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.

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 127	86.57 1	87.39 49	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

fmvsm_s_conf0.5_n_4		85.39 68	85.75 61	84.30 118	86.70 239	65.83 184	88.77 124	89.78 170	75.46 101	88.35 28	93.73 65	69.19 87	93.06 183	91.30 2	88.44 143	94.02 59

fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 176	87.08 231	65.21 200	89.09 113	90.21 158	79.67 18	89.98 18	95.02 18	73.17 38	91.71 236	91.30 2	91.60 89	92.34 140

fmvsm_s_conf0.5_n_8		86.56 42	87.17 33	84.73 103	87.76 208	65.62 191	89.20 104	92.21 89	79.94 16	89.74 20	94.86 20	68.63 96	94.20 122	90.83 4	91.39 94	94.38 42

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 179	82.14 3	86.65 57	94.28 38	68.28 101	97.46 6	90.81 5	95.31 34	95.15 7

test_fmvsmconf_n		85.92 54	86.04 55	85.57 76	85.03 277	69.51 93	89.62 89	90.58 142	73.42 160	87.75 42	94.02 52	72.85 43	93.24 167	90.37 6	90.75 104	93.96 61

test_fmvsmconf0.1_n		85.61 62	85.65 62	85.50 77	82.99 326	69.39 100	89.65 86	90.29 156	73.31 163	87.77 41	94.15 46	71.72 54	93.23 168	90.31 7	90.67 106	93.89 67

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 80	80.25 367	69.03 103	89.47 92	89.65 176	73.24 167	86.98 54	94.27 39	66.62 116	93.23 168	90.26 8	89.95 119	93.78 74

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 151	86.17 249	65.00 208	86.96 186	87.28 245	74.35 132	88.25 31	94.23 42	61.82 173	92.60 197	89.85 9	88.09 148	93.84 70

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 125	86.26 246	67.40 153	89.18 105	89.31 187	72.50 176	88.31 29	93.86 61	69.66 81	91.96 224	89.81 10	91.05 99	93.38 93

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 150	85.62 261	64.94 210	87.03 184	86.62 261	74.32 133	87.97 39	94.33 36	60.67 197	92.60 197	89.72 11	87.79 150	93.96 61

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 39

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 39

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
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
fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 155	87.32 224	65.13 203	88.86 120	91.63 112	75.41 102	88.23 32	93.45 72	68.56 97	92.47 204	89.52 15	92.78 73	93.20 104

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 35	92.78 4	95.74 6	82.45 3	97.49 4	89.42 16	96.68 2	94.95 11

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 9	89.42 16	96.57 7	94.67 28

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 97	91.06 16	96.03 1	76.84 14	97.03 17	89.09 18	95.65 27	94.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 53	92.12 9	95.78 4	80.98 9	97.40 9	89.08 19	96.41 12	93.33 97
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 19	96.41 12	94.21 50

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 56	93.10 1	95.72 8	82.99 1	97.44 7	89.07 21	96.63 4	94.88 15

test_241102_TWO								94.06 10	77.24 56	92.78 4	95.72 8	81.26 8	97.44 7	89.07 21	96.58 6	94.26 49

fmvsm_l_conf0.5_n_3		86.02 49	86.32 45	85.14 85	87.20 227	68.54 123	89.57 90	90.44 147	75.31 106	87.49 46	94.39 35	72.86 42	92.72 194	89.04 23	90.56 107	94.16 51

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 279	92.39 6				88.94 24	96.63 4	94.85 20

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 122	85.42 265	68.81 109	88.49 136	87.26 247	68.08 269	88.03 36	93.49 68	72.04 50	91.77 232	88.90 25	89.14 130	92.24 147

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 135	86.69 240	67.31 156	89.46 93	83.07 314	71.09 202	86.96 55	93.70 66	69.02 93	91.47 249	88.79 26	84.62 194	93.44 92

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 112	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 110	84.12 295	69.37 101	88.15 151	87.96 228	70.01 226	83.95 96	93.23 77	68.80 95	91.51 247	88.61 28	89.96 118	92.57 129

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 123	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
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
fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 129	84.86 279	67.28 157	89.40 98	83.01 315	70.67 210	87.08 52	93.96 58	68.38 99	91.45 250	88.56 30	84.50 195	93.56 87

test_fmvsm_n_1920		85.29 70	85.34 68	85.13 88	86.12 251	69.93 86	88.65 132	90.78 138	69.97 228	88.27 30	93.98 57	71.39 60	91.54 244	88.49 31	90.45 109	93.91 64

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 35	94.06 50	76.43 16	96.84 21	88.48 32	95.99 18	94.34 45

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 137	85.38 266	68.40 126	88.34 143	86.85 257	67.48 276	87.48 47	93.40 73	70.89 66	91.61 237	88.38 33	89.22 128	92.16 151

fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 120	86.14 250	68.12 133	89.43 94	82.87 319	70.27 221	87.27 51	93.80 64	69.09 88	91.58 239	88.21 34	83.65 215	93.14 108

fmvsm_s_conf0.1_n_a		83.32 104	82.99 102	84.28 120	83.79 303	68.07 135	89.34 101	82.85 320	69.80 232	87.36 50	94.06 50	68.34 100	91.56 242	87.95 35	83.46 221	93.21 103

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 127	88.90 24	93.85 62	75.75 20	96.00 54	87.80 36	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 87	88.14 33	95.09 17	71.06 65	96.67 29	87.67 37	96.37 14	94.09 55

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 142	87.63 38	94.27 59	93.65 81
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
SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 113

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 93	92.29 7	95.66 10	81.67 6	97.38 11	87.44 41	96.34 15	93.95 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 95	89.16 21	95.10 16	75.65 21	96.19 46	87.07 42	96.01 17	94.79 22

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 145	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

MTAPA		87.23 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 202	92.02 94	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 100

fmvsm_s_conf0.5_n_7		83.34 103	84.03 86	81.28 228	85.73 258	65.13 203	85.40 237	89.90 168	74.96 117	82.13 119	93.89 60	66.65 115	87.92 317	86.56 45	91.05 99	90.80 188

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 115	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 111

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 115	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 111

reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 124	88.80 25	95.61 11	70.29 74	96.44 39	86.20 48	93.08 69	93.16 106

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 49	89.79 19	94.12 47	78.98 12	96.58 35	85.66 49	95.72 24	94.58 33

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 45	83.84 98	94.40 34	72.24 47	96.28 43	85.65 50	95.30 35	93.62 84
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
balanced_conf03		86.78 37	86.99 35	86.15 63	91.24 83	67.61 146	90.51 62	92.90 56	77.26 55	87.44 48	91.63 114	71.27 62	96.06 49	85.62 51	95.01 37	94.78 23

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 69	85.24 68	94.32 37	71.76 53	96.93 19	85.53 52	95.79 22	94.32 46

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 90	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 126	91.89 102	68.69 260	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 115

test9_res														84.90 55	95.70 26	92.87 120

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 63	93.47 71	73.02 41	97.00 18	84.90 55	94.94 40	94.10 54

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 169	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 41

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 61	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 128	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 122

MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 174	84.64 81	91.71 110	71.85 51	96.03 50	84.77 60	94.45 54	94.49 37

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 205	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

PC_three_1452									68.21 268	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 71	84.91 73	94.44 32	70.78 68	96.61 32	84.53 63	94.89 42	93.66 77

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 71	84.66 80	94.52 25	68.81 94	96.65 30	84.53 63	94.90 41	94.00 60

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 74	84.45 85	94.52 25	69.09 88	96.70 27	84.37 65	94.83 45	94.03 58

CANet		86.45 43	86.10 53	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 130	91.43 122	70.34 72	97.23 14	84.26 66	93.36 68	94.37 43

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 171	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS		86.69 39	86.95 37	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

CP-MVS		87.11 33	86.92 38	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 77	83.68 101	94.46 29	67.93 104	95.95 57	84.20 69	94.39 55	93.23 100

GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 67	84.68 77	93.99 56	70.67 70	96.82 22	84.18 70	95.01 37	93.90 66

BP-MVS1		84.32 81	83.71 90	86.17 61	87.84 201	67.85 139	89.38 99	89.64 177	77.73 41	83.98 95	92.12 102	56.89 229	95.43 70	84.03 71	91.75 88	95.24 6

EC-MVSNet		86.01 50	86.38 44	84.91 97	89.31 139	66.27 175	92.32 30	93.63 21	79.37 22	84.17 91	91.88 106	69.04 92	95.43 70	83.93 72	93.77 63	93.01 116

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 52	82.45 3	96.87 20	83.77 73	96.48 8	94.88 15

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 161	88.69 130	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 112	83.49 74	91.14 98	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
dcpmvs_2		85.63 61	86.15 52	84.06 137	91.71 78	64.94 210	86.47 205	91.87 104	73.63 152	86.60 58	93.02 84	76.57 15	91.87 230	83.36 75	92.15 81	95.35 3

test_prior2							88.85 122		75.41 102	84.91 73	93.54 67	74.28 29		83.31 76	95.86 20

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 177	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 44

XVS		87.18 32	86.91 39	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	94.17 44	67.45 109	96.60 33	83.06 78	94.50 51	94.07 56

X-MVStestdata		80.37 165	77.83 201	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 438	67.45 109	96.60 33	83.06 78	94.50 51	94.07 56

mamv4		76.81 245	78.23 193	72.54 361	86.12 251	65.75 189	78.76 350	82.07 328	64.12 318	72.97 290	91.02 138	67.97 103	68.08 426	83.04 80	78.02 285	83.80 372

APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 156	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 90

agg_prior2														82.91 82	95.45 29	92.70 124

mPP-MVS		86.67 41	86.32 45	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 68	82.81 114	94.25 41	66.44 120	96.24 44	82.88 83	94.28 58	93.38 93

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 157	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 134

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 157	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 134

h-mvs33		83.15 106	82.19 114	86.02 69	90.56 98	70.85 73	88.15 151	89.16 195	76.02 91	84.67 78	91.39 123	61.54 178	95.50 66	82.71 86	75.48 322	91.72 160

hse-mvs2		81.72 128	80.94 134	84.07 135	88.72 163	67.68 144	85.87 223	87.26 247	76.02 91	84.67 78	88.22 207	61.54 178	93.48 157	82.71 86	73.44 350	91.06 179

PGM-MVS		86.68 40	86.27 47	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 99	83.86 97	94.42 33	67.87 106	96.64 31	82.70 88	94.57 50	93.66 77

ACMMP		85.89 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 71	80.73 141	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 100
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
diffmvs		82.10 120	81.88 122	82.76 198	83.00 324	63.78 236	83.68 275	89.76 172	72.94 172	82.02 121	89.85 159	65.96 129	90.79 269	82.38 90	87.30 158	93.71 76
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 92	84.54 79	80.99 237	90.06 113	65.83 184	84.21 267	88.74 214	71.60 192	85.01 70	92.44 96	74.51 25	83.50 361	82.15 91	92.15 81	93.64 83

SPE-MVS-test		86.29 48	86.48 43	85.71 73	91.02 88	67.21 162	92.36 29	93.78 18	78.97 30	83.51 105	91.20 129	70.65 71	95.15 84	81.96 92	94.89 42	94.77 24

TSAR-MVS + GP.		85.71 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 245	76.41 80	85.80 62	90.22 154	74.15 31	95.37 78	81.82 93	91.88 84	92.65 128

alignmvs		85.48 64	85.32 70	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 88	87.73 44	91.46 121	70.32 73	93.78 142	81.51 94	88.95 131	94.63 32

sasdasda		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 75	88.01 37	91.23 126	73.28 36	93.91 136	81.50 95	88.80 134	94.77 24

canonicalmvs		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 75	88.01 37	91.23 126	73.28 36	93.91 136	81.50 95	88.80 134	94.77 24

baseline		84.93 76	84.98 74	84.80 101	87.30 225	65.39 197	87.30 177	92.88 57	77.62 43	84.04 94	92.26 99	71.81 52	93.96 129	81.31 97	90.30 111	95.03 10

MGCFI-Net		85.06 75	85.51 65	83.70 153	89.42 131	63.01 254	89.43 94	92.62 73	76.43 79	87.53 45	91.34 124	72.82 44	93.42 162	81.28 98	88.74 137	94.66 31

casdiffmvs		85.11 73	85.14 73	85.01 91	87.20 227	65.77 188	87.75 163	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 135	81.27 99	90.48 108	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
MVS_111021_HR		85.14 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 219	90.33 153	76.11 89	82.08 120	91.61 116	71.36 61	94.17 125	81.02 100	92.58 76	92.08 153

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 113	73.89 146	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 86

CPTT-MVS		83.73 90	83.33 97	84.92 96	93.28 49	70.86 72	92.09 36	90.38 149	68.75 259	79.57 153	92.83 88	60.60 201	93.04 186	80.92 102	91.56 92	90.86 187

ETV-MVS		84.90 78	84.67 78	85.59 75	89.39 134	68.66 120	88.74 128	92.64 72	79.97 15	84.10 92	85.71 273	69.32 85	95.38 75	80.82 103	91.37 95	92.72 123

DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 77	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		83.88 86	83.53 92	84.96 93	86.77 237	69.28 102	90.46 67	92.67 67	74.79 122	82.95 109	91.33 125	72.70 45	93.09 181	80.79 105	79.28 273	92.50 134

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 81	88.18 182	67.85 139	87.66 165	89.73 174	80.05 14	82.95 109	89.59 168	70.74 69	94.82 101	80.66 106	84.72 192	93.28 99

MSLP-MVS++		85.43 66	85.76 60	84.45 111	91.93 75	70.24 79	90.71 59	92.86 58	77.46 51	84.22 89	92.81 90	67.16 113	92.94 188	80.36 107	94.35 57	90.16 217

MVS_111021_LR		82.61 115	82.11 115	84.11 128	88.82 157	71.58 55	85.15 240	86.16 269	74.69 124	80.47 143	91.04 135	62.29 166	90.55 273	80.33 108	90.08 116	90.20 216

DELS-MVS		85.41 67	85.30 71	85.77 72	88.49 170	67.93 138	85.52 236	93.44 27	78.70 31	83.63 104	89.03 183	74.57 24	95.71 61	80.26 109	94.04 61	93.66 77
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
GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 175	83.71 100	91.86 108	55.69 235	95.35 79	80.03 110	89.74 122	94.69 27

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 89	87.87 199	67.53 149	87.44 173	89.66 175	79.74 17	82.23 118	89.41 177	70.24 75	94.74 104	79.95 111	83.92 207	92.99 118

CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 143	83.16 108	91.07 134	75.94 18	95.19 82	79.94 112	94.38 56	93.55 88

RRT-MVS		82.60 117	82.10 116	84.10 129	87.98 195	62.94 259	87.45 172	91.27 123	77.42 52	79.85 149	90.28 150	56.62 232	94.70 107	79.87 113	88.15 147	94.67 28

AstraMVS		80.81 148	80.14 149	82.80 192	86.05 254	63.96 230	86.46 206	85.90 273	73.71 150	80.85 139	90.56 146	54.06 252	91.57 241	79.72 114	83.97 206	92.86 121

OPM-MVS		83.50 98	82.95 103	85.14 85	88.79 160	70.95 69	89.13 111	91.52 116	77.55 48	80.96 137	91.75 109	60.71 195	94.50 112	79.67 115	86.51 170	89.97 233
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 134	92.42 80	68.32 267	84.61 82	93.48 69	72.32 46	96.15 48	79.00 116	95.43 30	94.28 48

MVSFormer		82.85 112	82.05 118	85.24 83	87.35 218	70.21 80	90.50 64	90.38 149	68.55 262	81.32 130	89.47 171	61.68 175	93.46 159	78.98 117	90.26 112	92.05 154

test_djsdf		80.30 166	79.32 167	83.27 167	83.98 299	65.37 198	90.50 64	90.38 149	68.55 262	76.19 227	88.70 190	56.44 233	93.46 159	78.98 117	80.14 263	90.97 184

test_vis1_n_1920		75.52 267	75.78 243	74.75 340	79.84 373	57.44 328	83.26 286	85.52 277	62.83 335	79.34 157	86.17 266	45.10 346	79.71 381	78.75 119	81.21 247	87.10 318

HQP_MVS		83.64 93	83.14 98	85.14 85	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 162	91.00 139	60.42 203	95.38 75	78.71 120	86.32 172	91.33 171

plane_prior5								92.44 77					95.38 75	78.71 120	86.32 172	91.33 171

LPG-MVS_test		82.08 121	81.27 127	84.50 108	89.23 143	68.76 112	90.22 73	91.94 100	75.37 104	76.64 215	91.51 118	54.29 248	94.91 95	78.44 122	83.78 208	89.83 238

LGP-MVS_train				84.50 108	89.23 143	68.76 112		91.94 100	75.37 104	76.64 215	91.51 118	54.29 248	94.91 95	78.44 122	83.78 208	89.83 238

lupinMVS		81.39 137	80.27 147	84.76 102	87.35 218	70.21 80	85.55 232	86.41 263	62.85 334	81.32 130	88.61 194	61.68 175	92.24 216	78.41 124	90.26 112	91.83 157

jason		81.39 137	80.29 146	84.70 104	86.63 242	69.90 88	85.95 220	86.77 258	63.24 327	81.07 136	89.47 171	61.08 191	92.15 218	78.33 125	90.07 117	92.05 154
jason: jason.
xiu_mvs_v1_base_debu		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 229	88.77 210	69.06 252	81.83 122	88.16 208	50.91 289	92.85 190	78.29 126	87.56 152	89.06 257

xiu_mvs_v1_base		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 229	88.77 210	69.06 252	81.83 122	88.16 208	50.91 289	92.85 190	78.29 126	87.56 152	89.06 257

xiu_mvs_v1_base_debi		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 229	88.77 210	69.06 252	81.83 122	88.16 208	50.91 289	92.85 190	78.29 126	87.56 152	89.06 257

guyue		81.13 141	80.64 138	82.60 201	86.52 243	63.92 233	86.69 199	87.73 236	73.97 142	80.83 140	89.69 162	56.70 230	91.33 255	78.26 129	85.40 186	92.54 131

Effi-MVS+		83.62 95	83.08 99	85.24 83	88.38 176	67.45 150	88.89 119	89.15 196	75.50 100	82.27 117	88.28 204	69.61 82	94.45 114	77.81 130	87.84 149	93.84 70

PS-MVSNAJss		82.07 122	81.31 126	84.34 116	86.51 244	67.27 158	89.27 102	91.51 117	71.75 187	79.37 155	90.22 154	63.15 153	94.27 118	77.69 131	82.36 235	91.49 167

ACMP	74.13 6	81.51 136	80.57 139	84.36 114	89.42 131	68.69 119	89.97 77	91.50 120	74.46 130	75.04 262	90.41 149	53.82 254	94.54 109	77.56 132	82.91 227	89.86 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 133

HQP-MVS		82.61 115	82.02 119	84.37 113	89.33 136	66.98 165	89.17 106	92.19 91	76.41 80	77.23 200	90.23 153	60.17 206	95.11 87	77.47 133	85.99 180	91.03 181

MVS_Test		83.15 106	83.06 100	83.41 163	86.86 233	63.21 250	86.11 217	92.00 96	74.31 134	82.87 111	89.44 176	70.03 76	93.21 170	77.39 135	88.50 142	93.81 72

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 214	93.37 74	60.40 205	96.75 26	77.20 136	93.73 64	95.29 5

anonymousdsp		78.60 205	77.15 219	82.98 184	80.51 365	67.08 163	87.24 179	89.53 180	65.66 299	75.16 257	87.19 235	52.52 263	92.25 215	77.17 137	79.34 272	89.61 245

mmtdpeth		74.16 282	73.01 286	77.60 308	83.72 306	61.13 278	85.10 242	85.10 281	72.06 185	77.21 204	80.33 368	43.84 355	85.75 339	77.14 138	52.61 416	85.91 341

VDD-MVS		83.01 111	82.36 112	84.96 93	91.02 88	66.40 172	88.91 118	88.11 223	77.57 45	84.39 87	93.29 76	52.19 269	93.91 136	77.05 139	88.70 138	94.57 35

XVG-OURS-SEG-HR		80.81 148	79.76 156	83.96 147	85.60 262	68.78 111	83.54 282	90.50 145	70.66 213	76.71 213	91.66 111	60.69 196	91.26 256	76.94 140	81.58 243	91.83 157

jajsoiax		79.29 188	77.96 196	83.27 167	84.68 284	66.57 171	89.25 103	90.16 160	69.20 248	75.46 242	89.49 170	45.75 341	93.13 179	76.84 141	80.80 253	90.11 221

SDMVSNet		80.38 163	80.18 148	80.99 237	89.03 152	64.94 210	80.45 326	89.40 183	75.19 110	76.61 217	89.98 156	60.61 200	87.69 321	76.83 142	83.55 217	90.33 211

mvs_tets		79.13 192	77.77 205	83.22 171	84.70 283	66.37 173	89.17 106	90.19 159	69.38 241	75.40 245	89.46 173	44.17 353	93.15 177	76.78 143	80.70 255	90.14 218

DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 181	93.04 41	69.80 232	82.85 112	91.22 128	73.06 40	96.02 52	76.72 144	94.63 48	91.46 170

test_cas_vis1_n_1920		73.76 288	73.74 277	73.81 349	75.90 395	59.77 298	80.51 324	82.40 324	58.30 376	81.62 128	85.69 274	44.35 352	76.41 399	76.29 145	78.61 276	85.23 351

ET-MVSNet_ETH3D		78.63 204	76.63 234	84.64 105	86.73 238	69.47 95	85.01 244	84.61 287	69.54 238	66.51 367	86.59 253	50.16 298	91.75 233	76.26 146	84.24 203	92.69 126

v2v482		80.23 167	79.29 168	83.05 180	83.62 307	64.14 227	87.04 183	89.97 165	73.61 153	78.18 180	87.22 233	61.10 190	93.82 140	76.11 147	76.78 302	91.18 175

test_fmvs1_n		70.86 321	70.24 318	72.73 359	72.51 417	55.28 360	81.27 312	79.71 358	51.49 406	78.73 164	84.87 295	27.54 413	77.02 393	76.06 148	79.97 265	85.88 342

CLD-MVS		82.31 118	81.65 124	84.29 119	88.47 171	67.73 143	85.81 227	92.35 82	75.78 94	78.33 176	86.58 255	64.01 143	94.35 115	76.05 149	87.48 155	90.79 189
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		83.72 91	82.92 104	86.14 65	84.22 293	69.48 94	91.05 56	85.27 279	81.30 6	76.83 209	91.65 112	66.09 125	95.56 63	76.00 150	93.85 62	93.38 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs1		70.93 320	70.52 313	72.16 363	73.71 406	55.05 362	80.82 315	78.77 367	51.21 407	78.58 169	84.41 303	31.20 408	76.94 394	75.88 151	80.12 264	84.47 363

XVG-OURS		80.41 162	79.23 170	83.97 146	85.64 260	69.02 105	83.03 294	90.39 148	71.09 202	77.63 191	91.49 120	54.62 247	91.35 253	75.71 152	83.47 220	91.54 164

V42		79.38 187	78.24 191	82.83 189	81.10 359	65.50 194	85.55 232	89.82 169	71.57 193	78.21 178	86.12 267	60.66 198	93.18 176	75.64 153	75.46 324	89.81 240

PS-MVSNAJ		81.69 130	81.02 132	83.70 153	89.51 127	68.21 132	84.28 266	90.09 162	70.79 207	81.26 134	85.62 278	63.15 153	94.29 116	75.62 154	88.87 133	88.59 281

xiu_mvs_v2_base		81.69 130	81.05 131	83.60 155	89.15 146	68.03 137	84.46 260	90.02 163	70.67 210	81.30 133	86.53 258	63.17 152	94.19 124	75.60 155	88.54 140	88.57 282

EIA-MVS		83.31 105	82.80 106	84.82 99	89.59 123	65.59 192	88.21 147	92.68 66	74.66 126	78.96 160	86.42 260	69.06 90	95.26 80	75.54 156	90.09 115	93.62 84

AUN-MVS		79.21 190	77.60 211	84.05 140	88.71 164	67.61 146	85.84 225	87.26 247	69.08 251	77.23 200	88.14 212	53.20 261	93.47 158	75.50 157	73.45 349	91.06 179

mvsmamba		80.60 157	79.38 164	84.27 122	89.74 121	67.24 160	87.47 170	86.95 253	70.02 225	75.38 246	88.93 184	51.24 286	92.56 200	75.47 158	89.22 128	93.00 117

reproduce_monomvs		75.40 271	74.38 268	78.46 291	83.92 301	57.80 322	83.78 273	86.94 254	73.47 159	72.25 301	84.47 301	38.74 384	89.27 294	75.32 159	70.53 369	88.31 287

OMC-MVS		82.69 113	81.97 121	84.85 98	88.75 162	67.42 151	87.98 154	90.87 136	74.92 118	79.72 151	91.65 112	62.19 169	93.96 129	75.26 160	86.42 171	93.16 106

v1144		80.03 171	79.03 174	83.01 182	83.78 304	64.51 218	87.11 182	90.57 144	71.96 186	78.08 183	86.20 265	61.41 182	93.94 132	74.93 161	77.23 293	90.60 199

MVSTER		79.01 195	77.88 200	82.38 205	83.07 321	64.80 214	84.08 271	88.95 206	69.01 255	78.69 165	87.17 236	54.70 245	92.43 206	74.69 162	80.57 257	89.89 236

test_vis1_n		69.85 335	69.21 324	71.77 365	72.66 416	55.27 361	81.48 308	76.21 386	52.03 403	75.30 253	83.20 333	28.97 411	76.22 401	74.60 163	78.41 282	83.81 371

test_fmvs2		68.35 348	67.48 347	70.98 374	69.50 420	51.95 384	80.05 332	76.38 385	49.33 409	74.65 269	84.38 304	23.30 422	75.40 410	74.51 164	75.17 333	85.60 345

PVSNet_Blended_VisFu		82.62 114	81.83 123	84.96 93	90.80 94	69.76 90	88.74 128	91.70 111	69.39 240	78.96 160	88.46 199	65.47 132	94.87 100	74.42 165	88.57 139	90.24 215

v8		79.97 173	79.02 175	82.80 192	84.09 296	64.50 220	87.96 155	90.29 156	74.13 141	75.24 255	86.81 242	62.88 158	93.89 139	74.39 166	75.40 327	90.00 229

v144192		79.47 181	78.37 187	82.78 196	83.35 312	63.96 230	86.96 186	90.36 152	69.99 227	77.50 192	85.67 276	60.66 198	93.77 144	74.27 167	76.58 303	90.62 197

ACMM	73.20 8	80.78 154	79.84 155	83.58 157	89.31 139	68.37 127	89.99 76	91.60 114	70.28 220	77.25 198	89.66 164	53.37 259	93.53 155	74.24 168	82.85 228	88.85 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 203		58.10 379	87.04 53			88.98 301	74.07 169

v1192		79.59 178	78.43 186	83.07 179	83.55 309	64.52 217	86.93 189	90.58 142	70.83 206	77.78 188	85.90 269	59.15 209	93.94 132	73.96 170	77.19 295	90.76 191

v10		79.74 175	78.67 179	82.97 185	84.06 297	64.95 209	87.88 161	90.62 141	73.11 168	75.11 259	86.56 256	61.46 181	94.05 128	73.68 171	75.55 320	89.90 235

v1921920		79.22 189	78.03 195	82.80 192	83.30 314	63.94 232	86.80 193	90.33 153	69.91 230	77.48 193	85.53 280	58.44 213	93.75 146	73.60 172	76.85 300	90.71 195

cl22		78.07 218	77.01 221	81.23 230	82.37 340	61.83 272	83.55 280	87.98 227	68.96 256	75.06 261	83.87 315	61.40 183	91.88 229	73.53 173	76.39 307	89.98 232

Effi-MVS+-dtu		80.03 171	78.57 182	84.42 112	85.13 275	68.74 114	88.77 124	88.10 224	74.99 114	74.97 263	83.49 328	57.27 225	93.36 163	73.53 173	80.88 251	91.18 175

c3_l		78.75 200	77.91 198	81.26 229	82.89 328	61.56 275	84.09 270	89.13 198	69.97 228	75.56 238	84.29 307	66.36 121	92.09 220	73.47 175	75.48 322	90.12 220

VDDNet		81.52 134	80.67 137	84.05 140	90.44 101	64.13 228	89.73 84	85.91 272	71.11 201	83.18 107	93.48 69	50.54 295	93.49 156	73.40 176	88.25 145	94.54 36

CANet_DTU		80.61 156	79.87 154	82.83 189	85.60 262	63.17 253	87.36 174	88.65 216	76.37 84	75.88 233	88.44 200	53.51 257	93.07 182	73.30 177	89.74 122	92.25 145

miper_ehance_all_eth		78.59 206	77.76 206	81.08 235	82.66 333	61.56 275	83.65 276	89.15 196	68.87 257	75.55 239	83.79 319	66.49 119	92.03 221	73.25 178	76.39 307	89.64 244

3Dnovator	76.31 5	83.38 102	82.31 113	86.59 55	87.94 196	72.94 28	90.64 60	92.14 93	77.21 58	75.47 240	92.83 88	58.56 212	94.72 105	73.24 179	92.71 75	92.13 152

v1240		78.99 196	77.78 204	82.64 199	83.21 316	63.54 241	86.62 201	90.30 155	69.74 237	77.33 196	85.68 275	57.04 227	93.76 145	73.13 180	76.92 297	90.62 197

miper_enhance_ethall		77.87 225	76.86 225	80.92 240	81.65 347	61.38 277	82.68 295	88.98 203	65.52 301	75.47 240	82.30 348	65.76 131	92.00 223	72.95 181	76.39 307	89.39 250

MG-MVS		83.41 100	83.45 93	83.28 166	92.74 65	62.28 266	88.17 149	89.50 181	75.22 107	81.49 129	92.74 94	66.75 114	95.11 87	72.85 182	91.58 91	92.45 137

EPP-MVSNet		83.40 101	83.02 101	84.57 106	90.13 107	64.47 221	92.32 30	90.73 139	74.45 131	79.35 156	91.10 132	69.05 91	95.12 85	72.78 183	87.22 159	94.13 53

test_fmvs3		63.36 372	61.82 375	67.98 389	62.51 429	46.96 410	77.37 368	74.03 396	45.24 414	67.50 349	78.79 385	12.16 434	72.98 418	72.77 184	66.02 386	83.99 369

IterMVS-LS		80.06 170	79.38 164	82.11 208	85.89 255	63.20 251	86.79 194	89.34 185	74.19 138	75.45 243	86.72 245	66.62 116	92.39 208	72.58 185	76.86 299	90.75 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		78.73 201	77.83 201	81.43 222	85.17 271	60.30 293	89.41 97	90.90 134	71.21 199	77.17 205	88.73 189	46.38 330	93.21 170	72.57 186	78.96 275	90.79 189

EI-MVSNet		80.52 161	79.98 151	82.12 207	84.28 291	63.19 252	86.41 207	88.95 206	74.18 139	78.69 165	87.54 225	66.62 116	92.43 206	72.57 186	80.57 257	90.74 193

Vis-MVSNet		83.46 99	82.80 106	85.43 79	90.25 105	68.74 114	90.30 72	90.13 161	76.33 86	80.87 138	92.89 86	61.00 192	94.20 122	72.45 188	90.97 101	93.35 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS		81.82 127	81.23 128	83.57 158	91.89 76	63.43 246	89.84 78	81.85 331	77.04 64	83.21 106	93.10 79	52.26 268	93.43 161	71.98 189	89.95 119	93.85 68

v148		78.72 202	77.80 203	81.47 221	82.73 331	61.96 270	86.30 212	88.08 225	73.26 165	76.18 228	85.47 282	62.46 163	92.36 210	71.92 190	73.82 346	90.09 223

PVSNet_BlendedMVS		80.60 157	80.02 150	82.36 206	88.85 154	65.40 195	86.16 216	92.00 96	69.34 242	78.11 181	86.09 268	66.02 127	94.27 118	71.52 191	82.06 238	87.39 305

PVSNet_Blended		80.98 143	80.34 144	82.90 187	88.85 154	65.40 195	84.43 262	92.00 96	67.62 273	78.11 181	85.05 293	66.02 127	94.27 118	71.52 191	89.50 124	89.01 262

eth_miper_zixun_eth		77.92 223	76.69 232	81.61 219	83.00 324	61.98 269	83.15 288	89.20 194	69.52 239	74.86 265	84.35 306	61.76 174	92.56 200	71.50 193	72.89 354	90.28 214

UA-Net		85.08 74	84.96 75	85.45 78	92.07 73	68.07 135	89.78 82	90.86 137	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 194	90.88 103	93.07 110

FA-MVS(test-final)		80.96 144	79.91 153	84.10 129	88.30 179	65.01 207	84.55 257	90.01 164	73.25 166	79.61 152	87.57 222	58.35 214	94.72 105	71.29 195	86.25 174	92.56 130

cl____		77.72 228	76.76 229	80.58 246	82.49 337	60.48 290	83.09 290	87.87 231	69.22 246	74.38 274	85.22 288	62.10 170	91.53 245	71.09 196	75.41 326	89.73 243

DIV-MVS_self_test		77.72 228	76.76 229	80.58 246	82.48 338	60.48 290	83.09 290	87.86 232	69.22 246	74.38 274	85.24 286	62.10 170	91.53 245	71.09 196	75.40 327	89.74 242

MonoMVSNet		76.49 253	75.80 242	78.58 285	81.55 350	58.45 309	86.36 210	86.22 267	74.87 121	74.73 267	83.73 321	51.79 281	88.73 306	70.78 198	72.15 359	88.55 283

test_yl		81.17 139	80.47 142	83.24 169	89.13 147	63.62 237	86.21 214	89.95 166	72.43 180	81.78 126	89.61 166	57.50 222	93.58 150	70.75 199	86.90 163	92.52 132

DCV-MVSNet		81.17 139	80.47 142	83.24 169	89.13 147	63.62 237	86.21 214	89.95 166	72.43 180	81.78 126	89.61 166	57.50 222	93.58 150	70.75 199	86.90 163	92.52 132

VNet		82.21 119	82.41 110	81.62 217	90.82 93	60.93 282	84.47 258	89.78 170	76.36 85	84.07 93	91.88 106	64.71 139	90.26 275	70.68 201	88.89 132	93.66 77

mvs_anonymous		79.42 184	79.11 173	80.34 251	84.45 290	57.97 317	82.59 296	87.62 238	67.40 277	76.17 230	88.56 197	68.47 98	89.59 288	70.65 202	86.05 178	93.47 91

VPA-MVSNet		80.60 157	80.55 140	80.76 243	88.07 190	60.80 285	86.86 191	91.58 115	75.67 98	80.24 145	89.45 175	63.34 147	90.25 276	70.51 203	79.22 274	91.23 174

PAPM_NR		83.02 110	82.41 110	84.82 99	92.47 70	66.37 173	87.93 158	91.80 107	73.82 147	77.32 197	90.66 144	67.90 105	94.90 97	70.37 204	89.48 125	93.19 105

thisisatest0530		79.40 185	77.76 206	84.31 117	87.69 211	65.10 206	87.36 174	84.26 294	70.04 224	77.42 194	88.26 206	49.94 301	94.79 103	70.20 205	84.70 193	93.03 114

tttt0517		79.40 185	77.91 198	83.90 149	88.10 188	63.84 234	88.37 142	84.05 296	71.45 195	76.78 211	89.12 180	49.93 303	94.89 98	70.18 206	83.18 225	92.96 119

UniMVSNet_NR-MVSNet		81.88 125	81.54 125	82.92 186	88.46 172	63.46 244	87.13 180	92.37 81	80.19 12	78.38 174	89.14 179	71.66 57	93.05 184	70.05 207	76.46 305	92.25 145

DU-MVS		81.12 142	80.52 141	82.90 187	87.80 203	63.46 244	87.02 185	91.87 104	79.01 28	78.38 174	89.07 181	65.02 136	93.05 184	70.05 207	76.46 305	92.20 148

XVG-ACMP-BASELINE		76.11 259	74.27 270	81.62 217	83.20 317	64.67 216	83.60 279	89.75 173	69.75 235	71.85 305	87.09 238	32.78 403	92.11 219	69.99 209	80.43 259	88.09 291

GeoE		81.71 129	81.01 133	83.80 152	89.51 127	64.45 222	88.97 116	88.73 215	71.27 198	78.63 168	89.76 161	66.32 122	93.20 173	69.89 210	86.02 179	93.74 75

FIs		82.07 122	82.42 109	81.04 236	88.80 159	58.34 311	88.26 146	93.49 26	76.93 66	78.47 173	91.04 135	69.92 78	92.34 212	69.87 211	84.97 189	92.44 138

114514_t		80.68 155	79.51 161	84.20 126	94.09 38	67.27 158	89.64 87	91.11 130	58.75 374	74.08 276	90.72 143	58.10 215	95.04 92	69.70 212	89.42 126	90.30 213

Anonymous20231211		78.97 197	77.69 209	82.81 191	90.54 99	64.29 225	90.11 75	91.51 117	65.01 308	76.16 231	88.13 213	50.56 294	93.03 187	69.68 213	77.56 292	91.11 177

Patchmatch-RL test		70.24 329	67.78 342	77.61 306	77.43 390	59.57 302	71.16 398	70.33 404	62.94 333	68.65 340	72.77 410	50.62 293	85.49 344	69.58 214	66.58 384	87.77 297

UniMVSNet (Re)		81.60 133	81.11 130	83.09 176	88.38 176	64.41 223	87.60 166	93.02 45	78.42 34	78.56 170	88.16 208	69.78 79	93.26 166	69.58 214	76.49 304	91.60 161

IterMVS-SCA-FT		75.43 269	73.87 275	80.11 257	82.69 332	64.85 213	81.57 307	83.47 305	69.16 249	70.49 317	84.15 313	51.95 276	88.15 314	69.23 216	72.14 360	87.34 307

v7n		78.97 197	77.58 212	83.14 174	83.45 311	65.51 193	88.32 144	91.21 125	73.69 151	72.41 298	86.32 263	57.93 216	93.81 141	69.18 217	75.65 318	90.11 221

Anonymous20240529		80.19 169	78.89 177	84.10 129	90.60 97	64.75 215	88.95 117	90.90 134	65.97 296	80.59 142	91.17 131	49.97 300	93.73 148	69.16 218	82.70 232	93.81 72

miper_lstm_enhance		74.11 283	73.11 285	77.13 314	80.11 369	59.62 300	72.23 394	86.92 256	66.76 281	70.40 318	82.92 338	56.93 228	82.92 365	69.06 219	72.63 355	88.87 269

testdata				79.97 259	90.90 91	64.21 226		84.71 285	59.27 367	85.40 66	92.91 85	62.02 172	89.08 299	68.95 220	91.37 95	86.63 328

test1111		79.43 183	79.18 172	80.15 256	89.99 114	53.31 378	87.33 176	77.05 381	75.04 113	80.23 146	92.77 93	48.97 315	92.33 213	68.87 221	92.40 80	94.81 21

GA-MVS		76.87 244	75.17 258	81.97 212	82.75 330	62.58 261	81.44 310	86.35 266	72.16 184	74.74 266	82.89 339	46.20 335	92.02 222	68.85 222	81.09 248	91.30 173

test2506		77.30 238	76.49 235	79.74 264	90.08 109	52.02 382	87.86 162	63.10 424	74.88 119	80.16 147	92.79 91	38.29 388	92.35 211	68.74 223	92.50 78	94.86 18

ECVR-MVS		79.61 176	79.26 169	80.67 245	90.08 109	54.69 365	87.89 160	77.44 377	74.88 119	80.27 144	92.79 91	48.96 316	92.45 205	68.55 224	92.50 78	94.86 18

UGNet		80.83 147	79.59 160	84.54 107	88.04 191	68.09 134	89.42 96	88.16 222	76.95 65	76.22 226	89.46 173	49.30 310	93.94 132	68.48 225	90.31 110	91.60 161
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
FC-MVSNet-test		81.52 134	82.02 119	80.03 258	88.42 175	55.97 350	87.95 156	93.42 29	77.10 62	77.38 195	90.98 141	69.96 77	91.79 231	68.46 226	84.50 195	92.33 141

DP-MVS Recon		83.11 109	82.09 117	86.15 63	94.44 19	70.92 71	88.79 123	92.20 90	70.53 215	79.17 158	91.03 137	64.12 142	96.03 50	68.39 227	90.14 114	91.50 166

UniMVSNet_ETH3D		79.10 193	78.24 191	81.70 216	86.85 234	60.24 294	87.28 178	88.79 209	74.25 137	76.84 208	90.53 148	49.48 306	91.56 242	67.98 228	82.15 236	93.29 98

D2MVS		74.82 276	73.21 283	79.64 268	79.81 374	62.56 262	80.34 328	87.35 244	64.37 315	68.86 338	82.66 343	46.37 331	90.10 278	67.91 229	81.24 246	86.25 331

IS-MVSNet		83.15 106	82.81 105	84.18 127	89.94 116	63.30 248	91.59 43	88.46 220	79.04 27	79.49 154	92.16 100	65.10 135	94.28 117	67.71 230	91.86 87	94.95 11

Fast-Effi-MVS+-dtu		78.02 220	76.49 235	82.62 200	83.16 320	66.96 167	86.94 188	87.45 243	72.45 177	71.49 310	84.17 312	54.79 244	91.58 239	67.61 231	80.31 260	89.30 253

PAPR		81.66 132	80.89 135	83.99 145	90.27 104	64.00 229	86.76 197	91.77 110	68.84 258	77.13 207	89.50 169	67.63 107	94.88 99	67.55 232	88.52 141	93.09 109

cascas		76.72 247	74.64 262	82.99 183	85.78 257	65.88 183	82.33 298	89.21 193	60.85 353	72.74 292	81.02 359	47.28 323	93.75 146	67.48 233	85.02 188	89.34 252

1314		76.53 249	75.30 256	80.21 255	83.93 300	62.32 265	84.66 252	88.81 208	60.23 358	70.16 323	84.07 314	55.30 238	90.73 271	67.37 234	83.21 224	87.59 302

无先验							87.48 169	88.98 203	60.00 360				94.12 126	67.28 235		88.97 265

thisisatest0515		77.33 237	75.38 253	83.18 172	85.27 270	63.80 235	82.11 301	83.27 308	65.06 306	75.91 232	83.84 317	49.54 305	94.27 118	67.24 236	86.19 175	91.48 168

原ACMM1				84.35 115	93.01 60	68.79 110		92.44 77	63.96 324	81.09 135	91.57 117	66.06 126	95.45 68	67.19 237	94.82 46	88.81 272

Baseline_NR-MVSNet		78.15 216	78.33 189	77.61 306	85.79 256	56.21 348	86.78 195	85.76 275	73.60 154	77.93 186	87.57 222	65.02 136	88.99 300	67.14 238	75.33 329	87.63 299

TranMVSNet+NR-MVSNet		80.84 146	80.31 145	82.42 204	87.85 200	62.33 264	87.74 164	91.33 122	80.55 9	77.99 185	89.86 158	65.23 134	92.62 195	67.05 239	75.24 332	92.30 143

Fast-Effi-MVS+		80.81 148	79.92 152	83.47 159	88.85 154	64.51 218	85.53 234	89.39 184	70.79 207	78.49 172	85.06 292	67.54 108	93.58 150	67.03 240	86.58 168	92.32 142

VPNet		78.69 203	78.66 180	78.76 281	88.31 178	55.72 354	84.45 261	86.63 260	76.79 70	78.26 177	90.55 147	59.30 208	89.70 287	66.63 241	77.05 296	90.88 186

PM-MVS		66.41 360	64.14 363	73.20 355	73.92 405	56.45 341	78.97 347	64.96 421	63.88 325	64.72 378	80.24 370	19.84 426	83.44 362	66.24 242	64.52 391	79.71 402

test-LLR		72.94 303	72.43 292	74.48 341	81.35 355	58.04 315	78.38 355	77.46 375	66.66 283	69.95 327	79.00 382	48.06 319	79.24 382	66.13 243	84.83 190	86.15 334

test-mter		71.41 315	70.39 317	74.48 341	81.35 355	58.04 315	78.38 355	77.46 375	60.32 357	69.95 327	79.00 382	36.08 397	79.24 382	66.13 243	84.83 190	86.15 334

MVS		78.19 215	76.99 223	81.78 214	85.66 259	66.99 164	84.66 252	90.47 146	55.08 395	72.02 304	85.27 285	63.83 145	94.11 127	66.10 245	89.80 121	84.24 365

NR-MVSNet		80.23 167	79.38 164	82.78 196	87.80 203	63.34 247	86.31 211	91.09 131	79.01 28	72.17 302	89.07 181	67.20 112	92.81 193	66.08 246	75.65 318	92.20 148

CVMVSNet		72.99 302	72.58 291	74.25 344	84.28 291	50.85 396	86.41 207	83.45 306	44.56 415	73.23 287	87.54 225	49.38 308	85.70 340	65.90 247	78.44 280	86.19 333

IterMVS		74.29 279	72.94 287	78.35 292	81.53 351	63.49 243	81.58 306	82.49 323	68.06 270	69.99 326	83.69 323	51.66 283	85.54 343	65.85 248	71.64 363	86.01 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 280	72.42 293	79.80 263	83.76 305	59.59 301	85.92 222	86.64 259	66.39 290	66.96 357	87.58 221	39.46 379	91.60 238	65.76 249	69.27 374	88.22 288

tpmrst		72.39 305	72.13 296	73.18 356	80.54 364	49.91 400	79.91 335	79.08 365	63.11 329	71.69 307	79.95 373	55.32 237	82.77 366	65.66 250	73.89 344	86.87 321

MAR-MVS		81.84 126	80.70 136	85.27 82	91.32 82	71.53 56	89.82 79	90.92 133	69.77 234	78.50 171	86.21 264	62.36 165	94.52 111	65.36 251	92.05 83	89.77 241
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
Anonymous202405211		78.25 211	77.01 221	81.99 211	91.03 87	60.67 287	84.77 249	83.90 298	70.65 214	80.00 148	91.20 129	41.08 373	91.43 251	65.21 252	85.26 187	93.85 68

ab-mvs		79.51 179	78.97 176	81.14 233	88.46 172	60.91 283	83.84 272	89.24 192	70.36 217	79.03 159	88.87 187	63.23 151	90.21 277	65.12 253	82.57 233	92.28 144

IB-MVS	68.01 15	75.85 263	73.36 282	83.31 165	84.76 282	66.03 177	83.38 283	85.06 282	70.21 223	69.40 333	81.05 358	45.76 340	94.66 108	65.10 254	75.49 321	89.25 254
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
WR-MVS		79.49 180	79.22 171	80.27 253	88.79 160	58.35 310	85.06 243	88.61 218	78.56 32	77.65 190	88.34 202	63.81 146	90.66 272	64.98 255	77.22 294	91.80 159

CostFormer		75.24 273	73.90 274	79.27 273	82.65 334	58.27 312	80.80 316	82.73 322	61.57 348	75.33 252	83.13 334	55.52 236	91.07 265	64.98 255	78.34 283	88.45 284

API-MVS		81.99 124	81.23 128	84.26 124	90.94 90	70.18 85	91.10 55	89.32 186	71.51 194	78.66 167	88.28 204	65.26 133	95.10 90	64.74 257	91.23 97	87.51 303

新几何1				83.42 161	93.13 54	70.71 74		85.48 278	57.43 385	81.80 125	91.98 103	63.28 148	92.27 214	64.60 258	92.99 70	87.27 310

testing91		76.54 248	75.66 247	79.18 276	88.43 174	55.89 351	81.08 313	83.00 316	73.76 149	75.34 248	84.29 307	46.20 335	90.07 279	64.33 259	84.50 195	91.58 163

testing99		76.09 260	75.12 259	79.00 277	88.16 183	55.50 357	80.79 317	81.40 336	73.30 164	75.17 256	84.27 310	44.48 350	90.02 280	64.28 260	84.22 204	91.48 168

pm-mvs1		77.25 239	76.68 233	78.93 279	84.22 293	58.62 308	86.41 207	88.36 221	71.37 196	73.31 285	88.01 214	61.22 188	89.15 298	64.24 261	73.01 353	89.03 261

TESTMET0.1,1		69.89 334	69.00 326	72.55 360	79.27 383	56.85 334	78.38 355	74.71 394	57.64 382	68.09 344	77.19 395	37.75 390	76.70 395	63.92 262	84.09 205	84.10 368

QAPM		80.88 145	79.50 162	85.03 90	88.01 194	68.97 107	91.59 43	92.00 96	66.63 288	75.15 258	92.16 100	57.70 219	95.45 68	63.52 263	88.76 136	90.66 196

baseline2		75.70 264	73.83 276	81.30 227	83.26 315	61.79 273	82.57 297	80.65 343	66.81 279	66.88 358	83.42 329	57.86 218	92.19 217	63.47 264	79.57 267	89.91 234

LCM-MVSNet-Re		77.05 240	76.94 224	77.36 310	87.20 227	51.60 389	80.06 331	80.46 347	75.20 109	67.69 347	86.72 245	62.48 162	88.98 301	63.44 265	89.25 127	91.51 165

gm-plane-assit					81.40 353	53.83 373			62.72 338		80.94 361		92.39 208	63.40 266

baseline1		76.98 242	76.75 231	77.66 304	88.13 186	55.66 355	85.12 241	81.89 329	73.04 170	76.79 210	88.90 185	62.43 164	87.78 320	63.30 267	71.18 366	89.55 247

AdaColmap		80.58 160	79.42 163	84.06 137	93.09 57	68.91 108	89.36 100	88.97 205	69.27 243	75.70 236	89.69 162	57.20 226	95.77 59	63.06 268	88.41 144	87.50 304

test_vis1_rt		60.28 377	58.42 380	65.84 394	67.25 423	55.60 356	70.44 403	60.94 427	44.33 416	59.00 402	66.64 417	24.91 417	68.67 424	62.80 269	69.48 372	73.25 413

GBi-Net		78.40 208	77.40 214	81.40 224	87.60 213	63.01 254	88.39 139	89.28 188	71.63 189	75.34 248	87.28 229	54.80 241	91.11 259	62.72 270	79.57 267	90.09 223

test1		78.40 208	77.40 214	81.40 224	87.60 213	63.01 254	88.39 139	89.28 188	71.63 189	75.34 248	87.28 229	54.80 241	91.11 259	62.72 270	79.57 267	90.09 223

FMVSNet3		77.88 224	76.85 226	80.97 239	86.84 235	62.36 263	86.52 204	88.77 210	71.13 200	75.34 248	86.66 251	54.07 251	91.10 262	62.72 270	79.57 267	89.45 249

CMPMVS	51.72 21	70.19 330	68.16 332	76.28 319	73.15 413	57.55 326	79.47 338	83.92 297	48.02 411	56.48 411	84.81 297	43.13 359	86.42 334	62.67 273	81.81 242	84.89 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 230	77.40 214	78.60 284	89.03 152	60.02 296	79.00 346	85.83 274	75.19 110	76.61 217	89.98 156	54.81 240	85.46 345	62.63 274	83.55 217	90.33 211

FMVSNet2		78.20 214	77.21 218	81.20 231	87.60 213	62.89 260	87.47 170	89.02 201	71.63 189	75.29 254	87.28 229	54.80 241	91.10 262	62.38 275	79.38 271	89.61 245

testdata2													91.01 266	62.37 276

testing11		75.14 274	74.01 271	78.53 288	88.16 183	56.38 344	80.74 320	80.42 349	70.67 210	72.69 295	83.72 322	43.61 357	89.86 282	62.29 277	83.76 210	89.36 251

CP-MVSNet		78.22 212	78.34 188	77.84 301	87.83 202	54.54 367	87.94 157	91.17 127	77.65 42	73.48 284	88.49 198	62.24 168	88.43 311	62.19 278	74.07 341	90.55 201

XXY-MVS		75.41 270	75.56 248	74.96 335	83.59 308	57.82 321	80.59 323	83.87 299	66.54 289	74.93 264	88.31 203	63.24 150	80.09 380	62.16 279	76.85 300	86.97 320

pmmvs6		74.69 277	73.39 280	78.61 283	81.38 354	57.48 327	86.64 200	87.95 229	64.99 309	70.18 321	86.61 252	50.43 296	89.52 289	62.12 280	70.18 371	88.83 271

1112_ss		77.40 236	76.43 237	80.32 252	89.11 151	60.41 292	83.65 276	87.72 237	62.13 344	73.05 289	86.72 245	62.58 161	89.97 281	62.11 281	80.80 253	90.59 200

PS-CasMVS		78.01 221	78.09 194	77.77 303	87.71 209	54.39 369	88.02 153	91.22 124	77.50 50	73.26 286	88.64 193	60.73 194	88.41 312	61.88 282	73.88 345	90.53 202

CDS-MVSNet		79.07 194	77.70 208	83.17 173	87.60 213	68.23 131	84.40 264	86.20 268	67.49 275	76.36 223	86.54 257	61.54 178	90.79 269	61.86 283	87.33 157	90.49 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 174	78.33 189	84.09 133	85.17 271	69.91 87	90.57 61	90.97 132	66.70 282	72.17 302	91.91 104	54.70 245	93.96 129	61.81 284	90.95 102	88.41 286

K. test v3		71.19 316	68.51 328	79.21 275	83.04 323	57.78 323	84.35 265	76.91 382	72.90 173	62.99 389	82.86 340	39.27 380	91.09 264	61.65 285	52.66 415	88.75 275

CHOSEN 1792x2688		77.63 232	75.69 244	83.44 160	89.98 115	68.58 122	78.70 351	87.50 241	56.38 390	75.80 235	86.84 241	58.67 211	91.40 252	61.58 286	85.75 184	90.34 210

PCF-MVS	73.52 7	80.38 163	78.84 178	85.01 91	87.71 209	68.99 106	83.65 276	91.46 121	63.00 331	77.77 189	90.28 150	66.10 124	95.09 91	61.40 287	88.22 146	90.94 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 222	77.15 219	80.36 250	87.57 217	60.21 295	83.37 284	87.78 235	66.11 292	75.37 247	87.06 240	63.27 149	90.48 274	61.38 288	82.43 234	90.40 208

HyFIR lowres test		77.53 233	75.40 252	83.94 148	89.59 123	66.62 169	80.36 327	88.64 217	56.29 391	76.45 220	85.17 289	57.64 220	93.28 165	61.34 289	83.10 226	91.91 156

PMMVS		69.34 338	68.67 327	71.35 370	75.67 397	62.03 268	75.17 380	73.46 397	50.00 408	68.68 339	79.05 380	52.07 274	78.13 387	61.16 290	82.77 229	73.90 412

FMVSNet1		77.44 234	76.12 241	81.40 224	86.81 236	63.01 254	88.39 139	89.28 188	70.49 216	74.39 273	87.28 229	49.06 314	91.11 259	60.91 291	78.52 278	90.09 223

sss		73.60 290	73.64 278	73.51 351	82.80 329	55.01 363	76.12 372	81.69 332	62.47 340	74.68 268	85.85 272	57.32 224	78.11 388	60.86 292	80.93 249	87.39 305

Test_1112_low_res		76.40 255	75.44 250	79.27 273	89.28 141	58.09 313	81.69 305	87.07 251	59.53 365	72.48 297	86.67 250	61.30 185	89.33 292	60.81 293	80.15 262	90.41 207

sc_t1		72.19 310	69.51 321	80.23 254	84.81 280	61.09 280	84.68 251	80.22 353	60.70 354	71.27 311	83.58 326	36.59 394	89.24 295	60.41 294	63.31 394	90.37 209

BH-untuned		79.47 181	78.60 181	82.05 209	89.19 145	65.91 182	86.07 218	88.52 219	72.18 182	75.42 244	87.69 219	61.15 189	93.54 154	60.38 295	86.83 165	86.70 326

WTY-MVS		75.65 265	75.68 245	75.57 326	86.40 245	56.82 335	77.92 364	82.40 324	65.10 305	76.18 228	87.72 217	63.13 156	80.90 377	60.31 296	81.96 239	89.00 264

pmmvs4		74.03 286	71.91 297	80.39 249	81.96 343	68.32 128	81.45 309	82.14 326	59.32 366	69.87 329	85.13 290	52.40 266	88.13 315	60.21 297	74.74 337	84.73 361

PEN-MVS		77.73 227	77.69 209	77.84 301	87.07 232	53.91 372	87.91 159	91.18 126	77.56 47	73.14 288	88.82 188	61.23 187	89.17 297	59.95 298	72.37 356	90.43 206

CR-MVSNet		73.37 293	71.27 306	79.67 267	81.32 357	65.19 201	75.92 374	80.30 351	59.92 361	72.73 293	81.19 356	52.50 264	86.69 329	59.84 299	77.71 288	87.11 316

mvs5depth		69.45 337	67.45 348	75.46 330	73.93 404	55.83 352	79.19 343	83.23 309	66.89 278	71.63 308	83.32 330	33.69 402	85.09 348	59.81 300	55.34 412	85.46 347

lessismore_v0				78.97 278	81.01 360	57.15 331		65.99 417		61.16 395	82.82 341	39.12 382	91.34 254	59.67 301	46.92 422	88.43 285

CNLPA		78.08 217	76.79 228	81.97 212	90.40 102	71.07 65	87.59 167	84.55 288	66.03 295	72.38 299	89.64 165	57.56 221	86.04 337	59.61 302	83.35 222	88.79 273

BH-RMVSNet		79.61 176	78.44 185	83.14 174	89.38 135	65.93 181	84.95 246	87.15 250	73.56 155	78.19 179	89.79 160	56.67 231	93.36 163	59.53 303	86.74 166	90.13 219

MS-PatchMatch		73.83 287	72.67 289	77.30 312	83.87 302	66.02 178	81.82 302	84.66 286	61.37 351	68.61 341	82.82 341	47.29 322	88.21 313	59.27 304	84.32 202	77.68 406

test_post1							78.90 349				5.43 440	48.81 318	85.44 346	59.25 305

SCA		74.22 281	72.33 294	79.91 260	84.05 298	62.17 267	79.96 334	79.29 363	66.30 291	72.38 299	80.13 371	51.95 276	88.60 309	59.25 305	77.67 291	88.96 266

FE-MVS		77.78 226	75.68 245	84.08 134	88.09 189	66.00 179	83.13 289	87.79 234	68.42 266	78.01 184	85.23 287	45.50 344	95.12 85	59.11 307	85.83 183	91.11 177

SixPastTwentyTwo		73.37 293	71.26 307	79.70 265	85.08 276	57.89 319	85.57 228	83.56 303	71.03 204	65.66 371	85.88 270	42.10 367	92.57 199	59.11 307	63.34 393	88.65 279

WR-MVS_H		78.51 207	78.49 183	78.56 286	88.02 192	56.38 344	88.43 137	92.67 67	77.14 60	73.89 278	87.55 224	66.25 123	89.24 295	58.92 309	73.55 348	90.06 227

PLC	70.83 11	78.05 219	76.37 239	83.08 178	91.88 77	67.80 141	88.19 148	89.46 182	64.33 316	69.87 329	88.38 201	53.66 255	93.58 150	58.86 310	82.73 230	87.86 295
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 298	71.46 302	78.54 287	82.50 336	59.85 297	82.18 300	82.84 321	58.96 370	71.15 314	89.41 177	45.48 345	84.77 352	58.82 311	71.83 362	91.02 183

EU-MVSNet		68.53 346	67.61 345	71.31 371	78.51 387	47.01 409	84.47 258	84.27 293	42.27 418	66.44 368	84.79 298	40.44 376	83.76 357	58.76 312	68.54 379	83.17 377

pmmvs-eth3d		70.50 326	67.83 340	78.52 289	77.37 391	66.18 176	81.82 302	81.51 334	58.90 371	63.90 385	80.42 366	42.69 362	86.28 335	58.56 313	65.30 389	83.11 379

TAMVS		78.89 199	77.51 213	83.03 181	87.80 203	67.79 142	84.72 250	85.05 283	67.63 272	76.75 212	87.70 218	62.25 167	90.82 268	58.53 314	87.13 160	90.49 204

WBMVS		73.43 292	72.81 288	75.28 332	87.91 197	50.99 395	78.59 354	81.31 338	65.51 303	74.47 272	84.83 296	46.39 329	86.68 330	58.41 315	77.86 286	88.17 290

ACMH+	68.96 14	76.01 261	74.01 271	82.03 210	88.60 167	65.31 199	88.86 120	87.55 239	70.25 222	67.75 346	87.47 227	41.27 371	93.19 175	58.37 316	75.94 315	87.60 300

tpm		72.37 307	71.71 299	74.35 343	82.19 341	52.00 383	79.22 342	77.29 379	64.56 312	72.95 291	83.68 324	51.35 284	83.26 364	58.33 317	75.80 316	87.81 296

BH-w/o		78.21 213	77.33 217	80.84 241	88.81 158	65.13 203	84.87 247	87.85 233	69.75 235	74.52 271	84.74 299	61.34 184	93.11 180	58.24 318	85.84 182	84.27 364

Vis-MVSNet (Re-imp)		78.36 210	78.45 184	78.07 297	88.64 166	51.78 388	86.70 198	79.63 359	74.14 140	75.11 259	90.83 142	61.29 186	89.75 285	58.10 319	91.60 89	92.69 126

MVP-Stereo		76.12 258	74.46 267	81.13 234	85.37 267	69.79 89	84.42 263	87.95 229	65.03 307	67.46 350	85.33 284	53.28 260	91.73 235	58.01 320	83.27 223	81.85 391
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 333	73.16 412	50.51 398	63.05 426	87.47 242		64.28 380	77.81 392	17.80 428	89.73 286	57.88 321	60.64 401	85.49 346

TR-MVS		77.44 234	76.18 240	81.20 231	88.24 180	63.24 249	84.61 255	86.40 264	67.55 274	77.81 187	86.48 259	54.10 250	93.15 177	57.75 322	82.72 231	87.20 311

F-COLMAP		76.38 256	74.33 269	82.50 203	89.28 141	66.95 168	88.41 138	89.03 200	64.05 321	66.83 359	88.61 194	46.78 327	92.89 189	57.48 323	78.55 277	87.67 298

EG-PatchMatch MVS		74.04 284	71.82 298	80.71 244	84.92 278	67.42 151	85.86 224	88.08 225	66.04 294	64.22 381	83.85 316	35.10 399	92.56 200	57.44 324	80.83 252	82.16 390

PatchmatchNet		73.12 299	71.33 305	78.49 290	83.18 318	60.85 284	79.63 336	78.57 368	64.13 317	71.73 306	79.81 376	51.20 287	85.97 338	57.40 325	76.36 312	88.66 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 241	76.80 227	77.54 309	86.24 247	53.06 381	87.52 168	90.66 140	77.08 63	72.50 296	88.67 192	60.48 202	89.52 289	57.33 326	70.74 368	90.05 228

UnsupCasMVSNet_eth		67.33 353	65.99 357	71.37 368	73.48 409	51.47 391	75.16 381	85.19 280	65.20 304	60.78 396	80.93 363	42.35 363	77.20 392	57.12 327	53.69 414	85.44 348

pmmvs5		71.55 314	70.20 319	75.61 325	77.83 388	56.39 343	81.74 304	80.89 339	57.76 381	67.46 350	84.49 300	49.26 311	85.32 347	57.08 328	75.29 330	85.11 355

testing3-2		75.12 275	75.19 257	74.91 336	90.40 102	45.09 417	80.29 329	78.42 369	78.37 37	76.54 219	87.75 216	44.36 351	87.28 326	57.04 329	83.49 219	92.37 139

Anonymous20240521		68.80 342	67.22 351	73.55 350	74.33 402	54.11 370	83.18 287	85.61 276	58.15 377	61.68 393	80.94 361	30.71 409	81.27 375	57.00 330	73.34 352	85.28 350

mvsany_test1		62.30 374	61.26 378	65.41 395	69.52 419	54.86 364	66.86 415	49.78 435	46.65 412	68.50 343	83.21 332	49.15 312	66.28 427	56.93 331	60.77 400	75.11 411

TransMVSNet (Re)		75.39 272	74.56 264	77.86 300	85.50 264	57.10 332	86.78 195	86.09 271	72.17 183	71.53 309	87.34 228	63.01 157	89.31 293	56.84 332	61.83 397	87.17 312

tt0320-xc		70.11 331	67.45 348	78.07 297	85.33 268	59.51 303	83.28 285	78.96 366	58.77 372	67.10 356	80.28 369	36.73 393	87.42 324	56.83 333	59.77 404	87.29 309

test_vis3_rt		49.26 394	47.02 396	56.00 406	54.30 435	45.27 416	66.76 417	48.08 436	36.83 425	44.38 424	53.20 429	7.17 441	64.07 429	56.77 334	55.66 409	58.65 425

EPMVS		69.02 340	68.16 332	71.59 366	79.61 378	49.80 402	77.40 367	66.93 415	62.82 336	70.01 324	79.05 380	45.79 339	77.86 390	56.58 335	75.26 331	87.13 315

KD-MVS_self_test		68.81 341	67.59 346	72.46 362	74.29 403	45.45 412	77.93 363	87.00 252	63.12 328	63.99 384	78.99 384	42.32 364	84.77 352	56.55 336	64.09 392	87.16 314

tpm2		73.26 297	71.46 302	78.63 282	83.34 313	56.71 338	80.65 322	80.40 350	56.63 389	73.55 283	82.02 353	51.80 280	91.24 257	56.35 337	78.42 281	87.95 292

LTVRE_ROB	69.57 13	76.25 257	74.54 265	81.41 223	88.60 167	64.38 224	79.24 341	89.12 199	70.76 209	69.79 331	87.86 215	49.09 313	93.20 173	56.21 338	80.16 261	86.65 327
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
ACMH	67.68 16	75.89 262	73.93 273	81.77 215	88.71 164	66.61 170	88.62 133	89.01 202	69.81 231	66.78 360	86.70 249	41.95 369	91.51 247	55.64 339	78.14 284	87.17 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 359	64.71 361	71.90 364	81.45 352	63.52 242	57.98 428	68.95 411	53.57 398	62.59 391	76.70 396	46.22 334	75.29 411	55.25 340	79.68 266	76.88 408

tt0320		70.49 327	68.03 335	77.89 299	84.78 281	59.12 305	83.55 280	80.44 348	58.13 378	67.43 352	80.41 367	39.26 381	87.54 323	55.12 341	63.18 395	86.99 319

UBG		73.08 300	72.27 295	75.51 328	88.02 192	51.29 393	78.35 358	77.38 378	65.52 301	73.87 279	82.36 346	45.55 342	86.48 333	55.02 342	84.39 201	88.75 275

EPNet_dtu		75.46 268	74.86 260	77.23 313	82.57 335	54.60 366	86.89 190	83.09 313	71.64 188	66.25 369	85.86 271	55.99 234	88.04 316	54.92 343	86.55 169	89.05 260
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 385	51.45 390	61.61 400	55.51 434	44.74 419	63.52 424	45.41 439	43.69 417	58.11 406	76.45 398	17.99 427	63.76 430	54.77 344	47.59 421	76.34 409

PVSNet	64.34 18	72.08 312	70.87 311	75.69 324	86.21 248	56.44 342	74.37 388	80.73 342	62.06 345	70.17 322	82.23 350	42.86 361	83.31 363	54.77 344	84.45 199	87.32 308

ITE_SJBPF				78.22 293	81.77 346	60.57 288		83.30 307	69.25 245	67.54 348	87.20 234	36.33 396	87.28 326	54.34 346	74.62 338	86.80 323

SSC-MVS3.2		73.35 296	73.39 280	73.23 352	85.30 269	49.01 403	74.58 387	81.57 333	75.21 108	73.68 281	85.58 279	52.53 262	82.05 370	54.33 347	77.69 290	88.63 280

MDTV_nov1_ep13_2view						37.79 431	75.16 381		55.10 394	66.53 364		49.34 309		53.98 348		87.94 293

gg-mvs-nofinetune		69.95 333	67.96 336	75.94 321	83.07 321	54.51 368	77.23 369	70.29 405	63.11 329	70.32 319	62.33 419	43.62 356	88.69 307	53.88 349	87.76 151	84.62 362

PatchMatch-RL		72.38 306	70.90 310	76.80 317	88.60 167	67.38 154	79.53 337	76.17 387	62.75 337	69.36 334	82.00 354	45.51 343	84.89 351	53.62 350	80.58 256	78.12 405

test_f		52.09 390	50.82 391	55.90 407	53.82 437	42.31 426	59.42 427	58.31 431	36.45 426	56.12 413	70.96 414	12.18 433	57.79 433	53.51 351	56.57 408	67.60 418

Patchmtry		70.74 322	69.16 325	75.49 329	80.72 361	54.07 371	74.94 385	80.30 351	58.34 375	70.01 324	81.19 356	52.50 264	86.54 331	53.37 352	71.09 367	85.87 343

USDC		70.33 328	68.37 329	76.21 320	80.60 363	56.23 347	79.19 343	86.49 262	60.89 352	61.29 394	85.47 282	31.78 406	89.47 291	53.37 352	76.21 313	82.94 383

LF4IMVS		64.02 370	62.19 374	69.50 379	70.90 418	53.29 379	76.13 371	77.18 380	52.65 401	58.59 403	80.98 360	23.55 421	76.52 397	53.06 354	66.66 383	78.68 404

PAPM		77.68 231	76.40 238	81.51 220	87.29 226	61.85 271	83.78 273	89.59 178	64.74 310	71.23 312	88.70 190	62.59 160	93.66 149	52.66 355	87.03 162	89.01 262

dmvs_re		71.14 317	70.58 312	72.80 358	81.96 343	59.68 299	75.60 378	79.34 362	68.55 262	69.27 336	80.72 364	49.42 307	76.54 396	52.56 356	77.79 287	82.19 389

CL-MVSNet_self_test		72.37 307	71.46 302	75.09 334	79.49 380	53.53 374	80.76 319	85.01 284	69.12 250	70.51 316	82.05 352	57.92 217	84.13 355	52.27 357	66.00 387	87.60 300

tpm cat1		70.57 324	68.31 330	77.35 311	82.41 339	57.95 318	78.08 360	80.22 353	52.04 402	68.54 342	77.66 393	52.00 275	87.84 319	51.77 358	72.07 361	86.25 331

our_test_3		69.14 339	67.00 352	75.57 326	79.80 375	58.80 306	77.96 362	77.81 372	59.55 364	62.90 390	78.25 389	47.43 321	83.97 356	51.71 359	67.58 381	83.93 370

MDTV_nov1_ep13			69.97 320		83.18 318	53.48 375	77.10 370	80.18 355	60.45 355	69.33 335	80.44 365	48.89 317	86.90 328	51.60 360	78.51 279

myMVS_eth3d28		73.62 289	73.53 279	73.90 348	88.20 181	47.41 407	78.06 361	79.37 361	74.29 136	73.98 277	84.29 307	44.67 347	83.54 360	51.47 361	87.39 156	90.74 193

JIA-IIPM		66.32 361	62.82 373	76.82 316	77.09 392	61.72 274	65.34 421	75.38 388	58.04 380	64.51 379	62.32 420	42.05 368	86.51 332	51.45 362	69.22 375	82.21 388

testing222		74.04 284	72.66 290	78.19 294	87.89 198	55.36 358	81.06 314	79.20 364	71.30 197	74.65 269	83.57 327	39.11 383	88.67 308	51.43 363	85.75 184	90.53 202

MSDG		73.36 295	70.99 309	80.49 248	84.51 289	65.80 186	80.71 321	86.13 270	65.70 298	65.46 372	83.74 320	44.60 348	90.91 267	51.13 364	76.89 298	84.74 360

PatchT		68.46 347	67.85 338	70.29 376	80.70 362	43.93 420	72.47 393	74.88 391	60.15 359	70.55 315	76.57 397	49.94 301	81.59 372	50.58 365	74.83 336	85.34 349

GG-mvs-BLEND				75.38 331	81.59 349	55.80 353	79.32 340	69.63 407		67.19 354	73.67 408	43.24 358	88.90 305	50.41 366	84.50 195	81.45 393

KD-MVS_2432*1600		66.22 362	63.89 365	73.21 353	75.47 400	53.42 376	70.76 401	84.35 290	64.10 319	66.52 365	78.52 386	34.55 400	84.98 349	50.40 367	50.33 419	81.23 394

miper_refine_blended		66.22 362	63.89 365	73.21 353	75.47 400	53.42 376	70.76 401	84.35 290	64.10 319	66.52 365	78.52 386	34.55 400	84.98 349	50.40 367	50.33 419	81.23 394

AllTest		70.96 319	68.09 334	79.58 269	85.15 273	63.62 237	84.58 256	79.83 356	62.31 341	60.32 398	86.73 243	32.02 404	88.96 303	50.28 369	71.57 364	86.15 334

TestCases				79.58 269	85.15 273	63.62 237		79.83 356	62.31 341	60.32 398	86.73 243	32.02 404	88.96 303	50.28 369	71.57 364	86.15 334

TAPA-MVS	73.13 9	79.15 191	77.94 197	82.79 195	89.59 123	62.99 258	88.16 150	91.51 117	65.77 297	77.14 206	91.09 133	60.91 193	93.21 170	50.26 371	87.05 161	92.17 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 366	62.91 371	71.38 367	75.85 396	56.60 340	69.12 409	74.66 395	57.28 386	54.12 414	77.87 391	45.85 338	74.48 413	49.95 372	61.52 399	83.05 380

MDA-MVSNet_test_wron		65.03 366	62.92 370	71.37 368	75.93 394	56.73 336	69.09 410	74.73 393	57.28 386	54.03 415	77.89 390	45.88 337	74.39 414	49.89 373	61.55 398	82.99 382

tpmvs		71.09 318	69.29 323	76.49 318	82.04 342	56.04 349	78.92 348	81.37 337	64.05 321	67.18 355	78.28 388	49.74 304	89.77 284	49.67 374	72.37 356	83.67 373

ppachtmachnet_test		70.04 332	67.34 350	78.14 295	79.80 375	61.13 278	79.19 343	80.59 344	59.16 368	65.27 374	79.29 379	46.75 328	87.29 325	49.33 375	66.72 382	86.00 340

UnsupCasMVSNet_bld		63.70 371	61.53 377	70.21 377	73.69 407	51.39 392	72.82 392	81.89 329	55.63 393	57.81 407	71.80 412	38.67 385	78.61 385	49.26 376	52.21 417	80.63 398

UWE-MVS		72.13 311	71.49 301	74.03 346	86.66 241	47.70 405	81.40 311	76.89 383	63.60 326	75.59 237	84.22 311	39.94 378	85.62 342	48.98 377	86.13 177	88.77 274

dp		66.80 356	65.43 358	70.90 375	79.74 377	48.82 404	75.12 383	74.77 392	59.61 363	64.08 383	77.23 394	42.89 360	80.72 378	48.86 378	66.58 384	83.16 378

FMVSNet5		69.50 336	67.96 336	74.15 345	82.97 327	55.35 359	80.01 333	82.12 327	62.56 339	63.02 387	81.53 355	36.92 392	81.92 371	48.42 379	74.06 342	85.17 354

thres100view900		76.50 250	75.55 249	79.33 272	89.52 126	56.99 333	85.83 226	83.23 309	73.94 144	76.32 224	87.12 237	51.89 278	91.95 225	48.33 380	83.75 211	89.07 255

tfpn200view9		76.42 254	75.37 254	79.55 271	89.13 147	57.65 324	85.17 238	83.60 301	73.41 161	76.45 220	86.39 261	52.12 270	91.95 225	48.33 380	83.75 211	89.07 255

thres400		76.50 250	75.37 254	79.86 261	89.13 147	57.65 324	85.17 238	83.60 301	73.41 161	76.45 220	86.39 261	52.12 270	91.95 225	48.33 380	83.75 211	90.00 229

LCM-MVSNet		54.25 384	49.68 394	67.97 390	53.73 438	45.28 415	66.85 416	80.78 341	35.96 427	39.45 428	62.23 421	8.70 438	78.06 389	48.24 383	51.20 418	80.57 399

RPMNet		73.51 291	70.49 314	82.58 202	81.32 357	65.19 201	75.92 374	92.27 84	57.60 383	72.73 293	76.45 398	52.30 267	95.43 70	48.14 384	77.71 288	87.11 316

thres600view7		76.50 250	75.44 250	79.68 266	89.40 133	57.16 330	85.53 234	83.23 309	73.79 148	76.26 225	87.09 238	51.89 278	91.89 228	48.05 385	83.72 214	90.00 229

TDRefinement		67.49 351	64.34 362	76.92 315	73.47 410	61.07 281	84.86 248	82.98 317	59.77 362	58.30 405	85.13 290	26.06 414	87.89 318	47.92 386	60.59 402	81.81 392

thres200		75.55 266	74.47 266	78.82 280	87.78 206	57.85 320	83.07 292	83.51 304	72.44 179	75.84 234	84.42 302	52.08 273	91.75 233	47.41 387	83.64 216	86.86 322

PVSNet_0	57.27 20	61.67 376	59.27 379	68.85 383	79.61 378	57.44 328	68.01 411	73.44 398	55.93 392	58.54 404	70.41 415	44.58 349	77.55 391	47.01 388	35.91 427	71.55 415

DP-MVS		76.78 246	74.57 263	83.42 161	93.29 48	69.46 97	88.55 135	83.70 300	63.98 323	70.20 320	88.89 186	54.01 253	94.80 102	46.66 389	81.88 241	86.01 338

COLMAP_ROB	66.92 17	73.01 301	70.41 316	80.81 242	87.13 230	65.63 190	88.30 145	84.19 295	62.96 332	63.80 386	87.69 219	38.04 389	92.56 200	46.66 389	74.91 335	84.24 365
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 323	69.30 322	74.88 337	84.52 288	56.35 346	75.87 376	79.42 360	64.59 311	67.76 345	82.41 345	41.10 372	81.54 373	46.64 391	81.34 244	86.75 325

LS3D		76.95 243	74.82 261	83.37 164	90.45 100	67.36 155	89.15 110	86.94 254	61.87 347	69.52 332	90.61 145	51.71 282	94.53 110	46.38 392	86.71 167	88.21 289

ETVMVS		72.25 309	71.05 308	75.84 322	87.77 207	51.91 385	79.39 339	74.98 390	69.26 244	73.71 280	82.95 337	40.82 375	86.14 336	46.17 393	84.43 200	89.47 248

MDA-MVSNet-bldmvs		66.68 357	63.66 367	75.75 323	79.28 382	60.56 289	73.92 390	78.35 370	64.43 313	50.13 420	79.87 375	44.02 354	83.67 358	46.10 394	56.86 406	83.03 381

new-patchmatchnet		61.73 375	61.73 376	61.70 399	72.74 415	24.50 442	69.16 408	78.03 371	61.40 349	56.72 410	75.53 404	38.42 386	76.48 398	45.95 395	57.67 405	84.13 367

WB-MVSnew		71.96 313	71.65 300	72.89 357	84.67 287	51.88 386	82.29 299	77.57 374	62.31 341	73.67 282	83.00 336	53.49 258	81.10 376	45.75 396	82.13 237	85.70 344

TinyColmap		67.30 354	64.81 360	74.76 339	81.92 345	56.68 339	80.29 329	81.49 335	60.33 356	56.27 412	83.22 331	24.77 418	87.66 322	45.52 397	69.47 373	79.95 401

pmmvs3		57.79 380	54.26 385	68.37 386	64.02 428	56.72 337	75.12 383	65.17 419	40.20 420	52.93 416	69.86 416	20.36 425	75.48 408	45.45 398	55.25 413	72.90 414

OpenMVS_ROB	64.09 19	70.56 325	68.19 331	77.65 305	80.26 366	59.41 304	85.01 244	82.96 318	58.76 373	65.43 373	82.33 347	37.63 391	91.23 258	45.34 399	76.03 314	82.32 387

test0.0.03 1		68.00 350	67.69 343	68.90 382	77.55 389	47.43 406	75.70 377	72.95 401	66.66 283	66.56 363	82.29 349	48.06 319	75.87 405	44.97 400	74.51 339	83.41 375

testgi		66.67 358	66.53 355	67.08 392	75.62 398	41.69 427	75.93 373	76.50 384	66.11 292	65.20 377	86.59 253	35.72 398	74.71 412	43.71 401	73.38 351	84.84 359

Anonymous20231206		68.60 343	67.80 341	71.02 373	80.23 368	50.75 397	78.30 359	80.47 346	56.79 388	66.11 370	82.63 344	46.35 332	78.95 384	43.62 402	75.70 317	83.36 376

tfpnnormal		74.39 278	73.16 284	78.08 296	86.10 253	58.05 314	84.65 254	87.53 240	70.32 219	71.22 313	85.63 277	54.97 239	89.86 282	43.03 403	75.02 334	86.32 330

MIMVSNet1		68.58 344	66.78 354	73.98 347	80.07 370	51.82 387	80.77 318	84.37 289	64.40 314	59.75 401	82.16 351	36.47 395	83.63 359	42.73 404	70.33 370	86.48 329

ttmdpeth		59.91 378	57.10 382	68.34 387	67.13 424	46.65 411	74.64 386	67.41 414	48.30 410	62.52 392	85.04 294	20.40 424	75.93 404	42.55 405	45.90 425	82.44 386

test20.03		67.45 352	66.95 353	68.94 381	75.48 399	44.84 418	77.50 366	77.67 373	66.66 283	63.01 388	83.80 318	47.02 325	78.40 386	42.53 406	68.86 378	83.58 374

ADS-MVSNet2		66.20 364	63.33 368	74.82 338	79.92 371	58.75 307	67.55 413	75.19 389	53.37 399	65.25 375	75.86 401	42.32 364	80.53 379	41.57 407	68.91 376	85.18 352

ADS-MVSNet		64.36 369	62.88 372	68.78 384	79.92 371	47.17 408	67.55 413	71.18 403	53.37 399	65.25 375	75.86 401	42.32 364	73.99 415	41.57 407	68.91 376	85.18 352

Patchmatch-test		64.82 368	63.24 369	69.57 378	79.42 381	49.82 401	63.49 425	69.05 410	51.98 404	59.95 400	80.13 371	50.91 289	70.98 419	40.66 409	73.57 347	87.90 294

MVS-HIRNet		59.14 379	57.67 381	63.57 397	81.65 347	43.50 421	71.73 395	65.06 420	39.59 422	51.43 417	57.73 425	38.34 387	82.58 367	39.53 410	73.95 343	64.62 421

WAC-MVS						42.58 423								39.46 411

myMVS_eth3d		67.02 355	66.29 356	69.21 380	84.68 284	42.58 423	78.62 352	73.08 399	66.65 286	66.74 361	79.46 377	31.53 407	82.30 368	39.43 412	76.38 310	82.75 384

DSMNet-mixed		57.77 381	56.90 383	60.38 401	67.70 422	35.61 432	69.18 407	53.97 433	32.30 431	57.49 408	79.88 374	40.39 377	68.57 425	38.78 413	72.37 356	76.97 407

N_pmnet		52.79 389	53.26 387	51.40 413	78.99 384	7.68 447	69.52 405	3.89 446	51.63 405	57.01 409	74.98 405	40.83 374	65.96 428	37.78 414	64.67 390	80.56 400

testing3		68.56 345	67.67 344	71.22 372	87.33 223	42.87 422	83.06 293	71.54 402	70.36 217	69.08 337	84.38 304	30.33 410	85.69 341	37.50 415	75.45 325	85.09 356

MVStest1		56.63 382	52.76 388	68.25 388	61.67 430	53.25 380	71.67 396	68.90 412	38.59 423	50.59 419	83.05 335	25.08 416	70.66 420	36.76 416	38.56 426	80.83 397

test_0402		72.79 304	70.44 315	79.84 262	88.13 186	65.99 180	85.93 221	84.29 292	65.57 300	67.40 353	85.49 281	46.92 326	92.61 196	35.88 417	74.38 340	80.94 396

new_pmnet		50.91 392	50.29 392	52.78 412	68.58 421	34.94 434	63.71 423	56.63 432	39.73 421	44.95 423	65.47 418	21.93 423	58.48 432	34.98 418	56.62 407	64.92 420

APD_test1		53.31 388	49.93 393	63.42 398	65.68 425	50.13 399	71.59 397	66.90 416	34.43 428	40.58 427	71.56 413	8.65 439	76.27 400	34.64 419	55.36 411	63.86 422

Syy-MVS		68.05 349	67.85 338	68.67 385	84.68 284	40.97 428	78.62 352	73.08 399	66.65 286	66.74 361	79.46 377	52.11 272	82.30 368	32.89 420	76.38 310	82.75 384

dmvs_testset		62.63 373	64.11 364	58.19 403	78.55 386	24.76 441	75.28 379	65.94 418	67.91 271	60.34 397	76.01 400	53.56 256	73.94 416	31.79 421	67.65 380	75.88 410

UWE-MVS-28		65.32 365	64.93 359	66.49 393	78.70 385	38.55 430	77.86 365	64.39 422	62.00 346	64.13 382	83.60 325	41.44 370	76.00 403	31.39 422	80.89 250	84.92 357

ANet_high		50.57 393	46.10 397	63.99 396	48.67 441	39.13 429	70.99 400	80.85 340	61.39 350	31.18 430	57.70 426	17.02 429	73.65 417	31.22 423	15.89 438	79.18 403

EGC-MVSNET		52.07 391	47.05 395	67.14 391	83.51 310	60.71 286	80.50 325	67.75 413	0.07 441	0.43 442	75.85 403	24.26 419	81.54 373	28.82 424	62.25 396	59.16 424

PMMVS2		40.82 400	38.86 404	46.69 414	53.84 436	16.45 445	48.61 431	49.92 434	37.49 424	31.67 429	60.97 422	8.14 440	56.42 434	28.42 425	30.72 431	67.19 419

tmp_tt		18.61 407	21.40 410	10.23 423	4.82 446	10.11 446	34.70 433	30.74 444	1.48 440	23.91 436	26.07 437	28.42 412	13.41 442	27.12 426	15.35 439	7.17 437

test_method		31.52 403	29.28 407	38.23 417	27.03 445	6.50 448	20.94 436	62.21 425	4.05 439	22.35 437	52.50 430	13.33 431	47.58 437	27.04 427	34.04 429	60.62 423

testf1		45.72 395	41.96 399	57.00 404	56.90 432	45.32 413	66.14 418	59.26 429	26.19 432	30.89 431	60.96 423	4.14 442	70.64 421	26.39 428	46.73 423	55.04 427

APD_test2		45.72 395	41.96 399	57.00 404	56.90 432	45.32 413	66.14 418	59.26 429	26.19 432	30.89 431	60.96 423	4.14 442	70.64 421	26.39 428	46.73 423	55.04 427

FPMVS		53.68 387	51.64 389	59.81 402	65.08 426	51.03 394	69.48 406	69.58 408	41.46 419	40.67 426	72.32 411	16.46 430	70.00 423	24.24 430	65.42 388	58.40 426

Gipuma		45.18 398	41.86 401	55.16 410	77.03 393	51.52 390	32.50 434	80.52 345	32.46 430	27.12 433	35.02 434	9.52 437	75.50 407	22.31 431	60.21 403	38.45 433
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 397	45.38 398	45.55 415	73.36 411	26.85 439	67.72 412	34.19 441	54.15 397	49.65 421	56.41 428	25.43 415	62.94 431	19.45 432	28.09 432	46.86 431

DeepMVS_CX				27.40 421	40.17 444	26.90 438		24.59 445	17.44 437	23.95 435	48.61 432	9.77 436	26.48 440	18.06 433	24.47 434	28.83 434

WB-MVS		54.94 383	54.72 384	55.60 409	73.50 408	20.90 443	74.27 389	61.19 426	59.16 368	50.61 418	74.15 406	47.19 324	75.78 406	17.31 434	35.07 428	70.12 416

PMVS	37.38 22	44.16 399	40.28 403	55.82 408	40.82 443	42.54 425	65.12 422	63.99 423	34.43 428	24.48 434	57.12 427	3.92 444	76.17 402	17.10 435	55.52 410	48.75 429
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 405	25.89 409	43.81 416	44.55 442	35.46 433	28.87 435	39.07 440	18.20 436	18.58 438	40.18 433	2.68 445	47.37 438	17.07 436	23.78 435	48.60 430
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 386	53.59 386	54.75 411	72.87 414	19.59 444	73.84 391	60.53 428	57.58 384	49.18 422	73.45 409	46.34 333	75.47 409	16.20 437	32.28 430	69.20 417

E-PMN		31.77 402	30.64 405	35.15 419	52.87 439	27.67 436	57.09 429	47.86 437	24.64 434	16.40 439	33.05 435	11.23 435	54.90 435	14.46 438	18.15 436	22.87 435

EMVS		30.81 404	29.65 406	34.27 420	50.96 440	25.95 440	56.58 430	46.80 438	24.01 435	15.53 440	30.68 436	12.47 432	54.43 436	12.81 439	17.05 437	22.43 436

kuosan		39.70 401	40.40 402	37.58 418	64.52 427	26.98 437	65.62 420	33.02 442	46.12 413	42.79 425	48.99 431	24.10 420	46.56 439	12.16 440	26.30 433	39.20 432

wuyk23d		16.82 408	15.94 411	19.46 422	58.74 431	31.45 435	39.22 432	3.74 447	6.84 438	6.04 441	2.70 441	1.27 446	24.29 441	10.54 441	14.40 440	2.63 438

testmvs		6.04 411	8.02 414	0.10 425	0.08 447	0.03 450	69.74 404	0.04 448	0.05 442	0.31 443	1.68 442	0.02 448	0.04 443	0.24 442	0.02 441	0.25 440

test123		6.12 410	8.11 413	0.14 424	0.06 448	0.09 449	71.05 399	0.03 449	0.04 443	0.25 444	1.30 443	0.05 447	0.03 444	0.21 443	0.01 442	0.29 439

mmdepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

monomultidepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

test_blank		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uanet_test		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

DCPMVS		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

cdsmvs_eth3d_5k		19.96 406	26.61 408	0.00 426	0.00 449	0.00 451	0.00 437	89.26 191	0.00 444	0.00 445	88.61 194	61.62 177	0.00 445	0.00 444	0.00 443	0.00 441

pcd_1.5k_mvsjas		5.26 412	7.02 415	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	63.15 153	0.00 445	0.00 444	0.00 443	0.00 441

sosnet-low-res		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

sosnet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uncertanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

Regformer		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

ab-mvs-re		7.23 409	9.64 412	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	86.72 245	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 129	91.30 15

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 38	92.05 11	95.74 6	80.83 11

eth-test2					0.00 449

eth-test					0.00 449

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 59	93.10 1	95.39 14	82.99 1	97.27 12

save fliter					93.80 40	72.35 42	90.47 66	91.17 127	74.31 134

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 53	92.81 3	95.79 3	80.98 9

GSMVS																88.96 266

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 285				88.96 266

sam_mvs												50.01 299

MTGPA								92.02 94

test_post											5.46 439	50.36 297	84.24 354

patchmatchnet-post											74.00 407	51.12 288	88.60 309

MTMP							92.18 34	32.83 443

TEST9					93.26 52	72.96 25	88.75 126	91.89 102	68.44 265	85.00 71	93.10 79	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 131	91.84 106	68.69 260	84.87 75	93.10 79	74.43 26	95.16 83

agg_prior					92.85 62	71.94 50		91.78 109		84.41 86			94.93 94

test_prior4						72.60 34	89.01 115

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 64

新几何2							86.29 213

旧先验1					91.96 74	65.79 187		86.37 265			93.08 83	69.31 86			92.74 74	88.74 277

原ACMM2							86.86 191

test222					91.50 80	68.26 130	84.16 268	83.20 312	54.63 396	79.74 150	91.63 114	58.97 210			91.42 93	86.77 324

segment_acmp												73.08 39

testdata1							84.14 269		75.71 95

test12				86.80 52	92.63 67	70.70 75		91.79 108		82.71 115		71.67 56	96.16 47		94.50 51	93.54 89

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 203

plane_prior4											91.00 139

plane_prior3						68.60 121			78.44 33	78.92 162

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 117

plane_prior						68.71 116	90.38 70		77.62 43						86.16 176

n2								0.00 450

nn								0.00 450

door-mid								69.98 406

test11								92.23 87

door								69.44 409

HQP5-MVS						66.98 165

HQP-NCC					89.33 136		89.17 106		76.41 80	77.23 200

ACMP_Plane					89.33 136		89.17 106		76.41 80	77.23 200

HQP4-MVS										77.24 199			95.11 87			91.03 181

HQP3-MVS								92.19 91							85.99 180

HQP2-MVS												60.17 206

NP-MVS					89.62 122	68.32 128					90.24 152

ACMMP++_ref															81.95 240

ACMMP++															81.25 245

Test By Simon												64.33 140

High-resolution multi-view benchmark