 311	38.72 233	56.25 371	42.04 299	66.19 294	72.31 339

testing3		56.54 297	55.92 299	58.41 319	77.52 192	27.93 386	69.72 284	56.36 359	54.75 211	58.63 296	77.80 274	20.88 372	71.75 301	25.31 384	62.25 326	75.53 304

tpmrst		58.24 285	58.70 276	56.84 329	66.97 342	34.32 362	69.57 286	61.14 341	47.17 308	58.58 297	71.60 335	41.28 210	60.41 350	49.20 235	62.84 321	75.78 301

IB-MVS	56.42 12	65.40 220	62.73 239	73.40 136	74.89 237	52.78 167	73.09 239	75.13 224	55.69 187	58.48 298	73.73 321	32.86 294	86.32 82	50.63 223	70.11 247	81.10 235
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
CVMVSNet		59.63 278	59.14 271	61.08 307	74.47 250	38.84 323	75.20 200	68.74 287	31.15 374	58.24 299	76.51 295	32.39 306	68.58 317	49.77 228	65.84 296	75.81 300

D2MVS		62.30 255	60.29 265	68.34 236	66.46 348	48.42 234	65.70 310	73.42 248	47.71 299	58.16 300	75.02 312	30.51 315	77.71 252	53.96 196	71.68 226	78.90 269

RPSCF		55.80 306	54.22 315	60.53 308	65.13 355	42.91 290	64.30 325	57.62 353	36.84 367	58.05 301	82.28 190	28.01 337	56.24 372	37.14 324	58.61 345	82.44 208

tpm cat1		59.25 280	56.95 289	66.15 262	72.19 284	46.96 250	68.09 295	65.76 306	40.03 361	57.81 302	70.56 342	38.32 237	74.51 286	38.26 318	61.50 332	77.00 291

gg-mvs-nofinetune		57.86 289	56.43 295	62.18 298	72.62 274	35.35 355	66.57 304	56.33 360	50.65 262	57.64 303	57.10 383	30.65 314	76.36 277	37.38 322	78.88 128	74.82 314

ACMH+	57.40 11	66.12 210	64.06 217	72.30 159	77.79 179	52.83 166	80.39 95	78.03 178	57.30 152	57.47 304	82.55 181	27.68 340	84.17 125	45.54 268	69.78 256	79.90 254

dmvs_re		56.77 296	56.83 291	56.61 330	69.23 327	41.02 304	58.37 353	64.18 318	50.59 264	57.45 305	71.42 336	35.54 265	58.94 358	37.23 323	67.45 284	69.87 361

MS-PatchMatch		62.42 253	61.46 253	65.31 277	75.21 235	52.10 180	72.05 254	74.05 241	46.41 313	57.42 306	74.36 317	34.35 277	77.57 254	45.62 267	73.67 192	66.26 369

PVSNet	50.76 19	58.40 284	57.39 285	61.42 303	75.53 230	44.04 279	61.43 338	63.45 323	47.04 309	56.91 307	73.61 322	27.00 347	64.76 336	39.12 314	72.40 215	75.47 305

Patchmtry		57.16 293	56.47 294	59.23 312	69.17 329	34.58 361	62.98 330	63.15 326	44.53 328	56.83 308	74.84 313	35.83 263	68.71 316	40.03 309	60.91 334	74.39 319

LS3D		64.71 228	62.50 241	71.34 183	79.72 123	55.71 117	79.82 105	74.72 231	48.50 288	56.62 309	84.62 138	33.59 287	82.34 170	29.65 371	75.23 178	75.97 298

ACMH	55.70 15	65.20 223	63.57 226	70.07 207	78.07 169	52.01 184	79.48 113	79.69 138	55.75 186	56.59 310	80.98 218	27.12 345	80.94 196	42.90 294	71.58 227	77.25 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Syy-MVS		56.00 304	56.23 297	55.32 336	74.69 245	26.44 392	65.52 313	57.49 354	50.97 259	56.52 311	72.18 328	39.89 219	68.09 319	24.20 385	64.59 307	71.44 348

myMVS_eth3d		54.86 313	54.61 308	55.61 335	74.69 245	27.31 389	65.52 313	57.49 354	50.97 259	56.52 311	72.18 328	21.87 370	68.09 319	27.70 377	64.59 307	71.44 348

MVP-Stereo		65.41 219	63.80 222	70.22 203	77.62 188	55.53 124	76.30 177	78.53 164	50.59 264	56.47 313	78.65 259	39.84 220	82.68 161	44.10 281	72.12 222	72.44 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS_ROB	52.78 18	60.03 273	58.14 282	65.69 271	70.47 309	44.82 270	75.33 196	70.86 268	45.04 324	56.06 314	76.00 300	26.89 348	79.65 219	35.36 339	67.29 285	72.60 331

EG-PatchMatch MVS		64.71 228	62.87 236	70.22 203	77.68 181	53.48 151	77.99 136	78.82 154	53.37 231	56.03 315	77.41 282	24.75 360	84.04 128	46.37 259	73.42 200	73.14 326

PLC	56.13 14	65.09 224	63.21 233	70.72 197	81.04 99	54.87 134	78.57 123	77.47 187	48.51 287	55.71 316	81.89 200	33.71 284	79.71 218	41.66 302	70.37 241	77.58 282
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 315	53.69 318	54.79 340	66.12 351	31.96 376	62.34 335	49.05 378	44.42 331	55.54 317	71.33 338	30.22 318	56.70 367	41.65 303	62.54 324	75.71 302

MDTV_nov1_ep13			57.00 288		72.73 272	38.26 328	65.02 322	64.73 314	44.74 326	55.46 318	72.48 326	32.61 303	70.47 306	37.47 321	67.75 282

test-LLR		58.15 287	58.13 283	58.22 321	68.57 332	44.80 271	65.46 315	57.92 351	50.08 268	55.44 319	69.82 349	32.62 301	57.44 364	49.66 231	73.62 193	72.41 336

test-mter		56.42 300	55.82 300	58.22 321	68.57 332	44.80 271	65.46 315	57.92 351	39.94 362	55.44 319	69.82 349	21.92 367	57.44 364	49.66 231	73.62 193	72.41 336

ITE_SJBPF				62.09 299	66.16 350	44.55 276		64.32 316	47.36 304	55.31 321	80.34 230	19.27 373	62.68 343	36.29 335	62.39 325	79.04 266

MIMVSNet		57.35 291	57.07 287	58.22 321	74.21 257	37.18 338	62.46 333	60.88 342	48.88 282	55.29 322	75.99 302	31.68 309	62.04 345	31.87 355	72.35 216	75.43 306

Anonymous20231206		55.10 312	55.30 304	54.48 341	69.81 322	33.94 366	62.91 331	62.13 336	41.08 354	55.18 323	75.65 306	32.75 298	56.59 370	30.32 368	67.86 280	72.91 327

KD-MVS_2432*1600		53.45 319	51.50 327	59.30 310	62.82 364	37.14 339	55.33 365	71.79 262	47.34 305	55.09 324	70.52 343	21.91 368	70.45 307	35.72 337	42.97 381	70.31 357

miper_refine_blended		53.45 319	51.50 327	59.30 310	62.82 364	37.14 339	55.33 365	71.79 262	47.34 305	55.09 324	70.52 343	21.91 368	70.45 307	35.72 337	42.97 381	70.31 357

pmmvs-eth3d		58.81 282	56.31 296	66.30 258	67.61 339	52.42 177	72.30 251	64.76 313	43.55 338	54.94 326	74.19 319	28.95 329	72.60 294	43.31 287	57.21 350	73.88 324

baseline2		63.42 241	61.26 257	69.89 213	72.55 276	47.62 244	71.54 260	68.38 289	50.11 267	54.82 327	75.55 308	43.06 188	80.96 195	48.13 245	67.16 287	81.11 234

OurMVSNet-221017-0		61.37 267	58.63 277	69.61 216	72.05 286	48.06 238	73.93 227	72.51 255	47.23 307	54.74 328	80.92 220	21.49 371	81.24 189	48.57 241	56.22 355	79.53 261

GG-mvs-BLEND				62.34 297	71.36 298	37.04 342	69.20 289	57.33 356		54.73 329	65.48 371	30.37 316	77.82 249	34.82 340	74.93 179	72.17 340

tpmvs		58.47 283	56.95 289	63.03 294	70.20 313	41.21 303	67.90 297	67.23 296	49.62 273	54.73 329	70.84 340	34.14 278	76.24 279	36.64 331	61.29 333	71.64 344

EPNet_dtu		61.90 260	61.97 247	61.68 300	72.89 270	39.78 314	75.85 189	65.62 308	55.09 201	54.56 331	79.36 250	37.59 244	67.02 326	39.80 311	76.95 158	78.25 272
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		53.17 323	53.44 320	52.33 354	68.29 336	25.34 396	58.21 354	54.41 366	44.46 330	54.56 331	69.05 355	33.32 289	60.94 347	36.93 326	61.76 331	70.73 355

test0.0.03 1		53.32 322	53.59 319	52.50 353	62.81 366	29.45 381	59.51 349	54.11 367	50.08 268	54.40 333	74.31 318	32.62 301	55.92 373	30.50 367	63.95 312	72.15 341

ambc				65.13 279	63.72 362	37.07 341	47.66 382	78.78 157		54.37 334	71.42 336	11.24 389	80.94 196	45.64 266	53.85 363	77.38 284

SixPastTwentyTwo		61.65 263	58.80 275	70.20 205	75.80 224	47.22 248	75.59 192	69.68 276	54.61 213	54.11 335	79.26 252	27.07 346	82.96 149	43.27 288	49.79 373	80.41 246

ppachtmachnet_test		58.06 288	55.38 303	66.10 264	69.51 323	48.99 226	68.01 296	66.13 305	44.50 329	54.05 336	70.74 341	32.09 308	72.34 296	36.68 330	56.71 354	76.99 293

TESTMET0.1,1		55.28 309	54.90 306	56.42 331	66.56 346	43.67 282	65.46 315	56.27 361	39.18 364	53.83 337	67.44 361	24.21 361	55.46 375	48.04 246	73.11 206	70.13 359

pmmvs5		56.47 299	55.68 301	58.86 316	61.41 372	36.71 345	66.37 306	62.75 328	40.38 358	53.70 338	76.62 292	34.56 273	67.05 325	40.02 310	65.27 299	72.83 329

MSDG		61.81 262	59.23 270	69.55 220	72.64 273	52.63 170	70.45 277	75.81 209	51.38 251	53.70 338	76.11 299	29.52 324	81.08 194	37.70 320	65.79 297	74.93 312

test_fmvs3		44.30 345	42.55 348	49.55 360	42.83 396	27.15 391	53.03 371	44.93 388	22.03 391	53.69 340	64.94 372	4.21 401	49.63 386	47.47 247	49.82 372	71.88 342

K. test v3		60.47 271	57.11 286	70.56 199	73.74 260	48.22 236	75.10 204	62.55 329	58.27 136	53.62 341	76.31 298	27.81 339	81.59 181	47.42 248	39.18 386	81.88 219

PM-MVS		52.33 325	50.19 333	58.75 317	62.10 369	45.14 269	65.75 309	40.38 394	43.60 337	53.52 342	72.65 325	9.16 394	65.87 333	50.41 224	54.18 361	65.24 371

PMMVS		53.96 315	53.26 321	56.04 332	62.60 367	50.92 194	61.17 342	56.09 362	32.81 372	53.51 343	66.84 366	34.04 280	59.93 353	44.14 280	68.18 278	57.27 381

PatchMatch-RL		56.25 302	54.55 309	61.32 306	77.06 204	56.07 109	65.57 312	54.10 368	44.13 334	53.49 344	71.27 339	25.20 357	66.78 327	36.52 333	63.66 313	61.12 373

LCM-MVSNet-Re		61.88 261	61.35 254	63.46 288	74.58 248	31.48 377	61.42 339	58.14 350	58.71 127	53.02 345	79.55 246	43.07 187	76.80 267	45.69 265	77.96 142	82.11 215

F-COLMAP		63.05 248	60.87 263	69.58 219	76.99 207	53.63 148	78.12 133	76.16 204	47.97 296	52.41 346	81.61 206	27.87 338	78.11 245	40.07 308	66.66 290	77.00 291

test20.03		53.87 317	54.02 316	53.41 349	61.47 371	28.11 385	61.30 340	59.21 346	51.34 253	52.09 347	77.43 281	33.29 290	58.55 360	29.76 370	60.27 340	73.58 325

testgi		51.90 326	52.37 323	50.51 359	60.39 378	23.55 399	58.42 352	58.15 349	49.03 280	51.83 348	79.21 253	22.39 365	55.59 374	29.24 373	62.64 322	72.40 338

EU-MVSNet		55.61 307	54.41 311	59.19 314	65.41 354	33.42 369	72.44 249	71.91 261	28.81 376	51.27 349	73.87 320	24.76 359	69.08 315	43.04 291	58.20 346	75.06 308

MDTV_nov1_ep13_2view						25.89 394	61.22 341		40.10 360	51.10 350		32.97 293		38.49 316		78.61 270

COLMAP_ROB	52.97 17	61.27 268	58.81 273	68.64 231	74.63 247	52.51 174	78.42 126	73.30 249	49.92 271	50.96 351	81.51 209	23.06 364	79.40 223	31.63 360	65.85 295	74.01 323
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
KD-MVS_self_test		55.22 310	53.89 317	59.21 313	57.80 383	27.47 388	57.75 358	74.32 236	47.38 303	50.90 352	70.00 348	28.45 335	70.30 310	40.44 307	57.92 347	79.87 255

ADS-MVSNet2		51.33 330	48.76 337	59.07 315	66.02 352	44.60 274	50.90 375	59.76 344	36.90 365	50.74 353	66.18 369	26.38 349	63.11 341	27.17 378	54.76 359	69.50 363

ADS-MVSNet		48.48 339	47.77 340	50.63 358	66.02 352	29.92 380	50.90 375	50.87 377	36.90 365	50.74 353	66.18 369	26.38 349	52.47 382	27.17 378	54.76 359	69.50 363

our_test_3		56.49 298	54.42 310	62.68 296	69.51 323	45.48 266	66.08 308	61.49 339	44.11 335	50.73 355	69.60 352	33.05 291	68.15 318	38.38 317	56.86 351	74.40 318

FMVSNet5		55.86 305	54.93 305	58.66 318	71.05 303	36.35 348	64.18 327	62.48 330	46.76 311	50.66 356	74.73 315	25.80 354	64.04 338	33.11 348	65.57 298	75.59 303

lessismore_v0				69.91 211	71.42 296	47.80 240		50.90 376		50.39 357	75.56 307	27.43 343	81.33 186	45.91 263	34.10 392	80.59 243

UnsupCasMVSNet_eth		53.16 324	52.47 322	55.23 337	59.45 379	33.39 370	59.43 350	69.13 284	45.98 317	50.35 358	72.32 327	29.30 327	58.26 362	42.02 300	44.30 379	74.05 322

dmvs_testset		50.16 334	51.90 324	44.94 367	66.49 347	11.78 405	61.01 345	51.50 372	51.17 257	50.30 359	67.44 361	39.28 226	60.29 351	22.38 387	57.49 349	62.76 372

dp		51.89 327	51.60 326	52.77 352	68.44 335	32.45 374	62.36 334	54.57 365	44.16 333	49.31 360	67.91 357	28.87 331	56.61 369	33.89 343	54.89 358	69.24 366

Anonymous20240521		55.30 308	54.41 311	57.96 324	60.92 377	41.73 299	71.09 270	71.06 267	41.18 353	48.65 361	73.31 323	16.93 376	59.25 356	42.54 295	64.01 310	72.90 328

JIA-IIPM		51.56 328	47.68 342	63.21 291	64.61 357	50.73 198	47.71 381	58.77 348	42.90 344	48.46 362	51.72 387	24.97 358	70.24 311	36.06 336	53.89 362	68.64 367

USDC		56.35 301	54.24 314	62.69 295	64.74 356	40.31 309	65.05 321	73.83 244	43.93 336	47.58 363	77.71 278	15.36 380	75.05 284	38.19 319	61.81 330	72.70 330

UnsupCasMVSNet_bld		50.07 335	48.87 336	53.66 346	60.97 376	33.67 368	57.62 359	64.56 315	39.47 363	47.38 364	64.02 375	27.47 341	59.32 355	34.69 341	43.68 380	67.98 368

AllTest		57.08 294	54.65 307	64.39 284	71.44 294	49.03 223	69.92 283	67.30 293	45.97 318	47.16 365	79.77 240	17.47 374	67.56 323	33.65 344	59.16 343	76.57 295

TestCases				64.39 284	71.44 294	49.03 223		67.30 293	45.97 318	47.16 365	79.77 240	17.47 374	67.56 323	33.65 344	59.16 343	76.57 295

CMPMVS	42.80 21	57.81 290	55.97 298	63.32 289	60.98 375	47.38 247	64.66 324	69.50 280	32.06 373	46.83 367	77.80 274	29.50 325	71.36 302	48.68 239	73.75 191	71.21 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		55.17 311	54.31 313	57.77 326	70.03 317	32.01 375	65.68 311	64.81 312	49.19 278	46.75 368	76.00 300	25.53 356	64.04 338	28.65 374	62.13 327	77.26 287

mvsany_test1		39.38 354	38.16 357	43.02 370	49.05 390	34.28 363	44.16 389	25.94 405	22.74 389	46.57 369	62.21 378	23.85 363	41.16 397	33.01 349	35.91 389	53.63 384

PVSNet_0	43.31 20	47.46 342	45.64 345	52.92 351	67.60 340	44.65 273	54.06 369	54.64 364	41.59 351	46.15 370	58.75 380	30.99 312	58.66 359	32.18 351	24.81 395	55.46 383

Patchmatch-test		49.08 337	48.28 339	51.50 357	64.40 358	30.85 379	45.68 385	48.46 381	35.60 369	46.10 371	72.10 330	34.47 276	46.37 390	27.08 380	60.65 338	77.27 286

YYNet1		50.73 332	48.96 334	56.03 333	61.10 374	41.78 298	51.94 373	56.44 358	40.94 356	44.84 372	67.80 359	30.08 319	55.08 376	36.77 327	50.71 369	71.22 350

MDA-MVSNet_test_wron		50.71 333	48.95 335	56.00 334	61.17 373	41.84 297	51.90 374	56.45 357	40.96 355	44.79 373	67.84 358	30.04 320	55.07 377	36.71 329	50.69 370	71.11 353

TDRefinement		53.44 321	50.72 330	61.60 301	64.31 359	46.96 250	70.89 272	65.27 311	41.78 348	44.61 374	77.98 267	11.52 388	66.36 330	28.57 375	51.59 367	71.49 347

new-patchmatchnet		47.56 341	47.73 341	47.06 362	58.81 381	9.37 408	48.78 379	59.21 346	43.28 340	44.22 375	68.66 356	25.67 355	57.20 366	31.57 362	49.35 374	74.62 317

test_vis1_rt		41.35 352	39.45 354	47.03 363	46.65 395	37.86 331	47.76 380	38.65 395	23.10 387	44.21 376	51.22 389	11.20 390	44.08 392	39.27 313	53.02 364	59.14 376

N_pmnet		39.35 355	40.28 353	36.54 378	63.76 360	1.62 413	49.37 378	0.76 412	34.62 371	43.61 377	66.38 368	26.25 351	42.57 394	26.02 383	51.77 366	65.44 370

CHOSEN 280x420		47.83 340	46.36 344	52.24 356	67.37 341	49.78 214	38.91 393	43.11 392	35.00 370	43.27 378	63.30 376	28.95 329	49.19 387	36.53 332	60.80 336	57.76 380

TinyColmap		54.14 314	51.72 325	61.40 304	66.84 344	41.97 296	66.52 305	68.51 288	44.81 325	42.69 379	75.77 305	11.66 386	72.94 293	31.96 354	56.77 353	69.27 365

MDA-MVSNet-bldmvs		53.87 317	50.81 329	63.05 293	66.25 349	48.58 232	56.93 362	63.82 320	48.09 294	41.22 380	70.48 345	30.34 317	68.00 322	34.24 342	45.92 378	72.57 332

pmmvs3		44.92 344	41.95 351	53.86 344	52.58 388	43.55 283	62.11 336	46.90 386	26.05 383	40.63 381	60.19 379	11.08 391	57.91 363	31.83 359	46.15 377	60.11 374

LF4IMVS		42.95 347	42.26 349	45.04 365	48.30 393	32.50 373	54.80 367	48.49 380	28.03 379	40.51 382	70.16 346	9.24 393	43.89 393	31.63 360	49.18 375	58.72 377

WB-MVS		43.26 346	43.41 347	42.83 371	63.32 363	10.32 407	58.17 355	45.20 387	45.42 322	40.44 383	67.26 364	34.01 282	58.98 357	11.96 399	24.88 394	59.20 375

mvsany_test3		32.62 361	30.57 365	38.77 376	36.16 405	24.20 398	38.10 394	20.63 407	19.14 393	40.36 384	57.43 382	5.06 398	36.63 400	29.59 372	28.66 393	55.49 382

DSMNet-mixed		39.30 356	38.72 355	41.03 373	51.22 389	19.66 402	45.53 386	31.35 401	15.83 398	39.80 385	67.42 363	22.19 366	45.13 391	22.43 386	52.69 365	58.31 378

test_f		31.86 363	31.05 364	34.28 379	32.33 408	21.86 400	32.34 395	30.46 402	16.02 397	39.78 386	55.45 384	4.80 399	32.36 402	30.61 366	37.66 388	48.64 386

SSC-MVS		41.96 350	41.99 350	41.90 372	62.46 368	9.28 409	57.41 360	44.32 390	43.38 339	38.30 387	66.45 367	32.67 300	58.42 361	10.98 400	21.91 397	57.99 379

MVS-HIRNet		45.52 343	44.48 346	48.65 361	68.49 334	34.05 365	59.41 351	44.50 389	27.03 381	37.96 388	50.47 391	26.16 352	64.10 337	26.74 381	59.52 341	47.82 390

FPMVS		42.18 349	41.11 352	45.39 364	58.03 382	41.01 306	49.50 377	53.81 369	30.07 375	33.71 389	64.03 373	11.69 385	52.08 385	14.01 395	55.11 357	43.09 392

test_vis3_rt		32.09 362	30.20 366	37.76 377	35.36 406	27.48 387	40.60 392	28.29 404	16.69 396	32.52 390	40.53 395	1.96 407	37.40 399	33.64 346	42.21 383	48.39 387

new_pmnet		34.13 360	34.29 361	33.64 380	52.63 387	18.23 404	44.43 388	33.90 400	22.81 388	30.89 391	53.18 385	10.48 392	35.72 401	20.77 389	39.51 385	46.98 391

LCM-MVSNet		40.30 353	35.88 359	53.57 347	42.24 397	29.15 382	45.21 387	60.53 343	22.23 390	28.02 392	50.98 390	3.72 403	61.78 346	31.22 365	38.76 387	69.78 362

APD_test1		37.39 357	34.94 360	44.72 368	48.88 391	33.19 371	52.95 372	44.00 391	19.49 392	27.28 393	58.59 381	3.18 405	52.84 381	18.92 390	41.17 384	48.14 389

ANet_high		41.38 351	37.47 358	53.11 350	39.73 402	24.45 397	56.94 361	69.69 275	47.65 300	26.04 394	52.32 386	12.44 384	62.38 344	21.80 388	10.61 403	72.49 333

testf1		31.46 364	28.89 367	39.16 374	41.99 399	28.78 383	46.45 383	37.56 396	14.28 399	21.10 395	48.96 392	1.48 409	47.11 388	13.63 396	34.56 390	41.60 393

APD_test2		31.46 364	28.89 367	39.16 374	41.99 399	28.78 383	46.45 383	37.56 396	14.28 399	21.10 395	48.96 392	1.48 409	47.11 388	13.63 396	34.56 390	41.60 393

PMVS	28.69 22	36.22 358	33.29 362	45.02 366	36.82 404	35.98 353	54.68 368	48.74 379	26.31 382	21.02 397	51.61 388	2.88 406	60.10 352	9.99 403	47.58 376	38.99 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		27.40 366	25.91 369	31.87 382	39.46 403	6.57 410	31.17 396	28.52 403	23.96 385	20.45 398	48.94 394	4.20 402	37.94 398	16.51 392	19.97 398	51.09 385

Gipuma		34.77 359	31.91 363	43.33 369	62.05 370	37.87 330	20.39 398	67.03 297	23.23 386	18.41 399	25.84 399	4.24 400	62.73 342	14.71 394	51.32 368	29.38 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		9.43 373	11.14 376	4.30 388	2.38 411	4.40 411	13.62 400	16.08 409	0.39 405	15.89 400	13.06 402	15.80 379	5.54 407	12.63 398	10.46 404	2.95 402

MVE	17.77 23	21.41 369	17.77 374	32.34 381	34.34 407	25.44 395	16.11 399	24.11 406	11.19 401	13.22 401	31.92 397	1.58 408	30.95 403	10.47 401	17.03 399	40.62 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 370	18.10 373	24.41 385	13.68 410	3.11 412	12.06 401	42.37 393	2.00 404	11.97 402	36.38 396	5.77 397	29.35 404	15.06 393	23.65 396	40.76 395

DeepMVS_CX				12.03 387	17.97 409	10.91 406		10.60 410	7.46 402	11.07 403	28.36 398	3.28 404	11.29 406	8.01 405	9.74 405	13.89 401

E-PMN		23.77 367	22.73 371	26.90 383	42.02 398	20.67 401	42.66 390	35.70 398	17.43 394	10.28 404	25.05 400	6.42 396	42.39 395	10.28 402	14.71 400	17.63 399

EMVS		22.97 368	21.84 372	26.36 384	40.20 401	19.53 403	41.95 391	34.64 399	17.09 395	9.73 405	22.83 401	7.29 395	42.22 396	9.18 404	13.66 401	17.32 400

wuyk23d		13.32 372	12.52 375	15.71 386	47.54 394	26.27 393	31.06 397	1.98 411	4.93 403	5.18 406	1.94 406	0.45 411	18.54 405	6.81 406	12.83 402	2.33 403

EGC-MVSNET		42.47 348	38.48 356	54.46 342	74.33 254	48.73 230	70.33 279	51.10 374	0.03 406	0.18 407	67.78 360	13.28 383	66.49 329	18.91 391	50.36 371	48.15 388

testmvs		4.52 376	6.03 379	0.01 390	0.01 412	0.00 415	53.86 370	0.00 413	0.01 407	0.04 408	0.27 407	0.00 413	0.00 408	0.04 407	0.00 406	0.03 405

test123		4.73 375	6.30 378	0.02 389	0.01 412	0.01 414	56.36 363	0.00 413	0.01 407	0.04 408	0.21 408	0.01 412	0.00 408	0.03 408	0.00 406	0.04 404

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

cdsmvs_eth3d_5k		17.50 371	23.34 370	0.00 391	0.00 414	0.00 415	0.00 402	78.63 161	0.00 409	0.00 410	82.18 191	49.25 115	0.00 408	0.00 409	0.00 406	0.00 406

pcd_1.5k_mvsjas		3.92 377	5.23 380	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	47.05 146	0.00 408	0.00 409	0.00 406	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

ab-mvs-re		6.49 374	8.65 377	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	77.89 272	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 415	0.00 402	0.00 413	0.00 409	0.00 410	0.00 409	0.00 413	0.00 408	0.00 409	0.00 406	0.00 406

WAC-MVS						27.31 389								27.77 376

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

No_MVS				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 16

save fliter					86.17 33	61.30 28	83.98 47	79.66 140	59.00 121

test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 37

GSMVS																78.05 275

sam_mvs1												34.74 272				78.05 275

sam_mvs												33.43 288

MTGPA								80.97 123

test_post1							68.67 292				3.64 404	32.39 306	69.49 313	44.17 278

test_post											3.55 405	33.90 283	66.52 328

patchmatchnet-post											64.03 373	34.50 274	74.27 288

MTMP							86.03 19	17.08 408

gm-plane-assit					71.40 297	41.72 301			48.85 283		73.31 323		82.48 168	48.90 238

test9_res														75.28 37	88.31 32	83.81 169

agg_prior2														73.09 55	87.93 40	84.33 150

test_prior4						62.51 14	82.08 77

test_prior				76.69 53	84.20 61	57.27 88		84.88 37					86.43 78			86.38 72

新几何2							76.12 180

旧先验1					83.04 70	53.15 159		67.52 292			87.85 71	44.08 179			80.76 100	78.03 278

无先验							79.66 110	74.30 238	48.40 290				80.78 202	53.62 198		79.03 267

原ACMM2							79.02 116

testdata2													72.18 299	46.95 256

segment_acmp												54.23 54

testdata1							72.65 243		60.50 91

plane_prior7					81.41 90	55.96 111

plane_prior6					81.20 97	56.24 106						45.26 170

plane_prior5								84.01 49					87.21 53	68.16 81	80.58 103	84.65 144

plane_prior4											86.10 108

plane_prior2							84.22 40		64.52 25

plane_prior1					81.27 95

plane_prior						56.31 102	83.58 53		63.19 48						80.48 106

n2								0.00 413

nn								0.00 413

door-mid								47.19 385

test11								83.47 67

door								47.60 383

HQP5-MVS						54.94 131

BP-MVS														67.04 93

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 164

NP-MVS					80.98 100	56.05 110					85.54 126

ACMMP++_ref															74.07 186

ACMMP++															72.16 221

Test By Simon												48.33 126

High-resolution multi-view benchmark