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.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 57	91.63 1	86.34 1	97.97 1	94.77 3	66.57 122	95.38 1	87.74 1	97.72 1	93.00 7

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 124	84.80 35	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 100	97.05 2	96.93 1

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 14	79.37 15	84.79 69	74.51 53	96.15 3	92.88 8

ACMP	69.50 8	82.64 29	83.38 30	80.40 41	86.50 46	69.44 71	82.30 58	86.08 24	66.80 69	86.70 34	89.99 78	81.64 6	85.95 35	74.35 55	96.11 4	85.81 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	66.64 10	81.20 40	82.48 43	77.35 80	81.16 131	62.39 129	80.51 72	87.80 8	73.02 30	87.57 24	91.08 40	80.28 9	82.44 104	64.82 133	96.10 5	87.21 58

LPG-MVS_test		83.47 20	84.33 16	80.90 36	87.00 40	70.41 64	82.04 61	86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 72

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 72

ACMM	69.25 9	82.11 33	83.31 31	78.49 66	88.17 37	73.96 38	83.11 53	84.52 60	66.40 73	87.45 26	89.16 96	81.02 8	80.52 142	74.27 56	95.73 8	80.98 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NR-MVSNet		73.62 116	74.05 116	72.33 164	83.50 94	43.71 300	65.65 283	77.32 192	64.32 97	75.59 186	87.08 134	62.45 158	81.34 121	54.90 225	95.63 9	91.93 9

WR-MVS_H		80.22 54	82.17 45	74.39 115	89.46 15	42.69 312	78.24 101	82.24 97	78.21 13	89.57 10	92.10 19	68.05 101	85.59 50	66.04 124	95.62 10	94.88 5

TranMVSNet+NR-MVSNet		76.13 85	77.66 79	71.56 172	84.61 81	42.57 314	70.98 204	78.29 179	68.67 61	83.04 79	89.26 90	72.99 61	80.75 138	55.58 221	95.47 11	91.35 12

COLMAP_ROB	72.78 3	83.75 15	84.11 19	82.68 13	82.97 106	74.39 36	87.18 11	88.18 7	78.98 8	86.11 43	91.47 34	79.70 14	85.76 45	66.91 119	95.46 12	87.89 49
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVS		84.12 12	84.55 14	82.80 11	89.42 18	79.74 6	88.19 5	84.43 61	71.96 43	84.70 64	90.56 55	77.12 28	86.18 28	79.24 21	95.36 13	82.49 184

Baseline_NR-MVSNet		70.62 171	73.19 133	62.92 289	76.97 185	34.44 377	68.84 233	70.88 264	60.25 134	79.50 122	90.53 56	61.82 165	69.11 294	54.67 229	95.27 14	85.22 89

UniMVSNet (Re)		75.00 102	75.48 99	73.56 129	83.14 99	47.92 260	70.41 213	81.04 123	63.67 104	79.54 121	86.37 161	62.83 153	81.82 115	57.10 204	95.25 15	90.94 16

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 177

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 177

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 38	79.26 7	89.12 12	92.10 19	77.52 25	85.92 39	80.47 8	95.20 18	82.10 192

PS-CasMVS		80.41 51	82.86 40	73.07 138	89.93 7	39.21 339	77.15 115	81.28 115	79.74 6	90.87 5	92.73 12	75.03 46	84.93 65	63.83 145	95.19 19	95.07 3

ACMMP		84.22 10	84.84 12	82.35 18	89.23 22	76.66 26	87.65 7	85.89 26	71.03 47	85.85 45	90.58 54	78.77 18	85.78 44	79.37 19	95.17 20	84.62 111
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
PEN-MVS		80.46 50	82.91 38	73.11 137	89.83 9	39.02 342	77.06 117	82.61 93	80.04 5	90.60 7	92.85 10	74.93 47	85.21 60	63.15 153	95.15 21	95.09 2

CP-MVSNet		79.48 58	81.65 49	72.98 141	89.66 13	39.06 341	76.76 118	80.46 136	78.91 9	90.32 8	91.70 29	68.49 96	84.89 66	63.40 150	95.12 22	95.01 4

SteuartSystems-ACMMP		83.07 25	83.64 26	81.35 30	85.14 72	71.00 58	85.53 29	84.78 49	70.91 48	85.64 48	90.41 62	75.55 41	87.69 5	79.75 11	95.08 23	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
UniMVSNet_NR-MVSNet		74.90 105	75.65 96	72.64 157	83.04 104	45.79 284	69.26 228	78.81 165	66.66 71	81.74 97	86.88 141	63.26 149	81.07 129	56.21 212	94.98 24	91.05 14

DU-MVS		74.91 104	75.57 98	72.93 145	83.50 94	45.79 284	69.47 223	80.14 143	65.22 86	81.74 97	87.08 134	61.82 165	81.07 129	56.21 212	94.98 24	91.93 9

MP-MVS-pluss		82.54 30	83.46 29	79.76 45	88.88 31	68.44 80	81.57 64	86.33 19	63.17 112	85.38 55	91.26 37	76.33 33	84.67 71	83.30 2	94.96 26	86.17 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		84.12 12	84.63 13	82.60 14	88.21 36	74.40 35	85.24 31	87.21 14	70.69 50	85.14 57	90.42 61	78.99 17	86.62 15	80.83 6	94.93 27	86.79 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 16	73.69 27	86.17 40	91.70 29	78.23 21	85.20 61	79.45 16	94.91 28	88.15 48

MSC_two_6792asdad				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 138

No_MVS				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 138

MP-MVS		83.19 22	83.54 27	82.14 20	90.54 5	79.00 9	86.42 25	83.59 77	71.31 44	81.26 103	90.96 42	74.57 50	84.69 70	78.41 25	94.78 31	82.74 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		83.19 22	83.87 22	81.13 34	91.16 3	78.16 12	84.87 33	80.63 132	72.08 41	84.93 59	90.79 48	74.65 49	84.42 75	80.98 5	94.75 32	80.82 217

test_0728_THIRD									74.03 25	85.83 46	90.41 62	75.58 40	85.69 47	77.43 34	94.74 33	84.31 126

PGM-MVS		83.07 25	83.25 34	82.54 16	89.57 14	77.21 24	82.04 61	85.40 36	67.96 64	84.91 62	90.88 45	75.59 39	86.57 16	78.16 26	94.71 34	83.82 136

DTE-MVSNet		80.35 52	82.89 39	72.74 154	89.84 8	37.34 359	77.16 114	81.81 105	80.45 4	90.92 4	92.95 8	74.57 50	86.12 31	63.65 146	94.68 35	94.76 6

mPP-MVS		84.01 14	84.39 15	82.88 7	90.65 4	81.38 4	87.08 13	82.79 87	72.41 39	85.11 58	90.85 47	76.65 31	84.89 66	79.30 20	94.63 36	82.35 186

FC-MVSNet-test		73.32 123	74.78 104	68.93 223	79.21 151	36.57 361	71.82 191	79.54 155	57.63 160	82.57 88	90.38 67	59.38 196	78.99 165	57.91 198	94.56 37	91.23 13

DeepC-MVS	72.44 4	81.00 44	80.83 54	81.50 26	86.70 45	70.03 68	82.06 60	87.00 15	59.89 137	80.91 109	90.53 56	72.19 64	88.56 2	73.67 61	94.52 38	85.92 77
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	79.20 16	85.58 51	78.11 27	94.46 39	84.89 98

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	81.38 7		78.11 27	94.46 39	84.89 98

UA-Net		81.56 37	82.28 44	79.40 52	88.91 29	69.16 76	84.67 36	80.01 146	75.34 19	79.80 119	94.91 2	69.79 88	80.25 146	72.63 68	94.46 39	88.78 42

ACMH	63.62 14	77.50 76	80.11 58	69.68 204	79.61 143	56.28 189	78.81 93	83.62 76	63.41 110	87.14 33	90.23 74	76.11 35	73.32 245	67.58 106	94.44 42	79.44 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		83.12 24	83.68 25	81.45 28	89.14 25	73.28 46	86.32 26	85.97 25	67.39 65	84.02 71	90.39 65	74.73 48	86.46 17	80.73 7	94.43 43	84.60 114

SED-MVS		81.78 35	83.48 28	76.67 85	86.12 54	61.06 144	83.62 46	84.72 52	72.61 35	87.38 28	89.70 83	77.48 26	85.89 42	75.29 46	94.39 44	83.08 163

IU-MVS					86.12 54	60.90 148		80.38 138	45.49 306	81.31 102				75.64 45	94.39 44	84.65 108

DVP-MVS		81.15 41	83.12 36	75.24 107	86.16 52	60.78 150	83.77 44	80.58 134	72.48 37	85.83 46	90.41 62	78.57 19	85.69 47	75.86 42	94.39 44	79.24 247
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				76.57 87	86.20 49	60.57 153	83.77 44	85.49 32					85.90 40	75.86 42	94.39 44	83.25 157

SMA-MVS		82.12 32	82.68 42	80.43 40	88.90 30	69.52 69	85.12 32	84.76 50	63.53 106	84.23 69	91.47 34	72.02 67	87.16 8	79.74 13	94.36 48	84.61 112
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
ACMMP_NAP		82.33 31	83.28 32	79.46 51	89.28 19	69.09 78	83.62 46	84.98 45	64.77 94	83.97 72	91.02 41	75.53 42	85.93 38	82.00 3	94.36 48	83.35 155

APD-MVS_3200maxsize		83.57 17	84.33 16	81.31 32	82.83 109	73.53 44	85.50 30	87.45 13	74.11 23	86.45 38	90.52 58	80.02 10	84.48 73	77.73 31	94.34 50	85.93 76

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 78	66.72 94	86.54 23	85.11 42	72.00 42	86.65 35	91.75 28	78.20 22	87.04 11	77.93 29	94.32 51	83.47 149
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		83.62 16	83.93 21	82.69 12	89.78 11	77.51 22	87.01 17	84.19 68	70.23 51	84.49 66	90.67 53	75.15 44	86.37 20	79.58 14	94.26 52	84.18 129

region2R		83.54 18	83.86 23	82.58 15	89.82 10	77.53 18	87.06 16	84.23 67	70.19 53	83.86 73	90.72 52	75.20 43	86.27 23	79.41 18	94.25 53	83.95 134

HFP-MVS		83.39 21	84.03 20	81.48 27	89.25 21	75.69 28	87.01 17	84.27 64	70.23 51	84.47 67	90.43 60	76.79 29	85.94 36	79.58 14	94.23 54	82.82 173

test_241102_TWO								84.80 48	72.61 35	84.93 59	89.70 83	77.73 24	85.89 42	75.29 46	94.22 55	83.25 157

XVS		83.51 19	83.73 24	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	90.39 65	73.86 55	86.31 21	78.84 23	94.03 56	84.64 109

X-MVStestdata		76.81 81	74.79 103	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	9.95 433	73.86 55	86.31 21	78.84 23	94.03 56	84.64 109

DPE-MVS		82.00 34	83.02 37	78.95 60	85.36 69	67.25 89	82.91 54	84.98 45	73.52 28	85.43 54	90.03 77	76.37 32	86.97 13	74.56 51	94.02 58	82.62 181
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
GST-MVS		82.79 28	83.27 33	81.34 31	88.99 27	73.29 45	85.94 28	85.13 41	68.58 62	84.14 70	90.21 75	73.37 59	86.41 18	79.09 22	93.98 59	84.30 128

9.14			80.22 57		80.68 134		80.35 77	87.69 11	59.90 136	83.00 80	88.20 120	74.57 50	81.75 117	73.75 60	93.78 60

SF-MVS		80.72 47	81.80 46	77.48 77	82.03 119	64.40 115	83.41 50	88.46 6	65.28 85	84.29 68	89.18 94	73.73 58	83.22 92	76.01 41	93.77 61	84.81 105

IS-MVSNet		75.10 99	75.42 100	74.15 119	79.23 150	48.05 258	79.43 86	78.04 183	70.09 54	79.17 126	88.02 125	53.04 250	83.60 83	58.05 197	93.76 62	90.79 18

PMVS	70.70 6	81.70 36	83.15 35	77.36 79	90.35 6	82.82 3	82.15 59	79.22 159	74.08 24	87.16 32	91.97 21	84.80 2	76.97 202	64.98 131	93.61 63	72.28 327
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 40	77.42 17	86.15 41	90.24 73	81.69 5	85.94 36	77.77 30	93.58 64	83.09 162

OPM-MVS		80.99 45	81.63 50	79.07 56	86.86 44	69.39 72	79.41 88	84.00 73	65.64 77	85.54 52	89.28 89	76.32 34	83.47 88	74.03 58	93.57 65	84.35 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 39	72.91 47	80.23 80	85.56 31	66.56 72	85.64 48	89.57 85	69.12 92	80.55 141	72.51 70	93.37 66	83.48 148

FIs		72.56 145	73.80 120	68.84 226	78.74 162	37.74 355	71.02 203	79.83 148	56.12 176	80.88 111	89.45 87	58.18 206	78.28 184	56.63 206	93.36 67	90.51 20

WR-MVS		71.20 163	72.48 149	67.36 245	84.98 74	35.70 369	64.43 300	68.66 282	65.05 90	81.49 100	86.43 160	57.57 218	76.48 209	50.36 263	93.32 68	89.90 22

CLD-MVS		72.88 139	72.36 152	74.43 114	77.03 182	54.30 205	68.77 239	83.43 79	52.12 230	76.79 163	74.44 333	69.54 90	83.91 79	55.88 215	93.25 69	85.09 94
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		81.51 38	81.76 47	80.76 38	89.20 23	78.75 10	86.48 24	82.03 101	68.80 58	80.92 108	88.52 113	72.00 68	82.39 105	74.80 48	93.04 70	81.14 207

APD-MVS		81.13 42	81.73 48	79.36 53	84.47 83	70.53 63	83.85 42	83.70 75	69.43 57	83.67 75	88.96 103	75.89 37	86.41 18	72.62 69	92.95 71	81.14 207
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZD-MVS					83.91 90	69.36 73		81.09 121	58.91 147	82.73 87	89.11 97	75.77 38	86.63 14	72.73 67	92.93 72

OurMVSNet-221017-0		78.57 66	78.53 71	78.67 63	80.48 136	64.16 116	80.24 79	82.06 100	61.89 121	88.77 16	93.32 5	57.15 222	82.60 103	70.08 87	92.80 73	89.25 28

Anonymous20231211		75.54 92	77.19 83	70.59 185	77.67 176	45.70 287	74.73 148	80.19 141	68.80 58	82.95 82	92.91 9	66.26 124	76.76 207	58.41 195	92.77 74	89.30 27

test_prior2							75.57 136		58.92 146	76.53 173	86.78 144	67.83 107		69.81 88	92.76 75

CDPH-MVS		77.33 77	77.06 85	78.14 71	84.21 87	63.98 119	76.07 131	83.45 78	54.20 207	77.68 147	87.18 132	69.98 85	85.37 53	68.01 102	92.72 76	85.08 95

EPP-MVSNet		73.86 114	73.38 128	75.31 105	78.19 166	53.35 214	80.45 73	77.32 192	65.11 89	76.47 176	86.80 142	49.47 270	83.77 81	53.89 239	92.72 76	88.81 41

OMC-MVS		79.41 59	78.79 67	81.28 33	80.62 135	70.71 62	80.91 69	84.76 50	62.54 117	81.77 95	86.65 152	71.46 71	83.53 86	67.95 104	92.44 78	89.60 24

tt0805		76.12 86	78.43 72	69.20 213	81.32 128	41.37 320	76.72 119	77.64 188	63.78 103	82.06 91	87.88 126	79.78 11	79.05 163	64.33 137	92.40 79	87.17 61

DP-MVS		78.44 70	79.29 64	75.90 97	81.86 122	65.33 106	79.05 91	84.63 58	74.83 22	80.41 114	86.27 163	71.68 69	83.45 89	62.45 158	92.40 79	78.92 252

nrg030		74.87 107	75.99 94	71.52 173	74.90 218	49.88 242	74.10 160	82.58 94	54.55 199	83.50 77	89.21 92	71.51 70	75.74 215	61.24 165	92.34 81	88.94 37

SD-MVS		80.28 53	81.55 51	76.47 90	83.57 93	67.83 84	83.39 51	85.35 39	64.42 96	86.14 42	87.07 136	74.02 54	80.97 133	77.70 32	92.32 82	80.62 225
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
Anonymous20240521		63.55 265	66.07 240	55.99 340	66.18 348	44.04 298	68.77 239	68.80 280	46.99 293	72.57 236	85.84 177	39.87 327	50.22 384	53.40 246	92.23 83	73.71 310

LTVRE_ROB	75.46 1	84.22 10	84.98 11	81.94 24	84.82 76	75.40 29	91.60 3	87.80 8	73.52 28	88.90 15	93.06 7	71.39 73	81.53 119	81.53 4	92.15 84	88.91 38
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
ACMMP++															91.96 85

v7n		79.37 60	80.41 56	76.28 92	78.67 163	55.81 194	79.22 90	82.51 95	70.72 49	87.54 25	92.44 15	68.00 103	81.34 121	72.84 66	91.72 86	91.69 11

VDDNet		71.60 158	73.13 135	67.02 250	86.29 48	41.11 322	69.97 217	66.50 292	68.72 60	74.74 199	91.70 29	59.90 190	75.81 213	48.58 280	91.72 86	84.15 131

UniMVSNet_ETH3D		76.74 82	79.02 65	69.92 202	89.27 20	43.81 299	74.47 154	71.70 243	72.33 40	85.50 53	93.65 4	77.98 23	76.88 205	54.60 230	91.64 88	89.08 32

wuyk23d		61.97 282	66.25 237	49.12 379	58.19 401	60.77 152	66.32 274	52.97 372	55.93 180	90.62 6	86.91 140	73.07 60	35.98 427	20.63 429	91.63 89	50.62 416

CNVR-MVS		78.49 68	78.59 70	78.16 70	85.86 63	67.40 88	78.12 104	81.50 109	63.92 100	77.51 148	86.56 156	68.43 98	84.82 68	73.83 59	91.61 90	82.26 190

train_agg		76.38 84	76.55 88	75.86 98	85.47 67	69.32 74	76.42 123	78.69 170	54.00 212	76.97 154	86.74 146	66.60 120	81.10 127	72.50 71	91.56 91	77.15 277

Gipuma		69.55 188	72.83 143	59.70 317	63.63 367	53.97 208	80.08 82	75.93 207	64.24 98	73.49 225	88.93 104	57.89 216	62.46 343	59.75 185	91.55 92	62.67 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SixPastTwentyTwo		75.77 87	76.34 89	74.06 120	81.69 124	54.84 201	76.47 120	75.49 211	64.10 99	87.73 21	92.24 18	50.45 265	81.30 123	67.41 109	91.46 93	86.04 74

test9_res														72.12 75	91.37 94	77.40 272

3Dnovator+	73.19 2	81.08 43	80.48 55	82.87 8	81.41 127	72.03 49	84.38 38	86.23 23	77.28 18	80.65 112	90.18 76	59.80 193	87.58 6	73.06 64	91.34 95	89.01 34

DeepPCF-MVS	71.07 5	78.48 69	77.14 84	82.52 17	84.39 86	77.04 25	76.35 125	84.05 71	56.66 171	80.27 116	85.31 182	68.56 95	87.03 12	67.39 111	91.26 96	83.50 145

LS3D		80.99 45	80.85 53	81.41 29	78.37 164	71.37 54	87.45 8	85.87 27	77.48 16	81.98 92	89.95 80	69.14 91	85.26 57	66.15 121	91.24 97	87.61 53

HPM-MVS++		79.89 55	79.80 61	80.18 43	89.02 26	78.44 11	83.49 49	80.18 142	64.71 95	78.11 140	88.39 116	65.46 133	83.14 93	77.64 33	91.20 98	78.94 251

KD-MVS_self_test		66.38 236	67.51 221	62.97 287	61.76 375	34.39 378	58.11 350	75.30 212	50.84 250	77.12 153	85.42 180	56.84 227	69.44 291	51.07 257	91.16 99	85.08 95

test_djsdf		78.88 63	78.27 73	80.70 39	81.42 126	71.24 56	83.98 40	75.72 209	52.27 228	87.37 30	92.25 17	68.04 102	80.56 139	72.28 73	91.15 100	90.32 21

DeepC-MVS_fast	69.89 7	77.17 78	76.33 90	79.70 48	83.90 91	67.94 82	80.06 83	83.75 74	56.73 170	74.88 198	85.32 181	65.54 131	87.79 3	65.61 128	91.14 101	83.35 155
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testf1		75.66 90	76.57 86	72.95 142	67.07 339	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 270	60.46 173	91.13 102	79.56 241

APD_test2		75.66 90	76.57 86	72.95 142	67.07 339	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 270	60.46 173	91.13 102	79.56 241

ambc				70.10 198	77.74 174	50.21 233	74.28 158	77.93 186		79.26 124	88.29 119	54.11 245	79.77 153	64.43 135	91.10 104	80.30 231

原ACMM1				73.90 122	85.90 60	65.15 110		81.67 107	50.97 248	74.25 212	86.16 168	61.60 167	83.54 85	56.75 205	91.08 105	73.00 315

114514_t		73.40 121	73.33 132	73.64 126	84.15 89	57.11 185	78.20 102	80.02 145	43.76 322	72.55 237	86.07 173	64.00 146	83.35 91	60.14 179	91.03 106	80.45 228

HQP_MVS		78.77 64	78.78 68	78.72 62	85.18 70	65.18 108	82.74 55	85.49 32	65.45 80	78.23 137	89.11 97	60.83 180	86.15 29	71.09 78	90.94 107	84.82 103

plane_prior5								85.49 32					86.15 29	71.09 78	90.94 107	84.82 103

agg_prior2														70.70 82	90.93 109	78.55 256

PHI-MVS		74.92 103	74.36 110	76.61 86	76.40 196	62.32 130	80.38 75	83.15 82	54.16 209	73.23 230	80.75 255	62.19 162	83.86 80	68.02 101	90.92 110	83.65 142

AllTest		77.66 74	77.43 80	78.35 68	79.19 152	70.81 59	78.60 95	88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 178	55.60 218	90.90 111	85.81 78

TestCases				78.35 68	79.19 152	70.81 59		88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 178	55.60 218	90.90 111	85.81 78

NCCC		78.25 71	78.04 76	78.89 61	85.61 65	69.45 70	79.80 85	80.99 124	65.77 76	75.55 187	86.25 165	67.42 108	85.42 52	70.10 86	90.88 113	81.81 198

VPNet		65.58 243	67.56 220	59.65 318	79.72 142	30.17 399	60.27 332	62.14 322	54.19 208	71.24 260	86.63 153	58.80 201	67.62 308	44.17 316	90.87 114	81.18 206

DVP-MVS++		81.24 39	82.74 41	76.76 84	83.14 99	60.90 148	91.64 1	85.49 32	74.03 25	84.93 59	90.38 67	66.82 115	85.90 40	77.43 34	90.78 115	83.49 146

PC_three_1452									46.98 294	81.83 94	86.28 162	66.55 123	84.47 74	63.31 152	90.78 115	83.49 146

h-mvs33		73.08 128	71.61 164	77.48 77	83.89 92	72.89 48	70.47 211	71.12 261	54.28 203	77.89 141	83.41 212	49.04 274	80.98 132	63.62 147	90.77 117	78.58 255

XVG-OURS		79.51 57	79.82 60	78.58 65	86.11 57	74.96 32	76.33 127	84.95 47	66.89 67	82.75 86	88.99 102	66.82 115	78.37 181	74.80 48	90.76 118	82.40 185

PS-MVSNAJss		77.54 75	77.35 82	78.13 72	84.88 75	66.37 96	78.55 96	79.59 153	53.48 219	86.29 39	92.43 16	62.39 159	80.25 146	67.90 105	90.61 119	87.77 50

anonymousdsp		78.60 65	77.80 77	81.00 35	78.01 170	74.34 37	80.09 81	76.12 204	50.51 253	89.19 11	90.88 45	71.45 72	77.78 195	73.38 62	90.60 120	90.90 17

pmmvs6		71.82 155	73.66 123	66.31 257	75.94 205	42.01 316	66.99 265	72.53 237	63.45 108	76.43 177	92.78 11	72.95 62	69.69 289	51.41 254	90.46 121	87.22 57

test12				76.51 88	82.28 116	60.94 147		81.64 108		73.60 223		64.88 139	85.19 62		90.42 122	83.38 153

VDD-MVS		70.81 169	71.44 168	68.91 224	79.07 157	46.51 278	67.82 252	70.83 265	61.23 124	74.07 216	88.69 108	59.86 191	75.62 216	51.11 256	90.28 123	84.61 112

mvs_tets		78.93 62	78.67 69	79.72 47	84.81 77	73.93 39	80.65 71	76.50 201	51.98 233	87.40 27	91.86 26	76.09 36	78.53 173	68.58 95	90.20 124	86.69 66

EPNet		69.10 196	67.32 224	74.46 111	68.33 320	61.27 141	77.56 107	63.57 316	60.95 128	56.62 383	82.75 226	51.53 259	81.24 124	54.36 235	90.20 124	80.88 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		68.71 204	70.37 179	63.72 276	76.13 200	38.06 353	64.10 302	71.48 249	56.60 173	74.10 215	88.31 118	64.78 141	69.72 288	47.69 291	90.15 126	83.37 154

TAPA-MVS	65.27 12	75.16 98	74.29 111	77.77 75	74.86 219	68.08 81	77.89 105	84.04 72	55.15 187	76.19 181	83.39 213	66.91 113	80.11 150	60.04 181	90.14 127	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
jajsoiax		78.51 67	78.16 75	79.59 49	84.65 80	73.83 41	80.42 74	76.12 204	51.33 244	87.19 31	91.51 33	73.79 57	78.44 177	68.27 98	90.13 128	86.49 69

Anonymous20240529		72.56 145	73.79 121	68.86 225	76.89 190	45.21 290	68.80 238	77.25 194	67.16 66	76.89 158	90.44 59	65.95 127	74.19 238	50.75 259	90.00 129	87.18 60

AdaColmap		74.22 110	74.56 106	73.20 134	81.95 120	60.97 146	79.43 86	80.90 125	65.57 78	72.54 238	81.76 242	70.98 78	85.26 57	47.88 289	90.00 129	73.37 311

DP-MVS Recon		73.57 118	72.69 145	76.23 93	82.85 108	63.39 122	74.32 155	82.96 85	57.75 155	70.35 268	81.98 238	64.34 145	84.41 76	49.69 267	89.95 131	80.89 215

test1111		64.62 253	65.19 249	62.93 288	79.01 158	29.91 400	65.45 286	54.41 362	54.09 210	71.47 258	88.48 114	37.02 345	74.29 237	46.83 298	89.94 132	84.58 115

plane_prior						65.18 108	80.06 83		61.88 122						89.91 133

cl____		68.26 213	68.26 208	68.29 234	64.98 359	43.67 301	65.89 278	74.67 217	50.04 260	76.86 160	82.42 231	48.74 278	75.38 217	60.92 170	89.81 134	85.80 82

DIV-MVS_self_test		68.27 212	68.26 208	68.29 234	64.98 359	43.67 301	65.89 278	74.67 217	50.04 260	76.86 160	82.43 230	48.74 278	75.38 217	60.94 169	89.81 134	85.81 78

OPU-MVS				78.65 64	83.44 97	66.85 93	83.62 46				86.12 170	66.82 115	86.01 34	61.72 162	89.79 136	83.08 163

LFMVS		67.06 229	67.89 216	64.56 268	78.02 169	38.25 350	70.81 208	59.60 333	65.18 87	71.06 262	86.56 156	43.85 302	75.22 221	46.35 301	89.63 137	80.21 234

TSAR-MVS + GP.		73.08 128	71.60 165	77.54 76	78.99 159	70.73 61	74.96 141	69.38 276	60.73 131	74.39 209	78.44 296	57.72 217	82.78 100	60.16 177	89.60 138	79.11 249

EC-MVSNet		77.08 79	77.39 81	76.14 95	76.86 191	56.87 187	80.32 78	87.52 12	63.45 108	74.66 203	84.52 194	69.87 87	84.94 64	69.76 89	89.59 139	86.60 67

MIMVSNet1		66.57 234	69.23 192	58.59 326	81.26 130	37.73 356	64.06 303	57.62 338	57.02 164	78.40 136	90.75 49	62.65 154	58.10 364	41.77 330	89.58 140	79.95 236

mmtdpeth		68.76 202	70.55 178	63.40 282	67.06 341	56.26 190	68.73 241	71.22 259	55.47 184	70.09 273	88.64 111	65.29 136	56.89 367	58.94 191	89.50 141	77.04 282

TransMVSNet (Re)		69.62 186	71.63 162	63.57 278	76.51 194	35.93 367	65.75 282	71.29 255	61.05 126	75.02 195	89.90 81	65.88 129	70.41 283	49.79 266	89.48 142	84.38 124

ACMMP++_ref															89.47 143

test2506		61.23 289	60.85 290	62.38 293	78.80 160	27.88 408	67.33 261	37.42 427	54.23 205	67.55 308	88.68 109	17.87 431	74.39 235	46.33 302	89.41 144	84.86 101

ECVR-MVS		64.82 250	65.22 248	63.60 277	78.80 160	31.14 394	66.97 266	56.47 352	54.23 205	69.94 276	88.68 109	37.23 344	74.81 230	45.28 312	89.41 144	84.86 101

SPE-MVS-test		74.89 106	74.23 112	76.86 83	77.01 184	62.94 127	78.98 92	84.61 59	58.62 148	70.17 272	80.80 254	66.74 119	81.96 113	61.74 161	89.40 146	85.69 84

PCF-MVS	63.80 13	72.70 143	71.69 159	75.72 99	78.10 167	60.01 157	73.04 169	81.50 109	45.34 309	79.66 120	84.35 197	65.15 137	82.65 102	48.70 278	89.38 147	84.50 121
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP3-MVS								84.12 69							89.16 148

HQP-MVS		75.24 97	75.01 102	75.94 96	82.37 113	58.80 172	77.32 111	84.12 69	59.08 141	71.58 251	85.96 175	58.09 210	85.30 55	67.38 113	89.16 148	83.73 141

AUN-MVS		70.22 175	67.88 217	77.22 82	82.96 107	71.61 52	69.08 231	71.39 251	49.17 270	71.70 248	78.07 303	37.62 343	79.21 161	61.81 159	89.15 150	80.82 217

v10		75.69 89	76.20 91	74.16 118	74.44 229	48.69 249	75.84 135	82.93 86	59.02 145	85.92 44	89.17 95	58.56 203	82.74 101	70.73 81	89.14 151	91.05 14

MM		78.15 73	77.68 78	79.55 50	80.10 139	65.47 104	80.94 68	78.74 169	71.22 45	72.40 240	88.70 107	60.51 182	87.70 4	77.40 36	89.13 152	85.48 87

hse-mvs2		72.32 149	70.66 177	77.31 81	83.10 103	71.77 51	69.19 230	71.45 250	54.28 203	77.89 141	78.26 298	49.04 274	79.23 160	63.62 147	89.13 152	80.92 214

MCST-MVS		73.42 120	73.34 131	73.63 127	81.28 129	59.17 164	74.80 146	83.13 83	45.50 304	72.84 233	83.78 209	65.15 137	80.99 131	64.54 134	89.09 154	80.73 221

MVS_0304		75.45 93	74.66 105	77.83 74	75.58 210	61.53 137	78.29 99	77.18 195	63.15 114	69.97 275	87.20 131	57.54 219	87.05 10	74.05 57	88.96 155	84.89 98

ITE_SJBPF				80.35 42	76.94 186	73.60 42		80.48 135	66.87 68	83.64 76	86.18 166	70.25 83	79.90 152	61.12 168	88.95 156	87.56 54

ANet_high		67.08 228	69.94 183	58.51 327	57.55 402	27.09 410	58.43 347	76.80 199	63.56 105	82.40 89	91.93 23	59.82 192	64.98 334	50.10 265	88.86 157	83.46 150

test_0402		78.17 72	79.48 63	74.24 117	83.50 94	59.15 165	72.52 173	74.60 219	75.34 19	88.69 17	91.81 27	75.06 45	82.37 106	65.10 129	88.68 158	81.20 205

APD_test1		75.04 101	75.38 101	74.02 121	69.89 301	70.15 66	76.46 121	79.71 149	65.50 79	82.99 81	88.60 112	66.94 112	72.35 258	59.77 184	88.54 159	79.56 241

mvs5depth		66.35 238	67.98 214	61.47 302	62.43 371	51.05 224	69.38 225	69.24 278	56.74 169	73.62 222	89.06 100	46.96 287	58.63 360	55.87 216	88.49 160	74.73 298

casdiffmvs_mvg		75.26 96	76.18 92	72.52 159	72.87 261	49.47 243	72.94 171	84.71 54	59.49 139	80.90 110	88.81 106	70.07 84	79.71 154	67.40 110	88.39 161	88.40 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EGC-MVSNET		64.77 252	61.17 286	75.60 102	86.90 43	74.47 34	84.04 39	68.62 283	0.60 435	1.13 437	91.61 32	65.32 135	74.15 239	64.01 139	88.28 162	78.17 262

IterMVS-LS		73.01 132	73.12 136	72.66 156	73.79 241	49.90 238	71.63 193	78.44 175	58.22 150	80.51 113	86.63 153	58.15 208	79.62 155	62.51 156	88.20 163	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSLP-MVS++		74.48 109	75.78 95	70.59 185	84.66 79	62.40 128	78.65 94	84.24 66	60.55 132	77.71 146	81.98 238	63.12 150	77.64 197	62.95 154	88.14 164	71.73 332

CL-MVSNet_self_test		62.44 280	63.40 269	59.55 319	72.34 266	32.38 386	56.39 358	64.84 306	51.21 246	67.46 309	81.01 252	50.75 263	63.51 341	38.47 351	88.12 165	82.75 175

FMVSNet1		71.06 164	72.48 149	66.81 251	77.65 177	40.68 329	71.96 185	73.03 229	61.14 125	79.45 123	90.36 70	60.44 183	75.20 223	50.20 264	88.05 166	84.54 116

pm-mvs1		68.40 207	69.85 185	64.04 274	73.10 254	39.94 336	64.61 298	70.50 267	55.52 183	73.97 219	89.33 88	63.91 147	68.38 301	49.68 268	88.02 167	83.81 137

TinyColmap		67.98 214	69.28 189	64.08 272	67.98 326	46.82 275	70.04 215	75.26 213	53.05 221	77.36 150	86.79 143	59.39 195	72.59 255	45.64 307	88.01 168	72.83 319

v8		75.07 100	75.64 97	73.35 131	73.42 245	47.46 269	75.20 138	81.45 111	60.05 135	85.64 48	89.26 90	58.08 212	81.80 116	69.71 91	87.97 169	90.79 18

BP-MVS1		71.60 158	70.06 181	76.20 94	74.07 236	55.22 199	74.29 157	73.44 226	57.29 162	73.87 221	84.65 189	32.57 364	83.49 87	72.43 72	87.94 170	89.89 23

tttt0517		69.46 189	67.79 219	74.46 111	75.34 211	52.72 216	75.05 140	63.27 319	54.69 194	78.87 130	84.37 196	26.63 399	81.15 125	63.95 142	87.93 171	89.51 25

new-patchmatchnet		52.89 349	55.76 331	44.26 399	59.94 389	6.31 440	37.36 424	50.76 383	41.10 342	64.28 329	79.82 272	44.77 296	48.43 392	36.24 371	87.61 172	78.03 265

tfpnnormal		66.48 235	67.93 215	62.16 295	73.40 246	36.65 360	63.45 308	64.99 304	55.97 178	72.82 234	87.80 127	57.06 225	69.10 295	48.31 284	87.54 173	80.72 222

Anonymous202405211		66.02 240	66.89 233	63.43 281	74.22 232	38.14 351	59.00 340	66.13 294	63.33 111	69.76 279	85.95 176	51.88 255	70.50 280	44.23 315	87.52 174	81.64 202

c3_l		69.82 184	69.89 184	69.61 205	66.24 346	43.48 303	68.12 249	79.61 152	51.43 239	77.72 145	80.18 267	54.61 241	78.15 189	63.62 147	87.50 175	87.20 59

v144192		72.99 134	73.06 138	72.77 152	74.58 227	47.48 268	71.90 189	80.44 137	51.57 237	81.46 101	84.11 202	58.04 214	82.12 111	67.98 103	87.47 176	88.70 43

Patchmtry		60.91 291	63.01 274	54.62 347	66.10 349	26.27 416	67.47 256	56.40 353	54.05 211	72.04 246	86.66 150	33.19 359	60.17 352	43.69 317	87.45 177	77.42 271

v1921920		72.96 137	72.98 140	72.89 148	74.67 223	47.58 267	71.92 188	80.69 128	51.70 236	81.69 99	83.89 206	56.58 229	82.25 109	68.34 97	87.36 178	88.82 40

CSCG		74.12 111	74.39 108	73.33 132	79.35 147	61.66 136	77.45 110	81.98 102	62.47 119	79.06 128	80.19 266	61.83 164	78.79 169	59.83 183	87.35 179	79.54 244

MGCFI-Net		71.70 157	73.10 137	67.49 243	73.23 249	43.08 308	72.06 181	82.43 96	54.58 197	75.97 182	82.00 236	72.42 63	75.22 221	57.84 199	87.34 180	84.18 129

v1192		73.40 121	73.42 126	73.32 133	74.65 226	48.67 250	72.21 178	81.73 106	52.76 224	81.85 93	84.56 192	57.12 223	82.24 110	68.58 95	87.33 181	89.06 33

LCM-MVSNet-Re		69.10 196	71.57 166	61.70 298	70.37 293	34.30 379	61.45 320	79.62 150	56.81 167	89.59 9	88.16 123	68.44 97	72.94 248	42.30 324	87.33 181	77.85 269

sasdasda		72.29 150	73.38 128	69.04 217	74.23 230	47.37 270	73.93 162	83.18 80	54.36 201	76.61 168	81.64 245	72.03 65	75.34 219	57.12 202	87.28 183	84.40 122

canonicalmvs		72.29 150	73.38 128	69.04 217	74.23 230	47.37 270	73.93 162	83.18 80	54.36 201	76.61 168	81.64 245	72.03 65	75.34 219	57.12 202	87.28 183	84.40 122

baseline		73.10 127	73.96 118	70.51 187	71.46 276	46.39 281	72.08 180	84.40 62	55.95 179	76.62 167	86.46 159	67.20 109	78.03 190	64.22 138	87.27 185	87.11 62

test_fmvsmconf0.01_n		73.91 112	73.64 124	74.71 108	69.79 305	66.25 97	75.90 133	79.90 147	46.03 300	76.48 175	85.02 185	67.96 105	73.97 240	74.47 54	87.22 186	83.90 135

alignmvs		70.54 172	71.00 172	69.15 215	73.50 243	48.04 259	69.85 220	79.62 150	53.94 215	76.54 172	82.00 236	59.00 199	74.68 231	57.32 201	87.21 187	84.72 107

F-COLMAP		75.29 95	73.99 117	79.18 54	81.73 123	71.90 50	81.86 63	82.98 84	59.86 138	72.27 241	84.00 204	64.56 143	83.07 96	51.48 252	87.19 188	82.56 183

TSAR-MVS + MP.		79.05 61	78.81 66	79.74 46	88.94 28	67.52 87	86.61 22	81.38 113	51.71 235	77.15 152	91.42 36	65.49 132	87.20 7	79.44 17	87.17 189	84.51 120
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1240		73.06 130	73.14 134	72.84 151	74.74 222	47.27 273	71.88 190	81.11 119	51.80 234	82.28 90	84.21 198	56.22 233	82.34 107	68.82 94	87.17 189	88.91 38

GDP-MVS		70.84 168	69.24 191	75.62 101	76.44 195	55.65 196	74.62 153	82.78 89	49.63 263	72.10 245	83.79 208	31.86 372	82.84 99	64.93 132	87.01 191	88.39 47

test_fmvsmconf0.1_n		73.26 125	72.82 144	74.56 110	69.10 312	66.18 99	74.65 152	79.34 157	45.58 303	75.54 188	83.91 205	67.19 110	73.88 243	73.26 63	86.86 192	83.63 143

v1144		73.29 124	73.39 127	73.01 139	74.12 235	48.11 256	72.01 183	81.08 122	53.83 216	81.77 95	84.68 187	58.07 213	81.91 114	68.10 99	86.86 192	88.99 36

casdiffmvs		73.06 130	73.84 119	70.72 183	71.32 278	46.71 277	70.93 205	84.26 65	55.62 182	77.46 149	87.10 133	67.09 111	77.81 193	63.95 142	86.83 194	87.64 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet (Re-imp)		62.74 277	63.21 272	61.34 305	72.19 268	31.56 391	67.31 262	53.87 364	53.60 218	69.88 277	83.37 215	40.52 323	70.98 276	41.40 332	86.78 195	81.48 204

CS-MVS		76.51 83	76.00 93	78.06 73	77.02 183	64.77 112	80.78 70	82.66 92	60.39 133	74.15 213	83.30 219	69.65 89	82.07 112	69.27 92	86.75 196	87.36 56

K. test v3		73.67 115	73.61 125	73.87 123	79.78 141	55.62 198	74.69 150	62.04 326	66.16 75	84.76 63	93.23 6	49.47 270	80.97 133	65.66 127	86.67 197	85.02 97

test_fmvsmconf_n		72.91 138	72.40 151	74.46 111	68.62 316	66.12 100	74.21 159	78.80 167	45.64 302	74.62 204	83.25 221	66.80 118	73.86 244	72.97 65	86.66 198	83.39 152

thisisatest0530		67.05 230	65.16 250	72.73 155	73.10 254	50.55 228	71.26 201	63.91 314	50.22 257	74.46 208	80.75 255	26.81 398	80.25 146	59.43 187	86.50 199	87.37 55

lessismore_v0				72.75 153	79.60 144	56.83 188		57.37 341		83.80 74	89.01 101	47.45 285	78.74 170	64.39 136	86.49 200	82.69 179

MVSMamba_PlusPlus		76.88 80	78.21 74	72.88 149	80.83 132	48.71 248	83.28 52	82.79 87	72.78 31	79.17 126	91.94 22	56.47 231	83.95 78	70.51 85	86.15 201	85.99 75

MVS_111021_HR		72.98 135	72.97 141	72.99 140	80.82 133	65.47 104	68.81 236	72.77 234	57.67 157	75.76 183	82.38 232	71.01 77	77.17 200	61.38 164	86.15 201	76.32 285

LF4IMVS		67.50 220	67.31 225	68.08 237	58.86 396	61.93 132	71.43 195	75.90 208	44.67 316	72.42 239	80.20 265	57.16 221	70.44 281	58.99 190	86.12 203	71.88 330

FMVSNet2		67.48 221	68.21 211	65.29 263	73.14 251	38.94 343	68.81 236	71.21 260	54.81 189	76.73 164	86.48 158	48.63 280	74.60 232	47.98 288	86.11 204	82.35 186

EPNet_dtu		58.93 308	58.52 307	60.16 316	67.91 327	47.70 266	69.97 217	58.02 337	49.73 262	47.28 418	73.02 347	38.14 337	62.34 344	36.57 368	85.99 205	70.43 346
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS-SEG-HR		79.62 56	79.99 59	78.49 66	86.46 47	74.79 33	77.15 115	85.39 37	66.73 70	80.39 115	88.85 105	74.43 53	78.33 183	74.73 50	85.79 206	82.35 186

balanced_conf03		73.59 117	74.06 115	72.17 167	77.48 179	47.72 265	81.43 65	82.20 98	54.38 200	79.19 125	87.68 128	54.41 242	83.57 84	63.98 141	85.78 207	85.22 89

API-MVS		70.97 167	71.51 167	69.37 208	75.20 213	55.94 192	80.99 67	76.84 198	62.48 118	71.24 260	77.51 308	61.51 169	80.96 136	52.04 248	85.76 208	71.22 338

v2v482		72.55 147	72.58 147	72.43 161	72.92 260	46.72 276	71.41 196	79.13 160	55.27 185	81.17 105	85.25 183	55.41 237	81.13 126	67.25 117	85.46 209	89.43 26

GBi-Net		68.30 209	68.79 198	66.81 251	73.14 251	40.68 329	71.96 185	73.03 229	54.81 189	74.72 200	90.36 70	48.63 280	75.20 223	47.12 293	85.37 210	84.54 116

test1		68.30 209	68.79 198	66.81 251	73.14 251	40.68 329	71.96 185	73.03 229	54.81 189	74.72 200	90.36 70	48.63 280	75.20 223	47.12 293	85.37 210	84.54 116

FMVSNet3		65.00 249	65.16 250	64.52 269	69.47 307	37.56 358	66.63 271	70.38 268	51.55 238	74.72 200	83.27 220	37.89 341	74.44 234	47.12 293	85.37 210	81.57 203

CNLPA		73.44 119	73.03 139	74.66 109	78.27 165	75.29 30	75.99 132	78.49 174	65.39 82	75.67 185	83.22 224	61.23 173	66.77 322	53.70 241	85.33 213	81.92 196

Effi-MVS+-dtu		75.43 94	72.28 153	84.91 3	77.05 181	83.58 2	78.47 97	77.70 187	57.68 156	74.89 197	78.13 302	64.80 140	84.26 77	56.46 210	85.32 214	86.88 63

UGNet		70.20 176	69.05 194	73.65 125	76.24 198	63.64 120	75.87 134	72.53 237	61.48 123	60.93 359	86.14 169	52.37 253	77.12 201	50.67 260	85.21 215	80.17 235
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
VNet		64.01 264	65.15 252	60.57 312	73.28 248	35.61 370	57.60 352	67.08 289	54.61 196	66.76 314	83.37 215	56.28 232	66.87 318	42.19 326	85.20 216	79.23 248

TAMVS		65.31 245	63.75 264	69.97 201	82.23 117	59.76 160	66.78 270	63.37 318	45.20 310	69.79 278	79.37 283	47.42 286	72.17 260	34.48 380	85.15 217	77.99 267

test_yl		65.11 246	65.09 254	65.18 264	70.59 286	40.86 325	63.22 313	72.79 232	57.91 153	68.88 292	79.07 290	42.85 309	74.89 228	45.50 309	84.97 218	79.81 237

DCV-MVSNet		65.11 246	65.09 254	65.18 264	70.59 286	40.86 325	63.22 313	72.79 232	57.91 153	68.88 292	79.07 290	42.85 309	74.89 228	45.50 309	84.97 218	79.81 237

USDC		62.80 275	63.10 273	61.89 296	65.19 355	43.30 306	67.42 257	74.20 222	35.80 383	72.25 242	84.48 195	45.67 290	71.95 266	37.95 355	84.97 218	70.42 347

ETV-MVS		72.72 142	72.16 155	74.38 116	76.90 189	55.95 191	73.34 166	84.67 55	62.04 120	72.19 244	70.81 360	65.90 128	85.24 59	58.64 192	84.96 221	81.95 195

DPM-MVS		69.98 181	69.22 193	72.26 165	82.69 111	58.82 171	70.53 210	81.23 117	47.79 288	64.16 330	80.21 264	51.32 261	83.12 94	60.14 179	84.95 222	74.83 297

SDMVSNet		66.36 237	67.85 218	61.88 297	73.04 257	46.14 283	58.54 345	71.36 252	51.42 240	68.93 290	82.72 227	65.62 130	62.22 346	54.41 233	84.67 223	77.28 273

sd_testset		63.55 265	65.38 246	58.07 329	73.04 257	38.83 345	57.41 353	65.44 301	51.42 240	68.93 290	82.72 227	63.76 148	58.11 363	41.05 334	84.67 223	77.28 273

eth_miper_zixun_eth		69.42 190	68.73 202	71.50 174	67.99 325	46.42 279	67.58 254	78.81 165	50.72 251	78.13 139	80.34 263	50.15 267	80.34 144	60.18 176	84.65 225	87.74 51

miper_lstm_enhance		61.97 282	61.63 282	62.98 286	60.04 385	45.74 286	47.53 401	70.95 262	44.04 318	73.06 231	78.84 293	39.72 328	60.33 351	55.82 217	84.64 226	82.88 170

cl22		67.14 227	66.51 235	69.03 219	63.20 368	43.46 304	66.88 269	76.25 203	49.22 269	74.48 207	77.88 304	45.49 292	77.40 199	60.64 172	84.59 227	86.24 70

miper_ehance_all_eth		68.36 208	68.16 213	68.98 220	65.14 358	43.34 305	67.07 264	78.92 164	49.11 271	76.21 180	77.72 305	53.48 247	77.92 192	61.16 167	84.59 227	85.68 85

miper_enhance_ethall		65.86 241	65.05 257	68.28 236	61.62 377	42.62 313	64.74 294	77.97 184	42.52 332	73.42 227	72.79 348	49.66 268	77.68 196	58.12 196	84.59 227	84.54 116

CDS-MVSNet		64.33 260	62.66 277	69.35 210	80.44 137	58.28 178	65.26 288	65.66 298	44.36 317	67.30 311	75.54 321	43.27 305	71.77 267	37.68 357	84.44 230	78.01 266
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testing3-2		56.85 319	57.62 316	54.53 348	75.84 206	22.23 428	51.26 389	49.10 391	61.04 127	63.74 338	79.73 274	22.29 418	59.44 355	31.16 395	84.43 231	81.92 196

CANet		73.00 133	71.84 157	76.48 89	75.82 207	61.28 140	74.81 144	80.37 139	63.17 112	62.43 349	80.50 260	61.10 177	85.16 63	64.00 140	84.34 232	83.01 166

PLC	62.01 16	71.79 156	70.28 180	76.33 91	80.31 138	68.63 79	78.18 103	81.24 116	54.57 198	67.09 313	80.63 258	59.44 194	81.74 118	46.91 296	84.17 233	78.63 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_BlendedMVS		65.38 244	64.30 258	68.61 229	69.81 302	49.36 244	65.60 285	78.96 162	45.50 304	59.98 362	78.61 294	51.82 256	78.20 186	44.30 313	84.11 234	78.27 260

cascas		64.59 254	62.77 276	70.05 199	75.27 212	50.02 235	61.79 319	71.61 245	42.46 333	63.68 339	68.89 383	49.33 272	80.35 143	47.82 290	84.05 235	79.78 239

MSP-MVS		80.49 49	79.67 62	82.96 6	89.70 12	77.46 23	87.16 12	85.10 43	64.94 93	81.05 106	88.38 117	57.10 224	87.10 9	79.75 11	83.87 236	84.31 126
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
test20.03		55.74 326	57.51 318	50.42 368	59.89 390	32.09 388	50.63 390	49.01 392	50.11 258	65.07 324	83.23 222	45.61 291	48.11 393	30.22 398	83.82 237	71.07 342

fmvsm_s_conf0.5_n_6		70.08 178	69.97 182	70.39 188	72.99 259	58.93 170	68.84 233	76.40 202	49.08 272	68.75 296	81.65 244	57.34 220	71.97 265	70.91 80	83.81 238	80.26 232

D2MVS		62.58 279	61.05 288	67.20 247	63.85 364	47.92 260	56.29 359	69.58 274	39.32 357	70.07 274	78.19 300	34.93 353	72.68 250	53.44 244	83.74 239	81.00 212

MVS_111021_LR		72.10 152	71.82 158	72.95 142	79.53 145	73.90 40	70.45 212	66.64 291	56.87 165	76.81 162	81.76 242	68.78 93	71.76 268	61.81 159	83.74 239	73.18 313

patch_mono-2		62.73 278	64.08 261	58.68 325	70.36 294	55.87 193	60.84 327	64.11 313	41.23 341	64.04 331	78.22 299	60.00 187	48.80 388	54.17 237	83.71 241	71.37 335

dcpmvs_2		71.02 166	72.65 146	66.16 258	76.06 204	50.49 229	71.97 184	79.36 156	50.34 254	82.81 85	83.63 210	64.38 144	67.27 313	61.54 163	83.71 241	80.71 223

test_fmvsmvis_n_1920		72.36 148	72.49 148	71.96 168	71.29 279	64.06 118	72.79 172	81.82 104	40.23 353	81.25 104	81.04 251	70.62 80	68.69 297	69.74 90	83.60 243	83.14 161

thres600view7		61.82 284	61.38 285	63.12 284	71.81 272	34.93 374	64.64 296	56.99 346	54.78 193	70.33 269	79.74 273	32.07 369	72.42 257	38.61 349	83.46 244	82.02 193

旧先验1					84.55 82	60.36 155		63.69 315			87.05 137	54.65 240			83.34 245	69.66 353

SSC-MVS3.2		57.01 318	59.50 300	49.57 375	67.73 330	25.95 418	46.68 404	51.75 379	51.41 242	63.84 335	79.66 276	53.28 249	50.34 383	37.85 356	83.28 246	72.41 324

新几何1				69.99 200	88.37 35	71.34 55		62.08 324	43.85 319	74.99 196	86.11 171	52.85 251	70.57 279	50.99 258	83.23 247	68.05 366

Vis-MVSNet		74.85 108	74.56 106	75.72 99	81.63 125	64.64 113	76.35 125	79.06 161	62.85 115	73.33 228	88.41 115	62.54 157	79.59 157	63.94 144	82.92 248	82.94 167
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_8		72.87 140	72.85 142	72.93 145	72.25 267	59.01 169	72.35 175	80.13 144	56.32 174	75.74 184	84.12 200	60.14 186	75.05 226	71.71 76	82.90 249	84.75 106

ET-MVSNet_ETH3D		63.32 268	60.69 292	71.20 179	70.15 299	55.66 195	65.02 292	64.32 311	43.28 331	68.99 286	72.05 353	25.46 405	78.19 188	54.16 238	82.80 250	79.74 240

DELS-MVS		68.83 200	68.31 206	70.38 189	70.55 290	48.31 252	63.78 306	82.13 99	54.00 212	68.96 287	75.17 326	58.95 200	80.06 151	58.55 193	82.74 251	82.76 174
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
CMPMVS	48.73 20	61.54 288	60.89 289	63.52 279	61.08 379	51.55 221	68.07 250	68.00 286	33.88 392	65.87 317	81.25 248	37.91 340	67.71 306	49.32 273	82.60 252	71.31 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		60.26 298	59.61 299	62.20 294	67.70 331	44.33 296	58.18 349	60.96 329	40.75 349	65.80 318	72.57 349	41.23 316	63.92 338	46.87 297	82.42 253	78.33 258

v148		69.38 192	69.39 188	69.36 209	69.14 311	44.56 294	68.83 235	72.70 235	54.79 192	78.59 132	84.12 200	54.69 239	76.74 208	59.40 188	82.20 254	86.79 64

thisisatest0515		60.48 296	57.86 314	68.34 233	67.25 335	46.42 279	60.58 330	62.14 322	40.82 347	63.58 342	69.12 378	26.28 401	78.34 182	48.83 276	82.13 255	80.26 232

fmvsm_s_conf0.5_n_5		71.46 161	71.62 163	70.99 181	73.89 240	59.95 158	73.02 170	73.08 228	45.15 311	77.30 151	84.06 203	64.73 142	70.08 284	71.20 77	82.10 256	82.92 168

OpenMVS	62.51 15	68.76 202	68.75 200	68.78 227	70.56 288	53.91 209	78.29 99	77.35 191	48.85 276	70.22 270	83.52 211	52.65 252	76.93 203	55.31 222	81.99 257	75.49 290

MAR-MVS		67.72 218	66.16 238	72.40 162	74.45 228	64.99 111	74.87 142	77.50 190	48.67 278	65.78 319	68.58 387	57.01 226	77.79 194	46.68 299	81.92 258	74.42 304
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
Anonymous20231206		54.13 337	55.82 330	49.04 380	70.89 280	35.96 366	51.73 386	50.87 382	34.86 385	62.49 348	79.22 285	42.52 312	44.29 410	27.95 409	81.88 259	66.88 372

FE-MVS		68.29 211	66.96 231	72.26 165	74.16 234	54.24 206	77.55 108	73.42 227	57.65 159	72.66 235	84.91 186	32.02 371	81.49 120	48.43 282	81.85 260	81.04 209

GeoE		73.14 126	73.77 122	71.26 177	78.09 168	52.64 217	74.32 155	79.56 154	56.32 174	76.35 179	83.36 217	70.76 79	77.96 191	63.32 151	81.84 261	83.18 160

FA-MVS(test-final)		71.27 162	71.06 171	71.92 169	73.96 237	52.32 219	76.45 122	76.12 204	59.07 144	74.04 218	86.18 166	52.18 254	79.43 159	59.75 185	81.76 262	84.03 132

thres100view900		61.17 290	61.09 287	61.39 303	72.14 269	35.01 373	65.42 287	56.99 346	55.23 186	70.71 265	79.90 271	32.07 369	72.09 261	35.61 375	81.73 263	77.08 279

tfpn200view9		60.35 297	59.97 296	61.51 300	70.78 282	35.35 371	63.27 311	57.47 339	53.00 222	68.31 300	77.09 311	32.45 366	72.09 261	35.61 375	81.73 263	77.08 279

thres400		60.77 294	59.97 296	63.15 283	70.78 282	35.35 371	63.27 311	57.47 339	53.00 222	68.31 300	77.09 311	32.45 366	72.09 261	35.61 375	81.73 263	82.02 193

MG-MVS		70.47 173	71.34 169	67.85 239	79.26 149	40.42 333	74.67 151	75.15 215	58.41 149	68.74 297	88.14 124	56.08 234	83.69 82	59.90 182	81.71 266	79.43 246

PAPM_NR		73.91 112	74.16 113	73.16 135	81.90 121	53.50 212	81.28 66	81.40 112	66.17 74	73.30 229	83.31 218	59.96 188	83.10 95	58.45 194	81.66 267	82.87 171

FMVSNet5		55.08 333	55.54 332	53.71 350	65.80 350	33.50 383	56.22 360	52.50 374	43.72 324	61.06 356	83.38 214	25.46 405	54.87 372	30.11 399	81.64 268	72.75 320

fmvsm_s_conf0.5_n_3		72.97 136	74.13 114	69.47 207	71.40 277	58.36 177	73.07 168	80.64 131	56.86 166	75.49 190	84.67 188	67.86 106	72.33 259	75.68 44	81.54 269	77.73 270

PAPR		69.20 193	68.66 203	70.82 182	75.15 215	47.77 263	75.31 137	81.11 119	49.62 265	66.33 315	79.27 284	61.53 168	82.96 97	48.12 286	81.50 270	81.74 201

testdata				64.13 271	85.87 62	63.34 123		61.80 327	47.83 287	76.42 178	86.60 155	48.83 277	62.31 345	54.46 232	81.26 271	66.74 375

3Dnovator	65.95 11	71.50 160	71.22 170	72.34 163	73.16 250	63.09 125	78.37 98	78.32 177	57.67 157	72.22 243	84.61 191	54.77 238	78.47 175	60.82 171	81.07 272	75.45 291

testing3		58.28 312	58.38 310	58.00 330	77.45 180	26.12 417	60.78 328	43.00 413	56.02 177	70.18 271	75.76 318	13.27 439	67.24 314	48.02 287	80.89 273	80.65 224

RPSCF		75.76 88	74.37 109	79.93 44	74.81 220	77.53 18	77.53 109	79.30 158	59.44 140	78.88 129	89.80 82	71.26 74	73.09 247	57.45 200	80.89 273	89.17 31

EG-PatchMatch MVS		70.70 170	70.88 173	70.16 196	82.64 112	58.80 172	71.48 194	73.64 224	54.98 188	76.55 171	81.77 241	61.10 177	78.94 166	54.87 226	80.84 275	72.74 321

V42		71.06 164	70.83 174	71.72 170	67.25 335	47.14 274	65.94 277	80.35 140	51.35 243	83.40 78	83.23 222	59.25 197	78.80 168	65.91 125	80.81 276	89.23 29

fmvsm_s_conf0.5_n_7		67.30 226	66.92 232	68.43 231	72.78 263	58.22 179	60.90 326	72.51 239	49.62 265	63.66 340	80.65 257	58.56 203	68.63 299	62.83 155	80.76 277	78.45 257

ttmdpeth		56.40 322	55.45 333	59.25 320	55.63 412	40.69 328	58.94 342	49.72 387	36.22 379	65.39 320	86.97 138	23.16 414	56.69 368	42.30 324	80.74 278	80.36 230

fmvsm_s_conf0.1_n_2		69.14 195	68.42 205	71.28 176	68.30 321	57.60 183	65.06 291	69.91 271	48.24 280	74.56 206	82.84 225	55.55 236	69.73 287	70.66 83	80.69 279	86.52 68

test222					87.30 38	69.15 77	67.85 251	59.59 334	41.06 343	73.05 232	85.72 179	48.03 283			80.65 280	66.92 371

BH-untuned		69.39 191	69.46 187	69.18 214	77.96 171	56.88 186	68.47 246	77.53 189	56.77 168	77.79 144	79.63 277	60.30 185	80.20 149	46.04 304	80.65 280	70.47 345

pmmvs-eth3d		64.41 259	63.27 271	67.82 241	75.81 208	60.18 156	69.49 222	62.05 325	38.81 363	74.13 214	82.23 234	43.76 303	68.65 298	42.53 323	80.63 282	74.63 299

fmvsm_s_conf0.5_n_2		68.93 198	68.23 210	71.02 180	67.78 329	57.58 184	64.74 294	69.56 275	48.16 282	74.38 210	82.32 233	56.00 235	69.68 290	70.65 84	80.52 283	85.80 82

fmvsm_l_conf0.5_n_3		71.98 154	71.68 160	72.88 149	72.84 262	64.15 117	73.48 164	77.11 196	48.97 275	71.31 259	84.18 199	67.98 104	71.60 272	68.86 93	80.43 284	82.89 169

EI-MVSNet-Vis-set		72.78 141	71.87 156	75.54 103	74.77 221	59.02 168	72.24 177	71.56 247	63.92 100	78.59 132	71.59 355	66.22 125	78.60 172	67.58 106	80.32 285	89.00 35

diffmvs		67.42 224	67.50 222	67.20 247	62.26 373	45.21 290	64.87 293	77.04 197	48.21 281	71.74 247	79.70 275	58.40 205	71.17 275	64.99 130	80.27 286	85.22 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVStest1		55.38 330	54.97 337	56.58 337	43.72 434	40.07 335	59.13 338	47.09 400	34.83 386	76.53 173	84.65 189	13.55 438	53.30 377	55.04 224	80.23 287	76.38 284

EI-MVSNet-UG-set		72.63 144	71.68 160	75.47 104	74.67 223	58.64 175	72.02 182	71.50 248	63.53 106	78.58 134	71.39 359	65.98 126	78.53 173	67.30 116	80.18 288	89.23 29

MDA-MVSNet-bldmvs		62.34 281	61.73 279	64.16 270	61.64 376	49.90 238	48.11 399	57.24 344	53.31 220	80.95 107	79.39 282	49.00 276	61.55 348	45.92 305	80.05 289	81.03 210

reproduce_monomvs		58.94 307	58.14 312	61.35 304	59.70 392	40.98 324	60.24 333	63.51 317	45.85 301	68.95 288	75.31 325	18.27 429	65.82 327	51.47 253	79.97 290	77.26 276

IB-MVS	49.67 18	59.69 302	56.96 321	67.90 238	68.19 323	50.30 232	61.42 321	65.18 303	47.57 290	55.83 387	67.15 396	23.77 411	79.60 156	43.56 319	79.97 290	73.79 309
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
WBMVS		53.38 343	54.14 343	51.11 365	70.16 298	26.66 412	50.52 392	51.64 380	39.32 357	63.08 346	77.16 310	23.53 412	55.56 369	31.99 390	79.88 292	71.11 341

Patchmatch-RL test		59.95 300	59.12 302	62.44 292	72.46 265	54.61 204	59.63 336	47.51 398	41.05 344	74.58 205	74.30 335	31.06 381	65.31 331	51.61 251	79.85 293	67.39 368

EI-MVSNet		69.61 187	69.01 196	71.41 175	73.94 238	49.90 238	71.31 199	71.32 253	58.22 150	75.40 192	70.44 362	58.16 207	75.85 211	62.51 156	79.81 294	88.48 44

MVSTER		63.29 269	61.60 283	68.36 232	59.77 391	46.21 282	60.62 329	71.32 253	41.83 336	75.40 192	79.12 288	30.25 387	75.85 211	56.30 211	79.81 294	83.03 165

ab-mvs		64.11 262	65.13 253	61.05 307	71.99 270	38.03 354	67.59 253	68.79 281	49.08 272	65.32 322	86.26 164	58.02 215	66.85 320	39.33 342	79.79 296	78.27 260

PVSNet_Blended_VisFu		70.04 179	68.88 197	73.53 130	82.71 110	63.62 121	74.81 144	81.95 103	48.53 279	67.16 312	79.18 287	51.42 260	78.38 180	54.39 234	79.72 297	78.60 254

thres200		57.55 316	57.02 320	59.17 321	67.89 328	34.93 374	58.91 343	57.25 343	50.24 256	64.01 332	71.46 357	32.49 365	71.39 273	31.31 393	79.57 298	71.19 340

testgi		54.00 341	56.86 322	45.45 393	58.20 400	25.81 419	49.05 395	49.50 389	45.43 307	67.84 303	81.17 249	51.81 258	43.20 414	29.30 403	79.41 299	67.34 370

jason		64.47 257	62.84 275	69.34 211	76.91 187	59.20 161	67.15 263	65.67 297	35.29 384	65.16 323	76.74 314	44.67 297	70.68 277	54.74 228	79.28 300	78.14 263
jason: jason.
test_fmvsm_n_1920		69.63 185	68.45 204	73.16 135	70.56 288	65.86 102	70.26 214	78.35 176	37.69 370	74.29 211	78.89 292	61.10 177	68.10 304	65.87 126	79.07 301	85.53 86

Fast-Effi-MVS+-dtu		70.00 180	68.74 201	73.77 124	73.47 244	64.53 114	71.36 197	78.14 182	55.81 181	68.84 294	74.71 330	65.36 134	75.75 214	52.00 249	79.00 302	81.03 210

fmvsm_s_conf0.5_n_4		70.18 177	69.83 186	71.24 178	71.65 273	58.59 176	69.29 227	71.66 244	48.69 277	71.62 249	82.11 235	59.94 189	70.03 285	74.52 52	78.96 303	85.10 93

EU-MVSNet		60.82 292	60.80 291	60.86 310	68.37 318	41.16 321	72.27 176	68.27 285	26.96 415	69.08 284	75.71 319	32.09 368	67.44 311	55.59 220	78.90 304	73.97 306

MVS_Test		69.84 183	70.71 176	67.24 246	67.49 333	43.25 307	69.87 219	81.22 118	52.69 225	71.57 254	86.68 149	62.09 163	74.51 233	66.05 123	78.74 305	83.96 133

Fast-Effi-MVS+		68.81 201	68.30 207	70.35 191	74.66 225	48.61 251	66.06 276	78.32 177	50.62 252	71.48 257	75.54 321	68.75 94	79.59 157	50.55 262	78.73 306	82.86 172

MVSFormer		69.93 182	69.03 195	72.63 158	74.93 216	59.19 162	83.98 40	75.72 209	52.27 228	63.53 343	76.74 314	43.19 306	80.56 139	72.28 73	78.67 307	78.14 263

lupinMVS		63.36 267	61.49 284	68.97 221	74.93 216	59.19 162	65.80 281	64.52 310	34.68 390	63.53 343	74.25 336	43.19 306	70.62 278	53.88 240	78.67 307	77.10 278

mvsmamba		68.87 199	67.30 226	73.57 128	76.58 193	53.70 211	84.43 37	74.25 221	45.38 308	76.63 166	84.55 193	35.85 350	85.27 56	49.54 270	78.49 309	81.75 200

Effi-MVS+		72.10 152	72.28 153	71.58 171	74.21 233	50.33 231	74.72 149	82.73 90	62.62 116	70.77 264	76.83 313	69.96 86	80.97 133	60.20 175	78.43 310	83.45 151

CANet_DTU		64.04 263	63.83 263	64.66 267	68.39 317	42.97 310	73.45 165	74.50 220	52.05 232	54.78 393	75.44 324	43.99 301	70.42 282	53.49 243	78.41 311	80.59 226

myMVS_eth3d28		51.35 361	51.99 358	49.44 376	69.21 308	22.51 426	49.82 394	49.11 390	49.00 274	55.03 391	70.31 365	22.73 417	52.88 378	24.33 422	78.39 312	72.92 316

xiu_mvs_v1_base_debu		67.87 215	67.07 228	70.26 192	79.13 154	61.90 133	67.34 258	71.25 256	47.98 284	67.70 305	74.19 338	61.31 170	72.62 252	56.51 207	78.26 313	76.27 286

xiu_mvs_v1_base		67.87 215	67.07 228	70.26 192	79.13 154	61.90 133	67.34 258	71.25 256	47.98 284	67.70 305	74.19 338	61.31 170	72.62 252	56.51 207	78.26 313	76.27 286

xiu_mvs_v1_base_debi		67.87 215	67.07 228	70.26 192	79.13 154	61.90 133	67.34 258	71.25 256	47.98 284	67.70 305	74.19 338	61.31 170	72.62 252	56.51 207	78.26 313	76.27 286

fmvsm_l_conf0.5_n		67.48 221	66.88 234	69.28 212	67.41 334	62.04 131	70.69 209	69.85 272	39.46 356	69.59 280	81.09 250	58.15 208	68.73 296	67.51 108	78.16 316	77.07 281

BH-RMVSNet		68.69 205	68.20 212	70.14 197	76.40 196	53.90 210	64.62 297	73.48 225	58.01 152	73.91 220	81.78 240	59.09 198	78.22 185	48.59 279	77.96 317	78.31 259

RRT-MVS		70.33 174	70.73 175	69.14 216	71.93 271	45.24 289	75.10 139	75.08 216	60.85 130	78.62 131	87.36 130	49.54 269	78.64 171	60.16 177	77.90 318	83.55 144

IterMVS-SCA-FT		67.68 219	66.07 240	72.49 160	73.34 247	58.20 180	63.80 305	65.55 300	48.10 283	76.91 157	82.64 229	45.20 293	78.84 167	61.20 166	77.89 319	80.44 229

PVSNet_Blended		62.90 274	61.64 281	66.69 254	69.81 302	49.36 244	61.23 323	78.96 162	42.04 334	59.98 362	68.86 384	51.82 256	78.20 186	44.30 313	77.77 320	72.52 322

fmvsm_l_conf0.5_n_a		66.66 232	65.97 242	68.72 228	67.09 337	61.38 139	70.03 216	69.15 279	38.59 364	68.41 298	80.36 262	56.56 230	68.32 302	66.10 122	77.45 321	76.46 283

UWE-MVS		52.94 348	52.70 351	53.65 351	73.56 242	27.49 409	57.30 354	49.57 388	38.56 365	62.79 347	71.42 358	19.49 426	60.41 350	24.33 422	77.33 322	73.06 314

testing222		53.37 344	52.50 354	55.98 341	70.51 291	29.68 401	56.20 361	51.85 377	46.19 298	56.76 381	68.94 381	19.18 427	65.39 330	25.87 416	76.98 323	72.87 318

MSDG		67.47 223	67.48 223	67.46 244	70.70 284	54.69 203	66.90 268	78.17 180	60.88 129	70.41 267	74.76 328	61.22 175	73.18 246	47.38 292	76.87 324	74.49 302

testing91		55.74 326	55.29 336	57.08 333	70.63 285	30.85 396	54.94 371	56.31 355	50.34 254	57.08 377	70.10 370	24.50 409	65.86 326	36.98 365	76.75 325	74.53 301

E-PMN		45.17 383	45.36 386	44.60 397	50.07 426	42.75 311	38.66 421	42.29 418	46.39 297	39.55 429	51.15 425	26.00 402	45.37 403	37.68 357	76.41 326	45.69 422

PM-MVS		64.49 256	63.61 266	67.14 249	76.68 192	75.15 31	68.49 245	42.85 414	51.17 247	77.85 143	80.51 259	45.76 289	66.31 325	52.83 247	76.35 327	59.96 404

EIA-MVS		68.59 206	67.16 227	72.90 147	75.18 214	55.64 197	69.39 224	81.29 114	52.44 227	64.53 326	70.69 361	60.33 184	82.30 108	54.27 236	76.31 328	80.75 220

BH-w/o		64.81 251	64.29 259	66.36 256	76.08 203	54.71 202	65.61 284	75.23 214	50.10 259	71.05 263	71.86 354	54.33 243	79.02 164	38.20 353	76.14 329	65.36 381

testing99		55.16 332	54.56 341	56.98 335	70.13 300	30.58 398	54.55 374	54.11 363	49.53 267	56.76 381	70.14 369	22.76 416	65.79 328	36.99 364	76.04 330	74.57 300

MVS		60.62 295	59.97 296	62.58 291	68.13 324	47.28 272	68.59 242	73.96 223	32.19 399	59.94 364	68.86 384	50.48 264	77.64 197	41.85 329	75.74 331	62.83 393

TR-MVS		64.59 254	63.54 267	67.73 242	75.75 209	50.83 227	63.39 309	70.29 269	49.33 268	71.55 255	74.55 331	50.94 262	78.46 176	40.43 338	75.69 332	73.89 308

mvs_anonymous		65.08 248	65.49 245	63.83 275	63.79 365	37.60 357	66.52 273	69.82 273	43.44 327	73.46 226	86.08 172	58.79 202	71.75 269	51.90 250	75.63 333	82.15 191

QAPM		69.18 194	69.26 190	68.94 222	71.61 274	52.58 218	80.37 76	78.79 168	49.63 263	73.51 224	85.14 184	53.66 246	79.12 162	55.11 223	75.54 334	75.11 296

IterMVS		63.12 271	62.48 278	65.02 266	66.34 345	52.86 215	63.81 304	62.25 321	46.57 296	71.51 256	80.40 261	44.60 298	66.82 321	51.38 255	75.47 335	75.38 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 272	60.47 293	70.61 184	83.04 104	54.10 207	59.93 335	72.24 242	33.67 395	69.00 285	75.63 320	38.69 335	76.93 203	36.60 367	75.45 336	80.81 219

EMVS		44.61 387	44.45 392	45.10 396	48.91 429	43.00 309	37.92 422	41.10 424	46.75 295	38.00 431	48.43 428	26.42 400	46.27 397	37.11 363	75.38 337	46.03 421

MIMVSNet		54.39 336	56.12 328	49.20 377	72.57 264	30.91 395	59.98 334	48.43 395	41.66 337	55.94 386	83.86 207	41.19 318	50.42 382	26.05 413	75.38 337	66.27 376

our_test_3		56.46 321	56.51 324	56.30 338	67.70 331	39.66 338	55.36 367	52.34 376	40.57 352	63.85 334	69.91 373	40.04 326	58.22 362	43.49 320	75.29 339	71.03 343

pmmvs4		60.78 293	59.04 303	66.00 260	73.06 256	57.67 182	64.53 299	60.22 331	36.91 376	65.96 316	77.27 309	39.66 329	68.54 300	38.87 346	74.89 340	71.80 331

fmvsm_s_conf0.1_n_a		67.37 225	66.36 236	70.37 190	70.86 281	61.17 142	74.00 161	57.18 345	40.77 348	68.83 295	80.88 253	63.11 151	67.61 309	66.94 118	74.72 341	82.33 189

GA-MVS		62.91 273	61.66 280	66.66 255	67.09 337	44.49 295	61.18 324	69.36 277	51.33 244	69.33 283	74.47 332	36.83 346	74.94 227	50.60 261	74.72 341	80.57 227

KD-MVS_2432*1600		52.05 356	51.58 360	53.44 353	52.11 423	31.20 392	44.88 410	64.83 307	41.53 338	64.37 327	70.03 371	15.61 435	64.20 335	36.25 369	74.61 343	64.93 386

miper_refine_blended		52.05 356	51.58 360	53.44 353	52.11 423	31.20 392	44.88 410	64.83 307	41.53 338	64.37 327	70.03 371	15.61 435	64.20 335	36.25 369	74.61 343	64.93 386

fmvsm_s_conf0.5_n_a		67.00 231	65.95 243	70.17 195	69.72 306	61.16 143	73.34 166	56.83 348	40.96 345	68.36 299	80.08 269	62.84 152	67.57 310	66.90 120	74.50 345	81.78 199

fmvsm_s_conf0.1_n		66.60 233	65.54 244	69.77 203	68.99 313	59.15 165	72.12 179	56.74 350	40.72 350	68.25 302	80.14 268	61.18 176	66.92 316	67.34 115	74.40 346	83.23 159

pmmvs5		52.49 353	52.58 353	52.21 359	54.99 415	32.38 386	55.45 366	53.84 365	32.15 401	55.49 389	74.81 327	38.08 338	57.37 366	34.02 382	74.40 346	66.88 372

PatchT		53.35 345	56.47 325	43.99 400	64.19 363	17.46 431	59.15 337	43.10 412	52.11 231	54.74 394	86.95 139	29.97 390	49.98 385	43.62 318	74.40 346	64.53 390

fmvsm_s_conf0.5_n		66.34 239	65.27 247	69.57 206	68.20 322	59.14 167	71.66 192	56.48 351	40.92 346	67.78 304	79.46 279	61.23 173	66.90 317	67.39 111	74.32 349	82.66 180

SSC-MVS		61.79 285	66.08 239	48.89 381	76.91 187	10.00 439	53.56 378	47.37 399	68.20 63	76.56 170	89.21 92	54.13 244	57.59 365	54.75 227	74.07 350	79.08 250

xiu_mvs_v2_base		64.43 258	63.96 262	65.85 262	77.72 175	51.32 223	63.63 307	72.31 241	45.06 314	61.70 350	69.66 374	62.56 155	73.93 242	49.06 275	73.91 351	72.31 326

PS-MVSNAJ		64.27 261	63.73 265	65.90 261	77.82 173	51.42 222	63.33 310	72.33 240	45.09 313	61.60 351	68.04 389	62.39 159	73.95 241	49.07 274	73.87 352	72.34 325

OpenMVS_ROB	54.93 17	63.23 270	63.28 270	63.07 285	69.81 302	45.34 288	68.52 244	67.14 288	43.74 323	70.61 266	79.22 285	47.90 284	72.66 251	48.75 277	73.84 353	71.21 339

ETVMVS		50.32 367	49.87 375	51.68 361	70.30 296	26.66 412	52.33 385	43.93 409	43.54 326	54.91 392	67.95 390	20.01 424	60.17 352	22.47 425	73.40 354	68.22 363

test_fmvs3		56.78 320	55.99 329	59.12 322	53.96 421	48.09 257	58.76 344	66.22 293	27.54 413	76.66 165	68.69 386	25.32 407	51.31 380	53.42 245	73.38 355	77.97 268

MDTV_nov1_ep13			54.05 345		65.54 353	29.30 403	59.00 340	55.22 356	35.96 382	52.44 401	75.98 317	30.77 384	59.62 354	38.21 352	73.33 356

PAPM		61.79 285	60.37 294	66.05 259	76.09 201	41.87 317	69.30 226	76.79 200	40.64 351	53.80 398	79.62 278	44.38 299	82.92 98	29.64 402	73.11 357	73.36 312

WB-MVS		60.04 299	64.19 260	47.59 384	76.09 201	10.22 438	52.44 384	46.74 401	65.17 88	74.07 216	87.48 129	53.48 247	55.28 371	49.36 272	72.84 358	77.28 273

testing11		53.13 346	52.26 356	55.75 342	70.44 292	31.73 390	54.75 372	52.40 375	44.81 315	52.36 403	68.40 388	21.83 419	65.74 329	32.64 389	72.73 359	69.78 351

Patchmatch-test		47.93 375	49.96 374	41.84 404	57.42 403	24.26 421	48.75 396	41.49 421	39.30 359	56.79 380	73.48 342	30.48 386	33.87 428	29.29 404	72.61 360	67.39 368

gg-mvs-nofinetune		55.75 325	56.75 323	52.72 357	62.87 369	28.04 407	68.92 232	41.36 422	71.09 46	50.80 408	92.63 13	20.74 421	66.86 319	29.97 400	72.41 361	63.25 392

Syy-MVS		54.13 337	55.45 333	50.18 369	68.77 314	23.59 422	55.02 368	44.55 407	43.80 320	58.05 374	64.07 402	46.22 288	58.83 358	46.16 303	72.36 362	68.12 364

myMVS_eth3d		50.36 366	50.52 371	49.88 370	68.77 314	22.69 424	55.02 368	44.55 407	43.80 320	58.05 374	64.07 402	14.16 437	58.83 358	33.90 384	72.36 362	68.12 364

test_vis3_rt		51.94 358	51.04 365	54.65 346	46.32 432	50.13 234	44.34 412	78.17 180	23.62 426	68.95 288	62.81 406	21.41 420	38.52 425	41.49 331	72.22 364	75.30 295

test-LLR		50.43 365	50.69 370	49.64 373	60.76 380	41.87 317	53.18 379	45.48 405	43.41 328	49.41 413	60.47 415	29.22 393	44.73 407	42.09 327	72.14 365	62.33 399

test-mter		48.56 374	48.20 379	49.64 373	60.76 380	41.87 317	53.18 379	45.48 405	31.91 404	49.41 413	60.47 415	18.34 428	44.73 407	42.09 327	72.14 365	62.33 399

1112_ss		59.48 303	58.99 304	60.96 309	77.84 172	42.39 315	61.42 321	68.45 284	37.96 368	59.93 365	67.46 392	45.11 295	65.07 333	40.89 336	71.81 367	75.41 292

WB-MVSnew		53.94 342	54.76 339	51.49 363	71.53 275	28.05 406	58.22 348	50.36 384	37.94 369	59.16 369	70.17 368	49.21 273	51.94 379	24.49 420	71.80 368	74.47 303

UBG		49.18 372	49.35 376	48.66 382	70.36 294	26.56 414	50.53 391	45.61 404	37.43 372	53.37 399	65.97 397	23.03 415	54.20 375	26.29 411	71.54 369	65.20 383

N_pmnet		52.06 355	51.11 364	54.92 344	59.64 393	71.03 57	37.42 423	61.62 328	33.68 394	57.12 376	72.10 350	37.94 339	31.03 429	29.13 408	71.35 370	62.70 394

XXY-MVS		55.19 331	57.40 319	48.56 383	64.45 362	34.84 376	51.54 387	53.59 366	38.99 362	63.79 337	79.43 280	56.59 228	45.57 400	36.92 366	71.29 371	65.25 382

MDA-MVSNet_test_wron		52.57 352	53.49 348	49.81 372	54.24 417	36.47 362	40.48 418	46.58 402	38.13 366	75.47 191	73.32 344	41.05 321	43.85 412	40.98 335	71.20 372	69.10 360

YYNet1		52.58 351	53.50 346	49.85 371	54.15 418	36.45 363	40.53 417	46.55 403	38.09 367	75.52 189	73.31 345	41.08 320	43.88 411	41.10 333	71.14 373	69.21 358

HY-MVS	49.31 19	57.96 314	57.59 317	59.10 323	66.85 342	36.17 364	65.13 290	65.39 302	39.24 360	54.69 395	78.14 301	44.28 300	67.18 315	33.75 385	70.79 374	73.95 307

Test_1112_low_res		58.78 309	58.69 306	59.04 324	79.41 146	38.13 352	57.62 351	66.98 290	34.74 388	59.62 368	77.56 307	42.92 308	63.65 340	38.66 348	70.73 375	75.35 294

pmmvs3		46.71 378	45.09 388	51.55 362	56.76 406	48.25 253	55.78 365	39.53 426	24.13 425	50.35 411	63.40 404	15.90 434	51.08 381	29.29 404	70.69 376	55.33 413

test_fmvs2		54.80 334	54.11 344	56.88 336	51.76 425	49.95 237	56.70 357	65.80 296	26.22 418	69.42 281	65.25 400	31.82 373	49.98 385	49.63 269	70.36 377	70.71 344

SCA		58.57 311	58.04 313	60.17 315	70.17 297	41.07 323	65.19 289	53.38 370	43.34 330	61.00 358	73.48 342	45.20 293	69.38 292	40.34 339	70.31 378	70.05 348

CR-MVSNet		58.96 306	58.49 308	60.36 314	66.37 343	48.24 254	70.93 205	56.40 353	32.87 398	61.35 353	86.66 150	33.19 359	63.22 342	48.50 281	70.17 379	69.62 354

RPMNet		65.77 242	65.08 256	67.84 240	66.37 343	48.24 254	70.93 205	86.27 20	54.66 195	61.35 353	86.77 145	33.29 358	85.67 49	55.93 214	70.17 379	69.62 354

test0.0.03 1		47.72 376	48.31 378	45.93 391	55.53 413	29.39 402	46.40 406	41.21 423	43.41 328	55.81 388	67.65 391	29.22 393	43.77 413	25.73 417	69.87 381	64.62 388

PVSNet	43.83 21	51.56 359	51.17 363	52.73 356	68.34 319	38.27 349	48.22 398	53.56 368	36.41 378	54.29 396	64.94 401	34.60 354	54.20 375	30.34 397	69.87 381	65.71 379

tpm2		56.12 323	54.64 340	60.55 313	66.24 346	36.01 365	68.14 248	56.77 349	33.60 396	58.25 373	75.52 323	30.25 387	74.33 236	33.27 386	69.76 383	71.32 336

CostFormer		57.35 317	56.14 327	60.97 308	63.76 366	38.43 347	67.50 255	60.22 331	37.14 375	59.12 370	76.34 316	32.78 362	71.99 264	39.12 345	69.27 384	72.47 323

MonoMVSNet		62.75 276	63.42 268	60.73 311	65.60 352	40.77 327	72.49 174	70.56 266	52.49 226	75.07 194	79.42 281	39.52 331	69.97 286	46.59 300	69.06 385	71.44 334

baseline1		57.82 315	58.36 311	56.19 339	69.17 310	30.76 397	62.94 315	55.21 357	46.04 299	63.83 336	78.47 295	41.20 317	63.68 339	39.44 341	68.99 386	74.13 305

PatchMatch-RL		58.68 310	57.72 315	61.57 299	76.21 199	73.59 43	61.83 318	49.00 393	47.30 292	61.08 355	68.97 380	50.16 266	59.01 357	36.06 374	68.84 387	52.10 414

CVMVSNet		59.21 305	58.44 309	61.51 300	73.94 238	47.76 264	71.31 199	64.56 309	26.91 417	60.34 361	70.44 362	36.24 349	67.65 307	53.57 242	68.66 388	69.12 359

dmvs_re		49.91 370	50.77 369	47.34 385	59.98 386	38.86 344	53.18 379	53.58 367	39.75 355	55.06 390	61.58 411	36.42 348	44.40 409	29.15 407	68.23 389	58.75 407

TESTMET0.1,1		45.17 383	44.93 389	45.89 392	56.02 409	38.31 348	53.18 379	41.94 420	27.85 412	44.86 424	56.47 420	17.93 430	41.50 420	38.08 354	68.06 390	57.85 408

test_fmvs1_n		52.70 350	52.01 357	54.76 345	53.83 422	50.36 230	55.80 364	65.90 295	24.96 422	65.39 320	60.64 414	27.69 396	48.46 390	45.88 306	67.99 391	65.46 380

PMMVS2		37.74 396	40.87 396	28.36 413	42.41 436	5.35 441	24.61 428	27.75 433	32.15 401	47.85 417	70.27 366	35.85 350	29.51 431	19.08 430	67.85 392	50.22 417

1314		59.83 301	58.86 305	62.74 290	65.71 351	44.78 293	68.59 242	72.63 236	33.54 397	61.05 357	67.29 395	43.62 304	71.26 274	49.49 271	67.84 393	72.19 328

CHOSEN 1792x2688		58.09 313	56.30 326	63.45 280	79.95 140	50.93 226	54.07 376	65.59 299	28.56 411	61.53 352	74.33 334	41.09 319	66.52 324	33.91 383	67.69 394	72.92 316

test_fmvs1		51.51 360	50.86 368	53.48 352	49.72 428	49.35 246	54.11 375	64.96 305	24.64 424	63.66 340	59.61 417	28.33 395	48.45 391	45.38 311	67.30 395	62.66 396

tpm		50.60 364	52.42 355	45.14 395	65.18 356	26.29 415	60.30 331	43.50 410	37.41 373	57.01 378	79.09 289	30.20 389	42.32 415	32.77 388	66.36 396	66.81 374

FPMVS		59.43 304	60.07 295	57.51 332	77.62 178	71.52 53	62.33 317	50.92 381	57.40 161	69.40 282	80.00 270	39.14 333	61.92 347	37.47 360	66.36 396	39.09 427

GG-mvs-BLEND				52.24 358	60.64 382	29.21 404	69.73 221	42.41 415		45.47 421	52.33 424	20.43 422	68.16 303	25.52 418	65.42 398	59.36 406

UWE-MVS-28		44.18 388	44.37 393	43.61 401	60.10 384	16.96 432	52.62 383	33.27 431	36.79 377	48.86 415	69.47 377	19.96 425	45.65 399	13.40 432	64.83 399	68.23 362

tpmvs		55.84 324	55.45 333	57.01 334	60.33 383	33.20 384	65.89 278	59.29 335	47.52 291	56.04 385	73.60 341	31.05 382	68.06 305	40.64 337	64.64 400	69.77 352

WTY-MVS		49.39 371	50.31 373	46.62 389	61.22 378	32.00 389	46.61 405	49.77 386	33.87 393	54.12 397	69.55 376	41.96 313	45.40 402	31.28 394	64.42 401	62.47 397

baseline2		55.57 329	52.74 350	64.05 273	65.26 354	44.11 297	62.38 316	54.43 361	39.03 361	51.21 406	67.35 394	33.66 357	72.45 256	37.14 362	64.22 402	75.60 289

test_vis1_n		51.27 362	50.41 372	53.83 349	56.99 404	50.01 236	56.75 356	60.53 330	25.68 420	59.74 367	57.86 418	29.40 392	47.41 395	43.10 321	63.66 403	64.08 391

MS-PatchMatch		55.59 328	54.89 338	57.68 331	69.18 309	49.05 247	61.00 325	62.93 320	35.98 381	58.36 372	68.93 382	36.71 347	66.59 323	37.62 359	63.30 404	57.39 410

test_cas_vis1_n_1920		50.90 363	50.92 367	50.83 367	54.12 420	47.80 262	51.44 388	54.61 360	26.95 416	63.95 333	60.85 412	37.86 342	44.97 405	45.53 308	62.97 405	59.72 405

test_vis1_n_1920		52.96 347	53.50 346	51.32 364	59.15 394	44.90 292	56.13 362	64.29 312	30.56 409	59.87 366	60.68 413	40.16 325	47.47 394	48.25 285	62.46 406	61.58 401

test_f		43.79 390	45.63 384	38.24 411	42.29 437	38.58 346	34.76 426	47.68 397	22.22 429	67.34 310	63.15 405	31.82 373	30.60 430	39.19 344	62.28 407	45.53 423

sss		47.59 377	48.32 377	45.40 394	56.73 407	33.96 380	45.17 408	48.51 394	32.11 403	52.37 402	65.79 398	40.39 324	41.91 418	31.85 391	61.97 408	60.35 403

test_vis1_rt		46.70 379	45.24 387	51.06 366	44.58 433	51.04 225	39.91 419	67.56 287	21.84 430	51.94 404	50.79 426	33.83 356	39.77 422	35.25 378	61.50 409	62.38 398

PMMVS		44.69 385	43.95 394	46.92 387	50.05 427	53.47 213	48.08 400	42.40 416	22.36 428	44.01 427	53.05 423	42.60 311	45.49 401	31.69 392	61.36 410	41.79 425

UnsupCasMVSNet_bld		50.01 369	51.03 366	46.95 386	58.61 397	32.64 385	48.31 397	53.27 371	34.27 391	60.47 360	71.53 356	41.40 315	47.07 396	30.68 396	60.78 411	61.13 402

MVP-Stereo		61.56 287	59.22 301	68.58 230	79.28 148	60.44 154	69.20 229	71.57 246	43.58 325	56.42 384	78.37 297	39.57 330	76.46 210	34.86 379	60.16 412	68.86 361
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DSMNet-mixed		43.18 392	44.66 391	38.75 409	54.75 416	28.88 405	57.06 355	27.42 434	13.47 432	47.27 419	77.67 306	38.83 334	39.29 424	25.32 419	60.12 413	48.08 418

UnsupCasMVSNet_eth		52.26 354	53.29 349	49.16 378	55.08 414	33.67 382	50.03 393	58.79 336	37.67 371	63.43 345	74.75 329	41.82 314	45.83 398	38.59 350	59.42 414	67.98 367

tpm cat1		54.02 340	52.63 352	58.19 328	64.85 361	39.86 337	66.26 275	57.28 342	32.16 400	56.90 379	70.39 364	32.75 363	65.30 332	34.29 381	58.79 415	69.41 356

CHOSEN 280x420		41.62 393	39.89 398	46.80 388	61.81 374	51.59 220	33.56 427	35.74 429	27.48 414	37.64 432	53.53 421	23.24 413	42.09 416	27.39 410	58.64 416	46.72 420

tpmrst		50.15 368	51.38 362	46.45 390	56.05 408	24.77 420	64.40 301	49.98 385	36.14 380	53.32 400	69.59 375	35.16 352	48.69 389	39.24 343	58.51 417	65.89 377

PatchmatchNet		54.60 335	54.27 342	55.59 343	65.17 357	39.08 340	66.92 267	51.80 378	39.89 354	58.39 371	73.12 346	31.69 375	58.33 361	43.01 322	58.38 418	69.38 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		45.53 381	47.29 381	40.24 407	62.29 372	26.82 411	56.02 363	37.41 428	29.74 410	43.69 428	81.27 247	33.96 355	55.48 370	24.46 421	56.79 419	38.43 428

ADS-MVSNet2		48.76 373	47.25 382	53.29 355	55.90 410	40.54 332	47.34 402	54.99 359	31.41 406	50.48 409	72.06 351	31.23 378	54.26 374	25.93 414	55.93 420	65.07 384

ADS-MVSNet		44.62 386	45.58 385	41.73 405	55.90 410	20.83 429	47.34 402	39.94 425	31.41 406	50.48 409	72.06 351	31.23 378	39.31 423	25.93 414	55.93 420	65.07 384

EPMVS		45.74 380	46.53 383	43.39 402	54.14 419	22.33 427	55.02 368	35.00 430	34.69 389	51.09 407	70.20 367	25.92 403	42.04 417	37.19 361	55.50 422	65.78 378

JIA-IIPM		54.03 339	51.62 359	61.25 306	59.14 395	55.21 200	59.10 339	47.72 396	50.85 249	50.31 412	85.81 178	20.10 423	63.97 337	36.16 372	55.41 423	64.55 389

dmvs_testset		45.26 382	47.51 380	38.49 410	59.96 388	14.71 434	58.50 346	43.39 411	41.30 340	51.79 405	56.48 419	39.44 332	49.91 387	21.42 427	55.35 424	50.85 415

new_pmnet		37.55 397	39.80 399	30.79 412	56.83 405	16.46 433	39.35 420	30.65 432	25.59 421	45.26 422	61.60 410	24.54 408	28.02 432	21.60 426	52.80 425	47.90 419

dp		44.09 389	44.88 390	41.72 406	58.53 399	23.18 423	54.70 373	42.38 417	34.80 387	44.25 426	65.61 399	24.48 410	44.80 406	29.77 401	49.42 426	57.18 411

mvsany_test3		43.76 391	41.01 395	52.01 360	48.09 430	57.74 181	42.47 414	23.85 437	23.30 427	64.80 325	62.17 409	27.12 397	40.59 421	29.17 406	48.11 427	57.69 409

MVE	27.91 23	36.69 398	35.64 401	39.84 408	43.37 435	35.85 368	19.49 429	24.61 435	24.68 423	39.05 430	62.63 408	38.67 336	27.10 433	21.04 428	47.25 428	56.56 412
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test1		37.88 395	35.74 400	44.28 398	47.28 431	49.90 238	36.54 425	24.37 436	19.56 431	45.76 420	53.46 422	32.99 361	37.97 426	26.17 412	35.52 429	44.99 424

PVSNet_0	36.71 22	41.12 394	40.78 397	42.14 403	59.97 387	40.13 334	40.97 416	42.24 419	30.81 408	44.86 424	49.41 427	40.70 322	45.12 404	23.15 424	34.96 430	41.16 426

tmp_tt		11.98 403	14.73 406	3.72 418	2.28 441	4.62 442	19.44 430	14.50 439	0.47 436	21.55 434	9.58 434	25.78 404	4.57 437	11.61 434	27.37 431	1.96 433

test_method		19.26 401	19.12 405	19.71 415	9.09 440	1.91 443	7.79 431	53.44 369	1.42 434	10.27 436	35.80 430	17.42 432	25.11 434	12.44 433	24.38 432	32.10 429

dongtai		31.66 399	32.98 402	27.71 414	58.58 398	12.61 436	45.02 409	14.24 440	41.90 335	47.93 416	43.91 429	10.65 440	41.81 419	14.06 431	20.53 433	28.72 430

kuosan		22.02 400	23.52 404	17.54 416	41.56 438	11.24 437	41.99 415	13.39 441	26.13 419	28.87 433	30.75 431	9.72 441	21.94 435	4.77 436	14.49 434	19.43 431

DeepMVS_CX				11.83 417	15.51 439	13.86 435		11.25 442	5.76 433	20.85 435	26.46 432	17.06 433	9.22 436	9.69 435	13.82 435	12.42 432

test123		4.43 406	5.78 409	0.39 420	0.97 442	0.28 444	46.33 407	0.45 443	0.31 437	0.62 438	1.50 437	0.61 443	0.11 439	0.56 437	0.63 436	0.77 435

testmvs		4.06 407	5.28 410	0.41 419	0.64 443	0.16 445	42.54 413	0.31 444	0.26 438	0.50 439	1.40 438	0.77 442	0.17 438	0.56 437	0.55 437	0.90 434

mmdepth		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

monomultidepth		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

test_blank		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uanet_test		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

DCPMVS		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

cdsmvs_eth3d_5k		17.71 402	23.62 403	0.00 421	0.00 444	0.00 446	0.00 432	70.17 270	0.00 439	0.00 440	74.25 336	68.16 100	0.00 440	0.00 439	0.00 438	0.00 436

pcd_1.5k_mvsjas		5.20 405	6.93 408	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	62.39 159	0.00 440	0.00 439	0.00 438	0.00 436

sosnet-low-res		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

sosnet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uncertanet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

Regformer		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

ab-mvs-re		5.62 404	7.50 407	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	67.46 392	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uanet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

WAC-MVS						22.69 424								36.10 373

FOURS1					89.19 24	77.84 14	91.64 1	89.11 3	84.05 3	91.57 3

test_one_0601					85.84 64	61.45 138		85.63 30	75.27 21	85.62 51	90.38 67	76.72 30

eth-test2					0.00 444

eth-test					0.00 444

test_241102_ONE					86.12 54	61.06 144		84.72 52	72.64 34	87.38 28	89.47 86	77.48 26	85.74 46

save fliter					87.00 40	67.23 90	79.24 89	77.94 185	56.65 172

test0726					86.16 52	60.78 150	83.81 43	85.10 43	72.48 37	85.27 56	89.96 79	78.57 19

GSMVS																70.05 348

test_part2					85.90 60	66.44 95				84.61 65

sam_mvs1												31.41 376				70.05 348

sam_mvs												31.21 380

MTGPA								80.63 132

test_post1							66.63 271				2.08 435	30.66 385	59.33 356	40.34 339

test_post											1.99 436	30.91 383	54.76 373

patchmatchnet-post											68.99 379	31.32 377	69.38 292

MTMP							84.83 34	19.26 438

gm-plane-assit					62.51 370	33.91 381			37.25 374		62.71 407		72.74 249	38.70 347

TEST9					85.47 67	69.32 74	76.42 123	78.69 170	53.73 217	76.97 154	86.74 146	66.84 114	81.10 127

test_8					85.09 73	67.89 83	76.26 128	78.66 172	54.00 212	76.89 158	86.72 148	66.60 120	80.89 137

agg_prior					84.44 85	66.02 101		78.62 173		76.95 156			80.34 144

test_prior4						70.14 67	77.57 106

test_prior				75.27 106	82.15 118	59.85 159		84.33 63					83.39 90			82.58 182

旧先验2							71.17 202		45.11 312	78.54 135			61.28 349	59.19 189

新几何2							71.33 198

无先验							74.82 143	70.94 263	47.75 289				76.85 206	54.47 231		72.09 329

原ACMM2							74.78 147

testdata2													67.30 312	48.34 283

segment_acmp												68.30 99

testdata1							68.34 247		57.24 163

plane_prior7					85.18 70	66.21 98

plane_prior6					84.18 88	65.31 107						60.83 180

plane_prior4											89.11 97

plane_prior3						65.67 103			63.82 102	78.23 137

plane_prior2							82.74 55		65.45 80

plane_prior1					84.46 84

n2								0.00 445

nn								0.00 445

door-mid								55.02 358

test11								82.71 91

door								52.91 373

HQP5-MVS						58.80 172

HQP-NCC					82.37 113		77.32 111		59.08 141	71.58 251

ACMP_Plane					82.37 113		77.32 111		59.08 141	71.58 251

BP-MVS														67.38 113

HQP4-MVS										71.59 250			85.31 54			83.74 140

HQP2-MVS												58.09 210

NP-MVS					83.34 98	63.07 126					85.97 174

MDTV_nov1_ep13_2view						18.41 430	53.74 377		31.57 405	44.89 423		29.90 391		32.93 387		71.48 333

Test By Simon												62.56 155

High-resolution multi-view benchmark