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.

AdaColmap		93.82 117	93.06 124	96.10 109	99.88 1	89.07 164	98.33 194	97.55 118	86.81 233	90.39 185	98.65 98	75.09 232	99.98 9	93.32 141	97.53 124	99.26 99

DP-MVS Recon		95.85 61	95.15 76	97.95 30	99.87 2	94.38 52	99.60 38	97.48 134	86.58 237	94.42 124	99.13 44	87.36 86	99.98 9	93.64 135	98.33 107	99.48 79

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 10	99.70 26	97.98 53	97.18 3	95.96 94	99.33 19	92.62 26	100.00 1	98.99 25	99.93 1	99.98 6

CNVR-MVS		98.46 1	98.38 1	98.72 9	99.80 4	96.19 15	99.80 15	97.99 52	97.05 6	99.41 4	99.59 2	92.89 25	100.00 1	98.99 25	99.90 7	99.96 10

MG-MVS		97.24 19	96.83 30	98.47 15	99.79 5	95.71 18	99.07 109	99.06 10	94.45 40	96.42 88	98.70 95	88.81 61	99.74 88	95.35 101	99.86 12	99.97 7

NCCC		98.12 5	98.11 3	98.13 24	99.76 6	94.46 48	99.81 11	97.88 57	96.54 13	98.84 24	99.46 10	92.55 27	99.98 9	98.25 46	99.93 1	99.94 18

region2R		96.30 45	96.17 47	96.70 81	99.70 7	90.31 131	99.46 59	97.66 92	90.55 124	97.07 71	99.07 51	86.85 98	99.97 21	95.43 99	99.74 29	99.81 33

HFP-MVS		96.42 41	96.26 41	96.90 69	99.69 8	90.96 118	99.47 55	97.81 66	90.54 125	96.88 73	99.05 54	87.57 78	99.96 28	95.65 92	99.72 31	99.78 38

ACMMPR		96.28 46	96.14 51	96.73 78	99.68 9	90.47 129	99.47 55	97.80 68	90.54 125	96.83 78	99.03 56	86.51 109	99.95 31	95.65 92	99.72 31	99.75 46

ZD-MVS					99.67 10	93.28 71		97.61 105	87.78 209	97.41 60	99.16 36	90.15 49	99.56 105	98.35 41	99.70 35

CP-MVS		96.22 47	96.15 50	96.42 97	99.67 10	89.62 155	99.70 26	97.61 105	90.07 140	96.00 93	99.16 36	87.43 81	99.92 40	96.03 88	99.72 31	99.70 52

DVP-MVS		98.07 7	98.00 6	98.29 19	99.66 12	95.20 30	99.72 23	97.47 136	93.95 48	99.07 15	99.46 10	93.18 22	99.97 21	99.64 7	99.82 19	99.69 55
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				98.77 8	99.66 12	96.37 14	99.72 23	97.68 88					99.98 9	99.64 7	99.82 19	99.96 10

test0726					99.66 12	95.20 30	99.77 17	97.70 84	93.95 48	99.35 7	99.54 3	93.18 22

CPTT-MVS		94.60 97	94.43 87	95.09 147	99.66 12	86.85 220	99.44 62	97.47 136	83.22 293	94.34 126	98.96 66	82.50 175	99.55 106	94.81 113	99.50 53	98.88 133

MSLP-MVS++		97.50 16	97.45 17	97.63 38	99.65 16	93.21 72	99.70 26	98.13 42	94.61 35	97.78 55	99.46 10	89.85 51	99.81 79	97.97 50	99.91 6	99.88 26

OPU-MVS				99.49 4	99.64 17	98.51 4	99.77 17				99.19 30	95.12 8	99.97 21	99.90 1	99.92 3	99.99 1

SED-MVS		98.18 2	98.10 4	98.41 18	99.63 18	95.24 25	99.77 17	97.72 79	94.17 43	99.30 8	99.54 3	93.32 19	99.98 9	99.70 4	99.81 23	99.99 1

IU-MVS					99.63 18	95.38 22		97.73 78	95.54 26	99.54 3				99.69 6	99.81 23	99.99 1

test_241102_ONE					99.63 18	95.24 25		97.72 79	94.16 45	99.30 8	99.49 9	93.32 19	99.98 9

PAPR		96.35 42	95.82 57	97.94 31	99.63 18	94.19 56	99.42 67	97.55 118	92.43 82	93.82 136	99.12 46	87.30 88	99.91 45	94.02 126	99.06 75	99.74 47

XVS		96.47 40	96.37 39	96.77 74	99.62 22	90.66 126	99.43 65	97.58 113	92.41 85	96.86 74	98.96 66	87.37 83	99.87 58	95.65 92	99.43 59	99.78 38

X-MVStestdata		90.69 189	88.66 212	96.77 74	99.62 22	90.66 126	99.43 65	97.58 113	92.41 85	96.86 74	29.59 402	87.37 83	99.87 58	95.65 92	99.43 59	99.78 38

DVP-MVS++		98.18 2	98.09 5	98.44 16	99.61 24	95.38 22	99.55 44	97.68 88	93.01 70	99.23 10	99.45 14	95.12 8	99.98 9	99.25 18	99.92 3	99.97 7

MSC_two_6792asdad				99.51 2	99.61 24	98.60 2		97.69 86					99.98 9	99.55 12	99.83 15	99.96 10

No_MVS				99.51 2	99.61 24	98.60 2		97.69 86					99.98 9	99.55 12	99.83 15	99.96 10

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 24	99.61 24	94.45 49	98.85 131	97.64 98	96.51 16	95.88 97	99.39 18	87.35 87	99.99 5	96.61 77	99.69 36	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_one_0601					99.59 28	94.89 34		97.64 98	93.14 69	98.93 21	99.45 14	93.45 18

CDPH-MVS		96.56 38	96.18 44	97.70 36	99.59 28	93.92 60	99.13 104	97.44 142	89.02 167	97.90 53	99.22 27	88.90 60	99.49 112	94.63 119	99.79 27	99.68 56

test_prior				97.01 60	99.58 30	91.77 96		97.57 116					99.49 112			99.79 36

APDe-MVS		97.53 13	97.47 15	97.70 36	99.58 30	93.63 64	99.56 43	97.52 126	93.59 63	98.01 50	99.12 46	90.80 40	99.55 106	99.26 17	99.79 27	99.93 20
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		95.90 60	95.75 62	96.38 100	99.58 30	89.41 159	99.26 84	97.41 146	90.66 119	94.82 117	98.95 69	86.15 117	99.98 9	95.24 104	99.64 40	99.74 47

TEST9					99.57 33	93.17 73	99.38 71	97.66 92	89.57 152	98.39 35	99.18 33	90.88 38	99.66 94

train_agg		97.20 22	97.08 22	97.57 42	99.57 33	93.17 73	99.38 71	97.66 92	90.18 134	98.39 35	99.18 33	90.94 35	99.66 94	98.58 36	99.85 13	99.88 26

test_8					99.55 35	93.07 76	99.37 74	97.64 98	90.18 134	98.36 37	99.19 30	90.94 35	99.64 100

test_part2					99.54 36	95.42 20				98.13 42

MSP-MVS		97.77 9	98.18 2	96.53 92	99.54 36	90.14 137	99.41 68	97.70 84	95.46 28	98.60 29	99.19 30	95.71 4	99.49 112	98.15 48	99.85 13	99.95 15
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
agg_prior					99.54 36	92.66 85		97.64 98		97.98 51			99.61 102

CSCG		94.87 86	94.71 83	95.36 136	99.54 36	86.49 225	99.34 77	98.15 40	82.71 304	90.15 188	99.25 23	89.48 54	99.86 63	94.97 111	98.82 90	99.72 50

HPM-MVS++		97.72 10	97.59 13	98.14 23	99.53 40	94.76 42	99.19 87	97.75 74	95.66 24	98.21 40	99.29 20	91.10 33	99.99 5	97.68 55	99.87 9	99.68 56

APD-MVS		96.95 28	96.72 31	97.63 38	99.51 41	93.58 65	99.16 93	97.44 142	90.08 139	98.59 30	99.07 51	89.06 57	99.42 123	97.92 51	99.66 37	99.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
FOURS1					99.50 42	88.94 171	99.55 44	97.47 136	91.32 108	98.12 44

DPE-MVS		98.11 6	98.00 6	98.44 16	99.50 42	95.39 21	99.29 81	97.72 79	94.50 37	98.64 28	99.54 3	93.32 19	99.97 21	99.58 10	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PGM-MVS		95.85 61	95.65 67	96.45 95	99.50 42	89.77 152	98.22 202	98.90 13	89.19 162	96.74 81	98.95 69	85.91 121	99.92 40	93.94 128	99.46 55	99.66 60

GST-MVS		95.97 55	95.66 65	96.90 69	99.49 45	91.22 105	99.45 61	97.48 134	89.69 146	95.89 96	98.72 91	86.37 112	99.95 31	94.62 120	99.22 70	99.52 75

MP-MVS		96.00 52	95.82 57	96.54 91	99.47 46	90.13 139	99.36 75	97.41 146	90.64 122	95.49 107	98.95 69	85.51 126	99.98 9	96.00 89	99.59 49	99.52 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		96.09 50	95.81 59	96.95 68	99.42 47	91.19 107	99.55 44	97.53 122	89.72 145	95.86 99	98.94 72	86.59 105	99.97 21	95.13 105	99.56 50	99.68 56

SR-MVS		96.13 49	96.16 49	96.07 110	99.42 47	89.04 165	98.59 163	97.33 153	90.44 128	96.84 76	99.12 46	86.75 100	99.41 126	97.47 58	99.44 58	99.76 45

PAPM_NR		95.43 73	95.05 80	96.57 90	99.42 47	90.14 137	98.58 165	97.51 128	90.65 121	92.44 151	98.90 76	87.77 77	99.90 50	90.88 166	99.32 64	99.68 56

9.14			96.87 26		99.34 50		99.50 51	97.49 133	89.41 157	98.59 30	99.43 16	89.78 52	99.69 91	98.69 30	99.62 44

save fliter					99.34 50	93.85 62	99.65 35	97.63 102	95.69 22

PHI-MVS		96.65 36	96.46 37	97.21 54	99.34 50	91.77 96	99.70 26	98.05 46	86.48 242	98.05 47	99.20 29	89.33 55	99.96 28	98.38 39	99.62 44	99.90 22

test12				97.83 33	99.33 53	94.45 49		97.55 118		97.56 56		88.60 63	99.50 111		99.71 34	99.55 72

SMA-MVS		97.24 19	96.99 23	98.00 29	99.30 54	94.20 55	99.16 93	97.65 97	89.55 154	99.22 12	99.52 8	90.34 48	99.99 5	98.32 43	99.83 15	99.82 32
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
MTAPA		96.09 50	95.80 60	96.96 67	99.29 55	91.19 107	97.23 262	97.45 139	92.58 79	94.39 125	99.24 25	86.43 111	99.99 5	96.22 83	99.40 62	99.71 51

HPM-MVS		95.41 75	95.22 74	95.99 115	99.29 55	89.14 162	99.17 92	97.09 178	87.28 222	95.40 108	98.48 112	84.93 136	99.38 128	95.64 96	99.65 38	99.47 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		94.67 95	94.30 88	95.79 122	99.25 57	88.13 189	98.41 183	98.67 22	90.38 130	91.43 166	98.72 91	82.22 184	99.95 31	93.83 132	95.76 157	99.29 96
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_3200maxsize		95.64 70	95.65 67	95.62 129	99.24 58	87.80 195	98.42 181	97.22 161	88.93 172	96.64 86	98.98 61	85.49 127	99.36 130	96.68 74	99.27 68	99.70 52

SR-MVS-dyc-post		95.75 67	95.86 56	95.41 135	99.22 59	87.26 215	98.40 186	97.21 162	89.63 148	96.67 84	98.97 62	86.73 102	99.36 130	96.62 75	99.31 65	99.60 67

RE-MVS-def			95.70 63		99.22 59	87.26 215	98.40 186	97.21 162	89.63 148	96.67 84	98.97 62	85.24 133		96.62 75	99.31 65	99.60 67

patch_mono-2		97.10 25	97.97 8	94.49 169	99.21 61	83.73 286	99.62 37	98.25 32	95.28 30	99.38 6	98.91 75	92.28 28	99.94 34	99.61 9	99.22 70	99.78 38

API-MVS		94.78 89	94.18 94	96.59 87	99.21 61	90.06 144	98.80 137	97.78 71	83.59 288	93.85 134	99.21 28	83.79 149	99.97 21	92.37 153	99.00 79	99.74 47

PLC	91.07 3	94.23 105	94.01 98	94.87 155	99.17 63	87.49 204	99.25 85	96.55 208	88.43 187	91.26 170	98.21 124	85.92 119	99.86 63	89.77 181	97.57 121	97.24 200
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet-Vis-set		95.76 66	95.63 69	96.17 107	99.14 64	90.33 130	98.49 174	97.82 63	91.92 95	94.75 119	98.88 80	87.06 93	99.48 116	95.40 100	97.17 134	98.70 150

TSAR-MVS + MP.		97.44 17	97.46 16	97.39 48	99.12 65	93.49 69	98.52 168	97.50 131	94.46 38	98.99 17	98.64 99	91.58 30	99.08 148	98.49 37	99.83 15	99.60 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		97.22 21	96.92 24	98.12 26	99.11 66	94.88 35	99.44 62	97.45 139	89.60 150	98.70 26	99.42 17	90.42 45	99.72 89	98.47 38	99.65 38	99.77 43

HPM-MVS_fast		94.89 85	94.62 84	95.70 125	99.11 66	88.44 185	99.14 101	97.11 174	85.82 250	95.69 103	98.47 113	83.46 154	99.32 135	93.16 143	99.63 43	99.35 90

MAR-MVS		94.43 102	94.09 96	95.45 133	99.10 68	87.47 205	98.39 190	97.79 70	88.37 189	94.02 131	99.17 35	78.64 217	99.91 45	92.48 152	98.85 89	98.96 123
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
114514_t		94.06 107	93.05 125	97.06 58	99.08 69	92.26 92	98.97 123	97.01 186	82.58 306	92.57 149	98.22 122	80.68 200	99.30 136	89.34 187	99.02 78	99.63 64

EI-MVSNet-UG-set		95.43 73	95.29 72	95.86 120	99.07 70	89.87 149	98.43 180	97.80 68	91.78 97	94.11 129	98.77 85	86.25 115	99.48 116	94.95 112	96.45 143	98.22 175

原ACMM1				96.18 105	99.03 71	90.08 140		97.63 102	88.98 168	97.00 72	98.97 62	88.14 71	99.71 90	88.23 198	99.62 44	98.76 147

SD-MVS		97.51 15	97.40 18	97.81 34	99.01 72	93.79 63	99.33 78	97.38 149	93.73 59	98.83 25	99.02 58	90.87 39	99.88 54	98.69 30	99.74 29	99.77 43
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
旧先验1					98.97 73	92.90 83		97.74 75			99.15 39	91.05 34			99.33 63	99.60 67

LS3D		90.19 198	88.72 210	94.59 168	98.97 73	86.33 233	96.90 274	96.60 202	74.96 356	84.06 245	98.74 88	75.78 229	99.83 73	74.93 319	97.57 121	97.62 191

CNLPA		93.64 124	92.74 133	96.36 101	98.96 75	90.01 147	99.19 87	95.89 262	86.22 245	89.40 196	98.85 81	80.66 201	99.84 69	88.57 194	96.92 137	99.24 100

MP-MVS-pluss		95.80 63	95.30 71	97.29 50	98.95 76	92.66 85	98.59 163	97.14 170	88.95 170	93.12 143	99.25 23	85.62 123	99.94 34	96.56 79	99.48 54	99.28 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
新几何1				97.40 47	98.92 77	92.51 90		97.77 73	85.52 255	96.69 83	99.06 53	88.08 72	99.89 53	84.88 236	99.62 44	99.79 36

DP-MVS		88.75 228	86.56 248	95.34 137	98.92 77	87.45 206	97.64 246	93.52 343	70.55 368	81.49 287	97.25 161	74.43 238	99.88 54	71.14 342	94.09 173	98.67 152

TSAR-MVS + GP.		96.95 28	96.91 25	97.07 57	98.88 79	91.62 99	99.58 41	96.54 209	95.09 32	96.84 76	98.63 101	91.16 31	99.77 85	99.04 24	96.42 144	99.81 33

CANet		97.00 27	96.49 35	98.55 12	98.86 80	96.10 16	99.83 9	97.52 126	95.90 19	97.21 66	98.90 76	82.66 174	99.93 38	98.71 29	98.80 91	99.63 64

dcpmvs_2		95.67 69	96.18 44	94.12 186	98.82 81	84.22 279	97.37 254	95.45 288	90.70 118	95.77 101	98.63 101	90.47 43	98.68 164	99.20 20	99.22 70	99.45 81

ACMMP_NAP		96.59 37	96.18 44	97.81 34	98.82 81	93.55 66	98.88 130	97.59 111	90.66 119	97.98 51	99.14 42	86.59 105	100.00 1	96.47 81	99.46 55	99.89 25

PVSNet_BlendedMVS		93.36 132	93.20 121	93.84 197	98.77 83	91.61 100	99.47 55	98.04 48	91.44 104	94.21 127	92.63 273	83.50 152	99.87 58	97.41 59	83.37 269	90.05 332

PVSNet_Blended		95.94 58	95.66 65	96.75 76	98.77 83	91.61 100	99.88 3	98.04 48	93.64 62	94.21 127	97.76 135	83.50 152	99.87 58	97.41 59	97.75 119	98.79 143

DeepPCF-MVS	93.56 1	96.55 39	97.84 10	92.68 221	98.71 85	78.11 343	99.70 26	97.71 83	98.18 1	97.36 62	99.76 1	90.37 47	99.94 34	99.27 16	99.54 52	99.99 1

EPNet		96.82 31	96.68 33	97.25 53	98.65 86	93.10 75	99.48 53	98.76 15	96.54 13	97.84 54	98.22 122	87.49 80	99.66 94	95.35 101	97.78 118	99.00 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		93.90 114	93.62 111	94.73 162	98.63 87	87.00 218	98.04 221	96.56 207	92.19 90	92.46 150	98.73 89	79.49 209	99.14 145	92.16 155	94.34 172	98.03 180

MVS_111021_HR		96.69 34	96.69 32	96.72 80	98.58 88	91.00 117	99.14 101	99.45 1	93.86 54	95.15 113	98.73 89	88.48 64	99.76 86	97.23 63	99.56 50	99.40 85

test_yl		95.27 78	94.60 85	97.28 51	98.53 89	92.98 79	99.05 112	98.70 19	86.76 234	94.65 122	97.74 137	87.78 75	99.44 119	95.57 97	92.61 187	99.44 82

DCV-MVSNet		95.27 78	94.60 85	97.28 51	98.53 89	92.98 79	99.05 112	98.70 19	86.76 234	94.65 122	97.74 137	87.78 75	99.44 119	95.57 97	92.61 187	99.44 82

TAPA-MVS	87.50 9	90.35 193	89.05 203	94.25 181	98.48 91	85.17 266	98.42 181	96.58 206	82.44 311	87.24 214	98.53 105	82.77 169	98.84 155	59.09 377	97.88 114	98.72 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.32 92	91.21 106	98.08 218	97.58 113	83.74 284	95.87 98	99.02 58	86.74 101			99.64 40	99.81 33

DPM-MVS		97.86 8	97.25 20	99.68 1	98.25 93	99.10 1	99.76 20	97.78 71	96.61 12	98.15 41	99.53 7	93.62 17	100.00 1	91.79 158	99.80 26	99.94 18

LFMVS		92.23 160	90.84 173	96.42 97	98.24 94	91.08 114	98.24 201	96.22 228	83.39 291	94.74 120	98.31 118	61.12 332	98.85 154	94.45 122	92.82 183	99.32 93

testdata				95.26 142	98.20 95	87.28 212		97.60 107	85.21 259	98.48 33	99.15 39	88.15 70	98.72 162	90.29 174	99.45 57	99.78 38

PatchMatch-RL		91.47 171	90.54 180	94.26 180	98.20 95	86.36 231	96.94 272	97.14 170	87.75 211	88.98 199	95.75 213	71.80 265	99.40 127	80.92 278	97.39 128	97.02 208

MVS_111021_LR		95.78 64	95.94 53	95.28 141	98.19 97	87.69 196	98.80 137	99.26 7	93.39 65	95.04 115	98.69 96	84.09 146	99.76 86	96.96 69	99.06 75	98.38 165

F-COLMAP		92.07 164	91.75 155	93.02 211	98.16 98	82.89 297	98.79 141	95.97 245	86.54 239	87.92 206	97.80 132	78.69 216	99.65 98	85.97 222	95.93 156	96.53 222

Anonymous202405211		88.84 222	87.03 241	94.27 179	98.14 99	84.18 280	98.44 179	95.58 281	76.79 350	89.34 197	96.88 181	53.42 359	99.54 108	87.53 207	87.12 235	99.09 114

VNet		95.08 83	94.26 89	97.55 43	98.07 100	93.88 61	98.68 149	98.73 18	90.33 131	97.16 70	97.43 153	79.19 211	99.53 109	96.91 71	91.85 201	99.24 100

CS-MVS-test		95.98 54	96.34 40	94.90 154	98.06 101	87.66 199	99.69 33	96.10 237	93.66 60	98.35 38	99.05 54	86.28 113	97.66 215	96.96 69	98.90 87	99.37 88

DELS-MVS		97.12 24	96.60 34	98.68 10	98.03 102	96.57 11	99.84 8	97.84 60	96.36 18	95.20 112	98.24 121	88.17 68	99.83 73	96.11 86	99.60 48	99.64 62
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
PVSNet	87.13 12	93.69 120	92.83 132	96.28 103	97.99 103	90.22 135	99.38 71	98.93 12	91.42 106	93.66 137	97.68 140	71.29 270	99.64 100	87.94 203	97.20 131	98.98 121

test_fmvsm_n_1920		97.08 26	97.55 14	95.67 127	97.94 104	89.61 156	99.93 1	98.48 24	97.08 5	99.08 14	99.13 44	88.17 68	99.93 38	99.11 23	99.06 75	97.47 194

cl22		89.57 210	88.79 209	91.91 234	97.94 104	87.62 200	97.98 224	96.51 210	85.03 264	82.37 270	91.79 285	83.65 150	96.50 269	85.96 223	77.89 297	91.61 285

CS-MVS		95.75 67	96.19 42	94.40 173	97.88 106	86.22 236	99.66 34	96.12 236	92.69 78	98.07 46	98.89 78	87.09 91	97.59 221	96.71 72	98.62 99	99.39 87

CHOSEN 280x420		96.80 32	96.85 27	96.66 84	97.85 107	94.42 51	94.76 323	98.36 29	92.50 81	95.62 105	97.52 148	97.92 1	97.38 233	98.31 44	98.80 91	98.20 177

thres200		93.69 120	92.59 137	96.97 66	97.76 108	94.74 43	99.35 76	99.36 2	89.23 160	91.21 172	96.97 175	83.42 155	98.77 157	85.08 232	90.96 216	97.39 196

HY-MVS	88.56 7	95.29 77	94.23 90	98.48 14	97.72 109	96.41 13	94.03 331	98.74 16	92.42 84	95.65 104	94.76 232	86.52 108	99.49 112	95.29 103	92.97 182	99.53 74

Anonymous20231211		84.72 291	82.65 302	90.91 257	97.71 110	84.55 275	97.28 258	96.67 197	66.88 381	79.18 313	90.87 303	58.47 340	96.60 259	82.61 265	74.20 324	91.59 287

tfpn200view9		93.43 129	92.27 142	96.90 69	97.68 111	94.84 38	99.18 89	99.36 2	88.45 184	90.79 175	96.90 179	83.31 156	98.75 159	84.11 248	90.69 218	97.12 202

thres400		93.39 131	92.27 142	96.73 78	97.68 111	94.84 38	99.18 89	99.36 2	88.45 184	90.79 175	96.90 179	83.31 156	98.75 159	84.11 248	90.69 218	96.61 217

thres100view900		93.34 133	92.15 145	96.90 69	97.62 113	94.84 38	99.06 111	99.36 2	87.96 204	90.47 183	96.78 186	83.29 158	98.75 159	84.11 248	90.69 218	97.12 202

thres600view7		93.18 139	92.00 148	96.75 76	97.62 113	94.92 33	99.07 109	99.36 2	87.96 204	90.47 183	96.78 186	83.29 158	98.71 163	82.93 262	90.47 222	96.61 217

WTY-MVS		95.97 55	95.11 78	98.54 13	97.62 113	96.65 9	99.44 62	98.74 16	92.25 89	95.21 111	98.46 115	86.56 107	99.46 118	95.00 110	92.69 186	99.50 78

fmvsm_l_conf0.5_n_a		97.70 11	97.80 11	97.42 45	97.59 116	92.91 82	99.86 4	98.04 48	96.70 10	99.58 2	99.26 21	90.90 37	99.94 34	99.57 11	98.66 98	99.40 85

Anonymous20240529		87.66 249	85.58 262	93.92 194	97.59 116	85.01 269	98.13 210	97.13 172	66.69 382	88.47 203	96.01 209	55.09 353	99.51 110	87.00 210	84.12 260	97.23 201

HyFIR lowres test		93.68 122	93.29 119	94.87 155	97.57 118	88.04 191	98.18 206	98.47 25	87.57 217	91.24 171	95.05 226	85.49 127	97.46 228	93.22 142	92.82 183	99.10 113

canonicalmvs		95.02 84	93.96 103	98.20 21	97.53 119	95.92 17	98.71 145	96.19 231	91.78 97	95.86 99	98.49 110	79.53 208	99.03 149	96.12 85	91.42 213	99.66 60

fmvsm_l_conf0.5_n		97.65 12	97.72 12	97.41 46	97.51 120	92.78 84	99.85 7	98.05 46	96.78 8	99.60 1	99.23 26	90.42 45	99.92 40	99.55 12	98.50 103	99.55 72

CHOSEN 1792x2688		94.35 103	93.82 107	95.95 117	97.40 121	88.74 179	98.41 183	98.27 31	92.18 91	91.43 166	96.40 197	78.88 212	99.81 79	93.59 136	97.81 115	99.30 95

SteuartSystems-ACMMP		97.25 18	97.34 19	97.01 60	97.38 122	91.46 103	99.75 21	97.66 92	94.14 47	98.13 42	99.26 21	92.16 29	99.66 94	97.91 52	99.64 40	99.90 22
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n		96.19 48	96.49 35	95.30 140	97.37 123	89.16 161	99.86 4	98.47 25	95.68 23	98.87 22	99.15 39	82.44 181	99.92 40	99.14 21	97.43 127	96.83 213

alignmvs		95.77 65	95.00 81	98.06 28	97.35 124	95.68 19	99.71 25	97.50 131	91.50 102	96.16 92	98.61 103	86.28 113	99.00 150	96.19 84	91.74 203	99.51 77

PS-MVSNAJ		96.87 30	96.40 38	98.29 19	97.35 124	97.29 5	99.03 115	97.11 174	95.83 20	98.97 19	99.14 42	82.48 177	99.60 103	98.60 33	99.08 73	98.00 181

testing222		94.48 101	94.00 99	95.95 117	97.30 126	92.27 91	98.82 134	97.92 55	89.20 161	94.82 117	97.26 159	87.13 90	97.32 236	91.95 156	91.56 207	98.25 172

MVS_0304		97.53 13	97.15 21	98.67 11	97.30 126	96.52 12	99.60 38	98.88 14	97.14 4	97.21 66	98.94 72	86.89 97	99.91 45	99.43 15	98.91 86	99.59 71

EPNet_dtu		92.28 158	92.15 145	92.70 220	97.29 128	84.84 271	98.64 155	97.82 63	92.91 75	93.02 145	97.02 173	85.48 129	95.70 315	72.25 339	94.89 167	97.55 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSTER		92.71 146	92.32 140	93.86 196	97.29 128	92.95 81	99.01 118	96.59 203	90.09 138	85.51 231	94.00 243	94.61 16	96.56 264	90.77 170	83.03 272	92.08 272

EPMVS		92.59 151	91.59 157	95.59 131	97.22 130	90.03 145	91.78 351	98.04 48	90.42 129	91.66 160	90.65 311	86.49 110	97.46 228	81.78 273	96.31 147	99.28 97

test_fmvs1		92.35 155	92.94 130	90.57 267	97.19 131	75.43 352	99.55 44	94.97 308	95.20 31	96.82 79	97.57 147	59.59 337	99.84 69	97.30 61	98.29 110	96.46 224

tpmvs		89.16 214	87.76 228	93.35 205	97.19 131	84.75 273	90.58 366	97.36 151	81.99 316	84.56 238	89.31 339	83.98 148	98.17 180	74.85 321	90.00 225	97.12 202

DeepC-MVS	91.02 4	94.56 100	93.92 105	96.46 94	97.16 133	90.76 122	98.39 190	97.11 174	93.92 50	88.66 201	98.33 117	78.14 219	99.85 67	95.02 108	98.57 101	98.78 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
iter_conf05		93.48 126	93.18 122	94.39 176	97.15 134	94.17 57	99.30 80	92.97 348	92.38 88	86.70 223	95.42 219	95.67 5	96.59 260	94.67 118	84.32 258	92.39 255

PVSNet_Blended_VisFu		94.67 95	94.11 95	96.34 102	97.14 135	91.10 112	99.32 79	97.43 144	92.10 94	91.53 165	96.38 200	83.29 158	99.68 92	93.42 140	96.37 145	98.25 172

h-mvs33		92.47 154	91.95 150	94.05 190	97.13 136	85.01 269	98.36 192	98.08 44	93.85 55	96.27 90	96.73 188	83.19 161	99.43 122	95.81 90	68.09 356	97.70 187

miper_enhance_ethall		90.33 194	89.70 189	92.22 226	97.12 137	88.93 173	98.35 193	95.96 247	88.60 179	83.14 255	92.33 275	87.38 82	96.18 292	86.49 218	77.89 297	91.55 288

xiu_mvs_v2_base		96.66 35	96.17 47	98.11 27	97.11 138	96.96 6	99.01 118	97.04 181	95.51 27	98.86 23	99.11 50	82.19 185	99.36 130	98.59 35	98.14 111	98.00 181

VDD-MVS		91.24 178	90.18 184	94.45 172	97.08 139	85.84 252	98.40 186	96.10 237	86.99 225	93.36 140	98.16 125	54.27 356	99.20 138	96.59 78	90.63 221	98.31 171

UGNet		91.91 166	90.85 172	95.10 146	97.06 140	88.69 180	98.01 222	98.24 34	92.41 85	92.39 152	93.61 254	60.52 334	99.68 92	88.14 199	97.25 130	96.92 211
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
baseline1		92.61 150	91.28 163	96.58 88	97.05 141	94.63 46	97.72 240	96.20 229	89.82 143	88.56 202	96.85 182	86.85 98	97.82 201	88.42 195	80.10 288	97.30 198

iter_conf_final		93.22 138	93.04 126	93.76 199	97.03 142	92.22 93	99.05 112	93.31 345	92.11 93	86.93 218	95.42 219	95.01 10	96.59 260	93.98 127	84.48 255	92.46 254

CANet_DTU		94.31 104	93.35 116	97.20 55	97.03 142	94.71 44	98.62 157	95.54 283	95.61 25	97.21 66	98.47 113	71.88 263	99.84 69	88.38 196	97.46 126	97.04 207

MSDG		88.29 237	86.37 250	94.04 191	96.90 144	86.15 240	96.52 287	94.36 329	77.89 346	79.22 312	96.95 176	69.72 277	99.59 104	73.20 334	92.58 189	96.37 227

BH-w/o		92.32 156	91.79 153	93.91 195	96.85 145	86.18 238	99.11 106	95.74 271	88.13 198	84.81 235	97.00 174	77.26 224	97.91 194	89.16 192	98.03 112	97.64 188

AllTest		84.97 289	83.12 294	90.52 270	96.82 146	78.84 335	95.89 307	92.17 359	77.96 344	75.94 331	95.50 216	55.48 349	99.18 139	71.15 340	87.14 233	93.55 244

TestCases				90.52 270	96.82 146	78.84 335		92.17 359	77.96 344	75.94 331	95.50 216	55.48 349	99.18 139	71.15 340	87.14 233	93.55 244

SDMVSNet		91.09 179	89.91 187	94.65 164	96.80 148	90.54 128	97.78 234	97.81 66	88.34 191	85.73 227	95.26 223	66.44 305	98.26 177	94.25 125	86.75 236	95.14 235

sd_testset		89.23 213	88.05 227	92.74 219	96.80 148	85.33 262	95.85 312	97.03 183	88.34 191	85.73 227	95.26 223	61.12 332	97.76 210	85.61 228	86.75 236	95.14 235

PMMVS		93.62 125	93.90 106	92.79 216	96.79 150	81.40 314	98.85 131	96.81 193	91.25 109	96.82 79	98.15 126	77.02 225	98.13 182	93.15 144	96.30 148	98.83 139

BH-RMVSNet		91.25 177	89.99 186	95.03 151	96.75 151	88.55 182	98.65 153	94.95 309	87.74 212	87.74 208	97.80 132	68.27 287	98.14 181	80.53 283	97.49 125	98.41 162

MVS_Test		93.67 123	92.67 135	96.69 82	96.72 152	92.66 85	97.22 263	96.03 242	87.69 215	95.12 114	94.03 241	81.55 191	98.28 176	89.17 191	96.46 142	99.14 108

COLMAP_ROB	82.69 18	84.54 295	82.82 296	89.70 294	96.72 152	78.85 334	95.89 307	92.83 351	71.55 365	77.54 326	95.89 211	59.40 338	99.14 145	67.26 356	88.26 229	91.11 305
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		92.50 153	91.65 156	95.06 148	96.60 154	89.64 154	97.06 268	96.44 215	86.64 236	84.14 243	93.93 245	82.49 176	96.17 294	91.47 159	96.08 153	99.35 90

ETV-MVS		96.00 52	96.00 52	96.00 114	96.56 155	91.05 115	99.63 36	96.61 201	93.26 68	97.39 61	98.30 119	86.62 104	98.13 182	98.07 49	97.57 121	98.82 140

GG-mvs-BLEND				96.98 65	96.53 156	94.81 41	87.20 371	97.74 75		93.91 133	96.40 197	96.56 2	96.94 248	95.08 106	98.95 84	99.20 104

FMVSNet3		88.81 226	87.08 240	93.99 193	96.52 157	94.59 47	98.08 218	96.20 229	85.85 249	82.12 274	91.60 289	74.05 243	95.40 324	79.04 290	80.24 285	91.99 275

fmvsm_s_conf0.5_n_a		95.97 55	96.19 42	95.31 139	96.51 158	89.01 167	99.81 11	98.39 27	95.46 28	99.19 13	99.16 36	81.44 195	99.91 45	98.83 28	96.97 136	97.01 209

BH-untuned		91.46 172	90.84 173	93.33 206	96.51 158	84.83 272	98.84 133	95.50 285	86.44 244	83.50 247	96.70 189	75.49 231	97.77 205	86.78 216	97.81 115	97.40 195

FE-MVS		91.38 174	90.16 185	95.05 150	96.46 160	87.53 203	89.69 368	97.84 60	82.97 298	92.18 154	92.00 282	84.07 147	98.93 153	80.71 280	95.52 161	98.68 151

sss		94.85 87	93.94 104	97.58 40	96.43 161	94.09 59	98.93 125	99.16 8	89.50 155	95.27 110	97.85 129	81.50 192	99.65 98	92.79 150	94.02 174	98.99 120

test2506		94.80 88	94.21 91	96.58 88	96.41 162	92.18 94	98.01 222	98.96 11	90.82 116	93.46 139	97.28 157	85.92 119	98.45 169	89.82 179	97.19 132	99.12 111

ECVR-MVS		92.29 157	91.33 162	95.15 145	96.41 162	87.84 194	98.10 215	94.84 312	90.82 116	91.42 168	97.28 157	65.61 311	98.49 168	90.33 173	97.19 132	99.12 111

ET-MVSNet_ETH3D		92.56 152	91.45 160	95.88 119	96.39 164	94.13 58	99.46 59	96.97 189	92.18 91	66.94 371	98.29 120	94.65 15	94.28 344	94.34 123	83.82 265	99.24 100

dp		90.16 200	88.83 208	94.14 185	96.38 165	86.42 227	91.57 355	97.06 180	84.76 270	88.81 200	90.19 329	84.29 144	97.43 231	75.05 318	91.35 215	98.56 156

EIA-MVS		95.11 81	95.27 73	94.64 166	96.34 166	86.51 224	99.59 40	96.62 200	92.51 80	94.08 130	98.64 99	86.05 118	98.24 179	95.07 107	98.50 103	99.18 105

test_vis1_n_1920		93.08 142	93.42 115	92.04 233	96.31 167	79.36 331	99.83 9	96.06 241	96.72 9	98.53 32	98.10 127	58.57 339	99.91 45	97.86 53	98.79 94	96.85 212

TR-MVS		90.77 186	89.44 194	94.76 159	96.31 167	88.02 192	97.92 226	95.96 247	85.52 255	88.22 205	97.23 162	66.80 301	98.09 185	84.58 240	92.38 191	98.17 178

UA-Net		93.30 134	92.62 136	95.34 137	96.27 169	88.53 184	95.88 309	96.97 189	90.90 114	95.37 109	97.07 171	82.38 182	99.10 147	83.91 252	94.86 168	98.38 165

tpmrst		92.78 145	92.16 144	94.65 164	96.27 169	87.45 206	91.83 350	97.10 177	89.10 166	94.68 121	90.69 308	88.22 67	97.73 213	89.78 180	91.80 202	98.77 146

hse-mvs2		91.67 169	91.51 159	92.15 230	96.22 171	82.61 303	97.74 239	97.53 122	93.85 55	96.27 90	96.15 203	83.19 161	97.44 230	95.81 90	66.86 363	96.40 226

AUN-MVS		90.17 199	89.50 192	92.19 228	96.21 172	82.67 301	97.76 238	97.53 122	88.05 200	91.67 159	96.15 203	83.10 163	97.47 227	88.11 200	66.91 362	96.43 225

ADS-MVSNet2		87.62 250	86.88 243	89.86 288	96.21 172	79.14 333	87.15 372	92.99 347	83.01 296	89.91 191	87.27 352	78.87 213	92.80 357	74.20 326	92.27 194	97.64 188

ADS-MVSNet		88.99 216	87.30 236	94.07 188	96.21 172	87.56 202	87.15 372	96.78 195	83.01 296	89.91 191	87.27 352	78.87 213	97.01 245	74.20 326	92.27 194	97.64 188

PatchmatchNet		92.05 165	91.04 168	95.06 148	96.17 175	89.04 165	91.26 359	97.26 155	89.56 153	90.64 179	90.56 317	88.35 66	97.11 240	79.53 286	96.07 154	99.03 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test1111		92.12 162	91.19 165	94.94 153	96.15 176	87.36 209	98.12 212	94.84 312	90.85 115	90.97 173	97.26 159	65.60 312	98.37 171	89.74 182	97.14 135	99.07 117

gg-mvs-nofinetune		90.00 203	87.71 230	96.89 73	96.15 176	94.69 45	85.15 377	97.74 75	68.32 377	92.97 146	60.16 390	96.10 3	96.84 251	93.89 129	98.87 88	99.14 108

MDTV_nov1_ep13			90.47 182		96.14 178	88.55 182	91.34 358	97.51 128	89.58 151	92.24 153	90.50 321	86.99 96	97.61 220	77.64 301	92.34 192

IS-MVSNet		93.00 143	92.51 138	94.49 169	96.14 178	87.36 209	98.31 197	95.70 273	88.58 180	90.17 187	97.50 149	83.02 165	97.22 237	87.06 208	96.07 154	98.90 132

Vis-MVSNet (Re-imp)		93.26 137	93.00 129	94.06 189	96.14 178	86.71 223	98.68 149	96.70 196	88.30 193	89.71 195	97.64 143	85.43 130	96.39 276	88.06 201	96.32 146	99.08 115

thisisatest0515		94.75 90	94.19 92	96.43 96	96.13 181	92.64 88	99.47 55	97.60 107	87.55 218	93.17 142	97.59 145	94.71 13	98.42 170	88.28 197	93.20 179	98.24 174

FA-MVS(test-final)		92.22 161	91.08 167	95.64 128	96.05 182	88.98 168	91.60 354	97.25 156	86.99 225	91.84 156	92.12 276	83.03 164	99.00 150	86.91 213	93.91 175	98.93 129

test_fmvsmconf_n		96.78 33	96.84 28	96.61 85	95.99 183	90.25 132	99.90 2	98.13 42	96.68 11	98.42 34	98.92 74	85.34 132	99.88 54	99.12 22	99.08 73	99.70 52

ab-mvs		91.05 182	89.17 200	96.69 82	95.96 184	91.72 98	92.62 345	97.23 160	85.61 254	89.74 193	93.89 247	68.55 284	99.42 123	91.09 162	87.84 231	98.92 131

Fast-Effi-MVS+		91.72 168	90.79 176	94.49 169	95.89 185	87.40 208	99.54 49	95.70 273	85.01 266	89.28 198	95.68 214	77.75 221	97.57 225	83.22 257	95.06 166	98.51 158

EPP-MVSNet		93.75 119	93.67 110	94.01 192	95.86 186	85.70 254	98.67 151	97.66 92	84.46 273	91.36 169	97.18 166	91.16 31	97.79 203	92.93 146	93.75 176	98.53 157

mvsany_test1		94.57 99	95.09 79	92.98 212	95.84 187	82.07 307	98.76 143	95.24 301	92.87 77	96.45 87	98.71 94	84.81 139	99.15 141	97.68 55	95.49 162	97.73 186

Effi-MVS+		93.87 115	93.15 123	96.02 113	95.79 188	90.76 122	96.70 284	95.78 268	86.98 228	95.71 102	97.17 167	79.58 206	98.01 192	94.57 121	96.09 152	99.31 94

tpm cat1		88.89 220	87.27 237	93.76 199	95.79 188	85.32 263	90.76 364	97.09 178	76.14 352	85.72 229	88.59 342	82.92 166	98.04 190	76.96 305	91.43 212	97.90 184

thisisatest0530		94.00 109	93.52 112	95.43 134	95.76 190	90.02 146	98.99 120	97.60 107	86.58 237	91.74 158	97.36 156	94.78 12	98.34 172	86.37 219	92.48 190	97.94 183

3Dnovator+	87.72 8	93.43 129	91.84 152	98.17 22	95.73 191	95.08 32	98.92 127	97.04 181	91.42 106	81.48 288	97.60 144	74.60 235	99.79 82	90.84 167	98.97 81	99.64 62

MVS		93.92 112	92.28 141	98.83 7	95.69 192	96.82 8	96.22 299	98.17 37	84.89 268	84.34 242	98.61 103	79.32 210	99.83 73	93.88 130	99.43 59	99.86 29

cascas		90.93 184	89.33 198	95.76 123	95.69 192	93.03 78	98.99 120	96.59 203	80.49 331	86.79 222	94.45 236	65.23 315	98.60 167	93.52 137	92.18 196	95.66 234

QAPM		91.41 173	89.49 193	97.17 56	95.66 194	93.42 70	98.60 161	97.51 128	80.92 329	81.39 289	97.41 154	72.89 255	99.87 58	82.33 267	98.68 96	98.21 176

tttt0517		93.30 134	93.01 128	94.17 184	95.57 195	86.47 226	98.51 171	97.60 107	85.99 248	90.55 180	97.19 165	94.80 11	98.31 173	85.06 233	91.86 200	97.74 185

1112_ss		92.71 146	91.55 158	96.20 104	95.56 196	91.12 110	98.48 176	94.69 319	88.29 194	86.89 220	98.50 108	87.02 94	98.66 165	84.75 237	89.77 226	98.81 141

diffmvs		94.59 98	94.19 92	95.81 121	95.54 197	90.69 124	98.70 147	95.68 275	91.61 99	95.96 94	97.81 131	80.11 202	98.06 187	96.52 80	95.76 157	98.67 152
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LCM-MVSNet-Re		88.59 233	88.61 213	88.51 314	95.53 198	72.68 364	96.85 276	88.43 383	88.45 184	73.14 348	90.63 312	75.82 228	94.38 343	92.95 145	95.71 159	98.48 160

Test_1112_low_res		92.27 159	90.97 169	96.18 105	95.53 198	91.10 112	98.47 178	94.66 320	88.28 195	86.83 221	93.50 258	87.00 95	98.65 166	84.69 238	89.74 227	98.80 142

PCF-MVS	89.78 5	91.26 175	89.63 190	96.16 108	95.44 200	91.58 102	95.29 319	96.10 237	85.07 263	82.75 257	97.45 152	78.28 218	99.78 84	80.60 282	95.65 160	97.12 202
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EC-MVSNet		95.09 82	95.17 75	94.84 157	95.42 201	88.17 187	99.48 53	95.92 254	91.47 103	97.34 63	98.36 116	82.77 169	97.41 232	97.24 62	98.58 100	98.94 128

3Dnovator	87.35 11	93.17 140	91.77 154	97.37 49	95.41 202	93.07 76	98.82 134	97.85 59	91.53 101	82.56 263	97.58 146	71.97 262	99.82 76	91.01 164	99.23 69	99.22 103

IB-MVS	89.43 6	92.12 162	90.83 175	95.98 116	95.40 203	90.78 121	99.81 11	98.06 45	91.23 110	85.63 230	93.66 253	90.63 41	98.78 156	91.22 161	71.85 346	98.36 168
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
test_cas_vis1_n_1920		93.86 116	93.74 109	94.22 182	95.39 204	86.08 242	99.73 22	96.07 240	96.38 17	97.19 69	97.78 134	65.46 314	99.86 63	96.71 72	98.92 85	96.73 214

miper_ehance_all_eth		88.94 218	88.12 225	91.40 246	95.32 205	86.93 219	97.85 231	95.55 282	84.19 276	81.97 279	91.50 291	84.16 145	95.91 308	84.69 238	77.89 297	91.36 296

1314		93.44 128	91.98 149	97.84 32	95.24 206	94.38 52	96.22 299	97.92 55	90.18 134	82.28 271	97.71 139	77.63 222	99.80 81	91.94 157	98.67 97	99.34 92

XVG-OURS		90.83 185	90.49 181	91.86 235	95.23 207	81.25 318	95.79 314	95.92 254	88.96 169	90.02 190	98.03 128	71.60 267	99.35 133	91.06 163	87.78 232	94.98 238

casdiffmvs_mvg		94.00 109	93.33 117	96.03 112	95.22 208	90.90 120	99.09 107	95.99 243	90.58 123	91.55 164	97.37 155	79.91 204	98.06 187	95.01 109	95.22 164	99.13 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TESTMET0.1,1		93.82 117	93.26 120	95.49 132	95.21 209	90.25 132	99.15 98	97.54 121	89.18 163	91.79 157	94.87 229	89.13 56	97.63 218	86.21 220	96.29 149	98.60 155

xiu_mvs_v1_base_debu		94.73 91	93.98 100	96.99 62	95.19 210	95.24 25	98.62 157	96.50 211	92.99 72	97.52 57	98.83 82	72.37 258	99.15 141	97.03 65	96.74 139	96.58 219

xiu_mvs_v1_base		94.73 91	93.98 100	96.99 62	95.19 210	95.24 25	98.62 157	96.50 211	92.99 72	97.52 57	98.83 82	72.37 258	99.15 141	97.03 65	96.74 139	96.58 219

xiu_mvs_v1_base_debi		94.73 91	93.98 100	96.99 62	95.19 210	95.24 25	98.62 157	96.50 211	92.99 72	97.52 57	98.83 82	72.37 258	99.15 141	97.03 65	96.74 139	96.58 219

XVG-OURS-SEG-HR		90.95 183	90.66 179	91.83 236	95.18 213	81.14 321	95.92 306	95.92 254	88.40 188	90.33 186	97.85 129	70.66 273	99.38 128	92.83 148	88.83 228	94.98 238

Effi-MVS+-dtu		89.97 205	90.68 178	87.81 319	95.15 214	71.98 366	97.87 230	95.40 292	91.92 95	87.57 209	91.44 292	74.27 241	96.84 251	89.45 184	93.10 181	94.60 240

Syy-MVS		84.10 303	84.53 282	82.83 350	95.14 215	65.71 378	97.68 243	96.66 198	86.52 240	82.63 260	96.84 183	68.15 288	89.89 374	45.62 388	91.54 209	92.87 247

myMVS_eth3d		88.68 232	89.07 202	87.50 322	95.14 215	79.74 329	97.68 243	96.66 198	86.52 240	82.63 260	96.84 183	85.22 134	89.89 374	69.43 348	91.54 209	92.87 247

Vis-MVSNet		92.64 148	91.85 151	95.03 151	95.12 217	88.23 186	98.48 176	96.81 193	91.61 99	92.16 155	97.22 163	71.58 268	98.00 193	85.85 227	97.81 115	98.88 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		86.67 262	84.96 270	91.80 238	95.11 218	88.81 176	96.77 278	95.25 298	82.94 299	82.12 274	90.25 324	62.89 324	94.97 331	79.04 290	80.24 285	91.62 282

test1		86.67 262	84.96 270	91.80 238	95.11 218	88.81 176	96.77 278	95.25 298	82.94 299	82.12 274	90.25 324	62.89 324	94.97 331	79.04 290	80.24 285	91.62 282

FMVSNet2		86.90 257	84.79 276	93.24 207	95.11 218	92.54 89	97.67 245	95.86 266	82.94 299	80.55 295	91.17 298	62.89 324	95.29 326	77.23 302	79.71 291	91.90 276

GeoE		90.60 191	89.56 191	93.72 202	95.10 221	85.43 259	99.41 68	94.94 310	83.96 281	87.21 215	96.83 185	74.37 239	97.05 244	80.50 284	93.73 177	98.67 152

baseline		93.91 113	93.30 118	95.72 124	95.10 221	90.07 141	97.48 250	95.91 259	91.03 111	93.54 138	97.68 140	79.58 206	98.02 191	94.27 124	95.14 165	99.08 115

casdiffmvs		93.98 111	93.43 114	95.61 130	95.07 223	89.86 150	98.80 137	95.84 267	90.98 113	92.74 148	97.66 142	79.71 205	98.10 184	94.72 116	95.37 163	98.87 135
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVSFormer		94.71 94	94.08 97	96.61 85	95.05 224	94.87 36	97.77 236	96.17 233	86.84 231	98.04 48	98.52 106	85.52 124	95.99 301	89.83 177	98.97 81	98.96 123

lupinMVS		96.32 44	95.94 53	97.44 44	95.05 224	94.87 36	99.86 4	96.50 211	93.82 57	98.04 48	98.77 85	85.52 124	98.09 185	96.98 68	98.97 81	99.37 88

CostFormer		92.89 144	92.48 139	94.12 186	94.99 226	85.89 249	92.89 341	97.00 187	86.98 228	95.00 116	90.78 304	90.05 50	97.51 226	92.92 147	91.73 204	98.96 123

c3_l		88.19 239	87.23 238	91.06 253	94.97 227	86.17 239	97.72 240	95.38 293	83.43 290	81.68 286	91.37 293	82.81 168	95.72 314	84.04 251	73.70 328	91.29 300

SCA		90.64 190	89.25 199	94.83 158	94.95 228	88.83 175	96.26 296	97.21 162	90.06 141	90.03 189	90.62 313	66.61 302	96.81 253	83.16 258	94.36 171	98.84 136

test-LLR		93.11 141	92.68 134	94.40 173	94.94 229	87.27 213	99.15 98	97.25 156	90.21 132	91.57 161	94.04 239	84.89 137	97.58 222	85.94 224	96.13 150	98.36 168

test-mter		93.27 136	92.89 131	94.40 173	94.94 229	87.27 213	99.15 98	97.25 156	88.95 170	91.57 161	94.04 239	88.03 73	97.58 222	85.94 224	96.13 150	98.36 168

cl____		87.82 241	86.79 245	90.89 259	94.88 231	85.43 259	97.81 232	95.24 301	82.91 303	80.71 294	91.22 296	81.97 188	95.84 310	81.34 275	75.06 312	91.40 295

DIV-MVS_self_test		87.82 241	86.81 244	90.87 260	94.87 232	85.39 261	97.81 232	95.22 306	82.92 302	80.76 293	91.31 295	81.99 186	95.81 312	81.36 274	75.04 313	91.42 294

tpm2		91.77 167	91.09 166	93.82 198	94.83 233	85.56 258	92.51 346	97.16 169	84.00 279	93.83 135	90.66 310	87.54 79	97.17 238	87.73 205	91.55 208	98.72 148

PVSNet_0	83.28 16	87.31 253	85.16 268	93.74 201	94.78 234	84.59 274	98.91 128	98.69 21	89.81 144	78.59 319	93.23 263	61.95 328	99.34 134	94.75 114	55.72 383	97.30 198

CDS-MVSNet		93.47 127	93.04 126	94.76 159	94.75 235	89.45 158	98.82 134	97.03 183	87.91 206	90.97 173	96.48 195	89.06 57	96.36 278	89.50 183	92.81 185	98.49 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gm-plane-assit					94.69 236	88.14 188			88.22 196		97.20 164		98.29 175	90.79 169

eth_miper_zixun_eth		87.76 244	87.00 242	90.06 281	94.67 237	82.65 302	97.02 271	95.37 294	84.19 276	81.86 284	91.58 290	81.47 193	95.90 309	83.24 256	73.61 329	91.61 285

testing3		87.75 245	88.22 223	86.36 330	94.66 238	77.41 346	99.52 50	97.95 54	86.05 247	81.12 290	96.69 190	86.18 116	89.31 378	61.65 372	90.12 224	92.35 260

RPSCF		85.33 285	85.55 263	84.67 342	94.63 239	62.28 381	93.73 333	93.76 337	74.38 359	85.23 234	97.06 172	64.09 318	98.31 173	80.98 276	86.08 244	93.41 246

miper_lstm_enhance		86.90 257	86.20 253	89.00 309	94.53 240	81.19 319	96.74 282	95.24 301	82.33 312	80.15 300	90.51 320	81.99 186	94.68 340	80.71 280	73.58 330	91.12 304

Patchmatch-test		86.25 271	84.06 288	92.82 215	94.42 241	82.88 298	82.88 386	94.23 331	71.58 364	79.39 310	90.62 313	89.00 59	96.42 275	63.03 368	91.37 214	99.16 106

VDDNet		90.08 202	88.54 218	94.69 163	94.41 242	87.68 197	98.21 204	96.40 216	76.21 351	93.33 141	97.75 136	54.93 354	98.77 157	94.71 117	90.96 216	97.61 192

fmvsm_s_conf0.1_n		95.56 71	95.68 64	95.20 143	94.35 243	89.10 163	99.50 51	97.67 91	94.76 34	98.68 27	99.03 56	81.13 198	99.86 63	98.63 32	97.36 129	96.63 216

test_fmvsmvis_n_1920		95.47 72	95.40 70	95.70 125	94.33 244	90.22 135	99.70 26	96.98 188	96.80 7	92.75 147	98.89 78	82.46 180	99.92 40	98.36 40	98.33 107	96.97 210

KD-MVS_2432*1600		82.98 308	80.52 316	90.38 274	94.32 245	88.98 168	92.87 342	95.87 264	80.46 332	73.79 343	87.49 349	82.76 171	93.29 351	70.56 344	46.53 392	88.87 349

miper_refine_blended		82.98 308	80.52 316	90.38 274	94.32 245	88.98 168	92.87 342	95.87 264	80.46 332	73.79 343	87.49 349	82.76 171	93.29 351	70.56 344	46.53 392	88.87 349

EI-MVSNet		89.87 206	89.38 197	91.36 248	94.32 245	85.87 250	97.61 247	96.59 203	85.10 261	85.51 231	97.10 169	81.30 197	96.56 264	83.85 254	83.03 272	91.64 280

CVMVSNet		90.30 195	90.91 171	88.46 315	94.32 245	73.58 360	97.61 247	97.59 111	90.16 137	88.43 204	97.10 169	76.83 226	92.86 354	82.64 264	93.54 178	98.93 129

WB-MVSnew		88.69 230	88.34 220	89.77 292	94.30 249	85.99 247	98.14 209	97.31 154	87.15 224	87.85 207	96.07 207	69.91 274	95.52 319	72.83 337	91.47 211	87.80 356

test_fmvs1_n		91.07 180	91.41 161	90.06 281	94.10 250	74.31 356	99.18 89	94.84 312	94.81 33	96.37 89	97.46 151	50.86 367	99.82 76	97.14 64	97.90 113	96.04 231

IterMVS-LS		88.34 235	87.44 233	91.04 254	94.10 250	85.85 251	98.10 215	95.48 286	85.12 260	82.03 278	91.21 297	81.35 196	95.63 317	83.86 253	75.73 309	91.63 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		92.62 149	92.09 147	94.20 183	94.10 250	87.68 197	98.41 183	96.97 189	87.53 219	89.74 193	96.04 208	84.77 141	96.49 271	88.97 193	92.31 193	98.42 161

PAPM		96.35 42	95.94 53	97.58 40	94.10 250	95.25 24	98.93 125	98.17 37	94.26 42	93.94 132	98.72 91	89.68 53	97.88 197	96.36 82	99.29 67	99.62 66

CLD-MVS		91.06 181	90.71 177	92.10 231	94.05 254	86.10 241	99.55 44	96.29 225	94.16 45	84.70 237	97.17 167	69.62 279	97.82 201	94.74 115	86.08 244	92.39 255
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					93.95 255		99.16 93		93.92 50	87.57 209

ACMP_Plane					93.95 255		99.16 93		93.92 50	87.57 209

HQP-MVS		91.50 170	91.23 164	92.29 225	93.95 255	86.39 229	99.16 93	96.37 218	93.92 50	87.57 209	96.67 191	73.34 247	97.77 205	93.82 133	86.29 239	92.72 249

NP-MVS					93.94 258	86.22 236					96.67 191

plane_prior6					93.92 259	86.02 246						72.92 253

ACMP	87.39 10	88.71 229	88.24 222	90.12 280	93.91 260	81.06 322	98.50 172	95.67 276	89.43 156	80.37 297	95.55 215	65.67 309	97.83 200	90.55 171	84.51 253	91.47 290
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior1					93.90 261

HQP_MVS		91.26 175	90.95 170	92.16 229	93.84 262	86.07 244	99.02 116	96.30 222	93.38 66	86.99 216	96.52 193	72.92 253	97.75 211	93.46 138	86.17 242	92.67 251

plane_prior7					93.84 262	85.73 253

dmvs_re		88.69 230	88.06 226	90.59 266	93.83 264	78.68 337	95.75 315	96.18 232	87.99 203	84.48 241	96.32 201	67.52 295	96.94 248	84.98 235	85.49 248	96.14 229

MVS-HIRNet		79.01 327	75.13 339	90.66 265	93.82 265	81.69 310	85.16 376	93.75 338	54.54 386	74.17 341	59.15 392	57.46 343	96.58 263	63.74 365	94.38 170	93.72 243

FMVSNet5		82.29 311	80.54 315	87.52 321	93.79 266	84.01 282	93.73 333	92.47 355	76.92 349	74.27 340	86.15 360	63.69 322	89.24 379	69.07 349	74.79 316	89.29 344

ACMH+	83.78 15	84.21 299	82.56 304	89.15 306	93.73 267	79.16 332	96.43 289	94.28 330	81.09 326	74.00 342	94.03 241	54.58 355	97.67 214	76.10 312	78.81 293	90.63 320

ACMM	86.95 13	88.77 227	88.22 223	90.43 272	93.61 268	81.34 316	98.50 172	95.92 254	87.88 207	83.85 246	95.20 225	67.20 298	97.89 196	86.90 214	84.90 251	92.06 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	85.28 14	90.75 187	88.84 207	96.48 93	93.58 269	93.51 68	98.80 137	97.41 146	82.59 305	78.62 317	97.49 150	68.00 291	99.82 76	84.52 242	98.55 102	96.11 230

IterMVS		85.81 278	84.67 279	89.22 304	93.51 270	83.67 287	96.32 293	94.80 315	85.09 262	78.69 315	90.17 330	66.57 304	93.17 353	79.48 288	77.42 303	90.81 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		88.83 224	87.38 235	93.16 209	93.47 271	86.24 234	84.97 379	94.20 332	88.92 173	90.76 177	86.88 356	84.43 142	94.82 336	70.64 343	92.17 197	98.41 162

RPMNet		85.07 288	81.88 305	94.64 166	93.47 271	86.24 234	84.97 379	97.21 162	64.85 384	90.76 177	78.80 381	80.95 199	99.27 137	53.76 383	92.17 197	98.41 162

IterMVS-SCA-FT		85.73 281	84.64 280	89.00 309	93.46 273	82.90 296	96.27 294	94.70 318	85.02 265	78.62 317	90.35 322	66.61 302	93.33 350	79.38 289	77.36 304	90.76 315

Fast-Effi-MVS+-dtu		88.84 222	88.59 215	89.58 297	93.44 274	78.18 341	98.65 153	94.62 321	88.46 183	84.12 244	95.37 222	68.91 281	96.52 267	82.06 270	91.70 205	94.06 241

Patchmtry		83.61 307	81.64 307	89.50 299	93.36 275	82.84 299	84.10 382	94.20 332	69.47 374	79.57 308	86.88 356	84.43 142	94.78 337	68.48 352	74.30 322	90.88 310

LPG-MVS_test		88.86 221	88.47 219	90.06 281	93.35 276	80.95 323	98.22 202	95.94 250	87.73 213	83.17 253	96.11 205	66.28 306	97.77 205	90.19 175	85.19 249	91.46 291

LGP-MVS_train				90.06 281	93.35 276	80.95 323		95.94 250	87.73 213	83.17 253	96.11 205	66.28 306	97.77 205	90.19 175	85.19 249	91.46 291

JIA-IIPM		85.97 274	84.85 274	89.33 303	93.23 278	73.68 359	85.05 378	97.13 172	69.62 373	91.56 163	68.03 388	88.03 73	96.96 246	77.89 300	93.12 180	97.34 197

ACMH	83.09 17	84.60 293	82.61 303	90.57 267	93.18 279	82.94 294	96.27 294	94.92 311	81.01 327	72.61 354	93.61 254	56.54 345	97.79 203	74.31 324	81.07 283	90.99 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchT		85.44 284	83.19 293	92.22 226	93.13 280	83.00 293	83.80 385	96.37 218	70.62 367	90.55 180	79.63 380	84.81 139	94.87 334	58.18 379	91.59 206	98.79 143

baseline2		94.04 108	93.80 108	94.74 161	93.07 281	90.25 132	98.12 212	98.16 39	89.86 142	86.53 224	96.95 176	95.56 6	98.05 189	91.44 160	94.53 169	95.93 232

jason		95.40 76	94.86 82	97.03 59	92.91 282	94.23 54	99.70 26	96.30 222	93.56 64	96.73 82	98.52 106	81.46 194	97.91 194	96.08 87	98.47 105	98.96 123
jason: jason.
LTVRE_ROB	81.71 19	84.59 294	82.72 301	90.18 278	92.89 283	83.18 292	93.15 338	94.74 316	78.99 337	75.14 338	92.69 271	65.64 310	97.63 218	69.46 347	81.82 281	89.74 337
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
VPA-MVSNet		89.10 215	87.66 231	93.45 204	92.56 284	91.02 116	97.97 225	98.32 30	86.92 230	86.03 226	92.01 280	68.84 283	97.10 242	90.92 165	75.34 310	92.23 263

tpm		89.67 208	88.95 205	91.82 237	92.54 285	81.43 313	92.95 340	95.92 254	87.81 208	90.50 182	89.44 336	84.99 135	95.65 316	83.67 255	82.71 275	98.38 165

GA-MVS		90.10 201	88.69 211	94.33 177	92.44 286	87.97 193	99.08 108	96.26 226	89.65 147	86.92 219	93.11 266	68.09 289	96.96 246	82.54 266	90.15 223	98.05 179

test_fmvsmconf0.1_n		95.94 58	95.79 61	96.40 99	92.42 287	89.92 148	99.79 16	96.85 192	96.53 15	97.22 65	98.67 97	82.71 173	99.84 69	98.92 27	98.98 80	99.43 84

FIs		90.70 188	89.87 188	93.18 208	92.29 288	91.12 110	98.17 208	98.25 32	89.11 165	83.44 248	94.82 231	82.26 183	96.17 294	87.76 204	82.76 274	92.25 261

ITE_SJBPF				87.93 317	92.26 289	76.44 349		93.47 344	87.67 216	79.95 303	95.49 218	56.50 346	97.38 233	75.24 317	82.33 278	89.98 334

UniMVSNet (Re)		89.50 212	88.32 221	93.03 210	92.21 290	90.96 118	98.90 129	98.39 27	89.13 164	83.22 250	92.03 278	81.69 190	96.34 284	86.79 215	72.53 339	91.81 277

UniMVSNet_NR-MVSNet		89.60 209	88.55 217	92.75 218	92.17 291	90.07 141	98.74 144	98.15 40	88.37 189	83.21 251	93.98 244	82.86 167	95.93 305	86.95 211	72.47 340	92.25 261

TinyColmap		80.42 321	77.94 326	87.85 318	92.09 292	78.58 338	93.74 332	89.94 376	74.99 355	69.77 359	91.78 286	46.09 374	97.58 222	65.17 364	77.89 297	87.38 358

fmvsm_s_conf0.1_n_a		95.16 80	95.15 76	95.18 144	92.06 293	88.94 171	99.29 81	97.53 122	94.46 38	98.98 18	98.99 60	79.99 203	99.85 67	98.24 47	96.86 138	96.73 214

tt0805		86.50 267	84.79 276	91.63 244	91.97 294	81.49 312	96.49 288	97.38 149	82.24 313	82.44 265	95.82 212	51.22 364	98.25 178	84.55 241	80.96 284	95.13 237

MS-PatchMatch		86.75 260	85.92 257	89.22 304	91.97 294	82.47 304	96.91 273	96.14 235	83.74 284	77.73 324	93.53 257	58.19 341	97.37 235	76.75 308	98.35 106	87.84 354

VPNet		88.30 236	86.57 247	93.49 203	91.95 296	91.35 104	98.18 206	97.20 166	88.61 178	84.52 240	94.89 228	62.21 327	96.76 256	89.34 187	72.26 343	92.36 257

FMVSNet1		83.94 304	81.32 312	91.80 238	91.94 297	88.81 176	96.77 278	95.25 298	77.98 342	78.25 322	90.25 324	50.37 368	94.97 331	73.27 333	77.81 301	91.62 282

WR-MVS		88.54 234	87.22 239	92.52 223	91.93 298	89.50 157	98.56 166	97.84 60	86.99 225	81.87 282	93.81 248	74.25 242	95.92 307	85.29 230	74.43 320	92.12 270

D2MVS		87.96 240	87.39 234	89.70 294	91.84 299	83.40 289	98.31 197	98.49 23	88.04 201	78.23 323	90.26 323	73.57 245	96.79 255	84.21 245	83.53 267	88.90 348

FC-MVSNet-test		90.22 197	89.40 196	92.67 222	91.78 300	89.86 150	97.89 227	98.22 35	88.81 175	82.96 256	94.66 233	81.90 189	95.96 303	85.89 226	82.52 277	92.20 267

MIMVSNet		84.48 296	81.83 306	92.42 224	91.73 301	87.36 209	85.52 375	94.42 327	81.40 322	81.91 280	87.58 346	51.92 362	92.81 356	73.84 329	88.15 230	97.08 206

USDC		84.74 290	82.93 295	90.16 279	91.73 301	83.54 288	95.00 321	93.30 346	88.77 176	73.19 347	93.30 261	53.62 358	97.65 217	75.88 314	81.54 282	89.30 343

test_vis1_n		90.40 192	90.27 183	90.79 262	91.55 303	76.48 348	99.12 105	94.44 324	94.31 41	97.34 63	96.95 176	43.60 378	99.42 123	97.57 57	97.60 120	96.47 223

nrg030		90.23 196	88.87 206	94.32 178	91.53 304	93.54 67	98.79 141	95.89 262	88.12 199	84.55 239	94.61 234	78.80 215	96.88 250	92.35 154	75.21 311	92.53 253

DU-MVS		88.83 224	87.51 232	92.79 216	91.46 305	90.07 141	98.71 145	97.62 104	88.87 174	83.21 251	93.68 251	74.63 233	95.93 305	86.95 211	72.47 340	92.36 257

NR-MVSNet		87.74 248	86.00 256	92.96 213	91.46 305	90.68 125	96.65 285	97.42 145	88.02 202	73.42 345	93.68 251	77.31 223	95.83 311	84.26 244	71.82 347	92.36 257

tfpnnormal		83.65 305	81.35 311	90.56 269	91.37 307	88.06 190	97.29 257	97.87 58	78.51 341	76.20 328	90.91 301	64.78 316	96.47 272	61.71 371	73.50 331	87.13 363

test_vis1_rt		81.31 317	80.05 320	85.11 337	91.29 308	70.66 370	98.98 122	77.39 398	85.76 252	68.80 362	82.40 369	36.56 385	99.44 119	92.67 151	86.55 238	85.24 373

test_0402		78.81 329	76.33 334	86.26 331	91.18 309	78.44 340	95.88 309	91.34 370	68.55 375	70.51 358	89.91 331	52.65 361	94.99 330	47.14 387	79.78 290	85.34 372

test0.0.03 1		88.96 217	88.61 213	90.03 285	91.09 310	84.43 276	98.97 123	97.02 185	90.21 132	80.29 298	96.31 202	84.89 137	91.93 368	72.98 335	85.70 247	93.73 242

WR-MVS_H		86.53 266	85.49 264	89.66 296	91.04 311	83.31 291	97.53 249	98.20 36	84.95 267	79.64 306	90.90 302	78.01 220	95.33 325	76.29 311	72.81 336	90.35 324

CP-MVSNet		86.54 265	85.45 265	89.79 291	91.02 312	82.78 300	97.38 253	97.56 117	85.37 257	79.53 309	93.03 267	71.86 264	95.25 327	79.92 285	73.43 334	91.34 297

TranMVSNet+NR-MVSNet		87.75 245	86.31 251	92.07 232	90.81 313	88.56 181	98.33 194	97.18 167	87.76 210	81.87 282	93.90 246	72.45 257	95.43 322	83.13 260	71.30 350	92.23 263

PS-CasMVS		85.81 278	84.58 281	89.49 301	90.77 314	82.11 306	97.20 264	97.36 151	84.83 269	79.12 314	92.84 270	67.42 297	95.16 329	78.39 298	73.25 335	91.21 302

DeepMVS_CX				76.08 361	90.74 315	51.65 394		90.84 372	86.47 243	57.89 382	87.98 343	35.88 386	92.60 358	65.77 362	65.06 367	83.97 377

mvsmamba		89.99 204	89.42 195	91.69 243	90.64 316	86.34 232	98.40 186	92.27 357	91.01 112	84.80 236	94.93 227	76.12 227	96.51 268	92.81 149	83.84 262	92.21 265

OPM-MVS		89.76 207	89.15 201	91.57 245	90.53 317	85.58 257	98.11 214	95.93 253	92.88 76	86.05 225	96.47 196	67.06 300	97.87 198	89.29 190	86.08 244	91.26 301
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XXY-MVS		87.75 245	86.02 255	92.95 214	90.46 318	89.70 153	97.71 242	95.90 260	84.02 278	80.95 291	94.05 238	67.51 296	97.10 242	85.16 231	78.41 294	92.04 274

UniMVSNet_ETH3D		85.65 283	83.79 291	91.21 249	90.41 319	80.75 325	95.36 318	95.78 268	78.76 340	81.83 285	94.33 237	49.86 369	96.66 257	84.30 243	83.52 268	96.22 228

RRT_MVS		88.91 219	88.56 216	89.93 286	90.31 320	81.61 311	98.08 218	96.38 217	89.30 158	82.41 268	94.84 230	73.15 251	96.04 300	90.38 172	82.23 279	92.15 268

v10		85.73 281	84.01 289	90.87 260	90.03 321	86.73 222	97.20 264	95.22 306	81.25 324	79.85 305	89.75 333	73.30 249	96.28 290	76.87 306	72.64 338	89.61 340

v8		86.11 272	84.45 283	91.10 252	89.99 322	86.85 220	97.24 261	95.36 295	81.99 316	79.89 304	89.86 332	74.53 237	96.39 276	78.83 294	72.32 342	90.05 332

V42		87.00 256	85.68 261	90.98 256	89.91 323	86.08 242	98.32 196	95.61 279	83.67 287	82.72 258	90.67 309	74.00 244	96.53 266	81.94 272	74.28 323	90.32 325

XVG-ACMP-BASELINE		85.86 276	84.95 272	88.57 313	89.90 324	77.12 347	94.30 327	95.60 280	87.40 221	82.12 274	92.99 269	53.42 359	97.66 215	85.02 234	83.83 263	90.92 309

PEN-MVS		85.21 286	83.93 290	89.07 308	89.89 325	81.31 317	97.09 267	97.24 159	84.45 274	78.66 316	92.68 272	68.44 286	94.87 334	75.98 313	70.92 351	91.04 306

test_fmvs2		85.10 287	85.45 265	84.02 345	89.85 326	65.63 379	98.49 174	92.59 353	90.45 127	85.43 233	93.32 259	43.94 376	96.59 260	90.81 168	84.19 259	89.85 336

v1144		86.83 259	85.31 267	91.40 246	89.75 327	87.21 217	98.31 197	95.45 288	83.22 293	82.70 259	90.78 304	73.36 246	96.36 278	79.49 287	74.69 317	90.63 320

TransMVSNet (Re)		81.97 313	79.61 322	89.08 307	89.70 328	84.01 282	97.26 259	91.85 365	78.84 338	73.07 351	91.62 288	67.17 299	95.21 328	67.50 355	59.46 377	88.02 353

v2v482		87.27 254	85.76 259	91.78 242	89.59 329	87.58 201	98.56 166	95.54 283	84.53 272	82.51 264	91.78 286	73.11 252	96.47 272	82.07 269	74.14 326	91.30 299

pm-mvs1		84.68 292	82.78 299	90.40 273	89.58 330	85.18 265	97.31 256	94.73 317	81.93 318	76.05 330	92.01 280	65.48 313	96.11 297	78.75 295	69.14 353	89.91 335

pmmvs4		87.58 251	86.17 254	91.80 238	89.58 330	88.92 174	97.25 260	95.28 297	82.54 307	80.49 296	93.17 265	75.62 230	96.05 299	82.75 263	78.90 292	90.42 323

bld_raw_dy_0_64		87.82 241	86.71 246	91.15 251	89.54 332	85.61 255	97.37 254	89.16 381	89.26 159	83.42 249	94.50 235	65.79 308	96.18 292	88.00 202	83.37 269	91.67 279

v1192		86.32 270	84.71 278	91.17 250	89.53 333	86.40 228	98.13 210	95.44 290	82.52 308	82.42 267	90.62 313	71.58 268	96.33 285	77.23 302	74.88 314	90.79 313

v144192		86.40 268	84.89 273	90.91 257	89.48 334	85.59 256	98.21 204	95.43 291	82.45 310	82.62 262	90.58 316	72.79 256	96.36 278	78.45 297	74.04 327	90.79 313

v148		86.38 269	85.06 269	90.37 276	89.47 335	84.10 281	98.52 168	95.48 286	83.80 283	80.93 292	90.22 327	74.60 235	96.31 286	80.92 278	71.55 348	90.69 318

v1921920		86.02 273	84.44 284	90.77 263	89.32 336	85.20 264	98.10 215	95.35 296	82.19 314	82.25 272	90.71 306	70.73 271	96.30 289	76.85 307	74.49 319	90.80 312

v1240		85.77 280	84.11 287	90.73 264	89.26 337	85.15 267	97.88 229	95.23 305	81.89 319	82.16 273	90.55 318	69.60 280	96.31 286	75.59 316	74.87 315	90.72 317

our_test_3		84.47 297	82.80 297	89.50 299	89.01 338	83.90 284	97.03 269	94.56 322	81.33 323	75.36 337	90.52 319	71.69 266	94.54 342	68.81 350	76.84 305	90.07 330

ppachtmachnet_test		83.63 306	81.57 309	89.80 290	89.01 338	85.09 268	97.13 266	94.50 323	78.84 338	76.14 329	91.00 300	69.78 276	94.61 341	63.40 366	74.36 321	89.71 339

DTE-MVSNet		84.14 301	82.80 297	88.14 316	88.95 340	79.87 328	96.81 277	96.24 227	83.50 289	77.60 325	92.52 274	67.89 293	94.24 345	72.64 338	69.05 354	90.32 325

PS-MVSNAJss		89.54 211	89.05 203	91.00 255	88.77 341	84.36 277	97.39 251	95.97 245	88.47 181	81.88 281	93.80 249	82.48 177	96.50 269	89.34 187	83.34 271	92.15 268

Baseline_NR-MVSNet		85.83 277	84.82 275	88.87 312	88.73 342	83.34 290	98.63 156	91.66 366	80.41 334	82.44 265	91.35 294	74.63 233	95.42 323	84.13 247	71.39 349	87.84 354

MVP-Stereo		86.61 264	85.83 258	88.93 311	88.70 343	83.85 285	96.07 303	94.41 328	82.15 315	75.64 335	91.96 283	67.65 294	96.45 274	77.20 304	98.72 95	86.51 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		84.19 300	84.42 285	83.52 348	88.64 344	67.37 377	96.04 304	95.76 270	85.29 258	78.44 320	93.18 264	70.67 272	91.48 370	75.79 315	75.98 307	91.70 278

pmmvs5		85.87 275	84.40 286	90.30 277	88.53 345	84.23 278	98.60 161	93.71 339	81.53 321	80.29 298	92.02 279	64.51 317	95.52 319	82.04 271	78.34 295	91.15 303

MDA-MVSNet-bldmvs		77.82 335	74.75 341	87.03 326	88.33 346	78.52 339	96.34 292	92.85 350	75.57 353	48.87 388	87.89 344	57.32 344	92.49 362	60.79 373	64.80 368	90.08 329

N_pmnet		70.19 347	69.87 349	71.12 368	88.24 347	30.63 407	95.85 312	28.70 406	70.18 370	68.73 363	86.55 358	64.04 319	93.81 346	53.12 384	73.46 332	88.94 347

v7n		84.42 298	82.75 300	89.43 302	88.15 348	81.86 308	96.75 281	95.67 276	80.53 330	78.38 321	89.43 337	69.89 275	96.35 283	73.83 330	72.13 344	90.07 330

SixPastTwentyTwo		82.63 310	81.58 308	85.79 334	88.12 349	71.01 369	95.17 320	92.54 354	84.33 275	72.93 352	92.08 277	60.41 335	95.61 318	74.47 323	74.15 325	90.75 316

test_djsdf		88.26 238	87.73 229	89.84 289	88.05 350	82.21 305	97.77 236	96.17 233	86.84 231	82.41 268	91.95 284	72.07 261	95.99 301	89.83 177	84.50 254	91.32 298

mvs_tets		87.09 255	86.22 252	89.71 293	87.87 351	81.39 315	96.73 283	95.90 260	88.19 197	79.99 302	93.61 254	59.96 336	96.31 286	89.40 186	84.34 257	91.43 293

OurMVSNet-221017-0		84.13 302	83.59 292	85.77 335	87.81 352	70.24 371	94.89 322	93.65 341	86.08 246	76.53 327	93.28 262	61.41 330	96.14 296	80.95 277	77.69 302	90.93 308

YYNet1		79.64 326	77.04 331	87.43 324	87.80 353	79.98 327	96.23 298	94.44 324	73.83 361	51.83 385	87.53 347	67.96 292	92.07 367	66.00 361	67.75 360	90.23 327

MDA-MVSNet_test_wron		79.65 325	77.05 330	87.45 323	87.79 354	80.13 326	96.25 297	94.44 324	73.87 360	51.80 386	87.47 351	68.04 290	92.12 366	66.02 360	67.79 359	90.09 328

jajsoiax		87.35 252	86.51 249	89.87 287	87.75 355	81.74 309	97.03 269	95.98 244	88.47 181	80.15 300	93.80 249	61.47 329	96.36 278	89.44 185	84.47 256	91.50 289

K. test v3		81.04 318	79.77 321	84.83 340	87.41 356	70.23 372	95.60 317	93.93 336	83.70 286	67.51 369	89.35 338	55.76 347	93.58 349	76.67 309	68.03 357	90.67 319

dmvs_testset		77.17 337	78.99 324	71.71 366	87.25 357	38.55 403	91.44 356	81.76 394	85.77 251	69.49 360	95.94 210	69.71 278	84.37 386	52.71 385	76.82 306	92.21 265

testgi		82.29 311	81.00 314	86.17 332	87.24 358	74.84 355	97.39 251	91.62 367	88.63 177	75.85 334	95.42 219	46.07 375	91.55 369	66.87 359	79.94 289	92.12 270

LF4IMVS		81.94 314	81.17 313	84.25 344	87.23 359	68.87 376	93.35 337	91.93 364	83.35 292	75.40 336	93.00 268	49.25 372	96.65 258	78.88 293	78.11 296	87.22 362

EG-PatchMatch MVS		79.92 322	77.59 327	86.90 327	87.06 360	77.90 345	96.20 301	94.06 334	74.61 357	66.53 373	88.76 341	40.40 383	96.20 291	67.02 357	83.66 266	86.61 364

test_fmvsmconf0.01_n		94.14 106	93.51 113	96.04 111	86.79 361	89.19 160	99.28 83	95.94 250	95.70 21	95.50 106	98.49 110	73.27 250	99.79 82	98.28 45	98.32 109	99.15 107

Gipuma		54.77 359	52.22 363	62.40 377	86.50 362	59.37 385	50.20 395	90.35 375	36.52 393	41.20 394	49.49 395	18.33 396	81.29 388	32.10 394	65.34 366	46.54 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		86.69 261	85.75 260	89.53 298	86.46 363	82.94 294	96.39 290	95.71 272	83.97 280	79.63 307	90.70 307	68.85 282	95.94 304	86.01 221	84.02 261	89.72 338

EGC-MVSNET		60.70 354	55.37 358	76.72 360	86.35 364	71.08 367	89.96 367	84.44 391	0.38 403	1.50 404	84.09 365	37.30 384	88.10 382	40.85 392	73.44 333	70.97 388

test_method		70.10 348	68.66 351	74.41 365	86.30 365	55.84 387	94.47 324	89.82 377	35.18 394	66.15 374	84.75 364	30.54 388	77.96 395	70.40 346	60.33 375	89.44 342

lessismore_v0				85.08 338	85.59 366	69.28 374		90.56 374		67.68 368	90.21 328	54.21 357	95.46 321	73.88 328	62.64 371	90.50 322

CMPMVS	58.40 21	80.48 320	80.11 319	81.59 356	85.10 367	59.56 384	94.14 330	95.95 249	68.54 376	60.71 380	93.31 260	55.35 352	97.87 198	83.06 261	84.85 252	87.33 360
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		80.76 319	79.42 323	84.79 341	84.78 368	72.98 361	96.53 286	92.97 348	79.56 335	74.33 339	88.83 340	61.27 331	92.15 365	60.59 374	75.92 308	89.24 345

DSMNet-mixed		81.60 316	81.43 310	82.10 353	84.36 369	60.79 382	93.63 335	86.74 386	79.00 336	79.32 311	87.15 354	63.87 320	89.78 376	66.89 358	91.92 199	95.73 233

pmmvs6		79.90 323	77.31 329	87.67 320	84.17 370	78.13 342	95.86 311	93.68 340	67.94 378	72.67 353	89.62 335	50.98 366	95.75 313	74.80 322	66.04 364	89.14 346

new_pmnet		76.02 338	73.71 343	82.95 349	83.88 371	72.85 363	91.26 359	92.26 358	70.44 369	62.60 378	81.37 373	47.64 373	92.32 363	61.85 370	72.10 345	83.68 378

OpenMVS_ROB	73.86 20	77.99 334	75.06 340	86.77 328	83.81 372	77.94 344	96.38 291	91.53 369	67.54 379	68.38 364	87.13 355	43.94 376	96.08 298	55.03 382	81.83 280	86.29 367

test20.03		78.51 332	77.48 328	81.62 355	83.07 373	71.03 368	96.11 302	92.83 351	81.66 320	69.31 361	89.68 334	57.53 342	87.29 384	58.65 378	68.47 355	86.53 365

Anonymous20240521		78.63 331	76.90 332	83.82 346	82.82 374	72.86 362	95.72 316	93.57 342	73.55 362	72.17 355	84.79 363	49.69 370	92.51 361	65.29 363	74.50 318	86.09 368

UnsupCasMVSNet_eth		78.90 328	76.67 333	85.58 336	82.81 375	74.94 354	91.98 349	96.31 221	84.64 271	65.84 375	87.71 345	51.33 363	92.23 364	72.89 336	56.50 382	89.56 341

KD-MVS_self_test		77.47 336	75.88 336	82.24 351	81.59 376	68.93 375	92.83 344	94.02 335	77.03 348	73.14 348	83.39 366	55.44 351	90.42 371	67.95 353	57.53 380	87.38 358

CL-MVSNet_self_test		79.89 324	78.34 325	84.54 343	81.56 377	75.01 353	96.88 275	95.62 278	81.10 325	75.86 333	85.81 361	68.49 285	90.26 372	63.21 367	56.51 381	88.35 351

MIMVSNet1		75.92 339	73.30 344	83.81 347	81.29 378	75.57 351	92.26 347	92.05 362	73.09 363	67.48 370	86.18 359	40.87 382	87.64 383	55.78 381	70.68 352	88.21 352

Patchmatch-RL test		81.90 315	80.13 318	87.23 325	80.71 379	70.12 373	84.07 383	88.19 384	83.16 295	70.57 356	82.18 371	87.18 89	92.59 359	82.28 268	62.78 370	98.98 121

APD_test1		68.93 349	66.98 352	74.77 364	80.62 380	53.15 391	87.97 370	85.01 389	53.76 387	59.26 381	87.52 348	25.19 390	89.95 373	56.20 380	67.33 361	81.19 382

pmmvs-eth3d		78.71 330	76.16 335	86.38 329	80.25 381	81.19 319	94.17 329	92.13 361	77.97 343	66.90 372	82.31 370	55.76 347	92.56 360	73.63 332	62.31 373	85.38 370

UnsupCasMVSNet_bld		73.85 344	70.14 348	84.99 339	79.44 382	75.73 350	88.53 369	95.24 301	70.12 371	61.94 379	74.81 385	41.41 381	93.62 348	68.65 351	51.13 389	85.62 369

PM-MVS		74.88 342	72.85 345	80.98 357	78.98 383	64.75 380	90.81 363	85.77 387	80.95 328	68.23 366	82.81 367	29.08 389	92.84 355	76.54 310	62.46 372	85.36 371

new-patchmatchnet		74.80 343	72.40 346	81.99 354	78.36 384	72.20 365	94.44 325	92.36 356	77.06 347	63.47 377	79.98 379	51.04 365	88.85 380	60.53 375	54.35 384	84.92 375

test_fmvs3		75.09 341	75.19 338	74.81 363	77.45 385	54.08 389	95.93 305	90.64 373	82.51 309	73.29 346	81.19 374	22.29 392	86.29 385	85.50 229	67.89 358	84.06 376

WB-MVS		66.44 350	66.29 353	66.89 371	74.84 386	44.93 398	93.00 339	84.09 392	71.15 366	55.82 383	81.63 372	63.79 321	80.31 393	21.85 397	50.47 390	75.43 384

SSC-MVS		65.42 351	65.20 354	66.06 372	73.96 387	43.83 399	92.08 348	83.54 393	69.77 372	54.73 384	80.92 376	63.30 323	79.92 394	20.48 398	48.02 391	74.44 385

pmmvs3		72.86 345	69.76 350	82.17 352	73.86 388	74.19 357	94.20 328	89.01 382	64.23 385	67.72 367	80.91 377	41.48 380	88.65 381	62.40 369	54.02 385	83.68 378

mvsany_test3		75.85 340	74.52 342	79.83 358	73.53 389	60.64 383	91.73 352	87.87 385	83.91 282	70.55 357	82.52 368	31.12 387	93.66 347	86.66 217	62.83 369	85.19 374

test_f		71.94 346	70.82 347	75.30 362	72.77 390	53.28 390	91.62 353	89.66 379	75.44 354	64.47 376	78.31 382	20.48 393	89.56 377	78.63 296	66.02 365	83.05 381

ambc				79.60 359	72.76 391	56.61 386	76.20 390	92.01 363		68.25 365	80.23 378	23.34 391	94.73 338	73.78 331	60.81 374	87.48 357

TDRefinement		78.01 333	75.31 337	86.10 333	70.06 392	73.84 358	93.59 336	91.58 368	74.51 358	73.08 350	91.04 299	49.63 371	97.12 239	74.88 320	59.47 376	87.33 360

test_vis3_rt		61.29 353	58.75 356	68.92 370	67.41 393	52.84 392	91.18 361	59.23 405	66.96 380	41.96 393	58.44 393	11.37 401	94.72 339	74.25 325	57.97 379	59.20 392

testf1		56.38 357	53.73 360	64.31 375	64.84 394	45.11 396	80.50 388	75.94 400	38.87 390	42.74 390	75.07 383	11.26 402	81.19 389	41.11 390	53.27 386	66.63 389

APD_test2		56.38 357	53.73 360	64.31 375	64.84 394	45.11 396	80.50 388	75.94 400	38.87 390	42.74 390	75.07 383	11.26 402	81.19 389	41.11 390	53.27 386	66.63 389

PMMVS2		58.97 356	55.07 359	70.69 369	62.72 396	55.37 388	85.97 374	80.52 395	49.48 388	45.94 389	68.31 387	15.73 398	80.78 391	49.79 386	37.12 394	75.91 383

E-PMN		41.02 364	40.93 366	41.29 381	61.97 397	33.83 404	84.00 384	65.17 403	27.17 396	27.56 396	46.72 397	17.63 397	60.41 400	19.32 399	18.82 396	29.61 396

wuyk23d		16.71 368	16.73 372	16.65 383	60.15 398	25.22 408	41.24 396	5.17 407	6.56 400	5.48 403	3.61 403	3.64 405	22.72 402	15.20 401	9.52 400	1.99 400

FPMVS		61.57 352	60.32 355	65.34 373	60.14 399	42.44 401	91.02 362	89.72 378	44.15 389	42.63 392	80.93 375	19.02 394	80.59 392	42.50 389	72.76 337	73.00 386

EMVS		39.96 365	39.88 367	40.18 382	59.57 400	32.12 406	84.79 381	64.57 404	26.27 397	26.14 398	44.18 400	18.73 395	59.29 401	17.03 400	17.67 398	29.12 397

LCM-MVSNet		60.07 355	56.37 357	71.18 367	54.81 401	48.67 395	82.17 387	89.48 380	37.95 392	49.13 387	69.12 386	13.75 400	81.76 387	59.28 376	51.63 388	83.10 380

MVE	44.00 22	41.70 363	37.64 368	53.90 380	49.46 402	43.37 400	65.09 394	66.66 402	26.19 398	25.77 399	48.53 396	3.58 406	63.35 399	26.15 396	27.28 395	54.97 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		50.71 361	46.17 364	64.33 374	44.27 403	52.30 393	76.13 391	78.73 396	64.95 383	27.37 397	55.23 394	14.61 399	67.74 397	36.01 393	18.23 397	72.95 387

PMVS	41.42 23	45.67 362	42.50 365	55.17 379	34.28 404	32.37 405	66.24 393	78.71 397	30.72 395	22.04 400	59.59 391	4.59 404	77.85 396	27.49 395	58.84 378	55.29 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 360	52.86 362	56.05 378	32.75 405	41.97 402	73.42 392	76.12 399	21.91 399	39.68 395	96.39 199	42.59 379	65.10 398	78.00 299	14.92 399	61.08 391

testmvs		18.81 367	23.05 370	6.10 385	4.48 406	2.29 410	97.78 234	3.00 408	3.27 401	18.60 401	62.71 389	1.53 408	2.49 404	14.26 402	1.80 401	13.50 399

test123		16.58 369	19.47 371	7.91 384	3.59 407	5.37 409	94.32 326	1.39 409	2.49 402	13.98 402	44.60 399	2.91 407	2.65 403	11.35 403	0.57 402	15.70 398

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

eth-test2					0.00 408

eth-test					0.00 408

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		22.52 366	30.03 369	0.00 386	0.00 408	0.00 411	0.00 397	97.17 168	0.00 404	0.00 405	98.77 85	74.35 240	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		6.87 371	9.16 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	82.48 177	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		8.21 370	10.94 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	98.50 108	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				98.86 5		96.83 7	99.81 11	99.13 9	97.66 2	98.29 39	98.96 66	85.84 122	99.90 50	99.72 3	98.80 91	99.85 30

WAC-MVS						79.74 329								67.75 354

PC_three_1452									94.60 36	99.41 4	99.12 46	95.50 7	99.96 28	99.84 2	99.92 3	99.97 7

test_241102_TWO								97.72 79	94.17 43	99.23 10	99.54 3	93.14 24	99.98 9	99.70 4	99.82 19	99.99 1

test_0728_THIRD									93.01 70	99.07 15	99.46 10	94.66 14	99.97 21	99.25 18	99.82 19	99.95 15

GSMVS																98.84 136

sam_mvs1												88.39 65				98.84 136

sam_mvs												87.08 92

MTGPA								97.45 139

test_post1							90.74 365				41.37 401	85.38 131	96.36 278	83.16 258

test_post											46.00 398	87.37 83	97.11 240

patchmatchnet-post											84.86 362	88.73 62	96.81 253

MTMP							99.21 86	91.09 371

test9_res														98.60 33	99.87 9	99.90 22

agg_prior2														97.84 54	99.87 9	99.91 21

test_prior4						92.00 95	99.41 68

test_prior2							99.57 42		91.43 105	98.12 44	98.97 62	90.43 44		98.33 42	99.81 23

旧先验2							98.67 151		85.75 253	98.96 20			98.97 152	93.84 131

新几何2							98.26 200

无先验							98.52 168	97.82 63	87.20 223				99.90 50	87.64 206		99.85 30

原ACMM2							98.69 148

testdata2													99.88 54	84.16 246

segment_acmp												90.56 42

testdata1							97.89 227		92.43 82

plane_prior5								96.30 222					97.75 211	93.46 138	86.17 242	92.67 251

plane_prior4											96.52 193

plane_prior3						85.91 248			93.65 61	86.99 216

plane_prior2							99.02 116		93.38 66

plane_prior						86.07 244	99.14 101		93.81 58						86.26 241

n2								0.00 410

nn								0.00 410

door-mid								84.90 390

test11								97.68 88

door								85.30 388

HQP5-MVS						86.39 229

BP-MVS														93.82 133

HQP4-MVS										87.57 209			97.77 205			92.72 249

HQP3-MVS								96.37 218							86.29 239

HQP2-MVS												73.34 247

MDTV_nov1_ep13_2view						91.17 109	91.38 357		87.45 220	93.08 144		86.67 103		87.02 209		98.95 127

ACMMP++_ref															82.64 276

ACMMP++															83.83 263

Test By Simon												83.62 151

High-resolution multi-view benchmark