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	91.17 149	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	84.06 164	91.71 84	64.94 238	86.47 232	91.87 121	73.63 176	86.60 67	93.02 93	76.57 18	91.87 263	83.36 84	92.15 90	95.35 3

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 251	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	89.05 234	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	76.66 254	93.37 83	60.40 236	96.75 30	77.20 162	93.73 70	95.29 6

BP-MVS1		84.32 91	83.71 108	86.17 68	87.84 213	67.85 154	89.38 109	89.64 203	77.73 45	83.98 106	92.12 118	56.89 266	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	89.54 206	82.14 3	86.65 66	94.28 46	68.28 120	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	74.62 151	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	87.30 249	65.39 218	87.30 200	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									68.21 313	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

IS-MVSNet		83.15 129	82.81 126	84.18 153	89.94 123	63.30 285	91.59 51	88.46 261	79.04 30	79.49 188	92.16 115	65.10 159	94.28 130	67.71 272	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	74.77 147	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		77.30 278	76.49 275	79.74 310	90.08 116	52.02 434	87.86 178	63.10 477	74.88 143	80.16 181	92.79 100	38.29 439	92.35 243	68.74 265	92.50 84	94.86 19

ECVR-MVS		79.61 212	79.26 205	80.67 283	90.08 116	54.69 415	87.89 176	77.44 430	74.88 143	80.27 178	92.79 100	48.96 362	92.45 237	68.55 266	92.50 84	94.86 19

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 325	92.39 6				88.94 28	96.63 4	94.85 21

test1111		79.43 219	79.18 208	80.15 297	89.99 121	53.31 428	87.33 199	77.05 434	75.04 136	80.23 180	92.77 102	48.97 361	92.33 245	68.87 263	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	75.71 112	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	91.63 135	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

E4		84.10 98	83.99 101	84.45 132	87.58 239	64.99 234	86.54 230	92.25 96	76.38 94	83.37 121	92.09 119	69.88 90	93.58 166	79.78 130	88.03 171	94.77 25

viewmacassd2359aftdt		83.76 109	83.66 110	84.07 161	86.59 275	64.56 247	86.88 215	91.82 124	75.72 111	83.34 122	92.15 117	68.24 121	92.88 218	79.05 136	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	91.23 149	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	83.51 120	91.20 153	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	91.23 149	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	75.24 126	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 98	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 98	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

E5new		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

E6new		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

GDP-MVS		83.52 118	82.64 130	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 202	83.71 111	91.86 125	55.69 275	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 38

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 38
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		82.60 142	82.10 142	84.10 155	87.98 207	62.94 296	87.45 190	91.27 145	77.42 56	79.85 183	90.28 182	56.62 269	94.70 117	79.87 129	88.15 167	94.67 38

E2		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

E3		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

MGCFI-Net		85.06 85	85.51 74	83.70 183	89.42 139	63.01 291	89.43 104	92.62 78	76.43 89	87.53 53	91.34 147	72.82 49	93.42 187	81.28 108	88.74 153	94.66 41

viewmanbaseed2359cas		83.66 112	83.55 112	84.00 172	86.81 267	64.53 248	86.65 225	91.75 129	74.89 142	83.15 128	91.68 131	68.74 113	92.83 222	79.02 138	89.24 142	94.63 44

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 102	87.73 52	91.46 144	70.32 80	93.78 158	81.51 104	88.95 147	94.63 44

viewdifsd2359ckpt09		83.34 124	82.55 132	85.70 81	87.64 230	67.72 159	88.43 151	91.68 131	71.91 216	81.65 154	90.68 170	67.10 134	94.75 113	76.17 177	87.70 178	94.62 46

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 134	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 47
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 47

viewcassd2359sk11		83.89 103	83.74 107	84.34 140	87.76 221	64.91 241	86.30 241	92.22 100	75.47 119	83.04 129	91.52 140	70.15 83	93.53 174	79.26 135	87.96 172	94.57 49

VDD-MVS		83.01 134	82.36 136	84.96 107	91.02 95	66.40 191	88.91 128	88.11 264	77.57 49	84.39 96	93.29 85	52.19 309	93.91 152	77.05 165	88.70 154	94.57 49

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 105	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	94.65 52	94.56 51

KinetiMVS		83.31 127	82.61 131	85.39 91	87.08 260	67.56 165	88.06 168	91.65 132	77.80 44	82.21 143	91.79 126	57.27 261	94.07 142	77.77 155	89.89 132	94.56 51

VDDNet		81.52 164	80.67 164	84.05 167	90.44 108	64.13 260	89.73 93	85.91 322	71.11 233	83.18 126	93.48 78	50.54 339	93.49 179	73.40 210	88.25 165	94.54 53

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 54

E3new		83.78 108	83.60 111	84.31 142	87.76 221	64.89 242	86.24 244	92.20 103	75.15 135	82.87 132	91.23 149	70.11 84	93.52 176	79.05 136	87.79 175	94.51 55

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 201	84.64 90	91.71 130	71.85 58	96.03 55	84.77 69	94.45 60	94.49 56

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 114	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 57
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 58

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 58

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 196	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 60

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 102	79.94 17	89.74 27	94.86 26	68.63 114	94.20 136	90.83 5	91.39 104	94.38 61

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	81.32 158	91.43 145	70.34 79	97.23 17	84.26 75	93.36 74	94.37 62

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 204	85.22 78	91.90 122	69.47 95	96.42 44	83.28 86	95.94 23	94.35 63

viewdifsd2359ckpt07		82.83 137	82.78 129	82.99 214	86.51 277	62.58 299	85.09 277	90.83 161	75.22 128	82.28 140	91.63 135	69.43 96	92.03 253	77.71 156	86.32 202	94.34 64

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 64

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 66

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 104	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 66
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	68.32 312	84.61 91	93.48 78	72.32 52	96.15 53	79.00 140	95.43 34	94.28 68

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 69

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 70

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 246	67.30 174	89.50 101	90.98 154	76.25 101	90.56 22	94.75 29	68.38 117	94.24 135	90.80 7	92.32 89	94.19 71

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 251	68.54 130	89.57 99	90.44 172	75.31 125	87.49 54	94.39 42	72.86 47	92.72 225	89.04 27	90.56 118	94.16 72

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 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 122	83.02 122	84.57 123	90.13 114	64.47 253	92.32 35	90.73 164	74.45 155	79.35 193	91.10 156	69.05 108	95.12 92	72.78 217	87.22 186	94.13 74

viewdifsd2359ckpt13		82.91 135	82.29 138	84.77 118	86.96 263	66.90 187	87.47 187	91.62 134	72.19 209	81.68 153	90.71 169	66.92 135	93.28 190	75.90 182	87.15 188	94.12 75

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 76

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 100	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 77

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	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

X-MVStestdata		80.37 199	77.83 239	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 491	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

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	69.09 105	96.70 31	84.37 74	94.83 49	94.03 80

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 144	86.70 271	65.83 205	88.77 136	89.78 195	75.46 120	88.35 36	93.73 74	69.19 104	93.06 210	91.30 3	88.44 159	94.02 81

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	68.81 111	96.65 34	84.53 72	94.90 45	94.00 82

fmvsm_s_conf0.1_n_2		83.80 106	83.79 106	83.83 179	85.62 297	64.94 238	87.03 207	86.62 311	74.32 157	87.97 47	94.33 43	60.67 228	92.60 228	89.72 14	87.79 175	93.96 83

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 316	69.51 100	89.62 98	90.58 167	73.42 184	87.75 50	94.02 61	72.85 48	93.24 194	90.37 8	90.75 115	93.96 83

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 107	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 85
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	86.12 286	69.93 92	88.65 144	90.78 163	69.97 269	88.27 38	93.98 66	71.39 67	91.54 279	88.49 35	90.45 120	93.91 86

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 86

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 88

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 370	69.39 107	89.65 95	90.29 181	73.31 188	87.77 49	94.15 55	71.72 61	93.23 195	90.31 9	90.67 117	93.89 89

Anonymous202405211		78.25 250	77.01 261	81.99 249	91.03 94	60.67 336	84.77 284	83.90 349	70.65 250	80.00 182	91.20 153	41.08 423	91.43 286	65.21 294	85.26 225	93.85 90

LFMVS		81.82 154	81.23 154	83.57 188	91.89 82	63.43 283	89.84 87	81.85 382	77.04 70	83.21 123	93.10 88	52.26 308	93.43 186	71.98 229	89.95 130	93.85 90

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 181	86.17 284	65.00 233	86.96 210	87.28 289	74.35 156	88.25 39	94.23 50	61.82 204	92.60 228	89.85 12	88.09 168	93.84 92

Effi-MVS+		83.62 116	83.08 120	85.24 95	88.38 188	67.45 167	88.89 129	89.15 230	75.50 118	82.27 141	88.28 244	69.61 94	94.45 127	77.81 154	87.84 174	93.84 92

Anonymous20240529		80.19 205	78.89 214	84.10 155	90.60 104	64.75 245	88.95 127	90.90 157	65.97 342	80.59 174	91.17 155	49.97 346	93.73 164	69.16 260	82.70 272	93.81 94

MVS_Test		83.15 129	83.06 121	83.41 194	86.86 264	63.21 287	86.11 248	92.00 113	74.31 158	82.87 132	89.44 212	70.03 87	93.21 197	77.39 161	88.50 158	93.81 94

Elysia		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 373	94.82 108	76.85 167	89.57 136	93.80 96

StellarMVS		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 373	94.82 108	76.85 167	89.57 136	93.80 96

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 412	69.03 110	89.47 102	89.65 202	73.24 192	86.98 62	94.27 47	66.62 138	93.23 195	90.26 10	89.95 130	93.78 98

GeoE		81.71 156	81.01 159	83.80 182	89.51 134	64.45 254	88.97 126	88.73 254	71.27 230	78.63 205	89.76 197	66.32 144	93.20 200	69.89 252	86.02 210	93.74 99

diffmvs		82.10 146	81.88 148	82.76 231	83.00 366	63.78 269	83.68 316	89.76 197	72.94 199	82.02 146	89.85 191	65.96 153	90.79 311	82.38 100	87.30 185	93.71 100
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	93.70 101

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	93.66 102

VNet		82.21 145	82.41 134	81.62 255	90.82 100	60.93 329	84.47 293	89.78 195	76.36 96	84.07 104	91.88 123	64.71 163	90.26 321	70.68 241	88.89 148	93.66 102

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 117	83.86 108	94.42 40	67.87 126	96.64 35	82.70 98	94.57 56	93.66 102

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 268	93.44 32	78.70 34	83.63 115	89.03 219	74.57 27	95.71 66	80.26 121	94.04 67	93.66 102
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	93.65 106
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	82.82 135	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 106
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		83.65 113	84.54 89	80.99 275	90.06 120	65.83 205	84.21 304	88.74 253	71.60 222	85.01 79	92.44 105	74.51 29	83.50 411	82.15 101	92.15 90	93.64 108

EIA-MVS		83.31 127	82.80 127	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 150	78.96 197	86.42 302	69.06 107	95.26 87	75.54 188	90.09 126	93.62 109

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	93.62 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
diffmvs_AUTHOR		82.38 143	82.27 139	82.73 233	83.26 356	63.80 267	83.89 311	89.76 197	73.35 187	82.37 139	90.84 166	66.25 145	90.79 311	82.77 93	87.93 173	93.59 111

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 134	73.89 170	82.67 138	94.09 57	62.60 188	95.54 70	80.93 111	92.93 77	93.57 112

fmvsm_s_conf0.1_n		83.56 117	83.38 116	84.10 155	84.86 318	67.28 175	89.40 108	83.01 366	70.67 246	87.08 60	93.96 67	68.38 117	91.45 285	88.56 34	84.50 234	93.56 113

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 167	83.16 127	91.07 158	75.94 21	95.19 89	79.94 124	94.38 62	93.55 114

test12				86.80 58	92.63 73	70.70 81		91.79 126		82.71 137		71.67 63	96.16 52		94.50 57	93.54 115

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 180	85.94 69	94.51 35	65.80 154	95.61 67	83.04 89	92.51 83	93.53 116

mvs_anonymous		79.42 220	79.11 209	80.34 290	84.45 329	57.97 366	82.59 338	87.62 281	67.40 322	76.17 270	88.56 237	68.47 116	89.59 334	70.65 242	86.05 209	93.47 117

fmvsm_s_conf0.5_n		83.80 106	83.71 108	84.07 161	86.69 272	67.31 173	89.46 103	83.07 365	71.09 234	86.96 63	93.70 75	69.02 110	91.47 284	88.79 30	84.62 233	93.44 118

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 151	86.26 280	67.40 170	89.18 115	89.31 219	72.50 203	88.31 37	93.86 70	69.66 93	91.96 257	89.81 13	91.05 109	93.38 119

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 100	76.87 74	82.81 136	94.25 49	66.44 142	96.24 49	82.88 92	94.28 64	93.38 119

EPNet		83.72 111	82.92 125	86.14 72	84.22 332	69.48 101	91.05 64	85.27 329	81.30 6	76.83 249	91.65 133	66.09 149	95.56 68	76.00 181	93.85 68	93.38 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 120	82.80 127	85.43 90	90.25 112	68.74 121	90.30 80	90.13 186	76.33 97	80.87 169	92.89 95	61.00 223	94.20 136	72.45 226	90.97 111	93.35 122
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	93.33 123
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		79.10 230	78.24 228	81.70 254	86.85 265	60.24 343	87.28 201	88.79 246	74.25 161	76.84 248	90.53 177	49.48 352	91.56 275	67.98 270	82.15 276	93.29 124

EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 93	88.18 194	67.85 154	87.66 182	89.73 200	80.05 15	82.95 130	89.59 204	70.74 76	94.82 108	80.66 118	84.72 231	93.28 125

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 228	92.02 111	79.45 22	85.88 70	94.80 27	68.07 122	96.21 50	86.69 52	95.34 36	93.23 126

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	67.93 124	95.95 62	84.20 78	94.39 61	93.23 126

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	80.73 172	93.82 72	64.33 166	96.29 46	82.67 99	90.69 116	93.23 126
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	88.06 268	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	90.91 113	93.21 129

fmvsm_s_conf0.1_n_a		83.32 126	82.99 123	84.28 146	83.79 342	68.07 145	89.34 111	82.85 371	69.80 273	87.36 58	94.06 59	68.34 119	91.56 275	87.95 42	83.46 260	93.21 129

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 185	87.32 248	65.13 228	88.86 130	91.63 133	75.41 121	88.23 40	93.45 81	68.56 115	92.47 236	89.52 18	92.78 79	93.20 131

PAPM_NR		83.02 133	82.41 134	84.82 115	92.47 76	66.37 192	87.93 174	91.80 125	73.82 171	77.32 237	90.66 171	67.90 125	94.90 104	70.37 244	89.48 139	93.19 132

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 190	87.12 259	66.01 199	88.56 148	89.43 210	75.59 116	89.32 28	94.32 44	72.89 46	91.21 296	90.11 11	92.33 87	93.16 133

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 148	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 133

OMC-MVS		82.69 138	81.97 147	84.85 114	88.75 174	67.42 168	87.98 170	90.87 159	74.92 141	79.72 185	91.65 133	62.19 198	93.96 144	75.26 192	86.42 201	93.16 133

fmvsm_s_conf0.5_n_a		83.63 115	83.41 115	84.28 146	86.14 285	68.12 143	89.43 104	82.87 370	70.27 262	87.27 59	93.80 73	69.09 105	91.58 272	88.21 38	83.65 254	93.14 136

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 172	87.78 218	66.09 196	89.96 86	90.80 162	77.37 57	86.72 65	94.20 52	72.51 51	92.78 224	89.08 22	92.33 87	93.13 137

PAPR		81.66 159	80.89 161	83.99 174	90.27 111	64.00 261	86.76 222	91.77 128	68.84 303	77.13 247	89.50 205	67.63 127	94.88 106	67.55 274	88.52 157	93.09 138

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 160	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 234	90.88 114	93.07 139

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

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	93.05 142

thisisatest0530		79.40 221	77.76 244	84.31 142	87.69 228	65.10 231	87.36 197	84.26 345	70.04 265	77.42 234	88.26 246	49.94 347	94.79 112	70.20 247	84.70 232	93.03 143

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 119	68.69 305	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 144

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	91.88 123	69.04 109	95.43 77	83.93 81	93.77 69	93.01 145

mvsmamba		80.60 190	79.38 200	84.27 148	89.74 128	67.24 178	87.47 187	86.95 301	70.02 266	75.38 286	88.93 224	51.24 330	92.56 231	75.47 190	89.22 143	93.00 146

EI-MVSNet-UG-set		83.81 105	83.38 116	85.09 103	87.87 211	67.53 166	87.44 195	89.66 201	79.74 18	82.23 142	89.41 213	70.24 82	94.74 114	79.95 123	83.92 246	92.99 147

tttt0517		79.40 221	77.91 235	83.90 178	88.10 200	63.84 266	88.37 157	84.05 347	71.45 225	76.78 251	89.12 216	49.93 349	94.89 105	70.18 248	83.18 265	92.96 148

viewdifsd2359ckpt11		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 307	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 322	92.95 149

viewmsd2359difaftdt		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 307	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 322	92.95 149

test9_res														84.90 64	95.70 30	92.87 151

viewmambaseed2359dif		80.41 195	79.84 187	82.12 244	82.95 372	62.50 302	83.39 324	88.06 268	67.11 323	80.98 165	90.31 181	66.20 147	91.01 304	74.62 196	84.90 228	92.86 152

AstraMVS		80.81 178	80.14 179	82.80 225	86.05 289	63.96 262	86.46 233	85.90 323	73.71 174	80.85 170	90.56 175	54.06 292	91.57 274	79.72 131	83.97 245	92.86 152

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 152	86.84 64	94.65 31	67.31 131	95.77 64	84.80 68	92.85 78	92.84 154

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	85.71 315	69.32 98	95.38 82	80.82 113	91.37 105	92.72 155

agg_prior2														82.91 91	95.45 33	92.70 156

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 198	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 156
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 242	76.63 274	84.64 122	86.73 270	69.47 102	85.01 279	84.61 338	69.54 280	66.51 416	86.59 295	50.16 343	91.75 266	76.26 176	84.24 242	92.69 158

Vis-MVSNet (Re-imp)		78.36 249	78.45 221	78.07 346	88.64 178	51.78 440	86.70 223	79.63 412	74.14 164	75.11 299	90.83 167	61.29 217	89.75 331	58.10 369	91.60 99	92.69 158

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 289	76.41 90	85.80 71	90.22 186	74.15 35	95.37 85	81.82 103	91.88 94	92.65 160

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 131	84.12 334	69.37 108	88.15 166	87.96 271	70.01 267	83.95 107	93.23 86	68.80 112	91.51 282	88.61 32	89.96 129	92.57 161

FA-MVS(test-final)		80.96 174	79.91 184	84.10 155	88.30 191	65.01 232	84.55 292	90.01 189	73.25 191	79.61 186	87.57 264	58.35 250	94.72 115	71.29 235	86.25 205	92.56 162

guyue		81.13 171	80.64 165	82.60 236	86.52 276	63.92 265	86.69 224	87.73 279	73.97 166	80.83 171	89.69 198	56.70 267	91.33 290	78.26 153	85.40 224	92.54 163

test_yl		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

DCV-MVSNet		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	65.00 162	95.56 68	82.75 94	91.87 95	92.50 166

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	63.87 170		82.75 94	91.87 95	92.50 166

nrg030		83.88 104	83.53 113	84.96 107	86.77 269	69.28 109	90.46 75	92.67 72	74.79 146	82.95 130	91.33 148	72.70 50	93.09 208	80.79 115	79.28 315	92.50 166

SSM_0404		81.91 151	80.84 162	85.13 101	89.24 151	68.26 137	87.84 179	89.25 224	71.06 236	80.62 173	90.39 179	59.57 239	94.65 119	72.45 226	87.19 187	92.47 169

MG-MVS		83.41 121	83.45 114	83.28 197	92.74 71	62.28 308	88.17 164	89.50 208	75.22 128	81.49 156	92.74 103	66.75 136	95.11 94	72.85 216	91.58 101	92.45 170

FIs		82.07 148	82.42 133	81.04 274	88.80 171	58.34 360	88.26 161	93.49 31	76.93 72	78.47 211	91.04 159	69.92 89	92.34 244	69.87 253	84.97 227	92.44 171

testing3-2		75.12 318	75.19 300	74.91 386	90.40 109	45.09 470	80.29 378	78.42 422	78.37 40	76.54 259	87.75 258	44.36 400	87.28 373	57.04 379	83.49 258	92.37 172

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 207	87.08 260	65.21 225	89.09 123	90.21 183	79.67 19	89.98 24	95.02 24	73.17 42	91.71 269	91.30 3	91.60 99	92.34 173

FC-MVSNet-test		81.52 164	82.02 145	80.03 299	88.42 187	55.97 400	87.95 172	93.42 34	77.10 68	77.38 235	90.98 165	69.96 88	91.79 264	68.46 268	84.50 234	92.33 174

Fast-Effi-MVS+		80.81 178	79.92 183	83.47 189	88.85 163	64.51 250	85.53 266	89.39 212	70.79 243	78.49 209	85.06 335	67.54 128	93.58 166	67.03 282	86.58 198	92.32 175

TranMVSNet+NR-MVSNet		80.84 176	80.31 173	82.42 239	87.85 212	62.33 306	87.74 181	91.33 144	80.55 9	77.99 223	89.86 190	65.23 158	92.62 226	67.05 281	75.24 377	92.30 176

ab-mvs		79.51 215	78.97 212	81.14 271	88.46 184	60.91 330	83.84 312	89.24 226	70.36 257	79.03 196	88.87 227	63.23 178	90.21 323	65.12 295	82.57 273	92.28 177

CANet_DTU		80.61 188	79.87 186	82.83 222	85.60 298	63.17 290	87.36 197	88.65 257	76.37 95	75.88 273	88.44 240	53.51 297	93.07 209	73.30 211	89.74 134	92.25 178

UniMVSNet_NR-MVSNet		81.88 152	81.54 151	82.92 218	88.46 184	63.46 281	87.13 203	92.37 86	80.19 12	78.38 212	89.14 215	71.66 64	93.05 211	70.05 249	76.46 350	92.25 178

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 148	85.42 303	68.81 116	88.49 150	87.26 294	68.08 314	88.03 44	93.49 77	72.04 57	91.77 265	88.90 29	89.14 146	92.24 180

DU-MVS		81.12 172	80.52 168	82.90 219	87.80 215	63.46 281	87.02 208	91.87 121	79.01 31	78.38 212	89.07 217	65.02 160	93.05 211	70.05 249	76.46 350	92.20 181

NR-MVSNet		80.23 203	79.38 200	82.78 229	87.80 215	63.34 284	86.31 240	91.09 153	79.01 31	72.17 345	89.07 217	67.20 132	92.81 223	66.08 288	75.65 363	92.20 181

mamba_0408		79.37 224	77.52 251	84.93 110	88.81 167	67.96 149	65.03 475	88.66 255	70.96 240	79.48 189	89.80 194	58.69 245	94.65 119	70.35 245	85.93 213	92.18 183

SSM_04072		77.67 271	77.52 251	78.12 344	88.81 167	67.96 149	65.03 475	88.66 255	70.96 240	79.48 189	89.80 194	58.69 245	74.23 467	70.35 245	85.93 213	92.18 183

SSM_0407		81.58 161	80.48 169	84.87 113	88.81 167	67.96 149	87.37 196	89.25 224	71.06 236	79.48 189	90.39 179	59.57 239	94.48 126	72.45 226	85.93 213	92.18 183

TAPA-MVS	73.13 9	79.15 228	77.94 234	82.79 228	89.59 130	62.99 295	88.16 165	91.51 139	65.77 343	77.14 246	91.09 157	60.91 224	93.21 197	50.26 421	87.05 190	92.17 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a		84.13 97	84.16 94	84.06 164	85.38 304	68.40 133	88.34 158	86.85 305	67.48 321	87.48 55	93.40 82	70.89 73	91.61 270	88.38 37	89.22 143	92.16 187

3Dnovator	76.31 5	83.38 123	82.31 137	86.59 61	87.94 208	72.94 28	90.64 68	92.14 110	77.21 63	75.47 280	92.83 97	58.56 248	94.72 115	73.24 213	92.71 81	92.13 188

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 250	90.33 178	76.11 103	82.08 145	91.61 138	71.36 68	94.17 139	81.02 110	92.58 82	92.08 189

MVSFormer		82.85 136	82.05 144	85.24 95	87.35 241	70.21 86	90.50 72	90.38 174	68.55 307	81.32 158	89.47 207	61.68 206	93.46 184	78.98 141	90.26 123	92.05 190

jason		81.39 167	80.29 174	84.70 121	86.63 274	69.90 94	85.95 251	86.77 306	63.24 379	81.07 164	89.47 207	61.08 222	92.15 250	78.33 149	90.07 128	92.05 190
jason: jason.
HyFIR lowres test		77.53 273	75.40 293	83.94 177	89.59 130	66.62 188	80.36 376	88.64 258	56.29 443	76.45 260	85.17 332	57.64 256	93.28 190	61.34 338	83.10 266	91.91 192

XVG-OURS-SEG-HR		80.81 178	79.76 189	83.96 176	85.60 298	68.78 118	83.54 323	90.50 170	70.66 249	76.71 253	91.66 132	60.69 227	91.26 291	76.94 166	81.58 283	91.83 193

lupinMVS		81.39 167	80.27 175	84.76 119	87.35 241	70.21 86	85.55 264	86.41 313	62.85 386	81.32 158	88.61 234	61.68 206	92.24 248	78.41 148	90.26 123	91.83 193

WR-MVS		79.49 216	79.22 207	80.27 292	88.79 172	58.35 359	85.06 278	88.61 259	78.56 35	77.65 230	88.34 242	63.81 172	90.66 316	64.98 297	77.22 338	91.80 195

icg_test_0407_2		78.92 236	78.93 213	78.90 327	87.13 254	63.59 274	76.58 422	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	81.38 426	72.56 222	85.56 220	91.74 196

IMVS_0407		80.61 188	79.90 185	82.75 232	87.13 254	63.59 274	85.33 270	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	92.91 216	72.56 222	85.56 220	91.74 196

IMVS_0404		77.16 280	76.42 278	79.37 318	87.13 254	63.59 274	77.12 420	89.33 214	70.51 252	66.22 419	89.03 219	50.36 341	82.78 416	72.56 222	85.56 220	91.74 196

IMVS_0403		80.80 181	80.12 180	82.87 221	87.13 254	63.59 274	85.19 271	89.33 214	70.51 252	78.49 209	89.03 219	63.26 176	93.27 192	72.56 222	85.56 220	91.74 196

h-mvs33		83.15 129	82.19 140	86.02 76	90.56 105	70.85 79	88.15 166	89.16 229	76.02 105	84.67 87	91.39 146	61.54 209	95.50 73	82.71 96	75.48 367	91.72 200

UniMVSNet (Re)		81.60 160	81.11 156	83.09 207	88.38 188	64.41 255	87.60 183	93.02 50	78.42 37	78.56 207	88.16 248	69.78 91	93.26 193	69.58 256	76.49 349	91.60 201

UGNet		80.83 177	79.59 196	84.54 124	88.04 203	68.09 144	89.42 106	88.16 263	76.95 71	76.22 266	89.46 209	49.30 356	93.94 147	68.48 267	90.31 121	91.60 201
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		76.54 289	75.66 288	79.18 323	88.43 186	55.89 401	81.08 362	83.00 367	73.76 173	75.34 288	84.29 350	46.20 384	90.07 325	64.33 301	84.50 234	91.58 203

XVG-OURS		80.41 195	79.23 206	83.97 175	85.64 296	69.02 112	83.03 336	90.39 173	71.09 234	77.63 231	91.49 143	54.62 287	91.35 288	75.71 184	83.47 259	91.54 204

LCM-MVSNet-Re		77.05 281	76.94 264	77.36 360	87.20 251	51.60 441	80.06 381	80.46 400	75.20 131	67.69 396	86.72 287	62.48 191	88.98 347	63.44 307	89.25 141	91.51 205

DP-MVS Recon		83.11 132	82.09 143	86.15 70	94.44 23	70.92 76	88.79 135	92.20 103	70.53 251	79.17 195	91.03 161	64.12 168	96.03 55	68.39 269	90.14 125	91.50 206

PS-MVSNAJss		82.07 148	81.31 152	84.34 140	86.51 277	67.27 176	89.27 112	91.51 139	71.75 217	79.37 192	90.22 186	63.15 180	94.27 131	77.69 157	82.36 275	91.49 207

testing99		76.09 303	75.12 302	79.00 324	88.16 195	55.50 407	80.79 366	81.40 387	73.30 189	75.17 296	84.27 353	44.48 399	90.02 326	64.28 302	84.22 243	91.48 208

thisisatest0515		77.33 277	75.38 294	83.18 203	85.27 308	63.80 267	82.11 346	83.27 359	65.06 356	75.91 272	83.84 362	49.54 351	94.27 131	67.24 278	86.19 206	91.48 208

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 204	93.04 46	69.80 273	82.85 134	91.22 152	73.06 44	96.02 57	76.72 174	94.63 54	91.46 210

HQP_MVS		83.64 114	83.14 119	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 199	91.00 163	60.42 234	95.38 82	78.71 144	86.32 202	91.33 211

plane_prior5								92.44 82					95.38 82	78.71 144	86.32 202	91.33 211

GA-MVS		76.87 285	75.17 301	81.97 250	82.75 375	62.58 299	81.44 358	86.35 316	72.16 212	74.74 307	82.89 384	46.20 384	92.02 255	68.85 264	81.09 288	91.30 213

VPA-MVSNet		80.60 190	80.55 167	80.76 281	88.07 202	60.80 332	86.86 216	91.58 137	75.67 115	80.24 179	89.45 211	63.34 173	90.25 322	70.51 243	79.22 316	91.23 214

Effi-MVS+-dtu		80.03 207	78.57 219	84.42 134	85.13 313	68.74 121	88.77 136	88.10 265	74.99 137	74.97 304	83.49 373	57.27 261	93.36 188	73.53 207	80.88 291	91.18 215

v2v482		80.23 203	79.29 204	83.05 211	83.62 348	64.14 259	87.04 206	89.97 190	73.61 177	78.18 218	87.22 275	61.10 221	93.82 156	76.11 178	76.78 346	91.18 215

FE-MVS		77.78 265	75.68 286	84.08 160	88.09 201	66.00 200	83.13 331	87.79 277	68.42 311	78.01 222	85.23 330	45.50 393	95.12 92	59.11 357	85.83 217	91.11 217

Anonymous20231211		78.97 234	77.69 247	82.81 224	90.54 106	64.29 257	90.11 83	91.51 139	65.01 358	76.16 271	88.13 253	50.56 338	93.03 214	69.68 255	77.56 336	91.11 217

hse-mvs2		81.72 155	80.94 160	84.07 161	88.72 175	67.68 160	85.87 254	87.26 294	76.02 105	84.67 87	88.22 247	61.54 209	93.48 182	82.71 96	73.44 395	91.06 219

AUN-MVS		79.21 227	77.60 249	84.05 167	88.71 176	67.61 162	85.84 256	87.26 294	69.08 294	77.23 240	88.14 252	53.20 301	93.47 183	75.50 189	73.45 394	91.06 219

HQP4-MVS										77.24 239			95.11 94			91.03 221

HQP-MVS		82.61 140	82.02 145	84.37 137	89.33 144	66.98 183	89.17 116	92.19 105	76.41 90	77.23 240	90.23 185	60.17 237	95.11 94	77.47 159	85.99 211	91.03 221

RPSCF		73.23 345	71.46 346	78.54 335	82.50 381	59.85 346	82.18 345	82.84 372	58.96 422	71.15 357	89.41 213	45.48 394	84.77 400	58.82 361	71.83 407	91.02 223

LuminaMVS		80.68 186	79.62 195	83.83 179	85.07 315	68.01 148	86.99 209	88.83 244	70.36 257	81.38 157	87.99 255	50.11 344	92.51 235	79.02 138	86.89 194	90.97 224

test_djsdf		80.30 202	79.32 203	83.27 198	83.98 338	65.37 219	90.50 72	90.38 174	68.55 307	76.19 267	88.70 230	56.44 270	93.46 184	78.98 141	80.14 303	90.97 224

PCF-MVS	73.52 7	80.38 197	78.84 215	85.01 105	87.71 224	68.99 113	83.65 317	91.46 143	63.00 383	77.77 229	90.28 182	66.10 148	95.09 98	61.40 336	88.22 166	90.94 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 241	78.66 217	78.76 329	88.31 190	55.72 404	84.45 296	86.63 310	76.79 76	78.26 215	90.55 176	59.30 242	89.70 333	66.63 283	77.05 340	90.88 227

CPTT-MVS		83.73 110	83.33 118	84.92 111	93.28 53	70.86 78	92.09 41	90.38 174	68.75 304	79.57 187	92.83 97	60.60 232	93.04 213	80.92 112	91.56 102	90.86 228

fmvsm_s_conf0.5_n_7		83.34 124	84.03 100	81.28 266	85.73 294	65.13 228	85.40 269	89.90 193	74.96 140	82.13 144	93.89 69	66.65 137	87.92 364	86.56 53	91.05 109	90.80 229

tt0805		78.73 239	77.83 239	81.43 260	85.17 309	60.30 342	89.41 107	90.90 157	71.21 231	77.17 245	88.73 229	46.38 379	93.21 197	72.57 220	78.96 317	90.79 230

CLD-MVS		82.31 144	81.65 150	84.29 145	88.47 183	67.73 158	85.81 258	92.35 87	75.78 110	78.33 214	86.58 297	64.01 169	94.35 128	76.05 180	87.48 182	90.79 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 214	78.43 223	83.07 210	83.55 350	64.52 249	86.93 213	90.58 167	70.83 242	77.78 228	85.90 311	59.15 243	93.94 147	73.96 204	77.19 339	90.76 232

IterMVS-LS		80.06 206	79.38 200	82.11 246	85.89 290	63.20 288	86.79 219	89.34 213	74.19 162	75.45 283	86.72 287	66.62 138	92.39 240	72.58 219	76.86 343	90.75 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d28		73.62 333	73.53 323	73.90 400	88.20 193	47.41 460	78.06 411	79.37 414	74.29 160	73.98 318	84.29 350	44.67 396	83.54 410	51.47 411	87.39 183	90.74 234

EI-MVSNet		80.52 194	79.98 182	82.12 244	84.28 330	63.19 289	86.41 234	88.95 241	74.18 163	78.69 202	87.54 267	66.62 138	92.43 238	72.57 220	80.57 297	90.74 234

v1921920		79.22 226	78.03 232	82.80 225	83.30 355	63.94 264	86.80 218	90.33 178	69.91 271	77.48 233	85.53 322	58.44 249	93.75 162	73.60 206	76.85 344	90.71 236

QAPM		80.88 175	79.50 198	85.03 104	88.01 206	68.97 114	91.59 51	92.00 113	66.63 334	75.15 298	92.16 115	57.70 255	95.45 75	63.52 305	88.76 152	90.66 237

v144192		79.47 217	78.37 224	82.78 229	83.35 353	63.96 262	86.96 210	90.36 177	69.99 268	77.50 232	85.67 318	60.66 229	93.77 160	74.27 201	76.58 347	90.62 238

v1240		78.99 233	77.78 242	82.64 234	83.21 358	63.54 278	86.62 227	90.30 180	69.74 278	77.33 236	85.68 317	57.04 264	93.76 161	73.13 214	76.92 341	90.62 238

v1144		80.03 207	79.03 210	83.01 213	83.78 343	64.51 250	87.11 205	90.57 169	71.96 215	78.08 221	86.20 307	61.41 213	93.94 147	74.93 194	77.23 337	90.60 240

1112_ss		77.40 276	76.43 277	80.32 291	89.11 160	60.41 341	83.65 317	87.72 280	62.13 396	73.05 330	86.72 287	62.58 190	89.97 327	62.11 330	80.80 293	90.59 241

CP-MVSNet		78.22 251	78.34 225	77.84 350	87.83 214	54.54 417	87.94 173	91.17 149	77.65 46	73.48 325	88.49 238	62.24 197	88.43 358	62.19 327	74.07 386	90.55 242

testing222		74.04 328	72.66 334	78.19 342	87.89 210	55.36 408	81.06 363	79.20 417	71.30 229	74.65 310	83.57 372	39.11 434	88.67 354	51.43 413	85.75 218	90.53 243

PS-CasMVS		78.01 260	78.09 231	77.77 352	87.71 224	54.39 419	88.02 169	91.22 146	77.50 54	73.26 327	88.64 233	60.73 225	88.41 359	61.88 331	73.88 390	90.53 243

CDS-MVSNet		79.07 231	77.70 246	83.17 204	87.60 231	68.23 141	84.40 301	86.20 318	67.49 320	76.36 263	86.54 299	61.54 209	90.79 311	61.86 332	87.33 184	90.49 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 237	77.51 253	83.03 212	87.80 215	67.79 157	84.72 285	85.05 334	67.63 317	76.75 252	87.70 260	62.25 196	90.82 310	58.53 364	87.13 189	90.49 245

PEN-MVS		77.73 266	77.69 247	77.84 350	87.07 262	53.91 422	87.91 175	91.18 148	77.56 51	73.14 329	88.82 228	61.23 218	89.17 343	59.95 347	72.37 401	90.43 247

Test_1112_low_res		76.40 298	75.44 291	79.27 320	89.28 149	58.09 362	81.69 353	87.07 299	59.53 417	72.48 340	86.67 292	61.30 216	89.33 338	60.81 342	80.15 302	90.41 248

HY-MVS	69.67 12	77.95 261	77.15 259	80.36 289	87.57 240	60.21 344	83.37 326	87.78 278	66.11 338	75.37 287	87.06 282	63.27 175	90.48 318	61.38 337	82.43 274	90.40 249

sc_t1		72.19 360	69.51 371	80.23 294	84.81 319	61.09 326	84.68 286	80.22 406	60.70 406	71.27 354	83.58 371	36.59 445	89.24 341	60.41 343	63.31 445	90.37 250

CHOSEN 1792x2688		77.63 272	75.69 285	83.44 191	89.98 122	68.58 129	78.70 401	87.50 284	56.38 442	75.80 275	86.84 283	58.67 247	91.40 287	61.58 335	85.75 218	90.34 251

SDMVSNet		80.38 197	80.18 176	80.99 275	89.03 161	64.94 238	80.45 375	89.40 211	75.19 132	76.61 257	89.98 188	60.61 231	87.69 368	76.83 170	83.55 256	90.33 252

sd_testset		77.70 269	77.40 254	78.60 332	89.03 161	60.02 345	79.00 396	85.83 324	75.19 132	76.61 257	89.98 188	54.81 280	85.46 393	62.63 321	83.55 256	90.33 252

114514_t		80.68 186	79.51 197	84.20 152	94.09 42	67.27 176	89.64 96	91.11 152	58.75 426	74.08 317	90.72 168	58.10 251	95.04 99	69.70 254	89.42 140	90.30 254

eth_miper_zixun_eth		77.92 262	76.69 272	81.61 257	83.00 366	61.98 313	83.15 330	89.20 228	69.52 281	74.86 306	84.35 349	61.76 205	92.56 231	71.50 233	72.89 399	90.28 255

PVSNet_Blended_VisFu		82.62 139	81.83 149	84.96 107	90.80 101	69.76 97	88.74 140	91.70 130	69.39 282	78.96 197	88.46 239	65.47 156	94.87 107	74.42 199	88.57 155	90.24 256

MVS_111021_LR		82.61 140	82.11 141	84.11 154	88.82 166	71.58 57	85.15 274	86.16 319	74.69 148	80.47 177	91.04 159	62.29 195	90.55 317	80.33 120	90.08 127	90.20 257

MSLP-MVS++		85.43 75	85.76 69	84.45 132	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 133	92.94 215	80.36 119	94.35 63	90.16 258

mvs_tets		79.13 229	77.77 243	83.22 202	84.70 322	66.37 192	89.17 116	90.19 184	69.38 283	75.40 285	89.46 209	44.17 402	93.15 204	76.78 173	80.70 295	90.14 259

BH-RMVSNet		79.61 212	78.44 222	83.14 205	89.38 143	65.93 202	84.95 281	87.15 297	73.56 179	78.19 217	89.79 196	56.67 268	93.36 188	59.53 352	86.74 196	90.13 260

c3_l		78.75 238	77.91 235	81.26 267	82.89 373	61.56 319	84.09 309	89.13 232	69.97 269	75.56 278	84.29 350	66.36 143	92.09 252	73.47 209	75.48 367	90.12 261

v7n		78.97 234	77.58 250	83.14 205	83.45 352	65.51 214	88.32 159	91.21 147	73.69 175	72.41 341	86.32 305	57.93 252	93.81 157	69.18 259	75.65 363	90.11 262

jajsoiax		79.29 225	77.96 233	83.27 198	84.68 323	66.57 190	89.25 113	90.16 185	69.20 291	75.46 282	89.49 206	45.75 390	93.13 206	76.84 169	80.80 293	90.11 262

v148		78.72 240	77.80 241	81.47 259	82.73 376	61.96 314	86.30 241	88.08 266	73.26 190	76.18 268	85.47 324	62.46 192	92.36 242	71.92 230	73.82 391	90.09 264

GBi-Net		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 281	91.11 297	62.72 317	79.57 307	90.09 264

test1		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 281	91.11 297	62.72 317	79.57 307	90.09 264

FMVSNet1		77.44 274	76.12 282	81.40 262	86.81 267	63.01 291	88.39 154	89.28 220	70.49 256	74.39 314	87.28 271	49.06 360	91.11 297	60.91 340	78.52 320	90.09 264

WR-MVS_H		78.51 246	78.49 220	78.56 334	88.02 204	56.38 394	88.43 151	92.67 72	77.14 65	73.89 319	87.55 266	66.25 145	89.24 341	58.92 359	73.55 393	90.06 268

DTE-MVSNet		76.99 282	76.80 267	77.54 359	86.24 281	53.06 432	87.52 185	90.66 165	77.08 69	72.50 339	88.67 232	60.48 233	89.52 335	57.33 376	70.74 413	90.05 269

v8		79.97 209	79.02 211	82.80 225	84.09 335	64.50 252	87.96 171	90.29 181	74.13 165	75.24 295	86.81 284	62.88 187	93.89 155	74.39 200	75.40 372	90.00 270

thres600view7		76.50 291	75.44 291	79.68 312	89.40 141	57.16 380	85.53 266	83.23 360	73.79 172	76.26 265	87.09 280	51.89 320	91.89 261	48.05 436	83.72 253	90.00 270

thres400		76.50 291	75.37 295	79.86 304	89.13 156	57.65 374	85.17 272	83.60 352	73.41 185	76.45 260	86.39 303	52.12 310	91.95 258	48.33 431	83.75 250	90.00 270

cl22		78.07 257	77.01 261	81.23 268	82.37 385	61.83 316	83.55 321	87.98 270	68.96 301	75.06 301	83.87 360	61.40 214	91.88 262	73.53 207	76.39 352	89.98 273

OPM-MVS		83.50 119	82.95 124	85.14 98	88.79 172	70.95 74	89.13 121	91.52 138	77.55 52	80.96 166	91.75 129	60.71 226	94.50 124	79.67 132	86.51 200	89.97 274
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 307	73.83 320	81.30 265	83.26 356	61.79 317	82.57 339	80.65 395	66.81 325	66.88 407	83.42 374	57.86 254	92.19 249	63.47 306	79.57 307	89.91 275

v10		79.74 211	78.67 216	82.97 217	84.06 336	64.95 235	87.88 177	90.62 166	73.11 195	75.11 299	86.56 298	61.46 212	94.05 143	73.68 205	75.55 365	89.90 276

MVSTER		79.01 232	77.88 238	82.38 240	83.07 363	64.80 244	84.08 310	88.95 241	69.01 298	78.69 202	87.17 278	54.70 285	92.43 238	74.69 195	80.57 297	89.89 277

ACMP	74.13 6	81.51 166	80.57 166	84.36 138	89.42 139	68.69 126	89.97 85	91.50 142	74.46 154	75.04 302	90.41 178	53.82 294	94.54 121	77.56 158	82.91 267	89.86 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 147	81.27 153	84.50 129	89.23 152	68.76 119	90.22 81	91.94 117	75.37 123	76.64 255	91.51 141	54.29 288	94.91 102	78.44 146	83.78 247	89.83 279

LGP-MVS_train				84.50 129	89.23 152	68.76 119		91.94 117	75.37 123	76.64 255	91.51 141	54.29 288	94.91 102	78.44 146	83.78 247	89.83 279

V42		79.38 223	78.24 228	82.83 222	81.10 404	65.50 215	85.55 264	89.82 194	71.57 223	78.21 216	86.12 309	60.66 229	93.18 203	75.64 185	75.46 369	89.81 281

MAR-MVS		81.84 153	80.70 163	85.27 94	91.32 89	71.53 58	89.82 88	90.92 156	69.77 275	78.50 208	86.21 306	62.36 194	94.52 123	65.36 293	92.05 93	89.77 282
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		77.72 267	76.76 269	80.58 285	82.48 383	60.48 339	83.09 332	87.86 275	69.22 289	74.38 315	85.24 329	62.10 199	91.53 280	71.09 236	75.40 372	89.74 283

cl____		77.72 267	76.76 269	80.58 285	82.49 382	60.48 339	83.09 332	87.87 274	69.22 289	74.38 315	85.22 331	62.10 199	91.53 280	71.09 236	75.41 371	89.73 284

miper_ehance_all_eth		78.59 244	77.76 244	81.08 273	82.66 378	61.56 319	83.65 317	89.15 230	68.87 302	75.55 279	83.79 364	66.49 141	92.03 253	73.25 212	76.39 352	89.64 285

anonymousdsp		78.60 243	77.15 259	82.98 216	80.51 410	67.08 181	87.24 202	89.53 207	65.66 345	75.16 297	87.19 277	52.52 303	92.25 247	77.17 163	79.34 314	89.61 286

FMVSNet2		78.20 253	77.21 258	81.20 269	87.60 231	62.89 297	87.47 187	89.02 236	71.63 219	75.29 294	87.28 271	54.80 281	91.10 300	62.38 324	79.38 313	89.61 286

baseline1		76.98 283	76.75 271	77.66 354	88.13 198	55.66 405	85.12 275	81.89 380	73.04 197	76.79 250	88.90 225	62.43 193	87.78 367	63.30 309	71.18 411	89.55 288

ETVMVS		72.25 359	71.05 355	75.84 372	87.77 220	51.91 437	79.39 389	74.98 443	69.26 287	73.71 321	82.95 382	40.82 425	86.14 383	46.17 444	84.43 239	89.47 289

FMVSNet3		77.88 263	76.85 266	80.97 277	86.84 266	62.36 305	86.52 231	88.77 247	71.13 232	75.34 288	86.66 293	54.07 291	91.10 300	62.72 317	79.57 307	89.45 290

SD_0403		74.65 321	74.77 305	74.29 394	86.20 283	47.42 459	83.71 315	85.12 331	69.30 285	68.50 388	87.95 256	59.40 241	86.05 384	49.38 425	83.35 261	89.40 291

miper_enhance_ethall		77.87 264	76.86 265	80.92 278	81.65 392	61.38 323	82.68 337	88.98 238	65.52 347	75.47 280	82.30 393	65.76 155	92.00 256	72.95 215	76.39 352	89.39 292

testing11		75.14 317	74.01 315	78.53 336	88.16 195	56.38 394	80.74 369	80.42 402	70.67 246	72.69 338	83.72 367	43.61 406	89.86 328	62.29 326	83.76 249	89.36 293

cascas		76.72 288	74.64 306	82.99 214	85.78 293	65.88 204	82.33 342	89.21 227	60.85 405	72.74 335	81.02 405	47.28 369	93.75 162	67.48 275	85.02 226	89.34 294

Fast-Effi-MVS+-dtu		78.02 259	76.49 275	82.62 235	83.16 362	66.96 185	86.94 212	87.45 286	72.45 204	71.49 353	84.17 357	54.79 284	91.58 272	67.61 273	80.31 300	89.30 295

IB-MVS	68.01 15	75.85 306	73.36 326	83.31 196	84.76 321	66.03 197	83.38 325	85.06 333	70.21 264	69.40 376	81.05 404	45.76 389	94.66 118	65.10 296	75.49 366	89.25 296
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		76.50 291	75.55 290	79.33 319	89.52 133	56.99 383	85.83 257	83.23 360	73.94 168	76.32 264	87.12 279	51.89 320	91.95 258	48.33 431	83.75 250	89.07 297

tfpn200view9		76.42 297	75.37 295	79.55 317	89.13 156	57.65 374	85.17 272	83.60 352	73.41 185	76.45 260	86.39 303	52.12 310	91.95 258	48.33 431	83.75 250	89.07 297

xiu_mvs_v1_base_debu		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 333	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 333	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base_debi		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 333	92.85 219	78.29 150	87.56 179	89.06 299

EPNet_dtu		75.46 311	74.86 303	77.23 363	82.57 380	54.60 416	86.89 214	83.09 364	71.64 218	66.25 418	85.86 313	55.99 273	88.04 363	54.92 393	86.55 199	89.05 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 279	76.68 273	78.93 326	84.22 332	58.62 357	86.41 234	88.36 262	71.37 226	73.31 326	88.01 254	61.22 219	89.15 344	64.24 303	73.01 398	89.03 303

PVSNet_Blended		80.98 173	80.34 172	82.90 219	88.85 163	65.40 216	84.43 298	92.00 113	67.62 318	78.11 219	85.05 336	66.02 151	94.27 131	71.52 231	89.50 138	89.01 304

PAPM		77.68 270	76.40 279	81.51 258	87.29 250	61.85 315	83.78 313	89.59 205	64.74 360	71.23 355	88.70 230	62.59 189	93.66 165	52.66 405	87.03 191	89.01 304

WTY-MVS		75.65 308	75.68 286	75.57 376	86.40 279	56.82 385	77.92 414	82.40 375	65.10 355	76.18 268	87.72 259	63.13 183	80.90 429	60.31 345	81.96 279	89.00 306

无先验							87.48 186	88.98 238	60.00 412				94.12 140	67.28 277		88.97 307

GSMVS																88.96 308

sam_mvs1												51.32 327				88.96 308

SCA		74.22 325	72.33 338	79.91 303	84.05 337	62.17 309	79.96 384	79.29 416	66.30 337	72.38 342	80.13 417	51.95 316	88.60 355	59.25 355	77.67 335	88.96 308

miper_lstm_enhance		74.11 327	73.11 329	77.13 364	80.11 414	59.62 349	72.23 446	86.92 304	66.76 327	70.40 361	82.92 383	56.93 265	82.92 415	69.06 261	72.63 400	88.87 311

ACMM	73.20 8	80.78 185	79.84 187	83.58 187	89.31 147	68.37 134	89.99 84	91.60 136	70.28 261	77.25 238	89.66 200	53.37 299	93.53 174	74.24 202	82.85 268	88.85 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 320	73.39 324	78.61 331	81.38 399	57.48 377	86.64 226	87.95 272	64.99 359	70.18 364	86.61 294	50.43 340	89.52 335	62.12 329	70.18 416	88.83 313

原ACMM1				84.35 139	93.01 66	68.79 117		92.44 82	63.96 375	81.09 163	91.57 139	66.06 150	95.45 75	67.19 279	94.82 50	88.81 314

CNLPA		78.08 256	76.79 268	81.97 250	90.40 109	71.07 70	87.59 184	84.55 339	66.03 341	72.38 342	89.64 201	57.56 257	86.04 385	59.61 351	83.35 261	88.79 315

UWE-MVS		72.13 361	71.49 345	74.03 398	86.66 273	47.70 457	81.40 359	76.89 436	63.60 378	75.59 277	84.22 354	39.94 428	85.62 390	48.98 428	86.13 208	88.77 316

UBG		73.08 347	72.27 339	75.51 378	88.02 204	51.29 445	78.35 408	77.38 431	65.52 347	73.87 320	82.36 391	45.55 391	86.48 380	55.02 392	84.39 240	88.75 317

K. test v3		71.19 366	68.51 378	79.21 322	83.04 365	57.78 372	84.35 302	76.91 435	72.90 200	62.99 440	82.86 385	39.27 431	91.09 302	61.65 334	52.66 468	88.75 317

旧先验1					91.96 80	65.79 208		86.37 315			93.08 92	69.31 99			92.74 80	88.74 319

PatchmatchNet		73.12 346	71.33 349	78.49 338	83.18 360	60.85 331	79.63 386	78.57 421	64.13 368	71.73 349	79.81 422	51.20 331	85.97 386	57.40 375	76.36 357	88.66 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 339	71.26 352	79.70 311	85.08 314	57.89 368	85.57 260	83.56 354	71.03 238	65.66 421	85.88 312	42.10 416	92.57 230	59.11 357	63.34 444	88.65 321

SSC-MVS3.2		73.35 342	73.39 324	73.23 404	85.30 307	49.01 455	74.58 439	81.57 384	75.21 130	73.68 322	85.58 321	52.53 302	82.05 421	54.33 397	77.69 334	88.63 322

PS-MVSNAJ		81.69 157	81.02 158	83.70 183	89.51 134	68.21 142	84.28 303	90.09 187	70.79 243	81.26 162	85.62 320	63.15 180	94.29 129	75.62 186	88.87 149	88.59 323

xiu_mvs_v2_base		81.69 157	81.05 157	83.60 185	89.15 155	68.03 147	84.46 295	90.02 188	70.67 246	81.30 161	86.53 300	63.17 179	94.19 138	75.60 187	88.54 156	88.57 324

MonoMVSNet		76.49 294	75.80 283	78.58 333	81.55 395	58.45 358	86.36 239	86.22 317	74.87 145	74.73 308	83.73 366	51.79 323	88.73 352	70.78 238	72.15 404	88.55 325

CostFormer		75.24 316	73.90 318	79.27 320	82.65 379	58.27 361	80.80 365	82.73 373	61.57 400	75.33 292	83.13 379	55.52 276	91.07 303	64.98 297	78.34 327	88.45 326

lessismore_v0				78.97 325	81.01 405	57.15 381		65.99 470		61.16 446	82.82 386	39.12 433	91.34 289	59.67 350	46.92 475	88.43 327

OpenMVS	72.83 10	79.77 210	78.33 226	84.09 159	85.17 309	69.91 93	90.57 69	90.97 155	66.70 328	72.17 345	91.91 121	54.70 285	93.96 144	61.81 333	90.95 112	88.41 328

usedtu_dtu_shiyan1		76.43 295	75.32 297	79.76 308	83.00 366	60.72 333	81.74 350	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 319	62.39 322	79.40 311	88.31 329

FE-MVSNET3		76.43 295	75.32 297	79.76 308	83.00 366	60.72 333	81.74 350	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 319	62.39 322	79.40 311	88.31 329

reproduce_monomvs		75.40 314	74.38 312	78.46 339	83.92 340	57.80 371	83.78 313	86.94 302	73.47 183	72.25 344	84.47 344	38.74 435	89.27 340	75.32 191	70.53 414	88.31 329

VortexMVS		78.57 245	77.89 237	80.59 284	85.89 290	62.76 298	85.61 259	89.62 204	72.06 213	74.99 303	85.38 326	55.94 274	90.77 314	74.99 193	76.58 347	88.23 332

OurMVSNet-221017-0		74.26 324	72.42 337	79.80 306	83.76 344	59.59 350	85.92 253	86.64 309	66.39 336	66.96 406	87.58 263	39.46 430	91.60 271	65.76 291	69.27 419	88.22 333

LS3D		76.95 284	74.82 304	83.37 195	90.45 107	67.36 172	89.15 120	86.94 302	61.87 399	69.52 375	90.61 174	51.71 324	94.53 122	46.38 443	86.71 197	88.21 334

WBMVS		73.43 336	72.81 332	75.28 382	87.91 209	50.99 447	78.59 404	81.31 389	65.51 349	74.47 313	84.83 339	46.39 378	86.68 377	58.41 365	77.86 330	88.17 335

XVG-ACMP-BASELINE		76.11 302	74.27 314	81.62 255	83.20 359	64.67 246	83.60 320	89.75 199	69.75 276	71.85 348	87.09 280	32.78 454	92.11 251	69.99 251	80.43 299	88.09 336

tpm2		73.26 344	71.46 346	78.63 330	83.34 354	56.71 388	80.65 371	80.40 403	56.63 441	73.55 324	82.02 398	51.80 322	91.24 292	56.35 387	78.42 325	87.95 337

MDTV_nov1_ep13_2view						37.79 484	75.16 433		55.10 447	66.53 413		49.34 355		53.98 398		87.94 338

Patchmatch-test		64.82 419	63.24 420	69.57 431	79.42 426	49.82 453	63.49 479	69.05 463	51.98 457	59.95 452	80.13 417	50.91 333	70.98 472	40.66 462	73.57 392	87.90 339

PLC	70.83 11	78.05 258	76.37 280	83.08 209	91.88 83	67.80 156	88.19 163	89.46 209	64.33 367	69.87 372	88.38 241	53.66 295	93.58 166	58.86 360	82.73 270	87.86 340
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 356	71.71 343	74.35 393	82.19 386	52.00 435	79.22 392	77.29 432	64.56 362	72.95 334	83.68 369	51.35 326	83.26 414	58.33 367	75.80 361	87.81 341

Patchmatch-RL test		70.24 379	67.78 392	77.61 356	77.43 441	59.57 351	71.16 450	70.33 457	62.94 385	68.65 383	72.77 463	50.62 337	85.49 392	69.58 256	66.58 430	87.77 342

F-COLMAP		76.38 299	74.33 313	82.50 238	89.28 149	66.95 186	88.41 153	89.03 235	64.05 372	66.83 408	88.61 234	46.78 375	92.89 217	57.48 373	78.55 319	87.67 343

Baseline_NR-MVSNet		78.15 255	78.33 226	77.61 356	85.79 292	56.21 398	86.78 220	85.76 325	73.60 178	77.93 224	87.57 264	65.02 160	88.99 346	67.14 280	75.33 374	87.63 344

CL-MVSNet_self_test		72.37 356	71.46 346	75.09 384	79.49 425	53.53 424	80.76 368	85.01 335	69.12 293	70.51 359	82.05 397	57.92 253	84.13 404	52.27 407	66.00 433	87.60 345

ACMH+	68.96 14	76.01 304	74.01 315	82.03 248	88.60 179	65.31 224	88.86 130	87.55 282	70.25 263	67.75 395	87.47 269	41.27 421	93.19 202	58.37 366	75.94 360	87.60 345

1314		76.53 290	75.30 299	80.21 295	83.93 339	62.32 307	84.66 287	88.81 245	60.23 410	70.16 366	84.07 359	55.30 278	90.73 315	67.37 276	83.21 264	87.59 347

blended_shiyan6		73.38 337	71.17 353	80.01 301	78.36 433	61.48 322	82.43 340	87.27 292	65.40 351	68.56 386	77.55 441	51.94 318	91.01 304	63.27 311	65.76 434	87.55 348

blended_shiyan8		73.38 337	71.17 353	80.02 300	78.36 433	61.51 321	82.43 340	87.28 289	65.40 351	68.61 384	77.53 442	51.91 319	91.00 307	63.28 310	65.76 434	87.53 349

API-MVS		81.99 150	81.23 154	84.26 150	90.94 97	70.18 91	91.10 63	89.32 218	71.51 224	78.66 204	88.28 244	65.26 157	95.10 97	64.74 299	91.23 107	87.51 350

AdaColmap		80.58 193	79.42 199	84.06 164	93.09 63	68.91 115	89.36 110	88.97 240	69.27 286	75.70 276	89.69 198	57.20 263	95.77 64	63.06 314	88.41 160	87.50 351

PVSNet_BlendedMVS		80.60 190	80.02 181	82.36 241	88.85 163	65.40 216	86.16 247	92.00 113	69.34 284	78.11 219	86.09 310	66.02 151	94.27 131	71.52 231	82.06 278	87.39 352

sss		73.60 334	73.64 322	73.51 403	82.80 374	55.01 413	76.12 424	81.69 383	62.47 392	74.68 309	85.85 314	57.32 260	78.11 440	60.86 341	80.93 289	87.39 352

FE-blended-shiyan7		72.94 350	70.66 360	79.79 307	77.80 438	61.03 328	81.31 360	87.15 297	65.18 354	68.09 391	76.28 450	51.32 327	90.97 308	63.06 314	65.76 434	87.35 354

usedtu_blend_shiyan5		73.29 343	70.96 357	80.25 293	77.80 438	62.16 310	84.44 297	87.38 287	64.41 364	68.09 391	76.28 450	51.32 327	91.23 293	63.21 312	65.76 434	87.35 354

IterMVS-SCA-FT		75.43 312	73.87 319	80.11 298	82.69 377	64.85 243	81.57 355	83.47 356	69.16 292	70.49 360	84.15 358	51.95 316	88.15 361	69.23 258	72.14 405	87.34 356

PVSNet	64.34 18	72.08 362	70.87 359	75.69 374	86.21 282	56.44 392	74.37 440	80.73 394	62.06 397	70.17 365	82.23 395	42.86 410	83.31 413	54.77 394	84.45 238	87.32 357

tt0320-xc		70.11 381	67.45 398	78.07 346	85.33 306	59.51 352	83.28 327	78.96 419	58.77 424	67.10 405	80.28 415	36.73 444	87.42 371	56.83 383	59.77 457	87.29 358

新几何1				83.42 192	93.13 60	70.71 80		85.48 328	57.43 437	81.80 150	91.98 120	63.28 174	92.27 246	64.60 300	92.99 76	87.27 359

blend_shiyan4		72.29 358	69.65 370	80.21 295	78.24 436	62.16 310	82.29 343	87.27 292	65.41 350	68.43 390	76.42 449	39.91 429	91.23 293	63.21 312	65.66 438	87.22 360

TR-MVS		77.44 274	76.18 281	81.20 269	88.24 192	63.24 286	84.61 290	86.40 314	67.55 319	77.81 227	86.48 301	54.10 290	93.15 204	57.75 372	82.72 271	87.20 361

TransMVSNet (Re)		75.39 315	74.56 308	77.86 349	85.50 302	57.10 382	86.78 220	86.09 321	72.17 211	71.53 352	87.34 270	63.01 184	89.31 339	56.84 382	61.83 450	87.17 362

ACMH	67.68 16	75.89 305	73.93 317	81.77 253	88.71 176	66.61 189	88.62 145	89.01 237	69.81 272	66.78 409	86.70 291	41.95 418	91.51 282	55.64 389	78.14 328	87.17 362
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 391	67.59 396	72.46 415	74.29 455	45.45 465	77.93 413	87.00 300	63.12 380	63.99 435	78.99 431	42.32 413	84.77 400	56.55 386	64.09 443	87.16 364

EPMVS		69.02 390	68.16 382	71.59 419	79.61 423	49.80 454	77.40 417	66.93 468	62.82 388	70.01 367	79.05 427	45.79 388	77.86 442	56.58 385	75.26 376	87.13 365

CR-MVSNet		73.37 339	71.27 351	79.67 313	81.32 402	65.19 226	75.92 426	80.30 404	59.92 413	72.73 336	81.19 402	52.50 304	86.69 376	59.84 348	77.71 332	87.11 366

RPMNet		73.51 335	70.49 363	82.58 237	81.32 402	65.19 226	75.92 426	92.27 93	57.60 435	72.73 336	76.45 447	52.30 307	95.43 77	48.14 435	77.71 332	87.11 366

test_vis1_n_1920		75.52 310	75.78 284	74.75 390	79.84 418	57.44 378	83.26 328	85.52 327	62.83 387	79.34 194	86.17 308	45.10 395	79.71 433	78.75 143	81.21 287	87.10 368

tt0320		70.49 377	68.03 385	77.89 348	84.78 320	59.12 354	83.55 321	80.44 401	58.13 430	67.43 401	80.41 413	39.26 432	87.54 370	55.12 391	63.18 446	86.99 369

XXY-MVS		75.41 313	75.56 289	74.96 385	83.59 349	57.82 370	80.59 372	83.87 350	66.54 335	74.93 305	88.31 243	63.24 177	80.09 432	62.16 328	76.85 344	86.97 370

tpmrst		72.39 354	72.13 340	73.18 408	80.54 409	49.91 452	79.91 385	79.08 418	63.11 381	71.69 350	79.95 419	55.32 277	82.77 417	65.66 292	73.89 389	86.87 371

thres200		75.55 309	74.47 310	78.82 328	87.78 218	57.85 369	83.07 334	83.51 355	72.44 206	75.84 274	84.42 345	52.08 313	91.75 266	47.41 438	83.64 255	86.86 372

ITE_SJBPF				78.22 341	81.77 391	60.57 337		83.30 358	69.25 288	67.54 397	87.20 276	36.33 447	87.28 373	54.34 396	74.62 383	86.80 373

test222					91.50 86	68.26 137	84.16 307	83.20 363	54.63 449	79.74 184	91.63 135	58.97 244			91.42 103	86.77 374

MIMVSNet		70.69 373	69.30 372	74.88 387	84.52 327	56.35 396	75.87 428	79.42 413	64.59 361	67.76 394	82.41 390	41.10 422	81.54 424	46.64 442	81.34 284	86.75 375

BH-untuned		79.47 217	78.60 218	82.05 247	89.19 154	65.91 203	86.07 249	88.52 260	72.18 210	75.42 284	87.69 261	61.15 220	93.54 173	60.38 344	86.83 195	86.70 376

FE-MVSNET2		72.88 352	71.28 350	77.67 353	78.30 435	57.78 372	84.43 298	88.92 243	69.56 279	64.61 429	81.67 400	46.73 377	88.54 357	59.33 353	67.99 425	86.69 377

LTVRE_ROB	69.57 13	76.25 300	74.54 309	81.41 261	88.60 179	64.38 256	79.24 391	89.12 233	70.76 245	69.79 374	87.86 257	49.09 359	93.20 200	56.21 388	80.16 301	86.65 378
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				79.97 302	90.90 98	64.21 258		84.71 336	59.27 419	85.40 75	92.91 94	62.02 201	89.08 345	68.95 262	91.37 105	86.63 379

MIMVSNet1		68.58 394	66.78 404	73.98 399	80.07 415	51.82 439	80.77 367	84.37 340	64.40 365	59.75 453	82.16 396	36.47 446	83.63 408	42.73 456	70.33 415	86.48 380

tfpnnormal		74.39 322	73.16 328	78.08 345	86.10 288	58.05 363	84.65 289	87.53 283	70.32 260	71.22 356	85.63 319	54.97 279	89.86 328	43.03 455	75.02 379	86.32 381

D2MVS		74.82 319	73.21 327	79.64 314	79.81 419	62.56 301	80.34 377	87.35 288	64.37 366	68.86 381	82.66 388	46.37 380	90.10 324	67.91 271	81.24 286	86.25 382

tpm cat1		70.57 374	68.31 380	77.35 361	82.41 384	57.95 367	78.08 410	80.22 406	52.04 455	68.54 387	77.66 440	52.00 315	87.84 366	51.77 408	72.07 406	86.25 382

CVMVSNet		72.99 349	72.58 335	74.25 395	84.28 330	50.85 448	86.41 234	83.45 357	44.56 468	73.23 328	87.54 267	49.38 354	85.70 388	65.90 289	78.44 322	86.19 384

AllTest		70.96 369	68.09 384	79.58 315	85.15 311	63.62 270	84.58 291	79.83 409	62.31 393	60.32 450	86.73 285	32.02 455	88.96 349	50.28 419	71.57 409	86.15 385

TestCases				79.58 315	85.15 311	63.62 270		79.83 409	62.31 393	60.32 450	86.73 285	32.02 455	88.96 349	50.28 419	71.57 409	86.15 385

test-LLR		72.94 350	72.43 336	74.48 391	81.35 400	58.04 364	78.38 405	77.46 428	66.66 329	69.95 370	79.00 429	48.06 365	79.24 434	66.13 285	84.83 229	86.15 385

test-mter		71.41 365	70.39 366	74.48 391	81.35 400	58.04 364	78.38 405	77.46 428	60.32 409	69.95 370	79.00 429	36.08 448	79.24 434	66.13 285	84.83 229	86.15 385

IterMVS		74.29 323	72.94 331	78.35 340	81.53 396	63.49 280	81.58 354	82.49 374	68.06 315	69.99 369	83.69 368	51.66 325	85.54 391	65.85 290	71.64 408	86.01 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 287	74.57 307	83.42 192	93.29 52	69.46 104	88.55 149	83.70 351	63.98 374	70.20 363	88.89 226	54.01 293	94.80 111	46.66 440	81.88 281	86.01 389

ppachtmachnet_test		70.04 382	67.34 400	78.14 343	79.80 420	61.13 324	79.19 393	80.59 396	59.16 420	65.27 424	79.29 426	46.75 376	87.29 372	49.33 426	66.72 428	86.00 391

mmtdpeth		74.16 326	73.01 330	77.60 358	83.72 345	61.13 324	85.10 276	85.10 332	72.06 213	77.21 244	80.33 414	43.84 404	85.75 387	77.14 164	52.61 469	85.91 392

test_fmvs1_n		70.86 371	70.24 367	72.73 412	72.51 469	55.28 410	81.27 361	79.71 411	51.49 459	78.73 201	84.87 338	27.54 465	77.02 445	76.06 179	79.97 305	85.88 393

Patchmtry		70.74 372	69.16 375	75.49 379	80.72 406	54.07 421	74.94 437	80.30 404	58.34 427	70.01 367	81.19 402	52.50 304	86.54 378	53.37 402	71.09 412	85.87 394

WB-MVSnew		71.96 363	71.65 344	72.89 410	84.67 326	51.88 438	82.29 343	77.57 427	62.31 393	73.67 323	83.00 381	53.49 298	81.10 428	45.75 447	82.13 277	85.70 395

test_fmvs2		68.35 398	67.48 397	70.98 427	69.50 473	51.95 436	80.05 382	76.38 438	49.33 462	74.65 310	84.38 347	23.30 474	75.40 462	74.51 198	75.17 378	85.60 396

usedtu_dtu_shiyan2		64.75 420	61.63 428	74.10 397	70.64 471	53.18 431	82.10 347	81.27 390	56.22 444	56.39 464	74.67 458	27.94 464	83.56 409	42.71 457	62.73 447	85.57 397

ambc				75.24 383	73.16 464	50.51 450	63.05 480	87.47 285		64.28 431	77.81 439	17.80 480	89.73 332	57.88 371	60.64 454	85.49 398

mvs5depth		69.45 387	67.45 398	75.46 380	73.93 456	55.83 402	79.19 393	83.23 360	66.89 324	71.63 351	83.32 375	33.69 453	85.09 396	59.81 349	55.34 465	85.46 399

UnsupCasMVSNet_eth		67.33 403	65.99 407	71.37 421	73.48 461	51.47 443	75.16 433	85.19 330	65.20 353	60.78 447	80.93 409	42.35 412	77.20 444	57.12 377	53.69 467	85.44 400

PatchT		68.46 397	67.85 388	70.29 429	80.70 407	43.93 473	72.47 445	74.88 444	60.15 411	70.55 358	76.57 446	49.94 347	81.59 423	50.58 415	74.83 381	85.34 401

Anonymous20240521		68.80 392	67.22 401	73.55 402	74.33 454	54.11 420	83.18 329	85.61 326	58.15 429	61.68 444	80.94 407	30.71 460	81.27 427	57.00 380	73.34 397	85.28 402

test_cas_vis1_n_1920		73.76 332	73.74 321	73.81 401	75.90 446	59.77 347	80.51 373	82.40 375	58.30 428	81.62 155	85.69 316	44.35 401	76.41 451	76.29 175	78.61 318	85.23 403

ADS-MVSNet2		66.20 415	63.33 419	74.82 388	79.92 416	58.75 356	67.55 465	75.19 442	53.37 452	65.25 425	75.86 453	42.32 413	80.53 431	41.57 460	68.91 421	85.18 404

ADS-MVSNet		64.36 421	62.88 423	68.78 437	79.92 416	47.17 461	67.55 465	71.18 456	53.37 452	65.25 425	75.86 453	42.32 413	73.99 468	41.57 460	68.91 421	85.18 404

FMVSNet5		69.50 386	67.96 386	74.15 396	82.97 371	55.35 409	80.01 383	82.12 378	62.56 391	63.02 438	81.53 401	36.92 443	81.92 422	48.42 430	74.06 387	85.17 406

pmmvs5		71.55 364	70.20 368	75.61 375	77.83 437	56.39 393	81.74 350	80.89 391	57.76 433	67.46 399	84.49 343	49.26 357	85.32 395	57.08 378	75.29 375	85.11 407

testing3		68.56 395	67.67 394	71.22 425	87.33 246	42.87 475	83.06 335	71.54 455	70.36 257	69.08 380	84.38 347	30.33 461	85.69 389	37.50 468	75.45 370	85.09 408

UWE-MVS-28		65.32 416	64.93 410	66.49 446	78.70 430	38.55 483	77.86 415	64.39 475	62.00 398	64.13 433	83.60 370	41.44 419	76.00 455	31.39 475	80.89 290	84.92 409

CMPMVS	51.72 21	70.19 380	68.16 382	76.28 369	73.15 465	57.55 376	79.47 388	83.92 348	48.02 464	56.48 463	84.81 340	43.13 408	86.42 381	62.67 320	81.81 282	84.89 410
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 409	66.53 405	67.08 445	75.62 450	41.69 480	75.93 425	76.50 437	66.11 338	65.20 427	86.59 295	35.72 449	74.71 464	43.71 452	73.38 396	84.84 411

MSDG		73.36 341	70.99 356	80.49 287	84.51 328	65.80 207	80.71 370	86.13 320	65.70 344	65.46 422	83.74 365	44.60 397	90.91 309	51.13 414	76.89 342	84.74 412

pmmvs4		74.03 330	71.91 341	80.39 288	81.96 388	68.32 135	81.45 357	82.14 377	59.32 418	69.87 372	85.13 333	52.40 306	88.13 362	60.21 346	74.74 382	84.73 413

gg-mvs-nofinetune		69.95 383	67.96 386	75.94 371	83.07 363	54.51 418	77.23 419	70.29 458	63.11 381	70.32 362	62.33 472	43.62 405	88.69 353	53.88 399	87.76 177	84.62 414

test_fmvs1		70.93 370	70.52 362	72.16 416	73.71 458	55.05 412	80.82 364	78.77 420	51.21 460	78.58 206	84.41 346	31.20 459	76.94 446	75.88 183	80.12 304	84.47 415

BH-w/o		78.21 252	77.33 257	80.84 279	88.81 167	65.13 228	84.87 282	87.85 276	69.75 276	74.52 312	84.74 342	61.34 215	93.11 207	58.24 368	85.84 216	84.27 416

MVS		78.19 254	76.99 263	81.78 252	85.66 295	66.99 182	84.66 287	90.47 171	55.08 448	72.02 347	85.27 328	63.83 171	94.11 141	66.10 287	89.80 133	84.24 417

COLMAP_ROB	66.92 17	73.01 348	70.41 365	80.81 280	87.13 254	65.63 211	88.30 160	84.19 346	62.96 384	63.80 437	87.69 261	38.04 440	92.56 231	46.66 440	74.91 380	84.24 417
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 427	61.73 427	61.70 452	72.74 467	24.50 495	69.16 460	78.03 424	61.40 401	56.72 462	75.53 456	38.42 437	76.48 450	45.95 446	57.67 458	84.13 419

TESTMET0.1,1		69.89 384	69.00 376	72.55 413	79.27 428	56.85 384	78.38 405	74.71 447	57.64 434	68.09 391	77.19 444	37.75 441	76.70 447	63.92 304	84.09 244	84.10 420

test_fmvs3		63.36 424	61.82 426	67.98 442	62.51 482	46.96 463	77.37 418	74.03 449	45.24 467	67.50 398	78.79 432	12.16 486	72.98 471	72.77 218	66.02 432	83.99 421

our_test_3		69.14 389	67.00 402	75.57 376	79.80 420	58.80 355	77.96 412	77.81 425	59.55 416	62.90 441	78.25 436	47.43 367	83.97 405	51.71 409	67.58 427	83.93 422

test_vis1_n		69.85 385	69.21 374	71.77 418	72.66 468	55.27 411	81.48 356	76.21 439	52.03 456	75.30 293	83.20 378	28.97 462	76.22 453	74.60 197	78.41 326	83.81 423

mamv4		76.81 286	78.23 230	72.54 414	86.12 286	65.75 210	78.76 400	82.07 379	64.12 369	72.97 333	91.02 162	67.97 123	68.08 479	83.04 89	78.02 329	83.80 424

tpmvs		71.09 368	69.29 373	76.49 368	82.04 387	56.04 399	78.92 398	81.37 388	64.05 372	67.18 404	78.28 435	49.74 350	89.77 330	49.67 424	72.37 401	83.67 425

test20.03		67.45 402	66.95 403	68.94 434	75.48 451	44.84 471	77.50 416	77.67 426	66.66 329	63.01 439	83.80 363	47.02 371	78.40 438	42.53 459	68.86 423	83.58 426

test0.0.03 1		68.00 400	67.69 393	68.90 435	77.55 440	47.43 458	75.70 429	72.95 454	66.66 329	66.56 412	82.29 394	48.06 365	75.87 457	44.97 451	74.51 384	83.41 427

Anonymous20231206		68.60 393	67.80 391	71.02 426	80.23 413	50.75 449	78.30 409	80.47 399	56.79 440	66.11 420	82.63 389	46.35 381	78.95 436	43.62 453	75.70 362	83.36 428

EU-MVSNet		68.53 396	67.61 395	71.31 424	78.51 432	47.01 462	84.47 293	84.27 344	42.27 471	66.44 417	84.79 341	40.44 426	83.76 406	58.76 362	68.54 424	83.17 429

dp		66.80 407	65.43 408	70.90 428	79.74 422	48.82 456	75.12 435	74.77 445	59.61 415	64.08 434	77.23 443	42.89 409	80.72 430	48.86 429	66.58 430	83.16 430

pmmvs-eth3d		70.50 376	67.83 390	78.52 337	77.37 442	66.18 195	81.82 348	81.51 385	58.90 423	63.90 436	80.42 412	42.69 411	86.28 382	58.56 363	65.30 440	83.11 431

YYNet1		65.03 417	62.91 422	71.38 420	75.85 448	56.60 390	69.12 461	74.66 448	57.28 438	54.12 467	77.87 438	45.85 387	74.48 465	49.95 422	61.52 452	83.05 432

MDA-MVSNet-bldmvs		66.68 408	63.66 418	75.75 373	79.28 427	60.56 338	73.92 442	78.35 423	64.43 363	50.13 473	79.87 421	44.02 403	83.67 407	46.10 445	56.86 459	83.03 433

MDA-MVSNet_test_wron		65.03 417	62.92 421	71.37 421	75.93 445	56.73 386	69.09 462	74.73 446	57.28 438	54.03 468	77.89 437	45.88 386	74.39 466	49.89 423	61.55 451	82.99 434

USDC		70.33 378	68.37 379	76.21 370	80.60 408	56.23 397	79.19 393	86.49 312	60.89 404	61.29 445	85.47 324	31.78 457	89.47 337	53.37 402	76.21 358	82.94 435

Syy-MVS		68.05 399	67.85 388	68.67 438	84.68 323	40.97 481	78.62 402	73.08 452	66.65 332	66.74 410	79.46 424	52.11 312	82.30 419	32.89 473	76.38 355	82.75 436

myMVS_eth3d		67.02 406	66.29 406	69.21 433	84.68 323	42.58 476	78.62 402	73.08 452	66.65 332	66.74 410	79.46 424	31.53 458	82.30 419	39.43 465	76.38 355	82.75 436

ttmdpeth		59.91 430	57.10 434	68.34 440	67.13 477	46.65 464	74.64 438	67.41 467	48.30 463	62.52 443	85.04 337	20.40 476	75.93 456	42.55 458	45.90 478	82.44 438

OpenMVS_ROB	64.09 19	70.56 375	68.19 381	77.65 355	80.26 411	59.41 353	85.01 279	82.96 369	58.76 425	65.43 423	82.33 392	37.63 442	91.23 293	45.34 450	76.03 359	82.32 439

JIA-IIPM		66.32 412	62.82 424	76.82 366	77.09 443	61.72 318	65.34 473	75.38 441	58.04 432	64.51 430	62.32 473	42.05 417	86.51 379	51.45 412	69.22 420	82.21 440

dmvs_re		71.14 367	70.58 361	72.80 411	81.96 388	59.68 348	75.60 430	79.34 415	68.55 307	69.27 379	80.72 410	49.42 353	76.54 448	52.56 406	77.79 331	82.19 441

EG-PatchMatch MVS		74.04 328	71.82 342	80.71 282	84.92 317	67.42 168	85.86 255	88.08 266	66.04 340	64.22 432	83.85 361	35.10 450	92.56 231	57.44 374	80.83 292	82.16 442

FE-MVSNET		67.25 405	65.33 409	73.02 409	75.86 447	52.54 433	80.26 380	80.56 397	63.80 377	60.39 448	79.70 423	41.41 420	84.66 402	43.34 454	62.62 448	81.86 443

MVP-Stereo		76.12 301	74.46 311	81.13 272	85.37 305	69.79 95	84.42 300	87.95 272	65.03 357	67.46 399	85.33 327	53.28 300	91.73 268	58.01 370	83.27 263	81.85 444
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 401	64.34 413	76.92 365	73.47 462	61.07 327	84.86 283	82.98 368	59.77 414	58.30 457	85.13 333	26.06 466	87.89 365	47.92 437	60.59 455	81.81 445

GG-mvs-BLEND				75.38 381	81.59 394	55.80 403	79.32 390	69.63 460		67.19 403	73.67 461	43.24 407	88.90 351	50.41 416	84.50 234	81.45 446

KD-MVS_2432*1600		66.22 413	63.89 416	73.21 405	75.47 452	53.42 426	70.76 453	84.35 341	64.10 370	66.52 414	78.52 433	34.55 451	84.98 397	50.40 417	50.33 472	81.23 447

miper_refine_blended		66.22 413	63.89 416	73.21 405	75.47 452	53.42 426	70.76 453	84.35 341	64.10 370	66.52 414	78.52 433	34.55 451	84.98 397	50.40 417	50.33 472	81.23 447

test_0402		72.79 353	70.44 364	79.84 305	88.13 198	65.99 201	85.93 252	84.29 343	65.57 346	67.40 402	85.49 323	46.92 372	92.61 227	35.88 470	74.38 385	80.94 449

MVStest1		56.63 434	52.76 440	68.25 441	61.67 483	53.25 430	71.67 448	68.90 465	38.59 476	50.59 472	83.05 380	25.08 468	70.66 473	36.76 469	38.56 479	80.83 450

UnsupCasMVSNet_bld		63.70 423	61.53 429	70.21 430	73.69 459	51.39 444	72.82 444	81.89 380	55.63 446	57.81 459	71.80 465	38.67 436	78.61 437	49.26 427	52.21 470	80.63 451

LCM-MVSNet		54.25 436	49.68 446	67.97 443	53.73 491	45.28 468	66.85 468	80.78 393	35.96 480	39.45 481	62.23 474	8.70 490	78.06 441	48.24 434	51.20 471	80.57 452

N_pmnet		52.79 441	53.26 439	51.40 466	78.99 429	7.68 500	69.52 457	3.89 499	51.63 458	57.01 461	74.98 457	40.83 424	65.96 481	37.78 467	64.67 441	80.56 453

TinyColmap		67.30 404	64.81 411	74.76 389	81.92 390	56.68 389	80.29 378	81.49 386	60.33 408	56.27 465	83.22 376	24.77 470	87.66 369	45.52 448	69.47 418	79.95 454

PM-MVS		66.41 411	64.14 414	73.20 407	73.92 457	56.45 391	78.97 397	64.96 474	63.88 376	64.72 428	80.24 416	19.84 478	83.44 412	66.24 284	64.52 442	79.71 455

ANet_high		50.57 445	46.10 449	63.99 449	48.67 494	39.13 482	70.99 452	80.85 392	61.39 402	31.18 483	57.70 479	17.02 481	73.65 470	31.22 476	15.89 491	79.18 456

LF4IMVS		64.02 422	62.19 425	69.50 432	70.90 470	53.29 429	76.13 423	77.18 433	52.65 454	58.59 455	80.98 406	23.55 473	76.52 449	53.06 404	66.66 429	78.68 457

PatchMatch-RL		72.38 355	70.90 358	76.80 367	88.60 179	67.38 171	79.53 387	76.17 440	62.75 389	69.36 377	82.00 399	45.51 392	84.89 399	53.62 400	80.58 296	78.12 458

MS-PatchMatch		73.83 331	72.67 333	77.30 362	83.87 341	66.02 198	81.82 348	84.66 337	61.37 403	68.61 384	82.82 386	47.29 368	88.21 360	59.27 354	84.32 241	77.68 459

DSMNet-mixed		57.77 433	56.90 435	60.38 454	67.70 475	35.61 485	69.18 459	53.97 486	32.30 484	57.49 460	79.88 420	40.39 427	68.57 478	38.78 466	72.37 401	76.97 460

CHOSEN 280x420		66.51 410	64.71 412	71.90 417	81.45 397	63.52 279	57.98 482	68.95 464	53.57 451	62.59 442	76.70 445	46.22 383	75.29 463	55.25 390	79.68 306	76.88 461

mvsany_test3		53.99 437	51.45 442	61.61 453	55.51 487	44.74 472	63.52 478	45.41 492	43.69 470	58.11 458	76.45 447	17.99 479	63.76 483	54.77 394	47.59 474	76.34 462

dmvs_testset		62.63 425	64.11 415	58.19 456	78.55 431	24.76 494	75.28 431	65.94 471	67.91 316	60.34 449	76.01 452	53.56 296	73.94 469	31.79 474	67.65 426	75.88 463

mvsany_test1		62.30 426	61.26 430	65.41 448	69.52 472	54.86 414	66.86 467	49.78 488	46.65 465	68.50 388	83.21 377	49.15 358	66.28 480	56.93 381	60.77 453	75.11 464

PMMVS		69.34 388	68.67 377	71.35 423	75.67 449	62.03 312	75.17 432	73.46 450	50.00 461	68.68 382	79.05 427	52.07 314	78.13 439	61.16 339	82.77 269	73.90 465

test_vis1_rt		60.28 429	58.42 432	65.84 447	67.25 476	55.60 406	70.44 455	60.94 480	44.33 469	59.00 454	66.64 470	24.91 469	68.67 477	62.80 316	69.48 417	73.25 466

pmmvs3		57.79 432	54.26 437	68.37 439	64.02 481	56.72 387	75.12 435	65.17 472	40.20 473	52.93 469	69.86 469	20.36 477	75.48 460	45.45 449	55.25 466	72.90 467

PVSNet_0	57.27 20	61.67 428	59.27 431	68.85 436	79.61 423	57.44 378	68.01 463	73.44 451	55.93 445	58.54 456	70.41 468	44.58 398	77.55 443	47.01 439	35.91 480	71.55 468

WB-MVS		54.94 435	54.72 436	55.60 462	73.50 460	20.90 496	74.27 441	61.19 479	59.16 420	50.61 471	74.15 459	47.19 370	75.78 458	17.31 487	35.07 481	70.12 469

SSC-MVS		53.88 438	53.59 438	54.75 464	72.87 466	19.59 497	73.84 443	60.53 481	57.58 436	49.18 475	73.45 462	46.34 382	75.47 461	16.20 490	32.28 483	69.20 470

test_f		52.09 442	50.82 443	55.90 460	53.82 490	42.31 479	59.42 481	58.31 484	36.45 479	56.12 466	70.96 467	12.18 485	57.79 486	53.51 401	56.57 461	67.60 471

PMMVS2		40.82 452	38.86 456	46.69 467	53.84 489	16.45 498	48.61 485	49.92 487	37.49 477	31.67 482	60.97 475	8.14 492	56.42 487	28.42 478	30.72 484	67.19 472

new_pmnet		50.91 444	50.29 444	52.78 465	68.58 474	34.94 487	63.71 477	56.63 485	39.73 474	44.95 476	65.47 471	21.93 475	58.48 485	34.98 471	56.62 460	64.92 473

MVS-HIRNet		59.14 431	57.67 433	63.57 450	81.65 392	43.50 474	71.73 447	65.06 473	39.59 475	51.43 470	57.73 478	38.34 438	82.58 418	39.53 463	73.95 388	64.62 474

APD_test1		53.31 440	49.93 445	63.42 451	65.68 478	50.13 451	71.59 449	66.90 469	34.43 481	40.58 480	71.56 466	8.65 491	76.27 452	34.64 472	55.36 464	63.86 475

test_method		31.52 455	29.28 459	38.23 470	27.03 498	6.50 501	20.94 490	62.21 478	4.05 492	22.35 490	52.50 483	13.33 483	47.58 490	27.04 480	34.04 482	60.62 476

EGC-MVSNET		52.07 443	47.05 447	67.14 444	83.51 351	60.71 335	80.50 374	67.75 466	0.07 494	0.43 495	75.85 455	24.26 471	81.54 424	28.82 477	62.25 449	59.16 477

test_vis3_rt		49.26 446	47.02 448	56.00 459	54.30 488	45.27 469	66.76 469	48.08 489	36.83 478	44.38 477	53.20 482	7.17 493	64.07 482	56.77 384	55.66 462	58.65 478

FPMVS		53.68 439	51.64 441	59.81 455	65.08 479	51.03 446	69.48 458	69.58 461	41.46 472	40.67 479	72.32 464	16.46 482	70.00 476	24.24 483	65.42 439	58.40 479

testf1		45.72 447	41.96 451	57.00 457	56.90 485	45.32 466	66.14 470	59.26 482	26.19 485	30.89 484	60.96 476	4.14 494	70.64 474	26.39 481	46.73 476	55.04 480

APD_test2		45.72 447	41.96 451	57.00 457	56.90 485	45.32 466	66.14 470	59.26 482	26.19 485	30.89 484	60.96 476	4.14 494	70.64 474	26.39 481	46.73 476	55.04 480

PMVS	37.38 22	44.16 451	40.28 455	55.82 461	40.82 496	42.54 478	65.12 474	63.99 476	34.43 481	24.48 487	57.12 480	3.92 496	76.17 454	17.10 488	55.52 463	48.75 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 457	25.89 461	43.81 469	44.55 495	35.46 486	28.87 489	39.07 493	18.20 489	18.58 491	40.18 486	2.68 497	47.37 491	17.07 489	23.78 488	48.60 483
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 449	45.38 450	45.55 468	73.36 463	26.85 492	67.72 464	34.19 494	54.15 450	49.65 474	56.41 481	25.43 467	62.94 484	19.45 485	28.09 485	46.86 484

kuosan		39.70 453	40.40 454	37.58 471	64.52 480	26.98 490	65.62 472	33.02 495	46.12 466	42.79 478	48.99 484	24.10 472	46.56 492	12.16 493	26.30 486	39.20 485

Gipuma		45.18 450	41.86 453	55.16 463	77.03 444	51.52 442	32.50 488	80.52 398	32.46 483	27.12 486	35.02 487	9.52 489	75.50 459	22.31 484	60.21 456	38.45 486
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 474	40.17 497	26.90 491		24.59 498	17.44 490	23.95 488	48.61 485	9.77 488	26.48 493	18.06 486	24.47 487	28.83 487

E-PMN		31.77 454	30.64 457	35.15 472	52.87 492	27.67 489	57.09 483	47.86 490	24.64 487	16.40 492	33.05 488	11.23 487	54.90 488	14.46 491	18.15 489	22.87 488

EMVS		30.81 456	29.65 458	34.27 473	50.96 493	25.95 493	56.58 484	46.80 491	24.01 488	15.53 493	30.68 489	12.47 484	54.43 489	12.81 492	17.05 490	22.43 489

tmp_tt		18.61 459	21.40 462	10.23 476	4.82 499	10.11 499	34.70 487	30.74 497	1.48 493	23.91 489	26.07 490	28.42 463	13.41 495	27.12 479	15.35 492	7.17 490

wuyk23d		16.82 460	15.94 463	19.46 475	58.74 484	31.45 488	39.22 486	3.74 500	6.84 491	6.04 494	2.70 494	1.27 498	24.29 494	10.54 494	14.40 493	2.63 491

test123		6.12 462	8.11 465	0.14 477	0.06 501	0.09 502	71.05 451	0.03 502	0.04 496	0.25 497	1.30 496	0.05 499	0.03 497	0.21 496	0.01 495	0.29 492

testmvs		6.04 463	8.02 466	0.10 478	0.08 500	0.03 503	69.74 456	0.04 501	0.05 495	0.31 496	1.68 495	0.02 500	0.04 496	0.24 495	0.02 494	0.25 493

mmdepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

monomultidepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

test_blank		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

uanet_test		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

DCPMVS		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

cdsmvs_eth3d_5k		19.96 458	26.61 460	0.00 479	0.00 502	0.00 504	0.00 491	89.26 223	0.00 497	0.00 498	88.61 234	61.62 208	0.00 498	0.00 497	0.00 496	0.00 494

pcd_1.5k_mvsjas		5.26 464	7.02 467	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	63.15 180	0.00 498	0.00 497	0.00 496	0.00 494

sosnet-low-res		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

sosnet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

uncertanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

Regformer		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

ab-mvs-re		7.23 461	9.64 464	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	86.72 287	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

uanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 496	0.00 494

TestfortrainingZip							93.28 12

WAC-MVS						42.58 476								39.46 464

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 153	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					0.00 502

eth-test					0.00 502

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 239	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	73.93 169	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	91.17 149	74.31 158

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												50.01 345

MTGPA								92.02 111

test_post1							78.90 399				5.43 493	48.81 364	85.44 394	59.25 355

test_post											5.46 492	50.36 341	84.24 403

patchmatchnet-post											74.00 460	51.12 332	88.60 355

MTMP							92.18 39	32.83 496

gm-plane-assit					81.40 398	53.83 423			62.72 390		80.94 407		92.39 240	63.40 308

TEST9					93.26 56	72.96 25	88.75 138	91.89 119	68.44 310	85.00 80	93.10 88	74.36 32	95.41 80

test_8					93.13 60	72.57 35	88.68 143	91.84 123	68.69 305	84.87 84	93.10 88	74.43 30	95.16 90

agg_prior					92.85 68	71.94 52		91.78 127		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 121	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

旧先验2							86.56 229		58.10 431	87.04 61			88.98 347	74.07 203

新几何2							86.29 243

原ACMM2							86.86 216

testdata2													91.01 304	62.37 325

segment_acmp												73.08 43

testdata1							84.14 308		75.71 112

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 234

plane_prior4											91.00 163

plane_prior3						68.60 128			78.44 36	78.92 199

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 207

n2								0.00 503

nn								0.00 503

door-mid								69.98 459

test11								92.23 97

door								69.44 462

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 90	77.23 240

ACMP_Plane					89.33 144		89.17 116		76.41 90	77.23 240

BP-MVS														77.47 159

HQP3-MVS								92.19 105							85.99 211

HQP2-MVS												60.17 237

NP-MVS					89.62 129	68.32 135					90.24 184

MDTV_nov1_ep13			69.97 369		83.18 360	53.48 425	77.10 421	80.18 408	60.45 407	69.33 378	80.44 411	48.89 363	86.90 375	51.60 410	78.51 321

ACMMP++_ref															81.95 280

ACMMP++															81.25 285

Test By Simon												64.33 166

High-resolution multi-view benchmark