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 81

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
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

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

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

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

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

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.
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

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

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

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

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

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
test9_res														75.28 17	88.31 19	83.81 140

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

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

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

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
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

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

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

#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

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

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

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

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
agg_prior2														73.09 32	87.93 26	84.33 118

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

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

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

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
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

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	6.49 353	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

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

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

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

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

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

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

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

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

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

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
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_prior5								84.01 31					87.21 36	68.16 51	80.58 78	84.65 112

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

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

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

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

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

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
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

BP-MVS														67.04 61

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

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
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

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

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

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

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

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

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

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

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

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

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

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.
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	33.01 265	80.34 187	64.66 77	67.64 243	84.23 126

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

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
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

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

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

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

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

testing_2		66.02 185	63.77 192	72.76 133	66.03 308	50.48 170	72.93 211	80.36 117	54.41 191	54.25 286	76.76 259	30.89 282	83.16 131	64.19 88	74.08 145	84.65 112

V42		68.65 134	67.35 140	72.56 138	68.93 287	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

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

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

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

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

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	32.79 268	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	32.79 268	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	32.79 268	79.02 210	63.81 94	77.31 119	81.22 196

Test4		67.77 158	65.97 170	73.19 123	68.64 288	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

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

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	28.28 297	81.81 164	63.48 99	70.62 192	83.67 148

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	27.45 303	81.84 163	63.45 100	70.56 195	83.69 145

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

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

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

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

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

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
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

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

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

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

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

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

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

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

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

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

v52		67.09 167	65.16 181	72.87 128	66.77 302	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	66.76 303	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

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

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

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

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

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
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

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

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

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

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

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.
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

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

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

旧先验2							76.08 163		45.32 278	76.55 16			65.56 296	58.75 136

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

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

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
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

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

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

mvs_anonymous		68.03 156	67.51 134	69.59 191	72.08 254	44.57 237	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

Patchmatch-RL test		58.16 259	55.49 269	66.15 228	67.92 295	48.89 204	60.66 304	51.07 335	47.86 257	59.36 234	62.71 328	34.02 256	72.27 269	56.41 144	59.40 299	77.30 244

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

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

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

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

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

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	65.99 294	55.67 150	63.50 269	79.42 225

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	16.24 332	79.32 199	55.64 151	71.76 182	80.47 210

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

1314		64.61 201	63.21 198	68.80 200	71.87 259	47.46 218	73.95 198	78.39 165	42.88 300	59.97 227	76.60 262	38.11 210	79.39 198	54.84 153	72.32 177	79.55 223

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

UniMVSNet_NR-MVSNet		71.11 85	71.00 79	71.44 161	79.20 106	44.13 240	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

DU-MVS		70.01 109	69.53 98	71.44 161	78.05 135	44.13 240	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

FIs		70.82 88	71.43 70	68.98 198	78.33 126	38.14 284	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

VPA-MVSNet		69.02 127	69.47 104	67.69 210	77.42 153	41.00 264	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

UniMVSNet (Re)		70.63 93	70.20 87	71.89 153	78.55 121	45.29 230	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

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

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

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

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

VNet		69.68 114	70.19 88	68.16 206	79.73 102	41.63 260	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

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

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	66.26 292	53.31 166	78.48 112	74.78 276

MVS		67.37 161	66.33 167	70.51 180	75.46 185	50.94 153	73.95 198	81.85 75	41.57 307	62.54 187	78.57 229	47.98 111	85.47 77	52.97 167	82.05 67	75.14 268

IterMVS		62.79 217	61.27 224	67.35 213	69.37 285	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.
FC-MVSNet-test		69.80 112	70.58 84	67.46 211	77.61 150	34.73 309	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

TranMVSNet+NR-MVSNet		70.36 103	70.10 90	71.17 169	78.64 120	42.97 251	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

Baseline_NR-MVSNet		67.05 169	67.56 131	65.50 243	75.65 181	37.70 288	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

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
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

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	32.71 272	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
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
VPNet		67.52 160	68.11 123	65.74 237	79.18 107	36.80 296	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

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

NR-MVSNet		69.54 118	68.85 110	71.59 160	78.05 135	43.81 244	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

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	32.86 267	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
PM-MVS		52.33 289	50.19 292	58.75 285	62.10 322	45.14 231	65.75 277	40.38 348	43.60 293	53.52 293	72.65 288	9.16 348	65.87 295	50.41 181	54.18 314	65.24 324

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	32.57 276	85.82 70	50.38 182	76.75 129	82.52 171

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

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

WR-MVS		68.47 141	68.47 117	68.44 205	80.20 95	39.84 267	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

CVMVSNet		59.63 246	59.14 236	61.08 278	74.47 199	38.84 276	75.20 180	68.74 249	31.15 335	58.24 249	76.51 263	32.39 277	68.58 283	49.77 186	65.84 251	75.81 262

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

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

test-LLR		58.15 260	58.13 252	58.22 287	68.57 289	44.80 233	65.46 280	57.92 311	50.08 237	55.44 273	69.82 306	32.62 273	57.44 320	49.66 189	73.62 150	72.41 300

test-mter		56.42 269	55.82 267	58.22 287	68.57 289	44.80 233	65.46 280	57.92 311	39.94 317	55.44 273	69.82 306	21.92 325	57.44 320	49.66 189	73.62 150	72.41 300

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	30.21 287	77.63 230	49.28 191	67.91 238	79.78 221

tpmrst		58.24 258	58.70 246	56.84 294	66.97 299	34.32 311	69.57 255	61.14 301	47.17 263	58.58 246	71.60 293	41.28 184	60.41 311	49.20 192	62.84 274	75.78 263

pm-mvs1		65.24 194	64.97 184	66.04 231	72.38 249	39.40 272	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

gm-plane-assit					71.40 265	41.72 259			48.85 246		73.31 287		82.48 155	48.90 194

CMPMVS	42.80 21	57.81 263	55.97 266	63.32 258	60.98 328	47.38 219	64.66 287	69.50 239	32.06 334	46.83 315	77.80 238	29.50 291	71.36 271	48.68 195	73.75 148	71.21 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 177	66.42 164	67.37 212	76.17 175	41.73 258	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

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	21.49 326	81.24 172	48.57 197	56.22 307	79.53 224

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

TESTMET0.1,1		55.28 277	54.90 273	56.42 295	66.56 304	43.67 245	65.46 280	56.27 320	39.18 319	53.83 289	67.44 313	24.21 320	55.46 334	48.04 199	73.11 162	70.13 314

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	27.81 300	81.59 167	47.42 200	39.18 338	81.88 181

pmmvs6		63.69 206	62.82 203	66.27 226	70.63 271	39.27 273	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

GBi-Net		67.21 164	66.55 160	69.19 195	77.63 145	43.33 247	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	43.33 247	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

FMVSNet3		66.32 183	65.61 174	68.46 204	76.48 174	42.34 254	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

FMVSNet2		66.93 172	66.31 169	68.79 201	77.63 145	42.98 250	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

testdata2													72.18 270	46.95 206

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

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

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	24.75 319	84.04 110	46.37 209	73.42 155	73.14 291

1112_ss		64.00 205	63.36 197	65.93 234	79.28 105	42.58 253	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

testpf		44.11 309	45.40 304	40.26 331	60.52 330	27.34 338	33.26 347	54.33 329	45.87 276	41.08 332	60.26 331	16.46 331	59.14 315	46.09 211	50.68 325	34.31 347

FMVSNet1		66.70 176	65.87 171	69.19 195	77.49 152	43.33 247	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

HyFIR lowres test		65.67 188	63.01 200	73.67 98	79.97 98	55.65 102	69.07 259	75.52 195	42.68 301	63.53 174	77.95 233	40.43 190	81.64 165	46.01 213	71.91 181	83.73 144

lessismore_v0				69.91 187	71.42 264	47.80 213		50.90 336		50.39 306	75.56 272	27.43 304	81.33 171	45.91 214	34.10 341	80.59 209

CHOSEN 1792x2688		65.08 197	62.84 202	71.82 155	81.49 72	56.26 90	66.32 276	74.20 212	40.53 313	63.16 178	78.65 226	41.30 182	77.80 229	45.80 215	74.09 144	81.40 187

LCM-MVSNet-Re		61.88 231	61.35 223	63.46 255	74.58 197	31.48 329	61.42 299	58.14 310	58.71 123	53.02 296	79.55 214	43.07 163	76.80 240	45.69 216	77.96 115	82.11 178

ambc				65.13 247	63.72 318	37.07 290	47.66 335	78.78 151		54.37 285	71.42 295	11.24 344	80.94 177	45.64 217	53.85 316	77.38 243

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	66.26 322

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	27.68 301	84.17 107	45.54 219	69.78 216	79.90 218

CR-MVSNet		59.91 242	57.90 254	65.96 232	69.96 279	52.07 144	65.31 283	63.15 291	42.48 302	59.36 234	74.84 276	35.83 235	70.75 274	45.50 220	64.65 262	75.06 269

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
CP-MVSNet		66.49 181	66.41 165	66.72 217	77.67 144	36.33 300	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

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

PS-CasMVS		66.42 182	66.32 168	66.70 219	77.60 151	36.30 302	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

XXY-MVS		60.68 237	61.67 220	57.70 292	70.43 273	38.45 282	64.19 289	66.47 263	48.05 255	63.22 176	80.86 179	49.28 85	60.47 310	45.25 225	67.28 245	74.19 285

HY-MVS	56.14 13	64.55 202	63.89 189	66.55 220	74.73 195	41.02 262	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

PEN-MVS		66.60 178	66.45 162	67.04 215	77.11 163	36.56 298	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

test_post1							68.67 261				3.64 354	32.39 277	69.49 279	44.17 228

PMMVS		53.96 281	53.26 285	56.04 296	62.60 321	50.92 155	61.17 302	56.09 321	32.81 332	53.51 294	66.84 315	34.04 255	59.93 313	44.14 229	68.18 236	57.27 337

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	72.44 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
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

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

Test_1112_low_res		62.32 225	61.77 219	64.00 254	79.08 110	39.53 271	68.17 268	70.17 231	43.25 296	59.03 241	79.90 200	44.08 155	71.24 272	43.79 233	68.42 229	81.25 195

TransMVSNet (Re)		64.72 198	64.33 186	65.87 236	75.22 188	38.56 281	74.66 191	75.08 205	58.90 121	61.79 204	82.63 128	51.18 63	78.07 226	43.63 234	55.87 308	80.99 205

pmmvs-eth3d		58.81 254	56.31 264	66.30 223	67.61 296	52.42 141	72.30 223	64.76 274	43.55 294	54.94 278	74.19 282	28.95 294	72.60 266	43.31 235	57.21 304	73.88 288

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	27.07 306	82.96 135	43.27 236	49.79 326	80.41 212

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	74.27 280

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

EU-MVSNet		55.61 276	54.41 276	59.19 282	65.41 311	33.42 322	72.44 221	71.91 225	28.81 337	51.27 300	73.87 283	24.76 318	69.08 281	43.04 239	58.20 303	75.06 269

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.
WR-MVS_H		67.02 170	66.92 151	67.33 214	77.95 138	37.75 287	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

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	27.12 305	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
WTY-MVS		59.75 244	60.39 230	57.85 290	72.32 251	37.83 286	61.05 303	64.18 284	45.95 275	61.91 201	79.11 221	47.01 126	60.88 309	42.50 243	69.49 222	74.83 274

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

LTVRE_ROB	55.42 16	63.15 215	61.23 225	68.92 199	76.57 172	47.80 213	59.92 305	76.39 186	54.35 192	58.67 244	82.46 133	29.44 292	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
sss		56.17 272	56.57 261	54.96 301	66.93 300	36.32 301	57.94 311	61.69 300	41.67 305	58.64 245	75.32 274	38.72 203	56.25 329	42.04 246	66.19 249	72.31 303

UnsupCasMVSNet_eth		53.16 288	52.47 286	55.23 299	59.45 335	33.39 323	59.43 307	69.13 244	45.98 272	50.35 307	72.32 290	29.30 293	58.26 318	42.02 247	44.30 335	74.05 286

tpm2		62.07 228	60.10 232	67.99 207	72.79 243	43.86 243	71.05 240	66.85 262	43.14 298	62.77 180	75.39 273	38.32 207	80.80 181	41.69 248	68.88 225	79.32 226

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
EPMVS		53.96 281	53.69 281	54.79 303	66.12 307	31.96 328	62.34 296	49.05 338	44.42 288	55.54 271	71.33 296	30.22 286	56.70 324	41.65 250	62.54 277	75.71 264

DTE-MVSNet		65.58 189	65.34 177	66.31 222	76.06 178	34.79 307	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

Patchmatch-test1		59.75 244	58.00 253	64.98 249	74.14 212	48.06 211	63.35 291	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

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

tpm		57.34 265	58.16 250	54.86 302	71.80 260	34.77 308	67.47 272	56.04 322	48.20 253	60.10 226	76.92 255	37.17 219	53.41 337	40.76 254	65.01 257	76.40 257

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

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	27.87 299	78.11 225	40.07 256	66.66 246	77.00 251

Patchmtry		57.16 266	56.47 262	59.23 281	69.17 286	34.58 310	62.98 292	63.15 291	44.53 285	56.83 264	74.84 276	35.83 235	68.71 282	40.03 257	60.91 287	74.39 279

pmmvs5		56.47 268	55.68 268	58.86 284	61.41 325	36.71 297	66.37 275	62.75 294	40.38 314	53.70 290	76.62 260	34.56 249	67.05 287	40.02 258	65.27 255	72.83 293

EPNet_dtu		61.90 229	61.97 214	61.68 268	72.89 242	39.78 268	75.85 169	65.62 268	55.09 181	54.56 282	79.36 217	37.59 214	67.02 288	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
Vis-MVSNet (Re-imp)		63.69 206	63.88 190	63.14 260	74.75 194	31.04 330	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

PVSNet	50.76 19	58.40 257	57.39 255	61.42 270	75.53 184	44.04 242	61.43 298	63.45 288	47.04 264	56.91 263	73.61 285	27.00 307	64.76 297	39.12 260	72.40 175	75.47 266

MDTV_nov1_ep13_2view						25.89 341	61.22 301		40.10 315	51.10 301		32.97 266		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	40.03 316	57.81 258	70.56 301	38.32 207	74.51 262	38.26 263	61.50 283	77.00 251

USDC		56.35 270	54.24 278	62.69 264	64.74 313	40.31 265	65.05 285	73.83 214	43.93 292	47.58 311	77.71 241	15.36 334	75.05 260	38.19 264	61.81 280	72.70 294

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	29.52 290	81.08 176	37.70 265	65.79 252	74.93 273

MDTV_nov1_ep13			57.00 258		72.73 244	38.26 283	65.02 286	64.73 275	44.74 282	55.46 272	72.48 289	32.61 275	70.47 276	37.47 266	67.75 241

gg-mvs-nofinetune		57.86 262	56.43 263	62.18 266	72.62 246	35.35 306	66.57 273	56.33 319	50.65 232	57.64 259	57.10 335	30.65 283	76.36 245	37.38 267	78.88 105	74.82 275

RPSCF		55.80 275	54.22 279	60.53 280	65.13 312	42.91 252	64.30 288	57.62 313	36.84 326	58.05 250	82.28 137	28.01 298	56.24 330	37.14 268	58.61 302	82.44 174

PatchT		53.17 287	53.44 284	52.33 313	68.29 293	25.34 344	58.21 310	54.41 328	44.46 287	54.56 282	69.05 308	33.32 263	60.94 308	36.93 269	61.76 281	70.73 313

YYNet1		50.73 294	48.96 293	56.03 297	61.10 327	41.78 257	51.94 327	56.44 318	40.94 310	44.84 320	67.80 312	30.08 288	55.08 335	36.77 270	50.71 323	71.22 309

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	36.77 270	76.88 125	85.14 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 295	48.95 294	56.00 298	61.17 326	41.84 256	51.90 328	56.45 317	40.96 309	44.79 321	67.84 311	30.04 289	55.07 336	36.71 272	50.69 324	71.11 312

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	32.09 279	72.34 268	36.68 273	56.71 306	76.99 253

tpmvs		58.47 256	56.95 259	63.03 262	70.20 276	41.21 261	67.90 271	67.23 260	49.62 240	54.73 280	70.84 298	34.14 254	76.24 247	36.64 274	61.29 286	71.64 307

CHOSEN 280x420		47.83 300	46.36 301	52.24 314	67.37 298	49.78 191	38.91 345	43.11 347	35.00 329	43.27 328	63.30 327	28.95 294	49.19 342	36.53 275	60.80 290	57.76 336

PatchMatch-RL		56.25 271	54.55 275	61.32 272	77.06 164	56.07 94	65.57 279	54.10 331	44.13 291	53.49 295	71.27 297	25.20 316	66.78 289	36.52 276	63.66 267	61.12 331

RPMNet		58.70 255	56.29 265	65.96 232	69.96 279	52.07 144	65.31 283	62.15 298	43.20 297	59.36 234	70.15 305	35.37 243	70.75 274	36.42 277	64.65 262	75.06 269

ITE_SJBPF				62.09 267	66.16 306	44.55 238		64.32 277	47.36 261	55.31 275	80.34 192	19.27 328	62.68 304	36.29 278	62.39 278	79.04 229

JIA-IIPM		51.56 292	47.68 300	63.21 259	64.61 314	50.73 161	47.71 334	58.77 308	42.90 299	48.46 310	51.72 339	24.97 317	70.24 277	36.06 279	53.89 315	68.64 320

OpenMVS_ROB	52.78 18	60.03 239	58.14 251	65.69 238	70.47 272	44.82 232	75.33 175	70.86 228	45.04 279	56.06 268	76.00 267	26.89 308	79.65 194	35.36 280	67.29 244	72.60 295

GG-mvs-BLEND				62.34 265	71.36 266	37.04 291	69.20 258	57.33 314		54.73 280	65.48 321	30.37 284	77.82 228	34.82 281	74.93 137	72.17 305

UnsupCasMVSNet_bld		50.07 296	48.87 295	53.66 306	60.97 329	33.67 321	57.62 312	64.56 276	39.47 318	47.38 312	64.02 324	27.47 302	59.32 314	34.69 282	43.68 336	67.98 321

MDA-MVSNet-bldmvs		53.87 283	50.81 290	63.05 261	66.25 305	48.58 206	56.93 314	63.82 286	48.09 254	41.22 331	70.48 302	30.34 285	68.00 284	34.24 283	45.92 333	72.57 296

dp		51.89 291	51.60 289	52.77 311	68.44 292	32.45 325	62.36 295	54.57 327	44.16 290	49.31 308	67.91 310	28.87 296	56.61 325	33.89 284	54.89 311	69.24 319

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	47.16 313	79.77 204	17.47 329	67.56 285	33.65 285	59.16 300	76.57 255

TestCases				64.39 252	71.44 262	49.03 200		67.30 258	45.97 273	47.16 313	79.77 204	17.47 329	67.56 285	33.65 285	59.16 300	76.57 255

FMVSNet5		55.86 274	54.93 272	58.66 286	71.05 268	36.35 299	64.18 290	62.48 296	46.76 265	50.66 305	74.73 278	25.80 313	64.04 299	33.11 287	65.57 254	75.59 265

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	32.82 288	69.96 213	72.45 298

PVSNet_0	43.31 20	47.46 302	45.64 302	52.92 310	67.60 297	44.65 235	54.06 320	54.64 326	41.59 306	46.15 317	58.75 334	30.99 281	58.66 316	32.18 289	24.81 344	55.46 338

TinyColmap		54.14 280	51.72 288	61.40 271	66.84 301	41.97 255	66.52 274	68.51 251	44.81 281	42.69 330	75.77 270	11.66 342	72.94 265	31.96 290	56.77 305	69.27 318

MIMVSNet		57.35 264	57.07 257	58.22 287	74.21 205	37.18 289	62.46 294	60.88 302	48.88 245	55.29 276	75.99 269	31.68 280	62.04 306	31.87 291	72.35 176	75.43 267

conf200view11		63.38 208	62.41 207	66.29 225	77.31 154	38.66 278	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 292	73.25 158	81.03 201

thres100view900		63.28 212	62.41 207	65.89 235	77.31 154	38.66 278	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 292	73.25 158	79.83 219

tfpn200view9		63.18 214	62.18 212	66.21 227	76.85 168	39.62 269	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 292	73.25 158	79.83 219

thres400		63.31 210	62.18 212	66.72 217	76.85 168	39.62 269	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 292	73.25 158	81.36 188

pmmvs3		44.92 306	41.95 311	53.86 305	52.58 342	43.55 246	62.11 297	46.90 345	26.05 341	40.63 334	60.19 332	11.08 345	57.91 319	31.83 296	46.15 332	60.11 333

LF4IMVS		42.95 310	42.26 310	45.04 325	48.30 346	32.50 324	54.80 318	48.49 340	28.03 338	40.51 335	70.16 304	9.24 347	43.89 347	31.63 297	49.18 330	58.72 334

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	23.06 322	79.40 197	31.63 297	65.85 250	74.01 287
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 301	47.73 299	47.06 322	58.81 336	9.37 355	48.78 333	59.21 306	43.28 295	44.22 323	68.66 309	25.67 314	57.20 323	31.57 299	49.35 329	74.62 278

thres600view7		63.30 211	62.27 210	66.41 221	77.18 162	38.87 275	72.35 222	69.11 245	56.98 143	62.37 197	80.96 176	37.01 222	79.00 214	31.43 300	73.05 164	81.36 188

tfpn111		63.33 209	62.34 209	66.30 223	77.31 154	38.66 278	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.23 200	31.38 301	73.09 163	81.03 201

thres200		62.20 226	61.16 226	65.34 245	75.38 187	39.99 266	69.60 254	69.29 243	55.64 174	61.87 202	76.99 254	37.07 221	78.96 215	31.28 302	73.28 157	77.06 249

LCM-MVSNet		40.30 315	35.88 320	53.57 307	42.24 349	29.15 334	45.21 339	60.53 303	22.23 345	28.02 342	50.98 341	3.72 356	61.78 307	31.22 303	38.76 339	69.78 315

test0.0.03 1		53.32 286	53.59 283	52.50 312	62.81 320	29.45 333	59.51 306	54.11 330	50.08 237	54.40 284	74.31 281	32.62 273	55.92 331	30.50 304	63.95 266	72.15 306

tfpn_ndepth		59.57 247	59.02 237	61.23 273	73.81 213	35.60 304	69.40 257	65.59 269	50.96 231	57.96 257	77.72 240	34.81 246	75.91 249	30.36 305	70.57 194	72.18 304

Anonymous20231206		55.10 279	55.30 271	54.48 304	69.81 282	33.94 314	62.91 293	62.13 299	41.08 308	55.18 277	75.65 271	32.75 271	56.59 326	30.32 306	67.86 239	72.91 292

tfpnnormal		62.47 223	61.63 221	64.99 248	74.81 193	39.01 274	71.22 237	73.72 215	55.22 180	60.21 225	80.09 199	41.26 185	76.98 239	30.02 307	68.09 237	78.97 231

view600		62.77 218	61.84 215	65.55 239	77.28 157	36.87 292	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 308	72.46 171	81.36 188

view800		62.77 218	61.84 215	65.55 239	77.28 157	36.87 292	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 308	72.46 171	81.36 188

conf0.05thres1000		62.77 218	61.84 215	65.55 239	77.28 157	36.87 292	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 308	72.46 171	81.36 188

tfpn		62.77 218	61.84 215	65.55 239	77.28 157	36.87 292	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 308	72.46 171	81.36 188

test20.03		53.87 283	54.02 280	53.41 308	61.47 324	28.11 336	61.30 300	59.21 306	51.34 226	52.09 298	77.43 251	33.29 264	58.55 317	29.76 312	60.27 297	73.58 289

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	29.65 313	75.23 136	75.97 258

conf0.01		59.97 240	58.81 238	63.42 256	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	81.03 201

conf0.002		59.97 240	58.81 238	63.42 256	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	81.03 201

thresconf0.02		59.40 248	58.81 238	61.17 274	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	74.25 281

tfpn_n400		59.40 248	58.81 238	61.17 274	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	74.25 281

tfpnconf		59.40 248	58.81 238	61.17 274	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	74.25 281

tfpnview11		59.40 248	58.81 238	61.17 274	74.15 206	33.83 315	68.32 262	64.22 278	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 314	68.26 230	74.25 281

testgi		51.90 290	52.37 287	50.51 317	60.39 331	23.55 347	58.42 309	58.15 309	49.03 244	51.83 299	79.21 220	22.39 323	55.59 332	29.24 320	62.64 275	72.40 302

tfpn1000		59.24 253	58.70 246	60.86 279	73.75 214	33.99 313	68.86 260	63.98 285	51.25 229	57.29 262	79.51 216	34.58 248	75.26 257	29.08 321	69.99 211	73.32 290

Anonymous20231211		55.92 273	53.63 282	62.77 263	68.22 294	35.56 305	74.48 194	69.89 233	46.42 267	49.07 309	73.45 286	21.13 327	76.77 241	28.74 322	51.30 322	75.97 258

MIMVSNet1		55.17 278	54.31 277	57.77 291	70.03 278	32.01 327	65.68 278	64.81 273	49.19 242	46.75 316	76.00 267	25.53 315	64.04 299	28.65 323	62.13 279	77.26 247

TDRefinement		53.44 285	50.72 291	61.60 269	64.31 316	46.96 221	70.89 242	65.27 272	41.78 303	44.61 322	77.98 232	11.52 343	66.36 291	28.57 324	51.59 320	71.49 308

ADS-MVSNet2		51.33 293	48.76 296	59.07 283	66.02 309	44.60 236	50.90 329	59.76 305	36.90 324	50.74 303	66.18 319	26.38 309	63.11 301	27.17 325	54.76 312	69.50 316

ADS-MVSNet		48.48 299	47.77 298	50.63 316	66.02 309	29.92 332	50.90 329	50.87 337	36.90 324	50.74 303	66.18 319	26.38 309	52.47 339	27.17 325	54.76 312	69.50 316

Patchmatch-test		49.08 297	48.28 297	51.50 315	64.40 315	30.85 331	45.68 337	48.46 341	35.60 328	46.10 319	72.10 291	34.47 252	46.37 343	27.08 327	60.65 291	77.27 246

MVS-HIRNet		45.52 305	44.48 307	48.65 320	68.49 291	34.05 312	59.41 308	44.50 346	27.03 339	37.96 337	50.47 342	26.16 312	64.10 298	26.74 328	59.52 298	47.82 341

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	26.41 329	64.71 259	71.15 311

no-one		40.85 314	36.09 318	55.14 300	48.55 345	38.72 277	42.15 343	62.92 293	34.60 331	23.55 344	49.74 343	12.21 340	66.16 293	26.27 330	24.84 343	60.54 332

N_pmnet		39.35 316	40.28 313	36.54 333	63.76 317	1.62 359	49.37 332	0.76 360	34.62 330	43.61 327	66.38 318	26.25 311	42.57 349	26.02 331	51.77 319	65.44 323

LP		48.51 298	45.51 303	57.52 293	62.86 319	44.53 239	52.38 326	59.84 304	38.11 321	42.81 329	61.02 329	23.23 321	63.02 302	24.10 332	45.24 334	65.02 325

DSMNet-mixed		39.30 317	38.72 316	41.03 330	51.22 343	19.66 349	45.53 338	31.35 353	15.83 349	39.80 336	67.42 314	22.19 324	45.13 346	22.43 333	52.69 317	58.31 335

1111		44.40 308	45.00 305	42.61 329	57.55 338	17.33 352	53.82 323	57.05 315	40.78 311	44.11 324	66.57 316	13.37 337	45.77 344	22.15 334	49.58 327	64.73 326

.test1245		34.88 320	39.49 315	21.04 341	57.55 338	17.33 352	53.82 323	57.05 315	40.78 311	44.11 324	66.57 316	13.37 337	45.77 344	22.15 334	0.00 355	0.03 356

wuykxyi23d		28.12 324	22.54 329	44.87 327	34.97 355	32.11 326	37.96 346	47.31 343	13.32 350	9.29 354	23.72 350	0.45 361	56.58 327	21.85 336	13.98 348	45.93 343

ANet_high		41.38 313	37.47 317	53.11 309	39.73 352	24.45 346	56.94 313	69.69 235	47.65 258	26.04 343	52.32 338	12.44 339	62.38 305	21.80 337	10.61 352	72.49 297

test1235678		45.66 303	44.46 308	49.26 318	59.88 333	28.68 335	56.36 316	55.54 325	39.12 320	40.89 333	63.40 326	14.41 336	57.32 322	21.05 338	49.47 328	61.78 329

new_pmnet		34.13 322	34.29 321	33.64 334	52.63 341	18.23 351	44.43 341	33.90 351	22.81 344	30.89 340	53.18 337	10.48 346	35.72 353	20.77 339	39.51 337	46.98 342

testus		44.59 307	43.87 309	46.76 323	59.85 334	24.65 345	53.86 321	55.82 323	36.26 327	43.97 326	63.42 325	8.39 349	53.14 338	20.70 340	52.52 318	62.51 327

test2356		45.61 304	44.66 306	48.47 321	60.15 332	28.08 337	52.44 325	52.83 334	38.01 322	46.13 318	60.98 330	15.08 335	55.54 333	20.43 341	55.85 309	61.78 329

testmv		42.25 311	40.11 314	48.66 319	53.23 340	27.02 339	56.62 315	55.74 324	37.25 323	33.10 339	59.52 333	7.78 350	56.58 327	19.61 342	38.13 340	62.40 328

PNet_i23d		27.88 325	25.99 325	33.55 335	47.54 347	25.89 341	47.24 336	32.91 352	21.44 346	15.90 348	38.09 345	0.85 360	42.76 348	16.90 343	13.03 350	32.00 348

test12356		36.16 319	35.94 319	36.83 332	50.82 344	8.52 356	44.84 340	53.49 333	32.72 333	30.11 341	55.08 336	7.11 352	49.47 341	16.60 344	32.68 342	52.50 339

PMMVS2		27.40 326	25.91 326	31.87 337	39.46 353	6.57 357	31.17 348	28.52 354	23.96 342	20.45 346	48.94 344	4.20 355	37.94 352	16.51 345	19.97 345	51.09 340

Gipuma		34.77 321	31.91 323	43.33 328	62.05 323	37.87 285	20.39 350	67.03 261	23.23 343	18.41 347	25.84 348	4.24 354	62.73 303	14.71 346	51.32 321	29.38 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 312	41.11 312	45.39 324	58.03 337	41.01 263	49.50 331	53.81 332	30.07 336	33.71 338	64.03 322	11.69 341	52.08 340	14.01 347	55.11 310	43.09 344

tmp_tt		9.43 332	11.14 333	4.30 344	2.38 358	4.40 358	13.62 352	16.08 357	0.39 354	15.89 349	13.06 352	15.80 333	5.54 357	12.63 348	10.46 353	2.95 353

MVE	17.77 23	21.41 329	17.77 331	32.34 336	34.34 356	25.44 343	16.11 351	24.11 355	11.19 351	13.22 350	31.92 346	1.58 359	30.95 354	10.47 349	17.03 346	40.62 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 327	22.73 328	26.90 339	42.02 350	20.67 348	42.66 342	35.70 349	17.43 347	10.28 352	25.05 349	6.42 353	42.39 350	10.28 350	14.71 347	17.63 350

PMVS	28.69 22	36.22 318	33.29 322	45.02 326	36.82 354	35.98 303	54.68 319	48.74 339	26.31 340	21.02 345	51.61 340	2.88 358	60.10 312	9.99 351	47.58 331	38.99 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 328	21.84 330	26.36 340	40.20 351	19.53 350	41.95 344	34.64 350	17.09 348	9.73 353	22.83 351	7.29 351	42.22 351	9.18 352	13.66 349	17.32 351

DeepMVS_CX				12.03 343	17.97 357	10.91 354		10.60 358	7.46 352	11.07 351	28.36 347	3.28 357	11.29 356	8.01 353	9.74 354	13.89 352

wuyk23d		13.32 331	12.52 332	15.71 342	47.54 347	26.27 340	31.06 349	1.98 359	4.93 353	5.18 355	1.94 356	0.45 361	18.54 355	6.81 354	12.83 351	2.33 354

testmvs		4.52 335	6.03 336	0.01 346	0.01 359	0.00 361	53.86 321	0.00 361	0.01 355	0.04 356	0.27 357	0.00 364	0.00 358	0.04 355	0.00 355	0.03 356

test123		4.73 334	6.30 335	0.02 345	0.01 359	0.01 360	56.36 316	0.00 361	0.01 355	0.04 356	0.21 358	0.01 363	0.00 358	0.03 356	0.00 355	0.04 355

cdsmvs_eth3d_5k		17.50 330	23.34 327	0.00 347	0.00 361	0.00 361	0.00 353	78.63 154	0.00 357	0.00 358	82.18 138	49.25 89	0.00 358	0.00 357	0.00 355	0.00 358

pcd_1.5k_mvsjas		3.92 336	5.23 337	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	47.05 123	0.00 358	0.00 357	0.00 355	0.00 358

pcd1.5k->3k		30.06 323	30.56 324	28.55 338	78.81 115	0.00 361	0.00 353	82.07 71	0.00 357	0.00 358	0.00 359	39.61 195	0.00 358	0.00 357	74.56 139	85.66 72

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

ab-mvs-re		6.49 333	8.65 334	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	77.89 236	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 361	0.00 353	0.00 361	0.00 357	0.00 358	0.00 359	0.00 364	0.00 358	0.00 357	0.00 355	0.00 358

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

MTGPA								80.97 99

test_post											3.55 355	33.90 257	66.52 290

patchmatchnet-post											64.03 322	34.50 250	74.27 263

MTMP								17.08 356

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

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

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

test222					83.14 55	58.68 59	72.57 219	63.45 288	41.78 303	67.56 128	86.12 75	37.13 220			78.73 109	74.98 272

segment_acmp												54.23 36

testdata1							72.65 214		60.50 78

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_prior4											86.10 76

plane_prior3						56.09 93			63.92 30	69.27 98

plane_prior2							84.22 25		64.52 24

plane_prior1					81.27 78

plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

n2								0.00 361

nn								0.00 361

door-mid								47.19 344

test11								83.47 46

door								47.60 342

HQP5-MVS						54.94 109

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

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