High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		86.90 1	88.67 1	81.57 20	91.50 1	63.30 105	84.80 25	87.77 7	86.18 1	96.26 2	96.06 2	90.32 1	84.49 50	68.08 83	97.05 3	96.93 1

PMVS	70.70 6	81.70 29	83.15 27	77.36 70	90.35 6	82.82 3	82.15 42	79.22 130	74.08 17	87.16 27	91.97 19	84.80 2	76.97 181	64.98 115	93.61 52	72.28 254
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LPG-MVS_test		83.47 15	84.33 10	80.90 32	87.00 37	70.41 56	82.04 44	86.35 12	69.77 40	87.75 18	91.13 36	81.83 3	86.20 19	77.13 27	95.96 7	86.08 77

LGP-MVS_train				80.90 32	87.00 37	70.41 56		86.35 12	69.77 40	87.75 18	91.13 36	81.83 3	86.20 19	77.13 27	95.96 7	86.08 77

ACMP	69.50 8	82.64 23	83.38 23	80.40 37	86.50 42	69.44 63	82.30 41	86.08 16	66.80 52	86.70 32	89.99 70	81.64 5	85.95 26	74.35 36	96.11 5	85.81 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
abl_6		84.92 3	85.70 3	82.57 14	86.72 40	79.27 8	87.56 5	86.08 16	77.48 9	88.12 16	91.53 30	81.18 6	84.31 55	78.12 22	94.47 35	84.15 113

ACMM	69.25 9	82.11 28	83.31 24	78.49 58	88.17 34	73.96 34	83.11 37	84.52 37	66.40 56	87.45 23	89.16 85	81.02 7	80.52 130	74.27 37	95.73 9	80.98 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		77.74 66	80.26 47	70.19 169	83.05 84	43.39 238	75.86 113	76.74 169	75.91 12	85.92 43	96.14 1	80.85 8	75.59 196	53.58 189	94.27 42	91.58 13

ACMH+	66.64 10	81.20 33	82.48 34	77.35 71	81.16 109	62.39 109	80.51 52	87.80 5	73.02 22	87.57 21	91.08 38	80.28 9	82.44 79	64.82 116	96.10 6	87.21 70

APD-MVS_3200maxsize		83.57 12	84.33 10	81.31 26	82.83 89	73.53 40	85.50 21	87.45 8	74.11 16	86.45 35	90.52 55	80.02 10	84.48 51	77.73 24	94.34 40	85.93 81

COLMAP_ROB	72.78 3	83.75 10	84.11 13	82.68 11	82.97 87	74.39 32	87.18 7	88.18 4	78.98 5	86.11 41	91.47 32	79.70 11	85.76 31	66.91 99	95.46 13	87.89 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 29	81.19 6	88.84 2	90.72 1	78.27 7	87.95 17	92.53 13	79.37 12	84.79 47	74.51 35	96.15 4	92.88 9

HPM-MVS		84.12 7	84.63 7	82.60 12	88.21 33	74.40 31	85.24 22	87.21 9	70.69 35	85.14 53	90.42 59	78.99 13	86.62 12	80.83 6	94.93 25	86.79 72
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		84.22 5	84.84 6	82.35 17	89.23 22	76.66 22	87.65 4	85.89 18	71.03 32	85.85 45	90.58 51	78.77 14	85.78 30	79.37 15	95.17 18	84.62 99
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
pcd1.5k->3k		35.00 328	36.93 328	29.21 339	84.62 66	0.00 361	0.00 352	78.90 138	0.00 356	0.00 357	0.00 358	78.26 15	0.00 359	0.00 356	90.55 101	87.62 65

HPM-MVS_fast		84.59 4	85.10 4	83.06 4	88.60 30	75.83 23	86.27 19	86.89 11	73.69 18	86.17 39	91.70 25	78.23 16	85.20 40	79.45 12	94.91 26	88.15 61

APDe-MVS		82.88 22	84.14 12	79.08 50	84.80 63	66.72 78	86.54 16	85.11 26	72.00 28	86.65 33	91.75 24	78.20 17	87.04 8	77.93 23	94.32 41	83.47 124

CP-MVS		84.12 7	84.55 8	82.80 9	89.42 18	79.74 7	88.19 3	84.43 38	71.96 29	84.70 60	90.56 52	77.12 18	86.18 21	79.24 17	95.36 14	82.49 147

HFP-MVS		83.39 16	84.03 14	81.48 22	89.25 20	75.69 24	87.01 11	84.27 41	70.23 36	84.47 64	90.43 56	76.79 19	85.94 27	79.58 10	94.23 45	82.82 137

#test#		82.40 25	82.71 32	81.48 22	89.25 20	75.69 24	84.47 27	84.27 41	64.45 76	84.47 64	90.43 56	76.79 19	85.94 27	76.01 31	94.23 45	82.82 137

mPP-MVS		84.01 9	84.39 9	82.88 5	90.65 4	81.38 5	87.08 9	82.79 66	72.41 25	85.11 55	90.85 44	76.65 21	84.89 44	79.30 16	94.63 32	82.35 149

MP-MVS-pluss		82.54 24	83.46 22	79.76 41	88.88 28	68.44 71	81.57 47	86.33 14	63.17 93	85.38 52	91.26 35	76.33 22	84.67 49	83.30 1	94.96 24	86.17 76
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		80.99 37	81.63 40	79.07 51	86.86 39	69.39 64	79.41 66	84.00 50	65.64 60	85.54 50	89.28 78	76.32 23	83.47 65	74.03 38	93.57 53	84.35 110

ACMH	63.62 14	77.50 69	80.11 48	69.68 176	79.61 118	56.28 145	78.81 69	83.62 54	63.41 92	87.14 28	90.23 67	76.11 24	73.32 211	67.58 91	94.44 38	79.44 197
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		78.93 55	78.67 60	79.72 43	84.81 62	73.93 35	80.65 51	76.50 170	51.98 207	87.40 24	91.86 21	76.09 25	78.53 158	68.58 78	90.20 105	86.69 74

APD-MVS		81.13 34	81.73 38	79.36 48	84.47 69	70.53 55	83.85 31	83.70 52	69.43 42	83.67 71	88.96 93	75.89 26	86.41 14	72.62 45	92.95 59	81.14 170
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		83.07 19	83.25 26	82.54 15	89.57 14	77.21 20	82.04 44	85.40 23	67.96 47	84.91 58	90.88 42	75.59 27	86.57 13	78.16 21	94.71 30	83.82 117

SteuartSystems-ACMMP		83.07 19	83.64 19	81.35 25	85.14 57	71.00 50	85.53 20	84.78 32	70.91 33	85.64 46	90.41 60	75.55 28	87.69 3	79.75 7	95.08 21	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_Plus		82.33 26	83.28 25	79.46 46	89.28 19	69.09 69	83.62 32	84.98 27	64.77 73	83.97 68	91.02 39	75.53 29	85.93 29	82.00 2	94.36 39	83.35 130

region2R		83.54 13	83.86 17	82.58 13	89.82 10	77.53 16	87.06 10	84.23 44	70.19 38	83.86 69	90.72 49	75.20 30	86.27 18	79.41 14	94.25 44	83.95 116

test_part1								84.94 30				75.17 31			93.83 49	82.50 145

ESAPD		81.57 30	82.55 33	78.63 56	85.90 46	66.44 80	83.39 34	84.94 30	73.27 20	84.61 61	89.25 81	75.17 31	86.96 10	72.56 46	93.83 49	82.50 145

ACMMPR		83.62 11	83.93 15	82.69 10	89.78 11	77.51 18	87.01 11	84.19 45	70.23 36	84.49 63	90.67 50	75.15 33	86.37 15	79.58 10	94.26 43	84.18 112

test_0402		78.17 65	79.48 54	74.24 99	83.50 79	59.15 133	72.52 147	74.60 185	75.34 13	88.69 15	91.81 22	75.06 34	82.37 81	65.10 114	88.68 127	81.20 167

PS-CasMVS		80.41 41	82.86 30	73.07 128	89.93 7	39.21 265	77.15 91	81.28 91	79.74 4	90.87 6	92.73 11	75.03 35	84.93 43	63.83 122	95.19 17	95.07 3

PEN-MVS		80.46 40	82.91 28	73.11 127	89.83 9	39.02 268	77.06 93	82.61 69	80.04 3	90.60 8	92.85 9	74.93 36	85.21 39	63.15 125	95.15 19	95.09 2

zzz-MVS		83.01 21	83.63 20	81.13 29	91.16 2	78.16 12	82.72 40	80.63 107	72.08 26	84.93 56	90.79 45	74.65 37	84.42 52	80.98 4	94.75 28	80.82 177

MTAPA		83.19 17	83.87 16	81.13 29	91.16 2	78.16 12	84.87 24	80.63 107	72.08 26	84.93 56	90.79 45	74.65 37	84.42 52	80.98 4	94.75 28	80.82 177

MP-MVS		83.19 17	83.54 21	82.14 18	90.54 5	79.00 9	86.42 18	83.59 55	71.31 30	81.26 95	90.96 41	74.57 39	84.69 48	78.41 20	94.78 27	82.74 140

DTE-MVSNet		80.35 42	82.89 29	72.74 138	89.84 8	37.34 282	77.16 90	81.81 79	80.45 2	90.92 5	92.95 8	74.57 39	86.12 25	63.65 123	94.68 31	94.76 6

XVG-OURS-SEG-HR		79.62 47	79.99 49	78.49 58	86.46 43	74.79 30	77.15 91	85.39 24	66.73 53	80.39 110	88.85 95	74.43 41	78.33 168	74.73 34	85.79 165	82.35 149

SD-MVS		80.28 43	81.55 41	76.47 75	83.57 78	67.83 75	83.39 34	85.35 25	64.42 79	86.14 40	87.07 113	74.02 42	80.97 117	77.70 25	92.32 68	80.62 182

XVS		83.51 14	83.73 18	82.85 7	89.43 16	77.61 14	86.80 13	84.66 34	72.71 23	82.87 76	90.39 61	73.86 43	86.31 16	78.84 18	94.03 47	84.64 97

X-MVStestdata		76.81 73	74.79 98	82.85 7	89.43 16	77.61 14	86.80 13	84.66 34	72.71 23	82.87 76	9.95 352	73.86 43	86.31 16	78.84 18	94.03 47	84.64 97

jajsoiax		78.51 60	78.16 64	79.59 45	84.65 65	73.83 37	80.42 54	76.12 171	51.33 214	87.19 26	91.51 31	73.79 45	78.44 162	68.27 81	90.13 109	86.49 75

wuyk23d		61.97 231	66.25 199	49.12 305	58.19 331	60.77 120	66.32 232	52.97 299	55.93 155	90.62 7	86.91 116	73.07 46	35.98 347	20.63 346	91.63 73	50.62 338

wuykxyi23d		75.33 89	76.75 76	71.04 159	78.83 134	85.01 1	71.78 163	61.00 257	53.47 194	96.33 1	93.38 4	73.07 46	68.04 265	65.65 111	97.28 2	60.07 324

TranMVSNet+NR-MVSNet		76.13 78	77.66 68	71.56 156	84.61 67	42.57 245	70.98 178	78.29 150	68.67 45	83.04 75	89.26 79	72.99 48	80.75 126	55.58 174	95.47 12	91.35 15

pmmvs6		71.82 148	73.66 113	66.31 213	75.94 166	42.01 247	66.99 225	72.53 198	63.45 91	76.43 158	92.78 10	72.95 49	69.69 248	51.41 198	90.46 102	87.22 69

SMA-MVS		82.23 27	82.82 31	80.48 36	88.90 27	69.66 61	85.12 23	84.95 28	63.53 89	84.31 67	91.47 32	72.87 50	87.16 6	79.74 9	94.47 35	84.61 100

DeepC-MVS	72.44 4	81.00 36	80.83 44	81.50 21	86.70 41	70.03 60	82.06 43	87.00 10	59.89 121	80.91 104	90.53 53	72.19 51	88.56 1	73.67 40	94.52 34	85.92 82
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v52		78.96 53	79.79 52	76.46 76	73.03 226	54.90 150	78.48 73	83.48 56	64.43 77	91.19 4	91.54 28	72.08 52	81.11 110	76.45 29	87.47 142	93.38 7

V4		78.96 53	79.79 52	76.46 76	73.02 227	54.90 150	78.48 73	83.47 57	64.43 77	91.20 3	91.54 28	72.08 52	81.11 110	76.45 29	87.46 144	93.38 7

canonicalmvs		72.29 144	73.38 119	69.04 184	74.23 194	47.37 216	73.93 138	83.18 60	54.36 181	76.61 152	81.64 207	72.03 54	75.34 198	57.12 157	87.28 150	84.40 108

CPTT-MVS		81.51 32	81.76 37	80.76 34	89.20 23	78.75 10	86.48 17	82.03 75	68.80 43	80.92 103	88.52 97	72.00 55	82.39 80	74.80 32	93.04 58	81.14 170

DP-MVS		78.44 63	79.29 55	75.90 84	81.86 101	65.33 88	79.05 68	84.63 36	74.83 15	80.41 109	86.27 140	71.68 56	83.45 66	62.45 129	92.40 66	78.92 203

nrg030		74.87 102	75.99 87	71.52 157	74.90 175	49.88 180	74.10 137	82.58 70	54.55 179	83.50 73	89.21 83	71.51 57	75.74 194	61.24 133	92.34 67	88.94 46

OMC-MVS		79.41 50	78.79 58	81.28 27	80.62 111	70.71 54	80.91 50	84.76 33	62.54 98	81.77 85	86.65 130	71.46 58	83.53 64	67.95 89	92.44 65	89.60 35

anonymousdsp		78.60 58	77.80 67	81.00 31	78.01 142	74.34 33	80.09 59	76.12 171	50.51 227	89.19 12	90.88 42	71.45 59	77.78 176	73.38 41	90.60 100	90.90 27

LTVRE_ROB	75.46 1	84.22 5	84.98 5	81.94 19	84.82 61	75.40 26	91.60 1	87.80 5	73.52 19	88.90 13	93.06 7	71.39 60	81.53 92	81.53 3	92.15 69	88.91 47
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
RPSCF		75.76 83	74.37 104	79.93 40	74.81 177	77.53 16	77.53 85	79.30 129	59.44 123	78.88 124	89.80 73	71.26 61	73.09 213	57.45 154	80.89 235	89.17 40

MVS_111021_HR		72.98 129	72.97 133	72.99 130	80.82 110	65.47 87	68.81 201	72.77 195	57.67 137	75.76 162	82.38 196	71.01 62	77.17 179	61.38 132	86.15 161	76.32 224

AdaColmap		74.22 109	74.56 100	73.20 125	81.95 99	60.97 118	79.43 64	80.90 105	65.57 61	72.54 202	81.76 205	70.98 63	85.26 37	47.88 225	90.00 111	73.37 242

Regformer-2		75.32 90	74.47 102	77.88 65	74.22 195	66.65 79	72.77 144	77.54 160	68.47 46	80.44 108	72.08 293	70.60 64	80.97 117	70.08 68	84.02 195	86.01 80

AllTest		77.66 67	77.43 69	78.35 60	79.19 127	70.81 51	78.60 71	88.64 2	65.37 65	80.09 113	88.17 104	70.33 65	78.43 163	55.60 171	90.90 94	85.81 83

TestCases				78.35 60	79.19 127	70.81 51		88.64 2	65.37 65	80.09 113	88.17 104	70.33 65	78.43 163	55.60 171	90.90 94	85.81 83

ITE_SJBPF				80.35 38	76.94 155	73.60 38		80.48 111	66.87 50	83.64 72	86.18 143	70.25 67	79.90 140	61.12 135	88.95 124	87.56 67

CDPH-MVS		77.33 70	77.06 73	78.14 63	84.21 74	63.98 99	76.07 108	83.45 58	54.20 182	77.68 139	87.18 110	69.98 68	85.37 34	68.01 85	92.72 63	85.08 92

Effi-MVS+		72.10 145	72.28 144	71.58 155	74.21 197	50.33 173	74.72 132	82.73 67	62.62 97	70.77 221	76.83 248	69.96 69	80.97 117	60.20 137	78.43 260	83.45 126

UA-Net		81.56 31	82.28 35	79.40 47	88.91 26	69.16 67	84.67 26	80.01 122	75.34 13	79.80 115	94.91 3	69.79 70	80.25 134	72.63 44	94.46 37	88.78 51

CLD-MVS		72.88 134	72.36 142	74.43 96	77.03 153	54.30 156	68.77 204	83.43 59	52.12 204	76.79 150	74.44 277	69.54 71	83.91 57	55.88 170	93.25 57	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Regformer-1		74.28 108	73.63 114	76.21 82	74.22 195	64.12 98	72.77 144	75.46 179	66.86 51	79.27 120	72.08 293	69.29 72	78.74 154	68.73 77	84.02 195	85.77 86

LS3D		80.99 37	80.85 43	81.41 24	78.37 137	71.37 46	87.45 6	85.87 19	77.48 9	81.98 83	89.95 71	69.14 73	85.26 37	66.15 106	91.24 83	87.61 66

XVG-ACMP-BASELINE		80.54 39	81.06 42	78.98 52	87.01 36	72.91 41	80.23 58	85.56 20	66.56 55	85.64 46	89.57 75	69.12 74	80.55 129	72.51 48	93.37 54	83.48 123

Regformer-4		74.64 104	73.67 112	77.55 68	74.74 179	64.49 96	72.91 141	75.42 180	67.45 48	80.24 112	72.07 296	68.98 75	80.19 137	70.29 64	80.91 233	87.98 62

MVS_111021_LR		72.10 145	71.82 149	72.95 132	79.53 120	73.90 36	70.45 183	66.64 234	56.87 147	76.81 149	81.76 205	68.78 76	71.76 236	61.81 130	83.74 199	73.18 244

Fast-Effi-MVS+		68.81 180	68.30 186	70.35 165	74.66 184	48.61 190	66.06 238	78.32 148	50.62 226	71.48 215	75.54 263	68.75 77	79.59 144	50.55 205	78.73 257	82.86 136

DeepPCF-MVS	71.07 5	78.48 62	77.14 72	82.52 16	84.39 73	77.04 21	76.35 101	84.05 48	56.66 151	80.27 111	85.31 155	68.56 78	87.03 9	67.39 94	91.26 82	83.50 122

CP-MVSNet		79.48 49	81.65 39	72.98 131	89.66 13	39.06 267	76.76 95	80.46 112	78.91 6	90.32 9	91.70 25	68.49 79	84.89 44	63.40 124	95.12 20	95.01 4

LCM-MVSNet-Re		69.10 174	71.57 154	61.70 248	70.37 250	34.30 304	61.45 281	79.62 125	56.81 148	89.59 10	88.16 106	68.44 80	72.94 214	42.30 259	87.33 148	77.85 215

CNVR-MVS		78.49 61	78.59 61	78.16 62	85.86 50	67.40 77	78.12 81	81.50 83	63.92 83	77.51 140	86.56 134	68.43 81	84.82 46	73.83 39	91.61 74	82.26 152

segment_acmp												68.30 82

cdsmvs_eth3d_5k		17.71 330	23.62 331	0.00 346	0.00 360	0.00 361	0.00 352	70.17 222	0.00 356	0.00 357	74.25 280	68.16 83	0.00 359	0.00 356	0.00 357	0.00 357

WR-MVS_H		80.22 44	82.17 36	74.39 97	89.46 15	42.69 244	78.24 78	82.24 72	78.21 8	89.57 11	92.10 18	68.05 84	85.59 32	66.04 108	95.62 11	94.88 5

test_djsdf		78.88 56	78.27 63	80.70 35	81.42 105	71.24 48	83.98 29	75.72 175	52.27 202	87.37 25	92.25 16	68.04 85	80.56 127	72.28 52	91.15 85	90.32 33

v7n		79.37 51	80.41 46	76.28 79	78.67 136	55.81 147	79.22 67	82.51 71	70.72 34	87.54 22	92.44 14	68.00 86	81.34 102	72.84 43	91.72 71	91.69 12

test_prior3		76.71 74	77.19 71	75.27 91	82.15 97	59.85 126	75.57 116	84.33 39	58.92 128	76.53 155	86.78 121	67.83 87	83.39 67	69.81 70	92.76 61	82.58 142

test_prior2							75.57 116		58.92 128	76.53 155	86.78 121	67.83 87		69.81 70	92.76 61

NCCC		78.25 64	78.04 65	78.89 53	85.61 52	69.45 62	79.80 63	80.99 104	65.77 59	75.55 165	86.25 142	67.42 89	85.42 33	70.10 67	90.88 96	81.81 161

v748		76.93 72	77.95 66	73.87 104	73.94 200	52.44 166	75.90 111	79.98 123	65.34 67	86.97 30	91.77 23	67.40 90	78.40 165	70.23 65	90.01 110	90.76 31

Regformer-3		72.86 135	72.28 144	74.62 94	74.74 179	60.18 123	72.91 141	71.76 203	64.74 74	78.42 129	72.07 296	67.00 91	76.28 189	67.97 88	80.91 233	87.39 68

TAPA-MVS	65.27 12	75.16 93	74.29 106	77.77 67	74.86 176	68.08 72	77.89 82	84.04 49	55.15 165	76.19 160	83.39 178	66.91 92	80.11 138	60.04 140	90.14 108	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TEST9					85.47 53	69.32 65	76.42 99	78.69 141	53.73 191	76.97 143	86.74 124	66.84 93	81.10 112

XVG-OURS		79.51 48	79.82 50	78.58 57	86.11 45	74.96 29	76.33 103	84.95 28	66.89 49	82.75 78	88.99 91	66.82 94	78.37 167	74.80 32	90.76 98	82.40 148

train_agg		76.38 75	76.55 77	75.86 85	85.47 53	69.32 65	76.42 99	78.69 141	54.00 186	76.97 143	86.74 124	66.60 95	81.10 112	72.50 49	91.56 75	77.15 218

test_8					85.09 58	67.89 74	76.26 104	78.66 143	54.00 186	76.89 148	86.72 126	66.60 95	80.89 123

agg_prior3		76.32 76	76.33 82	76.28 79	85.86 50	70.13 59	76.50 97	78.26 151	53.41 195	75.78 161	86.49 136	66.58 97	81.57 91	72.50 49	91.56 75	77.15 218

agg_prior1		75.89 80	76.41 80	74.31 98	84.44 71	66.02 84	76.12 107	78.62 144	54.40 180	76.95 145	86.85 118	66.44 98	80.34 132	72.45 51	91.42 79	76.57 223

EI-MVSNet-Vis-set		72.78 136	71.87 147	75.54 88	74.77 178	59.02 134	72.24 149	71.56 206	63.92 83	78.59 125	71.59 303	66.22 99	78.60 156	67.58 91	80.32 241	89.00 43

EI-MVSNet-UG-set		72.63 138	71.68 151	75.47 89	74.67 182	58.64 138	72.02 154	71.50 207	63.53 89	78.58 127	71.39 306	65.98 100	78.53 158	67.30 97	80.18 242	89.23 38

TransMVSNet (Re)		69.62 166	71.63 152	63.57 230	76.51 158	35.93 291	65.75 242	71.29 211	61.05 110	75.02 170	89.90 72	65.88 101	70.41 246	49.79 209	89.48 117	84.38 109

DeepC-MVS_fast	69.89 7	77.17 71	76.33 82	79.70 44	83.90 77	67.94 73	80.06 61	83.75 51	56.73 150	74.88 173	85.32 154	65.54 102	87.79 2	65.61 112	91.14 86	83.35 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		79.05 52	78.81 57	79.74 42	88.94 25	67.52 76	86.61 15	81.38 90	51.71 209	77.15 142	91.42 34	65.49 103	87.20 5	79.44 13	87.17 153	84.51 105

HPM-MVS++		79.89 45	79.80 51	80.18 39	89.02 24	78.44 11	83.49 33	80.18 119	64.71 75	78.11 135	88.39 100	65.46 104	83.14 70	77.64 26	91.20 84	78.94 202

Fast-Effi-MVS+-dtu		70.00 163	68.74 183	73.77 107	73.47 208	64.53 95	71.36 172	78.14 154	55.81 156	68.84 237	74.71 274	65.36 105	75.75 193	52.00 194	79.00 254	81.03 172

MCST-MVS		73.42 119	73.34 121	73.63 112	81.28 107	59.17 132	74.80 130	83.13 62	45.50 261	72.84 195	83.78 176	65.15 106	80.99 116	64.54 117	89.09 123	80.73 180

PCF-MVS	63.80 13	72.70 137	71.69 150	75.72 86	78.10 140	60.01 125	73.04 140	81.50 83	45.34 264	79.66 116	84.35 169	65.15 106	82.65 77	48.70 217	89.38 119	84.50 106
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test12				76.51 73	82.28 95	60.94 119		81.64 82		73.60 187		64.88 108	85.19 41		90.42 103	83.38 127

Effi-MVS+-dtu		75.43 87	72.28 144	84.91 2	77.05 151	83.58 2	78.47 75	77.70 158	57.68 135	74.89 172	78.13 241	64.80 109	84.26 56	56.46 164	85.32 180	86.88 71

mvs-test1		73.81 113	70.69 163	83.18 3	77.05 151	81.39 4	75.39 121	77.70 158	57.68 135	71.19 218	74.72 273	64.80 109	83.66 61	56.46 164	81.19 231	84.50 106

VPA-MVSNet		68.71 182	70.37 164	63.72 229	76.13 163	38.06 276	64.10 258	71.48 208	56.60 152	74.10 183	88.31 102	64.78 111	69.72 247	47.69 227	90.15 107	83.37 129

F-COLMAP		75.29 91	73.99 109	79.18 49	81.73 102	71.90 43	81.86 46	82.98 63	59.86 122	72.27 204	84.00 173	64.56 112	83.07 72	51.48 197	87.19 152	82.56 144

DP-MVS Recon		73.57 117	72.69 138	76.23 81	82.85 88	63.39 103	74.32 135	82.96 64	57.75 134	70.35 227	81.98 201	64.34 113	84.41 54	49.69 210	89.95 112	80.89 175

114514_t		73.40 120	73.33 122	73.64 111	84.15 76	57.11 142	78.20 79	80.02 121	43.76 274	72.55 201	86.07 149	64.00 114	83.35 69	60.14 139	91.03 89	80.45 185

pm-mvs1		68.40 184	69.85 167	64.04 226	73.10 222	39.94 262	64.61 254	70.50 219	55.52 160	73.97 185	89.33 77	63.91 115	68.38 262	49.68 211	88.02 136	83.81 118

UniMVSNet_NR-MVSNet		74.90 100	75.65 89	72.64 140	83.04 85	45.79 230	69.26 196	78.81 139	66.66 54	81.74 87	86.88 117	63.26 116	81.07 114	56.21 167	94.98 22	91.05 21

MSLP-MVS++		74.48 107	75.78 88	70.59 163	84.66 64	62.40 108	78.65 70	84.24 43	60.55 117	77.71 138	81.98 201	63.12 117	77.64 177	62.95 126	88.14 134	71.73 259

UniMVSNet (Re)		75.00 97	75.48 93	73.56 113	83.14 83	47.92 202	70.41 184	81.04 102	63.67 86	79.54 117	86.37 139	62.83 118	81.82 88	57.10 158	95.25 16	90.94 26

MIMVSNet1		66.57 197	69.23 172	58.59 272	81.26 108	37.73 279	64.06 259	57.62 269	57.02 146	78.40 130	90.75 47	62.65 119	58.10 303	41.77 265	89.58 116	79.95 194

xiu_mvs_v2_base		64.43 210	63.96 208	65.85 217	77.72 147	51.32 170	63.63 262	72.31 201	45.06 268	61.70 276	69.66 312	62.56 120	73.93 210	49.06 215	73.91 285	72.31 253

Test By Simon												62.56 120

Vis-MVSNet		74.85 103	74.56 100	75.72 86	81.63 104	64.64 94	76.35 101	79.06 135	62.85 95	73.33 191	88.41 99	62.54 122	79.59 144	63.94 121	82.92 205	82.94 134
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
NR-MVSNet		73.62 115	74.05 108	72.33 149	83.50 79	43.71 237	65.65 243	77.32 164	64.32 80	75.59 164	87.08 111	62.45 123	81.34 102	54.90 177	95.63 10	91.93 10

pcd_1.5k_mvsjas		5.20 333	6.93 334	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	62.39 124	0.00 359	0.00 356	0.00 357	0.00 357

PS-MVSNAJss		77.54 68	77.35 70	78.13 64	84.88 60	66.37 82	78.55 72	79.59 127	53.48 193	86.29 38	92.43 15	62.39 124	80.25 134	67.90 90	90.61 99	87.77 64

PS-MVSNAJ		64.27 213	63.73 211	65.90 216	77.82 145	51.42 169	63.33 265	72.33 200	45.09 267	61.60 277	68.04 322	62.39 124	73.95 209	49.07 214	73.87 286	72.34 252

PHI-MVS		74.92 98	74.36 105	76.61 72	76.40 159	62.32 110	80.38 55	83.15 61	54.16 184	73.23 193	80.75 212	62.19 127	83.86 58	68.02 84	90.92 93	83.65 121

MVS_Test		69.84 165	70.71 162	67.24 203	67.49 278	43.25 240	69.87 189	81.22 94	52.69 201	71.57 212	86.68 127	62.09 128	74.51 206	66.05 107	78.74 256	83.96 115

CSCG		74.12 110	74.39 103	73.33 121	79.35 122	61.66 115	77.45 86	81.98 76	62.47 100	79.06 123	80.19 218	61.83 129	78.79 153	59.83 142	87.35 147	79.54 196

DU-MVS		74.91 99	75.57 92	72.93 133	83.50 79	45.79 230	69.47 194	80.14 120	65.22 68	81.74 87	87.08 111	61.82 130	81.07 114	56.21 167	94.98 22	91.93 10

Baseline_NR-MVSNet		70.62 158	73.19 123	62.92 236	76.97 154	34.44 303	68.84 199	70.88 217	60.25 118	79.50 118	90.53 53	61.82 130	69.11 250	54.67 180	95.27 15	85.22 89

原ACMM1				73.90 103	85.90 46	65.15 92		81.67 81	50.97 224	74.25 181	86.16 145	61.60 132	83.54 63	56.75 159	91.08 88	73.00 245

PAPR		69.20 172	68.66 184	70.82 161	75.15 172	47.77 205	75.31 122	81.11 95	49.62 233	66.33 256	79.27 231	61.53 133	82.96 73	48.12 223	81.50 223	81.74 162

API-MVS		70.97 155	71.51 155	69.37 177	75.20 171	55.94 146	80.99 49	76.84 166	62.48 99	71.24 216	77.51 244	61.51 134	80.96 121	52.04 193	85.76 166	71.22 263

xiu_mvs_v1_base_debu		67.87 188	67.07 196	70.26 166	79.13 129	61.90 112	67.34 220	71.25 212	47.98 244	67.70 242	74.19 282	61.31 135	72.62 222	56.51 161	78.26 262	76.27 225

xiu_mvs_v1_base		67.87 188	67.07 196	70.26 166	79.13 129	61.90 112	67.34 220	71.25 212	47.98 244	67.70 242	74.19 282	61.31 135	72.62 222	56.51 161	78.26 262	76.27 225

xiu_mvs_v1_base_debi		67.87 188	67.07 196	70.26 166	79.13 129	61.90 112	67.34 220	71.25 212	47.98 244	67.70 242	74.19 282	61.31 135	72.62 222	56.51 161	78.26 262	76.27 225

CNLPA		73.44 118	73.03 131	74.66 93	78.27 138	75.29 27	75.99 109	78.49 146	65.39 64	75.67 163	83.22 188	61.23 138	66.77 277	53.70 188	85.33 179	81.92 160

MSDG		67.47 194	67.48 193	67.46 201	70.70 247	54.69 154	66.90 227	78.17 153	60.88 112	70.41 226	74.76 271	61.22 139	73.18 212	47.38 228	76.87 269	74.49 236

CANet		73.00 127	71.84 148	76.48 74	75.82 167	61.28 116	74.81 128	80.37 114	63.17 93	62.43 274	80.50 215	61.10 140	85.16 42	64.00 120	84.34 191	83.01 133

EG-PatchMatch MVS		70.70 157	70.88 160	70.16 170	82.64 91	58.80 135	71.48 166	73.64 189	54.98 169	76.55 153	81.77 204	61.10 140	78.94 149	54.87 178	80.84 236	72.74 249

HQP_MVS		78.77 57	78.78 59	78.72 54	85.18 55	65.18 90	82.74 38	85.49 21	65.45 62	78.23 133	89.11 87	60.83 142	86.15 22	71.09 56	90.94 90	84.82 94

plane_prior6					84.18 75	65.31 89						60.83 142

FMVSNet1		71.06 153	72.48 140	66.81 207	77.65 148	40.68 255	71.96 156	73.03 191	61.14 109	79.45 119	90.36 64	60.44 144	75.20 200	50.20 207	88.05 135	84.54 102

BH-untuned		69.39 169	69.46 168	69.18 182	77.96 143	56.88 143	68.47 210	77.53 161	56.77 149	77.79 137	79.63 226	60.30 145	80.20 136	46.04 236	80.65 238	70.47 268

MVS_0304		74.55 106	73.47 116	77.80 66	77.41 150	63.88 100	75.75 115	83.67 53	63.55 88	66.12 257	82.16 199	60.20 146	86.15 22	65.37 113	86.98 155	83.38 127

PAPM_NR		73.91 111	74.16 107	73.16 126	81.90 100	53.50 161	81.28 48	81.40 89	66.17 57	73.30 192	83.31 183	59.96 147	83.10 71	58.45 150	81.66 221	82.87 135

VDDNet		71.60 150	73.13 125	67.02 205	86.29 44	41.11 253	69.97 187	66.50 235	68.72 44	74.74 175	91.70 25	59.90 148	75.81 192	48.58 219	91.72 71	84.15 113

VDD-MVS		70.81 156	71.44 156	68.91 189	79.07 132	46.51 228	67.82 215	70.83 218	61.23 107	74.07 184	88.69 96	59.86 149	75.62 195	51.11 200	90.28 104	84.61 100

ANet_high		67.08 195	69.94 166	58.51 273	57.55 333	27.09 337	58.43 298	76.80 167	63.56 87	82.40 80	91.93 20	59.82 150	64.98 284	50.10 208	88.86 125	83.46 125

3Dnovator+	73.19 2	81.08 35	80.48 45	82.87 6	81.41 106	72.03 42	84.38 28	86.23 15	77.28 11	80.65 106	90.18 68	59.80 151	87.58 4	73.06 42	91.34 81	89.01 42

v13		76.23 77	77.02 74	73.86 106	74.61 186	48.80 185	76.91 94	81.10 98	62.66 96	87.02 29	91.01 40	59.76 152	81.41 97	71.29 55	88.78 126	91.38 14

v12		76.03 79	76.79 75	73.76 108	74.45 188	48.60 191	76.59 96	81.11 95	62.22 101	86.79 31	90.74 48	59.51 153	81.40 99	71.01 58	88.67 128	91.29 16

PLC	62.01 16	71.79 149	70.28 165	76.33 78	80.31 114	68.63 70	78.18 80	81.24 93	54.57 178	67.09 254	80.63 213	59.44 154	81.74 90	46.91 232	84.17 192	78.63 204
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TinyColmap		67.98 187	69.28 170	64.08 225	67.98 275	46.82 225	70.04 186	75.26 181	53.05 197	77.36 141	86.79 120	59.39 155	72.59 225	45.64 238	88.01 137	72.83 247

FC-MVSNet-test		73.32 122	74.78 99	68.93 187	79.21 126	36.57 284	71.82 162	79.54 128	57.63 139	82.57 79	90.38 62	59.38 156	78.99 148	57.91 152	94.56 33	91.23 17

v11		75.76 83	76.51 79	73.48 116	74.28 192	47.81 204	76.16 106	81.28 91	61.56 105	86.39 36	90.38 62	59.32 157	81.41 97	70.85 59	88.41 131	91.23 17

V42		71.06 153	70.83 161	71.72 154	67.25 279	47.14 220	65.94 239	80.35 115	51.35 213	83.40 74	83.23 186	59.25 158	78.80 152	65.91 109	80.81 237	89.23 38

V9		75.82 81	76.53 78	73.66 109	74.28 192	48.37 192	76.26 104	81.10 98	61.73 104	86.59 34	90.43 56	59.16 159	81.42 96	70.71 61	88.56 129	91.21 19

BH-RMVSNet		68.69 183	68.20 189	70.14 171	76.40 159	53.90 160	64.62 253	73.48 190	58.01 133	73.91 186	81.78 203	59.09 160	78.22 171	48.59 218	77.96 265	78.31 207

alignmvs		70.54 159	71.00 159	69.15 183	73.50 207	48.04 201	69.85 190	79.62 125	53.94 189	76.54 154	82.00 200	59.00 161	74.68 204	57.32 155	87.21 151	84.72 96

DELS-MVS		68.83 179	68.31 185	70.38 164	70.55 249	48.31 193	63.78 261	82.13 73	54.00 186	68.96 236	75.17 269	58.95 162	80.06 139	58.55 148	82.74 206	82.76 139
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
V14		75.58 86	76.26 84	73.55 114	74.10 199	48.13 197	75.91 110	81.07 101	61.19 108	86.34 37	90.11 69	58.80 163	81.40 99	70.40 63	88.43 130	91.12 20

VPNet		65.58 200	67.56 192	59.65 267	79.72 117	30.17 330	60.27 289	62.14 250	54.19 183	71.24 216	86.63 131	58.80 163	67.62 268	44.17 242	90.87 97	81.18 168

DI_MVS_plusplus_test		69.01 177	69.04 174	68.93 187	69.54 255	46.74 226	70.14 185	75.49 177	46.64 254	78.30 131	83.18 189	58.80 163	78.86 150	57.14 156	82.15 210	81.18 168

mvs_anonymous		65.08 203	65.49 202	63.83 228	63.79 298	37.60 280	66.52 231	69.82 223	43.44 278	73.46 190	86.08 148	58.79 166	71.75 237	51.90 195	75.63 275	82.15 153

test_normal		68.88 178	68.88 178	68.88 190	69.43 258	47.03 221	69.85 190	74.83 184	46.06 257	78.30 131	83.29 184	58.76 167	78.23 170	57.51 153	81.90 214	81.36 166

v10		75.69 85	76.20 85	74.16 100	74.44 190	48.69 187	75.84 114	82.93 65	59.02 127	85.92 43	89.17 84	58.56 168	82.74 76	70.73 60	89.14 122	91.05 21

v15		75.37 88	76.01 86	73.44 117	73.91 203	47.87 203	75.55 118	81.04 102	60.76 113	86.11 41	89.76 74	58.53 169	81.40 99	70.11 66	88.32 132	91.04 23

v7		73.59 116	73.69 111	73.28 123	74.42 191	48.68 188	72.74 146	81.98 76	54.76 175	82.07 82	85.05 160	58.53 169	82.22 85	67.99 86	85.66 169	88.95 45

v17		75.03 96	75.59 91	73.36 118	73.56 205	47.66 208	75.48 119	81.45 86	60.58 115	85.55 49	89.02 89	58.36 171	81.47 93	69.69 73	86.59 159	90.96 24

v16		74.89 101	75.41 95	73.35 119	73.54 206	47.62 209	75.47 120	81.45 86	60.58 115	85.46 51	88.97 92	58.27 172	81.47 93	69.66 74	85.25 181	90.95 25

FIs		72.56 142	73.80 110	68.84 191	78.74 135	37.74 278	71.02 177	79.83 124	56.12 153	80.88 105	89.45 76	58.18 173	78.28 169	56.63 160	93.36 55	90.51 32

EI-MVSNet		69.61 167	69.01 176	71.41 158	73.94 200	49.90 177	71.31 174	71.32 209	58.22 131	75.40 168	70.44 307	58.16 174	75.85 190	62.51 127	79.81 247	88.48 59

IterMVS-LS		73.01 126	73.12 126	72.66 139	73.79 204	49.90 177	71.63 165	78.44 147	58.22 131	80.51 107	86.63 131	58.15 175	79.62 142	62.51 127	88.20 133	88.48 59
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP2-MVS												58.09 176

HQP-MVS		75.24 92	75.01 97	75.94 83	82.37 92	58.80 135	77.32 87	84.12 46	59.08 124	71.58 209	85.96 151	58.09 176	85.30 36	67.38 95	89.16 120	83.73 120

v8		75.07 95	75.64 90	73.35 119	73.42 209	47.46 213	75.20 123	81.45 86	60.05 119	85.64 46	89.26 79	58.08 178	81.80 89	69.71 72	87.97 138	90.79 29

v1144		73.29 123	73.39 118	73.01 129	74.12 198	48.11 198	72.01 155	81.08 100	53.83 190	81.77 85	84.68 165	58.07 179	81.91 87	68.10 82	86.86 156	88.99 44

v144192		72.99 128	73.06 130	72.77 136	74.58 187	47.48 211	71.90 160	80.44 113	51.57 211	81.46 94	84.11 172	58.04 180	82.12 86	67.98 87	87.47 142	88.70 52

ab-mvs		64.11 214	65.13 205	61.05 255	71.99 240	38.03 277	67.59 216	68.79 226	49.08 235	65.32 261	86.26 141	58.02 181	66.85 275	39.33 274	79.79 249	78.27 208

v1neww		72.93 131	73.07 128	72.48 143	73.41 211	47.46 213	72.17 151	80.26 116	55.63 157	81.63 91	85.07 158	57.97 182	81.28 105	66.55 104	84.98 187	88.70 52

v7new		72.93 131	73.07 128	72.48 143	73.41 211	47.46 213	72.17 151	80.26 116	55.63 157	81.63 91	85.07 158	57.97 182	81.28 105	66.55 104	84.98 187	88.70 52

diffmvs		66.15 199	65.86 201	67.01 206	62.31 305	44.43 236	68.81 201	72.93 194	48.13 242	62.12 275	83.33 182	57.96 184	72.29 227	59.83 142	77.31 268	84.33 111

v6		72.93 131	73.08 127	72.48 143	73.42 209	47.47 212	72.17 151	80.25 118	55.63 157	81.65 90	85.04 161	57.95 185	81.28 105	66.56 103	85.01 186	88.70 52

v18		74.60 105	75.06 96	73.22 124	73.29 215	47.36 217	75.02 124	81.47 85	60.01 120	85.13 54	88.44 98	57.93 186	81.47 93	69.26 75	85.02 185	90.84 28

testing_2		72.01 147	72.36 142	70.95 160	70.79 244	48.70 186	72.81 143	78.09 155	48.79 237	84.46 66	89.15 86	57.90 187	78.55 157	61.55 131	87.74 139	85.61 87

Gipuma		69.55 168	72.83 134	59.70 266	63.63 300	53.97 158	80.08 60	75.93 173	64.24 81	73.49 189	88.93 94	57.89 188	62.46 291	59.75 144	91.55 77	62.67 318
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TSAR-MVS + GP.		73.08 124	71.60 153	77.54 69	78.99 133	70.73 53	74.96 125	69.38 224	60.73 114	74.39 180	78.44 238	57.72 189	82.78 75	60.16 138	89.60 115	79.11 201

WR-MVS		71.20 152	72.48 140	67.36 202	84.98 59	35.70 293	64.43 256	68.66 227	65.05 71	81.49 93	86.43 138	57.57 190	76.48 187	50.36 206	93.32 56	89.90 34

v1		72.60 139	72.73 135	72.19 150	73.12 221	47.01 222	71.48 166	79.10 134	55.01 166	81.24 96	84.92 164	57.46 191	80.90 122	66.59 100	85.67 167	88.68 56

v1141		72.59 141	72.73 135	72.19 150	73.10 222	47.00 223	71.48 166	79.11 132	55.01 166	81.23 97	84.94 163	57.45 192	80.89 123	66.58 101	85.65 170	88.68 56

divwei89l23v2f112		72.60 139	72.73 135	72.19 150	73.10 222	47.00 223	71.48 166	79.11 132	55.01 166	81.23 97	84.95 162	57.45 192	80.89 123	66.58 101	85.67 167	88.68 56

LF4IMVS		67.50 192	67.31 195	68.08 196	58.86 325	61.93 111	71.43 170	75.90 174	44.67 269	72.42 203	80.20 217	57.16 194	70.44 244	58.99 147	86.12 162	71.88 257

OurMVSNet-221017-0		78.57 59	78.53 62	78.67 55	80.48 112	64.16 97	80.24 57	82.06 74	61.89 102	88.77 14	93.32 5	57.15 195	82.60 78	70.08 68	92.80 60	89.25 37

v1192		73.40 120	73.42 117	73.32 122	74.65 185	48.67 189	72.21 150	81.73 80	52.76 200	81.85 84	84.56 167	57.12 196	82.24 84	68.58 78	87.33 148	89.06 41

HSP-MVS		79.69 46	79.17 56	81.27 28	89.70 12	77.46 19	87.16 8	80.58 110	64.94 72	81.05 100	88.38 101	57.10 197	87.10 7	79.75 7	83.87 197	79.24 199

tfpnnormal		66.48 198	67.93 190	62.16 246	73.40 213	36.65 283	63.45 263	64.99 242	55.97 154	72.82 196	87.80 109	57.06 198	69.10 251	48.31 222	87.54 141	80.72 181

MAR-MVS		67.72 191	66.16 200	72.40 147	74.45 188	64.99 93	74.87 126	77.50 162	48.67 238	65.78 260	68.58 321	57.01 199	77.79 175	46.68 234	81.92 213	74.42 237
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
XXY-MVS		55.19 276	57.40 265	48.56 308	64.45 296	34.84 302	51.54 315	53.59 295	38.99 295	63.79 268	79.43 228	56.59 200	45.57 323	36.92 294	71.29 296	65.25 307

v1921920		72.96 130	72.98 132	72.89 134	74.67 182	47.58 210	71.92 159	80.69 106	51.70 210	81.69 89	83.89 174	56.58 201	82.25 83	68.34 80	87.36 146	88.82 49

VNet		64.01 216	65.15 204	60.57 259	73.28 216	35.61 294	57.60 300	67.08 232	54.61 177	66.76 255	83.37 180	56.28 202	66.87 273	42.19 260	85.20 183	79.23 200

v1240		73.06 125	73.14 124	72.84 135	74.74 179	47.27 219	71.88 161	81.11 95	51.80 208	82.28 81	84.21 170	56.22 203	82.34 82	68.82 76	87.17 153	88.91 47

MG-MVS		70.47 160	71.34 157	67.85 198	79.26 124	40.42 260	74.67 134	75.15 183	58.41 130	68.74 238	88.14 107	56.08 204	83.69 60	59.90 141	81.71 220	79.43 198

v2v482		72.55 143	72.58 139	72.43 146	72.92 232	46.72 227	71.41 171	79.13 131	55.27 161	81.17 99	85.25 156	55.41 205	81.13 109	67.25 98	85.46 175	89.43 36

3Dnovator	65.95 11	71.50 151	71.22 158	72.34 148	73.16 217	63.09 106	78.37 76	78.32 148	57.67 137	72.22 206	84.61 166	54.77 206	78.47 160	60.82 136	81.07 232	75.45 229

v148		69.38 170	69.39 169	69.36 178	69.14 260	44.56 234	68.83 200	72.70 196	54.79 173	78.59 125	84.12 171	54.69 207	76.74 186	59.40 145	82.20 209	86.79 72

旧先验1					84.55 68	60.36 122		63.69 245			87.05 114	54.65 208			83.34 202	69.66 280

BH-w/o		64.81 205	64.29 207	66.36 212	76.08 165	54.71 153	65.61 244	75.23 182	50.10 230	71.05 220	71.86 302	54.33 209	79.02 147	38.20 285	76.14 272	65.36 306

ambc				70.10 172	77.74 146	50.21 175	74.28 136	77.93 157		79.26 121	88.29 103	54.11 210	79.77 141	64.43 118	91.10 87	80.30 187

QAPM		69.18 173	69.26 171	68.94 186	71.61 242	52.58 165	80.37 56	78.79 140	49.63 232	73.51 188	85.14 157	53.66 211	79.12 146	55.11 176	75.54 276	75.11 233

IS-MVSNet		75.10 94	75.42 94	74.15 101	79.23 125	48.05 200	79.43 64	78.04 156	70.09 39	79.17 122	88.02 108	53.04 212	83.60 62	58.05 151	93.76 51	90.79 29

1121		69.23 171	68.26 187	72.12 153	88.36 32	71.40 45	68.59 205	62.06 252	43.80 273	74.75 174	86.18 143	52.92 213	76.85 184	54.47 181	83.27 203	68.12 290

新几何1				69.99 174	88.37 31	71.34 47		62.08 251	43.85 272	74.99 171	86.11 147	52.85 214	70.57 243	50.99 201	83.23 204	68.05 291

OpenMVS	62.51 15	68.76 181	68.75 182	68.78 192	70.56 248	53.91 159	78.29 77	77.35 163	48.85 236	70.22 229	83.52 177	52.65 215	76.93 182	55.31 175	81.99 212	75.49 228

UGNet		70.20 161	69.05 173	73.65 110	76.24 161	63.64 101	75.87 112	72.53 198	61.48 106	60.93 286	86.14 146	52.37 216	77.12 180	50.67 203	85.21 182	80.17 193
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
Test4		69.04 176	68.95 177	69.32 181	69.52 256	48.10 199	70.69 182	78.25 152	45.90 258	80.99 101	82.24 197	51.91 217	78.11 174	58.46 149	82.58 208	81.74 162

PVSNet_BlendedMVS		65.38 201	64.30 206	68.61 193	69.81 252	49.36 181	65.60 245	78.96 136	45.50 261	59.98 291	78.61 237	51.82 218	78.20 172	44.30 240	84.11 193	78.27 208

PVSNet_Blended		62.90 223	61.64 230	66.69 210	69.81 252	49.36 181	61.23 284	78.96 136	42.04 284	59.98 291	68.86 319	51.82 218	78.20 172	44.30 240	77.77 267	72.52 250

testgi		54.00 283	56.86 269	45.45 316	58.20 330	25.81 342	49.05 319	49.50 315	45.43 263	67.84 241	81.17 210	51.81 220	43.20 336	29.30 326	79.41 252	67.34 296

EPNet		69.10 174	67.32 194	74.46 95	68.33 271	61.27 117	77.56 84	63.57 246	60.95 111	56.62 307	82.75 190	51.53 221	81.24 108	54.36 185	90.20 105	80.88 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		70.04 162	68.88 178	73.53 115	82.71 90	63.62 102	74.81 128	81.95 78	48.53 239	67.16 253	79.18 234	51.42 222	78.38 166	54.39 184	79.72 250	78.60 205

TR-MVS		64.59 206	63.54 213	67.73 200	75.75 169	50.83 172	63.39 264	70.29 221	49.33 234	71.55 213	74.55 275	50.94 223	78.46 161	40.43 272	75.69 274	73.89 240

MVS		60.62 244	59.97 242	62.58 243	68.13 273	47.28 218	68.59 205	73.96 188	32.19 329	59.94 293	68.86 319	50.48 224	77.64 177	41.85 263	75.74 273	62.83 316

SixPastTwentyTwo		75.77 82	76.34 81	74.06 102	81.69 103	54.84 152	76.47 98	75.49 177	64.10 82	87.73 20	92.24 17	50.45 225	81.30 104	67.41 93	91.46 78	86.04 79

PatchMatch-RL		58.68 257	57.72 262	61.57 249	76.21 162	73.59 39	61.83 277	49.00 317	47.30 252	61.08 281	68.97 316	50.16 226	59.01 301	36.06 299	68.84 311	52.10 337

K. test v3		73.67 114	73.61 115	73.87 104	79.78 116	55.62 148	74.69 133	62.04 254	66.16 58	84.76 59	93.23 6	49.47 227	80.97 117	65.66 110	86.67 158	85.02 93

EPP-MVSNet		73.86 112	73.38 119	75.31 90	78.19 139	53.35 163	80.45 53	77.32 164	65.11 70	76.47 157	86.80 119	49.47 227	83.77 59	53.89 186	92.72 63	88.81 50

cascas		64.59 206	62.77 224	70.05 173	75.27 170	50.02 176	61.79 278	71.61 204	42.46 282	63.68 269	68.89 318	49.33 229	80.35 131	47.82 226	84.05 194	79.78 195

MDA-MVSNet-bldmvs		62.34 230	61.73 227	64.16 223	61.64 309	49.90 177	48.11 323	57.24 275	53.31 196	80.95 102	79.39 229	49.00 230	61.55 295	45.92 237	80.05 244	81.03 172

testdata				64.13 224	85.87 49	63.34 104		61.80 255	47.83 247	76.42 159	86.60 133	48.83 231	62.31 293	54.46 183	81.26 230	66.74 301

GBi-Net		68.30 185	68.79 180	66.81 207	73.14 218	40.68 255	71.96 156	73.03 191	54.81 170	74.72 176	90.36 64	48.63 232	75.20 200	47.12 229	85.37 176	84.54 102

test1		68.30 185	68.79 180	66.81 207	73.14 218	40.68 255	71.96 156	73.03 191	54.81 170	74.72 176	90.36 64	48.63 232	75.20 200	47.12 229	85.37 176	84.54 102

FMVSNet2		67.48 193	68.21 188	65.29 218	73.14 218	38.94 269	68.81 201	71.21 215	54.81 170	76.73 151	86.48 137	48.63 232	74.60 205	47.98 224	86.11 163	82.35 149

test222					87.30 35	69.15 68	67.85 214	59.59 262	41.06 288	73.05 194	85.72 153	48.03 235			80.65 238	66.92 297

OpenMVS_ROB	54.93 17	63.23 219	63.28 214	63.07 235	69.81 252	45.34 232	68.52 208	67.14 231	43.74 275	70.61 225	79.22 232	47.90 236	72.66 221	48.75 216	73.84 287	71.21 264

lessismore_v0				72.75 137	79.60 119	56.83 144		57.37 272		83.80 70	89.01 90	47.45 237	78.74 154	64.39 119	86.49 160	82.69 141

TAMVS		65.31 202	63.75 210	69.97 175	82.23 96	59.76 128	66.78 228	63.37 247	45.20 265	69.79 231	79.37 230	47.42 238	72.17 228	34.48 305	85.15 184	77.99 214

PM-MVS		64.49 208	63.61 212	67.14 204	76.68 157	75.15 28	68.49 209	42.85 335	51.17 217	77.85 136	80.51 214	45.76 239	66.31 280	52.83 192	76.35 271	59.96 326

USDC		62.80 228	63.10 217	61.89 247	65.19 292	43.30 239	67.42 219	74.20 187	35.80 311	72.25 205	84.48 168	45.67 240	71.95 234	37.95 287	84.97 189	70.42 270

test20.03		55.74 274	57.51 264	50.42 298	59.89 319	32.09 320	50.63 316	49.01 316	50.11 229	65.07 263	83.23 186	45.61 241	48.11 318	30.22 321	83.82 198	71.07 267

Patchmatch-test1		57.81 265	58.04 261	57.13 279	70.17 251	41.07 254	65.19 250	53.38 297	43.34 281	61.00 284	71.94 300	45.20 242	62.69 290	41.81 264	70.31 302	67.63 293

1112_ss		59.48 249	58.99 248	60.96 257	77.84 144	42.39 246	61.42 282	68.45 228	37.96 301	59.93 294	67.46 324	45.11 243	65.07 283	40.89 269	71.81 294	75.41 230

new-patchmatchnet		52.89 289	55.76 276	44.26 322	59.94 318	6.31 356	37.36 346	50.76 312	41.10 287	64.28 265	79.82 223	44.77 244	48.43 317	36.24 297	87.61 140	78.03 212

jason		64.47 209	62.84 223	69.34 180	76.91 156	59.20 129	67.15 224	65.67 236	35.29 313	65.16 262	76.74 249	44.67 245	70.68 241	54.74 179	79.28 253	78.14 210
jason: jason.
IterMVS		63.12 220	62.48 226	65.02 219	66.34 286	52.86 164	63.81 260	62.25 249	46.57 255	71.51 214	80.40 216	44.60 246	66.82 276	51.38 199	75.47 277	75.38 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PAPM		61.79 234	60.37 240	66.05 214	76.09 164	41.87 248	69.30 195	76.79 168	40.64 291	53.80 321	79.62 227	44.38 247	82.92 74	29.64 325	73.11 289	73.36 243

HY-MVS	49.31 19	57.96 264	57.59 263	59.10 269	66.85 282	36.17 288	65.13 251	65.39 240	39.24 294	54.69 317	78.14 240	44.28 248	67.18 272	33.75 310	70.79 299	73.95 239

CANet_DTU		64.04 215	63.83 209	64.66 220	68.39 268	42.97 242	73.45 139	74.50 186	52.05 206	54.78 315	75.44 268	43.99 249	70.42 245	53.49 191	78.41 261	80.59 183

LFMVS		67.06 196	67.89 191	64.56 221	78.02 141	38.25 274	70.81 181	59.60 261	65.18 69	71.06 219	86.56 134	43.85 250	75.22 199	46.35 235	89.63 114	80.21 188

pmmvs-eth3d		64.41 211	63.27 215	67.82 199	75.81 168	60.18 123	69.49 193	62.05 253	38.81 296	74.13 182	82.23 198	43.76 251	68.65 260	42.53 258	80.63 240	74.63 235

1314		59.83 247	58.86 255	62.74 241	65.71 290	44.78 233	68.59 205	72.63 197	33.54 327	61.05 283	67.29 326	43.62 252	71.26 239	49.49 212	67.84 316	72.19 255

CDS-MVSNet		64.33 212	62.66 225	69.35 179	80.44 113	58.28 139	65.26 249	65.66 237	44.36 270	67.30 252	75.54 263	43.27 253	71.77 235	37.68 288	84.44 190	78.01 213
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		69.93 164	69.03 175	72.63 141	74.93 173	59.19 130	83.98 29	75.72 175	52.27 202	63.53 270	76.74 249	43.19 254	80.56 127	72.28 52	78.67 258	78.14 210

lupinMVS		63.36 217	61.49 233	68.97 185	74.93 173	59.19 130	65.80 241	64.52 244	34.68 318	63.53 270	74.25 280	43.19 254	70.62 242	53.88 187	78.67 258	77.10 220

Test_1112_low_res		58.78 256	58.69 257	59.04 270	79.41 121	38.13 275	57.62 299	66.98 233	34.74 316	59.62 295	77.56 243	42.92 256	63.65 287	38.66 280	70.73 300	75.35 232

PMMVS		44.69 315	43.95 320	46.92 310	50.05 352	53.47 162	48.08 324	42.40 337	22.36 350	44.01 346	53.05 345	42.60 257	45.49 324	31.69 315	61.36 329	41.79 346

Anonymous20231206		54.13 280	55.82 275	49.04 306	70.89 243	35.96 290	51.73 314	50.87 311	34.86 314	62.49 273	79.22 232	42.52 258	44.29 332	27.95 330	81.88 215	66.88 298

semantic-postprocess				72.49 142	73.34 214	58.20 140		65.55 239	48.10 243	76.91 147	82.64 191	42.25 259	78.84 151	61.20 134	77.89 266	80.44 186

WTY-MVS		49.39 300	50.31 301	46.62 312	61.22 311	32.00 321	46.61 327	49.77 314	33.87 321	54.12 320	69.55 314	41.96 260	45.40 325	31.28 317	64.42 323	62.47 319

UnsupCasMVSNet_eth		52.26 293	53.29 288	49.16 304	55.08 344	33.67 308	50.03 318	58.79 265	37.67 302	63.43 272	74.75 272	41.82 261	45.83 322	38.59 282	59.42 334	67.98 292

UnsupCasMVSNet_bld		50.01 299	51.03 299	46.95 309	58.61 327	32.64 317	48.31 321	53.27 298	34.27 319	60.47 287	71.53 304	41.40 262	47.07 320	30.68 318	60.78 330	61.13 322

MIMVSNet		54.39 279	56.12 274	49.20 303	72.57 233	30.91 329	59.98 290	48.43 319	41.66 286	55.94 310	83.86 175	41.19 263	50.42 312	26.05 332	75.38 279	66.27 302

CHOSEN 1792x2688		58.09 263	56.30 272	63.45 232	79.95 115	50.93 171	54.07 309	65.59 238	28.56 342	61.53 278	74.33 278	41.09 264	66.52 279	33.91 309	67.69 317	72.92 246

YYNet1		52.58 290	53.50 286	49.85 299	54.15 348	36.45 286	40.53 337	46.55 325	38.09 300	75.52 166	73.31 288	41.08 265	43.88 333	41.10 267	71.14 298	69.21 285

MDA-MVSNet_test_wron		52.57 291	53.49 287	49.81 300	54.24 347	36.47 285	40.48 338	46.58 324	38.13 299	75.47 167	73.32 287	41.05 266	43.85 334	40.98 268	71.20 297	69.10 287

PVSNet_0	36.71 22	41.12 321	40.78 323	42.14 325	59.97 317	40.13 261	40.97 336	42.24 340	30.81 340	44.86 342	49.41 349	40.70 267	45.12 327	23.15 341	34.96 350	41.16 347

Vis-MVSNet (Re-imp)		62.74 229	63.21 216	61.34 253	72.19 235	31.56 328	67.31 223	53.87 293	53.60 192	69.88 230	83.37 180	40.52 268	70.98 240	41.40 266	86.78 157	81.48 165

sss		47.59 306	48.32 305	45.40 317	56.73 337	33.96 305	45.17 331	48.51 318	32.11 333	52.37 324	65.79 328	40.39 269	41.91 341	31.85 314	61.97 328	60.35 323

pmmvs4		60.78 242	59.04 247	66.00 215	73.06 225	57.67 141	64.53 255	60.22 259	36.91 306	65.96 258	77.27 245	39.66 270	68.54 261	38.87 277	74.89 281	71.80 258

MVP-Stereo		61.56 235	59.22 245	68.58 194	79.28 123	60.44 121	69.20 197	71.57 205	43.58 277	56.42 308	78.37 239	39.57 271	76.46 188	34.86 304	60.16 331	68.86 288
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FPMVS		59.43 250	60.07 241	57.51 278	77.62 149	71.52 44	62.33 269	50.92 310	57.40 140	69.40 232	80.00 219	39.14 272	61.92 294	37.47 291	66.36 319	39.09 348

DSMNet-mixed		43.18 319	44.66 318	38.75 333	54.75 346	28.88 334	57.06 301	27.42 355	13.47 351	47.27 337	77.67 242	38.83 273	39.29 345	25.32 337	60.12 332	48.08 340

HyFIR lowres test		63.01 221	60.47 239	70.61 162	83.04 85	54.10 157	59.93 291	72.24 202	33.67 324	69.00 235	75.63 262	38.69 274	76.93 182	36.60 295	75.45 278	80.81 179

MVE	27.91 23	36.69 327	35.64 330	39.84 332	43.37 354	35.85 292	19.49 350	24.61 356	24.68 348	39.05 350	62.63 335	38.67 275	27.10 354	21.04 345	47.25 349	56.56 332
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		52.91 288	54.31 283	48.71 307	72.13 239	36.18 287	50.26 317	47.78 320	44.15 271	64.61 264	79.78 224	38.18 276	50.20 314	21.96 343	69.93 305	59.75 327

EPNet_dtu		58.93 255	58.52 258	60.16 265	67.91 276	47.70 207	69.97 187	58.02 266	49.73 231	47.28 336	73.02 291	38.14 277	62.34 292	36.57 296	85.99 164	70.43 269
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		52.49 292	52.58 291	52.21 296	54.99 345	32.38 318	55.45 306	53.84 294	32.15 331	55.49 313	74.81 270	38.08 278	57.37 304	34.02 308	74.40 283	66.88 298

N_pmnet		52.06 294	51.11 298	54.92 289	59.64 321	71.03 49	37.42 345	61.62 256	33.68 323	57.12 302	72.10 292	37.94 279	31.03 351	29.13 329	71.35 295	62.70 317

CMPMVS	48.73 20	61.54 236	60.89 237	63.52 231	61.08 312	51.55 168	68.07 213	68.00 230	33.88 320	65.87 259	81.25 209	37.91 280	67.71 266	49.32 213	82.60 207	71.31 262
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		65.00 204	65.16 203	64.52 222	69.47 257	37.56 281	66.63 229	70.38 220	51.55 212	74.72 176	83.27 185	37.89 281	74.44 207	47.12 229	85.37 176	81.57 164

GA-MVS		62.91 222	61.66 228	66.66 211	67.09 281	44.49 235	61.18 285	69.36 225	51.33 214	69.33 233	74.47 276	36.83 282	74.94 203	50.60 204	74.72 282	80.57 184

MS-PatchMatch		55.59 275	54.89 281	57.68 276	69.18 259	49.05 184	61.00 286	62.93 248	35.98 309	58.36 299	68.93 317	36.71 283	66.59 278	37.62 290	63.30 325	57.39 330

CVMVSNet		59.21 253	58.44 260	61.51 250	73.94 200	47.76 206	71.31 174	64.56 243	26.91 346	60.34 288	70.44 307	36.24 284	67.65 267	53.57 190	68.66 313	69.12 286

no-one		56.11 271	55.62 277	57.60 277	62.68 302	49.23 183	39.12 342	58.99 264	33.72 322	60.98 285	80.90 211	36.07 285	60.36 297	30.68 318	97.40 1	63.22 315

PMMVS2		37.74 324	40.87 321	28.36 340	42.41 355	5.35 357	24.61 349	27.75 354	32.15 331	47.85 335	70.27 310	35.85 286	29.51 352	19.08 348	67.85 315	50.22 339

tpmrst		50.15 298	51.38 295	46.45 313	56.05 338	24.77 344	64.40 257	49.98 313	36.14 308	53.32 322	69.59 313	35.16 287	48.69 316	39.24 275	58.51 339	65.89 303

testus		45.03 314	46.49 312	40.65 330	62.53 303	25.24 343	42.54 334	46.23 326	31.16 339	57.69 301	62.90 333	34.60 288	42.33 337	17.72 349	63.01 326	54.37 334

PVSNet	43.83 21	51.56 295	51.17 296	52.73 293	68.34 270	38.27 273	48.22 322	53.56 296	36.41 307	54.29 319	64.94 330	34.60 288	54.20 309	30.34 320	69.87 306	65.71 305

MVS-HIRNet		45.53 309	47.29 309	40.24 331	62.29 306	26.82 339	56.02 303	37.41 349	29.74 341	43.69 347	81.27 208	33.96 290	55.48 305	24.46 338	56.79 341	38.43 349

LP		53.02 287	52.27 293	55.27 288	55.76 342	40.55 258	55.64 305	55.07 284	42.46 282	56.95 304	73.21 289	33.67 291	54.18 310	38.41 283	59.29 335	71.08 266

RPMNet		61.25 238	61.55 232	60.36 263	66.37 284	48.24 195	70.93 179	54.45 291	54.66 176	61.35 279	86.77 123	33.29 292	63.22 288	55.93 169	70.17 303	69.62 281

CR-MVSNet		58.96 254	58.49 259	60.36 263	66.37 284	48.24 195	70.93 179	56.40 281	32.87 328	61.35 279	86.66 128	33.19 293	63.22 288	48.50 220	70.17 303	69.62 281

Patchmtry		60.91 240	63.01 218	54.62 290	66.10 288	26.27 341	67.47 218	56.40 281	54.05 185	72.04 207	86.66 128	33.19 293	60.17 298	43.69 243	87.45 145	77.42 216

CostFormer		57.35 268	56.14 273	60.97 256	63.76 299	38.43 271	67.50 217	60.22 259	37.14 305	59.12 296	76.34 251	32.78 295	71.99 233	39.12 276	69.27 309	72.47 251

tpm cat1		54.02 282	52.63 290	58.19 274	64.85 295	39.86 263	66.26 233	57.28 273	32.16 330	56.90 305	70.39 309	32.75 296	65.30 282	34.29 307	58.79 336	69.41 283

thres200		57.55 267	57.02 267	59.17 268	67.89 277	34.93 300	58.91 296	57.25 274	50.24 228	64.01 267	71.46 305	32.49 297	71.39 238	31.31 316	79.57 251	71.19 265

tfpn200view9		60.35 245	59.97 242	61.51 250	70.78 245	35.35 295	63.27 266	57.47 270	53.00 198	68.31 239	77.09 246	32.45 298	72.09 229	35.61 300	81.73 216	77.08 221

thres400		60.77 243	59.97 242	63.15 233	70.78 245	35.35 295	63.27 266	57.47 270	53.00 198	68.31 239	77.09 246	32.45 298	72.09 229	35.61 300	81.73 216	82.02 154

EU-MVSNet		60.82 241	60.80 238	60.86 258	68.37 269	41.16 252	72.27 148	68.27 229	26.96 345	69.08 234	75.71 261	32.09 300	67.44 269	55.59 173	78.90 255	73.97 238

tfpn111		61.91 232	61.65 229	62.68 242	72.14 236	35.01 297	65.42 246	56.99 276	55.23 162	70.71 222	79.90 220	32.07 301	72.85 215	38.80 278	83.61 200	80.18 189

conf200view11		61.42 237	61.09 235	62.43 245	72.14 236	35.01 297	65.42 246	56.99 276	55.23 162	70.71 222	79.90 220	32.07 301	72.09 229	35.61 300	81.73 216	80.18 189

thres100view900		61.17 239	61.09 235	61.39 252	72.14 236	35.01 297	65.42 246	56.99 276	55.23 162	70.71 222	79.90 220	32.07 301	72.09 229	35.61 300	81.73 216	77.08 221

thres600view7		61.82 233	61.38 234	63.12 234	71.81 241	34.93 300	64.64 252	56.99 276	54.78 174	70.33 228	79.74 225	32.07 301	72.42 226	38.61 281	83.46 201	82.02 154

PatchmatchNet		54.60 278	54.27 284	55.59 287	65.17 294	39.08 266	66.92 226	51.80 308	39.89 292	58.39 298	73.12 290	31.69 305	58.33 302	43.01 247	58.38 340	69.38 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
view600		62.88 224	62.90 219	62.82 237	72.97 228	33.66 309	66.10 234	55.01 286	57.05 142	72.66 197	82.56 192	31.60 306	72.78 216	42.64 254	85.55 171	82.02 154

view800		62.88 224	62.90 219	62.82 237	72.97 228	33.66 309	66.10 234	55.01 286	57.05 142	72.66 197	82.56 192	31.60 306	72.78 216	42.64 254	85.55 171	82.02 154

conf0.05thres1000		62.88 224	62.90 219	62.82 237	72.97 228	33.66 309	66.10 234	55.01 286	57.05 142	72.66 197	82.56 192	31.60 306	72.78 216	42.64 254	85.55 171	82.02 154

tfpn		62.88 224	62.90 219	62.82 237	72.97 228	33.66 309	66.10 234	55.01 286	57.05 142	72.66 197	82.56 192	31.60 306	72.78 216	42.64 254	85.55 171	82.02 154

sam_mvs1												31.41 310				70.05 272

patchmatchnet-post											68.99 315	31.32 311	69.38 249

ADS-MVSNet2		48.76 301	47.25 310	53.29 292	55.90 340	40.54 259	47.34 325	54.99 290	31.41 337	50.48 328	72.06 298	31.23 312	54.26 308	25.93 333	55.93 342	65.07 309

ADS-MVSNet		44.62 316	45.58 313	41.73 328	55.90 340	20.83 348	47.34 325	39.94 347	31.41 337	50.48 328	72.06 298	31.23 312	39.31 344	25.93 333	55.93 342	65.07 309

sam_mvs												31.21 314

Patchmatch-RL test		59.95 246	59.12 246	62.44 244	72.46 234	54.61 155	59.63 292	47.51 322	41.05 289	74.58 179	74.30 279	31.06 315	65.31 281	51.61 196	79.85 246	67.39 294

tpmvs		55.84 272	55.45 280	57.01 280	60.33 316	33.20 315	65.89 240	59.29 263	47.52 251	56.04 309	73.60 285	31.05 316	68.06 264	40.64 270	64.64 322	69.77 275

test_post											1.99 355	30.91 317	54.76 307

MDTV_nov1_ep13			54.05 285		65.54 291	29.30 332	59.00 295	55.22 283	35.96 310	52.44 323	75.98 253	30.77 318	59.62 299	38.21 284	73.33 288

test_post1							66.63 229				2.08 354	30.66 319	59.33 300	40.34 273

Patchmatch-test		47.93 304	49.96 302	41.84 327	57.42 334	24.26 345	48.75 320	41.49 342	39.30 293	56.79 306	73.48 286	30.48 320	33.87 350	29.29 327	72.61 290	67.39 294

tpm2		56.12 270	54.64 282	60.55 260	66.24 287	36.01 289	68.14 212	56.77 280	33.60 326	58.25 300	75.52 265	30.25 321	74.33 208	33.27 311	69.76 308	71.32 261

MVSTER		63.29 218	61.60 231	68.36 195	59.77 320	46.21 229	60.62 287	71.32 209	41.83 285	75.40 168	79.12 235	30.25 321	75.85 190	56.30 166	79.81 247	83.03 132

tpm		50.60 296	52.42 292	45.14 318	65.18 293	26.29 340	60.30 288	43.50 332	37.41 303	57.01 303	79.09 236	30.20 323	42.32 338	32.77 313	66.36 319	66.81 300

PatchT		53.35 285	56.47 271	43.99 323	64.19 297	17.46 350	59.15 293	43.10 333	52.11 205	54.74 316	86.95 115	29.97 324	49.98 315	43.62 244	74.40 283	64.53 313

MDTV_nov1_ep13_2view						18.41 349	53.74 310		31.57 336	44.89 341		29.90 325		32.93 312		71.48 260

test1235678		48.41 303	49.60 303	44.83 320	68.52 267	33.81 307	46.33 329	45.89 327	38.72 297	58.46 297	72.08 293	29.85 326	47.82 319	19.67 347	66.91 318	52.88 335

tpmp4_e23		57.57 266	55.46 279	63.93 227	66.48 283	41.56 251	71.68 164	60.65 258	35.64 312	55.35 314	76.25 252	29.53 327	75.41 197	34.40 306	69.12 310	74.83 234

test-LLR		50.43 297	50.69 300	49.64 301	60.76 313	41.87 248	53.18 311	45.48 330	43.41 279	49.41 332	60.47 338	29.22 328	44.73 329	42.09 261	72.14 292	62.33 320

test0.0.03 1		47.72 305	48.31 306	45.93 314	55.53 343	29.39 331	46.40 328	41.21 344	43.41 279	55.81 312	67.65 323	29.22 328	43.77 335	25.73 335	69.87 306	64.62 311

PatchFormer-LS_test		53.94 284	52.64 289	57.85 275	61.87 307	39.59 264	61.60 280	57.63 268	40.65 290	54.52 318	58.64 341	29.07 330	64.18 285	46.78 233	62.98 327	69.78 274

conf0.01		59.26 251	58.88 249	60.40 261	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	80.18 189

conf0.002		59.26 251	58.88 249	60.40 261	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	80.18 189

thresconf0.02		58.38 258	58.88 249	56.91 281	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	69.70 276

tfpn_n400		58.38 258	58.88 249	56.91 281	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	69.70 276

tfpnconf		58.38 258	58.88 249	56.91 281	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	69.70 276

tfpnview11		58.38 258	58.88 249	56.91 281	68.66 261	31.96 322	62.04 271	51.95 302	50.99 218	67.57 245	75.91 255	28.59 331	69.07 252	42.77 248	81.40 224	69.70 276

tfpn_ndepth		56.91 269	57.30 266	55.71 286	67.22 280	33.26 314	61.72 279	53.98 292	48.49 240	64.16 266	71.94 300	27.65 337	68.71 259	40.49 271	80.08 243	65.17 308

tfpn1000		58.28 262	58.86 255	56.53 285	68.05 274	32.26 319	62.58 268	51.67 309	51.25 216	67.38 251	75.95 254	27.24 338	68.83 258	43.51 246	82.11 211	68.49 289

EMVS		44.61 317	44.45 319	45.10 319	48.91 353	43.00 241	37.92 344	41.10 345	46.75 253	38.00 351	48.43 350	26.42 339	46.27 321	37.11 293	75.38 279	46.03 343

E-PMN		45.17 311	45.36 314	44.60 321	50.07 351	42.75 243	38.66 343	42.29 339	46.39 256	39.55 349	51.15 348	26.00 340	45.37 326	37.68 288	76.41 270	45.69 344

EPMVS		45.74 308	46.53 311	43.39 324	54.14 349	22.33 347	55.02 307	35.00 351	34.69 317	51.09 326	70.20 311	25.92 341	42.04 340	37.19 292	55.50 344	65.78 304

tmp_tt		11.98 331	14.73 332	3.72 343	2.28 357	4.62 358	19.44 351	14.50 358	0.47 353	21.55 353	9.58 353	25.78 342	4.57 356	11.61 352	27.37 352	1.96 353

FMVSNet5		55.08 277	55.54 278	53.71 291	65.80 289	33.50 313	56.22 302	52.50 301	43.72 276	61.06 282	83.38 179	25.46 343	54.87 306	30.11 322	81.64 222	72.75 248

new_pmnet		37.55 325	39.80 327	30.79 338	56.83 335	16.46 351	39.35 341	30.65 353	25.59 347	45.26 340	61.60 336	24.54 344	28.02 353	21.60 344	52.80 347	47.90 341

dp		44.09 318	44.88 317	41.72 329	58.53 328	23.18 346	54.70 308	42.38 338	34.80 315	44.25 345	65.61 329	24.48 345	44.80 328	29.77 324	49.42 348	57.18 331

IB-MVS	49.67 18	59.69 248	56.96 268	67.90 197	68.19 272	50.30 174	61.42 282	65.18 241	47.57 250	55.83 311	67.15 327	23.77 346	79.60 143	43.56 245	79.97 245	73.79 241
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
CHOSEN 280x420		41.62 320	39.89 326	46.80 311	61.81 308	51.59 167	33.56 348	35.74 350	27.48 344	37.64 352	53.53 344	23.24 347	42.09 339	27.39 331	58.64 337	46.72 342

DWT-MVSNet_test		53.04 286	51.12 297	58.77 271	61.23 310	38.67 270	62.16 270	57.74 267	38.24 298	51.76 325	59.07 340	21.36 348	67.40 270	44.80 239	63.76 324	70.25 271

gg-mvs-nofinetune		55.75 273	56.75 270	52.72 294	62.87 301	28.04 336	68.92 198	41.36 343	71.09 31	50.80 327	92.63 12	20.74 349	66.86 274	29.97 323	72.41 291	63.25 314

GG-mvs-BLEND				52.24 295	60.64 315	29.21 333	69.73 192	42.41 336		45.47 339	52.33 346	20.43 350	68.16 263	25.52 336	65.42 321	59.36 328

JIA-IIPM		54.03 281	51.62 294	61.25 254	59.14 324	55.21 149	59.10 294	47.72 321	50.85 225	50.31 331	85.81 152	20.10 351	63.97 286	36.16 298	55.41 345	64.55 312

test-mter		48.56 302	48.20 307	49.64 301	60.76 313	41.87 248	53.18 311	45.48 330	31.91 335	49.41 332	60.47 338	18.34 352	44.73 329	42.09 261	72.14 292	62.33 320

TESTMET0.1,1		45.17 311	44.93 316	45.89 315	56.02 339	38.31 272	53.18 311	41.94 341	27.85 343	44.86 342	56.47 343	17.93 353	41.50 343	38.08 286	68.06 314	57.85 329

test12356		38.35 323	40.80 322	31.01 337	58.31 329	9.09 355	36.67 347	46.65 323	33.65 325	44.39 344	60.94 337	17.56 354	39.23 346	16.01 350	53.03 346	44.72 345

DeepMVS_CX				11.83 342	15.51 356	13.86 354		11.25 359	5.76 352	20.85 354	26.46 351	17.06 355	9.22 355	9.69 353	13.82 353	12.42 352

pmmvs3		46.71 307	45.09 315	51.55 297	56.76 336	48.25 194	55.78 304	39.53 348	24.13 349	50.35 330	63.40 332	15.90 356	51.08 311	29.29 327	70.69 301	55.33 333

testpf		45.32 310	48.47 304	35.88 336	53.56 350	26.84 338	58.86 297	42.95 334	47.78 248	46.18 338	63.70 331	13.73 357	50.29 313	50.81 202	58.61 338	30.51 351

test2356		40.85 322	40.47 324	41.98 326	58.78 326	28.65 335	39.45 340	40.98 346	31.95 334	48.47 334	56.63 342	12.54 358	44.41 331	15.84 351	59.58 333	52.88 335

1111		45.08 313	47.96 308	36.43 335	59.56 322	14.82 352	43.56 332	45.65 328	45.60 259	60.04 289	75.47 266	9.31 359	34.46 348	23.66 339	68.76 312	60.02 325

.test1245		34.47 329	40.38 325	16.73 341	59.56 322	14.82 352	43.56 332	45.65 328	45.60 259	60.04 289	75.47 266	9.31 359	34.46 348	23.66 339	0.55 355	0.90 354

PNet_i23d		36.76 326	36.63 329	37.12 334	58.19 331	33.00 316	39.86 339	32.55 352	48.44 241	39.64 348	51.31 347	6.89 361	41.83 342	22.29 342	30.55 351	36.54 350

testmvs		4.06 335	5.28 336	0.41 344	0.64 359	0.16 360	42.54 334	0.31 361	0.26 355	0.50 356	1.40 357	0.77 362	0.17 357	0.56 354	0.55 355	0.90 354

test123		4.43 334	5.78 335	0.39 345	0.97 358	0.28 359	46.33 329	0.45 360	0.31 354	0.62 355	1.50 356	0.61 363	0.11 358	0.56 354	0.63 354	0.77 356

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		5.62 332	7.50 333	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	67.46 324	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																70.05 272

test_part3							83.39 34		73.27 20		89.25 81		86.96 10	72.56 46

test_part2					85.90 46	66.44 80				84.61 61

MTGPA								80.63 107

MTMP								19.26 357

gm-plane-assit					62.51 304	33.91 306			37.25 304		62.71 334		72.74 220	38.70 279

test9_res														72.12 54	91.37 80	77.40 217

agg_prior2														70.70 62	90.93 92	78.55 206

agg_prior					84.44 71	66.02 84		78.62 144		76.95 145			80.34 132

test_prior4						70.14 58	77.57 83

test_prior				75.27 91	82.15 97	59.85 126		84.33 39					83.39 67			82.58 142

旧先验2							71.17 176		45.11 266	78.54 128			61.28 296	59.19 146

新几何2							71.33 173

无先验							74.82 127	70.94 216	47.75 249				76.85 184	54.47 181		72.09 256

原ACMM2							74.78 131

testdata2													67.30 271	48.34 221

testdata1							68.34 211		57.24 141

plane_prior7					85.18 55	66.21 83

plane_prior5								85.49 21					86.15 22	71.09 56	90.94 90	84.82 94

plane_prior4											89.11 87

plane_prior3						65.67 86			63.82 85	78.23 133

plane_prior2							82.74 38		65.45 62

plane_prior1					84.46 70

plane_prior						65.18 90	80.06 61		61.88 103						89.91 113

n2								0.00 362

nn								0.00 362

door-mid								55.02 285

test11								82.71 68

door								52.91 300

HQP5-MVS						58.80 135

HQP-NCC					82.37 92		77.32 87		59.08 124	71.58 209

ACMP_Plane					82.37 92		77.32 87		59.08 124	71.58 209

BP-MVS														67.38 95

HQP4-MVS										71.59 208			85.31 35			83.74 119

HQP3-MVS								84.12 46							89.16 120

NP-MVS					83.34 82	63.07 107					85.97 150

ACMMP++_ref															89.47 118

ACMMP++															91.96 70

High-resolution multi-view benchmark