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	98.61 168	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
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 53	98.46 27	94.62 59	98.84 127	94.64 21	99.53 36	98.99 55

DVP-MVS++		95.93 52	96.34 34	94.70 108	96.54 168	86.66 149	98.45 4	98.22 32	93.26 66	97.54 38	97.36 82	93.12 89	99.38 54	93.88 35	98.68 147	98.04 143

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

UA-Net		97.35 4	97.24 11	97.69 4	98.22 74	93.87 30	98.42 6	98.19 35	96.95 14	95.46 131	99.23 4	93.45 76	99.57 14	95.34 17	99.89 2	99.63 9

OurMVSNet-221017-0		96.80 12	96.75 17	96.96 35	99.03 11	91.85 57	97.98 7	98.01 67	94.15 48	98.93 3	99.07 5	88.07 175	99.57 14	95.86 9	99.69 14	99.46 18

UniMVSNet_ETH3D		97.13 5	97.72 3	95.35 84	99.51 2	87.38 129	97.70 8	97.54 106	98.16 2	98.94 2	99.33 2	97.84 4	99.08 92	90.73 128	99.73 13	99.59 13

tt0805		95.42 73	95.93 56	93.86 144	98.75 32	88.47 113	97.68 9	94.29 255	96.48 21	95.38 133	93.63 265	94.89 52	97.94 228	95.38 16	96.92 252	95.17 283

HPM-MVS		96.81 11	96.62 22	97.36 23	98.89 20	93.53 38	97.51 10	98.44 13	92.35 82	95.95 109	96.41 145	96.71 8	99.42 32	93.99 34	99.36 58	99.13 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RRT_MVS		95.41 74	95.20 88	96.05 55	98.86 22	88.92 101	97.49 11	94.48 251	93.12 68	97.94 26	98.54 22	81.19 255	99.63 6	95.48 12	99.69 14	99.60 12

EPP-MVSNet		93.91 125	93.68 132	94.59 116	98.08 82	85.55 175	97.44 12	94.03 260	94.22 47	94.94 156	96.19 164	82.07 244	99.57 14	87.28 211	98.89 119	98.65 98

LS3D		96.11 47	95.83 62	96.95 36	94.75 259	94.20 19	97.34 13	97.98 70	97.31 11	95.32 138	96.77 122	93.08 91	99.20 78	91.79 104	98.16 196	97.44 200

HPM-MVS_fast		97.01 6	96.89 14	97.39 21	99.12 8	93.92 28	97.16 14	98.17 41	93.11 69	96.48 84	97.36 82	96.92 6	99.34 61	94.31 27	99.38 57	98.92 68

MVSFormer		92.18 175	92.23 166	92.04 206	94.74 260	80.06 243	97.15 15	97.37 117	88.98 169	88.83 299	92.79 286	77.02 287	99.60 9	96.41 4	96.75 259	96.46 241

test_djsdf		96.62 23	96.49 26	97.01 32	98.55 45	91.77 59	97.15 15	97.37 117	88.98 169	98.26 21	98.86 10	93.35 81	99.60 9	96.41 4	99.45 45	99.66 6

mvsmamba		95.61 64	95.40 78	96.22 51	98.44 60	89.86 84	97.14 17	97.45 114	91.25 122	97.49 42	98.14 35	83.49 224	99.45 26	95.52 11	99.66 21	99.36 24

IS-MVSNet		94.49 107	94.35 114	94.92 98	98.25 73	86.46 154	97.13 18	94.31 254	96.24 25	96.28 95	96.36 153	82.88 232	99.35 58	88.19 193	99.52 39	98.96 61

Anonymous20231211		96.60 25	97.13 12	95.00 96	97.46 126	86.35 159	97.11 19	98.24 30	97.58 8	98.72 8	98.97 7	93.15 88	99.15 82	93.18 67	99.74 12	99.50 17

anonymousdsp		96.74 17	96.42 29	97.68 6	98.00 91	94.03 25	96.97 20	97.61 101	87.68 199	98.45 18	98.77 15	94.20 67	99.50 21	96.70 3	99.40 55	99.53 15

ACMMP		96.61 24	96.34 34	97.43 18	98.61 38	93.88 29	96.95 21	98.18 37	92.26 85	96.33 89	96.84 120	95.10 43	99.40 45	93.47 52	99.33 62	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
APDe-MVS		96.46 31	96.64 21	95.93 62	97.68 112	89.38 95	96.90 22	98.41 16	92.52 77	97.43 46	97.92 51	95.11 42	99.50 21	94.45 23	99.30 67	98.92 68

EGC-MVSNET		80.97 330	75.73 341	96.67 42	98.85 24	94.55 15	96.83 23	96.60 177	2.44 376	5.32 377	98.25 33	92.24 108	98.02 220	91.85 102	99.21 86	97.45 198

v7n		96.82 9	97.31 10	95.33 86	98.54 48	86.81 143	96.83 23	98.07 56	96.59 20	98.46 17	98.43 29	92.91 96	99.52 19	96.25 6	99.76 10	99.65 8

CP-MVS		96.44 34	96.08 48	97.54 11	98.29 68	94.62 14	96.80 25	98.08 53	92.67 75	95.08 152	96.39 150	94.77 55	99.42 32	93.17 68	99.44 48	98.58 109

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 20	96.69 17	96.86 70	97.56 67	95.48 26	98.77 144	90.11 150	99.44 48	98.31 125
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		96.60 25	97.05 13	95.24 90	99.02 12	86.44 155	96.78 27	98.08 53	97.42 9	98.48 16	97.86 55	91.76 118	99.63 6	94.23 29	99.84 3	99.66 6

FE-MVS		89.06 243	88.29 250	91.36 228	94.78 257	79.57 258	96.77 28	90.99 310	84.87 243	92.96 220	96.29 157	60.69 357	98.80 137	80.18 289	97.11 243	95.71 270

CS-MVS		95.77 58	95.58 71	96.37 50	96.84 152	91.72 61	96.73 29	99.06 5	94.23 46	92.48 235	94.79 227	93.56 73	99.49 24	93.47 52	99.05 102	97.89 163

pmmvs6		96.80 12	97.36 9	95.15 93	99.12 8	87.82 125	96.68 30	97.86 80	96.10 27	98.14 23	99.28 3	97.94 3	98.21 204	91.38 116	99.69 14	99.42 19

3Dnovator	92.54 3	94.80 97	94.90 96	94.47 123	95.47 237	87.06 136	96.63 31	97.28 131	91.82 104	94.34 176	97.41 76	90.60 146	98.65 165	92.47 87	98.11 200	97.70 182

PS-CasMVS		96.69 20	97.43 5	94.49 122	99.13 6	84.09 193	96.61 32	97.97 72	97.91 5	98.64 13	98.13 37	95.24 36	99.65 3	93.39 59	99.84 3	99.72 2

mvs_tets		96.83 8	96.71 18	97.17 27	98.83 25	92.51 48	96.58 33	97.61 101	87.57 201	98.80 7	98.90 9	96.50 9	99.59 13	96.15 7	99.47 41	99.40 21

PEN-MVS		96.69 20	97.39 8	94.61 112	99.16 4	84.50 185	96.54 34	98.05 59	98.06 4	98.64 13	98.25 33	95.01 48	99.65 3	92.95 76	99.83 5	99.68 4

DTE-MVSNet		96.74 17	97.43 5	94.67 109	99.13 6	84.68 184	96.51 35	97.94 78	98.14 3	98.67 12	98.32 31	95.04 45	99.69 2	93.27 64	99.82 7	99.62 10

XVS		96.49 29	96.18 42	97.44 16	98.56 42	93.99 26	96.50 36	97.95 75	94.58 41	94.38 174	96.49 140	94.56 60	99.39 48	93.57 45	99.05 102	98.93 64

X-MVStestdata		90.70 201	88.45 245	97.44 16	98.56 42	93.99 26	96.50 36	97.95 75	94.58 41	94.38 174	26.89 374	94.56 60	99.39 48	93.57 45	99.05 102	98.93 64

DROMVSNet		95.44 70	95.62 69	94.89 99	96.93 147	87.69 126	96.48 38	99.14 4	93.93 53	92.77 226	94.52 237	93.95 70	99.49 24	93.62 44	99.22 85	97.51 195

mPP-MVS		96.46 31	96.05 50	97.69 4	98.62 36	94.65 13	96.45 39	97.74 92	92.59 76	95.47 129	96.68 132	94.50 62	99.42 32	93.10 70	99.26 78	98.99 55

QAPM		92.88 152	92.77 152	93.22 164	95.82 219	83.31 200	96.45 39	97.35 123	83.91 250	93.75 191	96.77 122	89.25 164	98.88 119	84.56 250	97.02 246	97.49 196

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 31	92.49 49	96.44 41	97.42 115	86.96 210	98.71 10	98.72 17	95.36 31	99.56 17	95.92 8	99.45 45	99.32 27

Gipuma		95.31 81	95.80 64	93.81 146	97.99 94	90.91 70	96.42 42	97.95 75	96.69 17	91.78 255	98.85 12	91.77 117	95.49 319	91.72 106	99.08 98	95.02 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MSP-MVS		95.34 77	94.63 109	97.48 14	98.67 33	94.05 23	96.41 43	98.18 37	91.26 120	95.12 148	95.15 210	86.60 203	99.50 21	93.43 58	96.81 256	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
SR-MVS-dyc-post		96.84 7	96.60 24	97.56 10	98.07 83	95.27 9	96.37 44	98.12 47	95.66 32	97.00 64	97.03 107	94.85 53	99.42 32	93.49 49	98.84 126	98.00 148

RE-MVS-def			96.66 19		98.07 83	95.27 9	96.37 44	98.12 47	95.66 32	97.00 64	97.03 107	95.40 28		93.49 49	98.84 126	98.00 148

TSAR-MVS + MP.		94.96 91	94.75 102	95.57 78	98.86 22	88.69 105	96.37 44	96.81 165	85.23 233	94.75 164	97.12 102	91.85 116	99.40 45	93.45 54	98.33 179	98.62 106
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS-test		95.32 78	95.10 92	95.96 58	96.86 151	90.75 74	96.33 47	99.20 2	93.99 50	91.03 267	93.73 263	93.52 75	99.55 18	91.81 103	99.45 45	97.58 189

ACMH	88.36 12	96.59 27	97.43 5	94.07 134	98.56 42	85.33 178	96.33 47	98.30 23	94.66 40	98.72 8	98.30 32	97.51 5	98.00 222	94.87 18	99.59 28	98.86 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
region2R		96.41 36	96.09 47	97.38 22	98.62 36	93.81 35	96.32 49	97.96 73	92.26 85	95.28 141	96.57 138	95.02 47	99.41 38	93.63 43	99.11 97	98.94 63

testf1		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 18	94.96 36	97.30 51	97.93 48	96.05 16	97.90 229	89.32 166	99.23 82	98.19 133

APD_test2		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 18	94.96 36	97.30 51	97.93 48	96.05 16	97.90 229	89.32 166	99.23 82	98.19 133

APD-MVS_3200maxsize		96.82 9	96.65 20	97.32 25	97.95 95	93.82 33	96.31 50	98.25 27	95.51 34	96.99 66	97.05 106	95.63 22	99.39 48	93.31 61	98.88 121	98.75 87

CP-MVSNet		96.19 45	96.80 16	94.38 127	98.99 16	83.82 196	96.31 50	97.53 108	97.60 7	98.34 19	97.52 70	91.98 114	99.63 6	93.08 72	99.81 8	99.70 3

HFP-MVS		96.39 38	96.17 44	97.04 31	98.51 51	93.37 39	96.30 54	97.98 70	92.35 82	95.63 125	96.47 141	95.37 29	99.27 72	93.78 39	99.14 95	98.48 115

ACMMPR		96.46 31	96.14 45	97.41 20	98.60 39	93.82 33	96.30 54	97.96 73	92.35 82	95.57 127	96.61 136	94.93 51	99.41 38	93.78 39	99.15 94	99.00 53

3Dnovator+	92.74 2	95.86 56	95.77 65	96.13 53	96.81 155	90.79 73	96.30 54	97.82 85	96.13 26	94.74 165	97.23 93	91.33 125	99.16 81	93.25 65	98.30 182	98.46 116

MIMVSNet1		95.52 67	95.45 74	95.72 73	99.14 5	89.02 99	96.23 57	96.87 161	93.73 57	97.87 27	98.49 26	90.73 143	99.05 97	86.43 227	99.60 26	99.10 47

test2506		85.42 300	84.57 302	87.96 309	97.81 100	66.53 362	96.14 58	56.35 379	89.04 167	93.55 198	98.10 38	42.88 381	98.68 161	88.09 197	99.18 90	98.67 96

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 85	94.69 11	96.13 59	98.07 56	95.17 35	96.82 72	96.73 129	95.09 44	99.43 31	92.99 75	98.71 143	98.50 112

MP-MVS		96.14 46	95.68 67	97.51 13	98.81 28	94.06 21	96.10 60	97.78 91	92.73 72	93.48 199	96.72 130	94.23 66	99.42 32	91.99 97	99.29 70	99.05 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		96.42 35	96.20 41	97.07 30	98.80 30	92.79 46	96.08 61	98.16 44	91.74 109	95.34 137	96.36 153	95.68 20	99.44 28	94.41 25	99.28 75	98.97 60

FA-MVS(test-final)		91.81 180	91.85 176	91.68 217	94.95 250	79.99 247	96.00 62	93.44 272	87.80 194	94.02 184	97.29 89	77.60 279	98.45 187	88.04 198	97.49 231	96.61 233

GBi-Net		93.21 142	92.96 148	93.97 137	95.40 239	84.29 186	95.99 63	96.56 180	88.63 177	95.10 149	98.53 23	81.31 251	98.98 105	86.74 217	98.38 173	98.65 98

test1		93.21 142	92.96 148	93.97 137	95.40 239	84.29 186	95.99 63	96.56 180	88.63 177	95.10 149	98.53 23	81.31 251	98.98 105	86.74 217	98.38 173	98.65 98

FMVSNet1		94.84 95	95.13 90	93.97 137	97.60 116	84.29 186	95.99 63	96.56 180	92.38 79	97.03 63	98.53 23	90.12 153	98.98 105	88.78 185	99.16 93	98.65 98

RPSCF		95.58 66	94.89 97	97.62 7	97.58 118	96.30 7	95.97 66	97.53 108	92.42 78	93.41 200	97.78 56	91.21 130	97.77 246	91.06 119	97.06 244	98.80 82

SixPastTwentyTwo		94.91 92	95.21 86	93.98 136	98.52 50	83.19 204	95.93 67	94.84 241	94.86 39	98.49 15	98.74 16	81.45 249	99.60 9	94.69 20	99.39 56	99.15 39

ambc				92.98 168	96.88 149	83.01 208	95.92 68	96.38 190		96.41 86	97.48 74	88.26 171	97.80 242	89.96 155	98.93 118	98.12 139

FC-MVSNet-test		95.32 78	95.88 58	93.62 149	98.49 58	81.77 219	95.90 69	98.32 20	93.93 53	97.53 40	97.56 67	88.48 168	99.40 45	92.91 77	99.83 5	99.68 4

MTAPA		96.65 22	96.38 33	97.47 15	98.95 18	94.05 23	95.88 70	97.62 99	94.46 44	96.29 93	96.94 112	93.56 73	99.37 56	94.29 28	99.42 50	98.99 55

CPTT-MVS		94.74 98	94.12 121	96.60 43	98.15 78	93.01 42	95.84 71	97.66 96	89.21 166	93.28 206	95.46 197	88.89 166	98.98 105	89.80 157	98.82 132	97.80 174

ab-mvs		92.40 168	92.62 159	91.74 213	97.02 141	81.65 221	95.84 71	95.50 227	86.95 211	92.95 221	97.56 67	90.70 144	97.50 260	79.63 297	97.43 234	96.06 256

bld_raw_dy_0_64		94.27 114	94.15 120	94.65 111	98.55 45	86.28 161	95.80 73	95.55 224	88.41 183	97.09 58	98.08 40	78.69 269	98.87 123	95.63 10	99.53 36	98.81 80

nrg030		96.32 40	96.55 25	95.62 76	97.83 99	88.55 111	95.77 74	98.29 26	92.68 73	98.03 25	97.91 52	95.13 40	98.95 112	93.85 37	99.49 40	99.36 24

ECVR-MVS		90.12 222	90.16 214	90.00 274	97.81 100	72.68 338	95.76 75	78.54 371	89.04 167	95.36 136	98.10 38	70.51 313	98.64 166	87.10 213	99.18 90	98.67 96

SteuartSystems-ACMMP		96.40 37	96.30 36	96.71 40	98.63 35	91.96 55	95.70 76	98.01 67	93.34 65	96.64 79	96.57 138	94.99 49	99.36 57	93.48 51	99.34 60	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
OpenMVS	89.45 8	92.27 173	92.13 169	92.68 182	94.53 269	84.10 192	95.70 76	97.03 147	82.44 268	91.14 266	96.42 144	88.47 169	98.38 190	85.95 232	97.47 233	95.55 278

GST-MVS		96.24 43	95.99 53	97.00 33	98.65 34	92.71 47	95.69 78	98.01 67	92.08 90	95.74 120	96.28 159	95.22 37	99.42 32	93.17 68	99.06 99	98.88 73

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 22	97.52 70	96.90 7	98.62 167	90.30 141	99.60 26	98.72 92

APD_test1		95.91 53	95.42 77	97.36 23	98.82 26	96.62 6	95.64 80	97.64 97	93.38 64	95.89 114	97.23 93	93.35 81	97.66 254	88.20 192	98.66 151	97.79 175

test1111		90.39 212	90.61 205	89.74 278	98.04 88	71.50 344	95.59 81	79.72 369	89.41 159	95.94 110	98.14 35	70.79 312	98.81 134	88.52 190	99.32 64	98.90 70

canonicalmvs		94.59 103	94.69 105	94.30 128	95.60 234	87.03 138	95.59 81	98.24 30	91.56 115	95.21 147	92.04 303	94.95 50	98.66 163	91.45 114	97.57 229	97.20 213

SF-MVS		95.88 55	95.88 58	95.87 68	98.12 79	89.65 87	95.58 83	98.56 12	91.84 101	96.36 88	96.68 132	94.37 65	99.32 67	92.41 88	99.05 102	98.64 103

PS-MVSNAJss		96.01 50	96.04 51	95.89 67	98.82 26	88.51 112	95.57 84	97.88 79	88.72 175	98.81 6	98.86 10	90.77 139	99.60 9	95.43 15	99.53 36	99.57 14

PMVS	87.21 14	94.97 90	95.33 81	93.91 141	98.97 17	97.16 2	95.54 85	95.85 211	96.47 22	93.40 202	97.46 75	95.31 33	95.47 320	86.18 231	98.78 137	89.11 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VDDNet		94.03 123	94.27 118	93.31 161	98.87 21	82.36 214	95.51 86	91.78 304	97.19 12	96.32 90	98.60 19	84.24 220	98.75 145	87.09 214	98.83 131	98.81 80

pm-mvs1		95.43 71	95.94 54	93.93 140	98.38 63	85.08 181	95.46 87	97.12 142	91.84 101	97.28 53	98.46 27	95.30 34	97.71 251	90.17 148	99.42 50	98.99 55

Vis-MVSNet		95.50 68	95.48 73	95.56 79	98.11 80	89.40 94	95.35 88	98.22 32	92.36 81	94.11 177	98.07 41	92.02 112	99.44 28	93.38 60	97.67 225	97.85 168
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test0726					98.51 51	86.69 147	95.34 89	98.18 37	91.85 98	97.63 34	97.37 79	95.58 23

FIs		94.90 93	95.35 79	93.55 152	98.28 69	81.76 220	95.33 90	98.14 45	93.05 71	97.07 59	97.18 98	87.65 182	99.29 68	91.72 106	99.69 14	99.61 11

PGM-MVS		96.32 40	95.94 54	97.43 18	98.59 41	93.84 32	95.33 90	98.30 23	91.40 118	95.76 118	96.87 117	95.26 35	99.45 26	92.77 78	99.21 86	99.00 53

LPG-MVS_test		96.38 39	96.23 39	96.84 38	98.36 66	92.13 52	95.33 90	98.25 27	91.78 105	97.07 59	97.22 95	96.38 12	99.28 70	92.07 95	99.59 28	99.11 44

AllTest		94.88 94	94.51 111	96.00 56	98.02 89	92.17 50	95.26 93	98.43 14	90.48 139	95.04 153	96.74 127	92.54 105	97.86 237	85.11 242	98.98 109	97.98 152

SED-MVS		96.00 51	96.41 32	94.76 105	98.51 51	86.97 139	95.21 94	98.10 50	91.95 92	97.63 34	97.25 91	96.48 10	99.35 58	93.29 62	99.29 70	97.95 156

OPU-MVS				95.15 93	96.84 152	89.43 92	95.21 94				95.66 189	93.12 89	98.06 215	86.28 230	98.61 153	97.95 156

DVP-MVS		95.82 57	96.18 42	94.72 107	98.51 51	86.69 147	95.20 96	97.00 149	91.85 98	97.40 49	97.35 85	95.58 23	99.34 61	93.44 55	99.31 65	98.13 138
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	86.72 146	95.20 96	98.22 32					99.38 54	93.44 55	99.31 65	98.53 111

Anonymous20240529		95.50 68	95.83 62	94.50 120	97.33 131	85.93 168	95.19 98	96.77 169	96.64 19	97.61 37	98.05 42	93.23 85	98.79 138	88.60 189	99.04 107	98.78 84

SMA-MVS		95.77 58	95.54 72	96.47 49	98.27 70	91.19 66	95.09 99	97.79 90	86.48 213	97.42 48	97.51 72	94.47 64	99.29 68	93.55 47	99.29 70	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
NR-MVSNet		95.28 82	95.28 84	95.26 89	97.75 104	87.21 133	95.08 100	97.37 117	93.92 55	97.65 33	95.90 175	90.10 155	99.33 66	90.11 150	99.66 21	99.26 30

TransMVSNet (Re)		95.27 84	96.04 51	92.97 169	98.37 65	81.92 218	95.07 101	96.76 170	93.97 52	97.77 30	98.57 20	95.72 19	97.90 229	88.89 183	99.23 82	99.08 48

UGNet		93.08 145	92.50 162	94.79 104	93.87 284	87.99 121	95.07 101	94.26 257	90.64 136	87.33 326	97.67 61	86.89 198	98.49 181	88.10 196	98.71 143	97.91 160
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
tttt0517		89.81 232	88.90 239	92.55 189	97.00 142	79.73 255	95.03 103	83.65 358	89.88 150	95.30 139	94.79 227	53.64 369	99.39 48	91.99 97	98.79 136	98.54 110

LFMVS		91.33 191	91.16 194	91.82 210	96.27 189	79.36 262	95.01 104	85.61 346	96.04 30	94.82 161	97.06 105	72.03 309	98.46 186	84.96 245	98.70 145	97.65 186

CSCG		94.69 100	94.75 102	94.52 119	97.55 120	87.87 123	95.01 104	97.57 104	92.68 73	96.20 101	93.44 271	91.92 115	98.78 141	89.11 177	99.24 81	96.92 222

GG-mvs-BLEND				83.24 344	85.06 373	71.03 346	94.99 106	65.55 377		74.09 371	75.51 371	44.57 376	94.46 333	59.57 369	87.54 359	84.24 364

EU-MVSNet		87.39 280	86.71 284	89.44 282	93.40 290	76.11 309	94.93 107	90.00 317	57.17 370	95.71 123	97.37 79	64.77 340	97.68 253	92.67 83	94.37 313	94.52 303

KD-MVS_self_test		94.10 121	94.73 104	92.19 198	97.66 114	79.49 260	94.86 108	97.12 142	89.59 157	96.87 69	97.65 62	90.40 150	98.34 194	89.08 178	99.35 59	98.75 87

MTMP							94.82 109	54.62 380

PHI-MVS		94.34 112	93.80 126	95.95 59	95.65 230	91.67 62	94.82 109	97.86 80	87.86 193	93.04 217	94.16 248	91.58 120	98.78 141	90.27 143	98.96 115	97.41 201

gg-mvs-nofinetune		82.10 323	81.02 325	85.34 331	87.46 363	71.04 345	94.74 111	67.56 376	96.44 23	79.43 366	98.99 6	45.24 375	96.15 306	67.18 361	92.17 342	88.85 356

ACMM	88.83 9	96.30 42	96.07 49	96.97 34	98.39 62	92.95 44	94.74 111	98.03 64	90.82 131	97.15 56	96.85 118	96.25 14	99.00 104	93.10 70	99.33 62	98.95 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		95.19 85	95.73 66	93.55 152	96.62 163	88.88 104	94.67 113	98.05 59	91.26 120	97.25 55	96.40 146	95.42 27	94.36 336	92.72 82	99.19 88	97.40 204
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
API-MVS		91.52 187	91.61 180	91.26 233	94.16 275	86.26 162	94.66 114	94.82 242	91.17 124	92.13 250	91.08 316	90.03 158	97.06 280	79.09 304	97.35 237	90.45 353

v10		94.68 101	95.27 85	92.90 175	96.57 165	80.15 239	94.65 115	97.57 104	90.68 135	97.43 46	98.00 45	88.18 172	99.15 82	94.84 19	99.55 35	99.41 20

v8		94.65 102	95.29 83	92.74 180	96.65 159	79.77 254	94.59 116	97.17 137	91.86 97	97.47 45	97.93 48	88.16 173	99.08 92	94.32 26	99.47 41	99.38 22

APD-MVS		95.00 89	94.69 105	95.93 62	97.38 128	90.88 71	94.59 116	97.81 86	89.22 165	95.46 131	96.17 167	93.42 79	99.34 61	89.30 168	98.87 124	97.56 192
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPA-MVSNet		95.14 86	95.67 68	93.58 151	97.76 103	83.15 205	94.58 118	97.58 103	93.39 63	97.05 62	98.04 43	93.25 84	98.51 180	89.75 160	99.59 28	99.08 48

ACMMP_NAP		96.21 44	96.12 46	96.49 48	98.90 19	91.42 63	94.57 119	98.03 64	90.42 142	96.37 87	97.35 85	95.68 20	99.25 73	94.44 24	99.34 60	98.80 82

HQP_MVS		94.26 116	93.93 123	95.23 91	97.71 108	88.12 118	94.56 120	97.81 86	91.74 109	93.31 203	95.59 190	86.93 196	98.95 112	89.26 172	98.51 164	98.60 107

plane_prior2							94.56 120		91.74 109

tfpnnormal		94.27 114	94.87 98	92.48 191	97.71 108	80.88 234	94.55 122	95.41 229	93.70 58	96.67 78	97.72 59	91.40 124	98.18 208	87.45 207	99.18 90	98.36 121

XVG-ACMP-BASELINE		95.68 62	95.34 80	96.69 41	98.40 61	93.04 41	94.54 123	98.05 59	90.45 141	96.31 91	96.76 124	92.91 96	98.72 150	91.19 117	99.42 50	98.32 123

DP-MVS		95.62 63	95.84 61	94.97 97	97.16 137	88.62 108	94.54 123	97.64 97	96.94 15	96.58 82	97.32 88	93.07 92	98.72 150	90.45 134	98.84 126	97.57 190

MIMVSNet		87.13 288	86.54 287	88.89 293	96.05 206	76.11 309	94.39 125	88.51 322	81.37 274	88.27 314	96.75 126	72.38 306	95.52 317	65.71 364	95.47 286	95.03 288

K. test v3		93.37 135	93.27 145	93.66 148	98.05 85	82.62 210	94.35 126	86.62 336	96.05 29	97.51 41	98.85 12	76.59 293	99.65 3	93.21 66	98.20 194	98.73 91

Vis-MVSNet (Re-imp)		90.42 209	90.16 214	91.20 237	97.66 114	77.32 293	94.33 127	87.66 330	91.20 123	92.99 218	95.13 212	75.40 297	98.28 197	77.86 309	99.19 88	97.99 151

ANet_high		94.83 96	96.28 37	90.47 259	96.65 159	73.16 333	94.33 127	98.74 10	96.39 24	98.09 24	98.93 8	93.37 80	98.70 157	90.38 137	99.68 18	99.53 15

iter_conf_final		90.23 219	89.32 229	92.95 171	94.65 266	81.46 225	94.32 129	95.40 231	85.61 229	92.84 223	95.37 206	54.58 366	99.13 86	92.16 91	98.94 117	98.25 128

ACMP	88.15 13	95.71 61	95.43 76	96.54 45	98.17 77	91.73 60	94.24 130	98.08 53	89.46 158	96.61 81	96.47 141	95.85 18	99.12 89	90.45 134	99.56 34	98.77 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		90.32 217	88.87 240	94.66 110	94.82 254	91.85 57	94.22 131	94.75 245	80.91 275	87.52 324	88.07 348	86.63 202	97.87 236	76.67 320	96.21 270	94.25 309
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
FMVSNet2		92.78 156	92.73 156	92.95 171	95.40 239	81.98 217	94.18 132	95.53 226	88.63 177	96.05 107	97.37 79	81.31 251	98.81 134	87.38 210	98.67 149	98.06 140

Anonymous20240521		92.86 154	93.57 136	90.74 253	96.57 165	75.50 316	94.15 133	95.60 217	89.38 160	95.90 113	97.90 54	80.39 259	97.96 226	92.60 85	99.68 18	98.75 87

GeoE		94.55 105	94.68 107	94.15 131	97.23 133	85.11 180	94.14 134	97.34 124	88.71 176	95.26 142	95.50 195	94.65 58	99.12 89	90.94 123	98.40 169	98.23 129

9.14			94.81 99		97.49 123		94.11 135	98.37 17	87.56 202	95.38 133	96.03 171	94.66 57	99.08 92	90.70 129	98.97 113

HPM-MVS++		95.02 88	94.39 112	96.91 37	97.88 97	93.58 37	94.09 136	96.99 151	91.05 126	92.40 240	95.22 209	91.03 137	99.25 73	92.11 92	98.69 146	97.90 161

HY-MVS	82.50 18	86.81 292	85.93 294	89.47 281	93.63 288	77.93 283	94.02 137	91.58 307	75.68 312	83.64 347	93.64 264	77.40 281	97.42 266	71.70 347	92.07 343	93.05 334

Effi-MVS+-dtu		93.90 126	92.60 160	97.77 3	94.74 260	96.67 5	94.00 138	95.41 229	89.94 148	91.93 254	92.13 301	90.12 153	98.97 109	87.68 205	97.48 232	97.67 185

Effi-MVS+		92.79 155	92.74 154	92.94 173	95.10 247	83.30 201	94.00 138	97.53 108	91.36 119	89.35 295	90.65 325	94.01 69	98.66 163	87.40 209	95.30 292	96.88 225

VDD-MVS		94.37 109	94.37 113	94.40 126	97.49 123	86.07 166	93.97 140	93.28 274	94.49 43	96.24 97	97.78 56	87.99 178	98.79 138	88.92 181	99.14 95	98.34 122

h-mvs33		92.89 151	91.99 172	95.58 77	96.97 143	90.55 76	93.94 141	94.01 263	89.23 163	93.95 186	96.19 164	76.88 290	99.14 84	91.02 120	95.71 280	97.04 218

EPNet		89.80 233	88.25 252	94.45 124	83.91 375	86.18 163	93.87 142	87.07 334	91.16 125	80.64 363	94.72 229	78.83 267	98.89 118	85.17 237	98.89 119	98.28 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs3		92.42 167	92.40 165	92.46 193	93.80 287	87.28 131	93.86 143	97.05 146	76.86 308	96.25 96	98.66 18	82.87 233	91.26 353	95.44 14	96.83 255	98.82 78

DeepC-MVS	91.39 4	95.43 71	95.33 81	95.71 74	97.67 113	90.17 80	93.86 143	98.02 66	87.35 203	96.22 99	97.99 46	94.48 63	99.05 97	92.73 81	99.68 18	97.93 158
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LCM-MVSNet-Re		94.20 119	94.58 110	93.04 166	95.91 216	83.13 206	93.79 145	99.19 3	92.00 91	98.84 5	98.04 43	93.64 72	99.02 102	81.28 278	98.54 160	96.96 221

TranMVSNet+NR-MVSNet		96.07 49	96.26 38	95.50 80	98.26 71	87.69 126	93.75 146	97.86 80	95.96 31	97.48 44	97.14 101	95.33 32	99.44 28	90.79 126	99.76 10	99.38 22

PAPM_NR		91.03 195	90.81 200	91.68 217	96.73 157	81.10 231	93.72 147	96.35 191	88.19 187	88.77 305	92.12 302	85.09 216	97.25 273	82.40 268	93.90 320	96.68 232

baseline		94.26 116	94.80 100	92.64 183	96.08 204	80.99 232	93.69 148	98.04 63	90.80 132	94.89 159	96.32 155	93.19 86	98.48 185	91.68 108	98.51 164	98.43 118

dcpmvs_2		93.96 124	95.01 94	90.82 251	97.60 116	74.04 328	93.68 149	98.85 7	89.80 152	97.82 28	97.01 110	91.14 135	99.21 76	90.56 132	98.59 155	99.19 36

MVS_0304		90.96 196	90.15 217	93.37 159	93.17 294	87.06 136	93.62 150	92.43 293	89.60 156	82.25 355	95.50 195	82.56 240	97.83 240	84.41 252	97.83 218	95.22 282

F-COLMAP		92.28 172	91.06 195	95.95 59	97.52 121	91.90 56	93.53 151	97.18 136	83.98 249	88.70 307	94.04 251	88.41 170	98.55 177	80.17 290	95.99 274	97.39 205

FMVSNet5		87.82 269	86.56 286	91.62 219	92.31 307	79.81 253	93.49 152	94.81 244	83.26 254	91.36 260	96.93 113	52.77 371	97.49 262	76.07 324	98.03 207	97.55 193

DPE-MVS		95.89 54	95.88 58	95.92 64	97.93 96	89.83 85	93.46 153	98.30 23	92.37 80	97.75 31	96.95 111	95.14 39	99.51 20	91.74 105	99.28 75	98.41 119
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
alignmvs		93.26 139	92.85 151	94.50 120	95.70 226	87.45 128	93.45 154	95.76 212	91.58 114	95.25 144	92.42 297	81.96 246	98.72 150	91.61 109	97.87 216	97.33 209

114514_t		90.51 206	89.80 223	92.63 185	98.00 91	82.24 215	93.40 155	97.29 129	65.84 361	89.40 294	94.80 226	86.99 194	98.75 145	83.88 255	98.61 153	96.89 224

DeepC-MVS_fast	89.96 7	93.73 128	93.44 140	94.60 115	96.14 200	87.90 122	93.36 156	97.14 139	85.53 230	93.90 189	95.45 198	91.30 127	98.59 172	89.51 163	98.62 152	97.31 210
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS-pluss		96.08 48	95.92 57	96.57 44	99.06 10	91.21 65	93.25 157	98.32 20	87.89 192	96.86 70	97.38 78	95.55 25	99.39 48	95.47 13	99.47 41	99.11 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_0402		95.73 60	96.22 40	94.26 129	98.19 76	85.77 172	93.24 158	97.24 133	96.88 16	97.69 32	97.77 58	94.12 68	99.13 86	91.54 113	99.29 70	97.88 164

MSLP-MVS++		93.25 141	93.88 124	91.37 227	96.34 183	82.81 209	93.11 159	97.74 92	89.37 161	94.08 179	95.29 208	90.40 150	96.35 303	90.35 139	98.25 187	94.96 290

baseline1		87.62 274	87.31 269	88.54 300	94.71 263	74.27 326	93.10 160	88.20 326	86.20 217	92.18 249	93.04 279	73.21 304	95.52 317	79.32 301	85.82 362	95.83 265

plane_prior						88.12 118	93.01 161		88.98 169						98.06 204

thres100view900		87.35 281	86.89 280	88.72 296	96.14 200	73.09 334	93.00 162	85.31 349	92.13 89	93.26 208	90.96 318	63.42 346	98.28 197	71.27 350	96.54 264	94.79 296

Patchmtry		90.11 223	89.92 221	90.66 255	90.35 340	77.00 297	92.96 163	92.81 281	90.25 145	94.74 165	96.93 113	67.11 324	97.52 259	85.17 237	98.98 109	97.46 197

LF4IMVS		92.72 158	92.02 171	94.84 102	95.65 230	91.99 54	92.92 164	96.60 177	85.08 239	92.44 238	93.62 266	86.80 199	96.35 303	86.81 216	98.25 187	96.18 252

UniMVSNet (Re)		95.32 78	95.15 89	95.80 70	97.79 102	88.91 102	92.91 165	98.07 56	93.46 62	96.31 91	95.97 174	90.14 152	99.34 61	92.11 92	99.64 24	99.16 38

TAPA-MVS	88.58 10	92.49 165	91.75 179	94.73 106	96.50 172	89.69 86	92.91 165	97.68 95	78.02 301	92.79 225	94.10 249	90.85 138	97.96 226	84.76 248	98.16 196	96.54 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0530		88.69 257	87.52 267	92.20 197	96.33 184	79.36 262	92.81 167	84.01 357	86.44 214	93.67 194	92.68 290	53.62 370	99.25 73	89.65 162	98.45 167	98.00 148

EIA-MVS		92.35 170	92.03 170	93.30 162	95.81 221	83.97 194	92.80 168	98.17 41	87.71 197	89.79 289	87.56 349	91.17 134	99.18 80	87.97 200	97.27 238	96.77 229

iter_conf05		88.94 250	88.09 259	91.50 224	92.74 302	76.97 300	92.80 168	95.92 208	82.82 263	93.65 195	95.37 206	49.41 373	99.13 86	90.82 125	99.28 75	98.40 120

thres600view7		87.66 272	87.10 278	89.36 285	96.05 206	73.17 332	92.72 170	85.31 349	91.89 96	93.29 205	90.97 317	63.42 346	98.39 188	73.23 338	96.99 251	96.51 236

wuyk23d		87.83 268	90.79 201	78.96 352	90.46 339	88.63 107	92.72 170	90.67 314	91.65 113	98.68 11	97.64 63	96.06 15	77.53 373	59.84 368	99.41 54	70.73 371

test_fmvs2		90.62 205	90.40 211	91.29 232	91.93 318	85.46 176	92.70 172	96.48 186	74.44 320	94.91 158	97.59 65	75.52 296	90.57 355	93.44 55	96.56 263	97.84 169

V42		93.43 134	93.58 135	92.97 169	95.34 243	81.22 229	92.67 173	96.49 185	87.25 205	96.20 101	96.37 152	87.32 188	98.85 126	92.39 89	98.21 192	98.85 77

casdiffmvs_mvg		95.10 87	95.62 69	93.53 155	96.25 192	83.23 202	92.66 174	98.19 35	93.06 70	97.49 42	97.15 100	94.78 54	98.71 156	92.27 90	98.72 142	98.65 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
OPM-MVS		95.61 64	95.45 74	96.08 54	98.49 58	91.00 68	92.65 175	97.33 125	90.05 147	96.77 75	96.85 118	95.04 45	98.56 175	92.77 78	99.06 99	98.70 95
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_vis1_n		89.01 246	89.01 235	89.03 290	92.57 304	82.46 213	92.62 176	96.06 203	73.02 330	90.40 276	95.77 185	74.86 298	89.68 360	90.78 127	94.98 298	94.95 291

DU-MVS		95.28 82	95.12 91	95.75 72	97.75 104	88.59 109	92.58 177	97.81 86	93.99 50	96.80 73	95.90 175	90.10 155	99.41 38	91.60 110	99.58 32	99.26 30

FMVSNet3		90.78 199	90.32 213	92.16 202	93.03 299	79.92 249	92.54 178	94.95 238	86.17 219	95.10 149	96.01 172	69.97 315	98.75 145	86.74 217	98.38 173	97.82 172

hse-mvs2		92.24 174	91.20 191	95.38 83	96.16 198	90.65 75	92.52 179	92.01 302	89.23 163	93.95 186	92.99 281	76.88 290	98.69 159	91.02 120	96.03 272	96.81 227

MVS_Test		92.57 164	93.29 142	90.40 262	93.53 289	75.85 312	92.52 179	96.96 152	88.73 174	92.35 243	96.70 131	90.77 139	98.37 193	92.53 86	95.49 285	96.99 220

CR-MVSNet		87.89 266	87.12 277	90.22 267	91.01 331	78.93 269	92.52 179	92.81 281	73.08 329	89.10 296	96.93 113	67.11 324	97.64 255	88.80 184	92.70 336	94.08 310

RPMNet		90.31 218	90.14 218	90.81 252	91.01 331	78.93 269	92.52 179	98.12 47	91.91 95	89.10 296	96.89 116	68.84 317	99.41 38	90.17 148	92.70 336	94.08 310

XVG-OURS-SEG-HR		95.38 75	95.00 95	96.51 46	98.10 81	94.07 20	92.46 183	98.13 46	90.69 134	93.75 191	96.25 162	98.03 2	97.02 281	92.08 94	95.55 283	98.45 117

EI-MVSNet-Vis-set		94.36 110	94.28 116	94.61 112	92.55 305	85.98 167	92.44 184	94.69 247	93.70 58	96.12 105	95.81 180	91.24 128	98.86 124	93.76 42	98.22 191	98.98 59

Anonymous202405211		92.58 162	92.50 162	92.83 178	96.55 167	83.22 203	92.43 185	91.64 306	94.10 49	95.59 126	96.64 134	81.88 248	97.50 260	85.12 241	98.52 162	97.77 177

AUN-MVS		90.05 227	88.30 249	95.32 88	96.09 203	90.52 77	92.42 186	92.05 301	82.08 271	88.45 311	92.86 283	65.76 334	98.69 159	88.91 182	96.07 271	96.75 231

EI-MVSNet-UG-set		94.35 111	94.27 118	94.59 116	92.46 306	85.87 170	92.42 186	94.69 247	93.67 61	96.13 104	95.84 179	91.20 131	98.86 124	93.78 39	98.23 189	99.03 51

NCCC		94.08 122	93.54 138	95.70 75	96.49 173	89.90 83	92.39 188	96.91 158	90.64 136	92.33 246	94.60 234	90.58 147	98.96 110	90.21 147	97.70 223	98.23 129

casdiffmvs		94.32 113	94.80 100	92.85 177	96.05 206	81.44 226	92.35 189	98.05 59	91.53 116	95.75 119	96.80 121	93.35 81	98.49 181	91.01 122	98.32 181	98.64 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		92.99 148	92.74 154	93.72 147	95.86 218	86.30 160	92.33 190	97.84 83	91.70 112	92.81 224	86.17 359	92.22 109	99.19 79	88.03 199	97.73 220	95.66 274

EI-MVSNet		92.99 148	93.26 146	92.19 198	92.12 313	79.21 267	92.32 191	94.67 249	91.77 107	95.24 145	95.85 177	87.14 192	98.49 181	91.99 97	98.26 185	98.86 74

CVMVSNet		85.16 302	84.72 299	86.48 322	92.12 313	70.19 349	92.32 191	88.17 327	56.15 371	90.64 272	95.85 177	67.97 322	96.69 292	88.78 185	90.52 351	92.56 339

test_fmvs1_n		88.73 256	88.38 247	89.76 277	92.06 315	82.53 211	92.30 193	96.59 179	71.14 339	92.58 232	95.41 203	68.55 318	89.57 362	91.12 118	95.66 281	97.18 214

OMC-MVS		94.22 118	93.69 131	95.81 69	97.25 132	91.27 64	92.27 194	97.40 116	87.10 209	94.56 169	95.42 200	93.74 71	98.11 213	86.62 221	98.85 125	98.06 140

PM-MVS		93.33 136	92.67 158	95.33 86	96.58 164	94.06 21	92.26 195	92.18 295	85.92 223	96.22 99	96.61 136	85.64 214	95.99 312	90.35 139	98.23 189	95.93 260

UniMVSNet_NR-MVSNet		95.35 76	95.21 86	95.76 71	97.69 111	88.59 109	92.26 195	97.84 83	94.91 38	96.80 73	95.78 184	90.42 148	99.41 38	91.60 110	99.58 32	99.29 29

AdaColmap		91.63 184	91.36 188	92.47 192	95.56 235	86.36 158	92.24 197	96.27 193	88.88 173	89.90 286	92.69 289	91.65 119	98.32 195	77.38 316	97.64 226	92.72 338

PVSNet_Blended_VisFu		91.63 184	91.20 191	92.94 173	97.73 107	83.95 195	92.14 198	97.46 112	78.85 297	92.35 243	94.98 218	84.16 221	99.08 92	86.36 228	96.77 258	95.79 267

Baseline_NR-MVSNet		94.47 108	95.09 93	92.60 187	98.50 57	80.82 235	92.08 199	96.68 173	93.82 56	96.29 93	98.56 21	90.10 155	97.75 249	90.10 152	99.66 21	99.24 32

Fast-Effi-MVS+-dtu		92.77 157	92.16 167	94.58 118	94.66 265	88.25 116	92.05 200	96.65 175	89.62 155	90.08 281	91.23 313	92.56 104	98.60 170	86.30 229	96.27 269	96.90 223

save fliter					97.46 126	88.05 120	92.04 201	97.08 144	87.63 200

PatchT		87.51 277	88.17 257	85.55 329	90.64 334	66.91 359	92.02 202	86.09 340	92.20 87	89.05 298	97.16 99	64.15 342	96.37 302	89.21 175	92.98 334	93.37 329

EG-PatchMatch MVS		94.54 106	94.67 108	94.14 132	97.87 98	86.50 151	92.00 203	96.74 171	88.16 188	96.93 68	97.61 64	93.04 93	97.90 229	91.60 110	98.12 199	98.03 146

v144192		93.20 144	93.54 138	92.16 202	96.05 206	78.26 280	91.95 204	97.14 139	84.98 241	95.96 108	96.11 168	87.08 193	99.04 100	93.79 38	98.84 126	99.17 37

VNet		92.67 160	92.96 148	91.79 211	96.27 189	80.15 239	91.95 204	94.98 237	92.19 88	94.52 171	96.07 169	87.43 186	97.39 269	84.83 246	98.38 173	97.83 170

1314		86.46 294	86.33 291	86.87 320	91.65 323	74.54 321	91.94 206	94.10 259	74.28 321	84.78 340	87.33 353	83.03 231	95.00 329	78.72 305	91.16 349	91.06 350

MVS		84.98 304	84.30 304	87.01 318	91.03 330	77.69 289	91.94 206	94.16 258	59.36 369	84.23 344	87.50 351	85.66 212	96.80 289	71.79 345	93.05 333	86.54 362

tfpn200view9		87.05 289	86.52 288	88.67 297	95.77 222	72.94 335	91.89 208	86.00 341	90.84 129	92.61 230	89.80 329	63.93 343	98.28 197	71.27 350	96.54 264	94.79 296

thres400		87.20 285	86.52 288	89.24 289	95.77 222	72.94 335	91.89 208	86.00 341	90.84 129	92.61 230	89.80 329	63.93 343	98.28 197	71.27 350	96.54 264	96.51 236

v1921920		93.26 139	93.61 134	92.19 198	96.04 210	78.31 279	91.88 210	97.24 133	85.17 235	96.19 103	96.19 164	86.76 200	99.05 97	94.18 30	98.84 126	99.22 33

XXY-MVS		92.58 162	93.16 147	90.84 250	97.75 104	79.84 250	91.87 211	96.22 198	85.94 222	95.53 128	97.68 60	92.69 102	94.48 332	83.21 259	97.51 230	98.21 131

IterMVS-LS		93.78 127	94.28 116	92.27 195	96.27 189	79.21 267	91.87 211	96.78 167	91.77 107	96.57 83	97.07 104	87.15 191	98.74 148	91.99 97	99.03 108	98.86 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		93.50 131	93.81 125	92.57 188	96.28 188	79.61 257	91.86 213	96.96 152	86.95 211	95.91 112	96.32 155	87.65 182	98.96 110	93.51 48	98.88 121	99.13 41

v1192		93.49 132	93.78 127	92.62 186	96.16 198	79.62 256	91.83 214	97.22 135	86.07 220	96.10 106	96.38 151	87.22 189	99.02 102	94.14 31	98.88 121	99.22 33

v1240		93.29 137	93.71 130	92.06 205	96.01 211	77.89 285	91.81 215	97.37 117	85.12 237	96.69 77	96.40 146	86.67 201	99.07 96	94.51 22	98.76 139	99.22 33

CNVR-MVS		94.58 104	94.29 115	95.46 82	96.94 145	89.35 96	91.81 215	96.80 166	89.66 154	93.90 189	95.44 199	92.80 100	98.72 150	92.74 80	98.52 162	98.32 123

v2v482		93.29 137	93.63 133	92.29 194	96.35 182	78.82 273	91.77 217	96.28 192	88.45 181	95.70 124	96.26 161	86.02 209	98.90 116	93.02 73	98.81 134	99.14 40

EPNet_dtu		85.63 298	84.37 303	89.40 284	86.30 368	74.33 325	91.64 218	88.26 324	84.84 244	72.96 372	89.85 327	71.27 311	97.69 252	76.60 321	97.62 227	96.18 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	85.34 15	90.40 210	88.92 237	94.85 101	96.53 171	90.02 81	91.58 219	96.48 186	80.16 281	86.14 332	92.18 299	85.73 211	98.25 202	76.87 319	94.61 309	96.30 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VPNet		93.08 145	93.76 128	91.03 241	98.60 39	75.83 314	91.51 220	95.62 216	91.84 101	95.74 120	97.10 103	89.31 163	98.32 195	85.07 244	99.06 99	98.93 64

XVG-OURS		94.72 99	94.12 121	96.50 47	98.00 91	94.23 18	91.48 221	98.17 41	90.72 133	95.30 139	96.47 141	87.94 179	96.98 282	91.41 115	97.61 228	98.30 126

HQP-NCC					96.36 179		91.37 222		87.16 206	88.81 301

ACMP_Plane					96.36 179		91.37 222		87.16 206	88.81 301

HQP-MVS		92.09 176	91.49 185	93.88 142	96.36 179	84.89 182	91.37 222	97.31 126	87.16 206	88.81 301	93.40 272	84.76 217	98.60 170	86.55 224	97.73 220	98.14 137

MCST-MVS		92.91 150	92.51 161	94.10 133	97.52 121	85.72 173	91.36 225	97.13 141	80.33 280	92.91 222	94.24 244	91.23 129	98.72 150	89.99 154	97.93 213	97.86 166

v148		92.87 153	93.29 142	91.62 219	96.25 192	77.72 288	91.28 226	95.05 235	89.69 153	95.93 111	96.04 170	87.34 187	98.38 190	90.05 153	97.99 210	98.78 84

tpmvs		84.22 308	83.97 307	84.94 334	87.09 365	65.18 365	91.21 227	88.35 323	82.87 262	85.21 335	90.96 318	65.24 338	96.75 290	79.60 300	85.25 363	92.90 336

CANet		92.38 169	91.99 172	93.52 157	93.82 286	83.46 199	91.14 228	97.00 149	89.81 151	86.47 330	94.04 251	87.90 180	99.21 76	89.50 164	98.27 184	97.90 161

CNLPA		91.72 182	91.20 191	93.26 163	96.17 197	91.02 67	91.14 228	95.55 224	90.16 146	90.87 268	93.56 269	86.31 205	94.40 335	79.92 296	97.12 242	94.37 306

DP-MVS Recon		92.31 171	91.88 175	93.60 150	97.18 136	86.87 142	91.10 230	97.37 117	84.92 242	92.08 251	94.08 250	88.59 167	98.20 205	83.50 256	98.14 198	95.73 269

OpenMVS_ROB	85.12 16	89.52 236	89.05 233	90.92 246	94.58 268	81.21 230	91.10 230	93.41 273	77.03 307	93.41 200	93.99 255	83.23 228	97.80 242	79.93 294	94.80 304	93.74 321

TSAR-MVS + GP.		93.07 147	92.41 164	95.06 95	95.82 219	90.87 72	90.97 232	92.61 289	88.04 189	94.61 168	93.79 262	88.08 174	97.81 241	89.41 165	98.39 172	96.50 239

MVP-Stereo		90.07 226	88.92 237	93.54 154	96.31 186	86.49 152	90.93 233	95.59 221	79.80 282	91.48 258	95.59 190	80.79 256	97.39 269	78.57 307	91.19 348	96.76 230
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVSTER		89.32 238	88.75 241	91.03 241	90.10 343	76.62 304	90.85 234	94.67 249	82.27 269	95.24 145	95.79 181	61.09 355	98.49 181	90.49 133	98.26 185	97.97 155

pmmvs-eth3d		91.54 186	90.73 203	93.99 135	95.76 224	87.86 124	90.83 235	93.98 264	78.23 300	94.02 184	96.22 163	82.62 239	96.83 288	86.57 222	98.33 179	97.29 211

CANet_DTU		89.85 231	89.17 231	91.87 208	92.20 311	80.02 246	90.79 236	95.87 210	86.02 221	82.53 354	91.77 306	80.01 260	98.57 174	85.66 234	97.70 223	97.01 219

test_prior4						89.91 82	90.74 237

TinyColmap		92.00 178	92.76 153	89.71 279	95.62 233	77.02 296	90.72 238	96.17 201	87.70 198	95.26 142	96.29 157	92.54 105	96.45 298	81.77 273	98.77 138	95.66 274

CDPH-MVS		92.67 160	91.83 177	95.18 92	96.94 145	88.46 114	90.70 239	97.07 145	77.38 303	92.34 245	95.08 215	92.67 103	98.88 119	85.74 233	98.57 157	98.20 132

DSMNet-mixed		82.21 320	81.56 319	84.16 340	89.57 349	70.00 353	90.65 240	77.66 373	54.99 372	83.30 350	97.57 66	77.89 278	90.50 357	66.86 362	95.54 284	91.97 343

TEST9					96.45 175	89.46 90	90.60 241	96.92 156	79.09 293	90.49 273	94.39 240	91.31 126	98.88 119

train_agg		92.71 159	91.83 177	95.35 84	96.45 175	89.46 90	90.60 241	96.92 156	79.37 288	90.49 273	94.39 240	91.20 131	98.88 119	88.66 188	98.43 168	97.72 181

PatchmatchNet		85.22 301	84.64 300	86.98 319	89.51 350	69.83 354	90.52 243	87.34 332	78.87 296	87.22 327	92.74 288	66.91 326	96.53 294	81.77 273	86.88 360	94.58 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_8					96.37 177	89.14 97	90.51 244	96.89 159	79.37 288	90.42 275	94.36 242	91.20 131	98.82 129

test_yl		90.11 223	89.73 226	91.26 233	94.09 278	79.82 251	90.44 245	92.65 286	90.90 127	93.19 212	93.30 274	73.90 301	98.03 217	82.23 269	96.87 253	95.93 260

DCV-MVSNet		90.11 223	89.73 226	91.26 233	94.09 278	79.82 251	90.44 245	92.65 286	90.90 127	93.19 212	93.30 274	73.90 301	98.03 217	82.23 269	96.87 253	95.93 260

tpm2		81.46 325	80.35 332	84.80 335	89.90 344	65.14 366	90.44 245	85.36 348	65.82 362	82.05 358	92.44 295	57.94 360	96.69 292	70.71 353	88.49 357	92.56 339

test_fmvs1		87.59 275	87.27 271	88.54 300	88.32 358	81.26 228	90.43 248	95.72 214	70.55 345	91.70 256	94.63 232	68.13 319	89.42 363	90.59 131	95.34 291	94.94 293

test_vis3_rt		90.40 210	90.03 219	91.52 223	92.58 303	88.95 100	90.38 249	97.72 94	73.30 327	97.79 29	97.51 72	77.05 286	87.10 367	89.03 179	94.89 300	98.50 112

CostFormer		83.09 314	82.21 317	85.73 328	89.27 352	67.01 358	90.35 250	86.47 337	70.42 346	83.52 349	93.23 277	61.18 354	96.85 287	77.21 317	88.26 358	93.34 330

TAMVS		90.16 221	89.05 233	93.49 158	96.49 173	86.37 157	90.34 251	92.55 290	80.84 278	92.99 218	94.57 236	81.94 247	98.20 205	73.51 336	98.21 192	95.90 263

EPMVS		81.17 329	80.37 331	83.58 342	85.58 371	65.08 367	90.31 252	71.34 375	77.31 305	85.80 334	91.30 312	59.38 358	92.70 348	79.99 291	82.34 368	92.96 335

CMPMVS	68.83 22	87.28 282	85.67 296	92.09 204	88.77 356	85.42 177	90.31 252	94.38 253	70.02 348	88.00 317	93.30 274	73.78 303	94.03 340	75.96 326	96.54 264	96.83 226
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_post1							90.21 254				5.85 378	65.36 336	96.00 311	79.61 298

test_prior2							90.21 254		89.33 162	90.77 269	94.81 224	90.41 149		88.21 191	98.55 158

MVS_111021_LR		93.66 129	93.28 144	94.80 103	96.25 192	90.95 69	90.21 254	95.43 228	87.91 190	93.74 193	94.40 239	92.88 98	96.38 301	90.39 136	98.28 183	97.07 215

WR-MVS		93.49 132	93.72 129	92.80 179	97.57 119	80.03 245	90.14 257	95.68 215	93.70 58	96.62 80	95.39 204	87.21 190	99.04 100	87.50 206	99.64 24	99.33 26

tpmrst		82.85 317	82.93 314	82.64 345	87.65 360	58.99 375	90.14 257	87.90 329	75.54 314	83.93 345	91.63 309	66.79 329	95.36 323	81.21 280	81.54 369	93.57 328

PVSNet_BlendedMVS		90.35 215	89.96 220	91.54 222	94.81 255	78.80 275	90.14 257	96.93 154	79.43 287	88.68 308	95.06 216	86.27 206	98.15 211	80.27 286	98.04 206	97.68 184

BH-untuned		90.68 202	90.90 196	90.05 273	95.98 212	79.57 258	90.04 260	94.94 239	87.91 190	94.07 180	93.00 280	87.76 181	97.78 245	79.19 303	95.17 295	92.80 337

新几何2							90.02 261

旧先验2							90.00 262		68.65 353	92.71 228			96.52 295	85.15 239

无先验							89.94 263	95.75 213	70.81 343				98.59 172	81.17 281		94.81 294

xiu_mvs_v1_base_debu		91.47 188	91.52 182	91.33 229	95.69 227	81.56 222	89.92 264	96.05 205	83.22 255	91.26 262	90.74 320	91.55 121	98.82 129	89.29 169	95.91 275	93.62 325

xiu_mvs_v1_base		91.47 188	91.52 182	91.33 229	95.69 227	81.56 222	89.92 264	96.05 205	83.22 255	91.26 262	90.74 320	91.55 121	98.82 129	89.29 169	95.91 275	93.62 325

xiu_mvs_v1_base_debi		91.47 188	91.52 182	91.33 229	95.69 227	81.56 222	89.92 264	96.05 205	83.22 255	91.26 262	90.74 320	91.55 121	98.82 129	89.29 169	95.91 275	93.62 325

mvs_anonymous		90.37 214	91.30 190	87.58 314	92.17 312	68.00 357	89.84 267	94.73 246	83.82 252	93.22 211	97.40 77	87.54 184	97.40 268	87.94 201	95.05 297	97.34 208

test20.03		90.80 198	90.85 199	90.63 256	95.63 232	79.24 265	89.81 268	92.87 280	89.90 149	94.39 173	96.40 146	85.77 210	95.27 327	73.86 335	99.05 102	97.39 205

1112_ss		88.42 260	87.41 268	91.45 225	96.69 158	80.99 232	89.72 269	96.72 172	73.37 326	87.00 328	90.69 323	77.38 282	98.20 205	81.38 277	93.72 323	95.15 285

UnsupCasMVSNet_eth		90.33 216	90.34 212	90.28 264	94.64 267	80.24 237	89.69 270	95.88 209	85.77 225	93.94 188	95.69 188	81.99 245	92.98 347	84.21 253	91.30 347	97.62 187

MG-MVS		89.54 235	89.80 223	88.76 295	94.88 251	72.47 340	89.60 271	92.44 292	85.82 224	89.48 293	95.98 173	82.85 234	97.74 250	81.87 272	95.27 293	96.08 255

Patchmatch-test		86.10 296	86.01 293	86.38 326	90.63 335	74.22 327	89.57 272	86.69 335	85.73 227	89.81 288	92.83 284	65.24 338	91.04 354	77.82 312	95.78 279	93.88 318

Anonymous20231206		88.77 254	88.29 250	90.20 269	96.31 186	78.81 274	89.56 273	93.49 271	74.26 322	92.38 241	95.58 193	82.21 241	95.43 322	72.07 344	98.75 141	96.34 245

DeepPCF-MVS	90.46 6	94.20 119	93.56 137	96.14 52	95.96 213	92.96 43	89.48 274	97.46 112	85.14 236	96.23 98	95.42 200	93.19 86	98.08 214	90.37 138	98.76 139	97.38 207

SCA		87.43 279	87.21 273	88.10 308	92.01 317	71.98 342	89.43 275	88.11 328	82.26 270	88.71 306	92.83 284	78.65 270	97.59 256	79.61 298	93.30 327	94.75 298

testgi		90.38 213	91.34 189	87.50 315	97.49 123	71.54 343	89.43 275	95.16 234	88.38 184	94.54 170	94.68 231	92.88 98	93.09 346	71.60 348	97.85 217	97.88 164

JIA-IIPM		85.08 303	83.04 312	91.19 238	87.56 361	86.14 164	89.40 277	84.44 356	88.98 169	82.20 356	97.95 47	56.82 363	96.15 306	76.55 322	83.45 366	91.30 348

原ACMM2							89.34 278

tpm		84.38 307	84.08 306	85.30 332	90.47 338	63.43 372	89.34 278	85.63 345	77.24 306	87.62 322	95.03 217	61.00 356	97.30 272	79.26 302	91.09 350	95.16 284

MVS_111021_HR		93.63 130	93.42 141	94.26 129	96.65 159	86.96 141	89.30 280	96.23 196	88.36 185	93.57 197	94.60 234	93.45 76	97.77 246	90.23 146	98.38 173	98.03 146

tpm cat1		80.61 333	79.46 336	84.07 341	88.78 355	65.06 368	89.26 281	88.23 325	62.27 367	81.90 360	89.66 335	62.70 351	95.29 326	71.72 346	80.60 370	91.86 346

CDS-MVSNet		89.55 234	88.22 255	93.53 155	95.37 242	86.49 152	89.26 281	93.59 267	79.76 284	91.15 265	92.31 298	77.12 285	98.38 190	77.51 314	97.92 214	95.71 270
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		91.28 193	90.86 198	92.53 190	95.45 238	82.53 211	89.25 283	96.52 184	85.00 240	89.91 285	88.55 345	92.94 94	98.84 127	84.72 249	95.44 287	96.22 250

BH-RMVSNet		90.47 208	90.44 209	90.56 258	95.21 246	78.65 277	89.15 284	93.94 265	88.21 186	92.74 227	94.22 245	86.38 204	97.88 233	78.67 306	95.39 289	95.14 286

thres200		85.85 297	85.18 298	87.88 312	94.44 270	72.52 339	89.08 285	86.21 338	88.57 180	91.44 259	88.40 346	64.22 341	98.00 222	68.35 358	95.88 278	93.12 331

USDC		89.02 244	89.08 232	88.84 294	95.07 248	74.50 323	88.97 286	96.39 189	73.21 328	93.27 207	96.28 159	82.16 243	96.39 300	77.55 313	98.80 135	95.62 277

testdata1							88.96 287		88.44 182

pmmvs5		87.87 267	87.14 275	90.07 271	93.26 293	76.97 300	88.89 288	92.18 295	73.71 325	88.36 312	93.89 259	76.86 292	96.73 291	80.32 285	96.81 256	96.51 236

patch_mono-2		92.46 166	92.72 157	91.71 215	96.65 159	78.91 271	88.85 289	97.17 137	83.89 251	92.45 237	96.76 124	89.86 159	97.09 278	90.24 145	98.59 155	99.12 43

test222					96.95 144	85.27 179	88.83 290	93.61 266	65.09 363	90.74 270	94.85 223	84.62 219			97.36 236	93.91 316

baseline2		83.38 312	81.54 321	88.90 292	91.38 326	72.84 337	88.78 291	81.22 364	78.97 294	79.82 365	87.56 349	61.73 353	97.80 242	74.30 333	90.05 353	96.05 257

diffmvs		91.74 181	91.93 174	91.15 239	93.06 297	78.17 281	88.77 292	97.51 111	86.28 216	92.42 239	93.96 256	88.04 176	97.46 263	90.69 130	96.67 261	97.82 172
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MDTV_nov1_ep13			83.88 308		89.42 351	61.52 373	88.74 293	87.41 331	73.99 323	84.96 339	94.01 254	65.25 337	95.53 316	78.02 308	93.16 329

D2MVS		89.93 229	89.60 228	90.92 246	94.03 280	78.40 278	88.69 294	94.85 240	78.96 295	93.08 214	95.09 214	74.57 299	96.94 283	88.19 193	98.96 115	97.41 201

TR-MVS		87.70 270	87.17 274	89.27 287	94.11 277	79.26 264	88.69 294	91.86 303	81.94 272	90.69 271	89.79 331	82.82 235	97.42 266	72.65 342	91.98 344	91.14 349

PatchMatch-RL		89.18 239	88.02 261	92.64 183	95.90 217	92.87 45	88.67 296	91.06 309	80.34 279	90.03 283	91.67 308	83.34 226	94.42 334	76.35 323	94.84 303	90.64 352

PAPR		87.65 273	86.77 283	90.27 265	92.85 301	77.38 292	88.56 297	96.23 196	76.82 310	84.98 338	89.75 333	86.08 208	97.16 276	72.33 343	93.35 326	96.26 249

MDTV_nov1_ep13_2view						42.48 380	88.45 298		67.22 357	83.56 348		66.80 327		72.86 341		94.06 312

jason		89.17 240	88.32 248	91.70 216	95.73 225	80.07 242	88.10 299	93.22 275	71.98 335	90.09 280	92.79 286	78.53 273	98.56 175	87.43 208	97.06 244	96.46 241
jason: jason.
mvsany_test3		89.11 242	88.21 256	91.83 209	91.30 328	90.25 79	88.09 300	78.76 370	76.37 311	96.43 85	98.39 30	83.79 223	90.43 358	86.57 222	94.20 317	94.80 295

BH-w/o		87.21 284	87.02 279	87.79 313	94.77 258	77.27 294	87.90 301	93.21 277	81.74 273	89.99 284	88.39 347	83.47 225	96.93 285	71.29 349	92.43 340	89.15 354

MS-PatchMatch		88.05 265	87.75 263	88.95 291	93.28 291	77.93 283	87.88 302	92.49 291	75.42 315	92.57 233	93.59 268	80.44 258	94.24 339	81.28 278	92.75 335	94.69 301

DELS-MVS		92.05 177	92.16 167	91.72 214	94.44 270	80.13 241	87.62 303	97.25 132	87.34 204	92.22 248	93.18 278	89.54 162	98.73 149	89.67 161	98.20 194	96.30 247
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
ADS-MVSNet2		84.01 309	82.20 318	89.41 283	89.04 353	76.37 308	87.57 304	90.98 311	72.71 333	84.46 341	92.45 293	68.08 320	96.48 297	70.58 354	83.97 364	95.38 280

ADS-MVSNet		82.25 319	81.55 320	84.34 339	89.04 353	65.30 364	87.57 304	85.13 353	72.71 333	84.46 341	92.45 293	68.08 320	92.33 349	70.58 354	83.97 364	95.38 280

IterMVS-SCA-FT		91.65 183	91.55 181	91.94 207	93.89 283	79.22 266	87.56 306	93.51 270	91.53 116	95.37 135	96.62 135	78.65 270	98.90 116	91.89 101	94.95 299	97.70 182

IterMVS		90.18 220	90.16 214	90.21 268	93.15 295	75.98 311	87.56 306	92.97 279	86.43 215	94.09 178	96.40 146	78.32 274	97.43 265	87.87 202	94.69 307	97.23 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		87.50 278	86.58 285	90.25 266	96.80 156	77.75 287	87.53 308	96.25 194	69.73 350	86.47 330	93.61 267	75.67 295	97.88 233	79.95 292	93.20 328	95.11 287

c3_l		91.32 192	91.42 186	91.00 244	92.29 308	76.79 303	87.52 309	96.42 188	85.76 226	94.72 167	93.89 259	82.73 236	98.16 210	90.93 124	98.55 158	98.04 143

UnsupCasMVSNet_bld		88.50 259	88.03 260	89.90 275	95.52 236	78.88 272	87.39 310	94.02 262	79.32 291	93.06 215	94.02 253	80.72 257	94.27 337	75.16 329	93.08 332	96.54 234

lupinMVS		88.34 262	87.31 269	91.45 225	94.74 260	80.06 243	87.23 311	92.27 294	71.10 340	88.83 299	91.15 314	77.02 287	98.53 178	86.67 220	96.75 259	95.76 268

pmmvs4		88.95 249	87.70 265	92.70 181	94.30 273	85.60 174	87.22 312	92.16 297	74.62 319	89.75 291	94.19 246	77.97 277	96.41 299	82.71 263	96.36 268	96.09 254

WTY-MVS		86.93 291	86.50 290	88.24 306	94.96 249	74.64 319	87.19 313	92.07 300	78.29 299	88.32 313	91.59 310	78.06 276	94.27 337	74.88 330	93.15 330	95.80 266

ET-MVSNet_ETH3D		86.15 295	84.27 305	91.79 211	93.04 298	81.28 227	87.17 314	86.14 339	79.57 286	83.65 346	88.66 343	57.10 361	98.18 208	87.74 204	95.40 288	95.90 263

MVS-HIRNet		78.83 338	80.60 330	73.51 355	93.07 296	47.37 378	87.10 315	78.00 372	68.94 352	77.53 368	97.26 90	71.45 310	94.62 330	63.28 367	88.74 356	78.55 370

xiu_mvs_v2_base		89.00 247	89.19 230	88.46 304	94.86 253	74.63 320	86.97 316	95.60 217	80.88 276	87.83 319	88.62 344	91.04 136	98.81 134	82.51 267	94.38 312	91.93 344

DPM-MVS		89.35 237	88.40 246	92.18 201	96.13 202	84.20 190	86.96 317	96.15 202	75.40 316	87.36 325	91.55 311	83.30 227	98.01 221	82.17 271	96.62 262	94.32 308

eth_miper_zixun_eth		90.72 200	90.61 205	91.05 240	92.04 316	76.84 302	86.91 318	96.67 174	85.21 234	94.41 172	93.92 257	79.53 263	98.26 201	89.76 159	97.02 246	98.06 140

dp		79.28 336	78.62 338	81.24 348	85.97 370	56.45 376	86.91 318	85.26 351	72.97 331	81.45 362	89.17 342	56.01 365	95.45 321	73.19 339	76.68 371	91.82 347

sss		87.23 283	86.82 281	88.46 304	93.96 281	77.94 282	86.84 320	92.78 284	77.59 302	87.61 323	91.83 305	78.75 268	91.92 350	77.84 310	94.20 317	95.52 279

miper_ehance_all_eth		90.48 207	90.42 210	90.69 254	91.62 324	76.57 305	86.83 321	96.18 200	83.38 253	94.06 181	92.66 291	82.20 242	98.04 216	89.79 158	97.02 246	97.45 198

CLD-MVS		91.82 179	91.41 187	93.04 166	96.37 177	83.65 198	86.82 322	97.29 129	84.65 246	92.27 247	89.67 334	92.20 110	97.85 239	83.95 254	99.47 41	97.62 187
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cl____		90.65 203	90.56 207	90.91 248	91.85 319	76.98 299	86.75 323	95.36 232	85.53 230	94.06 181	94.89 221	77.36 284	97.98 225	90.27 143	98.98 109	97.76 178

DIV-MVS_self_test		90.65 203	90.56 207	90.91 248	91.85 319	76.99 298	86.75 323	95.36 232	85.52 232	94.06 181	94.89 221	77.37 283	97.99 224	90.28 142	98.97 113	97.76 178

PS-MVSNAJ		88.86 252	88.99 236	88.48 303	94.88 251	74.71 318	86.69 325	95.60 217	80.88 276	87.83 319	87.37 352	90.77 139	98.82 129	82.52 266	94.37 313	91.93 344

PVSNet_Blended		88.74 255	88.16 258	90.46 261	94.81 255	78.80 275	86.64 326	96.93 154	74.67 318	88.68 308	89.18 341	86.27 206	98.15 211	80.27 286	96.00 273	94.44 305

MSDG		90.82 197	90.67 204	91.26 233	94.16 275	83.08 207	86.63 327	96.19 199	90.60 138	91.94 253	91.89 304	89.16 165	95.75 314	80.96 283	94.51 310	94.95 291

cl22		89.02 244	88.50 244	90.59 257	89.76 345	76.45 306	86.62 328	94.03 260	82.98 261	92.65 229	92.49 292	72.05 308	97.53 258	88.93 180	97.02 246	97.78 176

CL-MVSNet_self_test		90.04 228	89.90 222	90.47 259	95.24 245	77.81 286	86.60 329	92.62 288	85.64 228	93.25 210	93.92 257	83.84 222	96.06 310	79.93 294	98.03 207	97.53 194

PCF-MVS	84.52 17	89.12 241	87.71 264	93.34 160	96.06 205	85.84 171	86.58 330	97.31 126	68.46 354	93.61 196	93.89 259	87.51 185	98.52 179	67.85 359	98.11 200	95.66 274
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_f		86.65 293	87.13 276	85.19 333	90.28 341	86.11 165	86.52 331	91.66 305	69.76 349	95.73 122	97.21 97	69.51 316	81.28 372	89.15 176	94.40 311	88.17 359

Patchmatch-RL test		88.81 253	88.52 243	89.69 280	95.33 244	79.94 248	86.22 332	92.71 285	78.46 298	95.80 117	94.18 247	66.25 332	95.33 325	89.22 174	98.53 161	93.78 319

FPMVS		84.50 306	83.28 310	88.16 307	96.32 185	94.49 16	85.76 333	85.47 347	83.09 258	85.20 336	94.26 243	63.79 345	86.58 368	63.72 366	91.88 346	83.40 365

IB-MVS	77.21 19	83.11 313	81.05 324	89.29 286	91.15 329	75.85 312	85.66 334	86.00 341	79.70 285	82.02 359	86.61 355	48.26 374	98.39 188	77.84 310	92.22 341	93.63 324
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
MDA-MVSNet-bldmvs		91.04 194	90.88 197	91.55 221	94.68 264	80.16 238	85.49 335	92.14 298	90.41 143	94.93 157	95.79 181	85.10 215	96.93 285	85.15 239	94.19 319	97.57 190

test_vis1_rt		85.58 299	84.58 301	88.60 299	87.97 359	86.76 144	85.45 336	93.59 267	66.43 358	87.64 321	89.20 340	79.33 264	85.38 369	81.59 275	89.98 354	93.66 323

new-patchmatchnet		88.97 248	90.79 201	83.50 343	94.28 274	55.83 377	85.34 337	93.56 269	86.18 218	95.47 129	95.73 187	83.10 229	96.51 296	85.40 236	98.06 204	98.16 135

miper_enhance_ethall		88.42 260	87.87 262	90.07 271	88.67 357	75.52 315	85.10 338	95.59 221	75.68 312	92.49 234	89.45 337	78.96 266	97.88 233	87.86 203	97.02 246	96.81 227

HyFIR lowres test		87.19 286	85.51 297	92.24 196	97.12 140	80.51 236	85.03 339	96.06 203	66.11 360	91.66 257	92.98 282	70.12 314	99.14 84	75.29 328	95.23 294	97.07 215

pmmvs3		80.83 331	78.96 337	86.45 323	87.23 364	77.48 291	84.87 340	82.31 361	63.83 365	85.03 337	89.50 336	49.66 372	93.10 345	73.12 340	95.10 296	88.78 358

test0.0.03 1		82.48 318	81.47 322	85.48 330	89.70 346	73.57 331	84.73 341	81.64 363	83.07 259	88.13 316	86.61 355	62.86 349	89.10 365	66.24 363	90.29 352	93.77 320

N_pmnet		88.90 251	87.25 272	93.83 145	94.40 272	93.81 35	84.73 341	87.09 333	79.36 290	93.26 208	92.43 296	79.29 265	91.68 351	77.50 315	97.22 240	96.00 258

GA-MVS		87.70 270	86.82 281	90.31 263	93.27 292	77.22 295	84.72 343	92.79 283	85.11 238	89.82 287	90.07 326	66.80 327	97.76 248	84.56 250	94.27 316	95.96 259

ppachtmachnet_test		88.61 258	88.64 242	88.50 302	91.76 321	70.99 347	84.59 344	92.98 278	79.30 292	92.38 241	93.53 270	79.57 262	97.45 264	86.50 226	97.17 241	97.07 215

CHOSEN 1792x2688		87.19 286	85.92 295	91.00 244	97.13 139	79.41 261	84.51 345	95.60 217	64.14 364	90.07 282	94.81 224	78.26 275	97.14 277	73.34 337	95.38 290	96.46 241

thisisatest0515		84.72 305	82.99 313	89.90 275	92.96 300	75.33 317	84.36 346	83.42 359	77.37 304	88.27 314	86.65 354	53.94 368	98.72 150	82.56 265	97.40 235	95.67 273

cascas		87.02 290	86.28 292	89.25 288	91.56 325	76.45 306	84.33 347	96.78 167	71.01 341	86.89 329	85.91 360	81.35 250	96.94 283	83.09 260	95.60 282	94.35 307

new_pmnet		81.22 327	81.01 326	81.86 347	90.92 333	70.15 350	84.03 348	80.25 368	70.83 342	85.97 333	89.78 332	67.93 323	84.65 370	67.44 360	91.90 345	90.78 351

PAPM		81.91 324	80.11 334	87.31 317	93.87 284	72.32 341	84.02 349	93.22 275	69.47 351	76.13 370	89.84 328	72.15 307	97.23 274	53.27 372	89.02 355	92.37 341

our_test_3		87.55 276	87.59 266	87.44 316	91.76 321	70.48 348	83.83 350	90.55 315	79.79 283	92.06 252	92.17 300	78.63 272	95.63 315	84.77 247	94.73 305	96.22 250

miper_lstm_enhance		89.90 230	89.80 223	90.19 270	91.37 327	77.50 290	83.82 351	95.00 236	84.84 244	93.05 216	94.96 219	76.53 294	95.20 328	89.96 155	98.67 149	97.86 166

test-LLR		83.58 311	83.17 311	84.79 336	89.68 347	66.86 360	83.08 352	84.52 354	83.07 259	82.85 352	84.78 363	62.86 349	93.49 343	82.85 261	94.86 301	94.03 313

TESTMET0.1,1		79.09 337	78.04 339	82.25 346	87.52 362	64.03 371	83.08 352	80.62 366	70.28 347	80.16 364	83.22 366	44.13 377	90.56 356	79.95 292	93.36 325	92.15 342

test-mter		81.21 328	80.01 335	84.79 336	89.68 347	66.86 360	83.08 352	84.52 354	73.85 324	82.85 352	84.78 363	43.66 378	93.49 343	82.85 261	94.86 301	94.03 313

test123		9.49 344	12.01 347	1.91 359	2.87 382	1.30 383	82.38 355	1.34 384	1.36 377	2.84 378	6.56 376	2.45 382	0.97 378	2.73 376	5.56 376	3.47 374

PMMVS		83.00 315	81.11 323	88.66 298	83.81 376	86.44 155	82.24 356	85.65 344	61.75 368	82.07 357	85.64 361	79.75 261	91.59 352	75.99 325	93.09 331	87.94 360

KD-MVS_2432*1600		82.17 321	80.75 328	86.42 324	82.04 377	70.09 351	81.75 357	90.80 312	82.56 264	90.37 277	89.30 338	42.90 379	96.11 308	74.47 331	92.55 338	93.06 332

miper_refine_blended		82.17 321	80.75 328	86.42 324	82.04 377	70.09 351	81.75 357	90.80 312	82.56 264	90.37 277	89.30 338	42.90 379	96.11 308	74.47 331	92.55 338	93.06 332

mvsany_test1		83.91 310	82.93 314	86.84 321	86.18 369	85.93 168	81.11 359	75.03 374	70.80 344	88.57 310	94.63 232	83.08 230	87.38 366	80.39 284	86.57 361	87.21 361

YYNet1		88.17 263	88.24 253	87.93 310	92.21 310	73.62 330	80.75 360	88.77 320	82.51 267	94.99 155	95.11 213	82.70 237	93.70 341	83.33 257	93.83 321	96.48 240

MDA-MVSNet_test_wron		88.16 264	88.23 254	87.93 310	92.22 309	73.71 329	80.71 361	88.84 319	82.52 266	94.88 160	95.14 211	82.70 237	93.61 342	83.28 258	93.80 322	96.46 241

testmvs		9.02 345	11.42 348	1.81 360	2.77 383	1.13 384	79.44 362	1.90 383	1.18 378	2.65 379	6.80 375	1.95 383	0.87 379	2.62 377	3.45 377	3.44 375

PVSNet	76.22 20	82.89 316	82.37 316	84.48 338	93.96 281	64.38 370	78.60 363	88.61 321	71.50 337	84.43 343	86.36 358	74.27 300	94.60 331	69.87 356	93.69 324	94.46 304

PVSNet_0	70.34 21	74.58 339	72.96 342	79.47 351	90.63 335	66.24 363	73.26 364	83.40 360	63.67 366	78.02 367	78.35 370	72.53 305	89.59 361	56.68 370	60.05 374	82.57 368

E-PMN		80.72 332	80.86 327	80.29 350	85.11 372	68.77 356	72.96 365	81.97 362	87.76 196	83.25 351	83.01 367	62.22 352	89.17 364	77.15 318	94.31 315	82.93 366

CHOSEN 280x420		80.04 335	77.97 340	86.23 327	90.13 342	74.53 322	72.87 366	89.59 318	66.38 359	76.29 369	85.32 362	56.96 362	95.36 323	69.49 357	94.72 306	88.79 357

EMVS		80.35 334	80.28 333	80.54 349	84.73 374	69.07 355	72.54 367	80.73 365	87.80 194	81.66 361	81.73 368	62.89 348	89.84 359	75.79 327	94.65 308	82.71 367

PMMVS2		81.31 326	83.44 309	74.92 354	90.52 337	46.49 379	69.19 368	85.23 352	84.30 248	87.95 318	94.71 230	76.95 289	84.36 371	64.07 365	98.09 202	93.89 317

tmp_tt		37.97 342	44.33 345	18.88 358	11.80 381	21.54 381	63.51 369	45.66 382	4.23 375	51.34 375	50.48 373	59.08 359	22.11 377	44.50 374	68.35 373	13.00 373

MVE	59.87 23	73.86 340	72.65 343	77.47 353	87.00 367	74.35 324	61.37 370	60.93 378	67.27 356	69.69 373	86.49 357	81.24 254	72.33 374	56.45 371	83.45 366	85.74 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 341	48.94 344	54.93 356	39.68 380	12.38 382	28.59 371	90.09 316	6.82 374	41.10 376	78.41 369	54.41 367	70.69 375	50.12 373	51.26 375	81.72 369

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		23.35 343	31.13 346	0.00 361	0.00 384	0.00 385	0.00 372	95.58 223	0.00 379	0.00 380	91.15 314	93.43 78	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		7.56 346	10.09 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	90.77 139	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		7.56 346	10.08 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	90.69 323	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

MSC_two_6792asdad				95.90 65	96.54 168	89.57 88		96.87 161					99.41 38	94.06 32	99.30 67	98.72 92

PC_three_1452									75.31 317	95.87 115	95.75 186	92.93 95	96.34 305	87.18 212	98.68 147	98.04 143

No_MVS				95.90 65	96.54 168	89.57 88		96.87 161					99.41 38	94.06 32	99.30 67	98.72 92

test_one_0601					98.26 71	87.14 134		98.18 37	94.25 45	96.99 66	97.36 82	95.13 40

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					97.23 133	90.32 78		97.54 106	84.40 247	94.78 163	95.79 181	92.76 101	99.39 48	88.72 187	98.40 169

IU-MVS					98.51 51	86.66 149		96.83 164	72.74 332	95.83 116				93.00 74	99.29 70	98.64 103

test_241102_TWO								98.10 50	91.95 92	97.54 38	97.25 91	95.37 29	99.35 58	93.29 62	99.25 79	98.49 114

test_241102_ONE					98.51 51	86.97 139		98.10 50	91.85 98	97.63 34	97.03 107	96.48 10	98.95 112

test_0728_THIRD									93.26 66	97.40 49	97.35 85	94.69 56	99.34 61	93.88 35	99.42 50	98.89 71

GSMVS																94.75 298

test_part2					98.21 75	89.41 93				96.72 76

sam_mvs1												66.64 330				94.75 298

sam_mvs												66.41 331

MTGPA								97.62 99

test_post											6.07 377	65.74 335	95.84 313

patchmatchnet-post											91.71 307	66.22 333	97.59 256

gm-plane-assit					87.08 366	59.33 374			71.22 338		83.58 365		97.20 275	73.95 334

test9_res														88.16 195	98.40 169	97.83 170

agg_prior2														87.06 215	98.36 178	97.98 152

agg_prior					96.20 195	88.89 103		96.88 160		90.21 279			98.78 141

TestCases				96.00 56	98.02 89	92.17 50		98.43 14	90.48 139	95.04 153	96.74 127	92.54 105	97.86 237	85.11 242	98.98 109	97.98 152

test_prior				94.61 112	95.95 214	87.23 132		97.36 122					98.68 161			97.93 158

新几何1				93.17 165	97.16 137	87.29 130		94.43 252	67.95 355	91.29 261	94.94 220	86.97 195	98.23 203	81.06 282	97.75 219	93.98 315

旧先验1					96.20 195	84.17 191		94.82 242			95.57 194	89.57 161			97.89 215	96.32 246

原ACMM1				92.87 176	96.91 148	84.22 189		97.01 148	76.84 309	89.64 292	94.46 238	88.00 177	98.70 157	81.53 276	98.01 209	95.70 272

testdata2													98.03 217	80.24 288

segment_acmp												92.14 111

testdata				91.03 241	96.87 150	82.01 216		94.28 256	71.55 336	92.46 236	95.42 200	85.65 213	97.38 271	82.64 264	97.27 238	93.70 322

test12				94.43 125	95.95 214	86.75 145		96.24 195		89.76 290		89.79 160	98.79 138		97.95 212	97.75 180

plane_prior7					97.71 108	88.68 106

plane_prior6					97.21 135	88.23 117						86.93 196

plane_prior5								97.81 86					98.95 112	89.26 172	98.51 164	98.60 107

plane_prior4											95.59 190

plane_prior3						88.43 115			90.35 144	93.31 203

plane_prior1					97.38 128

n2								0.00 385

nn								0.00 385

door-mid								92.13 299

lessismore_v0				93.87 143	98.05 85	83.77 197		80.32 367		97.13 57	97.91 52	77.49 280	99.11 91	92.62 84	98.08 203	98.74 90

LGP-MVS_train				96.84 38	98.36 66	92.13 52		98.25 27	91.78 105	97.07 59	97.22 95	96.38 12	99.28 70	92.07 95	99.59 28	99.11 44

test11								96.65 175

door								91.26 308

HQP5-MVS						84.89 182

BP-MVS														86.55 224

HQP4-MVS										88.81 301			98.61 168			98.15 136

HQP3-MVS								97.31 126							97.73 220

HQP2-MVS												84.76 217

NP-MVS					96.82 154	87.10 135					93.40 272

ACMMP++_ref															98.82 132

ACMMP++															99.25 79

Test By Simon												90.61 145

ITE_SJBPF				95.95 59	97.34 130	93.36 40		96.55 183	91.93 94	94.82 161	95.39 204	91.99 113	97.08 279	85.53 235	97.96 211	97.41 201

DeepMVS_CX				53.83 357	70.38 379	64.56 369		48.52 381	33.01 373	65.50 374	74.21 372	56.19 364	46.64 376	38.45 375	70.07 372	50.30 372

High-resolution multi-view benchmark