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.

MCST-MVS		83.01 1	83.30 2	82.15 8	92.84 2	57.58 14	93.77 1	91.10 5	75.95 2	77.10 19	93.09 12	54.15 18	95.57 6	85.80 1	85.87 25	93.31 7

CNVR-MVS		81.76 5	81.90 5	81.33 12	90.04 5	57.70 12	91.71 7	88.87 23	70.31 17	77.64 18	93.87 4	52.58 24	93.91 19	84.17 2	87.92 12	92.39 21

CANet		80.90 7	81.17 7	80.09 25	87.62 28	54.21 79	91.60 10	86.47 66	73.13 5	79.89 13	93.10 10	49.88 43	92.98 25	84.09 3	84.75 37	93.08 12

MSP-MVS		82.30 4	83.47 1	78.80 45	82.99 104	52.71 119	85.04 121	88.63 29	66.08 53	86.77 1	92.75 15	72.05 1	91.46 57	83.35 4	93.53 1	92.23 24

PS-MVSNAJ		80.06 11	79.52 12	81.68 10	85.58 49	60.97 3	91.69 8	87.02 56	70.62 14	80.75 10	93.22 9	37.77 156	92.50 35	82.75 5	86.25 22	91.57 42

xiu_mvs_v2_base		79.86 12	79.31 13	81.53 11	85.03 63	60.73 4	91.65 9	86.86 59	70.30 18	80.77 9	93.07 13	37.63 161	92.28 41	82.73 6	85.71 26	91.57 42

DeepPCF-MVS	69.37 1	80.65 8	81.56 6	77.94 73	85.46 54	49.56 183	90.99 17	86.66 64	70.58 15	80.07 12	95.30 1	56.18 11	90.97 64	82.57 7	86.22 23	93.28 8

DVP-MVS		81.30 6	81.00 8	82.20 6	89.40 11	57.45 15	92.34 3	89.99 12	57.71 179	81.91 5	93.64 6	55.17 12	96.44 1	81.68 8	87.13 14	92.72 18

test_0728_SECOND				82.20 6	89.50 9	57.73 11	92.34 3	88.88 22					96.39 3	81.68 8	87.13 14	92.47 20

test_0728_THIRD									58.00 171	81.91 5	93.64 6	56.54 9	96.44 1	81.64 10	86.86 17	92.23 24

9.14			78.19 19		85.67 46		88.32 47	88.84 24	59.89 132	74.58 31	92.62 18	46.80 61	92.66 32	81.40 11	85.62 28

lupinMVS		78.38 19	78.11 20	79.19 34	83.02 102	55.24 49	91.57 11	84.82 106	69.12 24	76.67 21	92.02 28	44.82 85	90.23 87	80.83 12	80.09 74	92.08 28

HPM-MVS++		80.50 9	80.71 9	79.88 27	87.34 30	55.20 52	89.93 25	87.55 51	66.04 56	79.46 14	93.00 14	53.10 22	91.76 50	80.40 13	89.56 5	92.68 19

save filter2										75.07 26	92.45 20	44.05 91	87.28 170	80.01 14	84.65 39	90.13 79

SMA-MVS		79.10 15	78.76 15	80.12 23	84.42 70	55.87 37	87.58 60	86.76 61	61.48 114	80.26 11	93.10 10	46.53 65	92.41 37	79.97 15	88.77 7	92.08 28

APDe-MVS		78.44 17	78.20 18	79.19 34	88.56 16	54.55 74	89.76 29	87.77 46	55.91 204	78.56 15	92.49 19	48.20 48	92.65 33	79.49 16	83.04 48	90.39 70

EIA-MVS		77.17 33	76.74 35	78.48 55	81.80 127	54.55 74	86.13 89	85.33 88	68.20 30	73.10 43	90.52 61	45.23 79	90.66 71	79.37 17	80.95 62	90.22 74

jason		77.01 36	76.45 38	78.69 49	79.69 165	54.74 66	90.56 20	83.99 128	68.26 29	74.10 34	90.91 51	42.14 115	89.99 91	79.30 18	79.12 82	91.36 50
jason: jason.
DELS-MVS		82.32 3	82.50 3	81.79 9	86.80 34	56.89 24	92.77 2	86.30 70	77.83 1	77.88 16	92.13 24	60.24 3	94.78 13	78.97 19	89.61 4	93.69 4
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
NCCC		79.57 14	79.23 14	80.59 15	89.50 9	56.99 22	91.38 12	88.17 39	67.71 37	73.81 35	92.75 15	46.88 60	93.28 23	78.79 20	84.07 44	91.50 46

test9_res														78.72 21	85.44 31	91.39 48

CSCG		80.41 10	79.72 10	82.49 4	89.12 15	57.67 13	89.29 35	91.54 3	59.19 148	71.82 58	90.05 73	59.72 5	96.04 4	78.37 22	88.40 10	93.75 3

DPE-MVS		79.82 13	79.66 11	80.29 18	89.27 14	55.08 57	88.70 44	87.92 43	55.55 209	81.21 8	93.69 5	56.51 10	94.27 16	78.36 23	85.70 27	91.51 45

CS-MVS		78.19 22	77.97 22	78.82 43	83.52 86	53.08 111	89.10 37	86.30 70	68.01 33	73.57 37	91.26 44	47.28 56	92.35 39	78.21 24	84.51 41	91.05 55

MP-MVS-pluss		75.54 59	75.03 54	77.04 91	81.37 143	52.65 121	84.34 134	84.46 115	61.16 115	69.14 74	91.76 34	39.98 140	88.99 116	78.19 25	84.89 36	89.48 89
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 38	76.40 39	78.45 57	85.68 44	55.42 43	87.59 58	84.00 126	57.84 176	72.99 44	90.98 47	44.99 81	88.58 127	78.19 25	85.32 32	91.34 52

canonicalmvs		78.17 23	77.86 24	79.12 37	84.30 72	54.22 78	87.71 53	84.57 114	67.70 38	77.70 17	92.11 27	50.90 35	89.95 92	78.18 27	77.54 94	93.20 10

VDD-MVS		76.08 51	74.97 55	79.44 30	84.27 75	53.33 103	91.13 16	85.88 77	65.33 64	72.37 54	89.34 84	32.52 218	92.76 30	77.90 28	75.96 107	92.22 26

agg_prior1		76.68 44	76.24 42	78.00 69	85.64 47	54.92 61	87.55 61	83.61 135	57.99 172	72.53 51	91.05 45	45.36 77	88.10 148	77.76 29	84.68 38	90.99 58

diffmvs		75.11 65	74.65 60	76.46 105	78.52 186	53.35 101	83.28 166	79.94 189	70.51 16	71.64 60	88.72 95	46.02 70	86.08 200	77.52 30	75.75 111	89.96 81

alignmvs		78.08 24	77.98 21	78.39 60	83.53 85	53.22 106	89.77 28	85.45 82	66.11 51	76.59 23	91.99 30	54.07 19	89.05 110	77.34 31	77.00 99	92.89 15

SteuartSystems-ACMMP		77.08 35	76.33 40	79.34 32	80.98 147	55.31 47	89.76 29	86.91 58	62.94 93	71.65 59	91.56 39	42.33 112	92.56 34	77.14 32	83.69 46	90.15 77
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 46	75.66 46	78.73 47	81.92 125	54.67 71	84.06 142	85.35 87	61.10 117	72.99 44	91.50 40	40.25 134	91.00 62	76.84 33	86.98 16	90.51 68

CLD-MVS		75.60 58	75.39 50	76.24 108	80.69 154	52.40 125	90.69 19	86.20 73	74.40 3	65.01 114	88.93 91	42.05 117	90.58 74	76.57 34	73.96 124	85.73 160
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS		74.99 67	74.33 63	76.95 96	82.89 108	53.05 113	85.63 102	83.50 137	57.86 175	67.25 88	90.24 67	43.38 105	88.85 122	76.03 35	82.23 55	88.96 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		77.36 32	76.85 33	78.88 41	80.40 160	54.66 72	87.06 72	85.88 77	72.11 8	71.57 61	88.63 99	50.89 36	90.35 80	76.00 36	79.11 83	91.63 39

TSAR-MVS + GP.		77.82 27	77.59 26	78.49 54	85.25 59	50.27 174	90.02 22	90.57 7	56.58 198	74.26 33	91.60 38	54.26 16	92.16 43	75.87 37	79.91 78	93.05 13

baseline		76.86 41	76.24 42	78.71 48	80.47 159	54.20 81	83.90 147	84.88 105	71.38 12	71.51 62	89.15 89	50.51 37	90.55 75	75.71 38	78.65 86	91.39 48

agg_prior2														75.65 39	85.11 34	91.01 56

DeepC-MVS	67.15 4	76.90 40	76.27 41	78.80 45	80.70 153	55.02 58	86.39 83	86.71 62	66.96 44	67.91 84	89.97 75	48.03 50	91.41 58	75.60 40	84.14 43	89.96 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_BlendedMVS		73.42 85	73.30 71	73.76 161	85.91 41	51.83 139	86.18 88	84.24 122	65.40 61	69.09 75	80.86 193	46.70 63	88.13 146	75.43 41	65.92 184	81.33 224

PVSNet_Blended		76.53 45	76.54 36	76.50 103	85.91 41	51.83 139	88.89 41	84.24 122	67.82 35	69.09 75	89.33 86	46.70 63	88.13 146	75.43 41	81.48 61	89.55 87

LFMVS		78.52 16	77.14 30	82.67 3	89.58 8	58.90 7	91.27 15	88.05 40	63.22 89	74.63 30	90.83 54	41.38 126	94.40 14	75.42 43	79.90 79	94.72 2

testtj		76.96 37	76.48 37	78.40 59	89.89 7	53.67 89	88.72 43	86.15 74	54.56 219	74.86 28	92.31 22	44.38 89	91.97 48	75.19 44	82.24 54	89.54 88

MVS_111021_HR		76.39 47	75.38 51	79.42 31	85.33 57	56.47 29	88.15 48	84.97 102	65.15 67	66.06 101	89.88 76	43.79 96	92.16 43	75.03 45	80.03 77	89.64 86

test_prior3		77.59 29	77.33 29	78.39 60	86.35 38	54.91 63	89.04 38	85.45 82	61.88 105	73.55 38	91.46 42	48.01 51	89.70 98	74.73 46	85.46 29	90.55 64

test_prior2							89.04 38		61.88 105	73.55 38	91.46 42	48.01 51		74.73 46	85.46 29

SD-MVS		76.18 49	74.85 57	80.18 21	85.39 55	56.90 23	85.75 98	82.45 155	56.79 193	74.48 32	91.81 31	43.72 99	90.75 69	74.61 48	78.65 86	92.91 14
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS		76.15 50	75.68 45	77.54 80	88.52 17	53.44 97	87.26 69	85.03 101	53.79 224	74.91 27	91.68 37	43.80 95	90.31 82	74.36 49	81.82 58	88.87 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDDNet		74.37 71	72.13 88	81.09 14	79.58 166	56.52 28	90.02 22	86.70 63	52.61 233	71.23 65	87.20 119	31.75 228	93.96 18	74.30 50	75.77 110	92.79 17

TSAR-MVS + MP.		78.31 21	78.26 17	78.48 55	81.33 144	56.31 33	81.59 200	86.41 67	69.61 22	81.72 7	88.16 105	55.09 14	88.04 150	74.12 51	86.31 21	91.09 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		82.39 2	82.36 4	82.49 4	80.12 162	59.50 5	92.24 6	90.72 6	69.37 23	83.22 2	94.47 2	63.81 2	93.18 24	74.02 52	93.25 2	94.80 1

PHI-MVS		77.49 30	77.00 31	78.95 38	85.33 57	50.69 159	88.57 45	88.59 33	58.14 168	73.60 36	93.31 8	43.14 108	93.79 20	73.81 53	88.53 9	92.37 22

zzz-MVS		74.15 76	73.11 75	77.27 87	81.54 137	53.57 91	84.02 144	81.31 171	59.41 140	68.39 80	90.96 49	36.07 185	89.01 112	73.80 54	82.45 52	89.23 93

MTAPA		72.73 92	71.22 101	77.27 87	81.54 137	53.57 91	67.06 291	81.31 171	59.41 140	68.39 80	90.96 49	36.07 185	89.01 112	73.80 54	82.45 52	89.23 93

Regformer-1		77.80 28	77.44 28	78.88 41	87.78 26	52.44 124	87.60 55	90.08 10	68.86 25	72.49 53	91.79 32	47.69 53	94.90 11	73.57 56	77.05 96	89.31 91

VNet		77.99 26	77.92 23	78.19 64	87.43 29	50.12 175	90.93 18	91.41 4	67.48 40	75.12 25	90.15 72	46.77 62	91.00 62	73.52 57	78.46 88	93.44 5

EPNet		78.36 20	78.49 16	77.97 71	85.49 51	52.04 132	89.36 33	84.07 125	73.22 4	77.03 20	91.72 35	49.32 46	90.17 89	73.46 58	82.77 49	91.69 36
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DI_MVS_plusplus_test		71.30 112	68.98 130	78.26 63	72.76 250	54.08 83	81.72 195	83.22 142	65.75 57	51.94 248	78.47 211	36.01 189	90.31 82	73.33 59	77.60 92	90.40 69

xiu_mvs_v1_base_debu		71.60 109	70.29 110	75.55 122	77.26 204	53.15 107	85.34 107	79.37 200	55.83 205	72.54 48	90.19 69	22.38 281	86.66 185	73.28 60	76.39 103	86.85 141

xiu_mvs_v1_base		71.60 109	70.29 110	75.55 122	77.26 204	53.15 107	85.34 107	79.37 200	55.83 205	72.54 48	90.19 69	22.38 281	86.66 185	73.28 60	76.39 103	86.85 141

xiu_mvs_v1_base_debi		71.60 109	70.29 110	75.55 122	77.26 204	53.15 107	85.34 107	79.37 200	55.83 205	72.54 48	90.19 69	22.38 281	86.66 185	73.28 60	76.39 103	86.85 141

Regformer-2		77.15 34	76.82 34	78.14 65	87.78 26	51.84 138	87.60 55	89.12 18	67.23 41	71.93 57	91.79 32	46.03 69	93.53 22	72.85 63	77.05 96	89.05 100

PMMVS		72.98 89	72.05 91	75.78 120	83.57 84	48.60 200	84.08 140	82.85 150	61.62 109	68.24 82	90.33 66	28.35 243	87.78 158	72.71 64	76.69 101	90.95 59

ET-MVSNet_ETH3D		75.23 62	74.08 65	78.67 50	84.52 69	55.59 39	88.92 40	89.21 17	68.06 32	53.13 239	90.22 68	49.71 44	87.62 162	72.12 65	70.82 150	92.82 16

MVS		76.91 38	75.48 48	81.23 13	84.56 68	55.21 51	80.23 219	91.64 2	58.65 161	65.37 109	91.48 41	45.72 74	95.05 9	72.11 66	89.52 6	93.44 5

#test#		74.86 69	73.78 69	78.10 67	84.30 72	53.68 87	86.95 75	84.36 116	59.00 156	65.78 104	90.56 58	40.70 132	90.90 65	71.48 67	80.88 63	89.71 83

nrg030		72.27 103	71.56 95	74.42 144	75.93 217	50.60 161	86.97 74	83.21 143	62.75 95	67.15 89	84.38 148	50.07 41	86.66 185	71.19 68	62.37 213	85.99 154

DeepC-MVS_fast	67.50 3	78.00 25	77.63 25	79.13 36	88.52 17	55.12 54	89.95 24	85.98 76	68.31 28	71.33 64	92.75 15	45.52 76	90.37 79	71.15 69	85.14 33	91.91 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		74.87 68	73.90 67	77.77 74	83.30 92	53.45 96	85.75 98	85.29 91	59.22 147	66.50 96	89.85 77	40.94 128	90.76 68	70.94 70	83.35 47	89.10 99

CHOSEN 1792x2688		76.24 48	74.03 66	82.88 1	83.09 98	62.84 2	85.73 100	85.39 85	69.79 20	64.87 116	83.49 160	41.52 125	93.69 21	70.55 71	81.82 58	92.12 27

CDPH-MVS		76.05 52	75.19 52	78.62 52	86.51 37	54.98 60	87.32 64	84.59 113	58.62 162	70.75 68	90.85 53	43.10 110	90.63 73	70.50 72	84.51 41	90.24 73

HPM-MVS		72.60 94	71.50 96	75.89 118	82.02 123	51.42 149	80.70 213	83.05 146	56.12 203	64.03 129	89.53 82	37.55 163	88.37 135	70.48 73	80.04 76	87.88 123
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-3		76.02 53	75.47 49	77.70 76	85.49 51	51.47 147	85.12 117	90.19 9	68.52 27	69.36 73	90.66 56	46.45 66	94.81 12	70.25 74	73.16 129	86.81 144

MVS_111021_LR		69.07 146	67.91 140	72.54 183	77.27 203	49.56 183	79.77 224	73.96 266	59.33 145	60.73 158	87.82 110	30.19 237	81.53 242	69.94 75	72.19 142	86.53 147

test_yl		75.85 55	74.83 58	78.91 39	88.08 24	51.94 134	91.30 13	89.28 15	57.91 173	71.19 66	89.20 87	42.03 118	92.77 28	69.41 76	75.07 118	92.01 31

DCV-MVSNet		75.85 55	74.83 58	78.91 39	88.08 24	51.94 134	91.30 13	89.28 15	57.91 173	71.19 66	89.20 87	42.03 118	92.77 28	69.41 76	75.07 118	92.01 31

Regformer-4		75.06 66	74.59 61	76.47 104	85.49 51	50.33 170	85.12 117	88.61 31	66.42 46	68.48 79	90.66 56	44.15 90	92.68 31	69.24 78	73.16 129	86.39 151

HFP-MVS		74.37 71	73.13 74	78.10 67	84.30 72	53.68 87	85.58 103	84.36 116	56.82 191	65.78 104	90.56 58	40.70 132	90.90 65	69.18 79	80.88 63	89.71 83

ACMMPR		73.76 80	72.61 76	77.24 90	83.92 81	52.96 116	85.58 103	84.29 118	56.82 191	65.12 110	90.45 62	37.24 171	90.18 88	69.18 79	80.84 65	88.58 111

region2R		73.75 81	72.55 78	77.33 84	83.90 82	52.98 115	85.54 106	84.09 124	56.83 190	65.10 111	90.45 62	37.34 169	90.24 86	68.89 81	80.83 66	88.77 107

CP-MVS		72.59 96	71.46 97	76.00 117	82.93 107	52.32 129	86.93 77	82.48 154	55.15 212	63.65 136	90.44 65	35.03 196	88.53 131	68.69 82	77.83 91	87.15 136

baseline2		75.15 64	74.54 62	76.98 95	81.67 130	51.74 141	83.84 148	91.94 1	69.97 19	58.98 177	86.02 134	59.73 4	91.73 51	68.37 83	70.40 153	87.48 130

Effi-MVS+		75.24 61	73.61 70	80.16 22	81.92 125	57.42 17	85.21 111	76.71 242	60.68 124	73.32 42	89.34 84	47.30 55	91.63 52	68.28 84	79.72 80	91.42 47

CostFormer		73.89 79	72.30 83	78.66 51	82.36 121	56.58 25	75.56 248	85.30 90	66.06 54	70.50 71	76.88 226	57.02 8	89.06 108	68.27 85	68.74 163	90.33 72

CANet_DTU		73.71 82	73.14 72	75.40 125	82.61 117	50.05 176	84.67 130	79.36 203	69.72 21	75.39 24	90.03 74	29.41 240	85.93 206	67.99 86	79.11 83	90.22 74

PVSNet_Blended_VisFu		73.40 86	72.44 80	76.30 106	81.32 145	54.70 69	85.81 94	78.82 211	63.70 81	64.53 121	85.38 141	47.11 59	87.38 168	67.75 87	77.55 93	86.81 144

MSLP-MVS++		74.21 73	72.25 85	80.11 24	81.45 141	56.47 29	86.32 85	79.65 197	58.19 167	66.36 97	92.29 23	36.11 184	90.66 71	67.39 88	82.49 51	93.18 11

PGM-MVS		72.60 94	71.20 102	76.80 100	82.95 105	52.82 118	83.07 170	82.14 156	56.51 199	63.18 140	89.81 78	35.68 191	89.76 97	67.30 89	80.19 73	87.83 124

ETV-MVS		75.92 54	75.18 53	78.13 66	85.14 60	51.60 143	87.17 70	85.32 89	64.69 69	68.56 78	90.53 60	45.79 73	91.58 53	67.21 90	82.18 56	91.20 53

HY-MVS	67.03 5	73.90 78	73.14 72	76.18 111	84.70 67	47.36 220	75.56 248	86.36 69	66.27 49	70.66 69	83.91 153	51.05 33	89.31 105	67.10 91	72.61 138	91.88 34

testing_2		63.60 212	59.86 225	74.82 136	61.87 311	52.39 126	73.06 265	82.76 151	61.49 113	39.96 297	67.39 291	21.06 290	88.34 137	67.07 92	64.10 192	83.72 194

BP-MVS														66.70 93

HQP-MVS		72.34 99	71.44 98	75.03 132	79.02 173	51.56 144	88.00 49	83.68 132	65.45 58	64.48 122	85.13 142	37.35 167	88.62 125	66.70 93	73.12 131	84.91 173

SR-MVS		70.92 119	69.73 119	74.50 141	83.38 91	50.48 165	84.27 136	79.35 204	48.96 256	66.57 95	90.45 62	33.65 209	87.11 173	66.42 95	74.56 121	85.91 157

gm-plane-assit					83.24 94	54.21 79			70.91 13		88.23 104		95.25 8	66.37 96

PAPR		75.20 63	74.13 64	78.41 58	88.31 21	55.10 56	84.31 135	85.66 80	63.76 80	67.55 85	90.73 55	43.48 104	89.40 104	66.36 97	77.03 98	90.73 62

WTY-MVS		77.47 31	77.52 27	77.30 85	88.33 20	46.25 236	88.46 46	90.32 8	71.40 11	72.32 55	91.72 35	53.44 20	92.37 38	66.28 98	75.42 113	93.28 8

tpmrst		71.04 116	69.77 118	74.86 135	83.19 95	55.86 38	75.64 247	78.73 214	67.88 34	64.99 115	73.73 252	49.96 42	79.56 265	65.92 99	67.85 171	89.14 98

MVS_Test		75.85 55	74.93 56	78.62 52	84.08 77	55.20 52	83.99 145	85.17 96	68.07 31	73.38 41	82.76 170	50.44 38	89.00 114	65.90 100	80.61 67	91.64 37

ACMMP		70.81 121	69.29 126	75.39 126	81.52 140	51.92 136	83.43 160	83.03 147	56.67 196	58.80 184	88.91 92	31.92 226	88.58 127	65.89 101	73.39 128	85.67 161
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
XVS		72.92 90	71.62 94	76.81 98	83.41 87	52.48 122	84.88 126	83.20 144	58.03 169	63.91 131	89.63 81	35.50 192	89.78 95	65.50 102	80.50 69	88.16 116

X-MVStestdata		65.85 201	62.20 207	76.81 98	83.41 87	52.48 122	84.88 126	83.20 144	58.03 169	63.91 131	4.82 334	35.50 192	89.78 95	65.50 102	80.50 69	88.16 116

PAPM		76.76 42	76.07 44	78.81 44	80.20 161	59.11 6	86.86 78	86.23 72	68.60 26	70.18 72	88.84 94	51.57 29	87.16 172	65.48 104	86.68 18	90.15 77

HQP_MVS		70.96 118	69.91 117	74.12 150	77.95 193	49.57 181	85.76 96	82.59 152	63.60 84	62.15 147	83.28 164	36.04 187	88.30 141	65.46 105	72.34 140	84.49 176

plane_prior5								82.59 152					88.30 141	65.46 105	72.34 140	84.49 176

mPP-MVS		71.79 108	70.38 108	76.04 115	82.65 116	52.06 131	84.45 132	81.78 164	55.59 208	62.05 150	89.68 80	33.48 210	88.28 143	65.45 107	78.24 90	87.77 126

OPM-MVS		70.75 122	69.58 120	74.26 148	75.55 221	51.34 151	86.05 91	83.29 141	61.94 104	62.95 143	85.77 136	34.15 202	88.44 133	65.44 108	71.07 147	82.99 206

Effi-MVS+-dtu		66.24 197	64.96 193	70.08 223	75.17 222	49.64 180	82.01 186	74.48 260	62.15 100	57.83 196	76.08 239	30.59 234	83.79 227	65.40 109	60.93 220	76.81 270

mvs-test1		69.04 147	67.57 148	73.44 169	75.17 222	51.68 142	86.57 82	74.48 260	62.15 100	62.07 149	85.79 135	30.59 234	87.48 165	65.40 109	65.94 183	81.18 228

EI-MVSNet-Vis-set		73.19 88	72.60 77	74.99 134	82.56 118	49.80 179	82.55 179	89.00 21	66.17 50	65.89 103	88.98 90	43.83 94	92.29 40	65.38 111	69.01 161	82.87 209

TESTMET0.1,1		72.86 91	72.33 81	74.46 142	81.98 124	50.77 157	85.13 114	85.47 81	66.09 52	67.30 86	83.69 158	37.27 170	83.57 230	65.06 112	78.97 85	89.05 100

MVSTER		73.25 87	72.33 81	76.01 116	85.54 50	53.76 86	83.52 153	87.16 54	67.06 43	63.88 133	81.66 187	52.77 23	90.44 76	64.66 113	64.69 189	83.84 192

CPTT-MVS		67.15 182	65.84 175	71.07 208	80.96 148	50.32 171	81.94 188	74.10 263	46.18 271	57.91 195	87.64 115	29.57 239	81.31 244	64.10 114	70.18 155	81.56 218

EI-MVSNet-UG-set		72.37 98	71.73 93	74.29 147	81.60 133	49.29 187	81.85 191	88.64 28	65.29 66	65.05 112	88.29 102	43.18 106	91.83 49	63.74 115	67.97 169	81.75 216

ab-mvs		70.65 123	69.11 128	75.29 128	80.87 150	46.23 237	73.48 261	85.24 95	59.99 131	66.65 91	80.94 192	43.13 109	88.69 123	63.58 116	68.07 167	90.95 59

VPA-MVSNet		71.12 114	70.66 105	72.49 185	78.75 178	44.43 253	87.64 54	90.02 11	63.97 76	65.02 113	81.58 188	42.14 115	87.42 167	63.42 117	63.38 201	85.63 164

APD-MVS_3200maxsize		69.62 142	68.23 137	73.80 160	81.58 135	48.22 212	81.91 189	79.50 199	48.21 258	64.24 127	89.75 79	31.91 227	87.55 164	63.08 118	73.85 126	85.64 163

v2v482		69.55 143	67.64 146	75.26 130	72.32 257	53.83 85	84.93 125	81.94 159	65.37 63	60.80 157	79.25 203	41.62 123	88.98 117	63.03 119	59.51 226	82.98 207

PS-MVSNAJss		68.78 155	67.17 156	73.62 166	73.01 246	48.33 211	84.95 124	84.81 107	59.30 146	58.91 181	79.84 199	37.77 156	88.86 121	62.83 120	63.12 207	83.67 195

V42		67.66 170	65.60 182	73.86 157	70.69 272	53.63 90	81.50 201	78.61 217	63.85 78	59.49 171	77.49 218	37.98 153	87.65 161	62.33 121	58.43 233	80.29 239

MG-MVS		78.42 18	76.99 32	82.73 2	93.17 1	64.46 1	89.93 25	88.51 35	64.83 68	73.52 40	88.09 106	48.07 49	92.19 42	62.24 122	84.53 40	91.53 44

Patchmatch-RL test		58.72 245	54.32 257	71.92 195	63.91 304	44.25 255	61.73 302	55.19 313	57.38 184	49.31 261	54.24 315	37.60 162	80.89 247	62.19 123	47.28 289	90.63 63

mvs_anonymous		72.29 101	70.74 104	76.94 97	82.85 109	54.72 68	78.43 235	81.54 167	63.77 79	61.69 152	79.32 202	51.11 32	85.31 212	62.15 124	75.79 109	90.79 61

HyFIR lowres test		69.94 136	67.58 147	77.04 91	77.11 209	57.29 18	81.49 203	79.11 209	58.27 166	58.86 182	80.41 196	42.33 112	86.96 178	61.91 125	68.68 164	86.87 139

sss		70.49 125	70.13 114	71.58 200	81.59 134	39.02 286	80.78 212	84.71 111	59.34 143	66.61 93	88.09 106	37.17 172	85.52 208	61.82 126	71.02 148	90.20 76

1314		71.11 115	69.41 122	76.22 109	79.32 169	50.49 164	80.23 219	85.14 99	59.44 139	58.93 179	88.89 93	33.83 208	89.60 102	61.49 127	77.42 95	88.57 112

GA-MVS		69.04 147	66.70 163	76.06 114	75.11 224	52.36 127	83.12 168	80.23 185	63.32 87	60.65 159	79.22 204	30.98 232	88.37 135	61.25 128	66.41 178	87.46 131

VPNet		72.07 104	71.42 99	74.04 152	78.64 183	47.17 224	89.91 27	87.97 41	72.56 7	64.66 118	85.04 144	41.83 122	88.33 139	61.17 129	60.97 219	86.62 146

ACMP	61.11 9	66.24 197	64.33 197	72.00 191	74.89 229	49.12 188	83.18 167	79.83 192	55.41 211	52.29 244	82.68 174	25.83 262	86.10 198	60.89 130	63.94 195	80.78 232
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 84	72.19 87	77.57 79	83.02 102	55.24 49	81.63 197	81.44 169	50.28 247	76.67 21	90.91 51	44.82 85	86.11 196	60.83 131	80.09 74	91.36 50

test_djsdf		63.84 209	61.56 212	70.70 213	68.78 281	44.69 250	81.63 197	81.44 169	50.28 247	52.27 245	76.26 234	26.72 257	86.11 196	60.83 131	55.84 260	81.29 227

v148		68.24 163	66.35 165	73.88 156	71.76 260	51.47 147	84.23 137	81.90 163	63.69 82	58.94 178	76.44 231	43.72 99	87.78 158	60.63 133	55.86 259	82.39 212

test-LLR		69.65 141	69.01 129	71.60 198	78.67 180	48.17 213	85.13 114	79.72 194	59.18 150	63.13 141	82.58 175	36.91 176	80.24 256	60.56 134	75.17 115	86.39 151

test-mter		68.36 159	67.29 153	71.60 198	78.67 180	48.17 213	85.13 114	79.72 194	53.38 227	63.13 141	82.58 175	27.23 254	80.24 256	60.56 134	75.17 115	86.39 151

IB-MVS	68.87 2	74.01 77	72.03 92	79.94 26	83.04 101	55.50 41	90.24 21	88.65 27	67.14 42	61.38 153	81.74 186	53.21 21	94.28 15	60.45 136	62.41 212	90.03 80
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
v1144		68.81 152	66.82 158	74.80 138	72.34 256	53.46 94	84.68 129	81.77 165	64.25 73	60.28 161	77.91 213	40.23 135	88.95 118	60.37 137	59.52 225	81.97 214

LPG-MVS_test		66.44 194	64.58 195	72.02 189	74.42 233	48.60 200	83.07 170	80.64 181	54.69 217	53.75 235	83.83 154	25.73 264	86.98 176	60.33 138	64.71 187	80.48 236

LGP-MVS_train				72.02 189	74.42 233	48.60 200		80.64 181	54.69 217	53.75 235	83.83 154	25.73 264	86.98 176	60.33 138	64.71 187	80.48 236

abl_6		68.03 164	66.15 170	73.66 163	78.54 185	48.48 206	79.77 224	78.04 223	47.39 262	63.70 135	88.25 103	28.21 244	89.06 108	60.17 140	71.25 146	83.45 197

MVP-Stereo		70.97 117	70.44 107	72.59 182	76.03 216	51.36 150	85.02 123	86.99 57	60.31 128	56.53 215	78.92 207	40.11 138	90.00 90	60.00 141	90.01 3	76.41 275
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 215	60.84 217	70.32 219	68.33 286	44.45 252	81.23 205	81.05 176	53.37 228	50.96 255	77.81 215	17.49 303	85.49 210	59.31 142	58.05 239	81.02 230

baseline1		72.51 97	72.12 89	73.69 162	85.05 61	44.46 251	83.51 157	86.13 75	71.61 10	64.64 119	87.97 109	55.00 15	89.48 103	59.07 143	56.05 256	87.13 137

mvs_tets		62.96 217	60.55 219	70.19 220	68.22 289	44.24 256	80.90 209	80.74 180	52.99 231	50.82 257	77.56 216	16.74 306	85.44 211	59.04 144	57.94 241	80.89 231

HPM-MVS_fast		67.86 166	66.28 167	72.61 181	80.67 155	48.34 210	81.18 206	75.95 250	50.81 246	59.55 170	88.05 108	27.86 249	85.98 202	58.83 145	73.58 127	83.51 196

v144192		67.86 166	65.76 177	74.16 149	71.68 261	53.09 110	84.14 139	80.83 179	62.85 94	59.21 175	77.28 222	39.30 144	88.00 151	58.67 146	57.88 244	81.40 223

thisisatest0515		73.64 83	72.20 86	77.97 71	81.63 131	53.01 114	86.69 80	88.81 25	62.53 97	64.06 128	85.65 137	52.15 27	92.50 35	58.43 147	69.84 156	88.39 115

v8		67.25 179	64.99 192	74.04 152	72.89 249	53.31 104	82.37 182	80.11 187	61.54 111	54.29 231	76.02 240	42.89 111	88.41 134	58.43 147	56.36 251	80.39 238

XXY-MVS		70.18 128	69.28 127	72.89 177	77.64 197	42.88 265	85.06 120	87.50 52	62.58 96	62.66 145	82.34 180	43.64 101	89.83 94	58.42 149	63.70 197	85.96 156

3Dnovator	64.70 6	74.46 70	72.48 79	80.41 17	82.84 110	55.40 46	83.08 169	88.61 31	67.61 39	59.85 163	88.66 96	34.57 199	93.97 17	58.42 149	88.70 8	91.85 35

旧先验2							81.73 194		45.53 274	74.66 29			70.48 309	58.31 151

v1192		67.96 165	65.74 178	74.63 139	71.79 259	53.43 99	84.06 142	80.99 177	63.19 90	59.56 169	77.46 219	37.50 166	88.65 124	58.20 152	58.93 231	81.79 215

EPP-MVSNet		71.14 113	70.07 115	74.33 146	79.18 172	46.52 230	83.81 149	86.49 65	56.32 202	57.95 194	84.90 146	54.23 17	89.14 107	58.14 153	69.65 158	87.33 133

OMC-MVS		65.97 200	65.06 191	68.71 239	72.97 247	42.58 270	78.61 233	75.35 255	54.72 216	59.31 173	86.25 133	33.30 211	77.88 276	57.99 154	67.05 174	85.66 162

MS-PatchMatch		72.34 99	71.26 100	75.61 121	82.38 120	55.55 40	88.00 49	89.95 13	65.38 62	56.51 216	80.74 195	32.28 221	92.89 26	57.95 155	88.10 11	78.39 255

MAR-MVS		76.76 42	75.60 47	80.21 20	90.87 3	54.68 70	89.14 36	89.11 19	62.95 92	70.54 70	92.33 21	41.05 127	94.95 10	57.90 156	86.55 20	91.00 57
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
anonymousdsp		60.46 234	57.65 237	68.88 233	63.63 305	45.09 245	72.93 266	78.63 216	46.52 268	51.12 252	72.80 264	21.46 288	83.07 235	57.79 157	53.97 269	78.47 252

Anonymous20240529		69.71 138	67.28 154	77.00 94	83.78 83	50.36 168	88.87 42	85.10 100	47.22 263	64.03 129	83.37 162	27.93 248	92.10 46	57.78 158	67.44 172	88.53 113

Fast-Effi-MVS+-dtu		66.53 192	64.10 200	73.84 158	72.41 255	52.30 130	84.73 128	75.66 251	59.51 137	56.34 217	79.11 206	28.11 246	85.85 207	57.74 159	63.29 202	83.35 198

v1921920		67.45 175	65.23 189	74.10 151	71.51 264	52.90 117	83.75 151	80.44 184	62.48 98	59.12 176	77.13 223	36.98 174	87.90 152	57.53 160	58.14 238	81.49 219

IterMVS-LS		66.63 190	65.36 188	70.42 217	75.10 225	48.90 195	81.45 204	76.69 243	61.05 118	55.71 221	77.10 225	45.86 72	83.65 229	57.44 161	57.88 244	78.70 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 140	68.70 131	72.68 180	75.00 227	48.90 195	79.54 227	87.16 54	61.05 118	63.88 133	83.74 156	45.87 71	90.44 76	57.42 162	64.68 190	78.70 248

CDS-MVSNet		70.48 126	69.43 121	73.64 164	77.56 199	48.83 197	83.51 157	77.45 233	63.27 88	62.33 146	85.54 140	43.85 93	83.29 234	57.38 163	74.00 123	88.79 106
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 101	70.50 106	77.65 78	83.40 90	51.29 153	87.32 64	86.40 68	59.01 155	58.49 190	88.32 101	32.40 219	91.27 59	57.04 164	82.15 57	90.38 71

miper_lstm_enhance		63.91 208	62.30 206	68.75 238	75.06 226	46.78 226	69.02 287	81.14 175	59.68 135	52.76 241	72.39 268	40.71 131	77.99 274	56.81 165	53.09 275	81.48 220

PAPM_NR		71.80 107	69.98 116	77.26 89	81.54 137	53.34 102	78.60 234	85.25 94	53.46 226	60.53 160	88.66 96	45.69 75	89.24 106	56.49 166	79.62 81	89.19 96

v10		66.61 191	64.20 199	73.83 159	72.59 253	53.37 100	81.88 190	79.91 191	61.11 116	54.09 233	75.60 242	40.06 139	88.26 144	56.47 167	56.10 255	79.86 243

v1240		66.99 186	64.68 194	73.93 154	71.38 267	52.66 120	83.39 164	79.98 188	61.97 103	58.44 192	77.11 224	35.25 194	87.81 154	56.46 168	58.15 236	81.33 224

Anonymous202405211		70.11 129	67.88 142	76.79 101	87.20 32	47.24 223	89.49 31	77.38 235	54.88 215	66.14 99	86.84 125	20.93 291	91.54 54	56.45 169	71.62 143	91.59 40

Fast-Effi-MVS+		72.73 92	71.15 103	77.48 81	82.75 112	54.76 65	86.77 79	80.64 181	63.05 91	65.93 102	84.01 151	44.42 88	89.03 111	56.45 169	76.36 106	88.64 109

114514_t		69.87 137	67.88 142	75.85 119	88.38 19	52.35 128	86.94 76	83.68 132	53.70 225	55.68 222	85.60 138	30.07 238	91.20 60	55.84 171	71.02 148	83.99 185

tpm2		70.82 120	68.44 134	77.98 70	80.78 151	56.11 35	74.21 258	81.28 174	60.24 129	68.04 83	75.27 244	52.26 26	88.50 132	55.82 172	68.03 168	89.33 90

PCF-MVS	61.03 10	70.10 130	68.40 135	75.22 131	77.15 208	51.99 133	79.30 230	82.12 157	56.47 200	61.88 151	86.48 132	43.98 92	87.24 171	55.37 173	72.79 136	86.43 150
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	62.49 8	69.27 145	67.81 145	73.64 164	84.41 71	51.85 137	84.63 131	77.80 226	66.42 46	59.80 164	84.95 145	22.14 285	80.44 253	55.03 174	75.11 117	88.62 110

CHOSEN 280x420		57.53 254	56.38 246	60.97 288	74.01 237	48.10 215	46.30 318	54.31 315	48.18 259	50.88 256	77.43 220	38.37 152	59.16 319	54.83 175	63.14 206	75.66 279

GG-mvs-BLEND				77.77 74	86.68 35	50.61 160	68.67 288	88.45 36		68.73 77	87.45 118	59.15 6	90.67 70	54.83 175	87.67 13	92.03 30

TAMVS		69.51 144	68.16 138	73.56 167	76.30 212	48.71 199	82.57 178	77.17 238	62.10 102	61.32 154	84.23 149	41.90 120	83.46 232	54.80 177	73.09 133	88.50 114

DWT-MVSNet_test		75.47 60	73.87 68	80.29 18	87.33 31	57.05 21	82.86 175	87.96 42	72.59 6	67.29 87	87.79 111	51.61 28	91.52 55	54.75 178	72.63 137	92.29 23

D2MVS		63.49 213	61.39 214	69.77 227	69.29 279	48.93 194	78.89 232	77.71 229	60.64 125	49.70 259	72.10 273	27.08 255	83.48 231	54.48 179	62.65 210	76.90 269

IterMVS		63.77 211	61.67 210	70.08 223	72.68 252	51.24 154	80.44 215	75.51 252	60.51 126	51.41 250	73.70 255	32.08 223	78.91 266	54.30 180	54.35 268	80.08 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchFormer-LS_test		74.17 74	72.30 83	79.77 28	86.61 36	57.26 19	82.02 185	84.80 108	71.85 9	64.73 117	87.52 116	50.33 40	90.40 78	54.23 181	68.63 165	91.64 37

DP-MVS Recon		71.99 105	70.31 109	77.01 93	90.65 4	53.44 97	89.37 32	82.97 148	56.33 201	63.56 138	89.47 83	34.02 203	92.15 45	54.05 182	72.41 139	85.43 167

tpm		68.36 159	67.48 151	70.97 210	79.93 163	51.34 151	76.58 244	78.75 213	67.73 36	63.54 139	74.86 246	48.33 47	72.36 305	53.93 183	63.71 196	89.21 95

XVG-OURS-SEG-HR		62.02 226	59.54 227	69.46 229	65.30 297	45.88 239	65.06 293	73.57 270	46.45 269	57.42 208	83.35 163	26.95 256	78.09 272	53.77 184	64.03 193	84.42 178

cascas		69.01 149	66.13 171	77.66 77	79.36 167	55.41 45	86.99 73	83.75 131	56.69 195	58.92 180	81.35 189	24.31 271	92.10 46	53.23 185	70.61 151	85.46 166

UniMVSNet_NR-MVSNet		68.82 151	68.29 136	70.40 218	75.71 220	42.59 268	84.23 137	86.78 60	66.31 48	58.51 187	82.45 177	51.57 29	84.64 221	53.11 186	55.96 257	83.96 189

DU-MVS		66.84 189	65.74 178	70.16 221	73.27 244	42.59 268	81.50 201	82.92 149	63.53 86	58.51 187	82.11 183	40.75 129	84.64 221	53.11 186	55.96 257	83.24 202

1112_ss		70.05 132	69.37 123	72.10 188	80.77 152	42.78 266	85.12 117	76.75 241	59.69 134	61.19 155	92.12 25	47.48 54	83.84 226	53.04 188	68.21 166	89.66 85

XVG-OURS		61.88 227	59.34 229	69.49 228	65.37 296	46.27 235	64.80 294	73.49 271	47.04 265	57.41 209	82.85 168	25.15 267	78.18 270	53.00 189	64.98 185	84.01 184

thisisatest0530		70.47 127	68.56 132	76.20 110	79.78 164	51.52 146	83.49 159	88.58 34	57.62 182	58.60 186	82.79 169	51.03 34	91.48 56	52.84 190	62.36 214	85.59 165

UGNet		68.71 156	67.11 157	73.50 168	80.55 158	47.61 218	84.08 140	78.51 219	59.45 138	65.68 107	82.73 173	23.78 273	85.08 217	52.80 191	76.40 102	87.80 125
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
Anonymous20231211		66.08 199	63.67 201	73.31 170	83.07 100	48.75 198	86.01 93	84.67 112	45.27 276	56.54 214	76.67 229	28.06 247	88.95 118	52.78 192	59.95 222	82.23 213

无先验							85.19 112	78.00 225	49.08 254				85.13 215	52.78 192		87.45 132

1121		68.79 154	66.77 160	74.82 136	83.08 99	53.46 94	80.23 219	71.53 283	45.47 275	66.31 98	87.19 120	34.02 203	85.13 215	52.78 192	80.36 71	85.87 159

PVSNet_0	57.04 13	61.19 230	57.24 240	73.02 173	77.45 201	50.31 172	79.43 229	77.36 236	63.96 77	47.51 270	72.45 267	25.03 268	83.78 228	52.76 195	19.22 325	84.96 172

FIs		70.00 134	70.24 113	69.30 230	77.93 195	38.55 288	83.99 145	87.72 48	66.86 45	57.66 201	84.17 150	52.28 25	85.31 212	52.72 196	68.80 162	84.02 183

Vis-MVSNet		70.61 124	69.34 124	74.42 144	80.95 149	48.49 205	86.03 92	77.51 232	58.74 160	65.55 108	87.78 112	34.37 200	85.95 205	52.53 197	80.61 67	88.80 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 251	77.59 198	45.46 243		69.20 293	44.47 281	71.50 63	88.34 100	31.21 231	70.76 308	52.20 198	75.88 108	85.03 170

test_normal		50.04 284	44.55 289	66.51 256	51.13 321	36.55 297	20.68 329	79.15 208	54.56 219	27.63 322	45.16 318	13.97 314	84.07 224	52.04 199	56.66 250	78.90 246

API-MVS		74.17 74	72.07 90	80.49 16	90.02 6	58.55 8	87.30 66	84.27 119	57.51 183	65.77 106	87.77 113	41.61 124	95.97 5	51.71 200	82.63 50	86.94 138

ACMM	58.35 12	64.35 206	62.01 209	71.38 202	74.21 236	48.51 204	82.25 183	79.66 196	47.61 260	54.54 228	80.11 197	25.26 266	86.00 201	51.26 201	63.16 205	79.64 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 112	84.89 65	54.59 73		85.26 93	51.98 237	66.70 90	87.07 123	40.15 137	89.70 98	51.23 202	85.06 35	84.10 181

UniMVSNet (Re)		67.71 169	66.80 159	70.45 216	74.44 232	42.93 264	82.42 181	84.90 104	63.69 82	59.63 167	80.99 191	47.18 57	85.23 214	51.17 203	56.75 249	83.19 204

IterMVS-SCA-FT		59.12 240	58.81 234	60.08 290	70.68 273	45.07 246	80.42 216	74.25 262	43.54 288	50.02 258	73.73 252	31.97 224	56.74 320	51.06 204	53.60 273	78.42 254

Test_1112_low_res		67.18 181	66.23 168	70.02 226	78.75 178	41.02 280	83.43 160	73.69 268	57.29 185	58.45 191	82.39 179	45.30 78	80.88 248	50.50 205	66.26 182	88.16 116

pmmvs4		63.34 214	61.07 216	70.16 221	70.14 274	50.53 163	79.97 223	71.41 285	55.08 213	54.12 232	78.58 209	32.79 216	82.09 240	50.33 206	57.22 248	77.86 261

Baseline_NR-MVSNet		65.49 203	64.27 198	69.13 231	74.37 235	41.65 275	83.39 164	78.85 210	59.56 136	59.62 168	76.88 226	40.75 129	87.44 166	49.99 207	55.05 263	78.28 257

UniMVSNet_ETH3D		62.51 221	60.49 220	68.57 242	68.30 287	40.88 282	73.89 259	79.93 190	51.81 241	54.77 225	79.61 200	24.80 269	81.10 245	49.93 208	61.35 217	83.73 193

BH-w/o		70.02 133	68.51 133	74.56 140	82.77 111	50.39 167	86.60 81	78.14 222	59.77 133	59.65 166	85.57 139	39.27 145	87.30 169	49.86 209	74.94 120	85.99 154

LCM-MVSNet-Re		58.82 244	56.54 244	65.68 260	79.31 170	29.09 318	61.39 305	45.79 321	60.73 123	37.65 303	72.47 266	31.42 230	81.08 246	49.66 210	70.41 152	86.87 139

gg-mvs-nofinetune		67.43 176	64.53 196	76.13 112	85.95 40	47.79 217	64.38 295	88.28 38	39.34 297	66.62 92	41.27 319	58.69 7	89.00 114	49.64 211	86.62 19	91.59 40

TranMVSNet+NR-MVSNet		66.94 187	65.61 181	70.93 211	73.45 241	43.38 262	83.02 172	84.25 120	65.31 65	58.33 193	81.90 185	39.92 141	85.52 208	49.43 212	54.89 265	83.89 191

tttt0517		68.33 161	66.29 166	74.46 142	78.08 192	49.06 189	80.88 210	89.08 20	54.40 222	54.75 226	80.77 194	51.31 31	90.33 81	49.35 213	58.01 240	83.99 185

WR-MVS		67.58 171	66.76 161	70.04 225	75.92 218	45.06 249	86.23 87	85.28 92	64.31 72	58.50 189	81.00 190	44.80 87	82.00 241	49.21 214	55.57 262	83.06 205

test_post1							70.84 280				14.72 333	34.33 201	83.86 225	48.80 215

SCA		63.84 209	60.01 224	75.32 127	78.58 184	57.92 10	61.61 303	77.53 231	56.71 194	57.75 200	70.77 277	31.97 224	79.91 262	48.80 215	56.36 251	88.13 119

pmmvs5		62.80 219	61.18 215	67.66 246	69.53 278	42.37 273	82.65 177	75.19 256	54.30 223	52.03 247	78.51 210	31.64 229	80.67 249	48.60 217	58.15 236	79.95 242

新几何1				73.30 171	83.10 96	53.48 93		71.43 284	45.55 273	66.14 99	87.17 121	33.88 207	80.54 251	48.50 218	80.33 72	85.88 158

pm-mvs1		64.12 207	62.56 204	68.78 237	71.68 261	38.87 287	82.89 174	81.57 166	55.54 210	53.89 234	77.82 214	37.73 159	86.74 182	48.46 219	53.49 274	80.72 233

PM-MVS		46.92 289	43.76 292	56.41 298	52.18 320	32.26 310	63.21 299	38.18 326	37.99 302	40.78 295	66.20 294	5.09 330	65.42 314	48.19 220	41.99 303	71.54 303

FC-MVSNet-test		67.49 174	67.91 140	66.21 258	76.06 214	33.06 306	80.82 211	87.18 53	64.44 71	54.81 224	82.87 167	50.40 39	82.60 236	48.05 221	66.55 177	82.98 207

CMPMVS	40.41 21	55.34 266	52.64 268	63.46 274	60.88 314	43.84 258	61.58 304	71.06 286	30.43 315	36.33 304	74.63 248	24.14 272	75.44 288	48.05 221	66.62 176	71.12 305
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 179	65.99 174	71.04 209	73.27 244	43.91 257	85.32 110	84.75 110	66.05 55	53.65 237	82.11 183	45.05 80	85.97 204	47.55 223	56.18 254	83.24 202

QAPM		71.88 106	69.33 125	79.52 29	82.20 122	54.30 77	86.30 86	88.77 26	56.61 197	59.72 165	87.48 117	33.90 206	95.36 7	47.48 224	81.49 60	88.90 103

EPMVS		68.45 158	65.44 186	77.47 82	84.91 64	56.17 34	71.89 276	81.91 162	61.72 108	60.85 156	72.49 265	36.21 183	87.06 175	47.32 225	71.62 143	89.17 97

GBi-Net		67.09 183	65.47 184	71.96 192	82.71 113	46.36 232	83.52 153	83.31 138	58.55 163	57.58 202	76.23 235	36.72 179	86.20 192	47.25 226	63.40 198	83.32 199

test1		67.09 183	65.47 184	71.96 192	82.71 113	46.36 232	83.52 153	83.31 138	58.55 163	57.58 202	76.23 235	36.72 179	86.20 192	47.25 226	63.40 198	83.32 199

FMVSNet3		68.84 150	67.40 152	73.19 172	85.05 61	48.53 203	85.71 101	85.36 86	60.90 120	57.58 202	79.15 205	42.16 114	86.77 181	47.25 226	63.40 198	84.27 180

v7n		62.50 222	59.27 230	72.20 187	67.25 292	49.83 178	77.87 237	80.12 186	52.50 234	48.80 263	73.07 260	32.10 222	87.90 152	46.83 229	54.92 264	78.86 247

CVMVSNet		60.85 232	60.44 221	62.07 279	75.00 227	32.73 308	79.54 227	73.49 271	36.98 305	56.28 218	83.74 156	29.28 242	69.53 311	46.48 230	63.23 203	83.94 190

TR-MVS		69.71 138	67.85 144	75.27 129	82.94 106	48.48 206	87.40 63	80.86 178	57.15 187	64.61 120	87.08 122	32.67 217	89.64 101	46.38 231	71.55 145	87.68 128

MDTV_nov1_ep13_2view						43.62 260	71.13 279		54.95 214	59.29 174		36.76 178		46.33 232		87.32 134

FMVSNet2		67.57 172	65.79 176	72.90 175	82.71 113	47.97 216	85.15 113	84.93 103	58.55 163	56.71 212	78.26 212	36.72 179	86.67 184	46.15 233	62.94 209	84.07 182

UnsupCasMVSNet_eth		57.56 253	55.15 253	64.79 269	64.57 302	33.12 305	73.17 264	83.87 130	58.98 157	41.75 291	70.03 281	22.54 280	79.92 260	46.12 234	35.31 312	81.32 226

testdata2													77.81 278	45.64 235

XVG-ACMP-BASELINE		56.03 263	52.85 266	65.58 261	61.91 310	40.95 281	63.36 296	72.43 274	45.20 277	46.02 276	74.09 249	9.20 322	78.12 271	45.13 236	58.27 234	77.66 264

AdaColmap		67.86 166	65.48 183	75.00 133	88.15 23	54.99 59	86.10 90	76.63 244	49.30 253	57.80 197	86.65 129	29.39 241	88.94 120	45.10 237	70.21 154	81.06 229

BH-untuned		68.28 162	66.40 164	73.91 155	81.62 132	50.01 177	85.56 105	77.39 234	57.63 181	57.47 207	83.69 158	36.36 182	87.08 174	44.81 238	73.08 134	84.65 175

BH-RMVSNet		70.08 131	68.01 139	76.27 107	84.21 76	51.22 155	87.29 67	79.33 206	58.96 158	63.63 137	86.77 126	33.29 212	90.30 85	44.63 239	73.96 124	87.30 135

IS-MVSNet		68.80 153	67.55 149	72.54 183	78.50 187	43.43 261	81.03 208	79.35 204	59.12 153	57.27 210	86.71 127	46.05 68	87.70 160	44.32 240	75.60 112	86.49 148

pmmvs-eth3d		55.97 264	52.78 267	65.54 262	61.02 313	46.44 231	75.36 252	67.72 296	49.61 252	43.65 282	67.58 290	21.63 287	77.04 281	44.11 241	44.33 299	73.15 297

pmmvs6		59.64 236	57.15 241	67.09 250	66.01 293	36.86 295	80.50 214	78.64 215	45.05 278	49.05 262	73.94 251	27.28 253	86.10 198	43.96 242	49.94 282	78.31 256

EPNet_dtu		66.25 196	66.71 162	64.87 268	78.66 182	34.12 301	82.80 176	75.51 252	61.75 107	64.47 125	86.90 124	37.06 173	72.46 304	43.65 243	69.63 159	88.02 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 195	62.78 203	76.77 102	81.40 142	57.14 20	70.03 283	77.19 237	53.00 230	58.76 185	70.73 279	46.17 67	86.73 183	43.27 244	64.46 191	86.44 149

OpenMVS	61.00 11	69.99 135	67.55 149	77.30 85	78.37 190	54.07 84	84.36 133	85.76 79	57.22 186	56.71 212	87.67 114	30.79 233	92.83 27	43.04 245	84.06 45	85.01 171

MVS_0304		56.72 256	55.17 252	61.37 286	70.71 270	36.80 296	75.74 245	68.75 294	44.11 285	52.53 242	68.20 288	15.05 312	74.53 292	42.98 246	58.44 232	72.79 298

PatchmatchNet		67.07 185	63.63 202	77.40 83	83.10 96	58.03 9	72.11 274	77.77 227	58.85 159	59.37 172	70.83 276	37.84 155	84.93 218	42.96 247	69.83 157	89.26 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 223	59.04 232	72.77 178	73.97 239	56.57 26	60.52 306	71.72 279	60.04 130	57.49 205	65.86 295	38.94 146	80.31 254	42.86 248	59.93 223	81.42 221

FMVSNet1		64.57 204	62.11 208	71.96 192	77.32 202	46.36 232	83.52 153	83.31 138	52.43 235	54.42 229	76.23 235	27.80 250	86.20 192	42.59 249	61.34 218	83.32 199

UA-Net		67.32 178	66.23 168	70.59 214	78.85 176	41.23 279	73.60 260	75.45 254	61.54 111	66.61 93	84.53 147	38.73 149	86.57 190	42.48 250	74.24 122	83.98 187

MIMVSNet		63.12 216	60.29 222	71.61 197	75.92 218	46.65 228	65.15 292	81.94 159	59.14 152	54.65 227	69.47 283	25.74 263	80.63 250	41.03 251	69.56 160	87.55 129

EG-PatchMatch MVS		62.40 225	59.59 226	70.81 212	73.29 243	49.05 190	85.81 94	84.78 109	51.85 240	44.19 279	73.48 258	15.52 311	89.85 93	40.16 252	67.24 173	73.54 293

UnsupCasMVSNet_bld		53.86 273	50.53 274	63.84 271	63.52 306	34.75 299	71.38 277	81.92 161	46.53 267	38.95 301	57.93 312	20.55 292	80.20 258	39.91 253	34.09 318	76.57 273

dp		64.41 205	61.58 211	72.90 175	82.40 119	54.09 82	72.53 268	76.59 245	60.39 127	55.68 222	70.39 280	35.18 195	76.90 284	39.34 254	61.71 216	87.73 127

TransMVSNet (Re)		62.82 218	60.76 218	69.02 232	73.98 238	41.61 276	86.36 84	79.30 207	56.90 188	52.53 242	76.44 231	41.85 121	87.60 163	38.83 255	40.61 306	77.86 261

USDC		54.36 270	51.23 272	63.76 272	64.29 303	37.71 292	62.84 301	73.48 273	56.85 189	35.47 307	71.94 274	9.23 321	78.43 268	38.43 256	48.57 283	75.13 283

PLC	52.38 18	60.89 231	58.97 233	66.68 255	81.77 128	45.70 241	78.96 231	74.04 265	43.66 287	47.63 267	83.19 166	23.52 276	77.78 279	37.47 257	60.46 221	76.55 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 220	62.44 205	62.86 278	72.28 258	29.51 315	82.93 173	78.78 212	59.18 150	53.07 240	82.41 178	36.91 176	77.39 280	37.45 258	58.96 230	81.66 217

OurMVSNet-221017-0		52.39 279	48.73 280	63.35 275	65.21 298	38.42 289	68.54 289	64.95 300	38.19 300	39.57 298	71.43 275	13.23 316	79.92 260	37.16 259	40.32 307	71.72 301

CNLPA		60.59 233	58.44 235	67.05 252	79.21 171	47.26 222	79.75 226	64.34 303	42.46 293	51.90 249	83.94 152	27.79 251	75.41 289	37.12 260	59.49 227	78.47 252

K. test v3		54.04 272	49.42 279	67.92 245	68.55 283	42.57 271	75.51 250	63.07 305	52.07 236	39.21 299	64.59 299	19.34 296	82.21 237	37.11 261	25.31 322	78.97 245

Vis-MVSNet (Re-imp)		65.52 202	65.63 180	65.17 266	77.49 200	30.54 313	75.49 251	77.73 228	59.34 143	52.26 246	86.69 128	49.38 45	80.53 252	37.07 262	75.28 114	84.42 178

PatchMatch-RL		56.66 257	53.75 260	65.37 265	77.91 196	45.28 244	69.78 285	60.38 308	41.35 294	47.57 268	73.73 252	16.83 305	76.91 283	36.99 263	59.21 229	73.92 290

Patchmtry		56.56 259	52.95 265	67.42 248	72.53 254	50.59 162	59.05 308	71.72 279	37.86 303	46.92 271	65.86 295	38.94 146	80.06 259	36.94 264	46.72 294	71.60 302

FMVSNet5		58.61 246	56.45 245	65.10 267	77.20 207	39.74 284	74.77 254	77.12 239	50.27 249	43.28 285	67.71 289	26.15 261	76.90 284	36.78 265	54.78 266	78.65 250

MDTV_nov1_ep13			61.56 212		81.68 129	55.12 54	72.41 270	78.18 221	59.19 148	58.85 183	69.29 284	34.69 198	86.16 195	36.76 266	62.96 208

JIA-IIPM		52.33 280	47.77 284	66.03 259	71.20 268	46.92 225	40.00 325	76.48 246	37.10 304	46.73 272	37.02 321	32.96 213	77.88 276	35.97 267	52.45 277	73.29 295

lessismore_v0				67.98 244	64.76 301	41.25 278		45.75 322		36.03 306	65.63 297	19.29 297	84.11 223	35.67 268	21.24 324	78.59 251

CP-MVSNet		58.54 249	57.57 239	61.46 285	68.50 284	33.96 302	76.90 242	78.60 218	51.67 242	47.83 265	76.60 230	34.99 197	72.79 302	35.45 269	47.58 286	77.64 265

ambc				62.06 280	53.98 319	29.38 316	35.08 327	79.65 197		41.37 292	59.96 308	6.27 328	82.15 238	35.34 270	38.22 310	74.65 285

PS-CasMVS		58.12 252	57.03 243	61.37 286	68.24 288	33.80 304	76.73 243	78.01 224	51.20 244	47.54 269	76.20 238	32.85 214	72.76 303	35.17 271	47.37 288	77.55 266

EU-MVSNet		52.63 278	50.72 273	58.37 295	62.69 309	28.13 320	72.60 267	75.97 249	30.94 314	40.76 296	72.11 272	20.16 293	70.80 307	35.11 272	46.11 295	76.19 277

ACMH+	54.58 15	58.55 248	55.24 251	68.50 243	74.68 231	45.80 240	80.27 217	70.21 290	47.15 264	42.77 287	75.48 243	16.73 307	85.98 202	35.10 273	54.78 266	73.72 291

pmmvs3		45.53 291	41.55 293	57.44 297	48.97 324	39.68 285	70.06 282	57.66 311	28.32 317	34.06 310	57.29 313	8.50 323	66.85 313	34.86 274	34.26 316	65.80 313

our_test_3		59.11 241	55.08 255	71.18 207	71.42 265	53.29 105	81.96 187	74.52 259	48.32 257	42.08 288	69.28 285	28.14 245	82.15 238	34.35 275	45.68 297	78.11 260

PEN-MVS		58.35 251	57.15 241	61.94 281	67.55 291	34.39 300	77.01 240	78.35 220	51.87 239	47.72 266	76.73 228	33.91 205	73.75 297	34.03 276	47.17 290	77.68 263

tpmvs		62.45 224	59.42 228	71.53 201	83.93 80	54.32 76	70.03 283	77.61 230	51.91 238	53.48 238	68.29 287	37.91 154	86.66 185	33.36 277	58.27 234	73.62 292

YYNet1		53.82 274	49.96 276	65.41 264	70.09 276	48.95 192	72.30 271	71.66 281	44.25 283	31.89 316	63.07 302	23.73 274	73.95 295	33.26 278	39.40 308	73.34 294

MDA-MVSNet_test_wron		53.82 274	49.95 277	65.43 263	70.13 275	49.05 190	72.30 271	71.65 282	44.23 284	31.85 317	63.13 301	23.68 275	74.01 294	33.25 279	39.35 309	73.23 296

Anonymous20231206		59.08 242	57.59 238	63.55 273	68.77 282	32.14 311	80.26 218	79.78 193	50.00 250	49.39 260	72.39 268	26.64 258	78.36 269	33.12 280	57.94 241	80.14 240

F-COLMAP		55.96 265	53.65 262	62.87 277	72.76 250	42.77 267	74.70 256	70.37 289	40.03 296	41.11 294	79.36 201	17.77 302	73.70 298	32.80 281	53.96 270	72.15 299

PatchT		56.60 258	52.97 264	67.48 247	72.94 248	46.16 238	57.30 310	73.78 267	38.77 299	54.37 230	57.26 314	37.52 164	78.06 273	32.02 282	52.79 276	78.23 259

SixPastTwentyTwo		54.37 269	50.10 275	67.21 249	70.70 271	41.46 277	74.73 255	64.69 301	47.56 261	39.12 300	69.49 282	18.49 300	84.69 220	31.87 283	34.20 317	75.48 280

WR-MVS_H		58.91 243	58.04 236	61.54 284	69.07 280	33.83 303	76.91 241	81.99 158	51.40 243	48.17 264	74.67 247	40.23 135	74.15 293	31.78 284	48.10 284	76.64 272

ACMH	53.70 16	59.78 235	55.94 249	71.28 203	76.59 211	48.35 209	80.15 222	76.11 248	49.74 251	41.91 290	73.45 259	16.50 308	90.31 82	31.42 285	57.63 246	75.17 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 237	55.14 254	72.32 186	74.69 230	50.71 158	74.39 257	73.58 269	44.44 282	43.40 284	77.52 217	19.45 295	90.87 67	31.31 286	57.49 247	75.38 281

thres200		68.71 156	67.27 155	73.02 173	84.73 66	46.76 227	85.03 122	87.73 47	62.34 99	59.87 162	83.45 161	43.15 107	88.32 140	31.25 287	67.91 170	83.98 187

DTE-MVSNet		57.03 255	55.73 250	60.95 289	65.94 294	32.57 309	75.71 246	77.09 240	51.16 245	46.65 274	76.34 233	32.84 215	73.22 301	30.94 288	44.87 298	77.06 268

ppachtmachnet_test		58.56 247	54.34 256	71.24 204	71.42 265	54.74 66	81.84 192	72.27 276	49.02 255	45.86 278	68.99 286	26.27 259	83.30 233	30.12 289	43.23 302	75.69 278

MVS-HIRNet		49.01 285	44.71 287	61.92 282	76.06 214	46.61 229	63.23 298	54.90 314	24.77 319	33.56 312	36.60 322	21.28 289	75.88 287	29.49 290	62.54 211	63.26 318

test20.03		55.22 267	54.07 258	58.68 294	63.14 307	25.00 322	77.69 238	74.78 258	52.64 232	43.43 283	72.39 268	26.21 260	74.76 291	29.31 291	47.05 292	76.28 276

testgi		54.25 271	52.57 269	59.29 292	62.76 308	21.65 326	72.21 273	70.47 288	53.25 229	41.94 289	77.33 221	14.28 313	77.95 275	29.18 292	51.72 279	78.28 257

thres100view900		66.87 188	65.42 187	71.24 204	83.29 93	43.15 263	81.67 196	87.78 44	59.04 154	55.92 220	82.18 182	43.73 97	87.80 155	28.80 293	66.36 179	82.78 210

tfpn200view9		67.57 172	66.13 171	71.89 196	84.05 78	45.07 246	83.40 162	87.71 49	60.79 121	57.79 198	82.76 170	43.53 102	87.80 155	28.80 293	66.36 179	82.78 210

thres400		67.40 177	66.13 171	71.19 206	84.05 78	45.07 246	83.40 162	87.71 49	60.79 121	57.79 198	82.76 170	43.53 102	87.80 155	28.80 293	66.36 179	80.71 234

ADS-MVSNet2		55.21 268	51.44 271	66.51 256	80.60 156	49.56 183	55.03 312	65.44 299	44.72 279	51.00 253	61.19 305	22.83 277	75.41 289	28.54 296	53.63 271	74.57 286

ADS-MVSNet		56.17 262	51.95 270	68.84 234	80.60 156	53.07 112	55.03 312	70.02 291	44.72 279	51.00 253	61.19 305	22.83 277	78.88 267	28.54 296	53.63 271	74.57 286

LTVRE_ROB	45.45 19	52.73 277	49.74 278	61.69 283	69.78 277	34.99 298	44.52 319	67.60 297	43.11 290	43.79 281	74.03 250	18.54 299	81.45 243	28.39 298	57.94 241	68.62 308
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
new-patchmatchnet		48.21 286	46.55 286	53.18 301	57.73 317	18.19 330	70.24 281	71.02 287	45.70 272	33.70 311	60.23 307	18.00 301	69.86 310	27.97 299	34.35 315	71.49 304

OpenMVS_ROB	53.19 17	59.20 239	56.00 248	68.83 235	71.13 269	44.30 254	83.64 152	75.02 257	46.42 270	46.48 275	73.03 261	18.69 298	88.14 145	27.74 300	61.80 215	74.05 289

RPSCF		45.77 290	44.13 291	50.68 303	57.67 318	29.66 314	54.92 314	45.25 323	26.69 318	45.92 277	75.92 241	17.43 304	45.70 328	27.44 301	45.95 296	76.67 271

MDA-MVSNet-bldmvs		51.56 281	47.75 285	63.00 276	71.60 263	47.32 221	69.70 286	72.12 277	43.81 286	27.65 321	63.38 300	21.97 286	75.96 286	27.30 302	32.19 319	65.70 314

RPMNet		58.49 250	53.74 261	72.77 178	73.97 239	56.57 26	60.52 306	72.39 275	35.72 308	57.49 205	58.87 311	37.73 159	80.31 254	27.01 303	59.93 223	81.42 221

thres600view7		66.46 193	65.12 190	70.47 215	83.41 87	43.80 259	82.15 184	87.78 44	59.37 142	56.02 219	82.21 181	43.73 97	86.90 179	26.51 304	64.94 186	80.71 234

TAPA-MVS	56.12 14	61.82 228	60.18 223	66.71 253	78.48 188	37.97 291	75.19 253	76.41 247	46.82 266	57.04 211	86.52 131	27.67 252	77.03 282	26.50 305	67.02 175	85.14 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 302	58.03 316	31.94 312		53.57 318	36.67 306	41.32 293	75.23 245	11.17 318	51.57 324	25.81 306	48.04 285	72.02 300

Patchmatch-test		53.33 276	48.17 281	68.81 236	73.31 242	42.38 272	42.98 321	58.23 310	32.53 313	38.79 302	70.77 277	39.66 142	73.51 299	25.18 307	52.06 278	90.55 64

TinyColmap		48.15 287	44.49 290	59.13 293	65.73 295	38.04 290	63.34 297	62.86 306	38.78 298	29.48 319	67.23 293	6.46 327	73.30 300	24.59 308	41.90 304	66.04 312

AllTest		47.32 288	44.66 288	55.32 299	65.08 299	37.50 293	62.96 300	54.25 316	35.45 310	33.42 313	72.82 262	9.98 319	59.33 317	24.13 309	43.84 300	69.13 306

TestCases				55.32 299	65.08 299	37.50 293		54.25 316	35.45 310	33.42 313	72.82 262	9.98 319	59.33 317	24.13 309	43.84 300	69.13 306

N_pmnet		41.25 292	39.77 294	45.66 307	68.50 284	0.82 338	72.51 269	0.38 339	35.61 309	35.26 308	61.51 304	20.07 294	67.74 312	23.51 311	40.63 305	68.42 309

DP-MVS		59.24 238	56.12 247	68.63 240	88.24 22	50.35 169	82.51 180	64.43 302	41.10 295	46.70 273	78.77 208	24.75 270	88.57 130	22.26 312	56.29 253	66.96 311

MIMVSNet1		50.35 283	47.81 283	57.96 296	61.53 312	27.80 321	67.40 290	74.06 264	43.25 289	33.31 315	65.38 298	16.03 309	71.34 306	21.80 313	47.55 287	74.75 284

tfpnnormal		61.47 229	59.09 231	68.62 241	76.29 213	41.69 274	81.14 207	85.16 97	54.48 221	51.32 251	73.63 256	32.32 220	86.89 180	21.78 314	55.71 261	77.29 267

LF4IMVS		33.04 298	32.55 298	34.52 313	40.96 327	22.03 325	44.45 320	35.62 329	20.42 321	28.12 320	62.35 303	5.03 331	31.88 333	21.61 315	34.42 314	49.63 322

COLMAP_ROB	43.60 20	50.90 282	48.05 282	59.47 291	67.81 290	40.57 283	71.25 278	62.72 307	36.49 307	36.19 305	73.51 257	13.48 315	73.92 296	20.71 316	50.26 281	63.92 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 299	23.85 303	40.71 308	27.46 334	18.93 329	30.82 328	46.19 320	12.76 327	16.40 325	34.70 325	1.90 336	48.69 327	20.25 317	24.22 323	54.51 320

DSMNet-mixed		38.35 294	35.36 296	47.33 306	48.11 325	14.91 332	37.87 326	36.60 328	19.18 323	34.37 309	59.56 310	15.53 310	53.01 323	20.14 318	46.89 293	74.07 288

new_pmnet		33.56 297	31.89 299	38.59 310	49.01 323	20.42 327	51.01 315	37.92 327	20.58 320	23.45 323	46.79 317	6.66 326	49.28 326	20.00 319	31.57 321	46.09 324

LS3D		56.40 261	53.82 259	64.12 270	81.12 146	45.69 242	73.42 262	66.14 298	35.30 312	43.24 286	79.88 198	22.18 284	79.62 264	19.10 320	64.00 194	67.05 310

TDRefinement		40.91 293	38.37 295	48.55 305	50.45 322	33.03 307	58.98 309	50.97 319	28.50 316	29.89 318	67.39 291	6.21 329	54.51 321	17.67 321	35.25 313	58.11 319

test_0402		56.45 260	53.03 263	66.69 254	76.78 210	50.31 172	81.76 193	69.61 292	42.79 291	43.88 280	72.13 271	22.82 279	86.46 191	16.57 322	50.94 280	63.31 317

PMMVS2		26.71 301	22.98 304	37.87 311	36.89 329	8.51 336	42.51 322	29.32 333	19.09 324	13.01 327	37.54 320	2.23 334	53.11 322	14.54 323	11.71 326	51.99 321

ANet_high		34.39 296	29.59 300	48.78 304	30.34 332	22.28 324	55.53 311	63.79 304	38.11 301	15.47 326	36.56 323	6.94 324	59.98 316	13.93 324	5.64 333	64.08 315

tmp_tt		9.44 307	10.68 309	5.73 320	2.49 337	4.21 337	10.48 333	18.04 335	0.34 333	12.59 328	20.49 328	11.39 317	7.03 336	13.84 325	6.46 332	5.95 331

FPMVS		35.40 295	33.67 297	40.57 309	46.34 326	28.74 319	41.05 323	57.05 312	20.37 322	22.27 324	53.38 316	6.87 325	44.94 329	8.62 326	47.11 291	48.01 323

Gipuma		27.47 300	24.26 302	37.12 312	60.55 315	29.17 317	11.68 332	60.00 309	14.18 326	10.52 330	15.12 331	2.20 335	63.01 315	8.39 327	35.65 311	19.18 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 306	13.39 308	29.16 315	28.43 333	19.72 328	13.73 331	23.63 334	7.23 331	7.96 331	21.41 327	0.80 339	36.08 332	6.97 328	10.39 327	31.69 325
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 318	21.34 336	8.99 335		10.02 337	10.59 329	7.53 332	30.55 326	1.82 337	14.55 334	6.83 329	7.52 329	15.75 328

E-PMN		19.16 303	18.40 306	21.44 316	36.19 330	13.63 333	47.59 316	30.89 331	10.73 328	5.91 333	16.59 329	3.66 333	39.77 330	5.95 330	8.14 328	10.92 329

PMVS	19.57 22	25.07 302	22.43 305	32.99 314	23.12 335	22.98 323	40.98 324	35.19 330	15.99 325	11.95 329	35.87 324	1.47 338	49.29 325	5.41 331	31.90 320	26.70 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 304	17.66 307	20.71 317	34.13 331	12.64 334	46.94 317	29.94 332	10.46 330	5.58 334	14.93 332	4.23 332	38.83 331	5.24 332	7.51 330	10.67 330

wuyk23d		9.11 308	8.77 311	10.15 319	40.18 328	16.76 331	20.28 330	1.01 338	2.58 332	2.66 335	0.98 335	0.23 340	12.49 335	4.08 333	6.90 331	1.19 332

testmvs		6.14 310	8.18 312	0.01 321	0.01 338	0.00 340	73.40 263	0.00 340	0.00 334	0.02 336	0.15 336	0.00 341	0.00 337	0.02 334	0.00 334	0.02 333

test123		6.01 311	8.01 313	0.01 321	0.00 339	0.01 339	71.93 275	0.00 340	0.00 334	0.02 336	0.11 337	0.00 341	0.00 337	0.02 334	0.00 334	0.02 333

test_part1				0.00 323		0.00 340	0.00 334	88.42 37				0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

cdsmvs_eth3d_5k		18.33 305	24.44 301	0.00 323	0.00 339	0.00 340	0.00 334	89.40 14	0.00 334	0.00 338	92.02 28	38.55 150	0.00 337	0.00 336	0.00 334	0.00 335

pcd_1.5k_mvsjas		3.15 312	4.20 314	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	37.77 156	0.00 337	0.00 336	0.00 334	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

ab-mvs-re		7.68 309	10.24 310	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	92.12 25	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 339	0.00 340	0.00 334	0.00 340	0.00 334	0.00 338	0.00 338	0.00 341	0.00 337	0.00 336	0.00 334	0.00 335

save fliter					85.35 56	56.34 32	89.31 34	81.46 168	61.55 110

test0726					89.40 11	57.45 15	92.32 5	88.63 29	57.71 179	83.14 3	93.96 3	55.17 12

GSMVS																88.13 119

test_part2					89.33 13	55.48 42				82.27 4

sam_mvs1												38.86 148				88.13 119

sam_mvs												35.99 190

MTGPA								81.31 171

test_post											16.22 330	37.52 164	84.72 219

patchmatchnet-post											59.74 309	38.41 151	79.91 262

MTMP							87.27 68	15.34 336

TEST9					85.68 44	55.42 43	87.59 58	84.00 126	57.72 178	72.99 44	90.98 47	44.87 84	88.58 127

test_8					85.72 43	55.31 47	87.60 55	83.88 129	57.84 176	72.84 47	90.99 46	44.99 81	88.34 137

agg_prior					85.64 47	54.92 61		83.61 135		72.53 51			88.10 148

test_prior4						56.39 31	87.15 71

test_prior				78.39 60	86.35 38	54.91 63		85.45 82					89.70 98			90.55 64

新几何2							81.61 199

旧先验1					81.57 136	47.48 219		71.83 278			88.66 96	36.94 175			78.34 89	88.67 108

原ACMM2							83.77 150

test222					79.36 167	50.97 156	77.99 236	67.84 295	42.54 292	62.84 144	86.53 130	30.26 236			76.91 100	85.23 168

segment_acmp												44.97 83

testdata1							77.55 239		64.14 74

test12				79.24 33	86.89 33	56.08 36		85.16 97		72.27 56		47.15 58	91.10 61		85.93 24	90.54 67

plane_prior7					77.95 193	48.46 208

plane_prior6					78.42 189	49.39 186						36.04 187

plane_prior4											83.28 164

plane_prior3						48.95 192			64.01 75	62.15 147

plane_prior2							85.76 96		63.60 84

plane_prior1					78.31 191

plane_prior						49.57 181	87.43 62		64.57 70						72.84 135

n2								0.00 340

nn								0.00 340

door-mid								41.31 325

test11								84.25 120

door								43.27 324

HQP5-MVS						51.56 144

HQP-NCC					79.02 173		88.00 49		65.45 58	64.48 122

ACMP_Plane					79.02 173		88.00 49		65.45 58	64.48 122

HQP4-MVS										64.47 125			88.61 126			84.91 173

HQP3-MVS								83.68 132							73.12 131

HQP2-MVS												37.35 167

NP-MVS					78.76 177	50.43 166					85.12 143

ACMMP++_ref															63.20 204

ACMMP++															59.38 228

Test By Simon												39.38 143

High-resolution multi-view benchmark