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 2	99.85 1

test_part1		98.39 2	98.94 2	96.75 42	99.23 3	90.73 73	98.63 3	99.28 2	99.01 2	99.60 2	99.55 2	98.75 2	99.68 3	96.41 4	99.97 1	99.84 2

UniMVSNet_ETH3D		97.13 7	97.72 4	95.35 83	99.51 2	87.38 128	97.70 7	97.54 105	98.16 3	98.94 3	99.33 3	97.84 5	99.08 92	90.73 119	99.73 14	99.59 13

pmmvs6		96.80 14	97.36 10	95.15 95	99.12 8	87.82 124	96.68 23	97.86 78	96.10 26	98.14 23	99.28 4	97.94 4	98.21 204	91.38 113	99.69 15	99.42 19

UA-Net		97.35 5	97.24 12	97.69 5	98.22 68	93.87 29	98.42 5	98.19 32	96.95 14	95.46 124	99.23 5	93.45 73	99.57 14	95.34 13	99.89 3	99.63 10

OurMVSNet-221017-0		96.80 14	96.75 19	96.96 36	99.03 11	91.85 57	97.98 6	98.01 64	94.15 48	98.93 4	99.07 6	88.07 178	99.57 14	95.86 10	99.69 15	99.46 18

gg-mvs-nofinetune		82.10 306	81.02 310	85.34 313	87.46 345	71.04 325	94.74 97	67.56 355	96.44 22	79.43 347	98.99 7	45.24 358	96.15 299	67.18 341	92.17 323	88.85 338

Anonymous20231211		96.60 26	97.13 13	95.00 99	97.46 117	86.35 155	97.11 15	98.24 28	97.58 8	98.72 9	98.97 8	93.15 84	99.15 83	93.18 64	99.74 13	99.50 17

ANet_high		94.83 94	96.28 37	90.47 250	96.65 150	73.16 315	94.33 113	98.74 6	96.39 23	98.09 24	98.93 9	93.37 77	98.70 159	90.38 126	99.68 18	99.53 15

mvs_tets		96.83 10	96.71 20	97.17 27	98.83 22	92.51 48	96.58 27	97.61 100	87.57 192	98.80 8	98.90 10	96.50 11	99.59 13	96.15 8	99.47 38	99.40 21

PS-MVSNAJss		96.01 52	96.04 52	95.89 64	98.82 23	88.51 110	95.57 69	97.88 77	88.72 166	98.81 7	98.86 11	90.77 141	99.60 9	95.43 12	99.53 34	99.57 14

test_djsdf		96.62 24	96.49 29	97.01 33	98.55 40	91.77 59	97.15 12	97.37 115	88.98 160	98.26 21	98.86 11	93.35 78	99.60 9	96.41 4	99.45 42	99.66 7

K. test v3		93.37 134	93.27 143	93.66 149	98.05 79	82.62 203	94.35 112	86.62 318	96.05 28	97.51 38	98.85 13	76.59 284	99.65 4	93.21 63	98.20 186	98.73 88

Gipuma		95.31 76	95.80 64	93.81 147	97.99 88	90.91 69	96.42 35	97.95 73	96.69 17	91.78 237	98.85 13	91.77 115	95.49 309	91.72 102	99.08 87	95.02 275
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LTVRE_ROB	93.87 1	97.93 3	98.16 3	97.26 26	98.81 24	93.86 30	99.07 2	98.98 4	97.01 13	98.92 5	98.78 15	95.22 38	98.61 168	96.85 2	99.77 10	99.31 27
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 18	96.42 30	97.68 7	98.00 85	94.03 24	96.97 16	97.61 100	87.68 189	98.45 19	98.77 16	94.20 66	99.50 20	96.70 3	99.40 52	99.53 15

SixPastTwentyTwo		94.91 87	95.21 82	93.98 137	98.52 45	83.19 197	95.93 56	94.84 233	94.86 38	98.49 16	98.74 17	81.45 248	99.60 9	94.69 17	99.39 53	99.15 37

jajsoiax		96.59 28	96.42 30	97.12 29	98.76 27	92.49 49	96.44 34	97.42 113	86.96 201	98.71 11	98.72 18	95.36 32	99.56 17	95.92 9	99.45 42	99.32 26

VDDNet		94.03 121	94.27 116	93.31 162	98.87 20	82.36 205	95.51 71	91.78 291	97.19 12	96.32 83	98.60 19	84.24 224	98.75 147	87.09 198	98.83 121	98.81 79

TransMVSNet (Re)		95.27 79	96.04 52	92.97 172	98.37 60	81.92 209	95.07 87	96.76 165	93.97 52	97.77 27	98.57 20	95.72 19	97.90 226	88.89 167	99.23 74	99.08 45

Baseline_NR-MVSNet		94.47 107	95.09 87	92.60 189	98.50 53	80.82 225	92.08 183	96.68 168	93.82 55	96.29 86	98.56 21	90.10 158	97.75 244	90.10 141	99.66 20	99.24 31

GBi-Net		93.21 142	92.96 146	93.97 138	95.40 227	84.29 179	95.99 52	96.56 174	88.63 167	95.10 139	98.53 22	81.31 250	98.98 107	86.74 201	98.38 160	98.65 91

test1		93.21 142	92.96 146	93.97 138	95.40 227	84.29 179	95.99 52	96.56 174	88.63 167	95.10 139	98.53 22	81.31 250	98.98 107	86.74 201	98.38 160	98.65 91

FMVSNet1		94.84 93	95.13 85	93.97 138	97.60 108	84.29 179	95.99 52	96.56 174	92.38 75	97.03 56	98.53 22	90.12 155	98.98 107	88.78 169	99.16 80	98.65 91

MIMVSNet1		95.52 66	95.45 72	95.72 73	99.14 5	89.02 96	96.23 47	96.87 158	93.73 56	97.87 26	98.49 25	90.73 145	99.05 97	86.43 210	99.60 24	99.10 44

pm-mvs1		95.43 69	95.94 55	93.93 141	98.38 58	85.08 173	95.46 72	97.12 139	91.84 97	97.28 46	98.46 26	95.30 35	97.71 246	90.17 137	99.42 46	98.99 53

TDRefinement		97.68 4	97.60 5	97.93 2	99.02 12	95.95 5	98.61 4	98.81 5	97.41 10	97.28 46	98.46 26	94.62 57	98.84 129	94.64 18	99.53 34	98.99 53

v7n		96.82 11	97.31 11	95.33 85	98.54 42	86.81 141	96.83 19	98.07 51	96.59 20	98.46 18	98.43 28	92.91 90	99.52 18	96.25 7	99.76 11	99.65 9

DTE-MVSNet		96.74 18	97.43 6	94.67 110	99.13 6	84.68 176	96.51 29	97.94 76	98.14 4	98.67 13	98.32 29	95.04 45	99.69 2	93.27 61	99.82 8	99.62 11

ACMH	88.36 12	96.59 28	97.43 6	94.07 135	98.56 37	85.33 171	96.33 40	98.30 21	94.66 39	98.72 9	98.30 30	97.51 6	98.00 221	94.87 15	99.59 26	98.86 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		96.69 21	97.39 9	94.61 112	99.16 4	84.50 177	96.54 28	98.05 55	98.06 5	98.64 14	98.25 31	95.01 48	99.65 4	92.95 74	99.83 6	99.68 5

PS-CasMVS		96.69 21	97.43 6	94.49 123	99.13 6	84.09 186	96.61 25	97.97 70	97.91 6	98.64 14	98.13 32	95.24 37	99.65 4	93.39 55	99.84 4	99.72 3

Vis-MVSNet		95.50 67	95.48 71	95.56 79	98.11 74	89.40 91	95.35 73	98.22 30	92.36 77	94.11 169	98.07 33	92.02 108	99.44 24	93.38 56	97.67 220	97.85 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous20240529		95.50 67	95.83 62	94.50 121	97.33 123	85.93 163	95.19 83	96.77 164	96.64 19	97.61 34	98.05 34	93.23 81	98.79 138	88.60 174	99.04 96	98.78 82

VPA-MVSNet		95.14 81	95.67 68	93.58 152	97.76 95	83.15 198	94.58 104	97.58 102	93.39 63	97.05 55	98.04 35	93.25 80	98.51 181	89.75 149	99.59 26	99.08 45

LCM-MVSNet-Re		94.20 118	94.58 103	93.04 168	95.91 203	83.13 199	93.79 127	99.19 3	92.00 87	98.84 6	98.04 35	93.64 70	99.02 103	81.28 262	98.54 146	96.96 209

v10		94.68 100	95.27 81	92.90 177	96.57 156	80.15 229	94.65 101	97.57 103	90.68 131	97.43 41	98.00 37	88.18 175	99.15 83	94.84 16	99.55 33	99.41 20

DeepC-MVS	91.39 4	95.43 69	95.33 77	95.71 74	97.67 105	90.17 77	93.86 126	98.02 62	87.35 194	96.22 92	97.99 38	94.48 61	99.05 97	92.73 79	99.68 18	97.93 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
JIA-IIPM		85.08 289	83.04 298	91.19 231	87.56 342	86.14 160	89.40 267	84.44 339	88.98 160	82.20 336	97.95 39	56.82 347	96.15 299	76.55 304	83.45 345	91.30 330

v8		94.65 101	95.29 79	92.74 182	96.65 150	79.77 243	94.59 102	97.17 135	91.86 93	97.47 40	97.93 40	88.16 176	99.08 92	94.32 23	99.47 38	99.38 22

testing_2		94.03 121	94.38 109	93.00 170	96.79 147	81.41 217	92.87 150	96.96 147	85.88 217	97.06 54	97.92 41	91.18 136	98.71 158	91.72 102	99.04 96	98.87 72

APDe-MVS		96.46 33	96.64 23	95.93 61	97.68 104	89.38 92	96.90 18	98.41 14	92.52 73	97.43 41	97.92 41	95.11 42	99.50 20	94.45 20	99.30 62	98.92 67

nrg030		96.32 42	96.55 27	95.62 76	97.83 93	88.55 108	95.77 62	98.29 24	92.68 69	98.03 25	97.91 43	95.13 41	98.95 114	93.85 34	99.49 37	99.36 24

lessismore_v0				93.87 145	98.05 79	83.77 191		80.32 350		97.13 50	97.91 43	77.49 274	99.11 89	92.62 82	98.08 197	98.74 86

WR-MVS_H		96.60 26	97.05 15	95.24 91	99.02 12	86.44 151	96.78 22	98.08 48	97.42 9	98.48 17	97.86 45	91.76 116	99.63 7	94.23 27	99.84 4	99.66 7

VDD-MVS		94.37 108	94.37 110	94.40 128	97.49 114	86.07 161	93.97 124	93.28 263	94.49 43	96.24 90	97.78 46	87.99 181	98.79 138	88.92 165	99.14 82	98.34 118

RPSCF		95.58 65	94.89 91	97.62 8	97.58 109	96.30 4	95.97 55	97.53 107	92.42 74	93.41 188	97.78 46	91.21 132	97.77 241	91.06 115	97.06 237	98.80 80

test_0402		95.73 60	96.22 40	94.26 131	98.19 70	85.77 166	93.24 141	97.24 131	96.88 16	97.69 29	97.77 48	94.12 67	99.13 86	91.54 110	99.29 63	97.88 159

tfpnnormal		94.27 114	94.87 92	92.48 193	97.71 100	80.88 224	94.55 108	95.41 220	93.70 57	96.67 70	97.72 49	91.40 124	98.18 208	87.45 193	99.18 79	98.36 117

XXY-MVS		92.58 162	93.16 145	90.84 243	97.75 96	79.84 239	91.87 197	96.22 192	85.94 215	95.53 121	97.68 50	92.69 96	94.48 322	83.21 243	97.51 225	98.21 128

UGNet		93.08 145	92.50 160	94.79 106	93.87 269	87.99 120	95.07 87	94.26 249	90.64 132	87.33 306	97.67 51	86.89 202	98.49 182	88.10 181	98.71 133	97.91 156
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
wuyk23d		87.83 258	90.79 199	78.96 333	90.46 322	88.63 104	92.72 153	90.67 297	91.65 109	98.68 12	97.64 52	96.06 17	77.53 353	59.84 348	99.41 51	70.73 350

EG-PatchMatch MVS		94.54 105	94.67 100	94.14 133	97.87 92	86.50 147	92.00 188	96.74 166	88.16 178	96.93 60	97.61 53	93.04 88	97.90 226	91.60 107	98.12 193	98.03 141

DSMNet-mixed		82.21 305	81.56 304	84.16 321	89.57 331	70.00 331	90.65 229	77.66 353	54.99 353	83.30 330	97.57 54	77.89 273	90.50 345	66.86 342	95.54 272	91.97 325

FC-MVSNet-test		95.32 74	95.88 58	93.62 150	98.49 54	81.77 210	95.90 58	98.32 18	93.93 53	97.53 37	97.56 55	88.48 171	99.40 40	92.91 75	99.83 6	99.68 5

ab-mvs		92.40 167	92.62 157	91.74 212	97.02 134	81.65 212	95.84 60	95.50 218	86.95 202	92.95 207	97.56 55	90.70 146	97.50 254	79.63 279	97.43 228	96.06 243

COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 28	98.38 58	94.31 15	96.79 21	98.32 18	96.69 17	96.86 62	97.56 55	95.48 26	98.77 146	90.11 139	99.44 44	98.31 121
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVSNet		96.19 47	96.80 18	94.38 129	98.99 14	83.82 190	96.31 42	97.53 107	97.60 7	98.34 20	97.52 58	91.98 112	99.63 7	93.08 70	99.81 9	99.70 4

ACMH+	88.43 11	96.48 31	96.82 17	95.47 81	98.54 42	89.06 95	95.65 66	98.61 7	96.10 26	98.16 22	97.52 58	96.90 8	98.62 167	90.30 131	99.60 24	98.72 89

SMA-MVS		95.77 59	95.54 69	96.47 51	98.27 65	91.19 65	95.09 85	97.79 89	86.48 205	97.42 43	97.51 60	94.47 62	99.29 68	93.55 43	99.29 63	98.93 63
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
ambc				92.98 171	96.88 140	83.01 201	95.92 57	96.38 184		96.41 77	97.48 61	88.26 174	97.80 237	89.96 144	98.93 108	98.12 135

PMVS	87.21 14	94.97 85	95.33 77	93.91 142	98.97 15	97.16 2	95.54 70	95.85 204	96.47 21	93.40 190	97.46 62	95.31 34	95.47 310	86.18 214	98.78 128	89.11 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
abl_6		97.31 6	97.12 14	97.86 3	98.54 42	95.32 7	96.61 25	98.35 17	95.81 31	97.55 35	97.44 63	96.51 10	99.40 40	94.06 31	99.23 74	98.85 76

3Dnovator	92.54 3	94.80 96	94.90 90	94.47 124	95.47 225	87.06 134	96.63 24	97.28 129	91.82 100	94.34 167	97.41 64	90.60 148	98.65 166	92.47 84	98.11 194	97.70 175

mvs_anonymous		90.37 210	91.30 188	87.58 298	92.17 297	68.00 335	89.84 257	94.73 238	83.82 243	93.22 198	97.40 65	87.54 187	97.40 262	87.94 185	95.05 284	97.34 198

MP-MVS-pluss		96.08 50	95.92 57	96.57 46	99.06 10	91.21 64	93.25 140	98.32 18	87.89 183	96.86 62	97.38 66	95.55 25	99.39 45	95.47 11	99.47 38	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test0726					98.51 46	86.69 144	95.34 74	98.18 33	91.85 94	97.63 31	97.37 67	95.58 23

EU-MVSNet		87.39 269	86.71 272	89.44 269	93.40 274	76.11 293	94.93 93	90.00 299	57.17 351	95.71 114	97.37 67	64.77 324	97.68 248	92.67 81	94.37 297	94.52 286

FMVSNet2		92.78 155	92.73 155	92.95 174	95.40 227	81.98 208	94.18 117	95.53 217	88.63 167	96.05 101	97.37 67	81.31 250	98.81 136	87.38 196	98.67 137	98.06 137

HPM-MVS_fast		97.01 8	96.89 16	97.39 22	99.12 8	93.92 27	97.16 11	98.17 36	93.11 66	96.48 76	97.36 70	96.92 7	99.34 59	94.31 24	99.38 54	98.92 67

DVP-MVS		95.82 58	96.18 42	94.72 109	98.51 46	86.69 144	95.20 81	97.00 144	91.85 94	97.40 44	97.35 71	95.58 23	99.34 59	93.44 52	99.31 60	98.13 134

test_0728_THIRD									93.26 65	97.40 44	97.35 71	94.69 55	99.34 59	93.88 33	99.42 46	98.89 69

ACMMP_NAP		96.21 46	96.12 47	96.49 50	98.90 18	91.42 62	94.57 105	98.03 60	90.42 138	96.37 79	97.35 71	95.68 20	99.25 74	94.44 21	99.34 56	98.80 80

DP-MVS		95.62 63	95.84 61	94.97 100	97.16 129	88.62 105	94.54 109	97.64 96	96.94 15	96.58 74	97.32 74	93.07 87	98.72 152	90.45 123	98.84 116	97.57 183

MVS-HIRNet		78.83 321	80.60 313	73.51 336	93.07 280	47.37 357	87.10 303	78.00 352	68.94 333	77.53 349	97.26 75	71.45 299	94.62 320	63.28 347	88.74 336	78.55 349

SED-MVS		96.00 53	96.41 33	94.76 107	98.51 46	86.97 137	95.21 79	98.10 45	91.95 88	97.63 31	97.25 76	96.48 12	99.35 55	93.29 59	99.29 63	97.95 151

test_241102_TWO								98.10 45	91.95 88	97.54 36	97.25 76	95.37 29	99.35 55	93.29 59	99.25 71	98.49 109

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 53	96.81 145	90.79 72	96.30 44	97.82 84	96.13 25	94.74 156	97.23 78	91.33 126	99.16 82	93.25 62	98.30 172	98.46 112

LPG-MVS_test		96.38 41	96.23 39	96.84 40	98.36 61	92.13 52	95.33 75	98.25 25	91.78 101	97.07 51	97.22 79	96.38 14	99.28 70	92.07 91	99.59 26	99.11 41

LGP-MVS_train				96.84 40	98.36 61	92.13 52		98.25 25	91.78 101	97.07 51	97.22 79	96.38 14	99.28 70	92.07 91	99.59 26	99.11 41

FIs		94.90 88	95.35 75	93.55 153	98.28 64	81.76 211	95.33 75	98.14 40	93.05 67	97.07 51	97.18 81	87.65 185	99.29 68	91.72 102	99.69 15	99.61 12

PatchT		87.51 266	88.17 247	85.55 310	90.64 317	66.91 337	92.02 187	86.09 322	92.20 83	89.05 280	97.16 82	64.15 326	96.37 296	89.21 162	92.98 317	93.37 312

TranMVSNet+NR-MVSNet		96.07 51	96.26 38	95.50 80	98.26 66	87.69 125	93.75 128	97.86 78	95.96 30	97.48 39	97.14 83	95.33 33	99.44 24	90.79 118	99.76 11	99.38 22

TSAR-MVS + MP.		94.96 86	94.75 96	95.57 78	98.86 21	88.69 102	96.37 37	96.81 160	85.23 225	94.75 155	97.12 84	91.85 114	99.40 40	93.45 50	98.33 167	98.62 99
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VPNet		93.08 145	93.76 126	91.03 234	98.60 34	75.83 298	91.51 209	95.62 209	91.84 97	95.74 112	97.10 85	89.31 165	98.32 195	85.07 227	99.06 88	98.93 63

IterMVS-LS		93.78 126	94.28 114	92.27 196	96.27 176	79.21 254	91.87 197	96.78 162	91.77 103	96.57 75	97.07 86	87.15 194	98.74 150	91.99 93	99.03 98	98.86 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LFMVS		91.33 190	91.16 192	91.82 209	96.27 176	79.36 249	95.01 90	85.61 329	96.04 29	94.82 152	97.06 87	72.03 298	98.46 188	84.96 228	98.70 135	97.65 179

APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 25	97.95 89	93.82 32	96.31 42	98.25 25	95.51 35	96.99 59	97.05 88	95.63 22	99.39 45	93.31 58	98.88 111	98.75 85

SR-MVS-dyc-post		96.84 9	96.60 26	97.56 10	98.07 77	95.27 8	96.37 37	98.12 42	95.66 33	97.00 57	97.03 89	94.85 52	99.42 28	93.49 45	98.84 116	98.00 143

RE-MVS-def			96.66 21		98.07 77	95.27 8	96.37 37	98.12 42	95.66 33	97.00 57	97.03 89	95.40 28		93.49 45	98.84 116	98.00 143

test_241102_ONE					98.51 46	86.97 137		98.10 45	91.85 94	97.63 31	97.03 89	96.48 12	98.95 114

DPE-MVS		95.89 54	95.88 58	95.92 63	97.93 90	89.83 83	93.46 136	98.30 21	92.37 76	97.75 28	96.95 92	95.14 40	99.51 19	91.74 101	99.28 68	98.41 116

zzz-MVS		96.47 32	96.14 45	97.47 15	98.95 16	94.05 21	93.69 130	97.62 97	94.46 44	96.29 86	96.94 93	93.56 71	99.37 52	94.29 25	99.42 46	98.99 53

MTAPA		96.65 23	96.38 34	97.47 15	98.95 16	94.05 21	95.88 59	97.62 97	94.46 44	96.29 86	96.94 93	93.56 71	99.37 52	94.29 25	99.42 46	98.99 53

CR-MVSNet		87.89 256	87.12 265	90.22 257	91.01 314	78.93 256	92.52 160	92.81 270	73.08 315	89.10 278	96.93 95	67.11 308	97.64 249	88.80 168	92.70 319	94.08 293

Patchmtry		90.11 218	89.92 216	90.66 246	90.35 323	77.00 282	92.96 146	92.81 270	90.25 141	94.74 156	96.93 95	67.11 308	97.52 253	85.17 220	98.98 100	97.46 188

FMVSNet5		87.82 259	86.56 274	91.62 216	92.31 292	79.81 242	93.49 135	94.81 236	83.26 245	91.36 241	96.93 95	52.77 353	97.49 256	76.07 306	98.03 202	97.55 186

RPMNet		90.31 214	90.14 213	90.81 244	91.01 314	78.93 256	92.52 160	98.12 42	91.91 91	89.10 278	96.89 98	68.84 303	99.41 35	90.17 137	92.70 319	94.08 293

PGM-MVS		96.32 42	95.94 55	97.43 19	98.59 36	93.84 31	95.33 75	98.30 21	91.40 114	95.76 110	96.87 99	95.26 36	99.45 23	92.77 76	99.21 76	99.00 51

test1172		96.79 16	96.52 28	97.60 9	98.03 82	94.87 10	96.07 51	98.06 54	95.76 32	96.89 61	96.85 100	94.85 52	99.42 28	93.35 57	98.81 124	98.53 106

OPM-MVS		95.61 64	95.45 72	96.08 54	98.49 54	91.00 67	92.65 157	97.33 123	90.05 143	96.77 67	96.85 100	95.04 45	98.56 176	92.77 76	99.06 88	98.70 90

ACMM	88.83 9	96.30 44	96.07 50	96.97 35	98.39 57	92.95 44	94.74 97	98.03 60	90.82 127	97.15 49	96.85 100	96.25 16	99.00 106	93.10 68	99.33 58	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP		96.61 25	96.34 35	97.43 19	98.61 33	93.88 28	96.95 17	98.18 33	92.26 81	96.33 82	96.84 103	95.10 43	99.40 40	93.47 49	99.33 58	99.02 50
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
casdiffmvs		94.32 112	94.80 94	92.85 179	96.05 192	81.44 216	92.35 172	98.05 55	91.53 112	95.75 111	96.80 104	93.35 78	98.49 182	91.01 116	98.32 169	98.64 95

QAPM		92.88 152	92.77 151	93.22 166	95.82 207	83.31 194	96.45 32	97.35 122	83.91 242	93.75 180	96.77 105	89.25 166	98.88 121	84.56 233	97.02 239	97.49 187

LS3D		96.11 49	95.83 62	96.95 37	94.75 244	94.20 17	97.34 10	97.98 67	97.31 11	95.32 129	96.77 105	93.08 86	99.20 79	91.79 99	98.16 188	97.44 190

XVG-ACMP-BASELINE		95.68 62	95.34 76	96.69 44	98.40 56	93.04 41	94.54 109	98.05 55	90.45 137	96.31 84	96.76 107	92.91 90	98.72 152	91.19 114	99.42 46	98.32 119

MIMVSNet		87.13 277	86.54 275	88.89 279	96.05 192	76.11 293	94.39 111	88.51 304	81.37 262	88.27 295	96.75 108	72.38 295	95.52 307	65.71 344	95.47 274	95.03 274

AllTest		94.88 90	94.51 106	96.00 56	98.02 83	92.17 50	95.26 78	98.43 11	90.48 135	95.04 144	96.74 109	92.54 100	97.86 232	85.11 225	98.98 100	97.98 147

TestCases				96.00 56	98.02 83	92.17 50		98.43 11	90.48 135	95.04 144	96.74 109	92.54 100	97.86 232	85.11 225	98.98 100	97.98 147

SR-MVS		96.70 20	96.42 30	97.54 11	98.05 79	94.69 11	96.13 48	98.07 51	95.17 36	96.82 64	96.73 111	95.09 44	99.43 27	92.99 73	98.71 133	98.50 108

MP-MVS		96.14 48	95.68 67	97.51 13	98.81 24	94.06 19	96.10 49	97.78 90	92.73 68	93.48 187	96.72 112	94.23 65	99.42 28	91.99 93	99.29 63	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_Test		92.57 164	93.29 140	90.40 252	93.53 273	75.85 296	92.52 160	96.96 147	88.73 165	92.35 224	96.70 113	90.77 141	98.37 194	92.53 83	95.49 273	96.99 208

xxxxxxxxxxxxxcwj		95.03 82	94.93 89	95.33 85	97.46 117	88.05 118	92.04 185	98.42 13	87.63 190	96.36 80	96.68 114	94.37 63	99.32 65	92.41 85	99.05 91	98.64 95

SF-MVS		95.88 56	95.88 58	95.87 65	98.12 73	89.65 86	95.58 68	98.56 8	91.84 97	96.36 80	96.68 114	94.37 63	99.32 65	92.41 85	99.05 91	98.64 95

mPP-MVS		96.46 33	96.05 51	97.69 5	98.62 31	94.65 12	96.45 32	97.74 91	92.59 72	95.47 122	96.68 114	94.50 60	99.42 28	93.10 68	99.26 70	98.99 53

Anonymous202405211		92.58 162	92.50 160	92.83 180	96.55 157	83.22 196	92.43 166	91.64 292	94.10 49	95.59 118	96.64 117	81.88 247	97.50 254	85.12 224	98.52 148	97.77 170

IterMVS-SCA-FT		91.65 181	91.55 179	91.94 207	93.89 268	79.22 253	87.56 294	93.51 260	91.53 112	95.37 127	96.62 118	78.65 265	98.90 118	91.89 98	94.95 285	97.70 175

ACMMPR		96.46 33	96.14 45	97.41 21	98.60 34	93.82 32	96.30 44	97.96 71	92.35 78	95.57 119	96.61 119	94.93 51	99.41 35	93.78 36	99.15 81	99.00 51

PM-MVS		93.33 135	92.67 156	95.33 85	96.58 155	94.06 19	92.26 177	92.18 283	85.92 216	96.22 92	96.61 119	85.64 218	95.99 302	90.35 128	98.23 181	95.93 248

region2R		96.41 38	96.09 48	97.38 23	98.62 31	93.81 34	96.32 41	97.96 71	92.26 81	95.28 132	96.57 121	95.02 47	99.41 35	93.63 40	99.11 86	98.94 62

SteuartSystems-ACMMP		96.40 39	96.30 36	96.71 43	98.63 30	91.96 55	95.70 63	98.01 64	93.34 64	96.64 71	96.57 121	94.99 49	99.36 54	93.48 48	99.34 56	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
XVS		96.49 30	96.18 42	97.44 17	98.56 37	93.99 25	96.50 30	97.95 73	94.58 40	94.38 165	96.49 123	94.56 58	99.39 45	93.57 41	99.05 91	98.93 63

HFP-MVS		96.39 40	96.17 44	97.04 31	98.51 46	93.37 38	96.30 44	97.98 67	92.35 78	95.63 116	96.47 124	95.37 29	99.27 72	93.78 36	99.14 82	98.48 110

#test#		95.89 54	95.51 70	97.04 31	98.51 46	93.37 38	95.14 84	97.98 67	89.34 155	95.63 116	96.47 124	95.37 29	99.27 72	91.99 93	99.14 82	98.48 110

XVG-OURS		94.72 98	94.12 119	96.50 49	98.00 85	94.23 16	91.48 210	98.17 36	90.72 129	95.30 130	96.47 124	87.94 182	96.98 275	91.41 112	97.61 223	98.30 122

ACMP	88.15 13	95.71 61	95.43 74	96.54 47	98.17 71	91.73 60	94.24 115	98.08 48	89.46 153	96.61 73	96.47 124	95.85 18	99.12 88	90.45 123	99.56 32	98.77 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OpenMVS	89.45 8	92.27 172	92.13 167	92.68 184	94.53 254	84.10 185	95.70 63	97.03 142	82.44 256	91.14 247	96.42 128	88.47 172	98.38 191	85.95 215	97.47 227	95.55 265

HPM-MVS		96.81 13	96.62 24	97.36 24	98.89 19	93.53 37	97.51 8	98.44 10	92.35 78	95.95 104	96.41 129	96.71 9	99.42 28	93.99 32	99.36 55	99.13 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v1240		93.29 136	93.71 128	92.06 205	96.01 197	77.89 271	91.81 203	97.37 115	85.12 229	96.69 69	96.40 130	86.67 205	99.07 96	94.51 19	98.76 130	99.22 32

SD-MVS		95.19 80	95.73 66	93.55 153	96.62 153	88.88 101	94.67 99	98.05 55	91.26 117	97.25 48	96.40 130	95.42 27	94.36 326	92.72 80	99.19 77	97.40 194
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
test20.03		90.80 197	90.85 197	90.63 247	95.63 220	79.24 252	89.81 258	92.87 269	89.90 146	94.39 164	96.40 130	85.77 214	95.27 317	73.86 315	99.05 91	97.39 195

IterMVS		90.18 216	90.16 210	90.21 258	93.15 279	75.98 295	87.56 294	92.97 268	86.43 207	94.09 170	96.40 130	78.32 269	97.43 259	87.87 186	94.69 292	97.23 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVS		96.44 36	96.08 49	97.54 11	98.29 63	94.62 13	96.80 20	98.08 48	92.67 71	95.08 142	96.39 134	94.77 54	99.42 28	93.17 65	99.44 44	98.58 104

v1192		93.49 131	93.78 125	92.62 188	96.16 185	79.62 245	91.83 202	97.22 133	86.07 213	96.10 100	96.38 135	87.22 192	99.02 103	94.14 30	98.88 111	99.22 32

V42		93.43 133	93.58 133	92.97 172	95.34 231	81.22 219	92.67 156	96.49 179	87.25 196	96.20 94	96.37 136	87.32 191	98.85 128	92.39 87	98.21 184	98.85 76

ZNCC-MVS		96.42 37	96.20 41	97.07 30	98.80 26	92.79 46	96.08 50	98.16 39	91.74 105	95.34 128	96.36 137	95.68 20	99.44 24	94.41 22	99.28 68	98.97 59

IS-MVSNet		94.49 106	94.35 111	94.92 101	98.25 67	86.46 150	97.13 14	94.31 247	96.24 24	96.28 89	96.36 137	82.88 232	99.35 55	88.19 178	99.52 36	98.96 60

v1144		93.50 130	93.81 123	92.57 190	96.28 175	79.61 246	91.86 201	96.96 147	86.95 202	95.91 107	96.32 139	87.65 185	98.96 112	93.51 44	98.88 111	99.13 39

baseline		94.26 115	94.80 94	92.64 185	96.08 190	80.99 222	93.69 130	98.04 59	90.80 128	94.89 150	96.32 139	93.19 82	98.48 186	91.68 105	98.51 150	98.43 114

TinyColmap		92.00 176	92.76 152	89.71 266	95.62 221	77.02 281	90.72 227	96.17 195	87.70 188	95.26 133	96.29 141	92.54 100	96.45 292	81.77 257	98.77 129	95.66 261

GST-MVS		96.24 45	95.99 54	97.00 34	98.65 29	92.71 47	95.69 65	98.01 64	92.08 86	95.74 112	96.28 142	95.22 38	99.42 28	93.17 65	99.06 88	98.88 71

USDC		89.02 236	89.08 226	88.84 280	95.07 235	74.50 306	88.97 276	96.39 183	73.21 314	93.27 195	96.28 142	82.16 242	96.39 294	77.55 295	98.80 126	95.62 264

v2v482		93.29 136	93.63 131	92.29 195	96.35 169	78.82 259	91.77 205	96.28 186	88.45 172	95.70 115	96.26 144	86.02 213	98.90 118	93.02 71	98.81 124	99.14 38

XVG-OURS-SEG-HR		95.38 71	95.00 88	96.51 48	98.10 75	94.07 18	92.46 164	98.13 41	90.69 130	93.75 180	96.25 145	98.03 3	97.02 274	92.08 90	95.55 271	98.45 113

pmmvs-eth3d		91.54 184	90.73 201	93.99 136	95.76 212	87.86 123	90.83 224	93.98 255	78.23 290	94.02 176	96.22 146	82.62 238	96.83 281	86.57 206	98.33 167	97.29 201

v1921920		93.26 139	93.61 132	92.19 199	96.04 196	78.31 265	91.88 196	97.24 131	85.17 227	96.19 96	96.19 147	86.76 204	99.05 97	94.18 29	98.84 116	99.22 32

EPP-MVSNet		93.91 124	93.68 130	94.59 117	98.08 76	85.55 169	97.44 9	94.03 252	94.22 47	94.94 147	96.19 147	82.07 243	99.57 14	87.28 197	98.89 109	98.65 91

APD-MVS		95.00 84	94.69 98	95.93 61	97.38 120	90.88 70	94.59 102	97.81 85	89.22 158	95.46 124	96.17 149	93.42 76	99.34 59	89.30 155	98.87 114	97.56 185
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v144192		93.20 144	93.54 135	92.16 202	96.05 192	78.26 266	91.95 189	97.14 136	84.98 233	95.96 103	96.11 150	87.08 196	99.04 100	93.79 35	98.84 116	99.17 35

VNet		92.67 159	92.96 146	91.79 210	96.27 176	80.15 229	91.95 189	94.98 228	92.19 84	94.52 162	96.07 151	87.43 189	97.39 263	84.83 229	98.38 160	97.83 164

v148		92.87 153	93.29 140	91.62 216	96.25 179	77.72 273	91.28 215	95.05 226	89.69 149	95.93 106	96.04 152	87.34 190	98.38 191	90.05 142	97.99 204	98.78 82

9.14			94.81 93		97.49 114		94.11 118	98.37 15	87.56 193	95.38 126	96.03 153	94.66 56	99.08 92	90.70 120	98.97 104

FMVSNet3		90.78 198	90.32 209	92.16 202	93.03 283	79.92 238	92.54 159	94.95 229	86.17 212	95.10 139	96.01 154	69.97 302	98.75 147	86.74 201	98.38 160	97.82 166

MG-MVS		89.54 229	89.80 217	88.76 281	94.88 237	72.47 321	89.60 261	92.44 280	85.82 218	89.48 275	95.98 155	82.85 233	97.74 245	81.87 256	95.27 280	96.08 242

UniMVSNet (Re)		95.32 74	95.15 84	95.80 68	97.79 94	88.91 98	92.91 148	98.07 51	93.46 62	96.31 84	95.97 156	90.14 154	99.34 59	92.11 88	99.64 22	99.16 36

DU-MVS		95.28 77	95.12 86	95.75 72	97.75 96	88.59 106	92.58 158	97.81 85	93.99 50	96.80 65	95.90 157	90.10 158	99.41 35	91.60 107	99.58 30	99.26 29

NR-MVSNet		95.28 77	95.28 80	95.26 90	97.75 96	87.21 132	95.08 86	97.37 115	93.92 54	97.65 30	95.90 157	90.10 158	99.33 64	90.11 139	99.66 20	99.26 29

ETH3D-3000-0.1		94.86 91	94.55 104	95.81 66	97.61 107	89.72 84	94.05 120	98.37 15	88.09 179	95.06 143	95.85 159	92.58 98	99.10 91	90.33 130	98.99 99	98.62 99

EI-MVSNet		92.99 149	93.26 144	92.19 199	92.12 298	79.21 254	92.32 174	94.67 242	91.77 103	95.24 135	95.85 159	87.14 195	98.49 182	91.99 93	98.26 175	98.86 73

CVMVSNet		85.16 288	84.72 287	86.48 305	92.12 298	70.19 329	92.32 174	88.17 309	56.15 352	90.64 254	95.85 159	67.97 306	96.69 285	88.78 169	90.52 332	92.56 321

EI-MVSNet-UG-set		94.35 110	94.27 116	94.59 117	92.46 291	85.87 164	92.42 167	94.69 240	93.67 61	96.13 98	95.84 162	91.20 133	98.86 126	93.78 36	98.23 181	99.03 49

EI-MVSNet-Vis-set		94.36 109	94.28 114	94.61 112	92.55 290	85.98 162	92.44 165	94.69 240	93.70 57	96.12 99	95.81 163	91.24 130	98.86 126	93.76 39	98.22 183	98.98 58

ZD-MVS					97.23 125	90.32 76		97.54 105	84.40 239	94.78 154	95.79 164	92.76 95	99.39 45	88.72 172	98.40 156

MDA-MVSNet-bldmvs		91.04 193	90.88 195	91.55 218	94.68 250	80.16 228	85.49 321	92.14 286	90.41 139	94.93 148	95.79 164	85.10 219	96.93 278	85.15 222	94.19 302	97.57 183

MVSTER		89.32 232	88.75 234	91.03 234	90.10 325	76.62 288	90.85 223	94.67 242	82.27 257	95.24 135	95.79 164	61.09 340	98.49 182	90.49 122	98.26 175	97.97 150

UniMVSNet_NR-MVSNet		95.35 72	95.21 82	95.76 71	97.69 103	88.59 106	92.26 177	97.84 82	94.91 37	96.80 65	95.78 167	90.42 150	99.41 35	91.60 107	99.58 30	99.29 28

new-patchmatchnet		88.97 239	90.79 199	83.50 324	94.28 259	55.83 356	85.34 322	93.56 259	86.18 211	95.47 122	95.73 168	83.10 230	96.51 290	85.40 219	98.06 198	98.16 130

UnsupCasMVSNet_eth		90.33 212	90.34 208	90.28 254	94.64 252	80.24 227	89.69 260	95.88 202	85.77 219	93.94 177	95.69 169	81.99 244	92.98 337	84.21 236	91.30 328	97.62 181

RRT_MVS		91.36 189	90.05 214	95.29 89	89.21 335	88.15 115	92.51 163	94.89 231	86.73 204	95.54 120	95.68 170	61.82 337	99.30 67	94.91 14	99.13 85	98.43 114

OPU-MVS				95.15 95	96.84 142	89.43 89	95.21 79				95.66 171	93.12 85	98.06 215	86.28 213	98.61 140	97.95 151

testtj		94.81 95	94.42 107	96.01 55	97.23 125	90.51 75	94.77 96	97.85 81	91.29 116	94.92 149	95.66 171	91.71 117	99.40 40	88.07 182	98.25 178	98.11 136

MVP-Stereo		90.07 221	88.92 230	93.54 155	96.31 173	86.49 148	90.93 222	95.59 213	79.80 270	91.48 239	95.59 173	80.79 254	97.39 263	78.57 289	91.19 329	96.76 217
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		94.26 115	93.93 121	95.23 92	97.71 100	88.12 116	94.56 106	97.81 85	91.74 105	93.31 191	95.59 173	86.93 199	98.95 114	89.26 159	98.51 150	98.60 102

plane_prior4											95.59 173

Anonymous20231206		88.77 244	88.29 242	90.20 259	96.31 173	78.81 260	89.56 263	93.49 261	74.26 308	92.38 222	95.58 176	82.21 240	95.43 312	72.07 324	98.75 132	96.34 231

旧先验1					96.20 181	84.17 184		94.82 234			95.57 177	89.57 163			97.89 209	96.32 232

Regformer-3		94.28 113	94.23 118	94.46 125	92.78 288	86.28 157	92.39 169	94.70 239	93.69 60	95.97 102	95.56 178	91.34 125	98.48 186	93.45 50	98.14 190	98.62 99

Regformer-4		94.90 88	94.67 100	95.59 77	92.78 288	89.02 96	92.39 169	95.91 201	94.50 42	96.41 77	95.56 178	92.10 107	99.01 105	94.23 27	98.14 190	98.74 86

ETH3D cwj APD-0.16		93.99 123	93.38 139	95.80 68	96.82 143	89.92 80	92.72 153	98.02 62	84.73 237	93.65 184	95.54 180	91.68 118	99.22 77	88.78 169	98.49 153	98.26 125

MVS_0304		90.96 195	90.15 212	93.37 159	93.17 278	87.06 134	93.62 132	92.43 281	89.60 152	82.25 335	95.50 181	82.56 239	97.83 235	84.41 235	97.83 212	95.22 269

CPTT-MVS		94.74 97	94.12 119	96.60 45	98.15 72	93.01 42	95.84 60	97.66 95	89.21 159	93.28 194	95.46 182	88.89 168	98.98 107	89.80 146	98.82 122	97.80 168

DeepC-MVS_fast	89.96 7	93.73 127	93.44 137	94.60 116	96.14 186	87.90 121	93.36 139	97.14 136	85.53 222	93.90 178	95.45 183	91.30 128	98.59 172	89.51 152	98.62 139	97.31 200
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNVR-MVS		94.58 103	94.29 113	95.46 82	96.94 137	89.35 93	91.81 203	96.80 161	89.66 150	93.90 178	95.44 184	92.80 94	98.72 152	92.74 78	98.52 148	98.32 119

testdata				91.03 234	96.87 141	82.01 207		94.28 248	71.55 321	92.46 218	95.42 185	85.65 217	97.38 265	82.64 248	97.27 232	93.70 306

DeepPCF-MVS	90.46 6	94.20 118	93.56 134	96.14 52	95.96 199	92.96 43	89.48 264	97.46 111	85.14 228	96.23 91	95.42 185	93.19 82	98.08 214	90.37 127	98.76 130	97.38 197

OMC-MVS		94.22 117	93.69 129	95.81 66	97.25 124	91.27 63	92.27 176	97.40 114	87.10 200	94.56 160	95.42 185	93.74 69	98.11 213	86.62 205	98.85 115	98.06 137

WR-MVS		93.49 131	93.72 127	92.80 181	97.57 110	80.03 235	90.14 246	95.68 208	93.70 57	96.62 72	95.39 188	87.21 193	99.04 100	87.50 192	99.64 22	99.33 25

ITE_SJBPF				95.95 58	97.34 122	93.36 40		96.55 177	91.93 90	94.82 152	95.39 188	91.99 111	97.08 272	85.53 218	97.96 205	97.41 191

RRT_test8_iter05		88.21 252	88.17 247	88.33 290	91.62 307	66.82 341	91.73 206	96.60 172	86.34 208	94.14 168	95.38 190	47.72 357	99.11 89	91.78 100	98.26 175	99.06 47

MSLP-MVS++		93.25 141	93.88 122	91.37 222	96.34 170	82.81 202	93.11 142	97.74 91	89.37 154	94.08 171	95.29 191	90.40 153	96.35 297	90.35 128	98.25 178	94.96 276

HPM-MVS++		95.02 83	94.39 108	96.91 38	97.88 91	93.58 36	94.09 119	96.99 146	91.05 122	92.40 221	95.22 192	91.03 139	99.25 74	92.11 88	98.69 136	97.90 157

MSP-MVS		95.34 73	94.63 102	97.48 14	98.67 28	94.05 21	96.41 36	98.18 33	91.26 117	95.12 138	95.15 193	86.60 207	99.50 20	93.43 54	96.81 247	98.89 69

MDA-MVSNet_test_wron		88.16 254	88.23 245	87.93 294	92.22 294	73.71 311	80.71 343	88.84 301	82.52 254	94.88 151	95.14 194	82.70 236	93.61 332	83.28 242	93.80 305	96.46 227

Vis-MVSNet (Re-imp)		90.42 207	90.16 210	91.20 230	97.66 106	77.32 278	94.33 113	87.66 312	91.20 119	92.99 205	95.13 195	75.40 287	98.28 197	77.86 291	99.19 77	97.99 146

YYNet1		88.17 253	88.24 244	87.93 294	92.21 295	73.62 312	80.75 342	88.77 302	82.51 255	94.99 146	95.11 196	82.70 236	93.70 331	83.33 241	93.83 304	96.48 226

D2MVS		89.93 223	89.60 222	90.92 239	94.03 265	78.40 264	88.69 283	94.85 232	78.96 284	93.08 201	95.09 197	74.57 288	96.94 276	88.19 178	98.96 106	97.41 191

CDPH-MVS		92.67 159	91.83 173	95.18 94	96.94 137	88.46 111	90.70 228	97.07 141	77.38 293	92.34 226	95.08 198	92.67 97	98.88 121	85.74 216	98.57 142	98.20 129

PVSNet_BlendedMVS		90.35 211	89.96 215	91.54 219	94.81 241	78.80 261	90.14 246	96.93 150	79.43 276	88.68 290	95.06 199	86.27 210	98.15 211	80.27 270	98.04 201	97.68 177

Regformer-1		94.55 104	94.33 112	95.19 93	92.83 286	88.54 109	91.87 197	95.84 205	93.99 50	95.95 104	95.04 200	92.00 109	98.79 138	93.14 67	98.31 170	98.23 126

Regformer-2		94.86 91	94.55 104	95.77 70	92.83 286	89.98 79	91.87 197	96.40 182	94.38 46	96.19 96	95.04 200	92.47 103	99.04 100	93.49 45	98.31 170	98.28 123

tpm		84.38 293	84.08 292	85.30 314	90.47 321	63.43 351	89.34 268	85.63 328	77.24 296	87.62 302	95.03 202	61.00 341	97.30 266	79.26 284	91.09 331	95.16 270

PVSNet_Blended_VisFu		91.63 182	91.20 190	92.94 175	97.73 99	83.95 189	92.14 181	97.46 111	78.85 286	92.35 224	94.98 203	84.16 225	99.08 92	86.36 211	96.77 249	95.79 255

miper_lstm_enhance		89.90 224	89.80 217	90.19 260	91.37 311	77.50 275	83.82 336	95.00 227	84.84 235	93.05 203	94.96 204	76.53 285	95.20 318	89.96 144	98.67 137	97.86 161

新几何1				93.17 167	97.16 129	87.29 129		94.43 244	67.95 336	91.29 242	94.94 205	86.97 198	98.23 203	81.06 267	97.75 213	93.98 299

1121		90.26 215	89.23 223	93.34 160	97.15 131	87.40 127	91.94 191	94.39 245	67.88 337	91.02 248	94.91 206	86.91 201	98.59 172	81.17 265	97.71 217	94.02 298

cl-mvsnet_		90.65 202	90.56 204	90.91 241	91.85 302	76.98 284	86.75 311	95.36 223	85.53 222	94.06 173	94.89 207	77.36 278	97.98 224	90.27 133	98.98 100	97.76 171

cl-mvsnet1		90.65 202	90.56 204	90.91 241	91.85 302	76.99 283	86.75 311	95.36 223	85.52 224	94.06 173	94.89 207	77.37 277	97.99 223	90.28 132	98.97 104	97.76 171

test222					96.95 136	85.27 172	88.83 279	93.61 257	65.09 344	90.74 252	94.85 209	84.62 223			97.36 230	93.91 300

test_prior3		93.29 136	92.85 149	94.61 112	95.95 200	87.23 130	90.21 242	97.36 120	89.33 156	90.77 250	94.81 210	90.41 151	98.68 162	88.21 176	98.55 143	97.93 153

test_prior2							90.21 242		89.33 156	90.77 250	94.81 210	90.41 151		88.21 176	98.55 143

CHOSEN 1792x2688		87.19 275	85.92 283	91.00 237	97.13 132	79.41 248	84.51 330	95.60 210	64.14 345	90.07 263	94.81 210	78.26 270	97.14 271	73.34 317	95.38 278	96.46 227

114514_t		90.51 204	89.80 217	92.63 187	98.00 85	82.24 206	93.40 138	97.29 127	65.84 342	89.40 276	94.80 213	86.99 197	98.75 147	83.88 238	98.61 140	96.89 212

tttt0517		89.81 226	88.90 232	92.55 191	97.00 135	79.73 244	95.03 89	83.65 341	89.88 147	95.30 130	94.79 214	53.64 351	99.39 45	91.99 93	98.79 127	98.54 105

EPNet		89.80 227	88.25 243	94.45 126	83.91 356	86.18 159	93.87 125	87.07 316	91.16 121	80.64 344	94.72 215	78.83 263	98.89 120	85.17 220	98.89 109	98.28 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		81.31 309	83.44 295	74.92 335	90.52 320	46.49 358	69.19 350	85.23 335	84.30 240	87.95 299	94.71 216	76.95 282	84.36 352	64.07 345	98.09 196	93.89 301

testgi		90.38 209	91.34 187	87.50 299	97.49 114	71.54 324	89.43 265	95.16 225	88.38 174	94.54 161	94.68 217	92.88 92	93.09 336	71.60 328	97.85 211	97.88 159

NCCC		94.08 120	93.54 135	95.70 75	96.49 160	89.90 82	92.39 169	96.91 154	90.64 132	92.33 227	94.60 218	90.58 149	98.96 112	90.21 136	97.70 218	98.23 126

MVS_111021_HR		93.63 129	93.42 138	94.26 131	96.65 150	86.96 139	89.30 270	96.23 190	88.36 175	93.57 186	94.60 218	93.45 73	97.77 241	90.23 135	98.38 160	98.03 141

TAMVS		90.16 217	89.05 227	93.49 158	96.49 160	86.37 153	90.34 239	92.55 278	80.84 266	92.99 205	94.57 220	81.94 246	98.20 205	73.51 316	98.21 184	95.90 251

原ACMM1				92.87 178	96.91 139	84.22 182		97.01 143	76.84 298	89.64 274	94.46 221	88.00 180	98.70 159	81.53 260	98.01 203	95.70 259

agg_prior1		92.60 161	91.76 176	95.10 97	96.20 181	88.89 99	90.37 237	96.88 156	79.67 274	90.21 259	94.41 222	91.30 128	98.78 142	88.46 175	98.37 165	97.64 180

MVS_111021_LR		93.66 128	93.28 142	94.80 105	96.25 179	90.95 68	90.21 242	95.43 219	87.91 181	93.74 182	94.40 223	92.88 92	96.38 295	90.39 125	98.28 173	97.07 204

TEST9					96.45 162	89.46 87	90.60 230	96.92 152	79.09 282	90.49 255	94.39 224	91.31 127	98.88 121

train_agg		92.71 158	91.83 173	95.35 83	96.45 162	89.46 87	90.60 230	96.92 152	79.37 277	90.49 255	94.39 224	91.20 133	98.88 121	88.66 173	98.43 155	97.72 174

test_8					96.37 164	89.14 94	90.51 233	96.89 155	79.37 277	90.42 257	94.36 226	91.20 133	98.82 131

FPMVS		84.50 292	83.28 296	88.16 292	96.32 172	94.49 14	85.76 319	85.47 330	83.09 249	85.20 316	94.26 227	63.79 329	86.58 350	63.72 346	91.88 327	83.40 345

MCST-MVS		92.91 151	92.51 159	94.10 134	97.52 112	85.72 167	91.36 214	97.13 138	80.33 268	92.91 208	94.24 228	91.23 131	98.72 152	89.99 143	97.93 207	97.86 161

BH-RMVSNet		90.47 206	90.44 206	90.56 249	95.21 233	78.65 263	89.15 274	93.94 256	88.21 176	92.74 211	94.22 229	86.38 208	97.88 228	78.67 288	95.39 277	95.14 272

pmmvs4		88.95 240	87.70 255	92.70 183	94.30 258	85.60 168	87.22 300	92.16 285	74.62 306	89.75 273	94.19 230	77.97 272	96.41 293	82.71 247	96.36 258	96.09 241

Patchmatch-RL test		88.81 243	88.52 236	89.69 267	95.33 232	79.94 237	86.22 318	92.71 274	78.46 288	95.80 109	94.18 231	66.25 316	95.33 315	89.22 161	98.53 147	93.78 303

PHI-MVS		94.34 111	93.80 124	95.95 58	95.65 218	91.67 61	94.82 94	97.86 78	87.86 184	93.04 204	94.16 232	91.58 120	98.78 142	90.27 133	98.96 106	97.41 191

TAPA-MVS	88.58 10	92.49 166	91.75 177	94.73 108	96.50 159	89.69 85	92.91 148	97.68 94	78.02 291	92.79 210	94.10 233	90.85 140	97.96 225	84.76 231	98.16 188	96.54 220
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DP-MVS Recon		92.31 170	91.88 172	93.60 151	97.18 128	86.87 140	91.10 219	97.37 115	84.92 234	92.08 232	94.08 234	88.59 170	98.20 205	83.50 240	98.14 190	95.73 257

CANet		92.38 168	91.99 170	93.52 157	93.82 271	83.46 193	91.14 217	97.00 144	89.81 148	86.47 310	94.04 235	87.90 183	99.21 78	89.50 153	98.27 174	97.90 157

F-COLMAP		92.28 171	91.06 193	95.95 58	97.52 112	91.90 56	93.53 134	97.18 134	83.98 241	88.70 289	94.04 235	88.41 173	98.55 178	80.17 273	95.99 263	97.39 195

UnsupCasMVSNet_bld		88.50 248	88.03 250	89.90 264	95.52 224	78.88 258	87.39 298	94.02 254	79.32 280	93.06 202	94.02 237	80.72 255	94.27 327	75.16 311	93.08 315	96.54 220

MDTV_nov1_ep13			83.88 294		89.42 333	61.52 352	88.74 282	87.41 313	73.99 310	84.96 319	94.01 238	65.25 321	95.53 306	78.02 290	93.16 312

OpenMVS_ROB	85.12 16	89.52 230	89.05 227	90.92 239	94.58 253	81.21 220	91.10 219	93.41 262	77.03 297	93.41 188	93.99 239	83.23 229	97.80 237	79.93 277	94.80 289	93.74 305

diffmvs		91.74 179	91.93 171	91.15 232	93.06 281	78.17 267	88.77 281	97.51 110	86.28 209	92.42 220	93.96 240	88.04 179	97.46 257	90.69 121	96.67 252	97.82 166

eth_miper_zixun_eth		90.72 199	90.61 203	91.05 233	92.04 300	76.84 286	86.91 306	96.67 169	85.21 226	94.41 163	93.92 241	79.53 260	98.26 201	89.76 148	97.02 239	98.06 137

cl_fuxian		91.32 191	91.42 184	91.00 237	92.29 293	76.79 287	87.52 297	96.42 181	85.76 220	94.72 158	93.89 242	82.73 235	98.16 210	90.93 117	98.55 143	98.04 140

pmmvs5		87.87 257	87.14 264	90.07 261	93.26 277	76.97 285	88.89 278	92.18 283	73.71 312	88.36 293	93.89 242	76.86 283	96.73 284	80.32 269	96.81 247	96.51 222

PCF-MVS	84.52 17	89.12 235	87.71 254	93.34 160	96.06 191	85.84 165	86.58 317	97.31 124	68.46 335	93.61 185	93.89 242	87.51 188	98.52 180	67.85 339	98.11 194	95.66 261
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + GP.		93.07 147	92.41 162	95.06 98	95.82 207	90.87 71	90.97 221	92.61 277	88.04 180	94.61 159	93.79 245	88.08 177	97.81 236	89.41 154	98.39 158	96.50 225

ETH3 D test6400		91.91 177	91.25 189	93.89 143	96.59 154	84.41 178	92.10 182	97.72 93	78.52 287	91.82 236	93.78 246	88.70 169	99.13 86	83.61 239	98.39 158	98.14 132

HY-MVS	82.50 18	86.81 281	85.93 282	89.47 268	93.63 272	77.93 269	94.02 121	91.58 293	75.68 300	83.64 327	93.64 247	77.40 275	97.42 260	71.70 327	92.07 324	93.05 315

LF4IMVS		92.72 157	92.02 169	94.84 104	95.65 218	91.99 54	92.92 147	96.60 172	85.08 231	92.44 219	93.62 248	86.80 203	96.35 297	86.81 200	98.25 178	96.18 239

Test_1112_low_res		87.50 267	86.58 273	90.25 256	96.80 146	77.75 272	87.53 296	96.25 188	69.73 331	86.47 310	93.61 249	75.67 286	97.88 228	79.95 275	93.20 311	95.11 273

MS-PatchMatch		88.05 255	87.75 253	88.95 277	93.28 275	77.93 269	87.88 290	92.49 279	75.42 303	92.57 216	93.59 250	80.44 256	94.24 329	81.28 262	92.75 318	94.69 284

CNLPA		91.72 180	91.20 190	93.26 164	96.17 184	91.02 66	91.14 217	95.55 216	90.16 142	90.87 249	93.56 251	86.31 209	94.40 325	79.92 278	97.12 236	94.37 289

ppachtmachnet_test		88.61 247	88.64 235	88.50 286	91.76 304	70.99 327	84.59 329	92.98 267	79.30 281	92.38 222	93.53 252	79.57 259	97.45 258	86.50 209	97.17 235	97.07 204

CSCG		94.69 99	94.75 96	94.52 120	97.55 111	87.87 122	95.01 90	97.57 103	92.68 69	96.20 94	93.44 253	91.92 113	98.78 142	89.11 163	99.24 73	96.92 210

NP-MVS					96.82 143	87.10 133					93.40 254

HQP-MVS		92.09 174	91.49 183	93.88 144	96.36 166	84.89 174	91.37 211	97.31 124	87.16 197	88.81 283	93.40 254	84.76 221	98.60 170	86.55 207	97.73 214	98.14 132

test_yl		90.11 218	89.73 220	91.26 226	94.09 263	79.82 240	90.44 234	92.65 275	90.90 123	93.19 199	93.30 256	73.90 290	98.03 217	82.23 253	96.87 245	95.93 248

DCV-MVSNet		90.11 218	89.73 220	91.26 226	94.09 263	79.82 240	90.44 234	92.65 275	90.90 123	93.19 199	93.30 256	73.90 290	98.03 217	82.23 253	96.87 245	95.93 248

CMPMVS	68.83 22	87.28 271	85.67 284	92.09 204	88.77 339	85.42 170	90.31 240	94.38 246	70.02 330	88.00 298	93.30 256	73.78 292	94.03 330	75.96 308	96.54 254	96.83 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CostFormer		83.09 299	82.21 302	85.73 309	89.27 334	67.01 336	90.35 238	86.47 319	70.42 328	83.52 329	93.23 259	61.18 339	96.85 280	77.21 299	88.26 338	93.34 313

DELS-MVS		92.05 175	92.16 165	91.72 213	94.44 255	80.13 231	87.62 291	97.25 130	87.34 195	92.22 229	93.18 260	89.54 164	98.73 151	89.67 150	98.20 186	96.30 233
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
baseline1		87.62 264	87.31 259	88.54 285	94.71 249	74.27 309	93.10 143	88.20 308	86.20 210	92.18 230	93.04 261	73.21 293	95.52 307	79.32 283	85.82 341	95.83 253

BH-untuned		90.68 201	90.90 194	90.05 263	95.98 198	79.57 247	90.04 249	94.94 230	87.91 181	94.07 172	93.00 262	87.76 184	97.78 240	79.19 285	95.17 282	92.80 318

HyFIR lowres test		87.19 275	85.51 285	92.24 197	97.12 133	80.51 226	85.03 324	96.06 197	66.11 341	91.66 238	92.98 263	70.12 301	99.14 85	75.29 310	95.23 281	97.07 204

AUN-MVS		90.05 222	88.30 241	95.32 88	96.09 189	90.52 74	92.42 167	92.05 289	82.08 259	88.45 292	92.86 264	65.76 318	98.69 161	88.91 166	96.07 261	96.75 218

SCA		87.43 268	87.21 262	88.10 293	92.01 301	71.98 323	89.43 265	88.11 310	82.26 258	88.71 288	92.83 265	78.65 265	97.59 250	79.61 280	93.30 310	94.75 281

Patchmatch-test		86.10 284	86.01 281	86.38 307	90.63 318	74.22 310	89.57 262	86.69 317	85.73 221	89.81 270	92.83 265	65.24 322	91.04 343	77.82 294	95.78 268	93.88 302

MVSFormer		92.18 173	92.23 164	92.04 206	94.74 245	80.06 233	97.15 12	97.37 115	88.98 160	88.83 281	92.79 267	77.02 280	99.60 9	96.41 4	96.75 250	96.46 227

jason		89.17 234	88.32 240	91.70 214	95.73 213	80.07 232	88.10 288	93.22 264	71.98 320	90.09 261	92.79 267	78.53 268	98.56 176	87.43 194	97.06 237	96.46 227
jason: jason.
PatchmatchNet		85.22 287	84.64 288	86.98 303	89.51 332	69.83 332	90.52 232	87.34 314	78.87 285	87.22 307	92.74 269	66.91 310	96.53 288	81.77 257	86.88 340	94.58 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AdaColmap		91.63 182	91.36 186	92.47 194	95.56 223	86.36 154	92.24 179	96.27 187	88.88 164	89.90 267	92.69 270	91.65 119	98.32 195	77.38 298	97.64 221	92.72 320

thisisatest0530		88.69 246	87.52 257	92.20 198	96.33 171	79.36 249	92.81 151	84.01 340	86.44 206	93.67 183	92.68 271	53.62 352	99.25 74	89.65 151	98.45 154	98.00 143

miper_ehance_all_eth		90.48 205	90.42 207	90.69 245	91.62 307	76.57 289	86.83 309	96.18 194	83.38 244	94.06 173	92.66 272	82.20 241	98.04 216	89.79 147	97.02 239	97.45 189

cl-mvsnet2		89.02 236	88.50 237	90.59 248	89.76 327	76.45 290	86.62 316	94.03 252	82.98 252	92.65 213	92.49 273	72.05 297	97.53 252	88.93 164	97.02 239	97.78 169

ADS-MVSNet2		84.01 295	82.20 303	89.41 270	89.04 336	76.37 292	87.57 292	90.98 296	72.71 318	84.46 321	92.45 274	68.08 304	96.48 291	70.58 334	83.97 343	95.38 267

ADS-MVSNet		82.25 304	81.55 305	84.34 320	89.04 336	65.30 343	87.57 292	85.13 336	72.71 318	84.46 321	92.45 274	68.08 304	92.33 339	70.58 334	83.97 343	95.38 267

tpm2		81.46 308	80.35 315	84.80 316	89.90 326	65.14 345	90.44 234	85.36 331	65.82 343	82.05 338	92.44 276	57.94 344	96.69 285	70.71 333	88.49 337	92.56 321

N_pmnet		88.90 241	87.25 261	93.83 146	94.40 257	93.81 34	84.73 326	87.09 315	79.36 279	93.26 196	92.43 277	79.29 261	91.68 341	77.50 297	97.22 234	96.00 245

alignmvs		93.26 139	92.85 149	94.50 121	95.70 214	87.45 126	93.45 137	95.76 206	91.58 110	95.25 134	92.42 278	81.96 245	98.72 152	91.61 106	97.87 210	97.33 199

CDS-MVSNet		89.55 228	88.22 246	93.53 156	95.37 230	86.49 148	89.26 271	93.59 258	79.76 272	91.15 246	92.31 279	77.12 279	98.38 191	77.51 296	97.92 208	95.71 258
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	85.34 15	90.40 208	88.92 230	94.85 103	96.53 158	90.02 78	91.58 208	96.48 180	80.16 269	86.14 312	92.18 280	85.73 215	98.25 202	76.87 301	94.61 294	96.30 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
our_test_3		87.55 265	87.59 256	87.44 300	91.76 304	70.48 328	83.83 335	90.55 298	79.79 271	92.06 233	92.17 281	78.63 267	95.63 305	84.77 230	94.73 290	96.22 237

Effi-MVS+-dtu		93.90 125	92.60 158	97.77 4	94.74 245	96.67 3	94.00 122	95.41 220	89.94 144	91.93 235	92.13 282	90.12 155	98.97 111	87.68 189	97.48 226	97.67 178

PAPM_NR		91.03 194	90.81 198	91.68 215	96.73 148	81.10 221	93.72 129	96.35 185	88.19 177	88.77 287	92.12 283	85.09 220	97.25 267	82.40 252	93.90 303	96.68 219

canonicalmvs		94.59 102	94.69 98	94.30 130	95.60 222	87.03 136	95.59 67	98.24 28	91.56 111	95.21 137	92.04 284	94.95 50	98.66 164	91.45 111	97.57 224	97.20 203

MSDG		90.82 196	90.67 202	91.26 226	94.16 260	83.08 200	86.63 315	96.19 193	90.60 134	91.94 234	91.89 285	89.16 167	95.75 304	80.96 268	94.51 295	94.95 277

sss		87.23 272	86.82 269	88.46 288	93.96 266	77.94 268	86.84 308	92.78 273	77.59 292	87.61 303	91.83 286	78.75 264	91.92 340	77.84 292	94.20 301	95.52 266

CANet_DTU		89.85 225	89.17 225	91.87 208	92.20 296	80.02 236	90.79 225	95.87 203	86.02 214	82.53 334	91.77 287	80.01 257	98.57 175	85.66 217	97.70 218	97.01 207

patchmatchnet-post											91.71 288	66.22 317	97.59 250

PatchMatch-RL		89.18 233	88.02 251	92.64 185	95.90 204	92.87 45	88.67 285	91.06 295	80.34 267	90.03 264	91.67 289	83.34 227	94.42 324	76.35 305	94.84 288	90.64 334

tpmrst		82.85 302	82.93 300	82.64 326	87.65 341	58.99 354	90.14 246	87.90 311	75.54 302	83.93 325	91.63 290	66.79 313	95.36 313	81.21 264	81.54 349	93.57 311

WTY-MVS		86.93 280	86.50 278	88.24 291	94.96 236	74.64 302	87.19 301	92.07 288	78.29 289	88.32 294	91.59 291	78.06 271	94.27 327	74.88 312	93.15 313	95.80 254

DPM-MVS		89.35 231	88.40 239	92.18 201	96.13 188	84.20 183	86.96 305	96.15 196	75.40 304	87.36 305	91.55 292	83.30 228	98.01 220	82.17 255	96.62 253	94.32 291

EPMVS		81.17 312	80.37 314	83.58 323	85.58 352	65.08 346	90.31 240	71.34 354	77.31 295	85.80 314	91.30 293	59.38 342	92.70 338	79.99 274	82.34 348	92.96 316

Fast-Effi-MVS+-dtu		92.77 156	92.16 165	94.58 119	94.66 251	88.25 113	92.05 184	96.65 170	89.62 151	90.08 262	91.23 294	92.56 99	98.60 170	86.30 212	96.27 259	96.90 211

cdsmvs_eth3d_5k		23.35 325	31.13 328	0.00 341	0.00 362	0.00 363	0.00 353	95.58 215	0.00 358	0.00 359	91.15 295	93.43 75	0.00 359	0.00 357	0.00 357	0.00 355

lupinMVS		88.34 251	87.31 259	91.45 220	94.74 245	80.06 233	87.23 299	92.27 282	71.10 324	88.83 281	91.15 295	77.02 280	98.53 179	86.67 204	96.75 250	95.76 256

API-MVS		91.52 185	91.61 178	91.26 226	94.16 260	86.26 158	94.66 100	94.82 234	91.17 120	92.13 231	91.08 297	90.03 161	97.06 273	79.09 286	97.35 231	90.45 335

thres600view7		87.66 262	87.10 266	89.36 272	96.05 192	73.17 314	92.72 153	85.31 332	91.89 92	93.29 193	90.97 298	63.42 330	98.39 189	73.23 318	96.99 244	96.51 222

thres100view900		87.35 270	86.89 268	88.72 282	96.14 186	73.09 316	93.00 145	85.31 332	92.13 85	93.26 196	90.96 299	63.42 330	98.28 197	71.27 330	96.54 254	94.79 279

tpmvs		84.22 294	83.97 293	84.94 315	87.09 347	65.18 344	91.21 216	88.35 305	82.87 253	85.21 315	90.96 299	65.24 322	96.75 283	79.60 282	85.25 342	92.90 317

xiu_mvs_v1_base_debu		91.47 186	91.52 180	91.33 223	95.69 215	81.56 213	89.92 253	96.05 198	83.22 246	91.26 243	90.74 301	91.55 121	98.82 131	89.29 156	95.91 264	93.62 308

xiu_mvs_v1_base		91.47 186	91.52 180	91.33 223	95.69 215	81.56 213	89.92 253	96.05 198	83.22 246	91.26 243	90.74 301	91.55 121	98.82 131	89.29 156	95.91 264	93.62 308

xiu_mvs_v1_base_debi		91.47 186	91.52 180	91.33 223	95.69 215	81.56 213	89.92 253	96.05 198	83.22 246	91.26 243	90.74 301	91.55 121	98.82 131	89.29 156	95.91 264	93.62 308

1112_ss		88.42 249	87.41 258	91.45 220	96.69 149	80.99 222	89.72 259	96.72 167	73.37 313	87.00 308	90.69 304	77.38 276	98.20 205	81.38 261	93.72 306	95.15 271

ab-mvs-re		7.56 328	10.08 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	90.69 304	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Effi-MVS+		92.79 154	92.74 153	92.94 175	95.10 234	83.30 195	94.00 122	97.53 107	91.36 115	89.35 277	90.65 306	94.01 68	98.66 164	87.40 195	95.30 279	96.88 213

mvs-test1		93.07 147	91.80 175	96.89 39	94.74 245	95.83 6	92.17 180	95.41 220	89.94 144	89.85 268	90.59 307	90.12 155	98.88 121	87.68 189	95.66 269	95.97 246

GA-MVS		87.70 260	86.82 269	90.31 253	93.27 276	77.22 280	84.72 328	92.79 272	85.11 230	89.82 269	90.07 308	66.80 311	97.76 243	84.56 233	94.27 300	95.96 247

EPNet_dtu		85.63 286	84.37 289	89.40 271	86.30 350	74.33 308	91.64 207	88.26 306	84.84 235	72.96 353	89.85 309	71.27 300	97.69 247	76.60 303	97.62 222	96.18 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM		81.91 307	80.11 317	87.31 301	93.87 269	72.32 322	84.02 334	93.22 264	69.47 332	76.13 351	89.84 310	72.15 296	97.23 268	53.27 352	89.02 335	92.37 323

tfpn200view9		87.05 278	86.52 276	88.67 283	95.77 210	72.94 317	91.89 194	86.00 324	90.84 125	92.61 214	89.80 311	63.93 327	98.28 197	71.27 330	96.54 254	94.79 279

thres400		87.20 274	86.52 276	89.24 276	95.77 210	72.94 317	91.89 194	86.00 324	90.84 125	92.61 214	89.80 311	63.93 327	98.28 197	71.27 330	96.54 254	96.51 222

TR-MVS		87.70 260	87.17 263	89.27 274	94.11 262	79.26 251	88.69 283	91.86 290	81.94 260	90.69 253	89.79 313	82.82 234	97.42 260	72.65 322	91.98 325	91.14 331

new_pmnet		81.22 310	81.01 311	81.86 328	90.92 316	70.15 330	84.03 333	80.25 351	70.83 326	85.97 313	89.78 314	67.93 307	84.65 351	67.44 340	91.90 326	90.78 333

PAPR		87.65 263	86.77 271	90.27 255	92.85 285	77.38 277	88.56 286	96.23 190	76.82 299	84.98 318	89.75 315	86.08 212	97.16 270	72.33 323	93.35 309	96.26 235

CLD-MVS		91.82 178	91.41 185	93.04 168	96.37 164	83.65 192	86.82 310	97.29 127	84.65 238	92.27 228	89.67 316	92.20 105	97.85 234	83.95 237	99.47 38	97.62 181
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpm cat1		80.61 316	79.46 319	84.07 322	88.78 338	65.06 347	89.26 271	88.23 307	62.27 348	81.90 340	89.66 317	62.70 335	95.29 316	71.72 326	80.60 350	91.86 328

pmmvs3		80.83 313	78.96 321	86.45 306	87.23 346	77.48 276	84.87 325	82.31 344	63.83 346	85.03 317	89.50 318	49.66 354	93.10 335	73.12 320	95.10 283	88.78 340

miper_enhance_ethall		88.42 249	87.87 252	90.07 261	88.67 340	75.52 299	85.10 323	95.59 213	75.68 300	92.49 217	89.45 319	78.96 262	97.88 228	87.86 187	97.02 239	96.81 215

PVSNet_Blended		88.74 245	88.16 249	90.46 251	94.81 241	78.80 261	86.64 314	96.93 150	74.67 305	88.68 290	89.18 320	86.27 210	98.15 211	80.27 270	96.00 262	94.44 288

dp		79.28 319	78.62 322	81.24 329	85.97 351	56.45 355	86.91 306	85.26 334	72.97 316	81.45 342	89.17 321	56.01 349	95.45 311	73.19 319	76.68 351	91.82 329

ET-MVSNet_ETH3D		86.15 283	84.27 291	91.79 210	93.04 282	81.28 218	87.17 302	86.14 321	79.57 275	83.65 326	88.66 322	57.10 345	98.18 208	87.74 188	95.40 276	95.90 251

xiu_mvs_v2_base		89.00 238	89.19 224	88.46 288	94.86 239	74.63 303	86.97 304	95.60 210	80.88 264	87.83 300	88.62 323	91.04 138	98.81 136	82.51 251	94.38 296	91.93 326

Fast-Effi-MVS+		91.28 192	90.86 196	92.53 192	95.45 226	82.53 204	89.25 273	96.52 178	85.00 232	89.91 266	88.55 324	92.94 89	98.84 129	84.72 232	95.44 275	96.22 237

thres200		85.85 285	85.18 286	87.88 296	94.44 255	72.52 320	89.08 275	86.21 320	88.57 171	91.44 240	88.40 325	64.22 325	98.00 221	68.35 338	95.88 267	93.12 314

BH-w/o		87.21 273	87.02 267	87.79 297	94.77 243	77.27 279	87.90 289	93.21 266	81.74 261	89.99 265	88.39 326	83.47 226	96.93 278	71.29 329	92.43 321	89.15 336

MAR-MVS		90.32 213	88.87 233	94.66 111	94.82 240	91.85 57	94.22 116	94.75 237	80.91 263	87.52 304	88.07 327	86.63 206	97.87 231	76.67 302	96.21 260	94.25 292
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
EIA-MVS		92.35 169	92.03 168	93.30 163	95.81 209	83.97 188	92.80 152	98.17 36	87.71 187	89.79 271	87.56 328	91.17 137	99.18 81	87.97 184	97.27 232	96.77 216

baseline2		83.38 297	81.54 306	88.90 278	91.38 310	72.84 319	88.78 280	81.22 347	78.97 283	79.82 346	87.56 328	61.73 338	97.80 237	74.30 313	90.05 334	96.05 244

MVS		84.98 290	84.30 290	87.01 302	91.03 313	77.69 274	91.94 191	94.16 250	59.36 350	84.23 324	87.50 330	85.66 216	96.80 282	71.79 325	93.05 316	86.54 342

PS-MVSNAJ		88.86 242	88.99 229	88.48 287	94.88 237	74.71 301	86.69 313	95.60 210	80.88 264	87.83 300	87.37 331	90.77 141	98.82 131	82.52 250	94.37 297	91.93 326

1314		86.46 282	86.33 279	86.87 304	91.65 306	74.54 304	91.94 191	94.10 251	74.28 307	84.78 320	87.33 332	83.03 231	95.00 319	78.72 287	91.16 330	91.06 332

thisisatest0515		84.72 291	82.99 299	89.90 264	92.96 284	75.33 300	84.36 331	83.42 342	77.37 294	88.27 295	86.65 333	53.94 350	98.72 152	82.56 249	97.40 229	95.67 260

test0.0.03 1		82.48 303	81.47 307	85.48 311	89.70 328	73.57 313	84.73 326	81.64 346	83.07 250	88.13 297	86.61 334	62.86 333	89.10 349	66.24 343	90.29 333	93.77 304

IB-MVS	77.21 19	83.11 298	81.05 309	89.29 273	91.15 312	75.85 296	85.66 320	86.00 324	79.70 273	82.02 339	86.61 334	48.26 356	98.39 189	77.84 292	92.22 322	93.63 307
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
MVE	59.87 23	73.86 323	72.65 326	77.47 334	87.00 349	74.35 307	61.37 352	60.93 357	67.27 338	69.69 354	86.49 336	81.24 253	72.33 354	56.45 351	83.45 345	85.74 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PVSNet	76.22 20	82.89 301	82.37 301	84.48 319	93.96 266	64.38 349	78.60 345	88.61 303	71.50 322	84.43 323	86.36 337	74.27 289	94.60 321	69.87 336	93.69 307	94.46 287

CS-MVS		92.54 165	92.31 163	93.23 165	95.89 205	84.07 187	93.58 133	98.48 9	88.60 170	90.41 258	86.23 338	92.00 109	99.35 55	87.54 191	98.06 198	96.26 235

ETV-MVS		92.99 149	92.74 153	93.72 148	95.86 206	86.30 156	92.33 173	97.84 82	91.70 108	92.81 209	86.17 339	92.22 104	99.19 80	88.03 183	97.73 214	95.66 261

cascas		87.02 279	86.28 280	89.25 275	91.56 309	76.45 290	84.33 332	96.78 162	71.01 325	86.89 309	85.91 340	81.35 249	96.94 276	83.09 244	95.60 270	94.35 290

PMMVS		83.00 300	81.11 308	88.66 284	83.81 357	86.44 151	82.24 341	85.65 327	61.75 349	82.07 337	85.64 341	79.75 258	91.59 342	75.99 307	93.09 314	87.94 341

CHOSEN 280x420		80.04 318	77.97 324	86.23 308	90.13 324	74.53 305	72.87 348	89.59 300	66.38 340	76.29 350	85.32 342	56.96 346	95.36 313	69.49 337	94.72 291	88.79 339

test-LLR		83.58 296	83.17 297	84.79 317	89.68 329	66.86 339	83.08 337	84.52 337	83.07 250	82.85 332	84.78 343	62.86 333	93.49 333	82.85 245	94.86 286	94.03 296

test-mter		81.21 311	80.01 318	84.79 317	89.68 329	66.86 339	83.08 337	84.52 337	73.85 311	82.85 332	84.78 343	43.66 361	93.49 333	82.85 245	94.86 286	94.03 296

gm-plane-assit					87.08 348	59.33 353			71.22 323		83.58 345		97.20 269	73.95 314

TESTMET0.1,1		79.09 320	78.04 323	82.25 327	87.52 343	64.03 350	83.08 337	80.62 349	70.28 329	80.16 345	83.22 346	44.13 360	90.56 344	79.95 275	93.36 308	92.15 324

E-PMN		80.72 315	80.86 312	80.29 331	85.11 353	68.77 334	72.96 347	81.97 345	87.76 186	83.25 331	83.01 347	62.22 336	89.17 348	77.15 300	94.31 299	82.93 346

EMVS		80.35 317	80.28 316	80.54 330	84.73 355	69.07 333	72.54 349	80.73 348	87.80 185	81.66 341	81.73 348	62.89 332	89.84 346	75.79 309	94.65 293	82.71 347

DWT-MVSNet_test		80.74 314	79.18 320	85.43 312	87.51 344	66.87 338	89.87 256	86.01 323	74.20 309	80.86 343	80.62 349	48.84 355	96.68 287	81.54 259	83.14 347	92.75 319

PVSNet_0	70.34 21	74.58 322	72.96 325	79.47 332	90.63 318	66.24 342	73.26 346	83.40 343	63.67 347	78.02 348	78.35 350	72.53 294	89.59 347	56.68 350	60.05 354	82.57 348

GG-mvs-BLEND				83.24 325	85.06 354	71.03 326	94.99 92	65.55 356		74.09 352	75.51 351	44.57 359	94.46 323	59.57 349	87.54 339	84.24 344

DeepMVS_CX				53.83 337	70.38 358	64.56 348		48.52 359	33.01 354	65.50 355	74.21 352	56.19 348	46.64 355	38.45 354	70.07 352	50.30 351

tmp_tt		37.97 324	44.33 327	18.88 338	11.80 359	21.54 360	63.51 351	45.66 360	4.23 355	51.34 356	50.48 353	59.08 343	22.11 356	44.50 353	68.35 353	13.00 352

X-MVStestdata		90.70 200	88.45 238	97.44 17	98.56 37	93.99 25	96.50 30	97.95 73	94.58 40	94.38 165	26.89 354	94.56 58	99.39 45	93.57 41	99.05 91	98.93 63

testmvs		9.02 327	11.42 330	1.81 340	2.77 361	1.13 362	79.44 344	1.90 361	1.18 357	2.65 358	6.80 355	1.95 363	0.87 358	2.62 356	3.45 356	3.44 354

test123		9.49 326	12.01 329	1.91 339	2.87 360	1.30 361	82.38 340	1.34 362	1.36 356	2.84 357	6.56 356	2.45 362	0.97 357	2.73 355	5.56 355	3.47 353

test_post											6.07 357	65.74 319	95.84 303

test_post1							90.21 242				5.85 358	65.36 320	96.00 301	79.61 280

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		7.56 328	10.09 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	90.77 141	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

IU-MVS					98.51 46	86.66 146		96.83 159	72.74 317	95.83 108				93.00 72	99.29 63	98.64 95

save fliter					97.46 117	88.05 118	92.04 185	97.08 140	87.63 190

test_0728_SECOND				94.88 102	98.55 40	86.72 143	95.20 81	98.22 30					99.38 51	93.44 52	99.31 60	98.53 106

GSMVS																94.75 281

test_part2					98.21 69	89.41 90				96.72 68

sam_mvs1												66.64 314				94.75 281

sam_mvs												66.41 315

MTGPA								97.62 97

MTMP							94.82 94	54.62 358

test9_res														88.16 180	98.40 156	97.83 164

agg_prior2														87.06 199	98.36 166	97.98 147

agg_prior					96.20 181	88.89 99		96.88 156		90.21 259			98.78 142

test_prior4						89.91 81	90.74 226

test_prior				94.61 112	95.95 200	87.23 130		97.36 120					98.68 162			97.93 153

旧先验2							90.00 251		68.65 334	92.71 212			96.52 289	85.15 222

新几何2							90.02 250

无先验							89.94 252	95.75 207	70.81 327				98.59 172	81.17 265		94.81 278

原ACMM2							89.34 268

testdata2													98.03 217	80.24 272

segment_acmp												92.14 106

testdata1							88.96 277		88.44 173

test12				94.43 127	95.95 200	86.75 142		96.24 189		89.76 272		89.79 162	98.79 138		97.95 206	97.75 173

plane_prior7					97.71 100	88.68 103

plane_prior6					97.21 127	88.23 114						86.93 199

plane_prior5								97.81 85					98.95 114	89.26 159	98.51 150	98.60 102

plane_prior3						88.43 112			90.35 140	93.31 191

plane_prior2							94.56 106		91.74 105

plane_prior1					97.38 120

plane_prior						88.12 116	93.01 144		88.98 160						98.06 198

n2								0.00 363

nn								0.00 363

door-mid								92.13 287

test11								96.65 170

door								91.26 294

HQP5-MVS						84.89 174

HQP-NCC					96.36 166		91.37 211		87.16 197	88.81 283

ACMP_Plane					96.36 166		91.37 211		87.16 197	88.81 283

BP-MVS														86.55 207

HQP4-MVS										88.81 283			98.61 168			98.15 131

HQP3-MVS								97.31 124							97.73 214

HQP2-MVS												84.76 221

MDTV_nov1_ep13_2view						42.48 359	88.45 287		67.22 339	83.56 328		66.80 311		72.86 321		94.06 295

ACMMP++_ref															98.82 122

ACMMP++															99.25 71

Test By Simon												90.61 147

High-resolution multi-view benchmark