High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

thres100view900		78.37 183	77.01 185	82.46 182	91.89 114	63.21 208	91.19 193	96.33 1	72.28 173	70.45 204	87.89 198	60.31 124	95.32 164	45.16 321	77.58 193	88.83 213

thres600view7		78.00 188	76.66 190	82.03 202	91.93 111	63.69 199	91.30 187	96.33 1	72.43 168	70.46 203	87.89 198	60.31 124	94.92 177	42.64 333	76.64 204	87.48 234

thres200		79.66 156	78.33 160	83.66 158	92.54 94	65.82 142	93.06 105	96.31 3	74.90 119	73.30 170	88.66 181	59.67 134	95.61 151	47.84 311	78.67 184	89.56 210

tfpn200view9		78.79 174	77.43 177	82.88 173	92.21 102	64.49 172	92.05 148	96.28 4	73.48 145	71.75 192	88.26 191	60.07 129	95.32 164	45.16 321	77.58 193	88.83 213

thres400		78.68 177	77.43 177	82.43 183	92.21 102	64.49 172	92.05 148	96.28 4	73.48 145	71.75 192	88.26 191	60.07 129	95.32 164	45.16 321	77.58 193	87.48 234

VNet		86.20 47	85.65 56	87.84 26	93.92 54	69.99 33	95.73 22	95.94 6	78.43 69	86.00 42	93.07 114	58.22 148	97.00 95	85.22 67	84.33 148	96.52 19

baseline2		83.68 91	83.42 82	84.48 135	87.37 214	66.00 135	90.06 225	95.93 7	79.71 48	69.08 220	90.39 163	77.92 6	96.28 122	78.91 119	81.38 166	91.16 191

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 27	99.38 1	92.64 6	95.93 10	97.08 9

MVS		84.66 70	82.86 94	90.06 2	90.93 136	74.56 6	87.91 265	95.54 9	68.55 247	72.35 186	94.71 74	59.78 133	98.90 18	81.29 102	94.69 32	96.74 12

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 158	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 4	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

CSCG		86.87 38	86.26 45	88.72 15	95.05 35	70.79 25	93.83 83	95.33 11	68.48 249	77.63 128	94.35 86	73.04 25	98.45 29	84.92 70	93.71 49	96.92 11

WTY-MVS		86.32 45	85.81 52	87.85 25	92.82 85	69.37 49	95.20 33	95.25 12	82.71 17	81.91 79	94.73 73	67.93 47	97.63 60	79.55 111	82.25 159	96.54 18

patch_mono-2		89.71 10	90.99 5	85.85 88	96.04 26	63.70 198	95.04 41	95.19 13	86.74 5	91.53 11	95.15 59	73.86 21	97.58 63	93.38 3	92.00 75	96.28 32

IU-MVS					96.46 13	69.91 37		95.18 14	80.75 37	95.28 1				92.34 8	95.36 14	96.47 24

IB-MVS	77.80 4	82.18 112	80.46 130	87.35 38	89.14 172	70.28 31	95.59 25	95.17 15	78.85 63	70.19 208	85.82 220	70.66 36	97.67 55	72.19 168	66.52 268	94.09 115
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
Regformer-1		87.24 30	87.60 27	86.15 79	95.14 32	65.83 141	93.95 72	95.12 16	82.11 22	84.25 61	95.73 36	67.88 48	98.35 33	85.60 63	88.64 111	94.26 105

Regformer-3		85.80 54	85.92 50	85.46 102	94.17 48	65.09 162	92.95 112	95.11 17	81.13 33	81.68 81	95.04 60	65.82 66	98.32 34	83.02 83	84.36 145	92.97 152

PHI-MVS		86.83 40	86.85 41	86.78 54	93.47 68	65.55 148	95.39 29	95.10 18	71.77 192	85.69 47	96.52 18	62.07 109	98.77 21	86.06 61	95.60 12	96.03 38

test_yl		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 239	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

DCV-MVSNet		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 239	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

Regformer-2		87.00 37	87.43 30	85.71 95	95.14 32	64.73 169	93.95 72	94.95 23	81.69 27	84.03 66	95.73 36	67.35 52	98.19 37	85.40 65	88.64 111	94.20 107

sss		82.71 106	82.38 103	83.73 154	89.25 169	59.58 274	92.24 139	94.89 24	77.96 75	79.86 103	92.38 132	56.70 168	97.05 90	77.26 131	80.86 170	94.55 94

EPNet		87.84 22	88.38 17	86.23 77	93.30 71	66.05 133	95.26 31	94.84 25	87.09 3	88.06 26	94.53 77	66.79 57	97.34 75	83.89 78	91.68 80	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		85.45 58	85.69 55	84.73 125	94.17 48	63.23 207	92.95 112	94.83 26	80.66 38	81.29 84	95.04 60	65.12 72	98.08 40	82.74 85	84.36 145	92.88 156

CNVR-MVS		90.32 5	90.89 6	88.61 19	96.76 10	70.65 26	96.47 12	94.83 26	84.83 9	89.07 23	96.80 15	70.86 35	99.06 15	92.64 6	95.71 11	96.12 35

EI-MVSNet-Vis-set		83.77 88	83.67 75	84.06 146	92.79 88	63.56 203	91.76 164	94.81 28	79.65 49	77.87 125	94.09 94	63.35 98	97.90 47	79.35 113	79.36 177	90.74 195

ETH3 D test6400		90.27 6	90.44 8	89.75 6	96.82 9	74.33 7	95.89 16	94.80 29	77.13 89	89.13 22	97.38 2	74.49 18	98.48 28	92.32 11	95.98 8	96.46 25

tttt0517		79.50 160	78.53 159	82.41 186	87.22 216	61.43 243	89.75 235	94.76 30	69.29 237	67.91 238	88.06 196	72.92 26	95.63 149	62.91 249	73.90 219	90.16 201

GG-mvs-BLEND				86.53 65	91.91 113	69.67 45	75.02 341	94.75 31		78.67 121	90.85 154	77.91 7	94.56 191	72.25 165	93.74 47	95.36 57

gg-mvs-nofinetune		77.18 201	74.31 219	85.80 90	91.42 127	68.36 70	71.78 343	94.72 32	49.61 347	77.12 135	45.92 364	77.41 8	93.98 219	67.62 208	93.16 57	95.05 76

thisisatest0515		83.41 92	82.49 101	86.16 78	89.46 164	68.26 74	93.54 92	94.70 33	74.31 125	75.75 145	90.92 152	72.62 29	96.52 119	69.64 188	81.50 165	93.71 130

EI-MVSNet-UG-set		83.14 98	82.96 90	83.67 157	92.28 99	63.19 209	91.38 182	94.68 34	79.22 55	76.60 140	93.75 100	62.64 104	97.76 53	78.07 126	78.01 188	90.05 203

VPA-MVSNet		79.03 166	78.00 166	82.11 200	85.95 236	64.48 174	93.22 102	94.66 35	75.05 117	74.04 165	84.95 228	52.17 217	93.52 231	74.90 148	67.04 264	88.32 226

NCCC		89.07 14	89.46 14	87.91 24	96.60 12	69.05 55	96.38 13	94.64 36	84.42 10	86.74 34	96.20 28	66.56 60	98.76 22	89.03 34	94.56 33	95.92 41

ET-MVSNet_ETH3D		84.01 83	83.15 89	86.58 62	90.78 141	70.89 24	94.74 47	94.62 37	81.44 30	58.19 309	93.64 103	73.64 24	92.35 270	82.66 86	78.66 185	96.50 23

thisisatest0530		81.15 129	80.07 132	84.39 137	88.26 194	65.63 146	91.40 178	94.62 37	71.27 209	70.93 199	89.18 177	72.47 30	96.04 132	65.62 230	76.89 203	91.49 181

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 17	69.99 33	96.64 8	94.52 39	71.92 181	90.55 16	96.93 10	73.77 22	99.08 11	91.91 14	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
HY-MVS	76.49 5	84.28 76	83.36 85	87.02 46	92.22 101	67.74 88	84.65 289	94.50 40	79.15 57	82.23 77	87.93 197	66.88 56	96.94 103	80.53 106	82.20 160	96.39 28

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 34	68.23 76	95.24 32	94.49 41	82.43 19	88.90 24	96.35 24	71.89 34	98.63 24	88.76 36	96.40 6	96.06 36

MG-MVS		87.11 33	86.27 44	89.62 8	97.79 1	76.27 4	94.96 44	94.49 41	78.74 67	83.87 68	92.94 117	64.34 83	96.94 103	75.19 141	94.09 40	95.66 47

SED-MVS		89.94 8	90.36 9	88.70 16	96.45 14	69.38 47	96.89 4	94.44 43	71.65 195	92.11 4	97.21 5	76.79 9	99.11 6	92.34 8	95.36 14	97.62 2

test_241102_ONE					96.45 14	69.38 47		94.44 43	71.65 195	92.11 4	97.05 8	76.79 9	99.11 6

test_241102_TWO								94.41 45	71.65 195	92.07 6	97.21 5	74.58 17	99.11 6	92.34 8	95.36 14	96.59 15

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 81	58.16 292	96.72 7	94.41 45	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 13	97.32 6

DELS-MVS		90.05 7	90.09 10	89.94 4	93.14 78	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 69	74.11 20	98.91 17	87.26 51	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
3Dnovator	73.91 6	82.69 107	80.82 122	88.31 22	89.57 160	71.26 20	92.60 128	94.39 48	78.84 64	67.89 239	92.48 130	48.42 250	98.52 26	68.80 200	94.40 37	95.15 71

DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 37	93.96 69	94.37 49	72.48 165	92.07 6	96.85 12	83.82 2	99.15 2	91.53 16	97.42 4	97.55 4

test_0728_SECOND				88.70 16	96.45 14	70.43 29	96.64 8	94.37 49					99.15 2	91.91 14	94.90 21	96.51 20

test0726					96.40 17	69.99 33	96.76 6	94.33 51	71.92 181	91.89 8	97.11 7	73.77 22

MSP-MVS		90.38 4	91.87 1	85.88 85	92.83 83	64.03 190	93.06 105	94.33 51	82.19 21	93.65 3	96.15 30	85.89 1	97.19 84	91.02 20	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
MAR-MVS		84.18 80	83.43 80	86.44 68	96.25 23	65.93 138	94.28 54	94.27 53	74.41 122	79.16 112	95.61 40	53.99 200	98.88 20	69.62 190	93.26 56	94.50 100
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
ETH3D cwj APD-0.16		87.06 34	87.18 35	86.71 56	91.99 109	67.48 97	92.97 110	94.21 54	71.48 206	85.72 45	96.32 26	68.13 43	98.00 42	89.06 32	94.70 31	94.65 92

test_one_0601					96.32 20	69.74 42		94.18 55	71.42 207	90.67 15	96.85 12	74.45 19

9.14			87.63 25		93.86 55		94.41 51	94.18 55	72.76 159	86.21 38	96.51 19	66.64 58	97.88 49	90.08 25	94.04 41

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 22	67.56 92	94.17 55	94.15 57	68.77 245	90.74 14	97.27 3	76.09 12	98.49 27	90.58 23	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
ETH3D-3000-0.1		87.61 24	87.89 21	86.75 55	93.58 64	67.21 103	94.31 53	94.14 58	72.92 156	87.13 30	96.62 17	67.81 49	97.94 43	90.13 24	94.42 36	95.09 74

DeepC-MVS_fast	79.48 2	87.95 20	88.00 20	87.79 27	95.86 29	68.32 71	95.74 20	94.11 59	83.82 12	83.49 69	96.19 29	64.53 81	98.44 30	83.42 81	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
SMA-MVS		88.14 16	88.29 19	87.67 28	93.21 75	68.72 63	93.85 79	94.03 60	74.18 127	91.74 9	96.67 16	65.61 69	98.42 32	89.24 30	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
FIs		79.47 161	79.41 147	79.67 253	85.95 236	59.40 276	91.68 168	93.94 61	78.06 74	68.96 223	88.28 189	66.61 59	91.77 282	66.20 224	74.99 212	87.82 230

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 62	90.42 144	66.38 126	96.09 15	93.87 62	77.73 80	84.01 67	95.66 38	63.39 97	97.94 43	87.40 48	93.55 52	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + GP.		87.96 19	88.37 18	86.70 58	93.51 67	65.32 152	95.15 35	93.84 63	78.17 73	85.93 43	94.80 72	75.80 13	98.21 35	89.38 27	88.78 108	96.59 15

CANet		89.61 11	89.99 11	88.46 20	94.39 44	69.71 43	96.53 11	93.78 64	86.89 4	89.68 19	95.78 34	65.94 64	99.10 9	92.99 4	93.91 44	96.58 17

APDe-MVS		87.54 25	87.84 22	86.65 59	96.07 25	66.30 129	94.84 46	93.78 64	69.35 236	88.39 25	96.34 25	67.74 50	97.66 58	90.62 22	93.44 53	96.01 39

TESTMET0.1,1		82.41 109	81.98 107	83.72 155	88.08 198	63.74 195	92.70 122	93.77 66	79.30 53	77.61 129	87.57 202	58.19 149	94.08 210	73.91 152	86.68 130	93.33 140

h-mvs33		83.01 100	82.56 100	84.35 139	89.34 165	62.02 231	92.72 120	93.76 67	81.45 28	82.73 74	92.25 136	60.11 127	97.13 88	87.69 43	62.96 292	93.91 124

SF-MVS		87.03 35	87.09 36	86.84 50	92.70 89	67.45 98	93.64 87	93.76 67	70.78 219	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

MVS_111021_HR		86.19 48	85.80 53	87.37 37	93.17 77	69.79 40	93.99 68	93.76 67	79.08 60	78.88 117	93.99 97	62.25 108	98.15 38	85.93 62	91.15 90	94.15 112

FC-MVSNet-test		77.99 189	78.08 165	77.70 277	84.89 253	55.51 317	90.27 219	93.75 70	76.87 91	66.80 254	87.59 201	65.71 68	90.23 303	62.89 250	73.94 217	87.37 237

QAPM		79.95 152	77.39 181	87.64 29	89.63 159	71.41 19	93.30 99	93.70 71	65.34 273	67.39 247	91.75 143	47.83 256	98.96 16	57.71 275	89.81 100	92.54 162

DeepC-MVS	77.85 3	85.52 57	85.24 59	86.37 72	88.80 180	66.64 120	92.15 141	93.68 72	81.07 34	76.91 138	93.64 103	62.59 105	98.44 30	85.50 64	92.84 62	94.03 119
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 121	81.52 112	82.61 180	88.77 181	60.21 266	93.02 109	93.66 73	68.52 248	72.90 174	90.39 163	72.19 32	94.96 174	74.93 146	79.29 179	92.67 158

PVSNet_BlendedMVS		83.38 93	83.43 80	83.22 168	93.76 58	67.53 94	94.06 64	93.61 74	79.13 58	81.00 91	85.14 225	63.19 100	97.29 78	87.08 52	73.91 218	84.83 287

PVSNet_Blended		86.73 42	86.86 40	86.31 75	93.76 58	67.53 94	96.33 14	93.61 74	82.34 20	81.00 91	93.08 112	63.19 100	97.29 78	87.08 52	91.38 86	94.13 113

alignmvs		87.28 29	86.97 38	88.24 23	91.30 130	71.14 23	95.61 24	93.56 76	79.30 53	87.07 33	95.25 54	68.43 39	96.93 105	87.87 41	84.33 148	96.65 13

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 64	91.73 118	68.04 80	90.36 216	93.55 77	82.89 14	91.29 12	92.89 120	72.27 31	96.03 133	87.99 40	94.77 25	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TEST9					94.18 46	67.28 101	94.16 56	93.51 78	71.75 193	85.52 48	95.33 47	68.01 44	97.27 82

train_agg		87.21 31	87.42 31	86.60 60	94.18 46	67.28 101	94.16 56	93.51 78	71.87 186	85.52 48	95.33 47	68.19 41	97.27 82	89.09 31	94.90 21	95.25 69

ZD-MVS					96.63 11	65.50 150		93.50 80	70.74 220	85.26 53	95.19 58	64.92 77	97.29 78	87.51 45	93.01 59

ACMMP_NAP		86.05 50	85.80 53	86.80 53	91.58 122	67.53 94	91.79 161	93.49 81	74.93 118	84.61 57	95.30 49	59.42 137	97.92 45	86.13 59	94.92 19	94.94 81

testtj		86.62 43	86.66 42	86.50 66	96.95 8	65.70 143	94.41 51	93.45 82	67.74 252	86.19 39	96.39 23	64.38 82	97.91 46	87.33 49	93.14 58	95.90 42

cdsmvs_eth3d_5k		19.86 343	26.47 342	0.00 362	0.00 385	0.00 386	0.00 373	93.45 82	0.00 380	0.00 381	95.27 52	49.56 239	0.00 381	0.00 379	0.00 378	0.00 377

3Dnovator+	73.60 7	82.10 116	80.60 127	86.60 60	90.89 138	66.80 117	95.20 33	93.44 84	74.05 129	67.42 245	92.49 129	49.46 240	97.65 59	70.80 178	91.68 80	95.33 58

test_8					94.19 45	67.19 104	94.15 59	93.42 85	71.87 186	85.38 51	95.35 46	68.19 41	96.95 102

ZNCC-MVS		85.33 60	85.08 61	86.06 80	93.09 80	65.65 145	93.89 77	93.41 86	73.75 138	79.94 101	94.68 75	60.61 123	98.03 41	82.63 88	93.72 48	94.52 98

原ACMM1				84.42 136	93.21 75	64.27 186		93.40 87	65.39 271	79.51 107	92.50 127	58.11 150	96.69 113	65.27 235	93.96 42	92.32 167

agg_prior1		87.02 36	87.26 33	86.28 76	94.16 50	66.97 112	94.08 63	93.31 88	71.85 188	84.49 59	95.39 45	68.91 38	96.75 111	88.84 35	94.32 38	95.13 72

agg_prior					94.16 50	66.97 112		93.31 88		84.49 59			96.75 111

PS-MVSNAJ		88.14 16	87.61 26	89.71 7	92.06 104	76.72 1	95.75 19	93.26 90	83.86 11	89.55 20	96.06 31	53.55 205	97.89 48	91.10 18	93.31 55	94.54 96

EI-MVSNet		78.97 168	78.22 163	81.25 215	85.33 244	62.73 222	89.53 239	93.21 91	72.39 170	72.14 187	90.13 169	60.99 118	94.72 182	67.73 207	72.49 229	86.29 256

MVSTER		82.47 108	82.05 105	83.74 152	92.68 90	69.01 56	91.90 156	93.21 91	79.83 44	72.14 187	85.71 222	74.72 16	94.72 182	75.72 138	72.49 229	87.50 233

UniMVSNet_NR-MVSNet		78.15 187	77.55 175	79.98 245	84.46 260	60.26 264	92.25 138	93.20 93	77.50 86	68.88 224	86.61 210	66.10 62	92.13 274	66.38 221	62.55 295	87.54 232

HFP-MVS		84.73 68	84.40 70	85.72 93	93.75 60	65.01 163	93.50 94	93.19 94	72.19 175	79.22 110	94.93 66	59.04 142	97.67 55	81.55 96	92.21 69	94.49 101

#test#		84.98 65	84.74 66	85.72 93	93.75 60	65.01 163	94.09 62	93.19 94	73.55 144	79.22 110	94.93 66	59.04 142	97.67 55	82.66 86	92.21 69	94.49 101

UniMVSNet (Re)		77.58 196	76.78 188	79.98 245	84.11 266	60.80 252	91.76 164	93.17 96	76.56 98	69.93 214	84.78 231	63.32 99	92.36 269	64.89 236	62.51 297	86.78 248

ACMMPR		84.37 73	84.06 72	85.28 109	93.56 65	64.37 181	93.50 94	93.15 97	72.19 175	78.85 119	94.86 70	56.69 169	97.45 67	81.55 96	92.20 71	94.02 120

GST-MVS		84.63 71	84.29 71	85.66 97	92.82 85	65.27 153	93.04 107	93.13 98	73.20 148	78.89 114	94.18 93	59.41 138	97.85 50	81.45 98	92.48 68	93.86 127

xiu_mvs_v2_base		87.92 21	87.38 32	89.55 12	91.41 129	76.43 3	95.74 20	93.12 99	83.53 13	89.55 20	95.95 32	53.45 209	97.68 54	91.07 19	92.62 64	94.54 96

test_prior3		87.38 27	87.70 24	86.42 69	94.71 39	67.35 99	95.10 37	93.10 100	75.40 110	85.25 54	95.61 40	67.94 45	96.84 107	87.47 46	94.77 25	95.05 76

test_prior				86.42 69	94.71 39	67.35 99		93.10 100					96.84 107			95.05 76

test11								93.01 102

CostFormer		82.33 110	81.15 115	85.86 87	89.01 175	68.46 68	82.39 307	93.01 102	75.59 105	80.25 98	81.57 267	72.03 33	94.96 174	79.06 117	77.48 197	94.16 111

PAPR		85.15 62	84.47 67	87.18 41	96.02 27	68.29 72	91.85 159	93.00 104	76.59 97	79.03 113	95.00 62	61.59 114	97.61 62	78.16 125	89.00 107	95.63 48

region2R		84.36 74	84.03 73	85.36 107	93.54 66	64.31 183	93.43 97	92.95 105	72.16 178	78.86 118	94.84 71	56.97 164	97.53 65	81.38 100	92.11 73	94.24 106

test12				87.09 44	94.60 41	68.86 59		92.91 106		82.67 76		65.44 70	97.55 64		93.69 50	94.84 85

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 170	71.18 21	96.57 10	92.90 107	82.70 18	87.13 30	95.27 52	64.99 74	95.80 138	89.34 28	91.80 78	95.93 40

PAPM_NR		82.97 101	81.84 108	86.37 72	94.10 52	66.76 118	87.66 270	92.84 108	69.96 229	74.07 164	93.57 105	63.10 102	97.50 66	70.66 181	90.58 96	94.85 82

CDPH-MVS		85.71 56	85.46 57	86.46 67	94.75 38	67.19 104	93.89 77	92.83 109	70.90 215	83.09 72	95.28 50	63.62 93	97.36 73	80.63 105	94.18 39	94.84 85

tfpnnormal		70.10 270	67.36 276	78.32 271	83.45 275	60.97 250	88.85 251	92.77 110	64.85 275	60.83 296	78.53 302	43.52 283	93.48 232	31.73 363	61.70 307	80.52 332

PAPM		85.89 53	85.46 57	87.18 41	88.20 197	72.42 14	92.41 135	92.77 110	82.11 22	80.34 97	93.07 114	68.27 40	95.02 172	78.39 124	93.59 51	94.09 115

MS-PatchMatch		77.90 193	76.50 191	82.12 197	85.99 235	69.95 36	91.75 166	92.70 112	73.97 132	62.58 288	84.44 235	41.11 289	95.78 139	63.76 243	92.17 72	80.62 331

MSLP-MVS++		86.27 46	85.91 51	87.35 38	92.01 107	68.97 58	95.04 41	92.70 112	79.04 61	81.50 83	96.50 20	58.98 144	96.78 109	83.49 80	93.93 43	96.29 30

ab-mvs		80.18 146	78.31 161	85.80 90	88.44 187	65.49 151	83.00 304	92.67 114	71.82 190	77.36 132	85.01 226	54.50 192	96.59 114	76.35 136	75.63 210	95.32 60

save fliter					93.84 56	67.89 84	95.05 39	92.66 115	78.19 71

XVS		83.87 86	83.47 78	85.05 114	93.22 73	63.78 193	92.92 115	92.66 115	73.99 130	78.18 123	94.31 89	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

X-MVStestdata		76.86 203	74.13 223	85.05 114	93.22 73	63.78 193	92.92 115	92.66 115	73.99 130	78.18 123	10.19 376	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

SD-MVS		87.49 26	87.49 29	87.50 35	93.60 63	68.82 61	93.90 76	92.63 118	76.86 92	87.90 27	95.76 35	66.17 61	97.63 60	89.06 32	91.48 84	96.05 37
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
无先验							92.71 121	92.61 119	62.03 298				97.01 93	66.63 215		93.97 121

APD-MVS		85.93 52	85.99 49	85.76 92	95.98 28	65.21 155	93.59 90	92.58 120	66.54 263	86.17 40	95.88 33	63.83 89	97.00 95	86.39 58	92.94 60	95.06 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
1314		80.70 138	78.95 155	85.94 84	87.77 207	67.56 92	87.91 265	92.55 121	72.17 177	67.44 244	93.09 111	50.27 233	97.04 92	71.68 174	87.64 119	93.23 144

MP-MVS-pluss		85.24 61	85.13 60	85.56 99	91.42 127	65.59 147	91.54 173	92.51 122	74.56 121	80.62 94	95.64 39	59.15 141	97.00 95	86.94 54	93.80 45	94.07 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
WR-MVS		76.76 208	75.74 202	79.82 250	84.60 256	62.27 229	92.60 128	92.51 122	76.06 101	67.87 240	85.34 223	56.76 166	90.24 302	62.20 254	63.69 291	86.94 246

OpenMVS	70.45 11	78.54 181	75.92 199	86.41 71	85.93 239	71.68 17	92.74 119	92.51 122	66.49 264	64.56 269	91.96 139	43.88 281	98.10 39	54.61 284	90.65 95	89.44 211

CHOSEN 1792x2688		84.98 65	83.45 79	89.57 11	89.94 153	75.14 5	92.07 147	92.32 125	81.87 25	75.68 147	88.27 190	60.18 126	98.60 25	80.46 107	90.27 99	94.96 80

CP-MVS		83.71 90	83.40 83	84.65 129	93.14 78	63.84 191	94.59 48	92.28 126	71.03 213	77.41 131	94.92 68	55.21 186	96.19 124	81.32 101	90.70 94	93.91 124

MP-MVS		85.02 63	84.97 63	85.17 113	92.60 93	64.27 186	93.24 100	92.27 127	73.13 150	79.63 106	94.43 80	61.90 110	97.17 85	85.00 68	92.56 65	94.06 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		84.73 68	84.47 67	85.50 100	91.89 114	65.16 157	91.55 172	92.23 128	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

MTGPA								92.23 128

MTAPA		83.91 85	83.38 84	85.50 100	91.89 114	65.16 157	81.75 309	92.23 128	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

VPNet		78.82 172	77.53 176	82.70 177	84.52 258	66.44 125	93.93 74	92.23 128	80.46 40	72.60 178	88.38 188	49.18 244	93.13 237	72.47 164	63.97 289	88.55 220

ACMMP		81.49 124	80.67 125	83.93 149	91.71 119	62.90 218	92.13 142	92.22 132	71.79 191	71.68 194	93.49 107	50.32 231	96.96 101	78.47 123	84.22 152	91.93 176
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PGM-MVS		83.25 96	82.70 97	84.92 119	92.81 87	64.07 189	90.44 212	92.20 133	71.28 208	77.23 134	94.43 80	55.17 187	97.31 77	79.33 114	91.38 86	93.37 137

jason		86.40 44	86.17 47	87.11 43	86.16 233	70.54 28	95.71 23	92.19 134	82.00 24	84.58 58	94.34 87	61.86 111	95.53 159	87.76 42	90.89 92	95.27 66
jason: jason.
CLD-MVS		82.73 104	82.35 104	83.86 150	87.90 204	67.65 91	95.45 27	92.18 135	85.06 8	72.58 179	92.27 135	52.46 215	95.78 139	84.18 74	79.06 180	88.16 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_Test		84.16 81	83.20 86	87.05 45	91.56 123	69.82 39	89.99 230	92.05 136	77.77 79	82.84 73	86.57 211	63.93 88	96.09 128	74.91 147	89.18 106	95.25 69

EIA-MVS		84.84 67	84.88 64	84.69 128	91.30 130	62.36 226	93.85 79	92.04 137	79.45 50	79.33 109	94.28 90	62.42 106	96.35 121	80.05 108	91.25 89	95.38 55

WR-MVS_H		70.59 267	69.94 262	72.53 320	81.03 294	51.43 335	87.35 274	92.03 138	67.38 257	60.23 298	80.70 281	55.84 181	83.45 347	46.33 317	58.58 325	82.72 310

FMVSNet3		77.73 194	76.04 197	82.80 174	91.20 133	68.99 57	91.87 157	91.99 139	73.35 147	67.04 250	83.19 248	56.62 170	92.14 273	59.80 268	69.34 248	87.28 241

DP-MVS Recon		82.73 104	81.65 111	85.98 82	97.31 4	67.06 108	95.15 35	91.99 139	69.08 242	76.50 142	93.89 99	54.48 195	98.20 36	70.76 179	85.66 136	92.69 157

EPNet_dtu		78.80 173	79.26 151	77.43 282	88.06 199	49.71 344	91.96 155	91.95 141	77.67 81	76.56 141	91.28 150	58.51 146	90.20 304	56.37 279	80.95 169	92.39 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FOURS1					93.95 53	61.77 236	93.96 69	91.92 142	62.14 297	86.57 35

ETV-MVS		86.01 51	86.11 48	85.70 96	90.21 149	67.02 111	93.43 97	91.92 142	81.21 32	84.13 65	94.07 96	60.93 120	95.63 149	89.28 29	89.81 100	94.46 103

CS-MVS-test		86.14 49	87.01 37	83.52 159	92.63 92	59.36 279	95.49 26	91.92 142	80.09 42	85.46 50	95.53 43	61.82 113	95.77 141	86.77 56	93.37 54	95.41 53

LFMVS		84.34 75	82.73 96	89.18 13	94.76 37	73.25 10	94.99 43	91.89 145	71.90 183	82.16 78	93.49 107	47.98 255	97.05 90	82.55 89	84.82 141	97.25 7

CS-MVS		85.80 54	86.65 43	83.27 167	92.00 108	58.92 284	95.31 30	91.86 146	79.97 43	84.82 56	95.40 44	62.26 107	95.51 160	86.11 60	92.08 74	95.37 56

HPM-MVS		83.25 96	82.95 91	84.17 144	92.25 100	62.88 219	90.91 198	91.86 146	70.30 225	77.12 135	93.96 98	56.75 167	96.28 122	82.04 92	91.34 88	93.34 138
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		82.96 102	82.44 102	84.52 133	92.83 83	62.92 217	92.76 118	91.85 148	71.52 203	75.61 150	94.24 91	53.48 208	96.99 98	78.97 118	90.73 93	93.64 133

XXY-MVS		77.94 191	76.44 192	82.43 183	82.60 283	64.44 176	92.01 150	91.83 149	73.59 143	70.00 211	85.82 220	54.43 196	94.76 179	69.63 189	68.02 259	88.10 229

baseline		85.01 64	84.44 69	86.71 56	88.33 192	68.73 62	90.24 221	91.82 150	81.05 35	81.18 87	92.50 127	63.69 92	96.08 130	84.45 73	86.71 129	95.32 60

casdiffmvs		85.37 59	84.87 65	86.84 50	88.25 195	69.07 54	93.04 107	91.76 151	81.27 31	80.84 93	92.07 138	64.23 84	96.06 131	84.98 69	87.43 121	95.39 54

NR-MVSNet		76.05 217	74.59 213	80.44 233	82.96 281	62.18 230	90.83 203	91.73 152	77.12 90	60.96 295	86.35 212	59.28 140	91.80 281	60.74 261	61.34 310	87.35 239

PVSNet_Blended_VisFu		83.97 84	83.50 77	85.39 106	90.02 151	66.59 123	93.77 84	91.73 152	77.43 88	77.08 137	89.81 173	63.77 91	96.97 100	79.67 110	88.21 114	92.60 160

canonicalmvs		86.85 39	86.25 46	88.66 18	91.80 117	71.92 15	93.54 92	91.71 154	80.26 41	87.55 28	95.25 54	63.59 95	96.93 105	88.18 39	84.34 147	97.11 8

HQP3-MVS								91.70 155							78.90 181

HQP-MVS		81.14 130	80.64 126	82.64 179	87.54 209	63.66 201	94.06 64	91.70 155	79.80 45	74.18 160	90.30 165	51.63 222	95.61 151	77.63 129	78.90 181	88.63 218

baseline1		81.84 120	81.03 120	84.28 142	91.60 121	66.62 121	91.08 195	91.66 157	81.87 25	74.86 155	91.67 145	69.98 37	94.92 177	71.76 172	64.75 281	91.29 190

FMVSNet2		76.07 214	74.01 225	82.26 191	88.85 177	67.66 90	91.33 185	91.61 158	70.84 216	65.98 257	82.25 256	48.03 252	92.00 278	58.46 273	68.73 254	87.10 243

114514_t		79.17 165	77.67 172	83.68 156	95.32 31	65.53 149	92.85 117	91.60 159	63.49 283	67.92 237	90.63 158	46.65 263	95.72 147	67.01 213	83.54 153	89.79 205

test-LLR		80.10 148	79.56 143	81.72 206	86.93 222	61.17 245	92.70 122	91.54 160	71.51 204	75.62 148	86.94 208	53.83 201	92.38 267	72.21 166	84.76 143	91.60 179

test-mter		79.96 151	79.38 149	81.72 206	86.93 222	61.17 245	92.70 122	91.54 160	73.85 135	75.62 148	86.94 208	49.84 238	92.38 267	72.21 166	84.76 143	91.60 179

DU-MVS		76.86 203	75.84 200	79.91 247	82.96 281	60.26 264	91.26 188	91.54 160	76.46 99	68.88 224	86.35 212	56.16 174	92.13 274	66.38 221	62.55 295	87.35 239

旧先验1					91.94 110	60.74 257		91.50 163			94.36 82	65.23 71			91.84 77	94.55 94

VDD-MVS		83.06 99	81.81 110	86.81 52	90.86 139	67.70 89	95.40 28	91.50 163	75.46 107	81.78 80	92.34 134	40.09 292	97.13 88	86.85 55	82.04 161	95.60 49

新几何1				84.73 125	92.32 97	64.28 185		91.46 165	59.56 315	79.77 104	92.90 119	56.95 165	96.57 116	63.40 244	92.91 61	93.34 138

tpm2		79.80 155	77.95 168	85.34 108	88.28 193	68.26 74	81.56 312	91.42 166	70.11 227	77.59 130	80.50 285	67.40 51	94.26 204	67.34 210	77.35 198	93.51 135

1121		81.25 128	80.05 133	84.87 122	92.30 98	64.31 183	87.91 265	91.39 167	59.44 316	79.94 101	92.91 118	57.09 158	97.01 93	66.63 215	92.81 63	93.29 141

TranMVSNet+NR-MVSNet		75.86 221	74.52 216	79.89 248	82.44 284	60.64 260	91.37 183	91.37 168	76.63 96	67.65 242	86.21 216	52.37 216	91.55 286	61.84 256	60.81 313	87.48 234

test2506		83.29 94	82.92 92	84.37 138	88.39 190	63.18 210	92.01 150	91.35 169	77.66 82	78.49 122	91.42 146	64.58 80	95.09 170	73.19 153	89.23 104	94.85 82

VDDNet		80.50 141	78.26 162	87.21 40	86.19 232	69.79 40	94.48 49	91.31 170	60.42 308	79.34 108	90.91 153	38.48 300	96.56 117	82.16 90	81.05 168	95.27 66

HQP_MVS		80.34 144	79.75 140	82.12 197	86.94 220	62.42 224	93.13 103	91.31 170	78.81 65	72.53 180	89.14 179	50.66 229	95.55 157	76.74 132	78.53 186	88.39 224

plane_prior5								91.31 170					95.55 157	76.74 132	78.53 186	88.39 224

SR-MVS		82.81 103	82.58 99	83.50 162	93.35 69	61.16 247	92.23 140	91.28 173	64.48 276	81.27 85	95.28 50	53.71 204	95.86 137	82.87 84	88.77 109	93.49 136

nrg030		80.93 135	79.86 138	84.13 145	83.69 271	68.83 60	93.23 101	91.20 174	75.55 106	75.06 154	88.22 194	63.04 103	94.74 181	81.88 93	66.88 265	88.82 216

EPMVS		78.49 182	75.98 198	86.02 81	91.21 132	69.68 44	80.23 322	91.20 174	75.25 114	72.48 182	78.11 306	54.65 191	93.69 228	57.66 276	83.04 154	94.69 88

hse-mvs2		81.12 132	81.11 119	81.16 218	86.52 226	57.48 303	89.40 242	91.16 176	81.45 28	82.73 74	90.49 161	60.11 127	94.58 188	87.69 43	60.41 318	91.41 184

AUN-MVS		78.37 183	77.43 177	81.17 217	86.60 225	57.45 304	89.46 241	91.16 176	74.11 128	74.40 159	90.49 161	55.52 182	94.57 189	74.73 150	60.43 317	91.48 182

cascas		78.18 186	75.77 201	85.41 105	87.14 218	69.11 52	92.96 111	91.15 178	66.71 262	70.47 202	86.07 217	37.49 310	96.48 120	70.15 184	79.80 174	90.65 196

tpm		78.58 180	77.03 184	83.22 168	85.94 238	64.56 170	83.21 302	91.14 179	78.31 70	73.67 168	79.68 296	64.01 86	92.09 276	66.07 225	71.26 239	93.03 150

PCF-MVS	73.15 9	79.29 163	77.63 174	84.29 141	86.06 234	65.96 137	87.03 276	91.10 180	69.86 231	69.79 215	90.64 156	57.54 155	96.59 114	64.37 239	82.29 158	90.32 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20240529		76.84 206	74.15 222	84.88 121	91.02 134	64.95 166	93.84 82	91.09 181	53.57 337	73.00 171	87.42 204	35.91 320	97.32 76	69.14 196	72.41 231	92.36 165

DROMVSNet		84.53 72	85.04 62	83.01 171	89.34 165	61.37 244	94.42 50	91.09 181	77.91 77	83.24 70	94.20 92	58.37 147	95.40 161	85.35 66	91.41 85	92.27 172

test_part1		79.63 157	77.86 171	84.93 118	92.50 95	71.43 18	94.15 59	91.08 183	72.51 164	70.66 200	84.98 227	59.84 131	95.07 171	72.07 169	62.94 293	88.30 227

PS-MVSNAJss		77.26 200	76.31 194	80.13 242	80.64 299	59.16 281	90.63 211	91.06 184	72.80 158	68.58 230	84.57 234	53.55 205	93.96 220	72.97 155	71.96 233	87.27 242

PVSNet	73.49 8	80.05 149	78.63 157	84.31 140	90.92 137	64.97 165	92.47 134	91.05 185	79.18 56	72.43 184	90.51 160	37.05 316	94.06 212	68.06 203	86.00 134	93.90 126

API-MVS		82.28 111	80.53 128	87.54 34	96.13 24	70.59 27	93.63 88	91.04 186	65.72 270	75.45 152	92.83 123	56.11 176	98.89 19	64.10 240	89.75 103	93.15 146

APD-MVS_3200maxsize		81.64 123	81.32 114	82.59 181	92.36 96	58.74 286	91.39 180	91.01 187	63.35 284	79.72 105	94.62 76	51.82 218	96.14 126	79.71 109	87.93 117	92.89 155

test1172		81.90 119	81.83 109	82.13 196	93.23 72	57.52 302	91.61 171	90.98 188	64.32 278	80.20 99	95.00 62	51.26 225	95.61 151	81.73 95	88.13 115	93.26 142

MVP-Stereo		77.12 202	76.23 195	79.79 251	81.72 290	66.34 128	89.29 243	90.88 189	70.56 223	62.01 291	82.88 249	49.34 241	94.13 207	65.55 232	93.80 45	78.88 344
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		79.87 153	78.68 156	83.45 164	89.96 152	61.51 241	92.13 142	90.79 190	76.83 94	78.85 119	86.33 214	38.16 302	96.17 125	67.93 205	87.17 122	92.67 158
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
TAMVS		80.37 143	79.45 146	83.13 170	85.14 248	63.37 204	91.23 189	90.76 191	74.81 120	72.65 177	88.49 183	60.63 122	92.95 241	69.41 192	81.95 162	93.08 149

MVSFormer		83.75 89	82.88 93	86.37 72	89.24 170	71.18 21	89.07 249	90.69 192	65.80 268	87.13 30	94.34 87	64.99 74	92.67 256	72.83 157	91.80 78	95.27 66

test_djsdf		73.76 246	72.56 242	77.39 283	77.00 337	53.93 324	89.07 249	90.69 192	65.80 268	63.92 275	82.03 259	43.14 284	92.67 256	72.83 157	68.53 255	85.57 276

PMMVS		81.98 118	82.04 106	81.78 204	89.76 157	56.17 312	91.13 194	90.69 192	77.96 75	80.09 100	93.57 105	46.33 268	94.99 173	81.41 99	87.46 120	94.17 110

dcpmvs_2		87.37 28	87.55 28	86.85 49	95.04 36	68.20 77	90.36 216	90.66 195	79.37 52	81.20 86	93.67 102	74.73 15	96.55 118	90.88 21	92.00 75	95.82 44

CDS-MVSNet		81.43 125	80.74 123	83.52 159	86.26 231	64.45 175	92.09 145	90.65 196	75.83 104	73.95 166	89.81 173	63.97 87	92.91 246	71.27 175	82.82 156	93.20 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 126	79.99 136	85.46 102	90.39 146	68.40 69	86.88 280	90.61 197	74.41 122	70.31 207	84.67 232	63.79 90	92.32 271	73.13 154	85.70 135	95.67 46

SR-MVS-dyc-post		81.06 133	80.70 124	82.15 194	92.02 105	58.56 288	90.90 199	90.45 198	62.76 290	78.89 114	94.46 78	51.26 225	95.61 151	78.77 121	86.77 127	92.28 169

RE-MVS-def			80.48 129		92.02 105	58.56 288	90.90 199	90.45 198	62.76 290	78.89 114	94.46 78	49.30 242		78.77 121	86.77 127	92.28 169

RPMNet		70.42 269	65.68 285	84.63 131	83.15 278	67.96 82	70.25 346	90.45 198	46.83 355	69.97 212	65.10 353	56.48 173	95.30 167	35.79 352	73.13 222	90.64 197

xiu_mvs_v1_base_debu		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 151	84.20 62	94.36 82	38.04 304	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 151	84.20 62	94.36 82	38.04 304	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base_debi		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 151	84.20 62	94.36 82	38.04 304	95.73 143	84.12 75	86.81 124	91.33 185

GBi-Net		75.65 224	73.83 227	81.10 222	88.85 177	65.11 159	90.01 227	90.32 204	70.84 216	67.04 250	80.25 290	48.03 252	91.54 287	59.80 268	69.34 248	86.64 249

test1		75.65 224	73.83 227	81.10 222	88.85 177	65.11 159	90.01 227	90.32 204	70.84 216	67.04 250	80.25 290	48.03 252	91.54 287	59.80 268	69.34 248	86.64 249

FMVSNet1		72.71 256	69.91 263	81.10 222	83.60 273	65.11 159	90.01 227	90.32 204	63.92 280	63.56 279	80.25 290	36.35 319	91.54 287	54.46 285	66.75 266	86.64 249

PVSNet_0	68.08 15	71.81 260	68.32 273	82.27 189	84.68 254	62.31 228	88.68 254	90.31 207	75.84 103	57.93 314	80.65 284	37.85 307	94.19 206	69.94 186	29.05 368	90.31 200

OPM-MVS		79.00 167	78.09 164	81.73 205	83.52 274	63.83 192	91.64 170	90.30 208	76.36 100	71.97 189	89.93 172	46.30 269	95.17 169	75.10 142	77.70 191	86.19 260
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CP-MVSNet		70.50 268	69.91 263	72.26 323	80.71 297	51.00 338	87.23 275	90.30 208	67.84 251	59.64 300	82.69 251	50.23 234	82.30 355	51.28 294	59.28 321	83.46 300

KD-MVS_2432*1600		69.03 279	66.37 281	77.01 289	85.56 242	61.06 248	81.44 313	90.25 210	67.27 258	58.00 312	76.53 319	54.49 193	87.63 326	48.04 308	35.77 363	82.34 316

miper_refine_blended		69.03 279	66.37 281	77.01 289	85.56 242	61.06 248	81.44 313	90.25 210	67.27 258	58.00 312	76.53 319	54.49 193	87.63 326	48.04 308	35.77 363	82.34 316

v148		76.19 212	74.47 217	81.36 213	80.05 307	64.44 176	91.75 166	90.23 212	73.68 141	67.13 249	80.84 280	55.92 180	93.86 226	68.95 198	61.73 306	85.76 274

v2v482		77.42 198	75.65 204	82.73 176	80.38 301	67.13 107	91.85 159	90.23 212	75.09 116	69.37 216	83.39 246	53.79 203	94.44 197	71.77 171	65.00 278	86.63 252

v1144		76.73 209	74.88 209	82.27 189	80.23 306	66.60 122	91.68 168	90.21 214	73.69 140	69.06 221	81.89 260	52.73 213	94.40 198	69.21 195	65.23 275	85.80 271

GA-MVS		78.33 185	76.23 195	84.65 129	83.65 272	66.30 129	91.44 174	90.14 215	76.01 102	70.32 206	84.02 239	42.50 285	94.72 182	70.98 176	77.00 202	92.94 153

MDTV_nov1_ep13			72.61 241		89.06 173	68.48 67	80.33 320	90.11 216	71.84 189	71.81 191	75.92 325	53.01 211	93.92 222	48.04 308	73.38 220

D2MVS		73.80 244	72.02 248	79.15 264	79.15 317	62.97 213	88.58 256	90.07 217	72.94 154	59.22 303	78.30 303	42.31 287	92.70 255	65.59 231	72.00 232	81.79 322

abl_6		79.82 154	79.20 152	81.70 208	89.85 154	58.34 290	88.47 258	90.07 217	62.56 293	77.71 127	93.08 112	47.65 259	96.78 109	77.94 127	85.45 138	89.99 204

TR-MVS		78.77 175	77.37 182	82.95 172	90.49 143	60.88 251	93.67 86	90.07 217	70.08 228	74.51 158	91.37 149	45.69 272	95.70 148	60.12 266	80.32 172	92.29 168

Anonymous20231211		73.08 248	70.39 259	81.13 220	90.62 142	63.33 205	91.40 178	90.06 220	51.84 341	64.46 272	80.67 283	36.49 318	94.07 211	63.83 242	64.17 286	85.98 267

jajsoiax		73.05 249	71.51 253	77.67 278	77.46 334	54.83 320	88.81 252	90.04 221	69.13 241	62.85 286	83.51 244	31.16 337	92.75 252	70.83 177	69.80 244	85.43 279

HyFIR lowres test		81.03 134	79.56 143	85.43 104	87.81 206	68.11 79	90.18 222	90.01 222	70.65 221	72.95 173	86.06 218	63.61 94	94.50 196	75.01 145	79.75 175	93.67 131

ACMM	69.62 13	74.34 238	72.73 239	79.17 262	84.25 265	57.87 295	90.36 216	89.93 223	63.17 287	65.64 259	86.04 219	37.79 308	94.10 208	65.89 226	71.52 236	85.55 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CL-MVSNet_self_test		69.92 272	68.09 274	75.41 300	73.25 348	55.90 315	90.05 226	89.90 224	69.96 229	61.96 292	76.54 318	51.05 227	87.64 325	49.51 302	50.59 344	82.70 312

UnsupCasMVSNet_eth		65.79 302	63.10 302	73.88 310	70.71 355	50.29 342	81.09 315	89.88 225	72.58 162	49.25 343	74.77 330	32.57 331	87.43 329	55.96 281	41.04 358	83.90 294

testdata				81.34 214	89.02 174	57.72 297		89.84 226	58.65 320	85.32 52	94.09 94	57.03 160	93.28 235	69.34 193	90.56 97	93.03 150

mvs_tets		72.71 256	71.11 254	77.52 279	77.41 335	54.52 322	88.45 259	89.76 227	68.76 246	62.70 287	83.26 247	29.49 341	92.71 253	70.51 183	69.62 246	85.34 281

v1192		75.98 219	73.92 226	82.15 194	79.73 308	66.24 131	91.22 190	89.75 228	72.67 160	68.49 231	81.42 270	49.86 237	94.27 202	67.08 212	65.02 277	85.95 268

PS-CasMVS		69.86 274	69.13 268	72.07 326	80.35 303	50.57 340	87.02 277	89.75 228	67.27 258	59.19 304	82.28 255	46.58 264	82.24 356	50.69 296	59.02 322	83.39 302

dp		75.01 233	72.09 247	83.76 151	89.28 168	66.22 132	79.96 327	89.75 228	71.16 210	67.80 241	77.19 314	51.81 219	92.54 262	50.39 297	71.44 238	92.51 163

LPG-MVS_test		75.82 222	74.58 214	79.56 257	84.31 263	59.37 277	90.44 212	89.73 231	69.49 234	64.86 264	88.42 184	38.65 298	94.30 200	72.56 162	72.76 226	85.01 285

LGP-MVS_train				79.56 257	84.31 263	59.37 277		89.73 231	69.49 234	64.86 264	88.42 184	38.65 298	94.30 200	72.56 162	72.76 226	85.01 285

tpmrst		80.57 139	79.14 154	84.84 123	90.10 150	68.28 73	81.70 310	89.72 233	77.63 84	75.96 144	79.54 298	64.94 76	92.71 253	75.43 139	77.28 200	93.55 134

v144192		76.05 217	74.03 224	82.12 197	79.50 312	66.55 124	91.39 180	89.71 234	72.30 172	68.17 233	81.33 272	51.75 220	94.03 217	67.94 204	64.19 285	85.77 272

TAPA-MVS	70.22 12	74.94 234	73.53 230	79.17 262	90.40 145	52.07 332	89.19 247	89.61 235	62.69 292	70.07 209	92.67 125	48.89 249	94.32 199	38.26 347	79.97 173	91.12 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		77.46 197	74.63 212	85.96 83	89.55 162	70.35 30	79.97 326	89.55 236	72.23 174	70.94 198	76.91 317	57.03 160	92.79 251	54.27 286	81.17 167	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1921920		75.63 226	73.49 231	82.06 201	79.38 313	66.35 127	91.07 197	89.48 237	71.98 180	67.99 234	81.22 275	49.16 246	93.90 223	66.56 217	64.56 284	85.92 270

v7n		71.31 264	68.65 269	79.28 260	76.40 339	60.77 254	86.71 281	89.45 238	64.17 279	58.77 308	78.24 304	44.59 279	93.54 230	57.76 274	61.75 305	83.52 298

test0.0.03 1		72.76 254	72.71 240	72.88 318	80.25 305	47.99 350	91.22 190	89.45 238	71.51 204	62.51 289	87.66 200	53.83 201	85.06 338	50.16 298	67.84 262	85.58 275

test222					89.77 156	61.60 240	89.55 237	89.42 240	56.83 329	77.28 133	92.43 131	52.76 212			91.14 91	93.09 148

V42		76.46 211	74.55 215	82.19 193	79.14 318	67.82 86	90.26 220	89.42 240	73.75 138	68.63 229	81.89 260	51.31 224	94.09 209	71.69 173	64.84 279	84.66 288

BH-w/o		80.49 142	79.30 150	84.05 147	90.83 140	64.36 182	93.60 89	89.42 240	74.35 124	69.09 219	90.15 168	55.23 185	95.61 151	64.61 237	86.43 133	92.17 174

pm-mvs1		72.89 252	71.09 255	78.26 273	79.10 319	57.62 300	90.80 204	89.30 243	67.66 254	62.91 285	81.78 262	49.11 247	92.95 241	60.29 265	58.89 323	84.22 291

v8		75.35 228	73.26 233	81.61 209	80.67 298	66.82 115	89.54 238	89.27 244	71.65 195	63.30 282	80.30 289	54.99 189	94.06 212	67.33 211	62.33 298	83.94 293

diffmvs		84.28 76	83.83 74	85.61 98	87.40 213	68.02 81	90.88 201	89.24 245	80.54 39	81.64 82	92.52 126	59.83 132	94.52 195	87.32 50	85.11 139	94.29 104

PEN-MVS		69.46 276	68.56 270	72.17 325	79.27 314	49.71 344	86.90 279	89.24 245	67.24 261	59.08 305	82.51 254	47.23 261	83.54 346	48.42 306	57.12 326	83.25 303

UniMVSNet_ETH3D		72.74 255	70.53 258	79.36 259	78.62 326	56.64 310	85.01 287	89.20 247	63.77 282	64.84 266	84.44 235	34.05 325	91.86 280	63.94 241	70.89 241	89.57 209

SCA		75.82 222	72.76 238	85.01 116	86.63 224	70.08 32	81.06 316	89.19 248	71.60 200	70.01 210	77.09 315	45.53 273	90.25 299	60.43 263	73.27 221	94.68 89

EG-PatchMatch MVS		68.55 283	65.41 288	77.96 276	78.69 324	62.93 215	89.86 232	89.17 249	60.55 307	50.27 339	77.73 309	22.60 356	94.06 212	47.18 314	72.65 228	76.88 351

HPM-MVS_fast		80.25 145	79.55 145	82.33 187	91.55 124	59.95 269	91.32 186	89.16 250	65.23 274	74.71 157	93.07 114	47.81 257	95.74 142	74.87 149	88.23 113	91.31 189

miper_enhance_ethall		78.86 171	77.97 167	81.54 210	88.00 202	65.17 156	91.41 176	89.15 251	75.19 115	68.79 226	83.98 240	67.17 53	92.82 248	72.73 160	65.30 272	86.62 253

Fast-Effi-MVS+		81.14 130	80.01 135	84.51 134	90.24 148	65.86 139	94.12 61	89.15 251	73.81 137	75.37 153	88.26 191	57.26 156	94.53 194	66.97 214	84.92 140	93.15 146

Vis-MVSNet (Re-imp)		79.24 164	79.57 142	78.24 274	88.46 186	52.29 331	90.41 214	89.12 253	74.24 126	69.13 218	91.91 140	65.77 67	90.09 306	59.00 272	88.09 116	92.33 166

v1240		75.21 231	72.98 235	81.88 203	79.20 315	66.00 135	90.75 206	89.11 254	71.63 199	67.41 246	81.22 275	47.36 260	93.87 224	65.46 233	64.72 282	85.77 272

v10		74.77 235	72.54 243	81.46 211	80.33 304	66.71 119	89.15 248	89.08 255	70.94 214	63.08 283	79.86 294	52.52 214	94.04 215	65.70 229	62.17 299	83.64 295

ACMP	71.68 10	75.58 227	74.23 221	79.62 255	84.97 252	59.64 272	90.80 204	89.07 256	70.39 224	62.95 284	87.30 206	38.28 301	93.87 224	72.89 156	71.45 237	85.36 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UnsupCasMVSNet_bld		61.60 317	57.71 321	73.29 315	68.73 360	51.64 333	78.61 330	89.05 257	57.20 326	46.11 348	61.96 357	28.70 344	88.60 314	50.08 299	38.90 360	79.63 339

CANet_DTU		84.09 82	83.52 76	85.81 89	90.30 147	66.82 115	91.87 157	89.01 258	85.27 7	86.09 41	93.74 101	47.71 258	96.98 99	77.90 128	89.78 102	93.65 132

UA-Net		80.02 150	79.65 141	81.11 221	89.33 167	57.72 297	86.33 283	89.00 259	77.44 87	81.01 90	89.15 178	59.33 139	95.90 136	61.01 260	84.28 150	89.73 207

MVS_111021_LR		82.02 117	81.52 112	83.51 161	88.42 188	62.88 219	89.77 234	88.93 260	76.78 95	75.55 151	93.10 110	50.31 232	95.38 163	83.82 79	87.02 123	92.26 173

miper_lstm_enhance		73.05 249	71.73 251	77.03 288	83.80 269	58.32 291	81.76 308	88.88 261	69.80 232	61.01 294	78.23 305	57.19 157	87.51 328	65.34 234	59.53 320	85.27 283

anonymousdsp		71.14 265	69.37 267	76.45 294	72.95 349	54.71 321	84.19 291	88.88 261	61.92 300	62.15 290	79.77 295	38.14 303	91.44 292	68.90 199	67.45 263	83.21 304

cl22		77.94 191	76.78 188	81.42 212	87.57 208	64.93 167	90.67 207	88.86 263	72.45 167	67.63 243	82.68 252	64.07 85	92.91 246	71.79 170	65.30 272	86.44 254

MIMVSNet		71.64 261	68.44 271	81.23 216	81.97 289	64.44 176	73.05 342	88.80 264	69.67 233	64.59 267	74.79 329	32.79 329	87.82 323	53.99 287	76.35 207	91.42 183

IterMVS-LS		76.49 210	75.18 208	80.43 234	84.49 259	62.74 221	90.64 209	88.80 264	72.40 169	65.16 263	81.72 263	60.98 119	92.27 272	67.74 206	64.65 283	86.29 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RRT_MVS		74.44 237	72.97 236	78.84 267	82.36 285	57.66 299	89.83 233	88.79 266	70.61 222	64.58 268	84.89 229	39.24 294	92.65 259	70.11 185	66.34 269	86.21 259

cl____		76.07 214	74.67 210	80.28 237	85.15 247	61.76 237	90.12 223	88.73 267	71.16 210	65.43 260	81.57 267	61.15 116	92.95 241	66.54 218	62.17 299	86.13 263

DIV-MVS_self_test		76.07 214	74.67 210	80.28 237	85.14 248	61.75 238	90.12 223	88.73 267	71.16 210	65.42 261	81.60 266	61.15 116	92.94 245	66.54 218	62.16 301	86.14 261

JIA-IIPM		66.06 300	62.45 307	76.88 292	81.42 293	54.45 323	57.49 364	88.67 269	49.36 348	63.86 276	46.86 363	56.06 177	90.25 299	49.53 301	68.83 252	85.95 268

OMC-MVS		78.67 179	77.91 170	80.95 228	85.76 240	57.40 305	88.49 257	88.67 269	73.85 135	72.43 184	92.10 137	49.29 243	94.55 192	72.73 160	77.89 189	90.91 194

miper_ehance_all_eth		77.60 195	76.44 192	81.09 225	85.70 241	64.41 179	90.65 208	88.64 271	72.31 171	67.37 248	82.52 253	64.77 78	92.64 260	70.67 180	65.30 272	86.24 258

BH-untuned		78.68 177	77.08 183	83.48 163	89.84 155	63.74 195	92.70 122	88.59 272	71.57 201	66.83 253	88.65 182	51.75 220	95.39 162	59.03 271	84.77 142	91.32 188

DTE-MVSNet		68.46 285	67.33 277	71.87 328	77.94 331	49.00 347	86.16 284	88.58 273	66.36 265	58.19 309	82.21 257	46.36 265	83.87 344	44.97 324	55.17 333	82.73 309

CPTT-MVS		79.59 158	79.16 153	80.89 230	91.54 125	59.80 271	92.10 144	88.54 274	60.42 308	72.96 172	93.28 109	48.27 251	92.80 250	78.89 120	86.50 132	90.06 202

CVMVSNet		74.04 241	74.27 220	73.33 314	85.33 244	43.94 360	89.53 239	88.39 275	54.33 336	70.37 205	90.13 169	49.17 245	84.05 341	61.83 257	79.36 177	91.99 175

1112_ss		80.56 140	79.83 139	82.77 175	88.65 182	60.78 253	92.29 137	88.36 276	72.58 162	72.46 183	94.95 64	65.09 73	93.42 234	66.38 221	77.71 190	94.10 114

tpmvs		72.88 253	69.76 265	82.22 192	90.98 135	67.05 109	78.22 334	88.30 277	63.10 288	64.35 274	74.98 328	55.09 188	94.27 202	43.25 327	69.57 247	85.34 281

PLC	68.80 14	75.23 230	73.68 229	79.86 249	92.93 82	58.68 287	90.64 209	88.30 277	60.90 305	64.43 273	90.53 159	42.38 286	94.57 189	56.52 278	76.54 205	86.33 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
eth_miper_zixun_eth		75.96 220	74.40 218	80.66 231	84.66 255	63.02 212	89.28 244	88.27 279	71.88 185	65.73 258	81.65 264	59.45 136	92.81 249	68.13 202	60.53 315	86.14 261

IS-MVSNet		80.14 147	79.41 147	82.33 187	87.91 203	60.08 268	91.97 154	88.27 279	72.90 157	71.44 196	91.73 144	61.44 115	93.66 229	62.47 253	86.53 131	93.24 143

Vis-MVSNet		80.92 136	79.98 137	83.74 152	88.48 185	61.80 235	93.44 96	88.26 281	73.96 133	77.73 126	91.76 142	49.94 236	94.76 179	65.84 227	90.37 98	94.65 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
c3_l		76.83 207	75.47 205	80.93 229	85.02 251	64.18 188	90.39 215	88.11 282	71.66 194	66.65 255	81.64 265	63.58 96	92.56 261	69.31 194	62.86 294	86.04 265

BH-RMVSNet		79.46 162	77.65 173	84.89 120	91.68 120	65.66 144	93.55 91	88.09 283	72.93 155	73.37 169	91.12 151	46.20 270	96.12 127	56.28 280	85.61 137	92.91 154

tpm cat1		75.30 229	72.21 246	84.58 132	88.52 183	67.77 87	78.16 335	88.02 284	61.88 301	68.45 232	76.37 321	60.65 121	94.03 217	53.77 289	74.11 215	91.93 176

Test_1112_low_res		79.56 159	78.60 158	82.43 183	88.24 196	60.39 263	92.09 145	87.99 285	72.10 179	71.84 190	87.42 204	64.62 79	93.04 238	65.80 228	77.30 199	93.85 128

AdaColmap		78.94 169	77.00 186	84.76 124	96.34 19	65.86 139	92.66 126	87.97 286	62.18 296	70.56 201	92.37 133	43.53 282	97.35 74	64.50 238	82.86 155	91.05 193

Effi-MVS+-dtu		76.14 213	75.28 207	78.72 268	83.22 276	55.17 319	89.87 231	87.78 287	75.42 108	67.98 235	81.43 269	45.08 276	92.52 263	75.08 143	71.63 234	88.48 221

mvs-test1		78.74 176	77.95 168	81.14 219	83.22 276	57.13 307	93.96 69	87.78 287	75.42 108	72.68 176	90.80 155	45.08 276	94.54 193	75.08 143	77.49 196	91.74 178

PatchT		69.11 278	65.37 289	80.32 235	82.07 288	63.68 200	67.96 355	87.62 289	50.86 344	69.37 216	65.18 352	57.09 158	88.53 316	41.59 336	66.60 267	88.74 217

XVG-OURS		74.25 240	72.46 244	79.63 254	78.45 327	57.59 301	80.33 320	87.39 290	63.86 281	68.76 227	89.62 175	40.50 291	91.72 283	69.00 197	74.25 214	89.58 208

Anonymous20231206		67.53 293	65.78 283	72.79 319	74.95 343	47.59 352	88.23 261	87.32 291	61.75 303	58.07 311	77.29 312	37.79 308	87.29 330	42.91 329	63.71 290	83.48 299

XVG-OURS-SEG-HR		74.70 236	73.08 234	79.57 256	78.25 328	57.33 306	80.49 318	87.32 291	63.22 286	68.76 227	90.12 171	44.89 278	91.59 285	70.55 182	74.09 216	89.79 205

pmmvs4		73.92 243	71.81 250	80.25 239	79.17 316	65.24 154	87.43 273	87.26 293	67.64 256	63.46 280	83.91 241	48.96 248	91.53 290	62.94 248	65.49 271	83.96 292

pmmvs5		73.35 247	71.52 252	78.86 266	78.64 325	60.61 261	91.08 195	86.90 294	67.69 253	63.32 281	83.64 242	44.33 280	90.53 296	62.04 255	66.02 270	85.46 278

test1111		80.84 137	80.02 134	83.33 165	87.87 205	60.76 255	92.62 127	86.86 295	77.86 78	75.73 146	91.39 148	46.35 266	94.70 185	72.79 159	88.68 110	94.52 98

ECVR-MVS		81.29 127	80.38 131	84.01 148	88.39 190	61.96 233	92.56 133	86.79 296	77.66 82	76.63 139	91.42 146	46.34 267	95.24 168	74.36 151	89.23 104	94.85 82

pmmvs6		67.57 292	64.76 291	76.00 298	72.82 351	53.37 326	88.71 253	86.78 297	53.19 338	57.58 316	78.03 307	35.33 322	92.41 266	55.56 282	54.88 335	82.21 319

MVS_0304		68.99 281	67.23 278	74.28 309	80.36 302	52.54 329	87.01 278	86.36 298	59.89 314	66.22 256	73.56 332	24.25 351	88.03 321	57.34 277	70.11 243	82.27 318

F-COLMAP		70.66 266	68.44 271	77.32 284	86.37 230	55.91 314	88.00 263	86.32 299	56.94 328	57.28 317	88.07 195	33.58 327	92.49 264	51.02 295	68.37 256	83.55 296

IterMVS		72.65 258	70.83 256	78.09 275	82.17 286	62.96 214	87.64 271	86.28 300	71.56 202	60.44 297	78.85 301	45.42 275	86.66 332	63.30 246	61.83 303	84.65 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 288	65.66 286	75.18 302	84.43 261	57.89 294	83.54 295	86.26 301	61.83 302	53.64 327	73.30 333	37.15 314	85.08 337	48.99 303	61.77 304	82.56 315

GeoE		78.90 170	77.43 177	83.29 166	88.95 176	62.02 231	92.31 136	86.23 302	70.24 226	71.34 197	89.27 176	54.43 196	94.04 215	63.31 245	80.81 171	93.81 129

EU-MVSNet		64.01 309	63.01 303	67.02 340	74.40 346	38.86 369	83.27 300	86.19 303	45.11 357	54.27 323	81.15 278	36.91 317	80.01 360	48.79 305	57.02 327	82.19 320

Effi-MVS+		83.82 87	82.76 95	86.99 47	89.56 161	69.40 46	91.35 184	86.12 304	72.59 161	83.22 71	92.81 124	59.60 135	96.01 135	81.76 94	87.80 118	95.56 50

IterMVS-SCA-FT		71.55 263	69.97 261	76.32 295	81.48 291	60.67 259	87.64 271	85.99 305	66.17 266	59.50 301	78.88 300	45.53 273	83.65 345	62.58 252	61.93 302	84.63 290

XVG-ACMP-BASELINE		68.04 288	65.53 287	75.56 299	74.06 347	52.37 330	78.43 331	85.88 306	62.03 298	58.91 307	81.21 277	20.38 360	91.15 293	60.69 262	68.18 257	83.16 305

ambc				69.61 332	61.38 368	41.35 362	49.07 367	85.86 307		50.18 341	66.40 350	10.16 370	88.14 320	45.73 320	44.20 353	79.32 342

CMPMVS	48.56 21	66.77 297	64.41 296	73.84 311	70.65 356	50.31 341	77.79 336	85.73 308	45.54 356	44.76 354	82.14 258	35.40 321	90.14 305	63.18 247	74.54 213	81.07 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+-dtu		75.04 232	73.37 232	80.07 243	80.86 295	59.52 275	91.20 192	85.38 309	71.90 183	65.20 262	84.84 230	41.46 288	92.97 240	66.50 220	72.96 224	87.73 231

Anonymous202405211		77.96 190	75.33 206	85.87 86	93.73 62	64.52 171	94.85 45	85.36 310	62.52 294	76.11 143	90.18 167	29.43 342	97.29 78	68.51 201	77.24 201	95.81 45

Anonymous20240521		62.09 315	59.08 318	71.10 329	67.19 361	48.72 348	83.91 293	85.23 311	50.38 345	47.84 346	71.22 344	20.74 359	85.51 336	46.47 316	58.75 324	79.06 343

our_test_3		68.29 286	64.69 292	79.11 265	78.92 320	64.85 168	88.40 260	85.06 312	60.32 310	52.68 329	76.12 323	40.81 290	89.80 309	44.25 326	55.65 331	82.67 314

USDC		67.43 295	64.51 294	76.19 296	77.94 331	55.29 318	78.38 332	85.00 313	73.17 149	48.36 345	80.37 287	21.23 358	92.48 265	52.15 293	64.02 288	80.81 329

TransMVSNet (Re)		70.07 271	67.66 275	77.31 285	80.62 300	59.13 283	91.78 163	84.94 314	65.97 267	60.08 299	80.44 286	50.78 228	91.87 279	48.84 304	45.46 352	80.94 327

xxxxxxxxxxxxxcwj		87.14 32	87.19 34	86.99 47	93.84 56	67.89 84	95.05 39	84.72 315	78.19 71	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

KD-MVS_self_test		60.87 319	58.60 319	67.68 337	66.13 362	39.93 366	75.63 340	84.70 316	57.32 325	49.57 342	68.45 348	29.55 340	82.87 351	48.09 307	47.94 348	80.25 336

ACMH	63.93 17	68.62 282	64.81 290	80.03 244	85.22 246	63.25 206	87.72 268	84.66 317	60.83 306	51.57 334	79.43 299	27.29 347	94.96 174	41.76 334	64.84 279	81.88 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Baseline_NR-MVSNet		73.99 242	72.83 237	77.48 281	80.78 296	59.29 280	91.79 161	84.55 318	68.85 243	68.99 222	80.70 281	56.16 174	92.04 277	62.67 251	60.98 312	81.11 325

MIMVSNet1		60.16 322	57.33 323	68.67 334	69.71 358	44.13 359	78.92 329	84.21 319	55.05 335	44.63 355	71.85 340	23.91 353	81.54 359	32.63 361	55.03 334	80.35 333

test20.03		63.83 310	62.65 306	67.38 339	70.58 357	39.94 365	86.57 282	84.17 320	63.29 285	51.86 332	77.30 311	37.09 315	82.47 353	38.87 346	54.13 337	79.73 338

MDA-MVSNet_test_wron		63.78 311	60.16 314	74.64 304	78.15 329	60.41 262	83.49 296	84.03 321	56.17 333	39.17 362	71.59 342	37.22 312	83.24 350	42.87 331	48.73 346	80.26 335

ADS-MVSNet		68.54 284	64.38 297	81.03 226	88.06 199	66.90 114	68.01 353	84.02 322	57.57 322	64.48 270	69.87 345	38.68 296	89.21 312	40.87 338	67.89 260	86.97 244

CR-MVSNet		73.79 245	70.82 257	82.70 177	83.15 278	67.96 82	70.25 346	84.00 323	73.67 142	69.97 212	72.41 336	57.82 152	89.48 310	52.99 292	73.13 222	90.64 197

Patchmtry		67.53 293	63.93 298	78.34 270	82.12 287	64.38 180	68.72 350	84.00 323	48.23 352	59.24 302	72.41 336	57.82 152	89.27 311	46.10 318	56.68 330	81.36 324

YYNet1		63.76 312	60.14 315	74.62 305	78.06 330	60.19 267	83.46 298	83.99 325	56.18 332	39.25 361	71.56 343	37.18 313	83.34 348	42.90 330	48.70 347	80.32 334

LTVRE_ROB	59.60 19	66.27 299	63.54 300	74.45 306	84.00 268	51.55 334	67.08 356	83.53 326	58.78 319	54.94 321	80.31 288	34.54 324	93.23 236	40.64 340	68.03 258	78.58 347
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
pmmvs-eth3d		65.53 303	62.32 308	75.19 301	69.39 359	59.59 273	82.80 305	83.43 327	62.52 294	51.30 336	72.49 334	32.86 328	87.16 331	55.32 283	50.73 343	78.83 345

OpenMVS_ROB	61.12 18	66.39 298	62.92 304	76.80 293	76.51 338	57.77 296	89.22 245	83.41 328	55.48 334	53.86 326	77.84 308	26.28 350	93.95 221	34.90 354	68.76 253	78.68 346

PatchMatch-RL		72.06 259	69.98 260	78.28 272	89.51 163	55.70 316	83.49 296	83.39 329	61.24 304	63.72 278	82.76 250	34.77 323	93.03 239	53.37 291	77.59 192	86.12 264

MSDG		69.54 275	65.73 284	80.96 227	85.11 250	63.71 197	84.19 291	83.28 330	56.95 327	54.50 322	84.03 238	31.50 335	96.03 133	42.87 331	69.13 251	83.14 306

CHOSEN 280x420		77.35 199	76.95 187	78.55 269	87.07 219	62.68 223	69.71 349	82.95 331	68.80 244	71.48 195	87.27 207	66.03 63	84.00 343	76.47 135	82.81 157	88.95 212

ppachtmachnet_test		67.72 290	63.70 299	79.77 252	78.92 320	66.04 134	88.68 254	82.90 332	60.11 312	55.45 320	75.96 324	39.19 295	90.55 295	39.53 342	52.55 340	82.71 311

iter_conf_final		81.74 122	80.93 121	84.18 143	92.66 91	69.10 53	92.94 114	82.80 333	79.01 62	74.85 156	88.40 186	61.83 112	94.61 186	79.36 112	76.52 206	88.83 213

new-patchmatchnet		59.30 324	56.48 325	67.79 336	65.86 363	44.19 358	82.47 306	81.77 334	59.94 313	43.65 358	66.20 351	27.67 346	81.68 358	39.34 343	41.40 357	77.50 350

iter_conf05		83.27 95	82.70 97	84.98 117	93.32 70	71.84 16	94.16 56	81.76 335	82.74 16	73.83 167	88.40 186	72.77 28	94.61 186	82.10 91	75.21 211	88.48 221

MDA-MVSNet-bldmvs		61.54 318	57.70 322	73.05 316	79.53 311	57.00 309	83.08 303	81.23 336	57.57 322	34.91 364	72.45 335	32.79 329	86.26 335	35.81 351	41.95 356	75.89 353

OurMVSNet-221017-0		64.68 305	62.17 309	72.21 324	76.08 342	47.35 353	80.67 317	81.02 337	56.19 331	51.60 333	79.66 297	27.05 348	88.56 315	53.60 290	53.63 338	80.71 330

ACMH+	65.35 16	67.65 291	64.55 293	76.96 291	84.59 257	57.10 308	88.08 262	80.79 338	58.59 321	53.00 328	81.09 279	26.63 349	92.95 241	46.51 315	61.69 308	80.82 328

CNLPA		74.31 239	72.30 245	80.32 235	91.49 126	61.66 239	90.85 202	80.72 339	56.67 330	63.85 277	90.64 156	46.75 262	90.84 294	53.79 288	75.99 209	88.47 223

mvsmamba		76.85 205	75.71 203	80.25 239	83.07 280	59.16 281	91.44 174	80.64 340	76.84 93	67.95 236	86.33 214	46.17 271	94.24 205	76.06 137	72.92 225	87.36 238

LS3D		69.17 277	66.40 280	77.50 280	91.92 112	56.12 313	85.12 286	80.37 341	46.96 353	56.50 319	87.51 203	37.25 311	93.71 227	32.52 362	79.40 176	82.68 313

testgi		64.48 307	62.87 305	69.31 333	71.24 352	40.62 364	85.49 285	79.92 342	65.36 272	54.18 324	83.49 245	23.74 354	84.55 339	41.60 335	60.79 314	82.77 308

test_0402		64.54 306	61.09 312	74.92 303	84.10 267	60.75 256	87.95 264	79.71 343	52.03 340	52.41 330	77.20 313	32.21 333	91.64 284	23.14 366	61.03 311	72.36 358

SixPastTwentyTwo		64.92 304	61.78 311	74.34 308	78.74 323	49.76 343	83.42 299	79.51 344	62.86 289	50.27 339	77.35 310	30.92 339	90.49 297	45.89 319	47.06 349	82.78 307

ITE_SJBPF				70.43 331	74.44 345	47.06 354		77.32 345	60.16 311	54.04 325	83.53 243	23.30 355	84.01 342	43.07 328	61.58 309	80.21 337

K. test v3		63.09 313	59.61 317	73.53 313	76.26 340	49.38 346	83.27 300	77.15 346	64.35 277	47.77 347	72.32 338	28.73 343	87.79 324	49.93 300	36.69 362	83.41 301

DP-MVS		69.90 273	66.48 279	80.14 241	95.36 30	62.93 215	89.56 236	76.11 347	50.27 346	57.69 315	85.23 224	39.68 293	95.73 143	33.35 357	71.05 240	81.78 323

RPSCF		64.24 308	61.98 310	71.01 330	76.10 341	45.00 357	75.83 339	75.94 348	46.94 354	58.96 306	84.59 233	31.40 336	82.00 357	47.76 312	60.33 319	86.04 265

TinyColmap		60.32 320	56.42 326	72.00 327	78.78 322	53.18 327	78.36 333	75.64 349	52.30 339	41.59 360	75.82 326	14.76 366	88.35 318	35.84 350	54.71 336	74.46 355

ADS-MVSNet2		66.90 296	63.44 301	77.26 286	88.06 199	60.70 258	68.01 353	75.56 350	57.57 322	64.48 270	69.87 345	38.68 296	84.10 340	40.87 338	67.89 260	86.97 244

COLMAP_ROB	57.96 20	62.98 314	59.65 316	72.98 317	81.44 292	53.00 328	83.75 294	75.53 351	48.34 351	48.81 344	81.40 271	24.14 352	90.30 298	32.95 359	60.52 316	75.65 354
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-test		65.86 301	60.94 313	80.62 232	83.75 270	58.83 285	58.91 363	75.26 352	44.50 359	50.95 338	77.09 315	58.81 145	87.90 322	35.13 353	64.03 287	95.12 73

MVS-HIRNet		60.25 321	55.55 327	74.35 307	84.37 262	56.57 311	71.64 344	74.11 353	34.44 364	45.54 353	42.24 367	31.11 338	89.81 307	40.36 341	76.10 208	76.67 352

bld_raw_dy_0_64		71.59 262	69.71 266	77.22 287	77.82 333	58.12 293	87.71 269	73.66 354	68.01 250	61.90 293	84.29 237	33.68 326	88.43 317	69.91 187	70.43 242	85.11 284

pmmvs3		55.51 326	51.50 331	67.53 338	57.90 370	50.93 339	80.37 319	73.66 354	40.63 362	44.15 357	64.75 354	16.30 363	78.97 361	44.77 325	40.98 359	72.69 356

TDRefinement		55.28 327	51.58 330	66.39 341	59.53 369	46.15 356	76.23 338	72.80 356	44.60 358	42.49 359	76.28 322	15.29 364	82.39 354	33.20 358	43.75 354	70.62 360

Gipuma		34.91 337	31.44 340	45.30 352	70.99 354	39.64 368	19.85 372	72.56 357	20.10 370	16.16 372	21.47 373	5.08 377	71.16 365	13.07 371	43.70 355	25.08 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		45.64 331	43.10 334	53.23 349	51.42 373	36.46 370	64.97 357	71.91 358	29.13 366	27.53 366	61.55 358	9.83 371	65.01 370	16.00 370	55.58 332	58.22 365

ANet_high		40.27 334	35.20 337	55.47 345	34.74 379	34.47 371	63.84 359	71.56 359	48.42 350	18.80 370	41.08 368	9.52 372	64.45 371	20.18 367	8.66 375	67.49 362

Patchmatch-RL test		68.17 287	64.49 295	79.19 261	71.22 353	53.93 324	70.07 348	71.54 360	69.22 238	56.79 318	62.89 355	56.58 171	88.61 313	69.53 191	52.61 339	95.03 79

LCM-MVSNet-Re		72.93 251	71.84 249	76.18 297	88.49 184	48.02 349	80.07 325	70.17 361	73.96 133	52.25 331	80.09 293	49.98 235	88.24 319	67.35 209	84.23 151	92.28 169

LCM-MVSNet		40.54 333	35.79 336	54.76 348	36.92 378	30.81 372	51.41 365	69.02 362	22.07 368	24.63 367	45.37 365	4.56 378	65.81 368	33.67 356	34.50 365	67.67 361

AllTest		61.66 316	58.06 320	72.46 321	79.57 309	51.42 336	80.17 323	68.61 363	51.25 342	45.88 349	81.23 273	19.86 361	86.58 333	38.98 344	57.01 328	79.39 340

TestCases				72.46 321	79.57 309	51.42 336		68.61 363	51.25 342	45.88 349	81.23 273	19.86 361	86.58 333	38.98 344	57.01 328	79.39 340

LF4IMVS		54.01 328	52.12 329	59.69 343	62.41 367	39.91 367	68.59 351	68.28 365	42.96 360	44.55 356	75.18 327	14.09 368	68.39 366	41.36 337	51.68 341	70.78 359

door								66.57 366

door-mid								66.01 367

DSMNet-mixed		56.78 325	54.44 328	63.79 342	63.21 365	29.44 373	64.43 358	64.10 368	42.12 361	51.32 335	71.60 341	31.76 334	75.04 362	36.23 349	65.20 276	86.87 247

PM-MVS		59.40 323	56.59 324	67.84 335	63.63 364	41.86 361	76.76 337	63.22 369	59.01 318	51.07 337	72.27 339	11.72 369	83.25 349	61.34 258	50.28 345	78.39 348

new_pmnet		49.31 330	46.44 333	57.93 344	62.84 366	40.74 363	68.47 352	62.96 370	36.48 363	35.09 363	57.81 359	14.97 365	72.18 364	32.86 360	46.44 350	60.88 364

lessismore_v0				73.72 312	72.93 350	47.83 351		61.72 371		45.86 351	73.76 331	28.63 345	89.81 307	47.75 313	31.37 367	83.53 297

EGC-MVSNET		42.35 332	38.09 335	55.11 346	74.57 344	46.62 355	71.63 345	55.77 372	0.04 377	0.24 378	62.70 356	14.24 367	74.91 363	17.59 369	46.06 351	43.80 366

test_method		38.59 335	35.16 338	48.89 351	54.33 371	21.35 378	45.32 368	53.71 373	7.41 374	28.74 365	51.62 361	8.70 373	52.87 372	33.73 355	32.89 366	72.47 357

PMMVS2		37.93 336	33.61 339	50.92 350	46.31 375	24.76 376	60.55 362	50.05 374	28.94 367	20.93 368	47.59 362	4.41 379	65.13 369	25.14 365	18.55 370	62.87 363

PMVS	26.43 22	31.84 338	28.16 341	42.89 353	25.87 381	27.58 374	50.92 366	49.78 375	21.37 369	14.17 373	40.81 369	2.01 380	66.62 367	9.61 373	38.88 361	34.49 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 339	24.00 343	26.45 356	43.74 376	18.44 380	60.86 360	39.66 376	15.11 371	9.53 375	22.10 372	6.52 375	46.94 374	8.31 374	10.14 372	13.98 372

tmp_tt		22.26 342	23.75 344	17.80 358	5.23 382	12.06 382	35.26 369	39.48 377	2.82 376	18.94 369	44.20 366	22.23 357	24.64 377	36.30 348	9.31 374	16.69 371

MVE	24.84 23	24.35 340	19.77 346	38.09 354	34.56 380	26.92 375	26.57 370	38.87 378	11.73 373	11.37 374	27.44 370	1.37 381	50.42 373	11.41 372	14.60 371	36.93 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 341	23.20 345	25.46 357	41.52 377	16.90 381	60.56 361	38.79 379	14.62 372	8.99 376	20.24 375	7.35 374	45.82 375	7.25 375	9.46 373	13.64 373

MTMP							93.77 84	32.52 380

DeepMVS_CX				34.71 355	51.45 372	24.73 377		28.48 381	31.46 365	17.49 371	52.75 360	5.80 376	42.60 376	18.18 368	19.42 369	36.81 368

N_pmnet		50.55 329	49.11 332	54.88 347	77.17 336	4.02 383	84.36 290	2.00 382	48.59 349	45.86 351	68.82 347	32.22 332	82.80 352	31.58 364	51.38 342	77.81 349

wuyk23d		11.30 344	10.95 347	12.33 359	48.05 374	19.89 379	25.89 371	1.92 383	3.58 375	3.12 377	1.37 377	0.64 382	15.77 378	6.23 376	7.77 376	1.35 374

testmvs		7.23 346	9.62 349	0.06 361	0.04 383	0.02 385	84.98 288	0.02 384	0.03 378	0.18 379	1.21 378	0.01 384	0.02 379	0.14 377	0.01 377	0.13 376

test123		6.92 347	9.21 350	0.08 360	0.03 384	0.05 384	81.65 311	0.01 385	0.02 379	0.14 380	0.85 379	0.03 383	0.02 379	0.12 378	0.00 378	0.16 375

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	0.00 378	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	0.00 378	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	0.00 378	0.00 377

pcd_1.5k_mvsjas		4.46 348	5.95 351	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	53.55 205	0.00 381	0.00 379	0.00 378	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	0.00 378	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	0.00 378	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	0.00 378	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	0.00 378	0.00 377

n2								0.00 386

nn								0.00 386

ab-mvs-re		7.91 345	10.55 348	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	94.95 64	0.00 385	0.00 381	0.00 379	0.00 378	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	0.00 378	0.00 377

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

eth-test2					0.00 385

eth-test					0.00 385

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

test_0728_THIRD									72.48 165	90.55 16	96.93 10	76.24 11	99.08 11	91.53 16	94.99 17	96.43 26

GSMVS																94.68 89

test_part2					96.29 21	68.16 78				90.78 13

sam_mvs1												57.85 151				94.68 89

sam_mvs												54.91 190

test_post1							78.95 328				20.70 374	53.05 210	91.50 291	60.43 263

test_post											23.01 371	56.49 172	92.67 256

patchmatchnet-post											67.62 349	57.62 154	90.25 299

gm-plane-assit					88.42 188	67.04 110			78.62 68		91.83 141		97.37 72	76.57 134

test9_res														89.41 26	94.96 18	95.29 62

agg_prior2														86.41 57	94.75 30	95.33 58

test_prior4						67.18 106	93.92 75

test_prior2							95.10 37		75.40 110	85.25 54	95.61 40	67.94 45		87.47 46	94.77 25

旧先验2							92.00 153		59.37 317	87.54 29			93.47 233	75.39 140

新几何2							91.41 176

原ACMM2							92.01 150

testdata2													96.09 128	61.26 259

segment_acmp												65.94 64

testdata1							89.21 246		77.55 85

plane_prior7					86.94 220	61.51 241

plane_prior6					87.23 215	62.32 227						50.66 229

plane_prior4											89.14 179

plane_prior3						61.95 234			79.09 59	72.53 180

plane_prior2							93.13 103		78.81 65

plane_prior1					87.15 217

plane_prior						62.42 224	93.85 79		79.38 51						78.80 183

HQP5-MVS						63.66 201

HQP-NCC					87.54 209		94.06 64		79.80 45	74.18 160

ACMP_Plane					87.54 209		94.06 64		79.80 45	74.18 160

BP-MVS														77.63 129

HQP4-MVS										74.18 160			95.61 151			88.63 218

HQP2-MVS												51.63 222

NP-MVS					87.41 212	63.04 211					90.30 165

MDTV_nov1_ep13_2view						59.90 270	80.13 324		67.65 255	72.79 175		54.33 198		59.83 267		92.58 161

ACMMP++_ref															71.63 234

ACMMP++															69.72 245

Test By Simon												54.21 199

High-resolution multi-view benchmark