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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 32	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 12	85.07 49	99.27 1	99.54 1

DTE-MVSNet		89.98 46	91.91 15	84.21 150	96.51 8	57.84 279	88.93 77	92.84 88	91.92 2	96.16 2	96.23 18	86.95 49	95.99 8	79.05 113	98.57 15	98.80 6

PS-CasMVS		90.06 42	91.92 13	84.47 144	96.56 7	58.83 274	89.04 74	92.74 91	91.40 4	96.12 3	96.06 22	87.23 46	95.57 33	79.42 111	98.74 6	99.00 2

LCM-MVSNet-Re		83.48 154	85.06 120	78.75 244	85.94 237	55.75 295	80.05 234	94.27 20	76.47 123	96.09 4	94.54 56	83.31 82	89.75 230	59.95 276	94.89 160	90.75 201

PEN-MVS		90.03 44	91.88 16	84.48 143	96.57 6	58.88 271	88.95 75	93.19 72	91.62 3	96.01 5	96.16 20	87.02 48	95.60 32	78.69 116	98.72 9	98.97 3

CP-MVSNet		89.27 60	90.91 42	84.37 145	96.34 9	58.61 276	88.66 84	92.06 105	90.78 5	95.67 6	95.17 39	81.80 107	95.54 38	79.00 114	98.69 10	98.95 4

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 63	85.72 31	96.79 1	95.51 5	88.86 13	95.63 7	96.99 8	84.81 68	93.16 134	91.10 1	97.53 71	96.58 29
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
WR-MVS_H		89.91 49	91.31 31	85.71 122	96.32 10	62.39 232	89.54 66	93.31 65	90.21 9	95.57 8	95.66 28	81.42 111	95.90 13	80.94 92	98.80 3	98.84 5

OurMVSNet-221017-0		90.01 45	89.74 54	90.83 37	93.16 76	80.37 68	91.91 32	93.11 74	81.10 74	95.32 9	97.24 5	72.94 196	94.85 68	85.07 49	97.78 53	97.26 15

anonymousdsp		89.73 52	88.88 69	92.27 9	89.82 164	86.67 15	90.51 45	90.20 159	69.87 209	95.06 10	96.14 21	84.28 72	93.07 139	87.68 13	96.34 109	97.09 19

wuyk23d		75.13 253	79.30 209	62.63 329	75.56 336	75.18 120	80.89 225	73.10 317	75.06 146	94.76 11	95.32 34	87.73 41	52.85 357	34.16 356	97.11 83	59.85 353

ACMH	76.49 14	89.34 59	91.14 34	83.96 156	92.50 92	70.36 163	89.55 64	93.84 45	81.89 66	94.70 12	95.44 33	90.69 9	88.31 249	83.33 68	98.30 26	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 84	87.45 83	86.45 102	92.52 91	69.19 175	87.84 94	88.05 195	81.66 68	94.64 13	96.53 14	65.94 236	94.75 70	83.02 72	96.83 92	95.41 50

mvs_tets		89.78 51	89.27 62	91.30 28	93.51 66	84.79 39	89.89 56	90.63 142	70.00 208	94.55 14	96.67 11	87.94 38	93.59 116	84.27 59	95.97 121	95.52 48

abl_6		93.02 4	93.16 4	92.60 4	94.73 42	88.99 7	93.26 10	94.19 27	89.11 11	94.43 15	95.27 36	91.86 3	95.09 60	87.54 18	98.02 38	93.71 108

jajsoiax		89.41 57	88.81 71	91.19 32	93.38 70	84.72 40	89.70 58	90.29 156	69.27 212	94.39 16	96.38 15	86.02 63	93.52 120	83.96 61	95.92 123	95.34 52

test_0402		88.65 69	89.58 58	85.88 118	92.55 90	72.22 146	84.01 152	89.44 174	88.63 17	94.38 17	95.77 24	86.38 59	93.59 116	79.84 104	95.21 146	91.82 179

UniMVSNet_ETH3D		89.12 63	90.72 45	84.31 148	97.00 2	64.33 210	89.67 61	88.38 189	88.84 14	94.29 18	97.57 3	90.48 14	91.26 185	72.57 184	97.65 60	97.34 14

v7n		90.13 40	90.96 40	87.65 88	91.95 109	71.06 158	89.99 53	93.05 77	86.53 26	94.29 18	96.27 17	82.69 87	94.08 95	86.25 37	97.63 61	97.82 8

test_djsdf		89.62 53	89.01 65	91.45 25	92.36 95	82.98 53	91.98 29	90.08 162	71.54 190	94.28 20	96.54 13	81.57 109	94.27 82	86.26 35	96.49 104	97.09 19

PS-MVSNAJss		88.31 72	87.90 77	89.56 59	93.31 72	77.96 90	87.94 92	91.97 108	70.73 199	94.19 21	96.67 11	76.94 154	94.57 78	83.07 70	96.28 111	96.15 32

SED-MVS		90.46 37	91.64 19	86.93 93	94.18 48	72.65 133	90.47 46	93.69 49	83.77 41	94.11 22	94.27 69	90.28 15	95.84 19	86.03 41	97.92 46	92.29 161

test_241102_ONE					94.18 48	72.65 133		93.69 49	83.62 43	94.11 22	93.78 97	90.28 15	95.50 45

DPE-MVS		90.53 35	91.08 35	88.88 66	93.38 70	78.65 85	89.15 73	94.05 35	84.68 35	93.90 24	94.11 79	88.13 35	96.30 3	84.51 57	97.81 52	91.70 182
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 80	78.04 89	92.84 14	94.14 32	83.33 48	93.90 24	95.73 25	88.77 26	96.41 1	87.60 16	97.98 42	92.98 133

ACMH+	77.89 11	90.73 30	91.50 23	88.44 76	93.00 79	76.26 115	89.65 62	95.55 4	87.72 22	93.89 26	94.94 44	91.62 4	93.44 124	78.35 120	98.76 4	95.61 47

ACMM	79.39 9	90.65 31	90.99 39	89.63 56	95.03 34	83.53 47	89.62 63	93.35 61	79.20 96	93.83 27	93.60 101	90.81 8	92.96 140	85.02 51	98.45 19	92.41 155
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 53	88.95 8	92.87 12	94.16 28	88.75 15	93.79 28	94.43 61	88.83 24	95.51 42	87.16 25	97.60 64	92.73 142

RE-MVS-def			92.61 5		94.13 53	88.95 8	92.87 12	94.16 28	88.75 15	93.79 28	94.43 61	90.64 11		87.16 25	97.60 64	92.73 142

DVP-MVS		90.06 42	91.32 30	86.29 106	94.16 51	72.56 138	90.54 43	91.01 134	83.61 44	93.75 30	94.65 53	89.76 19	95.78 24	86.42 31	97.97 43	90.55 208
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_THIRD									85.33 30	93.75 30	94.65 53	87.44 44	95.78 24	87.41 19	98.21 30	92.98 133

test0726					94.16 51	72.56 138	90.63 42	93.90 41	83.61 44	93.75 30	94.49 58	89.76 19

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 40	81.69 58	90.00 51	94.27 20	82.35 60	93.67 33	94.82 48	91.18 5	95.52 39	85.36 47	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 40	81.69 58		94.27 20	82.35 60	93.67 33	94.82 48	91.18 5	95.52 39	85.36 47	98.73 7	95.23 58

test1172		92.40 7	92.41 7	92.37 6	94.68 43	89.04 6	91.98 29	93.62 52	90.14 10	93.63 35	94.16 76	88.83 24	95.51 42	87.11 27	97.54 70	92.54 152

test_part1		87.15 85	87.82 78	85.15 131	88.88 177	63.04 222	87.98 90	94.85 14	82.52 58	93.61 36	95.73 25	67.51 227	95.71 28	80.48 100	98.83 2	96.69 25

test_241102_TWO								93.71 48	83.77 41	93.49 37	94.27 69	89.27 22	95.84 19	86.03 41	97.82 51	92.04 170

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 38	87.85 11	92.51 22	93.87 44	88.20 20	93.24 38	94.02 82	90.15 17	95.67 30	86.82 29	97.34 77	92.19 167

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 78	85.17 34	92.47 24	95.05 12	87.65 23	93.21 39	94.39 67	90.09 18	95.08 61	86.67 30	97.60 64	94.18 88

Anonymous20231211		88.40 71	89.62 57	84.73 138	90.46 153	65.27 201	88.86 78	93.02 81	87.15 24	93.05 40	97.10 6	82.28 95	92.02 165	76.70 140	97.99 40	96.88 23

MP-MVS-pluss		90.81 29	91.08 35	89.99 51	95.97 13	79.88 71	88.13 89	94.51 18	75.79 136	92.94 41	94.96 43	88.36 28	95.01 63	90.70 2	98.40 20	95.09 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 66	89.88 52	86.22 109	91.63 119	77.07 105	89.82 57	93.77 46	78.90 100	92.88 42	92.29 135	86.11 61	90.22 217	86.24 38	97.24 80	91.36 191
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
ACMP	79.16 10	90.54 34	90.60 47	90.35 46	94.36 45	80.98 64	89.16 72	94.05 35	79.03 99	92.87 43	93.74 98	90.60 12	95.21 57	82.87 73	98.76 4	94.87 63
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 2	96.46 2	90.58 7	92.86 44	96.29 16	88.16 34	94.17 91	86.07 40	98.48 18	97.22 17

SMA-MVS		90.31 38	90.48 48	89.83 52	95.31 30	79.52 77	90.98 39	93.24 71	75.37 143	92.84 45	95.28 35	85.58 65	96.09 7	87.92 10	97.76 54	93.88 99
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
test_part2					93.86 60	77.77 92				92.84 45

v10		86.54 92	87.10 87	84.84 135	88.16 191	63.28 219	86.64 114	92.20 102	75.42 142	92.81 47	94.50 57	74.05 181	94.06 96	83.88 62	96.28 111	97.17 18

v8		86.22 99	86.83 94	84.36 146	87.82 196	62.35 234	86.42 117	91.33 125	76.78 122	92.73 48	94.48 59	73.41 190	93.72 109	83.10 69	95.41 138	97.01 21

nrg030		87.85 78	88.49 73	85.91 116	90.07 160	69.73 166	87.86 93	94.20 25	74.04 155	92.70 49	94.66 52	85.88 64	91.50 176	79.72 105	97.32 78	96.50 30

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 59	80.97 65	91.49 35	93.48 58	82.82 55	92.60 50	93.97 84	88.19 32	96.29 4	87.61 15	98.20 32	94.39 81
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 50	89.97 51	89.27 62	94.76 39	79.86 72	86.76 111	92.78 90	78.78 102	92.51 51	93.64 100	88.13 35	93.84 105	84.83 54	97.55 67	94.10 92

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 15	85.81 30	92.99 11	94.23 23	85.21 31	92.51 51	95.13 40	90.65 10	95.34 50	88.06 9	98.15 33	95.95 40

K. test v3		85.14 114	84.73 126	86.37 103	91.13 138	69.63 168	85.45 130	76.68 292	84.06 39	92.44 53	96.99 8	62.03 254	94.65 73	80.58 98	93.24 195	94.83 67

ACMMP		91.91 12	91.87 17	92.03 13	95.53 26	85.91 25	93.35 9	94.16 28	82.52 58	92.39 54	94.14 77	89.15 23	95.62 31	87.35 20	98.24 28	94.56 72
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
xxxxxxxxxxxxxcwj		89.04 65	89.13 63	88.79 68	93.75 61	77.44 97	86.31 119	95.27 9	70.80 197	92.28 55	93.80 94	86.89 50	94.64 74	85.52 45	97.51 72	94.30 84

SF-MVS		90.27 39	90.80 44	88.68 72	92.86 84	77.09 104	91.19 38	95.74 3	81.38 71	92.28 55	93.80 94	86.89 50	94.64 74	85.52 45	97.51 72	94.30 84

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 64	86.15 21	93.37 8	95.10 11	90.28 8	92.11 57	95.03 42	89.75 21	94.93 65	79.95 103	98.27 27	95.04 62
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 46	89.84 53	90.41 44	94.91 37	84.50 44	89.49 68	93.98 37	79.68 89	92.09 58	93.89 92	83.80 76	93.10 138	82.67 75	98.04 35	93.64 112

TranMVSNet+NR-MVSNet		87.86 77	88.76 72	85.18 130	94.02 56	64.13 211	84.38 147	91.29 126	84.88 34	92.06 59	93.84 93	86.45 57	93.73 108	73.22 174	98.66 11	97.69 9

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 17	88.94 76	91.81 114	84.07 37	92.00 60	94.40 65	86.63 53	95.28 53	88.59 5	98.31 24	92.30 159

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 17	92.02 28	91.81 114	84.07 37	92.00 60	94.40 65	86.63 53	95.28 53	88.59 5	98.31 24	92.30 159

ACMMP_NAP		90.65 31	91.07 37	89.42 60	95.93 15	79.54 76	89.95 54	93.68 51	77.65 113	91.97 62	94.89 45	88.38 27	95.45 46	89.27 3	97.87 50	93.27 122

FC-MVSNet-test		85.93 105	87.05 89	82.58 187	92.25 100	56.44 290	85.75 125	93.09 75	77.33 116	91.94 63	94.65 53	74.78 173	93.41 126	75.11 157	98.58 14	97.88 7

lessismore_v0				85.95 115	91.10 139	70.99 159		70.91 330		91.79 64	94.42 63	61.76 255	92.93 142	79.52 110	93.03 201	93.93 97

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 25	84.67 41	93.51 6	94.85 14	82.88 54	91.77 65	93.94 91	90.55 13	95.73 27	88.50 7	98.23 29	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 25	90.95 41	91.93 15	95.67 22	85.85 28	90.00 51	93.90 41	80.32 82	91.74 66	94.41 64	88.17 33	95.98 9	86.37 33	97.99 40	93.96 96

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 12	88.48 10	92.72 16	92.60 94	83.09 51	91.54 67	94.25 73	87.67 42	95.51 42	87.21 24	98.11 34	93.12 128

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 28	84.66 42	92.58 20	93.29 68	81.99 63	91.47 68	93.96 87	88.35 29	95.56 34	87.74 11	97.74 56	92.85 137

#test#		90.49 36	90.31 50	91.02 33	95.43 28	84.66 42	90.65 41	93.29 68	77.00 120	91.47 68	93.96 87	88.35 29	95.56 34	84.88 52	97.74 56	92.85 137

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 19	85.88 27	92.58 20	93.25 70	81.99 63	91.40 70	94.17 75	87.51 43	95.87 15	87.74 11	97.76 54	93.99 94

GST-MVS		90.96 28	91.01 38	90.82 38	95.45 27	82.73 55	91.75 33	93.74 47	80.98 76	91.38 71	93.80 94	87.20 47	95.80 21	87.10 28	97.69 59	93.93 97

ANet_high		83.17 161	85.68 112	75.65 281	81.24 288	45.26 348	79.94 236	92.91 84	83.83 40	91.33 72	96.88 10	80.25 123	85.92 278	68.89 215	95.89 124	95.76 42

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 20	83.05 52	92.18 26	94.22 24	80.14 85	91.29 73	93.97 84	87.93 39	95.87 15	88.65 4	97.96 45	94.12 91

APD-MVS		89.54 55	89.63 56	89.26 63	92.57 89	81.34 63	90.19 49	93.08 76	80.87 77	91.13 74	93.19 105	86.22 60	95.97 10	82.23 79	97.18 82	90.45 210
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 61		91.84 115		88.80 80	95.32 8	75.14 145	91.07 75	92.89 115	87.27 45	93.78 107	83.69 65	97.55 67

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 31	87.62 12	93.38 7	93.36 60	83.16 50	91.06 76	94.00 83	88.26 31	95.71 28	87.28 23	98.39 21	92.55 151

FIs		85.35 111	86.27 100	82.60 186	91.86 114	57.31 283	85.10 134	93.05 77	75.83 135	91.02 77	93.97 84	73.57 186	92.91 144	73.97 165	98.02 38	97.58 12

UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 113	92.86 84	67.02 188	82.55 194	91.56 118	83.08 52	90.92 78	91.82 147	78.25 138	93.99 97	74.16 162	98.35 22	97.49 13

DU-MVS		86.80 89	86.99 90	86.21 111	93.24 74	67.02 188	83.16 181	92.21 101	81.73 67	90.92 78	91.97 141	77.20 148	93.99 97	74.16 162	98.35 22	97.61 10

V42		83.47 155	83.37 152	83.75 161	83.16 273	63.33 218	81.31 218	90.23 158	69.51 211	90.91 80	90.81 176	74.16 179	92.29 157	80.06 101	90.22 253	95.62 46

region2R		91.44 20	91.30 32	91.87 19	95.75 18	85.90 26	92.63 19	93.30 67	81.91 65	90.88 81	94.21 74	87.75 40	95.87 15	87.60 16	97.71 58	93.83 101

WR-MVS		83.56 152	84.40 138	81.06 211	93.43 69	54.88 301	78.67 257	85.02 242	81.24 72	90.74 82	91.56 154	72.85 197	91.08 191	68.00 223	98.04 35	97.23 16

v1240		84.30 134	84.51 135	83.65 163	87.65 201	61.26 243	82.85 188	91.54 119	67.94 228	90.68 83	90.65 182	71.71 210	93.64 111	82.84 74	94.78 162	96.07 35

ETH3D-3000-0.1		88.85 68	88.96 68	88.52 73	91.94 111	77.27 103	88.71 82	95.26 10	76.08 127	90.66 84	92.69 122	84.48 71	93.83 106	83.38 67	97.48 74	94.47 76

testtj		89.51 56	89.48 59	89.59 58	92.26 99	80.80 66	90.14 50	93.54 56	83.37 47	90.57 85	92.55 127	84.99 67	96.15 5	81.26 87	96.61 99	91.83 178

ZD-MVS					92.22 102	80.48 67		91.85 111	71.22 194	90.38 86	92.98 110	86.06 62	96.11 6	81.99 81	96.75 95

MIMVSNet1		83.63 151	84.59 132	80.74 216	94.06 55	62.77 226	82.72 190	84.53 248	77.57 115	90.34 87	95.92 23	76.88 160	85.83 280	61.88 262	97.42 75	93.62 113

LS3D		90.60 33	90.34 49	91.38 27	89.03 173	84.23 45	93.58 4	94.68 16	90.65 6	90.33 88	93.95 90	84.50 70	95.37 49	80.87 93	95.50 137	94.53 75

DIV-MVS_2432*1600		81.93 178	83.14 156	78.30 253	84.75 248	52.75 313	80.37 231	89.42 175	70.24 206	90.26 89	93.39 103	74.55 178	86.77 266	68.61 219	96.64 97	95.38 51

PMVS	80.48 6	90.08 41	90.66 46	88.34 79	96.71 3	92.97 1	90.31 48	89.57 172	88.51 18	90.11 90	95.12 41	90.98 7	88.92 239	77.55 132	97.07 84	83.13 304
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v1921920		84.23 138	84.37 139	83.79 159	87.64 202	61.71 238	82.91 187	91.20 129	67.94 228	90.06 91	90.34 187	72.04 207	93.59 116	82.32 78	94.91 158	96.07 35

ITE_SJBPF				90.11 50	90.72 147	84.97 36		90.30 153	81.56 69	90.02 92	91.20 161	82.40 91	90.81 201	73.58 171	94.66 166	94.56 72

XVS		91.54 15	91.36 26	92.08 10	95.64 23	86.25 19	92.64 17	93.33 62	85.07 32	89.99 93	94.03 81	86.57 55	95.80 21	87.35 20	97.62 62	94.20 86

X-MVStestdata		85.04 118	82.70 162	92.08 10	95.64 23	86.25 19	92.64 17	93.33 62	85.07 32	89.99 93	16.05 360	86.57 55	95.80 21	87.35 20	97.62 62	94.20 86

v1192		84.57 126	84.69 130	84.21 150	87.75 198	62.88 224	83.02 184	91.43 122	69.08 215	89.98 95	90.89 173	72.70 200	93.62 115	82.41 76	94.97 157	96.13 33

Anonymous20240529		86.20 100	87.13 86	83.42 168	90.19 157	64.55 208	84.55 142	90.71 139	85.85 29	89.94 96	95.24 38	82.13 97	90.40 212	69.19 212	96.40 107	95.31 54

Regformer-2		86.74 90	86.08 104	88.73 69	84.18 261	79.20 79	83.52 167	89.33 176	83.33 48	89.92 97	85.07 272	83.23 83	93.16 134	83.39 66	92.72 210	93.83 101

pmmvs6		86.52 93	88.06 76	81.90 196	92.22 102	62.28 235	84.66 140	89.15 178	83.54 46	89.85 98	97.32 4	88.08 37	86.80 265	70.43 201	97.30 79	96.62 27

Regformer-4		86.41 94	85.71 111	88.52 73	84.27 257	77.57 95	84.07 150	88.00 197	82.82 55	89.84 99	85.48 260	82.06 99	92.77 146	83.83 64	91.04 236	95.22 60

v144192		84.24 137	84.41 137	83.71 162	87.59 203	61.57 239	82.95 186	91.03 133	67.82 231	89.80 100	90.49 185	73.28 193	93.51 121	81.88 84	94.89 160	96.04 37

v1144		84.54 129	84.72 128	84.00 154	87.67 200	62.55 230	82.97 185	90.93 135	70.32 204	89.80 100	90.99 168	73.50 187	93.48 122	81.69 85	94.65 167	95.97 38

DeepC-MVS	82.31 4	89.15 62	89.08 64	89.37 61	93.64 65	79.07 80	88.54 85	94.20 25	73.53 161	89.71 102	94.82 48	85.09 66	95.77 26	84.17 60	98.03 37	93.26 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 75	86.93 92	91.22 31	90.08 159	89.30 5	89.68 60	91.11 131	79.26 95	89.68 103	94.81 51	82.44 90	87.74 253	76.54 142	88.74 269	96.61 28

IU-MVS					94.18 48	72.64 135		90.82 137	56.98 304	89.67 104				85.78 44	97.92 46	93.28 121

FMVSNet1		84.55 127	85.45 116	81.85 198	90.27 156	61.05 246	86.83 108	88.27 192	78.57 106	89.66 105	95.64 29	75.43 165	90.68 205	69.09 213	95.33 141	93.82 103

IterMVS-LS		84.73 123	84.98 122	83.96 156	87.35 206	63.66 214	83.25 177	89.88 166	76.06 128	89.62 106	92.37 133	73.40 192	92.52 151	78.16 124	94.77 164	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 70	89.01 65	87.36 91	91.30 130	77.50 96	87.55 96	92.97 83	87.95 21	89.62 106	92.87 116	84.56 69	93.89 102	77.65 130	96.62 98	90.70 202

UniMVSNet (Re)		86.87 86	86.98 91	86.55 100	93.11 77	68.48 179	83.80 161	92.87 85	80.37 80	89.61 108	91.81 148	77.72 142	94.18 89	75.00 158	98.53 16	96.99 22

IS-MVSNet		86.66 91	86.82 95	86.17 113	92.05 107	66.87 190	91.21 37	88.64 185	86.30 28	89.60 109	92.59 124	69.22 219	94.91 66	73.89 166	97.89 49	96.72 24

v2v482		84.09 140	84.24 141	83.62 164	87.13 211	61.40 240	82.71 191	89.71 168	72.19 185	89.55 110	91.41 157	70.70 215	93.20 131	81.02 90	93.76 184	96.25 31

Baseline_NR-MVSNet		84.00 144	85.90 107	78.29 254	91.47 128	53.44 309	82.29 202	87.00 217	79.06 98	89.55 110	95.72 27	77.20 148	86.14 276	72.30 186	98.51 17	95.28 55

CSCG		86.26 97	86.47 97	85.60 124	90.87 143	74.26 124	87.98 90	91.85 111	80.35 81	89.54 112	88.01 222	79.09 131	92.13 159	75.51 151	95.06 153	90.41 211

ambc				82.98 176	90.55 152	64.86 204	88.20 87	89.15 178		89.40 113	93.96 87	71.67 211	91.38 184	78.83 115	96.55 101	92.71 145

DeepPCF-MVS	81.24 5	87.28 83	86.21 102	90.49 43	91.48 127	84.90 37	83.41 172	92.38 99	70.25 205	89.35 114	90.68 180	82.85 86	94.57 78	79.55 107	95.95 122	92.00 172

Regformer-1		86.00 102	85.50 115	87.49 89	84.18 261	76.90 107	83.52 167	87.94 199	82.18 62	89.19 115	85.07 272	82.28 95	91.89 169	82.40 77	92.72 210	93.69 109

MSP-MVS		89.08 64	88.16 75	91.83 21	95.76 17	86.14 22	92.75 15	93.90 41	78.43 107	89.16 116	92.25 137	72.03 208	96.36 2	88.21 8	90.93 242	92.98 133
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
XVG-OURS		89.18 61	88.83 70	90.23 48	94.28 46	86.11 23	85.91 122	93.60 55	80.16 84	89.13 117	93.44 102	83.82 75	90.98 193	83.86 63	95.30 145	93.60 114

ETH3D cwj APD-0.16		87.83 79	87.62 81	88.47 75	91.21 133	78.20 87	87.26 100	94.54 17	72.05 186	88.89 118	92.31 134	83.86 74	94.24 85	81.59 86	96.87 89	92.97 136

MDA-MVSNet-bldmvs		77.47 230	76.90 234	79.16 240	79.03 315	64.59 205	66.58 336	75.67 298	73.15 172	88.86 119	88.99 210	66.94 230	81.23 309	64.71 244	88.22 276	91.64 184

EG-PatchMatch MVS		84.08 141	84.11 142	83.98 155	92.22 102	72.61 137	82.20 208	87.02 214	72.63 179	88.86 119	91.02 167	78.52 134	91.11 190	73.41 173	91.09 234	88.21 242

3Dnovator+	83.92 2	89.97 48	89.66 55	90.92 36	91.27 132	81.66 61	91.25 36	94.13 33	88.89 12	88.83 121	94.26 72	77.55 145	95.86 18	84.88 52	95.87 125	95.24 57

EI-MVSNet-UG-set		85.04 118	84.44 136	86.85 95	83.87 267	72.52 140	83.82 159	85.15 238	80.27 83	88.75 122	85.45 263	79.95 127	91.90 168	81.92 83	90.80 246	96.13 33

EI-MVSNet-Vis-set		85.12 115	84.53 134	86.88 94	84.01 264	72.76 132	83.91 157	85.18 237	80.44 79	88.75 122	85.49 259	80.08 124	91.92 167	82.02 80	90.85 245	95.97 38

OMC-MVS		88.19 73	87.52 82	90.19 49	91.94 111	81.68 60	87.49 98	93.17 73	76.02 130	88.64 124	91.22 159	84.24 73	93.37 127	77.97 128	97.03 85	95.52 48

Regformer-3		85.06 117	84.67 131	86.22 109	84.27 257	73.43 128	84.07 150	85.26 235	80.77 78	88.62 125	85.48 260	80.56 120	90.39 213	81.99 81	91.04 236	94.85 65

UA-Net		91.49 17	91.53 22	91.39 26	94.98 35	82.95 54	93.52 5	92.79 89	88.22 19	88.53 126	97.64 2	83.45 80	94.55 80	86.02 43	98.60 13	96.67 26

MP-MVS		91.14 27	90.91 42	91.83 21	96.18 11	86.88 14	92.20 25	93.03 80	82.59 57	88.52 127	94.37 68	86.74 52	95.41 48	86.32 34	98.21 30	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		85.50 109	86.14 103	83.58 165	87.97 192	67.13 187	87.55 96	94.32 19	73.44 163	88.47 128	87.54 231	86.45 57	91.06 192	75.76 150	93.76 184	92.54 152

NR-MVSNet		86.00 102	86.22 101	85.34 128	93.24 74	64.56 207	82.21 206	90.46 145	80.99 75	88.42 129	91.97 141	77.56 144	93.85 103	72.46 185	98.65 12	97.61 10

alignmvs		83.94 146	83.98 145	83.80 158	87.80 197	67.88 184	84.54 144	91.42 124	73.27 170	88.41 130	87.96 223	72.33 203	90.83 200	76.02 148	94.11 178	92.69 146

TransMVSNet (Re)		84.02 143	85.74 110	78.85 242	91.00 140	55.20 300	82.29 202	87.26 205	79.65 90	88.38 131	95.52 32	83.00 84	86.88 263	67.97 224	96.60 100	94.45 79

PM-MVS		80.20 205	79.00 211	83.78 160	88.17 190	86.66 16	81.31 218	66.81 342	69.64 210	88.33 132	90.19 191	64.58 239	83.63 299	71.99 189	90.03 254	81.06 330

tttt0517		81.07 186	79.58 207	85.52 125	88.99 175	66.45 195	87.03 105	75.51 300	73.76 159	88.32 133	90.20 190	37.96 351	94.16 94	79.36 112	95.13 149	95.93 41

casdiffmvs		85.21 112	85.85 108	83.31 170	86.17 234	62.77 226	83.03 183	93.93 39	74.69 149	88.21 134	92.68 123	82.29 94	91.89 169	77.87 129	93.75 186	95.27 56

v148		82.31 169	82.48 168	81.81 201	85.59 240	59.66 261	81.47 216	86.02 225	72.85 176	88.05 135	90.65 182	70.73 214	90.91 197	75.15 156	91.79 225	94.87 63

AllTest		87.97 76	87.40 85	89.68 54	91.59 120	83.40 48	89.50 67	95.44 6	79.47 91	88.00 136	93.03 108	82.66 88	91.47 177	70.81 193	96.14 117	94.16 89

TestCases				89.68 54	91.59 120	83.40 48		95.44 6	79.47 91	88.00 136	93.03 108	82.66 88	91.47 177	70.81 193	96.14 117	94.16 89

pm-mvs1		83.69 149	84.95 123	79.91 228	90.04 162	59.66 261	82.43 198	87.44 202	75.52 139	87.85 138	95.26 37	81.25 113	85.65 282	68.74 217	96.04 120	94.42 80

PCF-MVS	74.62 15	82.15 173	80.92 189	85.84 119	89.43 166	72.30 144	80.53 229	91.82 113	57.36 302	87.81 139	89.92 197	77.67 143	93.63 112	58.69 281	95.08 152	91.58 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 183	81.61 178	80.41 222	86.38 224	58.75 275	83.93 156	86.58 220	72.43 180	87.65 140	92.98 110	63.78 245	90.22 217	66.86 228	93.92 182	92.27 163

VPA-MVSNet		83.47 155	84.73 126	79.69 233	90.29 155	57.52 282	81.30 220	88.69 184	76.29 124	87.58 141	94.44 60	80.60 119	87.20 258	66.60 232	96.82 93	94.34 83

CPTT-MVS		89.39 58	88.98 67	90.63 41	95.09 33	86.95 13	92.09 27	92.30 100	79.74 88	87.50 142	92.38 130	81.42 111	93.28 129	83.07 70	97.24 80	91.67 183

VDDNet		84.35 132	85.39 117	81.25 206	95.13 32	59.32 264	85.42 131	81.11 266	86.41 27	87.41 143	96.21 19	73.61 185	90.61 208	66.33 233	96.85 90	93.81 106

cl_fuxian		81.64 180	81.59 179	81.79 202	80.86 294	59.15 268	78.61 258	90.18 160	68.36 221	87.20 144	87.11 240	69.39 217	91.62 174	78.16 124	94.43 172	94.60 71

VDD-MVS		84.23 138	84.58 133	83.20 172	91.17 137	65.16 203	83.25 177	84.97 245	79.79 87	87.18 145	94.27 69	74.77 174	90.89 198	69.24 209	96.54 102	93.55 118

MSLP-MVS++		85.00 120	86.03 105	81.90 196	91.84 115	71.56 156	86.75 112	93.02 81	75.95 133	87.12 146	89.39 201	77.98 139	89.40 235	77.46 133	94.78 162	84.75 281

baseline		85.20 113	85.93 106	83.02 175	86.30 229	62.37 233	84.55 142	93.96 38	74.48 152	87.12 146	92.03 140	82.30 93	91.94 166	78.39 118	94.21 175	94.74 68

YYNet1		70.06 292	70.44 288	68.90 311	73.76 346	53.42 310	58.99 350	67.20 338	58.42 293	87.10 148	85.39 265	59.82 267	67.32 343	59.79 277	83.50 315	85.96 267

MDA-MVSNet_test_wron		70.05 293	70.44 288	68.88 312	73.84 345	53.47 308	58.93 351	67.28 337	58.43 292	87.09 149	85.40 264	59.80 268	67.25 344	59.66 278	83.54 314	85.92 269

CNVR-MVS		87.81 80	87.68 80	88.21 81	92.87 82	77.30 102	85.25 132	91.23 128	77.31 117	87.07 150	91.47 156	82.94 85	94.71 71	84.67 55	96.27 113	92.62 149

EPP-MVSNet		85.47 110	85.04 121	86.77 97	91.52 126	69.37 169	91.63 34	87.98 198	81.51 70	87.05 151	91.83 146	66.18 235	95.29 51	70.75 196	96.89 88	95.64 45

RRT_MVS		83.25 158	81.08 186	89.74 53	80.55 301	79.32 78	86.41 118	86.69 218	72.33 184	87.00 152	91.08 164	44.98 335	95.55 37	84.47 58	96.24 114	94.36 82

TinyColmap		81.25 184	82.34 170	77.99 259	85.33 243	60.68 253	82.32 201	88.33 190	71.26 193	86.97 153	92.22 139	77.10 151	86.98 262	62.37 257	95.17 148	86.31 265

eth_miper_zixun_eth		80.84 190	80.22 199	82.71 184	81.41 286	60.98 249	77.81 267	90.14 161	67.31 235	86.95 154	87.24 237	64.26 241	92.31 155	75.23 155	91.61 228	94.85 65

Anonymous20240521		80.18 206	81.25 182	76.95 268	83.15 274	60.84 251	82.46 197	85.99 226	68.76 219	86.78 155	93.73 99	59.13 272	77.44 320	73.71 169	97.55 67	92.56 150

Patchmatch-RL test		74.48 261	73.68 261	76.89 271	84.83 247	66.54 194	72.29 316	69.16 335	57.70 298	86.76 156	86.33 248	45.79 325	82.59 302	69.63 206	90.65 251	81.54 321

XVG-OURS-SEG-HR		89.59 54	89.37 60	90.28 47	94.47 44	85.95 24	86.84 107	93.91 40	80.07 86	86.75 157	93.26 104	93.64 2	90.93 195	84.60 56	90.75 247	93.97 95

hse-mvs3		84.25 136	82.76 161	88.72 70	91.82 117	82.60 56	84.00 153	84.98 244	71.27 192	86.70 158	90.55 184	63.04 251	93.92 101	78.26 123	94.20 176	89.63 220

HPM-MVS++		88.93 67	88.45 74	90.38 45	94.92 36	85.85 28	89.70 58	91.27 127	78.20 109	86.69 159	92.28 136	80.36 122	95.06 62	86.17 39	96.49 104	90.22 213

TSAR-MVS + MP.		88.14 74	87.82 78	89.09 65	95.72 21	76.74 109	92.49 23	91.19 130	67.85 230	86.63 160	94.84 47	79.58 129	95.96 11	87.62 14	94.50 169	94.56 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet		82.61 165	82.42 169	83.20 172	83.25 271	63.66 214	83.50 170	85.07 239	76.06 128	86.55 161	85.10 269	73.41 190	90.25 214	78.15 126	90.67 249	95.68 44

HQP_MVS		87.75 81	87.43 84	88.70 71	93.45 67	76.42 113	89.45 69	93.61 53	79.44 93	86.55 161	92.95 113	74.84 171	95.22 55	80.78 95	95.83 126	94.46 77

plane_prior3						76.85 108			77.79 112	86.55 161

BH-untuned		80.96 188	80.99 187	80.84 215	88.55 182	68.23 180	80.33 232	88.46 186	72.79 177	86.55 161	86.76 243	74.72 175	91.77 173	61.79 263	88.99 264	82.52 310

MVSTER		77.09 234	75.70 245	81.25 206	75.27 340	61.08 245	77.49 274	85.07 239	60.78 283	86.55 161	88.68 214	43.14 341	90.25 214	73.69 170	90.67 249	92.42 154

旧先验2							81.73 211		56.88 305	86.54 166			84.90 288	72.81 182

IterMVS-SCA-FT		80.64 194	79.41 208	84.34 147	83.93 265	69.66 167	76.28 289	81.09 267	72.43 180	86.47 167	90.19 191	60.46 260	93.15 136	77.45 134	86.39 292	90.22 213

cl-mvsnet1		80.43 197	80.23 197	81.02 212	79.99 304	59.25 265	77.07 278	87.02 214	67.38 233	86.19 168	89.22 204	63.09 249	90.16 219	76.32 143	95.80 128	93.66 110

CDPH-MVS		86.17 101	85.54 114	88.05 84	92.25 100	75.45 118	83.85 158	92.01 106	65.91 245	86.19 168	91.75 150	83.77 77	94.98 64	77.43 135	96.71 96	93.73 107

cl-mvsnet_		80.42 198	80.23 197	81.02 212	79.99 304	59.25 265	77.07 278	87.02 214	67.37 234	86.18 170	89.21 205	63.08 250	90.16 219	76.31 144	95.80 128	93.65 111

ETH3 D test6400		85.09 116	84.87 124	85.75 121	90.80 145	69.34 170	85.90 123	93.31 65	65.43 252	86.11 171	89.95 195	80.92 115	94.86 67	75.90 149	95.57 135	93.05 130

MVS_111021_LR		84.28 135	83.76 148	85.83 120	89.23 170	83.07 51	80.99 224	83.56 251	72.71 178	86.07 172	89.07 209	81.75 108	86.19 275	77.11 138	93.36 190	88.24 241

GBi-Net		82.02 175	82.07 172	81.85 198	86.38 224	61.05 246	86.83 108	88.27 192	72.43 180	86.00 173	95.64 29	63.78 245	90.68 205	65.95 235	93.34 191	93.82 103

test1		82.02 175	82.07 172	81.85 198	86.38 224	61.05 246	86.83 108	88.27 192	72.43 180	86.00 173	95.64 29	63.78 245	90.68 205	65.95 235	93.34 191	93.82 103

FMVSNet3		78.80 217	78.55 215	79.57 235	82.89 277	56.89 288	81.76 210	85.77 228	69.04 216	86.00 173	90.44 186	51.75 303	90.09 225	65.95 235	93.34 191	91.72 181

miper_ehance_all_eth		80.34 201	80.04 204	81.24 208	79.82 306	58.95 270	77.66 269	89.66 169	65.75 249	85.99 176	85.11 268	68.29 224	91.42 181	76.03 147	92.03 221	93.33 119

tfpnnormal		81.79 179	82.95 159	78.31 252	88.93 176	55.40 296	80.83 227	82.85 255	76.81 121	85.90 177	94.14 77	74.58 177	86.51 270	66.82 231	95.68 134	93.01 132

TAPA-MVS	77.73 12	85.71 108	84.83 125	88.37 78	88.78 179	79.72 73	87.15 103	93.50 57	69.17 213	85.80 178	89.56 200	80.76 117	92.13 159	73.21 179	95.51 136	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 145	82.69 163	87.72 86	89.27 169	81.45 62	83.72 163	81.58 265	74.73 148	85.66 179	86.06 253	72.56 202	92.69 148	75.44 153	95.21 146	89.01 237

EU-MVSNet		75.12 254	74.43 256	77.18 266	83.11 275	59.48 263	85.71 127	82.43 258	39.76 356	85.64 180	88.76 212	44.71 337	87.88 252	73.86 167	85.88 296	84.16 287

LF4IMVS		82.75 164	81.93 175	85.19 129	82.08 279	80.15 70	85.53 129	88.76 183	68.01 225	85.58 181	87.75 227	71.80 209	86.85 264	74.02 164	93.87 183	88.58 240

Patchmtry		76.56 242	77.46 226	73.83 290	79.37 312	46.60 345	82.41 199	76.90 289	73.81 158	85.56 182	92.38 130	48.07 313	83.98 296	63.36 252	95.31 144	90.92 197

MVS_111021_HR		84.63 124	84.34 140	85.49 127	90.18 158	75.86 117	79.23 250	87.13 209	73.35 164	85.56 182	89.34 202	83.60 79	90.50 210	76.64 141	94.05 180	90.09 218

testdata				79.54 236	92.87 82	72.34 143		80.14 274	59.91 289	85.47 184	91.75 150	67.96 226	85.24 284	68.57 221	92.18 220	81.06 330

thisisatest0530		79.07 213	77.33 229	84.26 149	87.13 211	64.58 206	83.66 165	75.95 295	68.86 218	85.22 185	87.36 235	38.10 349	93.57 119	75.47 152	94.28 174	94.62 69

CLD-MVS		83.18 160	82.64 164	84.79 136	89.05 172	67.82 185	77.93 265	92.52 95	68.33 222	85.07 186	81.54 311	82.06 99	92.96 140	69.35 208	97.91 48	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	80.27 8	86.23 98	85.65 113	87.96 85	91.30 130	76.92 106	87.19 101	91.99 107	70.56 200	84.96 187	90.69 179	80.01 125	95.14 58	78.37 119	95.78 130	91.82 179
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 122	84.71 129	85.06 133	86.36 227	74.71 121	88.77 81	90.00 164	75.65 138	84.96 187	93.17 106	74.06 180	91.19 187	78.28 122	91.09 234	89.29 229

QAPM		82.59 166	82.59 166	82.58 187	86.44 222	66.69 192	89.94 55	90.36 149	67.97 227	84.94 189	92.58 126	72.71 199	92.18 158	70.63 199	87.73 281	88.85 238

VPNet		80.25 203	81.68 176	75.94 280	92.46 93	47.98 340	76.70 282	81.67 264	73.45 162	84.87 190	92.82 117	74.66 176	86.51 270	61.66 265	96.85 90	93.33 119

NCCC		87.36 82	86.87 93	88.83 67	92.32 98	78.84 83	86.58 115	91.09 132	78.77 103	84.85 191	90.89 173	80.85 116	95.29 51	81.14 89	95.32 142	92.34 157

PHI-MVS		86.38 95	85.81 109	88.08 82	88.44 185	77.34 100	89.35 71	93.05 77	73.15 172	84.76 192	87.70 228	78.87 133	94.18 89	80.67 97	96.29 110	92.73 142

pmmvs-eth3d		78.42 221	77.04 232	82.57 189	87.44 205	74.41 123	80.86 226	79.67 276	55.68 308	84.69 193	90.31 189	60.91 258	85.42 283	62.20 259	91.59 229	87.88 249

RRT_test8_iter05		78.08 224	77.52 225	79.75 231	80.84 295	52.54 317	80.61 228	88.96 180	67.77 232	84.62 194	89.29 203	33.89 356	92.10 162	77.59 131	94.15 177	94.62 69

test_prior3		86.31 96	86.31 99	86.32 104	90.59 150	71.99 148	83.37 173	92.85 86	75.43 140	84.58 195	91.57 152	81.92 105	94.17 91	79.54 108	96.97 86	92.80 139

test_prior2							83.37 173		75.43 140	84.58 195	91.57 152	81.92 105		79.54 108	96.97 86

TEST9					92.34 96	79.70 74	83.94 154	90.32 150	65.41 256	84.49 197	90.97 169	82.03 101	93.63 112

train_agg		85.98 104	85.28 118	88.07 83	92.34 96	79.70 74	83.94 154	90.32 150	65.79 246	84.49 197	90.97 169	81.93 103	93.63 112	81.21 88	96.54 102	90.88 198

Gipuma		84.44 130	86.33 98	78.78 243	84.20 260	73.57 127	89.55 64	90.44 146	84.24 36	84.38 199	94.89 45	76.35 163	80.40 313	76.14 146	96.80 94	82.36 312
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 105	78.87 82	83.82 159	90.31 152	65.79 246	84.36 200	90.96 171	81.93 103	93.44 124

cl-mvsnet2		78.97 214	78.21 220	81.24 208	77.74 320	59.01 269	77.46 275	87.13 209	65.79 246	84.32 201	85.10 269	58.96 274	90.88 199	75.36 154	92.03 221	93.84 100

agg_prior1		85.72 107	85.20 119	87.28 92	91.58 123	77.69 93	83.69 164	90.30 153	66.29 242	84.32 201	91.07 166	82.13 97	93.18 132	81.02 90	96.36 108	90.98 194

agg_prior					91.58 123	77.69 93		90.30 153		84.32 201			93.18 132

Anonymous202405211		80.51 196	81.19 185	78.49 249	88.48 183	57.26 284	76.63 283	82.49 257	81.21 73	84.30 204	92.24 138	67.99 225	86.24 274	62.22 258	95.13 149	91.98 175

LFMVS		80.15 207	80.56 191	78.89 241	89.19 171	55.93 292	85.22 133	73.78 311	82.96 53	84.28 205	92.72 121	57.38 284	90.07 226	63.80 249	95.75 131	90.68 203

Vis-MVSNet		86.86 87	86.58 96	87.72 86	92.09 105	77.43 99	87.35 99	92.09 104	78.87 101	84.27 206	94.05 80	78.35 137	93.65 110	80.54 99	91.58 230	92.08 169
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		84.31 133	83.91 147	85.52 125	88.58 181	70.40 162	84.50 146	93.37 59	78.76 104	84.07 207	78.72 330	80.39 121	95.13 59	73.82 168	92.98 203	91.04 193

MCST-MVS		84.36 131	83.93 146	85.63 123	91.59 120	71.58 155	83.52 167	92.13 103	61.82 273	83.96 208	89.75 199	79.93 128	93.46 123	78.33 121	94.34 173	91.87 177

新几何1				82.95 177	93.96 57	78.56 86		80.24 272	55.45 309	83.93 209	91.08 164	71.19 213	88.33 248	65.84 238	93.07 199	81.95 317

BH-RMVSNet		80.53 195	80.22 199	81.49 204	87.19 210	66.21 196	77.79 268	86.23 222	74.21 154	83.69 210	88.50 216	73.25 194	90.75 202	63.18 254	87.90 278	87.52 252

1121		80.86 189	79.81 206	84.02 153	93.93 58	78.70 84	81.64 213	80.18 273	55.43 310	83.67 211	91.15 162	71.29 212	91.41 182	67.95 225	93.06 200	81.96 316

USDC		76.63 240	76.73 236	76.34 277	83.46 269	57.20 285	80.02 235	88.04 196	52.14 328	83.65 212	91.25 158	63.24 248	86.65 269	54.66 306	94.11 178	85.17 276

miper_enhance_ethall		77.83 226	76.93 233	80.51 220	76.15 333	58.01 278	75.47 297	88.82 181	58.05 296	83.59 213	80.69 315	64.41 240	91.20 186	73.16 180	92.03 221	92.33 158

Effi-MVS+-dtu		85.82 106	83.38 151	93.14 3	87.13 211	91.15 2	87.70 95	88.42 187	74.57 150	83.56 214	85.65 257	78.49 135	94.21 87	72.04 187	92.88 205	94.05 93

CNLPA		83.55 153	83.10 157	84.90 134	89.34 168	83.87 46	84.54 144	88.77 182	79.09 97	83.54 215	88.66 215	74.87 170	81.73 307	66.84 230	92.29 215	89.11 231

bset_n11_16_dypcd		79.19 212	77.97 222	82.86 182	85.81 238	66.85 191	75.02 300	79.31 277	66.07 243	83.50 216	83.37 293	55.04 298	92.10 162	78.63 117	94.99 156	89.63 220

OpenMVS_ROB	70.19 17	77.77 229	77.46 226	78.71 245	84.39 254	61.15 244	81.18 222	82.52 256	62.45 270	83.34 217	87.37 234	66.20 234	88.66 245	64.69 245	85.02 303	86.32 264

thres100view900		75.45 250	75.05 250	76.66 274	87.27 207	51.88 322	81.07 223	73.26 315	75.68 137	83.25 218	86.37 247	45.54 326	88.80 240	51.98 319	90.99 238	89.31 227

miper_lstm_enhance		76.45 244	76.10 241	77.51 264	76.72 328	60.97 250	64.69 339	85.04 241	63.98 262	83.20 219	88.22 219	56.67 287	78.79 318	73.22 174	93.12 198	92.78 141

IterMVS		76.91 236	76.34 239	78.64 246	80.91 292	64.03 212	76.30 288	79.03 280	64.88 259	83.11 220	89.16 206	59.90 266	84.46 292	68.61 219	85.15 302	87.42 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 247	75.35 249	77.85 262	87.01 217	51.84 323	80.45 230	73.26 315	75.20 144	83.10 221	86.31 250	45.54 326	89.05 236	55.03 304	92.24 217	92.66 147

mvs_anonymous		78.13 223	78.76 213	76.23 279	79.24 313	50.31 334	78.69 256	84.82 246	61.60 277	83.09 222	92.82 117	73.89 183	87.01 259	68.33 222	86.41 291	91.37 190

MVS_Test		82.47 168	83.22 153	80.22 225	82.62 278	57.75 281	82.54 195	91.96 109	71.16 195	82.89 223	92.52 129	77.41 146	90.50 210	80.04 102	87.84 280	92.40 156

test12				86.57 99	90.74 146	72.63 136		90.69 140		82.76 224		79.20 130	94.80 69		95.32 142	92.27 163

原ACMM1				84.60 141	92.81 87	74.01 125		91.50 120	62.59 267	82.73 225	90.67 181	76.53 161	94.25 84	69.24 209	95.69 133	85.55 272

test_yl		78.71 219	78.51 216	79.32 238	84.32 255	58.84 272	78.38 259	85.33 233	75.99 131	82.49 226	86.57 244	58.01 278	90.02 227	62.74 255	92.73 208	89.10 232

DCV-MVSNet		78.71 219	78.51 216	79.32 238	84.32 255	58.84 272	78.38 259	85.33 233	75.99 131	82.49 226	86.57 244	58.01 278	90.02 227	62.74 255	92.73 208	89.10 232

diffmvs		80.40 199	80.48 194	80.17 226	79.02 316	60.04 257	77.54 272	90.28 157	66.65 240	82.40 228	87.33 236	73.50 187	87.35 257	77.98 127	89.62 258	93.13 127

test222					93.31 72	76.54 110	79.38 245	77.79 285	52.59 323	82.36 229	90.84 175	66.83 232			91.69 227	81.25 325

D2MVS		76.84 237	75.67 246	80.34 223	80.48 302	62.16 237	73.50 310	84.80 247	57.61 300	82.24 230	87.54 231	51.31 304	87.65 254	70.40 202	93.19 197	91.23 192

VNet		79.31 211	80.27 196	76.44 275	87.92 194	53.95 305	75.58 295	84.35 249	74.39 153	82.23 231	90.72 178	72.84 198	84.39 293	60.38 275	93.98 181	90.97 195

Vis-MVSNet (Re-imp)		77.82 227	77.79 224	77.92 260	88.82 178	51.29 327	83.28 175	71.97 324	74.04 155	82.23 231	89.78 198	57.38 284	89.41 234	57.22 289	95.41 138	93.05 130

API-MVS		82.28 170	82.61 165	81.30 205	86.29 230	69.79 164	88.71 82	87.67 201	78.42 108	82.15 233	84.15 284	77.98 139	91.59 175	65.39 241	92.75 207	82.51 311

DP-MVS Recon		84.05 142	83.22 153	86.52 101	91.73 118	75.27 119	83.23 179	92.40 97	72.04 187	82.04 234	88.33 218	77.91 141	93.95 100	66.17 234	95.12 151	90.34 212

MSDG		80.06 209	79.99 205	80.25 224	83.91 266	68.04 183	77.51 273	89.19 177	77.65 113	81.94 235	83.45 290	76.37 162	86.31 273	63.31 253	86.59 289	86.41 263

Fast-Effi-MVS+		81.04 187	80.57 190	82.46 191	87.50 204	63.22 220	78.37 261	89.63 170	68.01 225	81.87 236	82.08 306	82.31 92	92.65 149	67.10 227	88.30 275	91.51 189

testgi		72.36 276	74.61 252	65.59 323	80.56 300	42.82 354	68.29 329	73.35 314	66.87 238	81.84 237	89.93 196	72.08 206	66.92 346	46.05 341	92.54 212	87.01 259

tfpn200view9		74.86 258	74.23 257	76.74 273	86.24 231	52.12 319	79.24 248	73.87 309	73.34 165	81.82 238	84.60 280	46.02 320	88.80 240	51.98 319	90.99 238	89.31 227

thres400		75.14 252	74.23 257	77.86 261	86.24 231	52.12 319	79.24 248	73.87 309	73.34 165	81.82 238	84.60 280	46.02 320	88.80 240	51.98 319	90.99 238	92.66 147

CL-MVSNet_2432*1600		76.81 238	77.38 228	75.12 284	86.90 219	51.34 325	73.20 313	80.63 271	68.30 223	81.80 240	88.40 217	66.92 231	80.90 310	55.35 301	94.90 159	93.12 128

OpenMVS	76.72 13	81.98 177	82.00 174	81.93 195	84.42 253	68.22 181	88.50 86	89.48 173	66.92 237	81.80 240	91.86 143	72.59 201	90.16 219	71.19 192	91.25 233	87.40 254

AdaColmap		83.66 150	83.69 149	83.57 166	90.05 161	72.26 145	86.29 121	90.00 164	78.19 110	81.65 242	87.16 238	83.40 81	94.24 85	61.69 264	94.76 165	84.21 286

DELS-MVS		81.44 182	81.25 182	82.03 194	84.27 257	62.87 225	76.47 287	92.49 96	70.97 196	81.64 243	83.83 285	75.03 168	92.70 147	74.29 160	92.22 219	90.51 209
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
114514_t		83.10 162	82.54 167	84.77 137	92.90 81	69.10 177	86.65 113	90.62 143	54.66 313	81.46 244	90.81 176	76.98 153	94.38 81	72.62 183	96.18 115	90.82 200

TR-MVS		76.77 239	75.79 243	79.72 232	86.10 236	65.79 199	77.14 276	83.02 253	65.20 257	81.40 245	82.10 305	66.30 233	90.73 204	55.57 298	85.27 300	82.65 306

TAMVS		78.08 224	76.36 238	83.23 171	90.62 149	72.87 131	79.08 251	80.01 275	61.72 275	81.35 246	86.92 242	63.96 244	88.78 243	50.61 323	93.01 202	88.04 245

Effi-MVS+		83.90 147	84.01 144	83.57 166	87.22 209	65.61 200	86.55 116	92.40 97	78.64 105	81.34 247	84.18 283	83.65 78	92.93 142	74.22 161	87.87 279	92.17 168

CS-MVS		83.43 157	83.04 158	84.59 142	87.87 195	66.61 193	85.57 128	94.90 13	73.02 174	81.12 248	78.56 331	80.00 126	95.52 39	73.04 181	93.29 194	91.62 185

new-patchmatchnet		70.10 291	73.37 265	60.29 336	81.23 289	16.95 364	59.54 347	74.62 303	62.93 265	80.97 249	87.93 225	62.83 253	71.90 333	55.24 302	95.01 155	92.00 172

PVSNet_Blended_VisFu		81.55 181	80.49 193	84.70 140	91.58 123	73.24 130	84.21 148	91.67 117	62.86 266	80.94 250	87.16 238	67.27 229	92.87 145	69.82 205	88.94 266	87.99 246

BH-w/o		76.57 241	76.07 242	78.10 257	86.88 220	65.92 198	77.63 270	86.33 221	65.69 250	80.89 251	79.95 324	68.97 222	90.74 203	53.01 315	85.25 301	77.62 336

PAPM_NR		83.23 159	83.19 155	83.33 169	90.90 142	65.98 197	88.19 88	90.78 138	78.13 111	80.87 252	87.92 226	73.49 189	92.42 152	70.07 203	88.40 270	91.60 186

ab-mvs		79.67 210	80.56 191	76.99 267	88.48 183	56.93 286	84.70 139	86.06 224	68.95 217	80.78 253	93.08 107	75.30 167	84.62 291	56.78 290	90.90 243	89.43 225

mvs-test1		84.55 127	82.12 171	91.84 20	87.13 211	89.54 4	85.05 135	88.42 187	74.57 150	80.60 254	82.98 294	78.49 135	93.98 99	72.04 187	89.77 256	92.00 172

XXY-MVS		74.44 263	76.19 240	69.21 310	84.61 249	52.43 318	71.70 318	77.18 288	60.73 284	80.60 254	90.96 171	75.44 164	69.35 338	56.13 294	88.33 271	85.86 270

HQP4-MVS										80.56 256			94.61 76			93.56 116

HQP-NCC					91.19 134		84.77 136		73.30 167	80.55 257

ACMP_Plane					91.19 134		84.77 136		73.30 167	80.55 257

HQP-MVS		84.61 125	84.06 143	86.27 107	91.19 134	70.66 160	84.77 136	92.68 92	73.30 167	80.55 257	90.17 193	72.10 204	94.61 76	77.30 136	94.47 170	93.56 116

AUN-MVS		81.18 185	78.78 212	88.39 77	90.93 141	82.14 57	82.51 196	83.67 250	64.69 260	80.29 260	85.91 256	51.07 305	92.38 153	76.29 145	93.63 189	90.65 205

HyFIR lowres test		75.12 254	72.66 272	82.50 190	91.44 129	65.19 202	72.47 315	87.31 204	46.79 344	80.29 260	84.30 282	52.70 302	92.10 162	51.88 322	86.73 288	90.22 213

test20.03		73.75 266	74.59 254	71.22 303	81.11 290	51.12 329	70.15 324	72.10 323	70.42 201	80.28 262	91.50 155	64.21 242	74.72 330	46.96 339	94.58 168	87.82 251

F-COLMAP		84.97 121	83.42 150	89.63 56	92.39 94	83.40 48	88.83 79	91.92 110	73.19 171	80.18 263	89.15 207	77.04 152	93.28 129	65.82 239	92.28 216	92.21 166

GA-MVS		75.83 248	74.61 252	79.48 237	81.87 281	59.25 265	73.42 311	82.88 254	68.68 220	79.75 264	81.80 308	50.62 307	89.46 232	66.85 229	85.64 297	89.72 219

xiu_mvs_v1_base_debu		80.84 190	80.14 201	82.93 178	88.31 186	71.73 151	79.53 241	87.17 206	65.43 252	79.59 265	82.73 301	76.94 154	90.14 222	73.22 174	88.33 271	86.90 260

xiu_mvs_v1_base		80.84 190	80.14 201	82.93 178	88.31 186	71.73 151	79.53 241	87.17 206	65.43 252	79.59 265	82.73 301	76.94 154	90.14 222	73.22 174	88.33 271	86.90 260

xiu_mvs_v1_base_debi		80.84 190	80.14 201	82.93 178	88.31 186	71.73 151	79.53 241	87.17 206	65.43 252	79.59 265	82.73 301	76.94 154	90.14 222	73.22 174	88.33 271	86.90 260

UnsupCasMVSNet_eth		71.63 283	72.30 277	69.62 308	76.47 330	52.70 315	70.03 325	80.97 268	59.18 290	79.36 268	88.21 220	60.50 259	69.12 339	58.33 284	77.62 340	87.04 258

ppachtmachnet_test		74.73 260	74.00 259	76.90 270	80.71 298	56.89 288	71.53 319	78.42 282	58.24 294	79.32 269	82.92 298	57.91 281	84.26 294	65.60 240	91.36 232	89.56 222

MG-MVS		80.32 202	80.94 188	78.47 250	88.18 189	52.62 316	82.29 202	85.01 243	72.01 188	79.24 270	92.54 128	69.36 218	93.36 128	70.65 198	89.19 263	89.45 223

Fast-Effi-MVS+-dtu		82.54 167	81.41 181	85.90 117	85.60 239	76.53 112	83.07 182	89.62 171	73.02 174	79.11 271	83.51 288	80.74 118	90.24 216	68.76 216	89.29 260	90.94 196

CDS-MVSNet		77.32 232	75.40 247	83.06 174	89.00 174	72.48 141	77.90 266	82.17 260	60.81 282	78.94 272	83.49 289	59.30 270	88.76 244	54.64 307	92.37 214	87.93 248
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 268	73.54 263	72.43 299	84.92 246	47.79 341	79.89 237	74.00 308	65.93 244	78.81 273	86.28 251	56.36 289	81.63 308	56.63 291	79.04 336	87.87 250

EIA-MVS		82.19 172	81.23 184	85.10 132	87.95 193	69.17 176	83.22 180	93.33 62	70.42 201	78.58 274	79.77 327	77.29 147	94.20 88	71.51 190	88.96 265	91.93 176

thres200		72.34 277	71.55 283	74.70 287	83.48 268	51.60 324	75.02 300	73.71 312	70.14 207	78.56 275	80.57 318	46.20 318	88.20 250	46.99 338	89.29 260	84.32 285

our_test_3		71.85 280	71.59 281	72.62 297	80.71 298	53.78 306	69.72 326	71.71 328	58.80 291	78.03 276	80.51 320	56.61 288	78.84 317	62.20 259	86.04 295	85.23 275

KD-MVS_2432*1600		66.87 305	65.81 310	70.04 304	67.50 357	47.49 342	62.56 343	79.16 278	61.21 280	77.98 277	80.61 316	25.29 366	82.48 303	53.02 313	84.92 304	80.16 333

miper_refine_blended		66.87 305	65.81 310	70.04 304	67.50 357	47.49 342	62.56 343	79.16 278	61.21 280	77.98 277	80.61 316	25.29 366	82.48 303	53.02 313	84.92 304	80.16 333

jason		77.42 231	75.75 244	82.43 192	87.10 215	69.27 171	77.99 264	81.94 262	51.47 332	77.84 279	85.07 272	60.32 262	89.00 237	70.74 197	89.27 262	89.03 235
jason: jason.
MAR-MVS		80.24 204	78.74 214	84.73 138	86.87 221	78.18 88	85.75 125	87.81 200	65.67 251	77.84 279	78.50 332	73.79 184	90.53 209	61.59 267	90.87 244	85.49 274
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
FPMVS		72.29 278	72.00 278	73.14 293	88.63 180	85.00 35	74.65 304	67.39 336	71.94 189	77.80 281	87.66 229	50.48 308	75.83 326	49.95 325	79.51 331	58.58 355

pmmvs4		74.92 257	72.98 269	80.73 217	84.95 245	71.71 154	76.23 290	77.59 286	52.83 322	77.73 282	86.38 246	56.35 290	84.97 287	57.72 288	87.05 286	85.51 273

ET-MVSNet_ETH3D		75.28 251	72.77 270	82.81 183	83.03 276	68.11 182	77.09 277	76.51 293	60.67 285	77.60 283	80.52 319	38.04 350	91.15 189	70.78 195	90.68 248	89.17 230

UnsupCasMVSNet_bld		69.21 298	69.68 293	67.82 317	79.42 310	51.15 328	67.82 333	75.79 296	54.15 315	77.47 284	85.36 267	59.26 271	70.64 335	48.46 332	79.35 333	81.66 319

Anonymous20231206		71.38 284	71.88 279	69.88 306	86.31 228	54.37 302	70.39 323	74.62 303	52.57 324	76.73 285	88.76 212	59.94 265	72.06 332	44.35 344	93.23 196	83.23 302

CMPMVS	59.41 20	75.12 254	73.57 262	79.77 229	75.84 335	67.22 186	81.21 221	82.18 259	50.78 336	76.50 286	87.66 229	55.20 296	82.99 301	62.17 261	90.64 252	89.09 234
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 279	71.69 280	73.32 291	81.57 284	53.02 312	76.77 281	78.37 283	63.31 263	76.37 287	91.85 144	36.68 353	78.98 316	47.87 335	92.45 213	87.95 247

CVMVSNet		72.62 274	71.41 284	76.28 278	83.25 271	60.34 255	83.50 170	79.02 281	37.77 357	76.33 288	85.10 269	49.60 310	87.41 256	70.54 200	77.54 341	81.08 328

PLC	73.85 16	82.09 174	80.31 195	87.45 90	90.86 144	80.29 69	85.88 124	90.65 141	68.17 224	76.32 289	86.33 248	73.12 195	92.61 150	61.40 268	90.02 255	89.44 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 171	81.57 180	84.19 152	85.54 241	69.26 172	91.98 29	90.08 162	71.54 190	76.23 290	85.07 272	58.69 275	94.27 82	86.26 35	88.77 267	89.03 235

lupinMVS		76.37 245	74.46 255	82.09 193	85.54 241	69.26 172	76.79 280	80.77 270	50.68 338	76.23 290	82.82 299	58.69 275	88.94 238	69.85 204	88.77 267	88.07 243

PatchMatch-RL		74.48 261	73.22 266	78.27 255	87.70 199	85.26 33	75.92 292	70.09 332	64.34 261	76.09 292	81.25 313	65.87 237	78.07 319	53.86 309	83.82 313	71.48 344

thisisatest0515		73.00 272	70.52 287	80.46 221	81.45 285	59.90 259	73.16 314	74.31 307	57.86 297	76.08 293	77.78 334	37.60 352	92.12 161	65.00 242	91.45 231	89.35 226

MS-PatchMatch		70.93 286	70.22 290	73.06 294	81.85 282	62.50 231	73.82 309	77.90 284	52.44 325	75.92 294	81.27 312	55.67 293	81.75 306	55.37 300	77.70 339	74.94 340

CHOSEN 1792x2688		72.45 275	70.56 286	78.13 256	90.02 163	63.08 221	68.72 328	83.16 252	42.99 353	75.92 294	85.46 262	57.22 286	85.18 286	49.87 327	81.67 324	86.14 266

CR-MVSNet		74.00 264	73.04 268	76.85 272	79.58 307	62.64 228	82.58 192	76.90 289	50.50 339	75.72 296	92.38 130	48.07 313	84.07 295	68.72 218	82.91 319	83.85 291

RPMNet		78.88 215	78.28 219	80.68 219	79.58 307	62.64 228	82.58 192	94.16 28	74.80 147	75.72 296	92.59 124	48.69 311	95.56 34	73.48 172	82.91 319	83.85 291

DPM-MVS		80.10 208	79.18 210	82.88 181	90.71 148	69.74 165	78.87 254	90.84 136	60.29 287	75.64 298	85.92 255	67.28 228	93.11 137	71.24 191	91.79 225	85.77 271

PVSNet_BlendedMVS		78.80 217	77.84 223	81.65 203	84.43 251	63.41 216	79.49 244	90.44 146	61.70 276	75.43 299	87.07 241	69.11 220	91.44 179	60.68 273	92.24 217	90.11 217

PVSNet_Blended		76.49 243	75.40 247	79.76 230	84.43 251	63.41 216	75.14 299	90.44 146	57.36 302	75.43 299	78.30 333	69.11 220	91.44 179	60.68 273	87.70 282	84.42 284

PAPR		78.84 216	78.10 221	81.07 210	85.17 244	60.22 256	82.21 206	90.57 144	62.51 268	75.32 301	84.61 279	74.99 169	92.30 156	59.48 279	88.04 277	90.68 203

N_pmnet		70.20 289	68.80 298	74.38 288	80.91 292	84.81 38	59.12 349	76.45 294	55.06 311	75.31 302	82.36 304	55.74 292	54.82 356	47.02 337	87.24 285	83.52 295

cascas		76.29 246	74.81 251	80.72 218	84.47 250	62.94 223	73.89 308	87.34 203	55.94 307	75.16 303	76.53 342	63.97 243	91.16 188	65.00 242	90.97 241	88.06 244

SCA		73.32 267	72.57 274	75.58 282	81.62 283	55.86 293	78.89 253	71.37 329	61.73 274	74.93 304	83.42 291	60.46 260	87.01 259	58.11 286	82.63 323	83.88 288

xiu_mvs_v2_base		77.19 233	76.75 235	78.52 248	87.01 217	61.30 242	75.55 296	87.12 212	61.24 279	74.45 305	78.79 329	77.20 148	90.93 195	64.62 247	84.80 309	83.32 300

CANet		83.79 148	82.85 160	86.63 98	86.17 234	72.21 147	83.76 162	91.43 122	77.24 118	74.39 306	87.45 233	75.36 166	95.42 47	77.03 139	92.83 206	92.25 165

PS-MVSNAJ		77.04 235	76.53 237	78.56 247	87.09 216	61.40 240	75.26 298	87.13 209	61.25 278	74.38 307	77.22 339	76.94 154	90.94 194	64.63 246	84.83 308	83.35 299

MVP-Stereo		75.81 249	73.51 264	82.71 184	89.35 167	73.62 126	80.06 233	85.20 236	60.30 286	73.96 308	87.94 224	57.89 282	89.45 233	52.02 318	74.87 345	85.06 278
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 163	81.64 177	86.21 111	86.20 233	76.24 116	86.86 106	85.68 229	77.07 119	73.76 309	92.82 117	69.64 216	91.82 172	69.04 214	93.69 187	90.56 207
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
1112_ss		74.82 259	73.74 260	78.04 258	89.57 165	60.04 257	76.49 286	87.09 213	54.31 314	73.66 310	79.80 325	60.25 263	86.76 268	58.37 282	84.15 312	87.32 255

Test_1112_low_res		73.90 265	73.08 267	76.35 276	90.35 154	55.95 291	73.40 312	86.17 223	50.70 337	73.14 311	85.94 254	58.31 277	85.90 279	56.51 292	83.22 316	87.20 256

1314		73.22 269	72.56 275	75.20 283	80.41 303	57.84 279	81.64 213	85.36 232	51.68 331	73.10 312	76.65 341	61.45 256	85.19 285	63.54 250	79.21 335	82.59 307

Patchmatch-test		65.91 311	67.38 303	61.48 334	75.51 337	43.21 353	68.84 327	63.79 346	62.48 269	72.80 313	83.42 291	44.89 336	59.52 355	48.27 334	86.45 290	81.70 318

PatchmatchNet		69.71 296	68.83 297	72.33 300	77.66 322	53.60 307	79.29 246	69.99 333	57.66 299	72.53 314	82.93 297	46.45 317	80.08 315	60.91 271	72.09 348	83.31 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 301	68.08 302	67.55 318	78.74 318	43.53 352	75.60 294	67.10 341	54.92 312	72.23 315	88.10 221	42.87 342	75.97 325	52.21 317	80.95 330	83.15 303

pmmvs5		70.73 287	70.07 291	72.72 295	77.03 326	52.73 314	74.14 305	75.65 299	50.36 340	72.17 316	85.37 266	55.42 295	80.67 312	52.86 316	87.59 283	84.77 280

PatchT		70.52 288	72.76 271	63.79 328	79.38 311	33.53 360	77.63 270	65.37 344	73.61 160	71.77 317	92.79 120	44.38 338	75.65 327	64.53 248	85.37 299	82.18 314

MVS		73.21 270	72.59 273	75.06 285	80.97 291	60.81 252	81.64 213	85.92 227	46.03 347	71.68 318	77.54 335	68.47 223	89.77 229	55.70 297	85.39 298	74.60 341

MIMVSNet		71.09 285	71.59 281	69.57 309	87.23 208	50.07 335	78.91 252	71.83 325	60.20 288	71.26 319	91.76 149	55.08 297	76.09 324	41.06 349	87.02 287	82.54 309

WTY-MVS		67.91 302	68.35 300	66.58 321	80.82 296	48.12 339	65.96 337	72.60 319	53.67 318	71.20 320	81.68 310	58.97 273	69.06 340	48.57 331	81.67 324	82.55 308

test0.0.03 1		64.66 314	64.36 315	65.57 324	75.03 342	46.89 344	64.69 339	61.58 351	62.43 271	71.18 321	77.54 335	43.41 339	68.47 341	40.75 350	82.65 321	81.35 322

CostFormer		69.98 294	68.68 299	73.87 289	77.14 324	50.72 332	79.26 247	74.51 305	51.94 330	70.97 322	84.75 277	45.16 334	87.49 255	55.16 303	79.23 334	83.40 298

tpmvs		70.16 290	69.56 294	71.96 301	74.71 344	48.13 338	79.63 239	75.45 301	65.02 258	70.26 323	81.88 307	45.34 331	85.68 281	58.34 283	75.39 344	82.08 315

sss		66.92 304	67.26 304	65.90 322	77.23 323	51.10 330	64.79 338	71.72 327	52.12 329	70.13 324	80.18 322	57.96 280	65.36 351	50.21 324	81.01 329	81.25 325

tpm2		68.45 300	66.83 306	73.30 292	78.93 317	48.50 337	79.76 238	71.76 326	47.50 343	69.92 325	83.60 287	42.07 343	88.40 247	48.44 333	79.51 331	83.01 305

HY-MVS	64.64 18	73.03 271	72.47 276	74.71 286	83.36 270	54.19 303	82.14 209	81.96 261	56.76 306	69.57 326	86.21 252	60.03 264	84.83 290	49.58 328	82.65 321	85.11 277

tpm cat1		66.76 307	65.21 313	71.42 302	77.09 325	50.62 333	78.01 263	73.68 313	44.89 349	68.64 327	79.00 328	45.51 328	82.42 305	49.91 326	70.15 351	81.23 327

IB-MVS	62.13 19	71.64 282	68.97 296	79.66 234	80.80 297	62.26 236	73.94 307	76.90 289	63.27 264	68.63 328	76.79 340	33.83 357	91.84 171	59.28 280	87.26 284	84.88 279
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
EPNet		80.37 200	78.41 218	86.23 108	76.75 327	73.28 129	87.18 102	77.45 287	76.24 126	68.14 329	88.93 211	65.41 238	93.85 103	69.47 207	96.12 119	91.55 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 309	65.63 312	68.75 313	81.96 280	49.88 336	62.19 345	72.51 321	51.03 334	68.04 330	75.34 345	50.84 306	74.77 328	45.82 342	82.96 317	81.60 320

tpmrst		66.28 310	66.69 308	65.05 326	72.82 352	39.33 355	78.20 262	70.69 331	53.16 321	67.88 331	80.36 321	48.18 312	74.75 329	58.13 285	70.79 350	81.08 328

CANet_DTU		77.81 228	77.05 231	80.09 227	81.37 287	59.90 259	83.26 176	88.29 191	69.16 214	67.83 332	83.72 286	60.93 257	89.47 231	69.22 211	89.70 257	90.88 198

EPMVS		62.47 315	62.63 319	62.01 330	70.63 356	38.74 356	74.76 302	52.86 359	53.91 317	67.71 333	80.01 323	39.40 347	66.60 347	55.54 299	68.81 355	80.68 332

MVS_0304		78.17 222	77.23 230	80.99 214	84.13 263	69.07 178	81.39 217	80.81 269	76.28 125	67.53 334	89.11 208	62.87 252	86.77 266	60.90 272	92.01 224	87.13 257

MDTV_nov1_ep13			68.29 301		78.03 319	43.87 351	74.12 306	72.22 322	52.17 326	67.02 335	85.54 258	45.36 330	80.85 311	55.73 295	84.42 311

pmmvs3		62.47 315	60.02 327	69.80 307	71.58 355	64.00 213	70.52 322	58.44 355	39.77 355	66.05 336	75.84 343	27.10 365	72.28 331	46.15 340	84.77 310	73.11 342

ADS-MVSNet2		65.87 312	63.64 317	72.55 298	73.16 349	56.92 287	67.10 334	74.81 302	49.74 341	66.04 337	82.97 295	46.71 315	77.26 321	42.29 346	69.96 352	83.46 296

ADS-MVSNet		61.90 317	62.19 320	61.03 335	73.16 349	36.42 358	67.10 334	61.75 349	49.74 341	66.04 337	82.97 295	46.71 315	63.21 353	42.29 346	69.96 352	83.46 296

DWT-MVSNet_test		66.43 308	64.37 314	72.63 296	74.86 343	50.86 331	76.52 285	72.74 318	54.06 316	65.50 339	68.30 353	32.13 359	84.84 289	61.63 266	73.59 346	82.19 313

DSMNet-mixed		60.98 323	61.61 322	59.09 338	72.88 351	45.05 349	74.70 303	46.61 362	26.20 359	65.34 340	90.32 188	55.46 294	63.12 354	41.72 348	81.30 328	69.09 348

JIA-IIPM		69.41 297	66.64 309	77.70 263	73.19 348	71.24 157	75.67 293	65.56 343	70.42 201	65.18 341	92.97 112	33.64 358	83.06 300	53.52 311	69.61 354	78.79 335

test-LLR		67.21 303	66.74 307	68.63 314	76.45 331	55.21 298	67.89 330	67.14 339	62.43 271	65.08 342	72.39 347	43.41 339	69.37 336	61.00 269	84.89 306	81.31 323

test-mter		65.00 313	63.79 316	68.63 314	76.45 331	55.21 298	67.89 330	67.14 339	50.98 335	65.08 342	72.39 347	28.27 363	69.37 336	61.00 269	84.89 306	81.31 323

PMMVS2		55.64 328	59.27 328	44.74 342	64.30 362	12.32 365	40.60 356	49.79 361	53.19 320	65.06 344	84.81 276	53.60 301	49.76 358	32.68 358	89.41 259	72.15 343

baseline2		69.77 295	66.89 305	78.41 251	79.51 309	58.09 277	76.23 290	69.57 334	57.50 301	64.82 345	77.45 337	46.02 320	88.44 246	53.08 312	77.83 338	88.70 239

gg-mvs-nofinetune		68.96 299	69.11 295	68.52 316	76.12 334	45.32 347	83.59 166	55.88 357	86.68 25	64.62 346	97.01 7	30.36 361	83.97 297	44.78 343	82.94 318	76.26 338

PAPM		71.77 281	70.06 292	76.92 269	86.39 223	53.97 304	76.62 284	86.62 219	53.44 319	63.97 347	84.73 278	57.79 283	92.34 154	39.65 351	81.33 327	84.45 283

new_pmnet		55.69 327	57.66 329	49.76 341	75.47 338	30.59 361	59.56 346	51.45 360	43.62 352	62.49 348	75.48 344	40.96 345	49.15 359	37.39 354	72.52 347	69.55 347

MDTV_nov1_ep13_2view						27.60 363	70.76 321		46.47 346	61.27 349		45.20 332		49.18 329		83.75 293

dp		60.70 324	60.29 326	61.92 332	72.04 354	38.67 357	70.83 320	64.08 345	51.28 333	60.75 350	77.28 338	36.59 354	71.58 334	47.41 336	62.34 357	75.52 339

TESTMET0.1,1		61.29 320	60.32 325	64.19 327	72.06 353	51.30 326	67.89 330	62.09 347	45.27 348	60.65 351	69.01 350	27.93 364	64.74 352	56.31 293	81.65 326	76.53 337

PMMVS		61.65 318	60.38 324	65.47 325	65.40 361	69.26 172	63.97 341	61.73 350	36.80 358	60.11 352	68.43 351	59.42 269	66.35 348	48.97 330	78.57 337	60.81 352

PVSNet_0	51.08 22	56.10 326	54.97 331	59.48 337	75.12 341	53.28 311	55.16 352	61.89 348	44.30 350	59.16 353	62.48 356	54.22 299	65.91 350	35.40 355	47.01 358	59.25 354

MVS-HIRNet		61.16 321	62.92 318	55.87 339	79.09 314	35.34 359	71.83 317	57.98 356	46.56 345	59.05 354	91.14 163	49.95 309	76.43 323	38.74 352	71.92 349	55.84 356

E-PMN		61.59 319	61.62 321	61.49 333	66.81 359	55.40 296	53.77 353	60.34 352	66.80 239	58.90 355	65.50 354	40.48 346	66.12 349	55.72 296	86.25 293	62.95 351

GG-mvs-BLEND				67.16 319	73.36 347	46.54 346	84.15 149	55.04 358		58.64 356	61.95 357	29.93 362	83.87 298	38.71 353	76.92 342	71.07 345

EPNet_dtu		72.87 273	71.33 285	77.49 265	77.72 321	60.55 254	82.35 200	75.79 296	66.49 241	58.39 357	81.06 314	53.68 300	85.98 277	53.55 310	92.97 204	85.95 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 322	60.81 323	61.99 331	65.96 360	55.86 293	53.10 354	58.97 354	67.06 236	56.89 358	63.33 355	40.98 344	67.03 345	54.79 305	86.18 294	63.08 350

CHOSEN 280x420		59.08 325	56.52 330	66.76 320	76.51 329	64.39 209	49.62 355	59.00 353	43.86 351	55.66 359	68.41 352	35.55 355	68.21 342	43.25 345	76.78 343	67.69 349

MVE	40.22 23	51.82 329	50.47 332	55.87 339	62.66 363	51.91 321	31.61 358	39.28 363	40.65 354	50.76 360	74.98 346	56.24 291	44.67 360	33.94 357	64.11 356	71.04 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 343	32.95 364	29.49 362		21.63 366	12.07 360	37.95 361	45.07 358	30.84 360	19.21 361	17.94 360	33.06 360	23.69 357

tmp_tt		20.25 331	24.50 334	7.49 344	4.47 365	8.70 366	34.17 357	25.16 365	1.00 361	32.43 362	18.49 359	39.37 348	9.21 362	21.64 359	43.75 359	4.57 358

testmvs		5.91 335	7.65 338	0.72 346	1.20 366	0.37 368	59.14 348	0.67 368	0.49 363	1.11 363	2.76 363	0.94 369	0.24 364	1.02 362	1.47 361	1.55 360

test123		6.27 334	8.08 337	0.84 345	1.11 367	0.57 367	62.90 342	0.82 367	0.54 362	1.07 364	2.75 364	1.26 368	0.30 363	1.04 361	1.26 362	1.66 359

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		20.81 330	27.75 333	0.00 347	0.00 368	0.00 369	0.00 359	85.44 231	0.00 364	0.00 365	82.82 299	81.46 110	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		6.41 333	8.55 336	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	76.94 154	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		6.65 332	8.87 335	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	79.80 325	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

OPU-MVS				88.27 80	91.89 113	77.83 91	90.47 46				91.22 159	81.12 114	94.68 72	74.48 159	95.35 140	92.29 161

save fliter					93.75 61	77.44 97	86.31 119	89.72 167	70.80 197

test_0728_SECOND				86.79 96	94.25 47	72.45 142	90.54 43	94.10 34					95.88 14	86.42 31	97.97 43	92.02 171

GSMVS																83.88 288

sam_mvs1												46.11 319				83.88 288

sam_mvs												45.92 324

MTGPA								91.81 114

test_post1							78.85 255				3.13 361	45.19 333	80.13 314	58.11 286

test_post											3.10 362	45.43 329	77.22 322

patchmatchnet-post											81.71 309	45.93 323	87.01 259

MTMP							90.66 40	33.14 364

gm-plane-assit					75.42 339	44.97 350			52.17 326		72.36 349		87.90 251	54.10 308

test9_res														80.83 94	96.45 106	90.57 206

agg_prior2														79.68 106	96.16 116	90.22 213

test_prior4						78.97 81	84.59 141

test_prior				86.32 104	90.59 150	71.99 148		92.85 86					94.17 91			92.80 139

新几何2							81.72 212

旧先验1					91.97 108	71.77 150		81.78 263			91.84 145	73.92 182			93.65 188	83.61 294

无先验							82.81 189	85.62 230	58.09 295				91.41 182	67.95 225		84.48 282

原ACMM2							82.26 205

testdata2													86.43 272	63.52 251

segment_acmp												81.94 102

testdata1							79.62 240		73.95 157

plane_prior7					93.45 67	77.31 101

plane_prior6					92.61 88	76.54 110						74.84 171

plane_prior5								93.61 53					95.22 55	80.78 95	95.83 126	94.46 77

plane_prior4											92.95 113

plane_prior2							89.45 69		79.44 93

plane_prior1					92.83 86

plane_prior						76.42 113	87.15 103		75.94 134						95.03 154

n2								0.00 369

nn								0.00 369

door-mid								74.45 306

test11								91.46 121

door								72.57 320

HQP5-MVS						70.66 160

BP-MVS														77.30 136

HQP3-MVS								92.68 92							94.47 170

HQP2-MVS												72.10 204

NP-MVS					91.95 109	74.55 122					90.17 193

ACMMP++_ref															95.74 132

ACMMP++															97.35 76

Test By Simon												79.09 131

High-resolution multi-view benchmark