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.

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 82	96.94 102	75.19 138	94.09 40	95.66 46

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

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

DVP-MVS++.		90.53 3	91.09 4	88.87 14	97.31 4	69.91 38	93.96 67	94.37 49	72.48 161	92.07 6	96.85 12	83.82 2	99.15 2	91.53 15	97.42 4	97.55 4

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

DP-MVS Recon		82.73 101	81.65 108	85.98 83	97.31 4	67.06 107	95.15 35	91.99 141	69.08 238	76.50 139	93.89 98	54.48 192	98.20 36	70.76 175	85.66 132	92.69 154

testtj		86.62 42	86.66 40	86.50 67	96.95 8	65.70 143	94.41 50	93.45 83	67.74 247	86.19 38	96.39 23	64.38 81	97.91 46	87.33 48	93.14 58	95.90 42

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 84	89.13 21	97.38 2	74.49 17	98.48 28	92.32 10	95.98 8	96.46 25

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

ZD-MVS					96.63 11	65.50 150		93.50 81	70.74 217	85.26 52	95.19 56	64.92 77	97.29 77	87.51 44	93.01 59

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

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

SED-MVS		89.94 8	90.36 8	88.70 16	96.45 14	69.38 48	96.89 4	94.44 42	71.65 192	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	71.65 192	92.11 4	97.05 8	76.79 9	99.11 6

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

DVP-MVS		89.41 11	89.73 12	88.45 21	96.40 17	69.99 34	96.64 8	94.52 38	71.92 178	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	71.92 178	91.89 8	97.11 7	73.77 20

AdaColmap		78.94 165	77.00 182	84.76 123	96.34 19	65.86 139	92.66 124	87.97 285	62.18 291	70.56 197	92.37 131	43.53 278	97.35 73	64.50 233	82.86 151	91.05 190

test_one_0601					96.32 20	69.74 43		94.18 55	71.42 204	90.67 14	96.85 12	74.45 18

test_part2					96.29 21	68.16 77				90.78 12

DPE-MVS		88.77 14	89.21 14	87.45 36	96.26 22	67.56 91	94.17 54	94.15 57	68.77 241	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
MAR-MVS		84.18 78	83.43 78	86.44 69	96.25 23	65.93 138	94.28 53	94.27 53	74.41 117	79.16 110	95.61 40	53.99 197	98.88 20	69.62 184	93.26 56	94.50 95
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
API-MVS		82.28 108	80.53 124	87.54 34	96.13 24	70.59 26	93.63 86	91.04 186	65.72 265	75.45 149	92.83 121	56.11 173	98.89 19	64.10 235	89.75 101	93.15 142

APDe-MVS		87.54 24	87.84 21	86.65 60	96.07 25	66.30 129	94.84 45	93.78 64	69.35 232	88.39 24	96.34 25	67.74 48	97.66 58	90.62 20	93.44 54	96.01 38

PAPR		85.15 60	84.47 65	87.18 42	96.02 26	68.29 72	91.85 155	93.00 105	76.59 92	79.03 111	95.00 60	61.59 111	97.61 62	78.16 122	89.00 103	95.63 47

APD-MVS		85.93 50	85.99 46	85.76 92	95.98 27	65.21 156	93.59 88	92.58 121	66.54 258	86.17 39	95.88 33	63.83 88	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
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 80	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
DP-MVS		69.90 268	66.48 274	80.14 237	95.36 29	62.93 216	89.56 230	76.11 343	50.27 341	57.69 310	85.23 220	39.68 290	95.73 143	33.35 352	71.05 234	81.78 318

114514_t		79.17 161	77.67 168	83.68 153	95.32 30	65.53 149	92.85 115	91.60 159	63.49 278	67.92 234	90.63 155	46.65 260	95.72 147	67.01 207	83.54 149	89.79 202

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	88.64 107	94.26 100

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	88.64 107	94.20 102

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

CSCG		86.87 37	86.26 42	88.72 15	95.05 34	70.79 24	93.83 81	95.33 11	68.48 245	77.63 126	94.35 84	73.04 23	98.45 29	84.92 67	93.71 50	96.92 11

LFMVS		84.34 73	82.73 94	89.18 13	94.76 35	73.25 10	94.99 42	91.89 146	71.90 180	82.16 77	93.49 105	47.98 252	97.05 89	82.55 87	84.82 137	97.25 7

CDPH-MVS		85.71 53	85.46 55	86.46 68	94.75 36	67.19 103	93.89 75	92.83 110	70.90 212	83.09 71	95.28 48	63.62 92	97.36 72	80.63 102	94.18 39	94.84 80

test_prior3		87.38 27	87.70 24	86.42 70	94.71 37	67.35 98	95.10 37	93.10 101	75.40 105	85.25 53	95.61 40	67.94 43	96.84 106	87.47 45	94.77 25	95.05 73

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

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 80

test_yl		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 235	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 159	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	69.19 235	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 159	96.23 32

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

test_8					94.19 43	67.19 103	94.15 57	93.42 86	71.87 183	85.38 50	95.35 44	68.19 39	96.95 101

TEST9					94.18 44	67.28 100	94.16 55	93.51 79	71.75 190	85.52 48	95.33 45	68.01 42	97.27 81

train_agg		87.21 30	87.42 30	86.60 61	94.18 44	67.28 100	94.16 55	93.51 79	71.87 183	85.52 48	95.33 45	68.19 39	97.27 81	89.09 29	94.90 21	95.25 66

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	84.36 141	92.97 149

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	84.36 141	92.88 153

agg_prior1		87.02 35	87.26 32	86.28 77	94.16 48	66.97 111	94.08 61	93.31 89	71.85 185	84.49 57	95.39 43	68.91 36	96.75 110	88.84 34	94.32 38	95.13 69

agg_prior					94.16 48	66.97 111		93.31 89		84.49 57			96.75 110

PAPM_NR		82.97 98	81.84 105	86.37 73	94.10 50	66.76 118	87.66 265	92.84 109	69.96 225	74.07 160	93.57 103	63.10 101	97.50 65	70.66 177	90.58 94	94.85 79

FOURS1					93.95 51	61.77 236	93.96 67	91.92 144	62.14 292	86.57 34

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	58.22 145	97.00 94	85.22 64	84.33 144	96.52 19

9.14			87.63 25		93.86 53		94.41 50	94.18 55	72.76 155	86.21 37	96.51 19	66.64 57	97.88 49	90.08 23	94.04 41

xxxxxxxxxxxxxcwj		87.14 31	87.19 33	86.99 48	93.84 54	67.89 83	95.05 39	84.72 314	78.19 67	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

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

PVSNet_BlendedMVS		83.38 92	83.43 78	83.22 164	93.76 56	67.53 93	94.06 62	93.61 75	79.13 55	81.00 89	85.14 221	63.19 99	97.29 77	87.08 51	73.91 213	84.83 282

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 99	97.29 77	87.08 51	91.38 83	94.13 108

HFP-MVS		84.73 66	84.40 68	85.72 93	93.75 58	65.01 165	93.50 92	93.19 95	72.19 172	79.22 108	94.93 64	59.04 139	97.67 55	81.55 93	92.21 69	94.49 96

#test#		84.98 63	84.74 64	85.72 93	93.75 58	65.01 165	94.09 60	93.19 95	73.55 140	79.22 108	94.93 64	59.04 139	97.67 55	82.66 84	92.21 69	94.49 96

Anonymous202405211		77.96 186	75.33 202	85.87 87	93.73 60	64.52 173	94.85 44	85.36 309	62.52 289	76.11 140	90.18 164	29.43 337	97.29 77	68.51 195	77.24 198	95.81 44

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 88	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
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	72.92 152	87.13 29	96.62 17	67.81 47	97.94 43	90.13 22	94.42 36	95.09 71

ACMMPR		84.37 71	84.06 70	85.28 109	93.56 63	64.37 183	93.50 92	93.15 98	72.19 172	78.85 117	94.86 68	56.69 166	97.45 66	81.55 93	92.20 71	94.02 116

region2R		84.36 72	84.03 71	85.36 107	93.54 64	64.31 185	93.43 95	92.95 106	72.16 175	78.86 116	94.84 69	56.97 161	97.53 64	81.38 97	92.11 73	94.24 101

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	88.78 104	96.59 15

PHI-MVS		86.83 39	86.85 39	86.78 54	93.47 66	65.55 148	95.39 29	95.10 17	71.77 189	85.69 47	96.52 18	62.07 108	98.77 21	86.06 58	95.60 12	96.03 37

SR-MVS		82.81 100	82.58 96	83.50 159	93.35 67	61.16 247	92.23 137	91.28 173	64.48 271	81.27 84	95.28 48	53.71 201	95.86 137	82.87 81	88.77 105	93.49 132

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
test1172		81.90 116	81.83 106	82.13 192	93.23 69	57.52 297	91.61 167	90.98 188	64.32 273	80.20 97	95.00 60	51.26 222	95.61 151	81.73 92	88.13 111	93.26 138

XVS		83.87 85	83.47 76	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 126	78.18 120	94.31 88	55.25 180	97.41 69	79.16 111	91.58 79	93.95 118

X-MVStestdata		76.86 200	74.13 220	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 126	78.18 120	10.19 370	55.25 180	97.41 69	79.16 111	91.58 79	93.95 118

SMA-MVS		88.14 15	88.29 18	87.67 28	93.21 72	68.72 63	93.85 77	94.03 60	74.18 123	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
原ACMM1				84.42 136	93.21 72	64.27 188		93.40 88	65.39 266	79.51 105	92.50 125	58.11 147	96.69 112	65.27 229	93.96 42	92.32 164

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	62.25 107	98.15 38	85.93 59	91.15 87	94.15 107

CP-MVS		83.71 89	83.40 81	84.65 129	93.14 75	63.84 193	94.59 47	92.28 128	71.03 210	77.41 129	94.92 66	55.21 183	96.19 123	81.32 98	90.70 92	93.91 120

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
ZNCC-MVS		85.33 58	85.08 59	86.06 81	93.09 77	65.65 145	93.89 75	93.41 87	73.75 134	79.94 99	94.68 73	60.61 120	98.03 41	82.63 86	93.72 49	94.52 93

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 78	58.16 289	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

PLC	68.80 14	75.23 227	73.68 226	79.86 245	92.93 79	58.68 284	90.64 205	88.30 276	60.90 300	64.43 269	90.53 156	42.38 282	94.57 185	56.52 273	76.54 202	86.33 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DWT-MVSNet_test		83.95 83	82.80 92	87.41 37	92.90 80	70.07 33	89.12 243	94.42 44	82.15 20	77.64 125	91.77 140	70.81 33	96.22 122	65.03 230	81.36 163	95.94 39

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
mPP-MVS		82.96 99	82.44 99	84.52 133	92.83 81	62.92 218	92.76 116	91.85 148	71.52 200	75.61 147	94.24 90	53.48 205	96.99 97	78.97 114	90.73 91	93.64 129

GST-MVS		84.63 69	84.29 69	85.66 97	92.82 83	65.27 154	93.04 106	93.13 99	73.20 144	78.89 112	94.18 92	59.41 135	97.85 50	81.45 95	92.48 68	93.86 123

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	82.25 155	96.54 18

PGM-MVS		83.25 93	82.70 95	84.92 118	92.81 85	64.07 191	90.44 208	92.20 135	71.28 205	77.23 132	94.43 78	55.17 184	97.31 76	79.33 110	91.38 83	93.37 133

EI-MVSNet-Vis-set		83.77 87	83.67 73	84.06 144	92.79 86	63.56 205	91.76 160	94.81 27	79.65 47	77.87 122	94.09 93	63.35 97	97.90 47	79.35 109	79.36 174	90.74 192

SF-MVS		87.03 34	87.09 35	86.84 50	92.70 87	67.45 97	93.64 85	93.76 67	70.78 216	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

MVSTER		82.47 105	82.05 102	83.74 149	92.68 88	69.01 56	91.90 152	93.21 92	79.83 42	72.14 182	85.71 217	74.72 15	94.72 180	75.72 135	72.49 223	87.50 229

MP-MVS		85.02 61	84.97 61	85.17 113	92.60 89	64.27 188	93.24 99	92.27 129	73.13 146	79.63 104	94.43 78	61.90 109	97.17 84	85.00 65	92.56 65	94.06 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres200		79.66 152	78.33 156	83.66 155	92.54 90	65.82 142	93.06 104	96.31 3	74.90 114	73.30 165	88.66 178	59.67 131	95.61 151	47.84 306	78.67 181	89.56 207

test_part1		79.63 153	77.86 167	84.93 117	92.50 91	71.43 17	94.15 57	91.08 183	72.51 160	70.66 196	84.98 223	59.84 128	95.07 169	72.07 165	62.94 287	88.30 223

APD-MVS_3200maxsize		81.64 119	81.32 111	82.59 177	92.36 92	58.74 283	91.39 175	91.01 187	63.35 279	79.72 103	94.62 74	51.82 215	96.14 125	79.71 106	87.93 113	92.89 152

新几何1				84.73 125	92.32 93	64.28 187		91.46 166	59.56 310	79.77 102	92.90 117	56.95 162	96.57 115	63.40 239	92.91 61	93.34 134

1121		81.25 123	80.05 128	84.87 121	92.30 94	64.31 185	87.91 261	91.39 168	59.44 311	79.94 99	92.91 116	57.09 155	97.01 92	66.63 209	92.81 63	93.29 137

EI-MVSNet-UG-set		83.14 95	82.96 88	83.67 154	92.28 95	63.19 211	91.38 177	94.68 33	79.22 52	76.60 137	93.75 99	62.64 103	97.76 53	78.07 123	78.01 185	90.05 200

HPM-MVS		83.25 93	82.95 89	84.17 142	92.25 96	62.88 220	90.91 194	91.86 147	70.30 221	77.12 133	93.96 97	56.75 164	96.28 120	82.04 89	91.34 85	93.34 134
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 74	83.36 83	87.02 47	92.22 97	67.74 87	84.65 284	94.50 39	79.15 54	82.23 76	87.93 192	66.88 54	96.94 102	80.53 103	82.20 156	96.39 28

tfpn200view9		78.79 170	77.43 173	82.88 169	92.21 98	64.49 174	92.05 145	96.28 4	73.48 141	71.75 188	88.26 186	60.07 126	95.32 164	45.16 316	77.58 190	88.83 210

thres400		78.68 173	77.43 173	82.43 179	92.21 98	64.49 174	92.05 145	96.28 4	73.48 141	71.75 188	88.26 186	60.07 126	95.32 164	45.16 316	77.58 190	87.48 230

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	53.55 202	97.89 48	91.10 17	93.31 55	94.54 91

SR-MVS-dyc-post		81.06 128	80.70 120	82.15 190	92.02 101	58.56 285	90.90 195	90.45 198	62.76 285	78.89 112	94.46 76	51.26 222	95.61 151	78.77 117	86.77 123	92.28 166

RE-MVS-def			80.48 125		92.02 101	58.56 285	90.90 195	90.45 198	62.76 285	78.89 112	94.46 76	49.30 239		78.77 117	86.77 123	92.28 166

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	58.98 141	96.78 108	83.49 77	93.93 43	96.29 30

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	71.48 203	85.72 45	96.32 26	68.13 41	98.00 42	89.06 31	94.70 31	94.65 87

旧先验1					91.94 105	60.74 257		91.50 164			94.36 80	65.23 71			91.84 74	94.55 89

thres600view7		78.00 184	76.66 186	82.03 198	91.93 106	63.69 200	91.30 182	96.33 1	72.43 164	70.46 199	87.89 193	60.31 121	94.92 175	42.64 328	76.64 201	87.48 230

LS3D		69.17 272	66.40 275	77.50 276	91.92 107	56.12 308	85.12 281	80.37 337	46.96 348	56.50 314	87.51 198	37.25 307	93.71 222	32.52 357	79.40 173	82.68 308

GG-mvs-BLEND				86.53 66	91.91 108	69.67 46	75.02 336	94.75 30		78.67 119	90.85 151	77.91 7	94.56 187	72.25 161	93.74 48	95.36 54

thres100view900		78.37 179	77.01 181	82.46 178	91.89 109	63.21 210	91.19 188	96.33 1	72.28 169	70.45 200	87.89 193	60.31 121	95.32 164	45.16 316	77.58 190	88.83 210

zzz-MVS		84.73 66	84.47 65	85.50 100	91.89 109	65.16 158	91.55 168	92.23 130	75.32 107	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 103

MTAPA		83.91 84	83.38 82	85.50 100	91.89 109	65.16 158	81.75 304	92.23 130	75.32 107	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 103

RRT_test8_iter05		80.61 134	79.62 137	83.60 156	91.87 112	66.90 113	93.42 97	93.68 72	77.09 86	68.83 223	85.63 218	66.82 55	95.42 160	76.46 133	62.74 289	88.48 217

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 94	96.93 104	88.18 38	84.34 143	97.11 8

TSAR-MVS + MP.		88.11 17	88.64 15	86.54 65	91.73 114	68.04 79	90.36 212	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
ACMMP		81.49 120	80.67 121	83.93 146	91.71 115	62.90 219	92.13 139	92.22 134	71.79 188	71.68 190	93.49 105	50.32 228	96.96 100	78.47 119	84.22 148	91.93 173
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
BH-RMVSNet		79.46 158	77.65 169	84.89 119	91.68 116	65.66 144	93.55 89	88.09 282	72.93 151	73.37 164	91.12 148	46.20 266	96.12 126	56.28 275	85.61 133	92.91 151

baseline1		81.84 117	81.03 117	84.28 141	91.60 117	66.62 121	91.08 191	91.66 157	81.87 24	74.86 152	91.67 144	69.98 35	94.92 175	71.76 168	64.75 275	91.29 187

ACMMP_NAP		86.05 48	85.80 50	86.80 53	91.58 118	67.53 93	91.79 157	93.49 82	74.93 113	84.61 55	95.30 47	59.42 134	97.92 45	86.13 57	94.92 19	94.94 78

MVS_Test		84.16 79	83.20 84	87.05 46	91.56 119	69.82 40	89.99 225	92.05 138	77.77 76	82.84 72	86.57 207	63.93 87	96.09 127	74.91 144	89.18 102	95.25 66

HPM-MVS_fast		80.25 141	79.55 141	82.33 183	91.55 120	59.95 269	91.32 181	89.16 250	65.23 269	74.71 153	93.07 112	47.81 254	95.74 142	74.87 146	88.23 109	91.31 186

CPTT-MVS		79.59 154	79.16 149	80.89 227	91.54 121	59.80 271	92.10 141	88.54 273	60.42 303	72.96 167	93.28 107	48.27 248	92.80 245	78.89 116	86.50 128	90.06 199

CNLPA		74.31 235	72.30 241	80.32 232	91.49 122	61.66 239	90.85 198	80.72 336	56.67 325	63.85 273	90.64 153	46.75 259	90.84 289	53.79 283	75.99 205	88.47 219

MP-MVS-pluss		85.24 59	85.13 58	85.56 99	91.42 123	65.59 147	91.54 169	92.51 123	74.56 116	80.62 92	95.64 39	59.15 138	97.00 94	86.94 53	93.80 46	94.07 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 198	74.31 215	85.80 90	91.42 123	68.36 70	71.78 338	94.72 31	49.61 342	77.12 133	45.92 358	77.41 8	93.98 214	67.62 202	93.16 57	95.05 73

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	53.45 206	97.68 54	91.07 18	92.62 64	94.54 91

EIA-MVS		84.84 65	84.88 62	84.69 128	91.30 126	62.36 227	93.85 77	92.04 139	79.45 48	79.33 107	94.28 89	62.42 106	96.35 119	80.05 105	91.25 86	95.38 53

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	84.33 144	96.65 13

EPMVS		78.49 178	75.98 195	86.02 82	91.21 128	69.68 45	80.23 317	91.20 174	75.25 109	72.48 177	78.11 301	54.65 188	93.69 223	57.66 271	83.04 150	94.69 83

FMVSNet3		77.73 190	76.04 194	82.80 170	91.20 129	68.99 57	91.87 153	91.99 141	73.35 143	67.04 247	83.19 243	56.62 167	92.14 268	59.80 263	69.34 241	87.28 236

Anonymous20240529		76.84 202	74.15 219	84.88 120	91.02 130	64.95 168	93.84 80	91.09 181	53.57 332	73.00 166	87.42 199	35.91 316	97.32 75	69.14 190	72.41 225	92.36 162

tpmvs		72.88 249	69.76 261	82.22 188	90.98 131	67.05 108	78.22 329	88.30 276	63.10 283	64.35 270	74.98 323	55.09 185	94.27 198	43.25 322	69.57 240	85.34 277

MVS		84.66 68	82.86 91	90.06 2	90.93 132	74.56 6	87.91 261	95.54 9	68.55 243	72.35 181	94.71 72	59.78 130	98.90 18	81.29 99	94.69 32	96.74 12

PVSNet	73.49 8	80.05 145	78.63 153	84.31 139	90.92 133	64.97 167	92.47 131	91.05 185	79.18 53	72.43 179	90.51 157	37.05 312	94.06 207	68.06 197	86.00 130	93.90 122

RRT_MVS		77.38 195	76.59 187	79.77 248	90.91 134	63.61 204	91.15 189	90.91 189	72.28 169	72.06 184	87.28 202	43.92 276	89.04 308	73.32 149	67.47 256	86.67 244

3Dnovator+	73.60 7	82.10 113	80.60 123	86.60 61	90.89 135	66.80 117	95.20 33	93.44 85	74.05 125	67.42 242	92.49 127	49.46 237	97.65 59	70.80 174	91.68 77	95.33 55

VDD-MVS		83.06 96	81.81 107	86.81 52	90.86 136	67.70 88	95.40 28	91.50 164	75.46 102	81.78 79	92.34 132	40.09 289	97.13 87	86.85 54	82.04 157	95.60 48

BH-w/o		80.49 138	79.30 146	84.05 145	90.83 137	64.36 184	93.60 87	89.42 240	74.35 119	69.09 216	90.15 165	55.23 182	95.61 151	64.61 232	86.43 129	92.17 171

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	58.19 304	93.64 101	73.64 22	92.35 265	82.66 84	78.66 182	96.50 23

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	62.47 105	95.81 138	82.73 83	91.00 89	93.15 142

Anonymous20231211		73.08 244	70.39 255	81.13 217	90.62 140	63.33 207	91.40 173	90.06 220	51.84 336	64.46 268	80.67 278	36.49 314	94.07 206	63.83 237	64.17 280	85.98 263

TR-MVS		78.77 171	77.37 178	82.95 168	90.49 141	60.88 251	93.67 84	90.07 217	70.08 224	74.51 154	91.37 146	45.69 267	95.70 148	60.12 261	80.32 169	92.29 165

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	94.06 113

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 96	97.94 43	87.40 47	93.55 53	95.42 51
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 231	73.53 227	79.17 259	90.40 144	52.07 327	89.19 241	89.61 235	62.69 287	70.07 205	92.67 123	48.89 246	94.32 195	38.26 342	79.97 170	91.12 189
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 122	79.99 131	85.46 102	90.39 145	68.40 69	86.88 275	90.61 197	74.41 117	70.31 203	84.67 227	63.79 89	92.32 266	73.13 150	85.70 131	95.67 45

CANet_DTU		84.09 80	83.52 74	85.81 89	90.30 146	66.82 115	91.87 153	89.01 258	85.27 6	86.09 41	93.74 100	47.71 255	96.98 98	77.90 125	89.78 100	93.65 128

Fast-Effi-MVS+		81.14 125	80.01 130	84.51 134	90.24 147	65.86 139	94.12 59	89.15 251	73.81 133	75.37 150	88.26 186	57.26 153	94.53 190	66.97 208	84.92 136	93.15 142

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	60.93 117	95.63 149	89.28 27	89.81 98	94.46 98

tpmrst		80.57 135	79.14 150	84.84 122	90.10 149	68.28 73	81.70 305	89.72 233	77.63 79	75.96 141	79.54 293	64.94 76	92.71 248	75.43 136	77.28 197	93.55 130

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 83	77.08 135	89.81 170	63.77 90	96.97 99	79.67 107	88.21 110	92.60 157

UGNet		79.87 149	78.68 152	83.45 161	89.96 151	61.51 241	92.13 139	90.79 191	76.83 89	78.85 117	86.33 210	38.16 298	96.17 124	67.93 199	87.17 118	92.67 155
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
CHOSEN 1792x2688		84.98 63	83.45 77	89.57 11	89.94 152	75.14 5	92.07 144	92.32 126	81.87 24	75.68 144	88.27 185	60.18 123	98.60 25	80.46 104	90.27 97	94.96 77

abl_6		79.82 150	79.20 148	81.70 204	89.85 153	58.34 287	88.47 254	90.07 217	62.56 288	77.71 124	93.08 110	47.65 256	96.78 108	77.94 124	85.45 134	89.99 201

BH-untuned		78.68 173	77.08 179	83.48 160	89.84 154	63.74 197	92.70 120	88.59 271	71.57 198	66.83 250	88.65 179	51.75 217	95.39 162	59.03 266	84.77 138	91.32 185

test222					89.77 155	61.60 240	89.55 231	89.42 240	56.83 324	77.28 131	92.43 129	52.76 209			91.14 88	93.09 145

PMMVS		81.98 115	82.04 103	81.78 200	89.76 156	56.17 307	91.13 190	90.69 193	77.96 72	80.09 98	93.57 103	46.33 264	94.99 171	81.41 96	87.46 116	94.17 105

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

QAPM		79.95 148	77.39 177	87.64 29	89.63 158	71.41 18	93.30 98	93.70 71	65.34 268	67.39 244	91.75 142	47.83 253	98.96 16	57.71 270	89.81 98	92.54 159

3Dnovator	73.91 6	82.69 104	80.82 118	88.31 22	89.57 159	71.26 19	92.60 126	94.39 48	78.84 60	67.89 236	92.48 128	48.42 247	98.52 26	68.80 194	94.40 37	95.15 68

Effi-MVS+		83.82 86	82.76 93	86.99 48	89.56 160	69.40 47	91.35 179	86.12 303	72.59 157	83.22 70	92.81 122	59.60 132	96.01 134	81.76 91	87.80 114	95.56 49

PatchmatchNet		77.46 193	74.63 208	85.96 84	89.55 161	70.35 29	79.97 321	89.55 236	72.23 171	70.94 194	76.91 312	57.03 157	92.79 246	54.27 281	81.17 164	94.74 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 255	69.98 256	78.28 268	89.51 162	55.70 311	83.49 291	83.39 328	61.24 299	63.72 274	82.76 245	34.77 319	93.03 234	53.37 286	77.59 189	86.12 260

thisisatest0515		83.41 91	82.49 98	86.16 79	89.46 163	68.26 74	93.54 90	94.70 32	74.31 120	75.75 142	90.92 149	72.62 26	96.52 117	69.64 182	81.50 161	93.71 126

h-mvs33		83.01 97	82.56 97	84.35 138	89.34 164	62.02 232	92.72 118	93.76 67	81.45 27	82.73 73	92.25 134	60.11 124	97.13 87	87.69 42	62.96 286	93.91 120

DROMVSNet		84.53 70	85.04 60	83.01 167	89.34 164	61.37 244	94.42 49	91.09 181	77.91 74	83.24 69	94.20 91	58.37 144	95.40 161	85.35 63	91.41 82	92.27 169

UA-Net		80.02 146	79.65 136	81.11 218	89.33 166	57.72 293	86.33 278	89.00 259	77.44 82	81.01 88	89.15 175	59.33 136	95.90 136	61.01 255	84.28 146	89.73 204

dp		75.01 230	72.09 243	83.76 148	89.28 167	66.22 132	79.96 322	89.75 228	71.16 207	67.80 238	77.19 309	51.81 216	92.54 256	50.39 292	71.44 232	92.51 160

sss		82.71 103	82.38 100	83.73 151	89.25 168	59.58 274	92.24 136	94.89 23	77.96 72	79.86 101	92.38 130	56.70 165	97.05 89	77.26 128	80.86 167	94.55 89

MVSFormer		83.75 88	82.88 90	86.37 73	89.24 169	71.18 20	89.07 244	90.69 193	65.80 263	87.13 29	94.34 85	64.99 74	92.67 251	72.83 153	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

IB-MVS	77.80 4	82.18 109	80.46 126	87.35 39	89.14 171	70.28 30	95.59 26	95.17 14	78.85 59	70.19 204	85.82 215	70.66 34	97.67 55	72.19 164	66.52 262	94.09 110
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
MDTV_nov1_ep13			72.61 237		89.06 172	68.48 67	80.33 315	90.11 216	71.84 186	71.81 187	75.92 320	53.01 208	93.92 217	48.04 303	73.38 215

testdata				81.34 210	89.02 173	57.72 293		89.84 226	58.65 315	85.32 51	94.09 93	57.03 157	93.28 230	69.34 187	90.56 95	93.03 147

CostFormer		82.33 107	81.15 112	85.86 88	89.01 174	68.46 68	82.39 302	93.01 103	75.59 100	80.25 96	81.57 262	72.03 30	94.96 172	79.06 113	77.48 194	94.16 106

GeoE		78.90 166	77.43 173	83.29 163	88.95 175	62.02 232	92.31 133	86.23 301	70.24 222	71.34 193	89.27 173	54.43 193	94.04 210	63.31 240	80.81 168	93.81 125

GBi-Net		75.65 221	73.83 224	81.10 219	88.85 176	65.11 161	90.01 222	90.32 204	70.84 213	67.04 247	80.25 285	48.03 249	91.54 282	59.80 263	69.34 241	86.64 245

test1		75.65 221	73.83 224	81.10 219	88.85 176	65.11 161	90.01 222	90.32 204	70.84 213	67.04 247	80.25 285	48.03 249	91.54 282	59.80 263	69.34 241	86.64 245

FMVSNet2		76.07 210	74.01 222	82.26 187	88.85 176	67.66 89	91.33 180	91.61 158	70.84 213	65.98 254	82.25 251	48.03 249	92.00 273	58.46 268	68.73 247	87.10 238

DeepC-MVS	77.85 3	85.52 54	85.24 57	86.37 73	88.80 179	66.64 120	92.15 138	93.68 72	81.07 34	76.91 136	93.64 101	62.59 104	98.44 30	85.50 61	92.84 62	94.03 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 118	81.52 109	82.61 176	88.77 180	60.21 266	93.02 108	93.66 74	68.52 244	72.90 169	90.39 160	72.19 29	94.96 172	74.93 143	79.29 176	92.67 155

1112_ss		80.56 136	79.83 134	82.77 171	88.65 181	60.78 253	92.29 134	88.36 275	72.58 158	72.46 178	94.95 62	65.09 73	93.42 229	66.38 215	77.71 187	94.10 109

tpm cat1		75.30 226	72.21 242	84.58 132	88.52 182	67.77 86	78.16 330	88.02 283	61.88 296	68.45 230	76.37 316	60.65 118	94.03 212	53.77 284	74.11 210	91.93 173

LCM-MVSNet-Re		72.93 247	71.84 245	76.18 292	88.49 183	48.02 344	80.07 320	70.17 356	73.96 129	52.25 326	80.09 288	49.98 232	88.24 314	67.35 203	84.23 147	92.28 166

Vis-MVSNet		80.92 131	79.98 132	83.74 149	88.48 184	61.80 235	93.44 94	88.26 280	73.96 129	77.73 123	91.76 141	49.94 233	94.76 177	65.84 221	90.37 96	94.65 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 160	79.57 138	78.24 270	88.46 185	52.29 326	90.41 210	89.12 253	74.24 122	69.13 215	91.91 138	65.77 66	90.09 301	59.00 267	88.09 112	92.33 163

ab-mvs		80.18 142	78.31 157	85.80 90	88.44 186	65.49 151	83.00 299	92.67 115	71.82 187	77.36 130	85.01 222	54.50 189	96.59 113	76.35 134	75.63 206	95.32 57

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

MVS_111021_LR		82.02 114	81.52 109	83.51 158	88.42 187	62.88 220	89.77 228	88.93 260	76.78 90	75.55 148	93.10 108	50.31 229	95.38 163	83.82 76	87.02 119	92.26 170

baseline		85.01 62	84.44 67	86.71 57	88.33 189	68.73 62	90.24 216	91.82 150	81.05 35	81.18 85	92.50 125	63.69 91	96.08 129	84.45 70	86.71 125	95.32 57

tpm2		79.80 151	77.95 164	85.34 108	88.28 190	68.26 74	81.56 307	91.42 167	70.11 223	77.59 128	80.50 280	67.40 49	94.26 200	67.34 204	77.35 195	93.51 131

thisisatest0530		81.15 124	80.07 127	84.39 137	88.26 191	65.63 146	91.40 173	94.62 36	71.27 206	70.93 195	89.18 174	72.47 27	96.04 131	65.62 224	76.89 200	91.49 178

casdiffmvs		85.37 56	84.87 63	86.84 50	88.25 192	69.07 54	93.04 106	91.76 151	81.27 31	80.84 91	92.07 136	64.23 83	96.06 130	84.98 66	87.43 117	95.39 52

Test_1112_low_res		79.56 155	78.60 154	82.43 179	88.24 193	60.39 263	92.09 142	87.99 284	72.10 176	71.84 186	87.42 199	64.62 79	93.04 233	65.80 222	77.30 196	93.85 124

PAPM		85.89 51	85.46 55	87.18 42	88.20 194	72.42 14	92.41 132	92.77 111	82.11 21	80.34 95	93.07 112	68.27 38	95.02 170	78.39 120	93.59 52	94.09 110

TESTMET0.1,1		82.41 106	81.98 104	83.72 152	88.08 195	63.74 197	92.70 120	93.77 66	79.30 50	77.61 127	87.57 197	58.19 146	94.08 205	73.91 148	86.68 126	93.33 136

ADS-MVSNet2		66.90 291	63.44 296	77.26 282	88.06 196	60.70 258	68.01 347	75.56 346	57.57 317	64.48 266	69.87 340	38.68 292	84.10 335	40.87 333	67.89 253	86.97 239

ADS-MVSNet		68.54 279	64.38 292	81.03 223	88.06 196	66.90 113	68.01 347	84.02 321	57.57 317	64.48 266	69.87 340	38.68 292	89.21 307	40.87 333	67.89 253	86.97 239

EPNet_dtu		78.80 169	79.26 147	77.43 278	88.06 196	49.71 339	91.96 151	91.95 143	77.67 78	76.56 138	91.28 147	58.51 143	90.20 299	56.37 274	80.95 166	92.39 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		78.86 167	77.97 163	81.54 206	88.00 199	65.17 157	91.41 171	89.15 251	75.19 110	68.79 224	83.98 234	67.17 51	92.82 243	72.73 156	65.30 266	86.62 249

IS-MVSNet		80.14 143	79.41 143	82.33 183	87.91 200	60.08 268	91.97 150	88.27 278	72.90 153	71.44 192	91.73 143	61.44 112	93.66 224	62.47 248	86.53 127	93.24 139

CLD-MVS		82.73 101	82.35 101	83.86 147	87.90 201	67.65 90	95.45 27	92.18 137	85.06 7	72.58 174	92.27 133	52.46 212	95.78 140	84.18 71	79.06 177	88.16 224
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ECVR-MVS		80.84 132	80.02 129	83.33 162	87.87 202	60.76 255	92.62 125	86.86 295	77.86 75	75.73 143	91.39 145	46.35 263	94.70 183	72.79 155	88.68 106	94.52 93

HyFIR lowres test		81.03 129	79.56 139	85.43 104	87.81 203	68.11 78	90.18 217	90.01 222	70.65 218	72.95 168	86.06 213	63.61 93	94.50 192	75.01 142	79.75 172	93.67 127

1314		80.70 133	78.95 151	85.94 85	87.77 204	67.56 91	87.91 261	92.55 122	72.17 174	67.44 241	93.09 109	50.27 230	97.04 91	71.68 170	87.64 115	93.23 140

cl22		77.94 187	76.78 184	81.42 208	87.57 205	64.93 169	90.67 203	88.86 263	72.45 163	67.63 240	82.68 247	64.07 84	92.91 241	71.79 166	65.30 266	86.44 250

HQP-NCC					87.54 206		94.06 62		79.80 43	74.18 156

ACMP_Plane					87.54 206		94.06 62		79.80 43	74.18 156

HQP-MVS		81.14 125	80.64 122	82.64 175	87.54 206	63.66 202	94.06 62	91.70 155	79.80 43	74.18 156	90.30 162	51.63 219	95.61 151	77.63 126	78.90 178	88.63 214

NP-MVS					87.41 209	63.04 212					90.30 162

diffmvs		84.28 74	83.83 72	85.61 98	87.40 210	68.02 80	90.88 197	89.24 245	80.54 39	81.64 81	92.52 124	59.83 129	94.52 191	87.32 49	85.11 135	94.29 99

baseline2		83.68 90	83.42 80	84.48 135	87.37 211	66.00 135	90.06 220	95.93 7	79.71 46	69.08 217	90.39 160	77.92 6	96.28 120	78.91 115	81.38 162	91.16 188

plane_prior6					87.23 212	62.32 228						50.66 226

tttt0517		79.50 156	78.53 155	82.41 182	87.22 213	61.43 243	89.75 229	94.76 29	69.29 233	67.91 235	88.06 191	72.92 24	95.63 149	62.91 244	73.90 214	90.16 198

plane_prior1					87.15 214

cascas		78.18 182	75.77 198	85.41 105	87.14 215	69.11 53	92.96 110	91.15 178	66.71 257	70.47 198	86.07 212	37.49 306	96.48 118	70.15 180	79.80 171	90.65 193

CHOSEN 280x420		77.35 196	76.95 183	78.55 265	87.07 216	62.68 224	69.71 343	82.95 330	68.80 240	71.48 191	87.27 203	66.03 62	84.00 338	76.47 132	82.81 153	88.95 209

HQP_MVS		80.34 140	79.75 135	82.12 193	86.94 217	62.42 225	93.13 102	91.31 170	78.81 61	72.53 175	89.14 176	50.66 226	95.55 157	76.74 129	78.53 183	88.39 220

plane_prior7					86.94 217	61.51 241

test-LLR		80.10 144	79.56 139	81.72 202	86.93 219	61.17 245	92.70 120	91.54 160	71.51 201	75.62 145	86.94 204	53.83 198	92.38 262	72.21 162	84.76 139	91.60 176

test-mter		79.96 147	79.38 145	81.72 202	86.93 219	61.17 245	92.70 120	91.54 160	73.85 131	75.62 145	86.94 204	49.84 235	92.38 262	72.21 162	84.76 139	91.60 176

SCA		75.82 219	72.76 234	85.01 116	86.63 221	70.08 32	81.06 311	89.19 248	71.60 197	70.01 206	77.09 310	45.53 268	90.25 294	60.43 258	73.27 216	94.68 84

AUN-MVS		78.37 179	77.43 173	81.17 214	86.60 222	57.45 299	89.46 235	91.16 176	74.11 124	74.40 155	90.49 158	55.52 179	94.57 185	74.73 147	60.43 312	91.48 179

hse-mvs2		81.12 127	81.11 116	81.16 215	86.52 223	57.48 298	89.40 236	91.16 176	81.45 27	82.73 73	90.49 158	60.11 124	94.58 184	87.69 42	60.41 313	91.41 181

xiu_mvs_v1_base_debu		82.16 110	81.12 113	85.26 110	86.42 224	68.72 63	92.59 128	90.44 201	73.12 147	84.20 60	94.36 80	38.04 300	95.73 143	84.12 72	86.81 120	91.33 182

xiu_mvs_v1_base		82.16 110	81.12 113	85.26 110	86.42 224	68.72 63	92.59 128	90.44 201	73.12 147	84.20 60	94.36 80	38.04 300	95.73 143	84.12 72	86.81 120	91.33 182

xiu_mvs_v1_base_debi		82.16 110	81.12 113	85.26 110	86.42 224	68.72 63	92.59 128	90.44 201	73.12 147	84.20 60	94.36 80	38.04 300	95.73 143	84.12 72	86.81 120	91.33 182

F-COLMAP		70.66 261	68.44 266	77.32 280	86.37 227	55.91 309	88.00 259	86.32 298	56.94 323	57.28 312	88.07 190	33.58 322	92.49 258	51.02 290	68.37 249	83.55 291

CDS-MVSNet		81.43 121	80.74 119	83.52 157	86.26 228	64.45 177	92.09 142	90.65 196	75.83 99	73.95 162	89.81 170	63.97 86	92.91 241	71.27 171	82.82 152	93.20 141
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 137	78.26 158	87.21 41	86.19 229	69.79 41	94.48 48	91.31 170	60.42 303	79.34 106	90.91 150	38.48 296	96.56 116	82.16 88	81.05 165	95.27 63

jason		86.40 43	86.17 44	87.11 44	86.16 230	70.54 27	95.71 24	92.19 136	82.00 23	84.58 56	94.34 85	61.86 110	95.53 159	87.76 41	90.89 90	95.27 63
jason: jason.
PCF-MVS	73.15 9	79.29 159	77.63 170	84.29 140	86.06 231	65.96 137	87.03 271	91.10 180	69.86 227	69.79 212	90.64 153	57.54 152	96.59 113	64.37 234	82.29 154	90.32 196
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 189	76.50 188	82.12 193	85.99 232	69.95 37	91.75 162	92.70 113	73.97 128	62.58 284	84.44 230	41.11 286	95.78 140	63.76 238	92.17 72	80.62 326

FIs		79.47 157	79.41 143	79.67 250	85.95 233	59.40 276	91.68 164	93.94 61	78.06 70	68.96 220	88.28 184	66.61 58	91.77 277	66.20 218	74.99 207	87.82 226

VPA-MVSNet		79.03 162	78.00 162	82.11 196	85.95 233	64.48 176	93.22 101	94.66 34	75.05 112	74.04 161	84.95 224	52.17 214	93.52 226	74.90 145	67.04 258	88.32 222

tpm		78.58 176	77.03 180	83.22 164	85.94 235	64.56 172	83.21 297	91.14 179	78.31 66	73.67 163	79.68 291	64.01 85	92.09 271	66.07 219	71.26 233	93.03 147

OpenMVS	70.45 11	78.54 177	75.92 196	86.41 72	85.93 236	71.68 16	92.74 117	92.51 123	66.49 259	64.56 265	91.96 137	43.88 277	98.10 39	54.61 279	90.65 93	89.44 208

OMC-MVS		78.67 175	77.91 166	80.95 225	85.76 237	57.40 300	88.49 253	88.67 268	73.85 131	72.43 179	92.10 135	49.29 240	94.55 188	72.73 156	77.89 186	90.91 191

miper_ehance_all_eth		77.60 191	76.44 189	81.09 222	85.70 238	64.41 181	90.65 204	88.64 270	72.31 167	67.37 245	82.52 248	64.77 78	92.64 254	70.67 176	65.30 266	86.24 255

KD-MVS_2432*1600		69.03 274	66.37 276	77.01 284	85.56 239	61.06 248	81.44 308	90.25 210	67.27 253	58.00 307	76.53 314	54.49 190	87.63 321	48.04 303	35.77 357	82.34 311

miper_refine_blended		69.03 274	66.37 276	77.01 284	85.56 239	61.06 248	81.44 308	90.25 210	67.27 253	58.00 307	76.53 314	54.49 190	87.63 321	48.04 303	35.77 357	82.34 311

EI-MVSNet		78.97 164	78.22 159	81.25 212	85.33 241	62.73 223	89.53 233	93.21 92	72.39 166	72.14 182	90.13 166	60.99 115	94.72 180	67.73 201	72.49 223	86.29 253

CVMVSNet		74.04 237	74.27 216	73.33 309	85.33 241	43.94 354	89.53 233	88.39 274	54.33 331	70.37 201	90.13 166	49.17 242	84.05 336	61.83 252	79.36 174	91.99 172

ACMH	63.93 17	68.62 277	64.81 285	80.03 240	85.22 243	63.25 208	87.72 264	84.66 316	60.83 301	51.57 329	79.43 294	27.29 342	94.96 172	41.76 329	64.84 273	81.88 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl____		76.07 210	74.67 206	80.28 234	85.15 244	61.76 237	90.12 218	88.73 266	71.16 207	65.43 257	81.57 262	61.15 113	92.95 236	66.54 212	62.17 294	86.13 259

DIV-MVS_self_test		76.07 210	74.67 206	80.28 234	85.14 245	61.75 238	90.12 218	88.73 266	71.16 207	65.42 258	81.60 261	61.15 113	92.94 240	66.54 212	62.16 296	86.14 257

TAMVS		80.37 139	79.45 142	83.13 166	85.14 245	63.37 206	91.23 184	90.76 192	74.81 115	72.65 172	88.49 180	60.63 119	92.95 236	69.41 186	81.95 158	93.08 146

MSDG		69.54 270	65.73 279	80.96 224	85.11 247	63.71 199	84.19 286	83.28 329	56.95 322	54.50 317	84.03 232	31.50 330	96.03 132	42.87 326	69.13 244	83.14 301

c3_l		76.83 203	75.47 201	80.93 226	85.02 248	64.18 190	90.39 211	88.11 281	71.66 191	66.65 252	81.64 260	63.58 95	92.56 255	69.31 188	62.86 288	86.04 261

ACMP	71.68 10	75.58 224	74.23 217	79.62 252	84.97 249	59.64 272	90.80 200	89.07 256	70.39 220	62.95 280	87.30 201	38.28 297	93.87 219	72.89 152	71.45 231	85.36 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 185	78.08 161	77.70 273	84.89 250	55.51 312	90.27 214	93.75 70	76.87 87	66.80 251	87.59 196	65.71 67	90.23 298	62.89 245	73.94 212	87.37 233

PVSNet_0	68.08 15	71.81 256	68.32 268	82.27 185	84.68 251	62.31 229	88.68 250	90.31 207	75.84 98	57.93 309	80.65 279	37.85 303	94.19 201	69.94 181	29.05 362	90.31 197

eth_miper_zixun_eth		75.96 216	74.40 214	80.66 228	84.66 252	63.02 213	89.28 238	88.27 278	71.88 182	65.73 255	81.65 259	59.45 133	92.81 244	68.13 196	60.53 310	86.14 257

WR-MVS		76.76 204	75.74 199	79.82 246	84.60 253	62.27 230	92.60 126	92.51 123	76.06 96	67.87 237	85.34 219	56.76 163	90.24 297	62.20 249	63.69 285	86.94 241

ACMH+	65.35 16	67.65 286	64.55 288	76.96 286	84.59 254	57.10 303	88.08 258	80.79 335	58.59 316	53.00 323	81.09 274	26.63 344	92.95 236	46.51 310	61.69 303	80.82 323

VPNet		78.82 168	77.53 172	82.70 173	84.52 255	66.44 125	93.93 72	92.23 130	80.46 40	72.60 173	88.38 183	49.18 241	93.13 232	72.47 160	63.97 283	88.55 216

IterMVS-LS		76.49 206	75.18 204	80.43 231	84.49 256	62.74 222	90.64 205	88.80 264	72.40 165	65.16 260	81.72 258	60.98 116	92.27 267	67.74 200	64.65 277	86.29 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 183	77.55 171	79.98 241	84.46 257	60.26 264	92.25 135	93.20 94	77.50 81	68.88 221	86.61 206	66.10 61	92.13 269	66.38 215	62.55 290	87.54 228

FMVSNet5		68.04 283	65.66 281	75.18 297	84.43 258	57.89 290	83.54 290	86.26 300	61.83 297	53.64 322	73.30 328	37.15 310	85.08 332	48.99 298	61.77 299	82.56 310

MVS-HIRNet		60.25 316	55.55 322	74.35 302	84.37 259	56.57 306	71.64 339	74.11 349	34.44 359	45.54 348	42.24 361	31.11 333	89.81 302	40.36 336	76.10 204	76.67 347

LPG-MVS_test		75.82 219	74.58 210	79.56 254	84.31 260	59.37 277	90.44 208	89.73 231	69.49 230	64.86 261	88.42 181	38.65 294	94.30 196	72.56 158	72.76 220	85.01 280

LGP-MVS_train				79.56 254	84.31 260	59.37 277		89.73 231	69.49 230	64.86 261	88.42 181	38.65 294	94.30 196	72.56 158	72.76 220	85.01 280

ACMM	69.62 13	74.34 234	72.73 235	79.17 259	84.25 262	57.87 291	90.36 212	89.93 223	63.17 282	65.64 256	86.04 214	37.79 304	94.10 203	65.89 220	71.52 230	85.55 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 192	76.78 184	79.98 241	84.11 263	60.80 252	91.76 160	93.17 97	76.56 93	69.93 210	84.78 226	63.32 98	92.36 264	64.89 231	62.51 292	86.78 243

test_0402		64.54 301	61.09 307	74.92 298	84.10 264	60.75 256	87.95 260	79.71 339	52.03 335	52.41 325	77.20 308	32.21 328	91.64 279	23.14 361	61.03 306	72.36 353

LTVRE_ROB	59.60 19	66.27 294	63.54 295	74.45 301	84.00 265	51.55 329	67.08 350	83.53 325	58.78 314	54.94 316	80.31 283	34.54 320	93.23 231	40.64 335	68.03 251	78.58 342
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
miper_lstm_enhance		73.05 245	71.73 247	77.03 283	83.80 266	58.32 288	81.76 303	88.88 261	69.80 228	61.01 289	78.23 300	57.19 154	87.51 323	65.34 228	59.53 315	85.27 279

Patchmatch-test		65.86 296	60.94 308	80.62 229	83.75 267	58.83 282	58.91 357	75.26 348	44.50 354	50.95 333	77.09 310	58.81 142	87.90 317	35.13 348	64.03 281	95.12 70

nrg030		80.93 130	79.86 133	84.13 143	83.69 268	68.83 60	93.23 100	91.20 174	75.55 101	75.06 151	88.22 189	63.04 102	94.74 179	81.88 90	66.88 259	88.82 212

GA-MVS		78.33 181	76.23 192	84.65 129	83.65 269	66.30 129	91.44 170	90.14 215	76.01 97	70.32 202	84.02 233	42.50 281	94.72 180	70.98 172	77.00 199	92.94 150

FMVSNet1		72.71 252	69.91 259	81.10 219	83.60 270	65.11 161	90.01 222	90.32 204	63.92 275	63.56 275	80.25 285	36.35 315	91.54 282	54.46 280	66.75 260	86.64 245

OPM-MVS		79.00 163	78.09 160	81.73 201	83.52 271	63.83 194	91.64 166	90.30 208	76.36 95	71.97 185	89.93 169	46.30 265	95.17 168	75.10 139	77.70 188	86.19 256
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		70.10 265	67.36 271	78.32 267	83.45 272	60.97 250	88.85 247	92.77 111	64.85 270	60.83 291	78.53 297	43.52 279	93.48 227	31.73 358	61.70 302	80.52 327

Effi-MVS+-dtu		76.14 209	75.28 203	78.72 264	83.22 273	55.17 314	89.87 226	87.78 286	75.42 103	67.98 233	81.43 264	45.08 271	92.52 257	75.08 140	71.63 228	88.48 217

mvs-test1		78.74 172	77.95 164	81.14 216	83.22 273	57.13 302	93.96 67	87.78 286	75.42 103	72.68 171	90.80 152	45.08 271	94.54 189	75.08 140	77.49 193	91.74 175

CR-MVSNet		73.79 241	70.82 253	82.70 173	83.15 275	67.96 81	70.25 340	84.00 322	73.67 138	69.97 208	72.41 331	57.82 149	89.48 305	52.99 287	73.13 217	90.64 194

RPMNet		70.42 264	65.68 280	84.63 131	83.15 275	67.96 81	70.25 340	90.45 198	46.83 350	69.97 208	65.10 348	56.48 170	95.30 167	35.79 347	73.13 217	90.64 194

DU-MVS		76.86 200	75.84 197	79.91 243	82.96 277	60.26 264	91.26 183	91.54 160	76.46 94	68.88 221	86.35 208	56.16 171	92.13 269	66.38 215	62.55 290	87.35 234

NR-MVSNet		76.05 213	74.59 209	80.44 230	82.96 277	62.18 231	90.83 199	91.73 152	77.12 85	60.96 290	86.35 208	59.28 137	91.80 276	60.74 256	61.34 305	87.35 234

XXY-MVS		77.94 187	76.44 189	82.43 179	82.60 279	64.44 178	92.01 147	91.83 149	73.59 139	70.00 207	85.82 215	54.43 193	94.76 177	69.63 183	68.02 252	88.10 225

TranMVSNet+NR-MVSNet		75.86 218	74.52 212	79.89 244	82.44 280	60.64 260	91.37 178	91.37 169	76.63 91	67.65 239	86.21 211	52.37 213	91.55 281	61.84 251	60.81 308	87.48 230

IterMVS		72.65 254	70.83 252	78.09 271	82.17 281	62.96 215	87.64 266	86.28 299	71.56 199	60.44 292	78.85 296	45.42 270	86.66 327	63.30 241	61.83 298	84.65 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 288	63.93 293	78.34 266	82.12 282	64.38 182	68.72 344	84.00 322	48.23 347	59.24 297	72.41 331	57.82 149	89.27 306	46.10 313	56.68 325	81.36 319

PatchT		69.11 273	65.37 284	80.32 232	82.07 283	63.68 201	67.96 349	87.62 288	50.86 339	69.37 213	65.18 347	57.09 155	88.53 312	41.59 331	66.60 261	88.74 213

MIMVSNet		71.64 257	68.44 266	81.23 213	81.97 284	64.44 178	73.05 337	88.80 264	69.67 229	64.59 264	74.79 324	32.79 324	87.82 318	53.99 282	76.35 203	91.42 180

MVP-Stereo		77.12 199	76.23 192	79.79 247	81.72 285	66.34 128	89.29 237	90.88 190	70.56 219	62.01 287	82.88 244	49.34 238	94.13 202	65.55 226	93.80 46	78.88 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS-SCA-FT		71.55 258	69.97 257	76.32 290	81.48 286	60.67 259	87.64 266	85.99 304	66.17 261	59.50 296	78.88 295	45.53 268	83.65 340	62.58 247	61.93 297	84.63 285

COLMAP_ROB	57.96 20	62.98 309	59.65 311	72.98 312	81.44 287	53.00 323	83.75 289	75.53 347	48.34 346	48.81 339	81.40 266	24.14 347	90.30 293	32.95 354	60.52 311	75.65 349
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 295	62.45 302	76.88 287	81.42 288	54.45 318	57.49 358	88.67 268	49.36 343	63.86 272	46.86 357	56.06 174	90.25 294	49.53 296	68.83 245	85.95 264

WR-MVS_H		70.59 262	69.94 258	72.53 315	81.03 289	51.43 330	87.35 269	92.03 140	67.38 252	60.23 293	80.70 276	55.84 178	83.45 342	46.33 312	58.58 320	82.72 305

Fast-Effi-MVS+-dtu		75.04 229	73.37 229	80.07 239	80.86 290	59.52 275	91.20 187	85.38 308	71.90 180	65.20 259	84.84 225	41.46 285	92.97 235	66.50 214	72.96 219	87.73 227

Baseline_NR-MVSNet		73.99 238	72.83 233	77.48 277	80.78 291	59.29 279	91.79 157	84.55 317	68.85 239	68.99 219	80.70 276	56.16 171	92.04 272	62.67 246	60.98 307	81.11 320

CP-MVSNet		70.50 263	69.91 259	72.26 318	80.71 292	51.00 333	87.23 270	90.30 208	67.84 246	59.64 295	82.69 246	50.23 231	82.30 350	51.28 289	59.28 316	83.46 295

v8		75.35 225	73.26 230	81.61 205	80.67 293	66.82 115	89.54 232	89.27 244	71.65 192	63.30 278	80.30 284	54.99 186	94.06 207	67.33 205	62.33 293	83.94 288

PS-MVSNAJss		77.26 197	76.31 191	80.13 238	80.64 294	59.16 280	90.63 207	91.06 184	72.80 154	68.58 228	84.57 229	53.55 202	93.96 215	72.97 151	71.96 227	87.27 237

TransMVSNet (Re)		70.07 266	67.66 270	77.31 281	80.62 295	59.13 281	91.78 159	84.94 313	65.97 262	60.08 294	80.44 281	50.78 225	91.87 274	48.84 299	45.46 346	80.94 322

v2v482		77.42 194	75.65 200	82.73 172	80.38 296	67.13 106	91.85 155	90.23 212	75.09 111	69.37 213	83.39 241	53.79 200	94.44 193	71.77 167	65.00 272	86.63 248

MVS_0304		68.99 276	67.23 273	74.28 304	80.36 297	52.54 324	87.01 273	86.36 297	59.89 309	66.22 253	73.56 327	24.25 346	88.03 316	57.34 272	70.11 236	82.27 313

PS-CasMVS		69.86 269	69.13 263	72.07 321	80.35 298	50.57 335	87.02 272	89.75 228	67.27 253	59.19 299	82.28 250	46.58 261	82.24 351	50.69 291	59.02 317	83.39 297

v10		74.77 232	72.54 239	81.46 207	80.33 299	66.71 119	89.15 242	89.08 255	70.94 211	63.08 279	79.86 289	52.52 211	94.04 210	65.70 223	62.17 294	83.64 290

test0.0.03 1		72.76 250	72.71 236	72.88 313	80.25 300	47.99 345	91.22 185	89.45 238	71.51 201	62.51 285	87.66 195	53.83 198	85.06 333	50.16 293	67.84 255	85.58 271

v1144		76.73 205	74.88 205	82.27 185	80.23 301	66.60 122	91.68 164	90.21 214	73.69 136	69.06 218	81.89 255	52.73 210	94.40 194	69.21 189	65.23 269	85.80 267

v148		76.19 208	74.47 213	81.36 209	80.05 302	64.44 178	91.75 162	90.23 212	73.68 137	67.13 246	80.84 275	55.92 177	93.86 221	68.95 192	61.73 301	85.76 270

v1192		75.98 215	73.92 223	82.15 190	79.73 303	66.24 131	91.22 185	89.75 228	72.67 156	68.49 229	81.42 265	49.86 234	94.27 198	67.08 206	65.02 271	85.95 264

AllTest		61.66 311	58.06 315	72.46 316	79.57 304	51.42 331	80.17 318	68.61 358	51.25 337	45.88 344	81.23 268	19.86 356	86.58 328	38.98 339	57.01 323	79.39 335

TestCases				72.46 316	79.57 304	51.42 331		68.61 358	51.25 337	45.88 344	81.23 268	19.86 356	86.58 328	38.98 339	57.01 323	79.39 335

MDA-MVSNet-bldmvs		61.54 313	57.70 317	73.05 311	79.53 306	57.00 304	83.08 298	81.23 333	57.57 317	34.91 359	72.45 330	32.79 324	86.26 330	35.81 346	41.95 350	75.89 348

v144192		76.05 213	74.03 221	82.12 193	79.50 307	66.55 124	91.39 175	89.71 234	72.30 168	68.17 231	81.33 267	51.75 217	94.03 212	67.94 198	64.19 279	85.77 268

v1921920		75.63 223	73.49 228	82.06 197	79.38 308	66.35 127	91.07 193	89.48 237	71.98 177	67.99 232	81.22 270	49.16 243	93.90 218	66.56 211	64.56 278	85.92 266

PEN-MVS		69.46 271	68.56 265	72.17 320	79.27 309	49.71 339	86.90 274	89.24 245	67.24 256	59.08 300	82.51 249	47.23 258	83.54 341	48.42 301	57.12 321	83.25 298

v1240		75.21 228	72.98 232	81.88 199	79.20 310	66.00 135	90.75 202	89.11 254	71.63 196	67.41 243	81.22 270	47.36 257	93.87 219	65.46 227	64.72 276	85.77 268

pmmvs4		73.92 239	71.81 246	80.25 236	79.17 311	65.24 155	87.43 268	87.26 293	67.64 251	63.46 276	83.91 235	48.96 245	91.53 285	62.94 243	65.49 265	83.96 287

D2MVS		73.80 240	72.02 244	79.15 261	79.15 312	62.97 214	88.58 252	90.07 217	72.94 150	59.22 298	78.30 298	42.31 283	92.70 250	65.59 225	72.00 226	81.79 317

V42		76.46 207	74.55 211	82.19 189	79.14 313	67.82 85	90.26 215	89.42 240	73.75 134	68.63 227	81.89 255	51.31 221	94.09 204	71.69 169	64.84 273	84.66 283

pm-mvs1		72.89 248	71.09 251	78.26 269	79.10 314	57.62 295	90.80 200	89.30 243	67.66 249	62.91 281	81.78 257	49.11 244	92.95 236	60.29 260	58.89 318	84.22 286

our_test_3		68.29 281	64.69 287	79.11 262	78.92 315	64.85 170	88.40 256	85.06 311	60.32 305	52.68 324	76.12 318	40.81 287	89.80 304	44.25 321	55.65 326	82.67 309

ppachtmachnet_test		67.72 285	63.70 294	79.77 248	78.92 315	66.04 134	88.68 250	82.90 331	60.11 307	55.45 315	75.96 319	39.19 291	90.55 290	39.53 337	52.55 335	82.71 306

TinyColmap		60.32 315	56.42 321	72.00 322	78.78 317	53.18 322	78.36 328	75.64 345	52.30 334	41.59 355	75.82 321	14.76 361	88.35 313	35.84 345	54.71 331	74.46 350

SixPastTwentyTwo		64.92 299	61.78 306	74.34 303	78.74 318	49.76 338	83.42 294	79.51 340	62.86 284	50.27 334	77.35 305	30.92 334	90.49 292	45.89 314	47.06 344	82.78 302

EG-PatchMatch MVS		68.55 278	65.41 283	77.96 272	78.69 319	62.93 216	89.86 227	89.17 249	60.55 302	50.27 334	77.73 304	22.60 351	94.06 207	47.18 309	72.65 222	76.88 346

pmmvs5		73.35 243	71.52 248	78.86 263	78.64 320	60.61 261	91.08 191	86.90 294	67.69 248	63.32 277	83.64 236	44.33 275	90.53 291	62.04 250	66.02 264	85.46 274

UniMVSNet_ETH3D		72.74 251	70.53 254	79.36 256	78.62 321	56.64 305	85.01 282	89.20 247	63.77 277	64.84 263	84.44 230	34.05 321	91.86 275	63.94 236	70.89 235	89.57 206

XVG-OURS		74.25 236	72.46 240	79.63 251	78.45 322	57.59 296	80.33 315	87.39 290	63.86 276	68.76 225	89.62 172	40.50 288	91.72 278	69.00 191	74.25 209	89.58 205

XVG-OURS-SEG-HR		74.70 233	73.08 231	79.57 253	78.25 323	57.33 301	80.49 313	87.32 291	63.22 281	68.76 225	90.12 168	44.89 273	91.59 280	70.55 178	74.09 211	89.79 202

MDA-MVSNet_test_wron		63.78 306	60.16 309	74.64 299	78.15 324	60.41 262	83.49 291	84.03 320	56.17 328	39.17 357	71.59 337	37.22 308	83.24 345	42.87 326	48.73 341	80.26 330

YYNet1		63.76 307	60.14 310	74.62 300	78.06 325	60.19 267	83.46 293	83.99 324	56.18 327	39.25 356	71.56 338	37.18 309	83.34 343	42.90 325	48.70 342	80.32 329

DTE-MVSNet		68.46 280	67.33 272	71.87 323	77.94 326	49.00 342	86.16 279	88.58 272	66.36 260	58.19 304	82.21 252	46.36 262	83.87 339	44.97 319	55.17 328	82.73 304

USDC		67.43 290	64.51 289	76.19 291	77.94 326	55.29 313	78.38 327	85.00 312	73.17 145	48.36 340	80.37 282	21.23 353	92.48 259	52.15 288	64.02 282	80.81 324

jajsoiax		73.05 245	71.51 249	77.67 274	77.46 328	54.83 315	88.81 248	90.04 221	69.13 237	62.85 282	83.51 238	31.16 332	92.75 247	70.83 173	69.80 237	85.43 275

mvs_tets		72.71 252	71.11 250	77.52 275	77.41 329	54.52 317	88.45 255	89.76 227	68.76 242	62.70 283	83.26 242	29.49 336	92.71 248	70.51 179	69.62 239	85.34 277

N_pmnet		50.55 324	49.11 327	54.88 341	77.17 330	4.02 377	84.36 285	2.00 376	48.59 344	45.86 346	68.82 342	32.22 327	82.80 347	31.58 359	51.38 337	77.81 344

test_djsdf		73.76 242	72.56 238	77.39 279	77.00 331	53.93 319	89.07 244	90.69 193	65.80 263	63.92 271	82.03 254	43.14 280	92.67 251	72.83 153	68.53 248	85.57 272

bset_n11_16_dypcd		75.95 217	74.16 218	81.30 211	76.91 332	65.14 160	88.89 246	87.48 289	74.30 121	69.90 211	83.40 240	42.16 284	92.42 260	78.39 120	66.03 263	86.32 252

OpenMVS_ROB	61.12 18	66.39 293	62.92 299	76.80 288	76.51 333	57.77 292	89.22 239	83.41 327	55.48 329	53.86 321	77.84 303	26.28 345	93.95 216	34.90 349	68.76 246	78.68 341

v7n		71.31 259	68.65 264	79.28 257	76.40 334	60.77 254	86.71 276	89.45 238	64.17 274	58.77 303	78.24 299	44.59 274	93.54 225	57.76 269	61.75 300	83.52 293

K. test v3		63.09 308	59.61 312	73.53 308	76.26 335	49.38 341	83.27 295	77.15 342	64.35 272	47.77 342	72.32 333	28.73 338	87.79 319	49.93 295	36.69 356	83.41 296

RPSCF		64.24 303	61.98 305	71.01 325	76.10 336	45.00 351	75.83 334	75.94 344	46.94 349	58.96 301	84.59 228	31.40 331	82.00 352	47.76 307	60.33 314	86.04 261

OurMVSNet-221017-0		64.68 300	62.17 304	72.21 319	76.08 337	47.35 348	80.67 312	81.02 334	56.19 326	51.60 328	79.66 292	27.05 343	88.56 311	53.60 285	53.63 333	80.71 325

Anonymous20231206		67.53 288	65.78 278	72.79 314	74.95 338	47.59 347	88.23 257	87.32 291	61.75 298	58.07 306	77.29 307	37.79 304	87.29 325	42.91 324	63.71 284	83.48 294

ITE_SJBPF				70.43 326	74.44 339	47.06 349		77.32 341	60.16 306	54.04 320	83.53 237	23.30 350	84.01 337	43.07 323	61.58 304	80.21 332

EU-MVSNet		64.01 304	63.01 298	67.02 335	74.40 340	38.86 363	83.27 295	86.19 302	45.11 352	54.27 318	81.15 273	36.91 313	80.01 355	48.79 300	57.02 322	82.19 315

XVG-ACMP-BASELINE		68.04 283	65.53 282	75.56 294	74.06 341	52.37 325	78.43 326	85.88 305	62.03 293	58.91 302	81.21 272	20.38 355	91.15 288	60.69 257	68.18 250	83.16 300

CL-MVSNet_self_test		69.92 267	68.09 269	75.41 295	73.25 342	55.90 310	90.05 221	89.90 224	69.96 225	61.96 288	76.54 313	51.05 224	87.64 320	49.51 297	50.59 339	82.70 307

anonymousdsp		71.14 260	69.37 262	76.45 289	72.95 343	54.71 316	84.19 286	88.88 261	61.92 295	62.15 286	79.77 290	38.14 299	91.44 287	68.90 193	67.45 257	83.21 299

lessismore_v0				73.72 307	72.93 344	47.83 346		61.72 366		45.86 346	73.76 326	28.63 340	89.81 302	47.75 308	31.37 361	83.53 292

pmmvs6		67.57 287	64.76 286	76.00 293	72.82 345	53.37 321	88.71 249	86.78 296	53.19 333	57.58 311	78.03 302	35.33 318	92.41 261	55.56 277	54.88 330	82.21 314

testgi		64.48 302	62.87 300	69.31 328	71.24 346	40.62 358	85.49 280	79.92 338	65.36 267	54.18 319	83.49 239	23.74 349	84.55 334	41.60 330	60.79 309	82.77 303

Patchmatch-RL test		68.17 282	64.49 290	79.19 258	71.22 347	53.93 319	70.07 342	71.54 355	69.22 234	56.79 313	62.89 350	56.58 168	88.61 309	69.53 185	52.61 334	95.03 76

Gipuma		34.91 331	31.44 334	45.30 346	70.99 348	39.64 362	19.85 366	72.56 352	20.10 365	16.16 367	21.47 367	5.08 371	71.16 359	13.07 365	43.70 349	25.08 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 297	63.10 297	73.88 305	70.71 349	50.29 337	81.09 310	89.88 225	72.58 158	49.25 338	74.77 325	32.57 326	87.43 324	55.96 276	41.04 352	83.90 289

CMPMVS	48.56 21	66.77 292	64.41 291	73.84 306	70.65 350	50.31 336	77.79 331	85.73 307	45.54 351	44.76 349	82.14 253	35.40 317	90.14 300	63.18 242	74.54 208	81.07 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 305	62.65 301	67.38 334	70.58 351	39.94 359	86.57 277	84.17 319	63.29 280	51.86 327	77.30 306	37.09 311	82.47 348	38.87 341	54.13 332	79.73 333

MIMVSNet1		60.16 317	57.33 318	68.67 329	69.71 352	44.13 353	78.92 324	84.21 318	55.05 330	44.63 350	71.85 335	23.91 348	81.54 354	32.63 356	55.03 329	80.35 328

pmmvs-eth3d		65.53 298	62.32 303	75.19 296	69.39 353	59.59 273	82.80 300	83.43 326	62.52 289	51.30 331	72.49 329	32.86 323	87.16 326	55.32 278	50.73 338	78.83 340

UnsupCasMVSNet_bld		61.60 312	57.71 316	73.29 310	68.73 354	51.64 328	78.61 325	89.05 257	57.20 321	46.11 343	61.96 351	28.70 339	88.60 310	50.08 294	38.90 354	79.63 334

Anonymous20240521		62.09 310	59.08 313	71.10 324	67.19 355	48.72 343	83.91 288	85.23 310	50.38 340	47.84 341	71.22 339	20.74 354	85.51 331	46.47 311	58.75 319	79.06 338

KD-MVS_self_test		60.87 314	58.60 314	67.68 332	66.13 356	39.93 360	75.63 335	84.70 315	57.32 320	49.57 337	68.45 343	29.55 335	82.87 346	48.09 302	47.94 343	80.25 331

new-patchmatchnet		59.30 319	56.48 320	67.79 331	65.86 357	44.19 352	82.47 301	81.77 332	59.94 308	43.65 353	66.20 346	27.67 341	81.68 353	39.34 338	41.40 351	77.50 345

PM-MVS		59.40 318	56.59 319	67.84 330	63.63 358	41.86 355	76.76 332	63.22 364	59.01 313	51.07 332	72.27 334	11.72 363	83.25 344	61.34 253	50.28 340	78.39 343

DSMNet-mixed		56.78 320	54.44 323	63.79 337	63.21 359	29.44 367	64.43 352	64.10 363	42.12 356	51.32 330	71.60 336	31.76 329	75.04 357	36.23 344	65.20 270	86.87 242

new_pmnet		49.31 325	46.44 328	57.93 339	62.84 360	40.74 357	68.47 346	62.96 365	36.48 358	35.09 358	57.81 353	14.97 360	72.18 358	32.86 355	46.44 345	60.88 359

LF4IMVS		54.01 323	52.12 324	59.69 338	62.41 361	39.91 361	68.59 345	68.28 360	42.96 355	44.55 351	75.18 322	14.09 362	68.39 360	41.36 332	51.68 336	70.78 354

ambc				69.61 327	61.38 362	41.35 356	49.07 361	85.86 306		50.18 336	66.40 345	10.16 364	88.14 315	45.73 315	44.20 347	79.32 337

TDRefinement		55.28 322	51.58 325	66.39 336	59.53 363	46.15 350	76.23 333	72.80 351	44.60 353	42.49 354	76.28 317	15.29 359	82.39 349	33.20 353	43.75 348	70.62 355

pmmvs3		55.51 321	51.50 326	67.53 333	57.90 364	50.93 334	80.37 314	73.66 350	40.63 357	44.15 352	64.75 349	16.30 358	78.97 356	44.77 320	40.98 353	72.69 351

test_method		38.59 329	35.16 332	48.89 345	54.33 365	21.35 372	45.32 362	53.71 367	7.41 369	28.74 360	51.62 355	8.70 367	52.87 366	33.73 350	32.89 360	72.47 352

DeepMVS_CX				34.71 349	51.45 366	24.73 371		28.48 375	31.46 360	17.49 366	52.75 354	5.80 370	42.60 370	18.18 363	19.42 363	36.81 362

FPMVS		45.64 326	43.10 329	53.23 343	51.42 367	36.46 364	64.97 351	71.91 353	29.13 361	27.53 361	61.55 352	9.83 365	65.01 364	16.00 364	55.58 327	58.22 360

wuyk23d		11.30 338	10.95 341	12.33 353	48.05 368	19.89 373	25.89 365	1.92 377	3.58 370	3.12 372	1.37 371	0.64 376	15.77 372	6.23 370	7.77 370	1.35 368

PMMVS2		37.93 330	33.61 333	50.92 344	46.31 369	24.76 370	60.55 356	50.05 368	28.94 362	20.93 363	47.59 356	4.41 373	65.13 363	25.14 360	18.55 364	62.87 358

E-PMN		24.61 333	24.00 337	26.45 350	43.74 370	18.44 374	60.86 354	39.66 370	15.11 366	9.53 370	22.10 366	6.52 369	46.94 368	8.31 368	10.14 366	13.98 366

EMVS		23.76 335	23.20 339	25.46 351	41.52 371	16.90 375	60.56 355	38.79 373	14.62 367	8.99 371	20.24 369	7.35 368	45.82 369	7.25 369	9.46 367	13.64 367

LCM-MVSNet		40.54 327	35.79 330	54.76 342	36.92 372	30.81 366	51.41 359	69.02 357	22.07 363	24.63 362	45.37 359	4.56 372	65.81 362	33.67 351	34.50 359	67.67 356

ANet_high		40.27 328	35.20 331	55.47 340	34.74 373	34.47 365	63.84 353	71.56 354	48.42 345	18.80 365	41.08 362	9.52 366	64.45 365	20.18 362	8.66 369	67.49 357

MVE	24.84 23	24.35 334	19.77 340	38.09 348	34.56 374	26.92 369	26.57 364	38.87 372	11.73 368	11.37 369	27.44 364	1.37 375	50.42 367	11.41 366	14.60 365	36.93 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 332	28.16 335	42.89 347	25.87 375	27.58 368	50.92 360	49.78 369	21.37 364	14.17 368	40.81 363	2.01 374	66.62 361	9.61 367	38.88 355	34.49 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 336	23.75 338	17.80 352	5.23 376	12.06 376	35.26 363	39.48 371	2.82 371	18.94 364	44.20 360	22.23 352	24.64 371	36.30 343	9.31 368	16.69 365

testmvs		7.23 340	9.62 343	0.06 355	0.04 377	0.02 379	84.98 283	0.02 378	0.03 372	0.18 373	1.21 372	0.01 378	0.02 373	0.14 371	0.01 371	0.13 370

test123		6.92 341	9.21 344	0.08 354	0.03 378	0.05 378	81.65 306	0.01 379	0.02 373	0.14 374	0.85 373	0.03 377	0.02 373	0.12 372	0.00 372	0.16 369

test_blank		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

eth-test2					0.00 379

eth-test					0.00 379

uanet_test		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

cdsmvs_eth3d_5k		19.86 337	26.47 336	0.00 356	0.00 379	0.00 380	0.00 367	93.45 83	0.00 374	0.00 375	95.27 50	49.56 236	0.00 375	0.00 373	0.00 372	0.00 371

pcd_1.5k_mvsjas		4.46 342	5.95 345	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	53.55 202	0.00 375	0.00 373	0.00 372	0.00 371

sosnet-low-res		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

sosnet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

uncertanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

Regformer		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

ab-mvs-re		7.91 339	10.55 342	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	94.95 62	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

uanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 372	0.00 371

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

test_241102_TWO								94.41 45	71.65 192	92.07 6	97.21 5	74.58 16	99.11 6	92.34 7	95.36 14	96.59 15

test_0728_THIRD									72.48 161	90.55 15	96.93 10	76.24 11	99.08 11	91.53 15	94.99 17	96.43 26

GSMVS																94.68 84

sam_mvs1												57.85 148				94.68 84

sam_mvs												54.91 187

MTGPA								92.23 130

test_post1							78.95 323				20.70 368	53.05 207	91.50 286	60.43 258

test_post											23.01 365	56.49 169	92.67 251

patchmatchnet-post											67.62 344	57.62 151	90.25 294

MTMP							93.77 82	32.52 374

test9_res														89.41 24	94.96 18	95.29 59

agg_prior2														86.41 55	94.75 30	95.33 55

test_prior4						67.18 105	93.92 73

test_prior2							95.10 37		75.40 105	85.25 53	95.61 40	67.94 43		87.47 45	94.77 25

旧先验2							92.00 149		59.37 312	87.54 28			93.47 228	75.39 137

新几何2							91.41 171

无先验							92.71 119	92.61 120	62.03 293				97.01 92	66.63 209		93.97 117

原ACMM2							92.01 147

testdata2													96.09 127	61.26 254

segment_acmp												65.94 63

testdata1							89.21 240		77.55 80

plane_prior5								91.31 170					95.55 157	76.74 129	78.53 183	88.39 220

plane_prior4											89.14 176

plane_prior3						61.95 234			79.09 56	72.53 175

plane_prior2							93.13 102		78.81 61

plane_prior						62.42 225	93.85 77		79.38 49						78.80 180

n2								0.00 380

nn								0.00 380

door-mid								66.01 362

test11								93.01 103

door								66.57 361

HQP5-MVS						63.66 202

BP-MVS														77.63 126

HQP4-MVS										74.18 156			95.61 151			88.63 214

HQP3-MVS								91.70 155							78.90 178

HQP2-MVS												51.63 219

MDTV_nov1_ep13_2view						59.90 270	80.13 319		67.65 250	72.79 170		54.33 195		59.83 262		92.58 158

ACMMP++_ref															71.63 228

ACMMP++															69.72 238

Test By Simon												54.21 196

High-resolution multi-view benchmark