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 86	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 60	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 56	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 54	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 42	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 47	70.62 12	80.75 4	93.22 4	37.77 155	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 50	70.30 15	80.77 3	93.07 8	37.63 160	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 96	72.51 8	79.22 8	93.09 6	42.01 116	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	41.38 125	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 37	55.91 207	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 84	55.24 43	91.57 7	84.82 88	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	84.07 107	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	81.33 128	56.31 29	81.59 180	86.41 57	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 95	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 69	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 63	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	56.58 197	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 69	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	58.14 170	73.60 27	93.31 3	43.14 93	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	80.98 131	55.31 41	89.76 24	86.91 49	62.94 93	71.65 49	91.56 31	42.33 108	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	79.69 145	54.74 59	90.56 15	83.99 111	68.26 24	74.10 25	90.91 42	42.14 112	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	84.00 109	57.84 176	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	80.23 202	91.64 1	58.65 160	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	80.70 137	55.02 51	86.39 66	86.71 52	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	80.20 143	59.11 5	86.86 62	86.23 61	68.60 21	70.18 57	88.84 78	51.57 22	87.16 158	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	41.05 126	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	83.95 112	57.78 178	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	83.61 119	57.99 173	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	84.24 104	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	81.92 112	54.67 63	84.06 128	85.35 74	61.10 114	72.99 33	91.50 32	40.25 132	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 85	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 81	62.84 2	85.73 80	85.39 72	69.79 16	64.87 99	83.49 138	41.52 124	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	82.45 140	56.79 192	74.48 23	91.81 23	43.72 84	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 84	53.79 222	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 64	65.33 58	72.37 43	89.34 71	32.52 218	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 94	58.62 161	70.75 53	90.85 44	43.10 97	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 80	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	80.69 138	52.40 127	90.69 13	86.20 62	74.40 3	65.01 97	88.93 75	42.05 115	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	81.37 127	52.65 123	84.34 121	84.46 97	61.16 112	69.14 59	91.76 26	39.98 139	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	81.92 112	57.42 14	85.21 91	76.71 237	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 66	63.76 80	67.55 69	90.73 47	43.48 89	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 90	53.05 114	85.63 82	83.50 121	57.86 175	67.25 72	90.24 56	43.38 90	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 98	59.00 152	65.78 86	90.56 50	40.70 129	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 92	55.40 40	83.08 150	88.61 21	67.61 31	59.85 150	88.66 79	34.57 199	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 98	56.82 190	65.78 86	90.56 50	40.70 129	90.90 49	69.18 65	80.88 52	89.71 71

VDDNet		74.37 57	72.13 72	81.09 11	79.58 146	56.52 25	90.02 17	86.70 53	52.61 232	71.23 52	87.20 100	31.75 228	93.96 14	74.30 39	75.77 99	92.79 16

MSLP-MVS++		74.21 59	72.25 70	80.11 20	81.45 125	56.47 26	86.32 68	79.65 186	58.19 169	66.36 80	92.29 15	36.11 183	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 90	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	84.27 101	57.51 182	65.77 88	87.77 94	41.61 123	95.97 2	51.71 193	82.63 42	86.94 118

MPTG		74.15 62	73.11 60	77.27 76	81.54 121	53.57 81	84.02 130	81.31 159	59.41 136	68.39 64	90.96 40	36.07 184	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 83	55.50 36	90.24 16	88.65 19	67.14 34	61.38 133	81.74 162	53.21 14	94.28 12	60.45 125	62.41 198	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	75.56 241	86.36 59	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	82.36 103	56.58 22	75.56 241	85.30 75	66.06 46	70.50 56	76.88 213	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	84.29 100	56.82 190	65.12 93	90.45 52	37.24 170	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	84.09 106	56.83 189	65.10 94	90.45 52	37.34 168	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 99	50.05 172	84.67 117	79.36 192	69.72 17	75.39 18	90.03 62	29.41 240	85.93 192	67.99 71	79.11 72	90.22 64

MVSFormer		73.53 69	72.19 71	77.57 67	83.02 84	55.24 43	81.63 177	81.44 156	50.28 247	76.67 15	90.91 42	44.82 67	86.11 183	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	84.24 104	65.40 55	69.09 60	80.86 173	46.70 49	88.13 134	75.43 31	65.92 166	81.33 207

PVSNet_Blended_VisFu		73.40 71	72.44 65	76.30 91	81.32 129	54.70 61	85.81 75	78.82 199	63.70 81	64.53 103	85.38 119	47.11 46	87.38 155	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 45	67.06 35	63.88 113	81.66 163	52.77 16	90.44 57	64.66 96	64.69 171	83.84 170

EI-MVSNet-Vis-set		73.19 73	72.60 62	74.99 119	82.56 100	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	82.85 134	61.62 108	68.24 66	90.33 55	28.35 245	87.78 146	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	83.20 128	58.03 171	63.91 111	89.63 68	35.50 192	89.78 75	65.50 85	80.50 58	88.16 101

TESTMET0.1,1		72.86 76	72.33 66	74.46 133	81.98 110	50.77 155	85.13 94	85.47 68	66.09 44	67.30 70	83.69 136	37.27 169	83.57 230	65.06 95	78.97 73	89.05 87

Fast-Effi-MVS+		72.73 77	71.15 86	77.48 70	82.75 94	54.76 58	86.77 63	80.64 172	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	81.54 121	53.57 81	67.06 290	81.31 159	59.41 136	68.39 64	90.96 40	36.07 184	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 87	52.82 120	83.07 151	82.14 141	56.51 202	63.18 120	89.81 65	35.68 191	89.76 77	67.30 74	80.19 62	87.83 106

HPM-MVS		72.60 79	71.50 79	75.89 102	82.02 109	51.42 147	80.70 195	83.05 130	56.12 206	64.03 110	89.53 69	37.55 162	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 89	52.32 131	86.93 61	82.48 139	55.15 214	63.65 116	90.44 54	35.03 196	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	81.60 117	49.29 184	81.85 171	88.64 20	65.29 60	65.05 95	88.29 84	43.18 91	91.83 37	63.74 98	67.97 151	81.75 200

MS-PatchMatch		72.34 83	71.26 83	75.61 105	82.38 102	55.55 35	88.00 36	89.95 11	65.38 56	56.51 200	80.74 174	32.28 221	92.89 22	57.95 144	88.10 9	78.39 244

HQP-MVS		72.34 83	71.44 81	75.03 116	79.02 154	51.56 142	88.00 36	83.68 116	65.45 52	64.48 104	85.13 120	37.35 166	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 91	54.72 60	78.43 227	81.54 155	63.77 79	61.69 132	79.32 181	51.11 24	85.31 200	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 58	59.01 151	58.49 175	88.32 83	32.40 219	91.27 43	57.04 152	82.15 47	90.38 61

nrg030		72.27 87	71.56 78	74.42 134	75.93 196	50.60 159	86.97 58	83.21 127	62.75 95	67.15 73	84.38 126	50.07 30	86.66 171	71.19 56	62.37 199	85.99 135

VPNet		72.07 88	71.42 82	74.04 142	78.64 164	47.17 220	89.91 22	87.97 33	72.56 7	64.66 101	85.04 122	41.83 119	88.33 127	61.17 118	60.97 203	86.62 126

DP-MVS Recon		71.99 89	70.31 92	77.01 82	90.65 4	53.44 86	89.37 26	82.97 132	56.33 204	63.56 118	89.47 70	34.02 203	92.15 35	54.05 176	72.41 124	85.43 146

QAPM		71.88 90	69.33 107	79.52 25	82.20 104	54.30 68	86.30 69	88.77 18	56.61 196	59.72 152	87.48 98	33.90 206	95.36 4	47.48 215	81.49 50	88.90 90

PAPM_NR		71.80 91	69.98 99	77.26 78	81.54 121	53.34 94	78.60 225	85.25 78	53.46 224	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 98	52.06 133	84.45 119	81.78 152	55.59 211	62.05 130	89.68 67	33.48 209	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	77.26 183	53.15 107	85.34 87	79.37 189	55.83 208	72.54 37	90.19 57	22.38 281	86.66 171	73.28 48	76.39 92	86.85 121

xiu_mvs_v1_base		71.60 93	70.29 93	75.55 106	77.26 183	53.15 107	85.34 87	79.37 189	55.83 208	72.54 37	90.19 57	22.38 281	86.66 171	73.28 48	76.39 92	86.85 121

xiu_mvs_v1_base_debi		71.60 93	70.29 93	75.55 106	77.26 183	53.15 107	85.34 87	79.37 189	55.83 208	72.54 37	90.19 57	22.38 281	86.66 171	73.28 48	76.39 92	86.85 121

test_normal		71.31 96	68.95 113	78.39 50	72.30 253	54.25 69	81.67 175	84.05 108	65.94 50	51.31 235	78.09 199	36.06 186	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	72.76 235	54.08 74	81.72 174	83.22 126	65.75 51	51.94 231	78.47 193	36.01 189	90.31 62	73.33 47	77.60 80	90.40 59

EPP-MVSNet		71.14 98	70.07 98	74.33 136	79.18 153	46.52 225	83.81 133	86.49 55	56.32 205	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	78.75 159	44.43 247	87.64 41	90.02 9	63.97 75	65.02 96	81.58 164	42.14 112	87.42 154	63.42 100	63.38 183	85.63 144

1314		71.11 100	69.41 104	76.22 94	79.32 149	50.49 162	80.23 202	85.14 83	59.44 135	58.93 165	88.89 77	33.83 208	89.60 82	61.49 116	77.42 84	88.57 99

tpmrst		71.04 101	69.77 101	74.86 126	83.19 78	55.86 33	75.64 240	78.73 202	67.88 26	64.99 98	73.73 240	49.96 31	79.56 260	65.92 82	67.85 153	89.14 86

MVP-Stereo		70.97 102	70.44 90	72.59 170	76.03 195	51.36 148	85.02 109	86.99 48	60.31 125	56.53 199	78.92 186	40.11 136	90.00 70	60.00 131	90.01 2	76.41 269
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	77.95 172	49.57 177	85.76 77	82.59 137	63.60 84	62.15 127	83.28 141	36.04 187	88.30 129	65.46 88	72.34 126	84.49 155

tpm2		70.82 104	68.44 116	77.98 60	80.78 135	56.11 31	74.21 251	81.28 162	60.24 126	68.04 67	75.27 231	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	81.52 124	51.92 136	83.43 142	83.03 131	56.67 195	58.80 170	88.91 76	31.92 226	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	75.55 200	51.34 149	86.05 73	83.29 125	61.94 103	62.95 123	85.77 115	34.15 202	88.44 121	65.44 91	71.07 132	82.99 184

ab-mvs		70.65 107	69.11 110	75.29 112	80.87 134	46.23 232	73.48 254	85.24 79	59.99 128	66.65 76	80.94 172	43.13 94	88.69 109	63.58 99	68.07 149	90.95 47

Vis-MVSNet		70.61 108	69.34 106	74.42 134	80.95 133	48.49 201	86.03 74	77.51 228	58.74 159	65.55 90	87.78 93	34.37 200	85.95 191	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	81.59 118	39.02 283	80.78 194	84.71 93	59.34 139	66.61 78	88.09 88	37.17 171	85.52 196	61.82 115	71.02 133	90.20 65

CDS-MVSNet		70.48 110	69.43 103	73.64 153	77.56 178	48.83 192	83.51 141	77.45 229	63.27 88	62.33 126	85.54 118	43.85 78	83.29 232	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	77.64 176	42.88 259	85.06 106	87.50 43	62.58 96	62.66 125	82.34 156	43.64 86	89.83 74	58.42 138	63.70 179	85.96 137

PCF-MVS	61.03 10	70.10 112	68.40 117	75.22 115	77.15 187	51.99 135	79.30 222	82.12 145	56.47 203	61.88 131	86.48 112	43.98 77	87.24 157	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	79.33 194	58.96 154	63.63 117	86.77 106	33.29 211	90.30 65	44.63 231	73.96 109	87.30 116

1112_ss		70.05 114	69.37 105	72.10 176	80.77 136	42.78 260	85.12 97	76.75 236	59.69 131	61.19 135	92.12 17	47.48 42	83.84 226	53.04 182	68.21 148	89.66 73

diffmvs		70.02 115	68.35 118	75.03 116	79.19 152	51.48 144	78.50 226	76.65 239	59.71 130	67.10 74	80.32 176	42.81 104	87.12 159	58.48 136	72.37 125	86.49 128

BH-w/o		70.02 115	68.51 115	74.56 132	82.77 93	50.39 164	86.60 64	78.14 213	59.77 129	59.65 153	85.57 117	39.27 144	87.30 156	49.86 201	74.94 106	85.99 135

tpmp4_e23		70.01 117	67.13 144	78.65 42	81.93 111	57.90 10	73.99 252	81.35 158	60.61 122	65.28 92	73.78 239	52.48 18	88.60 113	48.40 210	66.35 163	89.44 77

FIs		70.00 118	70.24 96	69.30 227	77.93 174	38.55 285	83.99 131	87.72 39	66.86 37	57.66 186	84.17 128	52.28 19	85.31 200	52.72 188	68.80 144	84.02 162

OpenMVS	61.00 11	69.99 119	67.55 137	77.30 74	78.37 170	54.07 75	84.36 120	85.76 65	57.22 185	56.71 197	87.67 95	30.79 233	92.83 23	43.04 237	84.06 38	85.01 150

HyFIR lowres test		69.94 120	67.58 135	77.04 80	77.11 188	57.29 15	81.49 183	79.11 197	58.27 167	58.86 168	80.41 175	42.33 108	86.96 164	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	83.68 116	53.70 223	55.68 206	85.60 116	30.07 238	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 88	48.48 202	87.40 50	80.86 169	57.15 186	64.61 102	87.08 103	32.67 217	89.64 81	46.38 223	71.55 130	87.68 110

EI-MVSNet		69.70 123	68.70 114	72.68 168	75.00 205	48.90 190	79.54 214	87.16 45	61.05 115	63.88 113	83.74 134	45.87 55	90.44 57	57.42 150	64.68 172	78.70 234

test-LLR		69.65 124	69.01 111	71.60 195	78.67 161	48.17 209	85.13 94	79.72 183	59.18 145	63.13 121	82.58 151	36.91 175	80.24 252	60.56 123	75.17 103	86.39 132

APD-MVS_3200maxsize		69.62 125	68.23 120	73.80 150	81.58 119	48.22 208	81.91 169	79.50 188	48.21 255	64.24 109	89.75 66	31.91 227	87.55 151	63.08 101	73.85 111	85.64 143

v2v482		69.55 126	67.64 131	75.26 114	72.32 252	53.83 76	84.93 111	81.94 147	65.37 57	60.80 143	79.25 182	41.62 122	88.98 99	63.03 102	59.51 216	82.98 185

TAMVS		69.51 127	68.16 121	73.56 156	76.30 191	48.71 193	82.57 159	77.17 233	62.10 101	61.32 134	84.23 127	41.90 117	83.46 231	54.80 172	73.09 118	88.50 100

v1141		69.50 128	67.67 128	74.98 120	72.73 237	53.41 89	85.08 103	82.14 141	64.79 64	60.88 139	78.19 196	43.09 98	89.04 90	62.51 107	59.61 213	82.47 192

divwei89l23v2f112		69.50 128	67.67 128	74.98 120	72.72 238	53.41 89	85.08 103	82.14 141	64.79 64	60.88 139	78.19 196	43.11 95	89.04 90	62.51 107	59.62 212	82.48 191

v1		69.49 130	67.67 128	74.98 120	72.69 239	53.41 89	85.08 103	82.13 144	64.80 63	60.87 141	78.19 196	43.11 95	89.04 90	62.51 107	59.61 213	82.49 190

v1neww		69.43 131	67.62 132	74.89 123	72.90 230	53.31 97	85.12 97	81.11 163	64.29 69	61.00 136	78.53 189	42.88 101	88.98 99	62.66 105	60.06 207	82.37 194

v7new		69.43 131	67.62 132	74.89 123	72.90 230	53.31 97	85.12 97	81.11 163	64.29 69	61.00 136	78.53 189	42.88 101	88.98 99	62.66 105	60.06 207	82.37 194

v6		69.43 131	67.61 134	74.88 125	72.87 234	53.30 101	85.12 97	81.10 165	64.29 69	60.99 138	78.52 191	42.88 101	88.98 99	62.67 104	60.06 207	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	77.80 221	66.42 38	59.80 151	84.95 123	22.14 285	80.44 249	55.03 168	75.11 105	88.62 97

MVS_111021_LR		69.07 135	67.91 123	72.54 171	77.27 182	49.56 179	79.77 210	73.96 266	59.33 141	60.73 144	87.82 91	30.19 237	81.53 239	69.94 62	72.19 128	86.53 127

mvs-test1		69.04 136	67.57 136	73.44 158	75.17 201	51.68 141	86.57 65	74.48 259	62.15 99	62.07 129	85.79 114	30.59 234	87.48 152	65.40 92	65.94 165	81.18 211

GA-MVS		69.04 136	66.70 152	76.06 98	75.11 203	52.36 129	83.12 149	80.23 176	63.32 87	60.65 145	79.22 183	30.98 232	88.37 123	61.25 117	66.41 159	87.46 112

cascas		69.01 138	66.13 159	77.66 65	79.36 147	55.41 39	86.99 57	83.75 115	56.69 194	58.92 166	81.35 165	24.31 269	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 73	60.90 117	57.58 187	79.15 184	42.16 111	86.77 167	47.25 217	63.40 180	84.27 159

UniMVSNet_NR-MVSNet		68.82 140	68.29 119	70.40 214	75.71 199	42.59 262	84.23 123	86.78 51	66.31 40	58.51 172	82.45 153	51.57 22	84.64 213	53.11 180	55.96 243	83.96 167

v1144		68.81 141	66.82 146	74.80 129	72.34 251	53.46 84	84.68 116	81.77 153	64.25 72	60.28 148	77.91 200	40.23 133	88.95 103	60.37 126	59.52 215	81.97 197

IS-MVSNet		68.80 142	67.55 137	72.54 171	78.50 167	43.43 255	81.03 189	79.35 193	59.12 149	57.27 195	86.71 107	46.05 53	87.70 148	44.32 232	75.60 100	86.49 128

1121		68.79 143	66.77 149	74.82 127	83.08 82	53.46 84	80.23 202	71.53 283	45.47 272	66.31 81	87.19 101	34.02 203	85.13 203	52.78 185	80.36 60	85.87 139

PS-MVSNAJss		68.78 144	67.17 143	73.62 155	73.01 225	48.33 207	84.95 110	84.81 89	59.30 142	58.91 167	79.84 179	37.77 155	88.86 106	62.83 103	63.12 190	83.67 173

v7		68.76 145	66.79 148	74.68 130	72.60 242	53.37 92	84.72 115	80.88 168	63.80 78	60.43 147	78.21 195	40.05 138	88.89 105	60.34 127	60.07 206	81.77 199

thres200		68.71 146	67.27 142	73.02 161	84.73 55	46.76 222	85.03 108	87.73 38	62.34 98	59.87 149	83.45 139	43.15 92	88.32 128	31.25 279	67.91 152	83.98 165

UGNet		68.71 146	67.11 145	73.50 157	80.55 142	47.61 214	84.08 126	78.51 207	59.45 134	65.68 89	82.73 149	23.78 271	85.08 205	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	67.78 284	53.34 94	79.42 217	82.84 135	65.96 49	46.54 269	76.15 225	25.16 265	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	71.89 272	81.91 150	61.72 107	60.85 142	72.49 253	36.21 182	87.06 161	47.32 216	71.62 129	89.17 85

test-mter		68.36 150	67.29 141	71.60 195	78.67 161	48.17 209	85.13 94	79.72 183	53.38 225	63.13 121	82.58 151	27.23 253	80.24 252	60.56 123	75.17 103	86.39 132

tpm		68.36 150	67.48 139	70.97 205	79.93 144	51.34 149	76.58 237	78.75 201	67.73 28	63.54 119	74.86 233	48.33 35	72.36 303	53.93 177	63.71 178	89.21 83

BH-untuned		68.28 152	66.40 153	73.91 145	81.62 116	50.01 173	85.56 85	77.39 230	57.63 181	57.47 192	83.69 136	36.36 181	87.08 160	44.81 230	73.08 119	84.65 154

v148		68.24 153	66.35 154	73.88 146	71.76 257	51.47 145	84.23 123	81.90 151	63.69 82	58.94 164	76.44 217	43.72 84	87.78 146	60.63 122	55.86 245	82.39 193

abl_6		68.03 154	66.15 158	73.66 152	78.54 166	48.48 202	79.77 210	78.04 217	47.39 259	63.70 115	88.25 85	28.21 246	89.06 87	60.17 130	71.25 131	83.45 175

v1192		67.96 155	65.74 166	74.63 131	71.79 256	53.43 88	84.06 128	80.99 167	63.19 90	59.56 156	77.46 206	37.50 165	88.65 110	58.20 141	58.93 221	81.79 198

v144192		67.86 156	65.76 165	74.16 139	71.68 258	53.09 111	84.14 125	80.83 170	62.85 94	59.21 162	77.28 209	39.30 143	88.00 139	58.67 135	57.88 232	81.40 206

HPM-MVS_fast		67.86 156	66.28 155	72.61 169	80.67 139	48.34 206	81.18 186	75.95 248	50.81 246	59.55 157	88.05 90	27.86 248	85.98 188	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	76.63 240	49.30 253	57.80 182	86.65 109	29.39 241	88.94 104	45.10 229	70.21 137	81.06 212

UniMVSNet (Re)		67.71 159	66.80 147	70.45 212	74.44 210	42.93 258	82.42 162	84.90 87	63.69 82	59.63 154	80.99 171	47.18 44	85.23 202	51.17 196	56.75 237	83.19 182

V42		67.66 160	65.60 171	73.86 147	70.69 266	53.63 80	81.50 181	78.61 205	63.85 77	59.49 158	77.49 205	37.98 152	87.65 149	62.33 110	58.43 222	80.29 226

WR-MVS		67.58 161	66.76 150	70.04 223	75.92 197	45.06 244	86.23 70	85.28 76	64.31 68	58.50 174	81.00 170	44.80 69	82.00 238	49.21 205	55.57 248	83.06 183

tfpn200view9		67.57 162	66.13 159	71.89 189	84.05 65	45.07 241	83.40 144	87.71 40	60.79 118	57.79 183	82.76 146	43.53 87	87.80 143	28.80 284	66.36 160	82.78 188

FMVSNet2		67.57 162	65.79 164	72.90 163	82.71 95	47.97 212	85.15 93	84.93 86	58.55 162	56.71 197	78.26 194	36.72 178	86.67 170	46.15 225	62.94 192	84.07 161

FC-MVSNet-test		67.49 164	67.91 123	66.21 256	76.06 193	33.06 301	80.82 193	87.18 44	64.44 67	54.81 208	82.87 144	50.40 28	82.60 234	48.05 212	66.55 158	82.98 185

v1921920		67.45 165	65.23 178	74.10 141	71.51 261	52.90 119	83.75 135	80.44 175	62.48 97	59.12 163	77.13 210	36.98 173	87.90 140	57.53 148	58.14 227	81.49 203

gg-mvs-nofinetune		67.43 166	64.53 185	76.13 96	85.95 31	47.79 213	64.38 295	88.28 30	39.34 295	66.62 77	41.27 323	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 40	60.79 118	57.79 183	82.76 146	43.53 87	87.80 143	28.80 284	66.36 160	80.71 217

UA-Net		67.32 168	66.23 156	70.59 210	78.85 157	41.23 273	73.60 253	75.45 254	61.54 109	66.61 78	84.53 125	38.73 148	86.57 176	42.48 241	74.24 107	83.98 165

v8		67.25 169	64.99 181	74.04 142	72.89 232	53.31 97	82.37 163	80.11 178	61.54 109	54.29 213	76.02 227	42.89 100	88.41 122	58.43 137	56.36 238	80.39 225

NR-MVSNet		67.25 169	65.99 162	71.04 204	73.27 223	43.91 251	85.32 90	84.75 92	66.05 47	53.65 219	82.11 159	45.05 62	85.97 190	47.55 214	56.18 241	83.24 180

Test_1112_low_res		67.18 171	66.23 156	70.02 224	78.75 159	41.02 274	83.43 142	73.69 269	57.29 184	58.45 176	82.39 155	45.30 61	80.88 244	50.50 198	66.26 164	88.16 101

CPTT-MVS		67.15 172	65.84 163	71.07 203	80.96 132	50.32 167	81.94 168	74.10 263	46.18 268	57.91 180	87.64 96	29.57 239	81.31 241	64.10 97	70.18 138	81.56 202

GBi-Net		67.09 173	65.47 173	71.96 183	82.71 95	46.36 227	83.52 137	83.31 122	58.55 162	57.58 187	76.23 221	36.72 178	86.20 179	47.25 217	63.40 180	83.32 177

test1		67.09 173	65.47 173	71.96 183	82.71 95	46.36 227	83.52 137	83.31 122	58.55 162	57.58 187	76.23 221	36.72 178	86.20 179	47.25 217	63.40 180	83.32 177

PatchmatchNet		67.07 175	63.63 190	77.40 72	83.10 79	58.03 8	72.11 269	77.77 223	58.85 157	59.37 159	70.83 264	37.84 154	84.93 210	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	64.68 183	73.93 144	71.38 262	52.66 122	83.39 146	79.98 179	61.97 102	58.44 177	77.11 211	35.25 194	87.81 142	56.46 156	58.15 225	81.33 207

TranMVSNet+NR-MVSNet		66.94 177	65.61 170	70.93 206	73.45 220	43.38 256	83.02 153	84.25 102	65.31 59	58.33 178	81.90 161	39.92 140	85.52 196	49.43 204	54.89 251	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	70.16 217	73.27 223	42.59 262	81.50 181	82.92 133	63.53 86	58.51 172	82.11 159	40.75 127	84.64 213	53.11 180	55.96 243	83.24 180

IterMVS-LS		66.63 180	65.36 177	70.42 213	75.10 204	48.90 190	81.45 184	76.69 238	61.05 115	55.71 205	77.10 212	45.86 56	83.65 229	57.44 149	57.88 232	78.70 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 181	64.20 188	73.83 149	72.59 244	53.37 92	81.88 170	79.91 180	61.11 113	54.09 215	75.60 229	40.06 137	88.26 132	56.47 155	56.10 242	79.86 230

Fast-Effi-MVS+-dtu		66.53 182	64.10 189	73.84 148	72.41 249	52.30 132	84.73 114	75.66 251	59.51 133	56.34 201	79.11 185	28.11 247	85.85 193	57.74 147	63.29 185	83.35 176

thres600view7		66.46 183	65.12 179	70.47 211	83.41 72	43.80 253	82.15 165	87.78 35	59.37 138	56.02 203	82.21 157	43.73 82	86.90 165	26.51 296	64.94 168	80.71 217

LPG-MVS_test		66.44 184	64.58 184	72.02 180	74.42 211	48.60 196	83.07 151	80.64 172	54.69 218	53.75 217	83.83 132	25.73 262	86.98 162	60.33 128	64.71 169	80.48 223

tpm cat1		66.28 185	62.78 195	76.77 88	81.40 126	57.14 17	70.03 281	77.19 232	53.00 229	58.76 171	70.73 266	46.17 52	86.73 169	43.27 236	64.46 173	86.44 130

EPNet_dtu		66.25 186	66.71 151	64.87 266	78.66 163	34.12 296	82.80 157	75.51 252	61.75 106	64.47 107	86.90 105	37.06 172	72.46 302	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		66.24 187	64.96 182	70.08 219	75.17 201	49.64 176	82.01 167	74.48 259	62.15 99	57.83 181	76.08 226	30.59 234	83.79 227	65.40 92	60.93 204	76.81 264

ACMP	61.11 9	66.24 187	64.33 186	72.00 182	74.89 207	49.12 185	83.18 148	79.83 181	55.41 213	52.29 227	82.68 150	25.83 260	86.10 185	60.89 119	63.94 177	80.78 215
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		65.97 189	65.06 180	68.71 239	72.97 226	42.58 264	78.61 224	75.35 255	54.72 217	59.31 160	86.25 113	33.30 210	77.88 272	57.99 143	67.05 155	85.66 142

X-MVStestdata		65.85 190	62.20 198	76.81 85	83.41 72	52.48 124	84.88 112	83.20 128	58.03 171	63.91 111	4.82 341	35.50 192	89.78 75	65.50 85	80.50 58	88.16 101

Vis-MVSNet (Re-imp)		65.52 191	65.63 169	65.17 264	77.49 179	30.54 309	75.49 244	77.73 225	59.34 139	52.26 229	86.69 108	49.38 33	80.53 248	37.07 255	75.28 102	84.42 157

Baseline_NR-MVSNet		65.49 192	64.27 187	69.13 228	74.37 213	41.65 269	83.39 146	78.85 198	59.56 132	59.62 155	76.88 213	40.75 127	87.44 153	49.99 200	55.05 249	78.28 251

view600		64.79 193	63.45 191	68.82 233	82.13 105	40.75 276	79.41 218	88.29 26	56.54 198	53.26 221	81.30 166	44.26 72	85.01 206	22.97 306	62.85 193	80.71 217

view800		64.79 193	63.45 191	68.82 233	82.13 105	40.75 276	79.41 218	88.29 26	56.54 198	53.26 221	81.30 166	44.26 72	85.01 206	22.97 306	62.85 193	80.71 217

conf0.05thres1000		64.79 193	63.45 191	68.82 233	82.13 105	40.75 276	79.41 218	88.29 26	56.54 198	53.26 221	81.30 166	44.26 72	85.01 206	22.97 306	62.85 193	80.71 217

tfpn		64.79 193	63.45 191	68.82 233	82.13 105	40.75 276	79.41 218	88.29 26	56.54 198	53.26 221	81.30 166	44.26 72	85.01 206	22.97 306	62.85 193	80.71 217

FMVSNet1		64.57 197	62.11 199	71.96 183	77.32 181	46.36 227	83.52 137	83.31 122	52.43 236	54.42 211	76.23 221	27.80 249	86.20 179	42.59 240	61.34 202	83.32 177

dp		64.41 198	61.58 204	72.90 163	82.40 101	54.09 73	72.53 263	76.59 241	60.39 124	55.68 206	70.39 267	35.18 195	76.90 280	39.34 247	61.71 201	87.73 109

v18		64.36 199	61.80 201	72.05 177	72.97 226	53.31 97	81.16 187	77.76 224	59.14 147	48.50 247	68.97 272	42.91 99	84.38 215	56.62 153	48.17 273	78.47 238

ACMM	58.35 12	64.35 200	62.01 200	71.38 199	74.21 214	48.51 200	82.25 164	79.66 185	47.61 257	54.54 210	80.11 177	25.26 264	86.00 187	51.26 194	63.16 188	79.64 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v16		64.25 201	61.66 203	72.03 178	72.91 229	53.28 102	80.93 190	77.81 220	58.86 156	48.30 248	68.80 275	42.70 105	84.37 216	56.44 158	48.14 274	78.44 241

v17		64.19 202	61.58 204	72.03 178	72.89 232	53.28 102	80.91 191	77.80 221	58.87 155	48.22 249	68.77 276	42.69 106	84.37 216	56.43 159	47.66 277	78.43 242

pm-mvs1		64.12 203	62.56 196	68.78 238	71.68 258	38.87 284	82.89 155	81.57 154	55.54 212	53.89 216	77.82 201	37.73 158	86.74 168	48.46 209	53.49 260	80.72 216

test_djsdf		63.84 204	61.56 206	70.70 208	68.78 275	44.69 245	81.63 177	81.44 156	50.28 247	52.27 228	76.26 220	26.72 255	86.11 183	60.83 120	55.84 246	81.29 210

v15		63.83 205	61.13 209	71.93 186	72.60 242	53.21 105	80.44 197	78.22 209	58.80 158	47.57 254	68.22 278	42.50 107	84.18 218	55.82 161	46.02 289	78.39 244

IterMVS		63.77 206	61.67 202	70.08 219	72.68 240	51.24 152	80.44 197	75.51 252	60.51 123	51.41 233	73.70 243	32.08 224	78.91 261	54.30 174	54.35 254	80.08 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V14		63.72 207	60.99 211	71.91 188	72.58 245	53.18 106	80.24 201	78.19 210	58.53 165	47.35 260	68.10 279	42.28 110	84.18 218	55.68 163	45.97 290	78.36 247

testing_2		63.60 208	59.86 222	74.82 127	61.87 306	52.39 128	73.06 260	82.76 136	61.49 111	39.96 292	67.39 286	21.06 290	88.34 125	67.07 76	64.10 174	83.72 172

V9		63.60 208	60.84 212	71.87 190	72.51 247	53.12 110	80.04 206	78.15 212	58.25 168	47.14 262	67.98 280	42.08 114	84.18 218	55.47 164	45.92 292	78.32 248

v12		63.47 210	60.68 215	71.85 191	72.45 248	53.08 112	79.83 208	78.13 214	57.95 174	46.89 264	67.87 282	41.81 120	84.17 221	55.30 166	45.87 293	78.29 250

v11		63.44 211	60.66 216	71.79 193	72.61 241	53.02 116	79.80 209	78.08 216	58.30 166	47.27 261	67.91 281	40.67 131	84.14 223	54.93 169	46.39 287	78.23 254

v13		63.36 212	60.54 218	71.82 192	72.41 249	53.03 115	79.64 213	78.10 215	57.66 180	46.67 267	67.75 283	41.68 121	84.17 221	55.11 167	45.82 294	78.25 253

pmmvs4		63.34 213	61.07 210	70.16 217	70.14 268	50.53 161	79.97 207	71.41 285	55.08 215	54.12 214	78.58 188	32.79 216	82.09 237	50.33 199	57.22 236	77.86 256

Patchmatch-test1		63.23 214	59.16 228	75.43 109	78.58 165	57.92 9	61.61 303	77.53 227	56.71 193	57.75 185	70.98 263	31.97 225	78.19 265	40.97 244	56.36 238	90.18 66

jajsoiax		63.21 215	60.84 212	70.32 215	68.33 280	44.45 246	81.23 185	81.05 166	53.37 226	50.96 239	77.81 202	17.49 305	85.49 198	59.31 132	58.05 228	81.02 213

MIMVSNet		63.12 216	60.29 220	71.61 194	75.92 197	46.65 223	65.15 291	81.94 147	59.14 147	54.65 209	69.47 270	25.74 261	80.63 246	41.03 243	69.56 142	87.55 111

mvs_tets		62.96 217	60.55 217	70.19 216	68.22 282	44.24 250	80.90 192	80.74 171	52.99 230	50.82 241	77.56 203	16.74 308	85.44 199	59.04 133	57.94 229	80.89 214

TransMVSNet (Re)		62.82 218	60.76 214	69.02 229	73.98 216	41.61 270	86.36 67	79.30 195	56.90 187	52.53 226	76.44 217	41.85 118	87.60 150	38.83 248	40.61 304	77.86 256

pmmvs5		62.80 219	61.18 208	67.66 245	69.53 273	42.37 267	82.65 158	75.19 256	54.30 221	52.03 230	78.51 192	31.64 229	80.67 245	48.60 207	58.15 225	79.95 229

test0.0.03 1		62.54 220	62.44 197	62.86 276	72.28 254	29.51 312	82.93 154	78.78 200	59.18 145	53.07 225	82.41 154	36.91 175	77.39 276	37.45 251	58.96 220	81.66 201

v7n		62.50 221	59.27 227	72.20 175	67.25 286	49.83 174	77.87 230	80.12 177	52.50 235	48.80 246	73.07 248	32.10 223	87.90 140	46.83 220	54.92 250	78.86 233

CR-MVSNet		62.47 222	59.04 230	72.77 166	73.97 217	56.57 23	60.52 306	71.72 279	60.04 127	57.49 190	65.86 291	38.94 145	80.31 250	42.86 239	59.93 210	81.42 204

tpmvs		62.45 223	59.42 225	71.53 198	83.93 67	54.32 67	70.03 281	77.61 226	51.91 239	53.48 220	68.29 277	37.91 153	86.66 171	33.36 269	58.27 223	73.62 289

EG-PatchMatch MVS		62.40 224	59.59 223	70.81 207	73.29 222	49.05 186	85.81 75	84.78 91	51.85 241	44.19 274	73.48 246	15.52 313	89.85 73	40.16 245	67.24 154	73.54 290

XVG-OURS-SEG-HR		62.02 225	59.54 224	69.46 226	65.30 292	45.88 234	65.06 292	73.57 271	46.45 266	57.42 193	83.35 140	26.95 254	78.09 268	53.77 178	64.03 175	84.42 157

XVG-OURS		61.88 226	59.34 226	69.49 225	65.37 291	46.27 230	64.80 294	73.49 272	47.04 261	57.41 194	82.85 145	25.15 266	78.18 266	53.00 183	64.98 167	84.01 163

TAPA-MVS	56.12 14	61.82 227	60.18 221	66.71 252	78.48 168	37.97 288	75.19 246	76.41 243	46.82 263	57.04 196	86.52 111	27.67 251	77.03 278	26.50 297	67.02 156	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnnormal		61.47 228	59.09 229	68.62 241	76.29 192	41.69 268	81.14 188	85.16 81	54.48 220	51.32 234	73.63 244	32.32 220	86.89 166	21.78 313	55.71 247	77.29 262

v748		61.35 229	58.24 233	70.69 209	66.28 287	47.35 217	76.58 237	79.17 196	53.09 228	46.37 271	71.50 261	33.18 212	86.33 178	46.78 221	51.19 268	78.39 244

PVSNet_0	57.04 13	61.19 230	57.24 238	73.02 161	77.45 180	50.31 168	79.43 216	77.36 231	63.96 76	47.51 257	72.45 255	25.03 267	83.78 228	52.76 187	19.22 329	84.96 151

PLC	52.38 18	60.89 231	58.97 231	66.68 254	81.77 114	45.70 236	78.96 223	74.04 265	43.66 283	47.63 253	83.19 143	23.52 276	77.78 275	37.47 250	60.46 205	76.55 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 232	60.44 219	62.07 277	75.00 205	32.73 303	79.54 214	73.49 272	36.98 303	56.28 202	83.74 134	29.28 242	69.53 312	46.48 222	63.23 186	83.94 168

CNLPA		60.59 233	58.44 232	67.05 251	79.21 151	47.26 219	79.75 212	64.34 307	42.46 290	51.90 232	83.94 130	27.79 250	75.41 285	37.12 253	59.49 217	78.47 238

anonymousdsp		60.46 234	57.65 235	68.88 230	63.63 300	45.09 240	72.93 261	78.63 204	46.52 265	51.12 236	72.80 252	21.46 288	83.07 233	57.79 146	53.97 255	78.47 238

v52		59.82 235	56.41 244	70.06 221	61.49 309	48.67 194	69.46 285	75.80 249	52.55 233	47.49 258	68.82 274	28.60 243	85.70 194	52.13 191	51.34 267	75.80 272

V4		59.82 235	56.41 244	70.05 222	61.49 309	48.67 194	69.46 285	75.79 250	52.55 233	47.49 258	68.83 273	28.60 243	85.70 194	52.13 191	51.35 266	75.80 272

ACMH	53.70 16	59.78 237	55.94 249	71.28 200	76.59 190	48.35 205	80.15 205	76.11 244	49.74 251	41.91 285	73.45 247	16.50 310	90.31 62	31.42 277	57.63 234	75.17 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 238	57.15 239	67.09 249	66.01 288	36.86 292	80.50 196	78.64 203	45.05 274	49.05 245	73.94 238	27.28 252	86.10 185	43.96 234	49.94 271	78.31 249

MSDG		59.44 239	55.14 253	72.32 174	74.69 208	50.71 156	74.39 250	73.58 270	44.44 278	43.40 280	77.52 204	19.45 295	90.87 51	31.31 278	57.49 235	75.38 276

DP-MVS		59.24 240	56.12 247	68.63 240	88.24 16	50.35 165	82.51 161	64.43 306	41.10 292	46.70 266	78.77 187	24.75 268	88.57 117	22.26 311	56.29 240	66.96 309

OpenMVS_ROB	53.19 17	59.20 241	56.00 248	68.83 232	71.13 264	44.30 248	83.64 136	75.02 257	46.42 267	46.48 270	73.03 249	18.69 299	88.14 133	27.74 292	61.80 200	74.05 286

Anonymous20231206		59.08 242	57.59 236	63.55 271	68.77 276	32.14 306	80.26 200	79.78 182	50.00 250	49.39 243	72.39 256	26.64 256	78.36 264	33.12 272	57.94 229	80.14 227

WR-MVS_H		58.91 243	58.04 234	61.54 282	69.07 274	33.83 298	76.91 234	81.99 146	51.40 243	48.17 250	74.67 234	40.23 133	74.15 289	31.78 276	48.10 275	76.64 266

LCM-MVSNet-Re		58.82 244	56.54 242	65.68 258	79.31 150	29.09 315	61.39 305	45.79 327	60.73 120	37.65 298	72.47 254	31.42 230	81.08 242	49.66 202	70.41 136	86.87 119

Patchmatch-RL test		58.72 245	54.32 254	71.92 187	63.91 299	44.25 249	61.73 302	55.19 319	57.38 183	49.31 244	54.24 317	37.60 161	80.89 243	62.19 112	47.28 281	90.63 51

FMVSNet5		58.61 246	56.45 243	65.10 265	77.20 186	39.74 281	74.77 247	77.12 234	50.27 249	43.28 281	67.71 284	26.15 259	76.90 280	36.78 258	54.78 252	78.65 236

ACMH+	54.58 15	58.55 247	55.24 251	68.50 242	74.68 209	45.80 235	80.27 199	70.21 292	47.15 260	42.77 283	75.48 230	16.73 309	85.98 188	35.10 266	54.78 252	73.72 288

CP-MVSNet		58.54 248	57.57 237	61.46 284	68.50 278	33.96 297	76.90 235	78.60 206	51.67 242	47.83 251	76.60 216	34.99 197	72.79 300	35.45 262	47.58 278	77.64 260

RPMNet		58.49 249	53.74 258	72.77 166	73.97 217	56.57 23	60.52 306	72.39 276	35.72 308	57.49 190	58.87 311	37.73 158	80.31 250	27.01 295	59.93 210	81.42 204

PEN-MVS		58.35 250	57.15 239	61.94 279	67.55 285	34.39 295	77.01 233	78.35 208	51.87 240	47.72 252	76.73 215	33.91 205	73.75 294	34.03 268	47.17 282	77.68 258

PS-CasMVS		58.12 251	57.03 241	61.37 285	68.24 281	33.80 299	76.73 236	78.01 218	51.20 244	47.54 256	76.20 224	32.85 214	72.76 301	35.17 264	47.37 280	77.55 261

UnsupCasMVSNet_eth		57.56 252	55.15 252	64.79 267	64.57 297	33.12 300	73.17 258	83.87 114	58.98 153	41.75 286	70.03 268	22.54 280	79.92 256	46.12 226	35.31 313	81.32 209

CHOSEN 280x420		57.53 253	56.38 246	60.97 286	74.01 215	48.10 211	46.30 321	54.31 321	48.18 256	50.88 240	77.43 207	38.37 151	59.16 324	54.83 170	63.14 189	75.66 274

DTE-MVSNet		57.03 254	55.73 250	60.95 287	65.94 289	32.57 304	75.71 239	77.09 235	51.16 245	46.65 268	76.34 219	32.84 215	73.22 298	30.94 280	44.87 296	77.06 263

PatchMatch-RL		56.66 255	53.75 257	65.37 263	77.91 175	45.28 239	69.78 283	60.38 314	41.35 291	47.57 254	73.73 240	16.83 307	76.91 279	36.99 256	59.21 219	73.92 287

PatchT		56.60 256	52.97 261	67.48 246	72.94 228	46.16 233	57.30 312	73.78 268	38.77 297	54.37 212	57.26 314	37.52 163	78.06 269	32.02 274	52.79 261	78.23 254

Patchmtry		56.56 257	52.95 262	67.42 247	72.53 246	50.59 160	59.05 308	71.72 279	37.86 301	46.92 263	65.86 291	38.94 145	80.06 255	36.94 257	46.72 286	71.60 299

test_0402		56.45 258	53.03 260	66.69 253	76.78 189	50.31 168	81.76 172	69.61 294	42.79 288	43.88 276	72.13 258	22.82 279	86.46 177	16.57 327	50.94 269	63.31 318

LS3D		56.40 259	53.82 256	64.12 268	81.12 130	45.69 237	73.42 255	66.14 301	35.30 313	43.24 282	79.88 178	22.18 284	79.62 259	19.10 322	64.00 176	67.05 308

ADS-MVSNet		56.17 260	51.95 268	68.84 231	80.60 140	53.07 113	55.03 314	70.02 293	44.72 275	51.00 237	61.19 302	22.83 277	78.88 262	28.54 288	53.63 257	74.57 281

XVG-ACMP-BASELINE		56.03 261	52.85 263	65.58 259	61.91 305	40.95 275	63.36 296	72.43 275	45.20 273	46.02 272	74.09 236	9.20 324	78.12 267	45.13 228	58.27 223	77.66 259

pmmvs-eth3d		55.97 262	52.78 264	65.54 260	61.02 311	46.44 226	75.36 245	67.72 297	49.61 252	43.65 278	67.58 285	21.63 287	77.04 277	44.11 233	44.33 298	73.15 295

F-COLMAP		55.96 263	53.65 259	62.87 275	72.76 235	42.77 261	74.70 249	70.37 290	40.03 293	41.11 289	79.36 180	17.77 303	73.70 295	32.80 273	53.96 256	72.15 296

CMPMVS	40.41 21	55.34 264	52.64 265	63.46 272	60.88 312	43.84 252	61.58 304	71.06 286	30.43 320	36.33 300	74.63 235	24.14 270	75.44 284	48.05 212	66.62 157	71.12 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 265	54.07 255	58.68 292	63.14 302	25.00 321	77.69 231	74.78 258	52.64 231	43.43 279	72.39 256	26.21 258	74.76 288	29.31 282	47.05 284	76.28 270

ADS-MVSNet2		55.21 266	51.44 269	66.51 255	80.60 140	49.56 179	55.03 314	65.44 303	44.72 275	51.00 237	61.19 302	22.83 277	75.41 285	28.54 288	53.63 257	74.57 281

SixPastTwentyTwo		54.37 267	50.10 273	67.21 248	70.70 265	41.46 271	74.73 248	64.69 305	47.56 258	39.12 295	69.49 269	18.49 301	84.69 212	31.87 275	34.20 319	75.48 275

USDC		54.36 268	51.23 270	63.76 270	64.29 298	37.71 289	62.84 301	73.48 274	56.85 188	35.47 304	71.94 260	9.23 323	78.43 263	38.43 249	48.57 272	75.13 278

testgi		54.25 269	52.57 266	59.29 290	62.76 303	21.65 327	72.21 268	70.47 288	53.25 227	41.94 284	77.33 208	14.28 314	77.95 271	29.18 283	51.72 265	78.28 251

K. test v3		54.04 270	49.42 277	67.92 244	68.55 277	42.57 265	75.51 243	63.07 310	52.07 237	39.21 294	64.59 295	19.34 296	82.21 235	37.11 254	25.31 325	78.97 232

test2356		53.94 271	52.37 267	58.64 293	61.58 307	27.53 320	78.20 228	74.33 261	46.92 262	44.01 275	66.04 290	18.91 298	74.11 290	28.80 284	52.55 262	74.28 283

UnsupCasMVSNet_bld		53.86 272	50.53 272	63.84 269	63.52 301	34.75 294	71.38 273	81.92 149	46.53 264	38.95 296	57.93 312	20.55 292	80.20 254	39.91 246	34.09 320	76.57 267

YYNet1		53.82 273	49.96 274	65.41 262	70.09 270	48.95 188	72.30 266	71.66 281	44.25 279	31.89 317	63.07 299	23.73 272	73.95 292	33.26 270	39.40 306	73.34 292

MDA-MVSNet_test_wron		53.82 273	49.95 275	65.43 261	70.13 269	49.05 186	72.30 266	71.65 282	44.23 280	31.85 318	63.13 298	23.68 275	74.01 291	33.25 271	39.35 307	73.23 294

Patchmatch-test		53.33 275	48.17 279	68.81 237	73.31 221	42.38 266	42.98 325	58.23 316	32.53 316	38.79 297	70.77 265	39.66 141	73.51 296	25.18 299	52.06 264	90.55 52

LTVRE_ROB	45.45 19	52.73 276	49.74 276	61.69 281	69.78 271	34.99 293	44.52 323	67.60 298	43.11 287	43.79 277	74.03 237	18.54 300	81.45 240	28.39 290	57.94 229	68.62 306
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		52.63 277	50.72 271	58.37 294	62.69 304	28.13 317	72.60 262	75.97 247	30.94 319	40.76 291	72.11 259	20.16 293	70.80 308	35.11 265	46.11 288	76.19 271

OurMVSNet-221017-0		52.39 278	48.73 278	63.35 273	65.21 293	38.42 286	68.54 288	64.95 304	38.19 298	39.57 293	71.43 262	13.23 316	79.92 256	37.16 252	40.32 305	71.72 298

JIA-IIPM		52.33 279	47.77 282	66.03 257	71.20 263	46.92 221	40.00 329	76.48 242	37.10 302	46.73 265	37.02 325	32.96 213	77.88 272	35.97 260	52.45 263	73.29 293

MDA-MVSNet-bldmvs		51.56 280	47.75 283	63.00 274	71.60 260	47.32 218	69.70 284	72.12 277	43.81 282	27.65 323	63.38 297	21.97 286	75.96 282	27.30 294	32.19 321	65.70 312

COLMAP_ROB	43.60 20	50.90 281	48.05 280	59.47 289	67.81 283	40.57 280	71.25 274	62.72 312	36.49 307	36.19 301	73.51 245	13.48 315	73.92 293	20.71 317	50.26 270	63.92 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 282	47.81 281	57.96 295	61.53 308	27.80 319	67.40 289	74.06 264	43.25 286	33.31 316	65.38 294	16.03 311	71.34 307	21.80 312	47.55 279	74.75 279

MVS-HIRNet		49.01 283	44.71 288	61.92 280	76.06 193	46.61 224	63.23 298	54.90 320	24.77 324	33.56 313	36.60 326	21.28 289	75.88 283	29.49 281	62.54 197	63.26 319

testus		48.97 284	46.53 285	56.31 300	57.39 318	24.08 323	73.40 256	70.45 289	43.37 285	35.52 303	63.95 296	4.77 335	71.36 306	24.88 300	45.02 295	73.50 291

new-patchmatchnet		48.21 285	46.55 284	53.18 304	57.73 316	18.19 336	70.24 279	71.02 287	45.70 269	33.70 312	60.23 304	18.00 302	69.86 311	27.97 291	34.35 317	71.49 301

TinyColmap		48.15 286	44.49 290	59.13 291	65.73 290	38.04 287	63.34 297	62.86 311	38.78 296	29.48 321	67.23 288	6.46 330	73.30 297	24.59 301	41.90 302	66.04 310

1111		48.00 287	46.30 286	53.08 305	55.68 319	20.86 330	70.41 277	76.03 245	36.88 304	34.86 306	59.55 308	23.72 273	68.13 313	20.82 315	38.76 309	70.25 303

AllTest		47.32 288	44.66 289	55.32 301	65.08 294	37.50 290	62.96 300	54.25 322	35.45 311	33.42 314	72.82 250	9.98 320	59.33 322	24.13 302	43.84 299	69.13 304

test1235678		47.09 289	43.82 292	56.91 298	53.18 322	24.90 322	71.93 270	70.31 291	39.54 294	31.44 319	56.59 316	9.50 322	71.55 305	22.63 310	39.24 308	74.28 283

LP		47.05 290	42.23 295	61.53 283	72.04 255	49.37 183	49.48 318	65.50 302	34.57 314	34.29 310	52.30 319	17.73 304	75.32 287	17.56 325	36.57 311	59.91 320

PM-MVS		46.92 291	43.76 293	56.41 299	52.18 323	32.26 305	63.21 299	38.18 333	37.99 300	40.78 290	66.20 289	5.09 333	65.42 318	48.19 211	41.99 301	71.54 300

Anonymous20231211		46.87 292	43.27 294	57.67 296	57.88 315	30.12 310	73.14 259	64.16 308	33.43 315	34.34 309	59.42 310	12.15 317	77.99 270	19.64 321	35.23 315	64.90 314

testpf		45.92 293	45.81 287	46.27 312	69.56 272	27.86 318	23.18 334	73.91 267	44.10 281	36.99 299	57.16 315	20.56 291	71.77 304	42.17 242	44.64 297	39.18 328

RPSCF		45.77 294	44.13 291	50.68 307	57.67 317	29.66 311	54.92 316	45.25 329	26.69 323	45.92 273	75.92 228	17.43 306	45.70 334	27.44 293	45.95 291	76.67 265

pmmvs3		45.53 295	41.55 297	57.44 297	48.97 326	39.68 282	70.06 280	57.66 317	28.32 322	34.06 311	57.29 313	8.50 325	66.85 317	34.86 267	34.26 318	65.80 311

N_pmnet		41.25 296	39.77 298	45.66 314	68.50 278	0.82 345	72.51 264	0.38 347	35.61 309	35.26 305	61.51 301	20.07 294	67.74 315	23.51 304	40.63 303	68.42 307

TDRefinement		40.91 297	38.37 299	48.55 310	50.45 324	33.03 302	58.98 309	50.97 325	28.50 321	29.89 320	67.39 286	6.21 332	54.51 326	17.67 324	35.25 314	58.11 321

testmv		39.64 298	36.01 300	50.55 308	42.18 330	21.56 328	64.81 293	66.88 300	32.22 317	22.25 327	47.47 321	4.33 337	64.81 319	17.71 323	26.22 324	65.29 313

.test1245		38.91 299	41.99 296	29.67 324	55.68 319	20.86 330	70.41 277	76.03 245	36.88 304	34.86 306	59.55 308	23.72 273	68.13 313	20.82 315	0.00 341	0.02 339

DSMNet-mixed		38.35 300	35.36 301	47.33 311	48.11 327	14.91 338	37.87 330	36.60 335	19.18 328	34.37 308	59.56 307	15.53 312	53.01 328	20.14 319	46.89 285	74.07 285

test12356		37.84 301	35.07 302	46.18 313	45.03 329	8.02 343	57.70 311	62.67 313	31.83 318	22.78 325	50.25 320	4.46 336	66.95 316	17.25 326	23.62 327	63.57 317

no-one		37.21 302	31.48 306	54.40 303	39.62 334	31.91 308	45.68 322	67.42 299	35.54 310	14.59 330	35.91 328	7.35 326	73.20 299	22.98 305	14.23 330	58.09 322

FPMVS		35.40 303	33.67 303	40.57 316	46.34 328	28.74 316	41.05 327	57.05 318	20.37 327	22.27 326	53.38 318	6.87 328	44.94 335	8.62 333	47.11 283	48.01 326

ANet_high		34.39 304	29.59 307	48.78 309	30.34 338	22.28 325	55.53 313	63.79 309	38.11 299	15.47 329	36.56 327	6.94 327	59.98 321	13.93 329	5.64 340	64.08 315

new_pmnet		33.56 305	31.89 305	38.59 317	49.01 325	20.42 332	51.01 317	37.92 334	20.58 325	23.45 324	46.79 322	6.66 329	49.28 331	20.00 320	31.57 323	46.09 327

LF4IMVS		33.04 306	32.55 304	34.52 321	40.96 331	22.03 326	44.45 324	35.62 336	20.42 326	28.12 322	62.35 300	5.03 334	31.88 340	21.61 314	34.42 316	49.63 325

LCM-MVSNet		28.07 307	23.85 311	40.71 315	27.46 340	18.93 335	30.82 332	46.19 326	12.76 333	16.40 328	34.70 330	1.90 342	48.69 332	20.25 318	24.22 326	54.51 323

pcd1.5k->3k		27.74 308	27.68 308	27.93 326	73.75 219	0.00 347	0.00 339	85.50 67	0.00 341	0.00 344	0.00 345	26.52 257	0.00 344	0.00 343	63.37 184	83.79 171

Gipuma		27.47 309	24.26 310	37.12 319	60.55 313	29.17 314	11.68 337	60.00 315	14.18 331	10.52 334	15.12 337	2.20 341	63.01 320	8.39 334	35.65 312	19.18 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		26.71 310	22.98 313	37.87 318	36.89 335	8.51 342	42.51 326	29.32 341	19.09 329	13.01 331	37.54 324	2.23 340	53.11 327	14.54 328	11.71 331	51.99 324

PNet_i23d		25.11 311	23.09 312	31.17 323	40.18 332	21.30 329	57.99 310	33.28 338	13.77 332	9.94 335	30.29 332	0.45 346	43.74 336	13.61 331	8.28 333	28.46 331

PMVS	19.57 22	25.07 312	22.43 314	32.99 322	23.12 341	22.98 324	40.98 328	35.19 337	15.99 330	11.95 333	35.87 329	1.47 344	49.29 330	5.41 338	31.90 322	26.70 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		19.94 313	14.87 317	35.13 320	22.47 342	19.80 333	25.80 333	38.64 332	7.61 337	4.88 340	13.58 340	0.23 347	48.42 333	13.11 332	7.53 335	37.18 329

E-PMN		19.16 314	18.40 315	21.44 327	36.19 336	13.63 339	47.59 319	30.89 339	10.73 334	5.91 338	16.59 335	3.66 339	39.77 337	5.95 337	8.14 334	10.92 335

EMVS		18.42 315	17.66 316	20.71 328	34.13 337	12.64 340	46.94 320	29.94 340	10.46 336	5.58 339	14.93 338	4.23 338	38.83 338	5.24 339	7.51 337	10.67 336

cdsmvs_eth3d_5k		18.33 316	24.44 309	0.00 334	0.00 346	0.00 347	0.00 339	89.40 12	0.00 341	0.00 344	92.02 20	38.55 149	0.00 344	0.00 343	0.00 341	0.00 342

MVE	16.60 23	17.34 317	13.39 318	29.16 325	28.43 339	19.72 334	13.73 336	23.63 342	7.23 338	7.96 336	21.41 333	0.80 345	36.08 339	6.97 335	10.39 332	31.69 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 318	10.68 319	5.73 331	2.49 344	4.21 344	10.48 338	18.04 343	0.34 340	12.59 332	20.49 334	11.39 318	7.03 343	13.84 330	6.46 339	5.95 337

wuyk23d		9.11 319	8.77 321	10.15 330	40.18 332	16.76 337	20.28 335	1.01 346	2.58 339	2.66 341	0.98 342	0.23 347	12.49 342	4.08 340	6.90 338	1.19 338

ab-mvs-re		7.68 320	10.24 320	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	92.12 17	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

testmvs		6.14 321	8.18 322	0.01 332	0.01 345	0.00 347	73.40 256	0.00 348	0.00 341	0.02 342	0.15 343	0.00 349	0.00 344	0.02 341	0.00 341	0.02 339

test123		6.01 322	8.01 323	0.01 332	0.00 346	0.01 346	71.93 270	0.00 348	0.00 341	0.02 342	0.11 344	0.00 349	0.00 344	0.02 341	0.00 341	0.02 339

pcd_1.5k_mvsjas		3.15 323	4.20 324	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	37.77 155	0.00 344	0.00 343	0.00 341	0.00 342

sosnet-low-res		0.00 324	0.00 325	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

sosnet		0.00 324	0.00 325	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

uncertanet		0.00 324	0.00 325	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

Regformer		0.00 324	0.00 325	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

uanet		0.00 324	0.00 325	0.00 334	0.00 346	0.00 347	0.00 339	0.00 348	0.00 341	0.00 344	0.00 345	0.00 349	0.00 344	0.00 343	0.00 341	0.00 342

ESAPD								88.96 16

sam_mvs1												38.86 147

sam_mvs												35.99 190

semantic-postprocess				60.08 288	70.68 267	45.07 241		74.25 262	43.54 284	50.02 242	73.73 240	32.22 222	56.74 325	51.06 197	53.60 259	78.42 243

ambc				62.06 278	53.98 321	29.38 313	35.08 331	79.65 186		41.37 287	59.96 305	6.27 331	82.15 236	35.34 263	38.22 310	74.65 280

MTGPA								81.31 159

test_post1							70.84 276				14.72 339	34.33 201	83.86 225	48.80 206

test_post											16.22 336	37.52 163	84.72 211

patchmatchnet-post											59.74 306	38.41 150	79.91 258

GG-mvs-BLEND				77.77 63	86.68 26	50.61 158	68.67 287	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								15.34 344

gm-plane-assit					83.24 77	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	84.00 109	57.72 179	72.99 33	90.98 38	44.87 66	88.58 114

test_8					85.72 34	55.31 41	87.60 42	83.88 113	57.84 176	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		83.61 119		72.53 40			88.10 136

TestCases				55.32 301	65.08 294	37.50 290		54.25 322	35.45 311	33.42 314	72.82 250	9.98 320	59.33 322	24.13 302	43.84 299	69.13 304

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 69					89.70 78			90.55 52

旧先验2							81.73 173		45.53 271	74.66 21			70.48 310	58.31 140

新几何2							81.61 179

新几何1				73.30 159	83.10 79	53.48 83		71.43 284	45.55 270	66.14 82	87.17 102	33.88 207	80.54 247	48.50 208	80.33 61	85.88 138

旧先验1					81.57 120	47.48 215		71.83 278			88.66 79	36.94 174			78.34 76	88.67 95

无先验							85.19 92	78.00 219	49.08 254				85.13 203	52.78 185		87.45 113

原ACMM2							83.77 134

原ACMM1				76.13 96	84.89 54	54.59 65		85.26 77	51.98 238	66.70 75	87.07 104	40.15 135	89.70 78	51.23 195	85.06 30	84.10 160

test222					79.36 147	50.97 154	77.99 229	67.84 296	42.54 289	62.84 124	86.53 110	30.26 236			76.91 89	85.23 147

testdata2													77.81 274	45.64 227

segment_acmp												44.97 65

testdata				67.08 250	77.59 177	45.46 238		69.20 295	44.47 277	71.50 50	88.34 82	31.21 231	70.76 309	52.20 190	75.88 97	85.03 149

testdata1							77.55 232		64.14 73

test12				79.24 29	86.89 24	56.08 32		85.16 81		72.27 45		47.15 45	91.10 45		85.93 19	90.54 55

plane_prior7					77.95 172	48.46 204

plane_prior6					78.42 169	49.39 182						36.04 187

plane_prior5								82.59 137					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					78.31 171

plane_prior						49.57 177	87.43 49		64.57 66						72.84 120

n2								0.00 348

nn								0.00 348

door-mid								41.31 331

lessismore_v0				67.98 243	64.76 296	41.25 272		45.75 328		36.03 302	65.63 293	19.29 297	84.11 224	35.67 261	21.24 328	78.59 237

LGP-MVS_train				72.02 180	74.42 211	48.60 196		80.64 172	54.69 218	53.75 217	83.83 132	25.73 262	86.98 162	60.33 128	64.71 169	80.48 223

test11								84.25 102

door								43.27 330

HQP5-MVS						51.56 142

HQP-NCC					79.02 154		88.00 36		65.45 52	64.48 104

ACMP_Plane					79.02 154		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								83.68 116							73.12 116

HQP2-MVS												37.35 166

NP-MVS					78.76 158	50.43 163					85.12 121

MDTV_nov1_ep13_2view						43.62 254	71.13 275		54.95 216	59.29 161		36.76 177		46.33 224		87.32 115

MDTV_nov1_ep13			61.56 206		81.68 115	55.12 48	72.41 265	78.18 211	59.19 143	58.85 169	69.29 271	34.69 198	86.16 182	36.76 259	62.96 191

ACMMP++_ref															63.20 187

ACMMP++															59.38 218

Test By Simon												39.38 142

ITE_SJBPF				51.84 306	58.03 314	31.94 307		53.57 324	36.67 306	41.32 288	75.23 232	11.17 319	51.57 329	25.81 298	48.04 276	72.02 297

DeepMVS_CX				13.10 329	21.34 343	8.99 341		10.02 345	10.59 335	7.53 337	30.55 331	1.82 343	14.55 341	6.83 336	7.52 336	15.75 334

High-resolution multi-view benchmark