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.

AdaColmap		93.82 117	93.06 123	96.10 117	99.88 1	89.07 165	98.33 185	97.55 116	86.81 224	90.39 179	98.65 100	75.09 229	99.98 10	93.32 135	97.53 126	99.26 108

DP-MVS Recon		95.85 67	95.15 80	97.95 31	99.87 2	94.38 53	99.60 29	97.48 132	86.58 228	94.42 120	99.13 47	87.36 96	99.98 10	93.64 129	98.33 114	99.48 90

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 9	99.70 16	97.98 46	97.18 2	95.96 91	99.33 23	92.62 26	100.00 1	98.99 19	99.93 1	99.98 6

CNVR-MVS		98.46 1	98.38 1	98.72 8	99.80 4	96.19 14	99.80 7	97.99 45	97.05 3	99.41 2	99.59 2	92.89 25	100.00 1	98.99 19	99.90 7	99.96 10

MG-MVS		97.24 19	96.83 31	98.47 14	99.79 5	95.71 17	99.07 98	99.06 9	94.45 24	96.42 84	98.70 98	88.81 68	99.74 75	95.35 96	99.86 12	99.97 7

NCCC		98.12 5	98.11 3	98.13 23	99.76 6	94.46 49	99.81 5	97.88 51	96.54 5	98.84 18	99.46 11	92.55 27	99.98 10	98.25 40	99.93 1	99.94 18

testtj		97.23 21	97.05 22	97.75 38	99.75 7	93.34 73	99.16 82	97.74 70	91.28 98	98.40 30	99.29 24	89.95 54	99.98 10	98.20 41	99.70 39	99.94 18

region2R		96.30 52	96.17 52	96.70 91	99.70 8	90.31 139	99.46 50	97.66 88	90.55 115	97.07 64	99.07 54	86.85 106	99.97 23	95.43 94	99.74 32	99.81 35

ETH3 D test6400		97.67 11	97.33 18	98.69 9	99.69 9	96.43 11	99.63 26	97.73 74	91.05 101	98.66 23	99.53 7	90.59 42	99.71 78	99.32 12	99.80 27	99.91 22

HFP-MVS		96.42 48	96.26 47	96.90 77	99.69 9	90.96 127	99.47 45	97.81 60	90.54 116	96.88 67	99.05 57	87.57 87	99.96 30	95.65 87	99.72 34	99.78 42

#test#		96.48 45	96.34 44	96.90 77	99.69 9	90.96 127	99.53 40	97.81 60	90.94 105	96.88 67	99.05 57	87.57 87	99.96 30	95.87 84	99.72 34	99.78 42

ACMMPR		96.28 53	96.14 56	96.73 88	99.68 12	90.47 137	99.47 45	97.80 62	90.54 116	96.83 75	99.03 60	86.51 118	99.95 34	95.65 87	99.72 34	99.75 53

ZD-MVS					99.67 13	93.28 74		97.61 102	87.78 202	97.41 57	99.16 41	90.15 52	99.56 98	98.35 35	99.70 39

CP-MVS		96.22 54	96.15 55	96.42 106	99.67 13	89.62 160	99.70 16	97.61 102	90.07 133	96.00 88	99.16 41	87.43 91	99.92 41	96.03 82	99.72 34	99.70 62

DVP-MVS		98.07 7	98.00 6	98.29 18	99.66 15	95.20 29	99.72 13	97.47 134	93.95 31	99.07 11	99.46 11	93.18 22	99.97 23	99.64 6	99.82 19	99.69 65
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				98.77 7	99.66 15	96.37 13	99.72 13	97.68 85					99.98 10	99.64 6	99.82 19	99.96 10

test0726					99.66 15	95.20 29	99.77 8	97.70 81	93.95 31	99.35 5	99.54 3	93.18 22

CPTT-MVS		94.60 102	94.43 91	95.09 146	99.66 15	86.85 219	99.44 53	97.47 134	83.22 279	94.34 123	98.96 72	82.50 177	99.55 99	94.81 107	99.50 62	98.88 139

MSLP-MVS++		97.50 15	97.45 14	97.63 41	99.65 19	93.21 75	99.70 16	98.13 38	94.61 20	97.78 52	99.46 11	89.85 55	99.81 67	97.97 44	99.91 6	99.88 28

OPU-MVS				99.49 4	99.64 20	98.51 4	99.77 8				99.19 34	95.12 8	99.97 23	99.90 1	99.92 3	99.99 1

SED-MVS		98.18 2	98.10 4	98.41 17	99.63 21	95.24 24	99.77 8	97.72 76	94.17 26	99.30 6	99.54 3	93.32 19	99.98 10	99.70 3	99.81 23	99.99 1

IU-MVS					99.63 21	95.38 21		97.73 74	95.54 15	99.54 1				99.69 5	99.81 23	99.99 1

test_241102_ONE					99.63 21	95.24 24		97.72 76	94.16 28	99.30 6	99.49 10	93.32 19	99.98 10

PAPR		96.35 49	95.82 64	97.94 32	99.63 21	94.19 57	99.42 58	97.55 116	92.43 68	93.82 133	99.12 48	87.30 98	99.91 43	94.02 120	99.06 87	99.74 56

XVS		96.47 46	96.37 42	96.77 84	99.62 25	90.66 135	99.43 56	97.58 110	92.41 72	96.86 70	98.96 72	87.37 93	99.87 52	95.65 87	99.43 68	99.78 42

X-MVStestdata		90.69 184	88.66 205	96.77 84	99.62 25	90.66 135	99.43 56	97.58 110	92.41 72	96.86 70	29.59 380	87.37 93	99.87 52	95.65 87	99.43 68	99.78 42

DVP-MVS++		98.18 2	98.09 5	98.44 15	99.61 27	95.38 21	99.55 35	97.68 85	93.01 53	99.23 8	99.45 16	95.12 8	99.98 10	99.25 15	99.92 3	99.97 7

MSC_two_6792asdad				99.51 2	99.61 27	98.60 2		97.69 83					99.98 10	99.55 10	99.83 15	99.96 10

No_MVS				99.51 2	99.61 27	98.60 2		97.69 83					99.98 10	99.55 10	99.83 15	99.96 10

DeepC-MVS_fast	93.52 2	97.16 24	96.84 30	98.13 23	99.61 27	94.45 50	98.85 121	97.64 94	96.51 7	95.88 94	99.39 21	87.35 97	99.99 5	96.61 68	99.69 41	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_one_0601					99.59 31	94.89 34		97.64 94	93.14 52	98.93 16	99.45 16	93.45 18

CDPH-MVS		96.56 42	96.18 49	97.70 39	99.59 31	93.92 61	99.13 94	97.44 141	89.02 161	97.90 50	99.22 31	88.90 67	99.49 109	94.63 113	99.79 29	99.68 66

test_prior3		97.07 28	97.09 20	97.01 65	99.58 33	91.77 101	99.57 32	97.57 113	91.43 93	98.12 39	98.97 67	90.43 45	99.49 109	98.33 36	99.81 23	99.79 38

test_prior				97.01 65	99.58 33	91.77 101		97.57 113					99.49 109			99.79 38

APDe-MVS		97.53 12	97.47 12	97.70 39	99.58 33	93.63 66	99.56 34	97.52 123	93.59 46	98.01 46	99.12 48	90.80 40	99.55 99	99.26 14	99.79 29	99.93 21

mPP-MVS		95.90 66	95.75 68	96.38 108	99.58 33	89.41 163	99.26 74	97.41 145	90.66 111	94.82 114	98.95 74	86.15 126	99.98 10	95.24 99	99.64 47	99.74 56

TEST9					99.57 37	93.17 76	99.38 62	97.66 88	89.57 146	98.39 31	99.18 37	90.88 37	99.66 85

train_agg		97.20 23	97.08 21	97.57 45	99.57 37	93.17 76	99.38 62	97.66 88	90.18 127	98.39 31	99.18 37	90.94 35	99.66 85	98.58 28	99.85 13	99.88 28

test_8					99.55 39	93.07 80	99.37 65	97.64 94	90.18 127	98.36 33	99.19 34	90.94 35	99.64 91

test_part2					99.54 40	95.42 19				98.13 37

MSP-MVS		97.77 9	98.18 2	96.53 101	99.54 40	90.14 143	99.41 59	97.70 81	95.46 17	98.60 25	99.19 34	95.71 4	99.49 109	98.15 42	99.85 13	99.95 15
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
agg_prior1		97.12 25	97.03 23	97.38 53	99.54 40	92.66 88	99.35 67	97.64 94	90.38 120	97.98 47	99.17 39	90.84 39	99.61 94	98.57 29	99.78 31	99.87 31

agg_prior					99.54 40	92.66 88		97.64 94		97.98 47			99.61 94

CSCG		94.87 90	94.71 85	95.36 139	99.54 40	86.49 224	99.34 69	98.15 36	82.71 289	90.15 182	99.25 27	89.48 61	99.86 57	94.97 105	98.82 100	99.72 59

HPM-MVS++		97.72 10	97.59 11	98.14 22	99.53 45	94.76 42	99.19 77	97.75 68	95.66 13	98.21 35	99.29 24	91.10 33	99.99 5	97.68 48	99.87 9	99.68 66

APD-MVS		96.95 31	96.72 34	97.63 41	99.51 46	93.58 67	99.16 82	97.44 141	90.08 132	98.59 26	99.07 54	89.06 64	99.42 120	97.92 45	99.66 43	99.88 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
FOURS1					99.50 47	88.94 170	99.55 35	97.47 134	91.32 97	98.12 39

DPE-MVS		98.11 6	98.00 6	98.44 15	99.50 47	95.39 20	99.29 73	97.72 76	94.50 22	98.64 24	99.54 3	93.32 19	99.97 23	99.58 9	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PGM-MVS		95.85 67	95.65 72	96.45 104	99.50 47	89.77 157	98.22 193	98.90 12	89.19 156	96.74 77	98.95 74	85.91 130	99.92 41	93.94 122	99.46 64	99.66 70

GST-MVS		95.97 62	95.66 70	96.90 77	99.49 50	91.22 113	99.45 52	97.48 132	89.69 140	95.89 93	98.72 95	86.37 122	99.95 34	94.62 114	99.22 83	99.52 84

MP-MVS		96.00 59	95.82 64	96.54 100	99.47 51	90.13 145	99.36 66	97.41 145	90.64 114	95.49 104	98.95 74	85.51 134	99.98 10	96.00 83	99.59 58	99.52 84
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Regformer-1		96.97 30	96.80 32	97.47 47	99.46 52	93.11 78	98.89 118	97.94 47	92.89 59	96.90 66	99.02 61	89.78 56	99.53 102	97.06 55	99.26 80	99.75 53

Regformer-2		96.94 33	96.78 33	97.42 49	99.46 52	92.97 85	98.89 118	97.93 48	92.86 61	96.88 67	99.02 61	89.74 58	99.53 102	97.03 56	99.26 80	99.75 53

test1172		95.92 65	96.07 57	95.46 135	99.42 54	87.24 214	98.51 162	97.24 156	90.29 123	96.56 83	99.12 48	86.73 110	99.36 126	97.33 52	99.42 71	99.78 42

ZNCC-MVS		96.09 57	95.81 66	96.95 75	99.42 54	91.19 115	99.55 35	97.53 120	89.72 139	95.86 96	98.94 79	86.59 114	99.97 23	95.13 100	99.56 59	99.68 66

SR-MVS		96.13 56	96.16 54	96.07 118	99.42 54	89.04 166	98.59 154	97.33 152	90.44 118	96.84 73	99.12 48	86.75 108	99.41 122	97.47 49	99.44 67	99.76 52

PAPM_NR		95.43 77	95.05 82	96.57 99	99.42 54	90.14 143	98.58 156	97.51 126	90.65 113	92.44 147	98.90 81	87.77 86	99.90 45	90.88 159	99.32 75	99.68 66

xxxxxxxxxxxxxcwj		97.51 13	97.42 15	97.78 37	99.34 58	93.85 63	99.65 24	95.45 279	95.69 11	98.70 21	99.42 19	90.42 47	99.72 76	98.47 31	99.65 44	99.77 49

9.14			96.87 28		99.34 58		99.50 42	97.49 131	89.41 151	98.59 26	99.43 18	89.78 56	99.69 80	98.69 23	99.62 51

save fliter					99.34 58	93.85 63	99.65 24	97.63 99	95.69 11

Regformer-3		96.50 44	96.36 43	96.91 76	99.34 58	91.72 104	98.71 133	97.90 50	92.48 67	96.00 88	98.95 74	88.60 70	99.52 105	96.44 73	98.83 98	99.49 88

Regformer-4		96.45 47	96.33 46	96.81 83	99.34 58	91.44 111	98.71 133	97.88 51	92.43 68	95.97 90	98.95 74	88.42 74	99.51 106	96.40 74	98.83 98	99.49 88

PHI-MVS		96.65 40	96.46 40	97.21 59	99.34 58	91.77 101	99.70 16	98.05 41	86.48 231	98.05 43	99.20 33	89.33 62	99.96 30	98.38 34	99.62 51	99.90 24

test12				97.83 34	99.33 64	94.45 50		97.55 116		97.56 53		88.60 70	99.50 108		99.71 38	99.55 82

SMA-MVS		97.24 19	96.99 25	98.00 30	99.30 65	94.20 56	99.16 82	97.65 93	89.55 148	99.22 10	99.52 9	90.34 51	99.99 5	98.32 38	99.83 15	99.82 34
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
zzz-MVS		96.21 55	95.96 59	96.96 73	99.29 66	91.19 115	98.69 138	97.45 137	92.58 63	94.39 121	99.24 29	86.43 120	99.99 5	96.22 76	99.40 72	99.71 60

MTAPA		96.09 57	95.80 67	96.96 73	99.29 66	91.19 115	97.23 253	97.45 137	92.58 63	94.39 121	99.24 29	86.43 120	99.99 5	96.22 76	99.40 72	99.71 60

HPM-MVS		95.41 79	95.22 78	95.99 121	99.29 66	89.14 164	99.17 81	97.09 176	87.28 215	95.40 105	98.48 114	84.93 142	99.38 124	95.64 91	99.65 44	99.47 91
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		94.67 99	94.30 92	95.79 127	99.25 69	88.13 187	98.41 174	98.67 20	90.38 120	91.43 159	98.72 95	82.22 184	99.95 34	93.83 126	95.76 156	99.29 105
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
APD-MVS_3200maxsize		95.64 76	95.65 72	95.62 131	99.24 70	87.80 193	98.42 172	97.22 159	88.93 166	96.64 82	98.98 66	85.49 135	99.36 126	96.68 65	99.27 79	99.70 62

SR-MVS-dyc-post		95.75 73	95.86 63	95.41 138	99.22 71	87.26 212	98.40 177	97.21 160	89.63 142	96.67 80	98.97 67	86.73 110	99.36 126	96.62 66	99.31 76	99.60 78

RE-MVS-def			95.70 69		99.22 71	87.26 212	98.40 177	97.21 160	89.63 142	96.67 80	98.97 67	85.24 140		96.62 66	99.31 76	99.60 78

patch_mono-2		97.10 27	97.97 8	94.49 167	99.21 73	83.73 283	99.62 28	98.25 27	95.28 18	99.38 4	98.91 80	92.28 28	99.94 37	99.61 8	99.22 83	99.78 42

API-MVS		94.78 93	94.18 98	96.59 96	99.21 73	90.06 150	98.80 126	97.78 65	83.59 274	93.85 131	99.21 32	83.79 153	99.97 23	92.37 146	99.00 90	99.74 56

ETH3D-3000-0.1		97.29 17	97.01 24	98.12 25	99.18 75	94.97 32	99.47 45	97.52 123	89.85 136	98.79 20	99.46 11	90.41 49	99.69 80	98.78 21	99.67 42	99.70 62

PLC	91.07 3	94.23 108	94.01 102	94.87 154	99.17 76	87.49 201	99.25 75	96.55 202	88.43 182	91.26 163	98.21 126	85.92 128	99.86 57	89.77 173	97.57 123	97.24 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet-Vis-set		95.76 72	95.63 74	96.17 115	99.14 77	90.33 138	98.49 166	97.82 57	91.92 82	94.75 115	98.88 84	87.06 102	99.48 114	95.40 95	97.17 134	98.70 156

TSAR-MVS + MP.		97.44 16	97.46 13	97.39 52	99.12 78	93.49 71	98.52 159	97.50 129	94.46 23	98.99 13	98.64 101	91.58 30	99.08 144	98.49 30	99.83 15	99.60 78
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		97.22 22	96.92 26	98.12 25	99.11 79	94.88 35	99.44 53	97.45 137	89.60 144	98.70 21	99.42 19	90.42 47	99.72 76	98.47 31	99.65 44	99.77 49

HPM-MVS_fast		94.89 89	94.62 86	95.70 130	99.11 79	88.44 183	99.14 91	97.11 172	85.82 238	95.69 101	98.47 115	83.46 158	99.32 132	93.16 137	99.63 50	99.35 99

MAR-MVS		94.43 105	94.09 100	95.45 136	99.10 81	87.47 202	98.39 181	97.79 64	88.37 184	94.02 128	99.17 39	78.64 214	99.91 43	92.48 145	98.85 97	98.96 130
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
114514_t		94.06 109	93.05 124	97.06 63	99.08 82	92.26 97	98.97 111	97.01 183	82.58 291	92.57 145	98.22 124	80.68 198	99.30 133	89.34 180	99.02 89	99.63 74

EI-MVSNet-UG-set		95.43 77	95.29 76	95.86 125	99.07 83	89.87 154	98.43 171	97.80 62	91.78 85	94.11 126	98.77 89	86.25 125	99.48 114	94.95 106	96.45 141	98.22 180

原ACMM1				96.18 113	99.03 84	90.08 146		97.63 99	88.98 162	97.00 65	98.97 67	88.14 80	99.71 78	88.23 192	99.62 51	98.76 153

SD-MVS		97.51 13	97.40 16	97.81 35	99.01 85	93.79 65	99.33 70	97.38 148	93.73 42	98.83 19	99.02 61	90.87 38	99.88 49	98.69 23	99.74 32	99.77 49
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
旧先验1					98.97 86	92.90 87		97.74 70			99.15 43	91.05 34			99.33 74	99.60 78

LS3D		90.19 192	88.72 203	94.59 166	98.97 86	86.33 232	96.90 265	96.60 196	74.96 340	84.06 236	98.74 92	75.78 226	99.83 62	74.93 308	97.57 123	97.62 195

CNLPA		93.64 124	92.74 131	96.36 109	98.96 88	90.01 153	99.19 77	95.89 253	86.22 234	89.40 190	98.85 85	80.66 199	99.84 60	88.57 188	96.92 136	99.24 109

MP-MVS-pluss		95.80 69	95.30 75	97.29 55	98.95 89	92.66 88	98.59 154	97.14 168	88.95 164	93.12 140	99.25 27	85.62 131	99.94 37	96.56 70	99.48 63	99.28 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
新几何1				97.40 51	98.92 90	92.51 95		97.77 67	85.52 241	96.69 79	99.06 56	88.08 81	99.89 48	84.88 227	99.62 51	99.79 38

DP-MVS		88.75 222	86.56 240	95.34 140	98.92 90	87.45 203	97.64 237	93.52 332	70.55 351	81.49 276	97.25 157	74.43 236	99.88 49	71.14 329	94.09 169	98.67 157

1121		95.19 84	94.45 90	97.42 49	98.88 92	92.58 93	96.22 289	97.75 68	85.50 243	96.86 70	99.01 65	88.59 72	99.90 45	87.64 200	99.60 56	99.79 38

TSAR-MVS + GP.		96.95 31	96.91 27	97.07 62	98.88 92	91.62 106	99.58 31	96.54 204	95.09 19	96.84 73	98.63 103	91.16 31	99.77 72	99.04 18	96.42 142	99.81 35

CANet		97.00 29	96.49 39	98.55 11	98.86 94	96.10 15	99.83 4	97.52 123	95.90 9	97.21 61	98.90 81	82.66 176	99.93 40	98.71 22	98.80 101	99.63 74

dcpmvs_2		95.67 75	96.18 49	94.12 183	98.82 95	84.22 276	97.37 245	95.45 279	90.70 110	95.77 99	98.63 103	90.47 44	98.68 158	99.20 17	99.22 83	99.45 92

ACMMP_NAP		96.59 41	96.18 49	97.81 35	98.82 95	93.55 68	98.88 120	97.59 108	90.66 111	97.98 47	99.14 45	86.59 114	100.00 1	96.47 72	99.46 64	99.89 27

PVSNet_BlendedMVS		93.36 132	93.20 120	93.84 194	98.77 97	91.61 107	99.47 45	98.04 42	91.44 92	94.21 124	92.63 262	83.50 156	99.87 52	97.41 50	83.37 257	90.05 322

PVSNet_Blended		95.94 64	95.66 70	96.75 86	98.77 97	91.61 107	99.88 1	98.04 42	93.64 45	94.21 124	97.76 135	83.50 156	99.87 52	97.41 50	97.75 122	98.79 149

DeepPCF-MVS	93.56 1	96.55 43	97.84 10	92.68 217	98.71 99	78.11 336	99.70 16	97.71 80	98.18 1	97.36 59	99.76 1	90.37 50	99.94 37	99.27 13	99.54 61	99.99 1

EPNet		96.82 36	96.68 36	97.25 58	98.65 100	93.10 79	99.48 43	98.76 13	96.54 5	97.84 51	98.22 124	87.49 90	99.66 85	95.35 96	97.78 121	99.00 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		93.90 115	93.62 113	94.73 161	98.63 101	87.00 216	98.04 213	96.56 201	92.19 77	92.46 146	98.73 93	79.49 206	99.14 141	92.16 148	94.34 168	98.03 185

ETH3D cwj APD-0.16		96.94 33	96.58 38	98.01 29	98.62 102	94.73 44	99.13 94	97.38 148	88.44 181	98.53 28	99.39 21	89.66 60	99.69 80	98.43 33	99.61 55	99.61 77

abl_6		94.63 101	94.48 89	95.09 146	98.61 103	86.96 217	98.06 212	96.97 185	89.31 152	95.86 96	98.56 107	79.82 201	99.64 91	94.53 116	98.65 106	98.66 160

MVS_111021_HR		96.69 38	96.69 35	96.72 90	98.58 104	91.00 126	99.14 91	99.45 1	93.86 37	95.15 110	98.73 93	88.48 73	99.76 73	97.23 54	99.56 59	99.40 95

test_yl		95.27 82	94.60 87	97.28 56	98.53 105	92.98 83	99.05 101	98.70 17	86.76 225	94.65 118	97.74 137	87.78 84	99.44 117	95.57 92	92.61 182	99.44 93

DCV-MVSNet		95.27 82	94.60 87	97.28 56	98.53 105	92.98 83	99.05 101	98.70 17	86.76 225	94.65 118	97.74 137	87.78 84	99.44 117	95.57 92	92.61 182	99.44 93

TAPA-MVS	87.50 9	90.35 187	89.05 196	94.25 179	98.48 107	85.17 262	98.42 172	96.58 200	82.44 295	87.24 207	98.53 108	82.77 172	98.84 149	59.09 361	97.88 117	98.72 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.32 108	91.21 114	98.08 209	97.58 110	83.74 270	95.87 95	99.02 61	86.74 109			99.64 47	99.81 35

DPM-MVS		97.86 8	97.25 19	99.68 1	98.25 109	99.10 1	99.76 11	97.78 65	96.61 4	98.15 36	99.53 7	93.62 17	100.00 1	91.79 150	99.80 27	99.94 18

LFMVS		92.23 158	90.84 170	96.42 106	98.24 110	91.08 123	98.24 192	96.22 223	83.39 277	94.74 116	98.31 120	61.12 324	98.85 148	94.45 117	92.82 178	99.32 102

testdata				95.26 143	98.20 111	87.28 209		97.60 104	85.21 246	98.48 29	99.15 43	88.15 79	98.72 156	90.29 166	99.45 66	99.78 42

PatchMatch-RL		91.47 169	90.54 177	94.26 178	98.20 111	86.36 230	96.94 263	97.14 168	87.75 204	88.98 193	95.75 202	71.80 263	99.40 123	80.92 268	97.39 129	97.02 211

MVS_111021_LR		95.78 70	95.94 60	95.28 142	98.19 113	87.69 194	98.80 126	99.26 7	93.39 48	95.04 112	98.69 99	84.09 151	99.76 73	96.96 61	99.06 87	98.38 171

F-COLMAP		92.07 161	91.75 154	93.02 209	98.16 114	82.89 294	98.79 130	95.97 237	86.54 230	87.92 200	97.80 133	78.69 213	99.65 89	85.97 215	95.93 154	96.53 219

Anonymous202405211		88.84 215	87.03 232	94.27 177	98.14 115	84.18 277	98.44 170	95.58 272	76.79 335	89.34 191	96.88 177	53.42 348	99.54 101	87.53 202	87.12 225	99.09 121

VNet		95.08 87	94.26 93	97.55 46	98.07 116	93.88 62	98.68 140	98.73 16	90.33 122	97.16 63	97.43 151	79.19 208	99.53 102	96.91 63	91.85 196	99.24 109

CS-MVS-test		95.98 61	96.34 44	94.90 153	98.06 117	87.66 197	99.69 22	96.10 232	93.66 43	98.35 34	99.05 57	86.28 123	97.66 206	96.96 61	98.90 95	99.37 97

DELS-MVS		97.12 25	96.60 37	98.68 10	98.03 118	96.57 10	99.84 3	97.84 55	96.36 8	95.20 109	98.24 123	88.17 78	99.83 62	96.11 80	99.60 56	99.64 72
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
PVSNet	87.13 12	93.69 120	92.83 130	96.28 111	97.99 119	90.22 142	99.38 62	98.93 11	91.42 95	93.66 134	97.68 140	71.29 268	99.64 91	87.94 197	97.20 131	98.98 128

cl22		89.57 204	88.79 202	91.91 229	97.94 120	87.62 198	97.98 216	96.51 205	85.03 251	82.37 259	91.79 272	83.65 154	96.50 258	85.96 216	77.89 284	91.61 274

CS-MVS		95.75 73	96.19 48	94.40 171	97.88 121	86.22 235	99.66 23	96.12 231	92.69 62	98.07 42	98.89 83	87.09 100	97.59 212	96.71 64	98.62 107	99.39 96

CHOSEN 280x420		96.80 37	96.85 29	96.66 94	97.85 122	94.42 52	94.76 312	98.36 24	92.50 66	95.62 103	97.52 147	97.92 1	97.38 224	98.31 39	98.80 101	98.20 182

thres200		93.69 120	92.59 135	96.97 72	97.76 123	94.74 43	99.35 67	99.36 2	89.23 155	91.21 165	96.97 172	83.42 159	98.77 151	85.08 223	90.96 207	97.39 199

HY-MVS	88.56 7	95.29 81	94.23 94	98.48 13	97.72 124	96.41 12	94.03 320	98.74 14	92.42 71	95.65 102	94.76 221	86.52 117	99.49 109	95.29 98	92.97 177	99.53 83

Anonymous20231211		84.72 281	82.65 292	90.91 252	97.71 125	84.55 272	97.28 249	96.67 193	66.88 362	79.18 302	90.87 290	58.47 329	96.60 249	82.61 255	74.20 310	91.59 276

tfpn200view9		93.43 129	92.27 140	96.90 77	97.68 126	94.84 38	99.18 79	99.36 2	88.45 178	90.79 169	96.90 175	83.31 160	98.75 153	84.11 238	90.69 209	97.12 205

thres400		93.39 131	92.27 140	96.73 88	97.68 126	94.84 38	99.18 79	99.36 2	88.45 178	90.79 169	96.90 175	83.31 160	98.75 153	84.11 238	90.69 209	96.61 214

thres100view900		93.34 133	92.15 144	96.90 77	97.62 128	94.84 38	99.06 100	99.36 2	87.96 197	90.47 177	96.78 180	83.29 162	98.75 153	84.11 238	90.69 209	97.12 205

thres600view7		93.18 139	92.00 147	96.75 86	97.62 128	94.92 33	99.07 98	99.36 2	87.96 197	90.47 177	96.78 180	83.29 162	98.71 157	82.93 252	90.47 213	96.61 214

WTY-MVS		95.97 62	95.11 81	98.54 12	97.62 128	96.65 8	99.44 53	98.74 14	92.25 76	95.21 108	98.46 117	86.56 116	99.46 116	95.00 104	92.69 181	99.50 87

Anonymous20240529		87.66 241	85.58 254	93.92 191	97.59 131	85.01 265	98.13 200	97.13 170	66.69 363	88.47 197	96.01 200	55.09 342	99.51 106	87.00 205	84.12 246	97.23 204

HyFIR lowres test		93.68 122	93.29 118	94.87 154	97.57 132	88.04 189	98.18 197	98.47 22	87.57 210	91.24 164	95.05 214	85.49 135	97.46 219	93.22 136	92.82 178	99.10 120

test_part1		88.43 227	86.68 238	93.67 200	97.56 133	92.40 96	98.12 202	96.55 202	82.26 297	80.31 286	93.16 254	74.59 234	96.62 248	85.00 226	72.61 325	91.99 263

canonicalmvs		95.02 88	93.96 106	98.20 20	97.53 134	95.92 16	98.71 133	96.19 227	91.78 85	95.86 96	98.49 113	79.53 205	99.03 145	96.12 79	91.42 204	99.66 70

CHOSEN 1792x2688		94.35 106	93.82 110	95.95 123	97.40 135	88.74 177	98.41 174	98.27 26	92.18 78	91.43 159	96.40 190	78.88 209	99.81 67	93.59 130	97.81 118	99.30 104

SteuartSystems-ACMMP		97.25 18	97.34 17	97.01 65	97.38 136	91.46 110	99.75 12	97.66 88	94.14 30	98.13 37	99.26 26	92.16 29	99.66 85	97.91 46	99.64 47	99.90 24
Skip Steuart: Steuart Systems R&D Blog.
alignmvs		95.77 71	95.00 83	98.06 28	97.35 137	95.68 18	99.71 15	97.50 129	91.50 90	96.16 87	98.61 105	86.28 123	99.00 146	96.19 78	91.74 198	99.51 86

PS-MVSNAJ		96.87 35	96.40 41	98.29 18	97.35 137	97.29 5	99.03 104	97.11 172	95.83 10	98.97 14	99.14 45	82.48 179	99.60 96	98.60 25	99.08 86	98.00 186

EPNet_dtu		92.28 156	92.15 144	92.70 216	97.29 139	84.84 267	98.64 146	97.82 57	92.91 58	93.02 142	97.02 170	85.48 137	95.70 306	72.25 326	94.89 163	97.55 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSTER		92.71 145	92.32 138	93.86 193	97.29 139	92.95 86	99.01 107	96.59 197	90.09 131	85.51 223	94.00 232	94.61 16	96.56 253	90.77 162	83.03 260	92.08 259

EPMVS		92.59 150	91.59 156	95.59 133	97.22 141	90.03 151	91.78 338	98.04 42	90.42 119	91.66 154	90.65 298	86.49 119	97.46 219	81.78 263	96.31 145	99.28 106

tpmvs		89.16 207	87.76 218	93.35 203	97.19 142	84.75 269	90.58 348	97.36 150	81.99 300	84.56 229	89.31 326	83.98 152	98.17 172	74.85 310	90.00 215	97.12 205

DeepC-MVS	91.02 4	94.56 104	93.92 108	96.46 103	97.16 143	90.76 131	98.39 181	97.11 172	93.92 33	88.66 195	98.33 119	78.14 216	99.85 59	95.02 103	98.57 109	98.78 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
iter_conf05		93.48 126	93.18 121	94.39 174	97.15 144	94.17 58	99.30 72	92.97 337	92.38 75	86.70 217	95.42 208	95.67 5	96.59 250	94.67 112	84.32 245	92.39 243

PVSNet_Blended_VisFu		94.67 99	94.11 99	96.34 110	97.14 145	91.10 121	99.32 71	97.43 143	92.10 81	91.53 158	96.38 193	83.29 162	99.68 83	93.42 134	96.37 143	98.25 178

h-mvs33		92.47 153	91.95 149	94.05 187	97.13 146	85.01 265	98.36 183	98.08 39	93.85 38	96.27 85	96.73 182	83.19 165	99.43 119	95.81 85	68.09 343	97.70 191

miper_enhance_ethall		90.33 188	89.70 183	92.22 222	97.12 147	88.93 171	98.35 184	95.96 239	88.60 173	83.14 246	92.33 264	87.38 92	96.18 283	86.49 211	77.89 284	91.55 277

xiu_mvs_v2_base		96.66 39	96.17 52	98.11 27	97.11 148	96.96 6	99.01 107	97.04 179	95.51 16	98.86 17	99.11 53	82.19 185	99.36 126	98.59 27	98.14 115	98.00 186

VDD-MVS		91.24 175	90.18 180	94.45 170	97.08 149	85.84 249	98.40 177	96.10 232	86.99 217	93.36 137	98.16 127	54.27 345	99.20 135	96.59 69	90.63 212	98.31 177

UGNet		91.91 163	90.85 169	95.10 145	97.06 150	88.69 178	98.01 214	98.24 29	92.41 72	92.39 148	93.61 243	60.52 325	99.68 83	88.14 193	97.25 130	96.92 212
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
baseline1		92.61 149	91.28 161	96.58 97	97.05 151	94.63 47	97.72 233	96.20 225	89.82 137	88.56 196	96.85 178	86.85 106	97.82 193	88.42 189	80.10 275	97.30 201

iter_conf_final		93.22 138	93.04 125	93.76 196	97.03 152	92.22 98	99.05 101	93.31 334	92.11 80	86.93 211	95.42 208	95.01 10	96.59 250	93.98 121	84.48 242	92.46 242

CANet_DTU		94.31 107	93.35 116	97.20 60	97.03 152	94.71 45	98.62 148	95.54 274	95.61 14	97.21 61	98.47 115	71.88 261	99.84 60	88.38 190	97.46 128	97.04 210

MSDG		88.29 230	86.37 242	94.04 188	96.90 154	86.15 239	96.52 278	94.36 318	77.89 331	79.22 301	96.95 173	69.72 275	99.59 97	73.20 322	92.58 184	96.37 222

BH-w/o		92.32 154	91.79 152	93.91 192	96.85 155	86.18 237	99.11 96	95.74 262	88.13 192	84.81 226	97.00 171	77.26 221	97.91 186	89.16 185	98.03 116	97.64 192

AllTest		84.97 279	83.12 283	90.52 262	96.82 156	78.84 329	95.89 297	92.17 347	77.96 329	75.94 320	95.50 205	55.48 338	99.18 136	71.15 327	87.14 223	93.55 234

TestCases				90.52 262	96.82 156	78.84 329		92.17 347	77.96 329	75.94 320	95.50 205	55.48 338	99.18 136	71.15 327	87.14 223	93.55 234

PMMVS		93.62 125	93.90 109	92.79 213	96.79 158	81.40 310	98.85 121	96.81 189	91.25 99	96.82 76	98.15 128	77.02 222	98.13 174	93.15 138	96.30 146	98.83 145

BH-RMVSNet		91.25 174	89.99 181	95.03 150	96.75 159	88.55 180	98.65 144	94.95 300	87.74 205	87.74 201	97.80 133	68.27 284	98.14 173	80.53 272	97.49 127	98.41 168

MVS_Test		93.67 123	92.67 133	96.69 92	96.72 160	92.66 88	97.22 254	96.03 235	87.69 208	95.12 111	94.03 230	81.55 191	98.28 170	89.17 184	96.46 140	99.14 116

COLMAP_ROB	82.69 18	84.54 285	82.82 285	89.70 285	96.72 160	78.85 328	95.89 297	92.83 340	71.55 349	77.54 315	95.89 201	59.40 328	99.14 141	67.26 341	88.26 219	91.11 294
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		92.50 152	91.65 155	95.06 148	96.60 162	89.64 159	97.06 259	96.44 210	86.64 227	84.14 234	93.93 234	82.49 178	96.17 285	91.47 151	96.08 151	99.35 99

ETV-MVS		96.00 59	96.00 58	96.00 120	96.56 163	91.05 124	99.63 26	96.61 195	93.26 51	97.39 58	98.30 121	86.62 113	98.13 174	98.07 43	97.57 123	98.82 146

GG-mvs-BLEND				96.98 71	96.53 164	94.81 41	87.20 351	97.74 70		93.91 130	96.40 190	96.56 2	96.94 238	95.08 101	98.95 94	99.20 113

FMVSNet3		88.81 219	87.08 231	93.99 190	96.52 165	94.59 48	98.08 209	96.20 225	85.85 237	82.12 263	91.60 276	74.05 242	95.40 314	79.04 279	80.24 272	91.99 263

BH-untuned		91.46 170	90.84 170	93.33 204	96.51 166	84.83 268	98.84 123	95.50 276	86.44 233	83.50 238	96.70 183	75.49 228	97.77 197	86.78 210	97.81 118	97.40 198

sss		94.85 91	93.94 107	97.58 43	96.43 167	94.09 60	98.93 113	99.16 8	89.50 149	95.27 107	97.85 130	81.50 192	99.65 89	92.79 144	94.02 170	98.99 127

test2506		94.80 92	94.21 95	96.58 97	96.41 168	92.18 99	98.01 214	98.96 10	90.82 108	93.46 136	97.28 154	85.92 128	98.45 163	89.82 171	97.19 132	99.12 118

ECVR-MVS		92.29 155	91.33 160	95.15 144	96.41 168	87.84 192	98.10 206	94.84 303	90.82 108	91.42 161	97.28 154	65.61 306	98.49 162	90.33 165	97.19 132	99.12 118

ET-MVSNet_ETH3D		92.56 151	91.45 159	95.88 124	96.39 170	94.13 59	99.46 50	96.97 185	92.18 78	66.94 356	98.29 122	94.65 15	94.28 334	94.34 118	83.82 251	99.24 109

dp		90.16 194	88.83 201	94.14 182	96.38 171	86.42 226	91.57 339	97.06 178	84.76 257	88.81 194	90.19 316	84.29 149	97.43 222	75.05 307	91.35 206	98.56 162

EIA-MVS		95.11 85	95.27 77	94.64 164	96.34 172	86.51 223	99.59 30	96.62 194	92.51 65	94.08 127	98.64 101	86.05 127	98.24 171	95.07 102	98.50 111	99.18 114

TR-MVS		90.77 181	89.44 188	94.76 158	96.31 173	88.02 190	97.92 218	95.96 239	85.52 241	88.22 199	97.23 159	66.80 297	98.09 177	84.58 231	92.38 186	98.17 183

UA-Net		93.30 134	92.62 134	95.34 140	96.27 174	88.53 182	95.88 299	96.97 185	90.90 106	95.37 106	97.07 168	82.38 182	99.10 143	83.91 242	94.86 164	98.38 171

tpmrst		92.78 144	92.16 143	94.65 163	96.27 174	87.45 203	91.83 337	97.10 175	89.10 160	94.68 117	90.69 295	88.22 77	97.73 204	89.78 172	91.80 197	98.77 152

hse-mvs2		91.67 166	91.51 158	92.15 226	96.22 176	82.61 300	97.74 232	97.53 120	93.85 38	96.27 85	96.15 195	83.19 165	97.44 221	95.81 85	66.86 348	96.40 221

AUN-MVS		90.17 193	89.50 186	92.19 224	96.21 177	82.67 298	97.76 231	97.53 120	88.05 194	91.67 153	96.15 195	83.10 167	97.47 218	88.11 194	66.91 347	96.43 220

ADS-MVSNet2		87.62 242	86.88 234	89.86 280	96.21 177	79.14 327	87.15 352	92.99 336	83.01 282	89.91 185	87.27 338	78.87 210	92.80 346	74.20 314	92.27 189	97.64 192

ADS-MVSNet		88.99 209	87.30 227	94.07 185	96.21 177	87.56 200	87.15 352	96.78 191	83.01 282	89.91 185	87.27 338	78.87 210	97.01 235	74.20 314	92.27 189	97.64 192

PatchmatchNet		92.05 162	91.04 165	95.06 148	96.17 180	89.04 166	91.26 342	97.26 153	89.56 147	90.64 173	90.56 304	88.35 76	97.11 230	79.53 275	96.07 152	99.03 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test1111		92.12 159	91.19 163	94.94 152	96.15 181	87.36 206	98.12 202	94.84 303	90.85 107	90.97 167	97.26 156	65.60 307	98.37 165	89.74 174	97.14 135	99.07 124

gg-mvs-nofinetune		90.00 197	87.71 220	96.89 82	96.15 181	94.69 46	85.15 357	97.74 70	68.32 359	92.97 143	60.16 369	96.10 3	96.84 240	93.89 123	98.87 96	99.14 116

MDTV_nov1_ep13			90.47 179		96.14 183	88.55 180	91.34 341	97.51 126	89.58 145	92.24 149	90.50 308	86.99 105	97.61 211	77.64 289	92.34 187

IS-MVSNet		93.00 142	92.51 136	94.49 167	96.14 183	87.36 206	98.31 188	95.70 264	88.58 174	90.17 181	97.50 148	83.02 168	97.22 227	87.06 203	96.07 152	98.90 138

Vis-MVSNet (Re-imp)		93.26 137	93.00 128	94.06 186	96.14 183	86.71 222	98.68 140	96.70 192	88.30 186	89.71 189	97.64 143	85.43 138	96.39 266	88.06 195	96.32 144	99.08 122

thisisatest0515		94.75 94	94.19 96	96.43 105	96.13 186	92.64 92	99.47 45	97.60 104	87.55 211	93.17 139	97.59 145	94.71 13	98.42 164	88.28 191	93.20 174	98.24 179

ab-mvs		91.05 177	89.17 194	96.69 92	95.96 187	91.72 104	92.62 333	97.23 158	85.61 240	89.74 187	93.89 236	68.55 281	99.42 120	91.09 155	87.84 221	98.92 137

Fast-Effi-MVS+		91.72 165	90.79 173	94.49 167	95.89 188	87.40 205	99.54 39	95.70 264	85.01 253	89.28 192	95.68 203	77.75 218	97.57 216	83.22 247	95.06 162	98.51 164

EPP-MVSNet		93.75 119	93.67 112	94.01 189	95.86 189	85.70 251	98.67 142	97.66 88	84.46 260	91.36 162	97.18 163	91.16 31	97.79 195	92.93 140	93.75 171	98.53 163

Effi-MVS+		93.87 116	93.15 122	96.02 119	95.79 190	90.76 131	96.70 275	95.78 259	86.98 219	95.71 100	97.17 164	79.58 203	98.01 184	94.57 115	96.09 150	99.31 103

tpm cat1		88.89 213	87.27 228	93.76 196	95.79 190	85.32 259	90.76 346	97.09 176	76.14 337	85.72 221	88.59 329	82.92 169	98.04 182	76.96 293	91.43 203	97.90 189

thisisatest0530		94.00 111	93.52 114	95.43 137	95.76 192	90.02 152	98.99 109	97.60 104	86.58 228	91.74 152	97.36 153	94.78 12	98.34 166	86.37 212	92.48 185	97.94 188

3Dnovator+	87.72 8	93.43 129	91.84 151	98.17 21	95.73 193	95.08 31	98.92 115	97.04 179	91.42 95	81.48 277	97.60 144	74.60 232	99.79 70	90.84 160	98.97 91	99.64 72

MVS		93.92 113	92.28 139	98.83 6	95.69 194	96.82 7	96.22 289	98.17 33	84.89 255	84.34 232	98.61 105	79.32 207	99.83 62	93.88 124	99.43 68	99.86 32

cascas		90.93 179	89.33 192	95.76 128	95.69 194	93.03 82	98.99 109	96.59 197	80.49 315	86.79 216	94.45 225	65.23 309	98.60 161	93.52 131	92.18 191	95.66 227

QAPM		91.41 171	89.49 187	97.17 61	95.66 196	93.42 72	98.60 152	97.51 126	80.92 313	81.39 278	97.41 152	72.89 253	99.87 52	82.33 257	98.68 104	98.21 181

tttt0517		93.30 134	93.01 127	94.17 181	95.57 197	86.47 225	98.51 162	97.60 104	85.99 236	90.55 174	97.19 162	94.80 11	98.31 167	85.06 224	91.86 195	97.74 190

1112_ss		92.71 145	91.55 157	96.20 112	95.56 198	91.12 119	98.48 167	94.69 309	88.29 187	86.89 213	98.50 111	87.02 103	98.66 159	84.75 228	89.77 216	98.81 147

diffmvs		94.59 103	94.19 96	95.81 126	95.54 199	90.69 133	98.70 137	95.68 266	91.61 87	95.96 91	97.81 132	80.11 200	98.06 180	96.52 71	95.76 156	98.67 157

LCM-MVSNet-Re		88.59 224	88.61 206	88.51 307	95.53 200	72.68 354	96.85 267	88.43 370	88.45 178	73.14 336	90.63 299	75.82 225	94.38 333	92.95 139	95.71 158	98.48 166

Test_1112_low_res		92.27 157	90.97 166	96.18 113	95.53 200	91.10 121	98.47 169	94.66 310	88.28 188	86.83 215	93.50 247	87.00 104	98.65 160	84.69 229	89.74 217	98.80 148

PCF-MVS	89.78 5	91.26 172	89.63 184	96.16 116	95.44 202	91.58 109	95.29 308	96.10 232	85.07 250	82.75 249	97.45 150	78.28 215	99.78 71	80.60 271	95.65 159	97.12 205
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DROMVSNet		95.09 86	95.17 79	94.84 156	95.42 203	88.17 185	99.48 43	95.92 245	91.47 91	97.34 60	98.36 118	82.77 172	97.41 223	97.24 53	98.58 108	98.94 135

3Dnovator	87.35 11	93.17 140	91.77 153	97.37 54	95.41 204	93.07 80	98.82 124	97.85 54	91.53 89	82.56 253	97.58 146	71.97 260	99.82 65	91.01 157	99.23 82	99.22 112

IB-MVS	89.43 6	92.12 159	90.83 172	95.98 122	95.40 205	90.78 130	99.81 5	98.06 40	91.23 100	85.63 222	93.66 242	90.63 41	98.78 150	91.22 153	71.85 333	98.36 174
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
miper_ehance_all_eth		88.94 211	88.12 217	91.40 240	95.32 206	86.93 218	97.85 224	95.55 273	84.19 263	81.97 268	91.50 278	84.16 150	95.91 299	84.69 229	77.89 284	91.36 285

1314		93.44 128	91.98 148	97.84 33	95.24 207	94.38 53	96.22 289	97.92 49	90.18 127	82.28 260	97.71 139	77.63 219	99.80 69	91.94 149	98.67 105	99.34 101

XVG-OURS		90.83 180	90.49 178	91.86 230	95.23 208	81.25 314	95.79 304	95.92 245	88.96 163	90.02 184	98.03 129	71.60 265	99.35 130	91.06 156	87.78 222	94.98 228

TESTMET0.1,1		93.82 117	93.26 119	95.49 134	95.21 209	90.25 140	99.15 88	97.54 119	89.18 157	91.79 151	94.87 217	89.13 63	97.63 209	86.21 213	96.29 147	98.60 161

xiu_mvs_v1_base_debu		94.73 95	93.98 103	96.99 68	95.19 210	95.24 24	98.62 148	96.50 206	92.99 55	97.52 54	98.83 86	72.37 256	99.15 138	97.03 56	96.74 137	96.58 216

xiu_mvs_v1_base		94.73 95	93.98 103	96.99 68	95.19 210	95.24 24	98.62 148	96.50 206	92.99 55	97.52 54	98.83 86	72.37 256	99.15 138	97.03 56	96.74 137	96.58 216

xiu_mvs_v1_base_debi		94.73 95	93.98 103	96.99 68	95.19 210	95.24 24	98.62 148	96.50 206	92.99 55	97.52 54	98.83 86	72.37 256	99.15 138	97.03 56	96.74 137	96.58 216

XVG-OURS-SEG-HR		90.95 178	90.66 176	91.83 231	95.18 213	81.14 317	95.92 296	95.92 245	88.40 183	90.33 180	97.85 130	70.66 272	99.38 124	92.83 142	88.83 218	94.98 228

Effi-MVS+-dtu		89.97 199	90.68 175	87.81 312	95.15 214	71.98 356	97.87 222	95.40 284	91.92 82	87.57 202	91.44 279	74.27 239	96.84 240	89.45 176	93.10 176	94.60 230

mvs-test1		91.57 167	92.20 142	89.70 285	95.15 214	74.34 346	99.51 41	95.40 284	91.92 82	91.02 166	97.25 157	74.27 239	98.08 179	89.45 176	95.83 155	96.67 213

Vis-MVSNet		92.64 147	91.85 150	95.03 150	95.12 216	88.23 184	98.48 167	96.81 189	91.61 87	92.16 150	97.22 160	71.58 266	98.00 185	85.85 220	97.81 118	98.88 139
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		86.67 254	84.96 261	91.80 233	95.11 217	88.81 174	96.77 269	95.25 291	82.94 284	82.12 263	90.25 311	62.89 316	94.97 321	79.04 279	80.24 272	91.62 271

test1		86.67 254	84.96 261	91.80 233	95.11 217	88.81 174	96.77 269	95.25 291	82.94 284	82.12 263	90.25 311	62.89 316	94.97 321	79.04 279	80.24 272	91.62 271

FMVSNet2		86.90 249	84.79 267	93.24 205	95.11 217	92.54 94	97.67 236	95.86 257	82.94 284	80.55 283	91.17 285	62.89 316	95.29 316	77.23 290	79.71 278	91.90 265

GeoE		90.60 186	89.56 185	93.72 199	95.10 220	85.43 256	99.41 59	94.94 301	83.96 268	87.21 208	96.83 179	74.37 237	97.05 234	80.50 273	93.73 172	98.67 157

baseline		93.91 114	93.30 117	95.72 129	95.10 220	90.07 147	97.48 241	95.91 250	91.03 102	93.54 135	97.68 140	79.58 203	98.02 183	94.27 119	95.14 161	99.08 122

casdiffmvs		93.98 112	93.43 115	95.61 132	95.07 222	89.86 155	98.80 126	95.84 258	90.98 104	92.74 144	97.66 142	79.71 202	98.10 176	94.72 110	95.37 160	98.87 141

MVSFormer		94.71 98	94.08 101	96.61 95	95.05 223	94.87 36	97.77 229	96.17 228	86.84 222	98.04 44	98.52 109	85.52 132	95.99 292	89.83 169	98.97 91	98.96 130

lupinMVS		96.32 51	95.94 60	97.44 48	95.05 223	94.87 36	99.86 2	96.50 206	93.82 40	98.04 44	98.77 89	85.52 132	98.09 177	96.98 60	98.97 91	99.37 97

CostFormer		92.89 143	92.48 137	94.12 183	94.99 225	85.89 246	92.89 329	97.00 184	86.98 219	95.00 113	90.78 291	90.05 53	97.51 217	92.92 141	91.73 199	98.96 130

c3_l		88.19 232	87.23 229	91.06 248	94.97 226	86.17 238	97.72 233	95.38 286	83.43 276	81.68 275	91.37 280	82.81 171	95.72 305	84.04 241	73.70 314	91.29 289

SCA		90.64 185	89.25 193	94.83 157	94.95 227	88.83 173	96.26 286	97.21 160	90.06 134	90.03 183	90.62 300	66.61 298	96.81 242	83.16 248	94.36 167	98.84 142

test-LLR		93.11 141	92.68 132	94.40 171	94.94 228	87.27 210	99.15 88	97.25 154	90.21 124	91.57 155	94.04 228	84.89 143	97.58 213	85.94 217	96.13 148	98.36 174

test-mter		93.27 136	92.89 129	94.40 171	94.94 228	87.27 210	99.15 88	97.25 154	88.95 164	91.57 155	94.04 228	88.03 82	97.58 213	85.94 217	96.13 148	98.36 174

cl____		87.82 234	86.79 236	90.89 254	94.88 230	85.43 256	97.81 225	95.24 294	82.91 288	80.71 282	91.22 283	81.97 188	95.84 301	81.34 265	75.06 298	91.40 284

DIV-MVS_self_test		87.82 234	86.81 235	90.87 255	94.87 231	85.39 258	97.81 225	95.22 298	82.92 287	80.76 281	91.31 282	81.99 186	95.81 303	81.36 264	75.04 299	91.42 283

tpm2		91.77 164	91.09 164	93.82 195	94.83 232	85.56 255	92.51 334	97.16 167	84.00 266	93.83 132	90.66 297	87.54 89	97.17 228	87.73 199	91.55 202	98.72 154

PVSNet_0	83.28 16	87.31 245	85.16 259	93.74 198	94.78 233	84.59 271	98.91 116	98.69 19	89.81 138	78.59 308	93.23 251	61.95 320	99.34 131	94.75 108	55.72 365	97.30 201

CDS-MVSNet		93.47 127	93.04 125	94.76 158	94.75 234	89.45 162	98.82 124	97.03 181	87.91 199	90.97 167	96.48 188	89.06 64	96.36 268	89.50 175	92.81 180	98.49 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gm-plane-assit					94.69 235	88.14 186			88.22 190		97.20 161		98.29 169	90.79 161

eth_miper_zixun_eth		87.76 237	87.00 233	90.06 274	94.67 236	82.65 299	97.02 262	95.37 287	84.19 263	81.86 273	91.58 277	81.47 193	95.90 300	83.24 246	73.61 315	91.61 274

RPSCF		85.33 276	85.55 255	84.67 332	94.63 237	62.28 368	93.73 322	93.76 326	74.38 343	85.23 225	97.06 169	64.09 312	98.31 167	80.98 266	86.08 232	93.41 236

miper_lstm_enhance		86.90 249	86.20 245	89.00 301	94.53 238	81.19 315	96.74 273	95.24 294	82.33 296	80.15 289	90.51 307	81.99 186	94.68 330	80.71 270	73.58 316	91.12 293

Patchmatch-test		86.25 262	84.06 277	92.82 212	94.42 239	82.88 295	82.88 366	94.23 320	71.58 348	79.39 299	90.62 300	89.00 66	96.42 265	63.03 353	91.37 205	99.16 115

VDDNet		90.08 196	88.54 211	94.69 162	94.41 240	87.68 195	98.21 195	96.40 211	76.21 336	93.33 138	97.75 136	54.93 343	98.77 151	94.71 111	90.96 207	97.61 196

KD-MVS_2432*1600		82.98 298	80.52 306	90.38 266	94.32 241	88.98 168	92.87 330	95.87 255	80.46 316	73.79 332	87.49 335	82.76 174	93.29 340	70.56 331	46.53 370	88.87 338

miper_refine_blended		82.98 298	80.52 306	90.38 266	94.32 241	88.98 168	92.87 330	95.87 255	80.46 316	73.79 332	87.49 335	82.76 174	93.29 340	70.56 331	46.53 370	88.87 338

EI-MVSNet		89.87 200	89.38 191	91.36 242	94.32 241	85.87 247	97.61 238	96.59 197	85.10 248	85.51 223	97.10 166	81.30 196	96.56 253	83.85 244	83.03 260	91.64 269

CVMVSNet		90.30 189	90.91 168	88.46 308	94.32 241	73.58 350	97.61 238	97.59 108	90.16 130	88.43 198	97.10 166	76.83 223	92.86 343	82.64 254	93.54 173	98.93 136

IterMVS-LS		88.34 228	87.44 224	91.04 249	94.10 245	85.85 248	98.10 206	95.48 277	85.12 247	82.03 267	91.21 284	81.35 195	95.63 308	83.86 243	75.73 295	91.63 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		92.62 148	92.09 146	94.20 180	94.10 245	87.68 195	98.41 174	96.97 185	87.53 212	89.74 187	96.04 199	84.77 146	96.49 260	88.97 186	92.31 188	98.42 167

PAPM		96.35 49	95.94 60	97.58 43	94.10 245	95.25 23	98.93 113	98.17 33	94.26 25	93.94 129	98.72 95	89.68 59	97.88 189	96.36 75	99.29 78	99.62 76

CLD-MVS		91.06 176	90.71 174	92.10 227	94.05 248	86.10 240	99.55 35	96.29 220	94.16 28	84.70 228	97.17 164	69.62 276	97.82 193	94.74 109	86.08 232	92.39 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					93.95 249		99.16 82		93.92 33	87.57 202

ACMP_Plane					93.95 249		99.16 82		93.92 33	87.57 202

HQP-MVS		91.50 168	91.23 162	92.29 221	93.95 249	86.39 228	99.16 82	96.37 213	93.92 33	87.57 202	96.67 184	73.34 246	97.77 197	93.82 127	86.29 227	92.72 237

NP-MVS					93.94 252	86.22 235					96.67 184

plane_prior6					93.92 253	86.02 244						72.92 251

ACMP	87.39 10	88.71 223	88.24 215	90.12 273	93.91 254	81.06 318	98.50 164	95.67 267	89.43 150	80.37 285	95.55 204	65.67 304	97.83 192	90.55 163	84.51 240	91.47 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior1					93.90 255

HQP_MVS		91.26 172	90.95 167	92.16 225	93.84 256	86.07 242	99.02 105	96.30 217	93.38 49	86.99 209	96.52 186	72.92 251	97.75 202	93.46 132	86.17 230	92.67 239

plane_prior7					93.84 256	85.73 250

MVS-HIRNet		79.01 316	75.13 326	90.66 259	93.82 258	81.69 307	85.16 356	93.75 327	54.54 367	74.17 330	59.15 371	57.46 332	96.58 252	63.74 350	94.38 166	93.72 233

FMVSNet5		82.29 301	80.54 305	87.52 314	93.79 259	84.01 279	93.73 322	92.47 343	76.92 334	74.27 329	86.15 347	63.69 315	89.24 363	69.07 335	74.79 302	89.29 333

ACMH+	83.78 15	84.21 289	82.56 294	89.15 298	93.73 260	79.16 326	96.43 279	94.28 319	81.09 310	74.00 331	94.03 230	54.58 344	97.67 205	76.10 301	78.81 280	90.63 310

ACMM	86.95 13	88.77 221	88.22 216	90.43 264	93.61 261	81.34 312	98.50 164	95.92 245	87.88 200	83.85 237	95.20 212	67.20 294	97.89 188	86.90 208	84.90 238	92.06 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	85.28 14	90.75 182	88.84 200	96.48 102	93.58 262	93.51 70	98.80 126	97.41 145	82.59 290	78.62 306	97.49 149	68.00 287	99.82 65	84.52 232	98.55 110	96.11 224

IterMVS		85.81 269	84.67 269	89.22 296	93.51 263	83.67 284	96.32 283	94.80 305	85.09 249	78.69 304	90.17 317	66.57 300	93.17 342	79.48 277	77.42 290	90.81 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		88.83 217	87.38 226	93.16 207	93.47 264	86.24 233	84.97 359	94.20 321	88.92 167	90.76 171	86.88 342	84.43 147	94.82 326	70.64 330	92.17 192	98.41 168

RPMNet		85.07 278	81.88 295	94.64 164	93.47 264	86.24 233	84.97 359	97.21 160	64.85 365	90.76 171	78.80 363	80.95 197	99.27 134	53.76 366	92.17 192	98.41 168

IterMVS-SCA-FT		85.73 272	84.64 270	89.00 301	93.46 266	82.90 293	96.27 284	94.70 308	85.02 252	78.62 306	90.35 309	66.61 298	93.33 339	79.38 278	77.36 291	90.76 304

Fast-Effi-MVS+-dtu		88.84 215	88.59 208	89.58 289	93.44 267	78.18 334	98.65 144	94.62 311	88.46 177	84.12 235	95.37 211	68.91 278	96.52 256	82.06 260	91.70 200	94.06 231

Patchmtry		83.61 297	81.64 297	89.50 291	93.36 268	82.84 296	84.10 362	94.20 321	69.47 356	79.57 297	86.88 342	84.43 147	94.78 327	68.48 338	74.30 308	90.88 299

LPG-MVS_test		88.86 214	88.47 212	90.06 274	93.35 269	80.95 319	98.22 193	95.94 242	87.73 206	83.17 244	96.11 197	66.28 301	97.77 197	90.19 167	85.19 236	91.46 280

LGP-MVS_train				90.06 274	93.35 269	80.95 319		95.94 242	87.73 206	83.17 244	96.11 197	66.28 301	97.77 197	90.19 167	85.19 236	91.46 280

JIA-IIPM		85.97 265	84.85 265	89.33 295	93.23 271	73.68 349	85.05 358	97.13 170	69.62 355	91.56 157	68.03 367	88.03 82	96.96 236	77.89 288	93.12 175	97.34 200

ACMH	83.09 17	84.60 283	82.61 293	90.57 260	93.18 272	82.94 291	96.27 284	94.92 302	81.01 311	72.61 342	93.61 243	56.54 334	97.79 195	74.31 313	81.07 271	90.99 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchT		85.44 275	83.19 282	92.22 222	93.13 273	83.00 290	83.80 365	96.37 213	70.62 350	90.55 174	79.63 362	84.81 145	94.87 324	58.18 363	91.59 201	98.79 149

MVS_0304		84.13 292	82.66 291	88.52 306	93.07 274	80.15 322	95.81 303	98.21 31	79.27 320	86.85 214	86.40 345	41.33 367	94.69 329	76.36 299	86.69 226	90.73 306

baseline2		94.04 110	93.80 111	94.74 160	93.07 274	90.25 140	98.12 202	98.16 35	89.86 135	86.53 218	96.95 173	95.56 6	98.05 181	91.44 152	94.53 165	95.93 225

jason		95.40 80	94.86 84	97.03 64	92.91 276	94.23 55	99.70 16	96.30 217	93.56 47	96.73 78	98.52 109	81.46 194	97.91 186	96.08 81	98.47 112	98.96 130
jason: jason.
LTVRE_ROB	81.71 19	84.59 284	82.72 290	90.18 270	92.89 277	83.18 289	93.15 327	94.74 306	78.99 322	75.14 327	92.69 260	65.64 305	97.63 209	69.46 334	81.82 269	89.74 326
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
VPA-MVSNet		89.10 208	87.66 221	93.45 202	92.56 278	91.02 125	97.97 217	98.32 25	86.92 221	86.03 220	92.01 268	68.84 280	97.10 232	90.92 158	75.34 296	92.23 250

tpm		89.67 202	88.95 198	91.82 232	92.54 279	81.43 309	92.95 328	95.92 245	87.81 201	90.50 176	89.44 323	84.99 141	95.65 307	83.67 245	82.71 263	98.38 171

GA-MVS		90.10 195	88.69 204	94.33 175	92.44 280	87.97 191	99.08 97	96.26 221	89.65 141	86.92 212	93.11 255	68.09 285	96.96 236	82.54 256	90.15 214	98.05 184

FIs		90.70 183	89.87 182	93.18 206	92.29 281	91.12 119	98.17 199	98.25 27	89.11 159	83.44 239	94.82 219	82.26 183	96.17 285	87.76 198	82.76 262	92.25 248

ITE_SJBPF				87.93 310	92.26 282	76.44 340		93.47 333	87.67 209	79.95 292	95.49 207	56.50 335	97.38 224	75.24 306	82.33 266	89.98 324

UniMVSNet (Re)		89.50 206	88.32 214	93.03 208	92.21 283	90.96 127	98.90 117	98.39 23	89.13 158	83.22 241	92.03 266	81.69 190	96.34 274	86.79 209	72.53 326	91.81 266

UniMVSNet_NR-MVSNet		89.60 203	88.55 210	92.75 215	92.17 284	90.07 147	98.74 132	98.15 36	88.37 184	83.21 242	93.98 233	82.86 170	95.93 296	86.95 206	72.47 327	92.25 248

TinyColmap		80.42 310	77.94 314	87.85 311	92.09 285	78.58 331	93.74 321	89.94 364	74.99 339	69.77 346	91.78 273	46.09 361	97.58 213	65.17 349	77.89 284	87.38 346

MS-PatchMatch		86.75 252	85.92 249	89.22 296	91.97 286	82.47 301	96.91 264	96.14 230	83.74 270	77.73 313	93.53 246	58.19 330	97.37 226	76.75 296	98.35 113	87.84 343

VPNet		88.30 229	86.57 239	93.49 201	91.95 287	91.35 112	98.18 197	97.20 164	88.61 172	84.52 231	94.89 216	62.21 319	96.76 245	89.34 180	72.26 330	92.36 245

FMVSNet1		83.94 294	81.32 302	91.80 233	91.94 288	88.81 174	96.77 269	95.25 291	77.98 327	78.25 311	90.25 311	50.37 355	94.97 321	73.27 321	77.81 288	91.62 271

WR-MVS		88.54 225	87.22 230	92.52 219	91.93 289	89.50 161	98.56 157	97.84 55	86.99 217	81.87 271	93.81 237	74.25 241	95.92 298	85.29 221	74.43 306	92.12 256

D2MVS		87.96 233	87.39 225	89.70 285	91.84 290	83.40 286	98.31 188	98.49 21	88.04 195	78.23 312	90.26 310	73.57 244	96.79 244	84.21 235	83.53 255	88.90 337

FC-MVSNet-test		90.22 191	89.40 190	92.67 218	91.78 291	89.86 155	97.89 219	98.22 30	88.81 169	82.96 247	94.66 222	81.90 189	95.96 294	85.89 219	82.52 265	92.20 253

MIMVSNet		84.48 286	81.83 296	92.42 220	91.73 292	87.36 206	85.52 355	94.42 316	81.40 306	81.91 269	87.58 333	51.92 351	92.81 345	73.84 317	88.15 220	97.08 209

USDC		84.74 280	82.93 284	90.16 271	91.73 292	83.54 285	95.00 310	93.30 335	88.77 170	73.19 335	93.30 249	53.62 347	97.65 208	75.88 303	81.54 270	89.30 332

nrg030		90.23 190	88.87 199	94.32 176	91.53 294	93.54 69	98.79 130	95.89 253	88.12 193	84.55 230	94.61 223	78.80 212	96.88 239	92.35 147	75.21 297	92.53 241

DU-MVS		88.83 217	87.51 223	92.79 213	91.46 295	90.07 147	98.71 133	97.62 101	88.87 168	83.21 242	93.68 240	74.63 230	95.93 296	86.95 206	72.47 327	92.36 245

NR-MVSNet		87.74 240	86.00 248	92.96 210	91.46 295	90.68 134	96.65 276	97.42 144	88.02 196	73.42 334	93.68 240	77.31 220	95.83 302	84.26 234	71.82 334	92.36 245

tfpnnormal		83.65 295	81.35 301	90.56 261	91.37 297	88.06 188	97.29 248	97.87 53	78.51 326	76.20 317	90.91 288	64.78 310	96.47 261	61.71 356	73.50 317	87.13 351

test_0402		78.81 318	76.33 322	86.26 322	91.18 298	78.44 333	95.88 299	91.34 358	68.55 357	70.51 345	89.91 318	52.65 350	94.99 320	47.14 369	79.78 277	85.34 360

test0.0.03 1		88.96 210	88.61 206	90.03 277	91.09 299	84.43 273	98.97 111	97.02 182	90.21 124	80.29 287	96.31 194	84.89 143	91.93 357	72.98 323	85.70 235	93.73 232

WR-MVS_H		86.53 258	85.49 256	89.66 288	91.04 300	83.31 288	97.53 240	98.20 32	84.95 254	79.64 295	90.90 289	78.01 217	95.33 315	76.29 300	72.81 322	90.35 314

CP-MVSNet		86.54 257	85.45 257	89.79 283	91.02 301	82.78 297	97.38 244	97.56 115	85.37 244	79.53 298	93.03 256	71.86 262	95.25 317	79.92 274	73.43 320	91.34 286

TranMVSNet+NR-MVSNet		87.75 238	86.31 243	92.07 228	90.81 302	88.56 179	98.33 185	97.18 165	87.76 203	81.87 271	93.90 235	72.45 255	95.43 312	83.13 250	71.30 337	92.23 250

PS-CasMVS		85.81 269	84.58 271	89.49 293	90.77 303	82.11 304	97.20 255	97.36 150	84.83 256	79.12 303	92.84 259	67.42 292	95.16 319	78.39 286	73.25 321	91.21 291

DeepMVS_CX				76.08 348	90.74 304	51.65 376		90.84 360	86.47 232	57.89 365	87.98 330	35.88 371	92.60 347	65.77 347	65.06 351	83.97 362

mvsmamba		89.99 198	89.42 189	91.69 238	90.64 305	86.34 231	98.40 177	92.27 345	91.01 103	84.80 227	94.93 215	76.12 224	96.51 257	92.81 143	83.84 248	92.21 252

OPM-MVS		89.76 201	89.15 195	91.57 239	90.53 306	85.58 254	98.11 205	95.93 244	92.88 60	86.05 219	96.47 189	67.06 296	97.87 190	89.29 183	86.08 232	91.26 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XXY-MVS		87.75 238	86.02 247	92.95 211	90.46 307	89.70 158	97.71 235	95.90 251	84.02 265	80.95 279	94.05 227	67.51 291	97.10 232	85.16 222	78.41 281	92.04 262

UniMVSNet_ETH3D		85.65 274	83.79 280	91.21 243	90.41 308	80.75 321	95.36 307	95.78 259	78.76 325	81.83 274	94.33 226	49.86 356	96.66 246	84.30 233	83.52 256	96.22 223

RRT_MVS		88.91 212	88.56 209	89.93 278	90.31 309	81.61 308	98.08 209	96.38 212	89.30 153	82.41 257	94.84 218	73.15 249	96.04 291	90.38 164	82.23 267	92.15 254

bld_raw_conf005		88.44 226	87.56 222	91.09 247	90.18 310	84.69 270	97.81 225	90.17 363	90.20 126	82.77 248	94.81 220	67.23 293	96.46 263	91.13 154	83.71 253	92.11 258

v10		85.73 272	84.01 278	90.87 255	90.03 311	86.73 221	97.20 255	95.22 298	81.25 308	79.85 294	89.75 320	73.30 248	96.28 280	76.87 294	72.64 324	89.61 329

v8		86.11 263	84.45 272	91.10 246	89.99 312	86.85 219	97.24 252	95.36 288	81.99 300	79.89 293	89.86 319	74.53 235	96.39 266	78.83 283	72.32 329	90.05 322

V42		87.00 248	85.68 253	90.98 251	89.91 313	86.08 241	98.32 187	95.61 270	83.67 273	82.72 250	90.67 296	74.00 243	96.53 255	81.94 262	74.28 309	90.32 315

XVG-ACMP-BASELINE		85.86 267	84.95 263	88.57 305	89.90 314	77.12 339	94.30 316	95.60 271	87.40 214	82.12 263	92.99 258	53.42 348	97.66 206	85.02 225	83.83 249	90.92 298

PEN-MVS		85.21 277	83.93 279	89.07 300	89.89 315	81.31 313	97.09 258	97.24 156	84.45 261	78.66 305	92.68 261	68.44 283	94.87 324	75.98 302	70.92 338	91.04 295

test_low_dy_conf_001		88.79 220	88.33 213	90.16 271	89.83 316	82.22 302	97.87 222	96.22 223	88.25 189	84.24 233	95.09 213	71.11 269	96.19 282	88.63 187	83.76 252	92.06 260

v1144		86.83 251	85.31 258	91.40 240	89.75 317	87.21 215	98.31 188	95.45 279	83.22 279	82.70 251	90.78 291	73.36 245	96.36 268	79.49 276	74.69 303	90.63 310

TransMVSNet (Re)		81.97 303	79.61 311	89.08 299	89.70 318	84.01 279	97.26 250	91.85 353	78.84 323	73.07 339	91.62 275	67.17 295	95.21 318	67.50 340	59.46 360	88.02 342

v2v482		87.27 246	85.76 251	91.78 237	89.59 319	87.58 199	98.56 157	95.54 274	84.53 259	82.51 254	91.78 273	73.11 250	96.47 261	82.07 259	74.14 312	91.30 288

pm-mvs1		84.68 282	82.78 288	90.40 265	89.58 320	85.18 261	97.31 247	94.73 307	81.93 302	76.05 319	92.01 268	65.48 308	96.11 288	78.75 284	69.14 340	89.91 325

pmmvs4		87.58 243	86.17 246	91.80 233	89.58 320	88.92 172	97.25 251	95.28 290	82.54 292	80.49 284	93.17 253	75.62 227	96.05 290	82.75 253	78.90 279	90.42 313

bld_raw_dy_0_64		87.82 234	86.71 237	91.15 245	89.54 322	85.61 252	97.37 245	89.16 368	89.26 154	83.42 240	94.50 224	65.79 303	96.18 283	88.00 196	83.37 257	91.67 268

v1192		86.32 261	84.71 268	91.17 244	89.53 323	86.40 227	98.13 200	95.44 282	82.52 293	82.42 256	90.62 300	71.58 266	96.33 275	77.23 290	74.88 300	90.79 302

v144192		86.40 259	84.89 264	90.91 252	89.48 324	85.59 253	98.21 195	95.43 283	82.45 294	82.62 252	90.58 303	72.79 254	96.36 268	78.45 285	74.04 313	90.79 302

v148		86.38 260	85.06 260	90.37 268	89.47 325	84.10 278	98.52 159	95.48 277	83.80 269	80.93 280	90.22 314	74.60 232	96.31 276	80.92 268	71.55 335	90.69 308

v1921920		86.02 264	84.44 273	90.77 257	89.32 326	85.20 260	98.10 206	95.35 289	82.19 298	82.25 261	90.71 293	70.73 270	96.30 279	76.85 295	74.49 305	90.80 301

v1240		85.77 271	84.11 276	90.73 258	89.26 327	85.15 263	97.88 221	95.23 297	81.89 303	82.16 262	90.55 305	69.60 277	96.31 276	75.59 305	74.87 301	90.72 307

our_test_3		84.47 287	82.80 286	89.50 291	89.01 328	83.90 281	97.03 260	94.56 312	81.33 307	75.36 326	90.52 306	71.69 264	94.54 332	68.81 336	76.84 292	90.07 320

ppachtmachnet_test		83.63 296	81.57 299	89.80 282	89.01 328	85.09 264	97.13 257	94.50 313	78.84 323	76.14 318	91.00 287	69.78 274	94.61 331	63.40 351	74.36 307	89.71 328

DTE-MVSNet		84.14 291	82.80 286	88.14 309	88.95 330	79.87 325	96.81 268	96.24 222	83.50 275	77.60 314	92.52 263	67.89 289	94.24 335	72.64 325	69.05 341	90.32 315

PS-MVSNAJss		89.54 205	89.05 196	91.00 250	88.77 331	84.36 274	97.39 242	95.97 237	88.47 175	81.88 270	93.80 238	82.48 179	96.50 258	89.34 180	83.34 259	92.15 254

Baseline_NR-MVSNet		85.83 268	84.82 266	88.87 304	88.73 332	83.34 287	98.63 147	91.66 354	80.41 318	82.44 255	91.35 281	74.63 230	95.42 313	84.13 237	71.39 336	87.84 343

MVP-Stereo		86.61 256	85.83 250	88.93 303	88.70 333	83.85 282	96.07 294	94.41 317	82.15 299	75.64 324	91.96 270	67.65 290	96.45 264	77.20 292	98.72 103	86.51 354
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		84.19 290	84.42 274	83.52 337	88.64 334	67.37 366	96.04 295	95.76 261	85.29 245	78.44 309	93.18 252	70.67 271	91.48 359	75.79 304	75.98 293	91.70 267

pmmvs5		85.87 266	84.40 275	90.30 269	88.53 335	84.23 275	98.60 152	93.71 328	81.53 305	80.29 287	92.02 267	64.51 311	95.52 310	82.04 261	78.34 282	91.15 292

MDA-MVSNet-bldmvs		77.82 324	74.75 328	87.03 318	88.33 336	78.52 332	96.34 282	92.85 339	75.57 338	48.87 369	87.89 331	57.32 333	92.49 351	60.79 357	64.80 352	90.08 319

N_pmnet		70.19 332	69.87 334	71.12 351	88.24 337	30.63 384	95.85 302	28.70 384	70.18 353	68.73 348	86.55 344	64.04 313	93.81 336	53.12 367	73.46 318	88.94 336

v7n		84.42 288	82.75 289	89.43 294	88.15 338	81.86 305	96.75 272	95.67 267	80.53 314	78.38 310	89.43 324	69.89 273	96.35 273	73.83 318	72.13 331	90.07 320

SixPastTwentyTwo		82.63 300	81.58 298	85.79 325	88.12 339	71.01 359	95.17 309	92.54 342	84.33 262	72.93 340	92.08 265	60.41 326	95.61 309	74.47 312	74.15 311	90.75 305

test_djsdf		88.26 231	87.73 219	89.84 281	88.05 340	82.21 303	97.77 229	96.17 228	86.84 222	82.41 257	91.95 271	72.07 259	95.99 292	89.83 169	84.50 241	91.32 287

mvs_tets		87.09 247	86.22 244	89.71 284	87.87 341	81.39 311	96.73 274	95.90 251	88.19 191	79.99 291	93.61 243	59.96 327	96.31 276	89.40 179	84.34 244	91.43 282

OurMVSNet-221017-0		84.13 292	83.59 281	85.77 326	87.81 342	70.24 360	94.89 311	93.65 330	86.08 235	76.53 316	93.28 250	61.41 322	96.14 287	80.95 267	77.69 289	90.93 297

YYNet1		79.64 315	77.04 319	87.43 316	87.80 343	79.98 324	96.23 288	94.44 314	73.83 345	51.83 366	87.53 334	67.96 288	92.07 356	66.00 346	67.75 346	90.23 317

MDA-MVSNet_test_wron		79.65 314	77.05 318	87.45 315	87.79 344	80.13 323	96.25 287	94.44 314	73.87 344	51.80 367	87.47 337	68.04 286	92.12 355	66.02 345	67.79 345	90.09 318

jajsoiax		87.35 244	86.51 241	89.87 279	87.75 345	81.74 306	97.03 260	95.98 236	88.47 175	80.15 289	93.80 238	61.47 321	96.36 268	89.44 178	84.47 243	91.50 278

K. test v3		81.04 307	79.77 310	84.83 330	87.41 346	70.23 361	95.60 306	93.93 325	83.70 272	67.51 354	89.35 325	55.76 336	93.58 338	76.67 297	68.03 344	90.67 309

testgi		82.29 301	81.00 304	86.17 323	87.24 347	74.84 345	97.39 242	91.62 355	88.63 171	75.85 323	95.42 208	46.07 362	91.55 358	66.87 344	79.94 276	92.12 256

LF4IMVS		81.94 304	81.17 303	84.25 334	87.23 348	68.87 365	93.35 326	91.93 352	83.35 278	75.40 325	93.00 257	49.25 359	96.65 247	78.88 282	78.11 283	87.22 350

EG-PatchMatch MVS		79.92 311	77.59 315	86.90 319	87.06 349	77.90 338	96.20 292	94.06 323	74.61 341	66.53 358	88.76 328	40.40 369	96.20 281	67.02 342	83.66 254	86.61 352

Gipuma		54.77 338	52.22 342	62.40 355	86.50 350	59.37 371	50.20 373	90.35 362	36.52 371	41.20 372	49.49 373	18.33 377	81.29 370	32.10 373	65.34 350	46.54 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		86.69 253	85.75 252	89.53 290	86.46 351	82.94 291	96.39 280	95.71 263	83.97 267	79.63 296	90.70 294	68.85 279	95.94 295	86.01 214	84.02 247	89.72 327

EGC-MVSNET		60.70 335	55.37 339	76.72 347	86.35 352	71.08 357	89.96 349	84.44 376	0.38 381	1.50 382	84.09 352	37.30 370	88.10 366	40.85 371	73.44 319	70.97 369

test_method		70.10 333	68.66 336	74.41 349	86.30 353	55.84 373	94.47 313	89.82 365	35.18 372	66.15 359	84.75 351	30.54 372	77.96 373	70.40 333	60.33 358	89.44 331

lessismore_v0				85.08 328	85.59 354	69.28 363		90.56 361		67.68 353	90.21 315	54.21 346	95.46 311	73.88 316	62.64 354	90.50 312

CMPMVS	58.40 21	80.48 309	80.11 309	81.59 344	85.10 355	59.56 370	94.14 319	95.95 241	68.54 358	60.71 364	93.31 248	55.35 341	97.87 190	83.06 251	84.85 239	87.33 348
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		80.76 308	79.42 312	84.79 331	84.78 356	72.98 351	96.53 277	92.97 337	79.56 319	74.33 328	88.83 327	61.27 323	92.15 354	60.59 358	75.92 294	89.24 334

DSMNet-mixed		81.60 306	81.43 300	82.10 341	84.36 357	60.79 369	93.63 324	86.74 372	79.00 321	79.32 300	87.15 340	63.87 314	89.78 362	66.89 343	91.92 194	95.73 226

pmmvs6		79.90 312	77.31 317	87.67 313	84.17 358	78.13 335	95.86 301	93.68 329	67.94 360	72.67 341	89.62 322	50.98 354	95.75 304	74.80 311	66.04 349	89.14 335

new_pmnet		76.02 326	73.71 329	82.95 338	83.88 359	72.85 353	91.26 342	92.26 346	70.44 352	62.60 362	81.37 357	47.64 360	92.32 352	61.85 355	72.10 332	83.68 363

OpenMVS_ROB	73.86 20	77.99 323	75.06 327	86.77 320	83.81 360	77.94 337	96.38 281	91.53 357	67.54 361	68.38 349	87.13 341	43.94 363	96.08 289	55.03 365	81.83 268	86.29 355

test20.03		78.51 321	77.48 316	81.62 343	83.07 361	71.03 358	96.11 293	92.83 340	81.66 304	69.31 347	89.68 321	57.53 331	87.29 368	58.65 362	68.47 342	86.53 353

Anonymous20240521		78.63 320	76.90 320	83.82 335	82.82 362	72.86 352	95.72 305	93.57 331	73.55 346	72.17 343	84.79 350	49.69 357	92.51 350	65.29 348	74.50 304	86.09 356

UnsupCasMVSNet_eth		78.90 317	76.67 321	85.58 327	82.81 363	74.94 344	91.98 336	96.31 216	84.64 258	65.84 360	87.71 332	51.33 352	92.23 353	72.89 324	56.50 364	89.56 330

KD-MVS_self_test		77.47 325	75.88 324	82.24 339	81.59 364	68.93 364	92.83 332	94.02 324	77.03 333	73.14 336	83.39 353	55.44 340	90.42 360	67.95 339	57.53 362	87.38 346

CL-MVSNet_self_test		79.89 313	78.34 313	84.54 333	81.56 365	75.01 343	96.88 266	95.62 269	81.10 309	75.86 322	85.81 348	68.49 282	90.26 361	63.21 352	56.51 363	88.35 340

MIMVSNet1		75.92 327	73.30 330	83.81 336	81.29 366	75.57 342	92.26 335	92.05 350	73.09 347	67.48 355	86.18 346	40.87 368	87.64 367	55.78 364	70.68 339	88.21 341

Patchmatch-RL test		81.90 305	80.13 308	87.23 317	80.71 367	70.12 362	84.07 363	88.19 371	83.16 281	70.57 344	82.18 356	87.18 99	92.59 348	82.28 258	62.78 353	98.98 128

pmmvs-eth3d		78.71 319	76.16 323	86.38 321	80.25 368	81.19 315	94.17 318	92.13 349	77.97 328	66.90 357	82.31 355	55.76 336	92.56 349	73.63 320	62.31 356	85.38 358

UnsupCasMVSNet_bld		73.85 330	70.14 333	84.99 329	79.44 369	75.73 341	88.53 350	95.24 294	70.12 354	61.94 363	74.81 364	41.41 366	93.62 337	68.65 337	51.13 369	85.62 357

PM-MVS		74.88 328	72.85 331	80.98 345	78.98 370	64.75 367	90.81 345	85.77 373	80.95 312	68.23 351	82.81 354	29.08 373	92.84 344	76.54 298	62.46 355	85.36 359

new-patchmatchnet		74.80 329	72.40 332	81.99 342	78.36 371	72.20 355	94.44 314	92.36 344	77.06 332	63.47 361	79.98 361	51.04 353	88.85 364	60.53 359	54.35 366	84.92 361

pmmvs3		72.86 331	69.76 335	82.17 340	73.86 372	74.19 347	94.20 317	89.01 369	64.23 366	67.72 352	80.91 359	41.48 365	88.65 365	62.40 354	54.02 367	83.68 363

ambc				79.60 346	72.76 373	56.61 372	76.20 368	92.01 351		68.25 350	80.23 360	23.34 374	94.73 328	73.78 319	60.81 357	87.48 345

TDRefinement		78.01 322	75.31 325	86.10 324	70.06 374	73.84 348	93.59 325	91.58 356	74.51 342	73.08 338	91.04 286	49.63 358	97.12 229	74.88 309	59.47 359	87.33 348

PMMVS2		58.97 337	55.07 340	70.69 352	62.72 375	55.37 374	85.97 354	80.52 377	49.48 368	45.94 370	68.31 366	15.73 379	80.78 371	49.79 368	37.12 372	75.91 366

E-PMN		41.02 343	40.93 345	41.29 359	61.97 376	33.83 381	84.00 364	65.17 382	27.17 374	27.56 374	46.72 375	17.63 378	60.41 378	19.32 376	18.82 374	29.61 374

wuyk23d		16.71 347	16.73 351	16.65 361	60.15 377	25.22 385	41.24 374	5.17 385	6.56 378	5.48 381	3.61 381	3.64 383	22.72 380	15.20 378	9.52 378	1.99 378

FPMVS		61.57 334	60.32 337	65.34 353	60.14 378	42.44 379	91.02 344	89.72 366	44.15 369	42.63 371	80.93 358	19.02 375	80.59 372	42.50 370	72.76 323	73.00 367

EMVS		39.96 344	39.88 346	40.18 360	59.57 379	32.12 383	84.79 361	64.57 383	26.27 375	26.14 376	44.18 378	18.73 376	59.29 379	17.03 377	17.67 376	29.12 375

LCM-MVSNet		60.07 336	56.37 338	71.18 350	54.81 380	48.67 377	82.17 367	89.48 367	37.95 370	49.13 368	69.12 365	13.75 381	81.76 369	59.28 360	51.63 368	83.10 365

MVE	44.00 22	41.70 342	37.64 347	53.90 358	49.46 381	43.37 378	65.09 372	66.66 381	26.19 376	25.77 377	48.53 374	3.58 384	63.35 377	26.15 375	27.28 373	54.97 372
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		50.71 340	46.17 343	64.33 354	44.27 382	52.30 375	76.13 369	78.73 378	64.95 364	27.37 375	55.23 372	14.61 380	67.74 375	36.01 372	18.23 375	72.95 368

PMVS	41.42 23	45.67 341	42.50 344	55.17 357	34.28 383	32.37 382	66.24 371	78.71 379	30.72 373	22.04 378	59.59 370	4.59 382	77.85 374	27.49 374	58.84 361	55.29 371
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 339	52.86 341	56.05 356	32.75 384	41.97 380	73.42 370	76.12 380	21.91 377	39.68 373	96.39 192	42.59 364	65.10 376	78.00 287	14.92 377	61.08 370

testmvs		18.81 346	23.05 349	6.10 363	4.48 385	2.29 387	97.78 228	3.00 386	3.27 379	18.60 379	62.71 368	1.53 386	2.49 382	14.26 379	1.80 379	13.50 377

test123		16.58 348	19.47 350	7.91 362	3.59 386	5.37 386	94.32 315	1.39 387	2.49 380	13.98 380	44.60 377	2.91 385	2.65 381	11.35 380	0.57 380	15.70 376

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

eth-test2					0.00 387

eth-test					0.00 387

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		22.52 345	30.03 348	0.00 364	0.00 387	0.00 388	0.00 375	97.17 166	0.00 382	0.00 383	98.77 89	74.35 238	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		6.87 350	9.16 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	82.48 179	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.21 349	10.94 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	98.50 111	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

PC_three_1452									94.60 21	99.41 2	99.12 48	95.50 7	99.96 30	99.84 2	99.92 3	99.97 7

test_241102_TWO								97.72 76	94.17 26	99.23 8	99.54 3	93.14 24	99.98 10	99.70 3	99.82 19	99.99 1

test_0728_THIRD									93.01 53	99.07 11	99.46 11	94.66 14	99.97 23	99.25 15	99.82 19	99.95 15

GSMVS																98.84 142

sam_mvs1												88.39 75				98.84 142

sam_mvs												87.08 101

MTGPA								97.45 137

test_post1							90.74 347				41.37 379	85.38 139	96.36 268	83.16 248

test_post											46.00 376	87.37 93	97.11 230

patchmatchnet-post											84.86 349	88.73 69	96.81 242

MTMP							99.21 76	91.09 359

test9_res														98.60 25	99.87 9	99.90 24

agg_prior2														97.84 47	99.87 9	99.91 22

test_prior4						92.00 100	99.41 59

test_prior2							99.57 32		91.43 93	98.12 39	98.97 67	90.43 45		98.33 36	99.81 23

旧先验2							98.67 142		85.75 239	98.96 15			98.97 147	93.84 125

新几何2							98.26 191

无先验							98.52 159	97.82 57	87.20 216				99.90 45	87.64 200		99.85 33

原ACMM2							98.69 138

testdata2													99.88 49	84.16 236

segment_acmp												90.56 43

testdata1							97.89 219		92.43 68

plane_prior5								96.30 217					97.75 202	93.46 132	86.17 230	92.67 239

plane_prior4											96.52 186

plane_prior3						85.91 245			93.65 44	86.99 209

plane_prior2							99.02 105		93.38 49

plane_prior						86.07 242	99.14 91		93.81 41						86.26 229

n2								0.00 388

nn								0.00 388

door-mid								84.90 375

test11								97.68 85

door								85.30 374

HQP5-MVS						86.39 228

BP-MVS														93.82 127

HQP4-MVS										87.57 202			97.77 197			92.72 237

HQP3-MVS								96.37 213							86.29 227

HQP2-MVS												73.34 246

MDTV_nov1_ep13_2view						91.17 118	91.38 340		87.45 213	93.08 141		86.67 112		87.02 204		98.95 134

ACMMP++_ref															82.64 264

ACMMP++															83.83 249

Test By Simon												83.62 155

High-resolution multi-view benchmark