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 16	93.09 9	54.15 13	95.57 3	85.80 1	85.87 23	93.31 6

HSP-MVS		82.45 2	83.62 1	78.96 36	82.99 91	52.71 124	85.04 110	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 44	83.35 5	93.53 1	92.72 17

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 64	77.83 1	77.88 13	92.13 19	60.24 2	94.78 12	78.97 16	89.61 3	93.69 3
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 11	91.71 3	88.87 16	70.31 14	77.64 15	93.87 2	52.58 19	93.91 17	84.17 2	87.92 11	92.39 19

CANet		80.90 5	81.17 6	80.09 23	87.62 22	54.21 74	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 35	93.08 11

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 64	85.46 48	49.56 182	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 51	82.57 8	86.22 21	93.28 7

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

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

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

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

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

MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 67	90.58 14	84.48 100	72.51 8	79.22 11	93.09 9	42.01 129	93.28 21	84.00 4	85.84 24	92.87 15

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

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

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

APDe-MVS		78.44 16	78.20 17	79.19 32	88.56 12	54.55 69	89.76 24	87.77 40	55.91 212	78.56 12	92.49 16	48.20 38	92.65 28	79.49 14	83.04 43	90.39 63

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

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

EPNet		78.36 19	78.49 15	77.97 63	85.49 45	52.04 137	89.36 29	84.07 117	73.22 4	77.03 17	91.72 30	49.32 36	90.17 72	73.46 49	82.77 44	91.69 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 20	78.26 16	78.48 48	81.33 134	56.31 29	81.59 185	86.41 61	69.61 18	81.72 4	88.16 90	55.09 10	88.04 141	74.12 43	86.31 19	91.09 46

canonicalmvs		78.17 21	77.86 21	79.12 35	84.30 62	54.22 73	87.71 42	84.57 99	67.70 30	77.70 14	92.11 22	50.90 28	89.95 75	78.18 23	77.54 85	93.20 9

alignmvs		78.08 22	77.98 19	78.39 52	83.53 74	53.22 107	89.77 23	85.45 73	66.11 43	76.59 20	91.99 25	54.07 14	89.05 92	77.34 26	77.00 91	92.89 14

DeepC-MVS_fast	67.50 3	78.00 23	77.63 22	79.13 34	88.52 13	55.12 51	89.95 19	85.98 67	68.31 23	71.33 54	92.75 13	45.52 70	90.37 64	71.15 60	85.14 31	91.91 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		77.99 24	77.92 20	78.19 57	87.43 23	50.12 174	90.93 12	91.41 3	67.48 32	75.12 22	90.15 63	46.77 52	91.00 49	73.52 48	78.46 78	93.44 4

TSAR-MVS + GP.		77.82 25	77.59 23	78.49 47	85.25 52	50.27 173	90.02 17	90.57 5	56.58 200	74.26 27	91.60 33	54.26 11	92.16 36	75.87 31	79.91 70	93.05 12

Regformer-1		77.80 26	77.44 25	78.88 38	87.78 20	52.44 129	87.60 44	90.08 8	68.86 20	72.49 45	91.79 27	47.69 43	94.90 10	73.57 47	77.05 88	89.31 82

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

PHI-MVS		77.49 28	77.00 28	78.95 37	85.33 50	50.69 160	88.57 35	88.59 22	58.14 173	73.60 30	93.31 5	43.14 106	93.79 18	73.81 44	88.53 8	92.37 20

WTY-MVS		77.47 29	77.52 24	77.30 76	88.33 16	46.25 234	88.46 36	90.32 6	71.40 10	72.32 47	91.72 30	53.44 15	92.37 32	66.28 84	75.42 104	93.28 7

Regformer-2		77.15 30	76.82 30	78.14 58	87.78 20	51.84 141	87.60 44	89.12 13	67.23 33	71.93 49	91.79 27	46.03 64	93.53 20	72.85 55	77.05 88	89.05 90

SteuartSystems-ACMMP		77.08 31	76.33 34	79.34 30	80.98 137	55.31 44	89.76 24	86.91 52	62.94 93	71.65 52	91.56 34	42.33 121	92.56 29	77.14 27	83.69 42	90.15 70
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 32	76.45 32	78.69 42	79.69 158	54.74 62	90.56 15	83.99 121	68.26 24	74.10 28	90.91 45	42.14 125	89.99 74	79.30 15	79.12 74	91.36 42
jason: jason.
train_agg		76.91 33	76.40 33	78.45 50	85.68 37	55.42 40	87.59 47	84.00 119	57.84 179	72.99 36	90.98 41	44.99 74	88.58 117	78.19 20	85.32 29	91.34 44

MVS		76.91 33	75.48 42	81.23 10	84.56 59	55.21 48	80.23 207	91.64 1	58.65 163	65.37 94	91.48 36	45.72 68	95.05 8	72.11 57	89.52 5	93.44 4

DeepC-MVS	67.15 4	76.90 35	76.27 35	78.80 40	80.70 143	55.02 54	86.39 69	86.71 55	66.96 36	67.91 71	89.97 66	48.03 40	91.41 45	75.60 33	84.14 39	89.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPM		76.76 36	76.07 38	78.81 39	80.20 156	59.11 5	86.86 65	86.23 65	68.60 21	70.18 60	88.84 81	51.57 24	87.16 162	65.48 90	86.68 14	90.15 70

MAR-MVS		76.76 36	75.60 41	80.21 18	90.87 3	54.68 65	89.14 31	89.11 14	62.95 92	70.54 58	92.33 17	41.05 139	94.95 9	57.90 148	86.55 18	91.00 48
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
agg_prior3		76.73 38	76.15 37	78.48 48	85.66 39	55.59 35	87.54 51	83.95 123	57.78 181	71.78 51	90.81 49	44.33 82	88.52 122	78.19 20	85.32 29	91.34 44

agg_prior1		76.68 39	76.24 36	78.00 61	85.64 40	54.92 57	87.55 50	83.61 130	57.99 176	72.53 43	91.05 39	45.36 71	88.10 139	77.76 25	84.68 36	90.99 49

PVSNet_Blended		76.53 40	76.54 31	76.50 91	85.91 34	51.83 142	88.89 34	84.24 108	67.82 27	69.09 63	89.33 76	46.70 53	88.13 137	75.43 34	81.48 54	89.55 78

ACMMP_Plus		76.43 41	75.66 40	78.73 41	81.92 117	54.67 66	84.06 131	85.35 78	61.10 115	72.99 36	91.50 35	40.25 145	91.00 49	76.84 28	86.98 13	90.51 59

MVS_111021_HR		76.39 42	75.38 45	79.42 29	85.33 50	56.47 26	88.15 37	84.97 89	65.15 61	66.06 86	89.88 67	43.79 92	92.16 36	75.03 37	80.03 69	89.64 77

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

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

APD-MVS		76.15 45	75.68 39	77.54 70	88.52 13	53.44 89	87.26 58	85.03 88	53.79 227	74.91 23	91.68 32	43.80 91	90.31 65	74.36 41	81.82 51	88.87 94
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDD-MVS		76.08 46	74.97 48	79.44 28	84.27 65	53.33 99	91.13 10	85.88 68	65.33 58	72.37 46	89.34 74	32.52 231	92.76 26	77.90 24	75.96 99	92.22 22

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

Regformer-3		76.02 48	75.47 43	77.70 66	85.49 45	51.47 148	85.12 100	90.19 7	68.52 22	69.36 61	90.66 51	46.45 61	94.81 11	70.25 64	73.16 117	86.81 129

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

CLD-MVS		75.60 50	75.39 44	76.24 95	80.69 144	52.40 130	90.69 13	86.20 66	74.40 3	65.01 100	88.93 78	42.05 128	90.58 59	76.57 29	73.96 112	85.73 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS-pluss		75.54 51	75.03 47	77.04 82	81.37 133	52.65 126	84.34 124	84.46 101	61.16 113	69.14 62	91.76 29	39.98 152	88.99 101	78.19 20	84.89 34	89.48 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DWT-MVSNet_test		75.47 52	73.87 55	80.29 16	87.33 25	57.05 18	82.86 159	87.96 35	72.59 6	67.29 74	87.79 95	51.61 23	91.52 43	54.75 176	72.63 125	92.29 21

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

PAPR		75.20 54	74.13 53	78.41 51	88.31 17	55.10 53	84.31 125	85.66 70	63.76 80	67.55 72	90.73 50	43.48 102	89.40 86	66.36 83	77.03 90	90.73 53

Regformer-4		75.06 55	74.59 51	76.47 92	85.49 45	50.33 169	85.12 100	88.61 20	66.42 38	68.48 66	90.66 51	44.15 87	92.68 27	69.24 67	73.16 117	86.39 137

MP-MVS		74.99 56	74.33 52	76.95 85	82.89 95	53.05 117	85.63 85	83.50 133	57.86 178	67.25 75	90.24 59	43.38 103	88.85 110	76.03 30	82.23 49	88.96 92

#test#		74.86 57	73.78 56	78.10 59	84.30 62	53.68 81	86.95 62	84.36 102	59.00 155	65.78 89	90.56 53	40.70 142	90.90 52	71.48 58	80.88 55	89.71 74

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

HFP-MVS		74.37 59	73.13 61	78.10 59	84.30 62	53.68 81	85.58 86	84.36 102	56.82 193	65.78 89	90.56 53	40.70 142	90.90 52	69.18 68	80.88 55	89.71 74

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

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

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

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

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

IB-MVS	68.87 2	74.01 65	72.03 77	79.94 24	83.04 88	55.50 37	90.24 16	88.65 18	67.14 34	61.38 136	81.74 175	53.21 16	94.28 14	60.45 128	62.41 204	90.03 72
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
HY-MVS	67.03 5	73.90 66	73.14 59	76.18 97	84.70 58	47.36 219	75.56 254	86.36 63	66.27 41	70.66 57	83.91 135	51.05 27	89.31 87	67.10 78	72.61 126	91.88 28

CostFormer		73.89 67	72.30 70	78.66 43	82.36 108	56.58 22	75.56 254	85.30 79	66.06 46	70.50 59	76.88 226	57.02 8	89.06 90	68.27 73	68.74 148	90.33 65

ACMMPR		73.76 68	72.61 63	77.24 81	83.92 71	52.96 121	85.58 86	84.29 104	56.82 193	65.12 96	90.45 55	37.24 183	90.18 71	69.18 68	80.84 57	88.58 101

region2R		73.75 69	72.55 65	77.33 75	83.90 72	52.98 120	85.54 89	84.09 110	56.83 192	65.10 97	90.45 55	37.34 181	90.24 69	68.89 70	80.83 58	88.77 97

CANet_DTU		73.71 70	73.14 59	75.40 112	82.61 104	50.05 175	84.67 120	79.36 204	69.72 17	75.39 21	90.03 65	29.41 253	85.93 197	67.99 74	79.11 75	90.22 67

MVSFormer		73.53 71	72.19 73	77.57 69	83.02 89	55.24 46	81.63 182	81.44 168	50.28 260	76.67 18	90.91 45	44.82 78	86.11 188	60.83 123	80.09 66	91.36 42

PVSNet_BlendedMVS		73.42 72	73.30 58	73.76 153	85.91 34	51.83 142	86.18 74	84.24 108	65.40 55	69.09 63	80.86 186	46.70 53	88.13 137	75.43 34	65.92 170	81.33 216

PVSNet_Blended_VisFu		73.40 73	72.44 67	76.30 93	81.32 135	54.70 64	85.81 78	78.82 211	63.70 81	64.53 106	85.38 122	47.11 49	87.38 159	67.75 75	77.55 84	86.81 129

MVSTER		73.25 74	72.33 68	76.01 102	85.54 44	53.76 80	83.52 140	87.16 48	67.06 35	63.88 116	81.66 176	52.77 18	90.44 60	64.66 99	64.69 176	83.84 175

EI-MVSNet-Vis-set		73.19 75	72.60 64	74.99 121	82.56 105	49.80 178	82.55 163	89.00 15	66.17 42	65.89 88	88.98 77	43.83 90	92.29 33	65.38 97	69.01 146	82.87 192

PMMVS		72.98 76	72.05 76	75.78 106	83.57 73	48.60 199	84.08 129	82.85 146	61.62 108	68.24 69	90.33 58	28.35 258	87.78 150	72.71 56	76.69 93	90.95 50

XVS		72.92 77	71.62 79	76.81 87	83.41 75	52.48 127	84.88 115	83.20 140	58.03 174	63.91 114	89.63 71	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

TESTMET0.1,1		72.86 78	72.33 68	74.46 135	81.98 115	50.77 158	85.13 97	85.47 72	66.09 44	67.30 73	83.69 140	37.27 182	83.57 236	65.06 98	78.97 76	89.05 90

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

MTAPA		72.73 79	71.22 86	77.27 78	81.54 127	53.57 84	67.06 303	81.31 171	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

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

HPM-MVS		72.60 81	71.50 81	75.89 104	82.02 114	51.42 150	80.70 200	83.05 142	56.12 209	64.03 113	89.53 72	37.55 175	88.37 126	70.48 63	80.04 68	87.88 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 83	71.46 82	76.00 103	82.93 94	52.32 134	86.93 64	82.48 151	55.15 219	63.65 119	90.44 57	35.03 209	88.53 121	68.69 71	77.83 82	87.15 122

EI-MVSNet-UG-set		72.37 84	71.73 78	74.29 139	81.60 123	49.29 187	81.85 174	88.64 19	65.29 60	65.05 98	88.29 87	43.18 104	91.83 40	63.74 101	67.97 154	81.75 209

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

HQP-MVS		72.34 85	71.44 83	75.03 118	79.02 167	51.56 145	88.00 38	83.68 127	65.45 52	64.48 107	85.13 123	37.35 179	88.62 114	66.70 80	73.12 119	84.91 157

mvs_anonymous		72.29 87	70.74 89	76.94 86	82.85 96	54.72 63	78.43 233	81.54 167	63.77 79	61.69 135	79.32 194	51.11 26	85.31 205	62.15 116	75.79 101	90.79 52

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

nrg030		72.27 89	71.56 80	74.42 136	75.93 209	50.60 162	86.97 61	83.21 139	62.75 95	67.15 76	84.38 130	50.07 32	86.66 176	71.19 59	62.37 205	85.99 140

VPNet		72.07 90	71.42 84	74.04 144	78.64 177	47.17 223	89.91 22	87.97 34	72.56 7	64.66 104	85.04 125	41.83 132	88.33 130	61.17 121	60.97 210	86.62 131

DP-MVS Recon		71.99 91	70.31 94	77.01 84	90.65 4	53.44 89	89.37 28	82.97 144	56.33 207	63.56 121	89.47 73	34.02 216	92.15 38	54.05 179	72.41 127	85.43 151

QAPM		71.88 92	69.33 109	79.52 27	82.20 109	54.30 71	86.30 72	88.77 17	56.61 199	59.72 155	87.48 101	33.90 219	95.36 4	47.48 218	81.49 53	88.90 93

PAPM_NR		71.80 93	69.98 101	77.26 80	81.54 127	53.34 97	78.60 231	85.25 82	53.46 230	60.53 149	88.66 82	45.69 69	89.24 88	56.49 157	79.62 73	89.19 87

mPP-MVS		71.79 94	70.38 93	76.04 101	82.65 103	52.06 136	84.45 122	81.78 164	55.59 216	62.05 133	89.68 70	33.48 222	88.28 134	65.45 93	78.24 80	87.77 113

xiu_mvs_v1_base_debu		71.60 95	70.29 95	75.55 108	77.26 196	53.15 110	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 294	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base		71.60 95	70.29 95	75.55 108	77.26 196	53.15 110	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 294	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base_debi		71.60 95	70.29 95	75.55 108	77.26 196	53.15 110	85.34 90	79.37 201	55.83 213	72.54 40	90.19 60	22.38 294	86.66 176	73.28 51	76.39 95	86.85 126

test_normal		71.31 98	68.95 115	78.39 52	72.30 266	54.25 72	81.67 178	84.05 118	65.94 50	51.31 241	78.09 212	36.06 199	90.43 62	73.00 54	78.09 81	90.50 60

DI_MVS_plusplus_test		71.30 99	68.98 114	78.26 56	72.76 248	54.08 77	81.72 177	83.22 138	65.75 51	51.94 237	78.47 206	36.01 202	90.31 65	73.33 50	77.60 83	90.40 62

EPP-MVSNet		71.14 100	70.07 100	74.33 138	79.18 166	46.52 228	83.81 136	86.49 58	56.32 208	57.95 182	84.90 127	54.23 12	89.14 89	58.14 145	69.65 143	87.33 119

VPA-MVSNet		71.12 101	70.66 90	72.49 175	78.75 172	44.43 250	87.64 43	90.02 9	63.97 75	65.02 99	81.58 177	42.14 125	87.42 158	63.42 103	63.38 188	85.63 149

1314		71.11 102	69.41 106	76.22 96	79.32 162	50.49 165	80.23 207	85.14 87	59.44 136	58.93 168	88.89 80	33.83 221	89.60 85	61.49 119	77.42 87	88.57 102

tpmrst		71.04 103	69.77 103	74.86 128	83.19 83	55.86 33	75.64 253	78.73 214	67.88 26	64.99 101	73.73 253	49.96 33	79.56 273	65.92 85	67.85 156	89.14 89

MVP-Stereo		70.97 104	70.44 92	72.59 172	76.03 208	51.36 151	85.02 112	86.99 51	60.31 126	56.53 202	78.92 199	40.11 149	90.00 73	60.00 134	90.01 2	76.41 284
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 105	69.91 102	74.12 142	77.95 185	49.57 180	85.76 80	82.59 149	63.60 84	62.15 130	83.28 152	36.04 200	88.30 132	65.46 91	72.34 129	84.49 160

tpm2		70.82 106	68.44 118	77.98 62	80.78 141	56.11 31	74.21 264	81.28 174	60.24 127	68.04 70	75.27 244	52.26 22	88.50 123	55.82 164	68.03 153	89.33 81

ACMMP		70.81 107	69.29 110	75.39 113	81.52 130	51.92 139	83.43 145	83.03 143	56.67 198	58.80 173	88.91 79	31.92 239	88.58 117	65.89 87	73.39 116	85.67 146
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
OPM-MVS		70.75 108	69.58 104	74.26 140	75.55 213	51.34 152	86.05 76	83.29 137	61.94 103	62.95 126	85.77 118	34.15 215	88.44 124	65.44 94	71.07 135	82.99 189

ab-mvs		70.65 109	69.11 112	75.29 114	80.87 140	46.23 235	73.48 267	85.24 83	59.99 129	66.65 79	80.94 185	43.13 107	88.69 112	63.58 102	68.07 152	90.95 50

Vis-MVSNet		70.61 110	69.34 108	74.42 136	80.95 139	48.49 204	86.03 77	77.51 240	58.74 162	65.55 93	87.78 96	34.37 213	85.95 196	52.53 192	80.61 59	88.80 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 111	70.13 99	71.58 199	81.59 124	39.02 288	80.78 199	84.71 97	59.34 140	66.61 81	88.09 91	37.17 184	85.52 201	61.82 118	71.02 136	90.20 68

CDS-MVSNet		70.48 112	69.43 105	73.64 155	77.56 191	48.83 195	83.51 144	77.45 241	63.27 88	62.33 129	85.54 121	43.85 89	83.29 238	57.38 154	74.00 111	88.79 96
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		70.18 113	69.28 111	72.89 167	77.64 189	42.88 264	85.06 109	87.50 46	62.58 96	62.66 128	82.34 167	43.64 99	89.83 77	58.42 141	63.70 184	85.96 142

PCF-MVS	61.03 10	70.10 114	68.40 119	75.22 117	77.15 200	51.99 138	79.30 227	82.12 157	56.47 206	61.88 134	86.48 115	43.98 88	87.24 161	55.37 168	72.79 124	86.43 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 115	68.01 124	76.27 94	84.21 66	51.22 156	87.29 57	79.33 206	58.96 157	63.63 120	86.77 109	33.29 224	90.30 68	44.63 234	73.96 112	87.30 121

1112_ss		70.05 116	69.37 107	72.10 178	80.77 142	42.78 265	85.12 100	76.75 248	59.69 132	61.19 138	92.12 20	47.48 44	83.84 232	53.04 185	68.21 151	89.66 76

diffmvs		70.02 117	68.35 120	75.03 118	79.19 165	51.48 147	78.50 232	76.65 251	59.71 131	67.10 77	80.32 189	42.81 117	87.12 163	58.48 139	72.37 128	86.49 133

BH-w/o		70.02 117	68.51 117	74.56 134	82.77 98	50.39 167	86.60 67	78.14 225	59.77 130	59.65 156	85.57 120	39.27 157	87.30 160	49.86 204	74.94 109	85.99 140

tpmp4_e23		70.01 119	67.13 146	78.65 44	81.93 116	57.90 10	73.99 265	81.35 170	60.61 123	65.28 95	73.78 252	52.48 20	88.60 116	48.40 213	66.35 167	89.44 80

FIs		70.00 120	70.24 98	69.30 231	77.93 187	38.55 290	83.99 134	87.72 42	66.86 37	57.66 189	84.17 132	52.28 21	85.31 205	52.72 191	68.80 147	84.02 167

OpenMVS	61.00 11	69.99 121	67.55 139	77.30 76	78.37 183	54.07 78	84.36 123	85.76 69	57.22 188	56.71 200	87.67 98	30.79 246	92.83 25	43.04 240	84.06 41	85.01 155

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

114514_t		69.87 123	67.88 127	75.85 105	88.38 15	52.35 133	86.94 63	83.68 127	53.70 228	55.68 211	85.60 119	30.07 251	91.20 47	55.84 163	71.02 136	83.99 169

TR-MVS		69.71 124	67.85 128	75.27 115	82.94 93	48.48 205	87.40 53	80.86 181	57.15 189	64.61 105	87.08 106	32.67 230	89.64 84	46.38 226	71.55 133	87.68 115

EI-MVSNet		69.70 125	68.70 116	72.68 170	75.00 218	48.90 193	79.54 219	87.16 48	61.05 116	63.88 116	83.74 138	45.87 66	90.44 60	57.42 153	64.68 177	78.70 249

test-LLR		69.65 126	69.01 113	71.60 197	78.67 174	48.17 212	85.13 97	79.72 195	59.18 146	63.13 124	82.58 162	36.91 188	80.24 265	60.56 126	75.17 106	86.39 137

APD-MVS_3200maxsize		69.62 127	68.23 122	73.80 152	81.58 125	48.22 211	81.91 172	79.50 200	48.21 268	64.24 112	89.75 69	31.91 240	87.55 155	63.08 104	73.85 114	85.64 148

v2v482		69.55 128	67.64 133	75.26 116	72.32 265	53.83 79	84.93 114	81.94 159	65.37 57	60.80 146	79.25 195	41.62 135	88.98 102	63.03 105	59.51 223	82.98 190

TAMVS		69.51 129	68.16 123	73.56 158	76.30 204	48.71 196	82.57 162	77.17 245	62.10 101	61.32 137	84.23 131	41.90 130	83.46 237	54.80 175	73.09 121	88.50 103

v1141		69.50 130	67.67 130	74.98 122	72.73 250	53.41 92	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.09 111	89.04 93	62.51 110	59.61 220	82.47 197

divwei89l23v2f112		69.50 130	67.67 130	74.98 122	72.72 251	53.41 92	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.11 108	89.04 93	62.51 110	59.62 219	82.48 196

v1		69.49 132	67.67 130	74.98 122	72.69 252	53.41 92	85.08 106	82.13 156	64.80 63	60.87 144	78.19 209	43.11 108	89.04 93	62.51 110	59.61 220	82.49 195

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

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

v6		69.43 133	67.61 136	74.88 127	72.87 247	53.30 104	85.12 100	81.10 177	64.29 69	60.99 141	78.52 204	42.88 114	88.98 102	62.67 107	60.06 214	82.37 199

PVSNet	62.49 8	69.27 136	67.81 129	73.64 155	84.41 61	51.85 140	84.63 121	77.80 233	66.42 38	59.80 154	84.95 126	22.14 298	80.44 262	55.03 171	75.11 108	88.62 100

MVS_111021_LR		69.07 137	67.91 125	72.54 173	77.27 195	49.56 182	79.77 215	73.96 278	59.33 142	60.73 147	87.82 94	30.19 250	81.53 252	69.94 65	72.19 131	86.53 132

mvs-test1		69.04 138	67.57 138	73.44 160	75.17 214	51.68 144	86.57 68	74.48 271	62.15 99	62.07 132	85.79 117	30.59 247	87.48 156	65.40 95	65.94 169	81.18 220

GA-MVS		69.04 138	66.70 154	76.06 100	75.11 216	52.36 132	83.12 152	80.23 188	63.32 87	60.65 148	79.22 196	30.98 245	88.37 126	61.25 120	66.41 162	87.46 117

cascas		69.01 140	66.13 161	77.66 67	79.36 160	55.41 42	86.99 60	83.75 126	56.69 197	58.92 169	81.35 178	24.31 282	92.10 39	53.23 182	70.61 138	85.46 150

FMVSNet3		68.84 141	67.40 142	73.19 162	85.05 53	48.53 202	85.71 84	85.36 77	60.90 118	57.58 190	79.15 197	42.16 124	86.77 172	47.25 220	63.40 185	84.27 164

UniMVSNet_NR-MVSNet		68.82 142	68.29 121	70.40 218	75.71 212	42.59 267	84.23 126	86.78 54	66.31 40	58.51 175	82.45 164	51.57 24	84.64 219	53.11 183	55.96 256	83.96 172

v1144		68.81 143	66.82 148	74.80 131	72.34 264	53.46 87	84.68 119	81.77 165	64.25 72	60.28 151	77.91 213	40.23 146	88.95 106	60.37 129	59.52 222	81.97 202

IS-MVSNet		68.80 144	67.55 139	72.54 173	78.50 180	43.43 258	81.03 194	79.35 205	59.12 150	57.27 198	86.71 110	46.05 63	87.70 152	44.32 235	75.60 103	86.49 133

1121		68.79 145	66.77 151	74.82 129	83.08 87	53.46 87	80.23 207	71.53 295	45.47 285	66.31 84	87.19 104	34.02 216	85.13 208	52.78 188	80.36 63	85.87 144

PS-MVSNAJss		68.78 146	67.17 145	73.62 157	73.01 238	48.33 210	84.95 113	84.81 93	59.30 143	58.91 170	79.84 192	37.77 168	88.86 109	62.83 106	63.12 195	83.67 178

v7		68.76 147	66.79 150	74.68 132	72.60 255	53.37 95	84.72 118	80.88 180	63.80 78	60.43 150	78.21 208	40.05 151	88.89 108	60.34 130	60.07 213	81.77 208

thres200		68.71 148	67.27 144	73.02 163	84.73 57	46.76 225	85.03 111	87.73 41	62.34 98	59.87 152	83.45 149	43.15 105	88.32 131	31.25 282	67.91 155	83.98 170

UGNet		68.71 148	67.11 147	73.50 159	80.55 154	47.61 217	84.08 129	78.51 219	59.45 135	65.68 92	82.73 160	23.78 284	85.08 210	52.80 187	76.40 94	87.80 112
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Test4		68.64 150	65.68 170	77.53 71	67.78 297	53.34 97	79.42 222	82.84 147	65.96 49	46.54 282	76.15 238	25.16 278	88.83 111	69.74 66	77.53 86	90.43 61

EPMVS		68.45 151	65.44 177	77.47 73	84.91 55	56.17 30	71.89 285	81.91 162	61.72 107	60.85 145	72.49 266	36.21 195	87.06 165	47.32 219	71.62 132	89.17 88

test-mter		68.36 152	67.29 143	71.60 197	78.67 174	48.17 212	85.13 97	79.72 195	53.38 231	63.13 124	82.58 162	27.23 266	80.24 265	60.56 126	75.17 106	86.39 137

tpm		68.36 152	67.48 141	70.97 207	79.93 157	51.34 152	76.58 243	78.75 213	67.73 28	63.54 122	74.86 246	48.33 37	72.36 316	53.93 180	63.71 183	89.21 86

BH-untuned		68.28 154	66.40 155	73.91 147	81.62 122	50.01 176	85.56 88	77.39 242	57.63 184	57.47 195	83.69 140	36.36 194	87.08 164	44.81 233	73.08 122	84.65 159

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

abl_6		68.03 156	66.15 160	73.66 154	78.54 179	48.48 205	79.77 215	78.04 229	47.39 272	63.70 118	88.25 88	28.21 259	89.06 90	60.17 133	71.25 134	83.45 180

v1192		67.96 157	65.74 168	74.63 133	71.79 269	53.43 91	84.06 131	80.99 179	63.19 90	59.56 159	77.46 219	37.50 178	88.65 113	58.20 144	58.93 228	81.79 207

v144192		67.86 158	65.76 167	74.16 141	71.68 271	53.09 114	84.14 128	80.83 182	62.85 94	59.21 165	77.28 222	39.30 156	88.00 142	58.67 138	57.88 245	81.40 215

HPM-MVS_fast		67.86 158	66.28 157	72.61 171	80.67 145	48.34 209	81.18 191	75.95 260	50.81 259	59.55 160	88.05 93	27.86 261	85.98 193	58.83 137	73.58 115	83.51 179

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

UniMVSNet (Re)		67.71 161	66.80 149	70.45 216	74.44 223	42.93 263	82.42 165	84.90 91	63.69 82	59.63 157	80.99 184	47.18 47	85.23 207	51.17 199	56.75 250	83.19 187

V42		67.66 162	65.60 173	73.86 149	70.69 279	53.63 83	81.50 186	78.61 217	63.85 77	59.49 161	77.49 218	37.98 165	87.65 153	62.33 113	58.43 235	80.29 236

WR-MVS		67.58 163	66.76 152	70.04 227	75.92 210	45.06 247	86.23 73	85.28 80	64.31 68	58.50 177	81.00 183	44.80 80	82.00 251	49.21 208	55.57 261	83.06 188

tfpn200view9		67.57 164	66.13 161	71.89 191	84.05 68	45.07 244	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 287	66.36 163	82.78 193

FMVSNet2		67.57 164	65.79 166	72.90 165	82.71 100	47.97 215	85.15 96	84.93 90	58.55 165	56.71 200	78.26 207	36.72 191	86.67 175	46.15 228	62.94 198	84.07 166

FC-MVSNet-test		67.49 166	67.91 125	66.21 263	76.06 206	33.06 314	80.82 198	87.18 47	64.44 67	54.81 213	82.87 155	50.40 30	82.60 247	48.05 215	66.55 161	82.98 190

v1921920		67.45 167	65.23 181	74.10 143	71.51 274	52.90 122	83.75 138	80.44 187	62.48 97	59.12 166	77.13 223	36.98 186	87.90 143	57.53 151	58.14 240	81.49 212

gg-mvs-nofinetune		67.43 168	64.53 189	76.13 98	85.95 33	47.79 216	64.38 308	88.28 31	39.34 308	66.62 80	41.27 336	58.69 5	89.00 99	49.64 206	86.62 15	91.59 33

thres400		67.40 169	66.13 161	71.19 204	84.05 68	45.07 244	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 287	66.36 163	80.71 227

UA-Net		67.32 170	66.23 158	70.59 214	78.85 170	41.23 278	73.60 266	75.45 266	61.54 109	66.61 81	84.53 128	38.73 161	86.57 181	42.48 244	74.24 110	83.98 170

v8		67.25 171	64.99 184	74.04 144	72.89 245	53.31 100	82.37 166	80.11 190	61.54 109	54.29 218	76.02 240	42.89 113	88.41 125	58.43 140	56.36 251	80.39 235

NR-MVSNet		67.25 171	65.99 164	71.04 206	73.27 236	43.91 254	85.32 93	84.75 96	66.05 47	53.65 224	82.11 172	45.05 73	85.97 195	47.55 217	56.18 254	83.24 185

Test_1112_low_res		67.18 173	66.23 158	70.02 228	78.75 172	41.02 279	83.43 145	73.69 281	57.29 187	58.45 179	82.39 166	45.30 72	80.88 257	50.50 201	66.26 168	88.16 104

CPTT-MVS		67.15 174	65.84 165	71.07 205	80.96 138	50.32 170	81.94 171	74.10 275	46.18 281	57.91 183	87.64 99	29.57 252	81.31 254	64.10 100	70.18 141	81.56 211

GBi-Net		67.09 175	65.47 175	71.96 185	82.71 100	46.36 230	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

test1		67.09 175	65.47 175	71.96 185	82.71 100	46.36 230	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

PatchmatchNet		67.07 177	63.63 194	77.40 74	83.10 84	58.03 8	72.11 282	77.77 235	58.85 160	59.37 162	70.83 277	37.84 167	84.93 215	42.96 241	69.83 142	89.26 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 178	64.68 187	73.93 146	71.38 275	52.66 125	83.39 149	79.98 191	61.97 102	58.44 180	77.11 224	35.25 207	87.81 145	56.46 159	58.15 238	81.33 216

TranMVSNet+NR-MVSNet		66.94 179	65.61 172	70.93 209	73.45 233	43.38 259	83.02 156	84.25 106	65.31 59	58.33 181	81.90 174	39.92 153	85.52 201	49.43 207	54.89 264	83.89 174

thres100view900		66.87 180	65.42 178	71.24 203	83.29 79	43.15 260	81.67 178	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 287	66.36 163	82.78 193

DU-MVS		66.84 181	65.74 168	70.16 221	73.27 236	42.59 267	81.50 186	82.92 145	63.53 86	58.51 175	82.11 172	40.75 140	84.64 219	53.11 183	55.96 256	83.24 185

conf200view11		66.80 182	65.42 178	70.95 208	83.29 79	43.15 260	81.67 178	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 287	66.36 163	81.89 203

IterMVS-LS		66.63 183	65.36 180	70.42 217	75.10 217	48.90 193	81.45 189	76.69 250	61.05 116	55.71 210	77.10 225	45.86 67	83.65 235	57.44 152	57.88 245	78.70 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 184	64.20 192	73.83 151	72.59 257	53.37 95	81.88 173	79.91 192	61.11 114	54.09 220	75.60 242	40.06 150	88.26 135	56.47 158	56.10 255	79.86 240

Fast-Effi-MVS+-dtu		66.53 185	64.10 193	73.84 150	72.41 262	52.30 135	84.73 117	75.66 263	59.51 134	56.34 204	79.11 198	28.11 260	85.85 198	57.74 150	63.29 190	83.35 181

thres600view7		66.46 186	65.12 182	70.47 215	83.41 75	43.80 256	82.15 168	87.78 36	59.37 139	56.02 206	82.21 168	43.73 93	86.90 169	26.51 300	64.94 172	80.71 227

LPG-MVS_test		66.44 187	64.58 188	72.02 182	74.42 224	48.60 199	83.07 154	80.64 184	54.69 223	53.75 222	83.83 136	25.73 275	86.98 166	60.33 131	64.71 174	80.48 233

tfpn111		66.40 188	64.99 184	70.63 213	83.29 79	43.15 260	81.67 178	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	86.83 171	26.34 302	64.92 173	81.89 203

tpm cat1		66.28 189	62.78 200	76.77 90	81.40 132	57.14 17	70.03 294	77.19 244	53.00 235	58.76 174	70.73 279	46.17 62	86.73 174	43.27 239	64.46 178	86.44 135

EPNet_dtu		66.25 190	66.71 153	64.87 278	78.66 176	34.12 309	82.80 160	75.51 264	61.75 106	64.47 110	86.90 108	37.06 185	72.46 315	43.65 238	69.63 144	88.02 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 191	64.96 186	70.08 223	75.17 214	49.64 179	82.01 170	74.48 271	62.15 99	57.83 184	76.08 239	30.59 247	83.79 233	65.40 95	60.93 211	76.81 279

ACMP	61.11 9	66.24 191	64.33 190	72.00 184	74.89 220	49.12 188	83.18 151	79.83 193	55.41 218	52.29 233	82.68 161	25.83 273	86.10 190	60.89 122	63.94 182	80.78 225
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		65.97 193	65.06 183	68.71 243	72.97 239	42.58 269	78.61 230	75.35 267	54.72 222	59.31 163	86.25 116	33.30 223	77.88 285	57.99 146	67.05 158	85.66 147

X-MVStestdata		65.85 194	62.20 203	76.81 87	83.41 75	52.48 127	84.88 115	83.20 140	58.03 174	63.91 114	4.82 354	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

Vis-MVSNet (Re-imp)		65.52 195	65.63 171	65.17 276	77.49 192	30.54 322	75.49 257	77.73 237	59.34 140	52.26 235	86.69 111	49.38 35	80.53 261	37.07 258	75.28 105	84.42 162

Baseline_NR-MVSNet		65.49 196	64.27 191	69.13 232	74.37 226	41.65 274	83.39 149	78.85 210	59.56 133	59.62 158	76.88 226	40.75 140	87.44 157	49.99 203	55.05 262	78.28 266

view600		64.79 197	63.45 195	68.82 237	82.13 110	40.75 281	79.41 223	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 312	62.85 199	80.71 227

view800		64.79 197	63.45 195	68.82 237	82.13 110	40.75 281	79.41 223	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 312	62.85 199	80.71 227

conf0.05thres1000		64.79 197	63.45 195	68.82 237	82.13 110	40.75 281	79.41 223	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 312	62.85 199	80.71 227

tfpn		64.79 197	63.45 195	68.82 237	82.13 110	40.75 281	79.41 223	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 312	62.85 199	80.71 227

FMVSNet1		64.57 201	62.11 204	71.96 185	77.32 194	46.36 230	83.52 140	83.31 134	52.43 248	54.42 216	76.23 234	27.80 262	86.20 184	42.59 243	61.34 208	83.32 182

tfpn_ndepth		64.50 202	63.34 199	67.99 247	81.84 119	38.30 292	79.26 228	83.57 132	53.69 229	52.86 231	84.51 129	46.96 50	84.79 216	24.28 307	63.09 196	80.87 224

dp		64.41 203	61.58 216	72.90 165	82.40 106	54.09 76	72.53 276	76.59 253	60.39 125	55.68 211	70.39 280	35.18 208	76.90 293	39.34 250	61.71 207	87.73 114

v18		64.36 204	61.80 206	72.05 179	72.97 239	53.31 100	81.16 192	77.76 236	59.14 148	48.50 260	68.97 285	42.91 112	84.38 221	56.62 156	48.17 286	78.47 253

ACMM	58.35 12	64.35 205	62.01 205	71.38 201	74.21 227	48.51 203	82.25 167	79.66 197	47.61 270	54.54 215	80.11 190	25.26 277	86.00 192	51.26 197	63.16 193	79.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v16		64.25 206	61.66 215	72.03 180	72.91 242	53.28 105	80.93 195	77.81 232	58.86 159	48.30 261	68.80 288	42.70 118	84.37 222	56.44 161	48.14 287	78.44 256

v17		64.19 207	61.58 216	72.03 180	72.89 245	53.28 105	80.91 196	77.80 233	58.87 158	48.22 262	68.77 289	42.69 119	84.37 222	56.43 162	47.66 290	78.43 257

pm-mvs1		64.12 208	62.56 201	68.78 242	71.68 271	38.87 289	82.89 158	81.57 166	55.54 217	53.89 221	77.82 214	37.73 171	86.74 173	48.46 212	53.49 273	80.72 226

test_djsdf		63.84 209	61.56 218	70.70 211	68.78 288	44.69 248	81.63 182	81.44 168	50.28 260	52.27 234	76.26 233	26.72 268	86.11 188	60.83 123	55.84 259	81.29 219

v15		63.83 210	61.13 221	71.93 188	72.60 255	53.21 108	80.44 202	78.22 221	58.80 161	47.57 267	68.22 291	42.50 120	84.18 224	55.82 164	46.02 302	78.39 259

IterMVS		63.77 211	61.67 214	70.08 223	72.68 253	51.24 155	80.44 202	75.51 264	60.51 124	51.41 239	73.70 256	32.08 237	78.91 274	54.30 177	54.35 267	80.08 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V14		63.72 212	60.99 223	71.91 190	72.58 258	53.18 109	80.24 206	78.19 222	58.53 168	47.35 273	68.10 292	42.28 123	84.18 224	55.68 166	45.97 303	78.36 262

testing_2		63.60 213	59.86 234	74.82 129	61.87 319	52.39 131	73.06 273	82.76 148	61.49 111	39.96 305	67.39 299	21.06 303	88.34 128	67.07 79	64.10 179	83.72 177

V9		63.60 213	60.84 224	71.87 192	72.51 260	53.12 113	80.04 211	78.15 224	58.25 171	47.14 275	67.98 293	42.08 127	84.18 224	55.47 167	45.92 305	78.32 263

v12		63.47 215	60.68 227	71.85 193	72.45 261	53.08 115	79.83 213	78.13 226	57.95 177	46.89 277	67.87 295	41.81 133	84.17 227	55.30 169	45.87 306	78.29 265

v11		63.44 216	60.66 228	71.79 195	72.61 254	53.02 119	79.80 214	78.08 228	58.30 169	47.27 274	67.91 294	40.67 144	84.14 229	54.93 172	46.39 300	78.23 269

v13		63.36 217	60.54 230	71.82 194	72.41 262	53.03 118	79.64 218	78.10 227	57.66 183	46.67 280	67.75 296	41.68 134	84.17 227	55.11 170	45.82 307	78.25 268

pmmvs4		63.34 218	61.07 222	70.16 221	70.14 281	50.53 164	79.97 212	71.41 297	55.08 220	54.12 219	78.58 201	32.79 229	82.09 250	50.33 202	57.22 249	77.86 271

Patchmatch-test1		63.23 219	59.16 240	75.43 111	78.58 178	57.92 9	61.61 316	77.53 239	56.71 196	57.75 188	70.98 276	31.97 238	78.19 278	40.97 247	56.36 251	90.18 69

jajsoiax		63.21 220	60.84 224	70.32 219	68.33 293	44.45 249	81.23 190	81.05 178	53.37 232	50.96 245	77.81 215	17.49 318	85.49 203	59.31 135	58.05 241	81.02 222

MIMVSNet		63.12 221	60.29 232	71.61 196	75.92 210	46.65 226	65.15 304	81.94 159	59.14 148	54.65 214	69.47 283	25.74 274	80.63 259	41.03 246	69.56 145	87.55 116

conf0.01		63.04 222	61.74 207	66.95 257	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	81.89 203

conf0.002		63.04 222	61.74 207	66.95 257	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	81.89 203

mvs_tets		62.96 224	60.55 229	70.19 220	68.22 295	44.24 253	80.90 197	80.74 183	52.99 236	50.82 253	77.56 216	16.74 321	85.44 204	59.04 136	57.94 242	80.89 223

thresconf0.02		62.84 225	61.74 207	66.14 264	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	79.44 242

tfpn_n400		62.84 225	61.74 207	66.14 264	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	79.44 242

tfpnconf		62.84 225	61.74 207	66.14 264	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	79.44 242

tfpnview11		62.84 225	61.74 207	66.14 264	80.60 146	35.92 300	76.01 246	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 240	22.59 317	58.77 229	79.44 242

TransMVSNet (Re)		62.82 229	60.76 226	69.02 233	73.98 229	41.61 275	86.36 70	79.30 207	56.90 190	52.53 232	76.44 230	41.85 131	87.60 154	38.83 251	40.61 317	77.86 271

pmmvs5		62.80 230	61.18 220	67.66 250	69.53 286	42.37 272	82.65 161	75.19 268	54.30 226	52.03 236	78.51 205	31.64 242	80.67 258	48.60 210	58.15 238	79.95 239

tfpn1000		62.79 231	61.74 207	65.95 269	80.50 155	35.93 299	76.53 245	83.99 121	51.24 256	49.82 255	83.44 150	47.32 45	83.02 246	21.84 324	60.99 209	78.89 247

test0.0.03 1		62.54 232	62.44 202	62.86 288	72.28 267	29.51 325	82.93 157	78.78 212	59.18 146	53.07 230	82.41 165	36.91 188	77.39 289	37.45 254	58.96 227	81.66 210

v7n		62.50 233	59.27 239	72.20 177	67.25 299	49.83 177	77.87 236	80.12 189	52.50 247	48.80 259	73.07 261	32.10 236	87.90 143	46.83 223	54.92 263	78.86 248

CR-MVSNet		62.47 234	59.04 242	72.77 168	73.97 230	56.57 23	60.52 319	71.72 291	60.04 128	57.49 193	65.86 304	38.94 158	80.31 263	42.86 242	59.93 217	81.42 213

tpmvs		62.45 235	59.42 237	71.53 200	83.93 70	54.32 70	70.03 294	77.61 238	51.91 251	53.48 225	68.29 290	37.91 166	86.66 176	33.36 272	58.27 236	73.62 304

EG-PatchMatch MVS		62.40 236	59.59 235	70.81 210	73.29 235	49.05 189	85.81 78	84.78 95	51.85 253	44.19 287	73.48 259	15.52 326	89.85 76	40.16 248	67.24 157	73.54 305

XVG-OURS-SEG-HR		62.02 237	59.54 236	69.46 230	65.30 305	45.88 237	65.06 305	73.57 283	46.45 279	57.42 196	83.35 151	26.95 267	78.09 281	53.77 181	64.03 180	84.42 162

XVG-OURS		61.88 238	59.34 238	69.49 229	65.37 304	46.27 233	64.80 307	73.49 284	47.04 274	57.41 197	82.85 156	25.15 279	78.18 279	53.00 186	64.98 171	84.01 168

TAPA-MVS	56.12 14	61.82 239	60.18 233	66.71 259	78.48 181	37.97 294	75.19 259	76.41 255	46.82 276	57.04 199	86.52 114	27.67 264	77.03 291	26.50 301	67.02 159	85.14 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnnormal		61.47 240	59.09 241	68.62 245	76.29 205	41.69 273	81.14 193	85.16 85	54.48 225	51.32 240	73.63 257	32.32 233	86.89 170	21.78 326	55.71 260	77.29 277

v748		61.35 241	58.24 245	70.69 212	66.28 300	47.35 220	76.58 243	79.17 208	53.09 234	46.37 284	71.50 274	33.18 225	86.33 183	46.78 224	51.19 281	78.39 259

PVSNet_0	57.04 13	61.19 242	57.24 250	73.02 163	77.45 193	50.31 171	79.43 221	77.36 243	63.96 76	47.51 270	72.45 268	25.03 280	83.78 234	52.76 190	19.22 342	84.96 156

PLC	52.38 18	60.89 243	58.97 243	66.68 261	81.77 120	45.70 239	78.96 229	74.04 277	43.66 296	47.63 266	83.19 154	23.52 289	77.78 288	37.47 253	60.46 212	76.55 283
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 244	60.44 231	62.07 289	75.00 218	32.73 316	79.54 219	73.49 284	36.98 316	56.28 205	83.74 138	29.28 255	69.53 325	46.48 225	63.23 191	83.94 173

CNLPA		60.59 245	58.44 244	67.05 256	79.21 164	47.26 222	79.75 217	64.34 319	42.46 303	51.90 238	83.94 134	27.79 263	75.41 298	37.12 256	59.49 224	78.47 253

anonymousdsp		60.46 246	57.65 247	68.88 234	63.63 313	45.09 243	72.93 274	78.63 216	46.52 278	51.12 242	72.80 265	21.46 301	83.07 239	57.79 149	53.97 268	78.47 253

v52		59.82 247	56.41 256	70.06 225	61.49 322	48.67 197	69.46 298	75.80 261	52.55 245	47.49 271	68.82 287	28.60 256	85.70 199	52.13 194	51.34 280	75.80 287

V4		59.82 247	56.41 256	70.05 226	61.49 322	48.67 197	69.46 298	75.79 262	52.55 245	47.49 271	68.83 286	28.60 256	85.70 199	52.13 194	51.35 279	75.80 287

ACMH	53.70 16	59.78 249	55.94 261	71.28 202	76.59 203	48.35 208	80.15 210	76.11 256	49.74 264	41.91 298	73.45 260	16.50 323	90.31 65	31.42 280	57.63 247	75.17 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 250	57.15 251	67.09 254	66.01 301	36.86 298	80.50 201	78.64 215	45.05 287	49.05 258	73.94 251	27.28 265	86.10 190	43.96 237	49.94 284	78.31 264

MSDG		59.44 251	55.14 265	72.32 176	74.69 221	50.71 159	74.39 263	73.58 282	44.44 291	43.40 293	77.52 217	19.45 308	90.87 54	31.31 281	57.49 248	75.38 291

DP-MVS		59.24 252	56.12 259	68.63 244	88.24 18	50.35 168	82.51 164	64.43 318	41.10 305	46.70 279	78.77 200	24.75 281	88.57 120	22.26 323	56.29 253	66.96 324

OpenMVS_ROB	53.19 17	59.20 253	56.00 260	68.83 236	71.13 277	44.30 251	83.64 139	75.02 269	46.42 280	46.48 283	73.03 262	18.69 312	88.14 136	27.74 296	61.80 206	74.05 301

Anonymous20231206		59.08 254	57.59 248	63.55 283	68.77 289	32.14 319	80.26 205	79.78 194	50.00 263	49.39 256	72.39 269	26.64 269	78.36 277	33.12 275	57.94 242	80.14 237

WR-MVS_H		58.91 255	58.04 246	61.54 294	69.07 287	33.83 311	76.91 240	81.99 158	51.40 255	48.17 263	74.67 247	40.23 146	74.15 302	31.78 279	48.10 288	76.64 281

LCM-MVSNet-Re		58.82 256	56.54 254	65.68 270	79.31 163	29.09 328	61.39 318	45.79 339	60.73 121	37.65 311	72.47 267	31.42 243	81.08 255	49.66 205	70.41 139	86.87 124

Patchmatch-RL test		58.72 257	54.32 266	71.92 189	63.91 312	44.25 252	61.73 315	55.19 331	57.38 186	49.31 257	54.24 330	37.60 174	80.89 256	62.19 115	47.28 294	90.63 54

FMVSNet5		58.61 258	56.45 255	65.10 277	77.20 199	39.74 286	74.77 260	77.12 246	50.27 262	43.28 294	67.71 297	26.15 272	76.90 293	36.78 261	54.78 265	78.65 251

ACMH+	54.58 15	58.55 259	55.24 263	68.50 246	74.68 222	45.80 238	80.27 204	70.21 304	47.15 273	42.77 296	75.48 243	16.73 322	85.98 193	35.10 269	54.78 265	73.72 303

CP-MVSNet		58.54 260	57.57 249	61.46 296	68.50 291	33.96 310	76.90 241	78.60 218	51.67 254	47.83 264	76.60 229	34.99 210	72.79 313	35.45 265	47.58 291	77.64 275

RPMNet		58.49 261	53.74 270	72.77 168	73.97 230	56.57 23	60.52 319	72.39 288	35.72 321	57.49 193	58.87 324	37.73 171	80.31 263	27.01 299	59.93 217	81.42 213

PEN-MVS		58.35 262	57.15 251	61.94 291	67.55 298	34.39 308	77.01 239	78.35 220	51.87 252	47.72 265	76.73 228	33.91 218	73.75 307	34.03 271	47.17 295	77.68 273

PS-CasMVS		58.12 263	57.03 253	61.37 297	68.24 294	33.80 312	76.73 242	78.01 230	51.20 257	47.54 269	76.20 237	32.85 227	72.76 314	35.17 267	47.37 293	77.55 276

UnsupCasMVSNet_eth		57.56 264	55.15 264	64.79 279	64.57 310	33.12 313	73.17 271	83.87 125	58.98 156	41.75 299	70.03 281	22.54 293	79.92 269	46.12 229	35.31 326	81.32 218

CHOSEN 280x420		57.53 265	56.38 258	60.97 298	74.01 228	48.10 214	46.30 334	54.31 333	48.18 269	50.88 246	77.43 220	38.37 164	59.16 337	54.83 173	63.14 194	75.66 289

DTE-MVSNet		57.03 266	55.73 262	60.95 299	65.94 302	32.57 317	75.71 252	77.09 247	51.16 258	46.65 281	76.34 232	32.84 228	73.22 311	30.94 283	44.87 309	77.06 278

PatchMatch-RL		56.66 267	53.75 269	65.37 275	77.91 188	45.28 242	69.78 296	60.38 326	41.35 304	47.57 267	73.73 253	16.83 320	76.91 292	36.99 259	59.21 226	73.92 302

PatchT		56.60 268	52.97 273	67.48 251	72.94 241	46.16 236	57.30 325	73.78 280	38.77 310	54.37 217	57.26 327	37.52 176	78.06 282	32.02 277	52.79 274	78.23 269

Patchmtry		56.56 269	52.95 274	67.42 252	72.53 259	50.59 163	59.05 321	71.72 291	37.86 314	46.92 276	65.86 304	38.94 158	80.06 268	36.94 260	46.72 299	71.60 314

test_0402		56.45 270	53.03 272	66.69 260	76.78 202	50.31 171	81.76 175	69.61 306	42.79 301	43.88 289	72.13 271	22.82 292	86.46 182	16.57 340	50.94 282	63.31 333

LS3D		56.40 271	53.82 268	64.12 280	81.12 136	45.69 240	73.42 268	66.14 313	35.30 326	43.24 295	79.88 191	22.18 297	79.62 272	19.10 335	64.00 181	67.05 323

ADS-MVSNet		56.17 272	51.95 280	68.84 235	80.60 146	53.07 116	55.03 327	70.02 305	44.72 288	51.00 243	61.19 315	22.83 290	78.88 275	28.54 292	53.63 270	74.57 296

XVG-ACMP-BASELINE		56.03 273	52.85 275	65.58 271	61.91 318	40.95 280	63.36 309	72.43 287	45.20 286	46.02 285	74.09 249	9.20 337	78.12 280	45.13 231	58.27 236	77.66 274

pmmvs-eth3d		55.97 274	52.78 276	65.54 272	61.02 324	46.44 229	75.36 258	67.72 309	49.61 265	43.65 291	67.58 298	21.63 300	77.04 290	44.11 236	44.33 311	73.15 310

F-COLMAP		55.96 275	53.65 271	62.87 287	72.76 248	42.77 266	74.70 262	70.37 302	40.03 306	41.11 302	79.36 193	17.77 316	73.70 308	32.80 276	53.96 269	72.15 311

CMPMVS	40.41 21	55.34 276	52.64 277	63.46 284	60.88 325	43.84 255	61.58 317	71.06 298	30.43 333	36.33 313	74.63 248	24.14 283	75.44 297	48.05 215	66.62 160	71.12 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 277	54.07 267	58.68 304	63.14 315	25.00 334	77.69 237	74.78 270	52.64 237	43.43 292	72.39 269	26.21 271	74.76 301	29.31 285	47.05 297	76.28 285

ADS-MVSNet2		55.21 278	51.44 281	66.51 262	80.60 146	49.56 182	55.03 327	65.44 315	44.72 288	51.00 243	61.19 315	22.83 290	75.41 298	28.54 292	53.63 270	74.57 296

SixPastTwentyTwo		54.37 279	50.10 285	67.21 253	70.70 278	41.46 276	74.73 261	64.69 317	47.56 271	39.12 308	69.49 282	18.49 314	84.69 218	31.87 278	34.20 332	75.48 290

USDC		54.36 280	51.23 282	63.76 282	64.29 311	37.71 295	62.84 314	73.48 286	56.85 191	35.47 317	71.94 273	9.23 336	78.43 276	38.43 252	48.57 285	75.13 293

testgi		54.25 281	52.57 278	59.29 302	62.76 316	21.65 340	72.21 281	70.47 300	53.25 233	41.94 297	77.33 221	14.28 327	77.95 284	29.18 286	51.72 278	78.28 266

K. test v3		54.04 282	49.42 289	67.92 249	68.55 290	42.57 270	75.51 256	63.07 322	52.07 249	39.21 307	64.59 308	19.34 309	82.21 248	37.11 257	25.31 338	78.97 246

test2356		53.94 283	52.37 279	58.64 305	61.58 320	27.53 333	78.20 234	74.33 273	46.92 275	44.01 288	66.04 303	18.91 311	74.11 303	28.80 287	52.55 275	74.28 298

UnsupCasMVSNet_bld		53.86 284	50.53 284	63.84 281	63.52 314	34.75 307	71.38 286	81.92 161	46.53 277	38.95 309	57.93 325	20.55 305	80.20 267	39.91 249	34.09 333	76.57 282

YYNet1		53.82 285	49.96 286	65.41 274	70.09 283	48.95 191	72.30 279	71.66 293	44.25 292	31.89 330	63.07 312	23.73 285	73.95 305	33.26 273	39.40 319	73.34 307

MDA-MVSNet_test_wron		53.82 285	49.95 287	65.43 273	70.13 282	49.05 189	72.30 279	71.65 294	44.23 293	31.85 331	63.13 311	23.68 288	74.01 304	33.25 274	39.35 320	73.23 309

Patchmatch-test		53.33 287	48.17 291	68.81 241	73.31 234	42.38 271	42.98 338	58.23 328	32.53 329	38.79 310	70.77 278	39.66 154	73.51 309	25.18 304	52.06 277	90.55 55

LTVRE_ROB	45.45 19	52.73 288	49.74 288	61.69 293	69.78 284	34.99 306	44.52 336	67.60 310	43.11 300	43.79 290	74.03 250	18.54 313	81.45 253	28.39 294	57.94 242	68.62 321
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		52.63 289	50.72 283	58.37 306	62.69 317	28.13 330	72.60 275	75.97 259	30.94 332	40.76 304	72.11 272	20.16 306	70.80 321	35.11 268	46.11 301	76.19 286

OurMVSNet-221017-0		52.39 290	48.73 290	63.35 285	65.21 306	38.42 291	68.54 301	64.95 316	38.19 311	39.57 306	71.43 275	13.23 329	79.92 269	37.16 255	40.32 318	71.72 313

JIA-IIPM		52.33 291	47.77 294	66.03 268	71.20 276	46.92 224	40.00 342	76.48 254	37.10 315	46.73 278	37.02 338	32.96 226	77.88 285	35.97 263	52.45 276	73.29 308

MDA-MVSNet-bldmvs		51.56 292	47.75 295	63.00 286	71.60 273	47.32 221	69.70 297	72.12 289	43.81 295	27.65 336	63.38 310	21.97 299	75.96 295	27.30 298	32.19 334	65.70 327

COLMAP_ROB	43.60 20	50.90 293	48.05 292	59.47 301	67.81 296	40.57 285	71.25 287	62.72 324	36.49 320	36.19 314	73.51 258	13.48 328	73.92 306	20.71 330	50.26 283	63.92 331
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 294	47.81 293	57.96 307	61.53 321	27.80 332	67.40 302	74.06 276	43.25 299	33.31 329	65.38 307	16.03 324	71.34 320	21.80 325	47.55 292	74.75 294

MVS-HIRNet		49.01 295	44.71 300	61.92 292	76.06 206	46.61 227	63.23 311	54.90 332	24.77 337	33.56 326	36.60 339	21.28 302	75.88 296	29.49 284	62.54 203	63.26 334

testus		48.97 296	46.53 297	56.31 312	57.39 331	24.08 336	73.40 269	70.45 301	43.37 298	35.52 316	63.95 309	4.77 348	71.36 319	24.88 305	45.02 308	73.50 306

new-patchmatchnet		48.21 297	46.55 296	53.18 316	57.73 329	18.19 349	70.24 292	71.02 299	45.70 282	33.70 325	60.23 317	18.00 315	69.86 324	27.97 295	34.35 330	71.49 316

TinyColmap		48.15 298	44.49 302	59.13 303	65.73 303	38.04 293	63.34 310	62.86 323	38.78 309	29.48 334	67.23 301	6.46 343	73.30 310	24.59 306	41.90 315	66.04 325

1111		48.00 299	46.30 298	53.08 317	55.68 332	20.86 343	70.41 290	76.03 257	36.88 317	34.86 319	59.55 321	23.72 286	68.13 326	20.82 328	38.76 322	70.25 318

AllTest		47.32 300	44.66 301	55.32 313	65.08 307	37.50 296	62.96 313	54.25 334	35.45 324	33.42 327	72.82 263	9.98 333	59.33 335	24.13 308	43.84 312	69.13 319

test1235678		47.09 301	43.82 304	56.91 310	53.18 335	24.90 335	71.93 283	70.31 303	39.54 307	31.44 332	56.59 329	9.50 335	71.55 318	22.63 316	39.24 321	74.28 298

LP		47.05 302	42.23 307	61.53 295	72.04 268	49.37 186	49.48 331	65.50 314	34.57 327	34.29 323	52.30 332	17.73 317	75.32 300	17.56 338	36.57 324	59.91 335

PM-MVS		46.92 303	43.76 305	56.41 311	52.18 336	32.26 318	63.21 312	38.18 345	37.99 313	40.78 303	66.20 302	5.09 346	65.42 331	48.19 214	41.99 314	71.54 315

Anonymous20231211		46.87 304	43.27 306	57.67 308	57.88 328	30.12 323	73.14 272	64.16 320	33.43 328	34.34 322	59.42 323	12.15 330	77.99 283	19.64 334	35.23 328	64.90 329

testpf		45.92 305	45.81 299	46.27 324	69.56 285	27.86 331	23.18 347	73.91 279	44.10 294	36.99 312	57.16 328	20.56 304	71.77 317	42.17 245	44.64 310	39.18 343

RPSCF		45.77 306	44.13 303	50.68 319	57.67 330	29.66 324	54.92 329	45.25 341	26.69 336	45.92 286	75.92 241	17.43 319	45.70 347	27.44 297	45.95 304	76.67 280

pmmvs3		45.53 307	41.55 309	57.44 309	48.97 339	39.68 287	70.06 293	57.66 329	28.32 335	34.06 324	57.29 326	8.50 338	66.85 330	34.86 270	34.26 331	65.80 326

N_pmnet		41.25 308	39.77 310	45.66 326	68.50 291	0.82 358	72.51 277	0.38 359	35.61 322	35.26 318	61.51 314	20.07 307	67.74 328	23.51 310	40.63 316	68.42 322

TDRefinement		40.91 309	38.37 311	48.55 322	50.45 337	33.03 315	58.98 322	50.97 337	28.50 334	29.89 333	67.39 299	6.21 345	54.51 339	17.67 337	35.25 327	58.11 336

testmv		39.64 310	36.01 312	50.55 320	42.18 343	21.56 341	64.81 306	66.88 312	32.22 330	22.25 340	47.47 334	4.33 350	64.81 332	17.71 336	26.22 337	65.29 328

.test1245		38.91 311	41.99 308	29.67 336	55.68 332	20.86 343	70.41 290	76.03 257	36.88 317	34.86 319	59.55 321	23.72 286	68.13 326	20.82 328	0.00 354	0.02 354

DSMNet-mixed		38.35 312	35.36 313	47.33 323	48.11 340	14.91 351	37.87 343	36.60 347	19.18 341	34.37 321	59.56 320	15.53 325	53.01 341	20.14 332	46.89 298	74.07 300

test12356		37.84 313	35.07 314	46.18 325	45.03 342	8.02 356	57.70 324	62.67 325	31.83 331	22.78 338	50.25 333	4.46 349	66.95 329	17.25 339	23.62 340	63.57 332

no-one		37.21 314	31.48 318	54.40 315	39.62 347	31.91 321	45.68 335	67.42 311	35.54 323	14.59 343	35.91 341	7.35 339	73.20 312	22.98 311	14.23 343	58.09 337

FPMVS		35.40 315	33.67 315	40.57 328	46.34 341	28.74 329	41.05 340	57.05 330	20.37 340	22.27 339	53.38 331	6.87 341	44.94 348	8.62 346	47.11 296	48.01 341

ANet_high		34.39 316	29.59 319	48.78 321	30.34 351	22.28 338	55.53 326	63.79 321	38.11 312	15.47 342	36.56 340	6.94 340	59.98 334	13.93 342	5.64 353	64.08 330

new_pmnet		33.56 317	31.89 317	38.59 329	49.01 338	20.42 345	51.01 330	37.92 346	20.58 338	23.45 337	46.79 335	6.66 342	49.28 344	20.00 333	31.57 336	46.09 342

LF4IMVS		33.04 318	32.55 316	34.52 333	40.96 344	22.03 339	44.45 337	35.62 348	20.42 339	28.12 335	62.35 313	5.03 347	31.88 353	21.61 327	34.42 329	49.63 340

LCM-MVSNet		28.07 319	23.85 323	40.71 327	27.46 353	18.93 348	30.82 345	46.19 338	12.76 346	16.40 341	34.70 343	1.90 355	48.69 345	20.25 331	24.22 339	54.51 338

pcd1.5k->3k		27.74 320	27.68 320	27.93 338	73.75 232	0.00 360	0.00 352	85.50 71	0.00 354	0.00 357	0.00 358	26.52 270	0.00 357	0.00 356	63.37 189	83.79 176

Gipuma		27.47 321	24.26 322	37.12 331	60.55 326	29.17 327	11.68 350	60.00 327	14.18 344	10.52 347	15.12 350	2.20 354	63.01 333	8.39 347	35.65 325	19.18 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		26.71 322	22.98 325	37.87 330	36.89 348	8.51 355	42.51 339	29.32 353	19.09 342	13.01 344	37.54 337	2.23 353	53.11 340	14.54 341	11.71 344	51.99 339

PNet_i23d		25.11 323	23.09 324	31.17 335	40.18 345	21.30 342	57.99 323	33.28 350	13.77 345	9.94 348	30.29 345	0.45 359	43.74 349	13.61 344	8.28 346	28.46 346

PMVS	19.57 22	25.07 324	22.43 326	32.99 334	23.12 354	22.98 337	40.98 341	35.19 349	15.99 343	11.95 346	35.87 342	1.47 357	49.29 343	5.41 351	31.90 335	26.70 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		19.94 325	14.87 329	35.13 332	22.47 355	19.80 346	25.80 346	38.64 344	7.61 350	4.88 353	13.58 353	0.23 360	48.42 346	13.11 345	7.53 348	37.18 344

E-PMN		19.16 326	18.40 327	21.44 339	36.19 349	13.63 352	47.59 332	30.89 351	10.73 347	5.91 351	16.59 348	3.66 352	39.77 350	5.95 350	8.14 347	10.92 350

EMVS		18.42 327	17.66 328	20.71 340	34.13 350	12.64 353	46.94 333	29.94 352	10.46 349	5.58 352	14.93 351	4.23 351	38.83 351	5.24 352	7.51 350	10.67 351

cdsmvs_eth3d_5k		18.33 328	24.44 321	0.00 346	0.00 359	0.00 360	0.00 352	89.40 12	0.00 354	0.00 357	92.02 23	38.55 162	0.00 357	0.00 356	0.00 354	0.00 357

MVE	16.60 23	17.34 329	13.39 330	29.16 337	28.43 352	19.72 347	13.73 349	23.63 354	7.23 351	7.96 349	21.41 346	0.80 358	36.08 352	6.97 348	10.39 345	31.69 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 330	10.68 331	5.73 343	2.49 357	4.21 357	10.48 351	18.04 355	0.34 353	12.59 345	20.49 347	11.39 331	7.03 356	13.84 343	6.46 352	5.95 352

wuyk23d		9.11 331	8.77 333	10.15 342	40.18 345	16.76 350	20.28 348	1.01 358	2.58 352	2.66 354	0.98 355	0.23 360	12.49 355	4.08 353	6.90 351	1.19 353

ab-mvs-re		7.68 332	10.24 332	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	92.12 20	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

testmvs		6.14 333	8.18 334	0.01 344	0.01 358	0.00 360	73.40 269	0.00 360	0.00 354	0.02 355	0.15 356	0.00 362	0.00 357	0.02 354	0.00 354	0.02 354

test123		6.01 334	8.01 335	0.01 344	0.00 359	0.01 359	71.93 283	0.00 360	0.00 354	0.02 355	0.11 357	0.00 362	0.00 357	0.02 354	0.00 354	0.02 354

pcd_1.5k_mvsjas		3.15 335	4.20 336	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	37.77 168	0.00 357	0.00 356	0.00 354	0.00 357

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 359	0.00 360	0.00 352	0.00 360	0.00 354	0.00 357	0.00 358	0.00 362	0.00 357	0.00 356	0.00 354	0.00 357

GSMVS																88.13 107

test_part3							89.59 26		56.02 210		93.65 3		95.22 6	79.73 12

test_part2					89.33 9	55.48 38				82.27 2

test_part1								88.42 25				58.18 6			86.59 16	91.53 36

sam_mvs1												38.86 160				88.13 107

sam_mvs												35.99 203

semantic-postprocess				60.08 300	70.68 280	45.07 244		74.25 274	43.54 297	50.02 254	73.73 253	32.22 235	56.74 338	51.06 200	53.60 272	78.42 258

ambc				62.06 290	53.98 334	29.38 326	35.08 344	79.65 198		41.37 300	59.96 318	6.27 344	82.15 249	35.34 266	38.22 323	74.65 295

MTGPA								81.31 171

test_post1							70.84 289				14.72 352	34.33 214	83.86 231	48.80 209

test_post											16.22 349	37.52 176	84.72 217

patchmatchnet-post											59.74 319	38.41 163	79.91 271

GG-mvs-BLEND				77.77 65	86.68 28	50.61 161	68.67 300	88.45 24		68.73 65	87.45 102	59.15 4	90.67 56	54.83 173	87.67 12	92.03 26

MTMP								15.34 356

gm-plane-assit					83.24 82	54.21 74			70.91 11		88.23 89		95.25 5	66.37 82

test9_res														78.72 18	85.44 28	91.39 41

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

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

agg_prior2														75.65 32	85.11 32	91.01 47

agg_prior					85.64 40	54.92 57		83.61 130		72.53 43			88.10 139

TestCases				55.32 313	65.08 307	37.50 296		54.25 334	35.45 324	33.42 327	72.82 263	9.98 333	59.33 335	24.13 308	43.84 312	69.13 319

test_prior4						56.39 28	87.15 59

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

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

旧先验2							81.73 176		45.53 284	74.66 24			70.48 323	58.31 143

新几何2							81.61 184

新几何1				73.30 161	83.10 84	53.48 86		71.43 296	45.55 283	66.14 85	87.17 105	33.88 220	80.54 260	48.50 211	80.33 64	85.88 143

旧先验1					81.57 126	47.48 218		71.83 290			88.66 82	36.94 187			78.34 79	88.67 98

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

原ACMM2							83.77 137

原ACMM1				76.13 98	84.89 56	54.59 68		85.26 81	51.98 250	66.70 78	87.07 107	40.15 148	89.70 81	51.23 198	85.06 33	84.10 165

test222					79.36 160	50.97 157	77.99 235	67.84 308	42.54 302	62.84 127	86.53 113	30.26 249			76.91 92	85.23 152

testdata2													77.81 287	45.64 230

segment_acmp												44.97 76

testdata				67.08 255	77.59 190	45.46 241		69.20 307	44.47 290	71.50 53	88.34 85	31.21 244	70.76 322	52.20 193	75.88 100	85.03 154

testdata1							77.55 238		64.14 73

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

plane_prior7					77.95 185	48.46 207

plane_prior6					78.42 182	49.39 185						36.04 200

plane_prior5								82.59 149					88.30 132	65.46 91	72.34 129	84.49 160

plane_prior4											83.28 152

plane_prior3						48.95 191			64.01 74	62.15 130

plane_prior2							85.76 80		63.60 84

plane_prior1					78.31 184

plane_prior						49.57 180	87.43 52		64.57 66						72.84 123

n2								0.00 360

nn								0.00 360

door-mid								41.31 343

lessismore_v0				67.98 248	64.76 309	41.25 277		45.75 340		36.03 315	65.63 306	19.29 310	84.11 230	35.67 264	21.24 341	78.59 252

LGP-MVS_train				72.02 182	74.42 224	48.60 199		80.64 184	54.69 223	53.75 222	83.83 136	25.73 275	86.98 166	60.33 131	64.71 174	80.48 233

test11								84.25 106

door								43.27 342

HQP5-MVS						51.56 145

HQP-NCC					79.02 167		88.00 38		65.45 52	64.48 107

ACMP_Plane					79.02 167		88.00 38		65.45 52	64.48 107

BP-MVS														66.70 80

HQP4-MVS										64.47 110			88.61 115			84.91 157

HQP3-MVS								83.68 127							73.12 119

HQP2-MVS												37.35 179

NP-MVS					78.76 171	50.43 166					85.12 124

MDTV_nov1_ep13_2view						43.62 257	71.13 288		54.95 221	59.29 164		36.76 190		46.33 227		87.32 120

MDTV_nov1_ep13			61.56 218		81.68 121	55.12 51	72.41 278	78.18 223	59.19 144	58.85 172	69.29 284	34.69 211	86.16 187	36.76 262	62.96 197

ACMMP++_ref															63.20 192

ACMMP++															59.38 225

Test By Simon												39.38 155

ITE_SJBPF				51.84 318	58.03 327	31.94 320		53.57 336	36.67 319	41.32 301	75.23 245	11.17 332	51.57 342	25.81 303	48.04 289	72.02 312

DeepMVS_CX				13.10 341	21.34 356	8.99 354		10.02 357	10.59 348	7.53 350	30.55 344	1.82 356	14.55 354	6.83 349	7.52 349	15.75 349

High-resolution multi-view benchmark