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	72.28 255
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	43.39 239	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		35.00 329	36.93 329	29.21 340	84.62 66	0.00 362	0.00 353	78.90 138	0.00 357	0.00 358	0.00 359	78.26 15	0.00 360	0.00 357	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	39.21 266	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	39.02 269	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	37.34 283	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	9.95 353	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	49.12 306	58.19 332	60.77 120	66.32 232	52.97 300	55.93 155	90.62 7	86.91 116	73.07 46	35.98 348	20.63 347	91.63 73	50.62 339

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	60.07 325

TranMVSNet+NR-MVSNet		76.13 78	77.66 68	71.56 156	84.61 67	42.57 246	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	42.01 248	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	80.89 236	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	76.32 225

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	73.37 243

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	72.08 294	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	78.43 261	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	72.08 294	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	72.07 297	68.98 75	80.19 137	70.29 64	80.91 234	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	73.18 245

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	78.73 258	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	39.06 268	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	61.70 249	70.37 250	34.30 305	61.45 281	79.62 125	56.81 148	89.59 10	88.16 106	68.44 80	72.94 214	42.30 260	87.33 148	77.85 216

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		17.71 331	23.62 332	0.00 347	0.00 361	0.00 362	0.00 353	70.17 222	0.00 357	0.00 358	74.25 280	68.16 83	0.00 360	0.00 357	0.00 358	0.00 358

WR-MVS_H		80.22 44	82.17 36	74.39 97	89.46 15	42.69 245	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	72.07 297	67.00 91	76.28 189	67.97 88	80.91 234	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	77.15 219

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	77.15 219

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	76.57 224

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	71.59 304	66.22 99	78.60 156	67.58 91	80.32 242	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	71.39 307	65.98 100	78.53 158	67.30 97	80.18 243	89.23 38

TransMVSNet (Re)		69.62 166	71.63 152	63.57 230	76.51 158	35.93 292	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	79.00 255	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	81.19 232	84.50 106

VPA-MVSNet		68.71 182	70.37 164	63.72 229	76.13 163	38.06 277	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	39.94 263	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	71.73 260

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	58.59 273	81.26 108	37.73 280	64.06 259	57.62 270	57.02 146	78.40 130	90.75 47	62.65 119	58.10 304	41.77 266	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	61.70 277	69.66 313	62.56 120	73.93 210	49.06 215	73.91 286	72.31 254

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	43.71 238	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		5.20 334	6.93 335	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	62.39 124	0.00 360	0.00 357	0.00 358	0.00 358

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	61.60 278	68.04 323	62.39 124	73.95 209	49.07 214	73.87 287	72.34 253

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	67.49 279	43.25 241	69.87 189	81.22 94	52.69 201	71.57 212	86.68 127	62.09 128	74.51 206	66.05 107	78.74 257	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	34.44 304	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	73.00 246

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	81.50 224	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	71.22 264

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	78.26 263	76.27 226

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	78.26 263	76.27 226

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	78.26 263	76.27 226

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	76.87 270	74.49 237

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	62.43 275	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	80.84 237	72.74 250

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	40.68 256	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	46.04 237	80.65 239	70.47 269

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	81.66 222	82.87 135

VDDNet		71.60 150	73.13 125	67.02 205	86.29 44	41.11 254	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	58.51 274	57.55 334	27.09 338	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	45.64 239	88.01 137	72.83 248

FC-MVSNet-test		73.32 122	74.78 99	68.93 187	79.21 126	36.57 285	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	67.25 280	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	80.81 238	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	77.96 266	78.31 208

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	59.65 268	79.72 117	30.17 331	60.27 289	62.14 250	54.19 183	71.24 216	86.63 131	58.80 163	67.62 268	44.17 243	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	82.15 211	81.18 168

mvs_anonymous		65.08 203	65.49 202	63.83 228	63.79 299	37.60 281	66.52 231	69.82 223	43.44 278	73.46 190	86.08 148	58.79 166	71.75 237	51.90 195	75.63 276	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	81.90 215	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	37.74 279	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	70.44 308	58.16 174	75.85 190	62.51 127	79.81 248	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	61.05 256	71.99 240	38.03 278	67.59 216	68.79 226	49.08 235	65.32 262	86.26 141	58.02 181	66.85 275	39.33 275	79.79 250	78.27 209

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	62.31 306	44.43 236	68.81 201	72.93 194	48.13 242	62.12 276	83.33 182	57.96 184	72.29 227	59.83 142	77.31 269	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	59.70 267	63.63 301	53.97 158	80.08 60	75.93 173	64.24 81	73.49 189	88.93 94	57.89 188	62.46 292	59.75 144	91.55 77	62.67 319
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	35.70 294	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	58.86 326	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	71.88 258

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	62.16 247	73.40 213	36.65 284	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	65.78 261	68.58 322	57.01 199	77.79 175	46.68 235	81.92 214	74.42 238
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		55.19 277	57.40 266	48.56 309	64.45 297	34.84 303	51.54 316	53.59 296	38.99 296	63.79 269	79.43 228	56.59 200	45.57 324	36.92 295	71.29 297	65.25 308

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	60.57 260	73.28 216	35.61 295	57.60 301	67.08 232	54.61 177	66.76 255	83.37 180	56.28 202	66.87 273	42.19 261	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	40.42 261	74.67 134	75.15 183	58.41 130	68.74 238	88.14 107	56.08 204	83.69 60	59.90 141	81.71 221	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	81.07 233	75.45 230

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	82.20 210	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	69.66 281

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	71.86 303	54.33 209	79.02 147	38.20 286	76.14 273	65.36 307

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	75.54 277	75.11 234

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	68.12 291

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	68.05 292

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	81.99 213	75.49 229

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	60.93 287	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	59.98 292	78.61 237	51.82 218	78.20 172	44.30 241	84.11 193	78.27 209

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	59.98 292	68.86 320	51.82 218	78.20 172	44.30 241	77.77 268	72.52 251

testgi		54.00 284	56.86 270	45.45 317	58.20 331	25.81 343	49.05 320	49.50 316	45.43 263	67.84 241	81.17 210	51.81 220	43.20 337	29.30 327	79.41 253	67.34 297

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	56.62 308	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	79.72 251	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	40.43 273	75.69 275	73.89 241

MVS		60.62 244	59.97 242	62.58 243	68.13 273	47.28 218	68.59 205	73.96 188	32.19 330	59.94 294	68.86 320	50.48 224	77.64 177	41.85 264	75.74 274	62.83 317

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		58.68 258	57.72 263	61.57 250	76.21 162	73.59 39	61.83 277	49.00 318	47.30 252	61.08 282	68.97 317	50.16 226	59.01 302	36.06 300	68.84 312	52.10 338

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	63.68 270	68.89 319	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	61.64 310	49.90 177	48.11 324	57.24 276	53.31 196	80.95 102	79.39 229	49.00 230	61.55 296	45.92 238	80.05 245	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	62.31 294	54.46 183	81.26 231	66.74 302

GBi-Net		68.30 185	68.79 180	66.81 207	73.14 218	40.68 256	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	40.68 256	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	38.94 270	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	59.59 263	41.06 288	73.05 194	85.72 153	48.03 235			80.65 239	66.92 298

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	73.84 288	71.21 265

lessismore_v0				72.75 137	79.60 119	56.83 144		57.37 273		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	34.48 306	85.15 184	77.99 215

PM-MVS		64.49 208	63.61 212	67.14 204	76.68 157	75.15 28	68.49 209	42.85 336	51.17 217	77.85 136	80.51 214	45.76 239	66.31 280	52.83 192	76.35 272	59.96 327

USDC		62.80 228	63.10 217	61.89 248	65.19 293	43.30 240	67.42 219	74.20 187	35.80 312	72.25 205	84.48 168	45.67 240	71.95 234	37.95 288	84.97 189	70.42 271

test20.03		55.74 275	57.51 265	50.42 299	59.89 320	32.09 321	50.63 317	49.01 317	50.11 229	65.07 264	83.23 186	45.61 241	48.11 319	30.22 322	83.82 198	71.07 268

Patchmatch-test1		57.81 266	58.04 262	57.13 280	70.17 251	41.07 255	65.19 250	53.38 298	43.34 281	61.00 285	71.94 301	45.20 242	62.69 291	41.81 265	70.31 303	67.63 294

1112_ss		59.48 250	58.99 249	60.96 258	77.84 144	42.39 247	61.42 282	68.45 228	37.96 302	59.93 295	67.46 325	45.11 243	65.07 283	40.89 270	71.81 295	75.41 231

new-patchmatchnet		52.89 290	55.76 277	44.26 323	59.94 319	6.31 357	37.36 347	50.76 313	41.10 287	64.28 266	79.82 223	44.77 244	48.43 318	36.24 298	87.61 140	78.03 213

jason		64.47 209	62.84 223	69.34 180	76.91 156	59.20 129	67.15 224	65.67 236	35.29 314	65.16 263	76.74 249	44.67 245	70.68 241	54.74 179	79.28 254	78.14 211
jason: jason.
IterMVS		63.12 220	62.48 226	65.02 219	66.34 287	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	75.47 278	75.38 232
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	41.87 249	69.30 195	76.79 168	40.64 292	53.80 322	79.62 227	44.38 247	82.92 74	29.64 326	73.11 290	73.36 244

HY-MVS	49.31 19	57.96 265	57.59 264	59.10 270	66.85 283	36.17 289	65.13 251	65.39 240	39.24 295	54.69 318	78.14 240	44.28 248	67.18 272	33.75 311	70.79 300	73.95 240

CANet_DTU		64.04 215	63.83 209	64.66 220	68.39 268	42.97 243	73.45 139	74.50 186	52.05 206	54.78 316	75.44 268	43.99 249	70.42 245	53.49 191	78.41 262	80.59 183

LFMVS		67.06 196	67.89 191	64.56 221	78.02 141	38.25 275	70.81 181	59.60 262	65.18 69	71.06 219	86.56 134	43.85 250	75.22 199	46.35 236	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	38.81 297	74.13 182	82.23 198	43.76 251	68.65 260	42.53 259	80.63 241	74.63 236

1314		59.83 248	58.86 256	62.74 241	65.71 291	44.78 233	68.59 205	72.63 197	33.54 328	61.05 284	67.29 327	43.62 252	71.26 239	49.49 212	67.84 317	72.19 256

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	37.68 289	84.44 190	78.01 214
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	63.53 271	76.74 249	43.19 254	80.56 127	72.28 52	78.67 259	78.14 211

lupinMVS		63.36 217	61.49 233	68.97 185	74.93 173	59.19 130	65.80 241	64.52 244	34.68 319	63.53 271	74.25 280	43.19 254	70.62 242	53.88 187	78.67 259	77.10 221

Test_1112_low_res		58.78 257	58.69 258	59.04 271	79.41 121	38.13 276	57.62 300	66.98 233	34.74 317	59.62 296	77.56 243	42.92 256	63.65 288	38.66 281	70.73 301	75.35 233

PMMVS		44.69 316	43.95 321	46.92 311	50.05 353	53.47 162	48.08 325	42.40 338	22.36 351	44.01 347	53.05 346	42.60 257	45.49 325	31.69 316	61.36 330	41.79 347

Anonymous20231206		54.13 281	55.82 276	49.04 307	70.89 243	35.96 291	51.73 315	50.87 312	34.86 315	62.49 274	79.22 232	42.52 258	44.29 333	27.95 331	81.88 216	66.88 299

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	77.89 267	80.44 186

WTY-MVS		49.39 301	50.31 302	46.62 313	61.22 312	32.00 322	46.61 328	49.77 315	33.87 322	54.12 321	69.55 315	41.96 260	45.40 326	31.28 318	64.42 324	62.47 320

UnsupCasMVSNet_eth		52.26 294	53.29 289	49.16 305	55.08 345	33.67 309	50.03 319	58.79 266	37.67 303	63.43 273	74.75 272	41.82 261	45.83 323	38.59 283	59.42 335	67.98 293

UnsupCasMVSNet_bld		50.01 300	51.03 300	46.95 310	58.61 328	32.64 318	48.31 322	53.27 299	34.27 320	60.47 288	71.53 305	41.40 262	47.07 321	30.68 319	60.78 331	61.13 323

ppachtmachnet_test		60.26 246	59.61 245	62.20 246	67.70 278	44.33 237	58.18 299	60.96 258	40.75 290	65.80 260	72.57 292	41.23 263	63.92 287	46.87 233	82.42 209	78.33 207

MIMVSNet		54.39 280	56.12 275	49.20 304	72.57 233	30.91 330	59.98 290	48.43 320	41.66 286	55.94 311	83.86 175	41.19 264	50.42 313	26.05 333	75.38 280	66.27 303

CHOSEN 1792x2688		58.09 264	56.30 273	63.45 232	79.95 115	50.93 171	54.07 310	65.59 238	28.56 343	61.53 279	74.33 278	41.09 265	66.52 279	33.91 310	67.69 318	72.92 247

YYNet1		52.58 291	53.50 287	49.85 300	54.15 349	36.45 287	40.53 338	46.55 326	38.09 301	75.52 166	73.31 288	41.08 266	43.88 334	41.10 268	71.14 299	69.21 286

MDA-MVSNet_test_wron		52.57 292	53.49 288	49.81 301	54.24 348	36.47 286	40.48 339	46.58 325	38.13 300	75.47 167	73.32 287	41.05 267	43.85 335	40.98 269	71.20 298	69.10 288

PVSNet_0	36.71 22	41.12 322	40.78 324	42.14 326	59.97 318	40.13 262	40.97 337	42.24 341	30.81 341	44.86 343	49.41 350	40.70 268	45.12 328	23.15 342	34.96 351	41.16 348

Vis-MVSNet (Re-imp)		62.74 229	63.21 216	61.34 254	72.19 235	31.56 329	67.31 223	53.87 294	53.60 192	69.88 230	83.37 180	40.52 269	70.98 240	41.40 267	86.78 157	81.48 165

sss		47.59 307	48.32 306	45.40 318	56.73 338	33.96 306	45.17 332	48.51 319	32.11 334	52.37 325	65.79 329	40.39 270	41.91 342	31.85 315	61.97 329	60.35 324

pmmvs4		60.78 242	59.04 248	66.00 215	73.06 225	57.67 141	64.53 255	60.22 260	36.91 307	65.96 258	77.27 245	39.66 271	68.54 261	38.87 278	74.89 282	71.80 259

MVP-Stereo		61.56 235	59.22 246	68.58 194	79.28 123	60.44 121	69.20 197	71.57 205	43.58 277	56.42 309	78.37 239	39.57 272	76.46 188	34.86 305	60.16 332	68.86 289
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FPMVS		59.43 251	60.07 241	57.51 279	77.62 149	71.52 44	62.33 269	50.92 311	57.40 140	69.40 232	80.00 219	39.14 273	61.92 295	37.47 292	66.36 320	39.09 349

DSMNet-mixed		43.18 320	44.66 319	38.75 334	54.75 347	28.88 335	57.06 302	27.42 356	13.47 352	47.27 338	77.67 242	38.83 274	39.29 346	25.32 338	60.12 333	48.08 341

HyFIR lowres test		63.01 221	60.47 239	70.61 162	83.04 85	54.10 157	59.93 291	72.24 202	33.67 325	69.00 235	75.63 262	38.69 275	76.93 182	36.60 296	75.45 279	80.81 179

MVE	27.91 23	36.69 328	35.64 331	39.84 333	43.37 355	35.85 293	19.49 351	24.61 357	24.68 349	39.05 351	62.63 336	38.67 276	27.10 355	21.04 346	47.25 350	56.56 333
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		52.91 289	54.31 284	48.71 308	72.13 239	36.18 288	50.26 318	47.78 321	44.15 271	64.61 265	79.78 224	38.18 277	50.20 315	21.96 344	69.93 306	59.75 328

EPNet_dtu		58.93 256	58.52 259	60.16 266	67.91 276	47.70 207	69.97 187	58.02 267	49.73 231	47.28 337	73.02 291	38.14 278	62.34 293	36.57 297	85.99 164	70.43 270
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		52.49 293	52.58 292	52.21 297	54.99 346	32.38 319	55.45 307	53.84 295	32.15 332	55.49 314	74.81 270	38.08 279	57.37 305	34.02 309	74.40 284	66.88 299

N_pmnet		52.06 295	51.11 299	54.92 290	59.64 322	71.03 49	37.42 346	61.62 256	33.68 324	57.12 303	72.10 293	37.94 280	31.03 352	29.13 330	71.35 296	62.70 318

CMPMVS	48.73 20	61.54 236	60.89 237	63.52 231	61.08 313	51.55 168	68.07 213	68.00 230	33.88 321	65.87 259	81.25 209	37.91 281	67.71 266	49.32 213	82.60 207	71.31 263
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	37.56 282	66.63 229	70.38 220	51.55 212	74.72 176	83.27 185	37.89 282	74.44 207	47.12 229	85.37 176	81.57 164

GA-MVS		62.91 222	61.66 228	66.66 211	67.09 282	44.49 235	61.18 285	69.36 225	51.33 214	69.33 233	74.47 276	36.83 283	74.94 203	50.60 204	74.72 283	80.57 184

MS-PatchMatch		55.59 276	54.89 282	57.68 277	69.18 259	49.05 184	61.00 286	62.93 248	35.98 310	58.36 300	68.93 318	36.71 284	66.59 278	37.62 291	63.30 326	57.39 331

CVMVSNet		59.21 254	58.44 261	61.51 251	73.94 200	47.76 206	71.31 174	64.56 243	26.91 347	60.34 289	70.44 308	36.24 285	67.65 267	53.57 190	68.66 314	69.12 287

no-one		56.11 272	55.62 278	57.60 278	62.68 303	49.23 183	39.12 343	58.99 265	33.72 323	60.98 286	80.90 211	36.07 286	60.36 298	30.68 319	97.40 1	63.22 316

PMMVS2		37.74 325	40.87 322	28.36 341	42.41 356	5.35 358	24.61 350	27.75 355	32.15 332	47.85 336	70.27 311	35.85 287	29.51 353	19.08 349	67.85 316	50.22 340

tpmrst		50.15 299	51.38 296	46.45 314	56.05 339	24.77 345	64.40 257	49.98 314	36.14 309	53.32 323	69.59 314	35.16 288	48.69 317	39.24 276	58.51 340	65.89 304

testus		45.03 315	46.49 313	40.65 331	62.53 304	25.24 344	42.54 335	46.23 327	31.16 340	57.69 302	62.90 334	34.60 289	42.33 338	17.72 350	63.01 327	54.37 335

PVSNet	43.83 21	51.56 296	51.17 297	52.73 294	68.34 270	38.27 274	48.22 323	53.56 297	36.41 308	54.29 320	64.94 331	34.60 289	54.20 310	30.34 321	69.87 307	65.71 306

MVS-HIRNet		45.53 310	47.29 310	40.24 332	62.29 307	26.82 340	56.02 304	37.41 350	29.74 342	43.69 348	81.27 208	33.96 291	55.48 306	24.46 339	56.79 342	38.43 350

LP		53.02 288	52.27 294	55.27 289	55.76 343	40.55 259	55.64 306	55.07 285	42.46 282	56.95 305	73.21 289	33.67 292	54.18 311	38.41 284	59.29 336	71.08 267

RPMNet		61.25 238	61.55 232	60.36 264	66.37 285	48.24 195	70.93 179	54.45 292	54.66 176	61.35 280	86.77 123	33.29 293	63.22 289	55.93 169	70.17 304	69.62 282

CR-MVSNet		58.96 255	58.49 260	60.36 264	66.37 285	48.24 195	70.93 179	56.40 282	32.87 329	61.35 280	86.66 128	33.19 294	63.22 289	48.50 220	70.17 304	69.62 282

Patchmtry		60.91 240	63.01 218	54.62 291	66.10 289	26.27 342	67.47 218	56.40 282	54.05 185	72.04 207	86.66 128	33.19 294	60.17 299	43.69 244	87.45 145	77.42 217

CostFormer		57.35 269	56.14 274	60.97 257	63.76 300	38.43 272	67.50 217	60.22 260	37.14 306	59.12 297	76.34 251	32.78 296	71.99 233	39.12 277	69.27 310	72.47 252

tpm cat1		54.02 283	52.63 291	58.19 275	64.85 296	39.86 264	66.26 233	57.28 274	32.16 331	56.90 306	70.39 310	32.75 297	65.30 282	34.29 308	58.79 337	69.41 284

thres200		57.55 268	57.02 268	59.17 269	67.89 277	34.93 301	58.91 296	57.25 275	50.24 228	64.01 268	71.46 306	32.49 298	71.39 238	31.31 317	79.57 252	71.19 266

tfpn200view9		60.35 245	59.97 242	61.51 251	70.78 245	35.35 296	63.27 266	57.47 271	53.00 198	68.31 239	77.09 246	32.45 299	72.09 229	35.61 301	81.73 217	77.08 222

thres400		60.77 243	59.97 242	63.15 233	70.78 245	35.35 296	63.27 266	57.47 271	53.00 198	68.31 239	77.09 246	32.45 299	72.09 229	35.61 301	81.73 217	82.02 154

EU-MVSNet		60.82 241	60.80 238	60.86 259	68.37 269	41.16 253	72.27 148	68.27 229	26.96 346	69.08 234	75.71 261	32.09 301	67.44 269	55.59 173	78.90 256	73.97 239

tfpn111		61.91 232	61.65 229	62.68 242	72.14 236	35.01 298	65.42 246	56.99 277	55.23 162	70.71 222	79.90 220	32.07 302	72.85 215	38.80 279	83.61 200	80.18 189

conf200view11		61.42 237	61.09 235	62.43 245	72.14 236	35.01 298	65.42 246	56.99 277	55.23 162	70.71 222	79.90 220	32.07 302	72.09 229	35.61 301	81.73 217	80.18 189

thres100view900		61.17 239	61.09 235	61.39 253	72.14 236	35.01 298	65.42 246	56.99 277	55.23 162	70.71 222	79.90 220	32.07 302	72.09 229	35.61 301	81.73 217	77.08 222

thres600view7		61.82 233	61.38 234	63.12 234	71.81 241	34.93 301	64.64 252	56.99 277	54.78 174	70.33 228	79.74 225	32.07 302	72.42 226	38.61 282	83.46 201	82.02 154

PatchmatchNet		54.60 279	54.27 285	55.59 288	65.17 295	39.08 267	66.92 226	51.80 309	39.89 293	58.39 299	73.12 290	31.69 306	58.33 303	43.01 248	58.38 341	69.38 285
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	33.66 310	66.10 234	55.01 287	57.05 142	72.66 197	82.56 192	31.60 307	72.78 216	42.64 255	85.55 171	82.02 154

view800		62.88 224	62.90 219	62.82 237	72.97 228	33.66 310	66.10 234	55.01 287	57.05 142	72.66 197	82.56 192	31.60 307	72.78 216	42.64 255	85.55 171	82.02 154

conf0.05thres1000		62.88 224	62.90 219	62.82 237	72.97 228	33.66 310	66.10 234	55.01 287	57.05 142	72.66 197	82.56 192	31.60 307	72.78 216	42.64 255	85.55 171	82.02 154

tfpn		62.88 224	62.90 219	62.82 237	72.97 228	33.66 310	66.10 234	55.01 287	57.05 142	72.66 197	82.56 192	31.60 307	72.78 216	42.64 255	85.55 171	82.02 154

sam_mvs1												31.41 311				70.05 273

patchmatchnet-post											68.99 316	31.32 312	69.38 249

ADS-MVSNet2		48.76 302	47.25 311	53.29 293	55.90 341	40.54 260	47.34 326	54.99 291	31.41 338	50.48 329	72.06 299	31.23 313	54.26 309	25.93 334	55.93 343	65.07 310

ADS-MVSNet		44.62 317	45.58 314	41.73 329	55.90 341	20.83 349	47.34 326	39.94 348	31.41 338	50.48 329	72.06 299	31.23 313	39.31 345	25.93 334	55.93 343	65.07 310

sam_mvs												31.21 315

Patchmatch-RL test		59.95 247	59.12 247	62.44 244	72.46 234	54.61 155	59.63 292	47.51 323	41.05 289	74.58 179	74.30 279	31.06 316	65.31 281	51.61 196	79.85 247	67.39 295

tpmvs		55.84 273	55.45 281	57.01 281	60.33 317	33.20 316	65.89 240	59.29 264	47.52 251	56.04 310	73.60 285	31.05 317	68.06 264	40.64 271	64.64 323	69.77 276

test_post											1.99 356	30.91 318	54.76 308

MDTV_nov1_ep13			54.05 286		65.54 292	29.30 333	59.00 295	55.22 284	35.96 311	52.44 324	75.98 253	30.77 319	59.62 300	38.21 285	73.33 289

test_post1							66.63 229				2.08 355	30.66 320	59.33 301	40.34 274

Patchmatch-test		47.93 305	49.96 303	41.84 328	57.42 335	24.26 346	48.75 321	41.49 343	39.30 294	56.79 307	73.48 286	30.48 321	33.87 351	29.29 328	72.61 291	67.39 295

tpm2		56.12 271	54.64 283	60.55 261	66.24 288	36.01 290	68.14 212	56.77 281	33.60 327	58.25 301	75.52 265	30.25 322	74.33 208	33.27 312	69.76 309	71.32 262

MVSTER		63.29 218	61.60 231	68.36 195	59.77 321	46.21 229	60.62 287	71.32 209	41.83 285	75.40 168	79.12 235	30.25 322	75.85 190	56.30 166	79.81 248	83.03 132

tpm		50.60 297	52.42 293	45.14 319	65.18 294	26.29 341	60.30 288	43.50 333	37.41 304	57.01 304	79.09 236	30.20 324	42.32 339	32.77 314	66.36 320	66.81 301

PatchT		53.35 286	56.47 272	43.99 324	64.19 298	17.46 351	59.15 293	43.10 334	52.11 205	54.74 317	86.95 115	29.97 325	49.98 316	43.62 245	74.40 284	64.53 314

MDTV_nov1_ep13_2view						18.41 350	53.74 311		31.57 337	44.89 342		29.90 326		32.93 313		71.48 261

test1235678		48.41 304	49.60 304	44.83 321	68.52 267	33.81 308	46.33 330	45.89 328	38.72 298	58.46 298	72.08 294	29.85 327	47.82 320	19.67 348	66.91 319	52.88 336

tpmp4_e23		57.57 267	55.46 280	63.93 227	66.48 284	41.56 252	71.68 164	60.65 259	35.64 313	55.35 315	76.25 252	29.53 328	75.41 197	34.40 307	69.12 311	74.83 235

test-LLR		50.43 298	50.69 301	49.64 302	60.76 314	41.87 249	53.18 312	45.48 331	43.41 279	49.41 333	60.47 339	29.22 329	44.73 330	42.09 262	72.14 293	62.33 321

test0.0.03 1		47.72 306	48.31 307	45.93 315	55.53 344	29.39 332	46.40 329	41.21 345	43.41 279	55.81 313	67.65 324	29.22 329	43.77 336	25.73 336	69.87 307	64.62 312

PatchFormer-LS_test		53.94 285	52.64 290	57.85 276	61.87 308	39.59 265	61.60 280	57.63 269	40.65 291	54.52 319	58.64 342	29.07 331	64.18 285	46.78 234	62.98 328	69.78 275

conf0.01		59.26 252	58.88 250	60.40 262	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	80.18 189

conf0.002		59.26 252	58.88 250	60.40 262	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	80.18 189

thresconf0.02		58.38 259	58.88 250	56.91 282	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	69.70 277

tfpn_n400		58.38 259	58.88 250	56.91 282	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	69.70 277

tfpnconf		58.38 259	58.88 250	56.91 282	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	69.70 277

tfpnview11		58.38 259	58.88 250	56.91 282	68.66 261	31.96 323	62.04 271	51.95 303	50.99 218	67.57 245	75.91 255	28.59 332	69.07 252	42.77 249	81.40 225	69.70 277

tfpn_ndepth		56.91 270	57.30 267	55.71 287	67.22 281	33.26 315	61.72 279	53.98 293	48.49 240	64.16 267	71.94 301	27.65 338	68.71 259	40.49 272	80.08 244	65.17 309

tfpn1000		58.28 263	58.86 256	56.53 286	68.05 274	32.26 320	62.58 268	51.67 310	51.25 216	67.38 251	75.95 254	27.24 339	68.83 258	43.51 247	82.11 212	68.49 290

EMVS		44.61 318	44.45 320	45.10 320	48.91 354	43.00 242	37.92 345	41.10 346	46.75 253	38.00 352	48.43 351	26.42 340	46.27 322	37.11 294	75.38 280	46.03 344

E-PMN		45.17 312	45.36 315	44.60 322	50.07 352	42.75 244	38.66 344	42.29 340	46.39 256	39.55 350	51.15 349	26.00 341	45.37 327	37.68 289	76.41 271	45.69 345

EPMVS		45.74 309	46.53 312	43.39 325	54.14 350	22.33 348	55.02 308	35.00 352	34.69 318	51.09 327	70.20 312	25.92 342	42.04 341	37.19 293	55.50 345	65.78 305

tmp_tt		11.98 332	14.73 333	3.72 344	2.28 358	4.62 359	19.44 352	14.50 359	0.47 354	21.55 354	9.58 354	25.78 343	4.57 357	11.61 353	27.37 353	1.96 354

FMVSNet5		55.08 278	55.54 279	53.71 292	65.80 290	33.50 314	56.22 303	52.50 302	43.72 276	61.06 283	83.38 179	25.46 344	54.87 307	30.11 323	81.64 223	72.75 249

new_pmnet		37.55 326	39.80 328	30.79 339	56.83 336	16.46 352	39.35 342	30.65 354	25.59 348	45.26 341	61.60 337	24.54 345	28.02 354	21.60 345	52.80 348	47.90 342

dp		44.09 319	44.88 318	41.72 330	58.53 329	23.18 347	54.70 309	42.38 339	34.80 316	44.25 346	65.61 330	24.48 346	44.80 329	29.77 325	49.42 349	57.18 332

IB-MVS	49.67 18	59.69 249	56.96 269	67.90 197	68.19 272	50.30 174	61.42 282	65.18 241	47.57 250	55.83 312	67.15 328	23.77 347	79.60 143	43.56 246	79.97 246	73.79 242
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		41.62 321	39.89 327	46.80 312	61.81 309	51.59 167	33.56 349	35.74 351	27.48 345	37.64 353	53.53 345	23.24 348	42.09 340	27.39 332	58.64 338	46.72 343

DWT-MVSNet_test		53.04 287	51.12 298	58.77 272	61.23 311	38.67 271	62.16 270	57.74 268	38.24 299	51.76 326	59.07 341	21.36 349	67.40 270	44.80 240	63.76 325	70.25 272

gg-mvs-nofinetune		55.75 274	56.75 271	52.72 295	62.87 302	28.04 337	68.92 198	41.36 344	71.09 31	50.80 328	92.63 12	20.74 350	66.86 274	29.97 324	72.41 292	63.25 315

GG-mvs-BLEND				52.24 296	60.64 316	29.21 334	69.73 192	42.41 337		45.47 340	52.33 347	20.43 351	68.16 263	25.52 337	65.42 322	59.36 329

JIA-IIPM		54.03 282	51.62 295	61.25 255	59.14 325	55.21 149	59.10 294	47.72 322	50.85 225	50.31 332	85.81 152	20.10 352	63.97 286	36.16 299	55.41 346	64.55 313

test-mter		48.56 303	48.20 308	49.64 302	60.76 314	41.87 249	53.18 312	45.48 331	31.91 336	49.41 333	60.47 339	18.34 353	44.73 330	42.09 262	72.14 293	62.33 321

TESTMET0.1,1		45.17 312	44.93 317	45.89 316	56.02 340	38.31 273	53.18 312	41.94 342	27.85 344	44.86 343	56.47 344	17.93 354	41.50 344	38.08 287	68.06 315	57.85 330

test12356		38.35 324	40.80 323	31.01 338	58.31 330	9.09 356	36.67 348	46.65 324	33.65 326	44.39 345	60.94 338	17.56 355	39.23 347	16.01 351	53.03 347	44.72 346

DeepMVS_CX				11.83 343	15.51 357	13.86 355		11.25 360	5.76 353	20.85 355	26.46 352	17.06 356	9.22 356	9.69 354	13.82 354	12.42 353

pmmvs3		46.71 308	45.09 316	51.55 298	56.76 337	48.25 194	55.78 305	39.53 349	24.13 350	50.35 331	63.40 333	15.90 357	51.08 312	29.29 328	70.69 302	55.33 334

testpf		45.32 311	48.47 305	35.88 337	53.56 351	26.84 339	58.86 297	42.95 335	47.78 248	46.18 339	63.70 332	13.73 358	50.29 314	50.81 202	58.61 339	30.51 352

test2356		40.85 323	40.47 325	41.98 327	58.78 327	28.65 336	39.45 341	40.98 347	31.95 335	48.47 335	56.63 343	12.54 359	44.41 332	15.84 352	59.58 334	52.88 336

1111		45.08 314	47.96 309	36.43 336	59.56 323	14.82 353	43.56 333	45.65 329	45.60 259	60.04 290	75.47 266	9.31 360	34.46 349	23.66 340	68.76 313	60.02 326

.test1245		34.47 330	40.38 326	16.73 342	59.56 323	14.82 353	43.56 333	45.65 329	45.60 259	60.04 290	75.47 266	9.31 360	34.46 349	23.66 340	0.55 356	0.90 355

PNet_i23d		36.76 327	36.63 330	37.12 335	58.19 332	33.00 317	39.86 340	32.55 353	48.44 241	39.64 349	51.31 348	6.89 362	41.83 343	22.29 343	30.55 352	36.54 351

testmvs		4.06 336	5.28 337	0.41 345	0.64 360	0.16 361	42.54 335	0.31 362	0.26 356	0.50 357	1.40 358	0.77 363	0.17 358	0.56 355	0.55 356	0.90 355

test123		4.43 335	5.78 336	0.39 346	0.97 359	0.28 360	46.33 330	0.45 361	0.31 355	0.62 356	1.50 357	0.61 364	0.11 359	0.56 355	0.63 355	0.77 357

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		5.62 333	7.50 334	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	67.46 325	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																70.05 273

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								19.26 358

gm-plane-assit					62.51 305	33.91 307			37.25 305		62.71 335		72.74 220	38.70 280

test9_res														72.12 54	91.37 80	77.40 218

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			61.28 297	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		72.09 257

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								0.00 363

nn								0.00 363

door-mid								55.02 286

test11								82.71 68

door								52.91 301

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