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.

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 90	57.70 69	79.50 95	82.13 68	67.84 1	75.73 21	80.75 184	56.50 16	86.07 61	71.07 42	80.38 82	87.50 21

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 90	58.32 63	79.50 95	82.92 60	67.84 1	75.94 18	80.75 184	55.73 23	86.80 44	71.44 41	80.38 82	87.50 21

CANet		76.46 33	75.93 34	78.06 31	81.29 76	57.53 71	82.35 53	83.31 53	67.78 3	70.09 76	86.34 72	54.92 29	88.90 14	72.68 33	84.55 50	87.76 15

UA-Net		73.13 60	72.93 58	73.76 93	83.58 53	51.66 148	78.75 99	77.66 172	67.75 4	72.61 56	89.42 31	49.82 73	83.29 127	53.61 163	83.14 56	86.32 51

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 75	58.89 56	82.50 52	84.07 29	67.73 5	72.08 62	87.28 57	49.49 76	89.57 7	73.52 30	86.40 42	87.87 12

CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 23	85.03 15	66.96 6	77.58 13	90.06 23	59.47 10	89.13 11	78.67 7	89.73 6	87.03 34

TranMVSNet+NR-MVSNet		70.36 103	70.10 90	71.17 169	78.64 120	42.97 252	76.53 153	81.16 94	66.95 7	68.53 109	85.42 90	51.61 60	83.07 133	52.32 170	69.70 219	87.46 23

Regformer-4		74.25 51	73.48 52	76.57 50	79.75 99	56.54 86	78.54 106	81.49 82	66.93 8	73.90 38	80.30 194	53.84 41	85.98 66	69.76 44	76.84 126	87.17 31

3Dnovator+	66.72 4	75.84 40	74.57 43	79.66 4	82.40 62	59.92 45	85.83 13	86.32 7	66.92 9	67.80 125	89.24 35	42.03 168	89.38 8	64.07 89	86.50 41	89.69 1

NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 20	84.42 22	66.73 10	74.67 31	89.38 33	55.30 26	89.18 10	74.19 23	87.34 30	86.38 44

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 58	62.18 19	87.60 2	85.83 8	66.69 11	78.03 12	90.98 7	54.26 35	90.06 3	78.42 8	89.02 10	87.69 16
Skip Steuart: Steuart Systems R&D Blog.
Regformer-3		73.89 54	73.28 56	75.71 60	79.75 99	55.48 106	78.54 106	79.93 120	66.58 12	73.62 43	80.30 194	54.87 30	84.54 101	69.09 48	76.84 126	87.10 33

EPNet		73.09 61	72.16 63	75.90 55	75.95 179	56.28 89	83.05 39	72.39 222	66.53 13	65.27 154	87.00 58	50.40 70	85.47 77	62.48 106	86.32 43	85.94 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 85	71.00 79	71.44 161	79.20 106	44.13 241	76.02 167	82.60 65	66.48 14	68.20 113	84.60 100	56.82 15	82.82 142	54.62 155	70.43 196	87.36 28

HSP-MVS		80.69 2	81.20 1	79.14 10	86.21 19	62.73 12	86.09 11	85.03 15	65.51 15	83.81 1	90.51 14	63.71 3	89.23 9	81.51 1	88.44 14	85.45 81

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 41	65.37 16	78.78 10	90.64 10	58.63 13	87.24 34	79.00 5	90.37 3	85.26 94

NR-MVSNet		69.54 118	68.85 110	71.59 160	78.05 135	43.81 245	74.20 196	80.86 102	65.18 17	62.76 181	84.52 101	52.35 55	83.59 121	50.96 179	70.78 190	87.37 26

zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 23	63.57 2	83.37 36	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

MTAPA		76.90 28	76.42 29	78.35 26	86.08 23	63.57 2	74.92 187	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

EI-MVSNet-Vis-set		72.42 69	71.59 68	74.91 71	78.47 124	54.02 116	77.05 145	79.33 142	65.03 20	71.68 65	79.35 218	52.75 50	84.89 92	66.46 64	74.23 143	85.83 63

WR-MVS		68.47 141	68.47 117	68.44 205	80.20 95	39.84 268	73.75 203	76.07 190	64.68 21	68.11 117	83.63 114	50.39 71	79.14 208	49.78 185	69.66 220	86.34 49

XVS		77.17 25	76.56 28	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	90.01 25	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

X-MVStestdata		70.21 106	67.28 142	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	6.49 354	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

HQP_MVS		74.31 49	73.73 50	76.06 53	81.41 73	56.31 87	84.22 25	84.01 31	64.52 24	69.27 98	86.10 76	45.26 145	87.21 36	68.16 51	80.58 78	84.65 112

plane_prior2							84.22 25		64.52 24

EI-MVSNet-UG-set		71.92 76	71.06 78	74.52 81	77.98 137	53.56 122	76.62 151	79.16 144	64.40 26	71.18 67	78.95 223	52.19 56	84.66 100	65.47 72	73.57 152	85.32 91

DU-MVS		70.01 109	69.53 98	71.44 161	78.05 135	44.13 241	75.01 184	81.51 81	64.37 27	68.20 113	84.52 101	49.12 98	82.82 142	54.62 155	70.43 196	87.37 26

LFMVS		71.78 78	71.59 68	72.32 150	83.40 54	46.38 225	79.75 90	71.08 226	64.18 28	72.80 54	88.64 45	42.58 165	83.72 118	57.41 141	84.49 51	86.86 37

IS-MVSNet		71.57 80	71.00 79	73.27 120	78.86 113	45.63 229	80.22 83	78.69 153	64.14 29	66.46 139	87.36 54	49.30 80	85.60 71	50.26 183	83.71 55	88.59 3

plane_prior3						56.09 93			63.92 30	69.27 98

MP-MVS		78.35 13	78.26 13	78.64 23	86.54 14	63.47 5	86.02 12	83.55 44	63.89 31	73.60 44	90.60 11	54.85 31	86.72 47	77.20 11	88.06 23	85.74 69
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 45	74.46 44	75.65 62	77.84 140	52.25 142	75.59 171	84.17 27	63.76 32	73.15 49	82.79 124	59.58 9	86.80 44	67.24 59	86.04 44	87.89 10
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
OPM-MVS		74.73 46	74.25 46	76.19 52	80.81 84	59.01 54	82.60 49	83.64 42	63.74 33	72.52 57	87.49 52	47.18 122	85.88 69	69.47 46	80.78 74	83.66 149

UniMVSNet (Re)		70.63 93	70.20 87	71.89 153	78.55 121	45.29 231	75.94 168	82.92 60	63.68 34	68.16 115	83.59 115	53.89 40	83.49 123	53.97 159	71.12 188	86.89 36

test_part3							86.37 5		63.49 35		91.40 4		90.90 1	75.98 14

ESAPD		80.72 1	81.17 2	79.38 7	87.58 3	60.47 38	86.37 5	86.64 3	63.49 35	83.42 2	91.40 4	65.59 1	90.90 1	75.98 14	90.06 4	86.78 40

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 52	61.62 25	84.17 27	86.85 2	63.23 37	73.84 40	90.25 21	57.68 14	89.96 4	74.62 21	89.03 9	87.89 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 66	72.09 64	73.75 95	81.58 69	49.69 195	77.76 128	77.63 173	63.21 38	73.21 48	89.02 38	42.14 167	83.32 126	61.72 121	82.50 64	88.25 6

plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

ACMMP		76.02 37	75.33 39	78.07 30	85.20 36	61.91 23	85.49 19	84.44 21	63.04 40	69.80 86	89.74 30	45.43 141	87.16 38	72.01 37	82.87 62	85.14 96
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
PEN-MVS		66.60 178	66.45 162	67.04 215	77.11 163	36.56 299	77.03 146	80.42 115	62.95 41	62.51 189	84.03 110	46.69 129	79.07 209	44.22 227	63.08 273	85.51 77

APDe-MVS		80.16 4	80.59 3	78.86 20	86.64 11	60.02 42	88.12 1	86.42 6	62.94 42	82.40 4	92.12 2	59.64 8	89.76 5	78.70 6	88.32 18	86.79 39

mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 15	63.50 4	85.74 16	82.28 67	62.90 43	71.77 64	90.26 20	46.61 130	86.55 54	71.71 38	85.66 46	84.97 103

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 32	61.04 32	83.84 31	85.16 13	62.88 44	78.10 11	91.26 6	52.51 51	88.39 16	79.34 4	90.52 2	86.78 40

DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 44	60.32 40	83.03 40	85.33 12	62.86 45	80.17 6	90.03 24	61.76 4	88.95 13	74.21 22	88.67 13	88.12 8

HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 8	62.81 10	86.29 8	84.32 24	62.82 46	73.96 35	90.50 15	53.20 48	88.35 17	74.02 24	87.05 31	86.13 55

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 7	62.93 9	86.29 8	84.24 26	62.82 46	73.55 45	90.56 13	49.80 74	88.24 20	74.02 24	87.03 33	86.32 51

region2R		77.67 20	77.18 22	79.15 9	86.76 6	62.95 8	86.29 8	84.16 28	62.81 48	73.30 47	90.58 12	49.90 72	88.21 21	73.78 26	87.03 33	86.29 53

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 39	60.81 35	82.91 43	85.08 14	62.57 49	73.09 50	89.97 26	50.90 68	87.48 32	75.30 16	86.85 36	87.33 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 189	65.34 177	66.31 222	76.06 178	34.79 308	76.43 155	79.38 141	62.55 50	61.66 207	83.83 112	45.60 137	79.15 207	41.64 251	60.88 288	85.00 101

CP-MVSNet		66.49 181	66.41 165	66.72 217	77.67 144	36.33 301	76.83 150	79.52 136	62.45 51	62.54 187	83.47 119	46.32 131	78.37 221	45.47 222	63.43 270	85.45 81

CP-MVS		77.12 26	76.68 26	78.43 25	86.05 25	63.18 7	87.55 3	83.45 47	62.44 52	72.68 55	90.50 15	48.18 110	87.34 33	73.59 29	85.71 45	84.76 111

SMA-MVS		80.22 3	80.31 4	79.95 2	86.60 12	61.97 22	86.33 7	85.70 10	62.39 53	81.75 5	92.28 1	56.41 19	89.70 6	79.85 3	91.51 1	88.19 7

PS-CasMVS		66.42 182	66.32 168	66.70 219	77.60 151	36.30 303	76.94 148	79.61 125	62.36 54	62.43 196	83.66 113	45.69 135	78.37 221	45.35 224	63.26 271	85.42 84

3Dnovator	64.47 5	72.49 67	71.39 72	75.79 56	77.70 142	58.99 55	80.66 79	83.15 57	62.24 55	65.46 151	86.59 67	42.38 166	85.52 75	59.59 134	84.72 49	82.85 167

MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 40	59.52 47	82.93 42	85.39 11	62.15 56	76.41 17	91.51 3	52.47 53	86.78 46	80.66 2	89.64 8	87.80 13
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 85		82.31 55		62.10 57	67.85 120

ACMP_Plane					80.66 85		82.31 55		62.10 57	67.85 120

HQP-MVS		73.45 57	72.80 59	75.40 66	80.66 85	54.94 109	82.31 55	83.90 35	62.10 57	67.85 120	85.54 88	45.46 139	86.93 42	67.04 61	80.35 84	84.32 119

VPNet		67.52 160	68.11 123	65.74 237	79.18 107	36.80 297	72.17 225	72.83 220	62.04 60	67.79 126	85.83 82	48.88 102	76.60 243	51.30 177	72.97 165	83.81 140

WR-MVS_H		67.02 170	66.92 151	67.33 214	77.95 138	37.75 288	77.57 134	82.11 70	62.03 61	62.65 184	82.48 132	50.57 69	79.46 196	42.91 241	64.01 265	84.79 109

DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 21	60.86 34	84.71 21	84.85 19	61.98 62	73.06 51	88.88 41	53.72 42	89.06 12	68.27 50	88.04 24	87.42 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 22	62.73 12	82.55 50	83.74 40	61.71 63	72.45 59	90.34 18	48.48 107	88.13 22	72.32 34	86.85 36	85.78 64

Effi-MVS+		73.31 59	72.54 61	75.62 63	77.87 139	53.64 120	79.62 93	79.61 125	61.63 64	72.02 63	82.61 129	56.44 17	85.97 67	63.99 92	79.07 104	87.25 30

#test#		77.83 17	77.41 19	79.10 13	86.71 8	62.81 10	85.69 17	84.32 24	61.61 65	73.96 35	90.50 15	53.20 48	88.35 17	73.68 27	87.05 31	86.13 55

MG-MVS		73.96 53	73.89 49	74.16 84	85.65 28	49.69 195	81.59 68	81.29 89	61.45 66	71.05 68	88.11 48	51.77 58	87.73 29	61.05 124	83.09 57	85.05 100

LPG-MVS_test		72.74 64	71.74 67	75.76 57	80.22 93	57.51 72	82.55 50	83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 158

LGP-MVS_train				75.76 57	80.22 93	57.51 72		83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 158

CLD-MVS		73.33 58	72.68 60	75.29 69	78.82 114	53.33 126	78.23 112	84.79 20	61.30 69	70.41 72	81.04 171	52.41 54	87.12 39	64.61 79	82.49 65	85.41 88
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		74.02 52	73.46 54	75.69 61	83.01 58	60.63 37	77.29 141	78.40 164	61.18 70	70.58 70	85.97 79	54.18 37	84.00 114	67.52 58	82.98 60	82.45 173

FIs		70.82 88	71.43 70	68.98 198	78.33 126	38.14 285	76.96 147	83.59 43	61.02 71	67.33 131	86.73 60	55.07 27	81.64 165	54.61 157	79.22 101	87.14 32

FC-MVSNet-test		69.80 112	70.58 84	67.46 211	77.61 150	34.73 310	76.05 165	83.19 56	60.84 72	65.88 148	86.46 70	54.52 34	80.76 184	52.52 169	78.12 114	86.91 35

v8		70.33 104	69.28 107	73.49 107	73.15 229	50.22 177	78.62 103	80.78 103	60.79 73	66.45 140	82.11 143	49.35 78	84.98 85	63.58 98	68.71 227	85.28 92

CSCG		76.92 27	76.75 25	77.41 38	83.96 51	59.60 46	82.95 41	86.50 5	60.78 74	75.27 22	84.83 94	60.76 5	86.56 53	67.86 54	87.87 28	86.06 58

Vis-MVSNet		72.18 72	71.37 73	74.61 79	81.29 76	55.41 107	80.90 75	78.28 166	60.73 75	69.23 101	88.09 49	44.36 154	82.65 151	57.68 139	81.75 70	85.77 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
APD-MVS		78.02 15	78.04 15	77.98 33	86.44 16	60.81 35	85.52 18	84.36 23	60.61 76	79.05 9	90.30 19	55.54 25	88.32 19	73.48 31	87.03 33	84.83 106
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 70	71.20 77	75.59 65	80.28 92	57.54 70	82.74 46	82.84 64	60.58 77	65.24 156	86.18 74	39.25 198	86.03 64	66.95 63	76.79 128	83.22 156
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 214		60.50 78

UGNet		68.81 130	67.39 137	73.06 124	78.33 126	54.47 114	79.77 89	75.40 197	60.45 79	63.22 176	84.40 104	32.71 273	80.91 179	51.71 175	80.56 80	83.81 140
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
EPP-MVSNet		72.16 74	71.31 75	74.71 74	78.68 119	49.70 193	82.10 59	81.65 78	60.40 80	65.94 146	85.84 81	51.74 59	86.37 59	55.93 146	79.55 96	88.07 9

test_prior3		76.89 29	76.96 23	76.69 46	84.20 49	57.27 74	81.75 62	84.88 17	60.37 81	75.01 23	89.06 36	56.22 20	86.43 57	72.19 35	88.96 11	86.38 44

test_prior2							81.75 62		60.37 81	75.01 23	89.06 36	56.22 20		72.19 35	88.96 11

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 47	61.88 24	84.55 22	83.87 37	60.37 81	79.89 7	89.38 33	54.97 28	85.58 73	76.12 13	84.94 48	86.33 50

VNet		69.68 114	70.19 88	68.16 206	79.73 102	41.63 261	70.53 245	77.38 178	60.37 81	70.69 69	86.63 65	51.08 64	77.09 237	53.61 163	81.69 72	85.75 68

canonicalmvs		74.67 47	74.98 41	73.71 97	78.94 112	50.56 167	80.23 82	83.87 37	60.30 85	77.15 14	86.56 69	59.65 7	82.00 160	66.01 67	82.12 66	88.58 4

v7n		69.01 128	67.36 139	73.98 86	72.51 248	52.65 134	78.54 106	81.30 88	60.26 86	62.67 183	81.62 156	43.61 159	84.49 102	57.01 142	68.70 228	84.79 109

HPM-MVS_fast		74.30 50	73.46 54	76.80 45	84.45 48	59.04 53	83.65 33	81.05 95	60.15 87	70.43 71	89.84 28	41.09 186	85.59 72	67.61 57	82.90 61	85.77 66

VPA-MVSNet		69.02 127	69.47 104	67.69 210	77.42 153	41.00 265	74.04 197	79.68 123	60.06 88	69.26 100	84.81 95	51.06 65	77.58 231	54.44 158	74.43 141	84.48 117

v13		68.29 145	66.84 152	72.63 135	73.50 217	50.83 158	78.25 111	79.58 132	60.05 89	60.76 221	77.68 242	49.11 101	82.77 144	62.17 113	60.45 296	84.30 121

v10		70.21 106	69.02 109	73.81 90	73.51 216	50.92 155	78.74 100	81.39 85	60.05 89	66.39 141	81.83 150	47.58 116	85.41 80	62.80 103	68.86 226	85.09 99

v12		68.28 146	66.83 154	72.60 137	73.43 219	50.74 160	78.18 113	79.59 130	60.01 91	60.89 220	77.66 243	49.12 98	82.77 144	62.18 111	60.46 295	84.29 122

V9		68.27 147	66.84 152	72.56 138	73.39 222	50.63 163	78.10 118	79.60 127	59.94 92	61.05 218	77.62 244	49.18 94	82.77 144	62.17 113	60.48 294	84.27 123

v2v482		70.50 97	69.45 105	73.66 99	72.62 246	50.03 186	77.58 133	80.51 113	59.90 93	69.52 92	82.14 142	47.53 118	84.88 94	65.07 75	70.17 208	86.09 57

Baseline_NR-MVSNet		67.05 169	67.56 131	65.50 243	75.65 181	37.70 289	75.42 174	74.65 208	59.90 93	68.14 116	83.15 123	49.12 98	77.20 235	52.23 171	69.78 216	81.60 183

API-MVS		72.17 73	71.41 71	74.45 82	81.95 66	57.22 76	84.03 29	80.38 116	59.89 95	68.40 110	82.33 135	49.64 75	87.83 28	51.87 172	84.16 54	78.30 233

v11		68.15 155	66.73 157	72.42 147	73.43 219	50.28 175	77.94 124	79.65 124	59.88 96	61.11 217	77.55 248	48.25 109	82.75 149	61.88 120	60.85 289	84.23 126

Effi-MVS+-dtu		69.64 116	67.53 133	75.95 54	76.10 176	62.29 18	80.20 84	76.06 191	59.83 97	65.26 155	77.09 253	41.56 177	84.02 113	60.60 126	71.09 189	81.53 184

mvs-test1		70.44 101	68.19 121	77.18 40	76.10 176	63.22 6	80.59 80	76.06 191	59.83 97	66.32 142	79.87 201	41.56 177	85.53 74	60.60 126	72.77 166	82.80 168

V14		68.25 149	66.82 155	72.52 140	73.33 223	50.53 168	78.02 121	79.60 127	59.83 97	61.16 216	77.57 247	49.19 93	82.77 144	62.18 111	60.50 293	84.26 124

v15		68.22 152	66.81 156	72.47 145	73.25 224	50.40 171	77.92 125	79.60 127	59.77 100	61.28 214	77.52 249	49.25 89	82.77 144	62.16 115	60.51 292	84.24 125

v17		68.37 143	67.00 149	72.48 141	73.22 225	50.31 173	78.10 118	79.58 132	59.71 101	61.67 206	77.60 245	49.31 79	82.89 138	62.37 108	61.48 285	84.23 126

v16		68.38 142	67.01 148	72.47 145	73.22 225	50.29 174	78.10 118	79.59 130	59.71 101	61.72 205	77.60 245	49.28 85	82.89 138	62.36 109	61.54 282	84.23 126

v1neww		70.66 90	69.70 93	73.53 104	73.15 229	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 153	49.29 82	84.96 87	64.55 80	70.38 199	85.42 84

v7new		70.66 90	69.70 93	73.53 104	73.15 229	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 153	49.29 82	84.96 87	64.55 80	70.38 199	85.42 84

v6		70.66 90	69.70 93	73.53 104	73.14 232	50.21 180	78.11 115	80.67 106	59.65 103	69.82 85	81.65 155	49.29 82	84.96 87	64.55 80	70.39 198	85.42 84

CANet_DTU		68.18 153	67.71 130	69.59 191	74.83 192	46.24 226	78.66 102	76.85 184	59.60 106	63.45 175	82.09 144	35.25 244	77.41 233	59.88 131	78.76 108	85.14 96

EI-MVSNet		69.27 123	68.44 118	71.73 157	74.47 199	49.39 199	75.20 180	78.45 161	59.60 106	69.16 102	76.51 263	51.29 61	82.50 153	59.86 133	71.45 186	83.30 154

IterMVS-LS		69.22 126	68.48 116	71.43 163	74.44 201	49.40 198	76.23 160	77.55 174	59.60 106	65.85 149	81.59 159	51.28 62	81.58 168	59.87 132	69.90 214	83.30 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDDNet		71.81 77	71.33 74	73.26 121	82.80 60	47.60 217	78.74 100	75.27 198	59.59 109	72.94 52	89.40 32	41.51 180	83.91 115	58.75 136	82.99 59	88.26 5

v18		68.33 144	66.96 150	72.42 147	73.13 233	50.16 182	77.97 123	79.57 134	59.57 110	61.80 203	77.50 250	49.30 80	82.90 137	62.31 110	61.50 283	84.20 132

v1141		70.50 97	69.53 98	73.41 113	72.92 239	50.00 187	77.69 129	80.60 108	59.50 111	69.60 89	81.43 162	49.24 92	84.77 95	64.48 84	70.30 205	85.46 80

divwei89l23v2f112		70.50 97	69.53 98	73.41 113	72.91 240	50.00 187	77.69 129	80.59 109	59.50 111	69.60 89	81.43 162	49.26 87	84.77 95	64.48 84	70.31 204	85.47 78

v1		70.50 97	69.53 98	73.42 112	72.91 240	50.00 187	77.69 129	80.59 109	59.50 111	69.59 91	81.42 164	49.26 87	84.77 95	64.49 83	70.30 205	85.47 78

alignmvs		73.86 55	73.99 48	73.45 109	78.20 129	50.50 169	78.57 104	82.43 66	59.40 114	76.57 15	86.71 62	56.42 18	81.23 173	65.84 69	81.79 68	88.62 2

MVS_Test		72.45 68	72.46 62	72.42 147	74.88 191	48.50 207	76.28 158	83.14 58	59.40 114	72.46 58	84.68 96	55.66 24	81.12 174	65.98 68	79.66 93	87.63 18

TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 40	61.41 28	84.03 29	83.82 39	59.34 116	79.37 8	89.76 29	59.84 6	87.62 31	76.69 12	86.74 38	87.68 17

MSLP-MVS++		73.77 56	73.47 53	74.66 76	83.02 57	59.29 51	82.30 58	81.88 73	59.34 116	71.59 66	86.83 59	45.94 134	83.65 120	65.09 74	85.22 47	81.06 200

PAPM_NR		72.63 65	71.80 66	75.13 70	81.72 68	53.42 125	79.91 87	83.28 55	59.14 118	66.31 143	85.90 80	51.86 57	86.06 62	57.45 140	80.62 76	85.91 61

v7		70.57 94	69.48 103	73.85 88	73.50 217	50.92 155	78.27 110	81.43 83	58.93 119	69.61 88	81.49 161	47.56 117	85.43 79	63.94 93	70.62 192	85.21 95

v148		68.24 151	67.19 146	71.40 164	70.43 273	47.77 215	75.76 170	77.03 182	58.91 120	67.36 130	80.10 198	48.60 106	81.89 161	60.01 130	66.52 248	84.53 115

TransMVSNet (Re)		64.72 198	64.33 186	65.87 236	75.22 188	38.56 282	74.66 191	75.08 205	58.90 121	61.79 204	82.63 128	51.18 63	78.07 226	43.63 234	55.87 309	80.99 205

abl_6		74.34 48	73.50 51	76.86 43	82.43 61	60.16 41	83.48 35	81.86 74	58.81 122	73.95 37	89.86 27	41.87 171	86.62 50	67.98 53	81.23 73	83.80 143

LCM-MVSNet-Re		61.88 231	61.35 223	63.46 255	74.58 197	31.48 330	61.42 300	58.14 311	58.71 123	53.02 296	79.55 214	43.07 163	76.80 240	45.69 216	77.96 115	82.11 178

v1144		70.42 102	69.31 106	73.76 93	73.22 225	50.64 162	77.83 126	81.43 83	58.58 124	69.40 96	81.16 168	47.53 118	85.29 82	64.01 91	70.64 191	85.34 90

v748		67.26 163	65.67 173	72.02 152	69.90 281	49.77 192	76.24 159	79.57 134	58.58 124	60.49 224	80.38 189	44.47 153	82.17 158	56.16 145	65.26 256	84.12 134

TSAR-MVS + GP.		74.90 44	74.15 47	77.17 41	82.00 65	58.77 58	81.80 61	78.57 155	58.58 124	74.32 33	84.51 103	55.94 22	87.22 35	67.11 60	84.48 52	85.52 76

BH-RMVSNet		68.81 130	67.42 136	72.97 125	80.11 96	52.53 137	74.26 195	76.29 187	58.48 127	68.38 111	84.20 106	42.59 164	83.83 117	46.53 207	75.91 132	82.56 169

APD-MVS_3200maxsize		74.96 43	74.39 45	76.67 48	82.20 63	58.24 64	83.67 32	83.29 54	58.41 128	73.71 42	90.14 22	45.62 136	85.99 65	69.64 45	82.85 63	85.78 64

OMC-MVS		71.40 84	70.60 83	73.78 91	76.60 171	53.15 128	79.74 91	79.78 121	58.37 129	68.75 105	86.45 71	45.43 141	80.60 185	62.58 104	77.73 116	87.58 20

nrg030		72.96 62	73.01 57	72.84 130	75.41 186	50.24 176	80.02 85	82.89 63	58.36 130	74.44 32	86.73 60	58.90 12	80.83 180	65.84 69	74.46 140	87.44 24

K. test v3		60.47 238	57.11 256	70.56 179	73.74 215	48.22 209	75.10 183	62.55 295	58.27 131	53.62 292	76.31 265	27.81 301	81.59 167	47.42 200	39.18 339	81.88 181

MVS_111021_LR		69.50 119	68.78 112	71.65 158	78.38 125	59.33 50	74.82 189	70.11 232	58.08 132	67.83 124	84.68 96	41.96 169	76.34 246	65.62 71	77.54 117	79.30 227

v52		67.09 167	65.16 181	72.87 128	66.77 303	51.60 149	73.69 204	79.45 139	57.88 133	62.46 190	78.57 229	40.95 188	83.34 124	61.99 118	64.70 261	83.68 146

V4		67.09 167	65.16 181	72.87 128	66.76 304	51.60 149	73.69 204	79.45 139	57.88 133	62.45 191	78.58 228	40.96 187	83.34 124	61.99 118	64.71 259	83.68 146

Fast-Effi-MVS+-dtu		67.37 161	65.33 178	73.48 108	72.94 238	57.78 68	77.47 136	76.88 183	57.60 135	61.97 200	76.85 257	39.31 197	80.49 186	54.72 154	70.28 207	82.17 177

v1192		69.97 111	68.68 113	73.85 88	73.19 228	50.94 153	77.68 132	81.36 86	57.51 136	68.95 104	80.85 180	45.28 144	85.33 81	62.97 102	70.37 201	85.27 93

ACMH+	57.40 11	66.12 184	64.06 187	72.30 151	77.79 141	52.83 132	80.39 81	78.03 167	57.30 137	57.47 260	82.55 131	27.68 302	84.17 107	45.54 219	69.78 216	79.90 218

BH-untuned		68.27 147	67.29 141	71.21 167	79.74 101	53.22 127	76.06 164	77.46 177	57.19 138	66.10 144	81.61 157	45.37 143	83.50 122	45.42 223	76.68 130	76.91 254

tfpn111		63.33 209	62.34 209	66.30 223	77.31 154	38.66 279	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.23 200	31.38 302	73.09 163	81.03 201

conf200view11		63.38 208	62.41 207	66.29 225	77.31 154	38.66 279	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 293	73.25 158	81.03 201

thres100view900		63.28 212	62.41 207	65.89 235	77.31 154	38.66 279	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 293	73.25 158	79.83 219

DP-MVS Recon		72.15 75	70.73 82	76.40 51	86.57 13	57.99 66	81.15 74	82.96 59	57.03 142	66.78 136	85.56 86	44.50 151	88.11 23	51.77 174	80.23 87	83.10 162

thres600view7		63.30 211	62.27 210	66.41 221	77.18 162	38.87 276	72.35 222	69.11 245	56.98 143	62.37 197	80.96 176	37.01 222	79.00 214	31.43 301	73.05 164	81.36 188

V42		68.65 134	67.35 140	72.56 138	68.93 288	50.18 181	72.90 212	79.47 137	56.92 144	69.45 95	80.26 196	46.29 132	82.99 134	64.07 89	67.82 240	84.53 115

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 10	58.36 62	83.22 38	86.93 1	56.91 145	74.91 27	88.19 47	59.15 11	87.68 30	73.67 28	87.45 29	86.57 43

view600		62.77 218	61.84 215	65.55 239	77.28 157	36.87 293	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 309	72.46 171	81.36 188

view800		62.77 218	61.84 215	65.55 239	77.28 157	36.87 293	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 309	72.46 171	81.36 188

conf0.05thres1000		62.77 218	61.84 215	65.55 239	77.28 157	36.87 293	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 309	72.46 171	81.36 188

tfpn		62.77 218	61.84 215	65.55 239	77.28 157	36.87 293	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 309	72.46 171	81.36 188

GA-MVS		65.53 190	63.70 193	71.02 173	70.87 269	48.10 210	70.48 246	74.40 210	56.69 150	64.70 165	76.77 258	33.66 260	81.10 175	55.42 152	70.32 203	83.87 139

v144192		69.71 113	68.51 115	73.33 117	73.10 234	50.13 184	77.54 135	80.64 107	56.65 151	68.57 108	80.55 187	46.87 128	84.96 87	62.98 101	69.66 220	84.89 105

tfpn200view9		63.18 214	62.18 212	66.21 227	76.85 168	39.62 270	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 293	73.25 158	79.83 219

thres400		63.31 210	62.18 212	66.72 217	76.85 168	39.62 270	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 293	73.25 158	81.36 188

GBi-Net		67.21 164	66.55 160	69.19 195	77.63 145	43.33 248	77.31 138	77.83 169	56.62 154	65.04 159	82.70 125	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

test1		67.21 164	66.55 160	69.19 195	77.63 145	43.33 248	77.31 138	77.83 169	56.62 154	65.04 159	82.70 125	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

FMVSNet2		66.93 172	66.31 169	68.79 201	77.63 145	42.98 251	76.11 162	77.47 175	56.62 154	65.22 158	82.17 140	41.85 172	80.18 191	47.05 205	72.72 170	83.20 157

v1921920		69.47 120	68.17 122	73.36 116	73.06 235	50.10 185	77.39 137	80.56 111	56.58 157	68.59 106	80.37 190	44.72 147	84.98 85	62.47 107	69.82 215	85.00 101

FMVSNet1		66.70 176	65.87 171	69.19 195	77.49 152	43.33 248	77.31 138	77.83 169	56.45 158	64.60 166	82.70 125	38.08 211	80.33 188	46.08 212	72.31 178	83.92 136

v1240		69.24 125	67.91 125	73.25 122	73.02 237	49.82 190	77.21 142	80.54 112	56.43 159	68.34 112	80.51 188	43.33 162	84.99 83	62.03 117	69.77 218	84.95 104

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 35	59.14 52	81.31 72	84.02 30	56.32 160	74.05 34	88.98 39	53.34 46	87.92 27	69.23 47	88.42 15	87.59 19

Vis-MVSNet (Re-imp)		63.69 206	63.88 190	63.14 260	74.75 194	31.04 331	71.16 239	63.64 287	56.32 160	59.80 231	84.99 92	44.51 150	75.46 250	39.12 260	80.62 76	82.92 164

AdaColmap		69.99 110	68.66 114	73.97 87	84.94 42	57.83 67	82.63 48	78.71 152	56.28 162	64.34 168	84.14 107	41.57 176	87.06 41	46.45 208	78.88 105	77.02 250

PS-MVSNAJss		72.24 71	71.21 76	75.31 68	78.50 122	55.93 97	81.63 65	82.12 69	56.24 163	70.02 80	85.68 85	47.05 123	84.34 105	65.27 73	74.41 142	85.67 71

Fast-Effi-MVS+		70.28 105	69.12 108	73.73 96	78.50 122	51.50 151	75.01 184	79.46 138	56.16 164	68.59 106	79.55 214	53.97 38	84.05 109	53.34 165	77.53 118	85.65 73

agg_prior1		75.94 38	76.01 33	75.72 59	85.04 37	59.96 43	81.44 70	81.04 96	56.14 165	74.68 29	88.90 40	53.91 39	84.04 110	75.01 20	87.92 27	83.16 161

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 88	55.91 98	84.28 24	85.78 9	56.08 166	73.41 46	86.58 68	50.94 67	88.54 15	70.79 43	89.71 7	87.79 14

train_agg		76.27 35	76.15 30	76.64 49	85.58 30	61.59 26	81.62 66	81.26 90	55.86 167	74.93 25	88.81 42	53.70 43	84.68 98	75.24 18	88.33 16	83.65 150

test_8					85.40 33	60.96 33	81.54 69	81.18 93	55.86 167	74.81 28	88.80 44	53.70 43	84.45 103

FMVSNet3		66.32 183	65.61 174	68.46 204	76.48 174	42.34 255	74.98 186	77.15 181	55.83 169	65.04 159	81.16 168	39.91 191	80.14 192	47.18 202	72.76 167	82.90 166

PAPR		71.72 79	70.82 81	74.41 83	81.20 80	51.17 152	79.55 94	83.33 52	55.81 170	66.93 135	84.61 99	50.95 66	86.06 62	55.79 149	79.20 102	86.00 59

ACMH	55.70 15	65.20 195	63.57 195	70.07 185	78.07 134	52.01 147	79.48 97	79.69 122	55.75 171	56.59 266	80.98 175	27.12 306	80.94 177	42.90 242	71.58 184	77.25 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 193	62.73 204	73.40 115	74.89 190	52.78 133	73.09 210	75.13 201	55.69 172	58.48 248	73.73 284	32.86 268	86.32 60	50.63 180	70.11 209	81.10 199
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
TEST9					85.58 30	61.59 26	81.62 66	81.26 90	55.65 173	74.93 25	88.81 42	53.70 43	84.68 98

thres200		62.20 226	61.16 226	65.34 245	75.38 187	39.99 267	69.60 254	69.29 243	55.64 174	61.87 202	76.99 254	37.07 221	78.96 215	31.28 303	73.28 157	77.06 249

pm-mvs1		65.24 194	64.97 184	66.04 231	72.38 249	39.40 273	72.62 218	75.63 194	55.53 175	62.35 198	83.18 122	47.45 120	76.47 244	49.06 193	66.54 247	82.24 175

agg_prior3		76.13 36	75.89 36	76.85 44	85.76 26	62.02 21	81.65 64	81.01 98	55.51 176	73.73 41	88.60 46	53.23 47	84.90 91	75.24 18	88.33 16	83.65 150

ACMM	61.98 7	70.80 89	69.73 92	74.02 85	80.59 89	58.59 60	82.68 47	82.02 72	55.46 177	67.18 132	84.39 105	38.51 204	83.17 130	60.65 125	76.10 131	80.30 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CPTT-MVS		72.78 63	72.08 65	74.87 73	84.88 45	61.41 28	84.15 28	77.86 168	55.27 178	67.51 129	88.08 50	41.93 170	81.85 162	69.04 49	80.01 88	81.35 194

XVG-OURS		68.76 133	67.37 138	72.90 127	74.32 203	57.22 76	70.09 251	78.81 150	55.24 179	67.79 126	85.81 84	36.54 233	78.28 223	62.04 116	75.74 133	83.19 158

tfpnnormal		62.47 223	61.63 221	64.99 248	74.81 193	39.01 275	71.22 237	73.72 215	55.22 180	60.21 225	80.09 199	41.26 185	76.98 239	30.02 308	68.09 237	78.97 231

EPNet_dtu		61.90 229	61.97 214	61.68 269	72.89 242	39.78 269	75.85 169	65.62 268	55.09 181	54.56 282	79.36 217	37.59 214	67.02 289	39.80 259	76.95 124	78.25 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 83	70.39 85	74.65 77	82.01 64	58.82 57	79.93 86	80.35 118	55.09 181	65.82 150	82.16 141	49.17 95	82.64 152	60.34 128	78.62 111	82.50 172

PS-MVSNAJ		70.51 96	69.70 93	72.93 126	81.52 70	55.79 99	74.92 187	79.00 147	55.04 183	69.88 82	78.66 225	47.05 123	82.19 157	61.61 122	79.58 94	80.83 207

xiu_mvs_v2_base		70.52 95	69.75 91	72.84 130	81.21 79	55.63 103	75.11 182	78.92 148	54.92 184	69.96 81	79.68 207	47.00 127	82.09 159	61.60 123	79.37 97	80.81 208

MAR-MVS		71.51 81	70.15 89	75.60 64	81.84 67	59.39 49	81.38 71	82.90 62	54.90 185	68.08 118	78.70 224	47.73 114	85.51 76	51.68 176	84.17 53	81.88 181
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
XVG-OURS-SEG-HR		68.81 130	67.47 135	72.82 132	74.40 202	56.87 84	70.59 244	79.04 146	54.77 186	66.99 134	86.01 78	39.57 196	78.21 224	62.54 105	73.33 156	83.37 153

SixPastTwentyTwo		61.65 233	58.80 245	70.20 184	75.80 180	47.22 220	75.59 171	69.68 236	54.61 187	54.11 287	79.26 219	27.07 307	82.96 135	43.27 236	49.79 327	80.41 212

test_0402		63.25 213	61.01 227	69.96 186	80.00 97	54.37 115	76.86 149	72.02 224	54.58 188	58.71 243	80.79 182	35.00 245	84.36 104	26.41 330	64.71 259	71.15 312

DI_MVS_plusplus_test		69.35 122	68.03 124	73.30 119	71.11 267	50.14 183	75.49 173	79.16 144	54.57 189	62.45 191	80.76 183	44.67 149	84.20 106	64.23 87	79.81 91	85.54 75

BH-w/o		66.85 173	65.83 172	69.90 188	79.29 104	52.46 139	74.66 191	76.65 185	54.51 190	64.85 163	78.12 231	45.59 138	82.95 136	43.26 237	75.54 135	74.27 281

testing_2		66.02 185	63.77 192	72.76 133	66.03 309	50.48 170	72.93 211	80.36 117	54.41 191	54.25 286	76.76 259	30.89 283	83.16 131	64.19 88	74.08 145	84.65 112

LTVRE_ROB	55.42 16	63.15 215	61.23 225	68.92 199	76.57 172	47.80 213	59.92 306	76.39 186	54.35 192	58.67 244	82.46 133	29.44 293	81.49 169	42.12 245	71.14 187	77.46 242
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
test_normal		69.26 124	67.90 126	73.32 118	70.84 270	50.38 172	75.30 176	79.17 143	54.23 193	62.00 199	80.61 186	44.69 148	83.89 116	64.33 86	79.95 90	85.69 70

Test4		67.77 158	65.97 170	73.19 123	68.64 289	50.58 164	74.80 190	80.48 114	54.13 194	59.11 239	79.07 222	33.89 258	83.12 132	63.61 97	79.98 89	85.87 62

ab-mvs		66.65 177	66.42 164	67.37 212	76.17 175	41.73 259	70.41 248	76.14 189	53.99 195	65.98 145	83.51 117	49.48 77	76.24 247	48.60 196	73.46 154	84.14 133

XVG-ACMP-BASELINE		64.36 203	62.23 211	70.74 176	72.35 250	52.45 140	70.80 243	78.45 161	53.84 196	59.87 229	81.10 170	16.24 333	79.32 199	55.64 151	71.76 182	80.47 210

PVSNet_BlendedMVS		68.56 139	67.72 128	71.07 172	77.03 165	50.57 165	74.50 193	81.52 79	53.66 197	64.22 172	79.72 206	49.13 96	82.87 140	55.82 147	73.92 147	79.77 222

EG-PatchMatch MVS		64.71 199	62.87 201	70.22 182	77.68 143	53.48 123	77.99 122	78.82 149	53.37 198	56.03 269	77.41 252	24.75 320	84.04 110	46.37 209	73.42 155	73.14 292

diffmvs		67.72 159	66.73 157	70.70 178	69.74 283	47.69 216	73.33 207	74.74 206	53.30 199	64.51 167	81.80 151	49.25 89	79.02 210	59.15 135	74.75 138	85.39 89

DP-MVS		65.68 187	63.66 194	71.75 156	84.93 43	56.87 84	80.74 78	73.16 219	53.06 200	59.09 240	82.35 134	36.79 231	85.94 68	32.82 289	69.96 213	72.45 299

TR-MVS		66.59 180	65.07 183	71.17 169	79.18 107	49.63 197	73.48 206	75.20 200	52.95 201	67.90 119	80.33 193	39.81 193	83.68 119	43.20 238	73.56 153	80.20 214

QAPM		70.05 108	68.81 111	73.78 91	76.54 173	53.43 124	83.23 37	83.48 45	52.89 202	65.90 147	86.29 73	41.55 179	86.49 56	51.01 178	78.40 113	81.42 186

OpenMVS	61.03 9	68.85 129	67.56 131	72.70 134	74.26 204	53.99 117	81.21 73	81.34 87	52.70 203	62.75 182	85.55 87	38.86 202	84.14 108	48.41 198	83.01 58	79.97 217

pmmvs6		63.69 206	62.82 203	66.27 226	70.63 271	39.27 274	73.13 209	75.47 196	52.69 204	59.75 232	82.30 136	39.71 194	77.03 238	47.40 201	64.35 264	82.53 170

IterMVS		62.79 217	61.27 224	67.35 213	69.37 286	52.04 146	71.17 238	68.24 252	52.63 205	59.82 230	76.91 256	37.32 216	72.36 267	52.80 168	63.19 272	77.66 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 153	66.36 166	73.63 102	75.61 183	55.35 108	80.77 77	78.56 156	52.48 206	64.27 171	84.10 109	27.45 304	81.84 163	63.45 100	70.56 195	83.69 145

jajsoiax		68.25 149	66.45 162	73.66 99	75.62 182	55.49 105	80.82 76	78.51 158	52.33 207	64.33 169	84.11 108	28.28 298	81.81 164	63.48 99	70.62 192	83.67 148

TAMVS		66.78 175	65.27 179	71.33 166	79.16 109	53.67 119	73.84 202	69.59 238	52.32 208	65.28 153	81.72 152	44.49 152	77.40 234	42.32 244	78.66 110	82.92 164

CDS-MVSNet		66.80 174	65.37 176	71.10 171	78.98 111	53.13 130	73.27 208	71.07 227	52.15 209	64.72 164	80.23 197	43.56 160	77.10 236	45.48 221	78.88 105	83.05 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PatchFormer-LS_test		62.20 226	60.59 229	67.04 215	72.18 253	46.82 223	70.36 249	68.62 250	51.92 210	59.19 238	70.23 303	36.86 230	75.07 259	50.23 184	65.68 253	79.23 228

PVSNet_Blended		68.59 135	67.72 128	71.19 168	77.03 165	50.57 165	72.51 220	81.52 79	51.91 211	64.22 172	77.77 239	49.13 96	82.87 140	55.82 147	79.58 94	80.14 216

conf0.01		59.97 240	58.81 238	63.42 256	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	81.03 201

conf0.002		59.97 240	58.81 238	63.42 256	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	81.03 201

thresconf0.02		59.40 248	58.81 238	61.17 275	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	74.25 282

tfpn_n400		59.40 248	58.81 238	61.17 275	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	74.25 282

tfpnconf		59.40 248	58.81 238	61.17 275	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	74.25 282

tfpnview11		59.40 248	58.81 238	61.17 275	74.15 206	33.83 316	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 315	68.26 230	74.25 282

mvs_anonymous		68.03 156	67.51 134	69.59 191	72.08 254	44.57 238	71.99 231	75.23 199	51.67 218	67.06 133	82.57 130	54.68 32	77.94 227	56.56 143	75.71 134	86.26 54

xiu_mvs_v1_base_debu		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 269	79.02 210	63.81 94	77.31 119	81.22 196

xiu_mvs_v1_base		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 269	79.02 210	63.81 94	77.31 119	81.22 196

xiu_mvs_v1_base_debi		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 269	79.02 210	63.81 94	77.31 119	81.22 196

MVSTER		67.16 166	65.58 175	71.88 154	70.37 275	49.70 193	70.25 250	78.45 161	51.52 222	69.16 102	80.37 190	38.45 205	82.50 153	60.19 129	71.46 185	83.44 152

CNLPA		65.43 191	64.02 188	69.68 189	78.73 118	58.07 65	77.82 127	70.71 229	51.49 223	61.57 209	83.58 116	38.23 209	70.82 273	43.90 231	70.10 210	80.16 215

原ACMM1				74.69 75	85.39 34	59.40 48		83.42 48	51.47 224	70.27 75	86.61 66	48.61 105	86.51 55	53.85 160	87.96 25	78.16 235

MSDG		61.81 232	59.23 235	69.55 194	72.64 245	52.63 135	70.45 247	75.81 193	51.38 225	53.70 290	76.11 266	29.52 291	81.08 176	37.70 266	65.79 252	74.93 273

test20.03		53.87 284	54.02 281	53.41 309	61.47 325	28.11 337	61.30 301	59.21 307	51.34 226	52.09 298	77.43 251	33.29 264	58.55 318	29.76 313	60.27 297	73.58 290

MVSFormer		71.50 82	70.38 86	74.88 72	78.76 116	57.15 81	82.79 44	78.48 159	51.26 227	69.49 93	83.22 120	43.99 157	83.24 128	66.06 65	79.37 97	84.23 126

test_djsdf		69.45 121	67.74 127	74.58 80	74.57 198	54.92 111	82.79 44	78.48 159	51.26 227	65.41 152	83.49 118	38.37 206	83.24 128	66.06 65	69.25 223	85.56 74

tfpn1000		59.24 253	58.70 246	60.86 280	73.75 214	33.99 314	68.86 260	63.98 285	51.25 229	57.29 262	79.51 216	34.58 248	75.26 257	29.08 322	69.99 211	73.32 291

PAPM		67.92 157	66.69 159	71.63 159	78.09 133	49.02 202	77.09 144	81.24 92	51.04 230	60.91 219	83.98 111	47.71 115	84.99 83	40.81 253	79.32 100	80.90 206

tfpn_ndepth		59.57 247	59.02 237	61.23 274	73.81 213	35.60 305	69.40 257	65.59 269	50.96 231	57.96 257	77.72 240	34.81 246	75.91 249	30.36 306	70.57 194	72.18 305

gg-mvs-nofinetune		57.86 262	56.43 263	62.18 267	72.62 246	35.35 307	66.57 273	56.33 320	50.65 232	57.64 259	57.10 336	30.65 284	76.36 245	37.38 268	78.88 105	74.82 275

TAPA-MVS	59.36 10	66.60 178	65.20 180	70.81 174	76.63 170	48.75 205	76.52 154	80.04 119	50.64 233	65.24 156	84.93 93	39.15 199	78.54 216	36.77 271	76.88 125	85.14 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVP-Stereo		65.41 192	63.80 191	70.22 182	77.62 149	55.53 104	76.30 157	78.53 157	50.59 234	56.47 267	78.65 226	39.84 192	82.68 150	44.10 230	72.12 180	72.44 300
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 86	69.49 102	75.35 67	77.63 145	55.71 100	76.04 166	81.81 76	50.30 235	69.66 87	85.40 91	52.51 51	84.89 92	51.82 173	80.24 86	85.45 81
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DWT-MVSNet_test		61.90 229	59.93 233	67.83 208	71.98 257	46.09 227	71.03 241	69.71 234	50.09 236	58.51 247	70.62 300	30.21 288	77.63 230	49.28 191	67.91 238	79.78 221

test-LLR		58.15 260	58.13 252	58.22 288	68.57 290	44.80 234	65.46 281	57.92 312	50.08 237	55.44 273	69.82 306	32.62 274	57.44 321	49.66 189	73.62 150	72.41 301

test0.0.03 1		53.32 287	53.59 284	52.50 313	62.81 321	29.45 334	59.51 307	54.11 331	50.08 237	54.40 284	74.31 281	32.62 274	55.92 332	30.50 305	63.95 266	72.15 307

COLMAP_ROB	52.97 17	61.27 236	58.81 238	68.64 202	74.63 196	52.51 138	78.42 109	73.30 217	49.92 239	50.96 302	81.51 160	23.06 323	79.40 197	31.63 298	65.85 250	74.01 288
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpmvs		58.47 256	56.95 259	63.03 262	70.20 276	41.21 262	67.90 271	67.23 260	49.62 240	54.73 280	70.84 298	34.14 254	76.24 247	36.64 275	61.29 286	71.64 308

HY-MVS	56.14 13	64.55 202	63.89 189	66.55 220	74.73 195	41.02 263	69.96 252	74.43 209	49.29 241	61.66 207	80.92 177	47.43 121	76.68 242	44.91 226	71.69 183	81.94 179

MIMVSNet1		55.17 279	54.31 278	57.77 292	70.03 278	32.01 328	65.68 279	64.81 273	49.19 242	46.75 317	76.00 267	25.53 316	64.04 300	28.65 324	62.13 279	77.26 247

Patchmatch-test1		59.75 244	58.00 253	64.98 249	74.14 212	48.06 211	63.35 292	63.23 290	49.13 243	59.33 237	71.46 294	37.45 215	69.59 278	41.39 252	62.57 276	77.30 244

testgi		51.90 291	52.37 288	50.51 318	60.39 332	23.55 348	58.42 310	58.15 310	49.03 244	51.83 299	79.21 220	22.39 324	55.59 333	29.24 321	62.64 275	72.40 303

MIMVSNet		57.35 264	57.07 257	58.22 288	74.21 205	37.18 290	62.46 295	60.88 303	48.88 245	55.29 276	75.99 269	31.68 281	62.04 307	31.87 292	72.35 176	75.43 267

gm-plane-assit					71.40 265	41.72 260			48.85 246		73.31 287		82.48 155	48.90 194

cascas		65.98 186	63.42 196	73.64 101	77.26 161	52.58 136	72.26 224	77.21 180	48.56 247	61.21 215	74.60 279	32.57 277	85.82 70	50.38 182	76.75 129	82.52 171

PLC	56.13 14	65.09 196	63.21 198	70.72 177	81.04 82	54.87 112	78.57 104	77.47 175	48.51 248	55.71 270	81.89 149	33.71 259	79.71 193	41.66 249	70.37 201	77.58 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 199	62.50 206	71.34 165	79.72 103	55.71 100	79.82 88	74.72 207	48.50 249	56.62 265	84.62 98	33.59 261	82.34 156	29.65 314	75.23 136	75.97 258

anonymousdsp		67.00 171	64.82 185	73.57 103	70.09 277	56.13 92	76.35 156	77.35 179	48.43 250	64.99 162	80.84 181	33.01 266	80.34 187	64.66 77	67.64 243	84.23 126

无先验							79.66 92	74.30 211	48.40 251				80.78 182	53.62 161		79.03 230

114514_t		70.83 87	69.56 97	74.64 78	86.21 19	54.63 113	82.34 54	81.81 76	48.22 252	63.01 179	85.83 82	40.92 189	87.10 40	57.91 138	79.79 92	82.18 176

tpm		57.34 265	58.16 250	54.86 303	71.80 260	34.77 309	67.47 272	56.04 323	48.20 253	60.10 226	76.92 255	37.17 219	53.41 338	40.76 254	65.01 257	76.40 257

MDA-MVSNet-bldmvs		53.87 284	50.81 291	63.05 261	66.25 306	48.58 206	56.93 315	63.82 286	48.09 254	41.22 332	70.48 302	30.34 286	68.00 285	34.24 284	45.92 334	72.57 297

XXY-MVS		60.68 237	61.67 220	57.70 293	70.43 273	38.45 283	64.19 290	66.47 263	48.05 255	63.22 176	80.86 179	49.28 85	60.47 311	45.25 225	67.28 245	74.19 286

F-COLMAP		63.05 216	60.87 228	69.58 193	76.99 167	53.63 121	78.12 114	76.16 188	47.97 256	52.41 297	81.61 157	27.87 300	78.11 225	40.07 256	66.66 246	77.00 251

Patchmatch-RL test		58.16 259	55.49 269	66.15 228	67.92 296	48.89 204	60.66 305	51.07 336	47.86 257	59.36 234	62.71 329	34.02 256	72.27 269	56.41 144	59.40 299	77.30 244

ANet_high		41.38 314	37.47 318	53.11 310	39.73 353	24.45 347	56.94 314	69.69 235	47.65 258	26.04 344	52.32 339	12.44 340	62.38 306	21.80 338	10.61 353	72.49 298

CostFormer		64.04 204	62.51 205	68.61 203	71.88 258	45.77 228	71.30 236	70.60 230	47.55 259	64.31 170	76.61 261	41.63 175	79.62 195	49.74 187	69.00 224	80.42 211

PatchmatchNet		59.84 243	58.24 249	64.65 251	73.05 236	46.70 224	69.42 256	62.18 297	47.55 259	58.88 242	71.96 292	34.49 251	69.16 280	42.99 240	63.60 268	78.07 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ITE_SJBPF				62.09 268	66.16 307	44.55 239		64.32 277	47.36 261	55.31 275	80.34 192	19.27 329	62.68 305	36.29 279	62.39 278	79.04 229

OurMVSNet-221017-0		61.37 235	58.63 248	69.61 190	72.05 255	48.06 211	73.93 201	72.51 221	47.23 262	54.74 279	80.92 177	21.49 327	81.24 172	48.57 197	56.22 308	79.53 224

tpmrst		58.24 258	58.70 246	56.84 295	66.97 300	34.32 312	69.57 255	61.14 302	47.17 263	58.58 246	71.60 293	41.28 184	60.41 312	49.20 192	62.84 274	75.78 263

PVSNet	50.76 19	58.40 257	57.39 255	61.42 271	75.53 184	44.04 243	61.43 299	63.45 288	47.04 264	56.91 263	73.61 285	27.00 308	64.76 298	39.12 260	72.40 175	75.47 266

FMVSNet5		55.86 275	54.93 272	58.66 287	71.05 268	36.35 300	64.18 291	62.48 296	46.76 265	50.66 306	74.73 278	25.80 314	64.04 300	33.11 288	65.57 254	75.59 265

jason		69.65 115	68.39 119	73.43 111	78.27 128	56.88 83	77.12 143	73.71 216	46.53 266	69.34 97	83.22 120	43.37 161	79.18 202	64.77 76	79.20 102	84.23 126
jason: jason.
Anonymous20231211		55.92 274	53.63 283	62.77 263	68.22 295	35.56 306	74.48 194	69.89 233	46.42 267	49.07 310	73.45 286	21.13 328	76.77 241	28.74 323	51.30 323	75.97 258

MS-PatchMatch		62.42 224	61.46 222	65.31 246	75.21 189	52.10 143	72.05 230	74.05 213	46.41 268	57.42 261	74.36 280	34.35 253	77.57 232	45.62 218	73.67 149	66.26 323

1112_ss		64.00 205	63.36 197	65.93 234	79.28 105	42.58 254	71.35 235	72.36 223	46.41 268	60.55 223	77.89 236	46.27 133	73.28 264	46.18 210	69.97 212	81.92 180

lupinMVS		69.57 117	68.28 120	73.44 110	78.76 116	57.15 81	76.57 152	73.29 218	46.19 270	69.49 93	82.18 138	43.99 157	79.23 200	64.66 77	79.37 97	83.93 135

testdata				64.66 250	81.52 70	52.93 131		65.29 271	46.09 271	73.88 39	87.46 53	38.08 211	66.26 293	53.31 166	78.48 112	74.78 276

UnsupCasMVSNet_eth		53.16 289	52.47 287	55.23 300	59.45 336	33.39 324	59.43 308	69.13 244	45.98 272	50.35 308	72.32 290	29.30 294	58.26 319	42.02 247	44.30 336	74.05 287

AllTest		57.08 267	54.65 274	64.39 252	71.44 262	49.03 200	69.92 253	67.30 258	45.97 273	47.16 314	79.77 204	17.47 330	67.56 286	33.65 286	59.16 300	76.57 255

TestCases				64.39 252	71.44 262	49.03 200		67.30 258	45.97 273	47.16 314	79.77 204	17.47 330	67.56 286	33.65 286	59.16 300	76.57 255

WTY-MVS		59.75 244	60.39 230	57.85 291	72.32 251	37.83 287	61.05 304	64.18 284	45.95 275	61.91 201	79.11 221	47.01 126	60.88 310	42.50 243	69.49 222	74.83 274

testpf		44.11 310	45.40 305	40.26 332	60.52 331	27.34 339	33.26 348	54.33 330	45.87 276	41.08 333	60.26 332	16.46 332	59.14 316	46.09 211	50.68 326	34.31 348

semantic-postprocess				65.40 244	71.99 256	50.80 159		69.63 237	45.71 277	60.61 222	77.93 234	36.56 232	65.99 295	55.67 150	63.50 269	79.42 225

旧先验2							76.08 163		45.32 278	76.55 16			65.56 297	58.75 136

OpenMVS_ROB	52.78 18	60.03 239	58.14 251	65.69 238	70.47 272	44.82 233	75.33 175	70.86 228	45.04 279	56.06 268	76.00 267	26.89 309	79.65 194	35.36 281	67.29 244	72.60 296

tpmp4_e23		62.71 222	60.13 231	70.45 181	73.40 221	48.39 208	72.82 213	69.49 240	44.88 280	59.91 228	74.99 275	37.79 213	81.47 170	40.22 255	67.71 242	81.48 185

TinyColmap		54.14 281	51.72 289	61.40 272	66.84 302	41.97 256	66.52 274	68.51 251	44.81 281	42.69 331	75.77 270	11.66 343	72.94 265	31.96 291	56.77 306	69.27 319

MDTV_nov1_ep13			57.00 258		72.73 244	38.26 284	65.02 287	64.73 275	44.74 282	55.46 272	72.48 289	32.61 276	70.47 276	37.47 267	67.75 241

新几何1				70.76 175	85.66 27	61.13 31		66.43 265	44.68 283	70.29 73	86.64 64	41.29 183	75.23 258	49.72 188	81.75 70	75.93 261

1121		68.53 140	67.16 147	72.63 135	85.64 29	61.14 30	73.95 198	66.46 264	44.61 284	70.28 74	86.68 63	41.42 181	80.78 182	53.62 161	81.79 68	75.97 258

Patchmtry		57.16 266	56.47 262	59.23 282	69.17 287	34.58 311	62.98 293	63.15 291	44.53 285	56.83 264	74.84 276	35.83 235	68.71 282	40.03 257	60.91 287	74.39 280

ppachtmachnet_test		58.06 261	55.38 270	66.10 230	69.51 284	48.99 203	68.01 270	66.13 266	44.50 286	54.05 288	70.74 299	32.09 280	72.34 268	36.68 274	56.71 307	76.99 253

PatchT		53.17 288	53.44 285	52.33 314	68.29 294	25.34 345	58.21 311	54.41 329	44.46 287	54.56 282	69.05 309	33.32 263	60.94 309	36.93 270	61.76 281	70.73 314

EPMVS		53.96 282	53.69 282	54.79 304	66.12 308	31.96 329	62.34 297	49.05 339	44.42 288	55.54 271	71.33 296	30.22 287	56.70 325	41.65 250	62.54 277	75.71 264

pmmvs4		61.48 234	59.39 234	67.76 209	71.57 261	53.86 118	71.42 234	65.34 270	44.20 289	59.46 233	77.92 235	35.90 234	74.71 261	43.87 232	64.87 258	74.71 277

dp		51.89 292	51.60 290	52.77 312	68.44 293	32.45 326	62.36 296	54.57 328	44.16 290	49.31 309	67.91 311	28.87 297	56.61 326	33.89 285	54.89 312	69.24 320

PatchMatch-RL		56.25 272	54.55 275	61.32 273	77.06 164	56.07 94	65.57 280	54.10 332	44.13 291	53.49 295	71.27 297	25.20 317	66.78 290	36.52 277	63.66 267	61.12 332

our_test_3		56.49 268	54.42 276	62.68 265	69.51 284	45.48 230	66.08 277	61.49 301	44.11 292	50.73 305	69.60 308	33.05 265	68.15 284	38.38 263	56.86 305	74.40 279

USDC		56.35 271	54.24 279	62.69 264	64.74 314	40.31 266	65.05 286	73.83 214	43.93 293	47.58 312	77.71 241	15.36 335	75.05 260	38.19 265	61.81 280	72.70 295

PM-MVS		52.33 290	50.19 293	58.75 286	62.10 323	45.14 232	65.75 278	40.38 349	43.60 294	53.52 293	72.65 288	9.16 349	65.87 296	50.41 181	54.18 315	65.24 325

pmmvs-eth3d		58.81 254	56.31 264	66.30 223	67.61 297	52.42 141	72.30 223	64.76 274	43.55 295	54.94 278	74.19 282	28.95 295	72.60 266	43.31 235	57.21 304	73.88 289

new-patchmatchnet		47.56 302	47.73 300	47.06 323	58.81 337	9.37 356	48.78 334	59.21 307	43.28 296	44.22 324	68.66 310	25.67 315	57.20 324	31.57 300	49.35 330	74.62 278

Test_1112_low_res		62.32 225	61.77 219	64.00 254	79.08 110	39.53 272	68.17 268	70.17 231	43.25 297	59.03 241	79.90 200	44.08 155	71.24 272	43.79 233	68.42 229	81.25 195

RPMNet		58.70 255	56.29 265	65.96 232	69.96 279	52.07 144	65.31 284	62.15 298	43.20 298	59.36 234	70.15 305	35.37 243	70.75 274	36.42 278	64.65 262	75.06 269

tpm2		62.07 228	60.10 232	67.99 207	72.79 243	43.86 244	71.05 240	66.85 262	43.14 299	62.77 180	75.39 273	38.32 207	80.80 181	41.69 248	68.88 225	79.32 226

JIA-IIPM		51.56 293	47.68 301	63.21 259	64.61 315	50.73 161	47.71 335	58.77 309	42.90 300	48.46 311	51.72 340	24.97 318	70.24 277	36.06 280	53.89 316	68.64 321

1314		64.61 201	63.21 198	68.80 200	71.87 259	47.46 218	73.95 198	78.39 165	42.88 301	59.97 227	76.60 262	38.11 210	79.39 198	54.84 153	72.32 177	79.55 223

HyFIR lowres test		65.67 188	63.01 200	73.67 98	79.97 98	55.65 102	69.07 259	75.52 195	42.68 302	63.53 174	77.95 233	40.43 190	81.64 165	46.01 213	71.91 181	83.73 144

CR-MVSNet		59.91 242	57.90 254	65.96 232	69.96 279	52.07 144	65.31 284	63.15 291	42.48 303	59.36 234	74.84 276	35.83 235	70.75 274	45.50 220	64.65 262	75.06 269

test222					83.14 55	58.68 59	72.57 219	63.45 288	41.78 304	67.56 128	86.12 75	37.13 220			78.73 109	74.98 272

TDRefinement		53.44 286	50.72 292	61.60 270	64.31 317	46.96 221	70.89 242	65.27 272	41.78 304	44.61 323	77.98 232	11.52 344	66.36 292	28.57 325	51.59 321	71.49 309

sss		56.17 273	56.57 261	54.96 302	66.93 301	36.32 302	57.94 312	61.69 300	41.67 306	58.64 245	75.32 274	38.72 203	56.25 330	42.04 246	66.19 249	72.31 304

PVSNet_0	43.31 20	47.46 303	45.64 303	52.92 311	67.60 298	44.65 236	54.06 321	54.64 327	41.59 307	46.15 318	58.75 335	30.99 282	58.66 317	32.18 290	24.81 345	55.46 339

MVS		67.37 161	66.33 167	70.51 180	75.46 185	50.94 153	73.95 198	81.85 75	41.57 308	62.54 187	78.57 229	47.98 111	85.47 77	52.97 167	82.05 67	75.14 268

Anonymous20231206		55.10 280	55.30 271	54.48 305	69.81 282	33.94 315	62.91 294	62.13 299	41.08 309	55.18 277	75.65 271	32.75 272	56.59 327	30.32 307	67.86 239	72.91 293

MDA-MVSNet_test_wron		50.71 296	48.95 295	56.00 299	61.17 327	41.84 257	51.90 329	56.45 318	40.96 310	44.79 322	67.84 312	30.04 290	55.07 337	36.71 273	50.69 325	71.11 313

YYNet1		50.73 295	48.96 294	56.03 298	61.10 328	41.78 258	51.94 328	56.44 319	40.94 311	44.84 321	67.80 313	30.08 289	55.08 336	36.77 271	50.71 324	71.22 310

1111		44.40 309	45.00 306	42.61 330	57.55 339	17.33 353	53.82 324	57.05 316	40.78 312	44.11 325	66.57 317	13.37 338	45.77 345	22.15 335	49.58 328	64.73 327

.test1245		34.88 321	39.49 316	21.04 342	57.55 339	17.33 353	53.82 324	57.05 316	40.78 312	44.11 325	66.57 317	13.37 338	45.77 345	22.15 335	0.00 356	0.03 357

CHOSEN 1792x2688		65.08 197	62.84 202	71.82 155	81.49 72	56.26 90	66.32 276	74.20 212	40.53 314	63.16 178	78.65 226	41.30 182	77.80 229	45.80 215	74.09 144	81.40 187

pmmvs5		56.47 269	55.68 268	58.86 285	61.41 326	36.71 298	66.37 275	62.75 294	40.38 315	53.70 290	76.62 260	34.56 249	67.05 288	40.02 258	65.27 255	72.83 294

MDTV_nov1_ep13_2view						25.89 342	61.22 302		40.10 316	51.10 301		32.97 267		38.49 262		78.61 232

tpm cat1		59.25 252	56.95 259	66.15 228	72.19 252	46.96 221	68.09 269	65.76 267	40.03 317	57.81 258	70.56 301	38.32 207	74.51 262	38.26 264	61.50 283	77.00 251

test-mter		56.42 270	55.82 267	58.22 288	68.57 290	44.80 234	65.46 281	57.92 312	39.94 318	55.44 273	69.82 306	21.92 326	57.44 321	49.66 189	73.62 150	72.41 301

UnsupCasMVSNet_bld		50.07 297	48.87 296	53.66 307	60.97 330	33.67 322	57.62 313	64.56 276	39.47 319	47.38 313	64.02 325	27.47 303	59.32 315	34.69 283	43.68 337	67.98 322

TESTMET0.1,1		55.28 278	54.90 273	56.42 296	66.56 305	43.67 246	65.46 281	56.27 321	39.18 320	53.83 289	67.44 314	24.21 321	55.46 335	48.04 199	73.11 162	70.13 315

test1235678		45.66 304	44.46 309	49.26 319	59.88 334	28.68 336	56.36 317	55.54 326	39.12 321	40.89 334	63.40 327	14.41 337	57.32 323	21.05 339	49.47 329	61.78 330

LP		48.51 299	45.51 304	57.52 294	62.86 320	44.53 240	52.38 327	59.84 305	38.11 322	42.81 330	61.02 330	23.23 322	63.02 303	24.10 333	45.24 335	65.02 326

test2356		45.61 305	44.66 307	48.47 322	60.15 333	28.08 338	52.44 326	52.83 335	38.01 323	46.13 319	60.98 331	15.08 336	55.54 334	20.43 342	55.85 310	61.78 330

testmv		42.25 312	40.11 315	48.66 320	53.23 341	27.02 340	56.62 316	55.74 325	37.25 324	33.10 340	59.52 334	7.78 351	56.58 328	19.61 343	38.13 341	62.40 329

ADS-MVSNet2		51.33 294	48.76 297	59.07 284	66.02 310	44.60 237	50.90 330	59.76 306	36.90 325	50.74 303	66.18 320	26.38 310	63.11 302	27.17 326	54.76 313	69.50 317

ADS-MVSNet		48.48 300	47.77 299	50.63 317	66.02 310	29.92 333	50.90 330	50.87 338	36.90 325	50.74 303	66.18 320	26.38 310	52.47 340	27.17 326	54.76 313	69.50 317

RPSCF		55.80 276	54.22 280	60.53 281	65.13 313	42.91 253	64.30 289	57.62 314	36.84 327	58.05 250	82.28 137	28.01 299	56.24 331	37.14 269	58.61 302	82.44 174

testus		44.59 308	43.87 310	46.76 324	59.85 335	24.65 346	53.86 322	55.82 324	36.26 328	43.97 327	63.42 326	8.39 350	53.14 339	20.70 341	52.52 319	62.51 328

Patchmatch-test		49.08 298	48.28 298	51.50 316	64.40 316	30.85 332	45.68 338	48.46 342	35.60 329	46.10 320	72.10 291	34.47 252	46.37 344	27.08 328	60.65 291	77.27 246

CHOSEN 280x420		47.83 301	46.36 302	52.24 315	67.37 299	49.78 191	38.91 346	43.11 348	35.00 330	43.27 329	63.30 328	28.95 295	49.19 343	36.53 276	60.80 290	57.76 337

N_pmnet		39.35 317	40.28 314	36.54 334	63.76 318	1.62 360	49.37 333	0.76 361	34.62 331	43.61 328	66.38 319	26.25 312	42.57 350	26.02 332	51.77 320	65.44 324

no-one		40.85 315	36.09 319	55.14 301	48.55 346	38.72 278	42.15 344	62.92 293	34.60 332	23.55 345	49.74 344	12.21 341	66.16 294	26.27 331	24.84 344	60.54 333

PMMVS		53.96 282	53.26 286	56.04 297	62.60 322	50.92 155	61.17 303	56.09 322	32.81 333	53.51 294	66.84 316	34.04 255	59.93 314	44.14 229	68.18 236	57.27 338

test12356		36.16 320	35.94 320	36.83 333	50.82 345	8.52 357	44.84 341	53.49 334	32.72 334	30.11 342	55.08 337	7.11 353	49.47 342	16.60 345	32.68 343	52.50 340

CMPMVS	42.80 21	57.81 263	55.97 266	63.32 258	60.98 329	47.38 219	64.66 288	69.50 239	32.06 335	46.83 316	77.80 238	29.50 292	71.36 271	48.68 195	73.75 148	71.21 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		59.63 246	59.14 236	61.08 279	74.47 199	38.84 277	75.20 180	68.74 249	31.15 336	58.24 249	76.51 263	32.39 278	68.58 283	49.77 186	65.84 251	75.81 262

FPMVS		42.18 313	41.11 313	45.39 325	58.03 338	41.01 264	49.50 332	53.81 333	30.07 337	33.71 339	64.03 323	11.69 342	52.08 341	14.01 348	55.11 311	43.09 345

EU-MVSNet		55.61 277	54.41 277	59.19 283	65.41 312	33.42 323	72.44 221	71.91 225	28.81 338	51.27 300	73.87 283	24.76 319	69.08 281	43.04 239	58.20 303	75.06 269

LF4IMVS		42.95 311	42.26 311	45.04 326	48.30 347	32.50 325	54.80 319	48.49 341	28.03 339	40.51 336	70.16 304	9.24 348	43.89 348	31.63 298	49.18 331	58.72 335

MVS-HIRNet		45.52 306	44.48 308	48.65 321	68.49 292	34.05 313	59.41 309	44.50 347	27.03 340	37.96 338	50.47 343	26.16 313	64.10 299	26.74 329	59.52 298	47.82 342

PMVS	28.69 22	36.22 319	33.29 323	45.02 327	36.82 355	35.98 304	54.68 320	48.74 340	26.31 341	21.02 346	51.61 341	2.88 359	60.10 313	9.99 352	47.58 332	38.99 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 307	41.95 312	53.86 306	52.58 343	43.55 247	62.11 298	46.90 346	26.05 342	40.63 335	60.19 333	11.08 346	57.91 320	31.83 297	46.15 333	60.11 334

PMMVS2		27.40 327	25.91 327	31.87 338	39.46 354	6.57 358	31.17 349	28.52 355	23.96 343	20.45 347	48.94 345	4.20 356	37.94 353	16.51 346	19.97 346	51.09 341

Gipuma		34.77 322	31.91 324	43.33 329	62.05 324	37.87 286	20.39 351	67.03 261	23.23 344	18.41 348	25.84 349	4.24 355	62.73 304	14.71 347	51.32 322	29.38 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		34.13 323	34.29 322	33.64 335	52.63 342	18.23 352	44.43 342	33.90 352	22.81 345	30.89 341	53.18 338	10.48 347	35.72 354	20.77 340	39.51 338	46.98 343

LCM-MVSNet		40.30 316	35.88 321	53.57 308	42.24 350	29.15 335	45.21 340	60.53 304	22.23 346	28.02 343	50.98 342	3.72 357	61.78 308	31.22 304	38.76 340	69.78 316

PNet_i23d		27.88 326	25.99 326	33.55 336	47.54 348	25.89 342	47.24 337	32.91 353	21.44 347	15.90 349	38.09 346	0.85 361	42.76 349	16.90 344	13.03 351	32.00 349

E-PMN		23.77 328	22.73 329	26.90 340	42.02 351	20.67 349	42.66 343	35.70 350	17.43 348	10.28 353	25.05 350	6.42 354	42.39 351	10.28 351	14.71 348	17.63 351

EMVS		22.97 329	21.84 331	26.36 341	40.20 352	19.53 351	41.95 345	34.64 351	17.09 349	9.73 354	22.83 352	7.29 352	42.22 352	9.18 353	13.66 350	17.32 352

DSMNet-mixed		39.30 318	38.72 317	41.03 331	51.22 344	19.66 350	45.53 339	31.35 354	15.83 350	39.80 337	67.42 315	22.19 325	45.13 347	22.43 334	52.69 318	58.31 336

wuykxyi23d		28.12 325	22.54 330	44.87 328	34.97 356	32.11 327	37.96 347	47.31 344	13.32 351	9.29 355	23.72 351	0.45 362	56.58 328	21.85 337	13.98 349	45.93 344

MVE	17.77 23	21.41 330	17.77 332	32.34 337	34.34 357	25.44 344	16.11 352	24.11 356	11.19 352	13.22 351	31.92 347	1.58 360	30.95 355	10.47 350	17.03 347	40.62 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 344	17.97 358	10.91 355		10.60 359	7.46 353	11.07 352	28.36 348	3.28 358	11.29 357	8.01 354	9.74 355	13.89 353

wuyk23d		13.32 332	12.52 333	15.71 343	47.54 348	26.27 341	31.06 350	1.98 360	4.93 354	5.18 356	1.94 357	0.45 362	18.54 356	6.81 355	12.83 352	2.33 355

tmp_tt		9.43 333	11.14 334	4.30 345	2.38 359	4.40 359	13.62 353	16.08 358	0.39 355	15.89 350	13.06 353	15.80 334	5.54 358	12.63 349	10.46 354	2.95 354

testmvs		4.52 336	6.03 337	0.01 347	0.01 360	0.00 362	53.86 322	0.00 362	0.01 356	0.04 357	0.27 358	0.00 365	0.00 359	0.04 356	0.00 356	0.03 357

test123		4.73 335	6.30 336	0.02 346	0.01 360	0.01 361	56.36 317	0.00 362	0.01 356	0.04 357	0.21 359	0.01 364	0.00 359	0.03 357	0.00 356	0.04 356

cdsmvs_eth3d_5k		17.50 331	23.34 328	0.00 348	0.00 362	0.00 362	0.00 354	78.63 154	0.00 358	0.00 359	82.18 138	49.25 89	0.00 359	0.00 358	0.00 356	0.00 359

pcd_1.5k_mvsjas		3.92 337	5.23 338	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	47.05 123	0.00 359	0.00 358	0.00 356	0.00 359

pcd1.5k->3k		30.06 324	30.56 325	28.55 339	78.81 115	0.00 362	0.00 354	82.07 71	0.00 358	0.00 359	0.00 360	39.61 195	0.00 359	0.00 358	74.56 139	85.66 72

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

ab-mvs-re		6.49 334	8.65 335	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	77.89 236	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 362	0.00 354	0.00 362	0.00 358	0.00 359	0.00 360	0.00 365	0.00 359	0.00 358	0.00 356	0.00 359

GSMVS																78.05 237

test_part2					87.58 3	60.47 38				83.42 2

test_part1								86.64 3				65.59 1			90.06 4	86.78 40

sam_mvs1												34.74 247				78.05 237

sam_mvs												33.43 262

ambc				65.13 247	63.72 319	37.07 291	47.66 336	78.78 151		54.37 285	71.42 295	11.24 345	80.94 177	45.64 217	53.85 317	77.38 243

MTGPA								80.97 99

test_post1							68.67 261				3.64 355	32.39 278	69.49 279	44.17 228

test_post											3.55 356	33.90 257	66.52 291

patchmatchnet-post											64.03 323	34.50 250	74.27 263

GG-mvs-BLEND				62.34 266	71.36 266	37.04 292	69.20 258	57.33 315		54.73 280	65.48 322	30.37 285	77.82 228	34.82 282	74.93 137	72.17 306

MTMP								17.08 357

test9_res														75.28 17	88.31 19	83.81 140

agg_prior2														73.09 32	87.93 26	84.33 118

agg_prior					85.04 37	59.96 43		81.04 96		74.68 29			84.04 110

test_prior4						62.51 17	82.08 60

test_prior				76.69 46	84.20 49	57.27 74		84.88 17					86.43 57			86.38 44

新几何2							76.12 161

旧先验1					83.04 56	53.15 128		67.52 257			87.85 51	44.08 155			80.76 75	78.03 239

原ACMM2							79.02 98

testdata2													72.18 270	46.95 206

segment_acmp												54.23 36

test12				77.76 35	84.52 46	58.41 61		83.36 51		72.93 53		54.61 33	88.05 24		88.12 22	86.81 38

plane_prior7					81.41 73	55.96 96

plane_prior6					81.20 80	56.24 91						45.26 145

plane_prior5								84.01 31					87.21 36	68.16 51	80.58 78	84.65 112

plane_prior4											86.10 76

plane_prior1					81.27 78

n2								0.00 362

nn								0.00 362

door-mid								47.19 345

lessismore_v0				69.91 187	71.42 264	47.80 213		50.90 337		50.39 307	75.56 272	27.43 305	81.33 171	45.91 214	34.10 342	80.59 209

test11								83.47 46

door								47.60 343

HQP5-MVS						54.94 109

BP-MVS														67.04 61

HQP4-MVS										67.85 120			86.93 42			84.32 119

HQP3-MVS								83.90 35							80.35 84

HQP2-MVS												45.46 139

NP-MVS					80.98 83	56.05 95					85.54 88

ACMMP++_ref															74.07 146

ACMMP++															72.16 179

Test By Simon												48.33 108

High-resolution multi-view benchmark