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 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 52	99.27 1	99.54 1

test_part1		87.15 87	87.82 80	85.15 136	88.88 185	63.04 230	87.98 93	94.85 16	82.52 61	93.61 37	95.73 25	67.51 233	95.71 31	80.48 104	98.83 2	96.69 25

WR-MVS_H		89.91 50	91.31 31	85.71 127	96.32 10	62.39 240	89.54 69	93.31 70	90.21 10	95.57 9	95.66 28	81.42 114	95.90 15	80.94 96	98.80 3	98.84 5

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 46	80.98 66	89.16 75	94.05 40	79.03 103	92.87 46	93.74 100	90.60 12	95.21 61	82.87 76	98.76 4	94.87 63
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 30	91.50 23	88.44 79	93.00 81	76.26 119	89.65 65	95.55 7	87.72 23	93.89 27	94.94 44	91.62 4	93.44 129	78.35 124	98.76 4	95.61 47

PS-CasMVS		90.06 43	91.92 13	84.47 148	96.56 7	58.83 282	89.04 77	92.74 96	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 115	98.74 6	99.00 2

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 41	81.69 60	90.00 54	94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 41	81.69 60		94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

PEN-MVS		90.03 45	91.88 16	84.48 147	96.57 6	58.88 279	88.95 78	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 120	98.72 9	98.97 3

CP-MVSNet		89.27 61	90.91 43	84.37 149	96.34 9	58.61 284	88.66 87	92.06 110	90.78 6	95.67 7	95.17 39	81.80 110	95.54 41	79.00 118	98.69 10	98.95 4

TranMVSNet+NR-MVSNet		87.86 79	88.76 73	85.18 135	94.02 57	64.13 219	84.38 151	91.29 132	84.88 37	92.06 62	93.84 95	86.45 58	93.73 113	73.22 183	98.66 11	97.69 9

NR-MVSNet		86.00 104	86.22 103	85.34 133	93.24 76	64.56 215	82.21 212	90.46 153	80.99 78	88.42 133	91.97 143	77.56 149	93.85 108	72.46 193	98.65 12	97.61 10

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 55	93.52 7	92.79 94	88.22 20	88.53 130	97.64 2	83.45 83	94.55 85	86.02 46	98.60 13	96.67 26

FC-MVSNet-test		85.93 107	87.05 91	82.58 193	92.25 102	56.44 299	85.75 130	93.09 80	77.33 121	91.94 66	94.65 53	74.78 179	93.41 131	75.11 166	98.58 14	97.88 7

DTE-MVSNet		89.98 47	91.91 15	84.21 155	96.51 8	57.84 287	88.93 80	92.84 93	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 117	98.57 15	98.80 6

UniMVSNet (Re)		86.87 88	86.98 93	86.55 105	93.11 79	68.48 186	83.80 165	92.87 90	80.37 84	89.61 112	91.81 150	77.72 147	94.18 94	75.00 167	98.53 16	96.99 22

Baseline_NR-MVSNet		84.00 148	85.90 109	78.29 261	91.47 133	53.44 318	82.29 208	87.00 225	79.06 102	89.55 114	95.72 27	77.20 153	86.14 284	72.30 194	98.51 17	95.28 55

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 35	94.17 96	86.07 43	98.48 18	97.22 17

ACMM	79.39 9	90.65 31	90.99 40	89.63 56	95.03 35	83.53 48	89.62 66	93.35 66	79.20 100	93.83 28	93.60 103	90.81 8	92.96 146	85.02 54	98.45 19	92.41 159
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 73	88.13 92	94.51 21	75.79 141	92.94 44	94.96 43	88.36 29	95.01 67	90.70 2	98.40 20	95.09 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 79	94.00 85	88.26 32	95.71 31	87.28 26	98.39 21	92.55 153

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 118	92.86 86	67.02 196	82.55 199	91.56 123	83.08 55	90.92 81	91.82 149	78.25 143	93.99 102	74.16 171	98.35 22	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 116	93.24 76	67.02 196	83.16 185	92.21 106	81.73 70	90.92 81	91.97 143	77.20 153	93.99 102	74.16 171	98.35 22	97.61 10

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 18	88.94 79	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 165

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 18	92.02 30	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 165

ACMH	76.49 14	89.34 60	91.14 34	83.96 162	92.50 94	70.36 170	89.55 67	93.84 50	81.89 69	94.70 13	95.44 33	90.69 9	88.31 257	83.33 71	98.30 26	93.20 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 66	86.15 22	93.37 10	95.10 14	90.28 9	92.11 60	95.03 42	89.75 21	94.93 69	79.95 107	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
ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 26	93.35 11	94.16 31	82.52 61	92.39 57	94.14 79	89.15 23	95.62 34	87.35 23	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
HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 42	93.51 8	94.85 16	82.88 57	91.77 68	93.94 93	90.55 13	95.73 30	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
test_0728_THIRD									85.33 32	93.75 31	94.65 53	87.44 45	95.78 27	87.41 21	98.21 30	92.98 135

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 15	92.20 27	93.03 85	82.59 60	88.52 131	94.37 70	86.74 53	95.41 51	86.32 37	98.21 30	93.19 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 60	80.97 67	91.49 37	93.48 63	82.82 58	92.60 53	93.97 86	88.19 33	96.29 4	87.61 15	98.20 32	94.39 81
Skip Steuart: Steuart Systems R&D Blog.
MSC_two_6792asdad				88.81 68	91.55 128	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 160

No_MVS				88.81 68	91.55 128	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 160

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 31	92.99 13	94.23 26	85.21 34	92.51 54	95.13 40	90.65 10	95.34 54	88.06 9	98.15 35	95.95 40

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 99	83.09 54	91.54 70	94.25 75	87.67 43	95.51 44	87.21 27	98.11 36	93.12 130

WR-MVS		83.56 156	84.40 142	81.06 217	93.43 71	54.88 310	78.67 263	85.02 250	81.24 75	90.74 85	91.56 156	72.85 203	91.08 199	68.00 231	98.04 37	97.23 16

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 45	89.49 71	93.98 42	79.68 93	92.09 61	93.89 94	83.80 79	93.10 143	82.67 78	98.04 37	93.64 114

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 61	93.64 67	79.07 82	88.54 88	94.20 28	73.53 166	89.71 106	94.82 48	85.09 67	95.77 29	84.17 63	98.03 39	93.26 125
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		85.35 114	86.27 102	82.60 192	91.86 116	57.31 292	85.10 138	93.05 82	75.83 140	91.02 80	93.97 86	73.57 192	92.91 150	73.97 174	98.02 40	97.58 12

abl_6		93.02 4	93.16 4	92.60 4	94.73 43	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 36	91.86 3	95.09 64	87.54 18	98.02 40	93.71 109

Anonymous20231211		88.40 72	89.62 58	84.73 143	90.46 159	65.27 209	88.86 81	93.02 86	87.15 25	93.05 43	97.10 6	82.28 98	92.02 173	76.70 147	97.99 42	96.88 23

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 29	90.00 54	93.90 46	80.32 86	91.74 69	94.41 66	88.17 34	95.98 11	86.37 36	97.99 42	93.96 96

APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 82	78.04 91	92.84 16	94.14 35	83.33 51	93.90 25	95.73 25	88.77 26	96.41 1	87.60 16	97.98 44	92.98 135

DVP-MVS		90.06 43	91.32 30	86.29 111	94.16 52	72.56 144	90.54 45	91.01 140	83.61 47	93.75 31	94.65 53	89.76 19	95.78 27	86.42 34	97.97 45	90.55 215
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				86.79 101	94.25 48	72.45 148	90.54 45	94.10 38					95.88 16	86.42 34	97.97 45	92.02 177

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 53	92.18 28	94.22 27	80.14 89	91.29 76	93.97 86	87.93 40	95.87 18	88.65 4	97.96 47	94.12 91

SED-MVS		90.46 37	91.64 19	86.93 98	94.18 49	72.65 138	90.47 48	93.69 54	83.77 44	94.11 23	94.27 71	90.28 15	95.84 22	86.03 44	97.92 48	92.29 167

IU-MVS					94.18 49	72.64 140		90.82 145	56.98 312	89.67 108				85.78 47	97.92 48	93.28 123

CLD-MVS		83.18 164	82.64 167	84.79 141	89.05 180	67.82 192	77.93 271	92.52 100	68.33 229	85.07 193	81.54 319	82.06 102	92.96 146	69.35 216	97.91 50	93.57 117
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS-MVSNet		86.66 93	86.82 97	86.17 118	92.05 109	66.87 198	91.21 39	88.64 193	86.30 30	89.60 113	92.59 126	69.22 225	94.91 70	73.89 175	97.89 51	96.72 24

ACMMP_NAP		90.65 31	91.07 38	89.42 60	95.93 16	79.54 78	89.95 57	93.68 56	77.65 117	91.97 65	94.89 45	88.38 28	95.45 49	89.27 3	97.87 52	93.27 124

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 71	89.27 22	95.84 22	86.03 44	97.82 53	92.04 176

DPE-MVS		90.53 35	91.08 36	88.88 66	93.38 72	78.65 87	89.15 76	94.05 40	84.68 38	93.90 25	94.11 81	88.13 36	96.30 3	84.51 60	97.81 54	91.70 188
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 78	80.37 70	91.91 34	93.11 79	81.10 77	95.32 10	97.24 5	72.94 202	94.85 72	85.07 52	97.78 55	97.26 15

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 79	90.98 41	93.24 76	75.37 148	92.84 48	95.28 35	85.58 66	96.09 7	87.92 10	97.76 56	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
ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 28	92.58 22	93.25 75	81.99 66	91.40 73	94.17 77	87.51 44	95.87 18	87.74 11	97.76 56	93.99 94

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 43	92.58 22	93.29 73	81.99 66	91.47 71	93.96 89	88.35 30	95.56 37	87.74 11	97.74 58	92.85 139

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 43	90.65 43	93.29 73	77.00 125	91.47 71	93.96 89	88.35 30	95.56 37	84.88 55	97.74 58	92.85 139

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 27	92.63 21	93.30 72	81.91 68	90.88 84	94.21 76	87.75 41	95.87 18	87.60 16	97.71 60	93.83 101

GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 56	91.75 35	93.74 52	80.98 79	91.38 74	93.80 96	87.20 48	95.80 24	87.10 31	97.69 61	93.93 97

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 153	97.00 2	64.33 218	89.67 64	88.38 197	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 192	97.65 62	97.34 14

v7n		90.13 40	90.96 41	87.65 91	91.95 111	71.06 165	89.99 56	93.05 82	86.53 28	94.29 19	96.27 17	82.69 90	94.08 100	86.25 40	97.63 63	97.82 8

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	94.03 83	86.57 56	95.80 24	87.35 23	97.62 64	94.20 86

X-MVStestdata		85.04 121	82.70 165	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	16.05 368	86.57 56	95.80 24	87.35 23	97.62 64	94.20 86

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	88.83 24	95.51 44	87.16 28	97.60 66	92.73 144

RE-MVS-def			92.61 5		94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	90.64 11		87.16 28	97.60 66	92.73 144

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 80	85.17 35	92.47 26	95.05 15	87.65 24	93.21 42	94.39 69	90.09 18	95.08 65	86.67 33	97.60 66	94.18 88

Anonymous20240521		80.18 211	81.25 187	76.95 275	83.15 283	60.84 259	82.46 202	85.99 234	68.76 226	86.78 160	93.73 101	59.13 279	77.44 328	73.71 178	97.55 69	92.56 152

9.14			89.29 62		91.84 117		88.80 83	95.32 11	75.14 150	91.07 78	92.89 117	87.27 46	93.78 112	83.69 68	97.55 69

OPM-MVS		89.80 51	89.97 52	89.27 62	94.76 40	79.86 74	86.76 115	92.78 95	78.78 106	92.51 54	93.64 102	88.13 36	93.84 110	84.83 57	97.55 69	94.10 92
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test1172		92.40 7	92.41 7	92.37 6	94.68 44	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 78	88.83 24	95.51 44	87.11 30	97.54 72	92.54 154

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 65	85.72 32	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 139	91.10 1	97.53 73	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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 70	93.75 63	77.44 101	86.31 123	95.27 12	70.80 204	92.28 58	93.80 96	86.89 51	94.64 78	85.52 48	97.51 74	94.30 84

SF-MVS		90.27 39	90.80 45	88.68 74	92.86 86	77.09 108	91.19 40	95.74 5	81.38 74	92.28 58	93.80 96	86.89 51	94.64 78	85.52 48	97.51 74	94.30 84

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 75	91.94 113	77.27 107	88.71 85	95.26 13	76.08 132	90.66 87	92.69 124	84.48 73	93.83 111	83.38 70	97.48 76	94.47 76

MIMVSNet1		83.63 155	84.59 136	80.74 222	94.06 56	62.77 234	82.72 194	84.53 256	77.57 120	90.34 90	95.92 23	76.88 165	85.83 288	61.88 270	97.42 77	93.62 115

ACMMP++															97.35 78

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 41	94.02 84	90.15 17	95.67 33	86.82 32	97.34 79	92.19 173

nrg030		87.85 80	88.49 74	85.91 121	90.07 167	69.73 173	87.86 96	94.20 28	74.04 160	92.70 52	94.66 52	85.88 65	91.50 184	79.72 109	97.32 80	96.50 30

pmmvs6		86.52 95	88.06 78	81.90 202	92.22 104	62.28 243	84.66 144	89.15 186	83.54 49	89.85 102	97.32 4	88.08 38	86.80 273	70.43 209	97.30 81	96.62 27

SD-MVS		88.96 67	89.88 53	86.22 114	91.63 121	77.07 109	89.82 60	93.77 51	78.90 104	92.88 45	92.29 137	86.11 62	90.22 225	86.24 41	97.24 82	91.36 196
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
CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 14	92.09 29	92.30 105	79.74 92	87.50 147	92.38 132	81.42 114	93.28 134	83.07 73	97.24 82	91.67 189

APD-MVS		89.54 56	89.63 57	89.26 63	92.57 91	81.34 65	90.19 52	93.08 81	80.87 81	91.13 77	93.19 107	86.22 61	95.97 12	82.23 83	97.18 84	90.45 217
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 258	79.30 214	62.63 336	75.56 345	75.18 124	80.89 231	73.10 327	75.06 151	94.76 12	95.32 34	87.73 42	52.85 365	34.16 364	97.11 85	59.85 361

PMVS	80.48 6	90.08 41	90.66 47	88.34 82	96.71 3	92.97 1	90.31 50	89.57 180	88.51 19	90.11 93	95.12 41	90.98 7	88.92 247	77.55 139	97.07 86	83.13 312
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 74	87.52 84	90.19 49	91.94 113	81.68 62	87.49 101	93.17 78	76.02 135	88.64 128	91.22 162	84.24 76	93.37 132	77.97 135	97.03 87	95.52 48

test_prior3		86.31 98	86.31 101	86.32 109	90.59 156	71.99 155	83.37 177	92.85 91	75.43 145	84.58 202	91.57 154	81.92 108	94.17 96	79.54 112	96.97 88	92.80 141

test_prior2							83.37 177		75.43 145	84.58 202	91.57 154	81.92 108		79.54 112	96.97 88

EPP-MVSNet		85.47 112	85.04 125	86.77 102	91.52 131	69.37 176	91.63 36	87.98 206	81.51 73	87.05 156	91.83 148	66.18 241	95.29 55	70.75 204	96.89 90	95.64 45

ETH3D cwj APD-0.16		87.83 81	87.62 83	88.47 77	91.21 138	78.20 89	87.26 104	94.54 20	72.05 191	88.89 122	92.31 136	83.86 77	94.24 90	81.59 90	96.87 91	92.97 138

VDDNet		84.35 136	85.39 120	81.25 212	95.13 33	59.32 272	85.42 135	81.11 275	86.41 29	87.41 148	96.21 19	73.61 191	90.61 216	66.33 241	96.85 92	93.81 106

VPNet		80.25 208	81.68 181	75.94 287	92.46 95	47.98 349	76.70 288	81.67 273	73.45 167	84.87 197	92.82 119	74.66 182	86.51 278	61.66 273	96.85 92	93.33 121

SixPastTwentyTwo		87.20 86	87.45 85	86.45 107	92.52 93	69.19 182	87.84 97	88.05 203	81.66 71	94.64 14	96.53 14	65.94 242	94.75 74	83.02 75	96.83 94	95.41 50

VPA-MVSNet		83.47 159	84.73 130	79.69 240	90.29 161	57.52 291	81.30 226	88.69 192	76.29 129	87.58 146	94.44 62	80.60 125	87.20 266	66.60 240	96.82 95	94.34 83

Gipuma		84.44 134	86.33 100	78.78 250	84.20 269	73.57 131	89.55 67	90.44 154	84.24 39	84.38 206	94.89 45	76.35 168	80.40 321	76.14 153	96.80 96	82.36 320
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ZD-MVS					92.22 104	80.48 69		91.85 116	71.22 201	90.38 89	92.98 112	86.06 63	96.11 6	81.99 85	96.75 97

CDPH-MVS		86.17 103	85.54 117	88.05 87	92.25 102	75.45 122	83.85 162	92.01 111	65.91 252	86.19 174	91.75 152	83.77 80	94.98 68	77.43 142	96.71 98	93.73 108

DIV-MVS_2432*1600		81.93 183	83.14 160	78.30 260	84.75 257	52.75 322	80.37 237	89.42 183	70.24 213	90.26 92	93.39 105	74.55 184	86.77 274	68.61 227	96.64 99	95.38 51

DP-MVS		88.60 71	89.01 66	87.36 94	91.30 135	77.50 100	87.55 99	92.97 88	87.95 22	89.62 110	92.87 118	84.56 71	93.89 107	77.65 137	96.62 100	90.70 209

testtj		89.51 57	89.48 60	89.59 58	92.26 101	80.80 68	90.14 53	93.54 61	83.37 50	90.57 88	92.55 129	84.99 68	96.15 5	81.26 91	96.61 101	91.83 184

TransMVSNet (Re)		84.02 147	85.74 113	78.85 249	91.00 146	55.20 309	82.29 208	87.26 213	79.65 94	88.38 135	95.52 32	83.00 87	86.88 271	67.97 232	96.60 102	94.45 79

ambc				82.98 182	90.55 158	64.86 212	88.20 90	89.15 186		89.40 117	93.96 89	71.67 217	91.38 192	78.83 119	96.55 103	92.71 147

train_agg		85.98 106	85.28 121	88.07 86	92.34 98	79.70 76	83.94 158	90.32 158	65.79 253	84.49 204	90.97 172	81.93 106	93.63 117	81.21 92	96.54 104	90.88 204

VDD-MVS		84.23 142	84.58 137	83.20 178	91.17 142	65.16 211	83.25 181	84.97 253	79.79 91	87.18 150	94.27 71	74.77 180	90.89 206	69.24 217	96.54 104	93.55 120

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 29	89.70 61	91.27 133	78.20 113	86.69 165	92.28 138	80.36 128	95.06 66	86.17 42	96.49 106	90.22 220

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 97	82.98 54	91.98 31	90.08 170	71.54 195	94.28 21	96.54 13	81.57 112	94.27 87	86.26 38	96.49 106	97.09 19

test9_res														80.83 98	96.45 108	90.57 213

Anonymous20240529		86.20 102	87.13 88	83.42 174	90.19 163	64.55 216	84.55 146	90.71 147	85.85 31	89.94 100	95.24 38	82.13 100	90.40 220	69.19 220	96.40 109	95.31 54

agg_prior1		85.72 109	85.20 123	87.28 95	91.58 125	77.69 97	83.69 168	90.30 161	66.29 249	84.32 208	91.07 169	82.13 100	93.18 137	81.02 94	96.36 110	90.98 200

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 171	86.67 16	90.51 47	90.20 167	69.87 216	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 111	97.09 19

PHI-MVS		86.38 97	85.81 111	88.08 85	88.44 193	77.34 104	89.35 74	93.05 82	73.15 177	84.76 199	87.70 233	78.87 138	94.18 94	80.67 101	96.29 112	92.73 144

PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 74	77.96 94	87.94 95	91.97 113	70.73 206	94.19 22	96.67 11	76.94 159	94.57 82	83.07 73	96.28 113	96.15 32

v10		86.54 94	87.10 89	84.84 140	88.16 199	63.28 227	86.64 118	92.20 107	75.42 147	92.81 50	94.50 59	74.05 187	94.06 101	83.88 65	96.28 113	97.17 18

CNVR-MVS		87.81 82	87.68 82	88.21 84	92.87 84	77.30 106	85.25 136	91.23 134	77.31 122	87.07 155	91.47 158	82.94 88	94.71 75	84.67 58	96.27 115	92.62 151

DROMVSNet		88.01 76	88.32 76	87.09 96	89.28 176	72.03 154	90.31 50	96.31 3	80.88 80	85.12 192	89.67 204	84.47 74	95.46 48	82.56 79	96.26 116	93.77 107

RRT_MVS		83.25 162	81.08 191	89.74 53	80.55 310	79.32 80	86.41 122	86.69 226	72.33 188	87.00 157	91.08 167	44.98 342	95.55 40	84.47 61	96.24 117	94.36 82

114514_t		83.10 166	82.54 171	84.77 142	92.90 83	69.10 184	86.65 117	90.62 151	54.66 321	81.46 251	90.81 179	76.98 158	94.38 86	72.62 191	96.18 118	90.82 206

agg_prior2														79.68 110	96.16 119	90.22 220

AllTest		87.97 78	87.40 87	89.68 54	91.59 122	83.40 49	89.50 70	95.44 9	79.47 95	88.00 140	93.03 110	82.66 91	91.47 185	70.81 201	96.14 120	94.16 89

TestCases				89.68 54	91.59 122	83.40 49		95.44 9	79.47 95	88.00 140	93.03 110	82.66 91	91.47 185	70.81 201	96.14 120	94.16 89

EPNet		80.37 205	78.41 223	86.23 113	76.75 336	73.28 133	87.18 106	77.45 296	76.24 131	68.14 337	88.93 216	65.41 244	93.85 108	69.47 215	96.12 122	91.55 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		83.69 153	84.95 127	79.91 235	90.04 169	59.66 269	82.43 203	87.44 210	75.52 144	87.85 142	95.26 37	81.25 117	85.65 290	68.74 225	96.04 123	94.42 80

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 68	84.79 40	89.89 59	90.63 150	70.00 215	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 124	95.52 48

DeepPCF-MVS	81.24 5	87.28 85	86.21 104	90.49 43	91.48 132	84.90 38	83.41 176	92.38 104	70.25 212	89.35 118	90.68 183	82.85 89	94.57 82	79.55 111	95.95 125	92.00 178

DVP-MVS++.		90.07 42	91.09 35	87.00 97	91.55 128	72.64 140	96.19 2	94.10 38	85.33 32	93.49 38	94.64 56	81.12 118	95.88 16	87.41 21	95.94 126	92.48 156

PC_three_1452									58.96 298	90.06 94	91.33 160	80.66 124	93.03 145	75.78 158	95.94 126	92.48 156

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 72	84.72 41	89.70 61	90.29 164	69.27 219	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 128	95.34 52

ANet_high		83.17 165	85.68 115	75.65 288	81.24 297	45.26 357	79.94 242	92.91 89	83.83 43	91.33 75	96.88 10	80.25 129	85.92 286	68.89 223	95.89 129	95.76 42

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 137	81.66 63	91.25 38	94.13 36	88.89 13	88.83 125	94.26 74	77.55 150	95.86 21	84.88 55	95.87 130	95.24 57

HQP_MVS		87.75 83	87.43 86	88.70 73	93.45 69	76.42 117	89.45 72	93.61 58	79.44 97	86.55 167	92.95 115	74.84 177	95.22 59	80.78 99	95.83 131	94.46 77

plane_prior5								93.61 58					95.22 59	80.78 99	95.83 131	94.46 77

cl-mvsnet____		80.42 203	80.23 202	81.02 218	79.99 313	59.25 273	77.07 284	87.02 222	67.37 241	86.18 176	89.21 210	63.08 256	90.16 227	76.31 151	95.80 133	93.65 113

cl-mvsnet1		80.43 202	80.23 202	81.02 218	79.99 313	59.25 273	77.07 284	87.02 222	67.38 240	86.19 174	89.22 209	63.09 255	90.16 227	76.32 150	95.80 133	93.66 111

DeepC-MVS_fast	80.27 8	86.23 100	85.65 116	87.96 88	91.30 135	76.92 110	87.19 105	91.99 112	70.56 207	84.96 194	90.69 182	80.01 131	95.14 62	78.37 123	95.78 135	91.82 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 212	80.56 196	78.89 248	89.19 179	55.93 301	85.22 137	73.78 321	82.96 56	84.28 212	92.72 123	57.38 291	90.07 234	63.80 257	95.75 136	90.68 210

ACMMP++_ref															95.74 137

原ACMM1				84.60 146	92.81 89	74.01 129		91.50 125	62.59 274	82.73 232	90.67 184	76.53 166	94.25 89	69.24 217	95.69 138	85.55 280

tfpnnormal		81.79 184	82.95 162	78.31 259	88.93 184	55.40 305	80.83 233	82.85 264	76.81 126	85.90 183	94.14 79	74.58 183	86.51 278	66.82 239	95.68 139	93.01 134

ETH3 D test6400		85.09 119	84.87 128	85.75 126	90.80 151	69.34 177	85.90 127	93.31 70	65.43 259	86.11 177	89.95 199	80.92 120	94.86 71	75.90 157	95.57 140	93.05 132

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 81	88.78 187	79.72 75	87.15 107	93.50 62	69.17 220	85.80 184	89.56 205	80.76 122	92.13 167	73.21 188	95.51 141	93.25 126
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 33	90.34 50	91.38 27	89.03 181	84.23 46	93.58 6	94.68 19	90.65 7	90.33 91	93.95 92	84.50 72	95.37 53	80.87 97	95.50 142	94.53 75

v8		86.22 101	86.83 96	84.36 151	87.82 204	62.35 242	86.42 121	91.33 131	76.78 127	92.73 51	94.48 61	73.41 196	93.72 114	83.10 72	95.41 143	97.01 21

Vis-MVSNet (Re-imp)		77.82 232	77.79 229	77.92 267	88.82 186	51.29 336	83.28 179	71.97 334	74.04 160	82.23 238	89.78 202	57.38 291	89.41 242	57.22 297	95.41 143	93.05 132

OPU-MVS				88.27 83	91.89 115	77.83 95	90.47 48				91.22 162	81.12 118	94.68 76	74.48 168	95.35 145	92.29 167

FMVSNet1		84.55 131	85.45 119	81.85 204	90.27 162	61.05 254	86.83 112	88.27 200	78.57 110	89.66 109	95.64 29	75.43 171	90.68 213	69.09 221	95.33 146	93.82 103

test12				86.57 104	90.74 152	72.63 142		90.69 148		82.76 231		79.20 135	94.80 73		95.32 147	92.27 169

NCCC		87.36 84	86.87 95	88.83 67	92.32 100	78.84 85	86.58 119	91.09 138	78.77 107	84.85 198	90.89 176	80.85 121	95.29 55	81.14 93	95.32 147	92.34 163

Patchmtry		76.56 247	77.46 231	73.83 297	79.37 321	46.60 354	82.41 204	76.90 298	73.81 163	85.56 188	92.38 132	48.07 320	83.98 304	63.36 260	95.31 149	90.92 203

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 24	85.91 126	93.60 60	80.16 88	89.13 121	93.44 104	83.82 78	90.98 201	83.86 66	95.30 150	93.60 116

TSAR-MVS + GP.		83.95 149	82.69 166	87.72 89	89.27 177	81.45 64	83.72 167	81.58 274	74.73 153	85.66 185	86.06 258	72.56 208	92.69 154	75.44 162	95.21 151	89.01 244

test_0402		88.65 70	89.58 59	85.88 123	92.55 92	72.22 152	84.01 156	89.44 182	88.63 18	94.38 18	95.77 24	86.38 60	93.59 121	79.84 108	95.21 151	91.82 185

TinyColmap		81.25 189	82.34 174	77.99 266	85.33 251	60.68 261	82.32 207	88.33 198	71.26 199	86.97 158	92.22 141	77.10 156	86.98 270	62.37 265	95.17 153	86.31 273

Anonymous202405211		80.51 201	81.19 190	78.49 256	88.48 191	57.26 293	76.63 289	82.49 266	81.21 76	84.30 211	92.24 140	67.99 231	86.24 282	62.22 266	95.13 154	91.98 181

tttt0517		81.07 191	79.58 212	85.52 130	88.99 183	66.45 202	87.03 109	75.51 309	73.76 164	88.32 137	90.20 194	37.96 358	94.16 99	79.36 116	95.13 154	95.93 41

DP-MVS Recon		84.05 146	83.22 157	86.52 106	91.73 120	75.27 123	83.23 183	92.40 102	72.04 192	82.04 241	88.33 223	77.91 146	93.95 105	66.17 242	95.12 156	90.34 219

PCF-MVS	74.62 15	82.15 177	80.92 194	85.84 124	89.43 173	72.30 150	80.53 235	91.82 118	57.36 310	87.81 143	89.92 201	77.67 148	93.63 117	58.69 289	95.08 157	91.58 192
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 99	86.47 99	85.60 129	90.87 149	74.26 128	87.98 93	91.85 116	80.35 85	89.54 116	88.01 227	79.09 136	92.13 167	75.51 160	95.06 158	90.41 218

plane_prior						76.42 117	87.15 107		75.94 139						95.03 159

new-patchmatchnet		70.10 296	73.37 270	60.29 343	81.23 298	16.95 373	59.54 353	74.62 312	62.93 272	80.97 255	87.93 230	62.83 260	71.90 341	55.24 310	95.01 160	92.00 178

bset_n11_16_dypcd		79.19 217	77.97 227	82.86 188	85.81 246	66.85 199	75.02 306	79.31 286	66.07 250	83.50 223	83.37 301	55.04 305	92.10 170	78.63 121	94.99 161	89.63 227

v1192		84.57 130	84.69 134	84.21 155	87.75 206	62.88 232	83.02 188	91.43 127	69.08 222	89.98 99	90.89 176	72.70 206	93.62 120	82.41 80	94.97 162	96.13 33

v1921920		84.23 142	84.37 143	83.79 165	87.64 210	61.71 246	82.91 191	91.20 135	67.94 235	90.06 94	90.34 190	72.04 213	93.59 121	82.32 82	94.91 163	96.07 35

CL-MVSNet_2432*1600		76.81 243	77.38 233	75.12 291	86.90 227	51.34 334	73.20 319	80.63 280	68.30 230	81.80 247	88.40 222	66.92 237	80.90 318	55.35 309	94.90 164	93.12 130

v144192		84.24 141	84.41 141	83.71 168	87.59 211	61.57 247	82.95 190	91.03 139	67.82 238	89.80 104	90.49 188	73.28 199	93.51 126	81.88 88	94.89 165	96.04 37

LCM-MVSNet-Re		83.48 158	85.06 124	78.75 251	85.94 245	55.75 304	80.05 240	94.27 23	76.47 128	96.09 5	94.54 58	83.31 85	89.75 238	59.95 284	94.89 165	90.75 207

v1240		84.30 138	84.51 139	83.65 169	87.65 209	61.26 251	82.85 192	91.54 124	67.94 235	90.68 86	90.65 185	71.71 216	93.64 116	82.84 77	94.78 167	96.07 35

MSLP-MVS++		85.00 123	86.03 107	81.90 202	91.84 117	71.56 163	86.75 116	93.02 86	75.95 138	87.12 151	89.39 206	77.98 144	89.40 243	77.46 140	94.78 167	84.75 289

IterMVS-LS		84.73 127	84.98 126	83.96 162	87.35 214	63.66 222	83.25 181	89.88 174	76.06 133	89.62 110	92.37 135	73.40 198	92.52 157	78.16 130	94.77 169	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 154	83.69 153	83.57 172	90.05 168	72.26 151	86.29 125	90.00 172	78.19 114	81.65 249	87.16 243	83.40 84	94.24 90	61.69 272	94.76 170	84.21 294

ITE_SJBPF				90.11 50	90.72 153	84.97 37		90.30 161	81.56 72	90.02 96	91.20 164	82.40 94	90.81 209	73.58 180	94.66 171	94.56 72

v1144		84.54 133	84.72 132	84.00 160	87.67 208	62.55 238	82.97 189	90.93 143	70.32 211	89.80 104	90.99 171	73.50 193	93.48 127	81.69 89	94.65 172	95.97 38

test20.03		73.75 271	74.59 259	71.22 310	81.11 299	51.12 338	70.15 330	72.10 333	70.42 208	80.28 268	91.50 157	64.21 248	74.72 338	46.96 347	94.58 173	87.82 259

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 65	95.72 22	76.74 113	92.49 25	91.19 136	67.85 237	86.63 166	94.84 47	79.58 134	95.96 13	87.62 14	94.50 174	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
HQP3-MVS								92.68 97							94.47 175

HQP-MVS		84.61 129	84.06 147	86.27 112	91.19 139	70.66 167	84.77 140	92.68 97	73.30 172	80.55 263	90.17 197	72.10 210	94.61 80	77.30 143	94.47 175	93.56 118

cl_fuxian		81.64 185	81.59 184	81.79 208	80.86 303	59.15 276	78.61 264	90.18 168	68.36 228	87.20 149	87.11 245	69.39 223	91.62 182	78.16 130	94.43 177	94.60 71

MCST-MVS		84.36 135	83.93 150	85.63 128	91.59 122	71.58 162	83.52 171	92.13 108	61.82 280	83.96 215	89.75 203	79.93 133	93.46 128	78.33 125	94.34 178	91.87 183

thisisatest0530		79.07 218	77.33 234	84.26 154	87.13 219	64.58 214	83.66 169	75.95 304	68.86 225	85.22 191	87.36 240	38.10 356	93.57 124	75.47 161	94.28 179	94.62 69

baseline		85.20 116	85.93 108	83.02 181	86.30 237	62.37 241	84.55 146	93.96 43	74.48 157	87.12 151	92.03 142	82.30 96	91.94 174	78.39 122	94.21 180	94.74 68

hse-mvs3		84.25 140	82.76 164	88.72 72	91.82 119	82.60 57	84.00 157	84.98 252	71.27 197	86.70 163	90.55 187	63.04 257	93.92 106	78.26 127	94.20 181	89.63 227

RRT_test8_iter05		78.08 229	77.52 230	79.75 238	80.84 304	52.54 326	80.61 234	88.96 188	67.77 239	84.62 201	89.29 208	33.89 363	92.10 170	77.59 138	94.15 182	94.62 69

alignmvs		83.94 150	83.98 149	83.80 164	87.80 205	67.88 191	84.54 148	91.42 129	73.27 175	88.41 134	87.96 228	72.33 209	90.83 208	76.02 156	94.11 183	92.69 148

USDC		76.63 245	76.73 241	76.34 284	83.46 278	57.20 294	80.02 241	88.04 204	52.14 336	83.65 219	91.25 161	63.24 254	86.65 277	54.66 314	94.11 183	85.17 284

MVS_111021_HR		84.63 128	84.34 144	85.49 132	90.18 164	75.86 121	79.23 256	87.13 217	73.35 169	85.56 188	89.34 207	83.60 82	90.50 218	76.64 148	94.05 185	90.09 225

VNet		79.31 216	80.27 201	76.44 282	87.92 202	53.95 314	75.58 301	84.35 257	74.39 158	82.23 238	90.72 181	72.84 204	84.39 301	60.38 283	93.98 186	90.97 201

FMVSNet2		81.31 188	81.61 183	80.41 228	86.38 232	58.75 283	83.93 160	86.58 228	72.43 184	87.65 144	92.98 112	63.78 251	90.22 225	66.86 236	93.92 187	92.27 169

LF4IMVS		82.75 168	81.93 179	85.19 134	82.08 288	80.15 72	85.53 133	88.76 191	68.01 232	85.58 187	87.75 232	71.80 215	86.85 272	74.02 173	93.87 188	88.58 247

canonicalmvs		85.50 111	86.14 105	83.58 171	87.97 200	67.13 194	87.55 99	94.32 22	73.44 168	88.47 132	87.54 236	86.45 58	91.06 200	75.76 159	93.76 189	92.54 154

v2v482		84.09 144	84.24 145	83.62 170	87.13 219	61.40 248	82.71 195	89.71 176	72.19 190	89.55 114	91.41 159	70.70 221	93.20 136	81.02 94	93.76 189	96.25 31

casdiffmvs		85.21 115	85.85 110	83.31 176	86.17 242	62.77 234	83.03 187	93.93 44	74.69 154	88.21 138	92.68 125	82.29 97	91.89 177	77.87 136	93.75 191	95.27 56

CS-MVS-test		85.00 123	85.28 121	84.17 158	87.84 203	66.12 204	87.30 103	95.67 6	77.63 119	80.02 271	85.85 263	81.34 116	95.41 51	78.18 129	93.71 192	90.99 199

UGNet		82.78 167	81.64 182	86.21 116	86.20 241	76.24 120	86.86 110	85.68 237	77.07 124	73.76 317	92.82 119	69.64 222	91.82 180	69.04 222	93.69 193	90.56 214
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
旧先验1					91.97 110	71.77 157		81.78 272			91.84 147	73.92 188			93.65 194	83.61 302

AUN-MVS		81.18 190	78.78 217	88.39 80	90.93 147	82.14 59	82.51 201	83.67 259	64.69 267	80.29 266	85.91 262	51.07 312	92.38 160	76.29 152	93.63 195	90.65 212

hse-mvs2		83.47 159	81.81 180	88.47 77	91.03 145	82.27 58	82.61 196	83.69 258	71.27 197	86.70 163	86.05 259	63.04 257	92.41 159	78.26 127	93.62 196	90.71 208

CS-MVS		82.02 179	82.63 168	80.19 232	84.80 256	57.56 290	82.39 205	94.72 18	71.24 200	80.22 269	84.89 283	75.85 169	94.56 84	76.08 154	93.49 197	88.46 248

MVS_111021_LR		84.28 139	83.76 152	85.83 125	89.23 178	83.07 52	80.99 230	83.56 260	72.71 182	86.07 178	89.07 214	81.75 111	86.19 283	77.11 145	93.36 198	88.24 249

GBi-Net		82.02 179	82.07 176	81.85 204	86.38 232	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 199	93.82 103

test1		82.02 179	82.07 176	81.85 204	86.38 232	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 199	93.82 103

FMVSNet3		78.80 222	78.55 220	79.57 242	82.89 286	56.89 297	81.76 216	85.77 236	69.04 223	86.00 179	90.44 189	51.75 310	90.09 233	65.95 243	93.34 199	91.72 187

K. test v3		85.14 117	84.73 130	86.37 108	91.13 143	69.63 175	85.45 134	76.68 301	84.06 42	92.44 56	96.99 8	62.03 261	94.65 77	80.58 102	93.24 202	94.83 67

Anonymous20231206		71.38 289	71.88 284	69.88 313	86.31 236	54.37 311	70.39 329	74.62 312	52.57 332	76.73 293	88.76 217	59.94 272	72.06 340	44.35 352	93.23 203	83.23 310

D2MVS		76.84 242	75.67 251	80.34 229	80.48 311	62.16 245	73.50 316	84.80 255	57.61 308	82.24 237	87.54 236	51.31 311	87.65 262	70.40 210	93.19 204	91.23 197

miper_lstm_enhance		76.45 249	76.10 246	77.51 271	76.72 337	60.97 258	64.69 345	85.04 249	63.98 269	83.20 226	88.22 224	56.67 294	78.79 326	73.22 183	93.12 205	92.78 143

新几何1				82.95 183	93.96 58	78.56 88		80.24 281	55.45 317	83.93 216	91.08 167	71.19 219	88.33 256	65.84 246	93.07 206	81.95 325

1121		80.86 194	79.81 211	84.02 159	93.93 59	78.70 86	81.64 219	80.18 282	55.43 318	83.67 218	91.15 165	71.29 218	91.41 190	67.95 233	93.06 207	81.96 324

lessismore_v0				85.95 120	91.10 144	70.99 166		70.91 340		91.79 67	94.42 65	61.76 262	92.93 148	79.52 114	93.03 208	93.93 97

TAMVS		78.08 229	76.36 243	83.23 177	90.62 155	72.87 136	79.08 257	80.01 284	61.72 282	81.35 253	86.92 247	63.96 250	88.78 251	50.61 331	93.01 209	88.04 253

ETV-MVS		84.31 137	83.91 151	85.52 130	88.58 189	70.40 169	84.50 150	93.37 64	78.76 108	84.07 214	78.72 338	80.39 127	95.13 63	73.82 177	92.98 210	91.04 198

EPNet_dtu		72.87 278	71.33 290	77.49 272	77.72 330	60.55 262	82.35 206	75.79 305	66.49 248	58.39 365	81.06 322	53.68 307	85.98 285	53.55 318	92.97 211	85.95 276
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 108	83.38 155	93.14 3	87.13 219	91.15 2	87.70 98	88.42 195	74.57 155	83.56 221	85.65 264	78.49 140	94.21 92	72.04 195	92.88 212	94.05 93

CANet		83.79 152	82.85 163	86.63 103	86.17 242	72.21 153	83.76 166	91.43 127	77.24 123	74.39 314	87.45 238	75.36 172	95.42 50	77.03 146	92.83 213	92.25 171

API-MVS		82.28 174	82.61 169	81.30 211	86.29 238	69.79 171	88.71 85	87.67 209	78.42 112	82.15 240	84.15 292	77.98 144	91.59 183	65.39 249	92.75 214	82.51 319

test_yl		78.71 224	78.51 221	79.32 245	84.32 264	58.84 280	78.38 265	85.33 241	75.99 136	82.49 233	86.57 249	58.01 285	90.02 235	62.74 263	92.73 215	89.10 239

DCV-MVSNet		78.71 224	78.51 221	79.32 245	84.32 264	58.84 280	78.38 265	85.33 241	75.99 136	82.49 233	86.57 249	58.01 285	90.02 235	62.74 263	92.73 215	89.10 239

Regformer-1		86.00 104	85.50 118	87.49 92	84.18 270	76.90 111	83.52 171	87.94 207	82.18 65	89.19 119	85.07 279	82.28 98	91.89 177	82.40 81	92.72 217	93.69 110

Regformer-2		86.74 92	86.08 106	88.73 71	84.18 270	79.20 81	83.52 171	89.33 184	83.33 51	89.92 101	85.07 279	83.23 86	93.16 139	83.39 69	92.72 217	93.83 101

testgi		72.36 281	74.61 257	65.59 330	80.56 309	42.82 363	68.29 335	73.35 324	66.87 245	81.84 244	89.93 200	72.08 212	66.92 354	46.05 349	92.54 219	87.01 267

FMVSNet5		72.10 284	71.69 285	73.32 298	81.57 293	53.02 321	76.77 287	78.37 292	63.31 270	76.37 295	91.85 146	36.68 360	78.98 324	47.87 343	92.45 220	87.95 255

CDS-MVSNet		77.32 237	75.40 252	83.06 180	89.00 182	72.48 147	77.90 272	82.17 269	60.81 289	78.94 280	83.49 297	59.30 277	88.76 252	54.64 315	92.37 221	87.93 256
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		83.55 157	83.10 161	84.90 139	89.34 175	83.87 47	84.54 148	88.77 190	79.09 101	83.54 222	88.66 220	74.87 176	81.73 315	66.84 238	92.29 222	89.11 238

F-COLMAP		84.97 125	83.42 154	89.63 56	92.39 96	83.40 49	88.83 82	91.92 115	73.19 176	80.18 270	89.15 212	77.04 157	93.28 134	65.82 247	92.28 223	92.21 172

thres600view7		75.97 252	75.35 254	77.85 269	87.01 225	51.84 332	80.45 236	73.26 325	75.20 149	83.10 228	86.31 255	45.54 333	89.05 244	55.03 312	92.24 224	92.66 149

PVSNet_BlendedMVS		78.80 222	77.84 228	81.65 209	84.43 260	63.41 224	79.49 250	90.44 154	61.70 283	75.43 307	87.07 246	69.11 226	91.44 187	60.68 281	92.24 224	90.11 224

DELS-MVS		81.44 187	81.25 187	82.03 200	84.27 266	62.87 233	76.47 293	92.49 101	70.97 203	81.64 250	83.83 293	75.03 174	92.70 153	74.29 169	92.22 226	90.51 216
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
testdata				79.54 243	92.87 84	72.34 149		80.14 283	59.91 296	85.47 190	91.75 152	67.96 232	85.24 292	68.57 229	92.18 227	81.06 338

cl-mvsnet2		78.97 219	78.21 225	81.24 214	77.74 329	59.01 277	77.46 281	87.13 217	65.79 253	84.32 208	85.10 276	58.96 281	90.88 207	75.36 163	92.03 228	93.84 100

miper_ehance_all_eth		80.34 206	80.04 209	81.24 214	79.82 315	58.95 278	77.66 275	89.66 177	65.75 256	85.99 182	85.11 275	68.29 230	91.42 189	76.03 155	92.03 228	93.33 121

miper_enhance_ethall		77.83 231	76.93 238	80.51 226	76.15 342	58.01 286	75.47 303	88.82 189	58.05 304	83.59 220	80.69 323	64.41 246	91.20 194	73.16 189	92.03 228	92.33 164

MVS_0304		78.17 227	77.23 235	80.99 220	84.13 272	69.07 185	81.39 223	80.81 278	76.28 130	67.53 342	89.11 213	62.87 259	86.77 274	60.90 280	92.01 231	87.13 265

GeoE		85.45 113	85.81 111	84.37 149	90.08 165	67.07 195	85.86 129	91.39 130	72.33 188	87.59 145	90.25 193	84.85 69	92.37 161	78.00 133	91.94 232	93.66 111

DPM-MVS		80.10 213	79.18 215	82.88 187	90.71 154	69.74 172	78.87 260	90.84 144	60.29 294	75.64 306	85.92 261	67.28 234	93.11 142	71.24 199	91.79 233	85.77 279

v148		82.31 173	82.48 172	81.81 207	85.59 248	59.66 269	81.47 222	86.02 233	72.85 180	88.05 139	90.65 185	70.73 220	90.91 205	75.15 165	91.79 233	94.87 63

test222					93.31 74	76.54 114	79.38 251	77.79 294	52.59 331	82.36 236	90.84 178	66.83 238			91.69 235	81.25 333

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 190	81.41 295	60.98 257	77.81 273	90.14 169	67.31 242	86.95 159	87.24 242	64.26 247	92.31 163	75.23 164	91.61 236	94.85 65

pmmvs-eth3d		78.42 226	77.04 237	82.57 195	87.44 213	74.41 127	80.86 232	79.67 285	55.68 316	84.69 200	90.31 192	60.91 265	85.42 291	62.20 267	91.59 237	87.88 257

Vis-MVSNet		86.86 89	86.58 98	87.72 89	92.09 107	77.43 103	87.35 102	92.09 109	78.87 105	84.27 213	94.05 82	78.35 142	93.65 115	80.54 103	91.58 238	92.08 175
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		73.00 277	70.52 292	80.46 227	81.45 294	59.90 267	73.16 320	74.31 316	57.86 305	76.08 301	77.78 341	37.60 359	92.12 169	65.00 250	91.45 239	89.35 233

ppachtmachnet_test		74.73 265	74.00 264	76.90 277	80.71 307	56.89 297	71.53 325	78.42 291	58.24 302	79.32 277	82.92 306	57.91 288	84.26 302	65.60 248	91.36 240	89.56 229

OpenMVS	76.72 13	81.98 182	82.00 178	81.93 201	84.42 262	68.22 188	88.50 89	89.48 181	66.92 244	81.80 247	91.86 145	72.59 207	90.16 227	71.19 200	91.25 241	87.40 262

EG-PatchMatch MVS		84.08 145	84.11 146	83.98 161	92.22 104	72.61 143	82.20 214	87.02 222	72.63 183	88.86 123	91.02 170	78.52 139	91.11 198	73.41 182	91.09 242	88.21 250

3Dnovator	80.37 7	84.80 126	84.71 133	85.06 138	86.36 235	74.71 125	88.77 84	90.00 172	75.65 143	84.96 194	93.17 108	74.06 186	91.19 195	78.28 126	91.09 242	89.29 236

Regformer-3		85.06 120	84.67 135	86.22 114	84.27 266	73.43 132	84.07 154	85.26 243	80.77 82	88.62 129	85.48 267	80.56 126	90.39 221	81.99 85	91.04 244	94.85 65

Regformer-4		86.41 96	85.71 114	88.52 75	84.27 266	77.57 99	84.07 154	88.00 205	82.82 58	89.84 103	85.48 267	82.06 102	92.77 152	83.83 67	91.04 244	95.22 60

thres100view900		75.45 255	75.05 255	76.66 281	87.27 215	51.88 331	81.07 229	73.26 325	75.68 142	83.25 225	86.37 252	45.54 333	88.80 248	51.98 327	90.99 246	89.31 234

tfpn200view9		74.86 263	74.23 262	76.74 280	86.24 239	52.12 328	79.24 254	73.87 319	73.34 170	81.82 245	84.60 288	46.02 327	88.80 248	51.98 327	90.99 246	89.31 234

thres400		75.14 257	74.23 262	77.86 268	86.24 239	52.12 328	79.24 254	73.87 319	73.34 170	81.82 245	84.60 288	46.02 327	88.80 248	51.98 327	90.99 246	92.66 149

cascas		76.29 251	74.81 256	80.72 224	84.47 259	62.94 231	73.89 314	87.34 211	55.94 315	75.16 311	76.53 349	63.97 249	91.16 196	65.00 250	90.97 249	88.06 252

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 23	92.75 17	93.90 46	78.43 111	89.16 120	92.25 139	72.03 214	96.36 2	88.21 8	90.93 250	92.98 135
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
ab-mvs		79.67 215	80.56 196	76.99 274	88.48 191	56.93 295	84.70 143	86.06 232	68.95 224	80.78 259	93.08 109	75.30 173	84.62 299	56.78 298	90.90 251	89.43 232

MAR-MVS		80.24 209	78.74 219	84.73 143	86.87 229	78.18 90	85.75 130	87.81 208	65.67 258	77.84 287	78.50 339	73.79 190	90.53 217	61.59 275	90.87 252	85.49 282
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
EI-MVSNet-Vis-set		85.12 118	84.53 138	86.88 99	84.01 273	72.76 137	83.91 161	85.18 245	80.44 83	88.75 126	85.49 266	80.08 130	91.92 175	82.02 84	90.85 253	95.97 38

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 100	83.87 276	72.52 146	83.82 163	85.15 246	80.27 87	88.75 126	85.45 270	79.95 132	91.90 176	81.92 87	90.80 254	96.13 33

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 25	86.84 111	93.91 45	80.07 90	86.75 162	93.26 106	93.64 2	90.93 203	84.60 59	90.75 255	93.97 95

ET-MVSNet_ETH3D		75.28 256	72.77 275	82.81 189	83.03 285	68.11 189	77.09 283	76.51 302	60.67 292	77.60 291	80.52 327	38.04 357	91.15 197	70.78 203	90.68 256	89.17 237

EI-MVSNet		82.61 169	82.42 173	83.20 178	83.25 280	63.66 222	83.50 174	85.07 247	76.06 133	86.55 167	85.10 276	73.41 196	90.25 222	78.15 132	90.67 257	95.68 44

MVSTER		77.09 239	75.70 250	81.25 212	75.27 349	61.08 253	77.49 280	85.07 247	60.78 290	86.55 167	88.68 219	43.14 348	90.25 222	73.69 179	90.67 257	92.42 158

Patchmatch-RL test		74.48 266	73.68 266	76.89 278	84.83 255	66.54 201	72.29 322	69.16 345	57.70 306	86.76 161	86.33 253	45.79 332	82.59 310	69.63 214	90.65 259	81.54 329

CMPMVS	59.41 20	75.12 259	73.57 267	79.77 236	75.84 344	67.22 193	81.21 227	82.18 268	50.78 344	76.50 294	87.66 234	55.20 303	82.99 309	62.17 269	90.64 260	89.09 241
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V42		83.47 159	83.37 156	83.75 167	83.16 282	63.33 226	81.31 224	90.23 166	69.51 218	90.91 83	90.81 179	74.16 185	92.29 165	80.06 105	90.22 261	95.62 46

PM-MVS		80.20 210	79.00 216	83.78 166	88.17 198	86.66 17	81.31 224	66.81 352	69.64 217	88.33 136	90.19 195	64.58 245	83.63 307	71.99 197	90.03 262	81.06 338

PLC	73.85 16	82.09 178	80.31 200	87.45 93	90.86 150	80.29 71	85.88 128	90.65 149	68.17 231	76.32 297	86.33 253	73.12 201	92.61 156	61.40 276	90.02 263	89.44 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		84.55 131	82.12 175	91.84 20	87.13 219	89.54 4	85.05 139	88.42 195	74.57 155	80.60 260	82.98 302	78.49 140	93.98 104	72.04 195	89.77 264	92.00 178

CANet_DTU		77.81 233	77.05 236	80.09 234	81.37 296	59.90 267	83.26 180	88.29 199	69.16 221	67.83 340	83.72 294	60.93 264	89.47 239	69.22 219	89.70 265	90.88 204

diffmvs		80.40 204	80.48 199	80.17 233	79.02 325	60.04 265	77.54 278	90.28 165	66.65 247	82.40 235	87.33 241	73.50 193	87.35 265	77.98 134	89.62 266	93.13 129

PMMVS2		55.64 333	59.27 333	44.74 349	64.30 371	12.32 374	40.60 362	49.79 371	53.19 328	65.06 352	84.81 284	53.60 308	49.76 366	32.68 366	89.41 267	72.15 351

Fast-Effi-MVS+-dtu		82.54 171	81.41 186	85.90 122	85.60 247	76.53 116	83.07 186	89.62 179	73.02 179	79.11 279	83.51 296	80.74 123	90.24 224	68.76 224	89.29 268	90.94 202

thres200		72.34 282	71.55 288	74.70 294	83.48 277	51.60 333	75.02 306	73.71 322	70.14 214	78.56 283	80.57 326	46.20 325	88.20 258	46.99 346	89.29 268	84.32 293

jason		77.42 236	75.75 249	82.43 198	87.10 223	69.27 178	77.99 270	81.94 271	51.47 340	77.84 287	85.07 279	60.32 269	89.00 245	70.74 205	89.27 270	89.03 242
jason: jason.
MG-MVS		80.32 207	80.94 193	78.47 257	88.18 197	52.62 325	82.29 208	85.01 251	72.01 193	79.24 278	92.54 130	69.36 224	93.36 133	70.65 206	89.19 271	89.45 230

BH-untuned		80.96 193	80.99 192	80.84 221	88.55 190	68.23 187	80.33 238	88.46 194	72.79 181	86.55 167	86.76 248	74.72 181	91.77 181	61.79 271	88.99 272	82.52 318

EIA-MVS		82.19 176	81.23 189	85.10 137	87.95 201	69.17 183	83.22 184	93.33 67	70.42 208	78.58 282	79.77 335	77.29 152	94.20 93	71.51 198	88.96 273	91.93 182

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 145	91.58 125	73.24 135	84.21 152	91.67 122	62.86 273	80.94 256	87.16 243	67.27 235	92.87 151	69.82 213	88.94 274	87.99 254

MVSFormer		82.23 175	81.57 185	84.19 157	85.54 249	69.26 179	91.98 31	90.08 170	71.54 195	76.23 298	85.07 279	58.69 282	94.27 87	86.26 38	88.77 275	89.03 242

lupinMVS		76.37 250	74.46 260	82.09 199	85.54 249	69.26 179	76.79 286	80.77 279	50.68 346	76.23 298	82.82 307	58.69 282	88.94 246	69.85 212	88.77 275	88.07 251

RPSCF		88.00 77	86.93 94	91.22 31	90.08 165	89.30 5	89.68 63	91.11 137	79.26 99	89.68 107	94.81 51	82.44 93	87.74 261	76.54 149	88.74 277	96.61 28

PAPM_NR		83.23 163	83.19 159	83.33 175	90.90 148	65.98 205	88.19 91	90.78 146	78.13 115	80.87 258	87.92 231	73.49 195	92.42 158	70.07 211	88.40 278	91.60 191

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 184	88.31 194	71.73 158	79.53 247	87.17 214	65.43 259	79.59 273	82.73 309	76.94 159	90.14 230	73.22 183	88.33 279	86.90 268

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 184	88.31 194	71.73 158	79.53 247	87.17 214	65.43 259	79.59 273	82.73 309	76.94 159	90.14 230	73.22 183	88.33 279	86.90 268

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 184	88.31 194	71.73 158	79.53 247	87.17 214	65.43 259	79.59 273	82.73 309	76.94 159	90.14 230	73.22 183	88.33 279	86.90 268

XXY-MVS		74.44 268	76.19 245	69.21 317	84.61 258	52.43 327	71.70 324	77.18 297	60.73 291	80.60 260	90.96 174	75.44 170	69.35 346	56.13 302	88.33 279	85.86 278

Fast-Effi-MVS+		81.04 192	80.57 195	82.46 197	87.50 212	63.22 228	78.37 267	89.63 178	68.01 232	81.87 243	82.08 314	82.31 95	92.65 155	67.10 235	88.30 283	91.51 194

MDA-MVSNet-bldmvs		77.47 235	76.90 239	79.16 247	79.03 324	64.59 213	66.58 342	75.67 307	73.15 177	88.86 123	88.99 215	66.94 236	81.23 317	64.71 252	88.22 284	91.64 190

PAPR		78.84 221	78.10 226	81.07 216	85.17 252	60.22 264	82.21 212	90.57 152	62.51 275	75.32 309	84.61 287	74.99 175	92.30 164	59.48 287	88.04 285	90.68 210

BH-RMVSNet		80.53 200	80.22 204	81.49 210	87.19 218	66.21 203	77.79 274	86.23 230	74.21 159	83.69 217	88.50 221	73.25 200	90.75 210	63.18 262	87.90 286	87.52 260

Effi-MVS+		83.90 151	84.01 148	83.57 172	87.22 217	65.61 208	86.55 120	92.40 102	78.64 109	81.34 254	84.18 291	83.65 81	92.93 148	74.22 170	87.87 287	92.17 174

MVS_Test		82.47 172	83.22 157	80.22 231	82.62 287	57.75 289	82.54 200	91.96 114	71.16 202	82.89 230	92.52 131	77.41 151	90.50 218	80.04 106	87.84 288	92.40 160

QAPM		82.59 170	82.59 170	82.58 193	86.44 230	66.69 200	89.94 58	90.36 157	67.97 234	84.94 196	92.58 128	72.71 205	92.18 166	70.63 207	87.73 289	88.85 245

PVSNet_Blended		76.49 248	75.40 252	79.76 237	84.43 260	63.41 224	75.14 305	90.44 154	57.36 310	75.43 307	78.30 340	69.11 226	91.44 187	60.68 281	87.70 290	84.42 292

pmmvs5		70.73 292	70.07 296	72.72 302	77.03 335	52.73 323	74.14 311	75.65 308	50.36 348	72.17 324	85.37 273	55.42 302	80.67 320	52.86 324	87.59 291	84.77 288

IB-MVS	62.13 19	71.64 287	68.97 301	79.66 241	80.80 306	62.26 244	73.94 313	76.90 298	63.27 271	68.63 336	76.79 347	33.83 364	91.84 179	59.28 288	87.26 292	84.88 287
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
N_pmnet		70.20 294	68.80 303	74.38 295	80.91 301	84.81 39	59.12 355	76.45 303	55.06 319	75.31 310	82.36 312	55.74 299	54.82 364	47.02 345	87.24 293	83.52 303

pmmvs4		74.92 262	72.98 274	80.73 223	84.95 253	71.71 161	76.23 296	77.59 295	52.83 330	77.73 290	86.38 251	56.35 297	84.97 295	57.72 296	87.05 294	85.51 281

MIMVSNet		71.09 290	71.59 286	69.57 316	87.23 216	50.07 344	78.91 258	71.83 335	60.20 295	71.26 327	91.76 151	55.08 304	76.09 332	41.06 357	87.02 295	82.54 317

HyFIR lowres test		75.12 259	72.66 277	82.50 196	91.44 134	65.19 210	72.47 321	87.31 212	46.79 352	80.29 266	84.30 290	52.70 309	92.10 170	51.88 330	86.73 296	90.22 220

MSDG		80.06 214	79.99 210	80.25 230	83.91 275	68.04 190	77.51 279	89.19 185	77.65 117	81.94 242	83.45 298	76.37 167	86.31 281	63.31 261	86.59 297	86.41 271

Patchmatch-test		65.91 316	67.38 308	61.48 341	75.51 346	43.21 362	68.84 333	63.79 356	62.48 276	72.80 321	83.42 299	44.89 343	59.52 363	48.27 342	86.45 298	81.70 326

mvs_anonymous		78.13 228	78.76 218	76.23 286	79.24 322	50.31 343	78.69 262	84.82 254	61.60 284	83.09 229	92.82 119	73.89 189	87.01 267	68.33 230	86.41 299	91.37 195

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 152	83.93 274	69.66 174	76.28 295	81.09 276	72.43 184	86.47 173	90.19 195	60.46 267	93.15 141	77.45 141	86.39 300	90.22 220

E-PMN		61.59 324	61.62 326	61.49 340	66.81 368	55.40 305	53.77 359	60.34 362	66.80 246	58.90 363	65.50 361	40.48 353	66.12 357	55.72 304	86.25 301	62.95 359

EMVS		61.10 327	60.81 328	61.99 338	65.96 369	55.86 302	53.10 360	58.97 364	67.06 243	56.89 366	63.33 362	40.98 351	67.03 353	54.79 313	86.18 302	63.08 358

our_test_3		71.85 285	71.59 286	72.62 304	80.71 307	53.78 315	69.72 332	71.71 338	58.80 299	78.03 284	80.51 328	56.61 295	78.84 325	62.20 267	86.04 303	85.23 283

EU-MVSNet		75.12 259	74.43 261	77.18 273	83.11 284	59.48 271	85.71 132	82.43 267	39.76 364	85.64 186	88.76 217	44.71 344	87.88 260	73.86 176	85.88 304	84.16 295

GA-MVS		75.83 253	74.61 257	79.48 244	81.87 290	59.25 273	73.42 317	82.88 263	68.68 227	79.75 272	81.80 316	50.62 314	89.46 240	66.85 237	85.64 305	89.72 226

MVS		73.21 275	72.59 278	75.06 292	80.97 300	60.81 260	81.64 219	85.92 235	46.03 355	71.68 326	77.54 342	68.47 229	89.77 237	55.70 305	85.39 306	74.60 349

PatchT		70.52 293	72.76 276	63.79 335	79.38 320	33.53 369	77.63 276	65.37 354	73.61 165	71.77 325	92.79 122	44.38 345	75.65 335	64.53 256	85.37 307	82.18 322

TR-MVS		76.77 244	75.79 248	79.72 239	86.10 244	65.79 207	77.14 282	83.02 262	65.20 264	81.40 252	82.10 313	66.30 239	90.73 212	55.57 306	85.27 308	82.65 314

BH-w/o		76.57 246	76.07 247	78.10 264	86.88 228	65.92 206	77.63 276	86.33 229	65.69 257	80.89 257	79.95 332	68.97 228	90.74 211	53.01 323	85.25 309	77.62 344

IterMVS		76.91 241	76.34 244	78.64 253	80.91 301	64.03 220	76.30 294	79.03 289	64.88 266	83.11 227	89.16 211	59.90 273	84.46 300	68.61 227	85.15 310	87.42 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	70.19 17	77.77 234	77.46 231	78.71 252	84.39 263	61.15 252	81.18 228	82.52 265	62.45 277	83.34 224	87.37 239	66.20 240	88.66 253	64.69 253	85.02 311	86.32 272

KD-MVS_2432*1600		66.87 310	65.81 315	70.04 311	67.50 366	47.49 351	62.56 349	79.16 287	61.21 287	77.98 285	80.61 324	25.29 374	82.48 311	53.02 321	84.92 312	80.16 341

miper_refine_blended		66.87 310	65.81 315	70.04 311	67.50 366	47.49 351	62.56 349	79.16 287	61.21 287	77.98 285	80.61 324	25.29 374	82.48 311	53.02 321	84.92 312	80.16 341

test-LLR		67.21 308	66.74 312	68.63 321	76.45 340	55.21 307	67.89 336	67.14 349	62.43 278	65.08 350	72.39 354	43.41 346	69.37 344	61.00 277	84.89 314	81.31 331

test-mter		65.00 318	63.79 321	68.63 321	76.45 340	55.21 307	67.89 336	67.14 349	50.98 343	65.08 350	72.39 354	28.27 371	69.37 344	61.00 277	84.89 314	81.31 331

PS-MVSNAJ		77.04 240	76.53 242	78.56 254	87.09 224	61.40 248	75.26 304	87.13 217	61.25 285	74.38 315	77.22 346	76.94 159	90.94 202	64.63 254	84.83 316	83.35 307

xiu_mvs_v2_base		77.19 238	76.75 240	78.52 255	87.01 225	61.30 250	75.55 302	87.12 220	61.24 286	74.45 313	78.79 337	77.20 153	90.93 203	64.62 255	84.80 317	83.32 308

pmmvs3		62.47 320	60.02 332	69.80 314	71.58 364	64.00 221	70.52 328	58.44 365	39.77 363	66.05 344	75.84 350	27.10 373	72.28 339	46.15 348	84.77 318	73.11 350

MDTV_nov1_ep13			68.29 306		78.03 328	43.87 360	74.12 312	72.22 332	52.17 334	67.02 343	85.54 265	45.36 337	80.85 319	55.73 303	84.42 319

1112_ss		74.82 264	73.74 265	78.04 265	89.57 172	60.04 265	76.49 292	87.09 221	54.31 322	73.66 318	79.80 333	60.25 270	86.76 276	58.37 290	84.15 320	87.32 263

PatchMatch-RL		74.48 266	73.22 271	78.27 262	87.70 207	85.26 34	75.92 298	70.09 342	64.34 268	76.09 300	81.25 321	65.87 243	78.07 327	53.86 317	83.82 321	71.48 352

MDA-MVSNet_test_wron		70.05 298	70.44 293	68.88 319	73.84 354	53.47 317	58.93 357	67.28 347	58.43 300	87.09 154	85.40 271	59.80 275	67.25 352	59.66 286	83.54 322	85.92 277

YYNet1		70.06 297	70.44 293	68.90 318	73.76 355	53.42 319	58.99 356	67.20 348	58.42 301	87.10 153	85.39 272	59.82 274	67.32 351	59.79 285	83.50 323	85.96 275

Test_1112_low_res		73.90 270	73.08 272	76.35 283	90.35 160	55.95 300	73.40 318	86.17 231	50.70 345	73.14 319	85.94 260	58.31 284	85.90 287	56.51 300	83.22 324	87.20 264

PVSNet	58.17 21	66.41 314	65.63 317	68.75 320	81.96 289	49.88 345	62.19 351	72.51 331	51.03 342	68.04 338	75.34 352	50.84 313	74.77 336	45.82 350	82.96 325	81.60 328

gg-mvs-nofinetune		68.96 304	69.11 300	68.52 323	76.12 343	45.32 356	83.59 170	55.88 367	86.68 26	64.62 354	97.01 7	30.36 368	83.97 305	44.78 351	82.94 326	76.26 346

CR-MVSNet		74.00 269	73.04 273	76.85 279	79.58 316	62.64 236	82.58 197	76.90 298	50.50 347	75.72 304	92.38 132	48.07 320	84.07 303	68.72 226	82.91 327	83.85 299

RPMNet		78.88 220	78.28 224	80.68 225	79.58 316	62.64 236	82.58 197	94.16 31	74.80 152	75.72 304	92.59 126	48.69 318	95.56 37	73.48 181	82.91 327	83.85 299

test0.0.03 1		64.66 319	64.36 320	65.57 331	75.03 351	46.89 353	64.69 345	61.58 361	62.43 278	71.18 329	77.54 342	43.41 346	68.47 349	40.75 358	82.65 329	81.35 330

HY-MVS	64.64 18	73.03 276	72.47 281	74.71 293	83.36 279	54.19 312	82.14 215	81.96 270	56.76 314	69.57 334	86.21 257	60.03 271	84.83 298	49.58 336	82.65 329	85.11 285

SCA		73.32 272	72.57 279	75.58 289	81.62 292	55.86 302	78.89 259	71.37 339	61.73 281	74.93 312	83.42 299	60.46 267	87.01 267	58.11 294	82.63 331	83.88 296

CHOSEN 1792x2688		72.45 280	70.56 291	78.13 263	90.02 170	63.08 229	68.72 334	83.16 261	42.99 361	75.92 302	85.46 269	57.22 293	85.18 294	49.87 335	81.67 332	86.14 274

WTY-MVS		67.91 307	68.35 305	66.58 328	80.82 305	48.12 348	65.96 343	72.60 329	53.67 326	71.20 328	81.68 318	58.97 280	69.06 348	48.57 339	81.67 332	82.55 316

TESTMET0.1,1		61.29 325	60.32 330	64.19 334	72.06 362	51.30 335	67.89 336	62.09 357	45.27 356	60.65 359	69.01 357	27.93 372	64.74 360	56.31 301	81.65 334	76.53 345

PAPM		71.77 286	70.06 297	76.92 276	86.39 231	53.97 313	76.62 290	86.62 227	53.44 327	63.97 355	84.73 286	57.79 290	92.34 162	39.65 359	81.33 335	84.45 291

DSMNet-mixed		60.98 328	61.61 327	59.09 345	72.88 360	45.05 358	74.70 309	46.61 372	26.20 367	65.34 348	90.32 191	55.46 301	63.12 362	41.72 356	81.30 336	69.09 356

sss		66.92 309	67.26 309	65.90 329	77.23 332	51.10 339	64.79 344	71.72 337	52.12 337	70.13 332	80.18 330	57.96 287	65.36 359	50.21 332	81.01 337	81.25 333

tpm		67.95 306	68.08 307	67.55 325	78.74 327	43.53 361	75.60 300	67.10 351	54.92 320	72.23 323	88.10 226	42.87 349	75.97 333	52.21 325	80.95 338	83.15 311

tpm2		68.45 305	66.83 311	73.30 299	78.93 326	48.50 346	79.76 244	71.76 336	47.50 351	69.92 333	83.60 295	42.07 350	88.40 255	48.44 341	79.51 339	83.01 313

FPMVS		72.29 283	72.00 283	73.14 300	88.63 188	85.00 36	74.65 310	67.39 346	71.94 194	77.80 289	87.66 234	50.48 315	75.83 334	49.95 333	79.51 339	58.58 363

UnsupCasMVSNet_bld		69.21 303	69.68 298	67.82 324	79.42 319	51.15 337	67.82 339	75.79 305	54.15 323	77.47 292	85.36 274	59.26 278	70.64 343	48.46 340	79.35 341	81.66 327

CostFormer		69.98 299	68.68 304	73.87 296	77.14 333	50.72 341	79.26 253	74.51 314	51.94 338	70.97 330	84.75 285	45.16 341	87.49 263	55.16 311	79.23 342	83.40 306

1314		73.22 274	72.56 280	75.20 290	80.41 312	57.84 287	81.64 219	85.36 240	51.68 339	73.10 320	76.65 348	61.45 263	85.19 293	63.54 258	79.21 343	82.59 315

baseline1		73.26 273	73.54 268	72.43 306	84.92 254	47.79 350	79.89 243	74.00 317	65.93 251	78.81 281	86.28 256	56.36 296	81.63 316	56.63 299	79.04 344	87.87 258

PMMVS		61.65 323	60.38 329	65.47 332	65.40 370	69.26 179	63.97 347	61.73 360	36.80 366	60.11 360	68.43 358	59.42 276	66.35 356	48.97 338	78.57 345	60.81 360

baseline2		69.77 300	66.89 310	78.41 258	79.51 318	58.09 285	76.23 296	69.57 344	57.50 309	64.82 353	77.45 344	46.02 327	88.44 254	53.08 320	77.83 346	88.70 246

MS-PatchMatch		70.93 291	70.22 295	73.06 301	81.85 291	62.50 239	73.82 315	77.90 293	52.44 333	75.92 302	81.27 320	55.67 300	81.75 314	55.37 308	77.70 347	74.94 348

UnsupCasMVSNet_eth		71.63 288	72.30 282	69.62 315	76.47 339	52.70 324	70.03 331	80.97 277	59.18 297	79.36 276	88.21 225	60.50 266	69.12 347	58.33 292	77.62 348	87.04 266

CVMVSNet		72.62 279	71.41 289	76.28 285	83.25 280	60.34 263	83.50 174	79.02 290	37.77 365	76.33 296	85.10 276	49.60 317	87.41 264	70.54 208	77.54 349	81.08 336

GG-mvs-BLEND				67.16 326	73.36 356	46.54 355	84.15 153	55.04 368		58.64 364	61.95 364	29.93 369	83.87 306	38.71 361	76.92 350	71.07 353

CHOSEN 280x420		59.08 330	56.52 335	66.76 327	76.51 338	64.39 217	49.62 361	59.00 363	43.86 359	55.66 367	68.41 359	35.55 362	68.21 350	43.25 353	76.78 351	67.69 357

tpmvs		70.16 295	69.56 299	71.96 308	74.71 353	48.13 347	79.63 245	75.45 310	65.02 265	70.26 331	81.88 315	45.34 338	85.68 289	58.34 291	75.39 352	82.08 323

MVP-Stereo		75.81 254	73.51 269	82.71 190	89.35 174	73.62 130	80.06 239	85.20 244	60.30 293	73.96 316	87.94 229	57.89 289	89.45 241	52.02 326	74.87 353	85.06 286
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		66.43 313	64.37 319	72.63 303	74.86 352	50.86 340	76.52 291	72.74 328	54.06 324	65.50 347	68.30 360	32.13 366	84.84 297	61.63 274	73.59 354	82.19 321

new_pmnet		55.69 332	57.66 334	49.76 348	75.47 347	30.59 370	59.56 352	51.45 370	43.62 360	62.49 356	75.48 351	40.96 352	49.15 367	37.39 362	72.52 355	69.55 355

PatchmatchNet		69.71 301	68.83 302	72.33 307	77.66 331	53.60 316	79.29 252	69.99 343	57.66 307	72.53 322	82.93 305	46.45 324	80.08 323	60.91 279	72.09 356	83.31 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 326	62.92 323	55.87 346	79.09 323	35.34 368	71.83 323	57.98 366	46.56 353	59.05 362	91.14 166	49.95 316	76.43 331	38.74 360	71.92 357	55.84 364

tpmrst		66.28 315	66.69 313	65.05 333	72.82 361	39.33 364	78.20 268	70.69 341	53.16 329	67.88 339	80.36 329	48.18 319	74.75 337	58.13 293	70.79 358	81.08 336

tpm cat1		66.76 312	65.21 318	71.42 309	77.09 334	50.62 342	78.01 269	73.68 323	44.89 357	68.64 335	79.00 336	45.51 335	82.42 313	49.91 334	70.15 359	81.23 335

ADS-MVSNet2		65.87 317	63.64 322	72.55 305	73.16 358	56.92 296	67.10 340	74.81 311	49.74 349	66.04 345	82.97 303	46.71 322	77.26 329	42.29 354	69.96 360	83.46 304

ADS-MVSNet		61.90 322	62.19 325	61.03 342	73.16 358	36.42 367	67.10 340	61.75 359	49.74 349	66.04 345	82.97 303	46.71 322	63.21 361	42.29 354	69.96 360	83.46 304

JIA-IIPM		69.41 302	66.64 314	77.70 270	73.19 357	71.24 164	75.67 299	65.56 353	70.42 208	65.18 349	92.97 114	33.64 365	83.06 308	53.52 319	69.61 362	78.79 343

EPMVS		62.47 320	62.63 324	62.01 337	70.63 365	38.74 365	74.76 308	52.86 369	53.91 325	67.71 341	80.01 331	39.40 354	66.60 355	55.54 307	68.81 363	80.68 340

MVE	40.22 23	51.82 334	50.47 337	55.87 346	62.66 372	51.91 330	31.61 364	39.28 373	40.65 362	50.76 368	74.98 353	56.24 298	44.67 368	33.94 365	64.11 364	71.04 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 329	60.29 331	61.92 339	72.04 363	38.67 366	70.83 326	64.08 355	51.28 341	60.75 358	77.28 345	36.59 361	71.58 342	47.41 344	62.34 365	75.52 347

PVSNet_0	51.08 22	56.10 331	54.97 336	59.48 344	75.12 350	53.28 320	55.16 358	61.89 358	44.30 358	59.16 361	62.48 363	54.22 306	65.91 358	35.40 363	47.01 366	59.25 362

tmp_tt		20.25 337	24.50 340	7.49 352	4.47 375	8.70 375	34.17 363	25.16 375	1.00 370	32.43 370	18.49 367	39.37 355	9.21 371	21.64 368	43.75 367	4.57 367

test_method		30.46 335	29.60 338	33.06 350	17.99 374	3.84 376	13.62 365	73.92 318	2.79 369	18.29 371	53.41 365	28.53 370	43.25 369	22.56 367	35.27 368	52.11 365

DeepMVS_CX				24.13 351	32.95 373	29.49 371		21.63 376	12.07 368	37.95 369	45.07 366	30.84 367	19.21 370	17.94 369	33.06 369	23.69 366

testmvs		5.91 341	7.65 344	0.72 354	1.20 376	0.37 378	59.14 354	0.67 378	0.49 372	1.11 372	2.76 371	0.94 377	0.24 373	1.02 371	1.47 370	1.55 369

test123		6.27 340	8.08 343	0.84 353	1.11 377	0.57 377	62.90 348	0.82 377	0.54 371	1.07 373	2.75 372	1.26 376	0.30 372	1.04 370	1.26 371	1.66 368

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		20.81 336	27.75 339	0.00 355	0.00 378	0.00 379	0.00 366	85.44 239	0.00 373	0.00 374	82.82 307	81.46 113	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		6.41 339	8.55 342	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	76.94 159	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		6.65 338	8.87 341	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	79.80 333	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

test_one_0601					93.85 62	73.27 134		94.11 37	86.57 27	93.47 40	94.64 56	88.42 27

eth-test2					0.00 378

eth-test					0.00 378

test_241102_ONE					94.18 49	72.65 138		93.69 54	83.62 46	94.11 23	93.78 99	90.28 15	95.50 47

save fliter					93.75 63	77.44 101	86.31 123	89.72 175	70.80 204

test0726					94.16 52	72.56 144	90.63 44	93.90 46	83.61 47	93.75 31	94.49 60	89.76 19

GSMVS																83.88 296

test_part2					93.86 61	77.77 96				92.84 48

sam_mvs1												46.11 326				83.88 296

sam_mvs												45.92 331

MTGPA								91.81 119

test_post1							78.85 261				3.13 369	45.19 340	80.13 322	58.11 294

test_post											3.10 370	45.43 336	77.22 330

patchmatchnet-post											81.71 317	45.93 330	87.01 267

MTMP							90.66 42	33.14 374

gm-plane-assit					75.42 348	44.97 359			52.17 334		72.36 356		87.90 259	54.10 316

TEST9					92.34 98	79.70 76	83.94 158	90.32 158	65.41 263	84.49 204	90.97 172	82.03 104	93.63 117

test_8					92.09 107	78.87 84	83.82 163	90.31 160	65.79 253	84.36 207	90.96 174	81.93 106	93.44 129

agg_prior					91.58 125	77.69 97		90.30 161		84.32 208			93.18 137

test_prior4						78.97 83	84.59 145

test_prior				86.32 109	90.59 156	71.99 155		92.85 91					94.17 96			92.80 141

旧先验2							81.73 217		56.88 313	86.54 172			84.90 296	72.81 190

新几何2							81.72 218

无先验							82.81 193	85.62 238	58.09 303				91.41 190	67.95 233		84.48 290

原ACMM2							82.26 211

testdata2													86.43 280	63.52 259

segment_acmp												81.94 105

testdata1							79.62 246		73.95 162

plane_prior7					93.45 69	77.31 105

plane_prior6					92.61 90	76.54 114						74.84 177

plane_prior4											92.95 115

plane_prior3						76.85 112			77.79 116	86.55 167

plane_prior2							89.45 72		79.44 97

plane_prior1					92.83 88

n2								0.00 379

nn								0.00 379

door-mid								74.45 315

test11								91.46 126

door								72.57 330

HQP5-MVS						70.66 167

HQP-NCC					91.19 139		84.77 140		73.30 172	80.55 263

ACMP_Plane					91.19 139		84.77 140		73.30 172	80.55 263

BP-MVS														77.30 143

HQP4-MVS										80.56 262			94.61 80			93.56 118

HQP2-MVS												72.10 210

NP-MVS					91.95 111	74.55 126					90.17 197

MDTV_nov1_ep13_2view						27.60 372	70.76 327		46.47 354	61.27 357		45.20 339		49.18 337		83.75 301

Test By Simon												79.09 136

High-resolution multi-view benchmark