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.

TDRefinement		86.32 2	86.33 3	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 9	87.95 15	92.53 14	79.37 14	84.79 63	74.51 48	96.15 3	92.88 7

Effi-MVS+-dtu		75.43 93	72.28 142	84.91 2	77.05 172	83.58 1	78.47 93	77.70 169	57.68 146	74.89 181	78.13 249	64.80 129	84.26 72	56.46 177	85.32 190	86.88 61

test1172		84.85 4	85.39 6	83.21 3	88.34 37	80.50 6	85.12 29	85.22 39	81.06 2	87.20 28	90.28 67	79.20 15	85.58 46	78.04 27	94.08 54	83.55 129

SR-MVS-dyc-post		84.75 5	85.26 8	83.21 3	86.19 53	79.18 9	87.23 7	86.27 20	77.51 11	87.65 19	90.73 45	79.20 15	85.58 46	78.11 25	94.46 36	84.89 91

mvs-test1		73.81 113	70.69 163	83.18 5	77.05 172	81.39 3	75.39 128	77.70 169	57.68 146	71.19 230	74.72 273	64.80 129	83.66 79	56.46 177	81.19 239	84.50 110

HPM-MVS_fast		84.59 6	85.10 9	83.06 6	88.60 33	75.83 26	86.27 23	86.89 16	73.69 23	86.17 36	91.70 25	78.23 21	85.20 56	79.45 13	94.91 25	88.15 45

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 22	87.16 10	85.10 42	64.94 87	81.05 101	88.38 105	57.10 200	87.10 8	79.75 8	83.87 212	84.31 115

mPP-MVS		84.01 13	84.39 14	82.88 8	90.65 4	81.38 4	87.08 11	82.79 87	72.41 34	85.11 53	90.85 41	76.65 30	84.89 60	79.30 17	94.63 32	82.35 163

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 125	72.03 47	84.38 36	86.23 23	77.28 16	80.65 107	90.18 71	59.80 172	87.58 4	73.06 57	91.34 99	89.01 33

XVS		83.51 18	83.73 23	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 27	82.87 80	90.39 61	73.86 56	86.31 20	78.84 20	94.03 55	84.64 99

X-MVStestdata		76.81 82	74.79 101	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 27	82.87 80	9.95 352	73.86 56	86.31 20	78.84 20	94.03 55	84.64 99

CP-MVS		84.12 11	84.55 13	82.80 12	89.42 18	79.74 7	88.19 3	84.43 60	71.96 40	84.70 59	90.56 50	77.12 27	86.18 25	79.24 18	95.36 13	82.49 160

ACMMPR		83.62 15	83.93 20	82.69 13	89.78 11	77.51 21	87.01 13	84.19 69	70.23 47	84.49 61	90.67 48	75.15 43	86.37 19	79.58 11	94.26 49	84.18 118

COLMAP_ROB	72.78 3	83.75 14	84.11 18	82.68 14	82.97 103	74.39 35	87.18 9	88.18 7	78.98 6	86.11 39	91.47 30	79.70 13	85.76 39	66.91 105	95.46 12	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HPM-MVS		84.12 11	84.63 12	82.60 15	88.21 38	74.40 34	85.24 27	87.21 14	70.69 46	85.14 52	90.42 57	78.99 17	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
region2R		83.54 17	83.86 22	82.58 16	89.82 10	77.53 19	87.06 12	84.23 68	70.19 49	83.86 71	90.72 47	75.20 42	86.27 22	79.41 15	94.25 50	83.95 122

abl_6		84.92 3	85.70 4	82.57 17	86.72 47	79.27 8	87.56 5	86.08 25	77.48 13	88.12 14	91.53 28	81.18 9	84.31 71	78.12 24	94.47 35	84.15 119

PGM-MVS		83.07 24	83.25 34	82.54 18	89.57 14	77.21 23	82.04 58	85.40 35	67.96 60	84.91 57	90.88 39	75.59 38	86.57 15	78.16 23	94.71 30	83.82 123

DeepPCF-MVS	71.07 5	78.48 72	77.14 85	82.52 19	84.39 87	77.04 24	76.35 117	84.05 72	56.66 160	80.27 112	85.31 167	68.56 94	87.03 10	67.39 100	91.26 100	83.50 130

ACMMP		84.22 8	84.84 11	82.35 20	89.23 23	76.66 25	87.65 4	85.89 28	71.03 43	85.85 41	90.58 49	78.77 18	85.78 38	79.37 16	95.17 17	84.62 101
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
SR-MVS		84.51 7	85.27 7	82.25 21	88.52 34	77.71 16	86.81 15	85.25 38	77.42 15	86.15 37	90.24 68	81.69 6	85.94 31	77.77 29	93.58 65	83.09 142

MP-MVS		83.19 21	83.54 27	82.14 22	90.54 5	79.00 11	86.42 21	83.59 78	71.31 41	81.26 99	90.96 38	74.57 51	84.69 64	78.41 22	94.78 26	82.74 154
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LTVRE_ROB	75.46 1	84.22 8	84.98 10	81.94 23	84.82 76	75.40 29	91.60 1	87.80 8	73.52 24	88.90 11	93.06 7	71.39 75	81.53 115	81.53 3	92.15 86	88.91 37
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
RRT_MVS		73.80 114	71.19 158	81.60 24	71.04 247	70.33 63	78.78 89	74.91 197	56.96 155	77.83 138	85.56 165	32.82 311	87.39 5	71.16 71	91.68 90	87.07 60

LCM-MVSNet		86.90 1	88.67 1	81.57 25	91.50 1	63.30 117	84.80 33	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 66	68.08 90	97.05 1	96.93 1

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 48	70.03 65	82.06 57	87.00 15	59.89 128	80.91 105	90.53 51	72.19 65	88.56 1	73.67 54	94.52 34	85.92 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		83.39 20	84.03 19	81.48 27	89.25 21	75.69 27	87.01 13	84.27 64	70.23 47	84.47 62	90.43 55	76.79 28	85.94 31	79.58 11	94.23 51	82.82 151

#test#		82.40 31	82.71 41	81.48 27	89.25 21	75.69 27	84.47 35	84.27 64	64.45 91	84.47 62	90.43 55	76.79 28	85.94 31	76.01 36	94.23 51	82.82 151

ZNCC-MVS		83.12 23	83.68 24	81.45 29	89.14 25	73.28 45	86.32 22	85.97 27	67.39 62	84.02 69	90.39 61	74.73 48	86.46 16	80.73 7	94.43 40	84.60 104

LS3D		80.99 46	80.85 55	81.41 30	78.37 158	71.37 51	87.45 6	85.87 29	77.48 13	81.98 89	89.95 75	69.14 88	85.26 52	66.15 106	91.24 101	87.61 51

SteuartSystems-ACMMP		83.07 24	83.64 25	81.35 31	85.14 71	71.00 55	85.53 25	84.78 48	70.91 44	85.64 44	90.41 58	75.55 40	87.69 3	79.75 8	95.08 20	85.36 82
Skip Steuart: Steuart Systems R&D Blog.
GST-MVS		82.79 28	83.27 33	81.34 32	88.99 27	73.29 44	85.94 24	85.13 40	68.58 58	84.14 68	90.21 70	73.37 61	86.41 17	79.09 19	93.98 58	84.30 117

APD-MVS_3200maxsize		83.57 16	84.33 15	81.31 33	82.83 105	73.53 43	85.50 26	87.45 13	74.11 20	86.45 34	90.52 53	80.02 12	84.48 67	77.73 30	94.34 47	85.93 72

OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 132	70.71 59	80.91 64	84.76 49	62.54 110	81.77 91	86.65 139	71.46 73	83.53 82	67.95 95	92.44 82	89.60 23

zzz-MVS		83.01 26	83.63 26	81.13 35	91.16 2	78.16 14	82.72 54	80.63 121	72.08 37	84.93 54	90.79 42	74.65 49	84.42 68	80.98 4	94.75 27	80.82 188

MTAPA		83.19 21	83.87 21	81.13 35	91.16 2	78.16 14	84.87 31	80.63 121	72.08 37	84.93 54	90.79 42	74.65 49	84.42 68	80.98 4	94.75 27	80.82 188

anonymousdsp		78.60 68	77.80 79	81.00 37	78.01 163	74.34 36	80.09 75	76.12 184	50.51 228	89.19 10	90.88 39	71.45 74	77.78 187	73.38 55	90.60 118	90.90 17

LPG-MVS_test		83.47 19	84.33 15	80.90 38	87.00 42	70.41 61	82.04 58	86.35 17	69.77 51	87.75 16	91.13 34	81.83 4	86.20 23	77.13 34	95.96 6	86.08 68

LGP-MVS_train				80.90 38	87.00 42	70.41 61		86.35 17	69.77 51	87.75 16	91.13 34	81.83 4	86.20 23	77.13 34	95.96 6	86.08 68

CPTT-MVS		81.51 39	81.76 47	80.76 40	89.20 24	78.75 12	86.48 20	82.03 96	68.80 54	80.92 104	88.52 102	72.00 68	82.39 102	74.80 44	93.04 71	81.14 180

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 124	71.24 53	83.98 37	75.72 188	52.27 208	87.37 27	92.25 17	68.04 101	80.56 134	72.28 65	91.15 103	90.32 21

SMA-MVS		82.12 33	82.68 42	80.43 42	88.90 30	69.52 67	85.12 29	84.76 49	63.53 101	84.23 67	91.47 30	72.02 67	87.16 7	79.74 10	94.36 45	84.61 102
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
ACMP	69.50 8	82.64 29	83.38 30	80.40 43	86.50 49	69.44 69	82.30 55	86.08 25	66.80 67	86.70 32	89.99 73	81.64 7	85.95 30	74.35 49	96.11 4	85.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				80.35 44	76.94 176	73.60 41		80.48 125	66.87 65	83.64 74	86.18 152	70.25 82	79.90 149	61.12 142	88.95 145	87.56 52

HPM-MVS++		79.89 57	79.80 63	80.18 45	89.02 26	78.44 13	83.49 47	80.18 132	64.71 90	78.11 136	88.39 104	65.46 124	83.14 90	77.64 32	91.20 102	78.94 214

RPSCF		75.76 89	74.37 107	79.93 46	74.81 203	77.53 19	77.53 104	79.30 142	59.44 131	78.88 125	89.80 77	71.26 76	73.09 232	57.45 167	80.89 243	89.17 30

MP-MVS-pluss		82.54 30	83.46 29	79.76 47	88.88 31	68.44 78	81.57 61	86.33 19	63.17 106	85.38 50	91.26 33	76.33 32	84.67 65	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 28	67.52 83	86.61 18	81.38 107	51.71 215	77.15 146	91.42 32	65.49 123	87.20 6	79.44 14	87.17 170	84.51 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mvs_tets		78.93 65	78.67 73	79.72 49	84.81 77	73.93 38	80.65 65	76.50 182	51.98 213	87.40 24	91.86 22	76.09 35	78.53 166	68.58 85	90.20 122	86.69 64

DeepC-MVS_fast	69.89 7	77.17 81	76.33 90	79.70 50	83.90 93	67.94 80	80.06 77	83.75 75	56.73 159	74.88 182	85.32 166	65.54 122	87.79 2	65.61 111	91.14 104	83.35 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		81.19 41	81.70 49	79.67 51	83.95 91	69.77 66	83.58 46	84.63 56	72.13 36	82.85 82	88.36 106	75.00 46	86.79 12	71.99 69	92.84 74	82.44 161

jajsoiax		78.51 70	78.16 77	79.59 52	84.65 80	73.83 40	80.42 68	76.12 184	51.33 221	87.19 29	91.51 29	73.79 58	78.44 170	68.27 88	90.13 127	86.49 65

ACMMP_NAP		82.33 32	83.28 32	79.46 53	89.28 19	69.09 76	83.62 43	84.98 44	64.77 88	83.97 70	91.02 37	75.53 41	85.93 34	82.00 2	94.36 45	83.35 137

UA-Net		81.56 38	82.28 44	79.40 54	88.91 29	69.16 74	84.67 34	80.01 135	75.34 17	79.80 116	94.91 3	69.79 85	80.25 142	72.63 60	94.46 36	88.78 41

APD-MVS		81.13 43	81.73 48	79.36 55	84.47 83	70.53 60	83.85 39	83.70 76	69.43 53	83.67 73	88.96 98	75.89 36	86.41 17	72.62 61	92.95 72	81.14 180
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
F-COLMAP		75.29 95	73.99 112	79.18 56	81.73 121	71.90 48	81.86 60	82.98 84	59.86 129	72.27 214	84.00 180	64.56 132	83.07 93	51.48 214	87.19 169	82.56 159

APDe-MVS		82.88 27	84.14 17	79.08 57	84.80 78	66.72 89	86.54 19	85.11 41	72.00 39	86.65 33	91.75 24	78.20 22	87.04 9	77.93 28	94.32 48	83.47 132

OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 46	69.39 70	79.41 82	84.00 74	65.64 75	85.54 47	89.28 84	76.32 33	83.47 83	74.03 51	93.57 66	84.35 114

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 59	87.01 41	72.91 46	80.23 74	85.56 30	66.56 70	85.64 44	89.57 80	69.12 89	80.55 136	72.51 62	93.37 67	83.48 131

DPE-MVS		82.00 35	83.02 37	78.95 60	85.36 68	67.25 85	82.91 51	84.98 44	73.52 24	85.43 49	90.03 72	76.37 31	86.97 11	74.56 47	94.02 57	82.62 156

NCCC		78.25 75	78.04 78	78.89 61	85.61 65	69.45 68	79.80 79	80.99 118	65.77 74	75.55 174	86.25 151	67.42 105	85.42 48	70.10 77	90.88 114	81.81 173

HQP_MVS		78.77 67	78.78 71	78.72 62	85.18 69	65.18 101	82.74 52	85.49 32	65.45 77	78.23 133	89.11 93	60.83 163	86.15 26	71.09 72	90.94 108	84.82 93

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 63	80.48 133	64.16 110	80.24 73	82.06 95	61.89 114	88.77 12	93.32 5	57.15 198	82.60 100	70.08 78	92.80 76	89.25 27

OPU-MVS				78.65 64	83.44 98	66.85 88	83.62 43				86.12 156	66.82 111	86.01 29	61.72 135	89.79 134	83.08 143

XVG-OURS		79.51 59	79.82 62	78.58 65	86.11 60	74.96 32	76.33 119	84.95 46	66.89 64	82.75 83	88.99 97	66.82 111	78.37 174	74.80 44	90.76 116	82.40 162

XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 66	86.46 50	74.79 33	77.15 110	85.39 36	66.73 68	80.39 111	88.85 100	74.43 54	78.33 176	74.73 46	85.79 183	82.35 163

ACMM	69.25 9	82.11 34	83.31 31	78.49 66	88.17 39	73.96 37	83.11 50	84.52 59	66.40 71	87.45 23	89.16 91	81.02 10	80.52 137	74.27 50	95.73 8	80.98 185
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 68	87.00 42	67.23 86	79.24 83	88.61 5	56.65 161	84.29 65	89.18 88	73.73 59	83.22 88	76.01 36	93.77 60	84.81 95

AllTest		77.66 77	77.43 81	78.35 69	79.19 149	70.81 56	78.60 91	88.64 3	65.37 80	80.09 114	88.17 110	70.33 80	78.43 171	55.60 185	90.90 112	85.81 74

TestCases				78.35 69	79.19 149	70.81 56		88.64 3	65.37 80	80.09 114	88.17 110	70.33 80	78.43 171	55.60 185	90.90 112	85.81 74

CNVR-MVS		78.49 71	78.59 74	78.16 71	85.86 64	67.40 84	78.12 100	81.50 103	63.92 96	77.51 143	86.56 143	68.43 97	84.82 62	73.83 52	91.61 93	82.26 166

CDPH-MVS		77.33 80	77.06 86	78.14 72	84.21 88	63.98 112	76.07 122	83.45 79	54.20 188	77.68 142	87.18 118	69.98 83	85.37 49	68.01 92	92.72 80	85.08 89

PS-MVSNAJss		77.54 78	77.35 82	78.13 73	84.88 75	66.37 92	78.55 92	79.59 140	53.48 199	86.29 35	92.43 16	62.39 145	80.25 142	67.90 96	90.61 117	87.77 48

Regformer-2		75.32 94	74.47 105	77.88 74	74.22 216	66.65 90	72.77 155	77.54 171	68.47 59	80.44 109	72.08 295	70.60 79	80.97 127	70.08 78	84.02 210	86.01 71

test_part1		84.22 8	86.36 2	77.83 75	85.08 73	56.71 165	85.13 28	89.83 2	78.32 8	90.44 6	95.87 2	84.29 3	84.09 73	71.67 70	96.58 2	92.68 8

TAPA-MVS	65.27 12	75.16 97	74.29 109	77.77 76	74.86 202	68.08 79	77.89 101	84.04 73	55.15 174	76.19 170	83.39 185	66.91 109	80.11 147	60.04 152	90.14 126	85.13 86
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-4		74.64 106	73.67 117	77.55 77	74.74 205	64.49 108	72.91 152	75.42 192	67.45 61	80.24 113	72.07 297	68.98 90	80.19 146	70.29 76	80.91 241	87.98 46

TSAR-MVS + GP.		73.08 125	71.60 152	77.54 78	78.99 155	70.73 58	74.96 132	69.38 243	60.73 123	74.39 191	78.44 245	57.72 195	82.78 96	60.16 149	89.60 136	79.11 213

SF-MVS		80.72 48	81.80 46	77.48 79	82.03 116	64.40 109	83.41 48	88.46 6	65.28 82	84.29 65	89.18 88	73.73 59	83.22 88	76.01 36	93.77 60	84.81 95

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 80	80.09 136	66.16 94	79.08 86	86.13 24	57.55 151	80.93 103	87.76 117	71.98 69	82.73 98	72.11 68	92.83 75	83.25 139

PMVS	70.70 6	81.70 37	83.15 35	77.36 81	90.35 6	82.82 2	82.15 56	79.22 143	74.08 21	87.16 30	91.97 20	84.80 2	76.97 195	64.98 115	93.61 63	72.28 269
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMH+	66.64 10	81.20 40	82.48 43	77.35 82	81.16 128	62.39 121	80.51 66	87.80 8	73.02 26	87.57 21	91.08 36	80.28 11	82.44 101	64.82 116	96.10 5	87.21 56

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 83	80.67 131	64.57 106	80.26 72	87.60 12	60.74 122	82.47 86	88.03 114	71.73 70	81.81 111	73.12 56	93.61 63	85.09 87

SED-MVS		81.78 36	83.48 28	76.67 84	86.12 57	61.06 130	83.62 43	84.72 51	72.61 30	87.38 25	89.70 78	77.48 25	85.89 36	75.29 42	94.39 41	83.08 143

PHI-MVS		74.92 101	74.36 108	76.61 85	76.40 182	62.32 122	80.38 69	83.15 82	54.16 190	73.23 205	80.75 213	62.19 148	83.86 76	68.02 91	90.92 111	83.65 128

test_0728_SECOND				76.57 86	86.20 52	60.57 138	83.77 41	85.49 32					85.90 35	75.86 39	94.39 41	83.25 139

test12				76.51 87	82.28 112	60.94 133		81.64 102		73.60 198		64.88 128	85.19 57		90.42 120	83.38 135

CANet		73.00 129	71.84 147	76.48 88	75.82 190	61.28 128	74.81 135	80.37 129	63.17 106	62.43 286	80.50 217	61.10 161	85.16 58	64.00 121	84.34 206	83.01 146

SD-MVS		80.28 55	81.55 52	76.47 89	83.57 94	67.83 82	83.39 49	85.35 37	64.42 92	86.14 38	87.07 122	74.02 55	80.97 127	77.70 31	92.32 85	80.62 194
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
PLC	62.01 16	71.79 147	70.28 166	76.33 90	80.31 135	68.63 77	78.18 99	81.24 110	54.57 184	67.09 263	80.63 215	59.44 173	81.74 114	46.91 250	84.17 207	78.63 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		79.37 62	80.41 58	76.28 91	78.67 157	55.81 169	79.22 85	82.51 92	70.72 45	87.54 22	92.44 15	68.00 102	81.34 116	72.84 58	91.72 88	91.69 11

DP-MVS Recon		73.57 117	72.69 136	76.23 92	82.85 104	63.39 115	74.32 144	82.96 85	57.75 145	70.35 237	81.98 201	64.34 134	84.41 70	49.69 227	89.95 130	80.89 186

Regformer-1		74.28 108	73.63 119	76.21 93	74.22 216	64.12 111	72.77 155	75.46 191	66.86 66	79.27 121	72.08 295	69.29 87	78.74 162	68.73 84	84.02 210	85.77 79

HQP-MVS		75.24 96	75.01 100	75.94 94	82.37 109	58.80 154	77.32 106	84.12 70	59.08 132	71.58 221	85.96 161	58.09 187	85.30 51	67.38 101	89.16 141	83.73 127

DP-MVS		78.44 73	79.29 67	75.90 95	81.86 119	65.33 99	79.05 87	84.63 56	74.83 19	80.41 110	86.27 149	71.68 71	83.45 84	62.45 133	92.40 83	78.92 215

train_agg		76.38 85	76.55 87	75.86 96	85.47 66	69.32 72	76.42 115	78.69 152	54.00 192	76.97 148	86.74 133	66.60 113	81.10 122	72.50 63	91.56 94	77.15 231

Vis-MVSNet		74.85 105	74.56 103	75.72 97	81.63 123	64.64 105	76.35 117	79.06 145	62.85 108	73.33 203	88.41 103	62.54 143	79.59 153	63.94 124	82.92 220	82.94 147
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	63.80 13	72.70 137	71.69 149	75.72 97	78.10 161	60.01 143	73.04 151	81.50 103	45.34 266	79.66 117	84.35 176	65.15 126	82.65 99	48.70 235	89.38 140	84.50 110
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		72.78 135	71.87 146	75.54 99	74.77 204	59.02 152	72.24 159	71.56 223	63.92 96	78.59 127	71.59 303	66.22 117	78.60 164	67.58 97	80.32 249	89.00 34

EI-MVSNet-UG-set		72.63 138	71.68 150	75.47 100	74.67 208	58.64 157	72.02 162	71.50 224	63.53 101	78.58 129	71.39 306	65.98 118	78.53 166	67.30 103	80.18 251	89.23 28

EPP-MVSNet		73.86 112	73.38 123	75.31 101	78.19 160	53.35 185	80.45 67	77.32 175	65.11 85	76.47 166	86.80 128	49.47 235	83.77 77	53.89 203	92.72 80	88.81 40

test_prior3		76.71 84	77.19 83	75.27 102	82.15 114	59.85 144	75.57 126	84.33 62	58.92 136	76.53 164	86.78 130	67.83 103	83.39 85	69.81 80	92.76 78	82.58 157

test_prior				75.27 102	82.15 114	59.85 144		84.33 62					83.39 85			82.58 157

DVP-MVS		81.15 42	83.12 36	75.24 104	86.16 55	60.78 135	83.77 41	80.58 124	72.48 32	85.83 42	90.41 58	78.57 19	85.69 41	75.86 39	94.39 41	79.24 211

ETH3 D test6400		75.73 90	76.00 92	74.92 105	81.75 120	56.93 162	78.31 95	84.60 58	52.83 204	77.15 146	85.14 169	68.59 93	84.03 74	65.44 112	90.20 122	83.82 123

CNLPA		73.44 118	73.03 132	74.66 106	78.27 159	75.29 30	75.99 123	78.49 157	65.39 79	75.67 172	83.22 193	61.23 159	66.77 285	53.70 205	85.33 189	81.92 172

Regformer-3		72.86 134	72.28 142	74.62 107	74.74 205	60.18 141	72.91 152	71.76 219	64.74 89	78.42 131	72.07 297	67.00 108	76.28 205	67.97 94	80.91 241	87.39 53

tttt0517		69.46 170	67.79 196	74.46 108	75.34 193	52.72 187	75.05 131	63.27 270	54.69 181	78.87 126	84.37 175	26.63 340	81.15 120	63.95 122	87.93 154	89.51 24

EPNet		69.10 177	67.32 200	74.46 108	68.33 271	61.27 129	77.56 103	63.57 268	60.95 120	56.62 312	82.75 194	51.53 226	81.24 119	54.36 200	90.20 122	80.88 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CLD-MVS		72.88 133	72.36 140	74.43 110	77.03 174	54.30 178	68.77 208	83.43 80	52.12 210	76.79 158	74.44 277	69.54 86	83.91 75	55.88 183	93.25 70	85.09 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
WR-MVS_H		80.22 56	82.17 45	74.39 111	89.46 15	42.69 264	78.24 97	82.24 93	78.21 10	89.57 9	92.10 19	68.05 100	85.59 45	66.04 108	95.62 10	94.88 5

ETV-MVS		72.72 136	72.16 145	74.38 112	76.90 178	55.95 167	73.34 150	84.67 53	62.04 113	72.19 217	70.81 307	65.90 120	85.24 54	58.64 160	84.96 197	81.95 171

agg_prior1		75.89 87	76.41 88	74.31 113	84.44 85	66.02 95	76.12 121	78.62 155	54.40 186	76.95 150	86.85 127	66.44 115	80.34 139	72.45 64	91.42 97	76.57 235

test_0402		78.17 76	79.48 66	74.24 114	83.50 95	59.15 151	72.52 157	74.60 200	75.34 17	88.69 13	91.81 23	75.06 44	82.37 103	65.10 113	88.68 147	81.20 178

v10		75.69 91	76.20 91	74.16 115	74.44 214	48.69 209	75.84 125	82.93 86	59.02 135	85.92 40	89.17 90	58.56 182	82.74 97	70.73 74	89.14 143	91.05 14

IS-MVSNet		75.10 98	75.42 99	74.15 116	79.23 147	48.05 218	79.43 80	78.04 165	70.09 50	79.17 123	88.02 115	53.04 217	83.60 80	58.05 165	93.76 62	90.79 18

SixPastTwentyTwo		75.77 88	76.34 89	74.06 117	81.69 122	54.84 174	76.47 114	75.49 190	64.10 95	87.73 18	92.24 18	50.45 231	81.30 118	67.41 99	91.46 96	86.04 70

原ACMM1				73.90 118	85.90 61	65.15 103		81.67 101	50.97 224	74.25 192	86.16 154	61.60 153	83.54 81	56.75 172	91.08 106	73.00 260

K. test v3		73.67 115	73.61 120	73.87 119	79.78 138	55.62 172	74.69 141	62.04 279	66.16 73	84.76 58	93.23 6	49.47 235	80.97 127	65.66 110	86.67 176	85.02 90

Fast-Effi-MVS+-dtu		70.00 162	68.74 183	73.77 120	73.47 225	64.53 107	71.36 175	78.14 163	55.81 168	68.84 253	74.71 274	65.36 125	75.75 209	52.00 211	79.00 262	81.03 182

UGNet		70.20 160	69.05 176	73.65 121	76.24 184	63.64 113	75.87 124	72.53 214	61.48 116	60.93 296	86.14 155	52.37 221	77.12 194	50.67 220	85.21 191	80.17 202
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
114514_t		73.40 120	73.33 126	73.64 122	84.15 90	57.11 161	78.20 98	80.02 134	43.76 276	72.55 211	86.07 159	64.00 135	83.35 87	60.14 150	91.03 107	80.45 197

MCST-MVS		73.42 119	73.34 125	73.63 123	81.28 126	59.17 150	74.80 137	83.13 83	45.50 262	72.84 208	83.78 183	65.15 126	80.99 126	64.54 117	89.09 144	80.73 192

UniMVSNet (Re)		75.00 100	75.48 98	73.56 124	83.14 100	47.92 220	70.41 189	81.04 117	63.67 99	79.54 118	86.37 148	62.83 139	81.82 110	57.10 171	95.25 15	90.94 16

PVSNet_Blended_VisFu		70.04 161	68.88 179	73.53 125	82.71 106	63.62 114	74.81 135	81.95 98	48.53 246	67.16 262	79.18 237	51.42 227	78.38 173	54.39 199	79.72 258	78.60 217

v8		75.07 99	75.64 96	73.35 126	73.42 226	47.46 227	75.20 130	81.45 105	60.05 126	85.64 44	89.26 85	58.08 189	81.80 112	69.71 82	87.97 153	90.79 18

CSCG		74.12 110	74.39 106	73.33 127	79.35 144	61.66 127	77.45 105	81.98 97	62.47 112	79.06 124	80.19 222	61.83 150	78.79 161	59.83 154	87.35 163	79.54 208

v1192		73.40 120	73.42 121	73.32 128	74.65 211	48.67 210	72.21 160	81.73 100	52.76 205	81.85 90	84.56 173	57.12 199	82.24 107	68.58 85	87.33 164	89.06 32

CS-MVS		71.24 150	70.57 164	73.26 129	74.93 198	52.00 189	73.59 148	85.55 31	55.58 170	68.88 250	70.17 314	64.37 133	85.62 44	57.19 169	84.83 199	82.17 167

AdaColmap		74.22 109	74.56 103	73.20 130	81.95 117	60.97 132	79.43 80	80.90 119	65.57 76	72.54 212	81.76 205	70.98 78	85.26 52	47.88 243	90.00 128	73.37 257

PAPM_NR		73.91 111	74.16 110	73.16 131	81.90 118	53.50 183	81.28 62	81.40 106	66.17 72	73.30 204	83.31 189	59.96 168	83.10 92	58.45 162	81.66 235	82.87 149

PEN-MVS		80.46 51	82.91 38	73.11 132	89.83 9	39.02 290	77.06 112	82.61 90	80.04 4	90.60 5	92.85 10	74.93 47	85.21 55	63.15 129	95.15 18	95.09 2

PS-CasMVS		80.41 52	82.86 40	73.07 133	89.93 7	39.21 287	77.15 110	81.28 109	79.74 5	90.87 3	92.73 12	75.03 45	84.93 59	63.83 125	95.19 16	95.07 3

v1144		73.29 123	73.39 122	73.01 134	74.12 220	48.11 217	72.01 163	81.08 116	53.83 196	81.77 91	84.68 171	58.07 190	81.91 109	68.10 89	86.86 172	88.99 35

MVS_111021_HR		72.98 131	72.97 134	72.99 135	80.82 129	65.47 98	68.81 205	72.77 211	57.67 148	75.76 171	82.38 198	71.01 77	77.17 193	61.38 138	86.15 179	76.32 236

CP-MVSNet		79.48 60	81.65 50	72.98 136	89.66 13	39.06 289	76.76 113	80.46 126	78.91 7	90.32 7	91.70 25	68.49 95	84.89 60	63.40 128	95.12 19	95.01 4

MVS_111021_LR		72.10 143	71.82 148	72.95 137	79.53 142	73.90 39	70.45 188	66.64 253	56.87 156	76.81 157	81.76 205	68.78 91	71.76 250	61.81 134	83.74 214	73.18 259

DU-MVS		74.91 102	75.57 97	72.93 138	83.50 95	45.79 240	69.47 197	80.14 133	65.22 83	81.74 93	87.08 120	61.82 151	81.07 124	56.21 180	94.98 21	91.93 9

EIA-MVS		68.59 184	67.16 202	72.90 139	75.18 196	55.64 171	69.39 198	81.29 108	52.44 207	64.53 273	70.69 308	60.33 166	82.30 105	54.27 201	76.31 279	80.75 191

v1921920		72.96 132	72.98 133	72.89 140	74.67 208	47.58 225	71.92 167	80.69 120	51.70 216	81.69 95	83.89 181	56.58 204	82.25 106	68.34 87	87.36 162	88.82 39

v1240		73.06 126	73.14 128	72.84 141	74.74 205	47.27 230	71.88 169	81.11 113	51.80 214	82.28 88	84.21 177	56.22 206	82.34 104	68.82 83	87.17 170	88.91 37

v144192		72.99 130	73.06 131	72.77 142	74.58 212	47.48 226	71.90 168	80.44 127	51.57 217	81.46 97	84.11 179	58.04 191	82.12 108	67.98 93	87.47 160	88.70 42

lessismore_v0				72.75 143	79.60 141	56.83 164		57.37 294		83.80 72	89.01 96	47.45 247	78.74 162	64.39 119	86.49 178	82.69 155

DTE-MVSNet		80.35 53	82.89 39	72.74 144	89.84 8	37.34 305	77.16 109	81.81 99	80.45 3	90.92 2	92.95 8	74.57 51	86.12 28	63.65 126	94.68 31	94.76 6

thisisatest0530		67.05 204	65.16 212	72.73 145	73.10 234	50.55 196	71.26 179	63.91 266	50.22 230	74.46 190	80.75 213	26.81 339	80.25 142	59.43 156	86.50 177	87.37 54

IterMVS-LS		73.01 128	73.12 130	72.66 146	73.79 223	49.90 201	71.63 171	78.44 158	58.22 140	80.51 108	86.63 140	58.15 186	79.62 151	62.51 131	88.20 148	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		74.90 103	75.65 95	72.64 147	83.04 101	45.79 240	69.26 200	78.81 149	66.66 69	81.74 93	86.88 126	63.26 137	81.07 124	56.21 180	94.98 21	91.05 14

MVSFormer		69.93 164	69.03 177	72.63 148	74.93 198	59.19 148	83.98 37	75.72 188	52.27 208	63.53 282	76.74 259	43.19 267	80.56 134	72.28 65	78.67 266	78.14 223

IterMVS-SCA-FT		67.68 196	66.07 210	72.49 149	73.34 228	58.20 159	63.80 270	65.55 259	48.10 248	76.91 152	82.64 195	45.20 254	78.84 159	61.20 140	77.89 274	80.44 198

v2v482		72.55 141	72.58 137	72.43 150	72.92 237	46.72 233	71.41 174	79.13 144	55.27 172	81.17 100	85.25 168	55.41 208	81.13 121	67.25 104	85.46 185	89.43 25

MAR-MVS		67.72 195	66.16 209	72.40 151	74.45 213	64.99 104	74.87 133	77.50 173	48.67 245	65.78 269	68.58 326	57.01 202	77.79 186	46.68 252	81.92 229	74.42 251
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
3Dnovator	65.95 11	71.50 149	71.22 157	72.34 152	73.16 230	63.09 118	78.37 94	78.32 159	57.67 148	72.22 216	84.61 172	54.77 209	78.47 168	60.82 145	81.07 240	75.45 242

NR-MVSNet		73.62 116	74.05 111	72.33 153	83.50 95	43.71 253	65.65 250	77.32 175	64.32 93	75.59 173	87.08 120	62.45 144	81.34 116	54.90 191	95.63 9	91.93 9

DPM-MVS		69.98 163	69.22 175	72.26 154	82.69 107	58.82 153	70.53 187	81.23 111	47.79 253	64.16 275	80.21 220	51.32 228	83.12 91	60.14 150	84.95 198	74.83 248

1121		69.23 174	68.26 188	72.12 155	88.36 36	71.40 50	68.59 209	62.06 277	43.80 275	74.75 183	86.18 152	52.92 218	76.85 199	54.47 196	83.27 218	68.12 300

V42		71.06 152	70.83 161	71.72 156	67.25 281	47.14 231	65.94 244	80.35 130	51.35 220	83.40 76	83.23 191	59.25 176	78.80 160	65.91 109	80.81 245	89.23 28

Effi-MVS+		72.10 143	72.28 142	71.58 157	74.21 219	50.33 197	74.72 140	82.73 88	62.62 109	70.77 233	76.83 258	69.96 84	80.97 127	60.20 147	78.43 268	83.45 134

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 158	84.61 81	42.57 266	70.98 182	78.29 161	68.67 57	83.04 77	89.26 85	72.99 63	80.75 133	55.58 188	95.47 11	91.35 12

nrg030		74.87 104	75.99 93	71.52 159	74.90 201	49.88 204	74.10 146	82.58 91	54.55 185	83.50 75	89.21 87	71.51 72	75.74 210	61.24 139	92.34 84	88.94 36

eth_miper_zixun_eth		69.42 171	68.73 184	71.50 160	67.99 274	46.42 236	67.58 222	78.81 149	50.72 226	78.13 135	80.34 219	50.15 233	80.34 139	60.18 148	84.65 200	87.74 49

EI-MVSNet		69.61 168	69.01 178	71.41 161	73.94 221	49.90 201	71.31 177	71.32 226	58.22 140	75.40 177	70.44 309	58.16 185	75.85 206	62.51 131	79.81 255	88.48 43

ET-MVSNet_ETH3D		63.32 231	60.69 250	71.20 162	70.15 259	55.66 170	65.02 257	64.32 265	43.28 284	68.99 248	72.05 301	25.46 346	78.19 181	54.16 202	82.80 221	79.74 207

testing_2		72.01 145	72.36 140	70.95 163	70.79 249	48.70 208	72.81 154	78.09 164	48.79 244	84.46 64	89.15 92	57.90 193	78.55 165	61.55 137	87.74 155	85.61 81

PAPR		69.20 175	68.66 185	70.82 164	75.15 197	47.77 222	75.31 129	81.11 113	49.62 238	66.33 265	79.27 234	61.53 154	82.96 94	48.12 241	81.50 237	81.74 174

casdiffmvs		73.06 126	73.84 114	70.72 165	71.32 246	46.71 234	70.93 183	84.26 66	55.62 169	77.46 144	87.10 119	67.09 107	77.81 185	63.95 122	86.83 173	87.64 50

HyFIR lowres test		63.01 235	60.47 251	70.61 166	83.04 101	54.10 179	59.93 297	72.24 218	33.67 328	69.00 247	75.63 264	38.69 292	76.93 196	36.60 306	75.45 286	80.81 190

Anonymous20231211		75.54 92	77.19 83	70.59 167	77.67 169	45.70 243	74.73 139	80.19 131	68.80 54	82.95 79	92.91 9	66.26 116	76.76 201	58.41 163	92.77 77	89.30 26

MSLP-MVS++		74.48 107	75.78 94	70.59 167	84.66 79	62.40 120	78.65 90	84.24 67	60.55 124	77.71 141	81.98 201	63.12 138	77.64 189	62.95 130	88.14 149	71.73 274

baseline		73.10 124	73.96 113	70.51 169	71.46 245	46.39 238	72.08 161	84.40 61	55.95 166	76.62 160	86.46 146	67.20 106	78.03 183	64.22 120	87.27 167	87.11 59

DELS-MVS		68.83 179	68.31 186	70.38 170	70.55 256	48.31 213	63.78 271	82.13 94	54.00 192	68.96 249	75.17 269	58.95 179	80.06 148	58.55 161	82.74 222	82.76 153
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
Fast-Effi-MVS+		68.81 180	68.30 187	70.35 171	74.66 210	48.61 211	66.06 243	78.32 159	50.62 227	71.48 227	75.54 265	68.75 92	79.59 153	50.55 222	78.73 265	82.86 150

xiu_mvs_v1_base_debu		67.87 192	67.07 203	70.26 172	79.13 151	61.90 124	67.34 226	71.25 229	47.98 249	67.70 258	74.19 282	61.31 156	72.62 237	56.51 174	78.26 270	76.27 237

xiu_mvs_v1_base		67.87 192	67.07 203	70.26 172	79.13 151	61.90 124	67.34 226	71.25 229	47.98 249	67.70 258	74.19 282	61.31 156	72.62 237	56.51 174	78.26 270	76.27 237

xiu_mvs_v1_base_debi		67.87 192	67.07 203	70.26 172	79.13 151	61.90 124	67.34 226	71.25 229	47.98 249	67.70 258	74.19 282	61.31 156	72.62 237	56.51 174	78.26 270	76.27 237

EG-PatchMatch MVS		70.70 156	70.88 160	70.16 175	82.64 108	58.80 154	71.48 172	73.64 204	54.98 175	76.55 162	81.77 204	61.10 161	78.94 158	54.87 192	80.84 244	72.74 264

BH-RMVSNet		68.69 183	68.20 192	70.14 176	76.40 182	53.90 182	64.62 261	73.48 205	58.01 142	73.91 197	81.78 203	59.09 177	78.22 178	48.59 236	77.96 273	78.31 220

ambc				70.10 177	77.74 167	50.21 199	74.28 145	77.93 168		79.26 122	88.29 108	54.11 214	79.77 150	64.43 118	91.10 105	80.30 199

cascas		64.59 219	62.77 235	70.05 178	75.27 194	50.02 200	61.79 285	71.61 221	42.46 286	63.68 280	68.89 323	49.33 237	80.35 138	47.82 244	84.05 209	79.78 206

新几何1				69.99 179	88.37 35	71.34 52		62.08 276	43.85 274	74.99 180	86.11 157	52.85 219	70.57 258	50.99 218	83.23 219	68.05 301

TAMVS		65.31 213	63.75 225	69.97 180	82.23 113	59.76 146	66.78 237	63.37 269	45.20 267	69.79 242	79.37 233	47.42 248	72.17 243	34.48 315	85.15 193	77.99 227

UniMVSNet_ETH3D		76.74 83	79.02 68	69.92 181	89.27 20	43.81 252	74.47 143	71.70 220	72.33 35	85.50 48	93.65 4	77.98 23	76.88 198	54.60 195	91.64 91	89.08 31

ACMH	63.62 14	77.50 79	80.11 60	69.68 182	79.61 140	56.28 166	78.81 88	83.62 77	63.41 104	87.14 31	90.23 69	76.11 34	73.32 230	67.58 97	94.44 39	79.44 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl_fuxian		69.82 166	69.89 168	69.61 183	66.24 289	43.48 256	68.12 217	79.61 139	51.43 219	77.72 140	80.18 223	54.61 212	78.15 182	63.62 127	87.50 159	87.20 57

API-MVS		70.97 154	71.51 154	69.37 184	75.20 195	55.94 168	80.99 63	76.84 179	62.48 111	71.24 228	77.51 254	61.51 155	80.96 131	52.04 210	85.76 184	71.22 278

v148		69.38 173	69.39 171	69.36 185	69.14 267	44.56 248	68.83 204	72.70 212	54.79 179	78.59 127	84.12 178	54.69 210	76.74 202	59.40 157	82.20 226	86.79 62

CDS-MVSNet		64.33 225	62.66 236	69.35 186	80.44 134	58.28 158	65.26 254	65.66 257	44.36 272	67.30 261	75.54 265	43.27 266	71.77 249	37.68 298	84.44 205	78.01 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		64.47 222	62.84 234	69.34 187	76.91 177	59.20 147	67.15 230	65.67 256	35.29 318	65.16 271	76.74 259	44.67 258	70.68 256	54.74 193	79.28 261	78.14 223
jason: jason.
BH-untuned		69.39 172	69.46 170	69.18 188	77.96 164	56.88 163	68.47 214	77.53 172	56.77 158	77.79 139	79.63 229	60.30 167	80.20 145	46.04 254	80.65 246	70.47 283

alignmvs		70.54 158	71.00 159	69.15 189	73.50 224	48.04 219	69.85 194	79.62 137	53.94 195	76.54 163	82.00 200	59.00 178	74.68 222	57.32 168	87.21 168	84.72 97

canonicalmvs		72.29 142	73.38 123	69.04 190	74.23 215	47.37 228	73.93 147	83.18 81	54.36 187	76.61 161	81.64 207	72.03 66	75.34 214	57.12 170	87.28 166	84.40 112

cl-mvsnet2		67.14 201	66.51 207	69.03 191	63.20 309	43.46 257	66.88 236	76.25 183	49.22 240	74.48 189	77.88 250	45.49 253	77.40 191	60.64 146	84.59 202	86.24 66

miper_ehance_all_eth		68.36 186	68.16 193	68.98 192	65.14 299	43.34 258	67.07 232	78.92 148	49.11 241	76.21 169	77.72 251	53.48 216	77.92 184	61.16 141	84.59 202	85.68 80

lupinMVS		63.36 230	61.49 244	68.97 193	74.93 198	59.19 148	65.80 248	64.52 264	34.68 323	63.53 282	74.25 280	43.19 267	70.62 257	53.88 204	78.67 266	77.10 232

QAPM		69.18 176	69.26 173	68.94 194	71.61 244	52.58 188	80.37 70	78.79 151	49.63 237	73.51 199	85.14 169	53.66 215	79.12 155	55.11 190	75.54 284	75.11 246

FC-MVSNet-test		73.32 122	74.78 102	68.93 195	79.21 148	36.57 307	71.82 170	79.54 141	57.63 150	82.57 85	90.38 63	59.38 175	78.99 157	57.91 166	94.56 33	91.23 13

VDD-MVS		70.81 155	71.44 155	68.91 196	79.07 154	46.51 235	67.82 220	70.83 236	61.23 117	74.07 195	88.69 101	59.86 170	75.62 211	51.11 217	90.28 121	84.61 102

Anonymous20240529		72.56 139	73.79 116	68.86 197	76.89 179	45.21 245	68.80 207	77.25 177	67.16 63	76.89 153	90.44 54	65.95 119	74.19 227	50.75 219	90.00 128	87.18 58

FIs		72.56 139	73.80 115	68.84 198	78.74 156	37.74 301	71.02 181	79.83 136	56.12 164	80.88 106	89.45 82	58.18 184	78.28 177	56.63 173	93.36 68	90.51 20

OpenMVS	62.51 15	68.76 181	68.75 182	68.78 199	70.56 255	53.91 181	78.29 96	77.35 174	48.85 243	70.22 239	83.52 184	52.65 220	76.93 196	55.31 189	81.99 228	75.49 241

PVSNet_BlendedMVS		65.38 212	64.30 221	68.61 200	69.81 260	49.36 205	65.60 252	78.96 146	45.50 262	59.98 299	78.61 243	51.82 223	78.20 179	44.30 260	84.11 208	78.27 221

MVP-Stereo		61.56 247	59.22 258	68.58 201	79.28 145	60.44 139	69.20 201	71.57 222	43.58 279	56.42 313	78.37 246	39.57 289	76.46 204	34.86 314	60.16 336	68.86 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVSTER		63.29 232	61.60 243	68.36 202	59.77 328	46.21 239	60.62 292	71.32 226	41.83 288	75.40 177	79.12 238	30.25 332	75.85 206	56.30 179	79.81 255	83.03 145

thisisatest0515		60.48 255	57.86 269	68.34 203	67.25 281	46.42 236	60.58 293	62.14 274	40.82 293	63.58 281	69.12 319	26.28 342	78.34 175	48.83 233	82.13 227	80.26 200

cl-mvsnet_		68.26 190	68.26 188	68.29 204	64.98 300	43.67 254	65.89 245	74.67 198	50.04 233	76.86 155	82.42 197	48.74 240	75.38 212	60.92 144	89.81 132	85.80 78

cl-mvsnet1		68.27 189	68.26 188	68.29 204	64.98 300	43.67 254	65.89 245	74.67 198	50.04 233	76.86 155	82.43 196	48.74 240	75.38 212	60.94 143	89.81 132	85.81 74

miper_enhance_ethall		65.86 208	65.05 220	68.28 206	61.62 316	42.62 265	64.74 259	77.97 166	42.52 285	73.42 202	72.79 292	49.66 234	77.68 188	58.12 164	84.59 202	84.54 105

LF4IMVS		67.50 197	67.31 201	68.08 207	58.86 331	61.93 123	71.43 173	75.90 187	44.67 271	72.42 213	80.20 221	57.16 197	70.44 260	58.99 159	86.12 180	71.88 272

IB-MVS	49.67 18	59.69 260	56.96 275	67.90 208	68.19 272	50.30 198	61.42 287	65.18 261	47.57 255	55.83 316	67.15 333	23.77 351	79.60 152	43.56 266	79.97 253	73.79 256
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
MG-MVS		70.47 159	71.34 156	67.85 209	79.26 146	40.42 282	74.67 142	75.15 196	58.41 139	68.74 254	88.14 113	56.08 207	83.69 78	59.90 153	81.71 234	79.43 210

RPMNet		65.77 210	65.08 219	67.84 210	66.37 286	48.24 215	70.93 183	86.27 20	54.66 182	61.35 290	86.77 132	33.29 308	85.67 43	55.93 182	70.17 312	69.62 292

pmmvs-eth3d		64.41 224	63.27 230	67.82 211	75.81 191	60.18 141	69.49 196	62.05 278	38.81 303	74.13 193	82.23 199	43.76 264	68.65 269	42.53 269	80.63 248	74.63 249

RRT_test8_iter05		65.80 209	65.13 215	67.80 212	67.02 284	40.85 277	67.13 231	75.33 193	49.73 235	72.69 210	81.32 208	24.45 350	77.37 192	61.69 136	86.82 174	85.18 85

TR-MVS		64.59 219	63.54 228	67.73 213	75.75 192	50.83 195	63.39 274	70.29 239	49.33 239	71.55 225	74.55 275	50.94 229	78.46 169	40.43 282	75.69 282	73.89 255

MSDG		67.47 199	67.48 199	67.46 214	70.70 252	54.69 176	66.90 235	78.17 162	60.88 121	70.41 236	74.76 271	61.22 160	73.18 231	47.38 246	76.87 276	74.49 250

WR-MVS		71.20 151	72.48 138	67.36 215	84.98 74	35.70 315	64.43 264	68.66 246	65.05 86	81.49 96	86.43 147	57.57 196	76.48 203	50.36 223	93.32 69	89.90 22

MVS_Test		69.84 165	70.71 162	67.24 216	67.49 280	43.25 260	69.87 193	81.22 112	52.69 206	71.57 224	86.68 136	62.09 149	74.51 224	66.05 107	78.74 264	83.96 121

D2MVS		62.58 240	61.05 247	67.20 217	63.85 305	47.92 220	56.29 309	69.58 242	39.32 298	70.07 240	78.19 247	34.93 304	72.68 235	53.44 208	83.74 214	81.00 184

diffmvs		67.42 200	67.50 198	67.20 217	62.26 313	45.21 245	64.87 258	77.04 178	48.21 247	71.74 219	79.70 228	58.40 183	71.17 254	64.99 114	80.27 250	85.22 83

PM-MVS		64.49 221	63.61 227	67.14 219	76.68 180	75.15 31	68.49 213	42.85 337	51.17 223	77.85 137	80.51 216	45.76 249	66.31 288	52.83 209	76.35 278	59.96 331

VDDNet		71.60 148	73.13 129	67.02 220	86.29 51	41.11 273	69.97 191	66.50 254	68.72 56	74.74 184	91.70 25	59.90 169	75.81 208	48.58 237	91.72 88	84.15 119

GBi-Net		68.30 187	68.79 180	66.81 221	73.14 231	40.68 278	71.96 164	73.03 206	54.81 176	74.72 185	90.36 64	48.63 242	75.20 216	47.12 247	85.37 186	84.54 105

test1		68.30 187	68.79 180	66.81 221	73.14 231	40.68 278	71.96 164	73.03 206	54.81 176	74.72 185	90.36 64	48.63 242	75.20 216	47.12 247	85.37 186	84.54 105

FMVSNet1		71.06 152	72.48 138	66.81 221	77.65 170	40.68 278	71.96 164	73.03 206	61.14 118	79.45 120	90.36 64	60.44 165	75.20 216	50.20 224	88.05 150	84.54 105

PVSNet_Blended		62.90 237	61.64 241	66.69 224	69.81 260	49.36 205	61.23 289	78.96 146	42.04 287	59.98 299	68.86 324	51.82 223	78.20 179	44.30 260	77.77 275	72.52 265

GA-MVS		62.91 236	61.66 240	66.66 225	67.09 283	44.49 249	61.18 290	69.36 244	51.33 221	69.33 245	74.47 276	36.83 299	74.94 219	50.60 221	74.72 291	80.57 196

BH-w/o		64.81 218	64.29 222	66.36 226	76.08 188	54.71 175	65.61 251	75.23 195	50.10 232	71.05 232	71.86 302	54.33 213	79.02 156	38.20 295	76.14 280	65.36 315

pmmvs6		71.82 146	73.66 118	66.31 227	75.94 189	42.01 268	66.99 233	72.53 214	63.45 103	76.43 167	92.78 11	72.95 64	69.69 264	51.41 215	90.46 119	87.22 55

PAPM		61.79 246	60.37 252	66.05 228	76.09 187	41.87 269	69.30 199	76.79 181	40.64 295	53.80 325	79.62 230	44.38 260	82.92 95	29.64 333	73.11 298	73.36 258

pmmvs4		60.78 252	59.04 260	66.00 229	73.06 236	57.67 160	64.53 263	60.22 283	36.91 312	65.96 266	77.27 255	39.66 288	68.54 270	38.87 289	74.89 290	71.80 273

PS-MVSNAJ		64.27 226	63.73 226	65.90 230	77.82 166	51.42 192	63.33 275	72.33 216	45.09 269	61.60 288	68.04 327	62.39 145	73.95 228	49.07 231	73.87 295	72.34 267

xiu_mvs_v2_base		64.43 223	63.96 223	65.85 231	77.72 168	51.32 193	63.63 272	72.31 217	45.06 270	61.70 287	69.66 316	62.56 141	73.93 229	49.06 232	73.91 294	72.31 268

FMVSNet2		67.48 198	68.21 191	65.29 232	73.14 231	38.94 291	68.81 205	71.21 232	54.81 176	76.73 159	86.48 145	48.63 242	74.60 223	47.98 242	86.11 181	82.35 163

test_yl		65.11 214	65.09 217	65.18 233	70.59 253	40.86 275	63.22 278	72.79 209	57.91 143	68.88 250	79.07 240	42.85 270	74.89 220	45.50 257	84.97 194	79.81 204

DCV-MVSNet		65.11 214	65.09 217	65.18 233	70.59 253	40.86 275	63.22 278	72.79 209	57.91 143	68.88 250	79.07 240	42.85 270	74.89 220	45.50 257	84.97 194	79.81 204

IterMVS		63.12 234	62.48 237	65.02 235	66.34 288	52.86 186	63.81 269	62.25 273	46.57 259	71.51 226	80.40 218	44.60 259	66.82 284	51.38 216	75.47 285	75.38 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet_DTU		64.04 228	63.83 224	64.66 236	68.39 268	42.97 262	73.45 149	74.50 201	52.05 212	54.78 319	75.44 268	43.99 262	70.42 261	53.49 207	78.41 269	80.59 195

LFMVS		67.06 203	67.89 195	64.56 237	78.02 162	38.25 296	70.81 186	59.60 285	65.18 84	71.06 231	86.56 143	43.85 263	75.22 215	46.35 253	89.63 135	80.21 201

FMVSNet3		65.00 217	65.16 212	64.52 238	69.47 263	37.56 304	66.63 238	70.38 238	51.55 218	74.72 185	83.27 190	37.89 298	74.44 225	47.12 247	85.37 186	81.57 176

MDA-MVSNet-bldmvs		62.34 242	61.73 239	64.16 239	61.64 315	49.90 201	48.11 330	57.24 297	53.31 200	80.95 102	79.39 232	49.00 238	61.55 303	45.92 255	80.05 252	81.03 182

testdata				64.13 240	85.87 63	63.34 116		61.80 280	47.83 252	76.42 168	86.60 142	48.83 239	62.31 301	54.46 198	81.26 238	66.74 310

TinyColmap		67.98 191	69.28 172	64.08 241	67.98 275	46.82 232	70.04 190	75.26 194	53.05 201	77.36 145	86.79 129	59.39 174	72.59 240	45.64 256	88.01 152	72.83 262

baseline2		55.57 280	52.74 294	64.05 242	65.26 295	44.11 251	62.38 281	54.43 307	39.03 301	51.21 330	67.35 331	33.66 307	72.45 241	37.14 303	64.22 330	75.60 240

pm-mvs1		68.40 185	69.85 169	64.04 243	73.10 234	39.94 284	64.61 262	70.50 237	55.52 171	73.97 196	89.33 83	63.91 136	68.38 271	49.68 228	88.02 151	83.81 125

mvs_anonymous		65.08 216	65.49 211	63.83 244	63.79 306	37.60 303	66.52 240	69.82 241	43.44 280	73.46 201	86.08 158	58.79 181	71.75 251	51.90 212	75.63 283	82.15 168

VPA-MVSNet		68.71 182	70.37 165	63.72 245	76.13 186	38.06 299	64.10 266	71.48 225	56.60 163	74.10 194	88.31 107	64.78 131	69.72 263	47.69 245	90.15 125	83.37 136

TransMVSNet (Re)		69.62 167	71.63 151	63.57 246	76.51 181	35.93 313	65.75 249	71.29 228	61.05 119	75.02 179	89.90 76	65.88 121	70.41 262	49.79 226	89.48 138	84.38 113

CMPMVS	48.73 20	61.54 248	60.89 248	63.52 247	61.08 319	51.55 191	68.07 218	68.00 249	33.88 325	65.87 267	81.25 210	37.91 297	67.71 274	49.32 230	82.60 223	71.31 277
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CHOSEN 1792x2688		58.09 269	56.30 280	63.45 248	79.95 137	50.93 194	54.07 317	65.59 258	28.56 343	61.53 289	74.33 278	41.09 280	66.52 287	33.91 318	67.69 324	72.92 261

Anonymous202405211		66.02 207	66.89 206	63.43 249	74.22 216	38.14 297	59.00 301	66.13 255	63.33 105	69.76 243	85.95 162	51.88 222	70.50 259	44.23 262	87.52 158	81.64 175

MVS_0304		62.51 241	62.27 238	63.25 250	69.39 264	48.47 212	64.05 268	62.48 272	59.69 130	54.10 324	81.04 212	45.71 250	66.31 288	41.38 277	82.58 224	74.96 247

thres400		60.77 253	59.97 254	63.15 251	70.78 250	35.35 317	63.27 276	57.47 292	53.00 202	68.31 255	77.09 256	32.45 315	72.09 244	35.61 311	81.73 231	82.02 169

thres600view7		61.82 245	61.38 245	63.12 252	71.81 243	34.93 320	64.64 260	56.99 298	54.78 180	70.33 238	79.74 227	32.07 318	72.42 242	38.61 292	83.46 216	82.02 169

OpenMVS_ROB	54.93 17	63.23 233	63.28 229	63.07 253	69.81 260	45.34 244	68.52 212	67.14 250	43.74 277	70.61 235	79.22 235	47.90 246	72.66 236	48.75 234	73.84 296	71.21 279

miper_lstm_enhance		61.97 243	61.63 242	62.98 254	60.04 324	45.74 242	47.53 332	70.95 233	44.04 273	73.06 206	78.84 242	39.72 287	60.33 305	55.82 184	84.64 201	82.88 148

Baseline_NR-MVSNet		70.62 157	73.19 127	62.92 255	76.97 175	34.44 323	68.84 203	70.88 235	60.25 125	79.50 119	90.53 51	61.82 151	69.11 267	54.67 194	95.27 14	85.22 83

1314		59.83 259	58.86 262	62.74 256	65.71 293	44.78 247	68.59 209	72.63 213	33.54 330	61.05 294	67.29 332	43.62 265	71.26 253	49.49 229	67.84 323	72.19 270

MVS		60.62 254	59.97 254	62.58 257	68.13 273	47.28 229	68.59 209	73.96 203	32.19 332	59.94 301	68.86 324	50.48 230	77.64 189	41.85 274	75.74 281	62.83 323

Patchmatch-RL test		59.95 258	59.12 259	62.44 258	72.46 239	54.61 177	59.63 298	47.51 330	41.05 292	74.58 188	74.30 279	31.06 326	65.31 290	51.61 213	79.85 254	67.39 303

ppachtmachnet_test		60.26 257	59.61 257	62.20 259	67.70 278	44.33 250	58.18 305	60.96 282	40.75 294	65.80 268	72.57 293	41.23 277	63.92 295	46.87 251	82.42 225	78.33 219

tfpnnormal		66.48 206	67.93 194	62.16 260	73.40 227	36.65 306	63.45 273	64.99 262	55.97 165	72.82 209	87.80 116	57.06 201	69.10 268	48.31 240	87.54 157	80.72 193

USDC		62.80 238	63.10 232	61.89 261	65.19 296	43.30 259	67.42 225	74.20 202	35.80 317	72.25 215	84.48 174	45.67 251	71.95 248	37.95 297	84.97 194	70.42 285

LCM-MVSNet-Re		69.10 177	71.57 153	61.70 262	70.37 257	34.30 324	61.45 286	79.62 137	56.81 157	89.59 8	88.16 112	68.44 96	72.94 233	42.30 270	87.33 164	77.85 228

PatchMatch-RL		58.68 267	57.72 270	61.57 263	76.21 185	73.59 42	61.83 284	49.00 326	47.30 257	61.08 292	68.97 321	50.16 232	59.01 309	36.06 310	68.84 319	52.10 338

tfpn200view9		60.35 256	59.97 254	61.51 264	70.78 250	35.35 317	63.27 276	57.47 292	53.00 202	68.31 255	77.09 256	32.45 315	72.09 244	35.61 311	81.73 231	77.08 233

CVMVSNet		59.21 263	58.44 266	61.51 264	73.94 221	47.76 223	71.31 177	64.56 263	26.91 347	60.34 298	70.44 309	36.24 301	67.65 275	53.57 206	68.66 320	69.12 297

thres100view900		61.17 249	61.09 246	61.39 266	72.14 241	35.01 319	65.42 253	56.99 298	55.23 173	70.71 234	79.90 225	32.07 318	72.09 244	35.61 311	81.73 231	77.08 233

Vis-MVSNet (Re-imp)		62.74 239	63.21 231	61.34 267	72.19 240	31.56 334	67.31 229	53.87 308	53.60 198	69.88 241	83.37 187	40.52 284	70.98 255	41.40 276	86.78 175	81.48 177

JIA-IIPM		54.03 286	51.62 298	61.25 268	59.14 330	55.21 173	59.10 300	47.72 329	50.85 225	50.31 336	85.81 163	20.10 356	63.97 294	36.16 309	55.41 347	64.55 320

ab-mvs		64.11 227	65.13 215	61.05 269	71.99 242	38.03 300	67.59 221	68.79 245	49.08 242	65.32 270	86.26 150	58.02 192	66.85 283	39.33 286	79.79 257	78.27 221

CostFormer		57.35 273	56.14 281	60.97 270	63.76 307	38.43 293	67.50 223	60.22 283	37.14 311	59.12 304	76.34 261	32.78 312	71.99 247	39.12 288	69.27 317	72.47 266

1112_ss		59.48 261	58.99 261	60.96 271	77.84 165	42.39 267	61.42 287	68.45 247	37.96 307	59.93 302	67.46 329	45.11 256	65.07 292	40.89 280	71.81 303	75.41 243

EU-MVSNet		60.82 251	60.80 249	60.86 272	68.37 269	41.16 272	72.27 158	68.27 248	26.96 346	69.08 246	75.71 263	32.09 317	67.44 277	55.59 187	78.90 263	73.97 253

VNet		64.01 229	65.15 214	60.57 273	73.28 229	35.61 316	57.60 307	67.08 251	54.61 183	66.76 264	83.37 187	56.28 205	66.87 281	42.19 271	85.20 192	79.23 212

tpm2		56.12 275	54.64 288	60.55 274	66.24 289	36.01 311	68.14 216	56.77 300	33.60 329	58.25 307	75.52 267	30.25 332	74.33 226	33.27 320	69.76 316	71.32 276

CR-MVSNet		58.96 264	58.49 265	60.36 275	66.37 286	48.24 215	70.93 183	56.40 301	32.87 331	61.35 290	86.66 137	33.19 309	63.22 298	48.50 238	70.17 312	69.62 292

SCA		58.57 268	58.04 268	60.17 276	70.17 258	41.07 274	65.19 255	53.38 312	43.34 283	61.00 295	73.48 286	45.20 254	69.38 265	40.34 283	70.31 311	70.05 287

EPNet_dtu		58.93 265	58.52 264	60.16 277	67.91 276	47.70 224	69.97 191	58.02 289	49.73 235	47.28 340	73.02 291	38.14 294	62.34 300	36.57 307	85.99 182	70.43 284
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Gipuma		69.55 169	72.83 135	59.70 278	63.63 308	53.97 180	80.08 76	75.93 186	64.24 94	73.49 200	88.93 99	57.89 194	62.46 299	59.75 155	91.55 95	62.67 325
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
VPNet		65.58 211	67.56 197	59.65 279	79.72 139	30.17 337	60.27 295	62.14 274	54.19 189	71.24 228	86.63 140	58.80 180	67.62 276	44.17 263	90.87 115	81.18 179

thres200		57.55 272	57.02 274	59.17 280	67.89 277	34.93 320	58.91 303	57.25 296	50.24 229	64.01 276	71.46 305	32.49 314	71.39 252	31.31 325	79.57 259	71.19 280

HY-MVS	49.31 19	57.96 270	57.59 271	59.10 281	66.85 285	36.17 310	65.13 256	65.39 260	39.24 300	54.69 321	78.14 248	44.28 261	67.18 280	33.75 319	70.79 308	73.95 254

Test_1112_low_res		58.78 266	58.69 263	59.04 282	79.41 143	38.13 298	57.62 306	66.98 252	34.74 321	59.62 303	77.56 253	42.92 269	63.65 297	38.66 291	70.73 309	75.35 245

DWT-MVSNet_test		53.04 290	51.12 301	58.77 283	61.23 317	38.67 292	62.16 283	57.74 290	38.24 304	51.76 329	59.07 343	21.36 353	67.40 278	44.80 259	63.76 331	70.25 286

MIMVSNet1		66.57 205	69.23 174	58.59 284	81.26 127	37.73 302	64.06 267	57.62 291	57.02 154	78.40 132	90.75 44	62.65 140	58.10 312	41.77 275	89.58 137	79.95 203

ANet_high		67.08 202	69.94 167	58.51 285	57.55 336	27.09 343	58.43 304	76.80 180	63.56 100	82.40 87	91.93 21	59.82 171	64.98 293	50.10 225	88.86 146	83.46 133

tpm cat1		54.02 287	52.63 295	58.19 286	64.85 302	39.86 285	66.26 242	57.28 295	32.16 333	56.90 310	70.39 311	32.75 313	65.30 291	34.29 316	58.79 339	69.41 294

MS-PatchMatch		55.59 279	54.89 287	57.68 287	69.18 265	49.05 207	61.00 291	62.93 271	35.98 315	58.36 306	68.93 322	36.71 300	66.59 286	37.62 300	63.30 332	57.39 334

FPMVS		59.43 262	60.07 253	57.51 288	77.62 171	71.52 49	62.33 282	50.92 319	57.40 152	69.40 244	80.00 224	39.14 290	61.92 302	37.47 301	66.36 325	39.09 348

tpmvs		55.84 276	55.45 286	57.01 289	60.33 323	33.20 329	65.89 245	59.29 287	47.52 256	56.04 314	73.60 285	31.05 327	68.06 273	40.64 281	64.64 328	69.77 290

our_test_3		56.46 274	56.51 278	56.30 290	67.70 278	39.66 286	55.36 314	52.34 317	40.57 296	63.85 277	69.91 315	40.04 286	58.22 311	43.49 267	75.29 289	71.03 282

baseline1		57.82 271	58.36 267	56.19 291	69.17 266	30.76 336	62.94 280	55.21 304	46.04 261	63.83 278	78.47 244	41.20 278	63.68 296	39.44 285	68.99 318	74.13 252

PatchmatchNet		54.60 283	54.27 289	55.59 292	65.17 298	39.08 288	66.92 234	51.80 318	39.89 297	58.39 305	73.12 290	31.69 320	58.33 310	43.01 268	58.38 342	69.38 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		52.06 296	51.11 302	54.92 293	59.64 329	71.03 54	37.42 346	61.62 281	33.68 327	57.12 308	72.10 294	37.94 296	31.03 350	29.13 337	71.35 304	62.70 324

Patchmtry		60.91 250	63.01 233	54.62 294	66.10 291	26.27 346	67.47 224	56.40 301	54.05 191	72.04 218	86.66 137	33.19 309	60.17 306	43.69 264	87.45 161	77.42 229

FMVSNet5		55.08 282	55.54 285	53.71 295	65.80 292	33.50 328	56.22 310	52.50 316	43.72 278	61.06 293	83.38 186	25.46 346	54.87 315	30.11 330	81.64 236	72.75 263

ADS-MVSNet2		48.76 303	47.25 311	53.29 296	55.90 343	40.54 281	47.34 333	54.99 306	31.41 339	50.48 333	72.06 299	31.23 323	54.26 317	25.93 341	55.93 344	65.07 317

PVSNet	43.83 21	51.56 297	51.17 300	52.73 297	68.34 270	38.27 295	48.22 329	53.56 311	36.41 313	54.29 322	64.94 336	34.60 305	54.20 318	30.34 328	69.87 314	65.71 314

gg-mvs-nofinetune		55.75 277	56.75 277	52.72 298	62.87 310	28.04 342	68.92 202	41.36 345	71.09 42	50.80 332	92.63 13	20.74 354	66.86 282	29.97 331	72.41 300	63.25 322

GG-mvs-BLEND				52.24 299	60.64 322	29.21 340	69.73 195	42.41 338		45.47 342	52.33 347	20.43 355	68.16 272	25.52 344	65.42 327	59.36 332

pmmvs5		52.49 294	52.58 296	52.21 300	54.99 347	32.38 331	55.45 313	53.84 309	32.15 334	55.49 318	74.81 270	38.08 295	57.37 313	34.02 317	74.40 292	66.88 307

pmmvs3		46.71 308	45.09 315	51.55 301	56.76 339	48.25 214	55.78 312	39.53 349	24.13 350	50.35 335	63.40 337	15.90 360	51.08 319	29.29 335	70.69 310	55.33 337

test20.03		55.74 278	57.51 272	50.42 302	59.89 327	32.09 332	50.63 324	49.01 325	50.11 231	65.07 272	83.23 191	45.61 252	48.11 324	30.22 329	83.82 213	71.07 281

YYNet1		52.58 292	53.50 291	49.85 303	54.15 350	36.45 309	40.53 341	46.55 332	38.09 306	75.52 175	73.31 289	41.08 281	43.88 337	41.10 278	71.14 307	69.21 296

MDA-MVSNet_test_wron		52.57 293	53.49 292	49.81 304	54.24 349	36.47 308	40.48 342	46.58 331	38.13 305	75.47 176	73.32 288	41.05 282	43.85 338	40.98 279	71.20 306	69.10 298

test-LLR		50.43 299	50.69 304	49.64 305	60.76 320	41.87 269	53.18 319	45.48 333	43.41 281	49.41 337	60.47 341	29.22 337	44.73 334	42.09 272	72.14 301	62.33 327

test-mter		48.56 304	48.20 309	49.64 305	60.76 320	41.87 269	53.18 319	45.48 333	31.91 337	49.41 337	60.47 341	18.34 357	44.73 334	42.09 272	72.14 301	62.33 327

MIMVSNet		54.39 284	56.12 282	49.20 307	72.57 238	30.91 335	59.98 296	48.43 328	41.66 289	55.94 315	83.86 182	41.19 279	50.42 320	26.05 340	75.38 287	66.27 311

UnsupCasMVSNet_eth		52.26 295	53.29 293	49.16 308	55.08 346	33.67 327	50.03 325	58.79 288	37.67 308	63.43 284	74.75 272	41.82 275	45.83 327	38.59 293	59.42 338	67.98 302

wuyk23d		61.97 243	66.25 208	49.12 309	58.19 335	60.77 137	66.32 241	52.97 314	55.93 167	90.62 4	86.91 125	73.07 62	35.98 348	20.63 350	91.63 92	50.62 339

Anonymous20231206		54.13 285	55.82 283	49.04 310	70.89 248	35.96 312	51.73 322	50.87 320	34.86 319	62.49 285	79.22 235	42.52 273	44.29 336	27.95 338	81.88 230	66.88 307

XXY-MVS		55.19 281	57.40 273	48.56 311	64.45 303	34.84 322	51.54 323	53.59 310	38.99 302	63.79 279	79.43 231	56.59 203	45.57 328	36.92 305	71.29 305	65.25 316

UnsupCasMVSNet_bld		50.01 301	51.03 303	46.95 312	58.61 332	32.64 330	48.31 328	53.27 313	34.27 324	60.47 297	71.53 304	41.40 276	47.07 325	30.68 327	60.78 335	61.13 329

PMMVS		44.69 313	43.95 320	46.92 313	50.05 353	53.47 184	48.08 331	42.40 339	22.36 351	44.01 348	53.05 346	42.60 272	45.49 329	31.69 324	61.36 334	41.79 346

CHOSEN 280x420		41.62 318	39.89 323	46.80 314	61.81 314	51.59 190	33.56 348	35.74 351	27.48 345	37.64 353	53.53 345	23.24 352	42.09 342	27.39 339	58.64 340	46.72 343

WTY-MVS		49.39 302	50.31 305	46.62 315	61.22 318	32.00 333	46.61 335	49.77 323	33.87 326	54.12 323	69.55 318	41.96 274	45.40 330	31.28 326	64.42 329	62.47 326

tpmrst		50.15 300	51.38 299	46.45 316	56.05 341	24.77 348	64.40 265	49.98 322	36.14 314	53.32 326	69.59 317	35.16 303	48.69 322	39.24 287	58.51 341	65.89 312

test0.0.03 1		47.72 306	48.31 308	45.93 317	55.53 345	29.39 338	46.40 336	41.21 346	43.41 281	55.81 317	67.65 328	29.22 337	43.77 339	25.73 343	69.87 314	64.62 319

TESTMET0.1,1		45.17 311	44.93 316	45.89 318	56.02 342	38.31 294	53.18 319	41.94 343	27.85 344	44.86 345	56.47 344	17.93 358	41.50 345	38.08 296	68.06 321	57.85 333

testgi		54.00 288	56.86 276	45.45 319	58.20 334	25.81 347	49.05 326	49.50 324	45.43 265	67.84 257	81.17 211	51.81 225	43.20 340	29.30 334	79.41 260	67.34 305

sss		47.59 307	48.32 307	45.40 320	56.73 340	33.96 325	45.17 338	48.51 327	32.11 336	52.37 328	65.79 334	40.39 285	41.91 344	31.85 323	61.97 333	60.35 330

tpm		50.60 298	52.42 297	45.14 321	65.18 297	26.29 345	60.30 294	43.50 335	37.41 309	57.01 309	79.09 239	30.20 334	42.32 341	32.77 322	66.36 325	66.81 309

EMVS		44.61 315	44.45 319	45.10 322	48.91 354	43.00 261	37.92 345	41.10 347	46.75 258	38.00 352	48.43 350	26.42 341	46.27 326	37.11 304	75.38 287	46.03 344

E-PMN		45.17 311	45.36 314	44.60 323	50.07 352	42.75 263	38.66 344	42.29 341	46.39 260	39.55 350	51.15 348	26.00 343	45.37 331	37.68 298	76.41 277	45.69 345

new-patchmatchnet		52.89 291	55.76 284	44.26 324	59.94 326	6.31 357	37.36 347	50.76 321	41.10 290	64.28 274	79.82 226	44.77 257	48.43 323	36.24 308	87.61 156	78.03 225

PatchT		53.35 289	56.47 279	43.99 325	64.19 304	17.46 354	59.15 299	43.10 336	52.11 211	54.74 320	86.95 124	29.97 335	49.98 321	43.62 265	74.40 292	64.53 321

EPMVS		45.74 309	46.53 312	43.39 326	54.14 351	22.33 351	55.02 315	35.00 352	34.69 322	51.09 331	70.20 313	25.92 344	42.04 343	37.19 302	55.50 346	65.78 313

PVSNet_0	36.71 22	41.12 319	40.78 322	42.14 327	59.97 325	40.13 283	40.97 340	42.24 342	30.81 341	44.86 345	49.41 349	40.70 283	45.12 332	23.15 347	34.96 351	41.16 347

Patchmatch-test		47.93 305	49.96 306	41.84 328	57.42 337	24.26 349	48.75 327	41.49 344	39.30 299	56.79 311	73.48 286	30.48 331	33.87 349	29.29 335	72.61 299	67.39 303

ADS-MVSNet		44.62 314	45.58 313	41.73 329	55.90 343	20.83 352	47.34 333	39.94 348	31.41 339	50.48 333	72.06 299	31.23 323	39.31 346	25.93 341	55.93 344	65.07 317

dp		44.09 316	44.88 317	41.72 330	58.53 333	23.18 350	54.70 316	42.38 340	34.80 320	44.25 347	65.61 335	24.48 349	44.80 333	29.77 332	49.42 349	57.18 335

MVS-HIRNet		45.53 310	47.29 310	40.24 331	62.29 312	26.82 344	56.02 311	37.41 350	29.74 342	43.69 349	81.27 209	33.96 306	55.48 314	24.46 346	56.79 343	38.43 349

MVE	27.91 23	36.69 322	35.64 325	39.84 332	43.37 355	35.85 314	19.49 350	24.61 356	24.68 349	39.05 351	62.63 339	38.67 293	27.10 353	21.04 349	47.25 350	56.56 336
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed		43.18 317	44.66 318	38.75 333	54.75 348	28.88 341	57.06 308	27.42 355	13.47 352	47.27 341	77.67 252	38.83 291	39.29 347	25.32 345	60.12 337	48.08 341

new_pmnet		37.55 321	39.80 324	30.79 334	56.83 338	16.46 355	39.35 343	30.65 353	25.59 348	45.26 343	61.60 340	24.54 348	28.02 352	21.60 348	52.80 348	47.90 342

PMMVS2		37.74 320	40.87 321	28.36 335	42.41 356	5.35 358	24.61 349	27.75 354	32.15 334	47.85 339	70.27 312	35.85 302	29.51 351	19.08 351	67.85 322	50.22 340

DeepMVS_CX				11.83 336	15.51 357	13.86 356		11.25 359	5.76 353	20.85 355	26.46 351	17.06 359	9.22 354	9.69 353	13.82 353	12.42 350

tmp_tt		11.98 324	14.73 327	3.72 337	2.28 358	4.62 359	19.44 351	14.50 358	0.47 354	21.55 354	9.58 353	25.78 345	4.57 355	11.61 352	27.37 352	1.96 351

testmvs		4.06 328	5.28 331	0.41 338	0.64 360	0.16 361	42.54 339	0.31 361	0.26 356	0.50 357	1.40 357	0.77 361	0.17 356	0.56 354	0.55 355	0.90 352

test123		4.43 327	5.78 330	0.39 339	0.97 359	0.28 360	46.33 337	0.45 360	0.31 355	0.62 356	1.50 356	0.61 362	0.11 357	0.56 354	0.63 354	0.77 353

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		17.71 323	23.62 326	0.00 340	0.00 361	0.00 362	0.00 352	70.17 240	0.00 357	0.00 358	74.25 280	68.16 99	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		5.20 326	6.93 329	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	62.39 145	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		5.62 325	7.50 328	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	67.46 329	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ZD-MVS					83.91 92	69.36 71		81.09 115	58.91 138	82.73 84	89.11 93	75.77 37	86.63 13	72.73 59	92.93 73

RE-MVS-def			85.50 5		86.19 53	79.18 9	87.23 7	86.27 20	77.51 11	87.65 19	90.73 45	81.38 8		78.11 25	94.46 36	84.89 91

IU-MVS					86.12 57	60.90 134		80.38 128	45.49 264	81.31 98				75.64 41	94.39 41	84.65 98

test_241102_TWO								84.80 47	72.61 30	84.93 54	89.70 78	77.73 24	85.89 36	75.29 42	94.22 53	83.25 139

test_241102_ONE					86.12 57	61.06 130		84.72 51	72.64 29	87.38 25	89.47 81	77.48 25	85.74 40

9.14			80.22 59		80.68 130		80.35 71	87.69 11	59.90 127	83.00 78	88.20 109	74.57 51	81.75 113	73.75 53	93.78 59

save fliter					87.00 42	67.23 86	79.24 83	77.94 167	56.65 161

test_0728_THIRD									74.03 22	85.83 42	90.41 58	75.58 39	85.69 41	77.43 33	94.74 29	84.31 115

test0726					86.16 55	60.78 135	83.81 40	85.10 42	72.48 32	85.27 51	89.96 74	78.57 19

GSMVS																70.05 287

test_part2					85.90 61	66.44 91				84.61 60

sam_mvs1												31.41 321				70.05 287

sam_mvs												31.21 325

MTGPA								80.63 121

test_post1							66.63 238				2.08 354	30.66 330	59.33 308	40.34 283

test_post											1.99 355	30.91 328	54.76 316

patchmatchnet-post											68.99 320	31.32 322	69.38 265

MTMP							84.83 32	19.26 357

gm-plane-assit					62.51 311	33.91 326			37.25 310		62.71 338		72.74 234	38.70 290

test9_res														72.12 67	91.37 98	77.40 230

TEST9					85.47 66	69.32 72	76.42 115	78.69 152	53.73 197	76.97 148	86.74 133	66.84 110	81.10 122

test_8					85.09 72	67.89 81	76.26 120	78.66 154	54.00 192	76.89 153	86.72 135	66.60 113	80.89 132

agg_prior2														70.70 75	90.93 110	78.55 218

agg_prior					84.44 85	66.02 95		78.62 155		76.95 150			80.34 139

test_prior4						70.14 64	77.57 102

test_prior2							75.57 126		58.92 136	76.53 164	86.78 130	67.83 103		69.81 80	92.76 78

旧先验2							71.17 180		45.11 268	78.54 130			61.28 304	59.19 158

新几何2							71.33 176

旧先验1					84.55 82	60.36 140		63.69 267			87.05 123	54.65 211			83.34 217	69.66 291

无先验							74.82 134	70.94 234	47.75 254				76.85 199	54.47 196		72.09 271

原ACMM2							74.78 138

test222					87.30 40	69.15 75	67.85 219	59.59 286	41.06 291	73.05 207	85.72 164	48.03 245			80.65 246	66.92 306

testdata2													67.30 279	48.34 239

segment_acmp												68.30 98

testdata1							68.34 215		57.24 153

plane_prior7					85.18 69	66.21 93

plane_prior6					84.18 89	65.31 100						60.83 163

plane_prior5								85.49 32					86.15 26	71.09 72	90.94 108	84.82 93

plane_prior4											89.11 93

plane_prior3						65.67 97			63.82 98	78.23 133

plane_prior2							82.74 52		65.45 77

plane_prior1					84.46 84

plane_prior						65.18 101	80.06 77		61.88 115						89.91 131

n2								0.00 362

nn								0.00 362

door-mid								55.02 305

test11								82.71 89

door								52.91 315

HQP5-MVS						58.80 154

HQP-NCC					82.37 109		77.32 106		59.08 132	71.58 221

ACMP_Plane					82.37 109		77.32 106		59.08 132	71.58 221

BP-MVS														67.38 101

HQP4-MVS										71.59 220			85.31 50			83.74 126

HQP3-MVS								84.12 70							89.16 141

HQP2-MVS												58.09 187

NP-MVS					83.34 99	63.07 119					85.97 160

MDTV_nov1_ep13_2view						18.41 353	53.74 318		31.57 338	44.89 344		29.90 336		32.93 321		71.48 275

MDTV_nov1_ep13			54.05 290		65.54 294	29.30 339	59.00 301	55.22 303	35.96 316	52.44 327	75.98 262	30.77 329	59.62 307	38.21 294	73.33 297

ACMMP++_ref															89.47 139

ACMMP++															91.96 87

Test By Simon												62.56 141

High-resolution multi-view benchmark