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.

OPU-MVS				81.71 12	92.05 3	55.97 43	92.48 3				94.01 5	67.21 2	95.10 15	89.82 1	92.55 3	94.06 3

PC_three_1452									66.58 49	87.27 2	93.70 8	66.82 4	94.95 17	89.74 2	91.98 4	93.98 5

MVS_0304		81.58 8	82.05 6	80.20 26	82.36 128	54.70 75	91.13 18	88.95 22	74.49 4	80.04 21	93.64 10	52.40 35	93.27 29	88.85 3	86.56 29	92.61 25

test_fmvsm_n_1920		75.56 62	75.54 51	75.61 128	74.60 263	49.51 195	81.82 210	74.08 293	66.52 52	80.40 18	93.46 13	46.95 73	89.72 111	86.69 4	75.30 124	87.61 139

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 6	75.95 3	77.10 29	93.09 21	54.15 27	95.57 12	85.80 5	85.87 35	93.31 11

patch_mono-2		80.84 11	81.59 9	78.62 56	90.34 9	53.77 94	88.08 51	88.36 42	76.17 2	79.40 24	91.09 52	55.43 19	90.09 102	85.01 6	80.40 79	91.99 42

CNVR-MVS		81.76 7	81.90 7	81.33 17	90.04 10	57.70 12	91.71 9	88.87 27	70.31 18	77.64 28	93.87 7	52.58 34	93.91 25	84.17 7	87.92 15	92.39 29

dcpmvs_2		79.33 19	78.94 19	80.49 21	89.75 12	56.54 31	84.83 131	83.68 141	67.85 37	69.36 87	90.24 72	60.20 7	92.10 51	84.14 8	80.40 79	92.82 20

CANet		80.90 10	81.17 11	80.09 31	87.62 37	54.21 87	91.60 12	86.47 72	73.13 7	79.89 22	93.10 19	49.88 55	92.98 31	84.09 9	84.75 47	93.08 16

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 111	52.71 125	85.04 122	88.63 35	66.08 61	86.77 3	92.75 24	72.05 1	91.46 62	83.35 10	93.53 1	92.23 33
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
test_fmvsmvis_n_1920		71.29 119	70.38 115	74.00 164	71.04 302	48.79 212	79.19 252	64.62 335	62.75 108	66.73 102	91.99 38	40.94 147	88.35 152	83.00 11	73.18 140	84.85 193

IU-MVS					89.48 17	57.49 15		91.38 5	66.22 57	88.26 1				82.83 12	87.60 17	92.44 28

PS-MVSNAJ		80.06 15	79.52 16	81.68 13	85.58 55	60.97 3	91.69 10	87.02 62	70.62 15	80.75 17	93.22 18	37.77 177	92.50 41	82.75 13	86.25 32	91.57 52

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 14	85.03 67	60.73 4	91.65 11	86.86 65	70.30 19	80.77 16	93.07 22	37.63 182	92.28 46	82.73 14	85.71 36	91.57 52

DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 74	85.46 58	49.56 192	90.99 20	86.66 70	70.58 16	80.07 20	95.30 1	56.18 17	90.97 77	82.57 15	86.22 33	93.28 12

SED-MVS		81.92 6	81.75 8	82.44 7	89.48 17	56.89 25	92.48 3	88.94 23	57.50 208	84.61 4	94.09 3	58.81 11	96.37 6	82.28 16	87.60 17	94.06 3

test_241102_TWO								88.76 31	57.50 208	83.60 6	94.09 3	56.14 18	96.37 6	82.28 16	87.43 19	92.55 26

DVP-MVS		81.30 9	81.00 12	82.20 8	89.40 20	57.45 17	92.34 5	89.99 13	57.71 202	81.91 12	93.64 10	55.17 20	96.44 2	81.68 18	87.13 20	92.72 23
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				82.20 8	89.50 15	57.73 11	92.34 5	88.88 25					96.39 4	81.68 18	87.13 20	92.47 27

DVP-MVS++		82.44 2	82.38 4	82.62 4	91.77 4	57.49 15	84.98 125	88.88 25	58.00 194	83.60 6	93.39 14	67.21 2	96.39 4	81.64 20	91.98 4	93.98 5

test_0728_THIRD									58.00 194	81.91 12	93.64 10	56.54 15	96.44 2	81.64 20	86.86 23	92.23 33

MSC_two_6792asdad				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 22	86.80 25	92.34 31

No_MVS				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 22	86.80 25	92.34 31

9.14			78.19 24		85.67 53		88.32 49	88.84 28	59.89 153	74.58 40	92.62 27	46.80 75	92.66 38	81.40 24	85.62 38

lupinMVS		78.38 24	78.11 25	79.19 39	83.02 109	55.24 57	91.57 13	84.82 114	69.12 26	76.67 31	92.02 36	44.82 101	90.23 99	80.83 25	80.09 83	92.08 37

HPM-MVS++		80.50 13	80.71 13	79.88 33	87.34 39	55.20 60	89.93 28	87.55 57	66.04 64	79.46 23	93.00 23	53.10 31	91.76 56	80.40 26	89.56 8	92.68 24

SMA-MVS		79.10 20	78.76 20	80.12 29	84.42 75	55.87 45	87.58 63	86.76 67	61.48 130	80.26 19	93.10 19	46.53 79	92.41 43	79.97 27	88.77 10	92.08 37
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
APDe-MVS		78.44 22	78.20 23	79.19 39	88.56 26	54.55 81	89.76 32	87.77 52	55.91 233	78.56 25	92.49 28	48.20 61	92.65 39	79.49 28	83.04 56	90.39 79

ETV-MVS		77.17 38	76.74 39	78.48 60	81.80 136	54.55 81	86.13 92	85.33 95	68.20 31	73.10 53	90.52 66	45.23 93	90.66 85	79.37 29	80.95 71	90.22 83

jason		77.01 40	76.45 42	78.69 53	79.69 184	54.74 72	90.56 23	83.99 137	68.26 30	74.10 43	90.91 58	42.14 133	89.99 104	79.30 30	79.12 94	91.36 60
jason: jason.
test_vis1_n_1920		68.59 169	68.31 145	69.44 254	69.16 313	41.51 310	84.63 139	68.58 327	58.80 181	73.26 52	88.37 110	25.30 296	80.60 279	79.10 31	67.55 185	86.23 166

casdiffmvs_mvg		77.75 32	77.28 33	79.16 41	80.42 175	54.44 83	87.76 57	85.46 89	71.67 10	71.38 75	88.35 111	51.58 39	91.22 67	79.02 32	79.89 89	91.83 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		82.32 4	82.50 3	81.79 11	86.80 42	56.89 25	92.77 2	86.30 76	77.83 1	77.88 26	92.13 32	60.24 6	94.78 19	78.97 33	89.61 7	93.69 8
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
h-mvs33		73.95 78	72.89 77	77.15 91	80.17 178	50.37 173	84.68 136	83.33 147	68.08 32	71.97 67	88.65 108	42.50 127	91.15 70	78.82 34	57.78 269	89.91 93

hse-mvs2		71.44 118	70.68 109	73.73 174	76.34 237	47.44 241	79.45 249	79.47 216	68.08 32	71.97 67	86.01 148	42.50 127	86.93 197	78.82 34	53.46 306	86.83 156

NCCC		79.57 18	79.23 18	80.59 20	89.50 15	56.99 23	91.38 14	88.17 44	67.71 40	73.81 45	92.75 24	46.88 74	93.28 28	78.79 36	84.07 52	91.50 56

test9_res														78.72 37	85.44 40	91.39 58

test_cas_vis1_n_1920		67.10 201	66.60 178	68.59 267	65.17 335	43.23 294	83.23 179	69.84 323	55.34 241	70.67 82	87.71 124	24.70 302	76.66 317	78.57 38	64.20 209	85.89 175

CSCG		80.41 14	79.72 14	82.49 5	89.12 25	57.67 13	89.29 39	91.54 3	59.19 170	71.82 69	90.05 80	59.72 9	96.04 10	78.37 39	88.40 13	93.75 7

DPE-MVS		79.82 17	79.66 15	80.29 24	89.27 24	55.08 65	88.70 45	87.92 48	55.55 238	81.21 15	93.69 9	56.51 16	94.27 22	78.36 40	85.70 37	91.51 55
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		75.54 63	75.03 58	77.04 93	81.37 153	52.65 127	84.34 144	84.46 124	61.16 133	69.14 88	91.76 42	39.98 160	88.99 130	78.19 41	84.89 46	89.48 100
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 41	76.40 43	78.45 62	85.68 51	55.42 51	87.59 61	84.00 135	57.84 199	72.99 54	90.98 55	44.99 96	88.58 142	78.19 41	85.32 41	91.34 62

SF-MVS		77.64 33	77.42 32	78.32 66	83.75 89	52.47 130	86.63 84	87.80 49	58.78 182	74.63 38	92.38 29	47.75 65	91.35 64	78.18 43	86.85 24	91.15 65

canonicalmvs		78.17 27	77.86 28	79.12 43	84.30 77	54.22 86	87.71 58	84.57 123	67.70 41	77.70 27	92.11 35	50.90 46	89.95 105	78.18 43	77.54 106	93.20 14

VDD-MVS		76.08 54	74.97 60	79.44 35	84.27 79	53.33 110	91.13 18	85.88 82	65.33 72	72.37 64	89.34 93	32.52 245	92.76 37	77.90 45	75.96 117	92.22 35

diffmvs		75.11 69	74.65 65	76.46 108	78.52 208	53.35 108	83.28 178	79.94 204	70.51 17	71.64 71	88.72 104	46.02 84	86.08 223	77.52 46	75.75 121	89.96 91
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SDMVSNet		71.89 109	70.62 111	75.70 126	81.70 138	51.61 147	73.89 283	88.72 32	66.58 49	61.64 172	82.38 199	37.63 182	89.48 116	77.44 47	65.60 200	86.01 169

alignmvs		78.08 28	77.98 26	78.39 64	83.53 92	53.22 113	89.77 31	85.45 90	66.11 59	76.59 33	91.99 38	54.07 28	89.05 125	77.34 48	77.00 109	92.89 19

SteuartSystems-ACMMP		77.08 39	76.33 44	79.34 37	80.98 158	55.31 55	89.76 32	86.91 64	62.94 106	71.65 70	91.56 48	42.33 129	92.56 40	77.14 49	83.69 54	90.15 86
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 49	75.66 49	78.73 51	81.92 133	54.67 78	84.06 153	85.35 94	61.10 135	72.99 54	91.50 49	40.25 154	91.00 74	76.84 50	86.98 22	90.51 78

CLD-MVS		75.60 61	75.39 53	76.24 111	80.69 169	52.40 131	90.69 22	86.20 78	74.40 5	65.01 127	88.93 100	42.05 135	90.58 88	76.57 51	73.96 135	85.73 177
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS		74.99 70	74.33 67	76.95 99	82.89 115	53.05 119	85.63 105	83.50 146	57.86 198	67.25 100	90.24 72	43.38 119	88.85 136	76.03 52	82.23 62	88.96 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		77.36 36	76.85 38	78.88 47	80.40 176	54.66 79	87.06 75	85.88 82	72.11 9	71.57 72	88.63 109	50.89 48	90.35 93	76.00 53	79.11 95	91.63 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TSAR-MVS + GP.		77.82 31	77.59 30	78.49 59	85.25 63	50.27 179	90.02 25	90.57 10	56.58 227	74.26 42	91.60 47	54.26 25	92.16 48	75.87 54	79.91 87	93.05 17

baseline		76.86 44	76.24 46	78.71 52	80.47 174	54.20 89	83.90 157	84.88 113	71.38 13	71.51 73	89.15 98	50.51 49	90.55 89	75.71 55	78.65 98	91.39 58

agg_prior2														75.65 56	85.11 44	91.01 67

DeepC-MVS	67.15 4	76.90 43	76.27 45	78.80 49	80.70 168	55.02 66	86.39 86	86.71 68	66.96 47	67.91 96	89.97 82	48.03 63	91.41 63	75.60 57	84.14 51	89.96 91
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_BlendedMVS		73.42 87	73.30 73	73.76 172	85.91 48	51.83 143	86.18 91	84.24 131	65.40 69	69.09 89	80.86 217	46.70 77	88.13 161	75.43 58	65.92 199	81.33 254

PVSNet_Blended		76.53 48	76.54 41	76.50 107	85.91 48	51.83 143	88.89 43	84.24 131	67.82 38	69.09 89	89.33 95	46.70 77	88.13 161	75.43 58	81.48 70	89.55 98

LFMVS		78.52 21	77.14 35	82.67 3	89.58 13	58.90 7	91.27 17	88.05 46	63.22 102	74.63 38	90.83 61	41.38 145	94.40 20	75.42 60	79.90 88	94.72 2

ZD-MVS					89.55 14	53.46 101		84.38 125	57.02 216	73.97 44	91.03 53	44.57 104	91.17 69	75.41 61	81.78 68

MVS_111021_HR		76.39 50	75.38 54	79.42 36	85.33 61	56.47 33	88.15 50	84.97 110	65.15 75	66.06 114	89.88 83	43.79 110	92.16 48	75.03 62	80.03 86	89.64 97

CS-MVS-test		77.20 37	77.25 34	77.05 92	84.60 72	49.04 204	89.42 35	85.83 84	65.90 65	72.85 57	91.98 40	45.10 94	91.27 65	75.02 63	84.56 48	90.84 71

test_prior2							89.04 41		61.88 123	73.55 47	91.46 51	48.01 64		74.73 64	85.46 39

SD-MVS		76.18 52	74.85 62	80.18 27	85.39 59	56.90 24	85.75 101	82.45 165	56.79 222	74.48 41	91.81 41	43.72 113	90.75 83	74.61 65	78.65 98	92.91 18
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
CS-MVS		76.77 45	76.70 40	76.99 97	83.55 91	48.75 213	88.60 46	85.18 103	66.38 54	72.47 63	91.62 46	45.53 90	90.99 76	74.48 66	82.51 59	91.23 63

APD-MVS		76.15 53	75.68 48	77.54 80	88.52 27	53.44 104	87.26 72	85.03 109	53.79 255	74.91 36	91.68 45	43.80 109	90.31 95	74.36 67	81.82 66	88.87 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EC-MVSNet		75.30 64	75.20 55	75.62 127	80.98 158	49.00 205	87.43 64	84.68 120	63.49 99	70.97 80	90.15 78	42.86 126	91.14 71	74.33 68	81.90 65	86.71 158

VDDNet		74.37 73	72.13 91	81.09 19	79.58 185	56.52 32	90.02 25	86.70 69	52.61 264	71.23 77	87.20 132	31.75 255	93.96 24	74.30 69	75.77 120	92.79 22

TSAR-MVS + MP.		78.31 26	78.26 22	78.48 60	81.33 154	56.31 37	81.59 217	86.41 73	69.61 23	81.72 14	88.16 116	55.09 22	88.04 165	74.12 70	86.31 31	91.09 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 179	59.50 5	92.24 8	90.72 9	69.37 25	83.22 8	94.47 2	63.81 5	93.18 30	74.02 71	93.25 2	94.80 1

PHI-MVS		77.49 34	77.00 36	78.95 44	85.33 61	50.69 163	88.57 47	88.59 38	58.14 191	73.60 46	93.31 16	43.14 122	93.79 26	73.81 72	88.53 12	92.37 30

MTAPA		72.73 95	71.22 104	77.27 88	81.54 148	53.57 98	67.06 323	81.31 183	59.41 163	68.39 93	90.96 57	36.07 211	89.01 127	73.80 73	82.45 61	89.23 103

VNet		77.99 30	77.92 27	78.19 68	87.43 38	50.12 180	90.93 21	91.41 4	67.48 43	75.12 35	90.15 78	46.77 76	91.00 74	73.52 74	78.46 100	93.44 9

EPNet		78.36 25	78.49 21	77.97 72	85.49 57	52.04 137	89.36 37	84.07 134	73.22 6	77.03 30	91.72 43	49.32 59	90.17 101	73.46 75	82.77 57	91.69 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		71.60 114	70.29 118	75.55 130	77.26 227	53.15 114	85.34 110	79.37 217	55.83 234	72.54 59	90.19 75	22.38 314	86.66 204	73.28 76	76.39 113	86.85 153

xiu_mvs_v1_base		71.60 114	70.29 118	75.55 130	77.26 227	53.15 114	85.34 110	79.37 217	55.83 234	72.54 59	90.19 75	22.38 314	86.66 204	73.28 76	76.39 113	86.85 153

xiu_mvs_v1_base_debi		71.60 114	70.29 118	75.55 130	77.26 227	53.15 114	85.34 110	79.37 217	55.83 234	72.54 59	90.19 75	22.38 314	86.66 204	73.28 76	76.39 113	86.85 153

PMMVS		72.98 91	72.05 94	75.78 125	83.57 90	48.60 216	84.08 151	82.85 160	61.62 126	68.24 94	90.33 71	28.35 273	87.78 174	72.71 79	76.69 111	90.95 69

ZNCC-MVS		75.82 60	75.02 59	78.23 67	83.88 87	53.80 93	86.91 80	86.05 80	59.71 156	67.85 97	90.55 64	42.23 131	91.02 73	72.66 80	85.29 42	89.87 94

ET-MVSNet_ETH3D		75.23 66	74.08 69	78.67 54	84.52 74	55.59 47	88.92 42	89.21 18	68.06 35	53.13 274	90.22 74	49.71 56	87.62 181	72.12 81	70.82 161	92.82 20

MVS		76.91 41	75.48 52	81.23 18	84.56 73	55.21 59	80.23 240	91.64 2	58.65 184	65.37 122	91.48 50	45.72 88	95.05 16	72.11 82	89.52 9	93.44 9

nrg030		72.27 106	71.56 98	74.42 151	75.93 246	50.60 165	86.97 77	83.21 152	62.75 108	67.15 101	84.38 162	50.07 52	86.66 204	71.19 83	62.37 231	85.99 171

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 42	88.52 27	55.12 62	89.95 27	85.98 81	68.31 29	71.33 76	92.75 24	45.52 91	90.37 92	71.15 84	85.14 43	91.91 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
iter_conf05		73.51 86	72.24 88	77.33 84	87.93 36	55.97 43	87.90 56	70.81 318	68.72 27	64.04 142	84.36 164	47.54 67	90.87 79	71.11 85	67.75 184	85.13 187

GST-MVS		74.87 71	73.90 71	77.77 75	83.30 99	53.45 103	85.75 101	85.29 98	59.22 169	66.50 109	89.85 84	40.94 147	90.76 82	70.94 86	83.35 55	89.10 108

CHOSEN 1792x2688		76.24 51	74.03 70	82.88 1	83.09 106	62.84 2	85.73 103	85.39 92	69.79 21	64.87 129	83.49 178	41.52 144	93.69 27	70.55 87	81.82 66	92.12 36

CDPH-MVS		76.05 55	75.19 56	78.62 56	86.51 44	54.98 68	87.32 67	84.59 122	58.62 185	70.75 81	90.85 60	43.10 124	90.63 87	70.50 88	84.51 50	90.24 82

HPM-MVS		72.60 97	71.50 99	75.89 123	82.02 131	51.42 153	80.70 234	83.05 155	56.12 232	64.03 143	89.53 89	37.55 185	88.37 150	70.48 89	80.04 85	87.88 132
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		69.07 156	67.91 150	72.54 196	77.27 226	49.56 192	79.77 244	73.96 296	59.33 167	60.73 180	87.82 121	30.19 265	81.53 270	69.94 90	72.19 151	86.53 160

test_yl		75.85 57	74.83 63	78.91 45	88.08 34	51.94 139	91.30 15	89.28 16	57.91 196	71.19 78	89.20 96	42.03 136	92.77 35	69.41 91	75.07 129	92.01 40

DCV-MVSNet		75.85 57	74.83 63	78.91 45	88.08 34	51.94 139	91.30 15	89.28 16	57.91 196	71.19 78	89.20 96	42.03 136	92.77 35	69.41 91	75.07 129	92.01 40

HFP-MVS		74.37 73	73.13 76	78.10 70	84.30 77	53.68 96	85.58 106	84.36 126	56.82 220	65.78 118	90.56 63	40.70 152	90.90 78	69.18 93	80.88 72	89.71 95

ACMMPR		73.76 81	72.61 78	77.24 90	83.92 85	52.96 122	85.58 106	84.29 127	56.82 220	65.12 123	90.45 67	37.24 193	90.18 100	69.18 93	80.84 73	88.58 120

region2R		73.75 82	72.55 80	77.33 84	83.90 86	52.98 121	85.54 109	84.09 133	56.83 219	65.10 124	90.45 67	37.34 191	90.24 98	68.89 95	80.83 74	88.77 116

CP-MVS		72.59 99	71.46 100	76.00 122	82.93 114	52.32 134	86.93 79	82.48 164	55.15 242	63.65 149	90.44 70	35.03 223	88.53 146	68.69 96	77.83 104	87.15 147

baseline2		75.15 68	74.54 66	76.98 98	81.67 141	51.74 145	83.84 158	91.94 1	69.97 20	58.98 203	86.02 146	59.73 8	91.73 57	68.37 97	70.40 166	87.48 141

Effi-MVS+		75.24 65	73.61 72	80.16 28	81.92 133	57.42 19	85.21 114	76.71 270	60.68 144	73.32 51	89.34 93	47.30 69	91.63 58	68.28 98	79.72 90	91.42 57

CostFormer		73.89 80	72.30 86	78.66 55	82.36 128	56.58 28	75.56 271	85.30 97	66.06 62	70.50 85	76.88 260	57.02 14	89.06 124	68.27 99	68.74 176	90.33 81

CANet_DTU		73.71 83	73.14 74	75.40 133	82.61 124	50.05 181	84.67 138	79.36 220	69.72 22	75.39 34	90.03 81	29.41 269	85.93 229	67.99 100	79.11 95	90.22 83

PVSNet_Blended_VisFu		73.40 88	72.44 82	76.30 109	81.32 155	54.70 75	85.81 97	78.82 230	63.70 93	64.53 134	85.38 154	47.11 72	87.38 186	67.75 101	77.55 105	86.81 157

MSLP-MVS++		74.21 75	72.25 87	80.11 30	81.45 151	56.47 33	86.32 88	79.65 212	58.19 190	66.36 110	92.29 31	36.11 209	90.66 85	67.39 102	82.49 60	93.18 15

PGM-MVS		72.60 97	71.20 105	76.80 104	82.95 112	52.82 124	83.07 184	82.14 167	56.51 228	63.18 154	89.81 85	35.68 215	89.76 110	67.30 103	80.19 82	87.83 133

EIA-MVS		75.92 56	75.18 57	78.13 69	85.14 64	51.60 148	87.17 73	85.32 96	64.69 78	68.56 92	90.53 65	45.79 87	91.58 59	67.21 104	82.18 63	91.20 64

HY-MVS	67.03 5	73.90 79	73.14 74	76.18 116	84.70 71	47.36 242	75.56 271	86.36 75	66.27 56	70.66 83	83.91 170	51.05 44	89.31 119	67.10 105	72.61 147	91.88 44

BP-MVS														66.70 106

HQP-MVS		72.34 102	71.44 101	75.03 141	79.02 195	51.56 149	88.00 52	83.68 141	65.45 66	64.48 135	85.13 155	37.35 189	88.62 140	66.70 106	73.12 141	84.91 191

SR-MVS		70.92 127	69.73 127	74.50 148	83.38 98	50.48 169	84.27 146	79.35 221	48.96 289	66.57 108	90.45 67	33.65 236	87.11 190	66.42 108	74.56 132	85.91 174

gm-plane-assit					83.24 101	54.21 87			70.91 14		88.23 115		95.25 14	66.37 109

PAPR		75.20 67	74.13 68	78.41 63	88.31 31	55.10 64	84.31 145	85.66 86	63.76 92	67.55 98	90.73 62	43.48 118	89.40 118	66.36 110	77.03 108	90.73 73

WTY-MVS		77.47 35	77.52 31	77.30 86	88.33 30	46.25 260	88.46 48	90.32 11	71.40 12	72.32 65	91.72 43	53.44 29	92.37 44	66.28 111	75.42 123	93.28 12

tpmrst		71.04 124	69.77 126	74.86 144	83.19 103	55.86 46	75.64 270	78.73 234	67.88 36	64.99 128	73.73 289	49.96 54	79.56 293	65.92 112	67.85 183	89.14 107

MVS_Test		75.85 57	74.93 61	78.62 56	84.08 81	55.20 60	83.99 155	85.17 104	68.07 34	73.38 50	82.76 188	50.44 50	89.00 128	65.90 113	80.61 75	91.64 48

ACMMP		70.81 129	69.29 135	75.39 134	81.52 150	51.92 141	83.43 170	83.03 156	56.67 225	58.80 210	88.91 101	31.92 253	88.58 142	65.89 114	73.39 139	85.67 178
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
XVS		72.92 92	71.62 97	76.81 101	83.41 94	52.48 128	84.88 129	83.20 153	58.03 192	63.91 145	89.63 88	35.50 216	89.78 108	65.50 115	80.50 77	88.16 125

X-MVStestdata		65.85 222	62.20 229	76.81 101	83.41 94	52.48 128	84.88 129	83.20 153	58.03 192	63.91 145	4.82 383	35.50 216	89.78 108	65.50 115	80.50 77	88.16 125

PAPM		76.76 46	76.07 47	78.81 48	80.20 177	59.11 6	86.86 81	86.23 77	68.60 28	70.18 86	88.84 103	51.57 40	87.16 189	65.48 117	86.68 27	90.15 86

HQP_MVS		70.96 126	69.91 125	74.12 160	77.95 216	49.57 190	85.76 99	82.59 162	63.60 96	62.15 167	83.28 182	36.04 212	88.30 156	65.46 118	72.34 149	84.49 195

plane_prior5								82.59 162					88.30 156	65.46 118	72.34 149	84.49 195

mPP-MVS		71.79 113	70.38 115	76.04 120	82.65 123	52.06 136	84.45 141	81.78 176	55.59 237	62.05 169	89.68 87	33.48 237	88.28 158	65.45 120	78.24 103	87.77 135

OPM-MVS		70.75 130	69.58 129	74.26 157	75.55 251	51.34 155	86.05 94	83.29 151	61.94 122	62.95 158	85.77 149	34.15 230	88.44 148	65.44 121	71.07 158	82.99 227
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
iter_conf_final		71.46 117	69.68 128	76.81 101	86.03 46	53.49 99	84.73 133	74.37 290	60.27 149	66.28 111	84.36 164	35.14 221	90.87 79	65.41 122	70.51 164	86.05 168

Effi-MVS+-dtu		66.24 218	64.96 214	70.08 246	75.17 252	49.64 189	82.01 203	74.48 289	62.15 117	57.83 224	76.08 273	30.59 262	83.79 254	65.40 123	60.93 238	76.81 304

EI-MVSNet-Vis-set		73.19 90	72.60 79	74.99 143	82.56 125	49.80 188	82.55 194	89.00 21	66.17 58	65.89 117	88.98 99	43.83 108	92.29 45	65.38 124	69.01 174	82.87 230

TESTMET0.1,1		72.86 94	72.33 84	74.46 149	81.98 132	50.77 161	85.13 117	85.47 88	66.09 60	67.30 99	83.69 175	37.27 192	83.57 258	65.06 125	78.97 97	89.05 109

MVSTER		73.25 89	72.33 84	76.01 121	85.54 56	53.76 95	83.52 163	87.16 60	67.06 46	63.88 147	81.66 209	52.77 32	90.44 90	64.66 126	64.69 206	83.84 212

CPTT-MVS		67.15 200	65.84 195	71.07 231	80.96 160	50.32 176	81.94 205	74.10 292	46.18 307	57.91 223	87.64 126	29.57 268	81.31 272	64.10 127	70.18 168	81.56 244

miper_enhance_ethall		69.77 147	68.90 139	72.38 201	78.93 198	49.91 184	83.29 177	78.85 228	64.90 76	59.37 196	79.46 226	52.77 32	85.16 241	63.78 128	58.72 253	82.08 236

EI-MVSNet-UG-set		72.37 101	71.73 96	74.29 156	81.60 144	49.29 199	81.85 208	88.64 34	65.29 74	65.05 125	88.29 114	43.18 120	91.83 55	63.74 129	67.97 181	81.75 241

ab-mvs		70.65 131	69.11 137	75.29 137	80.87 164	46.23 261	73.48 287	85.24 102	59.99 152	66.65 104	80.94 216	43.13 123	88.69 138	63.58 130	68.07 179	90.95 69

VPA-MVSNet		71.12 121	70.66 110	72.49 198	78.75 201	44.43 281	87.64 59	90.02 12	63.97 88	65.02 126	81.58 211	42.14 133	87.42 185	63.42 131	63.38 219	85.63 181

mvsmamba		66.93 208	64.88 215	73.09 185	75.06 255	47.26 244	83.36 176	69.21 325	62.64 111	55.68 254	81.43 212	29.72 267	89.20 122	63.35 132	63.50 215	82.79 231

APD-MVS_3200maxsize		69.62 152	68.23 147	73.80 171	81.58 146	48.22 227	81.91 206	79.50 215	48.21 292	64.24 140	89.75 86	31.91 254	87.55 183	63.08 133	73.85 137	85.64 180

v2v482		69.55 153	67.64 158	75.26 139	72.32 289	53.83 92	84.93 128	81.94 171	65.37 71	60.80 179	79.25 230	41.62 141	88.98 131	63.03 134	59.51 246	82.98 228

PS-MVSNAJss		68.78 165	67.17 168	73.62 178	73.01 279	48.33 226	84.95 127	84.81 115	59.30 168	58.91 207	79.84 224	37.77 177	88.86 135	62.83 135	63.12 225	83.67 215

cl22		68.85 160	67.69 157	72.35 202	78.07 215	49.98 183	82.45 197	78.48 240	62.50 114	58.46 217	77.95 240	49.99 53	85.17 240	62.55 136	58.72 253	81.90 239

V42		67.66 185	65.60 202	73.86 168	70.69 304	53.63 97	81.50 220	78.61 237	63.85 90	59.49 195	77.49 247	37.98 174	87.65 177	62.33 137	58.43 256	80.29 268

AUN-MVS		68.20 177	66.35 181	73.76 172	76.37 236	47.45 240	79.52 248	79.52 214	60.98 138	62.34 163	86.02 146	36.59 206	86.94 196	62.32 138	53.47 305	86.89 150

MG-MVS		78.42 23	76.99 37	82.73 2	93.17 1	64.46 1	89.93 28	88.51 40	64.83 77	73.52 48	88.09 117	48.07 62	92.19 47	62.24 139	84.53 49	91.53 54

Patchmatch-RL test		58.72 273	54.32 285	71.92 216	63.91 342	44.25 283	61.73 336	55.19 349	57.38 210	49.31 296	54.24 356	37.60 184	80.89 275	62.19 140	47.28 325	90.63 74

mvs_anonymous		72.29 104	70.74 108	76.94 100	82.85 116	54.72 74	78.43 257	81.54 179	63.77 91	61.69 171	79.32 228	51.11 43	85.31 236	62.15 141	75.79 119	90.79 72

miper_ehance_all_eth		68.70 168	67.58 159	72.08 206	76.91 233	49.48 196	82.47 196	78.45 241	62.68 110	58.28 221	77.88 242	50.90 46	85.01 244	61.91 142	58.72 253	81.75 241

HyFIR lowres test		69.94 145	67.58 159	77.04 93	77.11 232	57.29 20	81.49 222	79.11 226	58.27 189	58.86 208	80.41 220	42.33 129	86.96 195	61.91 142	68.68 177	86.87 151

sss		70.49 133	70.13 122	71.58 223	81.59 145	39.02 322	80.78 233	84.71 119	59.34 165	66.61 106	88.09 117	37.17 194	85.52 232	61.82 144	71.02 159	90.20 85

1314		71.11 122	69.41 131	76.22 112	79.32 189	50.49 168	80.23 240	85.14 107	59.44 162	58.93 205	88.89 102	33.83 235	89.60 115	61.49 145	77.42 107	88.57 121

GA-MVS		69.04 157	66.70 175	76.06 119	75.11 253	52.36 132	83.12 182	80.23 199	63.32 100	60.65 181	79.22 231	30.98 260	88.37 150	61.25 146	66.41 194	87.46 142

ECVR-MVS		71.81 111	71.00 107	74.26 157	80.12 179	43.49 290	84.69 135	82.16 166	64.02 85	64.64 131	87.43 129	35.04 222	89.21 121	61.24 147	79.66 91	90.08 88

VPNet		72.07 107	71.42 102	74.04 162	78.64 206	47.17 247	89.91 30	87.97 47	72.56 8	64.66 130	85.04 157	41.83 140	88.33 154	61.17 148	60.97 237	86.62 159

ACMP	61.11 9	66.24 218	64.33 219	72.00 210	74.89 259	49.12 200	83.18 181	79.83 207	55.41 240	52.29 280	82.68 192	25.83 292	86.10 220	60.89 149	63.94 212	80.78 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 85	72.19 90	77.57 79	83.02 109	55.24 57	81.63 214	81.44 181	50.28 279	76.67 31	90.91 58	44.82 101	86.11 218	60.83 150	80.09 83	91.36 60

test_djsdf		63.84 231	61.56 234	70.70 236	68.78 315	44.69 278	81.63 214	81.44 181	50.28 279	52.27 281	76.26 268	26.72 286	86.11 218	60.83 150	55.84 286	81.29 257

v148		68.24 176	66.35 181	73.88 167	71.76 292	51.47 152	84.23 147	81.90 175	63.69 94	58.94 204	76.44 265	43.72 113	87.78 174	60.63 152	55.86 285	82.39 234

c3_l		67.97 178	66.66 176	71.91 217	76.20 242	49.31 198	82.13 202	78.00 247	61.99 120	57.64 230	76.94 257	49.41 57	84.93 245	60.62 153	57.01 273	81.49 245

test-LLR		69.65 151	69.01 138	71.60 221	78.67 203	48.17 228	85.13 117	79.72 209	59.18 172	63.13 155	82.58 193	36.91 198	80.24 284	60.56 154	75.17 126	86.39 164

test-mter		68.36 171	67.29 165	71.60 221	78.67 203	48.17 228	85.13 117	79.72 209	53.38 258	63.13 155	82.58 193	27.23 283	80.24 284	60.56 154	75.17 126	86.39 164

SR-MVS-dyc-post		68.27 175	66.87 170	72.48 199	80.96 160	48.14 230	81.54 218	76.98 264	46.42 304	62.75 160	89.42 91	31.17 259	86.09 222	60.52 156	72.06 152	83.19 223

RE-MVS-def			66.66 176		80.96 160	48.14 230	81.54 218	76.98 264	46.42 304	62.75 160	89.42 91	29.28 271		60.52 156	72.06 152	83.19 223

IB-MVS	68.87 2	74.01 77	72.03 95	79.94 32	83.04 108	55.50 49	90.24 24	88.65 33	67.14 45	61.38 174	81.74 208	53.21 30	94.28 21	60.45 158	62.41 230	90.03 90
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
v1144		68.81 163	66.82 171	74.80 145	72.34 288	53.46 101	84.68 136	81.77 177	64.25 82	60.28 183	77.91 241	40.23 155	88.95 132	60.37 159	59.52 245	81.97 237

LPG-MVS_test		66.44 215	64.58 217	72.02 208	74.42 265	48.60 216	83.07 184	80.64 192	54.69 249	53.75 270	83.83 171	25.73 294	86.98 193	60.33 160	64.71 204	80.48 265

LGP-MVS_train				72.02 208	74.42 265	48.60 216		80.64 192	54.69 249	53.75 270	83.83 171	25.73 294	86.98 193	60.33 160	64.71 204	80.48 265

MVP-Stereo		70.97 125	70.44 113	72.59 195	76.03 245	51.36 154	85.02 124	86.99 63	60.31 148	56.53 247	78.92 234	40.11 158	90.00 103	60.00 162	90.01 6	76.41 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 238	60.84 242	70.32 242	68.33 320	44.45 280	81.23 224	81.05 187	53.37 259	50.96 290	77.81 244	17.49 338	85.49 234	59.31 163	58.05 262	81.02 259

test2506		72.91 93	72.43 83	74.32 155	80.12 179	44.18 285	83.19 180	84.77 117	64.02 85	65.97 115	87.43 129	47.67 66	88.72 137	59.08 164	79.66 91	90.08 88

baseline1		72.51 100	72.12 92	73.69 175	85.05 65	44.46 279	83.51 167	86.13 79	71.61 11	64.64 131	87.97 120	55.00 23	89.48 116	59.07 165	56.05 282	87.13 148

mvs_tets		62.96 241	60.55 244	70.19 243	68.22 323	44.24 284	80.90 230	80.74 191	52.99 262	50.82 292	77.56 245	16.74 342	85.44 235	59.04 166	57.94 264	80.89 260

HPM-MVS_fast		67.86 180	66.28 184	72.61 194	80.67 170	48.34 225	81.18 226	75.95 279	50.81 277	59.55 193	88.05 119	27.86 278	85.98 225	58.83 167	73.58 138	83.51 216

eth_miper_zixun_eth		66.98 206	65.28 209	72.06 207	75.61 250	50.40 171	81.00 229	76.97 267	62.00 119	56.99 241	76.97 256	44.84 100	85.58 231	58.75 168	54.42 297	80.21 269

v144192		67.86 180	65.76 197	74.16 159	71.68 293	53.09 117	84.14 150	80.83 190	62.85 107	59.21 201	77.28 251	39.30 164	88.00 166	58.67 169	57.88 267	81.40 251

test1111		71.06 123	70.42 114	72.97 188	79.48 186	41.49 311	84.82 132	82.74 161	64.20 83	62.98 157	87.43 129	35.20 219	87.92 167	58.54 170	78.42 101	89.49 99

thisisatest0515		73.64 84	72.20 89	77.97 72	81.63 142	53.01 120	86.69 83	88.81 29	62.53 113	64.06 141	85.65 150	52.15 38	92.50 41	58.43 171	69.84 169	88.39 124

v8		67.25 197	64.99 213	74.04 162	72.89 282	53.31 111	82.37 199	80.11 201	61.54 128	54.29 265	76.02 274	42.89 125	88.41 149	58.43 171	56.36 275	80.39 267

XXY-MVS		70.18 136	69.28 136	72.89 191	77.64 220	42.88 298	85.06 121	87.50 58	62.58 112	62.66 162	82.34 201	43.64 115	89.83 107	58.42 173	63.70 214	85.96 173

3Dnovator	64.70 6	74.46 72	72.48 81	80.41 23	82.84 117	55.40 54	83.08 183	88.61 37	67.61 42	59.85 186	88.66 105	34.57 227	93.97 23	58.42 173	88.70 11	91.85 45

旧先验2							81.73 212		45.53 310	74.66 37			70.48 343	58.31 175

test_fmvs1		53.60 303	52.54 298	56.78 327	58.07 353	30.26 350	68.95 316	42.19 362	32.46 350	63.59 151	82.56 195	11.55 351	60.81 351	58.25 176	55.27 290	79.28 276

v1192		67.96 179	65.74 198	74.63 146	71.79 291	53.43 106	84.06 153	80.99 188	63.19 103	59.56 192	77.46 248	37.50 188	88.65 139	58.20 177	58.93 252	81.79 240

EPP-MVSNet		71.14 120	70.07 123	74.33 154	79.18 192	46.52 253	83.81 159	86.49 71	56.32 231	57.95 222	84.90 160	54.23 26	89.14 123	58.14 178	69.65 171	87.33 144

OMC-MVS		65.97 221	65.06 212	68.71 264	72.97 280	42.58 303	78.61 255	75.35 284	54.72 248	59.31 198	86.25 145	33.30 238	77.88 307	57.99 179	67.05 188	85.66 179

cl____		67.43 192	65.93 193	71.95 214	76.33 238	48.02 233	82.58 191	79.12 225	61.30 132	56.72 243	76.92 258	46.12 81	86.44 211	57.98 180	56.31 277	81.38 253

DIV-MVS_self_test		67.43 192	65.93 193	71.94 215	76.33 238	48.01 234	82.57 192	79.11 226	61.31 131	56.73 242	76.92 258	46.09 82	86.43 212	57.98 180	56.31 277	81.39 252

MS-PatchMatch		72.34 102	71.26 103	75.61 128	82.38 127	55.55 48	88.00 52	89.95 14	65.38 70	56.51 248	80.74 219	32.28 248	92.89 32	57.95 182	88.10 14	78.39 289

MAR-MVS		76.76 46	75.60 50	80.21 25	90.87 7	54.68 77	89.14 40	89.11 19	62.95 105	70.54 84	92.33 30	41.05 146	94.95 17	57.90 183	86.55 30	91.00 68
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
test_fmvs1_n		52.55 307	51.19 302	56.65 328	51.90 361	30.14 351	67.66 320	42.84 361	32.27 351	62.30 165	82.02 206	9.12 359	60.84 350	57.82 184	54.75 296	78.99 278

anonymousdsp		60.46 258	57.65 263	68.88 258	63.63 343	45.09 273	72.93 291	78.63 236	46.52 302	51.12 287	72.80 301	21.46 322	83.07 263	57.79 185	53.97 299	78.47 286

Anonymous20240529		69.71 148	67.28 166	77.00 96	83.78 88	50.36 174	88.87 44	85.10 108	47.22 297	64.03 143	83.37 180	27.93 277	92.10 51	57.78 186	67.44 186	88.53 122

Fast-Effi-MVS+-dtu		66.53 213	64.10 222	73.84 169	72.41 287	52.30 135	84.73 133	75.66 280	59.51 160	56.34 249	79.11 233	28.11 275	85.85 230	57.74 187	63.29 220	83.35 217

v1921920		67.45 191	65.23 210	74.10 161	71.51 296	52.90 123	83.75 161	80.44 195	62.48 115	59.12 202	77.13 252	36.98 196	87.90 168	57.53 188	58.14 261	81.49 245

IterMVS-LS		66.63 211	65.36 208	70.42 240	75.10 254	48.90 209	81.45 223	76.69 271	61.05 136	55.71 253	77.10 254	45.86 86	83.65 257	57.44 189	57.88 267	78.70 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 150	68.70 140	72.68 193	75.00 257	48.90 209	79.54 246	87.16 60	61.05 136	63.88 147	83.74 173	45.87 85	90.44 90	57.42 190	64.68 207	78.70 282

CDS-MVSNet		70.48 134	69.43 130	73.64 176	77.56 222	48.83 211	83.51 167	77.45 256	63.27 101	62.33 164	85.54 153	43.85 107	83.29 262	57.38 191	74.00 134	88.79 115
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 104	70.50 112	77.65 78	83.40 97	51.29 157	87.32 67	86.40 74	59.01 177	58.49 216	88.32 113	32.40 246	91.27 65	57.04 192	82.15 64	90.38 80

test_vis1_n		51.19 312	49.66 309	55.76 332	51.26 362	29.85 353	67.20 322	38.86 365	32.12 352	59.50 194	79.86 223	8.78 360	58.23 357	56.95 193	52.46 309	79.19 277

bld_raw_dy_0_64		59.75 260	57.01 270	67.96 272	66.73 327	45.30 271	77.59 262	59.97 345	50.49 278	47.15 310	77.03 255	17.45 339	79.06 294	56.92 194	59.76 244	79.51 275

miper_lstm_enhance		63.91 230	62.30 228	68.75 263	75.06 255	46.78 249	69.02 314	81.14 186	59.68 158	52.76 277	72.39 306	40.71 151	77.99 305	56.81 195	53.09 307	81.48 247

PAPM_NR		71.80 112	69.98 124	77.26 89	81.54 148	53.34 109	78.60 256	85.25 101	53.46 257	60.53 182	88.66 105	45.69 89	89.24 120	56.49 196	79.62 93	89.19 105

v10		66.61 212	64.20 221	73.83 170	72.59 285	53.37 107	81.88 207	79.91 206	61.11 134	54.09 267	75.60 276	40.06 159	88.26 159	56.47 197	56.10 281	79.86 273

v1240		66.99 205	64.68 216	73.93 165	71.38 299	52.66 126	83.39 174	79.98 203	61.97 121	58.44 219	77.11 253	35.25 218	87.81 170	56.46 198	58.15 259	81.33 254

Anonymous202405211		70.11 137	67.88 152	76.79 105	87.20 40	47.24 246	89.49 34	77.38 258	54.88 247	66.14 112	86.84 137	20.93 324	91.54 60	56.45 199	71.62 154	91.59 50

Fast-Effi-MVS+		72.73 95	71.15 106	77.48 81	82.75 119	54.76 71	86.77 82	80.64 192	63.05 104	65.93 116	84.01 168	44.42 105	89.03 126	56.45 199	76.36 116	88.64 118

sd_testset		67.79 183	65.95 192	73.32 181	81.70 138	46.33 258	68.99 315	80.30 198	66.58 49	61.64 172	82.38 199	30.45 263	87.63 179	55.86 201	65.60 200	86.01 169

114514_t		69.87 146	67.88 152	75.85 124	88.38 29	52.35 133	86.94 78	83.68 141	53.70 256	55.68 254	85.60 151	30.07 266	91.20 68	55.84 202	71.02 159	83.99 205

tpm2		70.82 128	68.44 143	77.98 71	80.78 166	56.11 39	74.21 282	81.28 185	60.24 150	68.04 95	75.27 278	52.26 37	88.50 147	55.82 203	68.03 180	89.33 101

PCF-MVS	61.03 10	70.10 138	68.40 144	75.22 140	77.15 231	51.99 138	79.30 251	82.12 168	56.47 229	61.88 170	86.48 144	43.98 106	87.24 188	55.37 204	72.79 146	86.43 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	62.49 8	69.27 155	67.81 156	73.64 176	84.41 76	51.85 142	84.63 139	77.80 249	66.42 53	59.80 187	84.95 159	22.14 319	80.44 282	55.03 205	75.11 128	88.62 119

CHOSEN 280x420		57.53 282	56.38 275	60.97 317	74.01 269	48.10 232	46.30 358	54.31 351	48.18 293	50.88 291	77.43 249	38.37 173	59.16 356	54.83 206	63.14 224	75.66 315

GG-mvs-BLEND				77.77 75	86.68 43	50.61 164	68.67 317	88.45 41		68.73 91	87.45 128	59.15 10	90.67 84	54.83 206	87.67 16	92.03 39

TAMVS		69.51 154	68.16 148	73.56 179	76.30 240	48.71 215	82.57 192	77.17 261	62.10 118	61.32 175	84.23 166	41.90 138	83.46 260	54.80 208	73.09 143	88.50 123

D2MVS		63.49 235	61.39 236	69.77 250	69.29 312	48.93 208	78.89 254	77.71 252	60.64 145	49.70 294	72.10 311	27.08 284	83.48 259	54.48 209	62.65 228	76.90 303

IterMVS		63.77 233	61.67 232	70.08 246	72.68 284	51.24 158	80.44 236	75.51 281	60.51 146	51.41 285	73.70 292	32.08 250	78.91 295	54.30 210	54.35 298	80.08 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RRT_MVS		63.68 234	61.01 241	71.70 219	73.48 273	45.98 263	81.19 225	76.08 277	54.33 253	52.84 276	79.27 229	22.21 317	87.65 177	54.13 211	55.54 289	81.46 248

DP-MVS Recon		71.99 108	70.31 117	77.01 95	90.65 8	53.44 104	89.37 36	82.97 158	56.33 230	63.56 152	89.47 90	34.02 231	92.15 50	54.05 212	72.41 148	85.43 184

tpm		68.36 171	67.48 163	70.97 233	79.93 182	51.34 155	76.58 268	78.75 233	67.73 39	63.54 153	74.86 280	48.33 60	72.36 337	53.93 213	63.71 213	89.21 104

XVG-OURS-SEG-HR		62.02 250	59.54 252	69.46 253	65.30 333	45.88 264	65.06 325	73.57 300	46.45 303	57.42 237	83.35 181	26.95 285	78.09 301	53.77 214	64.03 210	84.42 197

FA-MVS(test-final)		69.00 159	66.60 178	76.19 115	83.48 93	47.96 236	74.73 278	82.07 169	57.27 212	62.18 166	78.47 238	36.09 210	92.89 32	53.76 215	71.32 157	87.73 136

cascas		69.01 158	66.13 187	77.66 77	79.36 187	55.41 53	86.99 76	83.75 140	56.69 224	58.92 206	81.35 213	24.31 304	92.10 51	53.23 216	70.61 162	85.46 183

UniMVSNet_NR-MVSNet		68.82 162	68.29 146	70.40 241	75.71 249	42.59 301	84.23 147	86.78 66	66.31 55	58.51 213	82.45 196	51.57 40	84.64 249	53.11 217	55.96 283	83.96 209

DU-MVS		66.84 210	65.74 198	70.16 244	73.27 277	42.59 301	81.50 220	82.92 159	63.53 98	58.51 213	82.11 204	40.75 149	84.64 249	53.11 217	55.96 283	83.24 221

1112_ss		70.05 140	69.37 132	72.10 205	80.77 167	42.78 299	85.12 120	76.75 268	59.69 157	61.19 176	92.12 33	47.48 68	83.84 253	53.04 219	68.21 178	89.66 96

XVG-OURS		61.88 251	59.34 254	69.49 252	65.37 332	46.27 259	64.80 326	73.49 301	47.04 299	57.41 238	82.85 186	25.15 298	78.18 299	53.00 220	64.98 202	84.01 204

thisisatest0530		70.47 135	68.56 141	76.20 114	79.78 183	51.52 151	83.49 169	88.58 39	57.62 205	58.60 212	82.79 187	51.03 45	91.48 61	52.84 221	62.36 232	85.59 182

UGNet		68.71 166	67.11 169	73.50 180	80.55 173	47.61 238	84.08 151	78.51 239	59.45 161	65.68 120	82.73 191	23.78 306	85.08 243	52.80 222	76.40 112	87.80 134
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
Anonymous20231211		66.08 220	63.67 223	73.31 182	83.07 107	48.75 213	86.01 96	84.67 121	45.27 311	56.54 246	76.67 263	28.06 276	88.95 132	52.78 223	59.95 240	82.23 235

无先验							85.19 115	78.00 247	49.08 287				85.13 242	52.78 223		87.45 143

PVSNet_0	57.04 13	61.19 254	57.24 266	73.02 186	77.45 224	50.31 177	79.43 250	77.36 259	63.96 89	47.51 308	72.45 305	25.03 299	83.78 255	52.76 225	19.22 375	84.96 190

FIs		70.00 142	70.24 121	69.30 255	77.93 218	38.55 325	83.99 155	87.72 54	66.86 48	57.66 229	84.17 167	52.28 36	85.31 236	52.72 226	68.80 175	84.02 203

Vis-MVSNet		70.61 132	69.34 133	74.42 151	80.95 163	48.49 221	86.03 95	77.51 255	58.74 183	65.55 121	87.78 122	34.37 228	85.95 228	52.53 227	80.61 75	88.80 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 279	77.59 221	45.46 270		69.20 326	44.47 316	71.50 74	88.34 112	31.21 258	70.76 342	52.20 228	75.88 118	85.03 188

API-MVS		74.17 76	72.07 93	80.49 21	90.02 11	58.55 8	87.30 69	84.27 128	57.51 207	65.77 119	87.77 123	41.61 142	95.97 11	51.71 229	82.63 58	86.94 149

GeoE		69.96 144	67.88 152	76.22 112	81.11 157	51.71 146	84.15 149	76.74 269	59.83 154	60.91 177	84.38 162	41.56 143	88.10 163	51.67 230	70.57 163	88.84 113

dmvs_re		67.61 186	66.00 190	72.42 200	81.86 135	43.45 291	64.67 327	80.00 202	69.56 24	60.07 184	85.00 158	34.71 225	87.63 179	51.48 231	66.68 190	86.17 167

ACMM	58.35 12	64.35 227	62.01 231	71.38 225	74.21 268	48.51 220	82.25 200	79.66 211	47.61 295	54.54 262	80.11 221	25.26 297	86.00 224	51.26 232	63.16 223	79.64 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 117	84.89 69	54.59 80		85.26 100	51.98 268	66.70 103	87.07 135	40.15 157	89.70 112	51.23 233	85.06 45	84.10 201

UniMVSNet (Re)		67.71 184	66.80 172	70.45 239	74.44 264	42.93 297	82.42 198	84.90 112	63.69 94	59.63 190	80.99 215	47.18 70	85.23 239	51.17 234	56.75 274	83.19 223

IterMVS-SCA-FT		59.12 266	58.81 259	60.08 319	70.68 305	45.07 274	80.42 237	74.25 291	43.54 323	50.02 293	73.73 289	31.97 251	56.74 358	51.06 235	53.60 303	78.42 288

Test_1112_low_res		67.18 199	66.23 185	70.02 249	78.75 201	41.02 315	83.43 170	73.69 298	57.29 211	58.45 218	82.39 198	45.30 92	80.88 276	50.50 236	66.26 198	88.16 125

pmmvs4		63.34 237	61.07 240	70.16 244	70.14 306	50.53 167	79.97 243	71.41 315	55.08 243	54.12 266	78.58 236	32.79 243	82.09 268	50.33 237	57.22 272	77.86 295

Baseline_NR-MVSNet		65.49 224	64.27 220	69.13 256	74.37 267	41.65 308	83.39 174	78.85 228	59.56 159	59.62 191	76.88 260	40.75 149	87.44 184	49.99 238	55.05 291	78.28 291

UniMVSNet_ETH3D		62.51 245	60.49 245	68.57 268	68.30 321	40.88 317	73.89 283	79.93 205	51.81 272	54.77 259	79.61 225	24.80 300	81.10 273	49.93 239	61.35 235	83.73 213

BH-w/o		70.02 141	68.51 142	74.56 147	82.77 118	50.39 172	86.60 85	78.14 245	59.77 155	59.65 189	85.57 152	39.27 165	87.30 187	49.86 240	74.94 131	85.99 171

LCM-MVSNet-Re		58.82 272	56.54 271	65.68 289	79.31 190	29.09 358	61.39 339	45.79 357	60.73 143	37.65 345	72.47 304	31.42 257	81.08 274	49.66 241	70.41 165	86.87 151

gg-mvs-nofinetune		67.43 192	64.53 218	76.13 117	85.95 47	47.79 237	64.38 328	88.28 43	39.34 332	66.62 105	41.27 364	58.69 13	89.00 128	49.64 242	86.62 28	91.59 50

TranMVSNet+NR-MVSNet		66.94 207	65.61 201	70.93 234	73.45 274	43.38 293	83.02 186	84.25 129	65.31 73	58.33 220	81.90 207	39.92 161	85.52 232	49.43 243	54.89 293	83.89 211

tttt0517		68.33 173	66.29 183	74.46 149	78.08 214	49.06 201	80.88 231	89.08 20	54.40 252	54.75 260	80.77 218	51.31 42	90.33 94	49.35 244	58.01 263	83.99 205

test_fmvs2		45.89 321	44.32 323	50.62 338	45.85 370	24.70 364	58.87 346	37.84 368	25.22 359	52.46 279	74.56 283	7.07 363	54.69 359	49.28 245	47.70 321	72.48 335

WR-MVS		67.58 187	66.76 173	70.04 248	75.92 247	45.06 277	86.23 90	85.28 99	64.31 81	58.50 215	81.00 214	44.80 103	82.00 269	49.21 246	55.57 288	83.06 226

tt0805		63.39 236	61.31 237	69.64 251	69.36 311	38.87 323	78.00 258	85.48 87	48.82 290	55.66 257	81.66 209	24.38 303	86.37 213	49.04 247	59.36 249	83.68 214

test_post1							70.84 307				14.72 382	34.33 229	83.86 252	48.80 248

SCA		63.84 231	60.01 250	75.32 135	78.58 207	57.92 10	61.61 337	77.53 254	56.71 223	57.75 228	70.77 317	31.97 251	79.91 290	48.80 248	56.36 275	88.13 128

pmmvs5		62.80 243	61.18 238	67.66 274	69.53 310	42.37 306	82.65 190	75.19 285	54.30 254	52.03 283	78.51 237	31.64 256	80.67 277	48.60 250	58.15 259	79.95 272

新几何1				73.30 183	83.10 104	53.48 100		71.43 314	45.55 309	66.14 112	87.17 133	33.88 234	80.54 280	48.50 251	80.33 81	85.88 176

pm-mvs1		64.12 229	62.56 226	68.78 262	71.68 293	38.87 323	82.89 188	81.57 178	55.54 239	53.89 269	77.82 243	37.73 180	86.74 201	48.46 252	53.49 304	80.72 262

PM-MVS		46.92 320	43.76 325	56.41 330	52.18 360	32.26 346	63.21 333	38.18 366	37.99 337	40.78 336	66.20 332	5.09 371	65.42 348	48.19 253	41.99 341	71.54 341

FC-MVSNet-test		67.49 190	67.91 150	66.21 287	76.06 243	33.06 342	80.82 232	87.18 59	64.44 80	54.81 258	82.87 185	50.40 51	82.60 264	48.05 254	66.55 193	82.98 228

CMPMVS	40.41 21	55.34 293	52.64 296	63.46 303	60.88 351	43.84 287	61.58 338	71.06 316	30.43 354	36.33 347	74.63 282	24.14 305	75.44 321	48.05 254	66.62 192	71.12 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 197	65.99 191	71.04 232	73.27 277	43.91 286	85.32 113	84.75 118	66.05 63	53.65 272	82.11 204	45.05 95	85.97 227	47.55 256	56.18 280	83.24 221

QAPM		71.88 110	69.33 134	79.52 34	82.20 130	54.30 85	86.30 89	88.77 30	56.61 226	59.72 188	87.48 127	33.90 233	95.36 13	47.48 257	81.49 69	88.90 111

EPMVS		68.45 170	65.44 206	77.47 82	84.91 68	56.17 38	71.89 303	81.91 174	61.72 125	60.85 178	72.49 303	36.21 208	87.06 192	47.32 258	71.62 154	89.17 106

GBi-Net		67.09 202	65.47 204	71.96 211	82.71 120	46.36 255	83.52 163	83.31 148	58.55 186	57.58 231	76.23 269	36.72 203	86.20 214	47.25 259	63.40 216	83.32 218

test1		67.09 202	65.47 204	71.96 211	82.71 120	46.36 255	83.52 163	83.31 148	58.55 186	57.58 231	76.23 269	36.72 203	86.20 214	47.25 259	63.40 216	83.32 218

FMVSNet3		68.84 161	67.40 164	73.19 184	85.05 65	48.53 219	85.71 104	85.36 93	60.90 140	57.58 231	79.15 232	42.16 132	86.77 200	47.25 259	63.40 216	84.27 199

v7n		62.50 246	59.27 255	72.20 204	67.25 326	49.83 187	77.87 260	80.12 200	52.50 265	48.80 299	73.07 297	32.10 249	87.90 168	46.83 262	54.92 292	78.86 280

CVMVSNet		60.85 256	60.44 246	62.07 308	75.00 257	32.73 344	79.54 246	73.49 301	36.98 340	56.28 250	83.74 173	29.28 271	69.53 345	46.48 263	63.23 221	83.94 210

TR-MVS		69.71 148	67.85 155	75.27 138	82.94 113	48.48 222	87.40 66	80.86 189	57.15 215	64.61 133	87.08 134	32.67 244	89.64 114	46.38 264	71.55 156	87.68 138

MDTV_nov1_ep13_2view						43.62 289	71.13 306		54.95 246	59.29 200		36.76 200		46.33 265		87.32 145

FMVSNet2		67.57 188	65.79 196	72.90 189	82.71 120	47.97 235	85.15 116	84.93 111	58.55 186	56.71 244	78.26 239	36.72 203	86.67 203	46.15 266	62.94 227	84.07 202

UnsupCasMVSNet_eth		57.56 281	55.15 281	64.79 298	64.57 340	33.12 341	73.17 290	83.87 139	58.98 178	41.75 331	70.03 321	22.54 313	79.92 288	46.12 267	35.31 352	81.32 256

testdata2													77.81 309	45.64 268

XVG-ACMP-BASELINE		56.03 290	52.85 294	65.58 290	61.91 348	40.95 316	63.36 330	72.43 306	45.20 312	46.02 316	74.09 285	9.20 358	78.12 300	45.13 269	58.27 257	77.66 298

AdaColmap		67.86 180	65.48 203	75.00 142	88.15 33	54.99 67	86.10 93	76.63 272	49.30 286	57.80 225	86.65 141	29.39 270	88.94 134	45.10 270	70.21 167	81.06 258

BH-untuned		68.28 174	66.40 180	73.91 166	81.62 143	50.01 182	85.56 108	77.39 257	57.63 204	57.47 236	83.69 175	36.36 207	87.08 191	44.81 271	73.08 144	84.65 194

mvsany_test1		43.38 324	42.57 326	45.82 342	50.96 363	26.10 362	55.80 349	27.74 378	27.15 357	47.41 309	74.39 284	18.67 333	44.95 370	44.66 272	36.31 350	66.40 351

BH-RMVSNet		70.08 139	68.01 149	76.27 110	84.21 80	51.22 159	87.29 70	79.33 223	58.96 179	63.63 150	86.77 138	33.29 239	90.30 97	44.63 273	73.96 135	87.30 146

test_vis1_rt		40.29 327	38.64 329	45.25 344	48.91 367	30.09 352	59.44 343	27.07 379	24.52 361	38.48 343	51.67 360	6.71 366	49.44 364	44.33 274	46.59 331	56.23 360

IS-MVSNet		68.80 164	67.55 161	72.54 196	78.50 209	43.43 292	81.03 228	79.35 221	59.12 175	57.27 239	86.71 139	46.05 83	87.70 176	44.32 275	75.60 122	86.49 161

pmmvs-eth3d		55.97 291	52.78 295	65.54 291	61.02 350	46.44 254	75.36 275	67.72 329	49.61 285	43.65 322	67.58 329	21.63 321	77.04 312	44.11 276	44.33 336	73.15 334

pmmvs6		59.64 261	57.15 267	67.09 278	66.01 328	36.86 332	80.50 235	78.64 235	45.05 313	49.05 297	73.94 287	27.28 282	86.10 220	43.96 277	49.94 315	78.31 290

EPNet_dtu		66.25 217	66.71 174	64.87 297	78.66 205	34.12 337	82.80 189	75.51 281	61.75 124	64.47 138	86.90 136	37.06 195	72.46 336	43.65 278	69.63 172	88.02 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 216	62.78 225	76.77 106	81.40 152	57.14 22	70.03 310	77.19 260	53.00 261	58.76 211	70.73 319	46.17 80	86.73 202	43.27 279	64.46 208	86.44 162

OpenMVS	61.00 11	69.99 143	67.55 161	77.30 86	78.37 212	54.07 91	84.36 143	85.76 85	57.22 213	56.71 244	87.67 125	30.79 261	92.83 34	43.04 280	84.06 53	85.01 189

PatchmatchNet		67.07 204	63.63 224	77.40 83	83.10 104	58.03 9	72.11 301	77.77 250	58.85 180	59.37 196	70.83 316	37.84 176	84.93 245	42.96 281	69.83 170	89.26 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 247	59.04 257	72.77 192	73.97 271	56.57 29	60.52 340	71.72 310	60.04 151	57.49 234	65.86 333	38.94 167	80.31 283	42.86 282	59.93 241	81.42 249

test_fmvs3		37.95 329	35.75 331	44.55 345	35.50 376	18.92 374	48.32 355	34.00 373	18.36 367	41.31 334	61.58 343	2.29 378	48.06 368	42.72 283	37.71 349	66.66 350

FMVSNet1		64.57 225	62.11 230	71.96 211	77.32 225	46.36 255	83.52 163	83.31 148	52.43 266	54.42 263	76.23 269	27.80 279	86.20 214	42.59 284	61.34 236	83.32 218

UA-Net		67.32 196	66.23 185	70.59 237	78.85 199	41.23 314	73.60 285	75.45 283	61.54 128	66.61 106	84.53 161	38.73 170	86.57 209	42.48 285	74.24 133	83.98 207

CL-MVSNet_self_test		62.98 240	61.14 239	68.50 269	65.86 330	42.96 296	84.37 142	82.98 157	60.98 138	53.95 268	72.70 302	40.43 153	83.71 256	41.10 286	47.93 320	78.83 281

MIMVSNet		63.12 239	60.29 248	71.61 220	75.92 247	46.65 251	65.15 324	81.94 171	59.14 174	54.65 261	69.47 323	25.74 293	80.63 278	41.03 287	69.56 173	87.55 140

FE-MVS		64.15 228	60.43 247	75.30 136	80.85 165	49.86 186	68.28 319	78.37 242	50.26 282	59.31 198	73.79 288	26.19 290	91.92 54	40.19 288	66.67 191	84.12 200

EG-PatchMatch MVS		62.40 249	59.59 251	70.81 235	73.29 276	49.05 202	85.81 97	84.78 116	51.85 271	44.19 319	73.48 295	15.52 347	89.85 106	40.16 289	67.24 187	73.54 330

UnsupCasMVSNet_bld		53.86 300	50.53 304	63.84 300	63.52 344	34.75 335	71.38 304	81.92 173	46.53 301	38.95 341	57.93 353	20.55 325	80.20 286	39.91 290	34.09 359	76.57 309

dp		64.41 226	61.58 233	72.90 189	82.40 126	54.09 90	72.53 293	76.59 273	60.39 147	55.68 254	70.39 320	35.18 220	76.90 315	39.34 291	61.71 234	87.73 136

TransMVSNet (Re)		62.82 242	60.76 243	69.02 257	73.98 270	41.61 309	86.36 87	79.30 224	56.90 217	52.53 278	76.44 265	41.85 139	87.60 182	38.83 292	40.61 344	77.86 295

USDC		54.36 297	51.23 301	63.76 301	64.29 341	37.71 329	62.84 335	73.48 303	56.85 218	35.47 350	71.94 312	9.23 357	78.43 297	38.43 293	48.57 317	75.13 320

PLC	52.38 18	60.89 255	58.97 258	66.68 285	81.77 137	45.70 268	78.96 253	74.04 295	43.66 322	47.63 305	83.19 184	23.52 309	77.78 310	37.47 294	60.46 239	76.55 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 244	62.44 227	62.86 307	72.28 290	29.51 355	82.93 187	78.78 231	59.18 172	53.07 275	82.41 197	36.91 198	77.39 311	37.45 295	58.96 251	81.66 243

OurMVSNet-221017-0		52.39 308	48.73 311	63.35 304	65.21 334	38.42 326	68.54 318	64.95 333	38.19 335	39.57 338	71.43 313	13.23 350	79.92 288	37.16 296	40.32 345	71.72 339

CNLPA		60.59 257	58.44 260	67.05 280	79.21 191	47.26 244	79.75 245	64.34 337	42.46 328	51.90 284	83.94 169	27.79 280	75.41 322	37.12 297	59.49 247	78.47 286

K. test v3		54.04 299	49.42 310	67.92 273	68.55 317	42.57 304	75.51 273	63.07 340	52.07 267	39.21 339	64.59 337	19.34 329	82.21 265	37.11 298	25.31 368	78.97 279

Vis-MVSNet (Re-imp)		65.52 223	65.63 200	65.17 295	77.49 223	30.54 349	75.49 274	77.73 251	59.34 165	52.26 282	86.69 140	49.38 58	80.53 281	37.07 299	75.28 125	84.42 197

PatchMatch-RL		56.66 284	53.75 289	65.37 294	77.91 219	45.28 272	69.78 312	60.38 343	41.35 329	47.57 306	73.73 289	16.83 341	76.91 314	36.99 300	59.21 250	73.92 327

Patchmtry		56.56 286	52.95 293	67.42 276	72.53 286	50.59 166	59.05 344	71.72 310	37.86 338	46.92 311	65.86 333	38.94 167	80.06 287	36.94 301	46.72 330	71.60 340

FMVSNet5		58.61 274	56.45 272	65.10 296	77.20 230	39.74 319	74.77 277	77.12 262	50.27 281	43.28 325	67.71 328	26.15 291	76.90 315	36.78 302	54.78 294	78.65 284

MDTV_nov1_ep13			61.56 234		81.68 140	55.12 62	72.41 295	78.18 244	59.19 170	58.85 209	69.29 324	34.69 226	86.16 217	36.76 303	62.96 226

JIA-IIPM		52.33 309	47.77 316	66.03 288	71.20 300	46.92 248	40.00 366	76.48 274	37.10 339	46.73 312	37.02 366	32.96 240	77.88 307	35.97 304	52.45 310	73.29 332

lessismore_v0				67.98 271	64.76 339	41.25 313		45.75 358		36.03 349	65.63 335	19.29 330	84.11 251	35.67 305	21.24 373	78.59 285

CP-MVSNet		58.54 277	57.57 265	61.46 314	68.50 318	33.96 338	76.90 266	78.60 238	51.67 273	47.83 303	76.60 264	34.99 224	72.79 334	35.45 306	47.58 322	77.64 299

Anonymous20240521		51.65 310	48.42 312	61.34 316	56.43 357	39.65 321	73.57 286	73.47 304	36.64 342	36.59 346	63.98 338	10.75 354	72.25 338	35.35 307	49.01 316	72.11 337

ambc				62.06 309	53.98 359	29.38 356	35.08 369	79.65 212		41.37 332	59.96 348	6.27 369	82.15 266	35.34 308	38.22 348	74.65 322

KD-MVS_2432*1600		59.04 269	56.44 273	66.86 281	79.07 193	45.87 265	72.13 299	80.42 196	55.03 244	48.15 301	71.01 314	36.73 201	78.05 303	35.21 309	30.18 363	76.67 305

miper_refine_blended		59.04 269	56.44 273	66.86 281	79.07 193	45.87 265	72.13 299	80.42 196	55.03 244	48.15 301	71.01 314	36.73 201	78.05 303	35.21 309	30.18 363	76.67 305

PS-CasMVS		58.12 279	57.03 269	61.37 315	68.24 322	33.80 340	76.73 267	78.01 246	51.20 275	47.54 307	76.20 272	32.85 241	72.76 335	35.17 311	47.37 324	77.55 300

EU-MVSNet		52.63 306	50.72 303	58.37 324	62.69 347	28.13 360	72.60 292	75.97 278	30.94 353	40.76 337	72.11 310	20.16 326	70.80 341	35.11 312	46.11 332	76.19 313

ACMH+	54.58 15	58.55 276	55.24 280	68.50 269	74.68 261	45.80 267	80.27 238	70.21 321	47.15 298	42.77 327	75.48 277	16.73 343	85.98 225	35.10 313	54.78 294	73.72 328

pmmvs3		45.53 323	41.55 327	57.44 326	48.97 366	39.68 320	70.06 309	57.66 347	28.32 356	34.06 353	57.29 354	8.50 361	66.85 347	34.86 314	34.26 357	65.80 353

our_test_3		59.11 267	55.08 283	71.18 230	71.42 297	53.29 112	81.96 204	74.52 288	48.32 291	42.08 328	69.28 325	28.14 274	82.15 266	34.35 315	45.68 334	78.11 294

PEN-MVS		58.35 278	57.15 267	61.94 310	67.55 325	34.39 336	77.01 264	78.35 243	51.87 270	47.72 304	76.73 262	33.91 232	73.75 329	34.03 316	47.17 326	77.68 297

KD-MVS_self_test		49.24 315	46.85 318	56.44 329	54.32 358	22.87 366	57.39 347	73.36 305	44.36 318	37.98 344	59.30 351	18.97 331	71.17 340	33.48 317	42.44 340	75.26 318

tpmvs		62.45 248	59.42 253	71.53 224	83.93 84	54.32 84	70.03 310	77.61 253	51.91 269	53.48 273	68.29 327	37.91 175	86.66 204	33.36 318	58.27 257	73.62 329

YYNet1		53.82 301	49.96 306	65.41 293	70.09 308	48.95 206	72.30 296	71.66 312	44.25 319	31.89 359	63.07 341	23.73 307	73.95 327	33.26 319	39.40 346	73.34 331

MDA-MVSNet_test_wron		53.82 301	49.95 307	65.43 292	70.13 307	49.05 202	72.30 296	71.65 313	44.23 320	31.85 360	63.13 340	23.68 308	74.01 326	33.25 320	39.35 347	73.23 333

Anonymous20231206		59.08 268	57.59 264	63.55 302	68.77 316	32.14 347	80.26 239	79.78 208	50.00 283	49.39 295	72.39 306	26.64 287	78.36 298	33.12 321	57.94 264	80.14 270

F-COLMAP		55.96 292	53.65 290	62.87 306	72.76 283	42.77 300	74.70 280	70.37 320	40.03 331	41.11 335	79.36 227	17.77 337	73.70 330	32.80 322	53.96 300	72.15 336

PatchT		56.60 285	52.97 292	67.48 275	72.94 281	46.16 262	57.30 348	73.78 297	38.77 334	54.37 264	57.26 355	37.52 186	78.06 302	32.02 323	52.79 308	78.23 293

SixPastTwentyTwo		54.37 296	50.10 305	67.21 277	70.70 303	41.46 312	74.73 278	64.69 334	47.56 296	39.12 340	69.49 322	18.49 335	84.69 248	31.87 324	34.20 358	75.48 316

WR-MVS_H		58.91 271	58.04 262	61.54 313	69.07 314	33.83 339	76.91 265	81.99 170	51.40 274	48.17 300	74.67 281	40.23 155	74.15 325	31.78 325	48.10 318	76.64 308

ACMH	53.70 16	59.78 259	55.94 278	71.28 226	76.59 235	48.35 224	80.15 242	76.11 276	49.74 284	41.91 330	73.45 296	16.50 344	90.31 95	31.42 326	57.63 270	75.17 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 262	55.14 282	72.32 203	74.69 260	50.71 162	74.39 281	73.58 299	44.44 317	43.40 324	77.52 246	19.45 328	90.87 79	31.31 327	57.49 271	75.38 317

thres200		68.71 166	67.27 167	73.02 186	84.73 70	46.76 250	85.03 123	87.73 53	62.34 116	59.87 185	83.45 179	43.15 121	88.32 155	31.25 328	67.91 182	83.98 207

DTE-MVSNet		57.03 283	55.73 279	60.95 318	65.94 329	32.57 345	75.71 269	77.09 263	51.16 276	46.65 314	76.34 267	32.84 242	73.22 333	30.94 329	44.87 335	77.06 302

ppachtmachnet_test		58.56 275	54.34 284	71.24 227	71.42 297	54.74 72	81.84 209	72.27 307	49.02 288	45.86 318	68.99 326	26.27 288	83.30 261	30.12 330	43.23 339	75.69 314

mvsany_test3		28.00 336	25.98 338	34.05 353	28.97 381	15.31 380	34.54 370	18.17 384	16.24 368	29.30 363	53.37 359	2.79 376	33.38 381	30.01 331	20.41 374	53.45 363

MVS-HIRNet		49.01 316	44.71 320	61.92 311	76.06 243	46.61 252	63.23 332	54.90 350	24.77 360	33.56 355	36.60 368	21.28 323	75.88 320	29.49 332	62.54 229	63.26 358

test20.03		55.22 294	54.07 287	58.68 323	63.14 345	25.00 363	77.69 261	74.78 287	52.64 263	43.43 323	72.39 306	26.21 289	74.76 324	29.31 333	47.05 328	76.28 312

testgi		54.25 298	52.57 297	59.29 321	62.76 346	21.65 370	72.21 298	70.47 319	53.25 260	41.94 329	77.33 250	14.28 348	77.95 306	29.18 334	51.72 312	78.28 291

thres100view900		66.87 209	65.42 207	71.24 227	83.29 100	43.15 295	81.67 213	87.78 50	59.04 176	55.92 252	82.18 203	43.73 111	87.80 171	28.80 335	66.36 195	82.78 232

tfpn200view9		67.57 188	66.13 187	71.89 218	84.05 82	45.07 274	83.40 172	87.71 55	60.79 141	57.79 226	82.76 188	43.53 116	87.80 171	28.80 335	66.36 195	82.78 232

thres400		67.40 195	66.13 187	71.19 229	84.05 82	45.07 274	83.40 172	87.71 55	60.79 141	57.79 226	82.76 188	43.53 116	87.80 171	28.80 335	66.36 195	80.71 263

ADS-MVSNet2		55.21 295	51.44 300	66.51 286	80.60 171	49.56 192	55.03 351	65.44 332	44.72 314	51.00 288	61.19 345	22.83 310	75.41 322	28.54 338	53.63 301	74.57 323

ADS-MVSNet		56.17 289	51.95 299	68.84 259	80.60 171	53.07 118	55.03 351	70.02 322	44.72 314	51.00 288	61.19 345	22.83 310	78.88 296	28.54 338	53.63 301	74.57 323

LTVRE_ROB	45.45 19	52.73 305	49.74 308	61.69 312	69.78 309	34.99 334	44.52 359	67.60 330	43.11 325	43.79 321	74.03 286	18.54 334	81.45 271	28.39 340	57.94 264	68.62 346
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
test_vis3_rt		24.79 342	22.95 345	30.31 358	28.59 382	18.92 374	37.43 368	17.27 386	12.90 371	21.28 369	29.92 375	1.02 385	36.35 375	28.28 341	29.82 365	35.65 369

new-patchmatchnet		48.21 317	46.55 319	53.18 335	57.73 355	18.19 378	70.24 308	71.02 317	45.70 308	33.70 354	60.23 347	18.00 336	69.86 344	27.97 342	34.35 356	71.49 342

OpenMVS_ROB	53.19 17	59.20 265	56.00 277	68.83 260	71.13 301	44.30 282	83.64 162	75.02 286	46.42 304	46.48 315	73.03 298	18.69 332	88.14 160	27.74 343	61.80 233	74.05 326

RPSCF		45.77 322	44.13 324	50.68 337	57.67 356	29.66 354	54.92 353	45.25 359	26.69 358	45.92 317	75.92 275	17.43 340	45.70 369	27.44 344	45.95 333	76.67 305

MDA-MVSNet-bldmvs		51.56 311	47.75 317	63.00 305	71.60 295	47.32 243	69.70 313	72.12 308	43.81 321	27.65 366	63.38 339	21.97 320	75.96 319	27.30 345	32.19 360	65.70 354

RPMNet		59.29 263	54.25 286	74.42 151	73.97 271	56.57 29	60.52 340	76.98 264	35.72 344	57.49 234	58.87 352	37.73 180	85.26 238	27.01 346	59.93 241	81.42 249

thres600view7		66.46 214	65.12 211	70.47 238	83.41 94	43.80 288	82.15 201	87.78 50	59.37 164	56.02 251	82.21 202	43.73 111	86.90 198	26.51 347	64.94 203	80.71 263

TAPA-MVS	56.12 14	61.82 252	60.18 249	66.71 283	78.48 210	37.97 328	75.19 276	76.41 275	46.82 300	57.04 240	86.52 143	27.67 281	77.03 313	26.50 348	67.02 189	85.14 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 336	58.03 354	31.94 348		53.57 354	36.67 341	41.32 333	75.23 279	11.17 353	51.57 363	25.81 349	48.04 319	72.02 338

Patchmatch-test		53.33 304	48.17 313	68.81 261	73.31 275	42.38 305	42.98 361	58.23 346	32.53 349	38.79 342	70.77 317	39.66 162	73.51 331	25.18 350	52.06 311	90.55 75

test_f		27.12 338	24.85 339	33.93 354	26.17 386	15.25 381	30.24 374	22.38 383	12.53 373	28.23 364	49.43 361	2.59 377	34.34 380	25.12 351	26.99 366	52.20 364

TinyColmap		48.15 318	44.49 322	59.13 322	65.73 331	38.04 327	63.34 331	62.86 341	38.78 333	29.48 362	67.23 331	6.46 368	73.30 332	24.59 352	41.90 342	66.04 352

AllTest		47.32 319	44.66 321	55.32 333	65.08 336	37.50 330	62.96 334	54.25 352	35.45 346	33.42 356	72.82 299	9.98 355	59.33 354	24.13 353	43.84 337	69.13 344

TestCases				55.32 333	65.08 336	37.50 330		54.25 352	35.45 346	33.42 356	72.82 299	9.98 355	59.33 354	24.13 353	43.84 337	69.13 344

N_pmnet		41.25 325	39.77 328	45.66 343	68.50 318	0.82 390	72.51 294	0.38 390	35.61 345	35.26 351	61.51 344	20.07 327	67.74 346	23.51 355	40.63 343	68.42 347

dmvs_testset		57.65 280	58.21 261	55.97 331	74.62 262	9.82 385	63.75 329	63.34 339	67.23 44	48.89 298	83.68 177	39.12 166	76.14 318	23.43 356	59.80 243	81.96 238

DP-MVS		59.24 264	56.12 276	68.63 265	88.24 32	50.35 175	82.51 195	64.43 336	41.10 330	46.70 313	78.77 235	24.75 301	88.57 145	22.26 357	56.29 279	66.96 349

MIMVSNet1		50.35 314	47.81 315	57.96 325	61.53 349	27.80 361	67.40 321	74.06 294	43.25 324	33.31 358	65.38 336	16.03 345	71.34 339	21.80 358	47.55 323	74.75 321

tfpnnormal		61.47 253	59.09 256	68.62 266	76.29 241	41.69 307	81.14 227	85.16 105	54.48 251	51.32 286	73.63 293	32.32 247	86.89 199	21.78 359	55.71 287	77.29 301

LF4IMVS		33.04 334	32.55 334	34.52 352	40.96 371	22.03 368	44.45 360	35.62 370	20.42 363	28.12 365	62.35 342	5.03 372	31.88 382	21.61 360	34.42 355	49.63 366

COLMAP_ROB	43.60 20	50.90 313	48.05 314	59.47 320	67.81 324	40.57 318	71.25 305	62.72 342	36.49 343	36.19 348	73.51 294	13.48 349	73.92 328	20.71 361	50.26 314	63.92 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 335	23.85 343	40.71 347	27.46 385	18.93 373	30.82 373	46.19 356	12.76 372	16.40 370	34.70 371	1.90 381	48.69 367	20.25 362	24.22 369	54.51 362

DSMNet-mixed		38.35 328	35.36 332	47.33 341	48.11 368	14.91 382	37.87 367	36.60 369	19.18 365	34.37 352	59.56 350	15.53 346	53.01 362	20.14 363	46.89 329	74.07 325

new_pmnet		33.56 333	31.89 335	38.59 349	49.01 365	20.42 371	51.01 354	37.92 367	20.58 362	23.45 367	46.79 362	6.66 367	49.28 366	20.00 364	31.57 362	46.09 368

LS3D		56.40 288	53.82 288	64.12 299	81.12 156	45.69 269	73.42 288	66.14 331	35.30 348	43.24 326	79.88 222	22.18 318	79.62 292	19.10 365	64.00 211	67.05 348

test_method		24.09 343	21.07 347	33.16 355	27.67 384	8.35 388	26.63 375	35.11 372	3.40 381	14.35 373	36.98 367	3.46 375	35.31 377	19.08 366	22.95 370	55.81 361

TDRefinement		40.91 326	38.37 330	48.55 340	50.45 364	33.03 343	58.98 345	50.97 355	28.50 355	29.89 361	67.39 330	6.21 370	54.51 360	17.67 367	35.25 353	58.11 359

test_0402		56.45 287	53.03 291	66.69 284	76.78 234	50.31 177	81.76 211	69.61 324	42.79 326	43.88 320	72.13 309	22.82 312	86.46 210	16.57 368	50.94 313	63.31 357

PMMVS2		26.71 339	22.98 344	37.87 350	36.89 374	8.51 387	42.51 362	29.32 377	19.09 366	13.01 374	37.54 365	2.23 379	53.11 361	14.54 369	11.71 376	51.99 365

ANet_high		34.39 331	29.59 337	48.78 339	30.34 380	22.28 367	55.53 350	63.79 338	38.11 336	15.47 372	36.56 369	6.94 364	59.98 353	13.93 370	5.64 383	64.08 355

tmp_tt		9.44 350	10.68 353	5.73 366	2.49 389	4.21 389	10.48 379	18.04 385	0.34 383	12.59 375	20.49 377	11.39 352	7.03 385	13.84 371	6.46 382	5.95 380

APD_test1		26.46 340	24.41 341	32.62 357	37.58 373	21.74 369	40.50 365	30.39 375	11.45 374	16.33 371	43.76 363	1.63 383	41.62 372	11.24 372	26.82 367	34.51 371

EGC-MVSNET		33.75 332	30.42 336	43.75 346	64.94 338	36.21 333	60.47 342	40.70 364	0.02 384	0.10 385	53.79 357	7.39 362	60.26 352	11.09 373	35.23 354	34.79 370

FPMVS		35.40 330	33.67 333	40.57 348	46.34 369	28.74 359	41.05 363	57.05 348	20.37 364	22.27 368	53.38 358	6.87 365	44.94 371	8.62 374	47.11 327	48.01 367

Gipuma		27.47 337	24.26 342	37.12 351	60.55 352	29.17 357	11.68 378	60.00 344	14.18 370	10.52 379	15.12 380	2.20 380	63.01 349	8.39 375	35.65 351	19.18 376
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		21.11 344	19.08 348	27.18 360	30.56 378	18.28 376	33.43 371	24.48 380	8.02 378	12.02 376	33.50 372	0.75 387	35.09 378	7.68 376	21.32 371	28.17 373

APD_test2		21.11 344	19.08 348	27.18 360	30.56 378	18.28 376	33.43 371	24.48 380	8.02 378	12.02 376	33.50 372	0.75 387	35.09 378	7.68 376	21.32 371	28.17 373

MVE	16.60 23	17.34 349	13.39 352	29.16 359	28.43 383	19.72 372	13.73 377	23.63 382	7.23 380	7.96 380	21.41 376	0.80 386	36.08 376	6.97 378	10.39 377	31.69 372
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 364	21.34 388	8.99 386		10.02 388	10.59 376	7.53 381	30.55 374	1.82 382	14.55 383	6.83 379	7.52 379	15.75 377

E-PMN		19.16 346	18.40 350	21.44 362	36.19 375	13.63 383	47.59 356	30.89 374	10.73 375	5.91 382	16.59 378	3.66 374	39.77 373	5.95 380	8.14 378	10.92 378

PMVS	19.57 22	25.07 341	22.43 346	32.99 356	23.12 387	22.98 365	40.98 364	35.19 371	15.99 369	11.95 378	35.87 370	1.47 384	49.29 365	5.41 381	31.90 361	26.70 375
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 347	17.66 351	20.71 363	34.13 377	12.64 384	46.94 357	29.94 376	10.46 377	5.58 383	14.93 381	4.23 373	38.83 374	5.24 382	7.51 380	10.67 379

wuyk23d		9.11 351	8.77 355	10.15 365	40.18 372	16.76 379	20.28 376	1.01 389	2.58 382	2.66 384	0.98 384	0.23 389	12.49 384	4.08 383	6.90 381	1.19 381

testmvs		6.14 353	8.18 356	0.01 367	0.01 390	0.00 392	73.40 289	0.00 391	0.00 385	0.02 386	0.15 385	0.00 390	0.00 386	0.02 384	0.00 384	0.02 382

test123		6.01 354	8.01 357	0.01 367	0.00 391	0.01 391	71.93 302	0.00 391	0.00 385	0.02 386	0.11 386	0.00 390	0.00 386	0.02 384	0.00 384	0.02 382

test_blank		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

cdsmvs_eth3d_5k		18.33 348	24.44 340	0.00 369	0.00 391	0.00 392	0.00 380	89.40 15	0.00 385	0.00 388	92.02 36	38.55 171	0.00 386	0.00 386	0.00 384	0.00 384

pcd_1.5k_mvsjas		3.15 355	4.20 358	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	37.77 177	0.00 386	0.00 386	0.00 384	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

ab-mvs-re		7.68 352	10.24 354	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	92.12 33	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 391	0.00 392	0.00 380	0.00 391	0.00 385	0.00 388	0.00 387	0.00 390	0.00 386	0.00 386	0.00 384	0.00 384

FOURS1					83.24 101	49.90 185	84.98 125	78.76 232	47.71 294	73.42 49

test_one_0601					89.39 22	57.29 20		88.09 45	57.21 214	82.06 11	93.39 14	54.94 24

eth-test2					0.00 391

eth-test					0.00 391

test_241102_ONE					89.48 17	56.89 25		88.94 23	57.53 206	84.61 4	93.29 17	58.81 11	96.45 1

save fliter					85.35 60	56.34 36	89.31 38	81.46 180	61.55 127

test0726					89.40 20	57.45 17	92.32 7	88.63 35	57.71 202	83.14 9	93.96 6	55.17 20

GSMVS																88.13 128

test_part2					89.33 23	55.48 50				82.27 10

sam_mvs1												38.86 169				88.13 128

sam_mvs												35.99 214

MTGPA								81.31 183

test_post											16.22 379	37.52 186	84.72 247

patchmatchnet-post											59.74 349	38.41 172	79.91 290

MTMP							87.27 71	15.34 387

TEST9					85.68 51	55.42 51	87.59 61	84.00 135	57.72 201	72.99 54	90.98 55	44.87 99	88.58 142

test_8					85.72 50	55.31 55	87.60 60	83.88 138	57.84 199	72.84 58	90.99 54	44.99 96	88.34 153

agg_prior					85.64 54	54.92 69		83.61 145		72.53 62			88.10 163

test_prior4						56.39 35	87.15 74

test_prior				78.39 64	86.35 45	54.91 70		85.45 90					89.70 112			90.55 75

新几何2							81.61 216

旧先验1					81.57 147	47.48 239		71.83 309			88.66 105	36.94 197			78.34 102	88.67 117

原ACMM2							83.77 160

test222					79.36 187	50.97 160	77.99 259	67.84 328	42.54 327	62.84 159	86.53 142	30.26 264			76.91 110	85.23 185

segment_acmp												44.97 98

testdata1							77.55 263		64.14 84

test12				79.24 38	86.89 41	56.08 40		85.16 105		72.27 66		47.15 71	91.10 72		85.93 34	90.54 77

plane_prior7					77.95 216	48.46 223

plane_prior6					78.42 211	49.39 197						36.04 212

plane_prior4											83.28 182

plane_prior3						48.95 206			64.01 87	62.15 167

plane_prior2							85.76 99		63.60 96

plane_prior1					78.31 213

plane_prior						49.57 190	87.43 64		64.57 79						72.84 145

n2								0.00 391

nn								0.00 391

door-mid								41.31 363

test11								84.25 129

door								43.27 360

HQP5-MVS						51.56 149

HQP-NCC					79.02 195		88.00 52		65.45 66	64.48 135

ACMP_Plane					79.02 195		88.00 52		65.45 66	64.48 135

HQP4-MVS										64.47 138			88.61 141			84.91 191

HQP3-MVS								83.68 141							73.12 141

HQP2-MVS												37.35 189

NP-MVS					78.76 200	50.43 170					85.12 156

ACMMP++_ref															63.20 222

ACMMP++															59.38 248

Test By Simon												39.38 163

High-resolution multi-view benchmark