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.

PVSNet_Blended		93.13 44	92.98 50	93.57 64	97.47 89	83.86 81	99.32 1	96.73 61	91.02 25	89.53 114	96.21 127	76.42 127	99.57 54	94.29 48	95.81 113	97.29 143

DELS-MVS		94.98 13	94.49 21	96.44 6	96.42 111	90.59 7	99.21 2	97.02 29	94.40 5	91.46 82	97.08 106	83.32 47	99.69 41	92.83 71	98.70 33	99.04 26
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
NCCC		95.63 6	95.94 8	94.69 27	99.21 7	85.15 60	99.16 3	96.96 35	94.11 6	95.59 25	98.64 21	85.07 33	99.91 4	95.61 33	99.10 9	99.00 28

DPM-MVS		96.21 2	95.53 11	98.26 1	96.26 114	95.09 1	99.15 4	96.98 32	93.39 9	96.45 18	98.79 10	90.17 10	99.99 1	89.33 117	99.25 6	99.70 3

lupinMVS		93.87 38	93.58 40	94.75 26	93.00 202	88.08 16	99.15 4	95.50 166	91.03 24	94.90 36	97.66 74	78.84 88	97.56 164	94.64 46	97.46 77	98.62 46

SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 62	99.12 6	96.78 49	88.72 55	97.79 4	98.91 3	88.48 17	99.82 18	98.15 4	98.97 17	99.74 1

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

test0726					99.05 10	85.18 55	99.11 8	96.78 49	88.75 53	97.65 9	98.91 3	87.69 22

DVP-MVS		95.58 8	95.91 9	94.57 29	99.05 10	85.18 55	99.06 9	96.46 100	88.75 53	96.69 13	98.76 14	87.69 22	99.76 25	97.90 9	98.85 22	98.77 35
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
test_0728_SECOND				95.14 17	99.04 15	86.14 34	99.06 9	96.77 55					99.84 12	97.90 9	98.85 22	99.45 10

CANet		94.89 14	94.64 18	95.63 12	97.55 88	88.12 15	99.06 9	96.39 112	94.07 7	95.34 28	97.80 69	76.83 120	99.87 8	97.08 19	97.64 73	98.89 31

CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 24	94.66 3	96.79 12	98.78 11	86.42 28	99.95 3	97.59 13	99.18 7	99.00 28

SteuartSystems-ACMMP		94.13 29	94.44 23	93.20 79	95.41 135	81.35 137	99.02 13	96.59 84	89.50 43	94.18 50	98.36 30	83.68 45	99.45 65	94.77 42	98.45 44	98.81 34
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	89.82 1	94.61 18	96.17 5	89.91 193	97.09 104	70.21 322	98.99 14	96.69 68	95.57 1	95.08 32	99.23 1	86.40 29	99.87 8	97.84 11	98.66 34	99.65 6

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 26	95.17 2	92.11 73	98.46 26	87.33 24	99.97 2	97.21 17	99.31 4	99.63 7

IB-MVS	85.34 4	88.67 140	87.14 159	93.26 76	93.12 200	84.32 74	98.76 16	97.27 18	87.19 90	79.36 221	90.45 231	83.92 43	98.53 130	84.41 154	69.79 297	96.93 153
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
ETH3 D test6400		95.56 9	95.41 13	96.00 9	99.02 19	89.42 9	98.75 17	96.80 48	87.28 85	95.88 23	98.95 2	85.92 30	99.41 67	97.15 18	98.95 20	99.18 25

CS-MVS-test		92.98 49	93.67 37	90.90 161	96.52 110	76.87 250	98.68 18	94.73 206	90.36 34	94.84 38	97.89 62	77.94 101	97.15 193	94.28 50	97.80 70	98.70 42

alignmvs		92.97 50	92.26 65	95.12 18	95.54 132	87.77 19	98.67 19	96.38 113	88.04 69	93.01 63	97.45 87	79.20 84	98.60 126	93.25 65	88.76 173	98.99 30

jason		92.73 56	92.23 66	94.21 41	90.50 262	87.30 25	98.65 20	95.09 186	90.61 28	92.76 66	97.13 103	75.28 154	97.30 182	93.32 63	96.75 101	98.02 87
jason: jason.
MSLP-MVS++		94.28 24	94.39 25	93.97 47	98.30 56	84.06 79	98.64 21	96.93 38	90.71 27	93.08 62	98.70 18	79.98 75	99.21 85	94.12 51	99.07 11	98.63 45

PHI-MVS		93.59 40	93.63 38	93.48 70	98.05 69	81.76 128	98.64 21	97.13 23	82.60 196	94.09 52	98.49 25	80.35 69	99.85 10	94.74 44	98.62 35	98.83 33

xxxxxxxxxxxxxcwj		94.38 22	94.62 19	93.68 58	98.24 58	83.34 93	98.61 23	92.69 297	91.32 19	95.07 33	98.74 16	82.93 52	99.38 69	95.42 36	98.51 38	98.32 60

save fliter					98.24 58	83.34 93	98.61 23	96.57 86	91.32 19

CS-MVS		92.73 56	93.48 42	90.48 174	96.27 113	75.93 269	98.55 25	94.93 193	89.32 46	94.54 45	97.67 73	78.91 87	97.02 197	93.80 53	97.32 84	98.49 52

DP-MVS Recon		91.72 77	90.85 87	94.34 35	99.50 1	85.00 64	98.51 26	95.96 141	80.57 224	88.08 133	97.63 80	76.84 119	99.89 7	85.67 144	94.88 121	98.13 79

patch_mono-2		95.14 12	96.08 7	92.33 115	98.44 48	77.84 232	98.43 27	97.21 20	92.58 11	97.68 8	97.65 78	86.88 25	99.83 16	98.25 3	97.60 74	99.33 17

CP-MVS		92.54 65	92.60 59	92.34 114	98.50 45	79.90 170	98.40 28	96.40 109	84.75 137	90.48 101	98.09 46	77.40 111	99.21 85	91.15 88	98.23 59	97.92 100

test_prior3		94.03 33	94.34 26	93.09 84	98.68 29	81.91 120	98.37 29	96.40 109	86.08 106	94.57 43	98.02 52	83.14 48	99.06 102	95.05 40	98.79 27	98.29 65

test_prior2							98.37 29		86.08 106	94.57 43	98.02 52	83.14 48		95.05 40	98.79 27

EPNet		94.06 32	94.15 30	93.76 53	97.27 101	84.35 73	98.29 31	97.64 13	94.57 4	95.36 27	96.88 112	79.96 76	99.12 99	91.30 86	96.11 106	97.82 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+		87.93 160	86.94 164	90.92 160	94.04 174	79.16 190	98.26 32	93.72 264	81.29 212	83.94 171	92.90 196	69.83 209	96.68 215	76.70 229	91.74 155	96.93 153

WTY-MVS		92.65 62	91.68 76	95.56 13	96.00 121	88.90 12	98.23 33	97.65 12	88.57 58	89.82 108	97.22 100	79.29 80	99.06 102	89.57 113	88.73 174	98.73 40

PS-MVSNAJ		94.17 27	93.52 41	96.10 8	95.65 130	92.35 2	98.21 34	95.79 151	92.42 13	96.24 19	98.18 35	71.04 201	99.17 93	96.77 21	97.39 82	96.79 159

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 10	95.07 146	92.02 6	98.19 35	95.68 156	92.06 15	96.01 22	98.14 40	70.83 204	98.96 108	96.74 22	96.57 102	96.76 162

9.14			94.26 29		98.10 66		98.14 36	96.52 93	84.74 138	94.83 39	98.80 9	82.80 55	99.37 73	95.95 28	98.42 46

ET-MVSNet_ETH3D		90.01 114	89.03 119	92.95 91	94.38 166	86.77 29	98.14 36	96.31 120	89.30 47	63.33 337	96.72 121	90.09 11	93.63 319	90.70 96	82.29 228	98.46 54

CLD-MVS		87.97 159	87.48 149	89.44 201	92.16 228	80.54 156	98.14 36	94.92 194	91.41 18	79.43 220	95.40 146	62.34 250	97.27 185	90.60 97	82.90 222	90.50 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 10	85.34 50	98.13 39	96.77 55	88.38 62	97.70 6	98.77 12	92.06 3	99.84 12	97.47 14	99.37 1	99.70 3

FOURS1					98.51 44	78.01 224	98.13 39	96.21 126	83.04 184	94.39 46

TSAR-MVS + GP.		94.35 23	94.50 20	93.89 49	97.38 98	83.04 101	98.10 41	95.29 180	91.57 17	93.81 53	97.45 87	86.64 26	99.43 66	96.28 23	94.01 130	99.20 23

test_yl		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 216	89.85 106	96.14 128	75.61 141	98.81 119	90.42 103	88.56 176	98.74 36

DCV-MVSNet		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 216	89.85 106	96.14 128	75.61 141	98.81 119	90.42 103	88.56 176	98.74 36

DROMVSNet		91.73 75	92.11 69	90.58 170	93.54 185	77.77 234	98.07 44	94.40 227	87.44 81	92.99 64	97.11 105	74.59 165	96.87 206	93.75 54	97.08 89	97.11 148

EIA-MVS		91.73 75	92.05 70	90.78 166	94.52 161	76.40 258	98.06 45	95.34 178	89.19 48	88.90 122	97.28 98	77.56 108	97.73 158	90.77 94	96.86 98	98.20 71

DeepC-MVS_fast	89.06 2	94.48 20	94.30 28	95.02 19	98.86 23	85.68 45	98.06 45	96.64 77	93.64 8	91.74 79	98.54 22	80.17 74	99.90 5	92.28 78	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		94.56 19	94.75 16	93.96 48	98.84 24	83.40 92	98.04 47	96.41 106	85.79 112	95.00 35	98.28 32	84.32 40	99.18 92	97.35 16	98.77 29	99.28 20

PVSNet_BlendedMVS		90.05 113	89.96 105	90.33 179	97.47 89	83.86 81	98.02 48	96.73 61	87.98 70	89.53 114	89.61 242	76.42 127	99.57 54	94.29 48	79.59 240	87.57 309

ETH3D-3000-0.1		94.43 21	94.42 24	94.45 31	97.78 77	85.78 41	97.98 49	96.53 92	85.29 126	95.45 26	98.81 8	83.36 46	99.38 69	96.07 25	98.53 37	98.19 72

ETV-MVS		92.72 58	92.87 52	92.28 118	94.54 160	81.89 122	97.98 49	95.21 183	89.77 41	93.11 61	96.83 114	77.23 116	97.50 171	95.74 31	95.38 115	97.44 133

MG-MVS		94.25 26	93.72 36	95.85 11	99.38 3	89.35 11	97.98 49	98.09 8	89.99 37	92.34 70	96.97 109	81.30 63	98.99 106	88.54 122	98.88 21	99.20 23

thisisatest0515		90.95 96	90.26 97	93.01 88	94.03 176	84.27 77	97.91 52	96.67 70	83.18 180	86.87 143	95.51 144	88.66 16	97.85 154	80.46 191	89.01 170	96.92 155

VNet		92.11 70	91.22 82	94.79 24	96.91 105	86.98 26	97.91 52	97.96 9	86.38 101	93.65 55	95.74 135	70.16 208	98.95 111	93.39 60	88.87 172	98.43 56

mvs-test1		86.83 172	87.17 156	85.81 271	91.96 236	65.24 342	97.90 54	93.34 281	85.57 117	84.51 164	95.14 156	61.99 255	97.19 189	83.55 169	90.55 161	95.00 200

thres200		88.92 132	87.65 141	92.73 101	96.30 112	85.62 46	97.85 55	98.86 1	84.38 150	84.82 158	93.99 183	75.12 157	98.01 146	70.86 278	86.67 189	94.56 210

3Dnovator+	82.88 8	89.63 120	87.85 137	94.99 20	94.49 165	86.76 30	97.84 56	95.74 153	86.10 105	75.47 270	96.02 131	65.00 237	99.51 61	82.91 179	97.07 90	98.72 41

TEST9					98.64 35	83.71 85	97.82 57	96.65 74	84.29 154	95.16 29	98.09 46	84.39 36	99.36 75

train_agg		94.28 24	94.45 22	93.74 54	98.64 35	83.71 85	97.82 57	96.65 74	84.50 146	95.16 29	98.09 46	84.33 37	99.36 75	95.91 29	98.96 19	98.16 75

test_8					98.63 37	83.64 88	97.81 59	96.63 79	84.50 146	95.10 31	98.11 45	84.33 37	99.23 81

HPM-MVS++		95.32 10	95.48 12	94.85 23	98.62 38	86.04 35	97.81 59	96.93 38	92.45 12	95.69 24	98.50 24	85.38 31	99.85 10	94.75 43	99.18 7	98.65 44

DPE-MVS		95.32 10	95.55 10	94.64 28	98.79 25	84.87 67	97.77 61	96.74 60	86.11 104	96.54 17	98.89 7	88.39 19	99.74 33	97.67 12	99.05 12	99.31 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
agg_prior1		94.10 30	94.31 27	93.48 70	98.59 39	83.13 98	97.77 61	96.56 88	84.38 150	94.19 48	98.13 41	84.66 35	99.16 94	95.74 31	98.74 31	98.15 77

#test#		92.99 48	92.99 49	92.98 89	98.71 27	81.12 140	97.77 61	96.70 65	85.75 113	91.75 77	97.97 59	78.47 93	99.71 37	91.36 85	98.41 48	98.12 80

PVSNet_Blended_VisFu		91.24 90	90.77 89	92.66 103	95.09 144	82.40 112	97.77 61	95.87 148	88.26 65	86.39 145	93.94 184	76.77 121	99.27 78	88.80 121	94.00 131	96.31 176

SD-MVS		94.84 15	95.02 15	94.29 37	97.87 76	84.61 71	97.76 65	96.19 129	89.59 42	96.66 15	98.17 39	84.33 37	99.60 51	96.09 24	98.50 41	98.66 43
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
test_prior4						82.34 113	97.75 66

SF-MVS		94.17 27	94.05 32	94.55 30	97.56 87	85.95 36	97.73 67	96.43 104	84.02 159	95.07 33	98.74 16	82.93 52	99.38 69	95.42 36	98.51 38	98.32 60

3Dnovator	82.32 10	89.33 124	87.64 142	94.42 33	93.73 181	85.70 44	97.73 67	96.75 59	86.73 100	76.21 257	95.93 132	62.17 251	99.68 43	81.67 185	97.81 69	97.88 101

CPTT-MVS		89.72 118	89.87 109	89.29 203	98.33 54	73.30 293	97.70 69	95.35 177	75.68 292	87.40 137	97.44 90	70.43 205	98.25 140	89.56 114	96.90 94	96.33 175

PVSNet	82.34 9	89.02 129	87.79 139	92.71 102	95.49 133	81.50 135	97.70 69	97.29 17	87.76 76	85.47 152	95.12 158	56.90 293	98.90 115	80.33 192	94.02 129	97.71 116

iter_conf05		90.14 112	89.79 111	91.17 153	95.85 125	86.93 27	97.68 71	88.67 343	89.93 38	81.73 200	92.80 197	90.37 8	96.03 233	90.44 101	80.65 233	90.56 238

CDPH-MVS		93.12 45	92.91 51	93.74 54	98.65 34	83.88 80	97.67 72	96.26 122	83.00 186	93.22 60	98.24 33	81.31 62	99.21 85	89.12 118	98.74 31	98.14 78

ZNCC-MVS		92.75 53	92.60 59	93.23 78	98.24 58	81.82 126	97.63 73	96.50 96	85.00 134	91.05 93	97.74 71	78.38 95	99.80 24	90.48 98	98.34 55	98.07 83

HQP-NCC					92.08 230		97.63 73		90.52 29	82.30 188

ACMP_Plane					92.08 230		97.63 73		90.52 29	82.30 188

HQP-MVS		87.91 161	87.55 147	88.98 208	92.08 230	78.48 206	97.63 73	94.80 202	90.52 29	82.30 188	94.56 169	65.40 233	97.32 180	87.67 131	83.01 219	91.13 232

testtj		94.09 31	94.08 31	94.09 45	99.28 6	83.32 95	97.59 77	96.61 80	83.60 175	94.77 41	98.46 26	82.72 56	99.64 47	95.29 38	98.42 46	99.32 18

HFP-MVS		92.89 51	92.86 53	92.98 89	98.71 27	81.12 140	97.58 78	96.70 65	85.20 129	91.75 77	97.97 59	78.47 93	99.71 37	90.95 89	98.41 48	98.12 80

ACMMPR		92.69 60	92.67 57	92.75 99	98.66 32	80.57 154	97.58 78	96.69 68	85.20 129	91.57 81	97.92 61	77.01 117	99.67 45	90.95 89	98.41 48	98.00 92

MVS_111021_HR		93.41 42	93.39 44	93.47 73	97.34 99	82.83 104	97.56 80	98.27 6	89.16 49	89.71 109	97.14 102	79.77 77	99.56 56	93.65 56	97.94 66	98.02 87

VDD-MVS		88.28 152	87.02 162	92.06 125	95.09 144	80.18 166	97.55 81	94.45 225	83.09 182	89.10 120	95.92 134	47.97 327	98.49 132	93.08 70	86.91 188	97.52 130

GeoE		86.36 179	85.20 178	89.83 196	93.17 196	76.13 261	97.53 82	92.11 302	79.58 248	80.99 204	94.01 182	66.60 227	96.17 231	73.48 260	89.30 167	97.20 147

MTMP							97.53 82	68.16 375

region2R		92.72 58	92.70 56	92.79 98	98.68 29	80.53 157	97.53 82	96.51 94	85.22 127	91.94 75	97.98 57	77.26 112	99.67 45	90.83 93	98.37 53	98.18 73

plane_prior						77.96 226	97.52 85		90.36 34						82.96 221

API-MVS		90.18 111	88.97 121	93.80 52	98.66 32	82.95 103	97.50 86	95.63 160	75.16 296	86.31 146	97.69 72	72.49 185	99.90 5	81.26 187	96.07 107	98.56 48

SMA-MVS		94.70 17	94.68 17	94.76 25	98.02 70	85.94 38	97.47 87	96.77 55	85.32 123	97.92 3	98.70 18	83.09 51	99.84 12	95.79 30	99.08 10	98.49 52
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
CSCG		92.02 71	91.65 77	93.12 82	98.53 41	80.59 153	97.47 87	97.18 22	77.06 286	84.64 162	97.98 57	83.98 42	99.52 58	90.72 95	97.33 83	99.23 22

Anonymous202405211		84.41 210	81.93 231	91.85 134	96.78 107	78.41 210	97.44 89	91.34 314	70.29 328	84.06 166	94.26 175	41.09 349	98.96 108	79.46 202	82.65 226	98.17 74

tfpn200view9		88.48 145	87.15 157	92.47 109	96.21 115	85.30 53	97.44 89	98.85 2	83.37 177	83.99 168	93.82 186	75.36 151	97.93 148	69.04 284	86.24 195	94.17 212

thres400		88.42 148	87.15 157	92.23 119	96.21 115	85.30 53	97.44 89	98.85 2	83.37 177	83.99 168	93.82 186	75.36 151	97.93 148	69.04 284	86.24 195	93.45 225

OpenMVS	79.58 14	86.09 183	83.62 205	93.50 68	90.95 253	86.71 31	97.44 89	95.83 149	75.35 293	72.64 292	95.72 136	57.42 290	99.64 47	71.41 271	95.85 112	94.13 215

Regformer-1		94.00 34	94.04 33	93.87 50	98.41 49	84.29 75	97.43 93	97.04 28	89.50 43	92.75 67	98.13 41	82.60 58	99.26 80	93.55 58	96.99 91	98.06 84

Regformer-2		93.92 35	94.01 34	93.67 59	98.41 49	83.75 84	97.43 93	97.00 30	89.43 45	92.69 68	98.13 41	82.48 59	99.22 83	93.51 59	96.99 91	98.04 85

MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 55	80.10 167	97.42 95	96.78 49	92.20 14	97.11 11	98.29 31	93.46 1	99.10 100	96.01 26	99.30 5	99.38 14
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
BH-w/o		88.24 153	87.47 150	90.54 172	95.03 149	78.54 205	97.41 96	93.82 255	84.08 157	78.23 231	94.51 171	69.34 211	97.21 187	80.21 195	94.58 125	95.87 184

GST-MVS		92.43 67	92.22 67	93.04 87	98.17 63	81.64 133	97.40 97	96.38 113	84.71 140	90.90 95	97.40 92	77.55 109	99.76 25	89.75 111	97.74 71	97.72 114

XVS		92.69 60	92.71 54	92.63 105	98.52 42	80.29 160	97.37 98	96.44 102	87.04 92	91.38 83	97.83 68	77.24 114	99.59 52	90.46 99	98.07 62	98.02 87

X-MVStestdata		86.26 181	84.14 198	92.63 105	98.52 42	80.29 160	97.37 98	96.44 102	87.04 92	91.38 83	20.73 376	77.24 114	99.59 52	90.46 99	98.07 62	98.02 87

MP-MVS		92.61 63	92.67 57	92.42 112	98.13 65	79.73 176	97.33 100	96.20 127	85.63 116	90.53 99	97.66 74	78.14 99	99.70 40	92.12 80	98.30 57	97.85 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		91.88 73	91.82 73	92.07 124	98.38 51	78.63 204	97.29 101	96.09 134	85.12 131	88.45 127	97.66 74	75.53 144	99.68 43	89.83 109	98.02 65	97.88 101

ETH3D cwj APD-0.16		93.91 37	93.76 35	94.36 34	96.70 108	85.74 42	97.22 102	96.41 106	83.94 162	94.13 51	98.69 20	83.13 50	99.37 73	95.25 39	98.39 51	97.97 97

EPP-MVSNet		89.76 117	89.72 112	89.87 194	93.78 178	76.02 266	97.22 102	96.51 94	79.35 251	85.11 154	95.01 162	84.82 34	97.10 195	87.46 133	88.21 180	96.50 168

APD-MVS		93.61 39	93.59 39	93.69 57	98.76 26	83.26 96	97.21 104	96.09 134	82.41 198	94.65 42	98.21 34	81.96 61	98.81 119	94.65 45	98.36 54	99.01 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNLPA		86.96 168	85.37 176	91.72 137	97.59 85	79.34 186	97.21 104	91.05 319	74.22 303	78.90 223	96.75 120	67.21 222	98.95 111	74.68 248	90.77 160	96.88 157

PAPR		92.74 54	92.17 68	94.45 31	98.89 22	84.87 67	97.20 106	96.20 127	87.73 77	88.40 128	98.12 44	78.71 91	99.76 25	87.99 129	96.28 104	98.74 36

QAPM		86.88 170	84.51 190	93.98 46	94.04 174	85.89 39	97.19 107	96.05 137	73.62 307	75.12 273	95.62 141	62.02 254	99.74 33	70.88 277	96.06 108	96.30 177

LFMVS		89.27 126	87.64 142	94.16 44	97.16 102	85.52 48	97.18 108	94.66 211	79.17 257	89.63 112	96.57 123	55.35 305	98.22 141	89.52 115	89.54 165	98.74 36

HQP_MVS		87.50 164	87.09 160	88.74 213	91.86 240	77.96 226	97.18 108	94.69 207	89.89 39	81.33 201	94.15 179	64.77 239	97.30 182	87.08 135	82.82 223	90.96 234

plane_prior2							97.18 108		89.89 39

MAR-MVS		90.63 102	90.22 98	91.86 132	98.47 47	78.20 220	97.18 108	96.61 80	83.87 166	88.18 132	98.18 35	68.71 213	99.75 31	83.66 168	97.15 88	97.63 122
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
PLC	83.97 7	88.00 158	87.38 152	89.83 196	98.02 70	76.46 256	97.16 112	94.43 226	79.26 256	81.98 195	96.28 126	69.36 210	99.27 78	77.71 218	92.25 150	93.77 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HPM-MVS_fast		90.38 109	90.17 101	91.03 157	97.61 83	77.35 243	97.15 113	95.48 167	79.51 249	88.79 123	96.90 110	71.64 195	98.81 119	87.01 138	97.44 79	96.94 152

thres100view900		88.30 151	86.95 163	92.33 115	96.10 119	84.90 66	97.14 114	98.85 2	82.69 194	83.41 176	93.66 189	75.43 148	97.93 148	69.04 284	86.24 195	94.17 212

thres600view7		88.06 156	86.70 166	92.15 122	96.10 119	85.17 59	97.14 114	98.85 2	82.70 193	83.41 176	93.66 189	75.43 148	97.82 155	67.13 293	85.88 199	93.45 225

sss		90.87 98	89.96 105	93.60 63	94.15 170	83.84 83	97.14 114	98.13 7	85.93 110	89.68 110	96.09 130	71.67 193	99.30 77	87.69 130	89.16 168	97.66 119

test-LLR		88.48 145	87.98 135	89.98 189	92.26 221	77.23 245	97.11 117	95.96 141	83.76 170	86.30 147	91.38 215	72.30 188	96.78 212	80.82 188	91.92 153	95.94 182

TESTMET0.1,1		89.83 115	89.34 117	91.31 147	92.54 214	80.19 165	97.11 117	96.57 86	86.15 103	86.85 144	91.83 209	79.32 79	96.95 200	81.30 186	92.35 149	96.77 161

test-mter		88.95 130	88.60 127	89.98 189	92.26 221	77.23 245	97.11 117	95.96 141	85.32 123	86.30 147	91.38 215	76.37 129	96.78 212	80.82 188	91.92 153	95.94 182

VDDNet		86.44 178	84.51 190	92.22 120	91.56 243	81.83 125	97.10 120	94.64 214	69.50 332	87.84 134	95.19 152	48.01 326	97.92 153	89.82 110	86.92 187	96.89 156

canonicalmvs		92.27 68	91.22 82	95.41 15	95.80 126	88.31 13	97.09 121	94.64 214	88.49 60	92.99 64	97.31 94	72.68 184	98.57 128	93.38 62	88.58 175	99.36 16

CDS-MVSNet		89.50 121	88.96 122	91.14 155	91.94 239	80.93 145	97.09 121	95.81 150	84.26 155	84.72 160	94.20 178	80.31 70	95.64 262	83.37 174	88.96 171	96.85 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Regformer-3		93.19 43	93.19 47	93.19 80	98.10 66	83.01 102	97.08 123	96.98 32	88.98 50	91.35 87	97.89 62	80.80 65	99.23 81	92.30 77	95.20 117	97.32 139

Regformer-4		93.06 47	93.12 48	92.89 94	98.10 66	82.20 116	97.08 123	96.92 40	88.87 52	91.23 90	97.89 62	80.57 68	99.19 90	92.21 79	95.20 117	97.29 143

nrg030		86.79 174	85.43 174	90.87 163	88.76 285	85.34 50	97.06 125	94.33 229	84.31 152	80.45 211	91.98 204	72.36 186	96.36 224	88.48 125	71.13 284	90.93 236

cascas		86.50 177	84.48 192	92.55 108	92.64 212	85.95 36	97.04 126	95.07 188	75.32 294	80.50 209	91.02 222	54.33 312	97.98 147	86.79 140	87.62 183	93.71 221

xiu_mvs_v1_base_debu		90.54 104	89.54 114	93.55 65	92.31 216	87.58 22	96.99 127	94.87 197	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 205

xiu_mvs_v1_base		90.54 104	89.54 114	93.55 65	92.31 216	87.58 22	96.99 127	94.87 197	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 205

xiu_mvs_v1_base_debi		90.54 104	89.54 114	93.55 65	92.31 216	87.58 22	96.99 127	94.87 197	87.23 87	93.27 57	97.56 82	57.43 287	98.32 137	92.72 72	93.46 138	94.74 205

HPM-MVS		91.62 81	91.53 79	91.89 131	97.88 75	79.22 188	96.99 127	95.73 154	82.07 203	89.50 116	97.19 101	75.59 143	98.93 114	90.91 91	97.94 66	97.54 126
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		88.79 138	87.57 146	92.45 110	98.21 61	81.74 129	96.99 127	95.45 169	75.16 296	82.48 185	95.69 138	68.59 214	98.50 131	80.33 192	95.18 119	97.10 149

旧先验2							96.97 132		74.06 305	96.10 20			97.76 157	88.38 126

h-mvs33		89.30 125	88.95 123	90.36 177	95.07 146	76.04 263	96.96 133	97.11 25	90.39 32	92.22 71	95.10 159	74.70 161	98.86 116	93.14 67	65.89 330	96.16 178

BH-RMVSNet		86.84 171	85.28 177	91.49 144	95.35 137	80.26 163	96.95 134	92.21 301	82.86 190	81.77 199	95.46 145	59.34 272	97.64 160	69.79 282	93.81 134	96.57 167

Vis-MVSNet		88.67 140	87.82 138	91.24 151	92.68 208	78.82 198	96.95 134	93.85 254	87.55 80	87.07 142	95.13 157	63.43 245	97.21 187	77.58 220	96.15 105	97.70 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		88.88 134	88.87 125	88.91 209	93.89 177	74.43 284	96.93 136	94.19 236	84.39 149	83.22 179	95.67 139	78.24 97	94.70 300	78.88 210	94.40 127	97.61 124

GA-MVS		85.79 188	84.04 199	91.02 158	89.47 280	80.27 162	96.90 137	94.84 200	85.57 117	80.88 205	89.08 246	56.56 297	96.47 221	77.72 217	85.35 205	96.34 173

无先验							96.87 138	96.78 49	77.39 279				99.52 58	79.95 197		98.43 56

原ACMM2							96.84 139

casdiffmvs		90.95 96	90.39 94	92.63 105	92.82 207	82.53 108	96.83 140	94.47 223	87.69 78	88.47 126	95.56 143	74.04 170	97.54 168	90.90 92	92.74 143	97.83 107

ACMMP_NAP		93.46 41	93.23 46	94.17 42	97.16 102	84.28 76	96.82 141	96.65 74	86.24 102	94.27 47	97.99 55	77.94 101	99.83 16	93.39 60	98.57 36	98.39 58

Anonymous20240529		83.15 231	80.60 249	90.80 164	95.74 127	78.27 214	96.81 142	94.92 194	60.10 358	81.89 197	92.54 200	45.82 334	98.82 118	79.25 206	78.32 256	95.31 197

MVSTER		89.25 127	88.92 124	90.24 181	95.98 122	84.66 70	96.79 143	95.36 175	87.19 90	80.33 213	90.61 229	90.02 12	95.97 237	85.38 147	78.64 251	90.09 251

BH-untuned		86.95 169	85.94 170	89.99 188	94.52 161	77.46 240	96.78 144	93.37 280	81.80 207	76.62 249	93.81 188	66.64 226	97.02 197	76.06 236	93.88 133	95.48 193

ACMMP		90.39 107	89.97 104	91.64 139	97.58 86	78.21 219	96.78 144	96.72 63	84.73 139	84.72 160	97.23 99	71.22 198	99.63 49	88.37 127	92.41 148	97.08 150
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
IS-MVSNet		88.67 140	88.16 133	90.20 183	93.61 182	76.86 251	96.77 146	93.07 291	84.02 159	83.62 175	95.60 142	74.69 164	96.24 229	78.43 213	93.66 136	97.49 132

test_part1		84.72 203	82.85 217	90.34 178	95.73 129	84.79 69	96.75 147	94.10 242	79.05 263	75.97 261	89.51 243	67.69 215	95.94 241	79.34 203	67.50 320	90.30 245

UniMVSNet (Re)		85.31 195	84.23 196	88.55 216	89.75 273	80.55 155	96.72 148	96.89 41	85.42 121	78.40 229	88.93 249	75.38 150	95.52 269	78.58 211	68.02 314	89.57 261

EPNet_dtu		87.65 163	87.89 136	86.93 254	94.57 158	71.37 315	96.72 148	96.50 96	88.56 59	87.12 141	95.02 161	75.91 137	94.01 312	66.62 295	90.00 163	95.42 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPNet		84.69 205	82.92 215	90.01 187	89.01 284	83.45 91	96.71 150	95.46 168	85.71 115	79.65 219	92.18 203	56.66 296	96.01 236	83.05 178	67.84 317	90.56 238

UniMVSNet_NR-MVSNet		85.49 192	84.59 188	88.21 226	89.44 281	79.36 184	96.71 150	96.41 106	85.22 127	78.11 232	90.98 224	76.97 118	95.14 287	79.14 207	68.30 311	90.12 248

AdaColmap		88.81 136	87.61 145	92.39 113	99.33 4	79.95 168	96.70 152	95.58 161	77.51 278	83.05 182	96.69 122	61.90 258	99.72 36	84.29 155	93.47 137	97.50 131

SR-MVS		92.16 69	92.27 64	91.83 135	98.37 52	78.41 210	96.67 153	95.76 152	82.19 202	91.97 74	98.07 50	76.44 126	98.64 123	93.71 55	97.27 85	98.45 55

EI-MVSNet-Vis-set		91.84 74	91.77 75	92.04 126	97.60 84	81.17 139	96.61 154	96.87 42	88.20 66	89.19 118	97.55 85	78.69 92	99.14 96	90.29 105	90.94 159	95.80 185

WR-MVS		84.32 211	82.96 214	88.41 218	89.38 282	80.32 159	96.59 155	96.25 123	83.97 161	76.63 248	90.36 233	67.53 218	94.86 297	75.82 240	70.09 295	90.06 253

test1111		88.11 155	87.04 161	91.35 146	93.15 197	78.79 201	96.57 156	90.78 324	86.88 96	85.04 155	95.20 151	57.23 292	97.39 177	83.88 160	94.59 124	97.87 103

TR-MVS		86.30 180	84.93 186	90.42 175	94.63 157	77.58 238	96.57 156	93.82 255	80.30 233	82.42 187	95.16 154	58.74 277	97.55 166	74.88 246	87.82 182	96.13 180

ECVR-MVS		88.35 150	87.25 154	91.65 138	93.54 185	79.40 183	96.56 158	90.78 324	86.78 98	85.57 151	95.25 147	57.25 291	97.56 164	84.73 153	94.80 122	97.98 94

thisisatest0530		89.65 119	89.02 120	91.53 143	93.46 191	80.78 149	96.52 159	96.67 70	81.69 209	83.79 173	94.90 164	88.85 15	97.68 159	77.80 214	87.49 186	96.14 179

test0.0.03 1		82.79 238	82.48 224	83.74 303	86.81 307	72.22 300	96.52 159	95.03 190	83.76 170	73.00 288	93.20 192	72.30 188	88.88 354	64.15 308	77.52 259	90.12 248

Baseline_NR-MVSNet		81.22 260	80.07 257	84.68 288	85.32 329	75.12 277	96.48 161	88.80 339	76.24 290	77.28 239	86.40 290	67.61 216	94.39 306	75.73 241	66.73 328	84.54 340

zzz-MVS		92.74 54	92.71 54	92.86 95	97.90 72	80.85 147	96.47 162	96.33 117	87.92 71	90.20 104	98.18 35	76.71 123	99.76 25	92.57 75	98.09 60	97.96 98

EI-MVSNet-UG-set		91.35 88	91.22 82	91.73 136	97.39 95	80.68 151	96.47 162	96.83 45	87.92 71	88.30 131	97.36 93	77.84 104	99.13 98	89.43 116	89.45 166	95.37 195

1112_ss		88.60 143	87.47 150	92.00 128	93.21 194	80.97 144	96.47 162	92.46 299	83.64 173	80.86 206	97.30 96	80.24 72	97.62 161	77.60 219	85.49 203	97.40 136

test1172		91.64 79	92.00 71	90.54 172	98.20 62	74.48 283	96.45 165	95.65 157	81.97 206	91.63 80	98.02 52	75.76 139	98.61 124	93.16 66	97.17 87	98.52 51

TAMVS		88.48 145	87.79 139	90.56 171	91.09 251	79.18 189	96.45 165	95.88 146	83.64 173	83.12 180	93.33 191	75.94 136	95.74 257	82.40 180	88.27 179	96.75 163

MP-MVS-pluss		92.58 64	92.35 62	93.29 75	97.30 100	82.53 108	96.44 167	96.04 138	84.68 141	89.12 119	98.37 29	77.48 110	99.74 33	93.31 64	98.38 52	97.59 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Test_1112_low_res		88.03 157	86.73 165	91.94 130	93.15 197	80.88 146	96.44 167	92.41 300	83.59 176	80.74 208	91.16 220	80.18 73	97.59 162	77.48 222	85.40 204	97.36 138

DU-MVS		84.57 207	83.33 210	88.28 222	88.76 285	79.36 184	96.43 169	95.41 174	85.42 121	78.11 232	90.82 225	67.61 216	95.14 287	79.14 207	68.30 311	90.33 243

新几何2							96.42 170

PAPM		92.87 52	92.40 61	94.30 36	92.25 223	87.85 18	96.40 171	96.38 113	91.07 23	88.72 124	96.90 110	82.11 60	97.37 179	90.05 107	97.70 72	97.67 118

test2506		90.96 95	90.39 94	92.65 104	93.54 185	82.46 111	96.37 172	97.35 16	86.78 98	87.55 136	95.25 147	77.83 105	97.50 171	84.07 157	94.80 122	97.98 94

VPA-MVSNet		85.32 194	83.83 200	89.77 199	90.25 265	82.63 106	96.36 173	97.07 27	83.03 185	81.21 203	89.02 248	61.58 259	96.31 226	85.02 150	70.95 286	90.36 241

UGNet		87.73 162	86.55 167	91.27 150	95.16 143	79.11 192	96.35 174	96.23 124	88.14 67	87.83 135	90.48 230	50.65 317	99.09 101	80.13 196	94.03 128	95.60 190
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
v2v482		83.46 225	81.86 232	88.25 224	86.19 315	79.65 178	96.34 175	94.02 246	81.56 210	77.32 238	88.23 259	65.62 230	96.03 233	77.77 215	69.72 299	89.09 273

CANet_DTU		90.98 94	90.04 103	93.83 51	94.76 155	86.23 33	96.32 176	93.12 290	93.11 10	93.71 54	96.82 116	63.08 247	99.48 63	84.29 155	95.12 120	95.77 186

APD-MVS_3200maxsize		91.23 91	91.35 81	90.89 162	97.89 74	76.35 259	96.30 177	95.52 165	79.82 243	91.03 94	97.88 65	74.70 161	98.54 129	92.11 81	96.89 95	97.77 111

v148		82.41 246	80.89 243	86.99 253	86.18 316	76.81 252	96.27 178	93.82 255	80.49 227	75.28 272	86.11 295	67.32 221	95.75 254	75.48 242	67.03 326	88.42 292

CHOSEN 1792x2688		91.07 93	90.21 99	93.64 60	95.18 142	83.53 89	96.26 179	96.13 131	88.92 51	84.90 157	93.10 195	72.86 182	99.62 50	88.86 119	95.67 114	97.79 110

diffmvs		91.17 92	90.74 90	92.44 111	93.11 201	82.50 110	96.25 180	93.62 268	87.79 75	90.40 102	95.93 132	73.44 178	97.42 175	93.62 57	92.55 145	97.41 135

pmmvs5		81.34 258	79.54 262	86.73 258	85.02 331	76.91 249	96.22 181	91.65 309	77.65 276	73.55 281	88.61 253	55.70 303	94.43 305	74.12 255	73.35 276	88.86 285

mvsmamba		85.17 197	84.54 189	87.05 252	87.94 296	75.11 278	96.22 181	87.79 346	86.91 94	78.55 227	91.77 210	64.93 238	95.91 244	86.94 139	79.80 236	90.12 248

PMMVS		89.46 122	89.92 107	88.06 228	94.64 156	69.57 329	96.22 181	94.95 192	87.27 86	91.37 86	96.54 124	65.88 229	97.39 177	88.54 122	93.89 132	97.23 145

SR-MVS-dyc-post		91.29 89	91.45 80	90.80 164	97.76 80	76.03 264	96.20 184	95.44 170	80.56 225	90.72 97	97.84 66	75.76 139	98.61 124	91.99 82	96.79 99	97.75 112

RE-MVS-def			91.18 85		97.76 80	76.03 264	96.20 184	95.44 170	80.56 225	90.72 97	97.84 66	73.36 179		91.99 82	96.79 99	97.75 112

MVS_111021_LR		91.60 82	91.64 78	91.47 145	95.74 127	78.79 201	96.15 186	96.77 55	88.49 60	88.64 125	97.07 107	72.33 187	99.19 90	93.13 69	96.48 103	96.43 170

FIs		86.73 176	86.10 169	88.61 215	90.05 270	80.21 164	96.14 187	96.95 36	85.56 120	78.37 230	92.30 201	76.73 122	95.28 279	79.51 201	79.27 245	90.35 242

v1144		82.90 237	81.27 241	87.78 233	86.29 313	79.07 195	96.14 187	93.93 248	80.05 239	77.38 236	86.80 280	65.50 231	95.93 243	75.21 244	70.13 292	88.33 294

TranMVSNet+NR-MVSNet		83.24 230	81.71 234	87.83 231	87.71 299	78.81 200	96.13 189	94.82 201	84.52 145	76.18 258	90.78 227	64.07 242	94.60 302	74.60 251	66.59 329	90.09 251

Fast-Effi-MVS+-dtu		83.33 227	82.60 222	85.50 278	89.55 278	69.38 330	96.09 190	91.38 311	82.30 199	75.96 262	91.41 213	56.71 294	95.58 267	75.13 245	84.90 208	91.54 230

miper_enhance_ethall		85.95 185	85.20 178	88.19 227	94.85 153	79.76 172	96.00 191	94.06 245	82.98 187	77.74 234	88.76 251	79.42 78	95.46 271	80.58 190	72.42 279	89.36 267

v144192		82.43 243	80.73 246	87.54 240	85.81 322	78.22 216	95.98 192	93.78 260	79.09 259	77.11 241	86.49 285	64.66 241	95.91 244	74.20 254	69.42 300	88.49 288

PVSNet_0	77.72 15	81.70 253	78.95 267	89.94 192	90.77 259	76.72 254	95.96 193	96.95 36	85.01 133	70.24 308	88.53 256	52.32 314	98.20 142	86.68 141	44.08 366	94.89 201

F-COLMAP		84.50 209	83.44 209	87.67 234	95.22 140	72.22 300	95.95 194	93.78 260	75.74 291	76.30 255	95.18 153	59.50 270	98.45 134	72.67 264	86.59 191	92.35 229

DeepC-MVS	86.58 3	91.53 83	91.06 86	92.94 92	94.52 161	81.89 122	95.95 194	95.98 140	90.76 26	83.76 174	96.76 118	73.24 180	99.71 37	91.67 84	96.96 93	97.22 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FMVSNet3		84.71 204	82.71 220	90.70 168	94.55 159	87.71 20	95.92 196	94.67 210	81.73 208	75.82 265	88.08 262	66.99 223	94.47 304	71.23 273	75.38 266	89.91 256

TAPA-MVS	81.61 12	85.02 199	83.67 202	89.06 205	96.79 106	73.27 295	95.92 196	94.79 204	74.81 299	80.47 210	96.83 114	71.07 200	98.19 143	49.82 356	92.57 144	95.71 188
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	81.66 11	84.00 214	83.22 212	86.33 262	91.53 246	72.95 298	95.91 198	93.79 259	83.70 172	73.79 280	92.22 202	54.31 313	96.89 204	83.98 158	79.74 239	89.16 271
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	80.70 13	83.72 221	82.85 217	86.31 265	91.19 249	72.12 303	95.88 199	94.29 231	80.44 228	77.02 242	91.96 205	55.24 306	97.14 194	79.30 205	80.38 234	89.67 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					96.15 117	78.41 210	95.87 200	96.46 100	71.97 321	89.66 111	97.45 87	76.33 130			98.24 58	98.30 64

V42		83.04 234	81.53 237	87.57 239	86.27 314	79.09 194	95.87 200	94.11 241	80.35 232	77.22 240	86.79 281	65.32 235	96.02 235	77.74 216	70.14 291	87.61 308

TSAR-MVS + MP.		94.79 16	95.17 14	93.64 60	97.66 82	84.10 78	95.85 202	96.42 105	91.26 21	97.49 10	96.80 117	86.50 27	98.49 132	95.54 34	99.03 13	98.33 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1192		82.31 247	80.55 250	87.60 236	85.94 319	78.47 209	95.85 202	93.80 258	79.33 252	76.97 243	86.51 284	63.33 246	95.87 246	73.11 261	70.13 292	88.46 290

v1921920		82.02 250	80.23 254	87.41 243	85.62 323	77.92 229	95.79 204	93.69 265	78.86 264	76.67 247	86.44 287	62.50 249	95.83 249	72.69 263	69.77 298	88.47 289

OPM-MVS		85.84 186	85.10 183	88.06 228	88.34 291	77.83 233	95.72 205	94.20 235	87.89 74	80.45 211	94.05 181	58.57 278	97.26 186	83.88 160	82.76 225	89.09 273
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XXY-MVS		83.84 217	82.00 230	89.35 202	87.13 305	81.38 136	95.72 205	94.26 232	80.15 237	75.92 263	90.63 228	61.96 257	96.52 219	78.98 209	73.28 277	90.14 247

tttt0517		88.57 144	88.19 132	89.71 200	93.00 202	75.99 267	95.67 207	96.67 70	80.78 219	81.82 198	94.40 172	88.97 14	97.58 163	76.05 237	86.31 192	95.57 191

bld_raw_conf005		83.53 223	82.51 223	86.62 260	87.38 303	73.99 289	95.66 208	85.25 355	85.74 114	76.81 245	91.39 214	55.95 302	95.86 247	84.87 152	79.51 243	90.00 255

IterMVS-LS		83.93 215	82.80 219	87.31 246	91.46 247	77.39 242	95.66 208	93.43 275	80.44 228	75.51 269	87.26 272	73.72 174	95.16 286	76.99 225	70.72 288	89.39 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		85.96 184	85.39 175	87.66 235	89.38 282	78.02 223	95.65 210	96.87 42	85.12 131	77.34 237	91.94 207	76.28 131	94.74 299	77.09 224	78.82 249	90.21 246

HyFIR lowres test		89.36 123	88.60 127	91.63 141	94.91 152	80.76 150	95.60 211	95.53 163	82.56 197	84.03 167	91.24 219	78.03 100	96.81 210	87.07 137	88.41 178	97.32 139

testdata1							95.57 212		87.44 81

cl22		85.11 198	84.17 197	87.92 230	95.06 148	78.82 198	95.51 213	94.22 233	79.74 245	76.77 246	87.92 264	75.96 135	95.68 258	79.93 199	72.42 279	89.27 268

v1240		81.70 253	79.83 261	87.30 247	85.50 324	77.70 237	95.48 214	93.44 274	78.46 269	76.53 250	86.44 287	60.85 262	95.84 248	71.59 270	70.17 290	88.35 293

baseline1		88.85 135	87.49 148	92.93 93	95.21 141	86.85 28	95.47 215	94.61 216	87.29 84	83.11 181	94.99 163	80.70 66	96.89 204	82.28 181	73.72 272	95.05 199

AUN-MVS		86.25 182	85.57 172	88.26 223	93.57 184	73.38 291	95.45 216	95.88 146	83.94 162	85.47 152	94.21 177	73.70 176	96.67 216	83.54 171	64.41 334	94.73 208

FMVSNet2		82.79 238	80.44 251	89.83 196	92.66 209	85.43 49	95.42 217	94.35 228	79.06 260	74.46 277	87.28 270	56.38 299	94.31 307	69.72 283	74.68 269	89.76 259

hse-mvs2		88.22 154	88.21 131	88.25 224	93.54 185	73.41 290	95.41 218	95.89 145	90.39 32	92.22 71	94.22 176	74.70 161	96.66 217	93.14 67	64.37 335	94.69 209

miper_ehance_all_eth		84.57 207	83.60 206	87.50 241	92.64 212	78.25 215	95.40 219	93.47 273	79.28 255	76.41 252	87.64 267	76.53 125	95.24 282	78.58 211	72.42 279	89.01 278

RRT_MVS		83.88 216	83.27 211	85.71 274	87.53 302	72.12 303	95.35 220	94.33 229	83.81 168	75.86 264	91.28 218	60.55 263	95.09 292	83.93 159	76.76 261	89.90 257

PGM-MVS		91.93 72	91.80 74	92.32 117	98.27 57	79.74 175	95.28 221	97.27 18	83.83 167	90.89 96	97.78 70	76.12 133	99.56 56	88.82 120	97.93 68	97.66 119

TransMVSNet (Re)		76.94 295	74.38 299	84.62 291	85.92 320	75.25 276	95.28 221	89.18 336	73.88 306	67.22 317	86.46 286	59.64 267	94.10 310	59.24 328	52.57 357	84.50 341

LPG-MVS_test		84.20 213	83.49 208	86.33 262	90.88 254	73.06 296	95.28 221	94.13 239	82.20 200	76.31 253	93.20 192	54.83 310	96.95 200	83.72 165	80.83 231	88.98 279

c3_l		83.80 218	82.65 221	87.25 248	92.10 229	77.74 236	95.25 224	93.04 292	78.58 267	76.01 259	87.21 274	75.25 155	95.11 289	77.54 221	68.89 305	88.91 284

D2MVS		82.67 240	81.55 236	86.04 269	87.77 298	76.47 255	95.21 225	96.58 85	82.66 195	70.26 307	85.46 303	60.39 264	95.80 251	76.40 233	79.18 246	85.83 333

Effi-MVS+		90.70 100	89.90 108	93.09 84	93.61 182	83.48 90	95.20 226	92.79 295	83.22 179	91.82 76	95.70 137	71.82 192	97.48 173	91.25 87	93.67 135	98.32 60

baseline2		90.39 107	90.21 99	90.93 159	90.86 256	80.99 143	95.20 226	97.41 15	86.03 108	80.07 217	94.61 168	90.58 6	97.47 174	87.29 134	89.86 164	94.35 211

Anonymous20231211		79.72 272	77.19 278	87.33 244	95.59 131	77.16 248	95.18 228	94.18 237	59.31 360	72.57 293	86.20 293	47.89 328	95.66 259	74.53 252	69.24 303	89.18 270

EI-MVSNet		85.80 187	85.20 178	87.59 237	91.55 244	77.41 241	95.13 229	95.36 175	80.43 230	80.33 213	94.71 166	73.72 174	95.97 237	76.96 227	78.64 251	89.39 262

CVMVSNet		84.83 202	85.57 172	82.63 315	91.55 244	60.38 356	95.13 229	95.03 190	80.60 223	82.10 194	94.71 166	66.40 228	90.19 351	74.30 253	90.32 162	97.31 141

cl____		83.27 228	82.12 227	86.74 255	92.20 224	75.95 268	95.11 231	93.27 284	78.44 270	74.82 275	87.02 277	74.19 168	95.19 284	74.67 249	69.32 301	89.09 273

DIV-MVS_self_test		83.27 228	82.12 227	86.74 255	92.19 225	75.92 270	95.11 231	93.26 285	78.44 270	74.81 276	87.08 276	74.19 168	95.19 284	74.66 250	69.30 302	89.11 272

pm-mvs1		80.05 269	78.02 272	86.15 267	85.42 325	75.81 271	95.11 231	92.69 297	77.13 283	70.36 306	87.43 269	58.44 280	95.27 280	71.36 272	64.25 336	87.36 314

DP-MVS		81.47 256	78.28 270	91.04 156	98.14 64	78.48 206	95.09 234	86.97 348	61.14 354	71.12 301	92.78 199	59.59 268	99.38 69	53.11 348	86.61 190	95.27 198

test_low_dy_conf_001		83.73 220	83.18 213	85.40 280	86.81 307	71.09 317	95.06 235	94.22 233	82.79 191	77.42 235	91.69 211	59.07 275	95.26 281	82.20 183	79.32 244	89.86 258

PAPM_NR		91.46 84	90.82 88	93.37 74	98.50 45	81.81 127	95.03 236	96.13 131	84.65 142	86.10 149	97.65 78	79.24 83	99.75 31	83.20 175	96.88 96	98.56 48

Effi-MVS+-dtu		84.61 206	84.90 187	83.72 304	91.96 236	63.14 349	94.95 237	93.34 281	85.57 117	79.79 218	87.12 275	61.99 255	95.61 265	83.55 169	85.83 200	92.41 228

PS-MVSNAJss		84.91 201	84.30 195	86.74 255	85.89 321	74.40 285	94.95 237	94.16 238	83.93 164	76.45 251	90.11 239	71.04 201	95.77 252	83.16 176	79.02 248	90.06 253

MS-PatchMatch		83.05 233	81.82 233	86.72 259	89.64 276	79.10 193	94.88 239	94.59 218	79.70 246	70.67 304	89.65 241	50.43 319	96.82 209	70.82 280	95.99 110	84.25 343

dcpmvs_2		93.10 46	93.46 43	92.02 127	97.77 78	79.73 176	94.82 240	93.86 253	86.91 94	91.33 88	96.76 118	85.20 32	98.06 145	96.90 20	97.60 74	98.27 68

OMC-MVS		88.80 137	88.16 133	90.72 167	95.30 138	77.92 229	94.81 241	94.51 220	86.80 97	84.97 156	96.85 113	67.53 218	98.60 126	85.08 148	87.62 183	95.63 189

MVSFormer		91.36 87	90.57 91	93.73 56	93.00 202	88.08 16	94.80 242	94.48 221	80.74 220	94.90 36	97.13 103	78.84 88	95.10 290	83.77 163	97.46 77	98.02 87

test_djsdf		83.00 236	82.45 225	84.64 290	84.07 340	69.78 326	94.80 242	94.48 221	80.74 220	75.41 271	87.70 266	61.32 261	95.10 290	83.77 163	79.76 237	89.04 276

baseline		90.76 99	90.10 102	92.74 100	92.90 206	82.56 107	94.60 244	94.56 219	87.69 78	89.06 121	95.67 139	73.76 173	97.51 170	90.43 102	92.23 151	98.16 75

abl_6		89.80 116	89.71 113	90.07 185	96.53 109	75.52 273	94.48 245	95.04 189	81.12 214	89.22 117	97.00 108	68.83 212	98.96 108	89.86 108	95.27 116	95.73 187

WR-MVS_H		81.02 261	80.09 255	83.79 301	88.08 295	71.26 316	94.46 246	96.54 90	80.08 238	72.81 291	86.82 279	70.36 206	92.65 327	64.18 307	67.50 320	87.46 313

NR-MVSNet		83.35 226	81.52 238	88.84 210	88.76 285	81.31 138	94.45 247	95.16 184	84.65 142	67.81 316	90.82 225	70.36 206	94.87 296	74.75 247	66.89 327	90.33 243

tfpnnormal		78.14 284	75.42 290	86.31 265	88.33 292	79.24 187	94.41 248	96.22 125	73.51 308	69.81 310	85.52 302	55.43 304	95.75 254	47.65 360	67.86 316	83.95 346

v8		81.88 251	80.06 258	87.32 245	86.63 309	79.04 196	94.41 248	93.65 267	78.77 265	73.19 287	85.57 300	66.87 224	95.81 250	73.84 258	67.61 319	87.11 316

MVS_Test		90.29 110	89.18 118	93.62 62	95.23 139	84.93 65	94.41 248	94.66 211	84.31 152	90.37 103	91.02 222	75.13 156	97.82 155	83.11 177	94.42 126	98.12 80

eth_miper_zixun_eth		83.12 232	82.01 229	86.47 261	91.85 242	74.80 279	94.33 251	93.18 287	79.11 258	75.74 268	87.25 273	72.71 183	95.32 277	76.78 228	67.13 324	89.27 268

v10		81.43 257	79.53 263	87.11 250	86.38 310	78.87 197	94.31 252	93.43 275	77.88 273	73.24 286	85.26 304	65.44 232	95.75 254	72.14 267	67.71 318	86.72 320

GBi-Net		82.42 244	80.43 252	88.39 219	92.66 209	81.95 117	94.30 253	93.38 277	79.06 260	75.82 265	85.66 296	56.38 299	93.84 314	71.23 273	75.38 266	89.38 264

test1		82.42 244	80.43 252	88.39 219	92.66 209	81.95 117	94.30 253	93.38 277	79.06 260	75.82 265	85.66 296	56.38 299	93.84 314	71.23 273	75.38 266	89.38 264

FMVSNet1		79.50 274	76.54 284	88.39 219	88.47 290	81.95 117	94.30 253	93.38 277	73.14 312	72.04 297	85.66 296	43.86 337	93.84 314	65.48 302	72.53 278	89.38 264

CP-MVSNet		81.01 262	80.08 256	83.79 301	87.91 297	70.51 319	94.29 256	95.65 157	80.83 218	72.54 294	88.84 250	63.71 243	92.32 330	68.58 289	68.36 310	88.55 287

CL-MVSNet_self_test		75.81 301	74.14 303	80.83 325	78.33 356	67.79 335	94.22 257	93.52 272	77.28 282	69.82 309	81.54 333	61.47 260	89.22 353	57.59 333	53.51 353	85.48 335

jajsoiax		82.12 249	81.15 242	85.03 284	84.19 338	70.70 318	94.22 257	93.95 247	83.07 183	73.48 282	89.75 240	49.66 322	95.37 274	82.24 182	79.76 237	89.02 277

PS-CasMVS		80.27 268	79.18 264	83.52 308	87.56 301	69.88 324	94.08 259	95.29 180	80.27 235	72.08 296	88.51 257	59.22 274	92.23 332	67.49 291	68.15 313	88.45 291

ppachtmachnet_test		77.19 293	74.22 301	86.13 268	85.39 326	78.22 216	93.98 260	91.36 313	71.74 323	67.11 319	84.87 313	56.67 295	93.37 323	52.21 349	64.59 333	86.80 319

mvs_tets		81.74 252	80.71 247	84.84 285	84.22 337	70.29 321	93.91 261	93.78 260	82.77 192	73.37 283	89.46 244	47.36 331	95.31 278	81.99 184	79.55 242	88.92 283

PEN-MVS		79.47 275	78.26 271	83.08 311	86.36 311	68.58 332	93.85 262	94.77 205	79.76 244	71.37 298	88.55 254	59.79 266	92.46 328	64.50 306	65.40 331	88.19 296

testmvs		9.92 345	12.94 348	0.84 361	0.65 383	0.29 385	93.78 263	0.39 384	0.42 377	2.85 378	15.84 377	0.17 384	0.30 380	2.18 377	0.21 377	1.91 375

our_test_3		77.90 287	75.37 291	85.48 279	85.39 326	76.74 253	93.63 264	91.67 308	73.39 311	65.72 328	84.65 315	58.20 281	93.13 325	57.82 331	67.87 315	86.57 322

EG-PatchMatch MVS		74.92 305	72.02 310	83.62 305	83.76 343	73.28 294	93.62 265	92.04 304	68.57 334	58.88 351	83.80 321	31.87 364	95.57 268	56.97 337	78.67 250	82.00 357

OpenMVS_ROB	68.52 20	73.02 314	69.57 320	83.37 309	80.54 350	71.82 309	93.60 266	88.22 344	62.37 347	61.98 343	83.15 326	35.31 359	95.47 270	45.08 363	75.88 263	82.82 349

pmmvs4		82.54 242	80.79 244	87.79 232	86.11 317	80.49 158	93.55 267	93.18 287	77.29 281	73.35 284	89.40 245	65.26 236	95.05 294	75.32 243	73.61 273	87.83 302

mvs_anonymous		88.68 139	87.62 144	91.86 132	94.80 154	81.69 132	93.53 268	94.92 194	82.03 204	78.87 225	90.43 232	75.77 138	95.34 275	85.04 149	93.16 141	98.55 50

DTE-MVSNet		78.37 282	77.06 279	82.32 318	85.22 330	67.17 338	93.40 269	93.66 266	78.71 266	70.53 305	88.29 258	59.06 276	92.23 332	61.38 320	63.28 340	87.56 310

v7n		79.32 277	77.34 276	85.28 281	84.05 341	72.89 299	93.38 270	93.87 252	75.02 298	70.68 303	84.37 316	59.58 269	95.62 264	67.60 290	67.50 320	87.32 315

Anonymous20231206		75.29 304	73.64 305	80.22 327	80.75 347	63.38 348	93.36 271	90.71 326	73.09 313	67.12 318	83.70 322	50.33 320	90.85 346	53.63 347	70.10 294	86.44 323

MVP-Stereo		82.65 241	81.67 235	85.59 277	86.10 318	78.29 213	93.33 272	92.82 294	77.75 275	69.17 314	87.98 263	59.28 273	95.76 253	71.77 268	96.88 96	82.73 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		88.94 131	87.20 155	94.17 42	93.21 194	85.73 43	93.33 272	96.64 77	82.89 188	75.98 260	96.36 125	66.83 225	99.39 68	83.52 173	96.02 109	97.39 137

1121		90.66 101	89.82 110	93.16 81	97.39 95	81.71 131	93.33 272	96.66 73	74.45 302	91.38 83	97.55 85	79.27 81	99.52 58	79.95 197	98.43 45	98.26 69

MVS		90.60 103	88.64 126	96.50 5	94.25 168	90.53 8	93.33 272	97.21 20	77.59 277	78.88 224	97.31 94	71.52 196	99.69 41	89.60 112	98.03 64	99.27 21

pmmvs6		74.65 307	71.67 311	83.60 306	79.13 354	69.94 323	93.31 276	90.88 323	61.05 355	65.83 327	84.15 319	43.43 339	94.83 298	66.62 295	60.63 344	86.02 330

ACMH+	76.62 16	77.47 291	74.94 293	85.05 283	91.07 252	71.58 313	93.26 277	90.01 329	71.80 322	64.76 331	88.55 254	41.62 347	96.48 220	62.35 316	71.00 285	87.09 317

testgi		74.88 306	73.40 306	79.32 331	80.13 351	61.75 352	93.21 278	86.64 351	79.49 250	66.56 325	91.06 221	35.51 358	88.67 355	56.79 338	71.25 283	87.56 310

LS3D		82.22 248	79.94 260	89.06 205	97.43 92	74.06 288	93.20 279	92.05 303	61.90 349	73.33 285	95.21 150	59.35 271	99.21 85	54.54 344	92.48 147	93.90 219

ACMH	75.40 17	77.99 285	74.96 292	87.10 251	90.67 260	76.41 257	93.19 280	91.64 310	72.47 319	63.44 336	87.61 268	43.34 340	97.16 190	58.34 329	73.94 271	87.72 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		88.92 132	88.48 129	90.24 181	94.06 173	77.18 247	93.04 281	94.66 211	87.39 83	91.09 92	93.89 185	74.92 159	98.18 144	75.83 239	91.43 156	95.35 196

IterMVS-SCA-FT		80.51 267	79.10 266	84.73 287	89.63 277	74.66 280	92.98 282	91.81 307	80.05 239	71.06 302	85.18 307	58.04 282	91.40 340	72.48 266	70.70 289	88.12 298

IterMVS		80.67 265	79.16 265	85.20 282	89.79 272	76.08 262	92.97 283	91.86 305	80.28 234	71.20 300	85.14 309	57.93 285	91.34 341	72.52 265	70.74 287	88.18 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_0304		78.43 281	76.70 282	83.60 306	88.22 293	69.81 325	92.91 284	95.10 185	72.32 320	78.71 226	80.29 341	33.78 360	93.37 323	68.77 287	80.23 235	87.63 306

MTAPA		92.45 66	92.31 63	92.86 95	97.90 72	80.85 147	92.88 285	96.33 117	87.92 71	90.20 104	98.18 35	76.71 123	99.76 25	92.57 75	98.09 60	97.96 98

SCA		85.63 190	83.64 204	91.60 142	92.30 219	81.86 124	92.88 285	95.56 162	84.85 135	82.52 184	85.12 310	58.04 282	95.39 272	73.89 256	87.58 185	97.54 126

test_0402		72.68 315	69.54 321	82.09 319	88.67 288	71.81 310	92.72 287	86.77 350	61.52 351	62.21 342	83.91 320	43.22 341	93.76 317	34.60 367	72.23 282	80.72 359

LCM-MVSNet-Re		83.75 219	83.54 207	84.39 297	93.54 185	64.14 345	92.51 288	84.03 361	83.90 165	66.14 326	86.59 283	67.36 220	92.68 326	84.89 151	92.87 142	96.35 172

anonymousdsp		80.98 263	79.97 259	84.01 298	81.73 346	70.44 320	92.49 289	93.58 271	77.10 285	72.98 289	86.31 291	57.58 286	94.90 295	79.32 204	78.63 253	86.69 321

PatchMatch-RL		85.00 200	83.66 203	89.02 207	95.86 124	74.55 282	92.49 289	93.60 269	79.30 254	79.29 222	91.47 212	58.53 279	98.45 134	70.22 281	92.17 152	94.07 216

test20.03		72.36 317	71.15 313	75.98 341	77.79 357	59.16 360	92.40 291	89.35 334	74.09 304	61.50 345	84.32 317	48.09 325	85.54 364	50.63 354	62.15 342	83.24 347

MDA-MVSNet-bldmvs		71.45 320	67.94 324	81.98 320	85.33 328	68.50 333	92.35 292	88.76 340	70.40 327	42.99 365	81.96 330	46.57 332	91.31 342	48.75 359	54.39 351	86.11 328

PCF-MVS	84.09 5	86.77 175	85.00 184	92.08 123	92.06 233	83.07 100	92.14 293	94.47 223	79.63 247	76.90 244	94.78 165	71.15 199	99.20 89	72.87 262	91.05 158	93.98 217
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_ETH3D		80.86 264	78.75 268	87.22 249	86.31 312	72.02 305	91.95 294	93.76 263	73.51 308	75.06 274	90.16 237	43.04 343	95.66 259	76.37 234	78.55 254	93.98 217

miper_lstm_enhance		81.66 255	80.66 248	84.67 289	91.19 249	71.97 307	91.94 295	93.19 286	77.86 274	72.27 295	85.26 304	73.46 177	93.42 321	73.71 259	67.05 325	88.61 286

MSDG		80.62 266	77.77 274	89.14 204	93.43 192	77.24 244	91.89 296	90.18 328	69.86 331	68.02 315	91.94 207	52.21 315	98.84 117	59.32 327	83.12 217	91.35 231

COLMAP_ROB	73.24 19	75.74 302	73.00 308	83.94 299	92.38 215	69.08 331	91.85 297	86.93 349	61.48 352	65.32 329	90.27 234	42.27 345	96.93 203	50.91 353	75.63 265	85.80 334
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EU-MVSNet		76.92 296	76.95 280	76.83 337	84.10 339	54.73 367	91.77 298	92.71 296	72.74 316	69.57 311	88.69 252	58.03 284	87.43 359	64.91 305	70.00 296	88.33 294

MDA-MVSNet_test_wron		73.54 310	70.43 317	82.86 312	84.55 333	71.85 308	91.74 299	91.32 315	67.63 335	46.73 364	81.09 336	55.11 307	90.42 350	55.91 341	59.76 345	86.31 325

YYNet1		73.53 311	70.43 317	82.85 313	84.52 335	71.73 311	91.69 300	91.37 312	67.63 335	46.79 363	81.21 335	55.04 308	90.43 349	55.93 340	59.70 346	86.38 324

N_pmnet		61.30 330	60.20 333	64.60 348	84.32 336	17.00 383	91.67 301	10.98 382	61.77 350	58.45 353	78.55 346	49.89 321	91.83 337	42.27 365	63.94 337	84.97 338

Anonymous20240521		72.06 319	69.91 319	78.50 333	77.11 361	61.67 354	91.62 302	90.97 321	65.52 342	62.37 341	79.05 345	36.32 355	90.96 345	57.75 332	68.52 308	82.87 348

XVG-OURS-SEG-HR		85.74 189	85.16 181	87.49 242	90.22 266	71.45 314	91.29 303	94.09 243	81.37 211	83.90 172	95.22 149	60.30 265	97.53 169	85.58 145	84.42 210	93.50 223

SixPastTwentyTwo		76.04 299	74.32 300	81.22 322	84.54 334	61.43 355	91.16 304	89.30 335	77.89 272	64.04 333	86.31 291	48.23 324	94.29 308	63.54 312	63.84 338	87.93 301

AllTest		75.92 300	73.06 307	84.47 293	92.18 226	67.29 336	91.07 305	84.43 359	67.63 335	63.48 334	90.18 235	38.20 353	97.16 190	57.04 335	73.37 274	88.97 281

XVG-OURS		85.18 196	84.38 194	87.59 237	90.42 264	71.73 311	91.06 306	94.07 244	82.00 205	83.29 178	95.08 160	56.42 298	97.55 166	83.70 167	83.42 215	93.49 224

K. test v3		73.62 308	71.59 312	79.69 329	82.98 344	59.85 359	90.85 307	88.83 338	77.13 283	58.90 350	82.11 329	43.62 338	91.72 338	65.83 301	54.10 352	87.50 312

OurMVSNet-221017-0		77.18 294	76.06 286	80.55 326	83.78 342	60.00 358	90.35 308	91.05 319	77.01 287	66.62 324	87.92 264	47.73 329	94.03 311	71.63 269	68.44 309	87.62 307

HY-MVS	84.06 6	91.63 80	90.37 96	95.39 16	96.12 118	88.25 14	90.22 309	97.58 14	88.33 64	90.50 100	91.96 205	79.26 82	99.06 102	90.29 105	89.07 169	98.88 32

new-patchmatchnet		68.85 326	65.93 328	77.61 335	73.57 369	63.94 347	90.11 310	88.73 341	71.62 324	55.08 358	73.60 355	40.84 350	87.22 360	51.35 352	48.49 361	81.67 358

CMPMVS	54.94 21	75.71 303	74.56 298	79.17 332	79.69 352	55.98 363	89.59 311	93.30 283	60.28 356	53.85 360	89.07 247	47.68 330	96.33 225	76.55 230	81.02 230	85.22 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		76.46 298	74.16 302	83.35 310	90.05 270	76.17 260	89.58 312	89.85 330	71.39 325	65.29 330	80.42 338	50.61 318	87.70 358	61.05 322	69.24 303	86.18 327

USDC		78.65 280	76.25 285	85.85 270	87.58 300	74.60 281	89.58 312	90.58 327	84.05 158	63.13 338	88.23 259	40.69 351	96.86 208	66.57 297	75.81 264	86.09 329

test123		9.07 346	11.73 349	1.11 360	0.50 384	0.77 384	89.44 314	0.20 385	0.34 378	2.15 379	10.72 378	0.34 383	0.32 379	1.79 378	0.08 378	2.23 374

pmmvs-eth3d		73.59 309	70.66 315	82.38 316	76.40 364	73.38 291	89.39 315	89.43 333	72.69 317	60.34 349	77.79 348	46.43 333	91.26 343	66.42 299	57.06 348	82.51 352

XVG-ACMP-BASELINE		79.38 276	77.90 273	83.81 300	84.98 332	67.14 339	89.03 316	93.18 287	80.26 236	72.87 290	88.15 261	38.55 352	96.26 227	76.05 237	78.05 257	88.02 299

ab-mvs		87.08 167	84.94 185	93.48 70	93.34 193	83.67 87	88.82 317	95.70 155	81.18 213	84.55 163	90.14 238	62.72 248	98.94 113	85.49 146	82.54 227	97.85 105

tpm		85.55 191	84.47 193	88.80 212	90.19 267	75.39 275	88.79 318	94.69 207	84.83 136	83.96 170	85.21 306	78.22 98	94.68 301	76.32 235	78.02 258	96.34 173

pmmvs3		65.75 329	62.18 332	76.45 339	67.12 371	64.54 343	88.68 319	85.05 357	54.77 364	57.54 357	73.79 354	29.40 367	86.21 362	55.49 343	47.77 362	78.62 360

CostFormer		89.08 128	88.39 130	91.15 154	93.13 199	79.15 191	88.61 320	96.11 133	83.14 181	89.58 113	86.93 278	83.83 44	96.87 206	88.22 128	85.92 198	97.42 134

TinyColmap		72.41 316	68.99 323	82.68 314	88.11 294	69.59 328	88.41 321	85.20 356	65.55 341	57.91 354	84.82 314	30.80 366	95.94 241	51.38 350	68.70 306	82.49 354

TDRefinement		69.20 325	65.78 329	79.48 330	66.04 372	62.21 351	88.21 322	86.12 352	62.92 346	61.03 347	85.61 299	33.23 361	94.16 309	55.82 342	53.02 355	82.08 356

KD-MVS_2432*1600		77.63 289	74.92 294	85.77 272	90.86 256	79.44 181	88.08 323	93.92 249	76.26 288	67.05 320	82.78 327	72.15 190	91.92 335	61.53 317	41.62 367	85.94 331

miper_refine_blended		77.63 289	74.92 294	85.77 272	90.86 256	79.44 181	88.08 323	93.92 249	76.26 288	67.05 320	82.78 327	72.15 190	91.92 335	61.53 317	41.62 367	85.94 331

tpm2		87.35 166	86.26 168	90.62 169	92.93 205	78.67 203	88.06 325	95.99 139	79.33 252	87.40 137	86.43 289	80.28 71	96.40 222	80.23 194	85.73 202	96.79 159

CHOSEN 280x420		91.71 78	91.85 72	91.29 149	94.94 150	82.69 105	87.89 326	96.17 130	85.94 109	87.27 140	94.31 173	90.27 9	95.65 261	94.04 52	95.86 111	95.53 192

RPSCF		77.73 288	76.63 283	81.06 323	88.66 289	55.76 365	87.77 327	87.88 345	64.82 344	74.14 279	92.79 198	49.22 323	96.81 210	67.47 292	76.88 260	90.62 237

KD-MVS_self_test		70.97 322	69.31 322	75.95 342	76.24 366	55.39 366	87.45 328	90.94 322	70.20 329	62.96 340	77.48 349	44.01 336	88.09 356	61.25 321	53.26 354	84.37 342

MIMVSNet1		69.44 323	66.65 327	77.84 334	76.48 363	62.84 350	87.42 329	88.97 337	66.96 340	57.75 356	79.72 344	32.77 363	85.83 363	46.32 361	63.42 339	84.85 339

tpmrst		88.36 149	87.38 152	91.31 147	94.36 167	79.92 169	87.32 330	95.26 182	85.32 123	88.34 129	86.13 294	80.60 67	96.70 214	83.78 162	85.34 206	97.30 142

UnsupCasMVSNet_eth		73.25 312	70.57 316	81.30 321	77.53 358	66.33 340	87.24 331	93.89 251	80.38 231	57.90 355	81.59 332	42.91 344	90.56 348	65.18 304	48.51 360	87.01 318

EPMVS		87.47 165	85.90 171	92.18 121	95.41 135	82.26 115	87.00 332	96.28 121	85.88 111	84.23 165	85.57 300	75.07 158	96.26 227	71.14 276	92.50 146	98.03 86

MDTV_nov1_ep13_2view						81.74 129	86.80 333		80.65 222	85.65 150		74.26 167		76.52 231		96.98 151

MDTV_nov1_ep13			83.69 201		94.09 172	81.01 142	86.78 334	96.09 134	83.81 168	84.75 159	84.32 317	74.44 166	96.54 218	63.88 309	85.07 207

dp		84.30 212	82.31 226	90.28 180	94.24 169	77.97 225	86.57 335	95.53 163	79.94 242	80.75 207	85.16 308	71.49 197	96.39 223	63.73 310	83.36 216	96.48 169

PatchmatchNet		86.83 172	85.12 182	91.95 129	94.12 171	82.27 114	86.55 336	95.64 159	84.59 144	82.98 183	84.99 312	77.26 112	95.96 240	68.61 288	91.34 157	97.64 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LTVRE_ROB	73.68 18	77.99 285	75.74 289	84.74 286	90.45 263	72.02 305	86.41 337	91.12 316	72.57 318	66.63 323	87.27 271	54.95 309	96.98 199	56.29 339	75.98 262	85.21 337
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
LF4IMVS		72.36 317	70.82 314	76.95 336	79.18 353	56.33 362	86.12 338	86.11 353	69.30 333	63.06 339	86.66 282	33.03 362	92.25 331	65.33 303	68.64 307	82.28 355

PM-MVS		69.32 324	66.93 326	76.49 338	73.60 368	55.84 364	85.91 339	79.32 370	74.72 300	61.09 346	78.18 347	21.76 369	91.10 344	70.86 278	56.90 349	82.51 352

test_post1							85.88 340				30.24 375	73.77 172	95.07 293	73.89 256

tpmvs		83.04 234	80.77 245	89.84 195	95.43 134	77.96 226	85.59 341	95.32 179	75.31 295	76.27 256	83.70 322	73.89 171	97.41 176	59.53 324	81.93 229	94.14 214

tpm cat1		83.63 222	81.38 239	90.39 176	93.53 190	78.19 221	85.56 342	95.09 186	70.78 326	78.51 228	83.28 325	74.80 160	97.03 196	66.77 294	84.05 211	95.95 181

DSMNet-mixed		73.13 313	72.45 309	75.19 343	77.51 359	46.82 370	85.09 343	82.01 366	67.61 339	69.27 313	81.33 334	50.89 316	86.28 361	54.54 344	83.80 212	92.46 227

UnsupCasMVSNet_bld		68.60 327	64.50 330	80.92 324	74.63 367	67.80 334	83.97 344	92.94 293	65.12 343	54.63 359	68.23 362	35.97 356	92.17 334	60.13 323	44.83 364	82.78 350

new_pmnet		66.18 328	63.18 331	75.18 344	76.27 365	61.74 353	83.79 345	84.66 358	56.64 363	51.57 361	71.85 361	31.29 365	87.93 357	49.98 355	62.55 341	75.86 362

FPMVS		55.09 332	52.93 335	61.57 351	55.98 373	40.51 376	83.11 346	83.41 364	37.61 367	34.95 368	71.95 359	14.40 373	76.95 367	29.81 368	65.16 332	67.25 366

EGC-MVSNET		52.46 334	47.56 337	67.15 345	81.98 345	60.11 357	82.54 347	72.44 373	0.11 379	0.70 380	74.59 352	25.11 368	83.26 365	29.04 369	61.51 343	58.09 367

GG-mvs-BLEND				93.49 69	94.94 150	86.26 32	81.62 348	97.00 30		88.32 130	94.30 174	91.23 5	96.21 230	88.49 124	97.43 80	98.00 92

MIMVSNet		79.18 278	75.99 287	88.72 214	87.37 304	80.66 152	79.96 349	91.82 306	77.38 280	74.33 278	81.87 331	41.78 346	90.74 347	66.36 300	83.10 218	94.76 204

ADS-MVSNet2		79.57 273	77.53 275	85.71 274	93.78 178	72.13 302	79.48 350	86.11 353	73.09 313	80.14 215	79.99 342	62.15 252	90.14 352	59.49 325	83.52 213	94.85 202

ADS-MVSNet		81.26 259	78.36 269	89.96 191	93.78 178	79.78 171	79.48 350	93.60 269	73.09 313	80.14 215	79.99 342	62.15 252	95.24 282	59.49 325	83.52 213	94.85 202

gg-mvs-nofinetune		85.48 193	82.90 216	93.24 77	94.51 164	85.82 40	79.22 352	96.97 34	61.19 353	87.33 139	53.01 366	90.58 6	96.07 232	86.07 142	97.23 86	97.81 109

MVS-HIRNet		71.36 321	67.00 325	84.46 295	90.58 261	69.74 327	79.15 353	87.74 347	46.09 365	61.96 344	50.50 367	45.14 335	95.64 262	53.74 346	88.11 181	88.00 300

CR-MVSNet		83.53 223	81.36 240	90.06 186	90.16 268	79.75 173	79.02 354	91.12 316	84.24 156	82.27 192	80.35 339	75.45 146	93.67 318	63.37 313	86.25 193	96.75 163

RPMNet		79.85 270	75.92 288	91.64 139	90.16 268	79.75 173	79.02 354	95.44 170	58.43 362	82.27 192	72.55 358	73.03 181	98.41 136	46.10 362	86.25 193	96.75 163

Patchmatch-RL test		76.65 297	74.01 304	84.55 292	77.37 360	64.23 344	78.49 356	82.84 365	78.48 268	64.63 332	73.40 356	76.05 134	91.70 339	76.99 225	57.84 347	97.72 114

Patchmtry		77.36 292	74.59 297	85.67 276	89.75 273	75.75 272	77.85 357	91.12 316	60.28 356	71.23 299	80.35 339	75.45 146	93.56 320	57.94 330	67.34 323	87.68 305

PatchT		79.75 271	76.85 281	88.42 217	89.55 278	75.49 274	77.37 358	94.61 216	63.07 345	82.46 186	73.32 357	75.52 145	93.41 322	51.36 351	84.43 209	96.36 171

PMMVS2		50.90 335	46.31 338	64.67 347	55.53 374	46.67 371	77.30 359	71.02 374	40.89 366	34.16 369	59.32 363	9.83 378	76.14 370	40.09 366	28.63 370	71.21 363

test_method		56.77 331	54.53 334	63.49 350	76.49 362	40.70 375	75.68 360	74.24 372	19.47 373	48.73 362	71.89 360	19.31 370	65.80 373	57.46 334	47.51 363	83.97 345

JIA-IIPM		79.00 279	77.20 277	84.40 296	89.74 275	64.06 346	75.30 361	95.44 170	62.15 348	81.90 196	59.08 364	78.92 86	95.59 266	66.51 298	85.78 201	93.54 222

EMVS		31.70 342	31.45 344	32.48 358	50.72 377	23.95 381	74.78 362	52.30 381	20.36 372	16.08 376	31.48 374	12.80 374	53.60 376	11.39 375	13.10 375	19.88 373

E-PMN		32.70 341	32.39 343	33.65 357	53.35 376	25.70 380	74.07 363	53.33 380	21.08 371	17.17 375	33.63 373	11.85 376	54.84 375	12.98 374	14.04 372	20.42 372

Patchmatch-test		78.25 283	74.72 296	88.83 211	91.20 248	74.10 287	73.91 364	88.70 342	59.89 359	66.82 322	85.12 310	78.38 95	94.54 303	48.84 358	79.58 241	97.86 104

LCM-MVSNet		52.52 333	48.24 336	65.35 346	47.63 378	41.45 374	72.55 365	83.62 363	31.75 368	37.66 367	57.92 365	9.19 379	76.76 368	49.26 357	44.60 365	77.84 361

ANet_high		46.22 336	41.28 341	61.04 352	39.91 380	46.25 372	70.59 366	76.18 371	58.87 361	23.09 372	48.00 369	12.58 375	66.54 372	28.65 370	13.62 373	70.35 364

ambc				76.02 340	68.11 370	51.43 368	64.97 367	89.59 331		60.49 348	74.49 353	17.17 372	92.46 328	61.50 319	52.85 356	84.17 344

tmp_tt		41.54 338	41.93 340	40.38 356	20.10 382	26.84 379	61.93 368	59.09 378	14.81 375	28.51 370	80.58 337	35.53 357	48.33 377	63.70 311	13.11 374	45.96 370

PMVS	34.80 23	39.19 339	35.53 342	50.18 354	29.72 381	30.30 378	59.60 369	66.20 377	26.06 370	17.91 374	49.53 368	3.12 380	74.09 371	18.19 373	49.40 358	46.14 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 340	29.49 345	46.92 355	41.86 379	36.28 377	50.45 370	56.52 379	18.75 374	18.28 373	37.84 370	2.41 381	58.41 374	18.71 372	20.62 371	46.06 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.11 337	42.05 339	54.30 353	80.69 348	51.30 369	35.80 371	83.81 362	28.13 369	27.94 371	34.53 371	11.41 377	76.70 369	21.45 371	54.65 350	34.90 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		14.10 344	13.89 347	14.72 359	55.23 375	22.91 382	33.83 372	3.56 383	4.94 376	4.11 377	2.28 379	2.06 382	19.66 378	10.23 376	8.74 376	1.59 376

test_blank		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

cdsmvs_eth3d_5k		21.43 343	28.57 346	0.00 362	0.00 385	0.00 386	0.00 373	95.93 144	0.00 380	0.00 381	97.66 74	63.57 244	0.00 381	0.00 379	0.00 379	0.00 377

pcd_1.5k_mvsjas		5.92 348	7.89 351	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	71.04 201	0.00 381	0.00 379	0.00 379	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

ab-mvs-re		8.11 347	10.81 350	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	97.30 96	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

PC_three_1452									91.12 22	98.33 2	98.42 28	92.51 2	99.81 21	98.96 2	99.37 1	99.70 3

No_MVS				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

test_one_0601					98.91 20	84.56 72		96.70 65	88.06 68	96.57 16	98.77 12	88.04 20

eth-test2					0.00 385

eth-test					0.00 385

ZD-MVS					99.09 9	83.22 97		96.60 83	82.88 189	93.61 56	98.06 51	82.93 52	99.14 96	95.51 35	98.49 42

IU-MVS					99.03 16	85.34 50		96.86 44	92.05 16	98.74 1				98.15 4	98.97 17	99.42 13

test_241102_TWO								96.78 49	88.72 55	97.70 6	98.91 3	87.86 21	99.82 18	98.15 4	99.00 15	99.47 9

test_241102_ONE					99.03 16	85.03 62		96.78 49	88.72 55	97.79 4	98.90 6	88.48 17	99.82 18

test_0728_THIRD									88.38 62	96.69 13	98.76 14	89.64 13	99.76 25	97.47 14	98.84 24	99.38 14

GSMVS																97.54 126

test_part2					98.90 21	85.14 61				96.07 21

sam_mvs1												77.59 107				97.54 126

sam_mvs												75.35 153

MTGPA								96.33 117

test_post											33.80 372	76.17 132	95.97 237

patchmatchnet-post											77.09 350	77.78 106	95.39 272

gm-plane-assit					92.27 220	79.64 179			84.47 148		95.15 155		97.93 148	85.81 143

test9_res														96.00 27	99.03 13	98.31 63

agg_prior2														94.30 47	99.00 15	98.57 47

agg_prior					98.59 39	83.13 98		96.56 88		94.19 48			99.16 94

TestCases				84.47 293	92.18 226	67.29 336		84.43 359	67.63 335	63.48 334	90.18 235	38.20 353	97.16 190	57.04 335	73.37 274	88.97 281

test_prior				93.09 84	98.68 29	81.91 120		96.40 109					99.06 102			98.29 65

新几何1				93.12 82	97.44 91	81.60 134		96.71 64	74.54 301	91.22 91	97.57 81	79.13 85	99.51 61	77.40 223	98.46 43	98.26 69

旧先验1					97.39 95	79.58 180		96.54 90			98.08 49	84.00 41			97.42 81	97.62 123

原ACMM1				91.22 152	97.77 78	78.10 222		96.61 80	81.05 215	91.28 89	97.42 91	77.92 103	98.98 107	79.85 200	98.51 38	96.59 166

testdata2													99.48 63	76.45 232

segment_acmp												82.69 57

testdata				90.13 184	95.92 123	74.17 286		96.49 99	73.49 310	94.82 40	97.99 55	78.80 90	97.93 148	83.53 172	97.52 76	98.29 65

test12				94.25 38	98.34 53	85.55 47		96.35 116		92.36 69		80.84 64	99.22 83		98.31 56	97.98 94

plane_prior7					91.86 240	77.55 239

plane_prior6					91.98 235	77.92 229						64.77 239

plane_prior5								94.69 207					97.30 182	87.08 135	82.82 223	90.96 234

plane_prior4											94.15 179

plane_prior3						77.75 235			90.17 36	81.33 201

plane_prior1					91.95 238

n2								0.00 386

nn								0.00 386

door-mid								79.75 369

lessismore_v0				79.98 328	80.59 349	58.34 361		80.87 367		58.49 352	83.46 324	43.10 342	93.89 313	63.11 314	48.68 359	87.72 303

LGP-MVS_train				86.33 262	90.88 254	73.06 296		94.13 239	82.20 200	76.31 253	93.20 192	54.83 310	96.95 200	83.72 165	80.83 231	88.98 279

test11								96.50 96

door								80.13 368

HQP5-MVS						78.48 206

BP-MVS														87.67 131

HQP4-MVS										82.30 188			97.32 180			91.13 232

HQP3-MVS								94.80 202							83.01 219

HQP2-MVS												65.40 233

NP-MVS					92.04 234	78.22 216					94.56 169

ACMMP++_ref															78.45 255

ACMMP++															79.05 247

Test By Simon												71.65 194

ITE_SJBPF				82.38 316	87.00 306	65.59 341		89.55 332	79.99 241	69.37 312	91.30 217	41.60 348	95.33 276	62.86 315	74.63 270	86.24 326

DeepMVS_CX				64.06 349	78.53 355	43.26 373		68.11 376	69.94 330	38.55 366	76.14 351	18.53 371	79.34 366	43.72 364	41.62 367	69.57 365

High-resolution multi-view benchmark