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 11	85.07 45	99.27 1	99.54 1

UniMVSNet_ETH3D		89.12 60	90.72 41	84.31 142	97.00 2	64.33 202	89.67 57	88.38 183	88.84 13	94.29 18	97.57 3	90.48 13	91.26 177	72.57 175	97.65 58	97.34 14

PMVS	80.48 6	90.08 38	90.66 42	88.34 75	96.71 3	92.97 1	90.31 44	89.57 167	88.51 15	90.11 84	95.12 40	90.98 7	88.92 231	77.55 124	97.07 78	83.13 294
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		91.27 20	91.26 29	91.29 27	96.59 4	86.29 14	88.94 72	91.81 107	84.07 34	92.00 55	94.40 63	86.63 49	95.28 47	88.59 5	98.31 23	92.30 153

MTAPA		91.52 14	91.60 16	91.29 27	96.59 4	86.29 14	92.02 26	91.81 107	84.07 34	92.00 55	94.40 63	86.63 49	95.28 47	88.59 5	98.31 23	92.30 153

PEN-MVS		90.03 41	91.88 13	84.48 137	96.57 6	58.88 262	88.95 71	93.19 66	91.62 3	96.01 5	96.16 20	87.02 44	95.60 29	78.69 110	98.72 8	98.97 3

PS-CasMVS		90.06 39	91.92 10	84.47 138	96.56 7	58.83 265	89.04 70	92.74 85	91.40 4	96.12 3	96.06 22	87.23 42	95.57 30	79.42 105	98.74 5	99.00 2

DTE-MVSNet		89.98 43	91.91 12	84.21 144	96.51 8	57.84 270	88.93 73	92.84 82	91.92 2	96.16 2	96.23 18	86.95 45	95.99 7	79.05 107	98.57 14	98.80 6

CP-MVSNet		89.27 57	90.91 38	84.37 139	96.34 9	58.61 267	88.66 80	92.06 99	90.78 5	95.67 6	95.17 38	81.80 102	95.54 34	79.00 108	98.69 9	98.95 4

WR-MVS_H		89.91 46	91.31 27	85.71 117	96.32 10	62.39 224	89.54 62	93.31 59	90.21 9	95.57 8	95.66 27	81.42 106	95.90 12	80.94 87	98.80 2	98.84 5

MP-MVS		91.14 25	90.91 38	91.83 19	96.18 11	86.88 11	92.20 23	93.03 74	82.59 53	88.52 121	94.37 66	86.74 48	95.41 42	86.32 31	98.21 29	93.19 125
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		91.69 11	91.47 20	92.37 5	96.04 12	88.48 7	92.72 14	92.60 88	83.09 47	91.54 63	94.25 70	87.67 38	95.51 38	87.21 24	98.11 33	93.12 127

MP-MVS-pluss		90.81 27	91.08 31	89.99 49	95.97 13	79.88 65	88.13 85	94.51 16	75.79 131	92.94 36	94.96 42	88.36 24	95.01 57	90.70 2	98.40 19	95.09 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 2	96.46 2	90.58 7	92.86 39	96.29 16	88.16 30	94.17 85	86.07 37	98.48 17	97.22 17

ACMMP_NAP		90.65 29	91.07 33	89.42 58	95.93 15	79.54 70	89.95 50	93.68 46	77.65 108	91.97 57	94.89 44	88.38 23	95.45 40	89.27 3	97.87 48	93.27 121

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 15	85.81 27	92.99 11	94.23 21	85.21 28	92.51 46	95.13 39	90.65 10	95.34 44	88.06 9	98.15 32	95.95 39

DVP-MVS		89.08 61	88.16 71	91.83 19	95.76 17	86.14 19	92.75 13	93.90 37	78.43 102	89.16 110	92.25 130	72.03 203	96.36 2	88.21 8	90.93 231	92.98 131

region2R		91.44 18	91.30 28	91.87 17	95.75 18	85.90 23	92.63 17	93.30 61	81.91 60	90.88 77	94.21 71	87.75 36	95.87 14	87.60 16	97.71 56	93.83 100

ACMMPR		91.49 15	91.35 24	91.92 14	95.74 19	85.88 24	92.58 18	93.25 64	81.99 58	91.40 66	94.17 72	87.51 39	95.87 14	87.74 11	97.76 52	93.99 93

ZNCC-MVS		91.26 21	91.34 25	91.01 33	95.73 20	83.05 49	92.18 24	94.22 22	80.14 80	91.29 69	93.97 80	87.93 35	95.87 14	88.65 4	97.96 44	94.12 90

TSAR-MVS + MP.		88.14 71	87.82 74	89.09 63	95.72 21	76.74 103	92.49 21	91.19 123	67.85 220	86.63 152	94.84 46	79.58 124	95.96 10	87.62 14	94.50 160	94.56 70
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		91.20 23	90.95 37	91.93 13	95.67 22	85.85 25	90.00 47	93.90 37	80.32 77	91.74 61	94.41 62	88.17 29	95.98 8	86.37 30	97.99 39	93.96 95

XVS		91.54 13	91.36 22	92.08 8	95.64 23	86.25 16	92.64 15	93.33 56	85.07 29	89.99 87	94.03 77	86.57 51	95.80 19	87.35 20	97.62 60	94.20 85

X-MVStestdata		85.04 114	82.70 156	92.08 8	95.64 23	86.25 16	92.64 15	93.33 56	85.07 29	89.99 87	16.05 347	86.57 51	95.80 19	87.35 20	97.62 60	94.20 85

HPM-MVS		92.13 7	92.20 8	91.91 15	95.58 25	84.67 38	93.51 6	94.85 13	82.88 50	91.77 60	93.94 87	90.55 12	95.73 25	88.50 7	98.23 28	95.33 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		91.91 10	91.87 14	92.03 11	95.53 26	85.91 22	93.35 9	94.16 26	82.52 54	92.39 49	94.14 73	89.15 21	95.62 28	87.35 20	98.24 27	94.56 70
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
GST-MVS		90.96 26	91.01 34	90.82 36	95.45 27	82.73 52	91.75 30	93.74 43	80.98 71	91.38 67	93.80 90	87.20 43	95.80 19	87.10 25	97.69 57	93.93 96

HFP-MVS		91.30 19	91.39 21	91.02 31	95.43 28	84.66 39	92.58 18	93.29 62	81.99 58	91.47 64	93.96 83	88.35 25	95.56 31	87.74 11	97.74 54	92.85 135

#test#		90.49 34	90.31 46	91.02 31	95.43 28	84.66 39	90.65 38	93.29 62	77.00 115	91.47 64	93.96 83	88.35 25	95.56 31	84.88 48	97.74 54	92.85 135

SMA-MVS		90.31 35	90.48 44	89.83 50	95.31 30	79.52 71	90.98 36	93.24 65	75.37 138	92.84 40	95.28 34	85.58 60	96.09 6	87.92 10	97.76 52	93.88 98

CP-MVS		91.67 12	91.58 17	91.96 12	95.29 31	87.62 9	93.38 7	93.36 54	83.16 46	91.06 72	94.00 79	88.26 27	95.71 26	87.28 23	98.39 20	92.55 146

VDDNet		84.35 129	85.39 112	81.25 200	95.13 32	59.32 255	85.42 126	81.11 257	86.41 24	87.41 137	96.21 19	73.61 180	90.61 200	66.33 223	96.85 84	93.81 105

CPTT-MVS		89.39 55	88.98 62	90.63 39	95.09 33	86.95 10	92.09 25	92.30 94	79.74 83	87.50 136	92.38 123	81.42 106	93.28 122	83.07 66	97.24 74	91.67 176

ACMM	79.39 9	90.65 29	90.99 35	89.63 54	95.03 34	83.53 44	89.62 59	93.35 55	79.20 91	93.83 26	93.60 96	90.81 8	92.96 133	85.02 47	98.45 18	92.41 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 15	91.53 18	91.39 24	94.98 35	82.95 51	93.52 5	92.79 83	88.22 16	88.53 120	97.64 2	83.45 75	94.55 74	86.02 39	98.60 12	96.67 25

HPM-MVS++		88.93 63	88.45 70	90.38 43	94.92 36	85.85 25	89.70 54	91.27 120	78.20 104	86.69 151	92.28 129	80.36 117	95.06 56	86.17 36	96.49 96	90.22 205

XVG-ACMP-BASELINE		89.98 43	89.84 49	90.41 42	94.91 37	84.50 41	89.49 64	93.98 32	79.68 84	92.09 53	93.89 88	83.80 71	93.10 131	82.67 71	98.04 34	93.64 111

SR-MVS		92.23 6	92.34 6	91.91 15	94.89 38	87.85 8	92.51 20	93.87 40	88.20 17	93.24 33	94.02 78	90.15 15	95.67 27	86.82 26	97.34 71	92.19 160

OPM-MVS		89.80 47	89.97 47	89.27 60	94.76 39	79.86 66	86.76 106	92.78 84	78.78 97	92.51 46	93.64 95	88.13 31	93.84 98	84.83 50	97.55 63	94.10 91

LPG-MVS_test		91.47 17	91.68 15	90.82 36	94.75 40	81.69 53	90.00 47	94.27 18	82.35 55	93.67 30	94.82 47	91.18 5	95.52 35	85.36 43	98.73 6	95.23 56

LGP-MVS_train				90.82 36	94.75 40	81.69 53		94.27 18	82.35 55	93.67 30	94.82 47	91.18 5	95.52 35	85.36 43	98.73 6	95.23 56

abl_6		93.02 4	93.16 4	92.60 4	94.73 42	88.99 6	93.26 10	94.19 25	89.11 10	94.43 15	95.27 35	91.86 3	95.09 54	87.54 18	98.02 37	93.71 107

XVG-OURS-SEG-HR		89.59 51	89.37 56	90.28 45	94.47 43	85.95 21	86.84 102	93.91 36	80.07 81	86.75 150	93.26 98	93.64 2	90.93 187	84.60 52	90.75 236	93.97 94

ACMP	79.16 10	90.54 32	90.60 43	90.35 44	94.36 44	80.98 59	89.16 68	94.05 30	79.03 94	92.87 38	93.74 94	90.60 11	95.21 51	82.87 69	98.76 3	94.87 61
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 58	88.83 65	90.23 46	94.28 45	86.11 20	85.91 117	93.60 49	80.16 79	89.13 111	93.44 97	83.82 70	90.98 185	83.86 59	95.30 137	93.60 113

test_0728_SECOND				86.79 91	94.25 46	72.45 135	90.54 40	94.10 29					95.88 13	86.42 28	97.97 42	92.02 164

IU-MVS					94.18 47	72.64 128		90.82 130	56.98 291	89.67 98				85.78 40	97.92 45	93.28 120

test_241102_ONE					94.18 47	72.65 127		93.69 45	83.62 39	94.11 22	93.78 93	90.28 14	95.50 39

MSP-MVS		90.06 39	91.32 26	86.29 101	94.16 49	72.56 131	90.54 40	91.01 127	83.61 40	93.75 27	94.65 52	89.76 17	95.78 22	86.42 28	97.97 42	90.55 200

test0726					94.16 49	72.56 131	90.63 39	93.90 37	83.61 40	93.75 27	94.49 58	89.76 17

MIMVSNet1		83.63 147	84.59 128	80.74 210	94.06 51	62.77 218	82.72 185	84.53 240	77.57 110	90.34 82	95.92 23	76.88 155	85.83 271	61.88 252	97.42 69	93.62 112

TranMVSNet+NR-MVSNet		87.86 74	88.76 67	85.18 125	94.02 52	64.13 203	84.38 143	91.29 119	84.88 31	92.06 54	93.84 89	86.45 53	93.73 101	73.22 165	98.66 10	97.69 9

新几何1				82.95 172	93.96 53	78.56 80		80.24 262	55.45 296	83.93 201	91.08 157	71.19 208	88.33 240	65.84 228	93.07 188	81.95 307

1121		80.86 183	79.81 199	84.02 147	93.93 54	78.70 78	81.64 206	80.18 263	55.43 297	83.67 203	91.15 155	71.29 207	91.41 174	67.95 215	93.06 189	81.96 306

SteuartSystems-ACMMP		91.16 24	91.36 22	90.55 40	93.91 55	80.97 60	91.49 32	93.48 52	82.82 51	92.60 45	93.97 80	88.19 28	96.29 4	87.61 15	98.20 31	94.39 80
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 56	77.77 86				92.84 40

xxxxxxxxxxxxxcwj		88.58 67	88.51 68	88.79 66	93.75 57	77.44 91	86.31 114	89.72 161	70.80 190	92.28 50	93.80 90	86.89 46	94.64 68	85.52 41	97.51 66	94.30 83

save fliter					93.75 57	77.44 91	86.31 114	89.72 161	70.80 190

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 21	93.73 59	85.72 28	96.79 1	95.51 5	88.86 12	95.63 7	96.99 8	84.81 63	93.16 127	91.10 1	97.53 65	96.58 28
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
COLMAP_ROB	83.01 3	91.97 9	91.95 9	92.04 10	93.68 60	86.15 18	93.37 8	95.10 10	90.28 8	92.11 52	95.03 41	89.75 19	94.93 59	79.95 97	98.27 26	95.04 60
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	82.31 4	89.15 59	89.08 59	89.37 59	93.64 61	79.07 74	88.54 81	94.20 23	73.53 156	89.71 96	94.82 47	85.09 61	95.77 24	84.17 56	98.03 36	93.26 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 48	89.27 58	91.30 26	93.51 62	84.79 36	89.89 52	90.63 135	70.00 200	94.55 14	96.67 11	87.94 34	93.59 109	84.27 55	95.97 113	95.52 47

HQP_MVS		87.75 78	87.43 79	88.70 68	93.45 63	76.42 107	89.45 65	93.61 47	79.44 88	86.55 153	92.95 106	74.84 167	95.22 49	80.78 90	95.83 118	94.46 76

plane_prior7					93.45 63	77.31 95

WR-MVS		83.56 148	84.40 134	81.06 205	93.43 65	54.88 292	78.67 249	85.02 235	81.24 67	90.74 78	91.56 147	72.85 192	91.08 183	68.00 213	98.04 34	97.23 16

DPE-MVS		90.53 33	91.08 31	88.88 64	93.38 66	78.65 79	89.15 69	94.05 30	84.68 32	93.90 23	94.11 75	88.13 31	96.30 3	84.51 53	97.81 50	91.70 175

jajsoiax		89.41 54	88.81 66	91.19 30	93.38 66	84.72 37	89.70 54	90.29 150	69.27 204	94.39 16	96.38 15	86.02 58	93.52 113	83.96 57	95.92 115	95.34 50

PS-MVSNAJss		88.31 69	87.90 73	89.56 57	93.31 68	77.96 84	87.94 87	91.97 102	70.73 192	94.19 21	96.67 11	76.94 149	94.57 72	83.07 66	96.28 103	96.15 31

test222					93.31 68	76.54 104	79.38 237	77.79 272	52.59 310	82.36 220	90.84 168	66.83 225			91.69 216	81.25 315

DU-MVS		86.80 85	86.99 85	86.21 106	93.24 70	67.02 181	83.16 176	92.21 95	81.73 62	90.92 74	91.97 134	77.20 143	93.99 91	74.16 154	98.35 21	97.61 10

NR-MVSNet		86.00 98	86.22 96	85.34 123	93.24 70	64.56 199	82.21 199	90.46 138	80.99 70	88.42 123	91.97 134	77.56 139	93.85 96	72.46 176	98.65 11	97.61 10

OurMVSNet-221017-0		90.01 42	89.74 50	90.83 35	93.16 72	80.37 62	91.91 29	93.11 68	81.10 69	95.32 9	97.24 5	72.94 191	94.85 62	85.07 45	97.78 51	97.26 15

UniMVSNet (Re)		86.87 82	86.98 86	86.55 95	93.11 73	68.48 172	83.80 156	92.87 79	80.37 75	89.61 102	91.81 141	77.72 137	94.18 83	75.00 150	98.53 15	96.99 22

APD-MVS_3200maxsize		92.05 8	92.24 7	91.48 22	93.02 74	85.17 31	92.47 22	95.05 11	87.65 20	93.21 34	94.39 65	90.09 16	95.08 55	86.67 27	97.60 62	94.18 87

ACMH+	77.89 11	90.73 28	91.50 19	88.44 73	93.00 75	76.26 109	89.65 58	95.55 4	87.72 19	93.89 25	94.94 43	91.62 4	93.44 117	78.35 113	98.76 3	95.61 46

APDe-MVS		91.22 22	91.92 10	89.14 62	92.97 76	78.04 83	92.84 12	94.14 27	83.33 44	93.90 23	95.73 25	88.77 22	96.41 1	87.60 16	97.98 41	92.98 131

114514_t		83.10 158	82.54 161	84.77 131	92.90 77	69.10 170	86.65 108	90.62 136	54.66 300	81.46 234	90.81 169	76.98 148	94.38 75	72.62 174	96.18 107	90.82 193

testdata				79.54 229	92.87 78	72.34 136		80.14 264	59.91 276	85.47 176	91.75 143	67.96 221	85.24 275	68.57 211	92.18 209	81.06 320

CNVR-MVS		87.81 77	87.68 75	88.21 77	92.87 78	77.30 96	85.25 127	91.23 121	77.31 112	87.07 144	91.47 149	82.94 80	94.71 65	84.67 51	96.27 105	92.62 145

SF-MVS		90.27 36	90.80 40	88.68 69	92.86 80	77.09 98	91.19 35	95.74 3	81.38 66	92.28 50	93.80 90	86.89 46	94.64 68	85.52 41	97.51 66	94.30 83

UniMVSNet_NR-MVSNet		86.84 84	87.06 83	86.17 108	92.86 80	67.02 181	82.55 189	91.56 111	83.08 48	90.92 74	91.82 140	78.25 133	93.99 91	74.16 154	98.35 21	97.49 13

plane_prior1					92.83 82

原ACMM1				84.60 135	92.81 83	74.01 119		91.50 113	62.59 256	82.73 216	90.67 174	76.53 156	94.25 78	69.24 200	95.69 125	85.55 262

plane_prior6					92.61 84	76.54 104						74.84 167

APD-MVS		89.54 52	89.63 52	89.26 61	92.57 85	81.34 58	90.19 45	93.08 70	80.87 72	91.13 70	93.19 99	86.22 56	95.97 9	82.23 75	97.18 76	90.45 202
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 65	89.58 54	85.88 113	92.55 86	72.22 139	84.01 148	89.44 169	88.63 14	94.38 17	95.77 24	86.38 55	93.59 109	79.84 98	95.21 138	91.82 172

SixPastTwentyTwo		87.20 81	87.45 78	86.45 97	92.52 87	69.19 168	87.84 89	88.05 189	81.66 63	94.64 13	96.53 14	65.94 229	94.75 64	83.02 68	96.83 86	95.41 49

ACMH	76.49 14	89.34 56	91.14 30	83.96 150	92.50 88	70.36 156	89.55 60	93.84 41	81.89 61	94.70 12	95.44 32	90.69 9	88.31 241	83.33 64	98.30 25	93.20 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 197	81.68 170	75.94 272	92.46 89	47.98 329	76.70 274	81.67 255	73.45 157	84.87 182	92.82 110	74.66 172	86.51 261	61.66 255	96.85 84	93.33 118

F-COLMAP		84.97 117	83.42 146	89.63 54	92.39 90	83.40 45	88.83 75	91.92 104	73.19 166	80.18 252	89.15 199	77.04 147	93.28 122	65.82 229	92.28 205	92.21 159

test_djsdf		89.62 50	89.01 60	91.45 23	92.36 91	82.98 50	91.98 27	90.08 156	71.54 185	94.28 20	96.54 13	81.57 104	94.27 76	86.26 32	96.49 96	97.09 19

TEST9					92.34 92	79.70 68	83.94 149	90.32 143	65.41 246	84.49 189	90.97 162	82.03 96	93.63 105

train_agg		85.98 100	85.28 113	88.07 79	92.34 92	79.70 68	83.94 149	90.32 143	65.79 236	84.49 189	90.97 162	81.93 98	93.63 105	81.21 83	96.54 94	90.88 191

NCCC		87.36 79	86.87 88	88.83 65	92.32 94	78.84 77	86.58 110	91.09 125	78.77 98	84.85 183	90.89 166	80.85 111	95.29 45	81.14 84	95.32 134	92.34 151

testtj		89.51 53	89.48 55	89.59 56	92.26 95	80.80 61	90.14 46	93.54 50	83.37 43	90.57 81	92.55 120	84.99 62	96.15 5	81.26 82	96.61 91	91.83 171

FC-MVSNet-test		85.93 101	87.05 84	82.58 181	92.25 96	56.44 281	85.75 120	93.09 69	77.33 111	91.94 58	94.65 52	74.78 169	93.41 119	75.11 149	98.58 13	97.88 7

CDPH-MVS		86.17 97	85.54 109	88.05 80	92.25 96	75.45 112	83.85 153	92.01 100	65.91 235	86.19 160	91.75 143	83.77 72	94.98 58	77.43 127	96.71 89	93.73 106

pmmvs6		86.52 89	88.06 72	81.90 190	92.22 98	62.28 227	84.66 135	89.15 172	83.54 42	89.85 92	97.32 4	88.08 33	86.80 257	70.43 192	97.30 73	96.62 26

EG-PatchMatch MVS		84.08 137	84.11 138	83.98 149	92.22 98	72.61 130	82.20 201	87.02 208	72.63 174	88.86 113	91.02 160	78.52 129	91.11 182	73.41 164	91.09 223	88.21 232

test_8					92.09 100	78.87 76	83.82 154	90.31 145	65.79 236	84.36 192	90.96 164	81.93 98	93.44 117

Vis-MVSNet		86.86 83	86.58 91	87.72 82	92.09 100	77.43 93	87.35 94	92.09 98	78.87 96	84.27 198	94.05 76	78.35 132	93.65 103	80.54 94	91.58 219	92.08 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 87	86.82 90	86.17 108	92.05 102	66.87 183	91.21 34	88.64 179	86.30 25	89.60 103	92.59 117	69.22 214	94.91 60	73.89 158	97.89 47	96.72 24

旧先验1					91.97 103	71.77 143		81.78 254			91.84 138	73.92 177			93.65 178	83.61 284

v7n		90.13 37	90.96 36	87.65 84	91.95 104	71.06 151	89.99 49	93.05 71	86.53 23	94.29 18	96.27 17	82.69 82	94.08 89	86.25 34	97.63 59	97.82 8

NP-MVS					91.95 104	74.55 116					90.17 185

ETH3D-3000-0.1		88.85 64	88.96 63	88.52 70	91.94 106	77.27 97	88.71 78	95.26 9	76.08 122	90.66 80	92.69 115	84.48 66	93.83 99	83.38 63	97.48 68	94.47 75

OMC-MVS		88.19 70	87.52 77	90.19 47	91.94 106	81.68 55	87.49 93	93.17 67	76.02 125	88.64 118	91.22 152	84.24 68	93.37 120	77.97 120	97.03 79	95.52 47

OPU-MVS				88.27 76	91.89 108	77.83 85	90.47 43				91.22 152	81.12 109	94.68 66	74.48 151	95.35 132	92.29 155

FIs		85.35 107	86.27 95	82.60 180	91.86 109	57.31 274	85.10 129	93.05 71	75.83 130	91.02 73	93.97 80	73.57 181	92.91 137	73.97 157	98.02 37	97.58 12

9.14			89.29 57		91.84 110		88.80 76	95.32 8	75.14 140	91.07 71	92.89 108	87.27 41	93.78 100	83.69 61	97.55 63

MSLP-MVS++		85.00 116	86.03 100	81.90 190	91.84 110	71.56 149	86.75 107	93.02 75	75.95 128	87.12 140	89.39 193	77.98 134	89.40 227	77.46 125	94.78 153	84.75 271

DP-MVS Recon		84.05 138	83.22 149	86.52 96	91.73 112	75.27 113	83.23 174	92.40 91	72.04 182	82.04 225	88.33 209	77.91 136	93.95 94	66.17 224	95.12 143	90.34 204

SD-MVS		88.96 62	89.88 48	86.22 104	91.63 113	77.07 99	89.82 53	93.77 42	78.90 95	92.88 37	92.29 128	86.11 57	90.22 209	86.24 35	97.24 74	91.36 184
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
AllTest		87.97 73	87.40 80	89.68 52	91.59 114	83.40 45	89.50 63	95.44 6	79.47 86	88.00 130	93.03 102	82.66 83	91.47 169	70.81 184	96.14 109	94.16 88

TestCases				89.68 52	91.59 114	83.40 45		95.44 6	79.47 86	88.00 130	93.03 102	82.66 83	91.47 169	70.81 184	96.14 109	94.16 88

MCST-MVS		84.36 127	83.93 142	85.63 118	91.59 114	71.58 148	83.52 162	92.13 97	61.82 262	83.96 200	89.75 191	79.93 123	93.46 116	78.33 114	94.34 164	91.87 170

agg_prior1		85.72 103	85.20 114	87.28 88	91.58 117	77.69 87	83.69 159	90.30 147	66.29 232	84.32 193	91.07 159	82.13 92	93.18 125	81.02 85	96.36 100	90.98 187

agg_prior					91.58 117	77.69 87		90.30 147		84.32 193			93.18 125

PVSNet_Blended_VisFu		81.55 176	80.49 186	84.70 134	91.58 117	73.24 124	84.21 144	91.67 110	62.86 255	80.94 240	87.16 229	67.27 223	92.87 138	69.82 196	88.94 255	87.99 236

EPP-MVSNet		85.47 106	85.04 116	86.77 92	91.52 120	69.37 162	91.63 31	87.98 192	81.51 65	87.05 145	91.83 139	66.18 228	95.29 45	70.75 187	96.89 82	95.64 44

DeepPCF-MVS	81.24 5	87.28 80	86.21 97	90.49 41	91.48 121	84.90 34	83.41 167	92.38 93	70.25 198	89.35 108	90.68 173	82.85 81	94.57 72	79.55 101	95.95 114	92.00 165

Baseline_NR-MVSNet		84.00 140	85.90 102	78.29 246	91.47 122	53.44 300	82.29 195	87.00 211	79.06 93	89.55 104	95.72 26	77.20 143	86.14 267	72.30 177	98.51 16	95.28 53

HyFIR lowres test		75.12 245	72.66 262	82.50 184	91.44 123	65.19 194	72.47 305	87.31 198	46.79 331	80.29 250	84.30 272	52.70 292	92.10 154	51.88 309	86.73 277	90.22 205

DP-MVS		88.60 66	89.01 60	87.36 87	91.30 124	77.50 90	87.55 91	92.97 77	87.95 18	89.62 100	92.87 109	84.56 64	93.89 95	77.65 122	96.62 90	90.70 195

DeepC-MVS_fast	80.27 8	86.23 94	85.65 108	87.96 81	91.30 124	76.92 100	87.19 96	91.99 101	70.56 193	84.96 179	90.69 172	80.01 120	95.14 52	78.37 112	95.78 122	91.82 172
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 45	89.66 51	90.92 34	91.27 126	81.66 56	91.25 33	94.13 28	88.89 11	88.83 115	94.26 69	77.55 140	95.86 17	84.88 48	95.87 117	95.24 55

ETH3D cwj APD-0.16		87.83 76	87.62 76	88.47 72	91.21 127	78.20 81	87.26 95	94.54 15	72.05 181	88.89 112	92.31 127	83.86 69	94.24 79	81.59 81	96.87 83	92.97 134

HQP-NCC					91.19 128		84.77 131		73.30 162	80.55 247

ACMP_Plane					91.19 128		84.77 131		73.30 162	80.55 247

HQP-MVS		84.61 121	84.06 139	86.27 102	91.19 128	70.66 153	84.77 131	92.68 86	73.30 162	80.55 247	90.17 185	72.10 199	94.61 70	77.30 128	94.47 161	93.56 115

VDD-MVS		84.23 134	84.58 129	83.20 167	91.17 131	65.16 195	83.25 172	84.97 237	79.79 82	87.18 139	94.27 67	74.77 170	90.89 190	69.24 200	96.54 94	93.55 117

K. test v3		85.14 110	84.73 121	86.37 98	91.13 132	69.63 161	85.45 125	76.68 279	84.06 36	92.44 48	96.99 8	62.03 246	94.65 67	80.58 93	93.24 184	94.83 65

lessismore_v0				85.95 110	91.10 133	70.99 152		70.91 318		91.79 59	94.42 61	61.76 247	92.93 135	79.52 104	93.03 190	93.93 96

TransMVSNet (Re)		84.02 139	85.74 105	78.85 235	91.00 134	55.20 291	82.29 195	87.26 199	79.65 85	88.38 125	95.52 31	83.00 79	86.88 255	67.97 214	96.60 92	94.45 78

PAPM_NR		83.23 155	83.19 151	83.33 164	90.90 135	65.98 189	88.19 84	90.78 131	78.13 106	80.87 242	87.92 217	73.49 184	92.42 145	70.07 194	88.40 259	91.60 179

CSCG		86.26 93	86.47 92	85.60 119	90.87 136	74.26 118	87.98 86	91.85 105	80.35 76	89.54 106	88.01 213	79.09 126	92.13 151	75.51 143	95.06 145	90.41 203

PLC	73.85 16	82.09 170	80.31 188	87.45 86	90.86 137	80.29 63	85.88 119	90.65 134	68.17 214	76.32 276	86.33 239	73.12 190	92.61 143	61.40 258	90.02 244	89.44 214
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETH3 D test6400		85.09 112	84.87 119	85.75 116	90.80 138	69.34 163	85.90 118	93.31 59	65.43 242	86.11 163	89.95 187	80.92 110	94.86 61	75.90 141	95.57 127	93.05 128

test12				86.57 94	90.74 139	72.63 129		90.69 133		82.76 215		79.20 125	94.80 63		95.32 134	92.27 156

ITE_SJBPF				90.11 48	90.72 140	84.97 33		90.30 147	81.56 64	90.02 86	91.20 154	82.40 86	90.81 193	73.58 162	94.66 157	94.56 70

DPM-MVS		80.10 201	79.18 203	82.88 176	90.71 141	69.74 158	78.87 246	90.84 129	60.29 274	75.64 285	85.92 246	67.28 222	93.11 130	71.24 182	91.79 214	85.77 261

TAMVS		78.08 215	76.36 227	83.23 166	90.62 142	72.87 125	79.08 243	80.01 265	61.72 264	81.35 236	86.92 233	63.96 237	88.78 235	50.61 310	93.01 191	88.04 235

test_prior3		86.31 92	86.31 94	86.32 99	90.59 143	71.99 141	83.37 168	92.85 80	75.43 135	84.58 187	91.57 145	81.92 100	94.17 85	79.54 102	96.97 80	92.80 137

test_prior				86.32 99	90.59 143	71.99 141		92.85 80					94.17 85			92.80 137

ambc				82.98 171	90.55 145	64.86 196	88.20 83	89.15 172		89.40 107	93.96 83	71.67 206	91.38 176	78.83 109	96.55 93	92.71 141

Anonymous20231211		88.40 68	89.62 53	84.73 132	90.46 146	65.27 193	88.86 74	93.02 75	87.15 21	93.05 35	97.10 6	82.28 90	92.02 156	76.70 133	97.99 39	96.88 23

Test_1112_low_res		73.90 256	73.08 257	76.35 268	90.35 147	55.95 282	73.40 303	86.17 217	50.70 324	73.14 298	85.94 245	58.31 268	85.90 270	56.51 282	83.22 303	87.20 246

VPA-MVSNet		83.47 151	84.73 121	79.69 226	90.29 148	57.52 273	81.30 213	88.69 178	76.29 119	87.58 135	94.44 60	80.60 114	87.20 250	66.60 222	96.82 87	94.34 82

FMVSNet1		84.55 123	85.45 111	81.85 192	90.27 149	61.05 238	86.83 103	88.27 186	78.57 101	89.66 99	95.64 28	75.43 161	90.68 197	69.09 204	95.33 133	93.82 102

Anonymous20240529		86.20 96	87.13 81	83.42 163	90.19 150	64.55 200	84.55 138	90.71 132	85.85 26	89.94 90	95.24 37	82.13 92	90.40 204	69.19 203	96.40 99	95.31 52

MVS_111021_HR		84.63 120	84.34 136	85.49 122	90.18 151	75.86 111	79.23 242	87.13 203	73.35 159	85.56 174	89.34 194	83.60 74	90.50 202	76.64 134	94.05 170	90.09 210

RPSCF		88.00 72	86.93 87	91.22 29	90.08 152	89.30 5	89.68 56	91.11 124	79.26 90	89.68 97	94.81 50	82.44 85	87.74 245	76.54 135	88.74 258	96.61 27

nrg030		87.85 75	88.49 69	85.91 111	90.07 153	69.73 159	87.86 88	94.20 23	74.04 150	92.70 44	94.66 51	85.88 59	91.50 168	79.72 99	97.32 72	96.50 29

AdaColmap		83.66 146	83.69 145	83.57 160	90.05 154	72.26 138	86.29 116	90.00 158	78.19 105	81.65 232	87.16 229	83.40 76	94.24 79	61.69 254	94.76 156	84.21 276

pm-mvs1		83.69 145	84.95 118	79.91 221	90.04 155	59.66 252	82.43 191	87.44 196	75.52 134	87.85 132	95.26 36	81.25 108	85.65 273	68.74 208	96.04 112	94.42 79

CHOSEN 1792x2688		72.45 266	70.56 276	78.13 248	90.02 156	63.08 214	68.72 318	83.16 243	42.99 340	75.92 281	85.46 252	57.22 277	85.18 277	49.87 314	81.67 311	86.14 256

anonymousdsp		89.73 49	88.88 64	92.27 7	89.82 157	86.67 12	90.51 42	90.20 153	69.87 201	95.06 10	96.14 21	84.28 67	93.07 132	87.68 13	96.34 101	97.09 19

1112_ss		74.82 250	73.74 250	78.04 250	89.57 158	60.04 248	76.49 278	87.09 207	54.31 301	73.66 297	79.80 312	60.25 255	86.76 259	58.37 272	84.15 299	87.32 245

PCF-MVS	74.62 15	82.15 169	80.92 182	85.84 114	89.43 159	72.30 137	80.53 222	91.82 106	57.36 289	87.81 133	89.92 189	77.67 138	93.63 105	58.69 271	95.08 144	91.58 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 240	73.51 254	82.71 178	89.35 160	73.62 120	80.06 225	85.20 229	60.30 273	73.96 295	87.94 215	57.89 273	89.45 225	52.02 305	74.87 332	85.06 268
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 149	83.10 152	84.90 128	89.34 161	83.87 43	84.54 140	88.77 176	79.09 92	83.54 207	88.66 207	74.87 166	81.73 297	66.84 220	92.29 204	89.11 221

TSAR-MVS + GP.		83.95 141	82.69 157	87.72 82	89.27 162	81.45 57	83.72 158	81.58 256	74.73 143	85.66 171	86.06 244	72.56 197	92.69 141	75.44 145	95.21 138	89.01 227

MVS_111021_LR		84.28 132	83.76 144	85.83 115	89.23 163	83.07 48	80.99 217	83.56 242	72.71 173	86.07 164	89.07 201	81.75 103	86.19 266	77.11 130	93.36 179	88.24 231

LFMVS		80.15 200	80.56 184	78.89 234	89.19 164	55.93 283	85.22 128	73.78 298	82.96 49	84.28 197	92.72 114	57.38 275	90.07 218	63.80 239	95.75 123	90.68 196

CLD-MVS		83.18 156	82.64 158	84.79 130	89.05 165	67.82 178	77.93 257	92.52 89	68.33 213	85.07 178	81.54 300	82.06 94	92.96 133	69.35 199	97.91 46	93.57 114
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 31	90.34 45	91.38 25	89.03 166	84.23 42	93.58 4	94.68 14	90.65 6	90.33 83	93.95 86	84.50 65	95.37 43	80.87 88	95.50 129	94.53 74

CDS-MVSNet		77.32 223	75.40 237	83.06 169	89.00 167	72.48 134	77.90 258	82.17 251	60.81 269	78.94 261	83.49 279	59.30 262	88.76 236	54.64 296	92.37 203	87.93 238
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 180	79.58 200	85.52 120	88.99 168	66.45 187	87.03 100	75.51 287	73.76 154	88.32 127	90.20 182	37.96 340	94.16 88	79.36 106	95.13 141	95.93 40

tfpnnormal		81.79 174	82.95 154	78.31 245	88.93 169	55.40 287	80.83 220	82.85 246	76.81 116	85.90 169	94.14 73	74.58 173	86.51 261	66.82 221	95.68 126	93.01 130

Vis-MVSNet (Re-imp)		77.82 218	77.79 214	77.92 252	88.82 170	51.29 316	83.28 170	71.97 311	74.04 150	82.23 222	89.78 190	57.38 275	89.41 226	57.22 279	95.41 130	93.05 128

TAPA-MVS	77.73 12	85.71 104	84.83 120	88.37 74	88.78 171	79.72 67	87.15 98	93.50 51	69.17 205	85.80 170	89.56 192	80.76 112	92.13 151	73.21 170	95.51 128	93.25 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FPMVS		72.29 269	72.00 268	73.14 284	88.63 172	85.00 32	74.65 295	67.39 324	71.94 184	77.80 268	87.66 220	50.48 297	75.83 314	49.95 312	79.51 318	58.58 343

ETV-MVS		84.31 130	83.91 143	85.52 120	88.58 173	70.40 155	84.50 142	93.37 53	78.76 99	84.07 199	78.72 317	80.39 116	95.13 53	73.82 160	92.98 192	91.04 186

BH-untuned		80.96 182	80.99 180	80.84 209	88.55 174	68.23 173	80.33 224	88.46 180	72.79 172	86.55 153	86.76 234	74.72 171	91.77 165	61.79 253	88.99 253	82.52 300

Anonymous202405211		80.51 190	81.19 178	78.49 242	88.48 175	57.26 275	76.63 275	82.49 248	81.21 68	84.30 196	92.24 131	67.99 220	86.24 265	62.22 248	95.13 141	91.98 168

ab-mvs		79.67 203	80.56 184	76.99 259	88.48 175	56.93 277	84.70 134	86.06 218	68.95 209	80.78 243	93.08 101	75.30 163	84.62 282	56.78 280	90.90 232	89.43 215

PHI-MVS		86.38 91	85.81 104	88.08 78	88.44 177	77.34 94	89.35 67	93.05 71	73.15 167	84.76 184	87.70 219	78.87 128	94.18 83	80.67 92	96.29 102	92.73 140

xiu_mvs_v1_base_debu		80.84 184	80.14 194	82.93 173	88.31 178	71.73 144	79.53 233	87.17 200	65.43 242	79.59 254	82.73 290	76.94 149	90.14 214	73.22 165	88.33 260	86.90 250

xiu_mvs_v1_base		80.84 184	80.14 194	82.93 173	88.31 178	71.73 144	79.53 233	87.17 200	65.43 242	79.59 254	82.73 290	76.94 149	90.14 214	73.22 165	88.33 260	86.90 250

xiu_mvs_v1_base_debi		80.84 184	80.14 194	82.93 173	88.31 178	71.73 144	79.53 233	87.17 200	65.43 242	79.59 254	82.73 290	76.94 149	90.14 214	73.22 165	88.33 260	86.90 250

MG-MVS		80.32 196	80.94 181	78.47 243	88.18 181	52.62 306	82.29 195	85.01 236	72.01 183	79.24 259	92.54 121	69.36 213	93.36 121	70.65 189	89.19 252	89.45 213

PM-MVS		80.20 199	79.00 204	83.78 154	88.17 182	86.66 13	81.31 211	66.81 330	69.64 202	88.33 126	90.19 183	64.58 232	83.63 291	71.99 180	90.03 243	81.06 320

v10		86.54 88	87.10 82	84.84 129	88.16 183	63.28 212	86.64 109	92.20 96	75.42 137	92.81 42	94.50 57	74.05 176	94.06 90	83.88 58	96.28 103	97.17 18

canonicalmvs		85.50 105	86.14 98	83.58 159	87.97 184	67.13 180	87.55 91	94.32 17	73.44 158	88.47 122	87.54 222	86.45 53	91.06 184	75.76 142	93.76 174	92.54 147

EIA-MVS		82.19 168	81.23 177	85.10 126	87.95 185	69.17 169	83.22 175	93.33 56	70.42 194	78.58 263	79.77 314	77.29 142	94.20 82	71.51 181	88.96 254	91.93 169

VNet		79.31 204	80.27 189	76.44 267	87.92 186	53.95 296	75.58 287	84.35 241	74.39 148	82.23 222	90.72 171	72.84 193	84.39 284	60.38 265	93.98 171	90.97 188

CS-MVS		83.43 153	83.04 153	84.59 136	87.87 187	66.61 185	85.57 123	94.90 12	73.02 169	81.12 238	78.56 318	80.00 121	95.52 35	73.04 172	93.29 183	91.62 178

v8		86.22 95	86.83 89	84.36 140	87.82 188	62.35 226	86.42 112	91.33 118	76.78 117	92.73 43	94.48 59	73.41 185	93.72 102	83.10 65	95.41 130	97.01 21

alignmvs		83.94 142	83.98 141	83.80 152	87.80 189	67.88 177	84.54 140	91.42 117	73.27 165	88.41 124	87.96 214	72.33 198	90.83 192	76.02 140	94.11 168	92.69 142

v1192		84.57 122	84.69 125	84.21 144	87.75 190	62.88 216	83.02 179	91.43 115	69.08 207	89.98 89	90.89 166	72.70 195	93.62 108	82.41 72	94.97 148	96.13 32

PatchMatch-RL		74.48 252	73.22 256	78.27 247	87.70 191	85.26 30	75.92 284	70.09 320	64.34 250	76.09 279	81.25 302	65.87 230	78.07 308	53.86 298	83.82 300	71.48 332

v1144		84.54 125	84.72 123	84.00 148	87.67 192	62.55 222	82.97 180	90.93 128	70.32 197	89.80 94	90.99 161	73.50 182	93.48 115	81.69 80	94.65 158	95.97 37

v1240		84.30 131	84.51 131	83.65 157	87.65 193	61.26 235	82.85 183	91.54 112	67.94 218	90.68 79	90.65 175	71.71 205	93.64 104	82.84 70	94.78 153	96.07 34

v1921920		84.23 134	84.37 135	83.79 153	87.64 194	61.71 230	82.91 182	91.20 122	67.94 218	90.06 85	90.34 179	72.04 202	93.59 109	82.32 74	94.91 149	96.07 34

v144192		84.24 133	84.41 133	83.71 156	87.59 195	61.57 231	82.95 181	91.03 126	67.82 221	89.80 94	90.49 177	73.28 188	93.51 114	81.88 79	94.89 150	96.04 36

Fast-Effi-MVS+		81.04 181	80.57 183	82.46 185	87.50 196	63.22 213	78.37 253	89.63 165	68.01 215	81.87 227	82.08 295	82.31 87	92.65 142	67.10 217	88.30 264	91.51 182

pmmvs-eth3d		78.42 212	77.04 221	82.57 183	87.44 197	74.41 117	80.86 219	79.67 266	55.68 295	84.69 185	90.31 181	60.91 250	85.42 274	62.20 249	91.59 218	87.88 239

IterMVS-LS		84.73 119	84.98 117	83.96 150	87.35 198	63.66 207	83.25 172	89.88 160	76.06 123	89.62 100	92.37 126	73.40 187	92.52 144	78.16 116	94.77 155	95.69 42
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres100view900		75.45 241	75.05 240	76.66 266	87.27 199	51.88 312	81.07 216	73.26 302	75.68 132	83.25 209	86.37 238	45.54 315	88.80 232	51.98 306	90.99 227	89.31 217

MIMVSNet		71.09 276	71.59 271	69.57 298	87.23 200	50.07 324	78.91 244	71.83 312	60.20 275	71.26 306	91.76 142	55.08 288	76.09 312	41.06 336	87.02 276	82.54 299

Effi-MVS+		83.90 143	84.01 140	83.57 160	87.22 201	65.61 192	86.55 111	92.40 91	78.64 100	81.34 237	84.18 273	83.65 73	92.93 135	74.22 153	87.87 268	92.17 161

BH-RMVSNet		80.53 189	80.22 192	81.49 198	87.19 202	66.21 188	77.79 260	86.23 216	74.21 149	83.69 202	88.50 208	73.25 189	90.75 194	63.18 244	87.90 267	87.52 242

thisisatest0530		79.07 205	77.33 218	84.26 143	87.13 203	64.58 198	83.66 160	75.95 282	68.86 210	85.22 177	87.36 226	38.10 338	93.57 112	75.47 144	94.28 165	94.62 67

Effi-MVS+-dtu		85.82 102	83.38 147	93.14 3	87.13 203	91.15 2	87.70 90	88.42 181	74.57 145	83.56 206	85.65 247	78.49 130	94.21 81	72.04 178	92.88 194	94.05 92

mvs-test1		84.55 123	82.12 165	91.84 18	87.13 203	89.54 4	85.05 130	88.42 181	74.57 145	80.60 244	82.98 283	78.49 130	93.98 93	72.04 178	89.77 245	92.00 165

v2v482		84.09 136	84.24 137	83.62 158	87.13 203	61.40 232	82.71 186	89.71 163	72.19 180	89.55 104	91.41 150	70.70 210	93.20 124	81.02 85	93.76 174	96.25 30

jason		77.42 222	75.75 234	82.43 186	87.10 207	69.27 164	77.99 256	81.94 253	51.47 319	77.84 266	85.07 262	60.32 254	89.00 229	70.74 188	89.27 251	89.03 225
jason: jason.
PS-MVSNAJ		77.04 226	76.53 226	78.56 240	87.09 208	61.40 232	75.26 290	87.13 203	61.25 267	74.38 294	77.22 326	76.94 149	90.94 186	64.63 236	84.83 295	83.35 289

xiu_mvs_v2_base		77.19 224	76.75 224	78.52 241	87.01 209	61.30 234	75.55 288	87.12 206	61.24 268	74.45 292	78.79 316	77.20 143	90.93 187	64.62 237	84.80 296	83.32 290

thres600view7		75.97 238	75.35 239	77.85 254	87.01 209	51.84 313	80.45 223	73.26 302	75.20 139	83.10 212	86.31 241	45.54 315	89.05 228	55.03 293	92.24 206	92.66 143

BH-w/o		76.57 231	76.07 231	78.10 249	86.88 211	65.92 190	77.63 262	86.33 215	65.69 240	80.89 241	79.95 311	68.97 217	90.74 195	53.01 302	85.25 290	77.62 324

MAR-MVS		80.24 198	78.74 206	84.73 132	86.87 212	78.18 82	85.75 120	87.81 194	65.67 241	77.84 266	78.50 319	73.79 179	90.53 201	61.59 257	90.87 233	85.49 264
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
testing_2		84.36 127	84.64 127	83.50 162	86.74 213	63.97 206	84.56 137	90.31 145	66.22 233	91.62 62	94.55 55	75.88 159	91.95 157	77.02 132	94.89 150	94.56 70

QAPM		82.59 162	82.59 160	82.58 181	86.44 214	66.69 184	89.94 51	90.36 142	67.97 217	84.94 181	92.58 119	72.71 194	92.18 150	70.63 190	87.73 270	88.85 228

PAPM		71.77 272	70.06 282	76.92 260	86.39 215	53.97 295	76.62 276	86.62 213	53.44 306	63.97 334	84.73 268	57.79 274	92.34 146	39.65 338	81.33 314	84.45 273

GBi-Net		82.02 171	82.07 166	81.85 192	86.38 216	61.05 238	86.83 103	88.27 186	72.43 175	86.00 165	95.64 28	63.78 238	90.68 197	65.95 225	93.34 180	93.82 102

test1		82.02 171	82.07 166	81.85 192	86.38 216	61.05 238	86.83 103	88.27 186	72.43 175	86.00 165	95.64 28	63.78 238	90.68 197	65.95 225	93.34 180	93.82 102

FMVSNet2		81.31 178	81.61 172	80.41 215	86.38 216	58.75 266	83.93 151	86.58 214	72.43 175	87.65 134	92.98 104	63.78 238	90.22 209	66.86 218	93.92 172	92.27 156

3Dnovator	80.37 7	84.80 118	84.71 124	85.06 127	86.36 219	74.71 115	88.77 77	90.00 158	75.65 133	84.96 179	93.17 100	74.06 175	91.19 179	78.28 115	91.09 223	89.29 219

Anonymous20231206		71.38 275	71.88 269	69.88 295	86.31 220	54.37 293	70.39 313	74.62 290	52.57 311	76.73 272	88.76 204	59.94 257	72.06 320	44.35 331	93.23 185	83.23 292

baseline		85.20 109	85.93 101	83.02 170	86.30 221	62.37 225	84.55 138	93.96 33	74.48 147	87.12 140	92.03 133	82.30 88	91.94 158	78.39 111	94.21 166	94.74 66

API-MVS		82.28 166	82.61 159	81.30 199	86.29 222	69.79 157	88.71 78	87.67 195	78.42 103	82.15 224	84.15 274	77.98 134	91.59 167	65.39 231	92.75 196	82.51 301

tfpn200view9		74.86 249	74.23 247	76.74 265	86.24 223	52.12 309	79.24 240	73.87 296	73.34 160	81.82 229	84.60 270	46.02 309	88.80 232	51.98 306	90.99 227	89.31 217

thres400		75.14 243	74.23 247	77.86 253	86.24 223	52.12 309	79.24 240	73.87 296	73.34 160	81.82 229	84.60 270	46.02 309	88.80 232	51.98 306	90.99 227	92.66 143

UGNet		82.78 159	81.64 171	86.21 106	86.20 225	76.24 110	86.86 101	85.68 222	77.07 114	73.76 296	92.82 110	69.64 211	91.82 164	69.04 205	93.69 177	90.56 199
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
CANet		83.79 144	82.85 155	86.63 93	86.17 226	72.21 140	83.76 157	91.43 115	77.24 113	74.39 293	87.45 224	75.36 162	95.42 41	77.03 131	92.83 195	92.25 158

casdiffmvs		85.21 108	85.85 103	83.31 165	86.17 226	62.77 218	83.03 178	93.93 34	74.69 144	88.21 128	92.68 116	82.29 89	91.89 161	77.87 121	93.75 176	95.27 54

TR-MVS		76.77 229	75.79 232	79.72 225	86.10 228	65.79 191	77.14 268	83.02 244	65.20 247	81.40 235	82.10 294	66.30 226	90.73 196	55.57 288	85.27 289	82.65 296

LCM-MVSNet-Re		83.48 150	85.06 115	78.75 237	85.94 229	55.75 286	80.05 226	94.27 18	76.47 118	96.09 4	94.54 56	83.31 77	89.75 222	59.95 266	94.89 150	90.75 194

Fast-Effi-MVS+-dtu		82.54 163	81.41 175	85.90 112	85.60 230	76.53 106	83.07 177	89.62 166	73.02 169	79.11 260	83.51 278	80.74 113	90.24 208	68.76 207	89.29 249	90.94 189

v148		82.31 165	82.48 162	81.81 195	85.59 231	59.66 252	81.47 209	86.02 219	72.85 171	88.05 129	90.65 175	70.73 209	90.91 189	75.15 148	91.79 214	94.87 61

MVSFormer		82.23 167	81.57 174	84.19 146	85.54 232	69.26 165	91.98 27	90.08 156	71.54 185	76.23 277	85.07 262	58.69 266	94.27 76	86.26 32	88.77 256	89.03 225

lupinMVS		76.37 235	74.46 245	82.09 187	85.54 232	69.26 165	76.79 272	80.77 261	50.68 325	76.23 277	82.82 288	58.69 266	88.94 230	69.85 195	88.77 256	88.07 233

TinyColmap		81.25 179	82.34 164	77.99 251	85.33 234	60.68 244	82.32 194	88.33 184	71.26 187	86.97 147	92.22 132	77.10 146	86.98 254	62.37 247	95.17 140	86.31 255

PAPR		78.84 207	78.10 212	81.07 204	85.17 235	60.22 247	82.21 199	90.57 137	62.51 257	75.32 288	84.61 269	74.99 165	92.30 148	59.48 269	88.04 266	90.68 196

pmmvs4		74.92 248	72.98 259	80.73 211	84.95 236	71.71 147	76.23 282	77.59 273	52.83 309	77.73 269	86.38 237	56.35 281	84.97 278	57.72 278	87.05 275	85.51 263

baseline1		73.26 259	73.54 253	72.43 290	84.92 237	47.79 330	79.89 229	74.00 295	65.93 234	78.81 262	86.28 242	56.36 280	81.63 298	56.63 281	79.04 323	87.87 240

Patchmatch-RL test		74.48 252	73.68 251	76.89 262	84.83 238	66.54 186	72.29 306	69.16 323	57.70 285	86.76 149	86.33 239	45.79 314	82.59 294	69.63 197	90.65 240	81.54 311

XXY-MVS		74.44 254	76.19 229	69.21 299	84.61 239	52.43 308	71.70 308	77.18 275	60.73 271	80.60 244	90.96 164	75.44 160	69.35 326	56.13 284	88.33 260	85.86 260

cascas		76.29 236	74.81 241	80.72 212	84.47 240	62.94 215	73.89 299	87.34 197	55.94 294	75.16 290	76.53 329	63.97 236	91.16 180	65.00 232	90.97 230	88.06 234

PVSNet_BlendedMVS		78.80 208	77.84 213	81.65 197	84.43 241	63.41 209	79.49 236	90.44 139	61.70 265	75.43 286	87.07 232	69.11 215	91.44 171	60.68 263	92.24 206	90.11 209

PVSNet_Blended		76.49 233	75.40 237	79.76 223	84.43 241	63.41 209	75.14 291	90.44 139	57.36 289	75.43 286	78.30 320	69.11 215	91.44 171	60.68 263	87.70 271	84.42 274

OpenMVS	76.72 13	81.98 173	82.00 168	81.93 189	84.42 243	68.22 174	88.50 82	89.48 168	66.92 227	81.80 231	91.86 136	72.59 196	90.16 211	71.19 183	91.25 222	87.40 244

OpenMVS_ROB	70.19 17	77.77 220	77.46 216	78.71 238	84.39 244	61.15 236	81.18 215	82.52 247	62.45 259	83.34 208	87.37 225	66.20 227	88.66 237	64.69 235	85.02 292	86.32 254

test_yl		78.71 210	78.51 208	79.32 231	84.32 245	58.84 263	78.38 251	85.33 226	75.99 126	82.49 217	86.57 235	58.01 269	90.02 219	62.74 245	92.73 197	89.10 222

DCV-MVSNet		78.71 210	78.51 208	79.32 231	84.32 245	58.84 263	78.38 251	85.33 226	75.99 126	82.49 217	86.57 235	58.01 269	90.02 219	62.74 245	92.73 197	89.10 222

Regformer-3		85.06 113	84.67 126	86.22 104	84.27 247	73.43 122	84.07 146	85.26 228	80.77 73	88.62 119	85.48 250	80.56 115	90.39 205	81.99 77	91.04 225	94.85 63

Regformer-4		86.41 90	85.71 106	88.52 70	84.27 247	77.57 89	84.07 146	88.00 191	82.82 51	89.84 93	85.48 250	82.06 94	92.77 139	83.83 60	91.04 225	95.22 58

DELS-MVS		81.44 177	81.25 176	82.03 188	84.27 247	62.87 217	76.47 279	92.49 90	70.97 189	81.64 233	83.83 275	75.03 164	92.70 140	74.29 152	92.22 208	90.51 201
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
Gipuma		84.44 126	86.33 93	78.78 236	84.20 250	73.57 121	89.55 60	90.44 139	84.24 33	84.38 191	94.89 44	76.35 158	80.40 302	76.14 138	96.80 88	82.36 302
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-1		86.00 98	85.50 110	87.49 85	84.18 251	76.90 101	83.52 162	87.94 193	82.18 57	89.19 109	85.07 262	82.28 90	91.89 161	82.40 73	92.72 199	93.69 108

Regformer-2		86.74 86	86.08 99	88.73 67	84.18 251	79.20 73	83.52 162	89.33 170	83.33 44	89.92 91	85.07 262	83.23 78	93.16 127	83.39 62	92.72 199	93.83 100

MVS_0304		78.17 213	77.23 219	80.99 208	84.13 253	69.07 171	81.39 210	80.81 260	76.28 120	67.53 321	89.11 200	62.87 244	86.77 258	60.90 262	92.01 213	87.13 247

EI-MVSNet-Vis-set		85.12 111	84.53 130	86.88 89	84.01 254	72.76 126	83.91 152	85.18 230	80.44 74	88.75 116	85.49 249	80.08 119	91.92 159	82.02 76	90.85 234	95.97 37

IterMVS-SCA-FT		80.64 188	79.41 201	84.34 141	83.93 255	69.66 160	76.28 281	81.09 258	72.43 175	86.47 159	90.19 183	60.46 252	93.15 129	77.45 126	86.39 281	90.22 205

MSDG		80.06 202	79.99 198	80.25 217	83.91 256	68.04 176	77.51 265	89.19 171	77.65 108	81.94 226	83.45 280	76.37 157	86.31 264	63.31 243	86.59 278	86.41 253

EI-MVSNet-UG-set		85.04 114	84.44 132	86.85 90	83.87 257	72.52 133	83.82 154	85.15 231	80.27 78	88.75 116	85.45 253	79.95 122	91.90 160	81.92 78	90.80 235	96.13 32

thres200		72.34 268	71.55 273	74.70 278	83.48 258	51.60 314	75.02 292	73.71 299	70.14 199	78.56 264	80.57 305	46.20 307	88.20 242	46.99 325	89.29 249	84.32 275

USDC		76.63 230	76.73 225	76.34 269	83.46 259	57.20 276	80.02 227	88.04 190	52.14 315	83.65 204	91.25 151	63.24 241	86.65 260	54.66 295	94.11 168	85.17 266

HY-MVS	64.64 18	73.03 262	72.47 266	74.71 277	83.36 260	54.19 294	82.14 202	81.96 252	56.76 293	69.57 313	86.21 243	60.03 256	84.83 281	49.58 315	82.65 308	85.11 267

EI-MVSNet		82.61 161	82.42 163	83.20 167	83.25 261	63.66 207	83.50 165	85.07 232	76.06 123	86.55 153	85.10 259	73.41 185	90.25 206	78.15 118	90.67 238	95.68 43

CVMVSNet		72.62 265	71.41 274	76.28 270	83.25 261	60.34 246	83.50 165	79.02 268	37.77 344	76.33 275	85.10 259	49.60 299	87.41 248	70.54 191	77.54 328	81.08 318

V42		83.47 151	83.37 148	83.75 155	83.16 263	63.33 211	81.31 211	90.23 152	69.51 203	90.91 76	90.81 169	74.16 174	92.29 149	80.06 95	90.22 242	95.62 45

EU-MVSNet		75.12 245	74.43 246	77.18 258	83.11 264	59.48 254	85.71 122	82.43 249	39.76 343	85.64 172	88.76 204	44.71 326	87.88 244	73.86 159	85.88 285	84.16 277

ET-MVSNet_ETH3D		75.28 242	72.77 260	82.81 177	83.03 265	68.11 175	77.09 269	76.51 280	60.67 272	77.60 270	80.52 306	38.04 339	91.15 181	70.78 186	90.68 237	89.17 220

FMVSNet3		78.80 208	78.55 207	79.57 228	82.89 266	56.89 279	81.76 203	85.77 221	69.04 208	86.00 165	90.44 178	51.75 293	90.09 217	65.95 225	93.34 180	91.72 174

MVS_Test		82.47 164	83.22 149	80.22 218	82.62 267	57.75 272	82.54 190	91.96 103	71.16 188	82.89 214	92.52 122	77.41 141	90.50 202	80.04 96	87.84 269	92.40 150

LF4IMVS		82.75 160	81.93 169	85.19 124	82.08 268	80.15 64	85.53 124	88.76 177	68.01 215	85.58 173	87.75 218	71.80 204	86.85 256	74.02 156	93.87 173	88.58 230

PVSNet	58.17 21	66.41 298	65.63 300	68.75 302	81.96 269	49.88 325	62.19 333	72.51 308	51.03 321	68.04 317	75.34 332	50.84 295	74.77 316	45.82 329	82.96 304	81.60 310

GA-MVS		75.83 239	74.61 242	79.48 230	81.87 270	59.25 256	73.42 302	82.88 245	68.68 211	79.75 253	81.80 297	50.62 296	89.46 224	66.85 219	85.64 286	89.72 211

MS-PatchMatch		70.93 277	70.22 280	73.06 285	81.85 271	62.50 223	73.82 300	77.90 271	52.44 312	75.92 281	81.27 301	55.67 284	81.75 296	55.37 290	77.70 326	74.94 328

SCA		73.32 258	72.57 264	75.58 274	81.62 272	55.86 284	78.89 245	71.37 317	61.73 263	74.93 291	83.42 281	60.46 252	87.01 251	58.11 276	82.63 310	83.88 278

FMVSNet5		72.10 270	71.69 270	73.32 282	81.57 273	53.02 303	76.77 273	78.37 270	63.31 252	76.37 274	91.85 137	36.68 342	78.98 305	47.87 322	92.45 202	87.95 237

thisisatest0515		73.00 263	70.52 277	80.46 214	81.45 274	59.90 250	73.16 304	74.31 294	57.86 284	76.08 280	77.78 321	37.60 341	92.12 153	65.00 232	91.45 220	89.35 216

eth_miper_zixun_eth		80.84 184	80.22 192	82.71 178	81.41 275	60.98 241	77.81 259	90.14 155	67.31 225	86.95 148	87.24 228	64.26 234	92.31 147	75.23 147	91.61 217	94.85 63

CANet_DTU		77.81 219	77.05 220	80.09 220	81.37 276	59.90 250	83.26 171	88.29 185	69.16 206	67.83 319	83.72 276	60.93 249	89.47 223	69.22 202	89.70 246	90.88 191

ANet_high		83.17 157	85.68 107	75.65 273	81.24 277	45.26 335	79.94 228	92.91 78	83.83 37	91.33 68	96.88 10	80.25 118	85.92 269	68.89 206	95.89 116	95.76 41

new-patchmatchnet		70.10 282	73.37 255	60.29 325	81.23 278	16.95 351	59.54 335	74.62 290	62.93 254	80.97 239	87.93 216	62.83 245	71.90 321	55.24 291	95.01 147	92.00 165

test20.03		73.75 257	74.59 244	71.22 294	81.11 279	51.12 318	70.15 314	72.10 310	70.42 194	80.28 251	91.50 148	64.21 235	74.72 318	46.96 326	94.58 159	87.82 241

MVS		73.21 261	72.59 263	75.06 276	80.97 280	60.81 243	81.64 206	85.92 220	46.03 334	71.68 305	77.54 322	68.47 218	89.77 221	55.70 287	85.39 287	74.60 329

N_pmnet		70.20 280	68.80 288	74.38 279	80.91 281	84.81 35	59.12 337	76.45 281	55.06 298	75.31 289	82.36 293	55.74 283	54.82 344	47.02 324	87.24 274	83.52 285

IterMVS		76.91 227	76.34 228	78.64 239	80.91 281	64.03 204	76.30 280	79.03 267	64.88 249	83.11 211	89.16 198	59.90 258	84.46 283	68.61 210	85.15 291	87.42 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl_fuxian		81.64 175	81.59 173	81.79 196	80.86 283	59.15 259	78.61 250	90.18 154	68.36 212	87.20 138	87.11 231	69.39 212	91.62 166	78.16 116	94.43 163	94.60 69

RRT_test8_iter05		78.08 215	77.52 215	79.75 224	80.84 284	52.54 307	80.61 221	88.96 174	67.77 222	84.62 186	89.29 195	33.89 345	92.10 154	77.59 123	94.15 167	94.62 67

WTY-MVS		67.91 293	68.35 290	66.58 310	80.82 285	48.12 328	65.96 327	72.60 306	53.67 305	71.20 307	81.68 299	58.97 264	69.06 328	48.57 318	81.67 311	82.55 298

IB-MVS	62.13 19	71.64 273	68.97 286	79.66 227	80.80 286	62.26 228	73.94 298	76.90 276	63.27 253	68.63 315	76.79 327	33.83 346	91.84 163	59.28 270	87.26 273	84.88 269
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
our_test_3		71.85 271	71.59 271	72.62 288	80.71 287	53.78 297	69.72 316	71.71 316	58.80 278	78.03 265	80.51 307	56.61 279	78.84 306	62.20 249	86.04 284	85.23 265

ppachtmachnet_test		74.73 251	74.00 249	76.90 261	80.71 287	56.89 279	71.53 309	78.42 269	58.24 281	79.32 258	82.92 287	57.91 272	84.26 285	65.60 230	91.36 221	89.56 212

testgi		72.36 267	74.61 242	65.59 312	80.56 289	42.82 341	68.29 319	73.35 301	66.87 228	81.84 228	89.93 188	72.08 201	66.92 334	46.05 328	92.54 201	87.01 249

RRT_MVS		83.25 154	81.08 179	89.74 51	80.55 290	79.32 72	86.41 113	86.69 212	72.33 179	87.00 146	91.08 157	44.98 324	95.55 33	84.47 54	96.24 106	94.36 81

D2MVS		76.84 228	75.67 236	80.34 216	80.48 291	62.16 229	73.50 301	84.80 239	57.61 287	82.24 221	87.54 222	51.31 294	87.65 246	70.40 193	93.19 186	91.23 185

1314		73.22 260	72.56 265	75.20 275	80.41 292	57.84 270	81.64 206	85.36 225	51.68 318	73.10 299	76.65 328	61.45 248	85.19 276	63.54 240	79.21 322	82.59 297

cl-mvsnet_		80.42 192	80.23 190	81.02 206	79.99 293	59.25 256	77.07 270	87.02 208	67.37 224	86.18 162	89.21 197	63.08 243	90.16 211	76.31 137	95.80 120	93.65 110

cl-mvsnet1		80.43 191	80.23 190	81.02 206	79.99 293	59.25 256	77.07 270	87.02 208	67.38 223	86.19 160	89.22 196	63.09 242	90.16 211	76.32 136	95.80 120	93.66 109

miper_ehance_all_eth		80.34 195	80.04 197	81.24 202	79.82 295	58.95 261	77.66 261	89.66 164	65.75 239	85.99 168	85.11 258	68.29 219	91.42 173	76.03 139	92.03 210	93.33 118

CR-MVSNet		74.00 255	73.04 258	76.85 263	79.58 296	62.64 220	82.58 187	76.90 276	50.50 326	75.72 283	92.38 123	48.07 302	84.07 286	68.72 209	82.91 306	83.85 281

RPMNet		76.06 237	75.79 232	76.85 263	79.58 296	62.64 220	82.58 187	71.75 314	74.80 142	75.72 283	92.59 117	48.69 300	84.07 286	73.48 163	82.91 306	83.85 281

baseline2		69.77 286	66.89 295	78.41 244	79.51 298	58.09 268	76.23 282	69.57 322	57.50 288	64.82 332	77.45 324	46.02 309	88.44 238	53.08 301	77.83 325	88.70 229

UnsupCasMVSNet_bld		69.21 289	69.68 283	67.82 306	79.42 299	51.15 317	67.82 323	75.79 283	54.15 302	77.47 271	85.36 257	59.26 263	70.64 323	48.46 319	79.35 320	81.66 309

PatchT		70.52 279	72.76 261	63.79 317	79.38 300	33.53 347	77.63 262	65.37 332	73.61 155	71.77 304	92.79 113	44.38 327	75.65 315	64.53 238	85.37 288	82.18 304

Patchmtry		76.56 232	77.46 216	73.83 281	79.37 301	46.60 332	82.41 192	76.90 276	73.81 153	85.56 174	92.38 123	48.07 302	83.98 288	63.36 242	95.31 136	90.92 190

mvs_anonymous		78.13 214	78.76 205	76.23 271	79.24 302	50.31 323	78.69 248	84.82 238	61.60 266	83.09 213	92.82 110	73.89 178	87.01 251	68.33 212	86.41 280	91.37 183

MVS-HIRNet		61.16 310	62.92 306	55.87 328	79.09 303	35.34 346	71.83 307	57.98 344	46.56 332	59.05 341	91.14 156	49.95 298	76.43 311	38.74 339	71.92 336	55.84 344

MDA-MVSNet-bldmvs		77.47 221	76.90 223	79.16 233	79.03 304	64.59 197	66.58 326	75.67 285	73.15 167	88.86 113	88.99 202	66.94 224	81.23 299	64.71 234	88.22 265	91.64 177

diffmvs		80.40 193	80.48 187	80.17 219	79.02 305	60.04 248	77.54 264	90.28 151	66.65 230	82.40 219	87.33 227	73.50 182	87.35 249	77.98 119	89.62 247	93.13 126

tpm2		68.45 291	66.83 296	73.30 283	78.93 306	48.50 326	79.76 230	71.76 313	47.50 330	69.92 312	83.60 277	42.07 332	88.40 239	48.44 320	79.51 318	83.01 295

tpm		67.95 292	68.08 292	67.55 307	78.74 307	43.53 339	75.60 286	67.10 329	54.92 299	72.23 302	88.10 212	42.87 331	75.97 313	52.21 304	80.95 317	83.15 293

MDTV_nov1_ep13			68.29 291		78.03 308	43.87 338	74.12 297	72.22 309	52.17 313	67.02 322	85.54 248	45.36 319	80.85 300	55.73 285	84.42 298

cl-mvsnet2		78.97 206	78.21 211	81.24 202	77.74 309	59.01 260	77.46 267	87.13 203	65.79 236	84.32 193	85.10 259	58.96 265	90.88 191	75.36 146	92.03 210	93.84 99

EPNet_dtu		72.87 264	71.33 275	77.49 257	77.72 310	60.55 245	82.35 193	75.79 283	66.49 231	58.39 344	81.06 303	53.68 290	85.98 268	53.55 299	92.97 193	85.95 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 287	68.83 287	72.33 291	77.66 311	53.60 298	79.29 238	69.99 321	57.66 286	72.53 301	82.93 286	46.45 306	80.08 304	60.91 261	72.09 335	83.31 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 295	67.26 294	65.90 311	77.23 312	51.10 319	64.79 328	71.72 315	52.12 316	70.13 311	80.18 309	57.96 271	65.36 339	50.21 311	81.01 316	81.25 315

CostFormer		69.98 285	68.68 289	73.87 280	77.14 313	50.72 321	79.26 239	74.51 292	51.94 317	70.97 309	84.75 267	45.16 323	87.49 247	55.16 292	79.23 321	83.40 288

tpm cat1		66.76 296	65.21 301	71.42 293	77.09 314	50.62 322	78.01 255	73.68 300	44.89 336	68.64 314	79.00 315	45.51 317	82.42 295	49.91 313	70.15 338	81.23 317

pmmvs5		70.73 278	70.07 281	72.72 286	77.03 315	52.73 304	74.14 296	75.65 286	50.36 327	72.17 303	85.37 256	55.42 286	80.67 301	52.86 303	87.59 272	84.77 270

EPNet		80.37 194	78.41 210	86.23 103	76.75 316	73.28 123	87.18 97	77.45 274	76.24 121	68.14 316	88.93 203	65.41 231	93.85 96	69.47 198	96.12 111	91.55 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		76.45 234	76.10 230	77.51 256	76.72 317	60.97 242	64.69 329	85.04 234	63.98 251	83.20 210	88.22 210	56.67 278	78.79 307	73.22 165	93.12 187	92.78 139

CHOSEN 280x420		59.08 314	56.52 318	66.76 309	76.51 318	64.39 201	49.62 343	59.00 341	43.86 338	55.66 346	68.41 339	35.55 344	68.21 330	43.25 332	76.78 330	67.69 337

UnsupCasMVSNet_eth		71.63 274	72.30 267	69.62 297	76.47 319	52.70 305	70.03 315	80.97 259	59.18 277	79.36 257	88.21 211	60.50 251	69.12 327	58.33 274	77.62 327	87.04 248

test-LLR		67.21 294	66.74 297	68.63 303	76.45 320	55.21 289	67.89 320	67.14 327	62.43 260	65.08 329	72.39 334	43.41 328	69.37 324	61.00 259	84.89 293	81.31 313

test-mter		65.00 302	63.79 304	68.63 303	76.45 320	55.21 289	67.89 320	67.14 327	50.98 322	65.08 329	72.39 334	28.27 352	69.37 324	61.00 259	84.89 293	81.31 313

miper_enhance_ethall		77.83 217	76.93 222	80.51 213	76.15 322	58.01 269	75.47 289	88.82 175	58.05 283	83.59 205	80.69 304	64.41 233	91.20 178	73.16 171	92.03 210	92.33 152

gg-mvs-nofinetune		68.96 290	69.11 285	68.52 305	76.12 323	45.32 334	83.59 161	55.88 345	86.68 22	64.62 333	97.01 7	30.36 350	83.97 289	44.78 330	82.94 305	76.26 326

CMPMVS	59.41 20	75.12 245	73.57 252	79.77 222	75.84 324	67.22 179	81.21 214	82.18 250	50.78 323	76.50 273	87.66 220	55.20 287	82.99 293	62.17 251	90.64 241	89.09 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
wuyk23d		75.13 244	79.30 202	62.63 318	75.56 325	75.18 114	80.89 218	73.10 304	75.06 141	94.76 11	95.32 33	87.73 37	52.85 345	34.16 343	97.11 77	59.85 341

Patchmatch-test		65.91 300	67.38 293	61.48 323	75.51 326	43.21 340	68.84 317	63.79 334	62.48 258	72.80 300	83.42 281	44.89 325	59.52 343	48.27 321	86.45 279	81.70 308

new_pmnet		55.69 316	57.66 317	49.76 330	75.47 327	30.59 348	59.56 334	51.45 348	43.62 339	62.49 335	75.48 331	40.96 334	49.15 347	37.39 341	72.52 334	69.55 335

gm-plane-assit					75.42 328	44.97 337			52.17 313		72.36 336		87.90 243	54.10 297

MVSTER		77.09 225	75.70 235	81.25 200	75.27 329	61.08 237	77.49 266	85.07 232	60.78 270	86.55 153	88.68 206	43.14 330	90.25 206	73.69 161	90.67 238	92.42 148

PVSNet_0	51.08 22	56.10 315	54.97 319	59.48 326	75.12 330	53.28 302	55.16 340	61.89 336	44.30 337	59.16 340	62.48 343	54.22 289	65.91 338	35.40 342	47.01 345	59.25 342

test0.0.03 1		64.66 303	64.36 303	65.57 313	75.03 331	46.89 331	64.69 329	61.58 339	62.43 260	71.18 308	77.54 322	43.41 328	68.47 329	40.75 337	82.65 308	81.35 312

DWT-MVSNet_test		66.43 297	64.37 302	72.63 287	74.86 332	50.86 320	76.52 277	72.74 305	54.06 303	65.50 326	68.30 340	32.13 348	84.84 280	61.63 256	73.59 333	82.19 303

tpmvs		70.16 281	69.56 284	71.96 292	74.71 333	48.13 327	79.63 231	75.45 288	65.02 248	70.26 310	81.88 296	45.34 320	85.68 272	58.34 273	75.39 331	82.08 305

MDA-MVSNet_test_wron		70.05 284	70.44 278	68.88 301	73.84 334	53.47 299	58.93 339	67.28 325	58.43 279	87.09 143	85.40 254	59.80 260	67.25 332	59.66 268	83.54 301	85.92 259

YYNet1		70.06 283	70.44 278	68.90 300	73.76 335	53.42 301	58.99 338	67.20 326	58.42 280	87.10 142	85.39 255	59.82 259	67.32 331	59.79 267	83.50 302	85.96 257

GG-mvs-BLEND				67.16 308	73.36 336	46.54 333	84.15 145	55.04 346		58.64 343	61.95 344	29.93 351	83.87 290	38.71 340	76.92 329	71.07 333

JIA-IIPM		69.41 288	66.64 299	77.70 255	73.19 337	71.24 150	75.67 285	65.56 331	70.42 194	65.18 328	92.97 105	33.64 347	83.06 292	53.52 300	69.61 341	78.79 323

ADS-MVSNet2		65.87 301	63.64 305	72.55 289	73.16 338	56.92 278	67.10 324	74.81 289	49.74 328	66.04 324	82.97 284	46.71 304	77.26 309	42.29 333	69.96 339	83.46 286

ADS-MVSNet		61.90 306	62.19 308	61.03 324	73.16 338	36.42 345	67.10 324	61.75 337	49.74 328	66.04 324	82.97 284	46.71 304	63.21 341	42.29 333	69.96 339	83.46 286

DSMNet-mixed		60.98 312	61.61 310	59.09 327	72.88 340	45.05 336	74.70 294	46.61 350	26.20 346	65.34 327	90.32 180	55.46 285	63.12 342	41.72 335	81.30 315	69.09 336

tpmrst		66.28 299	66.69 298	65.05 315	72.82 341	39.33 342	78.20 254	70.69 319	53.16 308	67.88 318	80.36 308	48.18 301	74.75 317	58.13 275	70.79 337	81.08 318

TESTMET0.1,1		61.29 309	60.32 313	64.19 316	72.06 342	51.30 315	67.89 320	62.09 335	45.27 335	60.65 338	69.01 337	27.93 353	64.74 340	56.31 283	81.65 313	76.53 325

dp		60.70 313	60.29 314	61.92 321	72.04 343	38.67 344	70.83 310	64.08 333	51.28 320	60.75 337	77.28 325	36.59 343	71.58 322	47.41 323	62.34 344	75.52 327

pmmvs3		62.47 304	60.02 315	69.80 296	71.58 344	64.00 205	70.52 312	58.44 343	39.77 342	66.05 323	75.84 330	27.10 354	72.28 319	46.15 327	84.77 297	73.11 330

EPMVS		62.47 304	62.63 307	62.01 319	70.63 345	38.74 343	74.76 293	52.86 347	53.91 304	67.71 320	80.01 310	39.40 336	66.60 335	55.54 289	68.81 342	80.68 322

E-PMN		61.59 308	61.62 309	61.49 322	66.81 346	55.40 287	53.77 341	60.34 340	66.80 229	58.90 342	65.50 341	40.48 335	66.12 337	55.72 286	86.25 282	62.95 339

EMVS		61.10 311	60.81 311	61.99 320	65.96 347	55.86 284	53.10 342	58.97 342	67.06 226	56.89 345	63.33 342	40.98 333	67.03 333	54.79 294	86.18 283	63.08 338

PMMVS		61.65 307	60.38 312	65.47 314	65.40 348	69.26 165	63.97 331	61.73 338	36.80 345	60.11 339	68.43 338	59.42 261	66.35 336	48.97 317	78.57 324	60.81 340

PMMVS2		55.64 317	59.27 316	44.74 331	64.30 349	12.32 352	40.60 344	49.79 349	53.19 307	65.06 331	84.81 266	53.60 291	49.76 346	32.68 345	89.41 248	72.15 331

MVE	40.22 23	51.82 318	50.47 320	55.87 328	62.66 350	51.91 311	31.61 346	39.28 351	40.65 341	50.76 347	74.98 333	56.24 282	44.67 348	33.94 344	64.11 343	71.04 334
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 332	32.95 351	29.49 349		21.63 354	12.07 347	37.95 348	45.07 345	30.84 349	19.21 349	17.94 347	33.06 347	23.69 345

tmp_tt		20.25 320	24.50 322	7.49 333	4.47 352	8.70 353	34.17 345	25.16 353	1.00 348	32.43 349	18.49 346	39.37 337	9.21 350	21.64 346	43.75 346	4.57 346

testmvs		5.91 324	7.65 326	0.72 335	1.20 353	0.37 355	59.14 336	0.67 356	0.49 350	1.11 350	2.76 350	0.94 356	0.24 352	1.02 349	1.47 348	1.55 348

test123		6.27 323	8.08 325	0.84 334	1.11 354	0.57 354	62.90 332	0.82 355	0.54 349	1.07 351	2.75 351	1.26 355	0.30 351	1.04 348	1.26 349	1.66 347

uanet_test		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

cdsmvs_eth3d_5k		20.81 319	27.75 321	0.00 336	0.00 355	0.00 356	0.00 347	85.44 224	0.00 351	0.00 352	82.82 288	81.46 105	0.00 353	0.00 350	0.00 350	0.00 349

pcd_1.5k_mvsjas		6.41 322	8.55 324	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	76.94 149	0.00 353	0.00 350	0.00 350	0.00 349

sosnet-low-res		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sosnet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uncertanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

Regformer		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

ab-mvs-re		6.65 321	8.87 323	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	79.80 312	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

test_241102_TWO								93.71 44	83.77 38	93.49 32	94.27 67	89.27 20	95.84 18	86.03 38	97.82 49	92.04 163

test_0728_THIRD									85.33 27	93.75 27	94.65 52	87.44 40	95.78 22	87.41 19	98.21 29	92.98 131

GSMVS																83.88 278

test_part1				0.00 336		0.00 356	0.00 347	93.93 34				0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sam_mvs1												46.11 308				83.88 278

sam_mvs												45.92 313

MTGPA								91.81 107

test_post1							78.85 247				3.13 348	45.19 322	80.13 303	58.11 276

test_post											3.10 349	45.43 318	77.22 310

patchmatchnet-post											81.71 298	45.93 312	87.01 251

MTMP							90.66 37	33.14 352

test9_res														80.83 89	96.45 98	90.57 198

agg_prior2														79.68 100	96.16 108	90.22 205

test_prior4						78.97 75	84.59 136

test_prior2							83.37 168		75.43 135	84.58 187	91.57 145	81.92 100		79.54 102	96.97 80

旧先验2							81.73 204		56.88 292	86.54 158			84.90 279	72.81 173

新几何2							81.72 205

无先验							82.81 184	85.62 223	58.09 282				91.41 174	67.95 215		84.48 272

原ACMM2							82.26 198

testdata2													86.43 263	63.52 241

segment_acmp												81.94 97

testdata1							79.62 232		73.95 152

plane_prior5								93.61 47					95.22 49	80.78 90	95.83 118	94.46 76

plane_prior4											92.95 106

plane_prior3						76.85 102			77.79 107	86.55 153

plane_prior2							89.45 65		79.44 88

plane_prior						76.42 107	87.15 98		75.94 129						95.03 146

n2								0.00 357

nn								0.00 357

door-mid								74.45 293

test11								91.46 114

door								72.57 307

HQP5-MVS						70.66 153

BP-MVS														77.30 128

HQP4-MVS										80.56 246			94.61 70			93.56 115

HQP3-MVS								92.68 86							94.47 161

HQP2-MVS												72.10 199

MDTV_nov1_ep13_2view						27.60 350	70.76 311		46.47 333	61.27 336		45.20 321		49.18 316		83.75 283

ACMMP++_ref															95.74 124

ACMMP++															97.35 70

Test By Simon												79.09 126

High-resolution multi-view benchmark