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	81.64 115	68.35 2	75.77 45	90.38 30	53.98 65	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 82	67.78 3	70.09 139	86.34 126	54.92 55	88.90 25	72.68 69	84.55 69	87.76 43

UA-Net		73.13 85	72.93 85	73.76 134	83.58 67	51.66 212	78.75 125	77.66 213	67.75 4	72.61 109	89.42 52	49.82 132	83.29 158	53.61 246	83.14 83	86.32 104

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 72

TranMVSNet+NR-MVSNet		70.36 144	70.10 139	71.17 222	78.64 163	42.97 336	76.53 195	81.16 136	66.95 6	68.53 170	85.42 154	51.61 108	83.07 162	52.32 254	69.70 306	87.46 54

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	67.80 195	89.24 56	42.03 234	89.38 19	64.07 140	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 50	89.18 21	74.19 57	87.34 46	86.38 96

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 61	90.06 14	78.42 23	89.02 23	87.69 44
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 86	72.16 96	75.90 74	75.95 247	56.28 110	83.05 62	72.39 301	66.53 10	65.27 247	87.00 100	50.40 125	85.47 113	62.48 165	86.32 60	85.94 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 125	71.00 119	71.44 209	79.20 143	44.13 322	76.02 210	82.60 102	66.48 11	68.20 175	84.60 172	56.82 37	82.82 175	54.62 236	70.43 286	87.36 63

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 32
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	85.26 155

NR-MVSNet		69.54 169	68.85 161	71.59 203	78.05 186	43.81 327	74.20 250	80.86 143	65.18 14	62.76 291	84.52 173	52.35 94	83.59 152	50.96 269	70.78 281	87.37 61

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 235	80.97 141	65.13 15	75.77 45	90.88 20	48.63 149	86.66 74	77.23 29	88.17 33	84.81 171

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 20

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 32

EI-MVSNet-Vis-set		72.42 102	71.59 103	74.91 95	78.47 167	54.02 153	77.05 181	79.33 168	65.03 18	71.68 121	79.35 297	52.75 86	84.89 126	66.46 120	74.23 224	85.83 122

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 241	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	72.65 108	79.46 293	53.65 77	87.87 44	67.45 110	82.91 89	85.89 119

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 86	64.69 22	74.21 75	87.40 89	49.48 136	86.17 91	68.04 102	87.55 43	87.42 56

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 250	64.69 22	74.21 75	87.40 89	49.48 136	86.17 91	68.04 102	83.88 79	85.85 120

WR-MVS		68.47 199	68.47 172	68.44 274	80.20 121	39.84 364	73.75 262	76.07 238	64.68 24	68.11 183	83.63 195	50.39 126	79.14 258	49.78 274	69.66 307	86.34 100

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	72.17 114	90.01 45	47.95 156	88.01 40	71.55 82	86.74 55	86.37 98

X-MVStestdata		70.21 147	67.28 206	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 114	6.49 466	47.95 156	88.01 40	71.55 82	86.74 55	86.37 98

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	69.27 158	86.10 133	45.26 199	87.21 59	68.16 100	80.58 118	84.65 175

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 112	71.06 118	74.52 112	77.98 189	53.56 164	76.62 192	79.16 169	64.40 29	71.18 127	78.95 302	52.19 96	84.66 133	65.47 131	73.57 237	85.32 151

DU-MVS		70.01 152	69.53 146	71.44 209	78.05 186	44.13 322	75.01 231	81.51 118	64.37 30	68.20 175	84.52 173	49.12 146	82.82 175	54.62 236	70.43 286	87.37 61

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	85.45 143
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 27

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 46

LFMVS		71.78 114	71.59 103	72.32 183	83.40 71	46.38 298	79.75 112	71.08 310	64.18 34	72.80 105	88.64 67	42.58 229	83.72 148	57.41 212	84.49 72	86.86 77

IS-MVSNet		71.57 118	71.00 119	73.27 159	78.86 153	45.63 309	80.22 103	78.69 183	64.14 37	66.46 222	87.36 92	49.30 140	85.60 106	50.26 273	83.71 82	88.59 16

plane_prior3						56.09 114			63.92 38	69.27 158

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 71	63.89 39	73.60 84	90.60 23	54.85 56	86.72 72	77.20 30	88.06 36	85.74 129
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	75.59 218	84.17 50	63.76 40	73.15 93	82.79 210	59.58 20	86.80 70	67.24 111	86.04 61	87.89 35
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 68	63.74 41	72.52 110	87.49 86	47.18 172	85.88 101	69.47 93	80.78 112	83.66 216
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 137	70.20 134	71.89 190	78.55 164	45.29 312	75.94 211	82.92 96	63.68 42	68.16 178	83.59 196	53.89 68	83.49 155	53.97 242	71.12 277	86.89 76

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 88	88.53 29	74.79 53	88.34 29	86.63 89

testing3-2		62.06 305	62.36 288	61.17 357	79.29 138	30.31 438	64.09 379	63.49 378	63.50 44	62.84 288	82.22 232	32.35 360	69.02 363	40.01 362	73.43 242	84.17 192

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	72.77 106	87.25 97	53.13 81	87.93 42	71.97 77	85.57 64	86.66 87

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 67	88.68 27	76.48 35	89.63 20	87.16 69

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 78
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 52	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 35
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 98	72.09 97	73.75 136	81.58 93	49.69 250	77.76 157	77.63 214	63.21 50	73.21 90	89.02 58	42.14 233	83.32 157	61.72 172	82.50 95	88.25 25

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	69.80 149	89.74 51	45.43 195	87.16 61	72.01 75	82.87 91	85.14 157
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		66.60 244	66.45 224	67.04 288	77.11 222	36.56 397	77.03 182	80.42 150	62.95 53	62.51 299	84.03 184	46.69 180	79.07 260	44.22 324	63.08 370	85.51 138

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 80
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	82.28 105	62.90 55	71.77 119	90.26 35	46.61 181	86.55 80	71.71 80	85.66 63	84.97 166

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 89	88.39 30	79.34 9	90.52 13	86.78 81

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 31

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 80	88.35 31	74.02 59	87.05 47	86.13 111

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	49.80 133	88.24 33	74.02 59	87.03 48	86.32 104

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	49.90 130	88.21 34	73.78 61	87.03 48	86.29 108

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 255	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	75.70 250	49.99 241	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 98	89.97 46	50.90 121	87.48 53	75.30 47	86.85 53	87.33 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 258	65.34 250	66.31 299	76.06 246	34.79 410	76.43 197	79.38 167	62.55 64	61.66 310	83.83 189	45.60 189	79.15 257	41.64 354	60.88 385	85.00 163

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 29
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		66.49 247	66.41 228	66.72 290	77.67 200	36.33 400	76.83 190	79.52 164	62.45 66	62.54 297	83.47 202	46.32 183	78.37 272	45.47 319	63.43 367	85.45 143

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 74	62.44 67	72.68 107	90.50 27	48.18 154	87.34 54	73.59 63	85.71 62	84.76 174

PS-CasMVS		66.42 248	66.32 232	66.70 292	77.60 208	36.30 402	76.94 184	79.61 162	62.36 68	62.43 302	83.66 194	45.69 187	78.37 272	45.35 321	63.26 368	85.42 146

3Dnovator	64.47 5	72.49 99	71.39 109	75.79 77	77.70 198	58.99 73	80.66 99	83.15 91	62.24 69	65.46 243	86.59 116	42.38 232	85.52 109	59.59 192	84.72 67	82.85 239

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 91	86.78 71	80.66 4	89.64 19	87.80 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 189

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 189

HQP-MVS		73.45 78	72.80 88	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 189	85.54 152	45.46 193	86.93 67	67.04 114	80.35 122	84.32 185

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	71.49 125	86.03 136	53.83 69	86.36 87	67.74 105	86.91 52	88.19 29

VPNet		67.52 223	68.11 185	65.74 313	79.18 145	36.80 395	72.17 292	72.83 297	62.04 75	67.79 196	85.83 143	48.88 148	76.60 314	51.30 265	72.97 251	83.81 206

WR-MVS_H		67.02 235	66.92 216	67.33 287	77.95 190	37.75 384	77.57 160	82.11 108	62.03 76	62.65 294	82.48 225	50.57 124	79.46 248	42.91 342	64.01 360	84.79 172

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 99	88.88 62	53.72 73	89.06 23	68.27 97	88.04 37	87.42 56
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 37

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	72.45 113	90.34 33	48.48 152	88.13 37	72.32 72	86.85 53	85.78 123

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 125	75.33 261	52.89 184	78.24 139	77.32 222	61.65 80	78.13 27	88.90 61	52.82 85	81.54 202	78.46 22	78.67 156	87.60 49

Effi-MVS+		73.31 81	72.54 92	75.62 84	77.87 191	53.64 161	79.62 116	79.61 162	61.63 81	72.02 117	82.61 215	56.44 40	85.97 99	63.99 143	79.07 147	87.25 66

MG-MVS		73.96 73	73.89 72	74.16 122	85.65 42	49.69 250	81.59 88	81.29 129	61.45 82	71.05 128	88.11 72	51.77 105	87.73 48	61.05 178	83.09 84	85.05 162

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 172	73.95 282	61.40 83	79.46 19	90.14 37	57.07 34	81.15 212	80.00 5	79.31 139	88.51 19

LPG-MVS_test		72.74 92	71.74 102	75.76 78	80.22 119	57.51 92	82.55 73	83.40 76	61.32 84	66.67 219	87.33 94	39.15 274	86.59 75	67.70 106	77.30 183	83.19 229

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 76	61.32 84	66.67 219	87.33 94	39.15 274	86.59 75	67.70 106	77.30 183	83.19 229

CLD-MVS		73.33 80	72.68 90	75.29 91	78.82 155	53.33 173	78.23 140	84.79 42	61.30 86	70.41 136	81.04 259	52.41 92	87.12 62	64.61 139	82.49 96	85.41 147
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 121	70.70 125	73.74 137	77.76 196	49.30 258	76.60 193	80.45 149	61.25 87	68.17 177	84.78 162	44.64 207	84.90 125	64.79 135	77.88 172	87.03 72

viewcassd2359sk11		73.56 76	73.41 80	74.00 126	77.13 219	50.35 233	76.86 188	83.69 67	61.23 88	73.14 94	86.38 125	56.09 45	82.96 165	67.15 112	79.01 148	88.70 14

fmvsm_s_conf0.5_n_3		73.55 77	74.39 65	71.03 227	74.09 297	51.86 209	77.77 156	75.60 246	61.18 89	78.67 25	88.98 59	55.88 47	77.73 287	78.69 16	78.68 155	83.50 221

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 173	78.40 201	61.18 89	70.58 134	85.97 138	54.18 63	84.00 144	67.52 109	82.98 88	82.45 251

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 91	74.90 61	87.17 98	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

FIs		70.82 134	71.43 107	68.98 267	78.33 175	38.14 380	76.96 183	83.59 70	61.02 92	67.33 203	86.73 108	55.07 51	81.64 198	54.61 238	79.22 142	87.14 70

FOURS1					86.12 36	60.82 37	88.18 1	83.61 69	60.87 93	81.50 16

FC-MVSNet-test		69.80 159	70.58 128	67.46 283	77.61 207	34.73 413	76.05 208	83.19 90	60.84 94	65.88 237	86.46 122	54.52 60	80.76 227	52.52 253	78.12 168	86.91 75

v8		70.33 145	69.28 152	73.49 151	73.15 310	50.22 235	78.62 130	80.78 144	60.79 95	66.45 223	82.11 239	49.35 139	84.98 122	63.58 152	68.71 322	85.28 153

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 96	75.27 50	84.83 160	60.76 15	86.56 77	67.86 104	87.87 41	86.06 113

Vis-MVSNet		72.18 106	71.37 110	74.61 106	81.29 100	55.41 132	80.90 95	78.28 204	60.73 97	69.23 161	88.09 73	44.36 211	82.65 179	57.68 209	81.75 106	85.77 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 124	70.16 136	74.57 109	74.59 280	52.77 188	75.91 212	81.20 133	60.72 98	69.10 164	85.71 147	41.67 243	83.53 153	63.91 146	78.62 158	87.42 56

BP-MVS1		73.41 79	72.25 95	76.88 57	76.68 234	53.70 159	79.15 121	81.07 137	60.66 99	71.81 118	87.39 91	40.93 256	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	60.61 100	79.05 22	90.30 34	55.54 49	88.32 32	73.48 64	87.03 48	84.83 170
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 104	71.20 115	75.59 86	80.28 117	57.54 90	82.74 69	82.84 100	60.58 101	65.24 251	86.18 130	39.25 272	86.03 97	66.95 118	76.79 191	83.22 227
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	60.53 102	78.99 23	91.45 12	51.51 110	87.78 47	75.65 43	87.55 43	87.10 71

testdata1							72.65 281		60.50 103

UGNet		68.81 189	67.39 201	73.06 163	78.33 175	54.47 145	79.77 111	75.40 253	60.45 104	63.22 280	84.40 177	32.71 349	80.91 223	51.71 263	80.56 120	83.81 206
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 84	73.16 82	73.11 162	75.15 267	49.31 257	77.53 164	83.21 86	60.42 105	73.20 91	87.34 93	53.82 70	81.05 217	67.02 116	80.79 111	88.96 9

h-mvs33		72.71 93	71.49 106	76.40 68	81.99 88	59.58 57	76.92 185	76.74 231	60.40 106	74.81 63	85.95 139	45.54 191	85.76 104	70.41 89	70.61 284	83.86 205

hse-mvs2		71.04 126	69.86 140	74.60 107	79.58 133	57.12 102	73.96 254	75.25 256	60.40 106	74.81 63	81.95 241	45.54 191	82.90 168	70.41 89	66.83 339	83.77 210

EPP-MVSNet		72.16 109	71.31 112	74.71 100	78.68 159	49.70 248	82.10 81	81.65 114	60.40 106	65.94 233	85.84 142	51.74 106	86.37 86	55.93 222	79.55 134	88.07 34

UniMVSNet_ETH3D		67.60 222	67.07 215	69.18 264	77.39 213	42.29 341	74.18 251	75.59 247	60.37 109	66.77 215	86.06 135	37.64 290	78.93 267	52.16 256	73.49 239	86.32 104

test_prior2							81.75 84		60.37 109	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	60.37 109	79.89 18	89.38 54	54.97 54	85.58 108	76.12 39	84.94 66	86.33 102
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		69.68 163	70.19 135	68.16 277	79.73 130	41.63 350	70.53 316	77.38 219	60.37 109	70.69 131	86.63 113	51.08 117	77.09 299	53.61 246	81.69 108	85.75 128

sasdasda		74.67 63	74.98 58	73.71 139	78.94 151	50.56 229	80.23 101	83.87 61	60.30 113	77.15 36	86.56 118	59.65 17	82.00 192	66.01 125	82.12 97	88.58 17

canonicalmvs		74.67 63	74.98 58	73.71 139	78.94 151	50.56 229	80.23 101	83.87 61	60.30 113	77.15 36	86.56 118	59.65 17	82.00 192	66.01 125	82.12 97	88.58 17

v7n		69.01 185	67.36 203	73.98 127	72.51 324	52.65 190	78.54 134	81.30 128	60.26 115	62.67 293	81.62 248	43.61 217	84.49 134	57.01 213	68.70 323	84.79 172

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 79	60.22 116	77.85 31	91.42 14	50.67 122	87.69 49	72.46 70	84.53 70	85.46 141

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 79	60.22 116	77.85 31	91.42 14	50.67 122	87.69 49	72.46 70	84.53 70	85.46 141

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 138	60.15 118	70.43 135	89.84 48	41.09 255	85.59 107	67.61 108	82.90 90	85.77 126

VPA-MVSNet		69.02 184	69.47 148	67.69 281	77.42 212	41.00 357	74.04 252	79.68 160	60.06 119	69.26 160	84.81 161	51.06 118	77.58 289	54.44 239	74.43 222	84.48 182

v10		70.21 147	69.02 157	73.81 131	73.51 304	50.92 221	78.74 126	81.39 121	60.05 120	66.39 224	81.83 244	47.58 163	85.41 116	62.80 162	68.86 321	85.09 161

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 106	59.99 121	75.10 54	90.35 32	47.66 161	86.52 81	71.64 81	82.99 86	84.47 183

SSC-MVS3.2		60.57 318	61.39 300	58.12 380	74.29 290	32.63 428	59.52 404	65.53 359	59.90 122	62.45 300	79.75 286	41.96 235	63.90 394	39.47 366	69.65 309	77.84 331

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 122	78.57 26	90.36 31	57.51 32	86.86 69	77.39 28	89.52 21

v2v482		70.50 140	69.45 149	73.66 142	72.62 320	50.03 240	77.58 159	80.51 148	59.90 122	69.52 151	82.14 237	47.53 165	84.88 128	65.07 134	70.17 294	86.09 112

Baseline_NR-MVSNet		67.05 234	67.56 193	65.50 317	75.65 251	37.70 386	75.42 221	74.65 269	59.90 122	68.14 179	83.15 208	49.12 146	77.20 297	52.23 255	69.78 303	81.60 264

API-MVS		72.17 107	71.41 108	74.45 113	81.95 89	57.22 95	84.03 51	80.38 151	59.89 126	68.40 172	82.33 228	49.64 134	87.83 46	51.87 260	84.16 77	78.30 322

Effi-MVS+-dtu		69.64 165	67.53 196	75.95 73	76.10 245	62.29 15	80.20 104	76.06 239	59.83 127	65.26 250	77.09 335	41.56 246	84.02 143	60.60 183	71.09 280	81.53 265

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 97	59.65 128	77.31 34	91.43 13	49.62 135	87.24 55	71.99 76	83.75 81	85.14 157

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 130	85.13 33	59.65 128	71.53 124	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 11

CANet_DTU		68.18 207	67.71 192	69.59 255	74.83 273	46.24 300	78.66 129	76.85 228	59.60 130	63.45 278	82.09 240	35.25 314	77.41 292	59.88 189	78.76 153	85.14 157

EI-MVSNet		69.27 178	68.44 174	71.73 197	74.47 283	49.39 255	75.20 226	78.45 197	59.60 130	69.16 162	76.51 347	51.29 113	82.50 184	59.86 191	71.45 274	83.30 224

IterMVS-LS		69.22 180	68.48 170	71.43 211	74.44 285	49.40 254	76.23 202	77.55 215	59.60 130	65.85 238	81.59 251	51.28 114	81.58 201	59.87 190	69.90 301	83.30 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 100	73.34 81	69.81 252	77.77 195	43.21 333	75.84 215	81.18 134	59.59 133	75.45 48	86.64 111	57.74 28	77.94 279	63.92 144	81.90 102	88.30 23

VDDNet		71.81 113	71.33 111	73.26 160	82.80 79	47.60 289	78.74 126	75.27 255	59.59 133	72.94 101	89.40 53	41.51 248	83.91 145	58.75 204	82.99 86	88.26 24

viewmanbaseed2359cas		72.92 89	72.89 86	73.00 164	75.16 265	49.25 260	77.25 176	83.11 94	59.52 135	72.93 102	86.63 113	54.11 64	80.98 218	66.63 119	80.67 115	88.76 13

alignmvs		73.86 74	73.99 70	73.45 153	78.20 178	50.50 231	78.57 132	82.43 103	59.40 136	76.57 41	86.71 110	56.42 41	81.23 211	65.84 128	81.79 103	88.62 15

MVS_Test		72.45 100	72.46 93	72.42 181	74.88 270	48.50 275	76.28 200	83.14 92	59.40 136	72.46 111	84.68 165	55.66 48	81.12 213	65.98 127	79.66 131	87.63 47

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 138	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 45
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	81.88 110	59.34 138	71.59 122	86.83 104	45.94 186	83.65 150	65.09 133	85.22 65	81.06 280

PAPM_NR		72.63 96	71.80 100	75.13 92	81.72 92	53.42 171	79.91 109	83.28 84	59.14 140	66.31 226	85.90 140	51.86 102	86.06 95	57.45 211	80.62 116	85.91 118

testing91		64.46 275	63.80 266	66.47 296	78.43 169	40.06 362	67.63 344	69.59 324	59.06 141	63.18 282	78.05 315	34.05 327	76.99 304	48.30 290	75.87 204	82.37 253

myMVS_eth3d28		60.66 317	61.04 308	59.51 364	77.32 215	31.58 433	63.11 384	63.87 374	59.00 142	60.90 319	78.26 312	32.69 351	66.15 384	36.10 392	78.13 167	80.81 285

save fliter					86.17 33	61.30 28	83.98 53	79.66 161	59.00 142

v148		68.24 205	67.19 213	71.40 212	70.43 363	47.77 286	75.76 216	77.03 226	58.91 144	67.36 202	80.10 279	48.60 151	81.89 194	60.01 187	66.52 342	84.53 180

TransMVSNet (Re)		64.72 269	64.33 259	65.87 312	75.22 262	38.56 376	74.66 241	75.08 264	58.90 145	61.79 308	82.63 214	51.18 115	78.07 277	43.63 335	55.87 408	80.99 282

Anonymous202405211		66.84 239	65.99 238	69.40 259	80.19 122	42.21 343	71.11 309	71.31 309	58.80 146	67.90 187	86.39 124	29.83 377	79.65 245	49.60 280	78.78 152	86.33 102

test2506		65.33 263	64.61 257	67.50 282	79.46 136	34.19 418	74.43 247	51.92 429	58.72 147	66.75 216	88.05 75	25.99 411	80.92 222	51.94 259	84.25 74	87.39 59

ECVR-MVS		67.72 220	67.51 197	68.35 275	79.46 136	36.29 403	74.79 238	66.93 347	58.72 147	67.19 207	88.05 75	36.10 307	81.38 206	52.07 257	84.25 74	87.39 59

test1111		67.21 227	67.14 214	67.42 284	79.24 142	34.76 412	73.89 259	65.65 357	58.71 149	66.96 212	87.95 79	36.09 308	80.53 229	52.03 258	83.79 80	86.97 74

LCM-MVSNet-Re		61.88 308	61.35 301	63.46 337	74.58 281	31.48 434	61.42 394	58.14 407	58.71 149	53.02 402	79.55 291	43.07 223	76.80 308	45.69 312	77.96 170	82.11 259

testing99		64.05 279	63.29 277	66.34 298	78.17 182	39.76 366	67.33 349	68.00 338	58.60 151	63.03 285	78.10 314	32.57 356	76.94 306	48.22 291	75.58 208	82.34 254

v1144		70.42 142	69.31 151	73.76 134	73.22 308	50.64 226	77.83 154	81.43 120	58.58 152	69.40 155	81.16 256	47.53 165	85.29 118	64.01 142	70.64 282	85.34 150

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 190	58.58 152	74.32 73	84.51 175	55.94 46	87.22 58	67.11 113	84.48 73	85.52 137

BH-RMVSNet		68.81 189	67.42 200	72.97 165	80.11 125	52.53 194	74.26 249	76.29 234	58.48 154	68.38 173	84.20 179	42.59 228	83.83 146	46.53 304	75.91 203	82.56 245

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 83	58.41 155	73.71 83	90.14 37	45.62 188	85.99 98	69.64 91	82.85 92	85.78 123

OMC-MVS		71.40 123	70.60 126	73.78 132	76.60 237	53.15 176	79.74 113	79.78 158	58.37 156	68.75 166	86.45 123	45.43 195	80.60 228	62.58 163	77.73 173	87.58 51

nrg030		72.96 88	73.01 84	72.84 168	75.41 259	50.24 234	80.02 105	82.89 99	58.36 157	74.44 70	86.73 108	58.90 24	80.83 224	65.84 128	74.46 220	87.44 55

K. test v3		60.47 321	57.11 340	70.56 237	73.74 301	48.22 278	75.10 230	62.55 386	58.27 158	53.62 397	76.31 351	27.81 395	81.59 200	47.42 295	39.18 445	81.88 262

FA-MVS(test-final)		69.82 157	68.48 170	73.84 130	78.44 168	50.04 239	75.58 220	78.99 175	58.16 159	67.59 199	82.14 237	42.66 227	85.63 105	56.60 215	76.19 197	85.84 121

MVS_111021_LR		69.50 172	68.78 164	71.65 201	78.38 170	59.33 61	74.82 237	70.11 318	58.08 160	67.83 194	84.68 165	41.96 235	76.34 319	65.62 130	77.54 176	79.30 313

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 123	58.07 161	73.14 94	90.07 39	44.74 205	85.84 102	68.20 98	81.76 104	84.03 195

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 123	58.07 161	73.14 94	90.07 39	43.06 224		68.20 98	81.76 104	84.03 195

SDMVSNet		68.03 210	68.10 186	67.84 279	77.13 219	48.72 271	65.32 366	79.10 170	58.02 163	65.08 254	82.55 221	47.83 158	73.40 333	63.92 144	73.92 228	81.41 267

sd_testset		64.46 275	64.45 258	64.51 328	77.13 219	42.25 342	62.67 387	72.11 304	58.02 163	65.08 254	82.55 221	41.22 254	69.88 359	47.32 297	73.92 228	81.41 267

GeoE		71.01 128	70.15 137	73.60 147	79.57 134	52.17 201	78.93 124	78.12 206	58.02 163	67.76 198	83.87 188	52.36 93	82.72 177	56.90 214	75.79 205	85.92 117

ZD-MVS					86.64 21	60.38 45		82.70 101	57.95 166	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

EIA-MVS		71.78 114	70.60 126	75.30 90	79.85 128	53.54 165	77.27 175	83.26 85	57.92 167	66.49 221	79.39 295	52.07 99	86.69 73	60.05 186	79.14 146	85.66 133

test_yl		69.69 161	69.13 154	71.36 215	78.37 172	45.74 305	74.71 239	80.20 153	57.91 168	70.01 144	83.83 189	42.44 230	82.87 171	54.97 232	79.72 129	85.48 139

DCV-MVSNet		69.69 161	69.13 154	71.36 215	78.37 172	45.74 305	74.71 239	80.20 153	57.91 168	70.01 144	83.83 189	42.44 230	82.87 171	54.97 232	79.72 129	85.48 139

MonoMVSNet		64.15 278	63.31 276	66.69 293	70.51 361	44.12 324	74.47 245	74.21 277	57.81 170	63.03 285	76.62 343	38.33 283	77.31 295	54.22 240	60.59 390	78.64 320

dcpmvs_2		74.55 67	75.23 55	72.48 177	82.34 83	53.34 172	77.87 151	81.46 119	57.80 171	75.49 47	86.81 105	62.22 13	77.75 286	71.09 85	82.02 100	86.34 100

diffmvs_AUTHOR		71.02 127	70.87 121	71.45 208	69.89 374	48.97 266	73.16 275	78.33 203	57.79 172	72.11 116	85.26 157	51.84 103	77.89 282	71.00 86	78.47 163	87.49 53

viewdifsd2359ckpt11		69.13 181	68.38 177	71.38 213	71.57 341	48.61 272	73.22 273	73.18 292	57.65 173	70.67 132	84.73 163	50.03 128	79.80 242	63.25 155	71.10 278	85.74 129

viewmsd2359difaftdt		69.13 181	68.38 177	71.38 213	71.57 341	48.61 272	73.22 273	73.18 292	57.65 173	70.67 132	84.73 163	50.03 128	79.80 242	63.25 155	71.10 278	85.74 129

fmvsm_s_conf0.5_n_6		72.59 97	72.87 87	71.73 197	75.14 268	51.96 207	76.28 200	77.12 225	57.63 175	73.85 81	86.91 102	51.54 109	77.87 283	77.18 31	80.18 126	85.37 149

Fast-Effi-MVS+-dtu		67.37 225	65.33 251	73.48 152	72.94 315	57.78 88	77.47 165	76.88 227	57.60 176	61.97 305	76.85 339	39.31 270	80.49 232	54.72 235	70.28 292	82.17 258

v1192		69.97 154	68.68 166	73.85 129	73.19 309	50.94 219	77.68 158	81.36 123	57.51 177	68.95 165	80.85 266	45.28 198	85.33 117	62.97 161	70.37 288	85.27 154

ACMH+	57.40 11	66.12 252	64.06 261	72.30 184	77.79 194	52.83 186	80.39 100	78.03 207	57.30 178	57.47 357	82.55 221	27.68 397	84.17 138	45.54 315	69.78 303	79.90 302

diffmvs		70.69 136	70.43 129	71.46 206	69.45 381	48.95 267	72.93 278	78.46 196	57.27 179	71.69 120	83.97 187	51.48 111	77.92 281	70.70 88	77.95 171	87.53 52
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		68.27 203	67.29 205	71.21 219	79.74 129	53.22 174	76.06 207	77.46 218	57.19 180	66.10 230	81.61 249	45.37 197	83.50 154	45.42 320	76.68 193	76.91 347

viewdifsd2359ckpt13		72.40 103	71.79 101	74.22 120	75.63 252	51.77 211	78.67 128	83.13 93	57.08 181	71.59 122	85.36 156	53.10 82	82.64 180	63.07 159	78.51 160	88.24 26

thres100view900		63.28 288	62.41 287	65.89 310	77.31 216	38.66 375	72.65 281	69.11 331	57.07 182	62.45 300	81.03 260	37.01 302	79.17 254	31.84 413	73.25 246	79.83 305

fmvsm_s_conf0.5_n_7		69.54 169	69.67 144	69.15 266	73.47 306	51.41 214	70.35 320	73.34 288	57.05 183	68.41 171	85.83 143	49.86 131	72.84 336	71.86 78	76.83 190	83.19 229

DP-MVS Recon		72.15 110	70.73 124	76.40 68	86.57 24	57.99 84	81.15 93	82.96 95	57.03 184	66.78 214	85.56 149	44.50 209	88.11 38	51.77 262	80.23 125	83.10 234

thres600view7		63.30 287	62.27 289	66.41 297	77.18 218	38.87 373	72.35 288	69.11 331	56.98 185	62.37 303	80.96 262	37.01 302	79.00 265	31.43 420	73.05 250	81.36 270

V42		68.65 193	67.35 204	72.56 174	68.93 387	50.18 236	72.90 279	79.47 165	56.92 186	69.45 154	80.26 275	46.29 184	82.99 164	64.07 140	67.82 330	84.53 180

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 187	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 90

GA-MVS		65.53 259	63.70 268	71.02 228	70.87 356	48.10 280	70.48 317	74.40 271	56.69 188	64.70 263	76.77 340	33.66 335	81.10 214	55.42 231	70.32 291	83.87 204

v144192		69.71 160	68.51 169	73.33 158	73.10 311	50.13 237	77.54 162	80.64 145	56.65 189	68.57 169	80.55 269	46.87 179	84.96 124	62.98 160	69.66 307	84.89 169

fmvsm_l_conf0.5_n_3		73.23 83	73.13 83	73.55 149	74.40 286	55.13 137	78.97 123	74.96 265	56.64 190	74.76 66	88.75 66	55.02 53	78.77 269	76.33 37	78.31 166	86.74 82

tfpn200view9		63.18 290	62.18 291	66.21 302	76.85 231	39.62 367	71.96 296	69.44 327	56.63 191	62.61 295	79.83 282	37.18 296	79.17 254	31.84 413	73.25 246	79.83 305

thres400		63.31 286	62.18 291	66.72 290	76.85 231	39.62 367	71.96 296	69.44 327	56.63 191	62.61 295	79.83 282	37.18 296	79.17 254	31.84 413	73.25 246	81.36 270

GBi-Net		67.21 227	66.55 222	69.19 261	77.63 202	43.33 330	77.31 169	77.83 210	56.62 193	65.04 256	82.70 211	41.85 238	80.33 234	47.18 299	72.76 254	83.92 201

test1		67.21 227	66.55 222	69.19 261	77.63 202	43.33 330	77.31 169	77.83 210	56.62 193	65.04 256	82.70 211	41.85 238	80.33 234	47.18 299	72.76 254	83.92 201

FMVSNet2		66.93 237	66.31 233	68.79 270	77.63 202	42.98 335	76.11 205	77.47 216	56.62 193	65.22 253	82.17 235	41.85 238	80.18 240	47.05 302	72.72 257	83.20 228

fmvsm_l_conf0.5_n_9		73.27 82	73.66 76	72.09 186	73.82 298	52.72 189	77.45 166	74.28 275	56.61 196	77.10 38	88.16 71	56.17 43	77.09 299	78.27 24	81.13 110	86.48 94

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 197	72.46 111	86.76 106	56.89 36	87.86 45	66.36 121	88.91 25	83.64 218

v1921920		69.47 173	68.17 183	73.36 157	73.06 312	50.10 238	77.39 167	80.56 146	56.58 198	68.59 167	80.37 271	44.72 206	84.98 122	62.47 166	69.82 302	85.00 163

FMVSNet1		66.70 242	65.87 239	69.19 261	77.49 210	43.33 330	77.31 169	77.83 210	56.45 199	64.60 265	82.70 211	38.08 288	80.33 234	46.08 308	72.31 263	83.92 201

v1240		69.24 179	67.91 188	73.25 161	73.02 314	49.82 242	77.21 177	80.54 147	56.43 200	68.34 174	80.51 270	43.33 220	84.99 120	62.03 170	69.77 305	84.95 167

fmvsm_s_conf0.5_n_4		72.04 111	71.85 99	72.58 173	73.74 301	52.49 196	76.69 191	72.42 300	56.42 201	75.32 49	87.04 99	52.13 98	78.01 278	79.29 12	73.65 234	87.26 65

testing222		62.29 302	61.31 302	65.25 323	77.87 191	38.53 377	68.34 338	66.31 353	56.37 202	63.15 284	77.58 329	28.47 389	76.18 322	37.04 381	76.65 194	81.05 281

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 203	74.05 77	88.98 59	53.34 79	87.92 43	69.23 95	88.42 28	87.59 50

Vis-MVSNet (Re-imp)		63.69 283	63.88 264	63.14 341	74.75 275	31.04 436	71.16 307	63.64 377	56.32 203	59.80 331	84.99 158	44.51 208	75.46 324	39.12 368	80.62 116	82.92 236

AdaColmap		69.99 153	68.66 167	73.97 128	84.94 54	57.83 86	82.63 71	78.71 182	56.28 205	64.34 266	84.14 181	41.57 245	87.06 65	46.45 305	78.88 149	77.02 343

PS-MVSNAJss		72.24 105	71.21 114	75.31 89	78.50 165	55.93 118	81.63 85	82.12 107	56.24 206	70.02 143	85.68 148	47.05 174	84.34 137	65.27 132	74.41 223	85.67 132

c3_l		68.33 202	67.56 193	70.62 236	70.87 356	46.21 301	74.47 245	78.80 180	56.22 207	66.19 227	78.53 310	51.88 101	81.40 205	62.08 167	69.04 317	84.25 188

Fast-Effi-MVS+		70.28 146	69.12 156	73.73 138	78.50 165	51.50 213	75.01 231	79.46 166	56.16 208	68.59 167	79.55 291	53.97 66	84.05 140	53.34 248	77.53 177	85.65 134

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 209	73.41 86	86.58 117	50.94 120	88.54 28	70.79 87	89.71 17	87.79 42

baseline1		63.81 282	63.87 265	63.62 336	76.29 242	36.36 398	71.78 299	67.29 343	56.05 210	64.23 271	82.95 209	47.11 173	74.41 329	47.30 298	61.85 379	80.10 299

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 130	55.86 211	74.93 58	88.81 63	53.70 74	84.68 131	75.24 49	88.33 30	83.65 217

test_8					85.40 46	60.96 34	81.54 89	81.18 134	55.86 211	74.81 63	88.80 65	53.70 74	84.45 135

FMVSNet3		66.32 251	65.61 244	68.46 273	76.48 240	42.34 340	74.98 233	77.15 224	55.83 213	65.04 256	81.16 256	39.91 263	80.14 241	47.18 299	72.76 254	82.90 238

PAPR		71.72 117	70.82 122	74.41 114	81.20 104	51.17 215	79.55 118	83.33 81	55.81 214	66.93 213	84.61 169	50.95 119	86.06 95	55.79 225	79.20 143	86.00 114

eth_miper_zixun_eth		67.63 221	66.28 234	71.67 200	71.60 340	48.33 277	73.68 263	77.88 208	55.80 215	65.91 234	78.62 308	47.35 171	82.88 170	59.45 193	66.25 343	83.81 206

ACMH	55.70 15	65.20 265	63.57 270	70.07 245	78.07 185	52.01 206	79.48 119	79.69 159	55.75 216	56.59 364	80.98 261	27.12 402	80.94 220	42.90 343	71.58 272	77.25 341
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 262	62.73 284	73.40 156	74.89 269	52.78 187	73.09 277	75.13 260	55.69 217	58.48 349	73.73 380	32.86 344	86.32 88	50.63 270	70.11 295	81.10 279
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		61.53 311	60.94 310	63.30 339	68.95 386	36.93 394	67.60 345	72.80 298	55.67 218	59.95 328	76.63 342	45.01 204	72.22 343	39.74 365	62.09 378	80.74 287

TEST9					85.58 43	61.59 24	81.62 86	81.26 130	55.65 219	74.93 58	88.81 63	53.70 74	84.68 131

thres200		62.20 303	61.16 307	65.34 321	75.38 260	39.99 363	69.60 329	69.29 329	55.64 220	61.87 307	76.99 336	37.07 301	78.96 266	31.28 421	73.28 245	77.06 342

guyue		68.10 209	67.23 212	70.71 235	73.67 303	49.27 259	73.65 264	76.04 240	55.62 221	67.84 193	82.26 231	41.24 253	78.91 268	61.01 179	73.72 232	83.94 199

pm-mvs1		65.24 264	64.97 255	66.04 307	72.38 327	39.40 370	72.62 283	75.63 245	55.53 222	62.35 304	83.18 207	47.45 167	76.47 317	49.06 284	66.54 341	82.24 255

testing11		62.81 294	61.90 294	65.54 315	78.38 170	40.76 359	67.59 346	66.78 349	55.48 223	60.13 323	77.11 334	31.67 363	76.79 309	45.53 316	74.45 221	79.06 315

ACMM	61.98 7	70.80 135	69.73 142	74.02 124	80.59 116	58.59 79	82.68 70	82.02 109	55.46 224	67.18 208	84.39 178	38.51 280	83.17 161	60.65 182	76.10 201	80.30 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 216	66.83 217	70.93 229	73.50 305	49.34 256	73.28 271	74.01 280	55.45 225	68.10 184	83.28 203	38.93 277	79.14 258	63.22 157	71.74 269	84.30 187

Anonymous20240529		69.91 155	69.02 157	72.56 174	80.19 122	47.65 287	77.56 161	80.99 140	55.45 225	69.88 147	86.76 106	39.24 273	82.18 190	54.04 241	77.10 187	87.85 38

tt0805		67.77 219	67.24 210	69.34 260	74.87 271	40.08 361	77.36 168	81.37 122	55.31 227	66.33 225	84.65 167	37.35 294	82.55 183	55.65 228	72.28 264	85.39 148

GDP-MVS		72.64 95	71.28 113	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 228	71.38 126	86.97 101	39.94 262	87.00 66	67.02 116	79.20 143	88.89 10

CPTT-MVS		72.78 91	72.08 98	74.87 97	84.88 57	61.41 26	84.15 49	77.86 209	55.27 229	67.51 201	88.08 74	41.93 237	81.85 195	69.04 96	80.01 127	81.35 272

XVG-OURS		68.76 192	67.37 202	72.90 167	74.32 289	57.22 95	70.09 324	78.81 179	55.24 230	67.79 196	85.81 146	36.54 305	78.28 274	62.04 169	75.74 206	83.19 229

tfpnnormal		62.47 298	61.63 297	64.99 325	74.81 274	39.01 372	71.22 305	73.72 284	55.22 231	60.21 322	80.09 280	41.26 252	76.98 305	30.02 427	68.09 328	78.97 318

cl____		67.18 230	66.26 235	69.94 247	70.20 367	45.74 305	73.30 268	76.83 229	55.10 232	65.27 247	79.57 290	47.39 169	80.53 229	59.41 195	69.22 315	83.53 220

DIV-MVS_self_test		67.18 230	66.26 235	69.94 247	70.20 367	45.74 305	73.29 270	76.83 229	55.10 232	65.27 247	79.58 289	47.38 170	80.53 229	59.43 194	69.22 315	83.54 219

PC_three_1452									55.09 234	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 20

EPNet_dtu		61.90 307	61.97 293	61.68 350	72.89 316	39.78 365	75.85 214	65.62 358	55.09 234	54.56 387	79.36 296	37.59 291	67.02 378	39.80 364	76.95 188	78.25 323
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 122	70.39 130	74.65 104	82.01 86	58.82 76	79.93 108	80.35 152	55.09 234	65.82 239	82.16 236	49.17 143	82.64 180	60.34 184	78.62 158	82.50 250

cl22		67.47 224	66.45 224	70.54 238	69.85 376	46.49 297	73.85 260	77.35 220	55.07 237	65.51 242	77.92 319	47.64 162	81.10 214	61.58 175	69.32 311	84.01 197

miper_ehance_all_eth		68.03 210	67.24 210	70.40 240	70.54 360	46.21 301	73.98 253	78.68 184	55.07 237	66.05 231	77.80 323	52.16 97	81.31 208	61.53 177	69.32 311	83.67 214

fmvsm_s_conf0.5_n_2		69.82 157	69.27 153	71.46 206	72.00 334	51.08 216	73.30 268	67.79 339	55.06 239	75.24 51	87.51 85	44.02 214	77.00 303	75.67 42	72.86 252	86.31 107

Elysia		70.19 149	68.29 179	75.88 75	74.15 293	54.33 149	78.26 136	83.21 86	55.04 240	67.28 204	83.59 196	30.16 372	86.11 93	63.67 150	79.26 140	87.20 67

StellarMVS		70.19 149	68.29 179	75.88 75	74.15 293	54.33 149	78.26 136	83.21 86	55.04 240	67.28 204	83.59 196	30.16 372	86.11 93	63.67 150	79.26 140	87.20 67

PS-MVSNAJ		70.51 139	69.70 143	72.93 166	81.52 94	55.79 122	74.92 235	79.00 174	55.04 240	69.88 147	78.66 305	47.05 174	82.19 189	61.61 173	79.58 132	80.83 284

fmvsm_s_conf0.1_n_2		69.64 165	69.01 159	71.52 204	71.66 339	51.04 217	73.39 267	67.14 345	55.02 243	75.11 53	87.64 84	42.94 226	77.01 302	75.55 44	72.63 258	86.52 93

mmtdpeth		60.40 322	59.12 323	64.27 331	69.59 378	48.99 264	70.67 314	70.06 319	54.96 244	62.78 289	73.26 385	27.00 404	67.66 371	58.44 207	45.29 437	76.16 352

xiu_mvs_v2_base		70.52 138	69.75 141	72.84 168	81.21 103	55.63 126	75.11 228	78.92 176	54.92 245	69.96 146	79.68 288	47.00 178	82.09 191	61.60 174	79.37 135	80.81 285

MAR-MVS		71.51 119	70.15 137	75.60 85	81.84 90	59.39 60	81.38 90	82.90 97	54.90 246	68.08 185	78.70 303	47.73 159	85.51 110	51.68 264	84.17 76	81.88 262
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		62.56 296	61.20 306	66.62 294	70.62 359	44.30 321	70.13 323	73.13 295	54.78 247	61.13 316	76.37 350	25.63 414	75.63 323	58.75 204	60.29 391	79.93 301

XVG-OURS-SEG-HR		68.81 189	67.47 199	72.82 170	74.40 286	56.87 105	70.59 315	79.04 173	54.77 248	66.99 211	86.01 137	39.57 268	78.21 275	62.54 164	73.33 244	83.37 223

testing3		56.54 353	55.92 355	58.41 375	77.52 209	27.93 446	69.72 327	56.36 416	54.75 249	58.63 347	77.80 323	20.88 430	71.75 346	25.31 443	62.25 376	75.53 359

Anonymous20231211		69.28 177	68.47 172	71.73 197	80.28 117	47.18 293	79.98 106	82.37 104	54.61 250	67.24 206	84.01 185	39.43 269	82.41 187	55.45 230	72.83 253	85.62 135

SixPastTwentyTwo		61.65 310	58.80 327	70.20 243	75.80 248	47.22 292	75.59 218	69.68 322	54.61 250	54.11 391	79.26 298	27.07 403	82.96 165	43.27 337	49.79 430	80.41 292

test_0402		63.25 289	61.01 309	69.96 246	80.00 126	54.37 148	76.86 188	72.02 305	54.58 252	58.71 343	80.79 268	35.00 317	84.36 136	26.41 441	64.71 354	71.15 411

tttt0517		67.83 217	65.66 243	74.33 116	76.69 233	50.82 223	77.86 152	73.99 281	54.54 253	64.64 264	82.53 224	35.06 316	85.50 111	55.71 226	69.91 300	86.67 86

BH-w/o		66.85 238	65.83 240	69.90 250	79.29 138	52.46 197	74.66 241	76.65 232	54.51 254	64.85 261	78.12 313	45.59 190	82.95 167	43.26 338	75.54 209	74.27 377

AUN-MVS		68.45 201	66.41 228	74.57 109	79.53 135	57.08 103	73.93 257	75.23 257	54.44 255	66.69 217	81.85 243	37.10 300	82.89 169	62.07 168	66.84 338	83.75 211

LTVRE_ROB	55.42 16	63.15 291	61.23 305	68.92 268	76.57 238	47.80 284	59.92 403	76.39 233	54.35 256	58.67 345	82.46 226	29.44 381	81.49 203	42.12 347	71.14 276	77.46 335
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 87	72.59 91	74.27 118	71.28 351	55.88 120	78.21 142	75.56 248	54.31 257	74.86 62	87.80 82	54.72 57	80.23 238	78.07 26	78.48 161	86.70 83

test_fmvsmconf0.01_n		72.17 107	71.50 105	74.16 122	67.96 393	55.58 129	78.06 147	74.67 268	54.19 258	74.54 69	88.23 69	50.35 127	80.24 237	78.07 26	77.46 179	86.65 88

test_fmvsmconf0.1_n		72.81 90	72.33 94	74.24 119	69.89 374	55.81 121	78.22 141	75.40 253	54.17 259	75.00 57	88.03 78	53.82 70	80.23 238	78.08 25	78.34 165	86.69 84

ETVMVS		59.51 332	58.81 325	61.58 352	77.46 211	34.87 409	64.94 371	59.35 402	54.06 260	61.08 317	76.67 341	29.54 378	71.87 345	32.16 409	74.07 226	78.01 330

ab-mvs		66.65 243	66.42 227	67.37 285	76.17 244	41.73 347	70.41 319	76.14 237	53.99 261	65.98 232	83.51 200	49.48 136	76.24 320	48.60 287	73.46 241	84.14 193

fmvsm_s_conf0.5_n_5		72.69 94	72.80 88	72.37 182	74.11 296	53.21 175	78.12 144	73.31 289	53.98 262	76.81 40	88.05 75	53.38 78	77.37 294	76.64 34	80.78 112	86.53 92

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 263	84.64 3				79.07 13	90.87 5	88.37 22

SSM_0407		70.41 143	68.96 160	74.75 99	78.65 160	53.46 167	77.28 174	80.00 156	53.88 264	68.14 179	84.61 169	43.21 221	86.26 90	58.80 202	76.11 198	84.54 177

SSM_0404		70.84 131	69.41 150	75.12 93	79.20 143	53.86 155	77.89 150	80.00 156	53.88 264	69.40 155	84.61 169	43.21 221	86.56 77	58.80 202	77.68 175	84.95 167

XVG-ACMP-BASELINE		64.36 277	62.23 290	70.74 233	72.35 328	52.45 198	70.80 313	78.45 197	53.84 266	59.87 329	81.10 258	16.24 438	79.32 251	55.64 229	71.76 268	80.47 289

mamba_0408		67.78 218	65.42 247	74.85 98	78.65 160	53.46 167	50.83 438	79.09 171	53.75 267	68.14 179	83.83 189	41.79 241	86.56 77	56.58 216	76.11 198	84.54 177

SSM_04072		64.98 268	65.42 247	63.68 335	78.65 160	53.46 167	50.83 438	79.09 171	53.75 267	68.14 179	83.83 189	41.79 241	53.03 440	56.58 216	76.11 198	84.54 177

VortexMVS		66.41 249	65.50 246	69.16 265	73.75 299	48.14 279	73.41 266	78.28 204	53.73 269	64.98 260	78.33 311	40.62 258	79.07 260	58.88 201	67.50 333	80.26 295

FE-MVS		65.91 254	63.33 275	73.63 145	77.36 214	51.95 208	72.62 283	75.81 242	53.70 270	65.31 245	78.96 301	28.81 387	86.39 85	43.93 329	73.48 240	82.55 246

thisisatest0530		67.92 214	65.78 241	74.33 116	76.29 242	51.03 218	76.89 186	74.25 276	53.67 271	65.59 241	81.76 246	35.15 315	85.50 111	55.94 221	72.47 259	86.47 95

PVSNet_BlendedMVS		68.56 198	67.72 190	71.07 226	77.03 228	50.57 227	74.50 244	81.52 116	53.66 272	64.22 272	79.72 287	49.13 144	82.87 171	55.82 223	73.92 228	79.77 308

patch_mono-2		69.85 156	71.09 117	66.16 303	79.11 148	54.80 143	71.97 295	74.31 273	53.50 273	70.90 130	84.17 180	57.63 31	63.31 396	66.17 122	82.02 100	80.38 293

EG-PatchMatch MVS		64.71 270	62.87 281	70.22 241	77.68 199	53.48 166	77.99 148	78.82 178	53.37 274	56.03 371	77.41 331	24.75 419	84.04 141	46.37 306	73.42 243	73.14 383

SD_0403		63.07 292	63.49 272	61.82 349	75.16 265	31.14 435	71.89 298	73.47 286	53.34 275	58.22 351	81.81 245	45.17 201	73.86 332	37.43 377	74.87 218	80.45 290

DP-MVS		65.68 256	63.66 269	71.75 196	84.93 55	56.87 105	80.74 98	73.16 294	53.06 276	59.09 340	82.35 227	36.79 304	85.94 100	32.82 407	69.96 299	72.45 392

TR-MVS		66.59 246	65.07 254	71.17 222	79.18 145	49.63 252	73.48 265	75.20 259	52.95 277	67.90 187	80.33 274	39.81 266	83.68 149	43.20 339	73.56 238	80.20 296

ET-MVSNet_ETH3D		67.96 213	65.72 242	74.68 102	76.67 235	55.62 128	75.11 228	74.74 266	52.91 278	60.03 326	80.12 278	33.68 334	82.64 180	61.86 171	76.34 195	85.78 123

QAPM		70.05 151	68.81 163	73.78 132	76.54 239	53.43 170	83.23 60	83.48 72	52.89 279	65.90 235	86.29 127	41.55 247	86.49 83	51.01 267	78.40 164	81.42 266

LuminaMVS		68.24 205	66.82 218	72.51 176	73.46 307	53.60 163	76.23 202	78.88 177	52.78 280	68.08 185	80.13 277	32.70 350	81.41 204	63.16 158	75.97 202	82.53 247

icg_test_0407_2		66.41 249	66.75 219	65.37 320	77.06 223	49.73 244	63.79 380	78.60 186	52.70 281	66.19 227	82.58 216	45.17 201	63.65 395	59.20 197	75.46 211	82.74 241

IMVS_0407		68.90 187	67.93 187	71.82 193	77.06 223	49.73 244	74.40 248	78.60 186	52.70 281	66.19 227	82.58 216	45.17 201	83.00 163	59.20 197	75.46 211	82.74 241

IMVS_0404		64.63 272	64.22 260	65.88 311	77.06 223	49.73 244	64.40 374	78.60 186	52.70 281	53.16 401	82.58 216	34.82 319	65.16 389	59.20 197	75.46 211	82.74 241

IMVS_0403		69.09 183	68.14 184	71.95 188	77.06 223	49.73 244	74.51 243	78.60 186	52.70 281	66.69 217	82.58 216	46.43 182	83.38 156	59.20 197	75.46 211	82.74 241

OpenMVS	61.03 9	68.85 188	67.56 193	72.70 172	74.26 291	53.99 154	81.21 92	81.34 127	52.70 281	62.75 292	85.55 151	38.86 278	84.14 139	48.41 289	83.01 85	79.97 300

pmmvs6		63.69 283	62.82 283	66.27 301	70.63 358	39.27 371	73.13 276	75.47 252	52.69 286	59.75 333	82.30 229	39.71 267	77.03 301	47.40 296	64.35 359	82.53 247

IterMVS		62.79 295	61.27 303	67.35 286	69.37 382	52.04 205	71.17 306	68.24 337	52.63 287	59.82 330	76.91 338	37.32 295	72.36 339	52.80 252	63.19 369	77.66 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 207	66.36 230	73.63 145	75.61 254	55.35 135	80.77 97	78.56 191	52.48 288	64.27 269	84.10 183	27.45 399	81.84 196	63.45 154	70.56 285	83.69 213

jajsoiax		68.25 204	66.45 224	73.66 142	75.62 253	55.49 131	80.82 96	78.51 193	52.33 289	64.33 267	84.11 182	28.28 391	81.81 197	63.48 153	70.62 283	83.67 214

TAMVS		66.78 241	65.27 252	71.33 218	79.16 147	53.67 160	73.84 261	69.59 324	52.32 290	65.28 246	81.72 247	44.49 210	77.40 293	42.32 346	78.66 157	82.92 236

CDS-MVSNet		66.80 240	65.37 249	71.10 225	78.98 150	53.13 178	73.27 272	71.07 311	52.15 291	64.72 262	80.23 276	43.56 218	77.10 298	45.48 318	78.88 149	83.05 235
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 199	66.56 221	74.21 121	79.60 132	52.95 180	74.94 234	75.48 251	52.09 292	60.10 324	83.27 204	36.54 305	84.70 130	59.32 196	77.69 174	84.99 165

viewmambaseed2359dif		68.91 186	68.18 182	71.11 224	70.21 366	48.05 283	72.28 290	75.90 241	51.96 293	70.93 129	84.47 176	51.37 112	78.59 270	61.55 176	74.97 216	86.68 85

PVSNet_Blended		68.59 194	67.72 190	71.19 220	77.03 228	50.57 227	72.51 286	81.52 116	51.91 294	64.22 272	77.77 326	49.13 144	82.87 171	55.82 223	79.58 132	80.14 298

mvs_anonymous		68.03 210	67.51 197	69.59 255	72.08 332	44.57 319	71.99 294	75.23 257	51.67 295	67.06 210	82.57 220	54.68 58	77.94 279	56.56 218	75.71 207	86.26 109

xiu_mvs_v1_base_debu		68.58 195	67.28 206	72.48 177	78.19 179	57.19 97	75.28 223	75.09 261	51.61 296	70.04 140	81.41 253	32.79 345	79.02 262	63.81 147	77.31 180	81.22 275

xiu_mvs_v1_base		68.58 195	67.28 206	72.48 177	78.19 179	57.19 97	75.28 223	75.09 261	51.61 296	70.04 140	81.41 253	32.79 345	79.02 262	63.81 147	77.31 180	81.22 275

xiu_mvs_v1_base_debi		68.58 195	67.28 206	72.48 177	78.19 179	57.19 97	75.28 223	75.09 261	51.61 296	70.04 140	81.41 253	32.79 345	79.02 262	63.81 147	77.31 180	81.22 275

MVSTER		67.16 232	65.58 245	71.88 191	70.37 365	49.70 248	70.25 322	78.45 197	51.52 299	69.16 162	80.37 271	38.45 281	82.50 184	60.19 185	71.46 273	83.44 222

CNLPA		65.43 260	64.02 262	69.68 253	78.73 158	58.07 83	77.82 155	70.71 314	51.49 300	61.57 312	83.58 199	38.23 286	70.82 351	43.90 330	70.10 296	80.16 297

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 75	51.47 301	70.27 138	86.61 115	48.61 150	86.51 82	53.85 244	87.96 39	78.16 324

miper_enhance_ethall		67.11 233	66.09 237	70.17 244	69.21 384	45.98 303	72.85 280	78.41 200	51.38 302	65.65 240	75.98 357	51.17 116	81.25 209	60.82 181	69.32 311	83.29 226

MSDG		61.81 309	59.23 321	69.55 258	72.64 319	52.63 192	70.45 318	75.81 242	51.38 302	53.70 394	76.11 352	29.52 379	81.08 216	37.70 375	65.79 347	74.93 368

test20.03		53.87 375	54.02 372	53.41 407	61.47 429	28.11 445	61.30 395	59.21 403	51.34 304	52.09 405	77.43 330	33.29 339	58.55 417	29.76 428	60.27 392	73.58 382

MVSFormer		71.50 120	70.38 131	74.88 96	78.76 156	57.15 100	82.79 67	78.48 194	51.26 305	69.49 152	83.22 205	43.99 215	83.24 159	66.06 123	79.37 135	84.23 189

test_djsdf		69.45 174	67.74 189	74.58 108	74.57 282	54.92 141	82.79 67	78.48 194	51.26 305	65.41 244	83.49 201	38.37 282	83.24 159	66.06 123	69.25 314	85.56 136

dmvs_testset		50.16 393	51.90 383	44.94 428	66.49 404	11.78 468	61.01 400	51.50 430	51.17 307	50.30 417	67.44 422	39.28 271	60.29 407	22.38 447	57.49 401	62.76 433

PAPM		67.92 214	66.69 220	71.63 202	78.09 184	49.02 263	77.09 180	81.24 132	51.04 308	60.91 318	83.98 186	47.71 160	84.99 120	40.81 356	79.32 138	80.90 283

Syy-MVS		56.00 360	56.23 353	55.32 393	74.69 277	26.44 452	65.52 361	57.49 411	50.97 309	56.52 365	72.18 389	39.89 264	68.09 367	24.20 444	64.59 357	71.44 407

myMVS_eth3d		54.86 371	54.61 364	55.61 392	74.69 277	27.31 449	65.52 361	57.49 411	50.97 309	56.52 365	72.18 389	21.87 428	68.09 367	27.70 435	64.59 357	71.44 407

miper_lstm_enhance		62.03 306	60.88 311	65.49 318	66.71 402	46.25 299	56.29 422	75.70 244	50.68 311	61.27 314	75.48 364	40.21 261	68.03 369	56.31 220	65.25 350	82.18 256

gg-mvs-nofinetune		57.86 344	56.43 350	62.18 347	72.62 320	35.35 408	66.57 351	56.33 417	50.65 312	57.64 356	57.10 444	30.65 366	76.36 318	37.38 378	78.88 149	74.82 370

TAPA-MVS	59.36 10	66.60 244	65.20 253	70.81 231	76.63 236	48.75 269	76.52 196	80.04 155	50.64 313	65.24 251	84.93 159	39.15 274	78.54 271	36.77 383	76.88 189	85.14 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 352	56.83 345	56.61 387	69.23 383	41.02 354	58.37 409	64.18 370	50.59 314	57.45 358	71.42 397	35.54 312	58.94 415	37.23 379	67.45 334	69.87 420

MVP-Stereo		65.41 261	63.80 266	70.22 241	77.62 206	55.53 130	76.30 199	78.53 192	50.59 314	56.47 367	78.65 306	39.84 265	82.68 178	44.10 328	72.12 266	72.44 393
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 130	69.49 147	75.35 88	77.63 202	55.71 123	76.04 209	81.81 112	50.30 316	69.66 150	85.40 155	52.51 89	84.89 126	51.82 261	80.24 124	85.45 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 363	53.81 374	61.11 358	59.39 439	40.98 358	65.89 356	68.28 336	50.21 317	58.11 353	75.42 365	17.03 434	67.63 373	43.79 332	46.21 434	74.73 372

baseline2		63.42 285	61.26 304	69.89 251	72.55 322	47.62 288	71.54 300	68.38 335	50.11 318	54.82 383	75.55 362	43.06 224	80.96 219	48.13 292	67.16 337	81.11 278

test-LLR		58.15 342	58.13 335	58.22 377	68.57 388	44.80 315	65.46 363	57.92 408	50.08 319	55.44 375	69.82 410	32.62 353	57.44 422	49.66 278	73.62 235	72.41 394

test0.0.03 1		53.32 380	53.59 377	52.50 413	62.81 424	29.45 440	59.51 405	54.11 425	50.08 319	54.40 389	74.31 374	32.62 353	55.92 431	30.50 424	63.95 362	72.15 399

fmvsm_s_conf0.5_n		69.58 167	68.84 162	71.79 195	72.31 330	52.90 182	77.90 149	62.43 389	49.97 321	72.85 104	85.90 140	52.21 95	76.49 315	75.75 41	70.26 293	85.97 115

COLMAP_ROB	52.97 17	61.27 315	58.81 325	68.64 271	74.63 279	52.51 195	78.42 135	73.30 290	49.92 322	50.96 409	81.51 252	23.06 422	79.40 249	31.63 417	65.85 345	74.01 380
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		69.54 169	68.74 165	71.93 189	72.47 325	53.82 157	78.25 138	62.26 391	49.78 323	73.12 97	86.21 129	52.66 87	76.79 309	75.02 50	68.88 319	85.18 156

WBMVS		60.54 319	60.61 313	60.34 361	78.00 188	35.95 405	64.55 373	64.89 363	49.63 324	63.39 279	78.70 303	33.85 332	67.65 372	42.10 348	70.35 290	77.43 336

tpmvs		58.47 337	56.95 343	63.03 343	70.20 367	41.21 353	67.90 343	67.23 344	49.62 325	54.73 385	70.84 401	34.14 326	76.24 320	36.64 387	61.29 383	71.64 403

fmvsm_s_conf0.1_n		69.41 175	68.60 168	71.83 192	71.07 353	52.88 185	77.85 153	62.44 388	49.58 326	72.97 100	86.22 128	51.68 107	76.48 316	75.53 45	70.10 296	86.14 110

UBG		59.62 331	59.53 319	59.89 362	78.12 183	35.92 406	64.11 378	60.81 399	49.45 327	61.34 313	75.55 362	33.05 340	67.39 376	38.68 370	74.62 219	76.35 351

thisisatest0515		65.83 255	63.50 271	72.82 170	73.75 299	49.50 253	71.32 303	73.12 296	49.39 328	63.82 274	76.50 349	34.95 318	84.84 129	53.20 250	75.49 210	84.13 194

fmvsm_s_conf0.1_n_a		69.32 176	68.44 174	71.96 187	70.91 355	53.78 158	78.12 144	62.30 390	49.35 329	73.20 91	86.55 120	51.99 100	76.79 309	74.83 52	68.68 324	85.32 151

HY-MVS	56.14 13	64.55 274	63.89 263	66.55 295	74.73 276	41.02 354	69.96 325	74.43 270	49.29 330	61.66 310	80.92 263	47.43 168	76.68 313	44.91 323	71.69 270	81.94 260

MIMVSNet1		55.17 368	54.31 369	57.77 383	70.03 371	32.01 431	65.68 359	64.81 364	49.19 331	46.75 428	76.00 354	25.53 415	64.04 392	28.65 432	62.13 377	77.26 340

SCA		60.49 320	58.38 331	66.80 289	74.14 295	48.06 281	63.35 383	63.23 381	49.13 332	59.33 339	72.10 391	37.45 292	74.27 330	44.17 325	62.57 373	78.05 326

test_fmvsmvis_n_1920		70.84 131	70.38 131	72.22 185	71.16 352	55.39 133	75.86 213	72.21 303	49.03 333	73.28 89	86.17 131	51.83 104	77.29 296	75.80 40	78.05 169	83.98 198

testgi		51.90 385	52.37 381	50.51 420	60.39 437	23.55 459	58.42 408	58.15 406	49.03 333	51.83 406	79.21 299	22.39 423	55.59 432	29.24 431	62.64 372	72.40 396

sc_t1		59.76 327	57.84 338	65.54 315	74.87 271	42.95 337	69.61 328	64.16 372	48.90 335	58.68 344	77.12 333	28.19 392	72.35 340	43.75 334	55.28 410	81.31 273

MIMVSNet		57.35 346	57.07 341	58.22 377	74.21 292	37.18 389	62.46 388	60.88 398	48.88 336	55.29 378	75.99 356	31.68 362	62.04 401	31.87 412	72.35 261	75.43 361

gm-plane-assit					71.40 348	41.72 349			48.85 337		73.31 383		82.48 186	48.90 285

fmvsm_l_conf0.5_n		70.99 129	70.82 122	71.48 205	71.45 344	54.40 147	77.18 178	70.46 316	48.67 338	75.17 52	86.86 103	53.77 72	76.86 307	76.33 37	77.51 178	83.17 233

UWE-MVS		60.18 323	59.78 317	61.39 355	77.67 200	33.92 421	69.04 335	63.82 375	48.56 339	64.27 269	77.64 328	27.20 401	70.40 356	33.56 404	76.24 196	79.83 305

cascas		65.98 253	63.42 273	73.64 144	77.26 217	52.58 193	72.26 291	77.21 223	48.56 339	61.21 315	74.60 372	32.57 356	85.82 103	50.38 272	76.75 192	82.52 249

PLC	56.13 14	65.09 266	63.21 278	70.72 234	81.04 106	54.87 142	78.57 132	77.47 216	48.51 341	55.71 372	81.89 242	33.71 333	79.71 244	41.66 352	70.37 288	77.58 334
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 270	62.50 286	71.34 217	79.72 131	55.71 123	79.82 110	74.72 267	48.50 342	56.62 363	84.62 168	33.59 336	82.34 188	29.65 429	75.23 215	75.97 353

anonymousdsp		67.00 236	64.82 256	73.57 148	70.09 370	56.13 113	76.35 198	77.35 220	48.43 343	64.99 259	80.84 267	33.01 342	80.34 233	64.66 137	67.64 332	84.23 189

无先验							79.66 115	74.30 274	48.40 344				80.78 226	53.62 245		79.03 317

FE-MVSNET		55.16 369	53.75 375	59.41 365	65.29 412	33.20 425	67.21 350	66.21 354	48.39 345	49.56 419	73.53 382	29.03 383	72.51 338	30.38 425	54.10 416	72.52 390

114514_t		70.83 133	69.56 145	74.64 105	86.21 31	54.63 144	82.34 76	81.81 112	48.22 346	63.01 287	85.83 143	40.92 257	87.10 63	57.91 208	79.79 128	82.18 256

tpm		57.34 347	58.16 333	54.86 396	71.80 338	34.77 411	67.47 348	56.04 420	48.20 347	60.10 324	76.92 337	37.17 298	53.41 439	40.76 357	65.01 351	76.40 350

test_fmvsm_n_1920		71.73 116	71.14 116	73.50 150	72.52 323	56.53 107	75.60 217	76.16 235	48.11 348	77.22 35	85.56 149	53.10 82	77.43 291	74.86 51	77.14 185	86.55 91

MDA-MVSNet-bldmvs		53.87 375	50.81 388	63.05 342	66.25 406	48.58 274	56.93 420	63.82 375	48.09 349	41.22 440	70.48 406	30.34 369	68.00 370	34.24 399	45.92 436	72.57 389

XXY-MVS		60.68 316	61.67 296	57.70 384	70.43 363	38.45 378	64.19 376	66.47 350	48.05 350	63.22 280	80.86 265	49.28 141	60.47 405	45.25 322	67.28 336	74.19 378

F-COLMAP		63.05 293	60.87 312	69.58 257	76.99 230	53.63 162	78.12 144	76.16 235	47.97 351	52.41 404	81.61 249	27.87 394	78.11 276	40.07 359	66.66 340	77.00 344

tt0320-xc		58.33 339	56.41 351	64.08 332	75.79 249	41.34 351	68.30 339	62.72 385	47.90 352	56.29 368	74.16 377	28.53 388	71.04 350	41.50 355	52.50 422	79.88 303

fmvsm_l_conf0.5_n_a		70.50 140	70.27 133	71.18 221	71.30 350	54.09 152	76.89 186	69.87 320	47.90 352	74.37 72	86.49 121	53.07 84	76.69 312	75.41 46	77.11 186	82.76 240

Patchmatch-RL test		58.16 341	55.49 358	66.15 304	67.92 394	48.89 268	60.66 401	51.07 433	47.86 354	59.36 336	62.71 438	34.02 329	72.27 342	56.41 219	59.40 394	77.30 338

D2MVS		62.30 301	60.29 315	68.34 276	66.46 405	48.42 276	65.70 358	73.42 287	47.71 355	58.16 352	75.02 368	30.51 367	77.71 288	53.96 243	71.68 271	78.90 319

ANet_high		41.38 412	37.47 419	53.11 409	39.73 465	24.45 457	56.94 419	69.69 321	47.65 356	26.04 457	52.32 447	12.44 446	62.38 400	21.80 448	10.61 466	72.49 391

CostFormer		64.04 280	62.51 285	68.61 272	71.88 336	45.77 304	71.30 304	70.60 315	47.55 357	64.31 268	76.61 345	41.63 244	79.62 247	49.74 276	69.00 318	80.42 291

PatchmatchNet		59.84 326	58.24 332	64.65 327	73.05 313	46.70 296	69.42 331	62.18 392	47.55 357	58.88 342	71.96 393	34.49 323	69.16 361	42.99 341	63.60 364	78.07 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 367	53.89 373	59.21 369	57.80 443	27.47 448	57.75 415	74.32 272	47.38 359	50.90 410	70.00 409	28.45 390	70.30 357	40.44 358	57.92 399	79.87 304

ITE_SJBPF				62.09 348	66.16 407	44.55 320		64.32 368	47.36 360	55.31 377	80.34 273	19.27 431	62.68 399	36.29 391	62.39 375	79.04 316

KD-MVS_2432*1600		53.45 377	51.50 386	59.30 366	62.82 422	37.14 390	55.33 423	71.79 307	47.34 361	55.09 380	70.52 404	21.91 426	70.45 354	35.72 394	42.97 440	70.31 416

miper_refine_blended		53.45 377	51.50 386	59.30 366	62.82 422	37.14 390	55.33 423	71.79 307	47.34 361	55.09 380	70.52 404	21.91 426	70.45 354	35.72 394	42.97 440	70.31 416

OurMVSNet-221017-0		61.37 314	58.63 329	69.61 254	72.05 333	48.06 281	73.93 257	72.51 299	47.23 363	54.74 384	80.92 263	21.49 429	81.24 210	48.57 288	56.22 407	79.53 310

tpmrst		58.24 340	58.70 328	56.84 386	66.97 399	34.32 416	69.57 330	61.14 397	47.17 364	58.58 348	71.60 396	41.28 251	60.41 406	49.20 282	62.84 371	75.78 356

tt0320		58.59 336	56.81 346	63.92 334	75.46 257	41.32 352	68.63 337	64.06 373	47.05 365	56.19 369	74.19 375	30.34 369	71.36 347	39.92 363	55.45 409	79.09 314

PVSNet	50.76 19	58.40 338	57.39 339	61.42 353	75.53 256	44.04 325	61.43 393	63.45 379	47.04 366	56.91 361	73.61 381	27.00 404	64.76 390	39.12 368	72.40 260	75.47 360

WB-MVSnew		59.66 329	59.69 318	59.56 363	75.19 264	35.78 407	69.34 332	64.28 369	46.88 367	61.76 309	75.79 358	40.61 259	65.20 388	32.16 409	71.21 275	77.70 332

UWE-MVS-28		52.25 384	52.35 382	51.93 417	66.99 398	22.79 460	63.48 382	48.31 441	46.78 368	52.73 403	76.11 352	27.78 396	57.82 421	20.58 450	68.41 326	75.17 362

FMVSNet5		55.86 361	54.93 361	58.66 374	71.05 354	36.35 399	64.18 377	62.48 387	46.76 369	50.66 414	74.73 371	25.80 412	64.04 392	33.11 405	65.57 348	75.59 358

jason		69.65 164	68.39 176	73.43 155	78.27 177	56.88 104	77.12 179	73.71 285	46.53 370	69.34 157	83.22 205	43.37 219	79.18 253	64.77 136	79.20 143	84.23 189
jason: jason.
MS-PatchMatch		62.42 299	61.46 299	65.31 322	75.21 263	52.10 202	72.05 293	74.05 279	46.41 371	57.42 359	74.36 373	34.35 325	77.57 290	45.62 314	73.67 233	66.26 430

1112_ss		64.00 281	63.36 274	65.93 309	79.28 140	42.58 339	71.35 302	72.36 302	46.41 371	60.55 321	77.89 321	46.27 185	73.28 334	46.18 307	69.97 298	81.92 261

lupinMVS		69.57 168	68.28 181	73.44 154	78.76 156	57.15 100	76.57 194	73.29 291	46.19 373	69.49 152	82.18 233	43.99 215	79.23 252	64.66 137	79.37 135	83.93 200

testdata				64.66 326	81.52 94	52.93 181		65.29 361	46.09 374	73.88 80	87.46 88	38.08 288	66.26 383	53.31 249	78.48 161	74.78 371

UnsupCasMVSNet_eth		53.16 382	52.47 380	55.23 394	59.45 438	33.39 424	59.43 406	69.13 330	45.98 375	50.35 416	72.32 388	29.30 382	58.26 419	42.02 350	44.30 438	74.05 379

AllTest		57.08 349	54.65 363	64.39 329	71.44 345	49.03 261	69.92 326	67.30 341	45.97 376	47.16 425	79.77 284	17.47 432	67.56 374	33.65 401	59.16 395	76.57 348

TestCases				64.39 329	71.44 345	49.03 261		67.30 341	45.97 376	47.16 425	79.77 284	17.47 432	67.56 374	33.65 401	59.16 395	76.57 348

WTY-MVS		59.75 328	60.39 314	57.85 382	72.32 329	37.83 383	61.05 399	64.18 370	45.95 378	61.91 306	79.11 300	47.01 177	60.88 404	42.50 345	69.49 310	74.83 369

IterMVS-SCA-FT		62.49 297	61.52 298	65.40 319	71.99 335	50.80 224	71.15 308	69.63 323	45.71 379	60.61 320	77.93 318	37.45 292	65.99 385	55.67 227	63.50 366	79.42 311

WB-MVS		43.26 406	43.41 406	42.83 432	63.32 421	10.32 470	58.17 411	45.20 448	45.42 380	40.44 443	67.26 425	34.01 330	58.98 414	11.96 461	24.88 455	59.20 436

旧先验2							76.08 206		45.32 381	76.55 42			65.56 387	58.75 204

OpenMVS_ROB	52.78 18	60.03 324	58.14 334	65.69 314	70.47 362	44.82 314	75.33 222	70.86 313	45.04 382	56.06 370	76.00 354	26.89 406	79.65 245	35.36 396	67.29 335	72.60 388

TinyColmap		54.14 372	51.72 384	61.40 354	66.84 401	41.97 344	66.52 352	68.51 334	44.81 383	42.69 439	75.77 359	11.66 448	72.94 335	31.96 411	56.77 405	69.27 424

MDTV_nov1_ep13			57.00 342		72.73 318	38.26 379	65.02 370	64.73 366	44.74 384	55.46 374	72.48 387	32.61 355	70.47 353	37.47 376	67.75 331

新几何1				70.76 232	85.66 41	61.13 30		66.43 351	44.68 385	70.29 137	86.64 111	41.29 250	75.23 325	49.72 277	81.75 106	75.93 354

Patchmtry		57.16 348	56.47 349	59.23 368	69.17 385	34.58 414	62.98 385	63.15 382	44.53 386	56.83 362	74.84 369	35.83 310	68.71 364	40.03 360	60.91 384	74.39 376

ppachtmachnet_test		58.06 343	55.38 359	66.10 306	69.51 379	48.99 264	68.01 342	66.13 355	44.50 387	54.05 392	70.74 402	32.09 361	72.34 341	36.68 386	56.71 406	76.99 346

PatchT		53.17 381	53.44 378	52.33 414	68.29 392	25.34 456	58.21 410	54.41 424	44.46 388	54.56 387	69.05 416	33.32 338	60.94 403	36.93 382	61.76 381	70.73 414

EPMVS		53.96 373	53.69 376	54.79 397	66.12 408	31.96 432	62.34 390	49.05 437	44.42 389	55.54 373	71.33 399	30.22 371	56.70 425	41.65 353	62.54 374	75.71 357

pmmvs4		61.48 313	59.39 320	67.76 280	71.57 341	53.86 155	71.42 301	65.34 360	44.20 390	59.46 335	77.92 319	35.90 309	74.71 327	43.87 331	64.87 353	74.71 373

dp		51.89 386	51.60 385	52.77 411	68.44 391	32.45 430	62.36 389	54.57 423	44.16 391	49.31 420	67.91 418	28.87 386	56.61 427	33.89 400	54.89 412	69.24 425

PatchMatch-RL		56.25 358	54.55 365	61.32 356	77.06 223	56.07 115	65.57 360	54.10 426	44.13 392	53.49 400	71.27 400	25.20 416	66.78 379	36.52 389	63.66 363	61.12 434

our_test_3		56.49 354	54.42 366	62.68 345	69.51 379	45.48 310	66.08 355	61.49 395	44.11 393	50.73 413	69.60 413	33.05 340	68.15 366	38.38 372	56.86 403	74.40 375

USDC		56.35 357	54.24 370	62.69 344	64.74 414	40.31 360	65.05 369	73.83 283	43.93 394	47.58 423	77.71 327	15.36 441	75.05 326	38.19 374	61.81 380	72.70 387

PM-MVS		52.33 383	50.19 392	58.75 373	62.10 427	45.14 313	65.75 357	40.38 455	43.60 395	53.52 398	72.65 386	9.16 456	65.87 386	50.41 271	54.18 415	65.24 432

pmmvs-eth3d		58.81 335	56.31 352	66.30 300	67.61 395	52.42 199	72.30 289	64.76 365	43.55 396	54.94 382	74.19 375	28.95 384	72.60 337	43.31 336	57.21 402	73.88 381

SSC-MVS		41.96 411	41.99 410	41.90 433	62.46 426	9.28 472	57.41 418	44.32 451	43.38 397	38.30 449	66.45 428	32.67 352	58.42 418	10.98 462	21.91 458	57.99 440

new-patchmatchnet		47.56 400	47.73 400	47.06 423	58.81 441	9.37 471	48.78 442	59.21 403	43.28 398	44.22 435	68.66 417	25.67 413	57.20 424	31.57 419	49.35 431	74.62 374

Test_1112_low_res		62.32 300	61.77 295	64.00 333	79.08 149	39.53 369	68.17 340	70.17 317	43.25 399	59.03 341	79.90 281	44.08 212	71.24 349	43.79 332	68.42 325	81.25 274

RPMNet		61.53 311	58.42 330	70.86 230	69.96 372	52.07 203	65.31 367	81.36 123	43.20 400	59.36 336	70.15 408	35.37 313	85.47 113	36.42 390	64.65 355	75.06 364

tpm2		62.07 304	60.10 316	67.99 278	72.79 317	43.86 326	71.05 311	66.85 348	43.14 401	62.77 290	75.39 366	38.32 284	80.80 225	41.69 351	68.88 319	79.32 312

JIA-IIPM		51.56 387	47.68 401	63.21 340	64.61 415	50.73 225	47.71 444	58.77 405	42.90 402	48.46 422	51.72 448	24.97 417	70.24 358	36.06 393	53.89 417	68.64 426

1314		64.61 273	63.21 278	68.80 269	71.87 337	47.46 290	73.95 255	78.39 202	42.88 403	59.97 327	76.60 346	38.11 287	79.39 250	54.84 234	72.32 262	79.55 309

HyFIR lowres test		65.67 257	63.01 280	73.67 141	79.97 127	55.65 125	69.07 334	75.52 249	42.68 404	63.53 277	77.95 317	40.43 260	81.64 198	46.01 309	71.91 267	83.73 212

CR-MVSNet		59.91 325	57.90 337	65.96 308	69.96 372	52.07 203	65.31 367	63.15 382	42.48 405	59.36 336	74.84 369	35.83 310	70.75 352	45.50 317	64.65 355	75.06 364

test222					83.14 72	58.68 78	72.57 285	63.45 379	41.78 406	67.56 200	86.12 132	37.13 299			78.73 154	74.98 367

TDRefinement		53.44 379	50.72 389	61.60 351	64.31 417	46.96 294	70.89 312	65.27 362	41.78 406	44.61 434	77.98 316	11.52 450	66.36 382	28.57 433	51.59 424	71.49 406

sss		56.17 359	56.57 348	54.96 395	66.93 400	36.32 401	57.94 412	61.69 394	41.67 408	58.64 346	75.32 367	38.72 279	56.25 429	42.04 349	66.19 344	72.31 397

PVSNet_0	43.31 20	47.46 401	45.64 404	52.92 410	67.60 396	44.65 317	54.06 428	54.64 422	41.59 409	46.15 430	58.75 441	30.99 365	58.66 416	32.18 408	24.81 456	55.46 444

MVS		67.37 225	66.33 231	70.51 239	75.46 257	50.94 219	73.95 255	81.85 111	41.57 410	62.54 297	78.57 309	47.98 155	85.47 113	52.97 251	82.05 99	75.14 363

Anonymous20240521		55.30 365	54.41 367	57.96 381	60.92 436	41.73 347	71.09 310	71.06 312	41.18 411	48.65 421	73.31 383	16.93 435	59.25 412	42.54 344	64.01 360	72.90 385

Anonymous20231206		55.10 370	55.30 360	54.48 398	69.81 377	33.94 420	62.91 386	62.13 393	41.08 412	55.18 379	75.65 360	32.75 348	56.59 428	30.32 426	67.86 329	72.91 384

MDA-MVSNet_test_wron		50.71 392	48.95 394	56.00 391	61.17 431	41.84 345	51.90 434	56.45 414	40.96 413	44.79 433	67.84 419	30.04 375	55.07 436	36.71 385	50.69 427	71.11 412

YYNet1		50.73 391	48.96 393	56.03 390	61.10 432	41.78 346	51.94 433	56.44 415	40.94 414	44.84 432	67.80 420	30.08 374	55.08 435	36.77 383	50.71 426	71.22 409

dongtai		34.52 421	34.94 421	33.26 442	61.06 433	16.00 467	52.79 432	23.78 468	40.71 415	39.33 447	48.65 456	16.91 436	48.34 448	12.18 460	19.05 460	35.44 459

CHOSEN 1792x2688		65.08 267	62.84 282	71.82 193	81.49 96	56.26 111	66.32 354	74.20 278	40.53 416	63.16 283	78.65 306	41.30 249	77.80 285	45.80 311	74.09 225	81.40 269

pmmvs5		56.47 355	55.68 357	58.86 372	61.41 430	36.71 396	66.37 353	62.75 384	40.38 417	53.70 394	76.62 343	34.56 321	67.05 377	40.02 361	65.27 349	72.83 386

test_vis1_n_1920		58.86 334	59.06 324	58.25 376	63.76 418	43.14 334	67.49 347	66.36 352	40.22 418	65.89 236	71.95 394	31.04 364	59.75 410	59.94 188	64.90 352	71.85 401

MDTV_nov1_ep13_2view						25.89 454	61.22 396		40.10 419	51.10 408		32.97 343		38.49 371		78.61 321

tpm cat1		59.25 333	56.95 343	66.15 304	72.19 331	46.96 294	68.09 341	65.76 356	40.03 420	57.81 355	70.56 403	38.32 284	74.51 328	38.26 373	61.50 382	77.00 344

test-mter		56.42 356	55.82 356	58.22 377	68.57 388	44.80 315	65.46 363	57.92 408	39.94 421	55.44 375	69.82 410	21.92 425	57.44 422	49.66 278	73.62 235	72.41 394

UnsupCasMVSNet_bld		50.07 394	48.87 395	53.66 403	60.97 435	33.67 422	57.62 416	64.56 367	39.47 422	47.38 424	64.02 436	27.47 398	59.32 411	34.69 398	43.68 439	67.98 428

TESTMET0.1,1		55.28 366	54.90 362	56.42 388	66.56 403	43.67 328	65.46 363	56.27 418	39.18 423	53.83 393	67.44 422	24.21 420	55.46 433	48.04 293	73.11 249	70.13 418

mamv4		56.85 351	58.00 336	53.43 406	72.46 326	54.47 145	57.56 417	54.74 421	38.81 424	57.42 359	79.45 294	47.57 164	38.70 459	60.88 180	53.07 419	67.11 429

ADS-MVSNet2		51.33 389	48.76 396	59.07 371	66.02 409	44.60 318	50.90 436	59.76 401	36.90 425	50.74 411	66.18 430	26.38 407	63.11 397	27.17 437	54.76 413	69.50 422

ADS-MVSNet		48.48 398	47.77 399	50.63 419	66.02 409	29.92 439	50.90 436	50.87 435	36.90 425	50.74 411	66.18 430	26.38 407	52.47 442	27.17 437	54.76 413	69.50 422

RPSCF		55.80 362	54.22 371	60.53 360	65.13 413	42.91 338	64.30 375	57.62 410	36.84 427	58.05 354	82.28 230	28.01 393	56.24 430	37.14 380	58.61 397	82.44 252

test_cas_vis1_n_1920		56.91 350	56.71 347	57.51 385	59.13 440	45.40 311	63.58 381	61.29 396	36.24 428	67.14 209	71.85 395	29.89 376	56.69 426	57.65 210	63.58 365	70.46 415

Patchmatch-test		49.08 396	48.28 398	51.50 418	64.40 416	30.85 437	45.68 448	48.46 440	35.60 429	46.10 431	72.10 391	34.47 324	46.37 451	27.08 439	60.65 388	77.27 339

CHOSEN 280x420		47.83 399	46.36 403	52.24 416	67.37 397	49.78 243	38.91 456	43.11 453	35.00 430	43.27 438	63.30 437	28.95 384	49.19 447	36.53 388	60.80 386	57.76 441

N_pmnet		39.35 416	40.28 413	36.54 439	63.76 418	1.62 476	49.37 441	0.76 475	34.62 431	43.61 437	66.38 429	26.25 409	42.57 455	26.02 442	51.77 423	65.44 431

kuosan		29.62 428	30.82 427	26.02 447	52.99 446	16.22 466	51.09 435	22.71 469	33.91 432	33.99 451	40.85 457	15.89 439	33.11 464	7.59 468	18.37 461	28.72 461

PMMVS		53.96 373	53.26 379	56.04 389	62.60 425	50.92 221	61.17 397	56.09 419	32.81 433	53.51 399	66.84 427	34.04 328	59.93 409	44.14 327	68.18 327	57.27 442

CMPMVS	42.80 21	57.81 345	55.97 354	63.32 338	60.98 434	47.38 291	64.66 372	69.50 326	32.06 434	46.83 427	77.80 323	29.50 380	71.36 347	48.68 286	73.75 231	71.21 410
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 402	42.95 407	53.39 408	52.33 450	29.15 441	57.77 413	48.20 442	31.81 435	49.86 418	77.21 332	8.69 457	59.16 413	27.31 436	33.40 452	71.84 402

CVMVSNet		59.63 330	59.14 322	61.08 359	74.47 283	38.84 374	75.20 226	68.74 333	31.15 436	58.24 350	76.51 347	32.39 358	68.58 365	49.77 275	65.84 346	75.81 355

FPMVS		42.18 410	41.11 412	45.39 425	58.03 442	41.01 356	49.50 440	53.81 427	30.07 437	33.71 452	64.03 434	11.69 447	52.08 445	14.01 456	55.11 411	43.09 453

EU-MVSNet		55.61 364	54.41 367	59.19 370	65.41 411	33.42 423	72.44 287	71.91 306	28.81 438	51.27 407	73.87 379	24.76 418	69.08 362	43.04 340	58.20 398	75.06 364

test_vis1_n		49.89 395	48.69 397	53.50 405	53.97 444	37.38 388	61.53 392	47.33 445	28.54 439	59.62 334	67.10 426	13.52 443	52.27 443	49.07 283	57.52 400	70.84 413

test_fmvs1_n		51.37 388	50.35 391	54.42 400	52.85 447	37.71 385	61.16 398	51.93 428	28.15 440	63.81 275	69.73 412	13.72 442	53.95 437	51.16 266	60.65 388	71.59 404

LF4IMVS		42.95 407	42.26 409	45.04 426	48.30 455	32.50 429	54.80 425	48.49 439	28.03 441	40.51 442	70.16 407	9.24 455	43.89 454	31.63 417	49.18 432	58.72 438

test_fmvs1		51.32 390	50.48 390	53.81 402	53.57 445	37.51 387	60.63 402	51.16 431	28.02 442	63.62 276	69.23 415	16.41 437	53.93 438	51.01 267	60.70 387	69.99 419

MVS-HIRNet		45.52 403	44.48 405	48.65 422	68.49 390	34.05 419	59.41 407	44.50 450	27.03 443	37.96 450	50.47 452	26.16 410	64.10 391	26.74 440	59.52 393	47.82 451

PMVS	28.69 22	36.22 419	33.29 424	45.02 427	36.82 467	35.98 404	54.68 426	48.74 438	26.31 444	21.02 460	51.61 449	2.88 469	60.10 408	9.99 465	47.58 433	38.99 458
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 404	41.95 411	53.86 401	52.58 449	43.55 329	62.11 391	46.90 447	26.05 445	40.63 441	60.19 440	11.08 453	57.91 420	31.83 416	46.15 435	60.11 435

test_fmvs2		48.69 397	47.49 402	52.29 415	48.63 454	33.06 427	57.76 414	48.05 443	25.71 446	59.76 332	69.60 413	11.57 449	52.23 444	49.45 281	56.86 403	71.58 405

PMMVS2		27.40 429	25.91 432	31.87 444	39.46 466	6.57 473	31.17 459	28.52 464	23.96 447	20.45 461	48.94 455	4.20 465	37.94 460	16.51 453	19.97 459	51.09 446

MVStest1		42.65 408	39.29 415	52.71 412	47.26 457	34.58 414	54.41 427	50.84 436	23.35 448	39.31 448	74.08 378	12.57 445	55.09 434	23.32 445	28.47 454	68.47 427

Gipuma		34.77 420	31.91 425	43.33 430	62.05 428	37.87 381	20.39 461	67.03 346	23.23 449	18.41 462	25.84 462	4.24 463	62.73 398	14.71 455	51.32 425	29.38 460
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 413	39.45 414	47.03 424	46.65 458	37.86 382	47.76 443	38.65 456	23.10 450	44.21 436	51.22 450	11.20 452	44.08 453	39.27 367	53.02 420	59.14 437

new_pmnet		34.13 422	34.29 423	33.64 441	52.63 448	18.23 465	44.43 451	33.90 461	22.81 451	30.89 454	53.18 446	10.48 454	35.72 463	20.77 449	39.51 444	46.98 452

mvsany_test1		39.38 415	38.16 418	43.02 431	49.05 452	34.28 417	44.16 452	25.94 466	22.74 452	46.57 429	62.21 439	23.85 421	41.16 458	33.01 406	35.91 448	53.63 445

LCM-MVSNet		40.30 414	35.88 420	53.57 404	42.24 460	29.15 441	45.21 450	60.53 400	22.23 453	28.02 455	50.98 451	3.72 466	61.78 402	31.22 422	38.76 446	69.78 421

test_fmvs3		44.30 405	42.55 408	49.55 421	42.83 459	27.15 451	53.03 430	44.93 449	22.03 454	53.69 396	64.94 433	4.21 464	49.63 446	47.47 294	49.82 429	71.88 400

APD_test1		37.39 418	34.94 421	44.72 429	48.88 453	33.19 426	52.95 431	44.00 452	19.49 455	27.28 456	58.59 442	3.18 468	52.84 441	18.92 451	41.17 443	48.14 450

mvsany_test3		32.62 423	30.57 428	38.77 437	36.16 468	24.20 458	38.10 457	20.63 470	19.14 456	40.36 444	57.43 443	5.06 461	36.63 462	29.59 430	28.66 453	55.49 443

E-PMN		23.77 430	22.73 434	26.90 445	42.02 461	20.67 462	42.66 453	35.70 459	17.43 457	10.28 467	25.05 463	6.42 459	42.39 456	10.28 464	14.71 463	17.63 462

EMVS		22.97 431	21.84 435	26.36 446	40.20 464	19.53 464	41.95 454	34.64 460	17.09 458	9.73 468	22.83 464	7.29 458	42.22 457	9.18 466	13.66 464	17.32 463

test_vis3_rt		32.09 424	30.20 429	37.76 438	35.36 469	27.48 447	40.60 455	28.29 465	16.69 459	32.52 453	40.53 458	1.96 470	37.40 461	33.64 403	42.21 442	48.39 448

test_f		31.86 425	31.05 426	34.28 440	32.33 471	21.86 461	32.34 458	30.46 463	16.02 460	39.78 446	55.45 445	4.80 462	32.36 465	30.61 423	37.66 447	48.64 447

DSMNet-mixed		39.30 417	38.72 416	41.03 434	51.22 451	19.66 463	45.53 449	31.35 462	15.83 461	39.80 445	67.42 424	22.19 424	45.13 452	22.43 446	52.69 421	58.31 439

testf1		31.46 426	28.89 430	39.16 435	41.99 462	28.78 443	46.45 446	37.56 457	14.28 462	21.10 458	48.96 453	1.48 472	47.11 449	13.63 457	34.56 449	41.60 454

APD_test2		31.46 426	28.89 430	39.16 435	41.99 462	28.78 443	46.45 446	37.56 457	14.28 462	21.10 458	48.96 453	1.48 472	47.11 449	13.63 457	34.56 449	41.60 454

MVE	17.77 23	21.41 432	17.77 437	32.34 443	34.34 470	25.44 455	16.11 462	24.11 467	11.19 464	13.22 464	31.92 460	1.58 471	30.95 466	10.47 463	17.03 462	40.62 457
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 450	17.97 472	10.91 469		10.60 473	7.46 465	11.07 466	28.36 461	3.28 467	11.29 469	8.01 467	9.74 468	13.89 464

wuyk23d		13.32 435	12.52 438	15.71 449	47.54 456	26.27 453	31.06 460	1.98 474	4.93 466	5.18 469	1.94 469	0.45 474	18.54 468	6.81 469	12.83 465	2.33 466

test_method		19.68 433	18.10 436	24.41 448	13.68 473	3.11 475	12.06 464	42.37 454	2.00 467	11.97 465	36.38 459	5.77 460	29.35 467	15.06 454	23.65 457	40.76 456

tmp_tt		9.43 436	11.14 439	4.30 451	2.38 474	4.40 474	13.62 463	16.08 472	0.39 468	15.89 463	13.06 465	15.80 440	5.54 470	12.63 459	10.46 467	2.95 465

EGC-MVSNET		42.47 409	38.48 417	54.46 399	74.33 288	48.73 270	70.33 321	51.10 432	0.03 469	0.18 470	67.78 421	13.28 444	66.49 381	18.91 452	50.36 428	48.15 449

testmvs		4.52 439	6.03 442	0.01 453	0.01 475	0.00 478	53.86 429	0.00 476	0.01 470	0.04 471	0.27 470	0.00 476	0.00 471	0.04 470	0.00 469	0.03 468

test123		4.73 438	6.30 441	0.02 452	0.01 475	0.01 477	56.36 421	0.00 476	0.01 470	0.04 471	0.21 471	0.01 475	0.00 471	0.03 471	0.00 469	0.04 467

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

cdsmvs_eth3d_5k		17.50 434	23.34 433	0.00 454	0.00 477	0.00 478	0.00 465	78.63 185	0.00 472	0.00 473	82.18 233	49.25 142	0.00 471	0.00 472	0.00 469	0.00 469

pcd_1.5k_mvsjas		3.92 440	5.23 443	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	47.05 174	0.00 471	0.00 472	0.00 469	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

ab-mvs-re		6.49 437	8.65 440	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	77.89 321	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 478	0.00 465	0.00 476	0.00 472	0.00 473	0.00 472	0.00 476	0.00 471	0.00 472	0.00 469	0.00 469

WAC-MVS						27.31 449								27.77 434

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 38

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 38

eth-test2					0.00 477

eth-test					0.00 477

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 27

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 48

GSMVS																78.05 326

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 320				78.05 326

sam_mvs												33.43 337

ambc				65.13 324	63.72 420	37.07 392	47.66 445	78.78 181		54.37 390	71.42 397	11.24 451	80.94 220	45.64 313	53.85 418	77.38 337

MTGPA								80.97 141

test_post1							68.67 336				3.64 467	32.39 358	69.49 360	44.17 325

test_post											3.55 468	33.90 331	66.52 380

patchmatchnet-post											64.03 434	34.50 322	74.27 330

GG-mvs-BLEND				62.34 346	71.36 349	37.04 393	69.20 333	57.33 413		54.73 385	65.48 432	30.37 368	77.82 284	34.82 397	74.93 217	72.17 398

MTMP							86.03 19	17.08 471

test9_res														75.28 48	88.31 32	83.81 206

agg_prior2														73.09 66	87.93 40	84.33 184

agg_prior					85.04 50	59.96 50		81.04 139		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 96

新几何2							76.12 204

旧先验1					83.04 74	53.15 176		67.52 340			87.85 81	44.08 212			80.76 114	78.03 329

原ACMM2							79.02 122

testdata2													72.18 344	46.95 303

segment_acmp												54.23 62

test12				77.76 46	84.52 58	58.41 80		83.36 78		72.93 102		54.61 59	88.05 39		88.12 34	86.81 79

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 199

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 118	84.65 175

plane_prior4											86.10 133

plane_prior1					81.27 102

n2								0.00 476

nn								0.00 476

door-mid								47.19 446

lessismore_v0				69.91 249	71.42 347	47.80 284		50.90 434		50.39 415	75.56 361	27.43 400	81.33 207	45.91 310	34.10 451	80.59 288

test11								83.47 73

door								47.60 444

HQP5-MVS						54.94 139

BP-MVS														67.04 114

HQP4-MVS										67.85 189			86.93 67			84.32 185

HQP3-MVS								83.90 58							80.35 122

HQP2-MVS												45.46 193

NP-MVS					80.98 107	56.05 116					85.54 152

ACMMP++_ref															74.07 226

ACMMP++															72.16 265

Test By Simon												48.33 153

High-resolution multi-view benchmark