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.

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 42

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 52	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 11

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 248	92.39 6				88.94 11	96.63 4	94.85 16

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 56	93.10 1	95.39 11	82.99 1	97.27 10

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 50	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 86
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
test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 50	92.81 3	95.79 3	80.98 9

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 37	92.05 11	95.74 6	80.83 11

test_part2					95.06 8	72.65 33				91.80 13

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 60	80.26 13	87.78 30	94.27 36	75.89 20	96.81 22	87.45 19	96.44 9	93.05 97

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 119	91.30 15

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 44	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 66	93.00 47	80.90 9	88.06 28	94.06 46	76.43 17	96.84 20	88.48 14	95.99 19	94.34 37

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 68	84.66 67	94.52 23	68.81 86	96.65 29	84.53 41	94.90 45	94.00 52

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 71	84.45 71	94.52 23	69.09 82	96.70 26	84.37 44	94.83 51	94.03 49

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 54	96.48 8	94.88 11

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 68	84.91 58	94.44 30	70.78 64	96.61 32	84.53 41	94.89 46	93.66 68

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 29	93.24 37	75.23 100	84.91 58	94.44 30	70.78 64	96.61 32	83.75 55	94.89 46	93.66 68

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 33	93.49 29	74.75 113	88.33 25	94.43 32	73.27 44	97.02 16	84.18 50	94.84 49	93.82 62

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 85	93.82 19	73.07 153	84.86 63	92.89 72	76.22 18	96.33 39	84.89 35	95.13 41	94.40 34

APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 31

DP-MVS Recon		83.11 92	82.09 99	86.15 68	94.44 21	70.92 76	88.79 115	92.20 88	70.53 193	79.17 138	91.03 117	64.12 127	96.03 50	68.39 201	90.14 110	91.50 148

XVS		87.18 34	86.91 38	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	94.17 40	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

X-MVStestdata		80.37 148	77.83 185	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	12.47 381	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

mPP-MVS		86.67 43	86.32 47	87.72 32	94.41 24	73.55 13	92.74 20	92.22 87	76.87 65	82.81 98	94.25 38	66.44 104	96.24 42	82.88 66	94.28 63	93.38 83

NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 54	92.83 61	81.50 6	85.79 46	93.47 59	73.02 47	97.00 17	84.90 33	94.94 44	94.10 45

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 66	85.24 52	94.32 35	71.76 56	96.93 18	85.53 29	95.79 25	94.32 38

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 184	87.75 31	94.07 45	74.01 40	96.70 26	84.66 39	94.84 49

MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 22	92.65 70	77.57 43	83.84 83	94.40 34	72.24 52	96.28 41	85.65 28	95.30 40	93.62 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 35	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 7

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 65	94.67 24
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
SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 23	93.63 24	74.77 112	92.29 7	95.97 2	74.28 36	97.24 11	88.58 13	96.91 1	94.87 13
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
APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 41	92.83 61	73.01 155	88.58 23	94.52 23	73.36 42	96.49 37	84.26 46	95.01 42	92.70 108
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		86.68 42	86.27 48	87.90 21	94.22 35	73.38 19	90.22 77	93.04 43	75.53 94	83.86 82	94.42 33	67.87 92	96.64 30	82.70 71	94.57 56	93.66 68

CP-MVS		87.11 35	86.92 37	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 73	83.68 85	94.46 27	67.93 90	95.95 56	84.20 49	94.39 60	93.23 89

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 76	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 189	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 25	93.43 32	76.89 64	84.68 64	93.99 50	70.67 67	96.82 21	84.18 50	95.01 42	93.90 57

SR-MVS		86.73 39	86.67 41	86.91 52	94.11 40	72.11 51	92.37 27	92.56 73	74.50 118	86.84 37	94.65 20	67.31 97	95.77 60	84.80 37	92.85 75	92.84 106

114514_t		80.68 139	79.51 143	84.20 121	94.09 41	67.27 153	89.64 92	91.11 131	58.75 331	74.08 245	90.72 122	58.10 200	95.04 96	69.70 186	89.42 119	90.30 189

test1172		86.20 52	86.22 49	86.12 70	93.95 42	69.89 95	91.79 43	92.28 82	75.07 104	86.40 40	94.58 22	65.00 122	95.56 65	84.34 45	92.60 78	92.90 104

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 34	93.33 35	76.07 85	83.81 84	93.95 51	69.77 76	96.01 52	85.15 31	94.66 53	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 69	89.69 169	74.31 123	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 18

save fliter					93.80 44	72.35 45	90.47 69	91.17 129	74.31 123

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 64	94.05 15	70.80 186	87.59 33	93.51 56	77.57 14	96.63 31	83.31 57	95.77 26	94.72 23

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 59	93.59 26	76.27 82	88.14 26	95.09 15	71.06 62	96.67 28	87.67 16	96.37 14	94.09 46

HPM-MVS_fast		85.35 66	84.95 71	86.57 61	93.69 48	70.58 84	92.15 36	91.62 114	73.89 134	82.67 100	94.09 44	62.60 147	95.54 68	80.93 84	92.93 73	93.57 78

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 49	92.35 80	74.62 117	88.90 21	93.85 52	75.75 21	96.00 53	87.80 15	94.63 54	95.04 5
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 87	93.50 28	75.17 103	86.34 41	95.29 12	70.86 63	96.00 53	88.78 12	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		85.89 56	85.39 62	87.38 44	93.59 51	72.63 34	92.74 20	93.18 41	76.78 68	80.73 122	93.82 53	64.33 125	96.29 40	82.67 72	90.69 102	93.23 89
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
DeepC-MVS_fast	79.65 3	86.91 38	86.62 42	87.76 29	93.52 52	72.37 44	91.26 50	93.04 43	76.62 73	84.22 76	93.36 61	71.44 60	96.76 24	80.82 86	95.33 38	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		85.76 58	85.29 66	87.17 48	93.49 53	71.08 68	88.58 124	92.42 78	68.32 239	84.61 68	93.48 57	72.32 51	96.15 48	79.00 99	95.43 34	94.28 40

DP-MVS		76.78 229	74.57 241	83.42 151	93.29 54	69.46 106	88.55 125	83.70 283	63.98 287	70.20 280	88.89 169	54.01 234	94.80 109	46.66 346	81.88 213	86.01 305

CPTT-MVS		83.73 78	83.33 82	84.92 96	93.28 55	70.86 77	92.09 37	90.38 146	68.75 232	79.57 134	92.83 74	60.60 186	93.04 189	80.92 85	91.56 93	90.86 170

TEST9					93.26 56	72.96 26	88.75 117	91.89 103	68.44 237	85.00 56	93.10 65	74.36 35	95.41 76

train_agg		86.43 46	86.20 50	87.13 49	93.26 56	72.96 26	88.75 117	91.89 103	68.69 233	85.00 56	93.10 65	74.43 32	95.41 76	84.97 32	95.71 30	93.02 99

test_8					93.13 58	72.57 36	88.68 121	91.84 107	68.69 233	84.87 62	93.10 65	74.43 32	95.16 87

新几何1				83.42 151	93.13 58	70.71 80		85.48 259	57.43 339	81.80 108	91.98 88	63.28 135	92.27 212	64.60 233	92.99 72	87.27 278

1121		80.84 130	79.77 137	84.05 131	93.11 60	70.78 79	84.66 234	85.42 260	57.37 340	81.76 111	92.02 87	63.41 133	94.12 135	67.28 209	92.93 73	87.26 279

AdaColmap		80.58 144	79.42 145	84.06 129	93.09 61	68.91 114	89.36 95	88.97 195	69.27 215	75.70 214	89.69 144	57.20 212	95.77 60	63.06 241	88.41 131	87.50 273

SR-MVS-dyc-post		85.77 57	85.61 59	86.23 66	93.06 62	70.63 82	91.88 39	92.27 83	73.53 144	85.69 47	94.45 28	65.00 122	95.56 65	82.75 67	91.87 88	92.50 116

RE-MVS-def			85.48 60		93.06 62	70.63 82	91.88 39	92.27 83	73.53 144	85.69 47	94.45 28	63.87 129		82.75 67	91.87 88	92.50 116

原ACMM1				84.35 116	93.01 64	68.79 115		92.44 75	63.96 288	81.09 118	91.57 99	66.06 110	95.45 72	67.19 212	94.82 52	88.81 247

CSCG		86.41 48	86.19 51	87.07 50	92.91 65	72.48 38	90.81 60	93.56 27	73.95 131	83.16 92	91.07 114	75.94 19	95.19 86	79.94 95	94.38 61	93.55 79

agg_prior1		86.22 51	86.09 55	86.62 59	92.85 66	71.94 54	88.59 123	91.78 110	68.96 228	84.41 72	93.18 64	74.94 27	94.93 99	84.75 38	95.33 38	93.01 100

agg_prior					92.85 66	71.94 54		91.78 110		84.41 72			94.93 99

9.14			88.26 15		92.84 68		91.52 47	94.75 1	73.93 133	88.57 24	94.67 19	75.57 24	95.79 59	86.77 23	95.76 28

SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 23	94.74 2	75.71 90	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 18

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 48	94.70 3	74.47 120	88.86 22	94.61 21	75.23 25	95.84 58	86.62 26	95.92 21	94.78 20

MG-MVS		83.41 85	83.45 80	83.28 156	92.74 71	62.28 251	88.17 141	89.50 173	75.22 101	81.49 112	92.74 80	66.75 100	95.11 91	72.85 158	91.58 92	92.45 119

APD-MVS_3200maxsize		85.97 54	85.88 56	86.22 67	92.69 72	69.53 102	91.93 38	92.99 49	73.54 143	85.94 42	94.51 26	65.80 114	95.61 63	83.04 64	92.51 80	93.53 81

test12				86.80 55	92.63 73	70.70 81		91.79 109		82.71 99		71.67 57	96.16 47		94.50 57	93.54 80

test_prior3		86.73 39	86.86 40	86.33 63	92.61 74	69.59 100	88.85 112	92.97 54	75.41 96	84.91 58	93.54 54	74.28 36	95.48 70	83.31 57	95.86 22	93.91 55

test_prior				86.33 63	92.61 74	69.59 100		92.97 54					95.48 70			93.91 55

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 42	93.19 40	77.87 38	90.32 17	94.00 48	74.83 28	93.78 151	87.63 17	94.27 64	93.65 73
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
PAPM_NR		83.02 93	82.41 93	84.82 100	92.47 77	66.37 168	87.93 150	91.80 108	73.82 135	77.32 178	90.66 123	67.90 91	94.90 104	70.37 178	89.48 118	93.19 93

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 109	89.57 93	93.39 34	77.53 47	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 27

abl_6		85.23 67	84.95 71	86.07 71	92.23 79	70.48 85	90.80 61	92.08 92	73.51 146	85.26 51	94.16 41	62.75 146	95.92 57	82.46 74	91.30 97	91.81 140

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 23	80.19 14	88.10 27	94.80 16	73.76 41	97.11 13	87.51 18	95.82 24	94.90 10
Skip Steuart: Steuart Systems R&D Blog.
UA-Net		85.08 71	84.96 70	85.45 80	92.07 81	68.07 136	89.78 88	90.86 137	82.48 2	84.60 69	93.20 63	69.35 79	95.22 85	71.39 169	90.88 101	93.07 96

旧先验1					91.96 82	65.79 182		86.37 249			93.08 69	69.31 81			92.74 76	88.74 250

MSLP-MVS++		85.43 64	85.76 58	84.45 112	91.93 83	70.24 86	90.71 62	92.86 59	77.46 49	84.22 76	92.81 76	67.16 99	92.94 191	80.36 91	94.35 62	90.16 193

LFMVS		81.82 110	81.23 111	83.57 148	91.89 84	63.43 233	89.84 84	81.85 307	77.04 61	83.21 90	93.10 65	52.26 246	93.43 170	71.98 164	89.95 114	93.85 59

PLC	70.83 11	78.05 203	76.37 221	83.08 167	91.88 85	67.80 140	88.19 140	89.46 174	64.33 281	69.87 289	88.38 183	53.66 236	93.58 159	58.86 279	82.73 203	87.86 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dcpmvs_2		85.63 61	86.15 53	84.06 129	91.71 86	64.94 201	86.47 192	91.87 105	73.63 139	86.60 39	93.02 70	76.57 16	91.87 227	83.36 56	92.15 84	95.35 1

MVS_111021_HR		85.14 69	84.75 73	86.32 65	91.65 87	72.70 31	85.98 204	90.33 150	76.11 84	82.08 103	91.61 98	71.36 61	94.17 134	81.02 83	92.58 79	92.08 132

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 88	72.45 41	90.02 80	94.37 4	71.76 167	87.28 34	94.27 36	75.18 26	96.08 49	85.16 30	95.77 26	93.80 65

test222					91.50 89	68.26 132	84.16 251	83.20 295	54.63 351	79.74 131	91.63 97	58.97 195			91.42 94	86.77 291

TSAR-MVS + GP.		85.71 59	85.33 63	86.84 53	91.34 90	72.50 37	89.07 105	87.28 235	76.41 75	85.80 45	90.22 132	74.15 39	95.37 82	81.82 78	91.88 87	92.65 112

MAR-MVS		81.84 109	80.70 120	85.27 84	91.32 91	71.53 60	89.82 85	90.92 134	69.77 206	78.50 150	86.21 246	62.36 153	94.52 119	65.36 226	92.05 86	89.77 217
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
DeepC-MVS	79.81 2	87.08 37	86.88 39	87.69 36	91.16 92	72.32 47	90.31 74	93.94 17	77.12 58	82.82 97	94.23 39	72.13 54	97.09 14	84.83 36	95.37 35	93.65 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 62	84.47 76	88.51 6	91.08 93	73.49 17	93.18 11	93.78 21	80.79 10	76.66 193	93.37 60	60.40 190	96.75 25	77.20 117	93.73 69	95.29 3

Anonymous202405211		78.25 195	77.01 203	81.99 198	91.03 94	60.67 270	84.77 232	83.90 281	70.65 192	80.00 130	91.20 109	41.08 336	91.43 238	65.21 227	85.26 169	93.85 59

CS-MVS-test		86.29 50	86.48 44	85.71 77	91.02 95	67.21 155	92.36 28	93.78 21	78.97 30	83.51 89	91.20 109	70.65 68	95.15 88	81.96 77	94.89 46	94.77 21

VDD-MVS		83.01 94	82.36 95	84.96 93	91.02 95	66.40 167	88.91 109	88.11 214	77.57 43	84.39 74	93.29 62	52.19 247	93.91 146	77.05 119	88.70 126	94.57 29

API-MVS		81.99 107	81.23 111	84.26 120	90.94 97	70.18 92	91.10 55	89.32 177	71.51 176	78.66 147	88.28 186	65.26 117	95.10 94	64.74 232	91.23 98	87.51 272

testdata				79.97 243	90.90 98	64.21 215		84.71 267	59.27 326	85.40 49	92.91 71	62.02 160	89.08 280	68.95 194	91.37 95	86.63 295

PHI-MVS		86.43 46	86.17 52	87.24 46	90.88 99	70.96 72	92.27 32	94.07 11	72.45 158	85.22 53	91.90 90	69.47 78	96.42 38	83.28 60	95.94 20	94.35 36

VNet		82.21 102	82.41 93	81.62 204	90.82 100	60.93 265	84.47 241	89.78 165	76.36 80	84.07 80	91.88 91	64.71 124	90.26 261	70.68 175	88.89 122	93.66 68

PVSNet_Blended_VisFu		82.62 98	81.83 106	84.96 93	90.80 101	69.76 97	88.74 119	91.70 113	69.39 212	78.96 140	88.46 181	65.47 116	94.87 107	74.42 140	88.57 127	90.24 191

CS-MVS		86.69 41	86.95 36	85.90 75	90.76 102	67.57 146	92.83 17	93.30 36	79.67 19	84.57 70	92.27 83	71.47 59	95.02 97	84.24 48	93.46 70	95.13 4

test_part1		82.78 96	82.08 100	84.89 98	90.66 103	66.97 160	90.96 58	92.93 57	77.19 55	80.53 124	90.04 136	63.44 132	95.39 78	76.04 129	76.90 262	92.31 123

Anonymous20240529		80.19 153	78.89 161	84.10 124	90.60 104	64.75 204	88.95 108	90.90 135	65.97 263	80.59 123	91.17 111	49.97 275	93.73 157	69.16 192	82.70 205	93.81 63

h-mvs33		83.15 89	82.19 97	86.02 73	90.56 105	70.85 78	88.15 143	89.16 185	76.02 86	84.67 65	91.39 105	61.54 165	95.50 69	82.71 69	75.48 285	91.72 142

Anonymous20231211		78.97 182	77.69 192	82.81 180	90.54 106	64.29 214	90.11 79	91.51 118	65.01 273	76.16 209	88.13 195	50.56 269	93.03 190	69.68 187	77.56 256	91.11 159

LS3D		76.95 227	74.82 239	83.37 154	90.45 107	67.36 152	89.15 103	86.94 241	61.87 307	69.52 292	90.61 124	51.71 258	94.53 118	46.38 349	86.71 152	88.21 259

VDDNet		81.52 118	80.67 121	84.05 131	90.44 108	64.13 217	89.73 90	85.91 255	71.11 181	83.18 91	93.48 57	50.54 270	93.49 165	73.40 152	88.25 132	94.54 30

CNLPA		78.08 201	76.79 210	81.97 199	90.40 109	71.07 69	87.59 159	84.55 270	66.03 262	72.38 262	89.64 146	57.56 206	86.04 310	59.61 270	83.35 194	88.79 248

PAPR		81.66 116	80.89 118	83.99 137	90.27 110	64.00 218	86.76 185	91.77 112	68.84 231	77.13 186	89.50 150	67.63 93	94.88 106	67.55 206	88.52 129	93.09 95

Vis-MVSNet		83.46 84	82.80 90	85.43 81	90.25 111	68.74 119	90.30 75	90.13 156	76.33 81	80.87 121	92.89 72	61.00 179	94.20 131	72.45 163	90.97 99	93.35 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DPM-MVS		84.93 72	84.29 77	86.84 53	90.20 112	73.04 24	87.12 171	93.04 43	69.80 205	82.85 96	91.22 108	73.06 46	96.02 51	76.72 125	94.63 54	91.46 151

EPP-MVSNet		83.40 86	83.02 86	84.57 106	90.13 113	64.47 210	92.32 30	90.73 138	74.45 122	79.35 137	91.10 112	69.05 84	95.12 89	72.78 159	87.22 144	94.13 44

CANet		86.45 45	86.10 54	87.51 40	90.09 114	70.94 74	89.70 91	92.59 72	81.78 4	81.32 113	91.43 104	70.34 69	97.23 12	84.26 46	93.36 71	94.37 35

test2506		77.30 221	76.49 217	79.74 248	90.08 115	52.02 346	87.86 154	63.10 374	74.88 109	80.16 129	92.79 77	38.29 346	92.35 209	68.74 197	92.50 81	94.86 14

ECVR-MVS		79.61 161	79.26 152	80.67 230	90.08 115	54.69 330	87.89 152	77.44 340	74.88 109	80.27 126	92.79 77	48.96 290	92.45 203	68.55 198	92.50 81	94.86 14

HQP_MVS		83.64 81	83.14 83	85.14 87	90.08 115	68.71 121	91.25 52	92.44 75	79.12 25	78.92 142	91.00 118	60.42 188	95.38 79	78.71 102	86.32 157	91.33 152

plane_prior7					90.08 115	68.51 128

patch_mono-2		83.65 80	84.54 75	80.99 223	90.06 119	65.83 179	84.21 250	88.74 205	71.60 174	85.01 54	92.44 81	74.51 30	83.50 328	82.15 76	92.15 84	93.64 75

test1111		79.43 168	79.18 156	80.15 240	89.99 120	53.31 343	87.33 166	77.05 343	75.04 105	80.23 128	92.77 79	48.97 289	92.33 211	68.87 195	92.40 83	94.81 17

CHOSEN 1792x2688		77.63 215	75.69 225	83.44 150	89.98 121	68.58 127	78.70 313	87.50 231	56.38 345	75.80 213	86.84 222	58.67 196	91.40 239	61.58 256	85.75 168	90.34 188

IS-MVSNet		83.15 89	82.81 89	84.18 122	89.94 122	63.30 235	91.59 44	88.46 211	79.04 27	79.49 135	92.16 85	65.10 119	94.28 124	67.71 204	91.86 90	94.95 7

plane_prior1					89.90 123

canonicalmvs		85.91 55	85.87 57	86.04 72	89.84 124	69.44 107	90.45 72	93.00 47	76.70 72	88.01 29	91.23 107	73.28 43	93.91 146	81.50 80	88.80 124	94.77 21

plane_prior6					89.84 124	68.70 123						60.42 188

NP-MVS					89.62 126	68.32 130					90.24 130

EIA-MVS		83.31 88	82.80 90	84.82 100	89.59 127	65.59 185	88.21 139	92.68 66	74.66 115	78.96 140	86.42 242	69.06 83	95.26 84	75.54 135	90.09 111	93.62 76

HyFIR lowres test		77.53 216	75.40 232	83.94 140	89.59 127	66.62 164	80.36 295	88.64 208	56.29 346	76.45 196	85.17 268	57.64 205	93.28 173	61.34 259	83.10 199	91.91 136

TAPA-MVS	73.13 9	79.15 176	77.94 181	82.79 183	89.59 127	62.99 244	88.16 142	91.51 118	65.77 264	77.14 185	91.09 113	60.91 180	93.21 175	50.26 329	87.05 146	92.17 130
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thres100view900		76.50 232	75.55 229	79.33 256	89.52 130	56.99 308	85.83 211	83.23 293	73.94 132	76.32 202	87.12 218	51.89 255	91.95 222	48.33 337	83.75 187	89.07 230

GeoE		81.71 112	81.01 116	83.80 143	89.51 131	64.45 211	88.97 107	88.73 206	71.27 179	78.63 148	89.76 143	66.32 106	93.20 177	69.89 184	86.02 163	93.74 66

alignmvs		85.48 62	85.32 64	85.96 74	89.51 131	69.47 104	89.74 89	92.47 74	76.17 83	87.73 32	91.46 103	70.32 70	93.78 151	81.51 79	88.95 121	94.63 26

PS-MVSNAJ		81.69 113	81.02 115	83.70 145	89.51 131	68.21 134	84.28 249	90.09 157	70.79 187	81.26 117	85.62 258	63.15 140	94.29 123	75.62 133	88.87 123	88.59 253

ACMP	74.13 6	81.51 120	80.57 122	84.36 115	89.42 134	68.69 124	89.97 82	91.50 121	74.46 121	75.04 235	90.41 128	53.82 235	94.54 117	77.56 113	82.91 200	89.86 213
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 232	75.44 230	79.68 250	89.40 135	57.16 305	85.53 219	83.23 293	73.79 137	76.26 203	87.09 219	51.89 255	91.89 225	48.05 342	83.72 190	90.00 205

ETV-MVS		84.90 74	84.67 74	85.59 79	89.39 136	68.66 125	88.74 119	92.64 71	79.97 17	84.10 79	85.71 254	69.32 80	95.38 79	80.82 86	91.37 95	92.72 107

BH-RMVSNet		79.61 161	78.44 170	83.14 164	89.38 137	65.93 176	84.95 229	87.15 238	73.56 142	78.19 160	89.79 142	56.67 215	93.36 171	59.53 271	86.74 151	90.13 195

iter_conf_final		80.63 140	79.35 149	84.46 111	89.36 138	67.70 143	89.85 83	84.49 271	73.19 151	78.30 156	88.94 166	45.98 306	94.56 115	79.59 97	84.48 178	91.11 159

Regformer-1		86.41 48	86.33 46	86.64 58	89.33 139	70.93 75	88.43 126	91.39 123	82.14 3	86.65 38	90.09 134	74.39 34	95.01 98	83.97 52	90.63 103	93.97 53

Regformer-2		86.63 44	86.53 43	86.95 51	89.33 139	71.24 67	88.43 126	92.05 93	82.50 1	86.88 36	90.09 134	74.45 31	95.61 63	84.38 43	90.63 103	94.01 51

HQP-NCC					89.33 139		89.17 99		76.41 75	77.23 181

ACMP_Plane					89.33 139		89.17 99		76.41 75	77.23 181

HQP-MVS		82.61 99	82.02 102	84.37 114	89.33 139	66.98 158	89.17 99	92.19 89	76.41 75	77.23 181	90.23 131	60.17 191	95.11 91	77.47 114	85.99 164	91.03 164

DROMVSNet		86.01 53	86.38 45	84.91 97	89.31 144	66.27 170	92.32 30	93.63 24	79.37 22	84.17 78	91.88 91	69.04 85	95.43 74	83.93 53	93.77 68	93.01 100

ACMM	73.20 8	80.78 138	79.84 136	83.58 147	89.31 144	68.37 129	89.99 81	91.60 115	70.28 197	77.25 179	89.66 145	53.37 238	93.53 164	74.24 143	82.85 201	88.85 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 236	75.44 230	79.27 257	89.28 146	58.09 290	81.69 283	87.07 239	59.53 324	72.48 260	86.67 232	61.30 172	89.33 275	60.81 263	80.15 233	90.41 186

F-COLMAP		76.38 237	74.33 246	82.50 190	89.28 146	66.95 162	88.41 129	89.03 190	64.05 285	66.83 315	88.61 176	46.78 300	92.89 192	57.48 291	78.55 246	87.67 267

LPG-MVS_test		82.08 104	81.27 110	84.50 109	89.23 148	68.76 117	90.22 77	91.94 101	75.37 98	76.64 194	91.51 100	54.29 230	94.91 101	78.44 104	83.78 185	89.83 214

LGP-MVS_train				84.50 109	89.23 148	68.76 117		91.94 101	75.37 98	76.64 194	91.51 100	54.29 230	94.91 101	78.44 104	83.78 185	89.83 214

BH-untuned		79.47 166	78.60 166	82.05 196	89.19 150	65.91 177	86.07 203	88.52 210	72.18 163	75.42 221	87.69 200	61.15 176	93.54 163	60.38 264	86.83 150	86.70 293

xiu_mvs_v2_base		81.69 113	81.05 114	83.60 146	89.15 151	68.03 137	84.46 243	90.02 158	70.67 190	81.30 116	86.53 240	63.17 139	94.19 132	75.60 134	88.54 128	88.57 254

test_yl		81.17 123	80.47 125	83.24 159	89.13 152	63.62 224	86.21 199	89.95 161	72.43 161	81.78 109	89.61 147	57.50 207	93.58 159	70.75 173	86.90 148	92.52 114

DCV-MVSNet		81.17 123	80.47 125	83.24 159	89.13 152	63.62 224	86.21 199	89.95 161	72.43 161	81.78 109	89.61 147	57.50 207	93.58 159	70.75 173	86.90 148	92.52 114

tfpn200view9		76.42 235	75.37 234	79.55 255	89.13 152	57.65 300	85.17 222	83.60 284	73.41 147	76.45 196	86.39 243	52.12 248	91.95 222	48.33 337	83.75 187	89.07 230

thres400		76.50 232	75.37 234	79.86 245	89.13 152	57.65 300	85.17 222	83.60 284	73.41 147	76.45 196	86.39 243	52.12 248	91.95 222	48.33 337	83.75 187	90.00 205

1112_ss		77.40 219	76.43 219	80.32 237	89.11 156	60.41 275	83.65 259	87.72 227	62.13 305	73.05 254	86.72 226	62.58 149	89.97 265	62.11 251	80.80 224	90.59 180

Regformer-3		85.23 67	85.07 68	85.70 78	88.95 157	69.01 111	88.29 136	89.91 163	80.95 8	85.01 54	90.01 138	72.45 50	94.19 132	82.50 73	87.57 136	93.90 57

Regformer-4		85.68 60	85.45 61	86.35 62	88.95 157	69.67 99	88.29 136	91.29 125	81.73 5	85.36 50	90.01 138	72.62 49	95.35 83	83.28 60	87.57 136	94.03 49

Fast-Effi-MVS+		80.81 133	79.92 133	83.47 149	88.85 159	64.51 207	85.53 219	89.39 175	70.79 187	78.49 151	85.06 271	67.54 94	93.58 159	67.03 215	86.58 153	92.32 122

PVSNet_BlendedMVS		80.60 142	80.02 131	82.36 193	88.85 159	65.40 190	86.16 201	92.00 97	69.34 214	78.11 162	86.09 249	66.02 111	94.27 125	71.52 166	82.06 210	87.39 274

PVSNet_Blended		80.98 126	80.34 127	82.90 176	88.85 159	65.40 190	84.43 245	92.00 97	67.62 243	78.11 162	85.05 272	66.02 111	94.27 125	71.52 166	89.50 117	89.01 237

MVS_111021_LR		82.61 99	82.11 98	84.11 123	88.82 162	71.58 59	85.15 224	86.16 252	74.69 114	80.47 125	91.04 115	62.29 154	90.55 259	80.33 92	90.08 112	90.20 192

BH-w/o		78.21 197	77.33 199	80.84 226	88.81 163	65.13 197	84.87 230	87.85 224	69.75 207	74.52 241	84.74 275	61.34 171	93.11 184	58.24 286	85.84 166	84.27 323

FIs		82.07 105	82.42 92	81.04 222	88.80 164	58.34 288	88.26 138	93.49 29	76.93 63	78.47 152	91.04 115	69.92 74	92.34 210	69.87 185	84.97 171	92.44 120

OPM-MVS		83.50 83	82.95 87	85.14 87	88.79 165	70.95 73	89.13 104	91.52 117	77.55 46	80.96 120	91.75 93	60.71 182	94.50 120	79.67 96	86.51 155	89.97 209
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
WR-MVS		79.49 165	79.22 154	80.27 238	88.79 165	58.35 287	85.06 226	88.61 209	78.56 32	77.65 171	88.34 184	63.81 131	90.66 258	64.98 230	77.22 258	91.80 141

OMC-MVS		82.69 97	81.97 104	84.85 99	88.75 167	67.42 149	87.98 146	90.87 136	74.92 108	79.72 132	91.65 95	62.19 157	93.96 139	75.26 137	86.42 156	93.16 94

hse-mvs2		81.72 111	80.94 117	84.07 128	88.72 168	67.68 144	85.87 208	87.26 236	76.02 86	84.67 65	88.22 189	61.54 165	93.48 166	82.71 69	73.44 311	91.06 162

AUN-MVS		79.21 175	77.60 194	84.05 131	88.71 169	67.61 145	85.84 210	87.26 236	69.08 223	77.23 181	88.14 194	53.20 240	93.47 167	75.50 136	73.45 310	91.06 162

ACMH	67.68 16	75.89 242	73.93 249	81.77 202	88.71 169	66.61 165	88.62 122	89.01 192	69.81 204	66.78 316	86.70 231	41.95 333	91.51 237	55.64 303	78.14 252	87.17 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 194	78.45 169	78.07 274	88.64 171	51.78 349	86.70 186	79.63 328	74.14 129	75.11 232	90.83 121	61.29 173	89.75 268	58.10 287	91.60 91	92.69 110

PatchMatch-RL		72.38 276	70.90 276	76.80 292	88.60 172	67.38 151	79.53 303	76.17 346	62.75 299	69.36 294	82.00 310	45.51 312	84.89 319	53.62 311	80.58 227	78.12 357

ACMH+	68.96 14	76.01 241	74.01 248	82.03 197	88.60 172	65.31 194	88.86 111	87.55 229	70.25 198	67.75 303	87.47 208	41.27 334	93.19 179	58.37 284	75.94 278	87.60 269

LTVRE_ROB	69.57 13	76.25 238	74.54 243	81.41 209	88.60 172	64.38 213	79.24 306	89.12 189	70.76 189	69.79 291	87.86 197	49.09 286	93.20 177	56.21 302	80.16 232	86.65 294
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
DELS-MVS		85.41 65	85.30 65	85.77 76	88.49 175	67.93 138	85.52 221	93.44 31	78.70 31	83.63 88	89.03 165	74.57 29	95.71 62	80.26 93	94.04 66	93.66 68
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
CLD-MVS		82.31 101	81.65 107	84.29 119	88.47 176	67.73 142	85.81 212	92.35 80	75.78 89	78.33 155	86.58 237	64.01 128	94.35 122	76.05 128	87.48 141	90.79 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 108	81.54 108	82.92 175	88.46 177	63.46 231	87.13 170	92.37 79	80.19 14	78.38 153	89.14 161	71.66 58	93.05 187	70.05 181	76.46 270	92.25 126

ab-mvs		79.51 164	78.97 160	81.14 219	88.46 177	60.91 266	83.84 256	89.24 182	70.36 195	79.03 139	88.87 170	63.23 138	90.21 263	65.12 228	82.57 206	92.28 125

FC-MVSNet-test		81.52 118	82.02 102	80.03 242	88.42 179	55.97 324	87.95 148	93.42 33	77.10 59	77.38 176	90.98 120	69.96 73	91.79 228	68.46 200	84.50 176	92.33 121

Effi-MVS+		83.62 82	83.08 84	85.24 85	88.38 180	67.45 148	88.89 110	89.15 186	75.50 95	82.27 101	88.28 186	69.61 77	94.45 121	77.81 111	87.84 134	93.84 61

UniMVSNet (Re)		81.60 117	81.11 113	83.09 166	88.38 180	64.41 212	87.60 158	93.02 46	78.42 34	78.56 149	88.16 190	69.78 75	93.26 174	69.58 188	76.49 269	91.60 143

VPNet		78.69 187	78.66 165	78.76 263	88.31 182	55.72 326	84.45 244	86.63 245	76.79 67	78.26 157	90.55 126	59.30 193	89.70 270	66.63 216	77.05 260	90.88 169

FA-MVS(test-final)		80.96 127	79.91 134	84.10 124	88.30 183	65.01 199	84.55 240	90.01 159	73.25 150	79.61 133	87.57 203	58.35 199	94.72 112	71.29 170	86.25 159	92.56 113

TR-MVS		77.44 217	76.18 222	81.20 217	88.24 184	63.24 236	84.61 238	86.40 248	67.55 244	77.81 168	86.48 241	54.10 232	93.15 181	57.75 290	82.72 204	87.20 280

EI-MVSNet-Vis-set		84.19 75	83.81 78	85.31 82	88.18 185	67.85 139	87.66 157	89.73 168	80.05 16	82.95 93	89.59 149	70.74 66	94.82 108	80.66 90	84.72 174	93.28 88

baseline1		76.98 226	76.75 213	77.66 279	88.13 186	55.66 327	85.12 225	81.89 305	73.04 154	76.79 189	88.90 168	62.43 152	87.78 299	63.30 240	71.18 326	89.55 223

test_0402		72.79 273	70.44 280	79.84 246	88.13 186	65.99 175	85.93 206	84.29 275	65.57 267	67.40 309	85.49 260	46.92 299	92.61 198	35.88 367	74.38 301	80.94 349

tttt0517		79.40 170	77.91 182	83.90 142	88.10 188	63.84 221	88.37 133	84.05 279	71.45 177	76.78 190	89.12 162	49.93 278	94.89 105	70.18 180	83.18 197	92.96 103

FE-MVS		77.78 210	75.68 226	84.08 127	88.09 189	66.00 174	83.13 269	87.79 225	68.42 238	78.01 165	85.23 266	45.50 313	95.12 89	59.11 275	85.83 167	91.11 159

VPA-MVSNet		80.60 142	80.55 123	80.76 228	88.07 190	60.80 268	86.86 179	91.58 116	75.67 93	80.24 127	89.45 156	63.34 134	90.25 262	70.51 177	79.22 244	91.23 156

UGNet		80.83 132	79.59 142	84.54 108	88.04 191	68.09 135	89.42 94	88.16 213	76.95 62	76.22 204	89.46 154	49.30 284	93.94 142	68.48 199	90.31 106	91.60 143
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
WR-MVS_H		78.51 191	78.49 168	78.56 266	88.02 192	56.38 319	88.43 126	92.67 67	77.14 57	73.89 246	87.55 205	66.25 107	89.24 277	58.92 278	73.55 309	90.06 203

QAPM		80.88 128	79.50 144	85.03 90	88.01 193	68.97 113	91.59 44	92.00 97	66.63 255	75.15 231	92.16 85	57.70 204	95.45 72	63.52 236	88.76 125	90.66 176

3Dnovator	76.31 5	83.38 87	82.31 96	86.59 60	87.94 194	72.94 29	90.64 63	92.14 91	77.21 54	75.47 217	92.83 74	58.56 197	94.72 112	73.24 155	92.71 77	92.13 131

EI-MVSNet-UG-set		83.81 77	83.38 81	85.09 89	87.87 195	67.53 147	87.44 163	89.66 170	79.74 18	82.23 102	89.41 158	70.24 71	94.74 111	79.95 94	83.92 184	92.99 102

TranMVSNet+NR-MVSNet		80.84 130	80.31 128	82.42 191	87.85 196	62.33 249	87.74 156	91.33 124	80.55 11	77.99 166	89.86 140	65.23 118	92.62 197	67.05 214	75.24 294	92.30 124

iter_conf05		80.00 157	78.70 163	83.91 141	87.84 197	65.83 179	88.84 114	84.92 266	71.61 173	78.70 144	88.94 166	43.88 319	94.56 115	79.28 98	84.28 181	91.33 152

CP-MVSNet		78.22 196	78.34 173	77.84 276	87.83 198	54.54 332	87.94 149	91.17 129	77.65 40	73.48 249	88.49 180	62.24 156	88.43 291	62.19 248	74.07 302	90.55 181

DU-MVS		81.12 125	80.52 124	82.90 176	87.80 199	63.46 231	87.02 174	91.87 105	79.01 28	78.38 153	89.07 163	65.02 120	93.05 187	70.05 181	76.46 270	92.20 128

NR-MVSNet		80.23 151	79.38 147	82.78 184	87.80 199	63.34 234	86.31 196	91.09 132	79.01 28	72.17 264	89.07 163	67.20 98	92.81 196	66.08 221	75.65 281	92.20 128

TAMVS		78.89 184	77.51 196	83.03 170	87.80 199	67.79 141	84.72 233	85.05 264	67.63 242	76.75 191	87.70 199	62.25 155	90.82 254	58.53 283	87.13 145	90.49 183

thres200		75.55 246	74.47 244	78.82 262	87.78 202	57.85 297	83.07 272	83.51 287	72.44 160	75.84 212	84.42 277	52.08 250	91.75 229	47.41 344	83.64 191	86.86 289

PS-CasMVS		78.01 205	78.09 178	77.77 278	87.71 203	54.39 334	88.02 145	91.22 126	77.50 48	73.26 251	88.64 175	60.73 181	88.41 292	61.88 252	73.88 306	90.53 182

PCF-MVS	73.52 7	80.38 147	78.84 162	85.01 91	87.71 203	68.99 112	83.65 259	91.46 122	63.00 294	77.77 170	90.28 129	66.10 108	95.09 95	61.40 257	88.22 133	90.94 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thisisatest0530		79.40 170	77.76 189	84.31 118	87.69 205	65.10 198	87.36 164	84.26 277	70.04 200	77.42 175	88.26 188	49.94 276	94.79 110	70.20 179	84.70 175	93.03 98

RRT_MVS		80.35 149	79.22 154	83.74 144	87.63 206	65.46 189	91.08 56	88.92 198	73.82 135	76.44 199	90.03 137	49.05 288	94.25 130	76.84 121	79.20 245	91.51 146

GBi-Net		78.40 192	77.40 197	81.40 210	87.60 207	63.01 241	88.39 130	89.28 178	71.63 170	75.34 224	87.28 210	54.80 222	91.11 245	62.72 242	79.57 237	90.09 199

test1		78.40 192	77.40 197	81.40 210	87.60 207	63.01 241	88.39 130	89.28 178	71.63 170	75.34 224	87.28 210	54.80 222	91.11 245	62.72 242	79.57 237	90.09 199

FMVSNet2		78.20 198	77.21 200	81.20 217	87.60 207	62.89 245	87.47 162	89.02 191	71.63 170	75.29 228	87.28 210	54.80 222	91.10 248	62.38 246	79.38 241	89.61 221

CDS-MVSNet		79.07 179	77.70 191	83.17 163	87.60 207	68.23 133	84.40 247	86.20 251	67.49 245	76.36 201	86.54 239	61.54 165	90.79 255	61.86 253	87.33 142	90.49 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 206	77.15 201	80.36 235	87.57 211	60.21 277	83.37 266	87.78 226	66.11 259	75.37 223	87.06 221	63.27 136	90.48 260	61.38 258	82.43 207	90.40 187

mvsmamba		81.69 113	80.74 119	84.56 107	87.45 212	66.72 163	91.26 50	85.89 256	74.66 115	78.23 158	90.56 125	54.33 229	94.91 101	80.73 89	83.54 192	92.04 135

xiu_mvs_v1_base_debu		80.80 135	79.72 139	84.03 134	87.35 213	70.19 89	85.56 214	88.77 201	69.06 224	81.83 105	88.16 190	50.91 264	92.85 193	78.29 108	87.56 138	89.06 232

xiu_mvs_v1_base		80.80 135	79.72 139	84.03 134	87.35 213	70.19 89	85.56 214	88.77 201	69.06 224	81.83 105	88.16 190	50.91 264	92.85 193	78.29 108	87.56 138	89.06 232

xiu_mvs_v1_base_debi		80.80 135	79.72 139	84.03 134	87.35 213	70.19 89	85.56 214	88.77 201	69.06 224	81.83 105	88.16 190	50.91 264	92.85 193	78.29 108	87.56 138	89.06 232

MVSFormer		82.85 95	82.05 101	85.24 85	87.35 213	70.21 87	90.50 67	90.38 146	68.55 235	81.32 113	89.47 152	61.68 162	93.46 168	78.98 100	90.26 108	92.05 133

lupinMVS		81.39 121	80.27 130	84.76 103	87.35 213	70.21 87	85.55 217	86.41 247	62.85 297	81.32 113	88.61 176	61.68 162	92.24 214	78.41 106	90.26 108	91.83 138

baseline		84.93 72	84.98 69	84.80 102	87.30 218	65.39 192	87.30 167	92.88 58	77.62 41	84.04 81	92.26 84	71.81 55	93.96 139	81.31 81	90.30 107	95.03 6

PAPM		77.68 214	76.40 220	81.51 207	87.29 219	61.85 256	83.78 257	89.59 171	64.74 275	71.23 272	88.70 172	62.59 148	93.66 158	52.66 316	87.03 147	89.01 237

LCM-MVSNet-Re		77.05 224	76.94 206	77.36 284	87.20 220	51.60 350	80.06 298	80.46 320	75.20 102	67.69 304	86.72 226	62.48 150	88.98 282	63.44 238	89.25 120	91.51 146

casdiffmvs		85.11 70	85.14 67	85.01 91	87.20 220	65.77 183	87.75 155	92.83 61	77.84 39	84.36 75	92.38 82	72.15 53	93.93 145	81.27 82	90.48 105	95.33 2

COLMAP_ROB	66.92 17	73.01 270	70.41 281	80.81 227	87.13 222	65.63 184	88.30 135	84.19 278	62.96 295	63.80 339	87.69 200	38.04 347	92.56 200	46.66 346	74.91 296	84.24 324
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 211	77.69 192	77.84 276	87.07 223	53.91 337	87.91 151	91.18 128	77.56 45	73.14 253	88.82 171	61.23 174	89.17 278	59.95 267	72.37 317	90.43 185

MVS_Test		83.15 89	83.06 85	83.41 153	86.86 224	63.21 237	86.11 202	92.00 97	74.31 123	82.87 95	89.44 157	70.03 72	93.21 175	77.39 116	88.50 130	93.81 63

UniMVSNet_ETH3D		79.10 178	78.24 176	81.70 203	86.85 225	60.24 276	87.28 168	88.79 200	74.25 126	76.84 187	90.53 127	49.48 281	91.56 234	67.98 202	82.15 209	93.29 87

FMVSNet3		77.88 208	76.85 208	80.97 224	86.84 226	62.36 248	86.52 191	88.77 201	71.13 180	75.34 224	86.66 233	54.07 233	91.10 248	62.72 242	79.57 237	89.45 224

FMVSNet1		77.44 217	76.12 223	81.40 210	86.81 227	63.01 241	88.39 130	89.28 178	70.49 194	74.39 242	87.28 210	49.06 287	91.11 245	60.91 261	78.52 247	90.09 199

nrg030		83.88 76	83.53 79	84.96 93	86.77 228	69.28 108	90.46 71	92.67 67	74.79 111	82.95 93	91.33 106	72.70 48	93.09 185	80.79 88	79.28 243	92.50 116

ET-MVSNet_ETH3D		78.63 188	76.63 216	84.64 105	86.73 229	69.47 104	85.01 227	84.61 269	69.54 210	66.51 321	86.59 235	50.16 273	91.75 229	76.26 126	84.24 182	92.69 110

jason		81.39 121	80.29 129	84.70 104	86.63 230	69.90 94	85.95 205	86.77 243	63.24 290	81.07 119	89.47 152	61.08 178	92.15 216	78.33 107	90.07 113	92.05 133
jason: jason.
PS-MVSNAJss		82.07 105	81.31 109	84.34 117	86.51 231	67.27 153	89.27 97	91.51 118	71.75 168	79.37 136	90.22 132	63.15 140	94.27 125	77.69 112	82.36 208	91.49 149

WTY-MVS		75.65 245	75.68 226	75.57 300	86.40 232	56.82 310	77.92 321	82.40 302	65.10 270	76.18 206	87.72 198	63.13 143	80.90 340	60.31 265	81.96 211	89.00 239

DTE-MVSNet		76.99 225	76.80 209	77.54 283	86.24 233	53.06 345	87.52 160	90.66 139	77.08 60	72.50 259	88.67 174	60.48 187	89.52 272	57.33 294	70.74 328	90.05 204

PVSNet	64.34 18	72.08 279	70.87 278	75.69 298	86.21 234	56.44 317	74.37 340	80.73 315	62.06 306	70.17 282	82.23 306	42.86 324	83.31 330	54.77 306	84.45 179	87.32 277

tfpnnormal		74.39 254	73.16 257	78.08 273	86.10 235	58.05 291	84.65 237	87.53 230	70.32 196	71.22 273	85.63 257	54.97 221	89.86 266	43.03 358	75.02 295	86.32 297

IterMVS-LS		80.06 154	79.38 147	82.11 195	85.89 236	63.20 238	86.79 182	89.34 176	74.19 127	75.45 220	86.72 226	66.62 101	92.39 206	72.58 161	76.86 264	90.75 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 200	78.33 174	77.61 281	85.79 237	56.21 322	86.78 183	85.76 257	73.60 141	77.93 167	87.57 203	65.02 120	88.99 281	67.14 213	75.33 291	87.63 268

cascas		76.72 230	74.64 240	82.99 172	85.78 238	65.88 178	82.33 277	89.21 183	60.85 313	72.74 256	81.02 315	47.28 297	93.75 155	67.48 207	85.02 170	89.34 226

MVS		78.19 199	76.99 205	81.78 201	85.66 239	66.99 157	84.66 234	90.47 144	55.08 350	72.02 266	85.27 264	63.83 130	94.11 137	66.10 220	89.80 115	84.24 324

XVG-OURS		80.41 146	79.23 153	83.97 138	85.64 240	69.02 110	83.03 273	90.39 145	71.09 182	77.63 172	91.49 102	54.62 228	91.35 240	75.71 131	83.47 193	91.54 145

CANet_DTU		80.61 141	79.87 135	82.83 178	85.60 241	63.17 240	87.36 164	88.65 207	76.37 79	75.88 211	88.44 182	53.51 237	93.07 186	73.30 153	89.74 116	92.25 126

XVG-OURS-SEG-HR		80.81 133	79.76 138	83.96 139	85.60 241	68.78 116	83.54 264	90.50 143	70.66 191	76.71 192	91.66 94	60.69 183	91.26 242	76.94 120	81.58 216	91.83 138

TransMVSNet (Re)		75.39 250	74.56 242	77.86 275	85.50 243	57.10 307	86.78 183	86.09 254	72.17 164	71.53 270	87.34 209	63.01 144	89.31 276	56.84 298	61.83 352	87.17 281

MVP-Stereo		76.12 239	74.46 245	81.13 220	85.37 244	69.79 96	84.42 246	87.95 220	65.03 272	67.46 307	85.33 263	53.28 239	91.73 231	58.01 288	83.27 195	81.85 344
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thisisatest0515		77.33 220	75.38 233	83.18 162	85.27 245	63.80 222	82.11 279	83.27 292	65.06 271	75.91 210	83.84 285	49.54 280	94.27 125	67.24 211	86.19 160	91.48 150

OpenMVS	72.83 10	79.77 159	78.33 174	84.09 126	85.17 246	69.91 93	90.57 65	90.97 133	66.70 251	72.17 264	91.91 89	54.70 226	93.96 139	61.81 254	90.95 100	88.41 257

AllTest		70.96 284	68.09 296	79.58 253	85.15 247	63.62 224	84.58 239	79.83 326	62.31 303	60.32 349	86.73 224	32.02 360	88.96 284	50.28 327	71.57 324	86.15 301

TestCases				79.58 253	85.15 247	63.62 224		79.83 326	62.31 303	60.32 349	86.73 224	32.02 360	88.96 284	50.28 327	71.57 324	86.15 301

Effi-MVS+-dtu		80.03 155	78.57 167	84.42 113	85.13 249	68.74 119	88.77 116	88.10 215	74.99 106	74.97 236	83.49 291	57.27 210	93.36 171	73.53 148	80.88 222	91.18 157

mvs-test1		80.88 128	79.40 146	85.29 83	85.13 249	69.75 98	89.28 96	88.10 215	74.99 106	76.44 199	86.72 226	57.27 210	94.26 129	73.53 148	83.18 197	91.87 137

SixPastTwentyTwo		73.37 264	71.26 274	79.70 249	85.08 251	57.89 296	85.57 213	83.56 286	71.03 183	65.66 325	85.88 251	42.10 331	92.57 199	59.11 275	63.34 350	88.65 252

bld_raw_dy_0_64		77.29 222	75.98 224	81.22 216	85.04 252	65.47 188	88.14 144	77.56 337	69.20 219	73.77 247	89.40 160	42.24 330	88.85 287	76.78 123	81.64 215	89.33 227

EG-PatchMatch MVS		74.04 259	71.82 267	80.71 229	84.92 253	67.42 149	85.86 209	88.08 217	66.04 261	64.22 335	83.85 284	35.10 355	92.56 200	57.44 292	80.83 223	82.16 343

IB-MVS	68.01 15	75.85 243	73.36 255	83.31 155	84.76 254	66.03 172	83.38 265	85.06 263	70.21 199	69.40 293	81.05 314	45.76 310	94.66 114	65.10 229	75.49 284	89.25 229
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
mvs_tets		79.13 177	77.77 188	83.22 161	84.70 255	66.37 168	89.17 99	90.19 154	69.38 213	75.40 222	89.46 154	44.17 317	93.15 181	76.78 123	80.70 226	90.14 194

jajsoiax		79.29 173	77.96 180	83.27 157	84.68 256	66.57 166	89.25 98	90.16 155	69.20 219	75.46 219	89.49 151	45.75 311	93.13 183	76.84 121	80.80 224	90.11 197

MIMVSNet		70.69 286	69.30 285	74.88 307	84.52 257	56.35 320	75.87 332	79.42 329	64.59 276	67.76 302	82.41 302	41.10 335	81.54 337	46.64 348	81.34 217	86.75 292

MSDG		73.36 266	70.99 275	80.49 233	84.51 258	65.80 181	80.71 291	86.13 253	65.70 265	65.46 326	83.74 288	44.60 315	90.91 253	51.13 322	76.89 263	84.74 319

mvs_anonymous		79.42 169	79.11 157	80.34 236	84.45 259	57.97 294	82.59 275	87.62 228	67.40 246	76.17 208	88.56 179	68.47 87	89.59 271	70.65 176	86.05 162	93.47 82

EI-MVSNet		80.52 145	79.98 132	82.12 194	84.28 260	63.19 239	86.41 193	88.95 196	74.18 128	78.69 145	87.54 206	66.62 101	92.43 204	72.57 162	80.57 228	90.74 174

CVMVSNet		72.99 271	72.58 261	74.25 313	84.28 260	50.85 355	86.41 193	83.45 290	44.56 362	73.23 252	87.54 206	49.38 282	85.70 312	65.90 222	78.44 249	86.19 300

pm-mvs1		77.25 223	76.68 215	78.93 261	84.22 262	58.62 286	86.41 193	88.36 212	71.37 178	73.31 250	88.01 196	61.22 175	89.15 279	64.24 234	73.01 314	89.03 236

EPNet		83.72 79	82.92 88	86.14 69	84.22 262	69.48 103	91.05 57	85.27 261	81.30 7	76.83 188	91.65 95	66.09 109	95.56 65	76.00 130	93.85 67	93.38 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v8		79.97 158	79.02 159	82.80 181	84.09 264	64.50 209	87.96 147	90.29 153	74.13 130	75.24 229	86.81 223	62.88 145	93.89 148	74.39 141	75.40 289	90.00 205

v10		79.74 160	78.67 164	82.97 174	84.06 265	64.95 200	87.88 153	90.62 140	73.11 152	75.11 232	86.56 238	61.46 168	94.05 138	73.68 146	75.55 283	89.90 211

SCA		74.22 257	72.33 264	79.91 244	84.05 266	62.17 252	79.96 300	79.29 330	66.30 258	72.38 262	80.13 324	51.95 253	88.60 289	59.25 273	77.67 255	88.96 241

test_djsdf		80.30 150	79.32 150	83.27 157	83.98 267	65.37 193	90.50 67	90.38 146	68.55 235	76.19 205	88.70 172	56.44 216	93.46 168	78.98 100	80.14 234	90.97 167

1314		76.53 231	75.30 236	80.21 239	83.93 268	62.32 250	84.66 234	88.81 199	60.23 317	70.16 283	84.07 282	55.30 220	90.73 257	67.37 208	83.21 196	87.59 271

MS-PatchMatch		73.83 261	72.67 260	77.30 286	83.87 269	66.02 173	81.82 280	84.66 268	61.37 311	68.61 299	82.82 298	47.29 296	88.21 293	59.27 272	84.32 180	77.68 358

v1144		80.03 155	79.03 158	83.01 171	83.78 270	64.51 207	87.11 172	90.57 142	71.96 166	78.08 164	86.20 247	61.41 169	93.94 142	74.93 138	77.23 257	90.60 179

OurMVSNet-221017-0		74.26 256	72.42 263	79.80 247	83.76 271	59.59 281	85.92 207	86.64 244	66.39 257	66.96 312	87.58 202	39.46 340	91.60 232	65.76 224	69.27 332	88.22 258

v2v482		80.23 151	79.29 151	83.05 169	83.62 272	64.14 216	87.04 173	89.97 160	73.61 140	78.18 161	87.22 214	61.10 177	93.82 149	76.11 127	76.78 267	91.18 157

XXY-MVS		75.41 249	75.56 228	74.96 306	83.59 273	57.82 298	80.59 293	83.87 282	66.54 256	74.93 237	88.31 185	63.24 137	80.09 343	62.16 249	76.85 265	86.97 287

v1192		79.59 163	78.43 171	83.07 168	83.55 274	64.52 206	86.93 177	90.58 141	70.83 185	77.78 169	85.90 250	59.15 194	93.94 142	73.96 145	77.19 259	90.76 172

EGC-MVSNET		52.07 337	47.05 341	67.14 344	83.51 275	60.71 269	80.50 294	67.75 367	0.07 384	0.43 385	75.85 353	24.26 367	81.54 337	28.82 370	62.25 351	59.16 370

v7n		78.97 182	77.58 195	83.14 164	83.45 276	65.51 186	88.32 134	91.21 127	73.69 138	72.41 261	86.32 245	57.93 201	93.81 150	69.18 191	75.65 281	90.11 197

v144192		79.47 166	78.37 172	82.78 184	83.35 277	63.96 219	86.96 175	90.36 149	69.99 201	77.50 173	85.67 256	60.66 184	93.77 153	74.27 142	76.58 268	90.62 177

tpm2		73.26 267	71.46 269	78.63 264	83.34 278	56.71 313	80.65 292	80.40 321	56.63 344	73.55 248	82.02 309	51.80 257	91.24 243	56.35 301	78.42 250	87.95 261

v1921920		79.22 174	78.03 179	82.80 181	83.30 279	63.94 220	86.80 181	90.33 150	69.91 203	77.48 174	85.53 259	58.44 198	93.75 155	73.60 147	76.85 265	90.71 175

baseline2		75.70 244	73.83 252	81.30 213	83.26 280	61.79 258	82.57 276	80.65 316	66.81 248	66.88 313	83.42 292	57.86 203	92.19 215	63.47 237	79.57 237	89.91 210

v1240		78.99 181	77.78 187	82.64 187	83.21 281	63.54 228	86.62 188	90.30 152	69.74 209	77.33 177	85.68 255	57.04 213	93.76 154	73.13 156	76.92 261	90.62 177

XVG-ACMP-BASELINE		76.11 240	74.27 247	81.62 204	83.20 282	64.67 205	83.60 262	89.75 167	69.75 207	71.85 267	87.09 219	32.78 359	92.11 217	69.99 183	80.43 230	88.09 260

MDTV_nov1_ep13			69.97 284		83.18 283	53.48 340	77.10 326	80.18 325	60.45 314	69.33 295	80.44 321	48.89 291	86.90 304	51.60 320	78.51 248

PatchmatchNet		73.12 269	71.33 272	78.49 269	83.18 283	60.85 267	79.63 302	78.57 332	64.13 282	71.73 268	79.81 329	51.20 262	85.97 311	57.40 293	76.36 275	88.66 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 204	76.49 217	82.62 188	83.16 285	66.96 161	86.94 176	87.45 233	72.45 158	71.49 271	84.17 280	54.79 225	91.58 233	67.61 205	80.31 231	89.30 228

gg-mvs-nofinetune		69.95 294	67.96 297	75.94 296	83.07 286	54.51 333	77.23 325	70.29 360	63.11 292	70.32 279	62.33 365	43.62 320	88.69 288	53.88 310	87.76 135	84.62 322

MVSTER		79.01 180	77.88 184	82.38 192	83.07 286	64.80 203	84.08 255	88.95 196	69.01 227	78.69 145	87.17 217	54.70 226	92.43 204	74.69 139	80.57 228	89.89 212

K. test v3		71.19 282	68.51 290	79.21 259	83.04 288	57.78 299	84.35 248	76.91 344	72.90 157	62.99 342	82.86 297	39.27 341	91.09 250	61.65 255	52.66 365	88.75 249

eth_miper_zixun_eth		77.92 207	76.69 214	81.61 206	83.00 289	61.98 254	83.15 268	89.20 184	69.52 211	74.86 238	84.35 278	61.76 161	92.56 200	71.50 168	72.89 315	90.28 190

diffmvs		82.10 103	81.88 105	82.76 186	83.00 289	63.78 223	83.68 258	89.76 166	72.94 156	82.02 104	89.85 141	65.96 113	90.79 255	82.38 75	87.30 143	93.71 67

FMVSNet5		69.50 296	67.96 297	74.15 314	82.97 291	55.35 328	80.01 299	82.12 304	62.56 301	63.02 340	81.53 311	36.92 350	81.92 335	48.42 336	74.06 303	85.17 315

c3_l		78.75 185	77.91 182	81.26 214	82.89 292	61.56 260	84.09 254	89.13 188	69.97 202	75.56 215	84.29 279	66.36 105	92.09 218	73.47 151	75.48 285	90.12 196

sss		73.60 262	73.64 253	73.51 317	82.80 293	55.01 329	76.12 328	81.69 308	62.47 302	74.68 240	85.85 253	57.32 209	78.11 350	60.86 262	80.93 221	87.39 274

GA-MVS		76.87 228	75.17 237	81.97 199	82.75 294	62.58 246	81.44 287	86.35 250	72.16 165	74.74 239	82.89 296	46.20 305	92.02 220	68.85 196	81.09 220	91.30 155

v148		78.72 186	77.80 186	81.47 208	82.73 295	61.96 255	86.30 197	88.08 217	73.26 149	76.18 206	85.47 261	62.46 151	92.36 208	71.92 165	73.82 307	90.09 199

IterMVS-SCA-FT		75.43 248	73.87 251	80.11 241	82.69 296	64.85 202	81.57 285	83.47 289	69.16 221	70.49 277	84.15 281	51.95 253	88.15 294	69.23 190	72.14 320	87.34 276

miper_ehance_all_eth		78.59 190	77.76 189	81.08 221	82.66 297	61.56 260	83.65 259	89.15 186	68.87 230	75.55 216	83.79 287	66.49 103	92.03 219	73.25 154	76.39 272	89.64 220

CostFormer		75.24 251	73.90 250	79.27 257	82.65 298	58.27 289	80.80 288	82.73 300	61.57 308	75.33 227	83.13 294	55.52 218	91.07 251	64.98 230	78.34 251	88.45 255

EPNet_dtu		75.46 247	74.86 238	77.23 288	82.57 299	54.60 331	86.89 178	83.09 296	71.64 169	66.25 323	85.86 252	55.99 217	88.04 296	54.92 305	86.55 154	89.05 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 268	71.46 269	78.54 267	82.50 300	59.85 278	82.18 278	82.84 299	58.96 328	71.15 274	89.41 158	45.48 314	84.77 320	58.82 280	71.83 322	91.02 166

cl____		77.72 212	76.76 211	80.58 231	82.49 301	60.48 273	83.09 270	87.87 222	69.22 217	74.38 243	85.22 267	62.10 158	91.53 235	71.09 171	75.41 288	89.73 219

DIV-MVS_self_test		77.72 212	76.76 211	80.58 231	82.48 302	60.48 273	83.09 270	87.86 223	69.22 217	74.38 243	85.24 265	62.10 158	91.53 235	71.09 171	75.40 289	89.74 218

tpm cat1		70.57 287	68.31 292	77.35 285	82.41 303	57.95 295	78.08 318	80.22 324	52.04 356	68.54 300	77.66 344	52.00 252	87.84 298	51.77 318	72.07 321	86.25 298

cl22		78.07 202	77.01 203	81.23 215	82.37 304	61.83 257	83.55 263	87.98 219	68.96 228	75.06 234	83.87 283	61.40 170	91.88 226	73.53 148	76.39 272	89.98 208

MVS_0304		72.48 274	70.89 277	77.24 287	82.20 305	59.68 279	84.11 253	83.49 288	67.10 247	66.87 314	80.59 320	35.00 356	87.40 301	59.07 277	79.58 236	84.63 321

tpm		72.37 277	71.71 268	74.35 312	82.19 306	52.00 347	79.22 307	77.29 341	64.56 277	72.95 255	83.68 290	51.35 260	83.26 331	58.33 285	75.80 279	87.81 265

tpmvs		71.09 283	69.29 286	76.49 293	82.04 307	56.04 323	78.92 311	81.37 311	64.05 285	67.18 311	78.28 339	49.74 279	89.77 267	49.67 332	72.37 317	83.67 330

pmmvs4		74.03 260	71.91 266	80.39 234	81.96 308	68.32 130	81.45 286	82.14 303	59.32 325	69.87 289	85.13 269	52.40 244	88.13 295	60.21 266	74.74 298	84.73 320

TinyColmap		67.30 310	64.81 314	74.76 309	81.92 309	56.68 314	80.29 297	81.49 310	60.33 315	56.27 362	83.22 293	24.77 366	87.66 300	45.52 352	69.47 331	79.95 353

ITE_SJBPF				78.22 271	81.77 310	60.57 271		83.30 291	69.25 216	67.54 305	87.20 215	36.33 352	87.28 303	54.34 308	74.62 299	86.80 290

miper_enhance_ethall		77.87 209	76.86 207	80.92 225	81.65 311	61.38 262	82.68 274	88.98 193	65.52 268	75.47 217	82.30 304	65.76 115	92.00 221	72.95 157	76.39 272	89.39 225

MVS-HIRNet		59.14 329	57.67 332	63.57 347	81.65 311	43.50 373	71.73 345	65.06 371	39.59 368	51.43 367	57.73 371	38.34 345	82.58 334	39.53 364	73.95 304	64.62 368

GG-mvs-BLEND				75.38 303	81.59 313	55.80 325	79.32 305	69.63 362		67.19 310	73.67 357	43.24 321	88.90 286	50.41 324	84.50 176	81.45 346

IterMVS		74.29 255	72.94 259	78.35 270	81.53 314	63.49 230	81.58 284	82.49 301	68.06 241	69.99 286	83.69 289	51.66 259	85.54 313	65.85 223	71.64 323	86.01 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 314	64.71 315	71.90 324	81.45 315	63.52 229	57.98 371	68.95 366	53.57 352	62.59 344	76.70 347	46.22 304	75.29 362	55.25 304	79.68 235	76.88 360

gm-plane-assit					81.40 316	53.83 338			62.72 300		80.94 317		92.39 206	63.40 239

pmmvs6		74.69 253	73.39 254	78.61 265	81.38 317	57.48 303	86.64 187	87.95 220	64.99 274	70.18 281	86.61 234	50.43 271	89.52 272	62.12 250	70.18 330	88.83 246

test-LLR		72.94 272	72.43 262	74.48 310	81.35 318	58.04 292	78.38 314	77.46 338	66.66 252	69.95 287	79.00 333	48.06 293	79.24 344	66.13 218	84.83 172	86.15 301

test-mter		71.41 281	70.39 282	74.48 310	81.35 318	58.04 292	78.38 314	77.46 338	60.32 316	69.95 287	79.00 333	36.08 353	79.24 344	66.13 218	84.83 172	86.15 301

CR-MVSNet		73.37 264	71.27 273	79.67 251	81.32 320	65.19 195	75.92 330	80.30 322	59.92 320	72.73 257	81.19 312	52.50 242	86.69 305	59.84 268	77.71 253	87.11 285

RPMNet		73.51 263	70.49 279	82.58 189	81.32 320	65.19 195	75.92 330	92.27 83	57.60 338	72.73 257	76.45 349	52.30 245	95.43 74	48.14 341	77.71 253	87.11 285

V42		79.38 172	78.24 176	82.83 178	81.10 322	65.50 187	85.55 217	89.82 164	71.57 175	78.21 159	86.12 248	60.66 184	93.18 180	75.64 132	75.46 287	89.81 216

lessismore_v0				78.97 260	81.01 323	57.15 306		65.99 369		61.16 347	82.82 298	39.12 342	91.34 241	59.67 269	46.92 371	88.43 256

Patchmtry		70.74 285	69.16 287	75.49 302	80.72 324	54.07 336	74.94 339	80.30 322	58.34 332	70.01 284	81.19 312	52.50 242	86.54 306	53.37 313	71.09 327	85.87 308

PatchT		68.46 305	67.85 299	70.29 333	80.70 325	43.93 372	72.47 343	74.88 349	60.15 318	70.55 275	76.57 348	49.94 276	81.59 336	50.58 323	74.83 297	85.34 311

USDC		70.33 290	68.37 291	76.21 295	80.60 326	56.23 321	79.19 308	86.49 246	60.89 312	61.29 346	85.47 261	31.78 362	89.47 274	53.37 313	76.21 276	82.94 340

tpmrst		72.39 275	72.13 265	73.18 321	80.54 327	49.91 358	79.91 301	79.08 331	63.11 292	71.69 269	79.95 326	55.32 219	82.77 333	65.66 225	73.89 305	86.87 288

anonymousdsp		78.60 189	77.15 201	82.98 173	80.51 328	67.08 156	87.24 169	89.53 172	65.66 266	75.16 230	87.19 216	52.52 241	92.25 213	77.17 118	79.34 242	89.61 221

OpenMVS_ROB	64.09 19	70.56 288	68.19 293	77.65 280	80.26 329	59.41 283	85.01 227	82.96 298	58.76 330	65.43 327	82.33 303	37.63 349	91.23 244	45.34 354	76.03 277	82.32 341

Anonymous20231206		68.60 302	67.80 301	71.02 331	80.23 330	50.75 356	78.30 317	80.47 319	56.79 343	66.11 324	82.63 301	46.35 303	78.95 346	43.62 357	75.70 280	83.36 333

miper_lstm_enhance		74.11 258	73.11 258	77.13 289	80.11 331	59.62 280	72.23 344	86.92 242	66.76 250	70.40 278	82.92 295	56.93 214	82.92 332	69.06 193	72.63 316	88.87 244

MIMVSNet1		68.58 303	66.78 310	73.98 315	80.07 332	51.82 348	80.77 289	84.37 272	64.40 279	59.75 352	82.16 307	36.47 351	83.63 327	42.73 359	70.33 329	86.48 296

ADS-MVSNet2		66.20 319	63.33 321	74.82 308	79.92 333	58.75 285	67.55 359	75.19 348	53.37 353	65.25 329	75.86 351	42.32 327	80.53 342	41.57 361	68.91 334	85.18 313

ADS-MVSNet		64.36 323	62.88 325	68.78 340	79.92 333	47.17 363	67.55 359	71.18 358	53.37 353	65.25 329	75.86 351	42.32 327	73.99 366	41.57 361	68.91 334	85.18 313

D2MVS		74.82 252	73.21 256	79.64 252	79.81 335	62.56 247	80.34 296	87.35 234	64.37 280	68.86 296	82.66 300	46.37 302	90.10 264	67.91 203	81.24 219	86.25 298

our_test_3		69.14 298	67.00 308	75.57 300	79.80 336	58.80 284	77.96 319	77.81 335	59.55 323	62.90 343	78.25 340	47.43 295	83.97 324	51.71 319	67.58 338	83.93 329

ppachtmachnet_test		70.04 293	67.34 306	78.14 272	79.80 336	61.13 263	79.19 308	80.59 317	59.16 327	65.27 328	79.29 330	46.75 301	87.29 302	49.33 333	66.72 339	86.00 307

dp		66.80 311	65.43 313	70.90 332	79.74 338	48.82 361	75.12 337	74.77 350	59.61 322	64.08 336	77.23 345	42.89 323	80.72 341	48.86 335	66.58 341	83.16 335

EPMVS		69.02 299	68.16 294	71.59 325	79.61 339	49.80 360	77.40 323	66.93 368	62.82 298	70.01 284	79.05 331	45.79 309	77.86 352	56.58 299	75.26 293	87.13 284

PVSNet_0	57.27 20	61.67 328	59.27 331	68.85 339	79.61 339	57.44 304	68.01 358	73.44 356	55.93 347	58.54 354	70.41 362	44.58 316	77.55 353	47.01 345	35.91 374	71.55 364

CL-MVSNet_self_test		72.37 277	71.46 269	75.09 305	79.49 341	53.53 339	80.76 290	85.01 265	69.12 222	70.51 276	82.05 308	57.92 202	84.13 323	52.27 317	66.00 344	87.60 269

Patchmatch-test		64.82 322	63.24 322	69.57 335	79.42 342	49.82 359	63.49 368	69.05 365	51.98 357	59.95 351	80.13 324	50.91 264	70.98 369	40.66 363	73.57 308	87.90 263

MDA-MVSNet-bldmvs		66.68 312	63.66 320	75.75 297	79.28 343	60.56 272	73.92 341	78.35 333	64.43 278	50.13 368	79.87 328	44.02 318	83.67 326	46.10 350	56.86 359	83.03 338

TESTMET0.1,1		69.89 295	69.00 288	72.55 322	79.27 344	56.85 309	78.38 314	74.71 352	57.64 337	68.09 301	77.19 346	37.75 348	76.70 355	63.92 235	84.09 183	84.10 327

N_pmnet		52.79 335	53.26 335	51.40 358	78.99 345	7.68 389	69.52 352	3.89 389	51.63 358	57.01 359	74.98 355	40.83 337	65.96 374	37.78 366	64.67 347	80.56 352

EU-MVSNet		68.53 304	67.61 304	71.31 330	78.51 346	47.01 364	84.47 241	84.27 276	42.27 364	66.44 322	84.79 274	40.44 338	83.76 325	58.76 281	68.54 337	83.17 334

pmmvs5		71.55 280	70.20 283	75.61 299	77.83 347	56.39 318	81.74 282	80.89 312	57.76 336	67.46 307	84.49 276	49.26 285	85.32 316	57.08 296	75.29 292	85.11 316

test0.0.03 1		68.00 306	67.69 303	68.90 338	77.55 348	47.43 362	75.70 333	72.95 357	66.66 252	66.56 317	82.29 305	48.06 293	75.87 359	44.97 355	74.51 300	83.41 332

Patchmatch-RL test		70.24 291	67.78 302	77.61 281	77.43 349	59.57 282	71.16 346	70.33 359	62.94 296	68.65 298	72.77 358	50.62 268	85.49 314	69.58 188	66.58 341	87.77 266

pmmvs-eth3d		70.50 289	67.83 300	78.52 268	77.37 350	66.18 171	81.82 280	81.51 309	58.90 329	63.90 338	80.42 322	42.69 325	86.28 309	58.56 282	65.30 346	83.11 336

JIA-IIPM		66.32 316	62.82 326	76.82 291	77.09 351	61.72 259	65.34 364	75.38 347	58.04 335	64.51 333	62.32 366	42.05 332	86.51 307	51.45 321	69.22 333	82.21 342

Gipuma		45.18 342	41.86 345	55.16 356	77.03 352	51.52 351	32.50 377	80.52 318	32.46 373	27.12 376	35.02 377	9.52 382	75.50 360	22.31 377	60.21 357	38.45 376
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 320	62.92 323	71.37 327	75.93 353	56.73 311	69.09 357	74.73 351	57.28 341	54.03 365	77.89 341	45.88 307	74.39 365	49.89 331	61.55 353	82.99 339

YYNet1		65.03 320	62.91 324	71.38 326	75.85 354	56.60 315	69.12 356	74.66 353	57.28 341	54.12 364	77.87 342	45.85 308	74.48 364	49.95 330	61.52 354	83.05 337

PMMVS		69.34 297	68.67 289	71.35 329	75.67 355	62.03 253	75.17 334	73.46 355	50.00 359	68.68 297	79.05 331	52.07 251	78.13 349	61.16 260	82.77 202	73.90 362

testgi		66.67 313	66.53 311	67.08 345	75.62 356	41.69 376	75.93 329	76.50 345	66.11 259	65.20 331	86.59 235	35.72 354	74.71 363	43.71 356	73.38 312	84.84 318

test20.03		67.45 308	66.95 309	68.94 337	75.48 357	44.84 370	77.50 322	77.67 336	66.66 252	63.01 341	83.80 286	47.02 298	78.40 348	42.53 360	68.86 336	83.58 331

KD-MVS_2432*1600		66.22 317	63.89 318	73.21 318	75.47 358	53.42 341	70.76 349	84.35 273	64.10 283	66.52 319	78.52 337	34.55 357	84.98 317	50.40 325	50.33 368	81.23 347

miper_refine_blended		66.22 317	63.89 318	73.21 318	75.47 358	53.42 341	70.76 349	84.35 273	64.10 283	66.52 319	78.52 337	34.55 357	84.98 317	50.40 325	50.33 368	81.23 347

Anonymous20240521		68.80 301	67.22 307	73.55 316	74.33 360	54.11 335	83.18 267	85.61 258	58.15 333	61.68 345	80.94 317	30.71 363	81.27 339	57.00 297	73.34 313	85.28 312

KD-MVS_self_test		68.81 300	67.59 305	72.46 323	74.29 361	45.45 366	77.93 320	87.00 240	63.12 291	63.99 337	78.99 335	42.32 327	84.77 320	56.55 300	64.09 349	87.16 283

PM-MVS		66.41 315	64.14 317	73.20 320	73.92 362	56.45 316	78.97 310	64.96 372	63.88 289	64.72 332	80.24 323	19.84 371	83.44 329	66.24 217	64.52 348	79.71 354

UnsupCasMVSNet_bld		63.70 325	61.53 330	70.21 334	73.69 363	51.39 353	72.82 342	81.89 305	55.63 348	57.81 357	71.80 360	38.67 343	78.61 347	49.26 334	52.21 366	80.63 350

UnsupCasMVSNet_eth		67.33 309	65.99 312	71.37 327	73.48 364	51.47 352	75.16 335	85.19 262	65.20 269	60.78 348	80.93 319	42.35 326	77.20 354	57.12 295	53.69 364	85.44 310

TDRefinement		67.49 307	64.34 316	76.92 290	73.47 365	61.07 264	84.86 231	82.98 297	59.77 321	58.30 355	85.13 269	26.06 365	87.89 297	47.92 343	60.59 356	81.81 345

ambc				75.24 304	73.16 366	50.51 357	63.05 369	87.47 232		64.28 334	77.81 343	17.80 373	89.73 269	57.88 289	60.64 355	85.49 309

CMPMVS	51.72 21	70.19 292	68.16 294	76.28 294	73.15 367	57.55 302	79.47 304	83.92 280	48.02 360	56.48 361	84.81 273	43.13 322	86.42 308	62.67 245	81.81 214	84.89 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 327	61.73 329	61.70 348	72.74 368	24.50 386	69.16 355	78.03 334	61.40 309	56.72 360	75.53 354	38.42 344	76.48 357	45.95 351	57.67 358	84.13 326

LF4IMVS		64.02 324	62.19 327	69.50 336	70.90 369	53.29 344	76.13 327	77.18 342	52.65 355	58.59 353	80.98 316	23.55 368	76.52 356	53.06 315	66.66 340	78.68 356

new_pmnet		50.91 338	50.29 339	52.78 357	68.58 370	34.94 381	63.71 366	56.63 379	39.73 367	44.95 369	65.47 364	21.93 369	58.48 376	34.98 368	56.62 360	64.92 367

DSMNet-mixed		57.77 331	56.90 333	60.38 350	67.70 371	35.61 379	69.18 354	53.97 380	32.30 374	57.49 358	79.88 327	40.39 339	68.57 373	38.78 365	72.37 317	76.97 359

FPMVS		53.68 334	51.64 336	59.81 351	65.08 372	51.03 354	69.48 353	69.58 363	41.46 365	40.67 370	72.32 359	16.46 375	70.00 372	24.24 376	65.42 345	58.40 371

pmmvs3		57.79 330	54.26 334	68.37 341	64.02 373	56.72 312	75.12 337	65.17 370	40.20 366	52.93 366	69.86 363	20.36 370	75.48 361	45.45 353	55.25 363	72.90 363

FMVS2		63.36 326	61.82 328	67.98 342	62.51 374	46.96 365	77.37 324	74.03 354	45.24 361	67.50 306	78.79 336	12.16 379	72.98 368	72.77 160	66.02 343	83.99 328

wuyk23d		16.82 351	15.94 354	19.46 365	58.74 375	31.45 382	39.22 375	3.74 390	6.84 381	6.04 384	2.70 384	1.27 389	24.29 384	10.54 383	14.40 383	2.63 381

FMVS1		45.72 340	41.96 343	57.00 352	56.90 376	45.32 367	66.14 362	59.26 376	26.19 375	30.89 374	60.96 369	4.14 385	70.64 370	26.39 374	46.73 372	55.04 372

APD_test		45.72 340	41.96 343	57.00 352	56.90 376	45.32 367	66.14 362	59.26 376	26.19 375	30.89 374	60.96 369	4.14 385	70.64 370	26.39 374	46.73 372	55.04 372

mvsany_test		53.99 333	51.45 337	61.61 349	55.51 378	44.74 371	63.52 367	45.41 384	43.69 363	58.11 356	76.45 349	17.99 372	63.76 375	54.77 306	47.59 370	76.34 361

PMMVS2		40.82 344	38.86 347	46.69 359	53.84 379	16.45 387	48.61 374	49.92 381	37.49 369	31.67 372	60.97 368	8.14 384	56.42 378	28.42 371	30.72 376	67.19 366

FMVS		52.09 336	50.82 338	55.90 354	53.82 380	42.31 375	59.42 370	58.31 378	36.45 370	56.12 363	70.96 361	12.18 378	57.79 377	53.51 312	56.57 361	67.60 365

LCM-MVSNet		54.25 332	49.68 340	67.97 343	53.73 381	45.28 369	66.85 361	80.78 314	35.96 371	39.45 371	62.23 367	8.70 383	78.06 351	48.24 340	51.20 367	80.57 351

E-PMN		31.77 345	30.64 348	35.15 362	52.87 382	27.67 383	57.09 372	47.86 382	24.64 377	16.40 382	33.05 378	11.23 380	54.90 379	14.46 381	18.15 379	22.87 378

EMVS		30.81 347	29.65 349	34.27 363	50.96 383	25.95 385	56.58 373	46.80 383	24.01 378	15.53 383	30.68 379	12.47 377	54.43 380	12.81 382	17.05 380	22.43 379

ANet_high		50.57 339	46.10 342	63.99 346	48.67 384	39.13 377	70.99 348	80.85 313	61.39 310	31.18 373	57.70 372	17.02 374	73.65 367	31.22 369	15.89 381	79.18 355

MVE	26.22 23	30.37 348	25.89 352	43.81 360	44.55 385	35.46 380	28.87 378	39.07 385	18.20 379	18.58 381	40.18 376	2.68 388	47.37 382	17.07 380	23.78 378	48.60 375
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 343	40.28 346	55.82 355	40.82 386	42.54 374	65.12 365	63.99 373	34.43 372	24.48 377	57.12 373	3.92 387	76.17 358	17.10 379	55.52 362	48.75 374
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 364	40.17 387	26.90 384		24.59 388	17.44 380	23.95 378	48.61 375	9.77 381	26.48 383	18.06 378	24.47 377	28.83 377

test_method		31.52 346	29.28 350	38.23 361	27.03 388	6.50 390	20.94 379	62.21 375	4.05 382	22.35 380	52.50 374	13.33 376	47.58 381	27.04 373	34.04 375	60.62 369

tmp_tt		18.61 350	21.40 353	10.23 366	4.82 389	10.11 388	34.70 376	30.74 387	1.48 383	23.91 379	26.07 380	28.42 364	13.41 385	27.12 372	15.35 382	7.17 380

testmvs		6.04 354	8.02 357	0.10 368	0.08 390	0.03 392	69.74 351	0.04 391	0.05 385	0.31 386	1.68 385	0.02 391	0.04 386	0.24 384	0.02 384	0.25 383

test123		6.12 353	8.11 356	0.14 367	0.06 391	0.09 391	71.05 347	0.03 392	0.04 386	0.25 387	1.30 386	0.05 390	0.03 387	0.21 385	0.01 385	0.29 382

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

eth-test2					0.00 392

eth-test					0.00 392

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		19.96 349	26.61 351	0.00 369	0.00 392	0.00 393	0.00 380	89.26 181	0.00 387	0.00 388	88.61 176	61.62 164	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		5.26 355	7.02 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	63.15 140	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		7.23 352	9.64 355	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	86.72 226	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

PC_three_1452									68.21 240	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 7

test_241102_TWO								94.06 12	77.24 52	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 41

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 24

GSMVS																88.96 241

sam_mvs1												51.32 261				88.96 241

sam_mvs												50.01 274

MTGPA								92.02 94

test_post1							78.90 312				5.43 383	48.81 292	85.44 315	59.25 273

test_post											5.46 382	50.36 272	84.24 322

patchmatchnet-post											74.00 356	51.12 263	88.60 289

MTMP							92.18 35	32.83 386

test9_res														84.90 33	95.70 31	92.87 105

agg_prior2														82.91 65	95.45 33	92.70 108

test_prior4						72.60 35	89.01 106

test_prior2							88.85 112		75.41 96	84.91 58	93.54 54	74.28 36		83.31 57	95.86 22

旧先验2							86.56 190		58.10 334	87.04 35			88.98 282	74.07 144

新几何2							86.29 198

无先验							87.48 161	88.98 193	60.00 319				94.12 135	67.28 209		88.97 240

原ACMM2							86.86 179

testdata2													91.01 252	62.37 247

segment_acmp												73.08 45

testdata1							84.14 252		75.71 90

plane_prior5								92.44 75					95.38 79	78.71 102	86.32 157	91.33 152

plane_prior4											91.00 118

plane_prior3						68.60 126			78.44 33	78.92 142

plane_prior2							91.25 52		79.12 25

plane_prior						68.71 121	90.38 73		77.62 41						86.16 161

n2								0.00 393

nn								0.00 393

door-mid								69.98 361

test11								92.23 86

door								69.44 364

HQP5-MVS						66.98 158

BP-MVS														77.47 114

HQP4-MVS										77.24 180			95.11 91			91.03 164

HQP3-MVS								92.19 89							85.99 164

HQP2-MVS												60.17 191

MDTV_nov1_ep13_2view						37.79 378	75.16 335		55.10 349	66.53 318		49.34 283		53.98 309		87.94 262

ACMMP++_ref															81.95 212

ACMMP++															81.25 218

Test By Simon												64.33 125

High-resolution multi-view benchmark