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.

thres100view900		78.37 171	77.01 172	82.46 169	91.89 103	63.21 205	91.19 180	96.33 1	72.28 159	70.45 188	87.89 183	60.31 117	95.32 157	45.16 300	77.58 182	88.83 199

thres600view7		78.00 175	76.66 177	82.03 190	91.93 100	63.69 195	91.30 174	96.33 1	72.43 154	70.46 187	87.89 183	60.31 117	94.92 167	42.64 312	76.64 193	87.48 218

thres200		79.66 146	78.33 150	83.66 149	92.54 85	65.82 138	93.06 99	96.31 3	74.90 108	73.30 155	88.66 168	59.67 124	95.61 145	47.84 291	78.67 173	89.56 196

tfpn200view9		78.79 162	77.43 166	82.88 160	92.21 92	64.49 169	92.05 137	96.28 4	73.48 133	71.75 178	88.26 176	60.07 120	95.32 157	45.16 300	77.58 182	88.83 199

thres400		78.68 165	77.43 166	82.43 170	92.21 92	64.49 169	92.05 137	96.28 4	73.48 133	71.75 178	88.26 176	60.07 120	95.32 157	45.16 300	77.58 182	87.48 218

VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 30	95.73 22	95.94 6	78.43 62	86.00 37	93.07 107	58.22 137	97.00 89	85.22 57	84.33 137	96.52 17

baseline2		83.68 87	83.42 77	84.48 130	87.37 200	66.00 131	90.06 213	95.93 7	79.71 43	69.08 204	90.39 151	77.92 4	96.28 115	78.91 107	81.38 155	91.16 177

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

MVS		84.66 66	82.86 88	90.06 2	90.93 127	74.56 6	87.91 250	95.54 9	68.55 231	72.35 171	94.71 69	59.78 123	98.90 14	81.29 91	94.69 28	96.74 10

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 150	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 72	97.64 2	97.94 1

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 21	93.83 76	95.33 11	68.48 233	77.63 118	94.35 81	73.04 20	98.45 25	84.92 60	93.71 45	96.92 9

WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 44	95.20 32	95.25 12	82.71 15	81.91 70	94.73 68	67.93 42	97.63 56	79.55 100	82.25 148	96.54 16

IU-MVS					96.46 10	69.91 34		95.18 13	80.75 33	95.28 1				92.34 6	95.36 12	96.47 20

IB-MVS	77.80 4	82.18 105	80.46 121	87.35 36	89.14 162	70.28 27	95.59 25	95.17 14	78.85 56	70.19 192	85.82 205	70.66 31	97.67 51	72.19 154	66.52 255	94.09 106
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
Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 65	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 54	88.64 100	94.26 96

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 158	92.95 106	95.11 16	81.13 30	81.68 72	95.04 54	65.82 62	98.32 30	83.02 73	84.36 134	92.97 141

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 180	85.69 42	96.52 15	62.07 104	98.77 17	86.06 52	95.60 10	96.03 33

test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 223	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 223	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 65	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 56	88.64 100	94.20 98

sss		82.71 99	82.38 96	83.73 145	89.25 159	59.58 262	92.24 128	94.89 21	77.96 68	79.86 93	92.38 125	56.70 157	97.05 84	77.26 119	80.86 160	94.55 86

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 68	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		85.45 54	85.69 51	84.73 120	94.17 42	63.23 204	92.95 106	94.83 23	80.66 34	81.29 75	95.04 54	65.12 68	98.08 36	82.74 75	84.36 134	92.88 145

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 22	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 138	92.79 81	63.56 200	91.76 152	94.81 25	79.65 44	77.87 114	94.09 88	63.35 93	97.90 43	79.35 101	79.36 166	90.74 181

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 78	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

tttt0517		79.50 149	78.53 149	82.41 173	87.22 202	61.43 235	89.75 221	94.76 27	69.29 221	67.91 222	88.06 181	72.92 21	95.63 143	62.91 233	73.90 206	90.16 187

GG-mvs-BLEND				86.53 62	91.91 102	69.67 40	75.02 322	94.75 28		78.67 111	90.85 144	77.91 5	94.56 176	72.25 151	93.74 43	95.36 50

gg-mvs-nofinetune		77.18 189	74.31 206	85.80 87	91.42 118	68.36 64	71.78 324	94.72 29	49.61 326	77.12 125	45.92 342	77.41 6	93.98 203	67.62 191	93.16 52	95.05 70

thisisatest0515		83.41 88	82.49 94	86.16 75	89.46 156	68.26 68	93.54 85	94.70 30	74.31 114	75.75 134	90.92 142	72.62 23	96.52 112	69.64 171	81.50 154	93.71 119

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 148	92.28 89	63.19 206	91.38 169	94.68 31	79.22 49	76.60 129	93.75 94	62.64 100	97.76 49	78.07 114	78.01 177	90.05 189

VPA-MVSNet		79.03 155	78.00 156	82.11 188	85.95 220	64.48 171	93.22 96	94.66 32	75.05 106	74.04 151	84.95 213	52.17 202	93.52 216	74.90 137	67.04 251	88.32 211

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 49	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 133	70.89 20	94.74 45	94.62 34	81.44 27	58.19 291	93.64 96	73.64 19	92.35 254	82.66 76	78.66 174	96.50 19

thisisatest0530		81.15 120	80.07 122	84.39 132	88.26 181	65.63 142	91.40 165	94.62 34	71.27 196	70.93 184	89.18 164	72.47 24	96.04 127	65.62 214	76.89 192	91.49 169

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 30	96.64 8	94.52 36	71.92 169	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 91	67.74 81	84.65 273	94.50 37	79.15 51	82.23 68	87.93 182	66.88 51	96.94 97	80.53 95	82.20 149	96.39 24

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 70	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 60	83.87 62	92.94 110	64.34 78	96.94 97	75.19 130	94.09 36	95.66 42

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 42	96.89 4	94.44 40	71.65 183	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_241102_ONE					96.45 11	69.38 42		94.44 40	71.65 183	92.11 3	97.05 8	76.79 7	99.11 4

DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 29	89.12 233	94.42 42	82.15 20	77.64 117	91.77 134	70.81 30	96.22 117	65.03 220	81.36 156	95.94 35

test_241102_TWO								94.41 43	71.65 183	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 122	93.00 73	58.16 277	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 44	95.94 7	97.03 8
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
3Dnovator	73.91 6	82.69 100	80.82 113	88.31 19	89.57 152	71.26 16	92.60 119	94.39 46	78.84 57	67.89 223	92.48 123	48.42 234	98.52 22	68.80 183	94.40 33	95.15 65

test_0728_SECOND				88.70 13	96.45 11	70.43 25	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

test0726					96.40 14	69.99 30	96.76 6	94.33 48	71.92 169	91.89 6	97.11 7	73.77 17

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 99	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 111	79.16 102	95.61 37	53.99 185	98.88 16	69.62 173	93.26 51	94.50 91
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
ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 98	67.48 90	92.97 104	94.21 51	71.48 194	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 147	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 85	94.17 52	94.15 53	68.77 229	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 96	94.31 50	94.14 54	72.92 144	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 65	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 71	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 57	93.85 72	94.03 56	74.18 116	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
FIs		79.47 150	79.41 137	79.67 240	85.95 220	59.40 264	91.68 156	93.94 57	78.06 67	68.96 207	88.28 174	66.61 55	91.77 266	66.20 208	74.99 199	87.82 214

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 136	66.38 121	96.09 15	93.87 58	77.73 71	84.01 61	95.66 35	63.39 92	97.94 39	87.40 41	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 66	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 38	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 220	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

TESTMET0.1,1		82.41 102	81.98 100	83.72 146	88.08 185	63.74 192	92.70 114	93.77 62	79.30 47	77.61 119	87.57 187	58.19 138	94.08 194	73.91 139	86.68 119	93.33 129

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 91	93.64 80	93.76 63	70.78 206	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 36	93.99 63	93.76 63	79.08 54	78.88 107	93.99 91	62.25 103	98.15 34	85.93 53	91.15 82	94.15 103

FC-MVSNet-test		77.99 176	78.08 155	77.70 263	84.89 235	55.51 297	90.27 207	93.75 65	76.87 81	66.80 238	87.59 186	65.71 64	90.23 287	62.89 234	73.94 204	87.37 221

QAPM		79.95 142	77.39 168	87.64 26	89.63 151	71.41 15	93.30 93	93.70 66	65.34 254	67.39 231	91.75 136	47.83 240	98.96 12	57.71 259	89.81 92	92.54 151

RRT_test8_iter05		80.61 128	79.62 131	83.60 150	91.87 106	66.90 107	93.42 92	93.68 67	77.09 80	68.83 210	85.63 208	66.82 52	95.42 154	76.46 124	62.74 279	88.48 206

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 169	66.64 114	92.15 130	93.68 67	81.07 31	76.91 128	93.64 96	62.59 101	98.44 26	85.50 55	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 114	81.52 105	82.61 167	88.77 170	60.21 254	93.02 103	93.66 69	68.52 232	72.90 159	90.39 151	72.19 26	94.96 164	74.93 135	79.29 168	92.67 147

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 156	93.76 51	67.53 87	94.06 59	93.61 70	79.13 52	81.00 81	85.14 211	63.19 95	97.29 73	87.08 45	73.91 205	84.83 271

PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 87	96.33 14	93.61 70	82.34 18	81.00 81	93.08 105	63.19 95	97.29 73	87.08 45	91.38 78	94.13 104

alignmvs		87.28 26	86.97 34	88.24 20	91.30 121	71.14 19	95.61 24	93.56 72	79.30 47	87.07 29	95.25 49	68.43 34	96.93 99	87.87 36	84.33 137	96.65 11

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 108	68.04 73	90.36 205	93.55 73	82.89 13	91.29 9	92.89 113	72.27 25	96.03 128	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TEST9					94.18 40	67.28 94	94.16 53	93.51 74	71.75 181	85.52 43	95.33 42	68.01 39	97.27 77

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 94	94.16 53	93.51 74	71.87 174	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

ZD-MVS					96.63 8	65.50 146		93.50 76	70.74 207	85.26 47	95.19 53	64.92 73	97.29 73	87.51 38	93.01 54

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 112	67.53 87	91.79 149	93.49 77	74.93 107	84.61 50	95.30 44	59.42 127	97.92 41	86.13 51	94.92 17	94.94 75

testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 78	67.74 235	86.19 34	96.39 20	64.38 77	97.91 42	87.33 42	93.14 53	95.90 38

cdsmvs_eth3d_5k		19.86 323	26.47 322	0.00 340	0.00 361	0.00 362	0.00 352	93.45 78	0.00 357	0.00 358	95.27 47	49.56 223	0.00 358	0.00 356	0.00 355	0.00 354

3Dnovator+	73.60 7	82.10 109	80.60 118	86.60 57	90.89 130	66.80 111	95.20 32	93.44 80	74.05 117	67.42 229	92.49 122	49.46 224	97.65 55	70.80 163	91.68 73	95.33 51

test_8					94.19 39	67.19 97	94.15 55	93.42 81	71.87 174	85.38 45	95.35 41	68.19 36	96.95 96

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 70	93.41 82	73.75 126	79.94 91	94.68 70	60.61 116	98.03 37	82.63 78	93.72 44	94.52 90

原ACMM1				84.42 131	93.21 67	64.27 183		93.40 83	65.39 252	79.51 97	92.50 120	58.11 139	96.69 107	65.27 219	93.96 38	92.32 156

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 105	94.08 58	93.31 84	71.85 176	84.49 52	95.39 40	68.91 33	96.75 105	88.84 29	94.32 34	95.13 66

agg_prior					94.16 44	66.97 105		93.31 84		84.49 52			96.75 105

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 94	76.72 1	95.75 19	93.26 86	83.86 10	89.55 16	96.06 28	53.55 190	97.89 44	91.10 13	93.31 50	94.54 88

EI-MVSNet		78.97 157	78.22 153	81.25 204	85.33 226	62.73 218	89.53 225	93.21 87	72.39 156	72.14 172	90.13 157	60.99 111	94.72 172	67.73 190	72.49 215	86.29 241

MVSTER		82.47 101	82.05 98	83.74 143	92.68 83	69.01 50	91.90 144	93.21 87	79.83 39	72.14 172	85.71 207	74.72 13	94.72 172	75.72 126	72.49 215	87.50 217

UniMVSNet_NR-MVSNet		78.15 174	77.55 164	79.98 231	84.46 242	60.26 252	92.25 127	93.20 89	77.50 75	68.88 208	86.61 196	66.10 58	92.13 258	66.38 205	62.55 280	87.54 216

HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 87	93.19 90	72.19 163	79.22 100	94.93 60	59.04 132	97.67 51	81.55 85	92.21 64	94.49 92

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 57	93.19 90	73.55 132	79.22 100	94.93 60	59.04 132	97.67 51	82.66 76	92.21 64	94.49 92

UniMVSNet (Re)		77.58 183	76.78 175	79.98 231	84.11 248	60.80 241	91.76 152	93.17 92	76.56 87	69.93 198	84.78 215	63.32 94	92.36 253	64.89 221	62.51 282	86.78 232

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 87	93.15 93	72.19 163	78.85 109	94.86 64	56.69 158	97.45 62	81.55 85	92.20 66	94.02 111

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 101	93.13 94	73.20 136	78.89 104	94.18 87	59.41 128	97.85 46	81.45 87	92.48 63	93.86 117

xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 120	76.43 3	95.74 20	93.12 95	83.53 12	89.55 16	95.95 29	53.45 194	97.68 50	91.07 14	92.62 59	94.54 88

test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 92	95.10 36	93.10 96	75.40 99	85.25 48	95.61 37	67.94 40	96.84 101	87.47 39	94.77 23	95.05 70

test_prior				86.42 66	94.71 33	67.35 92		93.10 96					96.84 101			95.05 70

test11								93.01 98

CostFormer		82.33 103	81.15 108	85.86 85	89.01 165	68.46 62	82.39 291	93.01 98	75.59 94	80.25 88	81.57 250	72.03 27	94.96 164	79.06 105	77.48 186	94.16 102

PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 66	91.85 147	93.00 100	76.59 86	79.03 103	95.00 56	61.59 107	97.61 58	78.16 113	89.00 97	95.63 43

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 90	92.95 101	72.16 166	78.86 108	94.84 65	56.97 153	97.53 60	81.38 89	92.11 68	94.24 97

test12				87.09 42	94.60 35	68.86 53		92.91 102		82.67 67		65.44 66	97.55 59		93.69 46	94.84 77

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 160	71.18 17	96.57 10	92.90 103	82.70 16	87.13 26	95.27 47	64.99 70	95.80 133	89.34 22	91.80 71	95.93 36

PAPM_NR		82.97 94	81.84 101	86.37 69	94.10 46	66.76 112	87.66 254	92.84 104	69.96 214	74.07 150	93.57 98	63.10 97	97.50 61	70.66 166	90.58 88	94.85 76

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 97	93.89 70	92.83 105	70.90 202	83.09 65	95.28 45	63.62 88	97.36 68	80.63 94	94.18 35	94.84 77

tfpnnormal		70.10 257	67.36 261	78.32 257	83.45 257	60.97 239	88.85 236	92.77 106	64.85 256	60.83 278	78.53 286	43.52 265	93.48 217	31.73 341	61.70 292	80.52 313

PAPM		85.89 50	85.46 53	87.18 39	88.20 184	72.42 12	92.41 125	92.77 106	82.11 21	80.34 87	93.07 107	68.27 35	95.02 162	78.39 112	93.59 47	94.09 106

MS-PatchMatch		77.90 180	76.50 179	82.12 185	85.99 219	69.95 33	91.75 154	92.70 108	73.97 120	62.58 272	84.44 219	41.11 271	95.78 134	63.76 228	92.17 67	80.62 312

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 97	68.97 52	95.04 40	92.70 108	79.04 55	81.50 74	96.50 17	58.98 134	96.78 103	83.49 70	93.93 39	96.29 26

ab-mvs		80.18 136	78.31 151	85.80 87	88.44 176	65.49 147	83.00 288	92.67 110	71.82 178	77.36 122	85.01 212	54.50 180	96.59 108	76.35 125	75.63 198	95.32 53

save fliter					93.84 49	67.89 77	95.05 38	92.66 111	78.19 64

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 118	78.18 112	94.31 84	55.25 171	97.41 65	79.16 103	91.58 75	93.95 113

X-MVStestdata		76.86 191	74.13 210	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 118	78.18 112	10.19 354	55.25 171	97.41 65	79.16 103	91.58 75	93.95 113

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 55	93.90 69	92.63 114	76.86 82	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
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
无先验							92.71 113	92.61 115	62.03 278				97.01 87	66.63 199		93.97 112

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 83	92.58 116	66.54 244	86.17 35	95.88 30	63.83 84	97.00 89	86.39 50	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
1314		80.70 127	78.95 145	85.94 81	87.77 193	67.56 85	87.91 250	92.55 117	72.17 165	67.44 228	93.09 104	50.27 217	97.04 86	71.68 159	87.64 108	93.23 133

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 118	65.59 143	91.54 161	92.51 118	74.56 110	80.62 84	95.64 36	59.15 131	97.00 89	86.94 47	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
WR-MVS		76.76 195	75.74 190	79.82 236	84.60 238	62.27 225	92.60 119	92.51 118	76.06 90	67.87 224	85.34 209	56.76 155	90.24 286	62.20 238	63.69 277	86.94 230

OpenMVS	70.45 11	78.54 169	75.92 187	86.41 68	85.93 223	71.68 14	92.74 112	92.51 118	66.49 245	64.56 252	91.96 131	43.88 263	98.10 35	54.61 268	90.65 87	89.44 197

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 145	75.14 5	92.07 136	92.32 121	81.87 24	75.68 135	88.27 175	60.18 119	98.60 21	80.46 96	90.27 91	94.96 74

CP-MVS		83.71 86	83.40 78	84.65 124	93.14 70	63.84 188	94.59 46	92.28 122	71.03 200	77.41 121	94.92 62	55.21 174	96.19 118	81.32 90	90.70 86	93.91 115

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 94	92.27 123	73.13 138	79.63 96	94.43 75	61.90 105	97.17 80	85.00 58	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS		86.61 41	86.85 37	85.88 82	91.52 116	66.25 126	95.42 27	92.25 124	80.36 37	84.10 59	94.82 66	62.88 99	96.08 124	88.25 33	92.07 69	95.30 55

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 103	65.16 153	91.55 160	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

MTGPA								92.23 125

MTAPA		83.91 81	83.38 79	85.50 97	91.89 103	65.16 153	81.75 293	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

VPNet		78.82 160	77.53 165	82.70 164	84.52 240	66.44 120	93.93 67	92.23 125	80.46 36	72.60 163	88.38 173	49.18 228	93.13 222	72.47 150	63.97 275	88.55 205

ACMMP		81.49 116	80.67 116	83.93 140	91.71 109	62.90 214	92.13 131	92.22 129	71.79 179	71.68 180	93.49 100	50.32 215	96.96 95	78.47 111	84.22 141	91.93 164
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
PGM-MVS		83.25 90	82.70 92	84.92 114	92.81 80	64.07 186	90.44 201	92.20 130	71.28 195	77.23 124	94.43 75	55.17 175	97.31 72	79.33 102	91.38 78	93.37 126

jason		86.40 42	86.17 43	87.11 41	86.16 217	70.54 24	95.71 23	92.19 131	82.00 23	84.58 51	94.34 82	61.86 106	95.53 153	87.76 37	90.89 84	95.27 60
jason: jason.
CLD-MVS		82.73 97	82.35 97	83.86 141	87.90 191	67.65 84	95.45 26	92.18 132	85.06 7	72.58 164	92.27 128	52.46 200	95.78 134	84.18 64	79.06 169	88.16 212
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_Test		84.16 76	83.20 81	87.05 43	91.56 113	69.82 35	89.99 217	92.05 133	77.77 70	82.84 66	86.57 197	63.93 83	96.09 122	74.91 136	89.18 96	95.25 63

EIA-MVS		84.84 63	84.88 59	84.69 123	91.30 121	62.36 222	93.85 72	92.04 134	79.45 45	79.33 99	94.28 85	62.42 102	96.35 114	80.05 97	91.25 81	95.38 49

WR-MVS_H		70.59 254	69.94 248	72.53 302	81.03 274	51.43 315	87.35 258	92.03 135	67.38 240	60.23 280	80.70 264	55.84 170	83.45 327	46.33 296	58.58 308	82.72 294

FMVSNet3		77.73 181	76.04 185	82.80 161	91.20 124	68.99 51	91.87 145	91.99 136	73.35 135	67.04 234	83.19 231	56.62 159	92.14 257	59.80 252	69.34 234	87.28 224

DP-MVS Recon		82.73 97	81.65 104	85.98 79	97.31 4	67.06 101	95.15 34	91.99 136	69.08 226	76.50 131	93.89 93	54.48 181	98.20 32	70.76 164	85.66 125	92.69 146

EPNet_dtu		78.80 161	79.26 141	77.43 268	88.06 186	49.71 324	91.96 143	91.95 138	77.67 72	76.56 130	91.28 140	58.51 136	90.20 288	56.37 263	80.95 159	92.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 141	67.02 104	93.43 90	91.92 139	81.21 29	84.13 58	94.07 90	60.93 113	95.63 143	89.28 23	89.81 92	94.46 94

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 140	71.90 171	82.16 69	93.49 100	47.98 239	97.05 84	82.55 79	84.82 130	97.25 5

HPM-MVS		83.25 90	82.95 86	84.17 136	92.25 90	62.88 215	90.91 186	91.86 141	70.30 211	77.12 125	93.96 92	56.75 156	96.28 115	82.04 81	91.34 80	93.34 127
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		82.96 95	82.44 95	84.52 128	92.83 76	62.92 213	92.76 111	91.85 142	71.52 191	75.61 138	94.24 86	53.48 193	96.99 92	78.97 106	90.73 85	93.64 122

XXY-MVS		77.94 178	76.44 180	82.43 170	82.60 264	64.44 173	92.01 139	91.83 143	73.59 131	70.00 195	85.82 205	54.43 182	94.76 169	69.63 172	68.02 245	88.10 213

baseline		85.01 60	84.44 64	86.71 53	88.33 179	68.73 56	90.24 209	91.82 144	81.05 32	81.18 77	92.50 120	63.69 87	96.08 124	84.45 63	86.71 118	95.32 53

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 182	69.07 48	93.04 101	91.76 145	81.27 28	80.84 83	92.07 130	64.23 79	96.06 126	84.98 59	87.43 110	95.39 48

NR-MVSNet		76.05 204	74.59 200	80.44 220	82.96 262	62.18 226	90.83 191	91.73 146	77.12 79	60.96 277	86.35 198	59.28 130	91.80 265	60.74 245	61.34 295	87.35 222

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 143	66.59 117	93.77 77	91.73 146	77.43 77	77.08 127	89.81 161	63.77 86	96.97 94	79.67 99	88.21 103	92.60 149

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 107	71.92 13	93.54 85	91.71 148	80.26 38	87.55 24	95.25 49	63.59 90	96.93 99	88.18 34	84.34 136	97.11 6

HQP3-MVS								91.70 149							78.90 170

HQP-MVS		81.14 121	80.64 117	82.64 166	87.54 195	63.66 197	94.06 59	91.70 149	79.80 40	74.18 146	90.30 153	51.63 207	95.61 145	77.63 117	78.90 170	88.63 203

baseline1		81.84 113	81.03 112	84.28 135	91.60 111	66.62 115	91.08 183	91.66 151	81.87 24	74.86 143	91.67 138	69.98 32	94.92 167	71.76 157	64.75 267	91.29 176

FMVSNet2		76.07 201	74.01 212	82.26 179	88.85 166	67.66 83	91.33 172	91.61 152	70.84 203	65.98 241	82.25 239	48.03 236	92.00 262	58.46 257	68.73 240	87.10 226

114514_t		79.17 154	77.67 161	83.68 147	95.32 26	65.53 145	92.85 110	91.60 153	63.49 264	67.92 221	90.63 148	46.65 247	95.72 141	67.01 197	83.54 142	89.79 191

test-LLR		80.10 138	79.56 133	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	71.51 192	75.62 136	86.94 194	53.83 186	92.38 251	72.21 152	84.76 132	91.60 167

test-mter		79.96 141	79.38 139	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	73.85 123	75.62 136	86.94 194	49.84 222	92.38 251	72.21 152	84.76 132	91.60 167

DU-MVS		76.86 191	75.84 188	79.91 233	82.96 262	60.26 252	91.26 175	91.54 154	76.46 88	68.88 208	86.35 198	56.16 163	92.13 258	66.38 205	62.55 280	87.35 222

旧先验1					91.94 99	60.74 245		91.50 157			94.36 77	65.23 67			91.84 70	94.55 86

VDD-MVS		83.06 93	81.81 103	86.81 49	90.86 131	67.70 82	95.40 28	91.50 157	75.46 96	81.78 71	92.34 127	40.09 275	97.13 83	86.85 48	82.04 150	95.60 44

新几何1				84.73 120	92.32 87	64.28 182		91.46 159	59.56 295	79.77 94	92.90 112	56.95 154	96.57 110	63.40 229	92.91 56	93.34 127

tpm2		79.80 145	77.95 158	85.34 105	88.28 180	68.26 68	81.56 296	91.42 160	70.11 212	77.59 120	80.50 268	67.40 46	94.26 189	67.34 193	77.35 187	93.51 124

1121		81.25 119	80.05 123	84.87 117	92.30 88	64.31 180	87.91 250	91.39 161	59.44 296	79.94 91	92.91 111	57.09 147	97.01 87	66.63 199	92.81 58	93.29 130

TranMVSNet+NR-MVSNet		75.86 208	74.52 203	79.89 234	82.44 265	60.64 248	91.37 170	91.37 162	76.63 85	67.65 226	86.21 201	52.37 201	91.55 270	61.84 240	60.81 298	87.48 218

VDDNet		80.50 131	78.26 152	87.21 38	86.19 216	69.79 36	94.48 47	91.31 163	60.42 288	79.34 98	90.91 143	38.48 282	96.56 111	82.16 80	81.05 158	95.27 60

HQP_MVS		80.34 134	79.75 129	82.12 185	86.94 206	62.42 220	93.13 97	91.31 163	78.81 58	72.53 165	89.14 166	50.66 213	95.55 151	76.74 120	78.53 175	88.39 209

plane_prior5								91.31 163					95.55 151	76.74 120	78.53 175	88.39 209

SR-MVS		82.81 96	82.58 93	83.50 153	93.35 62	61.16 238	92.23 129	91.28 166	64.48 257	81.27 76	95.28 45	53.71 189	95.86 132	82.87 74	88.77 99	93.49 125

nrg030		80.93 125	79.86 127	84.13 137	83.69 253	68.83 54	93.23 95	91.20 167	75.55 95	75.06 142	88.22 179	63.04 98	94.74 171	81.88 82	66.88 252	88.82 201

EPMVS		78.49 170	75.98 186	86.02 78	91.21 123	69.68 39	80.23 304	91.20 167	75.25 103	72.48 167	78.11 290	54.65 179	93.69 213	57.66 260	83.04 143	94.69 80

cascas		78.18 173	75.77 189	85.41 102	87.14 204	69.11 47	92.96 105	91.15 169	66.71 243	70.47 186	86.07 202	37.49 292	96.48 113	70.15 169	79.80 163	90.65 182

tpm		78.58 168	77.03 171	83.22 156	85.94 222	64.56 167	83.21 286	91.14 170	78.31 63	73.67 153	79.68 279	64.01 81	92.09 260	66.07 209	71.26 225	93.03 139

PCF-MVS	73.15 9	79.29 152	77.63 163	84.29 134	86.06 218	65.96 133	87.03 260	91.10 171	69.86 215	69.79 199	90.64 146	57.54 144	96.59 108	64.37 224	82.29 147	90.32 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20240529		76.84 193	74.15 209	84.88 116	91.02 125	64.95 163	93.84 75	91.09 172	53.57 316	73.00 156	87.42 189	35.91 302	97.32 71	69.14 179	72.41 217	92.36 154

PS-MVSNAJss		77.26 188	76.31 182	80.13 228	80.64 279	59.16 268	90.63 200	91.06 173	72.80 146	68.58 215	84.57 218	53.55 190	93.96 204	72.97 142	71.96 219	87.27 225

PVSNet	73.49 8	80.05 139	78.63 147	84.31 133	90.92 128	64.97 162	92.47 124	91.05 174	79.18 50	72.43 169	90.51 150	37.05 298	94.06 196	68.06 186	86.00 123	93.90 116

API-MVS		82.28 104	80.53 119	87.54 31	96.13 20	70.59 23	93.63 81	91.04 175	65.72 251	75.45 140	92.83 116	56.11 165	98.89 15	64.10 225	89.75 95	93.15 135

APD-MVS_3200maxsize		81.64 115	81.32 107	82.59 168	92.36 86	58.74 271	91.39 167	91.01 176	63.35 265	79.72 95	94.62 71	51.82 203	96.14 120	79.71 98	87.93 106	92.89 144

test1172		81.90 112	81.83 102	82.13 184	93.23 64	57.52 285	91.61 159	90.98 177	64.32 259	80.20 89	95.00 56	51.26 210	95.61 145	81.73 84	88.13 104	93.26 131

RRT_MVS		77.38 186	76.59 178	79.77 238	90.91 129	63.61 199	91.15 181	90.91 178	72.28 159	72.06 174	87.28 192	43.92 262	89.04 297	73.32 140	67.47 249	86.67 233

MVP-Stereo		77.12 190	76.23 183	79.79 237	81.72 270	66.34 123	89.29 227	90.88 179	70.56 209	62.01 275	82.88 232	49.34 225	94.13 191	65.55 216	93.80 41	78.88 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		79.87 143	78.68 146	83.45 155	89.96 144	61.51 233	92.13 131	90.79 180	76.83 83	78.85 109	86.33 200	38.16 284	96.17 119	67.93 188	87.17 111	92.67 147
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
testing_2		71.09 252	67.32 263	82.40 174	69.82 336	66.52 119	83.64 278	90.77 181	72.21 162	45.12 332	71.07 325	27.60 327	93.74 211	75.71 127	69.96 229	86.95 229

TAMVS		80.37 133	79.45 136	83.13 158	85.14 230	63.37 201	91.23 176	90.76 182	74.81 109	72.65 162	88.49 170	60.63 115	92.95 226	69.41 175	81.95 151	93.08 138

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 160	71.18 17	89.07 234	90.69 183	65.80 249	87.13 26	94.34 82	64.99 70	92.67 241	72.83 144	91.80 71	95.27 60

test_djsdf		73.76 232	72.56 228	77.39 269	77.00 317	53.93 304	89.07 234	90.69 183	65.80 249	63.92 258	82.03 242	43.14 266	92.67 241	72.83 144	68.53 241	85.57 261

PMMVS		81.98 111	82.04 99	81.78 192	89.76 149	56.17 293	91.13 182	90.69 183	77.96 68	80.09 90	93.57 98	46.33 250	94.99 163	81.41 88	87.46 109	94.17 101

CDS-MVSNet		81.43 117	80.74 114	83.52 151	86.26 215	64.45 172	92.09 134	90.65 186	75.83 93	73.95 152	89.81 161	63.97 82	92.91 231	71.27 160	82.82 145	93.20 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 118	79.99 125	85.46 99	90.39 138	68.40 63	86.88 264	90.61 187	74.41 111	70.31 191	84.67 216	63.79 85	92.32 255	73.13 141	85.70 124	95.67 41

SR-MVS-dyc-post		81.06 123	80.70 115	82.15 182	92.02 95	58.56 273	90.90 187	90.45 188	62.76 271	78.89 104	94.46 73	51.26 210	95.61 145	78.77 109	86.77 116	92.28 158

RE-MVS-def			80.48 120		92.02 95	58.56 273	90.90 187	90.45 188	62.76 271	78.89 104	94.46 73	49.30 226		78.77 109	86.77 116	92.28 158

RPMNet		70.42 256	65.68 269	84.63 126	83.15 260	67.96 75	70.25 326	90.45 188	46.83 334	69.97 196	65.10 333	56.48 162	95.30 160	35.79 331	73.13 209	90.64 183

xiu_mvs_v1_base_debu		82.16 106	81.12 109	85.26 107	86.42 211	68.72 57	92.59 121	90.44 191	73.12 139	84.20 55	94.36 77	38.04 286	95.73 137	84.12 65	86.81 113	91.33 171

xiu_mvs_v1_base		82.16 106	81.12 109	85.26 107	86.42 211	68.72 57	92.59 121	90.44 191	73.12 139	84.20 55	94.36 77	38.04 286	95.73 137	84.12 65	86.81 113	91.33 171

xiu_mvs_v1_base_debi		82.16 106	81.12 109	85.26 107	86.42 211	68.72 57	92.59 121	90.44 191	73.12 139	84.20 55	94.36 77	38.04 286	95.73 137	84.12 65	86.81 113	91.33 171

GBi-Net		75.65 211	73.83 214	81.10 209	88.85 166	65.11 155	90.01 214	90.32 194	70.84 203	67.04 234	80.25 273	48.03 236	91.54 271	59.80 252	69.34 234	86.64 234

test1		75.65 211	73.83 214	81.10 209	88.85 166	65.11 155	90.01 214	90.32 194	70.84 203	67.04 234	80.25 273	48.03 236	91.54 271	59.80 252	69.34 234	86.64 234

FMVSNet1		72.71 242	69.91 249	81.10 209	83.60 255	65.11 155	90.01 214	90.32 194	63.92 261	63.56 262	80.25 273	36.35 301	91.54 271	54.46 269	66.75 253	86.64 234

PVSNet_0	68.08 15	71.81 247	68.32 259	82.27 177	84.68 236	62.31 224	88.68 239	90.31 197	75.84 92	57.93 294	80.65 267	37.85 289	94.19 190	69.94 170	29.05 345	90.31 186

OPM-MVS		79.00 156	78.09 154	81.73 193	83.52 256	63.83 189	91.64 158	90.30 198	76.36 89	71.97 175	89.93 160	46.30 251	95.17 161	75.10 131	77.70 180	86.19 244

CP-MVSNet		70.50 255	69.91 249	72.26 305	80.71 277	51.00 318	87.23 259	90.30 198	67.84 234	59.64 282	82.69 234	50.23 218	82.30 334	51.28 278	59.28 305	83.46 284

v148		76.19 199	74.47 204	81.36 202	80.05 287	64.44 173	91.75 154	90.23 200	73.68 129	67.13 233	80.84 263	55.92 169	93.86 210	68.95 181	61.73 291	85.76 259

v2v482		77.42 185	75.65 191	82.73 163	80.38 281	67.13 100	91.85 147	90.23 200	75.09 105	69.37 200	83.39 229	53.79 188	94.44 182	71.77 156	65.00 264	86.63 237

v1144		76.73 196	74.88 196	82.27 177	80.23 286	66.60 116	91.68 156	90.21 202	73.69 128	69.06 205	81.89 243	52.73 198	94.40 183	69.21 178	65.23 261	85.80 256

GA-MVS		78.33 172	76.23 183	84.65 124	83.65 254	66.30 124	91.44 162	90.14 203	76.01 91	70.32 190	84.02 222	42.50 267	94.72 172	70.98 161	77.00 191	92.94 142

MDTV_nov1_ep13			72.61 227		89.06 163	68.48 61	80.33 302	90.11 204	71.84 177	71.81 177	75.92 306	53.01 196	93.92 206	48.04 290	73.38 207

D2MVS		73.80 230	72.02 234	79.15 251	79.15 298	62.97 209	88.58 241	90.07 205	72.94 142	59.22 285	78.30 287	42.31 269	92.70 240	65.59 215	72.00 218	81.79 303

abl_6		79.82 144	79.20 142	81.70 196	89.85 146	58.34 275	88.47 243	90.07 205	62.56 274	77.71 116	93.08 105	47.65 243	96.78 103	77.94 115	85.45 127	89.99 190

TR-MVS		78.77 163	77.37 169	82.95 159	90.49 135	60.88 240	93.67 79	90.07 205	70.08 213	74.51 145	91.37 139	45.69 253	95.70 142	60.12 250	80.32 161	92.29 157

Anonymous20231211		73.08 234	70.39 245	81.13 207	90.62 134	63.33 202	91.40 165	90.06 208	51.84 321	64.46 255	80.67 266	36.49 300	94.07 195	63.83 227	64.17 272	85.98 251

jajsoiax		73.05 235	71.51 239	77.67 264	77.46 314	54.83 300	88.81 237	90.04 209	69.13 225	62.85 270	83.51 227	31.16 318	92.75 237	70.83 162	69.80 230	85.43 264

HyFIR lowres test		81.03 124	79.56 133	85.43 101	87.81 192	68.11 72	90.18 210	90.01 210	70.65 208	72.95 158	86.06 203	63.61 89	94.50 181	75.01 134	79.75 164	93.67 120

ACMM	69.62 13	74.34 224	72.73 225	79.17 249	84.25 247	57.87 279	90.36 205	89.93 211	63.17 268	65.64 243	86.04 204	37.79 290	94.10 192	65.89 210	71.52 222	85.55 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		65.79 286	63.10 286	73.88 292	70.71 333	50.29 322	81.09 297	89.88 212	72.58 150	49.25 322	74.77 311	32.57 312	87.43 310	55.96 265	41.04 338	83.90 278

testdata				81.34 203	89.02 164	57.72 281		89.84 213	58.65 300	85.32 46	94.09 88	57.03 149	93.28 220	69.34 176	90.56 89	93.03 139

mvs_tets		72.71 242	71.11 240	77.52 265	77.41 315	54.52 302	88.45 244	89.76 214	68.76 230	62.70 271	83.26 230	29.49 321	92.71 238	70.51 168	69.62 232	85.34 266

v1192		75.98 206	73.92 213	82.15 182	79.73 289	66.24 127	91.22 177	89.75 215	72.67 148	68.49 216	81.42 253	49.86 221	94.27 187	67.08 196	65.02 263	85.95 253

PS-CasMVS		69.86 260	69.13 254	72.07 308	80.35 283	50.57 320	87.02 261	89.75 215	67.27 241	59.19 286	82.28 238	46.58 248	82.24 335	50.69 280	59.02 306	83.39 286

dp		75.01 220	72.09 233	83.76 142	89.28 158	66.22 128	79.96 309	89.75 215	71.16 197	67.80 225	77.19 298	51.81 204	92.54 246	50.39 281	71.44 224	92.51 152

LPG-MVS_test		75.82 209	74.58 201	79.56 244	84.31 245	59.37 265	90.44 201	89.73 218	69.49 218	64.86 248	88.42 171	38.65 280	94.30 185	72.56 148	72.76 212	85.01 269

LGP-MVS_train				79.56 244	84.31 245	59.37 265		89.73 218	69.49 218	64.86 248	88.42 171	38.65 280	94.30 185	72.56 148	72.76 212	85.01 269

tpmrst		80.57 129	79.14 144	84.84 118	90.10 142	68.28 67	81.70 294	89.72 220	77.63 73	75.96 133	79.54 281	64.94 72	92.71 238	75.43 128	77.28 189	93.55 123

v144192		76.05 204	74.03 211	82.12 185	79.50 293	66.55 118	91.39 167	89.71 221	72.30 158	68.17 218	81.33 255	51.75 205	94.03 200	67.94 187	64.19 271	85.77 257

TAPA-MVS	70.22 12	74.94 221	73.53 217	79.17 249	90.40 137	52.07 312	89.19 231	89.61 222	62.69 273	70.07 193	92.67 118	48.89 233	94.32 184	38.26 326	79.97 162	91.12 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		77.46 184	74.63 199	85.96 80	89.55 154	70.35 26	79.97 308	89.55 223	72.23 161	70.94 183	76.91 301	57.03 149	92.79 236	54.27 270	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_part1		72.36 245	69.25 253	81.68 197	79.94 288	65.07 159	90.68 195	89.53 224	52.32 318	63.42 264	79.21 283	40.43 274	94.01 202	67.14 195	60.59 300	85.96 252

v1921920		75.63 213	73.49 218	82.06 189	79.38 294	66.35 122	91.07 185	89.48 225	71.98 168	67.99 219	81.22 258	49.16 230	93.90 207	66.56 201	64.56 270	85.92 255

v7n		71.31 250	68.65 255	79.28 247	76.40 319	60.77 243	86.71 265	89.45 226	64.17 260	58.77 290	78.24 288	44.59 260	93.54 215	57.76 258	61.75 290	83.52 282

test0.0.03 1		72.76 240	72.71 226	72.88 300	80.25 285	47.99 329	91.22 177	89.45 226	71.51 192	62.51 273	87.66 185	53.83 186	85.06 318	50.16 282	67.84 248	85.58 260

test222					89.77 148	61.60 232	89.55 223	89.42 228	56.83 308	77.28 123	92.43 124	52.76 197			91.14 83	93.09 137

V42		76.46 198	74.55 202	82.19 181	79.14 299	67.82 79	90.26 208	89.42 228	73.75 126	68.63 214	81.89 243	51.31 209	94.09 193	71.69 158	64.84 265	84.66 272

BH-w/o		80.49 132	79.30 140	84.05 139	90.83 132	64.36 179	93.60 82	89.42 228	74.35 113	69.09 203	90.15 156	55.23 173	95.61 145	64.61 222	86.43 122	92.17 162

pm-mvs1		72.89 238	71.09 241	78.26 259	79.10 300	57.62 283	90.80 192	89.30 231	67.66 237	62.91 269	81.78 245	49.11 231	92.95 226	60.29 249	58.89 307	84.22 275

v8		75.35 215	73.26 220	81.61 198	80.67 278	66.82 109	89.54 224	89.27 232	71.65 183	63.30 266	80.30 272	54.99 177	94.06 196	67.33 194	62.33 283	83.94 277

diffmvs		84.28 71	83.83 69	85.61 95	87.40 199	68.02 74	90.88 189	89.24 233	80.54 35	81.64 73	92.52 119	59.83 122	94.52 180	87.32 43	85.11 128	94.29 95

PEN-MVS		69.46 262	68.56 256	72.17 307	79.27 295	49.71 324	86.90 263	89.24 233	67.24 242	59.08 287	82.51 237	47.23 245	83.54 326	48.42 289	57.12 309	83.25 287

UniMVSNet_ETH3D		72.74 241	70.53 244	79.36 246	78.62 307	56.64 291	85.01 271	89.20 235	63.77 263	64.84 250	84.44 219	34.05 307	91.86 264	63.94 226	70.89 227	89.57 195

SCA		75.82 209	72.76 224	85.01 113	86.63 210	70.08 28	81.06 298	89.19 236	71.60 188	70.01 194	77.09 299	45.53 254	90.25 283	60.43 247	73.27 208	94.68 81

EG-PatchMatch MVS		68.55 267	65.41 272	77.96 262	78.69 305	62.93 211	89.86 219	89.17 237	60.55 287	50.27 319	77.73 293	22.60 337	94.06 196	47.18 294	72.65 214	76.88 330

HPM-MVS_fast		80.25 135	79.55 135	82.33 175	91.55 114	59.95 257	91.32 173	89.16 238	65.23 255	74.71 144	93.07 107	47.81 241	95.74 136	74.87 138	88.23 102	91.31 175

miper_enhance_ethall		78.86 159	77.97 157	81.54 199	88.00 189	65.17 152	91.41 163	89.15 239	75.19 104	68.79 211	83.98 223	67.17 48	92.82 233	72.73 146	65.30 258	86.62 238

Fast-Effi-MVS+		81.14 121	80.01 124	84.51 129	90.24 140	65.86 135	94.12 56	89.15 239	73.81 125	75.37 141	88.26 176	57.26 145	94.53 179	66.97 198	84.92 129	93.15 135

Vis-MVSNet (Re-imp)		79.24 153	79.57 132	78.24 260	88.46 175	52.29 311	90.41 203	89.12 241	74.24 115	69.13 202	91.91 132	65.77 63	90.09 290	59.00 256	88.09 105	92.33 155

v1240		75.21 218	72.98 222	81.88 191	79.20 296	66.00 131	90.75 194	89.11 242	71.63 187	67.41 230	81.22 258	47.36 244	93.87 208	65.46 217	64.72 268	85.77 257

v10		74.77 222	72.54 229	81.46 200	80.33 284	66.71 113	89.15 232	89.08 243	70.94 201	63.08 267	79.86 277	52.52 199	94.04 199	65.70 213	62.17 284	83.64 279

ACMP	71.68 10	75.58 214	74.23 208	79.62 242	84.97 234	59.64 260	90.80 192	89.07 244	70.39 210	62.95 268	87.30 191	38.28 283	93.87 208	72.89 143	71.45 223	85.36 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UnsupCasMVSNet_bld		61.60 300	57.71 303	73.29 297	68.73 339	51.64 313	78.61 312	89.05 245	57.20 305	46.11 326	61.96 336	28.70 324	88.60 299	50.08 283	38.90 340	79.63 319

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 139	66.82 109	91.87 145	89.01 246	85.27 6	86.09 36	93.74 95	47.71 242	96.98 93	77.90 116	89.78 94	93.65 121

UA-Net		80.02 140	79.65 130	81.11 208	89.33 157	57.72 281	86.33 267	89.00 247	77.44 76	81.01 80	89.15 165	59.33 129	95.90 131	61.01 244	84.28 139	89.73 193

MVS_111021_LR		82.02 110	81.52 105	83.51 152	88.42 177	62.88 215	89.77 220	88.93 248	76.78 84	75.55 139	93.10 103	50.31 216	95.38 156	83.82 69	87.02 112	92.26 161

miper_lstm_enhance		73.05 235	71.73 237	77.03 273	83.80 251	58.32 276	81.76 292	88.88 249	69.80 216	61.01 276	78.23 289	57.19 146	87.51 309	65.34 218	59.53 304	85.27 268

anonymousdsp		71.14 251	69.37 252	76.45 277	72.95 327	54.71 301	84.19 275	88.88 249	61.92 280	62.15 274	79.77 278	38.14 285	91.44 276	68.90 182	67.45 250	83.21 288

cl-mvsnet2		77.94 178	76.78 175	81.42 201	87.57 194	64.93 164	90.67 196	88.86 251	72.45 153	67.63 227	82.68 235	64.07 80	92.91 231	71.79 155	65.30 258	86.44 239

MIMVSNet		71.64 248	68.44 257	81.23 205	81.97 269	64.44 173	73.05 323	88.80 252	69.67 217	64.59 251	74.79 310	32.79 310	87.82 307	53.99 271	76.35 195	91.42 170

IterMVS-LS		76.49 197	75.18 195	80.43 221	84.49 241	62.74 217	90.64 198	88.80 252	72.40 155	65.16 247	81.72 246	60.98 112	92.27 256	67.74 189	64.65 269	86.29 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl-mvsnet_		76.07 201	74.67 197	80.28 224	85.15 229	61.76 229	90.12 211	88.73 254	71.16 197	65.43 244	81.57 250	61.15 109	92.95 226	66.54 202	62.17 284	86.13 247

cl-mvsnet1		76.07 201	74.67 197	80.28 224	85.14 230	61.75 230	90.12 211	88.73 254	71.16 197	65.42 245	81.60 249	61.15 109	92.94 230	66.54 202	62.16 286	86.14 245

JIA-IIPM		66.06 284	62.45 291	76.88 275	81.42 273	54.45 303	57.49 344	88.67 256	49.36 327	63.86 259	46.86 341	56.06 166	90.25 283	49.53 285	68.83 238	85.95 253

OMC-MVS		78.67 167	77.91 160	80.95 215	85.76 224	57.40 286	88.49 242	88.67 256	73.85 123	72.43 169	92.10 129	49.29 227	94.55 177	72.73 146	77.89 178	90.91 180

miper_ehance_all_eth		77.60 182	76.44 180	81.09 212	85.70 225	64.41 176	90.65 197	88.64 258	72.31 157	67.37 232	82.52 236	64.77 74	92.64 244	70.67 165	65.30 258	86.24 243

BH-untuned		78.68 165	77.08 170	83.48 154	89.84 147	63.74 192	92.70 114	88.59 259	71.57 189	66.83 237	88.65 169	51.75 205	95.39 155	59.03 255	84.77 131	91.32 174

DTE-MVSNet		68.46 269	67.33 262	71.87 310	77.94 312	49.00 327	86.16 268	88.58 260	66.36 246	58.19 291	82.21 240	46.36 249	83.87 324	44.97 303	55.17 316	82.73 293

CPTT-MVS		79.59 147	79.16 143	80.89 217	91.54 115	59.80 259	92.10 133	88.54 261	60.42 288	72.96 157	93.28 102	48.27 235	92.80 235	78.89 108	86.50 121	90.06 188

CVMVSNet		74.04 227	74.27 207	73.33 296	85.33 226	43.94 338	89.53 225	88.39 262	54.33 315	70.37 189	90.13 157	49.17 229	84.05 321	61.83 241	79.36 166	91.99 163

1112_ss		80.56 130	79.83 128	82.77 162	88.65 171	60.78 242	92.29 126	88.36 263	72.58 150	72.46 168	94.95 58	65.09 69	93.42 219	66.38 205	77.71 179	94.10 105

tpmvs		72.88 239	69.76 251	82.22 180	90.98 126	67.05 102	78.22 316	88.30 264	63.10 269	64.35 257	74.98 309	55.09 176	94.27 187	43.25 306	69.57 233	85.34 266

PLC	68.80 14	75.23 217	73.68 216	79.86 235	92.93 74	58.68 272	90.64 198	88.30 264	60.90 285	64.43 256	90.53 149	42.38 268	94.57 175	56.52 262	76.54 194	86.33 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
eth_miper_zixun_eth		75.96 207	74.40 205	80.66 218	84.66 237	63.02 208	89.28 228	88.27 266	71.88 173	65.73 242	81.65 247	59.45 126	92.81 234	68.13 185	60.53 301	86.14 245

IS-MVSNet		80.14 137	79.41 137	82.33 175	87.91 190	60.08 256	91.97 142	88.27 266	72.90 145	71.44 182	91.73 137	61.44 108	93.66 214	62.47 237	86.53 120	93.24 132

Vis-MVSNet		80.92 126	79.98 126	83.74 143	88.48 174	61.80 228	93.44 89	88.26 268	73.96 121	77.73 115	91.76 135	49.94 220	94.76 169	65.84 211	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
cl_fuxian		76.83 194	75.47 192	80.93 216	85.02 233	64.18 185	90.39 204	88.11 269	71.66 182	66.65 239	81.64 248	63.58 91	92.56 245	69.31 177	62.86 278	86.04 249

BH-RMVSNet		79.46 151	77.65 162	84.89 115	91.68 110	65.66 140	93.55 84	88.09 270	72.93 143	73.37 154	91.12 141	46.20 252	96.12 121	56.28 264	85.61 126	92.91 143

tpm cat1		75.30 216	72.21 232	84.58 127	88.52 172	67.77 80	78.16 317	88.02 271	61.88 281	68.45 217	76.37 302	60.65 114	94.03 200	53.77 273	74.11 202	91.93 164

Test_1112_low_res		79.56 148	78.60 148	82.43 170	88.24 183	60.39 251	92.09 134	87.99 272	72.10 167	71.84 176	87.42 189	64.62 75	93.04 223	65.80 212	77.30 188	93.85 118

AdaColmap		78.94 158	77.00 173	84.76 119	96.34 16	65.86 135	92.66 118	87.97 273	62.18 277	70.56 185	92.37 126	43.53 264	97.35 69	64.50 223	82.86 144	91.05 179

Effi-MVS+-dtu		76.14 200	75.28 194	78.72 254	83.22 258	55.17 299	89.87 218	87.78 274	75.42 97	67.98 220	81.43 252	45.08 257	92.52 247	75.08 132	71.63 220	88.48 206

mvs-test1		78.74 164	77.95 158	81.14 206	83.22 258	57.13 288	93.96 64	87.78 274	75.42 97	72.68 161	90.80 145	45.08 257	94.54 178	75.08 132	77.49 185	91.74 166

PatchT		69.11 264	65.37 273	80.32 222	82.07 268	63.68 196	67.96 335	87.62 276	50.86 324	69.37 200	65.18 332	57.09 147	88.53 301	41.59 315	66.60 254	88.74 202

XVG-OURS		74.25 226	72.46 230	79.63 241	78.45 308	57.59 284	80.33 302	87.39 277	63.86 262	68.76 212	89.62 163	40.50 273	91.72 267	69.00 180	74.25 201	89.58 194

Anonymous20231206		67.53 277	65.78 267	72.79 301	74.95 323	47.59 331	88.23 246	87.32 278	61.75 283	58.07 293	77.29 296	37.79 290	87.29 311	42.91 308	63.71 276	83.48 283

XVG-OURS-SEG-HR		74.70 223	73.08 221	79.57 243	78.25 309	57.33 287	80.49 300	87.32 278	63.22 267	68.76 212	90.12 159	44.89 259	91.59 269	70.55 167	74.09 203	89.79 191

pmmvs4		73.92 229	71.81 236	80.25 226	79.17 297	65.24 150	87.43 257	87.26 280	67.64 239	63.46 263	83.91 224	48.96 232	91.53 274	62.94 232	65.49 257	83.96 276

pmmvs5		73.35 233	71.52 238	78.86 253	78.64 306	60.61 249	91.08 183	86.90 281	67.69 236	63.32 265	83.64 225	44.33 261	90.53 280	62.04 239	66.02 256	85.46 263

pmmvs6		67.57 276	64.76 275	76.00 281	72.82 329	53.37 306	88.71 238	86.78 282	53.19 317	57.58 296	78.03 291	35.33 304	92.41 250	55.56 266	54.88 318	82.21 300

MVS_0304		68.99 265	67.23 264	74.28 291	80.36 282	52.54 309	87.01 262	86.36 283	59.89 294	66.22 240	73.56 313	24.25 332	88.03 305	57.34 261	70.11 228	82.27 299

F-COLMAP		70.66 253	68.44 257	77.32 270	86.37 214	55.91 295	88.00 248	86.32 284	56.94 307	57.28 297	88.07 180	33.58 308	92.49 248	51.02 279	68.37 242	83.55 280

IterMVS		72.65 244	70.83 242	78.09 261	82.17 266	62.96 210	87.64 255	86.28 285	71.56 190	60.44 279	78.85 285	45.42 256	86.66 313	63.30 230	61.83 288	84.65 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 272	65.66 270	75.18 284	84.43 243	57.89 278	83.54 279	86.26 286	61.83 282	53.64 307	73.30 314	37.15 296	85.08 317	48.99 286	61.77 289	82.56 298

EU-MVSNet		64.01 293	63.01 287	67.02 320	74.40 325	38.86 346	83.27 284	86.19 287	45.11 336	54.27 303	81.15 261	36.91 299	80.01 339	48.79 288	57.02 310	82.19 301

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 153	69.40 41	91.35 171	86.12 288	72.59 149	83.22 64	92.81 117	59.60 125	96.01 130	81.76 83	87.80 107	95.56 45

IterMVS-SCA-FT		71.55 249	69.97 247	76.32 278	81.48 271	60.67 247	87.64 255	85.99 289	66.17 247	59.50 283	78.88 284	45.53 254	83.65 325	62.58 236	61.93 287	84.63 274

XVG-ACMP-BASELINE		68.04 272	65.53 271	75.56 282	74.06 326	52.37 310	78.43 313	85.88 290	62.03 278	58.91 289	81.21 260	20.38 340	91.15 277	60.69 246	68.18 243	83.16 289

ambc				69.61 313	61.38 345	41.35 340	49.07 347	85.86 291		50.18 321	66.40 330	10.16 349	88.14 304	45.73 299	44.20 333	79.32 322

CMPMVS	48.56 21	66.77 281	64.41 280	73.84 293	70.65 334	50.31 321	77.79 318	85.73 292	45.54 335	44.76 333	82.14 241	35.40 303	90.14 289	63.18 231	74.54 200	81.07 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+-dtu		75.04 219	73.37 219	80.07 229	80.86 275	59.52 263	91.20 179	85.38 293	71.90 171	65.20 246	84.84 214	41.46 270	92.97 225	66.50 204	72.96 211	87.73 215

Anonymous202405211		77.96 177	75.33 193	85.87 84	93.73 55	64.52 168	94.85 43	85.36 294	62.52 275	76.11 132	90.18 155	29.43 322	97.29 73	68.51 184	77.24 190	95.81 40

our_test_3		68.29 270	64.69 276	79.11 252	78.92 301	64.85 165	88.40 245	85.06 295	60.32 290	52.68 309	76.12 304	40.81 272	89.80 293	44.25 305	55.65 314	82.67 297

USDC		67.43 279	64.51 278	76.19 279	77.94 312	55.29 298	78.38 314	85.00 296	73.17 137	48.36 324	80.37 270	21.23 339	92.48 249	52.15 277	64.02 274	80.81 310

TransMVSNet (Re)		70.07 258	67.66 260	77.31 271	80.62 280	59.13 269	91.78 151	84.94 297	65.97 248	60.08 281	80.44 269	50.78 212	91.87 263	48.84 287	45.46 332	80.94 308

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 77	95.05 38	84.72 298	78.19 64	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

ACMH	63.93 17	68.62 266	64.81 274	80.03 230	85.22 228	63.25 203	87.72 253	84.66 299	60.83 286	51.57 314	79.43 282	27.29 328	94.96 164	41.76 313	64.84 265	81.88 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Baseline_NR-MVSNet		73.99 228	72.83 223	77.48 267	80.78 276	59.29 267	91.79 149	84.55 300	68.85 227	68.99 206	80.70 264	56.16 163	92.04 261	62.67 235	60.98 297	81.11 306

MIMVSNet1		60.16 304	57.33 305	68.67 315	69.71 337	44.13 337	78.92 311	84.21 301	55.05 314	44.63 334	71.85 321	23.91 334	81.54 338	32.63 339	55.03 317	80.35 314

test20.03		63.83 294	62.65 290	67.38 319	70.58 335	39.94 343	86.57 266	84.17 302	63.29 266	51.86 312	77.30 295	37.09 297	82.47 332	38.87 325	54.13 320	79.73 318

MDA-MVSNet_test_wron		63.78 295	60.16 298	74.64 286	78.15 310	60.41 250	83.49 280	84.03 303	56.17 312	39.17 341	71.59 323	37.22 294	83.24 330	42.87 310	48.73 328	80.26 316

ADS-MVSNet		68.54 268	64.38 281	81.03 213	88.06 186	66.90 107	68.01 333	84.02 304	57.57 302	64.48 253	69.87 326	38.68 278	89.21 296	40.87 317	67.89 246	86.97 227

CR-MVSNet		73.79 231	70.82 243	82.70 164	83.15 260	67.96 75	70.25 326	84.00 305	73.67 130	69.97 196	72.41 317	57.82 141	89.48 294	52.99 276	73.13 209	90.64 183

Patchmtry		67.53 277	63.93 282	78.34 256	82.12 267	64.38 177	68.72 330	84.00 305	48.23 331	59.24 284	72.41 317	57.82 141	89.27 295	46.10 297	56.68 313	81.36 305

YYNet1		63.76 296	60.14 299	74.62 287	78.06 311	60.19 255	83.46 282	83.99 307	56.18 311	39.25 340	71.56 324	37.18 295	83.34 328	42.90 309	48.70 329	80.32 315

LTVRE_ROB	59.60 19	66.27 283	63.54 284	74.45 288	84.00 250	51.55 314	67.08 336	83.53 308	58.78 299	54.94 301	80.31 271	34.54 306	93.23 221	40.64 319	68.03 244	78.58 326
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
pmmvs-eth3d		65.53 287	62.32 292	75.19 283	69.39 338	59.59 261	82.80 289	83.43 309	62.52 275	51.30 316	72.49 315	32.86 309	87.16 312	55.32 267	50.73 326	78.83 324

OpenMVS_ROB	61.12 18	66.39 282	62.92 288	76.80 276	76.51 318	57.77 280	89.22 229	83.41 310	55.48 313	53.86 306	77.84 292	26.28 331	93.95 205	34.90 333	68.76 239	78.68 325

PatchMatch-RL		72.06 246	69.98 246	78.28 258	89.51 155	55.70 296	83.49 280	83.39 311	61.24 284	63.72 261	82.76 233	34.77 305	93.03 224	53.37 275	77.59 181	86.12 248

MSDG		69.54 261	65.73 268	80.96 214	85.11 232	63.71 194	84.19 275	83.28 312	56.95 306	54.50 302	84.03 221	31.50 316	96.03 128	42.87 310	69.13 237	83.14 290

CHOSEN 280x420		77.35 187	76.95 174	78.55 255	87.07 205	62.68 219	69.71 329	82.95 313	68.80 228	71.48 181	87.27 193	66.03 59	84.00 323	76.47 123	82.81 146	88.95 198

ppachtmachnet_test		67.72 274	63.70 283	79.77 238	78.92 301	66.04 130	88.68 239	82.90 314	60.11 292	55.45 300	75.96 305	39.19 277	90.55 279	39.53 321	52.55 323	82.71 295

new-patchmatchnet		59.30 306	56.48 307	67.79 317	65.86 340	44.19 336	82.47 290	81.77 315	59.94 293	43.65 337	66.20 331	27.67 326	81.68 337	39.34 322	41.40 337	77.50 329

MDA-MVSNet-bldmvs		61.54 301	57.70 304	73.05 298	79.53 292	57.00 290	83.08 287	81.23 316	57.57 302	34.91 343	72.45 316	32.79 310	86.26 316	35.81 330	41.95 336	75.89 332

OurMVSNet-221017-0		64.68 289	62.17 293	72.21 306	76.08 322	47.35 332	80.67 299	81.02 317	56.19 310	51.60 313	79.66 280	27.05 329	88.56 300	53.60 274	53.63 321	80.71 311

ACMH+	65.35 16	67.65 275	64.55 277	76.96 274	84.59 239	57.10 289	88.08 247	80.79 318	58.59 301	53.00 308	81.09 262	26.63 330	92.95 226	46.51 295	61.69 293	80.82 309

CNLPA		74.31 225	72.30 231	80.32 222	91.49 117	61.66 231	90.85 190	80.72 319	56.67 309	63.85 260	90.64 146	46.75 246	90.84 278	53.79 272	75.99 197	88.47 208

LS3D		69.17 263	66.40 266	77.50 266	91.92 101	56.12 294	85.12 270	80.37 320	46.96 332	56.50 299	87.51 188	37.25 293	93.71 212	32.52 340	79.40 165	82.68 296

testgi		64.48 291	62.87 289	69.31 314	71.24 330	40.62 342	85.49 269	79.92 321	65.36 253	54.18 304	83.49 228	23.74 335	84.55 319	41.60 314	60.79 299	82.77 292

test_0402		64.54 290	61.09 296	74.92 285	84.10 249	60.75 244	87.95 249	79.71 322	52.03 320	52.41 310	77.20 297	32.21 314	91.64 268	23.14 344	61.03 296	72.36 336

SixPastTwentyTwo		64.92 288	61.78 295	74.34 290	78.74 304	49.76 323	83.42 283	79.51 323	62.86 270	50.27 319	77.35 294	30.92 320	90.49 281	45.89 298	47.06 330	82.78 291

ITE_SJBPF				70.43 312	74.44 324	47.06 333		77.32 324	60.16 291	54.04 305	83.53 226	23.30 336	84.01 322	43.07 307	61.58 294	80.21 317

K. test v3		63.09 297	59.61 301	73.53 295	76.26 320	49.38 326	83.27 284	77.15 325	64.35 258	47.77 325	72.32 319	28.73 323	87.79 308	49.93 284	36.69 342	83.41 285

DP-MVS		69.90 259	66.48 265	80.14 227	95.36 25	62.93 211	89.56 222	76.11 326	50.27 325	57.69 295	85.23 210	39.68 276	95.73 137	33.35 335	71.05 226	81.78 304

RPSCF		64.24 292	61.98 294	71.01 311	76.10 321	45.00 335	75.83 321	75.94 327	46.94 333	58.96 288	84.59 217	31.40 317	82.00 336	47.76 292	60.33 303	86.04 249

TinyColmap		60.32 302	56.42 308	72.00 309	78.78 303	53.18 307	78.36 315	75.64 328	52.30 319	41.59 339	75.82 307	14.76 346	88.35 302	35.84 329	54.71 319	74.46 334

ADS-MVSNet2		66.90 280	63.44 285	77.26 272	88.06 186	60.70 246	68.01 333	75.56 329	57.57 302	64.48 253	69.87 326	38.68 278	84.10 320	40.87 317	67.89 246	86.97 227

COLMAP_ROB	57.96 20	62.98 298	59.65 300	72.98 299	81.44 272	53.00 308	83.75 277	75.53 330	48.34 330	48.81 323	81.40 254	24.14 333	90.30 282	32.95 337	60.52 302	75.65 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-test		65.86 285	60.94 297	80.62 219	83.75 252	58.83 270	58.91 343	75.26 331	44.50 338	50.95 318	77.09 299	58.81 135	87.90 306	35.13 332	64.03 273	95.12 67

MVS-HIRNet		60.25 303	55.55 309	74.35 289	84.37 244	56.57 292	71.64 325	74.11 332	34.44 343	45.54 331	42.24 345	31.11 319	89.81 291	40.36 320	76.10 196	76.67 331

pmmvs3		55.51 308	51.50 313	67.53 318	57.90 347	50.93 319	80.37 301	73.66 333	40.63 341	44.15 336	64.75 334	16.30 343	78.97 340	44.77 304	40.98 339	72.69 335

TDRefinement		55.28 309	51.58 312	66.39 321	59.53 346	46.15 334	76.23 320	72.80 334	44.60 337	42.49 338	76.28 303	15.29 344	82.39 333	33.20 336	43.75 334	70.62 338

Gipuma		34.91 317	31.44 320	45.30 330	70.99 332	39.64 345	19.85 351	72.56 335	20.10 349	16.16 350	21.47 351	5.08 355	71.16 343	13.07 348	43.70 335	25.08 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		45.64 313	43.10 316	53.23 328	51.42 349	36.46 347	64.97 337	71.91 336	29.13 345	27.53 344	61.55 337	9.83 350	65.01 348	16.00 347	55.58 315	58.22 343

ANet_high		40.27 315	35.20 318	55.47 325	34.74 355	34.47 348	63.84 339	71.56 337	48.42 329	18.80 348	41.08 346	9.52 351	64.45 349	20.18 345	8.66 352	67.49 340

Patchmatch-RL test		68.17 271	64.49 279	79.19 248	71.22 331	53.93 304	70.07 328	71.54 338	69.22 222	56.79 298	62.89 335	56.58 160	88.61 298	69.53 174	52.61 322	95.03 73

LCM-MVSNet-Re		72.93 237	71.84 235	76.18 280	88.49 173	48.02 328	80.07 307	70.17 339	73.96 121	52.25 311	80.09 276	49.98 219	88.24 303	67.35 192	84.23 140	92.28 158

LCM-MVSNet		40.54 314	35.79 317	54.76 327	36.92 354	30.81 349	51.41 345	69.02 340	22.07 347	24.63 345	45.37 343	4.56 356	65.81 346	33.67 334	34.50 343	67.67 339

AllTest		61.66 299	58.06 302	72.46 303	79.57 290	51.42 316	80.17 305	68.61 341	51.25 322	45.88 327	81.23 256	19.86 341	86.58 314	38.98 323	57.01 311	79.39 320

TestCases				72.46 303	79.57 290	51.42 316		68.61 341	51.25 322	45.88 327	81.23 256	19.86 341	86.58 314	38.98 323	57.01 311	79.39 320

LF4IMVS		54.01 310	52.12 311	59.69 323	62.41 344	39.91 344	68.59 331	68.28 343	42.96 339	44.55 335	75.18 308	14.09 347	68.39 344	41.36 316	51.68 324	70.78 337

door								66.57 344

door-mid								66.01 345

DSMNet-mixed		56.78 307	54.44 310	63.79 322	63.21 342	29.44 350	64.43 338	64.10 346	42.12 340	51.32 315	71.60 322	31.76 315	75.04 341	36.23 328	65.20 262	86.87 231

PM-MVS		59.40 305	56.59 306	67.84 316	63.63 341	41.86 339	76.76 319	63.22 347	59.01 298	51.07 317	72.27 320	11.72 348	83.25 329	61.34 242	50.28 327	78.39 327

new_pmnet		49.31 312	46.44 315	57.93 324	62.84 343	40.74 341	68.47 332	62.96 348	36.48 342	35.09 342	57.81 338	14.97 345	72.18 342	32.86 338	46.44 331	60.88 342

lessismore_v0				73.72 294	72.93 328	47.83 330		61.72 349		45.86 329	73.76 312	28.63 325	89.81 291	47.75 293	31.37 344	83.53 281

PMMVS2		37.93 316	33.61 319	50.92 329	46.31 351	24.76 353	60.55 342	50.05 350	28.94 346	20.93 346	47.59 340	4.41 357	65.13 347	25.14 343	18.55 347	62.87 341

PMVS	26.43 22	31.84 318	28.16 321	42.89 331	25.87 357	27.58 351	50.92 346	49.78 351	21.37 348	14.17 351	40.81 347	2.01 358	66.62 345	9.61 350	38.88 341	34.49 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 319	24.00 323	26.45 334	43.74 352	18.44 356	60.86 340	39.66 352	15.11 350	9.53 353	22.10 350	6.52 353	46.94 351	8.31 351	10.14 349	13.98 349

tmp_tt		22.26 322	23.75 324	17.80 336	5.23 358	12.06 358	35.26 348	39.48 353	2.82 354	18.94 347	44.20 344	22.23 338	24.64 354	36.30 327	9.31 351	16.69 348

MVE	24.84 23	24.35 320	19.77 326	38.09 332	34.56 356	26.92 352	26.57 349	38.87 354	11.73 352	11.37 352	27.44 348	1.37 359	50.42 350	11.41 349	14.60 348	36.93 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 321	23.20 325	25.46 335	41.52 353	16.90 357	60.56 341	38.79 355	14.62 351	8.99 354	20.24 353	7.35 352	45.82 352	7.25 352	9.46 350	13.64 350

MTMP							93.77 77	32.52 356

DeepMVS_CX				34.71 333	51.45 348	24.73 354		28.48 357	31.46 344	17.49 349	52.75 339	5.80 354	42.60 353	18.18 346	19.42 346	36.81 345

N_pmnet		50.55 311	49.11 314	54.88 326	77.17 316	4.02 359	84.36 274	2.00 358	48.59 328	45.86 329	68.82 328	32.22 313	82.80 331	31.58 342	51.38 325	77.81 328

wuyk23d		11.30 324	10.95 327	12.33 337	48.05 350	19.89 355	25.89 350	1.92 359	3.58 353	3.12 355	1.37 355	0.64 360	15.77 355	6.23 353	7.77 353	1.35 351

testmvs		7.23 326	9.62 329	0.06 339	0.04 359	0.02 361	84.98 272	0.02 360	0.03 355	0.18 356	1.21 356	0.01 362	0.02 356	0.14 354	0.01 354	0.13 353

test123		6.92 327	9.21 330	0.08 338	0.03 360	0.05 360	81.65 295	0.01 361	0.02 356	0.14 357	0.85 357	0.03 361	0.02 356	0.12 355	0.00 355	0.16 352

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

pcd_1.5k_mvsjas		4.46 328	5.95 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	53.55 190	0.00 358	0.00 356	0.00 355	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

n2								0.00 362

nn								0.00 362

ab-mvs-re		7.91 325	10.55 328	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	94.95 58	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 355	0.00 354

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_0728_THIRD									72.48 152	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

GSMVS																94.68 81

test_part2					96.29 17	68.16 71				90.78 10

sam_mvs1												57.85 140				94.68 81

sam_mvs												54.91 178

test_post1							78.95 310				20.70 352	53.05 195	91.50 275	60.43 247

test_post											23.01 349	56.49 161	92.67 241

patchmatchnet-post											67.62 329	57.62 143	90.25 283

gm-plane-assit					88.42 177	67.04 103			78.62 61		91.83 133		97.37 67	76.57 122

test9_res														89.41 20	94.96 16	95.29 56

agg_prior2														86.41 49	94.75 26	95.33 51

test_prior4						67.18 99	93.92 68

test_prior2							95.10 36		75.40 99	85.25 48	95.61 37	67.94 40		87.47 39	94.77 23

旧先验2							92.00 141		59.37 297	87.54 25			93.47 218	75.39 129

新几何2							91.41 163

原ACMM2							92.01 139

testdata2													96.09 122	61.26 243

segment_acmp												65.94 60

testdata1							89.21 230		77.55 74

plane_prior7					86.94 206	61.51 233

plane_prior6					87.23 201	62.32 223						50.66 213

plane_prior4											89.14 166

plane_prior3						61.95 227			79.09 53	72.53 165

plane_prior2							93.13 97		78.81 58

plane_prior1					87.15 203

plane_prior						62.42 220	93.85 72		79.38 46						78.80 172

HQP5-MVS						63.66 197

HQP-NCC					87.54 195		94.06 59		79.80 40	74.18 146

ACMP_Plane					87.54 195		94.06 59		79.80 40	74.18 146

BP-MVS														77.63 117

HQP4-MVS										74.18 146			95.61 145			88.63 203

HQP2-MVS												51.63 207

NP-MVS					87.41 198	63.04 207					90.30 153

MDTV_nov1_ep13_2view						59.90 258	80.13 306		67.65 238	72.79 160		54.33 183		59.83 251		92.58 150

ACMMP++_ref															71.63 220

ACMMP++															69.72 231

Test By Simon												54.21 184

High-resolution multi-view benchmark