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.

APDe-MVS		89.15 8	89.63 7	87.73 30	94.49 21	71.69 54	93.83 4	93.96 17	75.70 105	91.06 18	96.03 1	76.84 16	97.03 20	89.09 21	95.65 30	94.47 48
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 34	73.73 9	92.40 28	93.63 25	74.77 137	92.29 7	95.97 2	74.28 32	97.24 15	88.58 32	96.91 1	94.87 18
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
test0726					95.27 5	71.25 63	93.60 7	94.11 10	77.33 57	92.81 3	95.79 3	80.98 10

DVP-MVS		89.60 3	90.35 3	87.33 44	95.27 5	71.25 63	93.49 10	92.73 68	77.33 57	92.12 11	95.78 4	80.98 10	97.40 9	89.08 22	96.41 12	93.33 114
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									78.38 38	92.12 11	95.78 4	81.46 8	97.40 9	89.42 19	96.57 7	94.67 32

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 30	71.25 63	95.06 1	94.23 6	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_one_0601					95.07 7	71.46 59		94.14 9	78.27 41	92.05 13	95.74 6	80.83 12

SED-MVS		90.08 2	90.85 2	87.77 27	95.30 2	70.98 70	93.57 8	94.06 14	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 24	96.63 4	94.88 16

test_241102_TWO								94.06 14	77.24 60	92.78 4	95.72 8	81.26 9	97.44 7	89.07 24	96.58 6	94.26 60

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 50	94.10 12	75.90 100	92.29 7	95.66 10	81.67 6	97.38 13	87.44 46	96.34 15	93.95 76
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 34	87.39 32	86.95 53	93.10 61	71.24 67	91.60 49	93.19 39	74.69 138	88.80 32	95.61 11	70.29 79	96.44 42	86.20 54	93.08 74	93.16 124

reproduce-ours		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 128	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 130

our_new_method		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 128	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 130

lecture		88.09 16	88.59 15	86.58 61	93.26 55	69.77 95	93.70 6	94.16 8	77.13 65	89.76 25	95.52 14	72.26 51	96.27 47	86.87 48	94.65 51	93.70 92

fmvsm_s_conf0.5_n_9		87.39 32	87.95 22	85.70 80	89.48 136	67.88 152	88.59 144	89.05 224	80.19 12	90.70 19	95.40 15	74.56 27	93.92 150	91.54 2	92.07 90	95.31 5

test_241102_ONE					95.30 2	70.98 70		94.06 14	77.17 63	93.10 1	95.39 16	82.99 1	97.27 14

MP-MVS-pluss		87.67 24	87.72 24	87.54 39	93.64 47	72.04 50	89.80 88	93.50 29	75.17 125	86.34 66	95.29 17	70.86 72	96.00 58	88.78 30	96.04 16	94.58 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 14	88.74 14	87.64 37	92.78 69	71.95 51	92.40 28	94.74 2	75.71 103	89.16 28	95.10 18	75.65 23	96.19 50	87.07 47	96.01 17	94.79 23

ACMMP_NAP		88.05 19	88.08 20	87.94 19	93.70 44	73.05 22	90.86 64	93.59 27	76.27 93	88.14 40	95.09 19	71.06 70	96.67 32	87.67 42	96.37 14	94.09 68

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 117	92.25 9	95.03 20		97.39 11	88.15 38	95.96 19	94.75 29

TestfortrainingZip a		89.27 6	89.82 6	87.60 38	94.57 17	70.90 76	93.28 12	94.36 3	75.24 117	92.25 9	95.03 20	81.59 7	97.39 11	86.12 55	95.96 19	94.52 46

ME-MVS		88.98 11	89.39 8	87.75 29	94.54 19	71.43 60	91.61 48	94.25 5	76.30 92	90.62 20	95.03 20	78.06 15	97.07 19	88.15 38	95.96 19	94.75 29

fmvsm_s_conf0.5_n_3		86.36 52	87.46 31	83.09 197	87.08 250	65.21 219	89.09 121	90.21 173	79.67 19	89.98 23	95.02 23	73.17 41	91.71 259	91.30 3	91.60 97	92.34 163

MM		89.16 7	89.23 9	88.97 4	90.79 101	73.65 10	92.66 27	91.17 140	86.57 1	87.39 56	94.97 24	71.70 60	97.68 1	92.19 1	95.63 31	95.57 1

fmvsm_s_conf0.5_n_8		86.56 46	87.17 37	84.73 119	87.76 219	65.62 210	89.20 112	92.21 94	79.94 17	89.74 26	94.86 25	68.63 104	94.20 135	90.83 5	91.39 102	94.38 52

MTAPA		87.23 35	87.00 38	87.90 22	94.18 38	74.25 5	86.58 222	92.02 102	79.45 22	85.88 68	94.80 26	68.07 112	96.21 49	86.69 50	95.34 35	93.23 117

SteuartSystems-ACMMP		88.72 13	88.86 13	88.32 9	92.14 77	72.96 25	93.73 5	93.67 24	80.19 12	88.10 41	94.80 26	73.76 36	97.11 17	87.51 44	95.82 24	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_10		86.38 51	86.76 45	85.24 94	87.33 236	67.30 173	89.50 99	90.98 145	76.25 94	90.56 21	94.75 28	68.38 107	94.24 134	90.80 7	92.32 87	94.19 62

9.14			88.26 18		92.84 68		91.52 55	94.75 1	73.93 159	88.57 34	94.67 29	75.57 24	95.79 62	86.77 49	95.76 26

SR-MVS		86.73 42	86.67 46	86.91 54	94.11 40	72.11 49	92.37 32	92.56 79	74.50 142	86.84 63	94.65 30	67.31 121	95.77 63	84.80 66	92.85 77	92.84 144

region2R		87.42 30	87.20 36	88.09 14	94.63 14	73.55 13	93.03 18	93.12 44	76.73 79	84.45 92	94.52 31	69.09 95	96.70 30	84.37 72	94.83 48	94.03 71

ACMMPR		87.44 28	87.23 35	88.08 15	94.64 13	73.59 12	93.04 16	93.20 38	76.78 76	84.66 87	94.52 31	68.81 101	96.65 33	84.53 70	94.90 44	94.00 73

APD-MVS		87.44 28	87.52 29	87.19 46	94.24 35	72.39 41	91.86 44	92.83 64	73.01 188	88.58 33	94.52 31	73.36 37	96.49 41	84.26 73	95.01 40	92.70 146
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 59	85.88 63	86.22 66	92.69 71	69.53 98	91.93 41	92.99 53	73.54 170	85.94 67	94.51 34	65.80 144	95.61 66	83.04 87	92.51 82	93.53 107

CP-MVS		87.11 37	86.92 42	87.68 36	94.20 37	73.86 7	93.98 3	92.82 67	76.62 82	83.68 110	94.46 35	67.93 114	95.95 61	84.20 76	94.39 60	93.23 117

SR-MVS-dyc-post		85.77 65	85.61 70	86.23 65	93.06 63	70.63 81	91.88 42	92.27 88	73.53 171	85.69 71	94.45 36	65.00 152	95.56 67	82.75 92	91.87 93	92.50 156

RE-MVS-def			85.48 73		93.06 63	70.63 81	91.88 42	92.27 88	73.53 171	85.69 71	94.45 36	63.87 160		82.75 92	91.87 93	92.50 156

HFP-MVS		87.58 25	87.47 30	87.94 19	94.58 16	73.54 15	93.04 16	93.24 37	76.78 76	84.91 80	94.44 38	70.78 73	96.61 35	84.53 70	94.89 45	93.66 93

PGM-MVS		86.68 44	86.27 53	87.90 22	94.22 36	73.38 18	90.22 80	93.04 45	75.53 108	83.86 106	94.42 39	67.87 116	96.64 34	82.70 96	94.57 55	93.66 93

MP-MVS		87.71 22	87.64 25	87.93 21	94.36 29	73.88 6	92.71 26	92.65 74	77.57 49	83.84 107	94.40 40	72.24 52	96.28 46	85.65 57	95.30 38	93.62 100
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		86.02 55	86.32 51	85.14 97	87.20 241	68.54 129	89.57 97	90.44 162	75.31 116	87.49 53	94.39 41	72.86 46	92.72 215	89.04 26	90.56 116	94.16 63

fmvsm_s_conf0.1_n_2		83.80 97	83.79 97	83.83 169	85.62 287	64.94 229	87.03 201	86.62 293	74.32 147	87.97 46	94.33 42	60.67 218	92.60 218	89.72 14	87.79 166	93.96 74

fmvsm_l_conf0.5_n_9		85.84 64	86.63 47	83.46 180	87.12 249	66.01 197	88.56 146	89.43 200	75.59 107	89.32 27	94.32 43	72.89 45	91.21 284	90.11 11	92.33 86	93.16 124

ZNCC-MVS		87.94 21	87.85 23	88.20 12	94.39 27	73.33 19	93.03 18	93.81 21	76.81 74	85.24 75	94.32 43	71.76 58	96.93 22	85.53 59	95.79 25	94.32 57

MGCNet		87.69 23	87.55 28	88.12 13	89.45 137	71.76 53	91.47 56	89.54 196	82.14 3	86.65 64	94.28 45	68.28 110	97.46 6	90.81 6	95.31 37	95.15 8

test_fmvsmconf0.01_n		84.73 87	84.52 89	85.34 91	80.25 400	69.03 109	89.47 100	89.65 192	73.24 182	86.98 61	94.27 46	66.62 128	93.23 186	90.26 10	89.95 128	93.78 89

HPM-MVS++		89.02 10	89.15 11	88.63 5	95.01 9	76.03 1	92.38 31	92.85 63	80.26 11	87.78 47	94.27 46	75.89 21	96.81 26	87.45 45	96.44 9	93.05 132

mPP-MVS		86.67 45	86.32 51	87.72 32	94.41 25	73.55 13	92.74 24	92.22 92	76.87 73	82.81 126	94.25 48	66.44 132	96.24 48	82.88 90	94.28 63	93.38 110

fmvsm_s_conf0.5_n_2		84.04 92	84.11 93	83.81 171	86.17 274	65.00 227	86.96 204	87.28 275	74.35 146	88.25 38	94.23 49	61.82 194	92.60 218	89.85 12	88.09 162	93.84 83

DeepC-MVS	79.81 2	87.08 39	86.88 44	87.69 35	91.16 90	72.32 45	90.31 78	93.94 18	77.12 66	82.82 125	94.23 49	72.13 54	97.09 18	84.83 65	95.37 34	93.65 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 36	86.91 43	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	94.17 51	67.45 119	96.60 36	83.06 85	94.50 56	94.07 69

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 33	73.46 17	92.90 20	94.11 10	80.27 10	91.35 16	94.16 52	78.35 14	96.77 27	89.59 17	94.22 65	94.67 32
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_fmvsmconf0.1_n		85.61 69	85.65 69	85.50 87	82.99 358	69.39 106	89.65 93	90.29 171	73.31 178	87.77 48	94.15 53	71.72 59	93.23 186	90.31 9	90.67 115	93.89 80

DeepPCF-MVS	80.84 1	88.10 15	88.56 16	86.73 58	92.24 76	69.03 109	89.57 97	93.39 34	77.53 53	89.79 24	94.12 54	78.98 13	96.58 38	85.66 56	95.72 27	94.58 39

HPM-MVS_fast		85.35 77	84.95 83	86.57 62	93.69 45	70.58 83	92.15 39	91.62 125	73.89 160	82.67 128	94.09 55	62.60 178	95.54 69	80.93 109	92.93 76	93.57 103

ZD-MVS					94.38 28	72.22 46		92.67 71	70.98 229	87.75 49	94.07 56	74.01 35	96.70 30	84.66 68	94.84 47

fmvsm_s_conf0.1_n_a		83.32 116	82.99 113	84.28 137	83.79 332	68.07 144	89.34 109	82.85 353	69.80 263	87.36 57	94.06 57	68.34 109	91.56 265	87.95 40	83.46 250	93.21 120

CNVR-MVS		88.93 12	89.13 12	88.33 8	94.77 12	73.82 8	90.51 69	93.00 50	80.90 7	88.06 42	94.06 57	76.43 18	96.84 24	88.48 35	95.99 18	94.34 55

test_fmvsmconf_n		85.92 60	86.04 61	85.57 86	85.03 306	69.51 99	89.62 96	90.58 157	73.42 174	87.75 49	94.02 59	72.85 47	93.24 185	90.37 8	90.75 113	93.96 74

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 59	82.45 3	96.87 23	83.77 80	96.48 8	94.88 16

PC_three_1452									68.21 300	92.02 14	94.00 61	82.09 5	95.98 60	84.58 69	96.68 2	94.95 12

SD-MVS		88.06 17	88.50 17	86.71 59	92.60 74	72.71 29	91.81 45	93.19 39	77.87 42	90.32 22	94.00 61	74.83 25	93.78 157	87.63 43	94.27 64	93.65 97
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
GST-MVS		87.42 30	87.26 33	87.89 24	94.12 39	72.97 24	92.39 30	93.43 32	76.89 72	84.68 84	93.99 63	70.67 75	96.82 25	84.18 77	95.01 40	93.90 79

test_fmvsm_n_1920		85.29 78	85.34 75	85.13 100	86.12 276	69.93 91	88.65 142	90.78 153	69.97 259	88.27 37	93.98 64	71.39 65	91.54 269	88.49 34	90.45 118	93.91 77

fmvsm_s_conf0.1_n		83.56 107	83.38 106	84.10 146	84.86 308	67.28 174	89.40 106	83.01 348	70.67 236	87.08 59	93.96 65	68.38 107	91.45 275	88.56 33	84.50 224	93.56 104

HPM-MVS		87.11 37	86.98 40	87.50 42	93.88 42	72.16 47	92.19 37	93.33 35	76.07 97	83.81 108	93.95 66	69.77 86	96.01 57	85.15 60	94.66 50	94.32 57
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_7		83.34 114	84.03 94	81.28 256	85.73 284	65.13 222	85.40 259	89.90 183	74.96 130	82.13 134	93.89 67	66.65 127	87.92 346	86.56 51	91.05 107	90.80 219

fmvsm_s_conf0.5_n_5		85.22 79	85.55 71	84.25 142	86.26 270	67.40 169	89.18 113	89.31 209	72.50 193	88.31 36	93.86 68	69.66 87	91.96 247	89.81 13	91.05 107	93.38 110

TSAR-MVS + MP.		88.02 20	88.11 19	87.72 32	93.68 46	72.13 48	91.41 57	92.35 86	74.62 141	88.90 31	93.85 69	75.75 22	96.00 58	87.80 41	94.63 53	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 63	85.39 74	87.38 43	93.59 48	72.63 33	92.74 24	93.18 43	76.78 76	80.73 162	93.82 70	64.33 156	96.29 45	82.67 97	90.69 114	93.23 117
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
fmvsm_s_conf0.5_n_a		83.63 105	83.41 105	84.28 137	86.14 275	68.12 142	89.43 102	82.87 352	70.27 252	87.27 58	93.80 71	69.09 95	91.58 262	88.21 37	83.65 244	93.14 127

fmvsm_s_conf0.5_n_4		85.39 75	85.75 68	84.30 135	86.70 261	65.83 203	88.77 134	89.78 185	75.46 111	88.35 35	93.73 72	69.19 94	93.06 201	91.30 3	88.44 157	94.02 72

fmvsm_s_conf0.5_n		83.80 97	83.71 99	84.07 152	86.69 262	67.31 172	89.46 101	83.07 347	71.09 224	86.96 62	93.70 73	69.02 100	91.47 274	88.79 29	84.62 223	93.44 109

test_prior2							88.85 130		75.41 112	84.91 80	93.54 74	74.28 32		83.31 83	95.86 23

fmvsm_l_conf0.5_n		84.47 88	84.54 87	84.27 139	85.42 293	68.81 115	88.49 148	87.26 277	68.08 301	88.03 43	93.49 75	72.04 55	91.77 255	88.90 28	89.14 144	92.24 170

VDDNet		81.52 154	80.67 154	84.05 158	90.44 107	64.13 250	89.73 91	85.91 304	71.11 223	83.18 117	93.48 76	50.54 323	93.49 172	73.40 200	88.25 159	94.54 45

CDPH-MVS		85.76 66	85.29 79	87.17 47	93.49 50	71.08 68	88.58 145	92.42 84	68.32 299	84.61 89	93.48 76	72.32 50	96.15 52	79.00 130	95.43 33	94.28 59

NCCC		88.06 17	88.01 21	88.24 11	94.41 25	73.62 11	91.22 61	92.83 64	81.50 5	85.79 70	93.47 78	73.02 44	97.00 21	84.90 62	94.94 43	94.10 67

fmvsm_s_conf0.5_n_6		85.55 70	86.20 54	83.60 175	87.32 238	65.13 222	88.86 128	91.63 124	75.41 112	88.23 39	93.45 79	68.56 105	92.47 226	89.52 18	92.78 78	93.20 122

fmvsm_l_conf0.5_n_a		84.13 91	84.16 92	84.06 155	85.38 294	68.40 132	88.34 156	86.85 287	67.48 308	87.48 54	93.40 80	70.89 71	91.61 260	88.38 36	89.22 141	92.16 177

3Dnovator+	77.84 4	85.48 71	84.47 90	88.51 7	91.08 92	73.49 16	93.18 15	93.78 22	80.79 8	76.66 244	93.37 81	60.40 226	96.75 29	77.20 152	93.73 69	95.29 6

DeepC-MVS_fast	79.65 3	86.91 40	86.62 48	87.76 28	93.52 49	72.37 43	91.26 58	93.04 45	76.62 82	84.22 98	93.36 82	71.44 64	96.76 28	80.82 111	95.33 36	94.16 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 124	82.36 126	84.96 106	91.02 94	66.40 190	88.91 126	88.11 251	77.57 49	84.39 94	93.29 83	52.19 297	93.91 151	77.05 155	88.70 152	94.57 41

test_fmvsmvis_n_1920		84.02 93	83.87 95	84.49 126	84.12 324	69.37 107	88.15 164	87.96 258	70.01 257	83.95 105	93.23 84	68.80 102	91.51 272	88.61 31	89.96 127	92.57 151

UA-Net		85.08 82	84.96 82	85.45 88	92.07 78	68.07 144	89.78 89	90.86 151	82.48 2	84.60 90	93.20 85	69.35 91	95.22 87	71.39 224	90.88 112	93.07 129

TEST9					93.26 55	72.96 25	88.75 136	91.89 110	68.44 297	85.00 78	93.10 86	74.36 31	95.41 79

train_agg		86.43 48	86.20 54	87.13 48	93.26 55	72.96 25	88.75 136	91.89 110	68.69 292	85.00 78	93.10 86	74.43 29	95.41 79	84.97 61	95.71 28	93.02 134

test_8					93.13 59	72.57 35	88.68 141	91.84 114	68.69 292	84.87 82	93.10 86	74.43 29	95.16 89

LFMVS		81.82 144	81.23 144	83.57 178	91.89 81	63.43 273	89.84 85	81.85 364	77.04 69	83.21 116	93.10 86	52.26 296	93.43 177	71.98 219	89.95 128	93.85 81

旧先验1					91.96 79	65.79 206		86.37 297			93.08 90	69.31 93			92.74 79	88.74 309

dcpmvs_2		85.63 68	86.15 58	84.06 155	91.71 83	64.94 229	86.47 225	91.87 112	73.63 166	86.60 65	93.02 91	76.57 17	91.87 253	83.36 82	92.15 88	95.35 3

testdata				79.97 288	90.90 97	64.21 248		84.71 318	59.27 401	85.40 73	92.91 92	62.02 191	89.08 328	68.95 252	91.37 103	86.63 361

MCST-MVS		87.37 33	87.25 34	87.73 30	94.53 20	72.46 40	89.82 86	93.82 20	73.07 186	84.86 83	92.89 93	76.22 19	96.33 44	84.89 64	95.13 39	94.40 51

Vis-MVSNet		83.46 110	82.80 117	85.43 89	90.25 111	68.74 120	90.30 79	90.13 176	76.33 91	80.87 159	92.89 93	61.00 213	94.20 135	72.45 216	90.97 109	93.35 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 100	83.33 108	84.92 110	93.28 52	70.86 77	92.09 40	90.38 164	68.75 291	79.57 177	92.83 95	60.60 222	93.04 204	80.92 110	91.56 100	90.86 218

3Dnovator	76.31 5	83.38 113	82.31 127	86.59 60	87.94 207	72.94 28	90.64 67	92.14 101	77.21 62	75.47 270	92.83 95	58.56 238	94.72 114	73.24 203	92.71 80	92.13 178

MSLP-MVS++		85.43 73	85.76 67	84.45 127	91.93 80	70.24 84	90.71 66	92.86 62	77.46 55	84.22 98	92.81 97	67.16 123	92.94 206	80.36 117	94.35 62	90.16 248

test2506		77.30 268	76.49 265	79.74 293	90.08 115	52.02 415	87.86 176	63.10 458	74.88 133	80.16 171	92.79 98	38.29 421	92.35 233	68.74 255	92.50 83	94.86 19

ECVR-MVS		79.61 202	79.26 195	80.67 273	90.08 115	54.69 397	87.89 174	77.44 411	74.88 133	80.27 168	92.79 98	48.96 346	92.45 227	68.55 256	92.50 83	94.86 19

test1111		79.43 209	79.18 198	80.15 285	89.99 120	53.31 410	87.33 193	77.05 415	75.04 126	80.23 170	92.77 100	48.97 345	92.33 235	68.87 253	92.40 85	94.81 22

MG-MVS		83.41 111	83.45 104	83.28 187	92.74 70	62.28 298	88.17 162	89.50 198	75.22 119	81.49 146	92.74 101	66.75 126	95.11 93	72.85 206	91.58 99	92.45 160

casdiffmvs_mvg		85.99 57	86.09 60	85.70 80	87.65 224	67.22 178	88.69 140	93.04 45	79.64 21	85.33 74	92.54 102	73.30 38	94.50 123	83.49 81	91.14 106	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
patch_mono-2		83.65 103	84.54 87	80.99 265	90.06 119	65.83 203	84.21 292	88.74 240	71.60 212	85.01 77	92.44 103	74.51 28	83.50 392	82.15 99	92.15 88	93.64 99

casdiffmvs		85.11 81	85.14 80	85.01 104	87.20 241	65.77 207	87.75 178	92.83 64	77.84 43	84.36 97	92.38 104	72.15 53	93.93 149	81.27 107	90.48 117	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		86.69 43	86.95 41	85.90 77	90.76 102	67.57 163	92.83 21	93.30 36	79.67 19	84.57 91	92.27 105	71.47 63	95.02 99	84.24 75	93.46 72	95.13 9

baseline		84.93 84	84.98 81	84.80 116	87.30 239	65.39 216	87.30 194	92.88 61	77.62 47	84.04 103	92.26 106	71.81 57	93.96 143	81.31 105	90.30 120	95.03 11

NormalMVS		86.29 53	85.88 63	87.52 40	93.26 55	72.47 38	91.65 46	92.19 96	79.31 24	84.39 94	92.18 107	64.64 154	95.53 70	80.70 114	94.65 51	94.56 43

SymmetryMVS		85.38 76	84.81 84	87.07 49	91.47 86	72.47 38	91.65 46	88.06 255	79.31 24	84.39 94	92.18 107	64.64 154	95.53 70	80.70 114	90.91 111	93.21 120

QAPM		80.88 165	79.50 188	85.03 103	88.01 205	68.97 113	91.59 50	92.00 104	66.63 321	75.15 288	92.16 109	57.70 245	95.45 74	63.52 295	88.76 150	90.66 227

IS-MVSNet		83.15 119	82.81 116	84.18 144	89.94 122	63.30 275	91.59 50	88.46 248	79.04 30	79.49 178	92.16 109	65.10 149	94.28 129	67.71 262	91.86 95	94.95 12

viewmacassd2359aftdt		83.76 99	83.66 101	84.07 152	86.59 265	64.56 237	86.88 209	91.82 115	75.72 102	83.34 115	92.15 111	68.24 111	92.88 209	79.05 127	89.15 143	94.77 25

BP-MVS1		84.32 89	83.71 99	86.17 67	87.84 212	67.85 153	89.38 107	89.64 193	77.73 45	83.98 104	92.12 112	56.89 256	95.43 76	84.03 78	91.75 96	95.24 7

新几何1				83.42 182	93.13 59	70.71 79		85.48 310	57.43 419	81.80 140	91.98 113	63.28 164	92.27 236	64.60 290	92.99 75	87.27 343

OpenMVS	72.83 10	79.77 200	78.33 216	84.09 150	85.17 299	69.91 92	90.57 68	90.97 146	66.70 315	72.17 333	91.91 114	54.70 273	93.96 143	61.81 316	90.95 110	88.41 318

PHI-MVS		86.43 48	86.17 57	87.24 45	90.88 98	70.96 72	92.27 36	94.07 13	72.45 194	85.22 76	91.90 115	69.47 89	96.42 43	83.28 84	95.94 22	94.35 54

VNet		82.21 135	82.41 124	81.62 245	90.82 99	60.93 314	84.47 283	89.78 185	76.36 90	84.07 102	91.88 116	64.71 153	90.26 304	70.68 231	88.89 146	93.66 93

EC-MVSNet		86.01 56	86.38 50	84.91 111	89.31 146	66.27 193	92.32 34	93.63 25	79.37 23	84.17 100	91.88 116	69.04 99	95.43 76	83.93 79	93.77 68	93.01 135

GDP-MVS		83.52 108	82.64 120	86.16 68	88.14 196	68.45 131	89.13 119	92.69 69	72.82 192	83.71 109	91.86 118	55.69 263	95.35 85	80.03 120	89.74 132	94.69 31

KinetiMVS		83.31 117	82.61 121	85.39 90	87.08 250	67.56 164	88.06 166	91.65 123	77.80 44	82.21 133	91.79 119	57.27 251	94.07 141	77.77 145	89.89 130	94.56 43

OPM-MVS		83.50 109	82.95 114	85.14 97	88.79 171	70.95 73	89.13 119	91.52 129	77.55 52	80.96 156	91.75 120	60.71 216	94.50 123	79.67 125	86.51 190	89.97 264
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 57	85.96 62	86.05 72	91.09 91	67.64 160	89.63 95	92.65 74	72.89 191	84.64 88	91.71 121	71.85 56	96.03 54	84.77 67	94.45 59	94.49 47

viewmanbaseed2359cas		83.66 102	83.55 102	84.00 163	86.81 257	64.53 238	86.65 219	91.75 120	74.89 132	83.15 119	91.68 122	68.74 103	92.83 213	79.02 128	89.24 140	94.63 36

XVG-OURS-SEG-HR		80.81 168	79.76 179	83.96 166	85.60 288	68.78 117	83.54 311	90.50 160	70.66 239	76.71 243	91.66 123	60.69 217	91.26 281	76.94 156	81.58 273	91.83 183

EPNet		83.72 101	82.92 115	86.14 71	84.22 322	69.48 100	91.05 63	85.27 311	81.30 6	76.83 239	91.65 124	66.09 139	95.56 67	76.00 171	93.85 67	93.38 110
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 128	81.97 137	84.85 113	88.75 173	67.42 167	87.98 168	90.87 150	74.92 131	79.72 175	91.65 124	62.19 188	93.96 143	75.26 182	86.42 191	93.16 124

viewdifsd2359ckpt07		82.83 127	82.78 119	82.99 204	86.51 267	62.58 289	85.09 267	90.83 152	75.22 119	82.28 130	91.63 126	69.43 90	92.03 243	77.71 146	86.32 192	94.34 55

balanced_conf03		86.78 41	86.99 39	86.15 69	91.24 89	67.61 161	90.51 69	92.90 60	77.26 59	87.44 55	91.63 126	71.27 67	96.06 53	85.62 58	95.01 40	94.78 24

test222					91.50 85	68.26 136	84.16 295	83.20 345	54.63 430	79.74 174	91.63 126	58.97 234			91.42 101	86.77 357

MVS_111021_HR		85.14 80	84.75 85	86.32 64	91.65 84	72.70 30	85.98 240	90.33 168	76.11 96	82.08 135	91.61 129	71.36 66	94.17 138	81.02 108	92.58 81	92.08 179

原ACMM1				84.35 131	93.01 65	68.79 116		92.44 81	63.96 357	81.09 153	91.57 130	66.06 140	95.45 74	67.19 269	94.82 49	88.81 304

viewcassd2359sk11		83.89 94	83.74 98	84.34 132	87.76 219	64.91 232	86.30 232	92.22 92	75.47 110	83.04 120	91.52 131	70.15 81	93.53 170	79.26 126	87.96 163	94.57 41

LPG-MVS_test		82.08 137	81.27 143	84.50 124	89.23 151	68.76 118	90.22 80	91.94 108	75.37 114	76.64 245	91.51 132	54.29 276	94.91 101	78.44 136	83.78 237	89.83 269

LGP-MVS_train				84.50 124	89.23 151	68.76 118		91.94 108	75.37 114	76.64 245	91.51 132	54.29 276	94.91 101	78.44 136	83.78 237	89.83 269

XVG-OURS		80.41 185	79.23 196	83.97 165	85.64 286	69.02 111	83.03 324	90.39 163	71.09 224	77.63 221	91.49 134	54.62 275	91.35 278	75.71 174	83.47 249	91.54 194

alignmvs		85.48 71	85.32 77	85.96 76	89.51 133	69.47 101	89.74 90	92.47 80	76.17 95	87.73 51	91.46 135	70.32 78	93.78 157	81.51 102	88.95 145	94.63 36

CANet		86.45 47	86.10 59	87.51 41	90.09 114	70.94 74	89.70 92	92.59 78	81.78 4	81.32 148	91.43 136	70.34 77	97.23 16	84.26 73	93.36 73	94.37 53

h-mvs33		83.15 119	82.19 130	86.02 75	90.56 104	70.85 78	88.15 164	89.16 219	76.02 98	84.67 85	91.39 137	61.54 199	95.50 72	82.71 94	75.48 355	91.72 190

MGCFI-Net		85.06 83	85.51 72	83.70 173	89.42 138	63.01 281	89.43 102	92.62 77	76.43 84	87.53 52	91.34 138	72.82 48	93.42 178	81.28 106	88.74 151	94.66 35

nrg030		83.88 95	83.53 103	84.96 106	86.77 259	69.28 108	90.46 74	92.67 71	74.79 136	82.95 121	91.33 139	72.70 49	93.09 199	80.79 113	79.28 303	92.50 156

sasdasda		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 140	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

canonicalmvs		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 140	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

DPM-MVS		84.93 84	84.29 91	86.84 55	90.20 112	73.04 23	87.12 198	93.04 45	69.80 263	82.85 124	91.22 142	73.06 43	96.02 56	76.72 164	94.63 53	91.46 200

Anonymous202405211		78.25 240	77.01 251	81.99 239	91.03 93	60.67 319	84.77 274	83.90 331	70.65 240	80.00 172	91.20 143	41.08 406	91.43 276	65.21 284	85.26 215	93.85 81

SPE-MVS-test		86.29 53	86.48 49	85.71 79	91.02 94	67.21 179	92.36 33	93.78 22	78.97 33	83.51 114	91.20 143	70.65 76	95.15 90	81.96 100	94.89 45	94.77 25

Anonymous20240529		80.19 195	78.89 204	84.10 146	90.60 103	64.75 235	88.95 125	90.90 148	65.97 329	80.59 164	91.17 145	49.97 330	93.73 163	69.16 250	82.70 262	93.81 85

EPP-MVSNet		83.40 112	83.02 112	84.57 122	90.13 113	64.47 243	92.32 34	90.73 154	74.45 145	79.35 183	91.10 146	69.05 98	95.12 91	72.78 207	87.22 176	94.13 65

TAPA-MVS	73.13 9	79.15 218	77.94 224	82.79 218	89.59 129	62.99 285	88.16 163	91.51 130	65.77 330	77.14 236	91.09 147	60.91 214	93.21 188	50.26 403	87.05 180	92.17 176
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 50	86.19 56	87.07 49	92.91 66	72.48 37	90.81 65	93.56 28	73.95 157	83.16 118	91.07 148	75.94 20	95.19 88	79.94 122	94.38 61	93.55 105

FIs		82.07 138	82.42 123	81.04 264	88.80 170	58.34 343	88.26 159	93.49 30	76.93 71	78.47 201	91.04 149	69.92 84	92.34 234	69.87 243	84.97 217	92.44 161

MVS_111021_LR		82.61 130	82.11 131	84.11 145	88.82 165	71.58 57	85.15 264	86.16 301	74.69 138	80.47 167	91.04 149	62.29 185	90.55 302	80.33 118	90.08 125	90.20 247

DP-MVS Recon		83.11 122	82.09 133	86.15 69	94.44 22	70.92 75	88.79 133	92.20 95	70.53 241	79.17 185	91.03 151	64.12 158	96.03 54	68.39 259	90.14 123	91.50 196

mamv4		76.81 276	78.23 220	72.54 395	86.12 276	65.75 208	78.76 381	82.07 361	64.12 351	72.97 321	91.02 152	67.97 113	68.08 460	83.04 87	78.02 317	83.80 405

HQP_MVS		83.64 104	83.14 109	85.14 97	90.08 115	68.71 122	91.25 59	92.44 81	79.12 28	78.92 189	91.00 153	60.42 224	95.38 81	78.71 134	86.32 192	91.33 201

plane_prior4											91.00 153

FC-MVSNet-test		81.52 154	82.02 135	80.03 287	88.42 186	55.97 382	87.95 170	93.42 33	77.10 67	77.38 225	90.98 155	69.96 83	91.79 254	68.46 258	84.50 224	92.33 164

diffmvs_AUTHOR		82.38 133	82.27 129	82.73 223	83.26 346	63.80 257	83.89 299	89.76 187	73.35 177	82.37 129	90.84 156	66.25 135	90.79 296	82.77 91	87.93 164	93.59 102

Vis-MVSNet (Re-imp)		78.36 239	78.45 211	78.07 329	88.64 177	51.78 421	86.70 217	79.63 393	74.14 154	75.11 289	90.83 157	61.29 207	89.75 314	58.10 351	91.60 97	92.69 148

114514_t		80.68 176	79.51 187	84.20 143	94.09 41	67.27 175	89.64 94	91.11 143	58.75 408	74.08 307	90.72 158	58.10 241	95.04 98	69.70 244	89.42 138	90.30 244

viewdifsd2359ckpt13		82.91 125	82.29 128	84.77 117	86.96 253	66.90 186	87.47 185	91.62 125	72.19 199	81.68 143	90.71 159	66.92 125	93.28 181	75.90 172	87.15 178	94.12 66

viewdifsd2359ckpt09		83.34 114	82.55 122	85.70 80	87.64 225	67.72 158	88.43 149	91.68 122	71.91 206	81.65 144	90.68 160	67.10 124	94.75 112	76.17 167	87.70 168	94.62 38

PAPM_NR		83.02 123	82.41 124	84.82 114	92.47 75	66.37 191	87.93 172	91.80 116	73.82 161	77.32 227	90.66 161	67.90 115	94.90 103	70.37 234	89.48 137	93.19 123

viewdifsd2359ckpt11		80.37 189	79.73 180	82.30 232	83.70 336	62.39 293	84.20 293	86.67 289	73.22 183	80.90 157	90.62 162	63.00 175	91.56 265	76.81 161	78.44 310	92.95 139

viewmsd2359difaftdt		80.37 189	79.73 180	82.30 232	83.70 336	62.39 293	84.20 293	86.67 289	73.22 183	80.90 157	90.62 162	63.00 175	91.56 265	76.81 161	78.44 310	92.95 139

LS3D		76.95 274	74.82 292	83.37 185	90.45 106	67.36 171	89.15 118	86.94 284	61.87 381	69.52 363	90.61 164	51.71 310	94.53 121	46.38 425	86.71 187	88.21 322

AstraMVS		80.81 168	80.14 169	82.80 215	86.05 279	63.96 252	86.46 226	85.90 305	73.71 164	80.85 160	90.56 165	54.06 280	91.57 264	79.72 124	83.97 235	92.86 142

VPNet		78.69 231	78.66 207	78.76 312	88.31 189	55.72 386	84.45 286	86.63 292	76.79 75	78.26 205	90.55 166	59.30 232	89.70 316	66.63 273	77.05 328	90.88 217

UniMVSNet_ETH3D		79.10 220	78.24 218	81.70 244	86.85 255	60.24 326	87.28 195	88.79 235	74.25 151	76.84 238	90.53 167	49.48 336	91.56 265	67.98 260	82.15 266	93.29 115

ACMP	74.13 6	81.51 156	80.57 156	84.36 130	89.42 138	68.69 125	89.97 84	91.50 133	74.46 144	75.04 292	90.41 168	53.82 282	94.54 120	77.56 148	82.91 257	89.86 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_0407		81.58 151	80.48 159	84.87 112	88.81 166	67.96 148	87.37 190	89.25 214	71.06 226	79.48 179	90.39 169	59.57 229	94.48 125	72.45 216	85.93 203	92.18 173

SSM_0404		81.91 141	80.84 152	85.13 100	89.24 150	68.26 136	87.84 177	89.25 214	71.06 226	80.62 163	90.39 169	59.57 229	94.65 118	72.45 216	87.19 177	92.47 159

viewmambaseed2359dif		80.41 185	79.84 177	82.12 234	82.95 360	62.50 292	83.39 312	88.06 255	67.11 310	80.98 155	90.31 171	66.20 137	91.01 292	74.62 186	84.90 218	92.86 142

RRT-MVS		82.60 132	82.10 132	84.10 146	87.98 206	62.94 286	87.45 188	91.27 136	77.42 56	79.85 173	90.28 172	56.62 259	94.70 116	79.87 123	88.15 161	94.67 32

PCF-MVS	73.52 7	80.38 187	78.84 205	85.01 104	87.71 221	68.99 112	83.65 305	91.46 134	63.00 365	77.77 219	90.28 172	66.10 138	95.09 97	61.40 319	88.22 160	90.94 216
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 128	68.32 134					90.24 174

HQP-MVS		82.61 130	82.02 135	84.37 129	89.33 143	66.98 182	89.17 114	92.19 96	76.41 85	77.23 230	90.23 175	60.17 227	95.11 93	77.47 149	85.99 201	91.03 211

PS-MVSNAJss		82.07 138	81.31 142	84.34 132	86.51 267	67.27 175	89.27 110	91.51 130	71.75 207	79.37 182	90.22 176	63.15 170	94.27 130	77.69 147	82.36 265	91.49 197

TSAR-MVS + GP.		85.71 67	85.33 76	86.84 55	91.34 87	72.50 36	89.07 122	87.28 275	76.41 85	85.80 69	90.22 176	74.15 34	95.37 84	81.82 101	91.88 92	92.65 150

SDMVSNet		80.38 187	80.18 166	80.99 265	89.03 160	64.94 229	80.45 356	89.40 201	75.19 123	76.61 247	89.98 178	60.61 221	87.69 350	76.83 160	83.55 246	90.33 242

sd_testset		77.70 259	77.40 244	78.60 315	89.03 160	60.02 328	79.00 377	85.83 306	75.19 123	76.61 247	89.98 178	54.81 268	85.46 375	62.63 306	83.55 246	90.33 242

TranMVSNet+NR-MVSNet		80.84 166	80.31 163	82.42 229	87.85 211	62.33 296	87.74 179	91.33 135	80.55 9	77.99 213	89.86 180	65.23 148	92.62 216	67.05 271	75.24 365	92.30 166

diffmvs		82.10 136	81.88 138	82.76 221	83.00 356	63.78 259	83.68 304	89.76 187	72.94 189	82.02 136	89.85 181	65.96 143	90.79 296	82.38 98	87.30 175	93.71 91
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Elysia		81.53 152	80.16 167	85.62 83	85.51 290	68.25 138	88.84 131	92.19 96	71.31 217	80.50 165	89.83 182	46.89 357	94.82 107	76.85 157	89.57 134	93.80 87

StellarMVS		81.53 152	80.16 167	85.62 83	85.51 290	68.25 138	88.84 131	92.19 96	71.31 217	80.50 165	89.83 182	46.89 357	94.82 107	76.85 157	89.57 134	93.80 87

mamba_0408		79.37 214	77.52 241	84.93 109	88.81 166	67.96 148	65.03 456	88.66 242	70.96 230	79.48 179	89.80 184	58.69 235	94.65 118	70.35 235	85.93 203	92.18 173

SSM_04072		77.67 261	77.52 241	78.12 327	88.81 166	67.96 148	65.03 456	88.66 242	70.96 230	79.48 179	89.80 184	58.69 235	74.23 448	70.35 235	85.93 203	92.18 173

BH-RMVSNet		79.61 202	78.44 212	83.14 195	89.38 142	65.93 200	84.95 271	87.15 280	73.56 169	78.19 207	89.79 186	56.67 258	93.36 179	59.53 335	86.74 186	90.13 250

GeoE		81.71 146	81.01 149	83.80 172	89.51 133	64.45 244	88.97 124	88.73 241	71.27 220	78.63 195	89.76 187	66.32 134	93.20 191	69.89 242	86.02 200	93.74 90

guyue		81.13 161	80.64 155	82.60 226	86.52 266	63.92 255	86.69 218	87.73 266	73.97 156	80.83 161	89.69 188	56.70 257	91.33 280	78.26 143	85.40 214	92.54 153

AdaColmap		80.58 183	79.42 189	84.06 155	93.09 62	68.91 114	89.36 108	88.97 230	69.27 275	75.70 266	89.69 188	57.20 253	95.77 63	63.06 300	88.41 158	87.50 337

ACMM	73.20 8	80.78 175	79.84 177	83.58 177	89.31 146	68.37 133	89.99 83	91.60 127	70.28 251	77.25 228	89.66 190	53.37 287	93.53 170	74.24 192	82.85 258	88.85 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 246	76.79 258	81.97 240	90.40 108	71.07 69	87.59 182	84.55 321	66.03 328	72.38 330	89.64 191	57.56 247	86.04 367	59.61 334	83.35 251	88.79 305

test_yl		81.17 159	80.47 160	83.24 190	89.13 155	63.62 260	86.21 235	89.95 181	72.43 197	81.78 141	89.61 192	57.50 248	93.58 165	70.75 229	86.90 182	92.52 154

DCV-MVSNet		81.17 159	80.47 160	83.24 190	89.13 155	63.62 260	86.21 235	89.95 181	72.43 197	81.78 141	89.61 192	57.50 248	93.58 165	70.75 229	86.90 182	92.52 154

EI-MVSNet-Vis-set		84.19 90	83.81 96	85.31 92	88.18 193	67.85 153	87.66 180	89.73 190	80.05 15	82.95 121	89.59 194	70.74 74	94.82 107	80.66 116	84.72 221	93.28 116

PAPR		81.66 149	80.89 151	83.99 164	90.27 110	64.00 251	86.76 216	91.77 119	68.84 290	77.13 237	89.50 195	67.63 117	94.88 105	67.55 264	88.52 155	93.09 128

jajsoiax		79.29 215	77.96 223	83.27 188	84.68 313	66.57 189	89.25 111	90.16 175	69.20 280	75.46 272	89.49 196	45.75 373	93.13 197	76.84 159	80.80 283	90.11 252

MVSFormer		82.85 126	82.05 134	85.24 94	87.35 231	70.21 85	90.50 71	90.38 164	68.55 294	81.32 148	89.47 197	61.68 196	93.46 175	78.98 131	90.26 121	92.05 180

jason		81.39 157	80.29 164	84.70 120	86.63 264	69.90 93	85.95 241	86.77 288	63.24 361	81.07 154	89.47 197	61.08 212	92.15 240	78.33 139	90.07 126	92.05 180
jason: jason.
mvs_tets		79.13 219	77.77 233	83.22 192	84.70 312	66.37 191	89.17 114	90.19 174	69.38 272	75.40 275	89.46 199	44.17 385	93.15 195	76.78 163	80.70 285	90.14 249

UGNet		80.83 167	79.59 186	84.54 123	88.04 202	68.09 143	89.42 104	88.16 250	76.95 70	76.22 256	89.46 199	49.30 340	93.94 146	68.48 257	90.31 119	91.60 191
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
VPA-MVSNet		80.60 180	80.55 157	80.76 271	88.07 201	60.80 317	86.86 210	91.58 128	75.67 106	80.24 169	89.45 201	63.34 163	90.25 305	70.51 233	79.22 304	91.23 204

MVS_Test		83.15 119	83.06 111	83.41 184	86.86 254	63.21 277	86.11 238	92.00 104	74.31 148	82.87 123	89.44 202	70.03 82	93.21 188	77.39 151	88.50 156	93.81 85

EI-MVSNet-UG-set		83.81 96	83.38 106	85.09 102	87.87 210	67.53 165	87.44 189	89.66 191	79.74 18	82.23 132	89.41 203	70.24 80	94.74 113	79.95 121	83.92 236	92.99 137

RPSCF		73.23 330	71.46 334	78.54 318	82.50 369	59.85 329	82.18 330	82.84 354	58.96 404	71.15 345	89.41 203	45.48 377	84.77 382	58.82 343	71.83 395	91.02 213

UniMVSNet_NR-MVSNet		81.88 142	81.54 141	82.92 208	88.46 183	63.46 271	87.13 197	92.37 85	80.19 12	78.38 202	89.14 205	71.66 62	93.05 202	70.05 239	76.46 338	92.25 168

tttt0517		79.40 211	77.91 225	83.90 168	88.10 199	63.84 256	88.37 155	84.05 329	71.45 215	76.78 241	89.12 206	49.93 333	94.89 104	70.18 238	83.18 255	92.96 138

DU-MVS		81.12 162	80.52 158	82.90 209	87.80 214	63.46 271	87.02 202	91.87 112	79.01 31	78.38 202	89.07 207	65.02 150	93.05 202	70.05 239	76.46 338	92.20 171

NR-MVSNet		80.23 193	79.38 190	82.78 219	87.80 214	63.34 274	86.31 231	91.09 144	79.01 31	72.17 333	89.07 207	67.20 122	92.81 214	66.08 278	75.65 351	92.20 171

icg_test_0407_2		78.92 226	78.93 203	78.90 310	87.13 244	63.59 264	76.58 403	89.33 204	70.51 242	77.82 215	89.03 209	61.84 192	81.38 407	72.56 212	85.56 210	91.74 186

IMVS_0407		80.61 178	79.90 175	82.75 222	87.13 244	63.59 264	85.33 260	89.33 204	70.51 242	77.82 215	89.03 209	61.84 192	92.91 207	72.56 212	85.56 210	91.74 186

IMVS_0404		77.16 270	76.42 268	79.37 301	87.13 244	63.59 264	77.12 401	89.33 204	70.51 242	66.22 402	89.03 209	50.36 325	82.78 397	72.56 212	85.56 210	91.74 186

IMVS_0403		80.80 171	80.12 170	82.87 211	87.13 244	63.59 264	85.19 261	89.33 204	70.51 242	78.49 199	89.03 209	63.26 166	93.27 183	72.56 212	85.56 210	91.74 186

DELS-MVS		85.41 74	85.30 78	85.77 78	88.49 181	67.93 151	85.52 258	93.44 31	78.70 34	83.63 113	89.03 209	74.57 26	95.71 65	80.26 119	94.04 66	93.66 93
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
mvsmamba		80.60 180	79.38 190	84.27 139	89.74 127	67.24 177	87.47 185	86.95 283	70.02 256	75.38 276	88.93 214	51.24 314	92.56 221	75.47 180	89.22 141	93.00 136

baseline1		76.98 273	76.75 261	77.66 336	88.13 197	55.66 387	85.12 265	81.89 362	73.04 187	76.79 240	88.90 215	62.43 183	87.78 349	63.30 299	71.18 399	89.55 278

DP-MVS		76.78 277	74.57 295	83.42 182	93.29 51	69.46 103	88.55 147	83.70 333	63.98 356	70.20 351	88.89 216	54.01 281	94.80 110	46.66 422	81.88 271	86.01 371

ab-mvs		79.51 205	78.97 202	81.14 261	88.46 183	60.91 315	83.84 300	89.24 216	70.36 247	79.03 186	88.87 217	63.23 168	90.21 306	65.12 285	82.57 263	92.28 167

PEN-MVS		77.73 256	77.69 237	77.84 333	87.07 252	53.91 404	87.91 173	91.18 139	77.56 51	73.14 319	88.82 218	61.23 208	89.17 326	59.95 330	72.37 389	90.43 237

tt0805		78.73 229	77.83 229	81.43 250	85.17 299	60.30 325	89.41 105	90.90 148	71.21 221	77.17 235	88.73 219	46.38 362	93.21 188	72.57 210	78.96 305	90.79 220

test_djsdf		80.30 192	79.32 193	83.27 188	83.98 328	65.37 217	90.50 71	90.38 164	68.55 294	76.19 257	88.70 220	56.44 260	93.46 175	78.98 131	80.14 293	90.97 214

PAPM		77.68 260	76.40 269	81.51 248	87.29 240	61.85 303	83.78 301	89.59 195	64.74 343	71.23 343	88.70 220	62.59 179	93.66 164	52.66 387	87.03 181	89.01 294

DTE-MVSNet		76.99 272	76.80 257	77.54 341	86.24 271	53.06 413	87.52 183	90.66 155	77.08 68	72.50 327	88.67 222	60.48 223	89.52 318	57.33 358	70.74 401	90.05 259

PS-CasMVS		78.01 250	78.09 221	77.77 335	87.71 221	54.39 401	88.02 167	91.22 137	77.50 54	73.26 317	88.64 223	60.73 215	88.41 341	61.88 314	73.88 378	90.53 233

cdsmvs_eth3d_5k		19.96 439	26.61 441	0.00 460	0.00 483	0.00 485	0.00 472	89.26 213	0.00 478	0.00 479	88.61 224	61.62 198	0.00 479	0.00 478	0.00 477	0.00 475

lupinMVS		81.39 157	80.27 165	84.76 118	87.35 231	70.21 85	85.55 254	86.41 295	62.85 368	81.32 148	88.61 224	61.68 196	92.24 238	78.41 138	90.26 121	91.83 183

F-COLMAP		76.38 287	74.33 301	82.50 228	89.28 148	66.95 185	88.41 151	89.03 225	64.05 354	66.83 391	88.61 224	46.78 359	92.89 208	57.48 355	78.55 307	87.67 331

mvs_anonymous		79.42 210	79.11 199	80.34 280	84.45 319	57.97 349	82.59 326	87.62 268	67.40 309	76.17 260	88.56 227	68.47 106	89.59 317	70.65 232	86.05 199	93.47 108

CP-MVSNet		78.22 241	78.34 215	77.84 333	87.83 213	54.54 399	87.94 171	91.17 140	77.65 46	73.48 315	88.49 228	62.24 187	88.43 340	62.19 310	74.07 374	90.55 232

PVSNet_Blended_VisFu		82.62 129	81.83 139	84.96 106	90.80 100	69.76 96	88.74 138	91.70 121	69.39 271	78.96 187	88.46 229	65.47 146	94.87 106	74.42 189	88.57 153	90.24 246

CANet_DTU		80.61 178	79.87 176	82.83 212	85.60 288	63.17 280	87.36 191	88.65 244	76.37 89	75.88 263	88.44 230	53.51 285	93.07 200	73.30 201	89.74 132	92.25 168

PLC	70.83 11	78.05 248	76.37 270	83.08 199	91.88 82	67.80 155	88.19 161	89.46 199	64.33 349	69.87 360	88.38 231	53.66 283	93.58 165	58.86 342	82.73 260	87.86 328
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 206	79.22 197	80.27 282	88.79 171	58.35 342	85.06 268	88.61 246	78.56 35	77.65 220	88.34 232	63.81 162	90.66 301	64.98 287	77.22 326	91.80 185

XXY-MVS		75.41 301	75.56 279	74.96 367	83.59 339	57.82 353	80.59 353	83.87 332	66.54 322	74.93 295	88.31 233	63.24 167	80.09 413	62.16 311	76.85 332	86.97 353

Effi-MVS+		83.62 106	83.08 110	85.24 94	88.38 187	67.45 166	88.89 127	89.15 220	75.50 109	82.27 131	88.28 234	69.61 88	94.45 126	77.81 144	87.84 165	93.84 83

API-MVS		81.99 140	81.23 144	84.26 141	90.94 96	70.18 90	91.10 62	89.32 208	71.51 214	78.66 194	88.28 234	65.26 147	95.10 96	64.74 289	91.23 105	87.51 336

thisisatest0530		79.40 211	77.76 234	84.31 134	87.69 223	65.10 225	87.36 191	84.26 327	70.04 255	77.42 224	88.26 236	49.94 331	94.79 111	70.20 237	84.70 222	93.03 133

hse-mvs2		81.72 145	80.94 150	84.07 152	88.72 174	67.68 159	85.87 244	87.26 277	76.02 98	84.67 85	88.22 237	61.54 199	93.48 173	82.71 94	73.44 383	91.06 209

xiu_mvs_v1_base_debu		80.80 171	79.72 182	84.03 160	87.35 231	70.19 87	85.56 251	88.77 236	69.06 284	81.83 137	88.16 238	50.91 317	92.85 210	78.29 140	87.56 169	89.06 289

xiu_mvs_v1_base		80.80 171	79.72 182	84.03 160	87.35 231	70.19 87	85.56 251	88.77 236	69.06 284	81.83 137	88.16 238	50.91 317	92.85 210	78.29 140	87.56 169	89.06 289

xiu_mvs_v1_base_debi		80.80 171	79.72 182	84.03 160	87.35 231	70.19 87	85.56 251	88.77 236	69.06 284	81.83 137	88.16 238	50.91 317	92.85 210	78.29 140	87.56 169	89.06 289

UniMVSNet (Re)		81.60 150	81.11 146	83.09 197	88.38 187	64.41 245	87.60 181	93.02 49	78.42 37	78.56 197	88.16 238	69.78 85	93.26 184	69.58 246	76.49 337	91.60 191

AUN-MVS		79.21 217	77.60 239	84.05 158	88.71 175	67.61 161	85.84 246	87.26 277	69.08 283	77.23 230	88.14 242	53.20 289	93.47 174	75.50 179	73.45 382	91.06 209

Anonymous20231211		78.97 224	77.69 237	82.81 214	90.54 105	64.29 247	90.11 82	91.51 130	65.01 341	76.16 261	88.13 243	50.56 322	93.03 205	69.68 245	77.56 324	91.11 207

pm-mvs1		77.25 269	76.68 263	78.93 309	84.22 322	58.62 340	86.41 227	88.36 249	71.37 216	73.31 316	88.01 244	61.22 209	89.15 327	64.24 293	73.01 386	89.03 293

LuminaMVS		80.68 176	79.62 185	83.83 169	85.07 305	68.01 147	86.99 203	88.83 233	70.36 247	81.38 147	87.99 245	50.11 328	92.51 225	79.02 128	86.89 184	90.97 214

SD_0403		74.65 309	74.77 293	74.29 376	86.20 273	47.42 440	83.71 303	85.12 313	69.30 274	68.50 374	87.95 246	59.40 231	86.05 366	49.38 407	83.35 251	89.40 281

LTVRE_ROB	69.57 13	76.25 288	74.54 297	81.41 251	88.60 178	64.38 246	79.24 372	89.12 223	70.76 235	69.79 362	87.86 247	49.09 343	93.20 191	56.21 370	80.16 291	86.65 360
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
testing3-2		75.12 306	75.19 288	74.91 368	90.40 108	45.09 451	80.29 359	78.42 403	78.37 40	76.54 249	87.75 248	44.36 383	87.28 355	57.04 361	83.49 248	92.37 162

WTY-MVS		75.65 296	75.68 276	75.57 358	86.40 269	56.82 367	77.92 395	82.40 357	65.10 338	76.18 258	87.72 249	63.13 173	80.90 410	60.31 328	81.96 269	89.00 296

TAMVS		78.89 227	77.51 243	83.03 202	87.80 214	67.79 156	84.72 275	85.05 316	67.63 304	76.75 242	87.70 250	62.25 186	90.82 295	58.53 346	87.13 179	90.49 235

BH-untuned		79.47 207	78.60 208	82.05 237	89.19 153	65.91 201	86.07 239	88.52 247	72.18 200	75.42 274	87.69 251	61.15 210	93.54 169	60.38 327	86.83 185	86.70 359

COLMAP_ROB	66.92 17	73.01 333	70.41 348	80.81 270	87.13 244	65.63 209	88.30 158	84.19 328	62.96 366	63.80 419	87.69 251	38.04 422	92.56 221	46.66 422	74.91 368	84.24 398
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 312	72.42 325	79.80 292	83.76 334	59.59 333	85.92 243	86.64 291	66.39 323	66.96 389	87.58 253	39.46 412	91.60 261	65.76 281	69.27 407	88.22 321

FA-MVS(test-final)		80.96 164	79.91 174	84.10 146	88.30 190	65.01 226	84.55 282	90.01 179	73.25 181	79.61 176	87.57 254	58.35 240	94.72 114	71.29 225	86.25 195	92.56 152

Baseline_NR-MVSNet		78.15 245	78.33 216	77.61 338	85.79 282	56.21 380	86.78 214	85.76 307	73.60 168	77.93 214	87.57 254	65.02 150	88.99 329	67.14 270	75.33 362	87.63 332

WR-MVS_H		78.51 236	78.49 210	78.56 317	88.02 203	56.38 376	88.43 149	92.67 71	77.14 64	73.89 309	87.55 256	66.25 135	89.24 324	58.92 341	73.55 381	90.06 258

EI-MVSNet		80.52 184	79.98 172	82.12 234	84.28 320	63.19 279	86.41 227	88.95 231	74.18 153	78.69 192	87.54 257	66.62 128	92.43 228	72.57 210	80.57 287	90.74 224

CVMVSNet		72.99 334	72.58 323	74.25 377	84.28 320	50.85 429	86.41 227	83.45 339	44.56 449	73.23 318	87.54 257	49.38 338	85.70 370	65.90 279	78.44 310	86.19 366

ACMH+	68.96 14	76.01 292	74.01 303	82.03 238	88.60 178	65.31 218	88.86 128	87.55 269	70.25 253	67.75 378	87.47 259	41.27 404	93.19 193	58.37 348	75.94 348	87.60 333

TransMVSNet (Re)		75.39 303	74.56 296	77.86 332	85.50 292	57.10 364	86.78 214	86.09 303	72.17 201	71.53 340	87.34 260	63.01 174	89.31 322	56.84 364	61.83 431	87.17 345

GBi-Net		78.40 237	77.40 244	81.40 252	87.60 226	63.01 281	88.39 152	89.28 210	71.63 209	75.34 278	87.28 261	54.80 269	91.11 285	62.72 302	79.57 297	90.09 254

test1		78.40 237	77.40 244	81.40 252	87.60 226	63.01 281	88.39 152	89.28 210	71.63 209	75.34 278	87.28 261	54.80 269	91.11 285	62.72 302	79.57 297	90.09 254

FMVSNet2		78.20 243	77.21 248	81.20 259	87.60 226	62.89 287	87.47 185	89.02 226	71.63 209	75.29 284	87.28 261	54.80 269	91.10 288	62.38 307	79.38 301	89.61 276

FMVSNet1		77.44 264	76.12 272	81.40 252	86.81 257	63.01 281	88.39 152	89.28 210	70.49 246	74.39 304	87.28 261	49.06 344	91.11 285	60.91 323	78.52 308	90.09 254

v2v482		80.23 193	79.29 194	83.05 201	83.62 338	64.14 249	87.04 200	89.97 180	73.61 167	78.18 208	87.22 265	61.10 211	93.82 155	76.11 168	76.78 334	91.18 205

ITE_SJBPF				78.22 324	81.77 379	60.57 320		83.30 340	69.25 277	67.54 380	87.20 266	36.33 429	87.28 355	54.34 378	74.62 371	86.80 356

anonymousdsp		78.60 233	77.15 249	82.98 206	80.51 398	67.08 180	87.24 196	89.53 197	65.66 332	75.16 287	87.19 267	52.52 291	92.25 237	77.17 153	79.34 302	89.61 276

MVSTER		79.01 222	77.88 228	82.38 230	83.07 353	64.80 234	84.08 298	88.95 231	69.01 287	78.69 192	87.17 268	54.70 273	92.43 228	74.69 185	80.57 287	89.89 267

thres100view900		76.50 281	75.55 280	79.33 302	89.52 132	56.99 365	85.83 247	83.23 342	73.94 158	76.32 254	87.12 269	51.89 306	91.95 248	48.33 413	83.75 240	89.07 287

thres600view7		76.50 281	75.44 281	79.68 295	89.40 140	57.16 362	85.53 256	83.23 342	73.79 162	76.26 255	87.09 270	51.89 306	91.89 251	48.05 418	83.72 243	90.00 260

XVG-ACMP-BASELINE		76.11 290	74.27 302	81.62 245	83.20 349	64.67 236	83.60 308	89.75 189	69.75 266	71.85 336	87.09 270	32.78 436	92.11 241	69.99 241	80.43 289	88.09 324

HY-MVS	69.67 12	77.95 251	77.15 249	80.36 279	87.57 230	60.21 327	83.37 314	87.78 265	66.11 325	75.37 277	87.06 272	63.27 165	90.48 303	61.38 320	82.43 264	90.40 239

CHOSEN 1792x2688		77.63 262	75.69 275	83.44 181	89.98 121	68.58 128	78.70 382	87.50 271	56.38 424	75.80 265	86.84 273	58.67 237	91.40 277	61.58 318	85.75 208	90.34 241

v8		79.97 199	79.02 201	82.80 215	84.09 325	64.50 242	87.96 169	90.29 171	74.13 155	75.24 285	86.81 274	62.88 177	93.89 154	74.39 190	75.40 360	90.00 260

AllTest		70.96 351	68.09 366	79.58 298	85.15 301	63.62 260	84.58 281	79.83 390	62.31 375	60.32 432	86.73 275	32.02 437	88.96 332	50.28 401	71.57 397	86.15 367

TestCases				79.58 298	85.15 301	63.62 260		79.83 390	62.31 375	60.32 432	86.73 275	32.02 437	88.96 332	50.28 401	71.57 397	86.15 367

LCM-MVSNet-Re		77.05 271	76.94 254	77.36 342	87.20 241	51.60 422	80.06 362	80.46 381	75.20 122	67.69 379	86.72 277	62.48 181	88.98 330	63.44 297	89.25 139	91.51 195

1112_ss		77.40 266	76.43 267	80.32 281	89.11 159	60.41 324	83.65 305	87.72 267	62.13 378	73.05 320	86.72 277	62.58 180	89.97 310	62.11 313	80.80 283	90.59 231

ab-mvs-re		7.23 442	9.64 445	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	86.72 277	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

IterMVS-LS		80.06 196	79.38 190	82.11 236	85.89 280	63.20 278	86.79 213	89.34 203	74.19 152	75.45 273	86.72 277	66.62 128	92.39 230	72.58 209	76.86 331	90.75 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 293	73.93 305	81.77 243	88.71 175	66.61 188	88.62 143	89.01 227	69.81 262	66.78 392	86.70 281	41.95 401	91.51 272	55.64 371	78.14 316	87.17 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 286	75.44 281	79.27 303	89.28 148	58.09 345	81.69 335	87.07 281	59.53 399	72.48 328	86.67 282	61.30 206	89.33 321	60.81 325	80.15 292	90.41 238

FMVSNet3		77.88 253	76.85 256	80.97 267	86.84 256	62.36 295	86.52 224	88.77 236	71.13 222	75.34 278	86.66 283	54.07 279	91.10 288	62.72 302	79.57 297	89.45 280

pmmvs6		74.69 308	73.39 312	78.61 314	81.38 387	57.48 359	86.64 220	87.95 259	64.99 342	70.18 352	86.61 284	50.43 324	89.52 318	62.12 312	70.18 404	88.83 303

ET-MVSNet_ETH3D		78.63 232	76.63 264	84.64 121	86.73 260	69.47 101	85.01 269	84.61 320	69.54 269	66.51 399	86.59 285	50.16 327	91.75 256	76.26 166	84.24 232	92.69 148

testgi		66.67 391	66.53 387	67.08 426	75.62 432	41.69 461	75.93 406	76.50 418	66.11 325	65.20 410	86.59 285	35.72 431	74.71 445	43.71 434	73.38 384	84.84 392

CLD-MVS		82.31 134	81.65 140	84.29 136	88.47 182	67.73 157	85.81 248	92.35 86	75.78 101	78.33 204	86.58 287	64.01 159	94.35 127	76.05 170	87.48 172	90.79 220
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 201	78.67 206	82.97 207	84.06 326	64.95 228	87.88 175	90.62 156	73.11 185	75.11 289	86.56 288	61.46 202	94.05 142	73.68 195	75.55 353	89.90 266

CDS-MVSNet		79.07 221	77.70 236	83.17 194	87.60 226	68.23 140	84.40 289	86.20 300	67.49 307	76.36 253	86.54 289	61.54 199	90.79 296	61.86 315	87.33 174	90.49 235
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 147	81.05 147	83.60 175	89.15 154	68.03 146	84.46 285	90.02 178	70.67 236	81.30 151	86.53 290	63.17 169	94.19 137	75.60 177	88.54 154	88.57 314

TR-MVS		77.44 264	76.18 271	81.20 259	88.24 191	63.24 276	84.61 280	86.40 296	67.55 306	77.81 217	86.48 291	54.10 278	93.15 195	57.75 354	82.72 261	87.20 344

EIA-MVS		83.31 117	82.80 117	84.82 114	89.59 129	65.59 211	88.21 160	92.68 70	74.66 140	78.96 187	86.42 292	69.06 97	95.26 86	75.54 178	90.09 124	93.62 100

tfpn200view9		76.42 285	75.37 285	79.55 300	89.13 155	57.65 356	85.17 262	83.60 334	73.41 175	76.45 250	86.39 293	52.12 298	91.95 248	48.33 413	83.75 240	89.07 287

thres400		76.50 281	75.37 285	79.86 290	89.13 155	57.65 356	85.17 262	83.60 334	73.41 175	76.45 250	86.39 293	52.12 298	91.95 248	48.33 413	83.75 240	90.00 260

v7n		78.97 224	77.58 240	83.14 195	83.45 342	65.51 212	88.32 157	91.21 138	73.69 165	72.41 329	86.32 295	57.93 242	93.81 156	69.18 249	75.65 351	90.11 252

MAR-MVS		81.84 143	80.70 153	85.27 93	91.32 88	71.53 58	89.82 86	90.92 147	69.77 265	78.50 198	86.21 296	62.36 184	94.52 122	65.36 283	92.05 91	89.77 272
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
v1144		80.03 197	79.03 200	83.01 203	83.78 333	64.51 240	87.11 199	90.57 159	71.96 205	78.08 211	86.20 297	61.41 203	93.94 146	74.93 184	77.23 325	90.60 230

test_vis1_n_1920		75.52 298	75.78 274	74.75 372	79.84 406	57.44 360	83.26 316	85.52 309	62.83 369	79.34 184	86.17 298	45.10 378	79.71 414	78.75 133	81.21 277	87.10 351

V42		79.38 213	78.24 218	82.83 212	81.10 392	65.50 213	85.55 254	89.82 184	71.57 213	78.21 206	86.12 299	60.66 219	93.18 194	75.64 175	75.46 357	89.81 271

PVSNet_BlendedMVS		80.60 180	80.02 171	82.36 231	88.85 162	65.40 214	86.16 237	92.00 104	69.34 273	78.11 209	86.09 300	66.02 141	94.27 130	71.52 221	82.06 268	87.39 338

v1192		79.59 204	78.43 213	83.07 200	83.55 340	64.52 239	86.93 207	90.58 157	70.83 232	77.78 218	85.90 301	59.15 233	93.94 146	73.96 194	77.19 327	90.76 222

SixPastTwentyTwo		73.37 325	71.26 339	79.70 294	85.08 304	57.89 351	85.57 250	83.56 336	71.03 228	65.66 404	85.88 302	42.10 399	92.57 220	59.11 339	63.34 426	88.65 311

EPNet_dtu		75.46 299	74.86 291	77.23 345	82.57 368	54.60 398	86.89 208	83.09 346	71.64 208	66.25 401	85.86 303	55.99 261	88.04 345	54.92 375	86.55 189	89.05 292
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 322	73.64 310	73.51 384	82.80 362	55.01 395	76.12 405	81.69 365	62.47 374	74.68 299	85.85 304	57.32 250	78.11 421	60.86 324	80.93 279	87.39 338

ETV-MVS		84.90 86	84.67 86	85.59 85	89.39 141	68.66 126	88.74 138	92.64 76	79.97 16	84.10 101	85.71 305	69.32 92	95.38 81	80.82 111	91.37 103	92.72 145

test_cas_vis1_n_1920		73.76 320	73.74 309	73.81 382	75.90 428	59.77 330	80.51 354	82.40 357	58.30 410	81.62 145	85.69 306	44.35 384	76.41 432	76.29 165	78.61 306	85.23 384

v1240		78.99 223	77.78 232	82.64 224	83.21 348	63.54 268	86.62 221	90.30 170	69.74 268	77.33 226	85.68 307	57.04 254	93.76 160	73.13 204	76.92 329	90.62 228

v144192		79.47 207	78.37 214	82.78 219	83.35 343	63.96 252	86.96 204	90.36 167	69.99 258	77.50 222	85.67 308	60.66 219	93.77 159	74.27 191	76.58 335	90.62 228

tfpnnormal		74.39 310	73.16 316	78.08 328	86.10 278	58.05 346	84.65 279	87.53 270	70.32 250	71.22 344	85.63 309	54.97 267	89.86 311	43.03 437	75.02 367	86.32 363

PS-MVSNAJ		81.69 147	81.02 148	83.70 173	89.51 133	68.21 141	84.28 291	90.09 177	70.79 233	81.26 152	85.62 310	63.15 170	94.29 128	75.62 176	88.87 147	88.59 313

SSC-MVS3.2		73.35 328	73.39 312	73.23 385	85.30 297	49.01 436	74.58 420	81.57 366	75.21 121	73.68 312	85.58 311	52.53 290	82.05 402	54.33 379	77.69 322	88.63 312

v1921920		79.22 216	78.03 222	82.80 215	83.30 345	63.94 254	86.80 212	90.33 168	69.91 261	77.48 223	85.53 312	58.44 239	93.75 161	73.60 196	76.85 332	90.71 226

test_0402		72.79 336	70.44 347	79.84 291	88.13 197	65.99 199	85.93 242	84.29 325	65.57 333	67.40 385	85.49 313	46.92 356	92.61 217	35.88 451	74.38 373	80.94 430

v148		78.72 230	77.80 231	81.47 249	82.73 364	61.96 302	86.30 232	88.08 253	73.26 180	76.18 258	85.47 314	62.46 182	92.36 232	71.92 220	73.82 379	90.09 254

USDC		70.33 360	68.37 361	76.21 352	80.60 396	56.23 379	79.19 374	86.49 294	60.89 386	61.29 427	85.47 314	31.78 439	89.47 320	53.37 384	76.21 346	82.94 416

VortexMVS		78.57 235	77.89 227	80.59 274	85.89 280	62.76 288	85.61 249	89.62 194	72.06 203	74.99 293	85.38 316	55.94 262	90.77 299	74.99 183	76.58 335	88.23 320

MVP-Stereo		76.12 289	74.46 299	81.13 262	85.37 295	69.79 94	84.42 288	87.95 259	65.03 340	67.46 382	85.33 317	53.28 288	91.73 258	58.01 352	83.27 253	81.85 425
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 244	76.99 253	81.78 242	85.66 285	66.99 181	84.66 277	90.47 161	55.08 429	72.02 335	85.27 318	63.83 161	94.11 140	66.10 277	89.80 131	84.24 398

DIV-MVS_self_test		77.72 257	76.76 259	80.58 275	82.48 371	60.48 322	83.09 320	87.86 262	69.22 278	74.38 305	85.24 319	62.10 189	91.53 270	71.09 226	75.40 360	89.74 273

FE-MVS		77.78 255	75.68 276	84.08 151	88.09 200	66.00 198	83.13 319	87.79 264	68.42 298	78.01 212	85.23 320	45.50 376	95.12 91	59.11 339	85.83 207	91.11 207

cl____		77.72 257	76.76 259	80.58 275	82.49 370	60.48 322	83.09 320	87.87 261	69.22 278	74.38 305	85.22 321	62.10 189	91.53 270	71.09 226	75.41 359	89.73 274

HyFIR lowres test		77.53 263	75.40 283	83.94 167	89.59 129	66.62 187	80.36 357	88.64 245	56.29 425	76.45 250	85.17 322	57.64 246	93.28 181	61.34 321	83.10 256	91.91 182

pmmvs4		74.03 318	71.91 329	80.39 278	81.96 376	68.32 134	81.45 339	82.14 359	59.32 400	69.87 360	85.13 323	52.40 294	88.13 344	60.21 329	74.74 370	84.73 394

TDRefinement		67.49 383	64.34 395	76.92 347	73.47 444	61.07 313	84.86 273	82.98 350	59.77 396	58.30 439	85.13 323	26.06 447	87.89 347	47.92 419	60.59 436	81.81 426

Fast-Effi-MVS+		80.81 168	79.92 173	83.47 179	88.85 162	64.51 240	85.53 256	89.39 202	70.79 233	78.49 199	85.06 325	67.54 118	93.58 165	67.03 272	86.58 188	92.32 165

PVSNet_Blended		80.98 163	80.34 162	82.90 209	88.85 162	65.40 214	84.43 287	92.00 104	67.62 305	78.11 209	85.05 326	66.02 141	94.27 130	71.52 221	89.50 136	89.01 294

ttmdpeth		59.91 411	57.10 415	68.34 421	67.13 458	46.65 445	74.64 419	67.41 448	48.30 444	62.52 425	85.04 327	20.40 457	75.93 437	42.55 439	45.90 459	82.44 419

test_fmvs1_n		70.86 353	70.24 350	72.73 393	72.51 451	55.28 392	81.27 342	79.71 392	51.49 440	78.73 191	84.87 328	27.54 446	77.02 426	76.06 169	79.97 295	85.88 375

WBMVS		73.43 324	72.81 320	75.28 364	87.91 208	50.99 428	78.59 385	81.31 371	65.51 336	74.47 303	84.83 329	46.39 361	86.68 359	58.41 347	77.86 318	88.17 323

CMPMVS	51.72 21	70.19 362	68.16 364	76.28 351	73.15 447	57.55 358	79.47 369	83.92 330	48.02 445	56.48 445	84.81 330	43.13 391	86.42 363	62.67 305	81.81 272	84.89 391
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 378	67.61 377	71.31 405	78.51 420	47.01 443	84.47 283	84.27 326	42.27 452	66.44 400	84.79 331	40.44 409	83.76 388	58.76 344	68.54 412	83.17 410

BH-w/o		78.21 242	77.33 247	80.84 269	88.81 166	65.13 222	84.87 272	87.85 263	69.75 266	74.52 302	84.74 332	61.34 205	93.11 198	58.24 350	85.84 206	84.27 397

pmmvs5		71.55 346	70.20 351	75.61 357	77.83 421	56.39 375	81.74 334	80.89 372	57.76 415	67.46 382	84.49 333	49.26 341	85.32 377	57.08 360	75.29 363	85.11 388

reproduce_monomvs		75.40 302	74.38 300	78.46 322	83.92 330	57.80 354	83.78 301	86.94 284	73.47 173	72.25 332	84.47 334	38.74 417	89.27 323	75.32 181	70.53 402	88.31 319

thres200		75.55 297	74.47 298	78.82 311	87.78 217	57.85 352	83.07 322	83.51 337	72.44 196	75.84 264	84.42 335	52.08 301	91.75 256	47.41 420	83.64 245	86.86 355

test_fmvs1		70.93 352	70.52 345	72.16 397	73.71 440	55.05 394	80.82 345	78.77 401	51.21 441	78.58 196	84.41 336	31.20 441	76.94 427	75.88 173	80.12 294	84.47 396

testing3		68.56 377	67.67 376	71.22 406	87.33 236	42.87 456	83.06 323	71.54 436	70.36 247	69.08 368	84.38 337	30.33 443	85.69 371	37.50 449	75.45 358	85.09 389

test_fmvs2		68.35 380	67.48 379	70.98 408	69.50 454	51.95 417	80.05 363	76.38 419	49.33 443	74.65 300	84.38 337	23.30 455	75.40 443	74.51 188	75.17 366	85.60 378

eth_miper_zixun_eth		77.92 252	76.69 262	81.61 247	83.00 356	61.98 301	83.15 318	89.20 218	69.52 270	74.86 296	84.35 339	61.76 195	92.56 221	71.50 223	72.89 387	90.28 245

myMVS_eth3d28		73.62 321	73.53 311	73.90 381	88.20 192	47.41 441	78.06 392	79.37 395	74.29 150	73.98 308	84.29 340	44.67 379	83.54 391	51.47 393	87.39 173	90.74 224

testing91		76.54 279	75.66 278	79.18 306	88.43 185	55.89 383	81.08 343	83.00 349	73.76 163	75.34 278	84.29 340	46.20 367	90.07 308	64.33 291	84.50 224	91.58 193

c3_l		78.75 228	77.91 225	81.26 257	82.89 361	61.56 307	84.09 297	89.13 222	69.97 259	75.56 268	84.29 340	66.36 133	92.09 242	73.47 199	75.48 355	90.12 251

testing99		76.09 291	75.12 290	79.00 307	88.16 194	55.50 389	80.79 347	81.40 369	73.30 179	75.17 286	84.27 343	44.48 382	90.02 309	64.28 292	84.22 233	91.48 198

UWE-MVS		72.13 343	71.49 333	74.03 379	86.66 263	47.70 438	81.40 341	76.89 417	63.60 360	75.59 267	84.22 344	39.94 411	85.62 372	48.98 410	86.13 198	88.77 306

Fast-Effi-MVS+-dtu		78.02 249	76.49 265	82.62 225	83.16 352	66.96 184	86.94 206	87.45 273	72.45 194	71.49 341	84.17 345	54.79 272	91.58 262	67.61 263	80.31 290	89.30 285

IterMVS-SCA-FT		75.43 300	73.87 307	80.11 286	82.69 365	64.85 233	81.57 337	83.47 338	69.16 281	70.49 348	84.15 346	51.95 304	88.15 343	69.23 248	72.14 393	87.34 340

1314		76.53 280	75.30 287	80.21 284	83.93 329	62.32 297	84.66 277	88.81 234	60.23 392	70.16 354	84.07 347	55.30 266	90.73 300	67.37 266	83.21 254	87.59 335

cl22		78.07 247	77.01 251	81.23 258	82.37 373	61.83 304	83.55 309	87.98 257	68.96 288	75.06 291	83.87 348	61.40 204	91.88 252	73.53 197	76.39 340	89.98 263

EG-PatchMatch MVS		74.04 316	71.82 330	80.71 272	84.92 307	67.42 167	85.86 245	88.08 253	66.04 327	64.22 414	83.85 349	35.10 432	92.56 221	57.44 356	80.83 282	82.16 423

thisisatest0515		77.33 267	75.38 284	83.18 193	85.27 298	63.80 257	82.11 331	83.27 341	65.06 339	75.91 262	83.84 350	49.54 335	94.27 130	67.24 268	86.19 196	91.48 198

test20.03		67.45 384	66.95 385	68.94 415	75.48 433	44.84 452	77.50 397	77.67 407	66.66 316	63.01 421	83.80 351	47.02 355	78.40 419	42.53 440	68.86 411	83.58 407

miper_ehance_all_eth		78.59 234	77.76 234	81.08 263	82.66 366	61.56 307	83.65 305	89.15 220	68.87 289	75.55 269	83.79 352	66.49 131	92.03 243	73.25 202	76.39 340	89.64 275

MSDG		73.36 327	70.99 341	80.49 277	84.51 318	65.80 205	80.71 351	86.13 302	65.70 331	65.46 405	83.74 353	44.60 380	90.91 294	51.13 396	76.89 330	84.74 393

MonoMVSNet		76.49 284	75.80 273	78.58 316	81.55 383	58.45 341	86.36 230	86.22 299	74.87 135	74.73 298	83.73 354	51.79 309	88.73 335	70.78 228	72.15 392	88.55 315

testing11		75.14 305	74.01 303	78.53 319	88.16 194	56.38 376	80.74 350	80.42 383	70.67 236	72.69 326	83.72 355	43.61 389	89.86 311	62.29 309	83.76 239	89.36 283

IterMVS		74.29 311	72.94 319	78.35 323	81.53 384	63.49 270	81.58 336	82.49 356	68.06 302	69.99 357	83.69 356	51.66 311	85.54 373	65.85 280	71.64 396	86.01 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 339	71.71 331	74.35 375	82.19 374	52.00 416	79.22 373	77.29 413	64.56 345	72.95 322	83.68 357	51.35 312	83.26 395	58.33 349	75.80 349	87.81 329

UWE-MVS-28		65.32 398	64.93 392	66.49 427	78.70 418	38.55 464	77.86 396	64.39 456	62.00 380	64.13 415	83.60 358	41.44 402	76.00 436	31.39 456	80.89 280	84.92 390

sc_t1		72.19 342	69.51 353	80.23 283	84.81 309	61.09 312	84.68 276	80.22 387	60.70 388	71.27 342	83.58 359	36.59 427	89.24 324	60.41 326	63.31 427	90.37 240

testing222		74.04 316	72.66 322	78.19 325	87.89 209	55.36 390	81.06 344	79.20 398	71.30 219	74.65 300	83.57 360	39.11 416	88.67 337	51.43 395	85.75 208	90.53 233

Effi-MVS+-dtu		80.03 197	78.57 209	84.42 128	85.13 303	68.74 120	88.77 134	88.10 252	74.99 127	74.97 294	83.49 361	57.27 251	93.36 179	73.53 197	80.88 281	91.18 205

baseline2		75.70 295	73.83 308	81.30 255	83.26 346	61.79 305	82.57 327	80.65 376	66.81 312	66.88 390	83.42 362	57.86 244	92.19 239	63.47 296	79.57 297	89.91 265

mvs5depth		69.45 369	67.45 380	75.46 362	73.93 438	55.83 384	79.19 374	83.23 342	66.89 311	71.63 339	83.32 363	33.69 435	85.09 378	59.81 332	55.34 446	85.46 380

TinyColmap		67.30 386	64.81 393	74.76 371	81.92 378	56.68 371	80.29 359	81.49 368	60.33 390	56.27 446	83.22 364	24.77 451	87.66 351	45.52 430	69.47 406	79.95 435

mvsany_test1		62.30 407	61.26 411	65.41 429	69.52 453	54.86 396	66.86 448	49.78 469	46.65 446	68.50 374	83.21 365	49.15 342	66.28 461	56.93 363	60.77 434	75.11 445

test_vis1_n		69.85 367	69.21 356	71.77 399	72.66 450	55.27 393	81.48 338	76.21 420	52.03 437	75.30 283	83.20 366	28.97 444	76.22 434	74.60 187	78.41 314	83.81 404

CostFormer		75.24 304	73.90 306	79.27 303	82.65 367	58.27 344	80.80 346	82.73 355	61.57 382	75.33 282	83.13 367	55.52 264	91.07 291	64.98 287	78.34 315	88.45 316

MVStest1		56.63 415	52.76 421	68.25 422	61.67 464	53.25 412	71.67 429	68.90 446	38.59 457	50.59 453	83.05 368	25.08 449	70.66 454	36.76 450	38.56 460	80.83 431

WB-MVSnew		71.96 345	71.65 332	72.89 391	84.67 316	51.88 419	82.29 329	77.57 408	62.31 375	73.67 313	83.00 369	53.49 286	81.10 409	45.75 429	82.13 267	85.70 377

ETVMVS		72.25 341	71.05 340	75.84 354	87.77 218	51.91 418	79.39 370	74.98 424	69.26 276	73.71 311	82.95 370	40.82 408	86.14 365	46.17 426	84.43 229	89.47 279

miper_lstm_enhance		74.11 315	73.11 317	77.13 346	80.11 402	59.62 332	72.23 427	86.92 286	66.76 314	70.40 349	82.92 371	56.93 255	82.92 396	69.06 251	72.63 388	88.87 301

GA-MVS		76.87 275	75.17 289	81.97 240	82.75 363	62.58 289	81.44 340	86.35 298	72.16 202	74.74 297	82.89 372	46.20 367	92.02 245	68.85 254	81.09 278	91.30 203

K. test v3		71.19 348	68.51 360	79.21 305	83.04 355	57.78 355	84.35 290	76.91 416	72.90 190	62.99 422	82.86 373	39.27 413	91.09 290	61.65 317	52.66 449	88.75 307

MS-PatchMatch		73.83 319	72.67 321	77.30 344	83.87 331	66.02 196	81.82 332	84.66 319	61.37 385	68.61 372	82.82 374	47.29 352	88.21 342	59.27 336	84.32 231	77.68 440

lessismore_v0				78.97 308	81.01 393	57.15 363		65.99 451		61.16 428	82.82 374	39.12 415	91.34 279	59.67 333	46.92 456	88.43 317

D2MVS		74.82 307	73.21 315	79.64 297	79.81 407	62.56 291	80.34 358	87.35 274	64.37 348	68.86 369	82.66 376	46.37 363	90.10 307	67.91 261	81.24 276	86.25 364

Anonymous20231206		68.60 375	67.80 373	71.02 407	80.23 401	50.75 430	78.30 390	80.47 380	56.79 422	66.11 403	82.63 377	46.35 364	78.95 417	43.62 435	75.70 350	83.36 409

MIMVSNet		70.69 355	69.30 354	74.88 369	84.52 317	56.35 378	75.87 409	79.42 394	64.59 344	67.76 377	82.41 378	41.10 405	81.54 405	46.64 424	81.34 274	86.75 358

UBG		73.08 332	72.27 327	75.51 360	88.02 203	51.29 426	78.35 389	77.38 412	65.52 334	73.87 310	82.36 379	45.55 374	86.48 362	55.02 374	84.39 230	88.75 307

OpenMVS_ROB	64.09 19	70.56 357	68.19 363	77.65 337	80.26 399	59.41 336	85.01 269	82.96 351	58.76 407	65.43 406	82.33 380	37.63 424	91.23 283	45.34 432	76.03 347	82.32 420

miper_enhance_ethall		77.87 254	76.86 255	80.92 268	81.65 380	61.38 309	82.68 325	88.98 228	65.52 334	75.47 270	82.30 381	65.76 145	92.00 246	72.95 205	76.39 340	89.39 282

test0.0.03 1		68.00 382	67.69 375	68.90 416	77.55 422	47.43 439	75.70 410	72.95 435	66.66 316	66.56 395	82.29 382	48.06 349	75.87 438	44.97 433	74.51 372	83.41 408

PVSNet	64.34 18	72.08 344	70.87 343	75.69 356	86.21 272	56.44 374	74.37 421	80.73 375	62.06 379	70.17 353	82.23 383	42.86 393	83.31 394	54.77 376	84.45 228	87.32 341

MIMVSNet1		68.58 376	66.78 386	73.98 380	80.07 403	51.82 420	80.77 348	84.37 322	64.40 347	59.75 435	82.16 384	36.47 428	83.63 390	42.73 438	70.33 403	86.48 362

CL-MVSNet_self_test		72.37 339	71.46 334	75.09 366	79.49 413	53.53 406	80.76 349	85.01 317	69.12 282	70.51 347	82.05 385	57.92 243	84.13 386	52.27 389	66.00 420	87.60 333

tpm2		73.26 329	71.46 334	78.63 313	83.34 344	56.71 370	80.65 352	80.40 384	56.63 423	73.55 314	82.02 386	51.80 308	91.24 282	56.35 369	78.42 313	87.95 325

PatchMatch-RL		72.38 338	70.90 342	76.80 349	88.60 178	67.38 170	79.53 368	76.17 421	62.75 371	69.36 365	82.00 387	45.51 375	84.89 381	53.62 382	80.58 286	78.12 439

FMVSNet5		69.50 368	67.96 368	74.15 378	82.97 359	55.35 391	80.01 364	82.12 360	62.56 373	63.02 420	81.53 388	36.92 425	81.92 403	48.42 412	74.06 375	85.17 387

CR-MVSNet		73.37 325	71.27 338	79.67 296	81.32 390	65.19 220	75.92 407	80.30 385	59.92 395	72.73 324	81.19 389	52.50 292	86.69 358	59.84 331	77.71 320	87.11 349

Patchmtry		70.74 354	69.16 357	75.49 361	80.72 394	54.07 403	74.94 418	80.30 385	58.34 409	70.01 355	81.19 389	52.50 292	86.54 360	53.37 384	71.09 400	85.87 376

IB-MVS	68.01 15	75.85 294	73.36 314	83.31 186	84.76 311	66.03 195	83.38 313	85.06 315	70.21 254	69.40 364	81.05 391	45.76 372	94.66 117	65.10 286	75.49 354	89.25 286
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		76.72 278	74.64 294	82.99 204	85.78 283	65.88 202	82.33 328	89.21 217	60.85 387	72.74 323	81.02 392	47.28 353	93.75 161	67.48 265	85.02 216	89.34 284

LF4IMVS		64.02 403	62.19 407	69.50 413	70.90 452	53.29 411	76.13 404	77.18 414	52.65 435	58.59 437	80.98 393	23.55 454	76.52 430	53.06 386	66.66 416	78.68 438

Anonymous20240521		68.80 374	67.22 383	73.55 383	74.33 436	54.11 402	83.18 317	85.61 308	58.15 411	61.68 426	80.94 394	30.71 442	81.27 408	57.00 362	73.34 385	85.28 383

gm-plane-assit					81.40 386	53.83 405			62.72 372		80.94 394		92.39 230	63.40 298

UnsupCasMVSNet_eth		67.33 385	65.99 389	71.37 402	73.48 443	51.47 424	75.16 414	85.19 312	65.20 337	60.78 429	80.93 396	42.35 395	77.20 425	57.12 359	53.69 448	85.44 381

dmvs_re		71.14 349	70.58 344	72.80 392	81.96 376	59.68 331	75.60 411	79.34 396	68.55 294	69.27 367	80.72 397	49.42 337	76.54 429	52.56 388	77.79 319	82.19 422

MDTV_nov1_ep13			69.97 352		83.18 350	53.48 407	77.10 402	80.18 389	60.45 389	69.33 366	80.44 398	48.89 347	86.90 357	51.60 392	78.51 309

pmmvs-eth3d		70.50 358	67.83 372	78.52 320	77.37 424	66.18 194	81.82 332	81.51 367	58.90 405	63.90 418	80.42 399	42.69 394	86.28 364	58.56 345	65.30 422	83.11 412

tt0320		70.49 359	68.03 367	77.89 331	84.78 310	59.12 337	83.55 309	80.44 382	58.13 412	67.43 384	80.41 400	39.26 414	87.54 352	55.12 373	63.18 428	86.99 352

mmtdpeth		74.16 314	73.01 318	77.60 340	83.72 335	61.13 310	85.10 266	85.10 314	72.06 203	77.21 234	80.33 401	43.84 387	85.75 369	77.14 154	52.61 450	85.91 374

tt0320-xc		70.11 363	67.45 380	78.07 329	85.33 296	59.51 335	83.28 315	78.96 400	58.77 406	67.10 388	80.28 402	36.73 426	87.42 353	56.83 365	59.77 438	87.29 342

PM-MVS		66.41 393	64.14 396	73.20 388	73.92 439	56.45 373	78.97 378	64.96 455	63.88 358	64.72 411	80.24 403	19.84 459	83.44 393	66.24 274	64.52 424	79.71 436

SCA		74.22 313	72.33 326	79.91 289	84.05 327	62.17 299	79.96 365	79.29 397	66.30 324	72.38 330	80.13 404	51.95 304	88.60 338	59.25 337	77.67 323	88.96 298

Patchmatch-test		64.82 401	63.24 402	69.57 412	79.42 414	49.82 434	63.49 460	69.05 444	51.98 438	59.95 434	80.13 404	50.91 317	70.98 453	40.66 443	73.57 380	87.90 327

tpmrst		72.39 337	72.13 328	73.18 389	80.54 397	49.91 433	79.91 366	79.08 399	63.11 363	71.69 338	79.95 406	55.32 265	82.77 398	65.66 282	73.89 377	86.87 354

DSMNet-mixed		57.77 414	56.90 416	60.38 435	67.70 456	35.61 466	69.18 440	53.97 467	32.30 465	57.49 442	79.88 407	40.39 410	68.57 459	38.78 447	72.37 389	76.97 441

MDA-MVSNet-bldmvs		66.68 390	63.66 400	75.75 355	79.28 415	60.56 321	73.92 423	78.35 404	64.43 346	50.13 454	79.87 408	44.02 386	83.67 389	46.10 427	56.86 440	83.03 414

PatchmatchNet		73.12 331	71.33 337	78.49 321	83.18 350	60.85 316	79.63 367	78.57 402	64.13 350	71.73 337	79.81 409	51.20 315	85.97 368	57.40 357	76.36 345	88.66 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 387	65.33 391	73.02 390	75.86 429	52.54 414	80.26 361	80.56 378	63.80 359	60.39 430	79.70 410	41.41 403	84.66 384	43.34 436	62.62 429	81.86 424

Syy-MVS		68.05 381	67.85 370	68.67 419	84.68 313	40.97 462	78.62 383	73.08 433	66.65 319	66.74 393	79.46 411	52.11 300	82.30 400	32.89 454	76.38 343	82.75 417

myMVS_eth3d		67.02 388	66.29 388	69.21 414	84.68 313	42.58 457	78.62 383	73.08 433	66.65 319	66.74 393	79.46 411	31.53 440	82.30 400	39.43 446	76.38 343	82.75 417

ppachtmachnet_test		70.04 364	67.34 382	78.14 326	79.80 408	61.13 310	79.19 374	80.59 377	59.16 402	65.27 407	79.29 413	46.75 360	87.29 354	49.33 408	66.72 415	86.00 373

EPMVS		69.02 372	68.16 364	71.59 400	79.61 411	49.80 435	77.40 398	66.93 449	62.82 370	70.01 355	79.05 414	45.79 371	77.86 423	56.58 367	75.26 364	87.13 348

PMMVS		69.34 370	68.67 359	71.35 404	75.67 431	62.03 300	75.17 413	73.46 431	50.00 442	68.68 370	79.05 414	52.07 302	78.13 420	61.16 322	82.77 259	73.90 446

test-LLR		72.94 335	72.43 324	74.48 373	81.35 388	58.04 347	78.38 386	77.46 409	66.66 316	69.95 358	79.00 416	48.06 349	79.24 415	66.13 275	84.83 219	86.15 367

test-mter		71.41 347	70.39 349	74.48 373	81.35 388	58.04 347	78.38 386	77.46 409	60.32 391	69.95 358	79.00 416	36.08 430	79.24 415	66.13 275	84.83 219	86.15 367

KD-MVS_self_test		68.81 373	67.59 378	72.46 396	74.29 437	45.45 446	77.93 394	87.00 282	63.12 362	63.99 417	78.99 418	42.32 396	84.77 382	56.55 368	64.09 425	87.16 347

test_fmvs3		63.36 405	61.82 408	67.98 423	62.51 463	46.96 444	77.37 399	74.03 430	45.24 448	67.50 381	78.79 419	12.16 467	72.98 452	72.77 208	66.02 419	83.99 402

KD-MVS_2432*1600		66.22 395	63.89 398	73.21 386	75.47 434	53.42 408	70.76 434	84.35 323	64.10 352	66.52 397	78.52 420	34.55 433	84.98 379	50.40 399	50.33 453	81.23 428

miper_refine_blended		66.22 395	63.89 398	73.21 386	75.47 434	53.42 408	70.76 434	84.35 323	64.10 352	66.52 397	78.52 420	34.55 433	84.98 379	50.40 399	50.33 453	81.23 428

tpmvs		71.09 350	69.29 355	76.49 350	82.04 375	56.04 381	78.92 379	81.37 370	64.05 354	67.18 387	78.28 422	49.74 334	89.77 313	49.67 406	72.37 389	83.67 406

our_test_3		69.14 371	67.00 384	75.57 358	79.80 408	58.80 338	77.96 393	77.81 406	59.55 398	62.90 423	78.25 423	47.43 351	83.97 387	51.71 391	67.58 414	83.93 403

MDA-MVSNet_test_wron		65.03 399	62.92 403	71.37 402	75.93 427	56.73 368	69.09 443	74.73 427	57.28 420	54.03 449	77.89 424	45.88 369	74.39 447	49.89 405	61.55 432	82.99 415

YYNet1		65.03 399	62.91 404	71.38 401	75.85 430	56.60 372	69.12 442	74.66 429	57.28 420	54.12 448	77.87 425	45.85 370	74.48 446	49.95 404	61.52 433	83.05 413

ambc				75.24 365	73.16 446	50.51 431	63.05 461	87.47 272		64.28 413	77.81 426	17.80 461	89.73 315	57.88 353	60.64 435	85.49 379

tpm cat1		70.57 356	68.31 362	77.35 343	82.41 372	57.95 350	78.08 391	80.22 387	52.04 436	68.54 373	77.66 427	52.00 303	87.84 348	51.77 390	72.07 394	86.25 364

dp		66.80 389	65.43 390	70.90 409	79.74 410	48.82 437	75.12 416	74.77 426	59.61 397	64.08 416	77.23 428	42.89 392	80.72 411	48.86 411	66.58 417	83.16 411

TESTMET0.1,1		69.89 366	69.00 358	72.55 394	79.27 416	56.85 366	78.38 386	74.71 428	57.64 416	68.09 376	77.19 429	37.75 423	76.70 428	63.92 294	84.09 234	84.10 401

CHOSEN 280x420		66.51 392	64.71 394	71.90 398	81.45 385	63.52 269	57.98 463	68.95 445	53.57 432	62.59 424	76.70 430	46.22 366	75.29 444	55.25 372	79.68 296	76.88 442

PatchT		68.46 379	67.85 370	70.29 410	80.70 395	43.93 454	72.47 426	74.88 425	60.15 393	70.55 346	76.57 431	49.94 331	81.59 404	50.58 397	74.83 369	85.34 382

mvsany_test3		53.99 418	51.45 423	61.61 434	55.51 468	44.74 453	63.52 459	45.41 473	43.69 451	58.11 440	76.45 432	17.99 460	63.76 464	54.77 376	47.59 455	76.34 443

RPMNet		73.51 323	70.49 346	82.58 227	81.32 390	65.19 220	75.92 407	92.27 88	57.60 417	72.73 324	76.45 432	52.30 295	95.43 76	48.14 417	77.71 320	87.11 349

dmvs_testset		62.63 406	64.11 397	58.19 437	78.55 419	24.76 475	75.28 412	65.94 452	67.91 303	60.34 431	76.01 434	53.56 284	73.94 450	31.79 455	67.65 413	75.88 444

ADS-MVSNet2		66.20 397	63.33 401	74.82 370	79.92 404	58.75 339	67.55 446	75.19 423	53.37 433	65.25 408	75.86 435	42.32 396	80.53 412	41.57 441	68.91 409	85.18 385

ADS-MVSNet		64.36 402	62.88 405	68.78 418	79.92 404	47.17 442	67.55 446	71.18 437	53.37 433	65.25 408	75.86 435	42.32 396	73.99 449	41.57 441	68.91 409	85.18 385

EGC-MVSNET		52.07 424	47.05 428	67.14 425	83.51 341	60.71 318	80.50 355	67.75 447	0.07 475	0.43 476	75.85 437	24.26 452	81.54 405	28.82 458	62.25 430	59.16 458

new-patchmatchnet		61.73 408	61.73 409	61.70 433	72.74 449	24.50 476	69.16 441	78.03 405	61.40 383	56.72 444	75.53 438	38.42 419	76.48 431	45.95 428	57.67 439	84.13 400

N_pmnet		52.79 422	53.26 420	51.40 447	78.99 417	7.68 481	69.52 438	3.89 480	51.63 439	57.01 443	74.98 439	40.83 407	65.96 462	37.78 448	64.67 423	80.56 434

WB-MVS		54.94 416	54.72 417	55.60 443	73.50 442	20.90 477	74.27 422	61.19 460	59.16 402	50.61 452	74.15 440	47.19 354	75.78 439	17.31 468	35.07 462	70.12 450

patchmatchnet-post											74.00 441	51.12 316	88.60 338

GG-mvs-BLEND				75.38 363	81.59 382	55.80 385	79.32 371	69.63 441		67.19 386	73.67 442	43.24 390	88.90 334	50.41 398	84.50 224	81.45 427

SSC-MVS		53.88 419	53.59 419	54.75 445	72.87 448	19.59 478	73.84 424	60.53 462	57.58 418	49.18 456	73.45 443	46.34 365	75.47 442	16.20 471	32.28 464	69.20 451

Patchmatch-RL test		70.24 361	67.78 374	77.61 338	77.43 423	59.57 334	71.16 431	70.33 438	62.94 367	68.65 371	72.77 444	50.62 321	85.49 374	69.58 246	66.58 417	87.77 330

FPMVS		53.68 420	51.64 422	59.81 436	65.08 460	51.03 427	69.48 439	69.58 442	41.46 453	40.67 460	72.32 445	16.46 463	70.00 457	24.24 464	65.42 421	58.40 460

UnsupCasMVSNet_bld		63.70 404	61.53 410	70.21 411	73.69 441	51.39 425	72.82 425	81.89 362	55.63 427	57.81 441	71.80 446	38.67 418	78.61 418	49.26 409	52.21 451	80.63 432

APD_test1		53.31 421	49.93 426	63.42 432	65.68 459	50.13 432	71.59 430	66.90 450	34.43 462	40.58 461	71.56 447	8.65 472	76.27 433	34.64 453	55.36 445	63.86 456

test_f		52.09 423	50.82 424	55.90 441	53.82 471	42.31 460	59.42 462	58.31 465	36.45 460	56.12 447	70.96 448	12.18 466	57.79 467	53.51 383	56.57 442	67.60 452

PVSNet_0	57.27 20	61.67 409	59.27 412	68.85 417	79.61 411	57.44 360	68.01 444	73.44 432	55.93 426	58.54 438	70.41 449	44.58 381	77.55 424	47.01 421	35.91 461	71.55 449

pmmvs3		57.79 413	54.26 418	68.37 420	64.02 462	56.72 369	75.12 416	65.17 453	40.20 454	52.93 450	69.86 450	20.36 458	75.48 441	45.45 431	55.25 447	72.90 448

test_vis1_rt		60.28 410	58.42 413	65.84 428	67.25 457	55.60 388	70.44 436	60.94 461	44.33 450	59.00 436	66.64 451	24.91 450	68.67 458	62.80 301	69.48 405	73.25 447

new_pmnet		50.91 425	50.29 425	52.78 446	68.58 455	34.94 468	63.71 458	56.63 466	39.73 455	44.95 457	65.47 452	21.93 456	58.48 466	34.98 452	56.62 441	64.92 454

gg-mvs-nofinetune		69.95 365	67.96 368	75.94 353	83.07 353	54.51 400	77.23 400	70.29 439	63.11 363	70.32 350	62.33 453	43.62 388	88.69 336	53.88 381	87.76 167	84.62 395

JIA-IIPM		66.32 394	62.82 406	76.82 348	77.09 425	61.72 306	65.34 454	75.38 422	58.04 414	64.51 412	62.32 454	42.05 400	86.51 361	51.45 394	69.22 408	82.21 421

LCM-MVSNet		54.25 417	49.68 427	67.97 424	53.73 472	45.28 449	66.85 449	80.78 374	35.96 461	39.45 462	62.23 455	8.70 471	78.06 422	48.24 416	51.20 452	80.57 433

PMMVS2		40.82 433	38.86 437	46.69 448	53.84 470	16.45 479	48.61 466	49.92 468	37.49 458	31.67 463	60.97 456	8.14 473	56.42 468	28.42 459	30.72 465	67.19 453

testf1		45.72 428	41.96 432	57.00 438	56.90 466	45.32 447	66.14 451	59.26 463	26.19 466	30.89 465	60.96 457	4.14 475	70.64 455	26.39 462	46.73 457	55.04 461

APD_test2		45.72 428	41.96 432	57.00 438	56.90 466	45.32 447	66.14 451	59.26 463	26.19 466	30.89 465	60.96 457	4.14 475	70.64 455	26.39 462	46.73 457	55.04 461

MVS-HIRNet		59.14 412	57.67 414	63.57 431	81.65 380	43.50 455	71.73 428	65.06 454	39.59 456	51.43 451	57.73 459	38.34 420	82.58 399	39.53 444	73.95 376	64.62 455

ANet_high		50.57 426	46.10 430	63.99 430	48.67 475	39.13 463	70.99 433	80.85 373	61.39 384	31.18 464	57.70 460	17.02 462	73.65 451	31.22 457	15.89 472	79.18 437

PMVS	37.38 22	44.16 432	40.28 436	55.82 442	40.82 477	42.54 459	65.12 455	63.99 457	34.43 462	24.48 468	57.12 461	3.92 477	76.17 435	17.10 469	55.52 444	48.75 463
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 430	45.38 431	45.55 449	73.36 445	26.85 473	67.72 445	34.19 475	54.15 431	49.65 455	56.41 462	25.43 448	62.94 465	19.45 466	28.09 466	46.86 465

test_vis3_rt		49.26 427	47.02 429	56.00 440	54.30 469	45.27 450	66.76 450	48.08 470	36.83 459	44.38 458	53.20 463	7.17 474	64.07 463	56.77 366	55.66 443	58.65 459

test_method		31.52 436	29.28 440	38.23 451	27.03 479	6.50 482	20.94 471	62.21 459	4.05 473	22.35 471	52.50 464	13.33 464	47.58 471	27.04 461	34.04 463	60.62 457

kuosan		39.70 434	40.40 435	37.58 452	64.52 461	26.98 471	65.62 453	33.02 476	46.12 447	42.79 459	48.99 465	24.10 453	46.56 473	12.16 474	26.30 467	39.20 466

DeepMVS_CX				27.40 455	40.17 478	26.90 472		24.59 479	17.44 471	23.95 469	48.61 466	9.77 469	26.48 474	18.06 467	24.47 468	28.83 468

MVE	26.22 23	30.37 438	25.89 442	43.81 450	44.55 476	35.46 467	28.87 470	39.07 474	18.20 470	18.58 472	40.18 467	2.68 478	47.37 472	17.07 470	23.78 469	48.60 464
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 431	41.86 434	55.16 444	77.03 426	51.52 423	32.50 469	80.52 379	32.46 464	27.12 467	35.02 468	9.52 470	75.50 440	22.31 465	60.21 437	38.45 467
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 435	30.64 438	35.15 453	52.87 473	27.67 470	57.09 464	47.86 471	24.64 468	16.40 473	33.05 469	11.23 468	54.90 469	14.46 472	18.15 470	22.87 469

EMVS		30.81 437	29.65 439	34.27 454	50.96 474	25.95 474	56.58 465	46.80 472	24.01 469	15.53 474	30.68 470	12.47 465	54.43 470	12.81 473	17.05 471	22.43 470

tmp_tt		18.61 440	21.40 443	10.23 457	4.82 480	10.11 480	34.70 468	30.74 478	1.48 474	23.91 470	26.07 471	28.42 445	13.41 476	27.12 460	15.35 473	7.17 471

X-MVStestdata		80.37 189	77.83 229	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	12.47 472	67.45 119	96.60 36	83.06 85	94.50 56	94.07 69

test_post											5.46 473	50.36 325	84.24 385

test_post1							78.90 380				5.43 474	48.81 348	85.44 376	59.25 337

wuyk23d		16.82 441	15.94 444	19.46 456	58.74 465	31.45 469	39.22 467	3.74 481	6.84 472	6.04 475	2.70 475	1.27 479	24.29 475	10.54 475	14.40 474	2.63 472

testmvs		6.04 444	8.02 447	0.10 459	0.08 481	0.03 484	69.74 437	0.04 482	0.05 476	0.31 477	1.68 476	0.02 481	0.04 477	0.24 476	0.02 475	0.25 474

test123		6.12 443	8.11 446	0.14 458	0.06 482	0.09 483	71.05 432	0.03 483	0.04 477	0.25 478	1.30 477	0.05 480	0.03 478	0.21 477	0.01 476	0.29 473

mmdepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

monomultidepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

test_blank		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uanet_test		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

DCPMVS		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

pcd_1.5k_mvsjas		5.26 445	7.02 448	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	63.15 170	0.00 479	0.00 478	0.00 477	0.00 475

sosnet-low-res		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

sosnet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uncertanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

Regformer		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

TestfortrainingZip							93.28 12

WAC-MVS						42.58 457								39.46 445

FOURS1					95.00 10	72.39 41	95.06 1	93.84 19	74.49 143	91.30 17

MSC_two_6792asdad				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 49

No_MVS				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 49

eth-test2					0.00 483

eth-test					0.00 483

IU-MVS					95.30 2	71.25 63		92.95 59	66.81 312	92.39 6				88.94 27	96.63 4	94.85 21

save fliter					93.80 43	72.35 44	90.47 73	91.17 140	74.31 148

test_0728_SECOND				87.71 34	95.34 1	71.43 60	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 61

GSMVS																88.96 298

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 313				88.96 298

sam_mvs												50.01 329

MTGPA								92.02 102

MTMP							92.18 38	32.83 477

test9_res														84.90 62	95.70 29	92.87 141

agg_prior2														82.91 89	95.45 32	92.70 146

agg_prior					92.85 67	71.94 52		91.78 118		84.41 93			94.93 100

test_prior4						72.60 34	89.01 123

test_prior				86.33 63	92.61 73	69.59 97		92.97 58					95.48 73			93.91 77

旧先验2							86.56 223		58.10 413	87.04 60			88.98 330	74.07 193

新几何2							86.29 234

无先验							87.48 184	88.98 228	60.00 394				94.12 139	67.28 267		88.97 297

原ACMM2							86.86 210

testdata2													91.01 292	62.37 308

segment_acmp												73.08 42

testdata1							84.14 296		75.71 103

test12				86.80 57	92.63 72	70.70 80		91.79 117		82.71 127		71.67 61	96.16 51		94.50 56	93.54 106

plane_prior7					90.08 115	68.51 130

plane_prior6					89.84 124	68.70 124						60.42 224

plane_prior5								92.44 81					95.38 81	78.71 134	86.32 192	91.33 201

plane_prior3						68.60 127			78.44 36	78.92 189

plane_prior2							91.25 59		79.12 28

plane_prior1					89.90 123

plane_prior						68.71 122	90.38 77		77.62 47						86.16 197

n2								0.00 484

nn								0.00 484

door-mid								69.98 440

test11								92.23 91

door								69.44 443

HQP5-MVS						66.98 182

HQP-NCC					89.33 143		89.17 114		76.41 85	77.23 230

ACMP_Plane					89.33 143		89.17 114		76.41 85	77.23 230

BP-MVS														77.47 149

HQP4-MVS										77.24 229			95.11 93			91.03 211

HQP3-MVS								92.19 96							85.99 201

HQP2-MVS												60.17 227

MDTV_nov1_ep13_2view						37.79 465	75.16 414		55.10 428	66.53 396		49.34 339		53.98 380		87.94 326

ACMMP++_ref															81.95 270

ACMMP++															81.25 275

Test By Simon												64.33 156

High-resolution multi-view benchmark