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.

cashybrid2		86.09 56	86.04 63	86.24 67	88.17 197	68.05 148	89.44 104	92.79 70	80.30 10	84.71 87	92.78 103	72.83 50	95.05 100	82.81 93	90.57 121	95.62 1

MM		89.16 7	89.23 9	88.97 4	90.79 103	73.65 10	92.66 28	91.17 154	86.57 1	87.39 58	94.97 25	71.70 65	97.68 1	92.19 1	95.63 31	95.57 2

casdiffmvs_mvg		85.99 60	86.09 62	85.70 83	87.65 232	67.22 182	88.69 144	93.04 47	79.64 22	85.33 77	92.54 106	73.30 40	94.50 127	83.49 83	91.14 110	95.37 3
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 71	86.15 60	84.06 169	91.71 85	64.94 242	86.47 236	91.87 124	73.63 181	86.60 68	93.02 94	76.57 19	91.87 268	83.36 84	92.15 90	95.35 4

casdiffmvs		85.11 84	85.14 84	85.01 109	87.20 256	65.77 213	87.75 184	92.83 66	77.84 45	84.36 101	92.38 109	72.15 58	93.93 153	81.27 110	90.48 123	95.33 5
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 83	89.48 139	67.88 155	88.59 148	89.05 239	80.19 13	90.70 20	95.40 17	74.56 29	93.92 154	91.54 2	92.07 92	95.31 6

3Dnovator+	77.84 4	85.48 74	84.47 94	88.51 7	91.08 94	73.49 16	93.18 16	93.78 23	80.79 8	76.66 262	93.37 84	60.40 243	96.75 30	77.20 166	93.73 69	95.29 7

BP-MVS1		84.32 93	83.71 110	86.17 70	87.84 216	67.85 156	89.38 110	89.64 208	77.73 47	83.98 109	92.12 121	56.89 273	95.43 78	84.03 80	91.75 99	95.24 8

hybridcas		85.11 84	85.18 83	84.90 117	87.47 244	65.68 214	88.53 152	92.38 88	77.91 43	84.27 102	92.48 107	72.19 57	93.88 159	80.37 120	90.97 113	95.15 9

MGCNet		87.69 24	87.55 29	88.12 13	89.45 140	71.76 54	91.47 57	89.54 211	82.14 3	86.65 67	94.28 46	68.28 123	97.46 6	90.81 6	95.31 37	95.15 9

CS-MVS		86.69 44	86.95 42	85.90 80	90.76 104	67.57 166	92.83 22	93.30 38	79.67 20	84.57 95	92.27 110	71.47 68	95.02 102	84.24 77	93.46 72	95.13 11

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 90	74.62 155	88.90 33	93.85 71	75.75 24	96.00 60	87.80 43	94.63 53	95.04 12
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 88	84.98 85	84.80 122	87.30 254	65.39 222	87.30 204	92.88 63	77.62 49	84.04 108	92.26 111	71.81 62	93.96 147	81.31 108	90.30 126	95.03 13

casdiffseed414692147		83.62 119	83.02 125	85.40 93	87.31 253	67.50 169	88.70 143	91.72 133	76.97 75	82.77 140	91.72 133	66.85 138	93.71 169	73.06 219	88.12 172	94.98 14

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 65	95.06 1	94.23 6	78.38 39	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

PC_three_1452									68.21 320	92.02 14	94.00 63	82.09 5	95.98 62	84.58 71	96.68 2	94.95 15

IS-MVSNet		83.15 133	82.81 130	84.18 158	89.94 124	63.30 289	91.59 51	88.46 268	79.04 31	79.49 196	92.16 118	65.10 163	94.28 133	67.71 278	91.86 98	94.95 15

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 13	88.10 43	94.80 27	73.76 38	97.11 17	87.51 46	95.82 24	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 73	93.57 8	94.06 14	77.24 65	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

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 19

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 151	92.29 7	95.97 2	74.28 34	97.24 15	88.58 33	96.91 1	94.87 21
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		77.30 285	76.49 281	79.74 321	90.08 117	52.02 447	87.86 182	63.10 491	74.88 147	80.16 189	92.79 101	38.29 452	92.35 248	68.74 271	92.50 84	94.86 22

ECVR-MVS		79.61 219	79.26 212	80.67 291	90.08 117	54.69 428	87.89 180	77.44 443	74.88 147	80.27 186	92.79 101	48.96 373	92.45 242	68.55 272	92.50 84	94.86 22

MED-MVS		89.75 3	90.37 3	87.89 24	94.57 17	71.43 61	93.28 12	94.36 3	77.30 62	92.25 9	95.87 3	81.59 7	97.39 11	88.15 39	95.96 19	94.85 24

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 17	71.25 65	93.28 12	93.91 19	77.30 62	91.13 18	95.87 3	77.62 16	96.95 22	86.12 57	93.07 75	94.85 24

IU-MVS					95.30 2	71.25 65		92.95 61	66.81 333	92.39 6				88.94 28	96.63 4	94.85 24

test1111		79.43 226	79.18 215	80.15 306	89.99 122	53.31 441	87.33 203	77.05 447	75.04 140	80.23 188	92.77 104	48.97 372	92.33 250	68.87 269	92.40 86	94.81 27

SF-MVS		88.46 15	88.74 15	87.64 39	92.78 71	71.95 52	92.40 29	94.74 2	75.71 117	89.16 29	95.10 20	75.65 25	96.19 52	87.07 49	96.01 17	94.79 28

BridgeMVS		86.78 42	86.99 40	86.15 72	91.24 91	67.61 164	90.51 70	92.90 62	77.26 64	87.44 57	91.63 139	71.27 72	96.06 55	85.62 60	95.01 40	94.78 29

E4		84.10 100	83.99 103	84.45 137	87.58 242	64.99 238	86.54 234	92.25 99	76.38 100	83.37 124	92.09 122	69.88 93	93.58 171	79.78 133	88.03 176	94.77 30

viewmacassd2359aftdt		83.76 112	83.66 112	84.07 166	86.59 280	64.56 251	86.88 219	91.82 127	75.72 116	83.34 125	92.15 120	68.24 124	92.88 223	79.05 140	89.15 149	94.77 30

sasdasda		85.91 64	85.87 68	86.04 76	89.84 126	69.44 106	90.45 76	93.00 52	76.70 86	88.01 46	91.23 153	73.28 41	93.91 155	81.50 106	88.80 154	94.77 30

SPE-MVS-test		86.29 54	86.48 51	85.71 82	91.02 96	67.21 183	92.36 34	93.78 23	78.97 34	83.51 123	91.20 157	70.65 81	95.15 92	81.96 103	94.89 45	94.77 30

canonicalmvs		85.91 64	85.87 68	86.04 76	89.84 126	69.44 106	90.45 76	93.00 52	76.70 86	88.01 46	91.23 153	73.28 41	93.91 155	81.50 106	88.80 154	94.77 30

MED-MVS test				87.86 27	94.57 17	71.43 61	93.28 12	94.36 3	75.24 131	92.25 9	95.03 22		97.39 11	88.15 39	95.96 19	94.75 35

ME-MVS		88.98 11	89.39 8	87.75 30	94.54 20	71.43 61	91.61 49	94.25 5	76.30 104	90.62 21	95.03 22	78.06 15	97.07 19	88.15 39	95.96 19	94.75 35

E5new		84.22 94	84.12 97	84.51 132	87.60 234	65.36 224	87.45 194	92.31 92	76.51 91	83.53 119	92.26 111	69.25 105	93.50 182	79.88 128	88.26 165	94.69 37

E6new		84.22 94	84.12 97	84.52 130	87.60 234	65.36 224	87.45 194	92.30 94	76.51 91	83.53 119	92.26 111	69.26 103	93.49 184	79.88 128	88.26 165	94.69 37

E6		84.22 94	84.12 97	84.52 130	87.60 234	65.36 224	87.45 194	92.30 94	76.51 91	83.53 119	92.26 111	69.26 103	93.49 184	79.88 128	88.26 165	94.69 37

E5		84.22 94	84.12 97	84.51 132	87.60 234	65.36 224	87.45 194	92.31 92	76.51 91	83.53 119	92.26 111	69.25 105	93.50 182	79.88 128	88.26 165	94.69 37

GDP-MVS		83.52 122	82.64 134	86.16 71	88.14 200	68.45 133	89.13 122	92.69 72	72.82 207	83.71 114	91.86 128	55.69 282	95.35 87	80.03 125	89.74 138	94.69 37

test_0728_THIRD									78.38 39	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 10	80.27 11	91.35 16	94.16 54	78.35 14	96.77 28	89.59 17	94.22 65	94.67 42
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		82.60 146	82.10 146	84.10 160	87.98 210	62.94 302	87.45 194	91.27 150	77.42 58	79.85 191	90.28 189	56.62 276	94.70 120	79.87 132	88.15 171	94.67 42

E2		84.00 103	83.87 104	84.39 140	87.70 229	64.95 239	86.40 241	92.23 100	75.85 113	83.21 126	91.78 130	70.09 88	93.55 176	79.52 137	88.05 174	94.66 45

E3		84.00 103	83.87 104	84.39 140	87.70 229	64.95 239	86.40 241	92.23 100	75.85 113	83.21 126	91.78 130	70.09 88	93.55 176	79.52 137	88.05 174	94.66 45

MGCFI-Net		85.06 87	85.51 75	83.70 188	89.42 141	63.01 296	89.43 105	92.62 80	76.43 95	87.53 54	91.34 151	72.82 51	93.42 192	81.28 109	88.74 157	94.66 45

viewmanbaseed2359cas		83.66 115	83.55 115	84.00 177	86.81 272	64.53 252	86.65 229	91.75 132	74.89 146	83.15 131	91.68 135	68.74 116	92.83 227	79.02 142	89.24 146	94.63 48

alignmvs		85.48 74	85.32 80	85.96 79	89.51 136	69.47 103	89.74 92	92.47 83	76.17 107	87.73 53	91.46 148	70.32 83	93.78 162	81.51 105	88.95 151	94.63 48

viewdifsd2359ckpt09		83.34 128	82.55 136	85.70 83	87.64 233	67.72 161	88.43 154	91.68 136	71.91 222	81.65 158	90.68 175	67.10 136	94.75 116	76.17 181	87.70 183	94.62 50

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 51	89.80 90	93.50 30	75.17 138	86.34 69	95.29 19	70.86 77	96.00 60	88.78 31	96.04 16	94.58 51
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 55	89.79 25	94.12 56	78.98 13	96.58 40	85.66 58	95.72 27	94.58 51

viewcassd2359sk11		83.89 106	83.74 109	84.34 145	87.76 224	64.91 245	86.30 245	92.22 103	75.47 124	83.04 132	91.52 144	70.15 86	93.53 179	79.26 139	87.96 177	94.57 53

VDD-MVS		83.01 138	82.36 140	84.96 111	91.02 96	66.40 194	88.91 129	88.11 271	77.57 51	84.39 98	93.29 86	52.19 316	93.91 155	77.05 169	88.70 158	94.57 53

NormalMVS		86.29 54	85.88 66	87.52 41	93.26 56	72.47 38	91.65 47	92.19 108	79.31 25	84.39 98	92.18 116	64.64 169	95.53 72	80.70 117	94.65 51	94.56 55

KinetiMVS		83.31 131	82.61 135	85.39 94	87.08 265	67.56 167	88.06 172	91.65 137	77.80 46	82.21 147	91.79 129	57.27 268	94.07 145	77.77 159	89.89 136	94.56 55

VDDNet		81.52 168	80.67 169	84.05 172	90.44 109	64.13 264	89.73 93	85.91 332	71.11 239	83.18 129	93.48 79	50.54 348	93.49 184	73.40 214	88.25 169	94.54 57

E3new		83.78 111	83.60 114	84.31 147	87.76 224	64.89 246	86.24 248	92.20 106	75.15 139	82.87 135	91.23 153	70.11 87	93.52 181	79.05 140	87.79 180	94.51 58

MVSMamba_PlusPlus		85.99 60	85.96 65	86.05 75	91.09 93	67.64 163	89.63 97	92.65 77	72.89 206	84.64 92	91.71 134	71.85 61	96.03 56	84.77 69	94.45 59	94.49 59

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 22	71.69 55	93.83 4	93.96 17	75.70 119	91.06 19	96.03 1	76.84 18	97.03 20	89.09 21	95.65 30	94.47 60
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 61

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 61

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 201	84.86 86	92.89 96	76.22 21	96.33 46	84.89 66	95.13 39	94.40 63

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 224	65.62 216	89.20 115	92.21 105	79.94 18	89.74 27	94.86 26	68.63 117	94.20 139	90.83 5	91.39 105	94.38 64

CANet		86.45 48	86.10 61	87.51 42	90.09 116	70.94 77	89.70 94	92.59 81	81.78 4	81.32 162	91.43 149	70.34 82	97.23 16	84.26 75	93.36 73	94.37 65

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 100	70.96 75	92.27 37	94.07 13	72.45 210	85.22 79	91.90 125	69.47 98	96.42 45	83.28 86	95.94 22	94.35 66

viewdifsd2359ckpt07		82.83 141	82.78 133	82.99 219	86.51 282	62.58 306	85.09 281	90.83 166	75.22 132	82.28 144	91.63 139	69.43 99	92.03 258	77.71 160	86.32 208	94.34 67

CNVR-MVS		88.93 12	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 67

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 80	85.24 78	94.32 44	71.76 63	96.93 23	85.53 61	95.79 25	94.32 69

balanced_ft_v1		83.98 105	83.64 113	85.03 107	89.76 129	65.86 208	88.31 163	91.71 134	74.41 160	80.41 185	90.82 171	62.90 193	94.90 106	83.04 89	91.37 106	94.32 69

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 109	83.81 113	93.95 68	69.77 95	96.01 59	85.15 62	94.66 50	94.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		85.76 69	85.29 82	87.17 49	93.49 51	71.08 71	88.58 149	92.42 87	68.32 319	84.61 93	93.48 79	72.32 54	96.15 54	79.00 144	95.43 33	94.28 72

test_241102_TWO								94.06 14	77.24 65	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 74

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 98	87.33 250	67.30 177	89.50 101	90.98 159	76.25 106	90.56 22	94.75 29	68.38 120	94.24 138	90.80 7	92.32 89	94.19 75

fmvsm_l_conf0.5_n_3		86.02 58	86.32 53	85.14 101	87.20 256	68.54 131	89.57 99	90.44 177	75.31 130	87.49 55	94.39 42	72.86 48	92.72 230	89.04 27	90.56 122	94.16 76

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 88	84.22 103	93.36 85	71.44 69	96.76 29	80.82 114	95.33 36	94.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 126	83.02 125	84.57 128	90.13 115	64.47 257	92.32 35	90.73 169	74.45 159	79.35 201	91.10 160	69.05 111	95.12 93	72.78 222	87.22 191	94.13 78

viewdifsd2359ckpt13		82.91 139	82.29 142	84.77 123	86.96 268	66.90 190	87.47 191	91.62 139	72.19 215	81.68 157	90.71 174	66.92 137	93.28 195	75.90 186	87.15 193	94.12 79

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 21	84.90 64	94.94 43	94.10 80

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 105	88.14 42	95.09 21	71.06 75	96.67 33	87.67 44	96.37 14	94.09 81

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 27	83.67 116	94.17 53	67.45 131	96.60 38	83.06 87	94.50 56	94.07 82

X-MVStestdata		80.37 205	77.83 245	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 27	83.67 116	12.47 517	67.45 131	96.60 38	83.06 87	94.50 56	94.07 82

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 46	76.73 85	84.45 96	94.52 32	69.09 108	96.70 31	84.37 74	94.83 48	94.03 84

fmvsm_s_conf0.5_n_4		85.39 78	85.75 71	84.30 149	86.70 276	65.83 209	88.77 137	89.78 200	75.46 125	88.35 37	93.73 74	69.19 107	93.06 215	91.30 3	88.44 163	94.02 85

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 40	76.78 82	84.66 91	94.52 32	68.81 114	96.65 35	84.53 72	94.90 44	94.00 86

fmvsm_s_conf0.1_n_2		83.80 109	83.79 108	83.83 184	85.62 301	64.94 242	87.03 211	86.62 321	74.32 162	87.97 48	94.33 43	60.67 235	92.60 233	89.72 14	87.79 180	93.96 87

test_fmvsmconf_n		85.92 63	86.04 63	85.57 89	85.03 320	69.51 101	89.62 98	90.58 172	73.42 189	87.75 51	94.02 61	72.85 49	93.24 199	90.37 8	90.75 118	93.96 87

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 12	75.90 112	92.29 7	95.66 12	81.67 6	97.38 13	87.44 48	96.34 15	93.95 89
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 81	85.34 78	85.13 104	86.12 291	69.93 93	88.65 146	90.78 168	69.97 276	88.27 39	93.98 66	71.39 70	91.54 284	88.49 35	90.45 124	93.91 90

test_prior				86.33 65	92.61 75	69.59 99		92.97 60					95.48 75			93.91 90

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 78	84.68 88	93.99 65	70.67 80	96.82 26	84.18 79	95.01 40	93.90 92

test_fmvsmconf0.1_n		85.61 72	85.65 72	85.50 90	82.99 375	69.39 108	89.65 95	90.29 186	73.31 193	87.77 50	94.15 55	71.72 64	93.23 200	90.31 9	90.67 120	93.89 93

Anonymous202405211		78.25 257	77.01 267	81.99 256	91.03 95	60.67 347	84.77 288	83.90 359	70.65 257	80.00 190	91.20 157	41.08 434	91.43 292	65.21 300	85.26 232	93.85 94

LFMVS		81.82 158	81.23 158	83.57 193	91.89 83	63.43 287	89.84 87	81.85 394	77.04 74	83.21 126	93.10 89	52.26 315	93.43 191	71.98 234	89.95 134	93.85 94

fmvsm_s_conf0.5_n_2		84.04 101	84.11 101	83.81 186	86.17 289	65.00 237	86.96 214	87.28 297	74.35 161	88.25 40	94.23 50	61.82 211	92.60 233	89.85 12	88.09 173	93.84 96

Effi-MVS+		83.62 119	83.08 123	85.24 98	88.38 190	67.45 170	88.89 130	89.15 235	75.50 123	82.27 145	88.28 251	69.61 97	94.45 130	77.81 158	87.84 179	93.84 96

Anonymous20240529		80.19 211	78.89 221	84.10 160	90.60 105	64.75 249	88.95 128	90.90 162	65.97 351	80.59 180	91.17 159	49.97 355	93.73 168	69.16 266	82.70 280	93.81 98

MVS_Test		83.15 133	83.06 124	83.41 199	86.86 269	63.21 291	86.11 252	92.00 116	74.31 163	82.87 135	89.44 219	70.03 90	93.21 202	77.39 165	88.50 162	93.81 98

Elysia		81.53 166	80.16 183	85.62 86	85.51 304	68.25 140	88.84 134	92.19 108	71.31 233	80.50 182	89.83 199	46.89 384	94.82 111	76.85 171	89.57 140	93.80 100

StellarMVS		81.53 166	80.16 183	85.62 86	85.51 304	68.25 140	88.84 134	92.19 108	71.31 233	80.50 182	89.83 199	46.89 384	94.82 111	76.85 171	89.57 140	93.80 100

test_fmvsmconf0.01_n		84.73 91	84.52 93	85.34 95	80.25 419	69.03 111	89.47 102	89.65 207	73.24 197	86.98 63	94.27 47	66.62 141	93.23 200	90.26 10	89.95 134	93.78 102

GeoE		81.71 160	81.01 164	83.80 187	89.51 136	64.45 258	88.97 127	88.73 260	71.27 236	78.63 213	89.76 204	66.32 147	93.20 205	69.89 258	86.02 217	93.74 103

diffmvs		82.10 150	81.88 152	82.76 236	83.00 371	63.78 273	83.68 320	89.76 202	72.94 204	82.02 150	89.85 198	65.96 156	90.79 321	82.38 101	87.30 190	93.71 104
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 8	77.13 70	89.76 26	95.52 16	72.26 55	96.27 49	86.87 50	94.65 51	93.70 105

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 82	84.91 83	94.44 39	70.78 78	96.61 37	84.53 72	94.89 45	93.66 106

VNet		82.21 149	82.41 138	81.62 263	90.82 101	60.93 340	84.47 297	89.78 200	76.36 102	84.07 107	91.88 126	64.71 168	90.26 334	70.68 247	88.89 152	93.66 106

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 47	75.53 122	83.86 111	94.42 40	67.87 128	96.64 36	82.70 99	94.57 55	93.66 106

DELS-MVS		85.41 77	85.30 81	85.77 81	88.49 184	67.93 154	85.52 272	93.44 32	78.70 35	83.63 118	89.03 226	74.57 28	95.71 67	80.26 124	94.04 66	93.66 106
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 44	90.32 23	94.00 63	74.83 27	93.78 162	87.63 45	94.27 64	93.65 110
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 18	77.12 71	82.82 138	94.23 50	72.13 59	97.09 18	84.83 67	95.37 34	93.65 110
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		83.65 116	84.54 91	80.99 283	90.06 121	65.83 209	84.21 308	88.74 258	71.60 228	85.01 80	92.44 108	74.51 30	83.50 424	82.15 102	92.15 90	93.64 112

EIA-MVS		83.31 131	82.80 131	84.82 120	89.59 132	65.59 217	88.21 166	92.68 73	74.66 154	78.96 205	86.42 309	69.06 110	95.26 88	75.54 192	90.09 130	93.62 113

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 77	77.57 51	83.84 112	94.40 41	72.24 56	96.28 48	85.65 59	95.30 38	93.62 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
diffmvs_AUTHOR		82.38 147	82.27 143	82.73 238	83.26 360	63.80 271	83.89 315	89.76 202	73.35 192	82.37 143	90.84 169	66.25 148	90.79 321	82.77 94	87.93 178	93.59 115

HPM-MVS_fast		85.35 80	84.95 87	86.57 64	93.69 46	70.58 85	92.15 40	91.62 139	73.89 175	82.67 142	94.09 57	62.60 195	95.54 71	80.93 112	92.93 77	93.57 116

fmvsm_s_conf0.1_n		83.56 121	83.38 119	84.10 160	84.86 322	67.28 178	89.40 109	83.01 376	70.67 253	87.08 61	93.96 67	68.38 120	91.45 291	88.56 34	84.50 242	93.56 117

CSCG		86.41 51	86.19 58	87.07 51	92.91 67	72.48 37	90.81 66	93.56 29	73.95 172	83.16 130	91.07 162	75.94 22	95.19 90	79.94 127	94.38 61	93.55 118

test12				86.80 59	92.63 74	70.70 82		91.79 129		82.71 141		71.67 66	96.16 53		94.50 56	93.54 119

APD-MVS_3200maxsize		85.97 62	85.88 66	86.22 69	92.69 73	69.53 100	91.93 42	92.99 55	73.54 185	85.94 70	94.51 35	65.80 157	95.61 68	83.04 89	92.51 83	93.53 120

mvs_anonymous		79.42 227	79.11 216	80.34 299	84.45 333	57.97 378	82.59 346	87.62 289	67.40 330	76.17 278	88.56 244	68.47 119	89.59 347	70.65 248	86.05 216	93.47 121

fmvsm_s_conf0.5_n		83.80 109	83.71 110	84.07 166	86.69 277	67.31 176	89.46 103	83.07 375	71.09 240	86.96 64	93.70 75	69.02 113	91.47 290	88.79 30	84.62 241	93.44 122

fmvsm_s_conf0.5_n_5		85.22 82	85.55 74	84.25 156	86.26 285	67.40 173	89.18 116	89.31 224	72.50 209	88.31 38	93.86 70	69.66 96	91.96 262	89.81 13	91.05 111	93.38 123

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 103	76.87 79	82.81 139	94.25 49	66.44 145	96.24 50	82.88 92	94.28 63	93.38 123

EPNet		83.72 114	82.92 129	86.14 74	84.22 336	69.48 102	91.05 64	85.27 339	81.30 6	76.83 257	91.65 137	66.09 152	95.56 69	76.00 185	93.85 67	93.38 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 124	82.80 131	85.43 92	90.25 113	68.74 122	90.30 80	90.13 191	76.33 103	80.87 174	92.89 96	61.00 230	94.20 139	72.45 231	90.97 113	93.35 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 65	93.49 10	92.73 71	77.33 60	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 127
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		79.10 237	78.24 235	81.70 262	86.85 270	60.24 355	87.28 205	88.79 251	74.25 166	76.84 256	90.53 182	49.48 362	91.56 280	67.98 276	82.15 284	93.29 128

EI-MVSNet-Vis-set		84.19 98	83.81 107	85.31 96	88.18 196	67.85 156	87.66 186	89.73 205	80.05 16	82.95 133	89.59 211	70.74 79	94.82 111	80.66 119	84.72 239	93.28 129

hybridnocas07		81.44 171	81.13 160	82.37 246	82.13 392	63.11 295	83.45 329	88.74 258	72.54 208	80.71 178	90.73 172	65.14 162	90.74 326	80.35 122	86.41 207	93.27 130

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 232	92.02 114	79.45 23	85.88 71	94.80 27	68.07 125	96.21 51	86.69 52	95.34 35	93.23 131

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 88	83.68 115	94.46 36	67.93 126	95.95 63	84.20 78	94.39 60	93.23 131

ACMMP		85.89 66	85.39 77	87.38 44	93.59 49	72.63 33	92.74 25	93.18 45	76.78 82	80.73 177	93.82 72	64.33 172	96.29 47	82.67 100	90.69 119	93.23 131
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 79	84.81 88	87.07 51	91.47 88	72.47 38	91.65 47	88.06 275	79.31 25	84.39 98	92.18 116	64.64 169	95.53 72	80.70 117	90.91 116	93.21 134

fmvsm_s_conf0.1_n_a		83.32 130	82.99 127	84.28 151	83.79 346	68.07 146	89.34 112	82.85 381	69.80 280	87.36 59	94.06 59	68.34 122	91.56 280	87.95 42	83.46 268	93.21 134

fmvsm_s_conf0.5_n_6		85.55 73	86.20 56	83.60 190	87.32 252	65.13 232	88.86 131	91.63 138	75.41 126	88.23 41	93.45 82	68.56 118	92.47 241	89.52 18	92.78 79	93.20 136

hybrid		81.05 178	80.66 170	82.22 250	81.97 394	62.99 300	83.42 330	88.68 261	70.76 251	80.56 181	90.40 185	64.49 171	90.48 330	79.57 136	86.06 215	93.19 137

PAPM_NR		83.02 137	82.41 138	84.82 120	92.47 77	66.37 195	87.93 178	91.80 128	73.82 176	77.32 245	90.66 176	67.90 127	94.90 106	70.37 250	89.48 143	93.19 137

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 195	87.12 264	66.01 202	88.56 150	89.43 215	75.59 121	89.32 28	94.32 44	72.89 47	91.21 302	90.11 11	92.33 87	93.16 139

reproduce_model		87.28 35	87.39 33	86.95 55	93.10 62	71.24 70	91.60 50	93.19 41	74.69 152	88.80 34	95.61 13	70.29 84	96.44 44	86.20 56	93.08 74	93.16 139

OMC-MVS		82.69 142	81.97 151	84.85 119	88.75 176	67.42 171	87.98 174	90.87 164	74.92 145	79.72 193	91.65 137	62.19 205	93.96 147	75.26 196	86.42 206	93.16 139

fmvsm_s_conf0.5_n_a		83.63 118	83.41 118	84.28 151	86.14 290	68.12 144	89.43 105	82.87 380	70.27 269	87.27 60	93.80 73	69.09 108	91.58 277	88.21 38	83.65 262	93.14 142

fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 177	87.78 221	66.09 199	89.96 86	90.80 167	77.37 59	86.72 66	94.20 52	72.51 53	92.78 229	89.08 22	92.33 87	93.13 143

TestfortrainingZip				87.28 46	92.85 68	72.05 50	93.28 12	93.32 37	76.52 90	88.91 32	93.52 77	77.30 17	96.67 33		91.98 94	93.13 143

PAPR		81.66 163	80.89 166	83.99 179	90.27 112	64.00 265	86.76 226	91.77 131	68.84 310	77.13 255	89.50 212	67.63 129	94.88 109	67.55 280	88.52 161	93.09 145

UA-Net		85.08 86	84.96 86	85.45 91	92.07 80	68.07 146	89.78 91	90.86 165	82.48 2	84.60 94	93.20 88	69.35 100	95.22 89	71.39 239	90.88 117	93.07 146

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 142	88.96 30	95.54 14	71.20 73	96.54 41	86.28 54	93.49 70	93.06 147

our_new_method		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 142	88.96 30	95.54 14	71.20 73	96.54 41	86.28 54	93.49 70	93.06 147

HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 65	80.26 12	87.78 49	94.27 47	75.89 23	96.81 27	87.45 47	96.44 9	93.05 149

thisisatest0530		79.40 228	77.76 250	84.31 147	87.69 231	65.10 235	87.36 201	84.26 355	70.04 272	77.42 242	88.26 253	49.94 356	94.79 115	70.20 253	84.70 240	93.03 150

train_agg		86.43 49	86.20 56	87.13 50	93.26 56	72.96 25	88.75 139	91.89 122	68.69 312	85.00 81	93.10 89	74.43 31	95.41 81	84.97 63	95.71 28	93.02 151

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 149	66.27 197	92.32 35	93.63 26	79.37 24	84.17 105	91.88 126	69.04 112	95.43 78	83.93 81	93.77 68	93.01 152

mvsmamba		80.60 196	79.38 207	84.27 153	89.74 130	67.24 181	87.47 191	86.95 310	70.02 273	75.38 294	88.93 231	51.24 338	92.56 236	75.47 194	89.22 147	93.00 153

EI-MVSNet-UG-set		83.81 108	83.38 119	85.09 106	87.87 214	67.53 168	87.44 199	89.66 206	79.74 19	82.23 146	89.41 220	70.24 85	94.74 117	79.95 126	83.92 254	92.99 154

tttt0517		79.40 228	77.91 241	83.90 183	88.10 203	63.84 270	88.37 160	84.05 357	71.45 231	76.78 259	89.12 223	49.93 358	94.89 108	70.18 254	83.18 273	92.96 155

viewdifsd2359ckpt11		80.37 205	79.73 196	82.30 248	83.70 350	62.39 310	84.20 309	86.67 317	73.22 198	80.90 172	90.62 177	63.00 191	91.56 280	76.81 175	78.44 331	92.95 156

viewmsd2359difaftdt		80.37 205	79.73 196	82.30 248	83.70 350	62.39 310	84.20 309	86.67 317	73.22 198	80.90 172	90.62 177	63.00 191	91.56 280	76.81 175	78.44 331	92.95 156

test9_res														84.90 64	95.70 29	92.87 158

viewmambaseed2359dif		80.41 201	79.84 193	82.12 251	82.95 377	62.50 309	83.39 331	88.06 275	67.11 331	80.98 170	90.31 188	66.20 150	91.01 311	74.62 200	84.90 235	92.86 159

AstraMVS		80.81 184	80.14 185	82.80 230	86.05 293	63.96 266	86.46 237	85.90 333	73.71 179	80.85 175	90.56 180	54.06 299	91.57 279	79.72 134	83.97 253	92.86 159

SR-MVS		86.73 43	86.67 48	86.91 56	94.11 41	72.11 49	92.37 33	92.56 82	74.50 156	86.84 65	94.65 31	67.31 133	95.77 65	84.80 68	92.85 78	92.84 161

ETV-MVS		84.90 90	84.67 90	85.59 88	89.39 144	68.66 128	88.74 141	92.64 79	79.97 17	84.10 106	85.71 323	69.32 101	95.38 83	80.82 114	91.37 106	92.72 162

agg_prior2														82.91 91	95.45 32	92.70 163

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 36	72.39 41	91.86 45	92.83 66	73.01 203	88.58 35	94.52 32	73.36 39	96.49 43	84.26 75	95.01 40	92.70 163
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 249	76.63 280	84.64 127	86.73 275	69.47 103	85.01 283	84.61 348	69.54 287	66.51 427	86.59 302	50.16 352	91.75 271	76.26 180	84.24 250	92.69 165

Vis-MVSNet (Re-imp)		78.36 256	78.45 228	78.07 359	88.64 180	51.78 453	86.70 227	79.63 425	74.14 169	75.11 307	90.83 170	61.29 224	89.75 344	58.10 381	91.60 100	92.69 165

TSAR-MVS + GP.		85.71 70	85.33 79	86.84 57	91.34 89	72.50 36	89.07 125	87.28 297	76.41 96	85.80 72	90.22 193	74.15 36	95.37 86	81.82 104	91.88 95	92.65 167

test_fmvsmvis_n_1920		84.02 102	83.87 104	84.49 136	84.12 338	69.37 109	88.15 170	87.96 279	70.01 274	83.95 110	93.23 87	68.80 115	91.51 287	88.61 32	89.96 133	92.57 168

FA-MVS(test-final)		80.96 180	79.91 190	84.10 160	88.30 193	65.01 236	84.55 296	90.01 194	73.25 196	79.61 194	87.57 271	58.35 257	94.72 118	71.29 240	86.25 211	92.56 169

guyue		81.13 176	80.64 171	82.60 241	86.52 281	63.92 269	86.69 228	87.73 287	73.97 171	80.83 176	89.69 205	56.70 274	91.33 296	78.26 157	85.40 231	92.54 170

dtuplus		80.04 213	79.40 206	81.97 257	83.08 367	62.61 305	83.63 324	87.98 277	67.47 329	81.02 169	90.50 183	64.86 167	90.77 324	71.28 241	84.76 238	92.53 171

test_yl		81.17 174	80.47 176	83.24 205	89.13 158	63.62 274	86.21 249	89.95 196	72.43 213	81.78 155	89.61 209	57.50 265	93.58 171	70.75 245	86.90 197	92.52 172

DCV-MVSNet		81.17 174	80.47 176	83.24 205	89.13 158	63.62 274	86.21 249	89.95 196	72.43 213	81.78 155	89.61 209	57.50 265	93.58 171	70.75 245	86.90 197	92.52 172

SR-MVS-dyc-post		85.77 68	85.61 73	86.23 68	93.06 64	70.63 83	91.88 43	92.27 96	73.53 186	85.69 74	94.45 37	65.00 166	95.56 69	82.75 95	91.87 96	92.50 174

RE-MVS-def			85.48 76		93.06 64	70.63 83	91.88 43	92.27 96	73.53 186	85.69 74	94.45 37	63.87 176		82.75 95	91.87 96	92.50 174

nrg030		83.88 107	83.53 116	84.96 111	86.77 274	69.28 110	90.46 75	92.67 74	74.79 150	82.95 133	91.33 152	72.70 52	93.09 213	80.79 116	79.28 324	92.50 174

SSM_0404		81.91 155	80.84 167	85.13 104	89.24 153	68.26 138	87.84 183	89.25 229	71.06 242	80.62 179	90.39 186	59.57 246	94.65 122	72.45 231	87.19 192	92.47 177

MG-MVS		83.41 125	83.45 117	83.28 202	92.74 72	62.28 315	88.17 168	89.50 213	75.22 132	81.49 160	92.74 105	66.75 139	95.11 95	72.85 221	91.58 102	92.45 178

FIs		82.07 152	82.42 137	81.04 282	88.80 173	58.34 372	88.26 165	93.49 31	76.93 77	78.47 219	91.04 163	69.92 92	92.34 249	69.87 259	84.97 234	92.44 179

testing3-2		75.12 324	75.19 306	74.91 399	90.40 110	45.09 484	80.29 387	78.42 435	78.37 41	76.54 267	87.75 265	44.36 411	87.28 386	57.04 391	83.49 266	92.37 180

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 212	87.08 265	65.21 229	89.09 124	90.21 188	79.67 20	89.98 24	95.02 24	73.17 43	91.71 274	91.30 3	91.60 100	92.34 181

FC-MVSNet-test		81.52 168	82.02 149	80.03 308	88.42 189	55.97 412	87.95 176	93.42 34	77.10 72	77.38 243	90.98 168	69.96 91	91.79 269	68.46 274	84.50 242	92.33 182

Fast-Effi-MVS+		80.81 184	79.92 189	83.47 194	88.85 165	64.51 254	85.53 270	89.39 217	70.79 249	78.49 217	85.06 343	67.54 130	93.58 171	67.03 288	86.58 203	92.32 183

TranMVSNet+NR-MVSNet		80.84 182	80.31 179	82.42 244	87.85 215	62.33 313	87.74 185	91.33 149	80.55 9	77.99 231	89.86 197	65.23 161	92.62 231	67.05 287	75.24 385	92.30 184

ab-mvs		79.51 222	78.97 219	81.14 279	88.46 186	60.91 341	83.84 316	89.24 231	70.36 264	79.03 204	88.87 234	63.23 184	90.21 336	65.12 301	82.57 281	92.28 185

CANet_DTU		80.61 194	79.87 192	82.83 227	85.60 302	63.17 294	87.36 201	88.65 264	76.37 101	75.88 281	88.44 247	53.51 304	93.07 214	73.30 215	89.74 138	92.25 186

UniMVSNet_NR-MVSNet		81.88 156	81.54 155	82.92 223	88.46 186	63.46 285	87.13 207	92.37 89	80.19 13	78.38 220	89.14 222	71.66 67	93.05 216	70.05 255	76.46 358	92.25 186

fmvsm_l_conf0.5_n		84.47 92	84.54 91	84.27 153	85.42 307	68.81 117	88.49 153	87.26 302	68.08 321	88.03 45	93.49 78	72.04 60	91.77 270	88.90 29	89.14 150	92.24 188

DU-MVS		81.12 177	80.52 174	82.90 224	87.80 218	63.46 285	87.02 212	91.87 124	79.01 32	78.38 220	89.07 224	65.02 164	93.05 216	70.05 255	76.46 358	92.20 189

NR-MVSNet		80.23 209	79.38 207	82.78 234	87.80 218	63.34 288	86.31 244	91.09 158	79.01 32	72.17 352	89.07 224	67.20 134	92.81 228	66.08 294	75.65 371	92.20 189

mamba_0408		79.37 231	77.52 257	84.93 114	88.81 169	67.96 151	65.03 488	88.66 262	70.96 246	79.48 197	89.80 201	58.69 252	94.65 122	70.35 251	85.93 220	92.18 191

SSM_04072		77.67 278	77.52 257	78.12 357	88.81 169	67.96 151	65.03 488	88.66 262	70.96 246	79.48 197	89.80 201	58.69 252	74.23 481	70.35 251	85.93 220	92.18 191

SSM_0407		81.58 165	80.48 175	84.87 118	88.81 169	67.96 151	87.37 200	89.25 229	71.06 242	79.48 197	90.39 186	59.57 246	94.48 129	72.45 231	85.93 220	92.18 191

TAPA-MVS	73.13 9	79.15 235	77.94 240	82.79 233	89.59 132	62.99 300	88.16 169	91.51 144	65.77 352	77.14 254	91.09 161	60.91 231	93.21 202	50.26 434	87.05 195	92.17 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a		84.13 99	84.16 96	84.06 169	85.38 308	68.40 134	88.34 161	86.85 314	67.48 328	87.48 56	93.40 83	70.89 76	91.61 275	88.38 37	89.22 147	92.16 195

3Dnovator	76.31 5	83.38 127	82.31 141	86.59 62	87.94 211	72.94 28	90.64 68	92.14 113	77.21 67	75.47 288	92.83 98	58.56 255	94.72 118	73.24 217	92.71 81	92.13 196

MVS_111021_HR		85.14 83	84.75 89	86.32 66	91.65 86	72.70 30	85.98 254	90.33 183	76.11 108	82.08 149	91.61 142	71.36 71	94.17 142	81.02 111	92.58 82	92.08 197

MVSFormer		82.85 140	82.05 148	85.24 98	87.35 245	70.21 87	90.50 72	90.38 179	68.55 314	81.32 162	89.47 214	61.68 213	93.46 189	78.98 145	90.26 127	92.05 198

jason		81.39 172	80.29 180	84.70 126	86.63 279	69.90 95	85.95 255	86.77 315	63.24 389	81.07 168	89.47 214	61.08 229	92.15 255	78.33 153	90.07 132	92.05 198
jason: jason.
HyFIR lowres test		77.53 280	75.40 299	83.94 182	89.59 132	66.62 191	80.36 385	88.64 265	56.29 457	76.45 268	85.17 340	57.64 263	93.28 195	61.34 350	83.10 274	91.91 200

XVG-OURS-SEG-HR		80.81 184	79.76 195	83.96 181	85.60 302	68.78 119	83.54 328	90.50 175	70.66 256	76.71 261	91.66 136	60.69 234	91.26 297	76.94 170	81.58 292	91.83 201

lupinMVS		81.39 172	80.27 181	84.76 124	87.35 245	70.21 87	85.55 268	86.41 323	62.85 396	81.32 162	88.61 241	61.68 213	92.24 253	78.41 152	90.26 127	91.83 201

WR-MVS		79.49 223	79.22 214	80.27 301	88.79 174	58.35 371	85.06 282	88.61 266	78.56 36	77.65 238	88.34 249	63.81 178	90.66 328	64.98 303	77.22 346	91.80 203

icg_test_0407_2		78.92 243	78.93 220	78.90 340	87.13 259	63.59 278	76.58 434	89.33 219	70.51 259	77.82 233	89.03 226	61.84 209	81.38 440	72.56 227	85.56 227	91.74 204

IMVS_0407		80.61 194	79.90 191	82.75 237	87.13 259	63.59 278	85.33 274	89.33 219	70.51 259	77.82 233	89.03 226	61.84 209	92.91 221	72.56 227	85.56 227	91.74 204

IMVS_0404		77.16 287	76.42 284	79.37 331	87.13 259	63.59 278	77.12 431	89.33 219	70.51 259	66.22 430	89.03 226	50.36 350	82.78 429	72.56 227	85.56 227	91.74 204

IMVS_0403		80.80 187	80.12 186	82.87 226	87.13 259	63.59 278	85.19 275	89.33 219	70.51 259	78.49 217	89.03 226	63.26 182	93.27 197	72.56 227	85.56 227	91.74 204

h-mvs33		83.15 133	82.19 144	86.02 78	90.56 106	70.85 80	88.15 170	89.16 234	76.02 110	84.67 89	91.39 150	61.54 216	95.50 74	82.71 97	75.48 375	91.72 208

UniMVSNet (Re)		81.60 164	81.11 161	83.09 212	88.38 190	64.41 259	87.60 187	93.02 51	78.42 38	78.56 215	88.16 255	69.78 94	93.26 198	69.58 262	76.49 357	91.60 209

UGNet		80.83 183	79.59 202	84.54 129	88.04 206	68.09 145	89.42 107	88.16 270	76.95 76	76.22 274	89.46 216	49.30 367	93.94 150	68.48 273	90.31 125	91.60 209
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		76.54 295	75.66 294	79.18 336	88.43 188	55.89 413	81.08 371	83.00 377	73.76 178	75.34 296	84.29 358	46.20 395	90.07 338	64.33 307	84.50 242	91.58 211

XVG-OURS		80.41 201	79.23 213	83.97 180	85.64 300	69.02 113	83.03 344	90.39 178	71.09 240	77.63 239	91.49 147	54.62 294	91.35 294	75.71 188	83.47 267	91.54 212

LCM-MVSNet-Re		77.05 288	76.94 270	77.36 373	87.20 256	51.60 454	80.06 390	80.46 412	75.20 135	67.69 406	86.72 294	62.48 198	88.98 360	63.44 313	89.25 145	91.51 213

DP-MVS Recon		83.11 136	82.09 147	86.15 72	94.44 23	70.92 78	88.79 136	92.20 106	70.53 258	79.17 203	91.03 165	64.12 174	96.03 56	68.39 275	90.14 129	91.50 214

PS-MVSNAJss		82.07 152	81.31 156	84.34 145	86.51 282	67.27 179	89.27 113	91.51 144	71.75 223	79.37 200	90.22 193	63.15 186	94.27 134	77.69 161	82.36 283	91.49 215

testing99		76.09 309	75.12 308	79.00 337	88.16 198	55.50 419	80.79 375	81.40 399	73.30 194	75.17 304	84.27 361	44.48 410	90.02 339	64.28 308	84.22 251	91.48 216

thisisatest0515		77.33 284	75.38 300	83.18 208	85.27 312	63.80 271	82.11 354	83.27 369	65.06 366	75.91 280	83.84 370	49.54 361	94.27 134	67.24 284	86.19 212	91.48 216

DPM-MVS		84.93 88	84.29 95	86.84 57	90.20 114	73.04 23	87.12 208	93.04 47	69.80 280	82.85 137	91.22 156	73.06 45	96.02 58	76.72 178	94.63 53	91.46 218

HQP_MVS		83.64 117	83.14 122	85.14 101	90.08 117	68.71 124	91.25 60	92.44 84	79.12 29	78.92 207	91.00 166	60.42 241	95.38 83	78.71 148	86.32 208	91.33 219

plane_prior5								92.44 84					95.38 83	78.71 148	86.32 208	91.33 219

GA-MVS		76.87 292	75.17 307	81.97 257	82.75 380	62.58 306	81.44 366	86.35 326	72.16 218	74.74 315	82.89 392	46.20 395	92.02 260	68.85 270	81.09 297	91.30 221

VPA-MVSNet		80.60 196	80.55 173	80.76 289	88.07 205	60.80 343	86.86 220	91.58 142	75.67 120	80.24 187	89.45 218	63.34 179	90.25 335	70.51 249	79.22 325	91.23 222

Effi-MVS+-dtu		80.03 214	78.57 226	84.42 139	85.13 317	68.74 122	88.77 137	88.10 272	74.99 141	74.97 312	83.49 381	57.27 268	93.36 193	73.53 211	80.88 300	91.18 223

v2v482		80.23 209	79.29 211	83.05 216	83.62 352	64.14 263	87.04 210	89.97 195	73.61 182	78.18 226	87.22 282	61.10 228	93.82 160	76.11 182	76.78 354	91.18 223

FE-MVS		77.78 272	75.68 292	84.08 165	88.09 204	66.00 203	83.13 338	87.79 285	68.42 318	78.01 230	85.23 338	45.50 404	95.12 93	59.11 369	85.83 224	91.11 225

Anonymous20231211		78.97 241	77.69 253	82.81 229	90.54 107	64.29 261	90.11 83	91.51 144	65.01 368	76.16 279	88.13 260	50.56 347	93.03 219	69.68 261	77.56 344	91.11 225

hse-mvs2		81.72 159	80.94 165	84.07 166	88.72 177	67.68 162	85.87 258	87.26 302	76.02 110	84.67 89	88.22 254	61.54 216	93.48 187	82.71 97	73.44 403	91.06 227

AUN-MVS		79.21 234	77.60 255	84.05 172	88.71 178	67.61 164	85.84 260	87.26 302	69.08 301	77.23 248	88.14 259	53.20 308	93.47 188	75.50 193	73.45 402	91.06 227

HQP4-MVS										77.24 247			95.11 95			91.03 229

HQP-MVS		82.61 144	82.02 149	84.37 142	89.33 146	66.98 186	89.17 117	92.19 108	76.41 96	77.23 248	90.23 192	60.17 244	95.11 95	77.47 163	85.99 218	91.03 229

RPSCF		73.23 352	71.46 352	78.54 348	82.50 386	59.85 358	82.18 353	82.84 382	58.96 435	71.15 364	89.41 220	45.48 405	84.77 413	58.82 373	71.83 415	91.02 231

LuminaMVS		80.68 192	79.62 201	83.83 184	85.07 319	68.01 150	86.99 213	88.83 249	70.36 264	81.38 161	87.99 262	50.11 353	92.51 240	79.02 142	86.89 199	90.97 232

test_djsdf		80.30 208	79.32 210	83.27 203	83.98 342	65.37 223	90.50 72	90.38 179	68.55 314	76.19 275	88.70 237	56.44 277	93.46 189	78.98 145	80.14 312	90.97 232

PCF-MVS	73.52 7	80.38 203	78.84 222	85.01 109	87.71 227	68.99 114	83.65 321	91.46 148	63.00 393	77.77 237	90.28 189	66.10 151	95.09 99	61.40 348	88.22 170	90.94 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 248	78.66 224	78.76 342	88.31 192	55.72 416	84.45 300	86.63 320	76.79 81	78.26 223	90.55 181	59.30 249	89.70 346	66.63 289	77.05 348	90.88 235

CPTT-MVS		83.73 113	83.33 121	84.92 115	93.28 53	70.86 79	92.09 41	90.38 179	68.75 311	79.57 195	92.83 98	60.60 239	93.04 218	80.92 113	91.56 103	90.86 236

fmvsm_s_conf0.5_n_7		83.34 128	84.03 102	81.28 274	85.73 298	65.13 232	85.40 273	89.90 198	74.96 144	82.13 148	93.89 69	66.65 140	87.92 377	86.56 53	91.05 111	90.80 237

tt0805		78.73 246	77.83 245	81.43 268	85.17 313	60.30 354	89.41 108	90.90 162	71.21 237	77.17 253	88.73 236	46.38 390	93.21 202	72.57 225	78.96 326	90.79 238

CLD-MVS		82.31 148	81.65 154	84.29 150	88.47 185	67.73 160	85.81 262	92.35 90	75.78 115	78.33 222	86.58 304	64.01 175	94.35 131	76.05 184	87.48 187	90.79 238
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 221	78.43 230	83.07 215	83.55 354	64.52 253	86.93 217	90.58 172	70.83 248	77.78 236	85.90 319	59.15 250	93.94 150	73.96 208	77.19 347	90.76 240

IterMVS-LS		80.06 212	79.38 207	82.11 253	85.89 294	63.20 292	86.79 223	89.34 218	74.19 167	75.45 291	86.72 294	66.62 141	92.39 245	72.58 224	76.86 351	90.75 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d28		73.62 339	73.53 329	73.90 413	88.20 195	47.41 474	78.06 421	79.37 427	74.29 165	73.98 326	84.29 358	44.67 407	83.54 423	51.47 424	87.39 188	90.74 242

EI-MVSNet		80.52 200	79.98 188	82.12 251	84.28 334	63.19 293	86.41 238	88.95 246	74.18 168	78.69 210	87.54 274	66.62 141	92.43 243	72.57 225	80.57 306	90.74 242

v1921920		79.22 233	78.03 238	82.80 230	83.30 359	63.94 268	86.80 222	90.33 183	69.91 278	77.48 241	85.53 330	58.44 256	93.75 166	73.60 210	76.85 352	90.71 244

QAPM		80.88 181	79.50 204	85.03 107	88.01 209	68.97 115	91.59 51	92.00 116	66.63 342	75.15 306	92.16 118	57.70 262	95.45 76	63.52 311	88.76 156	90.66 245

v144192		79.47 224	78.37 231	82.78 234	83.35 357	63.96 266	86.96 214	90.36 182	69.99 275	77.50 240	85.67 326	60.66 236	93.77 164	74.27 205	76.58 355	90.62 246

v1240		78.99 240	77.78 248	82.64 239	83.21 362	63.54 282	86.62 231	90.30 185	69.74 285	77.33 244	85.68 325	57.04 271	93.76 165	73.13 218	76.92 349	90.62 246

v1144		80.03 214	79.03 217	83.01 218	83.78 347	64.51 254	87.11 209	90.57 174	71.96 221	78.08 229	86.20 315	61.41 220	93.94 150	74.93 198	77.23 345	90.60 248

1112_ss		77.40 283	76.43 283	80.32 300	89.11 162	60.41 353	83.65 321	87.72 288	62.13 408	73.05 338	86.72 294	62.58 197	89.97 340	62.11 339	80.80 302	90.59 249

CP-MVSNet		78.22 258	78.34 232	77.84 363	87.83 217	54.54 430	87.94 177	91.17 154	77.65 48	73.48 333	88.49 245	62.24 204	88.43 371	62.19 336	74.07 394	90.55 250

testing222		74.04 334	72.66 340	78.19 355	87.89 213	55.36 420	81.06 372	79.20 430	71.30 235	74.65 318	83.57 380	39.11 447	88.67 367	51.43 426	85.75 225	90.53 251

PS-CasMVS		78.01 267	78.09 237	77.77 365	87.71 227	54.39 432	88.02 173	91.22 151	77.50 56	73.26 335	88.64 240	60.73 232	88.41 372	61.88 341	73.88 398	90.53 251

CDS-MVSNet		79.07 238	77.70 252	83.17 209	87.60 234	68.23 142	84.40 305	86.20 328	67.49 327	76.36 271	86.54 306	61.54 216	90.79 321	61.86 342	87.33 189	90.49 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 244	77.51 259	83.03 217	87.80 218	67.79 159	84.72 289	85.05 344	67.63 324	76.75 260	87.70 267	62.25 203	90.82 320	58.53 376	87.13 194	90.49 253

PEN-MVS		77.73 273	77.69 253	77.84 363	87.07 267	53.91 435	87.91 179	91.18 153	77.56 53	73.14 337	88.82 235	61.23 225	89.17 356	59.95 359	72.37 409	90.43 255

Test_1112_low_res		76.40 304	75.44 297	79.27 333	89.28 151	58.09 374	81.69 361	87.07 308	59.53 430	72.48 347	86.67 299	61.30 223	89.33 351	60.81 354	80.15 311	90.41 256

HY-MVS	69.67 12	77.95 268	77.15 265	80.36 298	87.57 243	60.21 356	83.37 333	87.78 286	66.11 346	75.37 295	87.06 289	63.27 181	90.48 330	61.38 349	82.43 282	90.40 257

sc_t1		72.19 368	69.51 380	80.23 303	84.81 323	61.09 335	84.68 290	80.22 419	60.70 418	71.27 361	83.58 379	36.59 459	89.24 354	60.41 355	63.31 458	90.37 258

CHOSEN 1792x2688		77.63 279	75.69 291	83.44 196	89.98 123	68.58 130	78.70 411	87.50 292	56.38 456	75.80 283	86.84 290	58.67 254	91.40 293	61.58 346	85.75 225	90.34 259

SDMVSNet		80.38 203	80.18 182	80.99 283	89.03 163	64.94 242	80.45 384	89.40 216	75.19 136	76.61 265	89.98 195	60.61 238	87.69 381	76.83 174	83.55 264	90.33 260

sd_testset		77.70 276	77.40 260	78.60 345	89.03 163	60.02 357	79.00 406	85.83 334	75.19 136	76.61 265	89.98 195	54.81 287	85.46 406	62.63 329	83.55 264	90.33 260

114514_t		80.68 192	79.51 203	84.20 157	94.09 42	67.27 179	89.64 96	91.11 157	58.75 439	74.08 325	90.72 173	58.10 258	95.04 101	69.70 260	89.42 144	90.30 262

eth_miper_zixun_eth		77.92 269	76.69 278	81.61 265	83.00 371	61.98 320	83.15 337	89.20 233	69.52 288	74.86 314	84.35 357	61.76 212	92.56 236	71.50 238	72.89 407	90.28 263

PVSNet_Blended_VisFu		82.62 143	81.83 153	84.96 111	90.80 102	69.76 98	88.74 141	91.70 135	69.39 289	78.96 205	88.46 246	65.47 159	94.87 110	74.42 203	88.57 159	90.24 264

MVS_111021_LR		82.61 144	82.11 145	84.11 159	88.82 168	71.58 58	85.15 278	86.16 329	74.69 152	80.47 184	91.04 163	62.29 202	90.55 329	80.33 123	90.08 131	90.20 265

MSLP-MVS++		85.43 76	85.76 70	84.45 137	91.93 82	70.24 86	90.71 67	92.86 64	77.46 57	84.22 103	92.81 100	67.16 135	92.94 220	80.36 121	94.35 62	90.16 266

mvs_tets		79.13 236	77.77 249	83.22 207	84.70 326	66.37 195	89.17 117	90.19 189	69.38 290	75.40 293	89.46 216	44.17 413	93.15 209	76.78 177	80.70 304	90.14 267

BH-RMVSNet		79.61 219	78.44 229	83.14 210	89.38 145	65.93 205	84.95 285	87.15 305	73.56 184	78.19 225	89.79 203	56.67 275	93.36 193	59.53 364	86.74 201	90.13 268

c3_l		78.75 245	77.91 241	81.26 275	82.89 378	61.56 327	84.09 313	89.13 237	69.97 276	75.56 286	84.29 358	66.36 146	92.09 257	73.47 213	75.48 375	90.12 269

v7n		78.97 241	77.58 256	83.14 210	83.45 356	65.51 218	88.32 162	91.21 152	73.69 180	72.41 348	86.32 312	57.93 259	93.81 161	69.18 265	75.65 371	90.11 270

jajsoiax		79.29 232	77.96 239	83.27 203	84.68 327	66.57 193	89.25 114	90.16 190	69.20 298	75.46 290	89.49 213	45.75 401	93.13 211	76.84 173	80.80 302	90.11 270

v148		78.72 247	77.80 247	81.47 267	82.73 381	61.96 321	86.30 245	88.08 273	73.26 195	76.18 276	85.47 332	62.46 199	92.36 247	71.92 235	73.82 399	90.09 272

GBi-Net		78.40 254	77.40 260	81.40 270	87.60 234	63.01 296	88.39 157	89.28 225	71.63 225	75.34 296	87.28 278	54.80 288	91.11 303	62.72 325	79.57 316	90.09 272

test1		78.40 254	77.40 260	81.40 270	87.60 234	63.01 296	88.39 157	89.28 225	71.63 225	75.34 296	87.28 278	54.80 288	91.11 303	62.72 325	79.57 316	90.09 272

FMVSNet1		77.44 281	76.12 288	81.40 270	86.81 272	63.01 296	88.39 157	89.28 225	70.49 263	74.39 322	87.28 278	49.06 371	91.11 303	60.91 352	78.52 329	90.09 272

WR-MVS_H		78.51 253	78.49 227	78.56 347	88.02 207	56.38 406	88.43 154	92.67 74	77.14 69	73.89 327	87.55 273	66.25 148	89.24 354	58.92 371	73.55 401	90.06 276

DTE-MVSNet		76.99 289	76.80 273	77.54 372	86.24 286	53.06 445	87.52 189	90.66 170	77.08 73	72.50 346	88.67 239	60.48 240	89.52 348	57.33 388	70.74 421	90.05 277

v8		79.97 216	79.02 218	82.80 230	84.09 339	64.50 256	87.96 175	90.29 186	74.13 170	75.24 303	86.81 291	62.88 194	93.89 158	74.39 204	75.40 380	90.00 278

thres600view7		76.50 297	75.44 297	79.68 324	89.40 143	57.16 392	85.53 270	83.23 370	73.79 177	76.26 273	87.09 287	51.89 327	91.89 266	48.05 449	83.72 261	90.00 278

thres400		76.50 297	75.37 301	79.86 314	89.13 158	57.65 386	85.17 276	83.60 362	73.41 190	76.45 268	86.39 310	52.12 317	91.95 263	48.33 444	83.75 258	90.00 278

cl22		78.07 264	77.01 267	81.23 276	82.37 390	61.83 323	83.55 326	87.98 277	68.96 308	75.06 309	83.87 368	61.40 221	91.88 267	73.53 211	76.39 360	89.98 281

OPM-MVS		83.50 123	82.95 128	85.14 101	88.79 174	70.95 76	89.13 122	91.52 143	77.55 54	80.96 171	91.75 132	60.71 233	94.50 127	79.67 135	86.51 205	89.97 282
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 313	73.83 326	81.30 273	83.26 360	61.79 324	82.57 347	80.65 407	66.81 333	66.88 418	83.42 382	57.86 261	92.19 254	63.47 312	79.57 316	89.91 283

v10		79.74 218	78.67 223	82.97 222	84.06 340	64.95 239	87.88 181	90.62 171	73.11 200	75.11 307	86.56 305	61.46 219	94.05 146	73.68 209	75.55 373	89.90 284

MVSTER		79.01 239	77.88 244	82.38 245	83.07 368	64.80 248	84.08 314	88.95 246	69.01 305	78.69 210	87.17 285	54.70 292	92.43 243	74.69 199	80.57 306	89.89 285

ACMP	74.13 6	81.51 170	80.57 172	84.36 143	89.42 141	68.69 127	89.97 85	91.50 147	74.46 158	75.04 310	90.41 184	53.82 301	94.54 124	77.56 162	82.91 275	89.86 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 151	81.27 157	84.50 134	89.23 154	68.76 120	90.22 81	91.94 120	75.37 128	76.64 263	91.51 145	54.29 295	94.91 104	78.44 150	83.78 255	89.83 287

LGP-MVS_train				84.50 134	89.23 154	68.76 120		91.94 120	75.37 128	76.64 263	91.51 145	54.29 295	94.91 104	78.44 150	83.78 255	89.83 287

V42		79.38 230	78.24 235	82.83 227	81.10 411	65.50 219	85.55 268	89.82 199	71.57 229	78.21 224	86.12 317	60.66 236	93.18 208	75.64 189	75.46 377	89.81 289

MAR-MVS		81.84 157	80.70 168	85.27 97	91.32 90	71.53 59	89.82 88	90.92 161	69.77 282	78.50 216	86.21 314	62.36 201	94.52 126	65.36 299	92.05 93	89.77 290
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		77.72 274	76.76 275	80.58 293	82.48 388	60.48 351	83.09 340	87.86 283	69.22 296	74.38 323	85.24 337	62.10 206	91.53 285	71.09 242	75.40 380	89.74 291

cl____		77.72 274	76.76 275	80.58 293	82.49 387	60.48 351	83.09 340	87.87 282	69.22 296	74.38 323	85.22 339	62.10 206	91.53 285	71.09 242	75.41 379	89.73 292

miper_ehance_all_eth		78.59 251	77.76 250	81.08 281	82.66 383	61.56 327	83.65 321	89.15 235	68.87 309	75.55 287	83.79 372	66.49 144	92.03 258	73.25 216	76.39 360	89.64 293

anonymousdsp		78.60 250	77.15 265	82.98 221	80.51 417	67.08 184	87.24 206	89.53 212	65.66 354	75.16 305	87.19 284	52.52 310	92.25 252	77.17 167	79.34 323	89.61 294

FMVSNet2		78.20 260	77.21 264	81.20 277	87.60 234	62.89 303	87.47 191	89.02 241	71.63 225	75.29 302	87.28 278	54.80 288	91.10 306	62.38 333	79.38 322	89.61 294

baseline1		76.98 290	76.75 277	77.66 367	88.13 201	55.66 417	85.12 279	81.89 392	73.04 202	76.79 258	88.90 232	62.43 200	87.78 380	63.30 315	71.18 419	89.55 296

ETVMVS		72.25 367	71.05 361	75.84 385	87.77 223	51.91 450	79.39 399	74.98 457	69.26 294	73.71 329	82.95 390	40.82 436	86.14 396	46.17 457	84.43 247	89.47 297

FMVSNet3		77.88 270	76.85 272	80.97 285	86.84 271	62.36 312	86.52 235	88.77 252	71.13 238	75.34 296	86.66 300	54.07 298	91.10 306	62.72 325	79.57 316	89.45 298

SD_0403		74.65 327	74.77 311	74.29 407	86.20 288	47.42 473	83.71 319	85.12 341	69.30 292	68.50 396	87.95 263	59.40 248	86.05 397	49.38 438	83.35 269	89.40 299

miper_enhance_ethall		77.87 271	76.86 271	80.92 286	81.65 399	61.38 331	82.68 345	88.98 243	65.52 356	75.47 288	82.30 401	65.76 158	92.00 261	72.95 220	76.39 360	89.39 300

testing11		75.14 323	74.01 321	78.53 349	88.16 198	56.38 406	80.74 378	80.42 414	70.67 253	72.69 345	83.72 375	43.61 417	89.86 341	62.29 335	83.76 257	89.36 301

cascas		76.72 294	74.64 312	82.99 219	85.78 297	65.88 207	82.33 350	89.21 232	60.85 417	72.74 342	81.02 413	47.28 380	93.75 166	67.48 281	85.02 233	89.34 302

Fast-Effi-MVS+-dtu		78.02 266	76.49 281	82.62 240	83.16 366	66.96 188	86.94 216	87.45 294	72.45 210	71.49 360	84.17 365	54.79 291	91.58 277	67.61 279	80.31 309	89.30 303

IB-MVS	68.01 15	75.85 312	73.36 332	83.31 201	84.76 325	66.03 200	83.38 332	85.06 343	70.21 271	69.40 383	81.05 412	45.76 400	94.66 121	65.10 302	75.49 374	89.25 304
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		76.50 297	75.55 296	79.33 332	89.52 135	56.99 395	85.83 261	83.23 370	73.94 173	76.32 272	87.12 286	51.89 327	91.95 263	48.33 444	83.75 258	89.07 305

tfpn200view9		76.42 303	75.37 301	79.55 329	89.13 158	57.65 386	85.17 276	83.60 362	73.41 190	76.45 268	86.39 310	52.12 317	91.95 263	48.33 444	83.75 258	89.07 305

xiu_mvs_v1_base_debu		80.80 187	79.72 198	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 302	81.83 151	88.16 255	50.91 341	92.85 224	78.29 154	87.56 184	89.06 307

xiu_mvs_v1_base		80.80 187	79.72 198	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 302	81.83 151	88.16 255	50.91 341	92.85 224	78.29 154	87.56 184	89.06 307

xiu_mvs_v1_base_debi		80.80 187	79.72 198	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 302	81.83 151	88.16 255	50.91 341	92.85 224	78.29 154	87.56 184	89.06 307

EPNet_dtu		75.46 317	74.86 309	77.23 376	82.57 385	54.60 429	86.89 218	83.09 374	71.64 224	66.25 429	85.86 321	55.99 280	88.04 376	54.92 406	86.55 204	89.05 310
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 286	76.68 279	78.93 339	84.22 336	58.62 369	86.41 238	88.36 269	71.37 232	73.31 334	88.01 261	61.22 226	89.15 357	64.24 309	73.01 406	89.03 311

PVSNet_Blended		80.98 179	80.34 178	82.90 224	88.85 165	65.40 220	84.43 302	92.00 116	67.62 325	78.11 227	85.05 344	66.02 154	94.27 134	71.52 236	89.50 142	89.01 312

PAPM		77.68 277	76.40 285	81.51 266	87.29 255	61.85 322	83.78 317	89.59 210	64.74 370	71.23 362	88.70 237	62.59 196	93.66 170	52.66 418	87.03 196	89.01 312

WTY-MVS		75.65 314	75.68 292	75.57 389	86.40 284	56.82 397	77.92 424	82.40 385	65.10 365	76.18 276	87.72 266	63.13 189	80.90 443	60.31 357	81.96 287	89.00 314

无先验							87.48 190	88.98 243	60.00 425				94.12 143	67.28 283		88.97 315

GSMVS																88.96 316

sam_mvs1												51.32 334				88.96 316

SCA		74.22 331	72.33 344	79.91 312	84.05 341	62.17 316	79.96 393	79.29 429	66.30 345	72.38 349	80.13 425	51.95 323	88.60 368	59.25 367	77.67 343	88.96 316

miper_lstm_enhance		74.11 333	73.11 335	77.13 377	80.11 422	59.62 361	72.23 459	86.92 313	66.76 335	70.40 368	82.92 391	56.93 272	82.92 428	69.06 267	72.63 408	88.87 319

ACMM	73.20 8	80.78 191	79.84 193	83.58 192	89.31 149	68.37 135	89.99 84	91.60 141	70.28 268	77.25 246	89.66 207	53.37 306	93.53 179	74.24 206	82.85 276	88.85 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 326	73.39 330	78.61 344	81.38 406	57.48 389	86.64 230	87.95 280	64.99 369	70.18 371	86.61 301	50.43 349	89.52 348	62.12 338	70.18 424	88.83 321

原ACMM1				84.35 144	93.01 66	68.79 118		92.44 84	63.96 384	81.09 167	91.57 143	66.06 153	95.45 76	67.19 285	94.82 49	88.81 322

CNLPA		78.08 263	76.79 274	81.97 257	90.40 110	71.07 72	87.59 188	84.55 349	66.03 349	72.38 349	89.64 208	57.56 264	86.04 398	59.61 363	83.35 269	88.79 323

UWE-MVS		72.13 369	71.49 351	74.03 411	86.66 278	47.70 471	81.40 367	76.89 449	63.60 387	75.59 285	84.22 362	39.94 440	85.62 403	48.98 441	86.13 214	88.77 324

UBG		73.08 354	72.27 345	75.51 391	88.02 207	51.29 458	78.35 418	77.38 444	65.52 356	73.87 328	82.36 399	45.55 402	86.48 393	55.02 405	84.39 248	88.75 325

K. test v3		71.19 374	68.51 387	79.21 335	83.04 370	57.78 384	84.35 306	76.91 448	72.90 205	62.99 453	82.86 393	39.27 444	91.09 308	61.65 345	52.66 482	88.75 325

旧先验1					91.96 81	65.79 212		86.37 325			93.08 93	69.31 102			92.74 80	88.74 327

PatchmatchNet		73.12 353	71.33 355	78.49 351	83.18 364	60.85 342	79.63 396	78.57 434	64.13 378	71.73 356	79.81 430	51.20 339	85.97 399	57.40 387	76.36 365	88.66 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 345	71.26 358	79.70 323	85.08 318	57.89 380	85.57 264	83.56 364	71.03 244	65.66 433	85.88 320	42.10 427	92.57 235	59.11 369	63.34 457	88.65 329

SSC-MVS3.2		73.35 348	73.39 330	73.23 417	85.30 311	49.01 469	74.58 451	81.57 396	75.21 134	73.68 330	85.58 329	52.53 309	82.05 435	54.33 410	77.69 342	88.63 330

PS-MVSNAJ		81.69 161	81.02 163	83.70 188	89.51 136	68.21 143	84.28 307	90.09 192	70.79 249	81.26 166	85.62 328	63.15 186	94.29 132	75.62 190	88.87 153	88.59 331

xiu_mvs_v2_base		81.69 161	81.05 162	83.60 190	89.15 157	68.03 149	84.46 299	90.02 193	70.67 253	81.30 165	86.53 307	63.17 185	94.19 141	75.60 191	88.54 160	88.57 332

MonoMVSNet		76.49 300	75.80 289	78.58 346	81.55 402	58.45 370	86.36 243	86.22 327	74.87 149	74.73 316	83.73 374	51.79 330	88.73 365	70.78 244	72.15 412	88.55 333

CostFormer		75.24 322	73.90 324	79.27 333	82.65 384	58.27 373	80.80 374	82.73 383	61.57 412	75.33 300	83.13 387	55.52 283	91.07 309	64.98 303	78.34 336	88.45 334

lessismore_v0				78.97 338	81.01 412	57.15 393		65.99 484		61.16 460	82.82 394	39.12 446	91.34 295	59.67 362	46.92 489	88.43 335

OpenMVS	72.83 10	79.77 217	78.33 233	84.09 164	85.17 313	69.91 94	90.57 69	90.97 160	66.70 336	72.17 352	91.91 124	54.70 292	93.96 147	61.81 343	90.95 115	88.41 336

usedtu_dtu_shiyan1		76.43 301	75.32 303	79.76 319	83.00 371	60.72 344	81.74 358	88.76 256	68.99 306	72.98 339	84.19 363	56.41 278	90.27 332	62.39 331	79.40 320	88.31 337

FE-MVSNET3		76.43 301	75.32 303	79.76 319	83.00 371	60.72 344	81.74 358	88.76 256	68.99 306	72.98 339	84.19 363	56.41 278	90.27 332	62.39 331	79.40 320	88.31 337

reproduce_monomvs		75.40 320	74.38 318	78.46 352	83.92 344	57.80 383	83.78 317	86.94 311	73.47 188	72.25 351	84.47 352	38.74 448	89.27 353	75.32 195	70.53 422	88.31 337

VortexMVS		78.57 252	77.89 243	80.59 292	85.89 294	62.76 304	85.61 263	89.62 209	72.06 219	74.99 311	85.38 334	55.94 281	90.77 324	74.99 197	76.58 355	88.23 340

OurMVSNet-221017-0		74.26 330	72.42 343	79.80 316	83.76 348	59.59 362	85.92 257	86.64 319	66.39 344	66.96 417	87.58 270	39.46 443	91.60 276	65.76 297	69.27 427	88.22 341

LS3D		76.95 291	74.82 310	83.37 200	90.45 108	67.36 175	89.15 121	86.94 311	61.87 411	69.52 382	90.61 179	51.71 331	94.53 125	46.38 456	86.71 202	88.21 342

WBMVS		73.43 342	72.81 338	75.28 395	87.91 212	50.99 460	78.59 414	81.31 401	65.51 358	74.47 321	84.83 347	46.39 389	86.68 390	58.41 377	77.86 338	88.17 343

XVG-ACMP-BASELINE		76.11 308	74.27 320	81.62 263	83.20 363	64.67 250	83.60 325	89.75 204	69.75 283	71.85 355	87.09 287	32.78 468	92.11 256	69.99 257	80.43 308	88.09 344

gbinet_0.2-2-1-0.02		73.24 351	70.86 366	80.39 296	78.03 447	61.62 326	83.10 339	86.69 316	65.98 350	69.29 386	76.15 462	49.77 359	91.51 287	62.75 324	66.00 444	88.03 345

tpm2		73.26 350	71.46 352	78.63 343	83.34 358	56.71 400	80.65 380	80.40 415	56.63 455	73.55 332	82.02 406	51.80 329	91.24 298	56.35 399	78.42 334	87.95 346

MDTV_nov1_ep13_2view						37.79 498	75.16 445		55.10 461	66.53 424		49.34 365		53.98 411		87.94 347

Patchmatch-test		64.82 432	63.24 433	69.57 445	79.42 434	49.82 466	63.49 492	69.05 477	51.98 471	59.95 466	80.13 425	50.91 341	70.98 487	40.66 475	73.57 400	87.90 348

PLC	70.83 11	78.05 265	76.37 286	83.08 214	91.88 84	67.80 158	88.19 167	89.46 214	64.33 377	69.87 379	88.38 248	53.66 302	93.58 171	58.86 372	82.73 278	87.86 349
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 364	71.71 349	74.35 406	82.19 391	52.00 448	79.22 402	77.29 445	64.56 372	72.95 341	83.68 377	51.35 333	83.26 427	58.33 379	75.80 369	87.81 350

Patchmatch-RL test		70.24 388	67.78 402	77.61 369	77.43 454	59.57 363	71.16 463	70.33 471	62.94 395	68.65 391	72.77 474	50.62 346	85.49 405	69.58 262	66.58 441	87.77 351

F-COLMAP		76.38 305	74.33 319	82.50 243	89.28 151	66.95 189	88.41 156	89.03 240	64.05 381	66.83 419	88.61 241	46.78 386	92.89 222	57.48 385	78.55 328	87.67 352

Baseline_NR-MVSNet		78.15 262	78.33 233	77.61 369	85.79 296	56.21 410	86.78 224	85.76 335	73.60 183	77.93 232	87.57 271	65.02 164	88.99 359	67.14 286	75.33 382	87.63 353

CL-MVSNet_self_test		72.37 364	71.46 352	75.09 397	79.49 433	53.53 437	80.76 377	85.01 345	69.12 300	70.51 366	82.05 405	57.92 260	84.13 417	52.27 420	66.00 444	87.60 354

ACMH+	68.96 14	76.01 310	74.01 321	82.03 255	88.60 181	65.31 228	88.86 131	87.55 290	70.25 270	67.75 405	87.47 276	41.27 432	93.19 207	58.37 378	75.94 368	87.60 354

1314		76.53 296	75.30 305	80.21 304	83.93 343	62.32 314	84.66 291	88.81 250	60.23 422	70.16 373	84.07 367	55.30 285	90.73 327	67.37 282	83.21 272	87.59 356

blended_shiyan6		73.38 343	71.17 359	80.01 310	78.36 442	61.48 330	82.43 348	87.27 300	65.40 360	68.56 394	77.55 449	51.94 325	91.01 311	63.27 317	65.76 446	87.55 357

blended_shiyan8		73.38 343	71.17 359	80.02 309	78.36 442	61.51 329	82.43 348	87.28 297	65.40 360	68.61 392	77.53 450	51.91 326	91.00 314	63.28 316	65.76 446	87.53 358

API-MVS		81.99 154	81.23 158	84.26 155	90.94 98	70.18 92	91.10 63	89.32 223	71.51 230	78.66 212	88.28 251	65.26 160	95.10 98	64.74 305	91.23 109	87.51 359

AdaColmap		80.58 199	79.42 205	84.06 169	93.09 63	68.91 116	89.36 111	88.97 245	69.27 293	75.70 284	89.69 205	57.20 270	95.77 65	63.06 320	88.41 164	87.50 360

0.4-1-1-0.1		70.93 378	67.94 397	79.91 312	79.35 435	61.27 332	78.95 408	82.19 389	63.36 388	67.50 408	69.40 481	39.83 442	91.04 310	62.44 330	68.40 433	87.40 361

PVSNet_BlendedMVS		80.60 196	80.02 187	82.36 247	88.85 165	65.40 220	86.16 251	92.00 116	69.34 291	78.11 227	86.09 318	66.02 154	94.27 134	71.52 236	82.06 286	87.39 362

sss		73.60 340	73.64 328	73.51 416	82.80 379	55.01 425	76.12 436	81.69 395	62.47 403	74.68 317	85.85 322	57.32 267	78.11 454	60.86 353	80.93 298	87.39 362

wanda-best-256-512		72.94 357	70.66 367	79.79 317	77.80 449	61.03 338	81.31 368	87.15 305	65.18 363	68.09 399	76.28 459	51.32 334	90.97 315	63.06 320	65.76 446	87.35 364

FE-blended-shiyan7		72.94 357	70.66 367	79.79 317	77.80 449	61.03 338	81.31 368	87.15 305	65.18 363	68.09 399	76.28 459	51.32 334	90.97 315	63.06 320	65.76 446	87.35 364

usedtu_blend_shiyan5		73.29 349	70.96 363	80.25 302	77.80 449	62.16 317	84.44 301	87.38 295	64.41 374	68.09 399	76.28 459	51.32 334	91.23 299	63.21 318	65.76 446	87.35 364

IterMVS-SCA-FT		75.43 318	73.87 325	80.11 307	82.69 382	64.85 247	81.57 363	83.47 366	69.16 299	70.49 367	84.15 366	51.95 323	88.15 374	69.23 264	72.14 413	87.34 367

PVSNet	64.34 18	72.08 370	70.87 365	75.69 387	86.21 287	56.44 404	74.37 453	80.73 406	62.06 409	70.17 372	82.23 403	42.86 421	83.31 426	54.77 407	84.45 246	87.32 368

tt0320-xc		70.11 390	67.45 408	78.07 359	85.33 310	59.51 364	83.28 334	78.96 432	58.77 437	67.10 416	80.28 423	36.73 458	87.42 384	56.83 395	59.77 471	87.29 369

新几何1				83.42 197	93.13 60	70.71 81		85.48 338	57.43 451	81.80 154	91.98 123	63.28 180	92.27 251	64.60 306	92.99 76	87.27 370

blend_shiyan4		72.29 366	69.65 379	80.21 304	78.24 445	62.16 317	82.29 351	87.27 300	65.41 359	68.43 398	76.42 458	39.91 441	91.23 299	63.21 318	65.66 451	87.22 371

TR-MVS		77.44 281	76.18 287	81.20 277	88.24 194	63.24 290	84.61 294	86.40 324	67.55 326	77.81 235	86.48 308	54.10 297	93.15 209	57.75 384	82.72 279	87.20 372

0.3-1-1-0.015		70.03 392	66.80 416	79.72 322	78.18 446	61.07 336	77.63 426	82.32 388	62.65 401	65.50 434	67.29 482	37.62 456	90.91 317	61.99 340	68.04 435	87.19 373

TransMVSNet (Re)		75.39 321	74.56 314	77.86 362	85.50 306	57.10 394	86.78 224	86.09 331	72.17 217	71.53 359	87.34 277	63.01 190	89.31 352	56.84 394	61.83 463	87.17 374

ACMH	67.68 16	75.89 311	73.93 323	81.77 261	88.71 178	66.61 192	88.62 147	89.01 242	69.81 279	66.78 420	86.70 298	41.95 429	91.51 287	55.64 401	78.14 337	87.17 374
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 403	67.59 406	72.46 427	74.29 468	45.45 479	77.93 423	87.00 309	63.12 390	63.99 448	78.99 439	42.32 424	84.77 413	56.55 398	64.09 456	87.16 376

EPMVS		69.02 402	68.16 391	71.59 432	79.61 431	49.80 467	77.40 428	66.93 482	62.82 398	70.01 374	79.05 435	45.79 399	77.86 456	56.58 397	75.26 384	87.13 377

CR-MVSNet		73.37 345	71.27 357	79.67 325	81.32 409	65.19 230	75.92 438	80.30 417	59.92 426	72.73 343	81.19 410	52.50 311	86.69 389	59.84 360	77.71 340	87.11 378

RPMNet		73.51 341	70.49 371	82.58 242	81.32 409	65.19 230	75.92 438	92.27 96	57.60 448	72.73 343	76.45 455	52.30 314	95.43 78	48.14 448	77.71 340	87.11 378

test_vis1_n_1920		75.52 316	75.78 290	74.75 403	79.84 426	57.44 390	83.26 335	85.52 337	62.83 397	79.34 202	86.17 316	45.10 406	79.71 447	78.75 147	81.21 296	87.10 380

tt0320		70.49 386	68.03 394	77.89 361	84.78 324	59.12 366	83.55 326	80.44 413	58.13 443	67.43 412	80.41 421	39.26 445	87.54 383	55.12 403	63.18 459	86.99 381

XXY-MVS		75.41 319	75.56 295	74.96 398	83.59 353	57.82 382	80.59 381	83.87 360	66.54 343	74.93 313	88.31 250	63.24 183	80.09 446	62.16 337	76.85 352	86.97 382

tpmrst		72.39 362	72.13 346	73.18 421	80.54 416	49.91 465	79.91 394	79.08 431	63.11 391	71.69 357	79.95 427	55.32 284	82.77 430	65.66 298	73.89 397	86.87 383

0.4-1-1-0.2		70.01 393	66.86 415	79.44 330	77.61 452	60.64 348	76.77 433	82.34 387	62.40 404	65.91 432	66.65 483	40.05 439	90.83 319	61.77 344	68.24 434	86.86 384

thres200		75.55 315	74.47 316	78.82 341	87.78 221	57.85 381	83.07 342	83.51 365	72.44 212	75.84 282	84.42 353	52.08 320	91.75 271	47.41 451	83.64 263	86.86 384

ITE_SJBPF				78.22 354	81.77 398	60.57 349		83.30 368	69.25 295	67.54 407	87.20 283	36.33 461	87.28 386	54.34 409	74.62 391	86.80 386

test222					91.50 87	68.26 138	84.16 311	83.20 373	54.63 463	79.74 192	91.63 139	58.97 251			91.42 104	86.77 387

MIMVSNet		70.69 382	69.30 381	74.88 400	84.52 331	56.35 408	75.87 440	79.42 426	64.59 371	67.76 404	82.41 398	41.10 433	81.54 438	46.64 455	81.34 293	86.75 388

BH-untuned		79.47 224	78.60 225	82.05 254	89.19 156	65.91 206	86.07 253	88.52 267	72.18 216	75.42 292	87.69 268	61.15 227	93.54 178	60.38 356	86.83 200	86.70 389

FE-MVSNET2		72.88 360	71.28 356	77.67 366	78.30 444	57.78 384	84.43 302	88.92 248	69.56 286	64.61 442	81.67 408	46.73 388	88.54 370	59.33 365	67.99 436	86.69 390

LTVRE_ROB	69.57 13	76.25 306	74.54 315	81.41 269	88.60 181	64.38 260	79.24 401	89.12 238	70.76 251	69.79 381	87.86 264	49.09 370	93.20 205	56.21 400	80.16 310	86.65 391
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				79.97 311	90.90 99	64.21 262		84.71 346	59.27 432	85.40 76	92.91 95	62.02 208	89.08 358	68.95 268	91.37 106	86.63 392

MIMVSNet1		68.58 406	66.78 417	73.98 412	80.07 423	51.82 452	80.77 376	84.37 350	64.40 375	59.75 467	82.16 404	36.47 460	83.63 421	42.73 469	70.33 423	86.48 393

tfpnnormal		74.39 328	73.16 334	78.08 358	86.10 292	58.05 375	84.65 293	87.53 291	70.32 267	71.22 363	85.63 327	54.97 286	89.86 341	43.03 468	75.02 387	86.32 394

D2MVS		74.82 325	73.21 333	79.64 326	79.81 427	62.56 308	80.34 386	87.35 296	64.37 376	68.86 389	82.66 396	46.37 391	90.10 337	67.91 277	81.24 295	86.25 395

tpm cat1		70.57 383	68.31 389	77.35 374	82.41 389	57.95 379	78.08 420	80.22 419	52.04 469	68.54 395	77.66 448	52.00 322	87.84 379	51.77 421	72.07 414	86.25 395

CVMVSNet		72.99 356	72.58 341	74.25 408	84.28 334	50.85 461	86.41 238	83.45 367	44.56 482	73.23 336	87.54 274	49.38 364	85.70 401	65.90 295	78.44 331	86.19 397

AllTest		70.96 377	68.09 393	79.58 327	85.15 315	63.62 274	84.58 295	79.83 422	62.31 405	60.32 464	86.73 292	32.02 469	88.96 362	50.28 432	71.57 417	86.15 398

TestCases				79.58 327	85.15 315	63.62 274		79.83 422	62.31 405	60.32 464	86.73 292	32.02 469	88.96 362	50.28 432	71.57 417	86.15 398

test-LLR		72.94 357	72.43 342	74.48 404	81.35 407	58.04 376	78.38 415	77.46 441	66.66 337	69.95 377	79.00 437	48.06 376	79.24 448	66.13 291	84.83 236	86.15 398

test-mter		71.41 373	70.39 374	74.48 404	81.35 407	58.04 376	78.38 415	77.46 441	60.32 421	69.95 377	79.00 437	36.08 462	79.24 448	66.13 291	84.83 236	86.15 398

IterMVS		74.29 329	72.94 337	78.35 353	81.53 403	63.49 284	81.58 362	82.49 384	68.06 322	69.99 376	83.69 376	51.66 332	85.54 404	65.85 296	71.64 416	86.01 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 293	74.57 313	83.42 197	93.29 52	69.46 105	88.55 151	83.70 361	63.98 383	70.20 370	88.89 233	54.01 300	94.80 114	46.66 453	81.88 289	86.01 402

ppachtmachnet_test		70.04 391	67.34 410	78.14 356	79.80 428	61.13 333	79.19 403	80.59 408	59.16 433	65.27 437	79.29 434	46.75 387	87.29 385	49.33 439	66.72 439	86.00 404

mmtdpeth		74.16 332	73.01 336	77.60 371	83.72 349	61.13 333	85.10 280	85.10 342	72.06 219	77.21 252	80.33 422	43.84 415	85.75 400	77.14 168	52.61 483	85.91 405

test_fmvs1_n		70.86 380	70.24 375	72.73 425	72.51 483	55.28 422	81.27 370	79.71 424	51.49 473	78.73 209	84.87 346	27.54 479	77.02 459	76.06 183	79.97 314	85.88 406

Patchmtry		70.74 381	69.16 384	75.49 392	80.72 413	54.07 434	74.94 449	80.30 417	58.34 440	70.01 374	81.19 410	52.50 311	86.54 391	53.37 415	71.09 420	85.87 407

dtuonly		69.95 394	69.98 377	69.85 444	73.09 479	49.46 468	74.55 452	76.40 451	57.56 450	67.82 403	86.31 313	50.89 345	74.23 481	61.46 347	81.71 291	85.86 408

WB-MVSnew		71.96 371	71.65 350	72.89 423	84.67 330	51.88 451	82.29 351	77.57 440	62.31 405	73.67 331	83.00 389	53.49 305	81.10 442	45.75 460	82.13 285	85.70 409

test_fmvs2		68.35 411	67.48 407	70.98 440	69.50 487	51.95 449	80.05 391	76.38 452	49.33 476	74.65 318	84.38 355	23.30 488	75.40 476	74.51 202	75.17 386	85.60 410

usedtu_dtu_shiyan2		64.75 433	61.63 441	74.10 410	70.64 485	53.18 444	82.10 355	81.27 402	56.22 458	56.39 478	74.67 469	27.94 478	83.56 422	42.71 470	62.73 460	85.57 411

ambc				75.24 396	73.16 477	50.51 463	63.05 493	87.47 293		64.28 444	77.81 447	17.80 494	89.73 345	57.88 383	60.64 468	85.49 412

mvs5depth		69.45 399	67.45 408	75.46 393	73.93 469	55.83 414	79.19 403	83.23 370	66.89 332	71.63 358	83.32 383	33.69 467	85.09 409	59.81 361	55.34 479	85.46 413

UnsupCasMVSNet_eth		67.33 416	65.99 420	71.37 434	73.48 474	51.47 456	75.16 445	85.19 340	65.20 362	60.78 461	80.93 417	42.35 423	77.20 458	57.12 389	53.69 481	85.44 414

PatchT		68.46 409	67.85 398	70.29 442	80.70 414	43.93 487	72.47 458	74.88 458	60.15 423	70.55 365	76.57 454	49.94 356	81.59 437	50.58 428	74.83 389	85.34 415

Anonymous20240521		68.80 404	67.22 412	73.55 415	74.33 467	54.11 433	83.18 336	85.61 336	58.15 442	61.68 458	80.94 415	30.71 474	81.27 441	57.00 392	73.34 405	85.28 416

test_cas_vis1_n_1920		73.76 338	73.74 327	73.81 414	75.90 459	59.77 359	80.51 382	82.40 385	58.30 441	81.62 159	85.69 324	44.35 412	76.41 465	76.29 179	78.61 327	85.23 417

ADS-MVSNet2		66.20 428	63.33 432	74.82 401	79.92 424	58.75 368	67.55 478	75.19 456	53.37 466	65.25 438	75.86 464	42.32 424	80.53 445	41.57 473	68.91 429	85.18 418

ADS-MVSNet		64.36 434	62.88 436	68.78 451	79.92 424	47.17 475	67.55 478	71.18 470	53.37 466	65.25 438	75.86 464	42.32 424	73.99 483	41.57 473	68.91 429	85.18 418

FMVSNet5		69.50 398	67.96 395	74.15 409	82.97 376	55.35 421	80.01 392	82.12 391	62.56 402	63.02 451	81.53 409	36.92 457	81.92 436	48.42 443	74.06 395	85.17 420

pmmvs5		71.55 372	70.20 376	75.61 388	77.83 448	56.39 405	81.74 358	80.89 403	57.76 446	67.46 410	84.49 351	49.26 368	85.32 408	57.08 390	75.29 383	85.11 421

testing3		68.56 407	67.67 404	71.22 438	87.33 250	42.87 489	83.06 343	71.54 469	70.36 264	69.08 388	84.38 355	30.33 475	85.69 402	37.50 481	75.45 378	85.09 422

UWE-MVS-28		65.32 429	64.93 423	66.49 460	78.70 439	38.55 497	77.86 425	64.39 489	62.00 410	64.13 446	83.60 378	41.44 430	76.00 469	31.39 488	80.89 299	84.92 423

CMPMVS	51.72 21	70.19 389	68.16 391	76.28 382	73.15 478	57.55 388	79.47 398	83.92 358	48.02 478	56.48 477	84.81 348	43.13 419	86.42 394	62.67 328	81.81 290	84.89 424
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 422	66.53 418	67.08 459	75.62 463	41.69 494	75.93 437	76.50 450	66.11 346	65.20 440	86.59 302	35.72 463	74.71 478	43.71 465	73.38 404	84.84 425

MSDG		73.36 347	70.99 362	80.49 295	84.51 332	65.80 211	80.71 379	86.13 330	65.70 353	65.46 435	83.74 373	44.60 408	90.91 317	51.13 427	76.89 350	84.74 426

pmmvs4		74.03 336	71.91 347	80.39 296	81.96 395	68.32 136	81.45 365	82.14 390	59.32 431	69.87 379	85.13 341	52.40 313	88.13 375	60.21 358	74.74 390	84.73 427

gg-mvs-nofinetune		69.95 394	67.96 395	75.94 384	83.07 368	54.51 431	77.23 430	70.29 472	63.11 391	70.32 369	62.33 486	43.62 416	88.69 366	53.88 412	87.76 182	84.62 428

test_fmvs1		70.93 378	70.52 370	72.16 428	73.71 471	55.05 424	80.82 373	78.77 433	51.21 474	78.58 214	84.41 354	31.20 473	76.94 460	75.88 187	80.12 313	84.47 429

BH-w/o		78.21 259	77.33 263	80.84 287	88.81 169	65.13 232	84.87 286	87.85 284	69.75 283	74.52 320	84.74 350	61.34 222	93.11 212	58.24 380	85.84 223	84.27 430

MVS		78.19 261	76.99 269	81.78 260	85.66 299	66.99 185	84.66 291	90.47 176	55.08 462	72.02 354	85.27 336	63.83 177	94.11 144	66.10 293	89.80 137	84.24 431

COLMAP_ROB	66.92 17	73.01 355	70.41 373	80.81 288	87.13 259	65.63 215	88.30 164	84.19 356	62.96 394	63.80 450	87.69 268	38.04 453	92.56 236	46.66 453	74.91 388	84.24 431
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 440	61.73 440	61.70 466	72.74 481	24.50 509	69.16 473	78.03 437	61.40 413	56.72 476	75.53 467	38.42 450	76.48 464	45.95 459	57.67 472	84.13 433

TESTMET0.1,1		69.89 396	69.00 385	72.55 426	79.27 437	56.85 396	78.38 415	74.71 461	57.64 447	68.09 399	77.19 452	37.75 454	76.70 461	63.92 310	84.09 252	84.10 434

test_fmvs3		63.36 437	61.82 439	67.98 456	62.51 496	46.96 477	77.37 429	74.03 463	45.24 481	67.50 408	78.79 440	12.16 500	72.98 486	72.77 223	66.02 443	83.99 435

our_test_3		69.14 401	67.00 413	75.57 389	79.80 428	58.80 367	77.96 422	77.81 438	59.55 429	62.90 454	78.25 444	47.43 378	83.97 418	51.71 422	67.58 438	83.93 436

test_vis1_n		69.85 397	69.21 383	71.77 431	72.66 482	55.27 423	81.48 364	76.21 453	52.03 470	75.30 301	83.20 386	28.97 476	76.22 467	74.60 201	78.41 335	83.81 437

tpmvs		71.09 376	69.29 382	76.49 381	82.04 393	56.04 411	78.92 409	81.37 400	64.05 381	67.18 415	78.28 443	49.74 360	89.77 343	49.67 437	72.37 409	83.67 438

test20.03		67.45 415	66.95 414	68.94 448	75.48 464	44.84 485	77.50 427	77.67 439	66.66 337	63.01 452	83.80 371	47.02 382	78.40 452	42.53 472	68.86 431	83.58 439

test0.0.03 1		68.00 413	67.69 403	68.90 449	77.55 453	47.43 472	75.70 441	72.95 468	66.66 337	66.56 423	82.29 402	48.06 376	75.87 471	44.97 464	74.51 392	83.41 440

Anonymous20231206		68.60 405	67.80 401	71.02 439	80.23 420	50.75 462	78.30 419	80.47 411	56.79 454	66.11 431	82.63 397	46.35 392	78.95 450	43.62 466	75.70 370	83.36 441

EU-MVSNet		68.53 408	67.61 405	71.31 437	78.51 441	47.01 476	84.47 297	84.27 354	42.27 485	66.44 428	84.79 349	40.44 437	83.76 419	58.76 374	68.54 432	83.17 442

dp		66.80 420	65.43 421	70.90 441	79.74 430	48.82 470	75.12 447	74.77 459	59.61 428	64.08 447	77.23 451	42.89 420	80.72 444	48.86 442	66.58 441	83.16 443

pmmvs-eth3d		70.50 385	67.83 400	78.52 350	77.37 455	66.18 198	81.82 356	81.51 397	58.90 436	63.90 449	80.42 420	42.69 422	86.28 395	58.56 375	65.30 453	83.11 444

YYNet1		65.03 430	62.91 435	71.38 433	75.85 461	56.60 402	69.12 474	74.66 462	57.28 452	54.12 481	77.87 446	45.85 398	74.48 479	49.95 435	61.52 466	83.05 445

MDA-MVSNet-bldmvs		66.68 421	63.66 431	75.75 386	79.28 436	60.56 350	73.92 455	78.35 436	64.43 373	50.13 487	79.87 429	44.02 414	83.67 420	46.10 458	56.86 473	83.03 446

MDA-MVSNet_test_wron		65.03 430	62.92 434	71.37 434	75.93 458	56.73 398	69.09 475	74.73 460	57.28 452	54.03 482	77.89 445	45.88 397	74.39 480	49.89 436	61.55 465	82.99 447

USDC		70.33 387	68.37 388	76.21 383	80.60 415	56.23 409	79.19 403	86.49 322	60.89 416	61.29 459	85.47 332	31.78 471	89.47 350	53.37 415	76.21 366	82.94 448

Syy-MVS		68.05 412	67.85 398	68.67 452	84.68 327	40.97 495	78.62 412	73.08 466	66.65 340	66.74 421	79.46 432	52.11 319	82.30 433	32.89 486	76.38 363	82.75 449

myMVS_eth3d		67.02 419	66.29 419	69.21 447	84.68 327	42.58 490	78.62 412	73.08 466	66.65 340	66.74 421	79.46 432	31.53 472	82.30 433	39.43 478	76.38 363	82.75 449

ttmdpeth		59.91 443	57.10 447	68.34 454	67.13 491	46.65 478	74.64 450	67.41 481	48.30 477	62.52 457	85.04 345	20.40 490	75.93 470	42.55 471	45.90 492	82.44 451

OpenMVS_ROB	64.09 19	70.56 384	68.19 390	77.65 368	80.26 418	59.41 365	85.01 283	82.96 379	58.76 438	65.43 436	82.33 400	37.63 455	91.23 299	45.34 463	76.03 367	82.32 452

JIA-IIPM		66.32 425	62.82 437	76.82 379	77.09 456	61.72 325	65.34 486	75.38 455	58.04 445	64.51 443	62.32 487	42.05 428	86.51 392	51.45 425	69.22 428	82.21 453

dmvs_re		71.14 375	70.58 369	72.80 424	81.96 395	59.68 360	75.60 442	79.34 428	68.55 314	69.27 387	80.72 418	49.42 363	76.54 462	52.56 419	77.79 339	82.19 454

EG-PatchMatch MVS		74.04 334	71.82 348	80.71 290	84.92 321	67.42 171	85.86 259	88.08 273	66.04 348	64.22 445	83.85 369	35.10 464	92.56 236	57.44 386	80.83 301	82.16 455

FE-MVSNET		67.25 418	65.33 422	73.02 422	75.86 460	52.54 446	80.26 389	80.56 409	63.80 386	60.39 462	79.70 431	41.41 431	84.66 415	43.34 467	62.62 461	81.86 456

MVP-Stereo		76.12 307	74.46 317	81.13 280	85.37 309	69.79 96	84.42 304	87.95 280	65.03 367	67.46 410	85.33 335	53.28 307	91.73 273	58.01 382	83.27 271	81.85 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 414	64.34 426	76.92 378	73.47 475	61.07 336	84.86 287	82.98 378	59.77 427	58.30 471	85.13 341	26.06 480	87.89 378	47.92 450	60.59 469	81.81 458

GG-mvs-BLEND				75.38 394	81.59 401	55.80 415	79.32 400	69.63 474		67.19 414	73.67 472	43.24 418	88.90 364	50.41 429	84.50 242	81.45 459

KD-MVS_2432*1600		66.22 426	63.89 429	73.21 418	75.47 465	53.42 439	70.76 466	84.35 351	64.10 379	66.52 425	78.52 441	34.55 465	84.98 410	50.40 430	50.33 486	81.23 460

miper_refine_blended		66.22 426	63.89 429	73.21 418	75.47 465	53.42 439	70.76 466	84.35 351	64.10 379	66.52 425	78.52 441	34.55 465	84.98 410	50.40 430	50.33 486	81.23 460

test_0402		72.79 361	70.44 372	79.84 315	88.13 201	65.99 204	85.93 256	84.29 353	65.57 355	67.40 413	85.49 331	46.92 383	92.61 232	35.88 483	74.38 393	80.94 462

MVStest1		56.63 447	52.76 453	68.25 455	61.67 497	53.25 443	71.67 461	68.90 479	38.59 490	50.59 486	83.05 388	25.08 482	70.66 488	36.76 482	38.56 493	80.83 463

UnsupCasMVSNet_bld		63.70 436	61.53 442	70.21 443	73.69 472	51.39 457	72.82 457	81.89 392	55.63 460	57.81 473	71.80 476	38.67 449	78.61 451	49.26 440	52.21 484	80.63 464

LCM-MVSNet		54.25 449	49.68 459	67.97 457	53.73 505	45.28 482	66.85 481	80.78 405	35.96 494	39.45 495	62.23 488	8.70 504	78.06 455	48.24 447	51.20 485	80.57 465

N_pmnet		52.79 454	53.26 452	51.40 480	78.99 438	7.68 523	69.52 470	3.89 522	51.63 472	57.01 475	74.98 468	40.83 435	65.96 495	37.78 480	64.67 454	80.56 466

TinyColmap		67.30 417	64.81 424	74.76 402	81.92 397	56.68 401	80.29 387	81.49 398	60.33 420	56.27 479	83.22 384	24.77 484	87.66 382	45.52 461	69.47 426	79.95 467

PM-MVS		66.41 424	64.14 427	73.20 420	73.92 470	56.45 403	78.97 407	64.96 488	63.88 385	64.72 441	80.24 424	19.84 492	83.44 425	66.24 290	64.52 455	79.71 468

ANet_high		50.57 458	46.10 462	63.99 463	48.67 508	39.13 496	70.99 465	80.85 404	61.39 414	31.18 497	57.70 494	17.02 495	73.65 485	31.22 489	15.89 507	79.18 469

LF4IMVS		64.02 435	62.19 438	69.50 446	70.90 484	53.29 442	76.13 435	77.18 446	52.65 468	58.59 469	80.98 414	23.55 487	76.52 463	53.06 417	66.66 440	78.68 470

dtuonlycased		68.45 410	67.29 411	71.92 429	80.18 421	54.90 426	79.76 395	80.38 416	60.11 424	62.57 456	76.44 457	49.34 365	82.31 432	55.05 404	61.77 464	78.53 471

PatchMatch-RL		72.38 363	70.90 364	76.80 380	88.60 181	67.38 174	79.53 397	76.17 454	62.75 399	69.36 384	82.00 407	45.51 403	84.89 412	53.62 413	80.58 305	78.12 472

MS-PatchMatch		73.83 337	72.67 339	77.30 375	83.87 345	66.02 201	81.82 356	84.66 347	61.37 415	68.61 392	82.82 394	47.29 379	88.21 373	59.27 366	84.32 249	77.68 473

DSMNet-mixed		57.77 446	56.90 448	60.38 468	67.70 489	35.61 499	69.18 472	53.97 500	32.30 498	57.49 474	79.88 428	40.39 438	68.57 493	38.78 479	72.37 409	76.97 474

CHOSEN 280x420		66.51 423	64.71 425	71.90 430	81.45 404	63.52 283	57.98 495	68.95 478	53.57 465	62.59 455	76.70 453	46.22 394	75.29 477	55.25 402	79.68 315	76.88 475

mvsany_test3		53.99 450	51.45 455	61.61 467	55.51 501	44.74 486	63.52 491	45.41 506	43.69 484	58.11 472	76.45 455	17.99 493	63.76 497	54.77 407	47.59 488	76.34 476

dmvs_testset		62.63 438	64.11 428	58.19 470	78.55 440	24.76 508	75.28 443	65.94 485	67.91 323	60.34 463	76.01 463	53.56 303	73.94 484	31.79 487	67.65 437	75.88 477

mvsany_test1		62.30 439	61.26 443	65.41 462	69.52 486	54.86 427	66.86 480	49.78 502	46.65 479	68.50 396	83.21 385	49.15 369	66.28 494	56.93 393	60.77 467	75.11 478

PMMVS		69.34 400	68.67 386	71.35 436	75.67 462	62.03 319	75.17 444	73.46 464	50.00 475	68.68 390	79.05 435	52.07 321	78.13 453	61.16 351	82.77 277	73.90 479

test_vis1_rt		60.28 442	58.42 445	65.84 461	67.25 490	55.60 418	70.44 468	60.94 494	44.33 483	59.00 468	66.64 484	24.91 483	68.67 492	62.80 323	69.48 425	73.25 480

pmmvs3		57.79 445	54.26 450	68.37 453	64.02 495	56.72 399	75.12 447	65.17 486	40.20 487	52.93 483	69.86 480	20.36 491	75.48 474	45.45 462	55.25 480	72.90 481

PVSNet_0	57.27 20	61.67 441	59.27 444	68.85 450	79.61 431	57.44 390	68.01 476	73.44 465	55.93 459	58.54 470	70.41 479	44.58 409	77.55 457	47.01 452	35.91 494	71.55 482

WB-MVS		54.94 448	54.72 449	55.60 476	73.50 473	20.90 511	74.27 454	61.19 493	59.16 433	50.61 485	74.15 470	47.19 381	75.78 472	17.31 503	35.07 495	70.12 483

SSC-MVS		53.88 451	53.59 451	54.75 478	72.87 480	19.59 512	73.84 456	60.53 495	57.58 449	49.18 489	73.45 473	46.34 393	75.47 475	16.20 506	32.28 497	69.20 484

test_f		52.09 455	50.82 456	55.90 474	53.82 504	42.31 493	59.42 494	58.31 498	36.45 493	56.12 480	70.96 478	12.18 499	57.79 500	53.51 414	56.57 475	67.60 485

PMMVS2		40.82 465	38.86 469	46.69 481	53.84 503	16.45 516	48.61 498	49.92 501	37.49 491	31.67 496	60.97 489	8.14 506	56.42 501	28.42 491	30.72 498	67.19 486

new_pmnet		50.91 457	50.29 457	52.78 479	68.58 488	34.94 501	63.71 490	56.63 499	39.73 488	44.95 490	65.47 485	21.93 489	58.48 499	34.98 484	56.62 474	64.92 487

MVS-HIRNet		59.14 444	57.67 446	63.57 464	81.65 399	43.50 488	71.73 460	65.06 487	39.59 489	51.43 484	57.73 493	38.34 451	82.58 431	39.53 476	73.95 396	64.62 488

APD_test1		53.31 453	49.93 458	63.42 465	65.68 492	50.13 464	71.59 462	66.90 483	34.43 495	40.58 494	71.56 477	8.65 505	76.27 466	34.64 485	55.36 478	63.86 489

test_method		31.52 468	29.28 472	38.23 485	27.03 517	6.50 525	20.94 508	62.21 492	4.05 513	22.35 506	52.50 500	13.33 497	47.58 504	27.04 493	34.04 496	60.62 490

EGC-MVSNET		52.07 456	47.05 460	67.14 458	83.51 355	60.71 346	80.50 383	67.75 480	0.07 539	0.43 540	75.85 466	24.26 485	81.54 438	28.82 490	62.25 462	59.16 491

test_vis3_rt		49.26 459	47.02 461	56.00 473	54.30 502	45.27 483	66.76 482	48.08 503	36.83 492	44.38 491	53.20 499	7.17 507	64.07 496	56.77 396	55.66 476	58.65 492

FPMVS		53.68 452	51.64 454	59.81 469	65.08 493	51.03 459	69.48 471	69.58 475	41.46 486	40.67 493	72.32 475	16.46 496	70.00 491	24.24 498	65.42 452	58.40 493

testf1		45.72 460	41.96 464	57.00 471	56.90 499	45.32 480	66.14 483	59.26 496	26.19 499	30.89 498	60.96 490	4.14 508	70.64 489	26.39 496	46.73 490	55.04 494

APD_test2		45.72 460	41.96 464	57.00 471	56.90 499	45.32 480	66.14 483	59.26 496	26.19 499	30.89 498	60.96 490	4.14 508	70.64 489	26.39 496	46.73 490	55.04 494

LoFTR		27.52 472	24.27 476	37.29 487	34.75 513	19.27 513	33.78 503	21.60 513	12.42 508	21.61 507	56.59 496	0.91 515	40.37 508	13.94 508	22.80 503	52.22 496

RoMa-SfM		28.67 471	25.38 475	38.54 484	32.61 514	22.48 510	40.24 499	7.23 518	21.81 503	26.66 501	60.46 492	0.96 514	41.72 507	26.47 495	11.95 510	51.40 497

PMVS	37.38 22	44.16 464	40.28 468	55.82 475	40.82 510	42.54 492	65.12 487	63.99 490	34.43 495	24.48 502	57.12 495	3.92 510	76.17 468	17.10 504	55.52 477	48.75 498
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 470	25.89 474	43.81 483	44.55 509	35.46 500	28.87 507	39.07 507	18.20 506	18.58 509	40.18 505	2.68 511	47.37 505	17.07 505	23.78 502	48.60 499
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DKM		25.67 473	23.01 477	33.64 490	32.08 515	19.25 514	37.50 501	5.52 519	18.67 504	23.58 505	55.44 498	0.64 518	34.02 509	23.95 499	9.73 511	47.66 500

dongtai		45.42 462	45.38 463	45.55 482	73.36 476	26.85 506	67.72 477	34.19 508	54.15 464	49.65 488	56.41 497	25.43 481	62.94 498	19.45 501	28.09 499	46.86 501

PDCNetPlus		24.75 474	22.46 478	31.64 491	35.53 512	17.00 515	32.00 505	9.46 515	18.43 505	18.56 510	51.31 501	1.65 512	33.00 511	26.51 494	8.70 513	44.91 502

kuosan		39.70 466	40.40 467	37.58 486	64.52 494	26.98 504	65.62 485	33.02 509	46.12 480	42.79 492	48.99 502	24.10 486	46.56 506	12.16 510	26.30 500	39.20 503

Gipuma		45.18 463	41.86 466	55.16 477	77.03 457	51.52 455	32.50 504	80.52 410	32.46 497	27.12 500	35.02 507	9.52 503	75.50 473	22.31 500	60.21 470	38.45 504
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MatchFormer		22.13 475	19.86 480	28.93 492	28.66 516	15.74 517	31.91 506	17.10 514	7.75 509	18.87 508	47.50 504	0.62 520	33.92 510	7.49 513	18.87 504	37.14 505

ELoFTR		14.23 479	11.56 482	22.24 494	11.02 522	6.56 524	13.59 511	7.57 517	5.55 511	11.96 514	39.09 506	0.21 529	24.93 513	9.43 512	5.66 518	35.22 506

GLUNet-SfM		12.90 480	10.00 483	21.62 495	13.58 521	8.30 521	10.19 513	9.30 516	4.31 512	12.18 513	30.90 509	0.50 524	22.76 515	4.89 514	4.14 524	33.79 507

DeepMVS_CX				27.40 493	40.17 511	26.90 505		24.59 512	17.44 507	23.95 503	48.61 503	9.77 502	26.48 512	18.06 502	24.47 501	28.83 508

E-PMN		31.77 467	30.64 470	35.15 488	52.87 506	27.67 503	57.09 496	47.86 504	24.64 501	16.40 511	33.05 508	11.23 501	54.90 502	14.46 507	18.15 505	22.87 509

EMVS		30.81 469	29.65 471	34.27 489	50.96 507	25.95 507	56.58 497	46.80 505	24.01 502	15.53 512	30.68 510	12.47 498	54.43 503	12.81 509	17.05 506	22.43 510

ALIKED-LG		8.61 481	8.70 485	8.33 498	20.63 518	8.70 520	15.50 509	4.61 520	2.19 514	5.84 516	18.70 512	0.80 516	8.06 517	1.03 522	8.97 512	8.25 511

SP-MNN		4.14 492	4.24 495	3.82 503	10.32 525	1.83 539	8.11 516	1.99 529	0.82 523	2.23 524	8.27 521	0.47 526	2.14 522	1.20 520	4.77 522	7.49 512

SP-LightGlue		4.27 490	4.41 493	3.86 502	10.99 523	1.99 535	8.19 514	2.06 528	0.98 521	2.37 523	8.29 519	0.56 522	2.10 523	1.27 518	4.99 520	7.48 513

SP-SuperGlue		4.24 491	4.38 494	3.81 504	10.75 524	2.00 534	8.18 515	2.09 527	1.00 520	2.41 522	8.29 519	0.56 522	2.05 525	1.27 518	4.91 521	7.39 514

ALIKED-MNN		7.86 482	7.83 488	7.97 499	19.40 519	8.86 519	14.48 510	3.90 521	1.59 515	4.74 521	16.49 513	0.59 521	7.65 518	0.91 523	8.34 515	7.39 514

SP-DiffGlue		4.29 489	4.46 492	3.77 505	3.68 542	2.12 532	5.97 518	2.22 526	1.10 518	4.89 518	13.93 516	0.66 517	1.95 526	2.47 515	5.24 519	7.22 516

tmp_tt		18.61 477	21.40 479	10.23 497	4.82 541	10.11 518	34.70 502	30.74 511	1.48 517	23.91 504	26.07 511	28.42 477	13.41 516	27.12 492	15.35 508	7.17 517

SP-NN		4.00 493	4.12 496	3.63 506	9.92 526	1.81 540	7.94 517	1.90 531	0.86 522	2.15 525	8.00 522	0.50 524	2.09 524	1.20 520	4.63 523	6.98 518

ALIKED-NN		7.51 483	7.61 489	7.21 500	18.26 520	8.10 522	13.45 512	3.88 523	1.50 516	4.87 519	16.47 514	0.64 518	7.00 519	0.88 524	8.50 514	6.52 519

XFeat-MNN		4.39 488	4.49 491	4.10 501	2.88 543	1.91 538	5.86 519	2.57 525	1.06 519	5.04 517	13.99 515	0.43 527	4.47 520	2.00 516	6.55 516	5.92 520

XFeat-NN		3.78 494	3.96 497	3.23 507	2.65 544	1.53 543	4.99 520	1.92 530	0.81 524	4.77 520	12.37 518	0.38 528	3.39 521	1.64 517	6.13 517	4.77 521

wuyk23d		16.82 478	15.94 481	19.46 496	58.74 498	31.45 502	39.22 500	3.74 524	6.84 510	6.04 515	2.70 539	1.27 513	24.29 514	10.54 511	14.40 509	2.63 522

SIFT-NN		2.77 495	2.92 498	2.34 508	8.70 527	3.08 526	4.46 521	1.01 533	0.68 525	1.46 526	5.49 523	0.16 530	1.65 527	0.26 525	4.04 525	2.27 523

SIFT-MNN		2.63 496	2.75 499	2.25 509	8.10 528	2.84 527	4.08 522	1.02 532	0.68 525	1.28 527	5.34 526	0.15 531	1.64 528	0.26 525	3.88 527	2.27 523

SIFT-NN-CMatch		2.31 499	2.41 502	2.00 512	6.59 534	2.34 531	3.48 526	0.83 536	0.65 528	1.28 527	5.09 527	0.14 532	1.52 531	0.23 528	3.41 530	2.14 525

SIFT-NN-PointCN		2.07 503	2.18 506	1.74 515	5.75 537	1.65 542	3.27 528	0.73 539	0.60 535	1.07 530	4.62 533	0.13 535	1.43 535	0.21 533	3.22 531	2.12 526

SIFT-NN-UMatch		2.26 500	2.39 503	1.89 514	6.21 536	2.08 533	3.76 524	0.83 536	0.66 527	1.04 531	5.09 527	0.14 532	1.52 531	0.23 528	3.51 529	2.07 527

SIFT-NN-NCMNet		2.52 497	2.64 500	2.14 510	7.53 530	2.74 528	4.00 523	0.98 534	0.65 528	1.24 529	5.08 529	0.14 532	1.60 529	0.23 528	3.94 526	2.07 527

SIFT-NCM-Cal		2.40 498	2.52 501	2.05 511	7.74 529	2.54 529	3.75 525	0.84 535	0.65 528	0.89 534	4.78 532	0.13 535	1.60 529	0.19 536	3.71 528	2.01 529

SIFT-ConvMatch		2.25 501	2.37 504	1.90 513	7.29 531	2.37 530	3.21 529	0.75 538	0.65 528	1.03 532	4.91 530	0.12 538	1.51 533	0.22 531	3.13 532	1.81 530

SIFT-PCN-Cal		1.72 506	1.82 510	1.39 519	5.64 538	1.19 545	2.39 533	0.53 544	0.55 537	0.72 537	3.90 536	0.09 541	1.22 539	0.17 538	2.42 537	1.76 531

SIFT-UMatch		2.16 502	2.30 505	1.72 516	6.99 532	1.97 537	3.32 527	0.70 540	0.64 532	0.91 533	4.86 531	0.12 538	1.49 534	0.22 531	2.97 533	1.72 532

SIFT-PointCN		1.72 506	1.83 509	1.36 520	5.55 539	1.22 544	2.59 532	0.59 542	0.55 537	0.71 538	3.77 537	0.08 542	1.24 538	0.17 538	2.48 536	1.63 533

SIFT-CM-Cal		2.02 504	2.13 507	1.67 517	6.79 533	1.99 535	2.79 531	0.64 541	0.63 533	0.87 535	4.48 535	0.13 535	1.41 536	0.19 536	2.70 534	1.61 534

SIFT-UM-Cal		1.97 505	2.12 508	1.52 518	6.57 535	1.67 541	2.93 530	0.57 543	0.62 534	0.83 536	4.55 534	0.11 540	1.37 537	0.20 535	2.69 535	1.53 535

SIFT-NCMNet		1.44 508	1.56 511	1.08 521	5.14 540	1.07 546	1.97 534	0.32 545	0.56 536	0.64 539	3.23 538	0.07 543	1.01 540	0.14 540	1.95 538	1.15 536

test123		6.12 485	8.11 486	0.14 522	0.06 546	0.09 547	71.05 464	0.03 547	0.04 541	0.25 542	1.30 541	0.05 544	0.03 542	0.21 533	0.01 540	0.29 537

testmvs		6.04 486	8.02 487	0.10 523	0.08 545	0.03 548	69.74 469	0.04 546	0.05 540	0.31 541	1.68 540	0.02 545	0.04 541	0.24 527	0.02 539	0.25 538

mmdepth		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

monomultidepth		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

test_blank		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

uanet_test		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

DCPMVS		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

cdsmvs_eth3d_5k		19.96 476	26.61 473	0.00 524	0.00 547	0.00 549	0.00 535	89.26 228	0.00 542	0.00 543	88.61 241	61.62 215	0.00 543	0.00 541	0.00 541	0.00 539

pcd_1.5k_mvsjas		5.26 487	7.02 490	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	63.15 186	0.00 543	0.00 541	0.00 541	0.00 539

sosnet-low-res		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

sosnet		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

uncertanet		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

Regformer		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

ab-mvs-re		7.23 484	9.64 484	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	86.72 294	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

uanet		0.00 509	0.00 512	0.00 524	0.00 547	0.00 549	0.00 535	0.00 548	0.00 542	0.00 543	0.00 542	0.00 546	0.00 543	0.00 541	0.00 541	0.00 539

WAC-MVS						42.58 490								39.46 477

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 157	91.30 17

test_one_0601					95.07 7	71.46 60		94.14 9	78.27 42	92.05 13	95.74 8	80.83 12

eth-test2					0.00 547

eth-test					0.00 547

ZD-MVS					94.38 29	72.22 46		92.67 74	70.98 245	87.75 51	94.07 58	74.01 37	96.70 31	84.66 70	94.84 47

test_241102_ONE					95.30 2	70.98 73		94.06 14	77.17 68	93.10 1	95.39 18	82.99 1	97.27 14

9.14			88.26 19		92.84 70		91.52 56	94.75 1	73.93 174	88.57 36	94.67 30	75.57 26	95.79 64	86.77 51	95.76 26

save fliter					93.80 44	72.35 44	90.47 74	91.17 154	74.31 163

test0726					95.27 5	71.25 65	93.60 7	94.11 10	77.33 60	92.81 3	95.79 5	80.98 10

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs												50.01 354

MTGPA								92.02 114

test_post1							78.90 410				5.43 525	48.81 375	85.44 407	59.25 367

test_post											5.46 524	50.36 350	84.24 416

patchmatchnet-post											74.00 471	51.12 340	88.60 368

MTMP							92.18 39	32.83 510

gm-plane-assit					81.40 405	53.83 436			62.72 400		80.94 415		92.39 245	63.40 314

TEST9					93.26 56	72.96 25	88.75 139	91.89 122	68.44 317	85.00 81	93.10 89	74.36 33	95.41 81

test_8					93.13 60	72.57 35	88.68 145	91.84 126	68.69 312	84.87 85	93.10 89	74.43 31	95.16 91

agg_prior					92.85 68	71.94 53		91.78 130		84.41 97			94.93 103

test_prior4						72.60 34	89.01 126

test_prior2							88.85 133		75.41 126	84.91 83	93.54 76	74.28 34		83.31 85	95.86 23

旧先验2							86.56 233		58.10 444	87.04 62			88.98 360	74.07 207

新几何2							86.29 247

原ACMM2							86.86 220

testdata2													91.01 311	62.37 334

segment_acmp												73.08 44

testdata1							84.14 312		75.71 117

plane_prior7					90.08 117	68.51 132

plane_prior6					89.84 126	68.70 126						60.42 241

plane_prior4											91.00 166

plane_prior3						68.60 129			78.44 37	78.92 207

plane_prior2							91.25 60		79.12 29

plane_prior1					89.90 125

plane_prior						68.71 124	90.38 78		77.62 49						86.16 213

n2								0.00 548

nn								0.00 548

door-mid								69.98 473

test11								92.23 100

door								69.44 476

HQP5-MVS						66.98 186

HQP-NCC					89.33 146		89.17 117		76.41 96	77.23 248

ACMP_Plane					89.33 146		89.17 117		76.41 96	77.23 248

BP-MVS														77.47 163

HQP3-MVS								92.19 108							85.99 218

HQP2-MVS												60.17 244

NP-MVS					89.62 131	68.32 136					90.24 191

MDTV_nov1_ep13			69.97 378		83.18 364	53.48 438	77.10 432	80.18 421	60.45 419	69.33 385	80.44 419	48.89 374	86.90 388	51.60 423	78.51 330

ACMMP++_ref															81.95 288

ACMMP++															81.25 294

Test By Simon												64.33 172

High-resolution multi-view benchmark