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		82.45 2	83.62 1	78.96 36	82.99 91	52.71 126	85.04 110	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 44	83.35 5	93.53 1	92.72 17

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 13	93.77 1	91.10 4	75.95 2	77.10 16	93.09 9	54.15 13	95.57 3	85.80 1	85.87 23	93.31 6

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 64	77.83 1	77.88 13	92.13 19	60.24 2	94.78 12	78.97 16	89.61 3	93.69 3
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
CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 11	91.71 3	88.87 16	70.31 14	77.64 15	93.87 2	52.58 19	93.91 17	84.17 2	87.92 11	92.39 19

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 64	85.46 48	49.56 184	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 51	82.57 8	86.22 21	93.28 7

CANet		80.90 5	81.17 6	80.09 23	87.62 22	54.21 75	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 35	93.08 11

HPM-MVS++		80.50 7	80.71 7	79.88 25	87.34 24	55.20 49	89.93 20	87.55 45	66.04 48	79.46 9	93.00 12	53.10 17	91.76 41	80.40 10	89.56 4	92.68 18

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 38	89.59 26	88.42 25	56.02 210	82.27 2	93.65 3	58.18 6	95.22 6	79.73 12	86.59 16	91.53 36

CSCG		80.41 9	79.72 9	82.49 4	89.12 11	57.67 12	89.29 30	91.54 2	59.19 144	71.82 50	90.05 64	59.72 3	96.04 1	78.37 19	88.40 9	93.75 2

MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 68	90.58 14	84.48 100	72.51 8	79.22 11	93.09 9	42.01 129	93.28 21	84.00 4	85.84 24	92.87 15

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 43	60.97 3	91.69 4	87.02 50	70.62 12	80.75 6	93.22 6	37.77 168	92.50 30	82.75 6	86.25 20	91.57 34

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 54	60.73 4	91.65 5	86.86 53	70.30 15	80.77 5	93.07 11	37.63 173	92.28 34	82.73 7	85.71 25	91.57 34

NCCC		79.57 13	79.23 13	80.59 13	89.50 8	56.99 19	91.38 8	88.17 32	67.71 29	73.81 29	92.75 13	46.88 51	93.28 21	78.79 17	84.07 40	91.50 39

SMA-MVS		78.93 14	78.55 14	80.10 22	84.42 60	55.81 34	87.58 49	86.47 59	61.29 112	79.34 10	93.10 7	46.02 65	92.41 31	79.97 11	88.72 6	92.08 24

EPNet		78.36 19	78.49 15	77.97 63	85.49 45	52.04 139	89.36 29	84.07 117	73.22 4	77.03 17	91.72 30	49.32 36	90.17 72	73.46 49	82.77 44	91.69 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 20	78.26 16	78.48 48	81.33 134	56.31 29	81.59 187	86.41 61	69.61 18	81.72 4	88.16 90	55.09 10	88.04 141	74.12 43	86.31 19	91.09 46

APDe-MVS		78.44 16	78.20 17	79.19 32	88.56 12	54.55 70	89.76 24	87.77 40	55.91 212	78.56 12	92.49 16	48.20 38	92.65 28	79.49 14	83.04 43	90.39 63

lupinMVS		78.38 18	78.11 18	79.19 32	83.02 89	55.24 46	91.57 7	84.82 92	69.12 19	76.67 18	92.02 23	44.82 78	90.23 70	80.83 9	80.09 66	92.08 24

alignmvs		78.08 22	77.98 19	78.39 52	83.53 74	53.22 109	89.77 23	85.45 73	66.11 43	76.59 20	91.99 25	54.07 14	89.05 92	77.34 26	77.00 91	92.89 14

VNet		77.99 24	77.92 20	78.19 57	87.43 23	50.12 176	90.93 12	91.41 3	67.48 32	75.12 22	90.15 63	46.77 52	91.00 49	73.52 48	78.46 78	93.44 4

canonicalmvs		78.17 21	77.86 21	79.12 35	84.30 62	54.22 74	87.71 42	84.57 99	67.70 30	77.70 14	92.11 22	50.90 28	89.95 75	78.18 23	77.54 85	93.20 9

DeepC-MVS_fast	67.50 3	78.00 23	77.63 22	79.13 34	88.52 13	55.12 51	89.95 19	85.98 67	68.31 23	71.33 54	92.75 13	45.52 70	90.37 64	71.15 60	85.14 31	91.91 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		77.82 25	77.59 23	78.49 47	85.25 52	50.27 175	90.02 17	90.57 5	56.58 200	74.26 27	91.60 33	54.26 11	92.16 36	75.87 31	79.91 70	93.05 12

WTY-MVS		77.47 29	77.52 24	77.30 76	88.33 16	46.25 236	88.46 36	90.32 6	71.40 10	72.32 47	91.72 30	53.44 15	92.37 32	66.28 84	75.42 104	93.28 7

Regformer-1		77.80 26	77.44 25	78.88 38	87.78 20	52.44 131	87.60 44	90.08 8	68.86 20	72.49 45	91.79 27	47.69 43	94.90 10	73.57 47	77.05 88	89.31 82

test_prior3		77.59 27	77.33 26	78.39 52	86.35 31	54.91 59	89.04 32	85.45 73	61.88 104	73.55 31	91.46 37	48.01 41	89.70 81	74.73 38	85.46 26	90.55 55

LFMVS		78.52 15	77.14 27	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 89	74.63 25	90.83 48	41.38 138	94.40 13	75.42 36	79.90 71	94.72 1

PHI-MVS		77.49 28	77.00 28	78.95 37	85.33 50	50.69 162	88.57 35	88.59 22	58.14 173	73.60 30	93.31 5	43.14 106	93.79 18	73.81 44	88.53 8	92.37 20

MG-MVS		78.42 17	76.99 29	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 62	73.52 33	88.09 91	48.07 39	92.19 35	62.24 114	84.53 37	91.53 36

Regformer-2		77.15 30	76.82 30	78.14 58	87.78 20	51.84 143	87.60 44	89.12 13	67.23 33	71.93 49	91.79 27	46.03 64	93.53 20	72.85 55	77.05 88	89.05 90

PVSNet_Blended		76.53 40	76.54 31	76.50 91	85.91 34	51.83 144	88.89 34	84.24 108	67.82 27	69.09 63	89.33 76	46.70 53	88.13 137	75.43 34	81.48 54	89.55 78

jason		77.01 32	76.45 32	78.69 42	79.69 158	54.74 62	90.56 15	83.99 121	68.26 24	74.10 28	90.91 45	42.14 125	89.99 74	79.30 15	79.12 74	91.36 42
jason: jason.
train_agg		76.91 33	76.40 33	78.45 50	85.68 37	55.42 40	87.59 47	84.00 119	57.84 179	72.99 36	90.98 41	44.99 74	88.58 117	78.19 20	85.32 29	91.34 44

SteuartSystems-ACMMP		77.08 31	76.33 34	79.34 30	80.98 137	55.31 44	89.76 24	86.91 52	62.94 93	71.65 52	91.56 34	42.33 121	92.56 29	77.14 27	83.69 42	90.15 70
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	67.15 4	76.90 35	76.27 35	78.80 40	80.70 143	55.02 54	86.39 69	86.71 55	66.96 36	67.91 71	89.97 66	48.03 40	91.41 45	75.60 33	84.14 39	89.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		76.68 39	76.24 36	78.00 61	85.64 40	54.92 57	87.55 50	83.61 130	57.99 176	72.53 43	91.05 39	45.36 71	88.10 139	77.76 25	84.68 36	90.99 49

agg_prior3		76.73 38	76.15 37	78.48 48	85.66 39	55.59 35	87.54 51	83.95 123	57.78 181	71.78 51	90.81 49	44.33 82	88.52 122	78.19 20	85.32 29	91.34 44

PAPM		76.76 36	76.07 38	78.81 39	80.20 156	59.11 5	86.86 65	86.23 65	68.60 21	70.18 60	88.84 81	51.57 24	87.16 162	65.48 90	86.68 14	90.15 70

APD-MVS		76.15 45	75.68 39	77.54 70	88.52 13	53.44 90	87.26 58	85.03 88	53.79 227	74.91 23	91.68 32	43.80 91	90.31 65	74.36 41	81.82 51	88.87 94
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		76.43 41	75.66 40	78.73 41	81.92 117	54.67 67	84.06 131	85.35 78	61.10 115	72.99 36	91.50 35	40.25 145	91.00 49	76.84 28	86.98 13	90.51 59

MAR-MVS		76.76 36	75.60 41	80.21 18	90.87 3	54.68 66	89.14 31	89.11 14	62.95 92	70.54 58	92.33 17	41.05 139	94.95 9	57.90 148	86.55 18	91.00 48
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
MVS		76.91 33	75.48 42	81.23 10	84.56 59	55.21 48	80.23 209	91.64 1	58.65 163	65.37 94	91.48 36	45.72 68	95.05 8	72.11 57	89.52 5	93.44 4

Regformer-3		76.02 48	75.47 43	77.70 66	85.49 45	51.47 150	85.12 100	90.19 7	68.52 22	69.36 61	90.66 51	46.45 61	94.81 11	70.25 64	73.16 117	86.81 129

CLD-MVS		75.60 50	75.39 44	76.24 95	80.69 144	52.40 132	90.69 13	86.20 66	74.40 3	65.01 100	88.93 78	42.05 128	90.58 59	76.57 29	73.96 112	85.73 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		76.39 42	75.38 45	79.42 29	85.33 50	56.47 26	88.15 37	84.97 89	65.15 61	66.06 86	89.88 67	43.79 92	92.16 36	75.03 37	80.03 69	89.64 77

CDPH-MVS		76.05 47	75.19 46	78.62 45	86.51 30	54.98 56	87.32 54	84.59 98	58.62 164	70.75 56	90.85 47	43.10 110	90.63 58	70.50 62	84.51 38	90.24 66

MP-MVS-pluss		75.54 51	75.03 47	77.04 82	81.37 133	52.65 128	84.34 124	84.46 101	61.16 113	69.14 62	91.76 29	39.98 152	88.99 101	78.19 20	84.89 34	89.48 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		76.08 46	74.97 48	79.44 28	84.27 65	53.33 100	91.13 10	85.88 68	65.33 58	72.37 46	89.34 74	32.52 231	92.76 26	77.90 24	75.96 99	92.22 22

MVS_Test		75.85 49	74.93 49	78.62 45	84.08 67	55.20 49	83.99 134	85.17 84	68.07 25	73.38 34	82.76 157	50.44 29	89.00 99	65.90 86	80.61 59	91.64 31

SD-MVS		76.18 44	74.85 50	80.18 19	85.39 49	56.90 20	85.75 82	82.45 152	56.79 195	74.48 26	91.81 26	43.72 97	90.75 55	74.61 40	78.65 77	92.91 13

Regformer-4		75.06 55	74.59 51	76.47 92	85.49 45	50.33 171	85.12 100	88.61 20	66.42 38	68.48 66	90.66 51	44.15 87	92.68 27	69.24 67	73.16 117	86.39 137

MP-MVS		74.99 56	74.33 52	76.95 85	82.89 95	53.05 119	85.63 85	83.50 133	57.86 178	67.25 75	90.24 59	43.38 103	88.85 110	76.03 30	82.23 49	88.96 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PAPR		75.20 54	74.13 53	78.41 51	88.31 17	55.10 53	84.31 125	85.66 70	63.76 80	67.55 72	90.73 50	43.48 102	89.40 86	66.36 83	77.03 90	90.73 53

CHOSEN 1792x2688		76.24 43	74.03 54	82.88 1	83.09 86	62.84 2	85.73 83	85.39 76	69.79 16	64.87 102	83.49 148	41.52 137	93.69 19	70.55 61	81.82 51	92.12 23

DWT-MVSNet_test		75.47 52	73.87 55	80.29 16	87.33 25	57.05 18	82.86 159	87.96 35	72.59 6	67.29 74	87.79 95	51.61 23	91.52 43	54.75 176	72.63 125	92.29 21

#test#		74.86 57	73.78 56	78.10 59	84.30 62	53.68 82	86.95 62	84.36 102	59.00 155	65.78 89	90.56 53	40.70 142	90.90 52	71.48 58	80.88 55	89.71 74

Effi-MVS+		75.24 53	73.61 57	80.16 20	81.92 117	57.42 14	85.21 94	76.71 249	60.68 122	73.32 35	89.34 74	47.30 46	91.63 42	68.28 72	79.72 72	91.42 40

PVSNet_BlendedMVS		73.42 72	73.30 58	73.76 153	85.91 34	51.83 144	86.18 74	84.24 108	65.40 55	69.09 63	80.86 186	46.70 53	88.13 137	75.43 34	65.92 170	81.33 216

CANet_DTU		73.71 70	73.14 59	75.40 112	82.61 104	50.05 177	84.67 120	79.36 204	69.72 17	75.39 21	90.03 65	29.41 253	85.93 197	67.99 74	79.11 75	90.22 67

HY-MVS	67.03 5	73.90 66	73.14 59	76.18 97	84.70 58	47.36 221	75.56 256	86.36 63	66.27 41	70.66 57	83.91 135	51.05 27	89.31 87	67.10 78	72.61 126	91.88 28

HFP-MVS		74.37 59	73.13 61	78.10 59	84.30 62	53.68 82	85.58 86	84.36 102	56.82 193	65.78 89	90.56 53	40.70 142	90.90 52	69.18 68	80.88 55	89.71 74

zzz-MVS		74.15 64	73.11 62	77.27 78	81.54 127	53.57 85	84.02 133	81.31 171	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

ACMMPR		73.76 68	72.61 63	77.24 81	83.92 71	52.96 123	85.58 86	84.29 104	56.82 193	65.12 96	90.45 55	37.24 183	90.18 71	69.18 68	80.84 57	88.58 101

EI-MVSNet-Vis-set		73.19 75	72.60 64	74.99 121	82.56 105	49.80 180	82.55 163	89.00 15	66.17 42	65.89 88	88.98 77	43.83 90	92.29 33	65.38 97	69.01 146	82.87 192

region2R		73.75 69	72.55 65	77.33 75	83.90 72	52.98 122	85.54 89	84.09 110	56.83 192	65.10 97	90.45 55	37.34 181	90.24 69	68.89 70	80.83 58	88.77 97

3Dnovator	64.70 6	74.46 58	72.48 66	80.41 15	82.84 97	55.40 43	83.08 153	88.61 20	67.61 31	59.85 153	88.66 82	34.57 212	93.97 15	58.42 141	88.70 7	91.85 29

PVSNet_Blended_VisFu		73.40 73	72.44 67	76.30 93	81.32 135	54.70 65	85.81 78	78.82 211	63.70 81	64.53 106	85.38 122	47.11 49	87.38 159	67.75 75	77.55 84	86.81 129

TESTMET0.1,1		72.86 78	72.33 68	74.46 135	81.98 115	50.77 160	85.13 97	85.47 72	66.09 44	67.30 73	83.69 140	37.27 182	83.57 236	65.06 98	78.97 76	89.05 90

MVSTER		73.25 74	72.33 68	76.01 102	85.54 44	53.76 81	83.52 140	87.16 48	67.06 35	63.88 116	81.66 176	52.77 18	90.44 60	64.66 99	64.69 176	83.84 175

PatchFormer-LS_test		74.17 62	72.30 70	79.77 26	86.61 29	57.26 16	82.02 169	84.80 94	71.85 9	64.73 103	87.52 100	50.33 31	90.40 63	54.23 178	68.63 150	91.64 31

CostFormer		73.89 67	72.30 70	78.66 43	82.36 108	56.58 22	75.56 256	85.30 79	66.06 46	70.50 59	76.88 226	57.02 8	89.06 90	68.27 73	68.74 148	90.33 65

MSLP-MVS++		74.21 61	72.25 72	80.11 21	81.45 131	56.47 26	86.32 71	79.65 198	58.19 172	66.36 83	92.29 18	36.11 196	90.66 57	67.39 76	82.49 46	93.18 10

MVSFormer		73.53 71	72.19 73	77.57 69	83.02 89	55.24 46	81.63 184	81.44 168	50.28 260	76.67 18	90.91 45	44.82 78	86.11 188	60.83 123	80.09 66	91.36 42

VDDNet		74.37 59	72.13 74	81.09 11	79.58 159	56.52 25	90.02 17	86.70 56	52.61 244	71.23 55	87.20 103	31.75 241	93.96 16	74.30 42	75.77 102	92.79 16

API-MVS		74.17 62	72.07 75	80.49 14	90.02 6	58.55 7	87.30 56	84.27 105	57.51 185	65.77 91	87.77 97	41.61 136	95.97 2	51.71 196	82.63 45	86.94 123

PMMVS		72.98 76	72.05 76	75.78 106	83.57 73	48.60 201	84.08 129	82.85 146	61.62 108	68.24 69	90.33 58	28.35 258	87.78 150	72.71 56	76.69 93	90.95 50

IB-MVS	68.87 2	74.01 65	72.03 77	79.94 24	83.04 88	55.50 37	90.24 16	88.65 18	67.14 34	61.38 136	81.74 175	53.21 16	94.28 14	60.45 128	62.41 204	90.03 72
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
EI-MVSNet-UG-set		72.37 84	71.73 78	74.29 139	81.60 123	49.29 189	81.85 175	88.64 19	65.29 60	65.05 98	88.29 87	43.18 104	91.83 40	63.74 101	67.97 154	81.75 209

XVS		72.92 77	71.62 79	76.81 87	83.41 75	52.48 129	84.88 115	83.20 140	58.03 174	63.91 114	89.63 71	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

nrg030		72.27 89	71.56 80	74.42 136	75.93 209	50.60 164	86.97 61	83.21 139	62.75 95	67.15 76	84.38 130	50.07 32	86.66 176	71.19 59	62.37 205	85.99 140

HPM-MVS		72.60 81	71.50 81	75.89 104	82.02 114	51.42 152	80.70 202	83.05 142	56.12 209	64.03 113	89.53 72	37.55 175	88.37 126	70.48 63	80.04 68	87.88 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 83	71.46 82	76.00 103	82.93 94	52.32 136	86.93 64	82.48 151	55.15 219	63.65 119	90.44 57	35.03 209	88.53 121	68.69 71	77.83 82	87.15 122

HQP-MVS		72.34 85	71.44 83	75.03 118	79.02 167	51.56 147	88.00 38	83.68 127	65.45 52	64.48 107	85.13 123	37.35 179	88.62 114	66.70 80	73.12 119	84.91 157

VPNet		72.07 90	71.42 84	74.04 144	78.64 177	47.17 225	89.91 22	87.97 34	72.56 7	64.66 104	85.04 125	41.83 132	88.33 130	61.17 121	60.97 210	86.62 131

MS-PatchMatch		72.34 85	71.26 85	75.61 107	82.38 107	55.55 36	88.00 38	89.95 11	65.38 56	56.51 203	80.74 187	32.28 234	92.89 24	57.95 147	88.10 10	78.39 259

MTAPA		72.73 79	71.22 86	77.27 78	81.54 127	53.57 85	67.06 305	81.31 171	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

PGM-MVS		72.60 81	71.20 87	76.80 89	82.95 92	52.82 125	83.07 154	82.14 153	56.51 205	63.18 123	89.81 68	35.68 204	89.76 80	67.30 77	80.19 65	87.83 111

Fast-Effi-MVS+		72.73 79	71.15 88	77.48 72	82.75 99	54.76 61	86.77 66	80.64 184	63.05 91	65.93 87	84.01 133	44.42 81	89.03 96	56.45 160	76.36 98	88.64 99

mvs_anonymous		72.29 87	70.74 89	76.94 86	82.85 96	54.72 64	78.43 235	81.54 167	63.77 79	61.69 135	79.32 194	51.11 26	85.31 205	62.15 116	75.79 101	90.79 52

VPA-MVSNet		71.12 101	70.66 90	72.49 175	78.75 172	44.43 252	87.64 43	90.02 9	63.97 75	65.02 99	81.58 177	42.14 125	87.42 158	63.42 103	63.38 188	85.63 149

3Dnovator+	62.71 7	72.29 87	70.50 91	77.65 68	83.40 78	51.29 156	87.32 54	86.40 62	59.01 154	58.49 178	88.32 86	32.40 232	91.27 46	57.04 155	82.15 50	90.38 64

MVP-Stereo		70.97 104	70.44 92	72.59 172	76.03 208	51.36 153	85.02 112	86.99 51	60.31 126	56.53 202	78.92 199	40.11 149	90.00 73	60.00 134	90.01 2	76.41 285
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mPP-MVS		71.79 94	70.38 93	76.04 101	82.65 103	52.06 138	84.45 122	81.78 164	55.59 216	62.05 133	89.68 70	33.48 222	88.28 134	65.45 93	78.24 80	87.77 113

DP-MVS Recon		71.99 91	70.31 94	77.01 84	90.65 4	53.44 90	89.37 28	82.97 144	56.33 207	63.56 121	89.47 73	34.02 216	92.15 38	54.05 179	72.41 127	85.43 151

xiu_mvs_v1_base_debu		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 296	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 296	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base_debi		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 296	86.66 176	73.28 51	76.39 95	86.85 126

FIs		70.00 120	70.24 98	69.30 233	77.93 187	38.55 292	83.99 134	87.72 42	66.86 37	57.66 189	84.17 132	52.28 21	85.31 205	52.72 191	68.80 147	84.02 167

sss		70.49 111	70.13 99	71.58 199	81.59 124	39.02 290	80.78 201	84.71 97	59.34 140	66.61 81	88.09 91	37.17 184	85.52 201	61.82 118	71.02 136	90.20 68

EPP-MVSNet		71.14 100	70.07 100	74.33 138	79.18 166	46.52 230	83.81 136	86.49 58	56.32 208	57.95 182	84.90 127	54.23 12	89.14 89	58.14 145	69.65 143	87.33 119

PAPM_NR		71.80 93	69.98 101	77.26 80	81.54 127	53.34 98	78.60 233	85.25 82	53.46 230	60.53 149	88.66 82	45.69 69	89.24 88	56.49 157	79.62 73	89.19 87

HQP_MVS		70.96 105	69.91 102	74.12 142	77.95 185	49.57 182	85.76 80	82.59 149	63.60 84	62.15 130	83.28 152	36.04 200	88.30 132	65.46 91	72.34 129	84.49 160

tpmrst		71.04 103	69.77 103	74.86 128	83.19 83	55.86 33	75.64 255	78.73 214	67.88 26	64.99 101	73.73 253	49.96 33	79.56 275	65.92 85	67.85 156	89.14 89

OPM-MVS		70.75 108	69.58 104	74.26 140	75.55 213	51.34 154	86.05 76	83.29 137	61.94 103	62.95 126	85.77 118	34.15 215	88.44 124	65.44 94	71.07 135	82.99 189

CDS-MVSNet		70.48 112	69.43 105	73.64 155	77.56 191	48.83 197	83.51 144	77.45 241	63.27 88	62.33 129	85.54 121	43.85 89	83.29 239	57.38 154	74.00 111	88.79 96
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		71.11 102	69.41 106	76.22 96	79.32 162	50.49 167	80.23 209	85.14 87	59.44 136	58.93 168	88.89 80	33.83 221	89.60 85	61.49 119	77.42 87	88.57 102

1112_ss		70.05 116	69.37 107	72.10 178	80.77 142	42.78 267	85.12 100	76.75 248	59.69 132	61.19 138	92.12 20	47.48 44	83.84 232	53.04 185	68.21 151	89.66 76

Vis-MVSNet		70.61 110	69.34 108	74.42 136	80.95 139	48.49 206	86.03 77	77.51 240	58.74 162	65.55 93	87.78 96	34.37 213	85.95 196	52.53 192	80.61 59	88.80 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
QAPM		71.88 92	69.33 109	79.52 27	82.20 109	54.30 72	86.30 72	88.77 17	56.61 199	59.72 155	87.48 101	33.90 219	95.36 4	47.48 218	81.49 53	88.90 93

ACMMP		70.81 107	69.29 110	75.39 113	81.52 130	51.92 141	83.43 145	83.03 143	56.67 198	58.80 173	88.91 79	31.92 239	88.58 117	65.89 87	73.39 116	85.67 146
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
XXY-MVS		70.18 113	69.28 111	72.89 167	77.64 189	42.88 266	85.06 109	87.50 46	62.58 96	62.66 128	82.34 167	43.64 99	89.83 77	58.42 141	63.70 184	85.96 142

ab-mvs		70.65 109	69.11 112	75.29 114	80.87 140	46.23 237	73.48 269	85.24 83	59.99 129	66.65 79	80.94 185	43.13 107	88.69 112	63.58 102	68.07 152	90.95 50

test-LLR		69.65 126	69.01 113	71.60 197	78.67 174	48.17 214	85.13 97	79.72 195	59.18 146	63.13 124	82.58 162	36.91 188	80.24 267	60.56 126	75.17 106	86.39 137

DI_MVS_plusplus_test		71.30 99	68.98 114	78.26 56	72.76 248	54.08 78	81.72 179	83.22 138	65.75 51	51.94 237	78.47 206	36.01 202	90.31 65	73.33 50	77.60 83	90.40 62

test_normal		71.31 98	68.95 115	78.39 52	72.30 266	54.25 73	81.67 180	84.05 118	65.94 50	51.31 241	78.09 212	36.06 199	90.43 62	73.00 54	78.09 81	90.50 60

EI-MVSNet		69.70 125	68.70 116	72.68 170	75.00 218	48.90 195	79.54 221	87.16 48	61.05 116	63.88 116	83.74 138	45.87 66	90.44 60	57.42 153	64.68 177	78.70 249

BH-w/o		70.02 117	68.51 117	74.56 134	82.77 98	50.39 169	86.60 67	78.14 225	59.77 130	59.65 156	85.57 120	39.27 157	87.30 160	49.86 204	74.94 109	85.99 140

tpm2		70.82 106	68.44 118	77.98 62	80.78 141	56.11 31	74.21 266	81.28 174	60.24 127	68.04 70	75.27 244	52.26 22	88.50 123	55.82 164	68.03 153	89.33 81

PCF-MVS	61.03 10	70.10 114	68.40 119	75.22 117	77.15 200	51.99 140	79.30 229	82.12 157	56.47 206	61.88 134	86.48 115	43.98 88	87.24 161	55.37 168	72.79 124	86.43 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		70.02 117	68.35 120	75.03 118	79.19 165	51.48 149	78.50 234	76.65 251	59.71 131	67.10 77	80.32 189	42.81 117	87.12 163	58.48 139	72.37 128	86.49 133

UniMVSNet_NR-MVSNet		68.82 142	68.29 121	70.40 220	75.71 212	42.59 269	84.23 126	86.78 54	66.31 40	58.51 175	82.45 164	51.57 24	84.64 219	53.11 183	55.96 256	83.96 172

APD-MVS_3200maxsize		69.62 127	68.23 122	73.80 152	81.58 125	48.22 213	81.91 173	79.50 200	48.21 270	64.24 112	89.75 69	31.91 240	87.55 155	63.08 104	73.85 114	85.64 148

TAMVS		69.51 129	68.16 123	73.56 158	76.30 204	48.71 198	82.57 162	77.17 245	62.10 101	61.32 137	84.23 131	41.90 130	83.46 237	54.80 175	73.09 121	88.50 103

BH-RMVSNet		70.08 115	68.01 124	76.27 94	84.21 66	51.22 158	87.29 57	79.33 206	58.96 157	63.63 120	86.77 109	33.29 224	90.30 68	44.63 234	73.96 112	87.30 121

FC-MVSNet-test		67.49 166	67.91 125	66.21 265	76.06 206	33.06 316	80.82 200	87.18 47	64.44 67	54.81 213	82.87 155	50.40 30	82.60 248	48.05 215	66.55 161	82.98 190

MVS_111021_LR		69.07 137	67.91 125	72.54 173	77.27 195	49.56 184	79.77 217	73.96 279	59.33 142	60.73 147	87.82 94	30.19 250	81.53 254	69.94 65	72.19 131	86.53 132

114514_t		69.87 123	67.88 127	75.85 105	88.38 15	52.35 135	86.94 63	83.68 127	53.70 228	55.68 211	85.60 119	30.07 251	91.20 47	55.84 163	71.02 136	83.99 169

TR-MVS		69.71 124	67.85 128	75.27 115	82.94 93	48.48 207	87.40 53	80.86 181	57.15 189	64.61 105	87.08 106	32.67 230	89.64 84	46.38 226	71.55 133	87.68 115

PVSNet	62.49 8	69.27 136	67.81 129	73.64 155	84.41 61	51.85 142	84.63 121	77.80 233	66.42 38	59.80 154	84.95 126	22.14 300	80.44 264	55.03 171	75.11 108	88.62 100

v1141		69.50 130	67.67 130	74.98 122	72.73 250	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.09 111	89.04 93	62.51 110	59.61 220	82.47 197

divwei89l23v2f112		69.50 130	67.67 130	74.98 122	72.72 251	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.11 108	89.04 93	62.51 110	59.62 219	82.48 196

v1		69.49 132	67.67 130	74.98 122	72.69 252	53.41 93	85.08 106	82.13 156	64.80 63	60.87 144	78.19 209	43.11 108	89.04 93	62.51 110	59.61 220	82.49 195

v2v482		69.55 128	67.64 133	75.26 116	72.32 265	53.83 80	84.93 114	81.94 159	65.37 57	60.80 146	79.25 195	41.62 135	88.98 102	63.03 105	59.51 223	82.98 190

v1neww		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 175	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v7new		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 175	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v6		69.43 133	67.61 136	74.88 127	72.87 247	53.30 105	85.12 100	81.10 177	64.29 69	60.99 141	78.52 204	42.88 114	88.98 102	62.67 107	60.06 214	82.37 199

HyFIR lowres test		69.94 122	67.58 137	77.04 82	77.11 201	57.29 15	81.49 190	79.11 209	58.27 170	58.86 171	80.41 188	42.33 121	86.96 168	61.91 117	68.68 149	86.87 124

mvs-test1		69.04 138	67.57 138	73.44 160	75.17 214	51.68 146	86.57 68	74.48 272	62.15 99	62.07 132	85.79 117	30.59 247	87.48 156	65.40 95	65.94 169	81.18 220

IS-MVSNet		68.80 144	67.55 139	72.54 173	78.50 180	43.43 260	81.03 196	79.35 205	59.12 150	57.27 198	86.71 110	46.05 63	87.70 152	44.32 235	75.60 103	86.49 133

OpenMVS	61.00 11	69.99 121	67.55 139	77.30 76	78.37 183	54.07 79	84.36 123	85.76 69	57.22 188	56.71 200	87.67 98	30.79 246	92.83 25	43.04 240	84.06 41	85.01 155

tpm		68.36 152	67.48 141	70.97 209	79.93 157	51.34 154	76.58 245	78.75 213	67.73 28	63.54 122	74.86 246	48.33 37	72.36 318	53.93 180	63.71 183	89.21 86

FMVSNet3		68.84 141	67.40 142	73.19 162	85.05 53	48.53 204	85.71 84	85.36 77	60.90 118	57.58 190	79.15 197	42.16 124	86.77 172	47.25 220	63.40 185	84.27 164

test-mter		68.36 152	67.29 143	71.60 197	78.67 174	48.17 214	85.13 97	79.72 195	53.38 231	63.13 124	82.58 162	27.23 267	80.24 267	60.56 126	75.17 106	86.39 137

thres200		68.71 148	67.27 144	73.02 163	84.73 57	46.76 227	85.03 111	87.73 41	62.34 98	59.87 152	83.45 149	43.15 105	88.32 131	31.25 283	67.91 155	83.98 170

PS-MVSNAJss		68.78 146	67.17 145	73.62 157	73.01 238	48.33 212	84.95 113	84.81 93	59.30 143	58.91 170	79.84 192	37.77 168	88.86 109	62.83 106	63.12 195	83.67 178

tpmp4_e23		70.01 119	67.13 146	78.65 44	81.93 116	57.90 10	73.99 267	81.35 170	60.61 123	65.28 95	73.78 252	52.48 20	88.60 116	48.40 213	66.35 167	89.44 80

UGNet		68.71 148	67.11 147	73.50 159	80.55 154	47.61 219	84.08 129	78.51 219	59.45 135	65.68 92	82.73 160	23.78 286	85.08 210	52.80 187	76.40 94	87.80 112
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
v1144		68.81 143	66.82 148	74.80 131	72.34 264	53.46 88	84.68 119	81.77 165	64.25 72	60.28 151	77.91 213	40.23 146	88.95 106	60.37 129	59.52 222	81.97 202

UniMVSNet (Re)		67.71 161	66.80 149	70.45 218	74.44 223	42.93 265	82.42 165	84.90 91	63.69 82	59.63 157	80.99 184	47.18 47	85.23 207	51.17 199	56.75 250	83.19 187

v7		68.76 147	66.79 150	74.68 132	72.60 255	53.37 96	84.72 118	80.88 180	63.80 78	60.43 150	78.21 208	40.05 151	88.89 108	60.34 130	60.07 213	81.77 208

1121		68.79 145	66.77 151	74.82 129	83.08 87	53.46 88	80.23 209	71.53 297	45.47 287	66.31 84	87.19 104	34.02 216	85.13 208	52.78 188	80.36 63	85.87 144

WR-MVS		67.58 163	66.76 152	70.04 229	75.92 210	45.06 249	86.23 73	85.28 80	64.31 68	58.50 177	81.00 183	44.80 80	82.00 253	49.21 208	55.57 261	83.06 188

EPNet_dtu		66.25 190	66.71 153	64.87 280	78.66 176	34.12 311	82.80 160	75.51 264	61.75 106	64.47 110	86.90 108	37.06 185	72.46 317	43.65 238	69.63 144	88.02 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GA-MVS		69.04 138	66.70 154	76.06 100	75.11 216	52.36 134	83.12 152	80.23 188	63.32 87	60.65 148	79.22 196	30.98 245	88.37 126	61.25 120	66.41 162	87.46 117

BH-untuned		68.28 154	66.40 155	73.91 147	81.62 122	50.01 178	85.56 88	77.39 242	57.63 184	57.47 195	83.69 140	36.36 194	87.08 164	44.81 233	73.08 122	84.65 159

v148		68.24 155	66.35 156	73.88 148	71.76 270	51.47 150	84.23 126	81.90 163	63.69 82	58.94 167	76.44 230	43.72 97	87.78 150	60.63 125	55.86 258	82.39 198

HPM-MVS_fast		67.86 158	66.28 157	72.61 171	80.67 145	48.34 211	81.18 193	75.95 260	50.81 259	59.55 160	88.05 93	27.86 262	85.98 193	58.83 137	73.58 115	83.51 179

UA-Net		67.32 170	66.23 158	70.59 216	78.85 170	41.23 280	73.60 268	75.45 266	61.54 109	66.61 81	84.53 128	38.73 161	86.57 181	42.48 244	74.24 110	83.98 170

Test_1112_low_res		67.18 173	66.23 158	70.02 230	78.75 172	41.02 281	83.43 145	73.69 282	57.29 187	58.45 179	82.39 166	45.30 72	80.88 259	50.50 201	66.26 168	88.16 104

abl_6		68.03 156	66.15 160	73.66 154	78.54 179	48.48 207	79.77 217	78.04 229	47.39 274	63.70 118	88.25 88	28.21 259	89.06 90	60.17 133	71.25 134	83.45 180

tfpn200view9		67.57 164	66.13 161	71.89 191	84.05 68	45.07 246	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 289	66.36 163	82.78 193

thres400		67.40 169	66.13 161	71.19 205	84.05 68	45.07 246	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 289	66.36 163	80.71 227

cascas		69.01 140	66.13 161	77.66 67	79.36 160	55.41 42	86.99 60	83.75 126	56.69 197	58.92 169	81.35 178	24.31 284	92.10 39	53.23 182	70.61 138	85.46 150

NR-MVSNet		67.25 171	65.99 164	71.04 208	73.27 236	43.91 256	85.32 93	84.75 96	66.05 47	53.65 224	82.11 172	45.05 73	85.97 195	47.55 217	56.18 254	83.24 185

CPTT-MVS		67.15 174	65.84 165	71.07 207	80.96 138	50.32 172	81.94 172	74.10 276	46.18 283	57.91 183	87.64 99	29.57 252	81.31 256	64.10 100	70.18 141	81.56 211

FMVSNet2		67.57 164	65.79 166	72.90 165	82.71 100	47.97 217	85.15 96	84.93 90	58.55 165	56.71 200	78.26 207	36.72 191	86.67 175	46.15 228	62.94 198	84.07 166

v144192		67.86 158	65.76 167	74.16 141	71.68 271	53.09 116	84.14 128	80.83 182	62.85 94	59.21 165	77.28 222	39.30 156	88.00 142	58.67 138	57.88 245	81.40 215

v1192		67.96 157	65.74 168	74.63 133	71.79 269	53.43 92	84.06 131	80.99 179	63.19 90	59.56 159	77.46 219	37.50 178	88.65 113	58.20 144	58.93 228	81.79 207

DU-MVS		66.84 181	65.74 168	70.16 223	73.27 236	42.59 269	81.50 188	82.92 145	63.53 86	58.51 175	82.11 172	40.75 140	84.64 219	53.11 183	55.96 256	83.24 185

Test4		68.64 150	65.68 170	77.53 71	67.78 299	53.34 98	79.42 224	82.84 147	65.96 49	46.54 282	76.15 238	25.16 280	88.83 111	69.74 66	77.53 86	90.43 61

Vis-MVSNet (Re-imp)		65.52 195	65.63 171	65.17 278	77.49 192	30.54 324	75.49 259	77.73 237	59.34 140	52.26 235	86.69 111	49.38 35	80.53 263	37.07 258	75.28 105	84.42 162

TranMVSNet+NR-MVSNet		66.94 179	65.61 172	70.93 211	73.45 233	43.38 261	83.02 156	84.25 106	65.31 59	58.33 181	81.90 174	39.92 153	85.52 201	49.43 207	54.89 264	83.89 174

V42		67.66 162	65.60 173	73.86 149	70.69 281	53.63 84	81.50 188	78.61 217	63.85 77	59.49 161	77.49 218	37.98 165	87.65 153	62.33 113	58.43 235	80.29 236

AdaColmap		67.86 158	65.48 174	75.00 120	88.15 19	54.99 55	86.10 75	76.63 252	49.30 266	57.80 185	86.65 112	29.39 254	88.94 107	45.10 232	70.21 140	81.06 221

GBi-Net		67.09 175	65.47 175	71.96 185	82.71 100	46.36 232	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

test1		67.09 175	65.47 175	71.96 185	82.71 100	46.36 232	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

EPMVS		68.45 151	65.44 177	77.47 73	84.91 55	56.17 30	71.89 287	81.91 162	61.72 107	60.85 145	72.49 266	36.21 195	87.06 165	47.32 219	71.62 132	89.17 88

conf200view11		66.80 182	65.42 178	70.95 210	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 289	66.36 163	81.89 203

thres100view900		66.87 180	65.42 178	71.24 203	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 289	66.36 163	82.78 193

IterMVS-LS		66.63 183	65.36 180	70.42 219	75.10 217	48.90 195	81.45 191	76.69 250	61.05 116	55.71 210	77.10 225	45.86 67	83.65 235	57.44 152	57.88 245	78.70 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		67.45 167	65.23 181	74.10 143	71.51 274	52.90 124	83.75 138	80.44 187	62.48 97	59.12 166	77.13 223	36.98 186	87.90 143	57.53 151	58.14 240	81.49 212

thres600view7		66.46 186	65.12 182	70.47 217	83.41 75	43.80 258	82.15 168	87.78 36	59.37 139	56.02 206	82.21 168	43.73 93	86.90 169	26.51 302	64.94 172	80.71 227

OMC-MVS		65.97 193	65.06 183	68.71 245	72.97 239	42.58 271	78.61 232	75.35 267	54.72 222	59.31 163	86.25 116	33.30 223	77.88 287	57.99 146	67.05 158	85.66 147

tfpn111		66.40 188	64.99 184	70.63 215	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	86.83 171	26.34 304	64.92 173	81.89 203

v8		67.25 171	64.99 184	74.04 144	72.89 245	53.31 101	82.37 166	80.11 190	61.54 109	54.29 218	76.02 240	42.89 113	88.41 125	58.43 140	56.36 251	80.39 235

Effi-MVS+-dtu		66.24 191	64.96 186	70.08 225	75.17 214	49.64 181	82.01 170	74.48 272	62.15 99	57.83 184	76.08 239	30.59 247	83.79 233	65.40 95	60.93 211	76.81 280

v1240		66.99 178	64.68 187	73.93 146	71.38 277	52.66 127	83.39 149	79.98 191	61.97 102	58.44 180	77.11 224	35.25 207	87.81 145	56.46 159	58.15 238	81.33 216

LPG-MVS_test		66.44 187	64.58 188	72.02 182	74.42 224	48.60 201	83.07 154	80.64 184	54.69 223	53.75 222	83.83 136	25.73 277	86.98 166	60.33 131	64.71 174	80.48 233

gg-mvs-nofinetune		67.43 168	64.53 189	76.13 98	85.95 33	47.79 218	64.38 310	88.28 31	39.34 310	66.62 80	41.27 338	58.69 5	89.00 99	49.64 206	86.62 15	91.59 33

ACMP	61.11 9	66.24 191	64.33 190	72.00 184	74.89 220	49.12 190	83.18 151	79.83 193	55.41 218	52.29 233	82.68 161	25.83 275	86.10 190	60.89 122	63.94 182	80.78 225
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Baseline_NR-MVSNet		65.49 196	64.27 191	69.13 234	74.37 226	41.65 276	83.39 149	78.85 210	59.56 133	59.62 158	76.88 226	40.75 140	87.44 157	49.99 203	55.05 262	78.28 266

v10		66.61 184	64.20 192	73.83 151	72.59 257	53.37 96	81.88 174	79.91 192	61.11 114	54.09 220	75.60 242	40.06 150	88.26 135	56.47 158	56.10 255	79.86 240

Fast-Effi-MVS+-dtu		66.53 185	64.10 193	73.84 150	72.41 262	52.30 137	84.73 117	75.66 263	59.51 134	56.34 204	79.11 198	28.11 261	85.85 198	57.74 150	63.29 190	83.35 181

PatchmatchNet		67.07 177	63.63 194	77.40 74	83.10 84	58.03 8	72.11 284	77.77 235	58.85 160	59.37 162	70.83 277	37.84 167	84.93 215	42.96 241	69.83 142	89.26 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
view600		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 314	62.85 199	80.71 227

view800		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 314	62.85 199	80.71 227

conf0.05thres1000		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 314	62.85 199	80.71 227

tfpn		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 314	62.85 199	80.71 227

tfpn_ndepth		64.50 202	63.34 199	67.99 249	81.84 119	38.30 294	79.26 230	83.57 132	53.69 229	52.86 231	84.51 129	46.96 50	84.79 216	24.28 309	63.09 196	80.87 224

tpm cat1		66.28 189	62.78 200	76.77 90	81.40 132	57.14 17	70.03 296	77.19 244	53.00 235	58.76 174	70.73 279	46.17 62	86.73 174	43.27 239	64.46 178	86.44 135

pm-mvs1		64.12 208	62.56 201	68.78 244	71.68 271	38.87 291	82.89 158	81.57 166	55.54 217	53.89 221	77.82 214	37.73 171	86.74 173	48.46 212	53.49 273	80.72 226

test0.0.03 1		62.54 232	62.44 202	62.86 290	72.28 267	29.51 327	82.93 157	78.78 212	59.18 146	53.07 230	82.41 165	36.91 188	77.39 291	37.45 254	58.96 227	81.66 210

X-MVStestdata		65.85 194	62.20 203	76.81 87	83.41 75	52.48 129	84.88 115	83.20 140	58.03 174	63.91 114	4.82 356	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

FMVSNet1		64.57 201	62.11 204	71.96 185	77.32 194	46.36 232	83.52 140	83.31 134	52.43 248	54.42 216	76.23 234	27.80 263	86.20 184	42.59 243	61.34 208	83.32 182

ACMM	58.35 12	64.35 205	62.01 205	71.38 201	74.21 227	48.51 205	82.25 167	79.66 197	47.61 272	54.54 215	80.11 190	25.26 279	86.00 192	51.26 197	63.16 193	79.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v18		64.36 204	61.80 206	72.05 179	72.97 239	53.31 101	81.16 194	77.76 236	59.14 148	48.50 260	68.97 287	42.91 112	84.38 221	56.62 156	48.17 286	78.47 253

conf0.01		63.04 222	61.74 207	66.95 259	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	81.89 203

conf0.002		63.04 222	61.74 207	66.95 259	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	81.89 203

thresconf0.02		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	79.44 242

tfpn_n400		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	79.44 242

tfpnconf		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	79.44 242

tfpnview11		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 319	58.77 229	79.44 242

tfpn1000		62.79 231	61.74 207	65.95 271	80.50 155	35.93 301	76.53 247	83.99 121	51.24 256	49.82 255	83.44 150	47.32 45	83.02 247	21.84 326	60.99 209	78.89 247

IterMVS		63.77 211	61.67 214	70.08 225	72.68 253	51.24 157	80.44 204	75.51 264	60.51 124	51.41 239	73.70 256	32.08 237	78.91 276	54.30 177	54.35 267	80.08 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v16		64.25 206	61.66 215	72.03 180	72.91 242	53.28 107	80.93 197	77.81 232	58.86 159	48.30 261	68.80 290	42.70 118	84.37 222	56.44 161	48.14 287	78.44 256

v17		64.19 207	61.58 216	72.03 180	72.89 245	53.28 107	80.91 198	77.80 233	58.87 158	48.22 262	68.77 291	42.69 119	84.37 222	56.43 162	47.66 290	78.43 257

dp		64.41 203	61.58 216	72.90 165	82.40 106	54.09 77	72.53 278	76.59 253	60.39 125	55.68 211	70.39 280	35.18 208	76.90 295	39.34 250	61.71 207	87.73 114

test_djsdf		63.84 209	61.56 218	70.70 213	68.78 290	44.69 250	81.63 184	81.44 168	50.28 260	52.27 234	76.26 233	26.72 269	86.11 188	60.83 123	55.84 259	81.29 219

MDTV_nov1_ep13			61.56 218		81.68 121	55.12 51	72.41 280	78.18 223	59.19 144	58.85 172	69.29 284	34.69 211	86.16 187	36.76 262	62.96 197

pmmvs5		62.80 230	61.18 220	67.66 252	69.53 288	42.37 274	82.65 161	75.19 268	54.30 226	52.03 236	78.51 205	31.64 242	80.67 260	48.60 210	58.15 238	79.95 239

v15		63.83 210	61.13 221	71.93 188	72.60 255	53.21 110	80.44 204	78.22 221	58.80 161	47.57 267	68.22 293	42.50 120	84.18 224	55.82 164	46.02 302	78.39 259

pmmvs4		63.34 218	61.07 222	70.16 223	70.14 283	50.53 166	79.97 214	71.41 299	55.08 220	54.12 219	78.58 201	32.79 229	82.09 252	50.33 202	57.22 249	77.86 272

V14		63.72 212	60.99 223	71.91 190	72.58 258	53.18 111	80.24 208	78.19 222	58.53 168	47.35 273	68.10 294	42.28 123	84.18 224	55.68 166	45.97 303	78.36 262

jajsoiax		63.21 220	60.84 224	70.32 221	68.33 295	44.45 251	81.23 192	81.05 178	53.37 232	50.96 245	77.81 215	17.49 320	85.49 203	59.31 135	58.05 241	81.02 222

V9		63.60 213	60.84 224	71.87 192	72.51 260	53.12 115	80.04 213	78.15 224	58.25 171	47.14 275	67.98 295	42.08 127	84.18 224	55.47 167	45.92 305	78.32 263

TransMVSNet (Re)		62.82 229	60.76 226	69.02 235	73.98 229	41.61 277	86.36 70	79.30 207	56.90 190	52.53 232	76.44 230	41.85 131	87.60 154	38.83 251	40.61 319	77.86 272

v12		63.47 215	60.68 227	71.85 193	72.45 261	53.08 117	79.83 215	78.13 226	57.95 177	46.89 277	67.87 297	41.81 133	84.17 227	55.30 169	45.87 306	78.29 265

v11		63.44 216	60.66 228	71.79 195	72.61 254	53.02 121	79.80 216	78.08 228	58.30 169	47.27 274	67.91 296	40.67 144	84.14 229	54.93 172	46.39 300	78.23 269

mvs_tets		62.96 224	60.55 229	70.19 222	68.22 297	44.24 255	80.90 199	80.74 183	52.99 236	50.82 253	77.56 216	16.74 323	85.44 204	59.04 136	57.94 242	80.89 223

v13		63.36 217	60.54 230	71.82 194	72.41 262	53.03 120	79.64 220	78.10 227	57.66 183	46.67 280	67.75 298	41.68 134	84.17 227	55.11 170	45.82 307	78.25 268

CVMVSNet		60.85 244	60.44 231	62.07 291	75.00 218	32.73 318	79.54 221	73.49 285	36.98 318	56.28 205	83.74 138	29.28 255	69.53 327	46.48 225	63.23 191	83.94 173

MIMVSNet		63.12 221	60.29 232	71.61 196	75.92 210	46.65 228	65.15 306	81.94 159	59.14 148	54.65 214	69.47 283	25.74 276	80.63 261	41.03 246	69.56 145	87.55 116

TAPA-MVS	56.12 14	61.82 239	60.18 233	66.71 261	78.48 181	37.97 296	75.19 261	76.41 255	46.82 278	57.04 199	86.52 114	27.67 265	77.03 293	26.50 303	67.02 159	85.14 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testing_2		63.60 213	59.86 234	74.82 129	61.87 321	52.39 133	73.06 275	82.76 148	61.49 111	39.96 307	67.39 301	21.06 305	88.34 128	67.07 79	64.10 179	83.72 177

EG-PatchMatch MVS		62.40 236	59.59 235	70.81 212	73.29 235	49.05 191	85.81 78	84.78 95	51.85 253	44.19 288	73.48 259	15.52 328	89.85 76	40.16 248	67.24 157	73.54 307

XVG-OURS-SEG-HR		62.02 237	59.54 236	69.46 232	65.30 307	45.88 239	65.06 307	73.57 284	46.45 281	57.42 196	83.35 151	26.95 268	78.09 283	53.77 181	64.03 180	84.42 162

tpmvs		62.45 235	59.42 237	71.53 200	83.93 70	54.32 71	70.03 296	77.61 238	51.91 251	53.48 225	68.29 292	37.91 166	86.66 176	33.36 273	58.27 236	73.62 306

XVG-OURS		61.88 238	59.34 238	69.49 231	65.37 306	46.27 235	64.80 309	73.49 285	47.04 276	57.41 197	82.85 156	25.15 281	78.18 281	53.00 186	64.98 171	84.01 168

v7n		62.50 233	59.27 239	72.20 177	67.25 301	49.83 179	77.87 238	80.12 189	52.50 247	48.80 259	73.07 261	32.10 236	87.90 143	46.83 223	54.92 263	78.86 248

Patchmatch-test1		63.23 219	59.16 240	75.43 111	78.58 178	57.92 9	61.61 318	77.53 239	56.71 196	57.75 188	70.98 276	31.97 238	78.19 280	40.97 247	56.36 251	90.18 69

tfpnnormal		61.47 240	59.09 241	68.62 247	76.29 205	41.69 275	81.14 195	85.16 85	54.48 225	51.32 240	73.63 257	32.32 233	86.89 170	21.78 328	55.71 260	77.29 278

CR-MVSNet		62.47 234	59.04 242	72.77 168	73.97 230	56.57 23	60.52 321	71.72 293	60.04 128	57.49 193	65.86 306	38.94 158	80.31 265	42.86 242	59.93 217	81.42 213

PLC	52.38 18	60.89 243	58.97 243	66.68 263	81.77 120	45.70 241	78.96 231	74.04 278	43.66 298	47.63 266	83.19 154	23.52 291	77.78 290	37.47 253	60.46 212	76.55 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		60.59 245	58.44 244	67.05 258	79.21 164	47.26 224	79.75 219	64.34 321	42.46 305	51.90 238	83.94 134	27.79 264	75.41 300	37.12 256	59.49 224	78.47 253

v748		61.35 241	58.24 245	70.69 214	66.28 302	47.35 222	76.58 245	79.17 208	53.09 234	46.37 284	71.50 274	33.18 225	86.33 183	46.78 224	51.19 281	78.39 259

WR-MVS_H		58.91 256	58.04 246	61.54 296	69.07 289	33.83 313	76.91 242	81.99 158	51.40 255	48.17 263	74.67 247	40.23 146	74.15 304	31.78 280	48.10 288	76.64 282

anonymousdsp		60.46 246	57.65 247	68.88 236	63.63 315	45.09 245	72.93 276	78.63 216	46.52 280	51.12 242	72.80 265	21.46 303	83.07 240	57.79 149	53.97 268	78.47 253

Anonymous20231206		59.08 255	57.59 248	63.55 285	68.77 291	32.14 321	80.26 207	79.78 194	50.00 263	49.39 256	72.39 269	26.64 270	78.36 279	33.12 276	57.94 242	80.14 237

CP-MVSNet		58.54 262	57.57 249	61.46 298	68.50 293	33.96 312	76.90 243	78.60 218	51.67 254	47.83 264	76.60 229	34.99 210	72.79 315	35.45 265	47.58 291	77.64 276

PVSNet_0	57.04 13	61.19 242	57.24 250	73.02 163	77.45 193	50.31 173	79.43 223	77.36 243	63.96 76	47.51 270	72.45 268	25.03 282	83.78 234	52.76 190	19.22 344	84.96 156

pmmvs6		59.64 250	57.15 251	67.09 256	66.01 303	36.86 300	80.50 203	78.64 215	45.05 289	49.05 258	73.94 251	27.28 266	86.10 190	43.96 237	49.94 284	78.31 264

PEN-MVS		58.35 264	57.15 251	61.94 293	67.55 300	34.39 310	77.01 241	78.35 220	51.87 252	47.72 265	76.73 228	33.91 218	73.75 309	34.03 272	47.17 295	77.68 274

PS-CasMVS		58.12 265	57.03 253	61.37 299	68.24 296	33.80 314	76.73 244	78.01 230	51.20 257	47.54 269	76.20 237	32.85 227	72.76 316	35.17 267	47.37 293	77.55 277

LCM-MVSNet-Re		58.82 257	56.54 254	65.68 272	79.31 163	29.09 330	61.39 320	45.79 341	60.73 121	37.65 313	72.47 267	31.42 243	81.08 257	49.66 205	70.41 139	86.87 124

FMVSNet5		58.61 259	56.45 255	65.10 279	77.20 199	39.74 288	74.77 262	77.12 246	50.27 262	43.28 295	67.71 299	26.15 274	76.90 295	36.78 261	54.78 265	78.65 251

v52		59.82 247	56.41 256	70.06 227	61.49 324	48.67 199	69.46 300	75.80 261	52.55 245	47.49 271	68.82 289	28.60 256	85.70 199	52.13 194	51.34 280	75.80 288

V4		59.82 247	56.41 256	70.05 228	61.49 324	48.67 199	69.46 300	75.79 262	52.55 245	47.49 271	68.83 288	28.60 256	85.70 199	52.13 194	51.35 279	75.80 288

CHOSEN 280x420		57.53 267	56.38 258	60.97 300	74.01 228	48.10 216	46.30 336	54.31 335	48.18 271	50.88 246	77.43 220	38.37 164	59.16 339	54.83 173	63.14 194	75.66 291

DP-MVS		59.24 252	56.12 259	68.63 246	88.24 18	50.35 170	82.51 164	64.43 320	41.10 307	46.70 279	78.77 200	24.75 283	88.57 120	22.26 325	56.29 253	66.96 326

OpenMVS_ROB	53.19 17	59.20 253	56.00 260	68.83 238	71.13 279	44.30 253	83.64 139	75.02 269	46.42 282	46.48 283	73.03 262	18.69 314	88.14 136	27.74 298	61.80 206	74.05 303

ACMH	53.70 16	59.78 249	55.94 261	71.28 202	76.59 203	48.35 210	80.15 212	76.11 256	49.74 264	41.91 300	73.45 260	16.50 325	90.31 65	31.42 281	57.63 247	75.17 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		57.03 268	55.73 262	60.95 301	65.94 304	32.57 319	75.71 254	77.09 247	51.16 258	46.65 281	76.34 232	32.84 228	73.22 313	30.94 284	44.87 310	77.06 279

ACMH+	54.58 15	58.55 261	55.24 263	68.50 248	74.68 222	45.80 240	80.27 206	70.21 306	47.15 275	42.77 297	75.48 243	16.73 324	85.98 193	35.10 269	54.78 265	73.72 305

UnsupCasMVSNet_eth		57.56 266	55.15 264	64.79 281	64.57 312	33.12 315	73.17 273	83.87 125	58.98 156	41.75 301	70.03 281	22.54 295	79.92 271	46.12 229	35.31 328	81.32 218

MSDG		59.44 251	55.14 265	72.32 176	74.69 221	50.71 161	74.39 265	73.58 283	44.44 293	43.40 294	77.52 217	19.45 310	90.87 54	31.31 282	57.49 248	75.38 293

our_test_3		59.11 254	55.08 266	71.18 206	71.42 275	53.29 106	81.96 171	74.52 271	48.32 269	42.08 298	69.28 285	28.14 260	82.15 250	34.35 271	45.68 308	78.11 271

ppachtmachnet_test		58.56 260	54.34 267	71.24 203	71.42 275	54.74 62	81.84 176	72.27 290	49.02 268	45.86 287	68.99 286	26.27 272	83.30 238	30.12 285	43.23 315	75.69 290

Patchmatch-RL test		58.72 258	54.32 268	71.92 189	63.91 314	44.25 254	61.73 317	55.19 333	57.38 186	49.31 257	54.24 332	37.60 174	80.89 258	62.19 115	47.28 294	90.63 54

test20.03		55.22 279	54.07 269	58.68 306	63.14 317	25.00 336	77.69 239	74.78 270	52.64 237	43.43 293	72.39 269	26.21 273	74.76 303	29.31 287	47.05 297	76.28 286

LS3D		56.40 273	53.82 270	64.12 282	81.12 136	45.69 242	73.42 270	66.14 315	35.30 328	43.24 296	79.88 191	22.18 299	79.62 274	19.10 337	64.00 181	67.05 325

PatchMatch-RL		56.66 269	53.75 271	65.37 277	77.91 188	45.28 244	69.78 298	60.38 328	41.35 306	47.57 267	73.73 253	16.83 322	76.91 294	36.99 259	59.21 226	73.92 304

RPMNet		58.49 263	53.74 272	72.77 168	73.97 230	56.57 23	60.52 321	72.39 289	35.72 323	57.49 193	58.87 326	37.73 171	80.31 265	27.01 301	59.93 217	81.42 213

F-COLMAP		55.96 277	53.65 273	62.87 289	72.76 248	42.77 268	74.70 264	70.37 304	40.03 308	41.11 304	79.36 193	17.77 318	73.70 310	32.80 277	53.96 269	72.15 313

test_0402		56.45 272	53.03 274	66.69 262	76.78 202	50.31 173	81.76 177	69.61 308	42.79 303	43.88 290	72.13 271	22.82 294	86.46 182	16.57 342	50.94 282	63.31 335

PatchT		56.60 270	52.97 275	67.48 253	72.94 241	46.16 238	57.30 327	73.78 281	38.77 312	54.37 217	57.26 329	37.52 176	78.06 284	32.02 278	52.79 274	78.23 269

Patchmtry		56.56 271	52.95 276	67.42 254	72.53 259	50.59 165	59.05 323	71.72 293	37.86 316	46.92 276	65.86 306	38.94 158	80.06 270	36.94 260	46.72 299	71.60 316

XVG-ACMP-BASELINE		56.03 275	52.85 277	65.58 273	61.91 320	40.95 282	63.36 311	72.43 288	45.20 288	46.02 285	74.09 249	9.20 339	78.12 282	45.13 231	58.27 236	77.66 275

pmmvs-eth3d		55.97 276	52.78 278	65.54 274	61.02 326	46.44 231	75.36 260	67.72 311	49.61 265	43.65 292	67.58 300	21.63 302	77.04 292	44.11 236	44.33 312	73.15 312

CMPMVS	40.41 21	55.34 278	52.64 279	63.46 286	60.88 327	43.84 257	61.58 319	71.06 300	30.43 335	36.33 315	74.63 248	24.14 285	75.44 299	48.05 215	66.62 160	71.12 319
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		54.25 283	52.57 280	59.29 304	62.76 318	21.65 342	72.21 283	70.47 302	53.25 233	41.94 299	77.33 221	14.28 329	77.95 286	29.18 288	51.72 278	78.28 266

test2356		53.94 285	52.37 281	58.64 307	61.58 322	27.53 335	78.20 236	74.33 274	46.92 277	44.01 289	66.04 305	18.91 313	74.11 305	28.80 289	52.55 275	74.28 300

ADS-MVSNet		56.17 274	51.95 282	68.84 237	80.60 146	53.07 118	55.03 329	70.02 307	44.72 290	51.00 243	61.19 317	22.83 292	78.88 277	28.54 294	53.63 270	74.57 298

ADS-MVSNet2		55.21 280	51.44 283	66.51 264	80.60 146	49.56 184	55.03 329	65.44 317	44.72 290	51.00 243	61.19 317	22.83 292	75.41 300	28.54 294	53.63 270	74.57 298

USDC		54.36 282	51.23 284	63.76 284	64.29 313	37.71 297	62.84 316	73.48 287	56.85 191	35.47 319	71.94 273	9.23 338	78.43 278	38.43 252	48.57 285	75.13 295

EU-MVSNet		52.63 291	50.72 285	58.37 308	62.69 319	28.13 332	72.60 277	75.97 259	30.94 334	40.76 306	72.11 272	20.16 308	70.80 323	35.11 268	46.11 301	76.19 287

UnsupCasMVSNet_bld		53.86 286	50.53 286	63.84 283	63.52 316	34.75 309	71.38 288	81.92 161	46.53 279	38.95 311	57.93 327	20.55 307	80.20 269	39.91 249	34.09 335	76.57 283

SixPastTwentyTwo		54.37 281	50.10 287	67.21 255	70.70 280	41.46 278	74.73 263	64.69 319	47.56 273	39.12 310	69.49 282	18.49 316	84.69 218	31.87 279	34.20 334	75.48 292

YYNet1		53.82 287	49.96 288	65.41 276	70.09 285	48.95 193	72.30 281	71.66 295	44.25 294	31.89 332	63.07 314	23.73 287	73.95 307	33.26 274	39.40 321	73.34 309

MDA-MVSNet_test_wron		53.82 287	49.95 289	65.43 275	70.13 284	49.05 191	72.30 281	71.65 296	44.23 295	31.85 333	63.13 313	23.68 290	74.01 306	33.25 275	39.35 322	73.23 311

LTVRE_ROB	45.45 19	52.73 290	49.74 290	61.69 295	69.78 286	34.99 308	44.52 338	67.60 312	43.11 302	43.79 291	74.03 250	18.54 315	81.45 255	28.39 296	57.94 242	68.62 323
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
K. test v3		54.04 284	49.42 291	67.92 251	68.55 292	42.57 272	75.51 258	63.07 324	52.07 249	39.21 309	64.59 310	19.34 311	82.21 249	37.11 257	25.31 340	78.97 246

OurMVSNet-221017-0		52.39 292	48.73 292	63.35 287	65.21 308	38.42 293	68.54 303	64.95 318	38.19 313	39.57 308	71.43 275	13.23 331	79.92 271	37.16 255	40.32 320	71.72 315

Patchmatch-test		53.33 289	48.17 293	68.81 243	73.31 234	42.38 273	42.98 340	58.23 330	32.53 331	38.79 312	70.77 278	39.66 154	73.51 311	25.18 306	52.06 277	90.55 55

COLMAP_ROB	43.60 20	50.90 295	48.05 294	59.47 303	67.81 298	40.57 287	71.25 289	62.72 326	36.49 322	36.19 316	73.51 258	13.48 330	73.92 308	20.71 332	50.26 283	63.92 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 296	47.81 295	57.96 309	61.53 323	27.80 334	67.40 304	74.06 277	43.25 301	33.31 331	65.38 309	16.03 326	71.34 322	21.80 327	47.55 292	74.75 296

JIA-IIPM		52.33 293	47.77 296	66.03 270	71.20 278	46.92 226	40.00 344	76.48 254	37.10 317	46.73 278	37.02 340	32.96 226	77.88 287	35.97 263	52.45 276	73.29 310

MDA-MVSNet-bldmvs		51.56 294	47.75 297	63.00 288	71.60 273	47.32 223	69.70 299	72.12 291	43.81 297	27.65 338	63.38 312	21.97 301	75.96 297	27.30 300	32.19 336	65.70 329

new-patchmatchnet		48.21 299	46.55 298	53.18 318	57.73 331	18.19 351	70.24 294	71.02 301	45.70 284	33.70 327	60.23 319	18.00 317	69.86 326	27.97 297	34.35 332	71.49 318

testus		48.97 298	46.53 299	56.31 314	57.39 333	24.08 338	73.40 271	70.45 303	43.37 300	35.52 318	63.95 311	4.77 350	71.36 321	24.88 307	45.02 309	73.50 308

1111		48.00 301	46.30 300	53.08 319	55.68 334	20.86 345	70.41 292	76.03 257	36.88 319	34.86 321	59.55 323	23.72 288	68.13 328	20.82 330	38.76 324	70.25 320

testpf		45.92 307	45.81 301	46.27 326	69.56 287	27.86 333	23.18 349	73.91 280	44.10 296	36.99 314	57.16 330	20.56 306	71.77 319	42.17 245	44.64 311	39.18 345

MVS-HIRNet		49.01 297	44.71 302	61.92 294	76.06 206	46.61 229	63.23 313	54.90 334	24.77 339	33.56 328	36.60 341	21.28 304	75.88 298	29.49 286	62.54 203	63.26 336

AllTest		47.32 302	44.66 303	55.32 315	65.08 309	37.50 298	62.96 315	54.25 336	35.45 326	33.42 329	72.82 263	9.98 335	59.33 337	24.13 310	43.84 313	69.13 321

TinyColmap		48.15 300	44.49 304	59.13 305	65.73 305	38.04 295	63.34 312	62.86 325	38.78 311	29.48 336	67.23 303	6.46 345	73.30 312	24.59 308	41.90 317	66.04 327

RPSCF		45.77 308	44.13 305	50.68 321	57.67 332	29.66 326	54.92 331	45.25 343	26.69 338	45.92 286	75.92 241	17.43 321	45.70 349	27.44 299	45.95 304	76.67 281

test1235678		47.09 303	43.82 306	56.91 312	53.18 337	24.90 337	71.93 285	70.31 305	39.54 309	31.44 334	56.59 331	9.50 337	71.55 320	22.63 318	39.24 323	74.28 300

PM-MVS		46.92 305	43.76 307	56.41 313	52.18 338	32.26 320	63.21 314	38.18 347	37.99 315	40.78 305	66.20 304	5.09 348	65.42 333	48.19 214	41.99 316	71.54 317

Anonymous20231211		46.87 306	43.27 308	57.67 310	57.88 330	30.12 325	73.14 274	64.16 322	33.43 330	34.34 324	59.42 325	12.15 332	77.99 285	19.64 336	35.23 330	64.90 331

LP		47.05 304	42.23 309	61.53 297	72.04 268	49.37 188	49.48 333	65.50 316	34.57 329	34.29 325	52.30 334	17.73 319	75.32 302	17.56 340	36.57 326	59.91 337

.test1245		38.91 313	41.99 310	29.67 338	55.68 334	20.86 345	70.41 292	76.03 257	36.88 319	34.86 321	59.55 323	23.72 288	68.13 328	20.82 330	0.00 356	0.02 356

pmmvs3		45.53 309	41.55 311	57.44 311	48.97 341	39.68 289	70.06 295	57.66 331	28.32 337	34.06 326	57.29 328	8.50 340	66.85 332	34.86 270	34.26 333	65.80 328

N_pmnet		41.25 310	39.77 312	45.66 328	68.50 293	0.82 360	72.51 279	0.38 361	35.61 324	35.26 320	61.51 316	20.07 309	67.74 330	23.51 312	40.63 318	68.42 324

TDRefinement		40.91 311	38.37 313	48.55 324	50.45 339	33.03 317	58.98 324	50.97 339	28.50 336	29.89 335	67.39 301	6.21 347	54.51 341	17.67 339	35.25 329	58.11 338

testmv		39.64 312	36.01 314	50.55 322	42.18 345	21.56 343	64.81 308	66.88 314	32.22 332	22.25 342	47.47 336	4.33 352	64.81 334	17.71 338	26.22 339	65.29 330

DSMNet-mixed		38.35 314	35.36 315	47.33 325	48.11 342	14.91 353	37.87 345	36.60 349	19.18 343	34.37 323	59.56 322	15.53 327	53.01 343	20.14 334	46.89 298	74.07 302

test12356		37.84 315	35.07 316	46.18 327	45.03 344	8.02 358	57.70 326	62.67 327	31.83 333	22.78 340	50.25 335	4.46 351	66.95 331	17.25 341	23.62 342	63.57 334

FPMVS		35.40 317	33.67 317	40.57 330	46.34 343	28.74 331	41.05 342	57.05 332	20.37 342	22.27 341	53.38 333	6.87 343	44.94 350	8.62 348	47.11 296	48.01 343

LF4IMVS		33.04 320	32.55 318	34.52 335	40.96 346	22.03 341	44.45 339	35.62 350	20.42 341	28.12 337	62.35 315	5.03 349	31.88 355	21.61 329	34.42 331	49.63 342

new_pmnet		33.56 319	31.89 319	38.59 331	49.01 340	20.42 347	51.01 332	37.92 348	20.58 340	23.45 339	46.79 337	6.66 344	49.28 346	20.00 335	31.57 338	46.09 344

no-one		37.21 316	31.48 320	54.40 317	39.62 349	31.91 323	45.68 337	67.42 313	35.54 325	14.59 345	35.91 343	7.35 341	73.20 314	22.98 313	14.23 345	58.09 339

ANet_high		34.39 318	29.59 321	48.78 323	30.34 353	22.28 340	55.53 328	63.79 323	38.11 314	15.47 344	36.56 342	6.94 342	59.98 336	13.93 344	5.64 355	64.08 332

pcd1.5k->3k		27.74 322	27.68 322	27.93 340	73.75 232	0.00 362	0.00 354	85.50 71	0.00 356	0.00 359	0.00 360	26.52 271	0.00 359	0.00 358	63.37 189	83.79 176

cdsmvs_eth3d_5k		18.33 330	24.44 323	0.00 348	0.00 361	0.00 362	0.00 354	89.40 12	0.00 356	0.00 359	92.02 23	38.55 162	0.00 359	0.00 358	0.00 356	0.00 359

Gipuma		27.47 323	24.26 324	37.12 333	60.55 328	29.17 329	11.68 352	60.00 329	14.18 346	10.52 349	15.12 352	2.20 356	63.01 335	8.39 349	35.65 327	19.18 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 321	23.85 325	40.71 329	27.46 355	18.93 350	30.82 347	46.19 340	12.76 348	16.40 343	34.70 345	1.90 357	48.69 347	20.25 333	24.22 341	54.51 340

PNet_i23d		25.11 325	23.09 326	31.17 337	40.18 347	21.30 344	57.99 325	33.28 352	13.77 347	9.94 350	30.29 347	0.45 361	43.74 351	13.61 346	8.28 348	28.46 348

PMMVS2		26.71 324	22.98 327	37.87 332	36.89 350	8.51 357	42.51 341	29.32 355	19.09 344	13.01 346	37.54 339	2.23 355	53.11 342	14.54 343	11.71 346	51.99 341

PMVS	19.57 22	25.07 326	22.43 328	32.99 336	23.12 356	22.98 339	40.98 343	35.19 351	15.99 345	11.95 348	35.87 344	1.47 359	49.29 345	5.41 353	31.90 337	26.70 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		19.16 328	18.40 329	21.44 341	36.19 351	13.63 354	47.59 334	30.89 353	10.73 349	5.91 353	16.59 350	3.66 354	39.77 352	5.95 352	8.14 349	10.92 352

EMVS		18.42 329	17.66 330	20.71 342	34.13 352	12.64 355	46.94 335	29.94 354	10.46 351	5.58 354	14.93 353	4.23 353	38.83 353	5.24 354	7.51 352	10.67 353

wuykxyi23d		19.94 327	14.87 331	35.13 334	22.47 357	19.80 348	25.80 348	38.64 346	7.61 352	4.88 355	13.58 355	0.23 362	48.42 348	13.11 347	7.53 350	37.18 346

MVE	16.60 23	17.34 331	13.39 332	29.16 339	28.43 354	19.72 349	13.73 351	23.63 356	7.23 353	7.96 351	21.41 348	0.80 360	36.08 354	6.97 350	10.39 347	31.69 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 332	10.68 333	5.73 345	2.49 359	4.21 359	10.48 353	18.04 357	0.34 355	12.59 347	20.49 349	11.39 333	7.03 358	13.84 345	6.46 354	5.95 354

ab-mvs-re		7.68 334	10.24 334	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	92.12 20	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

wuyk23d		9.11 333	8.77 335	10.15 344	40.18 347	16.76 352	20.28 350	1.01 360	2.58 354	2.66 356	0.98 357	0.23 362	12.49 357	4.08 355	6.90 353	1.19 355

testmvs		6.14 335	8.18 336	0.01 346	0.01 360	0.00 362	73.40 271	0.00 362	0.00 356	0.02 357	0.15 358	0.00 364	0.00 359	0.02 356	0.00 356	0.02 356

test123		6.01 336	8.01 337	0.01 346	0.00 361	0.01 361	71.93 285	0.00 362	0.00 356	0.02 357	0.11 359	0.00 364	0.00 359	0.02 356	0.00 356	0.02 356

pcd_1.5k_mvsjas		3.15 337	4.20 338	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	37.77 168	0.00 359	0.00 358	0.00 356	0.00 359

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 361	0.00 362	0.00 354	0.00 362	0.00 356	0.00 359	0.00 360	0.00 364	0.00 359	0.00 358	0.00 356	0.00 359

GSMVS																88.13 107

test_part3							89.59 26		56.02 210		93.65 3		95.22 6	79.73 12

test_part2					89.33 9	55.48 38				82.27 2

test_part1								88.42 25				58.18 6			86.59 16	91.53 36

sam_mvs1												38.86 160				88.13 107

sam_mvs												35.99 203

semantic-postprocess				60.08 302	70.68 282	45.07 246		74.25 275	43.54 299	50.02 254	73.73 253	32.22 235	56.74 340	51.06 200	53.60 272	78.42 258

ambc				62.06 292	53.98 336	29.38 328	35.08 346	79.65 198		41.37 302	59.96 320	6.27 346	82.15 250	35.34 266	38.22 325	74.65 297

MTGPA								81.31 171

test_post1							70.84 291				14.72 354	34.33 214	83.86 231	48.80 209

test_post											16.22 351	37.52 176	84.72 217

patchmatchnet-post											59.74 321	38.41 163	79.91 273

GG-mvs-BLEND				77.77 65	86.68 28	50.61 163	68.67 302	88.45 24		68.73 65	87.45 102	59.15 4	90.67 56	54.83 173	87.67 12	92.03 26

MTMP								15.34 358

gm-plane-assit					83.24 82	54.21 75			70.91 11		88.23 89		95.25 5	66.37 82

test9_res														78.72 18	85.44 28	91.39 41

TEST9					85.68 37	55.42 40	87.59 47	84.00 119	57.72 182	72.99 36	90.98 41	44.87 77	88.58 117

test_8					85.72 36	55.31 44	87.60 44	83.88 124	57.84 179	72.84 39	90.99 40	44.99 74	88.34 128

agg_prior2														75.65 32	85.11 32	91.01 47

agg_prior					85.64 40	54.92 57		83.61 130		72.53 43			88.10 139

TestCases				55.32 315	65.08 309	37.50 298		54.25 336	35.45 326	33.42 329	72.82 263	9.98 335	59.33 337	24.13 310	43.84 313	69.13 321

test_prior4						56.39 28	87.15 59

test_prior2							89.04 32		61.88 104	73.55 31	91.46 37	48.01 41		74.73 38	85.46 26

test_prior				78.39 52	86.35 31	54.91 59		85.45 73					89.70 81			90.55 55

旧先验2							81.73 178		45.53 286	74.66 24			70.48 325	58.31 143

新几何2							81.61 186

新几何1				73.30 161	83.10 84	53.48 87		71.43 298	45.55 285	66.14 85	87.17 105	33.88 220	80.54 262	48.50 211	80.33 64	85.88 143

旧先验1					81.57 126	47.48 220		71.83 292			88.66 82	36.94 187			78.34 79	88.67 98

无先验							85.19 95	78.00 231	49.08 267				85.13 208	52.78 188		87.45 118

原ACMM2							83.77 137

原ACMM1				76.13 98	84.89 56	54.59 69		85.26 81	51.98 250	66.70 78	87.07 107	40.15 148	89.70 81	51.23 198	85.06 33	84.10 165

test222					79.36 160	50.97 159	77.99 237	67.84 310	42.54 304	62.84 127	86.53 113	30.26 249			76.91 92	85.23 152

testdata2													77.81 289	45.64 230

segment_acmp												44.97 76

testdata				67.08 257	77.59 190	45.46 243		69.20 309	44.47 292	71.50 53	88.34 85	31.21 244	70.76 324	52.20 193	75.88 100	85.03 154

testdata1							77.55 240		64.14 73

test12				79.24 31	86.89 26	56.08 32		85.16 85		72.27 48		47.15 48	91.10 48		85.93 22	90.54 58

plane_prior7					77.95 185	48.46 209

plane_prior6					78.42 182	49.39 187						36.04 200

plane_prior5								82.59 149					88.30 132	65.46 91	72.34 129	84.49 160

plane_prior4											83.28 152

plane_prior3						48.95 193			64.01 74	62.15 130

plane_prior2							85.76 80		63.60 84

plane_prior1					78.31 184

plane_prior						49.57 182	87.43 52		64.57 66						72.84 123

n2								0.00 362

nn								0.00 362

door-mid								41.31 345

lessismore_v0				67.98 250	64.76 311	41.25 279		45.75 342		36.03 317	65.63 308	19.29 312	84.11 230	35.67 264	21.24 343	78.59 252

LGP-MVS_train				72.02 182	74.42 224	48.60 201		80.64 184	54.69 223	53.75 222	83.83 136	25.73 277	86.98 166	60.33 131	64.71 174	80.48 233

test11								84.25 106

door								43.27 344

HQP5-MVS						51.56 147

HQP-NCC					79.02 167		88.00 38		65.45 52	64.48 107

ACMP_Plane					79.02 167		88.00 38		65.45 52	64.48 107

BP-MVS														66.70 80

HQP4-MVS										64.47 110			88.61 115			84.91 157

HQP3-MVS								83.68 127							73.12 119

HQP2-MVS												37.35 179

NP-MVS					78.76 171	50.43 168					85.12 124

MDTV_nov1_ep13_2view						43.62 259	71.13 290		54.95 221	59.29 164		36.76 190		46.33 227		87.32 120

ACMMP++_ref															63.20 192

ACMMP++															59.38 225

Test By Simon												39.38 155

ITE_SJBPF				51.84 320	58.03 329	31.94 322		53.57 338	36.67 321	41.32 303	75.23 245	11.17 334	51.57 344	25.81 305	48.04 289	72.02 314

DeepMVS_CX				13.10 343	21.34 358	8.99 356		10.02 359	10.59 350	7.53 352	30.55 346	1.82 358	14.55 356	6.83 351	7.52 351	15.75 351

High-resolution multi-view benchmark