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.

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 58	99.38 221	99.37 105	99.58 114	99.62 41	99.41 9	99.87 29	99.92 14	98.81 47	100.00 1	99.97 1	99.93 25	99.94 11

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 55	99.84 32	99.44 99	99.58 114	99.69 18	99.43 7	99.98 6	99.91 20	98.62 73	100.00 1	99.97 1	99.95 17	99.90 16

test_vis1_n_1920		98.63 166	98.40 173	99.31 152	99.86 20	97.94 252	99.67 69	99.62 41	99.43 7	99.99 2	99.91 20	87.29 375	100.00 1	99.92 11	99.92 27	99.98 2

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 31	99.86 20	99.61 70	99.56 127	99.63 39	99.48 3	99.98 6	99.83 72	98.75 58	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 31	99.84 32	99.63 67	99.56 127	99.63 39	99.47 4	99.98 6	99.82 81	98.75 58	99.99 4	99.97 1	99.97 7	99.94 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 14	99.80 52	99.66 56	99.48 185	99.64 36	99.45 5	99.92 16	99.92 14	98.62 73	99.99 4	99.96 6	99.99 1	99.96 7

patch_mono-2		99.26 74	99.62 5	98.16 304	99.81 46	94.59 371	99.52 154	99.64 36	99.33 13	99.73 68	99.90 27	99.00 22	99.99 4	99.69 21	99.98 4	99.89 19

h-mvs33		97.70 277	97.28 298	98.97 199	99.70 104	97.27 279	99.36 239	99.45 201	98.94 57	99.66 90	99.64 196	94.93 211	99.99 4	99.48 45	84.36 404	99.65 132

xiu_mvs_v1_base_debu		99.29 68	99.27 63	99.34 145	99.63 135	98.97 159	99.12 306	99.51 119	98.86 63	99.84 35	99.47 261	98.18 100	99.99 4	99.50 40	99.31 164	99.08 241

xiu_mvs_v1_base		99.29 68	99.27 63	99.34 145	99.63 135	98.97 159	99.12 306	99.51 119	98.86 63	99.84 35	99.47 261	98.18 100	99.99 4	99.50 40	99.31 164	99.08 241

xiu_mvs_v1_base_debi		99.29 68	99.27 63	99.34 145	99.63 135	98.97 159	99.12 306	99.51 119	98.86 63	99.84 35	99.47 261	98.18 100	99.99 4	99.50 40	99.31 164	99.08 241

EPNet		98.86 137	98.71 141	99.30 157	97.20 396	98.18 234	99.62 93	98.91 344	99.28 16	98.63 304	99.81 95	95.96 175	99.99 4	99.24 70	99.72 124	99.73 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		99.40 54	99.28 60	99.74 64	99.67 114	99.31 114	99.52 154	98.87 351	99.55 1	99.74 66	99.80 108	96.47 158	99.98 13	99.97 1	99.97 7	99.94 11

test_cas_vis1_n_1920		99.16 88	99.01 100	99.61 89	99.81 46	98.86 179	99.65 80	99.64 36	99.39 10	99.97 13	99.94 6	93.20 279	99.98 13	99.55 33	99.91 34	99.99 1

test_vis1_n		97.92 237	97.44 275	99.34 145	99.53 168	98.08 240	99.74 46	99.49 149	99.15 20	100.00 1	99.94 6	79.51 405	99.98 13	99.88 13	99.76 116	99.97 4

xiu_mvs_v2_base		99.26 74	99.25 67	99.29 160	99.53 168	98.91 173	99.02 329	99.45 201	98.80 72	99.71 75	99.26 318	98.94 32	99.98 13	99.34 58	99.23 169	98.98 255

PS-MVSNAJ		99.32 64	99.32 46	99.30 157	99.57 156	98.94 169	98.97 343	99.46 190	98.92 60	99.71 75	99.24 320	99.01 18	99.98 13	99.35 54	99.66 134	98.97 256

QAPM		98.67 162	98.30 180	99.80 49	99.20 268	99.67 54	99.77 34	99.72 11	94.74 368	98.73 284	99.90 27	95.78 185	99.98 13	96.96 301	99.88 57	99.76 90

3Dnovator	97.25 9	99.24 79	99.05 88	99.81 47	99.12 290	99.66 56	99.84 12	99.74 10	99.09 35	98.92 259	99.90 27	95.94 178	99.98 13	98.95 99	99.92 27	99.79 77

OpenMVS	96.50 16	98.47 171	98.12 192	99.52 116	99.04 308	99.53 86	99.82 16	99.72 11	94.56 371	98.08 338	99.88 39	94.73 227	99.98 13	97.47 269	99.76 116	99.06 247

reproduce_model		99.63 7	99.54 11	99.90 4	99.78 56	99.88 8	99.56 127	99.55 78	99.15 20	99.90 19	99.90 27	99.00 22	99.97 21	99.11 81	99.91 34	99.86 32

test_fmvsmconf0.1_n		99.55 17	99.45 24	99.86 24	99.44 203	99.65 60	99.50 170	99.61 48	99.45 5	99.87 29	99.92 14	97.31 126	99.97 21	99.95 7	99.99 1	99.97 4

test_fmvs1_n		98.41 177	98.14 189	99.21 172	99.82 42	97.71 265	99.74 46	99.49 149	99.32 14	99.99 2	99.95 3	85.32 386	99.97 21	99.82 16	99.84 83	99.96 7

CANet_DTU		98.97 127	98.87 122	99.25 167	99.33 233	98.42 226	99.08 315	99.30 283	99.16 19	99.43 150	99.75 142	95.27 202	99.97 21	98.56 166	99.95 17	99.36 215

MVS_0304		99.15 90	98.96 110	99.73 67	98.92 325	99.37 105	99.37 234	96.92 402	99.51 2	99.66 90	99.78 127	96.69 149	99.97 21	99.84 15	99.97 7	99.84 42

MTAPA		99.52 20	99.39 32	99.89 7	99.90 4	99.86 16	99.66 74	99.47 181	98.79 73	99.68 81	99.81 95	98.43 86	99.97 21	98.88 109	99.90 43	99.83 52

PGM-MVS		99.45 38	99.31 52	99.86 24	99.87 15	99.78 39	99.58 114	99.65 33	97.84 182	99.71 75	99.80 108	99.12 13	99.97 21	98.33 189	99.87 60	99.83 52

mPP-MVS		99.44 42	99.30 54	99.86 24	99.88 11	99.79 33	99.69 60	99.48 161	98.12 145	99.50 135	99.75 142	98.78 51	99.97 21	98.57 163	99.89 54	99.83 52

CP-MVS		99.45 38	99.32 46	99.85 31	99.83 39	99.75 42	99.69 60	99.52 105	98.07 155	99.53 130	99.63 202	98.93 36	99.97 21	98.74 134	99.91 34	99.83 52

SteuartSystems-ACMMP		99.54 18	99.42 25	99.87 14	99.82 42	99.81 28	99.59 106	99.51 119	98.62 88	99.79 48	99.83 72	99.28 4	99.97 21	98.48 173	99.90 43	99.84 42
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	97.12 13	99.18 84	98.97 106	99.82 44	99.17 282	99.68 51	99.81 20	99.51 119	99.20 18	98.72 285	99.89 32	95.68 189	99.97 21	98.86 117	99.86 68	99.81 64

reproduce-ours		99.61 8	99.52 12	99.90 4	99.76 66	99.88 8	99.52 154	99.54 87	99.13 23	99.89 21	99.89 32	98.96 25	99.96 32	99.04 89	99.90 43	99.85 36

our_new_method		99.61 8	99.52 12	99.90 4	99.76 66	99.88 8	99.52 154	99.54 87	99.13 23	99.89 21	99.89 32	98.96 25	99.96 32	99.04 89	99.90 43	99.85 36

fmvsm_s_conf0.5_n_a		99.56 16	99.47 20	99.85 31	99.83 39	99.64 66	99.52 154	99.65 33	99.10 30	99.98 6	99.92 14	97.35 125	99.96 32	99.94 9	99.92 27	99.95 9

fmvsm_s_conf0.5_n		99.51 21	99.40 30	99.85 31	99.84 32	99.65 60	99.51 163	99.67 23	99.13 23	99.98 6	99.92 14	96.60 152	99.96 32	99.95 7	99.96 12	99.95 9

mvsany_test1		99.50 23	99.46 23	99.62 88	99.61 145	99.09 142	98.94 349	99.48 161	99.10 30	99.96 14	99.91 20	98.85 42	99.96 32	99.72 19	99.58 144	99.82 57

test_fmvs1		98.88 133	98.79 134	99.16 177	99.69 108	97.61 269	99.55 141	99.49 149	99.32 14	99.98 6	99.91 20	91.41 327	99.96 32	99.82 16	99.92 27	99.90 16

DVP-MVS++		99.59 11	99.50 16	99.88 8	99.51 175	99.88 8	99.87 8	99.51 119	98.99 48	99.88 24	99.81 95	99.27 5	99.96 32	98.85 119	99.80 103	99.81 64

MSC_two_6792asdad				99.87 14	99.51 175	99.76 40		99.33 265					99.96 32	98.87 112	99.84 83	99.89 19

No_MVS				99.87 14	99.51 175	99.76 40		99.33 265					99.96 32	98.87 112	99.84 83	99.89 19

ZD-MVS					99.71 99	99.79 33		99.61 48	96.84 286	99.56 123	99.54 236	98.58 75	99.96 32	96.93 304	99.75 118

SED-MVS		99.61 8	99.52 12	99.88 8	99.84 32	99.90 2	99.60 100	99.48 161	99.08 36	99.91 17	99.81 95	99.20 7	99.96 32	98.91 106	99.85 75	99.79 77

test_241102_TWO								99.48 161	99.08 36	99.88 24	99.81 95	98.94 32	99.96 32	98.91 106	99.84 83	99.88 25

ZNCC-MVS		99.47 32	99.33 44	99.87 14	99.87 15	99.81 28	99.64 83	99.67 23	98.08 154	99.55 127	99.64 196	98.91 37	99.96 32	98.72 137	99.90 43	99.82 57

DVP-MVS		99.57 15	99.47 20	99.88 8	99.85 26	99.89 4	99.57 121	99.37 245	99.10 30	99.81 42	99.80 108	98.94 32	99.96 32	98.93 103	99.86 68	99.81 64
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_THIRD									98.99 48	99.81 42	99.80 108	99.09 14	99.96 32	98.85 119	99.90 43	99.88 25

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 121	99.51 119					99.96 32	98.93 103	99.86 68	99.88 25

SR-MVS		99.43 45	99.29 58	99.86 24	99.75 76	99.83 19	99.59 106	99.62 41	98.21 132	99.73 68	99.79 120	98.68 67	99.96 32	98.44 179	99.77 113	99.79 77

DPE-MVS		99.46 34	99.32 46	99.91 2	99.78 56	99.88 8	99.36 239	99.51 119	98.73 80	99.88 24	99.84 67	98.72 64	99.96 32	98.16 203	99.87 60	99.88 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 47	99.29 58	99.80 49	99.62 141	99.55 81	99.50 170	99.70 15	98.79 73	99.77 57	99.96 1	97.45 120	99.96 32	98.92 105	99.90 43	99.89 19

HFP-MVS		99.49 25	99.37 36	99.86 24	99.87 15	99.80 30	99.66 74	99.67 23	98.15 139	99.68 81	99.69 172	99.06 16	99.96 32	98.69 142	99.87 60	99.84 42

region2R		99.48 29	99.35 40	99.87 14	99.88 11	99.80 30	99.65 80	99.66 28	98.13 144	99.66 90	99.68 178	98.96 25	99.96 32	98.62 151	99.87 60	99.84 42

HPM-MVS++		99.39 56	99.23 70	99.87 14	99.75 76	99.84 18	99.43 205	99.51 119	98.68 85	99.27 192	99.53 240	98.64 72	99.96 32	98.44 179	99.80 103	99.79 77

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 63	99.89 4	99.75 42	99.56 70	99.02 41	99.88 24	99.85 57	99.18 10	99.96 32	99.22 71	99.92 27	99.90 16
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 25	99.36 38	99.86 24	99.87 15	99.79 33	99.66 74	99.67 23	98.15 139	99.67 85	99.69 172	98.95 30	99.96 32	98.69 142	99.87 60	99.84 42

MP-MVS		99.33 62	99.15 76	99.87 14	99.88 11	99.82 25	99.66 74	99.46 190	98.09 150	99.48 139	99.74 147	98.29 95	99.96 32	97.93 221	99.87 60	99.82 57
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 105	98.90 117	99.74 64	99.80 52	99.46 97	99.59 106	99.49 149	97.03 273	99.63 105	99.69 172	97.27 129	99.96 32	97.82 232	99.84 83	99.81 64

PVSNet_Blended_VisFu		99.36 59	99.28 60	99.61 89	99.86 20	99.07 147	99.47 191	99.93 2	97.66 205	99.71 75	99.86 52	97.73 115	99.96 32	99.47 47	99.82 96	99.79 77

UGNet		98.87 134	98.69 143	99.40 137	99.22 265	98.72 193	99.44 201	99.68 20	99.24 17	99.18 216	99.42 272	92.74 289	99.96 32	99.34 58	99.94 23	99.53 172
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
CSCG		99.32 64	99.32 46	99.32 151	99.85 26	98.29 229	99.71 55	99.66 28	98.11 147	99.41 157	99.80 108	98.37 92	99.96 32	98.99 95	99.96 12	99.72 106

ACMMP		99.45 38	99.32 46	99.82 44	99.89 8	99.67 54	99.62 93	99.69 18	98.12 145	99.63 105	99.84 67	98.73 63	99.96 32	98.55 169	99.83 92	99.81 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
test_fmvsmconf0.01_n		99.22 81	99.03 92	99.79 52	98.42 376	99.48 94	99.55 141	99.51 119	99.39 10	99.78 53	99.93 9	94.80 219	99.95 62	99.93 10	99.95 17	99.94 11

SR-MVS-dyc-post		99.45 38	99.31 52	99.85 31	99.76 66	99.82 25	99.63 88	99.52 105	98.38 109	99.76 62	99.82 81	98.53 79	99.95 62	98.61 154	99.81 99	99.77 85

GST-MVS		99.40 54	99.24 68	99.85 31	99.86 20	99.79 33	99.60 100	99.67 23	97.97 167	99.63 105	99.68 178	98.52 80	99.95 62	98.38 182	99.86 68	99.81 64

CANet		99.25 78	99.14 77	99.59 92	99.41 211	99.16 132	99.35 244	99.57 65	98.82 68	99.51 134	99.61 211	96.46 159	99.95 62	99.59 28	99.98 4	99.65 132

MP-MVS-pluss		99.37 58	99.20 72	99.88 8	99.90 4	99.87 15	99.30 255	99.52 105	97.18 255	99.60 115	99.79 120	98.79 50	99.95 62	98.83 125	99.91 34	99.83 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 47	99.27 63	99.88 8	99.89 8	99.80 30	99.67 69	99.50 139	98.70 82	99.77 57	99.49 253	98.21 98	99.95 62	98.46 177	99.77 113	99.88 25
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
testdata2													99.95 62	96.67 316

APD-MVS_3200maxsize		99.48 29	99.35 40	99.85 31	99.76 66	99.83 19	99.63 88	99.54 87	98.36 113	99.79 48	99.82 81	98.86 41	99.95 62	98.62 151	99.81 99	99.78 83

RPMNet		96.72 325	95.90 338	99.19 174	99.18 274	98.49 218	99.22 289	99.52 105	88.72 404	99.56 123	97.38 398	94.08 257	99.95 62	86.87 406	98.58 215	99.14 233

sss		99.17 86	99.05 88	99.53 110	99.62 141	98.97 159	99.36 239	99.62 41	97.83 183	99.67 85	99.65 190	97.37 124	99.95 62	99.19 73	99.19 172	99.68 122

MVSMamba_PlusPlus		99.46 34	99.41 29	99.64 82	99.68 112	99.50 91	99.75 42	99.50 139	98.27 122	99.87 29	99.92 14	98.09 104	99.94 72	99.65 25	99.95 17	99.47 192

fmvsm_s_conf0.1_n_a		99.26 74	99.06 87	99.85 31	99.52 172	99.62 68	99.54 145	99.62 41	98.69 83	99.99 2	99.96 1	94.47 243	99.94 72	99.88 13	99.92 27	99.98 2

fmvsm_s_conf0.1_n		99.29 68	99.10 81	99.86 24	99.70 104	99.65 60	99.53 153	99.62 41	98.74 79	99.99 2	99.95 3	94.53 241	99.94 72	99.89 12	99.96 12	99.97 4

TSAR-MVS + MP.		99.58 12	99.50 16	99.81 47	99.91 1	99.66 56	99.63 88	99.39 229	98.91 61	99.78 53	99.85 57	99.36 2	99.94 72	98.84 122	99.88 57	99.82 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 131	98.75 137	99.39 141	99.46 196	98.61 204	99.76 37	99.50 139	98.06 159	99.81 42	99.88 39	93.91 264	99.94 72	99.11 81	99.27 167	99.61 147

mamv4		99.33 62	99.42 25	99.07 185	99.67 114	97.73 260	99.42 212	99.60 54	98.15 139	99.94 15	99.91 20	98.42 88	99.94 72	99.72 19	99.96 12	99.54 166

XVS		99.53 19	99.42 25	99.87 14	99.85 26	99.83 19	99.69 60	99.68 20	98.98 51	99.37 168	99.74 147	98.81 47	99.94 72	98.79 130	99.86 68	99.84 42

X-MVStestdata		96.55 328	95.45 347	99.87 14	99.85 26	99.83 19	99.69 60	99.68 20	98.98 51	99.37 168	64.01 421	98.81 47	99.94 72	98.79 130	99.86 68	99.84 42

旧先验2							98.96 344		96.70 293	99.47 140			99.94 72	98.19 199

新几何1				99.75 61	99.75 76	99.59 73		99.54 87	96.76 289	99.29 186	99.64 196	98.43 86	99.94 72	96.92 306	99.66 134	99.72 106

testdata				99.54 102	99.75 76	98.95 166		99.51 119	97.07 267	99.43 150	99.70 162	98.87 40	99.94 72	97.76 239	99.64 137	99.72 106

HPM-MVS		99.42 47	99.28 60	99.83 43	99.90 4	99.72 45	99.81 20	99.54 87	97.59 210	99.68 81	99.63 202	98.91 37	99.94 72	98.58 160	99.91 34	99.84 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 82	99.10 81	99.45 130	99.89 8	98.52 214	99.39 227	99.94 1	98.73 80	99.11 225	99.89 32	95.50 194	99.94 72	99.50 40	99.97 7	99.89 19

APD-MVS		99.27 72	99.08 85	99.84 42	99.75 76	99.79 33	99.50 170	99.50 139	97.16 257	99.77 57	99.82 81	98.78 51	99.94 72	97.56 260	99.86 68	99.80 73
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 29	99.42 25	99.65 77	99.72 94	99.40 104	99.05 321	99.66 28	99.14 22	99.57 122	99.80 108	98.46 84	99.94 72	99.57 31	99.84 83	99.60 150
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
WTY-MVS		99.06 113	98.88 121	99.61 89	99.62 141	99.16 132	99.37 234	99.56 70	98.04 162	99.53 130	99.62 207	96.84 143	99.94 72	98.85 119	98.49 223	99.72 106

DeepC-MVS	98.35 2	99.30 66	99.19 73	99.64 82	99.82 42	99.23 125	99.62 93	99.55 78	98.94 57	99.63 105	99.95 3	95.82 184	99.94 72	99.37 53	99.97 7	99.73 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		99.27 72	99.12 79	99.74 64	99.18 274	99.75 42	99.56 127	99.57 65	98.45 102	99.49 138	99.85 57	97.77 114	99.94 72	98.33 189	99.84 83	99.52 173

SDMVSNet		99.11 105	98.90 117	99.75 61	99.81 46	99.59 73	99.81 20	99.65 33	98.78 76	99.64 102	99.88 39	94.56 237	99.93 90	99.67 23	98.26 235	99.72 106

FE-MVS		98.48 170	98.17 185	99.40 137	99.54 167	98.96 163	99.68 66	98.81 358	95.54 352	99.62 109	99.70 162	93.82 267	99.93 90	97.35 278	99.46 151	99.32 221

SF-MVS		99.38 57	99.24 68	99.79 52	99.79 54	99.68 51	99.57 121	99.54 87	97.82 187	99.71 75	99.80 108	98.95 30	99.93 90	98.19 199	99.84 83	99.74 95

dcpmvs_2		99.23 80	99.58 7	98.16 304	99.83 39	94.68 369	99.76 37	99.52 105	99.07 38	99.98 6	99.88 39	98.56 77	99.93 90	99.67 23	99.98 4	99.87 30

Anonymous20240529		98.09 207	97.68 243	99.34 145	99.66 124	98.44 223	99.40 223	99.43 215	93.67 378	99.22 203	99.89 32	90.23 343	99.93 90	99.26 69	98.33 229	99.66 128

ACMMP_NAP		99.47 32	99.34 42	99.88 8	99.87 15	99.86 16	99.47 191	99.48 161	98.05 161	99.76 62	99.86 52	98.82 46	99.93 90	98.82 129	99.91 34	99.84 42

EI-MVSNet-UG-set		99.58 12	99.57 8	99.64 82	99.78 56	99.14 137	99.60 100	99.45 201	99.01 43	99.90 19	99.83 72	98.98 24	99.93 90	99.59 28	99.95 17	99.86 32

无先验							98.99 337	99.51 119	96.89 283				99.93 90	97.53 263		99.72 106

VDDNet		97.55 291	97.02 310	99.16 177	99.49 186	98.12 239	99.38 232	99.30 283	95.35 354	99.68 81	99.90 27	82.62 398	99.93 90	99.31 61	98.13 246	99.42 204

ab-mvs		98.86 137	98.63 150	99.54 102	99.64 132	99.19 127	99.44 201	99.54 87	97.77 191	99.30 183	99.81 95	94.20 251	99.93 90	99.17 77	98.82 204	99.49 185

F-COLMAP		99.19 82	99.04 90	99.64 82	99.78 56	99.27 120	99.42 212	99.54 87	97.29 246	99.41 157	99.59 216	98.42 88	99.93 90	98.19 199	99.69 129	99.73 100

Anonymous202405211		98.30 188	97.98 209	99.26 166	99.57 156	98.16 235	99.41 215	98.55 380	96.03 346	99.19 212	99.74 147	91.87 314	99.92 101	99.16 78	98.29 234	99.70 116

EI-MVSNet-Vis-set		99.58 12	99.56 10	99.64 82	99.78 56	99.15 136	99.61 99	99.45 201	99.01 43	99.89 21	99.82 81	99.01 18	99.92 101	99.56 32	99.95 17	99.85 36

VDD-MVS		97.73 271	97.35 287	98.88 219	99.47 194	97.12 287	99.34 247	98.85 353	98.19 134	99.67 85	99.85 57	82.98 396	99.92 101	99.49 44	98.32 233	99.60 150

VNet		99.11 105	98.90 117	99.73 67	99.52 172	99.56 79	99.41 215	99.39 229	99.01 43	99.74 66	99.78 127	95.56 192	99.92 101	99.52 38	98.18 242	99.72 106

XVG-OURS-SEG-HR		98.69 160	98.62 155	98.89 217	99.71 99	97.74 259	99.12 306	99.54 87	98.44 105	99.42 153	99.71 158	94.20 251	99.92 101	98.54 170	98.90 198	99.00 252

mvsmamba		99.06 113	98.96 110	99.36 143	99.47 194	98.64 200	99.70 56	99.05 324	97.61 209	99.65 97	99.83 72	96.54 155	99.92 101	99.19 73	99.62 140	99.51 180

HPM-MVS_fast		99.51 21	99.40 30	99.85 31	99.91 1	99.79 33	99.76 37	99.56 70	97.72 196	99.76 62	99.75 142	99.13 12	99.92 101	99.07 87	99.92 27	99.85 36

HY-MVS	97.30 7	98.85 144	98.64 149	99.47 127	99.42 206	99.08 145	99.62 93	99.36 246	97.39 238	99.28 187	99.68 178	96.44 161	99.92 101	98.37 184	98.22 237	99.40 209

DP-MVS		99.16 88	98.95 112	99.78 55	99.77 63	99.53 86	99.41 215	99.50 139	97.03 273	99.04 241	99.88 39	97.39 121	99.92 101	98.66 146	99.90 43	99.87 30

IB-MVS	95.67 18	96.22 334	95.44 348	98.57 257	99.21 266	96.70 315	98.65 378	97.74 397	96.71 292	97.27 362	98.54 374	86.03 380	99.92 101	98.47 176	86.30 402	99.10 236
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
DeepC-MVS_fast	98.69 1	99.49 25	99.39 32	99.77 58	99.63 135	99.59 73	99.36 239	99.46 190	99.07 38	99.79 48	99.82 81	98.85 42	99.92 101	98.68 144	99.87 60	99.82 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		99.46 34	99.39 32	99.67 72	99.55 164	99.58 78	99.74 46	99.51 119	98.42 106	99.87 29	99.84 67	98.05 107	99.91 112	99.58 30	99.94 23	99.52 173

9.14			99.10 81		99.72 94		99.40 223	99.51 119	97.53 220	99.64 102	99.78 127	98.84 44	99.91 112	97.63 251	99.82 96

SMA-MVS		99.44 42	99.30 54	99.85 31	99.73 90	99.83 19	99.56 127	99.47 181	97.45 229	99.78 53	99.82 81	99.18 10	99.91 112	98.79 130	99.89 54	99.81 64
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
TEST9					99.67 114	99.65 60	99.05 321	99.41 220	96.22 331	98.95 255	99.49 253	98.77 54	99.91 112

train_agg		99.02 119	98.77 135	99.77 58	99.67 114	99.65 60	99.05 321	99.41 220	96.28 325	98.95 255	99.49 253	98.76 55	99.91 112	97.63 251	99.72 124	99.75 91

test_8					99.67 114	99.61 70	99.03 326	99.41 220	96.28 325	98.93 258	99.48 258	98.76 55	99.91 112

agg_prior					99.67 114	99.62 68		99.40 226		98.87 268			99.91 112

原ACMM1				99.65 77	99.73 90	99.33 109		99.47 181	97.46 226	99.12 223	99.66 189	98.67 69	99.91 112	97.70 248	99.69 129	99.71 115

LFMVS		97.90 240	97.35 287	99.54 102	99.52 172	99.01 154	99.39 227	98.24 387	97.10 265	99.65 97	99.79 120	84.79 389	99.91 112	99.28 65	98.38 226	99.69 118

XVG-OURS		98.73 158	98.68 144	98.88 219	99.70 104	97.73 260	98.92 351	99.55 78	98.52 97	99.45 143	99.84 67	95.27 202	99.91 112	98.08 210	98.84 202	99.00 252

PLC	97.94 4	99.02 119	98.85 126	99.53 110	99.66 124	99.01 154	99.24 283	99.52 105	96.85 285	99.27 192	99.48 258	98.25 97	99.91 112	97.76 239	99.62 140	99.65 132
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 284	97.06 309	99.47 127	99.61 145	99.09 142	98.04 404	99.25 295	91.24 395	98.51 314	99.70 162	94.55 239	99.91 112	92.76 383	99.85 75	99.42 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mmtdpeth		96.95 320	96.71 319	97.67 339	99.33 233	94.90 366	99.89 2	99.28 289	98.15 139	99.72 73	98.57 373	86.56 378	99.90 124	99.82 16	89.02 397	98.20 367

UWE-MVS		97.58 290	97.29 297	98.48 268	99.09 298	96.25 335	99.01 334	96.61 408	97.86 177	99.19 212	99.01 345	88.72 357	99.90 124	97.38 276	98.69 210	99.28 224

test_vis1_rt		95.81 344	95.65 343	96.32 370	99.67 114	91.35 397	99.49 181	96.74 406	98.25 125	95.24 385	98.10 391	74.96 406	99.90 124	99.53 36	98.85 201	97.70 391

FA-MVS(test-final)		98.75 155	98.53 166	99.41 136	99.55 164	99.05 150	99.80 25	99.01 329	96.59 307	99.58 119	99.59 216	95.39 197	99.90 124	97.78 235	99.49 150	99.28 224

MCST-MVS		99.43 45	99.30 54	99.82 44	99.79 54	99.74 44	99.29 260	99.40 226	98.79 73	99.52 132	99.62 207	98.91 37	99.90 124	98.64 148	99.75 118	99.82 57

CDPH-MVS		99.13 95	98.91 116	99.80 49	99.75 76	99.71 47	99.15 300	99.41 220	96.60 305	99.60 115	99.55 231	98.83 45	99.90 124	97.48 267	99.83 92	99.78 83

NCCC		99.34 61	99.19 73	99.79 52	99.61 145	99.65 60	99.30 255	99.48 161	98.86 63	99.21 206	99.63 202	98.72 64	99.90 124	98.25 195	99.63 139	99.80 73

114514_t		98.93 129	98.67 145	99.72 69	99.85 26	99.53 86	99.62 93	99.59 58	92.65 390	99.71 75	99.78 127	98.06 106	99.90 124	98.84 122	99.91 34	99.74 95

1112_ss		98.98 125	98.77 135	99.59 92	99.68 112	99.02 152	99.25 281	99.48 161	97.23 252	99.13 221	99.58 220	96.93 142	99.90 124	98.87 112	98.78 207	99.84 42

PHI-MVS		99.30 66	99.17 75	99.70 70	99.56 160	99.52 89	99.58 114	99.80 8	97.12 261	99.62 109	99.73 153	98.58 75	99.90 124	98.61 154	99.91 34	99.68 122

AdaColmap		99.01 123	98.80 131	99.66 73	99.56 160	99.54 83	99.18 295	99.70 15	98.18 137	99.35 174	99.63 202	96.32 164	99.90 124	97.48 267	99.77 113	99.55 164

COLMAP_ROB	97.56 6	98.86 137	98.75 137	99.17 176	99.88 11	98.53 210	99.34 247	99.59 58	97.55 216	98.70 292	99.89 32	95.83 183	99.90 124	98.10 205	99.90 43	99.08 241
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 184	98.03 204	99.31 152	99.63 135	98.56 207	99.54 145	96.75 405	97.53 220	99.73 68	99.65 190	91.25 331	99.89 136	98.62 151	99.56 145	99.48 186

tttt0517		98.42 175	98.14 189	99.28 164	99.66 124	98.38 227	99.74 46	96.85 403	97.68 202	99.79 48	99.74 147	91.39 328	99.89 136	98.83 125	99.56 145	99.57 161

test12				99.75 61	99.64 132	99.61 70		99.29 287		99.21 206		98.38 91	99.89 136		99.74 121	99.74 95

Test_1112_low_res		98.89 132	98.66 148	99.57 97	99.69 108	98.95 166	99.03 326	99.47 181	96.98 275	99.15 219	99.23 321	96.77 146	99.89 136	98.83 125	98.78 207	99.86 32

CNLPA		99.14 93	98.99 102	99.59 92	99.58 154	99.41 103	99.16 297	99.44 209	98.45 102	99.19 212	99.49 253	98.08 105	99.89 136	97.73 243	99.75 118	99.48 186

sd_testset		98.75 155	98.57 162	99.29 160	99.81 46	98.26 231	99.56 127	99.62 41	98.78 76	99.64 102	99.88 39	92.02 311	99.88 141	99.54 34	98.26 235	99.72 106

APD_test1		95.87 342	96.49 324	94.00 377	99.53 168	84.01 406	99.54 145	99.32 275	95.91 348	97.99 343	99.85 57	85.49 384	99.88 141	91.96 386	98.84 202	98.12 371

diffmvs		99.14 93	99.02 96	99.51 118	99.61 145	98.96 163	99.28 265	99.49 149	98.46 101	99.72 73	99.71 158	96.50 157	99.88 141	99.31 61	99.11 179	99.67 125
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 137	98.80 131	99.03 191	99.76 66	98.79 188	99.28 265	99.91 3	97.42 235	99.67 85	99.37 289	97.53 118	99.88 141	98.98 96	97.29 293	98.42 352

PVSNet_Blended		99.08 111	98.97 106	99.42 135	99.76 66	98.79 188	98.78 365	99.91 3	96.74 290	99.67 85	99.49 253	97.53 118	99.88 141	98.98 96	99.85 75	99.60 150

MVS		97.28 309	96.55 322	99.48 124	98.78 343	98.95 166	99.27 270	99.39 229	83.53 408	98.08 338	99.54 236	96.97 140	99.87 146	94.23 365	99.16 173	99.63 143

MG-MVS		99.13 95	99.02 96	99.45 130	99.57 156	98.63 201	99.07 316	99.34 258	98.99 48	99.61 112	99.82 81	97.98 109	99.87 146	97.00 297	99.80 103	99.85 36

MSDG		98.98 125	98.80 131	99.53 110	99.76 66	99.19 127	98.75 368	99.55 78	97.25 249	99.47 140	99.77 135	97.82 112	99.87 146	96.93 304	99.90 43	99.54 166

ETV-MVS		99.26 74	99.21 71	99.40 137	99.46 196	99.30 116	99.56 127	99.52 105	98.52 97	99.44 148	99.27 316	98.41 90	99.86 149	99.10 84	99.59 143	99.04 248

thisisatest0515		98.14 202	97.79 227	99.19 174	99.50 184	98.50 217	98.61 380	96.82 404	96.95 279	99.54 128	99.43 270	91.66 323	99.86 149	98.08 210	99.51 149	99.22 230

thres600view7		97.86 245	97.51 261	98.92 208	99.72 94	97.95 250	99.59 106	98.74 366	97.94 169	99.27 192	98.62 370	91.75 317	99.86 149	93.73 370	98.19 241	98.96 258

lupinMVS		99.13 95	99.01 100	99.46 129	99.51 175	98.94 169	99.05 321	99.16 309	97.86 177	99.80 46	99.56 228	97.39 121	99.86 149	98.94 100	99.85 75	99.58 158

PVSNet	96.02 17	98.85 144	98.84 128	98.89 217	99.73 90	97.28 278	98.32 396	99.60 54	97.86 177	99.50 135	99.57 225	96.75 147	99.86 149	98.56 166	99.70 128	99.54 166

MAR-MVS		98.86 137	98.63 150	99.54 102	99.37 224	99.66 56	99.45 195	99.54 87	96.61 302	99.01 244	99.40 280	97.09 133	99.86 149	97.68 250	99.53 148	99.10 236
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
testing91		97.44 303	97.02 310	98.71 245	99.18 274	96.89 309	99.19 293	99.04 325	97.78 190	98.31 325	98.29 383	85.41 385	99.85 155	98.01 216	97.95 251	99.39 210

test2506		96.81 324	96.65 320	97.29 351	99.74 83	92.21 394	99.60 100	85.06 425	99.13 23	99.77 57	99.93 9	87.82 373	99.85 155	99.38 52	99.38 156	99.80 73

AllTest		98.87 134	98.72 139	99.31 152	99.86 20	98.48 220	99.56 127	99.61 48	97.85 180	99.36 171	99.85 57	95.95 176	99.85 155	96.66 317	99.83 92	99.59 154

TestCases				99.31 152	99.86 20	98.48 220		99.61 48	97.85 180	99.36 171	99.85 57	95.95 176	99.85 155	96.66 317	99.83 92	99.59 154

jason		99.13 95	99.03 92	99.45 130	99.46 196	98.87 176	99.12 306	99.26 293	98.03 164	99.79 48	99.65 190	97.02 138	99.85 155	99.02 93	99.90 43	99.65 132
jason: jason.
CNVR-MVS		99.42 47	99.30 54	99.78 55	99.62 141	99.71 47	99.26 279	99.52 105	98.82 68	99.39 164	99.71 158	98.96 25	99.85 155	98.59 159	99.80 103	99.77 85

PAPM_NR		99.04 116	98.84 128	99.66 73	99.74 83	99.44 99	99.39 227	99.38 237	97.70 200	99.28 187	99.28 313	98.34 93	99.85 155	96.96 301	99.45 152	99.69 118

testing99		97.36 306	96.94 313	98.63 250	99.18 274	96.70 315	99.30 255	98.93 337	97.71 197	98.23 330	98.26 384	84.92 388	99.84 162	98.04 215	97.85 258	99.35 216

testing222		97.16 314	96.50 323	99.16 177	99.16 284	98.47 222	99.27 270	98.66 376	97.71 197	98.23 330	98.15 387	82.28 401	99.84 162	97.36 277	97.66 264	99.18 232

test1111		98.04 217	98.11 193	97.83 330	99.74 83	93.82 379	99.58 114	95.40 412	99.12 28	99.65 97	99.93 9	90.73 336	99.84 162	99.43 50	99.38 156	99.82 57

ECVR-MVS		98.04 217	98.05 202	98.00 317	99.74 83	94.37 374	99.59 106	94.98 413	99.13 23	99.66 90	99.93 9	90.67 337	99.84 162	99.40 51	99.38 156	99.80 73

test_yl		98.86 137	98.63 150	99.54 102	99.49 186	99.18 129	99.50 170	99.07 321	98.22 130	99.61 112	99.51 247	95.37 198	99.84 162	98.60 157	98.33 229	99.59 154

DCV-MVSNet		98.86 137	98.63 150	99.54 102	99.49 186	99.18 129	99.50 170	99.07 321	98.22 130	99.61 112	99.51 247	95.37 198	99.84 162	98.60 157	98.33 229	99.59 154

Fast-Effi-MVS+		98.70 159	98.43 170	99.51 118	99.51 175	99.28 118	99.52 154	99.47 181	96.11 341	99.01 244	99.34 299	96.20 168	99.84 162	97.88 224	98.82 204	99.39 210

TSAR-MVS + GP.		99.36 59	99.36 38	99.36 143	99.67 114	98.61 204	99.07 316	99.33 265	99.00 46	99.82 41	99.81 95	99.06 16	99.84 162	99.09 85	99.42 154	99.65 132

tpmrst		98.33 185	98.48 168	97.90 324	99.16 284	94.78 367	99.31 253	99.11 314	97.27 247	99.45 143	99.59 216	95.33 200	99.84 162	98.48 173	98.61 212	99.09 240

Vis-MVSNet		99.12 101	98.97 106	99.56 99	99.78 56	99.10 141	99.68 66	99.66 28	98.49 99	99.86 33	99.87 48	94.77 224	99.84 162	99.19 73	99.41 155	99.74 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 166	98.34 176	99.51 118	99.40 216	99.03 151	98.80 363	99.36 246	96.33 322	99.00 248	99.12 335	98.46 84	99.84 162	95.23 351	99.37 163	99.66 128

PatchMatch-RL		98.84 147	98.62 155	99.52 116	99.71 99	99.28 118	99.06 319	99.77 9	97.74 195	99.50 135	99.53 240	95.41 196	99.84 162	97.17 291	99.64 137	99.44 202

EPP-MVSNet		99.13 95	98.99 102	99.53 110	99.65 130	99.06 148	99.81 20	99.33 265	97.43 233	99.60 115	99.88 39	97.14 131	99.84 162	99.13 79	98.94 193	99.69 118

testing11		97.50 296	97.10 307	98.71 245	99.20 268	96.91 307	99.29 260	98.82 356	97.89 174	98.21 333	98.40 378	85.63 383	99.83 175	98.45 178	98.04 249	99.37 214

thres100view900		97.76 263	97.45 270	98.69 247	99.72 94	97.86 256	99.59 106	98.74 366	97.93 170	99.26 196	98.62 370	91.75 317	99.83 175	93.22 375	98.18 242	98.37 358

tfpn200view9		97.72 273	97.38 283	98.72 243	99.69 108	97.96 248	99.50 170	98.73 372	97.83 183	99.17 217	98.45 376	91.67 321	99.83 175	93.22 375	98.18 242	98.37 358

test_prior				99.68 71	99.67 114	99.48 94		99.56 70					99.83 175			99.74 95

1314		98.68 161	98.54 165	99.11 183	98.89 328	98.65 198	99.27 270	99.49 149	96.89 283	97.99 343	99.56 228	97.72 116	99.83 175	97.74 242	99.27 167	98.84 264

thres400		97.77 262	97.38 283	98.92 208	99.69 108	97.96 248	99.50 170	98.73 372	97.83 183	99.17 217	98.45 376	91.67 321	99.83 175	93.22 375	98.18 242	98.96 258

casdiffmvs		99.13 95	98.98 105	99.56 99	99.65 130	99.16 132	99.56 127	99.50 139	98.33 117	99.41 157	99.86 52	95.92 179	99.83 175	99.45 49	99.16 173	99.70 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS-test		99.49 25	99.48 18	99.54 102	99.78 56	99.30 116	99.89 2	99.58 62	98.56 93	99.73 68	99.69 172	98.55 78	99.82 182	99.69 21	99.85 75	99.48 186

MVS_Test		99.10 109	98.97 106	99.48 124	99.49 186	99.14 137	99.67 69	99.34 258	97.31 244	99.58 119	99.76 139	97.65 117	99.82 182	98.87 112	99.07 185	99.46 197

dp		97.75 267	97.80 226	97.59 343	99.10 295	93.71 382	99.32 250	98.88 349	96.48 314	99.08 232	99.55 231	92.67 295	99.82 182	96.52 321	98.58 215	99.24 229

RPSCF		98.22 192	98.62 155	96.99 357	99.82 42	91.58 396	99.72 52	99.44 209	96.61 302	99.66 90	99.89 32	95.92 179	99.82 182	97.46 270	99.10 182	99.57 161

PMMVS		98.80 151	98.62 155	99.34 145	99.27 251	98.70 194	98.76 367	99.31 279	97.34 241	99.21 206	99.07 337	97.20 130	99.82 182	98.56 166	98.87 199	99.52 173

UBG		97.85 246	97.48 264	98.95 202	99.25 257	97.64 267	99.24 283	98.74 366	97.90 173	98.64 302	98.20 386	88.65 361	99.81 187	98.27 194	98.40 225	99.42 204

EIA-MVS		99.18 84	99.09 84	99.45 130	99.49 186	99.18 129	99.67 69	99.53 100	97.66 205	99.40 162	99.44 268	98.10 103	99.81 187	98.94 100	99.62 140	99.35 216

Effi-MVS+		98.81 148	98.59 161	99.48 124	99.46 196	99.12 140	98.08 403	99.50 139	97.50 224	99.38 166	99.41 276	96.37 163	99.81 187	99.11 81	98.54 220	99.51 180

thres200		97.61 288	97.28 298	98.62 251	99.64 132	98.03 242	99.26 279	98.74 366	97.68 202	99.09 231	98.32 382	91.66 323	99.81 187	92.88 380	98.22 237	98.03 377

tpmvs		97.98 228	98.02 206	97.84 329	99.04 308	94.73 368	99.31 253	99.20 304	96.10 345	98.76 282	99.42 272	94.94 210	99.81 187	96.97 300	98.45 224	98.97 256

casdiffmvs_mvg		99.15 90	99.02 96	99.55 101	99.66 124	99.09 142	99.64 83	99.56 70	98.26 124	99.45 143	99.87 48	96.03 173	99.81 187	99.54 34	99.15 176	99.73 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepPCF-MVS	98.18 3	98.81 148	99.37 36	97.12 355	99.60 150	91.75 395	98.61 380	99.44 209	99.35 12	99.83 40	99.85 57	98.70 66	99.81 187	99.02 93	99.91 34	99.81 64

DPM-MVS		98.95 128	98.71 141	99.66 73	99.63 135	99.55 81	98.64 379	99.10 315	97.93 170	99.42 153	99.55 231	98.67 69	99.80 194	95.80 336	99.68 132	99.61 147

DP-MVS Recon		99.12 101	98.95 112	99.65 77	99.74 83	99.70 49	99.27 270	99.57 65	96.40 321	99.42 153	99.68 178	98.75 58	99.80 194	97.98 218	99.72 124	99.44 202

MVS_111021_LR		99.41 51	99.33 44	99.65 77	99.77 63	99.51 90	98.94 349	99.85 6	98.82 68	99.65 97	99.74 147	98.51 81	99.80 194	98.83 125	99.89 54	99.64 139

CS-MVS		99.50 23	99.48 18	99.54 102	99.76 66	99.42 101	99.90 1	99.55 78	98.56 93	99.78 53	99.70 162	98.65 71	99.79 197	99.65 25	99.78 110	99.41 207

Fast-Effi-MVS+-dtu		98.77 154	98.83 130	98.60 252	99.41 211	96.99 301	99.52 154	99.49 149	98.11 147	99.24 198	99.34 299	96.96 141	99.79 197	97.95 220	99.45 152	99.02 251

baseline1		98.31 186	97.95 213	99.38 142	99.50 184	98.74 191	99.59 106	98.93 337	98.41 107	99.14 220	99.60 214	94.59 235	99.79 197	98.48 173	93.29 373	99.61 147

baseline		99.15 90	99.02 96	99.53 110	99.66 124	99.14 137	99.72 52	99.48 161	98.35 114	99.42 153	99.84 67	96.07 171	99.79 197	99.51 39	99.14 177	99.67 125

PVSNet_0	94.43 19	96.09 339	95.47 346	97.94 321	99.31 241	94.34 376	97.81 405	99.70 15	97.12 261	97.46 356	98.75 367	89.71 348	99.79 197	97.69 249	81.69 408	99.68 122

API-MVS		99.04 116	99.03 92	99.06 187	99.40 216	99.31 114	99.55 141	99.56 70	98.54 95	99.33 178	99.39 284	98.76 55	99.78 202	96.98 299	99.78 110	98.07 374

OMC-MVS		99.08 111	99.04 90	99.20 173	99.67 114	98.22 233	99.28 265	99.52 105	98.07 155	99.66 90	99.81 95	97.79 113	99.78 202	97.79 234	99.81 99	99.60 150

GeoE		98.85 144	98.62 155	99.53 110	99.61 145	99.08 145	99.80 25	99.51 119	97.10 265	99.31 180	99.78 127	95.23 206	99.77 204	98.21 197	99.03 188	99.75 91

alignmvs		98.81 148	98.56 164	99.58 95	99.43 204	99.42 101	99.51 163	98.96 335	98.61 89	99.35 174	98.92 357	94.78 221	99.77 204	99.35 54	98.11 247	99.54 166

tpm cat1		97.39 305	97.36 285	97.50 346	99.17 282	93.73 381	99.43 205	99.31 279	91.27 394	98.71 286	99.08 336	94.31 249	99.77 204	96.41 325	98.50 222	99.00 252

CostFormer		97.72 273	97.73 239	97.71 337	99.15 288	94.02 378	99.54 145	99.02 328	94.67 369	99.04 241	99.35 295	92.35 307	99.77 204	98.50 172	97.94 252	99.34 219

MGCFI-Net		99.01 123	98.85 126	99.50 123	99.42 206	99.26 121	99.82 16	99.48 161	98.60 90	99.28 187	98.81 362	97.04 137	99.76 208	99.29 64	97.87 256	99.47 192

test_241102_ONE					99.84 32	99.90 2		99.48 161	99.07 38	99.91 17	99.74 147	99.20 7	99.76 208

MDTV_nov1_ep13			98.32 178		99.11 292	94.44 373	99.27 270	98.74 366	97.51 223	99.40 162	99.62 207	94.78 221	99.76 208	97.59 254	98.81 206

sasdasda		99.02 119	98.86 124	99.51 118	99.42 206	99.32 110	99.80 25	99.48 161	98.63 86	99.31 180	98.81 362	97.09 133	99.75 211	99.27 67	97.90 253	99.47 192

canonicalmvs		99.02 119	98.86 124	99.51 118	99.42 206	99.32 110	99.80 25	99.48 161	98.63 86	99.31 180	98.81 362	97.09 133	99.75 211	99.27 67	97.90 253	99.47 192

Effi-MVS+-dtu		98.78 152	98.89 120	98.47 273	99.33 233	96.91 307	99.57 121	99.30 283	98.47 100	99.41 157	98.99 347	96.78 145	99.74 213	98.73 136	99.38 156	98.74 277

patchmatchnet-post											98.70 368	94.79 220	99.74 213

SCA		98.19 196	98.16 186	98.27 299	99.30 242	95.55 349	99.07 316	98.97 333	97.57 213	99.43 150	99.57 225	92.72 290	99.74 213	97.58 255	99.20 171	99.52 173

BH-untuned		98.42 175	98.36 174	98.59 253	99.49 186	96.70 315	99.27 270	99.13 313	97.24 251	98.80 277	99.38 286	95.75 186	99.74 213	97.07 295	99.16 173	99.33 220

BH-RMVSNet		98.41 177	98.08 198	99.40 137	99.41 211	98.83 184	99.30 255	98.77 362	97.70 200	98.94 257	99.65 190	92.91 285	99.74 213	96.52 321	99.55 147	99.64 139

MVS_111021_HR		99.41 51	99.32 46	99.66 73	99.72 94	99.47 96	98.95 347	99.85 6	98.82 68	99.54 128	99.73 153	98.51 81	99.74 213	98.91 106	99.88 57	99.77 85

test_post											65.99 419	94.65 234	99.73 219

XVG-ACMP-BASELINE		97.83 252	97.71 241	98.20 301	99.11 292	96.33 331	99.41 215	99.52 105	98.06 159	99.05 240	99.50 250	89.64 350	99.73 219	97.73 243	97.38 291	98.53 340

HyFIR lowres test		99.11 105	98.92 114	99.65 77	99.90 4	99.37 105	99.02 329	99.91 3	97.67 204	99.59 118	99.75 142	95.90 181	99.73 219	99.53 36	99.02 190	99.86 32

DeepMVS_CX				93.34 380	99.29 246	82.27 409		99.22 300	85.15 406	96.33 377	99.05 340	90.97 334	99.73 219	93.57 372	97.77 261	98.01 378

Patchmatch-test		97.93 234	97.65 246	98.77 240	99.18 274	97.07 292	99.03 326	99.14 312	96.16 336	98.74 283	99.57 225	94.56 237	99.72 223	93.36 374	99.11 179	99.52 173

LPG-MVS_test		98.22 192	98.13 191	98.49 266	99.33 233	97.05 294	99.58 114	99.55 78	97.46 226	99.24 198	99.83 72	92.58 297	99.72 223	98.09 206	97.51 277	98.68 295

LGP-MVS_train				98.49 266	99.33 233	97.05 294		99.55 78	97.46 226	99.24 198	99.83 72	92.58 297	99.72 223	98.09 206	97.51 277	98.68 295

BH-w/o		98.00 226	97.89 222	98.32 291	99.35 228	96.20 337	99.01 334	98.90 346	96.42 319	98.38 321	99.00 346	95.26 204	99.72 223	96.06 329	98.61 212	99.03 249

ACMP	97.20 11	98.06 211	97.94 215	98.45 276	99.37 224	97.01 299	99.44 201	99.49 149	97.54 219	98.45 318	99.79 120	91.95 313	99.72 223	97.91 222	97.49 282	98.62 323
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 221	97.90 218	98.40 284	99.23 261	96.80 313	99.70 56	99.60 54	97.12 261	98.18 335	99.70 162	91.73 319	99.72 223	98.39 181	97.45 284	98.68 295
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_post1							99.23 285				65.14 420	94.18 254	99.71 229	97.58 255

ADS-MVSNet		98.20 195	98.08 198	98.56 260	99.33 233	96.48 326	99.23 285	99.15 310	96.24 329	99.10 228	99.67 184	94.11 255	99.71 229	96.81 309	99.05 186	99.48 186

JIA-IIPM		97.50 296	97.02 310	98.93 206	98.73 352	97.80 258	99.30 255	98.97 333	91.73 393	98.91 260	94.86 408	95.10 208	99.71 229	97.58 255	97.98 250	99.28 224

EPMVS		97.82 255	97.65 246	98.35 288	98.88 329	95.98 341	99.49 181	94.71 415	97.57 213	99.26 196	99.48 258	92.46 304	99.71 229	97.87 226	99.08 184	99.35 216

TDRefinement		95.42 348	94.57 355	97.97 319	89.83 418	96.11 340	99.48 185	98.75 363	96.74 290	96.68 374	99.88 39	88.65 361	99.71 229	98.37 184	82.74 407	98.09 373

ACMM	97.58 5	98.37 183	98.34 176	98.48 268	99.41 211	97.10 288	99.56 127	99.45 201	98.53 96	99.04 241	99.85 57	93.00 281	99.71 229	98.74 134	97.45 284	98.64 314
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 231	97.77 232	98.57 257	99.59 152	96.61 322	99.45 195	99.08 318	98.21 132	98.88 265	99.80 108	88.66 360	99.70 235	98.58 160	97.72 262	99.39 210

CHOSEN 280x420		99.12 101	99.13 78	99.08 184	99.66 124	97.89 253	98.43 390	99.71 13	98.88 62	99.62 109	99.76 139	96.63 151	99.70 235	99.46 48	99.99 1	99.66 128

EC-MVSNet		99.44 42	99.39 32	99.58 95	99.56 160	99.49 92	99.88 4	99.58 62	98.38 109	99.73 68	99.69 172	98.20 99	99.70 235	99.64 27	99.82 96	99.54 166

PatchmatchNet		98.31 186	98.36 174	98.19 302	99.16 284	95.32 357	99.27 270	98.92 340	97.37 239	99.37 168	99.58 220	94.90 214	99.70 235	97.43 273	99.21 170	99.54 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 206	97.99 208	98.44 279	99.41 211	96.96 305	99.60 100	99.56 70	98.09 150	98.15 336	99.91 20	90.87 335	99.70 235	98.88 109	97.45 284	98.67 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 296	96.90 314	99.29 160	99.23 261	98.78 190	99.32 250	98.90 346	97.52 222	98.56 311	98.09 392	84.72 390	99.69 240	97.86 227	97.88 255	99.39 210

HQP_MVS		98.27 191	98.22 184	98.44 279	99.29 246	96.97 303	99.39 227	99.47 181	98.97 54	99.11 225	99.61 211	92.71 292	99.69 240	97.78 235	97.63 265	98.67 302

plane_prior5								99.47 181					99.69 240	97.78 235	97.63 265	98.67 302

D2MVS		98.41 177	98.50 167	98.15 307	99.26 253	96.62 321	99.40 223	99.61 48	97.71 197	98.98 250	99.36 292	96.04 172	99.67 243	98.70 139	97.41 289	98.15 370

IS-MVSNet		99.05 115	98.87 122	99.57 97	99.73 90	99.32 110	99.75 42	99.20 304	98.02 165	99.56 123	99.86 52	96.54 155	99.67 243	98.09 206	99.13 178	99.73 100

CLD-MVS		98.16 200	98.10 194	98.33 289	99.29 246	96.82 312	98.75 368	99.44 209	97.83 183	99.13 221	99.55 231	92.92 283	99.67 243	98.32 191	97.69 263	98.48 344
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 311	97.30 295	97.09 356	99.43 204	93.31 387	99.73 50	98.87 351	98.83 67	99.28 187	99.80 108	84.45 391	99.66 246	97.88 224	97.45 284	98.30 360

AUN-MVS		96.88 322	96.31 328	98.59 253	99.48 193	97.04 297	99.27 270	99.22 300	97.44 232	98.51 314	99.41 276	91.97 312	99.66 246	97.71 246	83.83 405	99.07 246

UniMVSNet_ETH3D		97.32 308	96.81 316	98.87 223	99.40 216	97.46 272	99.51 163	99.53 100	95.86 349	98.54 313	99.77 135	82.44 399	99.66 246	98.68 144	97.52 276	99.50 184

OPM-MVS		98.19 196	98.10 194	98.45 276	98.88 329	97.07 292	99.28 265	99.38 237	98.57 92	99.22 203	99.81 95	92.12 309	99.66 246	98.08 210	97.54 274	98.61 332
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 237	97.78 230	98.32 291	99.46 196	96.68 319	99.56 127	99.54 87	98.41 107	97.79 352	99.87 48	90.18 344	99.66 246	98.05 214	97.18 298	98.62 323

hse-mvs2		97.50 296	97.14 304	98.59 253	99.49 186	97.05 294	99.28 265	99.22 300	98.94 57	99.66 90	99.42 272	94.93 211	99.65 251	99.48 45	83.80 406	99.08 241

VPA-MVSNet		98.29 189	97.95 213	99.30 157	99.16 284	99.54 83	99.50 170	99.58 62	98.27 122	99.35 174	99.37 289	92.53 299	99.65 251	99.35 54	94.46 355	98.72 279

TR-MVS		97.76 263	97.41 281	98.82 232	99.06 304	97.87 254	98.87 357	98.56 379	96.63 301	98.68 294	99.22 322	92.49 300	99.65 251	95.40 347	97.79 260	98.95 260

gm-plane-assit					98.54 372	92.96 389			94.65 370		99.15 330		99.64 254	97.56 260

HQP4-MVS										98.66 295			99.64 254			98.64 314

HQP-MVS		98.02 221	97.90 218	98.37 287	99.19 271	96.83 310	98.98 340	99.39 229	98.24 126	98.66 295	99.40 280	92.47 301	99.64 254	97.19 288	97.58 270	98.64 314

PAPM		97.59 289	97.09 308	99.07 185	99.06 304	98.26 231	98.30 397	99.10 315	94.88 364	98.08 338	99.34 299	96.27 166	99.64 254	89.87 394	98.92 196	99.31 222

TAPA-MVS	97.07 15	97.74 269	97.34 290	98.94 204	99.70 104	97.53 270	99.25 281	99.51 119	91.90 392	99.30 183	99.63 202	98.78 51	99.64 254	88.09 401	99.87 60	99.65 132
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 181	98.09 197	99.24 169	99.26 253	99.32 110	99.56 127	99.55 78	97.45 229	98.71 286	99.83 72	93.23 276	99.63 259	98.88 109	96.32 313	98.76 272

ITE_SJBPF				98.08 310	99.29 246	96.37 329		98.92 340	98.34 115	98.83 273	99.75 142	91.09 332	99.62 260	95.82 334	97.40 290	98.25 364

LF4IMVS		97.52 293	97.46 269	97.70 338	98.98 318	95.55 349	99.29 260	98.82 356	98.07 155	98.66 295	99.64 196	89.97 345	99.61 261	97.01 296	96.68 303	97.94 385

tpm		97.67 283	97.55 255	98.03 312	99.02 310	95.01 363	99.43 205	98.54 381	96.44 317	99.12 223	99.34 299	91.83 316	99.60 262	97.75 241	96.46 309	99.48 186

tpm2		97.44 303	97.34 290	97.74 336	99.15 288	94.36 375	99.45 195	98.94 336	93.45 383	98.90 262	99.44 268	91.35 329	99.59 263	97.31 279	98.07 248	99.29 223

baseline2		97.87 243	97.55 255	98.82 232	99.18 274	98.02 243	99.41 215	96.58 409	96.97 276	96.51 375	99.17 327	93.43 273	99.57 264	97.71 246	99.03 188	98.86 262

MS-PatchMatch		97.24 313	97.32 293	96.99 357	98.45 375	93.51 386	98.82 361	99.32 275	97.41 236	98.13 337	99.30 309	88.99 354	99.56 265	95.68 340	99.80 103	97.90 388

TinyColmap		97.12 316	96.89 315	97.83 330	99.07 302	95.52 352	98.57 383	98.74 366	97.58 212	97.81 351	99.79 120	88.16 368	99.56 265	95.10 352	97.21 296	98.39 356

USDC		97.34 307	97.20 302	97.75 335	99.07 302	95.20 359	98.51 387	99.04 325	97.99 166	98.31 325	99.86 52	89.02 353	99.55 267	95.67 341	97.36 292	98.49 343

MSLP-MVS++		99.46 34	99.47 20	99.44 134	99.60 150	99.16 132	99.41 215	99.71 13	98.98 51	99.45 143	99.78 127	99.19 9	99.54 268	99.28 65	99.84 83	99.63 143

TAMVS		99.12 101	99.08 85	99.24 169	99.46 196	98.55 208	99.51 163	99.46 190	98.09 150	99.45 143	99.82 81	98.34 93	99.51 269	98.70 139	98.93 194	99.67 125

EPNet_dtu		98.03 219	97.96 211	98.23 300	98.27 378	95.54 351	99.23 285	98.75 363	99.02 41	97.82 350	99.71 158	96.11 170	99.48 270	93.04 378	99.65 136	99.69 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 326	96.22 330	97.97 319	97.00 400	96.28 333	98.66 377	99.03 327	96.61 302	96.93 372	99.79 120	87.20 376	99.47 271	96.65 319	94.13 362	98.16 369

EG-PatchMatch MVS		95.97 341	95.69 342	96.81 364	97.78 385	92.79 390	99.16 297	98.93 337	96.16 336	94.08 393	99.22 322	82.72 397	99.47 271	95.67 341	97.50 279	98.17 368

MVP-Stereo		97.81 257	97.75 237	97.99 318	97.53 389	96.60 323	98.96 344	98.85 353	97.22 253	97.23 363	99.36 292	95.28 201	99.46 273	95.51 343	99.78 110	97.92 387
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 168	98.67 145	98.30 293	99.35 228	95.59 348	99.50 170	99.55 78	98.60 90	99.39 164	99.83 72	94.48 242	99.45 274	98.75 133	98.56 218	99.85 36

test-LLR		98.06 211	97.90 218	98.55 262	98.79 340	97.10 288	98.67 374	97.75 395	97.34 241	98.61 307	98.85 359	94.45 244	99.45 274	97.25 282	99.38 156	99.10 236

TESTMET0.1,1		97.55 291	97.27 301	98.40 284	98.93 323	96.53 324	98.67 374	97.61 398	96.96 277	98.64 302	99.28 313	88.63 363	99.45 274	97.30 280	99.38 156	99.21 231

test-mter		97.49 301	97.13 306	98.55 262	98.79 340	97.10 288	98.67 374	97.75 395	96.65 297	98.61 307	98.85 359	88.23 367	99.45 274	97.25 282	99.38 156	99.10 236

mvs_anonymous		99.03 118	98.99 102	99.16 177	99.38 221	98.52 214	99.51 163	99.38 237	97.79 188	99.38 166	99.81 95	97.30 127	99.45 274	99.35 54	98.99 191	99.51 180

tfpnnormal		97.84 250	97.47 267	98.98 197	99.20 268	99.22 126	99.64 83	99.61 48	96.32 323	98.27 329	99.70 162	93.35 275	99.44 279	95.69 339	95.40 338	98.27 362

v7n		97.87 243	97.52 259	98.92 208	98.76 350	98.58 206	99.84 12	99.46 190	96.20 332	98.91 260	99.70 162	94.89 215	99.44 279	96.03 330	93.89 367	98.75 274

jajsoiax		98.43 174	98.28 181	98.88 219	98.60 367	98.43 224	99.82 16	99.53 100	98.19 134	98.63 304	99.80 108	93.22 278	99.44 279	99.22 71	97.50 279	98.77 270

mvs_tets		98.40 180	98.23 183	98.91 212	98.67 360	98.51 216	99.66 74	99.53 100	98.19 134	98.65 301	99.81 95	92.75 287	99.44 279	99.31 61	97.48 283	98.77 270

Vis-MVSNet (Re-imp)		98.87 134	98.72 139	99.31 152	99.71 99	98.88 175	99.80 25	99.44 209	97.91 172	99.36 171	99.78 127	95.49 195	99.43 283	97.91 222	99.11 179	99.62 145

OPU-MVS				99.64 82	99.56 160	99.72 45	99.60 100				99.70 162	99.27 5	99.42 284	98.24 196	99.80 103	99.79 77

Anonymous20231211		97.88 241	97.54 258	98.90 214	99.71 99	98.53 210	99.48 185	99.57 65	94.16 374	98.81 275	99.68 178	93.23 276	99.42 284	98.84 122	94.42 357	98.76 272

ttmdpeth		97.80 259	97.63 250	98.29 294	98.77 348	97.38 275	99.64 83	99.36 246	98.78 76	96.30 378	99.58 220	92.34 308	99.39 286	98.36 186	95.58 333	98.10 372

VPNet		97.84 250	97.44 275	99.01 193	99.21 266	98.94 169	99.48 185	99.57 65	98.38 109	99.28 187	99.73 153	88.89 355	99.39 286	99.19 73	93.27 374	98.71 281

nrg030		98.64 165	98.42 171	99.28 164	99.05 307	99.69 50	99.81 20	99.46 190	98.04 162	99.01 244	99.82 81	96.69 149	99.38 288	99.34 58	94.59 354	98.78 267

GA-MVS		97.85 246	97.47 267	99.00 195	99.38 221	97.99 245	98.57 383	99.15 310	97.04 272	98.90 262	99.30 309	89.83 347	99.38 288	96.70 314	98.33 229	99.62 145

UniMVSNet (Re)		98.29 189	98.00 207	99.13 182	99.00 312	99.36 108	99.49 181	99.51 119	97.95 168	98.97 252	99.13 332	96.30 165	99.38 288	98.36 186	93.34 372	98.66 310

FIs		98.78 152	98.63 150	99.23 171	99.18 274	99.54 83	99.83 15	99.59 58	98.28 120	98.79 279	99.81 95	96.75 147	99.37 291	99.08 86	96.38 311	98.78 267

PS-MVSNAJss		98.92 130	98.92 114	98.90 214	98.78 343	98.53 210	99.78 32	99.54 87	98.07 155	99.00 248	99.76 139	99.01 18	99.37 291	99.13 79	97.23 295	98.81 265

CDS-MVSNet		99.09 110	99.03 92	99.25 167	99.42 206	98.73 192	99.45 195	99.46 190	98.11 147	99.46 142	99.77 135	98.01 108	99.37 291	98.70 139	98.92 196	99.66 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 345	95.16 350	97.51 345	99.30 242	93.69 383	98.88 355	95.78 410	85.09 407	98.78 280	92.65 410	91.29 330	99.37 291	94.85 357	99.85 75	99.46 197

v1192		97.81 257	97.44 275	98.91 212	98.88 329	98.68 195	99.51 163	99.34 258	96.18 334	99.20 209	99.34 299	94.03 258	99.36 295	95.32 349	95.18 342	98.69 290

EI-MVSNet		98.67 162	98.67 145	98.68 248	99.35 228	97.97 246	99.50 170	99.38 237	96.93 282	99.20 209	99.83 72	97.87 110	99.36 295	98.38 182	97.56 272	98.71 281

MVSTER		98.49 169	98.32 178	99.00 195	99.35 228	99.02 152	99.54 145	99.38 237	97.41 236	99.20 209	99.73 153	93.86 266	99.36 295	98.87 112	97.56 272	98.62 323

gg-mvs-nofinetune		96.17 337	95.32 349	98.73 242	98.79 340	98.14 237	99.38 232	94.09 416	91.07 397	98.07 341	91.04 414	89.62 351	99.35 298	96.75 311	99.09 183	98.68 295

pm-mvs1		97.68 280	97.28 298	98.88 219	99.06 304	98.62 202	99.50 170	99.45 201	96.32 323	97.87 348	99.79 120	92.47 301	99.35 298	97.54 262	93.54 371	98.67 302

OurMVSNet-221017-0		97.88 241	97.77 232	98.19 302	98.71 356	96.53 324	99.88 4	99.00 330	97.79 188	98.78 280	99.94 6	91.68 320	99.35 298	97.21 284	96.99 302	98.69 290

EGC-MVSNET		82.80 379	77.86 385	97.62 341	97.91 382	96.12 339	99.33 249	99.28 289	8.40 422	25.05 423	99.27 316	84.11 392	99.33 301	89.20 396	98.22 237	97.42 396

pmmvs6		96.53 329	96.09 334	97.82 332	98.69 358	95.47 353	99.37 234	99.47 181	93.46 382	97.41 357	99.78 127	87.06 377	99.33 301	96.92 306	92.70 381	98.65 312

V42		98.06 211	97.79 227	98.86 226	98.98 318	98.84 181	99.69 60	99.34 258	96.53 309	99.30 183	99.37 289	94.67 232	99.32 303	97.57 259	94.66 352	98.42 352

lessismore_v0				97.79 334	98.69 358	95.44 355		94.75 414		95.71 384	99.87 48	88.69 359	99.32 303	95.89 333	94.93 349	98.62 323

OpenMVS_ROB	92.34 20	94.38 359	93.70 365	96.41 369	97.38 391	93.17 388	99.06 319	98.75 363	86.58 405	94.84 391	98.26 384	81.53 402	99.32 303	89.01 397	97.87 256	96.76 399

v8		97.95 233	97.63 250	98.93 206	98.95 322	98.81 187	99.80 25	99.41 220	96.03 346	99.10 228	99.42 272	94.92 213	99.30 306	96.94 303	94.08 364	98.66 310

v1921920		97.80 259	97.45 270	98.84 230	98.80 339	98.53 210	99.52 154	99.34 258	96.15 338	99.24 198	99.47 261	93.98 260	99.29 307	95.40 347	95.13 344	98.69 290

anonymousdsp		98.44 173	98.28 181	98.94 204	98.50 373	98.96 163	99.77 34	99.50 139	97.07 267	98.87 268	99.77 135	94.76 225	99.28 308	98.66 146	97.60 268	98.57 338

MVSFormer		99.17 86	99.12 79	99.29 160	99.51 175	98.94 169	99.88 4	99.46 190	97.55 216	99.80 46	99.65 190	97.39 121	99.28 308	99.03 91	99.85 75	99.65 132

test_djsdf		98.67 162	98.57 162	98.98 197	98.70 357	98.91 173	99.88 4	99.46 190	97.55 216	99.22 203	99.88 39	95.73 187	99.28 308	99.03 91	97.62 267	98.75 274

cascas		97.69 278	97.43 279	98.48 268	98.60 367	97.30 277	98.18 401	99.39 229	92.96 386	98.41 319	98.78 366	93.77 269	99.27 311	98.16 203	98.61 212	98.86 262

v144192		97.92 237	97.60 253	98.87 223	98.83 338	98.65 198	99.55 141	99.34 258	96.20 332	99.32 179	99.40 280	94.36 246	99.26 312	96.37 326	95.03 346	98.70 286

dmvs_re		98.08 209	98.16 186	97.85 327	99.55 164	94.67 370	99.70 56	98.92 340	98.15 139	99.06 238	99.35 295	93.67 272	99.25 313	97.77 238	97.25 294	99.64 139

v2v482		98.06 211	97.77 232	98.92 208	98.90 327	98.82 185	99.57 121	99.36 246	96.65 297	99.19 212	99.35 295	94.20 251	99.25 313	97.72 245	94.97 347	98.69 290

v1240		97.69 278	97.32 293	98.79 238	98.85 336	98.43 224	99.48 185	99.36 246	96.11 341	99.27 192	99.36 292	93.76 270	99.24 315	94.46 361	95.23 341	98.70 286

WBMVS		97.74 269	97.50 262	98.46 274	99.24 259	97.43 273	99.21 291	99.42 217	97.45 229	98.96 254	99.41 276	88.83 356	99.23 316	98.94 100	96.02 318	98.71 281

v1144		97.98 228	97.69 242	98.85 229	98.87 332	98.66 197	99.54 145	99.35 253	96.27 327	99.23 202	99.35 295	94.67 232	99.23 316	96.73 312	95.16 343	98.68 295

v10		97.85 246	97.52 259	98.86 226	98.99 315	98.67 196	99.75 42	99.41 220	95.70 350	98.98 250	99.41 276	94.75 226	99.23 316	96.01 332	94.63 353	98.67 302

WR-MVS_H		98.13 203	97.87 223	98.90 214	99.02 310	98.84 181	99.70 56	99.59 58	97.27 247	98.40 320	99.19 326	95.53 193	99.23 316	98.34 188	93.78 369	98.61 332

miper_enhance_ethall		98.16 200	98.08 198	98.41 282	98.96 321	97.72 262	98.45 389	99.32 275	96.95 279	98.97 252	99.17 327	97.06 136	99.22 320	97.86 227	95.99 321	98.29 361

GG-mvs-BLEND				98.45 276	98.55 371	98.16 235	99.43 205	93.68 417		97.23 363	98.46 375	89.30 352	99.22 320	95.43 346	98.22 237	97.98 383

FC-MVSNet-test		98.75 155	98.62 155	99.15 181	99.08 301	99.45 98	99.86 11	99.60 54	98.23 129	98.70 292	99.82 81	96.80 144	99.22 320	99.07 87	96.38 311	98.79 266

UniMVSNet_NR-MVSNet		98.22 192	97.97 210	98.96 200	98.92 325	98.98 156	99.48 185	99.53 100	97.76 192	98.71 286	99.46 265	96.43 162	99.22 320	98.57 163	92.87 379	98.69 290

DU-MVS		98.08 209	97.79 227	98.96 200	98.87 332	98.98 156	99.41 215	99.45 201	97.87 176	98.71 286	99.50 250	94.82 217	99.22 320	98.57 163	92.87 379	98.68 295

cl____		98.01 224	97.84 225	98.55 262	99.25 257	97.97 246	98.71 372	99.34 258	96.47 316	98.59 310	99.54 236	95.65 190	99.21 325	97.21 284	95.77 327	98.46 349

WR-MVS		98.06 211	97.73 239	99.06 187	98.86 335	99.25 123	99.19 293	99.35 253	97.30 245	98.66 295	99.43 270	93.94 261	99.21 325	98.58 160	94.28 359	98.71 281

test_0402		96.64 327	96.24 329	97.85 327	98.85 336	96.43 328	99.44 201	99.26 293	93.52 380	96.98 370	99.52 243	88.52 364	99.20 327	92.58 385	97.50 279	97.93 386

SixPastTwentyTwo		97.50 296	97.33 292	98.03 312	98.65 361	96.23 336	99.77 34	98.68 375	97.14 258	97.90 346	99.93 9	90.45 338	99.18 328	97.00 297	96.43 310	98.67 302

cl22		97.85 246	97.64 249	98.48 268	99.09 298	97.87 254	98.60 382	99.33 265	97.11 264	98.87 268	99.22 322	92.38 306	99.17 329	98.21 197	95.99 321	98.42 352

WB-MVSnew		97.65 285	97.65 246	97.63 340	98.78 343	97.62 268	99.13 303	98.33 384	97.36 240	99.07 233	98.94 353	95.64 191	99.15 330	92.95 379	98.68 211	96.12 406

IterMVS-SCA-FT		97.82 255	97.75 237	98.06 311	99.57 156	96.36 330	99.02 329	99.49 149	97.18 255	98.71 286	99.72 157	92.72 290	99.14 331	97.44 272	95.86 326	98.67 302

pmmvs5		97.52 293	97.30 295	98.16 304	98.57 370	96.73 314	99.27 270	98.90 346	96.14 339	98.37 322	99.53 240	91.54 326	99.14 331	97.51 264	95.87 325	98.63 321

v148		97.79 261	97.55 255	98.50 265	98.74 351	97.72 262	99.54 145	99.33 265	96.26 328	98.90 262	99.51 247	94.68 231	99.14 331	97.83 231	93.15 376	98.63 321

miper_ehance_all_eth		98.18 198	98.10 194	98.41 282	99.23 261	97.72 262	98.72 371	99.31 279	96.60 305	98.88 265	99.29 311	97.29 128	99.13 334	97.60 253	95.99 321	98.38 357

NR-MVSNet		97.97 231	97.61 252	99.02 192	98.87 332	99.26 121	99.47 191	99.42 217	97.63 207	97.08 368	99.50 250	95.07 209	99.13 334	97.86 227	93.59 370	98.68 295

IterMVS		97.83 252	97.77 232	98.02 314	99.58 154	96.27 334	99.02 329	99.48 161	97.22 253	98.71 286	99.70 162	92.75 287	99.13 334	97.46 270	96.00 320	98.67 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 360	94.90 352	91.84 385	97.24 395	80.01 415	98.52 386	99.48 161	89.01 402	91.99 402	99.67 184	85.67 382	99.13 334	95.44 345	97.03 301	96.39 403
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 216	97.96 211	98.33 289	99.26 253	97.38 275	98.56 385	99.31 279	96.65 297	98.88 265	99.52 243	96.58 153	99.12 338	97.39 275	95.53 336	98.47 346

pmmvs4		98.13 203	97.90 218	98.81 235	98.61 366	98.87 176	98.99 337	99.21 303	96.44 317	99.06 238	99.58 220	95.90 181	99.11 339	97.18 290	96.11 317	98.46 349

TransMVSNet (Re)		97.15 315	96.58 321	98.86 226	99.12 290	98.85 180	99.49 181	98.91 344	95.48 353	97.16 366	99.80 108	93.38 274	99.11 339	94.16 367	91.73 385	98.62 323

ambc				93.06 383	92.68 414	82.36 408	98.47 388	98.73 372		95.09 389	97.41 397	55.55 415	99.10 341	96.42 324	91.32 386	97.71 389

Baseline_NR-MVSNet		97.76 263	97.45 270	98.68 248	99.09 298	98.29 229	99.41 215	98.85 353	95.65 351	98.63 304	99.67 184	94.82 217	99.10 341	98.07 213	92.89 378	98.64 314

test_vis3_rt		87.04 375	85.81 378	90.73 389	93.99 413	81.96 410	99.76 37	90.23 424	92.81 388	81.35 412	91.56 412	40.06 421	99.07 343	94.27 364	88.23 399	91.15 412

CP-MVSNet		98.09 207	97.78 230	99.01 193	98.97 320	99.24 124	99.67 69	99.46 190	97.25 249	98.48 317	99.64 196	93.79 268	99.06 344	98.63 150	94.10 363	98.74 277

PS-CasMVS		97.93 234	97.59 254	98.95 202	98.99 315	99.06 148	99.68 66	99.52 105	97.13 259	98.31 325	99.68 178	92.44 305	99.05 345	98.51 171	94.08 364	98.75 274

K. test v3		97.10 317	96.79 317	98.01 315	98.72 354	96.33 331	99.87 8	97.05 401	97.59 210	96.16 380	99.80 108	88.71 358	99.04 346	96.69 315	96.55 308	98.65 312

new_pmnet		96.38 333	96.03 335	97.41 347	98.13 381	95.16 362	99.05 321	99.20 304	93.94 375	97.39 360	98.79 365	91.61 325	99.04 346	90.43 392	95.77 327	98.05 376

DIV-MVS_self_test		98.01 224	97.85 224	98.48 268	99.24 259	97.95 250	98.71 372	99.35 253	96.50 310	98.60 309	99.54 236	95.72 188	99.03 348	97.21 284	95.77 327	98.46 349

IterMVS-LS		98.46 172	98.42 171	98.58 256	99.59 152	98.00 244	99.37 234	99.43 215	96.94 281	99.07 233	99.59 216	97.87 110	99.03 348	98.32 191	95.62 332	98.71 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 285	97.68 243	97.55 344	98.62 364	94.97 364	98.84 359	99.30 283	96.83 288	98.19 334	99.34 299	97.01 139	99.02 350	95.00 355	96.01 319	98.64 314

Patchmtry		97.75 267	97.40 282	98.81 235	99.10 295	98.87 176	99.11 312	99.33 265	94.83 366	98.81 275	99.38 286	94.33 247	99.02 350	96.10 328	95.57 334	98.53 340

N_pmnet		94.95 354	95.83 340	92.31 384	98.47 374	79.33 416	99.12 306	92.81 422	93.87 376	97.68 353	99.13 332	93.87 265	99.01 352	91.38 389	96.19 315	98.59 336

CR-MVSNet		98.17 199	97.93 216	98.87 223	99.18 274	98.49 218	99.22 289	99.33 265	96.96 277	99.56 123	99.38 286	94.33 247	99.00 353	94.83 358	98.58 215	99.14 233

c3_l		98.12 205	98.04 203	98.38 286	99.30 242	97.69 266	98.81 362	99.33 265	96.67 295	98.83 273	99.34 299	97.11 132	98.99 354	97.58 255	95.34 339	98.48 344

test0.0.03 1		97.71 276	97.42 280	98.56 260	98.41 377	97.82 257	98.78 365	98.63 377	97.34 241	98.05 342	98.98 349	94.45 244	98.98 355	95.04 354	97.15 299	98.89 261

PatchT		97.03 319	96.44 325	98.79 238	98.99 315	98.34 228	99.16 297	99.07 321	92.13 391	99.52 132	97.31 401	94.54 240	98.98 355	88.54 399	98.73 209	99.03 249

GBi-Net		97.68 280	97.48 264	98.29 294	99.51 175	97.26 281	99.43 205	99.48 161	96.49 311	99.07 233	99.32 306	90.26 340	98.98 355	97.10 292	96.65 304	98.62 323

test1		97.68 280	97.48 264	98.29 294	99.51 175	97.26 281	99.43 205	99.48 161	96.49 311	99.07 233	99.32 306	90.26 340	98.98 355	97.10 292	96.65 304	98.62 323

FMVSNet3		98.03 219	97.76 236	98.84 230	99.39 219	98.98 156	99.40 223	99.38 237	96.67 295	99.07 233	99.28 313	92.93 282	98.98 355	97.10 292	96.65 304	98.56 339

FMVSNet2		97.72 273	97.36 285	98.80 237	99.51 175	98.84 181	99.45 195	99.42 217	96.49 311	98.86 272	99.29 311	90.26 340	98.98 355	96.44 323	96.56 307	98.58 337

FMVSNet1		96.84 323	96.36 327	98.29 294	99.32 240	97.26 281	99.43 205	99.48 161	95.11 358	98.55 312	99.32 306	83.95 393	98.98 355	95.81 335	96.26 314	98.62 323

ppachtmachnet_test		97.49 301	97.45 270	97.61 342	98.62 364	95.24 358	98.80 363	99.46 190	96.11 341	98.22 332	99.62 207	96.45 160	98.97 362	93.77 369	95.97 324	98.61 332

TranMVSNet+NR-MVSNet		97.93 234	97.66 245	98.76 241	98.78 343	98.62 202	99.65 80	99.49 149	97.76 192	98.49 316	99.60 214	94.23 250	98.97 362	98.00 217	92.90 377	98.70 286

MVStest1		96.08 340	95.48 345	97.89 325	98.93 323	96.70 315	99.56 127	99.35 253	92.69 389	91.81 403	99.46 265	89.90 346	98.96 364	95.00 355	92.61 382	98.00 381

test_method		91.10 370	91.36 372	90.31 390	95.85 403	73.72 423	94.89 411	99.25 295	68.39 414	95.82 383	99.02 344	80.50 404	98.95 365	93.64 371	94.89 351	98.25 364

ADS-MVSNet2		98.02 221	98.07 201	97.87 326	99.33 233	95.19 360	99.23 285	99.08 318	96.24 329	99.10 228	99.67 184	94.11 255	98.93 366	96.81 309	99.05 186	99.48 186

ET-MVSNet_ETH3D		96.49 330	95.64 344	99.05 189	99.53 168	98.82 185	98.84 359	97.51 399	97.63 207	84.77 408	99.21 325	92.09 310	98.91 367	98.98 96	92.21 384	99.41 207

miper_lstm_enhance		98.00 226	97.91 217	98.28 298	99.34 232	97.43 273	98.88 355	99.36 246	96.48 314	98.80 277	99.55 231	95.98 174	98.91 367	97.27 281	95.50 337	98.51 342

MonoMVSNet		98.38 181	98.47 169	98.12 309	98.59 369	96.19 338	99.72 52	98.79 361	97.89 174	99.44 148	99.52 243	96.13 169	98.90 369	98.64 148	97.54 274	99.28 224

PEN-MVS		97.76 263	97.44 275	98.72 243	98.77 348	98.54 209	99.78 32	99.51 119	97.06 269	98.29 328	99.64 196	92.63 296	98.89 370	98.09 206	93.16 375	98.72 279

testing3		97.28 309	96.76 318	98.82 232	99.37 224	98.07 241	99.45 195	99.36 246	97.56 215	97.89 347	98.95 352	83.70 394	98.82 371	96.03 330	98.56 218	99.58 158

testgi		97.65 285	97.50 262	98.13 308	99.36 227	96.45 327	99.42 212	99.48 161	97.76 192	97.87 348	99.45 267	91.09 332	98.81 372	94.53 360	98.52 221	99.13 235

testf1		90.42 373	90.68 374	89.65 393	97.78 385	73.97 421	99.13 303	98.81 358	89.62 399	91.80 404	98.93 354	62.23 413	98.80 373	86.61 407	91.17 387	96.19 404

APD_test2		90.42 373	90.68 374	89.65 393	97.78 385	73.97 421	99.13 303	98.81 358	89.62 399	91.80 404	98.93 354	62.23 413	98.80 373	86.61 407	91.17 387	96.19 404

MIMVSNet		97.73 271	97.45 270	98.57 257	99.45 202	97.50 271	99.02 329	98.98 332	96.11 341	99.41 157	99.14 331	90.28 339	98.74 375	95.74 337	98.93 194	99.47 192

LCM-MVSNet-Re		97.83 252	98.15 188	96.87 363	99.30 242	92.25 393	99.59 106	98.26 385	97.43 233	96.20 379	99.13 332	96.27 166	98.73 376	98.17 202	98.99 191	99.64 139

Syy-MVS		97.09 318	97.14 304	96.95 360	99.00 312	92.73 391	99.29 260	99.39 229	97.06 269	97.41 357	98.15 387	93.92 263	98.68 377	91.71 387	98.34 227	99.45 200

myMVS_eth3d		96.89 321	96.37 326	98.43 281	99.00 312	97.16 285	99.29 260	99.39 229	97.06 269	97.41 357	98.15 387	83.46 395	98.68 377	95.27 350	98.34 227	99.45 200

DTE-MVSNet		97.51 295	97.19 303	98.46 274	98.63 363	98.13 238	99.84 12	99.48 161	96.68 294	97.97 345	99.67 184	92.92 283	98.56 379	96.88 308	92.60 383	98.70 286

PC_three_1452									98.18 137	99.84 35	99.70 162	99.31 3	98.52 380	98.30 193	99.80 103	99.81 64

mvsany_test3		93.77 362	93.45 366	94.74 375	95.78 404	88.01 401	99.64 83	98.25 386	98.28 120	94.31 392	97.97 394	68.89 409	98.51 381	97.50 265	90.37 392	97.71 389

UnsupCasMVSNet_bld		93.53 363	92.51 369	96.58 368	97.38 391	93.82 379	98.24 398	99.48 161	91.10 396	93.10 397	96.66 403	74.89 407	98.37 382	94.03 368	87.71 400	97.56 394

Anonymous20240521		96.20 336	95.89 339	97.13 354	97.72 388	94.96 365	99.79 31	99.29 287	93.01 385	97.20 365	99.03 342	89.69 349	98.36 383	91.16 390	96.13 316	98.07 374

test_f		91.90 369	91.26 373	93.84 378	95.52 408	85.92 403	99.69 60	98.53 382	95.31 355	93.87 394	96.37 405	55.33 416	98.27 384	95.70 338	90.98 390	97.32 397

MDA-MVSNet_test_wron		95.45 347	94.60 354	98.01 315	98.16 380	97.21 284	99.11 312	99.24 297	93.49 381	80.73 414	98.98 349	93.02 280	98.18 385	94.22 366	94.45 356	98.64 314

UnsupCasMVSNet_eth		96.44 331	96.12 332	97.40 348	98.65 361	95.65 346	99.36 239	99.51 119	97.13 259	96.04 382	98.99 347	88.40 365	98.17 386	96.71 313	90.27 393	98.40 355

KD-MVS_2432*1600		94.62 355	93.72 363	97.31 349	97.19 397	95.82 344	98.34 393	99.20 304	95.00 362	97.57 354	98.35 380	87.95 370	98.10 387	92.87 381	77.00 412	98.01 378

miper_refine_blended		94.62 355	93.72 363	97.31 349	97.19 397	95.82 344	98.34 393	99.20 304	95.00 362	97.57 354	98.35 380	87.95 370	98.10 387	92.87 381	77.00 412	98.01 378

YYNet1		95.36 349	94.51 356	97.92 322	97.89 383	97.10 288	99.10 314	99.23 298	93.26 384	80.77 413	99.04 341	92.81 286	98.02 389	94.30 362	94.18 361	98.64 314

EU-MVSNet		97.98 228	98.03 204	97.81 333	98.72 354	96.65 320	99.66 74	99.66 28	98.09 150	98.35 323	99.82 81	95.25 205	98.01 390	97.41 274	95.30 340	98.78 267

Gipuma		90.99 371	90.15 376	93.51 379	98.73 352	90.12 399	93.98 412	99.45 201	79.32 410	92.28 400	94.91 407	69.61 408	97.98 391	87.42 403	95.67 331	92.45 410
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 350	94.73 353	97.15 352	95.53 407	95.94 342	99.35 244	99.10 315	95.13 356	93.55 395	97.54 396	88.15 369	97.91 392	94.58 359	89.69 396	97.61 392

PM-MVS		92.96 366	92.23 370	95.14 374	95.61 405	89.98 400	99.37 234	98.21 388	94.80 367	95.04 390	97.69 395	65.06 410	97.90 393	94.30 362	89.98 395	97.54 395

MDA-MVSNet-bldmvs		94.96 353	93.98 360	97.92 322	98.24 379	97.27 279	99.15 300	99.33 265	93.80 377	80.09 415	99.03 342	88.31 366	97.86 394	93.49 373	94.36 358	98.62 323

Patchmatch-RL test		95.84 343	95.81 341	95.95 372	95.61 405	90.57 398	98.24 398	98.39 383	95.10 360	95.20 387	98.67 369	94.78 221	97.77 395	96.28 327	90.02 394	99.51 180

Anonymous20231206		96.22 334	96.03 335	96.79 365	97.31 394	94.14 377	99.63 88	99.08 318	96.17 335	97.04 369	99.06 339	93.94 261	97.76 396	86.96 405	95.06 345	98.47 346

SD-MVS		99.41 51	99.52 12	99.05 189	99.74 83	99.68 51	99.46 194	99.52 105	99.11 29	99.88 24	99.91 20	99.43 1	97.70 397	98.72 137	99.93 25	99.77 85
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
DSMNet-mixed		97.25 311	97.35 287	96.95 360	97.84 384	93.61 385	99.57 121	96.63 407	96.13 340	98.87 268	98.61 372	94.59 235	97.70 397	95.08 353	98.86 200	99.55 164

dongtai		93.26 364	92.93 368	94.25 376	99.39 219	85.68 404	97.68 407	93.27 418	92.87 387	96.85 373	99.39 284	82.33 400	97.48 399	76.78 412	97.80 259	99.58 158

pmmvs3		94.09 361	93.25 367	96.60 367	94.76 412	94.49 372	98.92 351	98.18 390	89.66 398	96.48 376	98.06 393	86.28 379	97.33 400	89.68 395	87.20 401	97.97 384

KD-MVS_self_test		95.00 352	94.34 357	96.96 359	97.07 399	95.39 356	99.56 127	99.44 209	95.11 358	97.13 367	97.32 400	91.86 315	97.27 401	90.35 393	81.23 409	98.23 366

FMVSNet5		96.43 332	96.19 331	97.15 352	99.11 292	95.89 343	99.32 250	99.52 105	94.47 373	98.34 324	99.07 337	87.54 374	97.07 402	92.61 384	95.72 330	98.47 346

new-patchmatchnet		94.48 358	94.08 359	95.67 373	95.08 410	92.41 392	99.18 295	99.28 289	94.55 372	93.49 396	97.37 399	87.86 372	97.01 403	91.57 388	88.36 398	97.61 392

LCM-MVSNet		86.80 377	85.22 381	91.53 387	87.81 419	80.96 413	98.23 400	98.99 331	71.05 412	90.13 407	96.51 404	48.45 420	96.88 404	90.51 391	85.30 403	96.76 399

CL-MVSNet_self_test		94.49 357	93.97 361	96.08 371	96.16 402	93.67 384	98.33 395	99.38 237	95.13 356	97.33 361	98.15 387	92.69 294	96.57 405	88.67 398	79.87 410	97.99 382

MIMVSNet1		95.51 346	95.04 351	96.92 362	97.38 391	95.60 347	99.52 154	99.50 139	93.65 379	96.97 371	99.17 327	85.28 387	96.56 406	88.36 400	95.55 335	98.60 335

test20.03		96.12 338	95.96 337	96.63 366	97.44 390	95.45 354	99.51 163	99.38 237	96.55 308	96.16 380	99.25 319	93.76 270	96.17 407	87.35 404	94.22 360	98.27 362

tmp_tt		82.80 379	81.52 382	86.66 395	66.61 425	68.44 424	92.79 414	97.92 392	68.96 413	80.04 416	99.85 57	85.77 381	96.15 408	97.86 227	43.89 418	95.39 408

test_fmvs3		92.10 368	91.77 371	93.08 382	96.19 401	86.25 402	99.82 16	98.62 378	96.65 297	95.19 388	96.90 402	55.05 417	95.93 409	96.63 320	90.92 391	97.06 398

kuosan		90.92 372	90.11 377	93.34 380	98.78 343	85.59 405	98.15 402	93.16 420	89.37 401	92.07 401	98.38 379	81.48 403	95.19 410	62.54 419	97.04 300	99.25 228

dmvs_testset		95.02 351	96.12 332	91.72 386	99.10 295	80.43 414	99.58 114	97.87 394	97.47 225	95.22 386	98.82 361	93.99 259	95.18 411	88.09 401	94.91 350	99.56 163

PMMVS2		86.87 376	85.37 380	91.35 388	90.21 417	83.80 407	98.89 354	97.45 400	83.13 409	91.67 406	95.03 406	48.49 419	94.70 412	85.86 409	77.62 411	95.54 407

PMVS	70.75 22	75.98 385	74.97 386	79.01 401	70.98 424	55.18 426	93.37 413	98.21 388	65.08 418	61.78 419	93.83 409	21.74 426	92.53 413	78.59 411	91.12 389	89.34 414
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 378	85.65 379	82.75 399	86.77 420	63.39 425	98.35 392	98.92 340	74.11 411	83.39 410	98.98 349	50.85 418	92.40 414	84.54 410	94.97 347	92.46 409

WB-MVS		93.10 365	94.10 358	90.12 391	95.51 409	81.88 411	99.73 50	99.27 292	95.05 361	93.09 398	98.91 358	94.70 230	91.89 415	76.62 413	94.02 366	96.58 401

SSC-MVS		92.73 367	93.73 362	89.72 392	95.02 411	81.38 412	99.76 37	99.23 298	94.87 365	92.80 399	98.93 354	94.71 229	91.37 416	74.49 415	93.80 368	96.42 402

MVE	76.82 21	76.91 384	74.31 388	84.70 396	85.38 422	76.05 420	96.88 410	93.17 419	67.39 415	71.28 417	89.01 416	21.66 427	87.69 417	71.74 416	72.29 414	90.35 413
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 381	79.88 383	82.81 398	90.75 416	76.38 419	97.69 406	95.76 411	66.44 416	83.52 409	92.25 411	62.54 412	87.16 418	68.53 417	61.40 415	84.89 416

EMVS		80.02 382	79.22 384	82.43 400	91.19 415	76.40 418	97.55 409	92.49 423	66.36 417	83.01 411	91.27 413	64.63 411	85.79 419	65.82 418	60.65 416	85.08 415

ANet_high		77.30 383	74.86 387	84.62 397	75.88 423	77.61 417	97.63 408	93.15 421	88.81 403	64.27 418	89.29 415	36.51 422	83.93 420	75.89 414	52.31 417	92.33 411

wuyk23d		40.18 386	41.29 391	36.84 402	86.18 421	49.12 427	79.73 415	22.81 427	27.64 419	25.46 422	28.45 422	21.98 425	48.89 421	55.80 420	23.56 421	12.51 419

test123		39.01 388	42.50 390	28.53 403	39.17 426	20.91 428	98.75 368	19.17 428	19.83 421	38.57 420	66.67 418	33.16 423	15.42 422	37.50 422	29.66 420	49.26 417

testmvs		39.17 387	43.78 389	25.37 404	36.04 427	16.84 429	98.36 391	26.56 426	20.06 420	38.51 421	67.32 417	29.64 424	15.30 423	37.59 421	39.90 419	43.98 418

mmdepth		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

monomultidepth		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

test_blank		0.13 392	0.17 395	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	1.57 423	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

uanet_test		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

DCPMVS		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

cdsmvs_eth3d_5k		24.64 389	32.85 392	0.00 405	0.00 428	0.00 430	0.00 416	99.51 119	0.00 423	0.00 424	99.56 228	96.58 153	0.00 424	0.00 423	0.00 422	0.00 420

pcd_1.5k_mvsjas		8.27 391	11.03 394	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	99.01 18	0.00 424	0.00 423	0.00 422	0.00 420

sosnet-low-res		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

sosnet		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

uncertanet		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

Regformer		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

ab-mvs-re		8.30 390	11.06 393	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	99.58 220	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

uanet		0.02 393	0.03 396	0.00 405	0.00 428	0.00 430	0.00 416	0.00 429	0.00 423	0.00 424	0.27 424	0.00 428	0.00 424	0.00 423	0.00 422	0.00 420

WAC-MVS						97.16 285								95.47 344

FOURS1					99.91 1	99.93 1	99.87 8	99.56 70	99.10 30	99.81 42

test_one_0601					99.81 46	99.88 8		99.49 149	98.97 54	99.65 97	99.81 95	99.09 14

eth-test2					0.00 428

eth-test					0.00 428

RE-MVS-def			99.34 42		99.76 66	99.82 25	99.63 88	99.52 105	98.38 109	99.76 62	99.82 81	98.75 58		98.61 154	99.81 99	99.77 85

IU-MVS					99.84 32	99.88 8		99.32 275	98.30 119	99.84 35				98.86 117	99.85 75	99.89 19

save fliter					99.76 66	99.59 73	99.14 302	99.40 226	99.00 46

test0726					99.85 26	99.89 4	99.62 93	99.50 139	99.10 30	99.86 33	99.82 81	98.94 32

GSMVS																99.52 173

test_part2					99.81 46	99.83 19				99.77 57

sam_mvs1												94.86 216				99.52 173

sam_mvs												94.72 228

MTGPA								99.47 181

MTMP							99.54 145	98.88 349

test9_res														97.49 266	99.72 124	99.75 91

agg_prior2														97.21 284	99.73 123	99.75 91

test_prior4						99.56 79	98.99 337

test_prior2							98.96 344		98.34 115	99.01 244	99.52 243	98.68 67		97.96 219	99.74 121

新几何2							99.01 334

旧先验1					99.74 83	99.59 73		99.54 87			99.69 172	98.47 83			99.68 132	99.73 100

原ACMM2							98.95 347

test222					99.75 76	99.49 92	98.91 353	99.49 149	96.42 319	99.34 177	99.65 190	98.28 96			99.69 129	99.72 106

segment_acmp												98.96 25

testdata1							98.85 358		98.32 118

plane_prior7					99.29 246	97.03 298

plane_prior6					99.27 251	96.98 302						92.71 292

plane_prior4											99.61 211

plane_prior3						97.00 300			98.69 83	99.11 225

plane_prior2							99.39 227		98.97 54

plane_prior1					99.26 253

plane_prior						96.97 303	99.21 291		98.45 102						97.60 268

n2								0.00 429

nn								0.00 429

door-mid								98.05 391

test11								99.35 253

door								97.92 392

HQP5-MVS						96.83 310

HQP-NCC					99.19 271		98.98 340		98.24 126	98.66 295

ACMP_Plane					99.19 271		98.98 340		98.24 126	98.66 295

BP-MVS														97.19 288

HQP3-MVS								99.39 229							97.58 270

HQP2-MVS												92.47 301

NP-MVS					99.23 261	96.92 306					99.40 280

MDTV_nov1_ep13_2view						95.18 361	99.35 244		96.84 286	99.58 119		95.19 207		97.82 232		99.46 197

ACMMP++_ref															97.19 297

ACMMP++															97.43 288

Test By Simon												98.75 58

High-resolution multi-view benchmark