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 47	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 61	90.34 10	53.77 97	88.08 57	88.36 48	76.17 2	79.40 23	91.09 56	55.43 20	90.09 109	85.01 5	80.40 87	91.99 43

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 11	57.70 14	91.71 10	88.87 33	70.31 17	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 34	84.83 143	83.68 150	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 32	87.62 40	54.21 90	91.60 13	86.47 80	73.13 6	79.89 21	93.10 18	49.88 56	92.98 35	84.09 8	84.75 51	93.08 16

MSP-MVS		82.30 6	83.47 1	78.80 52	82.99 124	52.71 132	85.04 134	88.63 40	66.08 59	86.77 3	92.75 24	72.05 1	91.46 70	83.35 9	93.53 1	92.23 34
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 55	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 69	70.62 14	80.75 17	93.22 17	37.77 186	92.50 46	82.75 11	86.25 35	91.57 52

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 78	60.73 4	91.65 12	86.86 72	70.30 18	80.77 16	93.07 21	37.63 191	92.28 52	82.73 12	85.71 39	91.57 52

DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 84	85.46 68	49.56 206	90.99 20	86.66 78	70.58 15	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 28	92.48 3	88.94 29	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 37	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 31					96.39 4	81.68 16	87.13 21	92.47 27

ETH3D-3000-0.1		78.73 21	78.71 21	78.78 55	85.58 62	52.40 140	88.42 54	89.03 26	60.01 152	76.06 33	92.80 23	48.34 62	92.88 37	81.66 18	86.48 33	91.04 69

DVP-MVS++		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 137	88.88 31	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 34

MSC_two_6792asdad				81.53 15	91.77 4	56.03 45		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 45		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 55	88.84 34	59.89 154	74.58 43	92.62 27	46.80 80	92.66 43	81.40 23	85.62 41

lupinMVS		78.38 25	78.11 27	79.19 41	83.02 122	55.24 60	91.57 14	84.82 122	69.12 24	76.67 30	92.02 39	44.82 109	90.23 106	80.83 24	80.09 92	92.08 38

ETH3D cwj APD-0.16		78.36 26	78.19 25	78.86 50	84.21 91	52.68 133	86.70 91	89.02 27	59.13 178	75.37 35	92.49 28	49.06 61	93.20 33	80.67 25	87.08 23	90.71 77

HPM-MVS++		80.50 13	80.71 13	79.88 34	87.34 42	55.20 63	89.93 29	87.55 64	66.04 62	79.46 22	93.00 22	53.10 32	91.76 63	80.40 26	89.56 9	92.68 25

SMA-MVS		79.10 20	78.76 20	80.12 30	84.42 85	55.87 48	87.58 69	86.76 75	61.48 131	80.26 19	93.10 18	46.53 84	92.41 49	79.97 27	88.77 11	92.08 38
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 41	88.56 28	54.55 85	89.76 34	87.77 59	55.91 237	78.56 24	92.49 28	48.20 64	92.65 44	79.49 28	83.04 60	90.39 84

ETV-MVS		77.17 41	76.74 43	78.48 65	81.80 148	54.55 85	86.13 102	85.33 103	68.20 30	73.10 56	90.52 76	45.23 102	90.66 92	79.37 29	80.95 78	90.22 88

jason		77.01 44	76.45 48	78.69 58	79.69 194	54.74 77	90.56 24	83.99 146	68.26 29	74.10 46	90.91 64	42.14 143	89.99 111	79.30 30	79.12 102	91.36 60
jason: jason.
DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 47	56.89 28	92.77 2	86.30 84	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 89	72.89 88	77.15 103	80.17 188	50.37 185	84.68 147	83.33 157	68.08 31	71.97 72	88.65 119	42.50 137	91.15 77	78.82 32	57.78 272	89.91 98

hse-mvs2		71.44 128	70.68 121	73.73 182	76.34 248	47.44 254	79.45 262	79.47 224	68.08 31	71.97 72	86.01 159	42.50 137	86.93 202	78.82 32	53.46 308	86.83 164

NCCC		79.57 18	79.23 18	80.59 21	89.50 17	56.99 26	91.38 15	88.17 50	67.71 39	73.81 48	92.75 24	46.88 79	93.28 32	78.79 34	84.07 56	91.50 56

test9_res														78.72 35	85.44 44	91.39 58

CSCG		80.41 14	79.72 14	82.49 6	89.12 27	57.67 15	89.29 41	91.54 3	59.19 172	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 55	55.55 242	81.21 15	93.69 10	56.51 17	94.27 25	78.36 37	85.70 40	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 70	75.03 65	77.04 104	81.37 165	52.65 135	84.34 154	84.46 132	61.16 134	69.14 93	91.76 45	39.98 170	88.99 137	78.19 38	84.89 50	89.48 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 47	76.40 49	78.45 67	85.68 57	55.42 54	87.59 67	84.00 144	57.84 204	72.99 57	90.98 60	44.99 104	88.58 149	78.19 38	85.32 45	91.34 62

xxxxxxxxxxxxxcwj		77.31 40	76.54 45	79.61 35	85.35 70	56.34 39	89.31 39	72.84 314	61.55 127	74.63 40	92.38 30	47.75 70	91.35 72	78.18 40	86.85 26	91.15 66

SF-MVS		77.64 35	77.42 35	78.32 73	83.75 101	52.47 138	86.63 93	87.80 56	58.78 186	74.63 40	92.38 30	47.75 70	91.35 72	78.18 40	86.85 26	91.15 66

canonicalmvs		78.17 29	77.86 30	79.12 44	84.30 87	54.22 89	87.71 62	84.57 131	67.70 40	77.70 26	92.11 38	50.90 47	89.95 112	78.18 40	77.54 114	93.20 14

VDD-MVS		76.08 61	74.97 67	79.44 37	84.27 90	53.33 117	91.13 19	85.88 91	65.33 70	72.37 69	89.34 104	32.52 252	92.76 41	77.90 43	75.96 127	92.22 36

agg_prior1		76.68 54	76.24 52	78.00 80	85.64 60	54.92 72	87.55 70	83.61 154	57.99 200	72.53 64	91.05 57	45.36 100	88.10 169	77.76 44	84.68 52	90.99 72

diffmvs		75.11 77	74.65 72	76.46 120	78.52 219	53.35 115	83.28 188	79.94 212	70.51 16	71.64 77	88.72 115	46.02 91	86.08 228	77.52 45	75.75 131	89.96 96

alignmvs		78.08 30	77.98 28	78.39 70	83.53 105	53.22 120	89.77 33	85.45 97	66.11 57	76.59 32	91.99 41	54.07 29	89.05 131	77.34 46	77.00 119	92.89 20

SteuartSystems-ACMMP		77.08 43	76.33 50	79.34 39	80.98 170	55.31 58	89.76 34	86.91 71	62.94 106	71.65 76	91.56 51	42.33 139	92.56 45	77.14 47	83.69 58	90.15 91
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 56	75.66 56	78.73 56	81.92 146	54.67 82	84.06 163	85.35 102	61.10 136	72.99 57	91.50 52	40.25 164	91.00 82	76.84 48	86.98 24	90.51 83

CLD-MVS		75.60 69	75.39 60	76.24 123	80.69 180	52.40 140	90.69 22	86.20 86	74.40 4	65.01 136	88.93 111	42.05 145	90.58 95	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 79	74.33 75	76.95 109	82.89 128	53.05 126	85.63 115	83.50 156	57.86 203	67.25 108	90.24 82	43.38 129	88.85 143	76.03 50	82.23 69	88.96 120
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 48	80.40 186	54.66 83	87.06 82	85.88 91	72.11 9	71.57 78	88.63 120	50.89 49	90.35 100	76.00 51	79.11 103	91.63 49

TSAR-MVS + GP.		77.82 33	77.59 32	78.49 64	85.25 74	50.27 192	90.02 26	90.57 11	56.58 231	74.26 45	91.60 50	54.26 26	92.16 55	75.87 52	79.91 96	93.05 17

baseline		76.86 50	76.24 52	78.71 57	80.47 185	54.20 92	83.90 168	84.88 121	71.38 12	71.51 79	89.15 109	50.51 50	90.55 96	75.71 53	78.65 106	91.39 58

agg_prior2														75.65 54	85.11 48	91.01 70

DeepC-MVS	67.15 4	76.90 49	76.27 51	78.80 52	80.70 179	55.02 69	86.39 96	86.71 76	66.96 46	67.91 103	89.97 93	48.03 66	91.41 71	75.60 55	84.14 55	89.96 96
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 83	73.76 180	85.91 54	51.83 154	86.18 101	84.24 140	65.40 67	69.09 94	80.86 222	46.70 82	88.13 167	75.43 56	65.92 207	81.33 257

PVSNet_Blended		76.53 55	76.54 45	76.50 118	85.91 54	51.83 154	88.89 46	84.24 140	67.82 37	69.09 94	89.33 106	46.70 82	88.13 167	75.43 56	81.48 77	89.55 104

LFMVS		78.52 22	77.14 37	82.67 4	89.58 15	58.90 8	91.27 18	88.05 52	63.22 102	74.63 40	90.83 67	41.38 155	94.40 23	75.42 58	79.90 97	94.72 2

ZD-MVS					89.55 16	53.46 107		84.38 133	57.02 220	73.97 47	91.03 58	44.57 112	91.17 76	75.41 59	81.78 75

testtj		76.96 45	76.48 47	78.40 69	89.89 13	53.67 101	88.72 48	86.15 87	54.56 255	74.86 38	92.31 33	44.38 114	91.97 61	75.19 60	82.24 68	89.54 105

MVS_111021_HR		76.39 57	75.38 61	79.42 38	85.33 72	56.47 36	88.15 56	84.97 118	65.15 74	66.06 121	89.88 94	43.79 120	92.16 55	75.03 61	80.03 95	89.64 103

test_prior3		77.59 36	77.33 36	78.39 70	86.35 50	54.91 74	89.04 43	85.45 97	61.88 122	73.55 50	91.46 54	48.01 67	89.70 118	74.73 62	85.46 42	90.55 79

test_prior2							89.04 43		61.88 122	73.55 50	91.46 54	48.01 67		74.73 62	85.46 42

SD-MVS		76.18 59	74.85 69	80.18 28	85.39 69	56.90 27	85.75 111	82.45 175	56.79 226	74.48 44	91.81 42	43.72 123	90.75 90	74.61 64	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-test		76.94 46	77.04 38	76.62 116	83.59 102	47.60 250	89.87 32	85.55 95	66.52 49	72.49 66	90.53 74	47.76 69	91.03 80	74.53 65	82.55 63	91.25 63

CS-MVS		76.69 53	76.72 44	76.60 117	83.54 104	47.58 251	88.59 51	85.23 111	66.38 52	72.48 68	91.62 49	45.57 98	91.00 82	74.50 66	82.55 63	91.23 64

APD-MVS		76.15 60	75.68 55	77.54 91	88.52 29	53.44 111	87.26 79	85.03 117	53.79 259	74.91 37	91.68 48	43.80 119	90.31 102	74.36 67	81.82 73	88.87 122
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DROMVSNet		75.30 72	75.20 62	75.62 137	80.98 170	49.00 217	87.43 71	84.68 128	63.49 99	70.97 85	90.15 88	42.86 136	91.14 78	74.33 68	81.90 72	86.71 167

VDDNet		74.37 83	72.13 101	81.09 20	79.58 195	56.52 35	90.02 26	86.70 77	52.61 268	71.23 82	87.20 142	31.75 262	93.96 27	74.30 69	75.77 130	92.79 23

TSAR-MVS + MP.		78.31 28	78.26 23	78.48 65	81.33 166	56.31 41	81.59 228	86.41 81	69.61 22	81.72 14	88.16 126	55.09 23	88.04 172	74.12 70	86.31 34	91.09 68
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	80.12 189	59.50 5	92.24 8	90.72 10	69.37 23	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 45	85.33 72	50.69 175	88.57 52	88.59 44	58.14 195	73.60 49	93.31 15	43.14 132	93.79 29	73.81 72	88.53 13	92.37 30

zzz-MVS		74.15 87	73.11 87	77.27 99	81.54 158	53.57 103	84.02 165	81.31 192	59.41 164	68.39 99	90.96 62	36.07 218	89.01 133	73.80 73	82.45 66	89.23 111

MTAPA		72.73 107	71.22 115	77.27 99	81.54 158	53.57 103	67.06 327	81.31 192	59.41 164	68.39 99	90.96 62	36.07 218	89.01 133	73.80 73	82.45 66	89.23 111

Regformer-1		77.80 34	77.44 34	78.88 48	87.78 38	52.44 139	87.60 64	90.08 14	68.86 25	72.49 66	91.79 43	47.69 72	94.90 20	73.57 75	77.05 116	89.31 109

VNet		77.99 32	77.92 29	78.19 75	87.43 41	50.12 195	90.93 21	91.41 4	67.48 42	75.12 36	90.15 88	46.77 81	91.00 82	73.52 76	78.46 108	93.44 9

EPNet		78.36 26	78.49 22	77.97 82	85.49 65	52.04 147	89.36 38	84.07 143	73.22 5	77.03 29	91.72 46	49.32 60	90.17 108	73.46 77	82.77 61	91.69 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		71.60 125	70.29 128	75.55 139	77.26 238	53.15 121	85.34 120	79.37 225	55.83 238	72.54 61	90.19 85	22.38 320	86.66 209	73.28 78	76.39 123	86.85 161

xiu_mvs_v1_base		71.60 125	70.29 128	75.55 139	77.26 238	53.15 121	85.34 120	79.37 225	55.83 238	72.54 61	90.19 85	22.38 320	86.66 209	73.28 78	76.39 123	86.85 161

xiu_mvs_v1_base_debi		71.60 125	70.29 128	75.55 139	77.26 238	53.15 121	85.34 120	79.37 225	55.83 238	72.54 61	90.19 85	22.38 320	86.66 209	73.28 78	76.39 123	86.85 161

Regformer-2		77.15 42	76.82 42	78.14 76	87.78 38	51.84 153	87.60 64	89.12 23	67.23 43	71.93 74	91.79 43	46.03 90	93.53 31	72.85 81	77.05 116	89.05 118

PMMVS		72.98 102	72.05 105	75.78 136	83.57 103	48.60 227	84.08 161	82.85 170	61.62 126	68.24 101	90.33 81	28.35 280	87.78 181	72.71 82	76.69 121	90.95 73

ZNCC-MVS		75.82 68	75.02 66	78.23 74	83.88 99	53.80 96	86.91 88	86.05 89	59.71 157	67.85 104	90.55 73	42.23 141	91.02 81	72.66 83	85.29 46	89.87 99

ET-MVSNet_ETH3D		75.23 74	74.08 77	78.67 59	84.52 84	55.59 50	88.92 45	89.21 22	68.06 34	53.13 278	90.22 84	49.71 57	87.62 185	72.12 84	70.82 173	92.82 21

MVS		76.91 47	75.48 58	81.23 19	84.56 83	55.21 62	80.23 251	91.64 2	58.65 188	65.37 130	91.48 53	45.72 96	95.05 17	72.11 85	89.52 10	93.44 9

#test#		74.86 81	73.78 81	78.10 78	84.30 87	53.68 99	86.95 85	84.36 134	59.00 182	65.78 125	90.56 71	40.70 161	90.90 86	71.48 86	80.88 79	89.71 100

nrg030		72.27 118	71.56 109	74.42 160	75.93 257	50.60 177	86.97 84	83.21 162	62.75 108	67.15 109	84.38 173	50.07 53	86.66 209	71.19 87	62.37 235	85.99 177

DeepC-MVS_fast	67.50 3	78.00 31	77.63 31	79.13 43	88.52 29	55.12 65	89.95 28	85.98 90	68.31 28	71.33 81	92.75 24	45.52 99	90.37 99	71.15 88	85.14 47	91.91 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		74.87 80	73.90 79	77.77 85	83.30 111	53.45 110	85.75 111	85.29 106	59.22 171	66.50 116	89.85 95	40.94 157	90.76 89	70.94 89	83.35 59	89.10 117

CHOSEN 1792x2688		76.24 58	74.03 78	82.88 1	83.09 118	62.84 2	85.73 113	85.39 100	69.79 20	64.87 138	83.49 186	41.52 154	93.69 30	70.55 90	81.82 73	92.12 37

CDPH-MVS		76.05 62	75.19 63	78.62 61	86.51 49	54.98 71	87.32 74	84.59 130	58.62 189	70.75 86	90.85 66	43.10 134	90.63 94	70.50 91	84.51 54	90.24 87

HPM-MVS		72.60 109	71.50 110	75.89 134	82.02 144	51.42 165	80.70 245	83.05 165	56.12 236	64.03 151	89.53 100	37.55 193	88.37 157	70.48 92	80.04 94	87.88 142
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 87	85.49 65	51.47 163	85.12 130	90.19 13	68.52 27	69.36 91	90.66 69	46.45 85	94.81 21	70.25 93	73.16 150	86.81 165

MVS_111021_LR		69.07 166	67.91 159	72.54 205	77.27 237	49.56 206	79.77 256	73.96 304	59.33 169	60.73 187	87.82 131	30.19 273	81.53 277	69.94 94	72.19 163	86.53 169

test_yl		75.85 65	74.83 70	78.91 46	88.08 36	51.94 149	91.30 16	89.28 20	57.91 201	71.19 83	89.20 107	42.03 146	92.77 39	69.41 95	75.07 138	92.01 41

DCV-MVSNet		75.85 65	74.83 70	78.91 46	88.08 36	51.94 149	91.30 16	89.28 20	57.91 201	71.19 83	89.20 107	42.03 146	92.77 39	69.41 95	75.07 138	92.01 41

Regformer-4		75.06 78	74.59 73	76.47 119	85.49 65	50.33 188	85.12 130	88.61 42	66.42 50	68.48 98	90.66 69	44.15 115	92.68 42	69.24 97	73.16 150	86.39 173

HFP-MVS		74.37 83	73.13 86	78.10 78	84.30 87	53.68 99	85.58 116	84.36 134	56.82 224	65.78 125	90.56 71	40.70 161	90.90 86	69.18 98	80.88 79	89.71 100

ACMMPR		73.76 93	72.61 89	77.24 102	83.92 97	52.96 129	85.58 116	84.29 136	56.82 224	65.12 132	90.45 77	37.24 201	90.18 107	69.18 98	80.84 81	88.58 130

region2R		73.75 94	72.55 91	77.33 96	83.90 98	52.98 128	85.54 119	84.09 142	56.83 223	65.10 133	90.45 77	37.34 199	90.24 105	68.89 100	80.83 82	88.77 126

CP-MVS		72.59 111	71.46 111	76.00 133	82.93 127	52.32 144	86.93 87	82.48 174	55.15 246	63.65 159	90.44 80	35.03 230	88.53 153	68.69 101	77.83 112	87.15 155

baseline2		75.15 76	74.54 74	76.98 108	81.67 151	51.74 156	83.84 169	91.94 1	69.97 19	58.98 209	86.02 157	59.73 8	91.73 64	68.37 102	70.40 177	87.48 149

Effi-MVS+		75.24 73	73.61 82	80.16 29	81.92 146	57.42 21	85.21 124	76.71 280	60.68 145	73.32 55	89.34 104	47.30 75	91.63 65	68.28 103	79.72 98	91.42 57

CostFormer		73.89 91	72.30 97	78.66 60	82.36 142	56.58 31	75.56 282	85.30 105	66.06 60	70.50 89	76.88 262	57.02 15	89.06 129	68.27 104	68.74 187	90.33 86

CANet_DTU		73.71 95	73.14 84	75.40 142	82.61 138	50.05 196	84.67 149	79.36 228	69.72 21	75.39 34	90.03 92	29.41 276	85.93 234	67.99 105	79.11 103	90.22 88

PVSNet_Blended_VisFu		73.40 99	72.44 93	76.30 121	81.32 167	54.70 80	85.81 107	78.82 238	63.70 93	64.53 143	85.38 166	47.11 78	87.38 191	67.75 106	77.55 113	86.81 165

MSLP-MVS++		74.21 85	72.25 98	80.11 31	81.45 163	56.47 36	86.32 98	79.65 220	58.19 194	66.36 117	92.29 34	36.11 217	90.66 92	67.39 107	82.49 65	93.18 15

PGM-MVS		72.60 109	71.20 116	76.80 113	82.95 125	52.82 131	83.07 193	82.14 177	56.51 232	63.18 163	89.81 96	35.68 223	89.76 117	67.30 108	80.19 91	87.83 143

EIA-MVS		75.92 64	75.18 64	78.13 77	85.14 75	51.60 159	87.17 80	85.32 104	64.69 77	68.56 97	90.53 74	45.79 95	91.58 66	67.21 109	82.18 70	91.20 65

HY-MVS	67.03 5	73.90 90	73.14 84	76.18 127	84.70 82	47.36 255	75.56 282	86.36 83	66.27 54	70.66 87	83.91 179	51.05 45	89.31 125	67.10 110	72.61 159	91.88 45

bset_n11_16_dypcd		65.51 231	63.21 233	72.41 209	68.84 321	50.15 193	81.25 235	72.40 316	59.17 176	59.20 207	78.66 239	25.69 303	85.27 243	66.80 111	56.88 277	81.80 243

BP-MVS														66.70 112

HQP-MVS		72.34 114	71.44 112	75.03 149	79.02 205	51.56 160	88.00 58	83.68 150	65.45 64	64.48 144	85.13 167	37.35 197	88.62 147	66.70 112	73.12 152	84.91 198

SR-MVS		70.92 136	69.73 137	74.50 157	83.38 110	50.48 181	84.27 156	79.35 229	48.96 291	66.57 115	90.45 77	33.65 243	87.11 195	66.42 114	74.56 141	85.91 180

gm-plane-assit					83.24 113	54.21 90			70.91 13		88.23 125		95.25 15	66.37 115

PAPR		75.20 75	74.13 76	78.41 68	88.31 33	55.10 67	84.31 155	85.66 94	63.76 92	67.55 105	90.73 68	43.48 128	89.40 124	66.36 116	77.03 118	90.73 76

WTY-MVS		77.47 38	77.52 33	77.30 97	88.33 32	46.25 271	88.46 53	90.32 12	71.40 11	72.32 70	91.72 46	53.44 30	92.37 50	66.28 117	75.42 133	93.28 12

RRT_test8_iter05		72.74 106	71.20 116	77.36 95	87.25 44	53.51 105	88.68 50	89.53 18	65.20 73	61.32 181	81.27 218	45.89 92	92.48 48	65.99 118	55.65 292	86.10 176

tpmrst		71.04 133	69.77 136	74.86 152	83.19 115	55.86 49	75.64 281	78.73 242	67.88 35	64.99 137	73.73 288	49.96 55	79.56 299	65.92 119	67.85 194	89.14 116

MVS_Test		75.85 65	74.93 68	78.62 61	84.08 93	55.20 63	83.99 166	85.17 112	68.07 33	73.38 54	82.76 196	50.44 51	89.00 135	65.90 120	80.61 83	91.64 48

ACMMP		70.81 138	69.29 144	75.39 143	81.52 162	51.92 151	83.43 181	83.03 166	56.67 229	58.80 216	88.91 112	31.92 260	88.58 149	65.89 121	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	76.81 111	83.41 106	52.48 136	84.88 141	83.20 163	58.03 196	63.91 154	89.63 99	35.50 224	89.78 115	65.50 122	80.50 85	88.16 135

X-MVStestdata		65.85 229	62.20 238	76.81 111	83.41 106	52.48 136	84.88 141	83.20 163	58.03 196	63.91 154	4.82 375	35.50 224	89.78 115	65.50 122	80.50 85	88.16 135

PAPM		76.76 51	76.07 54	78.81 51	80.20 187	59.11 7	86.86 89	86.23 85	68.60 26	70.18 90	88.84 114	51.57 41	87.16 194	65.48 124	86.68 30	90.15 91

HQP_MVS		70.96 135	69.91 135	74.12 169	77.95 227	49.57 204	85.76 109	82.59 172	63.60 96	62.15 174	83.28 190	36.04 220	88.30 162	65.46 125	72.34 161	84.49 201

plane_prior5								82.59 172					88.30 162	65.46 125	72.34 161	84.49 201

mPP-MVS		71.79 124	70.38 126	76.04 131	82.65 137	52.06 146	84.45 151	81.78 185	55.59 241	62.05 177	89.68 98	33.48 244	88.28 164	65.45 127	78.24 111	87.77 145

OPM-MVS		70.75 139	69.58 138	74.26 166	75.55 262	51.34 167	86.05 104	83.29 161	61.94 121	62.95 167	85.77 161	34.15 236	88.44 155	65.44 128	71.07 170	82.99 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Effi-MVS+-dtu		66.24 225	64.96 222	70.08 254	75.17 263	49.64 203	82.01 215	74.48 298	62.15 115	57.83 230	76.08 275	30.59 270	83.79 261	65.40 129	60.93 242	76.81 305

mvs-test1		69.04 167	67.57 170	73.44 190	75.17 263	51.68 158	86.57 95	74.48 298	62.15 115	62.07 176	85.79 160	30.59 270	87.48 188	65.40 129	65.94 206	81.18 261

EI-MVSNet-Vis-set		73.19 101	72.60 90	74.99 151	82.56 139	49.80 202	82.55 205	89.00 28	66.17 56	65.89 124	88.98 110	43.83 118	92.29 51	65.38 131	69.01 185	82.87 235

TESTMET0.1,1		72.86 105	72.33 95	74.46 158	81.98 145	50.77 173	85.13 127	85.47 96	66.09 58	67.30 106	83.69 184	37.27 200	83.57 265	65.06 132	78.97 105	89.05 118

test_part1		73.80 92	72.13 101	78.79 54	85.92 53	58.26 10	90.60 23	86.85 73	63.98 87	63.95 153	81.54 216	52.08 39	92.24 53	64.93 133	59.32 251	85.87 182

MVSTER		73.25 100	72.33 95	76.01 132	85.54 64	53.76 98	83.52 174	87.16 67	67.06 45	63.88 156	81.66 214	52.77 33	90.44 97	64.66 134	64.69 212	83.84 217

test1172		69.64 161	68.38 154	73.41 191	82.77 131	48.84 223	82.79 200	78.34 251	47.02 302	65.27 131	90.07 90	31.17 266	86.09 226	64.51 135	73.49 148	85.31 192

CPTT-MVS		67.15 209	65.84 203	71.07 239	80.96 172	50.32 189	81.94 217	74.10 301	46.18 310	57.91 229	87.64 136	29.57 275	81.31 279	64.10 136	70.18 179	81.56 248

miper_enhance_ethall		69.77 156	68.90 148	72.38 210	78.93 208	49.91 199	83.29 187	78.85 236	64.90 75	59.37 202	79.46 230	52.77 33	85.16 247	63.78 137	58.72 255	82.08 240

EI-MVSNet-UG-set		72.37 113	71.73 107	74.29 165	81.60 154	49.29 212	81.85 220	88.64 39	65.29 72	65.05 134	88.29 123	43.18 130	91.83 62	63.74 138	67.97 192	81.75 245

ab-mvs		70.65 140	69.11 146	75.29 145	80.87 176	46.23 272	73.48 296	85.24 110	59.99 153	66.65 111	80.94 221	43.13 133	88.69 145	63.58 139	68.07 190	90.95 73

VPA-MVSNet		71.12 130	70.66 122	72.49 207	78.75 211	44.43 290	87.64 63	90.02 15	63.97 88	65.02 135	81.58 215	42.14 143	87.42 190	63.42 140	63.38 223	85.63 188

APD-MVS_3200maxsize		69.62 162	68.23 156	73.80 179	81.58 156	48.22 239	81.91 218	79.50 223	48.21 293	64.24 149	89.75 97	31.91 261	87.55 187	63.08 141	73.85 146	85.64 187

v2v482		69.55 163	67.64 167	75.26 147	72.32 297	53.83 95	84.93 140	81.94 180	65.37 69	60.80 186	79.25 233	41.62 151	88.98 138	63.03 142	59.51 248	82.98 233

PS-MVSNAJss		68.78 176	67.17 178	73.62 187	73.01 287	48.33 238	84.95 139	84.81 123	59.30 170	58.91 213	79.84 228	37.77 186	88.86 142	62.83 143	63.12 229	83.67 219

cl22		68.85 170	67.69 166	72.35 211	78.07 226	49.98 198	82.45 208	78.48 248	62.50 112	58.46 223	77.95 243	49.99 54	85.17 246	62.55 144	58.72 255	81.90 242

V42		67.66 195	65.60 210	73.86 176	70.69 312	53.63 102	81.50 231	78.61 245	63.85 90	59.49 201	77.49 250	37.98 183	87.65 184	62.33 145	58.43 259	80.29 272

AUN-MVS		68.20 187	66.35 190	73.76 180	76.37 247	47.45 253	79.52 261	79.52 222	60.98 139	62.34 172	86.02 157	36.59 214	86.94 201	62.32 146	53.47 307	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 46	64.83 76	73.52 52	88.09 127	48.07 65	92.19 54	62.24 147	84.53 53	91.53 54

Patchmatch-RL test		58.72 278	54.32 290	71.92 225	63.91 347	44.25 292	61.73 338	55.19 354	57.38 215	49.31 299	54.24 355	37.60 192	80.89 282	62.19 148	47.28 326	90.63 78

mvs_anonymous		72.29 116	70.74 120	76.94 110	82.85 129	54.72 79	78.43 269	81.54 188	63.77 91	61.69 179	79.32 232	51.11 44	85.31 241	62.15 149	75.79 129	90.79 75

miper_ehance_all_eth		68.70 179	67.58 168	72.08 215	76.91 244	49.48 209	82.47 207	78.45 249	62.68 109	58.28 227	77.88 245	50.90 47	85.01 251	61.91 150	58.72 255	81.75 245

HyFIR lowres test		69.94 154	67.58 168	77.04 104	77.11 243	57.29 22	81.49 233	79.11 234	58.27 193	58.86 214	80.41 225	42.33 139	86.96 200	61.91 150	68.68 188	86.87 159

sss		70.49 142	70.13 132	71.58 231	81.59 155	39.02 328	80.78 244	84.71 127	59.34 167	66.61 113	88.09 127	37.17 202	85.52 237	61.82 152	71.02 171	90.20 90

1314		71.11 131	69.41 140	76.22 124	79.32 199	50.49 180	80.23 251	85.14 115	59.44 163	58.93 211	88.89 113	33.83 242	89.60 122	61.49 153	77.42 115	88.57 131

GA-MVS		69.04 167	66.70 186	76.06 130	75.11 265	52.36 142	83.12 191	80.23 208	63.32 100	60.65 188	79.22 234	30.98 268	88.37 157	61.25 154	66.41 201	87.46 150

ECVR-MVS		71.81 122	71.00 119	74.26 166	80.12 189	43.49 299	84.69 146	82.16 176	64.02 84	64.64 140	87.43 139	35.04 229	89.21 127	61.24 155	79.66 99	90.08 93

VPNet		72.07 119	71.42 113	74.04 171	78.64 216	47.17 259	89.91 31	87.97 53	72.56 8	64.66 139	85.04 169	41.83 150	88.33 160	61.17 156	60.97 241	86.62 168

RRT_MVS		65.43 233	64.01 230	69.68 259	81.54 158	50.15 193	82.31 211	76.78 277	55.25 245	60.64 189	82.00 211	25.18 305	79.00 300	60.96 157	51.45 314	79.89 277

ACMP	61.11 9	66.24 225	64.33 226	72.00 219	74.89 270	49.12 213	83.18 190	79.83 215	55.41 244	52.29 283	82.68 200	25.83 299	86.10 224	60.89 158	63.94 217	80.78 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 97	72.19 100	77.57 90	83.02 122	55.24 60	81.63 225	81.44 190	50.28 282	76.67 30	90.91 64	44.82 109	86.11 222	60.83 159	80.09 92	91.36 60

test_djsdf		63.84 239	61.56 243	70.70 244	68.78 322	44.69 287	81.63 225	81.44 190	50.28 282	52.27 284	76.26 270	26.72 294	86.11 222	60.83 159	55.84 290	81.29 260

v148		68.24 186	66.35 190	73.88 175	71.76 300	51.47 163	84.23 157	81.90 184	63.69 94	58.94 210	76.44 267	43.72 123	87.78 181	60.63 161	55.86 289	82.39 238

c3_l		67.97 189	66.66 187	71.91 226	76.20 253	49.31 211	82.13 214	78.00 256	61.99 119	57.64 236	76.94 259	49.41 58	84.93 252	60.62 162	57.01 276	81.49 249

test-LLR		69.65 160	69.01 147	71.60 229	78.67 213	48.17 240	85.13 127	79.72 217	59.18 174	63.13 164	82.58 201	36.91 206	80.24 290	60.56 163	75.17 135	86.39 173

test-mter		68.36 181	67.29 175	71.60 229	78.67 213	48.17 240	85.13 127	79.72 217	53.38 262	63.13 164	82.58 201	27.23 291	80.24 290	60.56 163	75.17 135	86.39 173

SR-MVS-dyc-post		68.27 185	66.87 180	72.48 208	80.96 172	48.14 242	81.54 229	76.98 273	46.42 307	62.75 169	89.42 102	31.17 266	86.09 226	60.52 165	72.06 164	83.19 228

RE-MVS-def			66.66 187		80.96 172	48.14 242	81.54 229	76.98 273	46.42 307	62.75 169	89.42 102	29.28 278		60.52 165	72.06 164	83.19 228

IB-MVS	68.87 2	74.01 88	72.03 106	79.94 33	83.04 121	55.50 52	90.24 25	88.65 38	67.14 44	61.38 180	81.74 213	53.21 31	94.28 24	60.45 167	62.41 234	90.03 95
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		68.81 173	66.82 181	74.80 154	72.34 296	53.46 107	84.68 147	81.77 186	64.25 81	60.28 191	77.91 244	40.23 165	88.95 139	60.37 168	59.52 247	81.97 241

LPG-MVS_test		66.44 222	64.58 224	72.02 217	74.42 274	48.60 227	83.07 193	80.64 202	54.69 253	53.75 274	83.83 180	25.73 301	86.98 198	60.33 169	64.71 210	80.48 269

LGP-MVS_train				72.02 217	74.42 274	48.60 227		80.64 202	54.69 253	53.75 274	83.83 180	25.73 301	86.98 198	60.33 169	64.71 210	80.48 269

abl_6		68.03 188	66.15 196	73.66 184	78.54 218	48.48 233	79.77 256	78.04 254	47.39 298	63.70 158	88.25 124	28.21 281	89.06 129	60.17 171	71.25 169	83.45 221

MVP-Stereo		70.97 134	70.44 124	72.59 204	76.03 256	51.36 166	85.02 136	86.99 70	60.31 149	56.53 253	78.92 237	40.11 168	90.00 110	60.00 172	90.01 6	76.41 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 244	60.84 249	70.32 250	68.33 327	44.45 289	81.23 236	81.05 197	53.37 263	50.96 293	77.81 247	17.49 342	85.49 239	59.31 173	58.05 265	81.02 263

test2506		72.91 104	72.43 94	74.32 164	80.12 189	44.18 294	83.19 189	84.77 125	64.02 84	65.97 122	87.43 139	47.67 73	88.72 144	59.08 174	79.66 99	90.08 93

baseline1		72.51 112	72.12 103	73.69 183	85.05 76	44.46 288	83.51 178	86.13 88	71.61 10	64.64 140	87.97 130	55.00 24	89.48 123	59.07 175	56.05 286	87.13 156

mvs_tets		62.96 247	60.55 251	70.19 251	68.22 330	44.24 293	80.90 241	80.74 201	52.99 266	50.82 295	77.56 248	16.74 345	85.44 240	59.04 176	57.94 267	80.89 264

HPM-MVS_fast		67.86 191	66.28 193	72.61 203	80.67 181	48.34 237	81.18 237	75.95 288	50.81 281	59.55 200	88.05 129	27.86 286	85.98 230	58.83 177	73.58 147	83.51 220

eth_miper_zixun_eth		66.98 214	65.28 217	72.06 216	75.61 261	50.40 183	81.00 240	76.97 276	62.00 118	56.99 247	76.97 258	44.84 108	85.58 236	58.75 178	54.42 299	80.21 273

v144192		67.86 191	65.76 205	74.16 168	71.68 301	53.09 124	84.14 160	80.83 200	62.85 107	59.21 206	77.28 254	39.30 174	88.00 173	58.67 179	57.88 270	81.40 254

test1111		71.06 132	70.42 125	72.97 197	79.48 196	41.49 317	84.82 144	82.74 171	64.20 82	62.98 166	87.43 139	35.20 227	87.92 174	58.54 180	78.42 109	89.49 106

thisisatest0515		73.64 96	72.20 99	77.97 82	81.63 152	53.01 127	86.69 92	88.81 35	62.53 111	64.06 150	85.65 162	52.15 38	92.50 46	58.43 181	69.84 180	88.39 134

v8		67.25 206	64.99 221	74.04 171	72.89 290	53.31 118	82.37 210	80.11 210	61.54 129	54.29 269	76.02 276	42.89 135	88.41 156	58.43 181	56.36 279	80.39 271

XXY-MVS		70.18 145	69.28 145	72.89 200	77.64 231	42.88 305	85.06 133	87.50 65	62.58 110	62.66 171	82.34 206	43.64 125	89.83 114	58.42 183	63.70 219	85.96 179

3Dnovator	64.70 6	74.46 82	72.48 92	80.41 24	82.84 130	55.40 57	83.08 192	88.61 42	67.61 41	59.85 193	88.66 116	34.57 233	93.97 26	58.42 183	88.70 12	91.85 46

旧先验2							81.73 223		45.53 313	74.66 39			70.48 348	58.31 185

v1192		67.96 190	65.74 206	74.63 155	71.79 299	53.43 113	84.06 163	80.99 198	63.19 103	59.56 199	77.46 251	37.50 196	88.65 146	58.20 186	58.93 254	81.79 244

EPP-MVSNet		71.14 129	70.07 133	74.33 163	79.18 202	46.52 265	83.81 170	86.49 79	56.32 235	57.95 228	84.90 171	54.23 27	89.14 128	58.14 187	69.65 182	87.33 152

OMC-MVS		65.97 228	65.06 220	68.71 271	72.97 288	42.58 310	78.61 267	75.35 293	54.72 252	59.31 204	86.25 156	33.30 245	77.88 313	57.99 188	67.05 197	85.66 186

cl____		67.43 201	65.93 201	71.95 223	76.33 249	48.02 245	82.58 202	79.12 233	61.30 133	56.72 249	76.92 260	46.12 87	86.44 216	57.98 189	56.31 281	81.38 256

DIV-MVS_self_test		67.43 201	65.93 201	71.94 224	76.33 249	48.01 246	82.57 203	79.11 234	61.31 132	56.73 248	76.92 260	46.09 88	86.43 217	57.98 189	56.31 281	81.39 255

MS-PatchMatch		72.34 114	71.26 114	75.61 138	82.38 141	55.55 51	88.00 58	89.95 17	65.38 68	56.51 254	80.74 224	32.28 255	92.89 36	57.95 191	88.10 15	78.39 290

MAR-MVS		76.76 51	75.60 57	80.21 27	90.87 8	54.68 81	89.14 42	89.11 24	62.95 105	70.54 88	92.33 32	41.05 156	94.95 18	57.90 192	86.55 32	91.00 71
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		60.46 264	57.65 268	68.88 265	63.63 348	45.09 282	72.93 300	78.63 244	46.52 305	51.12 290	72.80 300	21.46 327	83.07 270	57.79 193	53.97 301	78.47 287

Anonymous20240529		69.71 157	67.28 176	77.00 107	83.78 100	50.36 186	88.87 47	85.10 116	47.22 299	64.03 151	83.37 188	27.93 285	92.10 58	57.78 194	67.44 195	88.53 132

Fast-Effi-MVS+-dtu		66.53 220	64.10 229	73.84 177	72.41 295	52.30 145	84.73 145	75.66 289	59.51 161	56.34 255	79.11 236	28.11 283	85.85 235	57.74 195	63.29 224	83.35 222

v1921920		67.45 200	65.23 218	74.10 170	71.51 304	52.90 130	83.75 172	80.44 205	62.48 113	59.12 208	77.13 255	36.98 204	87.90 175	57.53 196	58.14 264	81.49 249

IterMVS-LS		66.63 218	65.36 216	70.42 248	75.10 266	48.90 221	81.45 234	76.69 281	61.05 137	55.71 259	77.10 257	45.86 94	83.65 264	57.44 197	57.88 270	78.70 283
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	72.68 202	75.00 268	48.90 221	79.54 259	87.16 67	61.05 137	63.88 156	83.74 182	45.87 93	90.44 97	57.42 198	64.68 213	78.70 283

CDS-MVSNet		70.48 143	69.43 139	73.64 185	77.56 233	48.83 224	83.51 178	77.45 265	63.27 101	62.33 173	85.54 165	43.85 117	83.29 269	57.38 199	74.00 143	88.79 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 116	70.50 123	77.65 89	83.40 109	51.29 169	87.32 74	86.40 82	59.01 181	58.49 222	88.32 122	32.40 253	91.27 74	57.04 200	82.15 71	90.38 85

miper_lstm_enhance		63.91 238	62.30 237	68.75 270	75.06 267	46.78 261	69.02 323	81.14 196	59.68 159	52.76 280	72.39 305	40.71 160	77.99 311	56.81 201	53.09 309	81.48 251

PAPM_NR		71.80 123	69.98 134	77.26 101	81.54 158	53.34 116	78.60 268	85.25 109	53.46 261	60.53 190	88.66 116	45.69 97	89.24 126	56.49 202	79.62 101	89.19 114

v10		66.61 219	64.20 228	73.83 178	72.59 293	53.37 114	81.88 219	79.91 214	61.11 135	54.09 271	75.60 278	40.06 169	88.26 165	56.47 203	56.10 285	79.86 278

v1240		66.99 213	64.68 223	73.93 173	71.38 307	52.66 134	83.39 185	79.98 211	61.97 120	58.44 225	77.11 256	35.25 226	87.81 177	56.46 204	58.15 262	81.33 257

Anonymous202405211		70.11 146	67.88 161	76.79 114	87.20 45	47.24 258	89.49 36	77.38 267	54.88 251	66.14 119	86.84 148	20.93 329	91.54 67	56.45 205	71.62 166	91.59 50

Fast-Effi-MVS+		72.73 107	71.15 118	77.48 92	82.75 133	54.76 76	86.77 90	80.64 202	63.05 104	65.93 123	84.01 177	44.42 113	89.03 132	56.45 205	76.36 126	88.64 128

114514_t		69.87 155	67.88 161	75.85 135	88.38 31	52.35 143	86.94 86	83.68 150	53.70 260	55.68 260	85.60 163	30.07 274	91.20 75	55.84 207	71.02 171	83.99 210

tpm2		70.82 137	68.44 152	77.98 81	80.78 177	56.11 43	74.21 292	81.28 195	60.24 150	68.04 102	75.27 280	52.26 37	88.50 154	55.82 208	68.03 191	89.33 108

PCF-MVS	61.03 10	70.10 147	68.40 153	75.22 148	77.15 242	51.99 148	79.30 264	82.12 178	56.47 233	61.88 178	86.48 155	43.98 116	87.24 193	55.37 209	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	73.64 185	84.41 86	51.85 152	84.63 150	77.80 258	66.42 50	59.80 194	84.95 170	22.14 324	80.44 288	55.03 210	75.11 137	88.62 129

CHOSEN 280x420		57.53 286	56.38 279	60.97 323	74.01 278	48.10 244	46.30 356	54.31 356	48.18 294	50.88 294	77.43 252	38.37 182	59.16 359	54.83 211	63.14 228	75.66 316

GG-mvs-BLEND				77.77 85	86.68 48	50.61 176	68.67 324	88.45 47		68.73 96	87.45 138	59.15 11	90.67 91	54.83 211	87.67 17	92.03 40

TAMVS		69.51 164	68.16 157	73.56 188	76.30 251	48.71 226	82.57 203	77.17 270	62.10 117	61.32 181	84.23 175	41.90 148	83.46 267	54.80 213	73.09 154	88.50 133

DWT-MVSNet_test		75.47 71	73.87 80	80.29 25	87.33 43	57.05 25	82.86 198	87.96 54	72.59 7	67.29 107	87.79 132	51.61 40	91.52 68	54.75 214	72.63 158	92.29 33

D2MVS		63.49 242	61.39 245	69.77 258	69.29 319	48.93 220	78.89 266	77.71 261	60.64 146	49.70 297	72.10 310	27.08 292	83.48 266	54.48 215	62.65 232	76.90 304

IterMVS		63.77 241	61.67 241	70.08 254	72.68 292	51.24 170	80.44 247	75.51 290	60.51 147	51.41 288	73.70 291	32.08 257	78.91 301	54.30 216	54.35 300	80.08 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		71.99 120	70.31 127	77.01 106	90.65 9	53.44 111	89.37 37	82.97 168	56.33 234	63.56 161	89.47 101	34.02 237	92.15 57	54.05 217	72.41 160	85.43 191

tpm		68.36 181	67.48 173	70.97 241	79.93 192	51.34 167	76.58 278	78.75 241	67.73 38	63.54 162	74.86 282	48.33 63	72.36 342	53.93 218	63.71 218	89.21 113

XVG-OURS-SEG-HR		62.02 256	59.54 258	69.46 261	65.30 339	45.88 274	65.06 329	73.57 308	46.45 306	57.42 243	83.35 189	26.95 293	78.09 307	53.77 219	64.03 215	84.42 203

cascas		69.01 169	66.13 197	77.66 88	79.36 197	55.41 56	86.99 83	83.75 149	56.69 228	58.92 212	81.35 217	24.31 310	92.10 58	53.23 220	70.61 174	85.46 190

UniMVSNet_NR-MVSNet		68.82 172	68.29 155	70.40 249	75.71 260	42.59 308	84.23 157	86.78 74	66.31 53	58.51 219	82.45 203	51.57 41	84.64 256	53.11 221	55.96 287	83.96 214

DU-MVS		66.84 217	65.74 206	70.16 252	73.27 285	42.59 308	81.50 231	82.92 169	63.53 98	58.51 219	82.11 209	40.75 158	84.64 256	53.11 221	55.96 287	83.24 226

1112_ss		70.05 149	69.37 141	72.10 214	80.77 178	42.78 306	85.12 130	76.75 278	59.69 158	61.19 183	92.12 36	47.48 74	83.84 260	53.04 223	68.21 189	89.66 102

XVG-OURS		61.88 257	59.34 260	69.49 260	65.37 338	46.27 270	64.80 330	73.49 309	47.04 301	57.41 244	82.85 194	25.15 306	78.18 305	53.00 224	64.98 208	84.01 209

thisisatest0530		70.47 144	68.56 150	76.20 126	79.78 193	51.52 162	83.49 180	88.58 45	57.62 210	58.60 218	82.79 195	51.03 46	91.48 69	52.84 225	62.36 236	85.59 189

UGNet		68.71 177	67.11 179	73.50 189	80.55 184	47.61 249	84.08 161	78.51 247	59.45 162	65.68 128	82.73 199	23.78 312	85.08 250	52.80 226	76.40 122	87.80 144
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		66.08 227	63.67 231	73.31 192	83.07 120	48.75 225	86.01 106	84.67 129	45.27 315	56.54 252	76.67 265	28.06 284	88.95 139	52.78 227	59.95 244	82.23 239

无先验							85.19 125	78.00 256	49.08 289				85.13 248	52.78 227		87.45 151

1121		68.79 175	66.77 183	74.82 153	83.08 119	53.46 107	80.23 251	71.53 324	45.47 314	66.31 118	87.19 143	34.02 237	85.13 248	52.78 227	80.36 89	85.87 182

PVSNet_0	57.04 13	61.19 260	57.24 271	73.02 195	77.45 235	50.31 190	79.43 263	77.36 268	63.96 89	47.51 310	72.45 304	25.03 307	83.78 262	52.76 230	19.22 367	84.96 197

FIs		70.00 151	70.24 131	69.30 262	77.93 229	38.55 330	83.99 166	87.72 61	66.86 47	57.66 235	84.17 176	52.28 36	85.31 241	52.72 231	68.80 186	84.02 208

Vis-MVSNet		70.61 141	69.34 142	74.42 160	80.95 175	48.49 232	86.03 105	77.51 264	58.74 187	65.55 129	87.78 133	34.37 234	85.95 233	52.53 232	80.61 83	88.80 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 284	77.59 232	45.46 280		69.20 334	44.47 320	71.50 80	88.34 121	31.21 265	70.76 347	52.20 233	75.88 128	85.03 195

API-MVS		74.17 86	72.07 104	80.49 22	90.02 12	58.55 9	87.30 76	84.27 137	57.51 212	65.77 127	87.77 134	41.61 152	95.97 11	51.71 234	82.63 62	86.94 157

GeoE		69.96 153	67.88 161	76.22 124	81.11 169	51.71 157	84.15 159	76.74 279	59.83 155	60.91 184	84.38 173	41.56 153	88.10 169	51.67 235	70.57 175	88.84 123

ACMM	58.35 12	64.35 236	62.01 240	71.38 233	74.21 277	48.51 231	82.25 212	79.66 219	47.61 296	54.54 266	80.11 226	25.26 304	86.00 229	51.26 236	63.16 227	79.64 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 128	84.89 80	54.59 84		85.26 108	51.98 272	66.70 110	87.07 146	40.15 167	89.70 118	51.23 237	85.06 49	84.10 206

UniMVSNet (Re)		67.71 194	66.80 182	70.45 247	74.44 273	42.93 304	82.42 209	84.90 120	63.69 94	59.63 197	80.99 220	47.18 76	85.23 245	51.17 238	56.75 278	83.19 228

IterMVS-SCA-FT		59.12 271	58.81 265	60.08 325	70.68 313	45.07 283	80.42 248	74.25 300	43.54 328	50.02 296	73.73 288	31.97 258	56.74 360	51.06 239	53.60 305	78.42 289

Test_1112_low_res		67.18 208	66.23 194	70.02 257	78.75 211	41.02 321	83.43 181	73.69 306	57.29 216	58.45 224	82.39 205	45.30 101	80.88 283	50.50 240	66.26 205	88.16 135

pmmvs4		63.34 243	61.07 248	70.16 252	70.14 314	50.53 179	79.97 255	71.41 326	55.08 247	54.12 270	78.58 240	32.79 250	82.09 275	50.33 241	57.22 275	77.86 296

Baseline_NR-MVSNet		65.49 232	64.27 227	69.13 263	74.37 276	41.65 315	83.39 185	78.85 236	59.56 160	59.62 198	76.88 262	40.75 158	87.44 189	49.99 242	55.05 294	78.28 292

UniMVSNet_ETH3D		62.51 251	60.49 252	68.57 274	68.30 328	40.88 323	73.89 293	79.93 213	51.81 276	54.77 263	79.61 229	24.80 308	81.10 280	49.93 243	61.35 239	83.73 218

BH-w/o		70.02 150	68.51 151	74.56 156	82.77 131	50.39 184	86.60 94	78.14 253	59.77 156	59.65 196	85.57 164	39.27 175	87.30 192	49.86 244	74.94 140	85.99 177

LCM-MVSNet-Re		58.82 277	56.54 275	65.68 294	79.31 200	29.09 360	61.39 341	45.79 362	60.73 144	37.65 343	72.47 303	31.42 264	81.08 281	49.66 245	70.41 176	86.87 159

gg-mvs-nofinetune		67.43 201	64.53 225	76.13 128	85.95 52	47.79 248	64.38 331	88.28 49	39.34 337	66.62 112	41.27 359	58.69 14	89.00 135	49.64 246	86.62 31	91.59 50

TranMVSNet+NR-MVSNet		66.94 215	65.61 209	70.93 242	73.45 282	43.38 301	83.02 195	84.25 138	65.31 71	58.33 226	81.90 212	39.92 171	85.52 237	49.43 247	54.89 296	83.89 216

tttt0517		68.33 183	66.29 192	74.46 158	78.08 225	49.06 214	80.88 242	89.08 25	54.40 257	54.75 264	80.77 223	51.31 43	90.33 101	49.35 248	58.01 266	83.99 210

WR-MVS		67.58 196	66.76 184	70.04 256	75.92 258	45.06 286	86.23 100	85.28 107	64.31 80	58.50 221	81.00 219	44.80 111	82.00 276	49.21 249	55.57 293	83.06 231

test_post1							70.84 316				14.72 374	34.33 235	83.86 259	48.80 250

SCA		63.84 239	60.01 256	75.32 144	78.58 217	57.92 12	61.61 339	77.53 263	56.71 227	57.75 234	70.77 316	31.97 258	79.91 296	48.80 250	56.36 279	88.13 138

pmmvs5		62.80 249	61.18 246	67.66 279	69.53 318	42.37 313	82.65 201	75.19 294	54.30 258	52.03 286	78.51 241	31.64 263	80.67 284	48.60 252	58.15 262	79.95 276

新几何1				73.30 193	83.10 116	53.48 106		71.43 325	45.55 312	66.14 119	87.17 144	33.88 241	80.54 286	48.50 253	80.33 90	85.88 181

pm-mvs1		64.12 237	62.56 235	68.78 269	71.68 301	38.87 329	82.89 197	81.57 187	55.54 243	53.89 273	77.82 246	37.73 189	86.74 206	48.46 254	53.49 306	80.72 266

PM-MVS		46.92 322	43.76 326	56.41 334	52.18 364	32.26 352	63.21 335	38.18 368	37.99 342	40.78 335	66.20 332	5.09 370	65.42 353	48.19 255	41.99 341	71.54 342

FC-MVSNet-test		67.49 199	67.91 159	66.21 292	76.06 254	33.06 348	80.82 243	87.18 66	64.44 79	54.81 262	82.87 193	50.40 52	82.60 271	48.05 256	66.55 200	82.98 233

CMPMVS	40.41 21	55.34 298	52.64 301	63.46 308	60.88 356	43.84 296	61.58 340	71.06 327	30.43 356	36.33 345	74.63 284	24.14 311	75.44 325	48.05 256	66.62 199	71.12 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 206	65.99 200	71.04 240	73.27 285	43.91 295	85.32 123	84.75 126	66.05 61	53.65 276	82.11 209	45.05 103	85.97 232	47.55 258	56.18 284	83.24 226

QAPM		71.88 121	69.33 143	79.52 36	82.20 143	54.30 88	86.30 99	88.77 36	56.61 230	59.72 195	87.48 137	33.90 240	95.36 14	47.48 259	81.49 76	88.90 121

EPMVS		68.45 180	65.44 214	77.47 93	84.91 79	56.17 42	71.89 312	81.91 183	61.72 125	60.85 185	72.49 302	36.21 216	87.06 197	47.32 260	71.62 166	89.17 115

GBi-Net		67.09 210	65.47 212	71.96 220	82.71 134	46.36 267	83.52 174	83.31 158	58.55 190	57.58 237	76.23 271	36.72 211	86.20 218	47.25 261	63.40 220	83.32 223

test1		67.09 210	65.47 212	71.96 220	82.71 134	46.36 267	83.52 174	83.31 158	58.55 190	57.58 237	76.23 271	36.72 211	86.20 218	47.25 261	63.40 220	83.32 223

FMVSNet3		68.84 171	67.40 174	73.19 194	85.05 76	48.53 230	85.71 114	85.36 101	60.90 141	57.58 237	79.15 235	42.16 142	86.77 205	47.25 261	63.40 220	84.27 205

v7n		62.50 252	59.27 261	72.20 213	67.25 333	49.83 201	77.87 271	80.12 209	52.50 269	48.80 301	73.07 296	32.10 256	87.90 175	46.83 264	54.92 295	78.86 281

CVMVSNet		60.85 262	60.44 253	62.07 313	75.00 268	32.73 350	79.54 259	73.49 309	36.98 345	56.28 256	83.74 182	29.28 278	69.53 350	46.48 265	63.23 225	83.94 215

TR-MVS		69.71 157	67.85 164	75.27 146	82.94 126	48.48 233	87.40 73	80.86 199	57.15 219	64.61 142	87.08 145	32.67 251	89.64 121	46.38 266	71.55 168	87.68 147

MDTV_nov1_ep13_2view						43.62 298	71.13 315		54.95 250	59.29 205		36.76 208		46.33 267		87.32 153

FMVSNet2		67.57 197	65.79 204	72.90 198	82.71 134	47.97 247	85.15 126	84.93 119	58.55 190	56.71 250	78.26 242	36.72 211	86.67 208	46.15 268	62.94 231	84.07 207

UnsupCasMVSNet_eth		57.56 285	55.15 286	64.79 303	64.57 345	33.12 347	73.17 299	83.87 148	58.98 183	41.75 331	70.03 320	22.54 319	79.92 294	46.12 269	35.31 350	81.32 259

testdata2													77.81 315	45.64 270

XVG-ACMP-BASELINE		56.03 295	52.85 299	65.58 295	61.91 353	40.95 322	63.36 332	72.43 315	45.20 316	46.02 316	74.09 285	9.20 361	78.12 306	45.13 271	58.27 260	77.66 299

AdaColmap		67.86 191	65.48 211	75.00 150	88.15 35	54.99 70	86.10 103	76.63 282	49.30 288	57.80 231	86.65 152	29.39 277	88.94 141	45.10 272	70.21 178	81.06 262

BH-untuned		68.28 184	66.40 189	73.91 174	81.62 153	50.01 197	85.56 118	77.39 266	57.63 209	57.47 242	83.69 184	36.36 215	87.08 196	44.81 273	73.08 155	84.65 200

BH-RMVSNet		70.08 148	68.01 158	76.27 122	84.21 91	51.22 171	87.29 77	79.33 231	58.96 184	63.63 160	86.77 149	33.29 246	90.30 104	44.63 274	73.96 144	87.30 154

IS-MVSNet		68.80 174	67.55 171	72.54 205	78.50 220	43.43 300	81.03 239	79.35 229	59.12 179	57.27 245	86.71 150	46.05 89	87.70 183	44.32 275	75.60 132	86.49 170

pmmvs-eth3d		55.97 296	52.78 300	65.54 296	61.02 355	46.44 266	75.36 286	67.72 337	49.61 287	43.65 322	67.58 329	21.63 326	77.04 318	44.11 276	44.33 336	73.15 335

pmmvs6		59.64 266	57.15 272	67.09 283	66.01 334	36.86 337	80.50 246	78.64 243	45.05 317	49.05 300	73.94 287	27.28 290	86.10 224	43.96 277	49.94 317	78.31 291

EPNet_dtu		66.25 224	66.71 185	64.87 302	78.66 215	34.12 343	82.80 199	75.51 290	61.75 124	64.47 147	86.90 147	37.06 203	72.46 341	43.65 278	69.63 183	88.02 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 223	62.78 234	76.77 115	81.40 164	57.14 24	70.03 319	77.19 269	53.00 265	58.76 217	70.73 318	46.17 86	86.73 207	43.27 279	64.46 214	86.44 171

OpenMVS	61.00 11	69.99 152	67.55 171	77.30 97	78.37 223	54.07 94	84.36 153	85.76 93	57.22 217	56.71 250	87.67 135	30.79 269	92.83 38	43.04 280	84.06 57	85.01 196

MVS_0304		56.72 288	55.17 285	61.37 320	70.71 310	36.80 338	75.74 279	68.75 335	44.11 325	52.53 281	68.20 327	15.05 351	74.53 329	42.98 281	58.44 258	72.79 336

PatchmatchNet		67.07 212	63.63 232	77.40 94	83.10 116	58.03 11	72.11 310	77.77 259	58.85 185	59.37 202	70.83 315	37.84 185	84.93 252	42.96 282	69.83 181	89.26 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 253	59.04 263	72.77 201	73.97 280	56.57 32	60.52 342	71.72 320	60.04 151	57.49 240	65.86 333	38.94 176	80.31 289	42.86 283	59.93 245	81.42 252

FMVSNet1		64.57 234	62.11 239	71.96 220	77.32 236	46.36 267	83.52 174	83.31 158	52.43 270	54.42 267	76.23 271	27.80 287	86.20 218	42.59 284	61.34 240	83.32 223

UA-Net		67.32 205	66.23 194	70.59 245	78.85 209	41.23 320	73.60 294	75.45 292	61.54 129	66.61 113	84.53 172	38.73 179	86.57 214	42.48 285	74.24 142	83.98 212

CL-MVSNet_self_test		62.98 246	61.14 247	68.50 275	65.86 336	42.96 303	84.37 152	82.98 167	60.98 139	53.95 272	72.70 301	40.43 163	83.71 263	41.10 286	47.93 322	78.83 282

MIMVSNet		63.12 245	60.29 254	71.61 228	75.92 258	46.65 263	65.15 328	81.94 180	59.14 177	54.65 265	69.47 322	25.74 300	80.63 285	41.03 287	69.56 184	87.55 148

EG-PatchMatch MVS		62.40 255	59.59 257	70.81 243	73.29 284	49.05 215	85.81 107	84.78 124	51.85 275	44.19 319	73.48 294	15.52 350	89.85 113	40.16 288	67.24 196	73.54 331

UnsupCasMVSNet_bld		53.86 305	50.53 307	63.84 305	63.52 349	34.75 341	71.38 313	81.92 182	46.53 304	38.95 340	57.93 352	20.55 330	80.20 292	39.91 289	34.09 357	76.57 310

dp		64.41 235	61.58 242	72.90 198	82.40 140	54.09 93	72.53 302	76.59 283	60.39 148	55.68 260	70.39 319	35.18 228	76.90 321	39.34 290	61.71 238	87.73 146

TransMVSNet (Re)		62.82 248	60.76 250	69.02 264	73.98 279	41.61 316	86.36 97	79.30 232	56.90 221	52.53 281	76.44 267	41.85 149	87.60 186	38.83 291	40.61 344	77.86 296

USDC		54.36 302	51.23 305	63.76 306	64.29 346	37.71 334	62.84 337	73.48 311	56.85 222	35.47 348	71.94 311	9.23 360	78.43 303	38.43 292	48.57 319	75.13 321

PLC	52.38 18	60.89 261	58.97 264	66.68 290	81.77 149	45.70 278	78.96 265	74.04 303	43.66 327	47.63 307	83.19 192	23.52 315	77.78 316	37.47 293	60.46 243	76.55 311
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 250	62.44 236	62.86 312	72.28 298	29.51 357	82.93 196	78.78 239	59.18 174	53.07 279	82.41 204	36.91 206	77.39 317	37.45 294	58.96 253	81.66 247

OurMVSNet-221017-0		52.39 311	48.73 313	63.35 309	65.21 340	38.42 331	68.54 325	64.95 341	38.19 340	39.57 337	71.43 312	13.23 354	79.92 294	37.16 295	40.32 345	71.72 340

CNLPA		60.59 263	58.44 266	67.05 285	79.21 201	47.26 257	79.75 258	64.34 344	42.46 333	51.90 287	83.94 178	27.79 288	75.41 326	37.12 296	59.49 249	78.47 287

K. test v3		54.04 304	49.42 312	67.92 278	68.55 324	42.57 311	75.51 284	63.07 346	52.07 271	39.21 338	64.59 337	19.34 334	82.21 272	37.11 297	25.31 363	78.97 280

Vis-MVSNet (Re-imp)		65.52 230	65.63 208	65.17 300	77.49 234	30.54 355	75.49 285	77.73 260	59.34 167	52.26 285	86.69 151	49.38 59	80.53 287	37.07 298	75.28 134	84.42 203

PatchMatch-RL		56.66 289	53.75 294	65.37 299	77.91 230	45.28 281	69.78 321	60.38 349	41.35 334	47.57 308	73.73 288	16.83 344	76.91 320	36.99 299	59.21 252	73.92 328

Patchmtry		56.56 291	52.95 298	67.42 281	72.53 294	50.59 178	59.05 345	71.72 320	37.86 343	46.92 311	65.86 333	38.94 176	80.06 293	36.94 300	46.72 331	71.60 341

FMVSNet5		58.61 279	56.45 276	65.10 301	77.20 241	39.74 325	74.77 288	77.12 271	50.27 284	43.28 325	67.71 328	26.15 298	76.90 321	36.78 301	54.78 297	78.65 285

MDTV_nov1_ep13			61.56 243		81.68 150	55.12 65	72.41 304	78.18 252	59.19 172	58.85 215	69.29 323	34.69 232	86.16 221	36.76 302	62.96 230

JIA-IIPM		52.33 312	47.77 318	66.03 293	71.20 308	46.92 260	40.00 363	76.48 284	37.10 344	46.73 312	37.02 361	32.96 247	77.88 313	35.97 303	52.45 311	73.29 333

lessismore_v0				67.98 277	64.76 344	41.25 319		45.75 363		36.03 347	65.63 335	19.29 335	84.11 258	35.67 304	21.24 366	78.59 286

CP-MVSNet		58.54 282	57.57 270	61.46 319	68.50 325	33.96 344	76.90 276	78.60 246	51.67 277	47.83 305	76.60 266	34.99 231	72.79 339	35.45 305	47.58 323	77.64 300

Anonymous20240521		51.65 313	48.42 314	61.34 322	56.43 361	39.65 327	73.57 295	73.47 312	36.64 347	36.59 344	63.98 338	10.75 357	72.25 343	35.35 306	49.01 318	72.11 338

ambc				62.06 314	53.98 363	29.38 358	35.08 365	79.65 220		41.37 332	59.96 347	6.27 368	82.15 273	35.34 307	38.22 348	74.65 323

KD-MVS_2432*1600		59.04 274	56.44 277	66.86 286	79.07 203	45.87 275	72.13 308	80.42 206	55.03 248	48.15 303	71.01 313	36.73 209	78.05 309	35.21 308	30.18 361	76.67 306

miper_refine_blended		59.04 274	56.44 277	66.86 286	79.07 203	45.87 275	72.13 308	80.42 206	55.03 248	48.15 303	71.01 313	36.73 209	78.05 309	35.21 308	30.18 361	76.67 306

PS-CasMVS		58.12 284	57.03 274	61.37 320	68.24 329	33.80 346	76.73 277	78.01 255	51.20 279	47.54 309	76.20 274	32.85 248	72.76 340	35.17 310	47.37 325	77.55 301

EU-MVSNet		52.63 310	50.72 306	58.37 330	62.69 352	28.13 362	72.60 301	75.97 287	30.94 355	40.76 336	72.11 309	20.16 331	70.80 346	35.11 311	46.11 332	76.19 314

ACMH+	54.58 15	58.55 281	55.24 284	68.50 275	74.68 272	45.80 277	80.27 249	70.21 331	47.15 300	42.77 327	75.48 279	16.73 346	85.98 230	35.10 312	54.78 297	73.72 329

pmmvs3		45.53 324	41.55 327	57.44 332	48.97 367	39.68 326	70.06 318	57.66 352	28.32 358	34.06 351	57.29 353	8.50 362	66.85 352	34.86 313	34.26 355	65.80 352

our_test_3		59.11 272	55.08 288	71.18 238	71.42 305	53.29 119	81.96 216	74.52 297	48.32 292	42.08 328	69.28 324	28.14 282	82.15 273	34.35 314	45.68 334	78.11 295

PEN-MVS		58.35 283	57.15 272	61.94 315	67.55 332	34.39 342	77.01 274	78.35 250	51.87 274	47.72 306	76.73 264	33.91 239	73.75 334	34.03 315	47.17 327	77.68 298

KD-MVS_self_test		49.24 317	46.85 320	56.44 333	54.32 362	22.87 366	57.39 347	73.36 313	44.36 322	37.98 342	59.30 350	18.97 336	71.17 345	33.48 316	42.44 340	75.26 319

tpmvs		62.45 254	59.42 259	71.53 232	83.93 96	54.32 87	70.03 319	77.61 262	51.91 273	53.48 277	68.29 326	37.91 184	86.66 209	33.36 317	58.27 260	73.62 330

YYNet1		53.82 306	49.96 309	65.41 298	70.09 316	48.95 218	72.30 305	71.66 322	44.25 323	31.89 357	63.07 341	23.73 313	73.95 332	33.26 318	39.40 346	73.34 332

MDA-MVSNet_test_wron		53.82 306	49.95 310	65.43 297	70.13 315	49.05 215	72.30 305	71.65 323	44.23 324	31.85 358	63.13 340	23.68 314	74.01 331	33.25 319	39.35 347	73.23 334

Anonymous20231206		59.08 273	57.59 269	63.55 307	68.77 323	32.14 353	80.26 250	79.78 216	50.00 285	49.39 298	72.39 305	26.64 295	78.36 304	33.12 320	57.94 267	80.14 274

F-COLMAP		55.96 297	53.65 295	62.87 311	72.76 291	42.77 307	74.70 290	70.37 330	40.03 336	41.11 334	79.36 231	17.77 341	73.70 335	32.80 321	53.96 302	72.15 337

PatchT		56.60 290	52.97 297	67.48 280	72.94 289	46.16 273	57.30 348	73.78 305	38.77 339	54.37 268	57.26 354	37.52 194	78.06 308	32.02 322	52.79 310	78.23 294

SixPastTwentyTwo		54.37 301	50.10 308	67.21 282	70.70 311	41.46 318	74.73 289	64.69 342	47.56 297	39.12 339	69.49 321	18.49 339	84.69 255	31.87 323	34.20 356	75.48 317

WR-MVS_H		58.91 276	58.04 267	61.54 318	69.07 320	33.83 345	76.91 275	81.99 179	51.40 278	48.17 302	74.67 283	40.23 165	74.15 330	31.78 324	48.10 320	76.64 309

ACMH	53.70 16	59.78 265	55.94 282	71.28 234	76.59 246	48.35 236	80.15 254	76.11 286	49.74 286	41.91 330	73.45 295	16.50 347	90.31 102	31.42 325	57.63 273	75.17 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 267	55.14 287	72.32 212	74.69 271	50.71 174	74.39 291	73.58 307	44.44 321	43.40 324	77.52 249	19.45 333	90.87 88	31.31 326	57.49 274	75.38 318

thres200		68.71 177	67.27 177	73.02 195	84.73 81	46.76 262	85.03 135	87.73 60	62.34 114	59.87 192	83.45 187	43.15 131	88.32 161	31.25 327	67.91 193	83.98 212

DTE-MVSNet		57.03 287	55.73 283	60.95 324	65.94 335	32.57 351	75.71 280	77.09 272	51.16 280	46.65 314	76.34 269	32.84 249	73.22 338	30.94 328	44.87 335	77.06 303

ppachtmachnet_test		58.56 280	54.34 289	71.24 235	71.42 305	54.74 77	81.84 221	72.27 317	49.02 290	45.86 318	68.99 325	26.27 296	83.30 268	30.12 329	43.23 339	75.69 315

MVS-HIRNet		49.01 318	44.71 322	61.92 316	76.06 254	46.61 264	63.23 334	54.90 355	24.77 360	33.56 353	36.60 363	21.28 328	75.88 324	29.49 330	62.54 233	63.26 357

test20.03		55.22 299	54.07 292	58.68 329	63.14 350	25.00 364	77.69 272	74.78 296	52.64 267	43.43 323	72.39 305	26.21 297	74.76 328	29.31 331	47.05 329	76.28 313

testgi		54.25 303	52.57 302	59.29 327	62.76 351	21.65 369	72.21 307	70.47 329	53.25 264	41.94 329	77.33 253	14.28 352	77.95 312	29.18 332	51.72 313	78.28 292

thres100view900		66.87 216	65.42 215	71.24 235	83.29 112	43.15 302	81.67 224	87.78 57	59.04 180	55.92 258	82.18 208	43.73 121	87.80 178	28.80 333	66.36 202	82.78 236

tfpn200view9		67.57 197	66.13 197	71.89 227	84.05 94	45.07 283	83.40 183	87.71 62	60.79 142	57.79 232	82.76 196	43.53 126	87.80 178	28.80 333	66.36 202	82.78 236

thres400		67.40 204	66.13 197	71.19 237	84.05 94	45.07 283	83.40 183	87.71 62	60.79 142	57.79 232	82.76 196	43.53 126	87.80 178	28.80 333	66.36 202	80.71 267

ADS-MVSNet2		55.21 300	51.44 304	66.51 291	80.60 182	49.56 206	55.03 350	65.44 340	44.72 318	51.00 291	61.19 344	22.83 316	75.41 326	28.54 336	53.63 303	74.57 324

ADS-MVSNet		56.17 294	51.95 303	68.84 266	80.60 182	53.07 125	55.03 350	70.02 332	44.72 318	51.00 291	61.19 344	22.83 316	78.88 302	28.54 336	53.63 303	74.57 324

LTVRE_ROB	45.45 19	52.73 309	49.74 311	61.69 317	69.78 317	34.99 340	44.52 357	67.60 338	43.11 330	43.79 321	74.03 286	18.54 338	81.45 278	28.39 338	57.94 267	68.62 347
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		48.21 319	46.55 321	53.18 337	57.73 359	18.19 373	70.24 317	71.02 328	45.70 311	33.70 352	60.23 346	18.00 340	69.86 349	27.97 339	34.35 354	71.49 343

OpenMVS_ROB	53.19 17	59.20 270	56.00 281	68.83 267	71.13 309	44.30 291	83.64 173	75.02 295	46.42 307	46.48 315	73.03 297	18.69 337	88.14 166	27.74 340	61.80 237	74.05 327

RPSCF		45.77 323	44.13 325	50.68 339	57.67 360	29.66 356	54.92 352	45.25 364	26.69 359	45.92 317	75.92 277	17.43 343	45.70 368	27.44 341	45.95 333	76.67 306

MDA-MVSNet-bldmvs		51.56 314	47.75 319	63.00 310	71.60 303	47.32 256	69.70 322	72.12 318	43.81 326	27.65 362	63.38 339	21.97 325	75.96 323	27.30 342	32.19 358	65.70 353

RPMNet		59.29 268	54.25 291	74.42 160	73.97 280	56.57 32	60.52 342	76.98 273	35.72 349	57.49 240	58.87 351	37.73 189	85.26 244	27.01 343	59.93 245	81.42 252

thres600view7		66.46 221	65.12 219	70.47 246	83.41 106	43.80 297	82.15 213	87.78 57	59.37 166	56.02 257	82.21 207	43.73 121	86.90 203	26.51 344	64.94 209	80.71 267

TAPA-MVS	56.12 14	61.82 258	60.18 255	66.71 288	78.48 221	37.97 333	75.19 287	76.41 285	46.82 303	57.04 246	86.52 154	27.67 289	77.03 319	26.50 345	67.02 198	85.14 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 338	58.03 358	31.94 354		53.57 359	36.67 346	41.32 333	75.23 281	11.17 356	51.57 364	25.81 346	48.04 321	72.02 339

Patchmatch-test		53.33 308	48.17 315	68.81 268	73.31 283	42.38 312	42.98 359	58.23 351	32.53 354	38.79 341	70.77 316	39.66 172	73.51 336	25.18 347	52.06 312	90.55 79

TinyColmap		48.15 320	44.49 324	59.13 328	65.73 337	38.04 332	63.34 333	62.86 347	38.78 338	29.48 360	67.23 331	6.46 367	73.30 337	24.59 348	41.90 342	66.04 351

AllTest		47.32 321	44.66 323	55.32 335	65.08 341	37.50 335	62.96 336	54.25 357	35.45 351	33.42 354	72.82 298	9.98 358	59.33 357	24.13 349	43.84 337	69.13 345

TestCases				55.32 335	65.08 341	37.50 335		54.25 357	35.45 351	33.42 354	72.82 298	9.98 358	59.33 357	24.13 349	43.84 337	69.13 345

N_pmnet		41.25 325	39.77 328	45.66 343	68.50 325	0.82 382	72.51 303	0.38 382	35.61 350	35.26 349	61.51 343	20.07 332	67.74 351	23.51 351	40.63 343	68.42 348

DP-MVS		59.24 269	56.12 280	68.63 272	88.24 34	50.35 187	82.51 206	64.43 343	41.10 335	46.70 313	78.77 238	24.75 309	88.57 152	22.26 352	56.29 283	66.96 350

MIMVSNet1		50.35 316	47.81 317	57.96 331	61.53 354	27.80 363	67.40 326	74.06 302	43.25 329	33.31 356	65.38 336	16.03 348	71.34 344	21.80 353	47.55 324	74.75 322

tfpnnormal		61.47 259	59.09 262	68.62 273	76.29 252	41.69 314	81.14 238	85.16 113	54.48 256	51.32 289	73.63 292	32.32 254	86.89 204	21.78 354	55.71 291	77.29 302

LF4IMVS		33.04 332	32.55 332	34.52 350	40.96 370	22.03 368	44.45 358	35.62 371	20.42 362	28.12 361	62.35 342	5.03 371	31.88 374	21.61 355	34.42 353	49.63 362

COLMAP_ROB	43.60 20	50.90 315	48.05 316	59.47 326	67.81 331	40.57 324	71.25 314	62.72 348	36.49 348	36.19 346	73.51 293	13.48 353	73.92 333	20.71 356	50.26 316	63.92 355
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 333	23.85 338	40.71 345	27.46 378	18.93 372	30.82 366	46.19 361	12.76 368	16.40 365	34.70 366	1.90 377	48.69 367	20.25 357	24.22 364	54.51 360

DSMNet-mixed		38.35 327	35.36 330	47.33 342	48.11 368	14.91 375	37.87 364	36.60 370	19.18 364	34.37 350	59.56 349	15.53 349	53.01 363	20.14 358	46.89 330	74.07 326

new_pmnet		33.56 331	31.89 333	38.59 347	49.01 366	20.42 370	51.01 353	37.92 369	20.58 361	23.45 363	46.79 358	6.66 366	49.28 366	20.00 359	31.57 360	46.09 364

LS3D		56.40 293	53.82 293	64.12 304	81.12 168	45.69 279	73.42 297	66.14 339	35.30 353	43.24 326	79.88 227	22.18 323	79.62 298	19.10 360	64.00 216	67.05 349

test_method		24.09 337	21.07 341	33.16 351	27.67 377	8.35 380	26.63 367	35.11 373	3.40 373	14.35 367	36.98 362	3.46 374	35.31 373	19.08 361	22.95 365	55.81 359

TDRefinement		40.91 326	38.37 329	48.55 341	50.45 365	33.03 349	58.98 346	50.97 360	28.50 357	29.89 359	67.39 330	6.21 369	54.51 361	17.67 362	35.25 351	58.11 358

test_0402		56.45 292	53.03 296	66.69 289	76.78 245	50.31 190	81.76 222	69.61 333	42.79 331	43.88 320	72.13 308	22.82 318	86.46 215	16.57 363	50.94 315	63.31 356

PMMVS2		26.71 335	22.98 339	37.87 348	36.89 372	8.51 379	42.51 360	29.32 376	19.09 365	13.01 368	37.54 360	2.23 375	53.11 362	14.54 364	11.71 368	51.99 361

ANet_high		34.39 329	29.59 335	48.78 340	30.34 375	22.28 367	55.53 349	63.79 345	38.11 341	15.47 366	36.56 364	6.94 364	59.98 356	13.93 365	5.64 375	64.08 354

tmp_tt		9.44 342	10.68 345	5.73 358	2.49 381	4.21 381	10.48 371	18.04 378	0.34 375	12.59 369	20.49 369	11.39 355	7.03 377	13.84 366	6.46 374	5.95 372

EGC-MVSNET		33.75 330	30.42 334	43.75 344	64.94 343	36.21 339	60.47 344	40.70 367	0.02 376	0.10 377	53.79 356	7.39 363	60.26 355	11.09 367	35.23 352	34.79 365

FPMVS		35.40 328	33.67 331	40.57 346	46.34 369	28.74 361	41.05 361	57.05 353	20.37 363	22.27 364	53.38 357	6.87 365	44.94 369	8.62 368	47.11 328	48.01 363

Gipuma		27.47 334	24.26 337	37.12 349	60.55 357	29.17 359	11.68 370	60.00 350	14.18 367	10.52 371	15.12 372	2.20 376	63.01 354	8.39 369	35.65 349	19.18 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 341	13.39 344	29.16 353	28.43 376	19.72 371	13.73 369	23.63 377	7.23 372	7.96 372	21.41 368	0.80 380	36.08 372	6.97 370	10.39 369	31.69 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 356	21.34 380	8.99 378		10.02 380	10.59 370	7.53 373	30.55 367	1.82 378	14.55 375	6.83 371	7.52 371	15.75 369

E-PMN		19.16 338	18.40 342	21.44 354	36.19 373	13.63 376	47.59 354	30.89 374	10.73 369	5.91 374	16.59 370	3.66 373	39.77 370	5.95 372	8.14 370	10.92 370

PMVS	19.57 22	25.07 336	22.43 340	32.99 352	23.12 379	22.98 365	40.98 362	35.19 372	15.99 366	11.95 370	35.87 365	1.47 379	49.29 365	5.41 373	31.90 359	26.70 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 339	17.66 343	20.71 355	34.13 374	12.64 377	46.94 355	29.94 375	10.46 371	5.58 375	14.93 373	4.23 372	38.83 371	5.24 374	7.51 372	10.67 371

wuyk23d		9.11 343	8.77 347	10.15 357	40.18 371	16.76 374	20.28 368	1.01 381	2.58 374	2.66 376	0.98 376	0.23 381	12.49 376	4.08 375	6.90 373	1.19 373

testmvs		6.14 345	8.18 348	0.01 359	0.01 382	0.00 384	73.40 298	0.00 383	0.00 377	0.02 378	0.15 377	0.00 382	0.00 378	0.02 376	0.00 376	0.02 374

test123		6.01 346	8.01 349	0.01 359	0.00 383	0.01 383	71.93 311	0.00 383	0.00 377	0.02 378	0.11 378	0.00 382	0.00 378	0.02 376	0.00 376	0.02 374

test_blank		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

cdsmvs_eth3d_5k		18.33 340	24.44 336	0.00 361	0.00 383	0.00 384	0.00 372	89.40 19	0.00 377	0.00 380	92.02 39	38.55 180	0.00 378	0.00 378	0.00 376	0.00 376

pcd_1.5k_mvsjas		3.15 347	4.20 350	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	37.77 186	0.00 378	0.00 378	0.00 376	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

ab-mvs-re		7.68 344	10.24 346	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	92.12 36	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 383	0.00 384	0.00 372	0.00 383	0.00 377	0.00 380	0.00 379	0.00 382	0.00 378	0.00 378	0.00 376	0.00 376

FOURS1					83.24 113	49.90 200	84.98 137	78.76 240	47.71 295	73.42 53

test_one_0601					89.39 24	57.29 22		88.09 51	57.21 218	82.06 11	93.39 13	54.94 25

eth-test2					0.00 383

eth-test					0.00 383

test_241102_ONE					89.48 19	56.89 28		88.94 29	57.53 211	84.61 4	93.29 16	58.81 12	96.45 1

save fliter					85.35 70	56.34 39	89.31 39	81.46 189	61.55 127

test0726					89.40 22	57.45 19	92.32 7	88.63 40	57.71 207	83.14 9	93.96 7	55.17 21

GSMVS																88.13 138

test_part2					89.33 25	55.48 53				82.27 10

sam_mvs1												38.86 178				88.13 138

sam_mvs												35.99 222

MTGPA								81.31 192

test_post											16.22 371	37.52 194	84.72 254

patchmatchnet-post											59.74 348	38.41 181	79.91 296

MTMP							87.27 78	15.34 379

TEST9					85.68 57	55.42 54	87.59 67	84.00 144	57.72 206	72.99 57	90.98 60	44.87 107	88.58 149

test_8					85.72 56	55.31 58	87.60 64	83.88 147	57.84 204	72.84 60	90.99 59	44.99 104	88.34 159

agg_prior					85.64 60	54.92 72		83.61 154		72.53 64			88.10 169

test_prior4						56.39 38	87.15 81

test_prior				78.39 70	86.35 50	54.91 74		85.45 97					89.70 118			90.55 79

新几何2							81.61 227

旧先验1					81.57 157	47.48 252		71.83 319			88.66 116	36.94 205			78.34 110	88.67 127

原ACMM2							83.77 171

test222					79.36 197	50.97 172	77.99 270	67.84 336	42.54 332	62.84 168	86.53 153	30.26 272			76.91 120	85.23 193

segment_acmp												44.97 106

testdata1							77.55 273		64.14 83

test12				79.24 40	86.89 46	56.08 44		85.16 113		72.27 71		47.15 77	91.10 79		85.93 37	90.54 82

plane_prior7					77.95 227	48.46 235

plane_prior6					78.42 222	49.39 210						36.04 220

plane_prior4											83.28 190

plane_prior3						48.95 218			64.01 86	62.15 174

plane_prior2							85.76 109		63.60 96

plane_prior1					78.31 224

plane_prior						49.57 204	87.43 71		64.57 78						72.84 156

n2								0.00 383

nn								0.00 383

door-mid								41.31 366

test11								84.25 138

door								43.27 365

HQP5-MVS						51.56 160

HQP-NCC					79.02 205		88.00 58		65.45 64	64.48 144

ACMP_Plane					79.02 205		88.00 58		65.45 64	64.48 144

HQP4-MVS										64.47 147			88.61 148			84.91 198

HQP3-MVS								83.68 150							73.12 152

HQP2-MVS												37.35 197

NP-MVS					78.76 210	50.43 182					85.12 168

ACMMP++_ref															63.20 226

ACMMP++															59.38 250

Test By Simon												39.38 173

High-resolution multi-view benchmark