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		91.08 1	91.46 1	89.94 2	97.66 2	73.37 6	97.13 1	95.58 2	89.33 1	85.77 17	96.26 7	72.84 7	99.38 1	92.64 2	95.93 3	97.08 4

CNVR-MVS		90.32 2	90.89 3	88.61 10	96.76 4	70.65 17	96.47 5	94.83 13	84.83 6	89.07 3	96.80 2	70.86 11	99.06 2	92.64 2	95.71 4	96.12 15

DELS-MVS		90.05 3	90.09 4	89.94 2	93.14 48	73.88 5	97.01 2	94.40 25	88.32 2	85.71 18	94.91 37	74.11 5	98.91 4	87.26 21	95.94 2	97.03 5
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
DeepPCF-MVS	81.17 1	89.72 4	91.38 2	84.72 87	93.00 50	58.16 221	96.72 3	94.41 24	86.50 4	90.25 1	97.83 1	75.46 3	98.67 10	92.78 1	95.49 6	97.32 1

HPM-MVS++		89.37 5	89.95 5	87.64 17	95.10 17	68.23 50	95.24 19	94.49 21	82.43 12	88.90 4	96.35 5	71.89 10	98.63 11	88.76 14	96.40 1	96.06 16

NCCC		89.07 6	89.46 6	87.91 13	96.60 5	69.05 34	96.38 6	94.64 19	84.42 7	86.74 13	96.20 8	66.56 32	98.76 9	89.03 12	94.56 15	95.92 22

PS-MVSNAJ		88.14 7	87.61 13	89.71 4	92.06 66	76.72 1	95.75 9	93.26 52	83.86 8	89.55 2	96.06 10	53.55 140	97.89 27	91.10 4	93.31 28	94.54 56

TSAR-MVS		87.96 8	88.37 8	86.70 36	93.51 41	65.32 122	95.15 22	93.84 31	78.17 49	85.93 16	94.80 40	75.80 2	98.21 19	89.38 7	88.78 66	96.59 10

DeepC-MVS_fast	79.48 2	87.95 9	88.00 9	87.79 16	95.86 12	68.32 46	95.74 10	94.11 28	83.82 9	83.49 34	96.19 9	64.53 48	98.44 15	83.42 39	94.88 10	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPNet		87.84 10	88.38 7	86.23 54	93.30 43	66.05 112	95.26 18	94.84 12	87.09 3	88.06 6	94.53 44	66.79 30	97.34 46	83.89 36	91.68 44	95.29 32
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 11	87.77 11	87.63 19	89.24 107	71.18 13	96.57 4	92.90 67	82.70 11	87.13 10	95.27 24	64.99 44	95.80 94	89.34 8	91.80 42	95.93 21

APDe-MVS		87.54 12	87.84 10	86.65 37	96.07 9	66.30 108	94.84 29	93.78 32	69.35 168	88.39 5	96.34 6	67.74 25	97.66 32	90.62 5	93.44 27	96.01 19

SD-MVS		87.49 13	87.49 15	87.50 21	93.60 38	68.82 40	93.90 48	92.63 76	76.86 65	87.90 7	95.76 12	66.17 33	97.63 34	89.06 11	91.48 48	96.05 17

test_prior3		87.38 14	87.70 12	86.42 46	94.71 21	67.35 63	95.10 24	93.10 60	75.40 80	85.25 24	95.61 16	67.94 21	96.84 70	87.47 18	94.77 11	95.05 43

alignmvs		87.28 15	86.97 20	88.24 12	91.30 78	71.14 15	95.61 13	93.56 41	79.30 32	87.07 12	95.25 26	68.43 16	96.93 68	87.87 16	84.33 95	96.65 8

Regformer-1		87.24 16	87.60 14	86.15 55	95.14 15	65.83 117	93.95 44	95.12 7	82.11 15	84.25 30	95.73 13	67.88 24	98.35 17	85.60 29	88.64 67	94.26 62

train_agg		87.21 17	87.42 17	86.60 39	94.18 26	67.28 65	94.16 33	93.51 42	71.87 132	85.52 20	95.33 21	68.19 18	97.27 50	89.09 9	94.90 8	95.25 37

MG-MVS		87.11 18	86.27 24	89.62 5	97.79 1	76.27 2	94.96 28	94.49 21	78.74 45	83.87 33	92.94 72	64.34 49	96.94 66	75.19 84	94.09 19	95.66 24

agg_prior1		87.02 19	87.26 18	86.28 53	94.16 30	66.97 74	94.08 38	93.31 50	71.85 134	84.49 28	95.39 19	68.91 14	96.75 73	88.84 13	94.32 17	95.13 41

Regformer-2		87.00 20	87.43 16	85.71 67	95.14 15	64.73 131	93.95 44	94.95 10	81.69 20	84.03 31	95.73 13	67.35 28	98.19 21	85.40 31	88.64 67	94.20 64

agg_prior3		86.93 21	87.08 19	86.48 43	94.21 24	66.95 76	94.14 36	93.40 47	71.80 137	84.86 26	95.13 28	66.16 34	97.25 52	89.09 9	94.90 8	95.25 37

CSCG		86.87 22	86.26 25	88.72 8	95.05 18	70.79 16	93.83 51	95.33 4	68.48 180	77.63 73	94.35 48	73.04 6	98.45 14	84.92 34	93.71 23	96.92 6

canonicalmvs		86.85 23	86.25 26	88.66 9	91.80 70	71.92 9	93.54 58	91.71 104	80.26 25	87.55 8	95.25 26	63.59 57	96.93 68	88.18 15	84.34 94	97.11 3

PHI-MVS		86.83 24	86.85 23	86.78 35	93.47 42	65.55 119	95.39 17	95.10 9	71.77 139	85.69 19	96.52 3	62.07 68	98.77 8	86.06 27	95.60 5	96.03 18

SteuartSystems-ACMMP		86.82 25	86.90 21	86.58 41	90.42 88	66.38 105	96.09 8	93.87 30	77.73 54	84.01 32	95.66 15	63.39 58	97.94 25	87.40 20	93.55 26	95.42 27
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 26	86.86 22	86.31 52	93.76 34	67.53 61	96.33 7	93.61 39	82.34 13	81.00 46	93.08 67	63.19 61	97.29 48	87.08 22	91.38 49	94.13 68

jason		86.40 27	86.17 27	87.11 30	86.16 151	70.54 19	95.71 12	92.19 88	82.00 18	84.58 27	94.34 49	61.86 70	95.53 106	87.76 17	90.89 54	95.27 34
jason: jason.
WTY-MVS		86.32 28	85.81 31	87.85 14	92.82 54	69.37 31	95.20 20	95.25 5	82.71 10	81.91 40	94.73 41	67.93 23	97.63 34	79.55 60	82.25 106	96.54 11

MSLP-MVS		86.27 29	85.91 30	87.35 24	92.01 67	68.97 37	95.04 26	92.70 71	79.04 40	81.50 43	96.50 4	58.98 83	96.78 72	83.49 38	93.93 21	96.29 14

VNet		86.20 30	85.65 34	87.84 15	93.92 33	69.99 23	95.73 11	95.94 1	78.43 47	86.00 15	93.07 69	58.22 85	97.00 60	85.22 32	84.33 95	96.52 12

MVS_111021_HR		86.19 31	85.80 32	87.37 23	93.17 47	69.79 27	93.99 43	93.76 34	79.08 39	78.88 62	93.99 55	62.25 67	98.15 22	85.93 28	91.15 52	94.15 67

APD-MVS		85.93 32	85.99 28	85.76 64	95.98 11	65.21 123	93.59 56	92.58 78	66.54 192	86.17 14	95.88 11	63.83 53	97.00 60	86.39 26	92.94 31	95.06 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 33	85.46 35	87.18 27	88.20 129	72.42 8	92.41 87	92.77 70	82.11 15	80.34 48	93.07 69	68.27 17	95.02 109	78.39 70	93.59 25	94.09 71

Regformer-3		85.80 34	85.92 29	85.46 68	94.17 28	65.09 127	92.95 71	95.11 8	81.13 21	81.68 42	95.04 29	65.82 37	98.32 18	83.02 40	84.36 92	92.97 99

CDPH-MVS		85.71 35	85.46 35	86.46 44	94.75 20	67.19 67	93.89 49	92.83 69	70.90 152	83.09 35	95.28 23	63.62 56	97.36 44	80.63 55	94.18 18	94.84 48

DeepC-MVS	77.85 3	85.52 36	85.24 37	86.37 49	88.80 116	66.64 95	92.15 91	93.68 37	81.07 22	76.91 83	93.64 59	62.59 66	98.44 15	85.50 30	92.84 33	94.03 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		85.45 37	85.69 33	84.73 85	94.17 28	63.23 159	92.95 71	94.83 13	80.66 23	81.29 44	95.04 29	65.12 42	98.08 24	82.74 41	84.36 92	92.88 103

PAPR		85.15 38	84.47 40	87.18 27	96.02 10	68.29 47	91.85 111	93.00 64	76.59 70	79.03 61	95.00 31	61.59 71	97.61 36	78.16 71	89.00 65	95.63 25

MP-MVS		85.02 39	84.97 38	85.17 76	92.60 58	64.27 144	93.24 64	92.27 84	73.13 113	79.63 55	94.43 45	61.90 69	97.17 53	85.00 33	92.56 35	94.06 73

#test#		84.98 40	84.74 39	85.72 65	93.75 36	65.01 128	94.09 37	93.19 56	73.55 109	79.22 58	94.93 34	59.04 81	97.67 29	82.66 42	92.21 36	94.49 59

HyFIR		84.98 40	83.45 47	89.57 6	89.94 94	75.14 3	92.07 97	92.32 82	81.87 19	75.68 88	88.27 122	60.18 77	98.60 12	80.46 57	90.27 61	94.96 46

HFP-MVS		84.73 42	84.40 41	85.72 65	93.75 36	65.01 128	93.50 59	93.19 56	72.19 125	79.22 58	94.93 34	59.04 81	97.67 29	81.55 49	92.21 36	94.49 59

HC-MVS		84.66 43	82.86 56	90.06 1	90.93 82	74.56 4	87.91 185	95.54 3	68.55 178	72.35 120	94.71 42	59.78 78	98.90 5	81.29 54	94.69 14	96.74 7

ACMMPR		84.37 44	84.06 42	85.28 75	93.56 39	64.37 139	93.50 59	93.15 59	72.19 125	78.85 64	94.86 38	56.69 101	97.45 40	81.55 49	92.20 38	94.02 75

region2R		84.36 45	84.03 43	85.36 73	93.54 40	64.31 141	93.43 62	92.95 65	72.16 128	78.86 63	94.84 39	56.97 95	97.53 38	81.38 52	92.11 40	94.24 63

LFMVS		84.34 46	82.73 58	89.18 7	94.76 19	73.25 7	94.99 27	91.89 97	71.90 131	82.16 39	93.49 62	47.98 185	97.05 55	82.55 43	84.82 88	97.25 2

HY-MVS	76.49 5	84.28 47	83.36 51	87.02 33	92.22 65	67.74 55	84.65 214	94.50 20	79.15 36	82.23 38	87.93 125	66.88 29	96.94 66	80.53 56	82.20 107	96.39 13

MAR-MVS		84.18 48	83.43 48	86.44 45	96.25 7	65.93 115	94.28 32	94.27 27	74.41 86	79.16 60	95.61 16	53.99 135	98.88 7	69.62 123	93.26 29	94.50 58
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS_Test		84.16 49	83.20 52	87.05 32	91.56 73	69.82 26	89.99 155	92.05 91	77.77 53	82.84 36	86.57 135	63.93 52	96.09 87	74.91 86	89.18 64	95.25 37

PVSNet_Blended_VisFu		83.97 50	83.50 45	85.39 72	90.02 92	66.59 98	93.77 52	91.73 102	77.43 60	77.08 82	89.81 110	63.77 55	96.97 63	79.67 59	88.21 70	92.60 107

DWT-MVSNet_test		83.95 51	82.80 57	87.41 22	92.90 52	70.07 22	89.12 168	94.42 23	82.15 14	77.64 72	91.77 89	70.81 12	96.22 82	65.03 159	81.36 110	95.94 20

XVS		83.87 52	83.47 46	85.05 78	93.22 44	63.78 148	92.92 73	92.66 74	73.99 97	78.18 67	94.31 51	55.25 110	97.41 41	79.16 63	91.58 46	93.95 77

EI-MVSNet-Vis-set		83.77 53	83.67 44	84.06 97	92.79 56	63.56 156	91.76 115	94.81 15	79.65 30	77.87 69	94.09 53	63.35 59	97.90 26	79.35 61	79.36 119	90.74 129

MVSFormer		83.75 54	82.88 55	86.37 49	89.24 107	71.18 13	89.07 169	90.69 136	65.80 196	87.13 10	94.34 49	64.99 44	92.67 183	72.83 93	91.80 42	95.27 34

CP-MVS		83.71 55	83.40 50	84.65 88	93.14 48	63.84 147	94.59 30	92.28 83	71.03 150	77.41 76	94.92 36	55.21 113	96.19 83	81.32 53	90.70 56	93.91 79

PVSNet_BlendedMVS		83.38 56	83.43 48	83.22 112	93.76 34	67.53 61	94.06 39	93.61 39	79.13 37	81.00 46	85.14 148	63.19 61	97.29 48	87.08 22	73.91 150	84.83 204

PGM-MVS		83.25 57	82.70 59	84.92 80	92.81 55	64.07 146	90.44 146	92.20 87	71.28 148	77.23 79	94.43 45	55.17 114	97.31 47	79.33 62	91.38 49	93.37 86

HPM-MVS		83.25 57	82.95 54	84.17 95	92.25 64	62.88 167	90.91 136	91.86 98	70.30 160	77.12 80	93.96 56	56.75 99	96.28 81	82.04 46	91.34 51	93.34 87

EI-MVSNet-UG-set		83.14 59	82.96 53	83.67 107	92.28 63	63.19 160	91.38 126	94.68 17	79.22 34	76.60 84	93.75 58	62.64 65	97.76 28	78.07 72	78.01 129	90.05 136

PatchFormer-LS_test		83.14 59	81.81 67	87.12 29	92.34 60	69.92 25	88.64 174	93.32 49	82.07 17	74.87 96	91.62 93	68.91 14	96.08 89	66.07 150	78.45 128	95.37 28

VDD-MVS		83.06 61	81.81 67	86.81 34	90.86 85	67.70 56	95.40 16	91.50 112	75.46 79	81.78 41	92.34 83	40.09 217	97.13 54	86.85 24	82.04 108	95.60 26

PAPM_NR		82.97 62	81.84 66	86.37 49	94.10 32	66.76 87	87.66 190	92.84 68	69.96 163	74.07 103	93.57 60	63.10 63	97.50 39	70.66 116	90.58 58	94.85 47

mPP-MVS		82.96 63	82.44 60	84.52 91	92.83 53	62.92 165	92.76 76	91.85 99	71.52 145	75.61 91	94.24 52	53.48 144	96.99 62	78.97 66	90.73 55	93.64 82

DP-MVS Recon		82.73 64	81.65 69	85.98 57	97.31 3	67.06 71	95.15 22	91.99 93	69.08 171	76.50 86	93.89 57	54.48 129	98.20 20	70.76 115	85.66 86	92.69 104

CLD-MVS		82.73 64	82.35 62	83.86 100	87.90 135	67.65 58	95.45 15	92.18 89	85.06 5	72.58 113	92.27 84	52.46 151	95.78 95	84.18 35	79.06 122	88.16 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 66	82.38 61	83.73 104	89.25 105	59.58 210	92.24 90	94.89 11	77.96 51	79.86 52	92.38 81	56.70 100	97.05 55	77.26 75	80.86 114	94.55 54

3Dnovator	73.91 6	82.69 67	80.82 74	88.31 11	89.57 100	71.26 12	92.60 84	94.39 26	78.84 42	67.89 165	92.48 79	48.42 180	98.52 13	68.80 130	94.40 16	95.15 40

MVSTER		82.47 68	82.05 63	83.74 102	92.68 57	69.01 35	91.90 109	93.21 53	79.83 26	72.14 121	85.71 144	74.72 4	94.72 119	75.72 80	72.49 157	87.50 156

TESTMET0.1,1		82.41 69	81.98 65	83.72 105	88.08 130	63.74 150	92.70 79	93.77 33	79.30 32	77.61 74	87.57 128	58.19 86	94.08 144	73.91 89	86.68 77	93.33 89

CostFormer		82.33 70	81.15 73	85.86 60	89.01 112	68.46 42	82.39 230	93.01 62	75.59 77	80.25 49	81.57 185	72.03 9	94.96 111	79.06 65	77.48 133	94.16 66

API-MVS		82.28 71	80.53 79	87.54 20	96.13 8	70.59 18	93.63 54	91.04 129	65.72 198	75.45 93	92.83 75	56.11 106	98.89 6	64.10 167	89.75 63	93.15 94

IB-MVS	77.80 4	82.18 72	80.46 80	87.35 24	89.14 109	70.28 21	95.59 14	95.17 6	78.85 41	70.19 133	85.82 142	70.66 13	97.67 29	72.19 101	66.52 191	94.09 71
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
3Dnovator+	73.60 7	82.10 73	80.60 78	86.60 39	90.89 84	66.80 86	95.20 20	93.44 45	74.05 96	67.42 170	92.49 78	49.46 173	97.65 33	70.80 114	91.68 44	95.33 29

MVS_111021_LR		82.02 74	81.52 70	83.51 109	88.42 125	62.88 167	89.77 157	88.93 191	76.78 67	75.55 92	93.10 65	50.31 165	95.38 108	83.82 37	87.02 76	92.26 116

PMMVS		81.98 75	82.04 64	81.78 149	89.76 98	56.17 234	91.13 134	90.69 136	77.96 51	80.09 50	93.57 60	46.33 197	94.99 110	81.41 51	87.46 74	94.17 65

EPP-MVSNet		81.79 76	81.52 70	82.61 123	88.77 117	60.21 200	93.02 69	93.66 38	68.52 179	72.90 109	90.39 103	72.19 8	94.96 111	74.93 85	79.29 121	92.67 105

APD-MVS_3200maxsize		81.64 77	81.32 72	82.59 124	92.36 59	58.74 218	91.39 124	91.01 130	63.35 212	79.72 54	94.62 43	51.82 154	96.14 85	79.71 58	87.93 72	92.89 102

ACMMP		81.49 78	80.67 76	83.93 99	91.71 71	62.90 166	92.13 92	92.22 86	71.79 138	71.68 125	93.49 62	50.32 164	96.96 64	78.47 68	84.22 99	91.93 119
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
CDS-MVSNet		81.43 79	80.74 75	83.52 108	86.26 149	64.45 134	92.09 95	90.65 139	75.83 76	73.95 105	89.81 110	63.97 51	92.91 175	71.27 108	82.82 104	93.20 93
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 80	79.99 82	85.46 68	90.39 90	68.40 43	86.88 202	90.61 140	74.41 86	70.31 132	84.67 152	63.79 54	92.32 191	73.13 90	85.70 85	95.67 23

liao		81.25 81	80.05 81	84.87 82	92.30 62	64.31 141	87.91 185	91.39 116	59.44 235	79.94 51	92.91 73	57.09 91	97.01 58	66.63 143	92.81 34	93.29 90

HQP-MVS		81.14 82	80.64 77	82.64 122	87.54 137	63.66 154	94.06 39	91.70 105	79.80 27	74.18 99	90.30 104	51.63 158	95.61 101	77.63 73	78.90 123	88.63 145

nrg030		80.93 83	79.86 84	84.13 96	83.69 181	68.83 39	93.23 65	91.20 122	75.55 78	75.06 95	88.22 123	63.04 64	94.74 118	81.88 47	66.88 188	88.82 143

Vis-MVSNet		80.92 84	79.98 83	83.74 102	88.48 122	61.80 179	93.44 61	88.26 205	73.96 100	77.73 70	91.76 90	49.94 169	94.76 116	65.84 153	90.37 60	94.65 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
liao1		80.70 85	78.95 99	85.94 59	87.77 136	67.56 60	87.91 185	92.55 79	72.17 127	67.44 169	93.09 66	50.27 166	97.04 57	71.68 104	87.64 73	93.23 92

tpmrst		80.57 86	79.14 98	84.84 83	90.10 91	68.28 48	81.70 233	89.72 170	77.63 56	75.96 87	79.54 213	64.94 46	92.71 181	75.43 82	77.28 136	93.55 83

1112_ss		80.56 87	79.83 85	82.77 117	88.65 118	60.78 189	92.29 88	88.36 201	72.58 118	72.46 117	94.95 32	65.09 43	93.42 166	66.38 146	77.71 131	94.10 70

VDDNet		80.50 88	78.26 105	87.21 26	86.19 150	69.79 27	94.48 31	91.31 119	60.42 230	79.34 57	90.91 97	38.48 223	96.56 79	82.16 44	81.05 112	95.27 34

BH-w/o		80.49 89	79.30 95	84.05 98	90.83 86	64.36 140	93.60 55	89.42 177	74.35 91	69.09 150	90.15 106	55.23 112	95.61 101	64.61 162	86.43 82	92.17 117

TAMVS		80.37 90	79.45 91	83.13 114	85.14 163	63.37 157	91.23 131	90.76 135	74.81 85	72.65 111	88.49 117	60.63 76	92.95 172	69.41 125	81.95 109	93.08 96

HQP_MVS		80.34 91	79.75 86	82.12 143	86.94 144	62.42 171	93.13 67	91.31 119	78.81 43	72.53 114	89.14 114	50.66 162	95.55 104	76.74 76	78.53 126	88.39 149

HPM-MVS_fast		80.25 92	79.55 90	82.33 134	91.55 74	59.95 205	91.32 129	89.16 184	65.23 203	74.71 97	93.07 69	47.81 187	95.74 97	74.87 88	88.23 69	91.31 125

diffmvs		80.18 93	78.55 103	85.07 77	88.56 119	66.93 77	86.70 205	88.62 196	70.42 157	78.69 66	85.26 146	56.93 97	94.77 115	68.68 131	83.09 101	93.51 84

ab-mvs		80.18 93	78.31 104	85.80 61	88.44 124	65.49 121	83.00 228	92.67 73	71.82 136	77.36 77	85.01 149	54.50 127	96.59 76	76.35 79	75.63 142	95.32 31

IS-MVSNet		80.14 95	79.41 92	82.33 134	87.91 134	60.08 204	91.97 101	88.27 204	72.90 115	71.44 126	91.73 92	61.44 72	93.66 162	62.47 182	86.53 80	93.24 91

test-LLR		80.10 96	79.56 89	81.72 150	86.93 146	61.17 185	92.70 79	91.54 109	71.51 146	75.62 89	86.94 132	53.83 136	92.38 188	72.21 99	84.76 90	91.60 121

PVSNet	73.49 8	80.05 97	78.63 101	84.31 93	90.92 83	64.97 130	92.47 86	91.05 128	79.18 35	72.43 118	90.51 102	37.05 235	94.06 145	68.06 132	86.00 84	93.90 80

UA-Net		80.02 98	79.65 87	81.11 158	89.33 103	57.72 225	86.33 207	89.00 190	77.44 59	81.01 45	89.15 113	59.33 79	95.90 91	61.01 188	84.28 97	89.73 140

test-mter		79.96 99	79.38 94	81.72 150	86.93 146	61.17 185	92.70 79	91.54 109	73.85 102	75.62 89	86.94 132	49.84 171	92.38 188	72.21 99	84.76 90	91.60 121

QAPM		79.95 100	77.39 117	87.64 17	89.63 99	71.41 11	93.30 63	93.70 36	65.34 202	67.39 172	91.75 91	47.83 186	98.96 3	57.71 203	89.81 62	92.54 109

UGNet		79.87 101	78.68 100	83.45 111	89.96 93	61.51 182	92.13 92	90.79 133	76.83 66	78.85 64	86.33 138	38.16 225	96.17 84	67.93 134	87.17 75	92.67 105
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
tpm2		79.80 102	77.95 109	85.34 74	88.28 127	68.26 49	81.56 237	91.42 115	70.11 161	77.59 75	80.50 200	67.40 26	94.26 139	67.34 139	77.35 134	93.51 84

DI_MVS_test_dynamic		79.78 103	77.50 116	86.62 38	80.90 199	69.46 30	90.69 141	91.97 95	77.00 62	59.07 212	82.34 171	46.82 191	95.88 92	82.14 45	86.59 79	94.53 57

DI_MVS_test_normal		79.66 104	77.36 119	86.54 42	80.72 202	69.21 32	90.68 142	92.16 90	76.99 63	58.63 216	82.03 180	46.70 193	95.86 93	81.74 48	86.63 78	94.56 53

CPTT-MVS		79.59 105	79.16 97	80.89 165	91.54 75	59.80 207	92.10 94	88.54 199	60.42 230	72.96 108	93.28 64	48.27 181	92.80 178	78.89 67	86.50 81	90.06 135

Test_1112_low_res		79.56 106	78.60 102	82.43 128	88.24 128	60.39 196	92.09 95	87.99 209	72.10 129	71.84 123	87.42 130	64.62 47	93.04 170	65.80 154	77.30 135	93.85 81

FIs		79.47 107	79.41 92	79.67 180	85.95 154	59.40 211	91.68 119	93.94 29	78.06 50	68.96 153	88.28 121	66.61 31	91.77 201	66.20 149	74.99 144	87.82 154

BH-RMVSNet		79.46 108	77.65 112	84.89 81	91.68 72	65.66 118	93.55 57	88.09 206	72.93 114	73.37 107	91.12 96	46.20 198	96.12 86	56.28 207	85.61 87	92.91 101

PCF-MVS	73.15 9	79.29 109	77.63 113	84.29 94	86.06 152	65.96 114	87.03 197	91.10 126	69.86 164	69.79 139	90.64 98	57.54 90	96.59 76	64.37 166	82.29 105	90.32 133
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 110	79.57 88	78.24 202	88.46 123	52.29 248	90.41 148	89.12 185	74.24 92	69.13 149	91.91 87	65.77 38	90.09 222	59.00 197	88.09 71	92.33 112

114514_t		79.17 111	77.67 111	83.68 106	95.32 14	65.53 120	92.85 75	91.60 108	63.49 211	67.92 164	90.63 100	46.65 194	95.72 99	67.01 142	83.54 100	89.79 138

VPA-MVSNet		79.03 112	78.00 108	82.11 146	85.95 154	64.48 133	93.22 66	94.66 18	75.05 84	74.04 104	84.95 150	52.17 153	93.52 164	74.90 87	67.04 187	88.32 151

EI-MVSNet		78.97 113	78.22 106	81.25 156	85.33 160	62.73 170	89.53 161	93.21 53	72.39 121	72.14 121	90.13 107	60.99 73	94.72 119	67.73 136	72.49 157	86.29 176

AdaColmap		78.94 114	77.00 122	84.76 84	96.34 6	65.86 116	92.66 83	87.97 210	62.18 220	70.56 128	92.37 82	43.53 206	97.35 45	64.50 164	82.86 103	91.05 127

tpmp4_e23		78.85 115	76.55 124	85.77 63	89.25 105	68.39 44	81.63 236	91.38 117	70.40 158	75.21 94	79.22 215	67.37 27	94.79 114	58.98 198	75.51 143	94.13 68

VPNet		78.82 116	77.53 115	82.70 119	84.52 170	66.44 104	93.93 46	92.23 85	80.46 24	72.60 112	88.38 120	49.18 176	93.13 169	72.47 98	63.97 213	88.55 147

EPNet_dtu		78.80 117	79.26 96	77.43 212	88.06 131	49.71 261	91.96 102	91.95 96	77.67 55	76.56 85	91.28 95	58.51 84	90.20 217	56.37 206	80.95 113	92.39 111
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TR-MVS		78.77 118	77.37 118	82.95 115	90.49 87	60.88 187	93.67 53	90.07 157	70.08 162	74.51 98	91.37 94	45.69 199	95.70 100	60.12 191	80.32 115	92.29 114

BH-untuned		78.68 119	77.08 120	83.48 110	89.84 96	63.74 150	92.70 79	88.59 197	71.57 143	66.83 177	88.65 116	51.75 156	95.39 107	59.03 196	84.77 89	91.32 124

OMC-MVS		78.67 120	77.91 110	80.95 164	85.76 159	57.40 228	88.49 175	88.67 194	73.85 102	72.43 118	92.10 85	49.29 175	94.55 123	72.73 95	77.89 130	90.91 128

tpm		78.58 121	77.03 121	83.22 112	85.94 156	64.56 132	83.21 226	91.14 125	78.31 48	73.67 106	79.68 210	64.01 50	92.09 196	66.07 150	71.26 165	93.03 97

OpenMVS	70.45 11	78.54 122	75.92 132	86.41 48	85.93 157	71.68 10	92.74 77	92.51 80	66.49 193	64.56 185	91.96 86	43.88 205	98.10 23	54.61 210	90.65 57	89.44 142

EPMVS		78.49 123	75.98 131	86.02 56	91.21 79	69.68 29	80.23 244	91.20 122	75.25 82	72.48 116	78.11 219	54.65 126	93.69 161	57.66 204	83.04 102	94.69 51

GA-MVS		78.33 124	76.23 128	84.65 88	83.65 182	66.30 108	91.44 122	90.14 155	76.01 74	70.32 131	84.02 157	42.50 208	94.72 119	70.98 112	77.00 137	92.94 100

cascas		78.18 125	75.77 134	85.41 71	87.14 143	69.11 33	92.96 70	91.15 124	66.71 191	70.47 129	86.07 140	37.49 229	96.48 80	70.15 119	79.80 117	90.65 130

UniMVSNet_NR-MVSNet		78.15 126	77.55 114	79.98 173	84.46 172	60.26 198	92.25 89	93.20 55	77.50 58	68.88 154	86.61 134	66.10 35	92.13 194	66.38 146	62.55 216	87.54 155

FC-MVSNet-test		77.99 127	78.08 107	77.70 207	84.89 166	55.51 238	90.27 150	93.75 35	76.87 64	66.80 178	87.59 127	65.71 39	90.23 216	62.89 178	73.94 149	87.37 158

XXY-MVS		77.94 128	76.44 126	82.43 128	82.60 188	64.44 135	92.01 98	91.83 100	73.59 108	70.00 135	85.82 142	54.43 130	94.76 116	69.63 122	68.02 183	88.10 153

MS-PatchMatch		77.90 129	76.50 125	82.12 143	85.99 153	69.95 24	91.75 117	92.70 71	73.97 99	62.58 200	84.44 155	41.11 214	95.78 95	63.76 168	92.17 39	80.62 249

FMVSNet3		77.73 130	76.04 130	82.80 116	91.20 80	68.99 36	91.87 110	91.99 93	73.35 111	67.04 174	83.19 163	56.62 102	92.14 193	59.80 193	69.34 172	87.28 162

UniMVSNet (Re)		77.58 131	76.78 123	79.98 173	84.11 178	60.80 188	91.76 115	93.17 58	76.56 71	69.93 138	84.78 151	63.32 60	92.36 190	64.89 160	62.51 218	86.78 170

PatchmatchNet		77.46 132	74.63 146	85.96 58	89.55 101	70.35 20	79.97 248	89.55 173	72.23 123	70.94 127	76.91 228	57.03 93	92.79 179	54.27 212	81.17 111	94.74 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 133	75.65 139	82.73 118	80.38 216	67.13 70	91.85 111	90.23 149	75.09 83	69.37 146	83.39 161	53.79 138	94.44 124	71.77 102	65.00 203	86.63 174

v1neww		77.39 134	75.71 136	82.44 125	80.69 204	66.83 79	91.94 106	90.18 152	74.19 93	69.60 140	82.51 167	54.99 121	94.44 124	71.68 104	65.60 193	86.05 182

v7new		77.39 134	75.71 136	82.44 125	80.69 204	66.83 79	91.94 106	90.18 152	74.19 93	69.60 140	82.51 167	54.99 121	94.44 124	71.68 104	65.60 193	86.05 182

v6		77.39 134	75.71 136	82.44 125	80.67 206	66.82 81	91.94 106	90.18 152	74.19 93	69.60 140	82.50 170	55.00 120	94.44 124	71.68 104	65.60 193	86.05 182

v1		77.29 137	75.57 140	82.42 130	80.61 214	66.73 88	91.96 102	90.42 141	74.41 86	69.46 143	82.12 177	55.14 115	94.40 129	71.00 109	65.04 200	86.13 178

v1141		77.28 138	75.57 140	82.42 130	80.63 210	66.73 88	91.96 102	90.42 141	74.41 86	69.46 143	82.12 177	55.09 117	94.40 129	70.99 111	65.05 199	86.12 179

divwei89l23v2f112		77.28 138	75.57 140	82.42 130	80.62 211	66.72 90	91.96 102	90.42 141	74.41 86	69.46 143	82.12 177	55.11 116	94.40 129	71.00 109	65.04 200	86.12 179

PS-MVSNAJss		77.26 140	76.31 127	80.13 171	80.64 209	59.16 215	90.63 145	91.06 127	72.80 116	68.58 159	84.57 154	53.55 140	93.96 152	72.97 91	71.96 160	87.27 163

gg-mvs-nofinetune		77.18 141	74.31 152	85.80 61	91.42 77	68.36 45	71.78 265	94.72 16	49.61 264	77.12 80	45.92 286	77.41 1	93.98 151	67.62 137	93.16 30	95.05 43

MVP-PatchMatch		77.12 142	76.23 128	79.79 179	81.72 194	66.34 107	89.29 163	90.88 131	70.56 156	62.01 202	82.88 164	49.34 174	94.13 141	65.55 156	93.80 22	78.88 260

X-MVStestdata		76.86 143	74.13 155	85.05 78	93.22 44	63.78 148	92.92 73	92.66 74	73.99 97	78.18 67	10.19 299	55.25 110	97.41 41	79.16 63	91.58 46	93.95 77

DU-MVS		76.86 143	75.84 133	79.91 175	82.96 186	60.26 198	91.26 130	91.54 109	76.46 72	68.88 154	86.35 136	56.16 104	92.13 194	66.38 146	62.55 216	87.35 160

v7		76.83 145	75.01 144	82.29 136	80.35 217	66.70 92	91.68 119	89.97 160	73.47 110	69.22 148	82.22 174	52.52 149	94.43 128	69.73 121	65.96 192	85.74 193

WR-MVS		76.76 146	75.74 135	79.82 178	84.60 168	62.27 175	92.60 84	92.51 80	76.06 73	67.87 166	85.34 145	56.76 98	90.24 215	62.20 183	63.69 215	86.94 168

v1144		76.73 147	74.88 145	82.27 137	80.23 224	66.60 96	91.68 119	90.21 151	73.69 105	69.06 151	81.89 182	52.73 148	94.40 129	69.21 127	65.23 196	85.80 189

IterMVS-LS		76.49 148	75.18 143	80.43 167	84.49 171	62.74 169	90.64 143	88.80 192	72.40 120	65.16 181	81.72 184	60.98 74	92.27 192	67.74 135	64.65 207	86.29 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 149	74.55 149	82.19 141	79.14 237	67.82 53	90.26 151	89.42 177	73.75 104	68.63 158	81.89 182	51.31 160	94.09 143	71.69 103	64.84 204	84.66 205

Test4		76.45 150	73.45 164	85.45 70	76.07 258	67.61 59	88.38 177	90.83 132	76.71 68	53.06 245	79.65 212	31.61 251	94.35 133	78.47 68	86.22 83	94.40 61

v148		76.19 151	74.47 151	81.36 154	80.05 228	64.44 135	91.75 117	90.23 149	73.68 106	67.13 173	80.84 196	55.92 108	93.86 158	68.95 129	61.73 224	85.76 192

FMVSNet2		76.07 152	74.01 157	82.26 139	88.85 113	67.66 57	91.33 128	91.61 107	70.84 153	65.98 179	82.25 173	48.03 182	92.00 198	58.46 199	68.73 178	87.10 164

v144192		76.05 153	74.03 156	82.12 143	79.50 233	66.55 100	91.39 124	89.71 171	72.30 122	68.17 162	81.33 188	51.75 156	94.03 149	67.94 133	64.19 210	85.77 190

NR-MVSNet		76.05 153	74.59 147	80.44 166	82.96 186	62.18 176	90.83 138	91.73 102	77.12 61	60.96 204	86.35 136	59.28 80	91.80 200	60.74 189	61.34 227	87.35 160

v1192		75.98 155	73.92 158	82.15 142	79.73 229	66.24 110	91.22 132	89.75 165	72.67 117	68.49 160	81.42 186	49.86 170	94.27 137	67.08 141	65.02 202	85.95 186

TranMVSNet+NR-MVSNet		75.86 156	74.52 150	79.89 176	82.44 189	60.64 194	91.37 127	91.37 118	76.63 69	67.65 168	86.21 139	52.37 152	91.55 205	61.84 184	60.81 230	87.48 157

LPG-MVS_test		75.82 157	74.58 148	79.56 184	84.31 175	59.37 212	90.44 146	89.73 168	69.49 166	64.86 182	88.42 118	38.65 221	94.30 135	72.56 96	72.76 154	85.01 202

GBi-Net		75.65 158	73.83 159	81.10 159	88.85 113	65.11 124	90.01 152	90.32 144	70.84 153	67.04 174	80.25 205	48.03 182	91.54 206	59.80 193	69.34 172	86.64 171

test1		75.65 158	73.83 159	81.10 159	88.85 113	65.11 124	90.01 152	90.32 144	70.84 153	67.04 174	80.25 205	48.03 182	91.54 206	59.80 193	69.34 172	86.64 171

v1921920		75.63 160	73.49 163	82.06 147	79.38 234	66.35 106	91.07 135	89.48 174	71.98 130	67.99 163	81.22 191	49.16 178	93.90 155	66.56 145	64.56 208	85.92 188

ACMP	71.68 10	75.58 161	74.23 154	79.62 182	84.97 165	59.64 208	90.80 139	89.07 188	70.39 159	62.95 197	87.30 131	38.28 224	93.87 156	72.89 92	71.45 163	85.36 199
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 162	73.26 165	81.61 152	80.67 206	66.82 81	89.54 160	89.27 180	71.65 141	63.30 195	80.30 204	54.99 121	94.06 145	67.33 140	62.33 219	83.94 207

tpm cat1		75.30 163	72.21 176	84.58 90	88.52 120	67.77 54	78.16 258	88.02 207	61.88 223	68.45 161	76.37 229	60.65 75	94.03 149	53.77 215	74.11 147	91.93 119

PLC	68.80 14	75.23 164	73.68 161	79.86 177	92.93 51	58.68 219	90.64 143	88.30 202	60.90 227	64.43 188	90.53 101	42.38 209	94.57 122	56.52 205	76.54 138	86.33 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 165	72.98 167	81.88 148	79.20 236	66.00 113	90.75 140	89.11 186	71.63 142	67.41 171	81.22 191	47.36 189	93.87 156	65.46 157	64.72 206	85.77 190

dp		75.01 166	72.09 177	83.76 101	89.28 104	66.22 111	79.96 249	89.75 165	71.16 149	67.80 167	77.19 226	51.81 155	92.54 185	50.39 224	71.44 164	92.51 110

TAPA-MVS	70.22 12	74.94 167	73.53 162	79.17 196	90.40 89	52.07 249	89.19 166	89.61 172	62.69 218	70.07 134	92.67 76	48.89 179	94.32 134	38.26 262	79.97 116	91.12 126
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 168	72.54 173	81.46 153	80.33 221	66.71 91	89.15 167	89.08 187	70.94 151	63.08 196	79.86 209	52.52 149	94.04 148	65.70 155	62.17 220	83.64 209

XVG-OURS-SEG-HR		74.70 169	73.08 166	79.57 183	78.25 244	57.33 229	80.49 240	87.32 215	63.22 214	68.76 156	90.12 109	44.89 203	91.59 204	70.55 117	74.09 148	89.79 138

ACMM	69.62 13	74.34 170	72.73 169	79.17 196	84.25 177	57.87 223	90.36 149	89.93 161	63.17 215	65.64 180	86.04 141	37.79 227	94.10 142	65.89 152	71.52 162	85.55 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 171	72.30 175	80.32 168	91.49 76	61.66 180	90.85 137	80.72 258	56.67 249	63.85 192	90.64 98	46.75 192	90.84 210	53.79 214	75.99 141	88.47 148

XVG-OURS		74.25 172	72.46 174	79.63 181	78.45 242	57.59 227	80.33 242	87.39 212	63.86 210	68.76 156	89.62 112	40.50 216	91.72 202	69.00 128	74.25 146	89.58 141

CVMVSNet		74.04 173	74.27 153	73.33 236	85.33 160	43.94 275	89.53 161	88.39 200	54.33 255	70.37 130	90.13 107	49.17 177	84.05 254	61.83 185	79.36 119	91.99 118

Baseline_NR-MVSNet		73.99 174	72.83 168	77.48 211	80.78 200	59.29 214	91.79 113	84.55 238	68.85 173	68.99 152	80.70 197	56.16 104	92.04 197	62.67 181	60.98 229	81.11 242

CR-MVSNet		73.79 175	70.82 182	82.70 119	83.15 184	67.96 51	70.25 268	84.00 243	73.67 107	69.97 136	72.41 249	57.82 88	89.48 231	52.99 219	73.13 152	90.64 131

test_djsdf		73.76 176	72.56 172	77.39 213	77.00 252	53.93 243	89.07 169	90.69 136	65.80 196	63.92 190	82.03 180	43.14 207	92.67 183	72.83 93	68.53 179	85.57 195

jajsoiax		73.05 177	71.51 179	77.67 208	77.46 249	54.83 240	88.81 172	90.04 159	69.13 170	62.85 198	83.51 159	31.16 254	92.75 180	70.83 113	69.80 168	85.43 197

LCM-MVSNet-Re		72.93 178	71.84 178	76.18 221	88.49 121	48.02 266	80.07 247	70.17 282	73.96 100	52.25 248	80.09 208	49.98 168	88.24 240	67.35 138	84.23 98	92.28 115

tpmvs		72.88 179	69.76 188	82.22 140	90.98 81	67.05 72	78.22 257	88.30 202	63.10 216	64.35 189	74.98 236	55.09 117	94.27 137	43.25 243	69.57 171	85.34 200

test0.0.03 1		72.76 180	72.71 170	72.88 240	80.25 223	47.99 267	91.22 132	89.45 175	71.51 146	62.51 201	87.66 126	53.83 136	85.06 251	50.16 225	67.84 186	85.58 194

mvs_tets		72.71 181	71.11 180	77.52 209	77.41 250	54.52 241	88.45 176	89.76 164	68.76 174	62.70 199	83.26 162	29.49 258	92.71 181	70.51 118	69.62 170	85.34 200

FMVSNet1		72.71 181	69.91 186	81.10 159	83.60 183	65.11 124	90.01 152	90.32 144	63.92 209	63.56 194	80.25 205	36.35 237	91.54 206	54.46 211	66.75 189	86.64 171

IterMVS		72.65 183	70.83 181	78.09 205	82.17 190	62.96 162	87.64 191	86.28 227	71.56 144	60.44 205	78.85 217	45.42 200	86.66 246	63.30 172	61.83 221	84.65 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pm-mvs1		72.28 184	69.97 184	79.20 195	78.43 243	61.48 184	88.92 171	88.01 208	65.63 199	61.92 203	79.10 216	45.18 201	90.70 211	58.24 200	64.29 209	85.43 197

PatchMatch-RL		72.06 185	69.98 183	78.28 200	89.51 102	55.70 237	83.49 220	83.39 249	61.24 226	63.72 193	82.76 165	34.77 241	93.03 171	53.37 218	77.59 132	86.12 179

v18		71.94 186	69.43 189	79.50 186	80.74 201	66.82 81	88.16 179	86.66 217	68.95 172	55.55 228	72.66 244	55.03 119	90.15 218	64.78 161	52.30 255	81.54 230

v16		71.81 187	69.26 190	79.47 187	80.66 208	66.81 84	87.93 183	86.63 219	68.70 176	55.35 229	72.51 245	54.75 124	90.12 220	64.51 163	52.28 256	81.47 231

PVSNet_0	68.08 15	71.81 187	68.32 200	82.27 137	84.68 167	62.31 174	88.68 173	90.31 147	75.84 75	57.93 220	80.65 199	37.85 226	94.19 140	69.94 120	29.05 288	90.31 134

v17		71.77 189	69.20 191	79.46 188	80.62 211	66.81 84	87.93 183	86.63 219	68.71 175	55.25 230	72.49 246	54.72 125	90.11 221	64.50 164	51.97 257	81.47 231

MIMVSNet		71.64 190	68.44 198	81.23 157	81.97 193	64.44 135	73.05 264	88.80 192	69.67 165	64.59 184	74.79 237	32.79 245	87.82 241	53.99 213	76.35 139	91.42 123

v15		71.40 191	68.75 193	79.35 189	80.39 215	66.70 92	87.57 192	86.64 218	68.66 177	54.68 232	72.00 253	54.50 127	89.98 223	63.69 169	50.66 262	81.38 235

v7n		71.31 192	68.65 194	79.28 191	76.40 254	60.77 190	86.71 204	89.45 175	64.17 208	58.77 215	78.24 218	44.59 204	93.54 163	57.76 202	61.75 223	83.52 212

V14		71.29 193	68.61 195	79.31 190	80.34 219	66.65 94	87.39 193	86.61 221	68.41 181	54.49 234	71.91 254	54.25 132	89.96 224	63.50 170	50.62 263	81.33 237

V9		71.16 194	68.46 197	79.27 192	80.26 222	66.60 96	87.21 196	86.56 222	68.17 182	54.26 237	71.81 256	54.00 134	89.93 225	63.28 173	50.57 264	81.27 238

testing_2		71.09 195	67.32 207	82.40 133	69.82 275	66.52 102	83.64 218	90.77 134	72.21 124	45.12 269	71.07 264	27.60 262	93.74 159	75.71 81	69.96 167	86.95 167

v11		71.05 196	68.32 200	79.23 193	80.34 219	66.57 99	87.01 199	86.55 223	68.11 183	54.40 235	71.66 258	52.94 146	89.91 226	62.71 180	51.12 260	81.21 239

v12		71.02 197	68.29 202	79.22 194	80.18 225	66.53 101	87.01 199	86.54 224	67.90 184	54.00 240	71.70 257	53.66 139	89.91 226	63.09 175	50.51 265	81.21 239

v13		70.90 198	68.15 203	79.15 198	80.08 226	66.45 103	86.83 203	86.50 225	67.62 187	53.78 242	71.61 259	53.51 143	89.87 228	62.89 178	50.50 266	81.14 241

F-COLMAP		70.66 199	68.44 198	77.32 214	86.37 148	55.91 236	88.00 181	86.32 226	56.94 247	57.28 224	88.07 124	33.58 243	92.49 186	51.02 222	68.37 180	83.55 210

WR-MVS_H		70.59 200	69.94 185	72.53 242	81.03 198	51.43 252	87.35 194	92.03 92	67.38 188	60.23 206	80.70 197	55.84 109	83.45 259	46.33 238	58.58 239	82.72 224

v748		70.55 201	67.97 204	78.27 201	75.75 259	58.78 217	86.29 208	89.25 181	65.12 204	56.66 226	77.17 227	45.05 202	92.95 172	58.13 201	58.33 240	83.10 220

CP-MVSNet		70.50 202	69.91 186	72.26 245	80.71 203	51.00 255	87.23 195	90.30 148	67.84 185	59.64 208	82.69 166	50.23 167	82.30 266	51.28 221	59.28 234	83.46 214

TransMVSNet (Re)		70.07 203	67.66 205	77.31 215	80.62 211	59.13 216	91.78 114	84.94 236	65.97 195	60.08 207	80.44 201	50.78 161	91.87 199	48.84 230	45.46 275	80.94 244

DP-MVS		69.90 204	66.48 210	80.14 170	95.36 13	62.93 163	89.56 158	76.11 266	50.27 263	57.69 223	85.23 147	39.68 218	95.73 98	33.35 271	71.05 166	81.78 229

PS-CasMVS		69.86 205	69.13 192	72.07 248	80.35 217	50.57 257	87.02 198	89.75 165	67.27 189	59.19 210	82.28 172	46.58 195	82.24 267	50.69 223	59.02 237	83.39 216

v52		69.80 206	67.01 209	78.15 203	71.84 268	60.10 202	82.02 231	87.39 212	64.48 205	57.80 221	75.97 232	41.47 213	92.90 176	63.00 176	59.13 236	81.45 233

V4		69.80 206	67.02 208	78.15 203	71.86 267	60.10 202	82.02 231	87.39 212	64.48 205	57.78 222	75.98 231	41.49 212	92.90 176	63.00 176	59.16 235	81.44 234

RPMNet		69.58 208	65.21 218	82.70 119	83.15 184	67.96 51	70.25 268	86.15 230	46.83 272	69.97 136	65.10 272	56.48 103	89.48 231	35.79 268	73.13 152	90.64 131

MSDG		69.54 209	65.73 213	80.96 163	85.11 164	63.71 152	84.19 216	83.28 250	56.95 246	54.50 233	84.03 156	31.50 252	96.03 90	42.87 248	69.13 175	83.14 219

PEN-MVS		69.46 210	68.56 196	72.17 247	79.27 235	49.71 261	86.90 201	89.24 182	67.24 190	59.08 211	82.51 167	47.23 190	83.54 258	48.42 232	57.12 241	83.25 217

LS3D		69.17 211	66.40 211	77.50 210	91.92 69	56.12 235	85.12 211	80.37 259	46.96 270	56.50 227	87.51 129	37.25 230	93.71 160	32.52 276	79.40 118	82.68 225

PatchT		69.11 212	65.37 217	80.32 168	82.07 192	63.68 153	67.96 276	87.62 211	50.86 262	69.37 146	65.18 271	57.09 91	88.53 238	41.59 252	66.60 190	88.74 144

ACMH	63.93 17	68.62 213	64.81 219	80.03 172	85.22 162	63.25 158	87.72 189	84.66 237	60.83 228	51.57 251	79.43 214	27.29 263	94.96 111	41.76 250	64.84 204	81.88 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 214	65.41 216	77.96 206	78.69 241	62.93 163	89.86 156	89.17 183	60.55 229	50.27 255	77.73 221	22.60 271	94.06 145	47.18 236	72.65 156	76.88 267

ADS-MVSNet		68.54 215	64.38 223	81.03 162	88.06 131	66.90 78	68.01 274	84.02 242	57.57 241	64.48 186	69.87 265	38.68 219	89.21 234	40.87 254	67.89 184	86.97 165

DTE-MVSNet		68.46 216	67.33 206	71.87 251	77.94 247	49.00 265	86.16 209	88.58 198	66.36 194	58.19 218	82.21 175	46.36 196	83.87 257	44.97 241	55.17 248	82.73 223

XVG-ACMP-BASELINE		68.04 217	65.53 215	75.56 222	74.06 264	52.37 247	78.43 254	85.88 233	62.03 221	58.91 214	81.21 193	20.38 275	91.15 209	60.69 190	68.18 181	83.16 218

FMVSNet5		68.04 217	65.66 214	75.18 224	84.43 173	57.89 222	83.54 219	86.26 228	61.83 224	53.64 243	73.30 241	37.15 233	85.08 250	48.99 229	61.77 222	82.56 226

ACMH+	65.35 16	67.65 219	64.55 220	76.96 217	84.59 169	57.10 230	88.08 180	80.79 257	58.59 240	53.00 246	81.09 195	26.63 265	92.95 172	46.51 237	61.69 225	80.82 245

Anonymous20231206		67.53 220	65.78 212	72.79 241	74.95 260	47.59 269	88.23 178	87.32 215	61.75 225	58.07 219	77.29 224	37.79 227	87.29 244	42.91 246	63.71 214	83.48 213

Patchmtry		67.53 220	63.93 224	78.34 199	82.12 191	64.38 138	68.72 271	84.00 243	48.23 269	59.24 209	72.41 249	57.82 88	89.27 233	46.10 239	56.68 245	81.36 236

USDC		67.43 222	64.51 221	76.19 220	77.94 247	55.29 239	78.38 255	85.00 235	73.17 112	48.36 259	80.37 202	21.23 274	92.48 187	52.15 220	64.02 211	80.81 246

ADS-MVSNet2		66.90 223	63.44 226	77.26 216	88.06 131	60.70 193	68.01 274	75.56 271	57.57 241	64.48 186	69.87 265	38.68 219	84.10 253	40.87 254	67.89 184	86.97 165

CMPMVS	48.56 21	66.77 224	64.41 222	73.84 233	70.65 273	50.31 258	77.79 259	85.73 234	45.54 275	44.76 270	82.14 176	35.40 239	90.14 219	63.18 174	74.54 145	81.07 243
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 225	62.92 230	76.80 219	76.51 253	57.77 224	89.22 164	83.41 248	55.48 253	53.86 241	77.84 220	26.28 266	93.95 153	34.90 269	68.76 177	78.68 262

LTVRE_ROB	59.60 19	66.27 226	63.54 225	74.45 228	84.00 180	51.55 251	67.08 277	83.53 246	58.78 238	54.94 231	80.31 203	34.54 242	93.23 168	40.64 256	68.03 182	78.58 263
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
JIA-IIPM		66.06 227	62.45 233	76.88 218	81.42 197	54.45 242	57.49 287	88.67 194	49.36 265	63.86 191	46.86 285	56.06 107	90.25 214	49.53 228	68.83 176	85.95 186

UnsupCasMVSNet_eth		65.79 228	63.10 228	73.88 232	70.71 272	50.29 259	81.09 238	89.88 162	72.58 118	49.25 257	74.77 238	32.57 247	87.43 243	55.96 208	41.04 279	83.90 208

pmmvs-eth3d		65.53 229	62.32 234	75.19 223	69.39 277	59.59 209	82.80 229	83.43 247	62.52 219	51.30 253	72.49 246	32.86 244	87.16 245	55.32 209	50.73 261	78.83 261

SixPastTwentyTwo		64.92 230	61.78 237	74.34 231	78.74 240	49.76 260	83.42 223	79.51 262	62.86 217	50.27 255	77.35 222	30.92 256	90.49 212	45.89 240	47.06 272	82.78 221

Patchmatch-RL test		64.87 231	61.69 238	74.44 229	71.22 270	53.93 243	70.07 270	71.54 281	69.22 169	56.79 225	74.43 239	19.77 278	88.61 235	69.53 124	52.61 254	80.79 247

OurMVSNet-221017-0		64.68 232	62.17 235	72.21 246	76.08 257	47.35 270	80.67 239	81.02 256	56.19 250	51.60 250	79.66 211	27.05 264	88.56 237	53.60 216	53.63 252	80.71 248

test_0402		64.54 233	61.09 239	74.92 225	84.10 179	60.75 191	87.95 182	79.71 261	52.03 258	52.41 247	77.20 225	32.21 249	91.64 203	23.14 285	61.03 228	72.36 274

testgi		64.48 234	62.87 231	69.31 254	71.24 269	40.62 280	85.49 210	79.92 260	65.36 201	54.18 238	83.49 160	23.74 269	84.55 252	41.60 251	60.79 231	82.77 222

RPSCF		64.24 235	61.98 236	71.01 252	76.10 256	45.00 273	75.83 262	75.94 268	46.94 271	58.96 213	84.59 153	31.40 253	82.00 268	47.76 234	60.33 233	86.04 185

test2356		64.16 236	63.28 227	66.81 260	69.37 278	39.86 283	87.76 188	86.02 231	59.83 233	53.54 244	73.23 242	34.94 240	80.67 270	39.66 258	65.20 197	79.89 254

EU-MVSNet		64.01 237	63.01 229	67.02 259	74.40 262	38.86 285	83.27 224	86.19 229	45.11 276	54.27 236	81.15 194	36.91 236	80.01 272	48.79 231	57.02 242	82.19 227

test20.03		63.83 238	62.65 232	67.38 258	70.58 274	39.94 281	86.57 206	84.17 240	63.29 213	51.86 249	77.30 223	37.09 234	82.47 264	38.87 261	54.13 251	79.73 256

MDA-MVSNet_test_wron		63.78 239	60.16 240	74.64 226	78.15 245	60.41 195	83.49 220	84.03 241	56.17 252	39.17 280	71.59 261	37.22 231	83.24 262	42.87 248	48.73 269	80.26 252

YYNet1		63.76 240	60.14 241	74.62 227	78.06 246	60.19 201	83.46 222	83.99 245	56.18 251	39.25 279	71.56 262	37.18 232	83.34 260	42.90 247	48.70 270	80.32 251

K. test v3		63.09 241	59.61 243	73.53 235	76.26 255	49.38 263	83.27 224	77.15 265	64.35 207	47.77 260	72.32 251	28.73 259	87.79 242	49.93 227	36.69 283	83.41 215

COLMAP_ROB	57.96 20	62.98 242	59.65 242	72.98 239	81.44 196	53.00 246	83.75 217	75.53 272	48.34 268	48.81 258	81.40 187	24.14 267	90.30 213	32.95 273	60.52 232	75.65 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		61.66 243	58.06 244	72.46 243	79.57 230	51.42 253	80.17 245	68.61 284	51.25 260	45.88 264	81.23 189	19.86 276	86.58 247	38.98 259	57.01 243	79.39 258

UnsupCasMVSNet_bld		61.60 244	57.71 245	73.29 237	68.73 279	51.64 250	78.61 253	89.05 189	57.20 245	46.11 263	61.96 277	28.70 260	88.60 236	50.08 226	38.90 281	79.63 257

MDA-MVSNet-bldmvs		61.54 245	57.70 246	73.05 238	79.53 232	57.00 231	83.08 227	81.23 254	57.57 241	34.91 284	72.45 248	32.79 245	86.26 249	35.81 267	41.95 278	75.89 269

TinyColmap		60.32 246	56.42 251	72.00 249	78.78 239	53.18 245	78.36 256	75.64 269	52.30 257	41.59 278	75.82 234	14.76 283	88.35 239	35.84 266	54.71 250	74.46 271

MVS-HIRNet		60.25 247	55.55 252	74.35 230	84.37 174	56.57 232	71.64 266	74.11 274	34.44 287	45.54 268	42.24 289	31.11 255	89.81 229	40.36 257	76.10 140	76.67 268

MIMVSNet1		60.16 248	57.33 248	68.67 255	69.71 276	44.13 274	78.92 252	84.21 239	55.05 254	44.63 271	71.85 255	23.91 268	81.54 269	32.63 275	55.03 249	80.35 250

PM-MVS		59.40 249	56.59 250	67.84 256	63.63 281	41.86 278	76.76 260	63.22 290	59.01 237	51.07 254	72.27 252	11.72 286	83.25 261	61.34 186	50.28 267	78.39 264

testus		59.36 250	57.51 247	64.90 263	66.72 280	37.56 286	84.98 212	81.09 255	57.46 244	47.72 261	72.76 243	11.43 288	78.78 278	36.56 263	58.91 238	78.36 265

testpf		57.17 251	56.93 249	57.88 270	79.13 238	42.40 276	34.23 294	85.97 232	52.64 256	47.66 262	66.50 268	36.33 238	79.65 274	53.60 216	56.31 246	51.60 287

DSMNet-mixed		56.78 252	54.44 254	63.79 265	63.21 282	29.44 294	64.43 280	64.10 289	42.12 284	51.32 252	71.60 260	31.76 250	75.04 281	36.23 265	65.20 197	86.87 169

LP		56.71 253	51.64 257	71.91 250	80.08 226	60.33 197	61.72 282	75.61 270	43.87 281	43.76 274	60.30 279	30.46 257	84.05 254	22.94 286	46.06 274	71.34 275

1111		56.66 254	54.98 253	61.69 266	61.99 285	31.38 290	79.81 250	83.17 251	45.66 273	41.94 276	65.44 269	41.50 210	79.56 275	27.64 280	47.68 271	74.14 272

test1235678		55.73 255	52.74 255	64.68 264	60.16 287	35.56 288	81.65 234	81.46 253	51.27 259	38.93 281	62.82 275	17.44 279	78.58 279	30.87 278	50.09 268	79.89 254

pmmvs3		55.51 256	51.50 259	67.53 257	57.90 289	50.93 256	80.37 241	73.66 276	40.63 285	44.15 273	64.75 273	16.30 280	78.97 277	44.77 242	40.98 280	72.69 273

TDRefinement		55.28 257	51.58 258	66.39 261	59.53 288	46.15 272	76.23 261	72.80 277	44.60 279	42.49 275	76.28 230	15.29 281	82.39 265	33.20 272	43.75 276	70.62 277

HyFIR lowres test		54.68 258	50.80 260	66.34 262	74.15 263	49.02 264	53.99 289	62.10 292	44.68 278	37.46 282	63.99 274	13.84 285	80.23 271	43.01 245	63.99 212	69.76 279

LF4IMVS		54.01 259	52.12 256	59.69 268	62.41 284	39.91 282	68.59 272	68.28 286	42.96 282	44.55 272	75.18 235	14.09 284	68.39 285	41.36 253	51.68 258	70.78 276

N_pmnet		50.55 260	49.11 262	54.88 274	77.17 251	4.02 306	84.36 215	2.00 303	48.59 266	45.86 266	68.82 267	32.22 248	82.80 263	31.58 277	51.38 259	77.81 266

new_pmnet		49.31 261	46.44 263	57.93 269	62.84 283	40.74 279	68.47 273	62.96 291	36.48 286	35.09 283	57.81 281	14.97 282	72.18 282	32.86 274	46.44 273	60.88 285

test12356		47.51 262	44.82 264	55.56 272	52.53 290	21.09 301	71.45 267	76.03 267	44.14 280	30.69 285	58.18 280	9.01 291	76.14 280	26.95 282	34.43 286	69.46 280

testmv		46.98 263	43.53 265	57.35 271	47.75 295	30.41 293	74.99 263	77.69 263	42.84 283	28.03 286	53.36 282	8.18 292	71.18 283	24.36 284	34.55 284	70.46 278

.test1245		46.52 264	49.68 261	37.02 283	61.99 285	31.38 290	79.81 250	83.17 251	45.66 273	41.94 276	65.44 269	41.50 210	79.56 275	27.64 280	0.01 300	0.13 299

FPMVS		45.64 265	43.10 266	53.23 276	51.42 292	36.46 287	64.97 279	71.91 279	29.13 289	27.53 287	61.55 278	9.83 289	65.01 289	16.00 291	55.58 247	58.22 286

no-one		44.13 266	38.39 267	61.34 267	45.91 297	41.94 277	61.67 283	75.07 273	45.05 277	20.07 290	40.68 292	11.58 287	79.82 273	30.18 279	15.30 291	62.26 284

LCM-MVSNet		40.54 267	35.79 268	54.76 275	36.92 301	30.81 292	51.41 290	69.02 283	22.07 291	24.63 288	45.37 287	4.56 297	65.81 287	33.67 270	34.50 285	67.67 281

ANet_high		40.27 268	35.20 269	55.47 273	34.74 302	34.47 289	63.84 281	71.56 280	48.42 267	18.80 292	41.08 290	9.52 290	64.45 290	20.18 288	8.66 298	67.49 282

PMMVS2		37.93 269	33.61 270	50.92 277	46.31 296	24.76 299	60.55 286	50.05 295	28.94 290	20.93 289	47.59 284	4.41 298	65.13 288	25.14 283	18.55 290	62.87 283

Gipuma		34.91 270	31.44 272	45.30 279	70.99 271	39.64 284	19.85 297	72.56 278	20.10 294	16.16 294	21.47 296	5.08 296	71.16 284	13.07 292	43.70 277	25.08 293
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 271	29.98 273	41.11 281	48.05 293	29.17 295	65.82 278	50.02 296	21.42 292	14.74 295	37.19 293	1.11 301	55.11 292	19.75 289	11.77 293	39.06 289

PMVS	26.43 22	31.84 272	28.16 274	42.89 280	25.87 305	27.58 297	50.92 291	49.78 297	21.37 293	14.17 296	40.81 291	2.01 299	66.62 286	9.61 294	38.88 282	34.49 292
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pcd1.5k->3k		31.17 273	31.85 271	29.12 285	81.48 195	0.00 309	0.00 298	91.79 101	0.00 303	0.00 304	0.00 303	41.05 215	0.00 301	0.00 300	72.34 159	87.36 159

wuykxyi23d		29.03 274	23.09 279	46.84 278	31.67 304	28.82 296	43.46 292	57.72 294	14.39 297	7.52 300	20.84 297	0.64 302	60.29 291	21.57 287	10.04 295	51.40 288

E-PMN		24.61 275	24.00 276	26.45 286	43.74 298	18.44 303	60.86 284	39.66 298	15.11 295	9.53 298	22.10 295	6.52 294	46.94 294	8.31 295	10.14 294	13.98 295

MVE	24.84 23	24.35 276	19.77 280	38.09 282	34.56 303	26.92 298	26.57 295	38.87 300	11.73 298	11.37 297	27.44 294	1.37 300	50.42 293	11.41 293	14.60 292	36.93 290
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 277	23.20 278	25.46 287	41.52 299	16.90 304	60.56 285	38.79 301	14.62 296	8.99 299	20.24 298	7.35 293	45.82 295	7.25 296	9.46 296	13.64 296

DUST3R		22.26 278	23.75 277	17.80 288	5.23 306	12.06 305	35.26 293	39.48 299	2.82 300	18.94 291	44.20 288	22.23 272	24.64 297	36.30 264	9.31 297	16.69 294

cdsmvs_eth3d_5k		19.86 279	26.47 275	0.00 292	0.00 309	0.00 309	0.00 298	93.45 44	0.00 303	0.00 304	95.27 24	49.56 172	0.00 301	0.00 300	0.00 302	0.00 301

wuyk23d		11.30 280	10.95 281	12.33 289	48.05 293	19.89 302	25.89 296	1.92 304	3.58 299	3.12 301	1.37 300	0.64 302	15.77 298	6.23 297	7.77 299	1.35 297

ab-mvs-re		7.91 281	10.55 282	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	94.95 32	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

testmvs		7.23 282	9.62 283	0.06 291	0.04 307	0.02 308	84.98 212	0.02 305	0.03 301	0.18 302	1.21 301	0.01 305	0.02 299	0.14 298	0.01 300	0.13 299

test123		6.92 283	9.21 284	0.08 290	0.03 308	0.05 307	81.65 234	0.01 306	0.02 302	0.14 303	0.85 302	0.03 304	0.02 299	0.12 299	0.00 302	0.16 298

pcd_1.5k_mvsjas		4.46 284	5.95 285	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	53.55 140	0.00 301	0.00 300	0.00 302	0.00 301

sosnet-low-res		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

sosnet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

uncertanet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

Regformer		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

uanet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 302	0.00 301

MTAPA					38.87 300

gm-plane-assit					88.42 125	67.04 73			78.62 46		91.83 88		97.37 43	76.57 78

test9_res														89.41 6	94.96 7	95.29 32

TEST9					94.18 26	67.28 65	94.16 33	93.51 42	71.75 140	85.52 20	95.33 21	68.01 20	97.27 50

Patchmatch-test1					72.70 266	56.44 233	57.17 288	74.00 275		58.57 217	71.23 263	21.52 273			52.64 253

Patchmatch-test					85.80 158	62.10 177					62.79 276

test_8					94.19 25	67.19 67	94.15 35	93.42 46	71.87 132	85.38 22	95.35 20	68.19 18	96.95 65

agg_prior2														86.41 25	94.75 13	95.33 29

agg_prior					94.16 30	66.97 74		93.31 50		84.49 28			96.75 73

TestCases				72.46 243	79.57 230	51.42 253		68.61 284	51.25 260	45.88 264	81.23 189	19.86 276	86.58 247	38.98 259	57.01 243	79.39 258

test_prior4						67.18 69	93.92 47

test_prior2							95.10 24		75.40 80	85.25 24	95.61 16	67.94 21		87.47 18	94.77 11

test_prior				86.42 46	94.71 21	67.35 63		93.10 60					96.84 70			95.05 43

旧先验2							92.00 100		59.37 236	87.54 9			93.47 165	75.39 83

新几何2							91.41 123

新几何1				84.73 85	92.32 61	64.28 143		91.46 114	59.56 234	79.77 53	92.90 74	56.95 96	96.57 78	63.40 171	92.91 32	93.34 87

旧先验1					91.94 68	60.74 192		91.50 112			94.36 47	65.23 41			91.84 41	94.55 54

无先验							92.71 78	92.61 77	62.03 221				97.01 58	66.63 143		93.97 76

原ACMM2							92.01 98

原ACMM1				84.42 92	93.21 46	64.27 144		93.40 47	65.39 200	79.51 56	92.50 77	58.11 87	96.69 75	65.27 158	93.96 20	92.32 113

test222					89.77 97	61.60 181	89.55 159	89.42 177	56.83 248	77.28 78	92.43 80	52.76 147			91.14 53	93.09 95

testdata2													96.09 87	61.26 187

segment_acmp												65.94 36

testdata				81.34 155	89.02 111	57.72 225		89.84 163	58.65 239	85.32 23	94.09 53	57.03 93	93.28 167	69.34 126	90.56 59	93.03 97

testdata1							89.21 165		77.55 57

test12				87.09 31	94.60 23	68.86 38		92.91 66		82.67 37		65.44 40	97.55 37		93.69 24	94.84 48

plane_prior7					86.94 144	61.51 182

plane_prior6					87.23 141	62.32 173						50.66 162

plane_prior5								91.31 119					95.55 104	76.74 76	78.53 126	88.39 149

plane_prior4											89.14 114

plane_prior3						61.95 178			79.09 38	72.53 114

plane_prior2							93.13 67		78.81 43

plane_prior1					87.15 142

plane_prior						62.42 171	93.85 50		79.38 31						78.80 125

abl_6					89.85 95	58.34 220		90.07 157		77.71 71	93.08 67	47.65 188				89.99 137

n2								0.00 307

nn								0.00 307

door-mid								66.01 288

lessismore_v0				73.72 234	72.93 265	47.83 268		61.72 293		45.86 266	73.76 240	28.63 261	89.81 229	47.75 235	31.37 287	83.53 211

LGP-MVS_train				79.56 184	84.31 175	59.37 212		89.73 168	69.49 166	64.86 182	88.42 118	38.65 221	94.30 135	72.56 96	72.76 154	85.01 202

test11								93.01 62

door								66.57 287

HQP5-MVS						63.66 154

HQP-NCC					87.54 137		94.06 39		79.80 27	74.18 99

ACMP_Plane					87.54 137		94.06 39		79.80 27	74.18 99

BP-MVS

High-resolution multi-view benchmark