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	91.17 125	86.57 1	87.39 45	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

fmvsm_s_conf0.5_n_3		86.36 46	87.46 27	83.09 172	87.08 228	65.21 197	89.09 111	90.21 156	79.67 17	89.98 18	95.02 18	73.17 38	91.71 232	91.30 2	91.60 88	92.34 133

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 175	82.14 3	86.65 53	94.28 37	68.28 97	97.46 6	90.81 3	95.31 34	95.15 7

test_fmvsmconf_n		85.92 53	86.04 53	85.57 76	85.03 267	69.51 93	89.62 89	90.58 140	73.42 152	87.75 38	94.02 51	72.85 43	93.24 166	90.37 4	90.75 100	93.96 59

test_fmvsmconf0.1_n		85.61 61	85.65 59	85.50 77	82.99 314	69.39 100	89.65 86	90.29 154	73.31 155	87.77 37	94.15 45	71.72 54	93.23 167	90.31 5	90.67 102	93.89 65

test_fmvsmconf0.01_n		84.73 75	84.52 77	85.34 80	80.25 355	69.03 103	89.47 92	89.65 172	73.24 159	86.98 50	94.27 38	66.62 111	93.23 167	90.26 6	89.95 115	93.78 72

fmvsm_s_conf0.5_n_2		84.04 80	84.11 81	83.81 148	86.17 243	65.00 203	86.96 182	87.28 239	74.35 126	88.25 28	94.23 41	61.82 168	92.60 195	89.85 7	88.09 143	93.84 68

fmvsm_s_conf0.1_n_2		83.80 84	83.79 84	83.83 147	85.62 253	64.94 205	87.03 180	86.62 255	74.32 127	87.97 35	94.33 35	60.67 192	92.60 195	89.72 8	87.79 145	93.96 59

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 9	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 9	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 44	78.35 13	96.77 24	89.59 11	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
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 34	92.78 4	95.74 6	82.45 3	97.49 4	89.42 12	96.68 2	94.95 11

test_0728_THIRD									78.38 34	92.12 9	95.78 4	81.46 7	97.40 9	89.42 12	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 95	91.06 16	96.03 1	76.84 14	97.03 17	89.09 14	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 51	92.12 9	95.78 4	80.98 9	97.40 9	89.08 15	96.41 12	93.33 94
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 15	96.41 12	94.21 49

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 54	93.10 1	95.72 8	82.99 1	97.44 7	89.07 17	96.63 4	94.88 15

test_241102_TWO								94.06 10	77.24 54	92.78 4	95.72 8	81.26 8	97.44 7	89.07 17	96.58 6	94.26 48

fmvsm_l_conf0.5_n_3		86.02 48	86.32 44	85.14 85	87.20 224	68.54 123	89.57 90	90.44 145	75.31 102	87.49 42	94.39 34	72.86 42	92.72 192	89.04 19	90.56 103	94.16 50

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 270	92.39 6				88.94 20	96.63 4	94.85 20

fmvsm_l_conf0.5_n		84.47 76	84.54 75	84.27 120	85.42 257	68.81 109	88.49 132	87.26 241	68.08 260	88.03 32	93.49 64	72.04 50	91.77 228	88.90 21	89.14 126	92.24 140

fmvsm_s_conf0.5_n		83.80 84	83.71 85	84.07 132	86.69 236	67.31 155	89.46 93	83.07 307	71.09 193	86.96 51	93.70 62	69.02 91	91.47 244	88.79 22	84.62 188	93.44 90

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 107	86.34 55	95.29 15	70.86 67	96.00 54	88.78 23	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_1920		84.02 81	83.87 82	84.49 109	84.12 283	69.37 101	88.15 147	87.96 223	70.01 217	83.95 92	93.23 72	68.80 93	91.51 242	88.61 24	89.96 114	92.57 124

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 117	92.29 7	95.97 2	74.28 29	97.24 13	88.58 25	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 92	83.38 90	84.10 126	84.86 269	67.28 156	89.40 98	83.01 308	70.67 201	87.08 48	93.96 57	68.38 95	91.45 245	88.56 26	84.50 189	93.56 85

test_fmvsm_n_1920		85.29 67	85.34 64	85.13 88	86.12 245	69.93 86	88.65 128	90.78 136	69.97 219	88.27 27	93.98 56	71.39 60	91.54 239	88.49 27	90.45 105	93.91 62

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 31	94.06 49	76.43 16	96.84 21	88.48 28	95.99 18	94.34 44

fmvsm_l_conf0.5_n_a		84.13 79	84.16 80	84.06 134	85.38 258	68.40 126	88.34 139	86.85 251	67.48 267	87.48 43	93.40 68	70.89 66	91.61 233	88.38 29	89.22 124	92.16 144

fmvsm_s_conf0.5_n_a		83.63 90	83.41 89	84.28 118	86.14 244	68.12 133	89.43 94	82.87 312	70.27 212	87.27 47	93.80 61	69.09 86	91.58 235	88.21 30	83.65 208	93.14 104

fmvsm_s_conf0.1_n_a		83.32 99	82.99 97	84.28 118	83.79 291	68.07 135	89.34 101	82.85 313	69.80 223	87.36 46	94.06 49	68.34 96	91.56 237	87.95 31	83.46 213	93.21 100

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 121	88.90 23	93.85 59	75.75 20	96.00 54	87.80 32	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 85	88.14 29	95.09 17	71.06 65	96.67 29	87.67 33	96.37 14	94.09 54

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 37	90.32 17	94.00 53	74.83 23	93.78 141	87.63 34	94.27 59	93.65 79
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 30	94.80 20	73.76 33	97.11 15	87.51 35	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 36	94.27 38	75.89 19	96.81 23	87.45 36	96.44 9	93.05 109

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 91	92.29 7	95.66 10	81.67 6	97.38 11	87.44 37	96.34 15	93.95 61
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 93	89.16 20	95.10 16	75.65 21	96.19 46	87.07 38	96.01 17	94.79 22

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 138	88.57 26	94.67 22	75.57 22	95.79 58	86.77 39	95.76 23

MTAPA		87.23 31	87.00 33	87.90 22	94.18 35	74.25 5	86.58 197	92.02 93	79.45 20	85.88 57	94.80 20	68.07 98	96.21 45	86.69 40	95.34 32	93.23 97

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 110	88.96 21	95.54 12	71.20 63	96.54 36	86.28 41	93.49 65	93.06 107

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 110	88.96 21	95.54 12	71.20 63	96.54 36	86.28 41	93.49 65	93.06 107

reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 118	88.80 24	95.61 11	70.29 74	96.44 39	86.20 43	93.08 69	93.16 102

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 47	89.79 19	94.12 46	78.98 12	96.58 35	85.66 44	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 43	83.84 94	94.40 33	72.24 47	96.28 43	85.65 45	95.30 35	93.62 82
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 34	86.15 63	91.24 83	67.61 146	90.51 62	92.90 56	77.26 53	87.44 44	91.63 109	71.27 62	96.06 49	85.62 46	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 67	85.24 64	94.32 36	71.76 53	96.93 19	85.53 47	95.79 22	94.32 45

HPM-MVS		87.11 33	86.98 35	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 88	83.81 95	93.95 58	69.77 80	96.01 53	85.15 48	94.66 47	94.32 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
train_agg		86.43 43	86.20 47	87.13 44	93.26 52	72.96 25	88.75 122	91.89 101	68.69 251	85.00 67	93.10 74	74.43 26	95.41 73	84.97 49	95.71 25	93.02 111

test9_res														84.90 50	95.70 26	92.87 116

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 59	93.47 67	73.02 41	97.00 18	84.90 50	94.94 40	94.10 53

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 161	84.86 72	92.89 81	76.22 17	96.33 41	84.89 52	95.13 36	94.40 41

DeepC-MVS	79.81 2	87.08 35	86.88 39	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 59	82.82 109	94.23 41	72.13 49	97.09 16	84.83 53	95.37 31	93.65 79
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 40	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 122	86.84 52	94.65 23	67.31 107	95.77 59	84.80 54	92.85 72	92.84 117

MVSMamba_PlusPlus		85.99 50	85.96 54	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 166	84.64 77	91.71 105	71.85 51	96.03 50	84.77 55	94.45 54	94.49 37

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 196	87.75 38	94.07 48	74.01 32	96.70 27	84.66 56	94.84 44

PC_three_1452									68.21 259	92.02 12	94.00 53	82.09 5	95.98 56	84.58 57	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 69	84.91 69	94.44 31	70.78 68	96.61 32	84.53 58	94.89 42	93.66 75

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 69	84.66 76	94.52 24	68.81 92	96.65 30	84.53 58	94.90 41	94.00 58

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 72	84.45 81	94.52 24	69.09 86	96.70 27	84.37 60	94.83 45	94.03 57

CANet		86.45 42	86.10 51	87.51 37	90.09 107	70.94 70	89.70 85	92.59 74	81.78 4	81.32 125	91.43 117	70.34 72	97.23 14	84.26 61	93.36 68	94.37 42

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 163	88.58 25	94.52 24	73.36 34	96.49 38	84.26 61	95.01 37	92.70 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS		86.69 39	86.95 36	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 17	84.57 80	92.27 93	71.47 58	95.02 93	84.24 63	93.46 67	95.13 8

CP-MVS		87.11 33	86.92 37	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 75	83.68 97	94.46 28	67.93 100	95.95 57	84.20 64	94.39 55	93.23 97

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 65	84.68 73	93.99 55	70.67 70	96.82 22	84.18 65	95.01 37	93.90 64

BP-MVS1		84.32 77	83.71 85	86.17 61	87.84 200	67.85 139	89.38 99	89.64 173	77.73 39	83.98 91	92.12 97	56.89 224	95.43 70	84.03 66	91.75 87	95.24 6

EC-MVSNet		86.01 49	86.38 43	84.91 97	89.31 138	66.27 174	92.32 30	93.63 21	79.37 21	84.17 87	91.88 101	69.04 90	95.43 70	83.93 67	93.77 63	93.01 112

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 51	82.45 3	96.87 20	83.77 68	96.48 8	94.88 15

casdiffmvs_mvg		85.99 50	86.09 52	85.70 74	87.65 210	67.22 160	88.69 126	93.04 41	79.64 19	85.33 63	92.54 90	73.30 35	94.50 112	83.49 69	91.14 96	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 60	86.15 50	84.06 134	91.71 78	64.94 205	86.47 200	91.87 103	73.63 144	86.60 54	93.02 79	76.57 15	91.87 226	83.36 70	92.15 80	95.35 3

test_prior2							88.85 119		75.41 99	84.91 69	93.54 63	74.28 29		83.31 71	95.86 20

PHI-MVS		86.43 43	86.17 49	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 168	85.22 65	91.90 100	69.47 82	96.42 40	83.28 72	95.94 19	94.35 43

XVS		87.18 32	86.91 38	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 98	94.17 43	67.45 105	96.60 33	83.06 73	94.50 51	94.07 55

X-MVStestdata		80.37 158	77.83 194	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 98	12.47 426	67.45 105	96.60 33	83.06 73	94.50 51	94.07 55

mamv4		76.81 238	78.23 186	72.54 349	86.12 245	65.75 187	78.76 339	82.07 321	64.12 309	72.97 281	91.02 133	67.97 99	68.08 414	83.04 75	78.02 277	83.80 360

APD-MVS_3200maxsize		85.97 52	85.88 55	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 148	85.94 56	94.51 27	65.80 125	95.61 62	83.04 75	92.51 76	93.53 88

agg_prior2														82.91 77	95.45 29	92.70 119

mPP-MVS		86.67 41	86.32 44	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 66	82.81 110	94.25 40	66.44 115	96.24 44	82.88 78	94.28 58	93.38 91

SR-MVS-dyc-post		85.77 57	85.61 60	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 149	85.69 60	94.45 29	65.00 133	95.56 63	82.75 79	91.87 84	92.50 128

RE-MVS-def			85.48 62		93.06 58	70.63 76	91.88 38	92.27 84	73.53 149	85.69 60	94.45 29	63.87 139		82.75 79	91.87 84	92.50 128

h-mvs33		83.15 101	82.19 109	86.02 69	90.56 98	70.85 73	88.15 147	89.16 190	76.02 89	84.67 74	91.39 118	61.54 173	95.50 66	82.71 81	75.48 313	91.72 153

hse-mvs2		81.72 123	80.94 129	84.07 132	88.72 162	67.68 144	85.87 217	87.26 241	76.02 89	84.67 74	88.22 200	61.54 173	93.48 156	82.71 81	73.44 341	91.06 172

PGM-MVS		86.68 40	86.27 46	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 97	83.86 93	94.42 32	67.87 102	96.64 31	82.70 83	94.57 50	93.66 75

ACMMP		85.89 56	85.39 63	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 69	80.73 134	93.82 60	64.33 135	96.29 42	82.67 84	90.69 101	93.23 97
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		82.10 115	81.88 117	82.76 193	83.00 312	63.78 229	83.68 267	89.76 168	72.94 164	82.02 116	89.85 153	65.96 124	90.79 263	82.38 85	87.30 153	93.71 74
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 88	84.54 75	80.99 230	90.06 112	65.83 183	84.21 259	88.74 209	71.60 183	85.01 66	92.44 91	74.51 25	83.50 350	82.15 86	92.15 80	93.64 81

SPE-MVS-test		86.29 47	86.48 42	85.71 73	91.02 88	67.21 161	92.36 29	93.78 18	78.97 29	83.51 101	91.20 124	70.65 71	95.15 84	81.96 87	94.89 42	94.77 24

TSAR-MVS + GP.		85.71 59	85.33 65	86.84 50	91.34 81	72.50 36	89.07 112	87.28 239	76.41 78	85.80 58	90.22 148	74.15 31	95.37 78	81.82 88	91.88 83	92.65 123

alignmvs		85.48 62	85.32 66	85.96 70	89.51 126	69.47 95	89.74 83	92.47 76	76.17 86	87.73 40	91.46 116	70.32 73	93.78 141	81.51 89	88.95 127	94.63 32

sasdasda		85.91 54	85.87 56	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 73	88.01 33	91.23 121	73.28 36	93.91 135	81.50 90	88.80 130	94.77 24

canonicalmvs		85.91 54	85.87 56	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 73	88.01 33	91.23 121	73.28 36	93.91 135	81.50 90	88.80 130	94.77 24

baseline		84.93 72	84.98 70	84.80 101	87.30 222	65.39 194	87.30 173	92.88 57	77.62 41	84.04 90	92.26 94	71.81 52	93.96 128	81.31 92	90.30 107	95.03 10

MGCFI-Net		85.06 71	85.51 61	83.70 150	89.42 130	63.01 247	89.43 94	92.62 73	76.43 77	87.53 41	91.34 119	72.82 44	93.42 161	81.28 93	88.74 133	94.66 31

casdiffmvs		85.11 69	85.14 69	85.01 91	87.20 224	65.77 186	87.75 159	92.83 60	77.84 38	84.36 84	92.38 92	72.15 48	93.93 134	81.27 94	90.48 104	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 68	84.75 73	86.32 58	91.65 79	72.70 30	85.98 213	90.33 151	76.11 87	82.08 115	91.61 111	71.36 61	94.17 124	81.02 95	92.58 75	92.08 146

HPM-MVS_fast		85.35 66	84.95 72	86.57 56	93.69 42	70.58 78	92.15 35	91.62 111	73.89 139	82.67 112	94.09 47	62.60 154	95.54 65	80.93 96	92.93 71	93.57 84

CPTT-MVS		83.73 86	83.33 92	84.92 96	93.28 49	70.86 72	92.09 36	90.38 147	68.75 250	79.57 146	92.83 83	60.60 196	93.04 184	80.92 97	91.56 91	90.86 180

ETV-MVS		84.90 74	84.67 74	85.59 75	89.39 133	68.66 120	88.74 124	92.64 72	79.97 15	84.10 88	85.71 265	69.32 84	95.38 75	80.82 98	91.37 93	92.72 118

DeepC-MVS_fast	79.65 3	86.91 36	86.62 41	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 75	84.22 85	93.36 70	71.44 59	96.76 25	80.82 98	95.33 33	94.16 50
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 82	83.53 87	84.96 93	86.77 234	69.28 102	90.46 67	92.67 67	74.79 116	82.95 105	91.33 120	72.70 45	93.09 180	80.79 100	79.28 265	92.50 128

EI-MVSNet-Vis-set		84.19 78	83.81 83	85.31 81	88.18 181	67.85 139	87.66 161	89.73 170	80.05 14	82.95 105	89.59 161	70.74 69	94.82 101	80.66 101	84.72 186	93.28 96

MSLP-MVS++		85.43 64	85.76 58	84.45 110	91.93 75	70.24 79	90.71 59	92.86 58	77.46 49	84.22 85	92.81 85	67.16 109	92.94 186	80.36 102	94.35 57	90.16 208

MVS_111021_LR		82.61 110	82.11 110	84.11 125	88.82 156	71.58 55	85.15 233	86.16 263	74.69 118	80.47 136	91.04 130	62.29 161	90.55 267	80.33 103	90.08 112	90.20 207

DELS-MVS		85.41 65	85.30 67	85.77 72	88.49 169	67.93 138	85.52 230	93.44 27	78.70 30	83.63 100	89.03 176	74.57 24	95.71 61	80.26 104	94.04 61	93.66 75
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 93	82.64 103	86.16 62	88.14 184	68.45 125	89.13 109	92.69 65	72.82 167	83.71 96	91.86 103	55.69 229	95.35 79	80.03 105	89.74 118	94.69 27

EI-MVSNet-UG-set		83.81 83	83.38 90	85.09 89	87.87 198	67.53 149	87.44 169	89.66 171	79.74 16	82.23 114	89.41 170	70.24 75	94.74 104	79.95 106	83.92 200	92.99 114

CSCG		86.41 45	86.19 48	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 136	83.16 104	91.07 129	75.94 18	95.19 82	79.94 107	94.38 56	93.55 86

RRT-MVS		82.60 112	82.10 111	84.10 126	87.98 194	62.94 252	87.45 168	91.27 121	77.42 50	79.85 142	90.28 144	56.62 226	94.70 107	79.87 108	88.15 142	94.67 28

OPM-MVS		83.50 94	82.95 98	85.14 85	88.79 159	70.95 69	89.13 109	91.52 114	77.55 46	80.96 132	91.75 104	60.71 190	94.50 112	79.67 109	86.51 165	89.97 224
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CDPH-MVS		85.76 58	85.29 68	87.17 43	93.49 47	71.08 64	88.58 130	92.42 80	68.32 258	84.61 78	93.48 65	72.32 46	96.15 48	79.00 110	95.43 30	94.28 47

MVSFormer		82.85 107	82.05 113	85.24 83	87.35 216	70.21 80	90.50 64	90.38 147	68.55 253	81.32 125	89.47 164	61.68 170	93.46 158	78.98 111	90.26 108	92.05 147

test_djsdf		80.30 159	79.32 160	83.27 163	83.98 287	65.37 195	90.50 64	90.38 147	68.55 253	76.19 219	88.70 183	56.44 227	93.46 158	78.98 111	80.14 255	90.97 177

test_vis1_n_1920		75.52 260	75.78 236	74.75 329	79.84 361	57.44 318	83.26 276	85.52 270	62.83 326	79.34 150	86.17 258	45.10 338	79.71 369	78.75 113	81.21 239	87.10 307

HQP_MVS		83.64 89	83.14 93	85.14 85	90.08 108	68.71 116	91.25 52	92.44 77	79.12 24	78.92 155	91.00 134	60.42 198	95.38 75	78.71 114	86.32 167	91.33 164

plane_prior5								92.44 77					95.38 75	78.71 114	86.32 167	91.33 164

LPG-MVS_test		82.08 116	81.27 122	84.50 107	89.23 142	68.76 112	90.22 73	91.94 99	75.37 100	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 201	89.83 229

LGP-MVS_train				84.50 107	89.23 142	68.76 112		91.94 99	75.37 100	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 201	89.83 229

lupinMVS		81.39 132	80.27 141	84.76 102	87.35 216	70.21 80	85.55 226	86.41 257	62.85 325	81.32 125	88.61 187	61.68 170	92.24 213	78.41 118	90.26 108	91.83 150

jason		81.39 132	80.29 140	84.70 103	86.63 238	69.90 88	85.95 214	86.77 252	63.24 318	81.07 131	89.47 164	61.08 186	92.15 215	78.33 119	90.07 113	92.05 147
jason: jason.
xiu_mvs_v1_base_debu		80.80 144	79.72 150	84.03 139	87.35 216	70.19 82	85.56 223	88.77 205	69.06 243	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 248

xiu_mvs_v1_base		80.80 144	79.72 150	84.03 139	87.35 216	70.19 82	85.56 223	88.77 205	69.06 243	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 248

xiu_mvs_v1_base_debi		80.80 144	79.72 150	84.03 139	87.35 216	70.19 82	85.56 223	88.77 205	69.06 243	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 248

Effi-MVS+		83.62 91	83.08 94	85.24 83	88.38 175	67.45 150	88.89 117	89.15 191	75.50 98	82.27 113	88.28 197	69.61 81	94.45 114	77.81 123	87.84 144	93.84 68

PS-MVSNAJss		82.07 117	81.31 121	84.34 115	86.51 239	67.27 157	89.27 102	91.51 115	71.75 178	79.37 148	90.22 148	63.15 148	94.27 118	77.69 124	82.36 227	91.49 160

ACMP	74.13 6	81.51 131	80.57 133	84.36 113	89.42 130	68.69 119	89.97 77	91.50 118	74.46 124	75.04 254	90.41 143	53.82 247	94.54 109	77.56 125	82.91 219	89.86 228
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 126

HQP-MVS		82.61 110	82.02 114	84.37 112	89.33 135	66.98 164	89.17 104	92.19 90	76.41 78	77.23 193	90.23 147	60.17 201	95.11 87	77.47 126	85.99 175	91.03 174

MVS_Test		83.15 101	83.06 95	83.41 159	86.86 230	63.21 243	86.11 211	92.00 95	74.31 128	82.87 107	89.44 169	70.03 76	93.21 169	77.39 128	88.50 138	93.81 70

3Dnovator+	77.84 4	85.48 62	84.47 78	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 207	93.37 69	60.40 200	96.75 26	77.20 129	93.73 64	95.29 5

anonymousdsp		78.60 198	77.15 212	82.98 180	80.51 353	67.08 162	87.24 175	89.53 176	65.66 290	75.16 249	87.19 227	52.52 255	92.25 212	77.17 130	79.34 264	89.61 236

mmtdpeth		74.16 274	73.01 277	77.60 298	83.72 294	61.13 271	85.10 235	85.10 274	72.06 176	77.21 197	80.33 357	43.84 346	85.75 328	77.14 131	52.61 404	85.91 329

VDD-MVS		83.01 106	82.36 107	84.96 93	91.02 88	66.40 171	88.91 116	88.11 218	77.57 43	84.39 83	93.29 71	52.19 261	93.91 135	77.05 132	88.70 134	94.57 35

XVG-OURS-SEG-HR		80.81 142	79.76 149	83.96 144	85.60 254	68.78 111	83.54 273	90.50 143	70.66 204	76.71 206	91.66 106	60.69 191	91.26 250	76.94 133	81.58 235	91.83 150

jajsoiax		79.29 181	77.96 189	83.27 163	84.68 272	66.57 170	89.25 103	90.16 158	69.20 239	75.46 234	89.49 163	45.75 333	93.13 178	76.84 134	80.80 245	90.11 212

SDMVSNet		80.38 156	80.18 142	80.99 230	89.03 151	64.94 205	80.45 316	89.40 179	75.19 105	76.61 210	89.98 150	60.61 195	87.69 313	76.83 135	83.55 210	90.33 202

mvs_tets		79.13 185	77.77 198	83.22 167	84.70 271	66.37 172	89.17 104	90.19 157	69.38 232	75.40 237	89.46 166	44.17 344	93.15 176	76.78 136	80.70 247	90.14 209

DPM-MVS		84.93 72	84.29 79	86.84 50	90.20 105	73.04 23	87.12 177	93.04 41	69.80 223	82.85 108	91.22 123	73.06 40	96.02 52	76.72 137	94.63 48	91.46 163

test_cas_vis1_n_1920		73.76 280	73.74 269	73.81 338	75.90 383	59.77 290	80.51 314	82.40 317	58.30 365	81.62 123	85.69 266	44.35 343	76.41 387	76.29 138	78.61 268	85.23 339

ET-MVSNet_ETH3D		78.63 197	76.63 227	84.64 104	86.73 235	69.47 95	85.01 237	84.61 280	69.54 229	66.51 355	86.59 245	50.16 290	91.75 229	76.26 139	84.24 197	92.69 121

v2v482		80.23 160	79.29 161	83.05 176	83.62 295	64.14 222	87.04 179	89.97 163	73.61 145	78.18 173	87.22 225	61.10 185	93.82 139	76.11 140	76.78 293	91.18 168

test_fmvs1_n		70.86 311	70.24 309	72.73 347	72.51 405	55.28 350	81.27 302	79.71 348	51.49 394	78.73 157	84.87 286	27.54 401	77.02 381	76.06 141	79.97 257	85.88 330

CLD-MVS		82.31 113	81.65 119	84.29 117	88.47 170	67.73 143	85.81 221	92.35 82	75.78 92	78.33 169	86.58 247	64.01 138	94.35 115	76.05 142	87.48 150	90.79 181
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 87	82.92 99	86.14 65	84.22 281	69.48 94	91.05 56	85.27 272	81.30 6	76.83 202	91.65 107	66.09 120	95.56 63	76.00 143	93.85 62	93.38 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs1		70.93 310	70.52 304	72.16 351	73.71 394	55.05 352	80.82 305	78.77 356	51.21 395	78.58 162	84.41 294	31.20 396	76.94 382	75.88 144	80.12 256	84.47 351

XVG-OURS		80.41 155	79.23 163	83.97 143	85.64 252	69.02 105	83.03 284	90.39 146	71.09 193	77.63 184	91.49 115	54.62 241	91.35 248	75.71 145	83.47 212	91.54 157

V42		79.38 180	78.24 184	82.83 185	81.10 347	65.50 191	85.55 226	89.82 166	71.57 184	78.21 171	86.12 259	60.66 193	93.18 175	75.64 146	75.46 315	89.81 231

PS-MVSNAJ		81.69 125	81.02 127	83.70 150	89.51 126	68.21 132	84.28 258	90.09 160	70.79 198	81.26 129	85.62 270	63.15 148	94.29 116	75.62 147	88.87 129	88.59 271

xiu_mvs_v2_base		81.69 125	81.05 126	83.60 152	89.15 145	68.03 137	84.46 252	90.02 161	70.67 201	81.30 128	86.53 250	63.17 147	94.19 123	75.60 148	88.54 136	88.57 272

EIA-MVS		83.31 100	82.80 101	84.82 99	89.59 122	65.59 189	88.21 143	92.68 66	74.66 120	78.96 153	86.42 252	69.06 88	95.26 80	75.54 149	90.09 111	93.62 82

AUN-MVS		79.21 183	77.60 204	84.05 137	88.71 163	67.61 146	85.84 219	87.26 241	69.08 242	77.23 193	88.14 205	53.20 254	93.47 157	75.50 150	73.45 340	91.06 172

mvsmamba		80.60 150	79.38 157	84.27 120	89.74 120	67.24 159	87.47 166	86.95 247	70.02 216	75.38 238	88.93 177	51.24 278	92.56 198	75.47 151	89.22 124	93.00 113

reproduce_monomvs		75.40 264	74.38 260	78.46 283	83.92 289	57.80 312	83.78 265	86.94 248	73.47 151	72.25 292	84.47 292	38.74 374	89.27 288	75.32 152	70.53 360	88.31 277

OMC-MVS		82.69 108	81.97 116	84.85 98	88.75 161	67.42 151	87.98 150	90.87 134	74.92 112	79.72 144	91.65 107	62.19 164	93.96 128	75.26 153	86.42 166	93.16 102

v1144		80.03 164	79.03 167	83.01 178	83.78 292	64.51 213	87.11 178	90.57 142	71.96 177	78.08 176	86.20 257	61.41 177	93.94 131	74.93 154	77.23 284	90.60 191

MVSTER		79.01 188	77.88 193	82.38 199	83.07 309	64.80 209	84.08 263	88.95 201	69.01 246	78.69 158	87.17 228	54.70 239	92.43 203	74.69 155	80.57 249	89.89 227

test_vis1_n		69.85 323	69.21 314	71.77 353	72.66 404	55.27 351	81.48 298	76.21 374	52.03 391	75.30 245	83.20 323	28.97 399	76.22 389	74.60 156	78.41 274	83.81 359

test_fmvs2		68.35 336	67.48 336	70.98 362	69.50 408	51.95 374	80.05 321	76.38 373	49.33 397	74.65 261	84.38 295	23.30 410	75.40 398	74.51 157	75.17 324	85.60 333

PVSNet_Blended_VisFu		82.62 109	81.83 118	84.96 93	90.80 94	69.76 90	88.74 124	91.70 110	69.39 231	78.96 153	88.46 192	65.47 127	94.87 100	74.42 158	88.57 135	90.24 206

v8		79.97 166	79.02 168	82.80 188	84.09 284	64.50 215	87.96 151	90.29 154	74.13 135	75.24 247	86.81 234	62.88 153	93.89 138	74.39 159	75.40 318	90.00 220

v144192		79.47 174	78.37 180	82.78 191	83.35 300	63.96 225	86.96 182	90.36 150	69.99 218	77.50 185	85.67 268	60.66 193	93.77 143	74.27 160	76.58 294	90.62 189

ACMM	73.20 8	80.78 147	79.84 148	83.58 153	89.31 138	68.37 127	89.99 76	91.60 112	70.28 211	77.25 191	89.66 157	53.37 252	93.53 154	74.24 161	82.85 220	88.85 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 198		58.10 367	87.04 49			88.98 294	74.07 162

v1192		79.59 171	78.43 179	83.07 175	83.55 297	64.52 212	86.93 185	90.58 140	70.83 197	77.78 181	85.90 261	59.15 204	93.94 131	73.96 163	77.19 286	90.76 183

v10		79.74 168	78.67 172	82.97 181	84.06 285	64.95 204	87.88 157	90.62 139	73.11 160	75.11 251	86.56 248	61.46 176	94.05 127	73.68 164	75.55 311	89.90 226

v1921920		79.22 182	78.03 188	82.80 188	83.30 302	63.94 226	86.80 189	90.33 151	69.91 221	77.48 186	85.53 271	58.44 208	93.75 145	73.60 165	76.85 291	90.71 187

cl22		78.07 211	77.01 214	81.23 223	82.37 328	61.83 265	83.55 272	87.98 222	68.96 247	75.06 253	83.87 306	61.40 178	91.88 225	73.53 166	76.39 298	89.98 223

Effi-MVS+-dtu		80.03 164	78.57 175	84.42 111	85.13 265	68.74 114	88.77 121	88.10 219	74.99 109	74.97 255	83.49 318	57.27 220	93.36 162	73.53 166	80.88 243	91.18 168

c3_l		78.75 193	77.91 191	81.26 222	82.89 316	61.56 268	84.09 262	89.13 193	69.97 219	75.56 230	84.29 298	66.36 116	92.09 217	73.47 168	75.48 313	90.12 211

VDDNet		81.52 129	80.67 132	84.05 137	90.44 101	64.13 223	89.73 84	85.91 266	71.11 192	83.18 103	93.48 65	50.54 287	93.49 155	73.40 169	88.25 140	94.54 36

CANet_DTU		80.61 149	79.87 147	82.83 185	85.60 254	63.17 246	87.36 170	88.65 211	76.37 82	75.88 225	88.44 193	53.51 250	93.07 181	73.30 170	89.74 118	92.25 138

miper_ehance_all_eth		78.59 199	77.76 199	81.08 228	82.66 321	61.56 268	83.65 268	89.15 191	68.87 248	75.55 231	83.79 310	66.49 114	92.03 218	73.25 171	76.39 298	89.64 235

3Dnovator	76.31 5	83.38 98	82.31 108	86.59 55	87.94 195	72.94 28	90.64 60	92.14 92	77.21 56	75.47 232	92.83 83	58.56 207	94.72 105	73.24 172	92.71 74	92.13 145

v1240		78.99 189	77.78 197	82.64 194	83.21 304	63.54 234	86.62 196	90.30 153	69.74 228	77.33 189	85.68 267	57.04 222	93.76 144	73.13 173	76.92 288	90.62 189

miper_enhance_ethall		77.87 218	76.86 218	80.92 233	81.65 335	61.38 270	82.68 285	88.98 198	65.52 292	75.47 232	82.30 338	65.76 126	92.00 220	72.95 174	76.39 298	89.39 241

MG-MVS		83.41 96	83.45 88	83.28 162	92.74 65	62.28 259	88.17 145	89.50 177	75.22 103	81.49 124	92.74 89	66.75 110	95.11 87	72.85 175	91.58 90	92.45 131

EPP-MVSNet		83.40 97	83.02 96	84.57 105	90.13 106	64.47 216	92.32 30	90.73 137	74.45 125	79.35 149	91.10 127	69.05 89	95.12 85	72.78 176	87.22 154	94.13 52

test_fmvs3		63.36 360	61.82 363	67.98 377	62.51 417	46.96 399	77.37 357	74.03 384	45.24 402	67.50 339	78.79 373	12.16 422	72.98 406	72.77 177	66.02 377	83.99 357

IterMVS-LS		80.06 163	79.38 157	82.11 202	85.89 248	63.20 244	86.79 190	89.34 181	74.19 132	75.45 235	86.72 237	66.62 111	92.39 205	72.58 178	76.86 290	90.75 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		78.73 194	77.83 194	81.43 216	85.17 261	60.30 285	89.41 97	90.90 132	71.21 190	77.17 198	88.73 182	46.38 322	93.21 169	72.57 179	78.96 267	90.79 181

EI-MVSNet		80.52 154	79.98 144	82.12 201	84.28 279	63.19 245	86.41 201	88.95 201	74.18 133	78.69 158	87.54 217	66.62 111	92.43 203	72.57 179	80.57 249	90.74 185

Vis-MVSNet		83.46 95	82.80 101	85.43 79	90.25 104	68.74 114	90.30 72	90.13 159	76.33 84	80.87 133	92.89 81	61.00 187	94.20 122	72.45 181	90.97 97	93.35 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS		81.82 122	81.23 123	83.57 154	91.89 76	63.43 239	89.84 78	81.85 324	77.04 62	83.21 102	93.10 74	52.26 260	93.43 160	71.98 182	89.95 115	93.85 66

v148		78.72 195	77.80 196	81.47 215	82.73 319	61.96 263	86.30 206	88.08 220	73.26 157	76.18 220	85.47 273	62.46 158	92.36 207	71.92 183	73.82 337	90.09 214

PVSNet_BlendedMVS		80.60 150	80.02 143	82.36 200	88.85 153	65.40 192	86.16 210	92.00 95	69.34 233	78.11 174	86.09 260	66.02 122	94.27 118	71.52 184	82.06 230	87.39 295

PVSNet_Blended		80.98 137	80.34 138	82.90 183	88.85 153	65.40 192	84.43 254	92.00 95	67.62 264	78.11 174	85.05 284	66.02 122	94.27 118	71.52 184	89.50 120	89.01 253

eth_miper_zixun_eth		77.92 216	76.69 225	81.61 213	83.00 312	61.98 262	83.15 278	89.20 189	69.52 230	74.86 257	84.35 297	61.76 169	92.56 198	71.50 186	72.89 345	90.28 205

UA-Net		85.08 70	84.96 71	85.45 78	92.07 73	68.07 135	89.78 82	90.86 135	82.48 2	84.60 79	93.20 73	69.35 83	95.22 81	71.39 187	90.88 99	93.07 106

FA-MVS(test-final)		80.96 138	79.91 146	84.10 126	88.30 178	65.01 202	84.55 249	90.01 162	73.25 158	79.61 145	87.57 214	58.35 209	94.72 105	71.29 188	86.25 169	92.56 125

cl____		77.72 221	76.76 222	80.58 239	82.49 325	60.48 282	83.09 280	87.87 226	69.22 237	74.38 266	85.22 279	62.10 165	91.53 240	71.09 189	75.41 317	89.73 234

DIV-MVS_self_test		77.72 221	76.76 222	80.58 239	82.48 326	60.48 282	83.09 280	87.86 227	69.22 237	74.38 266	85.24 277	62.10 165	91.53 240	71.09 189	75.40 318	89.74 233

MonoMVSNet		76.49 246	75.80 235	78.58 277	81.55 338	58.45 299	86.36 204	86.22 261	74.87 115	74.73 259	83.73 312	51.79 273	88.73 299	70.78 191	72.15 350	88.55 273

test_yl		81.17 134	80.47 136	83.24 165	89.13 146	63.62 230	86.21 208	89.95 164	72.43 171	81.78 121	89.61 159	57.50 217	93.58 149	70.75 192	86.90 158	92.52 126

DCV-MVSNet		81.17 134	80.47 136	83.24 165	89.13 146	63.62 230	86.21 208	89.95 164	72.43 171	81.78 121	89.61 159	57.50 217	93.58 149	70.75 192	86.90 158	92.52 126

VNet		82.21 114	82.41 105	81.62 211	90.82 93	60.93 274	84.47 250	89.78 167	76.36 83	84.07 89	91.88 101	64.71 134	90.26 269	70.68 194	88.89 128	93.66 75

mvs_anonymous		79.42 177	79.11 166	80.34 244	84.45 278	57.97 307	82.59 286	87.62 232	67.40 268	76.17 222	88.56 190	68.47 94	89.59 282	70.65 195	86.05 173	93.47 89

VPA-MVSNet		80.60 150	80.55 134	80.76 236	88.07 189	60.80 277	86.86 187	91.58 113	75.67 96	80.24 138	89.45 168	63.34 142	90.25 270	70.51 196	79.22 266	91.23 167

PAPM_NR		83.02 105	82.41 105	84.82 99	92.47 70	66.37 172	87.93 154	91.80 106	73.82 140	77.32 190	90.66 139	67.90 101	94.90 97	70.37 197	89.48 121	93.19 101

thisisatest0530		79.40 178	77.76 199	84.31 116	87.69 209	65.10 201	87.36 170	84.26 287	70.04 215	77.42 187	88.26 199	49.94 293	94.79 103	70.20 198	84.70 187	93.03 110

tttt0517		79.40 178	77.91 191	83.90 146	88.10 187	63.84 227	88.37 138	84.05 289	71.45 186	76.78 204	89.12 173	49.93 295	94.89 98	70.18 199	83.18 217	92.96 115

UniMVSNet_NR-MVSNet		81.88 120	81.54 120	82.92 182	88.46 171	63.46 237	87.13 176	92.37 81	80.19 12	78.38 167	89.14 172	71.66 57	93.05 182	70.05 200	76.46 296	92.25 138

DU-MVS		81.12 136	80.52 135	82.90 183	87.80 202	63.46 237	87.02 181	91.87 103	79.01 27	78.38 167	89.07 174	65.02 131	93.05 182	70.05 200	76.46 296	92.20 141

XVG-ACMP-BASELINE		76.11 252	74.27 262	81.62 211	83.20 305	64.67 211	83.60 271	89.75 169	69.75 226	71.85 296	87.09 230	32.78 391	92.11 216	69.99 202	80.43 251	88.09 281

GeoE		81.71 124	81.01 128	83.80 149	89.51 126	64.45 217	88.97 114	88.73 210	71.27 189	78.63 161	89.76 155	66.32 117	93.20 172	69.89 203	86.02 174	93.74 73

FIs		82.07 117	82.42 104	81.04 229	88.80 158	58.34 301	88.26 142	93.49 26	76.93 64	78.47 166	91.04 130	69.92 78	92.34 209	69.87 204	84.97 183	92.44 132

114514_t		80.68 148	79.51 154	84.20 123	94.09 38	67.27 157	89.64 87	91.11 128	58.75 363	74.08 268	90.72 138	58.10 210	95.04 92	69.70 205	89.42 122	90.30 204

Anonymous20231211		78.97 190	77.69 202	82.81 187	90.54 99	64.29 220	90.11 75	91.51 115	65.01 299	76.16 223	88.13 206	50.56 286	93.03 185	69.68 206	77.56 283	91.11 170

Patchmatch-RL test		70.24 318	67.78 331	77.61 296	77.43 378	59.57 294	71.16 386	70.33 392	62.94 324	68.65 330	72.77 398	50.62 285	85.49 333	69.58 207	66.58 375	87.77 287

UniMVSNet (Re)		81.60 128	81.11 125	83.09 172	88.38 175	64.41 218	87.60 162	93.02 45	78.42 33	78.56 163	88.16 201	69.78 79	93.26 165	69.58 207	76.49 295	91.60 154

IterMVS-SCA-FT		75.43 262	73.87 267	80.11 249	82.69 320	64.85 208	81.57 297	83.47 298	69.16 240	70.49 307	84.15 304	51.95 268	88.15 307	69.23 209	72.14 351	87.34 297

v7n		78.97 190	77.58 205	83.14 170	83.45 299	65.51 190	88.32 140	91.21 123	73.69 143	72.41 289	86.32 255	57.93 211	93.81 140	69.18 210	75.65 309	90.11 212

Anonymous20240529		80.19 162	78.89 170	84.10 126	90.60 97	64.75 210	88.95 115	90.90 132	65.97 287	80.59 135	91.17 126	49.97 292	93.73 147	69.16 211	82.70 224	93.81 70

miper_lstm_enhance		74.11 275	73.11 276	77.13 304	80.11 357	59.62 292	72.23 382	86.92 250	66.76 272	70.40 308	82.92 328	56.93 223	82.92 354	69.06 212	72.63 346	88.87 260

testdata				79.97 251	90.90 91	64.21 221		84.71 278	59.27 357	85.40 62	92.91 80	62.02 167	89.08 292	68.95 213	91.37 93	86.63 316

test1111		79.43 176	79.18 165	80.15 248	89.99 113	53.31 368	87.33 172	77.05 369	75.04 108	80.23 139	92.77 88	48.97 307	92.33 210	68.87 214	92.40 79	94.81 21

GA-MVS		76.87 237	75.17 250	81.97 206	82.75 318	62.58 254	81.44 300	86.35 260	72.16 175	74.74 258	82.89 329	46.20 327	92.02 219	68.85 215	81.09 240	91.30 166

test2506		77.30 231	76.49 228	79.74 256	90.08 108	52.02 372	87.86 158	63.10 412	74.88 113	80.16 140	92.79 86	38.29 378	92.35 208	68.74 216	92.50 77	94.86 18

ECVR-MVS		79.61 169	79.26 162	80.67 238	90.08 108	54.69 355	87.89 156	77.44 365	74.88 113	80.27 137	92.79 86	48.96 308	92.45 202	68.55 217	92.50 77	94.86 18

UGNet		80.83 141	79.59 153	84.54 106	88.04 190	68.09 134	89.42 96	88.16 217	76.95 63	76.22 218	89.46 166	49.30 302	93.94 131	68.48 218	90.31 106	91.60 154
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		81.52 129	82.02 114	80.03 250	88.42 174	55.97 340	87.95 152	93.42 29	77.10 60	77.38 188	90.98 136	69.96 77	91.79 227	68.46 219	84.50 189	92.33 134

DP-MVS Recon		83.11 104	82.09 112	86.15 63	94.44 19	70.92 71	88.79 120	92.20 89	70.53 206	79.17 151	91.03 132	64.12 137	96.03 50	68.39 220	90.14 110	91.50 159

UniMVSNet_ETH3D		79.10 186	78.24 184	81.70 210	86.85 231	60.24 286	87.28 174	88.79 204	74.25 131	76.84 201	90.53 142	49.48 298	91.56 237	67.98 221	82.15 228	93.29 95

D2MVS		74.82 268	73.21 274	79.64 260	79.81 362	62.56 255	80.34 318	87.35 238	64.37 306	68.86 328	82.66 333	46.37 323	90.10 272	67.91 222	81.24 238	86.25 319

IS-MVSNet		83.15 101	82.81 100	84.18 124	89.94 115	63.30 241	91.59 43	88.46 215	79.04 26	79.49 147	92.16 95	65.10 130	94.28 117	67.71 223	91.86 86	94.95 11

Fast-Effi-MVS+-dtu		78.02 213	76.49 228	82.62 195	83.16 308	66.96 166	86.94 184	87.45 237	72.45 168	71.49 301	84.17 303	54.79 238	91.58 235	67.61 224	80.31 252	89.30 244

PAPR		81.66 127	80.89 130	83.99 142	90.27 103	64.00 224	86.76 193	91.77 109	68.84 249	77.13 200	89.50 162	67.63 103	94.88 99	67.55 225	88.52 137	93.09 105

cascas		76.72 240	74.64 254	82.99 179	85.78 250	65.88 182	82.33 288	89.21 188	60.85 344	72.74 283	81.02 349	47.28 315	93.75 145	67.48 226	85.02 182	89.34 243

1314		76.53 242	75.30 249	80.21 247	83.93 288	62.32 258	84.66 244	88.81 203	60.23 348	70.16 313	84.07 305	55.30 232	90.73 265	67.37 227	83.21 216	87.59 292

无先验							87.48 165	88.98 198	60.00 350				94.12 125	67.28 228		88.97 256

thisisatest0515		77.33 230	75.38 246	83.18 168	85.27 260	63.80 228	82.11 291	83.27 301	65.06 297	75.91 224	83.84 308	49.54 297	94.27 118	67.24 229	86.19 170	91.48 161

原ACMM1				84.35 114	93.01 60	68.79 110		92.44 77	63.96 315	81.09 130	91.57 112	66.06 121	95.45 68	67.19 230	94.82 46	88.81 263

Baseline_NR-MVSNet		78.15 209	78.33 182	77.61 296	85.79 249	56.21 338	86.78 191	85.76 268	73.60 146	77.93 179	87.57 214	65.02 131	88.99 293	67.14 231	75.33 320	87.63 289

TranMVSNet+NR-MVSNet		80.84 140	80.31 139	82.42 198	87.85 199	62.33 257	87.74 160	91.33 120	80.55 9	77.99 178	89.86 152	65.23 129	92.62 193	67.05 232	75.24 323	92.30 136

Fast-Effi-MVS+		80.81 142	79.92 145	83.47 155	88.85 153	64.51 213	85.53 228	89.39 180	70.79 198	78.49 165	85.06 283	67.54 104	93.58 149	67.03 233	86.58 163	92.32 135

VPNet		78.69 196	78.66 173	78.76 273	88.31 177	55.72 344	84.45 253	86.63 254	76.79 68	78.26 170	90.55 141	59.30 203	89.70 281	66.63 234	77.05 287	90.88 179

PM-MVS		66.41 348	64.14 351	73.20 343	73.92 393	56.45 331	78.97 336	64.96 409	63.88 316	64.72 366	80.24 358	19.84 414	83.44 351	66.24 235	64.52 382	79.71 390

test-LLR		72.94 294	72.43 283	74.48 330	81.35 343	58.04 305	78.38 344	77.46 363	66.66 274	69.95 317	79.00 370	48.06 311	79.24 370	66.13 236	84.83 184	86.15 322

test-mter		71.41 305	70.39 308	74.48 330	81.35 343	58.04 305	78.38 344	77.46 363	60.32 347	69.95 317	79.00 370	36.08 385	79.24 370	66.13 236	84.83 184	86.15 322

MVS		78.19 208	76.99 216	81.78 208	85.66 251	66.99 163	84.66 244	90.47 144	55.08 383	72.02 295	85.27 276	63.83 140	94.11 126	66.10 238	89.80 117	84.24 353

NR-MVSNet		80.23 160	79.38 157	82.78 191	87.80 202	63.34 240	86.31 205	91.09 129	79.01 27	72.17 293	89.07 174	67.20 108	92.81 191	66.08 239	75.65 309	92.20 141

CVMVSNet		72.99 293	72.58 282	74.25 333	84.28 279	50.85 386	86.41 201	83.45 299	44.56 403	73.23 278	87.54 217	49.38 300	85.70 329	65.90 240	78.44 272	86.19 321

IterMVS		74.29 271	72.94 278	78.35 284	81.53 339	63.49 236	81.58 296	82.49 316	68.06 261	69.99 316	83.69 314	51.66 275	85.54 332	65.85 241	71.64 354	86.01 326
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 272	72.42 284	79.80 255	83.76 293	59.59 293	85.92 216	86.64 253	66.39 281	66.96 345	87.58 213	39.46 370	91.60 234	65.76 242	69.27 365	88.22 278

tpmrst		72.39 296	72.13 287	73.18 344	80.54 352	49.91 390	79.91 324	79.08 355	63.11 320	71.69 298	79.95 361	55.32 231	82.77 355	65.66 243	73.89 335	86.87 309

MAR-MVS		81.84 121	80.70 131	85.27 82	91.32 82	71.53 56	89.82 79	90.92 131	69.77 225	78.50 164	86.21 256	62.36 160	94.52 111	65.36 244	92.05 82	89.77 232
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		78.25 204	77.01 214	81.99 205	91.03 87	60.67 279	84.77 242	83.90 291	70.65 205	80.00 141	91.20 124	41.08 364	91.43 246	65.21 245	85.26 181	93.85 66

ab-mvs		79.51 172	78.97 169	81.14 226	88.46 171	60.91 275	83.84 264	89.24 187	70.36 208	79.03 152	88.87 180	63.23 146	90.21 271	65.12 246	82.57 225	92.28 137

IB-MVS	68.01 15	75.85 256	73.36 273	83.31 161	84.76 270	66.03 176	83.38 274	85.06 275	70.21 214	69.40 323	81.05 348	45.76 332	94.66 108	65.10 247	75.49 312	89.25 245
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		79.49 173	79.22 164	80.27 246	88.79 159	58.35 300	85.06 236	88.61 213	78.56 31	77.65 183	88.34 195	63.81 141	90.66 266	64.98 248	77.22 285	91.80 152

CostFormer		75.24 266	73.90 266	79.27 265	82.65 322	58.27 302	80.80 306	82.73 315	61.57 339	75.33 244	83.13 324	55.52 230	91.07 259	64.98 248	78.34 275	88.45 274

API-MVS		81.99 119	81.23 123	84.26 122	90.94 90	70.18 85	91.10 55	89.32 182	71.51 185	78.66 160	88.28 197	65.26 128	95.10 90	64.74 250	91.23 95	87.51 293

新几何1				83.42 157	93.13 54	70.71 74		85.48 271	57.43 373	81.80 120	91.98 98	63.28 143	92.27 211	64.60 251	92.99 70	87.27 299

testing91		76.54 241	75.66 240	79.18 268	88.43 173	55.89 341	81.08 303	83.00 309	73.76 142	75.34 240	84.29 298	46.20 327	90.07 273	64.33 252	84.50 189	91.58 156

testing99		76.09 253	75.12 251	79.00 269	88.16 182	55.50 347	80.79 307	81.40 328	73.30 156	75.17 248	84.27 301	44.48 342	90.02 274	64.28 253	84.22 198	91.48 161

pm-mvs1		77.25 232	76.68 226	78.93 271	84.22 281	58.62 298	86.41 201	88.36 216	71.37 187	73.31 276	88.01 207	61.22 183	89.15 291	64.24 254	73.01 344	89.03 252

TESTMET0.1,1		69.89 322	69.00 316	72.55 348	79.27 371	56.85 324	78.38 344	74.71 382	57.64 370	68.09 334	77.19 383	37.75 380	76.70 383	63.92 255	84.09 199	84.10 356

QAPM		80.88 139	79.50 155	85.03 90	88.01 193	68.97 107	91.59 43	92.00 95	66.63 279	75.15 250	92.16 95	57.70 214	95.45 68	63.52 256	88.76 132	90.66 188

baseline2		75.70 257	73.83 268	81.30 221	83.26 303	61.79 266	82.57 287	80.65 335	66.81 270	66.88 346	83.42 319	57.86 213	92.19 214	63.47 257	79.57 259	89.91 225

LCM-MVSNet-Re		77.05 233	76.94 217	77.36 300	87.20 224	51.60 379	80.06 320	80.46 339	75.20 104	67.69 337	86.72 237	62.48 157	88.98 294	63.44 258	89.25 123	91.51 158

gm-plane-assit					81.40 341	53.83 363			62.72 329		80.94 351		92.39 205	63.40 259

baseline1		76.98 235	76.75 224	77.66 294	88.13 185	55.66 345	85.12 234	81.89 322	73.04 162	76.79 203	88.90 178	62.43 159	87.78 312	63.30 260	71.18 357	89.55 238

AdaColmap		80.58 153	79.42 156	84.06 134	93.09 57	68.91 108	89.36 100	88.97 200	69.27 234	75.70 228	89.69 156	57.20 221	95.77 59	63.06 261	88.41 139	87.50 294

test_vis1_rt		60.28 365	58.42 368	65.84 382	67.25 411	55.60 346	70.44 391	60.94 415	44.33 404	59.00 390	66.64 405	24.91 405	68.67 412	62.80 262	69.48 363	73.25 401

GBi-Net		78.40 201	77.40 207	81.40 218	87.60 211	63.01 247	88.39 135	89.28 183	71.63 180	75.34 240	87.28 221	54.80 235	91.11 253	62.72 263	79.57 259	90.09 214

test1		78.40 201	77.40 207	81.40 218	87.60 211	63.01 247	88.39 135	89.28 183	71.63 180	75.34 240	87.28 221	54.80 235	91.11 253	62.72 263	79.57 259	90.09 214

FMVSNet3		77.88 217	76.85 219	80.97 232	86.84 232	62.36 256	86.52 199	88.77 205	71.13 191	75.34 240	86.66 243	54.07 245	91.10 256	62.72 263	79.57 259	89.45 240

CMPMVS	51.72 21	70.19 319	68.16 322	76.28 309	73.15 401	57.55 316	79.47 327	83.92 290	48.02 399	56.48 399	84.81 288	43.13 350	86.42 323	62.67 266	81.81 234	84.89 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 223	77.40 207	78.60 276	89.03 151	60.02 288	79.00 335	85.83 267	75.19 105	76.61 210	89.98 150	54.81 234	85.46 334	62.63 267	83.55 210	90.33 202

FMVSNet2		78.20 207	77.21 211	81.20 224	87.60 211	62.89 253	87.47 166	89.02 196	71.63 180	75.29 246	87.28 221	54.80 235	91.10 256	62.38 268	79.38 263	89.61 236

testdata2													91.01 260	62.37 269

testing11		75.14 267	74.01 263	78.53 280	88.16 182	56.38 334	80.74 310	80.42 340	70.67 201	72.69 286	83.72 313	43.61 348	89.86 276	62.29 270	83.76 203	89.36 242

CP-MVSNet		78.22 205	78.34 181	77.84 291	87.83 201	54.54 357	87.94 153	91.17 125	77.65 40	73.48 275	88.49 191	62.24 163	88.43 304	62.19 271	74.07 332	90.55 193

XXY-MVS		75.41 263	75.56 241	74.96 325	83.59 296	57.82 311	80.59 313	83.87 292	66.54 280	74.93 256	88.31 196	63.24 145	80.09 368	62.16 272	76.85 291	86.97 308

pmmvs6		74.69 269	73.39 272	78.61 275	81.38 342	57.48 317	86.64 195	87.95 224	64.99 300	70.18 311	86.61 244	50.43 288	89.52 283	62.12 273	70.18 362	88.83 262

1112_ss		77.40 229	76.43 230	80.32 245	89.11 150	60.41 284	83.65 268	87.72 231	62.13 335	73.05 280	86.72 237	62.58 156	89.97 275	62.11 274	80.80 245	90.59 192

PS-CasMVS		78.01 214	78.09 187	77.77 293	87.71 207	54.39 359	88.02 149	91.22 122	77.50 48	73.26 277	88.64 186	60.73 189	88.41 305	61.88 275	73.88 336	90.53 194

CDS-MVSNet		79.07 187	77.70 201	83.17 169	87.60 211	68.23 131	84.40 256	86.20 262	67.49 266	76.36 215	86.54 249	61.54 173	90.79 263	61.86 276	87.33 152	90.49 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 167	78.33 182	84.09 130	85.17 261	69.91 87	90.57 61	90.97 130	66.70 273	72.17 293	91.91 99	54.70 239	93.96 128	61.81 277	90.95 98	88.41 276

K. test v3		71.19 306	68.51 318	79.21 267	83.04 311	57.78 313	84.35 257	76.91 370	72.90 165	62.99 377	82.86 330	39.27 371	91.09 258	61.65 278	52.66 403	88.75 266

CHOSEN 1792x2688		77.63 225	75.69 237	83.44 156	89.98 114	68.58 122	78.70 340	87.50 235	56.38 378	75.80 227	86.84 233	58.67 206	91.40 247	61.58 279	85.75 179	90.34 201

PCF-MVS	73.52 7	80.38 156	78.84 171	85.01 91	87.71 207	68.99 106	83.65 268	91.46 119	63.00 322	77.77 182	90.28 144	66.10 119	95.09 91	61.40 280	88.22 141	90.94 178
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 215	77.15 212	80.36 243	87.57 215	60.21 287	83.37 275	87.78 230	66.11 283	75.37 239	87.06 232	63.27 144	90.48 268	61.38 281	82.43 226	90.40 200

HyFIR lowres test		77.53 226	75.40 245	83.94 145	89.59 122	66.62 168	80.36 317	88.64 212	56.29 379	76.45 212	85.17 280	57.64 215	93.28 164	61.34 282	83.10 218	91.91 149

PMMVS		69.34 326	68.67 317	71.35 358	75.67 385	62.03 261	75.17 369	73.46 385	50.00 396	68.68 329	79.05 368	52.07 266	78.13 375	61.16 283	82.77 221	73.90 400

FMVSNet1		77.44 227	76.12 234	81.40 218	86.81 233	63.01 247	88.39 135	89.28 183	70.49 207	74.39 265	87.28 221	49.06 306	91.11 253	60.91 284	78.52 270	90.09 214

sss		73.60 282	73.64 270	73.51 340	82.80 317	55.01 353	76.12 361	81.69 325	62.47 331	74.68 260	85.85 264	57.32 219	78.11 376	60.86 285	80.93 241	87.39 295

Test_1112_low_res		76.40 248	75.44 243	79.27 265	89.28 140	58.09 303	81.69 295	87.07 245	59.53 355	72.48 288	86.67 242	61.30 180	89.33 286	60.81 286	80.15 254	90.41 199

BH-untuned		79.47 174	78.60 174	82.05 203	89.19 144	65.91 181	86.07 212	88.52 214	72.18 173	75.42 236	87.69 211	61.15 184	93.54 153	60.38 287	86.83 160	86.70 314

WTY-MVS		75.65 258	75.68 238	75.57 316	86.40 240	56.82 325	77.92 353	82.40 317	65.10 296	76.18 220	87.72 209	63.13 151	80.90 365	60.31 288	81.96 231	89.00 255

pmmvs4		74.03 278	71.91 288	80.39 242	81.96 331	68.32 128	81.45 299	82.14 319	59.32 356	69.87 319	85.13 281	52.40 258	88.13 308	60.21 289	74.74 328	84.73 349

PEN-MVS		77.73 220	77.69 202	77.84 291	87.07 229	53.91 362	87.91 155	91.18 124	77.56 45	73.14 279	88.82 181	61.23 182	89.17 290	59.95 290	72.37 347	90.43 198

CR-MVSNet		73.37 285	71.27 297	79.67 259	81.32 345	65.19 198	75.92 363	80.30 342	59.92 351	72.73 284	81.19 346	52.50 256	86.69 318	59.84 291	77.71 280	87.11 305

mvs5depth		69.45 325	67.45 337	75.46 320	73.93 392	55.83 342	79.19 332	83.23 302	66.89 269	71.63 299	83.32 320	33.69 390	85.09 337	59.81 292	55.34 400	85.46 335

lessismore_v0				78.97 270	81.01 348	57.15 321		65.99 405		61.16 383	82.82 331	39.12 372	91.34 249	59.67 293	46.92 410	88.43 275

CNLPA		78.08 210	76.79 221	81.97 206	90.40 102	71.07 65	87.59 163	84.55 281	66.03 286	72.38 290	89.64 158	57.56 216	86.04 326	59.61 294	83.35 214	88.79 264

BH-RMVSNet		79.61 169	78.44 178	83.14 170	89.38 134	65.93 180	84.95 239	87.15 244	73.56 147	78.19 172	89.79 154	56.67 225	93.36 162	59.53 295	86.74 161	90.13 210

MS-PatchMatch		73.83 279	72.67 280	77.30 302	83.87 290	66.02 177	81.82 292	84.66 279	61.37 342	68.61 331	82.82 331	47.29 314	88.21 306	59.27 296	84.32 196	77.68 394

test_post1							78.90 338				5.43 428	48.81 310	85.44 335	59.25 297

SCA		74.22 273	72.33 285	79.91 252	84.05 286	62.17 260	79.96 323	79.29 353	66.30 282	72.38 290	80.13 359	51.95 268	88.60 302	59.25 297	77.67 282	88.96 257

FE-MVS		77.78 219	75.68 238	84.08 131	88.09 188	66.00 178	83.13 279	87.79 229	68.42 257	78.01 177	85.23 278	45.50 336	95.12 85	59.11 299	85.83 178	91.11 170

SixPastTwentyTwo		73.37 285	71.26 298	79.70 257	85.08 266	57.89 309	85.57 222	83.56 296	71.03 195	65.66 359	85.88 262	42.10 358	92.57 197	59.11 299	63.34 384	88.65 270

WR-MVS_H		78.51 200	78.49 176	78.56 278	88.02 191	56.38 334	88.43 133	92.67 67	77.14 58	73.89 270	87.55 216	66.25 118	89.24 289	58.92 301	73.55 339	90.06 218

PLC	70.83 11	78.05 212	76.37 232	83.08 174	91.88 77	67.80 141	88.19 144	89.46 178	64.33 307	69.87 319	88.38 194	53.66 248	93.58 149	58.86 302	82.73 222	87.86 285
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 289	71.46 293	78.54 279	82.50 324	59.85 289	82.18 290	82.84 314	58.96 360	71.15 304	89.41 170	45.48 337	84.77 341	58.82 303	71.83 353	91.02 176

EU-MVSNet		68.53 334	67.61 334	71.31 359	78.51 375	47.01 398	84.47 250	84.27 286	42.27 406	66.44 356	84.79 289	40.44 367	83.76 346	58.76 304	68.54 370	83.17 365

pmmvs-eth3d		70.50 316	67.83 329	78.52 281	77.37 379	66.18 175	81.82 292	81.51 326	58.90 361	63.90 373	80.42 356	42.69 353	86.28 324	58.56 305	65.30 380	83.11 367

TAMVS		78.89 192	77.51 206	83.03 177	87.80 202	67.79 142	84.72 243	85.05 276	67.63 263	76.75 205	87.70 210	62.25 162	90.82 262	58.53 306	87.13 155	90.49 196

WBMVS		73.43 284	72.81 279	75.28 322	87.91 196	50.99 385	78.59 343	81.31 330	65.51 294	74.47 264	84.83 287	46.39 321	86.68 319	58.41 307	77.86 278	88.17 280

ACMH+	68.96 14	76.01 254	74.01 263	82.03 204	88.60 166	65.31 196	88.86 118	87.55 233	70.25 213	67.75 336	87.47 219	41.27 362	93.19 174	58.37 308	75.94 306	87.60 290

tpm		72.37 298	71.71 290	74.35 332	82.19 329	52.00 373	79.22 331	77.29 367	64.56 303	72.95 282	83.68 315	51.35 276	83.26 353	58.33 309	75.80 307	87.81 286

BH-w/o		78.21 206	77.33 210	80.84 234	88.81 157	65.13 200	84.87 240	87.85 228	69.75 226	74.52 263	84.74 290	61.34 179	93.11 179	58.24 310	85.84 177	84.27 352

Vis-MVSNet (Re-imp)		78.36 203	78.45 177	78.07 289	88.64 165	51.78 378	86.70 194	79.63 349	74.14 134	75.11 251	90.83 137	61.29 181	89.75 279	58.10 311	91.60 88	92.69 121

MVP-Stereo		76.12 251	74.46 259	81.13 227	85.37 259	69.79 89	84.42 255	87.95 224	65.03 298	67.46 340	85.33 275	53.28 253	91.73 231	58.01 312	83.27 215	81.85 379
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 323	73.16 400	50.51 388	63.05 414	87.47 236		64.28 368	77.81 380	17.80 416	89.73 280	57.88 313	60.64 390	85.49 334

TR-MVS		77.44 227	76.18 233	81.20 224	88.24 179	63.24 242	84.61 247	86.40 258	67.55 265	77.81 180	86.48 251	54.10 244	93.15 176	57.75 314	82.72 223	87.20 300

F-COLMAP		76.38 249	74.33 261	82.50 197	89.28 140	66.95 167	88.41 134	89.03 195	64.05 312	66.83 347	88.61 187	46.78 319	92.89 187	57.48 315	78.55 269	87.67 288

EG-PatchMatch MVS		74.04 276	71.82 289	80.71 237	84.92 268	67.42 151	85.86 218	88.08 220	66.04 285	64.22 369	83.85 307	35.10 387	92.56 198	57.44 316	80.83 244	82.16 378

PatchmatchNet		73.12 290	71.33 296	78.49 282	83.18 306	60.85 276	79.63 325	78.57 357	64.13 308	71.73 297	79.81 364	51.20 279	85.97 327	57.40 317	76.36 303	88.66 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 234	76.80 220	77.54 299	86.24 241	53.06 371	87.52 164	90.66 138	77.08 61	72.50 287	88.67 185	60.48 197	89.52 283	57.33 318	70.74 359	90.05 219

UnsupCasMVSNet_eth		67.33 341	65.99 345	71.37 356	73.48 397	51.47 381	75.16 370	85.19 273	65.20 295	60.78 384	80.93 353	42.35 354	77.20 380	57.12 319	53.69 402	85.44 336

pmmvs5		71.55 304	70.20 310	75.61 315	77.83 376	56.39 333	81.74 294	80.89 331	57.76 369	67.46 340	84.49 291	49.26 303	85.32 336	57.08 320	75.29 321	85.11 343

Anonymous20240521		68.80 330	67.22 339	73.55 339	74.33 390	54.11 360	83.18 277	85.61 269	58.15 366	61.68 381	80.94 351	30.71 397	81.27 363	57.00 321	73.34 343	85.28 338

mvsany_test1		62.30 362	61.26 366	65.41 383	69.52 407	54.86 354	66.86 403	49.78 423	46.65 400	68.50 333	83.21 322	49.15 304	66.28 415	56.93 322	60.77 389	75.11 399

TransMVSNet (Re)		75.39 265	74.56 256	77.86 290	85.50 256	57.10 322	86.78 191	86.09 265	72.17 174	71.53 300	87.34 220	63.01 152	89.31 287	56.84 323	61.83 386	87.17 301

test_vis3_rt		49.26 382	47.02 384	56.00 394	54.30 423	45.27 405	66.76 405	48.08 424	36.83 413	44.38 412	53.20 417	7.17 429	64.07 417	56.77 324	55.66 397	58.65 413

EPMVS		69.02 328	68.16 322	71.59 354	79.61 366	49.80 392	77.40 356	66.93 403	62.82 327	70.01 314	79.05 368	45.79 331	77.86 378	56.58 325	75.26 322	87.13 304

KD-MVS_self_test		68.81 329	67.59 335	72.46 350	74.29 391	45.45 401	77.93 352	87.00 246	63.12 319	63.99 372	78.99 372	42.32 355	84.77 341	56.55 326	64.09 383	87.16 303

tpm2		73.26 288	71.46 293	78.63 274	83.34 301	56.71 328	80.65 312	80.40 341	56.63 377	73.55 274	82.02 343	51.80 272	91.24 251	56.35 327	78.42 273	87.95 282

LTVRE_ROB	69.57 13	76.25 250	74.54 257	81.41 217	88.60 166	64.38 219	79.24 330	89.12 194	70.76 200	69.79 321	87.86 208	49.09 305	93.20 172	56.21 328	80.16 253	86.65 315
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	75.89 255	73.93 265	81.77 209	88.71 163	66.61 169	88.62 129	89.01 197	69.81 222	66.78 348	86.70 241	41.95 360	91.51 242	55.64 329	78.14 276	87.17 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 347	64.71 349	71.90 352	81.45 340	63.52 235	57.98 416	68.95 399	53.57 386	62.59 379	76.70 384	46.22 326	75.29 399	55.25 330	79.68 258	76.88 396

UBG		73.08 291	72.27 286	75.51 318	88.02 191	51.29 383	78.35 347	77.38 366	65.52 292	73.87 271	82.36 336	45.55 334	86.48 322	55.02 331	84.39 195	88.75 266

EPNet_dtu		75.46 261	74.86 252	77.23 303	82.57 323	54.60 356	86.89 186	83.09 306	71.64 179	66.25 357	85.86 263	55.99 228	88.04 309	54.92 332	86.55 164	89.05 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 373	51.45 378	61.61 388	55.51 422	44.74 407	63.52 412	45.41 427	43.69 405	58.11 394	76.45 386	17.99 415	63.76 418	54.77 333	47.59 409	76.34 397

PVSNet	64.34 18	72.08 302	70.87 302	75.69 314	86.21 242	56.44 332	74.37 376	80.73 334	62.06 336	70.17 312	82.23 340	42.86 352	83.31 352	54.77 333	84.45 193	87.32 298

ITE_SJBPF				78.22 285	81.77 334	60.57 280		83.30 300	69.25 236	67.54 338	87.20 226	36.33 384	87.28 316	54.34 335	74.62 329	86.80 311

MDTV_nov1_ep13_2view						37.79 419	75.16 370		55.10 382	66.53 352		49.34 301		53.98 336		87.94 283

gg-mvs-nofinetune		69.95 321	67.96 325	75.94 311	83.07 309	54.51 358	77.23 358	70.29 393	63.11 320	70.32 309	62.33 407	43.62 347	88.69 300	53.88 337	87.76 146	84.62 350

PatchMatch-RL		72.38 297	70.90 301	76.80 307	88.60 166	67.38 153	79.53 326	76.17 375	62.75 328	69.36 324	82.00 344	45.51 335	84.89 340	53.62 338	80.58 248	78.12 393

test_f		52.09 378	50.82 379	55.90 395	53.82 425	42.31 414	59.42 415	58.31 419	36.45 414	56.12 401	70.96 402	12.18 421	57.79 421	53.51 339	56.57 396	67.60 406

Patchmtry		70.74 312	69.16 315	75.49 319	80.72 349	54.07 361	74.94 374	80.30 342	58.34 364	70.01 314	81.19 346	52.50 256	86.54 320	53.37 340	71.09 358	85.87 331

USDC		70.33 317	68.37 319	76.21 310	80.60 351	56.23 337	79.19 332	86.49 256	60.89 343	61.29 382	85.47 273	31.78 394	89.47 285	53.37 340	76.21 304	82.94 371

LF4IMVS		64.02 358	62.19 362	69.50 367	70.90 406	53.29 369	76.13 360	77.18 368	52.65 389	58.59 391	80.98 350	23.55 409	76.52 385	53.06 342	66.66 374	78.68 392

PAPM		77.68 224	76.40 231	81.51 214	87.29 223	61.85 264	83.78 265	89.59 174	64.74 301	71.23 302	88.70 183	62.59 155	93.66 148	52.66 343	87.03 157	89.01 253

dmvs_re		71.14 307	70.58 303	72.80 346	81.96 331	59.68 291	75.60 367	79.34 352	68.55 253	69.27 326	80.72 354	49.42 299	76.54 384	52.56 344	77.79 279	82.19 377

CL-MVSNet_self_test		72.37 298	71.46 293	75.09 324	79.49 368	53.53 364	80.76 309	85.01 277	69.12 241	70.51 306	82.05 342	57.92 212	84.13 344	52.27 345	66.00 378	87.60 290

tpm cat1		70.57 314	68.31 320	77.35 301	82.41 327	57.95 308	78.08 349	80.22 344	52.04 390	68.54 332	77.66 381	52.00 267	87.84 311	51.77 346	72.07 352	86.25 319

our_test_3		69.14 327	67.00 340	75.57 316	79.80 363	58.80 296	77.96 351	77.81 360	59.55 354	62.90 378	78.25 377	47.43 313	83.97 345	51.71 347	67.58 372	83.93 358

MDTV_nov1_ep13			69.97 311		83.18 306	53.48 365	77.10 359	80.18 345	60.45 345	69.33 325	80.44 355	48.89 309	86.90 317	51.60 348	78.51 271

myMVS_eth3d28		73.62 281	73.53 271	73.90 337	88.20 180	47.41 396	78.06 350	79.37 351	74.29 130	73.98 269	84.29 298	44.67 339	83.54 349	51.47 349	87.39 151	90.74 185

JIA-IIPM		66.32 349	62.82 361	76.82 306	77.09 380	61.72 267	65.34 409	75.38 376	58.04 368	64.51 367	62.32 408	42.05 359	86.51 321	51.45 350	69.22 366	82.21 376

testing222		74.04 276	72.66 281	78.19 286	87.89 197	55.36 348	81.06 304	79.20 354	71.30 188	74.65 261	83.57 317	39.11 373	88.67 301	51.43 351	85.75 179	90.53 194

MSDG		73.36 287	70.99 300	80.49 241	84.51 277	65.80 184	80.71 311	86.13 264	65.70 289	65.46 360	83.74 311	44.60 340	90.91 261	51.13 352	76.89 289	84.74 348

PatchT		68.46 335	67.85 327	70.29 364	80.70 350	43.93 408	72.47 381	74.88 379	60.15 349	70.55 305	76.57 385	49.94 293	81.59 360	50.58 353	74.83 327	85.34 337

GG-mvs-BLEND				75.38 321	81.59 337	55.80 343	79.32 329	69.63 395		67.19 343	73.67 396	43.24 349	88.90 298	50.41 354	84.50 189	81.45 381

KD-MVS_2432*1600		66.22 350	63.89 353	73.21 341	75.47 388	53.42 366	70.76 389	84.35 283	64.10 310	66.52 353	78.52 374	34.55 388	84.98 338	50.40 355	50.33 407	81.23 382

miper_refine_blended		66.22 350	63.89 353	73.21 341	75.47 388	53.42 366	70.76 389	84.35 283	64.10 310	66.52 353	78.52 374	34.55 388	84.98 338	50.40 355	50.33 407	81.23 382

AllTest		70.96 309	68.09 324	79.58 261	85.15 263	63.62 230	84.58 248	79.83 346	62.31 332	60.32 386	86.73 235	32.02 392	88.96 296	50.28 357	71.57 355	86.15 322

TestCases				79.58 261	85.15 263	63.62 230		79.83 346	62.31 332	60.32 386	86.73 235	32.02 392	88.96 296	50.28 357	71.57 355	86.15 322

TAPA-MVS	73.13 9	79.15 184	77.94 190	82.79 190	89.59 122	62.99 251	88.16 146	91.51 115	65.77 288	77.14 199	91.09 128	60.91 188	93.21 169	50.26 359	87.05 156	92.17 143
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 354	62.91 359	71.38 355	75.85 384	56.60 330	69.12 397	74.66 383	57.28 374	54.12 402	77.87 379	45.85 330	74.48 401	49.95 360	61.52 388	83.05 368

MDA-MVSNet_test_wron		65.03 354	62.92 358	71.37 356	75.93 382	56.73 326	69.09 398	74.73 381	57.28 374	54.03 403	77.89 378	45.88 329	74.39 402	49.89 361	61.55 387	82.99 370

tpmvs		71.09 308	69.29 313	76.49 308	82.04 330	56.04 339	78.92 337	81.37 329	64.05 312	67.18 344	78.28 376	49.74 296	89.77 278	49.67 362	72.37 347	83.67 361

ppachtmachnet_test		70.04 320	67.34 338	78.14 287	79.80 363	61.13 271	79.19 332	80.59 336	59.16 358	65.27 362	79.29 367	46.75 320	87.29 315	49.33 363	66.72 373	86.00 328

UnsupCasMVSNet_bld		63.70 359	61.53 365	70.21 365	73.69 395	51.39 382	72.82 380	81.89 322	55.63 381	57.81 395	71.80 400	38.67 375	78.61 373	49.26 364	52.21 405	80.63 386

UWE-MVS		72.13 301	71.49 292	74.03 335	86.66 237	47.70 394	81.40 301	76.89 371	63.60 317	75.59 229	84.22 302	39.94 369	85.62 331	48.98 365	86.13 172	88.77 265

dp		66.80 344	65.43 346	70.90 363	79.74 365	48.82 393	75.12 372	74.77 380	59.61 353	64.08 371	77.23 382	42.89 351	80.72 366	48.86 366	66.58 375	83.16 366

FMVSNet5		69.50 324	67.96 325	74.15 334	82.97 315	55.35 349	80.01 322	82.12 320	62.56 330	63.02 375	81.53 345	36.92 382	81.92 359	48.42 367	74.06 333	85.17 342

thres100view900		76.50 243	75.55 242	79.33 264	89.52 125	56.99 323	85.83 220	83.23 302	73.94 137	76.32 216	87.12 229	51.89 270	91.95 221	48.33 368	83.75 204	89.07 246

tfpn200view9		76.42 247	75.37 247	79.55 263	89.13 146	57.65 314	85.17 231	83.60 294	73.41 153	76.45 212	86.39 253	52.12 262	91.95 221	48.33 368	83.75 204	89.07 246

thres400		76.50 243	75.37 247	79.86 253	89.13 146	57.65 314	85.17 231	83.60 294	73.41 153	76.45 212	86.39 253	52.12 262	91.95 221	48.33 368	83.75 204	90.00 220

LCM-MVSNet		54.25 372	49.68 382	67.97 378	53.73 426	45.28 404	66.85 404	80.78 333	35.96 415	39.45 416	62.23 409	8.70 426	78.06 377	48.24 371	51.20 406	80.57 387

RPMNet		73.51 283	70.49 305	82.58 196	81.32 345	65.19 198	75.92 363	92.27 84	57.60 371	72.73 284	76.45 386	52.30 259	95.43 70	48.14 372	77.71 280	87.11 305

thres600view7		76.50 243	75.44 243	79.68 258	89.40 132	57.16 320	85.53 228	83.23 302	73.79 141	76.26 217	87.09 230	51.89 270	91.89 224	48.05 373	83.72 207	90.00 220

TDRefinement		67.49 339	64.34 350	76.92 305	73.47 398	61.07 273	84.86 241	82.98 310	59.77 352	58.30 393	85.13 281	26.06 402	87.89 310	47.92 374	60.59 391	81.81 380

thres200		75.55 259	74.47 258	78.82 272	87.78 205	57.85 310	83.07 282	83.51 297	72.44 170	75.84 226	84.42 293	52.08 265	91.75 229	47.41 375	83.64 209	86.86 310

PVSNet_0	57.27 20	61.67 364	59.27 367	68.85 371	79.61 366	57.44 318	68.01 399	73.44 386	55.93 380	58.54 392	70.41 403	44.58 341	77.55 379	47.01 376	35.91 415	71.55 403

DP-MVS		76.78 239	74.57 255	83.42 157	93.29 48	69.46 97	88.55 131	83.70 293	63.98 314	70.20 310	88.89 179	54.01 246	94.80 102	46.66 377	81.88 233	86.01 326

COLMAP_ROB	66.92 17	73.01 292	70.41 307	80.81 235	87.13 227	65.63 188	88.30 141	84.19 288	62.96 323	63.80 374	87.69 211	38.04 379	92.56 198	46.66 377	74.91 326	84.24 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 313	69.30 312	74.88 326	84.52 276	56.35 336	75.87 365	79.42 350	64.59 302	67.76 335	82.41 335	41.10 363	81.54 361	46.64 379	81.34 236	86.75 313

LS3D		76.95 236	74.82 253	83.37 160	90.45 100	67.36 154	89.15 108	86.94 248	61.87 338	69.52 322	90.61 140	51.71 274	94.53 110	46.38 380	86.71 162	88.21 279

ETVMVS		72.25 300	71.05 299	75.84 312	87.77 206	51.91 375	79.39 328	74.98 378	69.26 235	73.71 272	82.95 327	40.82 366	86.14 325	46.17 381	84.43 194	89.47 239

MDA-MVSNet-bldmvs		66.68 345	63.66 355	75.75 313	79.28 370	60.56 281	73.92 378	78.35 358	64.43 304	50.13 408	79.87 363	44.02 345	83.67 347	46.10 382	56.86 394	83.03 369

new-patchmatchnet		61.73 363	61.73 364	61.70 387	72.74 403	24.50 430	69.16 396	78.03 359	61.40 340	56.72 398	75.53 392	38.42 376	76.48 386	45.95 383	57.67 393	84.13 355

WB-MVSnew		71.96 303	71.65 291	72.89 345	84.67 275	51.88 376	82.29 289	77.57 362	62.31 332	73.67 273	83.00 326	53.49 251	81.10 364	45.75 384	82.13 229	85.70 332

TinyColmap		67.30 342	64.81 348	74.76 328	81.92 333	56.68 329	80.29 319	81.49 327	60.33 346	56.27 400	83.22 321	24.77 406	87.66 314	45.52 385	69.47 364	79.95 389

pmmvs3		57.79 368	54.26 373	68.37 374	64.02 416	56.72 327	75.12 372	65.17 407	40.20 408	52.93 404	69.86 404	20.36 413	75.48 396	45.45 386	55.25 401	72.90 402

OpenMVS_ROB	64.09 19	70.56 315	68.19 321	77.65 295	80.26 354	59.41 295	85.01 237	82.96 311	58.76 362	65.43 361	82.33 337	37.63 381	91.23 252	45.34 387	76.03 305	82.32 375

test0.0.03 1		68.00 338	67.69 332	68.90 370	77.55 377	47.43 395	75.70 366	72.95 389	66.66 274	66.56 351	82.29 339	48.06 311	75.87 393	44.97 388	74.51 330	83.41 363

testgi		66.67 346	66.53 343	67.08 380	75.62 386	41.69 415	75.93 362	76.50 372	66.11 283	65.20 365	86.59 245	35.72 386	74.71 400	43.71 389	73.38 342	84.84 347

Anonymous20231206		68.60 331	67.80 330	71.02 361	80.23 356	50.75 387	78.30 348	80.47 338	56.79 376	66.11 358	82.63 334	46.35 324	78.95 372	43.62 390	75.70 308	83.36 364

tfpnnormal		74.39 270	73.16 275	78.08 288	86.10 247	58.05 304	84.65 246	87.53 234	70.32 210	71.22 303	85.63 269	54.97 233	89.86 276	43.03 391	75.02 325	86.32 318

MIMVSNet1		68.58 332	66.78 342	73.98 336	80.07 358	51.82 377	80.77 308	84.37 282	64.40 305	59.75 389	82.16 341	36.47 383	83.63 348	42.73 392	70.33 361	86.48 317

ttmdpeth		59.91 366	57.10 370	68.34 375	67.13 412	46.65 400	74.64 375	67.41 402	48.30 398	62.52 380	85.04 285	20.40 412	75.93 392	42.55 393	45.90 413	82.44 374

test20.03		67.45 340	66.95 341	68.94 369	75.48 387	44.84 406	77.50 355	77.67 361	66.66 274	63.01 376	83.80 309	47.02 317	78.40 374	42.53 394	68.86 369	83.58 362

ADS-MVSNet2		66.20 352	63.33 356	74.82 327	79.92 359	58.75 297	67.55 401	75.19 377	53.37 387	65.25 363	75.86 389	42.32 355	80.53 367	41.57 395	68.91 367	85.18 340

ADS-MVSNet		64.36 357	62.88 360	68.78 372	79.92 359	47.17 397	67.55 401	71.18 391	53.37 387	65.25 363	75.86 389	42.32 355	73.99 403	41.57 395	68.91 367	85.18 340

Patchmatch-test		64.82 356	63.24 357	69.57 366	79.42 369	49.82 391	63.49 413	69.05 398	51.98 392	59.95 388	80.13 359	50.91 281	70.98 407	40.66 397	73.57 338	87.90 284

MVS-HIRNet		59.14 367	57.67 369	63.57 385	81.65 335	43.50 409	71.73 383	65.06 408	39.59 410	51.43 405	57.73 413	38.34 377	82.58 356	39.53 398	73.95 334	64.62 409

WAC-MVS						42.58 411								39.46 399

myMVS_eth3d		67.02 343	66.29 344	69.21 368	84.68 272	42.58 411	78.62 341	73.08 387	66.65 277	66.74 349	79.46 365	31.53 395	82.30 357	39.43 400	76.38 301	82.75 372

DSMNet-mixed		57.77 369	56.90 371	60.38 389	67.70 410	35.61 420	69.18 395	53.97 421	32.30 419	57.49 396	79.88 362	40.39 368	68.57 413	38.78 401	72.37 347	76.97 395

N_pmnet		52.79 377	53.26 375	51.40 401	78.99 372	7.68 435	69.52 393	3.89 434	51.63 393	57.01 397	74.98 393	40.83 365	65.96 416	37.78 402	64.67 381	80.56 388

testing3		68.56 333	67.67 333	71.22 360	87.33 221	42.87 410	83.06 283	71.54 390	70.36 208	69.08 327	84.38 295	30.33 398	85.69 330	37.50 403	75.45 316	85.09 344

MVStest1		56.63 370	52.76 376	68.25 376	61.67 418	53.25 370	71.67 384	68.90 400	38.59 411	50.59 407	83.05 325	25.08 404	70.66 408	36.76 404	38.56 414	80.83 385

test_0402		72.79 295	70.44 306	79.84 254	88.13 185	65.99 179	85.93 215	84.29 285	65.57 291	67.40 342	85.49 272	46.92 318	92.61 194	35.88 405	74.38 331	80.94 384

new_pmnet		50.91 380	50.29 380	52.78 400	68.58 409	34.94 422	63.71 411	56.63 420	39.73 409	44.95 411	65.47 406	21.93 411	58.48 420	34.98 406	56.62 395	64.92 408

APD_test1		53.31 376	49.93 381	63.42 386	65.68 413	50.13 389	71.59 385	66.90 404	34.43 416	40.58 415	71.56 401	8.65 427	76.27 388	34.64 407	55.36 399	63.86 410

Syy-MVS		68.05 337	67.85 327	68.67 373	84.68 272	40.97 416	78.62 341	73.08 387	66.65 277	66.74 349	79.46 365	52.11 264	82.30 357	32.89 408	76.38 301	82.75 372

dmvs_testset		62.63 361	64.11 352	58.19 391	78.55 374	24.76 429	75.28 368	65.94 406	67.91 262	60.34 385	76.01 388	53.56 249	73.94 404	31.79 409	67.65 371	75.88 398

UWE-MVS-28		65.32 353	64.93 347	66.49 381	78.70 373	38.55 418	77.86 354	64.39 410	62.00 337	64.13 370	83.60 316	41.44 361	76.00 391	31.39 410	80.89 242	84.92 345

ANet_high		50.57 381	46.10 385	63.99 384	48.67 429	39.13 417	70.99 388	80.85 332	61.39 341	31.18 418	57.70 414	17.02 417	73.65 405	31.22 411	15.89 426	79.18 391

EGC-MVSNET		52.07 379	47.05 383	67.14 379	83.51 298	60.71 278	80.50 315	67.75 401	0.07 429	0.43 430	75.85 391	24.26 407	81.54 361	28.82 412	62.25 385	59.16 412

PMMVS2		40.82 388	38.86 392	46.69 402	53.84 424	16.45 433	48.61 419	49.92 422	37.49 412	31.67 417	60.97 410	8.14 428	56.42 422	28.42 413	30.72 419	67.19 407

tmp_tt		18.61 395	21.40 398	10.23 411	4.82 434	10.11 434	34.70 421	30.74 432	1.48 428	23.91 424	26.07 425	28.42 400	13.41 430	27.12 414	15.35 427	7.17 425

test_method		31.52 391	29.28 395	38.23 405	27.03 433	6.50 436	20.94 424	62.21 413	4.05 427	22.35 425	52.50 418	13.33 419	47.58 425	27.04 415	34.04 417	60.62 411

testf1		45.72 383	41.96 387	57.00 392	56.90 420	45.32 402	66.14 406	59.26 417	26.19 420	30.89 419	60.96 411	4.14 430	70.64 409	26.39 416	46.73 411	55.04 415

APD_test2		45.72 383	41.96 387	57.00 392	56.90 420	45.32 402	66.14 406	59.26 417	26.19 420	30.89 419	60.96 411	4.14 430	70.64 409	26.39 416	46.73 411	55.04 415

FPMVS		53.68 375	51.64 377	59.81 390	65.08 414	51.03 384	69.48 394	69.58 396	41.46 407	40.67 414	72.32 399	16.46 418	70.00 411	24.24 418	65.42 379	58.40 414

Gipuma		45.18 386	41.86 389	55.16 398	77.03 381	51.52 380	32.50 422	80.52 337	32.46 418	27.12 421	35.02 422	9.52 425	75.50 395	22.31 419	60.21 392	38.45 421
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 385	45.38 386	45.55 403	73.36 399	26.85 427	67.72 400	34.19 429	54.15 385	49.65 409	56.41 416	25.43 403	62.94 419	19.45 420	28.09 420	46.86 419

DeepMVS_CX				27.40 409	40.17 432	26.90 426		24.59 433	17.44 425	23.95 423	48.61 420	9.77 424	26.48 428	18.06 421	24.47 422	28.83 422

WB-MVS		54.94 371	54.72 372	55.60 397	73.50 396	20.90 431	74.27 377	61.19 414	59.16 358	50.61 406	74.15 394	47.19 316	75.78 394	17.31 422	35.07 416	70.12 404

PMVS	37.38 22	44.16 387	40.28 391	55.82 396	40.82 431	42.54 413	65.12 410	63.99 411	34.43 416	24.48 422	57.12 415	3.92 432	76.17 390	17.10 423	55.52 398	48.75 417
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 393	25.89 397	43.81 404	44.55 430	35.46 421	28.87 423	39.07 428	18.20 424	18.58 426	40.18 421	2.68 433	47.37 426	17.07 424	23.78 423	48.60 418
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 374	53.59 374	54.75 399	72.87 402	19.59 432	73.84 379	60.53 416	57.58 372	49.18 410	73.45 397	46.34 325	75.47 397	16.20 425	32.28 418	69.20 405

E-PMN		31.77 390	30.64 393	35.15 407	52.87 427	27.67 424	57.09 417	47.86 425	24.64 422	16.40 427	33.05 423	11.23 423	54.90 423	14.46 426	18.15 424	22.87 423

EMVS		30.81 392	29.65 394	34.27 408	50.96 428	25.95 428	56.58 418	46.80 426	24.01 423	15.53 428	30.68 424	12.47 420	54.43 424	12.81 427	17.05 425	22.43 424

kuosan		39.70 389	40.40 390	37.58 406	64.52 415	26.98 425	65.62 408	33.02 430	46.12 401	42.79 413	48.99 419	24.10 408	46.56 427	12.16 428	26.30 421	39.20 420

wuyk23d		16.82 396	15.94 399	19.46 410	58.74 419	31.45 423	39.22 420	3.74 435	6.84 426	6.04 429	2.70 429	1.27 434	24.29 429	10.54 429	14.40 428	2.63 426

testmvs		6.04 399	8.02 402	0.10 413	0.08 435	0.03 438	69.74 392	0.04 436	0.05 430	0.31 431	1.68 430	0.02 436	0.04 431	0.24 430	0.02 429	0.25 428

test123		6.12 398	8.11 401	0.14 412	0.06 436	0.09 437	71.05 387	0.03 437	0.04 431	0.25 432	1.30 431	0.05 435	0.03 432	0.21 431	0.01 430	0.29 427

mmdepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

monomultidepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

test_blank		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet_test		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

DCPMVS		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

cdsmvs_eth3d_5k		19.96 394	26.61 396	0.00 414	0.00 437	0.00 439	0.00 425	89.26 186	0.00 432	0.00 433	88.61 187	61.62 172	0.00 433	0.00 432	0.00 431	0.00 429

pcd_1.5k_mvsjas		5.26 400	7.02 403	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	63.15 148	0.00 433	0.00 432	0.00 431	0.00 429

sosnet-low-res		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uncertanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

Regformer		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

ab-mvs-re		7.23 397	9.64 400	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	86.72 237	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 123	91.30 15

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 36	92.05 11	95.74 6	80.83 11

eth-test2					0.00 437

eth-test					0.00 437

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 57	93.10 1	95.39 14	82.99 1	97.27 12

save fliter					93.80 40	72.35 42	90.47 66	91.17 125	74.31 128

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 51	92.81 3	95.79 3	80.98 9

GSMVS																88.96 257

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 277				88.96 257

sam_mvs												50.01 291

MTGPA								92.02 93

test_post											5.46 427	50.36 289	84.24 343

patchmatchnet-post											74.00 395	51.12 280	88.60 302

MTMP							92.18 34	32.83 431

TEST9					93.26 52	72.96 25	88.75 122	91.89 101	68.44 256	85.00 67	93.10 74	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 127	91.84 105	68.69 251	84.87 71	93.10 74	74.43 26	95.16 83

agg_prior					92.85 62	71.94 50		91.78 108		84.41 82			94.93 94

test_prior4						72.60 34	89.01 113

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 62

新几何2							86.29 207

旧先验1					91.96 74	65.79 185		86.37 259			93.08 78	69.31 85			92.74 73	88.74 268

原ACMM2							86.86 187

test222					91.50 80	68.26 130	84.16 260	83.20 305	54.63 384	79.74 143	91.63 109	58.97 205			91.42 92	86.77 312

segment_acmp												73.08 39

testdata1							84.14 261		75.71 93

test12				86.80 52	92.63 67	70.70 75		91.79 107		82.71 111		71.67 56	96.16 47		94.50 51	93.54 87

plane_prior7					90.08 108	68.51 124

plane_prior6					89.84 117	68.70 118						60.42 198

plane_prior4											91.00 134

plane_prior3						68.60 121			78.44 32	78.92 155

plane_prior2							91.25 52		79.12 24

plane_prior1					89.90 116

plane_prior						68.71 116	90.38 70		77.62 41						86.16 171

n2								0.00 438

nn								0.00 438

door-mid								69.98 394

test11								92.23 87

door								69.44 397

HQP5-MVS						66.98 164

HQP-NCC					89.33 135		89.17 104		76.41 78	77.23 193

ACMP_Plane					89.33 135		89.17 104		76.41 78	77.23 193

HQP4-MVS										77.24 192			95.11 87			91.03 174

HQP3-MVS								92.19 90							85.99 175

HQP2-MVS												60.17 201

NP-MVS					89.62 121	68.32 128					90.24 146

ACMMP++_ref															81.95 232

ACMMP++															81.25 237

Test By Simon												64.33 135

High-resolution multi-view benchmark