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.

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 124	86.57 1	87.39 41	94.97 18	71.70 53	97.68 1	92.19 1	95.63 28	95.57 1

UA-Net		85.08 68	84.96 69	85.45 77	92.07 73	68.07 133	89.78 82	90.86 134	82.48 2	84.60 75	93.20 69	69.35 81	95.22 80	71.39 182	90.88 98	93.07 102

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 172	82.14 3	86.65 49	94.28 34	68.28 95	97.46 6	90.81 2	95.31 34	95.15 7

CANet		86.45 42	86.10 49	87.51 37	90.09 107	70.94 70	89.70 85	92.59 73	81.78 4	81.32 120	91.43 112	70.34 70	97.23 14	84.26 57	93.36 68	94.37 41

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 55	93.47 63	73.02 40	97.00 18	84.90 46	94.94 40	94.10 51

EPNet		83.72 83	82.92 95	86.14 64	84.22 275	69.48 94	91.05 56	85.27 267	81.30 6	76.83 197	91.65 102	66.09 118	95.56 63	76.00 138	93.85 62	93.38 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 29	94.06 45	76.43 16	96.84 21	88.48 24	95.99 18	94.34 43

3Dnovator+	77.84 4	85.48 60	84.47 76	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 202	93.37 65	60.40 196	96.75 26	77.20 124	93.73 64	95.29 5

TranMVSNet+NR-MVSNet		80.84 135	80.31 134	82.42 193	87.85 197	62.33 252	87.74 157	91.33 119	80.55 9	77.99 173	89.86 147	65.23 127	92.62 191	67.05 227	75.24 316	92.30 131

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 40	78.35 13	96.77 24	89.59 8	94.22 60	94.67 27
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
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 33	94.27 35	75.89 19	96.81 23	87.45 32	96.44 9	93.05 105

UniMVSNet_NR-MVSNet		81.88 115	81.54 115	82.92 177	88.46 171	63.46 232	87.13 173	92.37 80	80.19 12	78.38 162	89.14 167	71.66 55	93.05 181	70.05 195	76.46 289	92.25 133

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 28	94.80 19	73.76 33	97.11 15	87.51 31	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 76	83.81 80	85.31 80	88.18 180	67.85 137	87.66 158	89.73 167	80.05 14	82.95 100	89.59 156	70.74 67	94.82 100	80.66 97	84.72 180	93.28 92

ETV-MVS		84.90 72	84.67 72	85.59 74	89.39 133	68.66 120	88.74 121	92.64 71	79.97 15	84.10 84	85.71 260	69.32 82	95.38 75	80.82 94	91.37 92	92.72 114

EI-MVSNet-UG-set		83.81 80	83.38 86	85.09 87	87.87 196	67.53 147	87.44 166	89.66 168	79.74 16	82.23 109	89.41 165	70.24 73	94.74 103	79.95 101	83.92 194	92.99 110

CS-MVS		86.69 39	86.95 35	85.90 70	90.76 96	67.57 146	92.83 17	93.30 32	79.67 17	84.57 76	92.27 89	71.47 56	95.02 92	84.24 59	93.46 67	95.13 8

casdiffmvs_mvg		85.99 48	86.09 50	85.70 73	87.65 208	67.22 158	88.69 123	93.04 41	79.64 18	85.33 59	92.54 86	73.30 35	94.50 111	83.49 65	91.14 95	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 31	87.00 32	87.90 22	94.18 35	74.25 5	86.58 192	92.02 92	79.45 19	85.88 53	94.80 19	68.07 96	96.21 45	86.69 36	95.34 32	93.23 93

EC-MVSNet		86.01 47	86.38 42	84.91 95	89.31 138	66.27 172	92.32 30	93.63 21	79.37 20	84.17 83	91.88 97	69.04 88	95.43 70	83.93 63	93.77 63	93.01 108

XVS		87.18 32	86.91 37	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 93	94.17 39	67.45 103	96.60 33	83.06 69	94.50 51	94.07 53

X-MVStestdata		80.37 153	77.83 189	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 93	12.47 419	67.45 103	96.60 33	83.06 69	94.50 51	94.07 53

HQP_MVS		83.64 85	83.14 89	85.14 84	90.08 108	68.71 116	91.25 52	92.44 76	79.12 23	78.92 150	91.00 129	60.42 194	95.38 75	78.71 109	86.32 161	91.33 159

plane_prior2							91.25 52		79.12 23

IS-MVSNet		83.15 96	82.81 96	84.18 122	89.94 115	63.30 236	91.59 43	88.46 212	79.04 25	79.49 142	92.16 91	65.10 128	94.28 116	67.71 218	91.86 86	94.95 11

DU-MVS		81.12 131	80.52 130	82.90 178	87.80 200	63.46 232	87.02 177	91.87 102	79.01 26	78.38 162	89.07 169	65.02 129	93.05 181	70.05 195	76.46 289	92.20 136

NR-MVSNet		80.23 155	79.38 152	82.78 186	87.80 200	63.34 235	86.31 200	91.09 128	79.01 26	72.17 287	89.07 169	67.20 106	92.81 190	66.08 234	75.65 302	92.20 136

SPE-MVS-test		86.29 46	86.48 41	85.71 72	91.02 88	67.21 159	92.36 29	93.78 18	78.97 28	83.51 96	91.20 119	70.65 69	95.15 83	81.96 83	94.89 42	94.77 24

DELS-MVS		85.41 63	85.30 65	85.77 71	88.49 169	67.93 136	85.52 225	93.44 27	78.70 29	83.63 95	89.03 171	74.57 24	95.71 61	80.26 100	94.04 61	93.66 71
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
WR-MVS		79.49 168	79.22 159	80.27 241	88.79 159	58.35 295	85.06 231	88.61 210	78.56 30	77.65 178	88.34 190	63.81 139	90.66 261	64.98 243	77.22 278	91.80 147

plane_prior3						68.60 121			78.44 31	78.92 150

UniMVSNet (Re)		81.60 123	81.11 120	83.09 168	88.38 175	64.41 213	87.60 159	93.02 45	78.42 32	78.56 158	88.16 196	69.78 77	93.26 164	69.58 202	76.49 288	91.60 149

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 11

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 9	96.57 7	94.67 27

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 49	74.83 23	93.78 140	87.63 30	94.27 59	93.65 75
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
casdiffmvs		85.11 67	85.14 67	85.01 89	87.20 222	65.77 184	87.75 156	92.83 60	77.84 37	84.36 80	92.38 88	72.15 46	93.93 133	81.27 90	90.48 102	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BP-MVS1		84.32 75	83.71 81	86.17 61	87.84 198	67.85 137	89.38 98	89.64 170	77.73 38	83.98 87	92.12 93	56.89 220	95.43 70	84.03 62	91.75 87	95.24 6

CP-MVSNet		78.22 200	78.34 176	77.84 286	87.83 199	54.54 352	87.94 150	91.17 124	77.65 39	73.48 269	88.49 186	62.24 161	88.43 299	62.19 266	74.07 325	90.55 187

plane_prior						68.71 116	90.38 70		77.62 40						86.16 165

baseline		84.93 70	84.98 68	84.80 99	87.30 220	65.39 192	87.30 170	92.88 57	77.62 40	84.04 86	92.26 90	71.81 50	93.96 127	81.31 88	90.30 105	95.03 10

VDD-MVS		83.01 101	82.36 102	84.96 91	91.02 88	66.40 169	88.91 113	88.11 215	77.57 42	84.39 79	93.29 67	52.19 256	93.91 134	77.05 127	88.70 131	94.57 34

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 69	77.57 42	83.84 90	94.40 32	72.24 45	96.28 43	85.65 41	95.30 35	93.62 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 215	77.69 197	77.84 286	87.07 225	53.91 357	87.91 152	91.18 123	77.56 44	73.14 273	88.82 176	61.23 179	89.17 285	59.95 285	72.37 340	90.43 192

OPM-MVS		83.50 89	82.95 94	85.14 84	88.79 159	70.95 69	89.13 108	91.52 113	77.55 45	80.96 127	91.75 99	60.71 187	94.50 111	79.67 104	86.51 159	89.97 218
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 46	89.79 18	94.12 42	78.98 12	96.58 35	85.66 40	95.72 24	94.58 32

PS-CasMVS		78.01 209	78.09 182	77.77 288	87.71 205	54.39 354	88.02 146	91.22 121	77.50 47	73.26 271	88.64 181	60.73 186	88.41 300	61.88 270	73.88 329	90.53 188

MSLP-MVS++		85.43 62	85.76 56	84.45 108	91.93 75	70.24 79	90.71 59	92.86 58	77.46 48	84.22 81	92.81 81	67.16 107	92.94 185	80.36 98	94.35 57	90.16 202

RRT-MVS		82.60 107	82.10 106	84.10 124	87.98 192	62.94 247	87.45 165	91.27 120	77.42 49	79.85 137	90.28 139	56.62 222	94.70 106	79.87 103	88.15 139	94.67 27

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 50	92.12 9	95.78 4	80.98 9	97.40 9	89.08 12	96.41 12	93.33 90
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 50	92.81 3	95.79 3	80.98 9

balanced_conf03		86.78 37	86.99 33	86.15 62	91.24 83	67.61 144	90.51 62	92.90 56	77.26 52	87.44 40	91.63 104	71.27 60	96.06 49	85.62 42	95.01 37	94.78 23

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 53	93.10 1	95.72 8	82.99 1	97.44 7	89.07 14	96.63 4	94.88 15

test_241102_TWO								94.06 10	77.24 53	92.78 4	95.72 8	81.26 8	97.44 7	89.07 14	96.58 6	94.26 47

3Dnovator	76.31 5	83.38 93	82.31 103	86.59 55	87.94 193	72.94 28	90.64 60	92.14 91	77.21 55	75.47 227	92.83 79	58.56 203	94.72 104	73.24 167	92.71 74	92.13 140

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 56	93.10 1	95.39 14	82.99 1	97.27 12

WR-MVS_H		78.51 195	78.49 171	78.56 273	88.02 189	56.38 329	88.43 130	92.67 66	77.14 57	73.89 264	87.55 211	66.25 116	89.24 284	58.92 296	73.55 332	90.06 212

DeepC-MVS	79.81 2	87.08 35	86.88 38	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 58	82.82 104	94.23 38	72.13 47	97.09 16	84.83 49	95.37 31	93.65 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 124	82.02 109	80.03 245	88.42 174	55.97 335	87.95 149	93.42 29	77.10 59	77.38 183	90.98 131	69.96 75	91.79 223	68.46 214	84.50 183	92.33 129

DTE-MVSNet		76.99 229	76.80 215	77.54 294	86.24 237	53.06 366	87.52 161	90.66 137	77.08 60	72.50 281	88.67 180	60.48 193	89.52 278	57.33 313	70.74 352	90.05 213

LFMVS		81.82 117	81.23 118	83.57 150	91.89 76	63.43 234	89.84 78	81.85 319	77.04 61	83.21 97	93.10 70	52.26 255	93.43 159	71.98 177	89.95 113	93.85 63

UGNet		80.83 136	79.59 148	84.54 104	88.04 188	68.09 132	89.42 95	88.16 214	76.95 62	76.22 213	89.46 161	49.30 297	93.94 130	68.48 213	90.31 104	91.60 149
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
FIs		82.07 112	82.42 99	81.04 224	88.80 158	58.34 296	88.26 139	93.49 26	76.93 63	78.47 161	91.04 125	69.92 76	92.34 205	69.87 199	84.97 177	92.44 128

GST-MVS		87.42 27	87.26 28	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 64	84.68 69	93.99 51	70.67 68	96.82 22	84.18 61	95.01 37	93.90 61

mPP-MVS		86.67 41	86.32 43	87.72 30	94.41 22	73.55 13	92.74 20	92.22 87	76.87 65	82.81 105	94.25 37	66.44 113	96.24 44	82.88 74	94.28 58	93.38 87

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 66	85.24 60	94.32 33	71.76 51	96.93 19	85.53 43	95.79 22	94.32 44

VPNet		78.69 191	78.66 168	78.76 268	88.31 177	55.72 339	84.45 248	86.63 250	76.79 67	78.26 165	90.55 136	59.30 199	89.70 276	66.63 229	77.05 280	90.88 174

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 68	84.91 65	94.44 30	70.78 66	96.61 32	84.53 54	94.89 42	93.66 71

ACMMPR		87.44 25	87.23 30	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 68	84.66 72	94.52 23	68.81 90	96.65 30	84.53 54	94.90 41	94.00 56

ACMMP		85.89 54	85.39 61	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 68	80.73 129	93.82 56	64.33 133	96.29 42	82.67 80	90.69 100	93.23 93
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
region2R		87.42 27	87.20 31	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 71	84.45 77	94.52 23	69.09 84	96.70 27	84.37 56	94.83 45	94.03 55

sasdasda		85.91 52	85.87 54	86.04 66	89.84 117	69.44 98	90.45 68	93.00 46	76.70 72	88.01 31	91.23 116	73.28 36	93.91 134	81.50 86	88.80 127	94.77 24

canonicalmvs		85.91 52	85.87 54	86.04 66	89.84 117	69.44 98	90.45 68	93.00 46	76.70 72	88.01 31	91.23 116	73.28 36	93.91 134	81.50 86	88.80 127	94.77 24

CP-MVS		87.11 33	86.92 36	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 74	83.68 92	94.46 27	67.93 98	95.95 57	84.20 60	94.39 55	93.23 93

DeepC-MVS_fast	79.65 3	86.91 36	86.62 40	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 74	84.22 81	93.36 66	71.44 57	96.76 25	80.82 94	95.33 33	94.16 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		85.06 69	85.51 59	83.70 146	89.42 130	63.01 242	89.43 93	92.62 72	76.43 76	87.53 38	91.34 114	72.82 42	93.42 160	81.28 89	88.74 130	94.66 30

TSAR-MVS + GP.		85.71 57	85.33 63	86.84 50	91.34 81	72.50 36	89.07 109	87.28 236	76.41 77	85.80 54	90.22 143	74.15 31	95.37 78	81.82 84	91.88 83	92.65 119

HQP-NCC					89.33 135		89.17 103		76.41 77	77.23 188

ACMP_Plane					89.33 135		89.17 103		76.41 77	77.23 188

HQP-MVS		82.61 105	82.02 109	84.37 110	89.33 135	66.98 162	89.17 103	92.19 89	76.41 77	77.23 188	90.23 142	60.17 197	95.11 86	77.47 121	85.99 169	91.03 169

CANet_DTU		80.61 144	79.87 142	82.83 180	85.60 248	63.17 241	87.36 167	88.65 208	76.37 81	75.88 220	88.44 188	53.51 245	93.07 180	73.30 165	89.74 116	92.25 133

VNet		82.21 109	82.41 100	81.62 206	90.82 93	60.93 269	84.47 245	89.78 164	76.36 82	84.07 85	91.88 97	64.71 132	90.26 264	70.68 189	88.89 125	93.66 71

Vis-MVSNet		83.46 90	82.80 97	85.43 78	90.25 104	68.74 114	90.30 72	90.13 156	76.33 83	80.87 128	92.89 77	61.00 184	94.20 121	72.45 176	90.97 96	93.35 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 84	88.14 27	95.09 17	71.06 63	96.67 29	87.67 29	96.37 14	94.09 52

alignmvs		85.48 60	85.32 64	85.96 69	89.51 126	69.47 95	89.74 83	92.47 75	76.17 85	87.73 37	91.46 111	70.32 71	93.78 140	81.51 85	88.95 124	94.63 31

MVS_111021_HR		85.14 66	84.75 71	86.32 58	91.65 79	72.70 30	85.98 208	90.33 149	76.11 86	82.08 110	91.61 106	71.36 59	94.17 123	81.02 91	92.58 75	92.08 141

HPM-MVS		87.11 33	86.98 34	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 87	83.81 91	93.95 54	69.77 78	96.01 53	85.15 44	94.66 47	94.32 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 96	82.19 104	86.02 68	90.56 98	70.85 73	88.15 144	89.16 187	76.02 88	84.67 70	91.39 113	61.54 170	95.50 66	82.71 77	75.48 306	91.72 148

hse-mvs2		81.72 118	80.94 124	84.07 130	88.72 162	67.68 142	85.87 212	87.26 237	76.02 88	84.67 70	88.22 195	61.54 170	93.48 155	82.71 77	73.44 334	91.06 167

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 90	92.29 7	95.66 10	81.67 6	97.38 11	87.44 33	96.34 15	93.95 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CLD-MVS		82.31 108	81.65 114	84.29 115	88.47 170	67.73 141	85.81 216	92.35 81	75.78 91	78.33 164	86.58 242	64.01 136	94.35 114	76.05 137	87.48 145	90.79 176
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 92	89.16 19	95.10 16	75.65 21	96.19 46	87.07 34	96.01 17	94.79 22

testdata1							84.14 256		75.71 92

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 94	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 37
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 145	80.55 129	80.76 231	88.07 187	60.80 272	86.86 182	91.58 112	75.67 95	80.24 133	89.45 163	63.34 140	90.25 265	70.51 191	79.22 259	91.23 162

PGM-MVS		86.68 40	86.27 44	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 96	83.86 89	94.42 31	67.87 100	96.64 31	82.70 79	94.57 50	93.66 71

Effi-MVS+		83.62 87	83.08 90	85.24 82	88.38 175	67.45 148	88.89 114	89.15 188	75.50 97	82.27 108	88.28 192	69.61 79	94.45 113	77.81 118	87.84 140	93.84 65

test_prior2							88.85 116		75.41 98	84.91 65	93.54 59	74.28 29		83.31 67	95.86 20

LPG-MVS_test		82.08 111	81.27 117	84.50 105	89.23 142	68.76 112	90.22 73	91.94 98	75.37 99	76.64 203	91.51 108	54.29 237	94.91 94	78.44 111	83.78 195	89.83 223

LGP-MVS_train				84.50 105	89.23 142	68.76 112		91.94 98	75.37 99	76.64 203	91.51 108	54.29 237	94.91 94	78.44 111	83.78 195	89.83 223

MG-MVS		83.41 91	83.45 84	83.28 158	92.74 65	62.28 254	88.17 142	89.50 174	75.22 101	81.49 119	92.74 85	66.75 108	95.11 86	72.85 170	91.58 89	92.45 127

LCM-MVSNet-Re		77.05 228	76.94 212	77.36 295	87.20 222	51.60 374	80.06 315	80.46 334	75.20 102	67.69 331	86.72 232	62.48 155	88.98 289	63.44 253	89.25 120	91.51 153

SDMVSNet		80.38 151	80.18 137	80.99 225	89.03 151	64.94 201	80.45 311	89.40 176	75.19 103	76.61 205	89.98 145	60.61 191	87.69 308	76.83 130	83.55 204	90.33 196

sd_testset		77.70 218	77.40 202	78.60 271	89.03 151	60.02 283	79.00 330	85.83 262	75.19 103	76.61 205	89.98 145	54.81 229	85.46 329	62.63 262	83.55 204	90.33 196

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 105	86.34 51	95.29 15	70.86 65	96.00 54	88.78 19	96.04 16	94.58 32
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1111		79.43 171	79.18 160	80.15 243	89.99 113	53.31 363	87.33 169	77.05 363	75.04 106	80.23 134	92.77 84	48.97 302	92.33 206	68.87 209	92.40 79	94.81 21

Effi-MVS+-dtu		80.03 159	78.57 170	84.42 109	85.13 259	68.74 114	88.77 118	88.10 216	74.99 107	74.97 250	83.49 311	57.27 216	93.36 161	73.53 161	80.88 236	91.18 163

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 108	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 103

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 108	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 103

OMC-MVS		82.69 103	81.97 111	84.85 96	88.75 161	67.42 149	87.98 147	90.87 133	74.92 110	79.72 139	91.65 102	62.19 162	93.96 127	75.26 148	86.42 160	93.16 98

test2506		77.30 226	76.49 223	79.74 251	90.08 108	52.02 367	87.86 155	63.10 405	74.88 111	80.16 135	92.79 82	38.29 371	92.35 204	68.74 211	92.50 77	94.86 18

ECVR-MVS		79.61 164	79.26 157	80.67 233	90.08 108	54.69 350	87.89 153	77.44 359	74.88 111	80.27 132	92.79 82	48.96 303	92.45 198	68.55 212	92.50 77	94.86 18

MonoMVSNet		76.49 241	75.80 230	78.58 272	81.55 332	58.45 294	86.36 199	86.22 256	74.87 113	74.73 254	83.73 306	51.79 268	88.73 294	70.78 186	72.15 343	88.55 267

nrg030		83.88 79	83.53 83	84.96 91	86.77 230	69.28 102	90.46 67	92.67 66	74.79 114	82.95 100	91.33 115	72.70 43	93.09 179	80.79 96	79.28 258	92.50 124

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 115	92.29 7	95.97 2	74.28 29	97.24 13	88.58 21	96.91 1	94.87 17
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
reproduce_model		87.28 30	87.39 27	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 116	88.80 23	95.61 11	70.29 72	96.44 39	86.20 39	93.08 69	93.16 98

MVS_111021_LR		82.61 105	82.11 105	84.11 123	88.82 156	71.58 55	85.15 228	86.16 258	74.69 116	80.47 131	91.04 125	62.29 159	90.55 262	80.33 99	90.08 110	90.20 201

EIA-MVS		83.31 95	82.80 97	84.82 97	89.59 122	65.59 187	88.21 140	92.68 65	74.66 118	78.96 148	86.42 247	69.06 86	95.26 79	75.54 144	90.09 109	93.62 78

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 81	74.62 119	88.90 22	93.85 55	75.75 20	96.00 54	87.80 28	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 38	86.67 39	86.91 49	94.11 37	72.11 47	92.37 28	92.56 74	74.50 120	86.84 48	94.65 22	67.31 105	95.77 59	84.80 50	92.85 72	92.84 113

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 121	91.30 15

ACMP	74.13 6	81.51 126	80.57 128	84.36 111	89.42 130	68.69 119	89.97 77	91.50 117	74.46 122	75.04 249	90.41 138	53.82 242	94.54 108	77.56 120	82.91 213	89.86 222
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 92	83.02 92	84.57 103	90.13 106	64.47 211	92.32 30	90.73 136	74.45 123	79.35 144	91.10 122	69.05 87	95.12 84	72.78 171	87.22 148	94.13 50

save fliter					93.80 40	72.35 42	90.47 66	91.17 124	74.31 124

MVS_Test		83.15 96	83.06 91	83.41 155	86.86 226	63.21 238	86.11 206	92.00 94	74.31 124	82.87 102	89.44 164	70.03 74	93.21 168	77.39 123	88.50 135	93.81 66

UniMVSNet_ETH3D		79.10 181	78.24 179	81.70 205	86.85 227	60.24 281	87.28 171	88.79 201	74.25 126	76.84 196	90.53 137	49.48 293	91.56 232	67.98 216	82.15 222	93.29 91

IterMVS-LS		80.06 158	79.38 152	82.11 197	85.89 243	63.20 239	86.79 185	89.34 178	74.19 127	75.45 230	86.72 232	66.62 109	92.39 201	72.58 173	76.86 283	90.75 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 149	79.98 139	82.12 196	84.28 273	63.19 240	86.41 196	88.95 198	74.18 128	78.69 153	87.54 212	66.62 109	92.43 199	72.57 174	80.57 242	90.74 180

Vis-MVSNet (Re-imp)		78.36 198	78.45 172	78.07 284	88.64 165	51.78 373	86.70 189	79.63 344	74.14 129	75.11 246	90.83 132	61.29 178	89.75 274	58.10 306	91.60 88	92.69 117

v8		79.97 161	79.02 163	82.80 183	84.09 278	64.50 210	87.96 148	90.29 152	74.13 130	75.24 242	86.81 229	62.88 151	93.89 137	74.39 154	75.40 311	90.00 214

CSCG		86.41 45	86.19 46	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 131	83.16 99	91.07 124	75.94 18	95.19 81	79.94 102	94.38 56	93.55 82

thres100view900		76.50 238	75.55 237	79.33 259	89.52 125	56.99 318	85.83 215	83.23 297	73.94 132	76.32 211	87.12 224	51.89 265	91.95 217	48.33 362	83.75 198	89.07 240

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 133	88.57 25	94.67 21	75.57 22	95.79 58	86.77 35	95.76 23

HPM-MVS_fast		85.35 64	84.95 70	86.57 56	93.69 42	70.58 78	92.15 35	91.62 110	73.89 134	82.67 107	94.09 43	62.60 152	95.54 65	80.93 92	92.93 71	93.57 80

PAPM_NR		83.02 100	82.41 100	84.82 97	92.47 70	66.37 170	87.93 151	91.80 105	73.82 135	77.32 185	90.66 134	67.90 99	94.90 96	70.37 192	89.48 118	93.19 97

thres600view7		76.50 238	75.44 238	79.68 253	89.40 132	57.16 315	85.53 223	83.23 297	73.79 136	76.26 212	87.09 225	51.89 265	91.89 220	48.05 367	83.72 201	90.00 214

testing91		76.54 236	75.66 235	79.18 263	88.43 173	55.89 336	81.08 298	83.00 304	73.76 137	75.34 235	84.29 293	46.20 322	90.07 268	64.33 247	84.50 183	91.58 151

v7n		78.97 185	77.58 200	83.14 166	83.45 293	65.51 188	88.32 137	91.21 122	73.69 138	72.41 283	86.32 250	57.93 207	93.81 139	69.18 205	75.65 302	90.11 206

dcpmvs_2		85.63 58	86.15 48	84.06 132	91.71 78	64.94 201	86.47 195	91.87 102	73.63 139	86.60 50	93.02 75	76.57 15	91.87 222	83.36 66	92.15 80	95.35 3

v2v482		80.23 155	79.29 156	83.05 171	83.62 289	64.14 217	87.04 176	89.97 160	73.61 140	78.18 168	87.22 220	61.10 182	93.82 138	76.11 135	76.78 286	91.18 163

Baseline_NR-MVSNet		78.15 204	78.33 177	77.61 291	85.79 244	56.21 333	86.78 186	85.76 263	73.60 141	77.93 174	87.57 209	65.02 129	88.99 288	67.14 226	75.33 313	87.63 283

BH-RMVSNet		79.61 164	78.44 173	83.14 166	89.38 134	65.93 178	84.95 234	87.15 240	73.56 142	78.19 167	89.79 149	56.67 221	93.36 161	59.53 290	86.74 155	90.13 204

APD-MVS_3200maxsize		85.97 50	85.88 53	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 143	85.94 52	94.51 26	65.80 123	95.61 62	83.04 71	92.51 76	93.53 84

SR-MVS-dyc-post		85.77 55	85.61 58	86.23 59	93.06 58	70.63 76	91.88 38	92.27 83	73.53 144	85.69 56	94.45 28	65.00 131	95.56 63	82.75 75	91.87 84	92.50 124

RE-MVS-def			85.48 60		93.06 58	70.63 76	91.88 38	92.27 83	73.53 144	85.69 56	94.45 28	63.87 137		82.75 75	91.87 84	92.50 124

reproduce_monomvs		75.40 259	74.38 255	78.46 278	83.92 283	57.80 307	83.78 260	86.94 244	73.47 146	72.25 286	84.47 287	38.74 367	89.27 283	75.32 147	70.53 353	88.31 271

test_fmvsmconf_n		85.92 51	86.04 51	85.57 75	85.03 261	69.51 93	89.62 89	90.58 139	73.42 147	87.75 35	94.02 47	72.85 41	93.24 165	90.37 3	90.75 99	93.96 57

tfpn200view9		76.42 242	75.37 242	79.55 258	89.13 146	57.65 309	85.17 226	83.60 289	73.41 148	76.45 207	86.39 248	52.12 257	91.95 217	48.33 362	83.75 198	89.07 240

thres400		76.50 238	75.37 242	79.86 248	89.13 146	57.65 309	85.17 226	83.60 289	73.41 148	76.45 207	86.39 248	52.12 257	91.95 217	48.33 362	83.75 198	90.00 214

test_fmvsmconf0.1_n		85.61 59	85.65 57	85.50 76	82.99 308	69.39 100	89.65 86	90.29 152	73.31 150	87.77 34	94.15 41	71.72 52	93.23 166	90.31 4	90.67 101	93.89 62

testing99		76.09 248	75.12 246	79.00 264	88.16 181	55.50 342	80.79 302	81.40 323	73.30 151	75.17 243	84.27 295	44.48 336	90.02 269	64.28 248	84.22 192	91.48 156

v148		78.72 190	77.80 191	81.47 210	82.73 313	61.96 258	86.30 201	88.08 217	73.26 152	76.18 215	85.47 268	62.46 156	92.36 203	71.92 178	73.82 330	90.09 208

FA-MVS(test-final)		80.96 133	79.91 141	84.10 124	88.30 178	65.01 199	84.55 244	90.01 159	73.25 153	79.61 140	87.57 209	58.35 205	94.72 104	71.29 183	86.25 163	92.56 121

test_fmvsmconf0.01_n		84.73 73	84.52 75	85.34 79	80.25 349	69.03 103	89.47 91	89.65 169	73.24 154	86.98 46	94.27 35	66.62 109	93.23 166	90.26 5	89.95 113	93.78 68

v10		79.74 163	78.67 167	82.97 176	84.06 279	64.95 200	87.88 154	90.62 138	73.11 155	75.11 246	86.56 243	61.46 173	94.05 126	73.68 159	75.55 304	89.90 220

MCST-MVS		87.37 29	87.25 29	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 156	84.86 68	92.89 77	76.22 17	96.33 41	84.89 48	95.13 36	94.40 40

baseline1		76.98 230	76.75 219	77.66 289	88.13 183	55.66 340	85.12 229	81.89 317	73.04 157	76.79 198	88.90 173	62.43 157	87.78 307	63.30 255	71.18 350	89.55 232

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 158	88.58 24	94.52 23	73.36 34	96.49 38	84.26 57	95.01 37	92.70 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 110	81.88 112	82.76 188	83.00 306	63.78 224	83.68 262	89.76 165	72.94 159	82.02 111	89.85 148	65.96 122	90.79 258	82.38 81	87.30 147	93.71 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 300	68.51 312	79.21 262	83.04 305	57.78 308	84.35 252	76.91 364	72.90 160	62.99 370	82.86 323	39.27 364	91.09 253	61.65 273	52.66 396	88.75 260

MVSMamba_PlusPlus		85.99 48	85.96 52	86.05 65	91.09 85	67.64 143	89.63 88	92.65 69	72.89 161	84.64 73	91.71 100	71.85 49	96.03 50	84.77 51	94.45 54	94.49 36

Fast-Effi-MVS+-dtu		78.02 208	76.49 223	82.62 190	83.16 302	66.96 164	86.94 179	87.45 234	72.45 162	71.49 295	84.17 297	54.79 233	91.58 230	67.61 219	80.31 245	89.30 238

PHI-MVS		86.43 43	86.17 47	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 162	85.22 61	91.90 96	69.47 80	96.42 40	83.28 68	95.94 19	94.35 42

thres200		75.55 254	74.47 253	78.82 267	87.78 203	57.85 305	83.07 277	83.51 292	72.44 164	75.84 221	84.42 288	52.08 260	91.75 225	47.41 369	83.64 203	86.86 304

test_yl		81.17 129	80.47 131	83.24 161	89.13 146	63.62 225	86.21 203	89.95 161	72.43 165	81.78 116	89.61 154	57.50 213	93.58 148	70.75 187	86.90 152	92.52 122

DCV-MVSNet		81.17 129	80.47 131	83.24 161	89.13 146	63.62 225	86.21 203	89.95 161	72.43 165	81.78 116	89.61 154	57.50 213	93.58 148	70.75 187	86.90 152	92.52 122

BH-untuned		79.47 169	78.60 169	82.05 198	89.19 144	65.91 179	86.07 207	88.52 211	72.18 167	75.42 231	87.69 206	61.15 181	93.54 152	60.38 282	86.83 154	86.70 308

TransMVSNet (Re)		75.39 260	74.56 251	77.86 285	85.50 250	57.10 317	86.78 186	86.09 260	72.17 168	71.53 294	87.34 215	63.01 150	89.31 282	56.84 318	61.83 379	87.17 295

GA-MVS		76.87 232	75.17 245	81.97 201	82.75 312	62.58 249	81.44 295	86.35 255	72.16 169	74.74 253	82.89 322	46.20 322	92.02 215	68.85 210	81.09 234	91.30 161

mmtdpeth		74.16 269	73.01 271	77.60 293	83.72 288	61.13 266	85.10 230	85.10 269	72.06 170	77.21 192	80.33 350	43.84 340	85.75 323	77.14 126	52.61 397	85.91 323

v1144		80.03 159	79.03 162	83.01 173	83.78 286	64.51 208	87.11 175	90.57 141	71.96 171	78.08 171	86.20 252	61.41 174	93.94 130	74.93 149	77.23 277	90.60 185

PS-MVSNAJss		82.07 112	81.31 116	84.34 113	86.51 235	67.27 155	89.27 101	91.51 114	71.75 172	79.37 143	90.22 143	63.15 146	94.27 117	77.69 119	82.36 221	91.49 155

EPNet_dtu		75.46 256	74.86 247	77.23 298	82.57 317	54.60 351	86.89 181	83.09 301	71.64 173	66.25 351	85.86 258	55.99 224	88.04 304	54.92 327	86.55 158	89.05 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 196	77.40 202	81.40 213	87.60 209	63.01 242	88.39 132	89.28 180	71.63 174	75.34 235	87.28 216	54.80 230	91.11 248	62.72 258	79.57 252	90.09 208

test1		78.40 196	77.40 202	81.40 213	87.60 209	63.01 242	88.39 132	89.28 180	71.63 174	75.34 235	87.28 216	54.80 230	91.11 248	62.72 258	79.57 252	90.09 208

FMVSNet2		78.20 202	77.21 206	81.20 219	87.60 209	62.89 248	87.47 163	89.02 193	71.63 174	75.29 241	87.28 216	54.80 230	91.10 251	62.38 263	79.38 256	89.61 230

patch_mono-2		83.65 84	84.54 73	80.99 225	90.06 112	65.83 181	84.21 254	88.74 206	71.60 177	85.01 62	92.44 87	74.51 25	83.50 344	82.15 82	92.15 80	93.64 77

V42		79.38 175	78.24 179	82.83 180	81.10 341	65.50 189	85.55 221	89.82 163	71.57 178	78.21 166	86.12 254	60.66 189	93.18 174	75.64 141	75.46 308	89.81 225

API-MVS		81.99 114	81.23 118	84.26 120	90.94 90	70.18 85	91.10 55	89.32 179	71.51 179	78.66 155	88.28 192	65.26 126	95.10 89	64.74 245	91.23 94	87.51 287

tttt0517		79.40 173	77.91 186	83.90 144	88.10 185	63.84 222	88.37 135	84.05 284	71.45 180	76.78 199	89.12 168	49.93 290	94.89 97	70.18 194	83.18 211	92.96 111

pm-mvs1		77.25 227	76.68 221	78.93 266	84.22 275	58.62 293	86.41 196	88.36 213	71.37 181	73.31 270	88.01 202	61.22 180	89.15 286	64.24 249	73.01 337	89.03 246

testing222		74.04 271	72.66 275	78.19 281	87.89 195	55.36 343	81.06 299	79.20 348	71.30 182	74.65 256	83.57 310	39.11 366	88.67 296	51.43 345	85.75 173	90.53 188

GeoE		81.71 119	81.01 123	83.80 145	89.51 126	64.45 212	88.97 111	88.73 207	71.27 183	78.63 156	89.76 150	66.32 115	93.20 171	69.89 198	86.02 168	93.74 69

tt0805		78.73 189	77.83 189	81.43 211	85.17 255	60.30 280	89.41 96	90.90 131	71.21 184	77.17 193	88.73 177	46.38 317	93.21 168	72.57 174	78.96 260	90.79 176

FMVSNet3		77.88 212	76.85 214	80.97 227	86.84 228	62.36 251	86.52 194	88.77 202	71.13 185	75.34 235	86.66 238	54.07 240	91.10 251	62.72 258	79.57 252	89.45 234

VDDNet		81.52 124	80.67 127	84.05 135	90.44 101	64.13 218	89.73 84	85.91 261	71.11 186	83.18 98	93.48 61	50.54 282	93.49 154	73.40 164	88.25 137	94.54 35

fmvsm_s_conf0.5_n		83.80 81	83.71 81	84.07 130	86.69 232	67.31 153	89.46 92	83.07 302	71.09 187	86.96 47	93.70 58	69.02 89	91.47 239	88.79 18	84.62 182	93.44 86

XVG-OURS		80.41 150	79.23 158	83.97 141	85.64 247	69.02 105	83.03 279	90.39 144	71.09 187	77.63 179	91.49 110	54.62 236	91.35 243	75.71 140	83.47 206	91.54 152

SixPastTwentyTwo		73.37 279	71.26 292	79.70 252	85.08 260	57.89 304	85.57 217	83.56 291	71.03 189	65.66 353	85.88 257	42.10 352	92.57 193	59.11 294	63.34 377	88.65 264

ZD-MVS					94.38 25	72.22 44		92.67 66	70.98 190	87.75 35	94.07 44	74.01 32	96.70 27	84.66 52	94.84 44

v1192		79.59 166	78.43 174	83.07 170	83.55 291	64.52 207	86.93 180	90.58 139	70.83 191	77.78 176	85.90 256	59.15 200	93.94 130	73.96 158	77.19 279	90.76 178

Fast-Effi-MVS+		80.81 137	79.92 140	83.47 151	88.85 153	64.51 208	85.53 223	89.39 177	70.79 192	78.49 160	85.06 278	67.54 102	93.58 148	67.03 228	86.58 157	92.32 130

PS-MVSNAJ		81.69 120	81.02 122	83.70 146	89.51 126	68.21 130	84.28 253	90.09 157	70.79 192	81.26 124	85.62 265	63.15 146	94.29 115	75.62 142	88.87 126	88.59 265

LTVRE_ROB	69.57 13	76.25 245	74.54 252	81.41 212	88.60 166	64.38 214	79.24 325	89.12 191	70.76 194	69.79 315	87.86 203	49.09 300	93.20 171	56.21 323	80.16 246	86.65 309
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
testing11		75.14 262	74.01 258	78.53 275	88.16 181	56.38 329	80.74 305	80.42 335	70.67 195	72.69 280	83.72 307	43.61 342	89.86 271	62.29 265	83.76 197	89.36 236

fmvsm_s_conf0.1_n		83.56 88	83.38 86	84.10 124	84.86 263	67.28 154	89.40 97	83.01 303	70.67 195	87.08 44	93.96 53	68.38 93	91.45 240	88.56 22	84.50 183	93.56 81

xiu_mvs_v2_base		81.69 120	81.05 121	83.60 148	89.15 145	68.03 135	84.46 247	90.02 158	70.67 195	81.30 123	86.53 245	63.17 145	94.19 122	75.60 143	88.54 133	88.57 266

XVG-OURS-SEG-HR		80.81 137	79.76 144	83.96 142	85.60 248	68.78 111	83.54 268	90.50 142	70.66 198	76.71 201	91.66 101	60.69 188	91.26 245	76.94 128	81.58 229	91.83 145

Anonymous202405211		78.25 199	77.01 209	81.99 200	91.03 87	60.67 274	84.77 237	83.90 286	70.65 199	80.00 136	91.20 119	41.08 357	91.43 241	65.21 240	85.26 175	93.85 63

DP-MVS Recon		83.11 99	82.09 107	86.15 62	94.44 19	70.92 71	88.79 117	92.20 88	70.53 200	79.17 146	91.03 127	64.12 135	96.03 50	68.39 215	90.14 108	91.50 154

FMVSNet1		77.44 222	76.12 229	81.40 213	86.81 229	63.01 242	88.39 132	89.28 180	70.49 201	74.39 260	87.28 216	49.06 301	91.11 248	60.91 279	78.52 263	90.09 208

testing3		68.56 327	67.67 327	71.22 354	87.33 219	42.87 404	83.06 278	71.54 384	70.36 202	69.08 321	84.38 290	30.33 391	85.69 325	37.50 397	75.45 309	85.09 338

ab-mvs		79.51 167	78.97 164	81.14 221	88.46 171	60.91 270	83.84 259	89.24 184	70.36 202	79.03 147	88.87 175	63.23 144	90.21 266	65.12 241	82.57 219	92.28 132

tfpnnormal		74.39 265	73.16 269	78.08 283	86.10 242	58.05 299	84.65 241	87.53 231	70.32 204	71.22 297	85.63 264	54.97 228	89.86 271	43.03 385	75.02 318	86.32 312

ACMM	73.20 8	80.78 142	79.84 143	83.58 149	89.31 138	68.37 125	89.99 76	91.60 111	70.28 205	77.25 186	89.66 152	53.37 247	93.53 153	74.24 156	82.85 214	88.85 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 86	83.41 85	84.28 116	86.14 239	68.12 131	89.43 93	82.87 307	70.27 206	87.27 43	93.80 57	69.09 84	91.58 230	88.21 26	83.65 202	93.14 100

ACMH+	68.96 14	76.01 249	74.01 258	82.03 199	88.60 166	65.31 194	88.86 115	87.55 230	70.25 207	67.75 330	87.47 214	41.27 355	93.19 173	58.37 303	75.94 299	87.60 284

IB-MVS	68.01 15	75.85 251	73.36 267	83.31 157	84.76 264	66.03 174	83.38 269	85.06 270	70.21 208	69.40 317	81.05 341	45.76 327	94.66 107	65.10 242	75.49 305	89.25 239
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
thisisatest0530		79.40 173	77.76 194	84.31 114	87.69 207	65.10 198	87.36 167	84.26 282	70.04 209	77.42 182	88.26 194	49.94 288	94.79 102	70.20 193	84.70 181	93.03 106

mvsmamba		80.60 145	79.38 152	84.27 118	89.74 120	67.24 157	87.47 163	86.95 243	70.02 210	75.38 233	88.93 172	51.24 273	92.56 194	75.47 146	89.22 121	93.00 109

test_fmvsmvis_n_1920		84.02 78	83.87 79	84.49 107	84.12 277	69.37 101	88.15 144	87.96 220	70.01 211	83.95 88	93.23 68	68.80 91	91.51 237	88.61 20	89.96 112	92.57 120

v144192		79.47 169	78.37 175	82.78 186	83.35 294	63.96 220	86.96 178	90.36 148	69.99 212	77.50 180	85.67 263	60.66 189	93.77 142	74.27 155	76.58 287	90.62 183

test_fmvsm_n_1920		85.29 65	85.34 62	85.13 86	86.12 240	69.93 86	88.65 125	90.78 135	69.97 213	88.27 26	93.98 52	71.39 58	91.54 234	88.49 23	90.45 103	93.91 59

c3_l		78.75 188	77.91 186	81.26 217	82.89 310	61.56 263	84.09 257	89.13 190	69.97 213	75.56 225	84.29 293	66.36 114	92.09 213	73.47 163	75.48 306	90.12 205

v1921920		79.22 177	78.03 183	82.80 183	83.30 296	63.94 221	86.80 184	90.33 149	69.91 215	77.48 181	85.53 266	58.44 204	93.75 144	73.60 160	76.85 284	90.71 181

ACMH	67.68 16	75.89 250	73.93 260	81.77 204	88.71 163	66.61 167	88.62 126	89.01 194	69.81 216	66.78 342	86.70 236	41.95 354	91.51 237	55.64 324	78.14 269	87.17 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 94	82.99 93	84.28 116	83.79 285	68.07 133	89.34 100	82.85 308	69.80 217	87.36 42	94.06 45	68.34 94	91.56 232	87.95 27	83.46 207	93.21 96

DPM-MVS		84.93 70	84.29 77	86.84 50	90.20 105	73.04 23	87.12 174	93.04 41	69.80 217	82.85 103	91.22 118	73.06 39	96.02 52	76.72 132	94.63 48	91.46 158

MAR-MVS		81.84 116	80.70 126	85.27 81	91.32 82	71.53 56	89.82 79	90.92 130	69.77 219	78.50 159	86.21 251	62.36 158	94.52 110	65.36 239	92.05 82	89.77 226
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
XVG-ACMP-BASELINE		76.11 247	74.27 257	81.62 206	83.20 299	64.67 206	83.60 266	89.75 166	69.75 220	71.85 290	87.09 225	32.78 384	92.11 212	69.99 197	80.43 244	88.09 275

BH-w/o		78.21 201	77.33 205	80.84 229	88.81 157	65.13 197	84.87 235	87.85 225	69.75 220	74.52 258	84.74 285	61.34 176	93.11 178	58.24 305	85.84 171	84.27 345

v1240		78.99 184	77.78 192	82.64 189	83.21 298	63.54 229	86.62 191	90.30 151	69.74 222	77.33 184	85.68 262	57.04 218	93.76 143	73.13 168	76.92 281	90.62 183

ET-MVSNet_ETH3D		78.63 192	76.63 222	84.64 102	86.73 231	69.47 95	85.01 232	84.61 275	69.54 223	66.51 349	86.59 240	50.16 285	91.75 225	76.26 134	84.24 191	92.69 117

eth_miper_zixun_eth		77.92 211	76.69 220	81.61 208	83.00 306	61.98 257	83.15 273	89.20 186	69.52 224	74.86 252	84.35 292	61.76 166	92.56 194	71.50 181	72.89 338	90.28 199

PVSNet_Blended_VisFu		82.62 104	81.83 113	84.96 91	90.80 94	69.76 90	88.74 121	91.70 109	69.39 225	78.96 148	88.46 187	65.47 125	94.87 99	74.42 153	88.57 132	90.24 200

mvs_tets		79.13 180	77.77 193	83.22 163	84.70 265	66.37 170	89.17 103	90.19 154	69.38 226	75.40 232	89.46 161	44.17 338	93.15 175	76.78 131	80.70 240	90.14 203

PVSNet_BlendedMVS		80.60 145	80.02 138	82.36 195	88.85 153	65.40 190	86.16 205	92.00 94	69.34 227	78.11 169	86.09 255	66.02 120	94.27 117	71.52 179	82.06 224	87.39 289

AdaColmap		80.58 148	79.42 151	84.06 132	93.09 57	68.91 108	89.36 99	88.97 197	69.27 228	75.70 223	89.69 151	57.20 217	95.77 59	63.06 256	88.41 136	87.50 288

ETVMVS		72.25 294	71.05 293	75.84 307	87.77 204	51.91 370	79.39 323	74.98 372	69.26 229	73.71 266	82.95 320	40.82 359	86.14 320	46.17 375	84.43 188	89.47 233

ITE_SJBPF				78.22 280	81.77 328	60.57 275		83.30 295	69.25 230	67.54 332	87.20 221	36.33 377	87.28 311	54.34 330	74.62 322	86.80 305

cl____		77.72 216	76.76 217	80.58 234	82.49 319	60.48 277	83.09 275	87.87 223	69.22 231	74.38 261	85.22 274	62.10 163	91.53 235	71.09 184	75.41 310	89.73 228

DIV-MVS_self_test		77.72 216	76.76 217	80.58 234	82.48 320	60.48 277	83.09 275	87.86 224	69.22 231	74.38 261	85.24 272	62.10 163	91.53 235	71.09 184	75.40 311	89.74 227

jajsoiax		79.29 176	77.96 184	83.27 159	84.68 266	66.57 168	89.25 102	90.16 155	69.20 233	75.46 229	89.49 158	45.75 328	93.13 177	76.84 129	80.80 238	90.11 206

IterMVS-SCA-FT		75.43 257	73.87 262	80.11 244	82.69 314	64.85 203	81.57 292	83.47 293	69.16 234	70.49 301	84.15 298	51.95 263	88.15 302	69.23 204	72.14 344	87.34 291

CL-MVSNet_self_test		72.37 292	71.46 287	75.09 319	79.49 362	53.53 359	80.76 304	85.01 272	69.12 235	70.51 300	82.05 335	57.92 208	84.13 339	52.27 340	66.00 371	87.60 284

AUN-MVS		79.21 178	77.60 199	84.05 135	88.71 163	67.61 144	85.84 214	87.26 237	69.08 236	77.23 188	88.14 200	53.20 249	93.47 156	75.50 145	73.45 333	91.06 167

xiu_mvs_v1_base_debu		80.80 139	79.72 145	84.03 137	87.35 214	70.19 82	85.56 218	88.77 202	69.06 237	81.83 112	88.16 196	50.91 276	92.85 187	78.29 115	87.56 142	89.06 242

xiu_mvs_v1_base		80.80 139	79.72 145	84.03 137	87.35 214	70.19 82	85.56 218	88.77 202	69.06 237	81.83 112	88.16 196	50.91 276	92.85 187	78.29 115	87.56 142	89.06 242

xiu_mvs_v1_base_debi		80.80 139	79.72 145	84.03 137	87.35 214	70.19 82	85.56 218	88.77 202	69.06 237	81.83 112	88.16 196	50.91 276	92.85 187	78.29 115	87.56 142	89.06 242

MVSTER		79.01 183	77.88 188	82.38 194	83.07 303	64.80 204	84.08 258	88.95 198	69.01 240	78.69 153	87.17 223	54.70 234	92.43 199	74.69 150	80.57 242	89.89 221

cl22		78.07 206	77.01 209	81.23 218	82.37 322	61.83 260	83.55 267	87.98 219	68.96 241	75.06 248	83.87 300	61.40 175	91.88 221	73.53 161	76.39 291	89.98 217

miper_ehance_all_eth		78.59 194	77.76 194	81.08 223	82.66 315	61.56 263	83.65 263	89.15 188	68.87 242	75.55 226	83.79 304	66.49 112	92.03 214	73.25 166	76.39 291	89.64 229

PAPR		81.66 122	80.89 125	83.99 140	90.27 103	64.00 219	86.76 188	91.77 108	68.84 243	77.13 195	89.50 157	67.63 101	94.88 98	67.55 220	88.52 134	93.09 101

CPTT-MVS		83.73 82	83.33 88	84.92 94	93.28 49	70.86 72	92.09 36	90.38 145	68.75 244	79.57 141	92.83 79	60.60 192	93.04 183	80.92 93	91.56 90	90.86 175

train_agg		86.43 43	86.20 45	87.13 44	93.26 52	72.96 25	88.75 119	91.89 100	68.69 245	85.00 63	93.10 70	74.43 26	95.41 73	84.97 45	95.71 25	93.02 107

test_8					93.13 54	72.57 35	88.68 124	91.84 104	68.69 245	84.87 67	93.10 70	74.43 26	95.16 82

dmvs_re		71.14 301	70.58 297	72.80 340	81.96 325	59.68 286	75.60 360	79.34 346	68.55 247	69.27 320	80.72 347	49.42 294	76.54 378	52.56 339	77.79 272	82.19 370

MVSFormer		82.85 102	82.05 108	85.24 82	87.35 214	70.21 80	90.50 64	90.38 145	68.55 247	81.32 120	89.47 159	61.68 167	93.46 157	78.98 106	90.26 106	92.05 142

test_djsdf		80.30 154	79.32 155	83.27 159	83.98 281	65.37 193	90.50 64	90.38 145	68.55 247	76.19 214	88.70 178	56.44 223	93.46 157	78.98 106	80.14 248	90.97 172

TEST9					93.26 52	72.96 25	88.75 119	91.89 100	68.44 250	85.00 63	93.10 70	74.36 28	95.41 73

FE-MVS		77.78 214	75.68 233	84.08 129	88.09 186	66.00 176	83.13 274	87.79 226	68.42 251	78.01 172	85.23 273	45.50 331	95.12 84	59.11 294	85.83 172	91.11 165

CDPH-MVS		85.76 56	85.29 66	87.17 43	93.49 47	71.08 64	88.58 127	92.42 79	68.32 252	84.61 74	93.48 61	72.32 44	96.15 48	79.00 105	95.43 30	94.28 46

PC_three_1452									68.21 253	92.02 12	94.00 49	82.09 5	95.98 56	84.58 53	96.68 2	94.95 11

fmvsm_l_conf0.5_n		84.47 74	84.54 73	84.27 118	85.42 251	68.81 109	88.49 129	87.26 237	68.08 254	88.03 30	93.49 60	72.04 48	91.77 224	88.90 17	89.14 123	92.24 135

IterMVS		74.29 266	72.94 272	78.35 279	81.53 333	63.49 231	81.58 291	82.49 311	68.06 255	69.99 310	83.69 308	51.66 270	85.54 327	65.85 236	71.64 347	86.01 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 354	64.11 345	58.19 384	78.55 367	24.76 422	75.28 361	65.94 400	67.91 256	60.34 378	76.01 381	53.56 244	73.94 397	31.79 403	67.65 364	75.88 391

TAMVS		78.89 187	77.51 201	83.03 172	87.80 200	67.79 140	84.72 238	85.05 271	67.63 257	76.75 200	87.70 205	62.25 160	90.82 257	58.53 301	87.13 149	90.49 190

PVSNet_Blended		80.98 132	80.34 133	82.90 178	88.85 153	65.40 190	84.43 249	92.00 94	67.62 258	78.11 169	85.05 279	66.02 120	94.27 117	71.52 179	89.50 117	89.01 247

TR-MVS		77.44 222	76.18 228	81.20 219	88.24 179	63.24 237	84.61 242	86.40 253	67.55 259	77.81 175	86.48 246	54.10 239	93.15 175	57.75 309	82.72 217	87.20 294

CDS-MVSNet		79.07 182	77.70 196	83.17 165	87.60 209	68.23 129	84.40 251	86.20 257	67.49 260	76.36 210	86.54 244	61.54 170	90.79 258	61.86 271	87.33 146	90.49 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 77	84.16 78	84.06 132	85.38 252	68.40 124	88.34 136	86.85 247	67.48 261	87.48 39	93.40 64	70.89 64	91.61 228	88.38 25	89.22 121	92.16 139

mvs_anonymous		79.42 172	79.11 161	80.34 239	84.45 272	57.97 302	82.59 281	87.62 229	67.40 262	76.17 217	88.56 185	68.47 92	89.59 277	70.65 190	86.05 167	93.47 85

mvs5depth		69.45 319	67.45 331	75.46 315	73.93 385	55.83 337	79.19 327	83.23 297	66.89 263	71.63 293	83.32 313	33.69 383	85.09 332	59.81 287	55.34 393	85.46 329

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 264	92.39 6				88.94 16	96.63 4	94.85 20

baseline2		75.70 252	73.83 263	81.30 216	83.26 297	61.79 261	82.57 282	80.65 330	66.81 264	66.88 340	83.42 312	57.86 209	92.19 210	63.47 252	79.57 252	89.91 219

miper_lstm_enhance		74.11 270	73.11 270	77.13 299	80.11 351	59.62 287	72.23 375	86.92 246	66.76 266	70.40 302	82.92 321	56.93 219	82.92 348	69.06 207	72.63 339	88.87 254

OpenMVS	72.83 10	79.77 162	78.33 177	84.09 128	85.17 255	69.91 87	90.57 61	90.97 129	66.70 267	72.17 287	91.91 95	54.70 234	93.96 127	61.81 272	90.95 97	88.41 270

test-LLR		72.94 288	72.43 277	74.48 325	81.35 337	58.04 300	78.38 339	77.46 357	66.66 268	69.95 311	79.00 363	48.06 306	79.24 364	66.13 231	84.83 178	86.15 316

test20.03		67.45 334	66.95 335	68.94 363	75.48 380	44.84 400	77.50 348	77.67 355	66.66 268	63.01 369	83.80 303	47.02 312	78.40 368	42.53 388	68.86 362	83.58 355

test0.0.03 1		68.00 332	67.69 326	68.90 364	77.55 370	47.43 390	75.70 359	72.95 383	66.66 268	66.56 345	82.29 332	48.06 306	75.87 386	44.97 382	74.51 323	83.41 356

Syy-MVS		68.05 331	67.85 321	68.67 367	84.68 266	40.97 410	78.62 336	73.08 381	66.65 271	66.74 343	79.46 358	52.11 259	82.30 351	32.89 402	76.38 294	82.75 365

myMVS_eth3d		67.02 337	66.29 338	69.21 362	84.68 266	42.58 405	78.62 336	73.08 381	66.65 271	66.74 343	79.46 358	31.53 388	82.30 351	39.43 394	76.38 294	82.75 365

QAPM		80.88 134	79.50 150	85.03 88	88.01 191	68.97 107	91.59 43	92.00 94	66.63 273	75.15 245	92.16 91	57.70 210	95.45 68	63.52 251	88.76 129	90.66 182

XXY-MVS		75.41 258	75.56 236	74.96 320	83.59 290	57.82 306	80.59 308	83.87 287	66.54 274	74.93 251	88.31 191	63.24 143	80.09 362	62.16 267	76.85 284	86.97 302

OurMVSNet-221017-0		74.26 267	72.42 278	79.80 250	83.76 287	59.59 288	85.92 211	86.64 249	66.39 275	66.96 339	87.58 208	39.46 363	91.60 229	65.76 237	69.27 358	88.22 272

SCA		74.22 268	72.33 279	79.91 247	84.05 280	62.17 255	79.96 318	79.29 347	66.30 276	72.38 284	80.13 352	51.95 263	88.60 297	59.25 292	77.67 275	88.96 251

testgi		66.67 340	66.53 337	67.08 374	75.62 379	41.69 409	75.93 355	76.50 366	66.11 277	65.20 359	86.59 240	35.72 379	74.71 393	43.71 383	73.38 335	84.84 340

HY-MVS	69.67 12	77.95 210	77.15 207	80.36 238	87.57 213	60.21 282	83.37 270	87.78 227	66.11 277	75.37 234	87.06 227	63.27 142	90.48 263	61.38 276	82.43 220	90.40 194

EG-PatchMatch MVS		74.04 271	71.82 283	80.71 232	84.92 262	67.42 149	85.86 213	88.08 217	66.04 279	64.22 363	83.85 301	35.10 380	92.56 194	57.44 311	80.83 237	82.16 371

CNLPA		78.08 205	76.79 216	81.97 201	90.40 102	71.07 65	87.59 160	84.55 276	66.03 280	72.38 284	89.64 153	57.56 212	86.04 321	59.61 289	83.35 208	88.79 258

Anonymous20240529		80.19 157	78.89 165	84.10 124	90.60 97	64.75 205	88.95 112	90.90 131	65.97 281	80.59 130	91.17 121	49.97 287	93.73 146	69.16 206	82.70 218	93.81 66

TAPA-MVS	73.13 9	79.15 179	77.94 185	82.79 185	89.59 122	62.99 246	88.16 143	91.51 114	65.77 282	77.14 194	91.09 123	60.91 185	93.21 168	50.26 353	87.05 150	92.17 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 281	70.99 294	80.49 236	84.51 271	65.80 182	80.71 306	86.13 259	65.70 283	65.46 354	83.74 305	44.60 334	90.91 256	51.13 346	76.89 282	84.74 341

anonymousdsp		78.60 193	77.15 207	82.98 175	80.51 347	67.08 160	87.24 172	89.53 173	65.66 284	75.16 244	87.19 222	52.52 250	92.25 208	77.17 125	79.34 257	89.61 230

test_0402		72.79 289	70.44 300	79.84 249	88.13 183	65.99 177	85.93 210	84.29 280	65.57 285	67.40 336	85.49 267	46.92 313	92.61 192	35.88 399	74.38 324	80.94 377

UBG		73.08 285	72.27 280	75.51 313	88.02 189	51.29 378	78.35 342	77.38 360	65.52 286	73.87 265	82.36 329	45.55 329	86.48 317	55.02 326	84.39 189	88.75 260

miper_enhance_ethall		77.87 213	76.86 213	80.92 228	81.65 329	61.38 265	82.68 280	88.98 195	65.52 286	75.47 227	82.30 331	65.76 124	92.00 216	72.95 169	76.39 291	89.39 235

WBMVS		73.43 278	72.81 273	75.28 317	87.91 194	50.99 380	78.59 338	81.31 325	65.51 288	74.47 259	84.83 282	46.39 316	86.68 314	58.41 302	77.86 271	88.17 274

UnsupCasMVSNet_eth		67.33 335	65.99 339	71.37 350	73.48 390	51.47 376	75.16 363	85.19 268	65.20 289	60.78 377	80.93 346	42.35 348	77.20 374	57.12 314	53.69 395	85.44 330

WTY-MVS		75.65 253	75.68 233	75.57 311	86.40 236	56.82 320	77.92 347	82.40 312	65.10 290	76.18 215	87.72 204	63.13 149	80.90 359	60.31 283	81.96 225	89.00 249

thisisatest0515		77.33 225	75.38 241	83.18 164	85.27 254	63.80 223	82.11 286	83.27 296	65.06 291	75.91 219	83.84 302	49.54 292	94.27 117	67.24 224	86.19 164	91.48 156

MVP-Stereo		76.12 246	74.46 254	81.13 222	85.37 253	69.79 89	84.42 250	87.95 221	65.03 292	67.46 334	85.33 270	53.28 248	91.73 227	58.01 307	83.27 209	81.85 372
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 185	77.69 197	82.81 182	90.54 99	64.29 215	90.11 75	91.51 114	65.01 293	76.16 218	88.13 201	50.56 281	93.03 184	69.68 201	77.56 276	91.11 165

pmmvs6		74.69 264	73.39 266	78.61 270	81.38 336	57.48 312	86.64 190	87.95 221	64.99 294	70.18 305	86.61 239	50.43 283	89.52 278	62.12 268	70.18 355	88.83 256

PAPM		77.68 219	76.40 226	81.51 209	87.29 221	61.85 259	83.78 260	89.59 171	64.74 295	71.23 296	88.70 178	62.59 153	93.66 147	52.66 338	87.03 151	89.01 247

MIMVSNet		70.69 307	69.30 306	74.88 321	84.52 270	56.35 331	75.87 358	79.42 345	64.59 296	67.76 329	82.41 328	41.10 356	81.54 355	46.64 373	81.34 230	86.75 307

tpm		72.37 292	71.71 284	74.35 327	82.19 323	52.00 368	79.22 326	77.29 361	64.56 297	72.95 276	83.68 309	51.35 271	83.26 347	58.33 304	75.80 300	87.81 280

MDA-MVSNet-bldmvs		66.68 339	63.66 348	75.75 308	79.28 364	60.56 276	73.92 371	78.35 352	64.43 298	50.13 401	79.87 356	44.02 339	83.67 342	46.10 376	56.86 387	83.03 362

MIMVSNet1		68.58 326	66.78 336	73.98 331	80.07 352	51.82 372	80.77 303	84.37 277	64.40 299	59.75 382	82.16 334	36.47 376	83.63 343	42.73 386	70.33 354	86.48 311

D2MVS		74.82 263	73.21 268	79.64 255	79.81 356	62.56 250	80.34 313	87.35 235	64.37 300	68.86 322	82.66 326	46.37 318	90.10 267	67.91 217	81.24 232	86.25 313

PLC	70.83 11	78.05 207	76.37 227	83.08 169	91.88 77	67.80 139	88.19 141	89.46 175	64.33 301	69.87 313	88.38 189	53.66 243	93.58 148	58.86 297	82.73 216	87.86 279
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 284	71.33 290	78.49 277	83.18 300	60.85 271	79.63 320	78.57 351	64.13 302	71.73 291	79.81 357	51.20 274	85.97 322	57.40 312	76.36 296	88.66 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mamv4		76.81 233	78.23 181	72.54 343	86.12 240	65.75 185	78.76 334	82.07 316	64.12 303	72.97 275	91.02 128	67.97 97	68.08 407	83.04 71	78.02 270	83.80 353

KD-MVS_2432*1600		66.22 344	63.89 346	73.21 335	75.47 381	53.42 361	70.76 382	84.35 278	64.10 304	66.52 347	78.52 367	34.55 381	84.98 333	50.40 349	50.33 400	81.23 375

miper_refine_blended		66.22 344	63.89 346	73.21 335	75.47 381	53.42 361	70.76 382	84.35 278	64.10 304	66.52 347	78.52 367	34.55 381	84.98 333	50.40 349	50.33 400	81.23 375

tpmvs		71.09 302	69.29 307	76.49 303	82.04 324	56.04 334	78.92 332	81.37 324	64.05 306	67.18 338	78.28 369	49.74 291	89.77 273	49.67 356	72.37 340	83.67 354

F-COLMAP		76.38 244	74.33 256	82.50 192	89.28 140	66.95 165	88.41 131	89.03 192	64.05 306	66.83 341	88.61 182	46.78 314	92.89 186	57.48 310	78.55 262	87.67 282

DP-MVS		76.78 234	74.57 250	83.42 153	93.29 48	69.46 97	88.55 128	83.70 288	63.98 308	70.20 304	88.89 174	54.01 241	94.80 101	46.66 371	81.88 227	86.01 320

原ACMM1				84.35 112	93.01 60	68.79 110		92.44 76	63.96 309	81.09 125	91.57 107	66.06 119	95.45 68	67.19 225	94.82 46	88.81 257

PM-MVS		66.41 342	64.14 344	73.20 337	73.92 386	56.45 326	78.97 331	64.96 403	63.88 310	64.72 360	80.24 351	19.84 407	83.44 345	66.24 230	64.52 375	79.71 383

UWE-MVS		72.13 295	71.49 286	74.03 330	86.66 233	47.70 389	81.40 296	76.89 365	63.60 311	75.59 224	84.22 296	39.94 362	85.62 326	48.98 359	86.13 166	88.77 259

jason		81.39 127	80.29 135	84.70 101	86.63 234	69.90 88	85.95 209	86.77 248	63.24 312	81.07 126	89.47 159	61.08 183	92.15 211	78.33 114	90.07 111	92.05 142
jason: jason.
KD-MVS_self_test		68.81 323	67.59 329	72.46 344	74.29 384	45.45 395	77.93 346	87.00 242	63.12 313	63.99 365	78.99 365	42.32 349	84.77 336	56.55 321	64.09 376	87.16 297

gg-mvs-nofinetune		69.95 315	67.96 319	75.94 306	83.07 303	54.51 353	77.23 351	70.29 387	63.11 314	70.32 303	62.33 400	43.62 341	88.69 295	53.88 332	87.76 141	84.62 343

tpmrst		72.39 290	72.13 281	73.18 338	80.54 346	49.91 385	79.91 319	79.08 349	63.11 314	71.69 292	79.95 354	55.32 226	82.77 349	65.66 238	73.89 328	86.87 303

PCF-MVS	73.52 7	80.38 151	78.84 166	85.01 89	87.71 205	68.99 106	83.65 263	91.46 118	63.00 316	77.77 177	90.28 139	66.10 117	95.09 90	61.40 275	88.22 138	90.94 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 286	70.41 301	80.81 230	87.13 224	65.63 186	88.30 138	84.19 283	62.96 317	63.80 367	87.69 206	38.04 372	92.56 194	46.66 371	74.91 319	84.24 346
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 312	67.78 325	77.61 291	77.43 371	59.57 289	71.16 379	70.33 386	62.94 318	68.65 324	72.77 391	50.62 280	85.49 328	69.58 202	66.58 368	87.77 281

lupinMVS		81.39 127	80.27 136	84.76 100	87.35 214	70.21 80	85.55 221	86.41 252	62.85 319	81.32 120	88.61 182	61.68 167	92.24 209	78.41 113	90.26 106	91.83 145

test_vis1_n_1920		75.52 255	75.78 231	74.75 324	79.84 355	57.44 313	83.26 271	85.52 265	62.83 320	79.34 145	86.17 253	45.10 333	79.71 363	78.75 108	81.21 233	87.10 301

EPMVS		69.02 322	68.16 316	71.59 348	79.61 360	49.80 387	77.40 349	66.93 397	62.82 321	70.01 308	79.05 361	45.79 326	77.86 372	56.58 320	75.26 315	87.13 298

PatchMatch-RL		72.38 291	70.90 295	76.80 302	88.60 166	67.38 151	79.53 321	76.17 369	62.75 322	69.36 318	82.00 337	45.51 330	84.89 335	53.62 333	80.58 241	78.12 386

gm-plane-assit					81.40 335	53.83 358			62.72 323		80.94 344		92.39 201	63.40 254

FMVSNet5		69.50 318	67.96 319	74.15 329	82.97 309	55.35 344	80.01 317	82.12 315	62.56 324	63.02 368	81.53 338	36.92 375	81.92 353	48.42 361	74.06 326	85.17 336

sss		73.60 276	73.64 265	73.51 334	82.80 311	55.01 348	76.12 354	81.69 320	62.47 325	74.68 255	85.85 259	57.32 215	78.11 370	60.86 280	80.93 235	87.39 289

WB-MVSnew		71.96 297	71.65 285	72.89 339	84.67 269	51.88 371	82.29 284	77.57 356	62.31 326	73.67 267	83.00 319	53.49 246	81.10 358	45.75 378	82.13 223	85.70 326

AllTest		70.96 303	68.09 318	79.58 256	85.15 257	63.62 225	84.58 243	79.83 341	62.31 326	60.32 379	86.73 230	32.02 385	88.96 291	50.28 351	71.57 348	86.15 316

TestCases				79.58 256	85.15 257	63.62 225		79.83 341	62.31 326	60.32 379	86.73 230	32.02 385	88.96 291	50.28 351	71.57 348	86.15 316

1112_ss		77.40 224	76.43 225	80.32 240	89.11 150	60.41 279	83.65 263	87.72 228	62.13 329	73.05 274	86.72 232	62.58 154	89.97 270	62.11 269	80.80 238	90.59 186

PVSNet	64.34 18	72.08 296	70.87 296	75.69 309	86.21 238	56.44 327	74.37 369	80.73 329	62.06 330	70.17 306	82.23 333	42.86 346	83.31 346	54.77 328	84.45 187	87.32 292

LS3D		76.95 231	74.82 248	83.37 156	90.45 100	67.36 152	89.15 107	86.94 244	61.87 331	69.52 316	90.61 135	51.71 269	94.53 109	46.38 374	86.71 156	88.21 273

CostFormer		75.24 261	73.90 261	79.27 260	82.65 316	58.27 297	80.80 301	82.73 310	61.57 332	75.33 239	83.13 317	55.52 225	91.07 254	64.98 243	78.34 268	88.45 268

new-patchmatchnet		61.73 356	61.73 357	61.70 380	72.74 396	24.50 423	69.16 389	78.03 353	61.40 333	56.72 391	75.53 385	38.42 369	76.48 380	45.95 377	57.67 386	84.13 348

ANet_high		50.57 374	46.10 378	63.99 377	48.67 422	39.13 411	70.99 381	80.85 327	61.39 334	31.18 411	57.70 407	17.02 410	73.65 398	31.22 404	15.89 419	79.18 384

MS-PatchMatch		73.83 274	72.67 274	77.30 297	83.87 284	66.02 175	81.82 287	84.66 274	61.37 335	68.61 325	82.82 324	47.29 309	88.21 301	59.27 291	84.32 190	77.68 387

USDC		70.33 311	68.37 313	76.21 305	80.60 345	56.23 332	79.19 327	86.49 251	60.89 336	61.29 375	85.47 268	31.78 387	89.47 280	53.37 335	76.21 297	82.94 364

cascas		76.72 235	74.64 249	82.99 174	85.78 245	65.88 180	82.33 283	89.21 185	60.85 337	72.74 277	81.02 342	47.28 310	93.75 144	67.48 221	85.02 176	89.34 237

MDTV_nov1_ep13			69.97 305		83.18 300	53.48 360	77.10 352	80.18 340	60.45 338	69.33 319	80.44 348	48.89 304	86.90 312	51.60 343	78.51 264

TinyColmap		67.30 336	64.81 341	74.76 323	81.92 327	56.68 324	80.29 314	81.49 322	60.33 339	56.27 393	83.22 314	24.77 399	87.66 309	45.52 379	69.47 357	79.95 382

test-mter		71.41 299	70.39 302	74.48 325	81.35 337	58.04 300	78.38 339	77.46 357	60.32 340	69.95 311	79.00 363	36.08 378	79.24 364	66.13 231	84.83 178	86.15 316

1314		76.53 237	75.30 244	80.21 242	83.93 282	62.32 253	84.66 239	88.81 200	60.23 341	70.16 307	84.07 299	55.30 227	90.73 260	67.37 222	83.21 210	87.59 286

PatchT		68.46 329	67.85 321	70.29 358	80.70 344	43.93 402	72.47 374	74.88 373	60.15 342	70.55 299	76.57 378	49.94 288	81.59 354	50.58 347	74.83 320	85.34 331

无先验							87.48 162	88.98 195	60.00 343				94.12 124	67.28 223		88.97 250

CR-MVSNet		73.37 279	71.27 291	79.67 254	81.32 339	65.19 195	75.92 356	80.30 337	59.92 344	72.73 278	81.19 339	52.50 251	86.69 313	59.84 286	77.71 273	87.11 299

TDRefinement		67.49 333	64.34 343	76.92 300	73.47 391	61.07 268	84.86 236	82.98 305	59.77 345	58.30 386	85.13 276	26.06 395	87.89 305	47.92 368	60.59 384	81.81 373

dp		66.80 338	65.43 340	70.90 357	79.74 359	48.82 388	75.12 365	74.77 374	59.61 346	64.08 364	77.23 375	42.89 345	80.72 360	48.86 360	66.58 368	83.16 359

our_test_3		69.14 321	67.00 334	75.57 311	79.80 357	58.80 291	77.96 345	77.81 354	59.55 347	62.90 371	78.25 370	47.43 308	83.97 340	51.71 342	67.58 365	83.93 351

Test_1112_low_res		76.40 243	75.44 238	79.27 260	89.28 140	58.09 298	81.69 290	87.07 241	59.53 348	72.48 282	86.67 237	61.30 177	89.33 281	60.81 281	80.15 247	90.41 193

pmmvs4		74.03 273	71.91 282	80.39 237	81.96 325	68.32 126	81.45 294	82.14 314	59.32 349	69.87 313	85.13 276	52.40 253	88.13 303	60.21 284	74.74 321	84.73 342

testdata				79.97 246	90.90 91	64.21 216		84.71 273	59.27 350	85.40 58	92.91 76	62.02 165	89.08 287	68.95 208	91.37 92	86.63 310

WB-MVS		54.94 364	54.72 365	55.60 390	73.50 389	20.90 424	74.27 370	61.19 407	59.16 351	50.61 399	74.15 387	47.19 311	75.78 387	17.31 415	35.07 409	70.12 397

ppachtmachnet_test		70.04 314	67.34 332	78.14 282	79.80 357	61.13 266	79.19 327	80.59 331	59.16 351	65.27 356	79.29 360	46.75 315	87.29 310	49.33 357	66.72 366	86.00 322

RPSCF		73.23 283	71.46 287	78.54 274	82.50 318	59.85 284	82.18 285	82.84 309	58.96 353	71.15 298	89.41 165	45.48 332	84.77 336	58.82 298	71.83 346	91.02 171

pmmvs-eth3d		70.50 310	67.83 323	78.52 276	77.37 372	66.18 173	81.82 287	81.51 321	58.90 354	63.90 366	80.42 349	42.69 347	86.28 319	58.56 300	65.30 373	83.11 360

OpenMVS_ROB	64.09 19	70.56 309	68.19 315	77.65 290	80.26 348	59.41 290	85.01 232	82.96 306	58.76 355	65.43 355	82.33 330	37.63 374	91.23 247	45.34 381	76.03 298	82.32 368

114514_t		80.68 143	79.51 149	84.20 121	94.09 38	67.27 155	89.64 87	91.11 127	58.75 356	74.08 263	90.72 133	58.10 206	95.04 91	69.70 200	89.42 119	90.30 198

Patchmtry		70.74 306	69.16 309	75.49 314	80.72 343	54.07 356	74.94 367	80.30 337	58.34 357	70.01 308	81.19 339	52.50 251	86.54 315	53.37 335	71.09 351	85.87 325

test_cas_vis1_n_1920		73.76 275	73.74 264	73.81 332	75.90 376	59.77 285	80.51 309	82.40 312	58.30 358	81.62 118	85.69 261	44.35 337	76.41 381	76.29 133	78.61 261	85.23 333

Anonymous20240521		68.80 324	67.22 333	73.55 333	74.33 383	54.11 355	83.18 272	85.61 264	58.15 359	61.68 374	80.94 344	30.71 390	81.27 357	57.00 316	73.34 336	85.28 332

旧先验2							86.56 193		58.10 360	87.04 45			88.98 289	74.07 157

JIA-IIPM		66.32 343	62.82 354	76.82 301	77.09 373	61.72 262	65.34 402	75.38 370	58.04 361	64.51 361	62.32 401	42.05 353	86.51 316	51.45 344	69.22 359	82.21 369

pmmvs5		71.55 298	70.20 304	75.61 310	77.83 369	56.39 328	81.74 289	80.89 326	57.76 362	67.46 334	84.49 286	49.26 298	85.32 331	57.08 315	75.29 314	85.11 337

TESTMET0.1,1		69.89 316	69.00 310	72.55 342	79.27 365	56.85 319	78.38 339	74.71 376	57.64 363	68.09 328	77.19 376	37.75 373	76.70 377	63.92 250	84.09 193	84.10 349

RPMNet		73.51 277	70.49 299	82.58 191	81.32 339	65.19 195	75.92 356	92.27 83	57.60 364	72.73 278	76.45 379	52.30 254	95.43 70	48.14 366	77.71 273	87.11 299

SSC-MVS		53.88 367	53.59 367	54.75 392	72.87 395	19.59 425	73.84 372	60.53 409	57.58 365	49.18 403	73.45 390	46.34 320	75.47 390	16.20 418	32.28 411	69.20 398

新几何1				83.42 153	93.13 54	70.71 74		85.48 266	57.43 366	81.80 115	91.98 94	63.28 141	92.27 207	64.60 246	92.99 70	87.27 293

YYNet1		65.03 347	62.91 352	71.38 349	75.85 377	56.60 325	69.12 390	74.66 377	57.28 367	54.12 395	77.87 372	45.85 325	74.48 394	49.95 354	61.52 381	83.05 361

MDA-MVSNet_test_wron		65.03 347	62.92 351	71.37 350	75.93 375	56.73 321	69.09 391	74.73 375	57.28 367	54.03 396	77.89 371	45.88 324	74.39 395	49.89 355	61.55 380	82.99 363

Anonymous20231206		68.60 325	67.80 324	71.02 355	80.23 350	50.75 382	78.30 343	80.47 333	56.79 369	66.11 352	82.63 327	46.35 319	78.95 366	43.62 384	75.70 301	83.36 357

tpm2		73.26 282	71.46 287	78.63 269	83.34 295	56.71 323	80.65 307	80.40 336	56.63 370	73.55 268	82.02 336	51.80 267	91.24 246	56.35 322	78.42 266	87.95 276

CHOSEN 1792x2688		77.63 220	75.69 232	83.44 152	89.98 114	68.58 122	78.70 335	87.50 232	56.38 371	75.80 222	86.84 228	58.67 202	91.40 242	61.58 274	85.75 173	90.34 195

HyFIR lowres test		77.53 221	75.40 240	83.94 143	89.59 122	66.62 166	80.36 312	88.64 209	56.29 372	76.45 207	85.17 275	57.64 211	93.28 163	61.34 277	83.10 212	91.91 144

PVSNet_0	57.27 20	61.67 357	59.27 360	68.85 365	79.61 360	57.44 313	68.01 392	73.44 380	55.93 373	58.54 385	70.41 396	44.58 335	77.55 373	47.01 370	35.91 408	71.55 396

UnsupCasMVSNet_bld		63.70 352	61.53 358	70.21 359	73.69 388	51.39 377	72.82 373	81.89 317	55.63 374	57.81 388	71.80 393	38.67 368	78.61 367	49.26 358	52.21 398	80.63 379

MDTV_nov1_ep13_2view						37.79 412	75.16 363		55.10 375	66.53 346		49.34 296		53.98 331		87.94 277

MVS		78.19 203	76.99 211	81.78 203	85.66 246	66.99 161	84.66 239	90.47 143	55.08 376	72.02 289	85.27 271	63.83 138	94.11 125	66.10 233	89.80 115	84.24 346

test222					91.50 80	68.26 128	84.16 255	83.20 300	54.63 377	79.74 138	91.63 104	58.97 201			91.42 91	86.77 306

dongtai		45.42 378	45.38 379	45.55 396	73.36 392	26.85 420	67.72 393	34.19 422	54.15 378	49.65 402	56.41 409	25.43 396	62.94 412	19.45 413	28.09 413	46.86 412

CHOSEN 280x420		66.51 341	64.71 342	71.90 346	81.45 334	63.52 230	57.98 409	68.95 393	53.57 379	62.59 372	76.70 377	46.22 321	75.29 392	55.25 325	79.68 251	76.88 389

ADS-MVSNet2		66.20 346	63.33 349	74.82 322	79.92 353	58.75 292	67.55 394	75.19 371	53.37 380	65.25 357	75.86 382	42.32 349	80.53 361	41.57 389	68.91 360	85.18 334

ADS-MVSNet		64.36 350	62.88 353	68.78 366	79.92 353	47.17 391	67.55 394	71.18 385	53.37 380	65.25 357	75.86 382	42.32 349	73.99 396	41.57 389	68.91 360	85.18 334

LF4IMVS		64.02 351	62.19 355	69.50 361	70.90 399	53.29 364	76.13 353	77.18 362	52.65 382	58.59 384	80.98 343	23.55 402	76.52 379	53.06 337	66.66 367	78.68 385

tpm cat1		70.57 308	68.31 314	77.35 296	82.41 321	57.95 303	78.08 344	80.22 339	52.04 383	68.54 326	77.66 374	52.00 262	87.84 306	51.77 341	72.07 345	86.25 313

test_vis1_n		69.85 317	69.21 308	71.77 347	72.66 397	55.27 346	81.48 293	76.21 368	52.03 384	75.30 240	83.20 316	28.97 392	76.22 383	74.60 151	78.41 267	83.81 352

Patchmatch-test		64.82 349	63.24 350	69.57 360	79.42 363	49.82 386	63.49 406	69.05 392	51.98 385	59.95 381	80.13 352	50.91 276	70.98 400	40.66 391	73.57 331	87.90 278

N_pmnet		52.79 370	53.26 368	51.40 394	78.99 366	7.68 428	69.52 386	3.89 427	51.63 386	57.01 390	74.98 386	40.83 358	65.96 409	37.78 396	64.67 374	80.56 381

test_fmvs1_n		70.86 305	70.24 303	72.73 341	72.51 398	55.28 345	81.27 297	79.71 343	51.49 387	78.73 152	84.87 281	27.54 394	77.02 375	76.06 136	79.97 250	85.88 324

test_fmvs1		70.93 304	70.52 298	72.16 345	73.71 387	55.05 347	80.82 300	78.77 350	51.21 388	78.58 157	84.41 289	31.20 389	76.94 376	75.88 139	80.12 249	84.47 344

PMMVS		69.34 320	68.67 311	71.35 352	75.67 378	62.03 256	75.17 362	73.46 379	50.00 389	68.68 323	79.05 361	52.07 261	78.13 369	61.16 278	82.77 215	73.90 393

test_fmvs2		68.35 330	67.48 330	70.98 356	69.50 401	51.95 369	80.05 316	76.38 367	49.33 390	74.65 256	84.38 290	23.30 403	75.40 391	74.51 152	75.17 317	85.60 327

ttmdpeth		59.91 359	57.10 363	68.34 369	67.13 405	46.65 394	74.64 368	67.41 396	48.30 391	62.52 373	85.04 280	20.40 405	75.93 385	42.55 387	45.90 406	82.44 367

CMPMVS	51.72 21	70.19 313	68.16 316	76.28 304	73.15 394	57.55 311	79.47 322	83.92 285	48.02 392	56.48 392	84.81 283	43.13 344	86.42 318	62.67 261	81.81 228	84.89 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 355	61.26 359	65.41 376	69.52 400	54.86 349	66.86 396	49.78 416	46.65 393	68.50 327	83.21 315	49.15 299	66.28 408	56.93 317	60.77 382	75.11 392

kuosan		39.70 382	40.40 383	37.58 399	64.52 408	26.98 418	65.62 401	33.02 423	46.12 394	42.79 406	48.99 412	24.10 401	46.56 420	12.16 421	26.30 414	39.20 413

test_fmvs3		63.36 353	61.82 356	67.98 371	62.51 410	46.96 393	77.37 350	74.03 378	45.24 395	67.50 333	78.79 366	12.16 415	72.98 399	72.77 172	66.02 370	83.99 350

CVMVSNet		72.99 287	72.58 276	74.25 328	84.28 273	50.85 381	86.41 196	83.45 294	44.56 396	73.23 272	87.54 212	49.38 295	85.70 324	65.90 235	78.44 265	86.19 315

test_vis1_rt		60.28 358	58.42 361	65.84 375	67.25 404	55.60 341	70.44 384	60.94 408	44.33 397	59.00 383	66.64 398	24.91 398	68.67 405	62.80 257	69.48 356	73.25 394

mvsany_test3		53.99 366	51.45 371	61.61 381	55.51 415	44.74 401	63.52 405	45.41 420	43.69 398	58.11 387	76.45 379	17.99 408	63.76 411	54.77 328	47.59 402	76.34 390

EU-MVSNet		68.53 328	67.61 328	71.31 353	78.51 368	47.01 392	84.47 245	84.27 281	42.27 399	66.44 350	84.79 284	40.44 360	83.76 341	58.76 299	68.54 363	83.17 358

FPMVS		53.68 368	51.64 370	59.81 383	65.08 407	51.03 379	69.48 387	69.58 390	41.46 400	40.67 407	72.32 392	16.46 411	70.00 404	24.24 411	65.42 372	58.40 407

pmmvs3		57.79 361	54.26 366	68.37 368	64.02 409	56.72 322	75.12 365	65.17 401	40.20 401	52.93 397	69.86 397	20.36 406	75.48 389	45.45 380	55.25 394	72.90 395

new_pmnet		50.91 373	50.29 373	52.78 393	68.58 402	34.94 415	63.71 404	56.63 413	39.73 402	44.95 404	65.47 399	21.93 404	58.48 413	34.98 400	56.62 388	64.92 401

MVS-HIRNet		59.14 360	57.67 362	63.57 378	81.65 329	43.50 403	71.73 376	65.06 402	39.59 403	51.43 398	57.73 406	38.34 370	82.58 350	39.53 392	73.95 327	64.62 402

MVStest1		56.63 363	52.76 369	68.25 370	61.67 411	53.25 365	71.67 377	68.90 394	38.59 404	50.59 400	83.05 318	25.08 397	70.66 401	36.76 398	38.56 407	80.83 378

PMMVS2		40.82 381	38.86 385	46.69 395	53.84 417	16.45 426	48.61 412	49.92 415	37.49 405	31.67 410	60.97 403	8.14 421	56.42 415	28.42 406	30.72 412	67.19 400

test_vis3_rt		49.26 375	47.02 377	56.00 387	54.30 416	45.27 399	66.76 398	48.08 417	36.83 406	44.38 405	53.20 410	7.17 422	64.07 410	56.77 319	55.66 390	58.65 406

test_f		52.09 371	50.82 372	55.90 388	53.82 418	42.31 408	59.42 408	58.31 412	36.45 407	56.12 394	70.96 395	12.18 414	57.79 414	53.51 334	56.57 389	67.60 399

LCM-MVSNet		54.25 365	49.68 375	67.97 372	53.73 419	45.28 398	66.85 397	80.78 328	35.96 408	39.45 409	62.23 402	8.70 419	78.06 371	48.24 365	51.20 399	80.57 380

APD_test1		53.31 369	49.93 374	63.42 379	65.68 406	50.13 384	71.59 378	66.90 398	34.43 409	40.58 408	71.56 394	8.65 420	76.27 382	34.64 401	55.36 392	63.86 403

PMVS	37.38 22	44.16 380	40.28 384	55.82 389	40.82 424	42.54 407	65.12 403	63.99 404	34.43 409	24.48 415	57.12 408	3.92 425	76.17 384	17.10 416	55.52 391	48.75 410
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 379	41.86 382	55.16 391	77.03 374	51.52 375	32.50 415	80.52 332	32.46 411	27.12 414	35.02 415	9.52 418	75.50 388	22.31 412	60.21 385	38.45 414
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 362	56.90 364	60.38 382	67.70 403	35.61 413	69.18 388	53.97 414	32.30 412	57.49 389	79.88 355	40.39 361	68.57 406	38.78 395	72.37 340	76.97 388

testf1		45.72 376	41.96 380	57.00 385	56.90 413	45.32 396	66.14 399	59.26 410	26.19 413	30.89 412	60.96 404	4.14 423	70.64 402	26.39 409	46.73 404	55.04 408

APD_test2		45.72 376	41.96 380	57.00 385	56.90 413	45.32 396	66.14 399	59.26 410	26.19 413	30.89 412	60.96 404	4.14 423	70.64 402	26.39 409	46.73 404	55.04 408

E-PMN		31.77 383	30.64 386	35.15 400	52.87 420	27.67 417	57.09 410	47.86 418	24.64 415	16.40 420	33.05 416	11.23 416	54.90 416	14.46 419	18.15 417	22.87 416

EMVS		30.81 385	29.65 387	34.27 401	50.96 421	25.95 421	56.58 411	46.80 419	24.01 416	15.53 421	30.68 417	12.47 413	54.43 417	12.81 420	17.05 418	22.43 417

MVE	26.22 23	30.37 386	25.89 390	43.81 397	44.55 423	35.46 414	28.87 416	39.07 421	18.20 417	18.58 419	40.18 414	2.68 426	47.37 419	17.07 417	23.78 416	48.60 411
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 402	40.17 425	26.90 419		24.59 426	17.44 418	23.95 416	48.61 413	9.77 417	26.48 421	18.06 414	24.47 415	28.83 415

wuyk23d		16.82 389	15.94 392	19.46 403	58.74 412	31.45 416	39.22 413	3.74 428	6.84 419	6.04 422	2.70 422	1.27 427	24.29 422	10.54 422	14.40 421	2.63 419

test_method		31.52 384	29.28 388	38.23 398	27.03 426	6.50 429	20.94 417	62.21 406	4.05 420	22.35 418	52.50 411	13.33 412	47.58 418	27.04 408	34.04 410	60.62 404

tmp_tt		18.61 388	21.40 391	10.23 404	4.82 427	10.11 427	34.70 414	30.74 425	1.48 421	23.91 417	26.07 418	28.42 393	13.41 423	27.12 407	15.35 420	7.17 418

EGC-MVSNET		52.07 372	47.05 376	67.14 373	83.51 292	60.71 273	80.50 310	67.75 395	0.07 422	0.43 423	75.85 384	24.26 400	81.54 355	28.82 405	62.25 378	59.16 405

testmvs		6.04 392	8.02 395	0.10 406	0.08 428	0.03 431	69.74 385	0.04 429	0.05 423	0.31 424	1.68 423	0.02 429	0.04 424	0.24 423	0.02 422	0.25 421

test123		6.12 391	8.11 394	0.14 405	0.06 429	0.09 430	71.05 380	0.03 430	0.04 424	0.25 425	1.30 424	0.05 428	0.03 425	0.21 424	0.01 423	0.29 420

mmdepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

monomultidepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

test_blank		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet_test		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

DCPMVS		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

cdsmvs_eth3d_5k		19.96 387	26.61 389	0.00 407	0.00 430	0.00 432	0.00 418	89.26 183	0.00 425	0.00 426	88.61 182	61.62 169	0.00 426	0.00 425	0.00 424	0.00 422

pcd_1.5k_mvsjas		5.26 393	7.02 396	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	63.15 146	0.00 426	0.00 425	0.00 424	0.00 422

sosnet-low-res		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

sosnet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uncertanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

Regformer		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

ab-mvs-re		7.23 390	9.64 393	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	86.72 232	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

WAC-MVS						42.58 405								39.46 393

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 38

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 38

eth-test2					0.00 430

eth-test					0.00 430

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 20	83.77 64	96.48 8	94.88 15

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 48

GSMVS																88.96 251

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 272				88.96 251

sam_mvs												50.01 286

ambc				75.24 318	73.16 393	50.51 383	63.05 407	87.47 233		64.28 362	77.81 373	17.80 409	89.73 275	57.88 308	60.64 383	85.49 328

MTGPA								92.02 92

test_post1							78.90 333				5.43 421	48.81 305	85.44 330	59.25 292

test_post											5.46 420	50.36 284	84.24 338

patchmatchnet-post											74.00 388	51.12 275	88.60 297

GG-mvs-BLEND				75.38 316	81.59 331	55.80 338	79.32 324	69.63 389		67.19 337	73.67 389	43.24 343	88.90 293	50.41 348	84.50 183	81.45 374

MTMP							92.18 34	32.83 424

test9_res														84.90 46	95.70 26	92.87 112

agg_prior2														82.91 73	95.45 29	92.70 115

agg_prior					92.85 62	71.94 50		91.78 107		84.41 78			94.93 93

test_prior4						72.60 34	89.01 110

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 59

新几何2							86.29 202

旧先验1					91.96 74	65.79 183		86.37 254			93.08 74	69.31 83			92.74 73	88.74 262

原ACMM2							86.86 182

testdata2													91.01 255	62.37 264

segment_acmp												73.08 38

test12				86.80 52	92.63 67	70.70 75		91.79 106		82.71 106		71.67 54	96.16 47		94.50 51	93.54 83

plane_prior7					90.08 108	68.51 123

plane_prior6					89.84 117	68.70 118						60.42 194

plane_prior5								92.44 76					95.38 75	78.71 109	86.32 161	91.33 159

plane_prior4											91.00 129

plane_prior1					89.90 116

n2								0.00 431

nn								0.00 431

door-mid								69.98 388

lessismore_v0				78.97 265	81.01 342	57.15 316		65.99 399		61.16 376	82.82 324	39.12 365	91.34 244	59.67 288	46.92 403	88.43 269

test11								92.23 86

door								69.44 391

HQP5-MVS						66.98 162

BP-MVS														77.47 121

HQP4-MVS										77.24 187			95.11 86			91.03 169

HQP3-MVS								92.19 89							85.99 169

HQP2-MVS												60.17 197

NP-MVS					89.62 121	68.32 126					90.24 141

ACMMP++_ref															81.95 226

ACMMP++															81.25 231

Test By Simon												64.33 133

High-resolution multi-view benchmark