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	91.17 145	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	87.65 229	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	84.06 160	91.71 84	64.94 234	86.47 228	91.87 117	73.63 172	86.60 67	93.02 93	76.57 18	91.87 259	83.36 84	92.15 90	95.35 3

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 247	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	89.05 230	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	76.66 250	93.37 83	60.40 232	96.75 30	77.20 158	93.73 70	95.29 6

BP-MVS1		84.32 91	83.71 104	86.17 68	87.84 213	67.85 154	89.38 109	89.64 199	77.73 45	83.98 106	92.12 114	56.89 262	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	89.54 202	82.14 3	86.65 66	94.28 46	68.28 116	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	74.62 147	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	87.30 245	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									68.21 307	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

IS-MVSNet		83.15 125	82.81 122	84.18 149	89.94 123	63.30 281	91.59 51	88.46 255	79.04 30	79.49 184	92.16 111	65.10 155	94.28 130	67.71 268	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	74.77 143	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		77.30 274	76.49 271	79.74 299	90.08 116	52.02 422	87.86 178	63.10 465	74.88 139	80.16 177	92.79 100	38.29 428	92.35 239	68.74 261	92.50 84	94.86 19

ECVR-MVS		79.61 208	79.26 201	80.67 279	90.08 116	54.69 404	87.89 176	77.44 418	74.88 139	80.27 174	92.79 100	48.96 352	92.45 233	68.55 262	92.50 84	94.86 19

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 319	92.39 6				88.94 28	96.63 4	94.85 21

test1111		79.43 215	79.18 204	80.15 291	89.99 121	53.31 417	87.33 195	77.05 422	75.04 132	80.23 176	92.77 102	48.97 351	92.33 241	68.87 259	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	75.71 108	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	91.63 131	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

E4		84.10 94	83.99 97	84.45 128	87.58 235	64.99 230	86.54 226	92.25 92	76.38 90	83.37 117	92.09 115	69.88 90	93.58 166	79.78 126	88.03 167	94.77 25

viewmacassd2359aftdt		83.76 105	83.66 106	84.07 157	86.59 271	64.56 243	86.88 211	91.82 120	75.72 107	83.34 118	92.15 113	68.24 117	92.88 214	79.05 132	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	91.23 145	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	91.20 149	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	91.23 145	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	75.24 122	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

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 94	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

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 94	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

GDP-MVS		83.52 114	82.64 126	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 198	83.71 111	91.86 121	55.69 269	95.35 86	80.03 122	89.74 134	94.69 33

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 34

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 34
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		82.60 138	82.10 138	84.10 151	87.98 207	62.94 292	87.45 190	91.27 141	77.42 56	79.85 179	90.28 178	56.62 265	94.70 117	79.87 125	88.15 163	94.67 34

E2		84.00 97	83.87 98	84.39 131	87.70 226	64.95 231	86.40 233	92.23 93	75.85 104	83.21 119	91.78 123	70.09 85	93.55 171	79.52 129	88.05 165	94.66 37

E3		84.00 97	83.87 98	84.39 131	87.70 226	64.95 231	86.40 233	92.23 93	75.85 104	83.21 119	91.78 123	70.09 85	93.55 171	79.52 129	88.05 165	94.66 37

MGCFI-Net		85.06 85	85.51 74	83.70 179	89.42 139	63.01 287	89.43 104	92.62 78	76.43 85	87.53 53	91.34 143	72.82 49	93.42 183	81.28 108	88.74 153	94.66 37

viewmanbaseed2359cas		83.66 108	83.55 108	84.00 168	86.81 263	64.53 244	86.65 221	91.75 125	74.89 138	83.15 124	91.68 127	68.74 109	92.83 218	79.02 134	89.24 142	94.63 40

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 98	87.73 52	91.46 140	70.32 80	93.78 158	81.51 104	88.95 147	94.63 40

viewdifsd2359ckpt09		83.34 120	82.55 128	85.70 81	87.64 230	67.72 159	88.43 151	91.68 127	71.91 212	81.65 150	90.68 166	67.10 130	94.75 113	76.17 173	87.70 174	94.62 42

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 130	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 43
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 43

viewcassd2359sk11		83.89 99	83.74 103	84.34 136	87.76 221	64.91 237	86.30 237	92.22 96	75.47 115	83.04 125	91.52 136	70.15 83	93.53 174	79.26 131	87.96 168	94.57 45

VDD-MVS		83.01 130	82.36 132	84.96 107	91.02 95	66.40 191	88.91 128	88.11 258	77.57 49	84.39 96	93.29 85	52.19 303	93.91 152	77.05 161	88.70 154	94.57 45

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 101	79.31 24	84.39 96	92.18 109	64.64 160	95.53 71	80.70 116	94.65 52	94.56 47

KinetiMVS		83.31 123	82.61 127	85.39 91	87.08 256	67.56 165	88.06 168	91.65 128	77.80 44	82.21 139	91.79 122	57.27 257	94.07 142	77.77 151	89.89 132	94.56 47

VDDNet		81.52 160	80.67 160	84.05 163	90.44 108	64.13 256	89.73 93	85.91 311	71.11 229	83.18 122	93.48 78	50.54 329	93.49 177	73.40 206	88.25 161	94.54 49

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 122	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 50

E3new		83.78 104	83.60 107	84.31 138	87.76 221	64.89 238	86.24 240	92.20 99	75.15 131	82.87 128	91.23 145	70.11 84	93.52 176	79.05 132	87.79 171	94.51 51

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 197	84.64 90	91.71 126	71.85 58	96.03 55	84.77 69	94.45 60	94.49 52

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 110	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 53
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 54

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 54

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 192	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 56

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 98	79.94 17	89.74 27	94.86 26	68.63 110	94.20 136	90.83 5	91.39 104	94.38 57

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	81.32 154	91.43 141	70.34 79	97.23 17	84.26 75	93.36 74	94.37 58

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 200	85.22 78	91.90 118	69.47 95	96.42 44	83.28 86	95.94 23	94.35 59

viewdifsd2359ckpt07		82.83 133	82.78 125	82.99 210	86.51 273	62.58 295	85.09 273	90.83 157	75.22 124	82.28 136	91.63 131	69.43 96	92.03 249	77.71 152	86.32 198	94.34 60

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 60

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 62

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 100	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 62
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	68.32 306	84.61 91	93.48 78	72.32 52	96.15 53	79.00 136	95.43 34	94.28 64

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 65

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 66

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 242	67.30 174	89.50 101	90.98 150	76.25 97	90.56 22	94.75 29	68.38 113	94.24 135	90.80 7	92.32 89	94.19 67

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 247	68.54 130	89.57 99	90.44 168	75.31 121	87.49 54	94.39 42	72.86 47	92.72 221	89.04 27	90.56 118	94.16 68

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 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 118	83.02 118	84.57 123	90.13 114	64.47 249	92.32 35	90.73 160	74.45 151	79.35 189	91.10 152	69.05 104	95.12 92	72.78 213	87.22 182	94.13 70

viewdifsd2359ckpt13		82.91 131	82.29 134	84.77 118	86.96 259	66.90 187	87.47 187	91.62 130	72.19 205	81.68 149	90.71 165	66.92 131	93.28 186	75.90 178	87.15 184	94.12 71

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 72

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 96	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 73

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	67.45 125	96.60 37	83.06 87	94.50 57	94.07 74

X-MVStestdata		80.37 195	77.83 235	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 479	67.45 125	96.60 37	83.06 87	94.50 57	94.07 74

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	69.09 101	96.70 31	84.37 74	94.83 49	94.03 76

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 140	86.70 267	65.83 205	88.77 136	89.78 191	75.46 116	88.35 36	93.73 74	69.19 100	93.06 206	91.30 3	88.44 159	94.02 77

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	68.81 107	96.65 34	84.53 72	94.90 45	94.00 78

fmvsm_s_conf0.1_n_2		83.80 102	83.79 102	83.83 175	85.62 293	64.94 234	87.03 203	86.62 300	74.32 153	87.97 47	94.33 43	60.67 224	92.60 224	89.72 14	87.79 171	93.96 79

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 312	69.51 100	89.62 98	90.58 163	73.42 180	87.75 50	94.02 61	72.85 48	93.24 190	90.37 8	90.75 115	93.96 79

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 103	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 81
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	86.12 282	69.93 92	88.65 144	90.78 159	69.97 265	88.27 38	93.98 66	71.39 67	91.54 275	88.49 35	90.45 120	93.91 82

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 82

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 84

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 364	69.39 107	89.65 95	90.29 177	73.31 184	87.77 49	94.15 55	71.72 61	93.23 191	90.31 9	90.67 117	93.89 85

Anonymous202405211		78.25 246	77.01 257	81.99 245	91.03 94	60.67 325	84.77 280	83.90 338	70.65 246	80.00 178	91.20 149	41.08 413	91.43 282	65.21 290	85.26 221	93.85 86

LFMVS		81.82 150	81.23 150	83.57 184	91.89 82	63.43 279	89.84 87	81.85 371	77.04 70	83.21 119	93.10 88	52.26 302	93.43 182	71.98 225	89.95 130	93.85 86

fmvsm_s_conf0.5_n_2		84.04 95	84.11 95	83.81 177	86.17 280	65.00 229	86.96 206	87.28 282	74.35 152	88.25 39	94.23 50	61.82 200	92.60 224	89.85 12	88.09 164	93.84 88

Effi-MVS+		83.62 112	83.08 116	85.24 95	88.38 188	67.45 167	88.89 129	89.15 226	75.50 114	82.27 137	88.28 240	69.61 94	94.45 127	77.81 150	87.84 170	93.84 88

Anonymous20240529		80.19 201	78.89 210	84.10 151	90.60 104	64.75 241	88.95 127	90.90 153	65.97 336	80.59 170	91.17 151	49.97 336	93.73 164	69.16 256	82.70 268	93.81 90

MVS_Test		83.15 125	83.06 117	83.41 190	86.86 260	63.21 283	86.11 244	92.00 109	74.31 154	82.87 128	89.44 208	70.03 87	93.21 193	77.39 157	88.50 158	93.81 90

Elysia		81.53 158	80.16 173	85.62 84	85.51 296	68.25 139	88.84 133	92.19 101	71.31 223	80.50 171	89.83 188	46.89 363	94.82 108	76.85 163	89.57 136	93.80 92

StellarMVS		81.53 158	80.16 173	85.62 84	85.51 296	68.25 139	88.84 133	92.19 101	71.31 223	80.50 171	89.83 188	46.89 363	94.82 108	76.85 163	89.57 136	93.80 92

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 406	69.03 110	89.47 102	89.65 198	73.24 188	86.98 62	94.27 47	66.62 134	93.23 191	90.26 10	89.95 130	93.78 94

GeoE		81.71 152	81.01 155	83.80 178	89.51 134	64.45 250	88.97 126	88.73 248	71.27 226	78.63 201	89.76 193	66.32 140	93.20 196	69.89 248	86.02 206	93.74 95

diffmvs		82.10 142	81.88 144	82.76 227	83.00 362	63.78 265	83.68 311	89.76 193	72.94 195	82.02 142	89.85 187	65.96 149	90.79 302	82.38 100	87.30 181	93.71 96
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 97

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 98

VNet		82.21 141	82.41 130	81.62 251	90.82 100	60.93 320	84.47 289	89.78 191	76.36 92	84.07 104	91.88 119	64.71 159	90.26 310	70.68 237	88.89 148	93.66 98

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 113	83.86 108	94.42 40	67.87 122	96.64 35	82.70 98	94.57 56	93.66 98

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 264	93.44 32	78.70 34	83.63 115	89.03 215	74.57 27	95.71 66	80.26 121	94.04 67	93.66 98
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	93.65 102
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	82.82 131	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 102
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		83.65 109	84.54 89	80.99 271	90.06 120	65.83 205	84.21 299	88.74 247	71.60 218	85.01 79	92.44 105	74.51 29	83.50 399	82.15 101	92.15 90	93.64 104

EIA-MVS		83.31 123	82.80 123	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 146	78.96 193	86.42 298	69.06 103	95.26 87	75.54 184	90.09 126	93.62 105

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	93.62 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
diffmvs_AUTHOR		82.38 139	82.27 135	82.73 229	83.26 352	63.80 263	83.89 306	89.76 193	73.35 183	82.37 135	90.84 162	66.25 141	90.79 302	82.77 93	87.93 169	93.59 107

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 130	73.89 166	82.67 134	94.09 57	62.60 184	95.54 70	80.93 111	92.93 77	93.57 108

fmvsm_s_conf0.1_n		83.56 113	83.38 112	84.10 151	84.86 314	67.28 175	89.40 108	83.01 355	70.67 242	87.08 60	93.96 67	68.38 113	91.45 281	88.56 34	84.50 230	93.56 109

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 163	83.16 123	91.07 154	75.94 21	95.19 89	79.94 124	94.38 62	93.55 110

test12				86.80 58	92.63 73	70.70 81		91.79 122		82.71 133		71.67 63	96.16 52		94.50 57	93.54 111

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 176	85.94 69	94.51 35	65.80 150	95.61 67	83.04 89	92.51 83	93.53 112

mvs_anonymous		79.42 216	79.11 205	80.34 286	84.45 325	57.97 355	82.59 333	87.62 275	67.40 316	76.17 266	88.56 233	68.47 112	89.59 323	70.65 238	86.05 205	93.47 113

fmvsm_s_conf0.5_n		83.80 102	83.71 104	84.07 157	86.69 268	67.31 173	89.46 103	83.07 354	71.09 230	86.96 63	93.70 75	69.02 106	91.47 280	88.79 30	84.62 229	93.44 114

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 147	86.26 276	67.40 170	89.18 115	89.31 215	72.50 199	88.31 37	93.86 70	69.66 93	91.96 253	89.81 13	91.05 109	93.38 115

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 96	76.87 74	82.81 132	94.25 49	66.44 138	96.24 49	82.88 92	94.28 64	93.38 115

EPNet		83.72 107	82.92 121	86.14 72	84.22 328	69.48 101	91.05 64	85.27 318	81.30 6	76.83 245	91.65 129	66.09 145	95.56 68	76.00 177	93.85 68	93.38 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 116	82.80 123	85.43 90	90.25 112	68.74 121	90.30 80	90.13 182	76.33 93	80.87 165	92.89 95	61.00 219	94.20 136	72.45 222	90.97 111	93.35 118
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	93.33 119
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		79.10 226	78.24 224	81.70 250	86.85 261	60.24 332	87.28 197	88.79 242	74.25 157	76.84 244	90.53 173	49.48 342	91.56 271	67.98 266	82.15 272	93.29 120

EI-MVSNet-Vis-set		84.19 92	83.81 101	85.31 93	88.18 194	67.85 154	87.66 182	89.73 196	80.05 15	82.95 126	89.59 200	70.74 76	94.82 108	80.66 118	84.72 227	93.28 121

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 224	92.02 107	79.45 22	85.88 70	94.80 27	68.07 118	96.21 50	86.69 52	95.34 36	93.23 122

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	67.93 120	95.95 62	84.20 78	94.39 61	93.23 122

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	80.73 168	93.82 72	64.33 162	96.29 46	82.67 99	90.69 116	93.23 122
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	88.06 262	79.31 24	84.39 96	92.18 109	64.64 160	95.53 71	80.70 116	90.91 113	93.21 125

fmvsm_s_conf0.1_n_a		83.32 122	82.99 119	84.28 142	83.79 338	68.07 145	89.34 111	82.85 360	69.80 269	87.36 58	94.06 59	68.34 115	91.56 271	87.95 42	83.46 256	93.21 125

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 181	87.32 244	65.13 224	88.86 130	91.63 129	75.41 117	88.23 40	93.45 81	68.56 111	92.47 232	89.52 18	92.78 79	93.20 127

PAPM_NR		83.02 129	82.41 130	84.82 115	92.47 76	66.37 192	87.93 174	91.80 121	73.82 167	77.32 233	90.66 167	67.90 121	94.90 104	70.37 240	89.48 139	93.19 128

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 186	87.12 255	66.01 199	88.56 148	89.43 206	75.59 112	89.32 28	94.32 44	72.89 46	91.21 290	90.11 11	92.33 87	93.16 129

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 144	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 129

OMC-MVS		82.69 134	81.97 143	84.85 114	88.75 174	67.42 168	87.98 170	90.87 155	74.92 137	79.72 181	91.65 129	62.19 194	93.96 144	75.26 188	86.42 197	93.16 129

fmvsm_s_conf0.5_n_a		83.63 111	83.41 111	84.28 142	86.14 281	68.12 143	89.43 104	82.87 359	70.27 258	87.27 59	93.80 73	69.09 101	91.58 268	88.21 38	83.65 250	93.14 132

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 168	87.78 218	66.09 196	89.96 86	90.80 158	77.37 57	86.72 65	94.20 52	72.51 51	92.78 220	89.08 22	92.33 87	93.13 133

PAPR		81.66 155	80.89 157	83.99 170	90.27 111	64.00 257	86.76 218	91.77 124	68.84 297	77.13 243	89.50 201	67.63 123	94.88 106	67.55 270	88.52 157	93.09 134

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 156	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 230	90.88 114	93.07 135

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 134	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 136

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 134	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 136

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	93.05 138

thisisatest0530		79.40 217	77.76 240	84.31 138	87.69 228	65.10 227	87.36 193	84.26 334	70.04 261	77.42 230	88.26 242	49.94 337	94.79 112	70.20 243	84.70 228	93.03 139

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 115	68.69 299	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 140

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	91.88 119	69.04 105	95.43 77	83.93 81	93.77 69	93.01 141

mvsmamba		80.60 186	79.38 196	84.27 144	89.74 128	67.24 178	87.47 187	86.95 290	70.02 262	75.38 282	88.93 220	51.24 320	92.56 227	75.47 186	89.22 143	93.00 142

EI-MVSNet-UG-set		83.81 101	83.38 112	85.09 103	87.87 211	67.53 166	87.44 191	89.66 197	79.74 18	82.23 138	89.41 209	70.24 82	94.74 114	79.95 123	83.92 242	92.99 143

tttt0517		79.40 217	77.91 231	83.90 174	88.10 200	63.84 262	88.37 157	84.05 336	71.45 221	76.78 247	89.12 212	49.93 339	94.89 105	70.18 244	83.18 261	92.96 144

viewdifsd2359ckpt11		80.37 195	79.73 186	82.30 238	83.70 342	62.39 299	84.20 300	86.67 296	73.22 189	80.90 163	90.62 168	63.00 181	91.56 271	76.81 167	78.44 316	92.95 145

viewmsd2359difaftdt		80.37 195	79.73 186	82.30 238	83.70 342	62.39 299	84.20 300	86.67 296	73.22 189	80.90 163	90.62 168	63.00 181	91.56 271	76.81 167	78.44 316	92.95 145

test9_res														84.90 64	95.70 30	92.87 147

viewmambaseed2359dif		80.41 191	79.84 183	82.12 240	82.95 366	62.50 298	83.39 319	88.06 262	67.11 317	80.98 161	90.31 177	66.20 143	91.01 298	74.62 192	84.90 224	92.86 148

AstraMVS		80.81 174	80.14 175	82.80 221	86.05 285	63.96 258	86.46 229	85.90 312	73.71 170	80.85 166	90.56 171	54.06 286	91.57 270	79.72 127	83.97 241	92.86 148

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 148	86.84 64	94.65 31	67.31 127	95.77 64	84.80 68	92.85 78	92.84 150

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	85.71 311	69.32 98	95.38 82	80.82 113	91.37 105	92.72 151

agg_prior2														82.91 91	95.45 33	92.70 152

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 194	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 152
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 238	76.63 270	84.64 122	86.73 266	69.47 102	85.01 275	84.61 327	69.54 276	66.51 405	86.59 291	50.16 333	91.75 262	76.26 172	84.24 238	92.69 154

Vis-MVSNet (Re-imp)		78.36 245	78.45 217	78.07 335	88.64 178	51.78 428	86.70 219	79.63 400	74.14 160	75.11 295	90.83 163	61.29 213	89.75 320	58.10 358	91.60 99	92.69 154

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 282	76.41 86	85.80 71	90.22 182	74.15 35	95.37 85	81.82 103	91.88 94	92.65 156

test_fmvsmvis_n_1920		84.02 96	83.87 98	84.49 127	84.12 330	69.37 108	88.15 166	87.96 265	70.01 263	83.95 107	93.23 86	68.80 108	91.51 278	88.61 32	89.96 129	92.57 157

FA-MVS(test-final)		80.96 170	79.91 180	84.10 151	88.30 191	65.01 228	84.55 288	90.01 185	73.25 187	79.61 182	87.57 260	58.35 246	94.72 115	71.29 231	86.25 201	92.56 158

guyue		81.13 167	80.64 161	82.60 232	86.52 272	63.92 261	86.69 220	87.73 273	73.97 162	80.83 167	89.69 194	56.70 263	91.33 286	78.26 149	85.40 220	92.54 159

test_yl		81.17 165	80.47 166	83.24 196	89.13 156	63.62 266	86.21 241	89.95 187	72.43 203	81.78 147	89.61 198	57.50 254	93.58 166	70.75 235	86.90 188	92.52 160

DCV-MVSNet		81.17 165	80.47 166	83.24 196	89.13 156	63.62 266	86.21 241	89.95 187	72.43 203	81.78 147	89.61 198	57.50 254	93.58 166	70.75 235	86.90 188	92.52 160

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	73.53 177	85.69 73	94.45 37	65.00 158	95.56 68	82.75 94	91.87 95	92.50 162

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 89	73.53 177	85.69 73	94.45 37	63.87 166		82.75 94	91.87 95	92.50 162

nrg030		83.88 100	83.53 109	84.96 107	86.77 265	69.28 109	90.46 75	92.67 72	74.79 142	82.95 126	91.33 144	72.70 50	93.09 204	80.79 115	79.28 309	92.50 162

SSM_0404		81.91 147	80.84 158	85.13 101	89.24 151	68.26 137	87.84 179	89.25 220	71.06 232	80.62 169	90.39 175	59.57 235	94.65 119	72.45 222	87.19 183	92.47 165

MG-MVS		83.41 117	83.45 110	83.28 193	92.74 71	62.28 304	88.17 164	89.50 204	75.22 124	81.49 152	92.74 103	66.75 132	95.11 94	72.85 212	91.58 101	92.45 166

FIs		82.07 144	82.42 129	81.04 270	88.80 171	58.34 349	88.26 161	93.49 31	76.93 72	78.47 207	91.04 155	69.92 89	92.34 240	69.87 249	84.97 223	92.44 167

testing3-2		75.12 312	75.19 294	74.91 375	90.40 109	45.09 458	80.29 366	78.42 410	78.37 40	76.54 255	87.75 254	44.36 390	87.28 362	57.04 368	83.49 254	92.37 168

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 203	87.08 256	65.21 221	89.09 123	90.21 179	79.67 19	89.98 24	95.02 24	73.17 42	91.71 265	91.30 3	91.60 99	92.34 169

FC-MVSNet-test		81.52 160	82.02 141	80.03 293	88.42 187	55.97 389	87.95 172	93.42 34	77.10 68	77.38 231	90.98 161	69.96 88	91.79 260	68.46 264	84.50 230	92.33 170

Fast-Effi-MVS+		80.81 174	79.92 179	83.47 185	88.85 163	64.51 246	85.53 262	89.39 208	70.79 239	78.49 205	85.06 331	67.54 124	93.58 166	67.03 278	86.58 194	92.32 171

TranMVSNet+NR-MVSNet		80.84 172	80.31 169	82.42 235	87.85 212	62.33 302	87.74 181	91.33 140	80.55 9	77.99 219	89.86 186	65.23 154	92.62 222	67.05 277	75.24 371	92.30 172

ab-mvs		79.51 211	78.97 208	81.14 267	88.46 184	60.91 321	83.84 307	89.24 222	70.36 253	79.03 192	88.87 223	63.23 174	90.21 312	65.12 291	82.57 269	92.28 173

CANet_DTU		80.61 184	79.87 182	82.83 218	85.60 294	63.17 286	87.36 193	88.65 251	76.37 91	75.88 269	88.44 236	53.51 291	93.07 205	73.30 207	89.74 134	92.25 174

UniMVSNet_NR-MVSNet		81.88 148	81.54 147	82.92 214	88.46 184	63.46 277	87.13 199	92.37 86	80.19 12	78.38 208	89.14 211	71.66 64	93.05 207	70.05 245	76.46 344	92.25 174

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 144	85.42 299	68.81 116	88.49 150	87.26 284	68.08 308	88.03 44	93.49 77	72.04 57	91.77 261	88.90 29	89.14 146	92.24 176

DU-MVS		81.12 168	80.52 164	82.90 215	87.80 215	63.46 277	87.02 204	91.87 117	79.01 31	78.38 208	89.07 213	65.02 156	93.05 207	70.05 245	76.46 344	92.20 177

NR-MVSNet		80.23 199	79.38 196	82.78 225	87.80 215	63.34 280	86.31 236	91.09 149	79.01 31	72.17 339	89.07 213	67.20 128	92.81 219	66.08 284	75.65 357	92.20 177

mamba_0408		79.37 220	77.52 247	84.93 110	88.81 167	67.96 149	65.03 463	88.66 249	70.96 236	79.48 185	89.80 190	58.69 241	94.65 119	70.35 241	85.93 209	92.18 179

SSM_04072		77.67 267	77.52 247	78.12 333	88.81 167	67.96 149	65.03 463	88.66 249	70.96 236	79.48 185	89.80 190	58.69 241	74.23 455	70.35 241	85.93 209	92.18 179

SSM_0407		81.58 157	80.48 165	84.87 113	88.81 167	67.96 149	87.37 192	89.25 220	71.06 232	79.48 185	90.39 175	59.57 235	94.48 126	72.45 222	85.93 209	92.18 179

TAPA-MVS	73.13 9	79.15 224	77.94 230	82.79 224	89.59 130	62.99 291	88.16 165	91.51 135	65.77 337	77.14 242	91.09 153	60.91 220	93.21 193	50.26 410	87.05 186	92.17 182
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	84.06 160	85.38 300	68.40 133	88.34 158	86.85 294	67.48 315	87.48 55	93.40 82	70.89 73	91.61 266	88.38 37	89.22 143	92.16 183

3Dnovator	76.31 5	83.38 119	82.31 133	86.59 61	87.94 208	72.94 28	90.64 68	92.14 106	77.21 63	75.47 276	92.83 97	58.56 244	94.72 115	73.24 209	92.71 81	92.13 184

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 246	90.33 174	76.11 99	82.08 141	91.61 134	71.36 68	94.17 139	81.02 110	92.58 82	92.08 185

MVSFormer		82.85 132	82.05 140	85.24 95	87.35 237	70.21 86	90.50 72	90.38 170	68.55 301	81.32 154	89.47 203	61.68 202	93.46 180	78.98 137	90.26 123	92.05 186

jason		81.39 163	80.29 170	84.70 121	86.63 270	69.90 94	85.95 247	86.77 295	63.24 368	81.07 160	89.47 203	61.08 218	92.15 246	78.33 145	90.07 128	92.05 186
jason: jason.
HyFIR lowres test		77.53 269	75.40 289	83.94 173	89.59 130	66.62 188	80.36 364	88.64 252	56.29 432	76.45 256	85.17 328	57.64 252	93.28 186	61.34 327	83.10 262	91.91 188

XVG-OURS-SEG-HR		80.81 174	79.76 185	83.96 172	85.60 294	68.78 118	83.54 318	90.50 166	70.66 245	76.71 249	91.66 128	60.69 223	91.26 287	76.94 162	81.58 279	91.83 189

lupinMVS		81.39 163	80.27 171	84.76 119	87.35 237	70.21 86	85.55 260	86.41 302	62.85 375	81.32 154	88.61 230	61.68 202	92.24 244	78.41 144	90.26 123	91.83 189

WR-MVS		79.49 212	79.22 203	80.27 288	88.79 172	58.35 348	85.06 274	88.61 253	78.56 35	77.65 226	88.34 238	63.81 168	90.66 307	64.98 293	77.22 332	91.80 191

icg_test_0407_2		78.92 232	78.93 209	78.90 316	87.13 250	63.59 270	76.58 410	89.33 210	70.51 248	77.82 221	89.03 215	61.84 198	81.38 414	72.56 218	85.56 216	91.74 192

IMVS_0407		80.61 184	79.90 181	82.75 228	87.13 250	63.59 270	85.33 266	89.33 210	70.51 248	77.82 221	89.03 215	61.84 198	92.91 212	72.56 218	85.56 216	91.74 192

IMVS_0404		77.16 276	76.42 274	79.37 307	87.13 250	63.59 270	77.12 408	89.33 210	70.51 248	66.22 408	89.03 215	50.36 331	82.78 404	72.56 218	85.56 216	91.74 192

IMVS_0403		80.80 177	80.12 176	82.87 217	87.13 250	63.59 270	85.19 267	89.33 210	70.51 248	78.49 205	89.03 215	63.26 172	93.27 188	72.56 218	85.56 216	91.74 192

h-mvs33		83.15 125	82.19 136	86.02 76	90.56 105	70.85 79	88.15 166	89.16 225	76.02 101	84.67 87	91.39 142	61.54 205	95.50 73	82.71 96	75.48 361	91.72 196

UniMVSNet (Re)		81.60 156	81.11 152	83.09 203	88.38 188	64.41 251	87.60 183	93.02 50	78.42 37	78.56 203	88.16 244	69.78 91	93.26 189	69.58 252	76.49 343	91.60 197

UGNet		80.83 173	79.59 192	84.54 124	88.04 203	68.09 144	89.42 106	88.16 257	76.95 71	76.22 262	89.46 205	49.30 346	93.94 147	68.48 263	90.31 121	91.60 197
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		76.54 285	75.66 284	79.18 312	88.43 186	55.89 390	81.08 350	83.00 356	73.76 169	75.34 284	84.29 346	46.20 374	90.07 314	64.33 297	84.50 230	91.58 199

XVG-OURS		80.41 191	79.23 202	83.97 171	85.64 292	69.02 112	83.03 331	90.39 169	71.09 230	77.63 227	91.49 139	54.62 281	91.35 284	75.71 180	83.47 255	91.54 200

LCM-MVSNet-Re		77.05 277	76.94 260	77.36 349	87.20 247	51.60 429	80.06 369	80.46 388	75.20 127	67.69 385	86.72 283	62.48 187	88.98 336	63.44 303	89.25 141	91.51 201

DP-MVS Recon		83.11 128	82.09 139	86.15 70	94.44 23	70.92 76	88.79 135	92.20 99	70.53 247	79.17 191	91.03 157	64.12 164	96.03 55	68.39 265	90.14 125	91.50 202

PS-MVSNAJss		82.07 144	81.31 148	84.34 136	86.51 273	67.27 176	89.27 112	91.51 135	71.75 213	79.37 188	90.22 182	63.15 176	94.27 131	77.69 153	82.36 271	91.49 203

testing99		76.09 297	75.12 296	79.00 313	88.16 195	55.50 396	80.79 354	81.40 376	73.30 185	75.17 292	84.27 349	44.48 389	90.02 315	64.28 298	84.22 239	91.48 204

thisisatest0515		77.33 273	75.38 290	83.18 199	85.27 304	63.80 263	82.11 338	83.27 348	65.06 346	75.91 268	83.84 356	49.54 341	94.27 131	67.24 274	86.19 202	91.48 204

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 200	93.04 46	69.80 269	82.85 130	91.22 148	73.06 44	96.02 57	76.72 170	94.63 54	91.46 206

HQP_MVS		83.64 110	83.14 115	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 195	91.00 159	60.42 230	95.38 82	78.71 140	86.32 198	91.33 207

plane_prior5								92.44 82					95.38 82	78.71 140	86.32 198	91.33 207

GA-MVS		76.87 281	75.17 295	81.97 246	82.75 369	62.58 295	81.44 347	86.35 305	72.16 208	74.74 303	82.89 378	46.20 374	92.02 251	68.85 260	81.09 284	91.30 209

VPA-MVSNet		80.60 186	80.55 163	80.76 277	88.07 202	60.80 323	86.86 212	91.58 133	75.67 111	80.24 175	89.45 207	63.34 169	90.25 311	70.51 239	79.22 310	91.23 210

Effi-MVS+-dtu		80.03 203	78.57 215	84.42 130	85.13 309	68.74 121	88.77 136	88.10 259	74.99 133	74.97 300	83.49 367	57.27 257	93.36 184	73.53 203	80.88 287	91.18 211

v2v482		80.23 199	79.29 200	83.05 207	83.62 344	64.14 255	87.04 202	89.97 186	73.61 173	78.18 214	87.22 271	61.10 217	93.82 156	76.11 174	76.78 340	91.18 211

FE-MVS		77.78 261	75.68 282	84.08 156	88.09 201	66.00 200	83.13 326	87.79 271	68.42 305	78.01 218	85.23 326	45.50 383	95.12 92	59.11 346	85.83 213	91.11 213

Anonymous20231211		78.97 230	77.69 243	82.81 220	90.54 106	64.29 253	90.11 83	91.51 135	65.01 348	76.16 267	88.13 249	50.56 328	93.03 210	69.68 251	77.56 330	91.11 213

hse-mvs2		81.72 151	80.94 156	84.07 157	88.72 175	67.68 160	85.87 250	87.26 284	76.02 101	84.67 87	88.22 243	61.54 205	93.48 178	82.71 96	73.44 389	91.06 215

AUN-MVS		79.21 223	77.60 245	84.05 163	88.71 176	67.61 162	85.84 252	87.26 284	69.08 290	77.23 236	88.14 248	53.20 295	93.47 179	75.50 185	73.45 388	91.06 215

HQP4-MVS										77.24 235			95.11 94			91.03 217

HQP-MVS		82.61 136	82.02 141	84.37 133	89.33 144	66.98 183	89.17 116	92.19 101	76.41 86	77.23 236	90.23 181	60.17 233	95.11 94	77.47 155	85.99 207	91.03 217

RPSCF		73.23 336	71.46 340	78.54 324	82.50 375	59.85 335	82.18 337	82.84 361	58.96 411	71.15 351	89.41 209	45.48 384	84.77 389	58.82 350	71.83 401	91.02 219

LuminaMVS		80.68 182	79.62 191	83.83 175	85.07 311	68.01 148	86.99 205	88.83 240	70.36 253	81.38 153	87.99 251	50.11 334	92.51 231	79.02 134	86.89 190	90.97 220

test_djsdf		80.30 198	79.32 199	83.27 194	83.98 334	65.37 219	90.50 72	90.38 170	68.55 301	76.19 263	88.70 226	56.44 266	93.46 180	78.98 137	80.14 299	90.97 220

PCF-MVS	73.52 7	80.38 193	78.84 211	85.01 105	87.71 224	68.99 113	83.65 312	91.46 139	63.00 372	77.77 225	90.28 178	66.10 144	95.09 98	61.40 325	88.22 162	90.94 222
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 237	78.66 213	78.76 318	88.31 190	55.72 393	84.45 292	86.63 299	76.79 76	78.26 211	90.55 172	59.30 238	89.70 322	66.63 279	77.05 334	90.88 223

CPTT-MVS		83.73 106	83.33 114	84.92 111	93.28 53	70.86 78	92.09 41	90.38 170	68.75 298	79.57 183	92.83 97	60.60 228	93.04 209	80.92 112	91.56 102	90.86 224

fmvsm_s_conf0.5_n_7		83.34 120	84.03 96	81.28 262	85.73 290	65.13 224	85.40 265	89.90 189	74.96 136	82.13 140	93.89 69	66.65 133	87.92 353	86.56 53	91.05 109	90.80 225

tt0805		78.73 235	77.83 235	81.43 256	85.17 305	60.30 331	89.41 107	90.90 153	71.21 227	77.17 241	88.73 225	46.38 369	93.21 193	72.57 216	78.96 311	90.79 226

CLD-MVS		82.31 140	81.65 146	84.29 141	88.47 183	67.73 158	85.81 254	92.35 87	75.78 106	78.33 210	86.58 293	64.01 165	94.35 128	76.05 176	87.48 178	90.79 226
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 210	78.43 219	83.07 206	83.55 346	64.52 245	86.93 209	90.58 163	70.83 238	77.78 224	85.90 307	59.15 239	93.94 147	73.96 200	77.19 333	90.76 228

IterMVS-LS		80.06 202	79.38 196	82.11 242	85.89 286	63.20 284	86.79 215	89.34 209	74.19 158	75.45 279	86.72 283	66.62 134	92.39 236	72.58 215	76.86 337	90.75 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d28		73.62 327	73.53 317	73.90 388	88.20 193	47.41 448	78.06 399	79.37 402	74.29 156	73.98 314	84.29 346	44.67 386	83.54 398	51.47 400	87.39 179	90.74 230

EI-MVSNet		80.52 190	79.98 178	82.12 240	84.28 326	63.19 285	86.41 230	88.95 237	74.18 159	78.69 198	87.54 263	66.62 134	92.43 234	72.57 216	80.57 293	90.74 230

v1921920		79.22 222	78.03 228	82.80 221	83.30 351	63.94 260	86.80 214	90.33 174	69.91 267	77.48 229	85.53 318	58.44 245	93.75 162	73.60 202	76.85 338	90.71 232

QAPM		80.88 171	79.50 194	85.03 104	88.01 206	68.97 114	91.59 51	92.00 109	66.63 328	75.15 294	92.16 111	57.70 251	95.45 75	63.52 301	88.76 152	90.66 233

v144192		79.47 213	78.37 220	82.78 225	83.35 349	63.96 258	86.96 206	90.36 173	69.99 264	77.50 228	85.67 314	60.66 225	93.77 160	74.27 197	76.58 341	90.62 234

v1240		78.99 229	77.78 238	82.64 230	83.21 354	63.54 274	86.62 223	90.30 176	69.74 274	77.33 232	85.68 313	57.04 260	93.76 161	73.13 210	76.92 335	90.62 234

v1144		80.03 203	79.03 206	83.01 209	83.78 339	64.51 246	87.11 201	90.57 165	71.96 211	78.08 217	86.20 303	61.41 209	93.94 147	74.93 190	77.23 331	90.60 236

1112_ss		77.40 272	76.43 273	80.32 287	89.11 160	60.41 330	83.65 312	87.72 274	62.13 385	73.05 326	86.72 283	62.58 186	89.97 316	62.11 319	80.80 289	90.59 237

CP-MVSNet		78.22 247	78.34 221	77.84 339	87.83 214	54.54 406	87.94 173	91.17 145	77.65 46	73.48 321	88.49 234	62.24 193	88.43 347	62.19 316	74.07 380	90.55 238

testing222		74.04 322	72.66 328	78.19 331	87.89 210	55.36 397	81.06 351	79.20 405	71.30 225	74.65 306	83.57 366	39.11 423	88.67 343	51.43 402	85.75 214	90.53 239

PS-CasMVS		78.01 256	78.09 227	77.77 341	87.71 224	54.39 408	88.02 169	91.22 142	77.50 54	73.26 323	88.64 229	60.73 221	88.41 348	61.88 320	73.88 384	90.53 239

CDS-MVSNet		79.07 227	77.70 242	83.17 200	87.60 231	68.23 141	84.40 296	86.20 307	67.49 314	76.36 259	86.54 295	61.54 205	90.79 302	61.86 321	87.33 180	90.49 241
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 233	77.51 249	83.03 208	87.80 215	67.79 157	84.72 281	85.05 323	67.63 311	76.75 248	87.70 256	62.25 192	90.82 301	58.53 353	87.13 185	90.49 241

PEN-MVS		77.73 262	77.69 243	77.84 339	87.07 258	53.91 411	87.91 175	91.18 144	77.56 51	73.14 325	88.82 224	61.23 214	89.17 332	59.95 336	72.37 395	90.43 243

Test_1112_low_res		76.40 292	75.44 287	79.27 309	89.28 149	58.09 351	81.69 342	87.07 288	59.53 406	72.48 334	86.67 288	61.30 212	89.33 327	60.81 331	80.15 298	90.41 244

HY-MVS	69.67 12	77.95 257	77.15 255	80.36 285	87.57 236	60.21 333	83.37 321	87.78 272	66.11 332	75.37 283	87.06 278	63.27 171	90.48 309	61.38 326	82.43 270	90.40 245

sc_t1		72.19 349	69.51 360	80.23 289	84.81 315	61.09 318	84.68 282	80.22 394	60.70 395	71.27 348	83.58 365	36.59 434	89.24 330	60.41 332	63.31 434	90.37 246

CHOSEN 1792x2688		77.63 268	75.69 281	83.44 187	89.98 122	68.58 129	78.70 389	87.50 278	56.38 431	75.80 271	86.84 279	58.67 243	91.40 283	61.58 324	85.75 214	90.34 247

SDMVSNet		80.38 193	80.18 172	80.99 271	89.03 161	64.94 234	80.45 363	89.40 207	75.19 128	76.61 253	89.98 184	60.61 227	87.69 357	76.83 166	83.55 252	90.33 248

sd_testset		77.70 265	77.40 250	78.60 321	89.03 161	60.02 334	79.00 384	85.83 313	75.19 128	76.61 253	89.98 184	54.81 274	85.46 382	62.63 312	83.55 252	90.33 248

114514_t		80.68 182	79.51 193	84.20 148	94.09 42	67.27 176	89.64 96	91.11 148	58.75 415	74.08 313	90.72 164	58.10 247	95.04 99	69.70 250	89.42 140	90.30 250

eth_miper_zixun_eth		77.92 258	76.69 268	81.61 253	83.00 362	61.98 307	83.15 325	89.20 224	69.52 277	74.86 302	84.35 345	61.76 201	92.56 227	71.50 229	72.89 393	90.28 251

PVSNet_Blended_VisFu		82.62 135	81.83 145	84.96 107	90.80 101	69.76 97	88.74 140	91.70 126	69.39 278	78.96 193	88.46 235	65.47 152	94.87 107	74.42 195	88.57 155	90.24 252

MVS_111021_LR		82.61 136	82.11 137	84.11 150	88.82 166	71.58 57	85.15 270	86.16 308	74.69 144	80.47 173	91.04 155	62.29 191	90.55 308	80.33 120	90.08 127	90.20 253

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	67.16 129	92.94 211	80.36 119	94.35 63	90.16 254

mvs_tets		79.13 225	77.77 239	83.22 198	84.70 318	66.37 192	89.17 116	90.19 180	69.38 279	75.40 281	89.46 205	44.17 392	93.15 200	76.78 169	80.70 291	90.14 255

BH-RMVSNet		79.61 208	78.44 218	83.14 201	89.38 143	65.93 202	84.95 277	87.15 287	73.56 175	78.19 213	89.79 192	56.67 264	93.36 184	59.53 341	86.74 192	90.13 256

c3_l		78.75 234	77.91 231	81.26 263	82.89 367	61.56 313	84.09 304	89.13 228	69.97 265	75.56 274	84.29 346	66.36 139	92.09 248	73.47 205	75.48 361	90.12 257

v7n		78.97 230	77.58 246	83.14 201	83.45 348	65.51 214	88.32 159	91.21 143	73.69 171	72.41 335	86.32 301	57.93 248	93.81 157	69.18 255	75.65 357	90.11 258

jajsoiax		79.29 221	77.96 229	83.27 194	84.68 319	66.57 190	89.25 113	90.16 181	69.20 287	75.46 278	89.49 202	45.75 380	93.13 202	76.84 165	80.80 289	90.11 258

v148		78.72 236	77.80 237	81.47 255	82.73 370	61.96 308	86.30 237	88.08 260	73.26 186	76.18 264	85.47 320	62.46 188	92.36 238	71.92 226	73.82 385	90.09 260

GBi-Net		78.40 243	77.40 250	81.40 258	87.60 231	63.01 287	88.39 154	89.28 216	71.63 215	75.34 284	87.28 267	54.80 275	91.11 291	62.72 308	79.57 303	90.09 260

test1		78.40 243	77.40 250	81.40 258	87.60 231	63.01 287	88.39 154	89.28 216	71.63 215	75.34 284	87.28 267	54.80 275	91.11 291	62.72 308	79.57 303	90.09 260

FMVSNet1		77.44 270	76.12 278	81.40 258	86.81 263	63.01 287	88.39 154	89.28 216	70.49 252	74.39 310	87.28 267	49.06 350	91.11 291	60.91 329	78.52 314	90.09 260

WR-MVS_H		78.51 242	78.49 216	78.56 323	88.02 204	56.38 383	88.43 151	92.67 72	77.14 65	73.89 315	87.55 262	66.25 141	89.24 330	58.92 348	73.55 387	90.06 264

DTE-MVSNet		76.99 278	76.80 263	77.54 348	86.24 277	53.06 420	87.52 185	90.66 161	77.08 69	72.50 333	88.67 228	60.48 229	89.52 324	57.33 365	70.74 407	90.05 265

v8		79.97 205	79.02 207	82.80 221	84.09 331	64.50 248	87.96 171	90.29 177	74.13 161	75.24 291	86.81 280	62.88 183	93.89 155	74.39 196	75.40 366	90.00 266

thres600view7		76.50 287	75.44 287	79.68 301	89.40 141	57.16 369	85.53 262	83.23 349	73.79 168	76.26 261	87.09 276	51.89 312	91.89 257	48.05 425	83.72 249	90.00 266

thres400		76.50 287	75.37 291	79.86 296	89.13 156	57.65 363	85.17 268	83.60 341	73.41 181	76.45 256	86.39 299	52.12 304	91.95 254	48.33 420	83.75 246	90.00 266

cl22		78.07 253	77.01 257	81.23 264	82.37 379	61.83 310	83.55 316	87.98 264	68.96 295	75.06 297	83.87 354	61.40 210	91.88 258	73.53 203	76.39 346	89.98 269

OPM-MVS		83.50 115	82.95 120	85.14 98	88.79 172	70.95 74	89.13 121	91.52 134	77.55 52	80.96 162	91.75 125	60.71 222	94.50 124	79.67 128	86.51 196	89.97 270
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 301	73.83 314	81.30 261	83.26 352	61.79 311	82.57 334	80.65 383	66.81 319	66.88 396	83.42 368	57.86 250	92.19 245	63.47 302	79.57 303	89.91 271

v10		79.74 207	78.67 212	82.97 213	84.06 332	64.95 231	87.88 177	90.62 162	73.11 191	75.11 295	86.56 294	61.46 208	94.05 143	73.68 201	75.55 359	89.90 272

MVSTER		79.01 228	77.88 234	82.38 236	83.07 359	64.80 240	84.08 305	88.95 237	69.01 294	78.69 198	87.17 274	54.70 279	92.43 234	74.69 191	80.57 293	89.89 273

ACMP	74.13 6	81.51 162	80.57 162	84.36 134	89.42 139	68.69 126	89.97 85	91.50 138	74.46 150	75.04 298	90.41 174	53.82 288	94.54 121	77.56 154	82.91 263	89.86 274
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 143	81.27 149	84.50 125	89.23 152	68.76 119	90.22 81	91.94 113	75.37 119	76.64 251	91.51 137	54.29 282	94.91 102	78.44 142	83.78 243	89.83 275

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 113	75.37 119	76.64 251	91.51 137	54.29 282	94.91 102	78.44 142	83.78 243	89.83 275

V42		79.38 219	78.24 224	82.83 218	81.10 398	65.50 215	85.55 260	89.82 190	71.57 219	78.21 212	86.12 305	60.66 225	93.18 199	75.64 181	75.46 363	89.81 277

MAR-MVS		81.84 149	80.70 159	85.27 94	91.32 89	71.53 58	89.82 88	90.92 152	69.77 271	78.50 204	86.21 302	62.36 190	94.52 123	65.36 289	92.05 93	89.77 278
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		77.72 263	76.76 265	80.58 281	82.48 377	60.48 328	83.09 327	87.86 269	69.22 285	74.38 311	85.24 325	62.10 195	91.53 276	71.09 232	75.40 366	89.74 279

cl____		77.72 263	76.76 265	80.58 281	82.49 376	60.48 328	83.09 327	87.87 268	69.22 285	74.38 311	85.22 327	62.10 195	91.53 276	71.09 232	75.41 365	89.73 280

miper_ehance_all_eth		78.59 240	77.76 240	81.08 269	82.66 372	61.56 313	83.65 312	89.15 226	68.87 296	75.55 275	83.79 358	66.49 137	92.03 249	73.25 208	76.39 346	89.64 281

anonymousdsp		78.60 239	77.15 255	82.98 212	80.51 404	67.08 181	87.24 198	89.53 203	65.66 339	75.16 293	87.19 273	52.52 297	92.25 243	77.17 159	79.34 308	89.61 282

FMVSNet2		78.20 249	77.21 254	81.20 265	87.60 231	62.89 293	87.47 187	89.02 232	71.63 215	75.29 290	87.28 267	54.80 275	91.10 294	62.38 313	79.38 307	89.61 282

baseline1		76.98 279	76.75 267	77.66 343	88.13 198	55.66 394	85.12 271	81.89 369	73.04 193	76.79 246	88.90 221	62.43 189	87.78 356	63.30 305	71.18 405	89.55 284

ETVMVS		72.25 348	71.05 347	75.84 361	87.77 220	51.91 425	79.39 377	74.98 431	69.26 283	73.71 317	82.95 376	40.82 415	86.14 372	46.17 433	84.43 235	89.47 285

FMVSNet3		77.88 259	76.85 262	80.97 273	86.84 262	62.36 301	86.52 227	88.77 243	71.13 228	75.34 284	86.66 289	54.07 285	91.10 294	62.72 308	79.57 303	89.45 286

SD_0403		74.65 315	74.77 299	74.29 383	86.20 279	47.42 447	83.71 310	85.12 320	69.30 281	68.50 380	87.95 252	59.40 237	86.05 373	49.38 414	83.35 257	89.40 287

miper_enhance_ethall		77.87 260	76.86 261	80.92 274	81.65 386	61.38 315	82.68 332	88.98 234	65.52 341	75.47 276	82.30 387	65.76 151	92.00 252	72.95 211	76.39 346	89.39 288

testing11		75.14 311	74.01 309	78.53 325	88.16 195	56.38 383	80.74 357	80.42 390	70.67 242	72.69 332	83.72 361	43.61 396	89.86 317	62.29 315	83.76 245	89.36 289

cascas		76.72 284	74.64 300	82.99 210	85.78 289	65.88 204	82.33 335	89.21 223	60.85 394	72.74 329	81.02 399	47.28 359	93.75 162	67.48 271	85.02 222	89.34 290

Fast-Effi-MVS+-dtu		78.02 255	76.49 271	82.62 231	83.16 358	66.96 185	86.94 208	87.45 280	72.45 200	71.49 347	84.17 351	54.79 278	91.58 268	67.61 269	80.31 296	89.30 291

IB-MVS	68.01 15	75.85 300	73.36 320	83.31 192	84.76 317	66.03 197	83.38 320	85.06 322	70.21 260	69.40 370	81.05 398	45.76 379	94.66 118	65.10 292	75.49 360	89.25 292
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		76.50 287	75.55 286	79.33 308	89.52 133	56.99 372	85.83 253	83.23 349	73.94 164	76.32 260	87.12 275	51.89 312	91.95 254	48.33 420	83.75 246	89.07 293

tfpn200view9		76.42 291	75.37 291	79.55 306	89.13 156	57.65 363	85.17 268	83.60 341	73.41 181	76.45 256	86.39 299	52.12 304	91.95 254	48.33 420	83.75 246	89.07 293

xiu_mvs_v1_base_debu		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 323	92.85 215	78.29 146	87.56 175	89.06 295

xiu_mvs_v1_base		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 323	92.85 215	78.29 146	87.56 175	89.06 295

xiu_mvs_v1_base_debi		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 323	92.85 215	78.29 146	87.56 175	89.06 295

EPNet_dtu		75.46 305	74.86 297	77.23 352	82.57 374	54.60 405	86.89 210	83.09 353	71.64 214	66.25 407	85.86 309	55.99 267	88.04 352	54.92 382	86.55 195	89.05 298
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 275	76.68 269	78.93 315	84.22 328	58.62 346	86.41 230	88.36 256	71.37 222	73.31 322	88.01 250	61.22 215	89.15 333	64.24 299	73.01 392	89.03 299

PVSNet_Blended		80.98 169	80.34 168	82.90 215	88.85 163	65.40 216	84.43 293	92.00 109	67.62 312	78.11 215	85.05 332	66.02 147	94.27 131	71.52 227	89.50 138	89.01 300

PAPM		77.68 266	76.40 275	81.51 254	87.29 246	61.85 309	83.78 308	89.59 201	64.74 350	71.23 349	88.70 226	62.59 185	93.66 165	52.66 394	87.03 187	89.01 300

WTY-MVS		75.65 302	75.68 282	75.57 365	86.40 275	56.82 374	77.92 402	82.40 364	65.10 345	76.18 264	87.72 255	63.13 179	80.90 417	60.31 334	81.96 275	89.00 302

无先验							87.48 186	88.98 234	60.00 401				94.12 140	67.28 273		88.97 303

GSMVS																88.96 304

sam_mvs1												51.32 319				88.96 304

SCA		74.22 319	72.33 332	79.91 295	84.05 333	62.17 305	79.96 372	79.29 404	66.30 331	72.38 336	80.13 411	51.95 310	88.60 344	59.25 344	77.67 329	88.96 304

miper_lstm_enhance		74.11 321	73.11 323	77.13 353	80.11 408	59.62 338	72.23 434	86.92 293	66.76 321	70.40 355	82.92 377	56.93 261	82.92 403	69.06 257	72.63 394	88.87 307

ACMM	73.20 8	80.78 181	79.84 183	83.58 183	89.31 147	68.37 134	89.99 84	91.60 132	70.28 257	77.25 234	89.66 196	53.37 293	93.53 174	74.24 198	82.85 264	88.85 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 314	73.39 318	78.61 320	81.38 393	57.48 366	86.64 222	87.95 266	64.99 349	70.18 358	86.61 290	50.43 330	89.52 324	62.12 318	70.18 410	88.83 309

原ACMM1				84.35 135	93.01 66	68.79 117		92.44 82	63.96 364	81.09 159	91.57 135	66.06 146	95.45 75	67.19 275	94.82 50	88.81 310

CNLPA		78.08 252	76.79 264	81.97 246	90.40 109	71.07 70	87.59 184	84.55 328	66.03 335	72.38 336	89.64 197	57.56 253	86.04 374	59.61 340	83.35 257	88.79 311

UWE-MVS		72.13 350	71.49 339	74.03 386	86.66 269	47.70 445	81.40 348	76.89 424	63.60 367	75.59 273	84.22 350	39.94 418	85.62 379	48.98 417	86.13 204	88.77 312

UBG		73.08 338	72.27 333	75.51 367	88.02 204	51.29 433	78.35 396	77.38 419	65.52 341	73.87 316	82.36 385	45.55 381	86.48 369	55.02 381	84.39 236	88.75 313

K. test v3		71.19 355	68.51 367	79.21 311	83.04 361	57.78 361	84.35 297	76.91 423	72.90 196	62.99 429	82.86 379	39.27 420	91.09 296	61.65 323	52.66 456	88.75 313

旧先验1					91.96 80	65.79 208		86.37 304			93.08 92	69.31 99			92.74 80	88.74 315

PatchmatchNet		73.12 337	71.33 343	78.49 327	83.18 356	60.85 322	79.63 374	78.57 409	64.13 357	71.73 343	79.81 416	51.20 321	85.97 375	57.40 364	76.36 351	88.66 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 331	71.26 346	79.70 300	85.08 310	57.89 357	85.57 256	83.56 343	71.03 234	65.66 410	85.88 308	42.10 406	92.57 226	59.11 346	63.34 433	88.65 317

SSC-MVS3.2		73.35 334	73.39 318	73.23 392	85.30 303	49.01 443	74.58 427	81.57 373	75.21 126	73.68 318	85.58 317	52.53 296	82.05 409	54.33 386	77.69 328	88.63 318

PS-MVSNAJ		81.69 153	81.02 154	83.70 179	89.51 134	68.21 142	84.28 298	90.09 183	70.79 239	81.26 158	85.62 316	63.15 176	94.29 129	75.62 182	88.87 149	88.59 319

xiu_mvs_v2_base		81.69 153	81.05 153	83.60 181	89.15 155	68.03 147	84.46 291	90.02 184	70.67 242	81.30 157	86.53 296	63.17 175	94.19 138	75.60 183	88.54 156	88.57 320

MonoMVSNet		76.49 290	75.80 279	78.58 322	81.55 389	58.45 347	86.36 235	86.22 306	74.87 141	74.73 304	83.73 360	51.79 315	88.73 341	70.78 234	72.15 398	88.55 321

CostFormer		75.24 310	73.90 312	79.27 309	82.65 373	58.27 350	80.80 353	82.73 362	61.57 389	75.33 288	83.13 373	55.52 270	91.07 297	64.98 293	78.34 321	88.45 322

lessismore_v0				78.97 314	81.01 399	57.15 370		65.99 458		61.16 435	82.82 380	39.12 422	91.34 285	59.67 339	46.92 463	88.43 323

OpenMVS	72.83 10	79.77 206	78.33 222	84.09 155	85.17 305	69.91 93	90.57 69	90.97 151	66.70 322	72.17 339	91.91 117	54.70 279	93.96 144	61.81 322	90.95 112	88.41 324

reproduce_monomvs		75.40 308	74.38 306	78.46 328	83.92 336	57.80 360	83.78 308	86.94 291	73.47 179	72.25 338	84.47 340	38.74 424	89.27 329	75.32 187	70.53 408	88.31 325

VortexMVS		78.57 241	77.89 233	80.59 280	85.89 286	62.76 294	85.61 255	89.62 200	72.06 209	74.99 299	85.38 322	55.94 268	90.77 305	74.99 189	76.58 341	88.23 326

OurMVSNet-221017-0		74.26 318	72.42 331	79.80 298	83.76 340	59.59 339	85.92 249	86.64 298	66.39 330	66.96 395	87.58 259	39.46 419	91.60 267	65.76 287	69.27 413	88.22 327

LS3D		76.95 280	74.82 298	83.37 191	90.45 107	67.36 172	89.15 120	86.94 291	61.87 388	69.52 369	90.61 170	51.71 316	94.53 122	46.38 432	86.71 193	88.21 328

WBMVS		73.43 330	72.81 326	75.28 371	87.91 209	50.99 435	78.59 392	81.31 378	65.51 343	74.47 309	84.83 335	46.39 368	86.68 366	58.41 354	77.86 324	88.17 329

XVG-ACMP-BASELINE		76.11 296	74.27 308	81.62 251	83.20 355	64.67 242	83.60 315	89.75 195	69.75 272	71.85 342	87.09 276	32.78 443	92.11 247	69.99 247	80.43 295	88.09 330

tpm2		73.26 335	71.46 340	78.63 319	83.34 350	56.71 377	80.65 359	80.40 391	56.63 430	73.55 320	82.02 392	51.80 314	91.24 288	56.35 376	78.42 319	87.95 331

MDTV_nov1_ep13_2view						37.79 472	75.16 421		55.10 435	66.53 402		49.34 345		53.98 387		87.94 332

Patchmatch-test		64.82 408	63.24 409	69.57 419	79.42 420	49.82 441	63.49 467	69.05 451	51.98 445	59.95 441	80.13 411	50.91 323	70.98 460	40.66 450	73.57 386	87.90 333

PLC	70.83 11	78.05 254	76.37 276	83.08 205	91.88 83	67.80 156	88.19 163	89.46 205	64.33 356	69.87 366	88.38 237	53.66 289	93.58 166	58.86 349	82.73 266	87.86 334
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 346	71.71 337	74.35 382	82.19 380	52.00 423	79.22 380	77.29 420	64.56 352	72.95 328	83.68 363	51.35 318	83.26 402	58.33 356	75.80 355	87.81 335

Patchmatch-RL test		70.24 368	67.78 381	77.61 345	77.43 430	59.57 340	71.16 438	70.33 445	62.94 374	68.65 377	72.77 451	50.62 327	85.49 381	69.58 252	66.58 424	87.77 336

F-COLMAP		76.38 293	74.33 307	82.50 234	89.28 149	66.95 186	88.41 153	89.03 231	64.05 361	66.83 397	88.61 230	46.78 365	92.89 213	57.48 362	78.55 313	87.67 337

Baseline_NR-MVSNet		78.15 251	78.33 222	77.61 345	85.79 288	56.21 387	86.78 216	85.76 314	73.60 174	77.93 220	87.57 260	65.02 156	88.99 335	67.14 276	75.33 368	87.63 338

CL-MVSNet_self_test		72.37 346	71.46 340	75.09 373	79.49 419	53.53 413	80.76 356	85.01 324	69.12 289	70.51 353	82.05 391	57.92 249	84.13 393	52.27 396	66.00 427	87.60 339

ACMH+	68.96 14	76.01 298	74.01 309	82.03 244	88.60 179	65.31 220	88.86 130	87.55 276	70.25 259	67.75 384	87.47 265	41.27 411	93.19 198	58.37 355	75.94 354	87.60 339

1314		76.53 286	75.30 293	80.21 290	83.93 335	62.32 303	84.66 283	88.81 241	60.23 399	70.16 360	84.07 353	55.30 272	90.73 306	67.37 272	83.21 260	87.59 341

API-MVS		81.99 146	81.23 150	84.26 146	90.94 97	70.18 91	91.10 63	89.32 214	71.51 220	78.66 200	88.28 240	65.26 153	95.10 97	64.74 295	91.23 107	87.51 342

AdaColmap		80.58 189	79.42 195	84.06 160	93.09 63	68.91 115	89.36 110	88.97 236	69.27 282	75.70 272	89.69 194	57.20 259	95.77 64	63.06 306	88.41 160	87.50 343

PVSNet_BlendedMVS		80.60 186	80.02 177	82.36 237	88.85 163	65.40 216	86.16 243	92.00 109	69.34 280	78.11 215	86.09 306	66.02 147	94.27 131	71.52 227	82.06 274	87.39 344

sss		73.60 328	73.64 316	73.51 391	82.80 368	55.01 402	76.12 412	81.69 372	62.47 381	74.68 305	85.85 310	57.32 256	78.11 428	60.86 330	80.93 285	87.39 344

IterMVS-SCA-FT		75.43 306	73.87 313	80.11 292	82.69 371	64.85 239	81.57 344	83.47 345	69.16 288	70.49 354	84.15 352	51.95 310	88.15 350	69.23 254	72.14 399	87.34 346

PVSNet	64.34 18	72.08 351	70.87 350	75.69 363	86.21 278	56.44 381	74.37 428	80.73 382	62.06 386	70.17 359	82.23 389	42.86 400	83.31 401	54.77 383	84.45 234	87.32 347

tt0320-xc		70.11 370	67.45 387	78.07 335	85.33 302	59.51 341	83.28 322	78.96 407	58.77 413	67.10 394	80.28 409	36.73 433	87.42 360	56.83 372	59.77 445	87.29 348

新几何1				83.42 188	93.13 60	70.71 80		85.48 317	57.43 426	81.80 146	91.98 116	63.28 170	92.27 242	64.60 296	92.99 76	87.27 349

TR-MVS		77.44 270	76.18 277	81.20 265	88.24 192	63.24 282	84.61 286	86.40 303	67.55 313	77.81 223	86.48 297	54.10 284	93.15 200	57.75 361	82.72 267	87.20 350

TransMVSNet (Re)		75.39 309	74.56 302	77.86 338	85.50 298	57.10 371	86.78 216	86.09 310	72.17 207	71.53 346	87.34 266	63.01 180	89.31 328	56.84 371	61.83 438	87.17 351

ACMH	67.68 16	75.89 299	73.93 311	81.77 249	88.71 176	66.61 189	88.62 145	89.01 233	69.81 268	66.78 398	86.70 287	41.95 408	91.51 278	55.64 378	78.14 322	87.17 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 380	67.59 385	72.46 403	74.29 444	45.45 453	77.93 401	87.00 289	63.12 369	63.99 424	78.99 425	42.32 403	84.77 389	56.55 375	64.09 432	87.16 353

EPMVS		69.02 379	68.16 371	71.59 407	79.61 417	49.80 442	77.40 405	66.93 456	62.82 377	70.01 361	79.05 421	45.79 378	77.86 430	56.58 374	75.26 370	87.13 354

CR-MVSNet		73.37 331	71.27 345	79.67 302	81.32 396	65.19 222	75.92 414	80.30 392	59.92 402	72.73 330	81.19 396	52.50 298	86.69 365	59.84 337	77.71 326	87.11 355

RPMNet		73.51 329	70.49 353	82.58 233	81.32 396	65.19 222	75.92 414	92.27 89	57.60 424	72.73 330	76.45 439	52.30 301	95.43 77	48.14 424	77.71 326	87.11 355

test_vis1_n_1920		75.52 304	75.78 280	74.75 379	79.84 412	57.44 367	83.26 323	85.52 316	62.83 376	79.34 190	86.17 304	45.10 385	79.71 421	78.75 139	81.21 283	87.10 357

tt0320		70.49 366	68.03 374	77.89 337	84.78 316	59.12 343	83.55 316	80.44 389	58.13 419	67.43 390	80.41 407	39.26 421	87.54 359	55.12 380	63.18 435	86.99 358

XXY-MVS		75.41 307	75.56 285	74.96 374	83.59 345	57.82 359	80.59 360	83.87 339	66.54 329	74.93 301	88.31 239	63.24 173	80.09 420	62.16 317	76.85 338	86.97 359

tpmrst		72.39 344	72.13 334	73.18 396	80.54 403	49.91 440	79.91 373	79.08 406	63.11 370	71.69 344	79.95 413	55.32 271	82.77 405	65.66 288	73.89 383	86.87 360

thres200		75.55 303	74.47 304	78.82 317	87.78 218	57.85 358	83.07 329	83.51 344	72.44 202	75.84 270	84.42 341	52.08 307	91.75 262	47.41 427	83.64 251	86.86 361

ITE_SJBPF				78.22 330	81.77 385	60.57 326		83.30 347	69.25 284	67.54 386	87.20 272	36.33 436	87.28 362	54.34 385	74.62 377	86.80 362

test222					91.50 86	68.26 137	84.16 302	83.20 352	54.63 437	79.74 180	91.63 131	58.97 240			91.42 103	86.77 363

MIMVSNet		70.69 362	69.30 361	74.88 376	84.52 323	56.35 385	75.87 416	79.42 401	64.59 351	67.76 383	82.41 384	41.10 412	81.54 412	46.64 431	81.34 280	86.75 364

BH-untuned		79.47 213	78.60 214	82.05 243	89.19 154	65.91 203	86.07 245	88.52 254	72.18 206	75.42 280	87.69 257	61.15 216	93.54 173	60.38 333	86.83 191	86.70 365

FE-MVSNET2		72.88 342	71.28 344	77.67 342	78.30 427	57.78 361	84.43 293	88.92 239	69.56 275	64.61 418	81.67 394	46.73 367	88.54 346	59.33 342	67.99 419	86.69 366

LTVRE_ROB	69.57 13	76.25 294	74.54 303	81.41 257	88.60 179	64.38 252	79.24 379	89.12 229	70.76 241	69.79 368	87.86 253	49.09 349	93.20 196	56.21 377	80.16 297	86.65 367
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				79.97 294	90.90 98	64.21 254		84.71 325	59.27 408	85.40 75	92.91 94	62.02 197	89.08 334	68.95 258	91.37 105	86.63 368

MIMVSNet1		68.58 383	66.78 393	73.98 387	80.07 409	51.82 427	80.77 355	84.37 329	64.40 354	59.75 442	82.16 390	36.47 435	83.63 397	42.73 445	70.33 409	86.48 369

tfpnnormal		74.39 316	73.16 322	78.08 334	86.10 284	58.05 352	84.65 285	87.53 277	70.32 256	71.22 350	85.63 315	54.97 273	89.86 317	43.03 444	75.02 373	86.32 370

D2MVS		74.82 313	73.21 321	79.64 303	79.81 413	62.56 297	80.34 365	87.35 281	64.37 355	68.86 375	82.66 382	46.37 370	90.10 313	67.91 267	81.24 282	86.25 371

tpm cat1		70.57 363	68.31 369	77.35 350	82.41 378	57.95 356	78.08 398	80.22 394	52.04 443	68.54 379	77.66 434	52.00 309	87.84 355	51.77 397	72.07 400	86.25 371

CVMVSNet		72.99 340	72.58 329	74.25 384	84.28 326	50.85 436	86.41 230	83.45 346	44.56 456	73.23 324	87.54 263	49.38 344	85.70 377	65.90 285	78.44 316	86.19 373

AllTest		70.96 358	68.09 373	79.58 304	85.15 307	63.62 266	84.58 287	79.83 397	62.31 382	60.32 439	86.73 281	32.02 444	88.96 338	50.28 408	71.57 403	86.15 374

TestCases				79.58 304	85.15 307	63.62 266		79.83 397	62.31 382	60.32 439	86.73 281	32.02 444	88.96 338	50.28 408	71.57 403	86.15 374

test-LLR		72.94 341	72.43 330	74.48 380	81.35 394	58.04 353	78.38 393	77.46 416	66.66 323	69.95 364	79.00 423	48.06 355	79.24 422	66.13 281	84.83 225	86.15 374

test-mter		71.41 354	70.39 356	74.48 380	81.35 394	58.04 353	78.38 393	77.46 416	60.32 398	69.95 364	79.00 423	36.08 437	79.24 422	66.13 281	84.83 225	86.15 374

IterMVS		74.29 317	72.94 325	78.35 329	81.53 390	63.49 276	81.58 343	82.49 363	68.06 309	69.99 363	83.69 362	51.66 317	85.54 380	65.85 286	71.64 402	86.01 378
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 283	74.57 301	83.42 188	93.29 52	69.46 104	88.55 149	83.70 340	63.98 363	70.20 357	88.89 222	54.01 287	94.80 111	46.66 429	81.88 277	86.01 378

ppachtmachnet_test		70.04 371	67.34 389	78.14 332	79.80 414	61.13 316	79.19 381	80.59 384	59.16 409	65.27 413	79.29 420	46.75 366	87.29 361	49.33 415	66.72 422	86.00 380

mmtdpeth		74.16 320	73.01 324	77.60 347	83.72 341	61.13 316	85.10 272	85.10 321	72.06 209	77.21 240	80.33 408	43.84 394	85.75 376	77.14 160	52.61 457	85.91 381

test_fmvs1_n		70.86 360	70.24 357	72.73 400	72.51 458	55.28 399	81.27 349	79.71 399	51.49 447	78.73 197	84.87 334	27.54 453	77.02 433	76.06 175	79.97 301	85.88 382

Patchmtry		70.74 361	69.16 364	75.49 368	80.72 400	54.07 410	74.94 425	80.30 392	58.34 416	70.01 361	81.19 396	52.50 298	86.54 367	53.37 391	71.09 406	85.87 383

WB-MVSnew		71.96 352	71.65 338	72.89 398	84.67 322	51.88 426	82.29 336	77.57 415	62.31 382	73.67 319	83.00 375	53.49 292	81.10 416	45.75 436	82.13 273	85.70 384

test_fmvs2		68.35 387	67.48 386	70.98 415	69.50 461	51.95 424	80.05 370	76.38 426	49.33 450	74.65 306	84.38 343	23.30 462	75.40 450	74.51 194	75.17 372	85.60 385

ambc				75.24 372	73.16 453	50.51 438	63.05 468	87.47 279		64.28 420	77.81 433	17.80 468	89.73 321	57.88 360	60.64 442	85.49 386

mvs5depth		69.45 376	67.45 387	75.46 369	73.93 445	55.83 391	79.19 381	83.23 349	66.89 318	71.63 345	83.32 369	33.69 442	85.09 385	59.81 338	55.34 453	85.46 387

UnsupCasMVSNet_eth		67.33 392	65.99 396	71.37 409	73.48 450	51.47 431	75.16 421	85.19 319	65.20 344	60.78 436	80.93 403	42.35 402	77.20 432	57.12 366	53.69 455	85.44 388

PatchT		68.46 386	67.85 377	70.29 417	80.70 401	43.93 461	72.47 433	74.88 432	60.15 400	70.55 352	76.57 438	49.94 337	81.59 411	50.58 404	74.83 375	85.34 389

Anonymous20240521		68.80 381	67.22 390	73.55 390	74.33 443	54.11 409	83.18 324	85.61 315	58.15 418	61.68 433	80.94 401	30.71 449	81.27 415	57.00 369	73.34 391	85.28 390

test_cas_vis1_n_1920		73.76 326	73.74 315	73.81 389	75.90 435	59.77 336	80.51 361	82.40 364	58.30 417	81.62 151	85.69 312	44.35 391	76.41 439	76.29 171	78.61 312	85.23 391

ADS-MVSNet2		66.20 404	63.33 408	74.82 377	79.92 410	58.75 345	67.55 453	75.19 430	53.37 440	65.25 414	75.86 442	42.32 403	80.53 419	41.57 448	68.91 415	85.18 392

ADS-MVSNet		64.36 409	62.88 412	68.78 425	79.92 410	47.17 449	67.55 453	71.18 444	53.37 440	65.25 414	75.86 442	42.32 403	73.99 456	41.57 448	68.91 415	85.18 392

FMVSNet5		69.50 375	67.96 375	74.15 385	82.97 365	55.35 398	80.01 371	82.12 367	62.56 380	63.02 427	81.53 395	36.92 432	81.92 410	48.42 419	74.06 381	85.17 394

pmmvs5		71.55 353	70.20 358	75.61 364	77.83 428	56.39 382	81.74 341	80.89 379	57.76 422	67.46 388	84.49 339	49.26 347	85.32 384	57.08 367	75.29 369	85.11 395

testing3		68.56 384	67.67 383	71.22 413	87.33 242	42.87 463	83.06 330	71.54 443	70.36 253	69.08 374	84.38 343	30.33 450	85.69 378	37.50 456	75.45 364	85.09 396

UWE-MVS-28		65.32 405	64.93 399	66.49 434	78.70 424	38.55 471	77.86 403	64.39 463	62.00 387	64.13 422	83.60 364	41.44 409	76.00 443	31.39 463	80.89 286	84.92 397

CMPMVS	51.72 21	70.19 369	68.16 371	76.28 358	73.15 454	57.55 365	79.47 376	83.92 337	48.02 452	56.48 452	84.81 336	43.13 398	86.42 370	62.67 311	81.81 278	84.89 398
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 398	66.53 394	67.08 433	75.62 439	41.69 468	75.93 413	76.50 425	66.11 332	65.20 416	86.59 291	35.72 438	74.71 452	43.71 441	73.38 390	84.84 399

MSDG		73.36 333	70.99 348	80.49 283	84.51 324	65.80 207	80.71 358	86.13 309	65.70 338	65.46 411	83.74 359	44.60 387	90.91 300	51.13 403	76.89 336	84.74 400

pmmvs4		74.03 324	71.91 335	80.39 284	81.96 382	68.32 135	81.45 346	82.14 366	59.32 407	69.87 366	85.13 329	52.40 300	88.13 351	60.21 335	74.74 376	84.73 401

gg-mvs-nofinetune		69.95 372	67.96 375	75.94 360	83.07 359	54.51 407	77.23 407	70.29 446	63.11 370	70.32 356	62.33 460	43.62 395	88.69 342	53.88 388	87.76 173	84.62 402

test_fmvs1		70.93 359	70.52 352	72.16 404	73.71 447	55.05 401	80.82 352	78.77 408	51.21 448	78.58 202	84.41 342	31.20 448	76.94 434	75.88 179	80.12 300	84.47 403

BH-w/o		78.21 248	77.33 253	80.84 275	88.81 167	65.13 224	84.87 278	87.85 270	69.75 272	74.52 308	84.74 338	61.34 211	93.11 203	58.24 357	85.84 212	84.27 404

MVS		78.19 250	76.99 259	81.78 248	85.66 291	66.99 182	84.66 283	90.47 167	55.08 436	72.02 341	85.27 324	63.83 167	94.11 141	66.10 283	89.80 133	84.24 405

COLMAP_ROB	66.92 17	73.01 339	70.41 355	80.81 276	87.13 250	65.63 211	88.30 160	84.19 335	62.96 373	63.80 426	87.69 257	38.04 429	92.56 227	46.66 429	74.91 374	84.24 405
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 415	61.73 416	61.70 440	72.74 456	24.50 483	69.16 448	78.03 412	61.40 390	56.72 451	75.53 445	38.42 426	76.48 438	45.95 435	57.67 446	84.13 407

TESTMET0.1,1		69.89 373	69.00 365	72.55 401	79.27 422	56.85 373	78.38 393	74.71 435	57.64 423	68.09 382	77.19 436	37.75 430	76.70 435	63.92 300	84.09 240	84.10 408

test_fmvs3		63.36 412	61.82 415	67.98 430	62.51 470	46.96 451	77.37 406	74.03 437	45.24 455	67.50 387	78.79 426	12.16 474	72.98 459	72.77 214	66.02 426	83.99 409

our_test_3		69.14 378	67.00 391	75.57 365	79.80 414	58.80 344	77.96 400	77.81 413	59.55 405	62.90 430	78.25 430	47.43 357	83.97 394	51.71 398	67.58 421	83.93 410

test_vis1_n		69.85 374	69.21 363	71.77 406	72.66 457	55.27 400	81.48 345	76.21 427	52.03 444	75.30 289	83.20 372	28.97 451	76.22 441	74.60 193	78.41 320	83.81 411

mamv4		76.81 282	78.23 226	72.54 402	86.12 282	65.75 210	78.76 388	82.07 368	64.12 358	72.97 327	91.02 158	67.97 119	68.08 467	83.04 89	78.02 323	83.80 412

tpmvs		71.09 357	69.29 362	76.49 357	82.04 381	56.04 388	78.92 386	81.37 377	64.05 361	67.18 393	78.28 429	49.74 340	89.77 319	49.67 413	72.37 395	83.67 413

test20.03		67.45 391	66.95 392	68.94 422	75.48 440	44.84 459	77.50 404	77.67 414	66.66 323	63.01 428	83.80 357	47.02 361	78.40 426	42.53 447	68.86 417	83.58 414

test0.0.03 1		68.00 389	67.69 382	68.90 423	77.55 429	47.43 446	75.70 417	72.95 442	66.66 323	66.56 401	82.29 388	48.06 355	75.87 445	44.97 440	74.51 378	83.41 415

Anonymous20231206		68.60 382	67.80 380	71.02 414	80.23 407	50.75 437	78.30 397	80.47 387	56.79 429	66.11 409	82.63 383	46.35 371	78.95 424	43.62 442	75.70 356	83.36 416

EU-MVSNet		68.53 385	67.61 384	71.31 412	78.51 426	47.01 450	84.47 289	84.27 333	42.27 459	66.44 406	84.79 337	40.44 416	83.76 395	58.76 351	68.54 418	83.17 417

dp		66.80 396	65.43 397	70.90 416	79.74 416	48.82 444	75.12 423	74.77 433	59.61 404	64.08 423	77.23 435	42.89 399	80.72 418	48.86 418	66.58 424	83.16 418

pmmvs-eth3d		70.50 365	67.83 379	78.52 326	77.37 431	66.18 195	81.82 339	81.51 374	58.90 412	63.90 425	80.42 406	42.69 401	86.28 371	58.56 352	65.30 429	83.11 419

YYNet1		65.03 406	62.91 411	71.38 408	75.85 437	56.60 379	69.12 449	74.66 436	57.28 427	54.12 455	77.87 432	45.85 377	74.48 453	49.95 411	61.52 440	83.05 420

MDA-MVSNet-bldmvs		66.68 397	63.66 407	75.75 362	79.28 421	60.56 327	73.92 430	78.35 411	64.43 353	50.13 461	79.87 415	44.02 393	83.67 396	46.10 434	56.86 447	83.03 421

MDA-MVSNet_test_wron		65.03 406	62.92 410	71.37 409	75.93 434	56.73 375	69.09 450	74.73 434	57.28 427	54.03 456	77.89 431	45.88 376	74.39 454	49.89 412	61.55 439	82.99 422

USDC		70.33 367	68.37 368	76.21 359	80.60 402	56.23 386	79.19 381	86.49 301	60.89 393	61.29 434	85.47 320	31.78 446	89.47 326	53.37 391	76.21 352	82.94 423

Syy-MVS		68.05 388	67.85 377	68.67 426	84.68 319	40.97 469	78.62 390	73.08 440	66.65 326	66.74 399	79.46 418	52.11 306	82.30 407	32.89 461	76.38 349	82.75 424

myMVS_eth3d		67.02 395	66.29 395	69.21 421	84.68 319	42.58 464	78.62 390	73.08 440	66.65 326	66.74 399	79.46 418	31.53 447	82.30 407	39.43 453	76.38 349	82.75 424

ttmdpeth		59.91 418	57.10 422	68.34 428	67.13 465	46.65 452	74.64 426	67.41 455	48.30 451	62.52 432	85.04 333	20.40 464	75.93 444	42.55 446	45.90 466	82.44 426

OpenMVS_ROB	64.09 19	70.56 364	68.19 370	77.65 344	80.26 405	59.41 342	85.01 275	82.96 358	58.76 414	65.43 412	82.33 386	37.63 431	91.23 289	45.34 439	76.03 353	82.32 427

JIA-IIPM		66.32 401	62.82 413	76.82 355	77.09 432	61.72 312	65.34 461	75.38 429	58.04 421	64.51 419	62.32 461	42.05 407	86.51 368	51.45 401	69.22 414	82.21 428

dmvs_re		71.14 356	70.58 351	72.80 399	81.96 382	59.68 337	75.60 418	79.34 403	68.55 301	69.27 373	80.72 404	49.42 343	76.54 436	52.56 395	77.79 325	82.19 429

EG-PatchMatch MVS		74.04 322	71.82 336	80.71 278	84.92 313	67.42 168	85.86 251	88.08 260	66.04 334	64.22 421	83.85 355	35.10 439	92.56 227	57.44 363	80.83 288	82.16 430

FE-MVSNET		67.25 394	65.33 398	73.02 397	75.86 436	52.54 421	80.26 368	80.56 385	63.80 366	60.39 437	79.70 417	41.41 410	84.66 391	43.34 443	62.62 436	81.86 431

MVP-Stereo		76.12 295	74.46 305	81.13 268	85.37 301	69.79 95	84.42 295	87.95 266	65.03 347	67.46 388	85.33 323	53.28 294	91.73 264	58.01 359	83.27 259	81.85 432
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 390	64.34 402	76.92 354	73.47 451	61.07 319	84.86 279	82.98 357	59.77 403	58.30 446	85.13 329	26.06 454	87.89 354	47.92 426	60.59 443	81.81 433

GG-mvs-BLEND				75.38 370	81.59 388	55.80 392	79.32 378	69.63 448		67.19 392	73.67 449	43.24 397	88.90 340	50.41 405	84.50 230	81.45 434

KD-MVS_2432*1600		66.22 402	63.89 405	73.21 393	75.47 441	53.42 415	70.76 441	84.35 330	64.10 359	66.52 403	78.52 427	34.55 440	84.98 386	50.40 406	50.33 460	81.23 435

miper_refine_blended		66.22 402	63.89 405	73.21 393	75.47 441	53.42 415	70.76 441	84.35 330	64.10 359	66.52 403	78.52 427	34.55 440	84.98 386	50.40 406	50.33 460	81.23 435

test_0402		72.79 343	70.44 354	79.84 297	88.13 198	65.99 201	85.93 248	84.29 332	65.57 340	67.40 391	85.49 319	46.92 362	92.61 223	35.88 458	74.38 379	80.94 437

MVStest1		56.63 422	52.76 428	68.25 429	61.67 471	53.25 419	71.67 436	68.90 453	38.59 464	50.59 460	83.05 374	25.08 456	70.66 461	36.76 457	38.56 467	80.83 438

UnsupCasMVSNet_bld		63.70 411	61.53 417	70.21 418	73.69 448	51.39 432	72.82 432	81.89 369	55.63 434	57.81 448	71.80 453	38.67 425	78.61 425	49.26 416	52.21 458	80.63 439

LCM-MVSNet		54.25 424	49.68 434	67.97 431	53.73 479	45.28 456	66.85 456	80.78 381	35.96 468	39.45 469	62.23 462	8.70 478	78.06 429	48.24 423	51.20 459	80.57 440

N_pmnet		52.79 429	53.26 427	51.40 454	78.99 423	7.68 488	69.52 445	3.89 487	51.63 446	57.01 450	74.98 446	40.83 414	65.96 469	37.78 455	64.67 430	80.56 441

TinyColmap		67.30 393	64.81 400	74.76 378	81.92 384	56.68 378	80.29 366	81.49 375	60.33 397	56.27 453	83.22 370	24.77 458	87.66 358	45.52 437	69.47 412	79.95 442

PM-MVS		66.41 400	64.14 403	73.20 395	73.92 446	56.45 380	78.97 385	64.96 462	63.88 365	64.72 417	80.24 410	19.84 466	83.44 400	66.24 280	64.52 431	79.71 443

ANet_high		50.57 433	46.10 437	63.99 437	48.67 482	39.13 470	70.99 440	80.85 380	61.39 391	31.18 471	57.70 467	17.02 469	73.65 458	31.22 464	15.89 479	79.18 444

LF4IMVS		64.02 410	62.19 414	69.50 420	70.90 459	53.29 418	76.13 411	77.18 421	52.65 442	58.59 444	80.98 400	23.55 461	76.52 437	53.06 393	66.66 423	78.68 445

PatchMatch-RL		72.38 345	70.90 349	76.80 356	88.60 179	67.38 171	79.53 375	76.17 428	62.75 378	69.36 371	82.00 393	45.51 382	84.89 388	53.62 389	80.58 292	78.12 446

MS-PatchMatch		73.83 325	72.67 327	77.30 351	83.87 337	66.02 198	81.82 339	84.66 326	61.37 392	68.61 378	82.82 380	47.29 358	88.21 349	59.27 343	84.32 237	77.68 447

DSMNet-mixed		57.77 421	56.90 423	60.38 442	67.70 463	35.61 473	69.18 447	53.97 474	32.30 472	57.49 449	79.88 414	40.39 417	68.57 466	38.78 454	72.37 395	76.97 448

CHOSEN 280x420		66.51 399	64.71 401	71.90 405	81.45 391	63.52 275	57.98 470	68.95 452	53.57 439	62.59 431	76.70 437	46.22 373	75.29 451	55.25 379	79.68 302	76.88 449

mvsany_test3		53.99 425	51.45 430	61.61 441	55.51 475	44.74 460	63.52 466	45.41 480	43.69 458	58.11 447	76.45 439	17.99 467	63.76 471	54.77 383	47.59 462	76.34 450

dmvs_testset		62.63 413	64.11 404	58.19 444	78.55 425	24.76 482	75.28 419	65.94 459	67.91 310	60.34 438	76.01 441	53.56 290	73.94 457	31.79 462	67.65 420	75.88 451

mvsany_test1		62.30 414	61.26 418	65.41 436	69.52 460	54.86 403	66.86 455	49.78 476	46.65 453	68.50 380	83.21 371	49.15 348	66.28 468	56.93 370	60.77 441	75.11 452

PMMVS		69.34 377	68.67 366	71.35 411	75.67 438	62.03 306	75.17 420	73.46 438	50.00 449	68.68 376	79.05 421	52.07 308	78.13 427	61.16 328	82.77 265	73.90 453

test_vis1_rt		60.28 417	58.42 420	65.84 435	67.25 464	55.60 395	70.44 443	60.94 468	44.33 457	59.00 443	66.64 458	24.91 457	68.67 465	62.80 307	69.48 411	73.25 454

pmmvs3		57.79 420	54.26 425	68.37 427	64.02 469	56.72 376	75.12 423	65.17 460	40.20 461	52.93 457	69.86 457	20.36 465	75.48 448	45.45 438	55.25 454	72.90 455

PVSNet_0	57.27 20	61.67 416	59.27 419	68.85 424	79.61 417	57.44 367	68.01 451	73.44 439	55.93 433	58.54 445	70.41 456	44.58 388	77.55 431	47.01 428	35.91 468	71.55 456

WB-MVS		54.94 423	54.72 424	55.60 450	73.50 449	20.90 484	74.27 429	61.19 467	59.16 409	50.61 459	74.15 447	47.19 360	75.78 446	17.31 475	35.07 469	70.12 457

SSC-MVS		53.88 426	53.59 426	54.75 452	72.87 455	19.59 485	73.84 431	60.53 469	57.58 425	49.18 463	73.45 450	46.34 372	75.47 449	16.20 478	32.28 471	69.20 458

test_f		52.09 430	50.82 431	55.90 448	53.82 478	42.31 467	59.42 469	58.31 472	36.45 467	56.12 454	70.96 455	12.18 473	57.79 474	53.51 390	56.57 449	67.60 459

PMMVS2		40.82 440	38.86 444	46.69 455	53.84 477	16.45 486	48.61 473	49.92 475	37.49 465	31.67 470	60.97 463	8.14 480	56.42 475	28.42 466	30.72 472	67.19 460

new_pmnet		50.91 432	50.29 432	52.78 453	68.58 462	34.94 475	63.71 465	56.63 473	39.73 462	44.95 464	65.47 459	21.93 463	58.48 473	34.98 459	56.62 448	64.92 461

MVS-HIRNet		59.14 419	57.67 421	63.57 438	81.65 386	43.50 462	71.73 435	65.06 461	39.59 463	51.43 458	57.73 466	38.34 427	82.58 406	39.53 451	73.95 382	64.62 462

APD_test1		53.31 428	49.93 433	63.42 439	65.68 466	50.13 439	71.59 437	66.90 457	34.43 469	40.58 468	71.56 454	8.65 479	76.27 440	34.64 460	55.36 452	63.86 463

test_method		31.52 443	29.28 447	38.23 458	27.03 486	6.50 489	20.94 478	62.21 466	4.05 480	22.35 478	52.50 471	13.33 471	47.58 478	27.04 468	34.04 470	60.62 464

EGC-MVSNET		52.07 431	47.05 435	67.14 432	83.51 347	60.71 324	80.50 362	67.75 454	0.07 482	0.43 483	75.85 444	24.26 459	81.54 412	28.82 465	62.25 437	59.16 465

test_vis3_rt		49.26 434	47.02 436	56.00 447	54.30 476	45.27 457	66.76 457	48.08 477	36.83 466	44.38 465	53.20 470	7.17 481	64.07 470	56.77 373	55.66 450	58.65 466

FPMVS		53.68 427	51.64 429	59.81 443	65.08 467	51.03 434	69.48 446	69.58 449	41.46 460	40.67 467	72.32 452	16.46 470	70.00 464	24.24 471	65.42 428	58.40 467

testf1		45.72 435	41.96 439	57.00 445	56.90 473	45.32 454	66.14 458	59.26 470	26.19 473	30.89 472	60.96 464	4.14 482	70.64 462	26.39 469	46.73 464	55.04 468

APD_test2		45.72 435	41.96 439	57.00 445	56.90 473	45.32 454	66.14 458	59.26 470	26.19 473	30.89 472	60.96 464	4.14 482	70.64 462	26.39 469	46.73 464	55.04 468

PMVS	37.38 22	44.16 439	40.28 443	55.82 449	40.82 484	42.54 466	65.12 462	63.99 464	34.43 469	24.48 475	57.12 468	3.92 484	76.17 442	17.10 476	55.52 451	48.75 470
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 445	25.89 449	43.81 457	44.55 483	35.46 474	28.87 477	39.07 481	18.20 477	18.58 479	40.18 474	2.68 485	47.37 479	17.07 477	23.78 476	48.60 471
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 437	45.38 438	45.55 456	73.36 452	26.85 480	67.72 452	34.19 482	54.15 438	49.65 462	56.41 469	25.43 455	62.94 472	19.45 473	28.09 473	46.86 472

kuosan		39.70 441	40.40 442	37.58 459	64.52 468	26.98 478	65.62 460	33.02 483	46.12 454	42.79 466	48.99 472	24.10 460	46.56 480	12.16 481	26.30 474	39.20 473

Gipuma		45.18 438	41.86 441	55.16 451	77.03 433	51.52 430	32.50 476	80.52 386	32.46 471	27.12 474	35.02 475	9.52 477	75.50 447	22.31 472	60.21 444	38.45 474
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 462	40.17 485	26.90 479		24.59 486	17.44 478	23.95 476	48.61 473	9.77 476	26.48 481	18.06 474	24.47 475	28.83 475

E-PMN		31.77 442	30.64 445	35.15 460	52.87 480	27.67 477	57.09 471	47.86 478	24.64 475	16.40 480	33.05 476	11.23 475	54.90 476	14.46 479	18.15 477	22.87 476

EMVS		30.81 444	29.65 446	34.27 461	50.96 481	25.95 481	56.58 472	46.80 479	24.01 476	15.53 481	30.68 477	12.47 472	54.43 477	12.81 480	17.05 478	22.43 477

tmp_tt		18.61 447	21.40 450	10.23 464	4.82 487	10.11 487	34.70 475	30.74 485	1.48 481	23.91 477	26.07 478	28.42 452	13.41 483	27.12 467	15.35 480	7.17 478

wuyk23d		16.82 448	15.94 451	19.46 463	58.74 472	31.45 476	39.22 474	3.74 488	6.84 479	6.04 482	2.70 482	1.27 486	24.29 482	10.54 482	14.40 481	2.63 479

test123		6.12 450	8.11 453	0.14 465	0.06 489	0.09 490	71.05 439	0.03 490	0.04 484	0.25 485	1.30 484	0.05 487	0.03 485	0.21 484	0.01 483	0.29 480

testmvs		6.04 451	8.02 454	0.10 466	0.08 488	0.03 491	69.74 444	0.04 489	0.05 483	0.31 484	1.68 483	0.02 488	0.04 484	0.24 483	0.02 482	0.25 481

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

cdsmvs_eth3d_5k		19.96 446	26.61 448	0.00 467	0.00 490	0.00 492	0.00 479	89.26 219	0.00 485	0.00 486	88.61 230	61.62 204	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		5.26 452	7.02 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	63.15 176	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

ab-mvs-re		7.23 449	9.64 452	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	86.72 283	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

TestfortrainingZip							93.28 12

WAC-MVS						42.58 464								39.46 452

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 149	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					0.00 490

eth-test					0.00 490

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 235	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	73.93 165	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	91.17 145	74.31 154

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												50.01 335

MTGPA								92.02 107

test_post1							78.90 387				5.43 481	48.81 354	85.44 383	59.25 344

test_post											5.46 480	50.36 331	84.24 392

patchmatchnet-post											74.00 448	51.12 322	88.60 344

MTMP							92.18 39	32.83 484

gm-plane-assit					81.40 392	53.83 412			62.72 379		80.94 401		92.39 236	63.40 304

TEST9					93.26 56	72.96 25	88.75 138	91.89 115	68.44 304	85.00 80	93.10 88	74.36 32	95.41 80

test_8					93.13 60	72.57 35	88.68 143	91.84 119	68.69 299	84.87 84	93.10 88	74.43 30	95.16 90

agg_prior					92.85 68	71.94 52		91.78 123		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 117	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

旧先验2							86.56 225		58.10 420	87.04 61			88.98 336	74.07 199

新几何2							86.29 239

原ACMM2							86.86 212

testdata2													91.01 298	62.37 314

segment_acmp												73.08 43

testdata1							84.14 303		75.71 108

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 230

plane_prior4											91.00 159

plane_prior3						68.60 128			78.44 36	78.92 195

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 203

n2								0.00 491

nn								0.00 491

door-mid								69.98 447

test11								92.23 93

door								69.44 450

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 86	77.23 236

ACMP_Plane					89.33 144		89.17 116		76.41 86	77.23 236

BP-MVS														77.47 155

HQP3-MVS								92.19 101							85.99 207

HQP2-MVS												60.17 233

NP-MVS					89.62 129	68.32 135					90.24 180

MDTV_nov1_ep13			69.97 359		83.18 356	53.48 414	77.10 409	80.18 396	60.45 396	69.33 372	80.44 405	48.89 353	86.90 364	51.60 399	78.51 315

ACMMP++_ref															81.95 276

ACMMP++															81.25 281

Test By Simon												64.33 162

High-resolution multi-view benchmark