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 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 108	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 51
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 140	92.29 7	95.97 2	74.28 33	97.24 16	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 64	93.60 7	94.11 11	77.33 57	92.81 3	95.79 3	80.98 11

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 57	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 117
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 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 33

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	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 10	78.27 41	92.05 14	95.74 6	80.83 13

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	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 15	77.24 60	92.78 4	95.72 8	81.26 10	97.44 7	89.07 24	96.58 6	94.26 63

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 101	92.29 7	95.66 10	81.67 6	97.38 14	87.44 47	96.34 15	93.95 79
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 141	88.80 33	95.61 11	70.29 80	96.44 43	86.20 55	93.08 75	93.16 127

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 131	88.96 30	95.54 12	71.20 69	96.54 40	86.28 53	93.49 71	93.06 133

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 131	88.96 30	95.54 12	71.20 69	96.54 40	86.28 53	93.49 71	93.06 133

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 65	89.76 26	95.52 14	72.26 52	96.27 48	86.87 49	94.65 52	93.70 95

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 145	89.05 227	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 91	95.31 5

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 63	93.10 1	95.39 16	82.99 1	97.27 15

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 89	93.50 30	75.17 128	86.34 67	95.29 17	70.86 73	96.00 59	88.78 30	96.04 16	94.58 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 106	89.16 29	95.10 18	75.65 24	96.19 51	87.07 48	96.01 17	94.79 23

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 94	88.14 41	95.09 19	71.06 71	96.67 33	87.67 43	96.37 14	94.09 71

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 120	92.25 9	95.03 20		97.39 11	88.15 38	95.96 19	94.75 29

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 92	92.25 9	95.03 20	81.59 7	97.39 11	88.15 38	95.96 19	94.75 29

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 120	92.25 9	95.03 20	81.59 7	97.39 11	86.12 56	95.96 19	94.52 49

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 92	90.62 21	95.03 20	78.06 16	97.07 20	88.15 38	95.96 19	94.75 29

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 200	87.08 253	65.21 220	89.09 122	90.21 176	79.67 19	89.98 24	95.02 24	73.17 42	91.71 262	91.30 3	91.60 98	92.34 166

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 143	86.57 1	87.39 57	94.97 25	71.70 61	97.68 1	92.19 1	95.63 32	95.57 1

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 220	65.62 211	89.20 113	92.21 97	79.94 17	89.74 27	94.86 26	68.63 107	94.20 136	90.83 5	91.39 103	94.38 55

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 223	92.02 105	79.45 22	85.88 69	94.80 27	68.07 115	96.21 50	86.69 51	95.34 36	93.23 120

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 45	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 239	67.30 174	89.50 100	90.98 148	76.25 95	90.56 22	94.75 29	68.38 110	94.24 135	90.80 7	92.32 88	94.19 65

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 162	88.57 35	94.67 30	75.57 25	95.79 63	86.77 50	95.76 27

SR-MVS		86.73 43	86.67 47	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 145	86.84 64	94.65 31	67.31 124	95.77 64	84.80 67	92.85 78	92.84 147

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 79	84.45 93	94.52 32	69.09 98	96.70 31	84.37 73	94.83 49	94.03 74

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 76	84.66 88	94.52 32	68.81 104	96.65 34	84.53 71	94.90 45	94.00 76

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 191	88.58 34	94.52 32	73.36 38	96.49 42	84.26 74	95.01 41	92.70 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 60	85.88 64	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 173	85.94 68	94.51 35	65.80 147	95.61 67	83.04 88	92.51 83	93.53 110

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 82	83.68 111	94.46 36	67.93 117	95.95 62	84.20 77	94.39 61	93.23 120

SR-MVS-dyc-post		85.77 66	85.61 71	86.23 66	93.06 64	70.63 82	91.88 43	92.27 89	73.53 174	85.69 72	94.45 37	65.00 155	95.56 68	82.75 93	91.87 94	92.50 159

RE-MVS-def			85.48 74		93.06 64	70.63 82	91.88 43	92.27 89	73.53 174	85.69 72	94.45 37	63.87 163		82.75 93	91.87 94	92.50 159

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 76	84.91 81	94.44 39	70.78 74	96.61 36	84.53 71	94.89 46	93.66 96

PGM-MVS		86.68 45	86.27 54	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 111	83.86 107	94.42 40	67.87 119	96.64 35	82.70 97	94.57 56	93.66 96

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 108	94.40 41	72.24 53	96.28 47	85.65 58	95.30 39	93.62 103
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 56	86.32 52	85.14 98	87.20 244	68.54 130	89.57 98	90.44 165	75.31 119	87.49 54	94.39 42	72.86 47	92.72 218	89.04 26	90.56 117	94.16 66

fmvsm_s_conf0.1_n_2		83.80 100	83.79 100	83.83 172	85.62 290	64.94 232	87.03 202	86.62 296	74.32 150	87.97 47	94.33 43	60.67 221	92.60 221	89.72 14	87.79 169	93.96 77

fmvsm_l_conf0.5_n_9		85.84 65	86.63 48	83.46 183	87.12 252	66.01 198	88.56 147	89.43 203	75.59 110	89.32 28	94.32 44	72.89 46	91.21 287	90.11 11	92.33 87	93.16 127

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 74	85.24 76	94.32 44	71.76 59	96.93 23	85.53 60	95.79 26	94.32 60

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 199	82.14 3	86.65 65	94.28 46	68.28 113	97.46 6	90.81 6	95.31 38	95.15 8

test_fmvsmconf0.01_n		84.73 88	84.52 90	85.34 92	80.25 403	69.03 110	89.47 101	89.65 195	73.24 185	86.98 62	94.27 47	66.62 131	93.23 189	90.26 10	89.95 129	93.78 92

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 46	96.44 9	93.05 135

mPP-MVS		86.67 46	86.32 52	87.72 33	94.41 26	73.55 13	92.74 25	92.22 95	76.87 73	82.81 129	94.25 49	66.44 135	96.24 49	82.88 91	94.28 64	93.38 113

fmvsm_s_conf0.5_n_2		84.04 93	84.11 94	83.81 174	86.17 277	65.00 228	86.96 205	87.28 278	74.35 149	88.25 39	94.23 50	61.82 197	92.60 221	89.85 12	88.09 163	93.84 86

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 66	82.82 128	94.23 50	72.13 55	97.09 19	84.83 66	95.37 35	93.65 100
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 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 112	94.17 52	67.45 122	96.60 37	83.06 86	94.50 57	94.07 72

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 53	78.35 15	96.77 28	89.59 17	94.22 66	94.67 33
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 70	85.65 70	85.50 88	82.99 361	69.39 107	89.65 94	90.29 174	73.31 181	87.77 49	94.15 54	71.72 60	93.23 189	90.31 9	90.67 116	93.89 83

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 98	93.39 35	77.53 53	89.79 25	94.12 55	78.98 14	96.58 39	85.66 57	95.72 28	94.58 42

HPM-MVS_fast		85.35 78	84.95 84	86.57 63	93.69 46	70.58 84	92.15 40	91.62 128	73.89 163	82.67 131	94.09 56	62.60 181	95.54 70	80.93 110	92.93 77	93.57 106

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 232	87.75 50	94.07 57	74.01 36	96.70 31	84.66 69	94.84 48

fmvsm_s_conf0.1_n_a		83.32 119	82.99 116	84.28 140	83.79 335	68.07 145	89.34 110	82.85 356	69.80 266	87.36 58	94.06 58	68.34 112	91.56 268	87.95 41	83.46 253	93.21 123

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 58	76.43 19	96.84 25	88.48 35	95.99 18	94.34 58

test_fmvsmconf_n		85.92 61	86.04 62	85.57 87	85.03 309	69.51 100	89.62 97	90.58 160	73.42 177	87.75 50	94.02 60	72.85 48	93.24 188	90.37 8	90.75 114	93.96 77

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 60	82.45 3	96.87 24	83.77 81	96.48 8	94.88 16

PC_three_1452									68.21 303	92.02 15	94.00 62	82.09 5	95.98 61	84.58 70	96.68 2	94.95 12

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 62	74.83 26	93.78 158	87.63 44	94.27 65	93.65 100
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 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 72	84.68 85	93.99 64	70.67 76	96.82 26	84.18 78	95.01 41	93.90 82

test_fmvsm_n_1920		85.29 79	85.34 76	85.13 101	86.12 279	69.93 92	88.65 143	90.78 156	69.97 262	88.27 38	93.98 65	71.39 66	91.54 272	88.49 34	90.45 119	93.91 80

fmvsm_s_conf0.1_n		83.56 110	83.38 109	84.10 149	84.86 311	67.28 175	89.40 107	83.01 351	70.67 239	87.08 60	93.96 66	68.38 110	91.45 278	88.56 33	84.50 227	93.56 107

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 98	83.81 109	93.95 67	69.77 89	96.01 58	85.15 61	94.66 51	94.32 60
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		83.34 117	84.03 95	81.28 259	85.73 287	65.13 223	85.40 262	89.90 186	74.96 133	82.13 137	93.89 68	66.65 130	87.92 349	86.56 52	91.05 108	90.80 222

fmvsm_s_conf0.5_n_5		85.22 80	85.55 72	84.25 145	86.26 273	67.40 170	89.18 114	89.31 212	72.50 196	88.31 37	93.86 69	69.66 90	91.96 250	89.81 13	91.05 108	93.38 113

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 144	88.90 32	93.85 70	75.75 23	96.00 59	87.80 42	94.63 54	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 64	85.39 75	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 76	80.73 165	93.82 71	64.33 159	96.29 46	82.67 98	90.69 115	93.23 120
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		83.63 108	83.41 108	84.28 140	86.14 278	68.12 143	89.43 103	82.87 355	70.27 255	87.27 59	93.80 72	69.09 98	91.58 265	88.21 37	83.65 247	93.14 130

fmvsm_s_conf0.5_n_4		85.39 76	85.75 69	84.30 138	86.70 264	65.83 204	88.77 135	89.78 188	75.46 114	88.35 36	93.73 73	69.19 97	93.06 204	91.30 3	88.44 158	94.02 75

fmvsm_s_conf0.5_n		83.80 100	83.71 102	84.07 155	86.69 265	67.31 173	89.46 102	83.07 350	71.09 227	86.96 63	93.70 74	69.02 103	91.47 277	88.79 29	84.62 226	93.44 112

test_prior2							88.85 131		75.41 115	84.91 81	93.54 75	74.28 33		83.31 84	95.86 24

fmvsm_l_conf0.5_n		84.47 89	84.54 88	84.27 142	85.42 296	68.81 116	88.49 149	87.26 280	68.08 304	88.03 44	93.49 76	72.04 56	91.77 258	88.90 28	89.14 145	92.24 173

VDDNet		81.52 157	80.67 157	84.05 161	90.44 108	64.13 253	89.73 92	85.91 307	71.11 226	83.18 120	93.48 77	50.54 326	93.49 175	73.40 203	88.25 160	94.54 48

CDPH-MVS		85.76 67	85.29 80	87.17 48	93.49 51	71.08 69	88.58 146	92.42 85	68.32 302	84.61 90	93.48 77	72.32 51	96.15 53	79.00 133	95.43 34	94.28 62

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 71	93.47 79	73.02 45	97.00 22	84.90 63	94.94 44	94.10 70

fmvsm_s_conf0.5_n_6		85.55 71	86.20 55	83.60 178	87.32 241	65.13 223	88.86 129	91.63 127	75.41 115	88.23 40	93.45 80	68.56 108	92.47 229	89.52 18	92.78 79	93.20 125

fmvsm_l_conf0.5_n_a		84.13 92	84.16 93	84.06 158	85.38 297	68.40 133	88.34 157	86.85 290	67.48 311	87.48 55	93.40 81	70.89 72	91.61 263	88.38 36	89.22 142	92.16 180

3Dnovator+	77.84 4	85.48 72	84.47 91	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 247	93.37 82	60.40 229	96.75 30	77.20 155	93.73 70	95.29 6

DeepC-MVS_fast	79.65 3	86.91 41	86.62 49	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 82	84.22 99	93.36 83	71.44 65	96.76 29	80.82 112	95.33 37	94.16 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 127	82.36 129	84.96 107	91.02 95	66.40 191	88.91 127	88.11 254	77.57 49	84.39 95	93.29 84	52.19 300	93.91 152	77.05 158	88.70 153	94.57 44

test_fmvsmvis_n_1920		84.02 94	83.87 96	84.49 127	84.12 327	69.37 108	88.15 165	87.96 261	70.01 260	83.95 106	93.23 85	68.80 105	91.51 275	88.61 31	89.96 128	92.57 154

UA-Net		85.08 83	84.96 83	85.45 89	92.07 79	68.07 145	89.78 90	90.86 154	82.48 2	84.60 91	93.20 86	69.35 94	95.22 88	71.39 227	90.88 113	93.07 132

TEST9					93.26 56	72.96 25	88.75 137	91.89 113	68.44 300	85.00 79	93.10 87	74.36 32	95.41 80

train_agg		86.43 49	86.20 55	87.13 49	93.26 56	72.96 25	88.75 137	91.89 113	68.69 295	85.00 79	93.10 87	74.43 30	95.41 80	84.97 62	95.71 29	93.02 137

test_8					93.13 60	72.57 35	88.68 142	91.84 117	68.69 295	84.87 83	93.10 87	74.43 30	95.16 90

LFMVS		81.82 147	81.23 147	83.57 181	91.89 82	63.43 276	89.84 86	81.85 367	77.04 69	83.21 117	93.10 87	52.26 299	93.43 180	71.98 222	89.95 129	93.85 84

旧先验1					91.96 80	65.79 207		86.37 300			93.08 91	69.31 96			92.74 80	88.74 312

dcpmvs_2		85.63 69	86.15 59	84.06 158	91.71 84	64.94 232	86.47 226	91.87 115	73.63 169	86.60 66	93.02 92	76.57 18	91.87 256	83.36 83	92.15 89	95.35 3

testdata				79.97 291	90.90 98	64.21 251		84.71 321	59.27 404	85.40 74	92.91 93	62.02 194	89.08 331	68.95 255	91.37 104	86.63 364

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 87	93.82 21	73.07 189	84.86 84	92.89 94	76.22 20	96.33 45	84.89 65	95.13 40	94.40 54

Vis-MVSNet		83.46 113	82.80 120	85.43 90	90.25 112	68.74 121	90.30 80	90.13 179	76.33 91	80.87 162	92.89 94	61.00 216	94.20 136	72.45 219	90.97 110	93.35 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 103	83.33 111	84.92 111	93.28 53	70.86 78	92.09 41	90.38 167	68.75 294	79.57 180	92.83 96	60.60 225	93.04 207	80.92 111	91.56 101	90.86 221

3Dnovator	76.31 5	83.38 116	82.31 130	86.59 61	87.94 208	72.94 28	90.64 68	92.14 104	77.21 62	75.47 273	92.83 96	58.56 241	94.72 115	73.24 206	92.71 81	92.13 181

MSLP-MVS++		85.43 74	85.76 68	84.45 128	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 99	92.81 98	67.16 126	92.94 209	80.36 118	94.35 63	90.16 251

test2506		77.30 271	76.49 268	79.74 296	90.08 116	52.02 418	87.86 177	63.10 461	74.88 136	80.16 174	92.79 99	38.29 424	92.35 236	68.74 258	92.50 84	94.86 19

ECVR-MVS		79.61 205	79.26 198	80.67 276	90.08 116	54.69 400	87.89 175	77.44 414	74.88 136	80.27 171	92.79 99	48.96 349	92.45 230	68.55 259	92.50 84	94.86 19

test1111		79.43 212	79.18 201	80.15 288	89.99 121	53.31 413	87.33 194	77.05 418	75.04 129	80.23 173	92.77 101	48.97 348	92.33 238	68.87 256	92.40 86	94.81 22

MG-MVS		83.41 114	83.45 107	83.28 190	92.74 71	62.28 301	88.17 163	89.50 201	75.22 122	81.49 149	92.74 102	66.75 129	95.11 94	72.85 209	91.58 100	92.45 163

casdiffmvs_mvg		85.99 58	86.09 61	85.70 81	87.65 227	67.22 179	88.69 141	93.04 46	79.64 21	85.33 75	92.54 103	73.30 39	94.50 124	83.49 82	91.14 107	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		83.65 106	84.54 88	80.99 268	90.06 120	65.83 204	84.21 295	88.74 243	71.60 215	85.01 78	92.44 104	74.51 29	83.50 395	82.15 100	92.15 89	93.64 102

casdiffmvs		85.11 82	85.14 81	85.01 105	87.20 244	65.77 208	87.75 179	92.83 65	77.84 43	84.36 98	92.38 105	72.15 54	93.93 150	81.27 108	90.48 118	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 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 92	92.27 106	71.47 64	95.02 100	84.24 76	93.46 73	95.13 9

baseline		84.93 85	84.98 82	84.80 117	87.30 242	65.39 217	87.30 195	92.88 62	77.62 47	84.04 104	92.26 107	71.81 58	93.96 144	81.31 106	90.30 121	95.03 11

NormalMVS		86.29 54	85.88 64	87.52 41	93.26 56	72.47 38	91.65 47	92.19 99	79.31 24	84.39 95	92.18 108	64.64 157	95.53 71	80.70 115	94.65 52	94.56 46

SymmetryMVS		85.38 77	84.81 85	87.07 50	91.47 87	72.47 38	91.65 47	88.06 258	79.31 24	84.39 95	92.18 108	64.64 157	95.53 71	80.70 115	90.91 112	93.21 123

QAPM		80.88 168	79.50 191	85.03 104	88.01 206	68.97 114	91.59 51	92.00 107	66.63 324	75.15 291	92.16 110	57.70 248	95.45 75	63.52 298	88.76 151	90.66 230

IS-MVSNet		83.15 122	82.81 119	84.18 147	89.94 123	63.30 278	91.59 51	88.46 251	79.04 30	79.49 181	92.16 110	65.10 152	94.28 130	67.71 265	91.86 96	94.95 12

viewmacassd2359aftdt		83.76 102	83.66 104	84.07 155	86.59 268	64.56 240	86.88 210	91.82 118	75.72 105	83.34 116	92.15 112	68.24 114	92.88 212	79.05 130	89.15 144	94.77 25

BP-MVS1		84.32 90	83.71 102	86.17 68	87.84 213	67.85 154	89.38 108	89.64 196	77.73 45	83.98 105	92.12 113	56.89 259	95.43 77	84.03 79	91.75 97	95.24 7

新几何1				83.42 185	93.13 60	70.71 80		85.48 313	57.43 422	81.80 143	91.98 114	63.28 167	92.27 239	64.60 293	92.99 76	87.27 346

OpenMVS	72.83 10	79.77 203	78.33 219	84.09 153	85.17 302	69.91 93	90.57 69	90.97 149	66.70 318	72.17 336	91.91 115	54.70 276	93.96 144	61.81 319	90.95 111	88.41 321

PHI-MVS		86.43 49	86.17 58	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 197	85.22 77	91.90 116	69.47 92	96.42 44	83.28 85	95.94 23	94.35 57

VNet		82.21 138	82.41 127	81.62 248	90.82 100	60.93 317	84.47 286	89.78 188	76.36 90	84.07 103	91.88 117	64.71 156	90.26 307	70.68 234	88.89 147	93.66 96

EC-MVSNet		86.01 57	86.38 51	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 101	91.88 117	69.04 102	95.43 77	83.93 80	93.77 69	93.01 138

GDP-MVS		83.52 111	82.64 123	86.16 69	88.14 197	68.45 132	89.13 120	92.69 70	72.82 195	83.71 110	91.86 119	55.69 266	95.35 86	80.03 121	89.74 133	94.69 32

KinetiMVS		83.31 120	82.61 124	85.39 91	87.08 253	67.56 165	88.06 167	91.65 126	77.80 44	82.21 136	91.79 120	57.27 254	94.07 142	77.77 148	89.89 131	94.56 46

E2		84.00 95	83.87 96	84.39 130	87.70 224	64.95 229	86.40 231	92.23 92	75.85 102	83.21 117	91.78 121	70.09 83	93.55 170	79.52 127	88.05 164	94.66 36

E3		84.00 95	83.87 96	84.39 130	87.70 224	64.95 229	86.40 231	92.23 92	75.85 102	83.21 117	91.78 121	70.09 83	93.55 170	79.52 127	88.05 164	94.66 36

OPM-MVS		83.50 112	82.95 117	85.14 98	88.79 172	70.95 74	89.13 120	91.52 132	77.55 52	80.96 159	91.75 123	60.71 219	94.50 124	79.67 126	86.51 193	89.97 267
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 58	85.96 63	86.05 73	91.09 92	67.64 161	89.63 96	92.65 75	72.89 194	84.64 89	91.71 124	71.85 57	96.03 55	84.77 68	94.45 60	94.49 50

viewmanbaseed2359cas		83.66 105	83.55 105	84.00 166	86.81 260	64.53 241	86.65 220	91.75 123	74.89 135	83.15 122	91.68 125	68.74 106	92.83 216	79.02 131	89.24 141	94.63 39

XVG-OURS-SEG-HR		80.81 171	79.76 182	83.96 169	85.60 291	68.78 118	83.54 314	90.50 163	70.66 242	76.71 246	91.66 126	60.69 220	91.26 284	76.94 159	81.58 276	91.83 186

EPNet		83.72 104	82.92 118	86.14 72	84.22 325	69.48 101	91.05 64	85.27 314	81.30 6	76.83 242	91.65 127	66.09 142	95.56 68	76.00 174	93.85 68	93.38 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 131	81.97 140	84.85 114	88.75 174	67.42 168	87.98 169	90.87 153	74.92 134	79.72 178	91.65 127	62.19 191	93.96 144	75.26 185	86.42 194	93.16 127

viewdifsd2359ckpt07		82.83 130	82.78 122	82.99 207	86.51 270	62.58 292	85.09 270	90.83 155	75.22 122	82.28 133	91.63 129	69.43 93	92.03 246	77.71 149	86.32 195	94.34 58

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 59	87.44 56	91.63 129	71.27 68	96.06 54	85.62 59	95.01 41	94.78 24

test222					91.50 86	68.26 137	84.16 298	83.20 348	54.63 433	79.74 177	91.63 129	58.97 237			91.42 102	86.77 360

MVS_111021_HR		85.14 81	84.75 86	86.32 65	91.65 85	72.70 30	85.98 243	90.33 171	76.11 97	82.08 138	91.61 132	71.36 67	94.17 139	81.02 109	92.58 82	92.08 182

原ACMM1				84.35 134	93.01 66	68.79 117		92.44 82	63.96 360	81.09 156	91.57 133	66.06 143	95.45 75	67.19 272	94.82 50	88.81 307

viewcassd2359sk11		83.89 97	83.74 101	84.34 135	87.76 220	64.91 235	86.30 235	92.22 95	75.47 113	83.04 123	91.52 134	70.15 82	93.53 173	79.26 129	87.96 166	94.57 44

LPG-MVS_test		82.08 140	81.27 146	84.50 125	89.23 152	68.76 119	90.22 81	91.94 111	75.37 117	76.64 248	91.51 135	54.29 279	94.91 102	78.44 139	83.78 240	89.83 272

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 111	75.37 117	76.64 248	91.51 135	54.29 279	94.91 102	78.44 139	83.78 240	89.83 272

XVG-OURS		80.41 188	79.23 199	83.97 168	85.64 289	69.02 112	83.03 327	90.39 166	71.09 227	77.63 224	91.49 137	54.62 278	91.35 281	75.71 177	83.47 252	91.54 197

alignmvs		85.48 72	85.32 78	85.96 77	89.51 134	69.47 102	89.74 91	92.47 81	76.17 96	87.73 52	91.46 138	70.32 79	93.78 158	81.51 103	88.95 146	94.63 39

CANet		86.45 48	86.10 60	87.51 42	90.09 115	70.94 75	89.70 93	92.59 79	81.78 4	81.32 151	91.43 139	70.34 78	97.23 17	84.26 74	93.36 74	94.37 56

h-mvs33		83.15 122	82.19 133	86.02 76	90.56 105	70.85 79	88.15 165	89.16 222	76.02 99	84.67 86	91.39 140	61.54 202	95.50 73	82.71 95	75.48 358	91.72 193

MGCFI-Net		85.06 84	85.51 73	83.70 176	89.42 139	63.01 284	89.43 103	92.62 78	76.43 84	87.53 53	91.34 141	72.82 49	93.42 181	81.28 107	88.74 152	94.66 36

nrg030		83.88 98	83.53 106	84.96 107	86.77 262	69.28 109	90.46 75	92.67 72	74.79 139	82.95 124	91.33 142	72.70 50	93.09 202	80.79 114	79.28 306	92.50 159

sasdasda		85.91 62	85.87 66	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 80	88.01 45	91.23 143	73.28 40	93.91 152	81.50 104	88.80 149	94.77 25

canonicalmvs		85.91 62	85.87 66	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 80	88.01 45	91.23 143	73.28 40	93.91 152	81.50 104	88.80 149	94.77 25

DPM-MVS		84.93 85	84.29 92	86.84 56	90.20 113	73.04 23	87.12 199	93.04 46	69.80 266	82.85 127	91.22 145	73.06 44	96.02 57	76.72 167	94.63 54	91.46 203

Anonymous202405211		78.25 243	77.01 254	81.99 242	91.03 94	60.67 322	84.77 277	83.90 334	70.65 243	80.00 175	91.20 146	41.08 409	91.43 279	65.21 287	85.26 218	93.85 84

SPE-MVS-test		86.29 54	86.48 50	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 115	91.20 146	70.65 77	95.15 91	81.96 101	94.89 46	94.77 25

Anonymous20240529		80.19 198	78.89 207	84.10 149	90.60 104	64.75 238	88.95 126	90.90 151	65.97 332	80.59 167	91.17 148	49.97 333	93.73 164	69.16 253	82.70 265	93.81 88

EPP-MVSNet		83.40 115	83.02 115	84.57 123	90.13 114	64.47 246	92.32 35	90.73 157	74.45 148	79.35 186	91.10 149	69.05 101	95.12 92	72.78 210	87.22 179	94.13 68

TAPA-MVS	73.13 9	79.15 221	77.94 227	82.79 221	89.59 130	62.99 288	88.16 164	91.51 133	65.77 333	77.14 239	91.09 150	60.91 217	93.21 191	50.26 406	87.05 183	92.17 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 51	86.19 57	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 160	83.16 121	91.07 151	75.94 21	95.19 89	79.94 123	94.38 62	93.55 108

FIs		82.07 141	82.42 126	81.04 267	88.80 171	58.34 346	88.26 160	93.49 31	76.93 71	78.47 204	91.04 152	69.92 87	92.34 237	69.87 246	84.97 220	92.44 164

MVS_111021_LR		82.61 133	82.11 134	84.11 148	88.82 166	71.58 57	85.15 267	86.16 304	74.69 141	80.47 170	91.04 152	62.29 188	90.55 305	80.33 119	90.08 126	90.20 250

DP-MVS Recon		83.11 125	82.09 136	86.15 70	94.44 23	70.92 76	88.79 134	92.20 98	70.53 244	79.17 188	91.03 154	64.12 161	96.03 55	68.39 262	90.14 124	91.50 199

mamv4		76.81 279	78.23 223	72.54 398	86.12 279	65.75 209	78.76 384	82.07 364	64.12 354	72.97 324	91.02 155	67.97 116	68.08 463	83.04 88	78.02 320	83.80 408

HQP_MVS		83.64 107	83.14 112	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 192	91.00 156	60.42 227	95.38 82	78.71 137	86.32 195	91.33 204

plane_prior4											91.00 156

FC-MVSNet-test		81.52 157	82.02 138	80.03 290	88.42 187	55.97 385	87.95 171	93.42 34	77.10 67	77.38 228	90.98 158	69.96 86	91.79 257	68.46 261	84.50 227	92.33 167

diffmvs_AUTHOR		82.38 136	82.27 132	82.73 226	83.26 349	63.80 260	83.89 302	89.76 190	73.35 180	82.37 132	90.84 159	66.25 138	90.79 299	82.77 92	87.93 167	93.59 105

Vis-MVSNet (Re-imp)		78.36 242	78.45 214	78.07 332	88.64 178	51.78 424	86.70 218	79.63 396	74.14 157	75.11 292	90.83 160	61.29 210	89.75 317	58.10 354	91.60 98	92.69 151

114514_t		80.68 179	79.51 190	84.20 146	94.09 42	67.27 176	89.64 95	91.11 146	58.75 411	74.08 310	90.72 161	58.10 244	95.04 99	69.70 247	89.42 139	90.30 247

viewdifsd2359ckpt13		82.91 128	82.29 131	84.77 118	86.96 256	66.90 187	87.47 186	91.62 128	72.19 202	81.68 146	90.71 162	66.92 128	93.28 184	75.90 175	87.15 181	94.12 69

viewdifsd2359ckpt09		83.34 117	82.55 125	85.70 81	87.64 228	67.72 159	88.43 150	91.68 125	71.91 209	81.65 147	90.68 163	67.10 127	94.75 113	76.17 170	87.70 171	94.62 41

PAPM_NR		83.02 126	82.41 127	84.82 115	92.47 76	66.37 192	87.93 173	91.80 119	73.82 164	77.32 230	90.66 164	67.90 118	94.90 104	70.37 237	89.48 138	93.19 126

viewdifsd2359ckpt11		80.37 192	79.73 183	82.30 235	83.70 339	62.39 296	84.20 296	86.67 292	73.22 186	80.90 160	90.62 165	63.00 178	91.56 268	76.81 164	78.44 313	92.95 142

viewmsd2359difaftdt		80.37 192	79.73 183	82.30 235	83.70 339	62.39 296	84.20 296	86.67 292	73.22 186	80.90 160	90.62 165	63.00 178	91.56 268	76.81 164	78.44 313	92.95 142

LS3D		76.95 277	74.82 295	83.37 188	90.45 107	67.36 172	89.15 119	86.94 287	61.87 384	69.52 366	90.61 167	51.71 313	94.53 122	46.38 428	86.71 190	88.21 325

AstraMVS		80.81 171	80.14 172	82.80 218	86.05 282	63.96 255	86.46 227	85.90 308	73.71 167	80.85 163	90.56 168	54.06 283	91.57 267	79.72 125	83.97 238	92.86 145

VPNet		78.69 234	78.66 210	78.76 315	88.31 190	55.72 389	84.45 289	86.63 295	76.79 75	78.26 208	90.55 169	59.30 235	89.70 319	66.63 276	77.05 331	90.88 220

UniMVSNet_ETH3D		79.10 223	78.24 221	81.70 247	86.85 258	60.24 329	87.28 196	88.79 238	74.25 154	76.84 241	90.53 170	49.48 339	91.56 268	67.98 263	82.15 269	93.29 118

ACMP	74.13 6	81.51 159	80.57 159	84.36 133	89.42 139	68.69 126	89.97 85	91.50 136	74.46 147	75.04 295	90.41 171	53.82 285	94.54 121	77.56 151	82.91 260	89.86 271
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_0407		81.58 154	80.48 162	84.87 113	88.81 167	67.96 149	87.37 191	89.25 217	71.06 229	79.48 182	90.39 172	59.57 232	94.48 126	72.45 219	85.93 206	92.18 176

SSM_0404		81.91 144	80.84 155	85.13 101	89.24 151	68.26 137	87.84 178	89.25 217	71.06 229	80.62 166	90.39 172	59.57 232	94.65 119	72.45 219	87.19 180	92.47 162

viewmambaseed2359dif		80.41 188	79.84 180	82.12 237	82.95 363	62.50 295	83.39 315	88.06 258	67.11 313	80.98 158	90.31 174	66.20 140	91.01 295	74.62 189	84.90 221	92.86 145

RRT-MVS		82.60 135	82.10 135	84.10 149	87.98 207	62.94 289	87.45 189	91.27 139	77.42 56	79.85 176	90.28 175	56.62 262	94.70 117	79.87 124	88.15 162	94.67 33

PCF-MVS	73.52 7	80.38 190	78.84 208	85.01 105	87.71 222	68.99 113	83.65 308	91.46 137	63.00 368	77.77 222	90.28 175	66.10 141	95.09 98	61.40 322	88.22 161	90.94 219
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 129	68.32 135					90.24 177

HQP-MVS		82.61 133	82.02 138	84.37 132	89.33 144	66.98 183	89.17 115	92.19 99	76.41 85	77.23 233	90.23 178	60.17 230	95.11 94	77.47 152	85.99 204	91.03 214

PS-MVSNAJss		82.07 141	81.31 145	84.34 135	86.51 270	67.27 176	89.27 111	91.51 133	71.75 210	79.37 185	90.22 179	63.15 173	94.27 131	77.69 150	82.36 268	91.49 200

TSAR-MVS + GP.		85.71 68	85.33 77	86.84 56	91.34 88	72.50 36	89.07 123	87.28 278	76.41 85	85.80 70	90.22 179	74.15 35	95.37 85	81.82 102	91.88 93	92.65 153

SDMVSNet		80.38 190	80.18 169	80.99 268	89.03 161	64.94 232	80.45 359	89.40 204	75.19 126	76.61 250	89.98 181	60.61 224	87.69 353	76.83 163	83.55 249	90.33 245

sd_testset		77.70 262	77.40 247	78.60 318	89.03 161	60.02 331	79.00 380	85.83 309	75.19 126	76.61 250	89.98 181	54.81 271	85.46 378	62.63 309	83.55 249	90.33 245

TranMVSNet+NR-MVSNet		80.84 169	80.31 166	82.42 232	87.85 212	62.33 299	87.74 180	91.33 138	80.55 9	77.99 216	89.86 183	65.23 151	92.62 219	67.05 274	75.24 368	92.30 169

diffmvs		82.10 139	81.88 141	82.76 224	83.00 359	63.78 262	83.68 307	89.76 190	72.94 192	82.02 139	89.85 184	65.96 146	90.79 299	82.38 99	87.30 178	93.71 94
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		81.53 155	80.16 170	85.62 84	85.51 293	68.25 139	88.84 132	92.19 99	71.31 220	80.50 168	89.83 185	46.89 360	94.82 108	76.85 160	89.57 135	93.80 90

StellarMVS		81.53 155	80.16 170	85.62 84	85.51 293	68.25 139	88.84 132	92.19 99	71.31 220	80.50 168	89.83 185	46.89 360	94.82 108	76.85 160	89.57 135	93.80 90

mamba_0408		79.37 217	77.52 244	84.93 110	88.81 167	67.96 149	65.03 459	88.66 245	70.96 233	79.48 182	89.80 187	58.69 238	94.65 119	70.35 238	85.93 206	92.18 176

SSM_04072		77.67 264	77.52 244	78.12 330	88.81 167	67.96 149	65.03 459	88.66 245	70.96 233	79.48 182	89.80 187	58.69 238	74.23 451	70.35 238	85.93 206	92.18 176

BH-RMVSNet		79.61 205	78.44 215	83.14 198	89.38 143	65.93 201	84.95 274	87.15 283	73.56 172	78.19 210	89.79 189	56.67 261	93.36 182	59.53 338	86.74 189	90.13 253

GeoE		81.71 149	81.01 152	83.80 175	89.51 134	64.45 247	88.97 125	88.73 244	71.27 223	78.63 198	89.76 190	66.32 137	93.20 194	69.89 245	86.02 203	93.74 93

guyue		81.13 164	80.64 158	82.60 229	86.52 269	63.92 258	86.69 219	87.73 269	73.97 159	80.83 164	89.69 191	56.70 260	91.33 283	78.26 146	85.40 217	92.54 156

AdaColmap		80.58 186	79.42 192	84.06 158	93.09 63	68.91 115	89.36 109	88.97 233	69.27 278	75.70 269	89.69 191	57.20 256	95.77 64	63.06 303	88.41 159	87.50 340

ACMM	73.20 8	80.78 178	79.84 180	83.58 180	89.31 147	68.37 134	89.99 84	91.60 130	70.28 254	77.25 231	89.66 193	53.37 290	93.53 173	74.24 195	82.85 261	88.85 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 249	76.79 261	81.97 243	90.40 109	71.07 70	87.59 183	84.55 324	66.03 331	72.38 333	89.64 194	57.56 250	86.04 370	59.61 337	83.35 254	88.79 308

test_yl		81.17 162	80.47 163	83.24 193	89.13 156	63.62 263	86.21 238	89.95 184	72.43 200	81.78 144	89.61 195	57.50 251	93.58 166	70.75 232	86.90 185	92.52 157

DCV-MVSNet		81.17 162	80.47 163	83.24 193	89.13 156	63.62 263	86.21 238	89.95 184	72.43 200	81.78 144	89.61 195	57.50 251	93.58 166	70.75 232	86.90 185	92.52 157

EI-MVSNet-Vis-set		84.19 91	83.81 99	85.31 93	88.18 194	67.85 154	87.66 181	89.73 193	80.05 15	82.95 124	89.59 197	70.74 75	94.82 108	80.66 117	84.72 224	93.28 119

PAPR		81.66 152	80.89 154	83.99 167	90.27 111	64.00 254	86.76 217	91.77 122	68.84 293	77.13 240	89.50 198	67.63 120	94.88 106	67.55 267	88.52 156	93.09 131

jajsoiax		79.29 218	77.96 226	83.27 191	84.68 316	66.57 190	89.25 112	90.16 178	69.20 283	75.46 275	89.49 199	45.75 376	93.13 200	76.84 162	80.80 286	90.11 255

MVSFormer		82.85 129	82.05 137	85.24 95	87.35 234	70.21 86	90.50 72	90.38 167	68.55 297	81.32 151	89.47 200	61.68 199	93.46 178	78.98 134	90.26 122	92.05 183

jason		81.39 160	80.29 167	84.70 121	86.63 267	69.90 94	85.95 244	86.77 291	63.24 364	81.07 157	89.47 200	61.08 215	92.15 243	78.33 142	90.07 127	92.05 183
jason: jason.
mvs_tets		79.13 222	77.77 236	83.22 195	84.70 315	66.37 192	89.17 115	90.19 177	69.38 275	75.40 278	89.46 202	44.17 388	93.15 198	76.78 166	80.70 288	90.14 252

UGNet		80.83 170	79.59 189	84.54 124	88.04 203	68.09 144	89.42 105	88.16 253	76.95 70	76.22 259	89.46 202	49.30 343	93.94 147	68.48 260	90.31 120	91.60 194
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		80.60 183	80.55 160	80.76 274	88.07 202	60.80 320	86.86 211	91.58 131	75.67 109	80.24 172	89.45 204	63.34 166	90.25 308	70.51 236	79.22 307	91.23 207

MVS_Test		83.15 122	83.06 114	83.41 187	86.86 257	63.21 280	86.11 241	92.00 107	74.31 151	82.87 126	89.44 205	70.03 85	93.21 191	77.39 154	88.50 157	93.81 88

EI-MVSNet-UG-set		83.81 99	83.38 109	85.09 103	87.87 211	67.53 166	87.44 190	89.66 194	79.74 18	82.23 135	89.41 206	70.24 81	94.74 114	79.95 122	83.92 239	92.99 140

RPSCF		73.23 333	71.46 337	78.54 321	82.50 372	59.85 332	82.18 333	82.84 357	58.96 407	71.15 348	89.41 206	45.48 380	84.77 385	58.82 346	71.83 398	91.02 216

UniMVSNet_NR-MVSNet		81.88 145	81.54 144	82.92 211	88.46 184	63.46 274	87.13 198	92.37 86	80.19 12	78.38 205	89.14 208	71.66 63	93.05 205	70.05 242	76.46 341	92.25 171

tttt0517		79.40 214	77.91 228	83.90 171	88.10 200	63.84 259	88.37 156	84.05 332	71.45 218	76.78 244	89.12 209	49.93 336	94.89 105	70.18 241	83.18 258	92.96 141

DU-MVS		81.12 165	80.52 161	82.90 212	87.80 215	63.46 274	87.02 203	91.87 115	79.01 31	78.38 205	89.07 210	65.02 153	93.05 205	70.05 242	76.46 341	92.20 174

NR-MVSNet		80.23 196	79.38 193	82.78 222	87.80 215	63.34 277	86.31 234	91.09 147	79.01 31	72.17 336	89.07 210	67.20 125	92.81 217	66.08 281	75.65 354	92.20 174

icg_test_0407_2		78.92 229	78.93 206	78.90 313	87.13 247	63.59 267	76.58 406	89.33 207	70.51 245	77.82 218	89.03 212	61.84 195	81.38 410	72.56 215	85.56 213	91.74 189

IMVS_0407		80.61 181	79.90 178	82.75 225	87.13 247	63.59 267	85.33 263	89.33 207	70.51 245	77.82 218	89.03 212	61.84 195	92.91 210	72.56 215	85.56 213	91.74 189

IMVS_0404		77.16 273	76.42 271	79.37 304	87.13 247	63.59 267	77.12 404	89.33 207	70.51 245	66.22 405	89.03 212	50.36 328	82.78 400	72.56 215	85.56 213	91.74 189

IMVS_0403		80.80 174	80.12 173	82.87 214	87.13 247	63.59 267	85.19 264	89.33 207	70.51 245	78.49 202	89.03 212	63.26 169	93.27 186	72.56 215	85.56 213	91.74 189

DELS-MVS		85.41 75	85.30 79	85.77 79	88.49 182	67.93 152	85.52 261	93.44 32	78.70 34	83.63 114	89.03 212	74.57 27	95.71 66	80.26 120	94.04 67	93.66 96
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		80.60 183	79.38 193	84.27 142	89.74 128	67.24 178	87.47 186	86.95 286	70.02 259	75.38 279	88.93 217	51.24 317	92.56 224	75.47 183	89.22 142	93.00 139

baseline1		76.98 276	76.75 264	77.66 339	88.13 198	55.66 390	85.12 268	81.89 365	73.04 190	76.79 243	88.90 218	62.43 186	87.78 352	63.30 302	71.18 402	89.55 281

DP-MVS		76.78 280	74.57 298	83.42 185	93.29 52	69.46 104	88.55 148	83.70 336	63.98 359	70.20 354	88.89 219	54.01 284	94.80 111	46.66 425	81.88 274	86.01 374

ab-mvs		79.51 208	78.97 205	81.14 264	88.46 184	60.91 318	83.84 303	89.24 219	70.36 250	79.03 189	88.87 220	63.23 171	90.21 309	65.12 288	82.57 266	92.28 170

PEN-MVS		77.73 259	77.69 240	77.84 336	87.07 255	53.91 407	87.91 174	91.18 142	77.56 51	73.14 322	88.82 221	61.23 211	89.17 329	59.95 333	72.37 392	90.43 240

tt0805		78.73 232	77.83 232	81.43 253	85.17 302	60.30 328	89.41 106	90.90 151	71.21 224	77.17 238	88.73 222	46.38 365	93.21 191	72.57 213	78.96 308	90.79 223

test_djsdf		80.30 195	79.32 196	83.27 191	83.98 331	65.37 218	90.50 72	90.38 167	68.55 297	76.19 260	88.70 223	56.44 263	93.46 178	78.98 134	80.14 296	90.97 217

PAPM		77.68 263	76.40 272	81.51 251	87.29 243	61.85 306	83.78 304	89.59 198	64.74 346	71.23 346	88.70 223	62.59 182	93.66 165	52.66 390	87.03 184	89.01 297

DTE-MVSNet		76.99 275	76.80 260	77.54 344	86.24 274	53.06 416	87.52 184	90.66 158	77.08 68	72.50 330	88.67 225	60.48 226	89.52 321	57.33 361	70.74 404	90.05 262

PS-CasMVS		78.01 253	78.09 224	77.77 338	87.71 222	54.39 404	88.02 168	91.22 140	77.50 54	73.26 320	88.64 226	60.73 218	88.41 344	61.88 317	73.88 381	90.53 236

cdsmvs_eth3d_5k		19.96 442	26.61 444	0.00 463	0.00 486	0.00 488	0.00 475	89.26 216	0.00 481	0.00 482	88.61 227	61.62 201	0.00 482	0.00 481	0.00 480	0.00 478

lupinMVS		81.39 160	80.27 168	84.76 119	87.35 234	70.21 86	85.55 257	86.41 298	62.85 371	81.32 151	88.61 227	61.68 199	92.24 241	78.41 141	90.26 122	91.83 186

F-COLMAP		76.38 290	74.33 304	82.50 231	89.28 149	66.95 186	88.41 152	89.03 228	64.05 357	66.83 394	88.61 227	46.78 362	92.89 211	57.48 358	78.55 310	87.67 334

mvs_anonymous		79.42 213	79.11 202	80.34 283	84.45 322	57.97 352	82.59 329	87.62 271	67.40 312	76.17 263	88.56 230	68.47 109	89.59 320	70.65 235	86.05 202	93.47 111

CP-MVSNet		78.22 244	78.34 218	77.84 336	87.83 214	54.54 402	87.94 172	91.17 143	77.65 46	73.48 318	88.49 231	62.24 190	88.43 343	62.19 313	74.07 377	90.55 235

PVSNet_Blended_VisFu		82.62 132	81.83 142	84.96 107	90.80 101	69.76 97	88.74 139	91.70 124	69.39 274	78.96 190	88.46 232	65.47 149	94.87 107	74.42 192	88.57 154	90.24 249

CANet_DTU		80.61 181	79.87 179	82.83 215	85.60 291	63.17 283	87.36 192	88.65 247	76.37 89	75.88 266	88.44 233	53.51 288	93.07 203	73.30 204	89.74 133	92.25 171

PLC	70.83 11	78.05 251	76.37 273	83.08 202	91.88 83	67.80 156	88.19 162	89.46 202	64.33 352	69.87 363	88.38 234	53.66 286	93.58 166	58.86 345	82.73 263	87.86 331
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 209	79.22 200	80.27 285	88.79 172	58.35 345	85.06 271	88.61 249	78.56 35	77.65 223	88.34 235	63.81 165	90.66 304	64.98 290	77.22 329	91.80 188

XXY-MVS		75.41 304	75.56 282	74.96 370	83.59 342	57.82 356	80.59 356	83.87 335	66.54 325	74.93 298	88.31 236	63.24 170	80.09 416	62.16 314	76.85 335	86.97 356

Effi-MVS+		83.62 109	83.08 113	85.24 95	88.38 188	67.45 167	88.89 128	89.15 223	75.50 112	82.27 134	88.28 237	69.61 91	94.45 127	77.81 147	87.84 168	93.84 86

API-MVS		81.99 143	81.23 147	84.26 144	90.94 97	70.18 91	91.10 63	89.32 211	71.51 217	78.66 197	88.28 237	65.26 150	95.10 97	64.74 292	91.23 106	87.51 339

thisisatest0530		79.40 214	77.76 237	84.31 137	87.69 226	65.10 226	87.36 192	84.26 330	70.04 258	77.42 227	88.26 239	49.94 334	94.79 112	70.20 240	84.70 225	93.03 136

hse-mvs2		81.72 148	80.94 153	84.07 155	88.72 175	67.68 160	85.87 247	87.26 280	76.02 99	84.67 86	88.22 240	61.54 202	93.48 176	82.71 95	73.44 386	91.06 212

xiu_mvs_v1_base_debu		80.80 174	79.72 185	84.03 163	87.35 234	70.19 88	85.56 254	88.77 239	69.06 287	81.83 140	88.16 241	50.91 320	92.85 213	78.29 143	87.56 172	89.06 292

xiu_mvs_v1_base		80.80 174	79.72 185	84.03 163	87.35 234	70.19 88	85.56 254	88.77 239	69.06 287	81.83 140	88.16 241	50.91 320	92.85 213	78.29 143	87.56 172	89.06 292

xiu_mvs_v1_base_debi		80.80 174	79.72 185	84.03 163	87.35 234	70.19 88	85.56 254	88.77 239	69.06 287	81.83 140	88.16 241	50.91 320	92.85 213	78.29 143	87.56 172	89.06 292

UniMVSNet (Re)		81.60 153	81.11 149	83.09 200	88.38 188	64.41 248	87.60 182	93.02 50	78.42 37	78.56 200	88.16 241	69.78 88	93.26 187	69.58 249	76.49 340	91.60 194

AUN-MVS		79.21 220	77.60 242	84.05 161	88.71 176	67.61 162	85.84 249	87.26 280	69.08 286	77.23 233	88.14 245	53.20 292	93.47 177	75.50 182	73.45 385	91.06 212

Anonymous20231211		78.97 227	77.69 240	82.81 217	90.54 106	64.29 250	90.11 83	91.51 133	65.01 344	76.16 264	88.13 246	50.56 325	93.03 208	69.68 248	77.56 327	91.11 210

pm-mvs1		77.25 272	76.68 266	78.93 312	84.22 325	58.62 343	86.41 228	88.36 252	71.37 219	73.31 319	88.01 247	61.22 212	89.15 330	64.24 296	73.01 389	89.03 296

LuminaMVS		80.68 179	79.62 188	83.83 172	85.07 308	68.01 148	86.99 204	88.83 236	70.36 250	81.38 150	87.99 248	50.11 331	92.51 228	79.02 131	86.89 187	90.97 217

SD_0403		74.65 312	74.77 296	74.29 379	86.20 276	47.42 443	83.71 306	85.12 316	69.30 277	68.50 377	87.95 249	59.40 234	86.05 369	49.38 410	83.35 254	89.40 284

LTVRE_ROB	69.57 13	76.25 291	74.54 300	81.41 254	88.60 179	64.38 249	79.24 375	89.12 226	70.76 238	69.79 365	87.86 250	49.09 346	93.20 194	56.21 373	80.16 294	86.65 363
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		75.12 309	75.19 291	74.91 371	90.40 109	45.09 454	80.29 362	78.42 406	78.37 40	76.54 252	87.75 251	44.36 386	87.28 358	57.04 364	83.49 251	92.37 165

WTY-MVS		75.65 299	75.68 279	75.57 361	86.40 272	56.82 370	77.92 398	82.40 360	65.10 341	76.18 261	87.72 252	63.13 176	80.90 413	60.31 331	81.96 272	89.00 299

TAMVS		78.89 230	77.51 246	83.03 205	87.80 215	67.79 157	84.72 278	85.05 319	67.63 307	76.75 245	87.70 253	62.25 189	90.82 298	58.53 349	87.13 182	90.49 238

BH-untuned		79.47 210	78.60 211	82.05 240	89.19 154	65.91 202	86.07 242	88.52 250	72.18 203	75.42 277	87.69 254	61.15 213	93.54 172	60.38 330	86.83 188	86.70 362

COLMAP_ROB	66.92 17	73.01 336	70.41 351	80.81 273	87.13 247	65.63 210	88.30 159	84.19 331	62.96 369	63.80 422	87.69 254	38.04 425	92.56 224	46.66 425	74.91 371	84.24 401
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 315	72.42 328	79.80 295	83.76 337	59.59 336	85.92 246	86.64 294	66.39 326	66.96 392	87.58 256	39.46 415	91.60 264	65.76 284	69.27 410	88.22 324

FA-MVS(test-final)		80.96 167	79.91 177	84.10 149	88.30 191	65.01 227	84.55 285	90.01 182	73.25 184	79.61 179	87.57 257	58.35 243	94.72 115	71.29 228	86.25 198	92.56 155

Baseline_NR-MVSNet		78.15 248	78.33 219	77.61 341	85.79 285	56.21 383	86.78 215	85.76 310	73.60 171	77.93 217	87.57 257	65.02 153	88.99 332	67.14 273	75.33 365	87.63 335

WR-MVS_H		78.51 239	78.49 213	78.56 320	88.02 204	56.38 379	88.43 150	92.67 72	77.14 64	73.89 312	87.55 259	66.25 138	89.24 327	58.92 344	73.55 384	90.06 261

EI-MVSNet		80.52 187	79.98 175	82.12 237	84.28 323	63.19 282	86.41 228	88.95 234	74.18 156	78.69 195	87.54 260	66.62 131	92.43 231	72.57 213	80.57 290	90.74 227

CVMVSNet		72.99 337	72.58 326	74.25 380	84.28 323	50.85 432	86.41 228	83.45 342	44.56 452	73.23 321	87.54 260	49.38 341	85.70 373	65.90 282	78.44 313	86.19 369

ACMH+	68.96 14	76.01 295	74.01 306	82.03 241	88.60 179	65.31 219	88.86 129	87.55 272	70.25 256	67.75 381	87.47 262	41.27 407	93.19 196	58.37 351	75.94 351	87.60 336

TransMVSNet (Re)		75.39 306	74.56 299	77.86 335	85.50 295	57.10 367	86.78 215	86.09 306	72.17 204	71.53 343	87.34 263	63.01 177	89.31 325	56.84 367	61.83 434	87.17 348

GBi-Net		78.40 240	77.40 247	81.40 255	87.60 229	63.01 284	88.39 153	89.28 213	71.63 212	75.34 281	87.28 264	54.80 272	91.11 288	62.72 305	79.57 300	90.09 257

test1		78.40 240	77.40 247	81.40 255	87.60 229	63.01 284	88.39 153	89.28 213	71.63 212	75.34 281	87.28 264	54.80 272	91.11 288	62.72 305	79.57 300	90.09 257

FMVSNet2		78.20 246	77.21 251	81.20 262	87.60 229	62.89 290	87.47 186	89.02 229	71.63 212	75.29 287	87.28 264	54.80 272	91.10 291	62.38 310	79.38 304	89.61 279

FMVSNet1		77.44 267	76.12 275	81.40 255	86.81 260	63.01 284	88.39 153	89.28 213	70.49 249	74.39 307	87.28 264	49.06 347	91.11 288	60.91 326	78.52 311	90.09 257

v2v482		80.23 196	79.29 197	83.05 204	83.62 341	64.14 252	87.04 201	89.97 183	73.61 170	78.18 211	87.22 268	61.10 214	93.82 156	76.11 171	76.78 337	91.18 208

ITE_SJBPF				78.22 327	81.77 382	60.57 323		83.30 343	69.25 280	67.54 383	87.20 269	36.33 432	87.28 358	54.34 381	74.62 374	86.80 359

anonymousdsp		78.60 236	77.15 252	82.98 209	80.51 401	67.08 181	87.24 197	89.53 200	65.66 335	75.16 290	87.19 270	52.52 294	92.25 240	77.17 156	79.34 305	89.61 279

MVSTER		79.01 225	77.88 231	82.38 233	83.07 356	64.80 237	84.08 301	88.95 234	69.01 290	78.69 195	87.17 271	54.70 276	92.43 231	74.69 188	80.57 290	89.89 270

thres100view900		76.50 284	75.55 283	79.33 305	89.52 133	56.99 368	85.83 250	83.23 345	73.94 161	76.32 257	87.12 272	51.89 309	91.95 251	48.33 416	83.75 243	89.07 290

thres600view7		76.50 284	75.44 284	79.68 298	89.40 141	57.16 365	85.53 259	83.23 345	73.79 165	76.26 258	87.09 273	51.89 309	91.89 254	48.05 421	83.72 246	90.00 263

XVG-ACMP-BASELINE		76.11 293	74.27 305	81.62 248	83.20 352	64.67 239	83.60 311	89.75 192	69.75 269	71.85 339	87.09 273	32.78 439	92.11 244	69.99 244	80.43 292	88.09 327

HY-MVS	69.67 12	77.95 254	77.15 252	80.36 282	87.57 233	60.21 330	83.37 317	87.78 268	66.11 328	75.37 280	87.06 275	63.27 168	90.48 306	61.38 323	82.43 267	90.40 242

CHOSEN 1792x2688		77.63 265	75.69 278	83.44 184	89.98 122	68.58 129	78.70 385	87.50 274	56.38 427	75.80 268	86.84 276	58.67 240	91.40 280	61.58 321	85.75 211	90.34 244

v8		79.97 202	79.02 204	82.80 218	84.09 328	64.50 245	87.96 170	90.29 174	74.13 158	75.24 288	86.81 277	62.88 180	93.89 155	74.39 193	75.40 363	90.00 263

AllTest		70.96 354	68.09 369	79.58 301	85.15 304	63.62 263	84.58 284	79.83 393	62.31 378	60.32 435	86.73 278	32.02 440	88.96 335	50.28 404	71.57 400	86.15 370

TestCases				79.58 301	85.15 304	63.62 263		79.83 393	62.31 378	60.32 435	86.73 278	32.02 440	88.96 335	50.28 404	71.57 400	86.15 370

LCM-MVSNet-Re		77.05 274	76.94 257	77.36 345	87.20 244	51.60 425	80.06 365	80.46 384	75.20 125	67.69 382	86.72 280	62.48 184	88.98 333	63.44 300	89.25 140	91.51 198

1112_ss		77.40 269	76.43 270	80.32 284	89.11 160	60.41 327	83.65 308	87.72 270	62.13 381	73.05 323	86.72 280	62.58 183	89.97 313	62.11 316	80.80 286	90.59 234

ab-mvs-re		7.23 445	9.64 448	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	86.72 280	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

IterMVS-LS		80.06 199	79.38 193	82.11 239	85.89 283	63.20 281	86.79 214	89.34 206	74.19 155	75.45 276	86.72 280	66.62 131	92.39 233	72.58 212	76.86 334	90.75 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 296	73.93 308	81.77 246	88.71 176	66.61 189	88.62 144	89.01 230	69.81 265	66.78 395	86.70 284	41.95 404	91.51 275	55.64 374	78.14 319	87.17 348
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 289	75.44 284	79.27 306	89.28 149	58.09 348	81.69 338	87.07 284	59.53 402	72.48 331	86.67 285	61.30 209	89.33 324	60.81 328	80.15 295	90.41 241

FMVSNet3		77.88 256	76.85 259	80.97 270	86.84 259	62.36 298	86.52 225	88.77 239	71.13 225	75.34 281	86.66 286	54.07 282	91.10 291	62.72 305	79.57 300	89.45 283

pmmvs6		74.69 311	73.39 315	78.61 317	81.38 390	57.48 362	86.64 221	87.95 262	64.99 345	70.18 355	86.61 287	50.43 327	89.52 321	62.12 315	70.18 407	88.83 306

ET-MVSNet_ETH3D		78.63 235	76.63 267	84.64 122	86.73 263	69.47 102	85.01 272	84.61 323	69.54 272	66.51 402	86.59 288	50.16 330	91.75 259	76.26 169	84.24 235	92.69 151

testgi		66.67 394	66.53 390	67.08 429	75.62 435	41.69 464	75.93 409	76.50 421	66.11 328	65.20 413	86.59 288	35.72 434	74.71 448	43.71 437	73.38 387	84.84 395

CLD-MVS		82.31 137	81.65 143	84.29 139	88.47 183	67.73 158	85.81 251	92.35 87	75.78 104	78.33 207	86.58 290	64.01 162	94.35 128	76.05 173	87.48 175	90.79 223
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 204	78.67 209	82.97 210	84.06 329	64.95 229	87.88 176	90.62 159	73.11 188	75.11 292	86.56 291	61.46 205	94.05 143	73.68 198	75.55 356	89.90 269

CDS-MVSNet		79.07 224	77.70 239	83.17 197	87.60 229	68.23 141	84.40 292	86.20 303	67.49 310	76.36 256	86.54 292	61.54 202	90.79 299	61.86 318	87.33 177	90.49 238
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 150	81.05 150	83.60 178	89.15 155	68.03 147	84.46 288	90.02 181	70.67 239	81.30 154	86.53 293	63.17 172	94.19 138	75.60 180	88.54 155	88.57 317

TR-MVS		77.44 267	76.18 274	81.20 262	88.24 192	63.24 279	84.61 283	86.40 299	67.55 309	77.81 220	86.48 294	54.10 281	93.15 198	57.75 357	82.72 264	87.20 347

EIA-MVS		83.31 120	82.80 120	84.82 115	89.59 130	65.59 212	88.21 161	92.68 71	74.66 143	78.96 190	86.42 295	69.06 100	95.26 87	75.54 181	90.09 125	93.62 103

tfpn200view9		76.42 288	75.37 288	79.55 303	89.13 156	57.65 359	85.17 265	83.60 337	73.41 178	76.45 253	86.39 296	52.12 301	91.95 251	48.33 416	83.75 243	89.07 290

thres400		76.50 284	75.37 288	79.86 293	89.13 156	57.65 359	85.17 265	83.60 337	73.41 178	76.45 253	86.39 296	52.12 301	91.95 251	48.33 416	83.75 243	90.00 263

v7n		78.97 227	77.58 243	83.14 198	83.45 345	65.51 213	88.32 158	91.21 141	73.69 168	72.41 332	86.32 298	57.93 245	93.81 157	69.18 252	75.65 354	90.11 255

MAR-MVS		81.84 146	80.70 156	85.27 94	91.32 89	71.53 58	89.82 87	90.92 150	69.77 268	78.50 201	86.21 299	62.36 187	94.52 123	65.36 286	92.05 92	89.77 275
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		80.03 200	79.03 203	83.01 206	83.78 336	64.51 243	87.11 200	90.57 162	71.96 208	78.08 214	86.20 300	61.41 206	93.94 147	74.93 187	77.23 328	90.60 233

test_vis1_n_1920		75.52 301	75.78 277	74.75 375	79.84 409	57.44 363	83.26 319	85.52 312	62.83 372	79.34 187	86.17 301	45.10 381	79.71 417	78.75 136	81.21 280	87.10 354

V42		79.38 216	78.24 221	82.83 215	81.10 395	65.50 214	85.55 257	89.82 187	71.57 216	78.21 209	86.12 302	60.66 222	93.18 197	75.64 178	75.46 360	89.81 274

PVSNet_BlendedMVS		80.60 183	80.02 174	82.36 234	88.85 163	65.40 215	86.16 240	92.00 107	69.34 276	78.11 212	86.09 303	66.02 144	94.27 131	71.52 224	82.06 271	87.39 341

v1192		79.59 207	78.43 216	83.07 203	83.55 343	64.52 242	86.93 208	90.58 160	70.83 235	77.78 221	85.90 304	59.15 236	93.94 147	73.96 197	77.19 330	90.76 225

SixPastTwentyTwo		73.37 328	71.26 342	79.70 297	85.08 307	57.89 354	85.57 253	83.56 339	71.03 231	65.66 407	85.88 305	42.10 402	92.57 223	59.11 342	63.34 429	88.65 314

EPNet_dtu		75.46 302	74.86 294	77.23 348	82.57 371	54.60 401	86.89 209	83.09 349	71.64 211	66.25 404	85.86 306	55.99 264	88.04 348	54.92 378	86.55 192	89.05 295
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 325	73.64 313	73.51 387	82.80 365	55.01 398	76.12 408	81.69 368	62.47 377	74.68 302	85.85 307	57.32 253	78.11 424	60.86 327	80.93 282	87.39 341

ETV-MVS		84.90 87	84.67 87	85.59 86	89.39 142	68.66 127	88.74 139	92.64 77	79.97 16	84.10 102	85.71 308	69.32 95	95.38 82	80.82 112	91.37 104	92.72 148

test_cas_vis1_n_1920		73.76 323	73.74 312	73.81 385	75.90 431	59.77 333	80.51 357	82.40 360	58.30 413	81.62 148	85.69 309	44.35 387	76.41 435	76.29 168	78.61 309	85.23 387

v1240		78.99 226	77.78 235	82.64 227	83.21 351	63.54 271	86.62 222	90.30 173	69.74 271	77.33 229	85.68 310	57.04 257	93.76 161	73.13 207	76.92 332	90.62 231

v144192		79.47 210	78.37 217	82.78 222	83.35 346	63.96 255	86.96 205	90.36 170	69.99 261	77.50 225	85.67 311	60.66 222	93.77 160	74.27 194	76.58 338	90.62 231

tfpnnormal		74.39 313	73.16 319	78.08 331	86.10 281	58.05 349	84.65 282	87.53 273	70.32 253	71.22 347	85.63 312	54.97 270	89.86 314	43.03 440	75.02 370	86.32 366

PS-MVSNAJ		81.69 150	81.02 151	83.70 176	89.51 134	68.21 142	84.28 294	90.09 180	70.79 236	81.26 155	85.62 313	63.15 173	94.29 129	75.62 179	88.87 148	88.59 316

SSC-MVS3.2		73.35 331	73.39 315	73.23 388	85.30 300	49.01 439	74.58 423	81.57 369	75.21 124	73.68 315	85.58 314	52.53 293	82.05 405	54.33 382	77.69 325	88.63 315

v1921920		79.22 219	78.03 225	82.80 218	83.30 348	63.94 257	86.80 213	90.33 171	69.91 264	77.48 226	85.53 315	58.44 242	93.75 162	73.60 199	76.85 335	90.71 229

test_0402		72.79 339	70.44 350	79.84 294	88.13 198	65.99 200	85.93 245	84.29 328	65.57 336	67.40 388	85.49 316	46.92 359	92.61 220	35.88 454	74.38 376	80.94 433

v148		78.72 233	77.80 234	81.47 252	82.73 367	61.96 305	86.30 235	88.08 256	73.26 183	76.18 261	85.47 317	62.46 185	92.36 235	71.92 223	73.82 382	90.09 257

USDC		70.33 363	68.37 364	76.21 355	80.60 399	56.23 382	79.19 377	86.49 297	60.89 389	61.29 430	85.47 317	31.78 442	89.47 323	53.37 387	76.21 349	82.94 419

VortexMVS		78.57 238	77.89 230	80.59 277	85.89 283	62.76 291	85.61 252	89.62 197	72.06 206	74.99 296	85.38 319	55.94 265	90.77 302	74.99 186	76.58 338	88.23 323

MVP-Stereo		76.12 292	74.46 302	81.13 265	85.37 298	69.79 95	84.42 291	87.95 262	65.03 343	67.46 385	85.33 320	53.28 291	91.73 261	58.01 355	83.27 256	81.85 428
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 247	76.99 256	81.78 245	85.66 288	66.99 182	84.66 280	90.47 164	55.08 432	72.02 338	85.27 321	63.83 164	94.11 141	66.10 280	89.80 132	84.24 401

DIV-MVS_self_test		77.72 260	76.76 262	80.58 278	82.48 374	60.48 325	83.09 323	87.86 265	69.22 281	74.38 308	85.24 322	62.10 192	91.53 273	71.09 229	75.40 363	89.74 276

FE-MVS		77.78 258	75.68 279	84.08 154	88.09 201	66.00 199	83.13 322	87.79 267	68.42 301	78.01 215	85.23 323	45.50 379	95.12 92	59.11 342	85.83 210	91.11 210

cl____		77.72 260	76.76 262	80.58 278	82.49 373	60.48 325	83.09 323	87.87 264	69.22 281	74.38 308	85.22 324	62.10 192	91.53 273	71.09 229	75.41 362	89.73 277

HyFIR lowres test		77.53 266	75.40 286	83.94 170	89.59 130	66.62 188	80.36 360	88.64 248	56.29 428	76.45 253	85.17 325	57.64 249	93.28 184	61.34 324	83.10 259	91.91 185

pmmvs4		74.03 321	71.91 332	80.39 281	81.96 379	68.32 135	81.45 342	82.14 362	59.32 403	69.87 363	85.13 326	52.40 297	88.13 347	60.21 332	74.74 373	84.73 397

TDRefinement		67.49 386	64.34 398	76.92 350	73.47 447	61.07 316	84.86 276	82.98 353	59.77 399	58.30 442	85.13 326	26.06 450	87.89 350	47.92 422	60.59 439	81.81 429

Fast-Effi-MVS+		80.81 171	79.92 176	83.47 182	88.85 163	64.51 243	85.53 259	89.39 205	70.79 236	78.49 202	85.06 328	67.54 121	93.58 166	67.03 275	86.58 191	92.32 168

PVSNet_Blended		80.98 166	80.34 165	82.90 212	88.85 163	65.40 215	84.43 290	92.00 107	67.62 308	78.11 212	85.05 329	66.02 144	94.27 131	71.52 224	89.50 137	89.01 297

ttmdpeth		59.91 414	57.10 418	68.34 424	67.13 461	46.65 448	74.64 422	67.41 451	48.30 447	62.52 428	85.04 330	20.40 460	75.93 440	42.55 442	45.90 462	82.44 422

test_fmvs1_n		70.86 356	70.24 353	72.73 396	72.51 454	55.28 395	81.27 345	79.71 395	51.49 443	78.73 194	84.87 331	27.54 449	77.02 429	76.06 172	79.97 298	85.88 378

WBMVS		73.43 327	72.81 323	75.28 367	87.91 209	50.99 431	78.59 388	81.31 374	65.51 339	74.47 306	84.83 332	46.39 364	86.68 362	58.41 350	77.86 321	88.17 326

CMPMVS	51.72 21	70.19 365	68.16 367	76.28 354	73.15 450	57.55 361	79.47 372	83.92 333	48.02 448	56.48 448	84.81 333	43.13 394	86.42 366	62.67 308	81.81 275	84.89 394
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 381	67.61 380	71.31 408	78.51 423	47.01 446	84.47 286	84.27 329	42.27 455	66.44 403	84.79 334	40.44 412	83.76 391	58.76 347	68.54 415	83.17 413

BH-w/o		78.21 245	77.33 250	80.84 272	88.81 167	65.13 223	84.87 275	87.85 266	69.75 269	74.52 305	84.74 335	61.34 208	93.11 201	58.24 353	85.84 209	84.27 400

pmmvs5		71.55 349	70.20 354	75.61 360	77.83 424	56.39 378	81.74 337	80.89 375	57.76 418	67.46 385	84.49 336	49.26 344	85.32 380	57.08 363	75.29 366	85.11 391

reproduce_monomvs		75.40 305	74.38 303	78.46 325	83.92 333	57.80 357	83.78 304	86.94 287	73.47 176	72.25 335	84.47 337	38.74 420	89.27 326	75.32 184	70.53 405	88.31 322

thres200		75.55 300	74.47 301	78.82 314	87.78 218	57.85 355	83.07 325	83.51 340	72.44 199	75.84 267	84.42 338	52.08 304	91.75 259	47.41 423	83.64 248	86.86 358

test_fmvs1		70.93 355	70.52 348	72.16 400	73.71 443	55.05 397	80.82 348	78.77 404	51.21 444	78.58 199	84.41 339	31.20 444	76.94 430	75.88 176	80.12 297	84.47 399

testing3		68.56 380	67.67 379	71.22 409	87.33 239	42.87 459	83.06 326	71.54 439	70.36 250	69.08 371	84.38 340	30.33 446	85.69 374	37.50 452	75.45 361	85.09 392

test_fmvs2		68.35 383	67.48 382	70.98 411	69.50 457	51.95 420	80.05 366	76.38 422	49.33 446	74.65 303	84.38 340	23.30 458	75.40 446	74.51 191	75.17 369	85.60 381

eth_miper_zixun_eth		77.92 255	76.69 265	81.61 250	83.00 359	61.98 304	83.15 321	89.20 221	69.52 273	74.86 299	84.35 342	61.76 198	92.56 224	71.50 226	72.89 390	90.28 248

myMVS_eth3d28		73.62 324	73.53 314	73.90 384	88.20 193	47.41 444	78.06 395	79.37 398	74.29 153	73.98 311	84.29 343	44.67 382	83.54 394	51.47 396	87.39 176	90.74 227

testing91		76.54 282	75.66 281	79.18 309	88.43 186	55.89 386	81.08 346	83.00 352	73.76 166	75.34 281	84.29 343	46.20 370	90.07 311	64.33 294	84.50 227	91.58 196

c3_l		78.75 231	77.91 228	81.26 260	82.89 364	61.56 310	84.09 300	89.13 225	69.97 262	75.56 271	84.29 343	66.36 136	92.09 245	73.47 202	75.48 358	90.12 254

testing99		76.09 294	75.12 293	79.00 310	88.16 195	55.50 392	80.79 350	81.40 372	73.30 182	75.17 289	84.27 346	44.48 385	90.02 312	64.28 295	84.22 236	91.48 201

UWE-MVS		72.13 346	71.49 336	74.03 382	86.66 266	47.70 441	81.40 344	76.89 420	63.60 363	75.59 270	84.22 347	39.94 414	85.62 375	48.98 413	86.13 201	88.77 309

Fast-Effi-MVS+-dtu		78.02 252	76.49 268	82.62 228	83.16 355	66.96 185	86.94 207	87.45 276	72.45 197	71.49 344	84.17 348	54.79 275	91.58 265	67.61 266	80.31 293	89.30 288

IterMVS-SCA-FT		75.43 303	73.87 310	80.11 289	82.69 368	64.85 236	81.57 340	83.47 341	69.16 284	70.49 351	84.15 349	51.95 307	88.15 346	69.23 251	72.14 396	87.34 343

1314		76.53 283	75.30 290	80.21 287	83.93 332	62.32 300	84.66 280	88.81 237	60.23 395	70.16 357	84.07 350	55.30 269	90.73 303	67.37 269	83.21 257	87.59 338

cl22		78.07 250	77.01 254	81.23 261	82.37 376	61.83 307	83.55 312	87.98 260	68.96 291	75.06 294	83.87 351	61.40 207	91.88 255	73.53 200	76.39 343	89.98 266

EG-PatchMatch MVS		74.04 319	71.82 333	80.71 275	84.92 310	67.42 168	85.86 248	88.08 256	66.04 330	64.22 417	83.85 352	35.10 435	92.56 224	57.44 359	80.83 285	82.16 426

thisisatest0515		77.33 270	75.38 287	83.18 196	85.27 301	63.80 260	82.11 334	83.27 344	65.06 342	75.91 265	83.84 353	49.54 338	94.27 131	67.24 271	86.19 199	91.48 201

test20.03		67.45 387	66.95 388	68.94 418	75.48 436	44.84 455	77.50 400	77.67 410	66.66 319	63.01 424	83.80 354	47.02 358	78.40 422	42.53 443	68.86 414	83.58 410

miper_ehance_all_eth		78.59 237	77.76 237	81.08 266	82.66 369	61.56 310	83.65 308	89.15 223	68.87 292	75.55 272	83.79 355	66.49 134	92.03 246	73.25 205	76.39 343	89.64 278

MSDG		73.36 330	70.99 344	80.49 280	84.51 321	65.80 206	80.71 354	86.13 305	65.70 334	65.46 408	83.74 356	44.60 383	90.91 297	51.13 399	76.89 333	84.74 396

MonoMVSNet		76.49 287	75.80 276	78.58 319	81.55 386	58.45 344	86.36 233	86.22 302	74.87 138	74.73 301	83.73 357	51.79 312	88.73 338	70.78 231	72.15 395	88.55 318

testing11		75.14 308	74.01 306	78.53 322	88.16 195	56.38 379	80.74 353	80.42 386	70.67 239	72.69 329	83.72 358	43.61 392	89.86 314	62.29 312	83.76 242	89.36 286

IterMVS		74.29 314	72.94 322	78.35 326	81.53 387	63.49 273	81.58 339	82.49 359	68.06 305	69.99 360	83.69 359	51.66 314	85.54 376	65.85 283	71.64 399	86.01 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 342	71.71 334	74.35 378	82.19 377	52.00 419	79.22 376	77.29 416	64.56 348	72.95 325	83.68 360	51.35 315	83.26 398	58.33 352	75.80 352	87.81 332

UWE-MVS-28		65.32 401	64.93 395	66.49 430	78.70 421	38.55 467	77.86 399	64.39 459	62.00 383	64.13 418	83.60 361	41.44 405	76.00 439	31.39 459	80.89 283	84.92 393

sc_t1		72.19 345	69.51 356	80.23 286	84.81 312	61.09 315	84.68 279	80.22 390	60.70 391	71.27 345	83.58 362	36.59 430	89.24 327	60.41 329	63.31 430	90.37 243

testing222		74.04 319	72.66 325	78.19 328	87.89 210	55.36 393	81.06 347	79.20 401	71.30 222	74.65 303	83.57 363	39.11 419	88.67 340	51.43 398	85.75 211	90.53 236

Effi-MVS+-dtu		80.03 200	78.57 212	84.42 129	85.13 306	68.74 121	88.77 135	88.10 255	74.99 130	74.97 297	83.49 364	57.27 254	93.36 182	73.53 200	80.88 284	91.18 208

baseline2		75.70 298	73.83 311	81.30 258	83.26 349	61.79 308	82.57 330	80.65 379	66.81 315	66.88 393	83.42 365	57.86 247	92.19 242	63.47 299	79.57 300	89.91 268

mvs5depth		69.45 372	67.45 383	75.46 365	73.93 441	55.83 387	79.19 377	83.23 345	66.89 314	71.63 342	83.32 366	33.69 438	85.09 381	59.81 335	55.34 449	85.46 383

TinyColmap		67.30 389	64.81 396	74.76 374	81.92 381	56.68 374	80.29 362	81.49 371	60.33 393	56.27 449	83.22 367	24.77 454	87.66 354	45.52 433	69.47 409	79.95 438

mvsany_test1		62.30 410	61.26 414	65.41 432	69.52 456	54.86 399	66.86 451	49.78 472	46.65 449	68.50 377	83.21 368	49.15 345	66.28 464	56.93 366	60.77 437	75.11 448

test_vis1_n		69.85 370	69.21 359	71.77 402	72.66 453	55.27 396	81.48 341	76.21 423	52.03 440	75.30 286	83.20 369	28.97 447	76.22 437	74.60 190	78.41 317	83.81 407

CostFormer		75.24 307	73.90 309	79.27 306	82.65 370	58.27 347	80.80 349	82.73 358	61.57 385	75.33 285	83.13 370	55.52 267	91.07 294	64.98 290	78.34 318	88.45 319

MVStest1		56.63 418	52.76 424	68.25 425	61.67 467	53.25 415	71.67 432	68.90 449	38.59 460	50.59 456	83.05 371	25.08 452	70.66 457	36.76 453	38.56 463	80.83 434

WB-MVSnew		71.96 348	71.65 335	72.89 394	84.67 319	51.88 422	82.29 332	77.57 411	62.31 378	73.67 316	83.00 372	53.49 289	81.10 412	45.75 432	82.13 270	85.70 380

ETVMVS		72.25 344	71.05 343	75.84 357	87.77 219	51.91 421	79.39 373	74.98 427	69.26 279	73.71 314	82.95 373	40.82 411	86.14 368	46.17 429	84.43 232	89.47 282

miper_lstm_enhance		74.11 318	73.11 320	77.13 349	80.11 405	59.62 335	72.23 430	86.92 289	66.76 317	70.40 352	82.92 374	56.93 258	82.92 399	69.06 254	72.63 391	88.87 304

GA-MVS		76.87 278	75.17 292	81.97 243	82.75 366	62.58 292	81.44 343	86.35 301	72.16 205	74.74 300	82.89 375	46.20 370	92.02 248	68.85 257	81.09 281	91.30 206

K. test v3		71.19 351	68.51 363	79.21 308	83.04 358	57.78 358	84.35 293	76.91 419	72.90 193	62.99 425	82.86 376	39.27 416	91.09 293	61.65 320	52.66 452	88.75 310

MS-PatchMatch		73.83 322	72.67 324	77.30 347	83.87 334	66.02 197	81.82 335	84.66 322	61.37 388	68.61 375	82.82 377	47.29 355	88.21 345	59.27 339	84.32 234	77.68 443

lessismore_v0				78.97 311	81.01 396	57.15 366		65.99 454		61.16 431	82.82 377	39.12 418	91.34 282	59.67 336	46.92 459	88.43 320

D2MVS		74.82 310	73.21 318	79.64 300	79.81 410	62.56 294	80.34 361	87.35 277	64.37 351	68.86 372	82.66 379	46.37 366	90.10 310	67.91 264	81.24 279	86.25 367

Anonymous20231206		68.60 378	67.80 376	71.02 410	80.23 404	50.75 433	78.30 393	80.47 383	56.79 425	66.11 406	82.63 380	46.35 367	78.95 420	43.62 438	75.70 353	83.36 412

MIMVSNet		70.69 358	69.30 357	74.88 372	84.52 320	56.35 381	75.87 412	79.42 397	64.59 347	67.76 380	82.41 381	41.10 408	81.54 408	46.64 427	81.34 277	86.75 361

UBG		73.08 335	72.27 330	75.51 363	88.02 204	51.29 429	78.35 392	77.38 415	65.52 337	73.87 313	82.36 382	45.55 377	86.48 365	55.02 377	84.39 233	88.75 310

OpenMVS_ROB	64.09 19	70.56 360	68.19 366	77.65 340	80.26 402	59.41 339	85.01 272	82.96 354	58.76 410	65.43 409	82.33 383	37.63 427	91.23 286	45.34 435	76.03 350	82.32 423

miper_enhance_ethall		77.87 257	76.86 258	80.92 271	81.65 383	61.38 312	82.68 328	88.98 231	65.52 337	75.47 273	82.30 384	65.76 148	92.00 249	72.95 208	76.39 343	89.39 285

test0.0.03 1		68.00 385	67.69 378	68.90 419	77.55 425	47.43 442	75.70 413	72.95 438	66.66 319	66.56 398	82.29 385	48.06 352	75.87 441	44.97 436	74.51 375	83.41 411

PVSNet	64.34 18	72.08 347	70.87 346	75.69 359	86.21 275	56.44 377	74.37 424	80.73 378	62.06 382	70.17 356	82.23 386	42.86 396	83.31 397	54.77 379	84.45 231	87.32 344

MIMVSNet1		68.58 379	66.78 389	73.98 383	80.07 406	51.82 423	80.77 351	84.37 325	64.40 350	59.75 438	82.16 387	36.47 431	83.63 393	42.73 441	70.33 406	86.48 365

CL-MVSNet_self_test		72.37 342	71.46 337	75.09 369	79.49 416	53.53 409	80.76 352	85.01 320	69.12 285	70.51 350	82.05 388	57.92 246	84.13 389	52.27 392	66.00 423	87.60 336

tpm2		73.26 332	71.46 337	78.63 316	83.34 347	56.71 373	80.65 355	80.40 387	56.63 426	73.55 317	82.02 389	51.80 311	91.24 285	56.35 372	78.42 316	87.95 328

PatchMatch-RL		72.38 341	70.90 345	76.80 352	88.60 179	67.38 171	79.53 371	76.17 424	62.75 374	69.36 368	82.00 390	45.51 378	84.89 384	53.62 385	80.58 289	78.12 442

FMVSNet5		69.50 371	67.96 371	74.15 381	82.97 362	55.35 394	80.01 367	82.12 363	62.56 376	63.02 423	81.53 391	36.92 428	81.92 406	48.42 415	74.06 378	85.17 390

CR-MVSNet		73.37 328	71.27 341	79.67 299	81.32 393	65.19 221	75.92 410	80.30 388	59.92 398	72.73 327	81.19 392	52.50 295	86.69 361	59.84 334	77.71 323	87.11 352

Patchmtry		70.74 357	69.16 360	75.49 364	80.72 397	54.07 406	74.94 421	80.30 388	58.34 412	70.01 358	81.19 392	52.50 295	86.54 363	53.37 387	71.09 403	85.87 379

IB-MVS	68.01 15	75.85 297	73.36 317	83.31 189	84.76 314	66.03 196	83.38 316	85.06 318	70.21 257	69.40 367	81.05 394	45.76 375	94.66 118	65.10 289	75.49 357	89.25 289
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		76.72 281	74.64 297	82.99 207	85.78 286	65.88 203	82.33 331	89.21 220	60.85 390	72.74 326	81.02 395	47.28 356	93.75 162	67.48 268	85.02 219	89.34 287

LF4IMVS		64.02 406	62.19 410	69.50 416	70.90 455	53.29 414	76.13 407	77.18 417	52.65 438	58.59 440	80.98 396	23.55 457	76.52 433	53.06 389	66.66 419	78.68 441

Anonymous20240521		68.80 377	67.22 386	73.55 386	74.33 439	54.11 405	83.18 320	85.61 311	58.15 414	61.68 429	80.94 397	30.71 445	81.27 411	57.00 365	73.34 388	85.28 386

gm-plane-assit					81.40 389	53.83 408			62.72 375		80.94 397		92.39 233	63.40 301

UnsupCasMVSNet_eth		67.33 388	65.99 392	71.37 405	73.48 446	51.47 427	75.16 417	85.19 315	65.20 340	60.78 432	80.93 399	42.35 398	77.20 428	57.12 362	53.69 451	85.44 384

dmvs_re		71.14 352	70.58 347	72.80 395	81.96 379	59.68 334	75.60 414	79.34 399	68.55 297	69.27 370	80.72 400	49.42 340	76.54 432	52.56 391	77.79 322	82.19 425

MDTV_nov1_ep13			69.97 355		83.18 353	53.48 410	77.10 405	80.18 392	60.45 392	69.33 369	80.44 401	48.89 350	86.90 360	51.60 395	78.51 312

pmmvs-eth3d		70.50 361	67.83 375	78.52 323	77.37 427	66.18 195	81.82 335	81.51 370	58.90 408	63.90 421	80.42 402	42.69 397	86.28 367	58.56 348	65.30 425	83.11 415

tt0320		70.49 362	68.03 370	77.89 334	84.78 313	59.12 340	83.55 312	80.44 385	58.13 415	67.43 387	80.41 403	39.26 417	87.54 355	55.12 376	63.18 431	86.99 355

mmtdpeth		74.16 317	73.01 321	77.60 343	83.72 338	61.13 313	85.10 269	85.10 317	72.06 206	77.21 237	80.33 404	43.84 390	85.75 372	77.14 157	52.61 453	85.91 377

tt0320-xc		70.11 366	67.45 383	78.07 332	85.33 299	59.51 338	83.28 318	78.96 403	58.77 409	67.10 391	80.28 405	36.73 429	87.42 356	56.83 368	59.77 441	87.29 345

PM-MVS		66.41 396	64.14 399	73.20 391	73.92 442	56.45 376	78.97 381	64.96 458	63.88 361	64.72 414	80.24 406	19.84 462	83.44 396	66.24 277	64.52 427	79.71 439

SCA		74.22 316	72.33 329	79.91 292	84.05 330	62.17 302	79.96 368	79.29 400	66.30 327	72.38 333	80.13 407	51.95 307	88.60 341	59.25 340	77.67 326	88.96 301

Patchmatch-test		64.82 404	63.24 405	69.57 415	79.42 417	49.82 437	63.49 463	69.05 447	51.98 441	59.95 437	80.13 407	50.91 320	70.98 456	40.66 446	73.57 383	87.90 330

tpmrst		72.39 340	72.13 331	73.18 392	80.54 400	49.91 436	79.91 369	79.08 402	63.11 366	71.69 341	79.95 409	55.32 268	82.77 401	65.66 285	73.89 380	86.87 357

DSMNet-mixed		57.77 417	56.90 419	60.38 438	67.70 459	35.61 469	69.18 443	53.97 470	32.30 468	57.49 445	79.88 410	40.39 413	68.57 462	38.78 450	72.37 392	76.97 444

MDA-MVSNet-bldmvs		66.68 393	63.66 403	75.75 358	79.28 418	60.56 324	73.92 426	78.35 407	64.43 349	50.13 457	79.87 411	44.02 389	83.67 392	46.10 430	56.86 443	83.03 417

PatchmatchNet		73.12 334	71.33 340	78.49 324	83.18 353	60.85 319	79.63 370	78.57 405	64.13 353	71.73 340	79.81 412	51.20 318	85.97 371	57.40 360	76.36 348	88.66 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 390	65.33 394	73.02 393	75.86 432	52.54 417	80.26 364	80.56 381	63.80 362	60.39 433	79.70 413	41.41 406	84.66 387	43.34 439	62.62 432	81.86 427

Syy-MVS		68.05 384	67.85 373	68.67 422	84.68 316	40.97 465	78.62 386	73.08 436	66.65 322	66.74 396	79.46 414	52.11 303	82.30 403	32.89 457	76.38 346	82.75 420

myMVS_eth3d		67.02 391	66.29 391	69.21 417	84.68 316	42.58 460	78.62 386	73.08 436	66.65 322	66.74 396	79.46 414	31.53 443	82.30 403	39.43 449	76.38 346	82.75 420

ppachtmachnet_test		70.04 367	67.34 385	78.14 329	79.80 411	61.13 313	79.19 377	80.59 380	59.16 405	65.27 410	79.29 416	46.75 363	87.29 357	49.33 411	66.72 418	86.00 376

EPMVS		69.02 375	68.16 367	71.59 403	79.61 414	49.80 438	77.40 401	66.93 452	62.82 373	70.01 358	79.05 417	45.79 374	77.86 426	56.58 370	75.26 367	87.13 351

PMMVS		69.34 373	68.67 362	71.35 407	75.67 434	62.03 303	75.17 416	73.46 434	50.00 445	68.68 373	79.05 417	52.07 305	78.13 423	61.16 325	82.77 262	73.90 449

test-LLR		72.94 338	72.43 327	74.48 376	81.35 391	58.04 350	78.38 389	77.46 412	66.66 319	69.95 361	79.00 419	48.06 352	79.24 418	66.13 278	84.83 222	86.15 370

test-mter		71.41 350	70.39 352	74.48 376	81.35 391	58.04 350	78.38 389	77.46 412	60.32 394	69.95 361	79.00 419	36.08 433	79.24 418	66.13 278	84.83 222	86.15 370

KD-MVS_self_test		68.81 376	67.59 381	72.46 399	74.29 440	45.45 449	77.93 397	87.00 285	63.12 365	63.99 420	78.99 421	42.32 399	84.77 385	56.55 371	64.09 428	87.16 350

test_fmvs3		63.36 408	61.82 411	67.98 426	62.51 466	46.96 447	77.37 402	74.03 433	45.24 451	67.50 384	78.79 422	12.16 470	72.98 455	72.77 211	66.02 422	83.99 405

KD-MVS_2432*1600		66.22 398	63.89 401	73.21 389	75.47 437	53.42 411	70.76 437	84.35 326	64.10 355	66.52 400	78.52 423	34.55 436	84.98 382	50.40 402	50.33 456	81.23 431

miper_refine_blended		66.22 398	63.89 401	73.21 389	75.47 437	53.42 411	70.76 437	84.35 326	64.10 355	66.52 400	78.52 423	34.55 436	84.98 382	50.40 402	50.33 456	81.23 431

tpmvs		71.09 353	69.29 358	76.49 353	82.04 378	56.04 384	78.92 382	81.37 373	64.05 357	67.18 390	78.28 425	49.74 337	89.77 316	49.67 409	72.37 392	83.67 409

our_test_3		69.14 374	67.00 387	75.57 361	79.80 411	58.80 341	77.96 396	77.81 409	59.55 401	62.90 426	78.25 426	47.43 354	83.97 390	51.71 394	67.58 417	83.93 406

MDA-MVSNet_test_wron		65.03 402	62.92 406	71.37 405	75.93 430	56.73 371	69.09 446	74.73 430	57.28 423	54.03 452	77.89 427	45.88 372	74.39 450	49.89 408	61.55 435	82.99 418

YYNet1		65.03 402	62.91 407	71.38 404	75.85 433	56.60 375	69.12 445	74.66 432	57.28 423	54.12 451	77.87 428	45.85 373	74.48 449	49.95 407	61.52 436	83.05 416

ambc				75.24 368	73.16 449	50.51 434	63.05 464	87.47 275		64.28 416	77.81 429	17.80 464	89.73 318	57.88 356	60.64 438	85.49 382

tpm cat1		70.57 359	68.31 365	77.35 346	82.41 375	57.95 353	78.08 394	80.22 390	52.04 439	68.54 376	77.66 430	52.00 306	87.84 351	51.77 393	72.07 397	86.25 367

dp		66.80 392	65.43 393	70.90 412	79.74 413	48.82 440	75.12 419	74.77 429	59.61 400	64.08 419	77.23 431	42.89 395	80.72 414	48.86 414	66.58 420	83.16 414

TESTMET0.1,1		69.89 369	69.00 361	72.55 397	79.27 419	56.85 369	78.38 389	74.71 431	57.64 419	68.09 379	77.19 432	37.75 426	76.70 431	63.92 297	84.09 237	84.10 404

CHOSEN 280x420		66.51 395	64.71 397	71.90 401	81.45 388	63.52 272	57.98 466	68.95 448	53.57 435	62.59 427	76.70 433	46.22 369	75.29 447	55.25 375	79.68 299	76.88 445

PatchT		68.46 382	67.85 373	70.29 413	80.70 398	43.93 457	72.47 429	74.88 428	60.15 396	70.55 349	76.57 434	49.94 334	81.59 407	50.58 400	74.83 372	85.34 385

mvsany_test3		53.99 421	51.45 426	61.61 437	55.51 471	44.74 456	63.52 462	45.41 476	43.69 454	58.11 443	76.45 435	17.99 463	63.76 467	54.77 379	47.59 458	76.34 446

RPMNet		73.51 326	70.49 349	82.58 230	81.32 393	65.19 221	75.92 410	92.27 89	57.60 420	72.73 327	76.45 435	52.30 298	95.43 77	48.14 420	77.71 323	87.11 352

dmvs_testset		62.63 409	64.11 400	58.19 440	78.55 422	24.76 478	75.28 415	65.94 455	67.91 306	60.34 434	76.01 437	53.56 287	73.94 453	31.79 458	67.65 416	75.88 447

ADS-MVSNet2		66.20 400	63.33 404	74.82 373	79.92 407	58.75 342	67.55 449	75.19 426	53.37 436	65.25 411	75.86 438	42.32 399	80.53 415	41.57 444	68.91 412	85.18 388

ADS-MVSNet		64.36 405	62.88 408	68.78 421	79.92 407	47.17 445	67.55 449	71.18 440	53.37 436	65.25 411	75.86 438	42.32 399	73.99 452	41.57 444	68.91 412	85.18 388

EGC-MVSNET		52.07 427	47.05 431	67.14 428	83.51 344	60.71 321	80.50 358	67.75 450	0.07 478	0.43 479	75.85 440	24.26 455	81.54 408	28.82 461	62.25 433	59.16 461

new-patchmatchnet		61.73 411	61.73 412	61.70 436	72.74 452	24.50 479	69.16 444	78.03 408	61.40 386	56.72 447	75.53 441	38.42 422	76.48 434	45.95 431	57.67 442	84.13 403

N_pmnet		52.79 425	53.26 423	51.40 450	78.99 420	7.68 484	69.52 441	3.89 483	51.63 442	57.01 446	74.98 442	40.83 410	65.96 465	37.78 451	64.67 426	80.56 437

WB-MVS		54.94 419	54.72 420	55.60 446	73.50 445	20.90 480	74.27 425	61.19 463	59.16 405	50.61 455	74.15 443	47.19 357	75.78 442	17.31 471	35.07 465	70.12 453

patchmatchnet-post											74.00 444	51.12 319	88.60 341

GG-mvs-BLEND				75.38 366	81.59 385	55.80 388	79.32 374	69.63 444		67.19 389	73.67 445	43.24 393	88.90 337	50.41 401	84.50 227	81.45 430

SSC-MVS		53.88 422	53.59 422	54.75 448	72.87 451	19.59 481	73.84 427	60.53 465	57.58 421	49.18 459	73.45 446	46.34 368	75.47 445	16.20 474	32.28 467	69.20 454

Patchmatch-RL test		70.24 364	67.78 377	77.61 341	77.43 426	59.57 337	71.16 434	70.33 441	62.94 370	68.65 374	72.77 447	50.62 324	85.49 377	69.58 249	66.58 420	87.77 333

FPMVS		53.68 423	51.64 425	59.81 439	65.08 463	51.03 430	69.48 442	69.58 445	41.46 456	40.67 463	72.32 448	16.46 466	70.00 460	24.24 467	65.42 424	58.40 463

UnsupCasMVSNet_bld		63.70 407	61.53 413	70.21 414	73.69 444	51.39 428	72.82 428	81.89 365	55.63 430	57.81 444	71.80 449	38.67 421	78.61 421	49.26 412	52.21 454	80.63 435

APD_test1		53.31 424	49.93 429	63.42 435	65.68 462	50.13 435	71.59 433	66.90 453	34.43 465	40.58 464	71.56 450	8.65 475	76.27 436	34.64 456	55.36 448	63.86 459

test_f		52.09 426	50.82 427	55.90 444	53.82 474	42.31 463	59.42 465	58.31 468	36.45 463	56.12 450	70.96 451	12.18 469	57.79 470	53.51 386	56.57 445	67.60 455

PVSNet_0	57.27 20	61.67 412	59.27 415	68.85 420	79.61 414	57.44 363	68.01 447	73.44 435	55.93 429	58.54 441	70.41 452	44.58 384	77.55 427	47.01 424	35.91 464	71.55 452

pmmvs3		57.79 416	54.26 421	68.37 423	64.02 465	56.72 372	75.12 419	65.17 456	40.20 457	52.93 453	69.86 453	20.36 461	75.48 444	45.45 434	55.25 450	72.90 451

test_vis1_rt		60.28 413	58.42 416	65.84 431	67.25 460	55.60 391	70.44 439	60.94 464	44.33 453	59.00 439	66.64 454	24.91 453	68.67 461	62.80 304	69.48 408	73.25 450

new_pmnet		50.91 428	50.29 428	52.78 449	68.58 458	34.94 471	63.71 461	56.63 469	39.73 458	44.95 460	65.47 455	21.93 459	58.48 469	34.98 455	56.62 444	64.92 457

gg-mvs-nofinetune		69.95 368	67.96 371	75.94 356	83.07 356	54.51 403	77.23 403	70.29 442	63.11 366	70.32 353	62.33 456	43.62 391	88.69 339	53.88 384	87.76 170	84.62 398

JIA-IIPM		66.32 397	62.82 409	76.82 351	77.09 428	61.72 309	65.34 457	75.38 425	58.04 417	64.51 415	62.32 457	42.05 403	86.51 364	51.45 397	69.22 411	82.21 424

LCM-MVSNet		54.25 420	49.68 430	67.97 427	53.73 475	45.28 452	66.85 452	80.78 377	35.96 464	39.45 465	62.23 458	8.70 474	78.06 425	48.24 419	51.20 455	80.57 436

PMMVS2		40.82 436	38.86 440	46.69 451	53.84 473	16.45 482	48.61 469	49.92 471	37.49 461	31.67 466	60.97 459	8.14 476	56.42 471	28.42 462	30.72 468	67.19 456

testf1		45.72 431	41.96 435	57.00 441	56.90 469	45.32 450	66.14 454	59.26 466	26.19 469	30.89 468	60.96 460	4.14 478	70.64 458	26.39 465	46.73 460	55.04 464

APD_test2		45.72 431	41.96 435	57.00 441	56.90 469	45.32 450	66.14 454	59.26 466	26.19 469	30.89 468	60.96 460	4.14 478	70.64 458	26.39 465	46.73 460	55.04 464

MVS-HIRNet		59.14 415	57.67 417	63.57 434	81.65 383	43.50 458	71.73 431	65.06 457	39.59 459	51.43 454	57.73 462	38.34 423	82.58 402	39.53 447	73.95 379	64.62 458

ANet_high		50.57 429	46.10 433	63.99 433	48.67 478	39.13 466	70.99 436	80.85 376	61.39 387	31.18 467	57.70 463	17.02 465	73.65 454	31.22 460	15.89 475	79.18 440

PMVS	37.38 22	44.16 435	40.28 439	55.82 445	40.82 480	42.54 462	65.12 458	63.99 460	34.43 465	24.48 471	57.12 464	3.92 480	76.17 438	17.10 472	55.52 447	48.75 466
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 433	45.38 434	45.55 452	73.36 448	26.85 476	67.72 448	34.19 478	54.15 434	49.65 458	56.41 465	25.43 451	62.94 468	19.45 469	28.09 469	46.86 468

test_vis3_rt		49.26 430	47.02 432	56.00 443	54.30 472	45.27 453	66.76 453	48.08 473	36.83 462	44.38 461	53.20 466	7.17 477	64.07 466	56.77 369	55.66 446	58.65 462

test_method		31.52 439	29.28 443	38.23 454	27.03 482	6.50 485	20.94 474	62.21 462	4.05 476	22.35 474	52.50 467	13.33 467	47.58 474	27.04 464	34.04 466	60.62 460

kuosan		39.70 437	40.40 438	37.58 455	64.52 464	26.98 474	65.62 456	33.02 479	46.12 450	42.79 462	48.99 468	24.10 456	46.56 476	12.16 477	26.30 470	39.20 469

DeepMVS_CX				27.40 458	40.17 481	26.90 475		24.59 482	17.44 474	23.95 472	48.61 469	9.77 472	26.48 477	18.06 470	24.47 471	28.83 471

MVE	26.22 23	30.37 441	25.89 445	43.81 453	44.55 479	35.46 470	28.87 473	39.07 477	18.20 473	18.58 475	40.18 470	2.68 481	47.37 475	17.07 473	23.78 472	48.60 467
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 434	41.86 437	55.16 447	77.03 429	51.52 426	32.50 472	80.52 382	32.46 467	27.12 470	35.02 471	9.52 473	75.50 443	22.31 468	60.21 440	38.45 470
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 438	30.64 441	35.15 456	52.87 476	27.67 473	57.09 467	47.86 474	24.64 471	16.40 476	33.05 472	11.23 471	54.90 472	14.46 475	18.15 473	22.87 472

EMVS		30.81 440	29.65 442	34.27 457	50.96 477	25.95 477	56.58 468	46.80 475	24.01 472	15.53 477	30.68 473	12.47 468	54.43 473	12.81 476	17.05 474	22.43 473

tmp_tt		18.61 443	21.40 446	10.23 460	4.82 483	10.11 483	34.70 471	30.74 481	1.48 477	23.91 473	26.07 474	28.42 448	13.41 479	27.12 463	15.35 476	7.17 474

X-MVStestdata		80.37 192	77.83 232	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 112	12.47 475	67.45 122	96.60 37	83.06 86	94.50 57	94.07 72

test_post											5.46 476	50.36 328	84.24 388

test_post1							78.90 383				5.43 477	48.81 351	85.44 379	59.25 340

wuyk23d		16.82 444	15.94 447	19.46 459	58.74 468	31.45 472	39.22 470	3.74 484	6.84 475	6.04 478	2.70 478	1.27 482	24.29 478	10.54 478	14.40 477	2.63 475

testmvs		6.04 447	8.02 450	0.10 462	0.08 484	0.03 487	69.74 440	0.04 485	0.05 479	0.31 480	1.68 479	0.02 484	0.04 480	0.24 479	0.02 478	0.25 477

test123		6.12 446	8.11 449	0.14 461	0.06 485	0.09 486	71.05 435	0.03 486	0.04 480	0.25 481	1.30 480	0.05 483	0.03 481	0.21 480	0.01 479	0.29 476

mmdepth		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

monomultidepth		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

test_blank		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

uanet_test		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

DCPMVS		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

pcd_1.5k_mvsjas		5.26 448	7.02 451	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	63.15 173	0.00 482	0.00 481	0.00 480	0.00 478

sosnet-low-res		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

sosnet		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

uncertanet		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

Regformer		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

uanet		0.00 449	0.00 452	0.00 463	0.00 486	0.00 488	0.00 475	0.00 487	0.00 481	0.00 482	0.00 481	0.00 485	0.00 482	0.00 481	0.00 480	0.00 478

TestfortrainingZip							93.28 12

WAC-MVS						42.58 460								39.46 448

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 146	91.30 18

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 52

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 52

eth-test2					0.00 486

eth-test					0.00 486

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 315	92.39 6				88.94 27	96.63 4	94.85 21

save fliter					93.80 44	72.35 44	90.47 74	91.17 143	74.31 151

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 64

GSMVS																88.96 301

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 316				88.96 301

sam_mvs												50.01 332

MTGPA								92.02 105

MTMP							92.18 39	32.83 480

test9_res														84.90 63	95.70 30	92.87 144

agg_prior2														82.91 90	95.45 33	92.70 149

agg_prior					92.85 68	71.94 52		91.78 121		84.41 94			94.93 101

test_prior4						72.60 34	89.01 124

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 80

旧先验2							86.56 224		58.10 416	87.04 61			88.98 333	74.07 196

新几何2							86.29 237

无先验							87.48 185	88.98 231	60.00 397				94.12 140	67.28 270		88.97 300

原ACMM2							86.86 211

testdata2													91.01 295	62.37 311

segment_acmp												73.08 43

testdata1							84.14 299		75.71 106

test12				86.80 58	92.63 73	70.70 81		91.79 120		82.71 130		71.67 62	96.16 52		94.50 57	93.54 109

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 227

plane_prior5								92.44 82					95.38 82	78.71 137	86.32 195	91.33 204

plane_prior3						68.60 128			78.44 36	78.92 192

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 200

n2								0.00 487

nn								0.00 487

door-mid								69.98 443

test11								92.23 92

door								69.44 446

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 115		76.41 85	77.23 233

ACMP_Plane					89.33 144		89.17 115		76.41 85	77.23 233

BP-MVS														77.47 152

HQP4-MVS										77.24 232			95.11 94			91.03 214

HQP3-MVS								92.19 99							85.99 204

HQP2-MVS												60.17 230

MDTV_nov1_ep13_2view						37.79 468	75.16 417		55.10 431	66.53 399		49.34 342		53.98 383		87.94 329

ACMMP++_ref															81.95 273

ACMMP++															81.25 278

Test By Simon												64.33 159

High-resolution multi-view benchmark