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 13	92.05 3	55.97 46	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

PC_three_1452									66.58 48	87.27 2	93.70 9	66.82 4	94.95 18	89.74 2	91.98 4	93.98 5

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 7	59.33 6	92.10 9	90.95 9	65.68 63	80.67 18	94.42 3	59.41 10	95.89 12	86.74 3	89.75 7	92.94 18

MCST-MVS		83.01 2	83.30 3	82.15 11	92.84 2	57.58 16	93.77 1	91.10 6	75.95 3	77.10 28	93.09 20	54.15 28	95.57 13	85.80 4	85.87 38	93.31 11

patch_mono-2		80.84 11	81.59 9	78.62 60	90.34 10	53.77 97	88.08 56	88.36 47	76.17 2	79.40 23	91.09 57	55.43 20	90.09 111	85.01 5	80.40 87	91.99 42

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 11	57.70 14	91.71 10	88.87 32	70.31 16	77.64 27	93.87 8	52.58 35	93.91 28	84.17 6	87.92 16	92.39 29

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 14	56.54 33	84.83 143	83.68 148	67.85 36	69.36 91	90.24 82	60.20 7	92.10 58	84.14 7	80.40 87	92.82 21

CANet		80.90 10	81.17 11	80.09 31	87.62 41	54.21 90	91.60 13	86.47 78	73.13 6	79.89 21	93.10 18	49.88 55	92.98 35	84.09 8	84.75 51	93.08 16

MSP-MVS		82.30 6	83.47 1	78.80 51	82.99 125	52.71 132	85.04 134	88.63 39	66.08 58	86.77 3	92.75 24	72.05 1	91.46 70	83.35 9	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
IU-MVS					89.48 19	57.49 17		91.38 5	66.22 54	88.26 1				82.83 10	87.60 18	92.44 28

PS-MVSNAJ		80.06 15	79.52 16	81.68 14	85.58 62	60.97 3	91.69 11	87.02 67	70.62 13	80.75 17	93.22 17	37.77 185	92.50 47	82.75 11	86.25 35	91.57 51

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 78	60.73 4	91.65 12	86.86 70	70.30 17	80.77 16	93.07 21	37.63 190	92.28 52	82.73 12	85.71 39	91.57 51

DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 83	85.46 68	49.56 205	90.99 20	86.66 76	70.58 14	80.07 20	95.30 1	56.18 18	90.97 85	82.57 13	86.22 36	93.28 12

SED-MVS		81.92 7	81.75 8	82.44 8	89.48 19	56.89 27	92.48 3	88.94 28	57.50 213	84.61 4	94.09 4	58.81 12	96.37 6	82.28 14	87.60 18	94.06 3

test_241102_TWO								88.76 36	57.50 213	83.60 6	94.09 4	56.14 19	96.37 6	82.28 14	87.43 20	92.55 26

DVP-MVS		81.30 9	81.00 12	82.20 9	89.40 22	57.45 19	92.34 5	89.99 16	57.71 207	81.91 12	93.64 11	55.17 21	96.44 2	81.68 16	87.13 21	92.72 24
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 9	89.50 17	57.73 13	92.34 5	88.88 30					96.39 4	81.68 16	87.13 21	92.47 27

ETH3D-3000-0.1		78.73 21	78.71 21	78.78 54	85.58 62	52.40 140	88.42 53	89.03 25	60.01 153	76.06 33	92.80 23	48.34 61	92.88 38	81.66 18	86.48 33	91.04 67

DVP-MVS++		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 137	88.88 30	58.00 198	83.60 6	93.39 13	67.21 2	96.39 4	81.64 19	91.98 4	93.98 5

test_0728_THIRD									58.00 198	81.91 12	93.64 11	56.54 16	96.44 2	81.64 19	86.86 25	92.23 33

MSC_two_6792asdad				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

No_MVS				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

9.14			78.19 25		85.67 59		88.32 54	88.84 33	59.89 155	74.58 43	92.62 27	46.80 79	92.66 44	81.40 23	85.62 41

lupinMVS		78.38 25	78.11 27	79.19 40	83.02 123	55.24 60	91.57 14	84.82 120	69.12 23	76.67 30	92.02 39	44.82 108	90.23 108	80.83 24	80.09 92	92.08 37

ETH3D cwj APD-0.16		78.36 26	78.19 25	78.86 49	84.21 92	52.68 133	86.70 91	89.02 26	59.13 178	75.37 35	92.49 28	49.06 60	93.20 33	80.67 25	87.08 23	90.71 76

HPM-MVS++		80.50 13	80.71 13	79.88 33	87.34 43	55.20 63	89.93 29	87.55 62	66.04 61	79.46 22	93.00 22	53.10 32	91.76 64	80.40 26	89.56 9	92.68 25

SMA-MVS		79.10 20	78.76 20	80.12 29	84.42 86	55.87 48	87.58 69	86.76 73	61.48 131	80.26 19	93.10 18	46.53 83	92.41 49	79.97 27	88.77 11	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 23	78.20 24	79.19 40	88.56 28	54.55 85	89.76 33	87.77 57	55.91 238	78.56 24	92.49 28	48.20 63	92.65 45	79.49 28	83.04 61	90.39 83

ETV-MVS		77.17 42	76.74 43	78.48 64	81.80 149	54.55 85	86.13 102	85.33 101	68.20 30	73.10 56	90.52 76	45.23 100	90.66 94	79.37 29	80.95 78	90.22 87

jason		77.01 45	76.45 48	78.69 57	79.69 195	54.74 77	90.56 24	83.99 144	68.26 29	74.10 46	90.91 65	42.14 142	89.99 113	79.30 30	79.12 102	91.36 59
jason: jason.
DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 46	56.89 27	92.77 2	86.30 82	77.83 1	77.88 25	92.13 35	60.24 6	94.78 22	78.97 31	89.61 8	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 88	72.89 87	77.15 102	80.17 189	50.37 185	84.68 148	83.33 155	68.08 31	71.97 72	88.65 119	42.50 136	91.15 78	78.82 32	57.78 277	89.91 97

hse-mvs2		71.44 128	70.68 120	73.73 184	76.34 249	47.44 254	79.45 262	79.47 223	68.08 31	71.97 72	86.01 158	42.50 136	86.93 206	78.82 32	53.46 312	86.83 164

NCCC		79.57 18	79.23 18	80.59 21	89.50 17	56.99 25	91.38 15	88.17 49	67.71 39	73.81 48	92.75 24	46.88 78	93.28 32	78.79 34	84.07 57	91.50 55

test9_res														78.72 35	85.44 44	91.39 57

CSCG		80.41 14	79.72 14	82.49 6	89.12 27	57.67 15	89.29 41	91.54 3	59.19 173	71.82 75	90.05 91	59.72 9	96.04 10	78.37 36	88.40 14	93.75 7

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 26	55.08 68	88.70 49	87.92 53	55.55 243	81.21 15	93.69 10	56.51 17	94.27 25	78.36 37	85.70 40	91.51 54
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 70	75.03 65	77.04 104	81.37 165	52.65 135	84.34 155	84.46 130	61.16 134	69.14 93	91.76 46	39.98 169	88.99 140	78.19 38	84.89 50	89.48 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 47	76.40 49	78.45 66	85.68 57	55.42 54	87.59 67	84.00 142	57.84 204	72.99 57	90.98 61	44.99 103	88.58 152	78.19 38	85.32 45	91.34 61

xxxxxxxxxxxxxcwj		77.31 40	76.54 45	79.61 34	85.35 70	56.34 38	89.31 39	72.84 315	61.55 127	74.63 40	92.38 30	47.75 68	91.35 72	78.18 40	86.85 26	91.15 64

SF-MVS		77.64 35	77.42 35	78.32 72	83.75 102	52.47 138	86.63 93	87.80 54	58.78 186	74.63 40	92.38 30	47.75 68	91.35 72	78.18 40	86.85 26	91.15 64

canonicalmvs		78.17 29	77.86 30	79.12 43	84.30 88	54.22 89	87.71 62	84.57 129	67.70 40	77.70 26	92.11 38	50.90 46	89.95 114	78.18 40	77.54 114	93.20 14

VDD-MVS		76.08 61	74.97 67	79.44 36	84.27 91	53.33 117	91.13 19	85.88 89	65.33 70	72.37 69	89.34 104	32.52 253	92.76 42	77.90 43	75.96 127	92.22 35

agg_prior1		76.68 54	76.24 52	78.00 79	85.64 60	54.92 72	87.55 70	83.61 152	57.99 200	72.53 65	91.05 58	45.36 98	88.10 172	77.76 44	84.68 52	90.99 70

diffmvs		75.11 76	74.65 72	76.46 120	78.52 220	53.35 115	83.28 190	79.94 211	70.51 15	71.64 77	88.72 115	46.02 90	86.08 232	77.52 45	75.75 131	89.96 95

alignmvs		78.08 30	77.98 28	78.39 69	83.53 105	53.22 120	89.77 32	85.45 95	66.11 56	76.59 32	91.99 41	54.07 29	89.05 134	77.34 46	77.00 119	92.89 20

SteuartSystems-ACMMP		77.08 44	76.33 50	79.34 38	80.98 170	55.31 58	89.76 33	86.91 69	62.94 105	71.65 76	91.56 52	42.33 138	92.56 46	77.14 47	83.69 59	90.15 90
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 56	75.66 56	78.73 55	81.92 147	54.67 82	84.06 164	85.35 100	61.10 136	72.99 57	91.50 53	40.25 163	91.00 82	76.84 48	86.98 24	90.51 82

CLD-MVS		75.60 69	75.39 60	76.24 123	80.69 181	52.40 140	90.69 22	86.20 84	74.40 4	65.01 136	88.93 111	42.05 144	90.58 97	76.57 49	73.96 144	85.73 184
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 78	74.33 75	76.95 110	82.89 129	53.05 126	85.63 115	83.50 154	57.86 203	67.25 107	90.24 82	43.38 128	88.85 146	76.03 50	82.23 69	88.96 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		77.36 39	76.85 41	78.88 47	80.40 187	54.66 83	87.06 82	85.88 89	72.11 8	71.57 78	88.63 120	50.89 48	90.35 102	76.00 51	79.11 103	91.63 48

TSAR-MVS + GP.		77.82 33	77.59 32	78.49 63	85.25 74	50.27 192	90.02 26	90.57 11	56.58 232	74.26 45	91.60 51	54.26 26	92.16 55	75.87 52	79.91 96	93.05 17

baseline		76.86 50	76.24 52	78.71 56	80.47 186	54.20 92	83.90 169	84.88 119	71.38 11	71.51 79	89.15 109	50.51 49	90.55 98	75.71 53	78.65 106	91.39 57

agg_prior2														75.65 54	85.11 48	91.01 68

DeepC-MVS	67.15 4	76.90 49	76.27 51	78.80 51	80.70 180	55.02 69	86.39 96	86.71 74	66.96 46	67.91 103	89.97 93	48.03 65	91.41 71	75.60 55	84.14 56	89.96 95
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 98	73.30 82	73.76 182	85.91 54	51.83 154	86.18 101	84.24 138	65.40 67	69.09 94	80.86 222	46.70 81	88.13 170	75.43 56	65.92 210	81.33 260

PVSNet_Blended		76.53 55	76.54 45	76.50 118	85.91 54	51.83 154	88.89 46	84.24 138	67.82 37	69.09 94	89.33 106	46.70 81	88.13 170	75.43 56	81.48 77	89.55 103

LFMVS		78.52 22	77.14 38	82.67 4	89.58 15	58.90 8	91.27 18	88.05 51	63.22 101	74.63 40	90.83 68	41.38 154	94.40 23	75.42 58	79.90 97	94.72 2

ZD-MVS					89.55 16	53.46 107		84.38 131	57.02 221	73.97 47	91.03 59	44.57 111	91.17 77	75.41 59	81.78 75

testtj		76.96 46	76.48 47	78.40 68	89.89 13	53.67 101	88.72 48	86.15 85	54.56 255	74.86 38	92.31 33	44.38 113	91.97 61	75.19 60	82.24 68	89.54 104

MVS_111021_HR		76.39 57	75.38 61	79.42 37	85.33 72	56.47 35	88.15 55	84.97 116	65.15 73	66.06 121	89.88 94	43.79 119	92.16 55	75.03 61	80.03 95	89.64 102

CS-MVS-test		77.20 41	77.25 37	77.05 103	84.60 83	49.04 216	89.42 36	85.83 91	65.90 62	72.85 60	91.98 42	45.10 101	91.27 74	75.02 62	84.56 53	90.84 73

test_prior3		77.59 36	77.33 36	78.39 69	86.35 49	54.91 74	89.04 43	85.45 95	61.88 122	73.55 50	91.46 55	48.01 66	89.70 120	74.73 63	85.46 42	90.55 78

test_prior2							89.04 43		61.88 122	73.55 50	91.46 55	48.01 66		74.73 63	85.46 42

SD-MVS		76.18 59	74.85 69	80.18 27	85.39 69	56.90 26	85.75 111	82.45 173	56.79 227	74.48 44	91.81 43	43.72 122	90.75 92	74.61 65	78.65 106	92.91 19
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 51	76.70 44	76.99 108	83.55 104	48.75 225	88.60 50	85.18 109	66.38 51	72.47 68	91.62 50	45.53 96	90.99 84	74.48 66	82.51 64	91.23 62

APD-MVS		76.15 60	75.68 55	77.54 90	88.52 29	53.44 111	87.26 79	85.03 115	53.79 260	74.91 37	91.68 49	43.80 118	90.31 104	74.36 67	81.82 73	88.87 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DROMVSNet		75.30 71	75.20 62	75.62 138	80.98 170	49.00 217	87.43 71	84.68 126	63.49 98	70.97 85	90.15 88	42.86 135	91.14 79	74.33 68	81.90 72	86.71 167

VDDNet		74.37 82	72.13 101	81.09 20	79.58 196	56.52 34	90.02 26	86.70 75	52.61 269	71.23 82	87.20 141	31.75 263	93.96 27	74.30 69	75.77 130	92.79 23

TSAR-MVS + MP.		78.31 28	78.26 23	78.48 64	81.33 166	56.31 40	81.59 228	86.41 79	69.61 21	81.72 14	88.16 126	55.09 23	88.04 175	74.12 70	86.31 34	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 4	82.36 6	82.49 6	80.12 190	59.50 5	92.24 8	90.72 10	69.37 22	83.22 8	94.47 2	63.81 5	93.18 34	74.02 71	93.25 2	94.80 1

PHI-MVS		77.49 37	77.00 39	78.95 44	85.33 72	50.69 175	88.57 51	88.59 43	58.14 195	73.60 49	93.31 15	43.14 131	93.79 29	73.81 72	88.53 13	92.37 30

zzz-MVS		74.15 86	73.11 86	77.27 98	81.54 159	53.57 103	84.02 166	81.31 191	59.41 165	68.39 99	90.96 63	36.07 218	89.01 136	73.80 73	82.45 66	89.23 110

MTAPA		72.73 106	71.22 115	77.27 98	81.54 159	53.57 103	67.06 330	81.31 191	59.41 165	68.39 99	90.96 63	36.07 218	89.01 136	73.80 73	82.45 66	89.23 110

Regformer-1		77.80 34	77.44 34	78.88 47	87.78 39	52.44 139	87.60 64	90.08 14	68.86 24	72.49 67	91.79 44	47.69 70	94.90 20	73.57 75	77.05 116	89.31 108

VNet		77.99 32	77.92 29	78.19 74	87.43 42	50.12 193	90.93 21	91.41 4	67.48 42	75.12 36	90.15 88	46.77 80	91.00 82	73.52 76	78.46 108	93.44 9

EPNet		78.36 26	78.49 22	77.97 81	85.49 65	52.04 147	89.36 38	84.07 141	73.22 5	77.03 29	91.72 47	49.32 59	90.17 110	73.46 77	82.77 62	91.69 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		71.60 124	70.29 127	75.55 140	77.26 239	53.15 121	85.34 120	79.37 224	55.83 239	72.54 62	90.19 85	22.38 321	86.66 213	73.28 78	76.39 123	86.85 161

xiu_mvs_v1_base		71.60 124	70.29 127	75.55 140	77.26 239	53.15 121	85.34 120	79.37 224	55.83 239	72.54 62	90.19 85	22.38 321	86.66 213	73.28 78	76.39 123	86.85 161

xiu_mvs_v1_base_debi		71.60 124	70.29 127	75.55 140	77.26 239	53.15 121	85.34 120	79.37 224	55.83 239	72.54 62	90.19 85	22.38 321	86.66 213	73.28 78	76.39 123	86.85 161

Regformer-2		77.15 43	76.82 42	78.14 75	87.78 39	51.84 153	87.60 64	89.12 22	67.23 43	71.93 74	91.79 44	46.03 89	93.53 31	72.85 81	77.05 116	89.05 117

PMMVS		72.98 102	72.05 105	75.78 137	83.57 103	48.60 228	84.08 162	82.85 168	61.62 126	68.24 101	90.33 81	28.35 282	87.78 184	72.71 82	76.69 121	90.95 71

ZNCC-MVS		75.82 68	75.02 66	78.23 73	83.88 100	53.80 96	86.91 88	86.05 87	59.71 158	67.85 104	90.55 74	42.23 140	91.02 81	72.66 83	85.29 46	89.87 98

ET-MVSNet_ETH3D		75.23 73	74.08 77	78.67 58	84.52 85	55.59 50	88.92 45	89.21 21	68.06 34	53.13 279	90.22 84	49.71 56	87.62 189	72.12 84	70.82 173	92.82 21

MVS		76.91 47	75.48 58	81.23 19	84.56 84	55.21 62	80.23 251	91.64 2	58.65 188	65.37 130	91.48 54	45.72 94	95.05 17	72.11 85	89.52 10	93.44 9

#test#		74.86 80	73.78 80	78.10 77	84.30 88	53.68 99	86.95 85	84.36 132	59.00 182	65.78 125	90.56 72	40.70 160	90.90 86	71.48 86	80.88 79	89.71 99

nrg030		72.27 117	71.56 109	74.42 162	75.93 258	50.60 177	86.97 84	83.21 160	62.75 107	67.15 108	84.38 172	50.07 52	86.66 213	71.19 87	62.37 239	85.99 177

DeepC-MVS_fast	67.50 3	78.00 31	77.63 31	79.13 42	88.52 29	55.12 65	89.95 28	85.98 88	68.31 28	71.33 81	92.75 24	45.52 97	90.37 101	71.15 88	85.14 47	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 97	72.24 98	77.33 94	87.93 38	55.97 46	87.90 61	70.81 329	68.72 25	64.04 151	84.36 174	47.54 72	90.87 88	71.11 89	67.75 196	85.13 195

GST-MVS		74.87 79	73.90 79	77.77 84	83.30 112	53.45 110	85.75 111	85.29 104	59.22 172	66.50 115	89.85 95	40.94 156	90.76 91	70.94 90	83.35 60	89.10 116

CHOSEN 1792x2688		76.24 58	74.03 78	82.88 1	83.09 119	62.84 2	85.73 113	85.39 98	69.79 19	64.87 138	83.49 187	41.52 153	93.69 30	70.55 91	81.82 73	92.12 36

CDPH-MVS		76.05 62	75.19 63	78.62 60	86.51 48	54.98 71	87.32 74	84.59 128	58.62 189	70.75 86	90.85 67	43.10 133	90.63 96	70.50 92	84.51 55	90.24 86

HPM-MVS		72.60 108	71.50 110	75.89 135	82.02 145	51.42 165	80.70 245	83.05 163	56.12 237	64.03 152	89.53 100	37.55 192	88.37 160	70.48 93	80.04 94	87.88 141
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-3		76.02 63	75.47 59	77.70 86	85.49 65	51.47 163	85.12 130	90.19 13	68.52 27	69.36 91	90.66 70	46.45 84	94.81 21	70.25 94	73.16 150	86.81 165

MVS_111021_LR		69.07 166	67.91 159	72.54 208	77.27 238	49.56 205	79.77 256	73.96 305	59.33 170	60.73 188	87.82 131	30.19 274	81.53 280	69.94 95	72.19 162	86.53 169

test_yl		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 201	71.19 83	89.20 107	42.03 145	92.77 40	69.41 96	75.07 138	92.01 40

DCV-MVSNet		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 201	71.19 83	89.20 107	42.03 145	92.77 40	69.41 96	75.07 138	92.01 40

Regformer-4		75.06 77	74.59 73	76.47 119	85.49 65	50.33 188	85.12 130	88.61 41	66.42 49	68.48 98	90.66 70	44.15 114	92.68 43	69.24 98	73.16 150	86.39 173

HFP-MVS		74.37 82	73.13 85	78.10 77	84.30 88	53.68 99	85.58 116	84.36 132	56.82 225	65.78 125	90.56 72	40.70 160	90.90 86	69.18 99	80.88 79	89.71 99

ACMMPR		73.76 92	72.61 88	77.24 101	83.92 98	52.96 129	85.58 116	84.29 134	56.82 225	65.12 132	90.45 77	37.24 200	90.18 109	69.18 99	80.84 81	88.58 129

region2R		73.75 93	72.55 90	77.33 94	83.90 99	52.98 128	85.54 119	84.09 140	56.83 224	65.10 133	90.45 77	37.34 198	90.24 107	68.89 101	80.83 82	88.77 125

CP-MVS		72.59 110	71.46 111	76.00 134	82.93 128	52.32 144	86.93 87	82.48 172	55.15 246	63.65 160	90.44 80	35.03 231	88.53 156	68.69 102	77.83 112	87.15 155

baseline2		75.15 75	74.54 74	76.98 109	81.67 152	51.74 156	83.84 170	91.94 1	69.97 18	58.98 209	86.02 156	59.73 8	91.73 65	68.37 103	70.40 178	87.48 149

Effi-MVS+		75.24 72	73.61 81	80.16 28	81.92 147	57.42 21	85.21 124	76.71 279	60.68 145	73.32 55	89.34 104	47.30 74	91.63 66	68.28 104	79.72 98	91.42 56

CostFormer		73.89 90	72.30 96	78.66 59	82.36 143	56.58 30	75.56 283	85.30 103	66.06 59	70.50 89	76.88 264	57.02 15	89.06 132	68.27 105	68.74 188	90.33 85

CANet_DTU		73.71 94	73.14 83	75.40 143	82.61 139	50.05 194	84.67 150	79.36 227	69.72 20	75.39 34	90.03 92	29.41 278	85.93 238	67.99 106	79.11 103	90.22 87

PVSNet_Blended_VisFu		73.40 99	72.44 92	76.30 121	81.32 167	54.70 80	85.81 107	78.82 237	63.70 92	64.53 143	85.38 165	47.11 77	87.38 195	67.75 107	77.55 113	86.81 165

MSLP-MVS++		74.21 84	72.25 97	80.11 30	81.45 163	56.47 35	86.32 98	79.65 219	58.19 194	66.36 116	92.29 34	36.11 216	90.66 94	67.39 108	82.49 65	93.18 15

PGM-MVS		72.60 108	71.20 116	76.80 115	82.95 126	52.82 131	83.07 195	82.14 175	56.51 233	63.18 164	89.81 96	35.68 223	89.76 119	67.30 109	80.19 91	87.83 142

EIA-MVS		75.92 64	75.18 64	78.13 76	85.14 75	51.60 159	87.17 80	85.32 102	64.69 76	68.56 97	90.53 75	45.79 93	91.58 67	67.21 110	82.18 70	91.20 63

HY-MVS	67.03 5	73.90 89	73.14 83	76.18 128	84.70 82	47.36 255	75.56 283	86.36 81	66.27 53	70.66 87	83.91 180	51.05 44	89.31 127	67.10 111	72.61 158	91.88 44

BP-MVS														66.70 112

HQP-MVS		72.34 113	71.44 112	75.03 151	79.02 206	51.56 160	88.00 57	83.68 148	65.45 64	64.48 144	85.13 166	37.35 196	88.62 150	66.70 112	73.12 152	84.91 199

SR-MVS		70.92 136	69.73 136	74.50 159	83.38 111	50.48 181	84.27 157	79.35 228	48.96 294	66.57 114	90.45 77	33.65 244	87.11 199	66.42 114	74.56 141	85.91 180

gm-plane-assit					83.24 114	54.21 90			70.91 12		88.23 125		95.25 15	66.37 115

PAPR		75.20 74	74.13 76	78.41 67	88.31 33	55.10 67	84.31 156	85.66 93	63.76 91	67.55 105	90.73 69	43.48 127	89.40 126	66.36 116	77.03 118	90.73 75

WTY-MVS		77.47 38	77.52 33	77.30 96	88.33 32	46.25 272	88.46 52	90.32 12	71.40 10	72.32 70	91.72 47	53.44 30	92.37 50	66.28 117	75.42 133	93.28 12

tpmrst		71.04 133	69.77 135	74.86 154	83.19 116	55.86 49	75.64 282	78.73 241	67.88 35	64.99 137	73.73 291	49.96 54	79.56 302	65.92 118	67.85 195	89.14 115

MVS_Test		75.85 65	74.93 68	78.62 60	84.08 94	55.20 63	83.99 167	85.17 110	68.07 33	73.38 54	82.76 197	50.44 50	89.00 138	65.90 119	80.61 83	91.64 47

ACMMP		70.81 138	69.29 144	75.39 144	81.52 162	51.92 151	83.43 182	83.03 164	56.67 230	58.80 216	88.91 112	31.92 261	88.58 152	65.89 120	73.39 149	85.67 185
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		72.92 103	71.62 108	76.81 112	83.41 107	52.48 136	84.88 141	83.20 161	58.03 196	63.91 155	89.63 99	35.50 224	89.78 117	65.50 121	80.50 85	88.16 134

X-MVStestdata		65.85 231	62.20 238	76.81 112	83.41 107	52.48 136	84.88 141	83.20 161	58.03 196	63.91 155	4.82 383	35.50 224	89.78 117	65.50 121	80.50 85	88.16 134

PAPM		76.76 52	76.07 54	78.81 50	80.20 188	59.11 7	86.86 89	86.23 83	68.60 26	70.18 90	88.84 114	51.57 40	87.16 198	65.48 123	86.68 30	90.15 90

HQP_MVS		70.96 135	69.91 134	74.12 171	77.95 228	49.57 203	85.76 109	82.59 170	63.60 95	62.15 176	83.28 191	36.04 220	88.30 165	65.46 124	72.34 160	84.49 202

plane_prior5								82.59 170					88.30 165	65.46 124	72.34 160	84.49 202

mPP-MVS		71.79 123	70.38 125	76.04 132	82.65 138	52.06 146	84.45 152	81.78 184	55.59 242	62.05 179	89.68 98	33.48 245	88.28 167	65.45 126	78.24 111	87.77 144

OPM-MVS		70.75 139	69.58 138	74.26 168	75.55 263	51.34 167	86.05 104	83.29 159	61.94 121	62.95 168	85.77 160	34.15 237	88.44 158	65.44 127	71.07 170	82.99 234
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
iter_conf_final		71.46 127	69.68 137	76.81 112	86.03 51	53.49 105	84.73 145	74.37 300	60.27 150	66.28 118	84.36 174	35.14 229	90.87 88	65.41 128	70.51 176	86.05 176

Effi-MVS+-dtu		66.24 227	64.96 223	70.08 257	75.17 264	49.64 202	82.01 215	74.48 298	62.15 115	57.83 230	76.08 277	30.59 271	83.79 264	65.40 129	60.93 246	76.81 308

mvs-test1		69.04 167	67.57 170	73.44 192	75.17 264	51.68 158	86.57 95	74.48 298	62.15 115	62.07 178	85.79 159	30.59 271	87.48 192	65.40 129	65.94 209	81.18 264

EI-MVSNet-Vis-set		73.19 101	72.60 89	74.99 153	82.56 140	49.80 201	82.55 206	89.00 27	66.17 55	65.89 124	88.98 110	43.83 117	92.29 51	65.38 131	69.01 186	82.87 237

TESTMET0.1,1		72.86 105	72.33 94	74.46 160	81.98 146	50.77 173	85.13 127	85.47 94	66.09 57	67.30 106	83.69 185	37.27 199	83.57 268	65.06 132	78.97 105	89.05 117

test_part1		73.80 91	72.13 101	78.79 53	85.92 53	58.26 10	90.60 23	86.85 71	63.98 86	63.95 154	81.54 216	52.08 39	92.24 53	64.93 133	59.32 256	85.87 182

MVSTER		73.25 100	72.33 94	76.01 133	85.54 64	53.76 98	83.52 175	87.16 65	67.06 45	63.88 157	81.66 214	52.77 33	90.44 99	64.66 134	64.69 215	83.84 219

test1172		69.64 161	68.38 154	73.41 193	82.77 132	48.84 223	82.79 201	78.34 251	47.02 305	65.27 131	90.07 90	31.17 267	86.09 230	64.51 135	73.49 148	85.31 192

CPTT-MVS		67.15 210	65.84 204	71.07 242	80.96 172	50.32 189	81.94 217	74.10 302	46.18 313	57.91 229	87.64 135	29.57 277	81.31 282	64.10 136	70.18 180	81.56 250

miper_enhance_ethall		69.77 156	68.90 148	72.38 212	78.93 209	49.91 197	83.29 189	78.85 235	64.90 74	59.37 202	79.46 230	52.77 33	85.16 250	63.78 137	58.72 260	82.08 243

EI-MVSNet-UG-set		72.37 112	71.73 107	74.29 167	81.60 155	49.29 211	81.85 220	88.64 38	65.29 72	65.05 134	88.29 123	43.18 129	91.83 63	63.74 138	67.97 193	81.75 247

ab-mvs		70.65 140	69.11 146	75.29 147	80.87 176	46.23 273	73.48 298	85.24 108	59.99 154	66.65 110	80.94 221	43.13 132	88.69 148	63.58 139	68.07 191	90.95 71

VPA-MVSNet		71.12 130	70.66 121	72.49 210	78.75 212	44.43 293	87.64 63	90.02 15	63.97 87	65.02 135	81.58 215	42.14 142	87.42 194	63.42 140	63.38 227	85.63 188

mvsmamba		66.93 217	64.88 224	73.09 197	75.06 268	47.26 257	83.36 188	69.21 335	62.64 109	55.68 260	81.43 217	29.72 276	89.20 130	63.35 141	63.50 223	82.79 238

APD-MVS_3200maxsize		69.62 162	68.23 156	73.80 181	81.58 157	48.22 240	81.91 218	79.50 222	48.21 296	64.24 149	89.75 97	31.91 262	87.55 191	63.08 142	73.85 146	85.64 187

v2v482		69.55 163	67.64 167	75.26 149	72.32 300	53.83 95	84.93 140	81.94 179	65.37 69	60.80 187	79.25 234	41.62 150	88.98 141	63.03 143	59.51 253	82.98 235

PS-MVSNAJss		68.78 177	67.17 178	73.62 189	73.01 290	48.33 239	84.95 139	84.81 121	59.30 171	58.91 213	79.84 228	37.77 185	88.86 145	62.83 144	63.12 233	83.67 221

cl22		68.85 171	67.69 166	72.35 213	78.07 227	49.98 196	82.45 209	78.48 247	62.50 112	58.46 223	77.95 244	49.99 53	85.17 249	62.55 145	58.72 260	81.90 245

V42		67.66 196	65.60 211	73.86 178	70.69 315	53.63 102	81.50 231	78.61 244	63.85 89	59.49 201	77.49 251	37.98 182	87.65 187	62.33 146	58.43 264	80.29 275

AUN-MVS		68.20 188	66.35 191	73.76 182	76.37 248	47.45 253	79.52 261	79.52 221	60.98 139	62.34 173	86.02 156	36.59 213	86.94 205	62.32 147	53.47 311	86.89 158

MG-MVS		78.42 24	76.99 40	82.73 2	93.17 1	64.46 1	89.93 29	88.51 45	64.83 75	73.52 52	88.09 127	48.07 64	92.19 54	62.24 148	84.53 54	91.53 53

Patchmatch-RL test		58.72 281	54.32 293	71.92 227	63.91 350	44.25 295	61.73 341	55.19 357	57.38 215	49.31 301	54.24 359	37.60 191	80.89 285	62.19 149	47.28 329	90.63 77

mvs_anonymous		72.29 115	70.74 119	76.94 111	82.85 130	54.72 79	78.43 269	81.54 187	63.77 90	61.69 181	79.32 232	51.11 43	85.31 245	62.15 150	75.79 129	90.79 74

miper_ehance_all_eth		68.70 180	67.58 168	72.08 217	76.91 245	49.48 208	82.47 208	78.45 248	62.68 108	58.28 227	77.88 246	50.90 46	85.01 254	61.91 151	58.72 260	81.75 247

HyFIR lowres test		69.94 154	67.58 168	77.04 104	77.11 244	57.29 22	81.49 233	79.11 233	58.27 193	58.86 214	80.41 225	42.33 138	86.96 204	61.91 151	68.68 189	86.87 159

sss		70.49 142	70.13 131	71.58 234	81.59 156	39.02 331	80.78 244	84.71 125	59.34 168	66.61 112	88.09 127	37.17 201	85.52 241	61.82 153	71.02 171	90.20 89

1314		71.11 131	69.41 140	76.22 124	79.32 200	50.49 180	80.23 251	85.14 113	59.44 164	58.93 211	88.89 113	33.83 243	89.60 124	61.49 154	77.42 115	88.57 130

GA-MVS		69.04 167	66.70 186	76.06 131	75.11 266	52.36 142	83.12 193	80.23 207	63.32 99	60.65 189	79.22 235	30.98 269	88.37 160	61.25 155	66.41 204	87.46 150

ECVR-MVS		71.81 121	71.00 118	74.26 168	80.12 190	43.49 302	84.69 147	82.16 174	64.02 83	64.64 140	87.43 138	35.04 230	89.21 129	61.24 156	79.66 99	90.08 92

VPNet		72.07 118	71.42 113	74.04 173	78.64 217	47.17 260	89.91 31	87.97 52	72.56 7	64.66 139	85.04 168	41.83 149	88.33 163	61.17 157	60.97 245	86.62 168

ACMP	61.11 9	66.24 227	64.33 228	72.00 221	74.89 272	49.12 212	83.18 192	79.83 214	55.41 245	52.29 285	82.68 201	25.83 302	86.10 228	60.89 158	63.94 220	80.78 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 96	72.19 100	77.57 89	83.02 123	55.24 60	81.63 225	81.44 189	50.28 284	76.67 30	90.91 65	44.82 108	86.11 226	60.83 159	80.09 92	91.36 59

test_djsdf		63.84 240	61.56 243	70.70 247	68.78 324	44.69 290	81.63 225	81.44 189	50.28 284	52.27 286	76.26 272	26.72 296	86.11 226	60.83 159	55.84 294	81.29 263

v148		68.24 187	66.35 191	73.88 177	71.76 303	51.47 163	84.23 158	81.90 183	63.69 93	58.94 210	76.44 269	43.72 122	87.78 184	60.63 161	55.86 293	82.39 241

c3_l		67.97 190	66.66 187	71.91 228	76.20 254	49.31 210	82.13 214	78.00 256	61.99 119	57.64 236	76.94 261	49.41 57	84.93 255	60.62 162	57.01 281	81.49 251

test-LLR		69.65 160	69.01 147	71.60 232	78.67 214	48.17 241	85.13 127	79.72 216	59.18 175	63.13 165	82.58 202	36.91 205	80.24 293	60.56 163	75.17 135	86.39 173

test-mter		68.36 182	67.29 175	71.60 232	78.67 214	48.17 241	85.13 127	79.72 216	53.38 263	63.13 165	82.58 202	27.23 293	80.24 293	60.56 163	75.17 135	86.39 173

SR-MVS-dyc-post		68.27 186	66.87 180	72.48 211	80.96 172	48.14 243	81.54 229	76.98 273	46.42 310	62.75 170	89.42 102	31.17 267	86.09 230	60.52 165	72.06 163	83.19 230

RE-MVS-def			66.66 187		80.96 172	48.14 243	81.54 229	76.98 273	46.42 310	62.75 170	89.42 102	29.28 280		60.52 165	72.06 163	83.19 230

IB-MVS	68.87 2	74.01 87	72.03 106	79.94 32	83.04 122	55.50 52	90.24 25	88.65 37	67.14 44	61.38 182	81.74 213	53.21 31	94.28 24	60.45 167	62.41 238	90.03 94
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		68.81 174	66.82 181	74.80 156	72.34 299	53.46 107	84.68 148	81.77 185	64.25 80	60.28 191	77.91 245	40.23 164	88.95 142	60.37 168	59.52 252	81.97 244

LPG-MVS_test		66.44 224	64.58 226	72.02 219	74.42 276	48.60 228	83.07 195	80.64 201	54.69 253	53.75 275	83.83 181	25.73 304	86.98 202	60.33 169	64.71 213	80.48 272

LGP-MVS_train				72.02 219	74.42 276	48.60 228		80.64 201	54.69 253	53.75 275	83.83 181	25.73 304	86.98 202	60.33 169	64.71 213	80.48 272

abl_6		68.03 189	66.15 197	73.66 186	78.54 219	48.48 234	79.77 256	78.04 254	47.39 301	63.70 159	88.25 124	28.21 283	89.06 132	60.17 171	71.25 169	83.45 223

MVP-Stereo		70.97 134	70.44 123	72.59 207	76.03 257	51.36 166	85.02 136	86.99 68	60.31 149	56.53 253	78.92 238	40.11 167	90.00 112	60.00 172	90.01 6	76.41 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 246	60.84 250	70.32 253	68.33 329	44.45 292	81.23 235	81.05 196	53.37 264	50.96 295	77.81 248	17.49 344	85.49 243	59.31 173	58.05 270	81.02 266

test2506		72.91 104	72.43 93	74.32 166	80.12 190	44.18 297	83.19 191	84.77 123	64.02 83	65.97 122	87.43 138	47.67 71	88.72 147	59.08 174	79.66 99	90.08 92

baseline1		72.51 111	72.12 103	73.69 185	85.05 76	44.46 291	83.51 179	86.13 86	71.61 9	64.64 140	87.97 130	55.00 24	89.48 125	59.07 175	56.05 290	87.13 156

mvs_tets		62.96 249	60.55 252	70.19 254	68.22 332	44.24 296	80.90 241	80.74 200	52.99 267	50.82 297	77.56 249	16.74 348	85.44 244	59.04 176	57.94 272	80.89 267

HPM-MVS_fast		67.86 192	66.28 194	72.61 206	80.67 182	48.34 238	81.18 237	75.95 288	50.81 282	59.55 200	88.05 129	27.86 288	85.98 234	58.83 177	73.58 147	83.51 222

eth_miper_zixun_eth		66.98 215	65.28 218	72.06 218	75.61 262	50.40 183	81.00 240	76.97 276	62.00 118	56.99 247	76.97 260	44.84 107	85.58 240	58.75 178	54.42 303	80.21 276

v144192		67.86 192	65.76 206	74.16 170	71.68 304	53.09 124	84.14 161	80.83 199	62.85 106	59.21 207	77.28 255	39.30 173	88.00 176	58.67 179	57.88 275	81.40 257

test1111		71.06 132	70.42 124	72.97 200	79.48 197	41.49 320	84.82 144	82.74 169	64.20 81	62.98 167	87.43 138	35.20 227	87.92 177	58.54 180	78.42 109	89.49 105

thisisatest0515		73.64 95	72.20 99	77.97 81	81.63 153	53.01 127	86.69 92	88.81 34	62.53 111	64.06 150	85.65 161	52.15 38	92.50 47	58.43 181	69.84 181	88.39 133

v8		67.25 207	64.99 222	74.04 173	72.89 293	53.31 118	82.37 211	80.11 209	61.54 129	54.29 270	76.02 278	42.89 134	88.41 159	58.43 181	56.36 283	80.39 274

XXY-MVS		70.18 145	69.28 145	72.89 203	77.64 232	42.88 308	85.06 133	87.50 63	62.58 110	62.66 172	82.34 207	43.64 124	89.83 116	58.42 183	63.70 222	85.96 179

3Dnovator	64.70 6	74.46 81	72.48 91	80.41 24	82.84 131	55.40 57	83.08 194	88.61 41	67.61 41	59.85 193	88.66 116	34.57 234	93.97 26	58.42 183	88.70 12	91.85 45

旧先验2							81.73 223		45.53 316	74.66 39			70.48 351	58.31 185

v1192		67.96 191	65.74 207	74.63 157	71.79 302	53.43 113	84.06 164	80.99 197	63.19 102	59.56 199	77.46 252	37.50 195	88.65 149	58.20 186	58.93 259	81.79 246

EPP-MVSNet		71.14 129	70.07 132	74.33 165	79.18 203	46.52 266	83.81 171	86.49 77	56.32 236	57.95 228	84.90 170	54.23 27	89.14 131	58.14 187	69.65 183	87.33 152

OMC-MVS		65.97 230	65.06 221	68.71 273	72.97 291	42.58 313	78.61 267	75.35 293	54.72 252	59.31 204	86.25 155	33.30 246	77.88 316	57.99 188	67.05 199	85.66 186

cl____		67.43 202	65.93 202	71.95 225	76.33 250	48.02 246	82.58 203	79.12 232	61.30 133	56.72 249	76.92 262	46.12 86	86.44 220	57.98 189	56.31 285	81.38 259

DIV-MVS_self_test		67.43 202	65.93 202	71.94 226	76.33 250	48.01 247	82.57 204	79.11 233	61.31 132	56.73 248	76.92 262	46.09 87	86.43 221	57.98 189	56.31 285	81.39 258

MS-PatchMatch		72.34 113	71.26 114	75.61 139	82.38 142	55.55 51	88.00 57	89.95 17	65.38 68	56.51 254	80.74 224	32.28 256	92.89 36	57.95 191	88.10 15	78.39 293

MAR-MVS		76.76 52	75.60 57	80.21 26	90.87 8	54.68 81	89.14 42	89.11 23	62.95 104	70.54 88	92.33 32	41.05 155	94.95 18	57.90 192	86.55 32	91.00 69
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
anonymousdsp		60.46 266	57.65 270	68.88 267	63.63 351	45.09 285	72.93 302	78.63 243	46.52 308	51.12 292	72.80 303	21.46 329	83.07 273	57.79 193	53.97 305	78.47 290

Anonymous20240529		69.71 157	67.28 176	77.00 107	83.78 101	50.36 186	88.87 47	85.10 114	47.22 302	64.03 152	83.37 189	27.93 287	92.10 58	57.78 194	67.44 197	88.53 131

Fast-Effi-MVS+-dtu		66.53 222	64.10 231	73.84 179	72.41 298	52.30 145	84.73 145	75.66 289	59.51 162	56.34 255	79.11 237	28.11 285	85.85 239	57.74 195	63.29 228	83.35 224

v1921920		67.45 201	65.23 219	74.10 172	71.51 307	52.90 130	83.75 173	80.44 204	62.48 113	59.12 208	77.13 256	36.98 203	87.90 178	57.53 196	58.14 269	81.49 251

IterMVS-LS		66.63 220	65.36 217	70.42 251	75.10 267	48.90 221	81.45 234	76.69 280	61.05 137	55.71 259	77.10 258	45.86 92	83.65 267	57.44 197	57.88 275	78.70 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 159	68.70 149	72.68 205	75.00 270	48.90 221	79.54 259	87.16 65	61.05 137	63.88 157	83.74 183	45.87 91	90.44 99	57.42 198	64.68 216	78.70 286

CDS-MVSNet		70.48 143	69.43 139	73.64 187	77.56 234	48.83 224	83.51 179	77.45 265	63.27 100	62.33 174	85.54 164	43.85 116	83.29 272	57.38 199	74.00 143	88.79 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 115	70.50 122	77.65 88	83.40 110	51.29 169	87.32 74	86.40 80	59.01 181	58.49 222	88.32 122	32.40 254	91.27 74	57.04 200	82.15 71	90.38 84

bld_raw_dy_0_64		59.75 268	57.01 277	67.96 280	66.73 336	45.30 283	77.59 273	59.97 353	50.49 283	47.15 313	77.03 259	17.45 345	79.06 303	56.92 201	59.76 251	79.51 282

miper_lstm_enhance		63.91 239	62.30 237	68.75 272	75.06 268	46.78 262	69.02 325	81.14 195	59.68 160	52.76 282	72.39 308	40.71 159	77.99 314	56.81 202	53.09 313	81.48 253

PAPM_NR		71.80 122	69.98 133	77.26 100	81.54 159	53.34 116	78.60 268	85.25 107	53.46 262	60.53 190	88.66 116	45.69 95	89.24 128	56.49 203	79.62 101	89.19 113

v10		66.61 221	64.20 230	73.83 180	72.59 296	53.37 114	81.88 219	79.91 213	61.11 135	54.09 272	75.60 280	40.06 168	88.26 168	56.47 204	56.10 289	79.86 280

v1240		66.99 214	64.68 225	73.93 175	71.38 310	52.66 134	83.39 186	79.98 210	61.97 120	58.44 225	77.11 257	35.25 226	87.81 180	56.46 205	58.15 267	81.33 260

Anonymous202405211		70.11 146	67.88 161	76.79 116	87.20 44	47.24 259	89.49 35	77.38 267	54.88 251	66.14 119	86.84 147	20.93 331	91.54 68	56.45 206	71.62 165	91.59 49

Fast-Effi-MVS+		72.73 106	71.15 117	77.48 91	82.75 134	54.76 76	86.77 90	80.64 201	63.05 103	65.93 123	84.01 178	44.42 112	89.03 135	56.45 206	76.36 126	88.64 127

114514_t		69.87 155	67.88 161	75.85 136	88.38 31	52.35 143	86.94 86	83.68 148	53.70 261	55.68 260	85.60 162	30.07 275	91.20 76	55.84 208	71.02 171	83.99 212

tpm2		70.82 137	68.44 152	77.98 80	80.78 178	56.11 42	74.21 294	81.28 194	60.24 151	68.04 102	75.27 282	52.26 37	88.50 157	55.82 209	68.03 192	89.33 107

PCF-MVS	61.03 10	70.10 147	68.40 153	75.22 150	77.15 243	51.99 148	79.30 264	82.12 176	56.47 234	61.88 180	86.48 154	43.98 115	87.24 197	55.37 210	72.79 157	86.43 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	62.49 8	69.27 165	67.81 165	73.64 187	84.41 87	51.85 152	84.63 151	77.80 258	66.42 49	59.80 194	84.95 169	22.14 326	80.44 291	55.03 211	75.11 137	88.62 128

CHOSEN 280x420		57.53 289	56.38 282	60.97 326	74.01 280	48.10 245	46.30 360	54.31 359	48.18 297	50.88 296	77.43 253	38.37 181	59.16 362	54.83 212	63.14 232	75.66 319

GG-mvs-BLEND				77.77 84	86.68 47	50.61 176	68.67 326	88.45 46		68.73 96	87.45 137	59.15 11	90.67 93	54.83 212	87.67 17	92.03 39

TAMVS		69.51 164	68.16 157	73.56 190	76.30 252	48.71 227	82.57 204	77.17 270	62.10 117	61.32 183	84.23 176	41.90 147	83.46 270	54.80 214	73.09 154	88.50 132

D2MVS		63.49 244	61.39 245	69.77 261	69.29 322	48.93 220	78.89 266	77.71 261	60.64 146	49.70 299	72.10 313	27.08 294	83.48 269	54.48 215	62.65 236	76.90 307

IterMVS		63.77 242	61.67 241	70.08 257	72.68 295	51.24 170	80.44 247	75.51 290	60.51 147	51.41 290	73.70 294	32.08 258	78.91 304	54.30 216	54.35 304	80.08 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RRT_MVS		63.68 243	61.01 249	71.70 230	73.48 284	45.98 275	81.19 236	76.08 286	54.33 258	52.84 281	79.27 233	22.21 324	87.65 187	54.13 217	55.54 297	81.46 254

DP-MVS Recon		71.99 119	70.31 126	77.01 106	90.65 9	53.44 111	89.37 37	82.97 166	56.33 235	63.56 162	89.47 101	34.02 238	92.15 57	54.05 218	72.41 159	85.43 191

tpm		68.36 182	67.48 173	70.97 244	79.93 193	51.34 167	76.58 279	78.75 240	67.73 38	63.54 163	74.86 284	48.33 62	72.36 345	53.93 219	63.71 221	89.21 112

XVG-OURS-SEG-HR		62.02 258	59.54 260	69.46 263	65.30 342	45.88 276	65.06 332	73.57 309	46.45 309	57.42 243	83.35 190	26.95 295	78.09 310	53.77 220	64.03 218	84.42 204

FA-MVS(test-final)		69.00 170	66.60 189	76.19 127	83.48 106	47.96 249	74.73 290	82.07 177	57.27 217	62.18 175	78.47 242	36.09 217	92.89 36	53.76 221	71.32 168	87.73 145

cascas		69.01 169	66.13 198	77.66 87	79.36 198	55.41 56	86.99 83	83.75 147	56.69 229	58.92 212	81.35 218	24.31 311	92.10 58	53.23 222	70.61 174	85.46 190

UniMVSNet_NR-MVSNet		68.82 173	68.29 155	70.40 252	75.71 261	42.59 311	84.23 158	86.78 72	66.31 52	58.51 219	82.45 204	51.57 40	84.64 259	53.11 223	55.96 291	83.96 216

DU-MVS		66.84 219	65.74 207	70.16 255	73.27 288	42.59 311	81.50 231	82.92 167	63.53 97	58.51 219	82.11 210	40.75 157	84.64 259	53.11 223	55.96 291	83.24 228

1112_ss		70.05 149	69.37 141	72.10 216	80.77 179	42.78 309	85.12 130	76.75 277	59.69 159	61.19 184	92.12 36	47.48 73	83.84 263	53.04 225	68.21 190	89.66 101

XVG-OURS		61.88 259	59.34 262	69.49 262	65.37 341	46.27 271	64.80 333	73.49 310	47.04 304	57.41 244	82.85 195	25.15 307	78.18 308	53.00 226	64.98 211	84.01 211

thisisatest0530		70.47 144	68.56 150	76.20 126	79.78 194	51.52 162	83.49 181	88.58 44	57.62 210	58.60 218	82.79 196	51.03 45	91.48 69	52.84 227	62.36 240	85.59 189

UGNet		68.71 178	67.11 179	73.50 191	80.55 185	47.61 251	84.08 162	78.51 246	59.45 163	65.68 128	82.73 200	23.78 313	85.08 253	52.80 228	76.40 122	87.80 143
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
Anonymous20231211		66.08 229	63.67 232	73.31 194	83.07 121	48.75 225	86.01 106	84.67 127	45.27 318	56.54 252	76.67 267	28.06 286	88.95 142	52.78 229	59.95 248	82.23 242

无先验							85.19 125	78.00 256	49.08 292				85.13 251	52.78 229		87.45 151

1121		68.79 176	66.77 183	74.82 155	83.08 120	53.46 107	80.23 251	71.53 324	45.47 317	66.31 117	87.19 142	34.02 238	85.13 251	52.78 229	80.36 89	85.87 182

PVSNet_0	57.04 13	61.19 262	57.24 273	73.02 198	77.45 236	50.31 190	79.43 263	77.36 268	63.96 88	47.51 312	72.45 307	25.03 308	83.78 265	52.76 232	19.22 375	84.96 198

FIs		70.00 151	70.24 130	69.30 264	77.93 230	38.55 333	83.99 167	87.72 59	66.86 47	57.66 235	84.17 177	52.28 36	85.31 245	52.72 233	68.80 187	84.02 210

Vis-MVSNet		70.61 141	69.34 142	74.42 162	80.95 175	48.49 233	86.03 105	77.51 264	58.74 187	65.55 129	87.78 132	34.37 235	85.95 237	52.53 234	80.61 83	88.80 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 287	77.59 233	45.46 282		69.20 336	44.47 323	71.50 80	88.34 121	31.21 266	70.76 350	52.20 235	75.88 128	85.03 196

API-MVS		74.17 85	72.07 104	80.49 22	90.02 12	58.55 9	87.30 76	84.27 135	57.51 212	65.77 127	87.77 133	41.61 151	95.97 11	51.71 236	82.63 63	86.94 157

GeoE		69.96 153	67.88 161	76.22 124	81.11 169	51.71 157	84.15 160	76.74 278	59.83 156	60.91 185	84.38 172	41.56 152	88.10 172	51.67 237	70.57 175	88.84 122

ACMM	58.35 12	64.35 236	62.01 240	71.38 236	74.21 279	48.51 232	82.25 212	79.66 218	47.61 299	54.54 267	80.11 226	25.26 306	86.00 233	51.26 238	63.16 231	79.64 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 129	84.89 80	54.59 84		85.26 106	51.98 273	66.70 109	87.07 145	40.15 166	89.70 120	51.23 239	85.06 49	84.10 208

UniMVSNet (Re)		67.71 195	66.80 182	70.45 250	74.44 275	42.93 307	82.42 210	84.90 118	63.69 93	59.63 197	80.99 220	47.18 75	85.23 248	51.17 240	56.75 282	83.19 230

IterMVS-SCA-FT		59.12 274	58.81 267	60.08 328	70.68 316	45.07 286	80.42 248	74.25 301	43.54 331	50.02 298	73.73 291	31.97 259	56.74 363	51.06 241	53.60 309	78.42 292

Test_1112_low_res		67.18 209	66.23 195	70.02 260	78.75 212	41.02 324	83.43 182	73.69 307	57.29 216	58.45 224	82.39 206	45.30 99	80.88 286	50.50 242	66.26 208	88.16 134

pmmvs4		63.34 245	61.07 248	70.16 255	70.14 317	50.53 179	79.97 255	71.41 326	55.08 247	54.12 271	78.58 240	32.79 251	82.09 278	50.33 243	57.22 280	77.86 299

Baseline_NR-MVSNet		65.49 233	64.27 229	69.13 265	74.37 278	41.65 318	83.39 186	78.85 235	59.56 161	59.62 198	76.88 264	40.75 157	87.44 193	49.99 244	55.05 298	78.28 295

UniMVSNet_ETH3D		62.51 253	60.49 253	68.57 276	68.30 330	40.88 326	73.89 295	79.93 212	51.81 277	54.77 264	79.61 229	24.80 309	81.10 283	49.93 245	61.35 243	83.73 220

BH-w/o		70.02 150	68.51 151	74.56 158	82.77 132	50.39 184	86.60 94	78.14 253	59.77 157	59.65 196	85.57 163	39.27 174	87.30 196	49.86 246	74.94 140	85.99 177

LCM-MVSNet-Re		58.82 280	56.54 278	65.68 297	79.31 201	29.09 363	61.39 344	45.79 365	60.73 144	37.65 347	72.47 306	31.42 265	81.08 284	49.66 247	70.41 177	86.87 159

gg-mvs-nofinetune		67.43 202	64.53 227	76.13 129	85.95 52	47.79 250	64.38 334	88.28 48	39.34 340	66.62 111	41.27 365	58.69 14	89.00 138	49.64 248	86.62 31	91.59 49

TranMVSNet+NR-MVSNet		66.94 216	65.61 210	70.93 245	73.45 285	43.38 304	83.02 197	84.25 136	65.31 71	58.33 226	81.90 212	39.92 170	85.52 241	49.43 249	54.89 300	83.89 218

tttt0517		68.33 184	66.29 193	74.46 160	78.08 226	49.06 213	80.88 242	89.08 24	54.40 257	54.75 265	80.77 223	51.31 42	90.33 103	49.35 250	58.01 271	83.99 212

WR-MVS		67.58 197	66.76 184	70.04 259	75.92 259	45.06 289	86.23 100	85.28 105	64.31 79	58.50 221	81.00 219	44.80 110	82.00 279	49.21 251	55.57 296	83.06 233

test_post1							70.84 318				14.72 382	34.33 236	83.86 262	48.80 252

SCA		63.84 240	60.01 258	75.32 145	78.58 218	57.92 12	61.61 342	77.53 263	56.71 228	57.75 234	70.77 319	31.97 259	79.91 299	48.80 252	56.36 283	88.13 137

pmmvs5		62.80 251	61.18 246	67.66 282	69.53 321	42.37 316	82.65 202	75.19 294	54.30 259	52.03 288	78.51 241	31.64 264	80.67 287	48.60 254	58.15 267	79.95 279

新几何1				73.30 195	83.10 117	53.48 106		71.43 325	45.55 315	66.14 119	87.17 143	33.88 242	80.54 289	48.50 255	80.33 90	85.88 181

pm-mvs1		64.12 238	62.56 235	68.78 271	71.68 304	38.87 332	82.89 199	81.57 186	55.54 244	53.89 274	77.82 247	37.73 188	86.74 210	48.46 256	53.49 310	80.72 269

PM-MVS		46.92 325	43.76 329	56.41 337	52.18 367	32.26 355	63.21 338	38.18 371	37.99 345	40.78 339	66.20 335	5.09 373	65.42 356	48.19 257	41.99 344	71.54 345

FC-MVSNet-test		67.49 200	67.91 159	66.21 295	76.06 255	33.06 351	80.82 243	87.18 64	64.44 78	54.81 263	82.87 194	50.40 51	82.60 274	48.05 258	66.55 203	82.98 235

CMPMVS	40.41 21	55.34 301	52.64 304	63.46 311	60.88 359	43.84 299	61.58 343	71.06 327	30.43 359	36.33 349	74.63 286	24.14 312	75.44 328	48.05 258	66.62 202	71.12 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 207	65.99 201	71.04 243	73.27 288	43.91 298	85.32 123	84.75 124	66.05 60	53.65 277	82.11 210	45.05 102	85.97 236	47.55 260	56.18 288	83.24 228

QAPM		71.88 120	69.33 143	79.52 35	82.20 144	54.30 88	86.30 99	88.77 35	56.61 231	59.72 195	87.48 136	33.90 241	95.36 14	47.48 261	81.49 76	88.90 120

EPMVS		68.45 181	65.44 215	77.47 92	84.91 79	56.17 41	71.89 314	81.91 182	61.72 125	60.85 186	72.49 305	36.21 215	87.06 201	47.32 262	71.62 165	89.17 114

GBi-Net		67.09 211	65.47 213	71.96 222	82.71 135	46.36 268	83.52 175	83.31 156	58.55 190	57.58 237	76.23 273	36.72 210	86.20 222	47.25 263	63.40 224	83.32 225

test1		67.09 211	65.47 213	71.96 222	82.71 135	46.36 268	83.52 175	83.31 156	58.55 190	57.58 237	76.23 273	36.72 210	86.20 222	47.25 263	63.40 224	83.32 225

FMVSNet3		68.84 172	67.40 174	73.19 196	85.05 76	48.53 231	85.71 114	85.36 99	60.90 141	57.58 237	79.15 236	42.16 141	86.77 209	47.25 263	63.40 224	84.27 206

v7n		62.50 254	59.27 263	72.20 215	67.25 335	49.83 200	77.87 271	80.12 208	52.50 270	48.80 303	73.07 299	32.10 257	87.90 178	46.83 266	54.92 299	78.86 284

CVMVSNet		60.85 264	60.44 254	62.07 316	75.00 270	32.73 353	79.54 259	73.49 310	36.98 348	56.28 256	83.74 183	29.28 280	69.53 353	46.48 267	63.23 229	83.94 217

TR-MVS		69.71 157	67.85 164	75.27 148	82.94 127	48.48 234	87.40 73	80.86 198	57.15 220	64.61 142	87.08 144	32.67 252	89.64 123	46.38 268	71.55 167	87.68 147

MDTV_nov1_ep13_2view						43.62 301	71.13 317		54.95 250	59.29 206		36.76 207		46.33 269		87.32 153

FMVSNet2		67.57 198	65.79 205	72.90 201	82.71 135	47.97 248	85.15 126	84.93 117	58.55 190	56.71 250	78.26 243	36.72 210	86.67 212	46.15 270	62.94 235	84.07 209

UnsupCasMVSNet_eth		57.56 288	55.15 289	64.79 306	64.57 348	33.12 350	73.17 301	83.87 146	58.98 183	41.75 334	70.03 323	22.54 320	79.92 297	46.12 271	35.31 354	81.32 262

testdata2													77.81 318	45.64 272

XVG-ACMP-BASELINE		56.03 298	52.85 302	65.58 298	61.91 356	40.95 325	63.36 335	72.43 316	45.20 319	46.02 319	74.09 287	9.20 364	78.12 309	45.13 273	58.27 265	77.66 302

AdaColmap		67.86 192	65.48 212	75.00 152	88.15 35	54.99 70	86.10 103	76.63 281	49.30 291	57.80 231	86.65 151	29.39 279	88.94 144	45.10 274	70.21 179	81.06 265

BH-untuned		68.28 185	66.40 190	73.91 176	81.62 154	50.01 195	85.56 118	77.39 266	57.63 209	57.47 242	83.69 185	36.36 214	87.08 200	44.81 275	73.08 155	84.65 201

BH-RMVSNet		70.08 148	68.01 158	76.27 122	84.21 92	51.22 171	87.29 77	79.33 230	58.96 184	63.63 161	86.77 148	33.29 247	90.30 106	44.63 276	73.96 144	87.30 154

IS-MVSNet		68.80 175	67.55 171	72.54 208	78.50 221	43.43 303	81.03 239	79.35 228	59.12 179	57.27 245	86.71 149	46.05 88	87.70 186	44.32 277	75.60 132	86.49 170

pmmvs-eth3d		55.97 299	52.78 303	65.54 299	61.02 358	46.44 267	75.36 287	67.72 339	49.61 290	43.65 325	67.58 332	21.63 328	77.04 321	44.11 278	44.33 339	73.15 338

pmmvs6		59.64 269	57.15 274	67.09 286	66.01 337	36.86 340	80.50 246	78.64 242	45.05 320	49.05 302	73.94 289	27.28 292	86.10 228	43.96 279	49.94 320	78.31 294

EPNet_dtu		66.25 226	66.71 185	64.87 305	78.66 216	34.12 346	82.80 200	75.51 290	61.75 124	64.47 147	86.90 146	37.06 202	72.46 344	43.65 280	69.63 184	88.02 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 225	62.78 234	76.77 117	81.40 164	57.14 24	70.03 321	77.19 269	53.00 266	58.76 217	70.73 321	46.17 85	86.73 211	43.27 281	64.46 217	86.44 171

OpenMVS	61.00 11	69.99 152	67.55 171	77.30 96	78.37 224	54.07 94	84.36 154	85.76 92	57.22 218	56.71 250	87.67 134	30.79 270	92.83 39	43.04 282	84.06 58	85.01 197

MVS_0304		56.72 291	55.17 288	61.37 323	70.71 313	36.80 341	75.74 280	68.75 337	44.11 328	52.53 283	68.20 330	15.05 354	74.53 332	42.98 283	58.44 263	72.79 339

PatchmatchNet		67.07 213	63.63 233	77.40 93	83.10 117	58.03 11	72.11 312	77.77 259	58.85 185	59.37 202	70.83 318	37.84 184	84.93 255	42.96 284	69.83 182	89.26 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 255	59.04 265	72.77 204	73.97 282	56.57 31	60.52 345	71.72 320	60.04 152	57.49 240	65.86 336	38.94 175	80.31 292	42.86 285	59.93 249	81.42 255

FMVS2		37.95 331	35.75 333	44.55 347	35.50 377	18.92 376	48.32 357	34.00 377	18.36 369	41.31 337	61.58 346	2.29 380	48.06 371	42.72 286	37.71 352	66.66 354

FMVSNet1		64.57 234	62.11 239	71.96 222	77.32 237	46.36 268	83.52 175	83.31 156	52.43 271	54.42 268	76.23 273	27.80 289	86.20 222	42.59 287	61.34 244	83.32 225

UA-Net		67.32 206	66.23 195	70.59 248	78.85 210	41.23 323	73.60 296	75.45 292	61.54 129	66.61 112	84.53 171	38.73 178	86.57 218	42.48 288	74.24 142	83.98 214

CL-MVSNet_self_test		62.98 248	61.14 247	68.50 277	65.86 339	42.96 306	84.37 153	82.98 165	60.98 139	53.95 273	72.70 304	40.43 162	83.71 266	41.10 289	47.93 325	78.83 285

MIMVSNet		63.12 247	60.29 256	71.61 231	75.92 259	46.65 264	65.15 331	81.94 179	59.14 177	54.65 266	69.47 325	25.74 303	80.63 288	41.03 290	69.56 185	87.55 148

FE-MVS		64.15 237	60.43 255	75.30 146	80.85 177	49.86 199	68.28 328	78.37 249	50.26 287	59.31 204	73.79 290	26.19 300	91.92 62	40.19 291	66.67 201	84.12 207

EG-PatchMatch MVS		62.40 257	59.59 259	70.81 246	73.29 287	49.05 214	85.81 107	84.78 122	51.85 276	44.19 322	73.48 297	15.52 353	89.85 115	40.16 292	67.24 198	73.54 334

UnsupCasMVSNet_bld		53.86 308	50.53 310	63.84 308	63.52 352	34.75 344	71.38 315	81.92 181	46.53 307	38.95 344	57.93 356	20.55 332	80.20 295	39.91 293	34.09 361	76.57 313

dp		64.41 235	61.58 242	72.90 201	82.40 141	54.09 93	72.53 304	76.59 282	60.39 148	55.68 260	70.39 322	35.18 228	76.90 324	39.34 294	61.71 242	87.73 145

TransMVSNet (Re)		62.82 250	60.76 251	69.02 266	73.98 281	41.61 319	86.36 97	79.30 231	56.90 222	52.53 283	76.44 269	41.85 148	87.60 190	38.83 295	40.61 347	77.86 299

USDC		54.36 305	51.23 308	63.76 309	64.29 349	37.71 337	62.84 340	73.48 312	56.85 223	35.47 352	71.94 314	9.23 363	78.43 306	38.43 296	48.57 322	75.13 324

PLC	52.38 18	60.89 263	58.97 266	66.68 293	81.77 150	45.70 280	78.96 265	74.04 304	43.66 330	47.63 309	83.19 193	23.52 316	77.78 319	37.47 297	60.46 247	76.55 314
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 252	62.44 236	62.86 315	72.28 301	29.51 360	82.93 198	78.78 238	59.18 175	53.07 280	82.41 205	36.91 205	77.39 320	37.45 298	58.96 258	81.66 249

OurMVSNet-221017-0		52.39 314	48.73 316	63.35 312	65.21 343	38.42 334	68.54 327	64.95 343	38.19 343	39.57 341	71.43 315	13.23 357	79.92 297	37.16 299	40.32 348	71.72 343

CNLPA		60.59 265	58.44 268	67.05 288	79.21 202	47.26 257	79.75 258	64.34 346	42.46 336	51.90 289	83.94 179	27.79 290	75.41 329	37.12 300	59.49 254	78.47 290

K. test v3		54.04 307	49.42 315	67.92 281	68.55 326	42.57 314	75.51 285	63.07 348	52.07 272	39.21 342	64.59 340	19.34 336	82.21 275	37.11 301	25.31 368	78.97 283

Vis-MVSNet (Re-imp)		65.52 232	65.63 209	65.17 303	77.49 235	30.54 358	75.49 286	77.73 260	59.34 168	52.26 287	86.69 150	49.38 58	80.53 290	37.07 302	75.28 134	84.42 204

PatchMatch-RL		56.66 292	53.75 297	65.37 302	77.91 231	45.28 284	69.78 323	60.38 351	41.35 337	47.57 310	73.73 291	16.83 347	76.91 323	36.99 303	59.21 257	73.92 331

Patchmtry		56.56 294	52.95 301	67.42 284	72.53 297	50.59 178	59.05 348	71.72 320	37.86 346	46.92 314	65.86 336	38.94 175	80.06 296	36.94 304	46.72 334	71.60 344

FMVSNet5		58.61 282	56.45 279	65.10 304	77.20 242	39.74 328	74.77 289	77.12 271	50.27 286	43.28 328	67.71 331	26.15 301	76.90 324	36.78 305	54.78 301	78.65 288

MDTV_nov1_ep13			61.56 243		81.68 151	55.12 65	72.41 306	78.18 252	59.19 173	58.85 215	69.29 326	34.69 233	86.16 225	36.76 306	62.96 234

JIA-IIPM		52.33 315	47.77 321	66.03 296	71.20 311	46.92 261	40.00 367	76.48 283	37.10 347	46.73 315	37.02 367	32.96 248	77.88 316	35.97 307	52.45 315	73.29 336

lessismore_v0				67.98 279	64.76 347	41.25 322		45.75 366		36.03 351	65.63 338	19.29 337	84.11 261	35.67 308	21.24 373	78.59 289

CP-MVSNet		58.54 285	57.57 272	61.46 322	68.50 327	33.96 347	76.90 277	78.60 245	51.67 278	47.83 307	76.60 268	34.99 232	72.79 342	35.45 309	47.58 326	77.64 303

Anonymous20240521		51.65 316	48.42 317	61.34 325	56.43 364	39.65 330	73.57 297	73.47 313	36.64 350	36.59 348	63.98 341	10.75 360	72.25 346	35.35 310	49.01 321	72.11 341

ambc				62.06 317	53.98 366	29.38 361	35.08 369	79.65 219		41.37 335	59.96 351	6.27 371	82.15 276	35.34 311	38.22 351	74.65 326

KD-MVS_2432*1600		59.04 277	56.44 280	66.86 289	79.07 204	45.87 277	72.13 310	80.42 205	55.03 248	48.15 305	71.01 316	36.73 208	78.05 312	35.21 312	30.18 365	76.67 309

miper_refine_blended		59.04 277	56.44 280	66.86 289	79.07 204	45.87 277	72.13 310	80.42 205	55.03 248	48.15 305	71.01 316	36.73 208	78.05 312	35.21 312	30.18 365	76.67 309

PS-CasMVS		58.12 287	57.03 276	61.37 323	68.24 331	33.80 349	76.73 278	78.01 255	51.20 280	47.54 311	76.20 276	32.85 249	72.76 343	35.17 314	47.37 328	77.55 304

EU-MVSNet		52.63 313	50.72 309	58.37 333	62.69 355	28.13 365	72.60 303	75.97 287	30.94 358	40.76 340	72.11 312	20.16 333	70.80 349	35.11 315	46.11 335	76.19 317

ACMH+	54.58 15	58.55 284	55.24 287	68.50 277	74.68 274	45.80 279	80.27 249	70.21 332	47.15 303	42.77 330	75.48 281	16.73 349	85.98 234	35.10 316	54.78 301	73.72 332

pmmvs3		45.53 327	41.55 330	57.44 335	48.97 370	39.68 329	70.06 320	57.66 355	28.32 361	34.06 355	57.29 357	8.50 365	66.85 355	34.86 317	34.26 359	65.80 356

our_test_3		59.11 275	55.08 291	71.18 241	71.42 308	53.29 119	81.96 216	74.52 297	48.32 295	42.08 331	69.28 327	28.14 284	82.15 276	34.35 318	45.68 337	78.11 298

PEN-MVS		58.35 286	57.15 274	61.94 318	67.55 334	34.39 345	77.01 275	78.35 250	51.87 275	47.72 308	76.73 266	33.91 240	73.75 337	34.03 319	47.17 330	77.68 301

KD-MVS_self_test		49.24 320	46.85 323	56.44 336	54.32 365	22.87 369	57.39 350	73.36 314	44.36 325	37.98 346	59.30 354	18.97 338	71.17 348	33.48 320	42.44 343	75.26 322

tpmvs		62.45 256	59.42 261	71.53 235	83.93 97	54.32 87	70.03 321	77.61 262	51.91 274	53.48 278	68.29 329	37.91 183	86.66 213	33.36 321	58.27 265	73.62 333

YYNet1		53.82 309	49.96 312	65.41 301	70.09 319	48.95 218	72.30 307	71.66 322	44.25 326	31.89 361	63.07 344	23.73 314	73.95 335	33.26 322	39.40 349	73.34 335

MDA-MVSNet_test_wron		53.82 309	49.95 313	65.43 300	70.13 318	49.05 214	72.30 307	71.65 323	44.23 327	31.85 362	63.13 343	23.68 315	74.01 334	33.25 323	39.35 350	73.23 337

Anonymous20231206		59.08 276	57.59 271	63.55 310	68.77 325	32.14 356	80.26 250	79.78 215	50.00 288	49.39 300	72.39 308	26.64 297	78.36 307	33.12 324	57.94 272	80.14 277

F-COLMAP		55.96 300	53.65 298	62.87 314	72.76 294	42.77 310	74.70 292	70.37 331	40.03 339	41.11 338	79.36 231	17.77 343	73.70 338	32.80 325	53.96 306	72.15 340

PatchT		56.60 293	52.97 300	67.48 283	72.94 292	46.16 274	57.30 351	73.78 306	38.77 342	54.37 269	57.26 358	37.52 193	78.06 311	32.02 326	52.79 314	78.23 297

SixPastTwentyTwo		54.37 304	50.10 311	67.21 285	70.70 314	41.46 321	74.73 290	64.69 344	47.56 300	39.12 343	69.49 324	18.49 341	84.69 258	31.87 327	34.20 360	75.48 320

WR-MVS_H		58.91 279	58.04 269	61.54 321	69.07 323	33.83 348	76.91 276	81.99 178	51.40 279	48.17 304	74.67 285	40.23 164	74.15 333	31.78 328	48.10 323	76.64 312

ACMH	53.70 16	59.78 267	55.94 285	71.28 237	76.59 247	48.35 237	80.15 254	76.11 285	49.74 289	41.91 333	73.45 298	16.50 350	90.31 104	31.42 329	57.63 278	75.17 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 270	55.14 290	72.32 214	74.69 273	50.71 174	74.39 293	73.58 308	44.44 324	43.40 327	77.52 250	19.45 335	90.87 88	31.31 330	57.49 279	75.38 321

thres200		68.71 178	67.27 177	73.02 198	84.73 81	46.76 263	85.03 135	87.73 58	62.34 114	59.87 192	83.45 188	43.15 130	88.32 164	31.25 331	67.91 194	83.98 214

DTE-MVSNet		57.03 290	55.73 286	60.95 327	65.94 338	32.57 354	75.71 281	77.09 272	51.16 281	46.65 317	76.34 271	32.84 250	73.22 341	30.94 332	44.87 338	77.06 306

ppachtmachnet_test		58.56 283	54.34 292	71.24 238	71.42 308	54.74 77	81.84 221	72.27 317	49.02 293	45.86 321	68.99 328	26.27 298	83.30 271	30.12 333	43.23 342	75.69 318

mvsany_test		28.00 338	25.98 340	34.05 355	28.97 382	15.31 381	34.54 370	18.17 385	16.24 370	29.30 365	53.37 362	2.79 378	33.38 381	30.01 334	20.41 374	53.45 365

MVS-HIRNet		49.01 321	44.71 325	61.92 319	76.06 255	46.61 265	63.23 337	54.90 358	24.77 363	33.56 357	36.60 369	21.28 330	75.88 327	29.49 335	62.54 237	63.26 361

test20.03		55.22 302	54.07 295	58.68 332	63.14 353	25.00 367	77.69 272	74.78 296	52.64 268	43.43 326	72.39 308	26.21 299	74.76 331	29.31 336	47.05 332	76.28 316

testgi		54.25 306	52.57 305	59.29 330	62.76 354	21.65 372	72.21 309	70.47 330	53.25 265	41.94 332	77.33 254	14.28 355	77.95 315	29.18 337	51.72 317	78.28 295

thres100view900		66.87 218	65.42 216	71.24 238	83.29 113	43.15 305	81.67 224	87.78 55	59.04 180	55.92 258	82.18 209	43.73 120	87.80 181	28.80 338	66.36 205	82.78 239

tfpn200view9		67.57 198	66.13 198	71.89 229	84.05 95	45.07 286	83.40 184	87.71 60	60.79 142	57.79 232	82.76 197	43.53 125	87.80 181	28.80 338	66.36 205	82.78 239

thres400		67.40 205	66.13 198	71.19 240	84.05 95	45.07 286	83.40 184	87.71 60	60.79 142	57.79 232	82.76 197	43.53 125	87.80 181	28.80 338	66.36 205	80.71 270

ADS-MVSNet2		55.21 303	51.44 307	66.51 294	80.60 183	49.56 205	55.03 353	65.44 342	44.72 321	51.00 293	61.19 348	22.83 317	75.41 329	28.54 341	53.63 307	74.57 327

ADS-MVSNet		56.17 297	51.95 306	68.84 268	80.60 183	53.07 125	55.03 353	70.02 333	44.72 321	51.00 293	61.19 348	22.83 317	78.88 305	28.54 341	53.63 307	74.57 327

LTVRE_ROB	45.45 19	52.73 312	49.74 314	61.69 320	69.78 320	34.99 343	44.52 361	67.60 340	43.11 333	43.79 324	74.03 288	18.54 340	81.45 281	28.39 343	57.94 272	68.62 350
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
new-patchmatchnet		48.21 322	46.55 324	53.18 340	57.73 362	18.19 379	70.24 319	71.02 328	45.70 314	33.70 356	60.23 350	18.00 342	69.86 352	27.97 344	34.35 358	71.49 346

OpenMVS_ROB	53.19 17	59.20 273	56.00 284	68.83 269	71.13 312	44.30 294	83.64 174	75.02 295	46.42 310	46.48 318	73.03 300	18.69 339	88.14 169	27.74 345	61.80 241	74.05 330

RPSCF		45.77 326	44.13 328	50.68 342	57.67 363	29.66 359	54.92 355	45.25 367	26.69 362	45.92 320	75.92 279	17.43 346	45.70 372	27.44 346	45.95 336	76.67 309

MDA-MVSNet-bldmvs		51.56 317	47.75 322	63.00 313	71.60 306	47.32 256	69.70 324	72.12 318	43.81 329	27.65 368	63.38 342	21.97 327	75.96 326	27.30 347	32.19 362	65.70 357

RPMNet		59.29 271	54.25 294	74.42 162	73.97 282	56.57 31	60.52 345	76.98 273	35.72 352	57.49 240	58.87 355	37.73 188	85.26 247	27.01 348	59.93 249	81.42 255

thres600view7		66.46 223	65.12 220	70.47 249	83.41 107	43.80 300	82.15 213	87.78 55	59.37 167	56.02 257	82.21 208	43.73 120	86.90 207	26.51 349	64.94 212	80.71 270

TAPA-MVS	56.12 14	61.82 260	60.18 257	66.71 291	78.48 222	37.97 336	75.19 288	76.41 284	46.82 306	57.04 246	86.52 153	27.67 291	77.03 322	26.50 350	67.02 200	85.14 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 341	58.03 361	31.94 357		53.57 362	36.67 349	41.32 336	75.23 283	11.17 359	51.57 367	25.81 351	48.04 324	72.02 342

Patchmatch-test		53.33 311	48.17 318	68.81 270	73.31 286	42.38 315	42.98 363	58.23 354	32.53 357	38.79 345	70.77 319	39.66 171	73.51 339	25.18 352	52.06 316	90.55 78

FMVS		27.12 340	24.85 341	33.93 356	26.17 386	15.25 382	30.24 374	22.38 384	12.53 374	28.23 366	49.43 363	2.59 379	34.34 380	25.12 353	26.99 367	52.20 366

TinyColmap		48.15 323	44.49 327	59.13 331	65.73 340	38.04 335	63.34 336	62.86 349	38.78 341	29.48 364	67.23 334	6.46 370	73.30 340	24.59 354	41.90 345	66.04 355

AllTest		47.32 324	44.66 326	55.32 338	65.08 344	37.50 338	62.96 339	54.25 360	35.45 354	33.42 358	72.82 301	9.98 361	59.33 360	24.13 355	43.84 340	69.13 348

TestCases				55.32 338	65.08 344	37.50 338		54.25 360	35.45 354	33.42 358	72.82 301	9.98 361	59.33 360	24.13 355	43.84 340	69.13 348

N_pmnet		41.25 328	39.77 331	45.66 346	68.50 327	0.82 390	72.51 305	0.38 390	35.61 353	35.26 353	61.51 347	20.07 334	67.74 354	23.51 357	40.63 346	68.42 351

DP-MVS		59.24 272	56.12 283	68.63 274	88.24 34	50.35 187	82.51 207	64.43 345	41.10 338	46.70 316	78.77 239	24.75 310	88.57 155	22.26 358	56.29 287	66.96 353

MIMVSNet1		50.35 319	47.81 320	57.96 334	61.53 357	27.80 366	67.40 329	74.06 303	43.25 332	33.31 360	65.38 339	16.03 351	71.34 347	21.80 359	47.55 327	74.75 325

tfpnnormal		61.47 261	59.09 264	68.62 275	76.29 253	41.69 317	81.14 238	85.16 111	54.48 256	51.32 291	73.63 295	32.32 255	86.89 208	21.78 360	55.71 295	77.29 305

LF4IMVS		33.04 336	32.55 336	34.52 354	40.96 373	22.03 371	44.45 362	35.62 374	20.42 365	28.12 367	62.35 345	5.03 374	31.88 382	21.61 361	34.42 357	49.63 368

COLMAP_ROB	43.60 20	50.90 318	48.05 319	59.47 329	67.81 333	40.57 327	71.25 316	62.72 350	36.49 351	36.19 350	73.51 296	13.48 356	73.92 336	20.71 362	50.26 319	63.92 359
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 337	23.85 344	40.71 349	27.46 385	18.93 375	30.82 373	46.19 364	12.76 373	16.40 371	34.70 372	1.90 383	48.69 370	20.25 363	24.22 369	54.51 364

DSMNet-mixed		38.35 330	35.36 334	47.33 345	48.11 371	14.91 383	37.87 368	36.60 373	19.18 367	34.37 354	59.56 353	15.53 352	53.01 366	20.14 364	46.89 333	74.07 329

new_pmnet		33.56 335	31.89 337	38.59 351	49.01 369	20.42 373	51.01 356	37.92 372	20.58 364	23.45 369	46.79 364	6.66 369	49.28 369	20.00 365	31.57 364	46.09 370

LS3D		56.40 296	53.82 296	64.12 307	81.12 168	45.69 281	73.42 299	66.14 341	35.30 356	43.24 329	79.88 227	22.18 325	79.62 301	19.10 366	64.00 219	67.05 352

test_method		24.09 343	21.07 347	33.16 357	27.67 384	8.35 388	26.63 375	35.11 376	3.40 381	14.35 373	36.98 368	3.46 377	35.31 377	19.08 367	22.95 370	55.81 363

TDRefinement		40.91 329	38.37 332	48.55 344	50.45 368	33.03 352	58.98 349	50.97 363	28.50 360	29.89 363	67.39 333	6.21 372	54.51 364	17.67 368	35.25 355	58.11 362

test_0402		56.45 295	53.03 299	66.69 292	76.78 246	50.31 190	81.76 222	69.61 334	42.79 334	43.88 323	72.13 311	22.82 319	86.46 219	16.57 369	50.94 318	63.31 360

PMMVS2		26.71 341	22.98 345	37.87 352	36.89 375	8.51 387	42.51 364	29.32 380	19.09 368	13.01 374	37.54 366	2.23 381	53.11 365	14.54 370	11.71 376	51.99 367

ANet_high		34.39 333	29.59 339	48.78 343	30.34 381	22.28 370	55.53 352	63.79 347	38.11 344	15.47 372	36.56 370	6.94 367	59.98 359	13.93 371	5.64 383	64.08 358

tmp_tt		9.44 350	10.68 353	5.73 366	2.49 389	4.21 389	10.48 379	18.04 386	0.34 383	12.59 375	20.49 377	11.39 358	7.03 385	13.84 372	6.46 382	5.95 380

EGC-MVSNET		33.75 334	30.42 338	43.75 348	64.94 346	36.21 342	60.47 347	40.70 370	0.02 384	0.10 385	53.79 360	7.39 366	60.26 358	11.09 373	35.23 356	34.79 371

FPMVS		35.40 332	33.67 335	40.57 350	46.34 372	28.74 364	41.05 365	57.05 356	20.37 366	22.27 370	53.38 361	6.87 368	44.94 373	8.62 374	47.11 331	48.01 369

Gipuma		27.47 339	24.26 343	37.12 353	60.55 360	29.17 362	11.68 378	60.00 352	14.18 372	10.52 379	15.12 380	2.20 382	63.01 357	8.39 375	35.65 353	19.18 376
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FMVS1		21.11 344	19.08 348	27.18 360	30.56 379	18.28 377	33.43 371	24.48 381	8.02 378	12.02 376	33.50 373	0.75 387	35.09 378	7.68 376	21.32 371	28.17 373

APD_test		21.11 344	19.08 348	27.18 360	30.56 379	18.28 377	33.43 371	24.48 381	8.02 378	12.02 376	33.50 373	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	19.72 374	13.73 377	23.63 383	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	30.55 375	1.82 384	14.55 383	6.83 379	7.52 379	15.75 377

E-PMN		19.16 346	18.40 350	21.44 362	36.19 376	13.63 384	47.59 358	30.89 378	10.73 375	5.91 382	16.59 378	3.66 376	39.77 374	5.95 380	8.14 378	10.92 378

PMVS	19.57 22	25.07 342	22.43 346	32.99 358	23.12 387	22.98 368	40.98 366	35.19 375	15.99 371	11.95 378	35.87 371	1.47 385	49.29 368	5.41 381	31.90 363	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	34.13 378	12.64 385	46.94 359	29.94 379	10.46 377	5.58 383	14.93 381	4.23 375	38.83 375	5.24 382	7.51 380	10.67 379

wuyk23d		9.11 351	8.77 355	10.15 365	40.18 374	16.76 380	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	73.40 300	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	71.93 313	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	24.44 342	0.00 369	0.00 391	0.00 392	0.00 380	89.40 18	0.00 385	0.00 388	92.02 39	38.55 179	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	37.77 185	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 36	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					83.24 114	49.90 198	84.98 137	78.76 239	47.71 298	73.42 53

test_one_0601					89.39 24	57.29 22		88.09 50	57.21 219	82.06 11	93.39 13	54.94 25

eth-test2					0.00 391

eth-test					0.00 391

test_241102_ONE					89.48 19	56.89 27		88.94 28	57.53 211	84.61 4	93.29 16	58.81 12	96.45 1

save fliter					85.35 70	56.34 38	89.31 39	81.46 188	61.55 127

test0726					89.40 22	57.45 19	92.32 7	88.63 39	57.71 207	83.14 9	93.96 7	55.17 21

GSMVS																88.13 137

test_part2					89.33 25	55.48 53				82.27 10

sam_mvs1												38.86 177				88.13 137

sam_mvs												35.99 222

MTGPA								81.31 191

test_post											16.22 379	37.52 193	84.72 257

patchmatchnet-post											59.74 352	38.41 180	79.91 299

MTMP							87.27 78	15.34 387

TEST9					85.68 57	55.42 54	87.59 67	84.00 142	57.72 206	72.99 57	90.98 61	44.87 106	88.58 152

test_8					85.72 56	55.31 58	87.60 64	83.88 145	57.84 204	72.84 61	90.99 60	44.99 103	88.34 162

agg_prior					85.64 60	54.92 72		83.61 152		72.53 65			88.10 172

test_prior4						56.39 37	87.15 81

test_prior				78.39 69	86.35 49	54.91 74		85.45 95					89.70 120			90.55 78

新几何2							81.61 227

旧先验1					81.57 158	47.48 252		71.83 319			88.66 116	36.94 204			78.34 110	88.67 126

原ACMM2							83.77 172

test222					79.36 198	50.97 172	77.99 270	67.84 338	42.54 335	62.84 169	86.53 152	30.26 273			76.91 120	85.23 193

segment_acmp												44.97 105

testdata1							77.55 274		64.14 82

test12				79.24 39	86.89 45	56.08 43		85.16 111		72.27 71		47.15 76	91.10 80		85.93 37	90.54 81

plane_prior7					77.95 228	48.46 236

plane_prior6					78.42 223	49.39 209						36.04 220

plane_prior4											83.28 191

plane_prior3						48.95 218			64.01 85	62.15 176

plane_prior2							85.76 109		63.60 95

plane_prior1					78.31 225

plane_prior						49.57 203	87.43 71		64.57 77						72.84 156

n2								0.00 391

nn								0.00 391

door-mid								41.31 369

test11								84.25 136

door								43.27 368

HQP5-MVS						51.56 160

HQP-NCC					79.02 206		88.00 57		65.45 64	64.48 144

ACMP_Plane					79.02 206		88.00 57		65.45 64	64.48 144

HQP4-MVS										64.47 147			88.61 151			84.91 199

HQP3-MVS								83.68 148							73.12 152

HQP2-MVS												37.35 196

NP-MVS					78.76 211	50.43 182					85.12 167

ACMMP++_ref															63.20 230

ACMMP++															59.38 255

Test By Simon												39.38 172

High-resolution multi-view benchmark