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 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 143	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 228	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	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 70	86.15 60	84.06 158	91.71 84	64.94 233	86.47 227	91.87 115	73.63 170	86.60 67	93.02 93	76.57 18	91.87 257	83.36 84	92.15 90	95.35 3

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 245	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	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
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 228	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 248	93.37 83	60.40 230	96.75 30	77.20 156	93.73 70	95.29 6

BP-MVS1		84.32 91	83.71 103	86.17 68	87.84 213	67.85 154	89.38 109	89.64 197	77.73 45	83.98 106	92.12 114	56.89 260	95.43 77	84.03 80	91.75 98	95.24 7

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 200	82.14 3	86.65 66	94.28 46	68.28 114	97.46 6	90.81 6	95.31 38	95.15 8

CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 145	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
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 86	84.98 83	84.80 117	87.30 243	65.39 218	87.30 196	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

PC_three_1452									68.21 304	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

IS-MVSNet		83.15 123	82.81 120	84.18 147	89.94 123	63.30 279	91.59 51	88.46 252	79.04 30	79.49 182	92.16 111	65.10 153	94.28 130	67.71 266	91.86 97	94.95 12

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 141	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 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
test2506		77.30 272	76.49 269	79.74 297	90.08 116	52.02 419	87.86 178	63.10 462	74.88 137	80.16 175	92.79 100	38.29 425	92.35 237	68.74 259	92.50 84	94.86 19

ECVR-MVS		79.61 206	79.26 199	80.67 277	90.08 116	54.69 401	87.89 176	77.44 415	74.88 137	80.27 172	92.79 100	48.96 350	92.45 231	68.55 260	92.50 84	94.86 19

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 316	92.39 6				88.94 28	96.63 4	94.85 21

test1111		79.43 213	79.18 202	80.15 289	89.99 121	53.31 414	87.33 195	77.05 419	75.04 130	80.23 174	92.77 102	48.97 349	92.33 239	68.87 257	92.40 86	94.81 22

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 107	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 130	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

viewmacassd2359aftdt		83.76 103	83.66 105	84.07 155	86.59 269	64.56 241	86.88 211	91.82 118	75.72 106	83.34 117	92.15 113	68.24 115	92.88 212	79.05 131	89.15 145	94.77 25

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 144	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 116	91.20 147	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 144	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 121	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 29

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 93	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 29

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 93	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 29

GDP-MVS		83.52 112	82.64 124	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 196	83.71 111	91.86 120	55.69 267	95.35 86	80.03 122	89.74 134	94.69 32

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 33

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 33
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
RRT-MVS		82.60 136	82.10 136	84.10 149	87.98 207	62.94 290	87.45 190	91.27 139	77.42 56	79.85 177	90.28 176	56.62 263	94.70 117	79.87 125	88.15 163	94.67 33

E2		84.00 96	83.87 97	84.39 130	87.70 225	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 84	93.55 170	79.52 128	88.05 165	94.66 36

E3		84.00 96	83.87 97	84.39 130	87.70 225	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 84	93.55 170	79.52 128	88.05 165	94.66 36

MGCFI-Net		85.06 85	85.51 74	83.70 177	89.42 139	63.01 285	89.43 104	92.62 78	76.43 85	87.53 53	91.34 142	72.82 49	93.42 181	81.28 108	88.74 153	94.66 36

viewmanbaseed2359cas		83.66 106	83.55 106	84.00 166	86.81 261	64.53 242	86.65 221	91.75 123	74.89 136	83.15 123	91.68 126	68.74 107	92.83 216	79.02 132	89.24 142	94.63 39

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 97	87.73 52	91.46 139	70.32 80	93.78 158	81.51 104	88.95 147	94.63 39

viewdifsd2359ckpt09		83.34 118	82.55 126	85.70 81	87.64 229	67.72 159	88.43 151	91.68 125	71.91 210	81.65 148	90.68 164	67.10 128	94.75 113	76.17 171	87.70 172	94.62 41

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 129	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 42

viewcassd2359sk11		83.89 98	83.74 102	84.34 135	87.76 221	64.91 236	86.30 236	92.22 95	75.47 114	83.04 124	91.52 135	70.15 83	93.53 173	79.26 130	87.96 167	94.57 44

VDD-MVS		83.01 128	82.36 130	84.96 107	91.02 95	66.40 191	88.91 128	88.11 255	77.57 49	84.39 96	93.29 85	52.19 301	93.91 152	77.05 159	88.70 154	94.57 44

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 99	79.31 24	84.39 96	92.18 109	64.64 158	95.53 71	80.70 116	94.65 52	94.56 46

KinetiMVS		83.31 121	82.61 125	85.39 91	87.08 254	67.56 165	88.06 168	91.65 126	77.80 44	82.21 137	91.79 121	57.27 255	94.07 142	77.77 149	89.89 132	94.56 46

VDDNet		81.52 158	80.67 158	84.05 161	90.44 108	64.13 254	89.73 93	85.91 308	71.11 227	83.18 121	93.48 78	50.54 327	93.49 175	73.40 204	88.25 161	94.54 48

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 121	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 49

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 195	84.64 90	91.71 125	71.85 58	96.03 55	84.77 69	94.45 60	94.49 50

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 109	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 51
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 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 52

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 52

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 190	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 54

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 97	79.94 17	89.74 27	94.86 26	68.63 108	94.20 136	90.83 5	91.39 104	94.38 55

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 152	91.43 140	70.34 79	97.23 17	84.26 75	93.36 74	94.37 56

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 198	85.22 78	91.90 117	69.47 93	96.42 44	83.28 86	95.94 23	94.35 57

viewdifsd2359ckpt07		82.83 131	82.78 123	82.99 208	86.51 271	62.58 293	85.09 271	90.83 155	75.22 123	82.28 134	91.63 130	69.43 94	92.03 247	77.71 150	86.32 196	94.34 58

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 58

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 60

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 99	83.81 110	93.95 68	69.77 90	96.01 58	85.15 62	94.66 51	94.32 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 303	84.61 91	93.48 78	72.32 52	96.15 53	79.00 134	95.43 34	94.28 62

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 63

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 64

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 240	67.30 174	89.50 101	90.98 148	76.25 96	90.56 22	94.75 29	68.38 111	94.24 135	90.80 7	92.32 89	94.19 65

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 245	68.54 130	89.57 99	90.44 166	75.31 120	87.49 54	94.39 42	72.86 47	92.72 219	89.04 27	90.56 118	94.16 66

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 116	83.02 116	84.57 123	90.13 114	64.47 247	92.32 35	90.73 158	74.45 149	79.35 187	91.10 150	69.05 102	95.12 92	72.78 211	87.22 180	94.13 68

viewdifsd2359ckpt13		82.91 129	82.29 132	84.77 118	86.96 257	66.90 187	87.47 187	91.62 128	72.19 203	81.68 147	90.71 163	66.92 129	93.28 184	75.90 176	87.15 182	94.12 69

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 70

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 95	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 71

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 123	96.60 37	83.06 87	94.50 57	94.07 72

X-MVStestdata		80.37 193	77.83 233	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 476	67.45 123	96.60 37	83.06 87	94.50 57	94.07 72

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 99	96.70 31	84.37 74	94.83 49	94.03 74

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 138	86.70 265	65.83 205	88.77 136	89.78 189	75.46 115	88.35 36	93.73 74	69.19 98	93.06 204	91.30 3	88.44 159	94.02 75

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 105	96.65 34	84.53 72	94.90 45	94.00 76

fmvsm_s_conf0.1_n_2		83.80 101	83.79 101	83.83 173	85.62 291	64.94 233	87.03 203	86.62 297	74.32 151	87.97 47	94.33 43	60.67 222	92.60 222	89.72 14	87.79 170	93.96 77

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 310	69.51 100	89.62 98	90.58 161	73.42 178	87.75 50	94.02 61	72.85 48	93.24 188	90.37 8	90.75 115	93.96 77

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 102	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 79
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 80	85.34 77	85.13 101	86.12 280	69.93 92	88.65 144	90.78 157	69.97 263	88.27 38	93.98 66	71.39 67	91.54 273	88.49 35	90.45 120	93.91 80

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 80

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 82

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 362	69.39 107	89.65 95	90.29 175	73.31 182	87.77 49	94.15 55	71.72 61	93.23 189	90.31 9	90.67 117	93.89 83

Anonymous202405211		78.25 244	77.01 255	81.99 243	91.03 94	60.67 323	84.77 278	83.90 335	70.65 244	80.00 176	91.20 147	41.08 410	91.43 280	65.21 288	85.26 219	93.85 84

LFMVS		81.82 148	81.23 148	83.57 182	91.89 82	63.43 277	89.84 87	81.85 368	77.04 70	83.21 118	93.10 88	52.26 300	93.43 180	71.98 223	89.95 130	93.85 84

fmvsm_s_conf0.5_n_2		84.04 94	84.11 95	83.81 175	86.17 278	65.00 229	86.96 206	87.28 279	74.35 150	88.25 39	94.23 50	61.82 198	92.60 222	89.85 12	88.09 164	93.84 86

Effi-MVS+		83.62 110	83.08 114	85.24 95	88.38 188	67.45 167	88.89 129	89.15 224	75.50 113	82.27 135	88.28 238	69.61 92	94.45 127	77.81 148	87.84 169	93.84 86

Anonymous20240529		80.19 199	78.89 208	84.10 149	90.60 104	64.75 239	88.95 127	90.90 151	65.97 333	80.59 168	91.17 149	49.97 334	93.73 164	69.16 254	82.70 266	93.81 88

MVS_Test		83.15 123	83.06 115	83.41 188	86.86 258	63.21 281	86.11 242	92.00 107	74.31 152	82.87 127	89.44 206	70.03 86	93.21 191	77.39 155	88.50 158	93.81 88

Elysia		81.53 156	80.16 171	85.62 84	85.51 294	68.25 139	88.84 133	92.19 99	71.31 221	80.50 169	89.83 186	46.89 361	94.82 108	76.85 161	89.57 136	93.80 90

StellarMVS		81.53 156	80.16 171	85.62 84	85.51 294	68.25 139	88.84 133	92.19 99	71.31 221	80.50 169	89.83 186	46.89 361	94.82 108	76.85 161	89.57 136	93.80 90

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 404	69.03 110	89.47 102	89.65 196	73.24 186	86.98 62	94.27 47	66.62 132	93.23 189	90.26 10	89.95 130	93.78 92

GeoE		81.71 150	81.01 153	83.80 176	89.51 134	64.45 248	88.97 126	88.73 245	71.27 224	78.63 199	89.76 191	66.32 138	93.20 194	69.89 246	86.02 204	93.74 93

diffmvs		82.10 140	81.88 142	82.76 225	83.00 360	63.78 263	83.68 308	89.76 191	72.94 193	82.02 140	89.85 185	65.96 147	90.79 300	82.38 100	87.30 179	93.71 94
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 95

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 96

VNet		82.21 139	82.41 128	81.62 249	90.82 100	60.93 318	84.47 287	89.78 189	76.36 91	84.07 104	91.88 118	64.71 157	90.26 308	70.68 235	88.89 148	93.66 96

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 112	83.86 108	94.42 40	67.87 120	96.64 35	82.70 98	94.57 56	93.66 96

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 262	93.44 32	78.70 34	83.63 115	89.03 213	74.57 27	95.71 66	80.26 121	94.04 67	93.66 96
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 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 100
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 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 129	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 100
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		83.65 107	84.54 89	80.99 269	90.06 120	65.83 205	84.21 296	88.74 244	71.60 216	85.01 79	92.44 105	74.51 29	83.50 396	82.15 101	92.15 90	93.64 102

EIA-MVS		83.31 121	82.80 121	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 144	78.96 191	86.42 296	69.06 101	95.26 87	75.54 182	90.09 126	93.62 103

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
diffmvs_AUTHOR		82.38 137	82.27 133	82.73 227	83.26 350	63.80 261	83.89 303	89.76 191	73.35 181	82.37 133	90.84 160	66.25 139	90.79 300	82.77 93	87.93 168	93.59 105

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 128	73.89 164	82.67 132	94.09 57	62.60 182	95.54 70	80.93 111	92.93 77	93.57 106

fmvsm_s_conf0.1_n		83.56 111	83.38 110	84.10 149	84.86 312	67.28 175	89.40 108	83.01 352	70.67 240	87.08 60	93.96 67	68.38 111	91.45 279	88.56 34	84.50 228	93.56 107

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 161	83.16 122	91.07 152	75.94 21	95.19 89	79.94 124	94.38 62	93.55 108

test12				86.80 58	92.63 73	70.70 81		91.79 120		82.71 131		71.67 63	96.16 52		94.50 57	93.54 109

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 174	85.94 69	94.51 35	65.80 148	95.61 67	83.04 89	92.51 83	93.53 110

mvs_anonymous		79.42 214	79.11 203	80.34 284	84.45 323	57.97 353	82.59 330	87.62 272	67.40 313	76.17 264	88.56 231	68.47 110	89.59 321	70.65 236	86.05 203	93.47 111

fmvsm_s_conf0.5_n		83.80 101	83.71 103	84.07 155	86.69 266	67.31 173	89.46 103	83.07 351	71.09 228	86.96 63	93.70 75	69.02 104	91.47 278	88.79 30	84.62 227	93.44 112

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 145	86.26 274	67.40 170	89.18 115	89.31 213	72.50 197	88.31 37	93.86 70	69.66 91	91.96 251	89.81 13	91.05 109	93.38 113

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 95	76.87 74	82.81 130	94.25 49	66.44 136	96.24 49	82.88 92	94.28 64	93.38 113

EPNet		83.72 105	82.92 119	86.14 72	84.22 326	69.48 101	91.05 64	85.27 315	81.30 6	76.83 243	91.65 128	66.09 143	95.56 68	76.00 175	93.85 68	93.38 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 114	82.80 121	85.43 90	90.25 112	68.74 121	90.30 80	90.13 180	76.33 92	80.87 163	92.89 95	61.00 217	94.20 136	72.45 220	90.97 111	93.35 116
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 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 117
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		79.10 224	78.24 222	81.70 248	86.85 259	60.24 330	87.28 197	88.79 239	74.25 155	76.84 242	90.53 171	49.48 340	91.56 269	67.98 264	82.15 270	93.29 118

EI-MVSNet-Vis-set		84.19 92	83.81 100	85.31 93	88.18 194	67.85 154	87.66 182	89.73 194	80.05 15	82.95 125	89.59 198	70.74 76	94.82 108	80.66 118	84.72 225	93.28 119

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 224	92.02 105	79.45 22	85.88 70	94.80 27	68.07 116	96.21 50	86.69 52	95.34 36	93.23 120

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 118	95.95 62	84.20 78	94.39 61	93.23 120

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 166	93.82 72	64.33 160	96.29 46	82.67 99	90.69 116	93.23 120
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
SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 259	79.31 24	84.39 96	92.18 109	64.64 158	95.53 71	80.70 116	90.91 113	93.21 123

fmvsm_s_conf0.1_n_a		83.32 120	82.99 117	84.28 140	83.79 336	68.07 145	89.34 111	82.85 357	69.80 267	87.36 58	94.06 59	68.34 113	91.56 269	87.95 42	83.46 254	93.21 123

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 179	87.32 242	65.13 224	88.86 130	91.63 127	75.41 116	88.23 40	93.45 81	68.56 109	92.47 230	89.52 18	92.78 79	93.20 125

PAPM_NR		83.02 127	82.41 128	84.82 115	92.47 76	66.37 192	87.93 174	91.80 119	73.82 165	77.32 231	90.66 165	67.90 119	94.90 104	70.37 238	89.48 139	93.19 126

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 184	87.12 253	66.01 199	88.56 148	89.43 204	75.59 111	89.32 28	94.32 44	72.89 46	91.21 288	90.11 11	92.33 87	93.16 127

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 142	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 127

OMC-MVS		82.69 132	81.97 141	84.85 114	88.75 174	67.42 168	87.98 170	90.87 153	74.92 135	79.72 179	91.65 128	62.19 192	93.96 144	75.26 186	86.42 195	93.16 127

fmvsm_s_conf0.5_n_a		83.63 109	83.41 109	84.28 140	86.14 279	68.12 143	89.43 104	82.87 356	70.27 256	87.27 59	93.80 73	69.09 99	91.58 266	88.21 38	83.65 248	93.14 130

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 166	87.78 218	66.09 196	89.96 86	90.80 156	77.37 57	86.72 65	94.20 52	72.51 51	92.78 218	89.08 22	92.33 87	93.13 131

PAPR		81.66 153	80.89 155	83.99 168	90.27 111	64.00 255	86.76 218	91.77 122	68.84 294	77.13 241	89.50 199	67.63 121	94.88 106	67.55 268	88.52 157	93.09 132

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 154	82.48 2	84.60 92	93.20 87	69.35 95	95.22 88	71.39 228	90.88 114	93.07 133

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 132	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 134

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 132	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 134

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 136

thisisatest0530		79.40 215	77.76 238	84.31 137	87.69 227	65.10 227	87.36 193	84.26 331	70.04 259	77.42 228	88.26 240	49.94 335	94.79 112	70.20 241	84.70 226	93.03 137

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 113	68.69 296	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 138

EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 118	69.04 103	95.43 77	83.93 81	93.77 69	93.01 139

mvsmamba		80.60 184	79.38 194	84.27 142	89.74 128	67.24 178	87.47 187	86.95 287	70.02 260	75.38 280	88.93 218	51.24 318	92.56 225	75.47 184	89.22 143	93.00 140

EI-MVSNet-UG-set		83.81 100	83.38 110	85.09 103	87.87 211	67.53 166	87.44 191	89.66 195	79.74 18	82.23 136	89.41 207	70.24 82	94.74 114	79.95 123	83.92 240	92.99 141

tttt0517		79.40 215	77.91 229	83.90 172	88.10 200	63.84 260	88.37 157	84.05 333	71.45 219	76.78 245	89.12 210	49.93 337	94.89 105	70.18 242	83.18 259	92.96 142

viewdifsd2359ckpt11		80.37 193	79.73 184	82.30 236	83.70 340	62.39 297	84.20 297	86.67 293	73.22 187	80.90 161	90.62 166	63.00 179	91.56 269	76.81 165	78.44 314	92.95 143

viewmsd2359difaftdt		80.37 193	79.73 184	82.30 236	83.70 340	62.39 297	84.20 297	86.67 293	73.22 187	80.90 161	90.62 166	63.00 179	91.56 269	76.81 165	78.44 314	92.95 143

test9_res														84.90 64	95.70 30	92.87 145

viewmambaseed2359dif		80.41 189	79.84 181	82.12 238	82.95 364	62.50 296	83.39 316	88.06 259	67.11 314	80.98 159	90.31 175	66.20 141	91.01 296	74.62 190	84.90 222	92.86 146

AstraMVS		80.81 172	80.14 173	82.80 219	86.05 283	63.96 256	86.46 228	85.90 309	73.71 168	80.85 164	90.56 169	54.06 284	91.57 268	79.72 126	83.97 239	92.86 146

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 146	86.84 64	94.65 31	67.31 125	95.77 64	84.80 68	92.85 78	92.84 148

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 309	69.32 96	95.38 82	80.82 113	91.37 105	92.72 149

agg_prior2														82.91 91	95.45 33	92.70 150

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 192	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 150
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 236	76.63 268	84.64 122	86.73 264	69.47 102	85.01 273	84.61 324	69.54 273	66.51 403	86.59 289	50.16 331	91.75 260	76.26 170	84.24 236	92.69 152

Vis-MVSNet (Re-imp)		78.36 243	78.45 215	78.07 333	88.64 178	51.78 425	86.70 219	79.63 397	74.14 158	75.11 293	90.83 161	61.29 211	89.75 318	58.10 355	91.60 99	92.69 152

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 279	76.41 86	85.80 71	90.22 180	74.15 35	95.37 85	81.82 103	91.88 94	92.65 154

test_fmvsmvis_n_1920		84.02 95	83.87 97	84.49 127	84.12 328	69.37 108	88.15 166	87.96 262	70.01 261	83.95 107	93.23 86	68.80 106	91.51 276	88.61 32	89.96 129	92.57 155

FA-MVS(test-final)		80.96 168	79.91 178	84.10 149	88.30 191	65.01 228	84.55 286	90.01 183	73.25 185	79.61 180	87.57 258	58.35 244	94.72 115	71.29 229	86.25 199	92.56 156

guyue		81.13 165	80.64 159	82.60 230	86.52 270	63.92 259	86.69 220	87.73 270	73.97 160	80.83 165	89.69 192	56.70 261	91.33 284	78.26 147	85.40 218	92.54 157

test_yl		81.17 163	80.47 164	83.24 194	89.13 156	63.62 264	86.21 239	89.95 185	72.43 201	81.78 145	89.61 196	57.50 252	93.58 166	70.75 233	86.90 186	92.52 158

DCV-MVSNet		81.17 163	80.47 164	83.24 194	89.13 156	63.62 264	86.21 239	89.95 185	72.43 201	81.78 145	89.61 196	57.50 252	93.58 166	70.75 233	86.90 186	92.52 158

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 89	73.53 175	85.69 73	94.45 37	65.00 156	95.56 68	82.75 94	91.87 95	92.50 160

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 89	73.53 175	85.69 73	94.45 37	63.87 164		82.75 94	91.87 95	92.50 160

nrg030		83.88 99	83.53 107	84.96 107	86.77 263	69.28 109	90.46 75	92.67 72	74.79 140	82.95 125	91.33 143	72.70 50	93.09 202	80.79 115	79.28 307	92.50 160

SSM_0404		81.91 145	80.84 156	85.13 101	89.24 151	68.26 137	87.84 179	89.25 218	71.06 230	80.62 167	90.39 173	59.57 233	94.65 119	72.45 220	87.19 181	92.47 163

MG-MVS		83.41 115	83.45 108	83.28 191	92.74 71	62.28 302	88.17 164	89.50 202	75.22 123	81.49 150	92.74 103	66.75 130	95.11 94	72.85 210	91.58 101	92.45 164

FIs		82.07 142	82.42 127	81.04 268	88.80 171	58.34 347	88.26 161	93.49 31	76.93 72	78.47 205	91.04 153	69.92 88	92.34 238	69.87 247	84.97 221	92.44 165

testing3-2		75.12 310	75.19 292	74.91 372	90.40 109	45.09 455	80.29 363	78.42 407	78.37 40	76.54 253	87.75 252	44.36 387	87.28 359	57.04 365	83.49 252	92.37 166

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 201	87.08 254	65.21 221	89.09 123	90.21 177	79.67 19	89.98 24	95.02 24	73.17 42	91.71 263	91.30 3	91.60 99	92.34 167

FC-MVSNet-test		81.52 158	82.02 139	80.03 291	88.42 187	55.97 386	87.95 172	93.42 34	77.10 68	77.38 229	90.98 159	69.96 87	91.79 258	68.46 262	84.50 228	92.33 168

Fast-Effi-MVS+		80.81 172	79.92 177	83.47 183	88.85 163	64.51 244	85.53 260	89.39 206	70.79 237	78.49 203	85.06 329	67.54 122	93.58 166	67.03 276	86.58 192	92.32 169

TranMVSNet+NR-MVSNet		80.84 170	80.31 167	82.42 233	87.85 212	62.33 300	87.74 181	91.33 138	80.55 9	77.99 217	89.86 184	65.23 152	92.62 220	67.05 275	75.24 369	92.30 170

ab-mvs		79.51 209	78.97 206	81.14 265	88.46 184	60.91 319	83.84 304	89.24 220	70.36 251	79.03 190	88.87 221	63.23 172	90.21 310	65.12 289	82.57 267	92.28 171

CANet_DTU		80.61 182	79.87 180	82.83 216	85.60 292	63.17 284	87.36 193	88.65 248	76.37 90	75.88 267	88.44 234	53.51 289	93.07 203	73.30 205	89.74 134	92.25 172

UniMVSNet_NR-MVSNet		81.88 146	81.54 145	82.92 212	88.46 184	63.46 275	87.13 199	92.37 86	80.19 12	78.38 206	89.14 209	71.66 64	93.05 205	70.05 243	76.46 342	92.25 172

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 142	85.42 297	68.81 116	88.49 150	87.26 281	68.08 305	88.03 44	93.49 77	72.04 57	91.77 259	88.90 29	89.14 146	92.24 174

DU-MVS		81.12 166	80.52 162	82.90 213	87.80 215	63.46 275	87.02 204	91.87 115	79.01 31	78.38 206	89.07 211	65.02 154	93.05 205	70.05 243	76.46 342	92.20 175

NR-MVSNet		80.23 197	79.38 194	82.78 223	87.80 215	63.34 278	86.31 235	91.09 147	79.01 31	72.17 337	89.07 211	67.20 126	92.81 217	66.08 282	75.65 355	92.20 175

mamba_0408		79.37 218	77.52 245	84.93 110	88.81 167	67.96 149	65.03 460	88.66 246	70.96 234	79.48 183	89.80 188	58.69 239	94.65 119	70.35 239	85.93 207	92.18 177

SSM_04072		77.67 265	77.52 245	78.12 331	88.81 167	67.96 149	65.03 460	88.66 246	70.96 234	79.48 183	89.80 188	58.69 239	74.23 452	70.35 239	85.93 207	92.18 177

SSM_0407		81.58 155	80.48 163	84.87 113	88.81 167	67.96 149	87.37 192	89.25 218	71.06 230	79.48 183	90.39 173	59.57 233	94.48 126	72.45 220	85.93 207	92.18 177

TAPA-MVS	73.13 9	79.15 222	77.94 228	82.79 222	89.59 130	62.99 289	88.16 165	91.51 133	65.77 334	77.14 240	91.09 151	60.91 218	93.21 191	50.26 407	87.05 184	92.17 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a		84.13 93	84.16 94	84.06 158	85.38 298	68.40 133	88.34 158	86.85 291	67.48 312	87.48 55	93.40 82	70.89 73	91.61 264	88.38 37	89.22 143	92.16 181

3Dnovator	76.31 5	83.38 117	82.31 131	86.59 61	87.94 208	72.94 28	90.64 68	92.14 104	77.21 63	75.47 274	92.83 97	58.56 242	94.72 115	73.24 207	92.71 81	92.13 182

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 244	90.33 172	76.11 98	82.08 139	91.61 133	71.36 68	94.17 139	81.02 110	92.58 82	92.08 183

MVSFormer		82.85 130	82.05 138	85.24 95	87.35 235	70.21 86	90.50 72	90.38 168	68.55 298	81.32 152	89.47 201	61.68 200	93.46 178	78.98 135	90.26 123	92.05 184

jason		81.39 161	80.29 168	84.70 121	86.63 268	69.90 94	85.95 245	86.77 292	63.24 365	81.07 158	89.47 201	61.08 216	92.15 244	78.33 143	90.07 128	92.05 184
jason: jason.
HyFIR lowres test		77.53 267	75.40 287	83.94 171	89.59 130	66.62 188	80.36 361	88.64 249	56.29 429	76.45 254	85.17 326	57.64 250	93.28 184	61.34 325	83.10 260	91.91 186

XVG-OURS-SEG-HR		80.81 172	79.76 183	83.96 170	85.60 292	68.78 118	83.54 315	90.50 164	70.66 243	76.71 247	91.66 127	60.69 221	91.26 285	76.94 160	81.58 277	91.83 187

lupinMVS		81.39 161	80.27 169	84.76 119	87.35 235	70.21 86	85.55 258	86.41 299	62.85 372	81.32 152	88.61 228	61.68 200	92.24 242	78.41 142	90.26 123	91.83 187

WR-MVS		79.49 210	79.22 201	80.27 286	88.79 172	58.35 346	85.06 272	88.61 250	78.56 35	77.65 224	88.34 236	63.81 166	90.66 305	64.98 291	77.22 330	91.80 189

icg_test_0407_2		78.92 230	78.93 207	78.90 314	87.13 248	63.59 268	76.58 407	89.33 208	70.51 246	77.82 219	89.03 213	61.84 196	81.38 411	72.56 216	85.56 214	91.74 190

IMVS_0407		80.61 182	79.90 179	82.75 226	87.13 248	63.59 268	85.33 264	89.33 208	70.51 246	77.82 219	89.03 213	61.84 196	92.91 210	72.56 216	85.56 214	91.74 190

IMVS_0404		77.16 274	76.42 272	79.37 305	87.13 248	63.59 268	77.12 405	89.33 208	70.51 246	66.22 406	89.03 213	50.36 329	82.78 401	72.56 216	85.56 214	91.74 190

IMVS_0403		80.80 175	80.12 174	82.87 215	87.13 248	63.59 268	85.19 265	89.33 208	70.51 246	78.49 203	89.03 213	63.26 170	93.27 186	72.56 216	85.56 214	91.74 190

h-mvs33		83.15 123	82.19 134	86.02 76	90.56 105	70.85 79	88.15 166	89.16 223	76.02 100	84.67 87	91.39 141	61.54 203	95.50 73	82.71 96	75.48 359	91.72 194

UniMVSNet (Re)		81.60 154	81.11 150	83.09 201	88.38 188	64.41 249	87.60 183	93.02 50	78.42 37	78.56 201	88.16 242	69.78 89	93.26 187	69.58 250	76.49 341	91.60 195

UGNet		80.83 171	79.59 190	84.54 124	88.04 203	68.09 144	89.42 106	88.16 254	76.95 71	76.22 260	89.46 203	49.30 344	93.94 147	68.48 261	90.31 121	91.60 195
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		76.54 283	75.66 282	79.18 310	88.43 186	55.89 387	81.08 347	83.00 353	73.76 167	75.34 282	84.29 344	46.20 371	90.07 312	64.33 295	84.50 228	91.58 197

XVG-OURS		80.41 189	79.23 200	83.97 169	85.64 290	69.02 112	83.03 328	90.39 167	71.09 228	77.63 225	91.49 138	54.62 279	91.35 282	75.71 178	83.47 253	91.54 198

LCM-MVSNet-Re		77.05 275	76.94 258	77.36 346	87.20 245	51.60 426	80.06 366	80.46 385	75.20 126	67.69 383	86.72 281	62.48 185	88.98 334	63.44 301	89.25 141	91.51 199

DP-MVS Recon		83.11 126	82.09 137	86.15 70	94.44 23	70.92 76	88.79 135	92.20 98	70.53 245	79.17 189	91.03 155	64.12 162	96.03 55	68.39 263	90.14 125	91.50 200

PS-MVSNAJss		82.07 142	81.31 146	84.34 135	86.51 271	67.27 176	89.27 112	91.51 133	71.75 211	79.37 186	90.22 180	63.15 174	94.27 131	77.69 151	82.36 269	91.49 201

testing99		76.09 295	75.12 294	79.00 311	88.16 195	55.50 393	80.79 351	81.40 373	73.30 183	75.17 290	84.27 347	44.48 386	90.02 313	64.28 296	84.22 237	91.48 202

thisisatest0515		77.33 271	75.38 288	83.18 197	85.27 302	63.80 261	82.11 335	83.27 345	65.06 343	75.91 266	83.84 354	49.54 339	94.27 131	67.24 272	86.19 200	91.48 202

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 200	93.04 46	69.80 267	82.85 128	91.22 146	73.06 44	96.02 57	76.72 168	94.63 54	91.46 204

HQP_MVS		83.64 108	83.14 113	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 193	91.00 157	60.42 228	95.38 82	78.71 138	86.32 196	91.33 205

plane_prior5								92.44 82					95.38 82	78.71 138	86.32 196	91.33 205

GA-MVS		76.87 279	75.17 293	81.97 244	82.75 367	62.58 293	81.44 344	86.35 302	72.16 206	74.74 301	82.89 376	46.20 371	92.02 249	68.85 258	81.09 282	91.30 207

VPA-MVSNet		80.60 184	80.55 161	80.76 275	88.07 202	60.80 321	86.86 212	91.58 131	75.67 110	80.24 173	89.45 205	63.34 167	90.25 309	70.51 237	79.22 308	91.23 208

Effi-MVS+-dtu		80.03 201	78.57 213	84.42 129	85.13 307	68.74 121	88.77 136	88.10 256	74.99 131	74.97 298	83.49 365	57.27 255	93.36 182	73.53 201	80.88 285	91.18 209

v2v482		80.23 197	79.29 198	83.05 205	83.62 342	64.14 253	87.04 202	89.97 184	73.61 171	78.18 212	87.22 269	61.10 215	93.82 156	76.11 172	76.78 338	91.18 209

FE-MVS		77.78 259	75.68 280	84.08 154	88.09 201	66.00 200	83.13 323	87.79 268	68.42 302	78.01 216	85.23 324	45.50 380	95.12 92	59.11 343	85.83 211	91.11 211

Anonymous20231211		78.97 228	77.69 241	82.81 218	90.54 106	64.29 251	90.11 83	91.51 133	65.01 345	76.16 265	88.13 247	50.56 326	93.03 208	69.68 249	77.56 328	91.11 211

hse-mvs2		81.72 149	80.94 154	84.07 155	88.72 175	67.68 160	85.87 248	87.26 281	76.02 100	84.67 87	88.22 241	61.54 203	93.48 176	82.71 96	73.44 387	91.06 213

AUN-MVS		79.21 221	77.60 243	84.05 161	88.71 176	67.61 162	85.84 250	87.26 281	69.08 287	77.23 234	88.14 246	53.20 293	93.47 177	75.50 183	73.45 386	91.06 213

HQP4-MVS										77.24 233			95.11 94			91.03 215

HQP-MVS		82.61 134	82.02 139	84.37 132	89.33 144	66.98 183	89.17 116	92.19 99	76.41 86	77.23 234	90.23 179	60.17 231	95.11 94	77.47 153	85.99 205	91.03 215

RPSCF		73.23 334	71.46 338	78.54 322	82.50 373	59.85 333	82.18 334	82.84 358	58.96 408	71.15 349	89.41 207	45.48 381	84.77 386	58.82 347	71.83 399	91.02 217

LuminaMVS		80.68 180	79.62 189	83.83 173	85.07 309	68.01 148	86.99 205	88.83 237	70.36 251	81.38 151	87.99 249	50.11 332	92.51 229	79.02 132	86.89 188	90.97 218

test_djsdf		80.30 196	79.32 197	83.27 192	83.98 332	65.37 219	90.50 72	90.38 168	68.55 298	76.19 261	88.70 224	56.44 264	93.46 178	78.98 135	80.14 297	90.97 218

PCF-MVS	73.52 7	80.38 191	78.84 209	85.01 105	87.71 223	68.99 113	83.65 309	91.46 137	63.00 369	77.77 223	90.28 176	66.10 142	95.09 98	61.40 323	88.22 162	90.94 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 235	78.66 211	78.76 316	88.31 190	55.72 390	84.45 290	86.63 296	76.79 76	78.26 209	90.55 170	59.30 236	89.70 320	66.63 277	77.05 332	90.88 221

CPTT-MVS		83.73 104	83.33 112	84.92 111	93.28 53	70.86 78	92.09 41	90.38 168	68.75 295	79.57 181	92.83 97	60.60 226	93.04 207	80.92 112	91.56 102	90.86 222

fmvsm_s_conf0.5_n_7		83.34 118	84.03 96	81.28 260	85.73 288	65.13 224	85.40 263	89.90 187	74.96 134	82.13 138	93.89 69	66.65 131	87.92 350	86.56 53	91.05 109	90.80 223

tt0805		78.73 233	77.83 233	81.43 254	85.17 303	60.30 329	89.41 107	90.90 151	71.21 225	77.17 239	88.73 223	46.38 366	93.21 191	72.57 214	78.96 309	90.79 224

CLD-MVS		82.31 138	81.65 144	84.29 139	88.47 183	67.73 158	85.81 252	92.35 87	75.78 105	78.33 208	86.58 291	64.01 163	94.35 128	76.05 174	87.48 176	90.79 224
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 208	78.43 217	83.07 204	83.55 344	64.52 243	86.93 209	90.58 161	70.83 236	77.78 222	85.90 305	59.15 237	93.94 147	73.96 198	77.19 331	90.76 226

IterMVS-LS		80.06 200	79.38 194	82.11 240	85.89 284	63.20 282	86.79 215	89.34 207	74.19 156	75.45 277	86.72 281	66.62 132	92.39 234	72.58 213	76.86 335	90.75 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d28		73.62 325	73.53 315	73.90 385	88.20 193	47.41 445	78.06 396	79.37 399	74.29 154	73.98 312	84.29 344	44.67 383	83.54 395	51.47 397	87.39 177	90.74 228

EI-MVSNet		80.52 188	79.98 176	82.12 238	84.28 324	63.19 283	86.41 229	88.95 235	74.18 157	78.69 196	87.54 261	66.62 132	92.43 232	72.57 214	80.57 291	90.74 228

v1921920		79.22 220	78.03 226	82.80 219	83.30 349	63.94 258	86.80 214	90.33 172	69.91 265	77.48 227	85.53 316	58.44 243	93.75 162	73.60 200	76.85 336	90.71 230

QAPM		80.88 169	79.50 192	85.03 104	88.01 206	68.97 114	91.59 51	92.00 107	66.63 325	75.15 292	92.16 111	57.70 249	95.45 75	63.52 299	88.76 152	90.66 231

v144192		79.47 211	78.37 218	82.78 223	83.35 347	63.96 256	86.96 206	90.36 171	69.99 262	77.50 226	85.67 312	60.66 223	93.77 160	74.27 195	76.58 339	90.62 232

v1240		78.99 227	77.78 236	82.64 228	83.21 352	63.54 272	86.62 223	90.30 174	69.74 272	77.33 230	85.68 311	57.04 258	93.76 161	73.13 208	76.92 333	90.62 232

v1144		80.03 201	79.03 204	83.01 207	83.78 337	64.51 244	87.11 201	90.57 163	71.96 209	78.08 215	86.20 301	61.41 207	93.94 147	74.93 188	77.23 329	90.60 234

1112_ss		77.40 270	76.43 271	80.32 285	89.11 160	60.41 328	83.65 309	87.72 271	62.13 382	73.05 324	86.72 281	62.58 184	89.97 314	62.11 317	80.80 287	90.59 235

CP-MVSNet		78.22 245	78.34 219	77.84 337	87.83 214	54.54 403	87.94 173	91.17 143	77.65 46	73.48 319	88.49 232	62.24 191	88.43 344	62.19 314	74.07 378	90.55 236

testing222		74.04 320	72.66 326	78.19 329	87.89 210	55.36 394	81.06 348	79.20 402	71.30 223	74.65 304	83.57 364	39.11 420	88.67 341	51.43 399	85.75 212	90.53 237

PS-CasMVS		78.01 254	78.09 225	77.77 339	87.71 223	54.39 405	88.02 169	91.22 140	77.50 54	73.26 321	88.64 227	60.73 219	88.41 345	61.88 318	73.88 382	90.53 237

CDS-MVSNet		79.07 225	77.70 240	83.17 198	87.60 230	68.23 141	84.40 293	86.20 304	67.49 311	76.36 257	86.54 293	61.54 203	90.79 300	61.86 319	87.33 178	90.49 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 231	77.51 247	83.03 206	87.80 215	67.79 157	84.72 279	85.05 320	67.63 308	76.75 246	87.70 254	62.25 190	90.82 299	58.53 350	87.13 183	90.49 239

PEN-MVS		77.73 260	77.69 241	77.84 337	87.07 256	53.91 408	87.91 175	91.18 142	77.56 51	73.14 323	88.82 222	61.23 212	89.17 330	59.95 334	72.37 393	90.43 241

Test_1112_low_res		76.40 290	75.44 285	79.27 307	89.28 149	58.09 349	81.69 339	87.07 285	59.53 403	72.48 332	86.67 286	61.30 210	89.33 325	60.81 329	80.15 296	90.41 242

HY-MVS	69.67 12	77.95 255	77.15 253	80.36 283	87.57 234	60.21 331	83.37 318	87.78 269	66.11 329	75.37 281	87.06 276	63.27 169	90.48 307	61.38 324	82.43 268	90.40 243

sc_t1		72.19 346	69.51 357	80.23 287	84.81 313	61.09 316	84.68 280	80.22 391	60.70 392	71.27 346	83.58 363	36.59 431	89.24 328	60.41 330	63.31 431	90.37 244

CHOSEN 1792x2688		77.63 266	75.69 279	83.44 185	89.98 122	68.58 129	78.70 386	87.50 275	56.38 428	75.80 269	86.84 277	58.67 241	91.40 281	61.58 322	85.75 212	90.34 245

SDMVSNet		80.38 191	80.18 170	80.99 269	89.03 161	64.94 233	80.45 360	89.40 205	75.19 127	76.61 251	89.98 182	60.61 225	87.69 354	76.83 164	83.55 250	90.33 246

sd_testset		77.70 263	77.40 248	78.60 319	89.03 161	60.02 332	79.00 381	85.83 310	75.19 127	76.61 251	89.98 182	54.81 272	85.46 379	62.63 310	83.55 250	90.33 246

114514_t		80.68 180	79.51 191	84.20 146	94.09 42	67.27 176	89.64 96	91.11 146	58.75 412	74.08 311	90.72 162	58.10 245	95.04 99	69.70 248	89.42 140	90.30 248

eth_miper_zixun_eth		77.92 256	76.69 266	81.61 251	83.00 360	61.98 305	83.15 322	89.20 222	69.52 274	74.86 300	84.35 343	61.76 199	92.56 225	71.50 227	72.89 391	90.28 249

PVSNet_Blended_VisFu		82.62 133	81.83 143	84.96 107	90.80 101	69.76 97	88.74 140	91.70 124	69.39 275	78.96 191	88.46 233	65.47 150	94.87 107	74.42 193	88.57 155	90.24 250

MVS_111021_LR		82.61 134	82.11 135	84.11 148	88.82 166	71.58 57	85.15 268	86.16 305	74.69 142	80.47 171	91.04 153	62.29 189	90.55 306	80.33 120	90.08 127	90.20 251

MSLP-MVS++		85.43 75	85.76 69	84.45 128	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 127	92.94 209	80.36 119	94.35 63	90.16 252

mvs_tets		79.13 223	77.77 237	83.22 196	84.70 316	66.37 192	89.17 116	90.19 178	69.38 276	75.40 279	89.46 203	44.17 389	93.15 198	76.78 167	80.70 289	90.14 253

BH-RMVSNet		79.61 206	78.44 216	83.14 199	89.38 143	65.93 202	84.95 275	87.15 284	73.56 173	78.19 211	89.79 190	56.67 262	93.36 182	59.53 339	86.74 190	90.13 254

c3_l		78.75 232	77.91 229	81.26 261	82.89 365	61.56 311	84.09 301	89.13 226	69.97 263	75.56 272	84.29 344	66.36 137	92.09 246	73.47 203	75.48 359	90.12 255

v7n		78.97 228	77.58 244	83.14 199	83.45 346	65.51 214	88.32 159	91.21 141	73.69 169	72.41 333	86.32 299	57.93 246	93.81 157	69.18 253	75.65 355	90.11 256

jajsoiax		79.29 219	77.96 227	83.27 192	84.68 317	66.57 190	89.25 113	90.16 179	69.20 284	75.46 276	89.49 200	45.75 377	93.13 200	76.84 163	80.80 287	90.11 256

v148		78.72 234	77.80 235	81.47 253	82.73 368	61.96 306	86.30 236	88.08 257	73.26 184	76.18 262	85.47 318	62.46 186	92.36 236	71.92 224	73.82 383	90.09 258

GBi-Net		78.40 241	77.40 248	81.40 256	87.60 230	63.01 285	88.39 154	89.28 214	71.63 213	75.34 282	87.28 265	54.80 273	91.11 289	62.72 306	79.57 301	90.09 258

test1		78.40 241	77.40 248	81.40 256	87.60 230	63.01 285	88.39 154	89.28 214	71.63 213	75.34 282	87.28 265	54.80 273	91.11 289	62.72 306	79.57 301	90.09 258

FMVSNet1		77.44 268	76.12 276	81.40 256	86.81 261	63.01 285	88.39 154	89.28 214	70.49 250	74.39 308	87.28 265	49.06 348	91.11 289	60.91 327	78.52 312	90.09 258

WR-MVS_H		78.51 240	78.49 214	78.56 321	88.02 204	56.38 380	88.43 151	92.67 72	77.14 65	73.89 313	87.55 260	66.25 139	89.24 328	58.92 345	73.55 385	90.06 262

DTE-MVSNet		76.99 276	76.80 261	77.54 345	86.24 275	53.06 417	87.52 185	90.66 159	77.08 69	72.50 331	88.67 226	60.48 227	89.52 322	57.33 362	70.74 405	90.05 263

v8		79.97 203	79.02 205	82.80 219	84.09 329	64.50 246	87.96 171	90.29 175	74.13 159	75.24 289	86.81 278	62.88 181	93.89 155	74.39 194	75.40 364	90.00 264

thres600view7		76.50 285	75.44 285	79.68 299	89.40 141	57.16 366	85.53 260	83.23 346	73.79 166	76.26 259	87.09 274	51.89 310	91.89 255	48.05 422	83.72 247	90.00 264

thres400		76.50 285	75.37 289	79.86 294	89.13 156	57.65 360	85.17 266	83.60 338	73.41 179	76.45 254	86.39 297	52.12 302	91.95 252	48.33 417	83.75 244	90.00 264

cl22		78.07 251	77.01 255	81.23 262	82.37 377	61.83 308	83.55 313	87.98 261	68.96 292	75.06 295	83.87 352	61.40 208	91.88 256	73.53 201	76.39 344	89.98 267

OPM-MVS		83.50 113	82.95 118	85.14 98	88.79 172	70.95 74	89.13 121	91.52 132	77.55 52	80.96 160	91.75 124	60.71 220	94.50 124	79.67 127	86.51 194	89.97 268
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 299	73.83 312	81.30 259	83.26 350	61.79 309	82.57 331	80.65 380	66.81 316	66.88 394	83.42 366	57.86 248	92.19 243	63.47 300	79.57 301	89.91 269

v10		79.74 205	78.67 210	82.97 211	84.06 330	64.95 230	87.88 177	90.62 160	73.11 189	75.11 293	86.56 292	61.46 206	94.05 143	73.68 199	75.55 357	89.90 270

MVSTER		79.01 226	77.88 232	82.38 234	83.07 357	64.80 238	84.08 302	88.95 235	69.01 291	78.69 196	87.17 272	54.70 277	92.43 232	74.69 189	80.57 291	89.89 271

ACMP	74.13 6	81.51 160	80.57 160	84.36 133	89.42 139	68.69 126	89.97 85	91.50 136	74.46 148	75.04 296	90.41 172	53.82 286	94.54 121	77.56 152	82.91 261	89.86 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 141	81.27 147	84.50 125	89.23 152	68.76 119	90.22 81	91.94 111	75.37 118	76.64 249	91.51 136	54.29 280	94.91 102	78.44 140	83.78 241	89.83 273

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 111	75.37 118	76.64 249	91.51 136	54.29 280	94.91 102	78.44 140	83.78 241	89.83 273

V42		79.38 217	78.24 222	82.83 216	81.10 396	65.50 215	85.55 258	89.82 188	71.57 217	78.21 210	86.12 303	60.66 223	93.18 197	75.64 179	75.46 361	89.81 275

MAR-MVS		81.84 147	80.70 157	85.27 94	91.32 89	71.53 58	89.82 88	90.92 150	69.77 269	78.50 202	86.21 300	62.36 188	94.52 123	65.36 287	92.05 93	89.77 276
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		77.72 261	76.76 263	80.58 279	82.48 375	60.48 326	83.09 324	87.86 266	69.22 282	74.38 309	85.24 323	62.10 193	91.53 274	71.09 230	75.40 364	89.74 277

cl____		77.72 261	76.76 263	80.58 279	82.49 374	60.48 326	83.09 324	87.87 265	69.22 282	74.38 309	85.22 325	62.10 193	91.53 274	71.09 230	75.41 363	89.73 278

miper_ehance_all_eth		78.59 238	77.76 238	81.08 267	82.66 370	61.56 311	83.65 309	89.15 224	68.87 293	75.55 273	83.79 356	66.49 135	92.03 247	73.25 206	76.39 344	89.64 279

anonymousdsp		78.60 237	77.15 253	82.98 210	80.51 402	67.08 181	87.24 198	89.53 201	65.66 336	75.16 291	87.19 271	52.52 295	92.25 241	77.17 157	79.34 306	89.61 280

FMVSNet2		78.20 247	77.21 252	81.20 263	87.60 230	62.89 291	87.47 187	89.02 230	71.63 213	75.29 288	87.28 265	54.80 273	91.10 292	62.38 311	79.38 305	89.61 280

baseline1		76.98 277	76.75 265	77.66 340	88.13 198	55.66 391	85.12 269	81.89 366	73.04 191	76.79 244	88.90 219	62.43 187	87.78 353	63.30 303	71.18 403	89.55 282

ETVMVS		72.25 345	71.05 344	75.84 358	87.77 220	51.91 422	79.39 374	74.98 428	69.26 280	73.71 315	82.95 374	40.82 412	86.14 369	46.17 430	84.43 233	89.47 283

FMVSNet3		77.88 257	76.85 260	80.97 271	86.84 260	62.36 299	86.52 226	88.77 240	71.13 226	75.34 282	86.66 287	54.07 283	91.10 292	62.72 306	79.57 301	89.45 284

SD_0403		74.65 313	74.77 297	74.29 380	86.20 277	47.42 444	83.71 307	85.12 317	69.30 278	68.50 378	87.95 250	59.40 235	86.05 370	49.38 411	83.35 255	89.40 285

miper_enhance_ethall		77.87 258	76.86 259	80.92 272	81.65 384	61.38 313	82.68 329	88.98 232	65.52 338	75.47 274	82.30 385	65.76 149	92.00 250	72.95 209	76.39 344	89.39 286

testing11		75.14 309	74.01 307	78.53 323	88.16 195	56.38 380	80.74 354	80.42 387	70.67 240	72.69 330	83.72 359	43.61 393	89.86 315	62.29 313	83.76 243	89.36 287

cascas		76.72 282	74.64 298	82.99 208	85.78 287	65.88 204	82.33 332	89.21 221	60.85 391	72.74 327	81.02 396	47.28 357	93.75 162	67.48 269	85.02 220	89.34 288

Fast-Effi-MVS+-dtu		78.02 253	76.49 269	82.62 229	83.16 356	66.96 185	86.94 208	87.45 277	72.45 198	71.49 345	84.17 349	54.79 276	91.58 266	67.61 267	80.31 294	89.30 289

IB-MVS	68.01 15	75.85 298	73.36 318	83.31 190	84.76 315	66.03 197	83.38 317	85.06 319	70.21 258	69.40 368	81.05 395	45.76 376	94.66 118	65.10 290	75.49 358	89.25 290
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		76.50 285	75.55 284	79.33 306	89.52 133	56.99 369	85.83 251	83.23 346	73.94 162	76.32 258	87.12 273	51.89 310	91.95 252	48.33 417	83.75 244	89.07 291

tfpn200view9		76.42 289	75.37 289	79.55 304	89.13 156	57.65 360	85.17 266	83.60 338	73.41 179	76.45 254	86.39 297	52.12 302	91.95 252	48.33 417	83.75 244	89.07 291

xiu_mvs_v1_base_debu		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 255	88.77 240	69.06 288	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

xiu_mvs_v1_base		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 255	88.77 240	69.06 288	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

xiu_mvs_v1_base_debi		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 255	88.77 240	69.06 288	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

EPNet_dtu		75.46 303	74.86 295	77.23 349	82.57 372	54.60 402	86.89 210	83.09 350	71.64 212	66.25 405	85.86 307	55.99 265	88.04 349	54.92 379	86.55 193	89.05 296
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 273	76.68 267	78.93 313	84.22 326	58.62 344	86.41 229	88.36 253	71.37 220	73.31 320	88.01 248	61.22 213	89.15 331	64.24 297	73.01 390	89.03 297

PVSNet_Blended		80.98 167	80.34 166	82.90 213	88.85 163	65.40 216	84.43 291	92.00 107	67.62 309	78.11 213	85.05 330	66.02 145	94.27 131	71.52 225	89.50 138	89.01 298

PAPM		77.68 264	76.40 273	81.51 252	87.29 244	61.85 307	83.78 305	89.59 199	64.74 347	71.23 347	88.70 224	62.59 183	93.66 165	52.66 391	87.03 185	89.01 298

WTY-MVS		75.65 300	75.68 280	75.57 362	86.40 273	56.82 371	77.92 399	82.40 361	65.10 342	76.18 262	87.72 253	63.13 177	80.90 414	60.31 332	81.96 273	89.00 300

无先验							87.48 186	88.98 232	60.00 398				94.12 140	67.28 271		88.97 301

GSMVS																88.96 302

sam_mvs1												51.32 317				88.96 302

SCA		74.22 317	72.33 330	79.91 293	84.05 331	62.17 303	79.96 369	79.29 401	66.30 328	72.38 334	80.13 408	51.95 308	88.60 342	59.25 341	77.67 327	88.96 302

miper_lstm_enhance		74.11 319	73.11 321	77.13 350	80.11 406	59.62 336	72.23 431	86.92 290	66.76 318	70.40 353	82.92 375	56.93 259	82.92 400	69.06 255	72.63 392	88.87 305

ACMM	73.20 8	80.78 179	79.84 181	83.58 181	89.31 147	68.37 134	89.99 84	91.60 130	70.28 255	77.25 232	89.66 194	53.37 291	93.53 173	74.24 196	82.85 262	88.85 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 312	73.39 316	78.61 318	81.38 391	57.48 363	86.64 222	87.95 263	64.99 346	70.18 356	86.61 288	50.43 328	89.52 322	62.12 316	70.18 408	88.83 307

原ACMM1				84.35 134	93.01 66	68.79 117		92.44 82	63.96 361	81.09 157	91.57 134	66.06 144	95.45 75	67.19 273	94.82 50	88.81 308

CNLPA		78.08 250	76.79 262	81.97 244	90.40 109	71.07 70	87.59 184	84.55 325	66.03 332	72.38 334	89.64 195	57.56 251	86.04 371	59.61 338	83.35 255	88.79 309

UWE-MVS		72.13 347	71.49 337	74.03 383	86.66 267	47.70 442	81.40 345	76.89 421	63.60 364	75.59 271	84.22 348	39.94 415	85.62 376	48.98 414	86.13 202	88.77 310

UBG		73.08 336	72.27 331	75.51 364	88.02 204	51.29 430	78.35 393	77.38 416	65.52 338	73.87 314	82.36 383	45.55 378	86.48 366	55.02 378	84.39 234	88.75 311

K. test v3		71.19 352	68.51 364	79.21 309	83.04 359	57.78 359	84.35 294	76.91 420	72.90 194	62.99 426	82.86 377	39.27 417	91.09 294	61.65 321	52.66 453	88.75 311

旧先验1					91.96 80	65.79 208		86.37 301			93.08 92	69.31 97			92.74 80	88.74 313

PatchmatchNet		73.12 335	71.33 341	78.49 325	83.18 354	60.85 320	79.63 371	78.57 406	64.13 354	71.73 341	79.81 413	51.20 319	85.97 372	57.40 361	76.36 349	88.66 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 329	71.26 343	79.70 298	85.08 308	57.89 355	85.57 254	83.56 340	71.03 232	65.66 408	85.88 306	42.10 403	92.57 224	59.11 343	63.34 430	88.65 315

SSC-MVS3.2		73.35 332	73.39 316	73.23 389	85.30 301	49.01 440	74.58 424	81.57 370	75.21 125	73.68 316	85.58 315	52.53 294	82.05 406	54.33 383	77.69 326	88.63 316

PS-MVSNAJ		81.69 151	81.02 152	83.70 177	89.51 134	68.21 142	84.28 295	90.09 181	70.79 237	81.26 156	85.62 314	63.15 174	94.29 129	75.62 180	88.87 149	88.59 317

xiu_mvs_v2_base		81.69 151	81.05 151	83.60 179	89.15 155	68.03 147	84.46 289	90.02 182	70.67 240	81.30 155	86.53 294	63.17 173	94.19 138	75.60 181	88.54 156	88.57 318

MonoMVSNet		76.49 288	75.80 277	78.58 320	81.55 387	58.45 345	86.36 234	86.22 303	74.87 139	74.73 302	83.73 358	51.79 313	88.73 339	70.78 232	72.15 396	88.55 319

CostFormer		75.24 308	73.90 310	79.27 307	82.65 371	58.27 348	80.80 350	82.73 359	61.57 386	75.33 286	83.13 371	55.52 268	91.07 295	64.98 291	78.34 319	88.45 320

lessismore_v0				78.97 312	81.01 397	57.15 367		65.99 455		61.16 432	82.82 378	39.12 419	91.34 283	59.67 337	46.92 460	88.43 321

OpenMVS	72.83 10	79.77 204	78.33 220	84.09 153	85.17 303	69.91 93	90.57 69	90.97 149	66.70 319	72.17 337	91.91 116	54.70 277	93.96 144	61.81 320	90.95 112	88.41 322

reproduce_monomvs		75.40 306	74.38 304	78.46 326	83.92 334	57.80 358	83.78 305	86.94 288	73.47 177	72.25 336	84.47 338	38.74 421	89.27 327	75.32 185	70.53 406	88.31 323

VortexMVS		78.57 239	77.89 231	80.59 278	85.89 284	62.76 292	85.61 253	89.62 198	72.06 207	74.99 297	85.38 320	55.94 266	90.77 303	74.99 187	76.58 339	88.23 324

OurMVSNet-221017-0		74.26 316	72.42 329	79.80 296	83.76 338	59.59 337	85.92 247	86.64 295	66.39 327	66.96 393	87.58 257	39.46 416	91.60 265	65.76 285	69.27 411	88.22 325

LS3D		76.95 278	74.82 296	83.37 189	90.45 107	67.36 172	89.15 120	86.94 288	61.87 385	69.52 367	90.61 168	51.71 314	94.53 122	46.38 429	86.71 191	88.21 326

WBMVS		73.43 328	72.81 324	75.28 368	87.91 209	50.99 432	78.59 389	81.31 375	65.51 340	74.47 307	84.83 333	46.39 365	86.68 363	58.41 351	77.86 322	88.17 327

XVG-ACMP-BASELINE		76.11 294	74.27 306	81.62 249	83.20 353	64.67 240	83.60 312	89.75 193	69.75 270	71.85 340	87.09 274	32.78 440	92.11 245	69.99 245	80.43 293	88.09 328

tpm2		73.26 333	71.46 338	78.63 317	83.34 348	56.71 374	80.65 356	80.40 388	56.63 427	73.55 318	82.02 390	51.80 312	91.24 286	56.35 373	78.42 317	87.95 329

MDTV_nov1_ep13_2view						37.79 469	75.16 418		55.10 432	66.53 400		49.34 343		53.98 384		87.94 330

Patchmatch-test		64.82 405	63.24 406	69.57 416	79.42 418	49.82 438	63.49 464	69.05 448	51.98 442	59.95 438	80.13 408	50.91 321	70.98 457	40.66 447	73.57 384	87.90 331

PLC	70.83 11	78.05 252	76.37 274	83.08 203	91.88 83	67.80 156	88.19 163	89.46 203	64.33 353	69.87 364	88.38 235	53.66 287	93.58 166	58.86 346	82.73 264	87.86 332
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 343	71.71 335	74.35 379	82.19 378	52.00 420	79.22 377	77.29 417	64.56 349	72.95 326	83.68 361	51.35 316	83.26 399	58.33 353	75.80 353	87.81 333

Patchmatch-RL test		70.24 365	67.78 378	77.61 342	77.43 427	59.57 338	71.16 435	70.33 442	62.94 371	68.65 375	72.77 448	50.62 325	85.49 378	69.58 250	66.58 421	87.77 334

F-COLMAP		76.38 291	74.33 305	82.50 232	89.28 149	66.95 186	88.41 153	89.03 229	64.05 358	66.83 395	88.61 228	46.78 363	92.89 211	57.48 359	78.55 311	87.67 335

Baseline_NR-MVSNet		78.15 249	78.33 220	77.61 342	85.79 286	56.21 384	86.78 216	85.76 311	73.60 172	77.93 218	87.57 258	65.02 154	88.99 333	67.14 274	75.33 366	87.63 336

CL-MVSNet_self_test		72.37 343	71.46 338	75.09 370	79.49 417	53.53 410	80.76 353	85.01 321	69.12 286	70.51 351	82.05 389	57.92 247	84.13 390	52.27 393	66.00 424	87.60 337

ACMH+	68.96 14	76.01 296	74.01 307	82.03 242	88.60 179	65.31 220	88.86 130	87.55 273	70.25 257	67.75 382	87.47 263	41.27 408	93.19 196	58.37 352	75.94 352	87.60 337

1314		76.53 284	75.30 291	80.21 288	83.93 333	62.32 301	84.66 281	88.81 238	60.23 396	70.16 358	84.07 351	55.30 270	90.73 304	67.37 270	83.21 258	87.59 339

API-MVS		81.99 144	81.23 148	84.26 144	90.94 97	70.18 91	91.10 63	89.32 212	71.51 218	78.66 198	88.28 238	65.26 151	95.10 97	64.74 293	91.23 107	87.51 340

AdaColmap		80.58 187	79.42 193	84.06 158	93.09 63	68.91 115	89.36 110	88.97 234	69.27 279	75.70 270	89.69 192	57.20 257	95.77 64	63.06 304	88.41 160	87.50 341

PVSNet_BlendedMVS		80.60 184	80.02 175	82.36 235	88.85 163	65.40 216	86.16 241	92.00 107	69.34 277	78.11 213	86.09 304	66.02 145	94.27 131	71.52 225	82.06 272	87.39 342

sss		73.60 326	73.64 314	73.51 388	82.80 366	55.01 399	76.12 409	81.69 369	62.47 378	74.68 303	85.85 308	57.32 254	78.11 425	60.86 328	80.93 283	87.39 342

IterMVS-SCA-FT		75.43 304	73.87 311	80.11 290	82.69 369	64.85 237	81.57 341	83.47 342	69.16 285	70.49 352	84.15 350	51.95 308	88.15 347	69.23 252	72.14 397	87.34 344

PVSNet	64.34 18	72.08 348	70.87 347	75.69 360	86.21 276	56.44 378	74.37 425	80.73 379	62.06 383	70.17 357	82.23 387	42.86 397	83.31 398	54.77 380	84.45 232	87.32 345

tt0320-xc		70.11 367	67.45 384	78.07 333	85.33 300	59.51 339	83.28 319	78.96 404	58.77 410	67.10 392	80.28 406	36.73 430	87.42 357	56.83 369	59.77 442	87.29 346

新几何1				83.42 186	93.13 60	70.71 80		85.48 314	57.43 423	81.80 144	91.98 115	63.28 168	92.27 240	64.60 294	92.99 76	87.27 347

TR-MVS		77.44 268	76.18 275	81.20 263	88.24 192	63.24 280	84.61 284	86.40 300	67.55 310	77.81 221	86.48 295	54.10 282	93.15 198	57.75 358	82.72 265	87.20 348

TransMVSNet (Re)		75.39 307	74.56 300	77.86 336	85.50 296	57.10 368	86.78 216	86.09 307	72.17 205	71.53 344	87.34 264	63.01 178	89.31 326	56.84 368	61.83 435	87.17 349

ACMH	67.68 16	75.89 297	73.93 309	81.77 247	88.71 176	66.61 189	88.62 145	89.01 231	69.81 266	66.78 396	86.70 285	41.95 405	91.51 276	55.64 375	78.14 320	87.17 349
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 377	67.59 382	72.46 400	74.29 441	45.45 450	77.93 398	87.00 286	63.12 366	63.99 421	78.99 422	42.32 400	84.77 386	56.55 372	64.09 429	87.16 351

EPMVS		69.02 376	68.16 368	71.59 404	79.61 415	49.80 439	77.40 402	66.93 453	62.82 374	70.01 359	79.05 418	45.79 375	77.86 427	56.58 371	75.26 368	87.13 352

CR-MVSNet		73.37 329	71.27 342	79.67 300	81.32 394	65.19 222	75.92 411	80.30 389	59.92 399	72.73 328	81.19 393	52.50 296	86.69 362	59.84 335	77.71 324	87.11 353

RPMNet		73.51 327	70.49 350	82.58 231	81.32 394	65.19 222	75.92 411	92.27 89	57.60 421	72.73 328	76.45 436	52.30 299	95.43 77	48.14 421	77.71 324	87.11 353

test_vis1_n_1920		75.52 302	75.78 278	74.75 376	79.84 410	57.44 364	83.26 320	85.52 313	62.83 373	79.34 188	86.17 302	45.10 382	79.71 418	78.75 137	81.21 281	87.10 355

tt0320		70.49 363	68.03 371	77.89 335	84.78 314	59.12 341	83.55 313	80.44 386	58.13 416	67.43 388	80.41 404	39.26 418	87.54 356	55.12 377	63.18 432	86.99 356

XXY-MVS		75.41 305	75.56 283	74.96 371	83.59 343	57.82 357	80.59 357	83.87 336	66.54 326	74.93 299	88.31 237	63.24 171	80.09 417	62.16 315	76.85 336	86.97 357

tpmrst		72.39 341	72.13 332	73.18 393	80.54 401	49.91 437	79.91 370	79.08 403	63.11 367	71.69 342	79.95 410	55.32 269	82.77 402	65.66 286	73.89 381	86.87 358

thres200		75.55 301	74.47 302	78.82 315	87.78 218	57.85 356	83.07 326	83.51 341	72.44 200	75.84 268	84.42 339	52.08 305	91.75 260	47.41 424	83.64 249	86.86 359

ITE_SJBPF				78.22 328	81.77 383	60.57 324		83.30 344	69.25 281	67.54 384	87.20 270	36.33 433	87.28 359	54.34 382	74.62 375	86.80 360

test222					91.50 86	68.26 137	84.16 299	83.20 349	54.63 434	79.74 178	91.63 130	58.97 238			91.42 103	86.77 361

MIMVSNet		70.69 359	69.30 358	74.88 373	84.52 321	56.35 382	75.87 413	79.42 398	64.59 348	67.76 381	82.41 382	41.10 409	81.54 409	46.64 428	81.34 278	86.75 362

BH-untuned		79.47 211	78.60 212	82.05 241	89.19 154	65.91 203	86.07 243	88.52 251	72.18 204	75.42 278	87.69 255	61.15 214	93.54 172	60.38 331	86.83 189	86.70 363

LTVRE_ROB	69.57 13	76.25 292	74.54 301	81.41 255	88.60 179	64.38 250	79.24 376	89.12 227	70.76 239	69.79 366	87.86 251	49.09 347	93.20 194	56.21 374	80.16 295	86.65 364
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				79.97 292	90.90 98	64.21 252		84.71 322	59.27 405	85.40 75	92.91 94	62.02 195	89.08 332	68.95 256	91.37 105	86.63 365

MIMVSNet1		68.58 380	66.78 390	73.98 384	80.07 407	51.82 424	80.77 352	84.37 326	64.40 351	59.75 439	82.16 388	36.47 432	83.63 394	42.73 442	70.33 407	86.48 366

tfpnnormal		74.39 314	73.16 320	78.08 332	86.10 282	58.05 350	84.65 283	87.53 274	70.32 254	71.22 348	85.63 313	54.97 271	89.86 315	43.03 441	75.02 371	86.32 367

D2MVS		74.82 311	73.21 319	79.64 301	79.81 411	62.56 295	80.34 362	87.35 278	64.37 352	68.86 373	82.66 380	46.37 367	90.10 311	67.91 265	81.24 280	86.25 368

tpm cat1		70.57 360	68.31 366	77.35 347	82.41 376	57.95 354	78.08 395	80.22 391	52.04 440	68.54 377	77.66 431	52.00 307	87.84 352	51.77 394	72.07 398	86.25 368

CVMVSNet		72.99 338	72.58 327	74.25 381	84.28 324	50.85 433	86.41 229	83.45 343	44.56 453	73.23 322	87.54 261	49.38 342	85.70 374	65.90 283	78.44 314	86.19 370

AllTest		70.96 355	68.09 370	79.58 302	85.15 305	63.62 264	84.58 285	79.83 394	62.31 379	60.32 436	86.73 279	32.02 441	88.96 336	50.28 405	71.57 401	86.15 371

TestCases				79.58 302	85.15 305	63.62 264		79.83 394	62.31 379	60.32 436	86.73 279	32.02 441	88.96 336	50.28 405	71.57 401	86.15 371

test-LLR		72.94 339	72.43 328	74.48 377	81.35 392	58.04 351	78.38 390	77.46 413	66.66 320	69.95 362	79.00 420	48.06 353	79.24 419	66.13 279	84.83 223	86.15 371

test-mter		71.41 351	70.39 353	74.48 377	81.35 392	58.04 351	78.38 390	77.46 413	60.32 395	69.95 362	79.00 420	36.08 434	79.24 419	66.13 279	84.83 223	86.15 371

IterMVS		74.29 315	72.94 323	78.35 327	81.53 388	63.49 274	81.58 340	82.49 360	68.06 306	69.99 361	83.69 360	51.66 315	85.54 377	65.85 284	71.64 400	86.01 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 281	74.57 299	83.42 186	93.29 52	69.46 104	88.55 149	83.70 337	63.98 360	70.20 355	88.89 220	54.01 285	94.80 111	46.66 426	81.88 275	86.01 375

ppachtmachnet_test		70.04 368	67.34 386	78.14 330	79.80 412	61.13 314	79.19 378	80.59 381	59.16 406	65.27 411	79.29 417	46.75 364	87.29 358	49.33 412	66.72 419	86.00 377

mmtdpeth		74.16 318	73.01 322	77.60 344	83.72 339	61.13 314	85.10 270	85.10 318	72.06 207	77.21 238	80.33 405	43.84 391	85.75 373	77.14 158	52.61 454	85.91 378

test_fmvs1_n		70.86 357	70.24 354	72.73 397	72.51 455	55.28 396	81.27 346	79.71 396	51.49 444	78.73 195	84.87 332	27.54 450	77.02 430	76.06 173	79.97 299	85.88 379

Patchmtry		70.74 358	69.16 361	75.49 365	80.72 398	54.07 407	74.94 422	80.30 389	58.34 413	70.01 359	81.19 393	52.50 296	86.54 364	53.37 388	71.09 404	85.87 380

WB-MVSnew		71.96 349	71.65 336	72.89 395	84.67 320	51.88 423	82.29 333	77.57 412	62.31 379	73.67 317	83.00 373	53.49 290	81.10 413	45.75 433	82.13 271	85.70 381

test_fmvs2		68.35 384	67.48 383	70.98 412	69.50 458	51.95 421	80.05 367	76.38 423	49.33 447	74.65 304	84.38 341	23.30 459	75.40 447	74.51 192	75.17 370	85.60 382

ambc				75.24 369	73.16 450	50.51 435	63.05 465	87.47 276		64.28 417	77.81 430	17.80 465	89.73 319	57.88 357	60.64 439	85.49 383

mvs5depth		69.45 373	67.45 384	75.46 366	73.93 442	55.83 388	79.19 378	83.23 346	66.89 315	71.63 343	83.32 367	33.69 439	85.09 382	59.81 336	55.34 450	85.46 384

UnsupCasMVSNet_eth		67.33 389	65.99 393	71.37 406	73.48 447	51.47 428	75.16 418	85.19 316	65.20 341	60.78 433	80.93 400	42.35 399	77.20 429	57.12 363	53.69 452	85.44 385

PatchT		68.46 383	67.85 374	70.29 414	80.70 399	43.93 458	72.47 430	74.88 429	60.15 397	70.55 350	76.57 435	49.94 335	81.59 408	50.58 401	74.83 373	85.34 386

Anonymous20240521		68.80 378	67.22 387	73.55 387	74.33 440	54.11 406	83.18 321	85.61 312	58.15 415	61.68 430	80.94 398	30.71 446	81.27 412	57.00 366	73.34 389	85.28 387

test_cas_vis1_n_1920		73.76 324	73.74 313	73.81 386	75.90 432	59.77 334	80.51 358	82.40 361	58.30 414	81.62 149	85.69 310	44.35 388	76.41 436	76.29 169	78.61 310	85.23 388

ADS-MVSNet2		66.20 401	63.33 405	74.82 374	79.92 408	58.75 343	67.55 450	75.19 427	53.37 437	65.25 412	75.86 439	42.32 400	80.53 416	41.57 445	68.91 413	85.18 389

ADS-MVSNet		64.36 406	62.88 409	68.78 422	79.92 408	47.17 446	67.55 450	71.18 441	53.37 437	65.25 412	75.86 439	42.32 400	73.99 453	41.57 445	68.91 413	85.18 389

FMVSNet5		69.50 372	67.96 372	74.15 382	82.97 363	55.35 395	80.01 368	82.12 364	62.56 377	63.02 424	81.53 392	36.92 429	81.92 407	48.42 416	74.06 379	85.17 391

pmmvs5		71.55 350	70.20 355	75.61 361	77.83 425	56.39 379	81.74 338	80.89 376	57.76 419	67.46 386	84.49 337	49.26 345	85.32 381	57.08 364	75.29 367	85.11 392

testing3		68.56 381	67.67 380	71.22 410	87.33 240	42.87 460	83.06 327	71.54 440	70.36 251	69.08 372	84.38 341	30.33 447	85.69 375	37.50 453	75.45 362	85.09 393

UWE-MVS-28		65.32 402	64.93 396	66.49 431	78.70 422	38.55 468	77.86 400	64.39 460	62.00 384	64.13 419	83.60 362	41.44 406	76.00 440	31.39 460	80.89 284	84.92 394

CMPMVS	51.72 21	70.19 366	68.16 368	76.28 355	73.15 451	57.55 362	79.47 373	83.92 334	48.02 449	56.48 449	84.81 334	43.13 395	86.42 367	62.67 309	81.81 276	84.89 395
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 395	66.53 391	67.08 430	75.62 436	41.69 465	75.93 410	76.50 422	66.11 329	65.20 414	86.59 289	35.72 435	74.71 449	43.71 438	73.38 388	84.84 396

MSDG		73.36 331	70.99 345	80.49 281	84.51 322	65.80 207	80.71 355	86.13 306	65.70 335	65.46 409	83.74 357	44.60 384	90.91 298	51.13 400	76.89 334	84.74 397

pmmvs4		74.03 322	71.91 333	80.39 282	81.96 380	68.32 135	81.45 343	82.14 363	59.32 404	69.87 364	85.13 327	52.40 298	88.13 348	60.21 333	74.74 374	84.73 398

gg-mvs-nofinetune		69.95 369	67.96 372	75.94 357	83.07 357	54.51 404	77.23 404	70.29 443	63.11 367	70.32 354	62.33 457	43.62 392	88.69 340	53.88 385	87.76 171	84.62 399

test_fmvs1		70.93 356	70.52 349	72.16 401	73.71 444	55.05 398	80.82 349	78.77 405	51.21 445	78.58 200	84.41 340	31.20 445	76.94 431	75.88 177	80.12 298	84.47 400

BH-w/o		78.21 246	77.33 251	80.84 273	88.81 167	65.13 224	84.87 276	87.85 267	69.75 270	74.52 306	84.74 336	61.34 209	93.11 201	58.24 354	85.84 210	84.27 401

MVS		78.19 248	76.99 257	81.78 246	85.66 289	66.99 182	84.66 281	90.47 165	55.08 433	72.02 339	85.27 322	63.83 165	94.11 141	66.10 281	89.80 133	84.24 402

COLMAP_ROB	66.92 17	73.01 337	70.41 352	80.81 274	87.13 248	65.63 211	88.30 160	84.19 332	62.96 370	63.80 423	87.69 255	38.04 426	92.56 225	46.66 426	74.91 372	84.24 402
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 412	61.73 413	61.70 437	72.74 453	24.50 480	69.16 445	78.03 409	61.40 387	56.72 448	75.53 442	38.42 423	76.48 435	45.95 432	57.67 443	84.13 404

TESTMET0.1,1		69.89 370	69.00 362	72.55 398	79.27 420	56.85 370	78.38 390	74.71 432	57.64 420	68.09 380	77.19 433	37.75 427	76.70 432	63.92 298	84.09 238	84.10 405

test_fmvs3		63.36 409	61.82 412	67.98 427	62.51 467	46.96 448	77.37 403	74.03 434	45.24 452	67.50 385	78.79 423	12.16 471	72.98 456	72.77 212	66.02 423	83.99 406

our_test_3		69.14 375	67.00 388	75.57 362	79.80 412	58.80 342	77.96 397	77.81 410	59.55 402	62.90 427	78.25 427	47.43 355	83.97 391	51.71 395	67.58 418	83.93 407

test_vis1_n		69.85 371	69.21 360	71.77 403	72.66 454	55.27 397	81.48 342	76.21 424	52.03 441	75.30 287	83.20 370	28.97 448	76.22 438	74.60 191	78.41 318	83.81 408

mamv4		76.81 280	78.23 224	72.54 399	86.12 280	65.75 210	78.76 385	82.07 365	64.12 355	72.97 325	91.02 156	67.97 117	68.08 464	83.04 89	78.02 321	83.80 409

tpmvs		71.09 354	69.29 359	76.49 354	82.04 379	56.04 385	78.92 383	81.37 374	64.05 358	67.18 391	78.28 426	49.74 338	89.77 317	49.67 410	72.37 393	83.67 410

test20.03		67.45 388	66.95 389	68.94 419	75.48 437	44.84 456	77.50 401	77.67 411	66.66 320	63.01 425	83.80 355	47.02 359	78.40 423	42.53 444	68.86 415	83.58 411

test0.0.03 1		68.00 386	67.69 379	68.90 420	77.55 426	47.43 443	75.70 414	72.95 439	66.66 320	66.56 399	82.29 386	48.06 353	75.87 442	44.97 437	74.51 376	83.41 412

Anonymous20231206		68.60 379	67.80 377	71.02 411	80.23 405	50.75 434	78.30 394	80.47 384	56.79 426	66.11 407	82.63 381	46.35 368	78.95 421	43.62 439	75.70 354	83.36 413

EU-MVSNet		68.53 382	67.61 381	71.31 409	78.51 424	47.01 447	84.47 287	84.27 330	42.27 456	66.44 404	84.79 335	40.44 413	83.76 392	58.76 348	68.54 416	83.17 414

dp		66.80 393	65.43 394	70.90 413	79.74 414	48.82 441	75.12 420	74.77 430	59.61 401	64.08 420	77.23 432	42.89 396	80.72 415	48.86 415	66.58 421	83.16 415

pmmvs-eth3d		70.50 362	67.83 376	78.52 324	77.37 428	66.18 195	81.82 336	81.51 371	58.90 409	63.90 422	80.42 403	42.69 398	86.28 368	58.56 349	65.30 426	83.11 416

YYNet1		65.03 403	62.91 408	71.38 405	75.85 434	56.60 376	69.12 446	74.66 433	57.28 424	54.12 452	77.87 429	45.85 374	74.48 450	49.95 408	61.52 437	83.05 417

MDA-MVSNet-bldmvs		66.68 394	63.66 404	75.75 359	79.28 419	60.56 325	73.92 427	78.35 408	64.43 350	50.13 458	79.87 412	44.02 390	83.67 393	46.10 431	56.86 444	83.03 418

MDA-MVSNet_test_wron		65.03 403	62.92 407	71.37 406	75.93 431	56.73 372	69.09 447	74.73 431	57.28 424	54.03 453	77.89 428	45.88 373	74.39 451	49.89 409	61.55 436	82.99 419

USDC		70.33 364	68.37 365	76.21 356	80.60 400	56.23 383	79.19 378	86.49 298	60.89 390	61.29 431	85.47 318	31.78 443	89.47 324	53.37 388	76.21 350	82.94 420

Syy-MVS		68.05 385	67.85 374	68.67 423	84.68 317	40.97 466	78.62 387	73.08 437	66.65 323	66.74 397	79.46 415	52.11 304	82.30 404	32.89 458	76.38 347	82.75 421

myMVS_eth3d		67.02 392	66.29 392	69.21 418	84.68 317	42.58 461	78.62 387	73.08 437	66.65 323	66.74 397	79.46 415	31.53 444	82.30 404	39.43 450	76.38 347	82.75 421

ttmdpeth		59.91 415	57.10 419	68.34 425	67.13 462	46.65 449	74.64 423	67.41 452	48.30 448	62.52 429	85.04 331	20.40 461	75.93 441	42.55 443	45.90 463	82.44 423

OpenMVS_ROB	64.09 19	70.56 361	68.19 367	77.65 341	80.26 403	59.41 340	85.01 273	82.96 355	58.76 411	65.43 410	82.33 384	37.63 428	91.23 287	45.34 436	76.03 351	82.32 424

JIA-IIPM		66.32 398	62.82 410	76.82 352	77.09 429	61.72 310	65.34 458	75.38 426	58.04 418	64.51 416	62.32 458	42.05 404	86.51 365	51.45 398	69.22 412	82.21 425

dmvs_re		71.14 353	70.58 348	72.80 396	81.96 380	59.68 335	75.60 415	79.34 400	68.55 298	69.27 371	80.72 401	49.42 341	76.54 433	52.56 392	77.79 323	82.19 426

EG-PatchMatch MVS		74.04 320	71.82 334	80.71 276	84.92 311	67.42 168	85.86 249	88.08 257	66.04 331	64.22 418	83.85 353	35.10 436	92.56 225	57.44 360	80.83 286	82.16 427

FE-MVSNET		67.25 391	65.33 395	73.02 394	75.86 433	52.54 418	80.26 365	80.56 382	63.80 363	60.39 434	79.70 414	41.41 407	84.66 388	43.34 440	62.62 433	81.86 428

MVP-Stereo		76.12 293	74.46 303	81.13 266	85.37 299	69.79 95	84.42 292	87.95 263	65.03 344	67.46 386	85.33 321	53.28 292	91.73 262	58.01 356	83.27 257	81.85 429
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 387	64.34 399	76.92 351	73.47 448	61.07 317	84.86 277	82.98 354	59.77 400	58.30 443	85.13 327	26.06 451	87.89 351	47.92 423	60.59 440	81.81 430

GG-mvs-BLEND				75.38 367	81.59 386	55.80 389	79.32 375	69.63 445		67.19 390	73.67 446	43.24 394	88.90 338	50.41 402	84.50 228	81.45 431

KD-MVS_2432*1600		66.22 399	63.89 402	73.21 390	75.47 438	53.42 412	70.76 438	84.35 327	64.10 356	66.52 401	78.52 424	34.55 437	84.98 383	50.40 403	50.33 457	81.23 432

miper_refine_blended		66.22 399	63.89 402	73.21 390	75.47 438	53.42 412	70.76 438	84.35 327	64.10 356	66.52 401	78.52 424	34.55 437	84.98 383	50.40 403	50.33 457	81.23 432

test_0402		72.79 340	70.44 351	79.84 295	88.13 198	65.99 201	85.93 246	84.29 329	65.57 337	67.40 389	85.49 317	46.92 360	92.61 221	35.88 455	74.38 377	80.94 434

MVStest1		56.63 419	52.76 425	68.25 426	61.67 468	53.25 416	71.67 433	68.90 450	38.59 461	50.59 457	83.05 372	25.08 453	70.66 458	36.76 454	38.56 464	80.83 435

UnsupCasMVSNet_bld		63.70 408	61.53 414	70.21 415	73.69 445	51.39 429	72.82 429	81.89 366	55.63 431	57.81 445	71.80 450	38.67 422	78.61 422	49.26 413	52.21 455	80.63 436

LCM-MVSNet		54.25 421	49.68 431	67.97 428	53.73 476	45.28 453	66.85 453	80.78 378	35.96 465	39.45 466	62.23 459	8.70 475	78.06 426	48.24 420	51.20 456	80.57 437

N_pmnet		52.79 426	53.26 424	51.40 451	78.99 421	7.68 485	69.52 442	3.89 484	51.63 443	57.01 447	74.98 443	40.83 411	65.96 466	37.78 452	64.67 427	80.56 438

TinyColmap		67.30 390	64.81 397	74.76 375	81.92 382	56.68 375	80.29 363	81.49 372	60.33 394	56.27 450	83.22 368	24.77 455	87.66 355	45.52 434	69.47 410	79.95 439

PM-MVS		66.41 397	64.14 400	73.20 392	73.92 443	56.45 377	78.97 382	64.96 459	63.88 362	64.72 415	80.24 407	19.84 463	83.44 397	66.24 278	64.52 428	79.71 440

ANet_high		50.57 430	46.10 434	63.99 434	48.67 479	39.13 467	70.99 437	80.85 377	61.39 388	31.18 468	57.70 464	17.02 466	73.65 455	31.22 461	15.89 476	79.18 441

LF4IMVS		64.02 407	62.19 411	69.50 417	70.90 456	53.29 415	76.13 408	77.18 418	52.65 439	58.59 441	80.98 397	23.55 458	76.52 434	53.06 390	66.66 420	78.68 442

PatchMatch-RL		72.38 342	70.90 346	76.80 353	88.60 179	67.38 171	79.53 372	76.17 425	62.75 375	69.36 369	82.00 391	45.51 379	84.89 385	53.62 386	80.58 290	78.12 443

MS-PatchMatch		73.83 323	72.67 325	77.30 348	83.87 335	66.02 198	81.82 336	84.66 323	61.37 389	68.61 376	82.82 378	47.29 356	88.21 346	59.27 340	84.32 235	77.68 444

DSMNet-mixed		57.77 418	56.90 420	60.38 439	67.70 460	35.61 470	69.18 444	53.97 471	32.30 469	57.49 446	79.88 411	40.39 414	68.57 463	38.78 451	72.37 393	76.97 445

CHOSEN 280x420		66.51 396	64.71 398	71.90 402	81.45 389	63.52 273	57.98 467	68.95 449	53.57 436	62.59 428	76.70 434	46.22 370	75.29 448	55.25 376	79.68 300	76.88 446

mvsany_test3		53.99 422	51.45 427	61.61 438	55.51 472	44.74 457	63.52 463	45.41 477	43.69 455	58.11 444	76.45 436	17.99 464	63.76 468	54.77 380	47.59 459	76.34 447

dmvs_testset		62.63 410	64.11 401	58.19 441	78.55 423	24.76 479	75.28 416	65.94 456	67.91 307	60.34 435	76.01 438	53.56 288	73.94 454	31.79 459	67.65 417	75.88 448

mvsany_test1		62.30 411	61.26 415	65.41 433	69.52 457	54.86 400	66.86 452	49.78 473	46.65 450	68.50 378	83.21 369	49.15 346	66.28 465	56.93 367	60.77 438	75.11 449

PMMVS		69.34 374	68.67 363	71.35 408	75.67 435	62.03 304	75.17 417	73.46 435	50.00 446	68.68 374	79.05 418	52.07 306	78.13 424	61.16 326	82.77 263	73.90 450

test_vis1_rt		60.28 414	58.42 417	65.84 432	67.25 461	55.60 392	70.44 440	60.94 465	44.33 454	59.00 440	66.64 455	24.91 454	68.67 462	62.80 305	69.48 409	73.25 451

pmmvs3		57.79 417	54.26 422	68.37 424	64.02 466	56.72 373	75.12 420	65.17 457	40.20 458	52.93 454	69.86 454	20.36 462	75.48 445	45.45 435	55.25 451	72.90 452

PVSNet_0	57.27 20	61.67 413	59.27 416	68.85 421	79.61 415	57.44 364	68.01 448	73.44 436	55.93 430	58.54 442	70.41 453	44.58 385	77.55 428	47.01 425	35.91 465	71.55 453

WB-MVS		54.94 420	54.72 421	55.60 447	73.50 446	20.90 481	74.27 426	61.19 464	59.16 406	50.61 456	74.15 444	47.19 358	75.78 443	17.31 472	35.07 466	70.12 454

SSC-MVS		53.88 423	53.59 423	54.75 449	72.87 452	19.59 482	73.84 428	60.53 466	57.58 422	49.18 460	73.45 447	46.34 369	75.47 446	16.20 475	32.28 468	69.20 455

test_f		52.09 427	50.82 428	55.90 445	53.82 475	42.31 464	59.42 466	58.31 469	36.45 464	56.12 451	70.96 452	12.18 470	57.79 471	53.51 387	56.57 446	67.60 456

PMMVS2		40.82 437	38.86 441	46.69 452	53.84 474	16.45 483	48.61 470	49.92 472	37.49 462	31.67 467	60.97 460	8.14 477	56.42 472	28.42 463	30.72 469	67.19 457

new_pmnet		50.91 429	50.29 429	52.78 450	68.58 459	34.94 472	63.71 462	56.63 470	39.73 459	44.95 461	65.47 456	21.93 460	58.48 470	34.98 456	56.62 445	64.92 458

MVS-HIRNet		59.14 416	57.67 418	63.57 435	81.65 384	43.50 459	71.73 432	65.06 458	39.59 460	51.43 455	57.73 463	38.34 424	82.58 403	39.53 448	73.95 380	64.62 459

APD_test1		53.31 425	49.93 430	63.42 436	65.68 463	50.13 436	71.59 434	66.90 454	34.43 466	40.58 465	71.56 451	8.65 476	76.27 437	34.64 457	55.36 449	63.86 460

test_method		31.52 440	29.28 444	38.23 455	27.03 483	6.50 486	20.94 475	62.21 463	4.05 477	22.35 475	52.50 468	13.33 468	47.58 475	27.04 465	34.04 467	60.62 461

EGC-MVSNET		52.07 428	47.05 432	67.14 429	83.51 345	60.71 322	80.50 359	67.75 451	0.07 479	0.43 480	75.85 441	24.26 456	81.54 409	28.82 462	62.25 434	59.16 462

test_vis3_rt		49.26 431	47.02 433	56.00 444	54.30 473	45.27 454	66.76 454	48.08 474	36.83 463	44.38 462	53.20 467	7.17 478	64.07 467	56.77 370	55.66 447	58.65 463

FPMVS		53.68 424	51.64 426	59.81 440	65.08 464	51.03 431	69.48 443	69.58 446	41.46 457	40.67 464	72.32 449	16.46 467	70.00 461	24.24 468	65.42 425	58.40 464

testf1		45.72 432	41.96 436	57.00 442	56.90 470	45.32 451	66.14 455	59.26 467	26.19 470	30.89 469	60.96 461	4.14 479	70.64 459	26.39 466	46.73 461	55.04 465

APD_test2		45.72 432	41.96 436	57.00 442	56.90 470	45.32 451	66.14 455	59.26 467	26.19 470	30.89 469	60.96 461	4.14 479	70.64 459	26.39 466	46.73 461	55.04 465

PMVS	37.38 22	44.16 436	40.28 440	55.82 446	40.82 481	42.54 463	65.12 459	63.99 461	34.43 466	24.48 472	57.12 465	3.92 481	76.17 439	17.10 473	55.52 448	48.75 467
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 442	25.89 446	43.81 454	44.55 480	35.46 471	28.87 474	39.07 478	18.20 474	18.58 476	40.18 471	2.68 482	47.37 476	17.07 474	23.78 473	48.60 468
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 434	45.38 435	45.55 453	73.36 449	26.85 477	67.72 449	34.19 479	54.15 435	49.65 459	56.41 466	25.43 452	62.94 469	19.45 470	28.09 470	46.86 469

kuosan		39.70 438	40.40 439	37.58 456	64.52 465	26.98 475	65.62 457	33.02 480	46.12 451	42.79 463	48.99 469	24.10 457	46.56 477	12.16 478	26.30 471	39.20 470

Gipuma		45.18 435	41.86 438	55.16 448	77.03 430	51.52 427	32.50 473	80.52 383	32.46 468	27.12 471	35.02 472	9.52 474	75.50 444	22.31 469	60.21 441	38.45 471
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 459	40.17 482	26.90 476		24.59 483	17.44 475	23.95 473	48.61 470	9.77 473	26.48 478	18.06 471	24.47 472	28.83 472

E-PMN		31.77 439	30.64 442	35.15 457	52.87 477	27.67 474	57.09 468	47.86 475	24.64 472	16.40 477	33.05 473	11.23 472	54.90 473	14.46 476	18.15 474	22.87 473

EMVS		30.81 441	29.65 443	34.27 458	50.96 478	25.95 478	56.58 469	46.80 476	24.01 473	15.53 478	30.68 474	12.47 469	54.43 474	12.81 477	17.05 475	22.43 474

tmp_tt		18.61 444	21.40 447	10.23 461	4.82 484	10.11 484	34.70 472	30.74 482	1.48 478	23.91 474	26.07 475	28.42 449	13.41 480	27.12 464	15.35 477	7.17 475

wuyk23d		16.82 445	15.94 448	19.46 460	58.74 469	31.45 473	39.22 471	3.74 485	6.84 476	6.04 479	2.70 479	1.27 483	24.29 479	10.54 479	14.40 478	2.63 476

test123		6.12 447	8.11 450	0.14 462	0.06 486	0.09 487	71.05 436	0.03 487	0.04 481	0.25 482	1.30 481	0.05 484	0.03 482	0.21 481	0.01 480	0.29 477

testmvs		6.04 448	8.02 451	0.10 463	0.08 485	0.03 488	69.74 441	0.04 486	0.05 480	0.31 481	1.68 480	0.02 485	0.04 481	0.24 480	0.02 479	0.25 478

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

cdsmvs_eth3d_5k		19.96 443	26.61 445	0.00 464	0.00 487	0.00 489	0.00 476	89.26 217	0.00 482	0.00 483	88.61 228	61.62 202	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		5.26 449	7.02 452	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	63.15 174	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

ab-mvs-re		7.23 446	9.64 449	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	86.72 281	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

TestfortrainingZip							93.28 12

WAC-MVS						42.58 461								39.46 449

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 147	91.30 18

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

eth-test2					0.00 487

eth-test					0.00 487

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 233	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 163	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

save fliter					93.80 44	72.35 44	90.47 74	91.17 143	74.31 152

test0726					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs												50.01 333

MTGPA								92.02 105

test_post1							78.90 384				5.43 478	48.81 352	85.44 380	59.25 341

test_post											5.46 477	50.36 329	84.24 389

patchmatchnet-post											74.00 445	51.12 320	88.60 342

MTMP							92.18 39	32.83 481

gm-plane-assit					81.40 390	53.83 409			62.72 376		80.94 398		92.39 234	63.40 302

TEST9					93.26 56	72.96 25	88.75 138	91.89 113	68.44 301	85.00 80	93.10 88	74.36 32	95.41 80

test_8					93.13 60	72.57 35	88.68 143	91.84 117	68.69 296	84.87 84	93.10 88	74.43 30	95.16 90

agg_prior					92.85 68	71.94 52		91.78 121		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 116	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

旧先验2							86.56 225		58.10 417	87.04 61			88.98 334	74.07 197

新几何2							86.29 238

原ACMM2							86.86 212

testdata2													91.01 296	62.37 312

segment_acmp												73.08 43

testdata1							84.14 300		75.71 107

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 228

plane_prior4											91.00 157

plane_prior3						68.60 128			78.44 36	78.92 193

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 201

n2								0.00 488

nn								0.00 488

door-mid								69.98 444

test11								92.23 92

door								69.44 447

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 86	77.23 234

ACMP_Plane					89.33 144		89.17 116		76.41 86	77.23 234

BP-MVS														77.47 153

HQP3-MVS								92.19 99							85.99 205

HQP2-MVS												60.17 231

NP-MVS					89.62 129	68.32 135					90.24 178

MDTV_nov1_ep13			69.97 356		83.18 354	53.48 411	77.10 406	80.18 393	60.45 393	69.33 370	80.44 402	48.89 351	86.90 361	51.60 396	78.51 313

ACMMP++_ref															81.95 274

ACMMP++															81.25 279

Test By Simon												64.33 160

High-resolution multi-view benchmark