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.

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 125	86.57 1	87.39 45	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

casdiffmvs_mvg		85.99 50	86.09 52	85.70 74	87.65 209	67.22 160	88.69 126	93.04 41	79.64 19	85.33 63	92.54 90	73.30 35	94.50 112	83.49 69	91.14 96	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
dcpmvs_2		85.63 60	86.15 50	84.06 134	91.71 78	64.94 205	86.47 200	91.87 103	73.63 143	86.60 54	93.02 79	76.57 15	91.87 226	83.36 70	92.15 80	95.35 3

casdiffmvs		85.11 69	85.14 69	85.01 91	87.20 223	65.77 186	87.75 159	92.83 60	77.84 38	84.36 84	92.38 92	72.15 48	93.93 134	81.27 94	90.48 104	95.33 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
3Dnovator+	77.84 4	85.48 62	84.47 78	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 207	93.37 69	60.40 200	96.75 26	77.20 129	93.73 64	95.29 5

BP-MVS1		84.32 77	83.71 85	86.17 61	87.84 199	67.85 139	89.38 99	89.64 173	77.73 39	83.98 91	92.12 97	56.89 224	95.43 70	84.03 66	91.75 87	95.24 6

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 175	82.14 3	86.65 53	94.28 37	68.28 97	97.46 6	90.81 3	95.31 34	95.15 7

CS-MVS		86.69 39	86.95 36	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 17	84.57 80	92.27 93	71.47 58	95.02 93	84.24 63	93.46 67	95.13 8

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 121	88.90 23	93.85 59	75.75 20	96.00 54	87.80 32	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline		84.93 72	84.98 70	84.80 101	87.30 221	65.39 194	87.30 173	92.88 57	77.62 41	84.04 90	92.26 94	71.81 52	93.96 128	81.31 92	90.30 107	95.03 10

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 34	92.78 4	95.74 6	82.45 3	97.49 4	89.42 12	96.68 2	94.95 11

PC_three_1452									68.21 258	92.02 12	94.00 53	82.09 5	95.98 56	84.58 57	96.68 2	94.95 11

IS-MVSNet		83.15 101	82.81 100	84.18 124	89.94 115	63.30 241	91.59 43	88.46 215	79.04 26	79.49 147	92.16 95	65.10 130	94.28 117	67.71 223	91.86 86	94.95 11

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 30	94.80 20	73.76 33	97.11 15	87.51 35	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 54	93.10 1	95.72 8	82.99 1	97.44 7	89.07 17	96.63 4	94.88 15

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 51	82.45 3	96.87 20	83.77 68	96.48 8	94.88 15

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 117	92.29 7	95.97 2	74.28 29	97.24 13	88.58 25	96.91 1	94.87 17
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
test2506		77.30 231	76.49 228	79.74 256	90.08 108	52.02 372	87.86 158	63.10 410	74.88 113	80.16 140	92.79 86	38.29 376	92.35 208	68.74 216	92.50 77	94.86 18

ECVR-MVS		79.61 169	79.26 162	80.67 238	90.08 108	54.69 355	87.89 156	77.44 364	74.88 113	80.27 137	92.79 86	48.96 308	92.45 202	68.55 217	92.50 77	94.86 18

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 269	92.39 6				88.94 20	96.63 4	94.85 20

test1111		79.43 176	79.18 165	80.15 248	89.99 113	53.31 368	87.33 172	77.05 368	75.04 108	80.23 139	92.77 88	48.97 307	92.33 210	68.87 214	92.40 79	94.81 21

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 93	89.16 20	95.10 16	75.65 21	96.19 46	87.07 38	96.01 17	94.79 22

balanced_conf03		86.78 37	86.99 34	86.15 63	91.24 83	67.61 146	90.51 62	92.90 56	77.26 53	87.44 44	91.63 109	71.27 62	96.06 49	85.62 46	95.01 37	94.78 23

sasdasda		85.91 54	85.87 56	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 73	88.01 33	91.23 121	73.28 36	93.91 135	81.50 90	88.80 130	94.77 24

SPE-MVS-test		86.29 47	86.48 42	85.71 73	91.02 88	67.21 161	92.36 29	93.78 18	78.97 29	83.51 101	91.20 124	70.65 71	95.15 84	81.96 87	94.89 42	94.77 24

canonicalmvs		85.91 54	85.87 56	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 73	88.01 33	91.23 121	73.28 36	93.91 135	81.50 90	88.80 130	94.77 24

GDP-MVS		83.52 93	82.64 103	86.16 62	88.14 183	68.45 125	89.13 109	92.69 65	72.82 166	83.71 96	91.86 103	55.69 229	95.35 79	80.03 105	89.74 118	94.69 27

test_0728_THIRD									78.38 34	92.12 9	95.78 4	81.46 7	97.40 9	89.42 12	96.57 7	94.67 28

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 44	78.35 13	96.77 24	89.59 11	94.22 60	94.67 28
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
RRT-MVS		82.60 112	82.10 111	84.10 126	87.98 193	62.94 252	87.45 168	91.27 121	77.42 50	79.85 142	90.28 144	56.62 226	94.70 107	79.87 108	88.15 142	94.67 28

MGCFI-Net		85.06 71	85.51 61	83.70 150	89.42 130	63.01 247	89.43 94	92.62 73	76.43 77	87.53 41	91.34 119	72.82 44	93.42 161	81.28 93	88.74 133	94.66 31

alignmvs		85.48 62	85.32 66	85.96 70	89.51 126	69.47 95	89.74 83	92.47 76	76.17 86	87.73 40	91.46 116	70.32 73	93.78 141	81.51 89	88.95 127	94.63 32

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 107	86.34 55	95.29 15	70.86 67	96.00 54	88.78 23	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 47	89.79 19	94.12 46	78.98 12	96.58 35	85.66 44	95.72 24	94.58 33

VDD-MVS		83.01 106	82.36 107	84.96 93	91.02 88	66.40 171	88.91 116	88.11 218	77.57 43	84.39 83	93.29 71	52.19 261	93.91 135	77.05 132	88.70 134	94.57 35

VDDNet		81.52 129	80.67 132	84.05 137	90.44 101	64.13 223	89.73 84	85.91 266	71.11 191	83.18 103	93.48 65	50.54 287	93.49 155	73.40 169	88.25 140	94.54 36

MVSMamba_PlusPlus		85.99 50	85.96 54	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 165	84.64 77	91.71 105	71.85 51	96.03 50	84.77 55	94.45 54	94.49 37

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 95	91.06 16	96.03 1	76.84 14	97.03 17	89.09 14	95.65 27	94.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 9	96.44 9	94.41 39

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 9	96.44 9	94.41 39

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 160	84.86 72	92.89 81	76.22 17	96.33 41	84.89 52	95.13 36	94.40 41

CANet		86.45 42	86.10 51	87.51 37	90.09 107	70.94 70	89.70 85	92.59 74	81.78 4	81.32 125	91.43 117	70.34 72	97.23 14	84.26 61	93.36 68	94.37 42

PHI-MVS		86.43 43	86.17 49	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 167	85.22 65	91.90 100	69.47 82	96.42 40	83.28 72	95.94 19	94.35 43

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 31	94.06 49	76.43 16	96.84 21	88.48 28	95.99 18	94.34 44

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 67	85.24 64	94.32 36	71.76 53	96.93 19	85.53 47	95.79 22	94.32 45

HPM-MVS		87.11 33	86.98 35	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 88	83.81 95	93.95 58	69.77 80	96.01 53	85.15 48	94.66 47	94.32 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		85.76 58	85.29 68	87.17 43	93.49 47	71.08 64	88.58 130	92.42 80	68.32 257	84.61 78	93.48 65	72.32 46	96.15 48	79.00 110	95.43 30	94.28 47

test_241102_TWO								94.06 10	77.24 54	92.78 4	95.72 8	81.26 8	97.44 7	89.07 17	96.58 6	94.26 48

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 15	96.41 12	94.21 49

fmvsm_l_conf0.5_n_3		86.02 48	86.32 44	85.14 85	87.20 223	68.54 123	89.57 90	90.44 145	75.31 102	87.49 42	94.39 34	72.86 42	92.72 192	89.04 19	90.56 103	94.16 50

DeepC-MVS_fast	79.65 3	86.91 36	86.62 41	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 75	84.22 85	93.36 70	71.44 59	96.76 25	80.82 98	95.33 33	94.16 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 97	83.02 96	84.57 105	90.13 106	64.47 216	92.32 30	90.73 137	74.45 125	79.35 149	91.10 127	69.05 89	95.12 85	72.78 176	87.22 153	94.13 52

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 59	93.47 67	73.02 41	97.00 18	84.90 50	94.94 40	94.10 53

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 85	88.14 29	95.09 17	71.06 65	96.67 29	87.67 33	96.37 14	94.09 54

XVS		87.18 32	86.91 38	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 98	94.17 43	67.45 105	96.60 33	83.06 73	94.50 51	94.07 55

X-MVStestdata		80.37 158	77.83 194	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 98	12.47 424	67.45 105	96.60 33	83.06 73	94.50 51	94.07 55

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 72	84.45 81	94.52 24	69.09 86	96.70 27	84.37 60	94.83 45	94.03 57

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 69	84.66 76	94.52 24	68.81 92	96.65 30	84.53 58	94.90 41	94.00 58

fmvsm_s_conf0.1_n_2		83.80 84	83.79 84	83.83 147	85.62 252	64.94 205	87.03 180	86.62 255	74.32 127	87.97 35	94.33 35	60.67 192	92.60 195	89.72 8	87.79 145	93.96 59

test_fmvsmconf_n		85.92 53	86.04 53	85.57 76	85.03 266	69.51 93	89.62 89	90.58 140	73.42 151	87.75 38	94.02 51	72.85 43	93.24 166	90.37 4	90.75 100	93.96 59

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 91	92.29 7	95.66 10	81.67 6	97.38 11	87.44 37	96.34 15	93.95 61
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_fmvsm_n_1920		85.29 67	85.34 64	85.13 88	86.12 244	69.93 86	88.65 128	90.78 136	69.97 218	88.27 27	93.98 56	71.39 60	91.54 239	88.49 27	90.45 105	93.91 62

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 62

GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 65	84.68 73	93.99 55	70.67 70	96.82 22	84.18 65	95.01 37	93.90 64

test_fmvsmconf0.1_n		85.61 61	85.65 59	85.50 77	82.99 313	69.39 100	89.65 86	90.29 154	73.31 154	87.77 37	94.15 45	71.72 54	93.23 167	90.31 5	90.67 102	93.89 65

Anonymous202405211		78.25 204	77.01 214	81.99 205	91.03 87	60.67 279	84.77 242	83.90 291	70.65 204	80.00 141	91.20 124	41.08 362	91.43 246	65.21 245	85.26 180	93.85 66

LFMVS		81.82 122	81.23 123	83.57 154	91.89 76	63.43 239	89.84 78	81.85 324	77.04 62	83.21 102	93.10 74	52.26 260	93.43 160	71.98 182	89.95 115	93.85 66

fmvsm_s_conf0.5_n_2		84.04 80	84.11 81	83.81 148	86.17 242	65.00 203	86.96 182	87.28 239	74.35 126	88.25 28	94.23 41	61.82 168	92.60 195	89.85 7	88.09 143	93.84 68

Effi-MVS+		83.62 91	83.08 94	85.24 83	88.38 175	67.45 150	88.89 117	89.15 191	75.50 98	82.27 113	88.28 197	69.61 81	94.45 114	77.81 123	87.84 144	93.84 68

Anonymous20240529		80.19 162	78.89 170	84.10 126	90.60 97	64.75 210	88.95 115	90.90 132	65.97 286	80.59 135	91.17 126	49.97 292	93.73 147	69.16 211	82.70 223	93.81 70

MVS_Test		83.15 101	83.06 95	83.41 159	86.86 229	63.21 243	86.11 211	92.00 95	74.31 128	82.87 107	89.44 169	70.03 76	93.21 169	77.39 128	88.50 138	93.81 70

test_fmvsmconf0.01_n		84.73 75	84.52 77	85.34 80	80.25 354	69.03 103	89.47 92	89.65 172	73.24 158	86.98 50	94.27 38	66.62 111	93.23 167	90.26 6	89.95 115	93.78 72

GeoE		81.71 124	81.01 128	83.80 149	89.51 126	64.45 217	88.97 114	88.73 210	71.27 188	78.63 161	89.76 155	66.32 117	93.20 172	69.89 203	86.02 173	93.74 73

diffmvs		82.10 115	81.88 117	82.76 193	83.00 311	63.78 229	83.68 267	89.76 168	72.94 163	82.02 116	89.85 153	65.96 124	90.79 263	82.38 85	87.30 152	93.71 74
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 69	84.91 69	94.44 31	70.78 68	96.61 32	84.53 58	94.89 42	93.66 75

VNet		82.21 114	82.41 105	81.62 211	90.82 93	60.93 274	84.47 250	89.78 167	76.36 83	84.07 89	91.88 101	64.71 134	90.26 269	70.68 194	88.89 128	93.66 75

PGM-MVS		86.68 40	86.27 46	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 97	83.86 93	94.42 32	67.87 102	96.64 31	82.70 83	94.57 50	93.66 75

DELS-MVS		85.41 65	85.30 67	85.77 72	88.49 169	67.93 138	85.52 230	93.44 27	78.70 30	83.63 100	89.03 176	74.57 24	95.71 61	80.26 104	94.04 61	93.66 75
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
SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 37	90.32 17	94.00 53	74.83 23	93.78 141	87.63 34	94.27 59	93.65 79
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
DeepC-MVS	79.81 2	87.08 35	86.88 39	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 59	82.82 109	94.23 41	72.13 49	97.09 16	84.83 53	95.37 31	93.65 79
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
patch_mono-2		83.65 88	84.54 75	80.99 230	90.06 112	65.83 183	84.21 259	88.74 209	71.60 182	85.01 66	92.44 91	74.51 25	83.50 349	82.15 86	92.15 80	93.64 81

EIA-MVS		83.31 100	82.80 101	84.82 99	89.59 122	65.59 189	88.21 143	92.68 66	74.66 120	78.96 153	86.42 252	69.06 88	95.26 80	75.54 149	90.09 111	93.62 82

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 43	83.84 94	94.40 33	72.24 47	96.28 43	85.65 45	95.30 35	93.62 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		85.35 66	84.95 72	86.57 56	93.69 42	70.58 78	92.15 35	91.62 111	73.89 138	82.67 112	94.09 47	62.60 154	95.54 65	80.93 96	92.93 71	93.57 84

fmvsm_s_conf0.1_n		83.56 92	83.38 90	84.10 126	84.86 268	67.28 156	89.40 98	83.01 308	70.67 200	87.08 48	93.96 57	68.38 95	91.45 245	88.56 26	84.50 188	93.56 85

CSCG		86.41 45	86.19 48	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 135	83.16 104	91.07 129	75.94 18	95.19 82	79.94 107	94.38 56	93.55 86

test12				86.80 52	92.63 67	70.70 75		91.79 107		82.71 111		71.67 56	96.16 47		94.50 51	93.54 87

APD-MVS_3200maxsize		85.97 52	85.88 55	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 147	85.94 56	94.51 27	65.80 125	95.61 62	83.04 75	92.51 76	93.53 88

mvs_anonymous		79.42 177	79.11 166	80.34 244	84.45 277	57.97 307	82.59 286	87.62 232	67.40 267	76.17 222	88.56 190	68.47 94	89.59 282	70.65 195	86.05 172	93.47 89

fmvsm_s_conf0.5_n		83.80 84	83.71 85	84.07 132	86.69 235	67.31 155	89.46 93	83.07 307	71.09 192	86.96 51	93.70 62	69.02 91	91.47 244	88.79 22	84.62 187	93.44 90

mPP-MVS		86.67 41	86.32 44	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 66	82.81 110	94.25 40	66.44 115	96.24 44	82.88 78	94.28 58	93.38 91

EPNet		83.72 87	82.92 99	86.14 65	84.22 280	69.48 94	91.05 56	85.27 272	81.30 6	76.83 202	91.65 107	66.09 120	95.56 63	76.00 143	93.85 62	93.38 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 95	82.80 101	85.43 79	90.25 104	68.74 114	90.30 72	90.13 159	76.33 84	80.87 133	92.89 81	61.00 187	94.20 122	72.45 181	90.97 97	93.35 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 51	92.12 9	95.78 4	80.98 9	97.40 9	89.08 15	96.41 12	93.33 94
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
UniMVSNet_ETH3D		79.10 186	78.24 184	81.70 210	86.85 230	60.24 286	87.28 174	88.79 204	74.25 130	76.84 201	90.53 142	49.48 298	91.56 237	67.98 221	82.15 227	93.29 95

EI-MVSNet-Vis-set		84.19 78	83.81 83	85.31 81	88.18 180	67.85 139	87.66 161	89.73 170	80.05 14	82.95 105	89.59 161	70.74 69	94.82 101	80.66 101	84.72 185	93.28 96

MTAPA		87.23 31	87.00 33	87.90 22	94.18 35	74.25 5	86.58 197	92.02 93	79.45 20	85.88 57	94.80 20	68.07 98	96.21 45	86.69 40	95.34 32	93.23 97

CP-MVS		87.11 33	86.92 37	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 75	83.68 97	94.46 28	67.93 100	95.95 57	84.20 64	94.39 55	93.23 97

ACMMP		85.89 56	85.39 63	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 69	80.73 134	93.82 60	64.33 135	96.29 42	82.67 84	90.69 101	93.23 97
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
fmvsm_s_conf0.1_n_a		83.32 99	82.99 97	84.28 118	83.79 290	68.07 135	89.34 101	82.85 313	69.80 222	87.36 46	94.06 49	68.34 96	91.56 237	87.95 31	83.46 212	93.21 100

PAPM_NR		83.02 105	82.41 105	84.82 99	92.47 70	66.37 172	87.93 154	91.80 106	73.82 139	77.32 190	90.66 139	67.90 101	94.90 97	70.37 197	89.48 121	93.19 101

reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 118	88.80 24	95.61 11	70.29 74	96.44 39	86.20 43	93.08 69	93.16 102

OMC-MVS		82.69 108	81.97 116	84.85 98	88.75 161	67.42 151	87.98 150	90.87 134	74.92 112	79.72 144	91.65 107	62.19 164	93.96 128	75.26 153	86.42 165	93.16 102

fmvsm_s_conf0.5_n_a		83.63 90	83.41 89	84.28 118	86.14 243	68.12 133	89.43 94	82.87 312	70.27 211	87.27 47	93.80 61	69.09 86	91.58 235	88.21 30	83.65 207	93.14 104

PAPR		81.66 127	80.89 130	83.99 142	90.27 103	64.00 224	86.76 193	91.77 109	68.84 248	77.13 200	89.50 162	67.63 103	94.88 99	67.55 225	88.52 137	93.09 105

UA-Net		85.08 70	84.96 71	85.45 78	92.07 73	68.07 135	89.78 82	90.86 135	82.48 2	84.60 79	93.20 73	69.35 83	95.22 81	71.39 187	90.88 99	93.07 106

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 110	88.96 21	95.54 12	71.20 63	96.54 36	86.28 41	93.49 65	93.06 107

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 110	88.96 21	95.54 12	71.20 63	96.54 36	86.28 41	93.49 65	93.06 107

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 36	94.27 38	75.89 19	96.81 23	87.45 36	96.44 9	93.05 109

thisisatest0530		79.40 178	77.76 199	84.31 116	87.69 208	65.10 201	87.36 170	84.26 287	70.04 214	77.42 187	88.26 199	49.94 293	94.79 103	70.20 198	84.70 186	93.03 110

train_agg		86.43 43	86.20 47	87.13 44	93.26 52	72.96 25	88.75 122	91.89 101	68.69 250	85.00 67	93.10 74	74.43 26	95.41 73	84.97 49	95.71 25	93.02 111

EC-MVSNet		86.01 49	86.38 43	84.91 97	89.31 138	66.27 174	92.32 30	93.63 21	79.37 21	84.17 87	91.88 101	69.04 90	95.43 70	83.93 67	93.77 63	93.01 112

mvsmamba		80.60 150	79.38 157	84.27 120	89.74 120	67.24 159	87.47 166	86.95 247	70.02 215	75.38 238	88.93 177	51.24 278	92.56 198	75.47 151	89.22 124	93.00 113

EI-MVSNet-UG-set		83.81 83	83.38 90	85.09 89	87.87 197	67.53 149	87.44 169	89.66 171	79.74 16	82.23 114	89.41 170	70.24 75	94.74 104	79.95 106	83.92 199	92.99 114

tttt0517		79.40 178	77.91 191	83.90 146	88.10 186	63.84 227	88.37 138	84.05 289	71.45 185	76.78 204	89.12 173	49.93 295	94.89 98	70.18 199	83.18 216	92.96 115

test9_res														84.90 50	95.70 26	92.87 116

SR-MVS		86.73 38	86.67 40	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 122	86.84 52	94.65 23	67.31 107	95.77 59	84.80 54	92.85 72	92.84 117

ETV-MVS		84.90 74	84.67 74	85.59 75	89.39 133	68.66 120	88.74 124	92.64 72	79.97 15	84.10 88	85.71 265	69.32 84	95.38 75	80.82 98	91.37 93	92.72 118

agg_prior2														82.91 77	95.45 29	92.70 119

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 162	88.58 25	94.52 24	73.36 34	96.49 38	84.26 61	95.01 37	92.70 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 197	76.63 227	84.64 104	86.73 234	69.47 95	85.01 237	84.61 280	69.54 228	66.51 354	86.59 245	50.16 290	91.75 229	76.26 139	84.24 196	92.69 121

Vis-MVSNet (Re-imp)		78.36 203	78.45 177	78.07 289	88.64 165	51.78 378	86.70 194	79.63 349	74.14 133	75.11 251	90.83 137	61.29 181	89.75 279	58.10 311	91.60 88	92.69 121

TSAR-MVS + GP.		85.71 59	85.33 65	86.84 50	91.34 81	72.50 36	89.07 112	87.28 239	76.41 78	85.80 58	90.22 148	74.15 31	95.37 78	81.82 88	91.88 83	92.65 123

test_fmvsmvis_n_1920		84.02 81	83.87 82	84.49 109	84.12 282	69.37 101	88.15 147	87.96 223	70.01 216	83.95 92	93.23 72	68.80 93	91.51 242	88.61 24	89.96 114	92.57 124

FA-MVS(test-final)		80.96 138	79.91 146	84.10 126	88.30 178	65.01 202	84.55 249	90.01 162	73.25 157	79.61 145	87.57 214	58.35 209	94.72 105	71.29 188	86.25 168	92.56 125

test_yl		81.17 134	80.47 136	83.24 165	89.13 146	63.62 230	86.21 208	89.95 164	72.43 170	81.78 121	89.61 159	57.50 217	93.58 149	70.75 192	86.90 157	92.52 126

DCV-MVSNet		81.17 134	80.47 136	83.24 165	89.13 146	63.62 230	86.21 208	89.95 164	72.43 170	81.78 121	89.61 159	57.50 217	93.58 149	70.75 192	86.90 157	92.52 126

SR-MVS-dyc-post		85.77 57	85.61 60	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 148	85.69 60	94.45 29	65.00 133	95.56 63	82.75 79	91.87 84	92.50 128

RE-MVS-def			85.48 62		93.06 58	70.63 76	91.88 38	92.27 84	73.53 148	85.69 60	94.45 29	63.87 139		82.75 79	91.87 84	92.50 128

nrg030		83.88 82	83.53 87	84.96 93	86.77 233	69.28 102	90.46 67	92.67 67	74.79 116	82.95 105	91.33 120	72.70 45	93.09 180	80.79 100	79.28 263	92.50 128

MG-MVS		83.41 96	83.45 88	83.28 162	92.74 65	62.28 259	88.17 145	89.50 177	75.22 103	81.49 124	92.74 89	66.75 110	95.11 87	72.85 175	91.58 90	92.45 131

FIs		82.07 117	82.42 104	81.04 229	88.80 158	58.34 301	88.26 142	93.49 26	76.93 64	78.47 166	91.04 130	69.92 78	92.34 209	69.87 204	84.97 182	92.44 132

fmvsm_s_conf0.5_n_3		86.36 46	87.46 27	83.09 172	87.08 227	65.21 197	89.09 111	90.21 156	79.67 17	89.98 18	95.02 18	73.17 38	91.71 232	91.30 2	91.60 88	92.34 133

FC-MVSNet-test		81.52 129	82.02 114	80.03 250	88.42 174	55.97 340	87.95 152	93.42 29	77.10 60	77.38 188	90.98 136	69.96 77	91.79 227	68.46 219	84.50 188	92.33 134

Fast-Effi-MVS+		80.81 142	79.92 145	83.47 155	88.85 153	64.51 213	85.53 228	89.39 180	70.79 197	78.49 165	85.06 283	67.54 104	93.58 149	67.03 233	86.58 162	92.32 135

TranMVSNet+NR-MVSNet		80.84 140	80.31 139	82.42 198	87.85 198	62.33 257	87.74 160	91.33 120	80.55 9	77.99 178	89.86 152	65.23 129	92.62 193	67.05 232	75.24 321	92.30 136

ab-mvs		79.51 172	78.97 169	81.14 226	88.46 171	60.91 275	83.84 264	89.24 187	70.36 207	79.03 152	88.87 180	63.23 146	90.21 271	65.12 246	82.57 224	92.28 137

CANet_DTU		80.61 149	79.87 147	82.83 185	85.60 253	63.17 246	87.36 170	88.65 211	76.37 82	75.88 225	88.44 193	53.51 250	93.07 181	73.30 170	89.74 118	92.25 138

UniMVSNet_NR-MVSNet		81.88 120	81.54 120	82.92 182	88.46 171	63.46 237	87.13 176	92.37 81	80.19 12	78.38 167	89.14 172	71.66 57	93.05 182	70.05 200	76.46 294	92.25 138

fmvsm_l_conf0.5_n		84.47 76	84.54 75	84.27 120	85.42 256	68.81 109	88.49 132	87.26 241	68.08 259	88.03 32	93.49 64	72.04 50	91.77 228	88.90 21	89.14 126	92.24 140

DU-MVS		81.12 136	80.52 135	82.90 183	87.80 201	63.46 237	87.02 181	91.87 103	79.01 27	78.38 167	89.07 174	65.02 131	93.05 182	70.05 200	76.46 294	92.20 141

NR-MVSNet		80.23 160	79.38 157	82.78 191	87.80 201	63.34 240	86.31 205	91.09 129	79.01 27	72.17 292	89.07 174	67.20 108	92.81 191	66.08 239	75.65 307	92.20 141

TAPA-MVS	73.13 9	79.15 184	77.94 190	82.79 190	89.59 122	62.99 251	88.16 146	91.51 115	65.77 287	77.14 199	91.09 128	60.91 188	93.21 169	50.26 358	87.05 155	92.17 143
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a		84.13 79	84.16 80	84.06 134	85.38 257	68.40 126	88.34 139	86.85 251	67.48 266	87.48 43	93.40 68	70.89 66	91.61 233	88.38 29	89.22 124	92.16 144

3Dnovator	76.31 5	83.38 98	82.31 108	86.59 55	87.94 194	72.94 28	90.64 60	92.14 92	77.21 56	75.47 232	92.83 83	58.56 207	94.72 105	73.24 172	92.71 74	92.13 145

MVS_111021_HR		85.14 68	84.75 73	86.32 58	91.65 79	72.70 30	85.98 213	90.33 151	76.11 87	82.08 115	91.61 111	71.36 61	94.17 124	81.02 95	92.58 75	92.08 146

MVSFormer		82.85 107	82.05 113	85.24 83	87.35 215	70.21 80	90.50 64	90.38 147	68.55 252	81.32 125	89.47 164	61.68 170	93.46 158	78.98 111	90.26 108	92.05 147

jason		81.39 132	80.29 140	84.70 103	86.63 237	69.90 88	85.95 214	86.77 252	63.24 317	81.07 131	89.47 164	61.08 186	92.15 215	78.33 119	90.07 113	92.05 147
jason: jason.
HyFIR lowres test		77.53 226	75.40 245	83.94 145	89.59 122	66.62 168	80.36 317	88.64 212	56.29 377	76.45 212	85.17 280	57.64 215	93.28 164	61.34 282	83.10 217	91.91 149

XVG-OURS-SEG-HR		80.81 142	79.76 149	83.96 144	85.60 253	68.78 111	83.54 273	90.50 143	70.66 203	76.71 206	91.66 106	60.69 191	91.26 250	76.94 133	81.58 234	91.83 150

lupinMVS		81.39 132	80.27 141	84.76 102	87.35 215	70.21 80	85.55 226	86.41 257	62.85 324	81.32 125	88.61 187	61.68 170	92.24 213	78.41 118	90.26 108	91.83 150

WR-MVS		79.49 173	79.22 164	80.27 246	88.79 159	58.35 300	85.06 236	88.61 213	78.56 31	77.65 183	88.34 195	63.81 141	90.66 266	64.98 248	77.22 283	91.80 152

h-mvs33		83.15 101	82.19 109	86.02 69	90.56 98	70.85 73	88.15 147	89.16 190	76.02 89	84.67 74	91.39 118	61.54 173	95.50 66	82.71 81	75.48 311	91.72 153

UniMVSNet (Re)		81.60 128	81.11 125	83.09 172	88.38 175	64.41 218	87.60 162	93.02 45	78.42 33	78.56 163	88.16 201	69.78 79	93.26 165	69.58 207	76.49 293	91.60 154

UGNet		80.83 141	79.59 153	84.54 106	88.04 189	68.09 134	89.42 96	88.16 217	76.95 63	76.22 218	89.46 166	49.30 302	93.94 131	68.48 218	90.31 106	91.60 154
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
testing91		76.54 241	75.66 240	79.18 268	88.43 173	55.89 341	81.08 303	83.00 309	73.76 141	75.34 240	84.29 298	46.20 327	90.07 273	64.33 252	84.50 188	91.58 156

XVG-OURS		80.41 155	79.23 163	83.97 143	85.64 251	69.02 105	83.03 284	90.39 146	71.09 192	77.63 184	91.49 115	54.62 241	91.35 248	75.71 145	83.47 211	91.54 157

LCM-MVSNet-Re		77.05 233	76.94 217	77.36 300	87.20 223	51.60 379	80.06 320	80.46 339	75.20 104	67.69 336	86.72 237	62.48 157	88.98 294	63.44 258	89.25 123	91.51 158

DP-MVS Recon		83.11 104	82.09 112	86.15 63	94.44 19	70.92 71	88.79 120	92.20 89	70.53 205	79.17 151	91.03 132	64.12 137	96.03 50	68.39 220	90.14 110	91.50 159

PS-MVSNAJss		82.07 117	81.31 121	84.34 115	86.51 238	67.27 157	89.27 102	91.51 115	71.75 177	79.37 148	90.22 148	63.15 148	94.27 118	77.69 124	82.36 226	91.49 160

testing99		76.09 253	75.12 251	79.00 269	88.16 181	55.50 347	80.79 307	81.40 328	73.30 155	75.17 248	84.27 300	44.48 341	90.02 274	64.28 253	84.22 197	91.48 161

thisisatest0515		77.33 230	75.38 246	83.18 168	85.27 259	63.80 228	82.11 291	83.27 301	65.06 296	75.91 224	83.84 307	49.54 297	94.27 118	67.24 229	86.19 169	91.48 161

DPM-MVS		84.93 72	84.29 79	86.84 50	90.20 105	73.04 23	87.12 177	93.04 41	69.80 222	82.85 108	91.22 123	73.06 40	96.02 52	76.72 137	94.63 48	91.46 163

HQP_MVS		83.64 89	83.14 93	85.14 85	90.08 108	68.71 116	91.25 52	92.44 77	79.12 24	78.92 155	91.00 134	60.42 198	95.38 75	78.71 114	86.32 166	91.33 164

plane_prior5								92.44 77					95.38 75	78.71 114	86.32 166	91.33 164

GA-MVS		76.87 237	75.17 250	81.97 206	82.75 317	62.58 254	81.44 300	86.35 260	72.16 174	74.74 258	82.89 327	46.20 327	92.02 219	68.85 215	81.09 239	91.30 166

VPA-MVSNet		80.60 150	80.55 134	80.76 236	88.07 188	60.80 277	86.86 187	91.58 113	75.67 96	80.24 138	89.45 168	63.34 142	90.25 270	70.51 196	79.22 264	91.23 167

Effi-MVS+-dtu		80.03 164	78.57 175	84.42 111	85.13 264	68.74 114	88.77 121	88.10 219	74.99 109	74.97 255	83.49 316	57.27 220	93.36 162	73.53 166	80.88 241	91.18 168

v2v482		80.23 160	79.29 161	83.05 176	83.62 294	64.14 222	87.04 179	89.97 163	73.61 144	78.18 173	87.22 225	61.10 185	93.82 139	76.11 140	76.78 291	91.18 168

FE-MVS		77.78 219	75.68 238	84.08 131	88.09 187	66.00 178	83.13 279	87.79 229	68.42 256	78.01 177	85.23 278	45.50 336	95.12 85	59.11 299	85.83 177	91.11 170

Anonymous20231211		78.97 190	77.69 202	82.81 187	90.54 99	64.29 220	90.11 75	91.51 115	65.01 298	76.16 223	88.13 206	50.56 286	93.03 185	69.68 206	77.56 281	91.11 170

hse-mvs2		81.72 123	80.94 129	84.07 132	88.72 162	67.68 144	85.87 217	87.26 241	76.02 89	84.67 74	88.22 200	61.54 173	93.48 156	82.71 81	73.44 339	91.06 172

AUN-MVS		79.21 183	77.60 204	84.05 137	88.71 163	67.61 146	85.84 219	87.26 241	69.08 241	77.23 193	88.14 205	53.20 254	93.47 157	75.50 150	73.45 338	91.06 172

HQP4-MVS										77.24 192			95.11 87			91.03 174

HQP-MVS		82.61 110	82.02 114	84.37 112	89.33 135	66.98 164	89.17 104	92.19 90	76.41 78	77.23 193	90.23 147	60.17 201	95.11 87	77.47 126	85.99 174	91.03 174

RPSCF		73.23 288	71.46 292	78.54 279	82.50 323	59.85 289	82.18 290	82.84 314	58.96 358	71.15 303	89.41 170	45.48 337	84.77 341	58.82 303	71.83 351	91.02 176

test_djsdf		80.30 159	79.32 160	83.27 163	83.98 286	65.37 195	90.50 64	90.38 147	68.55 252	76.19 219	88.70 183	56.44 227	93.46 158	78.98 111	80.14 253	90.97 177

PCF-MVS	73.52 7	80.38 156	78.84 171	85.01 91	87.71 206	68.99 106	83.65 268	91.46 119	63.00 321	77.77 182	90.28 144	66.10 119	95.09 91	61.40 280	88.22 141	90.94 178
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 196	78.66 173	78.76 273	88.31 177	55.72 344	84.45 253	86.63 254	76.79 68	78.26 170	90.55 141	59.30 203	89.70 281	66.63 234	77.05 285	90.88 179

CPTT-MVS		83.73 86	83.33 92	84.92 96	93.28 49	70.86 72	92.09 36	90.38 147	68.75 249	79.57 146	92.83 83	60.60 196	93.04 184	80.92 97	91.56 91	90.86 180

tt0805		78.73 194	77.83 194	81.43 216	85.17 260	60.30 285	89.41 97	90.90 132	71.21 189	77.17 198	88.73 182	46.38 322	93.21 169	72.57 179	78.96 265	90.79 181

CLD-MVS		82.31 113	81.65 119	84.29 117	88.47 170	67.73 143	85.81 221	92.35 82	75.78 92	78.33 169	86.58 247	64.01 138	94.35 115	76.05 142	87.48 150	90.79 181
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 171	78.43 179	83.07 175	83.55 296	64.52 212	86.93 185	90.58 140	70.83 196	77.78 181	85.90 261	59.15 204	93.94 131	73.96 163	77.19 284	90.76 183

IterMVS-LS		80.06 163	79.38 157	82.11 202	85.89 247	63.20 244	86.79 190	89.34 181	74.19 131	75.45 235	86.72 237	66.62 111	92.39 205	72.58 178	76.86 288	90.75 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 154	79.98 144	82.12 201	84.28 278	63.19 245	86.41 201	88.95 201	74.18 132	78.69 158	87.54 217	66.62 111	92.43 203	72.57 179	80.57 247	90.74 185

v1921920		79.22 182	78.03 188	82.80 188	83.30 301	63.94 226	86.80 189	90.33 151	69.91 220	77.48 186	85.53 271	58.44 208	93.75 145	73.60 165	76.85 289	90.71 186

QAPM		80.88 139	79.50 155	85.03 90	88.01 192	68.97 107	91.59 43	92.00 95	66.63 278	75.15 250	92.16 95	57.70 214	95.45 68	63.52 256	88.76 132	90.66 187

v144192		79.47 174	78.37 180	82.78 191	83.35 299	63.96 225	86.96 182	90.36 150	69.99 217	77.50 185	85.67 268	60.66 193	93.77 143	74.27 160	76.58 292	90.62 188

v1240		78.99 189	77.78 197	82.64 194	83.21 303	63.54 234	86.62 196	90.30 153	69.74 227	77.33 189	85.68 267	57.04 222	93.76 144	73.13 173	76.92 286	90.62 188

v1144		80.03 164	79.03 167	83.01 178	83.78 291	64.51 213	87.11 178	90.57 142	71.96 176	78.08 176	86.20 257	61.41 177	93.94 131	74.93 154	77.23 282	90.60 190

1112_ss		77.40 229	76.43 230	80.32 245	89.11 150	60.41 284	83.65 268	87.72 231	62.13 334	73.05 279	86.72 237	62.58 156	89.97 275	62.11 274	80.80 243	90.59 191

CP-MVSNet		78.22 205	78.34 181	77.84 291	87.83 200	54.54 357	87.94 153	91.17 125	77.65 40	73.48 274	88.49 191	62.24 163	88.43 304	62.19 271	74.07 330	90.55 192

testing222		74.04 276	72.66 280	78.19 286	87.89 196	55.36 348	81.06 304	79.20 353	71.30 187	74.65 261	83.57 315	39.11 371	88.67 301	51.43 350	85.75 178	90.53 193

PS-CasMVS		78.01 214	78.09 187	77.77 293	87.71 206	54.39 359	88.02 149	91.22 122	77.50 48	73.26 276	88.64 186	60.73 189	88.41 305	61.88 275	73.88 334	90.53 193

CDS-MVSNet		79.07 187	77.70 201	83.17 169	87.60 210	68.23 131	84.40 256	86.20 262	67.49 265	76.36 215	86.54 249	61.54 173	90.79 263	61.86 276	87.33 151	90.49 195
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 192	77.51 206	83.03 177	87.80 201	67.79 142	84.72 243	85.05 276	67.63 262	76.75 205	87.70 210	62.25 162	90.82 262	58.53 306	87.13 154	90.49 195

PEN-MVS		77.73 220	77.69 202	77.84 291	87.07 228	53.91 362	87.91 155	91.18 124	77.56 45	73.14 278	88.82 181	61.23 182	89.17 290	59.95 290	72.37 345	90.43 197

Test_1112_low_res		76.40 248	75.44 243	79.27 265	89.28 140	58.09 303	81.69 295	87.07 245	59.53 353	72.48 287	86.67 242	61.30 180	89.33 286	60.81 286	80.15 252	90.41 198

HY-MVS	69.67 12	77.95 215	77.15 212	80.36 243	87.57 214	60.21 287	83.37 275	87.78 230	66.11 282	75.37 239	87.06 232	63.27 144	90.48 268	61.38 281	82.43 225	90.40 199

CHOSEN 1792x2688		77.63 225	75.69 237	83.44 156	89.98 114	68.58 122	78.70 340	87.50 235	56.38 376	75.80 227	86.84 233	58.67 206	91.40 247	61.58 279	85.75 178	90.34 200

SDMVSNet		80.38 156	80.18 142	80.99 230	89.03 151	64.94 205	80.45 316	89.40 179	75.19 105	76.61 210	89.98 150	60.61 195	87.69 313	76.83 135	83.55 209	90.33 201

sd_testset		77.70 223	77.40 207	78.60 276	89.03 151	60.02 288	79.00 335	85.83 267	75.19 105	76.61 210	89.98 150	54.81 234	85.46 334	62.63 267	83.55 209	90.33 201

114514_t		80.68 148	79.51 154	84.20 123	94.09 38	67.27 157	89.64 87	91.11 128	58.75 361	74.08 268	90.72 138	58.10 210	95.04 92	69.70 205	89.42 122	90.30 203

eth_miper_zixun_eth		77.92 216	76.69 225	81.61 213	83.00 311	61.98 262	83.15 278	89.20 189	69.52 229	74.86 257	84.35 297	61.76 169	92.56 198	71.50 186	72.89 343	90.28 204

PVSNet_Blended_VisFu		82.62 109	81.83 118	84.96 93	90.80 94	69.76 90	88.74 124	91.70 110	69.39 230	78.96 153	88.46 192	65.47 127	94.87 100	74.42 158	88.57 135	90.24 205

MVS_111021_LR		82.61 110	82.11 110	84.11 125	88.82 156	71.58 55	85.15 233	86.16 263	74.69 118	80.47 136	91.04 130	62.29 161	90.55 267	80.33 103	90.08 112	90.20 206

MSLP-MVS++		85.43 64	85.76 58	84.45 110	91.93 75	70.24 79	90.71 59	92.86 58	77.46 49	84.22 85	92.81 85	67.16 109	92.94 186	80.36 102	94.35 57	90.16 207

mvs_tets		79.13 185	77.77 198	83.22 167	84.70 270	66.37 172	89.17 104	90.19 157	69.38 231	75.40 237	89.46 166	44.17 343	93.15 176	76.78 136	80.70 245	90.14 208

BH-RMVSNet		79.61 169	78.44 178	83.14 170	89.38 134	65.93 180	84.95 239	87.15 244	73.56 146	78.19 172	89.79 154	56.67 225	93.36 162	59.53 295	86.74 160	90.13 209

c3_l		78.75 193	77.91 191	81.26 222	82.89 315	61.56 268	84.09 262	89.13 193	69.97 218	75.56 230	84.29 298	66.36 116	92.09 217	73.47 168	75.48 311	90.12 210

v7n		78.97 190	77.58 205	83.14 170	83.45 298	65.51 190	88.32 140	91.21 123	73.69 142	72.41 288	86.32 255	57.93 211	93.81 140	69.18 210	75.65 307	90.11 211

jajsoiax		79.29 181	77.96 189	83.27 163	84.68 271	66.57 170	89.25 103	90.16 158	69.20 238	75.46 234	89.49 163	45.75 333	93.13 178	76.84 134	80.80 243	90.11 211

v148		78.72 195	77.80 196	81.47 215	82.73 318	61.96 263	86.30 206	88.08 220	73.26 156	76.18 220	85.47 273	62.46 158	92.36 207	71.92 183	73.82 335	90.09 213

GBi-Net		78.40 201	77.40 207	81.40 218	87.60 210	63.01 247	88.39 135	89.28 183	71.63 179	75.34 240	87.28 221	54.80 235	91.11 253	62.72 263	79.57 257	90.09 213

test1		78.40 201	77.40 207	81.40 218	87.60 210	63.01 247	88.39 135	89.28 183	71.63 179	75.34 240	87.28 221	54.80 235	91.11 253	62.72 263	79.57 257	90.09 213

FMVSNet1		77.44 227	76.12 234	81.40 218	86.81 232	63.01 247	88.39 135	89.28 183	70.49 206	74.39 265	87.28 221	49.06 306	91.11 253	60.91 284	78.52 268	90.09 213

WR-MVS_H		78.51 200	78.49 176	78.56 278	88.02 190	56.38 334	88.43 133	92.67 67	77.14 58	73.89 269	87.55 216	66.25 118	89.24 289	58.92 301	73.55 337	90.06 217

DTE-MVSNet		76.99 234	76.80 220	77.54 299	86.24 240	53.06 371	87.52 164	90.66 138	77.08 61	72.50 286	88.67 185	60.48 197	89.52 283	57.33 318	70.74 357	90.05 218

v8		79.97 166	79.02 168	82.80 188	84.09 283	64.50 215	87.96 151	90.29 154	74.13 134	75.24 247	86.81 234	62.88 153	93.89 138	74.39 159	75.40 316	90.00 219

thres600view7		76.50 243	75.44 243	79.68 258	89.40 132	57.16 320	85.53 228	83.23 302	73.79 140	76.26 217	87.09 230	51.89 270	91.89 224	48.05 372	83.72 206	90.00 219

thres400		76.50 243	75.37 247	79.86 253	89.13 146	57.65 314	85.17 231	83.60 294	73.41 152	76.45 212	86.39 253	52.12 262	91.95 221	48.33 367	83.75 203	90.00 219

cl22		78.07 211	77.01 214	81.23 223	82.37 327	61.83 265	83.55 272	87.98 222	68.96 246	75.06 253	83.87 305	61.40 178	91.88 225	73.53 166	76.39 296	89.98 222

OPM-MVS		83.50 94	82.95 98	85.14 85	88.79 159	70.95 69	89.13 109	91.52 114	77.55 46	80.96 132	91.75 104	60.71 190	94.50 112	79.67 109	86.51 164	89.97 223
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 257	73.83 268	81.30 221	83.26 302	61.79 266	82.57 287	80.65 335	66.81 269	66.88 345	83.42 317	57.86 213	92.19 214	63.47 257	79.57 257	89.91 224

v10		79.74 168	78.67 172	82.97 181	84.06 284	64.95 204	87.88 157	90.62 139	73.11 159	75.11 251	86.56 248	61.46 176	94.05 127	73.68 164	75.55 309	89.90 225

MVSTER		79.01 188	77.88 193	82.38 199	83.07 308	64.80 209	84.08 263	88.95 201	69.01 245	78.69 158	87.17 228	54.70 239	92.43 203	74.69 155	80.57 247	89.89 226

ACMP	74.13 6	81.51 131	80.57 133	84.36 113	89.42 130	68.69 119	89.97 77	91.50 118	74.46 124	75.04 254	90.41 143	53.82 247	94.54 109	77.56 125	82.91 218	89.86 227
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 116	81.27 122	84.50 107	89.23 142	68.76 112	90.22 73	91.94 99	75.37 100	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 200	89.83 228

LGP-MVS_train				84.50 107	89.23 142	68.76 112		91.94 99	75.37 100	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 200	89.83 228

V42		79.38 180	78.24 184	82.83 185	81.10 346	65.50 191	85.55 226	89.82 166	71.57 183	78.21 171	86.12 259	60.66 193	93.18 175	75.64 146	75.46 313	89.81 230

MAR-MVS		81.84 121	80.70 131	85.27 82	91.32 82	71.53 56	89.82 79	90.92 131	69.77 224	78.50 164	86.21 256	62.36 160	94.52 111	65.36 244	92.05 82	89.77 231
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
DIV-MVS_self_test		77.72 221	76.76 222	80.58 239	82.48 325	60.48 282	83.09 280	87.86 227	69.22 236	74.38 266	85.24 277	62.10 165	91.53 240	71.09 189	75.40 316	89.74 232

cl____		77.72 221	76.76 222	80.58 239	82.49 324	60.48 282	83.09 280	87.87 226	69.22 236	74.38 266	85.22 279	62.10 165	91.53 240	71.09 189	75.41 315	89.73 233

miper_ehance_all_eth		78.59 199	77.76 199	81.08 228	82.66 320	61.56 268	83.65 268	89.15 191	68.87 247	75.55 231	83.79 309	66.49 114	92.03 218	73.25 171	76.39 296	89.64 234

anonymousdsp		78.60 198	77.15 212	82.98 180	80.51 352	67.08 162	87.24 175	89.53 176	65.66 289	75.16 249	87.19 227	52.52 255	92.25 212	77.17 130	79.34 262	89.61 235

FMVSNet2		78.20 207	77.21 211	81.20 224	87.60 210	62.89 253	87.47 166	89.02 196	71.63 179	75.29 246	87.28 221	54.80 235	91.10 256	62.38 268	79.38 261	89.61 235

baseline1		76.98 235	76.75 224	77.66 294	88.13 184	55.66 345	85.12 234	81.89 322	73.04 161	76.79 203	88.90 178	62.43 159	87.78 312	63.30 260	71.18 355	89.55 237

ETVMVS		72.25 299	71.05 298	75.84 312	87.77 205	51.91 375	79.39 328	74.98 377	69.26 234	73.71 271	82.95 325	40.82 364	86.14 325	46.17 380	84.43 193	89.47 238

FMVSNet3		77.88 217	76.85 219	80.97 232	86.84 231	62.36 256	86.52 199	88.77 205	71.13 190	75.34 240	86.66 243	54.07 245	91.10 256	62.72 263	79.57 257	89.45 239

miper_enhance_ethall		77.87 218	76.86 218	80.92 233	81.65 334	61.38 270	82.68 285	88.98 198	65.52 291	75.47 232	82.30 336	65.76 126	92.00 220	72.95 174	76.39 296	89.39 240

testing11		75.14 267	74.01 263	78.53 280	88.16 181	56.38 334	80.74 310	80.42 340	70.67 200	72.69 285	83.72 312	43.61 347	89.86 276	62.29 270	83.76 202	89.36 241

cascas		76.72 240	74.64 254	82.99 179	85.78 249	65.88 182	82.33 288	89.21 188	60.85 342	72.74 282	81.02 347	47.28 315	93.75 145	67.48 226	85.02 181	89.34 242

Fast-Effi-MVS+-dtu		78.02 213	76.49 228	82.62 195	83.16 307	66.96 166	86.94 184	87.45 237	72.45 167	71.49 300	84.17 302	54.79 238	91.58 235	67.61 224	80.31 250	89.30 243

IB-MVS	68.01 15	75.85 256	73.36 272	83.31 161	84.76 269	66.03 176	83.38 274	85.06 275	70.21 213	69.40 322	81.05 346	45.76 332	94.66 108	65.10 247	75.49 310	89.25 244
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
thres100view900		76.50 243	75.55 242	79.33 264	89.52 125	56.99 323	85.83 220	83.23 302	73.94 136	76.32 216	87.12 229	51.89 270	91.95 221	48.33 367	83.75 203	89.07 245

tfpn200view9		76.42 247	75.37 247	79.55 263	89.13 146	57.65 314	85.17 231	83.60 294	73.41 152	76.45 212	86.39 253	52.12 262	91.95 221	48.33 367	83.75 203	89.07 245

xiu_mvs_v1_base_debu		80.80 144	79.72 150	84.03 139	87.35 215	70.19 82	85.56 223	88.77 205	69.06 242	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 247

xiu_mvs_v1_base		80.80 144	79.72 150	84.03 139	87.35 215	70.19 82	85.56 223	88.77 205	69.06 242	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 247

xiu_mvs_v1_base_debi		80.80 144	79.72 150	84.03 139	87.35 215	70.19 82	85.56 223	88.77 205	69.06 242	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 247

EPNet_dtu		75.46 261	74.86 252	77.23 303	82.57 322	54.60 356	86.89 186	83.09 306	71.64 178	66.25 356	85.86 263	55.99 228	88.04 309	54.92 332	86.55 163	89.05 250
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 232	76.68 226	78.93 271	84.22 280	58.62 298	86.41 201	88.36 216	71.37 186	73.31 275	88.01 207	61.22 183	89.15 291	64.24 254	73.01 342	89.03 251

PVSNet_Blended		80.98 137	80.34 138	82.90 183	88.85 153	65.40 192	84.43 254	92.00 95	67.62 263	78.11 174	85.05 284	66.02 122	94.27 118	71.52 184	89.50 120	89.01 252

PAPM		77.68 224	76.40 231	81.51 214	87.29 222	61.85 264	83.78 265	89.59 174	64.74 300	71.23 301	88.70 183	62.59 155	93.66 148	52.66 343	87.03 156	89.01 252

WTY-MVS		75.65 258	75.68 238	75.57 316	86.40 239	56.82 325	77.92 352	82.40 317	65.10 295	76.18 220	87.72 209	63.13 151	80.90 364	60.31 288	81.96 230	89.00 254

无先验							87.48 165	88.98 198	60.00 348				94.12 125	67.28 228		88.97 255

GSMVS																88.96 256

sam_mvs1												51.32 277				88.96 256

SCA		74.22 273	72.33 284	79.91 252	84.05 285	62.17 260	79.96 323	79.29 352	66.30 281	72.38 289	80.13 357	51.95 268	88.60 302	59.25 297	77.67 280	88.96 256

miper_lstm_enhance		74.11 275	73.11 275	77.13 304	80.11 356	59.62 292	72.23 380	86.92 250	66.76 271	70.40 307	82.92 326	56.93 223	82.92 353	69.06 212	72.63 344	88.87 259

ACMM	73.20 8	80.78 147	79.84 148	83.58 153	89.31 138	68.37 127	89.99 76	91.60 112	70.28 210	77.25 191	89.66 157	53.37 252	93.53 154	74.24 161	82.85 219	88.85 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 269	73.39 271	78.61 275	81.38 341	57.48 317	86.64 195	87.95 224	64.99 299	70.18 310	86.61 244	50.43 288	89.52 283	62.12 273	70.18 360	88.83 261

原ACMM1				84.35 114	93.01 60	68.79 110		92.44 77	63.96 314	81.09 130	91.57 112	66.06 121	95.45 68	67.19 230	94.82 46	88.81 262

CNLPA		78.08 210	76.79 221	81.97 206	90.40 102	71.07 65	87.59 163	84.55 281	66.03 285	72.38 289	89.64 158	57.56 216	86.04 326	59.61 294	83.35 213	88.79 263

UWE-MVS		72.13 300	71.49 291	74.03 335	86.66 236	47.70 394	81.40 301	76.89 370	63.60 316	75.59 229	84.22 301	39.94 367	85.62 331	48.98 364	86.13 171	88.77 264

UBG		73.08 290	72.27 285	75.51 318	88.02 190	51.29 383	78.35 347	77.38 365	65.52 291	73.87 270	82.36 334	45.55 334	86.48 322	55.02 331	84.39 194	88.75 265

K. test v3		71.19 305	68.51 317	79.21 267	83.04 310	57.78 313	84.35 257	76.91 369	72.90 164	62.99 375	82.86 328	39.27 369	91.09 258	61.65 278	52.66 401	88.75 265

旧先验1					91.96 74	65.79 185		86.37 259			93.08 78	69.31 85			92.74 73	88.74 267

PatchmatchNet		73.12 289	71.33 295	78.49 282	83.18 305	60.85 276	79.63 325	78.57 356	64.13 307	71.73 296	79.81 362	51.20 279	85.97 327	57.40 317	76.36 301	88.66 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 284	71.26 297	79.70 257	85.08 265	57.89 309	85.57 222	83.56 296	71.03 194	65.66 358	85.88 262	42.10 357	92.57 197	59.11 299	63.34 382	88.65 269

PS-MVSNAJ		81.69 125	81.02 127	83.70 150	89.51 126	68.21 132	84.28 258	90.09 160	70.79 197	81.26 129	85.62 270	63.15 148	94.29 116	75.62 147	88.87 129	88.59 270

xiu_mvs_v2_base		81.69 125	81.05 126	83.60 152	89.15 145	68.03 137	84.46 252	90.02 161	70.67 200	81.30 128	86.53 250	63.17 147	94.19 123	75.60 148	88.54 136	88.57 271

MonoMVSNet		76.49 246	75.80 235	78.58 277	81.55 337	58.45 299	86.36 204	86.22 261	74.87 115	74.73 259	83.73 311	51.79 273	88.73 299	70.78 191	72.15 348	88.55 272

CostFormer		75.24 266	73.90 266	79.27 265	82.65 321	58.27 302	80.80 306	82.73 315	61.57 337	75.33 244	83.13 322	55.52 230	91.07 259	64.98 248	78.34 273	88.45 273

lessismore_v0				78.97 270	81.01 347	57.15 321		65.99 404		61.16 381	82.82 329	39.12 370	91.34 249	59.67 293	46.92 408	88.43 274

OpenMVS	72.83 10	79.77 167	78.33 182	84.09 130	85.17 260	69.91 87	90.57 61	90.97 130	66.70 272	72.17 292	91.91 99	54.70 239	93.96 128	61.81 277	90.95 98	88.41 275

reproduce_monomvs		75.40 264	74.38 260	78.46 283	83.92 288	57.80 312	83.78 265	86.94 248	73.47 150	72.25 291	84.47 292	38.74 372	89.27 288	75.32 152	70.53 358	88.31 276

OurMVSNet-221017-0		74.26 272	72.42 283	79.80 255	83.76 292	59.59 293	85.92 216	86.64 253	66.39 280	66.96 344	87.58 213	39.46 368	91.60 234	65.76 242	69.27 363	88.22 277

LS3D		76.95 236	74.82 253	83.37 160	90.45 100	67.36 154	89.15 108	86.94 248	61.87 336	69.52 321	90.61 140	51.71 274	94.53 110	46.38 379	86.71 161	88.21 278

WBMVS		73.43 283	72.81 278	75.28 322	87.91 195	50.99 385	78.59 343	81.31 330	65.51 293	74.47 264	84.83 287	46.39 321	86.68 319	58.41 307	77.86 276	88.17 279

XVG-ACMP-BASELINE		76.11 252	74.27 262	81.62 211	83.20 304	64.67 211	83.60 271	89.75 169	69.75 225	71.85 295	87.09 230	32.78 389	92.11 216	69.99 202	80.43 249	88.09 280

tpm2		73.26 287	71.46 292	78.63 274	83.34 300	56.71 328	80.65 312	80.40 341	56.63 375	73.55 273	82.02 341	51.80 272	91.24 251	56.35 327	78.42 271	87.95 281

MDTV_nov1_ep13_2view						37.79 417	75.16 368		55.10 380	66.53 351		49.34 301		53.98 336		87.94 282

Patchmatch-test		64.82 354	63.24 355	69.57 365	79.42 368	49.82 391	63.49 411	69.05 397	51.98 390	59.95 386	80.13 357	50.91 281	70.98 405	40.66 396	73.57 336	87.90 283

PLC	70.83 11	78.05 212	76.37 232	83.08 174	91.88 77	67.80 141	88.19 144	89.46 178	64.33 306	69.87 318	88.38 194	53.66 248	93.58 149	58.86 302	82.73 221	87.86 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 297	71.71 289	74.35 332	82.19 328	52.00 373	79.22 331	77.29 366	64.56 302	72.95 281	83.68 314	51.35 276	83.26 352	58.33 309	75.80 305	87.81 285

Patchmatch-RL test		70.24 317	67.78 330	77.61 296	77.43 376	59.57 294	71.16 384	70.33 391	62.94 323	68.65 329	72.77 396	50.62 285	85.49 333	69.58 207	66.58 373	87.77 286

F-COLMAP		76.38 249	74.33 261	82.50 197	89.28 140	66.95 167	88.41 134	89.03 195	64.05 311	66.83 346	88.61 187	46.78 319	92.89 187	57.48 315	78.55 267	87.67 287

Baseline_NR-MVSNet		78.15 209	78.33 182	77.61 296	85.79 248	56.21 338	86.78 191	85.76 268	73.60 145	77.93 179	87.57 214	65.02 131	88.99 293	67.14 231	75.33 318	87.63 288

CL-MVSNet_self_test		72.37 297	71.46 292	75.09 324	79.49 367	53.53 364	80.76 309	85.01 277	69.12 240	70.51 305	82.05 340	57.92 212	84.13 344	52.27 345	66.00 376	87.60 289

ACMH+	68.96 14	76.01 254	74.01 263	82.03 204	88.60 166	65.31 196	88.86 118	87.55 233	70.25 212	67.75 335	87.47 219	41.27 360	93.19 174	58.37 308	75.94 304	87.60 289

1314		76.53 242	75.30 249	80.21 247	83.93 287	62.32 258	84.66 244	88.81 203	60.23 346	70.16 312	84.07 304	55.30 232	90.73 265	67.37 227	83.21 215	87.59 291

API-MVS		81.99 119	81.23 123	84.26 122	90.94 90	70.18 85	91.10 55	89.32 182	71.51 184	78.66 160	88.28 197	65.26 128	95.10 90	64.74 250	91.23 95	87.51 292

AdaColmap		80.58 153	79.42 156	84.06 134	93.09 57	68.91 108	89.36 100	88.97 200	69.27 233	75.70 228	89.69 156	57.20 221	95.77 59	63.06 261	88.41 139	87.50 293

PVSNet_BlendedMVS		80.60 150	80.02 143	82.36 200	88.85 153	65.40 192	86.16 210	92.00 95	69.34 232	78.11 174	86.09 260	66.02 122	94.27 118	71.52 184	82.06 229	87.39 294

sss		73.60 281	73.64 270	73.51 339	82.80 316	55.01 353	76.12 359	81.69 325	62.47 330	74.68 260	85.85 264	57.32 219	78.11 375	60.86 285	80.93 240	87.39 294

IterMVS-SCA-FT		75.43 262	73.87 267	80.11 249	82.69 319	64.85 208	81.57 297	83.47 298	69.16 239	70.49 306	84.15 303	51.95 268	88.15 307	69.23 209	72.14 349	87.34 296

PVSNet	64.34 18	72.08 301	70.87 301	75.69 314	86.21 241	56.44 332	74.37 374	80.73 334	62.06 335	70.17 311	82.23 338	42.86 351	83.31 351	54.77 333	84.45 192	87.32 297

新几何1				83.42 157	93.13 54	70.71 74		85.48 271	57.43 371	81.80 120	91.98 98	63.28 143	92.27 211	64.60 251	92.99 70	87.27 298

TR-MVS		77.44 227	76.18 233	81.20 224	88.24 179	63.24 242	84.61 247	86.40 258	67.55 264	77.81 180	86.48 251	54.10 244	93.15 176	57.75 314	82.72 222	87.20 299

TransMVSNet (Re)		75.39 265	74.56 256	77.86 290	85.50 255	57.10 322	86.78 191	86.09 265	72.17 173	71.53 299	87.34 220	63.01 152	89.31 287	56.84 323	61.83 384	87.17 300

ACMH	67.68 16	75.89 255	73.93 265	81.77 209	88.71 163	66.61 169	88.62 129	89.01 197	69.81 221	66.78 347	86.70 241	41.95 359	91.51 242	55.64 329	78.14 274	87.17 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 328	67.59 334	72.46 349	74.29 389	45.45 400	77.93 351	87.00 246	63.12 318	63.99 370	78.99 370	42.32 354	84.77 341	56.55 326	64.09 381	87.16 302

EPMVS		69.02 327	68.16 321	71.59 353	79.61 365	49.80 392	77.40 354	66.93 402	62.82 326	70.01 313	79.05 366	45.79 331	77.86 377	56.58 325	75.26 320	87.13 303

CR-MVSNet		73.37 284	71.27 296	79.67 259	81.32 344	65.19 198	75.92 361	80.30 342	59.92 349	72.73 283	81.19 344	52.50 256	86.69 318	59.84 291	77.71 278	87.11 304

RPMNet		73.51 282	70.49 304	82.58 196	81.32 344	65.19 198	75.92 361	92.27 84	57.60 369	72.73 283	76.45 384	52.30 259	95.43 70	48.14 371	77.71 278	87.11 304

test_vis1_n_1920		75.52 260	75.78 236	74.75 329	79.84 360	57.44 318	83.26 276	85.52 270	62.83 325	79.34 150	86.17 258	45.10 338	79.71 368	78.75 113	81.21 238	87.10 306

XXY-MVS		75.41 263	75.56 241	74.96 325	83.59 295	57.82 311	80.59 313	83.87 292	66.54 279	74.93 256	88.31 196	63.24 145	80.09 367	62.16 272	76.85 289	86.97 307

tpmrst		72.39 295	72.13 286	73.18 343	80.54 351	49.91 390	79.91 324	79.08 354	63.11 319	71.69 297	79.95 359	55.32 231	82.77 354	65.66 243	73.89 333	86.87 308

thres200		75.55 259	74.47 258	78.82 272	87.78 204	57.85 310	83.07 282	83.51 297	72.44 169	75.84 226	84.42 293	52.08 265	91.75 229	47.41 374	83.64 208	86.86 309

ITE_SJBPF				78.22 285	81.77 333	60.57 280		83.30 300	69.25 235	67.54 337	87.20 226	36.33 382	87.28 316	54.34 335	74.62 327	86.80 310

test222					91.50 80	68.26 130	84.16 260	83.20 305	54.63 382	79.74 143	91.63 109	58.97 205			91.42 92	86.77 311

MIMVSNet		70.69 312	69.30 311	74.88 326	84.52 275	56.35 336	75.87 363	79.42 350	64.59 301	67.76 334	82.41 333	41.10 361	81.54 360	46.64 378	81.34 235	86.75 312

BH-untuned		79.47 174	78.60 174	82.05 203	89.19 144	65.91 181	86.07 212	88.52 214	72.18 172	75.42 236	87.69 211	61.15 184	93.54 153	60.38 287	86.83 159	86.70 313

LTVRE_ROB	69.57 13	76.25 250	74.54 257	81.41 217	88.60 166	64.38 219	79.24 330	89.12 194	70.76 199	69.79 320	87.86 208	49.09 305	93.20 172	56.21 328	80.16 251	86.65 314
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
testdata				79.97 251	90.90 91	64.21 221		84.71 278	59.27 355	85.40 62	92.91 80	62.02 167	89.08 292	68.95 213	91.37 93	86.63 315

MIMVSNet1		68.58 331	66.78 341	73.98 336	80.07 357	51.82 377	80.77 308	84.37 282	64.40 304	59.75 387	82.16 339	36.47 381	83.63 348	42.73 391	70.33 359	86.48 316

tfpnnormal		74.39 270	73.16 274	78.08 288	86.10 246	58.05 304	84.65 246	87.53 234	70.32 209	71.22 302	85.63 269	54.97 233	89.86 276	43.03 390	75.02 323	86.32 317

D2MVS		74.82 268	73.21 273	79.64 260	79.81 361	62.56 255	80.34 318	87.35 238	64.37 305	68.86 327	82.66 331	46.37 323	90.10 272	67.91 222	81.24 237	86.25 318

tpm cat1		70.57 313	68.31 319	77.35 301	82.41 326	57.95 308	78.08 349	80.22 344	52.04 388	68.54 331	77.66 379	52.00 267	87.84 311	51.77 346	72.07 350	86.25 318

CVMVSNet		72.99 292	72.58 281	74.25 333	84.28 278	50.85 386	86.41 201	83.45 299	44.56 401	73.23 277	87.54 217	49.38 300	85.70 329	65.90 240	78.44 270	86.19 320

AllTest		70.96 308	68.09 323	79.58 261	85.15 262	63.62 230	84.58 248	79.83 346	62.31 331	60.32 384	86.73 235	32.02 390	88.96 296	50.28 356	71.57 353	86.15 321

TestCases				79.58 261	85.15 262	63.62 230		79.83 346	62.31 331	60.32 384	86.73 235	32.02 390	88.96 296	50.28 356	71.57 353	86.15 321

test-LLR		72.94 293	72.43 282	74.48 330	81.35 342	58.04 305	78.38 344	77.46 362	66.66 273	69.95 316	79.00 368	48.06 311	79.24 369	66.13 236	84.83 183	86.15 321

test-mter		71.41 304	70.39 307	74.48 330	81.35 342	58.04 305	78.38 344	77.46 362	60.32 345	69.95 316	79.00 368	36.08 383	79.24 369	66.13 236	84.83 183	86.15 321

IterMVS		74.29 271	72.94 277	78.35 284	81.53 338	63.49 236	81.58 296	82.49 316	68.06 260	69.99 315	83.69 313	51.66 275	85.54 332	65.85 241	71.64 352	86.01 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 239	74.57 255	83.42 157	93.29 48	69.46 97	88.55 131	83.70 293	63.98 313	70.20 309	88.89 179	54.01 246	94.80 102	46.66 376	81.88 232	86.01 325

ppachtmachnet_test		70.04 319	67.34 337	78.14 287	79.80 362	61.13 271	79.19 332	80.59 336	59.16 356	65.27 361	79.29 365	46.75 320	87.29 315	49.33 362	66.72 371	86.00 327

mmtdpeth		74.16 274	73.01 276	77.60 298	83.72 293	61.13 271	85.10 235	85.10 274	72.06 175	77.21 197	80.33 355	43.84 345	85.75 328	77.14 131	52.61 402	85.91 328

test_fmvs1_n		70.86 310	70.24 308	72.73 346	72.51 403	55.28 350	81.27 302	79.71 348	51.49 392	78.73 157	84.87 286	27.54 399	77.02 380	76.06 141	79.97 255	85.88 329

Patchmtry		70.74 311	69.16 314	75.49 319	80.72 348	54.07 361	74.94 372	80.30 342	58.34 362	70.01 313	81.19 344	52.50 256	86.54 320	53.37 340	71.09 356	85.87 330

WB-MVSnew		71.96 302	71.65 290	72.89 344	84.67 274	51.88 376	82.29 289	77.57 361	62.31 331	73.67 272	83.00 324	53.49 251	81.10 363	45.75 383	82.13 228	85.70 331

test_fmvs2		68.35 335	67.48 335	70.98 361	69.50 406	51.95 374	80.05 321	76.38 372	49.33 395	74.65 261	84.38 295	23.30 408	75.40 396	74.51 157	75.17 322	85.60 332

ambc				75.24 323	73.16 398	50.51 388	63.05 412	87.47 236		64.28 367	77.81 378	17.80 414	89.73 280	57.88 313	60.64 388	85.49 333

mvs5depth		69.45 324	67.45 336	75.46 320	73.93 390	55.83 342	79.19 332	83.23 302	66.89 268	71.63 298	83.32 318	33.69 388	85.09 337	59.81 292	55.34 398	85.46 334

UnsupCasMVSNet_eth		67.33 340	65.99 344	71.37 355	73.48 395	51.47 381	75.16 368	85.19 273	65.20 294	60.78 382	80.93 351	42.35 353	77.20 379	57.12 319	53.69 400	85.44 335

PatchT		68.46 334	67.85 326	70.29 363	80.70 349	43.93 407	72.47 379	74.88 378	60.15 347	70.55 304	76.57 383	49.94 293	81.59 359	50.58 352	74.83 325	85.34 336

Anonymous20240521		68.80 329	67.22 338	73.55 338	74.33 388	54.11 360	83.18 277	85.61 269	58.15 364	61.68 379	80.94 349	30.71 395	81.27 362	57.00 321	73.34 341	85.28 337

test_cas_vis1_n_1920		73.76 280	73.74 269	73.81 337	75.90 381	59.77 290	80.51 314	82.40 317	58.30 363	81.62 123	85.69 266	44.35 342	76.41 386	76.29 138	78.61 266	85.23 338

ADS-MVSNet2		66.20 351	63.33 354	74.82 327	79.92 358	58.75 297	67.55 399	75.19 376	53.37 385	65.25 362	75.86 387	42.32 354	80.53 366	41.57 394	68.91 365	85.18 339

ADS-MVSNet		64.36 355	62.88 358	68.78 371	79.92 358	47.17 396	67.55 399	71.18 390	53.37 385	65.25 362	75.86 387	42.32 354	73.99 401	41.57 394	68.91 365	85.18 339

FMVSNet5		69.50 323	67.96 324	74.15 334	82.97 314	55.35 349	80.01 322	82.12 320	62.56 329	63.02 373	81.53 343	36.92 380	81.92 358	48.42 366	74.06 331	85.17 341

pmmvs5		71.55 303	70.20 309	75.61 315	77.83 374	56.39 333	81.74 294	80.89 331	57.76 367	67.46 339	84.49 291	49.26 303	85.32 336	57.08 320	75.29 319	85.11 342

testing3		68.56 332	67.67 332	71.22 359	87.33 220	42.87 409	83.06 283	71.54 389	70.36 207	69.08 326	84.38 295	30.33 396	85.69 330	37.50 402	75.45 314	85.09 343

CMPMVS	51.72 21	70.19 318	68.16 321	76.28 309	73.15 399	57.55 316	79.47 327	83.92 290	48.02 397	56.48 397	84.81 288	43.13 349	86.42 323	62.67 266	81.81 233	84.89 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 345	66.53 342	67.08 379	75.62 384	41.69 414	75.93 360	76.50 371	66.11 282	65.20 364	86.59 245	35.72 384	74.71 398	43.71 388	73.38 340	84.84 345

MSDG		73.36 286	70.99 299	80.49 241	84.51 276	65.80 184	80.71 311	86.13 264	65.70 288	65.46 359	83.74 310	44.60 339	90.91 261	51.13 351	76.89 287	84.74 346

pmmvs4		74.03 278	71.91 287	80.39 242	81.96 330	68.32 128	81.45 299	82.14 319	59.32 354	69.87 318	85.13 281	52.40 258	88.13 308	60.21 289	74.74 326	84.73 347

gg-mvs-nofinetune		69.95 320	67.96 324	75.94 311	83.07 308	54.51 358	77.23 356	70.29 392	63.11 319	70.32 308	62.33 405	43.62 346	88.69 300	53.88 337	87.76 146	84.62 348

test_fmvs1		70.93 309	70.52 303	72.16 350	73.71 392	55.05 352	80.82 305	78.77 355	51.21 393	78.58 162	84.41 294	31.20 394	76.94 381	75.88 144	80.12 254	84.47 349

BH-w/o		78.21 206	77.33 210	80.84 234	88.81 157	65.13 200	84.87 240	87.85 228	69.75 225	74.52 263	84.74 290	61.34 179	93.11 179	58.24 310	85.84 176	84.27 350

MVS		78.19 208	76.99 216	81.78 208	85.66 250	66.99 163	84.66 244	90.47 144	55.08 381	72.02 294	85.27 276	63.83 140	94.11 126	66.10 238	89.80 117	84.24 351

COLMAP_ROB	66.92 17	73.01 291	70.41 306	80.81 235	87.13 226	65.63 188	88.30 141	84.19 288	62.96 322	63.80 372	87.69 211	38.04 377	92.56 198	46.66 376	74.91 324	84.24 351
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 361	61.73 362	61.70 385	72.74 401	24.50 428	69.16 394	78.03 358	61.40 338	56.72 396	75.53 390	38.42 374	76.48 385	45.95 382	57.67 391	84.13 353

TESTMET0.1,1		69.89 321	69.00 315	72.55 347	79.27 370	56.85 324	78.38 344	74.71 381	57.64 368	68.09 333	77.19 381	37.75 378	76.70 382	63.92 255	84.09 198	84.10 354

test_fmvs3		63.36 358	61.82 361	67.98 376	62.51 415	46.96 398	77.37 355	74.03 383	45.24 400	67.50 338	78.79 371	12.16 420	72.98 404	72.77 177	66.02 375	83.99 355

our_test_3		69.14 326	67.00 339	75.57 316	79.80 362	58.80 296	77.96 350	77.81 359	59.55 352	62.90 376	78.25 375	47.43 313	83.97 345	51.71 347	67.58 370	83.93 356

test_vis1_n		69.85 322	69.21 313	71.77 352	72.66 402	55.27 351	81.48 298	76.21 373	52.03 389	75.30 245	83.20 321	28.97 397	76.22 388	74.60 156	78.41 272	83.81 357

mamv4		76.81 238	78.23 186	72.54 348	86.12 244	65.75 187	78.76 339	82.07 321	64.12 308	72.97 280	91.02 133	67.97 99	68.08 412	83.04 75	78.02 275	83.80 358

tpmvs		71.09 307	69.29 312	76.49 308	82.04 329	56.04 339	78.92 337	81.37 329	64.05 311	67.18 343	78.28 374	49.74 296	89.77 278	49.67 361	72.37 345	83.67 359

test20.03		67.45 339	66.95 340	68.94 368	75.48 385	44.84 405	77.50 353	77.67 360	66.66 273	63.01 374	83.80 308	47.02 317	78.40 373	42.53 393	68.86 367	83.58 360

test0.0.03 1		68.00 337	67.69 331	68.90 369	77.55 375	47.43 395	75.70 364	72.95 388	66.66 273	66.56 350	82.29 337	48.06 311	75.87 391	44.97 387	74.51 328	83.41 361

Anonymous20231206		68.60 330	67.80 329	71.02 360	80.23 355	50.75 387	78.30 348	80.47 338	56.79 374	66.11 357	82.63 332	46.35 324	78.95 371	43.62 389	75.70 306	83.36 362

EU-MVSNet		68.53 333	67.61 333	71.31 358	78.51 373	47.01 397	84.47 250	84.27 286	42.27 404	66.44 355	84.79 289	40.44 365	83.76 346	58.76 304	68.54 368	83.17 363

dp		66.80 343	65.43 345	70.90 362	79.74 364	48.82 393	75.12 370	74.77 379	59.61 351	64.08 369	77.23 380	42.89 350	80.72 365	48.86 365	66.58 373	83.16 364

pmmvs-eth3d		70.50 315	67.83 328	78.52 281	77.37 377	66.18 175	81.82 292	81.51 326	58.90 359	63.90 371	80.42 354	42.69 352	86.28 324	58.56 305	65.30 378	83.11 365

YYNet1		65.03 352	62.91 357	71.38 354	75.85 382	56.60 330	69.12 395	74.66 382	57.28 372	54.12 400	77.87 377	45.85 330	74.48 399	49.95 359	61.52 386	83.05 366

MDA-MVSNet-bldmvs		66.68 344	63.66 353	75.75 313	79.28 369	60.56 281	73.92 376	78.35 357	64.43 303	50.13 406	79.87 361	44.02 344	83.67 347	46.10 381	56.86 392	83.03 367

MDA-MVSNet_test_wron		65.03 352	62.92 356	71.37 355	75.93 380	56.73 326	69.09 396	74.73 380	57.28 372	54.03 401	77.89 376	45.88 329	74.39 400	49.89 360	61.55 385	82.99 368

USDC		70.33 316	68.37 318	76.21 310	80.60 350	56.23 337	79.19 332	86.49 256	60.89 341	61.29 380	85.47 273	31.78 392	89.47 285	53.37 340	76.21 302	82.94 369

Syy-MVS		68.05 336	67.85 326	68.67 372	84.68 271	40.97 415	78.62 341	73.08 386	66.65 276	66.74 348	79.46 363	52.11 264	82.30 356	32.89 407	76.38 299	82.75 370

myMVS_eth3d		67.02 342	66.29 343	69.21 367	84.68 271	42.58 410	78.62 341	73.08 386	66.65 276	66.74 348	79.46 363	31.53 393	82.30 356	39.43 399	76.38 299	82.75 370

ttmdpeth		59.91 364	57.10 368	68.34 374	67.13 410	46.65 399	74.64 373	67.41 401	48.30 396	62.52 378	85.04 285	20.40 410	75.93 390	42.55 392	45.90 411	82.44 372

OpenMVS_ROB	64.09 19	70.56 314	68.19 320	77.65 295	80.26 353	59.41 295	85.01 237	82.96 311	58.76 360	65.43 360	82.33 335	37.63 379	91.23 252	45.34 386	76.03 303	82.32 373

JIA-IIPM		66.32 348	62.82 359	76.82 306	77.09 378	61.72 267	65.34 407	75.38 375	58.04 366	64.51 366	62.32 406	42.05 358	86.51 321	51.45 349	69.22 364	82.21 374

dmvs_re		71.14 306	70.58 302	72.80 345	81.96 330	59.68 291	75.60 365	79.34 351	68.55 252	69.27 325	80.72 352	49.42 299	76.54 383	52.56 344	77.79 277	82.19 375

EG-PatchMatch MVS		74.04 276	71.82 288	80.71 237	84.92 267	67.42 151	85.86 218	88.08 220	66.04 284	64.22 368	83.85 306	35.10 385	92.56 198	57.44 316	80.83 242	82.16 376

MVP-Stereo		76.12 251	74.46 259	81.13 227	85.37 258	69.79 89	84.42 255	87.95 224	65.03 297	67.46 339	85.33 275	53.28 253	91.73 231	58.01 312	83.27 214	81.85 377
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 338	64.34 348	76.92 305	73.47 396	61.07 273	84.86 241	82.98 310	59.77 350	58.30 391	85.13 281	26.06 400	87.89 310	47.92 373	60.59 389	81.81 378

GG-mvs-BLEND				75.38 321	81.59 336	55.80 343	79.32 329	69.63 394		67.19 342	73.67 394	43.24 348	88.90 298	50.41 353	84.50 188	81.45 379

KD-MVS_2432*1600		66.22 349	63.89 351	73.21 340	75.47 386	53.42 366	70.76 387	84.35 283	64.10 309	66.52 352	78.52 372	34.55 386	84.98 338	50.40 354	50.33 405	81.23 380

miper_refine_blended		66.22 349	63.89 351	73.21 340	75.47 386	53.42 366	70.76 387	84.35 283	64.10 309	66.52 352	78.52 372	34.55 386	84.98 338	50.40 354	50.33 405	81.23 380

test_0402		72.79 294	70.44 305	79.84 254	88.13 184	65.99 179	85.93 215	84.29 285	65.57 290	67.40 341	85.49 272	46.92 318	92.61 194	35.88 404	74.38 329	80.94 382

MVStest1		56.63 368	52.76 374	68.25 375	61.67 416	53.25 370	71.67 382	68.90 399	38.59 409	50.59 405	83.05 323	25.08 402	70.66 406	36.76 403	38.56 412	80.83 383

UnsupCasMVSNet_bld		63.70 357	61.53 363	70.21 364	73.69 393	51.39 382	72.82 378	81.89 322	55.63 379	57.81 393	71.80 398	38.67 373	78.61 372	49.26 363	52.21 403	80.63 384

LCM-MVSNet		54.25 370	49.68 380	67.97 377	53.73 424	45.28 403	66.85 402	80.78 333	35.96 413	39.45 414	62.23 407	8.70 424	78.06 376	48.24 370	51.20 404	80.57 385

N_pmnet		52.79 375	53.26 373	51.40 399	78.99 371	7.68 433	69.52 391	3.89 432	51.63 391	57.01 395	74.98 391	40.83 363	65.96 414	37.78 401	64.67 379	80.56 386

TinyColmap		67.30 341	64.81 346	74.76 328	81.92 332	56.68 329	80.29 319	81.49 327	60.33 344	56.27 398	83.22 319	24.77 404	87.66 314	45.52 384	69.47 362	79.95 387

PM-MVS		66.41 347	64.14 349	73.20 342	73.92 391	56.45 331	78.97 336	64.96 408	63.88 315	64.72 365	80.24 356	19.84 412	83.44 350	66.24 235	64.52 380	79.71 388

ANet_high		50.57 379	46.10 383	63.99 382	48.67 427	39.13 416	70.99 386	80.85 332	61.39 339	31.18 416	57.70 412	17.02 415	73.65 403	31.22 409	15.89 424	79.18 389

LF4IMVS		64.02 356	62.19 360	69.50 366	70.90 404	53.29 369	76.13 358	77.18 367	52.65 387	58.59 389	80.98 348	23.55 407	76.52 384	53.06 342	66.66 372	78.68 390

PatchMatch-RL		72.38 296	70.90 300	76.80 307	88.60 166	67.38 153	79.53 326	76.17 374	62.75 327	69.36 323	82.00 342	45.51 335	84.89 340	53.62 338	80.58 246	78.12 391

MS-PatchMatch		73.83 279	72.67 279	77.30 302	83.87 289	66.02 177	81.82 292	84.66 279	61.37 340	68.61 330	82.82 329	47.29 314	88.21 306	59.27 296	84.32 195	77.68 392

DSMNet-mixed		57.77 367	56.90 369	60.38 387	67.70 408	35.61 418	69.18 393	53.97 419	32.30 417	57.49 394	79.88 360	40.39 366	68.57 411	38.78 400	72.37 345	76.97 393

CHOSEN 280x420		66.51 346	64.71 347	71.90 351	81.45 339	63.52 235	57.98 414	68.95 398	53.57 384	62.59 377	76.70 382	46.22 326	75.29 397	55.25 330	79.68 256	76.88 394

mvsany_test3		53.99 371	51.45 376	61.61 386	55.51 420	44.74 406	63.52 410	45.41 425	43.69 403	58.11 392	76.45 384	17.99 413	63.76 416	54.77 333	47.59 407	76.34 395

dmvs_testset		62.63 359	64.11 350	58.19 389	78.55 372	24.76 427	75.28 366	65.94 405	67.91 261	60.34 383	76.01 386	53.56 249	73.94 402	31.79 408	67.65 369	75.88 396

mvsany_test1		62.30 360	61.26 364	65.41 381	69.52 405	54.86 354	66.86 401	49.78 421	46.65 398	68.50 332	83.21 320	49.15 304	66.28 413	56.93 322	60.77 387	75.11 397

PMMVS		69.34 325	68.67 316	71.35 357	75.67 383	62.03 261	75.17 367	73.46 384	50.00 394	68.68 328	79.05 366	52.07 266	78.13 374	61.16 283	82.77 220	73.90 398

test_vis1_rt		60.28 363	58.42 366	65.84 380	67.25 409	55.60 346	70.44 389	60.94 413	44.33 402	59.00 388	66.64 403	24.91 403	68.67 410	62.80 262	69.48 361	73.25 399

pmmvs3		57.79 366	54.26 371	68.37 373	64.02 414	56.72 327	75.12 370	65.17 406	40.20 406	52.93 402	69.86 402	20.36 411	75.48 394	45.45 385	55.25 399	72.90 400

PVSNet_0	57.27 20	61.67 362	59.27 365	68.85 370	79.61 365	57.44 318	68.01 397	73.44 385	55.93 378	58.54 390	70.41 401	44.58 340	77.55 378	47.01 375	35.91 413	71.55 401

WB-MVS		54.94 369	54.72 370	55.60 395	73.50 394	20.90 429	74.27 375	61.19 412	59.16 356	50.61 404	74.15 392	47.19 316	75.78 392	17.31 420	35.07 414	70.12 402

SSC-MVS		53.88 372	53.59 372	54.75 397	72.87 400	19.59 430	73.84 377	60.53 414	57.58 370	49.18 408	73.45 395	46.34 325	75.47 395	16.20 423	32.28 416	69.20 403

test_f		52.09 376	50.82 377	55.90 393	53.82 423	42.31 413	59.42 413	58.31 417	36.45 412	56.12 399	70.96 400	12.18 419	57.79 419	53.51 339	56.57 394	67.60 404

PMMVS2		40.82 386	38.86 390	46.69 400	53.84 422	16.45 431	48.61 417	49.92 420	37.49 410	31.67 415	60.97 408	8.14 426	56.42 420	28.42 411	30.72 417	67.19 405

new_pmnet		50.91 378	50.29 378	52.78 398	68.58 407	34.94 420	63.71 409	56.63 418	39.73 407	44.95 409	65.47 404	21.93 409	58.48 418	34.98 405	56.62 393	64.92 406

MVS-HIRNet		59.14 365	57.67 367	63.57 383	81.65 334	43.50 408	71.73 381	65.06 407	39.59 408	51.43 403	57.73 411	38.34 375	82.58 355	39.53 397	73.95 332	64.62 407

APD_test1		53.31 374	49.93 379	63.42 384	65.68 411	50.13 389	71.59 383	66.90 403	34.43 414	40.58 413	71.56 399	8.65 425	76.27 387	34.64 406	55.36 397	63.86 408

test_method		31.52 389	29.28 393	38.23 403	27.03 431	6.50 434	20.94 422	62.21 411	4.05 425	22.35 423	52.50 416	13.33 417	47.58 423	27.04 413	34.04 415	60.62 409

EGC-MVSNET		52.07 377	47.05 381	67.14 378	83.51 297	60.71 278	80.50 315	67.75 400	0.07 427	0.43 428	75.85 389	24.26 405	81.54 360	28.82 410	62.25 383	59.16 410

test_vis3_rt		49.26 380	47.02 382	56.00 392	54.30 421	45.27 404	66.76 403	48.08 422	36.83 411	44.38 410	53.20 415	7.17 427	64.07 415	56.77 324	55.66 395	58.65 411

FPMVS		53.68 373	51.64 375	59.81 388	65.08 412	51.03 384	69.48 392	69.58 395	41.46 405	40.67 412	72.32 397	16.46 416	70.00 409	24.24 416	65.42 377	58.40 412

testf1		45.72 381	41.96 385	57.00 390	56.90 418	45.32 401	66.14 404	59.26 415	26.19 418	30.89 417	60.96 409	4.14 428	70.64 407	26.39 414	46.73 409	55.04 413

APD_test2		45.72 381	41.96 385	57.00 390	56.90 418	45.32 401	66.14 404	59.26 415	26.19 418	30.89 417	60.96 409	4.14 428	70.64 407	26.39 414	46.73 409	55.04 413

PMVS	37.38 22	44.16 385	40.28 389	55.82 394	40.82 429	42.54 412	65.12 408	63.99 409	34.43 414	24.48 420	57.12 413	3.92 430	76.17 389	17.10 421	55.52 396	48.75 415
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 391	25.89 395	43.81 402	44.55 428	35.46 419	28.87 421	39.07 426	18.20 422	18.58 424	40.18 419	2.68 431	47.37 424	17.07 422	23.78 421	48.60 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 383	45.38 384	45.55 401	73.36 397	26.85 425	67.72 398	34.19 427	54.15 383	49.65 407	56.41 414	25.43 401	62.94 417	19.45 418	28.09 418	46.86 417

kuosan		39.70 387	40.40 388	37.58 404	64.52 413	26.98 423	65.62 406	33.02 428	46.12 399	42.79 411	48.99 417	24.10 406	46.56 425	12.16 426	26.30 419	39.20 418

Gipuma		45.18 384	41.86 387	55.16 396	77.03 379	51.52 380	32.50 420	80.52 337	32.46 416	27.12 419	35.02 420	9.52 423	75.50 393	22.31 417	60.21 390	38.45 419
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 407	40.17 430	26.90 424		24.59 431	17.44 423	23.95 421	48.61 418	9.77 422	26.48 426	18.06 419	24.47 420	28.83 420

E-PMN		31.77 388	30.64 391	35.15 405	52.87 425	27.67 422	57.09 415	47.86 423	24.64 420	16.40 425	33.05 421	11.23 421	54.90 421	14.46 424	18.15 422	22.87 421

EMVS		30.81 390	29.65 392	34.27 406	50.96 426	25.95 426	56.58 416	46.80 424	24.01 421	15.53 426	30.68 422	12.47 418	54.43 422	12.81 425	17.05 423	22.43 422

tmp_tt		18.61 393	21.40 396	10.23 409	4.82 432	10.11 432	34.70 419	30.74 430	1.48 426	23.91 422	26.07 423	28.42 398	13.41 428	27.12 412	15.35 425	7.17 423

wuyk23d		16.82 394	15.94 397	19.46 408	58.74 417	31.45 421	39.22 418	3.74 433	6.84 424	6.04 427	2.70 427	1.27 432	24.29 427	10.54 427	14.40 426	2.63 424

test123		6.12 396	8.11 399	0.14 410	0.06 434	0.09 435	71.05 385	0.03 435	0.04 429	0.25 430	1.30 429	0.05 433	0.03 430	0.21 429	0.01 428	0.29 425

testmvs		6.04 397	8.02 400	0.10 411	0.08 433	0.03 436	69.74 390	0.04 434	0.05 428	0.31 429	1.68 428	0.02 434	0.04 429	0.24 428	0.02 427	0.25 426

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

cdsmvs_eth3d_5k		19.96 392	26.61 394	0.00 412	0.00 435	0.00 437	0.00 423	89.26 186	0.00 430	0.00 431	88.61 187	61.62 172	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		5.26 398	7.02 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	63.15 148	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		7.23 395	9.64 398	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	86.72 237	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						42.58 410								39.46 398

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 123	91.30 15

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 36	92.05 11	95.74 6	80.83 11

eth-test2					0.00 435

eth-test					0.00 435

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 195	87.75 38	94.07 48	74.01 32	96.70 27	84.66 56	94.84 44

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 57	93.10 1	95.39 14	82.99 1	97.27 12

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 137	88.57 26	94.67 22	75.57 22	95.79 58	86.77 39	95.76 23

save fliter					93.80 40	72.35 42	90.47 66	91.17 125	74.31 128

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 51	92.81 3	95.79 3	80.98 9

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs												50.01 291

MTGPA								92.02 93

test_post1							78.90 338				5.43 426	48.81 310	85.44 335	59.25 297

test_post											5.46 425	50.36 289	84.24 343

patchmatchnet-post											74.00 393	51.12 280	88.60 302

MTMP							92.18 34	32.83 429

gm-plane-assit					81.40 340	53.83 363			62.72 328		80.94 349		92.39 205	63.40 259

TEST9					93.26 52	72.96 25	88.75 122	91.89 101	68.44 255	85.00 67	93.10 74	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 127	91.84 105	68.69 250	84.87 71	93.10 74	74.43 26	95.16 83

agg_prior					92.85 62	71.94 50		91.78 108		84.41 82			94.93 94

test_prior4						72.60 34	89.01 113

test_prior2							88.85 119		75.41 99	84.91 69	93.54 63	74.28 29		83.31 71	95.86 20

旧先验2							86.56 198		58.10 365	87.04 49			88.98 294	74.07 162

新几何2							86.29 207

原ACMM2							86.86 187

testdata2													91.01 260	62.37 269

segment_acmp												73.08 39

testdata1							84.14 261		75.71 93

plane_prior7					90.08 108	68.51 124

plane_prior6					89.84 117	68.70 118						60.42 198

plane_prior4											91.00 134

plane_prior3						68.60 121			78.44 32	78.92 155

plane_prior2							91.25 52		79.12 24

plane_prior1					89.90 116

plane_prior						68.71 116	90.38 70		77.62 41						86.16 170

n2								0.00 436

nn								0.00 436

door-mid								69.98 393

test11								92.23 87

door								69.44 396

HQP5-MVS						66.98 164

HQP-NCC					89.33 135		89.17 104		76.41 78	77.23 193

ACMP_Plane					89.33 135		89.17 104		76.41 78	77.23 193

BP-MVS														77.47 126

HQP3-MVS								92.19 90							85.99 174

HQP2-MVS												60.17 201

NP-MVS					89.62 121	68.32 128					90.24 146

MDTV_nov1_ep13			69.97 310		83.18 305	53.48 365	77.10 357	80.18 345	60.45 343	69.33 324	80.44 353	48.89 309	86.90 317	51.60 348	78.51 269

ACMMP++_ref															81.95 231

ACMMP++															81.25 236

Test By Simon												64.33 135

High-resolution multi-view benchmark