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.

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 87

MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 40	59.52 47	82.93 43	85.39 11	62.15 56	76.41 17	91.51 3	52.47 54	86.78 46	80.66 2	89.64 8	87.80 14
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		80.16 3	80.31 4	79.72 3	86.49 14	61.95 22	86.33 7	85.75 10	62.49 51	82.20 5	92.00 2	56.53 16	89.54 7	79.51 3	91.48 1	87.68 18

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 32	61.04 32	83.84 32	85.16 13	62.88 44	78.10 11	91.26 6	52.51 52	88.39 16	79.34 4	90.52 2	86.78 42

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 42	65.37 16	78.78 10	90.64 10	58.63 13	87.24 34	79.00 5	90.37 3	85.26 99

APDe-MVS		80.16 3	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 1	59.64 8	89.76 5	78.70 6	88.32 18	86.79 41

CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 24	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 36

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 36	90.06 3	78.42 8	89.02 10	87.69 17
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 23	63.57 2	83.37 37	80.97 102	65.13 18	75.77 19	90.88 8	48.63 104	86.66 48	77.23 9	88.17 20	84.81 112

MTAPA		76.90 28	76.42 29	78.35 26	86.08 23	63.57 2	74.92 191	80.97 102	65.13 18	75.77 19	90.88 8	48.63 104	86.66 48	77.23 9	88.17 20	84.81 112

MP-MVS		78.35 13	78.26 13	78.64 23	86.54 13	63.47 5	86.02 13	83.55 45	63.89 31	73.60 44	90.60 11	54.85 32	86.72 47	77.20 11	88.06 23	85.74 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 40	61.41 28	84.03 30	83.82 40	59.34 117	79.37 8	89.76 29	59.84 6	87.62 31	76.69 12	86.74 38	87.68 18

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 47	61.88 24	84.55 23	83.87 38	60.37 81	79.89 7	89.38 33	54.97 29	85.58 74	76.12 13	84.94 48	86.33 53

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 42

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 39	60.81 35	82.91 44	85.08 14	62.57 49	73.09 51	89.97 26	50.90 70	87.48 32	75.30 16	86.85 36	87.33 31
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test9_res														75.28 17	88.31 19	83.81 145

train_agg		76.27 35	76.15 30	76.64 50	85.58 30	61.59 26	81.62 68	81.26 92	55.86 171	74.93 25	88.81 42	53.70 44	84.68 99	75.24 18	88.33 16	83.65 155

agg_prior3		76.13 36	75.89 36	76.85 45	85.76 26	62.02 21	81.65 66	81.01 100	55.51 180	73.73 41	88.60 46	53.23 48	84.90 92	75.24 18	88.33 16	83.65 155

agg_prior1		75.94 38	76.01 33	75.72 60	85.04 37	59.96 43	81.44 72	81.04 98	56.14 169	74.68 29	88.90 40	53.91 40	84.04 111	75.01 20	87.92 27	83.16 167

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 2	83.60 52	61.62 25	84.17 28	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 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 44	60.32 40	83.03 41	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 7

NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 21	84.42 23	66.73 10	74.67 31	89.38 33	55.30 27	89.18 10	74.19 23	87.34 30	86.38 47

HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 8	62.81 10	86.29 8	84.32 25	62.82 46	73.96 35	90.50 15	53.20 49	88.35 17	74.02 24	87.05 31	86.13 59

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 7	62.93 9	86.29 8	84.24 27	62.82 46	73.55 45	90.56 13	49.80 76	88.24 20	74.02 24	87.03 33	86.32 55

region2R		77.67 20	77.18 22	79.15 9	86.76 6	62.95 8	86.29 8	84.16 29	62.81 48	73.30 48	90.58 12	49.90 74	88.21 21	73.78 26	87.03 33	86.29 57

#test#		77.83 17	77.41 19	79.10 13	86.71 8	62.81 10	85.69 18	84.32 25	61.61 65	73.96 35	90.50 15	53.20 49	88.35 17	73.68 27	87.05 31	86.13 59

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 10	58.36 62	83.22 39	86.93 1	56.91 148	74.91 27	88.19 47	59.15 11	87.68 30	73.67 28	87.45 29	86.57 45

CP-MVS		77.12 26	76.68 26	78.43 25	86.05 25	63.18 7	87.55 3	83.45 48	62.44 53	72.68 56	90.50 15	48.18 111	87.34 33	73.59 29	85.71 45	84.76 116

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 75	58.89 56	82.50 53	84.07 30	67.73 5	72.08 63	87.28 57	49.49 78	89.57 6	73.52 30	86.40 42	87.87 11

APD-MVS		78.02 15	78.04 15	77.98 33	86.44 16	60.81 35	85.52 19	84.36 24	60.61 76	79.05 9	90.30 19	55.54 26	88.32 19	73.48 31	87.03 33	84.83 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
agg_prior2														73.09 32	87.93 26	84.33 123

CANet		76.46 33	75.93 34	78.06 31	81.29 76	57.53 71	82.35 55	83.31 54	67.78 3	70.09 77	86.34 74	54.92 30	88.90 14	72.68 33	84.55 50	87.76 16

PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 22	62.73 12	82.55 51	83.74 41	61.71 63	72.45 60	90.34 18	48.48 108	88.13 22	72.32 34	86.85 36	85.78 68

test_prior3		76.89 29	76.96 23	76.69 47	84.20 49	57.27 74	81.75 64	84.88 17	60.37 81	75.01 23	89.06 36	56.22 21	86.43 58	72.19 35	88.96 11	86.38 47

test_prior2							81.75 64		60.37 81	75.01 23	89.06 36	56.22 21		72.19 35	88.96 11

ACMMP		76.02 37	75.33 39	78.07 30	85.20 36	61.91 23	85.49 20	84.44 22	63.04 40	69.80 89	89.74 30	45.43 142	87.16 38	72.01 37	82.87 62	85.14 101
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
mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 15	63.50 4	85.74 17	82.28 69	62.90 43	71.77 65	90.26 20	46.61 131	86.55 55	71.71 38	85.66 46	84.97 108

XVS		77.17 25	76.56 28	79.00 16	86.32 17	62.62 14	85.83 14	83.92 34	64.55 22	72.17 61	90.01 25	47.95 113	88.01 25	71.55 39	86.74 38	86.37 50

X-MVStestdata		70.21 108	67.28 147	79.00 16	86.32 17	62.62 14	85.83 14	83.92 34	64.55 22	72.17 61	6.49 359	47.95 113	88.01 25	71.55 39	86.74 38	86.37 50

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 90	58.32 63	79.50 98	82.92 61	67.84 1	75.94 18	80.75 190	55.73 24	86.80 44	71.44 41	80.38 82	87.50 23

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 90	57.70 69	79.50 98	82.13 70	67.84 1	75.73 21	80.75 190	56.50 17	86.07 62	71.07 42	80.38 82	87.50 23

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 88	55.91 99	84.28 25	85.78 9	56.08 170	73.41 47	86.58 69	50.94 69	88.54 15	70.79 43	89.71 7	87.79 15

casdiffmvs		74.55 48	73.78 50	76.87 43	79.00 114	56.18 92	82.36 54	84.45 21	53.88 202	73.46 46	85.76 87	56.38 20	86.59 51	70.70 44	78.04 117	87.83 13

Regformer-4		74.25 52	73.48 53	76.57 51	79.75 102	56.54 86	78.54 109	81.49 84	66.93 8	73.90 38	80.30 200	53.84 42	85.98 67	69.76 45	76.84 131	87.17 33

APD-MVS_3200maxsize		74.96 43	74.39 45	76.67 49	82.20 63	58.24 64	83.67 33	83.29 55	58.41 130	73.71 42	90.14 22	45.62 137	85.99 66	69.64 46	82.85 63	85.78 68

OPM-MVS		74.73 46	74.25 46	76.19 53	80.81 84	59.01 54	82.60 50	83.64 43	63.74 33	72.52 58	87.49 52	47.18 123	85.88 70	69.47 47	80.78 74	83.66 154

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 35	59.14 52	81.31 74	84.02 31	56.32 164	74.05 34	88.98 39	53.34 47	87.92 27	69.23 48	88.42 15	87.59 21

Regformer-3		73.89 55	73.28 57	75.71 61	79.75 102	55.48 107	78.54 109	79.93 124	66.58 12	73.62 43	80.30 200	54.87 31	84.54 102	69.09 49	76.84 131	87.10 35

CPTT-MVS		72.78 64	72.08 66	74.87 74	84.88 45	61.41 28	84.15 29	77.86 173	55.27 183	67.51 134	88.08 50	41.93 172	81.85 166	69.04 50	80.01 88	81.35 200

DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 21	60.86 34	84.71 22	84.85 19	61.98 62	73.06 52	88.88 41	53.72 43	89.06 12	68.27 51	88.04 24	87.42 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP_MVS		74.31 50	73.73 51	76.06 54	81.41 73	56.31 87	84.22 26	84.01 32	64.52 24	69.27 101	86.10 78	45.26 146	87.21 36	68.16 52	80.58 78	84.65 117

plane_prior5								84.01 32					87.21 36	68.16 52	80.58 78	84.65 117

abl_6		74.34 49	73.50 52	76.86 44	82.43 61	60.16 41	83.48 36	81.86 76	58.81 123	73.95 37	89.86 27	41.87 173	86.62 50	67.98 54	81.23 73	83.80 148

diffmvs		70.36 104	69.99 92	71.46 164	70.48 278	48.19 211	74.59 198	76.30 192	60.36 85	67.75 132	83.81 118	51.22 64	79.77 198	67.92 55	77.50 122	86.42 46

CSCG		76.92 27	76.75 25	77.41 38	83.96 51	59.60 46	82.95 42	86.50 5	60.78 74	75.27 22	84.83 97	60.76 5	86.56 54	67.86 56	87.87 28	86.06 62

LPG-MVS_test		72.74 65	71.74 68	75.76 58	80.22 94	57.51 72	82.55 51	83.40 50	61.32 67	66.67 143	87.33 55	39.15 203	86.59 51	67.70 57	77.30 126	83.19 164

LGP-MVS_train				75.76 58	80.22 94	57.51 72		83.40 50	61.32 67	66.67 143	87.33 55	39.15 203	86.59 51	67.70 57	77.30 126	83.19 164

HPM-MVS_fast		74.30 51	73.46 55	76.80 46	84.45 48	59.04 53	83.65 34	81.05 97	60.15 88	70.43 72	89.84 28	41.09 188	85.59 73	67.61 59	82.90 61	85.77 70

MVS_111021_HR		74.02 53	73.46 55	75.69 62	83.01 58	60.63 37	77.29 145	78.40 169	61.18 70	70.58 71	85.97 81	54.18 38	84.00 115	67.52 60	82.98 60	82.45 179

DELS-MVS		74.76 45	74.46 44	75.65 63	77.84 145	52.25 143	75.59 175	84.17 28	63.76 32	73.15 50	82.79 131	59.58 9	86.80 44	67.24 61	86.04 44	87.89 9
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
TSAR-MVS + GP.		74.90 44	74.15 47	77.17 41	82.00 65	58.77 58	81.80 63	78.57 160	58.58 126	74.32 33	84.51 106	55.94 23	87.22 35	67.11 62	84.48 52	85.52 81

BP-MVS														67.04 63

HQP-MVS		73.45 58	72.80 60	75.40 67	80.66 85	54.94 110	82.31 57	83.90 36	62.10 57	67.85 124	85.54 91	45.46 140	86.93 42	67.04 63	80.35 84	84.32 124

ACMP	63.53 6	72.30 71	71.20 78	75.59 66	80.28 92	57.54 70	82.74 47	82.84 65	60.58 77	65.24 162	86.18 76	39.25 201	86.03 65	66.95 65	76.79 133	83.22 162
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EI-MVSNet-Vis-set		72.42 70	71.59 69	74.91 72	78.47 128	54.02 117	77.05 149	79.33 147	65.03 20	71.68 66	79.35 224	52.75 51	84.89 93	66.46 66	74.23 147	85.83 67

MVSFormer		71.50 83	70.38 87	74.88 73	78.76 120	57.15 81	82.79 45	78.48 164	51.26 233	69.49 96	83.22 127	43.99 158	83.24 129	66.06 67	79.37 98	84.23 131

test_djsdf		69.45 125	67.74 132	74.58 81	74.57 204	54.92 112	82.79 45	78.48 164	51.26 233	65.41 158	83.49 124	38.37 210	83.24 129	66.06 67	69.25 229	85.56 79

canonicalmvs		74.67 47	74.98 41	73.71 98	78.94 116	50.56 168	80.23 84	83.87 38	60.30 86	77.15 14	86.56 70	59.65 7	82.00 164	66.01 69	82.12 66	88.58 4

MVS_Test		72.45 69	72.46 63	72.42 149	74.88 196	48.50 208	76.28 162	83.14 59	59.40 115	72.46 59	84.68 99	55.66 25	81.12 178	65.98 70	79.66 94	87.63 20

alignmvs		73.86 56	73.99 48	73.45 110	78.20 134	50.50 170	78.57 107	82.43 67	59.40 115	76.57 15	86.71 63	56.42 19	81.23 177	65.84 71	81.79 68	88.62 2

nrg030		72.96 63	73.01 58	72.84 131	75.41 191	50.24 177	80.02 87	82.89 64	58.36 132	74.44 32	86.73 61	58.90 12	80.83 184	65.84 71	74.46 144	87.44 26

MVS_111021_LR		69.50 123	68.78 116	71.65 161	78.38 129	59.33 50	74.82 193	70.11 238	58.08 134	67.83 128	84.68 99	41.96 171	76.34 251	65.62 73	77.54 120	79.30 233

EI-MVSNet-UG-set		71.92 77	71.06 79	74.52 82	77.98 142	53.56 123	76.62 155	79.16 149	64.40 26	71.18 68	78.95 229	52.19 57	84.66 101	65.47 74	73.57 156	85.32 96

PS-MVSNAJss		72.24 72	71.21 77	75.31 69	78.50 126	55.93 98	81.63 67	82.12 71	56.24 167	70.02 81	85.68 88	47.05 124	84.34 106	65.27 75	74.41 146	85.67 75

MSLP-MVS++		73.77 57	73.47 54	74.66 77	83.02 57	59.29 51	82.30 60	81.88 75	59.34 117	71.59 67	86.83 59	45.94 135	83.65 121	65.09 76	85.22 47	81.06 206

v2v482		70.50 98	69.45 107	73.66 100	72.62 252	50.03 187	77.58 136	80.51 116	59.90 94	69.52 95	82.14 149	47.53 119	84.88 95	65.07 77	70.17 214	86.09 61

jason		69.65 119	68.39 124	73.43 112	78.27 133	56.88 83	77.12 147	73.71 221	46.53 272	69.34 100	83.22 127	43.37 162	79.18 209	64.77 78	79.20 103	84.23 131
jason: jason.
anonymousdsp		67.00 175	64.82 190	73.57 104	70.09 284	56.13 93	76.35 160	77.35 184	48.43 256	64.99 168	80.84 187	33.01 271	80.34 191	64.66 79	67.64 249	84.23 131

lupinMVS		69.57 121	68.28 125	73.44 111	78.76 120	57.15 81	76.57 156	73.29 223	46.19 275	69.49 96	82.18 145	43.99 158	79.23 207	64.66 79	79.37 98	83.93 140

CLD-MVS		73.33 59	72.68 61	75.29 70	78.82 118	53.33 127	78.23 115	84.79 20	61.30 69	70.41 73	81.04 177	52.41 55	87.12 39	64.61 81	82.49 65	85.41 94
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1neww		70.66 91	69.70 95	73.53 105	73.15 235	50.22 178	78.11 118	80.68 107	59.65 104	69.83 86	81.67 159	49.29 84	84.96 88	64.55 82	70.38 205	85.42 90

v7new		70.66 91	69.70 95	73.53 105	73.15 235	50.22 178	78.11 118	80.68 107	59.65 104	69.83 86	81.67 159	49.29 84	84.96 88	64.55 82	70.38 205	85.42 90

v6		70.66 91	69.70 95	73.53 105	73.14 238	50.21 181	78.11 118	80.67 109	59.65 104	69.82 88	81.65 161	49.29 84	84.96 88	64.55 82	70.39 204	85.42 90

v1		70.50 98	69.53 100	73.42 113	72.91 246	50.00 188	77.69 132	80.59 112	59.50 112	69.59 94	81.42 170	49.26 89	84.77 96	64.49 85	70.30 211	85.47 84

v1141		70.50 98	69.53 100	73.41 114	72.92 245	50.00 188	77.69 132	80.60 111	59.50 112	69.60 92	81.43 168	49.24 93	84.77 96	64.48 86	70.30 211	85.46 86

divwei89l23v2f112		70.50 98	69.53 100	73.41 114	72.91 246	50.00 188	77.69 132	80.59 112	59.50 112	69.60 92	81.43 168	49.26 89	84.77 96	64.48 86	70.31 210	85.47 84

test_normal		69.26 129	67.90 131	73.32 119	70.84 276	50.38 173	75.30 180	79.17 148	54.23 199	62.00 204	80.61 192	44.69 149	83.89 117	64.33 88	79.95 90	85.69 74

DI_MVS_plusplus_test		69.35 126	68.03 129	73.30 120	71.11 273	50.14 184	75.49 177	79.16 149	54.57 195	62.45 196	80.76 189	44.67 150	84.20 107	64.23 89	79.81 91	85.54 80

testing_2		66.02 190	63.77 198	72.76 134	66.03 314	50.48 171	72.93 216	80.36 120	54.41 197	54.25 292	76.76 265	30.89 288	83.16 132	64.19 90	74.08 149	84.65 117

V42		68.65 139	67.35 145	72.56 139	68.93 294	50.18 182	72.90 217	79.47 141	56.92 147	69.45 98	80.26 202	46.29 133	82.99 135	64.07 91	67.82 246	84.53 120

3Dnovator+	66.72 4	75.84 40	74.57 43	79.66 4	82.40 62	59.92 45	85.83 14	86.32 7	66.92 9	67.80 129	89.24 35	42.03 170	89.38 8	64.07 91	86.50 41	89.69 1

v1144		70.42 103	69.31 108	73.76 94	73.22 231	50.64 163	77.83 129	81.43 85	58.58 126	69.40 99	81.16 174	47.53 119	85.29 83	64.01 93	70.64 196	85.34 95

Effi-MVS+		73.31 60	72.54 62	75.62 64	77.87 144	53.64 121	79.62 96	79.61 129	61.63 64	72.02 64	82.61 136	56.44 18	85.97 68	63.99 94	79.07 105	87.25 32

v7		70.57 95	69.48 105	73.85 89	73.50 223	50.92 156	78.27 113	81.43 85	58.93 120	69.61 91	81.49 167	47.56 118	85.43 80	63.94 95	70.62 197	85.21 100

xiu_mvs_v1_base_debu		68.58 141	67.28 147	72.48 143	78.19 135	57.19 78	75.28 181	75.09 208	51.61 225	70.04 78	81.41 171	32.79 274	79.02 217	63.81 96	77.31 123	81.22 202

xiu_mvs_v1_base		68.58 141	67.28 147	72.48 143	78.19 135	57.19 78	75.28 181	75.09 208	51.61 225	70.04 78	81.41 171	32.79 274	79.02 217	63.81 96	77.31 123	81.22 202

xiu_mvs_v1_base_debi		68.58 141	67.28 147	72.48 143	78.19 135	57.19 78	75.28 181	75.09 208	51.61 225	70.04 78	81.41 171	32.79 274	79.02 217	63.81 96	77.31 123	81.22 202

Test4		67.77 163	65.97 175	73.19 124	68.64 295	50.58 165	74.80 194	80.48 117	54.13 200	59.11 244	79.07 228	33.89 263	83.12 133	63.61 99	79.98 89	85.87 66

v8		70.33 106	69.28 109	73.49 108	73.15 235	50.22 178	78.62 106	80.78 106	60.79 73	66.45 146	82.11 150	49.35 80	84.98 86	63.58 100	68.71 233	85.28 97

jajsoiax		68.25 154	66.45 166	73.66 100	75.62 187	55.49 106	80.82 78	78.51 163	52.33 213	64.33 174	84.11 111	28.28 304	81.81 168	63.48 101	70.62 197	83.67 153

mvs_tets		68.18 158	66.36 170	73.63 103	75.61 188	55.35 109	80.77 79	78.56 161	52.48 212	64.27 176	84.10 112	27.45 310	81.84 167	63.45 102	70.56 200	83.69 150

v144192		69.71 116	68.51 119	73.33 118	73.10 240	50.13 185	77.54 139	80.64 110	56.65 154	68.57 111	80.55 193	46.87 129	84.96 88	62.98 103	69.66 226	84.89 110

v1192		69.97 113	68.68 117	73.85 89	73.19 234	50.94 154	77.68 135	81.36 88	57.51 139	68.95 107	80.85 186	45.28 145	85.33 82	62.97 104	70.37 207	85.27 98

v10		70.21 108	69.02 112	73.81 91	73.51 222	50.92 156	78.74 103	81.39 87	60.05 90	66.39 147	81.83 157	47.58 117	85.41 81	62.80 105	68.86 232	85.09 104

OMC-MVS		71.40 85	70.60 84	73.78 92	76.60 176	53.15 129	79.74 94	79.78 125	58.37 131	68.75 108	86.45 72	45.43 142	80.60 189	62.58 106	77.73 119	87.58 22

XVG-OURS-SEG-HR		68.81 135	67.47 140	72.82 133	74.40 208	56.87 84	70.59 250	79.04 151	54.77 191	66.99 140	86.01 80	39.57 198	78.21 230	62.54 107	73.33 160	83.37 159

EPNet		73.09 62	72.16 64	75.90 56	75.95 184	56.28 89	83.05 40	72.39 227	66.53 13	65.27 160	87.00 58	50.40 72	85.47 78	62.48 108	86.32 43	85.94 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		69.47 124	68.17 127	73.36 117	73.06 241	50.10 186	77.39 141	80.56 114	56.58 160	68.59 109	80.37 196	44.72 148	84.98 86	62.47 109	69.82 221	85.00 106

v17		68.37 148	67.00 154	72.48 143	73.22 231	50.31 174	78.10 121	79.58 136	59.71 102	61.67 211	77.60 251	49.31 81	82.89 139	62.37 110	61.48 291	84.23 131

v16		68.38 147	67.01 153	72.47 147	73.22 231	50.29 175	78.10 121	79.59 134	59.71 102	61.72 210	77.60 251	49.28 87	82.89 139	62.36 111	61.54 288	84.23 131

v18		68.33 149	66.96 155	72.42 149	73.13 239	50.16 183	77.97 126	79.57 138	59.57 111	61.80 208	77.50 256	49.30 82	82.90 138	62.31 112	61.50 289	84.20 137

v12		68.28 151	66.83 159	72.60 138	73.43 225	50.74 161	78.18 116	79.59 134	60.01 92	60.89 225	77.66 249	49.12 99	82.77 146	62.18 113	60.46 301	84.29 127

V14		68.25 154	66.82 160	72.52 142	73.33 229	50.53 169	78.02 124	79.60 131	59.83 98	61.16 221	77.57 253	49.19 94	82.77 146	62.18 113	60.50 299	84.26 129

v13		68.29 150	66.84 157	72.63 136	73.50 223	50.83 159	78.25 114	79.58 136	60.05 90	60.76 226	77.68 248	49.11 102	82.77 146	62.17 115	60.45 302	84.30 126

V9		68.27 152	66.84 157	72.56 139	73.39 228	50.63 164	78.10 121	79.60 131	59.94 93	61.05 223	77.62 250	49.18 95	82.77 146	62.17 115	60.48 300	84.27 128

v15		68.22 157	66.81 161	72.47 147	73.25 230	50.40 172	77.92 128	79.60 131	59.77 101	61.28 219	77.52 255	49.25 91	82.77 146	62.16 117	60.51 298	84.24 130

XVG-OURS		68.76 138	67.37 143	72.90 128	74.32 209	57.22 76	70.09 257	78.81 155	55.24 184	67.79 130	85.81 86	36.54 238	78.28 229	62.04 118	75.74 138	83.19 164

v1240		69.24 130	67.91 130	73.25 123	73.02 243	49.82 191	77.21 146	80.54 115	56.43 163	68.34 115	80.51 194	43.33 163	84.99 84	62.03 119	69.77 224	84.95 109

v52		67.09 171	65.16 186	72.87 129	66.77 308	51.60 150	73.69 210	79.45 143	57.88 136	62.46 195	78.57 235	40.95 190	83.34 125	61.99 120	64.70 267	83.68 151

V4		67.09 171	65.16 186	72.87 129	66.76 309	51.60 150	73.69 210	79.45 143	57.88 136	62.45 196	78.58 234	40.96 189	83.34 125	61.99 120	64.71 265	83.68 151

v11		68.15 160	66.73 162	72.42 149	73.43 225	50.28 176	77.94 127	79.65 128	59.88 97	61.11 222	77.55 254	48.25 110	82.75 151	61.88 122	60.85 295	84.23 131

VDD-MVS		72.50 67	72.09 65	73.75 96	81.58 69	49.69 196	77.76 131	77.63 178	63.21 38	73.21 49	89.02 38	42.14 169	83.32 127	61.72 123	82.50 64	88.25 6

PS-MVSNAJ		70.51 97	69.70 95	72.93 127	81.52 70	55.79 100	74.92 191	79.00 152	55.04 188	69.88 84	78.66 231	47.05 124	82.19 160	61.61 124	79.58 95	80.83 213

xiu_mvs_v2_base		70.52 96	69.75 93	72.84 131	81.21 79	55.63 104	75.11 186	78.92 153	54.92 189	69.96 83	79.68 213	47.00 128	82.09 163	61.60 125	79.37 98	80.81 214

MG-MVS		73.96 54	73.89 49	74.16 85	85.65 28	49.69 196	81.59 70	81.29 91	61.45 66	71.05 69	88.11 48	51.77 59	87.73 29	61.05 126	83.09 57	85.05 105

ACMM	61.98 7	70.80 90	69.73 94	74.02 86	80.59 89	58.59 60	82.68 48	82.02 74	55.46 181	67.18 138	84.39 108	38.51 208	83.17 131	60.65 127	76.10 136	80.30 219
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		69.64 120	67.53 138	75.95 55	76.10 181	62.29 18	80.20 86	76.06 197	59.83 98	65.26 161	77.09 259	41.56 179	84.02 114	60.60 128	71.09 194	81.53 190

mvs-test1		70.44 102	68.19 126	77.18 40	76.10 181	63.22 6	80.59 82	76.06 197	59.83 98	66.32 148	79.87 207	41.56 179	85.53 75	60.60 128	72.77 171	82.80 174

PVSNet_Blended_VisFu		71.45 84	70.39 86	74.65 78	82.01 64	58.82 57	79.93 89	80.35 121	55.09 186	65.82 156	82.16 148	49.17 96	82.64 154	60.34 130	78.62 113	82.50 178

MVSTER		67.16 170	65.58 180	71.88 156	70.37 282	49.70 194	70.25 256	78.45 166	51.52 228	69.16 105	80.37 196	38.45 209	82.50 155	60.19 131	71.46 190	83.44 158

v148		68.24 156	67.19 151	71.40 168	70.43 280	47.77 217	75.76 174	77.03 187	58.91 121	67.36 135	80.10 204	48.60 107	81.89 165	60.01 132	66.52 254	84.53 120

CANet_DTU		68.18 158	67.71 135	69.59 195	74.83 198	46.24 229	78.66 105	76.85 189	59.60 107	63.45 180	82.09 151	35.25 249	77.41 239	59.88 133	78.76 110	85.14 101

IterMVS-LS		69.22 131	68.48 120	71.43 167	74.44 207	49.40 199	76.23 164	77.55 179	59.60 107	65.85 155	81.59 165	51.28 63	81.58 172	59.87 134	69.90 220	83.30 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 128	68.44 123	71.73 159	74.47 205	49.39 200	75.20 184	78.45 166	59.60 107	69.16 105	76.51 269	51.29 62	82.50 155	59.86 135	71.45 191	83.30 160

3Dnovator	64.47 5	72.49 68	71.39 73	75.79 57	77.70 147	58.99 55	80.66 81	83.15 58	62.24 55	65.46 157	86.59 68	42.38 168	85.52 76	59.59 136	84.72 49	82.85 173

旧先验2							76.08 167		45.32 283	76.55 16			65.56 302	58.75 137

VDDNet		71.81 78	71.33 75	73.26 122	82.80 60	47.60 219	78.74 103	75.27 204	59.59 110	72.94 53	89.40 32	41.51 182	83.91 116	58.75 137	82.99 59	88.26 5

114514_t		70.83 88	69.56 99	74.64 79	86.21 19	54.63 114	82.34 56	81.81 78	48.22 258	63.01 184	85.83 84	40.92 191	87.10 40	57.91 139	79.79 92	82.18 182

Vis-MVSNet		72.18 73	71.37 74	74.61 80	81.29 76	55.41 108	80.90 77	78.28 171	60.73 75	69.23 104	88.09 49	44.36 155	82.65 153	57.68 140	81.75 70	85.77 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPM_NR		72.63 66	71.80 67	75.13 71	81.72 68	53.42 126	79.91 90	83.28 56	59.14 119	66.31 149	85.90 82	51.86 58	86.06 63	57.45 141	80.62 76	85.91 65

LFMVS		71.78 79	71.59 69	72.32 152	83.40 54	46.38 228	79.75 93	71.08 232	64.18 28	72.80 55	88.64 45	42.58 166	83.72 119	57.41 142	84.49 51	86.86 39

v7n		69.01 133	67.36 144	73.98 87	72.51 254	52.65 135	78.54 109	81.30 90	60.26 87	62.67 188	81.62 162	43.61 160	84.49 103	57.01 143	68.70 234	84.79 114

mvs_anonymous		68.03 161	67.51 139	69.59 195	72.08 260	44.57 242	71.99 236	75.23 205	51.67 224	67.06 139	82.57 137	54.68 33	77.94 233	56.56 144	75.71 139	86.26 58

Patchmatch-RL test		58.16 265	55.49 275	66.15 233	67.92 301	48.89 205	60.66 311	51.07 341	47.86 263	59.36 239	62.71 334	34.02 261	72.27 274	56.41 145	59.40 305	77.30 250

v748		67.26 167	65.67 178	72.02 154	69.90 288	49.77 193	76.24 163	79.57 138	58.58 126	60.49 229	80.38 195	44.47 154	82.17 162	56.16 146	65.26 262	84.12 139

EPP-MVSNet		72.16 75	71.31 76	74.71 75	78.68 123	49.70 194	82.10 61	81.65 80	60.40 80	65.94 152	85.84 83	51.74 60	86.37 60	55.93 147	79.55 97	88.07 8

PVSNet_BlendedMVS		68.56 144	67.72 133	71.07 177	77.03 170	50.57 166	74.50 199	81.52 81	53.66 204	64.22 177	79.72 212	49.13 97	82.87 141	55.82 148	73.92 151	79.77 228

PVSNet_Blended		68.59 140	67.72 133	71.19 173	77.03 170	50.57 166	72.51 225	81.52 81	51.91 217	64.22 177	77.77 245	49.13 97	82.87 141	55.82 148	79.58 95	80.14 222

PAPR		71.72 80	70.82 82	74.41 84	81.20 80	51.17 153	79.55 97	83.33 53	55.81 174	66.93 141	84.61 102	50.95 68	86.06 63	55.79 150	79.20 103	86.00 63

semantic-postprocess				65.40 250	71.99 262	50.80 160		69.63 242	45.71 282	60.61 227	77.93 240	36.56 237	65.99 300	55.67 151	63.50 275	79.42 231

XVG-ACMP-BASELINE		64.36 209	62.23 217	70.74 181	72.35 256	52.45 141	70.80 249	78.45 166	53.84 203	59.87 234	81.10 176	16.24 338	79.32 206	55.64 152	71.76 187	80.47 216

Anonymous20231211		69.28 127	68.47 121	71.73 159	80.28 92	47.18 223	79.98 88	82.37 68	54.61 192	67.24 137	84.01 114	39.43 199	82.41 158	55.45 153	72.83 170	85.62 78

GA-MVS		65.53 195	63.70 199	71.02 178	70.87 275	48.10 212	70.48 252	74.40 215	56.69 153	64.70 171	76.77 264	33.66 265	81.10 179	55.42 154	70.32 209	83.87 144

0601test		69.69 117	69.13 110	71.36 169	78.37 130	45.74 232	74.71 195	80.20 122	57.91 135	70.01 82	83.83 116	42.44 167	82.87 141	54.97 155	79.72 93	85.48 83

1314		64.61 206	63.21 204	68.80 205	71.87 265	47.46 220	73.95 204	78.39 170	42.88 306	59.97 232	76.60 268	38.11 214	79.39 205	54.84 156	72.32 182	79.55 229

Fast-Effi-MVS+-dtu		67.37 165	65.33 183	73.48 109	72.94 244	57.78 68	77.47 140	76.88 188	57.60 138	61.97 205	76.85 263	39.31 200	80.49 190	54.72 157	70.28 213	82.17 183

UniMVSNet_NR-MVSNet		71.11 86	71.00 80	71.44 165	79.20 109	44.13 245	76.02 171	82.60 66	66.48 14	68.20 116	84.60 103	56.82 15	82.82 144	54.62 158	70.43 202	87.36 30

DU-MVS		70.01 111	69.53 100	71.44 165	78.05 140	44.13 245	75.01 188	81.51 83	64.37 27	68.20 116	84.52 104	49.12 99	82.82 144	54.62 158	70.43 202	87.37 28

FIs		70.82 89	71.43 71	68.98 203	78.33 131	38.14 290	76.96 151	83.59 44	61.02 71	67.33 136	86.73 61	55.07 28	81.64 169	54.61 160	79.22 102	87.14 34

VPA-MVSNet		69.02 132	69.47 106	67.69 215	77.42 158	41.00 270	74.04 203	79.68 127	60.06 89	69.26 103	84.81 98	51.06 67	77.58 237	54.44 161	74.43 145	84.48 122

Anonymous20240529		69.91 114	69.02 112	72.56 139	80.19 97	47.65 218	77.56 138	80.99 101	55.45 182	69.88 84	86.76 60	39.24 202	82.18 161	54.04 162	77.10 128	87.85 12

UniMVSNet (Re)		70.63 94	70.20 88	71.89 155	78.55 125	45.29 235	75.94 172	82.92 61	63.68 34	68.16 118	83.59 121	53.89 41	83.49 124	53.97 163	71.12 193	86.89 38

原ACMM1				74.69 76	85.39 34	59.40 48		83.42 49	51.47 230	70.27 76	86.61 67	48.61 106	86.51 56	53.85 164	87.96 25	78.16 241

无先验							79.66 95	74.30 216	48.40 257				80.78 186	53.62 165		79.03 236

1121		68.53 145	67.16 152	72.63 136	85.64 29	61.14 30	73.95 204	66.46 269	44.61 289	70.28 75	86.68 64	41.42 183	80.78 186	53.62 165	81.79 68	75.97 264

UA-Net		73.13 61	72.93 59	73.76 94	83.58 53	51.66 149	78.75 102	77.66 177	67.75 4	72.61 57	89.42 31	49.82 75	83.29 128	53.61 167	83.14 56	86.32 55

VNet		69.68 118	70.19 89	68.16 211	79.73 105	41.63 266	70.53 251	77.38 183	60.37 81	70.69 70	86.63 66	51.08 66	77.09 243	53.61 167	81.69 72	85.75 72

Fast-Effi-MVS+		70.28 107	69.12 111	73.73 97	78.50 126	51.50 152	75.01 188	79.46 142	56.16 168	68.59 109	79.55 220	53.97 39	84.05 110	53.34 169	77.53 121	85.65 77

testdata				64.66 256	81.52 70	52.93 132		65.29 276	46.09 276	73.88 39	87.46 53	38.08 215	66.26 298	53.31 170	78.48 114	74.78 281

MVS		67.37 165	66.33 171	70.51 184	75.46 190	50.94 154	73.95 204	81.85 77	41.57 313	62.54 192	78.57 235	47.98 112	85.47 78	52.97 171	82.05 67	75.14 273

IterMVS		62.79 223	61.27 230	67.35 218	69.37 292	52.04 147	71.17 243	68.24 257	52.63 211	59.82 235	76.91 262	37.32 220	72.36 272	52.80 172	63.19 278	77.66 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		69.80 115	70.58 85	67.46 216	77.61 155	34.73 315	76.05 169	83.19 57	60.84 72	65.88 154	86.46 71	54.52 35	80.76 188	52.52 173	78.12 116	86.91 37

TranMVSNet+NR-MVSNet		70.36 104	70.10 91	71.17 174	78.64 124	42.97 256	76.53 157	81.16 96	66.95 7	68.53 112	85.42 93	51.61 61	83.07 134	52.32 174	69.70 225	87.46 25

Baseline_NR-MVSNet		67.05 173	67.56 136	65.50 249	75.65 186	37.70 294	75.42 178	74.65 213	59.90 94	68.14 119	83.15 130	49.12 99	77.20 241	52.23 175	69.78 222	81.60 189

API-MVS		72.17 74	71.41 72	74.45 83	81.95 66	57.22 76	84.03 30	80.38 119	59.89 96	68.40 113	82.33 142	49.64 77	87.83 28	51.87 176	84.16 54	78.30 239

PCF-MVS	61.88 8	70.95 87	69.49 104	75.35 68	77.63 150	55.71 101	76.04 170	81.81 78	50.30 241	69.66 90	85.40 94	52.51 52	84.89 93	51.82 177	80.24 86	85.45 87
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 76	70.73 83	76.40 52	86.57 12	57.99 66	81.15 76	82.96 60	57.03 145	66.78 142	85.56 89	44.50 152	88.11 23	51.77 178	80.23 87	83.10 168

UGNet		68.81 135	67.39 142	73.06 125	78.33 131	54.47 115	79.77 92	75.40 203	60.45 79	63.22 181	84.40 107	32.71 278	80.91 183	51.71 179	80.56 80	83.81 145
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
MAR-MVS		71.51 82	70.15 90	75.60 65	81.84 67	59.39 49	81.38 73	82.90 63	54.90 190	68.08 121	78.70 230	47.73 115	85.51 77	51.68 180	84.17 53	81.88 187
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
VPNet		67.52 164	68.11 128	65.74 243	79.18 110	36.80 302	72.17 230	72.83 225	62.04 60	67.79 130	85.83 84	48.88 103	76.60 248	51.30 181	72.97 169	83.81 145

QAPM		70.05 110	68.81 115	73.78 92	76.54 178	53.43 125	83.23 38	83.48 46	52.89 208	65.90 153	86.29 75	41.55 181	86.49 57	51.01 182	78.40 115	81.42 192

NR-MVSNet		69.54 122	68.85 114	71.59 163	78.05 140	43.81 249	74.20 201	80.86 105	65.18 17	62.76 186	84.52 104	52.35 56	83.59 122	50.96 183	70.78 195	87.37 28

IB-MVS	56.42 12	65.40 198	62.73 210	73.40 116	74.89 195	52.78 134	73.09 215	75.13 207	55.69 176	58.48 254	73.73 290	32.86 273	86.32 61	50.63 184	70.11 215	81.10 205
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
PM-MVS		52.33 295	50.19 298	58.75 291	62.10 328	45.14 236	65.75 284	40.38 354	43.60 299	53.52 299	72.65 293	9.16 354	65.87 301	50.41 185	54.18 321	65.24 330

cascas		65.98 191	63.42 202	73.64 102	77.26 166	52.58 137	72.26 229	77.21 185	48.56 253	61.21 220	74.60 285	32.57 282	85.82 71	50.38 186	76.75 134	82.52 177

IS-MVSNet		71.57 81	71.00 80	73.27 121	78.86 117	45.63 233	80.22 85	78.69 158	64.14 29	66.46 145	87.36 54	49.30 82	85.60 72	50.26 187	83.71 55	88.59 3

PatchFormer-LS_test		62.20 232	60.59 235	67.04 220	72.18 259	46.82 226	70.36 255	68.62 255	51.92 216	59.19 243	70.23 308	36.86 235	75.07 264	50.23 188	65.68 259	79.23 234

WR-MVS		68.47 146	68.47 121	68.44 210	80.20 96	39.84 273	73.75 209	76.07 196	64.68 21	68.11 120	83.63 120	50.39 73	79.14 215	49.78 189	69.66 226	86.34 52

CVMVSNet		59.63 252	59.14 242	61.08 284	74.47 205	38.84 282	75.20 184	68.74 254	31.15 341	58.24 255	76.51 269	32.39 283	68.58 288	49.77 190	65.84 257	75.81 267

CostFormer		64.04 210	62.51 211	68.61 208	71.88 264	45.77 231	71.30 241	70.60 236	47.55 265	64.31 175	76.61 267	41.63 177	79.62 202	49.74 191	69.00 230	80.42 217

新几何1				70.76 180	85.66 27	61.13 31		66.43 270	44.68 288	70.29 74	86.64 65	41.29 185	75.23 263	49.72 192	81.75 70	75.93 266

test-LLR		58.15 266	58.13 258	58.22 293	68.57 296	44.80 238	65.46 287	57.92 317	50.08 243	55.44 279	69.82 311	32.62 279	57.44 326	49.66 193	73.62 154	72.41 306

test-mter		56.42 276	55.82 273	58.22 293	68.57 296	44.80 238	65.46 287	57.92 317	39.94 323	55.44 279	69.82 311	21.92 332	57.44 326	49.66 193	73.62 154	72.41 306

Anonymous202405211		66.84 178	65.99 174	69.40 199	80.19 97	42.21 260	71.11 245	71.31 231	58.80 124	67.90 122	86.39 73	29.83 296	79.65 200	49.60 195	78.78 109	86.33 53

DWT-MVSNet_test		61.90 235	59.93 239	67.83 213	71.98 263	46.09 230	71.03 247	69.71 239	50.09 242	58.51 253	70.62 305	30.21 293	77.63 236	49.28 196	67.91 244	79.78 227

tpmrst		58.24 264	58.70 252	56.84 300	66.97 305	34.32 317	69.57 261	61.14 307	47.17 269	58.58 252	71.60 298	41.28 186	60.41 317	49.20 197	62.84 280	75.78 268

pm-mvs1		65.24 199	64.97 189	66.04 237	72.38 255	39.40 278	72.62 223	75.63 200	55.53 179	62.35 203	83.18 129	47.45 121	76.47 249	49.06 198	66.54 253	82.24 181

gm-plane-assit					71.40 271	41.72 265			48.85 252		73.31 292		82.48 157	48.90 199

CMPMVS	42.80 21	57.81 269	55.97 272	63.32 264	60.98 334	47.38 221	64.66 294	69.50 244	32.06 340	46.83 321	77.80 244	29.50 298	71.36 276	48.68 200	73.75 152	71.21 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 182	66.42 168	67.37 217	76.17 180	41.73 264	70.41 254	76.14 195	53.99 201	65.98 151	83.51 123	49.48 79	76.24 252	48.60 201	73.46 158	84.14 138

OurMVSNet-221017-0		61.37 241	58.63 254	69.61 194	72.05 261	48.06 213	73.93 207	72.51 226	47.23 268	54.74 285	80.92 183	21.49 333	81.24 176	48.57 202	56.22 314	79.53 230

OpenMVS	61.03 9	68.85 134	67.56 136	72.70 135	74.26 210	53.99 118	81.21 75	81.34 89	52.70 209	62.75 187	85.55 90	38.86 206	84.14 109	48.41 203	83.01 58	79.97 223

TESTMET0.1,1		55.28 283	54.90 279	56.42 301	66.56 310	43.67 250	65.46 287	56.27 326	39.18 325	53.83 295	67.44 319	24.21 327	55.46 340	48.04 204	73.11 166	70.13 320

K. test v3		60.47 244	57.11 262	70.56 183	73.74 221	48.22 210	75.10 187	62.55 300	58.27 133	53.62 298	76.31 271	27.81 307	81.59 171	47.42 205	39.18 344	81.88 187

pmmvs6		63.69 212	62.82 209	66.27 231	70.63 277	39.27 279	73.13 214	75.47 202	52.69 210	59.75 237	82.30 143	39.71 196	77.03 244	47.40 206	64.35 270	82.53 176

GBi-Net		67.21 168	66.55 164	69.19 200	77.63 150	43.33 252	77.31 142	77.83 174	56.62 157	65.04 165	82.70 132	41.85 174	80.33 192	47.18 207	72.76 172	83.92 141

test1		67.21 168	66.55 164	69.19 200	77.63 150	43.33 252	77.31 142	77.83 174	56.62 157	65.04 165	82.70 132	41.85 174	80.33 192	47.18 207	72.76 172	83.92 141

FMVSNet3		66.32 188	65.61 179	68.46 209	76.48 179	42.34 259	74.98 190	77.15 186	55.83 173	65.04 165	81.16 174	39.91 193	80.14 196	47.18 207	72.76 172	82.90 172

FMVSNet2		66.93 176	66.31 173	68.79 206	77.63 150	42.98 255	76.11 166	77.47 180	56.62 157	65.22 164	82.17 147	41.85 174	80.18 195	47.05 210	72.72 175	83.20 163

testdata2													72.18 275	46.95 211

BH-RMVSNet		68.81 135	67.42 141	72.97 126	80.11 99	52.53 138	74.26 200	76.29 193	58.48 129	68.38 114	84.20 109	42.59 165	83.83 118	46.53 212	75.91 137	82.56 175

AdaColmap		69.99 112	68.66 118	73.97 88	84.94 42	57.83 67	82.63 49	78.71 157	56.28 166	64.34 173	84.14 110	41.57 178	87.06 41	46.45 213	78.88 106	77.02 256

EG-PatchMatch MVS		64.71 204	62.87 207	70.22 186	77.68 148	53.48 124	77.99 125	78.82 154	53.37 205	56.03 275	77.41 258	24.75 326	84.04 111	46.37 214	73.42 159	73.14 297

1112_ss		64.00 211	63.36 203	65.93 240	79.28 108	42.58 258	71.35 240	72.36 228	46.41 273	60.55 228	77.89 242	46.27 134	73.28 269	46.18 215	69.97 218	81.92 186

testpf		44.11 315	45.40 310	40.26 337	60.52 336	27.34 344	33.26 354	54.33 335	45.87 281	41.08 338	60.26 337	16.46 337	59.14 321	46.09 216	50.68 331	34.31 353

FMVSNet1		66.70 181	65.87 176	69.19 200	77.49 157	43.33 252	77.31 142	77.83 174	56.45 162	64.60 172	82.70 132	38.08 215	80.33 192	46.08 217	72.31 183	83.92 141

HyFIR lowres test		65.67 193	63.01 206	73.67 99	79.97 101	55.65 103	69.07 265	75.52 201	42.68 307	63.53 179	77.95 239	40.43 192	81.64 169	46.01 218	71.91 186	83.73 149

lessismore_v0				69.91 191	71.42 270	47.80 215		50.90 342		50.39 313	75.56 278	27.43 311	81.33 175	45.91 219	34.10 347	80.59 215

CHOSEN 1792x2688		65.08 202	62.84 208	71.82 157	81.49 72	56.26 90	66.32 282	74.20 217	40.53 319	63.16 183	78.65 232	41.30 184	77.80 235	45.80 220	74.09 148	81.40 193

LCM-MVSNet-Re		61.88 237	61.35 229	63.46 261	74.58 203	31.48 335	61.42 306	58.14 316	58.71 125	53.02 302	79.55 220	43.07 164	76.80 246	45.69 221	77.96 118	82.11 184

ambc				65.13 253	63.72 324	37.07 296	47.66 342	78.78 156		54.37 291	71.42 300	11.24 350	80.94 181	45.64 222	53.85 323	77.38 249

MS-PatchMatch		62.42 230	61.46 228	65.31 252	75.21 194	52.10 144	72.05 235	74.05 218	46.41 273	57.42 267	74.36 286	34.35 258	77.57 238	45.62 223	73.67 153	66.26 328

ACMH+	57.40 11	66.12 189	64.06 192	72.30 153	77.79 146	52.83 133	80.39 83	78.03 172	57.30 140	57.47 266	82.55 138	27.68 308	84.17 108	45.54 224	69.78 222	79.90 224

CR-MVSNet		59.91 248	57.90 260	65.96 238	69.96 286	52.07 145	65.31 290	63.15 296	42.48 308	59.36 239	74.84 282	35.83 240	70.75 279	45.50 225	64.65 268	75.06 274

CDS-MVSNet		66.80 179	65.37 181	71.10 176	78.98 115	53.13 131	73.27 213	71.07 233	52.15 215	64.72 170	80.23 203	43.56 161	77.10 242	45.48 226	78.88 106	83.05 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		66.49 186	66.41 169	66.72 222	77.67 149	36.33 306	76.83 154	79.52 140	62.45 52	62.54 192	83.47 125	46.32 132	78.37 227	45.47 227	63.43 276	85.45 87

BH-untuned		68.27 152	67.29 146	71.21 172	79.74 104	53.22 128	76.06 168	77.46 182	57.19 141	66.10 150	81.61 163	45.37 144	83.50 123	45.42 228	76.68 135	76.91 260

PS-CasMVS		66.42 187	66.32 172	66.70 224	77.60 156	36.30 308	76.94 152	79.61 129	62.36 54	62.43 201	83.66 119	45.69 136	78.37 227	45.35 229	63.26 277	85.42 90

XXY-MVS		60.68 243	61.67 226	57.70 298	70.43 280	38.45 288	64.19 296	66.47 268	48.05 261	63.22 181	80.86 185	49.28 87	60.47 316	45.25 230	67.28 251	74.19 291

HY-MVS	56.14 13	64.55 207	63.89 195	66.55 225	74.73 201	41.02 268	69.96 258	74.43 214	49.29 247	61.66 212	80.92 183	47.43 122	76.68 247	44.91 231	71.69 188	81.94 185

PEN-MVS		66.60 183	66.45 166	67.04 220	77.11 168	36.56 304	77.03 150	80.42 118	62.95 41	62.51 194	84.03 113	46.69 130	79.07 216	44.22 232	63.08 279	85.51 82

test_post1							68.67 267				3.64 360	32.39 283	69.49 284	44.17 233

PMMVS		53.96 287	53.26 291	56.04 302	62.60 327	50.92 156	61.17 309	56.09 327	32.81 338	53.51 300	66.84 321	34.04 260	59.93 319	44.14 234	68.18 242	57.27 343

MVP-Stereo		65.41 197	63.80 197	70.22 186	77.62 154	55.53 105	76.30 161	78.53 162	50.59 240	56.47 273	78.65 232	39.84 194	82.68 152	44.10 235	72.12 185	72.44 305
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		65.43 196	64.02 193	69.68 193	78.73 122	58.07 65	77.82 130	70.71 235	51.49 229	61.57 214	83.58 122	38.23 213	70.82 278	43.90 236	70.10 216	80.16 221

pmmvs4		61.48 240	59.39 240	67.76 214	71.57 267	53.86 119	71.42 239	65.34 275	44.20 294	59.46 238	77.92 241	35.90 239	74.71 266	43.87 237	64.87 264	74.71 282

Test_1112_low_res		62.32 231	61.77 225	64.00 260	79.08 113	39.53 277	68.17 274	70.17 237	43.25 302	59.03 246	79.90 206	44.08 156	71.24 277	43.79 238	68.42 235	81.25 201

TransMVSNet (Re)		64.72 203	64.33 191	65.87 242	75.22 193	38.56 287	74.66 196	75.08 211	58.90 122	61.79 209	82.63 135	51.18 65	78.07 232	43.63 239	55.87 315	80.99 211

pmmvs-eth3d		58.81 260	56.31 270	66.30 228	67.61 302	52.42 142	72.30 228	64.76 279	43.55 300	54.94 284	74.19 288	28.95 301	72.60 271	43.31 240	57.21 310	73.88 294

SixPastTwentyTwo		61.65 239	58.80 251	70.20 188	75.80 185	47.22 222	75.59 175	69.68 241	54.61 192	54.11 293	79.26 225	27.07 313	82.96 136	43.27 241	49.79 332	80.41 218

BH-w/o		66.85 177	65.83 177	69.90 192	79.29 107	52.46 140	74.66 196	76.65 190	54.51 196	64.85 169	78.12 237	45.59 139	82.95 137	43.26 242	75.54 140	74.27 286

TR-MVS		66.59 185	65.07 188	71.17 174	79.18 110	49.63 198	73.48 212	75.20 206	52.95 207	67.90 122	80.33 199	39.81 195	83.68 120	43.20 243	73.56 157	80.20 220

EU-MVSNet		55.61 282	54.41 283	59.19 288	65.41 317	33.42 328	72.44 226	71.91 230	28.81 343	51.27 306	73.87 289	24.76 325	69.08 286	43.04 244	58.20 309	75.06 274

PatchmatchNet		59.84 249	58.24 255	64.65 257	73.05 242	46.70 227	69.42 262	62.18 302	47.55 265	58.88 247	71.96 297	34.49 256	69.16 285	42.99 245	63.60 274	78.07 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		67.02 174	66.92 156	67.33 219	77.95 143	37.75 293	77.57 137	82.11 72	62.03 61	62.65 189	82.48 139	50.57 71	79.46 203	42.91 246	64.01 271	84.79 114

ACMH	55.70 15	65.20 200	63.57 201	70.07 189	78.07 139	52.01 148	79.48 100	79.69 126	55.75 175	56.59 272	80.98 181	27.12 312	80.94 181	42.90 247	71.58 189	77.25 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WTY-MVS		59.75 250	60.39 236	57.85 296	72.32 257	37.83 292	61.05 310	64.18 289	45.95 280	61.91 206	79.11 227	47.01 127	60.88 315	42.50 248	69.49 228	74.83 279

TAMVS		66.78 180	65.27 184	71.33 171	79.16 112	53.67 120	73.84 208	69.59 243	52.32 214	65.28 159	81.72 158	44.49 153	77.40 240	42.32 249	78.66 112	82.92 170

LTVRE_ROB	55.42 16	63.15 221	61.23 231	68.92 204	76.57 177	47.80 215	59.92 312	76.39 191	54.35 198	58.67 249	82.46 140	29.44 299	81.49 173	42.12 250	71.14 192	77.46 248
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
sss		56.17 279	56.57 267	54.96 307	66.93 306	36.32 307	57.94 318	61.69 305	41.67 311	58.64 251	75.32 280	38.72 207	56.25 335	42.04 251	66.19 255	72.31 309

UnsupCasMVSNet_eth		53.16 294	52.47 292	55.23 305	59.45 341	33.39 329	59.43 314	69.13 249	45.98 277	50.35 314	72.32 295	29.30 300	58.26 324	42.02 252	44.30 341	74.05 292

tpm2		62.07 234	60.10 238	67.99 212	72.79 249	43.86 248	71.05 246	66.85 267	43.14 304	62.77 185	75.39 279	38.32 211	80.80 185	41.69 253	68.88 231	79.32 232

PLC	56.13 14	65.09 201	63.21 204	70.72 182	81.04 82	54.87 113	78.57 107	77.47 180	48.51 254	55.71 276	81.89 156	33.71 264	79.71 199	41.66 254	70.37 207	77.58 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 287	53.69 288	54.79 309	66.12 313	31.96 334	62.34 303	49.05 344	44.42 293	55.54 277	71.33 301	30.22 292	56.70 330	41.65 255	62.54 283	75.71 269

DTE-MVSNet		65.58 194	65.34 182	66.31 227	76.06 183	34.79 313	76.43 159	79.38 145	62.55 50	61.66 212	83.83 116	45.60 138	79.15 214	41.64 256	60.88 294	85.00 106

Anonymous20240521		64.54 208	64.00 194	66.15 233	74.87 197	34.97 312	74.13 202	79.35 146	56.54 161	58.65 250	83.31 126	37.31 221	80.04 197	41.39 257	70.44 201	83.45 157

Patchmatch-test1		59.75 250	58.00 259	64.98 255	74.14 218	48.06 213	63.35 298	63.23 295	49.13 249	59.33 242	71.46 299	37.45 219	69.59 283	41.39 257	62.57 282	77.30 250

PAPM		67.92 162	66.69 163	71.63 162	78.09 138	49.02 203	77.09 148	81.24 94	51.04 236	60.91 224	83.98 115	47.71 116	84.99 84	40.81 259	79.32 101	80.90 212

tpm		57.34 271	58.16 256	54.86 308	71.80 266	34.77 314	67.47 278	56.04 328	48.20 259	60.10 231	76.92 261	37.17 224	53.41 343	40.76 260	65.01 263	76.40 263

tpmp4_e23		62.71 228	60.13 237	70.45 185	73.40 227	48.39 209	72.82 218	69.49 245	44.88 285	59.91 233	74.99 281	37.79 217	81.47 174	40.22 261	67.71 248	81.48 191

F-COLMAP		63.05 222	60.87 234	69.58 197	76.99 172	53.63 122	78.12 117	76.16 194	47.97 262	52.41 303	81.61 163	27.87 306	78.11 231	40.07 262	66.66 252	77.00 257

Patchmtry		57.16 272	56.47 268	59.23 287	69.17 293	34.58 316	62.98 299	63.15 296	44.53 290	56.83 270	74.84 282	35.83 240	68.71 287	40.03 263	60.91 293	74.39 285

pmmvs5		56.47 275	55.68 274	58.86 290	61.41 331	36.71 303	66.37 281	62.75 299	40.38 320	53.70 296	76.62 266	34.56 254	67.05 293	40.02 264	65.27 261	72.83 299

EPNet_dtu		61.90 235	61.97 220	61.68 274	72.89 248	39.78 274	75.85 173	65.62 273	55.09 186	54.56 288	79.36 223	37.59 218	67.02 294	39.80 265	76.95 129	78.25 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		63.69 212	63.88 196	63.14 266	74.75 200	31.04 336	71.16 244	63.64 292	56.32 164	59.80 236	84.99 95	44.51 151	75.46 255	39.12 266	80.62 76	82.92 170

PVSNet	50.76 19	58.40 263	57.39 261	61.42 276	75.53 189	44.04 247	61.43 305	63.45 293	47.04 270	56.91 269	73.61 291	27.00 314	64.76 303	39.12 266	72.40 180	75.47 271

MDTV_nov1_ep13_2view						25.89 347	61.22 308		40.10 321	51.10 307		32.97 272		38.49 268		78.61 238

our_test_3		56.49 274	54.42 282	62.68 270	69.51 290	45.48 234	66.08 283	61.49 306	44.11 297	50.73 311	69.60 313	33.05 270	68.15 289	38.38 269	56.86 311	74.40 284

tpm cat1		59.25 258	56.95 265	66.15 233	72.19 258	46.96 224	68.09 275	65.76 272	40.03 322	57.81 264	70.56 306	38.32 211	74.51 267	38.26 270	61.50 289	77.00 257

USDC		56.35 277	54.24 285	62.69 269	64.74 319	40.31 271	65.05 292	73.83 219	43.93 298	47.58 317	77.71 247	15.36 340	75.05 265	38.19 271	61.81 286	72.70 300

MSDG		61.81 238	59.23 241	69.55 198	72.64 251	52.63 136	70.45 253	75.81 199	51.38 231	53.70 296	76.11 272	29.52 297	81.08 180	37.70 272	65.79 258	74.93 278

MDTV_nov1_ep13			57.00 264		72.73 250	38.26 289	65.02 293	64.73 280	44.74 287	55.46 278	72.48 294	32.61 281	70.47 281	37.47 273	67.75 247

gg-mvs-nofinetune		57.86 268	56.43 269	62.18 272	72.62 252	35.35 311	66.57 279	56.33 325	50.65 238	57.64 265	57.10 341	30.65 289	76.36 250	37.38 274	78.88 106	74.82 280

RPSCF		55.80 281	54.22 286	60.53 286	65.13 318	42.91 257	64.30 295	57.62 319	36.84 332	58.05 256	82.28 144	28.01 305	56.24 336	37.14 275	58.61 308	82.44 180

PatchT		53.17 293	53.44 290	52.33 319	68.29 300	25.34 350	58.21 317	54.41 334	44.46 292	54.56 288	69.05 314	33.32 268	60.94 314	36.93 276	61.76 287	70.73 319

YYNet1		50.73 300	48.96 299	56.03 303	61.10 333	41.78 263	51.94 334	56.44 324	40.94 316	44.84 326	67.80 318	30.08 294	55.08 341	36.77 277	50.71 329	71.22 315

TAPA-MVS	59.36 10	66.60 183	65.20 185	70.81 179	76.63 175	48.75 206	76.52 158	80.04 123	50.64 239	65.24 162	84.93 96	39.15 203	78.54 222	36.77 277	76.88 130	85.14 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 301	48.95 300	56.00 304	61.17 332	41.84 262	51.90 335	56.45 323	40.96 315	44.79 327	67.84 317	30.04 295	55.07 342	36.71 279	50.69 330	71.11 318

ppachtmachnet_test		58.06 267	55.38 276	66.10 236	69.51 290	48.99 204	68.01 276	66.13 271	44.50 291	54.05 294	70.74 304	32.09 285	72.34 273	36.68 280	56.71 313	76.99 259

tpmvs		58.47 262	56.95 265	63.03 268	70.20 283	41.21 267	67.90 277	67.23 265	49.62 246	54.73 286	70.84 303	34.14 259	76.24 252	36.64 281	61.29 292	71.64 313

CHOSEN 280x420		47.83 306	46.36 307	52.24 320	67.37 304	49.78 192	38.91 352	43.11 353	35.00 335	43.27 334	63.30 333	28.95 301	49.19 348	36.53 282	60.80 296	57.76 342

PatchMatch-RL		56.25 278	54.55 281	61.32 278	77.06 169	56.07 95	65.57 286	54.10 337	44.13 296	53.49 301	71.27 302	25.20 323	66.78 295	36.52 283	63.66 273	61.12 337

RPMNet		58.70 261	56.29 271	65.96 238	69.96 286	52.07 145	65.31 290	62.15 303	43.20 303	59.36 239	70.15 310	35.37 248	70.75 279	36.42 284	64.65 268	75.06 274

ITE_SJBPF				62.09 273	66.16 312	44.55 243		64.32 282	47.36 267	55.31 281	80.34 198	19.27 334	62.68 310	36.29 285	62.39 284	79.04 235

JIA-IIPM		51.56 298	47.68 306	63.21 265	64.61 320	50.73 162	47.71 341	58.77 314	42.90 305	48.46 316	51.72 345	24.97 324	70.24 282	36.06 286	53.89 322	68.64 326

OpenMVS_ROB	52.78 18	60.03 245	58.14 257	65.69 244	70.47 279	44.82 237	75.33 179	70.86 234	45.04 284	56.06 274	76.00 273	26.89 315	79.65 200	35.36 287	67.29 250	72.60 301

GG-mvs-BLEND				62.34 271	71.36 272	37.04 297	69.20 264	57.33 320		54.73 286	65.48 327	30.37 290	77.82 234	34.82 288	74.93 142	72.17 311

UnsupCasMVSNet_bld		50.07 302	48.87 301	53.66 312	60.97 335	33.67 327	57.62 319	64.56 281	39.47 324	47.38 318	64.02 330	27.47 309	59.32 320	34.69 289	43.68 342	67.98 327

MDA-MVSNet-bldmvs		53.87 289	50.81 296	63.05 267	66.25 311	48.58 207	56.93 321	63.82 291	48.09 260	41.22 337	70.48 307	30.34 291	68.00 290	34.24 290	45.92 339	72.57 302

dp		51.89 297	51.60 295	52.77 317	68.44 299	32.45 331	62.36 302	54.57 333	44.16 295	49.31 315	67.91 316	28.87 303	56.61 331	33.89 291	54.89 318	69.24 325

AllTest		57.08 273	54.65 280	64.39 258	71.44 268	49.03 201	69.92 259	67.30 263	45.97 278	47.16 319	79.77 210	17.47 335	67.56 291	33.65 292	59.16 306	76.57 261

TestCases				64.39 258	71.44 268	49.03 201		67.30 263	45.97 278	47.16 319	79.77 210	17.47 335	67.56 291	33.65 292	59.16 306	76.57 261

FMVSNet5		55.86 280	54.93 278	58.66 292	71.05 274	36.35 305	64.18 297	62.48 301	46.76 271	50.66 312	74.73 284	25.80 320	64.04 305	33.11 294	65.57 260	75.59 270

DP-MVS		65.68 192	63.66 200	71.75 158	84.93 43	56.87 84	80.74 80	73.16 224	53.06 206	59.09 245	82.35 141	36.79 236	85.94 69	32.82 295	69.96 219	72.45 304

PVSNet_0	43.31 20	47.46 308	45.64 308	52.92 316	67.60 303	44.65 240	54.06 327	54.64 332	41.59 312	46.15 323	58.75 340	30.99 287	58.66 322	32.18 296	24.81 350	55.46 344

TinyColmap		54.14 286	51.72 294	61.40 277	66.84 307	41.97 261	66.52 280	68.51 256	44.81 286	42.69 336	75.77 276	11.66 348	72.94 270	31.96 297	56.77 312	69.27 324

MIMVSNet		57.35 270	57.07 263	58.22 293	74.21 211	37.18 295	62.46 301	60.88 308	48.88 251	55.29 282	75.99 275	31.68 286	62.04 312	31.87 298	72.35 181	75.43 272

conf200view11		63.38 214	62.41 213	66.29 230	77.31 159	38.66 284	72.65 219	69.11 250	57.07 142	62.45 196	81.03 178	37.01 227	79.17 210	31.84 299	73.25 162	81.03 207

thres100view900		63.28 218	62.41 213	65.89 241	77.31 159	38.66 284	72.65 219	69.11 250	57.07 142	62.45 196	81.03 178	37.01 227	79.17 210	31.84 299	73.25 162	79.83 225

tfpn200view9		63.18 220	62.18 218	66.21 232	76.85 173	39.62 275	71.96 237	69.44 246	56.63 155	62.61 190	79.83 208	37.18 222	79.17 210	31.84 299	73.25 162	79.83 225

thres400		63.31 216	62.18 218	66.72 222	76.85 173	39.62 275	71.96 237	69.44 246	56.63 155	62.61 190	79.83 208	37.18 222	79.17 210	31.84 299	73.25 162	81.36 194

pmmvs3		44.92 312	41.95 317	53.86 311	52.58 348	43.55 251	62.11 304	46.90 351	26.05 347	40.63 340	60.19 338	11.08 351	57.91 325	31.83 303	46.15 338	60.11 339

LF4IMVS		42.95 316	42.26 316	45.04 331	48.30 352	32.50 330	54.80 325	48.49 346	28.03 344	40.51 341	70.16 309	9.24 353	43.89 353	31.63 304	49.18 336	58.72 340

COLMAP_ROB	52.97 17	61.27 242	58.81 244	68.64 207	74.63 202	52.51 139	78.42 112	73.30 222	49.92 245	50.96 308	81.51 166	23.06 329	79.40 204	31.63 304	65.85 256	74.01 293
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 307	47.73 305	47.06 328	58.81 342	9.37 361	48.78 340	59.21 312	43.28 301	44.22 329	68.66 315	25.67 321	57.20 329	31.57 306	49.35 335	74.62 283

thres600view7		63.30 217	62.27 216	66.41 226	77.18 167	38.87 281	72.35 227	69.11 250	56.98 146	62.37 202	80.96 182	37.01 227	79.00 220	31.43 307	73.05 168	81.36 194

tfpn111		63.33 215	62.34 215	66.30 228	77.31 159	38.66 284	72.65 219	69.11 250	57.07 142	62.45 196	81.03 178	37.01 227	79.23 207	31.38 308	73.09 167	81.03 207

thres200		62.20 232	61.16 232	65.34 251	75.38 192	39.99 272	69.60 260	69.29 248	55.64 178	61.87 207	76.99 260	37.07 226	78.96 221	31.28 309	73.28 161	77.06 255

LCM-MVSNet		40.30 321	35.88 326	53.57 313	42.24 355	29.15 340	45.21 346	60.53 309	22.23 351	28.02 348	50.98 347	3.72 362	61.78 313	31.22 310	38.76 345	69.78 321

test0.0.03 1		53.32 292	53.59 289	52.50 318	62.81 326	29.45 339	59.51 313	54.11 336	50.08 243	54.40 290	74.31 287	32.62 279	55.92 337	30.50 311	63.95 272	72.15 312

tfpn_ndepth		59.57 253	59.02 243	61.23 279	73.81 219	35.60 310	69.40 263	65.59 274	50.96 237	57.96 263	77.72 246	34.81 251	75.91 254	30.36 312	70.57 199	72.18 310

Anonymous20231206		55.10 285	55.30 277	54.48 310	69.81 289	33.94 320	62.91 300	62.13 304	41.08 314	55.18 283	75.65 277	32.75 277	56.59 332	30.32 313	67.86 245	72.91 298

tfpnnormal		62.47 229	61.63 227	64.99 254	74.81 199	39.01 280	71.22 242	73.72 220	55.22 185	60.21 230	80.09 205	41.26 187	76.98 245	30.02 314	68.09 243	78.97 237

view600		62.77 224	61.84 221	65.55 245	77.28 162	36.87 298	72.15 231	67.78 258	56.79 149	61.46 215	81.92 152	36.88 231	78.42 223	29.86 315	72.46 176	81.36 194

view800		62.77 224	61.84 221	65.55 245	77.28 162	36.87 298	72.15 231	67.78 258	56.79 149	61.46 215	81.92 152	36.88 231	78.42 223	29.86 315	72.46 176	81.36 194

conf0.05thres1000		62.77 224	61.84 221	65.55 245	77.28 162	36.87 298	72.15 231	67.78 258	56.79 149	61.46 215	81.92 152	36.88 231	78.42 223	29.86 315	72.46 176	81.36 194

tfpn		62.77 224	61.84 221	65.55 245	77.28 162	36.87 298	72.15 231	67.78 258	56.79 149	61.46 215	81.92 152	36.88 231	78.42 223	29.86 315	72.46 176	81.36 194

test20.03		53.87 289	54.02 287	53.41 314	61.47 330	28.11 342	61.30 307	59.21 312	51.34 232	52.09 304	77.43 257	33.29 269	58.55 323	29.76 319	60.27 303	73.58 295

LS3D		64.71 204	62.50 212	71.34 170	79.72 106	55.71 101	79.82 91	74.72 212	48.50 255	56.62 271	84.62 101	33.59 266	82.34 159	29.65 320	75.23 141	75.97 264

conf0.01		59.97 246	58.81 244	63.42 262	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	81.03 207

conf0.002		59.97 246	58.81 244	63.42 262	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	81.03 207

thresconf0.02		59.40 254	58.81 244	61.17 280	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	74.25 287

tfpn_n400		59.40 254	58.81 244	61.17 280	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	74.25 287

tfpnconf		59.40 254	58.81 244	61.17 280	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	74.25 287

tfpnview11		59.40 254	58.81 244	61.17 280	74.15 212	33.83 321	68.32 268	64.22 283	51.79 218	58.04 257	79.57 214	35.41 242	75.41 256	29.57 321	68.26 236	74.25 287

testgi		51.90 296	52.37 293	50.51 323	60.39 337	23.55 353	58.42 316	58.15 315	49.03 250	51.83 305	79.21 226	22.39 330	55.59 338	29.24 327	62.64 281	72.40 308

tfpn1000		59.24 259	58.70 252	60.86 285	73.75 220	33.99 319	68.86 266	63.98 290	51.25 235	57.29 268	79.51 222	34.58 253	75.26 262	29.08 328	69.99 217	73.32 296

MIMVSNet1		55.17 284	54.31 284	57.77 297	70.03 285	32.01 333	65.68 285	64.81 278	49.19 248	46.75 322	76.00 273	25.53 322	64.04 305	28.65 329	62.13 285	77.26 253

TDRefinement		53.44 291	50.72 297	61.60 275	64.31 322	46.96 224	70.89 248	65.27 277	41.78 309	44.61 328	77.98 238	11.52 349	66.36 297	28.57 330	51.59 327	71.49 314

ADS-MVSNet2		51.33 299	48.76 302	59.07 289	66.02 315	44.60 241	50.90 336	59.76 311	36.90 330	50.74 309	66.18 325	26.38 316	63.11 307	27.17 331	54.76 319	69.50 322

ADS-MVSNet		48.48 305	47.77 304	50.63 322	66.02 315	29.92 338	50.90 336	50.87 343	36.90 330	50.74 309	66.18 325	26.38 316	52.47 345	27.17 331	54.76 319	69.50 322

Patchmatch-test		49.08 303	48.28 303	51.50 321	64.40 321	30.85 337	45.68 344	48.46 347	35.60 334	46.10 325	72.10 296	34.47 257	46.37 349	27.08 333	60.65 297	77.27 252

MVS-HIRNet		45.52 311	44.48 313	48.65 326	68.49 298	34.05 318	59.41 315	44.50 352	27.03 345	37.96 343	50.47 348	26.16 319	64.10 304	26.74 334	59.52 304	47.82 347

test_0402		63.25 219	61.01 233	69.96 190	80.00 100	54.37 116	76.86 153	72.02 229	54.58 194	58.71 248	80.79 188	35.00 250	84.36 105	26.41 335	64.71 265	71.15 317

no-one		40.85 320	36.09 324	55.14 306	48.55 351	38.72 283	42.15 350	62.92 298	34.60 337	23.55 350	49.74 349	12.21 346	66.16 299	26.27 336	24.84 349	60.54 338

N_pmnet		39.35 322	40.28 319	36.54 339	63.76 323	1.62 365	49.37 339	0.76 366	34.62 336	43.61 333	66.38 324	26.25 318	42.57 355	26.02 337	51.77 326	65.44 329

LP		48.51 304	45.51 309	57.52 299	62.86 325	44.53 244	52.38 333	59.84 310	38.11 327	42.81 335	61.02 335	23.23 328	63.02 308	24.10 338	45.24 340	65.02 331

DSMNet-mixed		39.30 323	38.72 322	41.03 336	51.22 349	19.66 355	45.53 345	31.35 359	15.83 355	39.80 342	67.42 320	22.19 331	45.13 352	22.43 339	52.69 324	58.31 341

1111		44.40 314	45.00 311	42.61 335	57.55 344	17.33 358	53.82 330	57.05 321	40.78 317	44.11 330	66.57 322	13.37 343	45.77 350	22.15 340	49.58 333	64.73 332

.test1245		34.88 326	39.49 321	21.04 347	57.55 344	17.33 358	53.82 330	57.05 321	40.78 317	44.11 330	66.57 322	13.37 343	45.77 350	22.15 340	0.00 361	0.03 362

wuykxyi23d		28.12 330	22.54 335	44.87 333	34.97 361	32.11 332	37.96 353	47.31 349	13.32 356	9.29 360	23.72 356	0.45 367	56.58 333	21.85 342	13.98 354	45.93 349

ANet_high		41.38 319	37.47 323	53.11 315	39.73 358	24.45 352	56.94 320	69.69 240	47.65 264	26.04 349	52.32 344	12.44 345	62.38 311	21.80 343	10.61 358	72.49 303

test1235678		45.66 309	44.46 314	49.26 324	59.88 339	28.68 341	56.36 323	55.54 331	39.12 326	40.89 339	63.40 332	14.41 342	57.32 328	21.05 344	49.47 334	61.78 335

new_pmnet		34.13 328	34.29 327	33.64 340	52.63 347	18.23 357	44.43 348	33.90 357	22.81 350	30.89 346	53.18 343	10.48 352	35.72 359	20.77 345	39.51 343	46.98 348

testus		44.59 313	43.87 315	46.76 329	59.85 340	24.65 351	53.86 328	55.82 329	36.26 333	43.97 332	63.42 331	8.39 355	53.14 344	20.70 346	52.52 325	62.51 333

test2356		45.61 310	44.66 312	48.47 327	60.15 338	28.08 343	52.44 332	52.83 340	38.01 328	46.13 324	60.98 336	15.08 341	55.54 339	20.43 347	55.85 316	61.78 335

testmv		42.25 317	40.11 320	48.66 325	53.23 346	27.02 345	56.62 322	55.74 330	37.25 329	33.10 345	59.52 339	7.78 356	56.58 333	19.61 348	38.13 346	62.40 334

PNet_i23d		27.88 331	25.99 331	33.55 341	47.54 353	25.89 347	47.24 343	32.91 358	21.44 352	15.90 354	38.09 351	0.85 366	42.76 354	16.90 349	13.03 356	32.00 354

test12356		36.16 325	35.94 325	36.83 338	50.82 350	8.52 362	44.84 347	53.49 339	32.72 339	30.11 347	55.08 342	7.11 358	49.47 347	16.60 350	32.68 348	52.50 345

PMMVS2		27.40 332	25.91 332	31.87 343	39.46 359	6.57 363	31.17 355	28.52 360	23.96 348	20.45 352	48.94 350	4.20 361	37.94 358	16.51 351	19.97 351	51.09 346

Gipuma		34.77 327	31.91 329	43.33 334	62.05 329	37.87 291	20.39 357	67.03 266	23.23 349	18.41 353	25.84 354	4.24 360	62.73 309	14.71 352	51.32 328	29.38 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 318	41.11 318	45.39 330	58.03 343	41.01 269	49.50 338	53.81 338	30.07 342	33.71 344	64.03 328	11.69 347	52.08 346	14.01 353	55.11 317	43.09 350

tmp_tt		9.43 338	11.14 339	4.30 350	2.38 364	4.40 364	13.62 359	16.08 363	0.39 360	15.89 355	13.06 358	15.80 339	5.54 363	12.63 354	10.46 359	2.95 359

MVE	17.77 23	21.41 335	17.77 337	32.34 342	34.34 362	25.44 349	16.11 358	24.11 361	11.19 357	13.22 356	31.92 352	1.58 365	30.95 360	10.47 355	17.03 352	40.62 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 333	22.73 334	26.90 345	42.02 356	20.67 354	42.66 349	35.70 355	17.43 353	10.28 358	25.05 355	6.42 359	42.39 356	10.28 356	14.71 353	17.63 356

PMVS	28.69 22	36.22 324	33.29 328	45.02 332	36.82 360	35.98 309	54.68 326	48.74 345	26.31 346	21.02 351	51.61 346	2.88 364	60.10 318	9.99 357	47.58 337	38.99 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 334	21.84 336	26.36 346	40.20 357	19.53 356	41.95 351	34.64 356	17.09 354	9.73 359	22.83 357	7.29 357	42.22 357	9.18 358	13.66 355	17.32 357

DeepMVS_CX				12.03 349	17.97 363	10.91 360		10.60 364	7.46 358	11.07 357	28.36 353	3.28 363	11.29 362	8.01 359	9.74 360	13.89 358

wuyk23d		13.32 337	12.52 338	15.71 348	47.54 353	26.27 346	31.06 356	1.98 365	4.93 359	5.18 361	1.94 362	0.45 367	18.54 361	6.81 360	12.83 357	2.33 360

testmvs		4.52 341	6.03 342	0.01 352	0.01 365	0.00 367	53.86 328	0.00 367	0.01 361	0.04 362	0.27 363	0.00 370	0.00 364	0.04 361	0.00 361	0.03 362

test123		4.73 340	6.30 341	0.02 351	0.01 365	0.01 366	56.36 323	0.00 367	0.01 361	0.04 362	0.21 364	0.01 369	0.00 364	0.03 362	0.00 361	0.04 361

cdsmvs_eth3d_5k		17.50 336	23.34 333	0.00 353	0.00 367	0.00 367	0.00 360	78.63 159	0.00 363	0.00 364	82.18 145	49.25 91	0.00 364	0.00 363	0.00 361	0.00 364

pcd_1.5k_mvsjas		3.92 342	5.23 343	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	47.05 124	0.00 364	0.00 363	0.00 361	0.00 364

pcd1.5k->3k		30.06 329	30.56 330	28.55 344	78.81 119	0.00 367	0.00 360	82.07 73	0.00 363	0.00 364	0.00 365	39.61 197	0.00 364	0.00 363	74.56 143	85.66 76

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

ab-mvs-re		6.49 339	8.65 340	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	77.89 242	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 367	0.00 360	0.00 367	0.00 363	0.00 364	0.00 365	0.00 370	0.00 364	0.00 363	0.00 361	0.00 364

GSMVS																78.05 243

test_part2					87.58 3	60.47 38				83.42 2

test_part1								86.64 3				65.59 1			90.06 4	86.78 42

sam_mvs1												34.74 252				78.05 243

sam_mvs												33.43 267

MTGPA								80.97 102

test_post											3.55 361	33.90 262	66.52 296

patchmatchnet-post											64.03 328	34.50 255	74.27 268

MTMP							86.03 12	17.08 362

TEST9					85.58 30	61.59 26	81.62 68	81.26 92	55.65 177	74.93 25	88.81 42	53.70 44	84.68 99

test_8					85.40 33	60.96 33	81.54 71	81.18 95	55.86 171	74.81 28	88.80 44	53.70 44	84.45 104

agg_prior					85.04 37	59.96 43		81.04 98		74.68 29			84.04 111

test_prior4						62.51 17	82.08 62

test_prior				76.69 47	84.20 49	57.27 74		84.88 17					86.43 58			86.38 47

新几何2							76.12 165

旧先验1					83.04 56	53.15 129		67.52 262			87.85 51	44.08 156			80.76 75	78.03 245

原ACMM2							79.02 101

test222					83.14 55	58.68 59	72.57 224	63.45 293	41.78 309	67.56 133	86.12 77	37.13 225			78.73 111	74.98 277

segment_acmp												54.23 37

testdata1							72.65 219		60.50 78

test12				77.76 35	84.52 46	58.41 61		83.36 52		72.93 54		54.61 34	88.05 24		88.12 22	86.81 40

plane_prior7					81.41 73	55.96 97

plane_prior6					81.20 80	56.24 91						45.26 146

plane_prior4											86.10 78

plane_prior3						56.09 94			63.92 30	69.27 101

plane_prior2							84.22 26		64.52 24

plane_prior1					81.27 78

plane_prior						56.31 87	83.58 35		63.19 39						80.48 81

n2								0.00 367

nn								0.00 367

door-mid								47.19 350

test11								83.47 47

door								47.60 348

HQP5-MVS						54.94 110

HQP-NCC					80.66 85		82.31 57		62.10 57	67.85 124

ACMP_Plane					80.66 85		82.31 57		62.10 57	67.85 124

HQP4-MVS										67.85 124			86.93 42			84.32 124

HQP3-MVS								83.90 36							80.35 84

HQP2-MVS												45.46 140

NP-MVS					80.98 83	56.05 96					85.54 91

ACMMP++_ref															74.07 150

ACMMP++															72.16 184

Test By Simon												48.33 109

High-resolution multi-view benchmark