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.

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 25	96.16 1	97.55 74	97.97 75	95.59 4	96.61 33	97.89 50	92.57 17	99.84 12	95.95 32	99.51 17	99.40 33

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 30	95.86 2	97.27 95	98.08 47	95.81 3	97.87 9	98.31 31	94.26 2	99.68 35	97.02 4	99.49 21	99.57 11

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 3	97.12 111	98.07 52	93.54 51	96.08 51	97.69 66	93.86 5	99.71 27	96.50 17	99.39 32	99.55 15

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 118	95.34 4	98.48 14	97.87 82	94.65 28	88.53 217	98.02 45	83.69 125	99.71 27	93.18 89	98.96 64	99.44 30

alignmvs		95.87 55	95.23 58	97.78 19	97.56 102	95.19 5	97.86 45	97.17 149	94.39 32	96.47 40	96.40 131	85.89 102	99.20 96	96.21 25	95.11 143	98.95 71

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 24	95.16 6	97.60 70	98.19 31	92.82 76	97.93 8	98.74 3	91.60 36	99.86 6	96.26 20	99.52 15	99.67 2

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 100	95.15 7	98.28 22	97.60 105	94.52 29	96.27 45	96.12 141	87.65 81	99.18 99	96.20 26	94.82 147	98.91 75

NCCC		97.30 8	97.03 9	98.11 6	98.77 33	95.06 8	97.34 89	98.04 61	95.96 2	97.09 25	97.88 52	93.18 9	99.71 27	95.84 35	99.17 51	99.56 13

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 27	94.93 9	97.72 58	98.10 44	91.50 104	98.01 6	98.32 30	92.33 21	99.58 53	94.85 58	99.51 17	99.53 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 23	94.82 10	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 6	99.68 35	97.93 1	99.69 1	99.75 1

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 21	94.71 11	96.96 121	98.06 54	90.67 126	95.55 72	98.78 2	91.07 42	99.86 6	96.58 15	99.55 12	99.38 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
nrg030		94.05 94	93.31 102	96.27 88	95.22 191	94.59 12	98.34 19	97.46 121	92.93 74	91.21 150	96.64 115	87.23 89	98.22 169	94.99 56	85.80 243	95.98 184

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 93	94.50 13	96.87 131	97.95 78	95.46 6	95.60 70	98.01 46	80.96 189	99.83 13	97.23 2	99.25 44	99.23 47

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 49	94.46 14	97.92 42	98.14 37	94.82 21	99.01 1	98.55 9	94.18 3	97.41 259	96.94 5	99.64 3	99.32 41

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 29	94.44 15	96.35 183	97.88 80	86.98 227	96.65 32	97.89 50	91.99 30	99.47 76	92.26 95	99.46 23	99.39 34

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 16	97.15 109	98.08 47	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 16	97.24 97	98.08 47	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 44	94.30 18	97.41 83	98.04 61	94.81 22	96.59 35	98.37 21	91.24 40	99.64 44	95.16 47	99.52 15	99.42 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test12				97.65 29	98.46 51	94.26 19		97.66 100		95.52 74		90.89 46	99.46 77		99.25 44	99.22 48

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 57	94.25 20	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 1	99.71 27	96.57 16	99.62 5	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 18	94.24 21	98.07 34	97.85 85	93.72 45	98.57 2	98.35 22	93.69 7	99.40 85	97.06 3	99.46 23	99.44 30

TEST9					98.70 36	94.19 22	96.41 175	98.02 64	88.17 200	96.03 52	97.56 81	92.74 12	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 36	94.19 22	96.41 175	98.02 64	88.58 185	96.03 52	97.56 81	92.73 13	99.59 50	95.04 51	99.37 37	99.39 34

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 22	94.19 22	97.03 114	98.08 47	88.35 193	95.09 78	97.65 70	89.97 57	99.48 75	92.08 104	98.59 73	98.44 107

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 25	98.52 10	98.32 19	93.21 58	97.18 18	98.29 34	92.08 26	99.83 13	95.63 39	99.59 7	99.54 17

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 25	98.15 29	98.32 19	92.57 81	97.18 18	98.29 34	92.08 26	99.83 13	95.12 49	99.59 7	99.54 17

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 55	94.07 27	98.21 28	98.45 15	89.86 144	97.11 24	98.01 46	92.52 19	99.69 33	96.03 31	99.53 14	99.36 39

test_8					98.67 38	94.06 28	96.37 182	98.01 66	88.58 185	95.98 57	97.55 83	92.73 13	99.58 53

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 29	98.59 7	98.20 29	94.85 17	96.59 35	98.29 34	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

X-MVStestdata		91.71 164	89.67 217	97.81 16	99.38 8	94.03 29	98.59 7	98.20 29	94.85 17	96.59 35	32.69 333	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 38	93.88 31	96.41 175	98.00 68	87.93 204	95.81 62	97.47 85	92.33 21	99.59 50	95.04 51	99.37 37	99.39 34

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 31	98.52 10	98.31 21	93.21 58	97.15 20	98.33 28	91.35 39	99.86 6	95.63 39	99.59 7	99.62 5

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 63	93.84 33	96.83 134	98.10 44	95.24 10	97.49 11	98.25 37	92.57 17	99.61 45	96.80 9	99.29 41	99.56 13

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 34	98.41 17	98.06 54	93.37 53	95.54 73	98.34 25	90.59 50	99.88 3	94.83 59	99.54 13	99.49 24

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 38	93.79 35	96.28 191	98.00 68	88.76 182	95.68 66	97.55 83	92.70 15	99.57 61	95.01 53	99.32 39	99.32 41

agg_prior					98.67 38	93.79 35		98.00 68		95.68 66			99.57 61

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 35	98.52 10	98.24 26	93.19 61	97.14 21	98.34 25	91.59 37	99.87 5	95.46 44	99.59 7	99.64 4

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 38	98.53 9	97.85 85	95.55 5	98.56 3	97.81 59	93.90 4	99.65 39	96.62 13	99.21 48	99.48 26

test_prior4						93.66 39	96.42 174

1121		94.71 80	93.83 83	97.34 41	98.57 49	93.64 40	96.04 205	97.73 91	81.56 289	95.68 66	97.85 56	90.23 53	99.65 39	87.68 179	99.12 57	98.73 85

新几何1				97.32 42	98.60 45	93.59 41		97.75 89	81.58 287	95.75 65	97.85 56	90.04 56	99.67 37	86.50 202	99.13 54	98.69 89

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 42	98.80 3	98.28 23	92.99 67	96.45 42	98.30 33	91.90 31	99.85 9	95.61 41	99.68 2	99.54 17

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 43	97.65 64	98.98 1	92.22 86	97.14 21	98.44 14	91.17 41	99.85 9	94.35 66	99.46 23	99.57 11

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 63	93.48 44	96.83 134	97.99 73	95.20 12	97.46 12	98.25 37	92.48 20	99.58 53	96.79 11	99.29 41	99.55 15

mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 45	98.50 13	98.09 46	93.27 57	95.95 58	98.33 28	91.04 43	99.88 3	95.20 46	99.57 11	99.60 8

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 65	93.39 46	96.79 141	96.72 193	94.17 36	97.44 13	97.66 69	92.76 11	99.33 90	96.86 8	97.76 93	99.08 60

CANet		96.39 41	96.02 43	97.50 37	97.62 98	93.38 47	97.02 116	97.96 76	95.42 8	94.86 80	97.81 59	87.38 87	99.82 16	96.88 7	99.20 49	99.29 43

旧先验1					98.38 58	93.38 47		97.75 89			98.09 41	92.30 25			99.01 62	99.16 51

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 132	93.36 49	98.65 6	98.36 16	94.12 37	89.25 207	98.06 43	82.20 171	99.77 20	93.41 86	99.32 39	99.18 50

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 178	93.34 50	97.39 85	98.71 5	93.14 63	90.10 172	94.83 198	87.71 79	98.03 199	91.67 117	83.99 268	95.46 206

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 91	93.19 51	95.96 210	98.18 33	95.23 11	95.87 59	97.65 70	91.45 38	99.70 32	95.87 33	99.44 27	99.00 67
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
DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 85	93.17 52	97.30 94	98.06 54	93.92 40	93.38 103	98.66 4	86.83 92	99.73 23	95.60 43	99.22 47	98.96 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 53	98.87 1	98.06 54	91.17 115	96.40 43	97.99 48	90.99 44	99.58 53	95.61 41	99.61 6	99.49 24

NR-MVSNet		92.34 147	91.27 160	95.53 116	94.95 204	93.05 54	97.39 85	98.07 52	92.65 80	84.46 263	95.71 163	85.00 112	97.77 235	89.71 137	83.52 276	95.78 193

test_prior3		96.46 39	96.20 41	97.23 48	98.67 38	92.99 55	96.35 183	98.00 68	92.80 77	96.03 52	97.59 77	92.01 28	99.41 83	95.01 53	99.38 33	99.29 43

test_prior				97.23 48	98.67 38	92.99 55		98.00 68					99.41 83			99.29 43

UA-Net		95.95 53	95.53 50	97.20 52	97.67 95	92.98 57	97.65 64	98.13 38	94.81 22	96.61 33	98.35 22	88.87 64	99.51 72	90.36 131	97.35 104	99.11 58

VNet		95.89 54	95.45 51	97.21 51	98.07 79	92.94 58	97.50 77	98.15 35	93.87 41	97.52 10	97.61 76	85.29 108	99.53 68	95.81 36	95.27 141	99.16 51

UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 181	92.83 59	97.17 107	98.58 10	92.98 72	90.13 168	95.80 155	88.37 73	97.85 226	91.71 113	83.93 269	95.73 199

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 204	92.83 59	97.16 108	98.24 26	93.02 66	90.13 168	95.71 163	83.47 127	97.85 226	91.71 113	83.93 269	95.78 193

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 61	98.74 4	98.06 54	90.57 135	96.77 28	98.35 22	90.21 54	99.53 68	94.80 61	99.63 4	99.38 37

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 181	92.73 62	98.27 23	98.12 39	84.86 257	85.78 255	97.75 63	78.89 228	99.74 22	87.50 186	98.65 71	96.73 166

EPNet		95.20 66	94.56 71	97.14 53	92.80 287	92.68 63	97.85 47	94.87 276	96.64 1	92.46 119	97.80 61	86.23 97	99.65 39	93.72 78	98.62 72	99.10 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		96.97 21	96.87 15	97.25 47	98.34 60	92.66 64	96.96 121	98.01 66	95.12 13	97.14 21	98.42 16	91.82 32	99.61 45	96.90 6	99.13 54	99.50 22

QAPM		93.45 113	92.27 131	96.98 58	96.77 124	92.62 65	98.39 18	98.12 39	84.50 262	88.27 223	97.77 62	82.39 167	99.81 17	85.40 221	98.81 67	98.51 98

ACMMP		96.27 44	95.93 44	97.28 45	99.24 19	92.62 65	98.25 25	98.81 3	92.99 67	94.56 84	98.39 20	88.96 63	99.85 9	94.57 65	97.63 94	99.36 39
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
CNLPA		94.28 85	93.53 93	96.52 69	98.38 58	92.55 67	96.59 166	96.88 187	90.13 140	91.91 132	97.24 91	85.21 109	99.09 113	87.64 182	97.83 89	97.92 127

PCF-MVS	89.48 11	91.56 176	89.95 206	96.36 82	96.60 128	92.52 68	92.51 291	97.26 144	79.41 298	88.90 209	96.56 124	84.04 122	99.55 63	77.01 291	97.30 105	97.01 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 111	92.49 69	95.64 225	96.64 201	89.05 168	93.00 111	95.79 158	85.77 105	99.45 79	89.16 151	94.35 152	97.96 125

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 169	92.39 70	97.86 45	98.66 9	92.30 85	92.09 130	95.37 181	80.49 201	98.40 158	93.95 71	85.86 242	95.75 197

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 28	92.31 71	97.98 40	98.06 54	93.11 64	97.44 13	98.55 9	90.93 45	99.55 63	96.06 29	99.25 44	99.51 21

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 51	92.31 71	96.20 198	98.90 2	94.30 35	95.86 60	97.74 64	92.33 21	99.38 88	96.04 30	99.42 28	99.28 46

FMVSNet3		91.78 163	90.69 182	95.03 137	96.53 134	92.27 73	97.02 116	96.93 182	89.79 149	89.35 201	94.65 206	77.01 244	97.47 254	86.12 208	88.82 219	95.35 216

test222					98.24 69	92.21 74	95.33 237	97.60 105	79.22 300	95.25 75	97.84 58	88.80 66			99.15 52	98.72 86

FMVSNet2		91.31 189	90.08 200	94.99 138	96.51 135	92.21 74	97.41 83	96.95 180	88.82 178	88.62 214	94.75 202	73.87 264	97.42 258	85.20 224	88.55 225	95.35 216

abl_6		96.40 40	96.21 40	96.98 58	98.89 31	92.20 76	97.89 43	98.03 63	93.34 56	97.22 17	98.42 16	87.93 77	99.72 26	95.10 50	99.07 59	99.02 62

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 80	92.19 77	97.67 61	97.47 119	88.13 202	93.00 111	95.84 152	84.86 115	99.51 72	87.99 171	98.17 82	97.83 133
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
CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 140	92.13 78	96.21 197	96.67 200	94.38 33	93.53 100	97.03 100	79.34 218	99.71 27	90.76 127	98.45 76	97.82 134

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 134	94.76 213	92.07 79	97.53 75	98.11 42	92.90 75	89.56 195	96.12 141	83.16 131	97.60 247	89.30 145	83.20 279	95.75 197

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 94	92.05 80	96.59 166	97.35 139	90.61 132	94.64 83	96.93 101	86.41 96	99.39 86	91.20 126	94.71 151	98.94 72

FIs		94.09 92	93.70 86	95.27 127	95.70 169	92.03 81	98.10 31	98.68 7	93.36 55	90.39 160	96.70 110	87.63 82	97.94 216	92.25 97	90.50 206	95.84 189

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 60	92.02 82	96.96 121	97.76 88	95.01 16	97.08 26	98.42 16	91.71 33	99.54 65	96.80 9	99.13 54	99.48 26

API-MVS		94.84 78	94.49 75	95.90 100	97.90 89	92.00 83	97.80 50	97.48 116	89.19 159	94.81 81	96.71 108	88.84 65	99.17 100	88.91 157	98.76 69	96.53 169

sss		94.51 82	93.80 84	96.64 62	97.07 112	91.97 84	96.32 187	98.06 54	88.94 173	94.50 85	96.78 105	84.60 117	99.27 93	91.90 107	96.02 129	98.68 90

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 105	91.96 85	95.40 235	97.45 125	89.81 148	93.22 104	96.28 135	79.62 215	99.46 77	90.74 128	93.11 166	98.50 100

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 35	91.86 86	97.67 61	98.49 12	94.66 27	97.24 16	98.41 19	92.31 24	98.94 118	96.61 14	99.46 23	98.96 69

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 129	91.71 87	96.25 193	97.35 139	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 180

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 129	91.71 87	96.25 193	97.35 139	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 180

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 129	91.71 87	96.25 193	97.35 139	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 180

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 46	91.68 90	96.59 166	97.81 87	89.87 143	92.15 128	97.06 99	83.62 126	99.54 65	89.34 144	98.07 84	97.70 138

114514_t		93.95 97	93.06 106	96.63 64	99.07 26	91.61 91	97.46 82	97.96 76	77.99 305	93.00 111	97.57 79	86.14 101	99.33 90	89.22 148	99.15 52	98.94 72

LS3D		93.57 110	92.61 121	96.47 75	97.59 100	91.61 91	97.67 61	97.72 94	85.17 252	90.29 162	98.34 25	84.60 117	99.73 23	83.85 247	98.27 79	98.06 124

MVS		91.71 164	90.44 187	95.51 117	95.20 193	91.59 93	96.04 205	97.45 125	73.44 317	87.36 239	95.60 169	85.42 107	99.10 111	85.97 212	97.46 97	95.83 190

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 108	91.58 94	98.26 24	98.12 39	94.38 33	94.90 79	98.15 39	82.28 168	98.92 119	91.45 121	98.58 74	99.01 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 17	91.48 95	98.33 20	98.11 42	87.79 207	95.17 77	98.03 44	87.09 90	99.61 45	93.51 81	99.42 28	99.02 62

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 127	91.47 96	96.41 175	97.41 132	91.02 120	94.50 85	95.92 148	87.53 84	98.78 131	93.89 74	96.81 115	98.84 82

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 151	91.46 97	96.33 186	97.04 168	88.97 172	93.56 98	96.51 126	87.55 83	97.89 224	89.80 135	95.95 131	98.44 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FC-MVSNet-test		93.94 98	93.57 90	95.04 136	95.48 175	91.45 98	98.12 30	98.71 5	93.37 53	90.23 163	96.70 110	87.66 80	97.85 226	91.49 119	90.39 207	95.83 190

PAPR		94.18 87	93.42 100	96.48 74	97.64 97	91.42 99	95.55 228	97.71 97	88.99 170	92.34 124	95.82 154	89.19 60	99.11 106	86.14 207	97.38 102	98.90 76

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 72	91.35 100	96.24 196	98.79 4	93.99 39	95.80 63	97.65 70	89.92 58	99.24 95	95.87 33	99.20 49	98.58 92

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 51	91.28 101	96.43 173	97.57 108	92.04 93	94.77 82	97.96 49	87.01 91	99.09 113	91.31 123	96.77 116	98.36 114

LFMVS		93.60 108	92.63 119	96.52 69	98.13 77	91.27 102	97.94 41	93.39 305	90.57 135	96.29 44	98.31 31	69.00 287	99.16 101	94.18 67	95.87 133	99.12 57

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 144	91.21 103	98.22 26	97.57 108	91.42 108	96.22 46	97.32 87	86.20 99	97.92 220	94.07 68	99.05 60	98.85 80

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 144	91.21 103	95.83 216	96.27 212	88.93 174	96.22 46	96.88 103	86.20 99	98.85 126	95.27 45	99.05 60	98.82 83

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 69	91.20 105	96.89 130	97.73 91	94.74 25	96.49 39	98.49 11	90.88 47	99.58 53	96.44 18	98.32 78	99.13 55

UGNet		94.04 95	93.28 103	96.31 84	96.85 119	91.19 106	97.88 44	97.68 99	94.40 31	93.00 111	96.18 138	73.39 270	99.61 45	91.72 112	98.46 75	98.13 119
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
GBi-Net		91.35 187	90.27 193	94.59 150	96.51 135	91.18 107	97.50 77	96.93 182	88.82 178	89.35 201	94.51 209	73.87 264	97.29 266	86.12 208	88.82 219	95.31 218

test1		91.35 187	90.27 193	94.59 150	96.51 135	91.18 107	97.50 77	96.93 182	88.82 178	89.35 201	94.51 209	73.87 264	97.29 266	86.12 208	88.82 219	95.31 218

FMVSNet1		89.88 229	88.31 236	94.59 150	95.41 177	91.18 107	97.50 77	96.93 182	86.62 236	87.41 237	94.51 209	65.94 301	97.29 266	83.04 254	87.43 233	95.31 218

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 48	91.15 110	96.69 156	97.39 133	87.29 219	91.37 140	96.71 108	88.39 72	99.52 71	87.33 190	97.13 109	97.73 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
原ACMM1				96.38 80	98.59 46	91.09 111		97.89 79	87.41 216	95.22 76	97.68 67	90.25 52	99.54 65	87.95 172	99.12 57	98.49 102

1112_ss		93.37 115	92.42 129	96.21 91	97.05 115	90.99 112	96.31 188	96.72 193	86.87 233	89.83 182	96.69 112	86.51 95	99.14 104	88.12 168	93.67 160	98.50 100

DP-MVS		92.76 137	91.51 154	96.52 69	98.77 33	90.99 112	97.38 87	96.08 221	82.38 280	89.29 204	97.87 53	83.77 124	99.69 33	81.37 271	96.69 120	98.89 78

VPNet		92.23 154	91.31 158	94.99 138	95.56 172	90.96 114	97.22 102	97.86 84	92.96 73	90.96 152	96.62 122	75.06 256	98.20 170	91.90 107	83.65 275	95.80 192

XXY-MVS		92.16 155	91.23 162	94.95 142	94.75 214	90.94 115	97.47 81	97.43 130	89.14 166	88.90 209	96.43 130	79.71 213	98.24 168	89.56 141	87.68 230	95.67 201

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 73	90.93 116	96.86 132	97.72 94	94.67 26	96.16 48	98.46 12	90.43 51	99.58 53	96.23 21	97.96 87	98.90 76

jason		94.84 78	94.39 79	96.18 92	95.52 173	90.93 116	96.09 202	96.52 205	89.28 156	96.01 56	97.32 87	84.70 116	98.77 133	95.15 48	98.91 66	98.85 80
jason: jason.
PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 73	90.86 118	97.27 95	98.25 25	90.21 138	94.18 91	97.27 89	87.48 85	99.73 23	93.53 80	97.77 92	98.55 93

WR-MVS		92.34 147	91.53 153	94.77 149	95.13 196	90.83 119	96.40 179	97.98 74	91.88 97	89.29 204	95.54 173	82.50 162	97.80 231	89.79 136	85.27 249	95.69 200

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 75	90.80 120	95.27 242	97.18 147	87.96 203	91.86 134	95.68 166	80.44 202	98.99 116	84.01 243	97.54 96	96.89 162

pmmvs4		90.93 201	89.85 210	94.17 166	93.34 272	90.79 121	94.60 251	96.02 222	84.62 260	87.45 235	95.15 189	81.88 178	97.45 255	87.70 177	87.87 229	94.27 270

OPM-MVS		93.28 118	92.76 112	94.82 144	94.63 218	90.77 122	96.65 159	97.18 147	93.72 45	91.68 135	97.26 90	79.33 219	98.63 141	92.13 101	92.28 174	95.07 231

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 31	90.68 123	97.24 97	97.73 91	91.80 98	92.93 116	96.62 122	89.13 62	99.14 104	89.21 149	97.78 91	98.97 68

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 104	90.66 124	95.31 239	97.48 116	93.85 42	96.51 38	95.70 165	88.65 68	99.65 39	94.80 61	98.27 79	96.17 175

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 95	90.56 125	98.44 15	96.22 216	93.21 58	93.99 93	97.74 64	85.55 106	98.45 156	89.98 132	97.86 88	99.14 54

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 54	90.53 126	96.04 205	97.48 116	93.47 52	95.67 69	98.10 40	89.17 61	99.25 94	91.27 124	98.77 68	99.13 55

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 106	90.50 127	95.44 234	97.44 128	93.70 47	96.46 41	96.18 138	88.59 71	99.53 68	94.79 63	97.81 90	96.17 175

CSCG		96.05 49	95.91 45	96.46 77	99.24 19	90.47 128	98.30 21	98.57 11	89.01 169	93.97 95	97.57 79	92.62 16	99.76 21	94.66 64	99.27 43	99.15 53

TAMVS		94.01 96	93.46 96	95.64 111	96.16 153	90.45 129	96.71 151	96.89 186	89.27 157	93.46 102	96.92 102	87.29 88	97.94 216	88.70 163	95.74 135	98.53 95

VDDNet		93.05 125	92.07 133	96.02 96	96.84 120	90.39 130	98.08 33	95.85 234	86.22 241	95.79 64	98.46 12	67.59 294	99.19 97	94.92 57	94.85 145	98.47 105

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 124	90.03 131	96.81 138	97.13 155	88.19 198	91.30 145	94.27 227	86.21 98	98.63 141	87.66 181	96.46 126	98.12 120

plane_prior6					96.10 158	90.00 132						81.32 185

plane_prior3						90.00 132			94.46 30	91.34 142

HQP_MVS		93.78 103	93.43 98	94.82 144	96.21 148	89.99 134	97.74 54	97.51 114	94.85 17	91.34 142	96.64 115	81.32 185	98.60 144	93.02 90	92.23 175	95.86 186

plane_prior						89.99 134	97.24 97		94.06 38						92.16 179

plane_prior7					96.21 148	89.98 136

test_normal		92.01 158	90.75 179	95.80 104	93.24 276	89.97 137	95.93 212	96.24 215	90.62 130	81.63 281	93.45 252	74.98 257	98.89 123	93.61 79	97.04 111	98.55 93

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 116	89.97 137	95.53 230	96.64 201	85.38 248	89.65 192	95.18 188	85.86 103	99.10 111	87.70 177	93.58 165	98.49 102

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 90	89.96 139	97.72 58	95.85 234	92.43 83	95.86 60	98.44 14	68.42 291	99.39 86	96.31 19	94.85 145	98.71 88

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 69	89.88 140	94.58 252	98.49 12	85.06 254	93.78 96	95.78 159	82.86 153	98.67 139	91.77 111	95.71 137	99.07 61

PAPM		91.52 179	90.30 191	95.20 128	95.30 185	89.83 141	93.38 277	96.85 189	86.26 240	88.59 216	95.80 155	84.88 113	98.15 175	75.67 294	95.93 132	97.63 139

NP-MVS					95.99 161	89.81 142					95.87 150

DI_MVS_plusplus_test		92.01 158	90.77 177	95.73 109	93.34 272	89.78 143	96.14 200	96.18 218	90.58 134	81.80 280	93.50 249	74.95 258	98.90 121	93.51 81	96.94 112	98.51 98

Test4		89.48 233	87.50 243	95.44 124	90.76 301	89.72 144	95.78 220	97.09 159	90.28 137	77.67 306	91.74 280	55.42 319	98.08 183	91.92 106	96.83 114	98.52 96

pm-mvs1		90.72 209	89.65 219	93.96 177	94.29 230	89.63 145	97.79 51	96.82 190	89.07 167	86.12 254	95.48 179	78.61 230	97.78 233	86.97 197	81.67 286	94.46 263

diffmvs		93.43 114	92.75 114	95.48 121	96.47 139	89.61 146	96.09 202	97.14 153	85.97 244	93.09 109	95.35 182	84.87 114	98.55 149	89.51 142	96.26 128	98.28 116

TAPA-MVS	90.10 7	92.30 150	91.22 163	95.56 114	98.33 62	89.60 147	96.79 141	97.65 102	81.83 284	91.52 137	97.23 92	87.94 76	98.91 120	71.31 305	98.37 77	98.17 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSTER		93.20 120	92.81 111	94.37 160	96.56 132	89.59 148	97.06 113	97.12 156	91.24 114	91.30 145	95.96 146	82.02 174	98.05 195	93.48 83	90.55 204	95.47 205

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 103	89.56 149	98.67 5	97.00 172	90.69 125	94.24 90	97.62 75	89.79 59	98.81 129	93.39 87	96.49 124	98.92 74

anonymousdsp		92.16 155	91.55 152	93.97 176	92.58 291	89.55 150	97.51 76	97.42 131	89.42 154	88.40 218	94.84 197	80.66 198	97.88 225	91.87 109	91.28 194	94.48 262

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 122	89.55 150	96.70 154	97.17 149	91.17 115	95.60 70	96.11 143	87.87 78	98.76 134	93.01 92	97.17 108	98.72 86

LTVRE_ROB	88.41 13	90.99 199	89.92 207	94.19 165	96.18 151	89.55 150	96.31 188	97.09 159	87.88 206	85.67 256	95.91 149	78.79 229	98.57 147	81.50 269	89.98 210	94.44 264
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
1314		92.81 136	92.03 135	95.14 134	95.33 184	89.52 153	96.04 205	97.44 128	87.72 210	86.25 253	95.33 183	83.84 123	98.79 130	89.26 146	97.05 110	97.11 153

WR-MVS_H		92.00 160	91.35 155	93.95 178	95.09 198	89.47 154	98.04 35	98.68 7	91.46 106	88.34 219	94.68 204	85.86 103	97.56 248	85.77 215	84.24 266	94.82 249

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 156	98.27 66	89.46 155	96.73 146	98.36 16	90.17 139	94.36 87	95.24 187	88.02 74	99.58 53	93.44 84	90.72 202	94.36 266

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 66	89.46 155	95.47 233	98.36 16	88.84 176	94.36 87	96.09 144	88.02 74	99.58 53	93.44 84	98.18 81	98.40 110

view600		92.55 140	91.68 146	95.18 129	97.98 81	89.44 157	98.00 36	94.57 279	92.09 88	93.17 105	95.52 174	78.14 236	99.11 106	81.61 265	94.04 154	96.98 155

view800		92.55 140	91.68 146	95.18 129	97.98 81	89.44 157	98.00 36	94.57 279	92.09 88	93.17 105	95.52 174	78.14 236	99.11 106	81.61 265	94.04 154	96.98 155

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 81	89.44 157	98.00 36	94.57 279	92.09 88	93.17 105	95.52 174	78.14 236	99.11 106	81.61 265	94.04 154	96.98 155

tfpn		92.55 140	91.68 146	95.18 129	97.98 81	89.44 157	98.00 36	94.57 279	92.09 88	93.17 105	95.52 174	78.14 236	99.11 106	81.61 265	94.04 154	96.98 155

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 66	89.38 161	95.18 245	98.48 14	85.60 247	93.76 97	97.11 97	83.15 132	99.61 45	91.33 122	98.72 70	99.19 49

HQP5-MVS						89.33 162

HQP-MVS		93.19 121	92.74 116	94.54 155	95.86 162	89.33 162	96.65 159	97.39 133	93.55 48	90.14 164	95.87 150	80.95 190	98.50 153	92.13 101	92.10 180	95.78 193

testing_2		87.33 263	85.03 270	94.22 164	87.77 313	89.32 164	94.97 247	97.11 158	89.22 158	71.64 315	88.73 301	55.16 320	97.94 216	91.95 105	88.73 223	95.41 208

PS-MVSNAJss		93.74 104	93.51 94	94.44 157	93.91 255	89.28 165	97.75 53	97.56 111	92.50 82	89.94 176	96.54 125	88.65 68	98.18 173	93.83 77	90.90 199	95.86 186

gg-mvs-nofinetune		87.82 259	85.61 266	94.44 157	94.46 223	89.27 166	91.21 302	84.61 330	80.88 292	89.89 179	74.98 322	71.50 275	97.53 250	85.75 216	97.21 107	96.51 170

GG-mvs-BLEND				93.62 200	93.69 262	89.20 167	92.39 294	83.33 331		87.98 228	89.84 287	71.00 279	96.87 276	82.08 264	95.40 139	94.80 251

CLD-MVS		92.98 127	92.53 125	94.32 163	96.12 157	89.20 167	95.28 240	97.47 119	92.66 79	89.90 177	95.62 168	80.58 199	98.40 158	92.73 93	92.40 173	95.38 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cascas		91.20 192	90.08 200	94.58 154	94.97 202	89.16 169	93.65 273	97.59 107	79.90 297	89.40 199	92.92 259	75.36 254	98.36 161	92.14 100	94.75 149	96.23 173

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 55	88.98 170	97.18 106	97.29 143	87.75 209	90.49 157	97.10 98	85.21 109	99.50 74	86.70 199	96.72 119	97.63 139

MSDG		91.42 183	90.24 195	94.96 141	97.15 110	88.91 171	93.69 271	96.32 210	85.72 246	86.93 248	96.47 128	80.24 206	98.98 117	80.57 273	95.05 144	96.98 155

testdata				95.46 123	98.18 76	88.90 172		97.66 100	82.73 278	97.03 27	98.07 42	90.06 55	98.85 126	89.67 138	98.98 63	98.64 91

ACMM	89.79 8	92.96 128	92.50 127	94.35 161	96.30 146	88.71 173	97.58 73	97.36 138	91.40 110	90.53 156	96.65 114	79.77 212	98.75 135	91.24 125	91.64 186	95.59 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		93.07 124	92.76 112	94.00 173	93.49 268	88.70 174	98.22 26	97.57 108	91.42 108	90.08 174	95.55 172	82.85 154	97.92 220	94.07 68	91.58 188	95.40 212

XVG-OURS		93.72 105	93.35 101	94.80 147	97.07 112	88.61 175	94.79 249	97.46 121	91.97 96	93.99 93	97.86 55	81.74 180	98.88 125	92.64 94	92.67 171	96.92 161

CP-MVSNet		91.89 162	91.24 161	93.82 184	95.05 199	88.57 176	97.82 49	98.19 31	91.70 100	88.21 224	95.76 160	81.96 175	97.52 251	87.86 173	84.65 263	95.37 215

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 146	97.06 114	88.53 177	95.28 240	97.45 125	91.68 101	94.08 92	97.68 67	82.41 166	98.90 121	93.84 76	92.47 172	96.98 155

jajsoiax		92.42 146	91.89 140	94.03 172	93.33 274	88.50 178	97.73 56	97.53 112	92.00 95	88.85 211	96.50 127	75.62 253	98.11 179	93.88 75	91.56 189	95.48 203

V42		91.58 175	90.87 172	93.73 193	94.05 249	88.50 178	97.32 92	96.97 176	88.80 181	89.71 188	94.33 220	82.54 161	98.05 195	89.01 155	85.07 255	94.64 259

TransMVSNet (Re)		88.94 238	87.56 241	93.08 223	94.35 227	88.45 180	97.73 56	95.23 260	87.47 214	84.26 266	95.29 184	79.86 211	97.33 264	79.44 281	74.44 314	93.45 279

mvs_tets		92.31 149	91.76 142	93.94 181	93.41 270	88.29 181	97.63 68	97.53 112	92.04 93	88.76 212	96.45 129	74.62 260	98.09 182	93.91 73	91.48 190	95.45 207

PS-CasMVS		91.55 177	90.84 176	93.69 197	94.96 203	88.28 182	97.84 48	98.24 26	91.46 106	88.04 226	95.80 155	79.67 214	97.48 253	87.02 196	84.54 264	95.31 218

LPG-MVS_test		92.94 129	92.56 122	94.10 168	96.16 153	88.26 183	97.65 64	97.46 121	91.29 111	90.12 170	97.16 94	79.05 222	98.73 136	92.25 97	91.89 183	95.31 218

LGP-MVS_train				94.10 168	96.16 153	88.26 183		97.46 121	91.29 111	90.12 170	97.16 94	79.05 222	98.73 136	92.25 97	91.89 183	95.31 218

v1141		91.61 171	90.89 169	93.78 187	94.01 250	88.24 185	96.96 121	96.96 177	89.17 163	89.75 186	94.29 223	82.99 146	98.03 199	88.85 159	85.00 258	95.07 231

v1144		91.37 186	90.60 184	93.68 198	93.89 256	88.23 186	96.84 133	97.03 170	88.37 192	89.69 190	94.39 216	82.04 173	97.98 207	87.80 175	85.37 247	94.84 245

divwei89l23v2f112		91.61 171	90.89 169	93.78 187	94.01 250	88.22 187	96.96 121	96.96 177	89.17 163	89.75 186	94.28 225	83.02 144	98.03 199	88.86 158	84.98 260	95.08 229

v1		91.61 171	90.89 169	93.78 187	94.01 250	88.21 188	96.96 121	96.96 177	89.17 163	89.78 185	94.29 223	82.97 148	98.05 195	88.85 159	84.99 259	95.08 229

MVP-Stereo		90.74 208	90.08 200	92.71 233	93.19 281	88.20 189	95.86 214	96.27 212	86.07 243	84.86 261	94.76 201	77.84 241	97.75 236	83.88 246	98.01 85	92.17 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	89.59 10	92.62 139	92.14 132	94.05 171	96.40 142	88.20 189	97.36 88	97.25 146	91.52 103	88.30 221	96.64 115	78.46 232	98.72 138	91.86 110	91.48 190	95.23 225
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		91.59 174	90.85 174	93.80 185	93.87 257	88.17 191	96.94 127	96.88 187	89.54 150	89.53 196	94.90 195	81.70 181	98.02 202	89.25 147	85.04 257	95.20 226

v7		91.47 181	90.73 180	93.68 198	94.13 238	88.16 192	97.09 112	97.05 165	88.38 191	89.80 183	94.52 208	82.21 170	98.01 203	88.00 170	85.42 246	94.87 243

v10		91.04 198	90.23 196	93.49 206	94.12 240	88.16 192	97.32 92	97.08 161	88.26 195	88.29 222	94.22 230	82.17 172	97.97 210	86.45 203	84.12 267	94.33 267

v1neww		91.70 167	91.01 166	93.75 190	94.19 232	88.14 194	97.20 103	96.98 173	89.18 161	89.87 180	94.44 213	83.10 136	98.06 192	89.06 153	85.09 253	95.06 234

v7new		91.70 167	91.01 166	93.75 190	94.19 232	88.14 194	97.20 103	96.98 173	89.18 161	89.87 180	94.44 213	83.10 136	98.06 192	89.06 153	85.09 253	95.06 234

v8		91.29 190	90.53 186	93.57 204	94.15 236	88.12 196	97.34 89	97.06 164	88.99 170	88.32 220	94.26 229	83.08 138	98.01 203	87.62 183	83.92 271	94.57 260

v6		91.69 169	91.00 168	93.75 190	94.14 237	88.12 196	97.20 103	96.98 173	89.19 159	89.90 177	94.42 215	83.04 142	98.07 187	89.07 152	85.10 252	95.07 231

v18		88.71 243	87.52 242	92.27 240	94.16 235	88.11 198	96.82 137	95.96 223	87.03 223	80.76 287	89.81 288	83.15 132	96.22 283	84.69 229	75.31 305	92.49 291

v16		88.69 244	87.50 243	92.26 242	94.19 232	88.11 198	96.81 138	95.95 224	87.01 225	80.71 289	89.80 289	83.08 138	96.20 284	84.61 232	75.34 304	92.48 293

v17		88.67 245	87.47 245	92.26 242	94.13 238	88.09 200	96.81 138	95.95 224	87.02 224	80.72 288	89.75 290	83.11 135	96.20 284	84.61 232	75.15 307	92.49 291

v15		88.53 247	87.31 247	92.20 245	94.09 244	88.05 201	96.72 149	95.90 228	87.01 225	80.53 292	89.60 294	83.02 144	96.13 286	84.29 237	74.64 308	92.41 297

Baseline_NR-MVSNet		91.20 192	90.62 183	92.95 226	93.83 258	88.03 202	97.01 118	95.12 265	88.42 190	89.70 189	95.13 191	83.47 127	97.44 256	89.66 139	83.24 278	93.37 281

v12		88.46 252	87.23 252	92.17 247	94.10 243	87.99 203	96.71 151	95.90 228	86.91 230	80.34 297	89.58 295	82.92 151	96.11 290	84.09 240	74.50 313	92.42 296

V14		88.52 248	87.30 248	92.17 247	94.12 240	87.99 203	96.72 149	95.91 227	86.98 227	80.50 293	89.63 291	83.03 143	96.12 288	84.23 238	74.60 310	92.40 298

BH-RMVSNet		92.72 138	91.97 138	94.97 140	97.16 109	87.99 203	96.15 199	95.60 242	90.62 130	91.87 133	97.15 96	78.41 233	98.57 147	83.16 252	97.60 95	98.36 114

V9		88.49 251	87.26 249	92.18 246	94.12 240	87.97 206	96.73 146	95.90 228	86.95 229	80.40 295	89.61 292	82.98 147	96.13 286	84.14 239	74.55 311	92.44 295

v13		88.45 253	87.22 253	92.16 249	94.08 246	87.95 207	96.71 151	95.90 228	86.86 234	80.27 299	89.55 296	82.92 151	96.12 288	84.02 242	74.63 309	92.40 298

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 142	97.61 99	87.92 208	98.10 31	95.80 237	92.22 86	93.02 110	97.45 86	84.53 119	97.91 223	88.24 166	97.97 86	99.02 62

ACMH	87.59 16	90.53 215	89.42 222	93.87 183	96.21 148	87.92 208	97.24 97	96.94 181	88.45 189	83.91 271	96.27 136	71.92 272	98.62 143	84.43 235	89.43 215	95.05 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		91.20 192	90.44 187	93.48 207	94.49 222	87.91 210	97.76 52	98.18 33	91.29 111	87.78 229	95.74 162	80.35 204	97.33 264	85.46 220	82.96 280	95.19 227

v1192		91.07 196	90.23 196	93.58 203	93.70 261	87.82 211	96.73 146	97.07 162	87.77 208	89.58 193	94.32 221	80.90 196	97.97 210	86.52 201	85.48 244	94.95 237

v11		88.41 254	87.19 256	92.08 252	94.08 246	87.77 212	96.75 144	95.85 234	86.74 235	80.50 293	89.50 297	82.49 163	96.08 291	83.55 248	75.20 306	92.38 300

MIMVSNet		88.50 250	86.76 258	93.72 195	94.84 210	87.77 212	91.39 298	94.05 295	86.41 238	87.99 227	92.59 264	63.27 305	95.82 296	77.44 287	92.84 169	97.57 146

IB-MVS	87.33 17	89.91 227	88.28 237	94.79 148	95.26 189	87.70 214	95.12 246	93.95 298	89.35 155	87.03 246	92.49 266	70.74 281	99.19 97	89.18 150	81.37 288	97.49 148
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
GA-MVS		91.38 185	90.31 190	94.59 150	94.65 217	87.62 215	94.34 257	96.19 217	90.73 124	90.35 161	93.83 238	71.84 273	97.96 214	87.22 192	93.61 163	98.21 117

v7n		90.76 205	89.86 209	93.45 210	93.54 265	87.60 216	97.70 60	97.37 136	88.85 175	87.65 233	94.08 233	81.08 187	98.10 180	84.68 230	83.79 274	94.66 258

TR-MVS		91.48 180	90.59 185	94.16 167	96.40 142	87.33 217	95.67 222	95.34 255	87.68 211	91.46 138	95.52 174	76.77 245	98.35 162	82.85 256	93.61 163	96.79 165

FMVSNet5		87.29 264	85.79 265	91.78 261	94.80 212	87.28 218	95.49 232	95.28 256	84.09 265	83.85 272	91.82 277	62.95 306	94.17 308	78.48 284	85.34 248	93.91 274

CHOSEN 280x420		93.12 122	92.72 117	94.34 162	96.71 126	87.27 219	90.29 307	97.72 94	86.61 237	91.34 142	95.29 184	84.29 121	98.41 157	93.25 88	98.94 65	97.35 151

pmmvs-eth3d		86.22 271	84.45 274	91.53 266	88.34 310	87.25 220	94.47 255	95.01 268	83.47 273	79.51 303	89.61 292	69.75 286	95.71 297	83.13 253	76.73 300	91.64 306

DTE-MVSNet		90.56 214	89.75 215	93.01 224	93.95 253	87.25 220	97.64 67	97.65 102	90.74 123	87.12 243	95.68 166	79.97 210	97.00 275	83.33 251	81.66 287	94.78 254

v144192		91.06 197	90.28 192	93.39 211	93.66 263	87.23 222	96.83 134	97.07 162	87.43 215	89.69 190	94.28 225	81.48 182	98.00 206	87.18 194	84.92 261	94.93 241

CR-MVSNet		90.82 204	89.77 213	93.95 178	94.45 224	87.19 223	90.23 308	95.68 240	86.89 232	92.40 120	92.36 271	80.91 193	97.05 270	81.09 272	93.95 158	97.60 144

RPMNet		88.52 248	86.72 260	93.95 178	94.45 224	87.19 223	90.23 308	94.99 270	77.87 307	92.40 120	87.55 312	80.17 208	97.05 270	68.84 309	93.95 158	97.60 144

COLMAP_ROB	87.81 15	90.40 217	89.28 224	93.79 186	97.95 86	87.13 225	96.92 128	95.89 233	82.83 277	86.88 250	97.18 93	73.77 267	99.29 92	78.44 285	93.62 162	94.95 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v52		90.70 211	90.00 204	92.82 227	93.24 276	87.03 226	97.60 70	97.14 153	88.21 196	87.69 231	93.94 235	80.91 193	98.07 187	87.39 187	83.87 273	93.36 282

V4		90.71 210	90.00 204	92.82 227	93.21 279	87.03 226	97.59 72	97.16 152	88.21 196	87.69 231	93.92 237	80.93 192	98.06 192	87.39 187	83.90 272	93.39 280

EI-MVSNet		93.03 126	92.88 110	93.48 207	95.77 167	86.98 228	96.44 171	97.12 156	90.66 128	91.30 145	97.64 73	86.56 94	98.05 195	89.91 133	90.55 204	95.41 208

IterMVS-LS		92.29 151	91.94 139	93.34 214	96.25 147	86.97 229	96.57 169	97.05 165	90.67 126	89.50 198	94.80 200	86.59 93	97.64 244	89.91 133	86.11 241	95.40 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 203	90.03 203	93.29 216	93.55 264	86.96 230	96.74 145	97.04 168	87.36 217	89.52 197	94.34 219	80.23 207	97.97 210	86.27 204	85.21 250	94.94 239

v1240		90.70 211	89.85 210	93.23 218	93.51 267	86.80 231	96.61 164	97.02 171	87.16 221	89.58 193	94.31 222	79.55 216	97.98 207	85.52 219	85.44 245	94.90 242

PMMVS		92.86 133	92.34 130	94.42 159	94.92 206	86.73 232	94.53 254	96.38 208	84.78 259	94.27 89	95.12 192	83.13 134	98.40 158	91.47 120	96.49 124	98.12 120

AllTest		90.23 221	88.98 228	93.98 174	97.94 87	86.64 233	96.51 170	95.54 245	85.38 248	85.49 258	96.77 106	70.28 283	99.15 102	80.02 276	92.87 167	96.15 177

TestCases				93.98 174	97.94 87	86.64 233		95.54 245	85.38 248	85.49 258	96.77 106	70.28 283	99.15 102	80.02 276	92.87 167	96.15 177

Patchmtry		88.64 246	87.25 250	92.78 231	94.09 244	86.64 233	89.82 311	95.68 240	80.81 294	87.63 234	92.36 271	80.91 193	97.03 272	78.86 283	85.12 251	94.67 257

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 133	98.09 78	86.63 236	96.00 209	98.15 35	95.43 7	97.95 7	98.56 7	93.40 8	99.36 89	96.77 12	99.48 22	99.45 28

ACMH+	87.92 14	90.20 222	89.18 226	93.25 217	96.48 138	86.45 237	96.99 119	96.68 198	88.83 177	84.79 262	96.22 137	70.16 285	98.53 150	84.42 236	88.04 227	94.77 255

pmmvs6		87.81 260	86.19 262	92.69 234	91.32 298	86.30 238	97.34 89	96.41 207	80.59 296	84.05 270	94.37 218	67.37 296	97.67 241	84.75 228	79.51 294	94.09 272

pmmvs5		89.86 230	88.87 230	92.82 227	92.86 285	86.23 239	96.26 192	95.39 249	84.24 263	87.12 243	94.51 209	74.27 262	97.36 263	87.61 184	87.57 231	94.86 244

BH-untuned		92.94 129	92.62 120	93.92 182	97.22 106	86.16 240	96.40 179	96.25 214	90.06 141	89.79 184	96.17 140	83.19 130	98.35 162	87.19 193	97.27 106	97.24 152

XVG-ACMP-BASELINE		90.93 201	90.21 198	93.09 222	94.31 229	85.89 241	95.33 237	97.26 144	91.06 119	89.38 200	95.44 180	68.61 289	98.60 144	89.46 143	91.05 197	94.79 253

v148		90.99 199	90.38 189	92.81 230	93.83 258	85.80 242	96.78 143	96.68 198	89.45 153	88.75 213	93.93 236	82.96 150	97.82 230	87.83 174	83.25 277	94.80 251

BH-w/o		92.14 157	91.75 143	93.31 215	96.99 117	85.73 243	95.67 222	95.69 239	88.73 183	89.26 206	94.82 199	82.97 148	98.07 187	85.26 223	96.32 127	96.13 179

v748		90.34 218	89.54 220	92.75 232	93.25 275	85.71 244	97.61 69	97.17 149	88.54 188	87.20 242	93.54 247	81.02 188	98.01 203	85.73 217	81.80 284	94.52 261

test0.0.03 1		89.37 236	88.70 231	91.41 269	92.47 292	85.63 245	95.22 244	92.70 310	91.11 117	86.91 249	93.65 244	79.02 224	93.19 313	78.00 286	89.18 217	95.41 208

test_0402		86.46 269	84.79 272	91.45 267	95.02 201	85.55 246	96.29 190	94.89 274	80.90 291	82.21 275	93.97 234	68.21 292	97.29 266	62.98 315	88.68 224	91.51 308

Fast-Effi-MVS+-dtu		92.29 151	91.99 137	93.21 220	95.27 186	85.52 247	97.03 114	96.63 203	92.09 88	89.11 208	95.14 190	80.33 205	98.08 183	87.54 185	94.74 150	96.03 183

mvs_anonymous		93.82 101	93.74 85	94.06 170	96.44 141	85.41 248	95.81 217	97.05 165	89.85 146	90.09 173	96.36 133	87.44 86	97.75 236	93.97 70	96.69 120	99.02 62

ITE_SJBPF				92.43 239	95.34 181	85.37 249		95.92 226	91.47 105	87.75 230	96.39 132	71.00 279	97.96 214	82.36 262	89.86 213	93.97 273

Patchmatch-test		89.42 235	87.99 239	93.70 196	95.27 186	85.11 250	88.98 314	94.37 287	81.11 290	87.10 245	93.69 241	82.28 168	97.50 252	74.37 296	94.76 148	98.48 104

PatchT		88.87 241	87.42 246	93.22 219	94.08 246	85.10 251	89.51 312	94.64 278	81.92 283	92.36 123	88.15 307	80.05 209	97.01 274	72.43 301	93.65 161	97.54 147

EG-PatchMatch MVS		87.02 266	85.44 267	91.76 263	92.67 289	85.00 252	96.08 204	96.45 206	83.41 274	79.52 302	93.49 250	57.10 315	97.72 238	79.34 282	90.87 200	92.56 289

USDC		88.94 238	87.83 240	92.27 240	94.66 216	84.96 253	93.86 268	95.90 228	87.34 218	83.40 273	95.56 171	67.43 295	98.19 172	82.64 260	89.67 214	93.66 276

Patchmatch-test1		91.54 178	90.85 174	93.59 201	95.59 171	84.95 254	94.72 250	95.58 244	90.82 121	92.25 126	93.58 246	75.80 250	97.41 259	83.35 249	95.98 130	98.40 110

ADS-MVSNet		89.89 228	88.68 232	93.53 205	95.86 162	84.89 255	90.93 303	95.07 267	83.23 275	91.28 148	91.81 278	79.01 226	97.85 226	79.52 278	91.39 192	97.84 131

MIMVSNet1		84.93 279	83.05 279	90.56 281	89.56 307	84.84 256	95.40 235	95.35 252	83.91 266	80.38 296	92.21 275	57.23 314	93.34 312	70.69 308	82.75 283	93.50 277

MS-PatchMatch		90.27 219	89.77 213	91.78 261	94.33 228	84.72 257	95.55 228	96.73 192	86.17 242	86.36 252	95.28 186	71.28 277	97.80 231	84.09 240	98.14 83	92.81 287

mvs-test1		93.63 107	93.69 87	93.46 209	96.02 159	84.61 258	97.24 97	96.72 193	93.85 42	92.30 125	95.76 160	83.08 138	98.89 123	91.69 115	96.54 123	96.87 163

TDRefinement		86.53 268	84.76 273	91.85 257	82.23 323	84.25 259	96.38 181	95.35 252	84.97 256	84.09 269	94.94 193	65.76 302	98.34 164	84.60 234	74.52 312	92.97 283

EPMVS		90.70 211	89.81 212	93.37 213	94.73 215	84.21 260	93.67 272	88.02 324	89.50 152	92.38 122	93.49 250	77.82 242	97.78 233	86.03 211	92.68 170	98.11 123

semantic-postprocess				91.82 258	95.52 173	84.20 261		96.15 219	90.61 132	87.39 238	94.27 227	75.63 252	96.44 279	87.34 189	86.88 238	94.82 249

PatchmatchNet		91.91 161	91.35 155	93.59 201	95.38 179	84.11 262	93.15 282	95.39 249	89.54 150	92.10 129	93.68 242	82.82 155	98.13 176	84.81 227	95.32 140	98.52 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OpenMVS_ROB	81.14 20	84.42 280	82.28 281	90.83 275	90.06 303	84.05 263	95.73 221	94.04 296	73.89 316	80.17 301	91.53 282	59.15 312	97.64 244	66.92 311	89.05 218	90.80 311

IterMVS		90.15 224	89.67 217	91.61 265	95.48 175	83.72 264	94.33 258	96.12 220	89.99 142	87.31 241	94.15 231	75.78 251	96.27 282	86.97 197	86.89 237	94.83 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPNet_dtu		91.71 164	91.28 159	92.99 225	93.76 260	83.71 265	96.69 156	95.28 256	93.15 62	87.02 247	95.95 147	83.37 129	97.38 262	79.46 280	96.84 113	97.88 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		90.76 205	89.89 208	93.38 212	95.04 200	83.70 266	95.85 215	94.30 290	88.19 198	90.46 158	92.80 260	73.61 268	98.50 153	88.16 167	90.58 203	97.95 126

PVSNet	86.66 18	92.24 153	91.74 145	93.73 193	97.77 92	83.69 267	92.88 286	96.72 193	87.91 205	93.00 111	94.86 196	78.51 231	99.05 115	86.53 200	97.45 101	98.47 105

PatchFormer-LS_test		91.68 170	91.18 165	93.19 221	95.24 190	83.63 268	95.53 230	95.44 248	89.82 147	91.37 140	92.58 265	80.85 197	98.52 151	89.65 140	90.16 209	97.42 150

MDA-MVSNet-bldmvs		85.00 278	82.95 280	91.17 271	93.13 283	83.33 269	94.56 253	95.00 269	84.57 261	65.13 321	92.65 262	70.45 282	95.85 294	73.57 299	77.49 297	94.33 267

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 235	96.02 159	83.25 270	97.14 110	96.72 193	93.85 42	91.20 151	93.44 253	83.08 138	98.30 166	91.69 115	95.73 136	96.50 171

TinyColmap		86.82 267	85.35 269	91.21 270	94.91 208	82.99 271	93.94 267	94.02 297	83.58 271	81.56 282	94.68 204	62.34 308	98.13 176	75.78 293	87.35 236	92.52 290

MDA-MVSNet_test_wron		85.87 274	84.23 276	90.80 278	92.38 293	82.57 272	93.17 280	95.15 263	82.15 281	67.65 317	92.33 274	78.20 234	95.51 301	77.33 288	79.74 292	94.31 269

UnsupCasMVSNet_bld		82.13 288	79.46 290	90.14 286	88.00 311	82.47 273	90.89 305	96.62 204	78.94 301	75.61 308	84.40 317	56.63 316	96.31 281	77.30 290	66.77 323	91.63 307

YYNet1		85.87 274	84.23 276	90.78 279	92.38 293	82.46 274	93.17 280	95.14 264	82.12 282	67.69 316	92.36 271	78.16 235	95.50 302	77.31 289	79.73 293	94.39 265

UnsupCasMVSNet_eth		85.99 273	84.45 274	90.62 280	89.97 304	82.40 275	93.62 274	97.37 136	89.86 144	78.59 305	92.37 268	65.25 303	95.35 303	82.27 263	70.75 317	94.10 271

ADS-MVSNet2		89.45 234	88.59 233	92.03 253	95.86 162	82.26 276	90.93 303	94.32 289	83.23 275	91.28 148	91.81 278	79.01 226	95.99 292	79.52 278	91.39 192	97.84 131

LP		84.13 281	81.85 286	90.97 273	93.20 280	82.12 277	87.68 318	94.27 292	76.80 308	81.93 278	88.52 302	72.97 271	95.95 293	59.53 320	81.73 285	94.84 245

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 238	96.82 123	81.89 278	96.92 128	93.71 300	92.41 84	84.30 265	94.60 207	85.08 111	97.03 272	91.51 118	97.36 103	98.40 110

CostFormer		91.18 195	90.70 181	92.62 236	94.84 210	81.76 279	94.09 265	94.43 284	84.15 264	92.72 118	93.77 240	79.43 217	98.20 170	90.70 129	92.18 178	97.90 128

tpmp4_e23		89.58 232	88.59 233	92.54 237	95.16 194	81.53 280	94.11 264	95.09 266	81.66 285	88.60 215	93.44 253	75.11 255	98.33 165	82.45 261	91.72 185	97.75 135

JIA-IIPM		88.26 256	87.04 257	91.91 255	93.52 266	81.42 281	89.38 313	94.38 286	80.84 293	90.93 153	80.74 319	79.22 220	97.92 220	82.76 257	91.62 187	96.38 172

OurMVSNet-221017-0		90.51 216	90.19 199	91.44 268	93.41 270	81.25 282	96.98 120	96.28 211	91.68 101	86.55 251	96.30 134	74.20 263	97.98 207	88.96 156	87.40 235	95.09 228

tpm2		89.96 226	89.21 225	92.23 244	94.91 208	81.25 282	93.78 269	94.42 285	80.62 295	91.56 136	93.44 253	76.44 247	97.94 216	85.60 218	92.08 182	97.49 148

testgi		87.97 257	87.21 254	90.24 285	92.86 285	80.76 284	96.67 158	94.97 271	91.74 99	85.52 257	95.83 153	62.66 307	94.47 307	76.25 292	88.36 226	95.48 203

test-LLR		91.42 183	91.19 164	92.12 250	94.59 219	80.66 285	94.29 259	92.98 307	91.11 117	90.76 154	92.37 268	79.02 224	98.07 187	88.81 161	96.74 117	97.63 139

test-mter		90.19 223	89.54 220	92.12 250	94.59 219	80.66 285	94.29 259	92.98 307	87.68 211	90.76 154	92.37 268	67.67 293	98.07 187	88.81 161	96.74 117	97.63 139

TESTMET0.1,1		90.06 225	89.42 222	91.97 254	94.41 226	80.62 287	94.29 259	91.97 314	87.28 220	90.44 159	92.47 267	68.79 288	97.67 241	88.50 165	96.60 122	97.61 143

tpm cat1		88.36 255	87.21 254	91.81 259	95.13 196	80.55 288	92.58 290	95.70 238	74.97 313	87.45 235	91.96 276	78.01 240	98.17 174	80.39 275	88.74 222	96.72 167

Anonymous20231206		87.09 265	86.14 263	89.93 288	91.22 299	80.35 289	96.11 201	95.35 252	83.57 272	84.16 267	93.02 258	73.54 269	95.61 298	72.16 302	86.14 240	93.84 275

MDTV_nov1_ep13			90.76 178		95.22 191	80.33 290	93.03 285	95.28 256	88.14 201	92.84 117	93.83 238	81.34 184	98.08 183	82.86 255	94.34 153

tpmvs		89.83 231	89.15 227	91.89 256	94.92 206	80.30 291	93.11 283	95.46 247	86.28 239	88.08 225	92.65 262	80.44 202	98.52 151	81.47 270	89.92 212	96.84 164

SixPastTwentyTwo		89.15 237	88.54 235	90.98 272	93.49 268	80.28 292	96.70 154	94.70 277	90.78 122	84.15 268	95.57 170	71.78 274	97.71 239	84.63 231	85.07 255	94.94 239

new_pmnet		82.89 284	81.12 289	88.18 293	89.63 306	80.18 293	91.77 297	92.57 311	76.79 309	75.56 309	88.23 306	61.22 310	94.48 306	71.43 304	82.92 281	89.87 313

test20.03		86.14 272	85.40 268	88.35 290	90.12 302	80.06 294	95.90 213	95.20 261	88.59 184	81.29 283	93.62 245	71.43 276	92.65 314	71.26 306	81.17 289	92.34 301

LF4IMVS		87.94 258	87.25 250	89.98 287	92.38 293	80.05 295	94.38 256	95.25 259	87.59 213	84.34 264	94.74 203	64.31 304	97.66 243	84.83 226	87.45 232	92.23 302

tpm		90.25 220	89.74 216	91.76 263	93.92 254	79.73 296	93.98 266	93.54 304	88.28 194	91.99 131	93.25 256	77.51 243	97.44 256	87.30 191	87.94 228	98.12 120

PVSNet_0	82.17 19	85.46 277	83.64 278	90.92 274	95.27 186	79.49 297	90.55 306	95.60 242	83.76 270	83.00 274	89.95 285	71.09 278	97.97 210	82.75 258	60.79 324	95.31 218

K. test v3		87.64 261	86.75 259	90.32 284	93.02 284	79.48 298	96.61 164	92.08 313	90.66 128	80.25 300	94.09 232	67.21 297	96.65 278	85.96 213	80.83 291	94.83 247

pmmvs3		79.97 290	77.50 294	87.39 295	82.80 321	79.38 299	92.70 289	90.75 319	70.69 319	78.66 304	87.47 313	51.34 323	93.40 311	73.39 300	69.65 319	89.38 314

tpmrst		91.44 182	91.32 157	91.79 260	95.15 195	79.20 300	93.42 276	95.37 251	88.55 187	93.49 101	93.67 243	82.49 163	98.27 167	90.41 130	89.34 216	97.90 128

lessismore_v0				90.45 282	91.96 296	79.09 301		87.19 327		80.32 298	94.39 216	66.31 299	97.55 249	84.00 244	76.84 299	94.70 256

gm-plane-assit					93.22 278	78.89 302			84.82 258		93.52 248		98.64 140	87.72 176

Patchmatch-RL test		87.38 262	86.24 261	90.81 276	88.74 309	78.40 303	88.12 317	93.17 306	87.11 222	82.17 276	89.29 298	81.95 176	95.60 299	88.64 164	77.02 298	98.41 109

PM-MVS		83.48 282	81.86 285	88.31 291	87.83 312	77.59 304	93.43 275	91.75 315	86.91 230	80.63 290	89.91 286	44.42 326	95.84 295	85.17 225	76.73 300	91.50 309

dp		88.90 240	88.26 238	90.81 276	94.58 221	76.62 305	92.85 287	94.93 273	85.12 253	90.07 175	93.07 257	75.81 249	98.12 178	80.53 274	87.42 234	97.71 137

RPSCF		90.75 207	90.86 173	90.42 283	96.84 120	76.29 306	95.61 227	96.34 209	83.89 267	91.38 139	97.87 53	76.45 246	98.78 131	87.16 195	92.23 175	96.20 174

new-patchmatchnet		83.18 283	81.87 284	87.11 296	86.88 315	75.99 307	93.70 270	95.18 262	85.02 255	77.30 307	88.40 304	65.99 300	93.88 310	74.19 298	70.18 318	91.47 310

CVMVSNet		91.23 191	91.75 143	89.67 289	95.77 167	74.69 308	96.44 171	94.88 275	85.81 245	92.18 127	97.64 73	79.07 221	95.58 300	88.06 169	95.86 134	98.74 84

EU-MVSNet		88.72 242	88.90 229	88.20 292	93.15 282	74.21 309	96.63 163	94.22 293	85.18 251	87.32 240	95.97 145	76.16 248	94.98 305	85.27 222	86.17 239	95.41 208

Anonymous20231211		78.22 294	75.30 295	86.99 298	86.14 316	74.16 310	95.62 226	93.88 299	66.43 320	74.44 310	87.86 309	41.39 327	95.11 304	62.49 316	69.46 320	91.71 305

Gipuma		67.86 301	65.41 302	75.18 314	92.66 290	73.45 311	66.50 331	94.52 283	53.33 326	57.80 325	66.07 328	30.81 330	89.20 324	48.15 329	78.88 295	62.90 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	62.92 21	85.62 276	84.92 271	87.74 294	89.14 308	73.12 312	94.17 262	96.80 191	73.98 315	73.65 311	94.93 194	66.36 298	97.61 246	83.95 245	91.28 194	92.48 293
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testpf		80.97 289	81.40 287	79.65 308	91.53 297	72.43 313	73.47 329	89.55 322	78.63 302	80.81 285	89.06 299	61.36 309	91.36 319	83.34 250	84.89 262	75.15 324

DSMNet-mixed		86.34 270	86.12 264	87.00 297	89.88 305	70.43 314	94.93 248	90.08 321	77.97 306	85.42 260	92.78 261	74.44 261	93.96 309	74.43 295	95.14 142	96.62 168

test2356		82.77 285	82.14 283	84.65 301	85.77 317	70.36 315	91.22 301	93.69 303	81.58 287	81.82 279	89.00 300	60.63 311	90.77 320	64.74 313	90.80 201	92.82 285

MDTV_nov1_ep13_2view						70.35 316	93.10 284		83.88 268	93.55 99		82.47 165		86.25 205		98.38 113

no-one		68.12 300	63.78 303	81.13 305	74.01 328	70.22 317	87.61 319	90.71 320	72.63 318	53.13 326	71.89 325	30.29 331	91.45 318	61.53 319	32.21 329	81.72 321

ambc				86.56 299	83.60 320	70.00 318	85.69 321	94.97 271		80.60 291	88.45 303	37.42 328	96.84 277	82.69 259	75.44 303	92.86 284

MVS-HIRNet		82.47 287	81.21 288	86.26 300	95.38 179	69.21 319	88.96 315	89.49 323	66.28 321	80.79 286	74.08 324	68.48 290	97.39 261	71.93 303	95.47 138	92.18 303

testus		82.63 286	82.15 282	84.07 302	87.31 314	67.67 320	93.18 278	94.29 291	82.47 279	82.14 277	90.69 283	53.01 321	91.94 317	66.30 312	89.96 211	92.62 288

test1235678		79.82 291	78.53 292	83.69 303	82.55 322	67.55 321	92.50 292	94.13 294	79.28 299	72.10 314	86.45 315	57.27 313	90.68 321	61.60 318	80.90 290	92.82 285

ANet_high		63.94 303	59.58 304	77.02 312	61.24 336	66.06 322	85.66 322	87.93 325	78.53 304	42.94 328	71.04 326	25.42 336	80.71 330	52.60 327	30.83 331	84.28 319

PMMVS2		70.19 299	66.92 301	80.01 307	76.35 325	65.67 323	86.22 320	87.58 326	64.83 323	62.38 322	80.29 321	26.78 335	88.49 326	63.79 314	54.07 325	85.88 318

LCM-MVSNet		72.55 296	69.39 299	82.03 304	70.81 333	65.42 324	90.12 310	94.36 288	55.02 325	65.88 320	81.72 318	24.16 337	89.96 322	74.32 297	68.10 321	90.71 312

DeepMVS_CX				74.68 315	90.84 300	64.34 325		81.61 334	65.34 322	67.47 319	88.01 308	48.60 324	80.13 331	62.33 317	73.68 316	79.58 322

testmv		72.22 297	70.02 297	78.82 309	73.06 331	61.75 326	91.24 300	92.31 312	74.45 314	61.06 323	80.51 320	34.21 329	88.63 325	55.31 325	68.07 322	86.06 317

wuykxyi23d		56.92 306	51.11 310	74.38 316	62.30 335	61.47 327	80.09 326	84.87 329	49.62 328	30.80 334	57.20 332	7.03 340	82.94 329	55.69 324	32.36 328	78.72 323

1111		78.29 293	77.55 293	80.50 306	83.89 318	59.98 328	91.89 295	93.71 300	75.06 311	73.60 312	87.67 310	55.66 317	92.60 315	58.54 322	77.92 296	88.93 315

.test1245		65.38 302	69.22 300	53.86 322	83.89 318	59.98 328	91.89 295	93.71 300	75.06 311	73.60 312	87.67 310	55.66 317	92.60 315	58.54 322	2.96 335	9.00 333

FPMVS		71.27 298	69.85 298	75.50 313	74.64 326	59.03 330	91.30 299	91.50 316	58.80 324	57.92 324	88.28 305	29.98 333	85.53 328	53.43 326	82.84 282	81.95 320

MVE	50.73 23	53.25 308	48.81 311	66.58 319	65.34 334	57.50 331	72.49 330	70.94 337	40.15 332	39.28 332	63.51 329	6.89 342	73.48 335	38.29 331	42.38 326	68.76 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test12356		74.97 295	74.13 296	77.49 311	78.81 324	56.23 332	88.53 316	92.75 309	75.14 310	67.50 318	85.07 316	44.88 325	89.96 322	58.71 321	75.75 302	86.26 316

PNet_i23d		59.01 304	55.87 305	68.44 317	73.98 329	51.37 333	81.36 325	82.41 332	52.37 327	42.49 330	70.39 327	11.39 338	79.99 332	49.77 328	38.71 327	73.97 325

PMVS	53.92 22	58.58 305	55.40 306	68.12 318	51.00 337	48.64 334	78.86 327	87.10 328	46.77 329	35.84 333	74.28 323	8.76 339	86.34 327	42.07 330	73.91 315	69.38 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 307	52.56 308	55.43 320	74.43 327	47.13 335	83.63 324	76.30 335	42.23 330	42.59 329	62.22 330	28.57 334	74.40 333	31.53 332	31.51 330	44.78 329

N_pmnet		78.73 292	78.71 291	78.79 310	92.80 287	46.50 336	94.14 263	43.71 339	78.61 303	80.83 284	91.66 281	74.94 259	96.36 280	67.24 310	84.45 265	93.50 277

EMVS		52.08 309	51.31 309	54.39 321	72.62 332	45.39 337	83.84 323	75.51 336	41.13 331	40.77 331	59.65 331	30.08 332	73.60 334	28.31 333	29.90 332	44.18 330

tmp_tt		51.94 310	53.82 307	46.29 323	33.73 338	45.30 338	78.32 328	67.24 338	18.02 333	50.93 327	87.05 314	52.99 322	53.11 336	70.76 307	25.29 333	40.46 331

wuyk23d		25.11 312	24.57 314	26.74 325	73.98 329	39.89 339	57.88 332	9.80 340	12.27 334	10.39 335	6.97 338	7.03 340	36.44 337	25.43 334	17.39 334	3.89 335

test123		13.04 315	15.66 316	5.18 326	4.51 340	3.45 340	92.50 292	1.81 342	2.50 336	7.58 337	20.15 335	3.67 343	2.18 339	7.13 336	1.07 337	9.90 332

testmvs		13.36 314	16.33 315	4.48 327	5.04 339	2.26 341	93.18 278	3.28 341	2.70 335	8.24 336	21.66 334	2.29 344	2.19 338	7.58 335	2.96 335	9.00 333

cdsmvs_eth3d_5k		23.24 313	30.99 313	0.00 328	0.00 341	0.00 342	0.00 333	97.63 104	0.00 337	0.00 338	96.88 103	84.38 120	0.00 340	0.00 337	0.00 338	0.00 336

pcd_1.5k_mvsjas		7.39 317	9.85 318	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	88.65 68	0.00 340	0.00 337	0.00 338	0.00 336

pcd1.5k->3k		38.37 311	40.51 312	31.96 324	94.29 230	0.00 342	0.00 333	97.69 98	0.00 337	0.00 338	0.00 339	81.45 183	0.00 340	0.00 337	91.11 196	95.89 185

sosnet-low-res		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

sosnet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uncertanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

Regformer		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

ab-mvs-re		8.06 316	10.74 317	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	96.69 112	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

sam_mvs1												82.76 156

sam_mvs												81.94 177

MTGPA								98.08 47

test_post1							92.81 288				16.58 337	80.53 200	97.68 240	86.20 206

test_post											17.58 336	81.76 179	98.08 183

patchmatchnet-post											90.45 284	82.65 160	98.10 180

MTMP								82.03 333

test9_res														94.81 60	99.38 33	99.45 28

agg_prior2														93.94 72	99.38 33	99.50 22

test_prior2							96.35 183		92.80 77	96.03 52	97.59 77	92.01 28		95.01 53	99.38 33

旧先验2							95.94 211		81.66 285	97.34 15			98.82 128	92.26 95

新几何2							95.79 218

无先验							95.79 218	97.87 82	83.87 269				99.65 39	87.68 179		98.89 78

原ACMM2							95.67 222

testdata2													99.67 37	85.96 213

segment_acmp												92.89 10

testdata1							95.26 243		93.10 65

plane_prior5								97.51 114					98.60 144	93.02 90	92.23 175	95.86 186

plane_prior4											96.64 115

plane_prior2							97.74 54		94.85 17

plane_prior1					96.14 156

n2								0.00 343

nn								0.00 343

door-mid								91.06 318

test11								97.88 80

door								91.13 317

HQP-NCC					95.86 162		96.65 159		93.55 48	90.14 164

ACMP_Plane					95.86 162		96.65 159		93.55 48	90.14 164

BP-MVS														92.13 101

HQP4-MVS										90.14 164			98.50 153			95.78 193

HQP3-MVS								97.39 133							92.10 180

HQP2-MVS												80.95 190

ACMMP++_ref															90.30 208

ACMMP++															91.02 198

Test By Simon												88.73 67

High-resolution multi-view benchmark