High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 8	85.07 36	99.27 3	99.54 1

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 26	80.07 70	86.75 148	93.26 97	93.64 2	90.93 176	84.60 44	90.75 223	93.97 100

Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 178	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 64	76.26 98	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 81	95.12 46	90.98 8	88.92 218	77.55 123	97.07 66	83.13 276
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 38	79.20 80	93.83 28	93.60 95	90.81 9	92.96 128	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 76	70.36 142	89.55 51	93.84 28	81.89 54	94.70 16	95.44 39	90.69 10	88.31 229	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 49	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 93	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 62	93.94 90	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 63	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 55	94.18 94

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 49	86.15 18	93.37 8	95.10 7	90.28 9	92.11 54	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pcd1.5k->3k		38.83 319	41.11 320	32.01 330	93.13 61	0.00 351	0.00 341	91.38 112	0.00 345	0.00 347	0.00 347	89.24 16	0.00 348	0.00 345	96.24 93	96.02 48

ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 53	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
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
APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 65	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 101	91.97 59	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 129	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 51	91.47 66	93.96 86	88.35 21	95.56 22	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 116	91.47 66	93.96 86	88.35 21	95.56 22	84.88 39	97.74 48	92.85 129

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 37	91.06 71	94.00 83	88.26 23	95.71 18	87.28 22	98.39 23	92.55 143

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 42	92.60 48	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 27	80.32 67	91.74 63	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 65	78.78 87	92.51 49	93.64 94	88.13 27	93.84 80	84.83 41	97.55 56	94.10 97

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 86	62.28 217	84.66 126	89.15 166	83.54 34	89.85 92	97.32 4	88.08 28	86.80 244	70.43 179	97.30 61	96.62 37

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 51	84.79 36	89.89 44	90.63 127	70.00 196	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 104	95.52 65

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 53	90.88 76	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

wuyk23d		75.13 235	79.30 197	62.63 310	75.56 315	75.18 103	80.89 221	73.10 288	75.06 138	94.76 15	95.32 40	87.73 31	52.85 340	34.16 334	97.11 65	59.85 334

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 119	93.03 1	85.85 111	81.96 237	74.58 141	98.58 2	97.29 5	87.73 31	87.31 237	82.84 67	99.41 1	81.99 289

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 38	91.54 65	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 51	91.40 68	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 249	89.04 60	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 248	88.95 61	93.19 47	91.62 4	96.01 6	96.16 22	87.02 37	95.60 20	78.69 113	98.72 10	98.97 3

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 252	88.93 63	92.84 63	91.92 2	96.16 3	96.23 20	86.95 38	95.99 4	79.05 110	98.57 16	98.80 6

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 44	88.52 123	94.37 72	86.74 39	95.41 30	86.32 26	98.21 32	93.19 124

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	94.03 82	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 159	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	16.05 342	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 173	67.13 162	87.55 81	94.32 12	73.44 153	88.47 124	87.54 224	86.45 44	91.06 173	75.76 136	93.76 166	92.54 144

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 180	84.38 131	91.29 114	84.88 27	92.06 56	93.84 92	86.45 44	93.73 81	73.22 156	98.66 12	97.69 12

test_0402		88.65 58	89.58 49	85.88 105	92.55 74	72.22 124	84.01 137	89.44 163	88.63 14	94.38 21	95.77 28	86.38 46	93.59 91	79.84 102	95.21 127	91.82 163

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 73	81.34 56	90.19 37	93.08 51	80.87 62	91.13 70	93.19 98	86.22 47	95.97 6	82.23 73	97.18 64	90.45 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 55	89.88 43	86.22 95	91.63 98	77.07 88	89.82 45	93.77 30	78.90 85	92.88 39	92.29 122	86.11 48	90.22 196	86.24 30	97.24 62	91.36 174

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 55	84.72 37	89.70 46	90.29 143	69.27 200	94.39 20	96.38 15	86.02 49	93.52 101	83.96 51	95.92 107	95.34 68

nrg030		87.85 68	88.49 61	85.91 103	90.07 134	69.73 144	87.86 76	94.20 17	74.04 146	92.70 46	94.66 58	85.88 50	91.50 161	79.72 103	97.32 60	96.50 40

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 50	79.07 70	88.54 69	94.20 17	73.53 151	89.71 96	94.82 54	85.09 51	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v52		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.97 8	96.31 16	84.82 52	93.98 73	88.59 4	94.83 141	98.23 8

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 48	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 53	93.16 120	91.10 1	97.53 57	96.58 39
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
V4		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.98 7	96.31 16	84.80 54	93.98 73	88.59 4	94.83 141	98.23 8

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 109	77.50 83	87.55 81	92.97 57	87.95 17	89.62 102	92.87 107	84.56 55	93.89 77	77.65 122	96.62 78	90.70 186

LS3D		90.60 28	90.34 40	91.38 23	89.03 148	84.23 42	93.58 4	94.68 10	90.65 7	90.33 80	93.95 89	84.50 56	95.37 31	80.87 86	95.50 119	94.53 85

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 139	86.67 12	90.51 35	90.20 148	69.87 197	95.06 14	96.14 23	84.28 57	93.07 127	87.68 13	96.34 88	97.09 30

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 94	81.68 53	87.49 83	93.17 48	76.02 125	88.64 120	91.22 145	84.24 58	93.37 110	77.97 121	97.03 67	95.52 65

XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 34	80.16 69	89.13 113	93.44 96	83.82 59	90.98 174	83.86 54	95.30 126	93.60 114

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 55	93.89 91	83.80 60	93.10 123	82.67 69	98.04 36	93.64 112

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 84	75.45 101	83.85 143	92.01 83	65.91 227	86.19 158	91.75 135	83.77 61	94.98 45	77.43 126	96.71 76	93.73 109

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 202	65.61 172	86.55 105	92.40 74	78.64 90	81.34 226	84.18 267	83.65 62	92.93 130	74.22 144	87.87 255	92.17 157

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 132	75.86 100	79.23 244	87.13 196	73.35 154	85.56 168	89.34 195	83.60 63	90.50 190	76.64 132	94.05 158	90.09 203

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 122	97.64 3	83.45 64	94.55 57	86.02 33	98.60 14	96.67 36

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 136	72.26 123	86.29 108	90.00 152	78.19 96	81.65 221	87.16 227	83.40 65	94.24 62	61.69 235	94.76 146	84.21 261

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 228	55.75 267	80.05 228	94.27 13	76.47 120	96.09 5	94.54 63	83.31 66	89.75 205	59.95 246	94.89 137	90.75 185

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 251	79.20 69	83.52 155	89.33 164	83.33 35	89.92 91	85.07 256	83.23 67	93.16 120	83.39 57	92.72 192	93.83 103

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 118	55.20 272	82.29 190	87.26 191	79.65 74	88.38 127	95.52 37	83.00 68	86.88 242	67.97 199	96.60 79	94.45 88

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 67	77.30 87	85.25 118	91.23 116	77.31 111	87.07 144	91.47 141	82.94 69	94.71 51	84.67 43	96.27 92	92.62 142

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 106	84.90 34	83.41 160	92.38 76	70.25 194	89.35 110	90.68 170	82.85 70	94.57 55	79.55 105	95.95 105	92.00 159

v7n		90.13 32	90.96 32	87.65 75	91.95 92	71.06 138	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 71	94.08 69	86.25 29	97.63 51	97.82 11

AllTest		87.97 66	87.40 76	89.68 47	91.59 99	83.40 45	89.50 54	95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

TestCases				89.68 47	91.59 99	83.40 45		95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

v748		88.91 57	89.82 45	86.19 99	90.06 135	68.53 154	88.81 65	91.48 99	84.36 28	94.19 24	95.98 25	82.52 74	92.67 137	84.30 47	96.67 77	97.37 20

RPSCF		88.00 65	86.93 84	91.22 27	90.08 133	89.30 6	89.68 48	91.11 119	79.26 79	89.68 97	94.81 57	82.44 75	87.74 234	76.54 133	88.74 245	96.61 38

ITE_SJBPF				90.11 45	90.72 124	84.97 33		90.30 140	81.56 57	90.02 84	91.20 148	82.40 76	90.81 181	73.58 152	94.66 148	94.56 81

Fast-Effi-MVS+		81.04 184	80.57 185	82.46 180	87.50 192	63.22 195	78.37 251	89.63 159	68.01 210	81.87 216	82.08 291	82.31 77	92.65 138	67.10 202	88.30 251	91.51 171

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 251	76.90 90	83.52 155	87.94 184	82.18 50	89.19 111	85.07 256	82.28 78	91.89 152	82.40 71	92.72 192	93.69 111

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 102	77.69 80	83.69 150	90.30 140	66.29 223	84.32 189	91.07 156	82.13 79	93.18 118	81.02 83	96.36 87	90.98 176

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 247	77.57 82	84.07 135	88.00 182	82.82 42	89.84 93	85.48 246	82.06 80	92.77 134	83.83 55	91.04 211	95.22 74

CLD-MVS		83.18 159	82.64 161	84.79 121	89.05 147	67.82 160	77.93 255	92.52 72	68.33 208	85.07 171	81.54 296	82.06 80	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TEST9					92.34 81	79.70 66	83.94 139	90.32 136	65.41 235	84.49 185	90.97 159	82.03 82	93.63 86

segment_acmp												81.94 83

train_agg		85.98 100	85.28 110	88.07 68	92.34 81	79.70 66	83.94 139	90.32 136	65.79 228	84.49 185	90.97 159	81.93 84	93.63 86	81.21 80	96.54 81	90.88 181

test_8					92.09 88	78.87 72	83.82 144	90.31 138	65.79 228	84.36 188	90.96 161	81.93 84	93.44 107

test_prior3		86.31 91	86.31 93	86.32 91	90.59 126	71.99 128	83.37 161	92.85 61	75.43 134	84.58 183	91.57 137	81.92 86	94.17 66	79.54 106	96.97 68	92.80 131

test_prior2							83.37 161		75.43 134	84.58 183	91.57 137	81.92 86		79.54 106	96.97 68

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 251	88.66 68	92.06 82	90.78 6	95.67 9	95.17 44	81.80 88	95.54 24	79.00 111	98.69 11	98.95 4

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 145	83.07 48	80.99 220	83.56 228	72.71 167	86.07 159	89.07 198	81.75 89	86.19 252	77.11 129	93.36 175	88.24 218

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 80	82.98 49	91.98 25	90.08 149	71.54 183	94.28 23	96.54 13	81.57 90	94.27 59	86.26 27	96.49 84	97.09 30

cdsmvs_eth3d_5k		20.81 320	27.75 321	0.00 336	0.00 350	0.00 351	0.00 341	85.44 214	0.00 345	0.00 347	82.82 283	81.46 91	0.00 348	0.00 345	0.00 347	0.00 345

WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 211	89.54 53	93.31 41	90.21 10	95.57 11	95.66 31	81.42 92	95.90 9	80.94 85	98.80 4	98.84 5

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 72	87.50 137	92.38 118	81.42 92	93.28 115	83.07 62	97.24 62	91.67 166

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 137	59.66 242	82.43 185	87.44 188	75.52 133	87.85 132	95.26 42	81.25 94	85.65 259	68.74 193	96.04 102	94.42 89

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 78	79.92 62	83.98 138	90.03 151	65.11 237	83.66 197	90.64 174	81.00 95	93.67 83	81.21 80	96.54 81	90.88 181

NCCC		87.36 71	86.87 85	88.83 58	92.32 83	78.84 73	86.58 104	91.09 120	78.77 88	84.85 176	90.89 163	80.85 96	95.29 33	81.14 82	95.32 123	92.34 150

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 153	79.72 65	87.15 89	93.50 35	69.17 202	85.80 164	89.56 192	80.76 97	92.13 146	73.21 160	95.51 118	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 166	81.41 177	85.90 104	85.60 229	76.53 95	83.07 169	89.62 160	73.02 164	79.11 247	83.51 273	80.74 98	90.24 195	68.76 192	89.29 237	90.94 178

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 130	57.52 255	81.30 215	88.69 170	76.29 121	87.58 135	94.44 66	80.60 99	87.20 238	66.60 208	96.82 74	94.34 91

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 247	73.43 112	84.07 135	85.26 216	80.77 63	88.62 121	85.48 246	80.56 100	90.39 192	81.99 75	91.04 211	94.85 79

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 95	86.69 149	92.28 123	80.36 101	95.06 43	86.17 31	96.49 84	90.22 198

ANet_high		83.17 160	85.68 104	75.65 261	81.24 274	45.26 323	79.94 230	92.91 59	83.83 33	91.33 69	96.88 10	80.25 102	85.92 255	68.89 191	95.89 108	95.76 53

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 253	72.76 116	83.91 142	85.18 218	80.44 64	88.75 118	85.49 245	80.08 103	91.92 150	82.02 74	90.85 221	95.97 49

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 109	76.92 89	87.19 87	91.99 84	70.56 190	84.96 172	90.69 169	80.01 104	95.14 40	78.37 114	95.78 113	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 256	72.52 119	83.82 144	85.15 219	80.27 68	88.75 118	85.45 249	79.95 105	91.90 151	81.92 76	90.80 222	96.13 43

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 99	71.58 135	83.52 155	92.13 80	61.82 258	83.96 193	89.75 189	79.93 106	93.46 106	78.33 116	94.34 154	91.87 162

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 92	92.49 20	91.19 118	67.85 215	86.63 150	94.84 53	79.58 107	95.96 7	87.62 14	94.50 151	94.56 81

test12				86.57 86	90.74 123	72.63 117		90.69 125		82.76 208		79.20 108	94.80 49		95.32 123	92.27 153

CSCG		86.26 92	86.47 91	85.60 110	90.87 121	74.26 108	87.98 74	91.85 88	80.35 66	89.54 108	88.01 215	79.09 109	92.13 146	75.51 137	95.06 132	90.41 195

Test By Simon												79.09 109

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 162	77.34 85	89.35 58	93.05 52	73.15 162	84.76 177	87.70 221	78.87 111	94.18 64	80.67 90	96.29 89	92.73 133

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 86	72.61 118	82.20 196	87.02 200	72.63 168	88.86 115	91.02 157	78.52 112	91.11 171	73.41 155	91.09 209	88.21 219

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 204	91.15 3	87.70 79	88.42 173	74.57 142	83.56 199	85.65 243	78.49 113	94.21 63	72.04 167	92.88 189	94.05 98

mvs-test1		84.55 123	82.12 168	91.84 17	87.13 204	89.54 5	85.05 121	88.42 173	74.57 142	80.60 232	82.98 279	78.49 113	93.98 73	72.04 167	89.77 234	92.00 159

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 88	77.43 84	87.35 84	92.09 81	78.87 86	84.27 192	94.05 81	78.35 115	93.65 84	80.54 92	91.58 206	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 69	67.02 163	82.55 183	91.56 95	83.08 39	90.92 73	91.82 132	78.25 116	93.99 71	74.16 145	98.35 24	97.49 16

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 96	71.56 136	86.75 98	93.02 56	75.95 126	87.12 141	89.39 194	77.98 117	89.40 210	77.46 124	94.78 143	84.75 255

API-MVS		82.28 169	82.61 162	81.30 195	86.29 221	69.79 143	88.71 67	87.67 186	78.42 93	82.15 213	84.15 269	77.98 117	91.59 160	65.39 216	92.75 191	82.51 283

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 97	75.27 102	83.23 167	92.40 74	72.04 177	82.04 214	88.33 211	77.91 119	93.95 76	66.17 210	95.12 130	90.34 197

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 62	68.48 155	83.80 146	92.87 60	80.37 65	89.61 104	91.81 133	77.72 120	94.18 64	75.00 142	98.53 17	96.99 34

PCF-MVS	74.62 15	82.15 171	80.92 184	85.84 106	89.43 141	72.30 122	80.53 224	91.82 89	57.36 277	87.81 133	89.92 186	77.67 121	93.63 86	58.69 252	95.08 131	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 58	64.56 177	82.21 194	90.46 131	80.99 61	88.42 125	91.97 126	77.56 122	93.85 78	72.46 164	98.65 13	97.61 13

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 111	81.66 54	91.25 32	94.13 22	88.89 12	88.83 117	94.26 74	77.55 123	95.86 12	84.88 39	95.87 109	95.24 71

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 266	57.75 254	82.54 184	91.96 86	71.16 186	82.89 207	92.52 117	77.41 124	90.50 190	80.04 100	87.84 256	92.40 147

xiu_mvs_v2_base		77.19 213	76.75 212	78.52 227	87.01 209	61.30 228	75.55 277	87.12 198	61.24 264	74.45 279	78.79 309	77.20 125	90.93 176	64.62 221	84.80 286	83.32 272

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 58	67.02 163	83.16 168	92.21 78	81.73 55	90.92 73	91.97 126	77.20 125	93.99 71	74.16 145	98.35 24	97.61 13

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 230	91.47 107	53.44 281	82.29 190	87.00 201	79.06 83	89.55 106	95.72 30	77.20 125	86.14 253	72.30 165	98.51 18	95.28 70

TinyColmap		81.25 182	82.34 167	77.99 235	85.33 233	60.68 236	82.32 189	88.33 175	71.26 185	86.97 146	92.22 125	77.10 128	86.98 241	62.37 230	95.17 129	86.31 240

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 79	83.40 45	88.83 64	91.92 87	73.19 161	80.18 240	89.15 197	77.04 129	93.28 115	65.82 215	92.28 198	92.21 156

114514_t		83.10 161	82.54 164	84.77 122	92.90 66	69.10 153	86.65 102	90.62 128	54.66 289	81.46 223	90.81 166	76.98 130	94.38 58	72.62 163	96.18 94	90.82 184

xiu_mvs_v1_base_debu		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 234	87.17 193	65.43 232	79.59 242	82.73 285	76.94 131	90.14 199	73.22 156	88.33 247	86.90 235

xiu_mvs_v1_base		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 234	87.17 193	65.43 232	79.59 242	82.73 285	76.94 131	90.14 199	73.22 156	88.33 247	86.90 235

xiu_mvs_v1_base_debi		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 234	87.17 193	65.43 232	79.59 242	82.73 285	76.94 131	90.14 199	73.22 156	88.33 247	86.90 235

pcd_1.5k_mvsjas		6.41 323	8.55 324	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	76.94 131	0.00 348	0.00 345	0.00 347	0.00 345

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 56	77.96 78	87.94 75	91.97 85	70.73 189	94.19 24	96.67 11	76.94 131	94.57 55	83.07 62	96.28 90	96.15 42

PS-MVSNAJ		77.04 215	76.53 218	78.56 226	87.09 208	61.40 226	75.26 278	87.13 196	61.25 263	74.38 281	77.22 315	76.94 131	90.94 175	64.63 220	84.83 285	83.35 271

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 202	82.72 179	84.53 226	77.57 103	90.34 79	95.92 26	76.88 137	85.83 257	61.88 233	97.42 58	93.62 113

原ACMM1				84.60 127	92.81 71	74.01 109		91.50 97	62.59 252	82.73 209	90.67 171	76.53 138	94.25 61	69.24 186	95.69 116	85.55 247

MSDG		80.06 197	79.99 195	80.25 206	83.91 255	68.04 158	77.51 260	89.19 165	77.65 101	81.94 215	83.45 275	76.37 139	86.31 251	63.31 228	86.59 267	86.41 238

Gipuma		84.44 126	86.33 92	78.78 222	84.20 250	73.57 111	89.55 51	90.44 132	84.24 29	84.38 187	94.89 51	76.35 140	80.40 288	76.14 134	96.80 75	82.36 284
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing_2		84.36 130	84.64 128	83.50 163	86.74 213	63.97 183	84.56 128	90.31 138	66.22 224	91.62 64	94.55 61	75.88 141	91.95 149	77.02 131	94.89 137	94.56 81

XXY-MVS		74.44 244	76.19 221	69.21 290	84.61 241	52.43 289	71.70 296	77.18 259	60.73 267	80.60 232	90.96 161	75.44 142	69.35 314	56.13 269	88.33 247	85.86 245

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 131	61.05 232	86.83 94	88.27 177	78.57 91	89.66 98	95.64 33	75.43 143	90.68 185	69.09 189	95.33 122	93.82 105

CANet		83.79 149	82.85 158	86.63 85	86.17 226	72.21 125	83.76 148	91.43 106	77.24 112	74.39 280	87.45 225	75.36 144	95.42 29	77.03 130	92.83 190	92.25 155

ab-mvs		79.67 198	80.56 186	76.99 247	88.48 161	56.93 258	84.70 125	86.06 208	68.95 206	80.78 231	93.08 100	75.30 145	84.62 268	56.78 266	90.90 219	89.43 207

v13		87.31 72	88.10 63	84.94 116	88.84 151	63.75 184	87.85 77	91.47 102	79.12 81	93.72 29	95.82 27	75.20 146	93.58 94	84.76 42	96.16 95	97.48 17

v11		86.96 76	87.78 69	84.51 128	88.50 160	62.60 207	87.21 86	91.63 94	78.08 98	93.40 34	95.56 36	75.07 147	93.57 95	84.46 46	96.08 100	97.36 21

DELS-MVS		81.44 178	81.25 179	82.03 183	84.27 247	62.87 201	76.47 269	92.49 73	70.97 188	81.64 222	83.83 270	75.03 148	92.70 135	74.29 143	92.22 201	90.51 193
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
PAPR		78.84 201	78.10 204	81.07 199	85.17 234	60.22 239	82.21 194	90.57 129	62.51 253	75.32 273	84.61 263	74.99 149	92.30 143	59.48 250	88.04 253	90.68 187

v12		87.15 75	87.91 65	84.84 119	88.69 154	63.52 187	87.58 80	91.46 103	78.74 89	93.57 32	95.66 31	74.94 150	93.57 95	84.50 45	96.08 100	97.43 18

CNLPA		83.55 154	83.10 156	84.90 118	89.34 143	83.87 43	84.54 129	88.77 168	79.09 82	83.54 200	88.66 204	74.87 151	81.73 284	66.84 206	92.29 197	89.11 211

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 52	76.42 96	89.45 56	93.61 32	79.44 77	86.55 151	92.95 105	74.84 152	95.22 37	80.78 88	95.83 111	94.46 86

plane_prior6					92.61 72	76.54 93						74.84 152

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 84	56.44 262	85.75 112	93.09 50	77.33 110	91.94 60	94.65 59	74.78 154	93.41 109	75.11 140	98.58 15	97.88 10

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 115	65.16 174	83.25 165	84.97 224	79.79 71	87.18 140	94.27 73	74.77 155	90.89 179	69.24 186	96.54 81	93.55 118

BH-untuned		80.96 185	80.99 182	80.84 201	88.55 158	68.23 156	80.33 226	88.46 172	72.79 166	86.55 151	86.76 231	74.72 156	91.77 156	61.79 234	88.99 241	82.52 282

VPNet		80.25 193	81.68 173	75.94 259	92.46 77	47.98 318	76.70 265	81.67 241	73.45 152	84.87 175	92.82 108	74.66 157	86.51 248	61.66 236	96.85 71	93.33 119

V9		86.96 76	87.70 70	84.74 123	88.52 159	63.27 193	87.31 85	91.45 105	78.28 94	93.43 33	95.45 38	74.59 158	93.57 95	84.23 49	96.01 103	97.38 19

tfpnnormal		81.79 176	82.95 157	78.31 229	88.93 150	55.40 268	80.83 223	82.85 232	76.81 117	85.90 163	94.14 79	74.58 159	86.51 248	66.82 207	95.68 117	93.01 127

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 194	74.83 104	84.18 133	93.30 42	77.48 104	77.39 257	88.46 206	74.53 160	95.74 16	78.09 120	94.75 147	92.36 149

V14		86.75 82	87.46 73	84.62 126	88.35 163	63.00 198	87.02 91	91.42 108	77.78 100	93.27 35	95.23 43	74.22 161	93.56 98	83.95 52	95.93 106	97.31 22

V42		83.47 156	83.37 152	83.75 150	83.16 262	63.33 191	81.31 213	90.23 147	69.51 199	90.91 75	90.81 166	74.16 162	92.29 144	80.06 99	90.22 231	95.62 60

3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 219	74.71 105	88.77 66	90.00 152	75.65 132	84.96 172	93.17 99	74.06 163	91.19 169	78.28 117	91.09 209	89.29 210

v10		86.54 86	87.10 78	84.84 119	88.16 169	63.28 192	86.64 103	92.20 79	75.42 136	92.81 43	94.50 64	74.05 164	94.06 70	83.88 53	96.28 90	97.17 28

v7		84.81 117	85.00 114	84.23 138	88.15 170	63.27 193	83.79 147	91.39 111	71.10 187	90.07 82	91.28 143	74.04 165	93.63 86	81.48 79	93.67 169	95.79 52

v15		86.56 85	87.25 77	84.51 128	88.15 170	62.72 203	86.72 101	91.40 110	77.38 105	93.11 37	95.00 48	73.93 166	93.55 99	83.67 56	95.86 110	97.26 23

旧先验1					91.97 91	71.77 130		81.78 240			91.84 130	73.92 167			93.65 170	83.61 266

mvs_anonymous		78.13 205	78.76 200	76.23 258	79.24 289	50.31 312	78.69 249	84.82 225	61.60 262	83.09 206	92.82 108	73.89 168	87.01 239	68.33 197	86.41 269	91.37 173

MAR-MVS		80.24 194	78.74 201	84.73 124	86.87 212	78.18 76	85.75 112	87.81 185	65.67 231	77.84 252	78.50 310	73.79 169	90.53 189	61.59 238	90.87 220	85.49 249
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
v17		86.32 90	86.95 83	84.44 132	88.00 172	62.62 206	86.74 99	91.48 99	77.17 113	92.74 44	94.56 60	73.74 170	93.53 100	83.27 59	94.87 140	97.18 27

v16		86.24 93	86.85 86	84.43 133	87.96 174	62.59 208	86.73 100	91.48 99	77.17 113	92.67 47	94.55 61	73.63 171	93.52 101	83.26 60	94.16 155	97.17 28

test_normal		81.23 183	81.16 180	81.43 194	84.77 240	61.99 219	81.46 212	86.95 202	63.16 248	87.22 139	89.63 190	73.62 172	91.65 159	72.92 161	90.70 224	90.65 189

DI_MVS_plusplus_test		81.27 181	81.26 178	81.29 196	84.98 235	61.65 224	81.98 199	87.25 192	63.56 243	87.56 136	89.60 191	73.62 172	91.83 154	72.20 166	90.59 229	90.38 196

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 245	85.42 117	81.11 243	86.41 22	87.41 138	96.21 21	73.61 174	90.61 188	66.33 209	96.85 71	93.81 108

FIs		85.35 108	86.27 94	82.60 175	91.86 95	57.31 256	85.10 120	93.05 52	75.83 128	91.02 72	93.97 84	73.57 175	92.91 132	73.97 148	98.02 39	97.58 15

v1144		84.54 125	84.72 121	84.00 143	87.67 188	62.55 209	82.97 171	90.93 123	70.32 193	89.80 94	90.99 158	73.50 176	93.48 105	81.69 78	94.65 149	95.97 49

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 120	65.98 169	88.19 72	90.78 124	78.13 97	80.87 230	87.92 219	73.49 177	92.42 141	70.07 180	88.40 246	91.60 168

v8		86.22 95	86.83 87	84.36 135	87.82 180	62.35 212	86.42 106	91.33 113	76.78 118	92.73 45	94.48 65	73.41 178	93.72 82	83.10 61	95.41 120	97.01 33

EI-MVSNet		82.61 164	82.42 166	83.20 166	83.25 260	63.66 185	83.50 158	85.07 220	76.06 123	86.55 151	85.10 254	73.41 178	90.25 193	78.15 119	90.67 225	95.68 58

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 195	63.66 185	83.25 165	89.88 154	76.06 123	89.62 102	92.37 121	73.40 180	92.52 140	78.16 118	94.77 145	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v6		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.26 146	72.33 173	89.94 88	91.19 149	73.30 181	93.29 112	80.26 96	93.26 179	95.62 60

v144192		84.24 135	84.41 134	83.71 154	87.59 191	61.57 225	82.95 172	91.03 121	67.82 216	89.80 94	90.49 175	73.28 182	93.51 104	81.88 77	94.89 137	96.04 47

v1neww		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

v18		85.99 99	86.55 90	84.30 137	87.73 186	62.29 216	86.40 107	91.49 98	76.64 119	92.40 52	94.20 77	73.28 182	93.52 101	82.87 65	93.99 159	97.09 30

BH-RMVSNet		80.53 190	80.22 191	81.49 193	87.19 203	66.21 168	77.79 257	86.23 206	74.21 145	83.69 195	88.50 205	73.25 186	90.75 182	63.18 229	87.90 254	87.52 228

PLC	73.85 16	82.09 172	80.31 189	87.45 78	90.86 122	80.29 60	85.88 110	90.65 126	68.17 209	76.32 263	86.33 237	73.12 187	92.61 139	61.40 239	90.02 233	89.44 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs		79.20 200	79.04 198	79.69 213	78.64 295	58.90 247	81.79 204	87.61 187	65.07 238	73.65 285	89.80 187	73.10 188	87.79 233	75.02 141	86.63 266	92.38 148

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 60	80.37 59	91.91 27	93.11 49	81.10 60	95.32 13	97.24 6	72.94 189	94.85 48	85.07 36	97.78 46	97.26 23

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 54	54.88 273	78.67 250	85.02 222	81.24 59	90.74 77	91.56 139	72.85 190	91.08 172	68.00 198	98.04 36	97.23 25

VNet		79.31 199	80.27 190	76.44 254	87.92 175	53.95 277	75.58 276	84.35 227	74.39 144	82.23 211	90.72 168	72.84 191	84.39 270	60.38 245	93.98 160	90.97 177

v1		84.16 138	84.38 136	83.52 160	87.33 196	61.71 220	82.79 176	89.73 157	71.89 182	89.64 99	91.11 154	72.72 192	93.10 123	80.40 93	93.79 165	95.75 54

QAPM		82.59 165	82.59 163	82.58 176	86.44 214	66.69 166	89.94 43	90.36 135	67.97 212	84.94 174	92.58 115	72.71 193	92.18 145	70.63 177	87.73 257	88.85 216

v1192		84.57 122	84.69 123	84.21 139	87.75 185	62.88 200	83.02 170	91.43 106	69.08 204	89.98 87	90.89 163	72.70 194	93.62 90	82.41 70	94.97 135	96.13 43

v1141		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 180	89.64 99	91.12 152	72.68 195	93.10 123	80.39 95	93.80 164	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 180	89.64 99	91.12 152	72.68 195	93.10 123	80.40 93	93.81 163	95.75 54

OpenMVS	76.72 13	81.98 175	82.00 171	81.93 184	84.42 245	68.22 157	88.50 70	89.48 162	66.92 219	81.80 220	91.86 128	72.59 197	90.16 198	71.19 171	91.25 207	87.40 230

TSAR-MVS + GP.		83.95 146	82.69 160	87.72 73	89.27 144	81.45 55	83.72 149	81.58 242	74.73 140	85.66 165	86.06 241	72.56 198	92.69 136	75.44 138	95.21 127	89.01 215

alignmvs		83.94 147	83.98 145	83.80 147	87.80 184	67.88 159	84.54 129	91.42 108	73.27 160	88.41 126	87.96 216	72.33 199	90.83 180	76.02 135	94.11 156	92.69 135

HQP2-MVS												72.10 200

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 112	70.66 140	84.77 122	92.68 67	73.30 157	80.55 235	90.17 183	72.10 200	94.61 53	77.30 127	94.47 152	93.56 116

testgi		72.36 256	74.61 233	65.59 303	80.56 282	42.82 331	68.29 305	73.35 284	66.87 220	81.84 217	89.93 185	72.08 202	66.92 322	46.05 315	92.54 194	87.01 234

v1921920		84.23 136	84.37 139	83.79 148	87.64 190	61.71 220	82.91 173	91.20 117	67.94 213	90.06 83	90.34 177	72.04 203	93.59 91	82.32 72	94.91 136	96.07 45

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 92	89.16 112	92.25 124	72.03 204	96.36 2	88.21 9	90.93 218	90.55 192

LF4IMVS		82.75 163	81.93 172	85.19 113	82.08 267	80.15 61	85.53 115	88.76 169	68.01 210	85.58 167	87.75 220	71.80 205	86.85 243	74.02 147	93.87 162	88.58 217

v1240		84.30 133	84.51 132	83.65 155	87.65 189	61.26 229	82.85 174	91.54 96	67.94 213	90.68 78	90.65 172	71.71 206	93.64 85	82.84 67	94.78 143	96.07 45

ambc				82.98 169	90.55 128	64.86 175	88.20 71	89.15 166		89.40 109	93.96 86	71.67 207	91.38 168	78.83 112	96.55 80	92.71 134

1121		80.86 186	79.81 196	84.02 142	93.93 45	78.70 74	81.64 208	80.18 248	55.43 286	83.67 196	91.15 150	71.29 208	91.41 166	67.95 200	93.06 185	81.96 290

新几何1				82.95 170	93.96 44	78.56 75		80.24 247	55.45 285	83.93 194	91.08 155	71.19 209	88.33 228	65.84 214	93.07 184	81.95 291

v148		82.31 168	82.48 165	81.81 191	85.59 230	59.66 242	81.47 211	86.02 209	72.85 165	88.05 129	90.65 172	70.73 210	90.91 178	75.15 139	91.79 203	94.87 77

v2v482		84.09 141	84.24 141	83.62 156	87.13 204	61.40 226	82.71 180	89.71 158	72.19 176	89.55 106	91.41 142	70.70 211	93.20 117	81.02 83	93.76 166	96.25 41

UGNet		82.78 162	81.64 174	86.21 97	86.20 225	76.24 99	86.86 92	85.68 212	77.07 115	73.76 283	92.82 108	69.64 212	91.82 155	69.04 190	93.69 168	90.56 191
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
MG-MVS		80.32 192	80.94 183	78.47 228	88.18 167	52.62 288	82.29 190	85.01 223	72.01 178	79.24 246	92.54 116	69.36 213	93.36 111	70.65 176	89.19 240	89.45 205

IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 90	66.87 165	91.21 33	88.64 171	86.30 23	89.60 105	92.59 113	69.22 214	94.91 47	73.89 149	97.89 44	96.72 35

PVSNet_BlendedMVS		78.80 202	77.84 205	81.65 192	84.43 243	63.41 188	79.49 237	90.44 132	61.70 261	75.43 271	87.07 229	69.11 215	91.44 164	60.68 243	92.24 199	90.11 202

PVSNet_Blended		76.49 225	75.40 227	79.76 211	84.43 243	63.41 188	75.14 279	90.44 132	57.36 277	75.43 271	78.30 311	69.11 215	91.44 164	60.68 243	87.70 258	84.42 258

BH-w/o		76.57 223	76.07 222	78.10 233	86.88 211	65.92 170	77.63 258	86.33 205	65.69 230	80.89 229	79.95 304	68.97 217	90.74 183	53.01 286	85.25 279	77.62 313

MVS		73.21 249	72.59 252	75.06 263	80.97 277	60.81 235	81.64 208	85.92 210	46.03 328	71.68 294	77.54 312	68.47 218	89.77 204	55.70 272	85.39 276	74.60 319

testdata				79.54 217	92.87 67	72.34 121		80.14 249	59.91 270	85.47 170	91.75 135	67.96 219	85.24 261	68.57 196	92.18 202	81.06 308

Test4		81.31 179	81.13 181	81.88 187	84.89 237	63.05 197	82.37 187	90.50 130	62.75 251	89.00 114	88.29 212	67.55 220	91.68 158	73.55 153	91.24 208	90.89 180

PVSNet_Blended_VisFu		81.55 177	80.49 188	84.70 125	91.58 102	73.24 114	84.21 132	91.67 93	62.86 250	80.94 228	87.16 227	67.27 221	92.87 133	69.82 182	88.94 242	87.99 223

MDA-MVSNet-bldmvs		77.47 210	76.90 211	79.16 219	79.03 291	64.59 176	66.58 313	75.67 268	73.15 162	88.86 115	88.99 199	66.94 222	81.23 285	64.71 218	88.22 252	91.64 167

test222					93.31 56	76.54 93	79.38 238	77.79 256	52.59 299	82.36 210	90.84 165	66.83 223			91.69 204	81.25 303

TR-MVS		76.77 221	75.79 223	79.72 212	86.10 227	65.79 171	77.14 262	83.02 230	65.20 236	81.40 224	82.10 290	66.30 224	90.73 184	55.57 273	85.27 278	82.65 278

OpenMVS_ROB	70.19 17	77.77 209	77.46 207	78.71 224	84.39 246	61.15 230	81.18 217	82.52 233	62.45 255	83.34 201	87.37 226	66.20 225	88.66 226	64.69 219	85.02 282	86.32 239

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 105	69.37 147	91.63 28	87.98 183	81.51 58	87.05 145	91.83 131	66.18 226	95.29 33	70.75 174	96.89 70	95.64 59

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 75	69.19 152	87.84 78	88.05 180	81.66 56	94.64 17	96.53 14	65.94 227	94.75 50	83.02 64	96.83 73	95.41 67

PatchMatch-RL		74.48 242	73.22 245	78.27 231	87.70 187	85.26 30	75.92 272	70.09 309	64.34 242	76.09 266	81.25 298	65.87 228	78.07 293	53.86 283	83.82 290	71.48 324

EPNet		80.37 191	78.41 203	86.23 94	76.75 307	73.28 113	87.18 88	77.45 258	76.24 122	68.14 308	88.93 200	65.41 229	93.85 78	69.47 184	96.12 99	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PM-MVS		80.20 195	79.00 199	83.78 149	88.17 168	86.66 13	81.31 213	66.81 323	69.64 198	88.33 128	90.19 181	64.58 230	83.63 276	71.99 169	90.03 232	81.06 308

test20.03		73.75 247	74.59 235	71.22 284	81.11 276	51.12 302	70.15 301	72.10 294	70.42 191	80.28 239	91.50 140	64.21 231	74.72 304	46.96 313	94.58 150	87.82 227

cascas		76.29 227	74.81 232	80.72 204	84.47 242	62.94 199	73.89 288	87.34 189	55.94 283	75.16 275	76.53 318	63.97 232	91.16 170	65.00 217	90.97 217	88.06 221

TAMVS		78.08 206	76.36 219	83.23 165	90.62 125	72.87 115	79.08 245	80.01 250	61.72 260	81.35 225	86.92 230	63.96 233	88.78 223	50.61 295	93.01 187	88.04 222

GBi-Net		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

test1		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

FMVSNet2		81.31 179	81.61 175	80.41 205	86.38 216	58.75 250	83.93 141	86.58 204	72.43 169	87.65 134	92.98 103	63.78 234	90.22 196	66.86 204	93.92 161	92.27 153

USDC		76.63 222	76.73 213	76.34 256	83.46 258	57.20 257	80.02 229	88.04 181	52.14 304	83.65 198	91.25 144	63.24 237	86.65 247	54.66 280	94.11 156	85.17 250

new-patchmatchnet		70.10 272	73.37 244	60.29 318	81.23 275	16.95 346	59.54 324	74.62 274	62.93 249	80.97 227	87.93 218	62.83 238	71.90 308	55.24 276	95.01 134	92.00 159

K. test v3		85.14 109	84.73 119	86.37 90	91.13 116	69.63 146	85.45 116	76.68 264	84.06 32	92.44 51	96.99 8	62.03 239	94.65 52	80.58 91	93.24 182	94.83 80

lessismore_v0				85.95 102	91.10 117	70.99 139		70.91 306		91.79 61	94.42 67	61.76 240	92.93 130	79.52 108	93.03 186	93.93 102

1314		73.22 248	72.56 253	75.20 262	80.41 283	57.84 252	81.64 208	85.36 215	51.68 307	73.10 287	76.65 317	61.45 241	85.19 262	63.54 225	79.21 314	82.59 279

CANet_DTU		77.81 208	77.05 209	80.09 208	81.37 273	59.90 241	83.26 164	88.29 176	69.16 203	67.83 311	83.72 271	60.93 242	89.47 206	69.22 188	89.70 235	90.88 181

pmmvs-eth3d		78.42 204	77.04 210	82.57 178	87.44 193	74.41 107	80.86 222	79.67 251	55.68 284	84.69 178	90.31 180	60.91 243	85.42 260	62.20 231	91.59 205	87.88 226

UnsupCasMVSNet_eth		71.63 262	72.30 255	69.62 288	76.47 309	52.70 287	70.03 302	80.97 245	59.18 271	79.36 245	88.21 213	60.50 244	69.12 315	58.33 259	77.62 318	87.04 233

Patchmatch-test1		72.75 252	72.61 251	73.19 273	81.62 271	55.86 265	78.89 247	71.37 301	61.73 259	74.93 276	82.15 289	60.46 245	81.80 282	59.68 248	82.63 300	81.92 292

jason		77.42 211	75.75 225	82.43 181	87.10 207	69.27 148	77.99 254	81.94 239	51.47 309	77.84 252	85.07 256	60.32 246	89.00 216	70.74 175	89.27 239	89.03 213
jason: jason.
1112_ss		74.82 241	73.74 240	78.04 234	89.57 140	60.04 240	76.49 268	87.09 199	54.31 290	73.66 284	79.80 305	60.25 247	86.76 246	58.37 257	84.15 289	87.32 231

HY-MVS	64.64 18	73.03 250	72.47 254	74.71 265	83.36 259	54.19 275	82.14 197	81.96 237	56.76 282	69.57 304	86.21 240	60.03 248	84.83 267	49.58 301	82.65 298	85.11 251

Anonymous20231206		71.38 264	71.88 258	69.88 285	86.31 220	54.37 274	70.39 300	74.62 274	52.57 300	76.73 259	88.76 201	59.94 249	72.06 307	44.35 318	93.23 183	83.23 274

IterMVS		76.91 216	76.34 220	78.64 225	80.91 278	64.03 181	76.30 270	79.03 252	64.88 240	83.11 204	89.16 196	59.90 250	84.46 269	68.61 195	85.15 281	87.42 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 273	70.44 266	68.90 291	73.76 327	53.42 282	58.99 328	67.20 319	58.42 273	87.10 142	85.39 251	59.82 251	67.32 319	59.79 247	83.50 292	85.96 242

MDA-MVSNet_test_wron		70.05 274	70.44 266	68.88 292	73.84 326	53.47 280	58.93 329	67.28 318	58.43 272	87.09 143	85.40 250	59.80 252	67.25 320	59.66 249	83.54 291	85.92 244

PMMVS		61.65 302	60.38 307	65.47 305	65.40 343	69.26 149	63.97 317	61.73 332	36.80 339	60.11 331	68.43 330	59.42 253	66.35 325	48.97 303	78.57 315	60.81 333

CDS-MVSNet		77.32 212	75.40 227	83.06 168	89.00 149	72.48 120	77.90 256	82.17 236	60.81 265	78.94 248	83.49 274	59.30 254	88.76 224	54.64 281	92.37 196	87.93 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 280	69.68 272	67.82 297	79.42 286	51.15 301	67.82 309	75.79 266	54.15 291	77.47 256	85.36 253	59.26 255	70.64 310	48.46 305	79.35 312	81.66 296

WTY-MVS		67.91 284	68.35 279	66.58 301	80.82 280	48.12 317	65.96 314	72.60 290	53.67 294	71.20 297	81.68 295	58.97 256	69.06 316	48.57 304	81.67 302	82.55 280

MVSFormer		82.23 170	81.57 176	84.19 141	85.54 231	69.26 149	91.98 25	90.08 149	71.54 183	76.23 264	85.07 256	58.69 257	94.27 59	86.26 27	88.77 243	89.03 213

lupinMVS		76.37 226	74.46 236	82.09 182	85.54 231	69.26 149	76.79 263	80.77 246	50.68 316	76.23 264	82.82 283	58.69 257	88.94 217	69.85 181	88.77 243	88.07 220

Test_1112_low_res		73.90 246	73.08 246	76.35 255	90.35 129	55.95 263	73.40 292	86.17 207	50.70 315	73.14 286	85.94 242	58.31 259	85.90 256	56.51 267	83.22 293	87.20 232

sss		66.92 287	67.26 285	65.90 302	77.23 303	51.10 303	64.79 315	71.72 300	52.12 305	70.13 302	80.18 302	57.96 260	65.36 329	50.21 296	81.01 307	81.25 303

MVP-Stereo		75.81 231	73.51 243	82.71 174	89.35 142	73.62 110	80.06 227	85.20 217	60.30 268	73.96 282	87.94 217	57.89 261	89.45 208	52.02 289	74.87 323	85.06 252
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 260	70.06 271	76.92 248	86.39 215	53.97 276	76.62 266	86.62 203	53.44 295	63.97 325	84.73 262	57.79 262	92.34 142	39.65 325	81.33 305	84.45 257

semantic-postprocess				84.34 136	83.93 254	69.66 145		81.09 244	72.43 169	86.47 157	90.19 181	57.56 263	93.15 122	77.45 125	86.39 270	90.22 198

LFMVS		80.15 196	80.56 186	78.89 220	89.19 146	55.93 264	85.22 119	73.78 280	82.96 40	84.28 191	92.72 112	57.38 264	90.07 203	63.80 224	95.75 114	90.68 187

Vis-MVSNet (Re-imp)		77.82 207	77.79 206	77.92 236	88.82 152	51.29 300	83.28 163	71.97 295	74.04 146	82.23 211	89.78 188	57.38 264	89.41 209	57.22 264	95.41 120	93.05 126

CHOSEN 1792x2688		72.45 255	70.56 265	78.13 232	90.02 138	63.08 196	68.72 304	83.16 229	42.99 334	75.92 267	85.46 248	57.22 266	85.18 263	49.87 299	81.67 302	86.14 241

pmmvs4		74.92 239	72.98 248	80.73 203	84.95 236	71.71 134	76.23 271	77.59 257	52.83 298	77.73 255	86.38 234	56.35 267	84.97 264	57.72 263	87.05 263	85.51 248

MVE	40.22 23	51.82 317	50.47 319	55.87 322	62.66 345	51.91 292	31.61 340	39.28 347	40.65 335	50.76 342	74.98 323	56.24 268	44.67 343	33.94 335	64.11 338	71.04 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		70.47 269	70.70 264	69.77 287	86.22 224	53.89 278	67.32 310	71.91 296	63.32 245	78.16 251	89.47 193	56.12 269	73.10 305	36.43 331	87.33 260	82.33 285

N_pmnet		70.20 270	68.80 277	74.38 267	80.91 278	84.81 35	59.12 327	76.45 265	55.06 287	75.31 274	82.36 288	55.74 270	54.82 339	47.02 311	87.24 262	83.52 267

MS-PatchMatch		70.93 266	70.22 269	73.06 275	81.85 270	62.50 210	73.82 289	77.90 255	52.44 301	75.92 267	81.27 297	55.67 271	81.75 283	55.37 275	77.70 317	74.94 318

DSMNet-mixed		60.98 307	61.61 304	59.09 321	72.88 333	45.05 325	74.70 282	46.61 346	26.20 340	65.34 319	90.32 179	55.46 272	63.12 334	41.72 322	81.30 306	69.09 328

pmmvs5		70.73 267	70.07 270	72.72 277	77.03 306	52.73 286	74.14 285	75.65 269	50.36 318	72.17 292	85.37 252	55.42 273	80.67 287	52.86 287	87.59 259	84.77 254

CMPMVS	59.41 20	75.12 236	73.57 242	79.77 210	75.84 313	67.22 161	81.21 216	82.18 235	50.78 314	76.50 260	87.66 222	55.20 274	82.99 278	62.17 232	90.64 228	89.09 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		71.09 265	71.59 260	69.57 289	87.23 201	50.07 313	78.91 246	71.83 297	60.20 269	71.26 296	91.76 134	55.08 275	76.09 298	41.06 323	87.02 264	82.54 281

no-one		71.52 263	70.43 268	74.81 264	78.45 297	63.41 188	57.73 330	77.03 260	51.46 310	77.17 258	90.33 178	54.96 276	80.35 289	47.41 309	99.29 2	80.68 310

PVSNet_0	51.08 22	56.10 312	54.97 316	59.48 320	75.12 322	53.28 283	55.16 331	61.89 330	44.30 331	59.16 334	62.48 338	54.22 277	65.91 327	35.40 333	47.01 340	59.25 335

EPNet_dtu		72.87 251	71.33 263	77.49 244	77.72 301	60.55 237	82.35 188	75.79 266	66.49 222	58.39 338	81.06 299	53.68 278	85.98 254	53.55 284	92.97 188	85.95 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 314	59.27 311	44.74 329	64.30 344	12.32 347	40.60 338	49.79 345	53.19 296	65.06 323	84.81 260	53.60 279	49.76 341	32.68 337	89.41 236	72.15 323

HyFIR lowres test		75.12 236	72.66 250	82.50 179	91.44 108	65.19 173	72.47 293	87.31 190	46.79 325	80.29 238	84.30 266	52.70 280	92.10 148	51.88 294	86.73 265	90.22 198

testus		62.33 299	63.03 299	60.20 319	78.78 293	40.74 332	59.14 325	69.80 311	49.26 321	71.41 295	74.72 324	52.33 281	63.52 331	29.84 338	82.01 301	76.36 315

FMVSNet3		78.80 202	78.55 202	79.57 216	82.89 264	56.89 260	81.76 205	85.77 211	69.04 205	86.00 160	90.44 176	51.75 282	90.09 202	65.95 211	93.34 176	91.72 165

PVSNet	58.17 21	66.41 290	65.63 292	68.75 293	81.96 268	49.88 314	62.19 320	72.51 292	51.03 312	68.04 309	75.34 322	50.84 283	74.77 302	45.82 316	82.96 294	81.60 297

GA-MVS		75.83 230	74.61 233	79.48 218	81.87 269	59.25 246	73.42 291	82.88 231	68.68 207	79.75 241	81.80 293	50.62 284	89.46 207	66.85 205	85.64 275	89.72 204

FPMVS		72.29 258	72.00 257	73.14 274	88.63 157	85.00 32	74.65 283	67.39 317	71.94 179	77.80 254	87.66 222	50.48 285	75.83 300	49.95 297	79.51 310	58.58 336

MVS-HIRNet		61.16 305	62.92 300	55.87 322	79.09 290	35.34 338	71.83 295	57.98 339	46.56 326	59.05 335	91.14 151	49.95 286	76.43 297	38.74 328	71.92 329	55.84 337

CVMVSNet		72.62 253	71.41 262	76.28 257	83.25 260	60.34 238	83.50 158	79.02 253	37.77 338	76.33 262	85.10 254	49.60 287	87.41 236	70.54 178	77.54 319	81.08 306

LP		69.42 278	68.30 280	72.77 276	71.48 338	56.84 261	73.66 290	74.84 272	63.52 244	70.95 300	83.35 277	49.55 288	77.15 296	57.13 265	70.21 331	84.33 259

RPMNet		76.06 228	75.79 223	76.85 250	79.58 284	62.64 204	82.58 181	71.75 299	74.80 139	75.72 269	92.59 113	48.69 289	84.07 271	73.48 154	82.91 296	83.85 263

tpmrst		66.28 291	66.69 288	65.05 307	72.82 334	39.33 334	78.20 252	70.69 307	53.16 297	67.88 310	80.36 301	48.18 290	74.75 303	58.13 260	70.79 330	81.08 306

CR-MVSNet		74.00 245	73.04 247	76.85 250	79.58 284	62.64 204	82.58 181	76.90 261	50.50 317	75.72 269	92.38 118	48.07 291	84.07 271	68.72 194	82.91 296	83.85 263

Patchmtry		76.56 224	77.46 207	73.83 269	79.37 288	46.60 320	82.41 186	76.90 261	73.81 149	85.56 168	92.38 118	48.07 291	83.98 273	63.36 227	95.31 125	90.92 179

ADS-MVSNet2		65.87 293	63.64 298	72.55 279	73.16 331	56.92 259	67.10 311	74.81 273	49.74 319	66.04 316	82.97 280	46.71 293	77.26 294	42.29 320	69.96 333	83.46 268

ADS-MVSNet		61.90 300	62.19 302	61.03 317	73.16 331	36.42 337	67.10 311	61.75 331	49.74 319	66.04 316	82.97 280	46.71 293	63.21 333	42.29 320	69.96 333	83.46 268

PatchmatchNet		69.71 276	68.83 276	72.33 280	77.66 302	53.60 279	79.29 239	69.99 310	57.66 276	72.53 289	82.93 282	46.45 295	80.08 291	60.91 242	72.09 328	83.31 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 257	71.55 261	74.70 266	83.48 257	51.60 297	75.02 280	73.71 281	70.14 195	78.56 249	80.57 300	46.20 296	88.20 230	46.99 312	89.29 237	84.32 260

sam_mvs1												46.11 297

tfpn200view9		74.86 240	74.23 238	76.74 252	86.24 222	52.12 290	79.24 241	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 290	90.99 213	89.31 208

thres400		75.14 234	74.23 238	77.86 237	86.24 222	52.12 290	79.24 241	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 290	90.99 213	92.66 136

test1235678		65.57 294	65.73 291	65.06 306	82.84 265	50.90 304	62.90 318	69.26 312	57.17 280	72.36 290	83.04 278	46.02 298	70.10 311	32.79 336	85.24 280	74.19 320

patchmatchnet-post											81.71 294	45.93 301	87.01 239

sam_mvs												45.92 302

Patchmatch-RL test		74.48 242	73.68 241	76.89 249	84.83 238	66.54 167	72.29 294	69.16 314	57.70 275	86.76 147	86.33 237	45.79 303	82.59 280	69.63 183	90.65 227	81.54 298

conf200view11		75.62 232	75.05 230	77.34 245	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 290	90.99 213	91.34 175

thres100view900		75.45 233	75.05 230	76.66 253	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 290	90.99 213	89.31 208

thres600view7		75.97 229	75.35 229	77.85 238	87.01 209	51.84 296	80.45 225	73.26 285	75.20 137	83.10 205	86.31 239	45.54 304	89.05 215	55.03 278	92.24 199	92.66 136

tpm cat1		66.76 288	65.21 293	71.42 283	77.09 305	50.62 311	78.01 253	73.68 282	44.89 330	68.64 305	79.00 308	45.51 307	82.42 281	49.91 298	70.15 332	81.23 305

test_post											3.10 344	45.43 308	77.22 295

MDTV_nov1_ep13			68.29 281		78.03 300	43.87 328	74.12 286	72.22 293	52.17 302	67.02 313	85.54 244	45.36 309	80.85 286	55.73 270	84.42 288

tpmvs		70.16 271	69.56 273	71.96 282	74.71 325	48.13 316	79.63 232	75.45 270	65.02 239	70.26 301	81.88 292	45.34 310	85.68 258	58.34 258	75.39 322	82.08 288

MDTV_nov1_ep13_2view						27.60 342	70.76 298		46.47 327	61.27 328		45.20 311		49.18 302		83.75 265

test_post1							78.85 248				3.13 343	45.19 312	80.13 290	58.11 261

view600		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 253	93.49 171	92.66 136

view800		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 253	93.49 171	92.66 136

conf0.05thres1000		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 253	93.49 171	92.66 136

tfpn		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 253	93.49 171	92.66 136

CostFormer		69.98 275	68.68 278	73.87 268	77.14 304	50.72 310	79.26 240	74.51 276	51.94 306	70.97 299	84.75 261	45.16 317	87.49 235	55.16 277	79.23 313	83.40 270

Patchmatch-test		65.91 292	67.38 283	61.48 315	75.51 317	43.21 330	68.84 303	63.79 327	62.48 254	72.80 288	83.42 276	44.89 318	59.52 336	48.27 307	86.45 268	81.70 294

EU-MVSNet		75.12 236	74.43 237	77.18 246	83.11 263	59.48 244	85.71 114	82.43 234	39.76 337	85.64 166	88.76 201	44.71 319	87.88 232	73.86 150	85.88 273	84.16 262

PatchT		70.52 268	72.76 249	63.79 309	79.38 287	33.53 339	77.63 258	65.37 325	73.61 150	71.77 293	92.79 111	44.38 320	75.65 301	64.53 222	85.37 277	82.18 287

test-LLR		67.21 286	66.74 287	68.63 294	76.45 310	55.21 270	67.89 306	67.14 320	62.43 256	65.08 321	72.39 326	43.41 321	69.37 312	61.00 240	84.89 283	81.31 301

test0.0.03 1		64.66 296	64.36 295	65.57 304	75.03 323	46.89 319	64.69 316	61.58 333	62.43 256	71.18 298	77.54 312	43.41 321	68.47 317	40.75 324	82.65 298	81.35 300

MVSTER		77.09 214	75.70 226	81.25 197	75.27 321	61.08 231	77.49 261	85.07 220	60.78 266	86.55 151	88.68 203	43.14 323	90.25 193	73.69 151	90.67 225	92.42 145

tpm		67.95 283	68.08 282	67.55 298	78.74 294	43.53 329	75.60 275	67.10 322	54.92 288	72.23 291	88.10 214	42.87 324	75.97 299	52.21 288	80.95 308	83.15 275

PatchFormer-LS_test		67.91 284	66.49 290	72.17 281	75.29 320	51.85 295	75.68 273	73.62 283	57.23 279	68.64 305	68.13 334	42.19 325	82.76 279	64.06 223	73.51 325	81.89 293

tpm2		68.45 282	66.83 286	73.30 271	78.93 292	48.50 315	79.76 231	71.76 298	47.50 324	69.92 303	83.60 272	42.07 326	88.40 227	48.44 306	79.51 310	83.01 277

tpmp4_e23		69.43 277	67.33 284	75.72 260	78.53 296	52.75 285	82.13 198	74.91 271	49.23 322	66.37 314	84.17 268	41.28 327	88.67 225	49.73 300	79.63 309	85.75 246

EMVS		61.10 306	60.81 306	61.99 312	65.96 342	55.86 265	53.10 335	58.97 336	67.06 217	56.89 340	63.33 337	40.98 328	67.03 321	54.79 279	86.18 272	63.08 331

new_pmnet		55.69 313	57.66 312	49.76 326	75.47 318	30.59 340	59.56 323	51.45 344	43.62 333	62.49 326	75.48 320	40.96 329	49.15 342	37.39 330	72.52 326	69.55 327

E-PMN		61.59 303	61.62 303	61.49 314	66.81 340	55.40 268	53.77 334	60.34 334	66.80 221	58.90 336	65.50 336	40.48 330	66.12 326	55.72 271	86.25 271	62.95 332

tfpn_ndepth		72.54 254	72.30 255	73.24 272	84.81 239	51.42 298	79.24 241	70.49 308	69.26 201	78.48 250	79.80 305	40.16 331	86.77 245	58.08 262	90.43 230	81.53 299

EPMVS		62.47 297	62.63 301	62.01 311	70.63 339	38.74 335	74.76 281	52.86 342	53.91 293	67.71 312	80.01 303	39.40 332	66.60 324	55.54 274	68.81 337	80.68 310

tmp_tt		20.25 321	24.50 322	7.49 333	4.47 347	8.70 348	34.17 339	25.16 349	1.00 342	32.43 344	18.49 341	39.37 333	9.21 345	21.64 341	43.75 341	4.57 341

FMVSNet5		72.10 259	71.69 259	73.32 270	81.57 272	53.02 284	76.77 264	78.37 254	63.31 246	76.37 261	91.85 129	36.68 334	78.98 292	47.87 308	92.45 195	87.95 224

dp		60.70 308	60.29 309	61.92 313	72.04 336	38.67 336	70.83 297	64.08 326	51.28 311	60.75 329	77.28 314	36.59 335	71.58 309	47.41 309	62.34 339	75.52 317

CHOSEN 280x420		59.08 309	56.52 314	66.76 300	76.51 308	64.39 179	49.62 337	59.00 335	43.86 332	55.66 341	68.41 332	35.55 336	68.21 318	43.25 319	76.78 321	67.69 329

IB-MVS	62.13 19	71.64 261	68.97 275	79.66 215	80.80 281	62.26 218	73.94 287	76.90 261	63.27 247	68.63 307	76.79 316	33.83 337	91.84 153	59.28 251	87.26 261	84.88 253
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
JIA-IIPM		69.41 279	66.64 289	77.70 239	73.19 330	71.24 137	75.67 274	65.56 324	70.42 191	65.18 320	92.97 104	33.64 338	83.06 277	53.52 285	69.61 335	78.79 312

DWT-MVSNet_test		66.43 289	64.37 294	72.63 278	74.86 324	50.86 305	76.52 267	72.74 289	54.06 292	65.50 318	68.30 333	32.13 339	84.84 266	61.63 237	73.59 324	82.19 286

DeepMVS_CX				24.13 332	32.95 346	29.49 341		21.63 350	12.07 341	37.95 343	45.07 340	30.84 340	19.21 344	17.94 342	33.06 343	23.69 340

gg-mvs-nofinetune		68.96 281	69.11 274	68.52 296	76.12 312	45.32 322	83.59 151	55.88 340	86.68 20	64.62 324	97.01 7	30.36 341	83.97 274	44.78 317	82.94 295	76.26 316

GG-mvs-BLEND				67.16 299	73.36 328	46.54 321	84.15 134	55.04 341		58.64 337	61.95 339	29.93 342	83.87 275	38.71 329	76.92 320	71.07 325

test12356		54.91 315	57.14 313	48.22 328	75.83 314	17.47 345	52.31 336	69.20 313	51.66 308	60.11 331	75.40 321	29.77 343	62.62 335	27.64 339	72.37 327	64.59 330

test-mter		65.00 295	63.79 296	68.63 294	76.45 310	55.21 270	67.89 306	67.14 320	50.98 313	65.08 321	72.39 326	28.27 344	69.37 312	61.00 240	84.89 283	81.31 301

TESTMET0.1,1		61.29 304	60.32 308	64.19 308	72.06 335	51.30 299	67.89 306	62.09 329	45.27 329	60.65 330	69.01 329	27.93 345	64.74 330	56.31 268	81.65 304	76.53 314

pmmvs3		62.47 297	60.02 310	69.80 286	71.58 337	64.00 182	70.52 299	58.44 337	39.77 336	66.05 315	75.84 319	27.10 346	72.28 306	46.15 314	84.77 287	73.11 322

testpf		58.55 310	61.58 305	49.48 327	66.03 341	40.05 333	74.40 284	58.07 338	64.72 241	59.36 333	72.67 325	22.76 347	66.92 322	67.07 203	69.15 336	41.46 339

test2356		56.69 311	55.15 315	61.32 316	73.20 329	44.11 327	54.95 332	62.52 328	48.75 323	62.45 327	68.42 331	21.10 348	65.67 328	26.86 340	78.08 316	74.19 320

1111		61.71 301	63.77 297	55.55 324	78.05 298	25.74 343	60.62 321	67.52 315	66.09 225	74.68 277	86.50 232	16.00 349	59.22 337	38.79 326	85.65 274	81.70 294

.test1245		48.02 318	54.41 317	28.84 331	78.05 298	25.74 343	60.62 321	67.52 315	66.09 225	74.68 277	86.50 232	16.00 349	59.22 337	38.79 326	1.47 344	1.55 343

PNet_i23d		52.13 316	51.24 318	54.79 325	75.56 315	45.26 323	54.54 333	52.55 343	66.95 218	57.19 339	65.82 335	13.15 351	63.40 332	36.39 332	39.04 342	55.71 338

test123		6.27 324	8.08 325	0.84 334	1.11 349	0.57 349	62.90 318	0.82 351	0.54 343	1.07 346	2.75 346	1.26 352	0.30 346	1.04 343	1.26 346	1.66 342

testmvs		5.91 325	7.65 326	0.72 335	1.20 348	0.37 350	59.14 325	0.67 352	0.49 344	1.11 345	2.76 345	0.94 353	0.24 347	1.02 344	1.47 344	1.55 343

sosnet-low-res		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

sosnet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

uncertanet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

Regformer		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

ab-mvs-re		6.65 322	8.87 323	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	79.80 305	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

uanet		0.00 326	0.00 327	0.00 336	0.00 350	0.00 351	0.00 341	0.00 353	0.00 345	0.00 347	0.00 347	0.00 354	0.00 348	0.00 345	0.00 347	0.00 345

test_part2					93.86 47	77.77 79				92.84 42

test11111								93.79 29

MTGPA								91.81 90

MTMP								33.14 348

gm-plane-assit					75.42 319	44.97 326			52.17 302		72.36 328		87.90 231	54.10 282

test9_res														80.83 87	96.45 86	90.57 190

agg_prior2														79.68 104	96.16 95	90.22 198

agg_prior					91.58 102	77.69 80		90.30 140		84.32 189			93.18 118

test_prior4						78.97 71	84.59 127

test_prior				86.32 91	90.59 126	71.99 128		92.85 61					94.17 66			92.80 131

旧先验2							81.73 206		56.88 281	86.54 156			84.90 265	72.81 162

新几何2							81.72 207

无先验							82.81 175	85.62 213	58.09 274				91.41 166	67.95 200		84.48 256

原ACMM2							82.26 193

testdata2													86.43 250	63.52 226

testdata1							79.62 233		73.95 148

plane_prior7					93.45 52	77.31 86

plane_prior5								93.61 32					95.22 37	80.78 88	95.83 111	94.46 86

plane_prior4											92.95 105

plane_prior3						76.85 91			77.79 99	86.55 151

plane_prior2							89.45 56		79.44 77

plane_prior1					92.83 70

plane_prior						76.42 96	87.15 89		75.94 127						95.03 133

n2								0.00 353

nn								0.00 353

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 140

HQP-NCC					91.19 112		84.77 122		73.30 157	80.55 235

ACMP_Plane					91.19 112		84.77 122		73.30 157	80.55 235

BP-MVS														77.30 127

HQP4-MVS										80.56 234			94.61 53			93.56 116

HQP3-MVS								92.68 67							94.47 152

NP-MVS					91.95 92	74.55 106					90.17 183

ACMMP++_ref															95.74 115

ACMMP++															97.35 59

High-resolution multi-view benchmark