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		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 39	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 114	68.35 2	75.77 45	90.38 30	53.98 64	90.26 13	81.30 3	87.68 42	88.77 12

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 138	86.34 125	54.92 54	88.90 25	72.68 69	84.55 69	87.76 42

UA-Net		73.13 84	72.93 84	73.76 133	83.58 67	51.66 212	78.75 125	77.66 212	67.75 4	72.61 108	89.42 52	49.82 131	83.29 158	53.61 245	83.14 83	86.32 103

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 71

TranMVSNet+NR-MVSNet		70.36 143	70.10 138	71.17 221	78.64 163	42.97 335	76.53 194	81.16 135	66.95 6	68.53 169	85.42 153	51.61 107	83.07 162	52.32 253	69.70 305	87.46 53

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 194	89.24 56	42.03 233	89.38 19	64.07 139	86.50 59	89.69 3

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 68	89.38 54	55.30 49	89.18 21	74.19 57	87.34 46	86.38 95

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 60	90.06 14	78.42 23	89.02 23	87.69 43
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 85	72.16 95	75.90 74	75.95 246	56.28 110	83.05 62	72.39 300	66.53 10	65.27 246	87.00 100	50.40 124	85.47 113	62.48 164	86.32 60	85.94 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 124	71.00 118	71.44 208	79.20 143	44.13 321	76.02 209	82.60 101	66.48 11	68.20 174	84.60 171	56.82 37	82.82 174	54.62 235	70.43 285	87.36 62

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 31
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++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 154

NR-MVSNet		69.54 168	68.85 160	71.59 202	78.05 186	43.81 326	74.20 249	80.86 142	65.18 14	62.76 290	84.52 172	52.35 93	83.59 152	50.96 268	70.78 280	87.37 60

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 234	80.97 140	65.13 15	75.77 45	90.88 20	48.63 148	86.66 74	77.23 29	88.17 33	84.81 170

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 19

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 31

EI-MVSNet-Vis-set		72.42 101	71.59 102	74.91 95	78.47 167	54.02 153	77.05 181	79.33 167	65.03 18	71.68 120	79.35 296	52.75 85	84.89 126	66.46 119	74.23 223	85.83 121

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 240	51.83 210	79.67 114	85.08 34	65.02 19	75.84 44	88.58 68	59.42 22	85.08 119	72.75 68	83.93 78	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 107	79.46 292	53.65 76	87.87 44	67.45 110	82.91 89	85.89 118

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 75	87.40 89	49.48 135	86.17 91	68.04 102	87.55 43	87.42 55

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 249	64.69 22	74.21 75	87.40 89	49.48 135	86.17 91	68.04 102	83.88 79	85.85 119

WR-MVS		68.47 198	68.47 171	68.44 273	80.20 121	39.84 363	73.75 261	76.07 237	64.68 24	68.11 182	83.63 194	50.39 125	79.14 257	49.78 273	69.66 306	86.34 99

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 113	90.01 45	47.95 155	88.01 40	71.55 82	86.74 55	86.37 97

X-MVStestdata		70.21 146	67.28 205	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 113	6.49 465	47.95 155	88.01 40	71.55 82	86.74 55	86.37 97

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 157	86.10 132	45.26 198	87.21 59	68.16 100	80.58 118	84.65 174

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 111	71.06 117	74.52 112	77.98 189	53.56 164	76.62 191	79.16 168	64.40 29	71.18 126	78.95 301	52.19 95	84.66 133	65.47 130	73.57 236	85.32 150

DU-MVS		70.01 151	69.53 145	71.44 208	78.05 186	44.13 321	75.01 230	81.51 117	64.37 30	68.20 174	84.52 172	49.12 145	82.82 174	54.62 235	70.43 285	87.37 60

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 142
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					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 26

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 45

LFMVS		71.78 113	71.59 102	72.32 182	83.40 71	46.38 297	79.75 112	71.08 309	64.18 34	72.80 104	88.64 67	42.58 228	83.72 148	57.41 211	84.49 72	86.86 76

IS-MVSNet		71.57 117	71.00 118	73.27 158	78.86 153	45.63 308	80.22 103	78.69 182	64.14 37	66.46 221	87.36 92	49.30 139	85.60 106	50.26 272	83.71 82	88.59 15

plane_prior3						56.09 114			63.92 38	69.27 157

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 84	90.60 23	54.85 55	86.72 72	77.20 30	88.06 36	85.74 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 193	52.25 200	75.59 217	84.17 50	63.76 40	73.15 93	82.79 209	59.58 20	86.80 70	67.24 111	86.04 61	87.89 34
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
OPM-MVS		74.73 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 109	87.49 86	47.18 171	85.88 101	69.47 93	80.78 112	83.66 215
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 136	70.20 133	71.89 189	78.55 164	45.29 311	75.94 210	82.92 95	63.68 42	68.16 177	83.59 195	53.89 67	83.49 155	53.97 241	71.12 276	86.89 75

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 74	90.03 43	52.56 87	88.53 29	74.79 53	88.34 29	86.63 88

testing3-2		62.06 304	62.36 287	61.17 356	79.29 138	30.31 437	64.09 378	63.49 377	63.50 44	62.84 287	82.22 231	32.35 359	69.02 362	40.01 361	73.43 241	84.17 191

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 190	83.73 56	86.08 18	63.47 45	72.77 105	87.25 97	53.13 80	87.93 42	71.97 77	85.57 64	86.66 86

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 55	90.47 29	53.96 66	88.68 27	76.48 35	89.63 20	87.16 68

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 36	89.67 18	86.84 77
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 87	87.27 96	55.06 51	86.30 89	71.78 79	84.58 68	89.25 5

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 82	90.25 36	57.68 29	89.96 15	74.62 54	89.03 22	87.89 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 97	72.09 96	73.75 135	81.58 93	49.69 249	77.76 157	77.63 213	63.21 50	73.21 90	89.02 58	42.14 232	83.32 157	61.72 171	82.50 95	88.25 24

plane_prior						56.31 108	83.58 59		63.19 51						80.48 121

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 148	89.74 51	45.43 194	87.16 61	72.01 75	82.87 91	85.14 156
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
PEN-MVS		66.60 243	66.45 223	67.04 287	77.11 221	36.56 396	77.03 182	80.42 149	62.95 53	62.51 298	84.03 183	46.69 179	79.07 259	44.22 323	63.08 369	85.51 137

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 79
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 104	62.90 55	71.77 118	90.26 35	46.61 180	86.55 80	71.71 80	85.66 63	84.97 165

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 88	88.39 30	79.34 9	90.52 13	86.78 80

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 56	88.67 26	88.12 30

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 79	90.50 27	53.20 79	88.35 31	74.02 59	87.05 47	86.13 110

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 85	90.56 25	49.80 132	88.24 33	74.02 59	87.03 48	86.32 103

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 87	90.58 24	49.90 129	88.21 34	73.78 61	87.03 48	86.29 107

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 254	50.37 232	78.17 143	85.06 36	62.80 61	74.40 71	87.86 80	57.88 27	83.61 151	69.46 94	82.79 93	89.59 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
baseline		74.61 65	74.70 61	74.34 115	75.70 249	49.99 240	77.54 162	84.63 43	62.73 62	73.98 78	87.79 83	57.67 30	83.82 147	69.49 92	82.74 94	89.20 7

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 97	89.97 46	50.90 120	87.48 53	75.30 47	86.85 53	87.33 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 257	65.34 249	66.31 298	76.06 245	34.79 409	76.43 196	79.38 166	62.55 64	61.66 309	83.83 188	45.60 188	79.15 256	41.64 353	60.88 384	85.00 162

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 28
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
CP-MVSNet		66.49 246	66.41 227	66.72 289	77.67 200	36.33 399	76.83 189	79.52 163	62.45 66	62.54 296	83.47 201	46.32 182	78.37 271	45.47 318	63.43 366	85.45 142

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 106	90.50 27	48.18 153	87.34 54	73.59 63	85.71 62	84.76 173

PS-CasMVS		66.42 247	66.32 231	66.70 291	77.60 208	36.30 401	76.94 184	79.61 161	62.36 68	62.43 301	83.66 193	45.69 186	78.37 271	45.35 320	63.26 367	85.42 145

3Dnovator	64.47 5	72.49 98	71.39 108	75.79 77	77.70 198	58.99 73	80.66 99	83.15 90	62.24 69	65.46 242	86.59 116	42.38 231	85.52 109	59.59 191	84.72 67	82.85 238

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 43	91.51 11	52.47 90	86.78 71	80.66 4	89.64 19	87.80 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 188

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 188

HQP-MVS		73.45 77	72.80 87	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 188	85.54 151	45.46 192	86.93 67	67.04 113	80.35 122	84.32 184

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 124	86.03 135	53.83 68	86.36 87	67.74 105	86.91 52	88.19 28

VPNet		67.52 222	68.11 184	65.74 312	79.18 145	36.80 394	72.17 291	72.83 296	62.04 75	67.79 195	85.83 142	48.88 147	76.60 313	51.30 264	72.97 250	83.81 205

WR-MVS_H		67.02 234	66.92 215	67.33 286	77.95 190	37.75 383	77.57 160	82.11 107	62.03 76	62.65 293	82.48 224	50.57 123	79.46 247	42.91 341	64.01 359	84.79 171

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 98	88.88 62	53.72 72	89.06 23	68.27 97	88.04 37	87.42 55
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 32	90.18 15	87.87 36

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 112	90.34 33	48.48 151	88.13 37	72.32 72	86.85 53	85.78 122

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 125	75.33 260	52.89 184	78.24 139	77.32 221	61.65 80	78.13 27	88.90 61	52.82 84	81.54 201	78.46 22	78.67 155	87.60 48

Effi-MVS+		73.31 80	72.54 91	75.62 84	77.87 191	53.64 161	79.62 116	79.61 161	61.63 81	72.02 116	82.61 214	56.44 40	85.97 99	63.99 142	79.07 147	87.25 65

MG-MVS		73.96 73	73.89 72	74.16 122	85.65 42	49.69 249	81.59 88	81.29 128	61.45 82	71.05 127	88.11 72	51.77 104	87.73 48	61.05 177	83.09 84	85.05 161

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 172	73.95 281	61.40 83	79.46 19	90.14 37	57.07 34	81.15 211	80.00 5	79.31 139	88.51 18

LPG-MVS_test		72.74 91	71.74 101	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 218	87.33 94	39.15 273	86.59 75	67.70 106	77.30 182	83.19 228

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 218	87.33 94	39.15 273	86.59 75	67.70 106	77.30 182	83.19 228

CLD-MVS		73.33 79	72.68 89	75.29 91	78.82 155	53.33 173	78.23 140	84.79 42	61.30 86	70.41 135	81.04 258	52.41 91	87.12 62	64.61 138	82.49 96	85.41 146
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 120	70.70 124	73.74 136	77.76 196	49.30 257	76.60 192	80.45 148	61.25 87	68.17 176	84.78 161	44.64 206	84.90 125	64.79 134	77.88 171	87.03 71

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 226	74.09 296	51.86 209	77.77 156	75.60 245	61.18 88	78.67 25	88.98 59	55.88 46	77.73 286	78.69 16	78.68 154	83.50 220

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 173	78.40 200	61.18 88	70.58 133	85.97 137	54.18 62	84.00 144	67.52 109	82.98 88	82.45 250

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 182	80.94 94	85.70 24	61.12 90	74.90 61	87.17 98	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

FIs		70.82 133	71.43 106	68.98 266	78.33 175	38.14 379	76.96 183	83.59 69	61.02 91	67.33 202	86.73 108	55.07 50	81.64 197	54.61 237	79.22 142	87.14 69

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	81.50 16

FC-MVSNet-test		69.80 158	70.58 127	67.46 282	77.61 207	34.73 412	76.05 207	83.19 89	60.84 93	65.88 236	86.46 122	54.52 59	80.76 226	52.52 252	78.12 167	86.91 74

v8		70.33 144	69.28 151	73.49 150	73.15 309	50.22 234	78.62 130	80.78 143	60.79 94	66.45 222	82.11 238	49.35 138	84.98 122	63.58 151	68.71 321	85.28 152

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 50	84.83 159	60.76 15	86.56 77	67.86 104	87.87 41	86.06 112

Vis-MVSNet		72.18 105	71.37 109	74.61 106	81.29 100	55.41 132	80.90 95	78.28 203	60.73 96	69.23 160	88.09 73	44.36 210	82.65 178	57.68 208	81.75 106	85.77 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 123	70.16 135	74.57 109	74.59 279	52.77 188	75.91 211	81.20 132	60.72 97	69.10 163	85.71 146	41.67 242	83.53 153	63.91 145	78.62 157	87.42 55

BP-MVS1		73.41 78	72.25 94	76.88 57	76.68 233	53.70 159	79.15 121	81.07 136	60.66 98	71.81 117	87.39 91	40.93 255	87.24 55	71.23 84	81.29 109	89.71 2

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 48	88.32 32	73.48 64	87.03 48	84.83 169
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 103	71.20 114	75.59 86	80.28 117	57.54 90	82.74 69	82.84 99	60.58 100	65.24 250	86.18 129	39.25 271	86.03 97	66.95 117	76.79 190	83.22 226
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 109	87.78 47	75.65 43	87.55 43	87.10 70

testdata1							72.65 280		60.50 102

UGNet		68.81 188	67.39 200	73.06 162	78.33 175	54.47 145	79.77 111	75.40 252	60.45 103	63.22 279	84.40 176	32.71 348	80.91 222	51.71 262	80.56 120	83.81 205
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
viewmacassd2359aftdt		73.15 83	73.16 81	73.11 161	75.15 266	49.31 256	77.53 164	83.21 85	60.42 104	73.20 91	87.34 93	53.82 69	81.05 216	67.02 115	80.79 111	88.96 9

h-mvs33		72.71 92	71.49 105	76.40 68	81.99 88	59.58 57	76.92 185	76.74 230	60.40 105	74.81 63	85.95 138	45.54 190	85.76 104	70.41 89	70.61 283	83.86 204

hse-mvs2		71.04 125	69.86 139	74.60 107	79.58 133	57.12 102	73.96 253	75.25 255	60.40 105	74.81 63	81.95 240	45.54 190	82.90 167	70.41 89	66.83 338	83.77 209

EPP-MVSNet		72.16 108	71.31 111	74.71 100	78.68 159	49.70 247	82.10 81	81.65 113	60.40 105	65.94 232	85.84 141	51.74 105	86.37 86	55.93 221	79.55 134	88.07 33

UniMVSNet_ETH3D		67.60 221	67.07 214	69.18 263	77.39 213	42.29 340	74.18 250	75.59 246	60.37 108	66.77 214	86.06 134	37.64 289	78.93 266	52.16 255	73.49 238	86.32 103

test_prior2							81.75 84		60.37 108	75.01 56	89.06 57	56.22 42		72.19 73	88.96 24

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 108	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 101
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
VNet		69.68 162	70.19 134	68.16 276	79.73 130	41.63 349	70.53 315	77.38 218	60.37 108	70.69 130	86.63 113	51.08 116	77.09 298	53.61 245	81.69 108	85.75 127

sasdasda		74.67 63	74.98 58	73.71 138	78.94 151	50.56 229	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 191	66.01 124	82.12 97	88.58 16

canonicalmvs		74.67 63	74.98 58	73.71 138	78.94 151	50.56 229	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 191	66.01 124	82.12 97	88.58 16

v7n		69.01 184	67.36 202	73.98 126	72.51 323	52.65 190	78.54 134	81.30 127	60.26 114	62.67 292	81.62 247	43.61 216	84.49 134	57.01 212	68.70 322	84.79 171

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 121	87.69 49	72.46 70	84.53 70	85.46 140

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 121	87.69 49	72.46 70	84.53 70	85.46 140

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 137	60.15 117	70.43 134	89.84 48	41.09 254	85.59 107	67.61 108	82.90 90	85.77 125

VPA-MVSNet		69.02 183	69.47 147	67.69 280	77.42 212	41.00 356	74.04 251	79.68 159	60.06 118	69.26 159	84.81 160	51.06 117	77.58 288	54.44 238	74.43 221	84.48 181

v10		70.21 146	69.02 156	73.81 130	73.51 303	50.92 221	78.74 126	81.39 120	60.05 119	66.39 223	81.83 243	47.58 162	85.41 116	62.80 161	68.86 320	85.09 160

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 105	59.99 120	75.10 54	90.35 32	47.66 160	86.52 81	71.64 81	82.99 86	84.47 182

SSC-MVS3.2		60.57 317	61.39 299	58.12 379	74.29 289	32.63 427	59.52 403	65.53 358	59.90 121	62.45 299	79.75 285	41.96 234	63.90 393	39.47 365	69.65 308	77.84 330

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 121	78.57 26	90.36 31	57.51 32	86.86 69	77.39 28	89.52 21

v2v482		70.50 139	69.45 148	73.66 141	72.62 319	50.03 239	77.58 159	80.51 147	59.90 121	69.52 150	82.14 236	47.53 164	84.88 128	65.07 133	70.17 293	86.09 111

Baseline_NR-MVSNet		67.05 233	67.56 192	65.50 316	75.65 250	37.70 385	75.42 220	74.65 268	59.90 121	68.14 178	83.15 207	49.12 145	77.20 296	52.23 254	69.78 302	81.60 263

API-MVS		72.17 106	71.41 107	74.45 113	81.95 89	57.22 95	84.03 51	80.38 150	59.89 125	68.40 171	82.33 227	49.64 133	87.83 46	51.87 259	84.16 77	78.30 321

Effi-MVS+-dtu		69.64 164	67.53 195	75.95 73	76.10 244	62.29 15	80.20 104	76.06 238	59.83 126	65.26 249	77.09 334	41.56 245	84.02 143	60.60 182	71.09 279	81.53 264

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 96	59.65 127	77.31 34	91.43 13	49.62 134	87.24 55	71.99 76	83.75 81	85.14 156

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 130	85.13 33	59.65 127	71.53 123	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 11

CANet_DTU		68.18 206	67.71 191	69.59 254	74.83 272	46.24 299	78.66 129	76.85 227	59.60 129	63.45 277	82.09 239	35.25 313	77.41 291	59.88 188	78.76 152	85.14 156

EI-MVSNet		69.27 177	68.44 173	71.73 196	74.47 282	49.39 254	75.20 225	78.45 196	59.60 129	69.16 161	76.51 346	51.29 112	82.50 183	59.86 190	71.45 273	83.30 223

IterMVS-LS		69.22 179	68.48 169	71.43 210	74.44 284	49.40 253	76.23 201	77.55 214	59.60 129	65.85 237	81.59 250	51.28 113	81.58 200	59.87 189	69.90 300	83.30 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 99	73.34 80	69.81 251	77.77 195	43.21 332	75.84 214	81.18 133	59.59 132	75.45 48	86.64 111	57.74 28	77.94 278	63.92 143	81.90 102	88.30 22

VDDNet		71.81 112	71.33 110	73.26 159	82.80 79	47.60 288	78.74 126	75.27 254	59.59 132	72.94 100	89.40 53	41.51 247	83.91 145	58.75 203	82.99 86	88.26 23

viewmanbaseed2359cas		72.92 88	72.89 85	73.00 163	75.16 264	49.25 259	77.25 176	83.11 93	59.52 134	72.93 101	86.63 113	54.11 63	80.98 217	66.63 118	80.67 115	88.76 13

alignmvs		73.86 74	73.99 70	73.45 152	78.20 178	50.50 231	78.57 132	82.43 102	59.40 135	76.57 41	86.71 110	56.42 41	81.23 210	65.84 127	81.79 103	88.62 14

MVS_Test		72.45 99	72.46 92	72.42 180	74.88 269	48.50 274	76.28 199	83.14 91	59.40 135	72.46 110	84.68 164	55.66 47	81.12 212	65.98 126	79.66 131	87.63 46

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 137	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 44
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 75	73.47 77	74.66 103	83.02 75	59.29 63	82.30 80	81.88 109	59.34 137	71.59 121	86.83 104	45.94 185	83.65 150	65.09 132	85.22 65	81.06 279

PAPM_NR		72.63 95	71.80 99	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 139	66.31 225	85.90 139	51.86 101	86.06 95	57.45 210	80.62 116	85.91 117

testing91		64.46 274	63.80 265	66.47 295	78.43 169	40.06 361	67.63 343	69.59 323	59.06 140	63.18 281	78.05 314	34.05 326	76.99 303	48.30 289	75.87 203	82.37 252

myMVS_eth3d28		60.66 316	61.04 307	59.51 363	77.32 215	31.58 432	63.11 383	63.87 373	59.00 141	60.90 318	78.26 311	32.69 350	66.15 383	36.10 391	78.13 166	80.81 284

save fliter					86.17 33	61.30 28	83.98 53	79.66 160	59.00 141

v148		68.24 204	67.19 212	71.40 211	70.43 362	47.77 285	75.76 215	77.03 225	58.91 143	67.36 201	80.10 278	48.60 150	81.89 193	60.01 186	66.52 341	84.53 179

TransMVSNet (Re)		64.72 268	64.33 258	65.87 311	75.22 261	38.56 375	74.66 240	75.08 263	58.90 144	61.79 307	82.63 213	51.18 114	78.07 276	43.63 334	55.87 407	80.99 281

Anonymous202405211		66.84 238	65.99 237	69.40 258	80.19 122	42.21 342	71.11 308	71.31 308	58.80 145	67.90 186	86.39 124	29.83 376	79.65 244	49.60 279	78.78 151	86.33 101

test2506		65.33 262	64.61 256	67.50 281	79.46 136	34.19 417	74.43 246	51.92 428	58.72 146	66.75 215	88.05 75	25.99 410	80.92 221	51.94 258	84.25 74	87.39 58

ECVR-MVS		67.72 219	67.51 196	68.35 274	79.46 136	36.29 402	74.79 237	66.93 346	58.72 146	67.19 206	88.05 75	36.10 306	81.38 205	52.07 256	84.25 74	87.39 58

test1111		67.21 226	67.14 213	67.42 283	79.24 142	34.76 411	73.89 258	65.65 356	58.71 148	66.96 211	87.95 79	36.09 307	80.53 228	52.03 257	83.79 80	86.97 73

LCM-MVSNet-Re		61.88 307	61.35 300	63.46 336	74.58 280	31.48 433	61.42 393	58.14 406	58.71 148	53.02 401	79.55 290	43.07 222	76.80 307	45.69 311	77.96 169	82.11 258

testing99		64.05 278	63.29 276	66.34 297	78.17 182	39.76 365	67.33 348	68.00 337	58.60 150	63.03 284	78.10 313	32.57 355	76.94 305	48.22 290	75.58 207	82.34 253

v1144		70.42 141	69.31 150	73.76 133	73.22 307	50.64 226	77.83 154	81.43 119	58.58 151	69.40 154	81.16 255	47.53 164	85.29 118	64.01 141	70.64 281	85.34 149

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 189	58.58 151	74.32 73	84.51 174	55.94 45	87.22 58	67.11 112	84.48 73	85.52 136

BH-RMVSNet		68.81 188	67.42 199	72.97 164	80.11 125	52.53 194	74.26 248	76.29 233	58.48 153	68.38 172	84.20 178	42.59 227	83.83 146	46.53 303	75.91 202	82.56 244

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 154	73.71 83	90.14 37	45.62 187	85.99 98	69.64 91	82.85 92	85.78 122

OMC-MVS		71.40 122	70.60 125	73.78 131	76.60 236	53.15 176	79.74 113	79.78 157	58.37 155	68.75 165	86.45 123	45.43 194	80.60 227	62.58 162	77.73 172	87.58 50

nrg030		72.96 87	73.01 83	72.84 167	75.41 258	50.24 233	80.02 105	82.89 98	58.36 156	74.44 70	86.73 108	58.90 24	80.83 223	65.84 127	74.46 219	87.44 54

K. test v3		60.47 320	57.11 339	70.56 236	73.74 300	48.22 277	75.10 229	62.55 385	58.27 157	53.62 396	76.31 350	27.81 394	81.59 199	47.42 294	39.18 444	81.88 261

FA-MVS(test-final)		69.82 156	68.48 169	73.84 129	78.44 168	50.04 238	75.58 219	78.99 174	58.16 158	67.59 198	82.14 236	42.66 226	85.63 105	56.60 214	76.19 196	85.84 120

MVS_111021_LR		69.50 171	68.78 163	71.65 200	78.38 170	59.33 61	74.82 236	70.11 317	58.08 159	67.83 193	84.68 164	41.96 234	76.34 318	65.62 129	77.54 175	79.30 312

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 122	58.07 160	73.14 94	90.07 39	44.74 204	85.84 102	68.20 98	81.76 104	84.03 194

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 122	58.07 160	73.14 94	90.07 39	43.06 223		68.20 98	81.76 104	84.03 194

SDMVSNet		68.03 209	68.10 185	67.84 278	77.13 219	48.72 270	65.32 365	79.10 169	58.02 162	65.08 253	82.55 220	47.83 157	73.40 332	63.92 143	73.92 227	81.41 266

sd_testset		64.46 274	64.45 257	64.51 327	77.13 219	42.25 341	62.67 386	72.11 303	58.02 162	65.08 253	82.55 220	41.22 253	69.88 358	47.32 296	73.92 227	81.41 266

GeoE		71.01 127	70.15 136	73.60 146	79.57 134	52.17 201	78.93 124	78.12 205	58.02 162	67.76 197	83.87 187	52.36 92	82.72 176	56.90 213	75.79 204	85.92 116

ZD-MVS					86.64 21	60.38 45		82.70 100	57.95 165	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

EIA-MVS		71.78 113	70.60 125	75.30 90	79.85 128	53.54 165	77.27 175	83.26 84	57.92 166	66.49 220	79.39 294	52.07 98	86.69 73	60.05 185	79.14 146	85.66 132

test_yl		69.69 160	69.13 153	71.36 214	78.37 172	45.74 304	74.71 238	80.20 152	57.91 167	70.01 143	83.83 188	42.44 229	82.87 170	54.97 231	79.72 129	85.48 138

DCV-MVSNet		69.69 160	69.13 153	71.36 214	78.37 172	45.74 304	74.71 238	80.20 152	57.91 167	70.01 143	83.83 188	42.44 229	82.87 170	54.97 231	79.72 129	85.48 138

MonoMVSNet		64.15 277	63.31 275	66.69 292	70.51 360	44.12 323	74.47 244	74.21 276	57.81 169	63.03 284	76.62 342	38.33 282	77.31 294	54.22 239	60.59 389	78.64 319

dcpmvs_2		74.55 67	75.23 55	72.48 176	82.34 83	53.34 172	77.87 151	81.46 118	57.80 170	75.49 47	86.81 105	62.22 13	77.75 285	71.09 85	82.02 100	86.34 99

diffmvs_AUTHOR		71.02 126	70.87 120	71.45 207	69.89 373	48.97 265	73.16 274	78.33 202	57.79 171	72.11 115	85.26 156	51.84 102	77.89 281	71.00 86	78.47 162	87.49 52

viewdifsd2359ckpt11		69.13 180	68.38 176	71.38 212	71.57 340	48.61 271	73.22 272	73.18 291	57.65 172	70.67 131	84.73 162	50.03 127	79.80 241	63.25 154	71.10 277	85.74 128

viewmsd2359difaftdt		69.13 180	68.38 176	71.38 212	71.57 340	48.61 271	73.22 272	73.18 291	57.65 172	70.67 131	84.73 162	50.03 127	79.80 241	63.25 154	71.10 277	85.74 128

fmvsm_s_conf0.5_n_6		72.59 96	72.87 86	71.73 196	75.14 267	51.96 207	76.28 199	77.12 224	57.63 174	73.85 81	86.91 102	51.54 108	77.87 282	77.18 31	80.18 126	85.37 148

Fast-Effi-MVS+-dtu		67.37 224	65.33 250	73.48 151	72.94 314	57.78 88	77.47 165	76.88 226	57.60 175	61.97 304	76.85 338	39.31 269	80.49 231	54.72 234	70.28 291	82.17 257

v1192		69.97 153	68.68 165	73.85 128	73.19 308	50.94 219	77.68 158	81.36 122	57.51 176	68.95 164	80.85 265	45.28 197	85.33 117	62.97 160	70.37 287	85.27 153

ACMH+	57.40 11	66.12 251	64.06 260	72.30 183	77.79 194	52.83 186	80.39 100	78.03 206	57.30 177	57.47 356	82.55 220	27.68 396	84.17 138	45.54 314	69.78 302	79.90 301

diffmvs		70.69 135	70.43 128	71.46 205	69.45 380	48.95 266	72.93 277	78.46 195	57.27 178	71.69 119	83.97 186	51.48 110	77.92 280	70.70 88	77.95 170	87.53 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 202	67.29 204	71.21 218	79.74 129	53.22 174	76.06 206	77.46 217	57.19 179	66.10 229	81.61 248	45.37 196	83.50 154	45.42 319	76.68 192	76.91 346

viewdifsd2359ckpt13		72.40 102	71.79 100	74.22 120	75.63 251	51.77 211	78.67 128	83.13 92	57.08 180	71.59 121	85.36 155	53.10 81	82.64 179	63.07 158	78.51 159	88.24 25

thres100view900		63.28 287	62.41 286	65.89 309	77.31 216	38.66 374	72.65 280	69.11 330	57.07 181	62.45 299	81.03 259	37.01 301	79.17 253	31.84 412	73.25 245	79.83 304

fmvsm_s_conf0.5_n_7		69.54 168	69.67 143	69.15 265	73.47 305	51.41 214	70.35 319	73.34 287	57.05 182	68.41 170	85.83 142	49.86 130	72.84 335	71.86 78	76.83 189	83.19 228

DP-MVS Recon		72.15 109	70.73 123	76.40 68	86.57 24	57.99 84	81.15 93	82.96 94	57.03 183	66.78 213	85.56 148	44.50 208	88.11 38	51.77 261	80.23 125	83.10 233

thres600view7		63.30 286	62.27 288	66.41 296	77.18 218	38.87 372	72.35 287	69.11 330	56.98 184	62.37 302	80.96 261	37.01 301	79.00 264	31.43 419	73.05 249	81.36 269

V42		68.65 192	67.35 203	72.56 173	68.93 386	50.18 235	72.90 278	79.47 164	56.92 185	69.45 153	80.26 274	46.29 183	82.99 164	64.07 139	67.82 329	84.53 179

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 186	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 89

GA-MVS		65.53 258	63.70 267	71.02 227	70.87 355	48.10 279	70.48 316	74.40 270	56.69 187	64.70 262	76.77 339	33.66 334	81.10 213	55.42 230	70.32 290	83.87 203

v144192		69.71 159	68.51 168	73.33 157	73.10 310	50.13 236	77.54 162	80.64 144	56.65 188	68.57 168	80.55 268	46.87 178	84.96 124	62.98 159	69.66 306	84.89 168

fmvsm_l_conf0.5_n_3		73.23 82	73.13 82	73.55 148	74.40 285	55.13 137	78.97 123	74.96 264	56.64 189	74.76 66	88.75 66	55.02 52	78.77 268	76.33 37	78.31 165	86.74 81

tfpn200view9		63.18 289	62.18 290	66.21 301	76.85 230	39.62 366	71.96 295	69.44 326	56.63 190	62.61 294	79.83 281	37.18 295	79.17 253	31.84 412	73.25 245	79.83 304

thres400		63.31 285	62.18 290	66.72 289	76.85 230	39.62 366	71.96 295	69.44 326	56.63 190	62.61 294	79.83 281	37.18 295	79.17 253	31.84 412	73.25 245	81.36 269

GBi-Net		67.21 226	66.55 221	69.19 260	77.63 202	43.33 329	77.31 169	77.83 209	56.62 192	65.04 255	82.70 210	41.85 237	80.33 233	47.18 298	72.76 253	83.92 200

test1		67.21 226	66.55 221	69.19 260	77.63 202	43.33 329	77.31 169	77.83 209	56.62 192	65.04 255	82.70 210	41.85 237	80.33 233	47.18 298	72.76 253	83.92 200

FMVSNet2		66.93 236	66.31 232	68.79 269	77.63 202	42.98 334	76.11 204	77.47 215	56.62 192	65.22 252	82.17 234	41.85 237	80.18 239	47.05 301	72.72 256	83.20 227

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 185	73.82 297	52.72 189	77.45 166	74.28 274	56.61 195	77.10 38	88.16 71	56.17 43	77.09 298	78.27 24	81.13 110	86.48 93

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 196	72.46 110	86.76 106	56.89 36	87.86 45	66.36 120	88.91 25	83.64 217

v1921920		69.47 172	68.17 182	73.36 156	73.06 311	50.10 237	77.39 167	80.56 145	56.58 197	68.59 166	80.37 270	44.72 205	84.98 122	62.47 165	69.82 301	85.00 162

FMVSNet1		66.70 241	65.87 238	69.19 260	77.49 210	43.33 329	77.31 169	77.83 209	56.45 198	64.60 264	82.70 210	38.08 287	80.33 233	46.08 307	72.31 262	83.92 200

v1240		69.24 178	67.91 187	73.25 160	73.02 313	49.82 241	77.21 177	80.54 146	56.43 199	68.34 173	80.51 269	43.33 219	84.99 120	62.03 169	69.77 304	84.95 166

fmvsm_s_conf0.5_n_4		72.04 110	71.85 98	72.58 172	73.74 300	52.49 196	76.69 190	72.42 299	56.42 200	75.32 49	87.04 99	52.13 97	78.01 277	79.29 12	73.65 233	87.26 64

testing222		62.29 301	61.31 301	65.25 322	77.87 191	38.53 376	68.34 337	66.31 352	56.37 201	63.15 283	77.58 328	28.47 388	76.18 321	37.04 380	76.65 193	81.05 280

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 202	74.05 77	88.98 59	53.34 78	87.92 43	69.23 95	88.42 28	87.59 49

Vis-MVSNet (Re-imp)		63.69 282	63.88 263	63.14 340	74.75 274	31.04 435	71.16 306	63.64 376	56.32 202	59.80 330	84.99 157	44.51 207	75.46 323	39.12 367	80.62 116	82.92 235

AdaColmap		69.99 152	68.66 166	73.97 127	84.94 54	57.83 86	82.63 71	78.71 181	56.28 204	64.34 265	84.14 180	41.57 244	87.06 65	46.45 304	78.88 148	77.02 342

PS-MVSNAJss		72.24 104	71.21 113	75.31 89	78.50 165	55.93 118	81.63 85	82.12 106	56.24 205	70.02 142	85.68 147	47.05 173	84.34 137	65.27 131	74.41 222	85.67 131

c3_l		68.33 201	67.56 192	70.62 235	70.87 355	46.21 300	74.47 244	78.80 179	56.22 206	66.19 226	78.53 309	51.88 100	81.40 204	62.08 166	69.04 316	84.25 187

Fast-Effi-MVS+		70.28 145	69.12 155	73.73 137	78.50 165	51.50 213	75.01 230	79.46 165	56.16 207	68.59 166	79.55 290	53.97 65	84.05 140	53.34 247	77.53 176	85.65 133

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 208	73.41 86	86.58 117	50.94 119	88.54 28	70.79 87	89.71 17	87.79 41

baseline1		63.81 281	63.87 264	63.62 335	76.29 241	36.36 397	71.78 298	67.29 342	56.05 209	64.23 270	82.95 208	47.11 172	74.41 328	47.30 297	61.85 378	80.10 298

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 129	55.86 210	74.93 58	88.81 63	53.70 73	84.68 131	75.24 49	88.33 30	83.65 216

test_8					85.40 46	60.96 34	81.54 89	81.18 133	55.86 210	74.81 63	88.80 65	53.70 73	84.45 135

FMVSNet3		66.32 250	65.61 243	68.46 272	76.48 239	42.34 339	74.98 232	77.15 223	55.83 212	65.04 255	81.16 255	39.91 262	80.14 240	47.18 298	72.76 253	82.90 237

PAPR		71.72 116	70.82 121	74.41 114	81.20 104	51.17 215	79.55 118	83.33 80	55.81 213	66.93 212	84.61 168	50.95 118	86.06 95	55.79 224	79.20 143	86.00 113

eth_miper_zixun_eth		67.63 220	66.28 233	71.67 199	71.60 339	48.33 276	73.68 262	77.88 207	55.80 214	65.91 233	78.62 307	47.35 170	82.88 169	59.45 192	66.25 342	83.81 205

ACMH	55.70 15	65.20 264	63.57 269	70.07 244	78.07 185	52.01 206	79.48 119	79.69 158	55.75 215	56.59 363	80.98 260	27.12 401	80.94 219	42.90 342	71.58 271	77.25 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 261	62.73 283	73.40 155	74.89 268	52.78 187	73.09 276	75.13 259	55.69 216	58.48 348	73.73 379	32.86 343	86.32 88	50.63 269	70.11 294	81.10 278
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
CL-MVSNet_self_test		61.53 310	60.94 309	63.30 338	68.95 385	36.93 393	67.60 344	72.80 297	55.67 217	59.95 327	76.63 341	45.01 203	72.22 342	39.74 364	62.09 377	80.74 286

TEST9					85.58 43	61.59 24	81.62 86	81.26 129	55.65 218	74.93 58	88.81 63	53.70 73	84.68 131

thres200		62.20 302	61.16 306	65.34 320	75.38 259	39.99 362	69.60 328	69.29 328	55.64 219	61.87 306	76.99 335	37.07 300	78.96 265	31.28 420	73.28 244	77.06 341

guyue		68.10 208	67.23 211	70.71 234	73.67 302	49.27 258	73.65 263	76.04 239	55.62 220	67.84 192	82.26 230	41.24 252	78.91 267	61.01 178	73.72 231	83.94 198

pm-mvs1		65.24 263	64.97 254	66.04 306	72.38 326	39.40 369	72.62 282	75.63 244	55.53 221	62.35 303	83.18 206	47.45 166	76.47 316	49.06 283	66.54 340	82.24 254

testing11		62.81 293	61.90 293	65.54 314	78.38 170	40.76 358	67.59 345	66.78 348	55.48 222	60.13 322	77.11 333	31.67 362	76.79 308	45.53 315	74.45 220	79.06 314

ACMM	61.98 7	70.80 134	69.73 141	74.02 124	80.59 116	58.59 79	82.68 70	82.02 108	55.46 223	67.18 207	84.39 177	38.51 279	83.17 161	60.65 181	76.10 200	80.30 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 215	66.83 216	70.93 228	73.50 304	49.34 255	73.28 270	74.01 279	55.45 224	68.10 183	83.28 202	38.93 276	79.14 257	63.22 156	71.74 268	84.30 186

Anonymous20240529		69.91 154	69.02 156	72.56 173	80.19 122	47.65 286	77.56 161	80.99 139	55.45 224	69.88 146	86.76 106	39.24 272	82.18 189	54.04 240	77.10 186	87.85 37

tt0805		67.77 218	67.24 209	69.34 259	74.87 270	40.08 360	77.36 168	81.37 121	55.31 226	66.33 224	84.65 166	37.35 293	82.55 182	55.65 227	72.28 263	85.39 147

GDP-MVS		72.64 94	71.28 112	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 227	71.38 125	86.97 101	39.94 261	87.00 66	67.02 115	79.20 143	88.89 10

CPTT-MVS		72.78 90	72.08 97	74.87 97	84.88 57	61.41 26	84.15 49	77.86 208	55.27 228	67.51 200	88.08 74	41.93 236	81.85 194	69.04 96	80.01 127	81.35 271

XVG-OURS		68.76 191	67.37 201	72.90 166	74.32 288	57.22 95	70.09 323	78.81 178	55.24 229	67.79 195	85.81 145	36.54 304	78.28 273	62.04 168	75.74 205	83.19 228

tfpnnormal		62.47 297	61.63 296	64.99 324	74.81 273	39.01 371	71.22 304	73.72 283	55.22 230	60.21 321	80.09 279	41.26 251	76.98 304	30.02 426	68.09 327	78.97 317

cl____		67.18 229	66.26 234	69.94 246	70.20 366	45.74 304	73.30 267	76.83 228	55.10 231	65.27 246	79.57 289	47.39 168	80.53 228	59.41 194	69.22 314	83.53 219

DIV-MVS_self_test		67.18 229	66.26 234	69.94 246	70.20 366	45.74 304	73.29 269	76.83 228	55.10 231	65.27 246	79.58 288	47.38 169	80.53 228	59.43 193	69.22 314	83.54 218

PC_three_1452									55.09 233	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 19

EPNet_dtu		61.90 306	61.97 292	61.68 349	72.89 315	39.78 364	75.85 213	65.62 357	55.09 233	54.56 386	79.36 295	37.59 290	67.02 377	39.80 363	76.95 187	78.25 322
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 121	70.39 129	74.65 104	82.01 86	58.82 76	79.93 108	80.35 151	55.09 233	65.82 238	82.16 235	49.17 142	82.64 179	60.34 183	78.62 157	82.50 249

cl22		67.47 223	66.45 223	70.54 237	69.85 375	46.49 296	73.85 259	77.35 219	55.07 236	65.51 241	77.92 318	47.64 161	81.10 213	61.58 174	69.32 310	84.01 196

miper_ehance_all_eth		68.03 209	67.24 209	70.40 239	70.54 359	46.21 300	73.98 252	78.68 183	55.07 236	66.05 230	77.80 322	52.16 96	81.31 207	61.53 176	69.32 310	83.67 213

fmvsm_s_conf0.5_n_2		69.82 156	69.27 152	71.46 205	72.00 333	51.08 216	73.30 267	67.79 338	55.06 238	75.24 51	87.51 85	44.02 213	77.00 302	75.67 42	72.86 251	86.31 106

Elysia		70.19 148	68.29 178	75.88 75	74.15 292	54.33 149	78.26 136	83.21 85	55.04 239	67.28 203	83.59 195	30.16 371	86.11 93	63.67 149	79.26 140	87.20 66

StellarMVS		70.19 148	68.29 178	75.88 75	74.15 292	54.33 149	78.26 136	83.21 85	55.04 239	67.28 203	83.59 195	30.16 371	86.11 93	63.67 149	79.26 140	87.20 66

PS-MVSNAJ		70.51 138	69.70 142	72.93 165	81.52 94	55.79 122	74.92 234	79.00 173	55.04 239	69.88 146	78.66 304	47.05 173	82.19 188	61.61 172	79.58 132	80.83 283

fmvsm_s_conf0.1_n_2		69.64 164	69.01 158	71.52 203	71.66 338	51.04 217	73.39 266	67.14 344	55.02 242	75.11 53	87.64 84	42.94 225	77.01 301	75.55 44	72.63 257	86.52 92

mmtdpeth		60.40 321	59.12 322	64.27 330	69.59 377	48.99 263	70.67 313	70.06 318	54.96 243	62.78 288	73.26 384	27.00 403	67.66 370	58.44 206	45.29 436	76.16 351

xiu_mvs_v2_base		70.52 137	69.75 140	72.84 167	81.21 103	55.63 126	75.11 227	78.92 175	54.92 244	69.96 145	79.68 287	47.00 177	82.09 190	61.60 173	79.37 135	80.81 284

MAR-MVS		71.51 118	70.15 136	75.60 85	81.84 90	59.39 60	81.38 90	82.90 96	54.90 245	68.08 184	78.70 302	47.73 158	85.51 110	51.68 263	84.17 76	81.88 261
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
reproduce_monomvs		62.56 295	61.20 305	66.62 293	70.62 358	44.30 320	70.13 322	73.13 294	54.78 246	61.13 315	76.37 349	25.63 413	75.63 322	58.75 203	60.29 390	79.93 300

XVG-OURS-SEG-HR		68.81 188	67.47 198	72.82 169	74.40 285	56.87 105	70.59 314	79.04 172	54.77 247	66.99 210	86.01 136	39.57 267	78.21 274	62.54 163	73.33 243	83.37 222

testing3		56.54 352	55.92 354	58.41 374	77.52 209	27.93 445	69.72 326	56.36 415	54.75 248	58.63 346	77.80 322	20.88 429	71.75 345	25.31 442	62.25 375	75.53 358

Anonymous20231211		69.28 176	68.47 171	71.73 196	80.28 117	47.18 292	79.98 106	82.37 103	54.61 249	67.24 205	84.01 184	39.43 268	82.41 186	55.45 229	72.83 252	85.62 134

SixPastTwentyTwo		61.65 309	58.80 326	70.20 242	75.80 247	47.22 291	75.59 217	69.68 321	54.61 249	54.11 390	79.26 297	27.07 402	82.96 165	43.27 336	49.79 429	80.41 291

test_0402		63.25 288	61.01 308	69.96 245	80.00 126	54.37 148	76.86 188	72.02 304	54.58 251	58.71 342	80.79 267	35.00 316	84.36 136	26.41 440	64.71 353	71.15 410

tttt0517		67.83 216	65.66 242	74.33 116	76.69 232	50.82 223	77.86 152	73.99 280	54.54 252	64.64 263	82.53 223	35.06 315	85.50 111	55.71 225	69.91 299	86.67 85

BH-w/o		66.85 237	65.83 239	69.90 249	79.29 138	52.46 197	74.66 240	76.65 231	54.51 253	64.85 260	78.12 312	45.59 189	82.95 166	43.26 337	75.54 208	74.27 376

AUN-MVS		68.45 200	66.41 227	74.57 109	79.53 135	57.08 103	73.93 256	75.23 256	54.44 254	66.69 216	81.85 242	37.10 299	82.89 168	62.07 167	66.84 337	83.75 210

LTVRE_ROB	55.42 16	63.15 290	61.23 304	68.92 267	76.57 237	47.80 283	59.92 402	76.39 232	54.35 255	58.67 344	82.46 225	29.44 380	81.49 202	42.12 346	71.14 275	77.46 334
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
test_fmvsmconf_n		73.01 86	72.59 90	74.27 118	71.28 350	55.88 120	78.21 142	75.56 247	54.31 256	74.86 62	87.80 82	54.72 56	80.23 237	78.07 26	78.48 160	86.70 82

test_fmvsmconf0.01_n		72.17 106	71.50 104	74.16 122	67.96 392	55.58 129	78.06 147	74.67 267	54.19 257	74.54 69	88.23 69	50.35 126	80.24 236	78.07 26	77.46 178	86.65 87

test_fmvsmconf0.1_n		72.81 89	72.33 93	74.24 119	69.89 373	55.81 121	78.22 141	75.40 252	54.17 258	75.00 57	88.03 78	53.82 69	80.23 237	78.08 25	78.34 164	86.69 83

ETVMVS		59.51 331	58.81 324	61.58 351	77.46 211	34.87 408	64.94 370	59.35 401	54.06 259	61.08 316	76.67 340	29.54 377	71.87 344	32.16 408	74.07 225	78.01 329

ab-mvs		66.65 242	66.42 226	67.37 284	76.17 243	41.73 346	70.41 318	76.14 236	53.99 260	65.98 231	83.51 199	49.48 135	76.24 319	48.60 286	73.46 240	84.14 192

fmvsm_s_conf0.5_n_5		72.69 93	72.80 87	72.37 181	74.11 295	53.21 175	78.12 144	73.31 288	53.98 261	76.81 40	88.05 75	53.38 77	77.37 293	76.64 34	80.78 112	86.53 91

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 262	84.64 3				79.07 13	90.87 5	88.37 21

SSM_0407		70.41 142	68.96 159	74.75 99	78.65 160	53.46 167	77.28 174	80.00 155	53.88 263	68.14 178	84.61 168	43.21 220	86.26 90	58.80 201	76.11 197	84.54 176

SSM_0404		70.84 130	69.41 149	75.12 93	79.20 143	53.86 155	77.89 150	80.00 155	53.88 263	69.40 154	84.61 168	43.21 220	86.56 77	58.80 201	77.68 174	84.95 166

XVG-ACMP-BASELINE		64.36 276	62.23 289	70.74 232	72.35 327	52.45 198	70.80 312	78.45 196	53.84 265	59.87 328	81.10 257	16.24 437	79.32 250	55.64 228	71.76 267	80.47 288

mamba_0408		67.78 217	65.42 246	74.85 98	78.65 160	53.46 167	50.83 437	79.09 170	53.75 266	68.14 178	83.83 188	41.79 240	86.56 77	56.58 215	76.11 197	84.54 176

SSM_04072		64.98 267	65.42 246	63.68 334	78.65 160	53.46 167	50.83 437	79.09 170	53.75 266	68.14 178	83.83 188	41.79 240	53.03 439	56.58 215	76.11 197	84.54 176

VortexMVS		66.41 248	65.50 245	69.16 264	73.75 298	48.14 278	73.41 265	78.28 203	53.73 268	64.98 259	78.33 310	40.62 257	79.07 259	58.88 200	67.50 332	80.26 294

FE-MVS		65.91 253	63.33 274	73.63 144	77.36 214	51.95 208	72.62 282	75.81 241	53.70 269	65.31 244	78.96 300	28.81 386	86.39 85	43.93 328	73.48 239	82.55 245

thisisatest0530		67.92 213	65.78 240	74.33 116	76.29 241	51.03 218	76.89 186	74.25 275	53.67 270	65.59 240	81.76 245	35.15 314	85.50 111	55.94 220	72.47 258	86.47 94

PVSNet_BlendedMVS		68.56 197	67.72 189	71.07 225	77.03 227	50.57 227	74.50 243	81.52 115	53.66 271	64.22 271	79.72 286	49.13 143	82.87 170	55.82 222	73.92 227	79.77 307

patch_mono-2		69.85 155	71.09 116	66.16 302	79.11 148	54.80 143	71.97 294	74.31 272	53.50 272	70.90 129	84.17 179	57.63 31	63.31 395	66.17 121	82.02 100	80.38 292

EG-PatchMatch MVS		64.71 269	62.87 280	70.22 240	77.68 199	53.48 166	77.99 148	78.82 177	53.37 273	56.03 370	77.41 330	24.75 418	84.04 141	46.37 305	73.42 242	73.14 382

SD_0403		63.07 291	63.49 271	61.82 348	75.16 264	31.14 434	71.89 297	73.47 285	53.34 274	58.22 350	81.81 244	45.17 200	73.86 331	37.43 376	74.87 217	80.45 289

DP-MVS		65.68 255	63.66 268	71.75 195	84.93 55	56.87 105	80.74 98	73.16 293	53.06 275	59.09 339	82.35 226	36.79 303	85.94 100	32.82 406	69.96 298	72.45 391

TR-MVS		66.59 245	65.07 253	71.17 221	79.18 145	49.63 251	73.48 264	75.20 258	52.95 276	67.90 186	80.33 273	39.81 265	83.68 149	43.20 338	73.56 237	80.20 295

ET-MVSNet_ETH3D		67.96 212	65.72 241	74.68 102	76.67 234	55.62 128	75.11 227	74.74 265	52.91 277	60.03 325	80.12 277	33.68 333	82.64 179	61.86 170	76.34 194	85.78 122

QAPM		70.05 150	68.81 162	73.78 131	76.54 238	53.43 170	83.23 60	83.48 71	52.89 278	65.90 234	86.29 126	41.55 246	86.49 83	51.01 266	78.40 163	81.42 265

LuminaMVS		68.24 204	66.82 217	72.51 175	73.46 306	53.60 163	76.23 201	78.88 176	52.78 279	68.08 184	80.13 276	32.70 349	81.41 203	63.16 157	75.97 201	82.53 246

icg_test_0407_2		66.41 248	66.75 218	65.37 319	77.06 222	49.73 243	63.79 379	78.60 185	52.70 280	66.19 226	82.58 215	45.17 200	63.65 394	59.20 196	75.46 210	82.74 240

IMVS_0407		68.90 186	67.93 186	71.82 192	77.06 222	49.73 243	74.40 247	78.60 185	52.70 280	66.19 226	82.58 215	45.17 200	83.00 163	59.20 196	75.46 210	82.74 240

IMVS_0404		64.63 271	64.22 259	65.88 310	77.06 222	49.73 243	64.40 373	78.60 185	52.70 280	53.16 400	82.58 215	34.82 318	65.16 388	59.20 196	75.46 210	82.74 240

IMVS_0403		69.09 182	68.14 183	71.95 187	77.06 222	49.73 243	74.51 242	78.60 185	52.70 280	66.69 216	82.58 215	46.43 181	83.38 156	59.20 196	75.46 210	82.74 240

OpenMVS	61.03 9	68.85 187	67.56 192	72.70 171	74.26 290	53.99 154	81.21 92	81.34 126	52.70 280	62.75 291	85.55 150	38.86 277	84.14 139	48.41 288	83.01 85	79.97 299

pmmvs6		63.69 282	62.82 282	66.27 300	70.63 357	39.27 370	73.13 275	75.47 251	52.69 285	59.75 332	82.30 228	39.71 266	77.03 300	47.40 295	64.35 358	82.53 246

IterMVS		62.79 294	61.27 302	67.35 285	69.37 381	52.04 205	71.17 305	68.24 336	52.63 286	59.82 329	76.91 337	37.32 294	72.36 338	52.80 251	63.19 368	77.66 332
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 206	66.36 229	73.63 144	75.61 253	55.35 135	80.77 97	78.56 190	52.48 287	64.27 268	84.10 182	27.45 398	81.84 195	63.45 153	70.56 284	83.69 212

jajsoiax		68.25 203	66.45 223	73.66 141	75.62 252	55.49 131	80.82 96	78.51 192	52.33 288	64.33 266	84.11 181	28.28 390	81.81 196	63.48 152	70.62 282	83.67 213

TAMVS		66.78 240	65.27 251	71.33 217	79.16 147	53.67 160	73.84 260	69.59 323	52.32 289	65.28 245	81.72 246	44.49 209	77.40 292	42.32 345	78.66 156	82.92 235

CDS-MVSNet		66.80 239	65.37 248	71.10 224	78.98 150	53.13 178	73.27 271	71.07 310	52.15 290	64.72 261	80.23 275	43.56 217	77.10 297	45.48 317	78.88 148	83.05 234
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 198	66.56 220	74.21 121	79.60 132	52.95 180	74.94 233	75.48 250	52.09 291	60.10 323	83.27 203	36.54 304	84.70 130	59.32 195	77.69 173	84.99 164

viewmambaseed2359dif		68.91 185	68.18 181	71.11 223	70.21 365	48.05 282	72.28 289	75.90 240	51.96 292	70.93 128	84.47 175	51.37 111	78.59 269	61.55 175	74.97 215	86.68 84

PVSNet_Blended		68.59 193	67.72 189	71.19 219	77.03 227	50.57 227	72.51 285	81.52 115	51.91 293	64.22 271	77.77 325	49.13 143	82.87 170	55.82 222	79.58 132	80.14 297

mvs_anonymous		68.03 209	67.51 196	69.59 254	72.08 331	44.57 318	71.99 293	75.23 256	51.67 294	67.06 209	82.57 219	54.68 57	77.94 278	56.56 217	75.71 206	86.26 108

xiu_mvs_v1_base_debu		68.58 194	67.28 205	72.48 176	78.19 179	57.19 97	75.28 222	75.09 260	51.61 295	70.04 139	81.41 252	32.79 344	79.02 261	63.81 146	77.31 179	81.22 274

xiu_mvs_v1_base		68.58 194	67.28 205	72.48 176	78.19 179	57.19 97	75.28 222	75.09 260	51.61 295	70.04 139	81.41 252	32.79 344	79.02 261	63.81 146	77.31 179	81.22 274

xiu_mvs_v1_base_debi		68.58 194	67.28 205	72.48 176	78.19 179	57.19 97	75.28 222	75.09 260	51.61 295	70.04 139	81.41 252	32.79 344	79.02 261	63.81 146	77.31 179	81.22 274

MVSTER		67.16 231	65.58 244	71.88 190	70.37 364	49.70 247	70.25 321	78.45 196	51.52 298	69.16 161	80.37 270	38.45 280	82.50 183	60.19 184	71.46 272	83.44 221

CNLPA		65.43 259	64.02 261	69.68 252	78.73 158	58.07 83	77.82 155	70.71 313	51.49 299	61.57 311	83.58 198	38.23 285	70.82 350	43.90 329	70.10 295	80.16 296

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 300	70.27 137	86.61 115	48.61 149	86.51 82	53.85 243	87.96 39	78.16 323

miper_enhance_ethall		67.11 232	66.09 236	70.17 243	69.21 383	45.98 302	72.85 279	78.41 199	51.38 301	65.65 239	75.98 356	51.17 115	81.25 208	60.82 180	69.32 310	83.29 225

MSDG		61.81 308	59.23 320	69.55 257	72.64 318	52.63 192	70.45 317	75.81 241	51.38 301	53.70 393	76.11 351	29.52 378	81.08 215	37.70 374	65.79 346	74.93 367

test20.03		53.87 374	54.02 371	53.41 406	61.47 428	28.11 444	61.30 394	59.21 402	51.34 303	52.09 404	77.43 329	33.29 338	58.55 416	29.76 427	60.27 391	73.58 381

MVSFormer		71.50 119	70.38 130	74.88 96	78.76 156	57.15 100	82.79 67	78.48 193	51.26 304	69.49 151	83.22 204	43.99 214	83.24 159	66.06 122	79.37 135	84.23 188

test_djsdf		69.45 173	67.74 188	74.58 108	74.57 281	54.92 141	82.79 67	78.48 193	51.26 304	65.41 243	83.49 200	38.37 281	83.24 159	66.06 122	69.25 313	85.56 135

dmvs_testset		50.16 392	51.90 382	44.94 427	66.49 403	11.78 467	61.01 399	51.50 429	51.17 306	50.30 416	67.44 421	39.28 270	60.29 406	22.38 446	57.49 400	62.76 432

PAPM		67.92 213	66.69 219	71.63 201	78.09 184	49.02 262	77.09 180	81.24 131	51.04 307	60.91 317	83.98 185	47.71 159	84.99 120	40.81 355	79.32 138	80.90 282

Syy-MVS		56.00 359	56.23 352	55.32 392	74.69 276	26.44 451	65.52 360	57.49 410	50.97 308	56.52 364	72.18 388	39.89 263	68.09 366	24.20 443	64.59 356	71.44 406

myMVS_eth3d		54.86 370	54.61 363	55.61 391	74.69 276	27.31 448	65.52 360	57.49 410	50.97 308	56.52 364	72.18 388	21.87 427	68.09 366	27.70 434	64.59 356	71.44 406

miper_lstm_enhance		62.03 305	60.88 310	65.49 317	66.71 401	46.25 298	56.29 421	75.70 243	50.68 310	61.27 313	75.48 363	40.21 260	68.03 368	56.31 219	65.25 349	82.18 255

gg-mvs-nofinetune		57.86 343	56.43 349	62.18 346	72.62 319	35.35 407	66.57 350	56.33 416	50.65 311	57.64 355	57.10 443	30.65 365	76.36 317	37.38 377	78.88 148	74.82 369

TAPA-MVS	59.36 10	66.60 243	65.20 252	70.81 230	76.63 235	48.75 268	76.52 195	80.04 154	50.64 312	65.24 250	84.93 158	39.15 273	78.54 270	36.77 382	76.88 188	85.14 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 351	56.83 344	56.61 386	69.23 382	41.02 353	58.37 408	64.18 369	50.59 313	57.45 357	71.42 396	35.54 311	58.94 414	37.23 378	67.45 333	69.87 419

MVP-Stereo		65.41 260	63.80 265	70.22 240	77.62 206	55.53 130	76.30 198	78.53 191	50.59 313	56.47 366	78.65 305	39.84 264	82.68 177	44.10 327	72.12 265	72.44 392
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 129	69.49 146	75.35 88	77.63 202	55.71 123	76.04 208	81.81 111	50.30 315	69.66 149	85.40 154	52.51 88	84.89 126	51.82 260	80.24 124	85.45 142
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 362	53.81 373	61.11 357	59.39 438	40.98 357	65.89 355	68.28 335	50.21 316	58.11 352	75.42 364	17.03 433	67.63 372	43.79 331	46.21 433	74.73 371

baseline2		63.42 284	61.26 303	69.89 250	72.55 321	47.62 287	71.54 299	68.38 334	50.11 317	54.82 382	75.55 361	43.06 223	80.96 218	48.13 291	67.16 336	81.11 277

test-LLR		58.15 341	58.13 334	58.22 376	68.57 387	44.80 314	65.46 362	57.92 407	50.08 318	55.44 374	69.82 409	32.62 352	57.44 421	49.66 277	73.62 234	72.41 393

test0.0.03 1		53.32 379	53.59 376	52.50 412	62.81 423	29.45 439	59.51 404	54.11 424	50.08 318	54.40 388	74.31 373	32.62 352	55.92 430	30.50 423	63.95 361	72.15 398

fmvsm_s_conf0.5_n		69.58 166	68.84 161	71.79 194	72.31 329	52.90 182	77.90 149	62.43 388	49.97 320	72.85 103	85.90 139	52.21 94	76.49 314	75.75 41	70.26 292	85.97 114

COLMAP_ROB	52.97 17	61.27 314	58.81 324	68.64 270	74.63 278	52.51 195	78.42 135	73.30 289	49.92 321	50.96 408	81.51 251	23.06 421	79.40 248	31.63 416	65.85 344	74.01 379
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 168	68.74 164	71.93 188	72.47 324	53.82 157	78.25 138	62.26 390	49.78 322	73.12 96	86.21 128	52.66 86	76.79 308	75.02 50	68.88 318	85.18 155

WBMVS		60.54 318	60.61 312	60.34 360	78.00 188	35.95 404	64.55 372	64.89 362	49.63 323	63.39 278	78.70 302	33.85 331	67.65 371	42.10 347	70.35 289	77.43 335

tpmvs		58.47 336	56.95 342	63.03 342	70.20 366	41.21 352	67.90 342	67.23 343	49.62 324	54.73 384	70.84 400	34.14 325	76.24 319	36.64 386	61.29 382	71.64 402

fmvsm_s_conf0.1_n		69.41 174	68.60 167	71.83 191	71.07 352	52.88 185	77.85 153	62.44 387	49.58 325	72.97 99	86.22 127	51.68 106	76.48 315	75.53 45	70.10 295	86.14 109

UBG		59.62 330	59.53 318	59.89 361	78.12 183	35.92 405	64.11 377	60.81 398	49.45 326	61.34 312	75.55 361	33.05 339	67.39 375	38.68 369	74.62 218	76.35 350

thisisatest0515		65.83 254	63.50 270	72.82 169	73.75 298	49.50 252	71.32 302	73.12 295	49.39 327	63.82 273	76.50 348	34.95 317	84.84 129	53.20 249	75.49 209	84.13 193

fmvsm_s_conf0.1_n_a		69.32 175	68.44 173	71.96 186	70.91 354	53.78 158	78.12 144	62.30 389	49.35 328	73.20 91	86.55 120	51.99 99	76.79 308	74.83 52	68.68 323	85.32 150

HY-MVS	56.14 13	64.55 273	63.89 262	66.55 294	74.73 275	41.02 353	69.96 324	74.43 269	49.29 329	61.66 309	80.92 262	47.43 167	76.68 312	44.91 322	71.69 269	81.94 259

MIMVSNet1		55.17 367	54.31 368	57.77 382	70.03 370	32.01 430	65.68 358	64.81 363	49.19 330	46.75 427	76.00 353	25.53 414	64.04 391	28.65 431	62.13 376	77.26 339

SCA		60.49 319	58.38 330	66.80 288	74.14 294	48.06 280	63.35 382	63.23 380	49.13 331	59.33 338	72.10 390	37.45 291	74.27 329	44.17 324	62.57 372	78.05 325

test_fmvsmvis_n_1920		70.84 130	70.38 130	72.22 184	71.16 351	55.39 133	75.86 212	72.21 302	49.03 332	73.28 89	86.17 130	51.83 103	77.29 295	75.80 40	78.05 168	83.98 197

testgi		51.90 384	52.37 380	50.51 419	60.39 436	23.55 458	58.42 407	58.15 405	49.03 332	51.83 405	79.21 298	22.39 422	55.59 431	29.24 430	62.64 371	72.40 395

sc_t1		59.76 326	57.84 337	65.54 314	74.87 270	42.95 336	69.61 327	64.16 371	48.90 334	58.68 343	77.12 332	28.19 391	72.35 339	43.75 333	55.28 409	81.31 272

MIMVSNet		57.35 345	57.07 340	58.22 376	74.21 291	37.18 388	62.46 387	60.88 397	48.88 335	55.29 377	75.99 355	31.68 361	62.04 400	31.87 411	72.35 260	75.43 360

gm-plane-assit					71.40 347	41.72 348			48.85 336		73.31 382		82.48 185	48.90 284

fmvsm_l_conf0.5_n		70.99 128	70.82 121	71.48 204	71.45 343	54.40 147	77.18 178	70.46 315	48.67 337	75.17 52	86.86 103	53.77 71	76.86 306	76.33 37	77.51 177	83.17 232

UWE-MVS		60.18 322	59.78 316	61.39 354	77.67 200	33.92 420	69.04 334	63.82 374	48.56 338	64.27 268	77.64 327	27.20 400	70.40 355	33.56 403	76.24 195	79.83 304

cascas		65.98 252	63.42 272	73.64 143	77.26 217	52.58 193	72.26 290	77.21 222	48.56 338	61.21 314	74.60 371	32.57 355	85.82 103	50.38 271	76.75 191	82.52 248

PLC	56.13 14	65.09 265	63.21 277	70.72 233	81.04 106	54.87 142	78.57 132	77.47 215	48.51 340	55.71 371	81.89 241	33.71 332	79.71 243	41.66 351	70.37 287	77.58 333
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 269	62.50 285	71.34 216	79.72 131	55.71 123	79.82 110	74.72 266	48.50 341	56.62 362	84.62 167	33.59 335	82.34 187	29.65 428	75.23 214	75.97 352

anonymousdsp		67.00 235	64.82 255	73.57 147	70.09 369	56.13 113	76.35 197	77.35 219	48.43 342	64.99 258	80.84 266	33.01 341	80.34 232	64.66 136	67.64 331	84.23 188

无先验							79.66 115	74.30 273	48.40 343				80.78 225	53.62 244		79.03 316

FE-MVSNET		55.16 368	53.75 374	59.41 364	65.29 411	33.20 424	67.21 349	66.21 353	48.39 344	49.56 418	73.53 381	29.03 382	72.51 337	30.38 424	54.10 415	72.52 389

114514_t		70.83 132	69.56 144	74.64 105	86.21 31	54.63 144	82.34 76	81.81 111	48.22 345	63.01 286	85.83 142	40.92 256	87.10 63	57.91 207	79.79 128	82.18 255

tpm		57.34 346	58.16 332	54.86 395	71.80 337	34.77 410	67.47 347	56.04 419	48.20 346	60.10 323	76.92 336	37.17 297	53.41 438	40.76 356	65.01 350	76.40 349

test_fmvsm_n_1920		71.73 115	71.14 115	73.50 149	72.52 322	56.53 107	75.60 216	76.16 234	48.11 347	77.22 35	85.56 148	53.10 81	77.43 290	74.86 51	77.14 184	86.55 90

MDA-MVSNet-bldmvs		53.87 374	50.81 387	63.05 341	66.25 405	48.58 273	56.93 419	63.82 374	48.09 348	41.22 439	70.48 405	30.34 368	68.00 369	34.24 398	45.92 435	72.57 388

XXY-MVS		60.68 315	61.67 295	57.70 383	70.43 362	38.45 377	64.19 375	66.47 349	48.05 349	63.22 279	80.86 264	49.28 140	60.47 404	45.25 321	67.28 335	74.19 377

F-COLMAP		63.05 292	60.87 311	69.58 256	76.99 229	53.63 162	78.12 144	76.16 234	47.97 350	52.41 403	81.61 248	27.87 393	78.11 275	40.07 358	66.66 339	77.00 343

tt0320-xc		58.33 338	56.41 350	64.08 331	75.79 248	41.34 350	68.30 338	62.72 384	47.90 351	56.29 367	74.16 376	28.53 387	71.04 349	41.50 354	52.50 421	79.88 302

fmvsm_l_conf0.5_n_a		70.50 139	70.27 132	71.18 220	71.30 349	54.09 152	76.89 186	69.87 319	47.90 351	74.37 72	86.49 121	53.07 83	76.69 311	75.41 46	77.11 185	82.76 239

Patchmatch-RL test		58.16 340	55.49 357	66.15 303	67.92 393	48.89 267	60.66 400	51.07 432	47.86 353	59.36 335	62.71 437	34.02 328	72.27 341	56.41 218	59.40 393	77.30 337

D2MVS		62.30 300	60.29 314	68.34 275	66.46 404	48.42 275	65.70 357	73.42 286	47.71 354	58.16 351	75.02 367	30.51 366	77.71 287	53.96 242	71.68 270	78.90 318

ANet_high		41.38 411	37.47 418	53.11 408	39.73 464	24.45 456	56.94 418	69.69 320	47.65 355	26.04 456	52.32 446	12.44 445	62.38 399	21.80 447	10.61 465	72.49 390

CostFormer		64.04 279	62.51 284	68.61 271	71.88 335	45.77 303	71.30 303	70.60 314	47.55 356	64.31 267	76.61 344	41.63 243	79.62 246	49.74 275	69.00 317	80.42 290

PatchmatchNet		59.84 325	58.24 331	64.65 326	73.05 312	46.70 295	69.42 330	62.18 391	47.55 356	58.88 341	71.96 392	34.49 322	69.16 360	42.99 340	63.60 363	78.07 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 366	53.89 372	59.21 368	57.80 442	27.47 447	57.75 414	74.32 271	47.38 358	50.90 409	70.00 408	28.45 389	70.30 356	40.44 357	57.92 398	79.87 303

ITE_SJBPF				62.09 347	66.16 406	44.55 319		64.32 367	47.36 359	55.31 376	80.34 272	19.27 430	62.68 398	36.29 390	62.39 374	79.04 315

KD-MVS_2432*1600		53.45 376	51.50 385	59.30 365	62.82 421	37.14 389	55.33 422	71.79 306	47.34 360	55.09 379	70.52 403	21.91 425	70.45 353	35.72 393	42.97 439	70.31 415

miper_refine_blended		53.45 376	51.50 385	59.30 365	62.82 421	37.14 389	55.33 422	71.79 306	47.34 360	55.09 379	70.52 403	21.91 425	70.45 353	35.72 393	42.97 439	70.31 415

OurMVSNet-221017-0		61.37 313	58.63 328	69.61 253	72.05 332	48.06 280	73.93 256	72.51 298	47.23 362	54.74 383	80.92 262	21.49 428	81.24 209	48.57 287	56.22 406	79.53 309

tpmrst		58.24 339	58.70 327	56.84 385	66.97 398	34.32 415	69.57 329	61.14 396	47.17 363	58.58 347	71.60 395	41.28 250	60.41 405	49.20 281	62.84 370	75.78 355

tt0320		58.59 335	56.81 345	63.92 333	75.46 256	41.32 351	68.63 336	64.06 372	47.05 364	56.19 368	74.19 374	30.34 368	71.36 346	39.92 362	55.45 408	79.09 313

PVSNet	50.76 19	58.40 337	57.39 338	61.42 352	75.53 255	44.04 324	61.43 392	63.45 378	47.04 365	56.91 360	73.61 380	27.00 403	64.76 389	39.12 367	72.40 259	75.47 359

WB-MVSnew		59.66 328	59.69 317	59.56 362	75.19 263	35.78 406	69.34 331	64.28 368	46.88 366	61.76 308	75.79 357	40.61 258	65.20 387	32.16 408	71.21 274	77.70 331

UWE-MVS-28		52.25 383	52.35 381	51.93 416	66.99 397	22.79 459	63.48 381	48.31 440	46.78 367	52.73 402	76.11 351	27.78 395	57.82 420	20.58 449	68.41 325	75.17 361

FMVSNet5		55.86 360	54.93 360	58.66 373	71.05 353	36.35 398	64.18 376	62.48 386	46.76 368	50.66 413	74.73 370	25.80 411	64.04 391	33.11 404	65.57 347	75.59 357

jason		69.65 163	68.39 175	73.43 154	78.27 177	56.88 104	77.12 179	73.71 284	46.53 369	69.34 156	83.22 204	43.37 218	79.18 252	64.77 135	79.20 143	84.23 188
jason: jason.
MS-PatchMatch		62.42 298	61.46 298	65.31 321	75.21 262	52.10 202	72.05 292	74.05 278	46.41 370	57.42 358	74.36 372	34.35 324	77.57 289	45.62 313	73.67 232	66.26 429

1112_ss		64.00 280	63.36 273	65.93 308	79.28 140	42.58 338	71.35 301	72.36 301	46.41 370	60.55 320	77.89 320	46.27 184	73.28 333	46.18 306	69.97 297	81.92 260

lupinMVS		69.57 167	68.28 180	73.44 153	78.76 156	57.15 100	76.57 193	73.29 290	46.19 372	69.49 151	82.18 232	43.99 214	79.23 251	64.66 136	79.37 135	83.93 199

testdata				64.66 325	81.52 94	52.93 181		65.29 360	46.09 373	73.88 80	87.46 88	38.08 287	66.26 382	53.31 248	78.48 160	74.78 370

UnsupCasMVSNet_eth		53.16 381	52.47 379	55.23 393	59.45 437	33.39 423	59.43 405	69.13 329	45.98 374	50.35 415	72.32 387	29.30 381	58.26 418	42.02 349	44.30 437	74.05 378

AllTest		57.08 348	54.65 362	64.39 328	71.44 344	49.03 260	69.92 325	67.30 340	45.97 375	47.16 424	79.77 283	17.47 431	67.56 373	33.65 400	59.16 394	76.57 347

TestCases				64.39 328	71.44 344	49.03 260		67.30 340	45.97 375	47.16 424	79.77 283	17.47 431	67.56 373	33.65 400	59.16 394	76.57 347

WTY-MVS		59.75 327	60.39 313	57.85 381	72.32 328	37.83 382	61.05 398	64.18 369	45.95 377	61.91 305	79.11 299	47.01 176	60.88 403	42.50 344	69.49 309	74.83 368

IterMVS-SCA-FT		62.49 296	61.52 297	65.40 318	71.99 334	50.80 224	71.15 307	69.63 322	45.71 378	60.61 319	77.93 317	37.45 291	65.99 384	55.67 226	63.50 365	79.42 310

WB-MVS		43.26 405	43.41 405	42.83 431	63.32 420	10.32 469	58.17 410	45.20 447	45.42 379	40.44 442	67.26 424	34.01 329	58.98 413	11.96 460	24.88 454	59.20 435

旧先验2							76.08 205		45.32 380	76.55 42			65.56 386	58.75 203

OpenMVS_ROB	52.78 18	60.03 323	58.14 333	65.69 313	70.47 361	44.82 313	75.33 221	70.86 312	45.04 381	56.06 369	76.00 353	26.89 405	79.65 244	35.36 395	67.29 334	72.60 387

TinyColmap		54.14 371	51.72 383	61.40 353	66.84 400	41.97 343	66.52 351	68.51 333	44.81 382	42.69 438	75.77 358	11.66 447	72.94 334	31.96 410	56.77 404	69.27 423

MDTV_nov1_ep13			57.00 341		72.73 317	38.26 378	65.02 369	64.73 365	44.74 383	55.46 373	72.48 386	32.61 354	70.47 352	37.47 375	67.75 330

新几何1				70.76 231	85.66 41	61.13 30		66.43 350	44.68 384	70.29 136	86.64 111	41.29 249	75.23 324	49.72 276	81.75 106	75.93 353

Patchmtry		57.16 347	56.47 348	59.23 367	69.17 384	34.58 413	62.98 384	63.15 381	44.53 385	56.83 361	74.84 368	35.83 309	68.71 363	40.03 359	60.91 383	74.39 375

ppachtmachnet_test		58.06 342	55.38 358	66.10 305	69.51 378	48.99 263	68.01 341	66.13 354	44.50 386	54.05 391	70.74 401	32.09 360	72.34 340	36.68 385	56.71 405	76.99 345

PatchT		53.17 380	53.44 377	52.33 413	68.29 391	25.34 455	58.21 409	54.41 423	44.46 387	54.56 386	69.05 415	33.32 337	60.94 402	36.93 381	61.76 380	70.73 413

EPMVS		53.96 372	53.69 375	54.79 396	66.12 407	31.96 431	62.34 389	49.05 436	44.42 388	55.54 372	71.33 398	30.22 370	56.70 424	41.65 352	62.54 373	75.71 356

pmmvs4		61.48 312	59.39 319	67.76 279	71.57 340	53.86 155	71.42 300	65.34 359	44.20 389	59.46 334	77.92 318	35.90 308	74.71 326	43.87 330	64.87 352	74.71 372

dp		51.89 385	51.60 384	52.77 410	68.44 390	32.45 429	62.36 388	54.57 422	44.16 390	49.31 419	67.91 417	28.87 385	56.61 426	33.89 399	54.89 411	69.24 424

PatchMatch-RL		56.25 357	54.55 364	61.32 355	77.06 222	56.07 115	65.57 359	54.10 425	44.13 391	53.49 399	71.27 399	25.20 415	66.78 378	36.52 388	63.66 362	61.12 433

our_test_3		56.49 353	54.42 365	62.68 344	69.51 378	45.48 309	66.08 354	61.49 394	44.11 392	50.73 412	69.60 412	33.05 339	68.15 365	38.38 371	56.86 402	74.40 374

USDC		56.35 356	54.24 369	62.69 343	64.74 413	40.31 359	65.05 368	73.83 282	43.93 393	47.58 422	77.71 326	15.36 440	75.05 325	38.19 373	61.81 379	72.70 386

PM-MVS		52.33 382	50.19 391	58.75 372	62.10 426	45.14 312	65.75 356	40.38 454	43.60 394	53.52 397	72.65 385	9.16 455	65.87 385	50.41 270	54.18 414	65.24 431

pmmvs-eth3d		58.81 334	56.31 351	66.30 299	67.61 394	52.42 199	72.30 288	64.76 364	43.55 395	54.94 381	74.19 374	28.95 383	72.60 336	43.31 335	57.21 401	73.88 380

SSC-MVS		41.96 410	41.99 409	41.90 432	62.46 425	9.28 471	57.41 417	44.32 450	43.38 396	38.30 448	66.45 427	32.67 351	58.42 417	10.98 461	21.91 457	57.99 439

new-patchmatchnet		47.56 399	47.73 399	47.06 422	58.81 440	9.37 470	48.78 441	59.21 402	43.28 397	44.22 434	68.66 416	25.67 412	57.20 423	31.57 418	49.35 430	74.62 373

Test_1112_low_res		62.32 299	61.77 294	64.00 332	79.08 149	39.53 368	68.17 339	70.17 316	43.25 398	59.03 340	79.90 280	44.08 211	71.24 348	43.79 331	68.42 324	81.25 273

RPMNet		61.53 310	58.42 329	70.86 229	69.96 371	52.07 203	65.31 366	81.36 122	43.20 399	59.36 335	70.15 407	35.37 312	85.47 113	36.42 389	64.65 354	75.06 363

tpm2		62.07 303	60.10 315	67.99 277	72.79 316	43.86 325	71.05 310	66.85 347	43.14 400	62.77 289	75.39 365	38.32 283	80.80 224	41.69 350	68.88 318	79.32 311

JIA-IIPM		51.56 386	47.68 400	63.21 339	64.61 414	50.73 225	47.71 443	58.77 404	42.90 401	48.46 421	51.72 447	24.97 416	70.24 357	36.06 392	53.89 416	68.64 425

1314		64.61 272	63.21 277	68.80 268	71.87 336	47.46 289	73.95 254	78.39 201	42.88 402	59.97 326	76.60 345	38.11 286	79.39 249	54.84 233	72.32 261	79.55 308

HyFIR lowres test		65.67 256	63.01 279	73.67 140	79.97 127	55.65 125	69.07 333	75.52 248	42.68 403	63.53 276	77.95 316	40.43 259	81.64 197	46.01 308	71.91 266	83.73 211

CR-MVSNet		59.91 324	57.90 336	65.96 307	69.96 371	52.07 203	65.31 366	63.15 381	42.48 404	59.36 335	74.84 368	35.83 309	70.75 351	45.50 316	64.65 354	75.06 363

test222					83.14 72	58.68 78	72.57 284	63.45 378	41.78 405	67.56 199	86.12 131	37.13 298			78.73 153	74.98 366

TDRefinement		53.44 378	50.72 388	61.60 350	64.31 416	46.96 293	70.89 311	65.27 361	41.78 405	44.61 433	77.98 315	11.52 449	66.36 381	28.57 432	51.59 423	71.49 405

sss		56.17 358	56.57 347	54.96 394	66.93 399	36.32 400	57.94 411	61.69 393	41.67 407	58.64 345	75.32 366	38.72 278	56.25 428	42.04 348	66.19 343	72.31 396

PVSNet_0	43.31 20	47.46 400	45.64 403	52.92 409	67.60 395	44.65 316	54.06 427	54.64 421	41.59 408	46.15 429	58.75 440	30.99 364	58.66 415	32.18 407	24.81 455	55.46 443

MVS		67.37 224	66.33 230	70.51 238	75.46 256	50.94 219	73.95 254	81.85 110	41.57 409	62.54 296	78.57 308	47.98 154	85.47 113	52.97 250	82.05 99	75.14 362

Anonymous20240521		55.30 364	54.41 366	57.96 380	60.92 435	41.73 346	71.09 309	71.06 311	41.18 410	48.65 420	73.31 382	16.93 434	59.25 411	42.54 343	64.01 359	72.90 384

Anonymous20231206		55.10 369	55.30 359	54.48 397	69.81 376	33.94 419	62.91 385	62.13 392	41.08 411	55.18 378	75.65 359	32.75 347	56.59 427	30.32 425	67.86 328	72.91 383

MDA-MVSNet_test_wron		50.71 391	48.95 393	56.00 390	61.17 430	41.84 344	51.90 433	56.45 413	40.96 412	44.79 432	67.84 418	30.04 374	55.07 435	36.71 384	50.69 426	71.11 411

YYNet1		50.73 390	48.96 392	56.03 389	61.10 431	41.78 345	51.94 432	56.44 414	40.94 413	44.84 431	67.80 419	30.08 373	55.08 434	36.77 382	50.71 425	71.22 408

dongtai		34.52 420	34.94 420	33.26 441	61.06 432	16.00 466	52.79 431	23.78 467	40.71 414	39.33 446	48.65 455	16.91 435	48.34 447	12.18 459	19.05 459	35.44 458

CHOSEN 1792x2688		65.08 266	62.84 281	71.82 192	81.49 96	56.26 111	66.32 353	74.20 277	40.53 415	63.16 282	78.65 305	41.30 248	77.80 284	45.80 310	74.09 224	81.40 268

pmmvs5		56.47 354	55.68 356	58.86 371	61.41 429	36.71 395	66.37 352	62.75 383	40.38 416	53.70 393	76.62 342	34.56 320	67.05 376	40.02 360	65.27 348	72.83 385

test_vis1_n_1920		58.86 333	59.06 323	58.25 375	63.76 417	43.14 333	67.49 346	66.36 351	40.22 417	65.89 235	71.95 393	31.04 363	59.75 409	59.94 187	64.90 351	71.85 400

MDTV_nov1_ep13_2view						25.89 453	61.22 395		40.10 418	51.10 407		32.97 342		38.49 370		78.61 320

tpm cat1		59.25 332	56.95 342	66.15 303	72.19 330	46.96 293	68.09 340	65.76 355	40.03 419	57.81 354	70.56 402	38.32 283	74.51 327	38.26 372	61.50 381	77.00 343

test-mter		56.42 355	55.82 355	58.22 376	68.57 387	44.80 314	65.46 362	57.92 407	39.94 420	55.44 374	69.82 409	21.92 424	57.44 421	49.66 277	73.62 234	72.41 393

UnsupCasMVSNet_bld		50.07 393	48.87 394	53.66 402	60.97 434	33.67 421	57.62 415	64.56 366	39.47 421	47.38 423	64.02 435	27.47 397	59.32 410	34.69 397	43.68 438	67.98 427

TESTMET0.1,1		55.28 365	54.90 361	56.42 387	66.56 402	43.67 327	65.46 362	56.27 417	39.18 422	53.83 392	67.44 421	24.21 419	55.46 432	48.04 292	73.11 248	70.13 417

mamv4		56.85 350	58.00 335	53.43 405	72.46 325	54.47 145	57.56 416	54.74 420	38.81 423	57.42 358	79.45 293	47.57 163	38.70 458	60.88 179	53.07 418	67.11 428

ADS-MVSNet2		51.33 388	48.76 395	59.07 370	66.02 408	44.60 317	50.90 435	59.76 400	36.90 424	50.74 410	66.18 429	26.38 406	63.11 396	27.17 436	54.76 412	69.50 421

ADS-MVSNet		48.48 397	47.77 398	50.63 418	66.02 408	29.92 438	50.90 435	50.87 434	36.90 424	50.74 410	66.18 429	26.38 406	52.47 441	27.17 436	54.76 412	69.50 421

RPSCF		55.80 361	54.22 370	60.53 359	65.13 412	42.91 337	64.30 374	57.62 409	36.84 426	58.05 353	82.28 229	28.01 392	56.24 429	37.14 379	58.61 396	82.44 251

test_cas_vis1_n_1920		56.91 349	56.71 346	57.51 384	59.13 439	45.40 310	63.58 380	61.29 395	36.24 427	67.14 208	71.85 394	29.89 375	56.69 425	57.65 209	63.58 364	70.46 414

Patchmatch-test		49.08 395	48.28 397	51.50 417	64.40 415	30.85 436	45.68 447	48.46 439	35.60 428	46.10 430	72.10 390	34.47 323	46.37 450	27.08 438	60.65 387	77.27 338

CHOSEN 280x420		47.83 398	46.36 402	52.24 415	67.37 396	49.78 242	38.91 455	43.11 452	35.00 429	43.27 437	63.30 436	28.95 383	49.19 446	36.53 387	60.80 385	57.76 440

N_pmnet		39.35 415	40.28 412	36.54 438	63.76 417	1.62 475	49.37 440	0.76 474	34.62 430	43.61 436	66.38 428	26.25 408	42.57 454	26.02 441	51.77 422	65.44 430

kuosan		29.62 427	30.82 426	26.02 446	52.99 445	16.22 465	51.09 434	22.71 468	33.91 431	33.99 450	40.85 456	15.89 438	33.11 463	7.59 467	18.37 460	28.72 460

PMMVS		53.96 372	53.26 378	56.04 388	62.60 424	50.92 221	61.17 396	56.09 418	32.81 432	53.51 398	66.84 426	34.04 327	59.93 408	44.14 326	68.18 326	57.27 441

CMPMVS	42.80 21	57.81 344	55.97 353	63.32 337	60.98 433	47.38 290	64.66 371	69.50 325	32.06 433	46.83 426	77.80 322	29.50 379	71.36 346	48.68 285	73.75 230	71.21 409
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 401	42.95 406	53.39 407	52.33 449	29.15 440	57.77 412	48.20 441	31.81 434	49.86 417	77.21 331	8.69 456	59.16 412	27.31 435	33.40 451	71.84 401

CVMVSNet		59.63 329	59.14 321	61.08 358	74.47 282	38.84 373	75.20 225	68.74 332	31.15 435	58.24 349	76.51 346	32.39 357	68.58 364	49.77 274	65.84 345	75.81 354

FPMVS		42.18 409	41.11 411	45.39 424	58.03 441	41.01 355	49.50 439	53.81 426	30.07 436	33.71 451	64.03 433	11.69 446	52.08 444	14.01 455	55.11 410	43.09 452

EU-MVSNet		55.61 363	54.41 366	59.19 369	65.41 410	33.42 422	72.44 286	71.91 305	28.81 437	51.27 406	73.87 378	24.76 417	69.08 361	43.04 339	58.20 397	75.06 363

test_vis1_n		49.89 394	48.69 396	53.50 404	53.97 443	37.38 387	61.53 391	47.33 444	28.54 438	59.62 333	67.10 425	13.52 442	52.27 442	49.07 282	57.52 399	70.84 412

test_fmvs1_n		51.37 387	50.35 390	54.42 399	52.85 446	37.71 384	61.16 397	51.93 427	28.15 439	63.81 274	69.73 411	13.72 441	53.95 436	51.16 265	60.65 387	71.59 403

LF4IMVS		42.95 406	42.26 408	45.04 425	48.30 454	32.50 428	54.80 424	48.49 438	28.03 440	40.51 441	70.16 406	9.24 454	43.89 453	31.63 416	49.18 431	58.72 437

test_fmvs1		51.32 389	50.48 389	53.81 401	53.57 444	37.51 386	60.63 401	51.16 430	28.02 441	63.62 275	69.23 414	16.41 436	53.93 437	51.01 266	60.70 386	69.99 418

MVS-HIRNet		45.52 402	44.48 404	48.65 421	68.49 389	34.05 418	59.41 406	44.50 449	27.03 442	37.96 449	50.47 451	26.16 409	64.10 390	26.74 439	59.52 392	47.82 450

PMVS	28.69 22	36.22 418	33.29 423	45.02 426	36.82 466	35.98 403	54.68 425	48.74 437	26.31 443	21.02 459	51.61 448	2.88 468	60.10 407	9.99 464	47.58 432	38.99 457
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 403	41.95 410	53.86 400	52.58 448	43.55 328	62.11 390	46.90 446	26.05 444	40.63 440	60.19 439	11.08 452	57.91 419	31.83 415	46.15 434	60.11 434

test_fmvs2		48.69 396	47.49 401	52.29 414	48.63 453	33.06 426	57.76 413	48.05 442	25.71 445	59.76 331	69.60 412	11.57 448	52.23 443	49.45 280	56.86 402	71.58 404

PMMVS2		27.40 428	25.91 431	31.87 443	39.46 465	6.57 472	31.17 458	28.52 463	23.96 446	20.45 460	48.94 454	4.20 464	37.94 459	16.51 452	19.97 458	51.09 445

MVStest1		42.65 407	39.29 414	52.71 411	47.26 456	34.58 413	54.41 426	50.84 435	23.35 447	39.31 447	74.08 377	12.57 444	55.09 433	23.32 444	28.47 453	68.47 426

Gipuma		34.77 419	31.91 424	43.33 429	62.05 427	37.87 380	20.39 460	67.03 345	23.23 448	18.41 461	25.84 461	4.24 462	62.73 397	14.71 454	51.32 424	29.38 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 412	39.45 413	47.03 423	46.65 457	37.86 381	47.76 442	38.65 455	23.10 449	44.21 435	51.22 449	11.20 451	44.08 452	39.27 366	53.02 419	59.14 436

new_pmnet		34.13 421	34.29 422	33.64 440	52.63 447	18.23 464	44.43 450	33.90 460	22.81 450	30.89 453	53.18 445	10.48 453	35.72 462	20.77 448	39.51 443	46.98 451

mvsany_test1		39.38 414	38.16 417	43.02 430	49.05 451	34.28 416	44.16 451	25.94 465	22.74 451	46.57 428	62.21 438	23.85 420	41.16 457	33.01 405	35.91 447	53.63 444

LCM-MVSNet		40.30 413	35.88 419	53.57 403	42.24 459	29.15 440	45.21 449	60.53 399	22.23 452	28.02 454	50.98 450	3.72 465	61.78 401	31.22 421	38.76 445	69.78 420

test_fmvs3		44.30 404	42.55 407	49.55 420	42.83 458	27.15 450	53.03 429	44.93 448	22.03 453	53.69 395	64.94 432	4.21 463	49.63 445	47.47 293	49.82 428	71.88 399

APD_test1		37.39 417	34.94 420	44.72 428	48.88 452	33.19 425	52.95 430	44.00 451	19.49 454	27.28 455	58.59 441	3.18 467	52.84 440	18.92 450	41.17 442	48.14 449

mvsany_test3		32.62 422	30.57 427	38.77 436	36.16 467	24.20 457	38.10 456	20.63 469	19.14 455	40.36 443	57.43 442	5.06 460	36.63 461	29.59 429	28.66 452	55.49 442

E-PMN		23.77 429	22.73 433	26.90 444	42.02 460	20.67 461	42.66 452	35.70 458	17.43 456	10.28 466	25.05 462	6.42 458	42.39 455	10.28 463	14.71 462	17.63 461

EMVS		22.97 430	21.84 434	26.36 445	40.20 463	19.53 463	41.95 453	34.64 459	17.09 457	9.73 467	22.83 463	7.29 457	42.22 456	9.18 465	13.66 463	17.32 462

test_vis3_rt		32.09 423	30.20 428	37.76 437	35.36 468	27.48 446	40.60 454	28.29 464	16.69 458	32.52 452	40.53 457	1.96 469	37.40 460	33.64 402	42.21 441	48.39 447

test_f		31.86 424	31.05 425	34.28 439	32.33 470	21.86 460	32.34 457	30.46 462	16.02 459	39.78 445	55.45 444	4.80 461	32.36 464	30.61 422	37.66 446	48.64 446

DSMNet-mixed		39.30 416	38.72 415	41.03 433	51.22 450	19.66 462	45.53 448	31.35 461	15.83 460	39.80 444	67.42 423	22.19 423	45.13 451	22.43 445	52.69 420	58.31 438

testf1		31.46 425	28.89 429	39.16 434	41.99 461	28.78 442	46.45 445	37.56 456	14.28 461	21.10 457	48.96 452	1.48 471	47.11 448	13.63 456	34.56 448	41.60 453

APD_test2		31.46 425	28.89 429	39.16 434	41.99 461	28.78 442	46.45 445	37.56 456	14.28 461	21.10 457	48.96 452	1.48 471	47.11 448	13.63 456	34.56 448	41.60 453

MVE	17.77 23	21.41 431	17.77 436	32.34 442	34.34 469	25.44 454	16.11 461	24.11 466	11.19 463	13.22 463	31.92 459	1.58 470	30.95 465	10.47 462	17.03 461	40.62 456
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 449	17.97 471	10.91 468		10.60 472	7.46 464	11.07 465	28.36 460	3.28 466	11.29 468	8.01 466	9.74 467	13.89 463

wuyk23d		13.32 434	12.52 437	15.71 448	47.54 455	26.27 452	31.06 459	1.98 473	4.93 465	5.18 468	1.94 468	0.45 473	18.54 467	6.81 468	12.83 464	2.33 465

test_method		19.68 432	18.10 435	24.41 447	13.68 472	3.11 474	12.06 463	42.37 453	2.00 466	11.97 464	36.38 458	5.77 459	29.35 466	15.06 453	23.65 456	40.76 455

tmp_tt		9.43 435	11.14 438	4.30 450	2.38 473	4.40 473	13.62 462	16.08 471	0.39 467	15.89 462	13.06 464	15.80 439	5.54 469	12.63 458	10.46 466	2.95 464

EGC-MVSNET		42.47 408	38.48 416	54.46 398	74.33 287	48.73 269	70.33 320	51.10 431	0.03 468	0.18 469	67.78 420	13.28 443	66.49 380	18.91 451	50.36 427	48.15 448

testmvs		4.52 438	6.03 441	0.01 452	0.01 474	0.00 477	53.86 428	0.00 475	0.01 469	0.04 470	0.27 469	0.00 475	0.00 470	0.04 469	0.00 468	0.03 467

test123		4.73 437	6.30 440	0.02 451	0.01 474	0.01 476	56.36 420	0.00 475	0.01 469	0.04 470	0.21 470	0.01 474	0.00 470	0.03 470	0.00 468	0.04 466

mmdepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

monomultidepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

test_blank		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

uanet_test		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

DCPMVS		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

cdsmvs_eth3d_5k		17.50 433	23.34 432	0.00 453	0.00 476	0.00 477	0.00 464	78.63 184	0.00 471	0.00 472	82.18 232	49.25 141	0.00 470	0.00 471	0.00 468	0.00 468

pcd_1.5k_mvsjas		3.92 439	5.23 442	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	47.05 173	0.00 470	0.00 471	0.00 468	0.00 468

sosnet-low-res		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

sosnet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

uncertanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

Regformer		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

ab-mvs-re		6.49 436	8.65 439	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	77.89 320	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

uanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 477	0.00 464	0.00 475	0.00 471	0.00 472	0.00 471	0.00 475	0.00 470	0.00 471	0.00 468	0.00 468

WAC-MVS						27.31 448								27.77 433

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 37

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 37

eth-test2					0.00 476

eth-test					0.00 476

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 65	91.15 4	88.23 26

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 47

GSMVS																78.05 325

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 319				78.05 325

sam_mvs												33.43 336

ambc				65.13 323	63.72 419	37.07 391	47.66 444	78.78 180		54.37 389	71.42 396	11.24 450	80.94 219	45.64 312	53.85 417	77.38 336

MTGPA								80.97 140

test_post1							68.67 335				3.64 466	32.39 357	69.49 359	44.17 324

test_post											3.55 467	33.90 330	66.52 379

patchmatchnet-post											64.03 433	34.50 321	74.27 329

GG-mvs-BLEND				62.34 345	71.36 348	37.04 392	69.20 332	57.33 412		54.73 384	65.48 431	30.37 367	77.82 283	34.82 396	74.93 216	72.17 397

MTMP							86.03 19	17.08 470

test9_res														75.28 48	88.31 32	83.81 205

agg_prior2														73.09 66	87.93 40	84.33 183

agg_prior					85.04 50	59.96 50		81.04 138		74.68 67			84.04 141

test_prior4						62.51 14	82.08 82

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 84			86.38 95

新几何2							76.12 203

旧先验1					83.04 74	53.15 176		67.52 339			87.85 81	44.08 211			80.76 114	78.03 328

原ACMM2							79.02 122

testdata2													72.18 343	46.95 302

segment_acmp												54.23 61

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 101		54.61 58	88.05 39		88.12 34	86.81 78

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 198

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 118	84.65 174

plane_prior4											86.10 132

plane_prior1					81.27 102

n2								0.00 475

nn								0.00 475

door-mid								47.19 445

lessismore_v0				69.91 248	71.42 346	47.80 283		50.90 433		50.39 414	75.56 360	27.43 399	81.33 206	45.91 309	34.10 450	80.59 287

test11								83.47 72

door								47.60 443

HQP5-MVS						54.94 139

BP-MVS														67.04 113

HQP4-MVS										67.85 188			86.93 67			84.32 184

HQP3-MVS								83.90 58							80.35 122

HQP2-MVS												45.46 192

NP-MVS					80.98 107	56.05 116					85.54 151

ACMMP++_ref															74.07 225

ACMMP++															72.16 264

Test By Simon												48.33 152

High-resolution multi-view benchmark