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.

PGM-MVS		96.81 41	96.53 45	97.65 47	99.35 22	93.53 67	97.65 89	98.98 1	92.22 116	97.14 45	98.44 30	91.17 67	99.85 17	94.35 99	99.46 42	99.57 23

MVS_111021_HR		96.68 48	96.58 43	96.99 76	98.46 79	92.31 102	96.20 228	98.90 2	94.30 50	95.86 94	97.74 90	92.33 38	99.38 115	96.04 49	99.42 47	99.28 69

ACMMP		96.27 60	95.93 63	97.28 62	99.24 30	92.62 92	98.25 32	98.81 3	92.99 91	94.56 122	98.39 37	88.96 94	99.85 17	94.57 98	97.63 125	99.36 62
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
MVS_111021_LR		96.24 61	96.19 59	96.39 101	98.23 102	91.35 133	96.24 226	98.79 4	93.99 55	95.80 96	97.65 98	89.92 88	99.24 124	95.87 52	99.20 74	98.58 124

FC-MVSNet-test		93.94 124	93.57 117	95.04 169	95.48 231	91.45 131	98.12 43	98.71 5	93.37 76	90.23 210	96.70 149	87.66 111	97.85 265	91.49 156	90.39 247	95.83 234

UniMVSNet (Re)		93.31 144	92.55 152	95.61 144	95.39 234	93.34 74	97.39 115	98.71 5	93.14 86	90.10 220	94.83 244	87.71 110	98.03 242	91.67 154	83.99 315	95.46 252

FIs		94.09 118	93.70 113	95.27 161	95.70 223	92.03 112	98.10 44	98.68 7	93.36 78	90.39 207	96.70 149	87.63 113	97.94 256	92.25 136	90.50 246	95.84 233

WR-MVS_H		92.00 193	91.35 189	93.95 220	95.09 258	89.47 195	98.04 49	98.68 7	91.46 139	88.34 267	94.68 252	85.86 139	97.56 291	85.77 264	84.24 312	94.82 293

VPA-MVSNet		93.24 146	92.48 157	95.51 151	95.70 223	92.39 98	97.86 63	98.66 9	92.30 114	92.09 177	95.37 224	80.49 227	98.40 199	93.95 107	85.86 287	95.75 241

UniMVSNet_NR-MVSNet		93.37 142	92.67 147	95.47 157	95.34 240	92.83 85	97.17 139	98.58 10	92.98 96	90.13 216	95.80 200	88.37 104	97.85 265	91.71 150	83.93 316	95.73 243

CSCG		96.05 65	95.91 64	96.46 96	99.24 30	90.47 166	98.30 27	98.57 11	89.01 209	93.97 134	97.57 107	92.62 31	99.76 33	94.66 95	99.27 65	99.15 76

MSLP-MVS++		96.94 33	97.06 14	96.59 86	98.72 61	91.86 117	97.67 86	98.49 12	94.66 40	97.24 40	98.41 36	92.31 40	98.94 153	96.61 26	99.46 42	98.96 96

HyFIR lowres test		93.66 133	92.92 139	95.87 128	98.24 98	89.88 182	94.58 282	98.49 12	85.06 299	93.78 137	95.78 204	82.86 185	98.67 176	91.77 148	95.71 169	99.07 86

CHOSEN 1792x2688		94.15 113	93.51 122	96.06 119	98.27 94	89.38 200	95.18 274	98.48 14	85.60 290	93.76 138	97.11 129	83.15 176	99.61 65	91.33 159	98.72 98	99.19 72

PHI-MVS		96.77 43	96.46 50	97.71 44	98.40 83	94.07 52	98.21 39	98.45 15	89.86 186	97.11 48	98.01 71	92.52 35	99.69 47	96.03 50	99.53 28	99.36 62

PVSNet_BlendedMVS		94.06 119	93.92 108	94.47 197	98.27 94	89.46 197	96.73 177	98.36 16	90.17 180	94.36 125	95.24 229	88.02 105	99.58 74	93.44 119	90.72 242	94.36 312

PVSNet_Blended		94.87 100	94.56 96	95.81 130	98.27 94	89.46 197	95.47 260	98.36 16	88.84 217	94.36 125	96.09 188	88.02 105	99.58 74	93.44 119	98.18 112	98.40 144

3Dnovator	91.36 5	95.19 90	94.44 103	97.44 55	96.56 182	93.36 73	98.65 9	98.36 16	94.12 52	89.25 249	98.06 67	82.20 201	99.77 32	93.41 121	99.32 57	99.18 73

FOURS1					99.55 1	93.34 74	99.29 1	98.35 19	94.98 25	98.49 15

DPE-MVS		97.86 4	97.65 5	98.47 5	99.17 34	95.78 7	97.21 136	98.35 19	95.16 16	98.71 12	98.80 11	95.05 10	99.89 3	96.70 24	99.73 1	99.73 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HFP-MVS		97.14 20	96.92 21	97.83 29	99.42 7	94.12 49	98.52 13	98.32 21	93.21 81	97.18 42	98.29 52	92.08 42	99.83 25	95.63 65	99.59 17	99.54 33

#test#		97.02 27	96.75 34	97.83 29	99.42 7	94.12 49	98.15 42	98.32 21	92.57 109	97.18 42	98.29 52	92.08 42	99.83 25	95.12 79	99.59 17	99.54 33

ACMMPR		97.07 23	96.84 26	97.79 35	99.44 6	93.88 56	98.52 13	98.31 23	93.21 81	97.15 44	98.33 46	91.35 62	99.86 8	95.63 65	99.59 17	99.62 15

APDe-MVS		97.82 5	97.73 4	98.08 18	99.15 35	94.82 29	98.81 5	98.30 24	94.76 37	98.30 17	98.90 3	93.77 17	99.68 50	97.93 1	99.69 3	99.75 5

test0726					99.45 3	95.36 13	98.31 26	98.29 25	94.92 26	98.99 4	98.92 2	95.08 8

MSP-MVS		97.59 8	97.54 6	97.73 41	99.40 12	93.77 62	98.53 12	98.29 25	95.55 5	98.56 14	97.81 85	93.90 15	99.65 56	96.62 25	99.21 73	99.77 1
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
DVP-MVS++.		98.06 1	97.99 1	98.28 9	98.67 64	95.39 11	99.29 1	98.28 27	94.78 35	98.93 6	98.87 6	96.04 2	99.86 8	97.45 8	99.58 22	99.59 19

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 39	98.28 27					99.86 8	97.52 4	99.67 6	99.75 5

CP-MVS		97.02 27	96.81 29	97.64 49	99.33 23	93.54 66	98.80 6	98.28 27	92.99 91	96.45 73	98.30 51	91.90 48	99.85 17	95.61 67	99.68 4	99.54 33

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 18	98.25 32	98.27 30	95.13 17	99.19 1	98.89 4	95.54 5	99.85 17	97.52 4	99.66 10	99.56 26

test_241102_TWO								98.27 30	95.13 17	98.93 6	98.89 4	94.99 11	99.85 17	97.52 4	99.65 12	99.74 7

test_241102_ONE					99.42 7	95.30 18		98.27 30	95.09 21	99.19 1	98.81 10	95.54 5	99.65 56

SF-MVS		97.39 11	97.13 12	98.17 14	99.02 45	95.28 20	98.23 36	98.27 30	92.37 113	98.27 18	98.65 15	93.33 20	99.72 38	96.49 30	99.52 29	99.51 38

SteuartSystems-ACMMP		97.62 7	97.53 7	97.87 27	98.39 85	94.25 42	98.43 20	98.27 30	95.34 10	98.11 20	98.56 19	94.53 12	99.71 41	96.57 28	99.62 15	99.65 11
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.32 24	95.20 21		98.25 35	95.13 17	98.48 16	98.87 6	95.16 7

PVSNet_Blended_VisFu		95.27 85	94.91 88	96.38 102	98.20 103	90.86 153	97.27 127	98.25 35	90.21 179	94.18 129	97.27 120	87.48 117	99.73 35	93.53 116	97.77 123	98.55 125

ETH3D-3000-0.1		97.07 23	96.71 37	98.14 16	98.90 53	95.33 17	97.68 85	98.24 37	91.57 135	97.90 25	98.37 38	92.61 32	99.66 55	95.59 70	99.51 33	99.43 53

region2R		97.07 23	96.84 26	97.77 38	99.46 2	93.79 59	98.52 13	98.24 37	93.19 84	97.14 45	98.34 43	91.59 57	99.87 7	95.46 73	99.59 17	99.64 12

PS-CasMVS		91.55 208	90.84 210	93.69 234	94.96 263	88.28 231	97.84 67	98.24 37	91.46 139	88.04 277	95.80 200	79.67 243	97.48 299	87.02 244	84.54 309	95.31 264

DU-MVS		92.90 161	92.04 166	95.49 154	94.95 264	92.83 85	97.16 140	98.24 37	93.02 89	90.13 216	95.71 208	83.47 170	97.85 265	91.71 150	83.93 316	95.78 237

9.14			96.75 34		98.93 49		97.73 78	98.23 41	91.28 149	97.88 26	98.44 30	93.00 24	99.65 56	95.76 58	99.47 40

testtj		96.93 34	96.56 44	98.05 20	99.10 36	94.66 31	97.78 72	98.22 42	92.74 104	97.59 28	98.20 60	91.96 47	99.86 8	94.21 101	99.25 69	99.63 13

ETH3 D test6400		96.16 63	95.52 71	98.07 19	98.90 53	95.06 26	97.03 146	98.21 43	88.16 240	96.64 61	97.70 92	91.18 66	99.67 52	92.44 133	99.47 40	99.48 45

D2MVS		91.30 224	90.95 204	92.35 279	94.71 278	85.52 287	96.18 229	98.21 43	88.89 215	86.60 302	93.82 292	79.92 239	97.95 255	89.29 195	90.95 239	93.56 326

XVS		97.18 17	96.96 19	97.81 33	99.38 15	94.03 54	98.59 10	98.20 45	94.85 28	96.59 65	98.29 52	91.70 53	99.80 30	95.66 60	99.40 49	99.62 15

X-MVStestdata		91.71 200	89.67 259	97.81 33	99.38 15	94.03 54	98.59 10	98.20 45	94.85 28	96.59 65	32.69 367	91.70 53	99.80 30	95.66 60	99.40 49	99.62 15

ACMMP_NAP		97.20 16	96.86 23	98.23 11	99.09 38	95.16 24	97.60 96	98.19 47	92.82 101	97.93 24	98.74 13	91.60 56	99.86 8	96.26 35	99.52 29	99.67 10

CP-MVSNet		91.89 196	91.24 196	93.82 227	95.05 259	88.57 224	97.82 68	98.19 47	91.70 132	88.21 273	95.76 205	81.96 205	97.52 297	87.86 219	84.65 305	95.37 261

ZNCC-MVS		96.96 31	96.67 39	97.85 28	99.37 17	94.12 49	98.49 17	98.18 49	92.64 108	96.39 75	98.18 61	91.61 55	99.88 4	95.59 70	99.55 25	99.57 23

SMA-MVS		97.35 13	97.03 15	98.30 8	99.06 42	95.42 10	97.94 58	98.18 49	90.57 174	98.85 9	98.94 1	93.33 20	99.83 25	96.72 23	99.68 4	99.63 13
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
PEN-MVS		91.20 228	90.44 225	93.48 243	94.49 286	87.91 244	97.76 74	98.18 49	91.29 146	87.78 282	95.74 207	80.35 230	97.33 310	85.46 268	82.96 326	95.19 274

DELS-MVS		96.61 49	96.38 53	97.30 60	97.79 126	93.19 77	95.96 240	98.18 49	95.23 12	95.87 93	97.65 98	91.45 58	99.70 46	95.87 52	99.44 46	99.00 94
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
tfpnnormal		89.70 272	88.40 277	93.60 237	95.15 254	90.10 173	97.56 99	98.16 53	87.28 266	86.16 306	94.63 255	77.57 278	98.05 238	74.48 340	84.59 308	92.65 338

VNet		95.89 70	95.45 74	97.21 68	98.07 112	92.94 84	97.50 103	98.15 54	93.87 57	97.52 29	97.61 104	85.29 145	99.53 92	95.81 57	95.27 175	99.16 74

DeepPCF-MVS	93.97 1	96.61 49	97.09 13	95.15 165	98.09 110	86.63 271	96.00 238	98.15 54	95.43 6	97.95 23	98.56 19	93.40 19	99.36 116	96.77 22	99.48 39	99.45 49

SD-MVS		97.41 10	97.53 7	97.06 74	98.57 77	94.46 34	97.92 60	98.14 56	94.82 32	99.01 3	98.55 21	94.18 14	97.41 306	96.94 14	99.64 13	99.32 64
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
GST-MVS		96.85 39	96.52 46	97.82 32	99.36 20	94.14 48	98.29 28	98.13 57	92.72 105	96.70 56	98.06 67	91.35 62	99.86 8	94.83 89	99.28 63	99.47 48

UA-Net		95.95 69	95.53 70	97.20 69	97.67 131	92.98 83	97.65 89	98.13 57	94.81 33	96.61 63	98.35 40	88.87 95	99.51 97	90.36 173	97.35 135	99.11 82

QAPM		93.45 140	92.27 162	96.98 77	96.77 171	92.62 92	98.39 23	98.12 59	84.50 307	88.27 271	97.77 88	82.39 198	99.81 29	85.40 269	98.81 95	98.51 130

Vis-MVSNet		95.23 87	94.81 89	96.51 91	97.18 146	91.58 125	98.26 31	98.12 59	94.38 48	94.90 117	98.15 62	82.28 199	98.92 154	91.45 158	98.58 104	99.01 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 163	91.68 179	96.40 99	95.34 240	92.73 88	98.27 30	98.12 59	84.86 302	85.78 308	97.75 89	78.89 259	99.74 34	87.50 235	98.65 101	96.73 207

TranMVSNet+NR-MVSNet		92.50 171	91.63 180	95.14 166	94.76 275	92.07 110	97.53 101	98.11 62	92.90 99	89.56 237	96.12 184	83.16 175	97.60 289	89.30 194	83.20 325	95.75 241

CPTT-MVS		95.57 79	95.19 82	96.70 80	99.27 28	91.48 128	98.33 25	98.11 62	87.79 251	95.17 115	98.03 69	87.09 123	99.61 65	93.51 117	99.42 47	99.02 87

Regformer-2		97.16 19	96.99 17	97.67 46	98.32 91	93.84 57	96.83 169	98.10 64	95.24 11	97.49 30	98.25 57	92.57 33	99.61 65	96.80 19	99.29 61	99.56 26

APD-MVS		96.95 32	96.60 41	98.01 22	99.03 44	94.93 28	97.72 81	98.10 64	91.50 137	98.01 22	98.32 48	92.33 38	99.58 74	94.85 87	99.51 33	99.53 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 38	96.60 41	97.64 49	99.40 12	93.44 69	98.50 16	98.09 66	93.27 80	95.95 92	98.33 46	91.04 69	99.88 4	95.20 76	99.57 24	99.60 18

ZD-MVS					99.05 43	94.59 32		98.08 67	89.22 204	97.03 51	98.10 63	92.52 35	99.65 56	94.58 97	99.31 59

zzz-MVS		97.07 23	96.77 33	97.97 25	99.37 17	94.42 36	97.15 142	98.08 67	95.07 22	96.11 82	98.59 17	90.88 73	99.90 1	96.18 44	99.50 36	99.58 21

MTGPA								98.08 67

MTAPA		97.08 22	96.78 32	97.97 25	99.37 17	94.42 36	97.24 129	98.08 67	95.07 22	96.11 82	98.59 17	90.88 73	99.90 1	96.18 44	99.50 36	99.58 21

CNVR-MVS		97.68 6	97.44 9	98.37 7	98.90 53	95.86 6	97.27 127	98.08 67	95.81 3	97.87 27	98.31 49	94.26 13	99.68 50	97.02 13	99.49 38	99.57 23

DP-MVS Recon		95.68 75	95.12 85	97.37 57	99.19 33	94.19 44	97.03 146	98.08 67	88.35 233	95.09 116	97.65 98	89.97 87	99.48 102	92.08 143	98.59 103	98.44 141

SR-MVS		97.01 29	96.86 23	97.47 54	99.09 38	93.27 76	97.98 52	98.07 73	93.75 62	97.45 32	98.48 27	91.43 59	99.59 71	96.22 38	99.27 65	99.54 33

MCST-MVS		97.18 17	96.84 26	98.20 13	99.30 26	95.35 15	97.12 144	98.07 73	93.54 71	96.08 84	97.69 93	93.86 16	99.71 41	96.50 29	99.39 51	99.55 30

NR-MVSNet		92.34 178	91.27 195	95.53 150	94.95 264	93.05 80	97.39 115	98.07 73	92.65 107	84.46 319	95.71 208	85.00 149	97.77 275	89.71 183	83.52 322	95.78 237

MP-MVS-pluss		96.70 45	96.27 55	97.98 24	99.23 32	94.71 30	96.96 157	98.06 76	90.67 165	95.55 107	98.78 12	91.07 68	99.86 8	96.58 27	99.55 25	99.38 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 41	96.71 37	97.12 72	99.01 48	92.31 102	97.98 52	98.06 76	93.11 87	97.44 33	98.55 21	90.93 71	99.55 87	96.06 46	99.25 69	99.51 38

MP-MVS		96.77 43	96.45 51	97.72 42	99.39 14	93.80 58	98.41 21	98.06 76	93.37 76	95.54 109	98.34 43	90.59 78	99.88 4	94.83 89	99.54 27	99.49 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 52	96.27 55	97.22 67	99.32 24	92.74 87	98.74 7	98.06 76	90.57 174	96.77 53	98.35 40	90.21 82	99.53 92	94.80 92	99.63 14	99.38 60

HPM-MVS		96.69 46	96.45 51	97.40 56	99.36 20	93.11 79	98.87 4	98.06 76	91.17 153	96.40 74	97.99 72	90.99 70	99.58 74	95.61 67	99.61 16	99.49 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 107	93.80 111	96.64 81	97.07 152	91.97 115	96.32 217	98.06 76	88.94 213	94.50 123	96.78 143	84.60 153	99.27 122	91.90 144	96.02 160	98.68 121

DeepC-MVS	93.07 3	96.06 64	95.66 69	97.29 61	97.96 114	93.17 78	97.30 125	98.06 76	93.92 56	93.38 147	98.66 14	86.83 125	99.73 35	95.60 69	99.22 72	98.96 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3D cwj APD-0.16		96.56 51	96.06 60	98.05 20	98.26 97	95.19 22	96.99 154	98.05 83	89.85 188	97.26 39	98.22 59	91.80 50	99.69 47	94.84 88	99.28 63	99.27 70

test1172		96.93 34	96.86 23	97.15 70	99.10 36	92.34 99	97.96 57	98.04 84	93.79 61	97.35 37	98.53 23	91.40 60	99.56 84	96.30 34	99.30 60	99.55 30

NCCC		97.30 15	97.03 15	98.11 17	98.77 59	95.06 26	97.34 119	98.04 84	95.96 2	97.09 49	97.88 77	93.18 23	99.71 41	95.84 56	99.17 76	99.56 26

DeepC-MVS_fast	93.89 2	96.93 34	96.64 40	97.78 36	98.64 72	94.30 38	97.41 111	98.04 84	94.81 33	96.59 65	98.37 38	91.24 64	99.64 64	95.16 77	99.52 29	99.42 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		96.88 37	96.80 30	97.11 73	99.02 45	92.34 99	97.98 52	98.03 87	93.52 72	97.43 35	98.51 24	91.40 60	99.56 84	96.05 47	99.26 67	99.43 53

RE-MVS-def			96.72 36		99.02 45	92.34 99	97.98 52	98.03 87	93.52 72	97.43 35	98.51 24	90.71 76		96.05 47	99.26 67	99.43 53

abl_6		96.40 56	96.21 57	96.98 77	98.89 56	92.20 107	97.89 61	98.03 87	93.34 79	97.22 41	98.42 33	87.93 108	99.72 38	95.10 80	99.07 86	99.02 87

RPMNet		88.98 277	87.05 292	94.77 187	94.45 288	87.19 257	90.23 346	98.03 87	77.87 351	92.40 164	87.55 352	80.17 234	99.51 97	68.84 356	93.95 196	97.60 184

save fliter					98.91 51	94.28 39	97.02 149	98.02 91	95.35 8

TEST9					98.70 62	94.19 44	96.41 205	98.02 91	88.17 238	96.03 86	97.56 109	92.74 27	99.59 71

train_agg		96.30 59	95.83 67	97.72 42	98.70 62	94.19 44	96.41 205	98.02 91	88.58 226	96.03 86	97.56 109	92.73 28	99.59 71	95.04 81	99.37 56	99.39 58

test_8					98.67 64	94.06 53	96.37 212	98.01 94	88.58 226	95.98 91	97.55 111	92.73 28	99.58 74

Regformer-4		96.97 30	96.87 22	97.25 64	98.34 88	92.66 90	96.96 157	98.01 94	95.12 20	97.14 45	98.42 33	91.82 49	99.61 65	96.90 15	99.13 79	99.50 41

agg_prior1		96.22 62	95.77 68	97.56 51	98.67 64	93.79 59	96.28 221	98.00 96	88.76 223	95.68 101	97.55 111	92.70 30	99.57 82	95.01 82	99.32 57	99.32 64

agg_prior					98.67 64	93.79 59		98.00 96		95.68 101			99.57 82

test_prior3		96.46 54	96.20 58	97.23 65	98.67 64	92.99 81	96.35 213	98.00 96	92.80 102	96.03 86	97.59 105	92.01 44	99.41 110	95.01 82	99.38 52	99.29 66

test_prior				97.23 65	98.67 64	92.99 81		98.00 96					99.41 110			99.29 66

Regformer-1		97.10 21	96.96 19	97.54 52	98.32 91	93.48 68	96.83 169	97.99 100	95.20 13	97.46 31	98.25 57	92.48 37	99.58 74	96.79 21	99.29 61	99.55 30

WR-MVS		92.34 178	91.53 184	94.77 187	95.13 256	90.83 154	96.40 208	97.98 101	91.88 129	89.29 246	95.54 219	82.50 194	97.80 270	89.79 182	85.27 296	95.69 244

HPM-MVS++		97.34 14	96.97 18	98.47 5	99.08 40	96.16 4	97.55 100	97.97 102	95.59 4	96.61 63	97.89 75	92.57 33	99.84 22	95.95 51	99.51 33	99.40 57

CANet		96.39 57	96.02 61	97.50 53	97.62 134	93.38 71	97.02 149	97.96 103	95.42 7	94.86 118	97.81 85	87.38 119	99.82 28	96.88 16	99.20 74	99.29 66

114514_t		93.95 123	93.06 135	96.63 83	99.07 41	91.61 122	97.46 110	97.96 103	77.99 349	93.00 155	97.57 107	86.14 137	99.33 117	89.22 198	99.15 77	98.94 99

IU-MVS					99.42 7	95.39 11		97.94 105	90.40 178	98.94 5				97.41 11	99.66 10	99.74 7

MSC_two_6792asdad				98.86 1	98.67 64	96.94 1		97.93 106					99.86 8	97.68 2	99.67 6	99.77 1

No_MVS				98.86 1	98.67 64	96.94 1		97.93 106					99.86 8	97.68 2	99.67 6	99.77 1

Anonymous20231211		90.63 252	89.42 263	94.27 206	98.24 98	89.19 211	98.05 48	97.89 108	79.95 341	88.25 272	94.96 236	72.56 308	98.13 222	89.70 184	85.14 298	95.49 248

原ACMM1				96.38 102	98.59 74	91.09 146		97.89 108	87.41 262	95.22 114	97.68 94	90.25 80	99.54 89	87.95 218	99.12 82	98.49 133

CDPH-MVS		95.97 68	95.38 77	97.77 38	98.93 49	94.44 35	96.35 213	97.88 110	86.98 270	96.65 60	97.89 75	91.99 46	99.47 103	92.26 134	99.46 42	99.39 58

test11								97.88 110

EIA-MVS		95.53 80	95.47 73	95.71 139	97.06 155	89.63 186	97.82 68	97.87 112	93.57 67	93.92 135	95.04 235	90.61 77	98.95 152	94.62 96	98.68 100	98.54 126

无先验							95.79 248	97.87 112	83.87 315				99.65 56	87.68 228		98.89 105

3Dnovator+	91.43 4	95.40 81	94.48 101	98.16 15	96.90 164	95.34 16	98.48 18	97.87 112	94.65 41	88.53 265	98.02 70	83.69 166	99.71 41	93.18 125	98.96 91	99.44 51

VPNet		92.23 186	91.31 192	94.99 171	95.56 227	90.96 149	97.22 135	97.86 115	92.96 97	90.96 199	96.62 161	75.06 295	98.20 215	91.90 144	83.65 321	95.80 236

DVP-MVS		97.91 3	97.81 3	98.22 12	99.45 3	95.36 13	98.21 39	97.85 116	94.92 26	98.73 10	98.87 6	95.08 8	99.84 22	97.52 4	99.67 6	99.48 45
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
TSAR-MVS + MP.		97.42 9	97.33 10	97.69 45	99.25 29	94.24 43	98.07 47	97.85 116	93.72 63	98.57 13	98.35 40	93.69 18	99.40 112	97.06 12	99.46 42	99.44 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		94.34 109	93.68 115	96.31 106	98.59 74	91.68 121	96.59 196	97.81 118	89.87 185	92.15 173	97.06 132	83.62 169	99.54 89	89.34 193	98.07 115	97.70 177

ETV-MVS		96.02 66	95.89 65	96.40 99	97.16 147	92.44 97	97.47 108	97.77 119	94.55 42	96.48 70	94.51 258	91.23 65	98.92 154	95.65 63	98.19 111	97.82 173

Regformer-3		96.85 39	96.80 30	97.01 75	98.34 88	92.02 113	96.96 157	97.76 120	95.01 24	97.08 50	98.42 33	91.71 52	99.54 89	96.80 19	99.13 79	99.48 45

新几何1				97.32 59	98.60 73	93.59 65		97.75 121	81.58 332	95.75 98	97.85 81	90.04 85	99.67 52	86.50 250	99.13 79	98.69 120

旧先验1					98.38 86	93.38 71		97.75 121			98.09 65	92.30 41			99.01 89	99.16 74

DROMVSNet		96.42 55	96.47 48	96.26 111	97.01 160	91.52 127	98.89 3	97.75 121	94.42 45	96.64 61	97.68 94	89.32 90	98.60 182	97.45 8	99.11 84	98.67 122

CS-MVS		95.88 71	95.98 62	95.58 146	96.44 190	90.56 162	97.78 72	97.73 124	93.01 90	96.07 85	96.77 144	90.13 83	98.57 187	96.83 18	99.10 85	97.60 184

EI-MVSNet-Vis-set		96.51 52	96.47 48	96.63 83	98.24 98	91.20 140	96.89 164	97.73 124	94.74 38	96.49 69	98.49 26	90.88 73	99.58 74	96.44 32	98.32 108	99.13 78

1121		94.71 105	93.83 110	97.34 58	98.57 77	93.64 64	96.04 234	97.73 124	81.56 333	95.68 101	97.85 81	90.23 81	99.65 56	87.68 228	99.12 82	98.73 116

PAPM_NR		95.01 92	94.59 95	96.26 111	98.89 56	90.68 159	97.24 129	97.73 124	91.80 130	92.93 160	96.62 161	89.13 93	99.14 134	89.21 199	97.78 122	98.97 95

Anonymous20240529		91.98 194	90.73 215	95.73 137	98.14 108	89.40 199	97.99 51	97.72 128	79.63 343	93.54 142	97.41 116	69.94 325	99.56 84	91.04 164	91.11 235	98.22 153

CHOSEN 280x420		93.12 150	92.72 146	94.34 204	96.71 175	87.27 253	90.29 345	97.72 128	86.61 277	91.34 188	95.29 226	84.29 160	98.41 198	93.25 124	98.94 92	97.35 192

EI-MVSNet-UG-set		96.34 58	96.30 54	96.47 94	98.20 103	90.93 151	96.86 165	97.72 128	94.67 39	96.16 81	98.46 28	90.43 79	99.58 74	96.23 37	97.96 118	98.90 103

LS3D		93.57 137	92.61 150	96.47 94	97.59 137	91.61 122	97.67 86	97.72 128	85.17 297	90.29 209	98.34 43	84.60 153	99.73 35	83.85 288	98.27 109	98.06 161

PAPR		94.18 112	93.42 128	96.48 93	97.64 133	91.42 132	95.55 256	97.71 132	88.99 210	92.34 169	95.82 199	89.19 91	99.11 136	86.14 256	97.38 133	98.90 103

test_part1		92.21 188	91.10 202	95.51 151	97.80 125	92.66 90	98.02 50	97.68 133	89.79 191	88.80 259	96.02 189	76.85 282	98.18 218	90.86 165	84.11 314	95.69 244

UGNet		94.04 121	93.28 131	96.31 106	96.85 165	91.19 141	97.88 62	97.68 133	94.40 46	93.00 155	96.18 180	73.39 307	99.61 65	91.72 149	98.46 105	98.13 156
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
testdata				95.46 158	98.18 107	88.90 217		97.66 135	82.73 325	97.03 51	98.07 66	90.06 84	98.85 160	89.67 185	98.98 90	98.64 123

test12				97.65 47	98.46 79	94.26 41		97.66 135		95.52 111		90.89 72	99.46 104		99.25 69	99.22 71

CS-MVS-test		95.86 73	95.88 66	95.80 131	96.76 173	90.59 161	98.40 22	97.65 137	93.52 72	95.53 110	96.79 142	89.98 86	98.59 186	95.59 70	98.69 99	98.23 152

DTE-MVSNet		90.56 253	89.75 257	93.01 261	93.95 301	87.25 254	97.64 93	97.65 137	90.74 162	87.12 293	95.68 211	79.97 238	97.00 321	83.33 289	81.66 331	94.78 300

TAPA-MVS	90.10 7	92.30 181	91.22 198	95.56 147	98.33 90	89.60 188	96.79 173	97.65 137	81.83 330	91.52 184	97.23 123	87.94 107	98.91 156	71.31 352	98.37 107	98.17 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
cdsmvs_eth3d_5k		23.24 337	30.99 339	0.00 355	0.00 378	0.00 379	0.00 366	97.63 140	0.00 373	0.00 374	96.88 140	84.38 157	0.00 374	0.00 372	0.00 372	0.00 370

DPM-MVS		95.69 74	94.92 87	98.01 22	98.08 111	95.71 9	95.27 270	97.62 141	90.43 177	95.55 107	97.07 131	91.72 51	99.50 100	89.62 187	98.94 92	98.82 111

canonicalmvs		96.02 66	95.45 74	97.75 40	97.59 137	95.15 25	98.28 29	97.60 142	94.52 43	96.27 78	96.12 184	87.65 112	99.18 129	96.20 43	94.82 183	98.91 102

test222					98.24 98	92.21 105	95.33 265	97.60 142	79.22 345	95.25 113	97.84 84	88.80 97			99.15 77	98.72 117

cascas		91.20 228	90.08 242	94.58 195	94.97 262	89.16 212	93.65 313	97.59 144	79.90 342	89.40 241	92.92 311	75.36 294	98.36 203	92.14 139	94.75 185	96.23 216

hse-mvs3		94.15 113	93.52 121	96.04 121	97.81 124	90.22 172	97.62 95	97.58 145	95.19 14	96.74 54	97.45 113	83.67 167	99.61 65	95.85 54	79.73 336	98.29 151

MVSFormer		95.37 82	95.16 83	95.99 124	96.34 196	91.21 138	98.22 37	97.57 146	91.42 141	96.22 79	97.32 118	86.20 135	97.92 259	94.07 104	99.05 87	98.85 108

test_djsdf		93.07 152	92.76 142	94.00 215	93.49 316	88.70 221	98.22 37	97.57 146	91.42 141	90.08 222	95.55 218	82.85 186	97.92 259	94.07 104	91.58 227	95.40 258

OMC-MVS		95.09 91	94.70 93	96.25 113	98.46 79	91.28 134	96.43 203	97.57 146	92.04 125	94.77 120	97.96 74	87.01 124	99.09 140	91.31 160	96.77 147	98.36 148

PS-MVSNAJss		93.74 131	93.51 122	94.44 198	93.91 303	89.28 207	97.75 75	97.56 149	92.50 110	89.94 224	96.54 164	88.65 99	98.18 218	93.83 113	90.90 240	95.86 230

jajsoiax		92.42 175	91.89 173	94.03 214	93.33 321	88.50 227	97.73 78	97.53 150	92.00 127	88.85 256	96.50 166	75.62 293	98.11 226	93.88 111	91.56 228	95.48 249

mvs_tets		92.31 180	91.76 175	93.94 222	93.41 318	88.29 230	97.63 94	97.53 150	92.04 125	88.76 260	96.45 168	74.62 297	98.09 231	93.91 109	91.48 229	95.45 254

HQP_MVS		93.78 130	93.43 126	94.82 180	96.21 200	89.99 177	97.74 76	97.51 152	94.85 28	91.34 188	96.64 154	81.32 215	98.60 182	93.02 128	92.23 215	95.86 230

plane_prior5								97.51 152					98.60 182	93.02 128	92.23 215	95.86 230

PS-MVSNAJ		95.37 82	95.33 79	95.49 154	97.35 141	90.66 160	95.31 267	97.48 154	93.85 58	96.51 68	95.70 210	88.65 99	99.65 56	94.80 92	98.27 109	96.17 219

API-MVS		94.84 101	94.49 100	95.90 127	97.90 120	92.00 114	97.80 70	97.48 154	89.19 205	94.81 119	96.71 147	88.84 96	99.17 130	88.91 205	98.76 97	96.53 210

MG-MVS		95.61 77	95.38 77	96.31 106	98.42 82	90.53 164	96.04 234	97.48 154	93.47 75	95.67 104	98.10 63	89.17 92	99.25 123	91.27 161	98.77 96	99.13 78

MAR-MVS		94.22 111	93.46 124	96.51 91	98.00 113	92.19 108	97.67 86	97.47 157	88.13 242	93.00 155	95.84 197	84.86 151	99.51 97	87.99 217	98.17 113	97.83 172
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
CLD-MVS		92.98 156	92.53 154	94.32 205	96.12 209	89.20 209	95.28 268	97.47 157	92.66 106	89.90 225	95.62 213	80.58 225	98.40 199	92.73 131	92.40 213	95.38 260
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 222	90.22 238	94.68 190	94.86 271	87.86 245	97.23 134	97.46 159	87.99 243	89.90 225	96.92 138	66.35 341	98.23 211	90.30 174	90.99 238	97.96 162

nrg030		94.05 120	93.31 130	96.27 110	95.22 251	94.59 32	98.34 24	97.46 159	92.93 98	91.21 197	96.64 154	87.23 122	98.22 212	94.99 85	85.80 288	95.98 228

RRT_test8_iter05		91.19 231	90.78 212	92.41 278	95.76 222	83.14 316	97.32 122	97.46 159	91.37 145	89.07 252	95.57 215	70.33 320	98.21 213	93.56 115	86.62 282	95.89 229

XVG-OURS		93.72 132	93.35 129	94.80 185	97.07 152	88.61 222	94.79 278	97.46 159	91.97 128	93.99 132	97.86 80	81.74 210	98.88 159	92.64 132	92.67 210	96.92 201

LPG-MVS_test		92.94 159	92.56 151	94.10 210	96.16 205	88.26 232	97.65 89	97.46 159	91.29 146	90.12 218	97.16 126	79.05 252	98.73 170	92.25 136	91.89 223	95.31 264

LGP-MVS_train				94.10 210	96.16 205	88.26 232		97.46 159	91.29 146	90.12 218	97.16 126	79.05 252	98.73 170	92.25 136	91.89 223	95.31 264

MVS		91.71 200	90.44 225	95.51 151	95.20 253	91.59 124	96.04 234	97.45 165	73.44 356	87.36 290	95.60 214	85.42 144	99.10 137	85.97 261	97.46 128	95.83 234

XVG-OURS-SEG-HR		93.86 127	93.55 118	94.81 182	97.06 155	88.53 226	95.28 268	97.45 165	91.68 133	94.08 131	97.68 94	82.41 197	98.90 157	93.84 112	92.47 212	96.98 197

baseline		95.58 78	95.42 76	96.08 117	96.78 170	90.41 169	97.16 140	97.45 165	93.69 66	95.65 105	97.85 81	87.29 120	98.68 175	95.66 60	97.25 139	99.13 78

ab-mvs		93.57 137	92.55 152	96.64 81	97.28 142	91.96 116	95.40 262	97.45 165	89.81 190	93.22 153	96.28 177	79.62 245	99.46 104	90.74 168	93.11 204	98.50 131

xiu_mvs_v2_base		95.32 84	95.29 80	95.40 159	97.22 143	90.50 165	95.44 261	97.44 169	93.70 65	96.46 72	96.18 180	88.59 102	99.53 92	94.79 94	97.81 121	96.17 219

1314		92.81 167	92.03 167	95.14 166	95.33 243	89.52 194	96.04 234	97.44 169	87.72 255	86.25 305	95.33 225	83.84 164	98.79 164	89.26 196	97.05 144	97.11 195

casdiffmvs		95.64 76	95.49 72	96.08 117	96.76 173	90.45 167	97.29 126	97.44 169	94.00 54	95.46 112	97.98 73	87.52 116	98.73 170	95.64 64	97.33 136	99.08 84

XXY-MVS		92.16 189	91.23 197	94.95 176	94.75 276	90.94 150	97.47 108	97.43 172	89.14 206	88.90 253	96.43 169	79.71 242	98.24 210	89.56 188	87.68 270	95.67 246

anonymousdsp		92.16 189	91.55 183	93.97 218	92.58 333	89.55 191	97.51 102	97.42 173	89.42 199	88.40 266	94.84 243	80.66 224	97.88 264	91.87 146	91.28 233	94.48 308

Effi-MVS+		94.93 97	94.45 102	96.36 104	96.61 176	91.47 129	96.41 205	97.41 174	91.02 158	94.50 123	95.92 193	87.53 115	98.78 165	93.89 110	96.81 146	98.84 110

HQP3-MVS								97.39 175							92.10 220

HQP-MVS		93.19 149	92.74 145	94.54 196	95.86 215	89.33 203	96.65 187	97.39 175	93.55 68	90.14 212	95.87 195	80.95 218	98.50 192	92.13 140	92.10 220	95.78 237

PLC	91.00 6	94.11 117	93.43 126	96.13 116	98.58 76	91.15 145	96.69 183	97.39 175	87.29 265	91.37 187	96.71 147	88.39 103	99.52 96	87.33 238	97.13 143	97.73 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 245	89.86 250	93.45 246	93.54 313	87.60 250	97.70 84	97.37 178	88.85 216	87.65 284	94.08 284	81.08 217	98.10 227	84.68 277	83.79 320	94.66 305

UnsupCasMVSNet_eth		85.99 307	84.45 311	90.62 316	89.97 350	82.40 322	93.62 314	97.37 178	89.86 186	78.59 350	92.37 319	65.25 347	95.35 346	82.27 300	70.75 354	94.10 319

ACMM	89.79 8	92.96 157	92.50 156	94.35 203	96.30 198	88.71 220	97.58 97	97.36 180	91.40 144	90.53 203	96.65 153	79.77 241	98.75 169	91.24 162	91.64 225	95.59 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 92	94.76 90	95.75 134	96.58 179	91.71 118	96.25 223	97.35 181	92.99 91	96.70 56	96.63 158	82.67 189	99.44 107	96.22 38	97.46 128	96.11 224

xiu_mvs_v1_base		95.01 92	94.76 90	95.75 134	96.58 179	91.71 118	96.25 223	97.35 181	92.99 91	96.70 56	96.63 158	82.67 189	99.44 107	96.22 38	97.46 128	96.11 224

xiu_mvs_v1_base_debi		95.01 92	94.76 90	95.75 134	96.58 179	91.71 118	96.25 223	97.35 181	92.99 91	96.70 56	96.63 158	82.67 189	99.44 107	96.22 38	97.46 128	96.11 224

diffmvs		95.25 86	95.13 84	95.63 142	96.43 192	89.34 202	95.99 239	97.35 181	92.83 100	96.31 76	97.37 117	86.44 130	98.67 176	96.26 35	97.19 141	98.87 107

WTY-MVS		94.71 105	94.02 107	96.79 79	97.71 130	92.05 111	96.59 196	97.35 181	90.61 171	94.64 121	96.93 135	86.41 131	99.39 113	91.20 163	94.71 187	98.94 99

F-COLMAP		93.58 136	92.98 137	95.37 160	98.40 83	88.98 215	97.18 138	97.29 186	87.75 254	90.49 204	97.10 130	85.21 146	99.50 100	86.70 247	96.72 150	97.63 179

XVG-ACMP-BASELINE		90.93 241	90.21 239	93.09 259	94.31 294	85.89 282	95.33 265	97.26 187	91.06 157	89.38 242	95.44 223	68.61 329	98.60 182	89.46 190	91.05 236	94.79 298

PCF-MVS	89.48 11	91.56 207	89.95 247	96.36 104	96.60 177	92.52 95	92.51 332	97.26 187	79.41 344	88.90 253	96.56 163	84.04 163	99.55 87	77.01 334	97.30 137	97.01 196
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 170	92.14 164	94.05 213	96.40 193	88.20 235	97.36 118	97.25 189	91.52 136	88.30 269	96.64 154	78.46 264	98.72 173	91.86 147	91.48 229	95.23 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
xxxxxxxxxxxxxcwj		97.36 12	97.20 11	97.83 29	98.91 51	94.28 39	97.02 149	97.22 190	95.35 8	98.27 18	98.65 15	93.33 20	99.72 38	96.49 30	99.52 29	99.51 38

OPM-MVS		93.28 145	92.76 142	94.82 180	94.63 282	90.77 157	96.65 187	97.18 191	93.72 63	91.68 182	97.26 121	79.33 249	98.63 179	92.13 140	92.28 214	95.07 276
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 161	92.02 168	95.56 147	98.19 105	90.80 155	95.27 270	97.18 191	87.96 244	91.86 181	95.68 211	80.44 228	98.99 150	84.01 284	97.54 127	96.89 202

MVS_0304		88.79 282	87.57 284	92.46 275	94.65 280	86.15 281	96.40 208	97.17 193	86.44 278	88.02 278	91.71 331	56.68 358	97.03 317	84.47 280	92.58 211	94.19 318

alignmvs		95.87 72	95.23 81	97.78 36	97.56 139	95.19 22	97.86 63	97.17 193	94.39 47	96.47 71	96.40 172	85.89 138	99.20 126	96.21 42	95.11 179	98.95 98

MVS_Test		94.89 99	94.62 94	95.68 140	96.83 168	89.55 191	96.70 181	97.17 193	91.17 153	95.60 106	96.11 187	87.87 109	98.76 168	93.01 130	97.17 142	98.72 117

Fast-Effi-MVS+		93.46 139	92.75 144	95.59 145	96.77 171	90.03 174	96.81 172	97.13 196	88.19 236	91.30 191	94.27 274	86.21 134	98.63 179	87.66 230	96.46 158	98.12 157

EI-MVSNet		93.03 154	92.88 140	93.48 243	95.77 220	86.98 262	96.44 201	97.12 197	90.66 167	91.30 191	97.64 101	86.56 127	98.05 238	89.91 178	90.55 244	95.41 255

MVSTER		93.20 148	92.81 141	94.37 202	96.56 182	89.59 189	97.06 145	97.12 197	91.24 150	91.30 191	95.96 191	82.02 204	98.05 238	93.48 118	90.55 244	95.47 251

test_yl		94.78 103	94.23 105	96.43 97	97.74 128	91.22 136	96.85 166	97.10 199	91.23 151	95.71 99	96.93 135	84.30 158	99.31 119	93.10 126	95.12 177	98.75 113

DCV-MVSNet		94.78 103	94.23 105	96.43 97	97.74 128	91.22 136	96.85 166	97.10 199	91.23 151	95.71 99	96.93 135	84.30 158	99.31 119	93.10 126	95.12 177	98.75 113

LTVRE_ROB	88.41 13	90.99 237	89.92 248	94.19 207	96.18 203	89.55 191	96.31 218	97.09 201	87.88 247	85.67 309	95.91 194	78.79 260	98.57 187	81.50 303	89.98 250	94.44 310
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
v10		91.04 235	90.23 236	93.49 242	94.12 297	88.16 238	97.32 122	97.08 202	88.26 235	88.29 270	94.22 279	82.17 202	97.97 249	86.45 251	84.12 313	94.33 313

v144192		91.06 234	90.28 232	93.39 247	93.66 311	87.23 256	96.83 169	97.07 203	87.43 261	89.69 232	94.28 273	81.48 213	98.00 245	87.18 242	84.92 304	94.93 284

v1192		91.07 233	90.23 236	93.58 239	93.70 309	87.82 246	96.73 177	97.07 203	87.77 252	89.58 235	94.32 271	80.90 222	97.97 249	86.52 249	85.48 291	94.95 280

v8		91.29 225	90.53 224	93.57 240	94.15 296	88.12 239	97.34 119	97.06 205	88.99 210	88.32 268	94.26 276	83.08 178	98.01 244	87.62 232	83.92 318	94.57 307

mvs_anonymous		93.82 128	93.74 112	94.06 212	96.44 190	85.41 289	95.81 247	97.05 206	89.85 188	90.09 221	96.36 174	87.44 118	97.75 276	93.97 106	96.69 151	99.02 87

IterMVS-LS		92.29 182	91.94 171	93.34 250	96.25 199	86.97 263	96.57 199	97.05 206	90.67 165	89.50 240	94.80 246	86.59 126	97.64 284	89.91 178	86.11 286	95.40 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 243	90.03 246	93.29 252	93.55 312	86.96 264	96.74 176	97.04 208	87.36 263	89.52 239	94.34 268	80.23 233	97.97 249	86.27 252	85.21 297	94.94 282

CDS-MVSNet		94.14 116	93.54 119	95.93 125	96.18 203	91.46 130	96.33 216	97.04 208	88.97 212	93.56 140	96.51 165	87.55 114	97.89 263	89.80 181	95.95 162	98.44 141
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1144		91.37 219	90.60 219	93.68 235	93.89 304	88.23 234	96.84 168	97.03 210	88.37 232	89.69 232	94.39 265	82.04 203	97.98 246	87.80 221	85.37 293	94.84 290

v1240		90.70 250	89.85 251	93.23 254	93.51 315	86.80 265	96.61 193	97.02 211	87.16 268	89.58 235	94.31 272	79.55 246	97.98 246	85.52 267	85.44 292	94.90 287

EPP-MVSNet		95.22 88	95.04 86	95.76 132	97.49 140	89.56 190	98.67 8	97.00 212	90.69 164	94.24 128	97.62 103	89.79 89	98.81 163	93.39 122	96.49 156	98.92 101

V42		91.58 206	90.87 206	93.73 230	94.05 300	88.50 227	97.32 122	96.97 213	88.80 222	89.71 230	94.33 269	82.54 193	98.05 238	89.01 203	85.07 300	94.64 306

FMVSNet2		91.31 223	90.08 242	94.99 171	96.51 185	92.21 105	97.41 111	96.95 214	88.82 219	88.62 262	94.75 248	73.87 301	97.42 305	85.20 272	88.55 264	95.35 262

ACMH	87.59 16	90.53 254	89.42 263	93.87 225	96.21 200	87.92 242	97.24 129	96.94 215	88.45 230	83.91 328	96.27 178	71.92 309	98.62 181	84.43 281	89.43 255	95.05 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 220	90.27 233	94.59 191	96.51 185	91.18 142	97.50 103	96.93 216	88.82 219	89.35 243	94.51 258	73.87 301	97.29 312	86.12 257	88.82 259	95.31 264

test1		91.35 220	90.27 233	94.59 191	96.51 185	91.18 142	97.50 103	96.93 216	88.82 219	89.35 243	94.51 258	73.87 301	97.29 312	86.12 257	88.82 259	95.31 264

FMVSNet3		91.78 198	90.69 217	95.03 170	96.53 184	92.27 104	97.02 149	96.93 216	89.79 191	89.35 243	94.65 254	77.01 281	97.47 300	86.12 257	88.82 259	95.35 262

FMVSNet1		89.88 269	88.31 278	94.59 191	95.41 233	91.18 142	97.50 103	96.93 216	86.62 276	87.41 288	94.51 258	65.94 345	97.29 312	83.04 292	87.43 273	95.31 264

GeoE		93.89 125	93.28 131	95.72 138	96.96 163	89.75 185	98.24 35	96.92 220	89.47 197	92.12 175	97.21 124	84.42 156	98.39 202	87.71 224	96.50 155	99.01 91

miper_enhance_ethall		91.54 210	91.01 203	93.15 257	95.35 239	87.07 261	93.97 303	96.90 221	86.79 274	89.17 250	93.43 307	86.55 128	97.64 284	89.97 177	86.93 277	94.74 302

eth_miper_zixun_eth		91.02 236	90.59 220	92.34 280	95.33 243	84.35 302	94.10 300	96.90 221	88.56 228	88.84 257	94.33 269	84.08 162	97.60 289	88.77 208	84.37 311	95.06 277

TAMVS		94.01 122	93.46 124	95.64 141	96.16 205	90.45 167	96.71 180	96.89 223	89.27 203	93.46 145	96.92 138	87.29 120	97.94 256	88.70 209	95.74 167	98.53 127

miper_ehance_all_eth		91.59 204	91.13 201	92.97 263	95.55 228	86.57 272	94.47 285	96.88 224	87.77 252	88.88 255	94.01 285	86.22 133	97.54 293	89.49 189	86.93 277	94.79 298

v2v482		91.59 204	90.85 209	93.80 228	93.87 305	88.17 237	96.94 160	96.88 224	89.54 194	89.53 238	94.90 240	81.70 211	98.02 243	89.25 197	85.04 302	95.20 273

CNLPA		94.28 110	93.53 120	96.52 88	98.38 86	92.55 94	96.59 196	96.88 224	90.13 182	91.91 179	97.24 122	85.21 146	99.09 140	87.64 231	97.83 120	97.92 165

RRT_MVS		93.21 147	92.32 161	95.91 126	94.92 266	94.15 47	96.92 161	96.86 227	91.42 141	91.28 194	96.43 169	79.66 244	98.10 227	93.29 123	90.06 249	95.46 252

PAPM		91.52 211	90.30 231	95.20 163	95.30 246	89.83 183	93.38 318	96.85 228	86.26 281	88.59 263	95.80 200	84.88 150	98.15 221	75.67 338	95.93 163	97.63 179

cl_fuxian		91.38 217	90.89 205	92.88 266	95.58 226	86.30 275	94.68 280	96.84 229	88.17 238	88.83 258	94.23 277	85.65 142	97.47 300	89.36 192	84.63 306	94.89 288

pm-mvs1		90.72 249	89.65 261	93.96 219	94.29 295	89.63 186	97.79 71	96.82 230	89.07 207	86.12 307	95.48 222	78.61 262	97.78 273	86.97 245	81.67 330	94.46 309

CMPMVS	62.92 21	85.62 311	84.92 308	87.74 333	89.14 355	73.12 359	94.17 298	96.80 231	73.98 354	73.65 355	94.93 238	66.36 340	97.61 288	83.95 286	91.28 233	92.48 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 259	89.77 255	91.78 294	94.33 292	84.72 300	95.55 256	96.73 232	86.17 283	86.36 304	95.28 228	71.28 314	97.80 270	84.09 283	98.14 114	92.81 335

Effi-MVS+-dtu		93.08 151	93.21 133	92.68 273	96.02 212	83.25 315	97.14 143	96.72 233	93.85 58	91.20 198	93.44 305	83.08 178	98.30 208	91.69 152	95.73 168	96.50 212

mvs-test1		93.63 134	93.69 114	93.46 245	96.02 212	84.61 301	97.24 129	96.72 233	93.85 58	92.30 170	95.76 205	83.08 178	98.89 158	91.69 152	96.54 154	96.87 203

TSAR-MVS + GP.		96.69 46	96.49 47	97.27 63	98.31 93	93.39 70	96.79 173	96.72 233	94.17 51	97.44 33	97.66 97	92.76 26	99.33 117	96.86 17	97.76 124	99.08 84

1112_ss		93.37 142	92.42 158	96.21 114	97.05 157	90.99 147	96.31 218	96.72 233	86.87 273	89.83 228	96.69 151	86.51 129	99.14 134	88.12 215	93.67 198	98.50 131

PVSNet	86.66 18	92.24 185	91.74 178	93.73 230	97.77 127	83.69 312	92.88 326	96.72 233	87.91 246	93.00 155	94.86 242	78.51 263	99.05 146	86.53 248	97.45 132	98.47 136

miper_lstm_enhance		90.50 256	90.06 245	91.83 290	95.33 243	83.74 308	93.86 306	96.70 238	87.56 259	87.79 281	93.81 293	83.45 172	96.92 323	87.39 236	84.62 307	94.82 293

v148		90.99 237	90.38 227	92.81 269	93.83 306	85.80 283	96.78 175	96.68 239	89.45 198	88.75 261	93.93 289	82.96 184	97.82 269	87.83 220	83.25 323	94.80 296

ACMH+	87.92 14	90.20 262	89.18 268	93.25 253	96.48 188	86.45 273	96.99 154	96.68 239	88.83 218	84.79 318	96.22 179	70.16 323	98.53 190	84.42 282	88.04 266	94.77 301

CANet_DTU		94.37 108	93.65 116	96.55 87	96.46 189	92.13 109	96.21 227	96.67 241	94.38 48	93.53 143	97.03 133	79.34 248	99.71 41	90.76 167	98.45 106	97.82 173

cl-mvsnet____		90.96 240	90.32 229	92.89 265	95.37 237	86.21 278	94.46 287	96.64 242	87.82 248	88.15 275	94.18 280	82.98 182	97.54 293	87.70 225	85.59 289	94.92 286

HY-MVS	89.66 9	93.87 126	92.95 138	96.63 83	97.10 151	92.49 96	95.64 254	96.64 242	89.05 208	93.00 155	95.79 203	85.77 141	99.45 106	89.16 202	94.35 189	97.96 162

Test_1112_low_res		92.84 165	91.84 174	95.85 129	97.04 158	89.97 180	95.53 258	96.64 242	85.38 293	89.65 234	95.18 230	85.86 139	99.10 137	87.70 225	93.58 203	98.49 133

cl-mvsnet1		90.97 239	90.33 228	92.88 266	95.36 238	86.19 279	94.46 287	96.63 245	87.82 248	88.18 274	94.23 277	82.99 181	97.53 295	87.72 222	85.57 290	94.93 284

Fast-Effi-MVS+-dtu		92.29 182	91.99 169	93.21 256	95.27 247	85.52 287	97.03 146	96.63 245	92.09 123	89.11 251	95.14 232	80.33 231	98.08 232	87.54 234	94.74 186	96.03 227

UnsupCasMVSNet_bld		82.13 322	79.46 325	90.14 322	88.00 359	82.47 320	90.89 343	96.62 247	78.94 346	75.61 352	84.40 355	56.63 359	96.31 331	77.30 331	66.77 358	91.63 348

cl-mvsnet2		91.21 227	90.56 223	93.14 258	96.09 211	86.80 265	94.41 289	96.58 248	87.80 250	88.58 264	93.99 287	80.85 223	97.62 287	89.87 180	86.93 277	94.99 279

jason		94.84 101	94.39 104	96.18 115	95.52 229	90.93 151	96.09 232	96.52 249	89.28 202	96.01 90	97.32 118	84.70 152	98.77 167	95.15 78	98.91 94	98.85 108
jason: jason.
AUN-MVS		91.76 199	90.75 214	94.81 182	97.00 161	88.57 224	96.65 187	96.49 250	89.63 193	92.15 173	96.12 184	78.66 261	98.50 192	90.83 166	79.18 339	97.36 191

hse-mvs2		93.45 140	92.99 136	94.81 182	97.02 159	88.59 223	96.69 183	96.47 251	95.19 14	96.74 54	96.16 183	83.67 167	98.48 196	95.85 54	79.13 340	97.35 192

EG-PatchMatch MVS		87.02 298	85.44 302	91.76 296	92.67 331	85.00 295	96.08 233	96.45 252	83.41 321	79.52 347	93.49 303	57.10 357	97.72 278	79.34 322	90.87 241	92.56 339

DIV-MVS_2432*1600		85.95 308	84.95 307	88.96 328	89.55 354	79.11 348	95.13 275	96.42 253	85.91 286	84.07 326	90.48 336	70.03 324	94.82 348	80.04 314	72.94 352	92.94 333

pmmvs6		87.81 293	86.19 297	92.69 272	91.32 342	86.30 275	97.34 119	96.41 254	80.59 340	84.05 327	94.37 267	67.37 336	97.67 281	84.75 276	79.51 338	94.09 321

PMMVS		92.86 163	92.34 159	94.42 201	94.92 266	86.73 267	94.53 284	96.38 255	84.78 304	94.27 127	95.12 234	83.13 177	98.40 199	91.47 157	96.49 156	98.12 157

RPSCF		90.75 247	90.86 207	90.42 319	96.84 166	76.29 354	95.61 255	96.34 256	83.89 313	91.38 186	97.87 78	76.45 285	98.78 165	87.16 243	92.23 215	96.20 217

MSDG		91.42 215	90.24 235	94.96 175	97.15 149	88.91 216	93.69 311	96.32 257	85.72 289	86.93 299	96.47 167	80.24 232	98.98 151	80.57 311	95.05 180	96.98 197

OurMVSNet-221017-0		90.51 255	90.19 240	91.44 302	93.41 318	81.25 328	96.98 156	96.28 258	91.68 133	86.55 303	96.30 176	74.20 300	97.98 246	88.96 204	87.40 275	95.09 275

MVP-Stereo		90.74 248	90.08 242	92.71 271	93.19 323	88.20 235	95.86 244	96.27 259	86.07 284	84.86 317	94.76 247	77.84 276	97.75 276	83.88 287	98.01 116	92.17 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 96	94.56 96	96.29 109	96.34 196	91.21 138	95.83 246	96.27 259	88.93 214	96.22 79	96.88 140	86.20 135	98.85 160	95.27 75	99.05 87	98.82 111

BH-untuned		92.94 159	92.62 149	93.92 224	97.22 143	86.16 280	96.40 208	96.25 261	90.06 183	89.79 229	96.17 182	83.19 174	98.35 204	87.19 241	97.27 138	97.24 194

CL-MVSNet_2432*1600		86.31 304	85.15 306	89.80 325	88.83 356	81.74 326	93.93 305	96.22 262	86.67 275	85.03 315	90.80 335	78.09 272	94.50 349	74.92 339	71.86 353	93.15 331

IS-MVSNet		94.90 98	94.52 99	96.05 120	97.67 131	90.56 162	98.44 19	96.22 262	93.21 81	93.99 132	97.74 90	85.55 143	98.45 197	89.98 176	97.86 119	99.14 77

GA-MVS		91.38 217	90.31 230	94.59 191	94.65 280	87.62 249	94.34 292	96.19 264	90.73 163	90.35 208	93.83 290	71.84 310	97.96 253	87.22 240	93.61 201	98.21 154

IterMVS-SCA-FT		90.31 258	89.81 253	91.82 291	95.52 229	84.20 305	94.30 294	96.15 265	90.61 171	87.39 289	94.27 274	75.80 290	96.44 329	87.34 237	86.88 281	94.82 293

IterMVS		90.15 264	89.67 259	91.61 298	95.48 231	83.72 309	94.33 293	96.12 266	89.99 184	87.31 292	94.15 282	75.78 292	96.27 332	86.97 245	86.89 280	94.83 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 168	91.51 187	96.52 88	98.77 59	90.99 147	97.38 117	96.08 267	82.38 326	89.29 246	97.87 78	83.77 165	99.69 47	81.37 308	96.69 151	98.89 105

pmmvs4		90.93 241	89.85 251	94.17 208	93.34 320	90.79 156	94.60 281	96.02 268	84.62 305	87.45 286	95.15 231	81.88 208	97.45 302	87.70 225	87.87 268	94.27 317

ppachtmachnet_test		88.35 288	87.29 287	91.53 299	92.45 335	83.57 313	93.75 309	95.97 269	84.28 308	85.32 314	94.18 280	79.00 258	96.93 322	75.71 337	84.99 303	94.10 319

Anonymous20240521		86.42 302	85.44 302	89.34 327	90.33 347	79.79 342	96.73 177	95.92 270	83.71 317	83.25 331	91.36 334	63.92 349	96.01 333	78.39 326	85.36 294	92.22 344

ITE_SJBPF				92.43 277	95.34 240	85.37 290		95.92 270	91.47 138	87.75 283	96.39 173	71.00 316	97.96 253	82.36 299	89.86 252	93.97 322

USDC		88.94 278	87.83 283	92.27 281	94.66 279	84.96 296	93.86 306	95.90 272	87.34 264	83.40 330	95.56 217	67.43 335	98.19 217	82.64 298	89.67 254	93.66 325

COLMAP_ROB	87.81 15	90.40 257	89.28 266	93.79 229	97.95 115	87.13 260	96.92 161	95.89 273	82.83 324	86.88 301	97.18 125	73.77 304	99.29 121	78.44 325	93.62 200	94.95 280
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 128	93.08 134	96.02 122	97.88 121	89.96 181	97.72 81	95.85 274	92.43 111	95.86 94	98.44 30	68.42 331	99.39 113	96.31 33	94.85 181	98.71 119

VDDNet		93.05 153	92.07 165	96.02 122	96.84 166	90.39 170	98.08 46	95.85 274	86.22 282	95.79 97	98.46 28	67.59 334	99.19 127	94.92 86	94.85 181	98.47 136

Vis-MVSNet (Re-imp)		94.15 113	93.88 109	94.95 176	97.61 135	87.92 242	98.10 44	95.80 276	92.22 116	93.02 154	97.45 113	84.53 155	97.91 262	88.24 213	97.97 117	99.02 87

KD-MVS_2432*1600		84.81 315	82.64 318	91.31 304	91.07 344	85.34 291	91.22 338	95.75 277	85.56 291	83.09 332	90.21 338	67.21 337	95.89 335	77.18 332	62.48 360	92.69 336

miper_refine_blended		84.81 315	82.64 318	91.31 304	91.07 344	85.34 291	91.22 338	95.75 277	85.56 291	83.09 332	90.21 338	67.21 337	95.89 335	77.18 332	62.48 360	92.69 336

tpm cat1		88.36 287	87.21 290	91.81 292	95.13 256	80.55 334	92.58 331	95.70 279	74.97 353	87.45 286	91.96 327	78.01 275	98.17 220	80.39 313	88.74 262	96.72 208

our_test_3		88.78 283	87.98 282	91.20 307	92.45 335	82.53 319	93.61 315	95.69 280	85.77 288	84.88 316	93.71 295	79.99 237	96.78 327	79.47 319	86.24 283	94.28 316

BH-w/o		92.14 191	91.75 176	93.31 251	96.99 162	85.73 284	95.67 251	95.69 280	88.73 224	89.26 248	94.82 245	82.97 183	98.07 235	85.26 271	96.32 159	96.13 223

CR-MVSNet		90.82 244	89.77 255	93.95 220	94.45 288	87.19 257	90.23 346	95.68 282	86.89 272	92.40 164	92.36 322	80.91 220	97.05 316	81.09 310	93.95 196	97.60 184

Patchmtry		88.64 285	87.25 288	92.78 270	94.09 298	86.64 268	89.82 349	95.68 282	80.81 338	87.63 285	92.36 322	80.91 220	97.03 317	78.86 323	85.12 299	94.67 304

BH-RMVSNet		92.72 169	91.97 170	94.97 174	97.16 147	87.99 241	96.15 230	95.60 284	90.62 170	91.87 180	97.15 128	78.41 266	98.57 187	83.16 290	97.60 126	98.36 148

PVSNet_0	82.17 19	85.46 312	83.64 315	90.92 310	95.27 247	79.49 344	90.55 344	95.60 284	83.76 316	83.00 334	89.95 340	71.09 315	97.97 249	82.75 296	60.79 362	95.31 264

SCA		91.84 197	91.18 200	93.83 226	95.59 225	84.95 297	94.72 279	95.58 286	90.82 159	92.25 171	93.69 296	75.80 290	98.10 227	86.20 254	95.98 161	98.45 138

AllTest		90.23 261	88.98 270	93.98 216	97.94 116	86.64 268	96.51 200	95.54 287	85.38 293	85.49 311	96.77 144	70.28 321	99.15 132	80.02 315	92.87 205	96.15 221

TestCases				93.98 216	97.94 116	86.64 268		95.54 287	85.38 293	85.49 311	96.77 144	70.28 321	99.15 132	80.02 315	92.87 205	96.15 221

tpmvs		89.83 271	89.15 269	91.89 288	94.92 266	80.30 337	93.11 323	95.46 289	86.28 280	88.08 276	92.65 314	80.44 228	98.52 191	81.47 304	89.92 251	96.84 204

pmmvs5		89.86 270	88.87 272	92.82 268	92.86 327	86.23 277	96.26 222	95.39 290	84.24 309	87.12 293	94.51 258	74.27 299	97.36 309	87.61 233	87.57 271	94.86 289

PatchmatchNet		91.91 195	91.35 189	93.59 238	95.38 235	84.11 306	93.15 322	95.39 290	89.54 194	92.10 176	93.68 298	82.82 187	98.13 222	84.81 275	95.32 174	98.52 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 214	91.32 191	91.79 293	95.15 254	79.20 347	93.42 317	95.37 292	88.55 229	93.49 144	93.67 299	82.49 195	98.27 209	90.41 171	89.34 256	97.90 166

Anonymous20231206		87.09 297	86.14 298	89.93 324	91.22 343	80.35 335	96.11 231	95.35 293	83.57 319	84.16 323	93.02 310	73.54 306	95.61 341	72.16 349	86.14 285	93.84 324

MIMVSNet1		84.93 314	83.05 316	90.56 317	89.56 353	84.84 299	95.40 262	95.35 293	83.91 312	80.38 343	92.21 326	57.23 356	93.34 356	70.69 355	82.75 329	93.50 327

TDRefinement		86.53 300	84.76 310	91.85 289	82.23 364	84.25 303	96.38 211	95.35 293	84.97 301	84.09 325	94.94 237	65.76 346	98.34 207	84.60 279	74.52 348	92.97 332

TR-MVS		91.48 213	90.59 220	94.16 209	96.40 193	87.33 251	95.67 251	95.34 296	87.68 256	91.46 185	95.52 220	76.77 283	98.35 204	82.85 294	93.61 201	96.79 206

EPNet_dtu		91.71 200	91.28 194	92.99 262	93.76 308	83.71 310	96.69 183	95.28 297	93.15 85	87.02 297	95.95 192	83.37 173	97.38 308	79.46 320	96.84 145	97.88 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 296	85.79 300	91.78 294	94.80 274	87.28 252	95.49 259	95.28 297	84.09 311	83.85 329	91.82 328	62.95 351	94.17 352	78.48 324	85.34 295	93.91 323

MDTV_nov1_ep13			90.76 213		95.22 251	80.33 336	93.03 325	95.28 297	88.14 241	92.84 161	93.83 290	81.34 214	98.08 232	82.86 293	94.34 190

LF4IMVS		87.94 291	87.25 288	89.98 323	92.38 337	80.05 341	94.38 290	95.25 300	87.59 258	84.34 320	94.74 249	64.31 348	97.66 283	84.83 274	87.45 272	92.23 343

TransMVSNet (Re)		88.94 278	87.56 285	93.08 260	94.35 291	88.45 229	97.73 78	95.23 301	87.47 260	84.26 322	95.29 226	79.86 240	97.33 310	79.44 321	74.44 349	93.45 329

test20.03		86.14 306	85.40 304	88.35 329	90.12 348	80.06 340	95.90 243	95.20 302	88.59 225	81.29 338	93.62 301	71.43 313	92.65 358	71.26 353	81.17 333	92.34 342

new-patchmatchnet		83.18 319	81.87 321	87.11 335	86.88 361	75.99 355	93.70 310	95.18 303	85.02 300	77.30 351	88.40 346	65.99 344	93.88 354	74.19 344	70.18 355	91.47 351

MDA-MVSNet_test_wron		85.87 309	84.23 313	90.80 314	92.38 337	82.57 318	93.17 320	95.15 304	82.15 327	67.65 357	92.33 325	78.20 268	95.51 344	77.33 329	79.74 335	94.31 315

YYNet1		85.87 309	84.23 313	90.78 315	92.38 337	82.46 321	93.17 320	95.14 305	82.12 328	67.69 356	92.36 322	78.16 271	95.50 345	77.31 330	79.73 336	94.39 311

Baseline_NR-MVSNet		91.20 228	90.62 218	92.95 264	93.83 306	88.03 240	97.01 153	95.12 306	88.42 231	89.70 231	95.13 233	83.47 170	97.44 303	89.66 186	83.24 324	93.37 330

thres200		92.23 186	91.39 188	94.75 189	97.61 135	89.03 214	96.60 195	95.09 307	92.08 124	93.28 150	94.00 286	78.39 267	99.04 148	81.26 309	94.18 191	96.19 218

ADS-MVSNet		89.89 268	88.68 274	93.53 241	95.86 215	84.89 298	90.93 341	95.07 308	83.23 322	91.28 194	91.81 329	79.01 256	97.85 265	79.52 317	91.39 231	97.84 170

pmmvs-eth3d		86.22 305	84.45 311	91.53 299	88.34 358	87.25 254	94.47 285	95.01 309	83.47 320	79.51 348	89.61 343	69.75 326	95.71 340	83.13 291	76.73 345	91.64 347

Anonymous202405211		92.07 192	90.83 211	95.76 132	98.19 105	88.75 219	97.58 97	95.00 310	86.00 285	93.64 139	97.45 113	66.24 343	99.53 92	90.68 170	92.71 208	99.01 91

MDA-MVSNet-bldmvs		85.00 313	82.95 317	91.17 308	93.13 325	83.33 314	94.56 283	95.00 310	84.57 306	65.13 361	92.65 314	70.45 319	95.85 337	73.57 345	77.49 342	94.33 313

ambc				86.56 337	83.60 362	70.00 362	85.69 356	94.97 312		80.60 342	88.45 345	37.42 365	96.84 325	82.69 297	75.44 347	92.86 334

testgi		87.97 290	87.21 290	90.24 321	92.86 327	80.76 330	96.67 186	94.97 312	91.74 131	85.52 310	95.83 198	62.66 352	94.47 351	76.25 335	88.36 265	95.48 249

dp		88.90 280	88.26 280	90.81 312	94.58 285	76.62 353	92.85 327	94.93 314	85.12 298	90.07 223	93.07 309	75.81 289	98.12 225	80.53 312	87.42 274	97.71 176

test_0402		86.46 301	84.79 309	91.45 301	95.02 261	85.55 286	96.29 220	94.89 315	80.90 335	82.21 335	93.97 288	68.21 332	97.29 312	62.98 360	88.68 263	91.51 349

tfpn200view9		92.38 177	91.52 185	94.95 176	97.85 122	89.29 205	97.41 111	94.88 316	92.19 120	93.27 151	94.46 263	78.17 269	99.08 142	81.40 305	94.08 192	96.48 213

CVMVSNet		91.23 226	91.75 176	89.67 326	95.77 220	74.69 356	96.44 201	94.88 316	85.81 287	92.18 172	97.64 101	79.07 251	95.58 343	88.06 216	95.86 165	98.74 115

thres400		92.42 175	91.52 185	95.12 168	97.85 122	89.29 205	97.41 111	94.88 316	92.19 120	93.27 151	94.46 263	78.17 269	99.08 142	81.40 305	94.08 192	96.98 197

EPNet		95.20 89	94.56 96	97.14 71	92.80 329	92.68 89	97.85 66	94.87 319	96.64 1	92.46 163	97.80 87	86.23 132	99.65 56	93.72 114	98.62 102	99.10 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		89.15 276	88.54 276	90.98 309	93.49 316	80.28 338	96.70 181	94.70 320	90.78 160	84.15 324	95.57 215	71.78 311	97.71 279	84.63 278	85.07 300	94.94 282

thres100view900		92.43 174	91.58 182	94.98 173	97.92 118	89.37 201	97.71 83	94.66 321	92.20 118	93.31 149	94.90 240	78.06 273	99.08 142	81.40 305	94.08 192	96.48 213

thres600view7		92.49 173	91.60 181	95.18 164	97.91 119	89.47 195	97.65 89	94.66 321	92.18 122	93.33 148	94.91 239	78.06 273	99.10 137	81.61 302	94.06 195	96.98 197

PatchT		88.87 281	87.42 286	93.22 255	94.08 299	85.10 294	89.51 350	94.64 323	81.92 329	92.36 167	88.15 349	80.05 236	97.01 320	72.43 348	93.65 199	97.54 188

baseline1		92.82 166	91.90 172	95.55 149	97.20 145	90.77 157	97.19 137	94.58 324	92.20 118	92.36 167	96.34 175	84.16 161	98.21 213	89.20 200	83.90 319	97.68 178

Gipuma		67.86 328	65.41 331	75.18 344	92.66 332	73.45 358	66.50 363	94.52 325	53.33 363	57.80 364	66.07 363	30.81 366	89.20 360	48.15 364	78.88 341	62.90 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CostFormer		91.18 232	90.70 216	92.62 274	94.84 272	81.76 325	94.09 301	94.43 326	84.15 310	92.72 162	93.77 294	79.43 247	98.20 215	90.70 169	92.18 218	97.90 166

tpm2		89.96 266	89.21 267	92.23 282	94.91 269	81.25 328	93.78 308	94.42 327	80.62 339	91.56 183	93.44 305	76.44 286	97.94 256	85.60 266	92.08 222	97.49 189

JIA-IIPM		88.26 289	87.04 293	91.91 287	93.52 314	81.42 327	89.38 351	94.38 328	80.84 337	90.93 200	80.74 357	79.22 250	97.92 259	82.76 295	91.62 226	96.38 215

Patchmatch-test		89.42 274	87.99 281	93.70 233	95.27 247	85.11 293	88.98 352	94.37 329	81.11 334	87.10 295	93.69 296	82.28 199	97.50 298	74.37 342	94.76 184	98.48 135

LCM-MVSNet		72.55 325	69.39 329	82.03 339	70.81 371	65.42 366	90.12 348	94.36 330	55.02 362	65.88 359	81.72 356	24.16 372	89.96 359	74.32 343	68.10 357	90.71 354

ADS-MVSNet2		89.45 273	88.59 275	92.03 285	95.86 215	82.26 323	90.93 341	94.32 331	83.23 322	91.28 194	91.81 329	79.01 256	95.99 334	79.52 317	91.39 231	97.84 170

DWT-MVSNet_test		90.76 245	89.89 249	93.38 248	95.04 260	83.70 311	95.85 245	94.30 332	88.19 236	90.46 205	92.80 312	73.61 305	98.50 192	88.16 214	90.58 243	97.95 164

EU-MVSNet		88.72 284	88.90 271	88.20 331	93.15 324	74.21 357	96.63 192	94.22 333	85.18 296	87.32 291	95.97 190	76.16 288	94.98 347	85.27 270	86.17 284	95.41 255

MIMVSNet		88.50 286	86.76 294	93.72 232	94.84 272	87.77 247	91.39 336	94.05 334	86.41 279	87.99 279	92.59 316	63.27 350	95.82 339	77.44 328	92.84 207	97.57 187

OpenMVS_ROB	81.14 20	84.42 317	82.28 320	90.83 311	90.06 349	84.05 307	95.73 250	94.04 335	73.89 355	80.17 346	91.53 333	59.15 355	97.64 284	66.92 358	89.05 258	90.80 353

TinyColmap		86.82 299	85.35 305	91.21 306	94.91 269	82.99 317	93.94 304	94.02 336	83.58 318	81.56 337	94.68 252	62.34 353	98.13 222	75.78 336	87.35 276	92.52 340

IB-MVS	87.33 17	89.91 267	88.28 279	94.79 186	95.26 250	87.70 248	95.12 276	93.95 337	89.35 201	87.03 296	92.49 317	70.74 318	99.19 127	89.18 201	81.37 332	97.49 189
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
LCM-MVSNet-Re		92.50 171	92.52 155	92.44 276	96.82 169	81.89 324	96.92 161	93.71 338	92.41 112	84.30 321	94.60 256	85.08 148	97.03 317	91.51 155	97.36 134	98.40 144

tpm		90.25 260	89.74 258	91.76 296	93.92 302	79.73 343	93.98 302	93.54 339	88.28 234	91.99 178	93.25 308	77.51 279	97.44 303	87.30 239	87.94 267	98.12 157

ET-MVSNet_ETH3D		91.49 212	90.11 241	95.63 142	96.40 193	91.57 126	95.34 264	93.48 340	90.60 173	75.58 353	95.49 221	80.08 235	96.79 326	94.25 100	89.76 253	98.52 128

bset_n11_16_dypcd		91.55 208	90.59 220	94.44 198	91.51 341	90.25 171	92.70 329	93.42 341	92.27 115	90.22 211	94.74 249	78.42 265	97.80 270	94.19 102	87.86 269	95.29 271

LFMVS		93.60 135	92.63 148	96.52 88	98.13 109	91.27 135	97.94 58	93.39 342	90.57 174	96.29 77	98.31 49	69.00 327	99.16 131	94.18 103	95.87 164	99.12 81

Patchmatch-RL test		87.38 295	86.24 296	90.81 312	88.74 357	78.40 351	88.12 354	93.17 343	87.11 269	82.17 336	89.29 344	81.95 206	95.60 342	88.64 210	77.02 343	98.41 143

test-LLR		91.42 215	91.19 199	92.12 283	94.59 283	80.66 331	94.29 295	92.98 344	91.11 155	90.76 201	92.37 319	79.02 254	98.07 235	88.81 206	96.74 148	97.63 179

test-mter		90.19 263	89.54 262	92.12 283	94.59 283	80.66 331	94.29 295	92.98 344	87.68 256	90.76 201	92.37 319	67.67 333	98.07 235	88.81 206	96.74 148	97.63 179

test_method		66.11 329	64.89 332	69.79 346	72.62 369	35.23 376	65.19 364	92.83 346	20.35 368	65.20 360	88.08 350	43.14 364	82.70 364	73.12 347	63.46 359	91.45 352

test0.0.03 1		89.37 275	88.70 273	91.41 303	92.47 334	85.63 285	95.22 273	92.70 347	91.11 155	86.91 300	93.65 300	79.02 254	93.19 357	78.00 327	89.18 257	95.41 255

new_pmnet		82.89 320	81.12 324	88.18 332	89.63 352	80.18 339	91.77 335	92.57 348	76.79 352	75.56 354	88.23 348	61.22 354	94.48 350	71.43 351	82.92 327	89.87 355

thisisatest0515		92.29 182	91.30 193	95.25 162	96.60 177	88.90 217	94.36 291	92.32 349	87.92 245	93.43 146	94.57 257	77.28 280	99.00 149	89.42 191	95.86 165	97.86 169

thisisatest0530		93.03 154	92.21 163	95.49 154	97.07 152	89.11 213	97.49 107	92.19 350	90.16 181	94.09 130	96.41 171	76.43 287	99.05 146	90.38 172	95.68 170	98.31 150

tttt0517		92.96 157	92.33 160	94.87 179	97.11 150	87.16 259	97.97 56	92.09 351	90.63 169	93.88 136	97.01 134	76.50 284	99.06 145	90.29 175	95.45 172	98.38 146

K. test v3		87.64 294	86.75 295	90.32 320	93.02 326	79.48 345	96.61 193	92.08 352	90.66 167	80.25 345	94.09 283	67.21 337	96.65 328	85.96 262	80.83 334	94.83 291

TESTMET0.1,1		90.06 265	89.42 263	91.97 286	94.41 290	80.62 333	94.29 295	91.97 353	87.28 266	90.44 206	92.47 318	68.79 328	97.67 281	88.50 212	96.60 153	97.61 183

PM-MVS		83.48 318	81.86 322	88.31 330	87.83 360	77.59 352	93.43 316	91.75 354	86.91 271	80.63 341	89.91 341	44.42 363	95.84 338	85.17 273	76.73 345	91.50 350

baseline2		91.63 203	90.86 207	93.94 222	94.33 292	86.32 274	95.92 242	91.64 355	89.37 200	86.94 298	94.69 251	81.62 212	98.69 174	88.64 210	94.57 188	96.81 205

FPMVS		71.27 326	69.85 328	75.50 343	74.64 366	59.03 368	91.30 337	91.50 356	58.80 361	57.92 363	88.28 347	29.98 368	85.53 363	53.43 362	82.84 328	81.95 359

door								91.13 357

door-mid								91.06 358

pmmvs3		79.97 323	77.50 327	87.39 334	82.80 363	79.38 346	92.70 329	90.75 359	70.69 357	78.66 349	87.47 353	51.34 361	93.40 355	73.39 346	69.65 356	89.38 356

DSMNet-mixed		86.34 303	86.12 299	87.00 336	89.88 351	70.43 360	94.93 277	90.08 360	77.97 350	85.42 313	92.78 313	74.44 298	93.96 353	74.43 341	95.14 176	96.62 209

MVS-HIRNet		82.47 321	81.21 323	86.26 338	95.38 235	69.21 363	88.96 353	89.49 361	66.28 358	80.79 340	74.08 361	68.48 330	97.39 307	71.93 350	95.47 171	92.18 345

EPMVS		90.70 250	89.81 253	93.37 249	94.73 277	84.21 304	93.67 312	88.02 362	89.50 196	92.38 166	93.49 303	77.82 277	97.78 273	86.03 260	92.68 209	98.11 160

ANet_high		63.94 330	59.58 333	77.02 342	61.24 373	66.06 364	85.66 357	87.93 363	78.53 348	42.94 366	71.04 362	25.42 371	80.71 365	52.60 363	30.83 366	84.28 358

PMMVS2		70.19 327	66.92 330	80.01 340	76.35 365	65.67 365	86.22 355	87.58 364	64.83 360	62.38 362	80.29 358	26.78 370	88.49 361	63.79 359	54.07 363	85.88 357

lessismore_v0				90.45 318	91.96 340	79.09 349		87.19 365		80.32 344	94.39 265	66.31 342	97.55 292	84.00 285	76.84 344	94.70 303

PMVS	53.92 22	58.58 331	55.40 334	68.12 347	51.00 374	48.64 370	78.86 360	87.10 366	46.77 364	35.84 370	74.28 360	8.76 373	86.34 362	42.07 365	73.91 350	69.38 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
gg-mvs-nofinetune		87.82 292	85.61 301	94.44 198	94.46 287	89.27 208	91.21 340	84.61 367	80.88 336	89.89 227	74.98 359	71.50 312	97.53 295	85.75 265	97.21 140	96.51 211

GG-mvs-BLEND				93.62 236	93.69 310	89.20 209	92.39 334	83.33 368		87.98 280	89.84 342	71.00 316	96.87 324	82.08 301	95.40 173	94.80 296

MTMP							97.86 63	82.03 369

DeepMVS_CX				74.68 345	90.84 346	64.34 367		81.61 370	65.34 359	67.47 358	88.01 351	48.60 362	80.13 366	62.33 361	73.68 351	79.58 360

E-PMN		53.28 332	52.56 336	55.43 349	74.43 367	47.13 371	83.63 359	76.30 371	42.23 365	42.59 367	62.22 365	28.57 369	74.40 367	31.53 367	31.51 365	44.78 364

EMVS		52.08 334	51.31 337	54.39 350	72.62 369	45.39 373	83.84 358	75.51 372	41.13 366	40.77 368	59.65 366	30.08 367	73.60 368	28.31 368	29.90 367	44.18 365

MVE	50.73 23	53.25 333	48.81 338	66.58 348	65.34 372	57.50 369	72.49 362	70.94 373	40.15 367	39.28 369	63.51 364	6.89 375	73.48 369	38.29 366	42.38 364	68.76 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 335	53.82 335	46.29 351	33.73 375	45.30 374	78.32 361	67.24 374	18.02 369	50.93 365	87.05 354	52.99 360	53.11 370	70.76 354	25.29 368	40.46 366

N_pmnet		78.73 324	78.71 326	78.79 341	92.80 329	46.50 372	94.14 299	43.71 375	78.61 347	80.83 339	91.66 332	74.94 296	96.36 330	67.24 357	84.45 310	93.50 327

wuyk23d		25.11 336	24.57 340	26.74 352	73.98 368	39.89 375	57.88 365	9.80 376	12.27 370	10.39 371	6.97 372	7.03 374	36.44 371	25.43 369	17.39 369	3.89 369

testmvs		13.36 338	16.33 341	4.48 354	5.04 376	2.26 378	93.18 319	3.28 377	2.70 371	8.24 372	21.66 368	2.29 377	2.19 372	7.58 370	2.96 370	9.00 368

test123		13.04 339	15.66 342	5.18 353	4.51 377	3.45 377	92.50 333	1.81 378	2.50 372	7.58 373	20.15 369	3.67 376	2.18 373	7.13 371	1.07 371	9.90 367

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		7.39 341	9.85 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	88.65 99	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

n2								0.00 379

nn								0.00 379

ab-mvs-re		8.06 340	10.74 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	96.69 151	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

PC_three_1452									90.77 161	98.89 8	98.28 55	96.24 1	98.35 204	95.76 58	99.58 22	99.59 19

eth-test2					0.00 378

eth-test					0.00 378

OPU-MVS				98.55 3	98.82 58	96.86 3	98.25 32				98.26 56	96.04 2	99.24 124	95.36 74	99.59 17	99.56 26

test_0728_THIRD									94.78 35	98.73 10	98.87 6	95.87 4	99.84 22	97.45 8	99.72 2	99.77 1

GSMVS																98.45 138

test_part2					99.28 27	95.74 8				98.10 21

sam_mvs1												82.76 188				98.45 138

sam_mvs												81.94 207

test_post1							92.81 328				16.58 371	80.53 226	97.68 280	86.20 254

test_post											17.58 370	81.76 209	98.08 232

patchmatchnet-post											90.45 337	82.65 192	98.10 227

gm-plane-assit					93.22 322	78.89 350			84.82 303		93.52 302		98.64 178	87.72 222

test9_res														94.81 91	99.38 52	99.45 49

agg_prior2														93.94 108	99.38 52	99.50 41

test_prior4						93.66 63	96.42 204

test_prior2							96.35 213		92.80 102	96.03 86	97.59 105	92.01 44		95.01 82	99.38 52

旧先验2							95.94 241		81.66 331	97.34 38			98.82 162	92.26 134

新几何2							95.79 248

原ACMM2							95.67 251

testdata2													99.67 52	85.96 262

segment_acmp												92.89 25

testdata1							95.26 272		93.10 88

plane_prior7					96.21 200	89.98 179

plane_prior6					96.10 210	90.00 175						81.32 215

plane_prior4											96.64 154

plane_prior3						90.00 175			94.46 44	91.34 188

plane_prior2							97.74 76		94.85 28

plane_prior1					96.14 208

plane_prior						89.99 177	97.24 129		94.06 53						92.16 219

HQP5-MVS						89.33 203

HQP-NCC					95.86 215		96.65 187		93.55 68	90.14 212

ACMP_Plane					95.86 215		96.65 187		93.55 68	90.14 212

BP-MVS														92.13 140

HQP4-MVS										90.14 212			98.50 192			95.78 237

HQP2-MVS												80.95 218

NP-MVS					95.99 214	89.81 184					95.87 195

MDTV_nov1_ep13_2view						70.35 361	93.10 324		83.88 314	93.55 141		82.47 196		86.25 253		98.38 146

ACMMP++_ref															90.30 248

ACMMP++															91.02 237

Test By Simon												88.73 98

High-resolution multi-view benchmark