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		97.29 23	96.98 31	98.23 11	98.01 112	95.03 26	98.07 55	95.76 298	97.78 1	97.52 48	98.80 29	88.09 108	99.86 9	99.44 1	99.37 62	99.80 1

fmvsm_s_conf0.5_n_2		96.62 58	96.82 43	96.02 132	97.98 115	90.43 175	97.50 134	98.59 19	96.59 5	99.31 2	99.08 4	84.47 166	99.75 46	99.37 2	98.45 118	97.88 191

fmvsm_s_conf0.5_n_3		97.15 27	97.36 19	96.52 90	97.98 115	91.19 145	97.84 86	98.65 17	97.08 2	99.25 5	99.10 3	87.88 114	99.79 37	99.32 3	99.18 79	98.59 136

fmvsm_l_conf0.5_n_a		97.63 9	97.76 5	97.26 63	98.25 89	92.59 90	97.81 93	98.68 13	94.93 37	99.24 6	98.87 22	93.52 20	99.79 37	99.32 3	99.21 75	99.40 58

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 56	98.21 95	92.75 84	97.83 89	98.73 9	95.04 35	99.30 3	98.84 27	93.34 22	99.78 40	99.32 3	99.13 85	99.50 44

fmvsm_s_conf0.1_n_2		96.33 70	96.44 66	96.00 136	97.30 153	90.37 178	97.53 131	97.92 113	96.52 6	99.14 9	99.08 4	83.21 188	99.74 47	99.22 6	98.06 134	97.88 191

fmvsm_l_conf0.5_n_3		97.64 8	97.60 9	97.79 30	98.14 102	93.94 52	97.93 75	98.65 17	96.70 3	99.38 1	99.07 7	89.92 86	99.81 30	99.16 7	99.43 48	99.61 23

test_fmvsm_n_1920		97.55 12	97.89 3	96.53 89	98.41 77	91.73 117	98.01 60	99.02 1	96.37 8	99.30 3	98.92 17	92.39 41	99.79 37	99.16 7	99.46 41	98.08 181

test_fmvsmconf_n		97.49 16	97.56 10	97.29 59	97.44 150	92.37 96	97.91 77	98.88 4	95.83 12	98.92 16	99.05 9	91.45 57	99.80 34	99.12 9	99.46 41	99.69 12

fmvsm_s_conf0.5_n		96.85 43	97.13 21	96.04 130	98.07 109	90.28 179	97.97 69	98.76 8	94.93 37	98.84 20	99.06 8	88.80 98	99.65 65	99.06 10	98.63 108	98.18 170

test_fmvsmconf0.1_n		97.09 29	97.06 24	97.19 68	95.67 258	92.21 103	97.95 72	98.27 42	95.78 16	98.40 29	99.00 11	89.99 84	99.78 40	99.06 10	99.41 54	99.59 25

MVS_0304		96.74 52	96.31 68	98.02 19	96.87 179	94.65 30	97.58 123	94.39 359	96.47 7	97.16 60	98.39 54	87.53 123	99.87 7	98.97 12	99.41 54	99.55 35

fmvsm_s_conf0.5_n_a		96.75 51	96.93 34	96.20 122	97.64 137	90.72 165	98.00 61	98.73 9	94.55 59	98.91 17	99.08 4	88.22 107	99.63 74	98.91 13	98.37 121	98.25 165

test_fmvsmvis_n_1920		96.70 53	96.84 39	96.31 111	96.62 199	91.73 117	97.98 63	98.30 35	96.19 9	96.10 106	98.95 15	89.42 89	99.76 43	98.90 14	99.08 89	97.43 218

fmvsm_s_conf0.1_n		96.58 61	96.77 47	96.01 135	96.67 197	90.25 180	97.91 77	98.38 26	94.48 63	98.84 20	99.14 1	88.06 109	99.62 75	98.82 15	98.60 110	98.15 174

test_fmvsmconf0.01_n		96.15 74	95.85 78	97.03 75	92.66 375	91.83 116	97.97 69	97.84 127	95.57 19	97.53 47	99.00 11	84.20 172	99.76 43	98.82 15	99.08 89	99.48 48

fmvsm_s_conf0.1_n_a		96.40 66	96.47 60	96.16 124	95.48 266	90.69 166	97.91 77	98.33 32	94.07 75	98.93 13	99.14 1	87.44 127	99.61 76	98.63 17	98.32 123	98.18 170

mamv4		94.66 120	96.10 73	90.37 357	98.01 112	73.41 406	96.82 202	97.78 132	89.95 221	94.52 147	97.43 137	92.91 27	99.09 156	98.28 18	99.16 82	98.60 134

MVSMamba_PlusPlus		96.51 62	96.48 59	96.59 86	98.07 109	91.97 112	98.14 49	97.79 131	90.43 210	97.34 56	97.52 133	91.29 63	99.19 137	98.12 19	99.64 14	98.60 134

APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 28	98.81 7	98.30 35	94.76 50	98.30 30	98.90 19	93.77 17	99.68 61	97.93 20	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_vis1_n_1920		94.17 130	94.58 111	92.91 292	97.42 151	82.02 359	97.83 89	97.85 123	94.68 53	98.10 34	98.49 44	70.15 353	99.32 124	97.91 21	98.82 100	97.40 220

reproduce_model		97.51 15	97.51 14	97.50 50	98.99 46	93.01 78	97.79 95	98.21 54	95.73 17	97.99 37	99.03 10	92.63 36	99.82 28	97.80 22	99.42 51	99.67 13

balanced_conf03		96.84 45	96.89 36	96.68 80	97.63 139	92.22 102	98.17 48	97.82 129	94.44 65	98.23 32	97.36 140	90.97 71	99.22 134	97.74 23	99.66 10	98.61 133

reproduce-ours		97.53 13	97.51 14	97.60 47	98.97 47	93.31 69	97.71 106	98.20 56	95.80 14	97.88 41	98.98 13	92.91 27	99.81 30	97.68 24	99.43 48	99.67 13

our_new_method		97.53 13	97.51 14	97.60 47	98.97 47	93.31 69	97.71 106	98.20 56	95.80 14	97.88 41	98.98 13	92.91 27	99.81 30	97.68 24	99.43 48	99.67 13

MSC_two_6792asdad				98.86 1	98.67 61	96.94 1		97.93 111					99.86 9	97.68 24	99.67 6	99.77 2

No_MVS				98.86 1	98.67 61	96.94 1		97.93 111					99.86 9	97.68 24	99.67 6	99.77 2

patch_mono-2		96.83 46	97.44 17	95.01 185	99.05 39	85.39 312	96.98 189	98.77 7	94.70 52	97.99 37	98.66 33	93.61 19	99.91 1	97.67 28	99.50 35	99.72 11

test_vis1_n		92.37 202	92.26 187	92.72 300	94.75 314	82.64 349	98.02 59	96.80 246	91.18 179	97.77 45	97.93 95	58.02 402	98.29 241	97.63 29	98.21 127	97.23 229

test_fmvs1_n		92.73 192	92.88 161	92.29 311	96.08 244	81.05 367	97.98 63	97.08 216	90.72 194	96.79 73	98.18 77	63.07 393	98.45 225	97.62 30	98.42 120	97.36 221

test_fmvs1		93.21 167	93.53 139	92.25 313	96.55 208	81.20 366	97.40 149	96.96 229	90.68 196	96.80 71	98.04 86	69.25 360	98.40 228	97.58 31	98.50 113	97.16 230

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 35	98.27 42	95.13 30	99.19 7	98.89 20	95.54 5	99.85 18	97.52 32	99.66 10	99.56 32

test_241102_TWO								98.27 42	95.13 30	98.93 13	98.89 20	94.99 11	99.85 18	97.52 32	99.65 13	99.74 8

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 42	97.85 123	94.92 39	98.73 22	98.87 22	95.08 8	99.84 23	97.52 32	99.67 6	99.48 48
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
test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 42	98.28 39					99.86 9	97.52 32	99.67 6	99.75 6

DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 61	95.39 11	99.29 1	98.28 39	94.78 48	98.93 13	98.87 22	96.04 2	99.86 9	97.45 36	99.58 23	99.59 25

test_0728_THIRD									94.78 48	98.73 22	98.87 22	95.87 4	99.84 23	97.45 36	99.72 2	99.77 2

EC-MVSNet		96.42 65	96.47 60	96.26 117	97.01 173	91.52 129	98.89 5	97.75 134	94.42 66	96.64 82	97.68 116	89.32 90	98.60 213	97.45 36	99.11 88	98.67 131

IU-MVS					99.42 7	95.39 11		97.94 110	90.40 212	98.94 12				97.41 39	99.66 10	99.74 8

mmtdpeth		89.70 310	88.96 308	91.90 321	95.84 253	84.42 328	97.46 143	95.53 314	90.27 213	94.46 150	90.50 383	69.74 358	98.95 173	97.39 40	69.48 408	92.34 385

dcpmvs_2		96.37 68	97.05 27	94.31 227	98.96 49	84.11 333	97.56 126	97.51 166	93.92 80	97.43 53	98.52 41	92.75 32	99.32 124	97.32 41	99.50 35	99.51 41

CS-MVS		96.86 41	97.06 24	96.26 117	98.16 101	91.16 150	99.09 3	97.87 118	95.30 25	97.06 66	98.03 87	91.72 50	98.71 203	97.10 42	99.17 80	98.90 109

TSAR-MVS + MP.		97.42 17	97.33 20	97.69 42	99.25 27	94.24 41	98.07 55	97.85 123	93.72 86	98.57 25	98.35 58	93.69 18	99.40 117	97.06 43	99.46 41	99.44 53
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		97.68 6	97.44 17	98.37 7	98.90 53	95.86 6	97.27 162	98.08 80	95.81 13	97.87 44	98.31 67	94.26 13	99.68 61	97.02 44	99.49 38	99.57 29

SD-MVS		97.41 18	97.53 12	97.06 74	98.57 72	94.46 34	97.92 76	98.14 70	94.82 45	99.01 10	98.55 39	94.18 14	97.41 342	96.94 45	99.64 14	99.32 66
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
SPE-MVS-test		96.89 39	97.04 28	96.45 101	98.29 85	91.66 123	99.03 4	97.85 123	95.84 11	96.90 69	97.97 93	91.24 64	98.75 196	96.92 46	99.33 64	98.94 102

CANet		96.39 67	96.02 74	97.50 50	97.62 140	93.38 64	97.02 183	97.96 108	95.42 22	94.86 139	97.81 108	87.38 129	99.82 28	96.88 47	99.20 77	99.29 67

TSAR-MVS + GP.		96.69 55	96.49 58	97.27 62	98.31 84	93.39 63	96.79 204	96.72 249	94.17 73	97.44 51	97.66 119	92.76 31	99.33 122	96.86 48	97.76 144	99.08 88

DeepPCF-MVS	93.97 1	96.61 59	97.09 23	95.15 177	98.09 105	86.63 288	96.00 267	98.15 68	95.43 21	97.95 39	98.56 37	93.40 21	99.36 121	96.77 49	99.48 39	99.45 51

BP-MVS1		95.89 83	95.49 83	97.08 73	96.67 197	93.20 73	98.08 53	96.32 273	94.56 58	96.32 96	97.84 105	84.07 175	99.15 146	96.75 50	98.78 102	98.90 109

test_cas_vis1_n_1920		94.48 124	94.55 115	94.28 229	96.78 190	86.45 293	97.63 119	97.64 149	93.32 105	97.68 46	98.36 57	73.75 329	99.08 159	96.73 51	99.05 91	97.31 225

SMA-MVS		97.35 20	97.03 29	98.30 8	99.06 38	95.42 10	97.94 73	98.18 63	90.57 206	98.85 19	98.94 16	93.33 23	99.83 26	96.72 52	99.68 4	99.63 19
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
DPE-MVS		97.86 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 170	98.35 30	95.16 29	98.71 24	98.80 29	95.05 10	99.89 3	96.70 53	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		97.59 11	97.54 11	97.73 38	99.40 11	93.77 57	98.53 14	98.29 37	95.55 20	98.56 26	97.81 108	93.90 15	99.65 65	96.62 54	99.21 75	99.77 2
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
MSLP-MVS++		96.94 37	97.06 24	96.59 86	98.72 58	91.86 115	97.67 110	98.49 22	94.66 55	97.24 58	98.41 53	92.31 44	98.94 175	96.61 55	99.46 41	98.96 99

MP-MVS-pluss		96.70 53	96.27 70	97.98 22	99.23 30	94.71 29	96.96 191	98.06 88	90.67 197	95.55 126	98.78 31	91.07 68	99.86 9	96.58 56	99.55 26	99.38 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		97.62 10	97.53 12	97.87 24	98.39 80	94.25 40	98.43 22	98.27 42	95.34 24	98.11 33	98.56 37	94.53 12	99.71 53	96.57 57	99.62 17	99.65 17
Skip Steuart: Steuart Systems R&D Blog.
MCST-MVS		97.18 25	96.84 39	98.20 14	99.30 24	95.35 15	97.12 177	98.07 85	93.54 95	96.08 107	97.69 115	93.86 16	99.71 53	96.50 58	99.39 58	99.55 35

SF-MVS		97.39 19	97.13 21	98.17 15	99.02 42	95.28 19	98.23 39	98.27 42	92.37 139	98.27 31	98.65 35	93.33 23	99.72 52	96.49 59	99.52 30	99.51 41

EI-MVSNet-Vis-set		96.51 62	96.47 60	96.63 83	98.24 90	91.20 144	96.89 195	97.73 137	94.74 51	96.49 89	98.49 44	90.88 74	99.58 84	96.44 60	98.32 123	99.13 81

VDD-MVS		93.82 148	93.08 154	96.02 132	97.88 124	89.96 190	97.72 104	95.85 294	92.43 137	95.86 115	98.44 50	68.42 368	99.39 118	96.31 61	94.85 208	98.71 128

ACMMP_NAP		97.20 24	96.86 37	98.23 11	99.09 34	95.16 22	97.60 122	98.19 61	92.82 130	97.93 40	98.74 32	91.60 55	99.86 9	96.26 62	99.52 30	99.67 13

diffmvs		95.25 100	95.13 98	95.63 155	96.43 222	89.34 212	95.99 268	97.35 197	92.83 129	96.31 97	97.37 139	86.44 140	98.67 206	96.26 62	97.19 163	98.87 115
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-UG-set		96.34 69	96.30 69	96.47 98	98.20 96	90.93 157	96.86 197	97.72 139	94.67 54	96.16 104	98.46 48	90.43 79	99.58 84	96.23 64	97.96 137	98.90 109

SR-MVS		97.01 34	96.86 37	97.47 52	99.09 34	93.27 71	97.98 63	98.07 85	93.75 85	97.45 50	98.48 47	91.43 59	99.59 81	96.22 65	99.27 68	99.54 37

xiu_mvs_v1_base_debu		95.01 106	94.76 105	95.75 147	96.58 203	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 262

xiu_mvs_v1_base		95.01 106	94.76 105	95.75 147	96.58 203	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 262

xiu_mvs_v1_base_debi		95.01 106	94.76 105	95.75 147	96.58 203	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 262

alignmvs		95.87 85	95.23 95	97.78 32	97.56 148	95.19 21	97.86 82	97.17 208	94.39 69	96.47 91	96.40 196	85.89 148	99.20 136	96.21 69	95.11 206	98.95 101

sasdasda		96.02 77	95.45 86	97.75 36	97.59 143	95.15 23	98.28 30	97.60 153	94.52 61	96.27 99	96.12 210	87.65 118	99.18 140	96.20 70	94.82 210	98.91 106

canonicalmvs		96.02 77	95.45 86	97.75 36	97.59 143	95.15 23	98.28 30	97.60 153	94.52 61	96.27 99	96.12 210	87.65 118	99.18 140	96.20 70	94.82 210	98.91 106

MGCFI-Net		95.94 82	95.40 90	97.56 49	97.59 143	94.62 31	98.21 42	97.57 158	94.41 67	96.17 103	96.16 208	87.54 122	99.17 142	96.19 72	94.73 215	98.91 106

RRT-MVS		94.51 122	94.35 122	94.98 188	96.40 223	86.55 291	97.56 126	97.41 189	93.19 110	94.93 137	97.04 158	79.12 269	99.30 128	96.19 72	97.32 158	99.09 87

MTAPA		97.08 30	96.78 46	97.97 23	99.37 16	94.42 36	97.24 164	98.08 80	95.07 34	96.11 105	98.59 36	90.88 74	99.90 2	96.18 74	99.50 35	99.58 28

APD-MVS_3200maxsize		96.81 47	96.71 50	97.12 70	99.01 45	92.31 99	97.98 63	98.06 88	93.11 116	97.44 51	98.55 39	90.93 72	99.55 94	96.06 75	99.25 72	99.51 41

SR-MVS-dyc-post		96.88 40	96.80 45	97.11 71	99.02 42	92.34 97	97.98 63	98.03 97	93.52 97	97.43 53	98.51 42	91.40 60	99.56 92	96.05 76	99.26 70	99.43 55

RE-MVS-def			96.72 49		99.02 42	92.34 97	97.98 63	98.03 97	93.52 97	97.43 53	98.51 42	90.71 76		96.05 76	99.26 70	99.43 55

MVS_111021_HR		96.68 57	96.58 55	96.99 76	98.46 73	92.31 99	96.20 258	98.90 3	94.30 72	95.86 115	97.74 113	92.33 42	99.38 120	96.04 78	99.42 51	99.28 69

PHI-MVS		96.77 49	96.46 63	97.71 41	98.40 78	94.07 48	98.21 42	98.45 25	89.86 223	97.11 64	98.01 90	92.52 39	99.69 59	96.03 79	99.53 29	99.36 64

casdiffmvs_mvg		95.81 86	95.57 81	96.51 94	96.87 179	91.49 130	97.50 134	97.56 162	93.99 78	95.13 135	97.92 96	87.89 113	98.78 191	95.97 80	97.33 156	99.26 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HPM-MVS++		97.34 21	96.97 32	98.47 5	99.08 36	96.16 4	97.55 130	97.97 107	95.59 18	96.61 83	97.89 97	92.57 38	99.84 23	95.95 81	99.51 33	99.40 58

DELS-MVS		96.61 59	96.38 67	97.30 58	97.79 128	93.19 74	95.96 269	98.18 63	95.23 26	95.87 114	97.65 120	91.45 57	99.70 58	95.87 82	99.44 47	99.00 97
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
MVS_111021_LR		96.24 73	96.19 72	96.39 106	98.23 94	91.35 137	96.24 256	98.79 6	93.99 78	95.80 117	97.65 120	89.92 86	99.24 132	95.87 82	99.20 77	98.58 137

h-mvs33		94.15 132	93.52 141	96.04 130	97.81 127	90.22 181	97.62 121	97.58 157	95.19 27	96.74 75	97.45 134	83.67 180	99.61 76	95.85 84	79.73 382	98.29 164

hse-mvs2		93.45 160	92.99 156	94.81 198	97.02 172	88.59 234	96.69 215	96.47 267	95.19 27	96.74 75	96.16 208	83.67 180	98.48 224	95.85 84	79.13 386	97.35 223

NCCC		97.30 22	97.03 29	98.11 17	98.77 56	95.06 25	97.34 155	98.04 95	95.96 10	97.09 65	97.88 99	93.18 25	99.71 53	95.84 86	99.17 80	99.56 32

VNet		95.89 83	95.45 86	97.21 66	98.07 109	92.94 81	97.50 134	98.15 68	93.87 82	97.52 48	97.61 126	85.29 155	99.53 98	95.81 87	95.27 201	99.16 77

PC_three_1452									90.77 191	98.89 18	98.28 72	96.24 1	98.35 236	95.76 88	99.58 23	99.59 25

9.14			96.75 48		98.93 50		97.73 101	98.23 53	91.28 175	97.88 41	98.44 50	93.00 26	99.65 65	95.76 88	99.47 40

XVS		97.18 25	96.96 33	97.81 28	99.38 14	94.03 50	98.59 12	98.20 56	94.85 41	96.59 85	98.29 70	91.70 52	99.80 34	95.66 90	99.40 56	99.62 20

X-MVStestdata		91.71 227	89.67 292	97.81 28	99.38 14	94.03 50	98.59 12	98.20 56	94.85 41	96.59 85	32.69 426	91.70 52	99.80 34	95.66 90	99.40 56	99.62 20

baseline		95.58 92	95.42 89	96.08 126	96.78 190	90.41 176	97.16 174	97.45 180	93.69 89	95.65 124	97.85 103	87.29 130	98.68 205	95.66 90	97.25 161	99.13 81

ETV-MVS		96.02 77	95.89 77	96.40 104	97.16 159	92.44 94	97.47 141	97.77 133	94.55 59	96.48 90	94.51 290	91.23 66	98.92 177	95.65 93	98.19 128	97.82 199

casdiffmvs		95.64 89	95.49 83	96.08 126	96.76 195	90.45 173	97.29 161	97.44 184	94.00 77	95.46 130	97.98 92	87.52 125	98.73 199	95.64 94	97.33 156	99.08 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HFP-MVS		97.14 28	96.92 35	97.83 26	99.42 7	94.12 46	98.52 15	98.32 33	93.21 107	97.18 59	98.29 70	92.08 46	99.83 26	95.63 95	99.59 19	99.54 37

ACMMPR		97.07 31	96.84 39	97.79 30	99.44 6	93.88 53	98.52 15	98.31 34	93.21 107	97.15 61	98.33 64	91.35 61	99.86 9	95.63 95	99.59 19	99.62 20

HPM-MVS		96.69 55	96.45 64	97.40 54	99.36 18	93.11 76	98.87 6	98.06 88	91.17 180	96.40 94	97.99 91	90.99 70	99.58 84	95.61 97	99.61 18	99.49 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		97.02 33	96.81 44	97.64 45	99.33 21	93.54 60	98.80 8	98.28 39	92.99 119	96.45 93	98.30 69	91.90 49	99.85 18	95.61 97	99.68 4	99.54 37

DeepC-MVS	93.07 3	96.06 75	95.66 80	97.29 59	97.96 117	93.17 75	97.30 160	98.06 88	93.92 80	93.38 175	98.66 33	86.83 135	99.73 49	95.60 99	99.22 74	98.96 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		96.96 35	96.67 51	97.85 25	99.37 16	94.12 46	98.49 19	98.18 63	92.64 135	96.39 95	98.18 77	91.61 54	99.88 4	95.59 100	99.55 26	99.57 29

region2R		97.07 31	96.84 39	97.77 34	99.46 2	93.79 55	98.52 15	98.24 50	93.19 110	97.14 62	98.34 61	91.59 56	99.87 7	95.46 101	99.59 19	99.64 18

OPU-MVS				98.55 3	98.82 55	96.86 3	98.25 35				98.26 73	96.04 2	99.24 132	95.36 102	99.59 19	99.56 32

lupinMVS		94.99 110	94.56 112	96.29 115	96.34 227	91.21 142	95.83 276	96.27 277	88.93 256	96.22 101	96.88 166	86.20 145	98.85 184	95.27 103	99.05 91	98.82 121

reproduce_monomvs		91.30 255	91.10 228	91.92 319	96.82 186	82.48 353	97.01 186	97.49 169	94.64 57	88.35 305	95.27 254	70.53 348	98.10 258	95.20 104	84.60 350	95.19 313

mPP-MVS		96.86 41	96.60 53	97.64 45	99.40 11	93.44 62	98.50 18	98.09 79	93.27 106	95.95 113	98.33 64	91.04 69	99.88 4	95.20 104	99.57 25	99.60 24

DeepC-MVS_fast	93.89 2	96.93 38	96.64 52	97.78 32	98.64 67	94.30 37	97.41 145	98.04 95	94.81 46	96.59 85	98.37 56	91.24 64	99.64 73	95.16 106	99.52 30	99.42 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
jason		94.84 115	94.39 121	96.18 123	95.52 264	90.93 157	96.09 262	96.52 264	89.28 242	96.01 111	97.32 141	84.70 162	98.77 194	95.15 107	98.91 99	98.85 117
jason: jason.
train_agg		96.30 71	95.83 79	97.72 39	98.70 59	94.19 42	96.41 237	98.02 100	88.58 268	96.03 108	97.56 130	92.73 34	99.59 81	95.04 108	99.37 62	99.39 60

mvsany_test1		93.93 144	93.98 127	93.78 257	94.94 304	86.80 281	94.62 326	92.55 390	88.77 265	96.85 70	98.49 44	88.98 94	98.08 263	95.03 109	95.62 195	96.46 250

test_prior2							96.35 245		92.80 131	96.03 108	97.59 127	92.01 47		95.01 110	99.38 59

nrg030		94.05 139	93.31 150	96.27 116	95.22 288	94.59 32	98.34 25	97.46 175	92.93 126	91.21 234	96.64 179	87.23 132	98.22 245	94.99 111	85.80 330	95.98 266

VDDNet		93.05 176	92.07 190	96.02 132	96.84 182	90.39 177	98.08 53	95.85 294	86.22 329	95.79 118	98.46 48	67.59 371	99.19 137	94.92 112	94.85 208	98.47 149

mvsmamba		94.57 121	94.14 125	95.87 140	97.03 171	89.93 191	97.84 86	95.85 294	91.34 171	94.79 141	96.80 168	80.67 240	98.81 188	94.85 113	98.12 132	98.85 117

APD-MVS		96.95 36	96.60 53	98.01 20	99.03 41	94.93 27	97.72 104	98.10 78	91.50 164	98.01 36	98.32 66	92.33 42	99.58 84	94.85 113	99.51 33	99.53 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		96.85 43	96.52 57	97.82 27	99.36 18	94.14 45	98.29 29	98.13 71	92.72 132	96.70 77	98.06 84	91.35 61	99.86 9	94.83 115	99.28 67	99.47 50

MP-MVS		96.77 49	96.45 64	97.72 39	99.39 13	93.80 54	98.41 23	98.06 88	93.37 102	95.54 128	98.34 61	90.59 78	99.88 4	94.83 115	99.54 28	99.49 46
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test9_res														94.81 117	99.38 59	99.45 51

PS-MVSNAJ		95.37 96	95.33 93	95.49 165	97.35 152	90.66 168	95.31 305	97.48 170	93.85 83	96.51 88	95.70 235	88.65 101	99.65 65	94.80 118	98.27 125	96.17 256

HPM-MVS_fast		96.51 62	96.27 70	97.22 65	99.32 22	92.74 85	98.74 9	98.06 88	90.57 206	96.77 74	98.35 58	90.21 81	99.53 98	94.80 118	99.63 16	99.38 62

xiu_mvs_v2_base		95.32 98	95.29 94	95.40 170	97.22 155	90.50 171	95.44 298	97.44 184	93.70 88	96.46 92	96.18 205	88.59 104	99.53 98	94.79 120	97.81 141	96.17 256

CSCG		96.05 76	95.91 76	96.46 100	99.24 28	90.47 172	98.30 28	98.57 21	89.01 251	93.97 162	97.57 128	92.62 37	99.76 43	94.66 121	99.27 68	99.15 79

test_fmvs2		89.77 309	89.93 281	89.31 371	93.68 350	76.37 398	97.64 117	95.90 291	89.84 226	91.49 221	96.26 203	58.77 401	97.10 352	94.65 122	91.13 275	94.46 349

EIA-MVS		95.53 94	95.47 85	95.71 152	97.06 167	89.63 196	97.82 91	97.87 118	93.57 91	93.92 163	95.04 263	90.61 77	98.95 173	94.62 123	98.68 106	98.54 139

SDMVSNet		94.17 130	93.61 135	95.86 142	98.09 105	91.37 136	97.35 154	98.20 56	93.18 112	91.79 214	97.28 143	79.13 268	98.93 176	94.61 124	92.84 246	97.28 226

ZD-MVS					99.05 39	94.59 32		98.08 80	89.22 244	97.03 67	98.10 80	92.52 39	99.65 65	94.58 125	99.31 66

ACMMP		96.27 72	95.93 75	97.28 61	99.24 28	92.62 88	98.25 35	98.81 5	92.99 119	94.56 146	98.39 54	88.96 95	99.85 18	94.57 126	97.63 145	99.36 64
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
GDP-MVS		95.62 90	95.13 98	97.09 72	96.79 189	93.26 72	97.89 80	97.83 128	93.58 90	96.80 71	97.82 107	83.06 195	99.16 144	94.40 127	97.95 138	98.87 115

PGM-MVS		96.81 47	96.53 56	97.65 43	99.35 20	93.53 61	97.65 113	98.98 2	92.22 142	97.14 62	98.44 50	91.17 67	99.85 18	94.35 128	99.46 41	99.57 29

ET-MVSNet_ETH3D		91.49 243	90.11 272	95.63 155	96.40 223	91.57 128	95.34 302	93.48 378	90.60 205	75.58 402	95.49 246	80.08 252	96.79 364	94.25 129	89.76 293	98.52 141

LFMVS		93.60 154	92.63 172	96.52 90	98.13 104	91.27 139	97.94 73	93.39 379	90.57 206	96.29 98	98.31 67	69.00 361	99.16 144	94.18 130	95.87 187	99.12 84

MVSFormer		95.37 96	95.16 97	95.99 137	96.34 227	91.21 142	98.22 40	97.57 158	91.42 168	96.22 101	97.32 141	86.20 145	97.92 294	94.07 131	99.05 91	98.85 117

test_djsdf		93.07 175	92.76 165	94.00 241	93.49 356	88.70 232	98.22 40	97.57 158	91.42 168	90.08 259	95.55 243	82.85 201	97.92 294	94.07 131	91.58 267	95.40 295

mvs_anonymous		93.82 148	93.74 131	94.06 237	96.44 221	85.41 310	95.81 277	97.05 221	89.85 225	90.09 258	96.36 198	87.44 127	97.75 312	93.97 133	96.69 174	99.02 91

VPA-MVSNet		93.24 166	92.48 181	95.51 163	95.70 256	92.39 95	97.86 82	98.66 16	92.30 140	92.09 206	95.37 249	80.49 244	98.40 228	93.95 134	85.86 329	95.75 279

agg_prior2														93.94 135	99.38 59	99.50 44

mvs_tets		92.31 205	91.76 201	93.94 248	93.41 359	88.29 243	97.63 119	97.53 164	92.04 151	88.76 297	96.45 193	74.62 321	98.09 262	93.91 136	91.48 269	95.45 291

Effi-MVS+		94.93 111	94.45 119	96.36 109	96.61 200	91.47 132	96.41 237	97.41 189	91.02 186	94.50 148	95.92 219	87.53 123	98.78 191	93.89 137	96.81 169	98.84 120

jajsoiax		92.42 199	91.89 198	94.03 240	93.33 362	88.50 239	97.73 101	97.53 164	92.00 153	88.85 294	96.50 191	75.62 313	98.11 257	93.88 138	91.56 268	95.48 287

XVG-OURS-SEG-HR		93.86 147	93.55 137	94.81 198	97.06 167	88.53 238	95.28 306	97.45 180	91.68 160	94.08 159	97.68 116	82.41 212	98.90 180	93.84 139	92.47 252	96.98 233

PS-MVSNAJss		93.74 151	93.51 142	94.44 218	93.91 342	89.28 217	97.75 98	97.56 162	92.50 136	89.94 261	96.54 189	88.65 101	98.18 250	93.83 140	90.90 281	95.86 267

EPNet		95.20 103	94.56 112	97.14 69	92.80 372	92.68 87	97.85 85	94.87 347	96.64 4	92.46 191	97.80 110	86.23 142	99.65 65	93.72 141	98.62 109	99.10 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		95.27 99	94.91 103	96.38 107	98.20 96	90.86 159	97.27 162	98.25 48	90.21 214	94.18 156	97.27 145	87.48 126	99.73 49	93.53 142	97.77 143	98.55 138

CPTT-MVS		95.57 93	95.19 96	96.70 79	99.27 26	91.48 131	98.33 26	98.11 76	87.79 295	95.17 134	98.03 87	87.09 133	99.61 76	93.51 143	99.42 51	99.02 91

MVSTER		93.20 168	92.81 164	94.37 221	96.56 206	89.59 199	97.06 180	97.12 211	91.24 176	91.30 228	95.96 217	82.02 219	98.05 270	93.48 144	90.55 285	95.47 289

PVSNet_BlendedMVS		94.06 138	93.92 128	94.47 216	98.27 86	89.46 207	96.73 209	98.36 27	90.17 215	94.36 151	95.24 257	88.02 110	99.58 84	93.44 145	90.72 283	94.36 353

PVSNet_Blended		94.87 114	94.56 112	95.81 144	98.27 86	89.46 207	95.47 297	98.36 27	88.84 259	94.36 151	96.09 215	88.02 110	99.58 84	93.44 145	98.18 129	98.40 157

3Dnovator	91.36 5	95.19 104	94.44 120	97.44 53	96.56 206	93.36 66	98.65 11	98.36 27	94.12 74	89.25 286	98.06 84	82.20 216	99.77 42	93.41 147	99.32 65	99.18 76

EPP-MVSNet		95.22 102	95.04 101	95.76 145	97.49 149	89.56 200	98.67 10	97.00 227	90.69 195	94.24 154	97.62 125	89.79 88	98.81 188	93.39 148	96.49 178	98.92 105

CHOSEN 280x420		93.12 172	92.72 170	94.34 224	96.71 196	87.27 269	90.29 400	97.72 139	86.61 321	91.34 225	95.29 251	84.29 171	98.41 227	93.25 149	98.94 97	97.35 223

3Dnovator+	91.43 4	95.40 95	94.48 118	98.16 16	96.90 178	95.34 16	98.48 20	97.87 118	94.65 56	88.53 302	98.02 89	83.69 179	99.71 53	93.18 150	98.96 96	99.44 53

test_yl		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 198	97.10 213	91.23 177	95.71 120	96.93 161	84.30 169	99.31 126	93.10 151	95.12 204	98.75 123

DCV-MVSNet		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 198	97.10 213	91.23 177	95.71 120	96.93 161	84.30 169	99.31 126	93.10 151	95.12 204	98.75 123

test_vis1_rt		86.16 349	85.06 350	89.46 367	93.47 358	80.46 374	96.41 237	86.61 418	85.22 343	79.15 395	88.64 397	52.41 410	97.06 353	93.08 153	90.57 284	90.87 401

test1111		93.19 169	92.82 163	94.30 228	97.58 147	84.56 327	98.21 42	89.02 409	93.53 96	94.58 145	98.21 74	72.69 332	99.05 166	93.06 154	98.48 116	99.28 69

ECVR-MVS		93.19 169	92.73 169	94.57 213	97.66 135	85.41 310	98.21 42	88.23 411	93.43 100	94.70 143	98.21 74	72.57 333	99.07 163	93.05 155	98.49 114	99.25 72

HQP_MVS		93.78 150	93.43 146	94.82 196	96.21 231	89.99 186	97.74 99	97.51 166	94.85 41	91.34 225	96.64 179	81.32 230	98.60 213	93.02 156	92.23 255	95.86 267

plane_prior5								97.51 166					98.60 213	93.02 156	92.23 255	95.86 267

MonoMVSNet		91.92 220	91.77 200	92.37 307	92.94 368	83.11 345	97.09 179	95.55 311	92.91 127	90.85 238	94.55 287	81.27 232	96.52 367	93.01 158	87.76 311	97.47 217

test2506		91.60 233	90.78 241	94.04 239	97.66 135	83.81 336	98.27 32	75.53 427	93.43 100	95.23 132	98.21 74	67.21 374	99.07 163	93.01 158	98.49 114	99.25 72

MVS_Test		94.89 113	94.62 109	95.68 153	96.83 184	89.55 201	96.70 213	97.17 208	91.17 180	95.60 125	96.11 214	87.87 115	98.76 195	93.01 158	97.17 164	98.72 126

CLD-MVS		92.98 179	92.53 178	94.32 225	96.12 241	89.20 220	95.28 306	97.47 173	92.66 133	89.90 262	95.62 239	80.58 242	98.40 228	92.73 161	92.40 253	95.38 297
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XVG-OURS		93.72 152	93.35 149	94.80 201	97.07 164	88.61 233	94.79 323	97.46 175	91.97 154	93.99 160	97.86 102	81.74 225	98.88 181	92.64 162	92.67 251	96.92 237

旧先验2							95.94 270		81.66 382	97.34 56			98.82 186	92.26 163

CDPH-MVS		95.97 80	95.38 91	97.77 34	98.93 50	94.44 35	96.35 245	97.88 116	86.98 314	96.65 81	97.89 97	91.99 48	99.47 109	92.26 163	99.46 41	99.39 60

FIs		94.09 137	93.70 132	95.27 173	95.70 256	92.03 110	98.10 51	98.68 13	93.36 104	90.39 245	96.70 174	87.63 120	97.94 291	92.25 165	90.50 287	95.84 270

LPG-MVS_test		92.94 182	92.56 175	94.10 235	96.16 236	88.26 245	97.65 113	97.46 175	91.29 172	90.12 255	97.16 151	79.05 271	98.73 199	92.25 165	91.89 263	95.31 302

LGP-MVS_train				94.10 235	96.16 236	88.26 245		97.46 175	91.29 172	90.12 255	97.16 151	79.05 271	98.73 199	92.25 165	91.89 263	95.31 302

cascas		91.20 260	90.08 273	94.58 212	94.97 300	89.16 223	93.65 365	97.59 156	79.90 393	89.40 278	92.92 353	75.36 314	98.36 235	92.14 168	94.75 213	96.23 252

OPM-MVS		93.28 165	92.76 165	94.82 196	94.63 320	90.77 163	96.65 219	97.18 206	93.72 86	91.68 218	97.26 146	79.33 266	98.63 210	92.13 169	92.28 254	95.07 316
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BP-MVS														92.13 169

HQP-MVS		93.19 169	92.74 168	94.54 214	95.86 248	89.33 213	96.65 219	97.39 191	93.55 92	90.14 249	95.87 221	80.95 234	98.50 221	92.13 169	92.10 260	95.78 275

DP-MVS Recon		95.68 88	95.12 100	97.37 55	99.19 31	94.19 42	97.03 181	98.08 80	88.35 277	95.09 136	97.65 120	89.97 85	99.48 108	92.08 172	98.59 111	98.44 154

VPNet		92.23 211	91.31 218	94.99 186	95.56 262	90.96 155	97.22 169	97.86 122	92.96 125	90.96 236	96.62 186	75.06 316	98.20 247	91.90 173	83.65 364	95.80 273

sss		94.51 122	93.80 130	96.64 81	97.07 164	91.97 112	96.32 248	98.06 88	88.94 255	94.50 148	96.78 169	84.60 163	99.27 130	91.90 173	96.02 183	98.68 130

anonymousdsp		92.16 213	91.55 209	93.97 244	92.58 377	89.55 201	97.51 133	97.42 188	89.42 239	88.40 304	94.84 272	80.66 241	97.88 299	91.87 175	91.28 273	94.48 348

test_fmvs3		83.21 365	83.02 362	83.78 388	86.77 412	68.34 414	96.76 207	94.91 342	86.49 322	84.14 368	89.48 393	36.04 420	91.73 410	91.86 176	80.77 379	91.26 400

ACMP	89.59 10	92.62 194	92.14 189	94.05 238	96.40 223	88.20 248	97.36 153	97.25 205	91.52 163	88.30 308	96.64 179	78.46 283	98.72 202	91.86 176	91.48 269	95.23 309
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		93.66 153	92.92 159	95.87 140	98.24 90	89.88 192	94.58 328	98.49 22	85.06 347	93.78 165	95.78 230	82.86 200	98.67 206	91.77 178	95.71 192	99.07 90

UGNet		94.04 140	93.28 151	96.31 111	96.85 181	91.19 145	97.88 81	97.68 144	94.40 68	93.00 183	96.18 205	73.39 331	99.61 76	91.72 179	98.46 117	98.13 175
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
UniMVSNet_NR-MVSNet		93.37 162	92.67 171	95.47 168	95.34 277	92.83 82	97.17 173	98.58 20	92.98 124	90.13 253	95.80 226	88.37 106	97.85 300	91.71 180	83.93 359	95.73 281

DU-MVS		92.90 184	92.04 191	95.49 165	94.95 302	92.83 82	97.16 174	98.24 50	93.02 118	90.13 253	95.71 233	83.47 183	97.85 300	91.71 180	83.93 359	95.78 275

Effi-MVS+-dtu		93.08 174	93.21 153	92.68 303	96.02 245	83.25 343	97.14 176	96.72 249	93.85 83	91.20 235	93.44 344	83.08 193	98.30 240	91.69 182	95.73 191	96.50 247

UniMVSNet (Re)		93.31 164	92.55 176	95.61 157	95.39 271	93.34 67	97.39 150	98.71 11	93.14 115	90.10 257	94.83 273	87.71 116	98.03 274	91.67 183	83.99 358	95.46 290

LCM-MVSNet-Re		92.50 195	92.52 179	92.44 305	96.82 186	81.89 360	96.92 193	93.71 376	92.41 138	84.30 364	94.60 285	85.08 158	97.03 355	91.51 184	97.36 154	98.40 157

FC-MVSNet-test		93.94 143	93.57 136	95.04 183	95.48 266	91.45 134	98.12 50	98.71 11	93.37 102	90.23 248	96.70 174	87.66 117	97.85 300	91.49 185	90.39 288	95.83 271

PMMVS		92.86 186	92.34 184	94.42 220	94.92 305	86.73 284	94.53 330	96.38 271	84.78 352	94.27 153	95.12 262	83.13 192	98.40 228	91.47 186	96.49 178	98.12 176

Vis-MVSNet		95.23 101	94.81 104	96.51 94	97.18 158	91.58 127	98.26 34	98.12 73	94.38 70	94.90 138	98.15 79	82.28 214	98.92 177	91.45 187	98.58 112	99.01 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		94.15 132	93.51 142	96.06 128	98.27 86	89.38 210	95.18 314	98.48 24	85.60 337	93.76 166	97.11 154	83.15 191	99.61 76	91.33 188	98.72 105	99.19 75

OMC-MVS		95.09 105	94.70 108	96.25 120	98.46 73	91.28 138	96.43 235	97.57 158	92.04 151	94.77 142	97.96 94	87.01 134	99.09 156	91.31 189	96.77 170	98.36 161

MG-MVS		95.61 91	95.38 91	96.31 111	98.42 76	90.53 170	96.04 264	97.48 170	93.47 99	95.67 123	98.10 80	89.17 92	99.25 131	91.27 190	98.77 103	99.13 81

ACMM	89.79 8	92.96 180	92.50 180	94.35 222	96.30 229	88.71 231	97.58 123	97.36 196	91.40 170	90.53 242	96.65 178	79.77 258	98.75 196	91.24 191	91.64 265	95.59 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WTY-MVS		94.71 119	94.02 126	96.79 78	97.71 132	92.05 109	96.59 228	97.35 197	90.61 203	94.64 144	96.93 161	86.41 141	99.39 118	91.20 192	94.71 216	98.94 102

testing11		91.68 230	90.75 244	94.47 216	96.53 211	86.56 290	95.76 281	94.51 356	91.10 184	91.24 233	93.59 338	68.59 365	98.86 182	91.10 193	94.29 221	98.00 185

tt0805		91.09 264	90.07 276	94.16 233	95.61 259	88.31 242	97.56 126	96.51 265	89.56 232	89.17 287	95.64 238	67.08 378	98.38 234	91.07 194	88.44 306	95.80 273

Anonymous20240529		91.98 219	90.73 246	95.73 150	98.14 102	89.40 209	97.99 62	97.72 139	79.63 394	93.54 170	97.41 138	69.94 355	99.56 92	91.04 195	91.11 276	98.22 167

AUN-MVS		91.76 226	90.75 244	94.81 198	97.00 174	88.57 235	96.65 219	96.49 266	89.63 230	92.15 202	96.12 210	78.66 280	98.50 221	90.83 196	79.18 385	97.36 221

mvsany_test3		83.59 363	82.44 367	87.03 382	83.80 415	73.82 404	93.70 361	90.92 403	86.42 323	82.51 380	90.26 386	46.76 415	95.71 379	90.82 197	76.76 392	91.57 395

CANet_DTU		94.37 125	93.65 134	96.55 88	96.46 220	92.13 107	96.21 257	96.67 256	94.38 70	93.53 171	97.03 159	79.34 265	99.71 53	90.76 198	98.45 118	97.82 199

ab-mvs		93.57 156	92.55 176	96.64 81	97.28 154	91.96 114	95.40 299	97.45 180	89.81 227	93.22 181	96.28 201	79.62 262	99.46 110	90.74 199	93.11 243	98.50 144

CostFormer		91.18 263	90.70 248	92.62 304	94.84 310	81.76 361	94.09 349	94.43 357	84.15 358	92.72 190	93.77 329	79.43 264	98.20 247	90.70 200	92.18 258	97.90 189

Anonymous202405211		92.07 216	90.83 240	95.76 145	98.19 98	88.75 230	97.58 123	95.00 336	86.00 332	93.64 167	97.45 134	66.24 383	99.53 98	90.68 201	92.71 249	99.01 94

testing99		91.62 232	90.72 247	94.32 225	96.48 217	86.11 302	95.81 277	94.76 348	91.55 162	91.75 216	93.44 344	68.55 366	98.82 186	90.43 202	93.69 236	98.04 183

tpmrst		91.44 245	91.32 217	91.79 327	95.15 293	79.20 390	93.42 370	95.37 318	88.55 271	93.49 172	93.67 335	82.49 210	98.27 242	90.41 203	89.34 297	97.90 189

thisisatest0530		93.03 177	92.21 188	95.49 165	97.07 164	89.11 224	97.49 140	92.19 392	90.16 216	94.09 158	96.41 195	76.43 306	99.05 166	90.38 204	95.68 193	98.31 163

UA-Net		95.95 81	95.53 82	97.20 67	97.67 133	92.98 80	97.65 113	98.13 71	94.81 46	96.61 83	98.35 58	88.87 96	99.51 103	90.36 205	97.35 155	99.11 85

UniMVSNet_ETH3D		91.34 253	90.22 269	94.68 206	94.86 309	87.86 259	97.23 168	97.46 175	87.99 286	89.90 262	96.92 164	66.35 381	98.23 244	90.30 206	90.99 279	97.96 186

tttt0517		92.96 180	92.33 185	94.87 195	97.11 162	87.16 275	97.97 69	92.09 393	90.63 201	93.88 164	97.01 160	76.50 303	99.06 165	90.29 207	95.45 198	98.38 159

testing91		91.90 222	91.02 230	94.53 215	96.54 209	86.55 291	95.86 274	95.64 307	91.77 157	91.89 211	93.47 343	69.94 355	98.86 182	90.23 208	93.86 235	98.18 170

FA-MVS(test-final)		93.52 158	92.92 159	95.31 172	96.77 192	88.54 237	94.82 322	96.21 282	89.61 231	94.20 155	95.25 256	83.24 187	99.14 149	90.01 209	96.16 182	98.25 165

IS-MVSNet		94.90 112	94.52 116	96.05 129	97.67 133	90.56 169	98.44 21	96.22 280	93.21 107	93.99 160	97.74 113	85.55 153	98.45 225	89.98 210	97.86 139	99.14 80

miper_enhance_ethall		91.54 240	91.01 231	93.15 284	95.35 276	87.07 277	93.97 351	96.90 237	86.79 318	89.17 287	93.43 347	86.55 138	97.64 320	89.97 211	86.93 320	94.74 342

EI-MVSNet		93.03 177	92.88 161	93.48 271	95.77 254	86.98 278	96.44 233	97.12 211	90.66 199	91.30 228	97.64 123	86.56 137	98.05 270	89.91 212	90.55 285	95.41 292

IterMVS-LS		92.29 207	91.94 196	93.34 276	96.25 230	86.97 279	96.57 231	97.05 221	90.67 197	89.50 277	94.80 275	86.59 136	97.64 320	89.91 212	86.11 328	95.40 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl22		91.21 259	90.56 254	93.14 285	96.09 243	86.80 281	94.41 336	96.58 263	87.80 294	88.58 301	93.99 322	80.85 239	97.62 323	89.87 214	86.93 320	94.99 319

CDS-MVSNet		94.14 135	93.54 138	95.93 138	96.18 234	91.46 133	96.33 247	97.04 223	88.97 254	93.56 168	96.51 190	87.55 121	97.89 298	89.80 215	95.95 185	98.44 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WR-MVS		92.34 203	91.53 210	94.77 203	95.13 295	90.83 160	96.40 241	97.98 106	91.88 155	89.29 283	95.54 244	82.50 209	97.80 306	89.79 216	85.27 338	95.69 282

NR-MVSNet		92.34 203	91.27 221	95.53 162	94.95 302	93.05 77	97.39 150	98.07 85	92.65 134	84.46 362	95.71 233	85.00 159	97.77 310	89.71 217	83.52 365	95.78 275

Anonymous20231211		90.63 284	89.42 299	94.27 230	98.24 90	89.19 222	98.05 57	97.89 114	79.95 392	88.25 311	94.96 265	72.56 334	98.13 253	89.70 218	85.14 340	95.49 286

testdata				95.46 169	98.18 100	88.90 228		97.66 145	82.73 374	97.03 67	98.07 83	90.06 82	98.85 184	89.67 219	98.98 95	98.64 132

Baseline_NR-MVSNet		91.20 260	90.62 250	92.95 291	93.83 345	88.03 253	97.01 186	95.12 332	88.42 275	89.70 268	95.13 261	83.47 183	97.44 339	89.66 220	83.24 367	93.37 370

DPM-MVS		95.69 87	94.92 102	98.01 20	98.08 108	95.71 9	95.27 308	97.62 152	90.43 210	95.55 126	97.07 156	91.72 50	99.50 106	89.62 221	98.94 97	98.82 121

XXY-MVS		92.16 213	91.23 223	94.95 192	94.75 314	90.94 156	97.47 141	97.43 187	89.14 246	88.90 291	96.43 194	79.71 259	98.24 243	89.56 222	87.68 312	95.67 283

miper_ehance_all_eth		91.59 234	91.13 227	92.97 290	95.55 263	86.57 289	94.47 332	96.88 240	87.77 296	88.88 293	94.01 320	86.22 143	97.54 329	89.49 223	86.93 320	94.79 338

WBMVS		90.69 283	89.99 279	92.81 297	96.48 217	85.00 320	95.21 313	96.30 275	89.46 237	89.04 290	94.05 319	72.45 335	97.82 304	89.46 224	87.41 317	95.61 284

XVG-ACMP-BASELINE		90.93 273	90.21 270	93.09 286	94.31 333	85.89 303	95.33 303	97.26 203	91.06 185	89.38 279	95.44 248	68.61 364	98.60 213	89.46 224	91.05 277	94.79 338

thisisatest0515		92.29 207	91.30 219	95.25 174	96.60 201	88.90 228	94.36 338	92.32 391	87.92 288	93.43 174	94.57 286	77.28 298	99.00 170	89.42 226	95.86 188	97.86 195

c3_l		91.38 248	90.89 234	92.88 294	95.58 261	86.30 296	94.68 325	96.84 244	88.17 281	88.83 296	94.23 310	85.65 152	97.47 336	89.36 227	84.63 348	94.89 328

AdaColmap		94.34 126	93.68 133	96.31 111	98.59 69	91.68 122	96.59 228	97.81 130	89.87 222	92.15 202	97.06 157	83.62 182	99.54 96	89.34 228	98.07 133	97.70 204

TranMVSNet+NR-MVSNet		92.50 195	91.63 206	95.14 178	94.76 313	92.07 108	97.53 131	98.11 76	92.90 128	89.56 274	96.12 210	83.16 190	97.60 325	89.30 229	83.20 368	95.75 279

D2MVS		91.30 255	90.95 233	92.35 308	94.71 317	85.52 308	96.18 259	98.21 54	88.89 257	86.60 345	93.82 327	79.92 256	97.95 290	89.29 230	90.95 280	93.56 366

1314		92.81 190	92.03 192	95.14 178	95.33 280	89.52 204	96.04 264	97.44 184	87.72 299	86.25 348	95.33 250	83.84 177	98.79 190	89.26 231	97.05 166	97.11 231

v2v482		91.59 234	90.85 238	93.80 255	93.87 344	88.17 250	96.94 192	96.88 240	89.54 233	89.53 275	94.90 269	81.70 226	98.02 275	89.25 232	85.04 344	95.20 310

114514_t		93.95 142	93.06 155	96.63 83	99.07 37	91.61 124	97.46 143	97.96 108	77.99 400	93.00 183	97.57 128	86.14 147	99.33 122	89.22 233	99.15 83	98.94 102

PAPM_NR		95.01 106	94.59 110	96.26 117	98.89 54	90.68 167	97.24 164	97.73 137	91.80 156	92.93 188	96.62 186	89.13 93	99.14 149	89.21 234	97.78 142	98.97 98

baseline1		92.82 189	91.90 197	95.55 161	97.20 157	90.77 163	97.19 171	94.58 353	92.20 144	92.36 195	96.34 199	84.16 173	98.21 246	89.20 235	83.90 362	97.68 205

IB-MVS	87.33 17	89.91 302	88.28 318	94.79 202	95.26 287	87.70 263	95.12 316	93.95 371	89.35 241	87.03 337	92.49 360	70.74 347	99.19 137	89.18 236	81.37 376	97.49 215
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
HY-MVS	89.66 9	93.87 146	92.95 158	96.63 83	97.10 163	92.49 93	95.64 289	96.64 257	89.05 250	93.00 183	95.79 229	85.77 151	99.45 112	89.16 237	94.35 218	97.96 186

V42		91.58 236	90.87 235	93.73 258	94.05 339	88.50 239	97.32 158	96.97 228	88.80 264	89.71 267	94.33 302	82.54 208	98.05 270	89.01 238	85.07 342	94.64 346

sd_testset		93.10 173	92.45 182	95.05 182	98.09 105	89.21 219	96.89 195	97.64 149	93.18 112	91.79 214	97.28 143	75.35 315	98.65 208	88.99 239	92.84 246	97.28 226

OurMVSNet-221017-0		90.51 288	90.19 271	91.44 336	93.41 359	81.25 364	96.98 189	96.28 276	91.68 160	86.55 346	96.30 200	74.20 324	97.98 279	88.96 240	87.40 318	95.09 315

API-MVS		94.84 115	94.49 117	95.90 139	97.90 123	92.00 111	97.80 94	97.48 170	89.19 245	94.81 140	96.71 172	88.84 97	99.17 142	88.91 241	98.76 104	96.53 245

test-LLR		91.42 246	91.19 225	92.12 315	94.59 321	80.66 370	94.29 343	92.98 383	91.11 182	90.76 240	92.37 363	79.02 273	98.07 267	88.81 242	96.74 171	97.63 206

test-mter		90.19 298	89.54 296	92.12 315	94.59 321	80.66 370	94.29 343	92.98 383	87.68 300	90.76 240	92.37 363	67.67 370	98.07 267	88.81 242	96.74 171	97.63 206

eth_miper_zixun_eth		91.02 268	90.59 252	92.34 310	95.33 280	84.35 329	94.10 348	96.90 237	88.56 270	88.84 295	94.33 302	84.08 174	97.60 325	88.77 244	84.37 355	95.06 317

myMVS_eth3d28		91.52 241	90.97 232	93.17 283	96.91 177	83.24 344	95.61 290	94.96 340	92.24 141	91.98 208	93.28 348	69.31 359	98.40 228	88.71 245	95.68 193	97.88 191

TAMVS		94.01 141	93.46 144	95.64 154	96.16 236	90.45 173	96.71 212	96.89 239	89.27 243	93.46 173	96.92 164	87.29 130	97.94 291	88.70 246	95.74 190	98.53 140

Patchmatch-RL test		87.38 334	86.24 338	90.81 349	88.74 405	78.40 394	88.12 413	93.17 381	87.11 313	82.17 382	89.29 394	81.95 221	95.60 383	88.64 247	77.02 390	98.41 156

baseline2		91.63 231	90.86 236	93.94 248	94.33 331	86.32 295	95.92 271	91.64 397	89.37 240	86.94 341	94.69 279	81.62 227	98.69 204	88.64 247	94.57 217	96.81 240

TESTMET0.1,1		90.06 300	89.42 299	91.97 318	94.41 329	80.62 372	94.29 343	91.97 395	87.28 310	90.44 244	92.47 362	68.79 362	97.67 317	88.50 249	96.60 176	97.61 210

Vis-MVSNet (Re-imp)		94.15 132	93.88 129	94.95 192	97.61 141	87.92 256	98.10 51	95.80 297	92.22 142	93.02 182	97.45 134	84.53 165	97.91 297	88.24 250	97.97 136	99.02 91

1112_ss		93.37 162	92.42 183	96.21 121	97.05 169	90.99 153	96.31 249	96.72 249	86.87 317	89.83 265	96.69 176	86.51 139	99.14 149	88.12 251	93.67 237	98.50 144

UBG		91.55 238	90.76 242	93.94 248	96.52 213	85.06 319	95.22 311	94.54 354	90.47 209	91.98 208	92.71 355	72.02 336	98.74 198	88.10 252	95.26 202	98.01 184

CVMVSNet		91.23 258	91.75 202	89.67 365	95.77 254	74.69 401	96.44 233	94.88 344	85.81 334	92.18 201	97.64 123	79.07 270	95.58 384	88.06 253	95.86 188	98.74 125

MAR-MVS		94.22 128	93.46 144	96.51 94	98.00 114	92.19 106	97.67 110	97.47 173	88.13 285	93.00 183	95.84 223	84.86 161	99.51 103	87.99 254	98.17 130	97.83 198
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
原ACMM1				96.38 107	98.59 69	91.09 152		97.89 114	87.41 306	95.22 133	97.68 116	90.25 80	99.54 96	87.95 255	99.12 87	98.49 146

CP-MVSNet		91.89 223	91.24 222	93.82 254	95.05 298	88.57 235	97.82 91	98.19 61	91.70 159	88.21 312	95.76 231	81.96 220	97.52 333	87.86 256	84.65 347	95.37 298

v148		90.99 269	90.38 258	92.81 297	93.83 345	85.80 304	96.78 206	96.68 254	89.45 238	88.75 298	93.93 324	82.96 199	97.82 304	87.83 257	83.25 366	94.80 336

v1144		91.37 250	90.60 251	93.68 263	93.89 343	88.23 247	96.84 200	97.03 225	88.37 276	89.69 269	94.39 297	82.04 218	97.98 279	87.80 258	85.37 335	94.84 330

DIV-MVS_self_test		90.97 271	90.33 259	92.88 294	95.36 275	86.19 300	94.46 334	96.63 260	87.82 292	88.18 313	94.23 310	82.99 196	97.53 331	87.72 259	85.57 332	94.93 324

gm-plane-assit					93.22 363	78.89 393			84.82 351		93.52 340		98.64 209	87.72 259

GeoE		93.89 145	93.28 151	95.72 151	96.96 176	89.75 195	98.24 38	96.92 236	89.47 236	92.12 204	97.21 149	84.42 167	98.39 233	87.71 261	96.50 177	99.01 94

cl____		90.96 272	90.32 260	92.89 293	95.37 274	86.21 299	94.46 334	96.64 257	87.82 292	88.15 314	94.18 313	82.98 197	97.54 329	87.70 262	85.59 331	94.92 326

pmmvs4		90.93 273	89.85 284	94.17 232	93.34 361	90.79 162	94.60 327	96.02 287	84.62 353	87.45 325	95.15 259	81.88 223	97.45 338	87.70 262	87.87 310	94.27 358

Test_1112_low_res		92.84 188	91.84 199	95.85 143	97.04 170	89.97 189	95.53 294	96.64 257	85.38 340	89.65 271	95.18 258	85.86 149	99.10 153	87.70 262	93.58 242	98.49 146

无先验							95.79 279	97.87 118	83.87 363				99.65 65	87.68 265		98.89 113

Fast-Effi-MVS+		93.46 159	92.75 167	95.59 158	96.77 192	90.03 183	96.81 203	97.13 210	88.19 280	91.30 228	94.27 307	86.21 144	98.63 210	87.66 266	96.46 180	98.12 176

CNLPA		94.28 127	93.53 139	96.52 90	98.38 81	92.55 91	96.59 228	96.88 240	90.13 218	91.91 210	97.24 147	85.21 156	99.09 156	87.64 267	97.83 140	97.92 188

v8		91.29 257	90.53 255	93.57 268	94.15 335	88.12 252	97.34 155	97.06 220	88.99 252	88.32 307	94.26 309	83.08 193	98.01 276	87.62 268	83.92 361	94.57 347

pmmvs5		89.86 307	88.87 311	92.82 296	92.86 370	86.23 298	96.26 252	95.39 316	84.24 357	87.12 333	94.51 290	74.27 323	97.36 345	87.61 269	87.57 313	94.86 329

Fast-Effi-MVS+-dtu		92.29 207	91.99 194	93.21 282	95.27 284	85.52 308	97.03 181	96.63 260	92.09 149	89.11 289	95.14 260	80.33 248	98.08 263	87.54 270	94.74 214	96.03 265

OpenMVS	89.19 12	92.86 186	91.68 205	96.40 104	95.34 277	92.73 86	98.27 32	98.12 73	84.86 350	85.78 351	97.75 112	78.89 278	99.74 47	87.50 271	98.65 107	96.73 242

miper_lstm_enhance		90.50 289	90.06 277	91.83 324	95.33 280	83.74 337	93.86 357	96.70 253	87.56 303	87.79 319	93.81 328	83.45 185	96.92 360	87.39 272	84.62 349	94.82 333

IterMVS-SCA-FT		90.31 291	89.81 286	91.82 325	95.52 264	84.20 332	94.30 342	96.15 284	90.61 203	87.39 328	94.27 307	75.80 310	96.44 368	87.34 273	86.88 324	94.82 333

PLC	91.00 6	94.11 136	93.43 146	96.13 125	98.58 71	91.15 151	96.69 215	97.39 191	87.29 309	91.37 224	96.71 172	88.39 105	99.52 102	87.33 274	97.13 165	97.73 202
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		90.25 294	89.74 291	91.76 330	93.92 341	79.73 384	93.98 350	93.54 377	88.28 278	91.99 207	93.25 349	77.51 297	97.44 339	87.30 275	87.94 309	98.12 176

GA-MVS		91.38 248	90.31 261	94.59 208	94.65 319	87.62 264	94.34 339	96.19 283	90.73 193	90.35 246	93.83 325	71.84 338	97.96 286	87.22 276	93.61 240	98.21 168

BH-untuned		92.94 182	92.62 173	93.92 251	97.22 155	86.16 301	96.40 241	96.25 279	90.06 219	89.79 266	96.17 207	83.19 189	98.35 236	87.19 277	97.27 160	97.24 228

v144192		91.06 266	90.28 263	93.39 274	93.66 351	87.23 272	96.83 201	97.07 218	87.43 305	89.69 269	94.28 306	81.48 228	98.00 277	87.18 278	84.92 346	94.93 324

RPSCF		90.75 278	90.86 236	90.42 356	96.84 182	76.29 399	95.61 290	96.34 272	83.89 361	91.38 223	97.87 100	76.45 304	98.78 191	87.16 279	92.23 255	96.20 254

test_f		80.57 372	79.62 374	83.41 389	83.38 418	67.80 416	93.57 368	93.72 375	80.80 389	77.91 399	87.63 405	33.40 421	92.08 409	87.14 280	79.04 387	90.34 404

PS-CasMVS		91.55 238	90.84 239	93.69 262	94.96 301	88.28 244	97.84 86	98.24 50	91.46 166	88.04 316	95.80 226	79.67 260	97.48 335	87.02 281	84.54 353	95.31 302

pm-mvs1		90.72 280	89.65 294	93.96 245	94.29 334	89.63 196	97.79 95	96.82 245	89.07 248	86.12 350	95.48 247	78.61 281	97.78 308	86.97 282	81.67 374	94.46 349

IterMVS		90.15 299	89.67 292	91.61 332	95.48 266	83.72 338	94.33 340	96.12 285	89.99 220	87.31 331	94.15 315	75.78 312	96.27 371	86.97 282	86.89 323	94.83 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		93.58 155	92.98 157	95.37 171	98.40 78	88.98 226	97.18 172	97.29 202	87.75 298	90.49 243	97.10 155	85.21 156	99.50 106	86.70 284	96.72 173	97.63 206

PVSNet	86.66 18	92.24 210	91.74 204	93.73 258	97.77 129	83.69 340	92.88 380	96.72 249	87.91 289	93.00 183	94.86 271	78.51 282	99.05 166	86.53 285	97.45 152	98.47 149

v1192		91.07 265	90.23 267	93.58 267	93.70 348	87.82 261	96.73 209	97.07 218	87.77 296	89.58 272	94.32 304	80.90 238	97.97 282	86.52 286	85.48 333	94.95 320

新几何1				97.32 57	98.60 68	93.59 59		97.75 134	81.58 383	95.75 119	97.85 103	90.04 83	99.67 63	86.50 287	99.13 85	98.69 129

v10		91.04 267	90.23 267	93.49 270	94.12 336	88.16 251	97.32 158	97.08 216	88.26 279	88.29 309	94.22 312	82.17 217	97.97 282	86.45 288	84.12 357	94.33 354

v1921920		90.85 275	90.03 278	93.29 278	93.55 352	86.96 280	96.74 208	97.04 223	87.36 307	89.52 276	94.34 301	80.23 250	97.97 282	86.27 289	85.21 339	94.94 322

MDTV_nov1_ep13_2view						70.35 410	93.10 377		83.88 362	93.55 169		82.47 211		86.25 290		98.38 159

test_post1							92.81 382				16.58 430	80.53 243	97.68 316	86.20 291

SCA		91.84 224	91.18 226	93.83 253	95.59 260	84.95 323	94.72 324	95.58 310	90.82 189	92.25 200	93.69 332	75.80 310	98.10 258	86.20 291	95.98 184	98.45 151

PAPR		94.18 129	93.42 148	96.48 97	97.64 137	91.42 135	95.55 292	97.71 143	88.99 252	92.34 198	95.82 225	89.19 91	99.11 152	86.14 293	97.38 153	98.90 109

GBi-Net		91.35 251	90.27 264	94.59 208	96.51 214	91.18 147	97.50 134	96.93 232	88.82 261	89.35 280	94.51 290	73.87 325	97.29 348	86.12 294	88.82 300	95.31 302

test1		91.35 251	90.27 264	94.59 208	96.51 214	91.18 147	97.50 134	96.93 232	88.82 261	89.35 280	94.51 290	73.87 325	97.29 348	86.12 294	88.82 300	95.31 302

FMVSNet3		91.78 225	90.69 249	95.03 184	96.53 211	92.27 101	97.02 183	96.93 232	89.79 228	89.35 280	94.65 283	77.01 299	97.47 336	86.12 294	88.82 300	95.35 299

EPMVS		90.70 281	89.81 286	93.37 275	94.73 316	84.21 331	93.67 364	88.02 412	89.50 235	92.38 194	93.49 341	77.82 295	97.78 308	86.03 297	92.68 250	98.11 179

MVS		91.71 227	90.44 256	95.51 163	95.20 290	91.59 126	96.04 264	97.45 180	73.44 410	87.36 329	95.60 240	85.42 154	99.10 153	85.97 298	97.46 148	95.83 271

testdata2													99.67 63	85.96 299

K. test v3		87.64 333	86.75 335	90.32 358	93.02 367	79.48 388	96.61 225	92.08 394	90.66 199	80.25 391	94.09 317	67.21 374	96.65 366	85.96 299	80.83 378	94.83 331

WR-MVS_H		92.00 218	91.35 215	93.95 246	95.09 297	89.47 205	98.04 58	98.68 13	91.46 166	88.34 306	94.68 280	85.86 149	97.56 327	85.77 301	84.24 356	94.82 333

gg-mvs-nofinetune		87.82 330	85.61 343	94.44 218	94.46 326	89.27 218	91.21 395	84.61 421	80.88 386	89.89 264	74.98 417	71.50 340	97.53 331	85.75 302	97.21 162	96.51 246

tpm2		89.96 301	89.21 303	92.23 314	94.91 307	81.25 364	93.78 359	94.42 358	80.62 390	91.56 219	93.44 344	76.44 305	97.94 291	85.60 303	92.08 262	97.49 215

v1240		90.70 281	89.85 284	93.23 280	93.51 355	86.80 281	96.61 225	97.02 226	87.16 312	89.58 272	94.31 305	79.55 263	97.98 279	85.52 304	85.44 334	94.90 327

PEN-MVS		91.20 260	90.44 256	93.48 271	94.49 325	87.91 258	97.76 97	98.18 63	91.29 172	87.78 320	95.74 232	80.35 247	97.33 346	85.46 305	82.96 369	95.19 313

QAPM		93.45 160	92.27 186	96.98 77	96.77 192	92.62 88	98.39 24	98.12 73	84.50 355	88.27 310	97.77 111	82.39 213	99.81 30	85.40 306	98.81 101	98.51 143

EU-MVSNet		88.72 322	88.90 310	88.20 375	93.15 365	74.21 403	96.63 224	94.22 366	85.18 344	87.32 330	95.97 216	76.16 307	94.98 390	85.27 307	86.17 326	95.41 292

BH-w/o		92.14 215	91.75 202	93.31 277	96.99 175	85.73 305	95.67 284	95.69 303	88.73 266	89.26 285	94.82 274	82.97 198	98.07 267	85.26 308	96.32 181	96.13 261

FMVSNet2		91.31 254	90.08 273	94.99 186	96.51 214	92.21 103	97.41 145	96.95 230	88.82 261	88.62 299	94.75 277	73.87 325	97.42 341	85.20 309	88.55 305	95.35 299

PM-MVS		83.48 364	81.86 370	88.31 374	87.83 409	77.59 396	93.43 369	91.75 396	86.91 315	80.63 387	89.91 390	44.42 416	95.84 377	85.17 310	76.73 393	91.50 397

LF4IMVS		87.94 329	87.25 326	89.98 362	92.38 382	80.05 382	94.38 337	95.25 326	87.59 302	84.34 363	94.74 278	64.31 390	97.66 319	84.83 311	87.45 314	92.23 388

PatchmatchNet		91.91 221	91.35 215	93.59 266	95.38 272	84.11 333	93.15 375	95.39 316	89.54 233	92.10 205	93.68 334	82.82 202	98.13 253	84.81 312	95.32 200	98.52 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs6		87.81 331	86.19 339	92.69 302	91.32 387	86.30 296	97.34 155	96.41 270	80.59 391	84.05 371	94.37 299	67.37 373	97.67 317	84.75 313	79.51 384	94.09 361

v7n		90.76 277	89.86 283	93.45 273	93.54 353	87.60 265	97.70 109	97.37 194	88.85 258	87.65 322	94.08 318	81.08 233	98.10 258	84.68 314	83.79 363	94.66 345

SixPastTwentyTwo		89.15 315	88.54 315	90.98 344	93.49 356	80.28 378	96.70 213	94.70 349	90.78 190	84.15 367	95.57 241	71.78 339	97.71 315	84.63 315	85.07 342	94.94 322

TDRefinement		86.53 342	84.76 354	91.85 323	82.23 420	84.25 330	96.38 243	95.35 319	84.97 349	84.09 369	94.94 266	65.76 387	98.34 239	84.60 316	74.52 398	92.97 373

ACMH	87.59 16	90.53 286	89.42 299	93.87 252	96.21 231	87.92 256	97.24 164	96.94 231	88.45 274	83.91 372	96.27 202	71.92 337	98.62 212	84.43 317	89.43 296	95.05 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	87.92 14	90.20 297	89.18 304	93.25 279	96.48 217	86.45 293	96.99 188	96.68 254	88.83 260	84.79 361	96.22 204	70.16 352	98.53 219	84.42 318	88.04 308	94.77 341

test_vis3_rt		72.73 378	70.55 381	79.27 392	80.02 421	68.13 415	93.92 355	74.30 429	76.90 403	58.99 420	73.58 420	20.29 429	95.37 387	84.16 319	72.80 403	74.31 417

FE-MVS		92.05 217	91.05 229	95.08 181	96.83 184	87.93 255	93.91 356	95.70 301	86.30 326	94.15 157	94.97 264	76.59 302	99.21 135	84.10 320	96.86 167	98.09 180

MS-PatchMatch		90.27 293	89.77 288	91.78 328	94.33 331	84.72 326	95.55 292	96.73 248	86.17 330	86.36 347	95.28 253	71.28 342	97.80 306	84.09 321	98.14 131	92.81 376

PatchMatch-RL		92.90 184	92.02 193	95.56 159	98.19 98	90.80 161	95.27 308	97.18 206	87.96 287	91.86 213	95.68 236	80.44 245	98.99 171	84.01 322	97.54 147	96.89 238

lessismore_v0				90.45 355	91.96 385	79.09 392		87.19 415		80.32 390	94.39 297	66.31 382	97.55 328	84.00 323	76.84 391	94.70 343

UWE-MVS		89.91 302	89.48 298	91.21 340	95.88 247	78.23 395	94.91 321	90.26 405	89.11 247	92.35 197	94.52 289	68.76 363	97.96 286	83.95 324	95.59 196	97.42 219

CMPMVS	62.92 21	85.62 356	84.92 352	87.74 378	89.14 400	73.12 408	94.17 346	96.80 246	73.98 407	73.65 406	94.93 267	66.36 380	97.61 324	83.95 324	91.28 273	92.48 384
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVP-Stereo		90.74 279	90.08 273	92.71 301	93.19 364	88.20 248	95.86 274	96.27 277	86.07 331	84.86 360	94.76 276	77.84 294	97.75 312	83.88 326	98.01 135	92.17 391
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LS3D		93.57 156	92.61 174	96.47 98	97.59 143	91.61 124	97.67 110	97.72 139	85.17 345	90.29 247	98.34 61	84.60 163	99.73 49	83.85 327	98.27 125	98.06 182

DTE-MVSNet		90.56 285	89.75 290	93.01 288	93.95 340	87.25 270	97.64 117	97.65 147	90.74 192	87.12 333	95.68 236	79.97 255	97.00 358	83.33 328	81.66 375	94.78 340

BH-RMVSNet		92.72 193	91.97 195	94.97 190	97.16 159	87.99 254	96.15 260	95.60 308	90.62 202	91.87 212	97.15 153	78.41 284	98.57 217	83.16 329	97.60 146	98.36 161

pmmvs-eth3d		86.22 348	84.45 356	91.53 333	88.34 407	87.25 270	94.47 332	95.01 335	83.47 369	79.51 394	89.61 392	69.75 357	95.71 379	83.13 330	76.73 393	91.64 393

FMVSNet1		89.88 305	88.31 317	94.59 208	95.41 270	91.18 147	97.50 134	96.93 232	86.62 320	87.41 327	94.51 290	65.94 386	97.29 348	83.04 331	87.43 315	95.31 302

testing222		90.31 291	88.96 308	94.35 222	96.54 209	87.29 267	95.50 295	93.84 374	90.97 187	91.75 216	92.96 352	62.18 398	98.00 277	82.86 332	94.08 228	97.76 201

MDTV_nov1_ep13			90.76 242		95.22 288	80.33 376	93.03 378	95.28 323	88.14 284	92.84 189	93.83 325	81.34 229	98.08 263	82.86 332	94.34 219

TR-MVS		91.48 244	90.59 252	94.16 233	96.40 223	87.33 266	95.67 284	95.34 322	87.68 300	91.46 222	95.52 245	76.77 301	98.35 236	82.85 334	93.61 240	96.79 241

dmvs_re		90.21 296	89.50 297	92.35 308	95.47 269	85.15 316	95.70 283	94.37 361	90.94 188	88.42 303	93.57 339	74.63 320	95.67 381	82.80 335	89.57 295	96.22 253

JIA-IIPM		88.26 327	87.04 331	91.91 320	93.52 354	81.42 363	89.38 407	94.38 360	80.84 387	90.93 237	80.74 414	79.22 267	97.92 294	82.76 336	91.62 266	96.38 251

PVSNet_0	82.17 19	85.46 357	83.64 360	90.92 345	95.27 284	79.49 387	90.55 399	95.60 308	83.76 365	83.00 379	89.95 389	71.09 343	97.97 282	82.75 337	60.79 419	95.31 302

ambc				86.56 384	83.60 417	70.00 411	85.69 415	94.97 338		80.60 388	88.45 398	37.42 419	96.84 363	82.69 338	75.44 397	92.86 375

USDC		88.94 317	87.83 322	92.27 312	94.66 318	84.96 322	93.86 357	95.90 291	87.34 308	83.40 374	95.56 242	67.43 372	98.19 249	82.64 339	89.67 294	93.66 365

ITE_SJBPF				92.43 306	95.34 277	85.37 313		95.92 289	91.47 165	87.75 321	96.39 197	71.00 344	97.96 286	82.36 340	89.86 292	93.97 362

UnsupCasMVSNet_eth		85.99 351	84.45 356	90.62 353	89.97 395	82.40 356	93.62 366	97.37 194	89.86 223	78.59 397	92.37 363	65.25 389	95.35 388	82.27 341	70.75 405	94.10 359

GG-mvs-BLEND				93.62 264	93.69 349	89.20 220	92.39 387	83.33 423		87.98 318	89.84 391	71.00 344	96.87 362	82.08 342	95.40 199	94.80 336

thres600view7		92.49 197	91.60 207	95.18 176	97.91 122	89.47 205	97.65 113	94.66 350	92.18 148	93.33 176	94.91 268	78.06 291	99.10 153	81.61 343	94.06 232	96.98 233

LTVRE_ROB	88.41 13	90.99 269	89.92 282	94.19 231	96.18 234	89.55 201	96.31 249	97.09 215	87.88 290	85.67 352	95.91 220	78.79 279	98.57 217	81.50 344	89.98 290	94.44 351
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
tpmvs		89.83 308	89.15 305	91.89 322	94.92 305	80.30 377	93.11 376	95.46 315	86.28 327	88.08 315	92.65 356	80.44 245	98.52 220	81.47 345	89.92 291	96.84 239

thres100view900		92.43 198	91.58 208	94.98 188	97.92 121	89.37 211	97.71 106	94.66 350	92.20 144	93.31 177	94.90 269	78.06 291	99.08 159	81.40 346	94.08 228	96.48 248

tfpn200view9		92.38 201	91.52 211	94.95 192	97.85 125	89.29 215	97.41 145	94.88 344	92.19 146	93.27 179	94.46 295	78.17 287	99.08 159	81.40 346	94.08 228	96.48 248

thres400		92.42 199	91.52 211	95.12 180	97.85 125	89.29 215	97.41 145	94.88 344	92.19 146	93.27 179	94.46 295	78.17 287	99.08 159	81.40 346	94.08 228	96.98 233

mvs5depth		86.53 342	85.08 349	90.87 346	88.74 405	82.52 352	91.91 389	94.23 365	86.35 325	87.11 335	93.70 331	66.52 379	97.76 311	81.37 349	75.80 395	92.31 387

ETVMVS		90.52 287	89.14 306	94.67 207	96.81 188	87.85 260	95.91 272	93.97 370	89.71 229	92.34 198	92.48 361	65.41 388	97.96 286	81.37 349	94.27 222	98.21 168

DP-MVS		92.76 191	91.51 213	96.52 90	98.77 56	90.99 153	97.38 152	96.08 286	82.38 376	89.29 283	97.87 100	83.77 178	99.69 59	81.37 349	96.69 174	98.89 113

thres200		92.23 211	91.39 214	94.75 205	97.61 141	89.03 225	96.60 227	95.09 333	92.08 150	93.28 178	94.00 321	78.39 285	99.04 169	81.26 352	94.18 224	96.19 255

CR-MVSNet		90.82 276	89.77 288	93.95 246	94.45 327	87.19 273	90.23 401	95.68 305	86.89 316	92.40 192	92.36 366	80.91 236	97.05 354	81.09 353	93.95 233	97.60 211

ttmdpeth		85.91 353	84.76 354	89.36 369	89.14 400	80.25 379	95.66 287	93.16 382	83.77 364	83.39 375	95.26 255	66.24 383	95.26 389	80.65 354	75.57 396	92.57 380

MSDG		91.42 246	90.24 266	94.96 191	97.15 161	88.91 227	93.69 363	96.32 273	85.72 336	86.93 342	96.47 192	80.24 249	98.98 172	80.57 355	95.05 207	96.98 233

dp		88.90 319	88.26 319	90.81 349	94.58 323	76.62 397	92.85 381	94.93 341	85.12 346	90.07 260	93.07 350	75.81 309	98.12 256	80.53 356	87.42 316	97.71 203

tpm cat1		88.36 325	87.21 328	91.81 326	95.13 295	80.55 373	92.58 384	95.70 301	74.97 406	87.45 325	91.96 373	78.01 293	98.17 251	80.39 357	88.74 303	96.72 243

KD-MVS_self_test		85.95 352	84.95 351	88.96 372	89.55 399	79.11 391	95.13 315	96.42 269	85.91 333	84.07 370	90.48 384	70.03 354	94.82 391	80.04 358	72.94 402	92.94 374

AllTest		90.23 295	88.98 307	93.98 242	97.94 119	86.64 285	96.51 232	95.54 312	85.38 340	85.49 354	96.77 170	70.28 350	99.15 146	80.02 359	92.87 244	96.15 259

TestCases				93.98 242	97.94 119	86.64 285		95.54 312	85.38 340	85.49 354	96.77 170	70.28 350	99.15 146	80.02 359	92.87 244	96.15 259

ADS-MVSNet2		89.45 312	88.59 314	92.03 317	95.86 248	82.26 357	90.93 396	94.32 364	83.23 371	91.28 231	91.81 375	79.01 275	95.99 373	79.52 361	91.39 271	97.84 196

ADS-MVSNet		89.89 304	88.68 313	93.53 269	95.86 248	84.89 324	90.93 396	95.07 334	83.23 371	91.28 231	91.81 375	79.01 275	97.85 300	79.52 361	91.39 271	97.84 196

our_test_3		88.78 321	87.98 321	91.20 342	92.45 380	82.53 351	93.61 367	95.69 303	85.77 335	84.88 359	93.71 330	79.99 254	96.78 365	79.47 363	86.24 325	94.28 357

EPNet_dtu		91.71 227	91.28 220	92.99 289	93.76 347	83.71 339	96.69 215	95.28 323	93.15 114	87.02 338	95.95 218	83.37 186	97.38 344	79.46 364	96.84 168	97.88 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		88.94 317	87.56 323	93.08 287	94.35 330	88.45 241	97.73 101	95.23 327	87.47 304	84.26 365	95.29 251	79.86 257	97.33 346	79.44 365	74.44 399	93.45 369

EG-PatchMatch MVS		87.02 339	85.44 344	91.76 330	92.67 374	85.00 320	96.08 263	96.45 268	83.41 370	79.52 393	93.49 341	57.10 404	97.72 314	79.34 366	90.87 282	92.56 381

Patchmtry		88.64 323	87.25 326	92.78 299	94.09 337	86.64 285	89.82 405	95.68 305	80.81 388	87.63 323	92.36 366	80.91 236	97.03 355	78.86 367	85.12 341	94.67 344

FMVSNet5		87.29 335	85.79 342	91.78 328	94.80 312	87.28 268	95.49 296	95.28 323	84.09 359	83.85 373	91.82 374	62.95 394	94.17 396	78.48 368	85.34 337	93.91 363

COLMAP_ROB	87.81 15	90.40 290	89.28 302	93.79 256	97.95 118	87.13 276	96.92 193	95.89 293	82.83 373	86.88 344	97.18 150	73.77 328	99.29 129	78.44 369	93.62 239	94.95 320
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		86.42 345	85.44 344	89.34 370	90.33 392	79.79 383	96.73 209	95.92 289	83.71 366	83.25 376	91.36 379	63.92 391	96.01 372	78.39 370	85.36 336	92.22 389

test0.0.03 1		89.37 314	88.70 312	91.41 337	92.47 379	85.63 306	95.22 311	92.70 388	91.11 182	86.91 343	93.65 336	79.02 273	93.19 407	78.00 371	89.18 298	95.41 292

MIMVSNet		88.50 324	86.76 334	93.72 260	94.84 310	87.77 262	91.39 391	94.05 367	86.41 324	87.99 317	92.59 359	63.27 392	95.82 378	77.44 372	92.84 246	97.57 213

MDA-MVSNet_test_wron		85.87 354	84.23 358	90.80 351	92.38 382	82.57 350	93.17 373	95.15 330	82.15 377	67.65 412	92.33 369	78.20 286	95.51 385	77.33 373	79.74 381	94.31 356

YYNet1		85.87 354	84.23 358	90.78 352	92.38 382	82.46 355	93.17 373	95.14 331	82.12 378	67.69 410	92.36 366	78.16 289	95.50 386	77.31 374	79.73 382	94.39 352

UnsupCasMVSNet_bld		82.13 370	79.46 375	90.14 360	88.00 408	82.47 354	90.89 398	96.62 262	78.94 397	75.61 401	84.40 412	56.63 405	96.31 370	77.30 375	66.77 413	91.63 394

KD-MVS_2432*1600		84.81 360	82.64 364	91.31 338	91.07 389	85.34 314	91.22 393	95.75 299	85.56 338	83.09 377	90.21 387	67.21 374	95.89 374	77.18 376	62.48 417	92.69 377

miper_refine_blended		84.81 360	82.64 364	91.31 338	91.07 389	85.34 314	91.22 393	95.75 299	85.56 338	83.09 377	90.21 387	67.21 374	95.89 374	77.18 376	62.48 417	92.69 377

PCF-MVS	89.48 11	91.56 237	89.95 280	96.36 109	96.60 201	92.52 92	92.51 385	97.26 203	79.41 395	88.90 291	96.56 188	84.04 176	99.55 94	77.01 378	97.30 159	97.01 232
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		89.88 305	89.56 295	90.82 348	94.57 324	83.06 346	95.65 288	92.85 385	87.86 291	90.83 239	94.10 316	79.66 261	96.88 361	76.34 379	94.19 223	92.54 382

testgi		87.97 328	87.21 328	90.24 359	92.86 370	80.76 368	96.67 218	94.97 338	91.74 158	85.52 353	95.83 224	62.66 396	94.47 394	76.25 380	88.36 307	95.48 287

TinyColmap		86.82 340	85.35 347	91.21 340	94.91 307	82.99 347	93.94 353	94.02 369	83.58 367	81.56 383	94.68 280	62.34 397	98.13 253	75.78 381	87.35 319	92.52 383

ppachtmachnet_test		88.35 326	87.29 325	91.53 333	92.45 380	83.57 341	93.75 360	95.97 288	84.28 356	85.32 357	94.18 313	79.00 277	96.93 359	75.71 382	84.99 345	94.10 359

PAPM		91.52 241	90.30 262	95.20 175	95.30 283	89.83 193	93.38 371	96.85 243	86.26 328	88.59 300	95.80 226	84.88 160	98.15 252	75.67 383	95.93 186	97.63 206

WAC-MVS						79.53 385								75.56 384

myMVS_eth3d		87.18 336	86.38 337	89.58 366	95.16 291	79.53 385	95.00 318	93.93 372	88.55 271	86.96 339	91.99 371	56.23 406	94.00 398	75.47 385	94.11 225	95.20 310

CL-MVSNet_self_test		86.31 347	85.15 348	89.80 364	88.83 403	81.74 362	93.93 354	96.22 280	86.67 319	85.03 358	90.80 382	78.09 290	94.50 392	74.92 386	71.86 404	93.15 372

tfpnnormal		89.70 310	88.40 316	93.60 265	95.15 293	90.10 182	97.56 126	98.16 67	87.28 310	86.16 349	94.63 284	77.57 296	98.05 270	74.48 387	84.59 351	92.65 379

DSMNet-mixed		86.34 346	86.12 341	87.00 383	89.88 396	70.43 409	94.93 320	90.08 406	77.97 401	85.42 356	92.78 354	74.44 322	93.96 400	74.43 388	95.14 203	96.62 244

Patchmatch-test		89.42 313	87.99 320	93.70 261	95.27 284	85.11 317	88.98 408	94.37 361	81.11 384	87.10 336	93.69 332	82.28 214	97.50 334	74.37 389	94.76 212	98.48 148

LCM-MVSNet		72.55 379	69.39 383	82.03 390	70.81 430	65.42 419	90.12 403	94.36 363	55.02 420	65.88 414	81.72 413	24.16 428	89.96 411	74.32 390	68.10 411	90.71 403

new-patchmatchnet		83.18 366	81.87 369	87.11 381	86.88 411	75.99 400	93.70 361	95.18 329	85.02 348	77.30 400	88.40 399	65.99 385	93.88 401	74.19 391	70.18 406	91.47 398

MVStest1		82.38 369	80.04 373	89.37 368	87.63 410	82.83 348	95.03 317	93.37 380	73.90 408	73.50 407	94.35 300	62.89 395	93.25 406	73.80 392	65.92 414	92.04 392

testing3		87.67 332	86.88 333	90.05 361	96.14 239	80.71 369	97.10 178	92.85 385	90.15 217	87.54 324	94.55 287	55.70 407	94.10 397	73.77 393	94.10 227	95.35 299

MDA-MVSNet-bldmvs		85.00 358	82.95 363	91.17 343	93.13 366	83.33 342	94.56 329	95.00 336	84.57 354	65.13 416	92.65 356	70.45 349	95.85 376	73.57 394	77.49 389	94.33 354

pmmvs3		79.97 373	77.50 378	87.39 380	82.80 419	79.38 389	92.70 383	90.75 404	70.69 411	78.66 396	87.47 407	51.34 411	93.40 403	73.39 395	69.65 407	89.38 406

test_method		66.11 387	64.89 389	69.79 404	72.62 428	35.23 436	65.19 423	92.83 387	20.35 426	65.20 415	88.08 403	43.14 417	82.70 421	73.12 396	63.46 416	91.45 399

PatchT		88.87 320	87.42 324	93.22 281	94.08 338	85.10 318	89.51 406	94.64 352	81.92 379	92.36 195	88.15 402	80.05 253	97.01 357	72.43 397	93.65 238	97.54 214

Anonymous20231206		87.09 338	86.14 340	89.93 363	91.22 388	80.35 375	96.11 261	95.35 319	83.57 368	84.16 366	93.02 351	73.54 330	95.61 382	72.16 398	86.14 327	93.84 364

MVS-HIRNet		82.47 368	81.21 371	86.26 385	95.38 272	69.21 412	88.96 409	89.49 407	66.28 414	80.79 386	74.08 419	68.48 367	97.39 343	71.93 399	95.47 197	92.18 390

new_pmnet		82.89 367	81.12 372	88.18 376	89.63 397	80.18 380	91.77 390	92.57 389	76.79 404	75.56 403	88.23 401	61.22 399	94.48 393	71.43 400	82.92 370	89.87 405

TAPA-MVS	90.10 7	92.30 206	91.22 224	95.56 159	98.33 83	89.60 198	96.79 204	97.65 147	81.83 380	91.52 220	97.23 148	87.94 112	98.91 179	71.31 401	98.37 121	98.17 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		86.14 350	85.40 346	88.35 373	90.12 393	80.06 381	95.90 273	95.20 328	88.59 267	81.29 384	93.62 337	71.43 341	92.65 408	71.26 402	81.17 377	92.34 385

tmp_tt		51.94 394	53.82 394	46.29 410	33.73 434	45.30 434	78.32 420	67.24 431	18.02 427	50.93 423	87.05 408	52.99 409	53.11 429	70.76 403	25.29 427	40.46 425

MIMVSNet1		84.93 359	83.05 361	90.56 354	89.56 398	84.84 325	95.40 299	95.35 319	83.91 360	80.38 389	92.21 370	57.23 403	93.34 404	70.69 404	82.75 372	93.50 367

APD_test1		79.31 374	77.70 377	84.14 387	89.11 402	69.07 413	92.36 388	91.50 398	69.07 412	73.87 405	92.63 358	39.93 418	94.32 395	70.54 405	80.25 380	89.02 407

RPMNet		88.98 316	87.05 330	94.77 203	94.45 327	87.19 273	90.23 401	98.03 97	77.87 402	92.40 192	87.55 406	80.17 251	99.51 103	68.84 406	93.95 233	97.60 211

UWE-MVS-28		86.81 341	86.41 336	88.02 377	92.87 369	74.60 402	95.38 301	86.70 417	88.17 281	87.28 332	94.67 282	70.83 346	93.30 405	67.45 407	94.31 220	96.17 256

N_pmnet		78.73 375	78.71 376	78.79 393	92.80 372	46.50 432	94.14 347	43.71 434	78.61 398	80.83 385	91.66 377	74.94 318	96.36 369	67.24 408	84.45 354	93.50 367

OpenMVS_ROB	81.14 20	84.42 362	82.28 368	90.83 347	90.06 394	84.05 335	95.73 282	94.04 368	73.89 409	80.17 392	91.53 378	59.15 400	97.64 320	66.92 409	89.05 299	90.80 402

PMMVS2		70.19 381	66.92 385	80.01 391	76.35 424	65.67 418	86.22 414	87.58 414	64.83 416	62.38 417	80.29 416	26.78 426	88.49 418	63.79 410	54.07 421	85.88 408

test_0402		86.46 344	84.79 353	91.45 335	95.02 299	85.55 307	96.29 251	94.89 343	80.90 385	82.21 381	93.97 323	68.21 369	97.29 348	62.98 411	88.68 304	91.51 396

DeepMVS_CX				74.68 402	90.84 391	64.34 420		81.61 425	65.34 415	67.47 413	88.01 404	48.60 414	80.13 424	62.33 412	73.68 401	79.58 414

Syy-MVS		87.13 337	87.02 332	87.47 379	95.16 291	73.21 407	95.00 318	93.93 372	88.55 271	86.96 339	91.99 371	75.90 308	94.00 398	61.59 413	94.11 225	95.20 310

testf1		69.31 383	66.76 386	76.94 397	78.61 422	61.93 421	88.27 411	86.11 419	55.62 418	59.69 418	85.31 410	20.19 430	89.32 412	57.62 414	69.44 409	79.58 414

APD_test2		69.31 383	66.76 386	76.94 397	78.61 422	61.93 421	88.27 411	86.11 419	55.62 418	59.69 418	85.31 410	20.19 430	89.32 412	57.62 414	69.44 409	79.58 414

EGC-MVSNET		68.77 385	63.01 391	86.07 386	92.49 378	82.24 358	93.96 352	90.96 402	0.71 431	2.62 432	90.89 381	53.66 408	93.46 402	57.25 416	84.55 352	82.51 412

dmvs_testset		81.38 371	82.60 366	77.73 394	91.74 386	51.49 429	93.03 378	84.21 422	89.07 248	78.28 398	91.25 380	76.97 300	88.53 417	56.57 417	82.24 373	93.16 371

FPMVS		71.27 380	69.85 382	75.50 400	74.64 425	59.03 425	91.30 392	91.50 398	58.80 417	57.92 421	88.28 400	29.98 424	85.53 420	53.43 418	82.84 371	81.95 413

ANet_high		63.94 389	59.58 392	77.02 396	61.24 432	66.06 417	85.66 416	87.93 413	78.53 399	42.94 424	71.04 421	25.42 427	80.71 423	52.60 419	30.83 425	84.28 411

Gipuma		67.86 386	65.41 388	75.18 401	92.66 375	73.45 405	66.50 422	94.52 355	53.33 421	57.80 422	66.07 422	30.81 422	89.20 414	48.15 420	78.88 388	62.90 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		69.99 382	69.33 384	71.98 403	88.78 404	61.64 423	89.86 404	59.93 433	75.67 405	74.96 404	85.45 409	50.19 412	81.66 422	43.86 421	55.27 420	72.63 418

PMVS	53.92 22	58.58 390	55.40 393	68.12 405	51.00 433	48.64 430	78.86 419	87.10 416	46.77 422	35.84 428	74.28 418	8.76 432	86.34 419	42.07 422	73.91 400	69.38 419
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 392	48.81 397	66.58 407	65.34 431	57.50 426	72.49 421	70.94 430	40.15 425	39.28 427	63.51 423	6.89 434	73.48 427	38.29 423	42.38 423	68.76 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVS		76.77 376	76.63 379	77.18 395	85.32 413	56.82 427	94.53 330	89.39 408	82.66 375	71.35 408	89.18 395	75.03 317	88.88 415	35.42 424	66.79 412	85.84 409

SSC-MVS		76.05 377	75.83 380	76.72 399	84.77 414	56.22 428	94.32 341	88.96 410	81.82 381	70.52 409	88.91 396	74.79 319	88.71 416	33.69 425	64.71 415	85.23 410

E-PMN		53.28 391	52.56 395	55.43 408	74.43 426	47.13 431	83.63 418	76.30 426	42.23 423	42.59 425	62.22 424	28.57 425	74.40 425	31.53 426	31.51 424	44.78 423

kuosan		65.27 388	64.66 390	67.11 406	83.80 415	61.32 424	88.53 410	60.77 432	68.22 413	67.67 411	80.52 415	49.12 413	70.76 428	29.67 427	53.64 422	69.26 420

EMVS		52.08 393	51.31 396	54.39 409	72.62 428	45.39 433	83.84 417	75.51 428	41.13 424	40.77 426	59.65 425	30.08 423	73.60 426	28.31 428	29.90 426	44.18 424

wuyk23d		25.11 395	24.57 399	26.74 411	73.98 427	39.89 435	57.88 424	9.80 435	12.27 428	10.39 429	6.97 431	7.03 433	36.44 430	25.43 429	17.39 428	3.89 428

testmvs		13.36 397	16.33 400	4.48 413	5.04 435	2.26 438	93.18 372	3.28 436	2.70 429	8.24 430	21.66 427	2.29 436	2.19 431	7.58 430	2.96 429	9.00 427

test123		13.04 398	15.66 401	5.18 412	4.51 436	3.45 437	92.50 386	1.81 437	2.50 430	7.58 431	20.15 428	3.67 435	2.18 432	7.13 431	1.07 430	9.90 426

mmdepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

monomultidepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

test_blank		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet_test		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

DCPMVS		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

cdsmvs_eth3d_5k		23.24 396	30.99 398	0.00 414	0.00 437	0.00 439	0.00 425	97.63 151	0.00 432	0.00 433	96.88 166	84.38 168	0.00 433	0.00 432	0.00 431	0.00 429

pcd_1.5k_mvsjas		7.39 400	9.85 403	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	88.65 101	0.00 433	0.00 432	0.00 431	0.00 429

sosnet-low-res		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uncertanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

Regformer		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

ab-mvs-re		8.06 399	10.74 402	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	96.69 176	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

FOURS1					99.55 1	93.34 67	99.29 1	98.35 30	94.98 36	98.49 27

test_one_0601					99.32 22	95.20 20		98.25 48	95.13 30	98.48 28	98.87 22	95.16 7

eth-test2					0.00 437

eth-test					0.00 437

test_241102_ONE					99.42 7	95.30 17		98.27 42	95.09 33	99.19 7	98.81 28	95.54 5	99.65 65

save fliter					98.91 52	94.28 38	97.02 183	98.02 100	95.35 23

test0726					99.45 3	95.36 13	98.31 27	98.29 37	94.92 39	98.99 11	98.92 17	95.08 8

GSMVS																98.45 151

test_part2					99.28 25	95.74 8				98.10 34

sam_mvs1												82.76 203				98.45 151

sam_mvs												81.94 222

MTGPA								98.08 80

test_post											17.58 429	81.76 224	98.08 263

patchmatchnet-post											90.45 385	82.65 207	98.10 258

MTMP							97.86 82	82.03 424

TEST9					98.70 59	94.19 42	96.41 237	98.02 100	88.17 281	96.03 108	97.56 130	92.74 33	99.59 81

test_8					98.67 61	94.06 49	96.37 244	98.01 103	88.58 268	95.98 112	97.55 132	92.73 34	99.58 84

agg_prior					98.67 61	93.79 55		98.00 104		95.68 122			99.57 91

test_prior4						93.66 58	96.42 236

test_prior				97.23 64	98.67 61	92.99 79		98.00 104					99.41 116			99.29 67

新几何2							95.79 279

旧先验1					98.38 81	93.38 64		97.75 134			98.09 82	92.30 45			99.01 94	99.16 77

原ACMM2							95.67 284

test222					98.24 90	92.21 103	95.33 303	97.60 153	79.22 396	95.25 131	97.84 105	88.80 98			99.15 83	98.72 126

segment_acmp												92.89 30

testdata1							95.26 310		93.10 117

test12				97.65 43	98.46 73	94.26 39		97.66 145		95.52 129		90.89 73	99.46 110		99.25 72	99.22 74

plane_prior7					96.21 231	89.98 188

plane_prior6					96.10 242	90.00 184						81.32 230

plane_prior4											96.64 179

plane_prior3						90.00 184			94.46 64	91.34 225

plane_prior2							97.74 99		94.85 41

plane_prior1					96.14 239

plane_prior						89.99 186	97.24 164		94.06 76						92.16 259

n2								0.00 438

nn								0.00 438

door-mid								91.06 401

test11								97.88 116

door								91.13 400

HQP5-MVS						89.33 213

HQP-NCC					95.86 248		96.65 219		93.55 92	90.14 249

ACMP_Plane					95.86 248		96.65 219		93.55 92	90.14 249

HQP4-MVS										90.14 249			98.50 221			95.78 275

HQP3-MVS								97.39 191							92.10 260

HQP2-MVS												80.95 234

NP-MVS					95.99 246	89.81 194					95.87 221

ACMMP++_ref															90.30 289

ACMMP++															91.02 278

Test By Simon												88.73 100

High-resolution multi-view benchmark