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.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 28	93.86 31	99.07 2	98.98 6	97.01 13	98.92 4	98.78 14	95.22 37	98.61 169	96.85 2	99.77 9	99.31 28
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
anonymousdsp		96.74 17	96.42 29	97.68 6	98.00 91	94.03 25	96.97 20	97.61 104	87.68 201	98.45 18	98.77 15	94.20 69	99.50 21	96.70 3	99.40 56	99.53 15

MVS_0304		93.92 129	93.68 136	94.64 113	95.94 220	85.83 173	94.34 127	88.14 333	92.98 74	91.09 272	97.68 62	86.73 205	99.36 57	96.64 4	99.59 28	98.72 92

MVSFormer		92.18 180	92.23 171	92.04 210	94.74 265	80.06 248	97.15 15	97.37 120	88.98 171	88.83 306	92.79 292	77.02 291	99.60 9	96.41 5	96.75 263	96.46 246

test_djsdf		96.62 23	96.49 26	97.01 32	98.55 45	91.77 59	97.15 15	97.37 120	88.98 171	98.26 22	98.86 10	93.35 84	99.60 9	96.41 5	99.45 46	99.66 6

test_fmvsmvis_n_1920		95.08 88	95.40 78	94.13 135	96.66 161	87.75 127	93.44 155	98.49 13	85.57 233	98.27 20	97.11 107	94.11 71	97.75 249	96.26 7	98.72 145	96.89 228

v7n		96.82 9	97.31 10	95.33 86	98.54 48	86.81 144	96.83 23	98.07 57	96.59 20	98.46 17	98.43 31	92.91 99	99.52 19	96.25 8	99.76 10	99.65 8

mvs_tets		96.83 8	96.71 18	97.17 27	98.83 25	92.51 48	96.58 33	97.61 104	87.57 203	98.80 7	98.90 9	96.50 9	99.59 13	96.15 9	99.47 42	99.40 21

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 31	92.49 49	96.44 41	97.42 118	86.96 212	98.71 10	98.72 17	95.36 31	99.56 17	95.92 10	99.45 46	99.32 27

OurMVSNet-221017-0		96.80 12	96.75 17	96.96 35	99.03 11	91.85 57	97.98 7	98.01 69	94.15 48	98.93 3	99.07 5	88.07 179	99.57 14	95.86 11	99.69 14	99.46 18

test_fmvsm_n_1920		94.72 100	94.74 106	94.67 109	96.30 191	88.62 108	93.19 160	98.07 57	85.63 231	97.08 60	97.35 88	90.86 141	97.66 255	95.70 12	98.48 171	97.74 184

bld_raw_dy_0_64		94.27 117	94.15 124	94.65 112	98.55 45	86.28 162	95.80 73	95.55 228	88.41 185	97.09 59	98.08 42	78.69 273	98.87 124	95.63 13	99.53 37	98.81 80

mvsmamba		95.61 64	95.40 78	96.22 51	98.44 60	89.86 84	97.14 17	97.45 117	91.25 125	97.49 43	98.14 37	83.49 229	99.45 26	95.52 14	99.66 21	99.36 24

RRT_MVS		95.41 74	95.20 89	96.05 55	98.86 22	88.92 101	97.49 11	94.48 257	93.12 70	97.94 27	98.54 24	81.19 259	99.63 6	95.48 15	99.69 14	99.60 12

MP-MVS-pluss		96.08 48	95.92 57	96.57 44	99.06 10	91.21 65	93.25 158	98.32 21	87.89 194	96.86 72	97.38 81	95.55 25	99.39 48	95.47 16	99.47 42	99.11 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvs3		92.42 172	92.40 170	92.46 197	93.80 292	87.28 133	93.86 144	97.05 149	76.86 315	96.25 98	98.66 18	82.87 238	91.26 358	95.44 17	96.83 259	98.82 78

PS-MVSNAJss		96.01 50	96.04 51	95.89 67	98.82 26	88.51 113	95.57 84	97.88 81	88.72 177	98.81 6	98.86 10	90.77 143	99.60 9	95.43 18	99.53 37	99.57 14

tt0805		95.42 73	95.93 56	93.86 147	98.75 32	88.47 114	97.68 9	94.29 261	96.48 21	95.38 137	93.63 271	94.89 52	97.94 229	95.38 19	96.92 256	95.17 289

UA-Net		97.35 4	97.24 11	97.69 4	98.22 74	93.87 30	98.42 6	98.19 36	96.95 14	95.46 135	99.23 4	93.45 79	99.57 14	95.34 20	99.89 2	99.63 9

ACMH	88.36 12	96.59 27	97.43 5	94.07 137	98.56 42	85.33 180	96.33 47	98.30 24	94.66 40	98.72 8	98.30 34	97.51 5	98.00 223	94.87 21	99.59 28	98.86 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v10		94.68 103	95.27 86	92.90 177	96.57 168	80.15 244	94.65 115	97.57 107	90.68 138	97.43 47	98.00 47	88.18 176	99.15 83	94.84 22	99.55 36	99.41 20

SixPastTwentyTwo		94.91 93	95.21 87	93.98 139	98.52 50	83.19 207	95.93 67	94.84 247	94.86 39	98.49 15	98.74 16	81.45 253	99.60 9	94.69 23	99.39 57	99.15 39

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 8	98.61 3	98.81 8	97.41 10	97.28 54	98.46 29	94.62 59	98.84 128	94.64 24	99.53 37	98.99 55

v1240		93.29 142	93.71 134	92.06 209	96.01 215	77.89 291	91.81 219	97.37 120	85.12 241	96.69 79	96.40 151	86.67 206	99.07 97	94.51 25	98.76 142	99.22 33

APDe-MVS		96.46 31	96.64 21	95.93 62	97.68 114	89.38 95	96.90 22	98.41 17	92.52 80	97.43 47	97.92 53	95.11 42	99.50 21	94.45 26	99.30 70	98.92 68

ACMMP_NAP		96.21 44	96.12 46	96.49 48	98.90 19	91.42 63	94.57 119	98.03 66	90.42 145	96.37 89	97.35 88	95.68 20	99.25 74	94.44 27	99.34 63	98.80 82

ZNCC-MVS		96.42 35	96.20 41	97.07 30	98.80 30	92.79 46	96.08 61	98.16 45	91.74 112	95.34 141	96.36 158	95.68 20	99.44 28	94.41 28	99.28 78	98.97 60

v8		94.65 104	95.29 84	92.74 182	96.65 162	79.77 259	94.59 116	97.17 140	91.86 100	97.47 46	97.93 50	88.16 177	99.08 93	94.32 29	99.47 42	99.38 22

HPM-MVS_fast		97.01 6	96.89 14	97.39 21	99.12 8	93.92 28	97.16 14	98.17 42	93.11 71	96.48 86	97.36 85	96.92 6	99.34 62	94.31 30	99.38 58	98.92 68

MTAPA		96.65 22	96.38 33	97.47 15	98.95 18	94.05 23	95.88 70	97.62 102	94.46 44	96.29 95	96.94 117	93.56 76	99.37 56	94.29 31	99.42 51	98.99 55

WR-MVS_H		96.60 25	97.05 13	95.24 90	99.02 12	86.44 156	96.78 27	98.08 54	97.42 9	98.48 16	97.86 57	91.76 121	99.63 6	94.23 32	99.84 3	99.66 6

v1921920		93.26 144	93.61 139	92.19 202	96.04 214	78.31 285	91.88 214	97.24 136	85.17 239	96.19 105	96.19 169	86.76 204	99.05 98	94.18 33	98.84 129	99.22 33

v1192		93.49 137	93.78 131	92.62 189	96.16 202	79.62 261	91.83 218	97.22 138	86.07 222	96.10 108	96.38 156	87.22 193	99.02 103	94.14 34	98.88 124	99.22 33

MSC_two_6792asdad				95.90 65	96.54 171	89.57 88		96.87 164					99.41 38	94.06 35	99.30 70	98.72 92

No_MVS				95.90 65	96.54 171	89.57 88		96.87 164					99.41 38	94.06 35	99.30 70	98.72 92

HPM-MVS		96.81 11	96.62 22	97.36 23	98.89 20	93.53 38	97.51 10	98.44 14	92.35 85	95.95 111	96.41 150	96.71 8	99.42 32	93.99 37	99.36 59	99.13 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DVP-MVS++		95.93 52	96.34 34	94.70 108	96.54 171	86.66 150	98.45 4	98.22 33	93.26 68	97.54 39	97.36 85	93.12 92	99.38 54	93.88 38	98.68 151	98.04 146

test_0728_THIRD									93.26 68	97.40 50	97.35 88	94.69 56	99.34 62	93.88 38	99.42 51	98.89 71

nrg030		96.32 40	96.55 25	95.62 76	97.83 101	88.55 112	95.77 74	98.29 27	92.68 76	98.03 26	97.91 54	95.13 40	98.95 113	93.85 40	99.49 41	99.36 24

v144192		93.20 149	93.54 143	92.16 206	96.05 210	78.26 286	91.95 207	97.14 142	84.98 245	95.96 110	96.11 173	87.08 197	99.04 101	93.79 41	98.84 129	99.17 37

HFP-MVS		96.39 38	96.17 44	97.04 31	98.51 51	93.37 39	96.30 54	97.98 72	92.35 85	95.63 127	96.47 146	95.37 29	99.27 73	93.78 42	99.14 98	98.48 118

EI-MVSNet-UG-set		94.35 114	94.27 122	94.59 118	92.46 311	85.87 171	92.42 189	94.69 253	93.67 61	96.13 106	95.84 185	91.20 134	98.86 125	93.78 42	98.23 194	99.03 51

ACMMPR		96.46 31	96.14 45	97.41 20	98.60 39	93.82 33	96.30 54	97.96 75	92.35 85	95.57 129	96.61 141	94.93 51	99.41 38	93.78 42	99.15 97	99.00 53

EI-MVSNet-Vis-set		94.36 113	94.28 120	94.61 114	92.55 310	85.98 168	92.44 187	94.69 253	93.70 58	96.12 107	95.81 186	91.24 131	98.86 125	93.76 45	98.22 196	98.98 59

region2R		96.41 36	96.09 47	97.38 22	98.62 36	93.81 35	96.32 49	97.96 75	92.26 88	95.28 145	96.57 143	95.02 47	99.41 38	93.63 46	99.11 100	98.94 63

EC-MVSNet		95.44 70	95.62 69	94.89 99	96.93 149	87.69 128	96.48 38	99.14 4	93.93 53	92.77 231	94.52 243	93.95 73	99.49 24	93.62 47	99.22 88	97.51 199

XVS		96.49 29	96.18 42	97.44 16	98.56 42	93.99 26	96.50 36	97.95 77	94.58 41	94.38 178	96.49 145	94.56 61	99.39 48	93.57 48	99.05 105	98.93 64

X-MVStestdata		90.70 205	88.45 250	97.44 16	98.56 42	93.99 26	96.50 36	97.95 77	94.58 41	94.38 178	26.89 382	94.56 61	99.39 48	93.57 48	99.05 105	98.93 64

SMA-MVS		95.77 58	95.54 72	96.47 49	98.27 70	91.19 66	95.09 99	97.79 92	86.48 215	97.42 49	97.51 75	94.47 66	99.29 69	93.55 50	99.29 73	98.93 64
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
v1144		93.50 136	93.81 129	92.57 192	96.28 192	79.61 262	91.86 217	96.96 155	86.95 213	95.91 114	96.32 160	87.65 186	98.96 111	93.51 51	98.88 124	99.13 41

SR-MVS-dyc-post		96.84 7	96.60 24	97.56 10	98.07 83	95.27 9	96.37 44	98.12 48	95.66 32	97.00 66	97.03 112	94.85 53	99.42 32	93.49 52	98.84 129	98.00 151

RE-MVS-def			96.66 19		98.07 83	95.27 9	96.37 44	98.12 48	95.66 32	97.00 66	97.03 112	95.40 28		93.49 52	98.84 129	98.00 151

SteuartSystems-ACMMP		96.40 37	96.30 36	96.71 40	98.63 35	91.96 55	95.70 76	98.01 69	93.34 67	96.64 81	96.57 143	94.99 49	99.36 57	93.48 54	99.34 63	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
CS-MVS		95.77 58	95.58 71	96.37 50	96.84 154	91.72 61	96.73 29	99.06 5	94.23 46	92.48 240	94.79 233	93.56 76	99.49 24	93.47 55	99.05 105	97.89 166

ACMMP		96.61 24	96.34 34	97.43 18	98.61 38	93.88 29	96.95 21	98.18 38	92.26 88	96.33 91	96.84 125	95.10 43	99.40 45	93.47 55	99.33 65	99.02 52
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
TSAR-MVS + MP.		94.96 92	94.75 104	95.57 78	98.86 22	88.69 105	96.37 44	96.81 168	85.23 237	94.75 168	97.12 106	91.85 119	99.40 45	93.45 57	98.33 184	98.62 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvs2		90.62 209	90.40 216	91.29 236	91.93 326	85.46 178	92.70 175	96.48 189	74.44 328	94.91 162	97.59 68	75.52 302	90.57 360	93.44 58	96.56 267	97.84 172

DVP-MVS		95.82 57	96.18 42	94.72 107	98.51 51	86.69 148	95.20 96	97.00 152	91.85 101	97.40 50	97.35 88	95.58 23	99.34 62	93.44 58	99.31 68	98.13 141
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
test_0728_SECOND				94.88 100	98.55 45	86.72 147	95.20 96	98.22 33					99.38 54	93.44 58	99.31 68	98.53 114

MSP-MVS		95.34 77	94.63 112	97.48 14	98.67 33	94.05 23	96.41 43	98.18 38	91.26 123	95.12 152	95.15 216	86.60 208	99.50 21	93.43 61	96.81 260	98.89 71
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
PS-CasMVS		96.69 20	97.43 5	94.49 124	99.13 6	84.09 195	96.61 32	97.97 74	97.91 5	98.64 13	98.13 39	95.24 36	99.65 3	93.39 62	99.84 3	99.72 2

Vis-MVSNet		95.50 68	95.48 73	95.56 79	98.11 80	89.40 94	95.35 88	98.22 33	92.36 84	94.11 181	98.07 43	92.02 115	99.44 28	93.38 63	97.67 229	97.85 171
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
APD-MVS_3200maxsize		96.82 9	96.65 20	97.32 25	97.95 95	93.82 33	96.31 50	98.25 28	95.51 34	96.99 68	97.05 111	95.63 22	99.39 48	93.31 64	98.88 124	98.75 87

SED-MVS		96.00 51	96.41 32	94.76 105	98.51 51	86.97 140	95.21 94	98.10 51	91.95 95	97.63 35	97.25 95	96.48 10	99.35 59	93.29 65	99.29 73	97.95 159

test_241102_TWO								98.10 51	91.95 95	97.54 39	97.25 95	95.37 29	99.35 59	93.29 65	99.25 82	98.49 117

DTE-MVSNet		96.74 17	97.43 5	94.67 109	99.13 6	84.68 186	96.51 35	97.94 80	98.14 3	98.67 12	98.32 33	95.04 45	99.69 2	93.27 67	99.82 7	99.62 10

3Dnovator+	92.74 2	95.86 56	95.77 65	96.13 53	96.81 157	90.79 73	96.30 54	97.82 87	96.13 26	94.74 169	97.23 97	91.33 128	99.16 82	93.25 68	98.30 187	98.46 119

K. test v3		93.37 140	93.27 150	93.66 151	98.05 85	82.62 215	94.35 126	86.62 343	96.05 29	97.51 42	98.85 12	76.59 298	99.65 3	93.21 69	98.20 199	98.73 91

Anonymous20231211		96.60 25	97.13 12	95.00 96	97.46 128	86.35 160	97.11 19	98.24 31	97.58 8	98.72 8	98.97 7	93.15 91	99.15 83	93.18 70	99.74 12	99.50 17

GST-MVS		96.24 43	95.99 53	97.00 33	98.65 34	92.71 47	95.69 78	98.01 69	92.08 93	95.74 122	96.28 164	95.22 37	99.42 32	93.17 71	99.06 102	98.88 73

CP-MVS		96.44 34	96.08 48	97.54 11	98.29 68	94.62 14	96.80 25	98.08 54	92.67 78	95.08 156	96.39 155	94.77 55	99.42 32	93.17 71	99.44 49	98.58 112

mPP-MVS		96.46 31	96.05 50	97.69 4	98.62 36	94.65 13	96.45 39	97.74 95	92.59 79	95.47 133	96.68 137	94.50 63	99.42 32	93.10 73	99.26 81	98.99 55

ACMM	88.83 9	96.30 42	96.07 49	96.97 34	98.39 62	92.95 44	94.74 111	98.03 66	90.82 134	97.15 57	96.85 123	96.25 14	99.00 105	93.10 73	99.33 65	98.95 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		96.19 45	96.80 16	94.38 129	98.99 16	83.82 198	96.31 50	97.53 111	97.60 7	98.34 19	97.52 73	91.98 117	99.63 6	93.08 75	99.81 8	99.70 3

v2v482		93.29 142	93.63 138	92.29 198	96.35 185	78.82 279	91.77 221	96.28 195	88.45 183	95.70 126	96.26 166	86.02 214	98.90 117	93.02 76	98.81 137	99.14 40

IU-MVS					98.51 51	86.66 150		96.83 167	72.74 340	95.83 118				93.00 77	99.29 73	98.64 106

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 85	94.69 11	96.13 59	98.07 57	95.17 35	96.82 74	96.73 134	95.09 44	99.43 31	92.99 78	98.71 147	98.50 115

PEN-MVS		96.69 20	97.39 8	94.61 114	99.16 4	84.50 187	96.54 34	98.05 61	98.06 4	98.64 13	98.25 35	95.01 48	99.65 3	92.95 79	99.83 5	99.68 4

FC-MVSNet-test		95.32 78	95.88 58	93.62 152	98.49 58	81.77 224	95.90 69	98.32 21	93.93 53	97.53 41	97.56 70	88.48 172	99.40 45	92.91 80	99.83 5	99.68 4

OPM-MVS		95.61 64	95.45 74	96.08 54	98.49 58	91.00 68	92.65 178	97.33 128	90.05 150	96.77 77	96.85 123	95.04 45	98.56 176	92.77 81	99.06 102	98.70 96
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		96.32 40	95.94 54	97.43 18	98.59 41	93.84 32	95.33 90	98.30 24	91.40 121	95.76 120	96.87 122	95.26 35	99.45 26	92.77 81	99.21 89	99.00 53

CNVR-MVS		94.58 106	94.29 119	95.46 82	96.94 147	89.35 96	91.81 219	96.80 169	89.66 157	93.90 193	95.44 205	92.80 103	98.72 151	92.74 83	98.52 166	98.32 126

DeepC-MVS	91.39 4	95.43 71	95.33 82	95.71 74	97.67 115	90.17 80	93.86 144	98.02 68	87.35 205	96.22 101	97.99 48	94.48 65	99.05 98	92.73 84	99.68 18	97.93 161
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		95.19 85	95.73 66	93.55 155	96.62 166	88.88 104	94.67 113	98.05 61	91.26 123	97.25 56	96.40 151	95.42 27	94.36 340	92.72 85	99.19 91	97.40 208
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
EU-MVSNet		87.39 286	86.71 290	89.44 286	93.40 296	76.11 316	94.93 107	90.00 322	57.17 378	95.71 125	97.37 82	64.77 348	97.68 254	92.67 86	94.37 317	94.52 309

lessismore_v0				93.87 146	98.05 85	83.77 199		80.32 374		97.13 58	97.91 54	77.49 284	99.11 92	92.62 87	98.08 208	98.74 90

Anonymous20240521		92.86 159	93.57 141	90.74 257	96.57 168	75.50 323	94.15 134	95.60 221	89.38 162	95.90 115	97.90 56	80.39 263	97.96 227	92.60 88	99.68 18	98.75 87

MVS_Test		92.57 169	93.29 147	90.40 266	93.53 295	75.85 319	92.52 182	96.96 155	88.73 176	92.35 248	96.70 136	90.77 143	98.37 194	92.53 89	95.49 289	96.99 224

3Dnovator	92.54 3	94.80 98	94.90 98	94.47 125	95.47 242	87.06 138	96.63 31	97.28 134	91.82 107	94.34 180	97.41 79	90.60 150	98.65 166	92.47 90	98.11 205	97.70 186

SF-MVS		95.88 55	95.88 58	95.87 68	98.12 79	89.65 87	95.58 83	98.56 12	91.84 104	96.36 90	96.68 137	94.37 67	99.32 68	92.41 91	99.05 105	98.64 106

V42		93.43 139	93.58 140	92.97 171	95.34 248	81.22 234	92.67 176	96.49 188	87.25 207	96.20 103	96.37 157	87.32 192	98.85 127	92.39 92	98.21 197	98.85 77

casdiffmvs_mvg		95.10 87	95.62 69	93.53 158	96.25 196	83.23 205	92.66 177	98.19 36	93.06 72	97.49 43	97.15 104	94.78 54	98.71 157	92.27 93	98.72 145	98.65 101
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
iter_conf_final		90.23 223	89.32 234	92.95 173	94.65 271	81.46 230	94.32 130	95.40 236	85.61 232	92.84 228	95.37 212	54.58 374	99.13 87	92.16 94	98.94 120	98.25 131

HPM-MVS++		95.02 89	94.39 115	96.91 37	97.88 99	93.58 37	94.09 137	96.99 154	91.05 129	92.40 245	95.22 215	91.03 140	99.25 74	92.11 95	98.69 150	97.90 164

UniMVSNet (Re)		95.32 78	95.15 90	95.80 70	97.79 104	88.91 102	92.91 168	98.07 57	93.46 64	96.31 93	95.97 180	90.14 156	99.34 62	92.11 95	99.64 24	99.16 38

XVG-OURS-SEG-HR		95.38 75	95.00 97	96.51 46	98.10 81	94.07 20	92.46 186	98.13 47	90.69 137	93.75 195	96.25 167	98.03 2	97.02 283	92.08 97	95.55 287	98.45 120

LPG-MVS_test		96.38 39	96.23 39	96.84 38	98.36 66	92.13 52	95.33 90	98.25 28	91.78 108	97.07 61	97.22 99	96.38 12	99.28 71	92.07 98	99.59 28	99.11 44

LGP-MVS_train				96.84 38	98.36 66	92.13 52		98.25 28	91.78 108	97.07 61	97.22 99	96.38 12	99.28 71	92.07 98	99.59 28	99.11 44

tttt0517		89.81 236	88.90 244	92.55 193	97.00 144	79.73 260	95.03 103	83.65 365	89.88 153	95.30 143	94.79 233	53.64 377	99.39 48	91.99 100	98.79 139	98.54 113

EI-MVSNet		92.99 153	93.26 151	92.19 202	92.12 320	79.21 272	92.32 194	94.67 255	91.77 110	95.24 149	95.85 183	87.14 196	98.49 182	91.99 100	98.26 190	98.86 74

MP-MVS		96.14 46	95.68 67	97.51 13	98.81 28	94.06 21	96.10 60	97.78 93	92.73 75	93.48 203	96.72 135	94.23 68	99.42 32	91.99 100	99.29 73	99.05 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
IterMVS-LS		93.78 132	94.28 120	92.27 199	96.27 193	79.21 272	91.87 215	96.78 170	91.77 110	96.57 85	97.07 109	87.15 195	98.74 149	91.99 100	99.03 111	98.86 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		91.65 188	91.55 186	91.94 211	93.89 288	79.22 271	87.56 314	93.51 276	91.53 119	95.37 139	96.62 140	78.65 274	98.90 117	91.89 104	94.95 303	97.70 186

EGC-MVSNET		80.97 337	75.73 349	96.67 42	98.85 24	94.55 15	96.83 23	96.60 180	2.44 384	5.32 385	98.25 35	92.24 111	98.02 221	91.85 105	99.21 89	97.45 202

CS-MVS-test		95.32 78	95.10 93	95.96 58	96.86 153	90.75 74	96.33 47	99.20 2	93.99 50	91.03 273	93.73 269	93.52 78	99.55 18	91.81 106	99.45 46	97.58 193

LS3D		96.11 47	95.83 62	96.95 36	94.75 264	94.20 19	97.34 13	97.98 72	97.31 11	95.32 142	96.77 127	93.08 94	99.20 79	91.79 107	98.16 201	97.44 204

DPE-MVS		95.89 54	95.88 58	95.92 64	97.93 96	89.83 85	93.46 153	98.30 24	92.37 83	97.75 32	96.95 116	95.14 39	99.51 20	91.74 108	99.28 78	98.41 122
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
FIs		94.90 94	95.35 80	93.55 155	98.28 69	81.76 225	95.33 90	98.14 46	93.05 73	97.07 61	97.18 102	87.65 186	99.29 69	91.72 109	99.69 14	99.61 11

Gipuma		95.31 81	95.80 64	93.81 149	97.99 94	90.91 70	96.42 42	97.95 77	96.69 17	91.78 260	98.85 12	91.77 120	95.49 323	91.72 109	99.08 101	95.02 295
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
baseline		94.26 119	94.80 102	92.64 186	96.08 208	80.99 237	93.69 149	98.04 65	90.80 135	94.89 163	96.32 160	93.19 89	98.48 186	91.68 111	98.51 168	98.43 121

alignmvs		93.26 144	92.85 156	94.50 122	95.70 231	87.45 130	93.45 154	95.76 216	91.58 117	95.25 148	92.42 303	81.96 250	98.72 151	91.61 112	97.87 221	97.33 213

UniMVSNet_NR-MVSNet		95.35 76	95.21 87	95.76 71	97.69 113	88.59 110	92.26 198	97.84 85	94.91 38	96.80 75	95.78 190	90.42 152	99.41 38	91.60 113	99.58 33	99.29 29

DU-MVS		95.28 82	95.12 92	95.75 72	97.75 106	88.59 110	92.58 180	97.81 88	93.99 50	96.80 75	95.90 181	90.10 159	99.41 38	91.60 113	99.58 33	99.26 30

EG-PatchMatch MVS		94.54 108	94.67 111	94.14 134	97.87 100	86.50 152	92.00 206	96.74 174	88.16 190	96.93 70	97.61 67	93.04 96	97.90 230	91.60 113	98.12 204	98.03 149

test_0402		95.73 60	96.22 40	94.26 131	98.19 76	85.77 174	93.24 159	97.24 136	96.88 16	97.69 33	97.77 60	94.12 70	99.13 87	91.54 116	99.29 73	97.88 167

canonicalmvs		94.59 105	94.69 108	94.30 130	95.60 239	87.03 139	95.59 81	98.24 31	91.56 118	95.21 151	92.04 309	94.95 50	98.66 164	91.45 117	97.57 233	97.20 217

XVG-OURS		94.72 100	94.12 125	96.50 47	98.00 91	94.23 18	91.48 225	98.17 42	90.72 136	95.30 143	96.47 146	87.94 183	96.98 284	91.41 118	97.61 232	98.30 129

pmmvs6		96.80 12	97.36 9	95.15 93	99.12 8	87.82 126	96.68 30	97.86 82	96.10 27	98.14 24	99.28 3	97.94 3	98.21 205	91.38 119	99.69 14	99.42 19

XVG-ACMP-BASELINE		95.68 62	95.34 81	96.69 41	98.40 61	93.04 41	94.54 123	98.05 61	90.45 144	96.31 93	96.76 129	92.91 99	98.72 151	91.19 120	99.42 51	98.32 126

test_fmvs1_n		88.73 261	88.38 252	89.76 281	92.06 322	82.53 216	92.30 196	96.59 182	71.14 347	92.58 237	95.41 209	68.55 326	89.57 368	91.12 121	95.66 285	97.18 218

RPSCF		95.58 66	94.89 99	97.62 7	97.58 120	96.30 7	95.97 66	97.53 111	92.42 81	93.41 204	97.78 58	91.21 133	97.77 246	91.06 122	97.06 248	98.80 82

h-mvs33		92.89 156	91.99 177	95.58 77	96.97 145	90.55 76	93.94 142	94.01 269	89.23 165	93.95 190	96.19 169	76.88 294	99.14 85	91.02 123	95.71 284	97.04 222

hse-mvs2		92.24 179	91.20 196	95.38 83	96.16 202	90.65 75	92.52 182	92.01 307	89.23 165	93.95 190	92.99 287	76.88 294	98.69 160	91.02 123	96.03 276	96.81 232

casdiffmvs		94.32 116	94.80 102	92.85 179	96.05 210	81.44 231	92.35 192	98.05 61	91.53 119	95.75 121	96.80 126	93.35 84	98.49 182	91.01 125	98.32 186	98.64 106
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.55 107	94.68 110	94.15 133	97.23 135	85.11 182	94.14 135	97.34 127	88.71 178	95.26 146	95.50 202	94.65 58	99.12 90	90.94 126	98.40 174	98.23 132

c3_l		91.32 197	91.42 191	91.00 248	92.29 313	76.79 309	87.52 317	96.42 191	85.76 228	94.72 171	93.89 265	82.73 241	98.16 211	90.93 127	98.55 162	98.04 146

iter_conf05		88.94 255	88.09 265	91.50 228	92.74 307	76.97 306	92.80 171	95.92 212	82.82 268	93.65 199	95.37 212	49.41 381	99.13 87	90.82 128	99.28 78	98.40 123

TranMVSNet+NR-MVSNet		96.07 49	96.26 38	95.50 80	98.26 71	87.69 128	93.75 147	97.86 82	95.96 31	97.48 45	97.14 105	95.33 32	99.44 28	90.79 129	99.76 10	99.38 22

test_vis1_n		89.01 251	89.01 240	89.03 294	92.57 309	82.46 218	92.62 179	96.06 206	73.02 338	90.40 282	95.77 191	74.86 304	89.68 366	90.78 130	94.98 302	94.95 297

UniMVSNet_ETH3D		97.13 5	97.72 3	95.35 84	99.51 2	87.38 131	97.70 8	97.54 109	98.16 2	98.94 2	99.33 2	97.84 4	99.08 93	90.73 131	99.73 13	99.59 13

9.14			94.81 101		97.49 125		94.11 136	98.37 18	87.56 204	95.38 137	96.03 177	94.66 57	99.08 93	90.70 132	98.97 116

diffmvs		91.74 186	91.93 179	91.15 243	93.06 302	78.17 287	88.77 300	97.51 114	86.28 218	92.42 244	93.96 262	88.04 180	97.46 265	90.69 133	96.67 265	97.82 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvs1		87.59 281	87.27 277	88.54 304	88.32 366	81.26 233	90.43 253	95.72 218	70.55 353	91.70 261	94.63 238	68.13 327	89.42 369	90.59 134	95.34 295	94.94 299

dcpmvs_2		93.96 127	95.01 96	90.82 255	97.60 118	74.04 335	93.68 150	98.85 7	89.80 155	97.82 29	97.01 115	91.14 138	99.21 77	90.56 135	98.59 159	99.19 36

MVSTER		89.32 243	88.75 246	91.03 245	90.10 351	76.62 311	90.85 238	94.67 255	82.27 275	95.24 149	95.79 187	61.09 363	98.49 182	90.49 136	98.26 190	97.97 158

DP-MVS		95.62 63	95.84 61	94.97 97	97.16 139	88.62 108	94.54 123	97.64 100	96.94 15	96.58 84	97.32 92	93.07 95	98.72 151	90.45 137	98.84 129	97.57 194

ACMP	88.15 13	95.71 61	95.43 76	96.54 45	98.17 77	91.73 60	94.24 131	98.08 54	89.46 160	96.61 83	96.47 146	95.85 18	99.12 90	90.45 137	99.56 35	98.77 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_111021_LR		93.66 134	93.28 149	94.80 103	96.25 196	90.95 69	90.21 259	95.43 233	87.91 192	93.74 197	94.40 245	92.88 101	96.38 304	90.39 139	98.28 188	97.07 219

ANet_high		94.83 97	96.28 37	90.47 263	96.65 162	73.16 340	94.33 128	98.74 10	96.39 24	98.09 25	98.93 8	93.37 83	98.70 158	90.38 140	99.68 18	99.53 15

DeepPCF-MVS	90.46 6	94.20 122	93.56 142	96.14 52	95.96 217	92.96 43	89.48 280	97.46 115	85.14 240	96.23 100	95.42 206	93.19 89	98.08 215	90.37 141	98.76 142	97.38 211

MSLP-MVS++		93.25 146	93.88 128	91.37 231	96.34 186	82.81 214	93.11 162	97.74 95	89.37 163	94.08 183	95.29 214	90.40 154	96.35 306	90.35 142	98.25 192	94.96 296

PM-MVS		93.33 141	92.67 163	95.33 86	96.58 167	94.06 21	92.26 198	92.18 300	85.92 225	96.22 101	96.61 141	85.64 219	95.99 315	90.35 142	98.23 194	95.93 266

test_vis1_n_1920		89.45 241	89.85 227	88.28 310	93.59 294	76.71 310	90.67 244	97.78 93	79.67 292	90.30 285	96.11 173	76.62 297	92.17 354	90.31 144	93.57 329	95.96 264

ACMH+	88.43 11	96.48 30	96.82 15	95.47 81	98.54 48	89.06 98	95.65 79	98.61 11	96.10 27	98.16 23	97.52 73	96.90 7	98.62 168	90.30 145	99.60 26	98.72 92

DIV-MVS_self_test		90.65 207	90.56 212	90.91 252	91.85 327	76.99 304	86.75 331	95.36 237	85.52 236	94.06 185	94.89 227	77.37 287	97.99 225	90.28 146	98.97 116	97.76 181

cl____		90.65 207	90.56 212	90.91 252	91.85 327	76.98 305	86.75 331	95.36 237	85.53 234	94.06 185	94.89 227	77.36 288	97.98 226	90.27 147	98.98 112	97.76 181

PHI-MVS		94.34 115	93.80 130	95.95 59	95.65 235	91.67 62	94.82 109	97.86 82	87.86 195	93.04 222	94.16 254	91.58 123	98.78 142	90.27 147	98.96 118	97.41 205

patch_mono-2		92.46 171	92.72 162	91.71 219	96.65 162	78.91 277	88.85 297	97.17 140	83.89 256	92.45 242	96.76 129	89.86 163	97.09 280	90.24 149	98.59 159	99.12 43

MVS_111021_HR		93.63 135	93.42 146	94.26 131	96.65 162	86.96 142	89.30 287	96.23 199	88.36 187	93.57 201	94.60 240	93.45 79	97.77 246	90.23 150	98.38 178	98.03 149

NCCC		94.08 125	93.54 143	95.70 75	96.49 176	89.90 83	92.39 191	96.91 161	90.64 139	92.33 251	94.60 240	90.58 151	98.96 111	90.21 151	97.70 227	98.23 132

pm-mvs1		95.43 71	95.94 54	93.93 143	98.38 63	85.08 183	95.46 87	97.12 145	91.84 104	97.28 54	98.46 29	95.30 34	97.71 252	90.17 152	99.42 51	98.99 55

RPMNet		90.31 222	90.14 222	90.81 256	91.01 339	78.93 274	92.52 182	98.12 48	91.91 98	89.10 303	96.89 121	68.84 325	99.41 38	90.17 152	92.70 342	94.08 316

NR-MVSNet		95.28 82	95.28 85	95.26 89	97.75 106	87.21 135	95.08 100	97.37 120	93.92 55	97.65 34	95.90 181	90.10 159	99.33 67	90.11 154	99.66 21	99.26 30

COLMAP_ROB	91.06 5	96.75 16	96.62 22	97.13 28	98.38 63	94.31 17	96.79 26	98.32 21	96.69 17	96.86 72	97.56 70	95.48 26	98.77 145	90.11 154	99.44 49	98.31 128
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		94.47 110	95.09 94	92.60 191	98.50 57	80.82 240	92.08 202	96.68 176	93.82 56	96.29 95	98.56 23	90.10 159	97.75 249	90.10 156	99.66 21	99.24 32

v148		92.87 158	93.29 147	91.62 223	96.25 196	77.72 294	91.28 230	95.05 240	89.69 156	95.93 113	96.04 176	87.34 191	98.38 191	90.05 157	97.99 215	98.78 84

MCST-MVS		92.91 155	92.51 166	94.10 136	97.52 123	85.72 175	91.36 229	97.13 144	80.33 286	92.91 227	94.24 250	91.23 132	98.72 151	89.99 158	97.93 218	97.86 169

miper_lstm_enhance		89.90 234	89.80 228	90.19 274	91.37 335	77.50 296	83.82 359	95.00 242	84.84 248	93.05 221	94.96 225	76.53 299	95.20 332	89.96 159	98.67 153	97.86 169

ambc				92.98 170	96.88 151	83.01 211	95.92 68	96.38 193		96.41 88	97.48 77	88.26 175	97.80 242	89.96 159	98.93 121	98.12 142

CPTT-MVS		94.74 99	94.12 125	96.60 43	98.15 78	93.01 42	95.84 71	97.66 99	89.21 168	93.28 210	95.46 203	88.89 170	98.98 106	89.80 161	98.82 135	97.80 177

miper_ehance_all_eth		90.48 211	90.42 215	90.69 258	91.62 332	76.57 312	86.83 329	96.18 203	83.38 258	94.06 185	92.66 297	82.20 246	98.04 217	89.79 162	97.02 250	97.45 202

eth_miper_zixun_eth		90.72 204	90.61 210	91.05 244	92.04 323	76.84 308	86.91 326	96.67 177	85.21 238	94.41 176	93.92 263	79.53 267	98.26 202	89.76 163	97.02 250	98.06 143

VPA-MVSNet		95.14 86	95.67 68	93.58 154	97.76 105	83.15 208	94.58 118	97.58 106	93.39 65	97.05 64	98.04 45	93.25 87	98.51 181	89.75 164	99.59 28	99.08 48

DELS-MVS		92.05 182	92.16 172	91.72 218	94.44 275	80.13 246	87.62 311	97.25 135	87.34 206	92.22 253	93.18 284	89.54 166	98.73 150	89.67 165	98.20 199	96.30 252
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
thisisatest0530		88.69 262	87.52 273	92.20 201	96.33 187	79.36 267	92.81 170	84.01 364	86.44 216	93.67 198	92.68 296	53.62 378	99.25 74	89.65 166	98.45 172	98.00 151

DeepC-MVS_fast	89.96 7	93.73 133	93.44 145	94.60 117	96.14 204	87.90 123	93.36 157	97.14 142	85.53 234	93.90 193	95.45 204	91.30 130	98.59 173	89.51 167	98.62 156	97.31 214
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		92.38 174	91.99 177	93.52 160	93.82 291	83.46 201	91.14 232	97.00 152	89.81 154	86.47 337	94.04 257	87.90 184	99.21 77	89.50 168	98.27 189	97.90 164

TSAR-MVS + GP.		93.07 152	92.41 169	95.06 95	95.82 224	90.87 72	90.97 236	92.61 295	88.04 191	94.61 172	93.79 268	88.08 178	97.81 241	89.41 169	98.39 177	96.50 244

testf1		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 19	94.96 36	97.30 52	97.93 50	96.05 16	97.90 230	89.32 170	99.23 85	98.19 136

APD_test2		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 19	94.96 36	97.30 52	97.93 50	96.05 16	97.90 230	89.32 170	99.23 85	98.19 136

APD-MVS		95.00 90	94.69 108	95.93 62	97.38 130	90.88 71	94.59 116	97.81 88	89.22 167	95.46 135	96.17 172	93.42 82	99.34 62	89.30 172	98.87 127	97.56 196
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
xiu_mvs_v1_base_debu		91.47 193	91.52 187	91.33 233	95.69 232	81.56 227	89.92 269	96.05 208	83.22 260	91.26 267	90.74 326	91.55 124	98.82 130	89.29 173	95.91 279	93.62 331

xiu_mvs_v1_base		91.47 193	91.52 187	91.33 233	95.69 232	81.56 227	89.92 269	96.05 208	83.22 260	91.26 267	90.74 326	91.55 124	98.82 130	89.29 173	95.91 279	93.62 331

xiu_mvs_v1_base_debi		91.47 193	91.52 187	91.33 233	95.69 232	81.56 227	89.92 269	96.05 208	83.22 260	91.26 267	90.74 326	91.55 124	98.82 130	89.29 173	95.91 279	93.62 331

HQP_MVS		94.26 119	93.93 127	95.23 91	97.71 110	88.12 119	94.56 120	97.81 88	91.74 112	93.31 207	95.59 197	86.93 200	98.95 113	89.26 176	98.51 168	98.60 110

plane_prior5								97.81 88					98.95 113	89.26 176	98.51 168	98.60 110

Patchmatch-RL test		88.81 258	88.52 248	89.69 284	95.33 249	79.94 253	86.22 340	92.71 291	78.46 305	95.80 119	94.18 253	66.25 340	95.33 329	89.22 178	98.53 165	93.78 325

PatchT		87.51 283	88.17 263	85.55 336	90.64 342	66.91 366	92.02 205	86.09 347	92.20 90	89.05 305	97.16 103	64.15 350	96.37 305	89.21 179	92.98 340	93.37 335

test_f		86.65 299	87.13 282	85.19 340	90.28 349	86.11 166	86.52 339	91.66 310	69.76 357	95.73 124	97.21 101	69.51 324	81.28 380	89.15 180	94.40 315	88.17 366

CSCG		94.69 102	94.75 104	94.52 121	97.55 122	87.87 124	95.01 104	97.57 107	92.68 76	96.20 103	93.44 277	91.92 118	98.78 142	89.11 181	99.24 84	96.92 226

KD-MVS_self_test		94.10 124	94.73 107	92.19 202	97.66 116	79.49 265	94.86 108	97.12 145	89.59 159	96.87 71	97.65 65	90.40 154	98.34 195	89.08 182	99.35 60	98.75 87

test_vis3_rt		90.40 214	90.03 223	91.52 227	92.58 308	88.95 100	90.38 254	97.72 97	73.30 335	97.79 30	97.51 75	77.05 290	87.10 373	89.03 183	94.89 304	98.50 115

cl22		89.02 249	88.50 249	90.59 261	89.76 353	76.45 313	86.62 336	94.03 266	82.98 266	92.65 234	92.49 298	72.05 315	97.53 260	88.93 184	97.02 250	97.78 179

VDD-MVS		94.37 112	94.37 117	94.40 128	97.49 125	86.07 167	93.97 141	93.28 280	94.49 43	96.24 99	97.78 58	87.99 182	98.79 139	88.92 185	99.14 98	98.34 125

AUN-MVS		90.05 231	88.30 254	95.32 88	96.09 207	90.52 77	92.42 189	92.05 306	82.08 277	88.45 318	92.86 289	65.76 342	98.69 160	88.91 186	96.07 275	96.75 236

TransMVSNet (Re)		95.27 84	96.04 51	92.97 171	98.37 65	81.92 223	95.07 101	96.76 173	93.97 52	97.77 31	98.57 22	95.72 19	97.90 230	88.89 187	99.23 85	99.08 48

CR-MVSNet		87.89 272	87.12 283	90.22 271	91.01 339	78.93 274	92.52 182	92.81 287	73.08 337	89.10 303	96.93 118	67.11 332	97.64 257	88.80 188	92.70 342	94.08 316

CVMVSNet		85.16 308	84.72 305	86.48 329	92.12 320	70.19 356	92.32 194	88.17 332	56.15 379	90.64 278	95.85 183	67.97 330	96.69 294	88.78 189	90.52 358	92.56 345

FMVSNet1		94.84 96	95.13 91	93.97 140	97.60 118	84.29 188	95.99 63	96.56 183	92.38 82	97.03 65	98.53 25	90.12 157	98.98 106	88.78 189	99.16 96	98.65 101

ZD-MVS					97.23 135	90.32 78		97.54 109	84.40 251	94.78 167	95.79 187	92.76 104	99.39 48	88.72 191	98.40 174

train_agg		92.71 164	91.83 182	95.35 84	96.45 178	89.46 90	90.60 246	96.92 159	79.37 295	90.49 279	94.39 246	91.20 134	98.88 120	88.66 192	98.43 173	97.72 185

Anonymous20240529		95.50 68	95.83 62	94.50 122	97.33 133	85.93 169	95.19 98	96.77 172	96.64 19	97.61 38	98.05 44	93.23 88	98.79 139	88.60 193	99.04 110	98.78 84

test1111		90.39 216	90.61 210	89.74 282	98.04 88	71.50 351	95.59 81	79.72 376	89.41 161	95.94 112	98.14 37	70.79 320	98.81 135	88.52 194	99.32 67	98.90 70

test_prior2							90.21 259		89.33 164	90.77 275	94.81 230	90.41 153		88.21 195	98.55 162

APD_test1		95.91 53	95.42 77	97.36 23	98.82 26	96.62 6	95.64 80	97.64 100	93.38 66	95.89 116	97.23 97	93.35 84	97.66 255	88.20 196	98.66 155	97.79 178

D2MVS		89.93 233	89.60 233	90.92 250	94.03 285	78.40 284	88.69 302	94.85 246	78.96 302	93.08 219	95.09 220	74.57 305	96.94 285	88.19 197	98.96 118	97.41 205

IS-MVSNet		94.49 109	94.35 118	94.92 98	98.25 73	86.46 155	97.13 18	94.31 260	96.24 25	96.28 97	96.36 158	82.88 237	99.35 59	88.19 197	99.52 40	98.96 61

test9_res														88.16 199	98.40 174	97.83 173

UGNet		93.08 150	92.50 167	94.79 104	93.87 289	87.99 122	95.07 101	94.26 263	90.64 139	87.33 333	97.67 64	86.89 202	98.49 182	88.10 200	98.71 147	97.91 163
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
test2506		85.42 306	84.57 308	87.96 315	97.81 102	66.53 369	96.14 58	56.35 387	89.04 169	93.55 202	98.10 40	42.88 389	98.68 162	88.09 201	99.18 93	98.67 99

test_cas_vis1_n_1920		88.25 268	88.27 257	88.20 312	92.19 318	78.92 276	89.45 281	95.44 232	75.29 325	93.23 215	95.65 196	71.58 317	90.23 364	88.05 202	93.55 330	95.44 286

FA-MVS(test-final)		91.81 185	91.85 181	91.68 221	94.95 255	79.99 252	96.00 62	93.44 278	87.80 196	94.02 188	97.29 93	77.60 283	98.45 188	88.04 203	97.49 235	96.61 238

ETV-MVS		92.99 153	92.74 159	93.72 150	95.86 223	86.30 161	92.33 193	97.84 85	91.70 115	92.81 229	86.17 365	92.22 112	99.19 80	88.03 204	97.73 224	95.66 280

EIA-MVS		92.35 175	92.03 175	93.30 164	95.81 226	83.97 196	92.80 171	98.17 42	87.71 199	89.79 296	87.56 355	91.17 137	99.18 81	87.97 205	97.27 242	96.77 234

mvs_anonymous		90.37 218	91.30 195	87.58 320	92.17 319	68.00 364	89.84 272	94.73 252	83.82 257	93.22 216	97.40 80	87.54 188	97.40 270	87.94 206	95.05 301	97.34 212

IterMVS		90.18 224	90.16 219	90.21 272	93.15 300	75.98 318	87.56 314	92.97 285	86.43 217	94.09 182	96.40 151	78.32 278	97.43 267	87.87 207	94.69 311	97.23 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		88.42 265	87.87 268	90.07 275	88.67 365	75.52 322	85.10 346	95.59 225	75.68 319	92.49 239	89.45 343	78.96 270	97.88 234	87.86 208	97.02 250	96.81 232

ET-MVSNet_ETH3D		86.15 301	84.27 311	91.79 215	93.04 303	81.28 232	87.17 322	86.14 346	79.57 293	83.65 354	88.66 349	57.10 369	98.18 209	87.74 209	95.40 292	95.90 269

Effi-MVS+-dtu		93.90 131	92.60 165	97.77 3	94.74 265	96.67 5	94.00 139	95.41 234	89.94 151	91.93 259	92.13 307	90.12 157	98.97 110	87.68 210	97.48 236	97.67 189

SDMVSNet		94.43 111	95.02 95	92.69 184	97.93 96	82.88 213	91.92 211	95.99 211	93.65 62	95.51 131	98.63 19	94.60 60	96.48 299	87.57 211	99.35 60	98.70 96

WR-MVS		93.49 137	93.72 133	92.80 181	97.57 121	80.03 250	90.14 262	95.68 219	93.70 58	96.62 82	95.39 210	87.21 194	99.04 101	87.50 212	99.64 24	99.33 26

tfpnnormal		94.27 117	94.87 100	92.48 195	97.71 110	80.88 239	94.55 122	95.41 234	93.70 58	96.67 80	97.72 61	91.40 127	98.18 209	87.45 213	99.18 93	98.36 124

jason		89.17 245	88.32 253	91.70 220	95.73 230	80.07 247	88.10 307	93.22 281	71.98 343	90.09 287	92.79 292	78.53 277	98.56 176	87.43 214	97.06 248	96.46 246
jason: jason.
Effi-MVS+		92.79 160	92.74 159	92.94 175	95.10 252	83.30 203	94.00 139	97.53 111	91.36 122	89.35 302	90.65 331	94.01 72	98.66 164	87.40 215	95.30 296	96.88 230

FMVSNet2		92.78 161	92.73 161	92.95 173	95.40 244	81.98 222	94.18 133	95.53 230	88.63 179	96.05 109	97.37 82	81.31 255	98.81 135	87.38 216	98.67 153	98.06 143

EPP-MVSNet		93.91 130	93.68 136	94.59 118	98.08 82	85.55 177	97.44 12	94.03 266	94.22 47	94.94 160	96.19 169	82.07 248	99.57 14	87.28 217	98.89 122	98.65 101

PC_three_1452									75.31 324	95.87 117	95.75 192	92.93 98	96.34 308	87.18 218	98.68 151	98.04 146

ECVR-MVS		90.12 226	90.16 219	90.00 278	97.81 102	72.68 345	95.76 75	78.54 379	89.04 169	95.36 140	98.10 40	70.51 321	98.64 167	87.10 219	99.18 93	98.67 99

VDDNet		94.03 126	94.27 122	93.31 163	98.87 21	82.36 219	95.51 86	91.78 309	97.19 12	96.32 92	98.60 21	84.24 225	98.75 146	87.09 220	98.83 134	98.81 80

agg_prior2														87.06 221	98.36 183	97.98 155

LF4IMVS		92.72 163	92.02 176	94.84 102	95.65 235	91.99 54	92.92 167	96.60 180	85.08 243	92.44 243	93.62 272	86.80 203	96.35 306	86.81 222	98.25 192	96.18 257

GBi-Net		93.21 147	92.96 153	93.97 140	95.40 244	84.29 188	95.99 63	96.56 183	88.63 179	95.10 153	98.53 25	81.31 255	98.98 106	86.74 223	98.38 178	98.65 101

test1		93.21 147	92.96 153	93.97 140	95.40 244	84.29 188	95.99 63	96.56 183	88.63 179	95.10 153	98.53 25	81.31 255	98.98 106	86.74 223	98.38 178	98.65 101

FMVSNet3		90.78 203	90.32 218	92.16 206	93.03 304	79.92 254	92.54 181	94.95 244	86.17 221	95.10 153	96.01 178	69.97 323	98.75 146	86.74 223	98.38 178	97.82 175

lupinMVS		88.34 267	87.31 275	91.45 229	94.74 265	80.06 248	87.23 319	92.27 299	71.10 348	88.83 306	91.15 320	77.02 291	98.53 179	86.67 226	96.75 263	95.76 274

OMC-MVS		94.22 121	93.69 135	95.81 69	97.25 134	91.27 64	92.27 197	97.40 119	87.10 211	94.56 173	95.42 206	93.74 74	98.11 214	86.62 227	98.85 128	98.06 143

mvsany_test3		89.11 247	88.21 262	91.83 213	91.30 336	90.25 79	88.09 308	78.76 377	76.37 318	96.43 87	98.39 32	83.79 228	90.43 363	86.57 228	94.20 321	94.80 301

pmmvs-eth3d		91.54 191	90.73 208	93.99 138	95.76 229	87.86 125	90.83 239	93.98 270	78.23 307	94.02 188	96.22 168	82.62 244	96.83 290	86.57 228	98.33 184	97.29 215

BP-MVS														86.55 230

HQP-MVS		92.09 181	91.49 190	93.88 145	96.36 182	84.89 184	91.37 226	97.31 129	87.16 208	88.81 308	93.40 278	84.76 222	98.60 171	86.55 230	97.73 224	98.14 140

ppachtmachnet_test		88.61 263	88.64 247	88.50 306	91.76 329	70.99 354	84.59 352	92.98 284	79.30 299	92.38 246	93.53 276	79.57 266	97.45 266	86.50 232	97.17 245	97.07 219

MIMVSNet1		95.52 67	95.45 74	95.72 73	99.14 5	89.02 99	96.23 57	96.87 164	93.73 57	97.87 28	98.49 28	90.73 147	99.05 98	86.43 233	99.60 26	99.10 47

PVSNet_Blended_VisFu		91.63 189	91.20 196	92.94 175	97.73 109	83.95 197	92.14 201	97.46 115	78.85 304	92.35 248	94.98 224	84.16 226	99.08 93	86.36 234	96.77 262	95.79 273

Fast-Effi-MVS+-dtu		92.77 162	92.16 172	94.58 120	94.66 270	88.25 117	92.05 203	96.65 178	89.62 158	90.08 288	91.23 319	92.56 107	98.60 171	86.30 235	96.27 273	96.90 227

OPU-MVS				95.15 93	96.84 154	89.43 92	95.21 94				95.66 195	93.12 92	98.06 216	86.28 236	98.61 157	97.95 159

PMVS	87.21 14	94.97 91	95.33 82	93.91 144	98.97 17	97.16 2	95.54 85	95.85 215	96.47 22	93.40 206	97.46 78	95.31 33	95.47 324	86.18 237	98.78 140	89.11 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OpenMVS	89.45 8	92.27 178	92.13 174	92.68 185	94.53 274	84.10 194	95.70 76	97.03 150	82.44 274	91.14 271	96.42 149	88.47 173	98.38 191	85.95 238	97.47 237	95.55 284

CDPH-MVS		92.67 165	91.83 182	95.18 92	96.94 147	88.46 115	90.70 243	97.07 148	77.38 310	92.34 250	95.08 221	92.67 106	98.88 120	85.74 239	98.57 161	98.20 135

CANet_DTU		89.85 235	89.17 236	91.87 212	92.20 317	80.02 251	90.79 240	95.87 214	86.02 223	82.53 362	91.77 312	80.01 264	98.57 175	85.66 240	97.70 227	97.01 223

ITE_SJBPF				95.95 59	97.34 132	93.36 40		96.55 186	91.93 97	94.82 165	95.39 210	91.99 116	97.08 281	85.53 241	97.96 216	97.41 205

new-patchmatchnet		88.97 253	90.79 206	83.50 350	94.28 279	55.83 384	85.34 345	93.56 275	86.18 220	95.47 133	95.73 193	83.10 234	96.51 298	85.40 242	98.06 209	98.16 138

EPNet		89.80 237	88.25 258	94.45 126	83.91 383	86.18 164	93.87 143	87.07 341	91.16 128	80.64 371	94.72 235	78.83 271	98.89 119	85.17 243	98.89 122	98.28 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		90.11 227	89.92 225	90.66 259	90.35 348	77.00 303	92.96 166	92.81 287	90.25 148	94.74 169	96.93 118	67.11 332	97.52 261	85.17 243	98.98 112	97.46 201

旧先验2							90.00 267		68.65 361	92.71 233			96.52 297	85.15 245

MDA-MVSNet-bldmvs		91.04 199	90.88 202	91.55 225	94.68 269	80.16 243	85.49 343	92.14 303	90.41 146	94.93 161	95.79 187	85.10 220	96.93 287	85.15 245	94.19 323	97.57 194

Anonymous202405211		92.58 167	92.50 167	92.83 180	96.55 170	83.22 206	92.43 188	91.64 311	94.10 49	95.59 128	96.64 139	81.88 252	97.50 262	85.12 247	98.52 166	97.77 180

AllTest		94.88 95	94.51 114	96.00 56	98.02 89	92.17 50	95.26 93	98.43 15	90.48 142	95.04 157	96.74 132	92.54 108	97.86 238	85.11 248	98.98 112	97.98 155

TestCases				96.00 56	98.02 89	92.17 50		98.43 15	90.48 142	95.04 157	96.74 132	92.54 108	97.86 238	85.11 248	98.98 112	97.98 155

VPNet		93.08 150	93.76 132	91.03 245	98.60 39	75.83 321	91.51 224	95.62 220	91.84 104	95.74 122	97.10 108	89.31 167	98.32 196	85.07 250	99.06 102	98.93 64

LFMVS		91.33 196	91.16 199	91.82 214	96.27 193	79.36 267	95.01 104	85.61 353	96.04 30	94.82 165	97.06 110	72.03 316	98.46 187	84.96 251	98.70 149	97.65 190

VNet		92.67 165	92.96 153	91.79 215	96.27 193	80.15 244	91.95 207	94.98 243	92.19 91	94.52 175	96.07 175	87.43 190	97.39 271	84.83 252	98.38 178	97.83 173

our_test_3		87.55 282	87.59 272	87.44 322	91.76 329	70.48 355	83.83 358	90.55 320	79.79 289	92.06 257	92.17 306	78.63 276	95.63 319	84.77 253	94.73 309	96.22 255

TAPA-MVS	88.58 10	92.49 170	91.75 184	94.73 106	96.50 175	89.69 86	92.91 168	97.68 98	78.02 308	92.79 230	94.10 255	90.85 142	97.96 227	84.76 254	98.16 201	96.54 239
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		91.28 198	90.86 203	92.53 194	95.45 243	82.53 216	89.25 290	96.52 187	85.00 244	89.91 292	88.55 351	92.94 97	98.84 128	84.72 255	95.44 291	96.22 255

GA-MVS		87.70 276	86.82 287	90.31 267	93.27 298	77.22 301	84.72 351	92.79 289	85.11 242	89.82 294	90.07 332	66.80 335	97.76 248	84.56 256	94.27 320	95.96 264

QAPM		92.88 157	92.77 157	93.22 166	95.82 224	83.31 202	96.45 39	97.35 126	83.91 255	93.75 195	96.77 127	89.25 168	98.88 120	84.56 256	97.02 250	97.49 200

UnsupCasMVSNet_eth		90.33 220	90.34 217	90.28 268	94.64 272	80.24 242	89.69 275	95.88 213	85.77 227	93.94 192	95.69 194	81.99 249	92.98 351	84.21 258	91.30 353	97.62 191

CLD-MVS		91.82 184	91.41 192	93.04 168	96.37 180	83.65 200	86.82 330	97.29 132	84.65 250	92.27 252	89.67 340	92.20 113	97.85 240	83.95 259	99.47 42	97.62 191
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
114514_t		90.51 210	89.80 228	92.63 188	98.00 91	82.24 220	93.40 156	97.29 132	65.84 369	89.40 301	94.80 232	86.99 198	98.75 146	83.88 260	98.61 157	96.89 228

DP-MVS Recon		92.31 176	91.88 180	93.60 153	97.18 138	86.87 143	91.10 234	97.37 120	84.92 246	92.08 256	94.08 256	88.59 171	98.20 206	83.50 261	98.14 203	95.73 275

YYNet1		88.17 269	88.24 259	87.93 316	92.21 316	73.62 337	80.75 368	88.77 325	82.51 273	94.99 159	95.11 219	82.70 242	93.70 345	83.33 262	93.83 325	96.48 245

MDA-MVSNet_test_wron		88.16 270	88.23 260	87.93 316	92.22 315	73.71 336	80.71 369	88.84 324	82.52 272	94.88 164	95.14 217	82.70 242	93.61 346	83.28 263	93.80 326	96.46 246

XXY-MVS		92.58 167	93.16 152	90.84 254	97.75 106	79.84 255	91.87 215	96.22 201	85.94 224	95.53 130	97.68 62	92.69 105	94.48 336	83.21 264	97.51 234	98.21 134

cascas		87.02 296	86.28 298	89.25 292	91.56 333	76.45 313	84.33 355	96.78 170	71.01 349	86.89 336	85.91 366	81.35 254	96.94 285	83.09 265	95.60 286	94.35 313

test-LLR		83.58 318	83.17 318	84.79 343	89.68 355	66.86 367	83.08 360	84.52 361	83.07 264	82.85 360	84.78 370	62.86 357	93.49 347	82.85 266	94.86 305	94.03 319

test-mter		81.21 335	80.01 342	84.79 343	89.68 355	66.86 367	83.08 360	84.52 361	73.85 332	82.85 360	84.78 370	43.66 386	93.49 347	82.85 266	94.86 305	94.03 319

pmmvs4		88.95 254	87.70 271	92.70 183	94.30 278	85.60 176	87.22 320	92.16 302	74.62 327	89.75 298	94.19 252	77.97 281	96.41 302	82.71 268	96.36 272	96.09 259

testdata				91.03 245	96.87 152	82.01 221		94.28 262	71.55 344	92.46 241	95.42 206	85.65 218	97.38 273	82.64 269	97.27 242	93.70 328

thisisatest0515		84.72 311	82.99 320	89.90 279	92.96 305	75.33 324	84.36 354	83.42 366	77.37 311	88.27 321	86.65 360	53.94 376	98.72 151	82.56 270	97.40 239	95.67 279

PS-MVSNAJ		88.86 257	88.99 241	88.48 307	94.88 256	74.71 325	86.69 333	95.60 221	80.88 282	87.83 326	87.37 358	90.77 143	98.82 130	82.52 271	94.37 317	91.93 350

xiu_mvs_v2_base		89.00 252	89.19 235	88.46 308	94.86 258	74.63 327	86.97 324	95.60 221	80.88 282	87.83 326	88.62 350	91.04 139	98.81 135	82.51 272	94.38 316	91.93 350

PAPM_NR		91.03 200	90.81 205	91.68 221	96.73 159	81.10 236	93.72 148	96.35 194	88.19 189	88.77 312	92.12 308	85.09 221	97.25 275	82.40 273	93.90 324	96.68 237

test_yl		90.11 227	89.73 231	91.26 237	94.09 283	79.82 256	90.44 250	92.65 292	90.90 130	93.19 217	93.30 280	73.90 307	98.03 218	82.23 274	96.87 257	95.93 266

DCV-MVSNet		90.11 227	89.73 231	91.26 237	94.09 283	79.82 256	90.44 250	92.65 292	90.90 130	93.19 217	93.30 280	73.90 307	98.03 218	82.23 274	96.87 257	95.93 266

DPM-MVS		89.35 242	88.40 251	92.18 205	96.13 206	84.20 192	86.96 325	96.15 205	75.40 323	87.36 332	91.55 317	83.30 232	98.01 222	82.17 276	96.62 266	94.32 314

MG-MVS		89.54 239	89.80 228	88.76 299	94.88 256	72.47 347	89.60 276	92.44 298	85.82 226	89.48 300	95.98 179	82.85 239	97.74 251	81.87 277	95.27 297	96.08 260

PatchmatchNet		85.22 307	84.64 306	86.98 325	89.51 358	69.83 361	90.52 248	87.34 339	78.87 303	87.22 334	92.74 294	66.91 334	96.53 296	81.77 278	86.88 367	94.58 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		92.00 183	92.76 158	89.71 283	95.62 238	77.02 302	90.72 242	96.17 204	87.70 200	95.26 146	96.29 162	92.54 108	96.45 301	81.77 278	98.77 141	95.66 280

sd_testset		93.94 128	94.39 115	92.61 190	97.93 96	83.24 204	93.17 161	95.04 241	93.65 62	95.51 131	98.63 19	94.49 64	95.89 316	81.72 280	99.35 60	98.70 96

test_vis1_rt		85.58 305	84.58 307	88.60 303	87.97 367	86.76 145	85.45 344	93.59 273	66.43 366	87.64 328	89.20 346	79.33 268	85.38 377	81.59 281	89.98 361	93.66 329

原ACMM1				92.87 178	96.91 150	84.22 191		97.01 151	76.84 316	89.64 299	94.46 244	88.00 181	98.70 158	81.53 282	98.01 214	95.70 278

1112_ss		88.42 265	87.41 274	91.45 229	96.69 160	80.99 237	89.72 274	96.72 175	73.37 334	87.00 335	90.69 329	77.38 286	98.20 206	81.38 283	93.72 327	95.15 291

MS-PatchMatch		88.05 271	87.75 269	88.95 295	93.28 297	77.93 289	87.88 310	92.49 297	75.42 322	92.57 238	93.59 274	80.44 262	94.24 343	81.28 284	92.75 341	94.69 307

LCM-MVSNet-Re		94.20 122	94.58 113	93.04 168	95.91 221	83.13 209	93.79 146	99.19 3	92.00 94	98.84 5	98.04 45	93.64 75	99.02 103	81.28 284	98.54 164	96.96 225

tpmrst		82.85 324	82.93 321	82.64 352	87.65 368	58.99 382	90.14 262	87.90 335	75.54 321	83.93 353	91.63 315	66.79 337	95.36 327	81.21 286	81.54 377	93.57 334

无先验							89.94 268	95.75 217	70.81 351				98.59 173	81.17 287		94.81 300

新几何1				93.17 167	97.16 139	87.29 132		94.43 258	67.95 363	91.29 266	94.94 226	86.97 199	98.23 204	81.06 288	97.75 223	93.98 321

MSDG		90.82 201	90.67 209	91.26 237	94.16 280	83.08 210	86.63 335	96.19 202	90.60 141	91.94 258	91.89 310	89.16 169	95.75 318	80.96 289	94.51 314	94.95 297

mvsany_test1		83.91 317	82.93 321	86.84 328	86.18 377	85.93 169	81.11 367	75.03 382	70.80 352	88.57 317	94.63 238	83.08 235	87.38 372	80.39 290	86.57 368	87.21 368

pmmvs5		87.87 273	87.14 281	90.07 275	93.26 299	76.97 306	88.89 295	92.18 300	73.71 333	88.36 319	93.89 265	76.86 296	96.73 293	80.32 291	96.81 260	96.51 241

PVSNet_BlendedMVS		90.35 219	89.96 224	91.54 226	94.81 260	78.80 281	90.14 262	96.93 157	79.43 294	88.68 315	95.06 222	86.27 211	98.15 212	80.27 292	98.04 211	97.68 188

PVSNet_Blended		88.74 260	88.16 264	90.46 265	94.81 260	78.80 281	86.64 334	96.93 157	74.67 326	88.68 315	89.18 347	86.27 211	98.15 212	80.27 292	96.00 277	94.44 311

testdata2													98.03 218	80.24 294

FE-MVS		89.06 248	88.29 255	91.36 232	94.78 262	79.57 263	96.77 28	90.99 315	84.87 247	92.96 225	96.29 162	60.69 365	98.80 138	80.18 295	97.11 247	95.71 276

F-COLMAP		92.28 177	91.06 200	95.95 59	97.52 123	91.90 56	93.53 151	97.18 139	83.98 254	88.70 314	94.04 257	88.41 174	98.55 178	80.17 296	95.99 278	97.39 209

EPMVS		81.17 336	80.37 338	83.58 349	85.58 379	65.08 374	90.31 257	71.34 383	77.31 312	85.80 341	91.30 318	59.38 366	92.70 352	79.99 297	82.34 376	92.96 341

TESTMET0.1,1		79.09 344	78.04 347	82.25 353	87.52 370	64.03 378	83.08 360	80.62 373	70.28 355	80.16 372	83.22 373	44.13 385	90.56 361	79.95 298	93.36 331	92.15 348

Test_1112_low_res		87.50 284	86.58 291	90.25 270	96.80 158	77.75 293	87.53 316	96.25 197	69.73 358	86.47 337	93.61 273	75.67 301	97.88 234	79.95 298	93.20 334	95.11 293

CL-MVSNet_self_test		90.04 232	89.90 226	90.47 263	95.24 250	77.81 292	86.60 337	92.62 294	85.64 230	93.25 214	93.92 263	83.84 227	96.06 313	79.93 300	98.03 212	97.53 198

OpenMVS_ROB	85.12 16	89.52 240	89.05 238	90.92 250	94.58 273	81.21 235	91.10 234	93.41 279	77.03 314	93.41 204	93.99 261	83.23 233	97.80 242	79.93 300	94.80 308	93.74 327

CNLPA		91.72 187	91.20 196	93.26 165	96.17 201	91.02 67	91.14 232	95.55 228	90.16 149	90.87 274	93.56 275	86.31 210	94.40 339	79.92 302	97.12 246	94.37 312

ab-mvs		92.40 173	92.62 164	91.74 217	97.02 143	81.65 226	95.84 71	95.50 231	86.95 213	92.95 226	97.56 70	90.70 148	97.50 262	79.63 303	97.43 238	96.06 261

test_post1							90.21 259				5.85 386	65.36 344	96.00 314	79.61 304

SCA		87.43 285	87.21 279	88.10 314	92.01 324	71.98 349	89.43 282	88.11 334	82.26 276	88.71 313	92.83 290	78.65 274	97.59 258	79.61 304	93.30 333	94.75 304

tpmvs		84.22 315	83.97 313	84.94 341	87.09 373	65.18 372	91.21 231	88.35 328	82.87 267	85.21 343	90.96 324	65.24 346	96.75 292	79.60 306	85.25 370	92.90 342

baseline1		87.62 280	87.31 275	88.54 304	94.71 268	74.27 333	93.10 163	88.20 331	86.20 219	92.18 254	93.04 285	73.21 310	95.52 321	79.32 307	85.82 369	95.83 271

tpm		84.38 314	84.08 312	85.30 339	90.47 346	63.43 379	89.34 285	85.63 352	77.24 313	87.62 329	95.03 223	61.00 364	97.30 274	79.26 308	91.09 356	95.16 290

BH-untuned		90.68 206	90.90 201	90.05 277	95.98 216	79.57 263	90.04 265	94.94 245	87.91 192	94.07 184	93.00 286	87.76 185	97.78 245	79.19 309	95.17 299	92.80 343

API-MVS		91.52 192	91.61 185	91.26 237	94.16 280	86.26 163	94.66 114	94.82 248	91.17 127	92.13 255	91.08 322	90.03 162	97.06 282	79.09 310	97.35 241	90.45 360

1314		86.46 300	86.33 297	86.87 327	91.65 331	74.54 328	91.94 209	94.10 265	74.28 329	84.78 348	87.33 359	83.03 236	95.00 333	78.72 311	91.16 355	91.06 357

BH-RMVSNet		90.47 212	90.44 214	90.56 262	95.21 251	78.65 283	89.15 291	93.94 271	88.21 188	92.74 232	94.22 251	86.38 209	97.88 234	78.67 312	95.39 293	95.14 292

MVP-Stereo		90.07 230	88.92 242	93.54 157	96.31 189	86.49 153	90.93 237	95.59 225	79.80 288	91.48 263	95.59 197	80.79 260	97.39 271	78.57 313	91.19 354	96.76 235
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDTV_nov1_ep13			83.88 315		89.42 359	61.52 380	88.74 301	87.41 337	73.99 331	84.96 347	94.01 260	65.25 345	95.53 320	78.02 314	93.16 335

Vis-MVSNet (Re-imp)		90.42 213	90.16 219	91.20 241	97.66 116	77.32 299	94.33 128	87.66 336	91.20 126	92.99 223	95.13 218	75.40 303	98.28 198	77.86 315	99.19 91	97.99 154

sss		87.23 289	86.82 287	88.46 308	93.96 286	77.94 288	86.84 328	92.78 290	77.59 309	87.61 330	91.83 311	78.75 272	91.92 355	77.84 316	94.20 321	95.52 285

IB-MVS	77.21 19	83.11 320	81.05 331	89.29 290	91.15 337	75.85 319	85.66 342	86.00 348	79.70 291	82.02 366	86.61 361	48.26 382	98.39 189	77.84 316	92.22 347	93.63 330
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
Patchmatch-test		86.10 302	86.01 299	86.38 333	90.63 343	74.22 334	89.57 277	86.69 342	85.73 229	89.81 295	92.83 290	65.24 346	91.04 359	77.82 318	95.78 283	93.88 324

USDC		89.02 249	89.08 237	88.84 298	95.07 253	74.50 330	88.97 293	96.39 192	73.21 336	93.27 211	96.28 164	82.16 247	96.39 303	77.55 319	98.80 138	95.62 283

CDS-MVSNet		89.55 238	88.22 261	93.53 158	95.37 247	86.49 153	89.26 288	93.59 273	79.76 290	91.15 270	92.31 304	77.12 289	98.38 191	77.51 320	97.92 219	95.71 276
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
N_pmnet		88.90 256	87.25 278	93.83 148	94.40 277	93.81 35	84.73 349	87.09 340	79.36 297	93.26 212	92.43 302	79.29 269	91.68 356	77.50 321	97.22 244	96.00 263

AdaColmap		91.63 189	91.36 193	92.47 196	95.56 240	86.36 159	92.24 200	96.27 196	88.88 175	89.90 293	92.69 295	91.65 122	98.32 196	77.38 322	97.64 230	92.72 344

CostFormer		83.09 321	82.21 324	85.73 335	89.27 360	67.01 365	90.35 255	86.47 344	70.42 354	83.52 357	93.23 283	61.18 362	96.85 289	77.21 323	88.26 365	93.34 336

E-PMN		80.72 339	80.86 334	80.29 357	85.11 380	68.77 363	72.96 373	81.97 369	87.76 198	83.25 359	83.01 374	62.22 360	89.17 370	77.15 324	94.31 319	82.93 374

PLC	85.34 15	90.40 214	88.92 242	94.85 101	96.53 174	90.02 81	91.58 223	96.48 189	80.16 287	86.14 339	92.18 305	85.73 216	98.25 203	76.87 325	94.61 313	96.30 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		90.32 221	88.87 245	94.66 111	94.82 259	91.85 57	94.22 132	94.75 251	80.91 281	87.52 331	88.07 354	86.63 207	97.87 237	76.67 326	96.21 274	94.25 315
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
EPNet_dtu		85.63 304	84.37 309	89.40 288	86.30 376	74.33 332	91.64 222	88.26 329	84.84 248	72.96 380	89.85 333	71.27 319	97.69 253	76.60 327	97.62 231	96.18 257
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		85.08 309	83.04 319	91.19 242	87.56 369	86.14 165	89.40 284	84.44 363	88.98 171	82.20 363	97.95 49	56.82 371	96.15 309	76.55 328	83.45 373	91.30 355

PatchMatch-RL		89.18 244	88.02 267	92.64 186	95.90 222	92.87 45	88.67 304	91.06 314	80.34 285	90.03 290	91.67 314	83.34 231	94.42 338	76.35 329	94.84 307	90.64 359

FMVSNet5		87.82 275	86.56 292	91.62 223	92.31 312	79.81 258	93.49 152	94.81 250	83.26 259	91.36 265	96.93 118	52.77 379	97.49 264	76.07 330	98.03 212	97.55 197

PMMVS		83.00 322	81.11 330	88.66 302	83.81 384	86.44 156	82.24 364	85.65 351	61.75 376	82.07 364	85.64 367	79.75 265	91.59 357	75.99 331	93.09 337	87.94 367

CMPMVS	68.83 22	87.28 288	85.67 302	92.09 208	88.77 364	85.42 179	90.31 257	94.38 259	70.02 356	88.00 324	93.30 280	73.78 309	94.03 344	75.96 332	96.54 268	96.83 231
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EMVS		80.35 341	80.28 340	80.54 356	84.73 382	69.07 362	72.54 375	80.73 372	87.80 196	81.66 368	81.73 375	62.89 356	89.84 365	75.79 333	94.65 312	82.71 375

HyFIR lowres test		87.19 292	85.51 303	92.24 200	97.12 142	80.51 241	85.03 347	96.06 206	66.11 368	91.66 262	92.98 288	70.12 322	99.14 85	75.29 334	95.23 298	97.07 219

UnsupCasMVSNet_bld		88.50 264	88.03 266	89.90 279	95.52 241	78.88 278	87.39 318	94.02 268	79.32 298	93.06 220	94.02 259	80.72 261	94.27 341	75.16 335	93.08 338	96.54 239

WTY-MVS		86.93 297	86.50 296	88.24 311	94.96 254	74.64 326	87.19 321	92.07 305	78.29 306	88.32 320	91.59 316	78.06 280	94.27 341	74.88 336	93.15 336	95.80 272

KD-MVS_2432*1600		82.17 328	80.75 335	86.42 331	82.04 385	70.09 358	81.75 365	90.80 317	82.56 270	90.37 283	89.30 344	42.90 387	96.11 311	74.47 337	92.55 344	93.06 338

miper_refine_blended		82.17 328	80.75 335	86.42 331	82.04 385	70.09 358	81.75 365	90.80 317	82.56 270	90.37 283	89.30 344	42.90 387	96.11 311	74.47 337	92.55 344	93.06 338

baseline2		83.38 319	81.54 328	88.90 296	91.38 334	72.84 344	88.78 299	81.22 371	78.97 301	79.82 373	87.56 355	61.73 361	97.80 242	74.30 339	90.05 360	96.05 262

gm-plane-assit					87.08 374	59.33 381			71.22 346		83.58 372		97.20 277	73.95 340

test20.03		90.80 202	90.85 204	90.63 260	95.63 237	79.24 270	89.81 273	92.87 286	89.90 152	94.39 177	96.40 151	85.77 215	95.27 331	73.86 341	99.05 105	97.39 209

TAMVS		90.16 225	89.05 238	93.49 161	96.49 176	86.37 158	90.34 256	92.55 296	80.84 284	92.99 223	94.57 242	81.94 251	98.20 206	73.51 342	98.21 197	95.90 269

CHOSEN 1792x2688		87.19 292	85.92 301	91.00 248	97.13 141	79.41 266	84.51 353	95.60 221	64.14 372	90.07 289	94.81 230	78.26 279	97.14 279	73.34 343	95.38 294	96.46 246

thres600view7		87.66 278	87.10 284	89.36 289	96.05 210	73.17 339	92.72 173	85.31 356	91.89 99	93.29 209	90.97 323	63.42 354	98.39 189	73.23 344	96.99 255	96.51 241

dp		79.28 343	78.62 346	81.24 355	85.97 378	56.45 383	86.91 326	85.26 358	72.97 339	81.45 370	89.17 348	56.01 373	95.45 325	73.19 345	76.68 379	91.82 353

pmmvs3		80.83 338	78.96 345	86.45 330	87.23 372	77.48 297	84.87 348	82.31 368	63.83 373	85.03 345	89.50 342	49.66 380	93.10 349	73.12 346	95.10 300	88.78 365

MDTV_nov1_ep13_2view						42.48 388	88.45 306		67.22 365	83.56 356		66.80 335		72.86 347		94.06 318

TR-MVS		87.70 276	87.17 280	89.27 291	94.11 282	79.26 269	88.69 302	91.86 308	81.94 278	90.69 277	89.79 337	82.82 240	97.42 268	72.65 348	91.98 350	91.14 356

PAPR		87.65 279	86.77 289	90.27 269	92.85 306	77.38 298	88.56 305	96.23 199	76.82 317	84.98 346	89.75 339	86.08 213	97.16 278	72.33 349	93.35 332	96.26 254

Anonymous20231206		88.77 259	88.29 255	90.20 273	96.31 189	78.81 280	89.56 278	93.49 277	74.26 330	92.38 246	95.58 200	82.21 245	95.43 326	72.07 350	98.75 144	96.34 250

MVS		84.98 310	84.30 310	87.01 324	91.03 338	77.69 295	91.94 209	94.16 264	59.36 377	84.23 352	87.50 357	85.66 217	96.80 291	71.79 351	93.05 339	86.54 370

tpm cat1		80.61 340	79.46 343	84.07 348	88.78 363	65.06 375	89.26 288	88.23 330	62.27 375	81.90 367	89.66 341	62.70 359	95.29 330	71.72 352	80.60 378	91.86 352

HY-MVS	82.50 18	86.81 298	85.93 300	89.47 285	93.63 293	77.93 289	94.02 138	91.58 312	75.68 319	83.64 355	93.64 270	77.40 285	97.42 268	71.70 353	92.07 349	93.05 340

testgi		90.38 217	91.34 194	87.50 321	97.49 125	71.54 350	89.43 282	95.16 239	88.38 186	94.54 174	94.68 237	92.88 101	93.09 350	71.60 354	97.85 222	97.88 167

BH-w/o		87.21 290	87.02 285	87.79 319	94.77 263	77.27 300	87.90 309	93.21 283	81.74 279	89.99 291	88.39 353	83.47 230	96.93 287	71.29 355	92.43 346	89.15 361

thres100view900		87.35 287	86.89 286	88.72 300	96.14 204	73.09 341	93.00 165	85.31 356	92.13 92	93.26 212	90.96 324	63.42 354	98.28 198	71.27 356	96.54 268	94.79 302

tfpn200view9		87.05 295	86.52 294	88.67 301	95.77 227	72.94 342	91.89 212	86.00 348	90.84 132	92.61 235	89.80 335	63.93 351	98.28 198	71.27 356	96.54 268	94.79 302

thres400		87.20 291	86.52 294	89.24 293	95.77 227	72.94 342	91.89 212	86.00 348	90.84 132	92.61 235	89.80 335	63.93 351	98.28 198	71.27 356	96.54 268	96.51 241

tpm2		81.46 332	80.35 339	84.80 342	89.90 352	65.14 373	90.44 250	85.36 355	65.82 370	82.05 365	92.44 301	57.94 368	96.69 294	70.71 359	88.49 364	92.56 345

ADS-MVSNet2		84.01 316	82.20 325	89.41 287	89.04 361	76.37 315	87.57 312	90.98 316	72.71 341	84.46 349	92.45 299	68.08 328	96.48 299	70.58 360	83.97 371	95.38 287

ADS-MVSNet		82.25 326	81.55 327	84.34 346	89.04 361	65.30 371	87.57 312	85.13 360	72.71 341	84.46 349	92.45 299	68.08 328	92.33 353	70.58 360	83.97 371	95.38 287

PVSNet	76.22 20	82.89 323	82.37 323	84.48 345	93.96 286	64.38 377	78.60 371	88.61 326	71.50 345	84.43 351	86.36 364	74.27 306	94.60 335	69.87 362	93.69 328	94.46 310

CHOSEN 280x420		80.04 342	77.97 348	86.23 334	90.13 350	74.53 329	72.87 374	89.59 323	66.38 367	76.29 377	85.32 368	56.96 370	95.36 327	69.49 363	94.72 310	88.79 364

thres200		85.85 303	85.18 304	87.88 318	94.44 275	72.52 346	89.08 292	86.21 345	88.57 182	91.44 264	88.40 352	64.22 349	98.00 223	68.35 364	95.88 282	93.12 337

dmvs_re		84.69 312	83.94 314	86.95 326	92.24 314	82.93 212	89.51 279	87.37 338	84.38 252	85.37 342	85.08 369	72.44 312	86.59 374	68.05 365	91.03 357	91.33 354

PCF-MVS	84.52 17	89.12 246	87.71 270	93.34 162	96.06 209	85.84 172	86.58 338	97.31 129	68.46 362	93.61 200	93.89 265	87.51 189	98.52 180	67.85 366	98.11 205	95.66 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
new_pmnet		81.22 334	81.01 333	81.86 354	90.92 341	70.15 357	84.03 356	80.25 375	70.83 350	85.97 340	89.78 338	67.93 331	84.65 378	67.44 367	91.90 351	90.78 358

gg-mvs-nofinetune		82.10 330	81.02 332	85.34 338	87.46 371	71.04 352	94.74 111	67.56 384	96.44 23	79.43 374	98.99 6	45.24 383	96.15 309	67.18 368	92.17 348	88.85 363

DSMNet-mixed		82.21 327	81.56 326	84.16 347	89.57 357	70.00 360	90.65 245	77.66 381	54.99 380	83.30 358	97.57 69	77.89 282	90.50 362	66.86 369	95.54 288	91.97 349

test0.0.03 1		82.48 325	81.47 329	85.48 337	89.70 354	73.57 338	84.73 349	81.64 370	83.07 264	88.13 323	86.61 361	62.86 357	89.10 371	66.24 370	90.29 359	93.77 326

MIMVSNet		87.13 294	86.54 293	88.89 297	96.05 210	76.11 316	94.39 125	88.51 327	81.37 280	88.27 321	96.75 131	72.38 313	95.52 321	65.71 371	95.47 290	95.03 294

PMMVS2		81.31 333	83.44 316	74.92 362	90.52 345	46.49 387	69.19 376	85.23 359	84.30 253	87.95 325	94.71 236	76.95 293	84.36 379	64.07 372	98.09 207	93.89 323

FPMVS		84.50 313	83.28 317	88.16 313	96.32 188	94.49 16	85.76 341	85.47 354	83.09 263	85.20 344	94.26 249	63.79 353	86.58 375	63.72 373	91.88 352	83.40 373

MVS-HIRNet		78.83 345	80.60 337	73.51 363	93.07 301	47.37 386	87.10 323	78.00 380	68.94 360	77.53 376	97.26 94	71.45 318	94.62 334	63.28 374	88.74 363	78.55 378

wuyk23d		87.83 274	90.79 206	78.96 359	90.46 347	88.63 107	92.72 173	90.67 319	91.65 116	98.68 11	97.64 66	96.06 15	77.53 381	59.84 375	99.41 55	70.73 379

GG-mvs-BLEND				83.24 351	85.06 381	71.03 353	94.99 106	65.55 385		74.09 379	75.51 379	44.57 384	94.46 337	59.57 376	87.54 366	84.24 372

PVSNet_0	70.34 21	74.58 347	72.96 350	79.47 358	90.63 343	66.24 370	73.26 372	83.40 367	63.67 374	78.02 375	78.35 378	72.53 311	89.59 367	56.68 377	60.05 382	82.57 376

MVE	59.87 23	73.86 348	72.65 351	77.47 360	87.00 375	74.35 331	61.37 378	60.93 386	67.27 364	69.69 381	86.49 363	81.24 258	72.33 382	56.45 378	83.45 373	85.74 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		81.91 331	80.11 341	87.31 323	93.87 289	72.32 348	84.02 357	93.22 281	69.47 359	76.13 378	89.84 334	72.15 314	97.23 276	53.27 379	89.02 362	92.37 347

test_method		50.44 349	48.94 352	54.93 364	39.68 388	12.38 390	28.59 379	90.09 321	6.82 382	41.10 384	78.41 377	54.41 375	70.69 383	50.12 380	51.26 383	81.72 377

dmvs_testset		78.23 346	78.99 344	75.94 361	91.99 325	55.34 385	88.86 296	78.70 378	82.69 269	81.64 369	79.46 376	75.93 300	85.74 376	48.78 381	82.85 375	86.76 369

tmp_tt		37.97 350	44.33 353	18.88 366	11.80 389	21.54 389	63.51 377	45.66 390	4.23 383	51.34 383	50.48 381	59.08 367	22.11 385	44.50 382	68.35 381	13.00 381

DeepMVS_CX				53.83 365	70.38 387	64.56 376		48.52 389	33.01 381	65.50 382	74.21 380	56.19 372	46.64 384	38.45 383	70.07 380	50.30 380

test123		9.49 352	12.01 355	1.91 367	2.87 390	1.30 391	82.38 363	1.34 392	1.36 385	2.84 386	6.56 384	2.45 390	0.97 386	2.73 384	5.56 384	3.47 382

testmvs		9.02 353	11.42 356	1.81 368	2.77 391	1.13 392	79.44 370	1.90 391	1.18 386	2.65 387	6.80 383	1.95 391	0.87 387	2.62 385	3.45 385	3.44 383

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		23.35 351	31.13 354	0.00 369	0.00 392	0.00 393	0.00 380	95.58 227	0.00 387	0.00 388	91.15 320	93.43 81	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		7.56 354	10.09 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	90.77 143	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		7.56 354	10.08 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	90.69 329	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

FOURS1					99.21 3	94.68 12	98.45 4	98.81 8	97.73 6	98.27 20

test_one_0601					98.26 71	87.14 136		98.18 38	94.25 45	96.99 68	97.36 85	95.13 40

eth-test2					0.00 392

eth-test					0.00 392

test_241102_ONE					98.51 51	86.97 140		98.10 51	91.85 101	97.63 35	97.03 112	96.48 10	98.95 113

save fliter					97.46 128	88.05 121	92.04 204	97.08 147	87.63 202

test0726					98.51 51	86.69 148	95.34 89	98.18 38	91.85 101	97.63 35	97.37 82	95.58 23

GSMVS																94.75 304

test_part2					98.21 75	89.41 93				96.72 78

sam_mvs1												66.64 338				94.75 304

sam_mvs												66.41 339

MTGPA								97.62 102

test_post											6.07 385	65.74 343	95.84 317

patchmatchnet-post											91.71 313	66.22 341	97.59 258

MTMP							94.82 109	54.62 388

TEST9					96.45 178	89.46 90	90.60 246	96.92 159	79.09 300	90.49 279	94.39 246	91.31 129	98.88 120

test_8					96.37 180	89.14 97	90.51 249	96.89 162	79.37 295	90.42 281	94.36 248	91.20 134	98.82 130

agg_prior					96.20 199	88.89 103		96.88 163		90.21 286			98.78 142

test_prior4						89.91 82	90.74 241

test_prior				94.61 114	95.95 218	87.23 134		97.36 125					98.68 162			97.93 161

新几何2							90.02 266

旧先验1					96.20 199	84.17 193		94.82 248			95.57 201	89.57 165			97.89 220	96.32 251

原ACMM2							89.34 285

test222					96.95 146	85.27 181	88.83 298	93.61 272	65.09 371	90.74 276	94.85 229	84.62 224			97.36 240	93.91 322

segment_acmp												92.14 114

testdata1							88.96 294		88.44 184

test12				94.43 127	95.95 218	86.75 146		96.24 198		89.76 297		89.79 164	98.79 139		97.95 217	97.75 183

plane_prior7					97.71 110	88.68 106

plane_prior6					97.21 137	88.23 118						86.93 200

plane_prior4											95.59 197

plane_prior3						88.43 116			90.35 147	93.31 207

plane_prior2							94.56 120		91.74 112

plane_prior1					97.38 130

plane_prior						88.12 119	93.01 164		88.98 171						98.06 209

n2								0.00 393

nn								0.00 393

door-mid								92.13 304

test11								96.65 178

door								91.26 313

HQP5-MVS						84.89 184

HQP-NCC					96.36 182		91.37 226		87.16 208	88.81 308

ACMP_Plane					96.36 182		91.37 226		87.16 208	88.81 308

HQP4-MVS										88.81 308			98.61 169			98.15 139

HQP3-MVS								97.31 129							97.73 224

HQP2-MVS												84.76 222

NP-MVS					96.82 156	87.10 137					93.40 278

ACMMP++_ref															98.82 135

ACMMP++															99.25 82

Test By Simon												90.61 149

High-resolution multi-view benchmark