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	84.21 149	96.51 8	57.84 277	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	84.47 143	96.56 7	58.83 272	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		83.48 153	85.06 120	78.75 243	85.94 236	55.75 293	80.05 232	94.27 20	76.47 123	96.09 4	94.54 56	83.31 82	89.75 229	59.95 274	94.89 159	90.75 200

PEN-MVS		90.03 44	91.88 16	84.48 142	96.57 6	58.88 269	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	84.37 144	96.34 9	58.61 274	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	93.16 133	91.10 1	97.53 70	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	85.71 121	96.32 10	62.39 231	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 67	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	89.82 163	86.67 15	90.51 45	90.20 159	69.87 208	95.06 10	96.14 21	84.28 72	93.07 138	87.68 13	96.34 108	97.09 19

wuyk23d		75.13 251	79.30 207	62.63 327	75.56 334	75.18 119	80.89 223	73.10 315	75.06 146	94.76 11	95.32 34	87.73 41	52.85 355	34.16 354	97.11 82	59.85 351

ACMH	76.49 14	89.34 59	91.14 34	83.96 155	92.50 92	70.36 162	89.55 64	93.84 45	81.89 66	94.70 12	95.44 33	90.69 9	88.31 248	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	86.45 101	92.52 91	69.19 174	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 91	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	70.00 207	94.55 14	96.67 11	87.94 38	93.59 115	84.27 59	95.97 120	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	69.27 211	94.39 16	96.38 15	86.02 63	93.52 119	83.96 61	95.92 122	95.34 52

test_0402		88.65 69	89.58 58	85.88 117	92.55 90	72.22 145	84.01 152	89.44 174	88.63 17	94.38 17	95.77 24	86.38 59	93.59 115	79.84 104	95.21 145	91.82 178

UniMVSNet_ETH3D		89.12 63	90.72 45	84.31 147	97.00 2	64.33 209	89.67 61	88.38 189	88.84 14	94.29 18	97.57 3	90.48 14	91.26 184	72.57 182	97.65 60	97.34 14

v7n		90.13 40	90.96 40	87.65 87	91.95 109	71.06 157	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	96.49 103	97.09 19

PS-MVSNAJss		88.31 72	87.90 77	89.56 59	93.31 72	77.96 89	87.94 92	91.97 108	70.73 198	94.19 21	96.67 11	76.94 154	94.57 78	83.07 70	96.28 110	96.15 32

SED-MVS		90.46 37	91.64 19	86.93 92	94.18 48	72.65 132	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	92.29 160

test_241102_ONE					94.18 48	72.65 132		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 84	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	91.70 181

APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 80	78.04 88	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 75	93.00 79	76.26 114	89.65 62	95.55 4	87.72 22	93.89 26	94.94 44	91.62 4	93.44 123	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	93.60 100	90.81 8	92.96 139	85.02 51	98.45 19	92.41 154
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	86.29 105	94.16 51	72.56 137	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	90.55 207
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	72.56 137	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 57	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 57		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 69	92.54 151

test_part1		87.15 85	87.82 78	85.15 130	88.88 176	63.04 221	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	92.04 169

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 76	92.19 166

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	84.73 137	90.46 152	65.27 200	88.86 78	93.02 81	87.15 24	93.05 40	97.10 6	82.28 95	92.02 164	76.70 139	97.99 40	96.88 23

MP-MVS-pluss		90.81 29	91.08 35	89.99 51	95.97 13	79.88 70	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	86.22 108	91.63 118	77.07 104	89.82 57	93.77 46	78.90 100	92.88 42	92.29 134	86.11 61	90.22 216	86.24 38	97.24 79	91.36 190
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 63	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 76	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 91				92.84 45

v10		86.54 92	87.10 87	84.84 134	88.16 190	63.28 218	86.64 114	92.20 102	75.42 142	92.81 47	94.50 57	74.05 181	94.06 96	83.88 62	96.28 110	97.17 18

v8		86.22 99	86.83 94	84.36 145	87.82 195	62.35 233	86.42 117	91.33 125	76.78 122	92.73 48	94.48 59	73.41 190	93.72 108	83.10 69	95.41 137	97.01 21

nrg030		87.85 78	88.49 73	85.91 115	90.07 159	69.73 165	87.86 93	94.20 25	74.04 155	92.70 49	94.66 52	85.88 64	91.50 175	79.72 105	97.32 77	96.50 30

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 59	80.97 64	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 71	86.76 111	92.78 90	78.78 102	92.51 51	93.64 99	88.13 35	93.84 104	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	86.37 102	91.13 137	69.63 167	85.45 130	76.68 290	84.06 39	92.44 53	96.99 8	62.03 253	94.65 73	80.58 98	93.24 193	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 96	86.31 119	95.27 9	70.80 196	92.28 55	93.80 94	86.89 50	94.64 74	85.52 45	97.51 71	94.30 84

SF-MVS		90.27 39	90.80 44	88.68 71	92.86 84	77.09 103	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 71	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	93.10 137	82.67 75	98.04 35	93.64 112

TranMVSNet+NR-MVSNet		87.86 77	88.76 72	85.18 129	94.02 56	64.13 210	84.38 147	91.29 126	84.88 34	92.06 59	93.84 93	86.45 57	93.73 107	73.22 172	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	92.30 158

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	92.30 158

ACMMP_NAP		90.65 31	91.07 37	89.42 60	95.93 15	79.54 75	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	82.58 186	92.25 100	56.44 288	85.75 125	93.09 75	77.33 116	91.94 63	94.65 53	74.78 173	93.41 125	75.11 156	98.58 14	97.88 7

lessismore_v0				85.95 114	91.10 138	70.99 158		70.91 328		91.79 64	94.42 63	61.76 254	92.93 141	79.52 110	93.03 199	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		83.17 160	85.68 112	75.65 279	81.24 286	45.26 346	79.94 234	92.91 84	83.83 40	91.33 72	96.88 10	80.25 123	85.92 277	68.89 213	95.89 123	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 62	90.19 49	93.08 76	80.87 77	91.13 74	93.19 104	86.22 60	95.97 10	82.23 79	97.18 81	90.45 209
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	92.89 114	87.27 45	93.78 106	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	92.55 150

FIs		85.35 111	86.27 100	82.60 185	91.86 114	57.31 281	85.10 134	93.05 77	75.83 135	91.02 77	93.97 84	73.57 186	92.91 143	73.97 164	98.02 38	97.58 12

UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 112	92.86 84	67.02 187	82.55 193	91.56 118	83.08 52	90.92 78	91.82 146	78.25 138	93.99 97	74.16 161	98.35 22	97.49 13

DU-MVS		86.80 89	86.99 90	86.21 110	93.24 74	67.02 187	83.16 180	92.21 101	81.73 67	90.92 78	91.97 140	77.20 148	93.99 97	74.16 161	98.35 22	97.61 10

V42		83.47 154	83.37 152	83.75 160	83.16 272	63.33 217	81.31 216	90.23 158	69.51 210	90.91 80	90.81 175	74.16 179	92.29 156	80.06 101	90.22 251	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		83.56 151	84.40 138	81.06 210	93.43 69	54.88 299	78.67 255	85.02 241	81.24 72	90.74 82	91.56 153	72.85 197	91.08 190	68.00 221	98.04 35	97.23 16

v1240		84.30 134	84.51 135	83.65 162	87.65 200	61.26 242	82.85 187	91.54 119	67.94 226	90.68 83	90.65 181	71.71 210	93.64 110	82.84 74	94.78 161	96.07 35

ETH3D-3000-0.1		88.85 68	88.96 68	88.52 72	91.94 111	77.27 102	88.71 82	95.26 10	76.08 127	90.66 84	92.69 121	84.48 71	93.83 105	83.38 67	97.48 73	94.47 76

testtj		89.51 56	89.48 59	89.59 58	92.26 99	80.80 65	90.14 50	93.54 56	83.37 47	90.57 85	92.55 126	84.99 67	96.15 5	81.26 87	96.61 98	91.83 177

ZD-MVS					92.22 102	80.48 66		91.85 111	71.22 193	90.38 86	92.98 109	86.06 62	96.11 6	81.99 81	96.75 94

MIMVSNet1		83.63 150	84.59 132	80.74 215	94.06 55	62.77 225	82.72 189	84.53 246	77.57 115	90.34 87	95.92 23	76.88 160	85.83 279	61.88 260	97.42 74	93.62 113

LS3D		90.60 33	90.34 49	91.38 27	89.03 172	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	95.50 136	94.53 75

DIV-MVS_2432*1600		81.93 177	83.14 156	78.30 252	84.75 247	52.75 311	80.37 229	89.42 175	70.24 205	90.26 89	93.39 102	74.55 178	86.77 265	68.61 217	96.64 96	95.38 51

PMVS	80.48 6	90.08 41	90.66 46	88.34 78	96.71 3	92.97 1	90.31 48	89.57 172	88.51 18	90.11 90	95.12 41	90.98 7	88.92 238	77.55 131	97.07 83	83.13 302
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v1921920		84.23 137	84.37 139	83.79 158	87.64 201	61.71 237	82.91 186	91.20 129	67.94 226	90.06 91	90.34 185	72.04 207	93.59 115	82.32 78	94.91 157	96.07 35

ITE_SJBPF				90.11 50	90.72 146	84.97 36		90.30 153	81.56 69	90.02 92	91.20 160	82.40 91	90.81 200	73.58 169	94.66 165	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	82.70 161	92.08 10	95.64 23	86.25 19	92.64 17	93.33 62	85.07 32	89.99 93	16.05 358	86.57 55	95.80 21	87.35 20	97.62 62	94.20 86

v1192		84.57 126	84.69 130	84.21 149	87.75 197	62.88 223	83.02 183	91.43 122	69.08 214	89.98 95	90.89 172	72.70 200	93.62 114	82.41 76	94.97 156	96.13 33

Anonymous20240529		86.20 100	87.13 86	83.42 167	90.19 156	64.55 207	84.55 142	90.71 139	85.85 29	89.94 96	95.24 38	82.13 97	90.40 211	69.19 210	96.40 106	95.31 54

Regformer-2		86.74 90	86.08 104	88.73 69	84.18 260	79.20 78	83.52 166	89.33 176	83.33 48	89.92 97	85.07 270	83.23 83	93.16 133	83.39 66	92.72 208	93.83 101

pmmvs6		86.52 93	88.06 76	81.90 195	92.22 102	62.28 234	84.66 140	89.15 178	83.54 46	89.85 98	97.32 4	88.08 37	86.80 264	70.43 199	97.30 78	96.62 27

Regformer-4		86.41 94	85.71 111	88.52 72	84.27 256	77.57 94	84.07 150	88.00 197	82.82 55	89.84 99	85.48 258	82.06 99	92.77 145	83.83 64	91.04 234	95.22 60

v144192		84.24 136	84.41 137	83.71 161	87.59 202	61.57 238	82.95 185	91.03 133	67.82 229	89.80 100	90.49 183	73.28 193	93.51 120	81.88 84	94.89 159	96.04 37

v1144		84.54 129	84.72 128	84.00 153	87.67 199	62.55 229	82.97 184	90.93 135	70.32 203	89.80 100	90.99 167	73.50 187	93.48 121	81.69 85	94.65 166	95.97 38

DeepC-MVS	82.31 4	89.15 62	89.08 64	89.37 61	93.64 65	79.07 79	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	90.08 158	89.30 5	89.68 60	91.11 131	79.26 95	89.68 103	94.81 51	82.44 90	87.74 252	76.54 141	88.74 267	96.61 28

IU-MVS					94.18 48	72.64 134		90.82 137	56.98 302	89.67 104				85.78 44	97.92 46	93.28 121

FMVSNet1		84.55 127	85.45 116	81.85 197	90.27 155	61.05 245	86.83 108	88.27 192	78.57 106	89.66 105	95.64 29	75.43 165	90.68 204	69.09 211	95.33 140	93.82 103

IterMVS-LS		84.73 123	84.98 122	83.96 155	87.35 205	63.66 213	83.25 176	89.88 166	76.06 128	89.62 106	92.37 132	73.40 192	92.52 150	78.16 123	94.77 163	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 90	91.30 129	77.50 95	87.55 96	92.97 83	87.95 21	89.62 106	92.87 115	84.56 69	93.89 101	77.65 129	96.62 97	90.70 201

UniMVSNet (Re)		86.87 86	86.98 91	86.55 99	93.11 77	68.48 178	83.80 160	92.87 85	80.37 80	89.61 108	91.81 147	77.72 142	94.18 89	75.00 157	98.53 16	96.99 22

IS-MVSNet		86.66 91	86.82 95	86.17 112	92.05 107	66.87 189	91.21 37	88.64 185	86.30 28	89.60 109	92.59 123	69.22 219	94.91 66	73.89 165	97.89 49	96.72 24

v2v482		84.09 139	84.24 141	83.62 163	87.13 210	61.40 239	82.71 190	89.71 168	72.19 185	89.55 110	91.41 156	70.70 215	93.20 130	81.02 90	93.76 182	96.25 31

Baseline_NR-MVSNet		84.00 143	85.90 107	78.29 253	91.47 127	53.44 307	82.29 200	87.00 217	79.06 98	89.55 110	95.72 27	77.20 148	86.14 275	72.30 184	98.51 17	95.28 55

CSCG		86.26 97	86.47 97	85.60 123	90.87 142	74.26 123	87.98 90	91.85 111	80.35 81	89.54 112	88.01 220	79.09 131	92.13 158	75.51 150	95.06 152	90.41 210

ambc				82.98 175	90.55 151	64.86 203	88.20 87	89.15 178		89.40 113	93.96 87	71.67 211	91.38 183	78.83 115	96.55 100	92.71 145

DeepPCF-MVS	81.24 5	87.28 83	86.21 102	90.49 43	91.48 126	84.90 37	83.41 171	92.38 99	70.25 204	89.35 114	90.68 179	82.85 86	94.57 78	79.55 107	95.95 121	92.00 171

Regformer-1		86.00 102	85.50 115	87.49 88	84.18 260	76.90 106	83.52 166	87.94 199	82.18 62	89.19 115	85.07 270	82.28 95	91.89 168	82.40 77	92.72 208	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	92.25 136	72.03 208	96.36 2	88.21 8	90.93 240	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	93.44 101	83.82 75	90.98 192	83.86 63	95.30 144	93.60 114

ETH3D cwj APD-0.16		87.83 79	87.62 81	88.47 74	91.21 132	78.20 86	87.26 100	94.54 17	72.05 186	88.89 118	92.31 133	83.86 74	94.24 85	81.59 86	96.87 88	92.97 136

MDA-MVSNet-bldmvs		77.47 228	76.90 232	79.16 239	79.03 313	64.59 204	66.58 334	75.67 296	73.15 172	88.86 119	88.99 208	66.94 230	81.23 308	64.71 242	88.22 274	91.64 183

EG-PatchMatch MVS		84.08 140	84.11 142	83.98 154	92.22 102	72.61 136	82.20 206	87.02 214	72.63 179	88.86 119	91.02 166	78.52 134	91.11 189	73.41 171	91.09 232	88.21 240

3Dnovator+	83.92 2	89.97 48	89.66 55	90.92 36	91.27 131	81.66 60	91.25 36	94.13 33	88.89 12	88.83 121	94.26 72	77.55 145	95.86 18	84.88 52	95.87 124	95.24 57

EI-MVSNet-UG-set		85.04 118	84.44 136	86.85 94	83.87 266	72.52 139	83.82 158	85.15 237	80.27 83	88.75 122	85.45 261	79.95 127	91.90 167	81.92 83	90.80 244	96.13 33

EI-MVSNet-Vis-set		85.12 115	84.53 134	86.88 93	84.01 263	72.76 131	83.91 156	85.18 236	80.44 79	88.75 122	85.49 257	80.08 124	91.92 166	82.02 80	90.85 243	95.97 38

OMC-MVS		88.19 73	87.52 82	90.19 49	91.94 111	81.68 59	87.49 98	93.17 73	76.02 130	88.64 124	91.22 158	84.24 73	93.37 126	77.97 127	97.03 84	95.52 48

Regformer-3		85.06 117	84.67 131	86.22 108	84.27 256	73.43 127	84.07 150	85.26 234	80.77 78	88.62 125	85.48 258	80.56 120	90.39 212	81.99 81	91.04 234	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	83.58 164	87.97 191	67.13 186	87.55 96	94.32 19	73.44 163	88.47 128	87.54 229	86.45 57	91.06 191	75.76 149	93.76 182	92.54 151

NR-MVSNet		86.00 102	86.22 101	85.34 127	93.24 74	64.56 206	82.21 204	90.46 145	80.99 75	88.42 129	91.97 140	77.56 144	93.85 102	72.46 183	98.65 12	97.61 10

alignmvs		83.94 145	83.98 145	83.80 157	87.80 196	67.88 183	84.54 144	91.42 124	73.27 170	88.41 130	87.96 221	72.33 203	90.83 199	76.02 147	94.11 176	92.69 146

TransMVSNet (Re)		84.02 142	85.74 110	78.85 241	91.00 139	55.20 298	82.29 200	87.26 205	79.65 90	88.38 131	95.52 32	83.00 84	86.88 262	67.97 222	96.60 99	94.45 79

PM-MVS		80.20 204	79.00 209	83.78 159	88.17 189	86.66 16	81.31 216	66.81 340	69.64 209	88.33 132	90.19 189	64.58 239	83.63 298	71.99 187	90.03 252	81.06 328

tttt0517		81.07 185	79.58 205	85.52 124	88.99 174	66.45 194	87.03 105	75.51 298	73.76 159	88.32 133	90.20 188	37.96 349	94.16 94	79.36 112	95.13 148	95.93 41

casdiffmvs		85.21 112	85.85 108	83.31 169	86.17 233	62.77 225	83.03 182	93.93 39	74.69 149	88.21 134	92.68 122	82.29 94	91.89 168	77.87 128	93.75 184	95.27 56

v148		82.31 168	82.48 167	81.81 200	85.59 239	59.66 259	81.47 214	86.02 225	72.85 176	88.05 135	90.65 181	70.73 214	90.91 196	75.15 155	91.79 223	94.87 63

AllTest		87.97 76	87.40 85	89.68 54	91.59 119	83.40 48	89.50 67	95.44 6	79.47 91	88.00 136	93.03 107	82.66 88	91.47 176	70.81 191	96.14 116	94.16 89

TestCases				89.68 54	91.59 119	83.40 48		95.44 6	79.47 91	88.00 136	93.03 107	82.66 88	91.47 176	70.81 191	96.14 116	94.16 89

pm-mvs1		83.69 148	84.95 123	79.91 227	90.04 161	59.66 259	82.43 196	87.44 202	75.52 139	87.85 138	95.26 37	81.25 113	85.65 281	68.74 215	96.04 119	94.42 80

PCF-MVS	74.62 15	82.15 172	80.92 187	85.84 118	89.43 165	72.30 143	80.53 227	91.82 113	57.36 300	87.81 139	89.92 195	77.67 143	93.63 111	58.69 279	95.08 151	91.58 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 182	81.61 177	80.41 221	86.38 223	58.75 273	83.93 155	86.58 220	72.43 180	87.65 140	92.98 109	63.78 245	90.22 216	66.86 226	93.92 180	92.27 162

VPA-MVSNet		83.47 154	84.73 126	79.69 232	90.29 154	57.52 280	81.30 218	88.69 184	76.29 124	87.58 141	94.44 60	80.60 119	87.20 257	66.60 230	96.82 92	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	92.38 129	81.42 111	93.28 128	83.07 70	97.24 79	91.67 182

VDDNet		84.35 132	85.39 117	81.25 205	95.13 32	59.32 262	85.42 131	81.11 264	86.41 27	87.41 143	96.21 19	73.61 185	90.61 207	66.33 231	96.85 89	93.81 106

cl_fuxian		81.64 179	81.59 178	81.79 201	80.86 292	59.15 266	78.61 256	90.18 160	68.36 219	87.20 144	87.11 238	69.39 217	91.62 173	78.16 123	94.43 171	94.60 71

VDD-MVS		84.23 137	84.58 133	83.20 171	91.17 136	65.16 202	83.25 176	84.97 243	79.79 87	87.18 145	94.27 69	74.77 174	90.89 197	69.24 207	96.54 101	93.55 118

MSLP-MVS++		85.00 120	86.03 105	81.90 195	91.84 115	71.56 155	86.75 112	93.02 81	75.95 133	87.12 146	89.39 199	77.98 139	89.40 234	77.46 132	94.78 161	84.75 279

baseline		85.20 113	85.93 106	83.02 174	86.30 228	62.37 232	84.55 142	93.96 38	74.48 152	87.12 146	92.03 139	82.30 93	91.94 165	78.39 118	94.21 174	94.74 68

YYNet1		70.06 290	70.44 286	68.90 309	73.76 344	53.42 308	58.99 348	67.20 336	58.42 291	87.10 148	85.39 263	59.82 266	67.32 341	59.79 275	83.50 313	85.96 265

MDA-MVSNet_test_wron		70.05 291	70.44 286	68.88 310	73.84 343	53.47 306	58.93 349	67.28 335	58.43 290	87.09 149	85.40 262	59.80 267	67.25 342	59.66 276	83.54 312	85.92 267

CNVR-MVS		87.81 80	87.68 80	88.21 80	92.87 82	77.30 101	85.25 132	91.23 128	77.31 117	87.07 150	91.47 155	82.94 85	94.71 71	84.67 55	96.27 112	92.62 149

EPP-MVSNet		85.47 110	85.04 121	86.77 96	91.52 125	69.37 168	91.63 34	87.98 198	81.51 70	87.05 151	91.83 145	66.18 235	95.29 51	70.75 194	96.89 87	95.64 45

RRT_MVS		83.25 157	81.08 184	89.74 53	80.55 299	79.32 77	86.41 118	86.69 218	72.33 184	87.00 152	91.08 163	44.98 333	95.55 37	84.47 58	96.24 113	94.36 82

TinyColmap		81.25 183	82.34 169	77.99 258	85.33 242	60.68 251	82.32 199	88.33 190	71.26 192	86.97 153	92.22 138	77.10 151	86.98 261	62.37 255	95.17 147	86.31 263

eth_miper_zixun_eth		80.84 189	80.22 197	82.71 183	81.41 284	60.98 248	77.81 265	90.14 161	67.31 233	86.95 154	87.24 235	64.26 241	92.31 154	75.23 154	91.61 226	94.85 65

Patchmatch-RL test		74.48 259	73.68 259	76.89 269	84.83 246	66.54 193	72.29 314	69.16 333	57.70 296	86.76 155	86.33 246	45.79 323	82.59 301	69.63 204	90.65 249	81.54 319

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	86.75 156	93.26 103	93.64 2	90.93 194	84.60 56	90.75 245	93.97 95

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	86.69 157	92.28 135	80.36 122	95.06 62	86.17 39	96.49 103	90.22 212

TSAR-MVS + MP.		88.14 74	87.82 78	89.09 65	95.72 21	76.74 108	92.49 23	91.19 130	67.85 228	86.63 158	94.84 47	79.58 129	95.96 11	87.62 14	94.50 168	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		82.61 164	82.42 168	83.20 171	83.25 270	63.66 213	83.50 169	85.07 238	76.06 128	86.55 159	85.10 267	73.41 190	90.25 213	78.15 125	90.67 247	95.68 44

HQP_MVS		87.75 81	87.43 84	88.70 70	93.45 67	76.42 112	89.45 69	93.61 53	79.44 93	86.55 159	92.95 112	74.84 171	95.22 55	80.78 95	95.83 125	94.46 77

plane_prior3						76.85 107			77.79 112	86.55 159

BH-untuned		80.96 187	80.99 185	80.84 214	88.55 181	68.23 179	80.33 230	88.46 186	72.79 177	86.55 159	86.76 241	74.72 175	91.77 172	61.79 261	88.99 262	82.52 308

MVSTER		77.09 232	75.70 243	81.25 205	75.27 338	61.08 244	77.49 272	85.07 238	60.78 281	86.55 159	88.68 212	43.14 339	90.25 213	73.69 168	90.67 247	92.42 153

旧先验2							81.73 209		56.88 303	86.54 164			84.90 287	72.81 180

IterMVS-SCA-FT		80.64 193	79.41 206	84.34 146	83.93 264	69.66 166	76.28 287	81.09 265	72.43 180	86.47 165	90.19 189	60.46 259	93.15 135	77.45 133	86.39 290	90.22 212

cl-mvsnet1		80.43 196	80.23 195	81.02 211	79.99 302	59.25 263	77.07 276	87.02 214	67.38 231	86.19 166	89.22 202	63.09 249	90.16 218	76.32 142	95.80 127	93.66 110

CDPH-MVS		86.17 101	85.54 114	88.05 83	92.25 100	75.45 117	83.85 157	92.01 106	65.91 243	86.19 166	91.75 149	83.77 77	94.98 64	77.43 134	96.71 95	93.73 107

cl-mvsnet_		80.42 197	80.23 195	81.02 211	79.99 302	59.25 263	77.07 276	87.02 214	67.37 232	86.18 168	89.21 203	63.08 250	90.16 218	76.31 143	95.80 127	93.65 111

ETH3 D test6400		85.09 116	84.87 124	85.75 120	90.80 144	69.34 169	85.90 123	93.31 65	65.43 250	86.11 169	89.95 193	80.92 115	94.86 67	75.90 148	95.57 134	93.05 130

MVS_111021_LR		84.28 135	83.76 148	85.83 119	89.23 169	83.07 51	80.99 222	83.56 249	72.71 178	86.07 170	89.07 207	81.75 108	86.19 274	77.11 137	93.36 188	88.24 239

GBi-Net		82.02 174	82.07 171	81.85 197	86.38 223	61.05 245	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 245	90.68 204	65.95 233	93.34 189	93.82 103

test1		82.02 174	82.07 171	81.85 197	86.38 223	61.05 245	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 245	90.68 204	65.95 233	93.34 189	93.82 103

FMVSNet3		78.80 215	78.55 213	79.57 234	82.89 275	56.89 286	81.76 208	85.77 227	69.04 215	86.00 171	90.44 184	51.75 301	90.09 224	65.95 233	93.34 189	91.72 180

miper_ehance_all_eth		80.34 200	80.04 202	81.24 207	79.82 304	58.95 268	77.66 267	89.66 169	65.75 247	85.99 174	85.11 266	68.29 224	91.42 180	76.03 146	92.03 219	93.33 119

tfpnnormal		81.79 178	82.95 159	78.31 251	88.93 175	55.40 294	80.83 225	82.85 253	76.81 121	85.90 175	94.14 77	74.58 177	86.51 269	66.82 229	95.68 133	93.01 132

TAPA-MVS	77.73 12	85.71 108	84.83 125	88.37 77	88.78 178	79.72 72	87.15 103	93.50 57	69.17 212	85.80 176	89.56 198	80.76 117	92.13 158	73.21 177	95.51 135	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 144	82.69 162	87.72 85	89.27 168	81.45 61	83.72 162	81.58 263	74.73 148	85.66 177	86.06 251	72.56 202	92.69 147	75.44 152	95.21 145	89.01 235

EU-MVSNet		75.12 252	74.43 254	77.18 265	83.11 273	59.48 261	85.71 127	82.43 256	39.76 354	85.64 178	88.76 210	44.71 335	87.88 251	73.86 166	85.88 294	84.16 285

LF4IMVS		82.75 163	81.93 174	85.19 128	82.08 277	80.15 69	85.53 129	88.76 183	68.01 223	85.58 179	87.75 225	71.80 209	86.85 263	74.02 163	93.87 181	88.58 238

Patchmtry		76.56 240	77.46 224	73.83 288	79.37 310	46.60 343	82.41 197	76.90 287	73.81 158	85.56 180	92.38 129	48.07 311	83.98 295	63.36 250	95.31 143	90.92 196

MVS_111021_HR		84.63 124	84.34 140	85.49 126	90.18 157	75.86 116	79.23 248	87.13 209	73.35 164	85.56 180	89.34 200	83.60 79	90.50 209	76.64 140	94.05 178	90.09 217

testdata				79.54 235	92.87 82	72.34 142		80.14 272	59.91 287	85.47 182	91.75 149	67.96 226	85.24 283	68.57 219	92.18 218	81.06 328

thisisatest0530		79.07 211	77.33 227	84.26 148	87.13 210	64.58 205	83.66 164	75.95 293	68.86 217	85.22 183	87.36 233	38.10 347	93.57 118	75.47 151	94.28 173	94.62 69

CLD-MVS		83.18 159	82.64 163	84.79 135	89.05 171	67.82 184	77.93 263	92.52 95	68.33 220	85.07 184	81.54 309	82.06 99	92.96 139	69.35 206	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 84	91.30 129	76.92 105	87.19 101	91.99 107	70.56 199	84.96 185	90.69 178	80.01 125	95.14 58	78.37 119	95.78 129	91.82 178
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	85.06 132	86.36 226	74.71 120	88.77 81	90.00 164	75.65 138	84.96 185	93.17 105	74.06 180	91.19 186	78.28 122	91.09 232	89.29 227

QAPM		82.59 165	82.59 165	82.58 186	86.44 221	66.69 191	89.94 55	90.36 149	67.97 225	84.94 187	92.58 125	72.71 199	92.18 157	70.63 197	87.73 279	88.85 236

VPNet		80.25 202	81.68 175	75.94 278	92.46 93	47.98 338	76.70 280	81.67 262	73.45 162	84.87 188	92.82 116	74.66 176	86.51 269	61.66 263	96.85 89	93.33 119

NCCC		87.36 82	86.87 93	88.83 67	92.32 98	78.84 82	86.58 115	91.09 132	78.77 103	84.85 189	90.89 172	80.85 116	95.29 51	81.14 89	95.32 141	92.34 156

PHI-MVS		86.38 95	85.81 109	88.08 81	88.44 184	77.34 99	89.35 71	93.05 77	73.15 172	84.76 190	87.70 226	78.87 133	94.18 89	80.67 97	96.29 109	92.73 142

pmmvs-eth3d		78.42 219	77.04 230	82.57 188	87.44 204	74.41 122	80.86 224	79.67 274	55.68 306	84.69 191	90.31 187	60.91 257	85.42 282	62.20 257	91.59 227	87.88 247

RRT_test8_iter05		78.08 222	77.52 223	79.75 230	80.84 293	52.54 315	80.61 226	88.96 180	67.77 230	84.62 192	89.29 201	33.89 354	92.10 161	77.59 130	94.15 175	94.62 69

test_prior3		86.31 96	86.31 99	86.32 103	90.59 149	71.99 147	83.37 172	92.85 86	75.43 140	84.58 193	91.57 151	81.92 105	94.17 91	79.54 108	96.97 85	92.80 139

test_prior2							83.37 172		75.43 140	84.58 193	91.57 151	81.92 105		79.54 108	96.97 85

TEST9					92.34 96	79.70 73	83.94 153	90.32 150	65.41 254	84.49 195	90.97 168	82.03 101	93.63 111

train_agg		85.98 104	85.28 118	88.07 82	92.34 96	79.70 73	83.94 153	90.32 150	65.79 244	84.49 195	90.97 168	81.93 103	93.63 111	81.21 88	96.54 101	90.88 197

Gipuma		84.44 130	86.33 98	78.78 242	84.20 259	73.57 126	89.55 64	90.44 146	84.24 36	84.38 197	94.89 45	76.35 163	80.40 312	76.14 145	96.80 93	82.36 310
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 105	78.87 81	83.82 158	90.31 152	65.79 244	84.36 198	90.96 170	81.93 103	93.44 123

cl-mvsnet2		78.97 212	78.21 218	81.24 207	77.74 318	59.01 267	77.46 273	87.13 209	65.79 244	84.32 199	85.10 267	58.96 272	90.88 198	75.36 153	92.03 219	93.84 100

agg_prior1		85.72 107	85.20 119	87.28 91	91.58 122	77.69 92	83.69 163	90.30 153	66.29 240	84.32 199	91.07 165	82.13 97	93.18 131	81.02 90	96.36 107	90.98 193

agg_prior					91.58 122	77.69 92		90.30 153		84.32 199			93.18 131

Anonymous202405211		80.51 195	81.19 183	78.49 248	88.48 182	57.26 282	76.63 281	82.49 255	81.21 73	84.30 202	92.24 137	67.99 225	86.24 273	62.22 256	95.13 148	91.98 174

LFMVS		80.15 205	80.56 189	78.89 240	89.19 170	55.93 290	85.22 133	73.78 309	82.96 53	84.28 203	92.72 120	57.38 282	90.07 225	63.80 247	95.75 130	90.68 202

Vis-MVSNet		86.86 87	86.58 96	87.72 85	92.09 105	77.43 98	87.35 99	92.09 104	78.87 101	84.27 204	94.05 80	78.35 137	93.65 109	80.54 99	91.58 228	92.08 168
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	85.52 124	88.58 180	70.40 161	84.50 146	93.37 59	78.76 104	84.07 205	78.72 328	80.39 121	95.13 59	73.82 167	92.98 201	91.04 192

MCST-MVS		84.36 131	83.93 146	85.63 122	91.59 119	71.58 154	83.52 166	92.13 103	61.82 271	83.96 206	89.75 197	79.93 128	93.46 122	78.33 121	94.34 172	91.87 176

新几何1				82.95 176	93.96 57	78.56 85		80.24 270	55.45 307	83.93 207	91.08 163	71.19 213	88.33 247	65.84 236	93.07 197	81.95 315

BH-RMVSNet		80.53 194	80.22 197	81.49 203	87.19 209	66.21 195	77.79 266	86.23 222	74.21 154	83.69 208	88.50 214	73.25 194	90.75 201	63.18 252	87.90 276	87.52 250

1121		80.86 188	79.81 204	84.02 152	93.93 58	78.70 83	81.64 211	80.18 271	55.43 308	83.67 209	91.15 161	71.29 212	91.41 181	67.95 223	93.06 198	81.96 314

USDC		76.63 238	76.73 234	76.34 275	83.46 268	57.20 283	80.02 233	88.04 196	52.14 326	83.65 210	91.25 157	63.24 248	86.65 268	54.66 304	94.11 176	85.17 274

miper_enhance_ethall		77.83 224	76.93 231	80.51 219	76.15 331	58.01 276	75.47 295	88.82 181	58.05 294	83.59 211	80.69 313	64.41 240	91.20 185	73.16 178	92.03 219	92.33 157

Effi-MVS+-dtu		85.82 106	83.38 151	93.14 3	87.13 210	91.15 2	87.70 95	88.42 187	74.57 150	83.56 212	85.65 255	78.49 135	94.21 87	72.04 185	92.88 203	94.05 93

CNLPA		83.55 152	83.10 157	84.90 133	89.34 167	83.87 46	84.54 144	88.77 182	79.09 97	83.54 213	88.66 213	74.87 170	81.73 306	66.84 228	92.29 213	89.11 229

bset_n11_16_dypcd		79.19 210	77.97 220	82.86 181	85.81 237	66.85 190	75.02 298	79.31 275	66.07 241	83.50 214	83.37 291	55.04 296	92.10 161	78.63 117	94.99 155	89.63 219

OpenMVS_ROB	70.19 17	77.77 227	77.46 224	78.71 244	84.39 253	61.15 243	81.18 220	82.52 254	62.45 268	83.34 215	87.37 232	66.20 234	88.66 244	64.69 243	85.02 301	86.32 262

thres100view900		75.45 248	75.05 248	76.66 272	87.27 206	51.88 320	81.07 221	73.26 313	75.68 137	83.25 216	86.37 245	45.54 324	88.80 239	51.98 317	90.99 236	89.31 225

miper_lstm_enhance		76.45 242	76.10 239	77.51 263	76.72 326	60.97 249	64.69 337	85.04 240	63.98 260	83.20 217	88.22 217	56.67 285	78.79 317	73.22 172	93.12 196	92.78 141

IterMVS		76.91 234	76.34 237	78.64 245	80.91 290	64.03 211	76.30 286	79.03 278	64.88 257	83.11 218	89.16 204	59.90 265	84.46 291	68.61 217	85.15 300	87.42 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 245	75.35 247	77.85 261	87.01 216	51.84 321	80.45 228	73.26 313	75.20 144	83.10 219	86.31 248	45.54 324	89.05 235	55.03 302	92.24 215	92.66 147

mvs_anonymous		78.13 221	78.76 211	76.23 277	79.24 311	50.31 332	78.69 254	84.82 244	61.60 275	83.09 220	92.82 116	73.89 183	87.01 258	68.33 220	86.41 289	91.37 189

MVS_Test		82.47 167	83.22 153	80.22 224	82.62 276	57.75 279	82.54 194	91.96 109	71.16 194	82.89 221	92.52 128	77.41 146	90.50 209	80.04 102	87.84 278	92.40 155

test12				86.57 98	90.74 145	72.63 135		90.69 140		82.76 222		79.20 130	94.80 69		95.32 141	92.27 162

原ACMM1				84.60 140	92.81 87	74.01 124		91.50 120	62.59 265	82.73 223	90.67 180	76.53 161	94.25 84	69.24 207	95.69 132	85.55 270

test_yl		78.71 217	78.51 214	79.32 237	84.32 254	58.84 270	78.38 257	85.33 232	75.99 131	82.49 224	86.57 242	58.01 276	90.02 226	62.74 253	92.73 206	89.10 230

DCV-MVSNet		78.71 217	78.51 214	79.32 237	84.32 254	58.84 270	78.38 257	85.33 232	75.99 131	82.49 224	86.57 242	58.01 276	90.02 226	62.74 253	92.73 206	89.10 230

diffmvs		80.40 198	80.48 192	80.17 225	79.02 314	60.04 255	77.54 270	90.28 157	66.65 238	82.40 226	87.33 234	73.50 187	87.35 256	77.98 126	89.62 256	93.13 127

test222					93.31 72	76.54 109	79.38 243	77.79 283	52.59 321	82.36 227	90.84 174	66.83 232			91.69 225	81.25 323

D2MVS		76.84 235	75.67 244	80.34 222	80.48 300	62.16 236	73.50 308	84.80 245	57.61 298	82.24 228	87.54 229	51.31 302	87.65 253	70.40 200	93.19 195	91.23 191

VNet		79.31 209	80.27 194	76.44 273	87.92 193	53.95 303	75.58 293	84.35 247	74.39 153	82.23 229	90.72 177	72.84 198	84.39 292	60.38 273	93.98 179	90.97 194

Vis-MVSNet (Re-imp)		77.82 225	77.79 222	77.92 259	88.82 177	51.29 325	83.28 174	71.97 322	74.04 155	82.23 229	89.78 196	57.38 282	89.41 233	57.22 287	95.41 137	93.05 130

API-MVS		82.28 169	82.61 164	81.30 204	86.29 229	69.79 163	88.71 82	87.67 201	78.42 108	82.15 231	84.15 282	77.98 139	91.59 174	65.39 239	92.75 205	82.51 309

DP-MVS Recon		84.05 141	83.22 153	86.52 100	91.73 117	75.27 118	83.23 178	92.40 97	72.04 187	82.04 232	88.33 216	77.91 141	93.95 100	66.17 232	95.12 150	90.34 211

MSDG		80.06 207	79.99 203	80.25 223	83.91 265	68.04 182	77.51 271	89.19 177	77.65 113	81.94 233	83.45 288	76.37 162	86.31 272	63.31 251	86.59 287	86.41 261

Fast-Effi-MVS+		81.04 186	80.57 188	82.46 190	87.50 203	63.22 219	78.37 259	89.63 170	68.01 223	81.87 234	82.08 304	82.31 92	92.65 148	67.10 225	88.30 273	91.51 188

testgi		72.36 274	74.61 250	65.59 321	80.56 298	42.82 352	68.29 327	73.35 312	66.87 236	81.84 235	89.93 194	72.08 206	66.92 344	46.05 339	92.54 210	87.01 257

tfpn200view9		74.86 256	74.23 255	76.74 271	86.24 230	52.12 317	79.24 246	73.87 307	73.34 165	81.82 236	84.60 278	46.02 318	88.80 239	51.98 317	90.99 236	89.31 225

thres400		75.14 250	74.23 255	77.86 260	86.24 230	52.12 317	79.24 246	73.87 307	73.34 165	81.82 236	84.60 278	46.02 318	88.80 239	51.98 317	90.99 236	92.66 147

CL-MVSNet_2432*1600		76.81 236	77.38 226	75.12 282	86.90 218	51.34 323	73.20 311	80.63 269	68.30 221	81.80 238	88.40 215	66.92 231	80.90 309	55.35 299	94.90 158	93.12 128

OpenMVS	76.72 13	81.98 176	82.00 173	81.93 194	84.42 252	68.22 180	88.50 86	89.48 173	66.92 235	81.80 238	91.86 142	72.59 201	90.16 218	71.19 190	91.25 231	87.40 252

AdaColmap		83.66 149	83.69 149	83.57 165	90.05 160	72.26 144	86.29 121	90.00 164	78.19 110	81.65 240	87.16 236	83.40 81	94.24 85	61.69 262	94.76 164	84.21 284

DELS-MVS		81.44 181	81.25 181	82.03 193	84.27 256	62.87 224	76.47 285	92.49 96	70.97 195	81.64 241	83.83 283	75.03 168	92.70 146	74.29 159	92.22 217	90.51 208
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		83.10 161	82.54 166	84.77 136	92.90 81	69.10 176	86.65 113	90.62 143	54.66 311	81.46 242	90.81 175	76.98 153	94.38 81	72.62 181	96.18 114	90.82 199

TR-MVS		76.77 237	75.79 241	79.72 231	86.10 235	65.79 198	77.14 274	83.02 251	65.20 255	81.40 243	82.10 303	66.30 233	90.73 203	55.57 296	85.27 298	82.65 304

TAMVS		78.08 222	76.36 236	83.23 170	90.62 148	72.87 130	79.08 249	80.01 273	61.72 273	81.35 244	86.92 240	63.96 244	88.78 242	50.61 321	93.01 200	88.04 243

Effi-MVS+		83.90 146	84.01 144	83.57 165	87.22 208	65.61 199	86.55 116	92.40 97	78.64 105	81.34 245	84.18 281	83.65 78	92.93 141	74.22 160	87.87 277	92.17 167

CS-MVS		83.43 156	83.04 158	84.59 141	87.87 194	66.61 192	85.57 128	94.90 13	73.02 174	81.12 246	78.56 329	80.00 126	95.52 39	73.04 179	93.29 192	91.62 184

new-patchmatchnet		70.10 289	73.37 263	60.29 334	81.23 287	16.95 362	59.54 345	74.62 301	62.93 263	80.97 247	87.93 223	62.83 252	71.90 331	55.24 300	95.01 154	92.00 171

PVSNet_Blended_VisFu		81.55 180	80.49 191	84.70 139	91.58 122	73.24 129	84.21 148	91.67 117	62.86 264	80.94 248	87.16 236	67.27 229	92.87 144	69.82 203	88.94 264	87.99 244

BH-w/o		76.57 239	76.07 240	78.10 256	86.88 219	65.92 197	77.63 268	86.33 221	65.69 248	80.89 249	79.95 322	68.97 222	90.74 202	53.01 313	85.25 299	77.62 334

PAPM_NR		83.23 158	83.19 155	83.33 168	90.90 141	65.98 196	88.19 88	90.78 138	78.13 111	80.87 250	87.92 224	73.49 189	92.42 151	70.07 201	88.40 268	91.60 185

ab-mvs		79.67 208	80.56 189	76.99 266	88.48 182	56.93 284	84.70 139	86.06 224	68.95 216	80.78 251	93.08 106	75.30 167	84.62 290	56.78 288	90.90 241	89.43 223

mvs-test1		84.55 127	82.12 170	91.84 20	87.13 210	89.54 4	85.05 135	88.42 187	74.57 150	80.60 252	82.98 292	78.49 135	93.98 99	72.04 185	89.77 254	92.00 171

XXY-MVS		74.44 261	76.19 238	69.21 308	84.61 248	52.43 316	71.70 316	77.18 286	60.73 282	80.60 252	90.96 170	75.44 164	69.35 336	56.13 292	88.33 269	85.86 268

HQP4-MVS										80.56 254			94.61 76			93.56 116

HQP-NCC					91.19 133		84.77 136		73.30 167	80.55 255

ACMP_Plane					91.19 133		84.77 136		73.30 167	80.55 255

HQP-MVS		84.61 125	84.06 143	86.27 106	91.19 133	70.66 159	84.77 136	92.68 92	73.30 167	80.55 255	90.17 191	72.10 204	94.61 76	77.30 135	94.47 169	93.56 116

AUN-MVS		81.18 184	78.78 210	88.39 76	90.93 140	82.14 56	82.51 195	83.67 248	64.69 258	80.29 258	85.91 254	51.07 303	92.38 152	76.29 144	93.63 187	90.65 204

HyFIR lowres test		75.12 252	72.66 270	82.50 189	91.44 128	65.19 201	72.47 313	87.31 204	46.79 342	80.29 258	84.30 280	52.70 300	92.10 161	51.88 320	86.73 286	90.22 212

test20.03		73.75 264	74.59 252	71.22 301	81.11 288	51.12 327	70.15 322	72.10 321	70.42 200	80.28 260	91.50 154	64.21 242	74.72 328	46.96 337	94.58 167	87.82 249

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	80.18 261	89.15 205	77.04 152	93.28 128	65.82 237	92.28 214	92.21 165

GA-MVS		75.83 246	74.61 250	79.48 236	81.87 279	59.25 263	73.42 309	82.88 252	68.68 218	79.75 262	81.80 306	50.62 305	89.46 231	66.85 227	85.64 295	89.72 218

xiu_mvs_v1_base_debu		80.84 189	80.14 199	82.93 177	88.31 185	71.73 150	79.53 239	87.17 206	65.43 250	79.59 263	82.73 299	76.94 154	90.14 221	73.22 172	88.33 269	86.90 258

xiu_mvs_v1_base		80.84 189	80.14 199	82.93 177	88.31 185	71.73 150	79.53 239	87.17 206	65.43 250	79.59 263	82.73 299	76.94 154	90.14 221	73.22 172	88.33 269	86.90 258

xiu_mvs_v1_base_debi		80.84 189	80.14 199	82.93 177	88.31 185	71.73 150	79.53 239	87.17 206	65.43 250	79.59 263	82.73 299	76.94 154	90.14 221	73.22 172	88.33 269	86.90 258

UnsupCasMVSNet_eth		71.63 281	72.30 275	69.62 306	76.47 328	52.70 313	70.03 323	80.97 266	59.18 288	79.36 266	88.21 218	60.50 258	69.12 337	58.33 282	77.62 338	87.04 256

ppachtmachnet_test		74.73 258	74.00 257	76.90 268	80.71 296	56.89 286	71.53 317	78.42 280	58.24 292	79.32 267	82.92 296	57.91 279	84.26 293	65.60 238	91.36 230	89.56 220

MG-MVS		80.32 201	80.94 186	78.47 249	88.18 188	52.62 314	82.29 200	85.01 242	72.01 188	79.24 268	92.54 127	69.36 218	93.36 127	70.65 196	89.19 261	89.45 221

Fast-Effi-MVS+-dtu		82.54 166	81.41 180	85.90 116	85.60 238	76.53 111	83.07 181	89.62 171	73.02 174	79.11 269	83.51 286	80.74 118	90.24 215	68.76 214	89.29 258	90.94 195

CDS-MVSNet		77.32 230	75.40 245	83.06 173	89.00 173	72.48 140	77.90 264	82.17 258	60.81 280	78.94 270	83.49 287	59.30 269	88.76 243	54.64 305	92.37 212	87.93 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 266	73.54 261	72.43 297	84.92 245	47.79 339	79.89 235	74.00 306	65.93 242	78.81 271	86.28 249	56.36 287	81.63 307	56.63 289	79.04 334	87.87 248

EIA-MVS		82.19 171	81.23 182	85.10 131	87.95 192	69.17 175	83.22 179	93.33 62	70.42 200	78.58 272	79.77 325	77.29 147	94.20 88	71.51 188	88.96 263	91.93 175

thres200		72.34 275	71.55 281	74.70 285	83.48 267	51.60 322	75.02 298	73.71 310	70.14 206	78.56 273	80.57 316	46.20 316	88.20 249	46.99 336	89.29 258	84.32 283

our_test_3		71.85 278	71.59 279	72.62 295	80.71 296	53.78 304	69.72 324	71.71 326	58.80 289	78.03 274	80.51 318	56.61 286	78.84 316	62.20 257	86.04 293	85.23 273

KD-MVS_2432*1600		66.87 303	65.81 308	70.04 302	67.50 355	47.49 340	62.56 341	79.16 276	61.21 278	77.98 275	80.61 314	25.29 364	82.48 302	53.02 311	84.92 302	80.16 331

miper_refine_blended		66.87 303	65.81 308	70.04 302	67.50 355	47.49 340	62.56 341	79.16 276	61.21 278	77.98 275	80.61 314	25.29 364	82.48 302	53.02 311	84.92 302	80.16 331

jason		77.42 229	75.75 242	82.43 191	87.10 214	69.27 170	77.99 262	81.94 260	51.47 330	77.84 277	85.07 270	60.32 261	89.00 236	70.74 195	89.27 260	89.03 233
jason: jason.
MAR-MVS		80.24 203	78.74 212	84.73 137	86.87 220	78.18 87	85.75 125	87.81 200	65.67 249	77.84 277	78.50 330	73.79 184	90.53 208	61.59 265	90.87 242	85.49 272
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		72.29 276	72.00 276	73.14 291	88.63 179	85.00 35	74.65 302	67.39 334	71.94 189	77.80 279	87.66 227	50.48 306	75.83 324	49.95 323	79.51 329	58.58 353

pmmvs4		74.92 255	72.98 267	80.73 216	84.95 244	71.71 153	76.23 288	77.59 284	52.83 320	77.73 280	86.38 244	56.35 288	84.97 286	57.72 286	87.05 284	85.51 271

ET-MVSNet_ETH3D		75.28 249	72.77 268	82.81 182	83.03 274	68.11 181	77.09 275	76.51 291	60.67 283	77.60 281	80.52 317	38.04 348	91.15 188	70.78 193	90.68 246	89.17 228

UnsupCasMVSNet_bld		69.21 296	69.68 291	67.82 315	79.42 308	51.15 326	67.82 331	75.79 294	54.15 313	77.47 282	85.36 265	59.26 270	70.64 333	48.46 330	79.35 331	81.66 317

Anonymous20231206		71.38 282	71.88 277	69.88 304	86.31 227	54.37 300	70.39 321	74.62 301	52.57 322	76.73 283	88.76 210	59.94 264	72.06 330	44.35 342	93.23 194	83.23 300

CMPMVS	59.41 20	75.12 252	73.57 260	79.77 228	75.84 333	67.22 185	81.21 219	82.18 257	50.78 334	76.50 284	87.66 227	55.20 294	82.99 300	62.17 259	90.64 250	89.09 232
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 277	71.69 278	73.32 289	81.57 282	53.02 310	76.77 279	78.37 281	63.31 261	76.37 285	91.85 143	36.68 351	78.98 315	47.87 333	92.45 211	87.95 245

CVMVSNet		72.62 272	71.41 282	76.28 276	83.25 270	60.34 253	83.50 169	79.02 279	37.77 355	76.33 286	85.10 267	49.60 308	87.41 255	70.54 198	77.54 339	81.08 326

PLC	73.85 16	82.09 173	80.31 193	87.45 89	90.86 143	80.29 68	85.88 124	90.65 141	68.17 222	76.32 287	86.33 246	73.12 195	92.61 149	61.40 266	90.02 253	89.44 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 170	81.57 179	84.19 151	85.54 240	69.26 171	91.98 29	90.08 162	71.54 190	76.23 288	85.07 270	58.69 273	94.27 82	86.26 35	88.77 265	89.03 233

lupinMVS		76.37 243	74.46 253	82.09 192	85.54 240	69.26 171	76.79 278	80.77 268	50.68 336	76.23 288	82.82 297	58.69 273	88.94 237	69.85 202	88.77 265	88.07 241

PatchMatch-RL		74.48 259	73.22 264	78.27 254	87.70 198	85.26 33	75.92 290	70.09 330	64.34 259	76.09 290	81.25 311	65.87 237	78.07 318	53.86 307	83.82 311	71.48 342

thisisatest0515		73.00 270	70.52 285	80.46 220	81.45 283	59.90 257	73.16 312	74.31 305	57.86 295	76.08 291	77.78 332	37.60 350	92.12 160	65.00 240	91.45 229	89.35 224

MS-PatchMatch		70.93 284	70.22 288	73.06 292	81.85 280	62.50 230	73.82 307	77.90 282	52.44 323	75.92 292	81.27 310	55.67 291	81.75 305	55.37 298	77.70 337	74.94 338

CHOSEN 1792x2688		72.45 273	70.56 284	78.13 255	90.02 162	63.08 220	68.72 326	83.16 250	42.99 351	75.92 292	85.46 260	57.22 284	85.18 285	49.87 325	81.67 322	86.14 264

CR-MVSNet		74.00 262	73.04 266	76.85 270	79.58 305	62.64 227	82.58 191	76.90 287	50.50 337	75.72 294	92.38 129	48.07 311	84.07 294	68.72 216	82.91 317	83.85 289

RPMNet		78.88 213	78.28 217	80.68 218	79.58 305	62.64 227	82.58 191	94.16 28	74.80 147	75.72 294	92.59 123	48.69 309	95.56 34	73.48 170	82.91 317	83.85 289

DPM-MVS		80.10 206	79.18 208	82.88 180	90.71 147	69.74 164	78.87 252	90.84 136	60.29 285	75.64 296	85.92 253	67.28 228	93.11 136	71.24 189	91.79 223	85.77 269

PVSNet_BlendedMVS		78.80 215	77.84 221	81.65 202	84.43 250	63.41 215	79.49 242	90.44 146	61.70 274	75.43 297	87.07 239	69.11 220	91.44 178	60.68 271	92.24 215	90.11 216

PVSNet_Blended		76.49 241	75.40 245	79.76 229	84.43 250	63.41 215	75.14 297	90.44 146	57.36 300	75.43 297	78.30 331	69.11 220	91.44 178	60.68 271	87.70 280	84.42 282

PAPR		78.84 214	78.10 219	81.07 209	85.17 243	60.22 254	82.21 204	90.57 144	62.51 266	75.32 299	84.61 277	74.99 169	92.30 155	59.48 277	88.04 275	90.68 202

N_pmnet		70.20 287	68.80 296	74.38 286	80.91 290	84.81 38	59.12 347	76.45 292	55.06 309	75.31 300	82.36 302	55.74 290	54.82 354	47.02 335	87.24 283	83.52 293

cascas		76.29 244	74.81 249	80.72 217	84.47 249	62.94 222	73.89 306	87.34 203	55.94 305	75.16 301	76.53 340	63.97 243	91.16 187	65.00 240	90.97 239	88.06 242

SCA		73.32 265	72.57 272	75.58 280	81.62 281	55.86 291	78.89 251	71.37 327	61.73 272	74.93 302	83.42 289	60.46 259	87.01 258	58.11 284	82.63 321	83.88 286

xiu_mvs_v2_base		77.19 231	76.75 233	78.52 247	87.01 216	61.30 241	75.55 294	87.12 212	61.24 277	74.45 303	78.79 327	77.20 148	90.93 194	64.62 245	84.80 307	83.32 298

CANet		83.79 147	82.85 160	86.63 97	86.17 233	72.21 146	83.76 161	91.43 122	77.24 118	74.39 304	87.45 231	75.36 166	95.42 47	77.03 138	92.83 204	92.25 164

PS-MVSNAJ		77.04 233	76.53 235	78.56 246	87.09 215	61.40 239	75.26 296	87.13 209	61.25 276	74.38 305	77.22 337	76.94 154	90.94 193	64.63 244	84.83 306	83.35 297

MVP-Stereo		75.81 247	73.51 262	82.71 183	89.35 166	73.62 125	80.06 231	85.20 235	60.30 284	73.96 306	87.94 222	57.89 280	89.45 232	52.02 316	74.87 343	85.06 276
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 162	81.64 176	86.21 110	86.20 232	76.24 115	86.86 106	85.68 228	77.07 119	73.76 307	92.82 116	69.64 216	91.82 171	69.04 212	93.69 185	90.56 206
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		74.82 257	73.74 258	78.04 257	89.57 164	60.04 255	76.49 284	87.09 213	54.31 312	73.66 308	79.80 323	60.25 262	86.76 267	58.37 280	84.15 310	87.32 253

Test_1112_low_res		73.90 263	73.08 265	76.35 274	90.35 153	55.95 289	73.40 310	86.17 223	50.70 335	73.14 309	85.94 252	58.31 275	85.90 278	56.51 290	83.22 314	87.20 254

1314		73.22 267	72.56 273	75.20 281	80.41 301	57.84 277	81.64 211	85.36 231	51.68 329	73.10 310	76.65 339	61.45 255	85.19 284	63.54 248	79.21 333	82.59 305

Patchmatch-test		65.91 309	67.38 301	61.48 332	75.51 335	43.21 351	68.84 325	63.79 344	62.48 267	72.80 311	83.42 289	44.89 334	59.52 353	48.27 332	86.45 288	81.70 316

PatchmatchNet		69.71 294	68.83 295	72.33 298	77.66 320	53.60 305	79.29 244	69.99 331	57.66 297	72.53 312	82.93 295	46.45 315	80.08 314	60.91 269	72.09 346	83.31 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 299	68.08 300	67.55 316	78.74 316	43.53 350	75.60 292	67.10 339	54.92 310	72.23 313	88.10 219	42.87 340	75.97 323	52.21 315	80.95 328	83.15 301

pmmvs5		70.73 285	70.07 289	72.72 293	77.03 324	52.73 312	74.14 303	75.65 297	50.36 338	72.17 314	85.37 264	55.42 293	80.67 311	52.86 314	87.59 281	84.77 278

PatchT		70.52 286	72.76 269	63.79 326	79.38 309	33.53 358	77.63 268	65.37 342	73.61 160	71.77 315	92.79 119	44.38 336	75.65 325	64.53 246	85.37 297	82.18 312

MVS		73.21 268	72.59 271	75.06 283	80.97 289	60.81 250	81.64 211	85.92 226	46.03 345	71.68 316	77.54 333	68.47 223	89.77 228	55.70 295	85.39 296	74.60 339

MIMVSNet		71.09 283	71.59 279	69.57 307	87.23 207	50.07 333	78.91 250	71.83 323	60.20 286	71.26 317	91.76 148	55.08 295	76.09 322	41.06 347	87.02 285	82.54 307

WTY-MVS		67.91 300	68.35 298	66.58 319	80.82 294	48.12 337	65.96 335	72.60 317	53.67 316	71.20 318	81.68 308	58.97 271	69.06 338	48.57 329	81.67 322	82.55 306

test0.0.03 1		64.66 312	64.36 313	65.57 322	75.03 340	46.89 342	64.69 337	61.58 349	62.43 269	71.18 319	77.54 333	43.41 337	68.47 339	40.75 348	82.65 319	81.35 320

CostFormer		69.98 292	68.68 297	73.87 287	77.14 322	50.72 330	79.26 245	74.51 303	51.94 328	70.97 320	84.75 275	45.16 332	87.49 254	55.16 301	79.23 332	83.40 296

tpmvs		70.16 288	69.56 292	71.96 299	74.71 342	48.13 336	79.63 237	75.45 299	65.02 256	70.26 321	81.88 305	45.34 329	85.68 280	58.34 281	75.39 342	82.08 313

sss		66.92 302	67.26 302	65.90 320	77.23 321	51.10 328	64.79 336	71.72 325	52.12 327	70.13 322	80.18 320	57.96 278	65.36 349	50.21 322	81.01 327	81.25 323

tpm2		68.45 298	66.83 304	73.30 290	78.93 315	48.50 335	79.76 236	71.76 324	47.50 341	69.92 323	83.60 285	42.07 341	88.40 246	48.44 331	79.51 329	83.01 303

HY-MVS	64.64 18	73.03 269	72.47 274	74.71 284	83.36 269	54.19 301	82.14 207	81.96 259	56.76 304	69.57 324	86.21 250	60.03 263	84.83 289	49.58 326	82.65 319	85.11 275

tpm cat1		66.76 305	65.21 311	71.42 300	77.09 323	50.62 331	78.01 261	73.68 311	44.89 347	68.64 325	79.00 326	45.51 326	82.42 304	49.91 324	70.15 349	81.23 325

IB-MVS	62.13 19	71.64 280	68.97 294	79.66 233	80.80 295	62.26 235	73.94 305	76.90 287	63.27 262	68.63 326	76.79 338	33.83 355	91.84 170	59.28 278	87.26 282	84.88 277
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		80.37 199	78.41 216	86.23 107	76.75 325	73.28 128	87.18 102	77.45 285	76.24 126	68.14 327	88.93 209	65.41 238	93.85 102	69.47 205	96.12 118	91.55 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 307	65.63 310	68.75 311	81.96 278	49.88 334	62.19 343	72.51 319	51.03 332	68.04 328	75.34 343	50.84 304	74.77 326	45.82 340	82.96 315	81.60 318

tpmrst		66.28 308	66.69 306	65.05 324	72.82 350	39.33 353	78.20 260	70.69 329	53.16 319	67.88 329	80.36 319	48.18 310	74.75 327	58.13 283	70.79 348	81.08 326

CANet_DTU		77.81 226	77.05 229	80.09 226	81.37 285	59.90 257	83.26 175	88.29 191	69.16 213	67.83 330	83.72 284	60.93 256	89.47 230	69.22 209	89.70 255	90.88 197

EPMVS		62.47 313	62.63 317	62.01 328	70.63 354	38.74 354	74.76 300	52.86 357	53.91 315	67.71 331	80.01 321	39.40 345	66.60 345	55.54 297	68.81 353	80.68 330

MVS_0304		78.17 220	77.23 228	80.99 213	84.13 262	69.07 177	81.39 215	80.81 267	76.28 125	67.53 332	89.11 206	62.87 251	86.77 265	60.90 270	92.01 222	87.13 255

MDTV_nov1_ep13			68.29 299		78.03 317	43.87 349	74.12 304	72.22 320	52.17 324	67.02 333	85.54 256	45.36 328	80.85 310	55.73 293	84.42 309

pmmvs3		62.47 313	60.02 325	69.80 305	71.58 353	64.00 212	70.52 320	58.44 353	39.77 353	66.05 334	75.84 341	27.10 363	72.28 329	46.15 338	84.77 308	73.11 340

ADS-MVSNet2		65.87 310	63.64 315	72.55 296	73.16 347	56.92 285	67.10 332	74.81 300	49.74 339	66.04 335	82.97 293	46.71 313	77.26 319	42.29 344	69.96 350	83.46 294

ADS-MVSNet		61.90 315	62.19 318	61.03 333	73.16 347	36.42 356	67.10 332	61.75 347	49.74 339	66.04 335	82.97 293	46.71 313	63.21 351	42.29 344	69.96 350	83.46 294

DWT-MVSNet_test		66.43 306	64.37 312	72.63 294	74.86 341	50.86 329	76.52 283	72.74 316	54.06 314	65.50 337	68.30 351	32.13 357	84.84 288	61.63 264	73.59 344	82.19 311

DSMNet-mixed		60.98 321	61.61 320	59.09 336	72.88 349	45.05 347	74.70 301	46.61 360	26.20 357	65.34 338	90.32 186	55.46 292	63.12 352	41.72 346	81.30 326	69.09 346

JIA-IIPM		69.41 295	66.64 307	77.70 262	73.19 346	71.24 156	75.67 291	65.56 341	70.42 200	65.18 339	92.97 111	33.64 356	83.06 299	53.52 309	69.61 352	78.79 333

test-LLR		67.21 301	66.74 305	68.63 312	76.45 329	55.21 296	67.89 328	67.14 337	62.43 269	65.08 340	72.39 345	43.41 337	69.37 334	61.00 267	84.89 304	81.31 321

test-mter		65.00 311	63.79 314	68.63 312	76.45 329	55.21 296	67.89 328	67.14 337	50.98 333	65.08 340	72.39 345	28.27 361	69.37 334	61.00 267	84.89 304	81.31 321

PMMVS2		55.64 326	59.27 326	44.74 340	64.30 360	12.32 363	40.60 354	49.79 359	53.19 318	65.06 342	84.81 274	53.60 299	49.76 356	32.68 356	89.41 257	72.15 341

baseline2		69.77 293	66.89 303	78.41 250	79.51 307	58.09 275	76.23 288	69.57 332	57.50 299	64.82 343	77.45 335	46.02 318	88.44 245	53.08 310	77.83 336	88.70 237

gg-mvs-nofinetune		68.96 297	69.11 293	68.52 314	76.12 332	45.32 345	83.59 165	55.88 355	86.68 25	64.62 344	97.01 7	30.36 359	83.97 296	44.78 341	82.94 316	76.26 336

PAPM		71.77 279	70.06 290	76.92 267	86.39 222	53.97 302	76.62 282	86.62 219	53.44 317	63.97 345	84.73 276	57.79 281	92.34 153	39.65 349	81.33 325	84.45 281

new_pmnet		55.69 325	57.66 327	49.76 339	75.47 336	30.59 359	59.56 344	51.45 358	43.62 350	62.49 346	75.48 342	40.96 343	49.15 357	37.39 352	72.52 345	69.55 345

MDTV_nov1_ep13_2view						27.60 361	70.76 319		46.47 344	61.27 347		45.20 330		49.18 327		83.75 291

dp		60.70 322	60.29 324	61.92 330	72.04 352	38.67 355	70.83 318	64.08 343	51.28 331	60.75 348	77.28 336	36.59 352	71.58 332	47.41 334	62.34 355	75.52 337

TESTMET0.1,1		61.29 318	60.32 323	64.19 325	72.06 351	51.30 324	67.89 328	62.09 345	45.27 346	60.65 349	69.01 348	27.93 362	64.74 350	56.31 291	81.65 324	76.53 335

PMMVS		61.65 316	60.38 322	65.47 323	65.40 359	69.26 171	63.97 339	61.73 348	36.80 356	60.11 350	68.43 349	59.42 268	66.35 346	48.97 328	78.57 335	60.81 350

PVSNet_0	51.08 22	56.10 324	54.97 329	59.48 335	75.12 339	53.28 309	55.16 350	61.89 346	44.30 348	59.16 351	62.48 354	54.22 297	65.91 348	35.40 353	47.01 356	59.25 352

MVS-HIRNet		61.16 319	62.92 316	55.87 337	79.09 312	35.34 357	71.83 315	57.98 354	46.56 343	59.05 352	91.14 162	49.95 307	76.43 321	38.74 350	71.92 347	55.84 354

E-PMN		61.59 317	61.62 319	61.49 331	66.81 357	55.40 294	53.77 351	60.34 350	66.80 237	58.90 353	65.50 352	40.48 344	66.12 347	55.72 294	86.25 291	62.95 349

GG-mvs-BLEND				67.16 317	73.36 345	46.54 344	84.15 149	55.04 356		58.64 354	61.95 355	29.93 360	83.87 297	38.71 351	76.92 340	71.07 343

EPNet_dtu		72.87 271	71.33 283	77.49 264	77.72 319	60.55 252	82.35 198	75.79 294	66.49 239	58.39 355	81.06 312	53.68 298	85.98 276	53.55 308	92.97 202	85.95 266
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 320	60.81 321	61.99 329	65.96 358	55.86 291	53.10 352	58.97 352	67.06 234	56.89 356	63.33 353	40.98 342	67.03 343	54.79 303	86.18 292	63.08 348

CHOSEN 280x420		59.08 323	56.52 328	66.76 318	76.51 327	64.39 208	49.62 353	59.00 351	43.86 349	55.66 357	68.41 350	35.55 353	68.21 340	43.25 343	76.78 341	67.69 347

MVE	40.22 23	51.82 327	50.47 330	55.87 337	62.66 361	51.91 319	31.61 356	39.28 361	40.65 352	50.76 358	74.98 344	56.24 289	44.67 358	33.94 355	64.11 354	71.04 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 341	32.95 362	29.49 360		21.63 364	12.07 358	37.95 359	45.07 356	30.84 358	19.21 359	17.94 358	33.06 358	23.69 355

tmp_tt		20.25 329	24.50 332	7.49 342	4.47 363	8.70 364	34.17 355	25.16 363	1.00 359	32.43 360	18.49 357	39.37 346	9.21 360	21.64 357	43.75 357	4.57 356

testmvs		5.91 333	7.65 336	0.72 344	1.20 364	0.37 366	59.14 346	0.67 366	0.49 361	1.11 361	2.76 361	0.94 367	0.24 362	1.02 360	1.47 359	1.55 358

test123		6.27 332	8.08 335	0.84 343	1.11 365	0.57 365	62.90 340	0.82 365	0.54 360	1.07 362	2.75 362	1.26 366	0.30 361	1.04 359	1.26 360	1.66 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		20.81 328	27.75 331	0.00 345	0.00 366	0.00 367	0.00 357	85.44 230	0.00 362	0.00 363	82.82 297	81.46 110	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		6.41 331	8.55 334	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	76.94 154	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		6.65 330	8.87 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	79.80 323	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

OPU-MVS				88.27 79	91.89 113	77.83 90	90.47 46				91.22 158	81.12 114	94.68 72	74.48 158	95.35 139	92.29 160

save fliter					93.75 61	77.44 96	86.31 119	89.72 167	70.80 196

test_0728_SECOND				86.79 95	94.25 47	72.45 141	90.54 43	94.10 34					95.88 14	86.42 31	97.97 43	92.02 170

GSMVS																83.88 286

sam_mvs1												46.11 317				83.88 286

sam_mvs												45.92 322

MTGPA								91.81 114

test_post1							78.85 253				3.13 359	45.19 331	80.13 313	58.11 284

test_post											3.10 360	45.43 327	77.22 320

patchmatchnet-post											81.71 307	45.93 321	87.01 258

MTMP							90.66 40	33.14 362

gm-plane-assit					75.42 337	44.97 348			52.17 324		72.36 347		87.90 250	54.10 306

test9_res														80.83 94	96.45 105	90.57 205

agg_prior2														79.68 106	96.16 115	90.22 212

test_prior4						78.97 80	84.59 141

test_prior				86.32 103	90.59 149	71.99 147		92.85 86					94.17 91			92.80 139

新几何2							81.72 210

旧先验1					91.97 108	71.77 149		81.78 261			91.84 144	73.92 182			93.65 186	83.61 292

无先验							82.81 188	85.62 229	58.09 293				91.41 181	67.95 223		84.48 280

原ACMM2							82.26 203

testdata2													86.43 271	63.52 249

segment_acmp												81.94 102

testdata1							79.62 238		73.95 157

plane_prior7					93.45 67	77.31 100

plane_prior6					92.61 88	76.54 109						74.84 171

plane_prior5								93.61 53					95.22 55	80.78 95	95.83 125	94.46 77

plane_prior4											92.95 112

plane_prior2							89.45 69		79.44 93

plane_prior1					92.83 86

plane_prior						76.42 112	87.15 103		75.94 134						95.03 153

n2								0.00 367

nn								0.00 367

door-mid								74.45 304

test11								91.46 121

door								72.57 318

HQP5-MVS						70.66 159

BP-MVS														77.30 135

HQP3-MVS								92.68 92							94.47 169

HQP2-MVS												72.10 204

NP-MVS					91.95 109	74.55 121					90.17 191

ACMMP++_ref															95.74 131

ACMMP++															97.35 75

Test By Simon												79.09 131

High-resolution multi-view benchmark