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 5	92.84 2	57.58 13	93.77 1	91.10 4	75.95 2	77.10 13	93.09 6	54.15 11	95.57 3	85.80 1	85.87 20	93.31 6

HSP-MVS		82.45 2	83.62 1	78.96 34	82.99 87	52.71 121	85.04 107	89.99 10	66.08 45	86.77 1	92.75 10	72.05 1	91.46 41	83.35 5	93.53 1	92.72 17

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 25	56.89 21	92.77 2	86.30 61	77.83 1	77.88 10	92.13 16	60.24 2	94.78 10	78.97 13	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 17	70.31 14	77.64 12	93.87 2	52.58 17	93.91 15	84.17 2	87.92 10	92.39 19

CANet		80.90 5	81.17 6	80.09 21	87.62 20	54.21 71	91.60 6	86.47 57	73.13 5	79.89 6	93.10 5	49.88 32	92.98 21	84.09 3	84.75 32	93.08 11

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 62	85.46 46	49.56 179	90.99 11	86.66 55	70.58 13	80.07 5	95.30 1	56.18 7	90.97 48	82.57 8	86.22 18	93.28 7

HPM-MVS++		80.50 7	80.71 7	79.88 23	87.34 22	55.20 46	89.93 20	87.55 43	66.04 48	79.46 7	93.00 9	53.10 15	91.76 38	80.40 10	89.56 4	92.68 18

CSCG		80.41 8	79.72 8	82.49 4	89.12 9	57.67 12	89.29 28	91.54 2	59.19 143	71.82 47	90.05 61	59.72 3	96.04 1	78.37 16	88.40 8	93.75 2

PS-MVSNAJ		80.06 9	79.52 10	81.68 7	85.58 41	60.97 3	91.69 4	87.02 48	70.62 12	80.75 4	93.22 4	37.77 156	92.50 28	82.75 6	86.25 17	91.57 33

xiu_mvs_v2_base		79.86 10	79.31 11	81.53 8	85.03 52	60.73 4	91.65 5	86.86 51	70.30 15	80.77 3	93.07 8	37.63 161	92.28 31	82.73 7	85.71 22	91.57 33

MVS_0304		79.84 11	79.71 9	80.25 16	85.64 38	54.62 64	90.58 14	84.48 97	72.51 8	79.22 8	93.09 6	42.01 117	93.28 19	84.00 4	85.84 21	92.87 15

NCCC		79.57 12	79.23 12	80.59 12	89.50 8	56.99 19	91.38 8	88.17 31	67.71 29	73.81 26	92.75 10	46.88 47	93.28 19	78.79 14	84.07 37	91.50 36

LFMVS		78.52 13	77.14 25	82.67 3	89.58 7	58.90 6	91.27 9	88.05 32	63.22 89	74.63 22	90.83 45	41.38 126	94.40 11	75.42 33	79.90 68	94.72 1

APDe-MVS		78.44 14	78.20 15	79.19 30	88.56 10	54.55 66	89.76 24	87.77 38	55.91 208	78.56 9	92.49 13	48.20 36	92.65 26	79.49 11	83.04 40	90.39 60

MG-MVS		78.42 15	76.99 27	82.73 2	93.17 1	64.46 1	89.93 20	88.51 24	64.83 62	73.52 30	88.09 88	48.07 37	92.19 32	62.24 111	84.53 34	91.53 35

lupinMVS		78.38 16	78.11 16	79.19 30	83.02 85	55.24 43	91.57 7	84.82 89	69.12 19	76.67 15	92.02 20	44.82 67	90.23 67	80.83 9	80.09 63	92.08 24

EPNet		78.36 17	78.49 13	77.97 61	85.49 43	52.04 134	89.36 27	84.07 108	73.22 4	77.03 14	91.72 27	49.32 34	90.17 69	73.46 46	82.77 41	91.69 29
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 18	78.26 14	78.48 46	81.33 129	56.31 29	81.59 181	86.41 58	69.61 18	81.72 2	88.16 87	55.09 8	88.04 138	74.12 40	86.31 16	91.09 43

canonicalmvs		78.17 19	77.86 19	79.12 33	84.30 59	54.22 70	87.71 40	84.57 96	67.70 30	77.70 11	92.11 19	50.90 26	89.95 72	78.18 20	77.54 82	93.20 9

alignmvs		78.08 20	77.98 17	78.39 50	83.53 71	53.22 104	89.77 23	85.45 70	66.11 43	76.59 17	91.99 22	54.07 12	89.05 89	77.34 23	77.00 88	92.89 14

DeepC-MVS_fast	67.50 3	78.00 21	77.63 20	79.13 32	88.52 11	55.12 48	89.95 19	85.98 64	68.31 23	71.33 51	92.75 10	45.52 59	90.37 61	71.15 57	85.14 28	91.91 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		77.99 22	77.92 18	78.19 55	87.43 21	50.12 171	90.93 12	91.41 3	67.48 32	75.12 19	90.15 60	46.77 48	91.00 46	73.52 45	78.46 75	93.44 4

TSAR-MVS + GP.		77.82 23	77.59 21	78.49 45	85.25 50	50.27 170	90.02 17	90.57 5	56.58 198	74.26 24	91.60 30	54.26 9	92.16 33	75.87 28	79.91 67	93.05 12

Regformer-1		77.80 24	77.44 23	78.88 36	87.78 18	52.44 126	87.60 42	90.08 8	68.86 20	72.49 42	91.79 24	47.69 41	94.90 8	73.57 44	77.05 85	89.31 79

test_prior3		77.59 25	77.33 24	78.39 50	86.35 29	54.91 56	89.04 30	85.45 70	61.88 104	73.55 28	91.46 34	48.01 39	89.70 78	74.73 35	85.46 23	90.55 52

PHI-MVS		77.49 26	77.00 26	78.95 35	85.33 48	50.69 157	88.57 33	88.59 23	58.14 171	73.60 27	93.31 3	43.14 94	93.79 16	73.81 41	88.53 7	92.37 20

WTY-MVS		77.47 27	77.52 22	77.30 74	88.33 14	46.25 231	88.46 34	90.32 6	71.40 10	72.32 44	91.72 27	53.44 13	92.37 29	66.28 81	75.42 101	93.28 7

Regformer-2		77.15 28	76.82 28	78.14 56	87.78 18	51.84 138	87.60 42	89.12 13	67.23 33	71.93 46	91.79 24	46.03 54	93.53 18	72.85 52	77.05 85	89.05 87

SteuartSystems-ACMMP		77.08 29	76.33 32	79.34 28	80.98 132	55.31 41	89.76 24	86.91 50	62.94 93	71.65 49	91.56 31	42.33 109	92.56 27	77.14 24	83.69 39	90.15 67
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 30	76.45 30	78.69 40	79.69 146	54.74 59	90.56 15	83.99 112	68.26 24	74.10 25	90.91 42	42.14 113	89.99 71	79.30 12	79.12 71	91.36 39
jason: jason.
train_agg		76.91 31	76.40 31	78.45 48	85.68 35	55.42 37	87.59 45	84.00 110	57.84 177	72.99 33	90.98 38	44.99 63	88.58 114	78.19 17	85.32 26	91.34 41

MVS		76.91 31	75.48 40	81.23 10	84.56 57	55.21 45	80.23 203	91.64 1	58.65 161	65.37 91	91.48 33	45.72 57	95.05 6	72.11 54	89.52 5	93.44 4

DeepC-MVS	67.15 4	76.90 33	76.27 33	78.80 38	80.70 138	55.02 51	86.39 66	86.71 53	66.96 36	67.91 68	89.97 63	48.03 38	91.41 42	75.60 30	84.14 36	89.96 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPM		76.76 34	76.07 36	78.81 37	80.20 144	59.11 5	86.86 62	86.23 62	68.60 21	70.18 57	88.84 78	51.57 22	87.16 159	65.48 87	86.68 13	90.15 67

MAR-MVS		76.76 34	75.60 39	80.21 17	90.87 3	54.68 62	89.14 29	89.11 14	62.95 92	70.54 55	92.33 14	41.05 127	94.95 7	57.90 145	86.55 15	91.00 45
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
agg_prior3		76.73 36	76.15 35	78.48 46	85.66 37	55.59 34	87.54 48	83.95 113	57.78 179	71.78 48	90.81 46	44.33 71	88.52 119	78.19 17	85.32 26	91.34 41

agg_prior1		76.68 37	76.24 34	78.00 59	85.64 38	54.92 54	87.55 47	83.61 120	57.99 174	72.53 40	91.05 36	45.36 60	88.10 136	77.76 22	84.68 33	90.99 46

PVSNet_Blended		76.53 38	76.54 29	76.50 89	85.91 32	51.83 139	88.89 32	84.24 105	67.82 27	69.09 60	89.33 73	46.70 49	88.13 134	75.43 31	81.48 51	89.55 75

ACMMP_Plus		76.43 39	75.66 38	78.73 39	81.92 113	54.67 63	84.06 128	85.35 75	61.10 114	72.99 33	91.50 32	40.25 133	91.00 46	76.84 25	86.98 12	90.51 56

MVS_111021_HR		76.39 40	75.38 43	79.42 27	85.33 48	56.47 26	88.15 35	84.97 86	65.15 61	66.06 83	89.88 64	43.79 81	92.16 33	75.03 34	80.03 66	89.64 74

CHOSEN 1792x2688		76.24 41	74.03 52	82.88 1	83.09 82	62.84 2	85.73 80	85.39 73	69.79 16	64.87 99	83.49 138	41.52 125	93.69 17	70.55 58	81.82 48	92.12 23

SD-MVS		76.18 42	74.85 48	80.18 18	85.39 47	56.90 20	85.75 79	82.45 141	56.79 193	74.48 23	91.81 23	43.72 85	90.75 52	74.61 37	78.65 74	92.91 13

APD-MVS		76.15 43	75.68 37	77.54 68	88.52 11	53.44 86	87.26 55	85.03 85	53.79 223	74.91 20	91.68 29	43.80 80	90.31 62	74.36 38	81.82 48	88.87 91
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDD-MVS		76.08 44	74.97 46	79.44 26	84.27 62	53.33 96	91.13 10	85.88 65	65.33 58	72.37 43	89.34 71	32.52 219	92.76 24	77.90 21	75.96 96	92.22 22

CDPH-MVS		76.05 45	75.19 44	78.62 43	86.51 28	54.98 53	87.32 51	84.59 95	58.62 162	70.75 53	90.85 44	43.10 98	90.63 55	70.50 59	84.51 35	90.24 63

Regformer-3		76.02 46	75.47 41	77.70 64	85.49 43	51.47 145	85.12 97	90.19 7	68.52 22	69.36 58	90.66 48	46.45 51	94.81 9	70.25 61	73.16 114	86.81 124

MVS_Test		75.85 47	74.93 47	78.62 43	84.08 64	55.20 46	83.99 131	85.17 81	68.07 25	73.38 31	82.76 146	50.44 27	89.00 96	65.90 83	80.61 56	91.64 30

CLD-MVS		75.60 48	75.39 42	76.24 93	80.69 139	52.40 127	90.69 13	86.20 63	74.40 3	65.01 97	88.93 75	42.05 116	90.58 56	76.57 26	73.96 109	85.73 140
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS-pluss		75.54 49	75.03 45	77.04 80	81.37 128	52.65 123	84.34 121	84.46 98	61.16 112	69.14 59	91.76 26	39.98 140	88.99 98	78.19 17	84.89 31	89.48 76
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DWT-MVSNet_test		75.47 50	73.87 53	80.29 15	87.33 23	57.05 18	82.86 156	87.96 34	72.59 6	67.29 71	87.79 92	51.61 21	91.52 40	54.75 173	72.63 122	92.29 21

Effi-MVS+		75.24 51	73.61 55	80.16 19	81.92 113	57.42 14	85.21 91	76.71 238	60.68 121	73.32 32	89.34 71	47.30 43	91.63 39	68.28 69	79.72 69	91.42 37

PAPR		75.20 52	74.13 51	78.41 49	88.31 15	55.10 50	84.31 122	85.66 67	63.76 80	67.55 69	90.73 47	43.48 90	89.40 83	66.36 80	77.03 87	90.73 50

Regformer-4		75.06 53	74.59 49	76.47 90	85.49 43	50.33 166	85.12 97	88.61 21	66.42 38	68.48 63	90.66 48	44.15 76	92.68 25	69.24 64	73.16 114	86.39 132

MP-MVS		74.99 54	74.33 50	76.95 83	82.89 91	53.05 114	85.63 82	83.50 122	57.86 176	67.25 72	90.24 56	43.38 91	88.85 107	76.03 27	82.23 46	88.96 89

#test#		74.86 55	73.78 54	78.10 57	84.30 59	53.68 78	86.95 59	84.36 99	59.00 153	65.78 86	90.56 50	40.70 130	90.90 49	71.48 55	80.88 52	89.71 71

3Dnovator	64.70 6	74.46 56	72.48 64	80.41 14	82.84 93	55.40 40	83.08 150	88.61 21	67.61 31	59.85 150	88.66 79	34.57 200	93.97 13	58.42 138	88.70 6	91.85 28

HFP-MVS		74.37 57	73.13 59	78.10 57	84.30 59	53.68 78	85.58 83	84.36 99	56.82 191	65.78 86	90.56 50	40.70 130	90.90 49	69.18 65	80.88 52	89.71 71

VDDNet		74.37 57	72.13 72	81.09 11	79.58 147	56.52 25	90.02 17	86.70 54	52.61 233	71.23 52	87.20 100	31.75 229	93.96 14	74.30 39	75.77 99	92.79 16

MSLP-MVS++		74.21 59	72.25 70	80.11 20	81.45 126	56.47 26	86.32 68	79.65 187	58.19 170	66.36 80	92.29 15	36.11 184	90.66 54	67.39 73	82.49 43	93.18 10

PatchFormer-LS_test		74.17 60	72.30 68	79.77 24	86.61 27	57.26 16	82.02 166	84.80 91	71.85 9	64.73 100	87.52 97	50.33 29	90.40 60	54.23 175	68.63 147	91.64 30

API-MVS		74.17 60	72.07 73	80.49 13	90.02 6	58.55 7	87.30 53	84.27 102	57.51 183	65.77 88	87.77 94	41.61 124	95.97 2	51.71 193	82.63 42	86.94 118

MPTG		74.15 62	73.11 60	77.27 76	81.54 122	53.57 81	84.02 130	81.31 160	59.41 136	68.39 64	90.96 40	36.07 185	89.01 94	73.80 42	82.45 44	89.23 81

IB-MVS	68.87 2	74.01 63	72.03 75	79.94 22	83.04 84	55.50 36	90.24 16	88.65 19	67.14 34	61.38 133	81.74 163	53.21 14	94.28 12	60.45 125	62.41 199	90.03 69
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
HY-MVS	67.03 5	73.90 64	73.14 57	76.18 95	84.70 56	47.36 216	75.56 242	86.36 60	66.27 41	70.66 54	83.91 131	51.05 25	89.31 84	67.10 75	72.61 123	91.88 27

CostFormer		73.89 65	72.30 68	78.66 41	82.36 104	56.58 22	75.56 242	85.30 76	66.06 46	70.50 56	76.88 214	57.02 6	89.06 87	68.27 70	68.74 145	90.33 62

ACMMPR		73.76 66	72.61 61	77.24 79	83.92 68	52.96 118	85.58 83	84.29 101	56.82 191	65.12 93	90.45 52	37.24 171	90.18 68	69.18 65	80.84 54	88.58 98

region2R		73.75 67	72.55 63	77.33 73	83.90 69	52.98 117	85.54 86	84.09 107	56.83 190	65.10 94	90.45 52	37.34 169	90.24 66	68.89 67	80.83 55	88.77 94

CANet_DTU		73.71 68	73.14 57	75.40 110	82.61 100	50.05 172	84.67 117	79.36 193	69.72 17	75.39 18	90.03 62	29.41 241	85.93 193	67.99 71	79.11 72	90.22 64

MVSFormer		73.53 69	72.19 71	77.57 67	83.02 85	55.24 43	81.63 178	81.44 157	50.28 248	76.67 15	90.91 42	44.82 67	86.11 184	60.83 120	80.09 63	91.36 39

PVSNet_BlendedMVS		73.42 70	73.30 56	73.76 151	85.91 32	51.83 139	86.18 71	84.24 105	65.40 55	69.09 60	80.86 174	46.70 49	88.13 134	75.43 31	65.92 167	81.33 208

PVSNet_Blended_VisFu		73.40 71	72.44 65	76.30 91	81.32 130	54.70 61	85.81 75	78.82 200	63.70 81	64.53 103	85.38 119	47.11 46	87.38 156	67.75 72	77.55 81	86.81 124

MVSTER		73.25 72	72.33 66	76.01 100	85.54 42	53.76 77	83.52 137	87.16 46	67.06 35	63.88 113	81.66 164	52.77 16	90.44 57	64.66 96	64.69 172	83.84 170

EI-MVSNet-Vis-set		73.19 73	72.60 62	74.99 119	82.56 101	49.80 175	82.55 160	89.00 15	66.17 42	65.89 85	88.98 74	43.83 79	92.29 30	65.38 94	69.01 143	82.87 187

PMMVS		72.98 74	72.05 74	75.78 104	83.57 70	48.60 196	84.08 126	82.85 135	61.62 108	68.24 66	90.33 55	28.35 246	87.78 147	72.71 53	76.69 90	90.95 47

XVS		72.92 75	71.62 77	76.81 85	83.41 72	52.48 124	84.88 112	83.20 129	58.03 172	63.91 111	89.63 68	35.50 193	89.78 75	65.50 85	80.50 58	88.16 101

TESTMET0.1,1		72.86 76	72.33 66	74.46 133	81.98 111	50.77 155	85.13 94	85.47 69	66.09 44	67.30 70	83.69 136	37.27 170	83.57 231	65.06 95	78.97 73	89.05 87

Fast-Effi-MVS+		72.73 77	71.15 86	77.48 70	82.75 95	54.76 58	86.77 63	80.64 173	63.05 91	65.93 84	84.01 129	44.42 70	89.03 93	56.45 157	76.36 95	88.64 96

MTAPA		72.73 77	71.22 84	77.27 76	81.54 122	53.57 81	67.06 291	81.31 160	59.41 136	68.39 64	90.96 40	36.07 185	89.01 94	73.80 42	82.45 44	89.23 81

PGM-MVS		72.60 79	71.20 85	76.80 87	82.95 88	52.82 120	83.07 151	82.14 142	56.51 203	63.18 120	89.81 65	35.68 192	89.76 77	67.30 74	80.19 62	87.83 106

HPM-MVS		72.60 79	71.50 79	75.89 102	82.02 110	51.42 147	80.70 196	83.05 131	56.12 207	64.03 110	89.53 69	37.55 163	88.37 123	70.48 60	80.04 65	87.88 105

CP-MVS		72.59 81	71.46 80	76.00 101	82.93 90	52.32 131	86.93 61	82.48 140	55.15 215	63.65 116	90.44 54	35.03 197	88.53 118	68.69 68	77.83 79	87.15 117

EI-MVSNet-UG-set		72.37 82	71.73 76	74.29 137	81.60 118	49.29 184	81.85 171	88.64 20	65.29 60	65.05 95	88.29 84	43.18 92	91.83 37	63.74 98	67.97 151	81.75 201

MS-PatchMatch		72.34 83	71.26 83	75.61 105	82.38 103	55.55 35	88.00 36	89.95 11	65.38 56	56.51 200	80.74 175	32.28 222	92.89 22	57.95 144	88.10 9	78.39 245

HQP-MVS		72.34 83	71.44 81	75.03 116	79.02 155	51.56 142	88.00 36	83.68 117	65.45 52	64.48 104	85.13 120	37.35 167	88.62 111	66.70 77	73.12 116	84.91 152

mvs_anonymous		72.29 85	70.74 87	76.94 84	82.85 92	54.72 60	78.43 228	81.54 156	63.77 79	61.69 132	79.32 182	51.11 24	85.31 201	62.15 113	75.79 98	90.79 49

3Dnovator+	62.71 7	72.29 85	70.50 89	77.65 66	83.40 75	51.29 151	87.32 51	86.40 59	59.01 152	58.49 175	88.32 83	32.40 220	91.27 43	57.04 152	82.15 47	90.38 61

nrg030		72.27 87	71.56 78	74.42 134	75.93 197	50.60 159	86.97 58	83.21 128	62.75 95	67.15 73	84.38 126	50.07 30	86.66 172	71.19 56	62.37 200	85.99 135

VPNet		72.07 88	71.42 82	74.04 142	78.64 165	47.17 220	89.91 22	87.97 33	72.56 7	64.66 101	85.04 122	41.83 120	88.33 127	61.17 118	60.97 204	86.62 126

DP-MVS Recon		71.99 89	70.31 92	77.01 82	90.65 4	53.44 86	89.37 26	82.97 133	56.33 205	63.56 118	89.47 70	34.02 204	92.15 35	54.05 176	72.41 124	85.43 146

QAPM		71.88 90	69.33 107	79.52 25	82.20 105	54.30 68	86.30 69	88.77 18	56.61 197	59.72 152	87.48 98	33.90 207	95.36 4	47.48 215	81.49 50	88.90 90

PAPM_NR		71.80 91	69.98 99	77.26 78	81.54 122	53.34 94	78.60 226	85.25 79	53.46 225	60.53 146	88.66 79	45.69 58	89.24 85	56.49 154	79.62 70	89.19 84

mPP-MVS		71.79 92	70.38 91	76.04 99	82.65 99	52.06 133	84.45 119	81.78 153	55.59 212	62.05 130	89.68 67	33.48 210	88.28 131	65.45 90	78.24 77	87.77 108

xiu_mvs_v1_base_debu		71.60 93	70.29 93	75.55 106	77.26 184	53.15 107	85.34 87	79.37 190	55.83 209	72.54 37	90.19 57	22.38 282	86.66 172	73.28 48	76.39 92	86.85 121

xiu_mvs_v1_base		71.60 93	70.29 93	75.55 106	77.26 184	53.15 107	85.34 87	79.37 190	55.83 209	72.54 37	90.19 57	22.38 282	86.66 172	73.28 48	76.39 92	86.85 121

xiu_mvs_v1_base_debi		71.60 93	70.29 93	75.55 106	77.26 184	53.15 107	85.34 87	79.37 190	55.83 209	72.54 37	90.19 57	22.38 282	86.66 172	73.28 48	76.39 92	86.85 121

test_normal		71.31 96	68.95 113	78.39 50	72.30 254	54.25 69	81.67 175	84.05 109	65.94 50	51.31 236	78.09 200	36.06 187	90.43 59	73.00 51	78.09 78	90.50 57

DI_MVS_plusplus_test		71.30 97	68.98 112	78.26 54	72.76 236	54.08 74	81.72 174	83.22 127	65.75 51	51.94 232	78.47 194	36.01 190	90.31 62	73.33 47	77.60 80	90.40 59

EPP-MVSNet		71.14 98	70.07 98	74.33 136	79.18 154	46.52 225	83.81 133	86.49 56	56.32 206	57.95 179	84.90 124	54.23 10	89.14 86	58.14 142	69.65 140	87.33 114

VPA-MVSNet		71.12 99	70.66 88	72.49 173	78.75 160	44.43 247	87.64 41	90.02 9	63.97 75	65.02 96	81.58 165	42.14 113	87.42 155	63.42 100	63.38 184	85.63 144

1314		71.11 100	69.41 104	76.22 94	79.32 150	50.49 162	80.23 203	85.14 84	59.44 135	58.93 165	88.89 77	33.83 209	89.60 82	61.49 116	77.42 84	88.57 99

tpmrst		71.04 101	69.77 101	74.86 126	83.19 79	55.86 33	75.64 241	78.73 203	67.88 26	64.99 98	73.73 241	49.96 31	79.56 261	65.92 82	67.85 153	89.14 86

MVP-Stereo		70.97 102	70.44 90	72.59 170	76.03 196	51.36 148	85.02 109	86.99 49	60.31 125	56.53 199	78.92 187	40.11 137	90.00 70	60.00 131	90.01 2	76.41 270
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 103	69.91 100	74.12 140	77.95 173	49.57 177	85.76 77	82.59 138	63.60 84	62.15 127	83.28 141	36.04 188	88.30 129	65.46 88	72.34 126	84.49 155

tpm2		70.82 104	68.44 116	77.98 60	80.78 136	56.11 31	74.21 252	81.28 163	60.24 126	68.04 67	75.27 232	52.26 20	88.50 120	55.82 161	68.03 150	89.33 78

ACMMP		70.81 105	69.29 108	75.39 111	81.52 125	51.92 136	83.43 142	83.03 132	56.67 196	58.80 170	88.91 76	31.92 227	88.58 114	65.89 84	73.39 113	85.67 141
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
OPM-MVS		70.75 106	69.58 102	74.26 138	75.55 201	51.34 149	86.05 73	83.29 126	61.94 103	62.95 123	85.77 115	34.15 203	88.44 121	65.44 91	71.07 132	82.99 184

ab-mvs		70.65 107	69.11 110	75.29 112	80.87 135	46.23 232	73.48 255	85.24 80	59.99 128	66.65 76	80.94 173	43.13 95	88.69 109	63.58 99	68.07 149	90.95 47

Vis-MVSNet		70.61 108	69.34 106	74.42 134	80.95 134	48.49 201	86.03 74	77.51 229	58.74 160	65.55 90	87.78 93	34.37 201	85.95 192	52.53 189	80.61 56	88.80 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 109	70.13 97	71.58 197	81.59 119	39.02 284	80.78 195	84.71 94	59.34 139	66.61 78	88.09 88	37.17 172	85.52 197	61.82 115	71.02 133	90.20 65

CDS-MVSNet		70.48 110	69.43 103	73.64 153	77.56 179	48.83 192	83.51 141	77.45 230	63.27 88	62.33 126	85.54 118	43.85 78	83.29 233	57.38 151	74.00 108	88.79 93
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		70.18 111	69.28 109	72.89 165	77.64 177	42.88 260	85.06 106	87.50 44	62.58 96	62.66 125	82.34 156	43.64 87	89.83 74	58.42 138	63.70 180	85.96 137

PCF-MVS	61.03 10	70.10 112	68.40 117	75.22 115	77.15 188	51.99 135	79.30 223	82.12 146	56.47 204	61.88 131	86.48 112	43.98 77	87.24 158	55.37 165	72.79 121	86.43 131
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 113	68.01 122	76.27 92	84.21 63	51.22 153	87.29 54	79.33 195	58.96 155	63.63 117	86.77 106	33.29 212	90.30 65	44.63 231	73.96 109	87.30 116

1112_ss		70.05 114	69.37 105	72.10 176	80.77 137	42.78 261	85.12 97	76.75 237	59.69 131	61.19 135	92.12 17	47.48 42	83.84 227	53.04 182	68.21 148	89.66 73

diffmvs		70.02 115	68.35 118	75.03 116	79.19 153	51.48 144	78.50 227	76.65 240	59.71 130	67.10 74	80.32 177	42.81 105	87.12 160	58.48 136	72.37 125	86.49 128

BH-w/o		70.02 115	68.51 115	74.56 132	82.77 94	50.39 164	86.60 64	78.14 214	59.77 129	59.65 153	85.57 117	39.27 145	87.30 157	49.86 201	74.94 106	85.99 135

tpmp4_e23		70.01 117	67.13 144	78.65 42	81.93 112	57.90 10	73.99 253	81.35 159	60.61 122	65.28 92	73.78 240	52.48 18	88.60 113	48.40 210	66.35 164	89.44 77

FIs		70.00 118	70.24 96	69.30 228	77.93 175	38.55 286	83.99 131	87.72 40	66.86 37	57.66 186	84.17 128	52.28 19	85.31 201	52.72 188	68.80 144	84.02 162

OpenMVS	61.00 11	69.99 119	67.55 137	77.30 74	78.37 171	54.07 75	84.36 120	85.76 66	57.22 186	56.71 197	87.67 95	30.79 234	92.83 23	43.04 237	84.06 38	85.01 150

HyFIR lowres test		69.94 120	67.58 135	77.04 80	77.11 189	57.29 15	81.49 184	79.11 198	58.27 168	58.86 168	80.41 176	42.33 109	86.96 165	61.91 114	68.68 146	86.87 119

114514_t		69.87 121	67.88 125	75.85 103	88.38 13	52.35 130	86.94 60	83.68 117	53.70 224	55.68 207	85.60 116	30.07 239	91.20 44	55.84 160	71.02 133	83.99 164

TR-MVS		69.71 122	67.85 126	75.27 113	82.94 89	48.48 202	87.40 50	80.86 170	57.15 187	64.61 102	87.08 103	32.67 218	89.64 81	46.38 223	71.55 130	87.68 110

EI-MVSNet		69.70 123	68.70 114	72.68 168	75.00 206	48.90 190	79.54 215	87.16 46	61.05 115	63.88 113	83.74 134	45.87 55	90.44 57	57.42 150	64.68 173	78.70 235

test-LLR		69.65 124	69.01 111	71.60 195	78.67 162	48.17 209	85.13 94	79.72 184	59.18 145	63.13 121	82.58 151	36.91 176	80.24 253	60.56 123	75.17 103	86.39 132

APD-MVS_3200maxsize		69.62 125	68.23 120	73.80 150	81.58 120	48.22 208	81.91 169	79.50 189	48.21 256	64.24 109	89.75 66	31.91 228	87.55 152	63.08 101	73.85 111	85.64 143

v2v482		69.55 126	67.64 131	75.26 114	72.32 253	53.83 76	84.93 111	81.94 148	65.37 57	60.80 143	79.25 183	41.62 123	88.98 99	63.03 102	59.51 217	82.98 185

TAMVS		69.51 127	68.16 121	73.56 156	76.30 192	48.71 193	82.57 159	77.17 234	62.10 101	61.32 134	84.23 127	41.90 118	83.46 232	54.80 172	73.09 118	88.50 100

v1141		69.50 128	67.67 128	74.98 120	72.73 238	53.41 89	85.08 103	82.14 142	64.79 64	60.88 139	78.19 197	43.09 99	89.04 90	62.51 107	59.61 214	82.47 192

divwei89l23v2f112		69.50 128	67.67 128	74.98 120	72.72 239	53.41 89	85.08 103	82.14 142	64.79 64	60.88 139	78.19 197	43.11 96	89.04 90	62.51 107	59.62 213	82.48 191

v1		69.49 130	67.67 128	74.98 120	72.69 240	53.41 89	85.08 103	82.13 145	64.80 63	60.87 141	78.19 197	43.11 96	89.04 90	62.51 107	59.61 214	82.49 190

v1neww		69.43 131	67.62 132	74.89 123	72.90 231	53.31 97	85.12 97	81.11 164	64.29 69	61.00 136	78.53 190	42.88 102	88.98 99	62.66 105	60.06 208	82.37 194

v7new		69.43 131	67.62 132	74.89 123	72.90 231	53.31 97	85.12 97	81.11 164	64.29 69	61.00 136	78.53 190	42.88 102	88.98 99	62.66 105	60.06 208	82.37 194

v6		69.43 131	67.61 134	74.88 125	72.87 235	53.30 101	85.12 97	81.10 166	64.29 69	60.99 138	78.52 192	42.88 102	88.98 99	62.67 104	60.06 208	82.37 194

PVSNet	62.49 8	69.27 134	67.81 127	73.64 153	84.41 58	51.85 137	84.63 118	77.80 222	66.42 38	59.80 151	84.95 123	22.14 286	80.44 250	55.03 168	75.11 105	88.62 97

MVS_111021_LR		69.07 135	67.91 123	72.54 171	77.27 183	49.56 179	79.77 211	73.96 267	59.33 141	60.73 144	87.82 91	30.19 238	81.53 240	69.94 62	72.19 128	86.53 127

mvs-test1		69.04 136	67.57 136	73.44 158	75.17 202	51.68 141	86.57 65	74.48 260	62.15 99	62.07 129	85.79 114	30.59 235	87.48 153	65.40 92	65.94 166	81.18 212

GA-MVS		69.04 136	66.70 152	76.06 98	75.11 204	52.36 129	83.12 149	80.23 177	63.32 87	60.65 145	79.22 184	30.98 233	88.37 123	61.25 117	66.41 159	87.46 112

cascas		69.01 138	66.13 159	77.66 65	79.36 148	55.41 39	86.99 57	83.75 116	56.69 195	58.92 166	81.35 166	24.31 270	92.10 36	53.23 179	70.61 135	85.46 145

FMVSNet3		68.84 139	67.40 140	73.19 160	85.05 51	48.53 199	85.71 81	85.36 74	60.90 117	57.58 187	79.15 185	42.16 112	86.77 168	47.25 217	63.40 181	84.27 159

UniMVSNet_NR-MVSNet		68.82 140	68.29 119	70.40 215	75.71 200	42.59 263	84.23 123	86.78 52	66.31 40	58.51 172	82.45 153	51.57 22	84.64 214	53.11 180	55.96 244	83.96 167

v1144		68.81 141	66.82 146	74.80 129	72.34 252	53.46 84	84.68 116	81.77 154	64.25 72	60.28 148	77.91 201	40.23 134	88.95 103	60.37 126	59.52 216	81.97 197

IS-MVSNet		68.80 142	67.55 137	72.54 171	78.50 168	43.43 255	81.03 190	79.35 194	59.12 149	57.27 195	86.71 107	46.05 53	87.70 149	44.32 232	75.60 100	86.49 128

1121		68.79 143	66.77 149	74.82 127	83.08 83	53.46 84	80.23 203	71.53 284	45.47 273	66.31 81	87.19 101	34.02 204	85.13 204	52.78 185	80.36 60	85.87 139

PS-MVSNAJss		68.78 144	67.17 143	73.62 155	73.01 226	48.33 207	84.95 110	84.81 90	59.30 142	58.91 167	79.84 180	37.77 156	88.86 106	62.83 103	63.12 191	83.67 173

v7		68.76 145	66.79 148	74.68 130	72.60 243	53.37 92	84.72 115	80.88 169	63.80 78	60.43 147	78.21 196	40.05 139	88.89 105	60.34 127	60.07 207	81.77 200

thres200		68.71 146	67.27 142	73.02 161	84.73 55	46.76 222	85.03 108	87.73 39	62.34 98	59.87 149	83.45 139	43.15 93	88.32 128	31.25 279	67.91 152	83.98 165

UGNet		68.71 146	67.11 145	73.50 157	80.55 143	47.61 214	84.08 126	78.51 208	59.45 134	65.68 89	82.73 149	23.78 272	85.08 206	52.80 184	76.40 91	87.80 107
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
Test4		68.64 148	65.68 168	77.53 69	67.78 285	53.34 94	79.42 218	82.84 136	65.96 49	46.54 270	76.15 226	25.16 266	88.83 108	69.74 63	77.53 83	90.43 58

EPMVS		68.45 149	65.44 175	77.47 71	84.91 53	56.17 30	71.89 273	81.91 151	61.72 107	60.85 142	72.49 254	36.21 183	87.06 162	47.32 216	71.62 129	89.17 85

test-mter		68.36 150	67.29 141	71.60 195	78.67 162	48.17 209	85.13 94	79.72 184	53.38 226	63.13 121	82.58 151	27.23 254	80.24 253	60.56 123	75.17 103	86.39 132

tpm		68.36 150	67.48 139	70.97 205	79.93 145	51.34 149	76.58 238	78.75 202	67.73 28	63.54 119	74.86 234	48.33 35	72.36 304	53.93 177	63.71 179	89.21 83

BH-untuned		68.28 152	66.40 153	73.91 145	81.62 117	50.01 173	85.56 85	77.39 231	57.63 182	57.47 192	83.69 136	36.36 182	87.08 161	44.81 230	73.08 119	84.65 154

v148		68.24 153	66.35 154	73.88 146	71.76 258	51.47 145	84.23 123	81.90 152	63.69 82	58.94 164	76.44 218	43.72 85	87.78 147	60.63 122	55.86 246	82.39 193

abl_6		68.03 154	66.15 158	73.66 152	78.54 167	48.48 202	79.77 211	78.04 218	47.39 260	63.70 115	88.25 85	28.21 247	89.06 87	60.17 130	71.25 131	83.45 175

v1192		67.96 155	65.74 166	74.63 131	71.79 257	53.43 88	84.06 128	80.99 168	63.19 90	59.56 156	77.46 207	37.50 166	88.65 110	58.20 141	58.93 222	81.79 199

v144192		67.86 156	65.76 165	74.16 139	71.68 259	53.09 111	84.14 125	80.83 171	62.85 94	59.21 162	77.28 210	39.30 144	88.00 139	58.67 135	57.88 233	81.40 207

HPM-MVS_fast		67.86 156	66.28 155	72.61 169	80.67 140	48.34 206	81.18 187	75.95 249	50.81 247	59.55 157	88.05 90	27.86 249	85.98 189	58.83 134	73.58 112	83.51 174

AdaColmap		67.86 156	65.48 172	75.00 118	88.15 17	54.99 52	86.10 72	76.63 241	49.30 254	57.80 182	86.65 109	29.39 242	88.94 104	45.10 229	70.21 137	81.06 213

UniMVSNet (Re)		67.71 159	66.80 147	70.45 213	74.44 211	42.93 259	82.42 162	84.90 88	63.69 82	59.63 154	80.99 172	47.18 44	85.23 203	51.17 196	56.75 238	83.19 182

V42		67.66 160	65.60 171	73.86 147	70.69 267	53.63 80	81.50 182	78.61 206	63.85 77	59.49 158	77.49 206	37.98 153	87.65 150	62.33 110	58.43 223	80.29 227

WR-MVS		67.58 161	66.76 150	70.04 224	75.92 198	45.06 244	86.23 70	85.28 77	64.31 68	58.50 174	81.00 171	44.80 69	82.00 239	49.21 205	55.57 249	83.06 183

tfpn200view9		67.57 162	66.13 159	71.89 189	84.05 65	45.07 241	83.40 144	87.71 41	60.79 118	57.79 183	82.76 146	43.53 88	87.80 143	28.80 284	66.36 160	82.78 188

FMVSNet2		67.57 162	65.79 164	72.90 163	82.71 96	47.97 212	85.15 93	84.93 87	58.55 163	56.71 197	78.26 195	36.72 179	86.67 171	46.15 225	62.94 193	84.07 161

FC-MVSNet-test		67.49 164	67.91 123	66.21 257	76.06 194	33.06 302	80.82 194	87.18 45	64.44 67	54.81 209	82.87 144	50.40 28	82.60 235	48.05 212	66.55 158	82.98 185

v1921920		67.45 165	65.23 179	74.10 141	71.51 262	52.90 119	83.75 135	80.44 176	62.48 97	59.12 163	77.13 211	36.98 174	87.90 140	57.53 148	58.14 228	81.49 204

gg-mvs-nofinetune		67.43 166	64.53 186	76.13 96	85.95 31	47.79 213	64.38 296	88.28 30	39.34 296	66.62 77	41.27 324	58.69 5	89.00 96	49.64 203	86.62 14	91.59 32

thres400		67.40 167	66.13 159	71.19 202	84.05 65	45.07 241	83.40 144	87.71 41	60.79 118	57.79 183	82.76 146	43.53 88	87.80 143	28.80 284	66.36 160	80.71 218

UA-Net		67.32 168	66.23 156	70.59 211	78.85 158	41.23 274	73.60 254	75.45 255	61.54 109	66.61 78	84.53 125	38.73 149	86.57 177	42.48 241	74.24 107	83.98 165

v8		67.25 169	64.99 182	74.04 142	72.89 233	53.31 97	82.37 163	80.11 179	61.54 109	54.29 214	76.02 228	42.89 101	88.41 122	58.43 137	56.36 239	80.39 226

NR-MVSNet		67.25 169	65.99 162	71.04 204	73.27 224	43.91 251	85.32 90	84.75 93	66.05 47	53.65 220	82.11 160	45.05 62	85.97 191	47.55 214	56.18 242	83.24 180

Test_1112_low_res		67.18 171	66.23 156	70.02 225	78.75 160	41.02 275	83.43 142	73.69 270	57.29 185	58.45 176	82.39 155	45.30 61	80.88 245	50.50 198	66.26 165	88.16 101

CPTT-MVS		67.15 172	65.84 163	71.07 203	80.96 133	50.32 167	81.94 168	74.10 264	46.18 269	57.91 180	87.64 96	29.57 240	81.31 242	64.10 97	70.18 138	81.56 203

GBi-Net		67.09 173	65.47 173	71.96 183	82.71 96	46.36 227	83.52 137	83.31 123	58.55 163	57.58 187	76.23 222	36.72 179	86.20 180	47.25 217	63.40 181	83.32 177

test1		67.09 173	65.47 173	71.96 183	82.71 96	46.36 227	83.52 137	83.31 123	58.55 163	57.58 187	76.23 222	36.72 179	86.20 180	47.25 217	63.40 181	83.32 177

PatchmatchNet		67.07 175	63.63 191	77.40 72	83.10 80	58.03 8	72.11 270	77.77 224	58.85 158	59.37 159	70.83 265	37.84 155	84.93 211	42.96 238	69.83 139	89.26 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 176	64.68 184	73.93 144	71.38 263	52.66 122	83.39 146	79.98 180	61.97 102	58.44 177	77.11 212	35.25 195	87.81 142	56.46 156	58.15 226	81.33 208

TranMVSNet+NR-MVSNet		66.94 177	65.61 170	70.93 207	73.45 221	43.38 256	83.02 153	84.25 103	65.31 59	58.33 178	81.90 162	39.92 141	85.52 197	49.43 204	54.89 252	83.89 169

thres100view900		66.87 178	65.42 176	71.24 201	83.29 76	43.15 257	81.67 175	87.78 35	59.04 150	55.92 204	82.18 158	43.73 82	87.80 143	28.80 284	66.36 160	82.78 188

DU-MVS		66.84 179	65.74 166	70.16 218	73.27 224	42.59 263	81.50 182	82.92 134	63.53 86	58.51 172	82.11 160	40.75 128	84.64 214	53.11 180	55.96 244	83.24 180

conf200view11		66.80 180	65.42 176	70.95 206	83.29 76	43.15 257	81.67 175	87.78 35	59.04 150	55.92 204	82.18 158	43.73 82	87.80 143	28.80 284	66.36 160	81.89 198

IterMVS-LS		66.63 181	65.36 178	70.42 214	75.10 205	48.90 190	81.45 185	76.69 239	61.05 115	55.71 206	77.10 213	45.86 56	83.65 230	57.44 149	57.88 233	78.70 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 182	64.20 189	73.83 149	72.59 245	53.37 92	81.88 170	79.91 181	61.11 113	54.09 216	75.60 230	40.06 138	88.26 132	56.47 155	56.10 243	79.86 231

Fast-Effi-MVS+-dtu		66.53 183	64.10 190	73.84 148	72.41 250	52.30 132	84.73 114	75.66 252	59.51 133	56.34 201	79.11 186	28.11 248	85.85 194	57.74 147	63.29 186	83.35 176

thres600view7		66.46 184	65.12 180	70.47 212	83.41 72	43.80 253	82.15 165	87.78 35	59.37 138	56.02 203	82.21 157	43.73 82	86.90 166	26.51 297	64.94 169	80.71 218

LPG-MVS_test		66.44 185	64.58 185	72.02 180	74.42 212	48.60 196	83.07 151	80.64 173	54.69 219	53.75 218	83.83 132	25.73 263	86.98 163	60.33 128	64.71 170	80.48 224

tpm cat1		66.28 186	62.78 196	76.77 88	81.40 127	57.14 17	70.03 282	77.19 233	53.00 230	58.76 171	70.73 267	46.17 52	86.73 170	43.27 236	64.46 174	86.44 130

EPNet_dtu		66.25 187	66.71 151	64.87 267	78.66 164	34.12 297	82.80 157	75.51 253	61.75 106	64.47 107	86.90 105	37.06 173	72.46 303	43.65 235	69.63 141	88.02 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 188	64.96 183	70.08 220	75.17 202	49.64 176	82.01 167	74.48 260	62.15 99	57.83 181	76.08 227	30.59 235	83.79 228	65.40 92	60.93 205	76.81 265

ACMP	61.11 9	66.24 188	64.33 187	72.00 182	74.89 208	49.12 185	83.18 148	79.83 182	55.41 214	52.29 228	82.68 150	25.83 261	86.10 186	60.89 119	63.94 178	80.78 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		65.97 190	65.06 181	68.71 240	72.97 227	42.58 265	78.61 225	75.35 256	54.72 218	59.31 160	86.25 113	33.30 211	77.88 273	57.99 143	67.05 155	85.66 142

X-MVStestdata		65.85 191	62.20 199	76.81 85	83.41 72	52.48 124	84.88 112	83.20 129	58.03 172	63.91 111	4.82 342	35.50 193	89.78 75	65.50 85	80.50 58	88.16 101

Vis-MVSNet (Re-imp)		65.52 192	65.63 169	65.17 265	77.49 180	30.54 310	75.49 245	77.73 226	59.34 139	52.26 230	86.69 108	49.38 33	80.53 249	37.07 255	75.28 102	84.42 157

Baseline_NR-MVSNet		65.49 193	64.27 188	69.13 229	74.37 214	41.65 270	83.39 146	78.85 199	59.56 132	59.62 155	76.88 214	40.75 128	87.44 154	49.99 200	55.05 250	78.28 252

view600		64.79 194	63.45 192	68.82 234	82.13 106	40.75 277	79.41 219	88.29 26	56.54 199	53.26 222	81.30 167	44.26 72	85.01 207	22.97 307	62.85 194	80.71 218

view800		64.79 194	63.45 192	68.82 234	82.13 106	40.75 277	79.41 219	88.29 26	56.54 199	53.26 222	81.30 167	44.26 72	85.01 207	22.97 307	62.85 194	80.71 218

conf0.05thres1000		64.79 194	63.45 192	68.82 234	82.13 106	40.75 277	79.41 219	88.29 26	56.54 199	53.26 222	81.30 167	44.26 72	85.01 207	22.97 307	62.85 194	80.71 218

tfpn		64.79 194	63.45 192	68.82 234	82.13 106	40.75 277	79.41 219	88.29 26	56.54 199	53.26 222	81.30 167	44.26 72	85.01 207	22.97 307	62.85 194	80.71 218

FMVSNet1		64.57 198	62.11 200	71.96 183	77.32 182	46.36 227	83.52 137	83.31 123	52.43 237	54.42 212	76.23 222	27.80 250	86.20 180	42.59 240	61.34 203	83.32 177

dp		64.41 199	61.58 205	72.90 163	82.40 102	54.09 73	72.53 264	76.59 242	60.39 124	55.68 207	70.39 268	35.18 196	76.90 281	39.34 247	61.71 202	87.73 109

v18		64.36 200	61.80 202	72.05 177	72.97 227	53.31 97	81.16 188	77.76 225	59.14 147	48.50 248	68.97 273	42.91 100	84.38 216	56.62 153	48.17 274	78.47 239

ACMM	58.35 12	64.35 201	62.01 201	71.38 199	74.21 215	48.51 200	82.25 164	79.66 186	47.61 258	54.54 211	80.11 178	25.26 265	86.00 188	51.26 194	63.16 189	79.64 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v16		64.25 202	61.66 204	72.03 178	72.91 230	53.28 102	80.93 191	77.81 221	58.86 157	48.30 249	68.80 276	42.70 106	84.37 217	56.44 158	48.14 275	78.44 242

v17		64.19 203	61.58 205	72.03 178	72.89 233	53.28 102	80.91 192	77.80 222	58.87 156	48.22 250	68.77 277	42.69 107	84.37 217	56.43 159	47.66 278	78.43 243

pm-mvs1		64.12 204	62.56 197	68.78 239	71.68 259	38.87 285	82.89 155	81.57 155	55.54 213	53.89 217	77.82 202	37.73 159	86.74 169	48.46 209	53.49 261	80.72 217

test_djsdf		63.84 205	61.56 207	70.70 209	68.78 276	44.69 245	81.63 178	81.44 157	50.28 248	52.27 229	76.26 221	26.72 256	86.11 184	60.83 120	55.84 247	81.29 211

v15		63.83 206	61.13 210	71.93 186	72.60 243	53.21 105	80.44 198	78.22 210	58.80 159	47.57 255	68.22 279	42.50 108	84.18 219	55.82 161	46.02 290	78.39 245

IterMVS		63.77 207	61.67 203	70.08 220	72.68 241	51.24 152	80.44 198	75.51 253	60.51 123	51.41 234	73.70 244	32.08 225	78.91 262	54.30 174	54.35 255	80.08 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V14		63.72 208	60.99 212	71.91 188	72.58 246	53.18 106	80.24 202	78.19 211	58.53 166	47.35 261	68.10 280	42.28 111	84.18 219	55.68 163	45.97 291	78.36 248

testing_2		63.60 209	59.86 223	74.82 127	61.87 307	52.39 128	73.06 261	82.76 137	61.49 111	39.96 293	67.39 287	21.06 291	88.34 125	67.07 76	64.10 175	83.72 172

V9		63.60 209	60.84 213	71.87 190	72.51 248	53.12 110	80.04 207	78.15 213	58.25 169	47.14 263	67.98 281	42.08 115	84.18 219	55.47 164	45.92 293	78.32 249

v12		63.47 211	60.68 216	71.85 191	72.45 249	53.08 112	79.83 209	78.13 215	57.95 175	46.89 265	67.87 283	41.81 121	84.17 222	55.30 166	45.87 294	78.29 251

v11		63.44 212	60.66 217	71.79 193	72.61 242	53.02 116	79.80 210	78.08 217	58.30 167	47.27 262	67.91 282	40.67 132	84.14 224	54.93 169	46.39 288	78.23 255

v13		63.36 213	60.54 219	71.82 192	72.41 250	53.03 115	79.64 214	78.10 216	57.66 181	46.67 268	67.75 284	41.68 122	84.17 222	55.11 167	45.82 295	78.25 254

pmmvs4		63.34 214	61.07 211	70.16 218	70.14 269	50.53 161	79.97 208	71.41 286	55.08 216	54.12 215	78.58 189	32.79 217	82.09 238	50.33 199	57.22 237	77.86 257

Patchmatch-test1		63.23 215	59.16 229	75.43 109	78.58 166	57.92 9	61.61 304	77.53 228	56.71 194	57.75 185	70.98 264	31.97 226	78.19 266	40.97 244	56.36 239	90.18 66

jajsoiax		63.21 216	60.84 213	70.32 216	68.33 281	44.45 246	81.23 186	81.05 167	53.37 227	50.96 240	77.81 203	17.49 306	85.49 199	59.31 132	58.05 229	81.02 214

MIMVSNet		63.12 217	60.29 221	71.61 194	75.92 198	46.65 223	65.15 292	81.94 148	59.14 147	54.65 210	69.47 271	25.74 262	80.63 247	41.03 243	69.56 142	87.55 111

mvs_tets		62.96 218	60.55 218	70.19 217	68.22 283	44.24 250	80.90 193	80.74 172	52.99 231	50.82 242	77.56 204	16.74 309	85.44 200	59.04 133	57.94 230	80.89 215

TransMVSNet (Re)		62.82 219	60.76 215	69.02 230	73.98 217	41.61 271	86.36 67	79.30 196	56.90 188	52.53 227	76.44 218	41.85 119	87.60 151	38.83 248	40.61 305	77.86 257

pmmvs5		62.80 220	61.18 209	67.66 246	69.53 274	42.37 268	82.65 158	75.19 257	54.30 222	52.03 231	78.51 193	31.64 230	80.67 246	48.60 207	58.15 226	79.95 230

test0.0.03 1		62.54 221	62.44 198	62.86 277	72.28 255	29.51 313	82.93 154	78.78 201	59.18 145	53.07 226	82.41 154	36.91 176	77.39 277	37.45 251	58.96 221	81.66 202

v7n		62.50 222	59.27 228	72.20 175	67.25 287	49.83 174	77.87 231	80.12 178	52.50 236	48.80 247	73.07 249	32.10 224	87.90 140	46.83 220	54.92 251	78.86 234

CR-MVSNet		62.47 223	59.04 231	72.77 166	73.97 218	56.57 23	60.52 307	71.72 280	60.04 127	57.49 190	65.86 292	38.94 146	80.31 251	42.86 239	59.93 211	81.42 205

tpmvs		62.45 224	59.42 226	71.53 198	83.93 67	54.32 67	70.03 282	77.61 227	51.91 240	53.48 221	68.29 278	37.91 154	86.66 172	33.36 269	58.27 224	73.62 290

EG-PatchMatch MVS		62.40 225	59.59 224	70.81 208	73.29 223	49.05 186	85.81 75	84.78 92	51.85 242	44.19 275	73.48 247	15.52 314	89.85 73	40.16 245	67.24 154	73.54 291

XVG-OURS-SEG-HR		62.02 226	59.54 225	69.46 227	65.30 293	45.88 234	65.06 293	73.57 272	46.45 267	57.42 193	83.35 140	26.95 255	78.09 269	53.77 178	64.03 176	84.42 157

XVG-OURS		61.88 227	59.34 227	69.49 226	65.37 292	46.27 230	64.80 295	73.49 273	47.04 262	57.41 194	82.85 145	25.15 267	78.18 267	53.00 183	64.98 168	84.01 163

TAPA-MVS	56.12 14	61.82 228	60.18 222	66.71 253	78.48 169	37.97 289	75.19 247	76.41 244	46.82 264	57.04 196	86.52 111	27.67 252	77.03 279	26.50 298	67.02 156	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnnormal		61.47 229	59.09 230	68.62 242	76.29 193	41.69 269	81.14 189	85.16 82	54.48 221	51.32 235	73.63 245	32.32 221	86.89 167	21.78 314	55.71 248	77.29 263

v748		61.35 230	58.24 234	70.69 210	66.28 288	47.35 217	76.58 238	79.17 197	53.09 229	46.37 272	71.50 262	33.18 213	86.33 179	46.78 221	51.19 269	78.39 245

PVSNet_0	57.04 13	61.19 231	57.24 239	73.02 161	77.45 181	50.31 168	79.43 217	77.36 232	63.96 76	47.51 258	72.45 256	25.03 268	83.78 229	52.76 187	19.22 330	84.96 151

PLC	52.38 18	60.89 232	58.97 232	66.68 255	81.77 115	45.70 236	78.96 224	74.04 266	43.66 284	47.63 254	83.19 143	23.52 277	77.78 276	37.47 250	60.46 206	76.55 269
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 233	60.44 220	62.07 278	75.00 206	32.73 304	79.54 215	73.49 273	36.98 304	56.28 202	83.74 134	29.28 243	69.53 313	46.48 222	63.23 187	83.94 168

CNLPA		60.59 234	58.44 233	67.05 252	79.21 152	47.26 219	79.75 213	64.34 308	42.46 291	51.90 233	83.94 130	27.79 251	75.41 286	37.12 253	59.49 218	78.47 239

anonymousdsp		60.46 235	57.65 236	68.88 231	63.63 301	45.09 240	72.93 262	78.63 205	46.52 266	51.12 237	72.80 253	21.46 289	83.07 234	57.79 146	53.97 256	78.47 239

v52		59.82 236	56.41 245	70.06 222	61.49 310	48.67 194	69.46 286	75.80 250	52.55 234	47.49 259	68.82 275	28.60 244	85.70 195	52.13 191	51.34 268	75.80 273

V4		59.82 236	56.41 245	70.05 223	61.49 310	48.67 194	69.46 286	75.79 251	52.55 234	47.49 259	68.83 274	28.60 244	85.70 195	52.13 191	51.35 267	75.80 273

ACMH	53.70 16	59.78 238	55.94 250	71.28 200	76.59 191	48.35 205	80.15 206	76.11 245	49.74 252	41.91 286	73.45 248	16.50 311	90.31 62	31.42 277	57.63 235	75.17 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 239	57.15 240	67.09 250	66.01 289	36.86 293	80.50 197	78.64 204	45.05 275	49.05 246	73.94 239	27.28 253	86.10 186	43.96 234	49.94 272	78.31 250

MSDG		59.44 240	55.14 254	72.32 174	74.69 209	50.71 156	74.39 251	73.58 271	44.44 279	43.40 281	77.52 205	19.45 296	90.87 51	31.31 278	57.49 236	75.38 277

DP-MVS		59.24 241	56.12 248	68.63 241	88.24 16	50.35 165	82.51 161	64.43 307	41.10 293	46.70 267	78.77 188	24.75 269	88.57 117	22.26 312	56.29 241	66.96 310

OpenMVS_ROB	53.19 17	59.20 242	56.00 249	68.83 233	71.13 265	44.30 248	83.64 136	75.02 258	46.42 268	46.48 271	73.03 250	18.69 300	88.14 133	27.74 293	61.80 201	74.05 287

Anonymous20231206		59.08 243	57.59 237	63.55 272	68.77 277	32.14 307	80.26 201	79.78 183	50.00 251	49.39 244	72.39 257	26.64 257	78.36 265	33.12 272	57.94 230	80.14 228

WR-MVS_H		58.91 244	58.04 235	61.54 283	69.07 275	33.83 299	76.91 235	81.99 147	51.40 244	48.17 251	74.67 235	40.23 134	74.15 290	31.78 276	48.10 276	76.64 267

LCM-MVSNet-Re		58.82 245	56.54 243	65.68 259	79.31 151	29.09 316	61.39 306	45.79 328	60.73 120	37.65 299	72.47 255	31.42 231	81.08 243	49.66 202	70.41 136	86.87 119

Patchmatch-RL test		58.72 246	54.32 255	71.92 187	63.91 300	44.25 249	61.73 303	55.19 320	57.38 184	49.31 245	54.24 318	37.60 162	80.89 244	62.19 112	47.28 282	90.63 51

FMVSNet5		58.61 247	56.45 244	65.10 266	77.20 187	39.74 282	74.77 248	77.12 235	50.27 250	43.28 282	67.71 285	26.15 260	76.90 281	36.78 258	54.78 253	78.65 237

ACMH+	54.58 15	58.55 248	55.24 252	68.50 243	74.68 210	45.80 235	80.27 200	70.21 293	47.15 261	42.77 284	75.48 231	16.73 310	85.98 189	35.10 266	54.78 253	73.72 289

CP-MVSNet		58.54 249	57.57 238	61.46 285	68.50 279	33.96 298	76.90 236	78.60 207	51.67 243	47.83 252	76.60 217	34.99 198	72.79 301	35.45 262	47.58 279	77.64 261

RPMNet		58.49 250	53.74 259	72.77 166	73.97 218	56.57 23	60.52 307	72.39 277	35.72 309	57.49 190	58.87 312	37.73 159	80.31 251	27.01 296	59.93 211	81.42 205

PEN-MVS		58.35 251	57.15 240	61.94 280	67.55 286	34.39 296	77.01 234	78.35 209	51.87 241	47.72 253	76.73 216	33.91 206	73.75 295	34.03 268	47.17 283	77.68 259

PS-CasMVS		58.12 252	57.03 242	61.37 286	68.24 282	33.80 300	76.73 237	78.01 219	51.20 245	47.54 257	76.20 225	32.85 215	72.76 302	35.17 264	47.37 281	77.55 262

UnsupCasMVSNet_eth		57.56 253	55.15 253	64.79 268	64.57 298	33.12 301	73.17 259	83.87 115	58.98 154	41.75 287	70.03 269	22.54 281	79.92 257	46.12 226	35.31 314	81.32 210

CHOSEN 280x420		57.53 254	56.38 247	60.97 287	74.01 216	48.10 211	46.30 322	54.31 322	48.18 257	50.88 241	77.43 208	38.37 152	59.16 325	54.83 170	63.14 190	75.66 275

DTE-MVSNet		57.03 255	55.73 251	60.95 288	65.94 290	32.57 305	75.71 240	77.09 236	51.16 246	46.65 269	76.34 220	32.84 216	73.22 299	30.94 280	44.87 297	77.06 264

PatchMatch-RL		56.66 256	53.75 258	65.37 264	77.91 176	45.28 239	69.78 284	60.38 315	41.35 292	47.57 255	73.73 241	16.83 308	76.91 280	36.99 256	59.21 220	73.92 288

PatchT		56.60 257	52.97 262	67.48 247	72.94 229	46.16 233	57.30 313	73.78 269	38.77 298	54.37 213	57.26 315	37.52 164	78.06 270	32.02 274	52.79 262	78.23 255

Patchmtry		56.56 258	52.95 263	67.42 248	72.53 247	50.59 160	59.05 309	71.72 280	37.86 302	46.92 264	65.86 292	38.94 146	80.06 256	36.94 257	46.72 287	71.60 300

test_0402		56.45 259	53.03 261	66.69 254	76.78 190	50.31 168	81.76 172	69.61 295	42.79 289	43.88 277	72.13 259	22.82 280	86.46 178	16.57 328	50.94 270	63.31 319

LS3D		56.40 260	53.82 257	64.12 269	81.12 131	45.69 237	73.42 256	66.14 302	35.30 314	43.24 283	79.88 179	22.18 285	79.62 260	19.10 323	64.00 177	67.05 309

ADS-MVSNet		56.17 261	51.95 269	68.84 232	80.60 141	53.07 113	55.03 315	70.02 294	44.72 276	51.00 238	61.19 303	22.83 278	78.88 263	28.54 289	53.63 258	74.57 282

XVG-ACMP-BASELINE		56.03 262	52.85 264	65.58 260	61.91 306	40.95 276	63.36 297	72.43 276	45.20 274	46.02 273	74.09 237	9.20 325	78.12 268	45.13 228	58.27 224	77.66 260

pmmvs-eth3d		55.97 263	52.78 265	65.54 261	61.02 312	46.44 226	75.36 246	67.72 298	49.61 253	43.65 279	67.58 286	21.63 288	77.04 278	44.11 233	44.33 299	73.15 296

F-COLMAP		55.96 264	53.65 260	62.87 276	72.76 236	42.77 262	74.70 250	70.37 291	40.03 294	41.11 290	79.36 181	17.77 304	73.70 296	32.80 273	53.96 257	72.15 297

CMPMVS	40.41 21	55.34 265	52.64 266	63.46 273	60.88 313	43.84 252	61.58 305	71.06 287	30.43 321	36.33 301	74.63 236	24.14 271	75.44 285	48.05 212	66.62 157	71.12 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 266	54.07 256	58.68 293	63.14 303	25.00 322	77.69 232	74.78 259	52.64 232	43.43 280	72.39 257	26.21 259	74.76 289	29.31 282	47.05 285	76.28 271

ADS-MVSNet2		55.21 267	51.44 270	66.51 256	80.60 141	49.56 179	55.03 315	65.44 304	44.72 276	51.00 238	61.19 303	22.83 278	75.41 286	28.54 289	53.63 258	74.57 282

SixPastTwentyTwo		54.37 268	50.10 274	67.21 249	70.70 266	41.46 272	74.73 249	64.69 306	47.56 259	39.12 296	69.49 270	18.49 302	84.69 213	31.87 275	34.20 320	75.48 276

USDC		54.36 269	51.23 271	63.76 271	64.29 299	37.71 290	62.84 302	73.48 275	56.85 189	35.47 305	71.94 261	9.23 324	78.43 264	38.43 249	48.57 273	75.13 279

testgi		54.25 270	52.57 267	59.29 291	62.76 304	21.65 328	72.21 269	70.47 289	53.25 228	41.94 285	77.33 209	14.28 315	77.95 272	29.18 283	51.72 266	78.28 252

K. test v3		54.04 271	49.42 278	67.92 245	68.55 278	42.57 266	75.51 244	63.07 311	52.07 238	39.21 295	64.59 296	19.34 297	82.21 236	37.11 254	25.31 326	78.97 233

test2356		53.94 272	52.37 268	58.64 294	61.58 308	27.53 321	78.20 229	74.33 262	46.92 263	44.01 276	66.04 291	18.91 299	74.11 291	28.80 284	52.55 263	74.28 284

UnsupCasMVSNet_bld		53.86 273	50.53 273	63.84 270	63.52 302	34.75 295	71.38 274	81.92 150	46.53 265	38.95 297	57.93 313	20.55 293	80.20 255	39.91 246	34.09 321	76.57 268

YYNet1		53.82 274	49.96 275	65.41 263	70.09 271	48.95 188	72.30 267	71.66 282	44.25 280	31.89 318	63.07 300	23.73 273	73.95 293	33.26 270	39.40 307	73.34 293

MDA-MVSNet_test_wron		53.82 274	49.95 276	65.43 262	70.13 270	49.05 186	72.30 267	71.65 283	44.23 281	31.85 319	63.13 299	23.68 276	74.01 292	33.25 271	39.35 308	73.23 295

Patchmatch-test		53.33 276	48.17 280	68.81 238	73.31 222	42.38 267	42.98 326	58.23 317	32.53 317	38.79 298	70.77 266	39.66 142	73.51 297	25.18 300	52.06 265	90.55 52

LTVRE_ROB	45.45 19	52.73 277	49.74 277	61.69 282	69.78 272	34.99 294	44.52 324	67.60 299	43.11 288	43.79 278	74.03 238	18.54 301	81.45 241	28.39 291	57.94 230	68.62 307
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
EU-MVSNet		52.63 278	50.72 272	58.37 295	62.69 305	28.13 318	72.60 263	75.97 248	30.94 320	40.76 292	72.11 260	20.16 294	70.80 309	35.11 265	46.11 289	76.19 272

OurMVSNet-221017-0		52.39 279	48.73 279	63.35 274	65.21 294	38.42 287	68.54 289	64.95 305	38.19 299	39.57 294	71.43 263	13.23 317	79.92 257	37.16 252	40.32 306	71.72 299

JIA-IIPM		52.33 280	47.77 283	66.03 258	71.20 264	46.92 221	40.00 330	76.48 243	37.10 303	46.73 266	37.02 326	32.96 214	77.88 273	35.97 260	52.45 264	73.29 294

MDA-MVSNet-bldmvs		51.56 281	47.75 284	63.00 275	71.60 261	47.32 218	69.70 285	72.12 278	43.81 283	27.65 324	63.38 298	21.97 287	75.96 283	27.30 295	32.19 322	65.70 313

COLMAP_ROB	43.60 20	50.90 282	48.05 281	59.47 290	67.81 284	40.57 281	71.25 275	62.72 313	36.49 308	36.19 302	73.51 246	13.48 316	73.92 294	20.71 318	50.26 271	63.92 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 283	47.81 282	57.96 296	61.53 309	27.80 320	67.40 290	74.06 265	43.25 287	33.31 317	65.38 295	16.03 312	71.34 308	21.80 313	47.55 280	74.75 280

MVS-HIRNet		49.01 284	44.71 289	61.92 281	76.06 194	46.61 224	63.23 299	54.90 321	24.77 325	33.56 314	36.60 327	21.28 290	75.88 284	29.49 281	62.54 198	63.26 320

testus		48.97 285	46.53 286	56.31 301	57.39 319	24.08 324	73.40 257	70.45 290	43.37 286	35.52 304	63.95 297	4.77 336	71.36 307	24.88 301	45.02 296	73.50 292

new-patchmatchnet		48.21 286	46.55 285	53.18 305	57.73 317	18.19 337	70.24 280	71.02 288	45.70 270	33.70 313	60.23 305	18.00 303	69.86 312	27.97 292	34.35 318	71.49 302

TinyColmap		48.15 287	44.49 291	59.13 292	65.73 291	38.04 288	63.34 298	62.86 312	38.78 297	29.48 322	67.23 289	6.46 331	73.30 298	24.59 302	41.90 303	66.04 311

1111		48.00 288	46.30 287	53.08 306	55.68 320	20.86 331	70.41 278	76.03 246	36.88 305	34.86 307	59.55 309	23.72 274	68.13 314	20.82 316	38.76 310	70.25 304

AllTest		47.32 289	44.66 290	55.32 302	65.08 295	37.50 291	62.96 301	54.25 323	35.45 312	33.42 315	72.82 251	9.98 321	59.33 323	24.13 303	43.84 300	69.13 305

test1235678		47.09 290	43.82 293	56.91 299	53.18 323	24.90 323	71.93 271	70.31 292	39.54 295	31.44 320	56.59 317	9.50 323	71.55 306	22.63 311	39.24 309	74.28 284

LP		47.05 291	42.23 296	61.53 284	72.04 256	49.37 183	49.48 319	65.50 303	34.57 315	34.29 311	52.30 320	17.73 305	75.32 288	17.56 326	36.57 312	59.91 321

PM-MVS		46.92 292	43.76 294	56.41 300	52.18 324	32.26 306	63.21 300	38.18 334	37.99 301	40.78 291	66.20 290	5.09 334	65.42 319	48.19 211	41.99 302	71.54 301

Anonymous20231211		46.87 293	43.27 295	57.67 297	57.88 316	30.12 311	73.14 260	64.16 309	33.43 316	34.34 310	59.42 311	12.15 318	77.99 271	19.64 322	35.23 316	64.90 315

testpf		45.92 294	45.81 288	46.27 313	69.56 273	27.86 319	23.18 335	73.91 268	44.10 282	36.99 300	57.16 316	20.56 292	71.77 305	42.17 242	44.64 298	39.18 329

RPSCF		45.77 295	44.13 292	50.68 308	57.67 318	29.66 312	54.92 317	45.25 330	26.69 324	45.92 274	75.92 229	17.43 307	45.70 335	27.44 294	45.95 292	76.67 266

pmmvs3		45.53 296	41.55 298	57.44 298	48.97 327	39.68 283	70.06 281	57.66 318	28.32 323	34.06 312	57.29 314	8.50 326	66.85 318	34.86 267	34.26 319	65.80 312

N_pmnet		41.25 297	39.77 299	45.66 315	68.50 279	0.82 346	72.51 265	0.38 348	35.61 310	35.26 306	61.51 302	20.07 295	67.74 316	23.51 305	40.63 304	68.42 308

TDRefinement		40.91 298	38.37 300	48.55 311	50.45 325	33.03 303	58.98 310	50.97 326	28.50 322	29.89 321	67.39 287	6.21 333	54.51 327	17.67 325	35.25 315	58.11 322

testmv		39.64 299	36.01 301	50.55 309	42.18 331	21.56 329	64.81 294	66.88 301	32.22 318	22.25 328	47.47 322	4.33 338	64.81 320	17.71 324	26.22 325	65.29 314

.test1245		38.91 300	41.99 297	29.67 325	55.68 320	20.86 331	70.41 278	76.03 246	36.88 305	34.86 307	59.55 309	23.72 274	68.13 314	20.82 316	0.00 342	0.02 340

DSMNet-mixed		38.35 301	35.36 302	47.33 312	48.11 328	14.91 339	37.87 331	36.60 336	19.18 329	34.37 309	59.56 308	15.53 313	53.01 329	20.14 320	46.89 286	74.07 286

test12356		37.84 302	35.07 303	46.18 314	45.03 330	8.02 344	57.70 312	62.67 314	31.83 319	22.78 326	50.25 321	4.46 337	66.95 317	17.25 327	23.62 328	63.57 318

no-one		37.21 303	31.48 307	54.40 304	39.62 335	31.91 309	45.68 323	67.42 300	35.54 311	14.59 331	35.91 329	7.35 327	73.20 300	22.98 306	14.23 331	58.09 323

FPMVS		35.40 304	33.67 304	40.57 317	46.34 329	28.74 317	41.05 328	57.05 319	20.37 328	22.27 327	53.38 319	6.87 329	44.94 336	8.62 334	47.11 284	48.01 327

ANet_high		34.39 305	29.59 308	48.78 310	30.34 339	22.28 326	55.53 314	63.79 310	38.11 300	15.47 330	36.56 328	6.94 328	59.98 322	13.93 330	5.64 341	64.08 316

new_pmnet		33.56 306	31.89 306	38.59 318	49.01 326	20.42 333	51.01 318	37.92 335	20.58 326	23.45 325	46.79 323	6.66 330	49.28 332	20.00 321	31.57 324	46.09 328

LF4IMVS		33.04 307	32.55 305	34.52 322	40.96 332	22.03 327	44.45 325	35.62 337	20.42 327	28.12 323	62.35 301	5.03 335	31.88 341	21.61 315	34.42 317	49.63 326

LCM-MVSNet		28.07 308	23.85 312	40.71 316	27.46 341	18.93 336	30.82 333	46.19 327	12.76 334	16.40 329	34.70 331	1.90 343	48.69 333	20.25 319	24.22 327	54.51 324

pcd1.5k->3k		27.74 309	27.68 309	27.93 327	73.75 220	0.00 348	0.00 340	85.50 68	0.00 342	0.00 345	0.00 346	26.52 258	0.00 345	0.00 344	63.37 185	83.79 171

Gipuma		27.47 310	24.26 311	37.12 320	60.55 314	29.17 315	11.68 338	60.00 316	14.18 332	10.52 335	15.12 338	2.20 342	63.01 321	8.39 335	35.65 313	19.18 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		26.71 311	22.98 314	37.87 319	36.89 336	8.51 343	42.51 327	29.32 342	19.09 330	13.01 332	37.54 325	2.23 341	53.11 328	14.54 329	11.71 332	51.99 325

PNet_i23d		25.11 312	23.09 313	31.17 324	40.18 333	21.30 330	57.99 311	33.28 339	13.77 333	9.94 336	30.29 333	0.45 347	43.74 337	13.61 332	8.28 334	28.46 332

PMVS	19.57 22	25.07 313	22.43 315	32.99 323	23.12 342	22.98 325	40.98 329	35.19 338	15.99 331	11.95 334	35.87 330	1.47 345	49.29 331	5.41 339	31.90 323	26.70 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		19.94 314	14.87 318	35.13 321	22.47 343	19.80 334	25.80 334	38.64 333	7.61 338	4.88 341	13.58 341	0.23 348	48.42 334	13.11 333	7.53 336	37.18 330

E-PMN		19.16 315	18.40 316	21.44 328	36.19 337	13.63 340	47.59 320	30.89 340	10.73 335	5.91 339	16.59 336	3.66 340	39.77 338	5.95 338	8.14 335	10.92 336

EMVS		18.42 316	17.66 317	20.71 329	34.13 338	12.64 341	46.94 321	29.94 341	10.46 337	5.58 340	14.93 339	4.23 339	38.83 339	5.24 340	7.51 338	10.67 337

cdsmvs_eth3d_5k		18.33 317	24.44 310	0.00 335	0.00 347	0.00 348	0.00 340	89.40 12	0.00 342	0.00 345	92.02 20	38.55 150	0.00 345	0.00 344	0.00 342	0.00 343

MVE	16.60 23	17.34 318	13.39 319	29.16 326	28.43 340	19.72 335	13.73 337	23.63 343	7.23 339	7.96 337	21.41 334	0.80 346	36.08 340	6.97 336	10.39 333	31.69 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 319	10.68 320	5.73 332	2.49 345	4.21 345	10.48 339	18.04 344	0.34 341	12.59 333	20.49 335	11.39 319	7.03 344	13.84 331	6.46 340	5.95 338

wuyk23d		9.11 320	8.77 322	10.15 331	40.18 333	16.76 338	20.28 336	1.01 347	2.58 340	2.66 342	0.98 343	0.23 348	12.49 343	4.08 341	6.90 339	1.19 339

ab-mvs-re		7.68 321	10.24 321	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	92.12 17	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

testmvs		6.14 322	8.18 323	0.01 333	0.01 346	0.00 348	73.40 257	0.00 349	0.00 342	0.02 343	0.15 344	0.00 350	0.00 345	0.02 342	0.00 342	0.02 340

test123		6.01 323	8.01 324	0.01 333	0.00 347	0.01 347	71.93 271	0.00 349	0.00 342	0.02 343	0.11 345	0.00 350	0.00 345	0.02 342	0.00 342	0.02 340

pcd_1.5k_mvsjas		3.15 324	4.20 325	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	37.77 156	0.00 345	0.00 344	0.00 342	0.00 343

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 347	0.00 348	0.00 340	0.00 349	0.00 342	0.00 345	0.00 346	0.00 350	0.00 345	0.00 344	0.00 342	0.00 343

ESAPD								88.96 16

sam_mvs1												38.86 148

sam_mvs												35.99 191

semantic-postprocess				60.08 289	70.68 268	45.07 241		74.25 263	43.54 285	50.02 243	73.73 241	32.22 223	56.74 326	51.06 197	53.60 260	78.42 244

ambc				62.06 279	53.98 322	29.38 314	35.08 332	79.65 187		41.37 288	59.96 306	6.27 332	82.15 237	35.34 263	38.22 311	74.65 281

MTGPA								81.31 160

test_post1							70.84 277				14.72 340	34.33 202	83.86 226	48.80 206

test_post											16.22 337	37.52 164	84.72 212

patchmatchnet-post											59.74 307	38.41 151	79.91 259

GG-mvs-BLEND				77.77 63	86.68 26	50.61 158	68.67 288	88.45 25		68.73 62	87.45 99	59.15 4	90.67 53	54.83 170	87.67 11	92.03 25

MTMP								15.34 345

gm-plane-assit					83.24 78	54.21 71			70.91 11		88.23 86		95.25 5	66.37 79

test9_res														78.72 15	85.44 25	91.39 38

TEST9					85.68 35	55.42 37	87.59 45	84.00 110	57.72 180	72.99 33	90.98 38	44.87 66	88.58 114

test_8					85.72 34	55.31 41	87.60 42	83.88 114	57.84 177	72.84 36	90.99 37	44.99 63	88.34 125

agg_prior2														75.65 29	85.11 29	91.01 44

agg_prior					85.64 38	54.92 54		83.61 120		72.53 40			88.10 136

TestCases				55.32 302	65.08 295	37.50 291		54.25 323	35.45 312	33.42 315	72.82 251	9.98 321	59.33 323	24.13 303	43.84 300	69.13 305

test_prior4						56.39 28	87.15 56

test_prior2							89.04 30		61.88 104	73.55 28	91.46 34	48.01 39		74.73 35	85.46 23

test_prior				78.39 50	86.35 29	54.91 56		85.45 70					89.70 78			90.55 52

旧先验2							81.73 173		45.53 272	74.66 21			70.48 311	58.31 140

新几何2							81.61 180

新几何1				73.30 159	83.10 80	53.48 83		71.43 285	45.55 271	66.14 82	87.17 102	33.88 208	80.54 248	48.50 208	80.33 61	85.88 138

旧先验1					81.57 121	47.48 215		71.83 279			88.66 79	36.94 175			78.34 76	88.67 95

无先验							85.19 92	78.00 220	49.08 255				85.13 204	52.78 185		87.45 113

原ACMM2							83.77 134

原ACMM1				76.13 96	84.89 54	54.59 65		85.26 78	51.98 239	66.70 75	87.07 104	40.15 136	89.70 78	51.23 195	85.06 30	84.10 160

test222					79.36 148	50.97 154	77.99 230	67.84 297	42.54 290	62.84 124	86.53 110	30.26 237			76.91 89	85.23 147

testdata2													77.81 275	45.64 227

segment_acmp												44.97 65

testdata				67.08 251	77.59 178	45.46 238		69.20 296	44.47 278	71.50 50	88.34 82	31.21 232	70.76 310	52.20 190	75.88 97	85.03 149

testdata1							77.55 233		64.14 73

test12				79.24 29	86.89 24	56.08 32		85.16 82		72.27 45		47.15 45	91.10 45		85.93 19	90.54 55

plane_prior7					77.95 173	48.46 204

plane_prior6					78.42 170	49.39 182						36.04 188

plane_prior5								82.59 138					88.30 129	65.46 88	72.34 126	84.49 155

plane_prior4											83.28 141

plane_prior3						48.95 188			64.01 74	62.15 127

plane_prior2							85.76 77		63.60 84

plane_prior1					78.31 172

plane_prior						49.57 177	87.43 49		64.57 66						72.84 120

n2								0.00 349

nn								0.00 349

door-mid								41.31 332

lessismore_v0				67.98 244	64.76 297	41.25 273		45.75 329		36.03 303	65.63 294	19.29 298	84.11 225	35.67 261	21.24 329	78.59 238

LGP-MVS_train				72.02 180	74.42 212	48.60 196		80.64 173	54.69 219	53.75 218	83.83 132	25.73 263	86.98 163	60.33 128	64.71 170	80.48 224

test11								84.25 103

door								43.27 331

HQP5-MVS						51.56 142

HQP-NCC					79.02 155		88.00 36		65.45 52	64.48 104

ACMP_Plane					79.02 155		88.00 36		65.45 52	64.48 104

BP-MVS														66.70 77

HQP4-MVS										64.47 107			88.61 112			84.91 152

HQP3-MVS								83.68 117							73.12 116

HQP2-MVS												37.35 167

NP-MVS					78.76 159	50.43 163					85.12 121

MDTV_nov1_ep13_2view						43.62 254	71.13 276		54.95 217	59.29 161		36.76 178		46.33 224		87.32 115

MDTV_nov1_ep13			61.56 207		81.68 116	55.12 48	72.41 266	78.18 212	59.19 143	58.85 169	69.29 272	34.69 199	86.16 183	36.76 259	62.96 192

ACMMP++_ref															63.20 188

ACMMP++															59.38 219

Test By Simon												39.38 143

ITE_SJBPF				51.84 307	58.03 315	31.94 308		53.57 325	36.67 307	41.32 289	75.23 233	11.17 320	51.57 330	25.81 299	48.04 277	72.02 298

DeepMVS_CX				13.10 330	21.34 344	8.99 342		10.02 346	10.59 336	7.53 338	30.55 332	1.82 344	14.55 342	6.83 337	7.52 337	15.75 335

High-resolution multi-view benchmark