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	90.79 103	73.65 10	92.66 28	91.17 151	86.57 1	87.39 58	94.97 25	71.70 63	97.68 1	92.19 1	95.63 32	95.57 1

casdiffmvs_mvg		85.99 59	86.09 62	85.70 82	87.65 231	67.22 180	88.69 142	93.04 47	79.64 21	85.33 77	92.54 105	73.30 40	94.50 126	83.49 83	91.14 110	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	84.06 166	91.71 85	64.94 239	86.47 233	91.87 122	73.63 178	86.60 68	93.02 94	76.57 19	91.87 265	83.36 84	92.15 90	95.35 3

casdiffmvs		85.11 83	85.14 82	85.01 107	87.20 253	65.77 211	87.75 181	92.83 66	77.84 43	84.36 100	92.38 107	72.15 56	93.93 152	81.27 109	90.48 121	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 82	89.48 139	67.88 154	88.59 146	89.05 236	80.19 12	90.70 20	95.40 15	74.56 29	93.92 153	91.54 2	92.07 92	95.31 5

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 94	73.49 16	93.18 16	93.78 23	80.79 8	76.66 256	93.37 84	60.40 237	96.75 30	77.20 162	93.73 70	95.29 6

BP-MVS1		84.32 91	83.71 108	86.17 69	87.84 215	67.85 155	89.38 109	89.64 205	77.73 45	83.98 107	92.12 119	56.89 267	95.43 78	84.03 80	91.75 99	95.24 7

MGCNet		87.69 24	87.55 29	88.12 13	89.45 140	71.76 54	91.47 57	89.54 208	82.14 3	86.65 67	94.28 46	68.28 121	97.46 6	90.81 6	95.31 38	95.15 8

CS-MVS		86.69 44	86.95 42	85.90 79	90.76 104	67.57 165	92.83 22	93.30 38	79.67 19	84.57 94	92.27 108	71.47 66	95.02 101	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 88	74.62 152	88.90 33	93.85 71	75.75 24	96.00 60	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	84.80 119	87.30 251	65.39 219	87.30 201	92.88 63	77.62 47	84.04 106	92.26 109	71.81 60	93.96 146	81.31 107	90.30 124	95.03 11

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 65	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									68.21 315	92.02 15	94.00 63	82.09 5	95.98 62	84.58 71	96.68 2	94.95 12

IS-MVSNet		83.15 130	82.81 127	84.18 155	89.94 124	63.30 286	91.59 51	88.46 263	79.04 30	79.49 190	92.16 116	65.10 159	94.28 132	67.71 272	91.86 98	94.95 12

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 79	72.96 25	93.73 5	93.67 25	80.19 12	88.10 43	94.80 27	73.76 38	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 72	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	74.77 148	92.29 7	95.97 2	74.28 34	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		77.30 279	76.49 275	79.74 315	90.08 117	52.02 440	87.86 179	63.10 483	74.88 144	80.16 183	92.79 101	38.29 444	92.35 245	68.74 265	92.50 84	94.86 19

ECVR-MVS		79.61 213	79.26 206	80.67 285	90.08 117	54.69 421	87.89 177	77.44 436	74.88 144	80.27 180	92.79 101	48.96 365	92.45 239	68.55 266	92.50 84	94.86 19

IU-MVS					95.30 2	71.25 65		92.95 61	66.81 327	92.39 6				88.94 28	96.63 4	94.85 21

test1111		79.43 220	79.18 209	80.15 300	89.99 122	53.31 434	87.33 200	77.05 440	75.04 137	80.23 182	92.77 103	48.97 364	92.33 247	68.87 263	92.40 86	94.81 22

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 71	71.95 52	92.40 29	94.74 2	75.71 113	89.16 29	95.10 18	75.65 25	96.19 52	87.07 49	96.01 17	94.79 23

balanced_conf03		86.78 42	86.99 40	86.15 71	91.24 91	67.61 163	90.51 70	92.90 62	77.26 60	87.44 57	91.63 136	71.27 70	96.06 55	85.62 60	95.01 41	94.78 24

E4		84.10 98	83.99 101	84.45 134	87.58 241	64.99 235	86.54 231	92.25 97	76.38 95	83.37 122	92.09 120	69.88 91	93.58 168	79.78 130	88.03 173	94.77 25

viewmacassd2359aftdt		83.76 110	83.66 110	84.07 163	86.59 277	64.56 248	86.88 216	91.82 125	75.72 112	83.34 123	92.15 118	68.24 122	92.88 220	79.05 136	89.15 147	94.77 25

sasdasda		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 81	88.01 46	91.23 150	73.28 41	93.91 154	81.50 105	88.80 152	94.77 25

SPE-MVS-test		86.29 54	86.48 51	85.71 81	91.02 96	67.21 181	92.36 34	93.78 23	78.97 33	83.51 121	91.20 154	70.65 79	95.15 92	81.96 102	94.89 46	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 81	88.01 46	91.23 150	73.28 41	93.91 154	81.50 105	88.80 152	94.77 25

MED-MVS test				87.86 26	94.57 17	71.43 61	93.28 12	94.36 3	75.24 127	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 61	93.28 12	94.36 3	76.30 99	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 61	91.61 49	94.25 6	76.30 99	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	84.51 129	87.60 233	65.36 221	87.45 191	92.31 90	76.51 86	83.53 117	92.26 109	69.25 103	93.50 179	79.88 125	88.26 163	94.69 33

E6new		84.22 92	84.12 95	84.52 127	87.60 233	65.36 221	87.45 191	92.30 92	76.51 86	83.53 117	92.26 109	69.26 101	93.49 181	79.88 125	88.26 163	94.69 33

E6		84.22 92	84.12 95	84.52 127	87.60 233	65.36 221	87.45 191	92.30 92	76.51 86	83.53 117	92.26 109	69.26 101	93.49 181	79.88 125	88.26 163	94.69 33

E5		84.22 92	84.12 95	84.51 129	87.60 233	65.36 221	87.45 191	92.31 90	76.51 86	83.53 117	92.26 109	69.25 103	93.50 179	79.88 125	88.26 163	94.69 33

GDP-MVS		83.52 119	82.64 131	86.16 70	88.14 199	68.45 133	89.13 121	92.69 71	72.82 204	83.71 112	91.86 126	55.69 276	95.35 87	80.03 122	89.74 136	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		82.60 143	82.10 143	84.10 157	87.98 209	62.94 297	87.45 191	91.27 147	77.42 56	79.85 185	90.28 183	56.62 270	94.70 119	79.87 129	88.15 169	94.67 38

E2		84.00 101	83.87 102	84.39 137	87.70 228	64.95 236	86.40 238	92.23 98	75.85 109	83.21 124	91.78 128	70.09 86	93.55 173	79.52 133	88.05 171	94.66 41

E3		84.00 101	83.87 102	84.39 137	87.70 228	64.95 236	86.40 238	92.23 98	75.85 109	83.21 124	91.78 128	70.09 86	93.55 173	79.52 133	88.05 171	94.66 41

MGCFI-Net		85.06 85	85.51 74	83.70 185	89.42 141	63.01 292	89.43 104	92.62 79	76.43 90	87.53 54	91.34 148	72.82 50	93.42 189	81.28 108	88.74 155	94.66 41

viewmanbaseed2359cas		83.66 113	83.55 113	84.00 174	86.81 269	64.53 249	86.65 226	91.75 130	74.89 143	83.15 129	91.68 132	68.74 114	92.83 224	79.02 138	89.24 144	94.63 44

alignmvs		85.48 73	85.32 79	85.96 78	89.51 136	69.47 103	89.74 92	92.47 82	76.17 103	87.73 53	91.46 145	70.32 81	93.78 160	81.51 104	88.95 149	94.63 44

viewdifsd2359ckpt09		83.34 125	82.55 133	85.70 82	87.64 232	67.72 160	88.43 151	91.68 133	71.91 218	81.65 155	90.68 171	67.10 134	94.75 115	76.17 177	87.70 180	94.62 46

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 51	89.80 90	93.50 30	75.17 135	86.34 69	95.29 17	70.86 75	96.00 60	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 60	92.24 78	69.03 111	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 40	85.66 58	95.72 28	94.58 47

viewcassd2359sk11		83.89 104	83.74 107	84.34 142	87.76 223	64.91 242	86.30 242	92.22 101	75.47 120	83.04 130	91.52 141	70.15 84	93.53 176	79.26 135	87.96 174	94.57 49

VDD-MVS		83.01 135	82.36 137	84.96 109	91.02 96	66.40 192	88.91 128	88.11 266	77.57 49	84.39 97	93.29 86	52.19 310	93.91 154	77.05 165	88.70 156	94.57 49

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 106	79.31 24	84.39 97	92.18 114	64.64 164	95.53 72	80.70 116	94.65 52	94.56 51

KinetiMVS		83.31 128	82.61 132	85.39 92	87.08 262	67.56 166	88.06 169	91.65 134	77.80 44	82.21 144	91.79 127	57.27 262	94.07 144	77.77 155	89.89 134	94.56 51

VDDNet		81.52 165	80.67 165	84.05 169	90.44 109	64.13 261	89.73 93	85.91 326	71.11 235	83.18 127	93.48 79	50.54 341	93.49 181	73.40 210	88.25 167	94.54 53

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 78	93.28 12	94.36 3	75.24 127	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 54

E3new		83.78 109	83.60 112	84.31 144	87.76 223	64.89 243	86.24 245	92.20 104	75.15 136	82.87 133	91.23 150	70.11 85	93.52 178	79.05 136	87.79 177	94.51 55

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 74	91.09 93	67.64 162	89.63 97	92.65 76	72.89 203	84.64 91	91.71 131	71.85 59	96.03 56	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 55	93.83 4	93.96 18	75.70 115	91.06 19	96.03 1	76.84 18	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 55					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 55					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	73.07 198	84.86 86	92.89 96	76.22 21	96.33 46	84.89 66	95.13 40	94.40 60

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 122	87.76 223	65.62 213	89.20 114	92.21 103	79.94 17	89.74 27	94.86 26	68.63 115	94.20 138	90.83 5	91.39 105	94.38 61

CANet		86.45 48	86.10 61	87.51 42	90.09 116	70.94 76	89.70 94	92.59 80	81.78 4	81.32 159	91.43 146	70.34 80	97.23 17	84.26 75	93.36 74	94.37 62

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 100	70.96 74	92.27 37	94.07 14	72.45 206	85.22 79	91.90 123	69.47 96	96.42 45	83.28 86	95.94 23	94.35 63

viewdifsd2359ckpt07		82.83 138	82.78 130	82.99 216	86.51 279	62.58 300	85.09 278	90.83 163	75.22 129	82.28 141	91.63 136	69.43 97	92.03 255	77.71 156	86.32 204	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 52	80.90 7	88.06 44	94.06 59	76.43 20	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 78	94.32 44	71.76 61	96.93 23	85.53 61	95.79 26	94.32 66

balanced_ft_v1		83.98 103	83.64 111	85.03 105	89.76 129	65.86 206	88.31 160	91.71 131	74.41 157	80.41 179	90.82 168	62.90 187	94.90 105	83.04 89	91.37 106	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	76.07 105	83.81 111	93.95 68	69.77 93	96.01 59	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 49	93.49 51	71.08 70	88.58 147	92.42 86	68.32 314	84.61 92	93.48 79	72.32 53	96.15 54	79.00 140	95.43 34	94.28 69

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 70

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 71

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 96	87.33 248	67.30 175	89.50 101	90.98 156	76.25 102	90.56 22	94.75 29	68.38 118	94.24 137	90.80 7	92.32 89	94.19 72

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 99	87.20 253	68.54 131	89.57 99	90.44 174	75.31 126	87.49 55	94.39 42	72.86 48	92.72 227	89.04 27	90.56 120	94.16 73

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 47	76.62 83	84.22 101	93.36 85	71.44 67	96.76 29	80.82 113	95.33 37	94.16 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 123	83.02 123	84.57 125	90.13 115	64.47 254	92.32 35	90.73 166	74.45 156	79.35 195	91.10 157	69.05 109	95.12 93	72.78 217	87.22 188	94.13 75

viewdifsd2359ckpt13		82.91 136	82.29 139	84.77 120	86.96 265	66.90 188	87.47 188	91.62 136	72.19 211	81.68 154	90.71 170	66.92 135	93.28 192	75.90 182	87.15 190	94.12 76

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 66	81.50 5	85.79 73	93.47 81	73.02 46	97.00 22	84.90 64	94.94 44	94.10 77

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 101	88.14 42	95.09 19	71.06 73	96.67 33	87.67 44	96.37 14	94.09 78

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 114	94.17 53	67.45 129	96.60 38	83.06 87	94.50 57	94.07 79

X-MVStestdata		80.37 200	77.83 239	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 114	12.47 497	67.45 129	96.60 38	83.06 87	94.50 57	94.07 79

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 46	76.73 80	84.45 95	94.52 32	69.09 106	96.70 31	84.37 74	94.83 49	94.03 81

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 146	86.70 273	65.83 207	88.77 136	89.78 197	75.46 121	88.35 37	93.73 74	69.19 105	93.06 212	91.30 3	88.44 161	94.02 82

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 40	76.78 77	84.66 90	94.52 32	68.81 112	96.65 35	84.53 72	94.90 45	94.00 83

fmvsm_s_conf0.1_n_2		83.80 107	83.79 106	83.83 181	85.62 298	64.94 239	87.03 208	86.62 315	74.32 159	87.97 48	94.33 43	60.67 229	92.60 230	89.72 14	87.79 177	93.96 84

test_fmvsmconf_n		85.92 62	86.04 63	85.57 88	85.03 317	69.51 101	89.62 98	90.58 169	73.42 186	87.75 51	94.02 61	72.85 49	93.24 196	90.37 8	90.75 117	93.96 84

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 108	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 86
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	85.13 102	86.12 288	69.93 93	88.65 144	90.78 165	69.97 271	88.27 39	93.98 66	71.39 68	91.54 281	88.49 35	90.45 122	93.91 87

test_prior				86.33 65	92.61 75	69.59 99		92.97 60					95.48 75			93.91 87

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 87	93.99 65	70.67 78	96.82 26	84.18 79	95.01 41	93.90 89

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 89	82.99 371	69.39 108	89.65 95	90.29 183	73.31 190	87.77 50	94.15 55	71.72 62	93.23 197	90.31 9	90.67 119	93.89 90

Anonymous202405211		78.25 251	77.01 261	81.99 251	91.03 95	60.67 341	84.77 285	83.90 353	70.65 252	80.00 184	91.20 154	41.08 426	91.43 289	65.21 294	85.26 227	93.85 91

LFMVS		81.82 155	81.23 155	83.57 190	91.89 83	63.43 284	89.84 87	81.85 388	77.04 70	83.21 124	93.10 89	52.26 309	93.43 188	71.98 229	89.95 132	93.85 91

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 183	86.17 286	65.00 234	86.96 211	87.28 291	74.35 158	88.25 40	94.23 50	61.82 205	92.60 230	89.85 12	88.09 170	93.84 93

Effi-MVS+		83.62 117	83.08 121	85.24 96	88.38 190	67.45 168	88.89 129	89.15 232	75.50 119	82.27 142	88.28 245	69.61 95	94.45 129	77.81 154	87.84 176	93.84 93

Anonymous20240529		80.19 206	78.89 215	84.10 157	90.60 105	64.75 246	88.95 127	90.90 159	65.97 345	80.59 175	91.17 156	49.97 348	93.73 166	69.16 260	82.70 274	93.81 95

MVS_Test		83.15 130	83.06 122	83.41 196	86.86 266	63.21 288	86.11 249	92.00 114	74.31 160	82.87 133	89.44 213	70.03 88	93.21 199	77.39 161	88.50 160	93.81 95

Elysia		81.53 163	80.16 178	85.62 85	85.51 301	68.25 140	88.84 133	92.19 106	71.31 229	80.50 176	89.83 193	46.89 376	94.82 110	76.85 167	89.57 138	93.80 97

StellarMVS		81.53 163	80.16 178	85.62 85	85.51 301	68.25 140	88.84 133	92.19 106	71.31 229	80.50 176	89.83 193	46.89 376	94.82 110	76.85 167	89.57 138	93.80 97

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 93	80.25 413	69.03 111	89.47 102	89.65 204	73.24 194	86.98 63	94.27 47	66.62 138	93.23 197	90.26 10	89.95 132	93.78 99

GeoE		81.71 157	81.01 160	83.80 184	89.51 136	64.45 255	88.97 126	88.73 256	71.27 232	78.63 207	89.76 198	66.32 144	93.20 202	69.89 252	86.02 212	93.74 100

diffmvs		82.10 147	81.88 149	82.76 233	83.00 367	63.78 270	83.68 317	89.76 199	72.94 201	82.02 147	89.85 192	65.96 153	90.79 318	82.38 100	87.30 187	93.71 101
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 63	93.26 56	69.77 97	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 54	96.27 49	86.87 50	94.65 52	93.70 102

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 39	76.78 77	84.91 83	94.44 39	70.78 76	96.61 37	84.53 72	94.89 46	93.66 103

VNet		82.21 146	82.41 135	81.62 257	90.82 101	60.93 334	84.47 294	89.78 197	76.36 97	84.07 105	91.88 124	64.71 163	90.26 328	70.68 241	88.89 150	93.66 103

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 47	75.53 118	83.86 109	94.42 40	67.87 126	96.64 36	82.70 98	94.57 56	93.66 103

DELS-MVS		85.41 76	85.30 80	85.77 80	88.49 184	67.93 153	85.52 269	93.44 32	78.70 34	83.63 116	89.03 220	74.57 28	95.71 67	80.26 121	94.04 67	93.66 103
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 61	92.60 76	72.71 29	91.81 46	93.19 41	77.87 42	90.32 23	94.00 63	74.83 27	93.78 160	87.63 45	94.27 65	93.65 107
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 92	72.32 45	90.31 79	93.94 19	77.12 67	82.82 136	94.23 50	72.13 57	97.09 19	84.83 67	95.37 35	93.65 107
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		83.65 114	84.54 89	80.99 277	90.06 121	65.83 207	84.21 305	88.74 255	71.60 224	85.01 80	92.44 106	74.51 30	83.50 418	82.15 101	92.15 90	93.64 109

EIA-MVS		83.31 128	82.80 128	84.82 117	89.59 132	65.59 214	88.21 163	92.68 72	74.66 151	78.96 199	86.42 303	69.06 108	95.26 88	75.54 188	90.09 128	93.62 110

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 76	77.57 49	83.84 110	94.40 41	72.24 55	96.28 48	85.65 59	95.30 39	93.62 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
diffmvs_AUTHOR		82.38 144	82.27 140	82.73 235	83.26 357	63.80 268	83.89 312	89.76 199	73.35 189	82.37 140	90.84 166	66.25 145	90.79 318	82.77 93	87.93 175	93.59 112

HPM-MVS_fast		85.35 79	84.95 85	86.57 64	93.69 46	70.58 85	92.15 40	91.62 136	73.89 172	82.67 139	94.09 57	62.60 189	95.54 71	80.93 111	92.93 77	93.57 113

fmvsm_s_conf0.1_n		83.56 118	83.38 117	84.10 157	84.86 319	67.28 176	89.40 108	83.01 370	70.67 248	87.08 61	93.96 67	68.38 118	91.45 288	88.56 34	84.50 236	93.56 114

CSCG		86.41 51	86.19 58	87.07 51	92.91 67	72.48 37	90.81 66	93.56 29	73.95 169	83.16 128	91.07 159	75.94 22	95.19 90	79.94 124	94.38 62	93.55 115

test12				86.80 59	92.63 74	70.70 82		91.79 127		82.71 138		71.67 64	96.16 53		94.50 57	93.54 116

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 68	92.69 73	69.53 100	91.93 42	92.99 55	73.54 182	85.94 70	94.51 35	65.80 154	95.61 68	83.04 89	92.51 83	93.53 117

mvs_anonymous		79.42 221	79.11 210	80.34 293	84.45 330	57.97 372	82.59 340	87.62 283	67.40 324	76.17 272	88.56 238	68.47 117	89.59 341	70.65 242	86.05 211	93.47 118

fmvsm_s_conf0.5_n		83.80 107	83.71 108	84.07 163	86.69 274	67.31 174	89.46 103	83.07 369	71.09 236	86.96 64	93.70 75	69.02 111	91.47 287	88.79 30	84.62 235	93.44 119

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 153	86.26 282	67.40 171	89.18 115	89.31 221	72.50 205	88.31 38	93.86 70	69.66 94	91.96 259	89.81 13	91.05 111	93.38 120

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 101	76.87 74	82.81 137	94.25 49	66.44 142	96.24 50	82.88 92	94.28 64	93.38 120

EPNet		83.72 112	82.92 126	86.14 73	84.22 333	69.48 102	91.05 64	85.27 333	81.30 6	76.83 251	91.65 134	66.09 149	95.56 69	76.00 181	93.85 68	93.38 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 121	82.80 128	85.43 91	90.25 113	68.74 122	90.30 80	90.13 188	76.33 98	80.87 170	92.89 96	61.00 224	94.20 138	72.45 226	90.97 113	93.35 123
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 65	93.49 10	92.73 70	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 124
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		79.10 231	78.24 229	81.70 256	86.85 267	60.24 349	87.28 202	88.79 248	74.25 163	76.84 250	90.53 178	49.48 355	91.56 277	67.98 270	82.15 278	93.29 125

EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 94	88.18 196	67.85 155	87.66 183	89.73 202	80.05 15	82.95 131	89.59 205	70.74 77	94.82 110	80.66 118	84.72 233	93.28 126

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 229	92.02 112	79.45 22	85.88 71	94.80 27	68.07 123	96.21 51	86.69 52	95.34 36	93.23 127

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 69	76.62 83	83.68 113	94.46 36	67.93 124	95.95 63	84.20 78	94.39 61	93.23 127

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 45	76.78 77	80.73 173	93.82 72	64.33 166	96.29 47	82.67 99	90.69 118	93.23 127
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 51	91.47 88	72.47 38	91.65 47	88.06 270	79.31 24	84.39 97	92.18 114	64.64 164	95.53 72	80.70 116	90.91 115	93.21 130

fmvsm_s_conf0.1_n_a		83.32 127	82.99 124	84.28 148	83.79 343	68.07 146	89.34 111	82.85 375	69.80 275	87.36 59	94.06 59	68.34 120	91.56 277	87.95 42	83.46 262	93.21 130

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 187	87.32 250	65.13 229	88.86 130	91.63 135	75.41 122	88.23 41	93.45 82	68.56 116	92.47 238	89.52 18	92.78 79	93.20 132

PAPM_NR		83.02 134	82.41 135	84.82 117	92.47 77	66.37 193	87.93 175	91.80 126	73.82 173	77.32 239	90.66 172	67.90 125	94.90 105	70.37 244	89.48 141	93.19 133

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 192	87.12 261	66.01 200	88.56 148	89.43 212	75.59 117	89.32 28	94.32 44	72.89 47	91.21 299	90.11 11	92.33 87	93.16 134

reproduce_model		87.28 35	87.39 33	86.95 55	93.10 62	71.24 69	91.60 50	93.19 41	74.69 149	88.80 34	95.61 11	70.29 82	96.44 44	86.20 56	93.08 75	93.16 134

OMC-MVS		82.69 139	81.97 148	84.85 116	88.75 176	67.42 169	87.98 171	90.87 161	74.92 142	79.72 187	91.65 134	62.19 199	93.96 146	75.26 192	86.42 203	93.16 134

fmvsm_s_conf0.5_n_a		83.63 116	83.41 116	84.28 148	86.14 287	68.12 144	89.43 104	82.87 374	70.27 264	87.27 60	93.80 73	69.09 106	91.58 274	88.21 38	83.65 256	93.14 137

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 174	87.78 220	66.09 197	89.96 86	90.80 164	77.37 57	86.72 66	94.20 52	72.51 52	92.78 226	89.08 22	92.33 87	93.13 138

TestfortrainingZip				87.28 46	92.85 68	72.05 50	93.28 12	93.32 37	76.52 85	88.91 32	93.52 77	77.30 17	96.67 33		91.98 94	93.13 138

PAPR		81.66 160	80.89 162	83.99 176	90.27 112	64.00 262	86.76 223	91.77 129	68.84 305	77.13 249	89.50 206	67.63 127	94.88 108	67.55 274	88.52 159	93.09 140

UA-Net		85.08 84	84.96 84	85.45 90	92.07 80	68.07 146	89.78 91	90.86 162	82.48 2	84.60 93	93.20 88	69.35 98	95.22 89	71.39 234	90.88 116	93.07 141

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 139	88.96 30	95.54 12	71.20 71	96.54 41	86.28 54	93.49 71	93.06 142

our_new_method		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 139	88.96 30	95.54 12	71.20 71	96.54 41	86.28 54	93.49 71	93.06 142

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 65	80.26 11	87.78 49	94.27 47	75.89 23	96.81 27	87.45 47	96.44 9	93.05 144

thisisatest0530		79.40 222	77.76 244	84.31 144	87.69 230	65.10 232	87.36 198	84.26 349	70.04 267	77.42 236	88.26 247	49.94 349	94.79 114	70.20 247	84.70 234	93.03 145

train_agg		86.43 49	86.20 56	87.13 50	93.26 56	72.96 25	88.75 138	91.89 120	68.69 307	85.00 81	93.10 89	74.43 31	95.41 81	84.97 63	95.71 29	93.02 146

EC-MVSNet		86.01 58	86.38 52	84.91 114	89.31 149	66.27 195	92.32 35	93.63 26	79.37 23	84.17 103	91.88 124	69.04 110	95.43 78	83.93 81	93.77 69	93.01 147

mvsmamba		80.60 191	79.38 201	84.27 150	89.74 130	67.24 179	87.47 188	86.95 304	70.02 268	75.38 288	88.93 225	51.24 332	92.56 233	75.47 190	89.22 145	93.00 148

EI-MVSNet-UG-set		83.81 106	83.38 117	85.09 104	87.87 213	67.53 167	87.44 196	89.66 203	79.74 18	82.23 143	89.41 214	70.24 83	94.74 116	79.95 123	83.92 248	92.99 149

tttt0517		79.40 222	77.91 235	83.90 180	88.10 202	63.84 267	88.37 157	84.05 351	71.45 227	76.78 253	89.12 217	49.93 351	94.89 107	70.18 248	83.18 267	92.96 150

viewdifsd2359ckpt11		80.37 200	79.73 191	82.30 244	83.70 347	62.39 304	84.20 306	86.67 311	73.22 195	80.90 168	90.62 173	63.00 185	91.56 277	76.81 171	78.44 324	92.95 151

viewmsd2359difaftdt		80.37 200	79.73 191	82.30 244	83.70 347	62.39 304	84.20 306	86.67 311	73.22 195	80.90 168	90.62 173	63.00 185	91.56 277	76.81 171	78.44 324	92.95 151

test9_res														84.90 64	95.70 30	92.87 153

viewmambaseed2359dif		80.41 196	79.84 188	82.12 246	82.95 373	62.50 303	83.39 325	88.06 270	67.11 325	80.98 166	90.31 182	66.20 147	91.01 308	74.62 196	84.90 230	92.86 154

AstraMVS		80.81 179	80.14 180	82.80 227	86.05 290	63.96 263	86.46 234	85.90 327	73.71 176	80.85 171	90.56 176	54.06 293	91.57 276	79.72 131	83.97 247	92.86 154

SR-MVS		86.73 43	86.67 48	86.91 56	94.11 41	72.11 49	92.37 33	92.56 81	74.50 153	86.84 65	94.65 31	67.31 131	95.77 65	84.80 68	92.85 78	92.84 156

ETV-MVS		84.90 88	84.67 88	85.59 87	89.39 144	68.66 128	88.74 140	92.64 78	79.97 16	84.10 104	85.71 316	69.32 99	95.38 83	80.82 113	91.37 106	92.72 157

agg_prior2														82.91 91	95.45 33	92.70 158

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 36	72.39 41	91.86 45	92.83 66	73.01 200	88.58 35	94.52 32	73.36 39	96.49 43	84.26 75	95.01 41	92.70 158
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 243	76.63 274	84.64 124	86.73 272	69.47 103	85.01 280	84.61 342	69.54 282	66.51 420	86.59 296	50.16 345	91.75 268	76.26 176	84.24 244	92.69 160

Vis-MVSNet (Re-imp)		78.36 250	78.45 222	78.07 353	88.64 180	51.78 446	86.70 224	79.63 418	74.14 166	75.11 301	90.83 167	61.29 218	89.75 338	58.10 374	91.60 100	92.69 160

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 57	91.34 89	72.50 36	89.07 124	87.28 291	76.41 91	85.80 72	90.22 187	74.15 36	95.37 86	81.82 103	91.88 95	92.65 162

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 133	84.12 335	69.37 109	88.15 167	87.96 273	70.01 269	83.95 108	93.23 87	68.80 113	91.51 284	88.61 32	89.96 131	92.57 163

FA-MVS(test-final)		80.96 175	79.91 185	84.10 157	88.30 193	65.01 233	84.55 293	90.01 191	73.25 193	79.61 188	87.57 265	58.35 251	94.72 117	71.29 235	86.25 207	92.56 164

guyue		81.13 172	80.64 166	82.60 238	86.52 278	63.92 266	86.69 225	87.73 281	73.97 168	80.83 172	89.69 199	56.70 268	91.33 293	78.26 153	85.40 226	92.54 165

test_yl		81.17 170	80.47 171	83.24 202	89.13 158	63.62 271	86.21 246	89.95 193	72.43 209	81.78 152	89.61 203	57.50 259	93.58 168	70.75 239	86.90 194	92.52 166

DCV-MVSNet		81.17 170	80.47 171	83.24 202	89.13 158	63.62 271	86.21 246	89.95 193	72.43 209	81.78 152	89.61 203	57.50 259	93.58 168	70.75 239	86.90 194	92.52 166

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 67	93.06 64	70.63 83	91.88 43	92.27 94	73.53 183	85.69 74	94.45 37	65.00 162	95.56 69	82.75 94	91.87 96	92.50 168

RE-MVS-def			85.48 75		93.06 64	70.63 83	91.88 43	92.27 94	73.53 183	85.69 74	94.45 37	63.87 170		82.75 94	91.87 96	92.50 168

nrg030		83.88 105	83.53 114	84.96 109	86.77 271	69.28 110	90.46 75	92.67 73	74.79 147	82.95 131	91.33 149	72.70 51	93.09 210	80.79 115	79.28 317	92.50 168

SSM_0404		81.91 152	80.84 163	85.13 102	89.24 153	68.26 138	87.84 180	89.25 226	71.06 238	80.62 174	90.39 180	59.57 240	94.65 121	72.45 226	87.19 189	92.47 171

MG-MVS		83.41 122	83.45 115	83.28 199	92.74 72	62.28 309	88.17 165	89.50 210	75.22 129	81.49 157	92.74 104	66.75 136	95.11 95	72.85 216	91.58 102	92.45 172

FIs		82.07 149	82.42 134	81.04 276	88.80 173	58.34 366	88.26 162	93.49 31	76.93 72	78.47 213	91.04 160	69.92 90	92.34 246	69.87 253	84.97 229	92.44 173

testing3-2		75.12 318	75.19 300	74.91 393	90.40 110	45.09 476	80.29 381	78.42 428	78.37 40	76.54 261	87.75 259	44.36 403	87.28 380	57.04 384	83.49 260	92.37 174

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 209	87.08 262	65.21 226	89.09 123	90.21 185	79.67 19	89.98 24	95.02 24	73.17 43	91.71 271	91.30 3	91.60 100	92.34 175

FC-MVSNet-test		81.52 165	82.02 146	80.03 302	88.42 189	55.97 406	87.95 173	93.42 34	77.10 68	77.38 237	90.98 165	69.96 89	91.79 266	68.46 268	84.50 236	92.33 176

Fast-Effi-MVS+		80.81 179	79.92 184	83.47 191	88.85 165	64.51 251	85.53 267	89.39 214	70.79 245	78.49 211	85.06 336	67.54 128	93.58 168	67.03 282	86.58 200	92.32 177

TranMVSNet+NR-MVSNet		80.84 177	80.31 174	82.42 241	87.85 214	62.33 307	87.74 182	91.33 146	80.55 9	77.99 225	89.86 191	65.23 158	92.62 228	67.05 281	75.24 378	92.30 178

ab-mvs		79.51 216	78.97 213	81.14 273	88.46 186	60.91 335	83.84 313	89.24 228	70.36 259	79.03 198	88.87 228	63.23 178	90.21 330	65.12 295	82.57 275	92.28 179

CANet_DTU		80.61 189	79.87 187	82.83 224	85.60 299	63.17 291	87.36 198	88.65 259	76.37 96	75.88 275	88.44 241	53.51 298	93.07 211	73.30 211	89.74 136	92.25 180

UniMVSNet_NR-MVSNet		81.88 153	81.54 152	82.92 220	88.46 186	63.46 282	87.13 204	92.37 87	80.19 12	78.38 214	89.14 216	71.66 65	93.05 213	70.05 249	76.46 351	92.25 180

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 150	85.42 304	68.81 117	88.49 150	87.26 296	68.08 316	88.03 45	93.49 78	72.04 58	91.77 267	88.90 29	89.14 148	92.24 182

DU-MVS		81.12 173	80.52 169	82.90 221	87.80 217	63.46 282	87.02 209	91.87 122	79.01 31	78.38 214	89.07 218	65.02 160	93.05 213	70.05 249	76.46 351	92.20 183

NR-MVSNet		80.23 204	79.38 201	82.78 231	87.80 217	63.34 285	86.31 241	91.09 155	79.01 31	72.17 346	89.07 218	67.20 132	92.81 225	66.08 288	75.65 364	92.20 183

mamba_0408		79.37 225	77.52 251	84.93 112	88.81 169	67.96 150	65.03 480	88.66 257	70.96 242	79.48 191	89.80 195	58.69 246	94.65 121	70.35 245	85.93 215	92.18 185

SSM_04072		77.67 272	77.52 251	78.12 351	88.81 169	67.96 150	65.03 480	88.66 257	70.96 242	79.48 191	89.80 195	58.69 246	74.23 474	70.35 245	85.93 215	92.18 185

SSM_0407		81.58 162	80.48 170	84.87 115	88.81 169	67.96 150	87.37 197	89.25 226	71.06 238	79.48 191	90.39 180	59.57 240	94.48 128	72.45 226	85.93 215	92.18 185

TAPA-MVS	73.13 9	79.15 229	77.94 234	82.79 230	89.59 132	62.99 296	88.16 166	91.51 141	65.77 346	77.14 248	91.09 158	60.91 225	93.21 199	50.26 426	87.05 192	92.17 188
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	84.06 166	85.38 305	68.40 134	88.34 158	86.85 308	67.48 323	87.48 56	93.40 83	70.89 74	91.61 272	88.38 37	89.22 145	92.16 189

3Dnovator	76.31 5	83.38 124	82.31 138	86.59 62	87.94 210	72.94 28	90.64 68	92.14 111	77.21 63	75.47 282	92.83 98	58.56 249	94.72 117	73.24 213	92.71 81	92.13 190

MVS_111021_HR		85.14 82	84.75 87	86.32 66	91.65 86	72.70 30	85.98 251	90.33 180	76.11 104	82.08 146	91.61 139	71.36 69	94.17 141	81.02 110	92.58 82	92.08 191

MVSFormer		82.85 137	82.05 145	85.24 96	87.35 243	70.21 87	90.50 72	90.38 176	68.55 309	81.32 159	89.47 208	61.68 207	93.46 186	78.98 141	90.26 125	92.05 192

jason		81.39 168	80.29 175	84.70 123	86.63 276	69.90 95	85.95 252	86.77 309	63.24 383	81.07 165	89.47 208	61.08 223	92.15 252	78.33 149	90.07 130	92.05 192
jason: jason.
HyFIR lowres test		77.53 274	75.40 293	83.94 179	89.59 132	66.62 189	80.36 379	88.64 260	56.29 449	76.45 262	85.17 333	57.64 257	93.28 192	61.34 343	83.10 268	91.91 194

XVG-OURS-SEG-HR		80.81 179	79.76 190	83.96 178	85.60 299	68.78 119	83.54 324	90.50 172	70.66 251	76.71 255	91.66 133	60.69 228	91.26 294	76.94 166	81.58 285	91.83 195

lupinMVS		81.39 168	80.27 176	84.76 121	87.35 243	70.21 87	85.55 265	86.41 317	62.85 390	81.32 159	88.61 235	61.68 207	92.24 250	78.41 148	90.26 125	91.83 195

WR-MVS		79.49 217	79.22 208	80.27 295	88.79 174	58.35 365	85.06 279	88.61 261	78.56 35	77.65 232	88.34 243	63.81 172	90.66 323	64.98 297	77.22 339	91.80 197

icg_test_0407_2		78.92 237	78.93 214	78.90 334	87.13 256	63.59 275	76.58 427	89.33 216	70.51 254	77.82 227	89.03 220	61.84 203	81.38 433	72.56 222	85.56 222	91.74 198

IMVS_0407		80.61 189	79.90 186	82.75 234	87.13 256	63.59 275	85.33 271	89.33 216	70.51 254	77.82 227	89.03 220	61.84 203	92.91 218	72.56 222	85.56 222	91.74 198

IMVS_0404		77.16 281	76.42 278	79.37 325	87.13 256	63.59 275	77.12 424	89.33 216	70.51 254	66.22 423	89.03 220	50.36 343	82.78 423	72.56 222	85.56 222	91.74 198

IMVS_0403		80.80 182	80.12 181	82.87 223	87.13 256	63.59 275	85.19 272	89.33 216	70.51 254	78.49 211	89.03 220	63.26 176	93.27 194	72.56 222	85.56 222	91.74 198

h-mvs33		83.15 130	82.19 141	86.02 77	90.56 106	70.85 80	88.15 167	89.16 231	76.02 106	84.67 88	91.39 147	61.54 210	95.50 74	82.71 96	75.48 368	91.72 202

UniMVSNet (Re)		81.60 161	81.11 157	83.09 209	88.38 190	64.41 256	87.60 184	93.02 51	78.42 37	78.56 209	88.16 249	69.78 92	93.26 195	69.58 256	76.49 350	91.60 203

UGNet		80.83 178	79.59 197	84.54 126	88.04 205	68.09 145	89.42 106	88.16 265	76.95 71	76.22 268	89.46 210	49.30 359	93.94 149	68.48 267	90.31 123	91.60 203
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	79.18 330	88.43 188	55.89 407	81.08 365	83.00 371	73.76 175	75.34 290	84.29 351	46.20 387	90.07 332	64.33 301	84.50 236	91.58 205

XVG-OURS		80.41 196	79.23 207	83.97 177	85.64 297	69.02 113	83.03 338	90.39 175	71.09 236	77.63 233	91.49 144	54.62 288	91.35 291	75.71 184	83.47 261	91.54 206

LCM-MVSNet-Re		77.05 282	76.94 264	77.36 367	87.20 253	51.60 447	80.06 384	80.46 406	75.20 132	67.69 399	86.72 288	62.48 192	88.98 354	63.44 307	89.25 143	91.51 207

DP-MVS Recon		83.11 133	82.09 144	86.15 71	94.44 23	70.92 77	88.79 135	92.20 104	70.53 253	79.17 197	91.03 162	64.12 168	96.03 56	68.39 269	90.14 127	91.50 208

PS-MVSNAJss		82.07 149	81.31 153	84.34 142	86.51 279	67.27 177	89.27 112	91.51 141	71.75 219	79.37 194	90.22 187	63.15 180	94.27 133	77.69 157	82.36 277	91.49 209

testing99		76.09 303	75.12 302	79.00 331	88.16 197	55.50 413	80.79 369	81.40 393	73.30 191	75.17 298	84.27 354	44.48 402	90.02 333	64.28 302	84.22 245	91.48 210

thisisatest0515		77.33 278	75.38 294	83.18 205	85.27 309	63.80 268	82.11 348	83.27 363	65.06 360	75.91 274	83.84 363	49.54 354	94.27 133	67.24 278	86.19 208	91.48 210

DPM-MVS		84.93 86	84.29 93	86.84 57	90.20 114	73.04 23	87.12 205	93.04 47	69.80 275	82.85 135	91.22 153	73.06 45	96.02 58	76.72 174	94.63 54	91.46 212

HQP_MVS		83.64 115	83.14 120	85.14 99	90.08 117	68.71 124	91.25 60	92.44 83	79.12 28	78.92 201	91.00 163	60.42 235	95.38 83	78.71 144	86.32 204	91.33 213

plane_prior5								92.44 83					95.38 83	78.71 144	86.32 204	91.33 213

GA-MVS		76.87 286	75.17 301	81.97 252	82.75 376	62.58 300	81.44 360	86.35 320	72.16 214	74.74 309	82.89 385	46.20 387	92.02 257	68.85 264	81.09 290	91.30 215

VPA-MVSNet		80.60 191	80.55 168	80.76 283	88.07 204	60.80 337	86.86 217	91.58 139	75.67 116	80.24 181	89.45 212	63.34 173	90.25 329	70.51 243	79.22 318	91.23 216

Effi-MVS+-dtu		80.03 208	78.57 220	84.42 136	85.13 314	68.74 122	88.77 136	88.10 267	74.99 138	74.97 306	83.49 374	57.27 262	93.36 190	73.53 207	80.88 293	91.18 217

v2v482		80.23 204	79.29 205	83.05 213	83.62 349	64.14 260	87.04 207	89.97 192	73.61 179	78.18 220	87.22 276	61.10 222	93.82 158	76.11 178	76.78 347	91.18 217

FE-MVS		77.78 266	75.68 286	84.08 162	88.09 203	66.00 201	83.13 332	87.79 279	68.42 313	78.01 224	85.23 331	45.50 396	95.12 93	59.11 362	85.83 219	91.11 219

Anonymous20231211		78.97 235	77.69 247	82.81 226	90.54 107	64.29 258	90.11 83	91.51 141	65.01 362	76.16 273	88.13 254	50.56 340	93.03 216	69.68 255	77.56 337	91.11 219

hse-mvs2		81.72 156	80.94 161	84.07 163	88.72 177	67.68 161	85.87 255	87.26 296	76.02 106	84.67 88	88.22 248	61.54 210	93.48 184	82.71 96	73.44 396	91.06 221

AUN-MVS		79.21 228	77.60 249	84.05 169	88.71 178	67.61 163	85.84 257	87.26 296	69.08 296	77.23 242	88.14 253	53.20 302	93.47 185	75.50 189	73.45 395	91.06 221

HQP4-MVS										77.24 241			95.11 95			91.03 223

HQP-MVS		82.61 141	82.02 146	84.37 139	89.33 146	66.98 184	89.17 116	92.19 106	76.41 91	77.23 242	90.23 186	60.17 238	95.11 95	77.47 159	85.99 213	91.03 223

RPSCF		73.23 346	71.46 346	78.54 342	82.50 382	59.85 352	82.18 347	82.84 376	58.96 428	71.15 358	89.41 214	45.48 397	84.77 407	58.82 366	71.83 408	91.02 225

LuminaMVS		80.68 187	79.62 196	83.83 181	85.07 316	68.01 149	86.99 210	88.83 246	70.36 259	81.38 158	87.99 256	50.11 346	92.51 237	79.02 138	86.89 196	90.97 226

test_djsdf		80.30 203	79.32 204	83.27 200	83.98 339	65.37 220	90.50 72	90.38 176	68.55 309	76.19 269	88.70 231	56.44 271	93.46 186	78.98 141	80.14 305	90.97 226

PCF-MVS	73.52 7	80.38 198	78.84 216	85.01 107	87.71 226	68.99 114	83.65 318	91.46 145	63.00 387	77.77 231	90.28 183	66.10 148	95.09 99	61.40 341	88.22 168	90.94 228
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 242	78.66 218	78.76 336	88.31 192	55.72 410	84.45 297	86.63 314	76.79 76	78.26 217	90.55 177	59.30 243	89.70 340	66.63 283	77.05 341	90.88 229

CPTT-MVS		83.73 111	83.33 119	84.92 113	93.28 53	70.86 79	92.09 41	90.38 176	68.75 306	79.57 189	92.83 98	60.60 233	93.04 215	80.92 112	91.56 103	90.86 230

fmvsm_s_conf0.5_n_7		83.34 125	84.03 100	81.28 268	85.73 295	65.13 229	85.40 270	89.90 195	74.96 141	82.13 145	93.89 69	66.65 137	87.92 371	86.56 53	91.05 111	90.80 231

tt0805		78.73 240	77.83 239	81.43 262	85.17 310	60.30 348	89.41 107	90.90 159	71.21 233	77.17 247	88.73 230	46.38 382	93.21 199	72.57 220	78.96 319	90.79 232

CLD-MVS		82.31 145	81.65 151	84.29 147	88.47 185	67.73 159	85.81 259	92.35 88	75.78 111	78.33 216	86.58 298	64.01 169	94.35 130	76.05 180	87.48 184	90.79 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 215	78.43 224	83.07 212	83.55 351	64.52 250	86.93 214	90.58 169	70.83 244	77.78 230	85.90 312	59.15 244	93.94 149	73.96 204	77.19 340	90.76 234

IterMVS-LS		80.06 207	79.38 201	82.11 248	85.89 291	63.20 289	86.79 220	89.34 215	74.19 164	75.45 285	86.72 288	66.62 138	92.39 242	72.58 219	76.86 344	90.75 235
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	73.90 407	88.20 195	47.41 466	78.06 414	79.37 420	74.29 162	73.98 320	84.29 351	44.67 399	83.54 417	51.47 416	87.39 185	90.74 236

EI-MVSNet		80.52 195	79.98 183	82.12 246	84.28 331	63.19 290	86.41 235	88.95 243	74.18 165	78.69 204	87.54 268	66.62 138	92.43 240	72.57 220	80.57 299	90.74 236

v1921920		79.22 227	78.03 232	82.80 227	83.30 356	63.94 265	86.80 219	90.33 180	69.91 273	77.48 235	85.53 323	58.44 250	93.75 164	73.60 206	76.85 345	90.71 238

QAPM		80.88 176	79.50 199	85.03 105	88.01 208	68.97 115	91.59 51	92.00 114	66.63 336	75.15 300	92.16 116	57.70 256	95.45 76	63.52 305	88.76 154	90.66 239

v144192		79.47 218	78.37 225	82.78 231	83.35 354	63.96 263	86.96 211	90.36 179	69.99 270	77.50 234	85.67 319	60.66 230	93.77 162	74.27 201	76.58 348	90.62 240

v1240		78.99 234	77.78 242	82.64 236	83.21 359	63.54 279	86.62 228	90.30 182	69.74 280	77.33 238	85.68 318	57.04 265	93.76 163	73.13 214	76.92 342	90.62 240

v1144		80.03 208	79.03 211	83.01 215	83.78 344	64.51 251	87.11 206	90.57 171	71.96 217	78.08 223	86.20 308	61.41 214	93.94 149	74.93 194	77.23 338	90.60 242

1112_ss		77.40 277	76.43 277	80.32 294	89.11 162	60.41 347	83.65 318	87.72 282	62.13 402	73.05 332	86.72 288	62.58 191	89.97 334	62.11 333	80.80 295	90.59 243

CP-MVSNet		78.22 252	78.34 226	77.84 357	87.83 216	54.54 423	87.94 174	91.17 151	77.65 46	73.48 327	88.49 239	62.24 198	88.43 365	62.19 330	74.07 387	90.55 244

testing222		74.04 328	72.66 334	78.19 349	87.89 212	55.36 414	81.06 366	79.20 423	71.30 231	74.65 312	83.57 373	39.11 439	88.67 361	51.43 418	85.75 220	90.53 245

PS-CasMVS		78.01 261	78.09 231	77.77 359	87.71 226	54.39 425	88.02 170	91.22 148	77.50 54	73.26 329	88.64 234	60.73 226	88.41 366	61.88 335	73.88 391	90.53 245

CDS-MVSNet		79.07 232	77.70 246	83.17 206	87.60 233	68.23 142	84.40 302	86.20 322	67.49 322	76.36 265	86.54 300	61.54 210	90.79 318	61.86 336	87.33 186	90.49 247
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 238	77.51 253	83.03 214	87.80 217	67.79 158	84.72 286	85.05 338	67.63 319	76.75 254	87.70 261	62.25 197	90.82 317	58.53 369	87.13 191	90.49 247

PEN-MVS		77.73 267	77.69 247	77.84 357	87.07 264	53.91 428	87.91 176	91.18 150	77.56 51	73.14 331	88.82 229	61.23 219	89.17 350	59.95 352	72.37 402	90.43 249

Test_1112_low_res		76.40 298	75.44 291	79.27 327	89.28 151	58.09 368	81.69 355	87.07 302	59.53 423	72.48 341	86.67 293	61.30 217	89.33 345	60.81 347	80.15 304	90.41 250

HY-MVS	69.67 12	77.95 262	77.15 259	80.36 292	87.57 242	60.21 350	83.37 327	87.78 280	66.11 340	75.37 289	87.06 283	63.27 175	90.48 325	61.38 342	82.43 276	90.40 251

sc_t1		72.19 362	69.51 373	80.23 297	84.81 320	61.09 329	84.68 287	80.22 412	60.70 412	71.27 355	83.58 372	36.59 451	89.24 348	60.41 348	63.31 451	90.37 252

CHOSEN 1792x2688		77.63 273	75.69 285	83.44 193	89.98 123	68.58 130	78.70 404	87.50 286	56.38 448	75.80 277	86.84 284	58.67 248	91.40 290	61.58 340	85.75 220	90.34 253

SDMVSNet		80.38 198	80.18 177	80.99 277	89.03 163	64.94 239	80.45 378	89.40 213	75.19 133	76.61 259	89.98 189	60.61 232	87.69 375	76.83 170	83.55 258	90.33 254

sd_testset		77.70 270	77.40 254	78.60 339	89.03 163	60.02 351	79.00 399	85.83 328	75.19 133	76.61 259	89.98 189	54.81 281	85.46 400	62.63 323	83.55 258	90.33 254

114514_t		80.68 187	79.51 198	84.20 154	94.09 42	67.27 177	89.64 96	91.11 154	58.75 432	74.08 319	90.72 169	58.10 252	95.04 100	69.70 254	89.42 142	90.30 256

eth_miper_zixun_eth		77.92 263	76.69 272	81.61 259	83.00 367	61.98 314	83.15 331	89.20 230	69.52 283	74.86 308	84.35 350	61.76 206	92.56 233	71.50 233	72.89 400	90.28 257

PVSNet_Blended_VisFu		82.62 140	81.83 150	84.96 109	90.80 102	69.76 98	88.74 140	91.70 132	69.39 284	78.96 199	88.46 240	65.47 156	94.87 109	74.42 199	88.57 157	90.24 258

MVS_111021_LR		82.61 141	82.11 142	84.11 156	88.82 168	71.58 58	85.15 275	86.16 323	74.69 149	80.47 178	91.04 160	62.29 196	90.55 324	80.33 120	90.08 129	90.20 259

MSLP-MVS++		85.43 75	85.76 69	84.45 134	91.93 82	70.24 86	90.71 67	92.86 64	77.46 55	84.22 101	92.81 100	67.16 133	92.94 217	80.36 119	94.35 63	90.16 260

mvs_tets		79.13 230	77.77 243	83.22 204	84.70 323	66.37 193	89.17 116	90.19 186	69.38 285	75.40 287	89.46 210	44.17 405	93.15 206	76.78 173	80.70 297	90.14 261

BH-RMVSNet		79.61 213	78.44 223	83.14 207	89.38 145	65.93 203	84.95 282	87.15 299	73.56 181	78.19 219	89.79 197	56.67 269	93.36 190	59.53 357	86.74 198	90.13 262

c3_l		78.75 239	77.91 235	81.26 269	82.89 374	61.56 321	84.09 310	89.13 234	69.97 271	75.56 280	84.29 351	66.36 143	92.09 254	73.47 209	75.48 368	90.12 263

v7n		78.97 235	77.58 250	83.14 207	83.45 353	65.51 215	88.32 159	91.21 149	73.69 177	72.41 342	86.32 306	57.93 253	93.81 159	69.18 259	75.65 364	90.11 264

jajsoiax		79.29 226	77.96 233	83.27 200	84.68 324	66.57 191	89.25 113	90.16 187	69.20 293	75.46 284	89.49 207	45.75 393	93.13 208	76.84 169	80.80 295	90.11 264

v148		78.72 241	77.80 241	81.47 261	82.73 377	61.96 315	86.30 242	88.08 268	73.26 192	76.18 270	85.47 325	62.46 193	92.36 244	71.92 230	73.82 392	90.09 266

GBi-Net		78.40 248	77.40 254	81.40 264	87.60 233	63.01 292	88.39 154	89.28 222	71.63 221	75.34 290	87.28 272	54.80 282	91.11 300	62.72 319	79.57 309	90.09 266

test1		78.40 248	77.40 254	81.40 264	87.60 233	63.01 292	88.39 154	89.28 222	71.63 221	75.34 290	87.28 272	54.80 282	91.11 300	62.72 319	79.57 309	90.09 266

FMVSNet1		77.44 275	76.12 282	81.40 264	86.81 269	63.01 292	88.39 154	89.28 222	70.49 258	74.39 316	87.28 272	49.06 363	91.11 300	60.91 345	78.52 322	90.09 266

WR-MVS_H		78.51 247	78.49 221	78.56 341	88.02 206	56.38 400	88.43 151	92.67 73	77.14 65	73.89 321	87.55 267	66.25 145	89.24 348	58.92 364	73.55 394	90.06 270

DTE-MVSNet		76.99 283	76.80 267	77.54 366	86.24 283	53.06 438	87.52 186	90.66 167	77.08 69	72.50 340	88.67 233	60.48 234	89.52 342	57.33 381	70.74 414	90.05 271

v8		79.97 210	79.02 212	82.80 227	84.09 336	64.50 253	87.96 172	90.29 183	74.13 167	75.24 297	86.81 285	62.88 188	93.89 157	74.39 200	75.40 373	90.00 272

thres600view7		76.50 291	75.44 291	79.68 318	89.40 143	57.16 386	85.53 267	83.23 364	73.79 174	76.26 267	87.09 281	51.89 321	91.89 263	48.05 441	83.72 255	90.00 272

thres400		76.50 291	75.37 295	79.86 308	89.13 158	57.65 380	85.17 273	83.60 356	73.41 187	76.45 262	86.39 304	52.12 311	91.95 260	48.33 436	83.75 252	90.00 272

cl22		78.07 258	77.01 261	81.23 270	82.37 386	61.83 317	83.55 322	87.98 272	68.96 303	75.06 303	83.87 361	61.40 215	91.88 264	73.53 207	76.39 353	89.98 275

OPM-MVS		83.50 120	82.95 125	85.14 99	88.79 174	70.95 75	89.13 121	91.52 140	77.55 52	80.96 167	91.75 130	60.71 227	94.50 126	79.67 132	86.51 202	89.97 276
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 307	73.83 320	81.30 267	83.26 357	61.79 318	82.57 341	80.65 401	66.81 327	66.88 411	83.42 375	57.86 255	92.19 251	63.47 306	79.57 309	89.91 277

v10		79.74 212	78.67 217	82.97 219	84.06 337	64.95 236	87.88 178	90.62 168	73.11 197	75.11 301	86.56 299	61.46 213	94.05 145	73.68 205	75.55 366	89.90 278

MVSTER		79.01 233	77.88 238	82.38 242	83.07 364	64.80 245	84.08 311	88.95 243	69.01 300	78.69 204	87.17 279	54.70 286	92.43 240	74.69 195	80.57 299	89.89 279

ACMP	74.13 6	81.51 167	80.57 167	84.36 140	89.42 141	68.69 127	89.97 85	91.50 144	74.46 155	75.04 304	90.41 179	53.82 295	94.54 123	77.56 158	82.91 269	89.86 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 148	81.27 154	84.50 131	89.23 154	68.76 120	90.22 81	91.94 118	75.37 124	76.64 257	91.51 142	54.29 289	94.91 103	78.44 146	83.78 249	89.83 281

LGP-MVS_train				84.50 131	89.23 154	68.76 120		91.94 118	75.37 124	76.64 257	91.51 142	54.29 289	94.91 103	78.44 146	83.78 249	89.83 281

V42		79.38 224	78.24 229	82.83 224	81.10 405	65.50 216	85.55 265	89.82 196	71.57 225	78.21 218	86.12 310	60.66 230	93.18 205	75.64 185	75.46 370	89.81 283

MAR-MVS		81.84 154	80.70 164	85.27 95	91.32 90	71.53 59	89.82 88	90.92 158	69.77 277	78.50 210	86.21 307	62.36 195	94.52 125	65.36 293	92.05 93	89.77 284
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		77.72 268	76.76 269	80.58 287	82.48 384	60.48 345	83.09 334	87.86 277	69.22 291	74.38 317	85.24 330	62.10 200	91.53 282	71.09 236	75.40 373	89.74 285

cl____		77.72 268	76.76 269	80.58 287	82.49 383	60.48 345	83.09 334	87.87 276	69.22 291	74.38 317	85.22 332	62.10 200	91.53 282	71.09 236	75.41 372	89.73 286

miper_ehance_all_eth		78.59 245	77.76 244	81.08 275	82.66 379	61.56 321	83.65 318	89.15 232	68.87 304	75.55 281	83.79 365	66.49 141	92.03 255	73.25 212	76.39 353	89.64 287

anonymousdsp		78.60 244	77.15 259	82.98 218	80.51 411	67.08 182	87.24 203	89.53 209	65.66 348	75.16 299	87.19 278	52.52 304	92.25 249	77.17 163	79.34 316	89.61 288

FMVSNet2		78.20 254	77.21 258	81.20 271	87.60 233	62.89 298	87.47 188	89.02 238	71.63 221	75.29 296	87.28 272	54.80 282	91.10 303	62.38 327	79.38 315	89.61 288

baseline1		76.98 284	76.75 271	77.66 361	88.13 200	55.66 411	85.12 276	81.89 386	73.04 199	76.79 252	88.90 226	62.43 194	87.78 374	63.30 309	71.18 412	89.55 290

ETVMVS		72.25 361	71.05 355	75.84 379	87.77 222	51.91 443	79.39 392	74.98 449	69.26 289	73.71 323	82.95 383	40.82 428	86.14 390	46.17 449	84.43 241	89.47 291

FMVSNet3		77.88 264	76.85 266	80.97 279	86.84 268	62.36 306	86.52 232	88.77 249	71.13 234	75.34 290	86.66 294	54.07 292	91.10 303	62.72 319	79.57 309	89.45 292

SD_0403		74.65 321	74.77 305	74.29 401	86.20 285	47.42 465	83.71 316	85.12 335	69.30 287	68.50 390	87.95 257	59.40 242	86.05 391	49.38 430	83.35 263	89.40 293

miper_enhance_ethall		77.87 265	76.86 265	80.92 280	81.65 393	61.38 325	82.68 339	88.98 240	65.52 350	75.47 282	82.30 394	65.76 155	92.00 258	72.95 215	76.39 353	89.39 294

testing11		75.14 317	74.01 315	78.53 343	88.16 197	56.38 400	80.74 372	80.42 408	70.67 248	72.69 339	83.72 368	43.61 409	89.86 335	62.29 329	83.76 251	89.36 295

cascas		76.72 288	74.64 306	82.99 216	85.78 294	65.88 205	82.33 344	89.21 229	60.85 411	72.74 336	81.02 406	47.28 372	93.75 164	67.48 275	85.02 228	89.34 296

Fast-Effi-MVS+-dtu		78.02 260	76.49 275	82.62 237	83.16 363	66.96 186	86.94 213	87.45 288	72.45 206	71.49 354	84.17 358	54.79 285	91.58 274	67.61 273	80.31 302	89.30 297

IB-MVS	68.01 15	75.85 306	73.36 326	83.31 198	84.76 322	66.03 198	83.38 326	85.06 337	70.21 266	69.40 377	81.05 405	45.76 392	94.66 120	65.10 296	75.49 367	89.25 298
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	79.33 326	89.52 135	56.99 389	85.83 258	83.23 364	73.94 170	76.32 266	87.12 280	51.89 321	91.95 260	48.33 436	83.75 252	89.07 299

tfpn200view9		76.42 297	75.37 295	79.55 323	89.13 158	57.65 380	85.17 273	83.60 356	73.41 187	76.45 262	86.39 304	52.12 311	91.95 260	48.33 436	83.75 252	89.07 299

xiu_mvs_v1_base_debu		80.80 182	79.72 193	84.03 171	87.35 243	70.19 89	85.56 262	88.77 249	69.06 297	81.83 148	88.16 249	50.91 335	92.85 221	78.29 150	87.56 181	89.06 301

xiu_mvs_v1_base		80.80 182	79.72 193	84.03 171	87.35 243	70.19 89	85.56 262	88.77 249	69.06 297	81.83 148	88.16 249	50.91 335	92.85 221	78.29 150	87.56 181	89.06 301

xiu_mvs_v1_base_debi		80.80 182	79.72 193	84.03 171	87.35 243	70.19 89	85.56 262	88.77 249	69.06 297	81.83 148	88.16 249	50.91 335	92.85 221	78.29 150	87.56 181	89.06 301

EPNet_dtu		75.46 311	74.86 303	77.23 370	82.57 381	54.60 422	86.89 215	83.09 368	71.64 220	66.25 422	85.86 314	55.99 274	88.04 370	54.92 398	86.55 201	89.05 304
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 280	76.68 273	78.93 333	84.22 333	58.62 363	86.41 235	88.36 264	71.37 228	73.31 328	88.01 255	61.22 220	89.15 351	64.24 303	73.01 399	89.03 305

PVSNet_Blended		80.98 174	80.34 173	82.90 221	88.85 165	65.40 217	84.43 299	92.00 114	67.62 320	78.11 221	85.05 337	66.02 151	94.27 133	71.52 231	89.50 140	89.01 306

PAPM		77.68 271	76.40 279	81.51 260	87.29 252	61.85 316	83.78 314	89.59 207	64.74 364	71.23 356	88.70 231	62.59 190	93.66 167	52.66 410	87.03 193	89.01 306

WTY-MVS		75.65 308	75.68 286	75.57 383	86.40 281	56.82 391	77.92 417	82.40 379	65.10 359	76.18 270	87.72 260	63.13 183	80.90 436	60.31 350	81.96 281	89.00 308

无先验							87.48 187	88.98 240	60.00 418				94.12 142	67.28 277		88.97 309

GSMVS																88.96 310

sam_mvs1												51.32 328				88.96 310

SCA		74.22 325	72.33 338	79.91 306	84.05 338	62.17 310	79.96 387	79.29 422	66.30 339	72.38 343	80.13 418	51.95 317	88.60 362	59.25 360	77.67 336	88.96 310

miper_lstm_enhance		74.11 327	73.11 329	77.13 371	80.11 415	59.62 355	72.23 451	86.92 307	66.76 329	70.40 362	82.92 384	56.93 266	82.92 422	69.06 261	72.63 401	88.87 313

ACMM	73.20 8	80.78 186	79.84 188	83.58 189	89.31 149	68.37 135	89.99 84	91.60 138	70.28 263	77.25 240	89.66 201	53.37 300	93.53 176	74.24 202	82.85 270	88.85 314
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 320	73.39 324	78.61 338	81.38 400	57.48 383	86.64 227	87.95 274	64.99 363	70.18 365	86.61 295	50.43 342	89.52 342	62.12 332	70.18 417	88.83 315

原ACMM1				84.35 141	93.01 66	68.79 118		92.44 83	63.96 378	81.09 164	91.57 140	66.06 150	95.45 76	67.19 279	94.82 50	88.81 316

CNLPA		78.08 257	76.79 268	81.97 252	90.40 110	71.07 71	87.59 185	84.55 343	66.03 343	72.38 343	89.64 202	57.56 258	86.04 392	59.61 356	83.35 263	88.79 317

UWE-MVS		72.13 363	71.49 345	74.03 405	86.66 275	47.70 463	81.40 361	76.89 442	63.60 381	75.59 279	84.22 355	39.94 432	85.62 397	48.98 433	86.13 210	88.77 318

UBG		73.08 348	72.27 339	75.51 385	88.02 206	51.29 451	78.35 411	77.38 437	65.52 350	73.87 322	82.36 392	45.55 394	86.48 387	55.02 397	84.39 242	88.75 319

K. test v3		71.19 368	68.51 380	79.21 329	83.04 366	57.78 378	84.35 303	76.91 441	72.90 202	62.99 446	82.86 386	39.27 436	91.09 305	61.65 339	52.66 474	88.75 319

旧先验1					91.96 81	65.79 210		86.37 319			93.08 93	69.31 100			92.74 80	88.74 321

PatchmatchNet		73.12 347	71.33 349	78.49 345	83.18 361	60.85 336	79.63 389	78.57 427	64.13 372	71.73 350	79.81 423	51.20 333	85.97 393	57.40 380	76.36 358	88.66 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 339	71.26 352	79.70 317	85.08 315	57.89 374	85.57 261	83.56 358	71.03 240	65.66 426	85.88 313	42.10 419	92.57 232	59.11 362	63.34 450	88.65 323

SSC-MVS3.2		73.35 342	73.39 324	73.23 411	85.30 308	49.01 461	74.58 444	81.57 390	75.21 131	73.68 324	85.58 322	52.53 303	82.05 428	54.33 402	77.69 335	88.63 324

PS-MVSNAJ		81.69 158	81.02 159	83.70 185	89.51 136	68.21 143	84.28 304	90.09 189	70.79 245	81.26 163	85.62 321	63.15 180	94.29 131	75.62 186	88.87 151	88.59 325

xiu_mvs_v2_base		81.69 158	81.05 158	83.60 187	89.15 157	68.03 148	84.46 296	90.02 190	70.67 248	81.30 162	86.53 301	63.17 179	94.19 140	75.60 187	88.54 158	88.57 326

MonoMVSNet		76.49 294	75.80 283	78.58 340	81.55 396	58.45 364	86.36 240	86.22 321	74.87 146	74.73 310	83.73 367	51.79 324	88.73 359	70.78 238	72.15 405	88.55 327

CostFormer		75.24 316	73.90 318	79.27 327	82.65 380	58.27 367	80.80 368	82.73 377	61.57 406	75.33 294	83.13 380	55.52 277	91.07 306	64.98 297	78.34 329	88.45 328

lessismore_v0				78.97 332	81.01 406	57.15 387		65.99 476		61.16 452	82.82 387	39.12 438	91.34 292	59.67 355	46.92 481	88.43 329

OpenMVS	72.83 10	79.77 211	78.33 227	84.09 161	85.17 310	69.91 94	90.57 69	90.97 157	66.70 330	72.17 346	91.91 122	54.70 286	93.96 146	61.81 337	90.95 114	88.41 330

usedtu_dtu_shiyan1		76.43 295	75.32 297	79.76 313	83.00 367	60.72 338	81.74 352	88.76 253	68.99 301	72.98 333	84.19 356	56.41 272	90.27 326	62.39 325	79.40 313	88.31 331

FE-MVSNET3		76.43 295	75.32 297	79.76 313	83.00 367	60.72 338	81.74 352	88.76 253	68.99 301	72.98 333	84.19 356	56.41 272	90.27 326	62.39 325	79.40 313	88.31 331

reproduce_monomvs		75.40 314	74.38 312	78.46 346	83.92 341	57.80 377	83.78 314	86.94 305	73.47 185	72.25 345	84.47 345	38.74 440	89.27 347	75.32 191	70.53 415	88.31 331

VortexMVS		78.57 246	77.89 237	80.59 286	85.89 291	62.76 299	85.61 260	89.62 206	72.06 215	74.99 305	85.38 327	55.94 275	90.77 321	74.99 193	76.58 348	88.23 334

OurMVSNet-221017-0		74.26 324	72.42 337	79.80 310	83.76 345	59.59 356	85.92 254	86.64 313	66.39 338	66.96 410	87.58 264	39.46 435	91.60 273	65.76 291	69.27 420	88.22 335

LS3D		76.95 285	74.82 304	83.37 197	90.45 108	67.36 173	89.15 120	86.94 305	61.87 405	69.52 376	90.61 175	51.71 325	94.53 124	46.38 448	86.71 199	88.21 336

WBMVS		73.43 336	72.81 332	75.28 389	87.91 211	50.99 453	78.59 407	81.31 395	65.51 352	74.47 315	84.83 340	46.39 381	86.68 384	58.41 370	77.86 331	88.17 337

XVG-ACMP-BASELINE		76.11 302	74.27 314	81.62 257	83.20 360	64.67 247	83.60 321	89.75 201	69.75 278	71.85 349	87.09 281	32.78 460	92.11 253	69.99 251	80.43 301	88.09 338

gbinet_0.2-2-1-0.02		73.24 345	70.86 360	80.39 290	78.03 440	61.62 320	83.10 333	86.69 310	65.98 344	69.29 380	76.15 454	49.77 352	91.51 284	62.75 318	66.00 437	88.03 339

tpm2		73.26 344	71.46 346	78.63 337	83.34 355	56.71 394	80.65 374	80.40 409	56.63 447	73.55 326	82.02 399	51.80 323	91.24 295	56.35 392	78.42 327	87.95 340

MDTV_nov1_ep13_2view						37.79 490	75.16 438		55.10 453	66.53 417		49.34 358		53.98 403		87.94 341

Patchmatch-test		64.82 424	63.24 425	69.57 437	79.42 427	49.82 459	63.49 484	69.05 469	51.98 463	59.95 458	80.13 418	50.91 335	70.98 479	40.66 467	73.57 393	87.90 342

PLC	70.83 11	78.05 259	76.37 280	83.08 211	91.88 84	67.80 157	88.19 164	89.46 211	64.33 371	69.87 373	88.38 242	53.66 296	93.58 168	58.86 365	82.73 272	87.86 343
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 358	71.71 343	74.35 400	82.19 387	52.00 441	79.22 395	77.29 438	64.56 366	72.95 335	83.68 370	51.35 327	83.26 421	58.33 372	75.80 362	87.81 344

Patchmatch-RL test		70.24 382	67.78 395	77.61 363	77.43 447	59.57 357	71.16 455	70.33 463	62.94 389	68.65 385	72.77 466	50.62 339	85.49 399	69.58 256	66.58 434	87.77 345

F-COLMAP		76.38 299	74.33 313	82.50 240	89.28 151	66.95 187	88.41 153	89.03 237	64.05 375	66.83 412	88.61 235	46.78 378	92.89 219	57.48 378	78.55 321	87.67 346

Baseline_NR-MVSNet		78.15 256	78.33 227	77.61 363	85.79 293	56.21 404	86.78 221	85.76 329	73.60 180	77.93 226	87.57 265	65.02 160	88.99 353	67.14 280	75.33 375	87.63 347

CL-MVSNet_self_test		72.37 358	71.46 346	75.09 391	79.49 426	53.53 430	80.76 371	85.01 339	69.12 295	70.51 360	82.05 398	57.92 254	84.13 411	52.27 412	66.00 437	87.60 348

ACMH+	68.96 14	76.01 304	74.01 315	82.03 250	88.60 181	65.31 225	88.86 130	87.55 284	70.25 265	67.75 398	87.47 270	41.27 424	93.19 204	58.37 371	75.94 361	87.60 348

1314		76.53 290	75.30 299	80.21 298	83.93 340	62.32 308	84.66 288	88.81 247	60.23 416	70.16 367	84.07 360	55.30 279	90.73 322	67.37 276	83.21 266	87.59 350

blended_shiyan6		73.38 337	71.17 353	80.01 304	78.36 435	61.48 324	82.43 342	87.27 294	65.40 354	68.56 388	77.55 442	51.94 319	91.01 308	63.27 311	65.76 439	87.55 351

blended_shiyan8		73.38 337	71.17 353	80.02 303	78.36 435	61.51 323	82.43 342	87.28 291	65.40 354	68.61 386	77.53 443	51.91 320	91.00 311	63.28 310	65.76 439	87.53 352

API-MVS		81.99 151	81.23 155	84.26 152	90.94 98	70.18 92	91.10 63	89.32 220	71.51 226	78.66 206	88.28 245	65.26 157	95.10 98	64.74 299	91.23 109	87.51 353

AdaColmap		80.58 194	79.42 200	84.06 166	93.09 63	68.91 116	89.36 110	88.97 242	69.27 288	75.70 278	89.69 199	57.20 264	95.77 65	63.06 314	88.41 162	87.50 354

0.4-1-1-0.1		70.93 372	67.94 390	79.91 306	79.35 428	61.27 326	78.95 401	82.19 383	63.36 382	67.50 401	69.40 473	39.83 434	91.04 307	62.44 324	68.40 426	87.40 355

PVSNet_BlendedMVS		80.60 191	80.02 182	82.36 243	88.85 165	65.40 217	86.16 248	92.00 114	69.34 286	78.11 221	86.09 311	66.02 151	94.27 133	71.52 231	82.06 280	87.39 356

sss		73.60 334	73.64 322	73.51 410	82.80 375	55.01 419	76.12 429	81.69 389	62.47 397	74.68 311	85.85 315	57.32 261	78.11 447	60.86 346	80.93 291	87.39 356

wanda-best-256-512		72.94 351	70.66 361	79.79 311	77.80 442	61.03 332	81.31 362	87.15 299	65.18 357	68.09 393	76.28 451	51.32 328	90.97 312	63.06 314	65.76 439	87.35 358

FE-blended-shiyan7		72.94 351	70.66 361	79.79 311	77.80 442	61.03 332	81.31 362	87.15 299	65.18 357	68.09 393	76.28 451	51.32 328	90.97 312	63.06 314	65.76 439	87.35 358

usedtu_blend_shiyan5		73.29 343	70.96 357	80.25 296	77.80 442	62.16 311	84.44 298	87.38 289	64.41 368	68.09 393	76.28 451	51.32 328	91.23 296	63.21 312	65.76 439	87.35 358

IterMVS-SCA-FT		75.43 312	73.87 319	80.11 301	82.69 378	64.85 244	81.57 357	83.47 360	69.16 294	70.49 361	84.15 359	51.95 317	88.15 368	69.23 258	72.14 406	87.34 361

PVSNet	64.34 18	72.08 364	70.87 359	75.69 381	86.21 284	56.44 398	74.37 445	80.73 400	62.06 403	70.17 366	82.23 396	42.86 413	83.31 420	54.77 399	84.45 240	87.32 362

tt0320-xc		70.11 384	67.45 401	78.07 353	85.33 307	59.51 358	83.28 328	78.96 425	58.77 430	67.10 409	80.28 416	36.73 450	87.42 378	56.83 388	59.77 463	87.29 363

新几何1				83.42 194	93.13 60	70.71 81		85.48 332	57.43 443	81.80 151	91.98 121	63.28 174	92.27 248	64.60 300	92.99 76	87.27 364

blend_shiyan4		72.29 360	69.65 372	80.21 298	78.24 438	62.16 311	82.29 345	87.27 294	65.41 353	68.43 392	76.42 450	39.91 433	91.23 296	63.21 312	65.66 444	87.22 365

TR-MVS		77.44 275	76.18 281	81.20 271	88.24 194	63.24 287	84.61 291	86.40 318	67.55 321	77.81 229	86.48 302	54.10 291	93.15 206	57.75 377	82.72 273	87.20 366

0.3-1-1-0.015		70.03 386	66.80 408	79.72 316	78.18 439	61.07 330	77.63 419	82.32 382	62.65 395	65.50 427	67.29 474	37.62 448	90.91 314	61.99 334	68.04 428	87.19 367

TransMVSNet (Re)		75.39 315	74.56 308	77.86 356	85.50 303	57.10 388	86.78 221	86.09 325	72.17 213	71.53 353	87.34 271	63.01 184	89.31 346	56.84 387	61.83 456	87.17 368

ACMH	67.68 16	75.89 305	73.93 317	81.77 255	88.71 178	66.61 190	88.62 145	89.01 239	69.81 274	66.78 413	86.70 292	41.95 421	91.51 284	55.64 394	78.14 330	87.17 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 396	67.59 399	72.46 421	74.29 461	45.45 471	77.93 416	87.00 303	63.12 384	63.99 441	78.99 432	42.32 416	84.77 407	56.55 391	64.09 449	87.16 370

EPMVS		69.02 395	68.16 384	71.59 425	79.61 424	49.80 460	77.40 421	66.93 474	62.82 392	70.01 368	79.05 428	45.79 391	77.86 449	56.58 390	75.26 377	87.13 371

CR-MVSNet		73.37 339	71.27 351	79.67 319	81.32 403	65.19 227	75.92 431	80.30 410	59.92 419	72.73 337	81.19 403	52.50 305	86.69 383	59.84 353	77.71 333	87.11 372

RPMNet		73.51 335	70.49 365	82.58 239	81.32 403	65.19 227	75.92 431	92.27 94	57.60 441	72.73 337	76.45 448	52.30 308	95.43 78	48.14 440	77.71 333	87.11 372

test_vis1_n_1920		75.52 310	75.78 284	74.75 397	79.84 419	57.44 384	83.26 329	85.52 331	62.83 391	79.34 196	86.17 309	45.10 398	79.71 440	78.75 143	81.21 289	87.10 374

tt0320		70.49 380	68.03 387	77.89 355	84.78 321	59.12 360	83.55 322	80.44 407	58.13 436	67.43 405	80.41 414	39.26 437	87.54 377	55.12 396	63.18 452	86.99 375

XXY-MVS		75.41 313	75.56 289	74.96 392	83.59 350	57.82 376	80.59 375	83.87 354	66.54 337	74.93 307	88.31 244	63.24 177	80.09 439	62.16 331	76.85 345	86.97 376

tpmrst		72.39 356	72.13 340	73.18 415	80.54 410	49.91 458	79.91 388	79.08 424	63.11 385	71.69 351	79.95 420	55.32 278	82.77 424	65.66 292	73.89 390	86.87 377

0.4-1-1-0.2		70.01 387	66.86 407	79.44 324	77.61 445	60.64 342	76.77 426	82.34 381	62.40 398	65.91 425	66.65 475	40.05 431	90.83 316	61.77 338	68.24 427	86.86 378

thres200		75.55 309	74.47 310	78.82 335	87.78 220	57.85 375	83.07 336	83.51 359	72.44 208	75.84 276	84.42 346	52.08 314	91.75 268	47.41 443	83.64 257	86.86 378

ITE_SJBPF				78.22 348	81.77 392	60.57 343		83.30 362	69.25 290	67.54 400	87.20 277	36.33 453	87.28 380	54.34 401	74.62 384	86.80 380

test222					91.50 87	68.26 138	84.16 308	83.20 367	54.63 455	79.74 186	91.63 136	58.97 245			91.42 104	86.77 381

MIMVSNet		70.69 376	69.30 374	74.88 394	84.52 328	56.35 402	75.87 433	79.42 419	64.59 365	67.76 397	82.41 391	41.10 425	81.54 431	46.64 447	81.34 286	86.75 382

BH-untuned		79.47 218	78.60 219	82.05 249	89.19 156	65.91 204	86.07 250	88.52 262	72.18 212	75.42 286	87.69 262	61.15 221	93.54 175	60.38 349	86.83 197	86.70 383

FE-MVSNET2		72.88 354	71.28 350	77.67 360	78.30 437	57.78 378	84.43 299	88.92 245	69.56 281	64.61 435	81.67 401	46.73 380	88.54 364	59.33 358	67.99 429	86.69 384

LTVRE_ROB	69.57 13	76.25 300	74.54 309	81.41 263	88.60 181	64.38 257	79.24 394	89.12 235	70.76 247	69.79 375	87.86 258	49.09 362	93.20 202	56.21 393	80.16 303	86.65 385
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				79.97 305	90.90 99	64.21 259		84.71 340	59.27 425	85.40 76	92.91 95	62.02 202	89.08 352	68.95 262	91.37 106	86.63 386

MIMVSNet1		68.58 399	66.78 409	73.98 406	80.07 416	51.82 445	80.77 370	84.37 344	64.40 369	59.75 459	82.16 397	36.47 452	83.63 415	42.73 461	70.33 416	86.48 387

tfpnnormal		74.39 322	73.16 328	78.08 352	86.10 289	58.05 369	84.65 290	87.53 285	70.32 262	71.22 357	85.63 320	54.97 280	89.86 335	43.03 460	75.02 380	86.32 388

D2MVS		74.82 319	73.21 327	79.64 320	79.81 420	62.56 302	80.34 380	87.35 290	64.37 370	68.86 383	82.66 389	46.37 383	90.10 331	67.91 271	81.24 288	86.25 389

tpm cat1		70.57 377	68.31 382	77.35 368	82.41 385	57.95 373	78.08 413	80.22 412	52.04 461	68.54 389	77.66 441	52.00 316	87.84 373	51.77 413	72.07 407	86.25 389

CVMVSNet		72.99 350	72.58 335	74.25 402	84.28 331	50.85 454	86.41 235	83.45 361	44.56 474	73.23 330	87.54 268	49.38 357	85.70 395	65.90 289	78.44 324	86.19 391

AllTest		70.96 371	68.09 386	79.58 321	85.15 312	63.62 271	84.58 292	79.83 415	62.31 399	60.32 456	86.73 286	32.02 461	88.96 356	50.28 424	71.57 410	86.15 392

TestCases				79.58 321	85.15 312	63.62 271		79.83 415	62.31 399	60.32 456	86.73 286	32.02 461	88.96 356	50.28 424	71.57 410	86.15 392

test-LLR		72.94 351	72.43 336	74.48 398	81.35 401	58.04 370	78.38 408	77.46 434	66.66 331	69.95 371	79.00 430	48.06 368	79.24 441	66.13 285	84.83 231	86.15 392

test-mter		71.41 367	70.39 368	74.48 398	81.35 401	58.04 370	78.38 408	77.46 434	60.32 415	69.95 371	79.00 430	36.08 454	79.24 441	66.13 285	84.83 231	86.15 392

IterMVS		74.29 323	72.94 331	78.35 347	81.53 397	63.49 281	81.58 356	82.49 378	68.06 317	69.99 370	83.69 369	51.66 326	85.54 398	65.85 290	71.64 409	86.01 396
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	83.42 194	93.29 52	69.46 105	88.55 149	83.70 355	63.98 377	70.20 364	88.89 227	54.01 294	94.80 113	46.66 445	81.88 283	86.01 396

ppachtmachnet_test		70.04 385	67.34 403	78.14 350	79.80 421	61.13 327	79.19 396	80.59 402	59.16 426	65.27 430	79.29 427	46.75 379	87.29 379	49.33 431	66.72 432	86.00 398

mmtdpeth		74.16 326	73.01 330	77.60 365	83.72 346	61.13 327	85.10 277	85.10 336	72.06 215	77.21 246	80.33 415	43.84 407	85.75 394	77.14 164	52.61 475	85.91 399

test_fmvs1_n		70.86 374	70.24 369	72.73 419	72.51 475	55.28 416	81.27 364	79.71 417	51.49 465	78.73 203	84.87 339	27.54 471	77.02 452	76.06 179	79.97 307	85.88 400

Patchmtry		70.74 375	69.16 377	75.49 386	80.72 407	54.07 427	74.94 442	80.30 410	58.34 433	70.01 368	81.19 403	52.50 305	86.54 385	53.37 407	71.09 413	85.87 401

WB-MVSnew		71.96 365	71.65 344	72.89 417	84.67 327	51.88 444	82.29 345	77.57 433	62.31 399	73.67 325	83.00 382	53.49 299	81.10 435	45.75 452	82.13 279	85.70 402

test_fmvs2		68.35 403	67.48 400	70.98 433	69.50 479	51.95 442	80.05 385	76.38 444	49.33 468	74.65 312	84.38 348	23.30 480	75.40 469	74.51 198	75.17 379	85.60 403

usedtu_dtu_shiyan2		64.75 425	61.63 433	74.10 404	70.64 477	53.18 437	82.10 349	81.27 396	56.22 450	56.39 470	74.67 461	27.94 470	83.56 416	42.71 462	62.73 453	85.57 404

ambc				75.24 390	73.16 470	50.51 456	63.05 485	87.47 287		64.28 437	77.81 440	17.80 486	89.73 339	57.88 376	60.64 460	85.49 405

mvs5depth		69.45 392	67.45 401	75.46 387	73.93 462	55.83 408	79.19 396	83.23 364	66.89 326	71.63 352	83.32 376	33.69 459	85.09 403	59.81 354	55.34 471	85.46 406

UnsupCasMVSNet_eth		67.33 408	65.99 412	71.37 427	73.48 467	51.47 449	75.16 438	85.19 334	65.20 356	60.78 453	80.93 410	42.35 415	77.20 451	57.12 382	53.69 473	85.44 407

PatchT		68.46 402	67.85 391	70.29 435	80.70 408	43.93 479	72.47 450	74.88 450	60.15 417	70.55 359	76.57 447	49.94 349	81.59 430	50.58 420	74.83 382	85.34 408

Anonymous20240521		68.80 397	67.22 404	73.55 409	74.33 460	54.11 426	83.18 330	85.61 330	58.15 435	61.68 450	80.94 408	30.71 466	81.27 434	57.00 385	73.34 398	85.28 409

test_cas_vis1_n_1920		73.76 332	73.74 321	73.81 408	75.90 452	59.77 353	80.51 376	82.40 379	58.30 434	81.62 156	85.69 317	44.35 404	76.41 458	76.29 175	78.61 320	85.23 410

ADS-MVSNet2		66.20 420	63.33 424	74.82 395	79.92 417	58.75 362	67.55 470	75.19 448	53.37 458	65.25 431	75.86 456	42.32 416	80.53 438	41.57 465	68.91 422	85.18 411

ADS-MVSNet		64.36 426	62.88 428	68.78 443	79.92 417	47.17 467	67.55 470	71.18 462	53.37 458	65.25 431	75.86 456	42.32 416	73.99 475	41.57 465	68.91 422	85.18 411

FMVSNet5		69.50 391	67.96 388	74.15 403	82.97 372	55.35 415	80.01 386	82.12 385	62.56 396	63.02 444	81.53 402	36.92 449	81.92 429	48.42 435	74.06 388	85.17 413

pmmvs5		71.55 366	70.20 370	75.61 382	77.83 441	56.39 399	81.74 352	80.89 397	57.76 439	67.46 403	84.49 344	49.26 360	85.32 402	57.08 383	75.29 376	85.11 414

testing3		68.56 400	67.67 397	71.22 431	87.33 248	42.87 481	83.06 337	71.54 461	70.36 259	69.08 382	84.38 348	30.33 467	85.69 396	37.50 473	75.45 371	85.09 415

UWE-MVS-28		65.32 421	64.93 415	66.49 452	78.70 432	38.55 489	77.86 418	64.39 481	62.00 404	64.13 439	83.60 371	41.44 422	76.00 462	31.39 480	80.89 292	84.92 416

CMPMVS	51.72 21	70.19 383	68.16 384	76.28 376	73.15 471	57.55 382	79.47 391	83.92 352	48.02 470	56.48 469	84.81 341	43.13 411	86.42 388	62.67 322	81.81 284	84.89 417
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 414	66.53 410	67.08 451	75.62 456	41.69 486	75.93 430	76.50 443	66.11 340	65.20 433	86.59 296	35.72 455	74.71 471	43.71 457	73.38 397	84.84 418

MSDG		73.36 341	70.99 356	80.49 289	84.51 329	65.80 209	80.71 373	86.13 324	65.70 347	65.46 428	83.74 366	44.60 400	90.91 314	51.13 419	76.89 343	84.74 419

pmmvs4		74.03 330	71.91 341	80.39 290	81.96 389	68.32 136	81.45 359	82.14 384	59.32 424	69.87 373	85.13 334	52.40 307	88.13 369	60.21 351	74.74 383	84.73 420

gg-mvs-nofinetune		69.95 388	67.96 388	75.94 378	83.07 364	54.51 424	77.23 423	70.29 464	63.11 385	70.32 363	62.33 478	43.62 408	88.69 360	53.88 404	87.76 179	84.62 421

test_fmvs1		70.93 372	70.52 364	72.16 422	73.71 464	55.05 418	80.82 367	78.77 426	51.21 466	78.58 208	84.41 347	31.20 465	76.94 453	75.88 183	80.12 306	84.47 422

BH-w/o		78.21 253	77.33 257	80.84 281	88.81 169	65.13 229	84.87 283	87.85 278	69.75 278	74.52 314	84.74 343	61.34 216	93.11 209	58.24 373	85.84 218	84.27 423

MVS		78.19 255	76.99 263	81.78 254	85.66 296	66.99 183	84.66 288	90.47 173	55.08 454	72.02 348	85.27 329	63.83 171	94.11 143	66.10 287	89.80 135	84.24 424

COLMAP_ROB	66.92 17	73.01 349	70.41 367	80.81 282	87.13 256	65.63 212	88.30 161	84.19 350	62.96 388	63.80 443	87.69 262	38.04 445	92.56 233	46.66 445	74.91 381	84.24 424
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 432	61.73 432	61.70 458	72.74 473	24.50 501	69.16 465	78.03 430	61.40 407	56.72 468	75.53 459	38.42 442	76.48 457	45.95 451	57.67 464	84.13 426

TESTMET0.1,1		69.89 389	69.00 378	72.55 420	79.27 430	56.85 390	78.38 408	74.71 453	57.64 440	68.09 393	77.19 445	37.75 446	76.70 454	63.92 304	84.09 246	84.10 427

test_fmvs3		63.36 429	61.82 431	67.98 448	62.51 488	46.96 469	77.37 422	74.03 455	45.24 473	67.50 401	78.79 433	12.16 492	72.98 478	72.77 218	66.02 436	83.99 428

our_test_3		69.14 394	67.00 405	75.57 383	79.80 421	58.80 361	77.96 415	77.81 431	59.55 422	62.90 447	78.25 437	47.43 370	83.97 412	51.71 414	67.58 431	83.93 429

test_vis1_n		69.85 390	69.21 376	71.77 424	72.66 474	55.27 417	81.48 358	76.21 445	52.03 462	75.30 295	83.20 379	28.97 468	76.22 460	74.60 197	78.41 328	83.81 430

tpmvs		71.09 370	69.29 375	76.49 375	82.04 388	56.04 405	78.92 402	81.37 394	64.05 375	67.18 408	78.28 436	49.74 353	89.77 337	49.67 429	72.37 402	83.67 431

test20.03		67.45 407	66.95 406	68.94 440	75.48 457	44.84 477	77.50 420	77.67 432	66.66 331	63.01 445	83.80 364	47.02 374	78.40 445	42.53 464	68.86 424	83.58 432

test0.0.03 1		68.00 405	67.69 396	68.90 441	77.55 446	47.43 464	75.70 434	72.95 460	66.66 331	66.56 416	82.29 395	48.06 368	75.87 464	44.97 456	74.51 385	83.41 433

Anonymous20231206		68.60 398	67.80 394	71.02 432	80.23 414	50.75 455	78.30 412	80.47 405	56.79 446	66.11 424	82.63 390	46.35 384	78.95 443	43.62 458	75.70 363	83.36 434

EU-MVSNet		68.53 401	67.61 398	71.31 430	78.51 434	47.01 468	84.47 294	84.27 348	42.27 477	66.44 421	84.79 342	40.44 429	83.76 413	58.76 367	68.54 425	83.17 435

dp		66.80 412	65.43 413	70.90 434	79.74 423	48.82 462	75.12 440	74.77 451	59.61 421	64.08 440	77.23 444	42.89 412	80.72 437	48.86 434	66.58 434	83.16 436

pmmvs-eth3d		70.50 379	67.83 393	78.52 344	77.37 448	66.18 196	81.82 350	81.51 391	58.90 429	63.90 442	80.42 413	42.69 414	86.28 389	58.56 368	65.30 446	83.11 437

YYNet1		65.03 422	62.91 427	71.38 426	75.85 454	56.60 396	69.12 466	74.66 454	57.28 444	54.12 473	77.87 439	45.85 390	74.48 472	49.95 427	61.52 458	83.05 438

MDA-MVSNet-bldmvs		66.68 413	63.66 423	75.75 380	79.28 429	60.56 344	73.92 447	78.35 429	64.43 367	50.13 479	79.87 422	44.02 406	83.67 414	46.10 450	56.86 465	83.03 439

MDA-MVSNet_test_wron		65.03 422	62.92 426	71.37 427	75.93 451	56.73 392	69.09 467	74.73 452	57.28 444	54.03 474	77.89 438	45.88 389	74.39 473	49.89 428	61.55 457	82.99 440

USDC		70.33 381	68.37 381	76.21 377	80.60 409	56.23 403	79.19 396	86.49 316	60.89 410	61.29 451	85.47 325	31.78 463	89.47 344	53.37 407	76.21 359	82.94 441

Syy-MVS		68.05 404	67.85 391	68.67 444	84.68 324	40.97 487	78.62 405	73.08 458	66.65 334	66.74 414	79.46 425	52.11 313	82.30 426	32.89 478	76.38 356	82.75 442

myMVS_eth3d		67.02 411	66.29 411	69.21 439	84.68 324	42.58 482	78.62 405	73.08 458	66.65 334	66.74 414	79.46 425	31.53 464	82.30 426	39.43 470	76.38 356	82.75 442

ttmdpeth		59.91 435	57.10 439	68.34 446	67.13 483	46.65 470	74.64 443	67.41 473	48.30 469	62.52 449	85.04 338	20.40 482	75.93 463	42.55 463	45.90 484	82.44 444

OpenMVS_ROB	64.09 19	70.56 378	68.19 383	77.65 362	80.26 412	59.41 359	85.01 280	82.96 373	58.76 431	65.43 429	82.33 393	37.63 447	91.23 296	45.34 455	76.03 360	82.32 445

JIA-IIPM		66.32 417	62.82 429	76.82 373	77.09 449	61.72 319	65.34 478	75.38 447	58.04 438	64.51 436	62.32 479	42.05 420	86.51 386	51.45 417	69.22 421	82.21 446

dmvs_re		71.14 369	70.58 363	72.80 418	81.96 389	59.68 354	75.60 435	79.34 421	68.55 309	69.27 381	80.72 411	49.42 356	76.54 455	52.56 411	77.79 332	82.19 447

EG-PatchMatch MVS		74.04 328	71.82 342	80.71 284	84.92 318	67.42 169	85.86 256	88.08 268	66.04 342	64.22 438	83.85 362	35.10 456	92.56 233	57.44 379	80.83 294	82.16 448

FE-MVSNET		67.25 410	65.33 414	73.02 416	75.86 453	52.54 439	80.26 383	80.56 403	63.80 380	60.39 454	79.70 424	41.41 423	84.66 409	43.34 459	62.62 454	81.86 449

MVP-Stereo		76.12 301	74.46 311	81.13 274	85.37 306	69.79 96	84.42 301	87.95 274	65.03 361	67.46 403	85.33 328	53.28 301	91.73 270	58.01 375	83.27 265	81.85 450
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 406	64.34 418	76.92 372	73.47 468	61.07 330	84.86 284	82.98 372	59.77 420	58.30 463	85.13 334	26.06 472	87.89 372	47.92 442	60.59 461	81.81 451

GG-mvs-BLEND				75.38 388	81.59 395	55.80 409	79.32 393	69.63 466		67.19 407	73.67 464	43.24 410	88.90 358	50.41 421	84.50 236	81.45 452

KD-MVS_2432*1600		66.22 418	63.89 421	73.21 412	75.47 458	53.42 432	70.76 458	84.35 345	64.10 373	66.52 418	78.52 434	34.55 457	84.98 404	50.40 422	50.33 478	81.23 453

miper_refine_blended		66.22 418	63.89 421	73.21 412	75.47 458	53.42 432	70.76 458	84.35 345	64.10 373	66.52 418	78.52 434	34.55 457	84.98 404	50.40 422	50.33 478	81.23 453

test_0402		72.79 355	70.44 366	79.84 309	88.13 200	65.99 202	85.93 253	84.29 347	65.57 349	67.40 406	85.49 324	46.92 375	92.61 229	35.88 475	74.38 386	80.94 455

MVStest1		56.63 439	52.76 445	68.25 447	61.67 489	53.25 436	71.67 453	68.90 471	38.59 482	50.59 478	83.05 381	25.08 474	70.66 480	36.76 474	38.56 485	80.83 456

UnsupCasMVSNet_bld		63.70 428	61.53 434	70.21 436	73.69 465	51.39 450	72.82 449	81.89 386	55.63 452	57.81 465	71.80 468	38.67 441	78.61 444	49.26 432	52.21 476	80.63 457

LCM-MVSNet		54.25 441	49.68 451	67.97 449	53.73 497	45.28 474	66.85 473	80.78 399	35.96 486	39.45 487	62.23 480	8.70 496	78.06 448	48.24 439	51.20 477	80.57 458

N_pmnet		52.79 446	53.26 444	51.40 472	78.99 431	7.68 506	69.52 462	3.89 505	51.63 464	57.01 467	74.98 460	40.83 427	65.96 487	37.78 472	64.67 447	80.56 459

TinyColmap		67.30 409	64.81 416	74.76 396	81.92 391	56.68 395	80.29 381	81.49 392	60.33 414	56.27 471	83.22 377	24.77 476	87.66 376	45.52 453	69.47 419	79.95 460

PM-MVS		66.41 416	64.14 419	73.20 414	73.92 463	56.45 397	78.97 400	64.96 480	63.88 379	64.72 434	80.24 417	19.84 484	83.44 419	66.24 284	64.52 448	79.71 461

ANet_high		50.57 450	46.10 454	63.99 455	48.67 500	39.13 488	70.99 457	80.85 398	61.39 408	31.18 489	57.70 485	17.02 487	73.65 477	31.22 481	15.89 497	79.18 462

LF4IMVS		64.02 427	62.19 430	69.50 438	70.90 476	53.29 435	76.13 428	77.18 439	52.65 460	58.59 461	80.98 407	23.55 479	76.52 456	53.06 409	66.66 433	78.68 463

PatchMatch-RL		72.38 357	70.90 358	76.80 374	88.60 181	67.38 172	79.53 390	76.17 446	62.75 393	69.36 378	82.00 400	45.51 395	84.89 406	53.62 405	80.58 298	78.12 464

MS-PatchMatch		73.83 331	72.67 333	77.30 369	83.87 342	66.02 199	81.82 350	84.66 341	61.37 409	68.61 386	82.82 387	47.29 371	88.21 367	59.27 359	84.32 243	77.68 465

DSMNet-mixed		57.77 438	56.90 440	60.38 460	67.70 481	35.61 491	69.18 464	53.97 492	32.30 490	57.49 466	79.88 421	40.39 430	68.57 485	38.78 471	72.37 402	76.97 466

CHOSEN 280x420		66.51 415	64.71 417	71.90 423	81.45 398	63.52 280	57.98 487	68.95 470	53.57 457	62.59 448	76.70 446	46.22 386	75.29 470	55.25 395	79.68 308	76.88 467

mvsany_test3		53.99 442	51.45 447	61.61 459	55.51 493	44.74 478	63.52 483	45.41 498	43.69 476	58.11 464	76.45 448	17.99 485	63.76 489	54.77 399	47.59 480	76.34 468

dmvs_testset		62.63 430	64.11 420	58.19 462	78.55 433	24.76 500	75.28 436	65.94 477	67.91 318	60.34 455	76.01 455	53.56 297	73.94 476	31.79 479	67.65 430	75.88 469

mvsany_test1		62.30 431	61.26 435	65.41 454	69.52 478	54.86 420	66.86 472	49.78 494	46.65 471	68.50 390	83.21 378	49.15 361	66.28 486	56.93 386	60.77 459	75.11 470

PMMVS		69.34 393	68.67 379	71.35 429	75.67 455	62.03 313	75.17 437	73.46 456	50.00 467	68.68 384	79.05 428	52.07 315	78.13 446	61.16 344	82.77 271	73.90 471

test_vis1_rt		60.28 434	58.42 437	65.84 453	67.25 482	55.60 412	70.44 460	60.94 486	44.33 475	59.00 460	66.64 476	24.91 475	68.67 484	62.80 317	69.48 418	73.25 472

pmmvs3		57.79 437	54.26 442	68.37 445	64.02 487	56.72 393	75.12 440	65.17 478	40.20 479	52.93 475	69.86 472	20.36 483	75.48 467	45.45 454	55.25 472	72.90 473

PVSNet_0	57.27 20	61.67 433	59.27 436	68.85 442	79.61 424	57.44 384	68.01 468	73.44 457	55.93 451	58.54 462	70.41 471	44.58 401	77.55 450	47.01 444	35.91 486	71.55 474

WB-MVS		54.94 440	54.72 441	55.60 468	73.50 466	20.90 502	74.27 446	61.19 485	59.16 426	50.61 477	74.15 462	47.19 373	75.78 465	17.31 492	35.07 487	70.12 475

SSC-MVS		53.88 443	53.59 443	54.75 470	72.87 472	19.59 503	73.84 448	60.53 487	57.58 442	49.18 481	73.45 465	46.34 385	75.47 468	16.20 495	32.28 489	69.20 476

test_f		52.09 447	50.82 448	55.90 466	53.82 496	42.31 485	59.42 486	58.31 490	36.45 485	56.12 472	70.96 470	12.18 491	57.79 492	53.51 406	56.57 467	67.60 477

PMMVS2		40.82 457	38.86 461	46.69 473	53.84 495	16.45 504	48.61 490	49.92 493	37.49 483	31.67 488	60.97 481	8.14 498	56.42 493	28.42 483	30.72 490	67.19 478

new_pmnet		50.91 449	50.29 449	52.78 471	68.58 480	34.94 493	63.71 482	56.63 491	39.73 480	44.95 482	65.47 477	21.93 481	58.48 491	34.98 476	56.62 466	64.92 479

MVS-HIRNet		59.14 436	57.67 438	63.57 456	81.65 393	43.50 480	71.73 452	65.06 479	39.59 481	51.43 476	57.73 484	38.34 443	82.58 425	39.53 468	73.95 389	64.62 480

APD_test1		53.31 445	49.93 450	63.42 457	65.68 484	50.13 457	71.59 454	66.90 475	34.43 487	40.58 486	71.56 469	8.65 497	76.27 459	34.64 477	55.36 470	63.86 481

test_method		31.52 460	29.28 464	38.23 476	27.03 504	6.50 507	20.94 495	62.21 484	4.05 498	22.35 496	52.50 489	13.33 489	47.58 496	27.04 485	34.04 488	60.62 482

EGC-MVSNET		52.07 448	47.05 452	67.14 450	83.51 352	60.71 340	80.50 377	67.75 472	0.07 500	0.43 501	75.85 458	24.26 477	81.54 431	28.82 482	62.25 455	59.16 483

test_vis3_rt		49.26 451	47.02 453	56.00 465	54.30 494	45.27 475	66.76 474	48.08 495	36.83 484	44.38 483	53.20 488	7.17 499	64.07 488	56.77 389	55.66 468	58.65 484

FPMVS		53.68 444	51.64 446	59.81 461	65.08 485	51.03 452	69.48 463	69.58 467	41.46 478	40.67 485	72.32 467	16.46 488	70.00 483	24.24 488	65.42 445	58.40 485

testf1		45.72 452	41.96 456	57.00 463	56.90 491	45.32 472	66.14 475	59.26 488	26.19 491	30.89 490	60.96 482	4.14 500	70.64 481	26.39 486	46.73 482	55.04 486

APD_test2		45.72 452	41.96 456	57.00 463	56.90 491	45.32 472	66.14 475	59.26 488	26.19 491	30.89 490	60.96 482	4.14 500	70.64 481	26.39 486	46.73 482	55.04 486

PMVS	37.38 22	44.16 456	40.28 460	55.82 467	40.82 502	42.54 484	65.12 479	63.99 482	34.43 487	24.48 493	57.12 486	3.92 502	76.17 461	17.10 493	55.52 469	48.75 488
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 462	25.89 466	43.81 475	44.55 501	35.46 492	28.87 494	39.07 499	18.20 495	18.58 497	40.18 492	2.68 503	47.37 497	17.07 494	23.78 494	48.60 489
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 454	45.38 455	45.55 474	73.36 469	26.85 498	67.72 469	34.19 500	54.15 456	49.65 480	56.41 487	25.43 473	62.94 490	19.45 490	28.09 491	46.86 490

kuosan		39.70 458	40.40 459	37.58 477	64.52 486	26.98 496	65.62 477	33.02 501	46.12 472	42.79 484	48.99 490	24.10 478	46.56 498	12.16 498	26.30 492	39.20 491

Gipuma		45.18 455	41.86 458	55.16 469	77.03 450	51.52 448	32.50 493	80.52 404	32.46 489	27.12 492	35.02 493	9.52 495	75.50 466	22.31 489	60.21 462	38.45 492
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 480	40.17 503	26.90 497		24.59 504	17.44 496	23.95 494	48.61 491	9.77 494	26.48 499	18.06 491	24.47 493	28.83 493

E-PMN		31.77 459	30.64 462	35.15 478	52.87 498	27.67 495	57.09 488	47.86 496	24.64 493	16.40 498	33.05 494	11.23 493	54.90 494	14.46 496	18.15 495	22.87 494

EMVS		30.81 461	29.65 463	34.27 479	50.96 499	25.95 499	56.58 489	46.80 497	24.01 494	15.53 499	30.68 495	12.47 490	54.43 495	12.81 497	17.05 496	22.43 495

tmp_tt		18.61 464	21.40 467	10.23 482	4.82 505	10.11 505	34.70 492	30.74 503	1.48 499	23.91 495	26.07 496	28.42 469	13.41 501	27.12 484	15.35 498	7.17 496

wuyk23d		16.82 465	15.94 468	19.46 481	58.74 490	31.45 494	39.22 491	3.74 506	6.84 497	6.04 500	2.70 500	1.27 504	24.29 500	10.54 499	14.40 499	2.63 497

test123		6.12 467	8.11 470	0.14 483	0.06 507	0.09 508	71.05 456	0.03 508	0.04 502	0.25 503	1.30 502	0.05 505	0.03 503	0.21 501	0.01 501	0.29 498

testmvs		6.04 468	8.02 471	0.10 484	0.08 506	0.03 509	69.74 461	0.04 507	0.05 501	0.31 502	1.68 501	0.02 506	0.04 502	0.24 500	0.02 500	0.25 499

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

cdsmvs_eth3d_5k		19.96 463	26.61 465	0.00 485	0.00 508	0.00 510	0.00 496	89.26 225	0.00 503	0.00 504	88.61 235	61.62 209	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		5.26 469	7.02 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	63.15 180	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		7.23 466	9.64 469	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	86.72 288	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

WAC-MVS						42.58 482								39.46 469

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 154	91.30 18

test_one_0601					95.07 7	71.46 60		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

eth-test2					0.00 508

eth-test					0.00 508

ZD-MVS					94.38 29	72.22 46		92.67 73	70.98 241	87.75 51	94.07 58	74.01 37	96.70 31	84.66 70	94.84 48

test_241102_ONE					95.30 2	70.98 72		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 70		91.52 56	94.75 1	73.93 171	88.57 36	94.67 30	75.57 26	95.79 64	86.77 51	95.76 27

save fliter					93.80 44	72.35 44	90.47 74	91.17 151	74.31 160

test0726					95.27 5	71.25 65	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												50.01 347

MTGPA								92.02 112

test_post1							78.90 403				5.43 499	48.81 367	85.44 401	59.25 360

test_post											5.46 498	50.36 343	84.24 410

patchmatchnet-post											74.00 463	51.12 334	88.60 362

MTMP							92.18 39	32.83 502

gm-plane-assit					81.40 399	53.83 429			62.72 394		80.94 408		92.39 242	63.40 308

TEST9					93.26 56	72.96 25	88.75 138	91.89 120	68.44 312	85.00 81	93.10 89	74.36 33	95.41 81

test_8					93.13 60	72.57 35	88.68 143	91.84 124	68.69 307	84.87 85	93.10 89	74.43 31	95.16 91

agg_prior					92.85 68	71.94 53		91.78 128		84.41 96			94.93 102

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 122	84.91 83	93.54 76	74.28 34		83.31 85	95.86 24

旧先验2							86.56 230		58.10 437	87.04 62			88.98 354	74.07 203

新几何2							86.29 244

原ACMM2							86.86 217

testdata2													91.01 308	62.37 328

segment_acmp												73.08 44

testdata1							84.14 309		75.71 113

plane_prior7					90.08 117	68.51 132

plane_prior6					89.84 126	68.70 126						60.42 235

plane_prior4											91.00 163

plane_prior3						68.60 129			78.44 36	78.92 201

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 125

plane_prior						68.71 124	90.38 78		77.62 47						86.16 209

n2								0.00 509

nn								0.00 509

door-mid								69.98 465

test11								92.23 98

door								69.44 468

HQP5-MVS						66.98 184

HQP-NCC					89.33 146		89.17 116		76.41 91	77.23 242

ACMP_Plane					89.33 146		89.17 116		76.41 91	77.23 242

BP-MVS														77.47 159

HQP3-MVS								92.19 106							85.99 213

HQP2-MVS												60.17 238

NP-MVS					89.62 131	68.32 136					90.24 185

MDTV_nov1_ep13			69.97 371		83.18 361	53.48 431	77.10 425	80.18 414	60.45 413	69.33 379	80.44 412	48.89 366	86.90 382	51.60 415	78.51 323

ACMMP++_ref															81.95 282

ACMMP++															81.25 287

Test By Simon												64.33 166

High-resolution multi-view benchmark