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 15	93.77 1	91.10 4	75.95 2	77.10 18	93.09 9	54.15 12	95.57 3	85.80 1	85.87 21	93.31 6

HSP-MVS		82.45 2	83.62 1	78.96 38	82.99 98	52.71 128	85.04 115	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 46	83.35 5	93.53 1	92.72 18

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 30	56.89 23	92.77 2	86.30 68	77.83 1	77.88 15	92.13 19	60.24 2	94.78 10	78.97 15	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 13	91.71 3	88.87 17	70.31 14	77.64 17	93.87 2	52.58 19	93.91 16	84.17 2	87.92 11	92.39 21

CANet		80.90 5	81.17 6	80.09 25	87.62 24	54.21 77	91.60 6	86.47 63	73.13 5	79.89 10	93.10 7	49.88 35	92.98 22	84.09 3	84.75 34	93.08 11

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 67	85.46 51	49.56 189	90.99 12	86.66 60	70.58 13	80.07 9	95.30 1	56.18 8	90.97 55	82.57 8	86.22 19	93.28 7

HPM-MVS++		80.50 7	80.71 7	79.88 27	87.34 26	55.20 50	89.93 21	87.55 47	66.04 48	79.46 12	93.00 12	53.10 17	91.76 42	80.40 11	89.56 4	92.68 19

CSCG		80.41 8	79.72 8	82.49 4	89.12 12	57.67 14	89.29 30	91.54 2	59.19 147	71.82 53	90.05 64	59.72 3	96.04 1	78.37 18	88.40 9	93.75 2

PS-MVSNAJ		80.06 9	79.52 11	81.68 7	85.58 46	60.97 3	91.69 4	87.02 52	70.62 12	80.75 7	93.22 6	37.77 171	92.50 30	82.75 6	86.25 18	91.57 38

xiu_mvs_v2_base		79.86 10	79.31 12	81.53 8	85.03 57	60.73 4	91.65 5	86.86 55	70.30 15	80.77 6	93.07 11	37.63 176	92.28 34	82.73 7	85.71 23	91.57 38

MVS_0304		79.84 11	79.71 9	80.25 19	85.64 43	54.62 70	90.58 15	84.48 106	72.51 8	79.22 13	93.09 9	42.01 132	93.28 20	84.00 4	85.84 22	92.87 16

ESAPD		79.82 12	79.66 10	80.29 17	89.27 11	55.08 55	88.70 36	87.92 37	55.55 220	81.21 5	93.69 3	56.51 7	94.27 13	78.36 19	85.70 24	91.51 41

NCCC		79.57 13	79.23 13	80.59 14	89.50 8	56.99 21	91.38 8	88.17 33	67.71 29	73.81 32	92.75 13	46.88 52	93.28 20	78.79 16	84.07 39	91.50 42

SMA-MVS		79.10 14	78.76 14	80.12 23	84.42 63	55.87 35	87.58 51	86.76 57	61.48 116	80.26 8	93.10 7	46.53 56	92.41 31	79.97 12	88.77 6	92.08 26

LFMVS		78.52 15	77.14 28	82.67 3	89.58 7	58.90 6	91.27 10	88.05 34	63.22 93	74.63 28	90.83 48	41.38 141	94.40 11	75.42 37	79.90 71	94.72 1

APDe-MVS		78.44 16	78.20 17	79.19 34	88.56 13	54.55 72	89.76 25	87.77 42	55.91 215	78.56 14	92.49 16	48.20 39	92.65 28	79.49 13	83.04 42	90.39 66

MG-MVS		78.42 17	76.99 30	82.73 2	93.17 1	64.46 1	89.93 21	88.51 24	64.83 65	73.52 36	88.09 92	48.07 40	92.19 35	62.24 118	84.53 36	91.53 40

lupinMVS		78.38 18	78.11 18	79.19 34	83.02 96	55.24 47	91.57 7	84.82 97	69.12 19	76.67 20	92.02 23	44.82 80	90.23 74	80.83 9	80.09 66	92.08 26

casdiffmvs1		78.37 19	77.57 24	80.75 12	83.13 89	58.05 8	84.81 122	86.65 61	63.81 81	79.75 11	84.50 132	53.76 14	91.28 49	80.50 10	81.23 54	92.91 13

EPNet		78.36 20	78.49 15	77.97 66	85.49 48	52.04 141	89.36 29	84.07 123	73.22 4	77.03 19	91.72 30	49.32 37	90.17 76	73.46 50	82.77 43	91.69 33
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 21	78.26 16	78.48 51	81.33 141	56.31 31	81.59 193	86.41 64	69.61 18	81.72 4	88.16 91	55.09 9	88.04 146	74.12 44	86.31 17	91.09 49

canonicalmvs		78.17 22	77.86 21	79.12 37	84.30 65	54.22 76	87.71 44	84.57 105	67.70 30	77.70 16	92.11 22	50.90 28	89.95 79	78.18 23	77.54 86	93.20 9

alignmvs		78.08 23	77.98 19	78.39 55	83.53 78	53.22 111	89.77 24	85.45 77	66.11 43	76.59 22	91.99 25	54.07 13	89.05 96	77.34 27	77.00 92	92.89 15

DeepC-MVS_fast	67.50 3	78.00 24	77.63 22	79.13 36	88.52 14	55.12 52	89.95 20	85.98 71	68.31 23	71.33 57	92.75 13	45.52 72	90.37 68	71.15 61	85.14 30	91.91 30
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 25	77.92 20	78.19 60	87.43 25	50.12 181	90.93 13	91.41 3	67.48 32	75.12 25	90.15 63	46.77 53	91.00 53	73.52 49	78.46 79	93.44 4

TSAR-MVS + GP.		77.82 26	77.59 23	78.49 50	85.25 55	50.27 180	90.02 18	90.57 5	56.58 204	74.26 30	91.60 33	54.26 10	92.16 36	75.87 32	79.91 70	93.05 12

Regformer-1		77.80 27	77.44 26	78.88 41	87.78 22	52.44 133	87.60 46	90.08 8	68.86 20	72.49 48	91.79 27	47.69 44	94.90 8	73.57 48	77.05 89	89.31 86

test_prior3		77.59 28	77.33 27	78.39 55	86.35 34	54.91 61	89.04 32	85.45 77	61.88 108	73.55 34	91.46 37	48.01 42	89.70 85	74.73 39	85.46 25	90.55 58

casdiffmvs		77.54 29	76.52 33	80.60 13	83.43 79	58.01 10	85.16 100	86.39 66	65.71 53	76.20 23	83.87 139	50.75 29	91.33 48	77.37 26	79.79 72	92.46 20

PHI-MVS		77.49 30	77.00 29	78.95 39	85.33 53	50.69 166	88.57 37	88.59 23	58.14 176	73.60 33	93.31 5	43.14 109	93.79 17	73.81 45	88.53 8	92.37 22

WTY-MVS		77.47 31	77.52 25	77.30 79	88.33 17	46.25 243	88.46 38	90.32 6	71.40 10	72.32 50	91.72 30	53.44 15	92.37 32	66.28 88	75.42 106	93.28 7

Regformer-2		77.15 32	76.82 31	78.14 61	87.78 22	51.84 146	87.60 46	89.12 14	67.23 33	71.93 52	91.79 27	46.03 67	93.53 19	72.85 56	77.05 89	89.05 94

SteuartSystems-ACMMP		77.08 33	76.33 36	79.34 32	80.98 144	55.31 45	89.76 25	86.91 54	62.94 97	71.65 55	91.56 34	42.33 123	92.56 29	77.14 28	83.69 41	90.15 73
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 34	76.45 34	78.69 45	79.69 165	54.74 64	90.56 16	83.99 127	68.26 24	74.10 31	90.91 45	42.14 127	89.99 78	79.30 14	79.12 75	91.36 45
jason: jason.
train_agg		76.91 35	76.40 35	78.45 53	85.68 40	55.42 41	87.59 49	84.00 125	57.84 183	72.99 39	90.98 41	44.99 76	88.58 122	78.19 20	85.32 28	91.34 47

MVS		76.91 35	75.48 44	81.23 10	84.56 62	55.21 49	80.23 215	91.64 1	58.65 166	65.37 100	91.48 36	45.72 70	95.05 6	72.11 58	89.52 5	93.44 4

DeepC-MVS	67.15 4	76.90 37	76.27 37	78.80 43	80.70 150	55.02 56	86.39 72	86.71 58	66.96 36	67.91 76	89.97 66	48.03 41	91.41 47	75.60 34	84.14 38	89.96 76
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 38	76.07 40	78.81 42	80.20 163	59.11 5	86.86 68	86.23 69	68.60 21	70.18 64	88.84 82	51.57 24	87.16 167	65.48 94	86.68 14	90.15 73

MAR-MVS		76.76 38	75.60 43	80.21 20	90.87 3	54.68 68	89.14 31	89.11 15	62.95 96	70.54 62	92.33 17	41.05 142	94.95 7	57.90 151	86.55 16	91.00 51
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 40	76.15 39	78.48 51	85.66 42	55.59 37	87.54 53	83.95 129	57.78 185	71.78 54	90.81 49	44.33 84	88.52 127	78.19 20	85.32 28	91.34 47

agg_prior1		76.68 41	76.24 38	78.00 64	85.64 43	54.92 59	87.55 52	83.61 136	57.99 179	72.53 46	91.05 39	45.36 73	88.10 144	77.76 25	84.68 35	90.99 52

PVSNet_Blended		76.53 42	76.54 32	76.50 96	85.91 37	51.83 147	88.89 34	84.24 114	67.82 27	69.09 68	89.33 76	46.70 54	88.13 142	75.43 35	81.48 53	89.55 82

ACMMP_Plus		76.43 43	75.66 42	78.73 44	81.92 124	54.67 69	84.06 137	85.35 82	61.10 119	72.99 39	91.50 35	40.25 148	91.00 53	76.84 29	86.98 13	90.51 62

MVS_111021_HR		76.39 44	75.38 47	79.42 31	85.33 53	56.47 28	88.15 39	84.97 94	65.15 64	66.06 92	89.88 67	43.79 95	92.16 36	75.03 38	80.03 69	89.64 81

CHOSEN 1792x2688		76.24 45	74.03 57	82.88 1	83.09 92	62.84 2	85.73 87	85.39 80	69.79 16	64.87 108	83.49 152	41.52 140	93.69 18	70.55 63	81.82 50	92.12 25

SD-MVS		76.18 46	74.85 52	80.18 21	85.39 52	56.90 22	85.75 86	82.45 158	56.79 199	74.48 29	91.81 26	43.72 100	90.75 59	74.61 41	78.65 78	92.91 13

APD-MVS		76.15 47	75.68 41	77.54 73	88.52 14	53.44 92	87.26 61	85.03 93	53.79 232	74.91 26	91.68 32	43.80 94	90.31 69	74.36 42	81.82 50	88.87 99
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDD-MVS		76.08 48	74.97 50	79.44 30	84.27 68	53.33 102	91.13 11	85.88 72	65.33 60	72.37 49	89.34 74	32.52 234	92.76 26	77.90 24	75.96 100	92.22 24

CDPH-MVS		76.05 49	75.19 48	78.62 48	86.51 33	54.98 58	87.32 56	84.59 104	58.62 167	70.75 60	90.85 47	43.10 113	90.63 62	70.50 64	84.51 37	90.24 69

Regformer-3		76.02 50	75.47 45	77.70 69	85.49 48	51.47 154	85.12 105	90.19 7	68.52 22	69.36 66	90.66 51	46.45 63	94.81 9	70.25 66	73.16 121	86.81 135

0601test		75.85 51	74.83 53	78.91 40	88.08 21	51.94 143	91.30 9	89.28 13	57.91 181	71.19 59	89.20 77	42.03 131	92.77 25	69.41 69	75.07 111	92.01 29

MVS_Test		75.85 51	74.93 51	78.62 48	84.08 70	55.20 50	83.99 140	85.17 88	68.07 25	73.38 37	82.76 163	50.44 30	89.00 103	65.90 90	80.61 59	91.64 34

CLD-MVS		75.60 53	75.39 46	76.24 100	80.69 151	52.40 134	90.69 14	86.20 70	74.40 3	65.01 106	88.93 79	42.05 130	90.58 63	76.57 30	73.96 116	85.73 150
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 54	75.03 49	77.04 85	81.37 140	52.65 130	84.34 130	84.46 107	61.16 117	69.14 67	91.76 29	39.98 155	88.99 105	78.19 20	84.89 33	89.48 83
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DWT-MVSNet_test		75.47 55	73.87 58	80.29 17	87.33 27	57.05 20	82.86 165	87.96 36	72.59 6	67.29 80	87.79 96	51.61 23	91.52 45	54.75 181	72.63 129	92.29 23

Effi-MVS+		75.24 56	73.61 60	80.16 22	81.92 124	57.42 16	85.21 98	76.71 257	60.68 126	73.32 38	89.34 74	47.30 47	91.63 43	68.28 75	79.72 73	91.42 43

PAPR		75.20 57	74.13 56	78.41 54	88.31 18	55.10 54	84.31 131	85.66 74	63.76 84	67.55 78	90.73 50	43.48 105	89.40 90	66.36 87	77.03 91	90.73 56

Regformer-4		75.06 58	74.59 54	76.47 97	85.49 48	50.33 176	85.12 105	88.61 21	66.42 38	68.48 71	90.66 51	44.15 89	92.68 27	69.24 70	73.16 121	86.39 142

MP-MVS		74.99 59	74.33 55	76.95 89	82.89 102	53.05 121	85.63 89	83.50 139	57.86 182	67.25 81	90.24 59	43.38 106	88.85 115	76.03 31	82.23 48	88.96 97
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
#test#		74.86 60	73.78 59	78.10 62	84.30 65	53.68 84	86.95 65	84.36 108	59.00 158	65.78 95	90.56 53	40.70 145	90.90 56	71.48 59	80.88 55	89.71 77

3Dnovator	64.70 6	74.46 61	72.48 69	80.41 16	82.84 104	55.40 44	83.08 159	88.61 21	67.61 31	59.85 160	88.66 83	34.57 215	93.97 14	58.42 144	88.70 7	91.85 32

HFP-MVS		74.37 62	73.13 64	78.10 62	84.30 65	53.68 84	85.58 90	84.36 108	56.82 197	65.78 95	90.56 53	40.70 145	90.90 56	69.18 71	80.88 55	89.71 77

VDDNet		74.37 62	72.13 77	81.09 11	79.58 166	56.52 27	90.02 18	86.70 59	52.61 249	71.23 58	87.20 104	31.75 244	93.96 15	74.30 43	75.77 104	92.79 17

MSLP-MVS++		74.21 64	72.25 75	80.11 24	81.45 138	56.47 28	86.32 74	79.65 205	58.19 175	66.36 88	92.29 18	36.11 199	90.66 61	67.39 80	82.49 45	93.18 10

PatchFormer-LS_test		74.17 65	72.30 73	79.77 28	86.61 32	57.26 18	82.02 175	84.80 99	71.85 9	64.73 109	87.52 101	50.33 32	90.40 67	54.23 183	68.63 154	91.64 34

API-MVS		74.17 65	72.07 78	80.49 15	90.02 6	58.55 7	87.30 58	84.27 111	57.51 189	65.77 97	87.77 98	41.61 139	95.97 2	51.71 202	82.63 44	86.94 129

zzz-MVS		74.15 67	73.11 65	77.27 81	81.54 134	53.57 87	84.02 139	81.31 178	59.41 140	68.39 72	90.96 43	36.07 200	89.01 101	73.80 46	82.45 46	89.23 88

IB-MVS	68.87 2	74.01 68	72.03 80	79.94 26	83.04 95	55.50 39	90.24 17	88.65 19	67.14 34	61.38 143	81.74 181	53.21 16	94.28 12	60.45 132	62.41 209	90.03 75
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 69	73.14 62	76.18 102	84.70 61	47.36 227	75.56 263	86.36 67	66.27 41	70.66 61	83.91 138	51.05 27	89.31 91	67.10 82	72.61 130	91.88 31

CostFormer		73.89 70	72.30 73	78.66 46	82.36 115	56.58 24	75.56 263	85.30 83	66.06 46	70.50 63	76.88 232	57.02 6	89.06 94	68.27 76	68.74 152	90.33 68

ACMMPR		73.76 71	72.61 66	77.24 84	83.92 74	52.96 125	85.58 90	84.29 110	56.82 197	65.12 102	90.45 55	37.24 186	90.18 75	69.18 71	80.84 57	88.58 106

region2R		73.75 72	72.55 68	77.33 78	83.90 75	52.98 124	85.54 93	84.09 116	56.83 196	65.10 103	90.45 55	37.34 184	90.24 73	68.89 73	80.83 58	88.77 102

CANet_DTU		73.71 73	73.14 62	75.40 119	82.61 111	50.05 182	84.67 126	79.36 211	69.72 17	75.39 24	90.03 65	29.41 256	85.93 203	67.99 77	79.11 76	90.22 70

MVSFormer		73.53 74	72.19 76	77.57 72	83.02 96	55.24 47	81.63 190	81.44 175	50.28 265	76.67 20	90.91 45	44.82 80	86.11 194	60.83 127	80.09 66	91.36 45

PVSNet_BlendedMVS		73.42 75	73.30 61	73.76 159	85.91 37	51.83 147	86.18 77	84.24 114	65.40 57	69.09 68	80.86 193	46.70 54	88.13 142	75.43 35	65.92 175	81.33 222

PVSNet_Blended_VisFu		73.40 76	72.44 70	76.30 98	81.32 142	54.70 67	85.81 82	78.82 218	63.70 85	64.53 112	85.38 124	47.11 50	87.38 164	67.75 79	77.55 85	86.81 135

MVSTER		73.25 77	72.33 71	76.01 108	85.54 47	53.76 83	83.52 146	87.16 50	67.06 35	63.88 123	81.66 182	52.77 18	90.44 64	64.66 103	64.69 181	83.84 180

EI-MVSNet-Vis-set		73.19 78	72.60 67	74.99 127	82.56 112	49.80 185	82.55 169	89.00 16	66.17 42	65.89 94	88.98 78	43.83 93	92.29 33	65.38 101	69.01 150	82.87 197

PMMVS		72.98 79	72.05 79	75.78 113	83.57 77	48.60 207	84.08 135	82.85 152	61.62 112	68.24 74	90.33 58	28.35 261	87.78 155	72.71 57	76.69 94	90.95 53

XVS		72.92 80	71.62 83	76.81 91	83.41 80	52.48 131	84.88 120	83.20 146	58.03 177	63.91 121	89.63 71	35.50 208	89.78 82	65.50 92	80.50 61	88.16 110

TESTMET0.1,1		72.86 81	72.33 71	74.46 141	81.98 122	50.77 164	85.13 102	85.47 76	66.09 44	67.30 79	83.69 144	37.27 185	83.57 242	65.06 102	78.97 77	89.05 94

diffmvs1		72.81 82	71.73 81	76.06 105	79.03 173	51.59 150	79.01 237	76.46 262	65.23 63	69.81 65	83.07 160	46.12 65	86.94 173	70.95 62	75.87 102	89.66 79

Fast-Effi-MVS+		72.73 83	71.15 92	77.48 75	82.75 106	54.76 63	86.77 69	80.64 191	63.05 95	65.93 93	84.01 136	44.42 83	89.03 100	56.45 164	76.36 99	88.64 104

MTAPA		72.73 83	71.22 90	77.27 81	81.54 134	53.57 87	67.06 312	81.31 178	59.41 140	68.39 72	90.96 43	36.07 200	89.01 101	73.80 46	82.45 46	89.23 88

PGM-MVS		72.60 85	71.20 91	76.80 93	82.95 99	52.82 127	83.07 160	82.14 159	56.51 209	63.18 130	89.81 68	35.68 207	89.76 84	67.30 81	80.19 65	87.83 117

HPM-MVS		72.60 85	71.50 85	75.89 110	82.02 121	51.42 156	80.70 208	83.05 148	56.12 213	64.03 119	89.53 72	37.55 178	88.37 131	70.48 65	80.04 68	87.88 116
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 87	71.46 86	76.00 109	82.93 101	52.32 138	86.93 67	82.48 157	55.15 223	63.65 126	90.44 57	35.03 212	88.53 126	68.69 74	77.83 83	87.15 128

EI-MVSNet-UG-set		72.37 88	71.73 81	74.29 145	81.60 130	49.29 194	81.85 181	88.64 20	65.29 62	65.05 104	88.29 88	43.18 107	91.83 41	63.74 105	67.97 158	81.75 215

MS-PatchMatch		72.34 89	71.26 89	75.61 114	82.38 114	55.55 38	88.00 40	89.95 11	65.38 58	56.51 211	80.74 194	32.28 237	92.89 23	57.95 150	88.10 10	78.39 265

HQP-MVS		72.34 89	71.44 87	75.03 125	79.02 174	51.56 151	88.00 40	83.68 133	65.45 54	64.48 113	85.13 125	37.35 182	88.62 119	66.70 84	73.12 123	84.91 162

mvs_anonymous		72.29 91	70.74 93	76.94 90	82.85 103	54.72 66	78.43 241	81.54 174	63.77 83	61.69 142	79.32 200	51.11 26	85.31 211	62.15 120	75.79 103	90.79 55

3Dnovator+	62.71 7	72.29 91	70.50 95	77.65 71	83.40 83	51.29 160	87.32 56	86.40 65	59.01 157	58.49 185	88.32 87	32.40 235	91.27 50	57.04 159	82.15 49	90.38 67

nrg030		72.27 93	71.56 84	74.42 142	75.93 217	50.60 168	86.97 64	83.21 145	62.75 99	67.15 82	84.38 133	50.07 33	86.66 182	71.19 60	62.37 210	85.99 145

VPNet		72.07 94	71.42 88	74.04 150	78.64 185	47.17 232	89.91 23	87.97 35	72.56 7	64.66 110	85.04 127	41.83 135	88.33 135	61.17 125	60.97 215	86.62 137

DP-MVS Recon		71.99 95	70.31 99	77.01 87	90.65 4	53.44 92	89.37 28	82.97 150	56.33 211	63.56 128	89.47 73	34.02 219	92.15 38	54.05 184	72.41 131	85.43 156

QAPM		71.88 96	69.33 114	79.52 29	82.20 116	54.30 74	86.30 75	88.77 18	56.61 203	59.72 162	87.48 102	33.90 222	95.36 4	47.48 224	81.49 52	88.90 98

diffmvs		71.81 97	70.47 96	75.80 112	78.97 177	51.56 151	78.25 242	76.39 264	65.75 51	67.61 77	81.37 184	44.14 90	86.84 176	67.81 78	73.97 115	89.00 96

PAPM_NR		71.80 98	69.98 106	77.26 83	81.54 134	53.34 100	78.60 240	85.25 86	53.46 235	60.53 156	88.66 83	45.69 71	89.24 92	56.49 161	79.62 74	89.19 91

mPP-MVS		71.79 99	70.38 98	76.04 107	82.65 110	52.06 140	84.45 128	81.78 170	55.59 219	62.05 140	89.68 70	33.48 225	88.28 139	65.45 97	78.24 81	87.77 119

xiu_mvs_v1_base_debu		71.60 100	70.29 100	75.55 115	77.26 204	53.15 114	85.34 94	79.37 208	55.83 216	72.54 43	90.19 60	22.38 302	86.66 182	73.28 52	76.39 96	86.85 132

xiu_mvs_v1_base		71.60 100	70.29 100	75.55 115	77.26 204	53.15 114	85.34 94	79.37 208	55.83 216	72.54 43	90.19 60	22.38 302	86.66 182	73.28 52	76.39 96	86.85 132

xiu_mvs_v1_base_debi		71.60 100	70.29 100	75.55 115	77.26 204	53.15 114	85.34 94	79.37 208	55.83 216	72.54 43	90.19 60	22.38 302	86.66 182	73.28 52	76.39 96	86.85 132

test_normal		71.31 103	68.95 120	78.39 55	72.30 274	54.25 75	81.67 186	84.05 124	65.94 50	51.31 249	78.09 218	36.06 202	90.43 66	73.00 55	78.09 82	90.50 63

DI_MVS_plusplus_test		71.30 104	68.98 119	78.26 59	72.76 256	54.08 80	81.72 185	83.22 144	65.75 51	51.94 245	78.47 212	36.01 205	90.31 69	73.33 51	77.60 84	90.40 65

EPP-MVSNet		71.14 105	70.07 105	74.33 144	79.18 172	46.52 237	83.81 142	86.49 62	56.32 212	57.95 189	84.90 129	54.23 11	89.14 93	58.14 148	69.65 147	87.33 125

VPA-MVSNet		71.12 106	70.66 94	72.49 182	78.75 180	44.43 259	87.64 45	90.02 9	63.97 78	65.02 105	81.58 183	42.14 127	87.42 163	63.42 107	63.38 193	85.63 154

1314		71.11 107	69.41 111	76.22 101	79.32 169	50.49 171	80.23 215	85.14 91	59.44 139	58.93 175	88.89 81	33.83 224	89.60 89	61.49 123	77.42 88	88.57 107

tpmrst		71.04 108	69.77 108	74.86 134	83.19 88	55.86 36	75.64 262	78.73 221	67.88 26	64.99 107	73.73 261	49.96 34	79.56 282	65.92 89	67.85 160	89.14 93

MVP-Stereo		70.97 109	70.44 97	72.59 179	76.03 216	51.36 157	85.02 117	86.99 53	60.31 130	56.53 210	78.92 205	40.11 152	90.00 77	60.00 138	90.01 2	76.41 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 110	69.91 107	74.12 148	77.95 193	49.57 187	85.76 84	82.59 155	63.60 88	62.15 137	83.28 157	36.04 203	88.30 137	65.46 95	72.34 132	84.49 165

tpm2		70.82 111	68.44 123	77.98 65	80.78 148	56.11 33	74.21 273	81.28 181	60.24 131	68.04 75	75.27 252	52.26 22	88.50 128	55.82 169	68.03 157	89.33 85

ACMMP		70.81 112	69.29 115	75.39 120	81.52 137	51.92 144	83.43 151	83.03 149	56.67 202	58.80 180	88.91 80	31.92 242	88.58 122	65.89 91	73.39 120	85.67 151
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		70.75 113	69.58 109	74.26 146	75.55 221	51.34 158	86.05 79	83.29 143	61.94 107	62.95 133	85.77 120	34.15 218	88.44 129	65.44 98	71.07 139	82.99 194

ab-mvs		70.65 114	69.11 117	75.29 121	80.87 147	46.23 244	73.48 276	85.24 87	59.99 133	66.65 84	80.94 192	43.13 110	88.69 117	63.58 106	68.07 156	90.95 53

Vis-MVSNet		70.61 115	69.34 113	74.42 142	80.95 146	48.49 212	86.03 80	77.51 247	58.74 165	65.55 99	87.78 97	34.37 216	85.95 202	52.53 198	80.61 59	88.80 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 116	70.13 104	71.58 206	81.59 131	39.02 297	80.78 207	84.71 102	59.34 143	66.61 86	88.09 92	37.17 187	85.52 207	61.82 122	71.02 140	90.20 71

CDS-MVSNet		70.48 117	69.43 110	73.64 161	77.56 199	48.83 202	83.51 150	77.45 248	63.27 92	62.33 136	85.54 123	43.85 92	83.29 245	57.38 158	74.00 114	88.79 101
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		70.18 118	69.28 116	72.89 174	77.64 197	42.88 273	85.06 114	87.50 48	62.58 100	62.66 135	82.34 173	43.64 102	89.83 81	58.42 144	63.70 189	85.96 147

Anonymous202405211		70.11 119	67.88 131	76.79 94	87.20 28	47.24 231	89.49 27	77.38 250	54.88 226	66.14 90	86.84 110	20.93 312	91.54 44	56.45 164	71.62 135	91.59 36

PCF-MVS	61.03 10	70.10 120	68.40 124	75.22 124	77.15 208	51.99 142	79.30 235	82.12 163	56.47 210	61.88 141	86.48 117	43.98 91	87.24 166	55.37 173	72.79 128	86.43 141
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 121	68.01 128	76.27 99	84.21 69	51.22 162	87.29 59	79.33 213	58.96 160	63.63 127	86.77 111	33.29 227	90.30 72	44.63 240	73.96 116	87.30 127

1112_ss		70.05 122	69.37 112	72.10 185	80.77 149	42.78 274	85.12 105	76.75 256	59.69 135	61.19 145	92.12 20	47.48 45	83.84 238	53.04 190	68.21 155	89.66 79

BH-w/o		70.02 123	68.51 122	74.56 140	82.77 105	50.39 173	86.60 70	78.14 232	59.77 134	59.65 163	85.57 122	39.27 160	87.30 165	49.86 210	74.94 112	85.99 145

tpmp4_e23		70.01 124	67.13 152	78.65 47	81.93 123	57.90 12	73.99 274	81.35 177	60.61 127	65.28 101	73.78 260	52.48 20	88.60 121	48.40 219	66.35 172	89.44 84

FIs		70.00 125	70.24 103	69.30 240	77.93 195	38.55 299	83.99 140	87.72 44	66.86 37	57.66 196	84.17 135	52.28 21	85.31 211	52.72 197	68.80 151	84.02 172

OpenMVS	61.00 11	69.99 126	67.55 144	77.30 79	78.37 191	54.07 81	84.36 129	85.76 73	57.22 192	56.71 207	87.67 99	30.79 249	92.83 24	43.04 246	84.06 40	85.01 160

HyFIR lowres test		69.94 127	67.58 142	77.04 85	77.11 209	57.29 17	81.49 196	79.11 216	58.27 173	58.86 178	80.41 195	42.33 123	86.96 172	61.91 121	68.68 153	86.87 130

114514_t		69.87 128	67.88 131	75.85 111	88.38 16	52.35 137	86.94 66	83.68 133	53.70 233	55.68 219	85.60 121	30.07 254	91.20 51	55.84 168	71.02 140	83.99 174

Anonymous20240529		69.71 129	67.28 149	77.00 88	83.78 76	50.36 174	88.87 35	85.10 92	47.22 281	64.03 119	83.37 155	27.93 266	92.10 39	57.78 153	67.44 161	88.53 108

TR-MVS		69.71 129	67.85 133	75.27 122	82.94 100	48.48 213	87.40 55	80.86 188	57.15 193	64.61 111	87.08 107	32.67 233	89.64 88	46.38 232	71.55 137	87.68 121

EI-MVSNet		69.70 131	68.70 121	72.68 177	75.00 226	48.90 200	79.54 227	87.16 50	61.05 120	63.88 123	83.74 142	45.87 68	90.44 64	57.42 157	64.68 182	78.70 255

test-LLR		69.65 132	69.01 118	71.60 204	78.67 182	48.17 220	85.13 102	79.72 202	59.18 149	63.13 131	82.58 168	36.91 191	80.24 274	60.56 130	75.17 108	86.39 142

APD-MVS_3200maxsize		69.62 133	68.23 126	73.80 158	81.58 132	48.22 219	81.91 179	79.50 207	48.21 276	64.24 118	89.75 69	31.91 243	87.55 160	63.08 108	73.85 118	85.64 153

v2v482		69.55 134	67.64 138	75.26 123	72.32 273	53.83 82	84.93 119	81.94 165	65.37 59	60.80 153	79.25 201	41.62 138	88.98 106	63.03 109	59.51 229	82.98 195

TAMVS		69.51 135	68.16 127	73.56 164	76.30 212	48.71 204	82.57 168	77.17 253	62.10 105	61.32 144	84.23 134	41.90 133	83.46 243	54.80 180	73.09 125	88.50 109

v1141		69.50 136	67.67 135	74.98 128	72.73 258	53.41 95	85.08 111	82.14 159	64.79 67	60.88 149	78.19 215	43.09 114	89.04 97	62.51 114	59.61 226	82.47 202

divwei89l23v2f112		69.50 136	67.67 135	74.98 128	72.72 259	53.41 95	85.08 111	82.14 159	64.79 67	60.88 149	78.19 215	43.11 111	89.04 97	62.51 114	59.62 225	82.48 201

v1		69.49 138	67.67 135	74.98 128	72.69 260	53.41 95	85.08 111	82.13 162	64.80 66	60.87 151	78.19 215	43.11 111	89.04 97	62.51 114	59.61 226	82.49 200

v1neww		69.43 139	67.62 139	74.89 131	72.90 251	53.31 103	85.12 105	81.11 182	64.29 72	61.00 146	78.53 208	42.88 117	88.98 106	62.66 112	60.06 219	82.37 204

v7new		69.43 139	67.62 139	74.89 131	72.90 251	53.31 103	85.12 105	81.11 182	64.29 72	61.00 146	78.53 208	42.88 117	88.98 106	62.66 112	60.06 219	82.37 204

v6		69.43 139	67.61 141	74.88 133	72.87 255	53.30 107	85.12 105	81.10 184	64.29 72	60.99 148	78.52 210	42.88 117	88.98 106	62.67 111	60.06 219	82.37 204

PVSNet	62.49 8	69.27 142	67.81 134	73.64 161	84.41 64	51.85 145	84.63 127	77.80 240	66.42 38	59.80 161	84.95 128	22.14 306	80.44 271	55.03 176	75.11 110	88.62 105

MVS_111021_LR		69.07 143	67.91 129	72.54 180	77.27 203	49.56 189	79.77 223	73.96 288	59.33 145	60.73 154	87.82 95	30.19 253	81.53 260	69.94 67	72.19 134	86.53 138

mvs-test1		69.04 144	67.57 143	73.44 166	75.17 222	51.68 149	86.57 71	74.48 281	62.15 103	62.07 139	85.79 119	30.59 250	87.48 161	65.40 99	65.94 174	81.18 226

GA-MVS		69.04 144	66.70 160	76.06 105	75.11 224	52.36 136	83.12 158	80.23 195	63.32 91	60.65 155	79.22 202	30.98 248	88.37 131	61.25 124	66.41 167	87.46 123

cascas		69.01 146	66.13 167	77.66 70	79.36 167	55.41 43	86.99 63	83.75 132	56.69 201	58.92 176	81.35 185	24.31 290	92.10 39	53.23 187	70.61 142	85.46 155

FMVSNet3		68.84 147	67.40 147	73.19 169	85.05 56	48.53 210	85.71 88	85.36 81	60.90 122	57.58 197	79.15 203	42.16 126	86.77 178	47.25 226	63.40 190	84.27 169

UniMVSNet_NR-MVSNet		68.82 148	68.29 125	70.40 227	75.71 220	42.59 276	84.23 132	86.78 56	66.31 40	58.51 182	82.45 170	51.57 24	84.64 225	53.11 188	55.96 263	83.96 177

v1144		68.81 149	66.82 154	74.80 137	72.34 272	53.46 90	84.68 125	81.77 171	64.25 75	60.28 158	77.91 219	40.23 149	88.95 110	60.37 133	59.52 228	81.97 208

IS-MVSNet		68.80 150	67.55 144	72.54 180	78.50 188	43.43 267	81.03 202	79.35 212	59.12 153	57.27 205	86.71 112	46.05 66	87.70 157	44.32 241	75.60 105	86.49 139

1121		68.79 151	66.77 157	74.82 135	83.08 93	53.46 90	80.23 215	71.53 306	45.47 294	66.31 89	87.19 105	34.02 219	85.13 214	52.78 193	80.36 63	85.87 149

PS-MVSNAJss		68.78 152	67.17 151	73.62 163	73.01 246	48.33 218	84.95 118	84.81 98	59.30 146	58.91 177	79.84 198	37.77 171	88.86 114	62.83 110	63.12 200	83.67 183

v7		68.76 153	66.79 156	74.68 138	72.60 263	53.37 98	84.72 124	80.88 187	63.80 82	60.43 157	78.21 214	40.05 154	88.89 113	60.34 134	60.07 218	81.77 214

thres200		68.71 154	67.27 150	73.02 170	84.73 60	46.76 234	85.03 116	87.73 43	62.34 102	59.87 159	83.45 153	43.15 108	88.32 136	31.25 290	67.91 159	83.98 175

UGNet		68.71 154	67.11 153	73.50 165	80.55 161	47.61 225	84.08 135	78.51 226	59.45 138	65.68 98	82.73 166	23.78 292	85.08 216	52.80 192	76.40 95	87.80 118
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		68.64 156	65.68 176	77.53 74	67.78 308	53.34 100	79.42 230	82.84 153	65.96 49	46.54 290	76.15 245	25.16 286	88.83 116	69.74 68	77.53 87	90.43 64

EPMVS		68.45 157	65.44 183	77.47 76	84.91 58	56.17 32	71.89 294	81.91 168	61.72 111	60.85 152	72.49 274	36.21 198	87.06 169	47.32 225	71.62 135	89.17 92

test-mter		68.36 158	67.29 148	71.60 204	78.67 182	48.17 220	85.13 102	79.72 202	53.38 236	63.13 131	82.58 168	27.23 272	80.24 274	60.56 130	75.17 108	86.39 142

tpm		68.36 158	67.48 146	70.97 216	79.93 164	51.34 158	76.58 252	78.75 220	67.73 28	63.54 129	74.86 254	48.33 38	72.36 324	53.93 185	63.71 188	89.21 90

BH-untuned		68.28 160	66.40 161	73.91 153	81.62 129	50.01 183	85.56 92	77.39 249	57.63 188	57.47 202	83.69 144	36.36 197	87.08 168	44.81 239	73.08 126	84.65 164

v148		68.24 161	66.35 162	73.88 154	71.76 279	51.47 154	84.23 132	81.90 169	63.69 86	58.94 174	76.44 237	43.72 100	87.78 155	60.63 129	55.86 265	82.39 203

abl_6		68.03 162	66.15 166	73.66 160	78.54 187	48.48 213	79.77 223	78.04 236	47.39 280	63.70 125	88.25 89	28.21 262	89.06 94	60.17 137	71.25 138	83.45 185

v1192		67.96 163	65.74 174	74.63 139	71.79 278	53.43 94	84.06 137	80.99 186	63.19 94	59.56 166	77.46 225	37.50 181	88.65 118	58.20 147	58.93 234	81.79 213

v144192		67.86 164	65.76 173	74.16 147	71.68 280	53.09 118	84.14 134	80.83 189	62.85 98	59.21 172	77.28 228	39.30 159	88.00 147	58.67 142	57.88 251	81.40 221

HPM-MVS_fast		67.86 164	66.28 163	72.61 178	80.67 152	48.34 217	81.18 199	75.95 269	50.81 264	59.55 167	88.05 94	27.86 267	85.98 199	58.83 141	73.58 119	83.51 184

AdaColmap		67.86 164	65.48 180	75.00 126	88.15 20	54.99 57	86.10 78	76.63 259	49.30 272	57.80 192	86.65 114	29.39 257	88.94 112	45.10 238	70.21 144	81.06 227

UniMVSNet (Re)		67.71 167	66.80 155	70.45 225	74.44 231	42.93 272	82.42 171	84.90 96	63.69 86	59.63 164	80.99 191	47.18 48	85.23 213	51.17 205	56.75 257	83.19 192

V42		67.66 168	65.60 179	73.86 155	70.69 290	53.63 86	81.50 194	78.61 224	63.85 80	59.49 168	77.49 224	37.98 168	87.65 158	62.33 117	58.43 241	80.29 242

WR-MVS		67.58 169	66.76 158	70.04 236	75.92 218	45.06 256	86.23 76	85.28 84	64.31 71	58.50 184	81.00 190	44.80 82	82.00 259	49.21 214	55.57 268	83.06 193

tfpn200view9		67.57 170	66.13 167	71.89 198	84.05 71	45.07 253	83.40 153	87.71 45	60.79 123	57.79 193	82.76 163	43.53 103	87.80 151	28.80 296	66.36 168	82.78 198

FMVSNet2		67.57 170	65.79 172	72.90 172	82.71 107	47.97 223	85.15 101	84.93 95	58.55 168	56.71 207	78.26 213	36.72 194	86.67 181	46.15 234	62.94 203	84.07 171

FC-MVSNet-test		67.49 172	67.91 129	66.21 272	76.06 214	33.06 323	80.82 206	87.18 49	64.44 70	54.81 221	82.87 161	50.40 31	82.60 254	48.05 221	66.55 166	82.98 195

v1921920		67.45 173	65.23 187	74.10 149	71.51 283	52.90 126	83.75 144	80.44 194	62.48 101	59.12 173	77.13 229	36.98 189	87.90 148	57.53 155	58.14 246	81.49 218

gg-mvs-nofinetune		67.43 174	64.53 195	76.13 103	85.95 36	47.79 224	64.38 317	88.28 32	39.34 318	66.62 85	41.27 345	58.69 5	89.00 103	49.64 212	86.62 15	91.59 36

thres400		67.40 175	66.13 167	71.19 212	84.05 71	45.07 253	83.40 153	87.71 45	60.79 123	57.79 193	82.76 163	43.53 103	87.80 151	28.80 296	66.36 168	80.71 233

UA-Net		67.32 176	66.23 164	70.59 223	78.85 178	41.23 287	73.60 275	75.45 275	61.54 113	66.61 86	84.53 130	38.73 164	86.57 187	42.48 250	74.24 113	83.98 175

v8		67.25 177	64.99 190	74.04 150	72.89 253	53.31 103	82.37 172	80.11 197	61.54 113	54.29 226	76.02 248	42.89 116	88.41 130	58.43 143	56.36 258	80.39 241

NR-MVSNet		67.25 177	65.99 170	71.04 215	73.27 244	43.91 263	85.32 97	84.75 101	66.05 47	53.65 232	82.11 178	45.05 75	85.97 201	47.55 223	56.18 261	83.24 190

Test_1112_low_res		67.18 179	66.23 164	70.02 237	78.75 180	41.02 288	83.43 151	73.69 291	57.29 191	58.45 186	82.39 172	45.30 74	80.88 265	50.50 207	66.26 173	88.16 110

CPTT-MVS		67.15 180	65.84 171	71.07 214	80.96 145	50.32 177	81.94 178	74.10 285	46.18 290	57.91 190	87.64 100	29.57 255	81.31 262	64.10 104	70.18 145	81.56 217

GBi-Net		67.09 181	65.47 181	71.96 192	82.71 107	46.36 239	83.52 146	83.31 140	58.55 168	57.58 197	76.23 241	36.72 194	86.20 190	47.25 226	63.40 190	83.32 187

test1		67.09 181	65.47 181	71.96 192	82.71 107	46.36 239	83.52 146	83.31 140	58.55 168	57.58 197	76.23 241	36.72 194	86.20 190	47.25 226	63.40 190	83.32 187

PatchmatchNet		67.07 183	63.63 201	77.40 77	83.10 90	58.03 9	72.11 291	77.77 242	58.85 163	59.37 169	70.83 285	37.84 170	84.93 221	42.96 247	69.83 146	89.26 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 184	64.68 193	73.93 152	71.38 286	52.66 129	83.39 155	79.98 198	61.97 106	58.44 187	77.11 230	35.25 210	87.81 150	56.46 163	58.15 244	81.33 222

TranMVSNet+NR-MVSNet		66.94 185	65.61 178	70.93 218	73.45 241	43.38 268	83.02 162	84.25 112	65.31 61	58.33 188	81.90 180	39.92 156	85.52 207	49.43 213	54.89 271	83.89 179

thres100view900		66.87 186	65.42 184	71.24 210	83.29 84	43.15 269	81.67 186	87.78 38	59.04 154	55.92 215	82.18 175	43.73 96	87.80 151	28.80 296	66.36 168	82.78 198

DU-MVS		66.84 187	65.74 174	70.16 230	73.27 244	42.59 276	81.50 194	82.92 151	63.53 90	58.51 182	82.11 178	40.75 143	84.64 225	53.11 188	55.96 263	83.24 190

conf200view11		66.80 188	65.42 184	70.95 217	83.29 84	43.15 269	81.67 186	87.78 38	59.04 154	55.92 215	82.18 175	43.73 96	87.80 151	28.80 296	66.36 168	81.89 209

IterMVS-LS		66.63 189	65.36 186	70.42 226	75.10 225	48.90 200	81.45 197	76.69 258	61.05 120	55.71 218	77.10 231	45.86 69	83.65 241	57.44 156	57.88 251	78.70 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 190	64.20 198	73.83 157	72.59 265	53.37 98	81.88 180	79.91 199	61.11 118	54.09 228	75.60 250	40.06 153	88.26 140	56.47 162	56.10 262	79.86 246

Fast-Effi-MVS+-dtu		66.53 191	64.10 199	73.84 156	72.41 270	52.30 139	84.73 123	75.66 272	59.51 137	56.34 212	79.11 204	28.11 264	85.85 204	57.74 154	63.29 195	83.35 186

thres600view7		66.46 192	65.12 188	70.47 224	83.41 80	43.80 265	82.15 174	87.78 38	59.37 142	56.02 214	82.21 174	43.73 96	86.90 174	26.51 309	64.94 177	80.71 233

LPG-MVS_test		66.44 193	64.58 194	72.02 189	74.42 232	48.60 207	83.07 160	80.64 191	54.69 228	53.75 230	83.83 140	25.73 283	86.98 170	60.33 135	64.71 179	80.48 239

tfpn111		66.40 194	64.99 190	70.63 222	83.29 84	43.15 269	81.67 186	87.78 38	59.04 154	55.92 215	82.18 175	43.73 96	86.83 177	26.34 311	64.92 178	81.89 209

tpm cat1		66.28 195	62.78 207	76.77 95	81.40 139	57.14 19	70.03 303	77.19 252	53.00 240	58.76 181	70.73 287	46.17 64	86.73 180	43.27 245	64.46 183	86.44 140

EPNet_dtu		66.25 196	66.71 159	64.87 287	78.66 184	34.12 318	82.80 166	75.51 273	61.75 110	64.47 116	86.90 109	37.06 188	72.46 323	43.65 244	69.63 148	88.02 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 197	64.96 192	70.08 232	75.17 222	49.64 186	82.01 176	74.48 281	62.15 103	57.83 191	76.08 247	30.59 250	83.79 239	65.40 99	60.93 216	76.81 287

ACMP	61.11 9	66.24 197	64.33 196	72.00 191	74.89 228	49.12 195	83.18 157	79.83 200	55.41 222	52.29 241	82.68 167	25.83 281	86.10 196	60.89 126	63.94 187	80.78 231
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 199	63.67 200	73.31 167	83.07 94	48.75 203	86.01 81	84.67 103	45.27 295	56.54 209	76.67 235	28.06 265	88.95 110	52.78 193	59.95 222	82.23 207

OMC-MVS		65.97 200	65.06 189	68.71 252	72.97 247	42.58 278	78.61 239	75.35 276	54.72 227	59.31 170	86.25 118	33.30 226	77.88 293	57.99 149	67.05 163	85.66 152

X-MVStestdata		65.85 201	62.20 210	76.81 91	83.41 80	52.48 131	84.88 120	83.20 146	58.03 177	63.91 121	4.82 363	35.50 208	89.78 82	65.50 92	80.50 61	88.16 110

Vis-MVSNet (Re-imp)		65.52 202	65.63 177	65.17 285	77.49 200	30.54 331	75.49 266	77.73 244	59.34 143	52.26 243	86.69 113	49.38 36	80.53 270	37.07 264	75.28 107	84.42 167

Baseline_NR-MVSNet		65.49 203	64.27 197	69.13 241	74.37 234	41.65 283	83.39 155	78.85 217	59.56 136	59.62 165	76.88 232	40.75 143	87.44 162	49.99 209	55.05 269	78.28 272

view600		64.79 204	63.45 202	68.82 246	82.13 117	40.75 290	79.41 231	88.29 28	56.54 205	53.26 234	81.30 186	44.26 85	85.01 217	22.97 321	62.85 204	80.71 233

view800		64.79 204	63.45 202	68.82 246	82.13 117	40.75 290	79.41 231	88.29 28	56.54 205	53.26 234	81.30 186	44.26 85	85.01 217	22.97 321	62.85 204	80.71 233

conf0.05thres1000		64.79 204	63.45 202	68.82 246	82.13 117	40.75 290	79.41 231	88.29 28	56.54 205	53.26 234	81.30 186	44.26 85	85.01 217	22.97 321	62.85 204	80.71 233

tfpn		64.79 204	63.45 202	68.82 246	82.13 117	40.75 290	79.41 231	88.29 28	56.54 205	53.26 234	81.30 186	44.26 85	85.01 217	22.97 321	62.85 204	80.71 233

FMVSNet1		64.57 208	62.11 211	71.96 192	77.32 202	46.36 239	83.52 146	83.31 140	52.43 253	54.42 224	76.23 241	27.80 268	86.20 190	42.59 249	61.34 213	83.32 187

tfpn_ndepth		64.50 209	63.34 206	67.99 256	81.84 126	38.30 301	79.26 236	83.57 138	53.69 234	52.86 239	84.51 131	46.96 51	84.79 222	24.28 316	63.09 201	80.87 230

dp		64.41 210	61.58 223	72.90 172	82.40 113	54.09 79	72.53 285	76.59 260	60.39 129	55.68 219	70.39 288	35.18 211	76.90 301	39.34 256	61.71 212	87.73 120

v18		64.36 211	61.80 213	72.05 186	72.97 247	53.31 103	81.16 200	77.76 243	59.14 151	48.50 268	68.97 295	42.91 115	84.38 227	56.62 160	48.17 293	78.47 259

ACMM	58.35 12	64.35 212	62.01 212	71.38 208	74.21 235	48.51 211	82.25 173	79.66 204	47.61 278	54.54 223	80.11 196	25.26 285	86.00 198	51.26 203	63.16 198	79.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v16		64.25 213	61.66 222	72.03 187	72.91 250	53.28 109	80.93 203	77.81 239	58.86 162	48.30 269	68.80 298	42.70 120	84.37 228	56.44 166	48.14 294	78.44 262

v17		64.19 214	61.58 223	72.03 187	72.89 253	53.28 109	80.91 204	77.80 240	58.87 161	48.22 270	68.77 299	42.69 121	84.37 228	56.43 167	47.66 297	78.43 263

pm-mvs1		64.12 215	62.56 208	68.78 251	71.68 280	38.87 298	82.89 164	81.57 173	55.54 221	53.89 229	77.82 220	37.73 174	86.74 179	48.46 218	53.49 280	80.72 232

test_djsdf		63.84 216	61.56 225	70.70 220	68.78 299	44.69 257	81.63 190	81.44 175	50.28 265	52.27 242	76.26 240	26.72 275	86.11 194	60.83 127	55.84 266	81.29 225

v15		63.83 217	61.13 228	71.93 195	72.60 263	53.21 112	80.44 210	78.22 228	58.80 164	47.57 275	68.22 301	42.50 122	84.18 230	55.82 169	46.02 309	78.39 265

IterMVS		63.77 218	61.67 221	70.08 232	72.68 261	51.24 161	80.44 210	75.51 273	60.51 128	51.41 247	73.70 264	32.08 240	78.91 283	54.30 182	54.35 274	80.08 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V14		63.72 219	60.99 230	71.91 197	72.58 266	53.18 113	80.24 214	78.19 229	58.53 171	47.35 281	68.10 302	42.28 125	84.18 230	55.68 171	45.97 310	78.36 268

testing_2		63.60 220	59.86 241	74.82 135	61.87 330	52.39 135	73.06 281	82.76 154	61.49 115	39.96 316	67.39 309	21.06 311	88.34 133	67.07 83	64.10 184	83.72 182

V9		63.60 220	60.84 231	71.87 199	72.51 268	53.12 117	80.04 219	78.15 231	58.25 174	47.14 283	67.98 303	42.08 129	84.18 230	55.47 172	45.92 312	78.32 269

v12		63.47 222	60.68 234	71.85 200	72.45 269	53.08 119	79.83 221	78.13 233	57.95 180	46.89 285	67.87 305	41.81 136	84.17 233	55.30 174	45.87 313	78.29 271

v11		63.44 223	60.66 235	71.79 202	72.61 262	53.02 123	79.80 222	78.08 235	58.30 172	47.27 282	67.91 304	40.67 147	84.14 235	54.93 177	46.39 307	78.23 275

v13		63.36 224	60.54 237	71.82 201	72.41 270	53.03 122	79.64 226	78.10 234	57.66 187	46.67 288	67.75 306	41.68 137	84.17 233	55.11 175	45.82 314	78.25 274

pmmvs4		63.34 225	61.07 229	70.16 230	70.14 292	50.53 170	79.97 220	71.41 308	55.08 224	54.12 227	78.58 207	32.79 232	82.09 258	50.33 208	57.22 255	77.86 278

Patchmatch-test1		63.23 226	59.16 247	75.43 118	78.58 186	57.92 11	61.61 325	77.53 246	56.71 200	57.75 195	70.98 284	31.97 241	78.19 287	40.97 253	56.36 258	90.18 72

jajsoiax		63.21 227	60.84 231	70.32 228	68.33 304	44.45 258	81.23 198	81.05 185	53.37 237	50.96 253	77.81 221	17.49 327	85.49 209	59.31 139	58.05 247	81.02 228

MIMVSNet		63.12 228	60.29 239	71.61 203	75.92 218	46.65 235	65.15 313	81.94 165	59.14 151	54.65 222	69.47 291	25.74 282	80.63 268	41.03 252	69.56 149	87.55 122

conf0.01		63.04 229	61.74 214	66.95 266	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	81.89 209

conf0.002		63.04 229	61.74 214	66.95 266	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	81.89 209

mvs_tets		62.96 231	60.55 236	70.19 229	68.22 306	44.24 262	80.90 205	80.74 190	52.99 241	50.82 261	77.56 222	16.74 330	85.44 210	59.04 140	57.94 248	80.89 229

thresconf0.02		62.84 232	61.74 214	66.14 273	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	79.44 248

tfpn_n400		62.84 232	61.74 214	66.14 273	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	79.44 248

tfpnconf		62.84 232	61.74 214	66.14 273	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	79.44 248

tfpnview11		62.84 232	61.74 214	66.14 273	80.60 153	35.92 309	76.01 255	84.09 116	52.62 243	50.87 255	83.60 146	46.49 57	83.04 247	22.59 326	58.77 235	79.44 248

TransMVSNet (Re)		62.82 236	60.76 233	69.02 242	73.98 237	41.61 284	86.36 73	79.30 214	56.90 194	52.53 240	76.44 237	41.85 134	87.60 159	38.83 257	40.61 326	77.86 278

pmmvs5		62.80 237	61.18 227	67.66 259	69.53 297	42.37 281	82.65 167	75.19 277	54.30 231	52.03 244	78.51 211	31.64 245	80.67 267	48.60 216	58.15 244	79.95 245

tfpn1000		62.79 238	61.74 214	65.95 278	80.50 162	35.93 308	76.53 254	83.99 127	51.24 261	49.82 263	83.44 154	47.32 46	83.02 253	21.84 333	60.99 214	78.89 253

test0.0.03 1		62.54 239	62.44 209	62.86 297	72.28 275	29.51 333	82.93 163	78.78 219	59.18 149	53.07 238	82.41 171	36.91 191	77.39 297	37.45 260	58.96 233	81.66 216

v7n		62.50 240	59.27 246	72.20 184	67.25 310	49.83 184	77.87 245	80.12 196	52.50 252	48.80 267	73.07 269	32.10 239	87.90 148	46.83 229	54.92 270	78.86 254

CR-MVSNet		62.47 241	59.04 249	72.77 175	73.97 238	56.57 25	60.52 328	71.72 302	60.04 132	57.49 200	65.86 314	38.94 161	80.31 272	42.86 248	59.93 223	81.42 219

tpmvs		62.45 242	59.42 244	71.53 207	83.93 73	54.32 73	70.03 303	77.61 245	51.91 256	53.48 233	68.29 300	37.91 169	86.66 182	33.36 279	58.27 242	73.62 313

EG-PatchMatch MVS		62.40 243	59.59 242	70.81 219	73.29 243	49.05 196	85.81 82	84.78 100	51.85 258	44.19 297	73.48 267	15.52 335	89.85 80	40.16 254	67.24 162	73.54 314

XVG-OURS-SEG-HR		62.02 244	59.54 243	69.46 239	65.30 316	45.88 246	65.06 314	73.57 293	46.45 288	57.42 203	83.35 156	26.95 274	78.09 290	53.77 186	64.03 185	84.42 167

XVG-OURS		61.88 245	59.34 245	69.49 238	65.37 315	46.27 242	64.80 316	73.49 294	47.04 283	57.41 204	82.85 162	25.15 287	78.18 288	53.00 191	64.98 176	84.01 173

TAPA-MVS	56.12 14	61.82 246	60.18 240	66.71 268	78.48 189	37.97 303	75.19 268	76.41 263	46.82 285	57.04 206	86.52 116	27.67 270	77.03 299	26.50 310	67.02 164	85.14 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnnormal		61.47 247	59.09 248	68.62 254	76.29 213	41.69 282	81.14 201	85.16 89	54.48 230	51.32 248	73.63 265	32.32 236	86.89 175	21.78 335	55.71 267	77.29 285

v748		61.35 248	58.24 252	70.69 221	66.28 311	47.35 228	76.58 252	79.17 215	53.09 239	46.37 292	71.50 282	33.18 228	86.33 189	46.78 230	51.19 288	78.39 265

PVSNet_0	57.04 13	61.19 249	57.24 257	73.02 170	77.45 201	50.31 178	79.43 229	77.36 251	63.96 79	47.51 278	72.45 276	25.03 288	83.78 240	52.76 196	19.22 350	84.96 161

PLC	52.38 18	60.89 250	58.97 250	66.68 270	81.77 127	45.70 248	78.96 238	74.04 287	43.66 306	47.63 274	83.19 159	23.52 297	77.78 296	37.47 259	60.46 217	76.55 291
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 251	60.44 238	62.07 299	75.00 226	32.73 325	79.54 227	73.49 294	36.98 326	56.28 213	83.74 142	29.28 258	69.53 333	46.48 231	63.23 196	83.94 178

CNLPA		60.59 252	58.44 251	67.05 265	79.21 171	47.26 230	79.75 225	64.34 330	42.46 313	51.90 246	83.94 137	27.79 269	75.41 306	37.12 262	59.49 230	78.47 259

anonymousdsp		60.46 253	57.65 254	68.88 243	63.63 324	45.09 252	72.93 283	78.63 223	46.52 287	51.12 250	72.80 273	21.46 309	83.07 246	57.79 152	53.97 275	78.47 259

v52		59.82 254	56.41 264	70.06 234	61.49 333	48.67 205	69.46 307	75.80 270	52.55 250	47.49 279	68.82 297	28.60 259	85.70 205	52.13 200	51.34 287	75.80 295

V4		59.82 254	56.41 264	70.05 235	61.49 333	48.67 205	69.46 307	75.79 271	52.55 250	47.49 279	68.83 296	28.60 259	85.70 205	52.13 200	51.35 286	75.80 295

ACMH	53.70 16	59.78 256	55.94 269	71.28 209	76.59 211	48.35 216	80.15 218	76.11 265	49.74 269	41.91 309	73.45 268	16.50 332	90.31 69	31.42 288	57.63 253	75.17 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 257	57.15 258	67.09 263	66.01 312	36.86 307	80.50 209	78.64 222	45.05 297	49.05 266	73.94 259	27.28 271	86.10 196	43.96 243	49.94 291	78.31 270

MSDG		59.44 258	55.14 273	72.32 183	74.69 229	50.71 165	74.39 272	73.58 292	44.44 301	43.40 303	77.52 223	19.45 317	90.87 58	31.31 289	57.49 254	75.38 300

DP-MVS		59.24 259	56.12 267	68.63 253	88.24 19	50.35 175	82.51 170	64.43 329	41.10 315	46.70 287	78.77 206	24.75 289	88.57 125	22.26 332	56.29 260	66.96 333

OpenMVS_ROB	53.19 17	59.20 260	56.00 268	68.83 245	71.13 288	44.30 260	83.64 145	75.02 278	46.42 289	46.48 291	73.03 270	18.69 321	88.14 141	27.74 305	61.80 211	74.05 310

our_test_3		59.11 261	55.08 274	71.18 213	71.42 284	53.29 108	81.96 177	74.52 280	48.32 275	42.08 307	69.28 293	28.14 263	82.15 256	34.35 277	45.68 315	78.11 277

Anonymous20231206		59.08 262	57.59 255	63.55 292	68.77 300	32.14 328	80.26 213	79.78 201	50.00 268	49.39 264	72.39 277	26.64 276	78.36 286	33.12 282	57.94 248	80.14 243

WR-MVS_H		58.91 263	58.04 253	61.54 304	69.07 298	33.83 320	76.91 249	81.99 164	51.40 260	48.17 271	74.67 255	40.23 149	74.15 310	31.78 286	48.10 295	76.64 289

LCM-MVSNet-Re		58.82 264	56.54 262	65.68 279	79.31 170	29.09 336	61.39 327	45.79 349	60.73 125	37.65 322	72.47 275	31.42 246	81.08 263	49.66 211	70.41 143	86.87 130

Patchmatch-RL test		58.72 265	54.32 276	71.92 196	63.91 323	44.25 261	61.73 324	55.19 341	57.38 190	49.31 265	54.24 339	37.60 177	80.89 264	62.19 119	47.28 301	90.63 57

FMVSNet5		58.61 266	56.45 263	65.10 286	77.20 207	39.74 295	74.77 269	77.12 254	50.27 267	43.28 304	67.71 307	26.15 280	76.90 301	36.78 267	54.78 272	78.65 257

ppachtmachnet_test		58.56 267	54.34 275	71.24 210	71.42 284	54.74 64	81.84 182	72.27 299	49.02 274	45.86 295	68.99 294	26.27 278	83.30 244	30.12 292	43.23 322	75.69 297

ACMH+	54.58 15	58.55 268	55.24 271	68.50 255	74.68 230	45.80 247	80.27 212	70.21 315	47.15 282	42.77 306	75.48 251	16.73 331	85.98 199	35.10 275	54.78 272	73.72 312

CP-MVSNet		58.54 269	57.57 256	61.46 306	68.50 302	33.96 319	76.90 250	78.60 225	51.67 259	47.83 272	76.60 236	34.99 213	72.79 321	35.45 271	47.58 298	77.64 282

RPMNet		58.49 270	53.74 280	72.77 175	73.97 238	56.57 25	60.52 328	72.39 298	35.72 331	57.49 200	58.87 333	37.73 174	80.31 272	27.01 308	59.93 223	81.42 219

PEN-MVS		58.35 271	57.15 258	61.94 301	67.55 309	34.39 317	77.01 248	78.35 227	51.87 257	47.72 273	76.73 234	33.91 221	73.75 315	34.03 278	47.17 302	77.68 280

Anonymous20240521		58.32 272	56.96 261	62.41 298	71.99 277	28.83 337	73.05 282	81.66 172	49.54 271	44.80 296	76.15 245	27.02 273	80.79 266	31.63 287	56.78 256	77.60 283

PS-CasMVS		58.12 273	57.03 260	61.37 307	68.24 305	33.80 321	76.73 251	78.01 237	51.20 262	47.54 277	76.20 244	32.85 230	72.76 322	35.17 273	47.37 300	77.55 284

UnsupCasMVSNet_eth		57.56 274	55.15 272	64.79 288	64.57 321	33.12 322	73.17 280	83.87 131	58.98 159	41.75 310	70.03 289	22.54 301	79.92 278	46.12 235	35.31 335	81.32 224

CHOSEN 280x420		57.53 275	56.38 266	60.97 308	74.01 236	48.10 222	46.30 343	54.31 343	48.18 277	50.88 254	77.43 226	38.37 167	59.16 345	54.83 178	63.14 199	75.66 298

DTE-MVSNet		57.03 276	55.73 270	60.95 309	65.94 313	32.57 326	75.71 261	77.09 255	51.16 263	46.65 289	76.34 239	32.84 231	73.22 319	30.94 291	44.87 317	77.06 286

PatchMatch-RL		56.66 277	53.75 279	65.37 284	77.91 196	45.28 251	69.78 305	60.38 336	41.35 314	47.57 275	73.73 261	16.83 329	76.91 300	36.99 265	59.21 232	73.92 311

PatchT		56.60 278	52.97 283	67.48 260	72.94 249	46.16 245	57.30 334	73.78 290	38.77 320	54.37 225	57.26 336	37.52 179	78.06 291	32.02 284	52.79 281	78.23 275

Patchmtry		56.56 279	52.95 284	67.42 261	72.53 267	50.59 169	59.05 330	71.72 302	37.86 324	46.92 284	65.86 314	38.94 161	80.06 277	36.94 266	46.72 306	71.60 323

test_0402		56.45 280	53.03 282	66.69 269	76.78 210	50.31 178	81.76 183	69.61 317	42.79 311	43.88 299	72.13 279	22.82 300	86.46 188	16.57 348	50.94 289	63.31 341

LS3D		56.40 281	53.82 278	64.12 289	81.12 143	45.69 249	73.42 277	66.14 324	35.30 336	43.24 305	79.88 197	22.18 305	79.62 281	19.10 343	64.00 186	67.05 332

ADS-MVSNet		56.17 282	51.95 290	68.84 244	80.60 153	53.07 120	55.03 336	70.02 316	44.72 298	51.00 251	61.19 325	22.83 298	78.88 284	28.54 301	53.63 277	74.57 305

XVG-ACMP-BASELINE		56.03 283	52.85 285	65.58 280	61.91 329	40.95 289	63.36 318	72.43 297	45.20 296	46.02 293	74.09 257	9.20 345	78.12 289	45.13 237	58.27 242	77.66 281

pmmvs-eth3d		55.97 284	52.78 286	65.54 281	61.02 335	46.44 238	75.36 267	67.72 320	49.61 270	43.65 301	67.58 308	21.63 308	77.04 298	44.11 242	44.33 319	73.15 319

F-COLMAP		55.96 285	53.65 281	62.87 296	72.76 256	42.77 275	74.70 271	70.37 313	40.03 316	41.11 313	79.36 199	17.77 325	73.70 316	32.80 283	53.96 276	72.15 320

CMPMVS	40.41 21	55.34 286	52.64 287	63.46 293	60.88 336	43.84 264	61.58 326	71.06 309	30.43 342	36.33 324	74.63 256	24.14 291	75.44 305	48.05 221	66.62 165	71.12 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 287	54.07 277	58.68 314	63.14 326	25.00 343	77.69 246	74.78 279	52.64 242	43.43 302	72.39 277	26.21 279	74.76 309	29.31 294	47.05 304	76.28 293

ADS-MVSNet2		55.21 288	51.44 291	66.51 271	80.60 153	49.56 189	55.03 336	65.44 326	44.72 298	51.00 251	61.19 325	22.83 298	75.41 306	28.54 301	53.63 277	74.57 305

SixPastTwentyTwo		54.37 289	50.10 295	67.21 262	70.70 289	41.46 285	74.73 270	64.69 328	47.56 279	39.12 319	69.49 290	18.49 323	84.69 224	31.87 285	34.20 340	75.48 299

USDC		54.36 290	51.23 292	63.76 291	64.29 322	37.71 304	62.84 323	73.48 296	56.85 195	35.47 328	71.94 281	9.23 344	78.43 285	38.43 258	48.57 292	75.13 302

testgi		54.25 291	52.57 288	59.29 312	62.76 327	21.65 349	72.21 290	70.47 311	53.25 238	41.94 308	77.33 227	14.28 336	77.95 292	29.18 295	51.72 285	78.28 272

K. test v3		54.04 292	49.42 299	67.92 258	68.55 301	42.57 279	75.51 265	63.07 332	52.07 254	39.21 318	64.59 318	19.34 318	82.21 255	37.11 263	25.31 346	78.97 252

test2356		53.94 293	52.37 289	58.64 315	61.58 331	27.53 342	78.20 243	74.33 283	46.92 284	44.01 298	66.04 313	18.91 320	74.11 311	28.80 296	52.55 282	74.28 307

UnsupCasMVSNet_bld		53.86 294	50.53 294	63.84 290	63.52 325	34.75 316	71.38 295	81.92 167	46.53 286	38.95 320	57.93 334	20.55 314	80.20 276	39.91 255	34.09 341	76.57 290

YYNet1		53.82 295	49.96 296	65.41 283	70.09 294	48.95 198	72.30 288	71.66 304	44.25 302	31.89 340	63.07 322	23.73 293	73.95 313	33.26 280	39.40 328	73.34 316

MDA-MVSNet_test_wron		53.82 295	49.95 297	65.43 282	70.13 293	49.05 196	72.30 288	71.65 305	44.23 303	31.85 341	63.13 321	23.68 296	74.01 312	33.25 281	39.35 329	73.23 318

Patchmatch-test		53.33 297	48.17 301	68.81 250	73.31 242	42.38 280	42.98 347	58.23 338	32.53 338	38.79 321	70.77 286	39.66 157	73.51 317	25.18 313	52.06 284	90.55 58

LTVRE_ROB	45.45 19	52.73 298	49.74 298	61.69 303	69.78 295	34.99 315	44.52 345	67.60 321	43.11 310	43.79 300	74.03 258	18.54 322	81.45 261	28.39 303	57.94 248	68.62 330
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		52.63 299	50.72 293	58.37 316	62.69 328	28.13 339	72.60 284	75.97 268	30.94 341	40.76 315	72.11 280	20.16 315	70.80 329	35.11 274	46.11 308	76.19 294

OurMVSNet-221017-0		52.39 300	48.73 300	63.35 294	65.21 317	38.42 300	68.54 310	64.95 327	38.19 321	39.57 317	71.43 283	13.23 338	79.92 278	37.16 261	40.32 327	71.72 322

JIA-IIPM		52.33 301	47.77 304	66.03 277	71.20 287	46.92 233	40.00 351	76.48 261	37.10 325	46.73 286	37.02 347	32.96 229	77.88 293	35.97 269	52.45 283	73.29 317

MDA-MVSNet-bldmvs		51.56 302	47.75 305	63.00 295	71.60 282	47.32 229	69.70 306	72.12 300	43.81 305	27.65 346	63.38 320	21.97 307	75.96 303	27.30 307	32.19 342	65.70 336

COLMAP_ROB	43.60 20	50.90 303	48.05 302	59.47 311	67.81 307	40.57 294	71.25 296	62.72 334	36.49 330	36.19 325	73.51 266	13.48 337	73.92 314	20.71 339	50.26 290	63.92 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 304	47.81 303	57.96 317	61.53 332	27.80 341	67.40 311	74.06 286	43.25 309	33.31 339	65.38 317	16.03 333	71.34 328	21.80 334	47.55 299	74.75 303

MVS-HIRNet		49.01 305	44.71 311	61.92 302	76.06 214	46.61 236	63.23 320	54.90 342	24.77 346	33.56 336	36.60 348	21.28 310	75.88 304	29.49 293	62.54 208	63.26 342

testus		48.97 306	46.53 307	56.31 321	57.39 341	24.08 345	73.40 278	70.45 312	43.37 308	35.52 327	63.95 319	4.77 356	71.36 327	24.88 314	45.02 316	73.50 315

new-patchmatchnet		48.21 307	46.55 306	53.18 325	57.73 339	18.19 358	70.24 301	71.02 310	45.70 291	33.70 335	60.23 327	18.00 324	69.86 332	27.97 304	34.35 338	71.49 325

TinyColmap		48.15 308	44.49 313	59.13 313	65.73 314	38.04 302	63.34 319	62.86 333	38.78 319	29.48 344	67.23 311	6.46 351	73.30 318	24.59 315	41.90 324	66.04 334

1111		48.00 309	46.30 308	53.08 326	55.68 342	20.86 352	70.41 299	76.03 266	36.88 327	34.86 330	59.55 331	23.72 294	68.13 334	20.82 337	38.76 331	70.25 327

AllTest		47.32 310	44.66 312	55.32 322	65.08 318	37.50 305	62.96 322	54.25 344	35.45 334	33.42 337	72.82 271	9.98 341	59.33 343	24.13 317	43.84 320	69.13 328

test1235678		47.09 311	43.82 315	56.91 319	53.18 345	24.90 344	71.93 292	70.31 314	39.54 317	31.44 342	56.59 338	9.50 343	71.55 326	22.63 325	39.24 330	74.28 307

LP		47.05 312	42.23 317	61.53 305	72.04 276	49.37 193	49.48 340	65.50 325	34.57 337	34.29 333	52.30 341	17.73 326	75.32 308	17.56 346	36.57 333	59.91 343

PM-MVS		46.92 313	43.76 316	56.41 320	52.18 346	32.26 327	63.21 321	38.18 355	37.99 323	40.78 314	66.20 312	5.09 354	65.42 339	48.19 220	41.99 323	71.54 324

testpf		45.92 314	45.81 309	46.27 333	69.56 296	27.86 340	23.18 356	73.91 289	44.10 304	36.99 323	57.16 337	20.56 313	71.77 325	42.17 251	44.64 318	39.18 351

RPSCF		45.77 315	44.13 314	50.68 328	57.67 340	29.66 332	54.92 338	45.25 351	26.69 345	45.92 294	75.92 249	17.43 328	45.70 355	27.44 306	45.95 311	76.67 288

pmmvs3		45.53 316	41.55 319	57.44 318	48.97 349	39.68 296	70.06 302	57.66 339	28.32 344	34.06 334	57.29 335	8.50 346	66.85 338	34.86 276	34.26 339	65.80 335

N_pmnet		41.25 317	39.77 320	45.66 335	68.50 302	0.82 367	72.51 286	0.38 369	35.61 332	35.26 329	61.51 324	20.07 316	67.74 336	23.51 319	40.63 325	68.42 331

TDRefinement		40.91 318	38.37 321	48.55 331	50.45 347	33.03 324	58.98 331	50.97 347	28.50 343	29.89 343	67.39 309	6.21 353	54.51 347	17.67 345	35.25 336	58.11 344

testmv		39.64 319	36.01 322	50.55 329	42.18 353	21.56 350	64.81 315	66.88 323	32.22 339	22.25 350	47.47 343	4.33 358	64.81 340	17.71 344	26.22 345	65.29 337

.test1245		38.91 320	41.99 318	29.67 345	55.68 342	20.86 352	70.41 299	76.03 266	36.88 327	34.86 330	59.55 331	23.72 294	68.13 334	20.82 337	0.00 362	0.02 362

DSMNet-mixed		38.35 321	35.36 323	47.33 332	48.11 350	14.91 360	37.87 352	36.60 357	19.18 350	34.37 332	59.56 330	15.53 334	53.01 349	20.14 341	46.89 305	74.07 309

test12356		37.84 322	35.07 324	46.18 334	45.03 352	8.02 365	57.70 333	62.67 335	31.83 340	22.78 348	50.25 342	4.46 357	66.95 337	17.25 347	23.62 348	63.57 340

no-one		37.21 323	31.48 328	54.40 324	39.62 357	31.91 330	45.68 344	67.42 322	35.54 333	14.59 353	35.91 350	7.35 347	73.20 320	22.98 320	14.23 351	58.09 345

FPMVS		35.40 324	33.67 325	40.57 337	46.34 351	28.74 338	41.05 349	57.05 340	20.37 349	22.27 349	53.38 340	6.87 349	44.94 356	8.62 354	47.11 303	48.01 349

ANet_high		34.39 325	29.59 329	48.78 330	30.34 361	22.28 347	55.53 335	63.79 331	38.11 322	15.47 352	36.56 349	6.94 348	59.98 342	13.93 350	5.64 361	64.08 338

v1.0		34.14 326	45.52 310	0.00 355	89.33 9	0.00 369	0.00 361	88.42 26	56.02 214	82.27 2	93.65 4	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

new_pmnet		33.56 327	31.89 327	38.59 338	49.01 348	20.42 354	51.01 339	37.92 356	20.58 347	23.45 347	46.79 344	6.66 350	49.28 352	20.00 342	31.57 344	46.09 350

LF4IMVS		33.04 328	32.55 326	34.52 342	40.96 354	22.03 348	44.45 346	35.62 358	20.42 348	28.12 345	62.35 323	5.03 355	31.88 361	21.61 336	34.42 337	49.63 348

LCM-MVSNet		28.07 329	23.85 333	40.71 336	27.46 363	18.93 357	30.82 354	46.19 348	12.76 355	16.40 351	34.70 352	1.90 363	48.69 353	20.25 340	24.22 347	54.51 346

pcd1.5k->3k		27.74 330	27.68 330	27.93 347	73.75 240	0.00 369	0.00 361	85.50 75	0.00 363	0.00 367	0.00 367	26.52 277	0.00 365	0.00 364	63.37 194	83.79 181

Gipuma		27.47 331	24.26 332	37.12 340	60.55 337	29.17 335	11.68 359	60.00 337	14.18 353	10.52 357	15.12 359	2.20 362	63.01 341	8.39 355	35.65 334	19.18 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		26.71 332	22.98 335	37.87 339	36.89 358	8.51 364	42.51 348	29.32 363	19.09 351	13.01 354	37.54 346	2.23 361	53.11 348	14.54 349	11.71 352	51.99 347

PNet_i23d		25.11 333	23.09 334	31.17 344	40.18 355	21.30 351	57.99 332	33.28 360	13.77 354	9.94 358	30.29 354	0.45 367	43.74 357	13.61 352	8.28 354	28.46 354

PMVS	19.57 22	25.07 334	22.43 336	32.99 343	23.12 364	22.98 346	40.98 350	35.19 359	15.99 352	11.95 356	35.87 351	1.47 365	49.29 351	5.41 359	31.90 343	26.70 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		19.94 335	14.87 339	35.13 341	22.47 365	19.80 355	25.80 355	38.64 354	7.61 359	4.88 363	13.58 362	0.23 368	48.42 354	13.11 353	7.53 356	37.18 352

E-PMN		19.16 336	18.40 337	21.44 348	36.19 359	13.63 361	47.59 341	30.89 361	10.73 356	5.91 361	16.59 357	3.66 360	39.77 358	5.95 358	8.14 355	10.92 358

EMVS		18.42 337	17.66 338	20.71 349	34.13 360	12.64 362	46.94 342	29.94 362	10.46 358	5.58 362	14.93 360	4.23 359	38.83 359	5.24 360	7.51 358	10.67 359

cdsmvs_eth3d_5k		18.33 338	24.44 331	0.00 355	0.00 369	0.00 369	0.00 361	89.40 12	0.00 363	0.00 367	92.02 23	38.55 165	0.00 365	0.00 364	0.00 362	0.00 365

MVE	16.60 23	17.34 339	13.39 340	29.16 346	28.43 362	19.72 356	13.73 358	23.63 364	7.23 360	7.96 359	21.41 355	0.80 366	36.08 360	6.97 356	10.39 353	31.69 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 340	10.68 341	5.73 352	2.49 367	4.21 366	10.48 360	18.04 365	0.34 362	12.59 355	20.49 356	11.39 339	7.03 364	13.84 351	6.46 360	5.95 360

wuyk23d		9.11 341	8.77 343	10.15 351	40.18 355	16.76 359	20.28 357	1.01 368	2.58 361	2.66 364	0.98 364	0.23 368	12.49 363	4.08 361	6.90 359	1.19 361

ab-mvs-re		7.68 342	10.24 342	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	92.12 20	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

testmvs		6.14 343	8.18 344	0.01 353	0.01 368	0.00 369	73.40 278	0.00 370	0.00 363	0.02 365	0.15 365	0.00 370	0.00 365	0.02 362	0.00 362	0.02 362

test123		6.01 344	8.01 345	0.01 353	0.00 369	0.01 368	71.93 292	0.00 370	0.00 363	0.02 365	0.11 366	0.00 370	0.00 365	0.02 362	0.00 362	0.02 362

pcd_1.5k_mvsjas		3.15 345	4.20 346	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	37.77 171	0.00 365	0.00 364	0.00 362	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 369	0.00 369	0.00 361	0.00 370	0.00 363	0.00 367	0.00 367	0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

GSMVS																88.13 113

test_part2					89.33 9	55.48 40				82.27 2

test_part1				0.00 355		0.00 369	0.00 361	88.42 26				0.00 370	0.00 365	0.00 364	0.00 362	0.00 365

sam_mvs1												38.86 163				88.13 113

sam_mvs												35.99 206

semantic-postprocess				60.08 310	70.68 291	45.07 253		74.25 284	43.54 307	50.02 262	73.73 261	32.22 238	56.74 346	51.06 206	53.60 279	78.42 264

ambc				62.06 300	53.98 344	29.38 334	35.08 353	79.65 205		41.37 311	59.96 328	6.27 352	82.15 256	35.34 272	38.22 332	74.65 304

MTGPA								81.31 178

test_post1							70.84 298				14.72 361	34.33 217	83.86 237	48.80 215

test_post											16.22 358	37.52 179	84.72 223

patchmatchnet-post											59.74 329	38.41 166	79.91 280

GG-mvs-BLEND				77.77 68	86.68 31	50.61 167	68.67 309	88.45 25		68.73 70	87.45 103	59.15 4	90.67 60	54.83 178	87.67 12	92.03 28

MTMP							87.27 60	15.34 366

gm-plane-assit					83.24 87	54.21 77			70.91 11		88.23 90		95.25 5	66.37 86

test9_res														78.72 17	85.44 27	91.39 44

TEST9					85.68 40	55.42 41	87.59 49	84.00 125	57.72 186	72.99 39	90.98 41	44.87 79	88.58 122

test_8					85.72 39	55.31 45	87.60 46	83.88 130	57.84 183	72.84 42	90.99 40	44.99 76	88.34 133

agg_prior2														75.65 33	85.11 31	91.01 50

agg_prior					85.64 43	54.92 59		83.61 136		72.53 46			88.10 144

TestCases				55.32 322	65.08 318	37.50 305		54.25 344	35.45 334	33.42 337	72.82 271	9.98 341	59.33 343	24.13 317	43.84 320	69.13 328

test_prior4						56.39 30	87.15 62

test_prior2							89.04 32		61.88 108	73.55 34	91.46 37	48.01 42		74.73 39	85.46 25

test_prior				78.39 55	86.35 34	54.91 61		85.45 77					89.70 85			90.55 58

旧先验2							81.73 184		45.53 293	74.66 27			70.48 331	58.31 146

新几何2							81.61 192

新几何1				73.30 168	83.10 90	53.48 89		71.43 307	45.55 292	66.14 90	87.17 106	33.88 223	80.54 269	48.50 217	80.33 64	85.88 148

旧先验1					81.57 133	47.48 226		71.83 301			88.66 83	36.94 190			78.34 80	88.67 103

无先验							85.19 99	78.00 238	49.08 273				85.13 214	52.78 193		87.45 124

原ACMM2							83.77 143

原ACMM1				76.13 103	84.89 59	54.59 71		85.26 85	51.98 255	66.70 83	87.07 108	40.15 151	89.70 85	51.23 204	85.06 32	84.10 170

test222					79.36 167	50.97 163	77.99 244	67.84 319	42.54 312	62.84 134	86.53 115	30.26 252			76.91 93	85.23 157

testdata2													77.81 295	45.64 236

segment_acmp												44.97 78

testdata				67.08 264	77.59 198	45.46 250		69.20 318	44.47 300	71.50 56	88.34 86	31.21 247	70.76 330	52.20 199	75.88 101	85.03 159

testdata1							77.55 247		64.14 76

test12				79.24 33	86.89 29	56.08 34		85.16 89		72.27 51		47.15 49	91.10 52		85.93 20	90.54 61

plane_prior7					77.95 193	48.46 215

plane_prior6					78.42 190	49.39 192						36.04 203

plane_prior5								82.59 155					88.30 137	65.46 95	72.34 132	84.49 165

plane_prior4											83.28 157

plane_prior3						48.95 198			64.01 77	62.15 137

plane_prior2							85.76 84		63.60 88

plane_prior1					78.31 192

plane_prior						49.57 187	87.43 54		64.57 69						72.84 127

n2								0.00 370

nn								0.00 370

door-mid								41.31 353

lessismore_v0				67.98 257	64.76 320	41.25 286		45.75 350		36.03 326	65.63 316	19.29 319	84.11 236	35.67 270	21.24 349	78.59 258

LGP-MVS_train				72.02 189	74.42 232	48.60 207		80.64 191	54.69 228	53.75 230	83.83 140	25.73 283	86.98 170	60.33 135	64.71 179	80.48 239

test11								84.25 112

door								43.27 352

HQP5-MVS						51.56 151

HQP-NCC					79.02 174		88.00 40		65.45 54	64.48 113

ACMP_Plane					79.02 174		88.00 40		65.45 54	64.48 113

BP-MVS														66.70 84

HQP4-MVS										64.47 116			88.61 120			84.91 162

HQP3-MVS								83.68 133							73.12 123

HQP2-MVS												37.35 182

NP-MVS					78.76 179	50.43 172					85.12 126

MDTV_nov1_ep13_2view						43.62 266	71.13 297		54.95 225	59.29 171		36.76 193		46.33 233		87.32 126

MDTV_nov1_ep13			61.56 225		81.68 128	55.12 52	72.41 287	78.18 230	59.19 147	58.85 179	69.29 292	34.69 214	86.16 193	36.76 268	62.96 202

ACMMP++_ref															63.20 197

ACMMP++															59.38 231

Test By Simon												39.38 158

ITE_SJBPF				51.84 327	58.03 338	31.94 329		53.57 346	36.67 329	41.32 312	75.23 253	11.17 340	51.57 350	25.81 312	48.04 296	72.02 321

DeepMVS_CX				13.10 350	21.34 366	8.99 363		10.02 367	10.59 357	7.53 360	30.55 353	1.82 364	14.55 362	6.83 357	7.52 357	15.75 357

High-resolution multi-view benchmark