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.

RA-MVS			95.45 11		99.00 1	87.45 19	97.37 8	97.63 6	86.14 211	97.01 24	98.09 19	89.62 16	99.58 14	95.23 45	98.96 15

aaatest				94.84 34	98.88 2	85.89 67	97.32 11	97.86 1	88.11 135	97.21 15	97.54 48		99.67 1	95.27 42	98.85 23	98.95 13

MED-MVS		95.95 2	96.31 2	94.90 25	98.88 2	85.89 67	97.32 11	97.86 1	90.76 30	97.21 15	98.09 19	92.42 4	99.67 1	95.27 42	98.95 16	99.14 2

TestfortrainingZip a		95.33 9	95.44 12	94.99 20	98.88 2	86.26 50	97.32 11	97.43 26	90.76 30	96.80 28	98.09 19	89.00 25	99.58 14	93.66 63	96.99 114	99.14 2

FOURS1					98.86 5	85.54 76	98.29 1	97.49 12	89.79 67	96.29 34

test_0728_SECOND				95.01 18	98.79 6	86.43 42	97.09 22	97.49 12					99.61 7	95.62 36	99.08 7	98.99 11

DVP-MVS		95.67 4	96.02 4	94.64 44	98.78 7	85.93 61	97.09 22	96.73 100	90.27 49	97.04 22	98.05 29	91.47 9	99.55 22	95.62 36	99.08 7	98.45 42
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					98.78 7	85.93 61	97.19 17	97.47 17	90.27 49	97.64 7	98.13 8	91.47 9

SED-MVS		95.91 3	96.28 3	94.80 38	98.77 9	85.99 58	97.13 20	97.44 21	90.31 45	97.71 3	98.07 24	92.31 5	99.58 14	95.66 32	99.13 3	98.84 19

IU-MVS					98.77 9	86.00 56		96.84 84	81.26 355	97.26 14				95.50 38	99.13 3	99.03 10

test_241102_ONE					98.77 9	85.99 58		97.44 21	90.26 51	97.71 3	97.96 35	92.31 5	99.38 37

region2R		94.43 37	94.27 49	94.92 22	98.65 12	86.67 33	96.92 30	97.23 45	88.60 116	93.58 83	97.27 60	85.22 67	99.54 26	92.21 97	98.74 36	98.56 30

ACMMPR		94.43 37	94.28 47	94.91 23	98.63 13	86.69 31	96.94 26	97.32 36	88.63 113	93.53 86	97.26 62	85.04 71	99.54 26	92.35 92	98.78 31	98.50 32

HFP-MVS		94.52 32	94.40 40	94.86 27	98.61 14	86.81 28	96.94 26	97.34 32	88.63 113	93.65 81	97.21 64	86.10 56	99.49 32	92.35 92	98.77 33	98.30 56

test_one_0601					98.58 15	85.83 70		97.44 21	91.05 24	96.78 29	98.06 26	91.45 12

test_part2					98.55 16	87.22 21				96.40 33

XVS		94.45 35	94.32 43	94.85 28	98.54 17	86.60 37	96.93 28	97.19 46	90.66 37	92.85 99	97.16 70	85.02 72	99.49 32	91.99 108	98.56 56	98.47 38

X-MVStestdata		88.31 244	86.13 294	94.85 28	98.54 17	86.60 37	96.93 28	97.19 46	90.66 37	92.85 99	23.41 542	85.02 72	99.49 32	91.99 108	98.56 56	98.47 38

ZNCC-MVS		94.47 34	94.28 47	95.03 17	98.52 19	86.96 22	96.85 34	97.32 36	88.24 127	93.15 91	97.04 75	86.17 55	99.62 5	92.40 89	98.81 28	98.52 31

mPP-MVS		93.99 57	93.78 68	94.63 45	98.50 20	85.90 66	96.87 32	96.91 77	88.70 111	91.83 138	97.17 69	83.96 88	99.55 22	91.44 123	98.64 50	98.43 44

MSP-MVS		95.42 7	95.56 8	94.98 21	98.49 21	86.52 39	96.91 31	97.47 17	91.73 15	96.10 38	96.69 89	89.90 14	99.30 50	94.70 50	98.04 82	99.13 4
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
test-260524					98.47 22	86.91 24		97.38 28		95.81 46		89.60 17	99.63 4	95.95 30	98.95 16

MP-MVS		94.25 43	94.07 58	94.77 40	98.47 22	86.31 47	96.71 37	96.98 66	89.04 96	91.98 128	97.19 67	85.43 64	99.56 18	92.06 106	98.79 29	98.44 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		94.45 35	94.20 53	95.19 14	98.46 24	87.50 17	95.00 158	97.12 57	87.13 179	92.51 116	96.30 108	89.24 22	99.34 44	93.46 66	98.62 51	98.73 23

PGM-MVS		93.96 59	93.72 72	94.68 43	98.43 25	86.22 51	95.30 131	97.78 3	87.45 168	93.26 88	97.33 58	84.62 81	99.51 30	90.75 139	98.57 54	98.32 55

MTAPA		94.42 40	94.22 50	95.00 19	98.42 26	86.95 23	94.36 214	96.97 67	91.07 23	93.14 92	97.56 47	84.30 84	99.56 18	93.43 67	98.75 35	98.47 38

GST-MVS		94.21 46	93.97 62	94.90 25	98.41 27	86.82 27	96.54 42	97.19 46	88.24 127	93.26 88	96.83 84	85.48 63	99.59 11	91.43 124	98.40 60	98.30 56

NormalMVS		93.46 73	93.16 86	94.37 57	98.40 28	86.20 52	96.30 48	96.27 139	91.65 18	92.68 109	96.13 123	77.97 195	98.84 108	90.75 139	98.26 65	98.07 84

lecture		95.10 14	95.46 10	94.01 66	98.40 28	84.36 109	97.70 3	97.78 3	91.19 21	96.22 36	98.08 23	86.64 47	99.37 39	94.91 48	98.26 65	98.29 61

aaEdge-Enhanced		95.17 12	95.29 16	94.81 36	98.39 30	85.89 67	95.91 89	97.55 9	89.01 100	95.86 44	97.54 48	89.24 22	99.59 11	95.27 42	98.85 23	98.95 13

HPM-MVS		94.02 55	93.88 63	94.43 52	98.39 30	85.78 72	97.25 16	97.07 62	86.90 190	92.62 113	96.80 88	84.85 78	99.17 59	92.43 87	98.65 49	98.33 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		94.34 41	94.21 52	94.74 42	98.39 30	86.64 35	97.60 5	97.24 43	88.53 118	92.73 107	97.23 63	85.20 68	99.32 48	92.15 100	98.83 27	98.25 70

DPE-MVS		95.57 5	95.67 6	95.25 12	98.36 33	87.28 20	95.56 120	97.51 11	89.13 92	97.14 18	97.91 36	91.64 8	99.62 5	94.61 52	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS_fast		93.40 81	93.22 84	93.94 70	98.36 33	84.83 89	97.15 19	96.80 91	85.77 220	92.47 117	97.13 71	82.38 111	99.07 67	90.51 144	98.40 60	97.92 110

DP-MVS Recon		91.95 113	91.28 134	93.96 69	98.33 35	85.92 63	94.66 185	96.66 107	82.69 315	90.03 195	95.82 148	82.30 115	99.03 72	84.57 245	96.48 132	96.91 197

APDe-MVS		95.46 6	95.64 7	94.91 23	98.26 36	86.29 49	97.46 7	97.40 27	89.03 98	96.20 37	98.10 15	89.39 20	99.34 44	95.88 31	99.03 11	99.10 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
TSAR-MVS + MP.		94.85 20	94.94 26	94.58 47	98.25 37	86.33 45	96.11 68	96.62 111	88.14 132	96.10 38	96.96 78	89.09 24	98.94 94	94.48 53	98.68 42	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		95.14 13	94.91 28	95.83 4	98.25 37	89.65 4	95.92 88	96.96 70	91.75 14	94.02 75	96.83 84	88.12 31	99.55 22	93.41 69	98.94 19	98.28 62

CPTT-MVS		91.99 112	91.80 113	92.55 148	98.24 39	81.98 196	96.76 36	96.49 122	81.89 337	90.24 184	96.44 105	78.59 185	98.61 138	89.68 161	97.85 91	97.06 182

SR-MVS		94.23 45	94.17 56	94.43 52	98.21 40	85.78 72	96.40 44	96.90 78	88.20 130	94.33 65	97.40 55	84.75 80	99.03 72	93.35 70	97.99 84	98.48 35

MP-MVS-pluss		94.21 46	94.00 61	94.85 28	98.17 41	86.65 34	94.82 171	97.17 51	86.26 207	92.83 101	97.87 38	85.57 62	99.56 18	94.37 55	98.92 20	98.34 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZD-MVS					98.15 42	86.62 36		97.07 62	83.63 286	94.19 68	96.91 80	87.57 38	99.26 53	91.99 108	98.44 59

SMA-MVS		95.20 10	95.07 22	95.59 6	98.14 43	88.48 9	96.26 55	97.28 42	85.90 216	97.67 5	98.10 15	88.41 27	99.56 18	94.66 51	99.19 1	98.71 25
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
CNVR-MVS		95.40 8	95.37 13	95.50 8	98.11 44	88.51 8	95.29 133	96.96 70	92.09 10	95.32 53	97.08 72	89.49 19	99.33 47	95.10 46	98.85 23	98.66 26

114514_t		89.51 201	88.50 218	92.54 149	98.11 44	81.99 195	95.16 148	96.36 131	70.19 479	85.81 285	95.25 183	76.70 213	98.63 135	82.07 289	96.86 121	97.00 189

ACMMP		93.24 85	92.88 92	94.30 60	98.09 46	85.33 81	96.86 33	97.45 20	88.33 122	90.15 193	97.03 76	81.44 134	99.51 30	90.85 136	95.74 149	98.04 91
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
APD-MVS		94.24 44	94.07 58	94.75 41	98.06 47	86.90 26	95.88 91	96.94 73	85.68 223	95.05 59	97.18 68	87.31 42	99.07 67	91.90 114	98.61 53	98.28 62
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		93.23 86	93.05 88	93.76 78	98.04 48	84.07 115	96.22 57	97.37 29	84.15 273	90.05 194	95.66 159	87.77 33	99.15 63	89.91 155	98.27 64	98.07 84

ACMMP_NAP		94.74 26	94.56 35	95.28 11	98.02 49	87.70 12	95.68 108	97.34 32	88.28 126	95.30 54	97.67 45	85.90 58	99.54 26	93.91 59	98.95 16	98.60 28

OPU-MVS				96.21 3	98.00 50	90.85 3	97.13 20				97.08 72	92.59 2	98.94 94	92.25 95	98.99 14	98.84 19

reproduce_model		94.76 25	94.92 27	94.29 61	97.92 51	85.18 83	95.95 86	97.19 46	89.67 71	95.27 55	98.16 7	86.53 51	99.36 42	95.42 39	98.15 75	98.33 51

SR-MVS-dyc-post		93.82 63	93.82 65	93.82 74	97.92 51	84.57 96	96.28 52	96.76 95	87.46 166	93.75 79	97.43 53	84.24 85	99.01 77	92.73 79	97.80 94	97.88 114

RE-MVS-def			93.68 74		97.92 51	84.57 96	96.28 52	96.76 95	87.46 166	93.75 79	97.43 53	82.94 103		92.73 79	97.80 94	97.88 114

APD-MVS_3200maxsize		93.78 64	93.77 69	93.80 76	97.92 51	84.19 113	96.30 48	96.87 81	86.96 186	93.92 77	97.47 51	83.88 89	98.96 91	92.71 82	97.87 90	98.26 69

reproduce-ours		94.82 21	94.97 24	94.38 55	97.91 55	85.46 77	95.86 92	97.15 53	89.82 61	95.23 56	98.10 15	87.09 44	99.37 39	95.30 40	98.25 69	98.30 56

our_new_method		94.82 21	94.97 24	94.38 55	97.91 55	85.46 77	95.86 92	97.15 53	89.82 61	95.23 56	98.10 15	87.09 44	99.37 39	95.30 40	98.25 69	98.30 56

save fliter					97.85 57	85.63 75	95.21 143	96.82 87	89.44 78

SF-MVS		94.97 18	94.90 30	95.20 13	97.84 58	87.76 11	96.65 40	97.48 16	87.76 157	95.71 47	97.70 44	88.28 30	99.35 43	93.89 60	98.78 31	98.48 35

NCCC		94.81 23	94.69 34	95.17 15	97.83 59	87.46 18	95.66 111	96.93 74	92.34 7	93.94 76	96.58 99	87.74 34	99.44 35	92.83 78	98.40 60	98.62 27

9.14			94.47 37		97.79 60		96.08 70	97.44 21	86.13 214	95.10 58	97.40 55	88.34 29	99.22 55	93.25 71	98.70 39

CDPH-MVS		92.83 95	92.30 105	94.44 50	97.79 60	86.11 55	94.06 237	96.66 107	80.09 369	92.77 104	96.63 96	86.62 48	99.04 71	87.40 198	98.66 46	98.17 75

DVP-MVS++		95.98 1	96.36 1	94.82 35	97.78 62	86.00 56	98.29 1	97.49 12	90.75 32	97.62 9	98.06 26	92.59 2	99.61 7	95.64 34	99.02 12	98.86 16

MSC_two_6792asdad				96.52 1	97.78 62	90.86 1		96.85 82					99.61 7	96.03 28	99.06 9	99.07 7

No_MVS				96.52 1	97.78 62	90.86 1		96.85 82					99.61 7	96.03 28	99.06 9	99.07 7

dcpmvs_2		93.49 71	94.19 54	91.38 230	97.69 65	76.78 381	94.25 219	96.29 135	88.33 122	94.46 63	96.88 81	88.07 32	98.64 133	93.62 65	98.09 79	98.73 23

DP-MVS		87.25 285	85.36 326	92.90 118	97.65 66	83.24 143	94.81 172	92.00 392	74.99 442	81.92 386	95.00 197	72.66 281	99.05 69	66.92 458	92.33 258	96.40 222

PAPM_NR		91.22 142	90.78 148	92.52 151	97.60 67	81.46 216	94.37 212	96.24 146	86.39 204	87.41 248	94.80 209	82.06 124	98.48 146	82.80 274	95.37 161	97.61 137

patch_mono-2		93.74 66	94.32 43	92.01 186	97.54 68	78.37 337	93.40 280	97.19 46	88.02 141	94.99 60	97.21 64	88.35 28	98.44 156	94.07 57	98.09 79	99.23 1

TEST9					97.53 69	86.49 40	94.07 235	96.78 92	81.61 347	92.77 104	96.20 112	87.71 35	99.12 65

train_agg		93.44 76	93.08 87	94.52 49	97.53 69	86.49 40	94.07 235	96.78 92	81.86 338	92.77 104	96.20 112	87.63 36	99.12 65	92.14 101	98.69 40	97.94 101

test_8					97.49 71	86.30 48	94.02 241	96.76 95	81.86 338	92.70 108	96.20 112	87.63 36	99.02 75

DeepC-MVS_fast	89.43 2	94.04 54	93.79 67	94.80 38	97.48 72	86.78 29	95.65 113	96.89 79	89.40 80	92.81 102	96.97 77	85.37 65	99.24 54	90.87 135	98.69 40	98.38 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		89.89 190	89.07 199	92.37 162	97.41 73	83.03 157	94.42 201	95.92 188	82.81 312	86.34 274	94.65 218	73.89 263	99.02 75	80.69 317	95.51 154	95.05 281

agg_prior					97.38 74	85.92 63		96.72 102		92.16 124			98.97 89

原ACMM1				92.01 186	97.34 75	81.05 232		96.81 90	78.89 385	90.45 178	95.92 138	82.65 108	98.84 108	80.68 318	98.26 65	96.14 235

TestfortrainingZip				95.40 9	97.32 76	88.97 6	97.32 11	96.82 87	89.07 93	95.69 48	96.49 102	89.27 21	99.29 52		95.80 146	97.95 100

MSLP-MVS++		93.72 67	94.08 57	92.65 142	97.31 77	83.43 136	95.79 99	97.33 34	90.03 54	93.58 83	96.96 78	84.87 77	97.76 235	92.19 99	98.66 46	96.76 207

新几何1				93.10 103	97.30 78	84.35 110		95.56 223	71.09 475	91.26 155	96.24 110	82.87 105	98.86 104	79.19 350	98.10 78	96.07 241

test_prior				93.82 74	97.29 79	84.49 100		96.88 80					98.87 102			98.11 83

PLC	84.53 7	89.06 221	88.03 231	92.15 184	97.27 80	82.69 171	94.29 217	95.44 236	79.71 374	84.01 346	94.18 242	76.68 214	98.75 119	77.28 371	93.41 226	95.02 282
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		94.96 19	95.33 14	93.88 71	97.25 81	86.69 31	96.19 58	97.11 60	90.42 41	96.95 25	97.27 60	89.53 18	96.91 329	94.38 54	98.85 23	98.03 92
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
test12				94.34 58	97.13 82	86.15 54		96.29 135		91.04 167		85.08 70	99.01 77		98.13 77	97.86 116

MG-MVS		91.77 121	91.70 115	92.00 189	97.08 83	80.03 282	93.60 273	95.18 258	87.85 153	90.89 171	96.47 104	82.06 124	98.36 163	85.07 233	97.04 112	97.62 135

SteuartSystems-ACMMP		95.20 10	95.32 15	94.85 28	96.99 84	86.33 45	97.33 9	97.30 39	91.38 20	95.39 52	97.46 52	88.98 26	99.40 36	94.12 56	98.89 21	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		93.45 75	93.31 81	93.84 73	96.99 84	84.84 88	93.24 293	97.24 43	88.76 108	91.60 145	95.85 145	86.07 57	98.66 128	91.91 112	98.16 73	98.03 92

CNLPA		89.07 220	87.98 233	92.34 166	96.87 86	84.78 91	94.08 234	93.24 353	81.41 351	84.46 330	95.13 193	75.57 235	96.62 346	77.21 372	93.84 207	95.61 264

PHI-MVS		93.89 61	93.65 76	94.62 46	96.84 87	86.43 42	96.69 38	97.49 12	85.15 247	93.56 85	96.28 109	85.60 61	99.31 49	92.45 86	98.79 29	98.12 82

旧先验1					96.79 88	81.81 202		95.67 214			96.81 86	86.69 46			97.66 100	96.97 191

LFMVS		90.08 180	89.13 196	92.95 116	96.71 89	82.32 187	96.08 70	89.91 451	86.79 191	92.15 125	96.81 86	62.60 403	98.34 166	87.18 202	93.90 204	98.19 73

SPE-MVS-test		94.02 55	94.29 46	93.24 93	96.69 90	83.24 143	97.49 6	96.92 75	92.14 9	92.90 97	95.77 154	85.02 72	98.33 168	93.03 75	98.62 51	98.13 79

Anonymous202405211		87.68 260	86.13 294	92.31 169	96.66 91	80.74 250	94.87 166	91.49 409	80.47 365	89.46 206	95.44 171	54.72 463	98.23 174	82.19 285	89.89 295	97.97 98

TAPA-MVS	84.62 6	88.16 248	87.01 259	91.62 214	96.64 92	80.65 252	94.39 208	96.21 152	76.38 426	86.19 278	95.44 171	79.75 163	98.08 195	62.75 476	95.29 163	96.13 236
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		90.30 173	89.37 190	93.07 107	96.61 93	84.48 101	95.68 108	95.67 214	82.36 320	87.85 237	92.85 290	76.63 215	98.80 113	80.01 330	96.68 126	95.91 247
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
VNet		92.24 108	91.91 112	93.24 93	96.59 94	83.43 136	94.84 170	96.44 123	89.19 90	94.08 74	95.90 139	77.85 201	98.17 179	88.90 175	93.38 227	98.13 79

TSAR-MVS + GP.		93.66 68	93.41 80	94.41 54	96.59 94	86.78 29	94.40 206	93.93 328	89.77 68	94.21 67	95.59 163	87.35 41	98.61 138	92.72 81	96.15 139	97.83 121

MVSMamba_PlusPlus		93.44 76	93.54 78	93.14 101	96.58 96	83.05 156	96.06 74	96.50 121	84.42 269	94.09 71	95.56 165	85.01 75	98.69 127	94.96 47	98.66 46	97.67 133

CS-MVS		94.12 52	94.44 39	93.17 99	96.55 97	83.08 155	97.63 4	96.95 72	91.71 16	93.50 87	96.21 111	85.61 60	98.24 173	93.64 64	98.17 72	98.19 73

test222					96.55 97	81.70 207	92.22 343	95.01 266	68.36 484	90.20 186	96.14 122	80.26 151			97.80 94	96.05 244

Anonymous20240529		88.09 250	86.59 276	92.58 146	96.53 99	81.92 199	95.99 80	95.84 198	74.11 452	89.06 213	95.21 187	61.44 413	98.81 112	83.67 262	87.47 336	97.01 188

Anonymous20231211		86.59 316	85.13 332	90.98 253	96.52 100	81.50 211	96.14 65	96.16 163	73.78 455	83.65 355	92.15 315	63.26 396	97.37 288	82.82 273	81.74 401	94.06 331

DeepPCF-MVS	89.96 1	94.20 48	94.77 33	92.49 153	96.52 100	80.00 284	94.00 244	97.08 61	90.05 53	95.65 50	97.29 59	89.66 15	98.97 89	93.95 58	98.71 37	98.50 32

fmvsm_s_conf0.5_n_9		94.99 17	95.50 9	93.44 86	96.51 102	82.25 188	95.76 103	96.92 75	93.37 3	97.63 8	98.43 2	84.82 79	99.16 62	98.15 1	97.92 87	98.90 15

testdata				90.49 276	96.40 103	77.89 353		95.37 243	72.51 467	93.63 82	96.69 89	82.08 123	97.65 244	83.08 266	97.39 104	95.94 246

PVSNet_Blended_VisFu		91.38 136	90.91 144	92.80 125	96.39 104	83.17 147	94.87 166	96.66 107	83.29 297	89.27 209	94.46 230	80.29 149	99.17 59	87.57 195	95.37 161	96.05 244

API-MVS		90.66 163	90.07 167	92.45 156	96.36 105	84.57 96	96.06 74	95.22 255	82.39 318	89.13 210	94.27 239	80.32 148	98.46 150	80.16 328	96.71 125	94.33 318

F-COLMAP		87.95 253	86.80 265	91.40 229	96.35 106	80.88 241	94.73 179	95.45 234	79.65 375	82.04 384	94.61 220	71.13 299	98.50 144	76.24 384	91.05 274	94.80 296

VDD-MVS		90.74 156	89.92 173	93.20 95	96.27 107	83.02 158	95.73 105	93.86 332	88.42 121	92.53 114	96.84 83	62.09 405	98.64 133	90.95 133	92.62 253	97.93 109

OMC-MVS		91.23 140	90.62 153	93.08 105	96.27 107	84.07 115	93.52 275	95.93 187	86.95 187	89.51 203	96.13 123	78.50 189	98.35 165	85.84 223	92.90 242	96.83 206

DPM-MVS		92.58 101	91.74 114	95.08 16	96.19 109	89.31 5	92.66 321	96.56 116	83.44 292	91.68 144	95.04 195	86.60 50	98.99 84	85.60 225	97.92 87	96.93 195

SymmetryMVS		92.81 98	92.31 104	94.32 59	96.15 110	86.20 52	96.30 48	94.43 306	91.65 18	92.68 109	96.13 123	77.97 195	98.84 108	90.75 139	94.72 174	97.92 110

CHOSEN 1792x2688		88.84 227	87.69 241	92.30 172	96.14 111	81.42 218	90.01 411	95.86 197	74.52 447	87.41 248	93.94 252	75.46 236	98.36 163	80.36 323	95.53 153	97.12 178

BridgeMVS		93.98 58	94.22 50	93.26 92	96.13 112	83.29 142	96.27 54	96.52 119	89.82 61	95.56 51	95.51 168	84.50 82	98.79 115	94.83 49	98.86 22	97.72 130

thres100view900		87.63 265	86.71 268	90.38 285	96.12 113	78.55 330	95.03 157	91.58 405	87.15 177	88.06 233	92.29 311	68.91 341	98.10 184	70.13 436	91.10 269	94.48 313

PVSNet_BlendedMVS		89.98 184	89.70 178	90.82 260	96.12 113	81.25 222	93.92 250	96.83 85	83.49 291	89.10 211	92.26 312	81.04 140	98.85 106	86.72 210	87.86 331	92.35 418

PVSNet_Blended		90.73 157	90.32 158	91.98 190	96.12 113	81.25 222	92.55 325	96.83 85	82.04 330	89.10 211	92.56 302	81.04 140	98.85 106	86.72 210	95.91 143	95.84 252

testing3-2		86.72 311	86.71 268	86.74 417	96.11 116	65.92 481	93.39 281	89.65 458	89.46 77	87.84 238	92.79 296	59.17 436	97.60 249	81.31 305	90.72 280	96.70 211

UA-Net		92.83 95	92.54 100	93.68 82	96.10 117	84.71 92	95.66 111	96.39 128	91.92 11	93.22 90	96.49 102	83.16 98	98.87 102	84.47 247	95.47 157	97.45 150

MM		95.10 14	94.91 28	95.68 5	96.09 118	88.34 10	96.68 39	94.37 310	95.08 1	94.68 61	97.72 43	82.94 103	99.64 3	97.85 5	98.76 34	99.06 9

thres600view7		87.65 262	86.67 271	90.59 264	96.08 119	78.72 324	94.88 165	91.58 405	87.06 182	88.08 232	92.30 310	68.91 341	98.10 184	70.05 439	91.10 269	94.96 286

DeepC-MVS	88.79 3	93.31 82	92.99 90	94.26 62	96.07 120	85.83 70	94.89 164	96.99 65	89.02 99	89.56 202	97.37 57	82.51 110	99.38 37	92.20 98	98.30 63	97.57 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		87.89 254	86.32 287	92.59 145	96.07 120	82.92 162	95.23 138	94.92 280	75.66 434	82.89 372	95.98 133	72.48 285	99.21 57	68.43 446	95.23 166	95.64 261

fmvsm_s_conf0.5_n_11		94.60 29	95.23 18	92.69 139	96.05 122	82.00 194	96.31 47	96.71 103	92.27 8	96.68 32	98.39 3	85.32 66	98.92 97	97.20 14	98.16 73	97.17 169

h-mvs33		90.80 154	90.15 164	92.75 132	96.01 123	82.66 172	95.43 124	95.53 227	89.80 64	93.08 93	95.64 160	75.77 228	99.00 82	92.07 103	78.05 439	96.60 215

SDMVSNet		90.19 176	89.61 182	91.93 195	96.00 124	83.09 154	92.89 310	95.98 181	88.73 109	86.85 261	95.20 188	72.09 292	97.08 314	88.90 175	89.85 297	95.63 262

sd_testset		88.59 236	87.85 239	90.83 258	96.00 124	80.42 265	92.35 333	94.71 294	88.73 109	86.85 261	95.20 188	67.31 351	96.43 370	79.64 337	89.85 297	95.63 262

HyFIR lowres test		88.09 250	86.81 264	91.93 195	96.00 124	80.63 253	90.01 411	95.79 201	73.42 459	87.68 243	92.10 320	73.86 264	97.96 219	80.75 316	91.70 263	97.19 168

tfpn200view9		87.58 270	86.64 272	90.41 282	95.99 127	78.64 327	94.58 188	91.98 394	86.94 188	88.09 230	91.77 332	69.18 337	98.10 184	70.13 436	91.10 269	94.48 313

thres400		87.62 267	86.64 272	90.57 265	95.99 127	78.64 327	94.58 188	91.98 394	86.94 188	88.09 230	91.77 332	69.18 337	98.10 184	70.13 436	91.10 269	94.96 286

MVS_111021_LR		92.47 104	92.29 106	92.98 112	95.99 127	84.43 105	93.08 299	96.09 172	88.20 130	91.12 160	95.72 157	81.33 136	97.76 235	91.74 116	97.37 105	96.75 208

fmvsm_s_conf0.5_n_8		94.56 31	95.12 20	92.87 120	95.96 130	81.32 220	95.76 103	97.57 8	93.48 2	97.53 11	98.32 4	81.78 131	99.13 64	97.91 2	97.81 93	98.16 76

fmvsm_l_conf0.5_n_9		94.65 28	95.28 17	92.77 127	95.95 131	81.83 201	95.53 121	97.12 57	91.68 17	97.89 2	98.06 26	85.71 59	98.65 130	97.32 12	98.26 65	97.83 121

PatchMatch-RL		86.77 310	85.54 320	90.47 280	95.88 132	82.71 170	90.54 393	92.31 380	79.82 373	84.32 338	91.57 344	68.77 343	96.39 372	73.16 412	93.48 224	92.32 419

EPP-MVSNet		91.70 130	91.56 121	92.13 185	95.88 132	80.50 263	97.33 9	95.25 252	86.15 210	89.76 200	95.60 162	83.42 94	98.32 170	87.37 200	93.25 231	97.56 142

IS-MVSNet		91.43 135	91.09 140	92.46 154	95.87 134	81.38 219	96.95 25	93.69 345	89.72 70	89.50 205	95.98 133	78.57 186	97.77 234	83.02 268	96.50 131	98.22 72

test_fmvsm_n_1920		94.71 27	95.11 21	93.50 85	95.79 135	84.62 94	96.15 63	97.64 5	89.85 60	97.19 17	97.89 37	86.28 54	98.71 125	97.11 16	98.08 81	97.17 169

PAPR		90.02 183	89.27 195	92.29 174	95.78 136	80.95 237	92.68 320	96.22 149	81.91 334	86.66 265	93.75 264	82.23 117	98.44 156	79.40 349	94.79 173	97.48 148

Vis-MVSNet (Re-imp)		89.59 199	89.44 186	90.03 302	95.74 137	75.85 395	95.61 116	90.80 430	87.66 162	87.83 239	95.40 174	76.79 211	96.46 367	78.37 357	96.73 124	97.80 124

fmvsm_s_conf0.5_n_10		94.43 37	94.84 31	93.20 95	95.73 138	83.19 146	95.99 80	97.31 38	91.08 22	97.67 5	98.11 12	81.87 128	99.22 55	97.86 4	97.91 89	97.20 167

test_yl		90.69 159	90.02 171	92.71 136	95.72 139	82.41 184	94.11 229	95.12 260	85.63 224	91.49 148	94.70 211	74.75 243	98.42 159	86.13 218	92.53 255	97.31 154

DCV-MVSNet		90.69 159	90.02 171	92.71 136	95.72 139	82.41 184	94.11 229	95.12 260	85.63 224	91.49 148	94.70 211	74.75 243	98.42 159	86.13 218	92.53 255	97.31 154

sasdasda		93.27 83	92.75 94	94.85 28	95.70 141	87.66 13	96.33 45	96.41 126	90.00 55	94.09 71	94.60 221	82.33 113	98.62 136	92.40 89	92.86 243	98.27 65

canonicalmvs		93.27 83	92.75 94	94.85 28	95.70 141	87.66 13	96.33 45	96.41 126	90.00 55	94.09 71	94.60 221	82.33 113	98.62 136	92.40 89	92.86 243	98.27 65

CANet		93.54 70	93.20 85	94.55 48	95.65 143	85.73 74	94.94 161	96.69 106	91.89 13	90.69 173	95.88 141	81.99 126	99.54 26	93.14 73	97.95 86	98.39 46

fmvsm_l_conf0.5_n_3		94.80 24	95.01 23	94.15 64	95.64 144	85.08 84	96.09 69	97.36 30	90.98 25	97.09 20	98.12 11	84.98 76	98.94 94	97.07 17	97.80 94	98.43 44

3Dnovator+	87.14 4	92.42 106	91.37 130	95.55 7	95.63 145	88.73 7	97.07 24	96.77 94	90.84 27	84.02 345	96.62 97	75.95 226	99.34 44	87.77 191	97.68 99	98.59 29

MGCFI-Net		93.03 92	92.63 98	94.23 63	95.62 146	85.92 63	96.08 70	96.33 133	89.86 59	93.89 78	94.66 217	82.11 121	98.50 144	92.33 94	92.82 246	98.27 65

fmvsm_s_conf0.5_n		93.76 65	94.06 60	92.86 121	95.62 146	83.17 147	96.14 65	96.12 169	88.13 133	95.82 45	98.04 32	83.43 92	98.48 146	96.97 21	96.23 136	96.92 196

test2506		87.21 289	86.28 289	90.02 304	95.62 146	73.64 420	96.25 56	71.38 515	87.89 151	90.45 178	96.65 93	55.29 457	98.09 192	86.03 220	96.94 115	98.33 51

ECVR-MVS		89.09 219	88.53 216	90.77 262	95.62 146	75.89 394	96.16 61	84.22 490	87.89 151	90.20 186	96.65 93	63.19 398	98.10 184	85.90 221	96.94 115	98.33 51

alignmvs		93.08 91	92.50 101	94.81 36	95.62 146	87.61 16	95.99 80	96.07 174	89.77 68	94.12 70	94.87 204	80.56 146	98.66 128	92.42 88	93.10 238	98.15 77

fmvsm_l_mol_unc0.5_1		95.04 16	95.73 5	92.96 115	95.59 151	82.16 190	94.15 225	96.64 110	91.92 11	98.69 1	98.92 1	90.35 13	98.76 118	96.75 22	98.57 54	97.98 97

testing915		90.59 168	90.24 160	91.63 213	95.58 152	80.71 251	95.14 149	92.25 384	87.37 171	90.97 170	94.37 231	77.06 208	97.29 295	85.51 227	93.93 203	96.88 199

test1111		89.10 217	88.64 213	90.48 277	95.53 153	74.97 404	96.08 70	84.89 488	88.13 133	90.16 192	96.65 93	63.29 395	98.10 184	86.14 216	96.90 118	98.39 46

fmvsm_s_conf0.5_n_3		94.49 33	95.13 19	92.56 147	95.49 154	81.10 230	95.93 87	97.16 52	92.96 4	97.39 13	98.13 8	83.63 91	98.80 113	97.89 3	97.61 101	97.78 126

WTY-MVS		89.60 198	88.92 206	91.67 212	95.47 155	81.15 227	92.38 330	94.78 291	83.11 301	89.06 213	94.32 234	78.67 184	96.61 349	81.57 301	90.89 278	97.24 162

DELS-MVS		93.43 80	93.25 83	93.97 68	95.42 156	85.04 85	93.06 302	97.13 56	90.74 34	91.84 136	95.09 194	86.32 53	99.21 57	91.22 126	98.45 58	97.65 134
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
balanced_ft_v1		92.23 109	92.05 109	92.77 127	95.40 157	81.78 205	95.80 97	95.69 213	87.94 145	91.92 133	95.04 195	75.91 227	98.71 125	93.83 61	96.94 115	97.82 123

fmvsm_s_conf0.5_n_2		93.47 72	93.83 64	92.39 161	95.36 158	81.19 226	95.20 145	96.56 116	90.37 43	97.13 19	98.03 33	77.47 204	98.96 91	97.79 6	96.58 128	97.03 185

thres200		87.21 289	86.24 291	90.12 295	95.36 158	78.53 331	93.26 291	92.10 388	86.42 202	88.00 235	91.11 357	69.24 336	98.00 211	69.58 440	91.04 276	93.83 347

Vis-MVSNet		91.75 123	91.23 135	93.29 90	95.32 160	83.78 125	96.14 65	95.98 181	89.89 57	90.45 178	96.58 99	75.09 239	98.31 171	84.75 239	96.90 118	97.78 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_l_conf0.5_n_a		94.20 48	94.40 40	93.60 83	95.29 161	84.98 86	95.61 116	96.28 138	86.31 205	96.75 30	97.86 39	87.40 40	98.74 122	97.07 17	97.02 113	97.07 181

fmvsm_l_conf0.5_n		94.29 42	94.46 38	93.79 77	95.28 162	85.43 79	95.68 108	96.43 124	86.56 198	96.84 27	97.81 41	87.56 39	98.77 117	97.14 15	96.82 122	97.16 176

BH-RMVSNet		88.37 242	87.48 246	91.02 248	95.28 162	79.45 305	92.89 310	93.07 359	85.45 234	86.91 257	94.84 208	70.35 314	97.76 235	73.97 406	94.59 181	95.85 251

COLMAP_ROB	80.39 16	83.96 372	82.04 382	89.74 319	95.28 162	79.75 295	94.25 219	92.28 381	75.17 440	78.02 440	93.77 262	58.60 440	97.84 230	65.06 467	85.92 349	91.63 431
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PS-MVSNAJ		91.18 144	90.92 143	91.96 192	95.26 165	82.60 178	92.09 348	95.70 211	86.27 206	91.84 136	92.46 304	79.70 165	98.99 84	89.08 170	95.86 145	94.29 319

BH-untuned		88.60 235	88.13 230	90.01 305	95.24 166	78.50 333	93.29 289	94.15 321	84.75 261	84.46 330	93.40 271	75.76 230	97.40 282	77.59 368	94.52 184	94.12 326

EC-MVSNet		93.44 76	93.71 73	92.63 143	95.21 167	82.43 181	97.27 15	96.71 103	90.57 40	92.88 98	95.80 150	83.16 98	98.16 180	93.68 62	98.14 76	97.31 154

ETV-MVS		92.74 99	92.66 97	92.97 113	95.20 168	84.04 119	95.07 153	96.51 120	90.73 35	92.96 96	91.19 351	84.06 86	98.34 166	91.72 117	96.54 129	96.54 220

mvsmamba		90.33 172	89.69 179	92.25 179	95.17 169	81.64 208	95.27 136	93.36 351	84.88 255	89.51 203	94.27 239	69.29 335	97.42 274	89.34 166	96.12 140	97.68 132

GeoE		90.05 181	89.43 187	91.90 200	95.16 170	80.37 266	95.80 97	94.65 297	83.90 278	87.55 247	94.75 210	78.18 194	97.62 248	81.28 306	93.63 216	97.71 131

EIA-MVS		91.95 113	91.94 111	91.98 190	95.16 170	80.01 283	95.36 126	96.73 100	88.44 119	89.34 207	92.16 314	83.82 90	98.45 154	89.35 165	97.06 111	97.48 148

ab-mvs		89.41 208	88.35 222	92.60 144	95.15 172	82.65 176	92.20 344	95.60 221	83.97 277	88.55 223	93.70 266	74.16 258	98.21 177	82.46 279	89.37 305	96.94 194

fmvsm_s_conf0.5_n_4		93.86 62	94.37 42	92.33 167	95.13 173	80.95 237	95.64 114	96.97 67	89.60 73	96.85 26	97.77 42	83.08 101	98.92 97	97.49 8	96.78 123	97.13 177

VDDNet		89.56 200	88.49 220	92.76 130	95.07 174	82.09 192	96.30 48	93.19 356	81.05 360	91.88 134	96.86 82	61.16 422	98.33 168	88.43 182	92.49 257	97.84 120

fmvsm_s_conf0.5_n_a		93.57 69	93.76 70	93.00 111	95.02 175	83.67 128	96.19 58	96.10 171	87.27 173	95.98 42	98.05 29	83.07 102	98.45 154	96.68 24	95.51 154	96.88 199

AllTest		83.42 379	81.39 386	89.52 336	95.01 176	77.79 358	93.12 295	90.89 428	77.41 409	76.12 455	93.34 272	54.08 466	97.51 257	68.31 447	84.27 366	93.26 373

TestCases				89.52 336	95.01 176	77.79 358		90.89 428	77.41 409	76.12 455	93.34 272	54.08 466	97.51 257	68.31 447	84.27 366	93.26 373

EI-MVSNet-Vis-set		93.01 93	92.92 91	93.29 90	95.01 176	83.51 135	94.48 194	95.77 202	90.87 26	92.52 115	96.67 91	84.50 82	99.00 82	91.99 108	94.44 187	97.36 153

SSM_0404		90.73 157	90.08 166	92.69 139	95.00 179	83.13 149	94.32 215	95.00 270	85.41 235	89.84 196	95.35 178	76.13 218	97.98 215	85.46 229	94.18 196	96.95 192

fmvsm_s_conf0.5_n_6		94.11 53	94.56 35	92.76 130	94.98 180	81.96 198	95.79 99	97.29 41	89.31 84	97.52 12	97.61 46	83.25 97	98.88 101	97.05 19	98.22 71	97.43 152

xiu_mvs_v2_base		91.13 146	90.89 145	91.86 201	94.97 181	82.42 182	92.24 341	95.64 219	86.11 215	91.74 143	93.14 283	79.67 170	98.89 100	89.06 171	95.46 158	94.28 320

tttt0517		88.61 234	87.78 240	91.11 243	94.96 182	77.81 356	95.35 127	89.69 455	85.09 250	88.05 234	94.59 223	66.93 357	98.48 146	83.27 265	92.13 260	97.03 185

baseline1		88.10 249	87.28 252	90.57 265	94.96 182	80.07 277	94.27 218	91.29 415	86.74 193	87.41 248	94.00 249	76.77 212	96.20 381	80.77 315	79.31 435	95.44 266

Test_1112_low_res		87.65 262	86.51 280	91.08 244	94.94 184	79.28 317	91.77 356	94.30 313	76.04 432	83.51 359	92.37 307	77.86 200	97.73 240	78.69 356	89.13 311	96.22 230

1112_ss		88.42 239	87.33 250	91.72 210	94.92 185	80.98 235	92.97 307	94.54 301	78.16 403	83.82 349	93.88 257	78.78 182	97.91 225	79.45 345	89.41 304	96.26 229

QAPM		89.51 201	88.15 229	93.59 84	94.92 185	84.58 95	96.82 35	96.70 105	78.43 397	83.41 364	96.19 116	73.18 276	99.30 50	77.11 374	96.54 129	96.89 198

MGCNet		94.18 51	93.80 66	95.34 10	94.91 187	87.62 15	95.97 83	93.01 361	92.58 6	94.22 66	97.20 66	80.56 146	99.59 11	97.04 20	98.68 42	98.81 22

BH-w/o		87.57 271	87.05 257	89.12 346	94.90 188	77.90 352	92.41 328	93.51 348	82.89 311	83.70 353	91.34 345	75.75 231	97.07 316	75.49 389	93.49 222	92.39 416

thisisatest0530		88.67 232	87.61 243	91.86 201	94.87 189	80.07 277	94.63 186	89.90 452	84.00 276	88.46 225	93.78 261	66.88 359	98.46 150	83.30 264	92.65 248	97.06 182

EI-MVSNet-UG-set		92.74 99	92.62 99	93.12 102	94.86 190	83.20 145	94.40 206	95.74 205	90.71 36	92.05 126	96.60 98	84.00 87	98.99 84	91.55 120	93.63 216	97.17 169

HY-MVS	83.01 12	89.03 223	87.94 235	92.29 174	94.86 190	82.77 164	92.08 349	94.49 304	81.52 350	86.93 255	92.79 296	78.32 193	98.23 174	79.93 331	90.55 282	95.88 250

hse-mvs2		89.88 191	89.34 191	91.51 222	94.83 192	81.12 229	93.94 248	93.91 331	89.80 64	93.08 93	93.60 267	75.77 228	97.66 243	92.07 103	77.07 447	95.74 257

AUN-MVS		87.78 258	86.54 279	91.48 224	94.82 193	81.05 232	93.91 252	93.93 328	83.00 306	86.93 255	93.53 269	69.50 329	97.67 241	86.14 216	77.12 446	95.73 259

Casviewmamba		92.82 97	92.75 94	93.03 108	94.79 194	82.44 180	95.39 125	96.24 146	90.58 39	91.79 140	96.43 106	82.73 107	98.19 178	91.31 125	95.54 152	98.46 41

fmvsm_s_conf0.5_n_5		93.96 59	94.18 55	93.30 89	94.79 194	83.81 124	95.77 101	96.74 99	88.02 141	96.23 35	97.84 40	83.36 96	98.83 111	97.49 8	97.34 107	97.25 161

mamba_0408		89.06 221	87.92 236	92.50 152	94.76 196	82.66 172	79.84 502	94.64 298	85.18 240	88.96 215	95.00 197	76.00 223	97.98 215	83.74 259	93.15 235	96.85 202

SSM_04072		88.57 238	87.92 236	90.51 274	94.76 196	82.66 172	79.84 502	94.64 298	85.18 240	88.96 215	95.00 197	76.00 223	92.03 469	83.74 259	93.15 235	96.85 202

SSM_0407		90.47 171	89.80 176	92.46 154	94.76 196	82.66 172	93.98 246	95.00 270	85.41 235	88.96 215	95.35 178	76.13 218	97.88 229	85.46 229	93.15 235	96.85 202

FBQ-MVS		87.19 291	85.74 315	91.52 220	94.74 199	80.62 255	93.91 252	92.20 385	84.27 271	87.61 244	88.77 421	61.17 420	97.29 295	78.01 364	91.03 277	96.64 214

Fast-Effi-MVS+		89.41 208	88.64 213	91.71 211	94.74 199	80.81 247	93.54 274	95.10 262	83.11 301	86.82 263	90.67 374	79.74 164	97.75 239	80.51 321	93.55 218	96.57 218

myMVS_eth3d28		85.80 336	85.26 330	87.42 395	94.73 201	69.92 465	90.60 391	90.95 425	87.21 176	86.06 281	90.04 393	59.47 431	96.02 388	74.89 398	93.35 230	96.33 224

ACMP	84.23 8	89.01 225	88.35 222	90.99 251	94.73 201	81.27 221	95.07 153	95.89 193	86.48 199	83.67 354	94.30 235	69.33 331	97.99 212	87.10 207	88.55 316	93.72 358
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
KinetiMVS		91.82 115	91.30 132	93.39 87	94.72 203	83.36 140	95.45 123	96.37 130	90.33 44	92.17 123	96.03 130	72.32 288	98.75 119	87.94 188	96.34 134	98.07 84

PVSNet	78.82 18	85.55 339	84.65 344	88.23 374	94.72 203	71.93 441	87.12 462	92.75 369	78.80 389	84.95 318	90.53 376	64.43 385	96.71 337	74.74 399	93.86 205	96.06 243

LCM-MVSNet-Re		88.30 245	88.32 225	88.27 371	94.71 205	72.41 440	93.15 294	90.98 423	87.77 156	79.25 426	91.96 327	78.35 192	95.75 404	83.04 267	95.62 151	96.65 213

HQP_MVS		90.60 167	90.19 162	91.82 205	94.70 206	82.73 168	95.85 94	96.22 149	90.81 28	86.91 257	94.86 205	74.23 254	98.12 182	88.15 183	89.99 291	94.63 299

plane_prior7					94.70 206	82.74 167

E3new		91.76 122	91.58 119	92.28 178	94.69 208	80.90 240	93.68 271	96.17 161	87.15 177	91.09 166	95.70 158	81.75 132	98.05 202	89.67 162	94.35 189	97.90 113

ACMH+	81.04 14	85.05 352	83.46 366	89.82 313	94.66 209	79.37 310	94.44 199	94.12 324	82.19 325	78.04 439	92.82 293	58.23 441	97.54 254	73.77 409	82.90 386	92.54 408

ACMM	84.12 9	89.14 216	88.48 221	91.12 240	94.65 210	81.22 224	95.31 129	96.12 169	85.31 239	85.92 283	94.34 232	70.19 317	98.06 198	85.65 224	88.86 314	94.08 330
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
viewcassd2359sk11		91.79 116	91.62 116	92.29 174	94.62 211	80.88 241	93.70 268	96.18 160	87.38 170	91.13 159	95.85 145	81.62 133	98.06 198	89.71 159	94.40 188	97.94 101

fmvsm_s_conf0.1_n_2		93.16 89	93.42 79	92.37 162	94.62 211	81.13 228	95.23 138	95.89 193	90.30 47	96.74 31	98.02 34	76.14 217	98.95 93	97.64 7	96.21 137	97.03 185

test_fmvsmconf_n		94.60 29	94.81 32	93.98 67	94.62 211	84.96 87	96.15 63	97.35 31	89.37 81	96.03 41	98.11 12	86.36 52	99.01 77	97.45 10	97.83 92	97.96 99

viewdifsd2359ckpt09		91.18 144	90.65 152	92.75 132	94.61 214	82.36 186	94.32 215	95.74 205	84.72 262	89.66 201	95.15 192	79.69 168	98.04 203	87.70 192	94.27 194	97.85 119

guyue		91.12 147	90.84 146	91.96 192	94.59 215	80.57 261	94.87 166	93.71 344	88.96 102	91.14 158	95.22 184	73.22 275	97.76 235	92.01 107	93.81 208	97.54 146

plane_prior1					94.59 215

casdiffmvs_mvg		92.96 94	92.83 93	93.35 88	94.59 215	83.40 138	95.00 158	96.34 132	90.30 47	92.05 126	96.05 127	83.43 92	98.15 181	92.07 103	95.67 150	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
3Dnovator	86.66 5	91.73 125	90.82 147	94.44 50	94.59 215	86.37 44	97.18 18	97.02 64	89.20 89	84.31 340	96.66 92	73.74 267	99.17 59	86.74 208	97.96 85	97.79 125

FA-MVS(test-final)		89.66 196	88.91 207	91.93 195	94.57 219	80.27 267	91.36 369	94.74 293	84.87 256	89.82 197	92.61 301	74.72 246	98.47 149	83.97 254	93.53 220	97.04 184

FE-MVS		87.40 278	86.02 300	91.57 218	94.56 220	79.69 299	90.27 398	93.72 343	80.57 363	88.80 219	91.62 340	65.32 375	98.59 140	74.97 397	94.33 191	96.44 221

GDP-MVS		92.04 111	91.46 127	93.75 79	94.55 221	84.69 93	95.60 119	96.56 116	87.83 154	93.07 95	95.89 140	73.44 271	98.65 130	90.22 148	96.03 142	97.91 112

plane_prior6					94.52 222	82.75 165						74.23 254

hybridcas		92.43 105	92.33 103	92.74 134	94.51 223	81.84 200	95.05 156	96.16 163	89.60 73	91.40 152	96.20 112	82.23 117	98.09 192	89.95 154	95.87 144	98.28 62

UGNet		89.95 187	88.95 205	92.95 116	94.51 223	83.31 141	95.70 107	95.23 253	89.37 81	87.58 245	93.94 252	64.00 390	98.78 116	83.92 255	96.31 135	96.74 209
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
PRO-TEST		92.11 110	92.00 110	92.44 157	94.50 225	81.48 215	94.67 183	96.19 153	88.04 140	92.23 122	94.64 219	80.86 143	97.82 232	90.78 138	96.11 141	98.02 94

BP-MVS1		92.48 103	92.07 108	93.72 80	94.50 225	84.39 108	95.90 90	94.30 313	90.39 42	92.67 111	95.94 136	74.46 250	98.65 130	93.14 73	97.35 106	98.13 79

E2		91.79 116	91.61 117	92.31 169	94.49 227	80.86 244	93.74 263	96.19 153	87.63 163	91.16 156	95.94 136	81.31 137	98.06 198	89.76 157	94.29 192	97.99 95

E3		91.78 119	91.61 117	92.30 172	94.48 228	80.86 244	93.73 264	96.19 153	87.63 163	91.16 156	95.95 135	81.30 138	98.06 198	89.76 157	94.29 192	97.99 95

LPG-MVS_test		89.45 204	88.90 208	91.12 240	94.47 229	81.49 213	95.30 131	96.14 165	86.73 194	85.45 300	95.16 190	69.89 321	98.10 184	87.70 192	89.23 309	93.77 353

LGP-MVS_train				91.12 240	94.47 229	81.49 213		96.14 165	86.73 194	85.45 300	95.16 190	69.89 321	98.10 184	87.70 192	89.23 309	93.77 353

baseline		92.39 107	92.29 106	92.69 139	94.46 231	81.77 206	94.14 226	96.27 139	89.22 88	91.88 134	96.00 131	82.35 112	97.99 212	91.05 128	95.27 165	98.30 56

ACMH	80.38 17	85.36 344	83.68 363	90.39 283	94.45 232	80.63 253	94.73 179	94.85 285	82.09 326	77.24 446	92.65 299	60.01 428	97.58 251	72.25 417	84.87 361	92.96 390
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	82.13 13	86.26 327	84.90 338	90.34 287	94.44 233	81.50 211	92.31 339	94.89 281	83.03 305	79.63 420	92.67 298	69.69 324	97.79 233	71.20 423	86.26 348	91.72 429
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
testing91		87.11 295	86.18 292	89.92 308	94.43 234	75.38 403	91.53 364	92.27 382	86.48 199	86.50 266	90.24 384	61.19 419	97.53 255	82.10 287	90.88 279	96.84 205

fmvsm_s_conf0.5_n_7		93.15 90	93.76 70	91.31 233	94.42 235	79.48 303	94.52 192	97.14 55	89.33 83	94.17 69	98.09 19	81.83 129	97.49 262	96.33 27	98.02 83	96.95 192

casdiffmvs		92.51 102	92.43 102	92.74 134	94.41 236	81.98 196	94.54 191	96.23 148	89.57 75	91.96 130	96.17 117	82.58 109	98.01 210	90.95 133	95.45 159	98.23 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
ETVMVS		84.43 365	82.92 376	88.97 352	94.37 237	74.67 407	91.23 376	88.35 469	83.37 295	86.06 281	89.04 412	55.38 455	95.67 408	67.12 454	91.34 267	96.58 217

MVS_Test		91.31 139	91.11 137	91.93 195	94.37 237	80.14 272	93.46 278	95.80 200	86.46 201	91.35 154	93.77 262	82.21 119	98.09 192	87.57 195	94.95 169	97.55 144

NP-MVS					94.37 237	82.42 182					93.98 250

TR-MVS		86.78 307	85.76 313	89.82 313	94.37 237	78.41 335	92.47 327	92.83 365	81.11 359	86.36 272	92.40 306	68.73 344	97.48 263	73.75 410	89.85 297	93.57 362

Effi-MVS+		91.59 133	91.11 137	93.01 110	94.35 241	83.39 139	94.60 187	95.10 262	87.10 180	90.57 177	93.10 285	81.43 135	98.07 197	89.29 167	94.48 185	97.59 140

E5new		91.71 126	91.55 122	92.20 180	94.33 242	80.62 255	94.41 202	96.19 153	88.06 136	91.11 161	96.16 118	79.92 156	98.03 206	90.00 149	93.80 209	97.94 101

E5		91.71 126	91.55 122	92.20 180	94.33 242	80.62 255	94.41 202	96.19 153	88.06 136	91.11 161	96.16 118	79.92 156	98.03 206	90.00 149	93.80 209	97.94 101

E6new		91.71 126	91.55 122	92.20 180	94.32 244	80.62 255	94.41 202	96.19 153	88.06 136	91.11 161	96.16 118	79.92 156	98.03 206	90.00 149	93.80 209	97.94 101

E6		91.71 126	91.55 122	92.20 180	94.32 244	80.62 255	94.41 202	96.19 153	88.06 136	91.11 161	96.16 118	79.92 156	98.03 206	90.00 149	93.80 209	97.94 101

viewmanbaseed2359cas		91.78 119	91.58 119	92.37 162	94.32 244	81.07 231	93.76 261	95.96 185	87.26 174	91.50 147	95.88 141	80.92 142	97.97 217	89.70 160	94.92 170	98.07 84

viewdifsd2359ckpt13		91.20 143	90.75 149	92.54 149	94.30 247	82.13 191	94.03 239	95.89 193	85.60 226	90.20 186	95.36 177	79.69 168	97.90 227	87.85 190	93.86 205	97.61 137

testing11		86.44 323	85.35 327	89.69 324	94.29 248	75.40 402	91.30 371	90.53 436	84.76 260	85.06 315	90.13 390	58.95 439	97.45 268	82.08 288	91.09 273	96.21 232

casdiffseed414692147		91.11 148	90.55 154	92.81 123	94.27 249	82.58 179	94.81 172	96.03 179	87.93 147	90.17 191	95.62 161	78.51 188	97.90 227	84.18 251	93.45 225	97.94 101

E4		91.74 124	91.55 122	92.31 169	94.27 249	80.80 248	93.81 258	96.17 161	87.97 143	91.11 161	96.05 127	80.75 144	98.08 195	89.78 156	94.02 199	98.06 89

RRT-MVS		90.85 153	90.70 151	91.30 234	94.25 251	76.83 380	94.85 169	96.13 168	89.04 96	90.23 185	94.88 203	70.15 318	98.72 123	91.86 115	94.88 171	98.34 49

testing99		86.72 311	85.73 317	89.69 324	94.23 252	74.91 406	91.35 370	90.97 424	86.14 211	86.36 272	90.22 385	59.41 433	97.48 263	82.24 284	90.66 281	96.69 212

CLD-MVS		89.47 203	88.90 208	91.18 239	94.22 253	82.07 193	92.13 346	96.09 172	87.90 149	85.37 309	92.45 305	74.38 252	97.56 253	87.15 203	90.43 284	93.93 337
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt07		91.11 148	91.02 141	91.41 228	94.21 254	78.37 337	92.91 309	95.71 210	87.50 165	90.32 183	95.88 141	80.27 150	97.99 212	88.78 178	93.55 218	97.86 116

UBG		85.51 340	84.57 348	88.35 367	94.21 254	71.78 445	90.07 409	89.66 457	82.28 323	85.91 284	89.01 413	61.30 414	97.06 317	76.58 380	92.06 261	96.22 230

HQP-NCC					94.17 256		94.39 208		88.81 105	85.43 303

ACMP_Plane					94.17 256		94.39 208		88.81 105	85.43 303

HQP-MVS		89.80 193	89.28 194	91.34 232	94.17 256	81.56 209	94.39 208	96.04 177	88.81 105	85.43 303	93.97 251	73.83 265	97.96 219	87.11 205	89.77 300	94.50 310

testing222		84.84 358	83.32 367	89.43 340	94.15 259	75.94 393	91.09 379	89.41 464	84.90 254	85.78 286	89.44 407	52.70 471	96.28 379	70.80 430	91.57 265	96.07 241

WBMVS		84.97 355	84.18 353	87.34 396	94.14 260	71.62 449	90.20 405	92.35 377	81.61 347	84.06 343	90.76 370	61.82 408	96.52 361	78.93 353	83.81 370	93.89 338

XVG-OURS		89.40 210	88.70 212	91.52 220	94.06 261	81.46 216	91.27 374	96.07 174	86.14 211	88.89 218	95.77 154	68.73 344	97.26 300	87.39 199	89.96 293	95.83 253

sss		88.93 226	88.26 228	90.94 255	94.05 262	80.78 249	91.71 358	95.38 240	81.55 349	88.63 222	93.91 256	75.04 240	95.47 417	82.47 278	91.61 264	96.57 218

PCF-MVS	84.11 10	87.74 259	86.08 298	92.70 138	94.02 263	84.43 105	89.27 425	95.87 196	73.62 457	84.43 332	94.33 233	78.48 191	98.86 104	70.27 432	94.45 186	94.81 295
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 283	85.98 302	91.08 244	94.01 264	83.10 151	95.14 149	94.94 275	83.57 287	84.37 333	91.64 336	66.59 364	96.34 376	78.23 361	85.36 355	93.79 348

test1		87.26 283	85.98 302	91.08 244	94.01 264	83.10 151	95.14 149	94.94 275	83.57 287	84.37 333	91.64 336	66.59 364	96.34 376	78.23 361	85.36 355	93.79 348

FMVSNet2		87.19 291	85.82 309	91.30 234	94.01 264	83.67 128	94.79 174	94.94 275	83.57 287	83.88 348	92.05 324	66.59 364	96.51 362	77.56 369	85.01 358	93.73 357

XVG-OURS-SEG-HR		89.95 187	89.45 185	91.47 225	94.00 267	81.21 225	91.87 353	96.06 176	85.78 219	88.55 223	95.73 156	74.67 247	97.27 298	88.71 179	89.64 302	95.91 247

FIs		90.51 170	90.35 157	90.99 251	93.99 268	80.98 235	95.73 105	97.54 10	89.15 91	86.72 264	94.68 213	81.83 129	97.24 302	85.18 231	88.31 324	94.76 297

xiu_mvs_v1_base_debu		90.64 164	90.05 168	92.40 158	93.97 269	84.46 102	93.32 284	95.46 231	85.17 242	92.25 119	94.03 244	70.59 309	98.57 141	90.97 129	94.67 176	94.18 322

xiu_mvs_v1_base		90.64 164	90.05 168	92.40 158	93.97 269	84.46 102	93.32 284	95.46 231	85.17 242	92.25 119	94.03 244	70.59 309	98.57 141	90.97 129	94.67 176	94.18 322

xiu_mvs_v1_base_debi		90.64 164	90.05 168	92.40 158	93.97 269	84.46 102	93.32 284	95.46 231	85.17 242	92.25 119	94.03 244	70.59 309	98.57 141	90.97 129	94.67 176	94.18 322

viewmacassd2359aftdt		91.67 132	91.43 129	92.37 162	93.95 272	81.00 234	93.90 255	95.97 184	87.75 158	91.45 150	96.04 129	79.92 156	97.97 217	89.26 168	94.67 176	98.14 78

VortexMVS		88.42 239	88.01 232	89.63 330	93.89 273	78.82 323	93.82 257	95.47 230	86.67 196	84.53 328	91.99 326	72.62 283	96.65 340	89.02 172	84.09 368	93.41 370

VPA-MVSNet		89.62 197	88.96 204	91.60 216	93.86 274	82.89 163	95.46 122	97.33 34	87.91 148	88.43 226	93.31 275	74.17 257	97.40 282	87.32 201	82.86 387	94.52 307

MVSFormer		91.68 131	91.30 132	92.80 125	93.86 274	83.88 122	95.96 84	95.90 191	84.66 265	91.76 141	94.91 201	77.92 198	97.30 292	89.64 163	97.11 109	97.24 162

lupinMVS		90.92 152	90.21 161	93.03 108	93.86 274	83.88 122	92.81 315	93.86 332	79.84 372	91.76 141	94.29 236	77.92 198	98.04 203	90.48 145	97.11 109	97.17 169

AstraMVS		90.69 159	90.30 159	91.84 204	93.81 277	79.85 291	94.76 177	92.39 376	88.96 102	91.01 169	95.87 144	70.69 307	97.94 222	92.49 85	92.70 247	97.73 129

IterMVS-LS		88.36 243	87.91 238	89.70 322	93.80 278	78.29 341	93.73 264	95.08 264	85.73 221	84.75 321	91.90 330	79.88 161	96.92 328	83.83 256	82.51 388	93.89 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 357	83.09 372	90.14 294	93.80 278	80.05 279	89.18 428	93.09 358	78.89 385	78.19 437	91.91 329	65.86 374	97.27 298	68.47 445	88.45 320	93.11 385

FMVSNet3		87.40 278	86.11 296	91.30 234	93.79 280	83.64 130	94.20 223	94.81 289	83.89 279	84.37 333	91.87 331	68.45 347	96.56 358	78.23 361	85.36 355	93.70 359

fmvsm_s_conf0.1_n		93.46 73	93.66 75	92.85 122	93.75 281	83.13 149	96.02 78	95.74 205	87.68 160	95.89 43	98.17 6	82.78 106	98.46 150	96.71 23	96.17 138	96.98 190

icg_test_0407_2		89.15 215	88.97 203	89.68 328	93.72 282	77.75 361	88.26 444	95.34 246	85.53 230	88.34 228	94.49 226	77.69 202	93.99 444	84.75 239	92.65 248	97.28 157

IMVS_0407		89.85 192	89.51 184	90.88 256	93.72 282	77.75 361	93.07 301	95.34 246	85.53 230	88.34 228	94.49 226	77.69 202	97.60 249	84.75 239	92.65 248	97.28 157

IMVS_0404		87.60 269	86.84 263	89.89 309	93.72 282	77.75 361	88.56 438	95.34 246	85.53 230	79.98 412	94.49 226	66.54 367	94.64 430	84.75 239	92.65 248	97.28 157

IMVS_0403		89.97 185	89.64 180	90.96 254	93.72 282	77.75 361	93.00 304	95.34 246	85.53 230	88.77 220	94.49 226	78.49 190	97.84 230	84.75 239	92.65 248	97.28 157

FC-MVSNet-test		90.27 174	90.18 163	90.53 269	93.71 286	79.85 291	95.77 101	97.59 7	89.31 84	86.27 275	94.67 216	81.93 127	97.01 322	84.26 249	88.09 327	94.71 298

TAMVS		89.21 214	88.29 226	91.96 192	93.71 286	82.62 177	93.30 288	94.19 318	82.22 324	87.78 241	93.94 252	78.83 180	96.95 326	77.70 367	92.98 240	96.32 225

ET-MVSNet_ETH3D		87.51 273	85.91 306	92.32 168	93.70 288	83.93 120	92.33 336	90.94 426	84.16 272	72.09 478	92.52 303	69.90 320	95.85 398	89.20 169	88.36 323	97.17 169

test_fmvsmvis_n_1920		93.44 76	93.55 77	93.10 103	93.67 289	84.26 111	95.83 96	96.14 165	89.00 101	92.43 118	97.50 50	83.37 95	98.72 123	96.61 25	97.44 103	96.32 225

reproduce_monomvs		86.37 325	85.87 307	87.87 383	93.66 290	73.71 418	93.44 279	95.02 265	88.61 115	82.64 376	91.94 328	57.88 443	96.68 338	89.96 153	79.71 431	93.22 377

CDS-MVSNet		89.45 204	88.51 217	92.29 174	93.62 291	83.61 133	93.01 303	94.68 296	81.95 332	87.82 240	93.24 279	78.69 183	96.99 323	80.34 324	93.23 232	96.28 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UniMVSNet (Re)		89.80 193	89.07 199	92.01 186	93.60 292	84.52 99	94.78 175	97.47 17	89.26 87	86.44 271	92.32 309	82.10 122	97.39 285	84.81 238	80.84 416	94.12 326

VPNet		88.20 247	87.47 247	90.39 283	93.56 293	79.46 304	94.04 238	95.54 226	88.67 112	86.96 254	94.58 224	69.33 331	97.15 307	84.05 253	80.53 421	94.56 305

thisisatest0515		87.33 281	85.99 301	91.37 231	93.49 294	79.55 301	90.63 390	89.56 460	80.17 367	87.56 246	90.86 364	67.07 356	98.28 172	81.50 302	93.02 239	96.29 227

mvs_anonymous		89.37 212	89.32 192	89.51 338	93.47 295	74.22 413	91.65 361	94.83 287	82.91 310	85.45 300	93.79 260	81.23 139	96.36 375	86.47 212	94.09 197	97.94 101

CANet_DTU		90.26 175	89.41 189	92.81 123	93.46 296	83.01 159	93.48 276	94.47 305	89.43 79	87.76 242	94.23 241	70.54 313	99.03 72	84.97 234	96.39 133	96.38 223

testing3		80.46 420	79.59 413	83.06 455	93.44 297	64.64 488	93.33 283	85.47 485	84.34 270	79.93 414	90.84 366	44.35 493	92.39 466	57.06 493	87.56 335	92.16 423

UniMVSNet_NR-MVSNet		89.92 189	89.29 193	91.81 207	93.39 298	83.72 126	94.43 200	97.12 57	89.80 64	86.46 268	93.32 274	83.16 98	97.23 303	84.92 235	81.02 412	94.49 312

Effi-MVS+-dtu		88.65 233	88.35 222	89.54 333	93.33 299	76.39 388	94.47 197	94.36 311	87.70 159	85.43 303	89.56 406	73.45 270	97.26 300	85.57 226	91.28 268	94.97 283

WR-MVS		88.38 241	87.67 242	90.52 273	93.30 300	80.18 270	93.26 291	95.96 185	88.57 117	85.47 299	92.81 294	76.12 220	96.91 329	81.24 307	82.29 392	94.47 315

WR-MVS_H		87.80 257	87.37 249	89.10 347	93.23 301	78.12 344	95.61 116	97.30 39	87.90 149	83.72 352	92.01 325	79.65 171	96.01 390	76.36 381	80.54 420	93.16 381

test_0402		81.30 411	79.17 420	87.67 387	93.19 302	78.17 343	92.98 306	91.71 399	75.25 439	76.02 458	90.31 383	59.23 434	96.37 373	50.22 502	83.63 375	88.47 482

Elysia		90.12 177	89.10 197	93.18 97	93.16 303	84.05 117	95.22 140	96.27 139	85.16 245	90.59 175	94.68 213	64.64 382	98.37 161	86.38 214	95.77 147	97.12 178

StellarMVS		90.12 177	89.10 197	93.18 97	93.16 303	84.05 117	95.22 140	96.27 139	85.16 245	90.59 175	94.68 213	64.64 382	98.37 161	86.38 214	95.77 147	97.12 178

OPM-MVS		90.12 177	89.56 183	91.82 205	93.14 305	83.90 121	94.16 224	95.74 205	88.96 102	87.86 236	95.43 173	72.48 285	97.91 225	88.10 187	90.18 289	93.65 360
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CP-MVSNet		87.63 265	87.26 254	88.74 358	93.12 306	76.59 385	95.29 133	96.58 114	88.43 120	83.49 362	92.98 288	75.28 237	95.83 399	78.97 352	81.15 408	93.79 348

mmtdpeth		85.04 354	84.15 355	87.72 386	93.11 307	75.74 397	94.37 212	92.83 365	84.98 252	89.31 208	86.41 455	61.61 411	97.14 310	92.63 84	62.11 496	90.29 458

diffmvs_AUTHOR		91.51 134	91.44 128	91.73 209	93.09 308	80.27 267	92.51 326	95.58 222	87.22 175	91.80 139	95.57 164	79.96 155	97.48 263	92.23 96	94.97 168	97.45 150

diffmvs		91.37 138	91.23 135	91.77 208	93.09 308	80.27 267	92.36 331	95.52 228	87.03 183	91.40 152	94.93 200	80.08 152	97.44 271	92.13 102	94.56 182	97.61 137
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
onestephybrid01		91.23 140	91.10 139	91.61 215	93.07 310	79.86 289	92.83 313	95.34 246	87.07 181	91.04 167	95.53 166	80.01 154	97.43 272	90.96 132	94.08 198	97.56 142

nrg030		91.08 150	90.39 156	93.17 99	93.07 310	86.91 24	96.41 43	96.26 143	88.30 124	88.37 227	94.85 207	82.19 120	97.64 246	91.09 127	82.95 382	94.96 286

UWE-MVS		83.69 378	83.09 372	85.48 432	93.06 312	65.27 486	90.92 384	86.14 480	79.90 371	86.26 276	90.72 373	57.17 447	95.81 401	71.03 428	92.62 253	95.35 271

PAPM		86.68 313	85.39 324	90.53 269	93.05 313	79.33 316	89.79 414	94.77 292	78.82 388	81.95 385	93.24 279	76.81 210	97.30 292	66.94 456	93.16 234	94.95 290

DU-MVS		89.34 213	88.50 218	91.85 203	93.04 314	83.72 126	94.47 197	96.59 113	89.50 76	86.46 268	93.29 277	77.25 206	97.23 303	84.92 235	81.02 412	94.59 302

NR-MVSNet		88.58 237	87.47 247	91.93 195	93.04 314	84.16 114	94.77 176	96.25 145	89.05 95	80.04 411	93.29 277	79.02 178	97.05 319	81.71 300	80.05 426	94.59 302

jason		90.80 154	90.10 165	92.90 118	93.04 314	83.53 134	93.08 299	94.15 321	80.22 366	91.41 151	94.91 201	76.87 209	97.93 223	90.28 146	96.90 118	97.24 162
jason: jason.
viewmamba		91.38 136	91.32 131	91.58 217	93.02 317	79.63 300	92.83 313	95.38 240	88.29 125	90.66 174	95.81 149	80.63 145	97.50 261	91.52 121	93.71 214	97.62 135

PS-CasMVS		87.32 282	86.88 260	88.63 361	92.99 318	76.33 390	95.33 128	96.61 112	88.22 129	83.30 368	93.07 286	73.03 278	95.79 403	78.36 358	81.00 414	93.75 355

test_vis1_n_1920		89.39 211	89.84 174	88.04 378	92.97 319	72.64 435	94.71 181	96.03 179	86.18 209	91.94 132	96.56 101	61.63 409	95.74 405	93.42 68	95.11 167	95.74 257

SD_0403		84.71 361	84.65 344	84.92 441	92.95 320	65.95 480	92.07 350	93.23 354	83.82 282	79.03 427	93.73 265	73.90 262	92.91 462	63.02 475	90.05 290	95.89 249

MVSTER		88.84 227	88.29 226	90.51 274	92.95 320	80.44 264	93.73 264	95.01 266	84.66 265	87.15 252	93.12 284	72.79 280	97.21 305	87.86 189	87.36 339	93.87 342

RPSCF		85.07 351	84.27 351	87.48 393	92.91 322	70.62 459	91.69 360	92.46 374	76.20 431	82.67 375	95.22 184	63.94 391	97.29 295	77.51 370	85.80 350	94.53 306

viewdifsd2359ckpt11		89.43 206	89.05 201	90.56 267	92.89 323	77.00 376	92.81 315	94.52 302	87.03 183	89.77 198	95.79 151	74.67 247	97.51 257	88.97 173	84.98 359	97.17 169

viewmsd2359difaftdt		89.43 206	89.05 201	90.56 267	92.89 323	77.00 376	92.81 315	94.52 302	87.03 183	89.77 198	95.79 151	74.67 247	97.51 257	88.97 173	84.98 359	97.17 169

viewmambaseed2359dif		90.04 182	89.78 177	90.83 258	92.85 325	77.92 349	92.23 342	95.01 266	81.90 335	90.20 186	95.45 170	79.64 172	97.34 289	87.52 197	93.17 233	97.23 166

dtuplus		89.78 195	89.43 187	90.85 257	92.83 326	77.91 350	92.32 338	94.97 272	82.33 322	90.20 186	95.53 166	78.56 187	97.38 287	85.15 232	92.95 241	97.24 162

FMVSNet1		85.85 334	84.11 356	91.08 244	92.81 327	83.10 151	95.14 149	94.94 275	81.64 345	82.68 374	91.64 336	59.01 438	96.34 376	75.37 391	83.78 371	93.79 348

tfpnnormal		84.72 360	83.23 370	89.20 344	92.79 328	80.05 279	94.48 194	95.81 199	82.38 319	81.08 395	91.21 350	69.01 340	96.95 326	61.69 478	80.59 419	90.58 457

LuminaMVS		90.55 169	89.81 175	92.77 127	92.78 329	84.21 112	94.09 233	94.17 320	85.82 217	91.54 146	94.14 243	69.93 319	97.92 224	91.62 119	94.21 195	96.18 233

hybridnocas07		90.93 151	90.72 150	91.54 219	92.75 330	79.72 297	92.35 333	95.21 256	86.41 203	90.44 181	95.40 174	79.17 177	97.39 285	90.83 137	93.94 202	97.50 147

SSC-MVS3.2		84.60 363	84.19 352	85.85 429	92.74 331	68.07 471	88.15 446	93.81 338	87.42 169	83.76 351	91.07 359	62.91 400	95.73 406	74.56 403	83.24 381	93.75 355

nomal-1		86.20 328	84.90 338	90.11 299	92.72 332	80.88 241	89.79 414	91.03 422	82.96 308	83.49 362	88.82 417	62.88 401	94.38 435	81.35 304	91.05 274	95.07 279

OpenMVS	83.78 11	88.74 231	87.29 251	93.08 105	92.70 333	85.39 80	96.57 41	96.43 124	78.74 391	80.85 397	96.07 126	69.64 325	99.01 77	78.01 364	96.65 127	94.83 294

TranMVSNet+NR-MVSNet		88.84 227	87.95 234	91.49 223	92.68 334	83.01 159	94.92 163	96.31 134	89.88 58	85.53 294	93.85 259	76.63 215	96.96 325	81.91 293	79.87 429	94.50 310

hybrid		90.69 159	90.45 155	91.43 227	92.67 335	79.42 308	92.28 340	95.21 256	85.15 247	90.39 182	95.37 176	78.93 179	97.32 291	90.27 147	93.74 213	97.55 144

MVS		87.44 276	86.10 297	91.44 226	92.61 336	83.62 131	92.63 322	95.66 216	67.26 487	81.47 389	92.15 315	77.95 197	98.22 176	79.71 334	95.48 156	92.47 411

fmvsm_s_conf0.1_n_a		93.19 87	93.26 82	92.97 113	92.49 337	83.62 131	96.02 78	95.72 209	86.78 192	96.04 40	98.19 5	82.30 115	98.43 158	96.38 26	95.42 160	96.86 201

CHOSEN 280x420		85.15 350	83.99 359	88.65 360	92.47 338	78.40 336	79.68 504	92.76 368	74.90 444	81.41 391	89.59 404	69.85 323	95.51 413	79.92 332	95.29 163	92.03 424

test_fmvsmconf0.1_n		94.20 48	94.31 45	93.88 71	92.46 339	84.80 90	96.18 60	96.82 87	89.29 86	95.68 49	98.11 12	85.10 69	98.99 84	97.38 11	97.75 98	97.86 116

UniMVSNet_ETH3D		87.53 272	86.37 284	91.00 250	92.44 340	78.96 322	94.74 178	95.61 220	84.07 275	85.36 310	94.52 225	59.78 430	97.34 289	82.93 269	87.88 330	96.71 210

1314		87.51 273	86.57 277	90.34 287	92.42 341	79.74 296	92.63 322	95.35 245	78.35 398	80.14 408	91.62 340	74.05 259	97.15 307	81.05 308	93.53 220	94.12 326

cl22		86.78 307	85.98 302	89.18 345	92.34 342	77.62 367	90.84 386	94.13 323	81.33 353	83.97 347	90.15 389	73.96 261	96.60 353	84.19 250	82.94 383	93.33 371

PEN-MVS		86.80 306	86.27 290	88.40 365	92.32 343	75.71 398	95.18 146	96.38 129	87.97 143	82.82 373	93.15 282	73.39 273	95.92 394	76.15 385	79.03 437	93.59 361

tt0805		86.92 300	85.74 315	90.48 277	92.22 344	79.98 285	95.63 115	94.88 283	83.83 281	84.74 322	92.80 295	57.61 445	97.67 241	85.48 228	84.42 364	93.79 348

c3_l		87.14 294	86.50 281	89.04 349	92.20 345	77.26 372	91.22 377	94.70 295	82.01 331	84.34 337	90.43 379	78.81 181	96.61 349	83.70 261	81.09 409	93.25 375

SCA		86.32 326	85.18 331	89.73 321	92.15 346	76.60 384	91.12 378	91.69 401	83.53 290	85.50 297	88.81 418	66.79 360	96.48 364	76.65 377	90.35 286	96.12 237

XXY-MVS		87.65 262	86.85 262	90.03 302	92.14 347	80.60 260	93.76 261	95.23 253	82.94 309	84.60 324	94.02 247	74.27 253	95.49 416	81.04 309	83.68 374	94.01 334

miper_ehance_all_eth		87.22 288	86.62 275	89.02 350	92.13 348	77.40 370	90.91 385	94.81 289	81.28 354	84.32 338	90.08 392	79.26 174	96.62 346	83.81 257	82.94 383	93.04 388

IB-MVS	80.51 15	85.24 349	83.26 369	91.19 238	92.13 348	79.86 289	91.75 357	91.29 415	83.28 298	80.66 401	88.49 425	61.28 415	98.46 150	80.99 312	79.46 433	95.25 274
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
cascas		86.43 324	84.98 335	90.80 261	92.10 350	80.92 239	90.24 402	95.91 190	73.10 462	83.57 358	88.39 426	65.15 377	97.46 267	84.90 237	91.43 266	94.03 333

Fast-Effi-MVS+-dtu		87.44 276	86.72 267	89.63 330	92.04 351	77.68 366	94.03 239	93.94 327	85.81 218	82.42 377	91.32 348	70.33 315	97.06 317	80.33 325	90.23 288	94.14 325

cl____		86.52 319	85.78 310	88.75 356	92.03 352	76.46 386	90.74 387	94.30 313	81.83 340	83.34 366	90.78 369	75.74 233	96.57 356	81.74 298	81.54 403	93.22 377

DIV-MVS_self_test		86.53 318	85.78 310	88.75 356	92.02 353	76.45 387	90.74 387	94.30 313	81.83 340	83.34 366	90.82 367	75.75 231	96.57 356	81.73 299	81.52 404	93.24 376

eth_miper_zixun_eth		86.50 320	85.77 312	88.68 359	91.94 354	75.81 396	90.47 396	94.89 281	82.05 328	84.05 344	90.46 378	75.96 225	96.77 333	82.76 275	79.36 434	93.46 368

Syy-MVS		80.07 425	79.78 408	80.94 465	91.92 355	59.93 502	89.75 417	87.40 477	81.72 342	78.82 432	87.20 443	66.29 369	91.29 479	47.06 507	87.84 332	91.60 432

myMVS_eth3d		79.67 430	78.79 426	82.32 461	91.92 355	64.08 489	89.75 417	87.40 477	81.72 342	78.82 432	87.20 443	45.33 491	91.29 479	59.09 488	87.84 332	91.60 432

PS-MVSNAJss		89.97 185	89.62 181	91.02 248	91.90 357	80.85 246	95.26 137	95.98 181	86.26 207	86.21 277	94.29 236	79.70 165	97.65 244	88.87 177	88.10 325	94.57 304

ITE_SJBPF				88.24 373	91.88 358	77.05 375		92.92 362	85.54 228	80.13 409	93.30 276	57.29 446	96.20 381	72.46 416	84.71 362	91.49 437

EI-MVSNet		89.10 217	88.86 210	89.80 316	91.84 359	78.30 340	93.70 268	95.01 266	85.73 221	87.15 252	95.28 181	79.87 162	97.21 305	83.81 257	87.36 339	93.88 341

CVMVSNet		84.69 362	84.79 342	84.37 446	91.84 359	64.92 487	93.70 268	91.47 411	66.19 492	86.16 279	95.28 181	67.18 355	93.33 455	80.89 314	90.42 285	94.88 292

dmvs_re		84.20 369	83.22 371	87.14 407	91.83 361	77.81 356	90.04 410	90.19 442	84.70 264	81.49 388	89.17 410	64.37 386	91.13 481	71.58 420	85.65 352	92.46 412

MVS-HIRNet		73.70 453	72.20 456	78.18 474	91.81 362	56.42 510	82.94 493	82.58 494	55.24 503	68.88 487	66.48 515	55.32 456	95.13 423	58.12 490	88.42 321	83.01 493

PatchmatchNet		85.85 334	84.70 343	89.29 342	91.76 363	75.54 399	88.49 440	91.30 414	81.63 346	85.05 316	88.70 423	71.71 293	96.24 380	74.61 402	89.05 312	96.08 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		84.43 365	83.06 374	88.54 362	91.72 364	78.44 334	95.18 146	92.82 367	82.73 314	79.67 419	92.12 317	73.49 269	95.96 392	71.10 427	68.73 484	91.21 444

IterMVS-SCA-FT		85.45 341	84.53 349	88.18 375	91.71 365	76.87 379	90.19 406	92.65 372	85.40 237	81.44 390	90.54 375	66.79 360	95.00 427	81.04 309	81.05 410	92.66 401

TinyColmap		79.76 429	77.69 433	85.97 425	91.71 365	73.12 426	89.55 419	90.36 439	75.03 441	72.03 479	90.19 387	46.22 490	96.19 383	63.11 473	81.03 411	88.59 481

MDTV_nov1_ep13			83.56 365		91.69 367	69.93 464	87.75 454	91.54 407	78.60 393	84.86 319	88.90 416	69.54 327	96.03 387	70.25 433	88.93 313

miper_enhance_ethall		86.90 301	86.18 292	89.06 348	91.66 368	77.58 368	90.22 404	94.82 288	79.16 381	84.48 329	89.10 411	79.19 176	96.66 339	84.06 252	82.94 383	92.94 391

DTE-MVSNet		86.11 329	85.48 322	87.98 379	91.65 369	74.92 405	94.93 162	95.75 204	87.36 172	82.26 379	93.04 287	72.85 279	95.82 400	74.04 405	77.46 443	93.20 379

MIMVSNet		82.59 387	80.53 391	88.76 355	91.51 370	78.32 339	86.57 468	90.13 444	79.32 377	80.70 400	88.69 424	52.98 470	93.07 460	66.03 462	88.86 314	94.90 291

WB-MVSnew		83.77 376	83.28 368	85.26 437	91.48 371	71.03 454	91.89 351	87.98 470	78.91 383	84.78 320	90.22 385	69.11 339	94.02 443	64.70 468	90.44 283	90.71 452

pm-mvs1		86.61 314	85.54 320	89.82 313	91.44 372	80.18 270	95.28 135	94.85 285	83.84 280	81.66 387	92.62 300	72.45 287	96.48 364	79.67 336	78.06 438	92.82 396

Baseline_NR-MVSNet		87.07 296	86.63 274	88.40 365	91.44 372	77.87 354	94.23 222	92.57 373	84.12 274	85.74 288	92.08 321	77.25 206	96.04 386	82.29 283	79.94 427	91.30 442

IterMVS		84.88 356	83.98 360	87.60 388	91.44 372	76.03 392	90.18 407	92.41 375	83.24 299	81.06 396	90.42 380	66.60 363	94.28 439	79.46 344	80.98 415	92.48 410
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		85.05 352	84.16 354	87.73 385	91.42 375	78.51 332	91.25 375	93.53 346	77.50 408	80.15 407	91.58 342	61.99 406	95.51 413	75.69 388	94.35 189	89.16 473

tpm2		84.08 370	82.94 375	87.48 393	91.39 376	71.27 450	89.23 427	90.37 438	71.95 471	84.64 323	89.33 408	67.30 352	96.55 360	75.17 393	87.09 343	94.63 299

v8		87.50 275	86.71 268	89.89 309	91.37 377	79.40 309	94.50 193	95.38 240	84.81 259	83.60 357	91.33 346	76.05 221	97.42 274	82.84 272	80.51 423	92.84 395

ADS-MVSNet2		81.66 402	79.71 411	87.50 391	91.35 378	74.19 414	83.33 490	88.48 468	72.90 464	82.24 380	85.77 462	64.98 378	93.20 458	64.57 469	83.74 372	95.12 277

ADS-MVSNet		81.56 404	79.78 408	86.90 412	91.35 378	71.82 443	83.33 490	89.16 466	72.90 464	82.24 380	85.77 462	64.98 378	93.76 449	64.57 469	83.74 372	95.12 277

GA-MVS		86.61 314	85.27 329	90.66 263	91.33 380	78.71 326	90.40 397	93.81 338	85.34 238	85.12 313	89.57 405	61.25 416	97.11 312	80.99 312	89.59 303	96.15 234

miper_lstm_enhance		85.27 348	84.59 347	87.31 398	91.28 381	74.63 408	87.69 455	94.09 325	81.20 358	81.36 392	89.85 400	74.97 242	94.30 438	81.03 311	79.84 430	93.01 389

XVG-ACMP-BASELINE		86.00 330	84.84 341	89.45 339	91.20 382	78.00 347	91.70 359	95.55 224	85.05 251	82.97 371	92.25 313	54.49 464	97.48 263	82.93 269	87.45 338	92.89 393

v10		87.25 285	86.38 283	89.85 311	91.19 383	79.50 302	94.48 194	95.45 234	83.79 283	83.62 356	91.19 351	75.13 238	97.42 274	81.94 292	80.60 418	92.63 402

FMVSNet5		81.52 407	79.60 412	87.27 399	91.17 384	77.95 348	91.49 365	92.26 383	76.87 421	76.16 454	87.91 435	51.67 473	92.34 467	67.74 451	81.16 406	91.52 435

USDC		82.76 384	81.26 388	87.26 400	91.17 384	74.55 409	89.27 425	93.39 350	78.26 401	75.30 462	92.08 321	54.43 465	96.63 343	71.64 419	85.79 351	90.61 454

CostFormer		85.77 337	84.94 337	88.26 372	91.16 386	72.58 438	89.47 423	91.04 421	76.26 429	86.45 270	89.97 396	70.74 306	96.86 332	82.35 281	87.07 344	95.34 272

test_cas_vis1_n_1920		88.83 230	88.85 211	88.78 354	91.15 387	76.72 382	93.85 256	94.93 279	83.23 300	92.81 102	96.00 131	61.17 420	94.45 431	91.67 118	94.84 172	95.17 276

baseline2		86.50 320	85.39 324	89.84 312	91.12 388	76.70 383	91.88 352	88.58 467	82.35 321	79.95 413	90.95 362	73.42 272	97.63 247	80.27 326	89.95 294	95.19 275

tpm cat1		81.96 394	80.27 397	87.01 408	91.09 389	71.02 455	87.38 460	91.53 408	66.25 491	80.17 406	86.35 457	68.22 349	96.15 384	69.16 441	82.29 392	93.86 344

tpmvs		83.35 381	82.07 381	87.20 405	91.07 390	71.00 456	88.31 443	91.70 400	78.91 383	80.49 404	87.18 445	69.30 334	97.08 314	68.12 450	83.56 376	93.51 366

tt0320-xc		79.63 432	76.66 442	88.52 363	91.03 391	78.72 324	93.00 304	89.53 462	66.37 490	76.11 457	87.11 447	46.36 489	95.32 421	72.78 414	67.67 485	91.51 436

v1144		87.61 268	86.79 266	90.06 300	91.01 392	79.34 313	93.95 247	95.42 239	83.36 296	85.66 290	91.31 349	74.98 241	97.42 274	83.37 263	82.06 394	93.42 369

v2v482		87.84 255	87.06 256	90.17 291	90.99 393	79.23 320	94.00 244	95.13 259	84.87 256	85.53 294	92.07 323	74.45 251	97.45 268	84.71 244	81.75 400	93.85 345

SixPastTwentyTwo		83.91 374	82.90 377	86.92 411	90.99 393	70.67 458	93.48 276	91.99 393	85.54 228	77.62 445	92.11 319	60.59 424	96.87 331	76.05 386	77.75 440	93.20 379

test-LLR		85.87 333	85.41 323	87.25 401	90.95 395	71.67 447	89.55 419	89.88 453	83.41 293	84.54 326	87.95 433	67.25 353	95.11 424	81.82 295	93.37 228	94.97 283

test-mter		84.54 364	83.64 364	87.25 401	90.95 395	71.67 447	89.55 419	89.88 453	79.17 380	84.54 326	87.95 433	55.56 452	95.11 424	81.82 295	93.37 228	94.97 283

v148		87.04 297	86.32 287	89.21 343	90.94 397	77.26 372	93.71 267	94.43 306	84.84 258	84.36 336	90.80 368	76.04 222	97.05 319	82.12 286	79.60 432	93.31 372

mvs_tets		88.06 252	87.28 252	90.38 285	90.94 397	79.88 288	95.22 140	95.66 216	85.10 249	84.21 342	93.94 252	63.53 393	97.40 282	88.50 181	88.40 322	93.87 342

MVP-Stereo		85.97 331	84.86 340	89.32 341	90.92 399	82.19 189	92.11 347	94.19 318	78.76 390	78.77 435	91.63 339	68.38 348	96.56 358	75.01 396	93.95 201	89.20 472
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		81.37 409	79.30 416	87.58 389	90.92 399	74.16 415	80.99 497	87.68 474	70.52 477	76.63 452	88.81 418	71.21 298	92.76 464	60.01 485	86.93 345	95.83 253

jajsoiax		88.24 246	87.50 245	90.48 277	90.89 401	80.14 272	95.31 129	95.65 218	84.97 253	84.24 341	94.02 247	65.31 376	97.42 274	88.56 180	88.52 318	93.89 338

tpmrst		85.35 345	84.99 334	86.43 421	90.88 402	67.88 474	88.71 435	91.43 412	80.13 368	86.08 280	88.80 420	73.05 277	96.02 388	82.48 277	83.40 380	95.40 268

gg-mvs-nofinetune		81.77 399	79.37 414	88.99 351	90.85 403	77.73 365	86.29 469	79.63 501	74.88 445	83.19 370	69.05 513	60.34 425	96.11 385	75.46 390	94.64 180	93.11 385

D2MVS		85.90 332	85.09 333	88.35 367	90.79 404	77.42 369	91.83 355	95.70 211	80.77 362	80.08 410	90.02 394	66.74 362	96.37 373	81.88 294	87.97 329	91.26 443

sc_t1		81.53 406	78.67 428	90.12 295	90.78 405	78.64 327	93.91 252	90.20 441	68.42 483	80.82 398	89.88 398	46.48 487	96.76 334	76.03 387	71.47 466	94.96 286

OurMVSNet-221017-0		85.35 345	84.64 346	87.49 392	90.77 406	72.59 437	94.01 242	94.40 309	84.72 262	79.62 421	93.17 281	61.91 407	96.72 335	81.99 291	81.16 406	93.16 381

v1192		87.25 285	86.33 286	90.00 306	90.76 407	79.04 321	93.80 259	95.48 229	82.57 316	85.48 298	91.18 353	73.38 274	97.42 274	82.30 282	82.06 394	93.53 363

test_djsdf		89.03 223	88.64 213	90.21 290	90.74 408	79.28 317	95.96 84	95.90 191	84.66 265	85.33 311	92.94 289	74.02 260	97.30 292	89.64 163	88.53 317	94.05 332

v7n		86.81 305	85.76 313	89.95 307	90.72 409	79.25 319	95.07 153	95.92 188	84.45 268	82.29 378	90.86 364	72.60 284	97.53 255	79.42 348	80.52 422	93.08 387

PVSNet_0	73.20 20	77.22 445	74.83 452	84.37 446	90.70 410	71.10 453	83.09 492	89.67 456	72.81 466	73.93 470	83.13 476	60.79 423	93.70 451	68.54 444	50.84 508	88.30 483

v144192		87.19 291	86.35 285	89.74 319	90.64 411	78.24 342	93.92 250	95.43 237	81.93 333	85.51 296	91.05 360	74.21 256	97.45 268	82.86 271	81.56 402	93.53 363

test_fmvs1		87.34 280	87.56 244	86.68 418	90.59 412	71.80 444	94.01 242	94.04 326	78.30 399	91.97 129	95.22 184	56.28 450	93.71 450	92.89 77	94.71 175	94.52 307

V42		87.68 260	86.86 261	90.15 293	90.58 413	80.14 272	94.24 221	95.28 251	83.66 285	85.67 289	91.33 346	74.73 245	97.41 280	84.43 248	81.83 398	92.89 393

CR-MVSNet		85.35 345	83.76 362	90.12 295	90.58 413	79.34 313	85.24 478	91.96 396	78.27 400	85.55 292	87.87 436	71.03 301	95.61 409	73.96 407	89.36 306	95.40 268

RPMNet		83.95 373	81.53 385	91.21 237	90.58 413	79.34 313	85.24 478	96.76 95	71.44 473	85.55 292	82.97 479	70.87 304	98.91 99	61.01 480	89.36 306	95.40 268

v1921920		86.97 299	86.06 299	89.69 324	90.53 416	78.11 345	93.80 259	95.43 237	81.90 335	85.33 311	91.05 360	72.66 281	97.41 280	82.05 290	81.80 399	93.53 363

usedtu_dtu_shiyan1		86.84 303	85.61 318	90.53 269	90.50 417	81.80 203	90.97 382	94.96 273	83.05 303	83.50 360	90.32 381	72.15 289	96.65 340	79.49 342	85.55 353	93.15 383

FE-MVSNET3		86.84 303	85.61 318	90.53 269	90.50 417	81.80 203	90.97 382	94.96 273	83.05 303	83.50 360	90.32 381	72.15 289	96.65 340	79.49 342	85.55 353	93.15 383

tt0320		80.13 424	77.41 435	88.29 370	90.50 417	78.02 346	93.10 298	90.71 433	66.06 493	76.75 450	86.97 448	49.56 479	95.40 418	71.65 418	71.41 467	91.46 439

v1240		86.78 307	85.85 308	89.56 332	90.45 420	77.79 358	93.61 272	95.37 243	81.65 344	85.43 303	91.15 355	71.50 296	97.43 272	81.47 303	82.05 396	93.47 367

tpm		84.73 359	84.02 358	86.87 414	90.33 421	68.90 468	89.06 430	89.94 450	80.85 361	85.75 287	89.86 399	68.54 346	95.97 391	77.76 366	84.05 369	95.75 256

EG-PatchMatch MVS		82.37 393	80.34 396	88.46 364	90.27 422	79.35 311	92.80 318	94.33 312	77.14 413	73.26 474	90.18 388	47.47 484	96.72 335	70.25 433	87.32 341	89.30 469

EPNet_dtu		86.49 322	85.94 305	88.14 376	90.24 423	72.82 430	94.11 229	92.20 385	86.66 197	79.42 422	92.36 308	73.52 268	95.81 401	71.26 422	93.66 215	95.80 255
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		83.90 375	82.70 380	87.51 390	90.23 424	72.67 433	88.62 437	81.96 496	81.37 352	85.01 317	88.34 427	66.31 368	94.45 431	75.30 392	87.12 342	95.43 267

EPNet		91.79 116	91.02 141	94.10 65	90.10 425	85.25 82	96.03 77	92.05 390	92.83 5	87.39 251	95.78 153	79.39 173	99.01 77	88.13 185	97.48 102	98.05 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		82.68 386	81.27 387	86.89 413	90.09 426	70.94 457	84.06 487	90.15 443	74.91 443	85.63 291	83.57 474	69.37 330	94.87 429	65.19 464	88.50 319	94.84 293

Patchmtry		82.71 385	80.93 390	88.06 377	90.05 427	76.37 389	84.74 484	91.96 396	72.28 470	81.32 393	87.87 436	71.03 301	95.50 415	68.97 442	80.15 425	92.32 419

pmmvs4		85.43 342	83.86 361	90.16 292	90.02 428	82.97 161	90.27 398	92.67 371	75.93 433	80.73 399	91.74 334	71.05 300	95.73 406	78.85 355	83.46 378	91.78 428

TESTMET0.1,1		83.74 377	82.85 378	86.42 422	89.96 429	71.21 452	89.55 419	87.88 471	77.41 409	83.37 365	87.31 441	56.71 448	93.65 452	80.62 319	92.85 245	94.40 316

dp		81.47 408	80.23 398	85.17 438	89.92 430	65.49 484	86.74 466	90.10 445	76.30 428	81.10 394	87.12 446	62.81 402	95.92 394	68.13 449	79.88 428	94.09 329

K. test v3		81.59 403	80.15 401	85.91 428	89.89 431	69.42 467	92.57 324	87.71 473	85.56 227	73.44 473	89.71 403	55.58 451	95.52 412	77.17 373	69.76 472	92.78 398

MDA-MVSNet-bldmvs		78.85 438	76.31 444	86.46 419	89.76 432	73.88 416	88.79 434	90.42 437	79.16 381	59.18 501	88.33 428	60.20 426	94.04 442	62.00 477	68.96 477	91.48 438

test_fmvs1_n		87.03 298	87.04 258	86.97 409	89.74 433	71.86 442	94.55 190	94.43 306	78.47 395	91.95 131	95.50 169	51.16 475	93.81 448	93.02 76	94.56 182	95.26 273

GG-mvs-BLEND				87.94 381	89.73 434	77.91 350	87.80 450	78.23 506		80.58 402	83.86 470	59.88 429	95.33 420	71.20 423	92.22 259	90.60 456

EGC-MVSNET		61.97 467	56.37 473	78.77 471	89.63 435	73.50 421	89.12 429	82.79 493	0.21 561	1.24 563	84.80 467	39.48 496	90.04 487	44.13 509	75.94 452	72.79 506

gm-plane-assit					89.60 436	68.00 472			77.28 412		88.99 414		97.57 252	79.44 346

MonoMVSNet		86.89 302	86.55 278	87.92 382	89.46 437	73.75 417	94.12 227	93.10 357	87.82 155	85.10 314	90.76 370	69.59 326	94.94 428	86.47 212	82.50 389	95.07 279

test_fmvsmconf0.01_n		93.19 87	93.02 89	93.71 81	89.25 438	84.42 107	96.06 74	96.29 135	89.06 94	94.68 61	98.13 8	79.22 175	98.98 88	97.22 13	97.24 108	97.74 128

anonymousdsp		87.84 255	87.09 255	90.12 295	89.13 439	80.54 262	94.67 183	95.55 224	82.05 328	83.82 349	92.12 317	71.47 297	97.15 307	87.15 203	87.80 334	92.67 400

N_pmnet		68.89 461	68.44 462	70.23 485	89.07 440	28.79 537	88.06 447	19.50 538	69.47 480	71.86 481	84.93 466	61.24 417	91.75 475	54.70 495	77.15 445	90.15 461

pmmvs5		84.21 368	82.84 379	88.34 369	88.95 441	76.94 378	92.41 328	91.91 398	75.63 435	80.28 405	91.18 353	64.59 384	95.57 410	77.09 375	83.47 377	92.53 409

PMMVS		85.71 338	84.96 336	87.95 380	88.90 442	77.09 374	88.68 436	90.06 446	72.32 469	86.47 267	90.76 370	72.15 289	94.40 434	81.78 297	93.49 222	92.36 417

JIA-IIPM		81.04 412	78.98 425	87.25 401	88.64 443	73.48 422	81.75 496	89.61 459	73.19 461	82.05 383	73.71 506	66.07 373	95.87 397	71.18 425	84.60 363	92.41 414

test_vis1_n		86.56 317	86.49 282	86.78 416	88.51 444	72.69 432	94.68 182	93.78 340	79.55 376	90.70 172	95.31 180	48.75 481	93.28 456	93.15 72	93.99 200	94.38 317

Gipuma		57.99 473	54.91 476	67.24 491	88.51 444	65.59 483	52.21 522	90.33 440	43.58 514	42.84 515	51.18 526	20.29 513	85.07 503	34.77 519	70.45 468	51.05 524
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		81.32 410	80.95 389	82.42 460	88.50 446	63.67 491	93.32 284	91.33 413	64.02 496	80.57 403	92.83 292	61.21 418	92.27 468	76.34 382	80.38 424	91.32 441

our_test_3		81.93 396	80.46 395	86.33 423	88.46 447	73.48 422	88.46 441	91.11 417	76.46 423	76.69 451	88.25 429	66.89 358	94.36 436	68.75 443	79.08 436	91.14 446

ppachtmachnet_test		81.84 397	80.07 402	87.15 406	88.46 447	74.43 412	89.04 431	92.16 387	75.33 438	77.75 443	88.99 414	66.20 370	95.37 419	65.12 466	77.60 441	91.65 430

lessismore_v0				86.04 424	88.46 447	68.78 469		80.59 499		73.01 476	90.11 391	55.39 454	96.43 370	75.06 395	65.06 491	92.90 392

dtuonlycased		79.67 430	79.05 424	81.54 463	88.34 450	68.44 470	88.96 433	90.65 435	78.48 394	73.21 475	85.88 461	63.18 399	91.00 483	70.40 431	72.32 459	85.19 489

test0.0.03 1		82.41 391	81.69 383	84.59 444	88.23 451	72.89 429	90.24 402	87.83 472	83.41 293	79.86 415	89.78 401	67.25 353	88.99 494	65.18 465	83.42 379	91.90 427

MDA-MVSNet_test_wron		79.21 436	77.19 439	85.29 435	88.22 452	72.77 431	85.87 472	90.06 446	74.34 448	62.62 498	87.56 439	66.14 371	91.99 472	66.90 459	73.01 456	91.10 449

YYNet1		79.22 435	77.20 438	85.28 436	88.20 453	72.66 434	85.87 472	90.05 448	74.33 449	62.70 496	87.61 438	66.09 372	92.03 469	66.94 456	72.97 457	91.15 445

UWE-MVS-28		78.98 437	78.38 430	80.80 466	88.18 454	60.66 501	90.65 389	78.51 503	78.84 387	77.93 441	90.93 363	59.08 437	89.02 493	50.96 500	90.33 287	92.72 399

pmmvs6		83.42 379	81.60 384	88.87 353	88.01 455	77.87 354	94.96 160	94.24 317	74.67 446	78.80 434	91.09 358	60.17 427	96.49 363	77.06 376	75.40 453	92.23 421

testgi		80.94 416	80.20 399	83.18 453	87.96 456	66.29 479	91.28 373	90.70 434	83.70 284	78.12 438	92.84 291	51.37 474	90.82 484	63.34 472	82.46 390	92.43 413

mvsany_test1		85.42 343	85.30 328	85.77 430	87.95 457	75.41 401	87.61 458	80.97 498	76.82 422	88.68 221	95.83 147	77.44 205	90.82 484	85.90 221	86.51 346	91.08 450

Anonymous20231206		81.03 413	79.77 410	84.82 442	87.85 458	70.26 462	91.42 366	92.08 389	73.67 456	77.75 443	89.25 409	62.43 404	93.08 459	61.50 479	82.00 397	91.12 447

dmvs_testset		74.57 452	75.81 450	70.86 483	87.72 459	40.47 526	87.05 463	77.90 508	82.75 313	71.15 484	85.47 464	67.98 350	84.12 507	45.26 508	76.98 448	88.00 484

0.4-1-1-0.1		81.55 405	78.59 429	90.42 281	87.55 460	79.90 287	88.56 438	89.19 465	77.01 418	79.72 418	77.71 495	54.84 460	97.11 312	80.50 322	72.20 461	94.26 321

test_fmvs2		83.98 371	84.03 357	83.83 452	87.16 461	67.53 478	93.93 249	92.89 363	77.62 406	86.89 260	93.53 269	47.18 485	92.02 471	90.54 142	86.51 346	91.93 426

OpenMVS_ROB	74.94 19	79.51 433	77.03 441	86.93 410	87.00 462	76.23 391	92.33 336	90.74 432	68.93 481	74.52 467	88.23 430	49.58 478	96.62 346	57.64 491	84.29 365	87.94 485

LF4IMVS		80.37 422	79.07 423	84.27 448	86.64 463	69.87 466	89.39 424	91.05 420	76.38 426	74.97 464	90.00 395	47.85 483	94.25 440	74.55 404	80.82 417	88.69 479

dtuonly		84.33 367	84.48 350	83.87 451	86.63 464	63.54 492	86.79 464	91.48 410	78.02 405	83.20 369	93.56 268	69.53 328	94.11 441	79.08 351	92.02 262	93.97 336

0.3-1-1-0.015		80.75 418	77.58 434	90.25 289	86.55 465	79.72 297	87.46 459	89.48 463	76.43 425	77.93 441	75.94 498	52.31 472	97.05 319	80.25 327	71.85 465	93.99 335

MIMVSNet1		79.38 434	77.28 437	85.69 431	86.35 466	73.67 419	91.61 362	92.75 369	78.11 404	72.64 477	88.12 431	48.16 482	91.97 473	60.32 482	77.49 442	91.43 440

KD-MVS_2432*1600		78.50 439	76.02 448	85.93 426	86.22 467	74.47 410	84.80 482	92.33 378	79.29 378	76.98 448	85.92 459	53.81 468	93.97 445	67.39 452	57.42 501	89.36 467

miper_refine_blended		78.50 439	76.02 448	85.93 426	86.22 467	74.47 410	84.80 482	92.33 378	79.29 378	76.98 448	85.92 459	53.81 468	93.97 445	67.39 452	57.42 501	89.36 467

0.4-1-1-0.2		80.84 417	77.77 432	90.06 300	86.18 469	79.35 311	86.75 465	89.54 461	76.23 430	78.59 436	75.46 501	55.03 459	96.99 323	80.11 329	72.05 463	93.85 345

CL-MVSNet_self_test		81.74 400	80.53 391	85.36 434	85.96 470	72.45 439	90.25 400	93.07 359	81.24 356	79.85 416	87.29 442	70.93 303	92.52 465	66.95 455	69.23 474	91.11 448

test_vis1_rt		77.96 443	76.46 443	82.48 459	85.89 471	71.74 446	90.25 400	78.89 502	71.03 476	71.30 483	81.35 489	42.49 495	91.05 482	84.55 246	82.37 391	84.65 490

test20.03		79.95 427	79.08 422	82.55 457	85.79 472	67.74 476	91.09 379	91.08 418	81.23 357	74.48 468	89.96 397	61.63 409	90.15 486	60.08 483	76.38 449	89.76 464

Anonymous20240521		80.44 421	79.21 418	84.11 449	85.75 473	67.89 473	92.86 312	93.23 354	75.61 436	75.59 461	87.47 440	50.03 476	94.33 437	71.14 426	81.21 405	90.12 462

KD-MVS_self_test		80.20 423	79.24 417	83.07 454	85.64 474	65.29 485	91.01 381	93.93 328	78.71 392	76.32 453	86.40 456	59.20 435	92.93 461	72.59 415	69.35 473	91.00 451

blended_shiyan6		82.78 383	80.48 394	89.67 329	85.53 475	79.76 294	91.37 368	93.82 335	77.14 413	79.30 425	83.73 472	64.96 380	96.63 343	79.68 335	68.75 480	92.63 402

blend_shiyan4		81.94 395	79.35 415	89.70 322	85.52 476	80.08 275	91.29 372	93.82 335	77.12 416	79.31 424	82.94 480	54.81 461	96.60 353	79.60 338	69.78 471	92.41 414

blended_shiyan8		82.79 382	80.49 393	89.69 324	85.50 477	79.83 293	91.38 367	93.82 335	77.14 413	79.39 423	83.73 472	64.95 381	96.63 343	79.75 333	68.77 479	92.62 404

Patchmatch-RL test		81.67 401	79.96 406	86.81 415	85.42 478	71.23 451	82.17 495	87.50 476	78.47 395	77.19 447	82.50 486	70.81 305	93.48 453	82.66 276	72.89 458	95.71 260

UnsupCasMVSNet_eth		80.07 425	78.27 431	85.46 433	85.24 479	72.63 436	88.45 442	94.87 284	82.99 307	71.64 482	88.07 432	56.34 449	91.75 475	73.48 411	63.36 494	92.01 425

gbinet_0.2-2-1-0.02		82.59 387	80.19 400	89.77 317	85.23 480	80.05 279	91.59 363	93.52 347	77.60 407	79.78 417	82.87 481	63.26 396	96.45 368	78.93 353	68.97 476	92.81 397

wanda-best-256-512		82.44 389	80.07 402	89.53 334	85.12 481	79.44 306	90.49 394	93.75 341	76.97 419	79.00 428	82.72 482	64.29 387	96.61 349	79.56 340	68.75 480	92.55 405

FE-blended-shiyan7		82.44 389	80.07 402	89.53 334	85.12 481	79.44 306	90.49 394	93.75 341	76.97 419	79.00 428	82.72 482	64.29 387	96.61 349	79.56 340	68.75 480	92.55 405

usedtu_blend_shiyan5		82.39 392	79.93 407	89.75 318	85.12 481	80.08 275	92.36 331	93.26 352	74.29 450	79.00 428	82.72 482	64.29 387	96.60 353	79.60 338	68.75 480	92.55 405

pmmvs-eth3d		80.97 415	78.72 427	87.74 384	84.99 484	79.97 286	90.11 408	91.65 403	75.36 437	73.51 472	86.03 458	59.45 432	93.96 447	75.17 393	72.21 460	89.29 471

FE-MVSNET2		81.82 398	79.99 405	87.34 396	84.74 485	77.36 371	92.72 319	94.55 300	82.09 326	73.79 471	86.46 452	57.80 444	94.45 431	74.65 400	73.10 455	90.20 459

mvs5depth		80.98 414	79.15 421	86.45 420	84.57 486	73.29 425	87.79 451	91.67 402	80.52 364	82.20 382	89.72 402	55.14 458	95.93 393	73.93 408	66.83 487	90.12 462

CMPMVS	59.16 21	80.52 419	79.20 419	84.48 445	83.98 487	67.63 477	89.95 413	93.84 334	64.79 495	66.81 492	91.14 356	57.93 442	95.17 422	76.25 383	88.10 325	90.65 453
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		76.23 449	73.27 454	85.09 439	83.79 488	72.92 428	85.65 475	93.47 349	71.52 472	68.84 488	79.08 493	49.77 477	93.21 457	66.81 460	60.52 498	89.13 475

FE-MVSNET		78.19 441	76.03 447	84.69 443	83.70 489	73.31 424	90.58 392	90.00 449	77.11 417	71.91 480	85.47 464	55.53 453	91.94 474	59.69 486	70.24 469	88.83 477

PM-MVS		78.11 442	76.12 446	84.09 450	83.54 490	70.08 463	88.97 432	85.27 487	79.93 370	74.73 466	86.43 454	34.70 502	93.48 453	79.43 347	72.06 462	88.72 478

dongtai		58.82 472	58.24 471	60.56 496	83.13 491	45.09 522	82.32 494	48.22 527	67.61 486	61.70 500	69.15 512	38.75 497	76.05 517	32.01 522	41.31 513	60.55 519

DSMNet-mixed		76.94 446	76.29 445	78.89 470	83.10 492	56.11 511	87.78 452	79.77 500	60.65 500	75.64 460	88.71 422	61.56 412	88.34 495	60.07 484	89.29 308	92.21 422

new_pmnet		72.15 455	70.13 459	78.20 473	82.95 493	65.68 482	83.91 488	82.40 495	62.94 498	64.47 495	79.82 492	42.85 494	86.26 500	57.41 492	74.44 454	82.65 495

new-patchmatchnet		76.41 448	75.17 451	80.13 467	82.65 494	59.61 503	87.66 456	91.08 418	78.23 402	69.85 486	83.22 475	54.76 462	91.63 478	64.14 471	64.89 492	89.16 473

ttmdpeth		76.55 447	74.64 453	82.29 462	82.25 495	67.81 475	89.76 416	85.69 483	70.35 478	75.76 459	91.69 335	46.88 486	89.77 488	66.16 461	63.23 495	89.30 469

ArgMatch-Sym		69.79 459	67.05 465	77.99 475	81.59 496	61.16 497	84.99 481	71.84 514	67.17 489	67.90 491	86.60 450	19.89 515	85.00 504	70.93 429	52.57 505	87.82 486

ArgMatch-SfM		70.39 458	67.69 463	78.49 472	81.44 497	60.73 499	84.71 485	75.65 513	68.09 485	66.71 493	86.79 449	20.42 512	86.05 501	71.50 421	53.87 503	88.67 480

WB-MVS		67.92 462	67.49 464	69.21 488	81.09 498	41.17 525	88.03 448	78.00 507	73.50 458	62.63 497	83.11 478	63.94 391	86.52 498	25.66 527	51.45 507	79.94 499

SSC-MVS		67.06 463	66.56 466	68.56 490	80.54 499	40.06 527	87.77 453	77.37 510	72.38 468	61.75 499	82.66 485	63.37 394	86.45 499	24.48 529	48.69 510	79.16 502

APD_test1		69.04 460	66.26 467	77.36 477	80.51 500	62.79 495	85.46 477	83.51 492	54.11 505	59.14 502	84.79 468	23.40 509	89.61 489	55.22 494	70.24 469	79.68 500

ambc				83.06 455	79.99 501	63.51 493	77.47 505	92.86 364		74.34 469	84.45 469	28.74 503	95.06 426	73.06 413	68.89 478	90.61 454

test_fmvs3		77.67 444	77.16 440	79.22 469	79.52 502	61.14 498	92.34 335	91.64 404	73.98 453	78.86 431	86.59 451	27.38 506	87.03 496	88.12 186	75.97 451	89.50 466

TDRefinement		79.81 428	77.34 436	87.22 404	79.24 503	75.48 400	93.12 295	92.03 391	76.45 424	75.01 463	91.58 342	49.19 480	96.44 369	70.22 435	69.18 475	89.75 465

MVStest1		72.91 454	69.70 460	82.54 458	78.14 504	73.05 427	88.21 445	86.21 479	60.69 499	64.70 494	90.53 376	46.44 488	85.70 502	58.78 489	53.62 504	88.87 476

usedtu_dtu_shiyan2		74.72 451	71.30 457	84.98 440	77.78 505	70.58 460	91.85 354	90.76 431	67.24 488	68.06 490	82.17 487	37.13 499	92.78 463	60.69 481	66.03 488	91.59 434

kuosan		53.51 477	53.30 478	54.13 504	76.06 506	45.36 521	80.11 501	48.36 526	59.63 501	54.84 503	63.43 521	37.41 498	62.07 527	20.73 531	39.10 515	54.96 523

pmmvs3		71.81 457	68.71 461	81.11 464	75.86 507	70.42 461	86.74 466	83.66 491	58.95 502	68.64 489	80.89 491	36.93 500	89.52 490	63.10 474	63.59 493	83.39 491

mvsany_test3		74.95 450	73.26 455	80.02 468	74.61 508	63.16 494	85.53 476	78.42 504	74.16 451	74.89 465	86.46 452	36.02 501	89.09 492	82.39 280	66.91 486	87.82 486

DenseAffine		56.77 475	52.17 480	70.54 484	74.27 509	53.25 513	77.23 506	50.43 525	49.87 507	47.26 511	77.37 496	7.99 526	79.10 514	50.35 501	34.79 518	79.28 501

DeepMVS_CX				56.31 502	74.23 510	51.81 514		56.67 523	44.85 512	48.54 508	75.16 503	27.87 505	58.74 528	40.92 516	52.22 506	58.39 522

test_f		71.95 456	70.87 458	75.21 478	74.21 511	59.37 504	85.07 480	85.82 482	65.25 494	70.42 485	83.13 476	23.62 507	82.93 509	78.32 359	71.94 464	83.33 492

LoFTR		57.22 474	52.62 479	71.00 481	72.03 512	48.57 517	72.00 513	70.08 516	44.40 513	40.92 518	76.42 497	8.12 525	82.76 510	42.28 515	47.33 511	81.66 497

test_vis3_rt		65.12 465	62.60 468	72.69 480	71.44 513	60.71 500	87.17 461	65.55 517	63.80 497	53.22 505	65.65 518	14.54 518	89.44 491	76.65 377	65.38 490	67.91 515

FPMVS		64.63 466	62.55 469	70.88 482	70.80 514	56.71 506	84.42 486	84.42 489	51.78 506	49.57 506	81.61 488	23.49 508	81.48 511	40.61 517	76.25 450	74.46 505

testf1		59.54 469	56.11 474	69.85 486	69.28 515	56.61 508	80.37 499	76.55 511	42.58 515	45.68 512	75.61 499	11.26 519	84.18 505	43.20 513	60.44 499	68.75 512

APD_test2		59.54 469	56.11 474	69.85 486	69.28 515	56.61 508	80.37 499	76.55 511	42.58 515	45.68 512	75.61 499	11.26 519	84.18 505	43.20 513	60.44 499	68.75 512

PMMVS2		59.60 468	56.40 472	69.21 488	68.83 517	46.58 518	73.02 512	77.48 509	55.07 504	49.21 507	72.95 508	17.43 516	80.04 512	49.32 504	44.33 512	80.99 498

wuyk23d		21.27 500	20.48 504	23.63 515	68.59 518	36.41 531	49.57 523	6.85 555	9.37 533	7.89 546	4.46 561	4.03 543	31.37 537	17.47 534	16.07 543	3.12 556

RoMa-SfM		53.80 476	49.39 481	67.06 492	67.87 519	48.86 515	75.04 507	38.06 532	47.23 510	47.40 510	78.96 494	7.40 527	76.66 516	48.89 505	33.62 519	75.64 504

MatchFormer		51.11 479	46.66 484	64.46 494	67.11 520	43.39 523	70.54 514	63.67 519	33.19 521	37.22 523	70.30 511	6.67 530	78.17 515	30.29 523	40.94 514	71.81 509

DKM		50.92 480	46.13 485	65.30 493	66.27 521	45.98 520	73.05 511	31.91 534	45.08 511	42.04 516	75.01 504	4.95 536	73.81 518	47.90 506	28.96 522	76.09 503

E-PMN		43.23 487	42.29 488	46.03 508	65.58 522	37.41 530	73.51 509	64.62 518	33.99 520	28.47 530	47.87 528	19.90 514	67.91 521	22.23 530	24.45 525	32.77 530

LCM-MVSNet		66.00 464	62.16 470	77.51 476	64.51 523	58.29 505	83.87 489	90.90 427	48.17 508	54.69 504	73.31 507	16.83 517	86.75 497	65.47 463	61.67 497	87.48 488

EMVS		42.07 488	41.12 491	44.92 510	63.45 524	35.56 532	73.65 508	63.48 520	33.05 522	26.88 532	45.45 529	21.27 511	67.14 522	19.80 532	23.02 529	32.06 531

PDCNetPlus		48.34 482	45.15 486	57.91 499	61.43 525	41.85 524	65.98 517	38.30 531	47.59 509	37.96 521	71.85 509	10.18 522	66.85 524	52.94 498	20.14 538	65.03 517

DKM-HiRes		45.90 484	41.41 490	59.36 497	59.55 526	39.90 528	67.13 515	23.25 536	39.95 519	38.74 520	71.81 510	3.67 545	66.42 525	43.82 510	24.82 524	71.77 510

ALIKED-LG		28.00 494	26.54 500	32.41 511	58.12 527	31.80 533	47.26 525	21.21 537	14.15 529	19.16 535	41.93 531	6.72 529	35.73 534	5.96 543	24.32 526	29.69 532

RoMa-HiRes		46.47 483	42.20 489	59.28 498	57.74 528	39.86 529	66.76 516	24.64 535	39.96 518	41.50 517	75.37 502	5.40 533	69.26 519	43.35 512	25.09 523	68.71 514

MVE	39.65 23	43.39 486	38.59 493	57.77 500	56.52 529	48.77 516	55.38 520	58.64 522	29.33 525	28.96 529	52.65 525	4.68 539	64.62 526	28.11 525	33.07 520	59.93 520
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		58.88 471	54.22 477	72.86 479	56.50 530	56.67 507	80.75 498	86.00 481	73.09 463	37.39 522	64.63 519	22.17 510	79.49 513	43.51 511	23.96 527	82.43 496

ALIKED-MNN		26.28 496	24.57 503	31.39 512	56.22 531	31.73 534	45.54 526	19.13 540	11.12 530	17.11 538	39.35 533	5.01 535	34.53 535	5.54 545	22.12 531	27.92 533

ALIKED-NN		26.07 497	24.75 502	30.02 513	55.08 532	30.61 535	44.20 528	19.22 539	10.98 531	17.98 536	40.71 532	5.39 534	32.83 536	5.59 544	23.63 528	26.63 534

test_method		50.52 481	48.47 482	56.66 501	52.26 533	18.98 543	41.51 529	81.40 497	10.10 532	44.59 514	75.01 504	28.51 504	68.16 520	53.54 497	49.31 509	82.83 494

PMVS	47.18 22	52.22 478	48.46 483	63.48 495	45.72 534	46.20 519	73.41 510	78.31 505	41.03 517	30.06 528	65.68 517	6.05 531	83.43 508	30.04 524	65.86 489	60.80 518
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MASt3R-SfM		45.78 485	43.96 487	51.24 506	45.04 535	29.83 536	57.88 519	38.83 530	31.88 523	47.48 509	81.30 490	7.16 528	51.15 531	49.56 503	36.51 516	72.74 507

PMatch-SfM		38.18 490	33.34 495	52.72 505	43.67 536	28.18 538	52.96 521	16.29 542	29.70 524	31.24 526	68.56 514	1.08 560	57.70 529	38.73 518	17.80 541	72.30 508

SP-LightGlue		20.24 501	20.15 506	20.49 517	43.51 537	12.27 553	38.68 531	14.56 545	7.54 538	12.90 543	30.07 538	4.75 537	14.38 542	7.60 538	21.75 533	34.82 525

SP-SuperGlue		20.22 502	20.18 505	20.36 518	43.26 538	12.27 553	38.71 530	14.77 544	7.64 537	13.04 542	30.21 537	4.73 538	14.21 544	7.59 539	21.65 534	34.59 526

SP-MNN		19.61 504	19.42 508	20.19 520	42.15 539	11.42 559	38.15 532	14.24 546	6.55 543	11.64 545	29.88 540	4.16 541	14.56 541	7.09 541	20.92 536	34.58 527

SP-NN		19.44 505	19.37 509	19.67 521	41.70 540	11.48 558	37.75 533	13.72 548	6.86 539	11.86 544	29.97 539	4.23 540	14.25 543	7.13 540	21.07 535	33.30 529

ELoFTR		40.15 489	35.08 494	55.36 503	41.27 541	28.17 539	47.70 524	43.76 528	29.15 526	30.35 527	65.97 516	2.17 547	66.90 523	34.51 520	20.83 537	71.00 511

PMatch-Up-SfM		32.59 492	28.46 498	44.98 509	37.19 542	22.27 542	44.73 527	10.63 549	23.85 527	27.52 531	64.10 520	0.78 564	47.14 532	34.15 521	13.22 548	65.53 516

SIFT-NN		12.98 508	13.18 512	12.37 525	36.49 543	16.03 544	22.41 540	7.69 551	4.89 544	7.41 547	20.48 543	1.69 548	11.46 547	1.88 549	15.70 544	9.61 542

GLUNet-SfM		31.36 493	26.25 501	46.70 507	35.51 544	24.89 540	33.71 534	36.36 533	19.08 528	23.78 533	52.69 524	3.82 544	56.26 530	19.75 533	11.56 552	58.95 521

MVS_clip		24.79 498	27.71 499	16.02 524	35.36 545	15.85 545	27.38 537	5.39 561	6.70 541	40.04 519	63.09 522	10.55 521	8.72 559	27.86 526	33.03 521	23.49 535

SIFT-MNN		12.44 509	12.55 513	12.11 526	34.55 546	15.21 546	20.91 541	7.74 550	4.86 545	6.54 549	20.09 544	1.51 549	11.47 546	1.88 549	14.87 546	9.64 541

SIFT-NCM-Cal		11.58 511	11.64 516	11.40 528	33.45 547	14.10 548	19.75 544	6.89 553	4.68 550	4.55 556	18.60 550	1.34 554	11.28 548	1.53 557	13.95 547	8.82 548

SIFT-NN-NCMNet		12.12 510	12.25 514	11.75 527	32.82 548	14.83 547	20.73 542	7.58 552	4.72 547	6.60 548	19.53 545	1.49 550	11.15 549	1.74 551	15.02 545	9.28 543

SIFT-ConvMatch		10.91 515	10.94 521	10.84 530	32.07 549	13.57 549	17.23 548	6.35 557	4.71 548	5.18 553	18.94 548	1.30 555	10.76 550	1.65 555	11.02 554	8.19 549

SIFT-UMatch		10.58 516	10.73 522	10.15 532	31.05 550	11.65 557	18.01 546	5.92 559	4.65 551	4.72 554	18.93 549	1.25 557	10.62 553	1.66 554	10.39 555	8.16 550

SIFT-CM-Cal		10.08 518	10.13 525	9.92 533	30.71 551	11.88 556	15.35 550	5.44 560	4.59 552	4.72 554	18.04 553	1.26 556	10.19 554	1.46 559	9.60 556	7.69 551

SIFT-NN-CMatch		11.26 512	11.31 518	11.13 529	30.21 552	13.40 550	18.43 545	6.79 556	4.71 548	6.47 550	19.53 545	1.43 552	10.72 551	1.71 552	12.49 551	9.26 544

SIFT-UM-Cal		9.80 519	10.00 526	9.22 535	30.05 553	10.15 561	16.31 549	4.85 564	4.54 553	4.19 557	18.23 552	1.19 558	9.95 556	1.52 558	9.11 558	7.57 552

SIFT-NN-UMatch		11.06 513	11.19 520	10.66 531	28.66 554	12.16 555	19.79 543	6.86 554	4.73 546	5.21 552	19.47 547	1.46 551	10.70 552	1.71 552	12.79 550	9.13 545

SIFT-NN-PointCN		10.26 517	10.46 523	9.65 534	27.18 555	9.89 562	17.89 547	6.17 558	4.40 554	5.65 551	18.29 551	1.43 552	10.09 555	1.61 556	11.55 553	8.99 547

SIFT-PCN-Cal		8.65 523	8.88 528	7.98 537	26.74 556	7.47 565	13.90 552	4.61 565	4.09 556	3.82 558	15.86 554	1.01 561	8.94 557	1.34 560	8.52 559	7.53 553

SIFT-PointCN		8.76 521	9.03 527	7.96 538	26.50 557	7.60 564	14.94 551	5.08 563	4.10 555	3.74 559	15.46 555	0.94 562	8.92 558	1.33 561	9.14 557	7.37 554

SIFT-NCMNet		7.46 525	7.71 531	6.72 539	25.03 558	6.86 566	11.42 553	2.98 566	4.05 557	3.38 560	13.68 556	0.84 563	7.65 560	1.13 562	6.87 560	5.66 555

VLMVS_CLIP		27.58 495	28.97 497	23.41 516	23.47 559	13.17 551	30.64 535	40.90 529	9.21 534	36.34 525	50.75 527	8.75 524	38.05 533	25.18 528	35.53 517	19.03 539

SP-DiffGlue		20.02 503	19.96 507	20.21 519	19.64 560	13.14 552	30.51 536	15.49 543	8.39 535	19.98 534	43.75 530	5.48 532	13.72 545	13.75 535	22.65 530	33.78 528

XFeat-MNN		17.43 506	16.95 510	18.86 522	16.90 561	11.28 560	27.31 538	17.08 541	8.08 536	15.61 540	35.73 534	4.06 542	22.95 539	10.20 536	17.59 542	22.35 536

XFeat-NN		15.96 507	15.86 511	16.25 523	15.78 562	9.87 563	25.17 539	13.83 547	6.76 540	15.68 539	34.83 535	3.61 546	19.28 540	9.22 537	17.90 540	19.58 538

tmp_tt		35.64 491	39.24 492	24.84 514	14.87 563	23.90 541	62.71 518	51.51 524	6.58 542	36.66 524	62.08 523	44.37 492	30.34 538	52.40 499	22.00 532	20.27 537

VLMVS		10.93 514	11.73 515	8.51 536	11.99 564	6.47 567	9.10 554	5.11 562	0.73 558	17.62 537	25.59 541	9.61 523	6.56 561	6.19 542	19.64 539	12.50 540

MVS_baseline		7.30 526	8.69 530	3.12 540	8.45 565	0.31 570	3.27 555	0.80 567	0.16 562	14.50 541	32.51 536	1.15 559	0.00 564	4.24 546	13.11 549	9.06 546

testmvs		8.92 520	11.52 517	1.12 542	1.06 566	0.46 569	86.02 470	0.65 568	0.62 559	2.74 561	9.52 559	0.31 566	0.45 563	2.38 547	0.39 561	2.46 558

test123		8.76 521	11.22 519	1.39 541	0.85 567	0.97 568	85.76 474	0.35 569	0.54 560	2.45 562	8.14 560	0.60 565	0.48 562	2.16 548	0.17 562	2.71 557

PatchmatchNet2					0.00 568	62.07 496	85.98 471	87.63 475	68.79 482
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

eth-test2					0.00 568

eth-test					0.00 568

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

cdsmvs_eth3d_5k		22.14 499	29.52 496	0.00 543	0.00 568	0.00 571	0.00 556	95.76 203	0.00 563	0.00 564	94.29 236	75.66 234	0.00 564	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		6.64 527	8.86 529	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	79.70 165	0.00 564	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

ab-mvs-re		7.82 524	10.43 524	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	93.88 257	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet1														54.59 496	77.20 444	90.17 460
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.68 477
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						64.08 489								59.14 487

PC_three_1452									82.47 317	97.09 20	97.07 74	92.72 1	98.04 203	92.70 83	99.02 12	98.86 16

test_241102_TWO								97.44 21	90.31 45	97.62 9	98.07 24	91.46 11	99.58 14	95.66 32	99.12 6	98.98 12

test_0728_THIRD									90.75 32	97.04 22	98.05 29	92.09 7	99.55 22	95.64 34	99.13 3	99.13 4

GSMVS																96.12 237

sam_mvs1												71.70 294				96.12 237

sam_mvs												70.60 308

MTGPA								96.97 67

test_post1							88.00 449				9.81 558	69.31 333	95.53 411	76.65 377

test_post											10.29 557	70.57 312	95.91 396

patchmatchnet-post											83.76 471	71.53 295	96.48 364

MTMP							96.16 61	60.64 521

test9_res														91.91 112	98.71 37	98.07 84

agg_prior2														90.54 142	98.68 42	98.27 65

test_prior4						85.96 60	94.11 229

test_prior2							94.12 227		87.67 161	92.63 112	96.39 107	86.62 48		91.50 122	98.67 45

旧先验2							93.36 282		71.25 474	94.37 64			97.13 311	86.74 208

新几何2							93.11 297

无先验							93.28 290	96.26 143	73.95 454				99.05 69	80.56 320		96.59 216

原ACMM2							92.94 308

testdata2													98.75 119	78.30 360

segment_acmp												87.16 43

testdata1							92.15 345		87.94 145

plane_prior5								96.22 149					98.12 182	88.15 183	89.99 291	94.63 299

plane_prior4											94.86 205

plane_prior3						82.75 165			90.26 51	86.91 257

plane_prior2							95.85 94		90.81 28

plane_prior						82.73 168	95.21 143		89.66 72						89.88 296

n2								0.00 570

nn								0.00 570

door-mid								85.49 484

test11								96.57 115

door								85.33 486

HQP5-MVS						81.56 209

BP-MVS														87.11 205

HQP4-MVS										85.43 303			97.96 219			94.51 309

HQP3-MVS								96.04 177							89.77 300

HQP2-MVS												73.83 265

MDTV_nov1_ep13_2view						55.91 512	87.62 457		73.32 460	84.59 325		70.33 315		74.65 400		95.50 265

ACMMP++_ref															87.47 336

ACMMP++															88.01 328

Test By Simon												80.02 153

High-resolution multi-view benchmark