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.

MSP-MVS		90.38 4	91.87 1	85.88 86	92.83 81	64.03 192	93.06 104	94.33 51	82.19 19	93.65 3	96.15 30	85.89 1	97.19 83	91.02 19	97.75 1	96.43 26
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
DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 38	93.96 67	94.37 49	72.48 163	92.07 6	96.85 12	83.82 2	99.15 2	91.53 15	97.42 4	97.55 4

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

DPM-MVS		90.70 2	90.52 6	91.24 1	89.68 157	76.68 2	97.29 1	95.35 10	82.87 14	91.58 10	97.22 4	79.93 5	99.10 9	83.12 79	97.64 2	97.94 1

baseline2		83.68 90	83.42 80	84.48 135	87.37 213	66.00 135	90.06 222	95.93 7	79.71 46	69.08 219	90.39 162	77.92 6	96.28 120	78.91 115	81.38 164	91.16 190

GG-mvs-BLEND				86.53 66	91.91 108	69.67 46	75.02 338	94.75 30		78.67 119	90.85 153	77.91 7	94.56 189	72.25 163	93.74 48	95.36 54

gg-mvs-nofinetune		77.18 200	74.31 217	85.80 90	91.42 123	68.36 70	71.78 340	94.72 31	49.61 344	77.12 134	45.92 360	77.41 8	93.98 216	67.62 204	93.16 57	95.05 73

SED-MVS		89.94 8	90.36 8	88.70 16	96.45 14	69.38 48	96.89 4	94.44 42	71.65 194	92.11 4	97.21 5	76.79 9	99.11 6	92.34 7	95.36 14	97.62 2

test_241102_ONE					96.45 14	69.38 48		94.44 42	71.65 194	92.11 4	97.05 8	76.79 9	99.11 6

test_0728_THIRD									72.48 163	90.55 15	96.93 10	76.24 11	99.08 11	91.53 15	94.99 17	96.43 26

DPE-MVS		88.77 14	89.21 14	87.45 36	96.26 22	67.56 91	94.17 54	94.15 57	68.77 243	90.74 13	97.27 3	76.09 12	98.49 27	90.58 21	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + GP.		87.96 18	88.37 17	86.70 59	93.51 65	65.32 152	95.15 35	93.84 63	78.17 69	85.93 43	94.80 70	75.80 13	98.21 35	89.38 25	88.78 106	96.59 15

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 78	58.16 291	96.72 7	94.41 45	86.50 5	90.25 17	97.83 1	75.46 14	98.67 23	92.78 4	95.49 13	97.32 6

MVSTER		82.47 106	82.05 103	83.74 151	92.68 88	69.01 56	91.90 154	93.21 92	79.83 42	72.14 184	85.71 219	74.72 15	94.72 182	75.72 135	72.49 225	87.50 231

test_241102_TWO								94.41 45	71.65 194	92.07 6	97.21 5	74.58 16	99.11 6	92.34 7	95.36 14	96.59 15

ETH3 D test6400		90.27 6	90.44 7	89.75 6	96.82 9	74.33 7	95.89 17	94.80 28	77.13 86	89.13 21	97.38 2	74.49 17	98.48 28	92.32 10	95.98 8	96.46 25

test_one_0601					96.32 20	69.74 43		94.18 55	71.42 206	90.67 14	96.85 12	74.45 18

DELS-MVS		90.05 7	90.09 9	89.94 4	93.14 75	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 67	74.11 19	98.91 17	87.26 50	95.94 9	97.03 10
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
DVP-MVS		89.41 11	89.73 12	88.45 21	96.40 17	69.99 34	96.64 8	94.52 38	71.92 180	90.55 15	96.93 10	73.77 20	99.08 11	91.91 13	94.90 21	96.29 30
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
test0726					96.40 17	69.99 34	96.76 6	94.33 51	71.92 180	91.89 8	97.11 7	73.77 20

ET-MVSNet_ETH3D		84.01 81	83.15 87	86.58 63	90.78 138	70.89 23	94.74 46	94.62 36	81.44 29	58.19 306	93.64 101	73.64 22	92.35 267	82.66 84	78.66 184	96.50 23

CSCG		86.87 37	86.26 42	88.72 15	95.05 34	70.79 24	93.83 81	95.33 11	68.48 247	77.63 127	94.35 84	73.04 23	98.45 29	84.92 67	93.71 50	96.92 11

tttt0517		79.50 158	78.53 157	82.41 184	87.22 215	61.43 245	89.75 231	94.76 29	69.29 235	67.91 237	88.06 193	72.92 24	95.63 149	62.91 246	73.90 216	90.16 200

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 44	96.26 27	72.84 25	99.38 1	92.64 5	95.93 10	97.08 9

thisisatest0515		83.41 91	82.49 99	86.16 79	89.46 163	68.26 74	93.54 90	94.70 32	74.31 122	75.75 144	90.92 151	72.62 26	96.52 117	69.64 184	81.50 163	93.71 128

thisisatest0530		81.15 126	80.07 129	84.39 137	88.26 193	65.63 146	91.40 175	94.62 36	71.27 208	70.93 197	89.18 176	72.47 27	96.04 131	65.62 226	76.89 202	91.49 180

TSAR-MVS + MP.		88.11 17	88.64 15	86.54 65	91.73 114	68.04 79	90.36 214	93.55 78	82.89 13	91.29 11	92.89 118	72.27 28	96.03 132	87.99 39	94.77 25	95.54 50
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPP-MVSNet		81.79 119	81.52 110	82.61 178	88.77 180	60.21 268	93.02 108	93.66 74	68.52 246	72.90 171	90.39 162	72.19 29	94.96 174	74.93 143	79.29 178	92.67 157

CostFormer		82.33 108	81.15 113	85.86 88	89.01 174	68.46 68	82.39 304	93.01 103	75.59 102	80.25 96	81.57 264	72.03 30	94.96 174	79.06 113	77.48 196	94.16 108

HPM-MVS++		89.37 12	89.95 11	87.64 29	95.10 33	68.23 76	95.24 32	94.49 40	82.43 17	88.90 23	96.35 24	71.89 31	98.63 24	88.76 35	96.40 6	96.06 35

CNVR-MVS		90.32 5	90.89 5	88.61 19	96.76 10	70.65 25	96.47 12	94.83 25	84.83 8	89.07 22	96.80 15	70.86 32	99.06 15	92.64 5	95.71 11	96.12 34

DWT-MVSNet_test		83.95 83	82.80 93	87.41 37	92.90 80	70.07 33	89.12 245	94.42 44	82.15 20	77.64 126	91.77 140	70.81 33	96.22 122	65.03 232	81.36 165	95.94 39

IB-MVS	77.80 4	82.18 110	80.46 127	87.35 39	89.14 171	70.28 30	95.59 26	95.17 14	78.85 59	70.19 206	85.82 217	70.66 34	97.67 55	72.19 166	66.52 264	94.09 112
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
baseline1		81.84 118	81.03 118	84.28 142	91.60 117	66.62 121	91.08 193	91.66 157	81.87 24	74.86 154	91.67 144	69.98 35	94.92 177	71.76 170	64.75 277	91.29 189

agg_prior1		87.02 35	87.26 32	86.28 77	94.16 48	66.97 111	94.08 61	93.31 89	71.85 187	84.49 57	95.39 43	68.91 36	96.75 110	88.84 34	94.32 38	95.13 69

alignmvs		87.28 28	86.97 36	88.24 23	91.30 126	71.14 22	95.61 25	93.56 77	79.30 50	87.07 32	95.25 52	68.43 37	96.93 104	87.87 40	84.33 146	96.65 13

PAPM		85.89 51	85.46 55	87.18 42	88.20 196	72.42 14	92.41 133	92.77 111	82.11 21	80.34 95	93.07 112	68.27 38	95.02 172	78.39 120	93.59 52	94.09 112

train_agg		87.21 30	87.42 30	86.60 61	94.18 44	67.28 100	94.16 55	93.51 79	71.87 185	85.52 48	95.33 45	68.19 39	97.27 81	89.09 29	94.90 21	95.25 66

test_8					94.19 43	67.19 103	94.15 57	93.42 86	71.87 185	85.38 50	95.35 44	68.19 39	96.95 101

ETH3D cwj APD-0.16		87.06 33	87.18 34	86.71 57	91.99 104	67.48 96	92.97 109	94.21 54	71.48 205	85.72 45	96.32 26	68.13 41	98.00 42	89.06 31	94.70 31	94.65 89

TEST9					94.18 44	67.28 100	94.16 55	93.51 79	71.75 192	85.52 48	95.33 45	68.01 42	97.27 81

test_prior3		87.38 27	87.70 24	86.42 70	94.71 37	67.35 98	95.10 37	93.10 101	75.40 107	85.25 53	95.61 40	67.94 43	96.84 106	87.47 45	94.77 25	95.05 73

test_prior2							95.10 37		75.40 107	85.25 53	95.61 40	67.94 43		87.47 45	94.77 25

WTY-MVS		86.32 44	85.81 49	87.85 25	92.82 83	69.37 50	95.20 33	95.25 12	82.71 15	81.91 78	94.73 71	67.93 45	97.63 60	79.55 108	82.25 157	96.54 18

Regformer-1		87.24 29	87.60 27	86.15 80	95.14 31	65.83 141	93.95 70	95.12 15	82.11 21	84.25 59	95.73 36	67.88 46	98.35 33	85.60 60	88.64 109	94.26 102

ETH3D-3000-0.1		87.61 23	87.89 20	86.75 55	93.58 62	67.21 102	94.31 52	94.14 58	72.92 154	87.13 29	96.62 17	67.81 47	97.94 43	90.13 22	94.42 36	95.09 71

APDe-MVS		87.54 24	87.84 21	86.65 60	96.07 25	66.30 129	94.84 45	93.78 64	69.35 234	88.39 24	96.34 25	67.74 48	97.66 58	90.62 20	93.44 54	96.01 38

tpm2		79.80 153	77.95 166	85.34 108	88.28 192	68.26 74	81.56 309	91.42 167	70.11 225	77.59 129	80.50 282	67.40 49	94.26 202	67.34 206	77.35 197	93.51 133

Regformer-2		87.00 36	87.43 29	85.71 95	95.14 31	64.73 171	93.95 70	94.95 22	81.69 26	84.03 64	95.73 36	67.35 50	98.19 37	85.40 62	88.64 109	94.20 104

miper_enhance_ethall		78.86 169	77.97 165	81.54 208	88.00 201	65.17 157	91.41 173	89.15 252	75.19 112	68.79 226	83.98 236	67.17 51	92.82 245	72.73 158	65.30 268	86.62 251

xxxxxxxxxxxxxcwj		87.14 31	87.19 33	86.99 48	93.84 54	67.89 83	95.05 39	84.72 316	78.19 67	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

SF-MVS		87.03 34	87.09 35	86.84 50	92.70 87	67.45 97	93.64 85	93.76 67	70.78 218	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

HY-MVS	76.49 5	84.28 74	83.36 83	87.02 47	92.22 97	67.74 87	84.65 286	94.50 39	79.15 54	82.23 76	87.93 194	66.88 54	96.94 102	80.53 103	82.20 158	96.39 28

RRT_test8_iter05		80.61 136	79.62 139	83.60 158	91.87 112	66.90 113	93.42 97	93.68 72	77.09 88	68.83 225	85.63 220	66.82 55	95.42 160	76.46 133	62.74 291	88.48 219

EPNet		87.84 21	88.38 16	86.23 78	93.30 68	66.05 133	95.26 31	94.84 24	87.09 3	88.06 25	94.53 75	66.79 56	97.34 74	83.89 75	91.68 77	95.29 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
9.14			87.63 25		93.86 53		94.41 50	94.18 55	72.76 157	86.21 37	96.51 19	66.64 57	97.88 49	90.08 23	94.04 41

FIs		79.47 159	79.41 145	79.67 252	85.95 235	59.40 278	91.68 166	93.94 61	78.06 70	68.96 222	88.28 186	66.61 58	91.77 279	66.20 220	74.99 209	87.82 228

NCCC		89.07 13	89.46 13	87.91 24	96.60 12	69.05 55	96.38 13	94.64 35	84.42 9	86.74 33	96.20 28	66.56 59	98.76 22	89.03 33	94.56 33	95.92 41

SD-MVS		87.49 26	87.49 28	87.50 35	93.60 61	68.82 61	93.90 74	92.63 119	76.86 90	87.90 26	95.76 35	66.17 60	97.63 60	89.06 31	91.48 81	96.05 36
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
UniMVSNet_NR-MVSNet		78.15 185	77.55 173	79.98 243	84.46 259	60.26 266	92.25 136	93.20 94	77.50 83	68.88 223	86.61 208	66.10 61	92.13 271	66.38 217	62.55 292	87.54 230

CHOSEN 280x420		77.35 198	76.95 185	78.55 267	87.07 218	62.68 225	69.71 345	82.95 332	68.80 242	71.48 193	87.27 205	66.03 62	84.00 340	76.47 132	82.81 155	88.95 211

CANet		89.61 10	89.99 10	88.46 20	94.39 42	69.71 44	96.53 11	93.78 64	86.89 4	89.68 18	95.78 34	65.94 63	99.10 9	92.99 3	93.91 44	96.58 17

segment_acmp												65.94 63

Regformer-3		85.80 52	85.92 47	85.46 102	94.17 46	65.09 164	92.95 111	95.11 16	81.13 33	81.68 80	95.04 58	65.82 65	98.32 34	83.02 80	84.36 143	92.97 151

Vis-MVSNet (Re-imp)		79.24 162	79.57 140	78.24 272	88.46 185	52.29 328	90.41 212	89.12 254	74.24 124	69.13 217	91.91 138	65.77 66	90.09 303	59.00 269	88.09 114	92.33 165

FC-MVSNet-test		77.99 187	78.08 163	77.70 275	84.89 252	55.51 314	90.27 216	93.75 70	76.87 89	66.80 253	87.59 198	65.71 67	90.23 300	62.89 247	73.94 214	87.37 235

SMA-MVS		88.14 15	88.29 18	87.67 28	93.21 72	68.72 63	93.85 77	94.03 60	74.18 125	91.74 9	96.67 16	65.61 68	98.42 32	89.24 28	96.08 7	95.88 43
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
CS-MVS		87.54 24	87.81 22	86.74 56	90.46 142	70.23 31	96.34 14	92.31 127	81.40 30	86.14 40	95.17 57	65.49 69	95.92 135	89.09 29	93.91 44	94.06 115

test12				87.09 45	94.60 39	68.86 59		92.91 107		82.67 75		65.44 70	97.55 63		93.69 51	94.84 82

旧先验1					91.94 105	60.74 259		91.50 164			94.36 80	65.23 71			91.84 74	94.55 91

Regformer-4		85.45 55	85.69 52	84.73 125	94.17 46	63.23 209	92.95 111	94.83 25	80.66 38	81.29 83	95.04 58	65.12 72	98.08 40	82.74 82	84.36 143	92.88 155

1112_ss		80.56 138	79.83 136	82.77 173	88.65 181	60.78 255	92.29 135	88.36 276	72.58 160	72.46 180	94.95 62	65.09 73	93.42 231	66.38 217	77.71 189	94.10 111

MVSFormer		83.75 88	82.88 91	86.37 73	89.24 169	71.18 20	89.07 246	90.69 194	65.80 265	87.13 29	94.34 85	64.99 74	92.67 253	72.83 155	91.80 75	95.27 63

lupinMVS		87.74 22	87.77 23	87.63 33	89.24 169	71.18 20	96.57 10	92.90 108	82.70 16	87.13 29	95.27 50	64.99 74	95.80 139	89.34 26	91.80 75	95.93 40

tpmrst		80.57 137	79.14 152	84.84 122	90.10 149	68.28 73	81.70 307	89.72 234	77.63 81	75.96 143	79.54 295	64.94 76	92.71 250	75.43 136	77.28 199	93.55 132

ZD-MVS					96.63 11	65.50 150		93.50 81	70.74 219	85.26 52	95.19 56	64.92 77	97.29 77	87.51 44	93.01 59

miper_ehance_all_eth		77.60 193	76.44 191	81.09 224	85.70 240	64.41 181	90.65 206	88.64 271	72.31 169	67.37 247	82.52 250	64.77 78	92.64 256	70.67 178	65.30 268	86.24 257

Test_1112_low_res		79.56 157	78.60 156	82.43 181	88.24 195	60.39 265	92.09 143	87.99 285	72.10 178	71.84 188	87.42 201	64.62 79	93.04 235	65.80 224	77.30 198	93.85 126

test2506		83.29 93	82.92 90	84.37 138	88.39 189	63.18 212	92.01 148	91.35 170	77.66 79	78.49 120	91.42 145	64.58 80	95.09 170	73.19 151	89.23 102	94.85 79

DeepC-MVS_fast	79.48 2	87.95 19	88.00 19	87.79 27	95.86 28	68.32 71	95.74 21	94.11 59	83.82 11	83.49 68	96.19 29	64.53 81	98.44 30	83.42 78	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		86.62 42	86.66 40	86.50 67	96.95 8	65.70 143	94.41 50	93.45 83	67.74 249	86.19 38	96.39 23	64.38 82	97.91 46	87.33 48	93.14 58	95.90 42

MG-MVS		87.11 32	86.27 41	89.62 8	97.79 1	76.27 4	94.96 43	94.49 40	78.74 63	83.87 66	92.94 115	64.34 83	96.94 102	75.19 138	94.09 40	95.66 46

casdiffmvs		85.37 56	84.87 63	86.84 50	88.25 194	69.07 54	93.04 106	91.76 151	81.27 31	80.84 91	92.07 136	64.23 84	96.06 130	84.98 66	87.43 119	95.39 52

cl22		77.94 189	76.78 186	81.42 210	87.57 207	64.93 169	90.67 205	88.86 264	72.45 165	67.63 242	82.68 249	64.07 85	92.91 243	71.79 168	65.30 268	86.44 252

tpm		78.58 178	77.03 182	83.22 166	85.94 237	64.56 172	83.21 299	91.14 180	78.31 66	73.67 165	79.68 293	64.01 86	92.09 273	66.07 221	71.26 235	93.03 149

CDS-MVSNet		81.43 122	80.74 120	83.52 159	86.26 230	64.45 177	92.09 143	90.65 197	75.83 101	73.95 164	89.81 172	63.97 87	92.91 243	71.27 173	82.82 154	93.20 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 79	83.20 84	87.05 46	91.56 119	69.82 40	89.99 227	92.05 138	77.77 76	82.84 72	86.57 209	63.93 88	96.09 127	74.91 144	89.18 104	95.25 66

APD-MVS		85.93 50	85.99 46	85.76 92	95.98 27	65.21 156	93.59 88	92.58 121	66.54 260	86.17 39	95.88 33	63.83 89	97.00 94	86.39 56	92.94 60	95.06 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 123	79.99 133	85.46 102	90.39 145	68.40 69	86.88 277	90.61 198	74.41 119	70.31 205	84.67 229	63.79 90	92.32 268	73.13 152	85.70 133	95.67 45

PVSNet_Blended_VisFu		83.97 82	83.50 75	85.39 106	90.02 150	66.59 123	93.77 82	91.73 152	77.43 85	77.08 136	89.81 172	63.77 91	96.97 99	79.67 107	88.21 112	92.60 159

baseline		85.01 62	84.44 67	86.71 57	88.33 191	68.73 62	90.24 218	91.82 150	81.05 35	81.18 85	92.50 125	63.69 92	96.08 129	84.45 70	86.71 127	95.32 57

CDPH-MVS		85.71 53	85.46 55	86.46 68	94.75 36	67.19 103	93.89 75	92.83 110	70.90 214	83.09 71	95.28 48	63.62 93	97.36 72	80.63 102	94.18 39	94.84 82

HyFIR lowres test		81.03 131	79.56 141	85.43 104	87.81 205	68.11 78	90.18 219	90.01 223	70.65 220	72.95 170	86.06 215	63.61 94	94.50 194	75.01 142	79.75 174	93.67 129

canonicalmvs		86.85 38	86.25 43	88.66 18	91.80 113	71.92 15	93.54 90	91.71 154	80.26 41	87.55 27	95.25 52	63.59 95	96.93 104	88.18 38	84.34 145	97.11 8

c3_l		76.83 205	75.47 203	80.93 228	85.02 250	64.18 190	90.39 213	88.11 282	71.66 193	66.65 254	81.64 262	63.58 96	92.56 257	69.31 190	62.86 290	86.04 263

SteuartSystems-ACMMP		86.82 40	86.90 37	86.58 63	90.42 143	66.38 126	96.09 16	93.87 62	77.73 77	84.01 65	95.66 38	63.39 97	97.94 43	87.40 47	93.55 53	95.42 51
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 87	83.67 73	84.06 145	92.79 86	63.56 205	91.76 162	94.81 27	79.65 47	77.87 123	94.09 93	63.35 98	97.90 47	79.35 109	79.36 176	90.74 194

UniMVSNet (Re)		77.58 194	76.78 186	79.98 243	84.11 265	60.80 254	91.76 162	93.17 97	76.56 95	69.93 212	84.78 228	63.32 99	92.36 266	64.89 233	62.51 294	86.78 245

PVSNet_BlendedMVS		83.38 92	83.43 78	83.22 166	93.76 56	67.53 93	94.06 62	93.61 75	79.13 55	81.00 89	85.14 223	63.19 100	97.29 77	87.08 51	73.91 215	84.83 284

PVSNet_Blended		86.73 41	86.86 38	86.31 76	93.76 56	67.53 93	96.33 15	93.61 75	82.34 18	81.00 89	93.08 110	63.19 100	97.29 77	87.08 51	91.38 83	94.13 110

PAPM_NR		82.97 99	81.84 106	86.37 73	94.10 50	66.76 118	87.66 267	92.84 109	69.96 227	74.07 162	93.57 103	63.10 102	97.50 65	70.66 179	90.58 94	94.85 79

nrg030		80.93 132	79.86 135	84.13 144	83.69 270	68.83 60	93.23 100	91.20 175	75.55 103	75.06 153	88.22 191	63.04 103	94.74 181	81.88 90	66.88 261	88.82 214

EI-MVSNet-UG-set		83.14 96	82.96 88	83.67 156	92.28 95	63.19 211	91.38 179	94.68 33	79.22 52	76.60 139	93.75 99	62.64 104	97.76 53	78.07 123	78.01 187	90.05 202

DeepC-MVS	77.85 3	85.52 54	85.24 57	86.37 73	88.80 179	66.64 120	92.15 139	93.68 72	81.07 34	76.91 137	93.64 101	62.59 105	98.44 30	85.50 61	92.84 62	94.03 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		85.35 57	85.55 54	84.75 124	90.77 139	65.29 153	95.38 30	91.54 160	78.03 71	83.67 67	94.32 87	62.47 106	95.81 138	82.73 83	91.00 89	93.15 144

EIA-MVS		84.84 65	84.88 62	84.69 128	91.30 126	62.36 228	93.85 77	92.04 139	79.45 48	79.33 107	94.28 89	62.42 107	96.35 119	80.05 105	91.25 86	95.38 53

MVS_111021_HR		86.19 47	85.80 50	87.37 38	93.17 74	69.79 41	93.99 66	93.76 67	79.08 57	78.88 115	93.99 96	62.25 108	98.15 38	85.93 59	91.15 87	94.15 109

PHI-MVS		86.83 39	86.85 39	86.78 54	93.47 66	65.55 148	95.39 29	95.10 17	71.77 191	85.69 47	96.52 18	62.07 109	98.77 21	86.06 58	95.60 12	96.03 37

MP-MVS		85.02 61	84.97 61	85.17 113	92.60 89	64.27 188	93.24 99	92.27 129	73.13 148	79.63 104	94.43 78	61.90 110	97.17 84	85.00 65	92.56 65	94.06 115
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 43	86.17 44	87.11 44	86.16 232	70.54 27	95.71 24	92.19 136	82.00 23	84.58 56	94.34 85	61.86 111	95.53 159	87.76 41	90.89 90	95.27 63
jason: jason.
PAPR		85.15 60	84.47 65	87.18 42	96.02 26	68.29 72	91.85 157	93.00 105	76.59 94	79.03 111	95.00 60	61.59 112	97.61 62	78.16 122	89.00 105	95.63 47

IS-MVSNet		80.14 145	79.41 145	82.33 185	87.91 202	60.08 270	91.97 152	88.27 279	72.90 155	71.44 194	91.73 143	61.44 113	93.66 226	62.47 250	86.53 129	93.24 141

cl____		76.07 212	74.67 208	80.28 236	85.15 246	61.76 239	90.12 220	88.73 267	71.16 209	65.43 259	81.57 264	61.15 114	92.95 238	66.54 214	62.17 296	86.13 261

DIV-MVS_self_test		76.07 212	74.67 208	80.28 236	85.14 247	61.75 240	90.12 220	88.73 267	71.16 209	65.42 260	81.60 263	61.15 114	92.94 242	66.54 214	62.16 298	86.14 259

EI-MVSNet		78.97 166	78.22 161	81.25 214	85.33 243	62.73 224	89.53 235	93.21 92	72.39 168	72.14 184	90.13 168	60.99 116	94.72 182	67.73 203	72.49 225	86.29 255

IterMVS-LS		76.49 208	75.18 206	80.43 233	84.49 258	62.74 223	90.64 207	88.80 265	72.40 167	65.16 262	81.72 260	60.98 117	92.27 269	67.74 202	64.65 279	86.29 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ETV-MVS		86.01 49	86.11 45	85.70 96	90.21 148	67.02 110	93.43 95	91.92 144	81.21 32	84.13 63	94.07 95	60.93 118	95.63 149	89.28 27	89.81 98	94.46 100

tpm cat1		75.30 228	72.21 244	84.58 132	88.52 182	67.77 86	78.16 332	88.02 284	61.88 298	68.45 232	76.37 318	60.65 119	94.03 214	53.77 286	74.11 212	91.93 175

TAMVS		80.37 141	79.45 144	83.13 168	85.14 247	63.37 206	91.23 186	90.76 193	74.81 117	72.65 174	88.49 182	60.63 120	92.95 238	69.41 188	81.95 160	93.08 148

ZNCC-MVS		85.33 58	85.08 59	86.06 81	93.09 77	65.65 145	93.89 75	93.41 87	73.75 136	79.94 99	94.68 73	60.61 121	98.03 41	82.63 86	93.72 49	94.52 95

thres100view900		78.37 181	77.01 183	82.46 180	91.89 109	63.21 210	91.19 190	96.33 1	72.28 171	70.45 202	87.89 195	60.31 122	95.32 164	45.16 318	77.58 192	88.83 212

thres600view7		78.00 186	76.66 188	82.03 200	91.93 106	63.69 200	91.30 184	96.33 1	72.43 166	70.46 201	87.89 195	60.31 122	94.92 177	42.64 330	76.64 203	87.48 232

CHOSEN 1792x2688		84.98 63	83.45 77	89.57 11	89.94 152	75.14 5	92.07 145	92.32 126	81.87 24	75.68 146	88.27 187	60.18 124	98.60 25	80.46 104	90.27 97	94.96 77

h-mvs33		83.01 98	82.56 98	84.35 139	89.34 164	62.02 233	92.72 118	93.76 67	81.45 27	82.73 73	92.25 134	60.11 125	97.13 87	87.69 42	62.96 288	93.91 122

hse-mvs2		81.12 129	81.11 117	81.16 217	86.52 225	57.48 300	89.40 238	91.16 177	81.45 27	82.73 73	90.49 160	60.11 125	94.58 186	87.69 42	60.41 315	91.41 183

tfpn200view9		78.79 172	77.43 175	82.88 171	92.21 98	64.49 174	92.05 146	96.28 4	73.48 143	71.75 190	88.26 188	60.07 127	95.32 164	45.16 318	77.58 192	88.83 212

thres400		78.68 175	77.43 175	82.43 181	92.21 98	64.49 174	92.05 146	96.28 4	73.48 143	71.75 190	88.26 188	60.07 127	95.32 164	45.16 318	77.58 192	87.48 232

test_part1		79.63 155	77.86 169	84.93 117	92.50 91	71.43 17	94.15 57	91.08 184	72.51 162	70.66 198	84.98 225	59.84 129	95.07 171	72.07 167	62.94 289	88.30 225

diffmvs		84.28 74	83.83 72	85.61 98	87.40 212	68.02 80	90.88 199	89.24 246	80.54 39	81.64 81	92.52 124	59.83 130	94.52 193	87.32 49	85.11 137	94.29 101

MVS		84.66 68	82.86 92	90.06 2	90.93 132	74.56 6	87.91 263	95.54 9	68.55 245	72.35 183	94.71 72	59.78 131	98.90 18	81.29 99	94.69 32	96.74 12

thres200		79.66 154	78.33 158	83.66 157	92.54 90	65.82 142	93.06 104	96.31 3	74.90 116	73.30 167	88.66 180	59.67 132	95.61 151	47.84 308	78.67 183	89.56 209

Effi-MVS+		83.82 86	82.76 94	86.99 48	89.56 160	69.40 47	91.35 181	86.12 305	72.59 159	83.22 70	92.81 122	59.60 133	96.01 134	81.76 91	87.80 116	95.56 49

eth_miper_zixun_eth		75.96 218	74.40 216	80.66 230	84.66 254	63.02 214	89.28 240	88.27 279	71.88 184	65.73 257	81.65 261	59.45 134	92.81 246	68.13 198	60.53 312	86.14 259

ACMMP_NAP		86.05 48	85.80 50	86.80 53	91.58 118	67.53 93	91.79 159	93.49 82	74.93 115	84.61 55	95.30 47	59.42 135	97.92 45	86.13 57	94.92 19	94.94 78

GST-MVS		84.63 69	84.29 69	85.66 97	92.82 83	65.27 154	93.04 106	93.13 99	73.20 146	78.89 112	94.18 92	59.41 136	97.85 50	81.45 95	92.48 68	93.86 125

UA-Net		80.02 148	79.65 138	81.11 220	89.33 166	57.72 295	86.33 280	89.00 260	77.44 84	81.01 88	89.15 177	59.33 137	95.90 136	61.01 257	84.28 148	89.73 206

NR-MVSNet		76.05 215	74.59 211	80.44 232	82.96 279	62.18 232	90.83 201	91.73 152	77.12 87	60.96 292	86.35 210	59.28 138	91.80 278	60.74 258	61.34 307	87.35 236

MP-MVS-pluss		85.24 59	85.13 58	85.56 99	91.42 123	65.59 147	91.54 171	92.51 123	74.56 118	80.62 92	95.64 39	59.15 139	97.00 94	86.94 53	93.80 46	94.07 114
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 66	84.40 68	85.72 93	93.75 58	65.01 165	93.50 92	93.19 95	72.19 174	79.22 108	94.93 64	59.04 140	97.67 55	81.55 93	92.21 69	94.49 98

#test#		84.98 63	84.74 64	85.72 93	93.75 58	65.01 165	94.09 60	93.19 95	73.55 142	79.22 108	94.93 64	59.04 140	97.67 55	82.66 84	92.21 69	94.49 98

MSLP-MVS++		86.27 45	85.91 48	87.35 39	92.01 103	68.97 58	95.04 41	92.70 113	79.04 58	81.50 82	96.50 20	58.98 142	96.78 108	83.49 77	93.93 43	96.29 30

Patchmatch-test		65.86 298	60.94 310	80.62 231	83.75 269	58.83 284	58.91 359	75.26 350	44.50 356	50.95 335	77.09 312	58.81 143	87.90 319	35.13 350	64.03 283	95.12 70

EPNet_dtu		78.80 171	79.26 149	77.43 280	88.06 198	49.71 341	91.96 153	91.95 143	77.67 78	76.56 140	91.28 149	58.51 144	90.20 301	56.37 276	80.95 168	92.39 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DROMVSNet		84.53 70	85.04 60	83.01 169	89.34 164	61.37 246	94.42 49	91.09 182	77.91 74	83.24 69	94.20 91	58.37 145	95.40 161	85.35 63	91.41 82	92.27 171

VNet		86.20 46	85.65 53	87.84 26	93.92 52	69.99 34	95.73 23	95.94 6	78.43 65	86.00 42	93.07 112	58.22 146	97.00 94	85.22 64	84.33 146	96.52 19

TESTMET0.1,1		82.41 107	81.98 105	83.72 154	88.08 197	63.74 197	92.70 120	93.77 66	79.30 50	77.61 128	87.57 199	58.19 147	94.08 207	73.91 149	86.68 128	93.33 138

原ACMM1				84.42 136	93.21 72	64.27 188		93.40 88	65.39 268	79.51 105	92.50 125	58.11 148	96.69 112	65.27 231	93.96 42	92.32 166

sam_mvs1												57.85 149				94.68 86

CR-MVSNet		73.79 243	70.82 255	82.70 175	83.15 277	67.96 81	70.25 342	84.00 324	73.67 140	69.97 210	72.41 333	57.82 150	89.48 307	52.99 289	73.13 219	90.64 196

Patchmtry		67.53 290	63.93 295	78.34 268	82.12 284	64.38 182	68.72 346	84.00 324	48.23 349	59.24 299	72.41 333	57.82 150	89.27 308	46.10 315	56.68 327	81.36 321

patchmatchnet-post											67.62 346	57.62 152	90.25 296

PCF-MVS	73.15 9	79.29 161	77.63 172	84.29 141	86.06 233	65.96 137	87.03 273	91.10 181	69.86 229	69.79 214	90.64 155	57.54 153	96.59 113	64.37 236	82.29 156	90.32 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 127	80.01 132	84.51 134	90.24 147	65.86 139	94.12 59	89.15 252	73.81 135	75.37 152	88.26 188	57.26 154	94.53 192	66.97 210	84.92 138	93.15 144

miper_lstm_enhance		73.05 247	71.73 249	77.03 285	83.80 268	58.32 290	81.76 305	88.88 262	69.80 230	61.01 291	78.23 302	57.19 155	87.51 325	65.34 230	59.53 317	85.27 281

1121		81.25 125	80.05 130	84.87 121	92.30 94	64.31 185	87.91 263	91.39 168	59.44 313	79.94 99	92.91 116	57.09 156	97.01 92	66.63 211	92.81 63	93.29 139

PatchT		69.11 275	65.37 286	80.32 234	82.07 285	63.68 201	67.96 351	87.62 289	50.86 341	69.37 215	65.18 349	57.09 156	88.53 314	41.59 333	66.60 263	88.74 215

testdata				81.34 212	89.02 173	57.72 295		89.84 227	58.65 317	85.32 51	94.09 93	57.03 158	93.28 232	69.34 189	90.56 95	93.03 149

PatchmatchNet		77.46 195	74.63 210	85.96 84	89.55 161	70.35 29	79.97 323	89.55 237	72.23 173	70.94 196	76.91 314	57.03 158	92.79 248	54.27 283	81.17 166	94.74 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_yl		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 237	81.09 86	92.88 119	57.00 160	97.44 67	81.11 100	81.76 161	96.23 32

DCV-MVSNet		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 237	81.09 86	92.88 119	57.00 160	97.44 67	81.11 100	81.76 161	96.23 32

region2R		84.36 72	84.03 71	85.36 107	93.54 64	64.31 185	93.43 95	92.95 106	72.16 177	78.86 116	94.84 69	56.97 162	97.53 64	81.38 97	92.11 73	94.24 103

新几何1				84.73 125	92.32 93	64.28 187		91.46 166	59.56 312	79.77 102	92.90 117	56.95 163	96.57 115	63.40 241	92.91 61	93.34 136

WR-MVS		76.76 206	75.74 201	79.82 248	84.60 255	62.27 231	92.60 126	92.51 123	76.06 98	67.87 239	85.34 221	56.76 164	90.24 299	62.20 251	63.69 287	86.94 243

HPM-MVS		83.25 94	82.95 89	84.17 143	92.25 96	62.88 221	90.91 196	91.86 147	70.30 223	77.12 134	93.96 97	56.75 165	96.28 120	82.04 89	91.34 85	93.34 136
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 104	82.38 101	83.73 153	89.25 168	59.58 276	92.24 137	94.89 23	77.96 72	79.86 101	92.38 130	56.70 166	97.05 89	77.26 128	80.86 169	94.55 91

ACMMPR		84.37 71	84.06 70	85.28 109	93.56 63	64.37 183	93.50 92	93.15 98	72.19 174	78.85 117	94.86 68	56.69 167	97.45 66	81.55 93	92.20 71	94.02 118

FMVSNet3		77.73 192	76.04 196	82.80 172	91.20 129	68.99 57	91.87 155	91.99 141	73.35 145	67.04 249	83.19 245	56.62 168	92.14 270	59.80 265	69.34 243	87.28 238

Patchmatch-RL test		68.17 284	64.49 292	79.19 260	71.22 349	53.93 321	70.07 344	71.54 357	69.22 236	56.79 315	62.89 352	56.58 169	88.61 311	69.53 187	52.61 336	95.03 76

test_post											23.01 367	56.49 170	92.67 253

RPMNet		70.42 266	65.68 282	84.63 131	83.15 277	67.96 81	70.25 342	90.45 199	46.83 352	69.97 210	65.10 350	56.48 171	95.30 167	35.79 349	73.13 219	90.64 196

DU-MVS		76.86 202	75.84 199	79.91 245	82.96 279	60.26 266	91.26 185	91.54 160	76.46 96	68.88 223	86.35 210	56.16 172	92.13 271	66.38 217	62.55 292	87.35 236

Baseline_NR-MVSNet		73.99 240	72.83 235	77.48 279	80.78 293	59.29 281	91.79 159	84.55 319	68.85 241	68.99 221	80.70 278	56.16 172	92.04 274	62.67 248	60.98 309	81.11 322

API-MVS		82.28 109	80.53 125	87.54 34	96.13 24	70.59 26	93.63 86	91.04 187	65.72 267	75.45 151	92.83 121	56.11 174	98.89 19	64.10 237	89.75 101	93.15 144

zzz-MVS		84.73 66	84.47 65	85.50 100	91.89 109	65.16 158	91.55 170	92.23 130	75.32 109	80.53 93	95.21 54	56.06 175	97.16 85	84.86 68	92.55 66	94.18 105

MTAPA		83.91 84	83.38 82	85.50 100	91.89 109	65.16 158	81.75 306	92.23 130	75.32 109	80.53 93	95.21 54	56.06 175	97.16 85	84.86 68	92.55 66	94.18 105

JIA-IIPM		66.06 297	62.45 304	76.88 289	81.42 290	54.45 320	57.49 360	88.67 269	49.36 345	63.86 274	46.86 359	56.06 175	90.25 296	49.53 298	68.83 247	85.95 266

v148		76.19 210	74.47 215	81.36 211	80.05 304	64.44 178	91.75 164	90.23 213	73.68 139	67.13 248	80.84 277	55.92 178	93.86 223	68.95 194	61.73 303	85.76 272

WR-MVS_H		70.59 264	69.94 260	72.53 317	81.03 291	51.43 332	87.35 271	92.03 140	67.38 254	60.23 295	80.70 278	55.84 179	83.45 344	46.33 314	58.58 322	82.72 307

AUN-MVS		78.37 181	77.43 175	81.17 216	86.60 224	57.45 301	89.46 237	91.16 177	74.11 126	74.40 157	90.49 160	55.52 180	94.57 187	74.73 147	60.43 314	91.48 181

XVS		83.87 85	83.47 76	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 128	78.18 121	94.31 88	55.25 181	97.41 69	79.16 111	91.58 79	93.95 120

X-MVStestdata		76.86 202	74.13 222	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 128	78.18 121	10.19 372	55.25 181	97.41 69	79.16 111	91.58 79	93.95 120

BH-w/o		80.49 140	79.30 148	84.05 146	90.83 137	64.36 184	93.60 87	89.42 241	74.35 121	69.09 218	90.15 167	55.23 183	95.61 151	64.61 234	86.43 131	92.17 173

CP-MVS		83.71 89	83.40 81	84.65 129	93.14 75	63.84 193	94.59 47	92.28 128	71.03 212	77.41 130	94.92 66	55.21 184	96.19 123	81.32 98	90.70 92	93.91 122

PGM-MVS		83.25 94	82.70 96	84.92 118	92.81 85	64.07 191	90.44 210	92.20 135	71.28 207	77.23 133	94.43 78	55.17 185	97.31 76	79.33 110	91.38 83	93.37 135

tpmvs		72.88 251	69.76 263	82.22 190	90.98 131	67.05 108	78.22 331	88.30 277	63.10 285	64.35 272	74.98 325	55.09 186	94.27 200	43.25 324	69.57 242	85.34 279

v8		75.35 227	73.26 232	81.61 207	80.67 295	66.82 115	89.54 234	89.27 245	71.65 194	63.30 280	80.30 286	54.99 187	94.06 209	67.33 207	62.33 295	83.94 290

sam_mvs												54.91 188

EPMVS		78.49 180	75.98 197	86.02 82	91.21 128	69.68 45	80.23 319	91.20 175	75.25 111	72.48 179	78.11 303	54.65 189	93.69 225	57.66 273	83.04 152	94.69 85

ab-mvs		80.18 144	78.31 159	85.80 90	88.44 186	65.49 151	83.00 301	92.67 115	71.82 189	77.36 131	85.01 224	54.50 190	96.59 113	76.35 134	75.63 208	95.32 57

KD-MVS_2432*1600		69.03 276	66.37 278	77.01 286	85.56 241	61.06 250	81.44 310	90.25 211	67.27 255	58.00 309	76.53 316	54.49 191	87.63 323	48.04 305	35.77 359	82.34 313

miper_refine_blended		69.03 276	66.37 278	77.01 286	85.56 241	61.06 250	81.44 310	90.25 211	67.27 255	58.00 309	76.53 316	54.49 191	87.63 323	48.04 305	35.77 359	82.34 313

DP-MVS Recon		82.73 102	81.65 109	85.98 83	97.31 4	67.06 107	95.15 35	91.99 141	69.08 240	76.50 141	93.89 98	54.48 193	98.20 36	70.76 177	85.66 134	92.69 156

GeoE		78.90 168	77.43 175	83.29 165	88.95 175	62.02 233	92.31 134	86.23 303	70.24 224	71.34 195	89.27 175	54.43 194	94.04 212	63.31 242	80.81 170	93.81 127

XXY-MVS		77.94 189	76.44 191	82.43 181	82.60 281	64.44 178	92.01 148	91.83 149	73.59 141	70.00 209	85.82 217	54.43 194	94.76 179	69.63 185	68.02 254	88.10 227

MDTV_nov1_ep13_2view						59.90 272	80.13 321		67.65 252	72.79 172		54.33 196		59.83 264		92.58 160

Test By Simon												54.21 197

MAR-MVS		84.18 78	83.43 78	86.44 69	96.25 23	65.93 138	94.28 53	94.27 53	74.41 119	79.16 110	95.61 40	53.99 198	98.88 20	69.62 186	93.26 56	94.50 97
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
test-LLR		80.10 146	79.56 141	81.72 204	86.93 221	61.17 247	92.70 120	91.54 160	71.51 203	75.62 147	86.94 206	53.83 199	92.38 264	72.21 164	84.76 141	91.60 178

test0.0.03 1		72.76 252	72.71 238	72.88 315	80.25 302	47.99 347	91.22 187	89.45 239	71.51 203	62.51 287	87.66 197	53.83 199	85.06 335	50.16 295	67.84 257	85.58 273

v2v482		77.42 196	75.65 202	82.73 174	80.38 298	67.13 106	91.85 157	90.23 213	75.09 113	69.37 215	83.39 243	53.79 201	94.44 195	71.77 169	65.00 274	86.63 250

SR-MVS		82.81 101	82.58 97	83.50 161	93.35 67	61.16 249	92.23 138	91.28 174	64.48 273	81.27 84	95.28 48	53.71 202	95.86 137	82.87 81	88.77 107	93.49 134

pcd_1.5k_mvsjas		4.46 344	5.95 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	53.55 203	0.00 377	0.00 375	0.00 374	0.00 373

PS-MVSNAJss		77.26 199	76.31 193	80.13 240	80.64 296	59.16 282	90.63 209	91.06 185	72.80 156	68.58 230	84.57 231	53.55 203	93.96 217	72.97 153	71.96 229	87.27 239

PS-MVSNAJ		88.14 15	87.61 26	89.71 7	92.06 100	76.72 1	95.75 20	93.26 91	83.86 10	89.55 19	96.06 31	53.55 203	97.89 48	91.10 17	93.31 55	94.54 93

mPP-MVS		82.96 100	82.44 100	84.52 133	92.83 81	62.92 219	92.76 116	91.85 148	71.52 202	75.61 149	94.24 90	53.48 206	96.99 97	78.97 114	90.73 91	93.64 131

xiu_mvs_v2_base		87.92 20	87.38 31	89.55 12	91.41 125	76.43 3	95.74 21	93.12 100	83.53 12	89.55 19	95.95 32	53.45 207	97.68 54	91.07 18	92.62 64	94.54 93

test_post1							78.95 325				20.70 370	53.05 208	91.50 288	60.43 260

MDTV_nov1_ep13			72.61 239		89.06 172	68.48 67	80.33 317	90.11 217	71.84 188	71.81 189	75.92 322	53.01 209	93.92 219	48.04 305	73.38 217

test222					89.77 155	61.60 242	89.55 233	89.42 241	56.83 326	77.28 132	92.43 129	52.76 210			91.14 88	93.09 147

v1144		76.73 207	74.88 207	82.27 187	80.23 303	66.60 122	91.68 166	90.21 215	73.69 138	69.06 220	81.89 257	52.73 211	94.40 196	69.21 191	65.23 271	85.80 269

v10		74.77 234	72.54 241	81.46 209	80.33 301	66.71 119	89.15 244	89.08 256	70.94 213	63.08 281	79.86 291	52.52 212	94.04 212	65.70 225	62.17 296	83.64 292

CLD-MVS		82.73 102	82.35 102	83.86 149	87.90 203	67.65 90	95.45 27	92.18 137	85.06 7	72.58 176	92.27 133	52.46 213	95.78 140	84.18 71	79.06 179	88.16 226
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		75.86 220	74.52 214	79.89 246	82.44 282	60.64 262	91.37 180	91.37 169	76.63 93	67.65 241	86.21 213	52.37 214	91.55 283	61.84 253	60.81 310	87.48 232

VPA-MVSNet		79.03 164	78.00 164	82.11 198	85.95 235	64.48 176	93.22 101	94.66 34	75.05 114	74.04 163	84.95 226	52.17 215	93.52 228	74.90 145	67.04 260	88.32 224

APD-MVS_3200maxsize		81.64 120	81.32 112	82.59 179	92.36 92	58.74 285	91.39 177	91.01 188	63.35 281	79.72 103	94.62 74	51.82 216	96.14 125	79.71 106	87.93 115	92.89 154

dp		75.01 232	72.09 245	83.76 150	89.28 167	66.22 132	79.96 324	89.75 229	71.16 209	67.80 240	77.19 311	51.81 217	92.54 258	50.39 294	71.44 234	92.51 162

v144192		76.05 215	74.03 223	82.12 195	79.50 309	66.55 124	91.39 177	89.71 235	72.30 170	68.17 233	81.33 269	51.75 218	94.03 214	67.94 200	64.19 281	85.77 270

BH-untuned		78.68 175	77.08 181	83.48 162	89.84 154	63.74 197	92.70 120	88.59 272	71.57 200	66.83 252	88.65 181	51.75 218	95.39 162	59.03 268	84.77 140	91.32 187

HQP2-MVS												51.63 220

HQP-MVS		81.14 127	80.64 123	82.64 177	87.54 208	63.66 202	94.06 62	91.70 155	79.80 43	74.18 158	90.30 164	51.63 220	95.61 151	77.63 126	78.90 180	88.63 216

V42		76.46 209	74.55 213	82.19 191	79.14 315	67.82 85	90.26 217	89.42 241	73.75 136	68.63 229	81.89 257	51.31 222	94.09 206	71.69 171	64.84 275	84.66 285

test1172		81.90 117	81.83 107	82.13 194	93.23 69	57.52 299	91.61 169	90.98 189	64.32 275	80.20 97	95.00 60	51.26 223	95.61 151	81.73 92	88.13 113	93.26 140

SR-MVS-dyc-post		81.06 130	80.70 121	82.15 192	92.02 101	58.56 287	90.90 197	90.45 199	62.76 287	78.89 112	94.46 76	51.26 223	95.61 151	78.77 117	86.77 125	92.28 168

CL-MVSNet_self_test		69.92 269	68.09 271	75.41 297	73.25 344	55.90 312	90.05 223	89.90 225	69.96 227	61.96 290	76.54 315	51.05 225	87.64 322	49.51 299	50.59 341	82.70 309

TransMVSNet (Re)		70.07 268	67.66 272	77.31 283	80.62 297	59.13 283	91.78 161	84.94 315	65.97 264	60.08 296	80.44 283	50.78 226	91.87 276	48.84 301	45.46 348	80.94 324

HQP_MVS		80.34 142	79.75 137	82.12 195	86.94 219	62.42 226	93.13 102	91.31 171	78.81 61	72.53 177	89.14 178	50.66 227	95.55 157	76.74 129	78.53 185	88.39 222

plane_prior6					87.23 214	62.32 229						50.66 227

ACMMP		81.49 121	80.67 122	83.93 148	91.71 115	62.90 220	92.13 140	92.22 134	71.79 190	71.68 192	93.49 105	50.32 229	96.96 100	78.47 119	84.22 150	91.93 175
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
MVS_111021_LR		82.02 115	81.52 110	83.51 160	88.42 187	62.88 221	89.77 230	88.93 261	76.78 92	75.55 150	93.10 108	50.31 230	95.38 163	83.82 76	87.02 121	92.26 172

1314		80.70 135	78.95 153	85.94 85	87.77 206	67.56 91	87.91 263	92.55 122	72.17 176	67.44 243	93.09 109	50.27 231	97.04 91	71.68 172	87.64 117	93.23 142

CP-MVSNet		70.50 265	69.91 261	72.26 320	80.71 294	51.00 335	87.23 272	90.30 209	67.84 248	59.64 297	82.69 248	50.23 232	82.30 352	51.28 291	59.28 318	83.46 297

LCM-MVSNet-Re		72.93 249	71.84 247	76.18 294	88.49 183	48.02 346	80.07 322	70.17 358	73.96 131	52.25 328	80.09 290	49.98 233	88.24 316	67.35 205	84.23 149	92.28 168

Vis-MVSNet		80.92 133	79.98 134	83.74 151	88.48 184	61.80 237	93.44 94	88.26 281	73.96 131	77.73 124	91.76 141	49.94 234	94.76 179	65.84 223	90.37 96	94.65 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 217	73.92 225	82.15 192	79.73 305	66.24 131	91.22 187	89.75 229	72.67 158	68.49 231	81.42 267	49.86 235	94.27 200	67.08 208	65.02 273	85.95 266

test-mter		79.96 149	79.38 147	81.72 204	86.93 221	61.17 247	92.70 120	91.54 160	73.85 133	75.62 147	86.94 206	49.84 236	92.38 264	72.21 164	84.76 141	91.60 178

cdsmvs_eth3d_5k		19.86 339	26.47 338	0.00 358	0.00 381	0.00 382	0.00 369	93.45 83	0.00 376	0.00 377	95.27 50	49.56 237	0.00 377	0.00 375	0.00 374	0.00 373

3Dnovator+	73.60 7	82.10 114	80.60 124	86.60 61	90.89 135	66.80 117	95.20 33	93.44 85	74.05 127	67.42 244	92.49 127	49.46 238	97.65 59	70.80 176	91.68 77	95.33 55

MVP-Stereo		77.12 201	76.23 194	79.79 249	81.72 287	66.34 128	89.29 239	90.88 191	70.56 221	62.01 289	82.88 246	49.34 239	94.13 204	65.55 228	93.80 46	78.88 341
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
RE-MVS-def			80.48 126		92.02 101	58.56 287	90.90 197	90.45 199	62.76 287	78.89 112	94.46 76	49.30 240		78.77 117	86.77 125	92.28 168

OMC-MVS		78.67 177	77.91 168	80.95 227	85.76 239	57.40 302	88.49 255	88.67 269	73.85 133	72.43 181	92.10 135	49.29 241	94.55 190	72.73 158	77.89 188	90.91 193

VPNet		78.82 170	77.53 174	82.70 175	84.52 257	66.44 125	93.93 72	92.23 130	80.46 40	72.60 175	88.38 185	49.18 242	93.13 234	72.47 162	63.97 285	88.55 218

CVMVSNet		74.04 239	74.27 218	73.33 311	85.33 243	43.94 356	89.53 235	88.39 275	54.33 333	70.37 203	90.13 168	49.17 243	84.05 338	61.83 254	79.36 176	91.99 174

v1921920		75.63 225	73.49 230	82.06 199	79.38 310	66.35 127	91.07 195	89.48 238	71.98 179	67.99 234	81.22 272	49.16 244	93.90 220	66.56 213	64.56 280	85.92 268

pm-mvs1		72.89 250	71.09 253	78.26 271	79.10 316	57.62 297	90.80 202	89.30 244	67.66 251	62.91 283	81.78 259	49.11 245	92.95 238	60.29 262	58.89 320	84.22 288

pmmvs4		73.92 241	71.81 248	80.25 238	79.17 313	65.24 155	87.43 270	87.26 294	67.64 253	63.46 278	83.91 237	48.96 246	91.53 287	62.94 245	65.49 267	83.96 289

TAPA-MVS	70.22 12	74.94 233	73.53 229	79.17 261	90.40 144	52.07 329	89.19 243	89.61 236	62.69 289	70.07 207	92.67 123	48.89 247	94.32 197	38.26 344	79.97 172	91.12 191
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 105	80.82 119	88.31 22	89.57 159	71.26 19	92.60 126	94.39 48	78.84 60	67.89 238	92.48 128	48.42 248	98.52 26	68.80 196	94.40 37	95.15 68

CPTT-MVS		79.59 156	79.16 151	80.89 229	91.54 121	59.80 273	92.10 142	88.54 274	60.42 305	72.96 169	93.28 107	48.27 249	92.80 247	78.89 116	86.50 130	90.06 201

GBi-Net		75.65 223	73.83 226	81.10 221	88.85 176	65.11 161	90.01 224	90.32 205	70.84 215	67.04 249	80.25 287	48.03 250	91.54 284	59.80 265	69.34 243	86.64 247

test1		75.65 223	73.83 226	81.10 221	88.85 176	65.11 161	90.01 224	90.32 205	70.84 215	67.04 249	80.25 287	48.03 250	91.54 284	59.80 265	69.34 243	86.64 247

FMVSNet2		76.07 212	74.01 224	82.26 189	88.85 176	67.66 89	91.33 182	91.61 158	70.84 215	65.98 256	82.25 253	48.03 250	92.00 275	58.46 270	68.73 249	87.10 240

LFMVS		84.34 73	82.73 95	89.18 13	94.76 35	73.25 10	94.99 42	91.89 146	71.90 182	82.16 77	93.49 105	47.98 253	97.05 89	82.55 87	84.82 139	97.25 7

QAPM		79.95 150	77.39 179	87.64 29	89.63 158	71.41 18	93.30 98	93.70 71	65.34 270	67.39 246	91.75 142	47.83 254	98.96 16	57.71 272	89.81 98	92.54 161

HPM-MVS_fast		80.25 143	79.55 143	82.33 185	91.55 120	59.95 271	91.32 183	89.16 251	65.23 271	74.71 155	93.07 112	47.81 255	95.74 142	74.87 146	88.23 111	91.31 188

CANet_DTU		84.09 80	83.52 74	85.81 89	90.30 146	66.82 115	91.87 155	89.01 259	85.27 6	86.09 41	93.74 100	47.71 256	96.98 98	77.90 125	89.78 100	93.65 130

abl_6		79.82 152	79.20 150	81.70 206	89.85 153	58.34 289	88.47 256	90.07 218	62.56 290	77.71 125	93.08 110	47.65 257	96.78 108	77.94 124	85.45 136	89.99 203

v1240		75.21 230	72.98 234	81.88 201	79.20 312	66.00 135	90.75 204	89.11 255	71.63 198	67.41 245	81.22 272	47.36 258	93.87 221	65.46 229	64.72 278	85.77 270

PEN-MVS		69.46 273	68.56 267	72.17 322	79.27 311	49.71 341	86.90 276	89.24 246	67.24 258	59.08 302	82.51 251	47.23 259	83.54 343	48.42 303	57.12 323	83.25 300

CNLPA		74.31 237	72.30 243	80.32 234	91.49 122	61.66 241	90.85 200	80.72 338	56.67 327	63.85 275	90.64 155	46.75 260	90.84 291	53.79 285	75.99 207	88.47 221

114514_t		79.17 163	77.67 170	83.68 155	95.32 30	65.53 149	92.85 115	91.60 159	63.49 280	67.92 236	90.63 157	46.65 261	95.72 147	67.01 209	83.54 151	89.79 204

PS-CasMVS		69.86 271	69.13 265	72.07 323	80.35 300	50.57 337	87.02 274	89.75 229	67.27 255	59.19 301	82.28 252	46.58 262	82.24 353	50.69 293	59.02 319	83.39 299

DTE-MVSNet		68.46 282	67.33 274	71.87 325	77.94 328	49.00 344	86.16 281	88.58 273	66.36 262	58.19 306	82.21 254	46.36 263	83.87 341	44.97 321	55.17 330	82.73 306

test1111		80.84 134	80.02 131	83.33 164	87.87 204	60.76 257	92.62 125	86.86 296	77.86 75	75.73 145	91.39 147	46.35 264	94.70 185	72.79 157	88.68 108	94.52 95

ECVR-MVS		81.29 124	80.38 128	84.01 147	88.39 189	61.96 235	92.56 131	86.79 297	77.66 79	76.63 138	91.42 145	46.34 265	95.24 168	74.36 148	89.23 102	94.85 79

PMMVS		81.98 116	82.04 104	81.78 202	89.76 156	56.17 309	91.13 192	90.69 194	77.96 72	80.09 98	93.57 103	46.33 266	94.99 173	81.41 96	87.46 118	94.17 107

OPM-MVS		79.00 165	78.09 162	81.73 203	83.52 273	63.83 194	91.64 168	90.30 209	76.36 97	71.97 187	89.93 171	46.30 267	95.17 169	75.10 139	77.70 190	86.19 258
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BH-RMVSNet		79.46 160	77.65 171	84.89 119	91.68 116	65.66 144	93.55 89	88.09 283	72.93 153	73.37 166	91.12 150	46.20 268	96.12 126	56.28 277	85.61 135	92.91 153

TR-MVS		78.77 173	77.37 180	82.95 170	90.49 141	60.88 253	93.67 84	90.07 218	70.08 226	74.51 156	91.37 148	45.69 269	95.70 148	60.12 263	80.32 171	92.29 167

IterMVS-SCA-FT		71.55 260	69.97 259	76.32 292	81.48 288	60.67 261	87.64 268	85.99 306	66.17 263	59.50 298	78.88 297	45.53 270	83.65 342	62.58 249	61.93 299	84.63 287

SCA		75.82 221	72.76 236	85.01 116	86.63 223	70.08 32	81.06 313	89.19 249	71.60 199	70.01 208	77.09 312	45.53 270	90.25 296	60.43 260	73.27 218	94.68 86

IterMVS		72.65 256	70.83 254	78.09 273	82.17 283	62.96 216	87.64 268	86.28 301	71.56 201	60.44 294	78.85 298	45.42 272	86.66 329	63.30 243	61.83 300	84.65 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 211	75.28 205	78.72 266	83.22 275	55.17 316	89.87 228	87.78 287	75.42 105	67.98 235	81.43 266	45.08 273	92.52 259	75.08 140	71.63 230	88.48 219

mvs-test1		78.74 174	77.95 166	81.14 218	83.22 275	57.13 304	93.96 67	87.78 287	75.42 105	72.68 173	90.80 154	45.08 273	94.54 191	75.08 140	77.49 195	91.74 177

XVG-OURS-SEG-HR		74.70 235	73.08 233	79.57 255	78.25 325	57.33 303	80.49 315	87.32 292	63.22 283	68.76 227	90.12 170	44.89 275	91.59 282	70.55 180	74.09 213	89.79 204

v7n		71.31 261	68.65 266	79.28 259	76.40 336	60.77 256	86.71 278	89.45 239	64.17 276	58.77 305	78.24 301	44.59 276	93.54 227	57.76 271	61.75 302	83.52 295

pmmvs5		73.35 245	71.52 250	78.86 265	78.64 322	60.61 263	91.08 193	86.90 295	67.69 250	63.32 279	83.64 238	44.33 277	90.53 293	62.04 252	66.02 266	85.46 276

RRT_MVS		77.38 197	76.59 189	79.77 250	90.91 134	63.61 204	91.15 191	90.91 190	72.28 171	72.06 186	87.28 204	43.92 278	89.04 310	73.32 150	67.47 258	86.67 246

OpenMVS	70.45 11	78.54 179	75.92 198	86.41 72	85.93 238	71.68 16	92.74 117	92.51 123	66.49 261	64.56 267	91.96 137	43.88 279	98.10 39	54.61 281	90.65 93	89.44 210

AdaColmap		78.94 167	77.00 184	84.76 123	96.34 19	65.86 139	92.66 124	87.97 286	62.18 293	70.56 199	92.37 131	43.53 280	97.35 73	64.50 235	82.86 153	91.05 192

tfpnnormal		70.10 267	67.36 273	78.32 269	83.45 274	60.97 252	88.85 249	92.77 111	64.85 272	60.83 293	78.53 299	43.52 281	93.48 229	31.73 360	61.70 304	80.52 329

test_djsdf		73.76 244	72.56 240	77.39 281	77.00 333	53.93 321	89.07 246	90.69 194	65.80 265	63.92 273	82.03 256	43.14 282	92.67 253	72.83 155	68.53 250	85.57 274

GA-MVS		78.33 183	76.23 194	84.65 129	83.65 271	66.30 129	91.44 172	90.14 216	76.01 99	70.32 204	84.02 235	42.50 283	94.72 182	70.98 174	77.00 201	92.94 152

PLC	68.80 14	75.23 229	73.68 228	79.86 247	92.93 79	58.68 286	90.64 207	88.30 277	60.90 302	64.43 271	90.53 158	42.38 284	94.57 187	56.52 275	76.54 204	86.33 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		73.80 242	72.02 246	79.15 263	79.15 314	62.97 215	88.58 254	90.07 218	72.94 152	59.22 300	78.30 300	42.31 285	92.70 252	65.59 227	72.00 228	81.79 319

bset_n11_16_dypcd		75.95 219	74.16 220	81.30 213	76.91 334	65.14 160	88.89 248	87.48 290	74.30 123	69.90 213	83.40 242	42.16 286	92.42 262	78.39 120	66.03 265	86.32 254

Fast-Effi-MVS+-dtu		75.04 231	73.37 231	80.07 241	80.86 292	59.52 277	91.20 189	85.38 310	71.90 182	65.20 261	84.84 227	41.46 287	92.97 237	66.50 216	72.96 221	87.73 229

MS-PatchMatch		77.90 191	76.50 190	82.12 195	85.99 234	69.95 37	91.75 164	92.70 113	73.97 130	62.58 286	84.44 232	41.11 288	95.78 140	63.76 240	92.17 72	80.62 328

our_test_3		68.29 283	64.69 289	79.11 264	78.92 317	64.85 170	88.40 258	85.06 313	60.32 307	52.68 326	76.12 320	40.81 289	89.80 306	44.25 323	55.65 328	82.67 311

XVG-OURS		74.25 238	72.46 242	79.63 253	78.45 324	57.59 298	80.33 317	87.39 291	63.86 278	68.76 227	89.62 174	40.50 290	91.72 280	69.00 193	74.25 211	89.58 207

VDD-MVS		83.06 97	81.81 108	86.81 52	90.86 136	67.70 88	95.40 28	91.50 164	75.46 104	81.78 79	92.34 132	40.09 291	97.13 87	86.85 54	82.04 159	95.60 48

DP-MVS		69.90 270	66.48 276	80.14 239	95.36 29	62.93 217	89.56 232	76.11 345	50.27 343	57.69 312	85.23 222	39.68 292	95.73 143	33.35 354	71.05 236	81.78 320

ppachtmachnet_test		67.72 287	63.70 296	79.77 250	78.92 317	66.04 134	88.68 252	82.90 333	60.11 309	55.45 317	75.96 321	39.19 293	90.55 292	39.53 339	52.55 337	82.71 308

ADS-MVSNet2		66.90 293	63.44 298	77.26 284	88.06 198	60.70 260	68.01 349	75.56 348	57.57 319	64.48 268	69.87 342	38.68 294	84.10 337	40.87 335	67.89 255	86.97 241

ADS-MVSNet		68.54 281	64.38 294	81.03 225	88.06 198	66.90 113	68.01 349	84.02 323	57.57 319	64.48 268	69.87 342	38.68 294	89.21 309	40.87 335	67.89 255	86.97 241

LPG-MVS_test		75.82 221	74.58 212	79.56 256	84.31 262	59.37 279	90.44 210	89.73 232	69.49 232	64.86 263	88.42 183	38.65 296	94.30 198	72.56 160	72.76 222	85.01 282

LGP-MVS_train				79.56 256	84.31 262	59.37 279		89.73 232	69.49 232	64.86 263	88.42 183	38.65 296	94.30 198	72.56 160	72.76 222	85.01 282

VDDNet		80.50 139	78.26 160	87.21 41	86.19 231	69.79 41	94.48 48	91.31 171	60.42 305	79.34 106	90.91 152	38.48 298	96.56 116	82.16 88	81.05 167	95.27 63

ACMP	71.68 10	75.58 226	74.23 219	79.62 254	84.97 251	59.64 274	90.80 202	89.07 257	70.39 222	62.95 282	87.30 203	38.28 299	93.87 221	72.89 154	71.45 233	85.36 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 151	78.68 154	83.45 163	89.96 151	61.51 243	92.13 140	90.79 192	76.83 91	78.85 117	86.33 212	38.16 300	96.17 124	67.93 201	87.17 120	92.67 157
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
anonymousdsp		71.14 262	69.37 264	76.45 291	72.95 345	54.71 318	84.19 288	88.88 262	61.92 297	62.15 288	79.77 292	38.14 301	91.44 289	68.90 195	67.45 259	83.21 301

xiu_mvs_v1_base_debu		82.16 111	81.12 114	85.26 110	86.42 226	68.72 63	92.59 128	90.44 202	73.12 149	84.20 60	94.36 80	38.04 302	95.73 143	84.12 72	86.81 122	91.33 184

xiu_mvs_v1_base		82.16 111	81.12 114	85.26 110	86.42 226	68.72 63	92.59 128	90.44 202	73.12 149	84.20 60	94.36 80	38.04 302	95.73 143	84.12 72	86.81 122	91.33 184

xiu_mvs_v1_base_debi		82.16 111	81.12 114	85.26 110	86.42 226	68.72 63	92.59 128	90.44 202	73.12 149	84.20 60	94.36 80	38.04 302	95.73 143	84.12 72	86.81 122	91.33 184

PVSNet_0	68.08 15	71.81 258	68.32 270	82.27 187	84.68 253	62.31 230	88.68 252	90.31 208	75.84 100	57.93 311	80.65 281	37.85 305	94.19 203	69.94 183	29.05 364	90.31 199

Anonymous20231206		67.53 290	65.78 280	72.79 316	74.95 340	47.59 349	88.23 259	87.32 292	61.75 300	58.07 308	77.29 309	37.79 306	87.29 327	42.91 326	63.71 286	83.48 296

ACMM	69.62 13	74.34 236	72.73 237	79.17 261	84.25 264	57.87 293	90.36 214	89.93 224	63.17 284	65.64 258	86.04 216	37.79 306	94.10 205	65.89 222	71.52 232	85.55 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 184	75.77 200	85.41 105	87.14 217	69.11 53	92.96 110	91.15 179	66.71 259	70.47 200	86.07 214	37.49 308	96.48 118	70.15 182	79.80 173	90.65 195

LS3D		69.17 274	66.40 277	77.50 278	91.92 107	56.12 310	85.12 283	80.37 339	46.96 350	56.50 316	87.51 200	37.25 309	93.71 224	32.52 359	79.40 175	82.68 310

MDA-MVSNet_test_wron		63.78 308	60.16 311	74.64 301	78.15 326	60.41 264	83.49 293	84.03 322	56.17 330	39.17 359	71.59 339	37.22 310	83.24 347	42.87 328	48.73 343	80.26 332

YYNet1		63.76 309	60.14 312	74.62 302	78.06 327	60.19 269	83.46 295	83.99 326	56.18 329	39.25 358	71.56 340	37.18 311	83.34 345	42.90 327	48.70 344	80.32 331

FMVSNet5		68.04 285	65.66 283	75.18 299	84.43 260	57.89 292	83.54 292	86.26 302	61.83 299	53.64 324	73.30 330	37.15 312	85.08 334	48.99 300	61.77 301	82.56 312

test20.03		63.83 307	62.65 303	67.38 336	70.58 353	39.94 361	86.57 279	84.17 321	63.29 282	51.86 329	77.30 308	37.09 313	82.47 350	38.87 343	54.13 334	79.73 335

PVSNet	73.49 8	80.05 147	78.63 155	84.31 140	90.92 133	64.97 167	92.47 132	91.05 186	79.18 53	72.43 181	90.51 159	37.05 314	94.06 209	68.06 199	86.00 132	93.90 124

EU-MVSNet		64.01 306	63.01 300	67.02 337	74.40 342	38.86 365	83.27 297	86.19 304	45.11 354	54.27 320	81.15 275	36.91 315	80.01 357	48.79 302	57.02 324	82.19 317

Anonymous20231211		73.08 246	70.39 257	81.13 219	90.62 140	63.33 207	91.40 175	90.06 221	51.84 338	64.46 270	80.67 280	36.49 316	94.07 208	63.83 239	64.17 282	85.98 265

FMVSNet1		72.71 254	69.91 261	81.10 221	83.60 272	65.11 161	90.01 224	90.32 205	63.92 277	63.56 277	80.25 287	36.35 317	91.54 284	54.46 282	66.75 262	86.64 247

Anonymous20240529		76.84 204	74.15 221	84.88 120	91.02 130	64.95 168	93.84 80	91.09 182	53.57 334	73.00 168	87.42 201	35.91 318	97.32 75	69.14 192	72.41 227	92.36 164

CMPMVS	48.56 21	66.77 294	64.41 293	73.84 308	70.65 352	50.31 338	77.79 333	85.73 309	45.54 353	44.76 351	82.14 255	35.40 319	90.14 302	63.18 244	74.54 210	81.07 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 289	64.76 288	76.00 295	72.82 347	53.37 323	88.71 251	86.78 298	53.19 335	57.58 313	78.03 304	35.33 320	92.41 263	55.56 279	54.88 332	82.21 316

PatchMatch-RL		72.06 257	69.98 258	78.28 270	89.51 162	55.70 313	83.49 293	83.39 330	61.24 301	63.72 276	82.76 247	34.77 321	93.03 236	53.37 288	77.59 191	86.12 262

LTVRE_ROB	59.60 19	66.27 296	63.54 297	74.45 303	84.00 267	51.55 331	67.08 352	83.53 327	58.78 316	54.94 318	80.31 285	34.54 322	93.23 233	40.64 337	68.03 253	78.58 344
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
UniMVSNet_ETH3D		72.74 253	70.53 256	79.36 258	78.62 323	56.64 307	85.01 284	89.20 248	63.77 279	64.84 265	84.44 232	34.05 323	91.86 277	63.94 238	70.89 237	89.57 208

F-COLMAP		70.66 263	68.44 268	77.32 282	86.37 229	55.91 311	88.00 261	86.32 300	56.94 325	57.28 314	88.07 192	33.58 324	92.49 260	51.02 292	68.37 251	83.55 293

pmmvs-eth3d		65.53 300	62.32 305	75.19 298	69.39 355	59.59 275	82.80 302	83.43 328	62.52 291	51.30 333	72.49 331	32.86 325	87.16 328	55.32 280	50.73 340	78.83 342

MDA-MVSNet-bldmvs		61.54 315	57.70 319	73.05 313	79.53 308	57.00 306	83.08 300	81.23 335	57.57 319	34.91 361	72.45 332	32.79 326	86.26 332	35.81 348	41.95 352	75.89 350

MIMVSNet		71.64 259	68.44 268	81.23 215	81.97 286	64.44 178	73.05 339	88.80 265	69.67 231	64.59 266	74.79 326	32.79 326	87.82 320	53.99 284	76.35 205	91.42 182

UnsupCasMVSNet_eth		65.79 299	63.10 299	73.88 307	70.71 351	50.29 339	81.09 312	89.88 226	72.58 160	49.25 340	74.77 327	32.57 328	87.43 326	55.96 278	41.04 354	83.90 291

N_pmnet		50.55 326	49.11 329	54.88 343	77.17 332	4.02 379	84.36 287	2.00 378	48.59 346	45.86 348	68.82 344	32.22 329	82.80 349	31.58 361	51.38 339	77.81 346

test_0402		64.54 303	61.09 309	74.92 300	84.10 266	60.75 258	87.95 262	79.71 341	52.03 337	52.41 327	77.20 310	32.21 330	91.64 281	23.14 363	61.03 308	72.36 355

DSMNet-mixed		56.78 322	54.44 325	63.79 339	63.21 361	29.44 369	64.43 354	64.10 365	42.12 358	51.32 332	71.60 338	31.76 331	75.04 359	36.23 346	65.20 272	86.87 244

MSDG		69.54 272	65.73 281	80.96 226	85.11 249	63.71 199	84.19 288	83.28 331	56.95 324	54.50 319	84.03 234	31.50 332	96.03 132	42.87 328	69.13 246	83.14 303

RPSCF		64.24 305	61.98 307	71.01 327	76.10 338	45.00 353	75.83 336	75.94 346	46.94 351	58.96 303	84.59 230	31.40 333	82.00 354	47.76 309	60.33 316	86.04 263

jajsoiax		73.05 247	71.51 251	77.67 276	77.46 330	54.83 317	88.81 250	90.04 222	69.13 239	62.85 284	83.51 240	31.16 334	92.75 249	70.83 175	69.80 239	85.43 277

MVS-HIRNet		60.25 318	55.55 324	74.35 304	84.37 261	56.57 308	71.64 341	74.11 351	34.44 361	45.54 350	42.24 363	31.11 335	89.81 304	40.36 338	76.10 206	76.67 349

SixPastTwentyTwo		64.92 301	61.78 308	74.34 305	78.74 320	49.76 340	83.42 296	79.51 342	62.86 286	50.27 336	77.35 307	30.92 336	90.49 294	45.89 316	47.06 346	82.78 304

KD-MVS_self_test		60.87 316	58.60 316	67.68 334	66.13 358	39.93 362	75.63 337	84.70 317	57.32 322	49.57 339	68.45 345	29.55 337	82.87 348	48.09 304	47.94 345	80.25 333

mvs_tets		72.71 254	71.11 252	77.52 277	77.41 331	54.52 319	88.45 257	89.76 228	68.76 244	62.70 285	83.26 244	29.49 338	92.71 250	70.51 181	69.62 241	85.34 279

Anonymous202405211		77.96 188	75.33 204	85.87 87	93.73 60	64.52 173	94.85 44	85.36 311	62.52 291	76.11 142	90.18 166	29.43 339	97.29 77	68.51 197	77.24 200	95.81 44

K. test v3		63.09 310	59.61 314	73.53 310	76.26 337	49.38 343	83.27 297	77.15 344	64.35 274	47.77 344	72.32 335	28.73 340	87.79 321	49.93 297	36.69 358	83.41 298

UnsupCasMVSNet_bld		61.60 314	57.71 318	73.29 312	68.73 356	51.64 330	78.61 327	89.05 258	57.20 323	46.11 345	61.96 353	28.70 341	88.60 312	50.08 296	38.90 356	79.63 336

lessismore_v0				73.72 309	72.93 346	47.83 348		61.72 368		45.86 348	73.76 328	28.63 342	89.81 304	47.75 310	31.37 363	83.53 294

new-patchmatchnet		59.30 321	56.48 322	67.79 333	65.86 359	44.19 354	82.47 303	81.77 334	59.94 310	43.65 355	66.20 348	27.67 343	81.68 355	39.34 340	41.40 353	77.50 347

ACMH	63.93 17	68.62 279	64.81 287	80.03 242	85.22 245	63.25 208	87.72 266	84.66 318	60.83 303	51.57 331	79.43 296	27.29 344	94.96 174	41.76 331	64.84 275	81.88 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 302	62.17 306	72.21 321	76.08 339	47.35 350	80.67 314	81.02 336	56.19 328	51.60 330	79.66 294	27.05 345	88.56 313	53.60 287	53.63 335	80.71 327

ACMH+	65.35 16	67.65 288	64.55 290	76.96 288	84.59 256	57.10 305	88.08 260	80.79 337	58.59 318	53.00 325	81.09 276	26.63 346	92.95 238	46.51 312	61.69 305	80.82 325

OpenMVS_ROB	61.12 18	66.39 295	62.92 301	76.80 290	76.51 335	57.77 294	89.22 241	83.41 329	55.48 331	53.86 323	77.84 305	26.28 347	93.95 218	34.90 351	68.76 248	78.68 343

MVS_0304		68.99 278	67.23 275	74.28 306	80.36 299	52.54 326	87.01 275	86.36 299	59.89 311	66.22 255	73.56 329	24.25 348	88.03 318	57.34 274	70.11 238	82.27 315

COLMAP_ROB	57.96 20	62.98 311	59.65 313	72.98 314	81.44 289	53.00 325	83.75 291	75.53 349	48.34 348	48.81 341	81.40 268	24.14 349	90.30 295	32.95 356	60.52 313	75.65 351
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 319	57.33 320	68.67 331	69.71 354	44.13 355	78.92 326	84.21 320	55.05 332	44.63 352	71.85 337	23.91 350	81.54 356	32.63 358	55.03 331	80.35 330

testgi		64.48 304	62.87 302	69.31 330	71.24 348	40.62 360	85.49 282	79.92 340	65.36 269	54.18 321	83.49 241	23.74 351	84.55 336	41.60 332	60.79 311	82.77 305

ITE_SJBPF				70.43 328	74.44 341	47.06 351		77.32 343	60.16 308	54.04 322	83.53 239	23.30 352	84.01 339	43.07 325	61.58 306	80.21 334

EG-PatchMatch MVS		68.55 280	65.41 285	77.96 274	78.69 321	62.93 217	89.86 229	89.17 250	60.55 304	50.27 336	77.73 306	22.60 353	94.06 209	47.18 311	72.65 224	76.88 348

tmp_tt		22.26 338	23.75 340	17.80 354	5.23 378	12.06 378	35.26 365	39.48 373	2.82 373	18.94 366	44.20 362	22.23 354	24.64 373	36.30 345	9.31 370	16.69 367

USDC		67.43 292	64.51 291	76.19 293	77.94 328	55.29 315	78.38 329	85.00 314	73.17 147	48.36 342	80.37 284	21.23 355	92.48 261	52.15 290	64.02 284	80.81 326

Anonymous20240521		62.09 312	59.08 315	71.10 326	67.19 357	48.72 345	83.91 290	85.23 312	50.38 342	47.84 343	71.22 341	20.74 356	85.51 333	46.47 313	58.75 321	79.06 340

XVG-ACMP-BASELINE		68.04 285	65.53 284	75.56 296	74.06 343	52.37 327	78.43 328	85.88 307	62.03 295	58.91 304	81.21 274	20.38 357	91.15 290	60.69 259	68.18 252	83.16 302

AllTest		61.66 313	58.06 317	72.46 318	79.57 306	51.42 333	80.17 320	68.61 360	51.25 339	45.88 346	81.23 270	19.86 358	86.58 330	38.98 341	57.01 325	79.39 337

TestCases				72.46 318	79.57 306	51.42 333		68.61 360	51.25 339	45.88 346	81.23 270	19.86 358	86.58 330	38.98 341	57.01 325	79.39 337

pmmvs3		55.51 323	51.50 328	67.53 335	57.90 366	50.93 336	80.37 316	73.66 352	40.63 359	44.15 354	64.75 351	16.30 360	78.97 358	44.77 322	40.98 355	72.69 353

TDRefinement		55.28 324	51.58 327	66.39 338	59.53 365	46.15 352	76.23 335	72.80 353	44.60 355	42.49 356	76.28 319	15.29 361	82.39 351	33.20 355	43.75 350	70.62 357

new_pmnet		49.31 327	46.44 330	57.93 341	62.84 362	40.74 359	68.47 348	62.96 367	36.48 360	35.09 360	57.81 355	14.97 362	72.18 360	32.86 357	46.44 347	60.88 361

TinyColmap		60.32 317	56.42 323	72.00 324	78.78 319	53.18 324	78.36 330	75.64 347	52.30 336	41.59 357	75.82 323	14.76 363	88.35 315	35.84 347	54.71 333	74.46 352

LF4IMVS		54.01 325	52.12 326	59.69 340	62.41 363	39.91 363	68.59 347	68.28 362	42.96 357	44.55 353	75.18 324	14.09 364	68.39 362	41.36 334	51.68 338	70.78 356

PM-MVS		59.40 320	56.59 321	67.84 332	63.63 360	41.86 357	76.76 334	63.22 366	59.01 315	51.07 334	72.27 336	11.72 365	83.25 346	61.34 255	50.28 342	78.39 345

ambc				69.61 329	61.38 364	41.35 358	49.07 363	85.86 308		50.18 338	66.40 347	10.16 366	88.14 317	45.73 317	44.20 349	79.32 339

FPMVS		45.64 328	43.10 331	53.23 345	51.42 369	36.46 366	64.97 353	71.91 355	29.13 363	27.53 363	61.55 354	9.83 367	65.01 366	16.00 366	55.58 329	58.22 362

ANet_high		40.27 330	35.20 333	55.47 342	34.74 375	34.47 367	63.84 355	71.56 356	48.42 347	18.80 367	41.08 364	9.52 368	64.45 367	20.18 364	8.66 371	67.49 359

test_method		38.59 331	35.16 334	48.89 347	54.33 367	21.35 374	45.32 364	53.71 369	7.41 371	28.74 362	51.62 357	8.70 369	52.87 368	33.73 352	32.89 362	72.47 354

EMVS		23.76 337	23.20 341	25.46 353	41.52 373	16.90 377	60.56 357	38.79 375	14.62 369	8.99 373	20.24 371	7.35 370	45.82 371	7.25 371	9.46 369	13.64 369

E-PMN		24.61 335	24.00 339	26.45 352	43.74 372	18.44 376	60.86 356	39.66 372	15.11 368	9.53 372	22.10 368	6.52 371	46.94 370	8.31 370	10.14 368	13.98 368

DeepMVS_CX				34.71 351	51.45 368	24.73 373		28.48 377	31.46 362	17.49 368	52.75 356	5.80 372	42.60 372	18.18 365	19.42 365	36.81 364

Gipuma		34.91 333	31.44 336	45.30 348	70.99 350	39.64 364	19.85 368	72.56 354	20.10 367	16.16 369	21.47 369	5.08 373	71.16 361	13.07 367	43.70 351	25.08 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 329	35.79 332	54.76 344	36.92 374	30.81 368	51.41 361	69.02 359	22.07 365	24.63 364	45.37 361	4.56 374	65.81 364	33.67 353	34.50 361	67.67 358

PMMVS2		37.93 332	33.61 335	50.92 346	46.31 371	24.76 372	60.55 358	50.05 370	28.94 364	20.93 365	47.59 358	4.41 375	65.13 365	25.14 362	18.55 366	62.87 360

PMVS	26.43 22	31.84 334	28.16 337	42.89 349	25.87 377	27.58 370	50.92 362	49.78 371	21.37 366	14.17 370	40.81 365	2.01 376	66.62 363	9.61 369	38.88 357	34.49 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 336	19.77 342	38.09 350	34.56 376	26.92 371	26.57 366	38.87 374	11.73 370	11.37 371	27.44 366	1.37 377	50.42 369	11.41 368	14.60 367	36.93 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 340	10.95 343	12.33 355	48.05 370	19.89 375	25.89 367	1.92 379	3.58 372	3.12 374	1.37 373	0.64 378	15.77 374	6.23 372	7.77 372	1.35 370

test123		6.92 343	9.21 346	0.08 356	0.03 380	0.05 380	81.65 308	0.01 381	0.02 375	0.14 376	0.85 375	0.03 379	0.02 375	0.12 374	0.00 374	0.16 371

testmvs		7.23 342	9.62 345	0.06 357	0.04 379	0.02 381	84.98 285	0.02 380	0.03 374	0.18 375	1.21 374	0.01 380	0.02 375	0.14 373	0.01 373	0.13 372

test_blank		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

uanet_test		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

sosnet-low-res		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

sosnet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

uncertanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

Regformer		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

ab-mvs-re		7.91 341	10.55 344	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	94.95 62	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

uanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 374	0.00 373

FOURS1					93.95 51	61.77 238	93.96 67	91.92 144	62.14 294	86.57 34

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

eth-test2					0.00 381

eth-test					0.00 381

IU-MVS					96.46 13	69.91 38		95.18 13	80.75 37	95.28 1				92.34 7	95.36 14	96.47 24

save fliter					93.84 54	67.89 83	95.05 39	92.66 116	78.19 67

test_0728_SECOND				88.70 16	96.45 14	70.43 28	96.64 8	94.37 49					99.15 2	91.91 13	94.90 21	96.51 20

GSMVS																94.68 86

test_part2					96.29 21	68.16 77				90.78 12

MTGPA								92.23 130

MTMP							93.77 82	32.52 376

gm-plane-assit					88.42 187	67.04 109			78.62 64		91.83 139		97.37 71	76.57 131

test9_res														89.41 24	94.96 18	95.29 59

agg_prior2														86.41 55	94.75 30	95.33 55

agg_prior					94.16 48	66.97 111		93.31 89		84.49 57			96.75 110

test_prior4						67.18 105	93.92 73

test_prior				86.42 70	94.71 37	67.35 98		93.10 101					96.84 106			95.05 73

旧先验2							92.00 151		59.37 314	87.54 28			93.47 230	75.39 137

新几何2							91.41 173

无先验							92.71 119	92.61 120	62.03 295				97.01 92	66.63 211		93.97 119

原ACMM2							92.01 148

testdata2													96.09 127	61.26 256

testdata1							89.21 242		77.55 82

plane_prior7					86.94 219	61.51 243

plane_prior5								91.31 171					95.55 157	76.74 129	78.53 185	88.39 222

plane_prior4											89.14 178

plane_prior3						61.95 236			79.09 56	72.53 177

plane_prior2							93.13 102		78.81 61

plane_prior1					87.15 216

plane_prior						62.42 226	93.85 77		79.38 49						78.80 182

n2								0.00 382

nn								0.00 382

door-mid								66.01 364

test11								93.01 103

door								66.57 363

HQP5-MVS						63.66 202

HQP-NCC					87.54 208		94.06 62		79.80 43	74.18 158

ACMP_Plane					87.54 208		94.06 62		79.80 43	74.18 158

BP-MVS														77.63 126

HQP4-MVS										74.18 158			95.61 151			88.63 216

HQP3-MVS								91.70 155							78.90 180

NP-MVS					87.41 211	63.04 213					90.30 164

ACMMP++_ref															71.63 230

ACMMP++															69.72 240

High-resolution multi-view benchmark