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.

thres200		88.92 130	87.65 139	92.73 101	96.30 109	85.62 45	97.85 54	98.86 1	84.38 148	84.82 157	93.99 182	75.12 156	98.01 144	70.86 275	86.67 188	94.56 209

thres100view900		88.30 149	86.95 161	92.33 117	96.10 115	84.90 65	97.14 112	98.85 2	82.69 191	83.41 175	93.66 188	75.43 147	97.93 146	69.04 281	86.24 194	94.17 211

tfpn200view9		88.48 143	87.15 155	92.47 111	96.21 111	85.30 52	97.44 87	98.85 2	83.37 175	83.99 167	93.82 185	75.36 150	97.93 146	69.04 281	86.24 194	94.17 211

thres600view7		88.06 154	86.70 164	92.15 123	96.10 115	85.17 58	97.14 112	98.85 2	82.70 190	83.41 175	93.66 188	75.43 147	97.82 153	67.13 290	85.88 198	93.45 224

thres400		88.42 146	87.15 155	92.23 120	96.21 111	85.30 52	97.44 87	98.85 2	83.37 175	83.99 167	93.82 185	75.36 150	97.93 146	69.04 281	86.24 194	93.45 224

MVS_111021_HR		93.41 41	93.39 40	93.47 73	97.34 97	82.83 105	97.56 78	98.27 6	89.16 45	89.71 107	97.14 100	79.77 76	99.56 55	93.65 53	97.94 66	98.02 84

sss		90.87 96	89.96 103	93.60 63	94.15 168	83.84 83	97.14 112	98.13 7	85.93 107	89.68 108	96.09 128	71.67 192	99.30 76	87.69 127	89.16 166	97.66 117

MG-MVS		94.25 25	93.72 35	95.85 11	99.38 3	89.35 11	97.98 48	98.09 8	89.99 35	92.34 69	96.97 108	81.30 62	98.99 105	88.54 119	98.88 21	99.20 22

VNet		92.11 67	91.22 80	94.79 24	96.91 103	86.98 26	97.91 51	97.96 9	86.38 98	93.65 53	95.74 133	70.16 208	98.95 110	93.39 57	88.87 170	98.43 53

test_yl		91.46 82	90.53 90	94.24 39	97.41 91	85.18 54	98.08 41	97.72 10	80.94 213	89.85 104	96.14 126	75.61 140	98.81 118	90.42 100	88.56 175	98.74 35

DCV-MVSNet		91.46 82	90.53 90	94.24 39	97.41 91	85.18 54	98.08 41	97.72 10	80.94 213	89.85 104	96.14 126	75.61 140	98.81 118	90.42 100	88.56 175	98.74 35

WTY-MVS		92.65 59	91.68 74	95.56 13	96.00 117	88.90 12	98.23 32	97.65 12	88.57 55	89.82 106	97.22 98	79.29 79	99.06 101	89.57 110	88.73 172	98.73 39

EPNet		94.06 31	94.15 29	93.76 53	97.27 99	84.35 72	98.29 30	97.64 13	94.57 4	95.36 26	96.88 111	79.96 75	99.12 98	91.30 84	96.11 104	97.82 105
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	84.06 6	91.63 78	90.37 94	95.39 16	96.12 114	88.25 14	90.22 306	97.58 14	88.33 62	90.50 98	91.96 204	79.26 81	99.06 101	90.29 102	89.07 167	98.88 31

baseline2		90.39 106	90.21 97	90.93 159	90.86 256	80.99 147	95.20 223	97.41 15	86.03 105	80.07 216	94.61 167	90.58 6	97.47 173	87.29 131	89.86 162	94.35 210

test2506		90.96 93	90.39 92	92.65 105	93.54 185	82.46 112	96.37 171	97.35 16	86.78 95	87.55 134	95.25 146	77.83 103	97.50 170	84.07 154	94.80 120	97.98 91

PVSNet	82.34 9	89.02 127	87.79 137	92.71 102	95.49 129	81.50 138	97.70 68	97.29 17	87.76 74	85.47 151	95.12 157	56.90 291	98.90 114	80.33 188	94.02 127	97.71 114

PGM-MVS		91.93 69	91.80 72	92.32 118	98.27 56	79.74 179	95.28 218	97.27 18	83.83 166	90.89 94	97.78 69	76.12 132	99.56 55	88.82 117	97.93 68	97.66 117

IB-MVS	85.34 4	88.67 138	87.14 157	93.26 76	93.12 200	84.32 73	98.76 17	97.27 18	87.19 88	79.36 220	90.45 227	83.92 41	98.53 129	84.41 151	69.79 294	96.93 152
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
MVS		90.60 101	88.64 124	96.50 5	94.25 166	90.53 8	93.33 269	97.21 20	77.59 274	78.88 223	97.31 92	71.52 195	99.69 40	89.60 109	98.03 64	99.27 20

CSCG		92.02 68	91.65 75	93.12 82	98.53 41	80.59 157	97.47 85	97.18 21	77.06 283	84.64 161	97.98 57	83.98 40	99.52 57	90.72 93	97.33 81	99.23 21

PHI-MVS		93.59 39	93.63 36	93.48 70	98.05 68	81.76 131	98.64 21	97.13 22	82.60 193	94.09 50	98.49 25	80.35 68	99.85 10	94.74 43	98.62 35	98.83 32

CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 23	94.66 3	96.79 11	98.78 11	86.42 27	99.95 3	97.59 12	99.18 7	99.00 27

h-mvs33		89.30 123	88.95 121	90.36 175	95.07 142	76.04 265	96.96 132	97.11 24	90.39 31	92.22 70	95.10 158	74.70 160	98.86 115	93.14 64	65.89 327	96.16 177

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 25	95.17 2	92.11 72	98.46 26	87.33 23	99.97 2	97.21 16	99.31 4	99.63 7

VPA-MVSNet		85.32 194	83.83 199	89.77 197	90.25 265	82.63 107	96.36 172	97.07 26	83.03 183	81.21 201	89.02 244	61.58 259	96.31 226	85.02 148	70.95 282	90.36 240

Regformer-1		94.00 33	94.04 32	93.87 50	98.41 48	84.29 74	97.43 91	97.04 27	89.50 40	92.75 66	98.13 41	82.60 57	99.26 79	93.55 55	96.99 88	98.06 81

DELS-MVS		94.98 12	94.49 20	96.44 6	96.42 108	90.59 7	99.21 2	97.02 28	94.40 5	91.46 81	97.08 104	83.32 46	99.69 40	92.83 68	98.70 33	99.04 25
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
GG-mvs-BLEND				93.49 69	94.94 147	86.26 31	81.62 345	97.00 29		88.32 128	94.30 173	91.23 5	96.21 230	88.49 121	97.43 78	98.00 89

Regformer-2		93.92 34	94.01 33	93.67 59	98.41 48	83.75 84	97.43 91	97.00 29	89.43 42	92.69 67	98.13 41	82.48 58	99.22 82	93.51 56	96.99 88	98.04 82

DPM-MVS		96.21 2	95.53 10	98.26 1	96.26 110	95.09 1	99.15 4	96.98 31	93.39 9	96.45 17	98.79 10	90.17 9	99.99 1	89.33 114	99.25 6	99.70 3

Regformer-3		93.19 42	93.19 44	93.19 80	98.10 65	83.01 103	97.08 121	96.98 31	88.98 46	91.35 86	97.89 62	80.80 64	99.23 80	92.30 74	95.20 115	97.32 138

gg-mvs-nofinetune		85.48 193	82.90 213	93.24 77	94.51 161	85.82 39	79.22 349	96.97 33	61.19 350	87.33 137	53.01 363	90.58 6	96.07 232	86.07 139	97.23 83	97.81 106

NCCC		95.63 6	95.94 7	94.69 27	99.21 7	85.15 59	99.16 3	96.96 34	94.11 6	95.59 24	98.64 21	85.07 31	99.91 4	95.61 31	99.10 9	99.00 27

FIs		86.73 176	86.10 168	88.61 216	90.05 270	80.21 168	96.14 186	96.95 35	85.56 116	78.37 229	92.30 199	76.73 121	95.28 276	79.51 197	79.27 240	90.35 241

PVSNet_0	77.72 15	81.70 250	78.95 264	89.94 190	90.77 259	76.72 256	95.96 192	96.95 35	85.01 130	70.24 305	88.53 253	52.32 311	98.20 141	86.68 138	44.08 363	94.89 200

HPM-MVS++		95.32 10	95.48 11	94.85 23	98.62 38	86.04 34	97.81 58	96.93 37	92.45 11	95.69 23	98.50 24	85.38 30	99.85 10	94.75 42	99.18 7	98.65 42

MSLP-MVS++		94.28 23	94.39 24	93.97 47	98.30 55	84.06 79	98.64 21	96.93 37	90.71 26	93.08 60	98.70 18	79.98 74	99.21 84	94.12 49	99.07 11	98.63 43

Regformer-4		93.06 46	93.12 45	92.89 94	98.10 65	82.20 117	97.08 121	96.92 39	88.87 48	91.23 88	97.89 62	80.57 67	99.19 89	92.21 76	95.20 115	97.29 142

UniMVSNet (Re)		85.31 195	84.23 195	88.55 217	89.75 273	80.55 159	96.72 147	96.89 40	85.42 117	78.40 228	88.93 246	75.38 149	95.52 266	78.58 207	68.02 311	89.57 257

FC-MVSNet-test		85.96 184	85.39 175	87.66 236	89.38 282	78.02 227	95.65 208	96.87 41	85.12 128	77.34 235	91.94 206	76.28 130	94.74 296	77.09 220	78.82 244	90.21 245

EI-MVSNet-Vis-set		91.84 72	91.77 73	92.04 127	97.60 82	81.17 142	96.61 153	96.87 41	88.20 64	89.19 116	97.55 82	78.69 91	99.14 95	90.29 102	90.94 157	95.80 184

IU-MVS					99.03 16	85.34 49		96.86 43	92.05 15	98.74 1				98.15 3	98.97 17	99.42 13

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 44					99.81 20	98.08 6	98.81 25	99.43 11

No_MVS				97.14 3	99.05 10	92.19 4		96.83 44					99.81 20	98.08 6	98.81 25	99.43 11

EI-MVSNet-UG-set		91.35 86	91.22 80	91.73 136	97.39 93	80.68 155	96.47 161	96.83 44	87.92 69	88.30 129	97.36 91	77.84 102	99.13 97	89.43 113	89.45 164	95.37 194

ETH3 D test6400		95.56 9	95.41 12	96.00 9	99.02 19	89.42 9	98.75 18	96.80 47	87.28 83	95.88 22	98.95 2	85.92 29	99.41 66	97.15 17	98.95 20	99.18 24

SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 61	99.12 6	96.78 48	88.72 51	97.79 4	98.91 3	88.48 16	99.82 17	98.15 3	98.97 17	99.74 1

test_241102_TWO								96.78 48	88.72 51	97.70 6	98.91 3	87.86 20	99.82 17	98.15 3	99.00 15	99.47 9

test_241102_ONE					99.03 16	85.03 61		96.78 48	88.72 51	97.79 4	98.90 6	88.48 16	99.82 17

test0726					99.05 10	85.18 54	99.11 8	96.78 48	88.75 49	97.65 8	98.91 3	87.69 21

MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 54	80.10 171	97.42 93	96.78 48	92.20 13	97.11 10	98.29 31	93.46 1	99.10 99	96.01 24	99.30 5	99.38 14
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
无先验							96.87 137	96.78 48	77.39 276				99.52 57	79.95 193		98.43 53

DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 10	85.34 49	98.13 38	96.77 54	88.38 59	97.70 6	98.77 12	92.06 3	99.84 12	97.47 13	99.37 1	99.70 3

test_0728_SECOND				95.14 17	99.04 15	86.14 33	99.06 9	96.77 54					99.84 12	97.90 8	98.85 22	99.45 10

SMA-MVS		94.70 16	94.68 16	94.76 25	98.02 69	85.94 37	97.47 85	96.77 54	85.32 120	97.92 3	98.70 18	83.09 50	99.84 12	95.79 28	99.08 10	98.49 50
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
MVS_111021_LR		91.60 80	91.64 76	91.47 145	95.74 122	78.79 205	96.15 185	96.77 54	88.49 57	88.64 123	97.07 105	72.33 186	99.19 89	93.13 66	96.48 101	96.43 169

3Dnovator	82.32 10	89.33 122	87.64 140	94.42 33	93.73 181	85.70 43	97.73 66	96.75 58	86.73 97	76.21 255	95.93 130	62.17 251	99.68 42	81.67 181	97.81 69	97.88 98

DPE-MVS		95.32 10	95.55 9	94.64 28	98.79 25	84.87 66	97.77 60	96.74 59	86.11 101	96.54 16	98.89 7	88.39 18	99.74 32	97.67 11	99.05 12	99.31 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PVSNet_BlendedMVS		90.05 111	89.96 103	90.33 177	97.47 87	83.86 81	98.02 47	96.73 60	87.98 68	89.53 112	89.61 238	76.42 126	99.57 53	94.29 47	79.59 237	87.57 306

PVSNet_Blended		93.13 43	92.98 47	93.57 64	97.47 87	83.86 81	99.32 1	96.73 60	91.02 24	89.53 112	96.21 125	76.42 126	99.57 53	94.29 47	95.81 111	97.29 142

ACMMP		90.39 106	89.97 102	91.64 139	97.58 84	78.21 223	96.78 143	96.72 62	84.73 136	84.72 159	97.23 97	71.22 197	99.63 48	88.37 124	92.41 146	97.08 149
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
新几何1				93.12 82	97.44 89	81.60 137		96.71 63	74.54 298	91.22 89	97.57 78	79.13 84	99.51 60	77.40 219	98.46 43	98.26 66

test_one_0601					98.91 20	84.56 71		96.70 64	88.06 66	96.57 15	98.77 12	88.04 19

HFP-MVS		92.89 49	92.86 50	92.98 89	98.71 27	81.12 143	97.58 76	96.70 64	85.20 126	91.75 76	97.97 59	78.47 92	99.71 36	90.95 87	98.41 48	98.12 77

#test#		92.99 47	92.99 46	92.98 89	98.71 27	81.12 143	97.77 60	96.70 64	85.75 110	91.75 76	97.97 59	78.47 92	99.71 36	91.36 83	98.41 48	98.12 77

ACMMPR		92.69 57	92.67 54	92.75 99	98.66 32	80.57 158	97.58 76	96.69 67	85.20 126	91.57 80	97.92 61	77.01 116	99.67 44	90.95 87	98.41 48	98.00 89

DeepPCF-MVS	89.82 1	94.61 17	96.17 5	89.91 191	97.09 102	70.21 319	98.99 14	96.69 67	95.57 1	95.08 31	99.23 1	86.40 28	99.87 8	97.84 10	98.66 34	99.65 6

thisisatest0530		89.65 117	89.02 118	91.53 143	93.46 191	80.78 153	96.52 158	96.67 69	81.69 206	83.79 172	94.90 163	88.85 14	97.68 157	77.80 210	87.49 185	96.14 178

tttt0517		88.57 142	88.19 130	89.71 198	93.00 202	75.99 269	95.67 206	96.67 69	80.78 216	81.82 197	94.40 171	88.97 13	97.58 161	76.05 234	86.31 191	95.57 190

thisisatest0515		90.95 94	90.26 95	93.01 88	94.03 174	84.27 76	97.91 51	96.67 69	83.18 178	86.87 142	95.51 142	88.66 15	97.85 152	80.46 187	89.01 168	96.92 154

1121		90.66 99	89.82 108	93.16 81	97.39 93	81.71 134	93.33 269	96.66 72	74.45 299	91.38 82	97.55 82	79.27 80	99.52 57	79.95 193	98.43 45	98.26 66

ACMMP_NAP		93.46 40	93.23 43	94.17 42	97.16 100	84.28 75	96.82 140	96.65 73	86.24 99	94.27 44	97.99 55	77.94 100	99.83 16	93.39 57	98.57 36	98.39 55

TEST9					98.64 35	83.71 85	97.82 56	96.65 73	84.29 152	95.16 28	98.09 46	84.39 34	99.36 74

train_agg		94.28 23	94.45 21	93.74 54	98.64 35	83.71 85	97.82 56	96.65 73	84.50 144	95.16 28	98.09 46	84.33 35	99.36 74	95.91 27	98.96 19	98.16 72

1314		88.94 129	87.20 153	94.17 42	93.21 194	85.73 42	93.33 269	96.64 76	82.89 186	75.98 258	96.36 123	66.83 225	99.39 67	83.52 169	96.02 107	97.39 136

DeepC-MVS_fast	89.06 2	94.48 19	94.30 27	95.02 19	98.86 23	85.68 44	98.06 44	96.64 76	93.64 8	91.74 78	98.54 22	80.17 73	99.90 5	92.28 75	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_8					98.63 37	83.64 88	97.81 58	96.63 78	84.50 144	95.10 30	98.11 45	84.33 35	99.23 80

testtj		94.09 30	94.08 30	94.09 45	99.28 6	83.32 95	97.59 75	96.61 79	83.60 173	94.77 39	98.46 26	82.72 55	99.64 46	95.29 36	98.42 46	99.32 17

原ACMM1				91.22 153	97.77 77	78.10 226		96.61 79	81.05 212	91.28 87	97.42 88	77.92 101	98.98 106	79.85 196	98.51 38	96.59 165

MAR-MVS		90.63 100	90.22 96	91.86 132	98.47 47	78.20 224	97.18 106	96.61 79	83.87 165	88.18 130	98.18 35	68.71 213	99.75 30	83.66 164	97.15 85	97.63 120
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
ZD-MVS					99.09 9	83.22 97		96.60 82	82.88 187	93.61 54	98.06 51	82.93 51	99.14 95	95.51 33	98.49 42

SteuartSystems-ACMMP		94.13 28	94.44 22	93.20 79	95.41 131	81.35 140	99.02 13	96.59 83	89.50 40	94.18 48	98.36 30	83.68 43	99.45 64	94.77 41	98.45 44	98.81 33
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D2MVS		82.67 237	81.55 233	86.04 268	87.77 297	76.47 257	95.21 222	96.58 84	82.66 192	70.26 304	85.46 300	60.39 263	95.80 248	76.40 229	79.18 241	85.83 330

save fliter					98.24 57	83.34 93	98.61 23	96.57 85	91.32 18

TESTMET0.1,1		89.83 113	89.34 115	91.31 148	92.54 214	80.19 169	97.11 115	96.57 85	86.15 100	86.85 143	91.83 208	79.32 78	96.95 199	81.30 182	92.35 147	96.77 160

agg_prior1		94.10 29	94.31 26	93.48 70	98.59 39	83.13 98	97.77 60	96.56 87	84.38 148	94.19 45	98.13 41	84.66 33	99.16 93	95.74 29	98.74 31	98.15 74

agg_prior					98.59 39	83.13 98		96.56 87		94.19 45			99.16 93

DWT-MVSNet_test		90.52 105	89.80 109	92.70 103	95.73 124	82.20 117	93.69 260	96.55 89	88.34 61	87.04 141	95.34 145	86.53 25	97.55 164	76.32 231	88.66 173	98.34 56

旧先验1					97.39 93	79.58 184		96.54 90			98.08 49	84.00 39			97.42 79	97.62 121

WR-MVS_H		81.02 258	80.09 252	83.79 298	88.08 295	71.26 314	94.46 241	96.54 90	80.08 235	72.81 288	86.82 276	70.36 206	92.65 324	64.18 304	67.50 317	87.46 310

ETH3D-3000-0.1		94.43 20	94.42 23	94.45 31	97.78 76	85.78 40	97.98 48	96.53 92	85.29 123	95.45 25	98.81 8	83.36 45	99.38 68	96.07 23	98.53 37	98.19 69

9.14			94.26 28		98.10 65		98.14 35	96.52 93	84.74 135	94.83 37	98.80 9	82.80 54	99.37 72	95.95 26	98.42 46

region2R		92.72 55	92.70 53	92.79 98	98.68 29	80.53 161	97.53 80	96.51 94	85.22 124	91.94 74	97.98 57	77.26 110	99.67 44	90.83 91	98.37 53	98.18 70

EPP-MVSNet		89.76 115	89.72 110	89.87 192	93.78 178	76.02 268	97.22 100	96.51 94	79.35 248	85.11 153	95.01 161	84.82 32	97.10 193	87.46 130	88.21 179	96.50 167

ZNCC-MVS		92.75 51	92.60 56	93.23 78	98.24 57	81.82 129	97.63 71	96.50 96	85.00 131	91.05 91	97.74 70	78.38 94	99.80 23	90.48 96	98.34 55	98.07 80

test11								96.50 96

EPNet_dtu		87.65 161	87.89 134	86.93 254	94.57 155	71.37 313	96.72 147	96.50 96	88.56 56	87.12 139	95.02 160	75.91 136	94.01 309	66.62 292	90.00 161	95.42 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testdata				90.13 182	95.92 119	74.17 286		96.49 99	73.49 307	94.82 38	97.99 55	78.80 89	97.93 146	83.53 168	97.52 74	98.29 63

DVP-MVS		95.58 8	95.91 8	94.57 29	99.05 10	85.18 54	99.06 9	96.46 100	88.75 49	96.69 12	98.76 14	87.69 21	99.76 24	97.90 8	98.85 22	98.77 34
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test222					96.15 113	78.41 214	95.87 199	96.46 100	71.97 318	89.66 109	97.45 84	76.33 129			98.24 58	98.30 62

XVS		92.69 57	92.71 51	92.63 107	98.52 42	80.29 164	97.37 96	96.44 102	87.04 91	91.38 82	97.83 67	77.24 112	99.59 51	90.46 97	98.07 62	98.02 84

X-MVStestdata		86.26 181	84.14 197	92.63 107	98.52 42	80.29 164	97.37 96	96.44 102	87.04 91	91.38 82	20.73 373	77.24 112	99.59 51	90.46 97	98.07 62	98.02 84

SF-MVS		94.17 26	94.05 31	94.55 30	97.56 85	85.95 35	97.73 66	96.43 104	84.02 158	95.07 32	98.74 16	82.93 51	99.38 68	95.42 34	98.51 38	98.32 58

TSAR-MVS + MP.		94.79 15	95.17 13	93.64 60	97.66 80	84.10 78	95.85 201	96.42 105	91.26 20	97.49 9	96.80 116	86.50 26	98.49 131	95.54 32	99.03 13	98.33 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ETH3D cwj APD-0.16		93.91 36	93.76 34	94.36 34	96.70 106	85.74 41	97.22 100	96.41 106	83.94 161	94.13 49	98.69 20	83.13 49	99.37 72	95.25 37	98.39 51	97.97 94

APDe-MVS		94.56 18	94.75 15	93.96 48	98.84 24	83.40 92	98.04 46	96.41 106	85.79 109	95.00 34	98.28 32	84.32 38	99.18 91	97.35 15	98.77 29	99.28 19

UniMVSNet_NR-MVSNet		85.49 192	84.59 188	88.21 227	89.44 281	79.36 188	96.71 149	96.41 106	85.22 124	78.11 231	90.98 219	76.97 117	95.14 283	79.14 203	68.30 308	90.12 247

test_prior3		94.03 32	94.34 25	93.09 84	98.68 29	81.91 123	98.37 28	96.40 109	86.08 103	94.57 41	98.02 52	83.14 47	99.06 101	95.05 38	98.79 27	98.29 63

test_prior				93.09 84	98.68 29	81.91 123		96.40 109					99.06 101			98.29 63

CP-MVS		92.54 62	92.60 56	92.34 116	98.50 45	79.90 174	98.40 26	96.40 109	84.75 134	90.48 99	98.09 46	77.40 109	99.21 84	91.15 86	98.23 59	97.92 97

CANet		94.89 13	94.64 17	95.63 12	97.55 86	88.12 15	99.06 9	96.39 112	94.07 7	95.34 27	97.80 68	76.83 119	99.87 8	97.08 18	97.64 73	98.89 30

GST-MVS		92.43 64	92.22 64	93.04 87	98.17 62	81.64 136	97.40 95	96.38 113	84.71 137	90.90 93	97.40 90	77.55 107	99.76 24	89.75 108	97.74 71	97.72 112

alignmvs		92.97 48	92.26 62	95.12 18	95.54 128	87.77 19	98.67 19	96.38 113	88.04 67	93.01 61	97.45 84	79.20 83	98.60 125	93.25 62	88.76 171	98.99 29

PAPM		92.87 50	92.40 58	94.30 36	92.25 223	87.85 18	96.40 170	96.38 113	91.07 22	88.72 122	96.90 109	82.11 59	97.37 178	90.05 104	97.70 72	97.67 116

test12				94.25 38	98.34 52	85.55 46		96.35 116		92.36 68		80.84 63	99.22 82		98.31 56	97.98 91

zzz-MVS		92.74 52	92.71 51	92.86 95	97.90 71	80.85 151	96.47 161	96.33 117	87.92 69	90.20 102	98.18 35	76.71 122	99.76 24	92.57 72	98.09 60	97.96 95

MTGPA								96.33 117

MTAPA		92.45 63	92.31 60	92.86 95	97.90 71	80.85 151	92.88 282	96.33 117	87.92 69	90.20 102	98.18 35	76.71 122	99.76 24	92.57 72	98.09 60	97.96 95

ET-MVSNet_ETH3D		90.01 112	89.03 117	92.95 91	94.38 164	86.77 28	98.14 35	96.31 120	89.30 43	63.33 334	96.72 119	90.09 10	93.63 316	90.70 94	82.29 227	98.46 51

EPMVS		87.47 163	85.90 171	92.18 122	95.41 131	82.26 116	87.00 329	96.28 121	85.88 108	84.23 164	85.57 297	75.07 157	96.26 227	71.14 273	92.50 144	98.03 83

CDPH-MVS		93.12 44	92.91 48	93.74 54	98.65 34	83.88 80	97.67 70	96.26 122	83.00 184	93.22 58	98.24 33	81.31 61	99.21 84	89.12 115	98.74 31	98.14 75

WR-MVS		84.32 211	82.96 211	88.41 219	89.38 282	80.32 163	96.59 154	96.25 123	83.97 160	76.63 245	90.36 229	67.53 218	94.86 294	75.82 237	70.09 292	90.06 251

UGNet		87.73 160	86.55 165	91.27 151	95.16 139	79.11 196	96.35 173	96.23 124	88.14 65	87.83 133	90.48 225	50.65 314	99.09 100	80.13 192	94.03 126	95.60 189
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
tfpnnormal		78.14 281	75.42 287	86.31 264	88.33 292	79.24 191	94.41 243	96.22 125	73.51 305	69.81 307	85.52 299	55.43 301	95.75 251	47.65 357	67.86 313	83.95 343

FOURS1					98.51 44	78.01 228	98.13 38	96.21 126	83.04 182	94.39 43

MP-MVS		92.61 60	92.67 54	92.42 114	98.13 64	79.73 180	97.33 98	96.20 127	85.63 112	90.53 97	97.66 72	78.14 98	99.70 39	92.12 77	98.30 57	97.85 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PAPR		92.74 52	92.17 65	94.45 31	98.89 22	84.87 66	97.20 104	96.20 127	87.73 75	88.40 126	98.12 44	78.71 90	99.76 24	87.99 126	96.28 102	98.74 35

SD-MVS		94.84 14	95.02 14	94.29 37	97.87 75	84.61 70	97.76 64	96.19 129	89.59 39	96.66 14	98.17 39	84.33 35	99.60 50	96.09 22	98.50 41	98.66 41
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
CHOSEN 280x420		91.71 76	91.85 70	91.29 150	94.94 147	82.69 106	87.89 323	96.17 130	85.94 106	87.27 138	94.31 172	90.27 8	95.65 258	94.04 50	95.86 109	95.53 191

CHOSEN 1792x2688		91.07 91	90.21 97	93.64 60	95.18 138	83.53 89	96.26 179	96.13 131	88.92 47	84.90 156	93.10 194	72.86 181	99.62 49	88.86 116	95.67 112	97.79 107

PAPM_NR		91.46 82	90.82 86	93.37 74	98.50 45	81.81 130	95.03 232	96.13 131	84.65 140	86.10 148	97.65 76	79.24 82	99.75 30	83.20 172	96.88 93	98.56 46

CostFormer		89.08 126	88.39 128	91.15 154	93.13 199	79.15 195	88.61 317	96.11 133	83.14 179	89.58 111	86.93 275	83.83 42	96.87 205	88.22 125	85.92 197	97.42 133

mPP-MVS		91.88 71	91.82 71	92.07 125	98.38 50	78.63 208	97.29 99	96.09 134	85.12 128	88.45 125	97.66 72	75.53 143	99.68 42	89.83 106	98.02 65	97.88 98

APD-MVS		93.61 38	93.59 37	93.69 57	98.76 26	83.26 96	97.21 102	96.09 134	82.41 195	94.65 40	98.21 34	81.96 60	98.81 118	94.65 44	98.36 54	99.01 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MDTV_nov1_ep13			83.69 200		94.09 170	81.01 146	86.78 331	96.09 134	83.81 167	84.75 158	84.32 314	74.44 165	96.54 217	63.88 306	85.07 206

QAPM		86.88 170	84.51 189	93.98 46	94.04 172	85.89 38	97.19 105	96.05 137	73.62 304	75.12 270	95.62 139	62.02 254	99.74 32	70.88 274	96.06 106	96.30 176

MP-MVS-pluss		92.58 61	92.35 59	93.29 75	97.30 98	82.53 109	96.44 166	96.04 138	84.68 138	89.12 117	98.37 29	77.48 108	99.74 32	93.31 61	98.38 52	97.59 123
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
tpm2		87.35 164	86.26 167	90.62 168	92.93 205	78.67 207	88.06 322	95.99 139	79.33 249	87.40 135	86.43 286	80.28 70	96.40 221	80.23 190	85.73 201	96.79 158

DeepC-MVS	86.58 3	91.53 81	91.06 84	92.94 92	94.52 158	81.89 125	95.95 193	95.98 140	90.76 25	83.76 173	96.76 117	73.24 179	99.71 36	91.67 81	96.96 90	97.22 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test-LLR		88.48 143	87.98 133	89.98 187	92.26 221	77.23 248	97.11 115	95.96 141	83.76 168	86.30 146	91.38 211	72.30 187	96.78 211	80.82 184	91.92 151	95.94 181

test-mter		88.95 128	88.60 125	89.98 187	92.26 221	77.23 248	97.11 115	95.96 141	85.32 120	86.30 146	91.38 211	76.37 128	96.78 211	80.82 184	91.92 151	95.94 181

DP-MVS Recon		91.72 75	90.85 85	94.34 35	99.50 1	85.00 63	98.51 25	95.96 141	80.57 221	88.08 131	97.63 77	76.84 118	99.89 7	85.67 141	94.88 119	98.13 76

cdsmvs_eth3d_5k		21.43 340	28.57 343	0.00 359	0.00 382	0.00 383	0.00 370	95.93 144	0.00 377	0.00 378	97.66 72	63.57 243	0.00 378	0.00 376	0.00 376	0.00 374

hse-mvs2		88.22 152	88.21 129	88.25 225	93.54 185	73.41 289	95.41 216	95.89 145	90.39 31	92.22 70	94.22 175	74.70 160	96.66 216	93.14 64	64.37 332	94.69 208

AUN-MVS		86.25 182	85.57 172	88.26 224	93.57 184	73.38 290	95.45 214	95.88 146	83.94 161	85.47 151	94.21 176	73.70 175	96.67 215	83.54 167	64.41 331	94.73 207

TAMVS		88.48 143	87.79 137	90.56 170	91.09 251	79.18 193	96.45 164	95.88 146	83.64 171	83.12 179	93.33 190	75.94 135	95.74 254	82.40 177	88.27 178	96.75 162

PVSNet_Blended_VisFu		91.24 88	90.77 87	92.66 104	95.09 140	82.40 113	97.77 60	95.87 148	88.26 63	86.39 144	93.94 183	76.77 120	99.27 77	88.80 118	94.00 129	96.31 175

OpenMVS	79.58 14	86.09 183	83.62 204	93.50 68	90.95 253	86.71 30	97.44 87	95.83 149	75.35 290	72.64 289	95.72 134	57.42 288	99.64 46	71.41 268	95.85 110	94.13 214

CDS-MVSNet		89.50 119	88.96 120	91.14 155	91.94 239	80.93 149	97.09 119	95.81 150	84.26 153	84.72 159	94.20 177	80.31 69	95.64 259	83.37 170	88.96 169	96.85 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		94.17 26	93.52 39	96.10 8	95.65 126	92.35 2	98.21 33	95.79 151	92.42 12	96.24 18	98.18 35	71.04 200	99.17 92	96.77 19	97.39 80	96.79 158

SR-MVS		92.16 66	92.27 61	91.83 135	98.37 51	78.41 214	96.67 152	95.76 152	82.19 199	91.97 73	98.07 50	76.44 125	98.64 122	93.71 52	97.27 82	98.45 52

3Dnovator+	82.88 8	89.63 118	87.85 135	94.99 20	94.49 162	86.76 29	97.84 55	95.74 153	86.10 102	75.47 267	96.02 129	65.00 237	99.51 60	82.91 176	97.07 87	98.72 40

HPM-MVS		91.62 79	91.53 77	91.89 131	97.88 74	79.22 192	96.99 126	95.73 154	82.07 200	89.50 114	97.19 99	75.59 142	98.93 113	90.91 89	97.94 66	97.54 124
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ab-mvs		87.08 166	84.94 185	93.48 70	93.34 193	83.67 87	88.82 314	95.70 155	81.18 210	84.55 162	90.14 234	62.72 247	98.94 112	85.49 143	82.54 226	97.85 102

xiu_mvs_v2_base		93.92 34	93.26 42	95.91 10	95.07 142	92.02 6	98.19 34	95.68 156	92.06 14	96.01 21	98.14 40	70.83 203	98.96 107	96.74 20	96.57 99	96.76 161

test1172		91.64 77	92.00 69	90.54 171	98.20 61	74.48 283	96.45 164	95.65 157	81.97 203	91.63 79	98.02 52	75.76 138	98.61 123	93.16 63	97.17 84	98.52 49

CP-MVSNet		81.01 259	80.08 253	83.79 298	87.91 296	70.51 316	94.29 251	95.65 157	80.83 215	72.54 291	88.84 247	63.71 242	92.32 327	68.58 286	68.36 307	88.55 284

PatchmatchNet		86.83 172	85.12 182	91.95 129	94.12 169	82.27 115	86.55 333	95.64 159	84.59 142	82.98 182	84.99 309	77.26 110	95.96 239	68.61 285	91.34 155	97.64 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
API-MVS		90.18 110	88.97 119	93.80 52	98.66 32	82.95 104	97.50 84	95.63 160	75.16 293	86.31 145	97.69 71	72.49 184	99.90 5	81.26 183	96.07 105	98.56 46

AdaColmap		88.81 134	87.61 143	92.39 115	99.33 4	79.95 172	96.70 151	95.58 161	77.51 275	83.05 181	96.69 120	61.90 258	99.72 35	84.29 152	93.47 135	97.50 129

SCA		85.63 190	83.64 203	91.60 142	92.30 219	81.86 127	92.88 282	95.56 162	84.85 132	82.52 183	85.12 307	58.04 280	95.39 269	73.89 253	87.58 184	97.54 124

dp		84.30 212	82.31 223	90.28 178	94.24 167	77.97 229	86.57 332	95.53 163	79.94 239	80.75 205	85.16 305	71.49 196	96.39 222	63.73 307	83.36 215	96.48 168

HyFIR lowres test		89.36 121	88.60 125	91.63 141	94.91 149	80.76 154	95.60 209	95.53 163	82.56 194	84.03 166	91.24 214	78.03 99	96.81 209	87.07 134	88.41 177	97.32 138

APD-MVS_3200maxsize		91.23 89	91.35 79	90.89 161	97.89 73	76.35 261	96.30 177	95.52 165	79.82 240	91.03 92	97.88 64	74.70 160	98.54 128	92.11 78	96.89 92	97.77 109

lupinMVS		93.87 37	93.58 38	94.75 26	93.00 202	88.08 16	99.15 4	95.50 166	91.03 23	94.90 35	97.66 72	78.84 87	97.56 162	94.64 45	97.46 75	98.62 44

HPM-MVS_fast		90.38 108	90.17 99	91.03 157	97.61 81	77.35 246	97.15 111	95.48 167	79.51 246	88.79 121	96.90 109	71.64 194	98.81 118	87.01 135	97.44 77	96.94 151

VPNet		84.69 204	82.92 212	90.01 185	89.01 284	83.45 91	96.71 149	95.46 168	85.71 111	79.65 218	92.18 201	56.66 294	96.01 235	83.05 175	67.84 314	90.56 237

114514_t		88.79 136	87.57 144	92.45 112	98.21 60	81.74 132	96.99 126	95.45 169	75.16 293	82.48 184	95.69 136	68.59 214	98.50 130	80.33 188	95.18 117	97.10 148

SR-MVS-dyc-post		91.29 87	91.45 78	90.80 163	97.76 78	76.03 266	96.20 183	95.44 170	80.56 222	90.72 95	97.84 65	75.76 138	98.61 123	91.99 79	96.79 96	97.75 110

RE-MVS-def			91.18 83		97.76 78	76.03 266	96.20 183	95.44 170	80.56 222	90.72 95	97.84 65	73.36 178		91.99 79	96.79 96	97.75 110

JIA-IIPM		79.00 276	77.20 274	84.40 293	89.74 275	64.06 343	75.30 358	95.44 170	62.15 345	81.90 195	59.08 361	78.92 86	95.59 263	66.51 295	85.78 200	93.54 221

RPMNet		79.85 267	75.92 285	91.64 139	90.16 268	79.75 177	79.02 351	95.44 170	58.43 359	82.27 191	72.55 355	73.03 180	98.41 135	46.10 359	86.25 192	96.75 162

DU-MVS		84.57 206	83.33 209	88.28 223	88.76 285	79.36 188	96.43 168	95.41 174	85.42 117	78.11 231	90.82 220	67.61 216	95.14 283	79.14 203	68.30 308	90.33 242

EI-MVSNet		85.80 187	85.20 178	87.59 238	91.55 244	77.41 244	95.13 226	95.36 175	80.43 227	80.33 211	94.71 165	73.72 173	95.97 236	76.96 223	78.64 246	89.39 259

MVSTER		89.25 125	88.92 122	90.24 179	95.98 118	84.66 69	96.79 142	95.36 175	87.19 88	80.33 211	90.61 224	90.02 11	95.97 236	85.38 144	78.64 246	90.09 249

CPTT-MVS		89.72 116	89.87 107	89.29 203	98.33 53	73.30 292	97.70 68	95.35 177	75.68 289	87.40 135	97.44 87	70.43 205	98.25 139	89.56 111	96.90 91	96.33 174

EIA-MVS		91.73 73	92.05 68	90.78 165	94.52 158	76.40 260	98.06 44	95.34 178	89.19 44	88.90 120	97.28 96	77.56 106	97.73 156	90.77 92	96.86 95	98.20 68

RRT_test8_iter05		87.14 165	86.41 166	89.32 202	94.41 163	81.10 145	97.06 123	95.33 179	84.67 139	76.27 253	90.48 225	83.60 44	96.33 224	85.10 145	70.78 283	90.53 238

tpmvs		83.04 231	80.77 242	89.84 193	95.43 130	77.96 230	85.59 338	95.32 180	75.31 292	76.27 253	83.70 319	73.89 170	97.41 175	59.53 321	81.93 228	94.14 213

PS-CasMVS		80.27 265	79.18 261	83.52 305	87.56 300	69.88 321	94.08 255	95.29 181	80.27 232	72.08 293	88.51 254	59.22 273	92.23 329	67.49 288	68.15 310	88.45 288

TSAR-MVS + GP.		94.35 22	94.50 19	93.89 49	97.38 96	83.04 102	98.10 40	95.29 181	91.57 16	93.81 51	97.45 84	86.64 24	99.43 65	96.28 21	94.01 128	99.20 22

tpmrst		88.36 147	87.38 150	91.31 148	94.36 165	79.92 173	87.32 327	95.26 183	85.32 120	88.34 127	86.13 291	80.60 66	96.70 213	83.78 158	85.34 205	97.30 141

ETV-MVS		92.72 55	92.87 49	92.28 119	94.54 157	81.89 125	97.98 48	95.21 184	89.77 38	93.11 59	96.83 113	77.23 114	97.50 170	95.74 29	95.38 113	97.44 132

NR-MVSNet		83.35 223	81.52 235	88.84 211	88.76 285	81.31 141	94.45 242	95.16 185	84.65 140	67.81 313	90.82 220	70.36 206	94.87 293	74.75 244	66.89 324	90.33 242

MVS_0304		78.43 278	76.70 279	83.60 303	88.22 293	69.81 322	92.91 281	95.10 186	72.32 317	78.71 225	80.29 338	33.78 357	93.37 320	68.77 284	80.23 233	87.63 303

jason		92.73 54	92.23 63	94.21 41	90.50 262	87.30 25	98.65 20	95.09 187	90.61 27	92.76 65	97.13 101	75.28 153	97.30 181	93.32 60	96.75 98	98.02 84
jason: jason.
tpm cat1		83.63 220	81.38 236	90.39 174	93.53 190	78.19 225	85.56 339	95.09 187	70.78 323	78.51 227	83.28 322	74.80 159	97.03 194	66.77 291	84.05 210	95.95 180

cascas		86.50 177	84.48 191	92.55 110	92.64 212	85.95 35	97.04 125	95.07 189	75.32 291	80.50 207	91.02 217	54.33 309	97.98 145	86.79 136	87.62 182	93.71 220

abl_6		89.80 114	89.71 111	90.07 183	96.53 107	75.52 274	94.48 240	95.04 190	81.12 211	89.22 115	97.00 107	68.83 212	98.96 107	89.86 105	95.27 114	95.73 186

CVMVSNet		84.83 201	85.57 172	82.63 312	91.55 244	60.38 353	95.13 226	95.03 191	80.60 220	82.10 193	94.71 165	66.40 228	90.19 348	74.30 250	90.32 160	97.31 140

test0.0.03 1		82.79 235	82.48 221	83.74 300	86.81 304	72.22 299	96.52 158	95.03 191	83.76 168	73.00 285	93.20 191	72.30 187	88.88 351	64.15 305	77.52 254	90.12 247

RRT_MVS		86.89 169	85.96 169	89.68 199	95.01 146	84.13 77	96.33 175	94.98 193	84.20 155	80.10 215	92.07 202	70.52 204	95.01 291	83.30 171	77.14 255	89.91 253

PMMVS		89.46 120	89.92 105	88.06 229	94.64 153	69.57 326	96.22 181	94.95 194	87.27 84	91.37 85	96.54 122	65.88 229	97.39 176	88.54 119	93.89 130	97.23 144

Anonymous20240529		83.15 228	80.60 246	90.80 163	95.74 122	78.27 218	96.81 141	94.92 195	60.10 355	81.89 196	92.54 198	45.82 331	98.82 117	79.25 202	78.32 251	95.31 196

mvs_anonymous		88.68 137	87.62 142	91.86 132	94.80 151	81.69 135	93.53 265	94.92 195	82.03 201	78.87 224	90.43 228	75.77 137	95.34 272	85.04 147	93.16 139	98.55 48

CLD-MVS		87.97 157	87.48 147	89.44 200	92.16 228	80.54 160	98.14 35	94.92 195	91.41 17	79.43 219	95.40 144	62.34 249	97.27 184	90.60 95	82.90 221	90.50 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
xiu_mvs_v1_base_debu		90.54 102	89.54 112	93.55 65	92.31 216	87.58 22	96.99 126	94.87 198	87.23 85	93.27 55	97.56 79	57.43 285	98.32 136	92.72 69	93.46 136	94.74 204

xiu_mvs_v1_base		90.54 102	89.54 112	93.55 65	92.31 216	87.58 22	96.99 126	94.87 198	87.23 85	93.27 55	97.56 79	57.43 285	98.32 136	92.72 69	93.46 136	94.74 204

xiu_mvs_v1_base_debi		90.54 102	89.54 112	93.55 65	92.31 216	87.58 22	96.99 126	94.87 198	87.23 85	93.27 55	97.56 79	57.43 285	98.32 136	92.72 69	93.46 136	94.74 204

CS-MVS		93.12 44	93.27 41	92.64 106	93.86 177	83.12 100	98.85 16	94.85 201	88.61 54	94.19 45	97.42 88	79.02 85	97.02 195	94.89 40	97.77 70	97.78 108

GA-MVS		85.79 188	84.04 198	91.02 158	89.47 280	80.27 166	96.90 136	94.84 202	85.57 113	80.88 203	89.08 242	56.56 295	96.47 220	77.72 213	85.35 204	96.34 172

TranMVSNet+NR-MVSNet		83.24 227	81.71 231	87.83 232	87.71 298	78.81 204	96.13 188	94.82 203	84.52 143	76.18 256	90.78 222	64.07 241	94.60 299	74.60 248	66.59 326	90.09 249

HQP3-MVS								94.80 204							83.01 218

HQP-MVS		87.91 159	87.55 145	88.98 208	92.08 230	78.48 210	97.63 71	94.80 204	90.52 28	82.30 187	94.56 168	65.40 233	97.32 179	87.67 128	83.01 218	91.13 231

TAPA-MVS	81.61 12	85.02 198	83.67 201	89.06 205	96.79 104	73.27 294	95.92 195	94.79 206	74.81 296	80.47 208	96.83 113	71.07 199	98.19 142	49.82 353	92.57 142	95.71 187
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PEN-MVS		79.47 272	78.26 268	83.08 308	86.36 307	68.58 329	93.85 258	94.77 207	79.76 241	71.37 295	88.55 251	59.79 265	92.46 325	64.50 303	65.40 328	88.19 293

HQP_MVS		87.50 162	87.09 158	88.74 214	91.86 240	77.96 230	97.18 106	94.69 208	89.89 36	81.33 199	94.15 178	64.77 238	97.30 181	87.08 132	82.82 222	90.96 233

plane_prior5								94.69 208					97.30 181	87.08 132	82.82 222	90.96 233

tpm		85.55 191	84.47 192	88.80 213	90.19 267	75.39 276	88.79 315	94.69 208	84.83 133	83.96 169	85.21 303	78.22 97	94.68 298	76.32 231	78.02 253	96.34 172

FMVSNet3		84.71 203	82.71 218	90.70 167	94.55 156	87.71 20	95.92 195	94.67 211	81.73 205	75.82 262	88.08 259	66.99 223	94.47 301	71.23 270	75.38 261	89.91 253

UA-Net		88.92 130	88.48 127	90.24 179	94.06 171	77.18 250	93.04 278	94.66 212	87.39 81	91.09 90	93.89 184	74.92 158	98.18 143	75.83 236	91.43 154	95.35 195

LFMVS		89.27 124	87.64 140	94.16 44	97.16 100	85.52 47	97.18 106	94.66 212	79.17 254	89.63 110	96.57 121	55.35 302	98.22 140	89.52 112	89.54 163	98.74 35

MVS_Test		90.29 109	89.18 116	93.62 62	95.23 135	84.93 64	94.41 243	94.66 212	84.31 150	90.37 101	91.02 217	75.13 155	97.82 153	83.11 174	94.42 124	98.12 77

canonicalmvs		92.27 65	91.22 80	95.41 15	95.80 121	88.31 13	97.09 119	94.64 215	88.49 57	92.99 62	97.31 92	72.68 183	98.57 127	93.38 59	88.58 174	99.36 16

VDDNet		86.44 178	84.51 189	92.22 121	91.56 243	81.83 128	97.10 118	94.64 215	69.50 329	87.84 132	95.19 151	48.01 323	97.92 151	89.82 107	86.92 186	96.89 155

baseline1		88.85 133	87.49 146	92.93 93	95.21 137	86.85 27	95.47 213	94.61 217	87.29 82	83.11 180	94.99 162	80.70 65	96.89 203	82.28 178	73.72 267	95.05 198

PatchT		79.75 268	76.85 278	88.42 218	89.55 278	75.49 275	77.37 355	94.61 217	63.07 342	82.46 185	73.32 354	75.52 144	93.41 319	51.36 348	84.43 208	96.36 170

MS-PatchMatch		83.05 230	81.82 230	86.72 259	89.64 276	79.10 197	94.88 235	94.59 219	79.70 243	70.67 301	89.65 237	50.43 316	96.82 208	70.82 277	95.99 108	84.25 340

baseline		90.76 97	90.10 100	92.74 100	92.90 206	82.56 108	94.60 239	94.56 220	87.69 76	89.06 119	95.67 137	73.76 172	97.51 169	90.43 99	92.23 149	98.16 72

OMC-MVS		88.80 135	88.16 131	90.72 166	95.30 134	77.92 233	94.81 236	94.51 221	86.80 94	84.97 155	96.85 112	67.53 218	98.60 125	85.08 146	87.62 182	95.63 188

MVSFormer		91.36 85	90.57 89	93.73 56	93.00 202	88.08 16	94.80 237	94.48 222	80.74 217	94.90 35	97.13 101	78.84 87	95.10 287	83.77 159	97.46 75	98.02 84

test_djsdf		83.00 233	82.45 222	84.64 287	84.07 336	69.78 323	94.80 237	94.48 222	80.74 217	75.41 268	87.70 263	61.32 261	95.10 287	83.77 159	79.76 234	89.04 273

casdiffmvs		90.95 94	90.39 92	92.63 107	92.82 207	82.53 109	96.83 139	94.47 224	87.69 76	88.47 124	95.56 141	74.04 169	97.54 167	90.90 90	92.74 141	97.83 104

PCF-MVS	84.09 5	86.77 175	85.00 184	92.08 124	92.06 233	83.07 101	92.14 290	94.47 224	79.63 244	76.90 242	94.78 164	71.15 198	99.20 88	72.87 259	91.05 156	93.98 216
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDD-MVS		88.28 150	87.02 160	92.06 126	95.09 140	80.18 170	97.55 79	94.45 226	83.09 180	89.10 118	95.92 132	47.97 324	98.49 131	93.08 67	86.91 187	97.52 128

PLC	83.97 7	88.00 156	87.38 150	89.83 194	98.02 69	76.46 258	97.16 110	94.43 227	79.26 253	81.98 194	96.28 124	69.36 210	99.27 77	77.71 214	92.25 148	93.77 219
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DROMVSNet		91.73 73	92.11 66	90.58 169	93.54 185	77.77 237	98.07 43	94.40 228	87.44 79	92.99 62	97.11 103	74.59 164	96.87 205	93.75 51	97.08 86	97.11 147

CS-MVS-test		91.92 70	92.11 66	91.37 146	94.00 175	79.66 181	98.39 27	94.38 229	87.14 90	92.87 64	97.05 106	77.17 115	96.97 198	91.44 82	96.55 100	97.47 131

FMVSNet2		82.79 235	80.44 248	89.83 194	92.66 209	85.43 48	95.42 215	94.35 230	79.06 257	74.46 274	87.28 267	56.38 297	94.31 304	69.72 280	74.68 264	89.76 255

nrg030		86.79 174	85.43 174	90.87 162	88.76 285	85.34 49	97.06 123	94.33 231	84.31 150	80.45 209	91.98 203	72.36 185	96.36 223	88.48 122	71.13 280	90.93 235

ACMM	80.70 13	83.72 219	82.85 214	86.31 264	91.19 249	72.12 302	95.88 198	94.29 232	80.44 225	77.02 240	91.96 204	55.24 303	97.14 192	79.30 201	80.38 232	89.67 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		83.84 216	82.00 227	89.35 201	87.13 302	81.38 139	95.72 204	94.26 233	80.15 234	75.92 261	90.63 223	61.96 257	96.52 218	78.98 205	73.28 273	90.14 246

cl22		85.11 197	84.17 196	87.92 231	95.06 144	78.82 202	95.51 211	94.22 234	79.74 242	76.77 243	87.92 261	75.96 134	95.68 255	79.93 195	72.42 275	89.27 265

OPM-MVS		85.84 186	85.10 183	88.06 229	88.34 291	77.83 236	95.72 204	94.20 235	87.89 72	80.45 209	94.05 180	58.57 276	97.26 185	83.88 156	82.76 224	89.09 270
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet (Re-imp)		88.88 132	88.87 123	88.91 209	93.89 176	74.43 284	96.93 135	94.19 236	84.39 147	83.22 178	95.67 137	78.24 96	94.70 297	78.88 206	94.40 125	97.61 122

Anonymous20231211		79.72 269	77.19 275	87.33 245	95.59 127	77.16 251	95.18 225	94.18 237	59.31 357	72.57 290	86.20 290	47.89 325	95.66 256	74.53 249	69.24 300	89.18 267

PS-MVSNAJss		84.91 200	84.30 194	86.74 255	85.89 317	74.40 285	94.95 233	94.16 238	83.93 163	76.45 248	90.11 235	71.04 200	95.77 249	83.16 173	79.02 243	90.06 251

LPG-MVS_test		84.20 213	83.49 207	86.33 261	90.88 254	73.06 295	95.28 218	94.13 239	82.20 197	76.31 250	93.20 191	54.83 307	96.95 199	83.72 161	80.83 230	88.98 276

LGP-MVS_train				86.33 261	90.88 254	73.06 295		94.13 239	82.20 197	76.31 250	93.20 191	54.83 307	96.95 199	83.72 161	80.83 230	88.98 276

V42		83.04 231	81.53 234	87.57 240	86.27 310	79.09 198	95.87 199	94.11 241	80.35 229	77.22 238	86.79 278	65.32 235	96.02 234	77.74 212	70.14 288	87.61 305

test_part1		84.72 202	82.85 214	90.34 176	95.73 124	84.79 68	96.75 146	94.10 242	79.05 260	75.97 259	89.51 239	67.69 215	95.94 240	79.34 199	67.50 317	90.30 244

XVG-OURS-SEG-HR		85.74 189	85.16 181	87.49 243	90.22 266	71.45 312	91.29 300	94.09 243	81.37 208	83.90 171	95.22 148	60.30 264	97.53 168	85.58 142	84.42 209	93.50 222

XVG-OURS		85.18 196	84.38 193	87.59 238	90.42 264	71.73 309	91.06 303	94.07 244	82.00 202	83.29 177	95.08 159	56.42 296	97.55 164	83.70 163	83.42 214	93.49 223

miper_enhance_ethall		85.95 185	85.20 178	88.19 228	94.85 150	79.76 176	96.00 190	94.06 245	82.98 185	77.74 233	88.76 248	79.42 77	95.46 268	80.58 186	72.42 275	89.36 264

v2v482		83.46 222	81.86 229	88.25 225	86.19 311	79.65 182	96.34 174	94.02 246	81.56 207	77.32 236	88.23 256	65.62 230	96.03 233	77.77 211	69.72 296	89.09 270

jajsoiax		82.12 246	81.15 239	85.03 281	84.19 334	70.70 315	94.22 252	93.95 247	83.07 181	73.48 279	89.75 236	49.66 319	95.37 271	82.24 179	79.76 234	89.02 274

v1144		82.90 234	81.27 238	87.78 234	86.29 309	79.07 199	96.14 186	93.93 248	80.05 236	77.38 234	86.80 277	65.50 231	95.93 242	75.21 241	70.13 289	88.33 291

KD-MVS_2432*1600		77.63 286	74.92 291	85.77 271	90.86 256	79.44 185	88.08 320	93.92 249	76.26 285	67.05 317	82.78 324	72.15 189	91.92 332	61.53 314	41.62 364	85.94 328

miper_refine_blended		77.63 286	74.92 291	85.77 271	90.86 256	79.44 185	88.08 320	93.92 249	76.26 285	67.05 317	82.78 324	72.15 189	91.92 332	61.53 314	41.62 364	85.94 328

UnsupCasMVSNet_eth		73.25 309	70.57 313	81.30 318	77.53 355	66.33 337	87.24 328	93.89 251	80.38 228	57.90 352	81.59 329	42.91 341	90.56 345	65.18 301	48.51 357	87.01 315

v7n		79.32 274	77.34 273	85.28 278	84.05 337	72.89 298	93.38 267	93.87 252	75.02 295	70.68 300	84.37 313	59.58 268	95.62 261	67.60 287	67.50 317	87.32 312

Vis-MVSNet		88.67 138	87.82 136	91.24 152	92.68 208	78.82 202	96.95 133	93.85 253	87.55 78	87.07 140	95.13 156	63.43 244	97.21 186	77.58 216	96.15 103	97.70 115
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v148		82.41 243	80.89 240	86.99 253	86.18 312	76.81 254	96.27 178	93.82 254	80.49 224	75.28 269	86.11 292	67.32 221	95.75 251	75.48 239	67.03 323	88.42 289

BH-w/o		88.24 151	87.47 148	90.54 171	95.03 145	78.54 209	97.41 94	93.82 254	84.08 156	78.23 230	94.51 170	69.34 211	97.21 186	80.21 191	94.58 123	95.87 183

TR-MVS		86.30 180	84.93 186	90.42 173	94.63 154	77.58 241	96.57 155	93.82 254	80.30 230	82.42 186	95.16 153	58.74 275	97.55 164	74.88 243	87.82 181	96.13 179

v1192		82.31 244	80.55 247	87.60 237	85.94 315	78.47 213	95.85 201	93.80 257	79.33 249	76.97 241	86.51 281	63.33 245	95.87 244	73.11 258	70.13 289	88.46 287

ACMP	81.66 11	84.00 214	83.22 210	86.33 261	91.53 246	72.95 297	95.91 197	93.79 258	83.70 170	73.79 277	92.22 200	54.31 310	96.89 203	83.98 155	79.74 236	89.16 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v144192		82.43 240	80.73 243	87.54 241	85.81 318	78.22 220	95.98 191	93.78 259	79.09 256	77.11 239	86.49 282	64.66 240	95.91 243	74.20 251	69.42 297	88.49 285

mvs_tets		81.74 249	80.71 244	84.84 282	84.22 333	70.29 318	93.91 257	93.78 259	82.77 189	73.37 280	89.46 240	47.36 328	95.31 275	81.99 180	79.55 239	88.92 280

F-COLMAP		84.50 208	83.44 208	87.67 235	95.22 136	72.22 299	95.95 193	93.78 259	75.74 288	76.30 252	95.18 152	59.50 269	98.45 133	72.67 261	86.59 190	92.35 228

UniMVSNet_ETH3D		80.86 261	78.75 265	87.22 250	86.31 308	72.02 303	91.95 291	93.76 262	73.51 305	75.06 271	90.16 233	43.04 340	95.66 256	76.37 230	78.55 249	93.98 216

Fast-Effi-MVS+		87.93 158	86.94 162	90.92 160	94.04 172	79.16 194	98.26 31	93.72 263	81.29 209	83.94 170	92.90 195	69.83 209	96.68 214	76.70 225	91.74 153	96.93 152

v1921920		82.02 247	80.23 251	87.41 244	85.62 319	77.92 233	95.79 203	93.69 264	78.86 261	76.67 244	86.44 284	62.50 248	95.83 246	72.69 260	69.77 295	88.47 286

DTE-MVSNet		78.37 279	77.06 276	82.32 315	85.22 326	67.17 335	93.40 266	93.66 265	78.71 263	70.53 302	88.29 255	59.06 274	92.23 329	61.38 317	63.28 337	87.56 307

v8		81.88 248	80.06 255	87.32 246	86.63 305	79.04 200	94.41 243	93.65 266	78.77 262	73.19 284	85.57 297	66.87 224	95.81 247	73.84 255	67.61 316	87.11 313

diffmvs		91.17 90	90.74 88	92.44 113	93.11 201	82.50 111	96.25 180	93.62 267	87.79 73	90.40 100	95.93 130	73.44 177	97.42 174	93.62 54	92.55 143	97.41 134

ADS-MVSNet		81.26 256	78.36 266	89.96 189	93.78 178	79.78 175	79.48 347	93.60 268	73.09 310	80.14 213	79.99 339	62.15 252	95.24 278	59.49 322	83.52 212	94.85 201

PatchMatch-RL		85.00 199	83.66 202	89.02 207	95.86 120	74.55 282	92.49 286	93.60 268	79.30 251	79.29 221	91.47 209	58.53 277	98.45 133	70.22 278	92.17 150	94.07 215

anonymousdsp		80.98 260	79.97 256	84.01 295	81.73 343	70.44 317	92.49 286	93.58 270	77.10 282	72.98 286	86.31 288	57.58 284	94.90 292	79.32 200	78.63 248	86.69 318

CL-MVSNet_self_test		75.81 298	74.14 300	80.83 322	78.33 353	67.79 332	94.22 252	93.52 271	77.28 279	69.82 306	81.54 330	61.47 260	89.22 350	57.59 330	53.51 350	85.48 332

miper_ehance_all_eth		84.57 206	83.60 205	87.50 242	92.64 212	78.25 219	95.40 217	93.47 272	79.28 252	76.41 249	87.64 264	76.53 124	95.24 278	78.58 207	72.42 275	89.01 275

bset_n11_16_dypcd		84.35 210	82.83 216	88.91 209	82.54 341	82.07 119	94.12 254	93.47 272	85.39 119	78.55 226	88.98 245	62.23 250	95.11 285	86.75 137	73.42 269	89.55 258

v1240		81.70 250	79.83 258	87.30 248	85.50 320	77.70 240	95.48 212	93.44 274	78.46 266	76.53 247	86.44 284	60.85 262	95.84 245	71.59 267	70.17 287	88.35 290

v10		81.43 254	79.53 260	87.11 251	86.38 306	78.87 201	94.31 247	93.43 275	77.88 270	73.24 283	85.26 301	65.44 232	95.75 251	72.14 264	67.71 315	86.72 317

IterMVS-LS		83.93 215	82.80 217	87.31 247	91.46 247	77.39 245	95.66 207	93.43 275	80.44 225	75.51 266	87.26 269	73.72 173	95.16 282	76.99 221	70.72 285	89.39 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		82.42 241	80.43 249	88.39 220	92.66 209	81.95 120	94.30 248	93.38 277	79.06 257	75.82 262	85.66 293	56.38 297	93.84 311	71.23 270	75.38 261	89.38 261

test1		82.42 241	80.43 249	88.39 220	92.66 209	81.95 120	94.30 248	93.38 277	79.06 257	75.82 262	85.66 293	56.38 297	93.84 311	71.23 270	75.38 261	89.38 261

FMVSNet1		79.50 271	76.54 281	88.39 220	88.47 290	81.95 120	94.30 248	93.38 277	73.14 309	72.04 294	85.66 293	43.86 334	93.84 311	65.48 299	72.53 274	89.38 261

BH-untuned		86.95 168	85.94 170	89.99 186	94.52 158	77.46 243	96.78 143	93.37 280	81.80 204	76.62 246	93.81 187	66.64 226	97.02 195	76.06 233	93.88 131	95.48 192

Effi-MVS+-dtu		84.61 205	84.90 187	83.72 301	91.96 236	63.14 346	94.95 233	93.34 281	85.57 113	79.79 217	87.12 272	61.99 255	95.61 262	83.55 165	85.83 199	92.41 227

mvs-test1		86.83 172	87.17 154	85.81 270	91.96 236	65.24 339	97.90 53	93.34 281	85.57 113	84.51 163	95.14 155	61.99 255	97.19 188	83.55 165	90.55 159	95.00 199

CMPMVS	54.94 21	75.71 300	74.56 295	79.17 329	79.69 349	55.98 360	89.59 308	93.30 283	60.28 353	53.85 357	89.07 243	47.68 327	96.33 224	76.55 226	81.02 229	85.22 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
cl____		83.27 225	82.12 224	86.74 255	92.20 224	75.95 270	95.11 228	93.27 284	78.44 267	74.82 272	87.02 274	74.19 167	95.19 280	74.67 246	69.32 298	89.09 270

DIV-MVS_self_test		83.27 225	82.12 224	86.74 255	92.19 225	75.92 271	95.11 228	93.26 285	78.44 267	74.81 273	87.08 273	74.19 167	95.19 280	74.66 247	69.30 299	89.11 269

miper_lstm_enhance		81.66 252	80.66 245	84.67 286	91.19 249	71.97 305	91.94 292	93.19 286	77.86 271	72.27 292	85.26 301	73.46 176	93.42 318	73.71 256	67.05 322	88.61 283

eth_miper_zixun_eth		83.12 229	82.01 226	86.47 260	91.85 242	74.80 279	94.33 246	93.18 287	79.11 255	75.74 265	87.25 270	72.71 182	95.32 274	76.78 224	67.13 321	89.27 265

pmmvs4		82.54 239	80.79 241	87.79 233	86.11 313	80.49 162	93.55 264	93.18 287	77.29 278	73.35 281	89.40 241	65.26 236	95.05 290	75.32 240	73.61 268	87.83 299

XVG-ACMP-BASELINE		79.38 273	77.90 270	83.81 297	84.98 328	67.14 336	89.03 313	93.18 287	80.26 233	72.87 287	88.15 258	38.55 349	96.26 227	76.05 234	78.05 252	88.02 296

CANet_DTU		90.98 92	90.04 101	93.83 51	94.76 152	86.23 32	96.32 176	93.12 290	93.11 10	93.71 52	96.82 115	63.08 246	99.48 62	84.29 152	95.12 118	95.77 185

IS-MVSNet		88.67 138	88.16 131	90.20 181	93.61 182	76.86 253	96.77 145	93.07 291	84.02 158	83.62 174	95.60 140	74.69 163	96.24 229	78.43 209	93.66 134	97.49 130

c3_l		83.80 217	82.65 219	87.25 249	92.10 229	77.74 239	95.25 221	93.04 292	78.58 264	76.01 257	87.21 271	75.25 154	95.11 285	77.54 217	68.89 302	88.91 281

UnsupCasMVSNet_bld		68.60 324	64.50 327	80.92 321	74.63 364	67.80 331	83.97 341	92.94 293	65.12 340	54.63 356	68.23 359	35.97 353	92.17 331	60.13 320	44.83 361	82.78 347

MVP-Stereo		82.65 238	81.67 232	85.59 275	86.10 314	78.29 217	93.33 269	92.82 294	77.75 272	69.17 311	87.98 260	59.28 272	95.76 250	71.77 265	96.88 93	82.73 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Effi-MVS+		90.70 98	89.90 106	93.09 84	93.61 182	83.48 90	95.20 223	92.79 295	83.22 177	91.82 75	95.70 135	71.82 191	97.48 172	91.25 85	93.67 133	98.32 58

EU-MVSNet		76.92 293	76.95 277	76.83 334	84.10 335	54.73 364	91.77 295	92.71 296	72.74 313	69.57 308	88.69 249	58.03 282	87.43 356	64.91 302	70.00 293	88.33 291

xxxxxxxxxxxxxcwj		94.38 21	94.62 18	93.68 58	98.24 57	83.34 93	98.61 23	92.69 297	91.32 18	95.07 32	98.74 16	82.93 51	99.38 68	95.42 34	98.51 38	98.32 58

pm-mvs1		80.05 266	78.02 269	86.15 266	85.42 321	75.81 272	95.11 228	92.69 297	77.13 280	70.36 303	87.43 266	58.44 278	95.27 277	71.36 269	64.25 333	87.36 311

1112_ss		88.60 141	87.47 148	92.00 128	93.21 194	80.97 148	96.47 161	92.46 299	83.64 171	80.86 204	97.30 94	80.24 71	97.62 159	77.60 215	85.49 202	97.40 135

Test_1112_low_res		88.03 155	86.73 163	91.94 130	93.15 197	80.88 150	96.44 166	92.41 300	83.59 174	80.74 206	91.16 215	80.18 72	97.59 160	77.48 218	85.40 203	97.36 137

BH-RMVSNet		86.84 171	85.28 177	91.49 144	95.35 133	80.26 167	96.95 133	92.21 301	82.86 188	81.77 198	95.46 143	59.34 271	97.64 158	69.79 279	93.81 132	96.57 166

GeoE		86.36 179	85.20 178	89.83 194	93.17 196	76.13 263	97.53 80	92.11 302	79.58 245	80.99 202	94.01 181	66.60 227	96.17 231	73.48 257	89.30 165	97.20 146

LS3D		82.22 245	79.94 257	89.06 205	97.43 90	74.06 288	93.20 276	92.05 303	61.90 346	73.33 282	95.21 149	59.35 270	99.21 84	54.54 341	92.48 145	93.90 218

EG-PatchMatch MVS		74.92 302	72.02 307	83.62 302	83.76 339	73.28 293	93.62 262	92.04 304	68.57 331	58.88 348	83.80 318	31.87 361	95.57 265	56.97 334	78.67 245	82.00 354

IterMVS		80.67 262	79.16 262	85.20 279	89.79 272	76.08 264	92.97 280	91.86 305	80.28 231	71.20 297	85.14 306	57.93 283	91.34 338	72.52 262	70.74 284	88.18 294
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		79.18 275	75.99 284	88.72 215	87.37 301	80.66 156	79.96 346	91.82 306	77.38 277	74.33 275	81.87 328	41.78 343	90.74 344	66.36 297	83.10 217	94.76 203

IterMVS-SCA-FT		80.51 264	79.10 263	84.73 284	89.63 277	74.66 280	92.98 279	91.81 307	80.05 236	71.06 299	85.18 304	58.04 280	91.40 337	72.48 263	70.70 286	88.12 295

our_test_3		77.90 284	75.37 288	85.48 277	85.39 322	76.74 255	93.63 261	91.67 308	73.39 308	65.72 325	84.65 312	58.20 279	93.13 322	57.82 328	67.87 312	86.57 319

pmmvs5		81.34 255	79.54 259	86.73 258	85.02 327	76.91 252	96.22 181	91.65 309	77.65 273	73.55 278	88.61 250	55.70 300	94.43 302	74.12 252	73.35 272	88.86 282

ACMH	75.40 17	77.99 282	74.96 289	87.10 252	90.67 260	76.41 259	93.19 277	91.64 310	72.47 316	63.44 333	87.61 265	43.34 337	97.16 189	58.34 326	73.94 266	87.72 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+-dtu		83.33 224	82.60 220	85.50 276	89.55 278	69.38 327	96.09 189	91.38 311	82.30 196	75.96 260	91.41 210	56.71 292	95.58 264	75.13 242	84.90 207	91.54 229

YYNet1		73.53 308	70.43 314	82.85 310	84.52 331	71.73 309	91.69 297	91.37 312	67.63 332	46.79 360	81.21 332	55.04 305	90.43 346	55.93 337	59.70 343	86.38 321

ppachtmachnet_test		77.19 290	74.22 298	86.13 267	85.39 322	78.22 220	93.98 256	91.36 313	71.74 320	67.11 316	84.87 310	56.67 293	93.37 320	52.21 346	64.59 330	86.80 316

Anonymous202405211		84.41 209	81.93 228	91.85 134	96.78 105	78.41 214	97.44 87	91.34 314	70.29 325	84.06 165	94.26 174	41.09 346	98.96 107	79.46 198	82.65 225	98.17 71

MDA-MVSNet_test_wron		73.54 307	70.43 314	82.86 309	84.55 329	71.85 306	91.74 296	91.32 315	67.63 332	46.73 361	81.09 333	55.11 304	90.42 347	55.91 338	59.76 342	86.31 322

CR-MVSNet		83.53 221	81.36 237	90.06 184	90.16 268	79.75 177	79.02 351	91.12 316	84.24 154	82.27 191	80.35 336	75.45 145	93.67 315	63.37 310	86.25 192	96.75 162

Patchmtry		77.36 289	74.59 294	85.67 274	89.75 273	75.75 273	77.85 354	91.12 316	60.28 353	71.23 296	80.35 336	75.45 145	93.56 317	57.94 327	67.34 320	87.68 302

LTVRE_ROB	73.68 18	77.99 282	75.74 286	84.74 283	90.45 263	72.02 303	86.41 334	91.12 316	72.57 315	66.63 320	87.27 268	54.95 306	96.98 197	56.29 336	75.98 257	85.21 334
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
OurMVSNet-221017-0		77.18 291	76.06 283	80.55 323	83.78 338	60.00 355	90.35 305	91.05 319	77.01 284	66.62 321	87.92 261	47.73 326	94.03 308	71.63 266	68.44 306	87.62 304

CNLPA		86.96 167	85.37 176	91.72 137	97.59 83	79.34 190	97.21 102	91.05 319	74.22 300	78.90 222	96.75 118	67.21 222	98.95 110	74.68 245	90.77 158	96.88 156

Anonymous20240521		72.06 316	69.91 316	78.50 330	77.11 358	61.67 351	91.62 299	90.97 321	65.52 339	62.37 338	79.05 342	36.32 352	90.96 342	57.75 329	68.52 305	82.87 345

KD-MVS_self_test		70.97 319	69.31 319	75.95 339	76.24 363	55.39 363	87.45 325	90.94 322	70.20 326	62.96 337	77.48 346	44.01 333	88.09 353	61.25 318	53.26 351	84.37 339

pmmvs6		74.65 304	71.67 308	83.60 303	79.13 351	69.94 320	93.31 273	90.88 323	61.05 352	65.83 324	84.15 316	43.43 336	94.83 295	66.62 292	60.63 341	86.02 327

test1111		88.11 153	87.04 159	91.35 147	93.15 197	78.79 205	96.57 155	90.78 324	86.88 93	85.04 154	95.20 150	57.23 290	97.39 176	83.88 156	94.59 122	97.87 100

ECVR-MVS		88.35 148	87.25 152	91.65 138	93.54 185	79.40 187	96.56 157	90.78 324	86.78 95	85.57 150	95.25 146	57.25 289	97.56 162	84.73 150	94.80 120	97.98 91

Anonymous20231206		75.29 301	73.64 302	80.22 324	80.75 344	63.38 345	93.36 268	90.71 326	73.09 310	67.12 315	83.70 319	50.33 317	90.85 343	53.63 344	70.10 291	86.44 320

USDC		78.65 277	76.25 282	85.85 269	87.58 299	74.60 281	89.58 309	90.58 327	84.05 157	63.13 335	88.23 256	40.69 348	96.86 207	66.57 294	75.81 259	86.09 326

MSDG		80.62 263	77.77 271	89.14 204	93.43 192	77.24 247	91.89 293	90.18 328	69.86 328	68.02 312	91.94 206	52.21 312	98.84 116	59.32 324	83.12 216	91.35 230

ACMH+	76.62 16	77.47 288	74.94 290	85.05 280	91.07 252	71.58 311	93.26 274	90.01 329	71.80 319	64.76 328	88.55 251	41.62 344	96.48 219	62.35 313	71.00 281	87.09 314

FMVSNet5		76.46 295	74.16 299	83.35 307	90.05 270	76.17 262	89.58 309	89.85 330	71.39 322	65.29 327	80.42 335	50.61 315	87.70 355	61.05 319	69.24 300	86.18 324

ambc				76.02 337	68.11 367	51.43 365	64.97 364	89.59 331		60.49 345	74.49 350	17.17 369	92.46 325	61.50 316	52.85 353	84.17 341

ITE_SJBPF				82.38 313	87.00 303	65.59 338		89.55 332	79.99 238	69.37 309	91.30 213	41.60 345	95.33 273	62.86 312	74.63 265	86.24 323

pmmvs-eth3d		73.59 306	70.66 312	82.38 313	76.40 361	73.38 290	89.39 312	89.43 333	72.69 314	60.34 346	77.79 345	46.43 330	91.26 340	66.42 296	57.06 345	82.51 349

test20.03		72.36 314	71.15 310	75.98 338	77.79 354	59.16 357	92.40 288	89.35 334	74.09 301	61.50 342	84.32 314	48.09 322	85.54 361	50.63 351	62.15 339	83.24 344

SixPastTwentyTwo		76.04 296	74.32 297	81.22 319	84.54 330	61.43 352	91.16 301	89.30 335	77.89 269	64.04 330	86.31 288	48.23 321	94.29 305	63.54 309	63.84 335	87.93 298

TransMVSNet (Re)		76.94 292	74.38 296	84.62 288	85.92 316	75.25 277	95.28 218	89.18 336	73.88 303	67.22 314	86.46 283	59.64 266	94.10 307	59.24 325	52.57 354	84.50 338

MIMVSNet1		69.44 320	66.65 324	77.84 331	76.48 360	62.84 347	87.42 326	88.97 337	66.96 337	57.75 353	79.72 341	32.77 360	85.83 360	46.32 358	63.42 336	84.85 336

K. test v3		73.62 305	71.59 309	79.69 326	82.98 340	59.85 356	90.85 304	88.83 338	77.13 280	58.90 347	82.11 326	43.62 335	91.72 335	65.83 298	54.10 349	87.50 309

Baseline_NR-MVSNet		81.22 257	80.07 254	84.68 285	85.32 325	75.12 278	96.48 160	88.80 339	76.24 287	77.28 237	86.40 287	67.61 216	94.39 303	75.73 238	66.73 325	84.54 337

MDA-MVSNet-bldmvs		71.45 317	67.94 321	81.98 317	85.33 324	68.50 330	92.35 289	88.76 340	70.40 324	42.99 362	81.96 327	46.57 329	91.31 339	48.75 356	54.39 348	86.11 325

new-patchmatchnet		68.85 323	65.93 325	77.61 332	73.57 366	63.94 344	90.11 307	88.73 341	71.62 321	55.08 355	73.60 352	40.84 347	87.22 357	51.35 349	48.49 358	81.67 355

Patchmatch-test		78.25 280	74.72 293	88.83 212	91.20 248	74.10 287	73.91 361	88.70 342	59.89 356	66.82 319	85.12 307	78.38 94	94.54 300	48.84 355	79.58 238	97.86 101

OpenMVS_ROB	68.52 20	73.02 311	69.57 317	83.37 306	80.54 347	71.82 307	93.60 263	88.22 343	62.37 344	61.98 340	83.15 323	35.31 356	95.47 267	45.08 360	75.88 258	82.82 346

RPSCF		77.73 285	76.63 280	81.06 320	88.66 289	55.76 362	87.77 324	87.88 344	64.82 341	74.14 276	92.79 196	49.22 320	96.81 209	67.47 289	76.88 256	90.62 236

MVS-HIRNet		71.36 318	67.00 322	84.46 292	90.58 261	69.74 324	79.15 350	87.74 345	46.09 362	61.96 341	50.50 364	45.14 332	95.64 259	53.74 343	88.11 180	88.00 297

DP-MVS		81.47 253	78.28 267	91.04 156	98.14 63	78.48 210	95.09 231	86.97 346	61.14 351	71.12 298	92.78 197	59.59 267	99.38 68	53.11 345	86.61 189	95.27 197

COLMAP_ROB	73.24 19	75.74 299	73.00 305	83.94 296	92.38 215	69.08 328	91.85 294	86.93 347	61.48 349	65.32 326	90.27 230	42.27 342	96.93 202	50.91 350	75.63 260	85.80 331
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_0402		72.68 312	69.54 318	82.09 316	88.67 288	71.81 308	92.72 284	86.77 348	61.52 348	62.21 339	83.91 317	43.22 338	93.76 314	34.60 364	72.23 278	80.72 356

testgi		74.88 303	73.40 303	79.32 328	80.13 348	61.75 349	93.21 275	86.64 349	79.49 247	66.56 322	91.06 216	35.51 355	88.67 352	56.79 335	71.25 279	87.56 307

TDRefinement		69.20 322	65.78 326	79.48 327	66.04 369	62.21 348	88.21 319	86.12 350	62.92 343	61.03 344	85.61 296	33.23 358	94.16 306	55.82 339	53.02 352	82.08 353

ADS-MVSNet2		79.57 270	77.53 272	85.71 273	93.78 178	72.13 301	79.48 347	86.11 351	73.09 310	80.14 213	79.99 339	62.15 252	90.14 349	59.49 322	83.52 212	94.85 201

LF4IMVS		72.36 314	70.82 311	76.95 333	79.18 350	56.33 359	86.12 335	86.11 351	69.30 330	63.06 336	86.66 279	33.03 359	92.25 328	65.33 300	68.64 304	82.28 352

TinyColmap		72.41 313	68.99 320	82.68 311	88.11 294	69.59 325	88.41 318	85.20 353	65.55 338	57.91 351	84.82 311	30.80 363	95.94 240	51.38 347	68.70 303	82.49 351

pmmvs3		65.75 326	62.18 329	76.45 336	67.12 368	64.54 340	88.68 316	85.05 354	54.77 361	57.54 354	73.79 351	29.40 364	86.21 359	55.49 340	47.77 359	78.62 357

new_pmnet		66.18 325	63.18 328	75.18 341	76.27 362	61.74 350	83.79 342	84.66 355	56.64 360	51.57 358	71.85 358	31.29 362	87.93 354	49.98 352	62.55 338	75.86 359

AllTest		75.92 297	73.06 304	84.47 290	92.18 226	67.29 333	91.07 302	84.43 356	67.63 332	63.48 331	90.18 231	38.20 350	97.16 189	57.04 332	73.37 270	88.97 278

TestCases				84.47 290	92.18 226	67.29 333		84.43 356	67.63 332	63.48 331	90.18 231	38.20 350	97.16 189	57.04 332	73.37 270	88.97 278

LCM-MVSNet-Re		83.75 218	83.54 206	84.39 294	93.54 185	64.14 342	92.51 285	84.03 358	83.90 164	66.14 323	86.59 280	67.36 220	92.68 323	84.89 149	92.87 140	96.35 171

Gipuma		45.11 334	42.05 336	54.30 350	80.69 345	51.30 366	35.80 368	83.81 359	28.13 366	27.94 368	34.53 368	11.41 374	76.70 366	21.45 368	54.65 347	34.90 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		52.52 330	48.24 333	65.35 343	47.63 375	41.45 371	72.55 362	83.62 360	31.75 365	37.66 364	57.92 362	9.19 376	76.76 365	49.26 354	44.60 362	77.84 358

FPMVS		55.09 329	52.93 332	61.57 348	55.98 370	40.51 373	83.11 343	83.41 361	37.61 364	34.95 365	71.95 356	14.40 370	76.95 364	29.81 365	65.16 329	67.25 363

Patchmatch-RL test		76.65 294	74.01 301	84.55 289	77.37 357	64.23 341	78.49 353	82.84 362	78.48 265	64.63 329	73.40 353	76.05 133	91.70 336	76.99 221	57.84 344	97.72 112

DSMNet-mixed		73.13 310	72.45 306	75.19 340	77.51 356	46.82 367	85.09 340	82.01 363	67.61 336	69.27 310	81.33 331	50.89 313	86.28 358	54.54 341	83.80 211	92.46 226

lessismore_v0				79.98 325	80.59 346	58.34 358		80.87 364		58.49 349	83.46 321	43.10 339	93.89 310	63.11 311	48.68 356	87.72 300

door								80.13 365

door-mid								79.75 366

PM-MVS		69.32 321	66.93 323	76.49 335	73.60 365	55.84 361	85.91 336	79.32 367	74.72 297	61.09 343	78.18 344	21.76 366	91.10 341	70.86 275	56.90 346	82.51 349

ANet_high		46.22 333	41.28 338	61.04 349	39.91 377	46.25 369	70.59 363	76.18 368	58.87 358	23.09 369	48.00 366	12.58 372	66.54 369	28.65 367	13.62 370	70.35 361

test_method		56.77 328	54.53 331	63.49 347	76.49 359	40.70 372	75.68 357	74.24 369	19.47 370	48.73 359	71.89 357	19.31 367	65.80 370	57.46 331	47.51 360	83.97 342

EGC-MVSNET		52.46 331	47.56 334	67.15 342	81.98 342	60.11 354	82.54 344	72.44 370	0.11 376	0.70 377	74.59 349	25.11 365	83.26 362	29.04 366	61.51 340	58.09 364

PMMVS2		50.90 332	46.31 335	64.67 344	55.53 371	46.67 368	77.30 356	71.02 371	40.89 363	34.16 366	59.32 360	9.83 375	76.14 367	40.09 363	28.63 367	71.21 360

MTMP							97.53 80	68.16 372

DeepMVS_CX				64.06 346	78.53 352	43.26 370		68.11 373	69.94 327	38.55 363	76.14 348	18.53 368	79.34 363	43.72 361	41.62 364	69.57 362

PMVS	34.80 23	39.19 336	35.53 339	50.18 351	29.72 378	30.30 375	59.60 366	66.20 374	26.06 367	17.91 371	49.53 365	3.12 377	74.09 368	18.19 370	49.40 355	46.14 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 335	41.93 337	40.38 353	20.10 379	26.84 376	61.93 365	59.09 375	14.81 372	28.51 367	80.58 334	35.53 354	48.33 374	63.70 308	13.11 371	45.96 367

MVE	35.65 22	33.85 337	29.49 342	46.92 352	41.86 376	36.28 374	50.45 367	56.52 376	18.75 371	18.28 370	37.84 367	2.41 378	58.41 371	18.71 369	20.62 368	46.06 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 338	32.39 340	33.65 354	53.35 373	25.70 377	74.07 360	53.33 377	21.08 368	17.17 372	33.63 370	11.85 373	54.84 372	12.98 371	14.04 369	20.42 369

EMVS		31.70 339	31.45 341	32.48 355	50.72 374	23.95 378	74.78 359	52.30 378	20.36 369	16.08 373	31.48 371	12.80 371	53.60 373	11.39 372	13.10 372	19.88 370

N_pmnet		61.30 327	60.20 330	64.60 345	84.32 332	17.00 380	91.67 298	10.98 379	61.77 347	58.45 350	78.55 343	49.89 318	91.83 334	42.27 362	63.94 334	84.97 335

wuyk23d		14.10 341	13.89 344	14.72 356	55.23 372	22.91 379	33.83 369	3.56 380	4.94 373	4.11 374	2.28 376	2.06 379	19.66 375	10.23 373	8.74 373	1.59 373

testmvs		9.92 342	12.94 345	0.84 358	0.65 380	0.29 382	93.78 259	0.39 381	0.42 374	2.85 375	15.84 374	0.17 381	0.30 377	2.18 374	0.21 374	1.91 372

test123		9.07 343	11.73 346	1.11 357	0.50 381	0.77 381	89.44 311	0.20 382	0.34 375	2.15 376	10.72 375	0.34 380	0.32 376	1.79 375	0.08 375	2.23 371

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		5.92 345	7.89 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	71.04 200	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

n2								0.00 383

nn								0.00 383

ab-mvs-re		8.11 344	10.81 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	97.30 94	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

PC_three_1452									91.12 21	98.33 2	98.42 28	92.51 2	99.81 20	98.96 2	99.37 1	99.70 3

eth-test2					0.00 382

eth-test					0.00 382

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 22	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

test_0728_THIRD									88.38 59	96.69 12	98.76 14	89.64 12	99.76 24	97.47 13	98.84 24	99.38 14

GSMVS																97.54 124

test_part2					98.90 21	85.14 60				96.07 20

sam_mvs1												77.59 105				97.54 124

sam_mvs												75.35 152

test_post1							85.88 337				30.24 372	73.77 171	95.07 289	73.89 253

test_post											33.80 369	76.17 131	95.97 236

patchmatchnet-post											77.09 347	77.78 104	95.39 269

gm-plane-assit					92.27 220	79.64 183			84.47 146		95.15 154		97.93 146	85.81 140

test9_res														96.00 25	99.03 13	98.31 61

agg_prior2														94.30 46	99.00 15	98.57 45

test_prior4						82.34 114	97.75 65

test_prior2							98.37 28		86.08 103	94.57 41	98.02 52	83.14 47		95.05 38	98.79 27

旧先验2							96.97 131		74.06 302	96.10 19			97.76 155	88.38 123

新几何2							96.42 169

原ACMM2							96.84 138

testdata2													99.48 62	76.45 228

segment_acmp												82.69 56

testdata1							95.57 210		87.44 79

plane_prior7					91.86 240	77.55 242

plane_prior6					91.98 235	77.92 233						64.77 238

plane_prior4											94.15 178

plane_prior3						77.75 238			90.17 34	81.33 199

plane_prior2							97.18 106		89.89 36

plane_prior1					91.95 238

plane_prior						77.96 230	97.52 83		90.36 33						82.96 220

HQP5-MVS						78.48 210

HQP-NCC					92.08 230		97.63 71		90.52 28	82.30 187

ACMP_Plane					92.08 230		97.63 71		90.52 28	82.30 187

BP-MVS														87.67 128

HQP4-MVS										82.30 187			97.32 179			91.13 231

HQP2-MVS												65.40 233

NP-MVS					92.04 234	78.22 220					94.56 168

MDTV_nov1_ep13_2view						81.74 132	86.80 330		80.65 219	85.65 149		74.26 166		76.52 227		96.98 150

ACMMP++_ref															78.45 250

ACMMP++															79.05 242

Test By Simon												71.65 193

High-resolution multi-view benchmark