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	87.08 229	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	92.34 134

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	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	85.03 268	69.51 93	89.62 89	90.58 140	73.42 153	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	82.99 315	69.39 100	89.65 86	90.29 154	73.31 156	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	80.25 356	69.03 103	89.47 92	89.65 172	73.24 160	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	86.17 244	65.00 203	86.96 182	87.28 239	74.35 127	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	85.62 254	64.94 205	87.03 180	86.62 255	74.32 128	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 96	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 52	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 55	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 55	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	87.20 225	68.54 123	89.57 90	90.44 145	75.31 103	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	66.81 271	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	85.42 258	68.81 109	88.49 132	87.26 241	68.08 261	88.03 32	93.49 64	72.04 50	91.77 228	88.90 21	89.14 126	92.24 141

fmvsm_s_conf0.5_n		83.80 84	83.71 85	84.07 132	86.69 237	67.31 155	89.46 93	83.07 307	71.09 194	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	75.17 108	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	84.12 284	69.37 101	88.15 147	87.96 223	70.01 218	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	74.77 118	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	84.86 270	67.28 156	89.40 98	83.01 308	70.67 202	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	86.12 246	69.93 86	88.65 128	90.78 136	69.97 220	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	85.38 259	68.40 126	88.34 139	86.85 251	67.48 268	87.48 43	93.40 68	70.89 66	91.61 233	88.38 29	89.22 124	92.16 145

fmvsm_s_conf0.5_n_a		83.63 90	83.41 89	84.28 118	86.14 245	68.12 133	89.43 94	82.87 312	70.27 213	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	83.79 292	68.07 135	89.34 101	82.85 313	69.80 224	87.36 46	94.06 49	68.34 96	91.56 237	87.95 31	83.46 214	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	74.62 122	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 86	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 38	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 92	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 94	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	73.93 139	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	74.98 111	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	74.98 111	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	74.69 119	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 48	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 44	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 54	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 68	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 89	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	68.69 252	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	73.07 162	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 60	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	74.50 123	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	72.89 167	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	70.98 197	87.75 38	94.07 48	74.01 32	96.70 27	84.66 56	94.84 44

PC_three_1452									68.21 260	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 70	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 70	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 73	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	90.09 108	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	73.01 164	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 76	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 66	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	87.84 201	67.85 139	89.38 99	89.64 173	77.73 40	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	89.31 139	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	87.65 211	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	73.63 145	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		75.41 100	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	72.45 169	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	12.47 427	67.45 105	96.60 33	83.06 73	94.50 51	94.07 55

mamv4		76.81 238	78.23 186	72.54 350	86.12 246	65.75 187	78.76 340	82.07 321	64.12 310	72.97 282	91.02 133	67.97 99	68.08 415	83.04 75	78.02 278	83.80 361

APD-MVS_3200maxsize		85.97 52	85.88 55	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 149	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 67	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	73.53 150	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	73.53 150	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 90	84.67 74	91.39 118	61.54 173	95.50 66	82.71 81	75.48 314	91.72 154

hse-mvs2		81.72 123	80.94 129	84.07 132	88.72 163	67.68 144	85.87 217	87.26 241	76.02 90	84.67 74	88.22 200	61.54 173	93.48 156	82.71 81	73.44 342	91.06 173

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 98	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 70	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	83.00 313	63.78 229	83.68 267	89.76 168	72.94 165	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	90.06 113	65.83 183	84.21 259	88.74 209	71.60 184	85.01 66	92.44 91	74.51 25	83.50 351	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 79	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	89.51 127	69.47 95	89.74 83	92.47 76	76.17 87	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	89.84 118	69.44 98	90.45 68	93.00 46	76.70 74	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	89.84 118	69.44 98	90.45 68	93.00 46	76.70 74	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	87.30 223	65.39 194	87.30 173	92.88 57	77.62 42	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	89.42 131	63.01 247	89.43 94	92.62 73	76.43 78	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	87.20 225	65.77 186	87.75 159	92.83 60	77.84 39	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 88	82.08 115	91.61 111	71.36 61	94.17 124	81.02 95	92.58 75	92.08 147

HPM-MVS_fast		85.35 66	84.95 72	86.57 56	93.69 42	70.58 78	92.15 35	91.62 111	73.89 140	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	68.75 251	79.57 146	92.83 83	60.60 196	93.04 184	80.92 97	91.56 91	90.86 181

ETV-MVS		84.90 74	84.67 74	85.59 75	89.39 134	68.66 120	88.74 124	92.64 72	79.97 15	84.10 88	85.71 266	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 76	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	86.77 235	69.28 102	90.46 67	92.67 67	74.79 117	82.95 105	91.33 120	72.70 45	93.09 180	80.79 100	79.28 266	92.50 128

EI-MVSNet-Vis-set		84.19 78	83.81 83	85.31 81	88.18 182	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 50	84.22 85	92.81 85	67.16 109	92.94 186	80.36 102	94.35 57	90.16 209

MVS_111021_LR		82.61 110	82.11 110	84.11 125	88.82 157	71.58 55	85.15 233	86.16 263	74.69 119	80.47 136	91.04 130	62.29 161	90.55 267	80.33 103	90.08 112	90.20 208

DELS-MVS		85.41 65	85.30 67	85.77 72	88.49 170	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	88.14 185	68.45 125	89.13 109	92.69 65	72.82 168	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	87.87 199	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	73.95 137	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	87.98 195	62.94 252	87.45 168	91.27 121	77.42 51	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	88.79 160	70.95 69	89.13 109	91.52 114	77.55 47	80.96 132	91.75 104	60.71 190	94.50 112	79.67 109	86.51 165	89.97 225
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	68.32 259	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	87.35 217	70.21 80	90.50 64	90.38 147	68.55 254	81.32 125	89.47 164	61.68 170	93.46 158	78.98 111	90.26 108	92.05 148

test_djsdf		80.30 159	79.32 160	83.27 163	83.98 288	65.37 195	90.50 64	90.38 147	68.55 254	76.19 220	88.70 183	56.44 227	93.46 158	78.98 111	80.14 256	90.97 178

test_vis1_n_1920		75.52 260	75.78 236	74.75 330	79.84 362	57.44 318	83.26 276	85.52 270	62.83 327	79.34 150	86.17 259	45.10 338	79.71 370	78.75 113	81.21 240	87.10 308

HQP_MVS		83.64 89	83.14 93	85.14 85	90.08 109	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	91.33 165

plane_prior5								92.44 77					95.38 75	78.71 114	86.32 167	91.33 165

LPG-MVS_test		82.08 116	81.27 122	84.50 107	89.23 143	68.76 112	90.22 73	91.94 99	75.37 101	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 201	89.83 230

LGP-MVS_train				84.50 107	89.23 143	68.76 112		91.94 99	75.37 101	76.64 208	91.51 113	54.29 242	94.91 95	78.44 116	83.78 201	89.83 230

lupinMVS		81.39 132	80.27 141	84.76 102	87.35 217	70.21 80	85.55 226	86.41 257	62.85 326	81.32 125	88.61 187	61.68 170	92.24 213	78.41 118	90.26 108	91.83 151

jason		81.39 132	80.29 140	84.70 103	86.63 239	69.90 88	85.95 214	86.77 252	63.24 319	81.07 131	89.47 164	61.08 186	92.15 215	78.33 119	90.07 113	92.05 148
jason: jason.
xiu_mvs_v1_base_debu		80.80 144	79.72 150	84.03 139	87.35 217	70.19 82	85.56 223	88.77 205	69.06 244	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 249

xiu_mvs_v1_base		80.80 144	79.72 150	84.03 139	87.35 217	70.19 82	85.56 223	88.77 205	69.06 244	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 249

xiu_mvs_v1_base_debi		80.80 144	79.72 150	84.03 139	87.35 217	70.19 82	85.56 223	88.77 205	69.06 244	81.83 117	88.16 201	50.91 281	92.85 188	78.29 120	87.56 147	89.06 249

Effi-MVS+		83.62 91	83.08 94	85.24 83	88.38 176	67.45 150	88.89 117	89.15 191	75.50 99	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	86.51 240	67.27 157	89.27 102	91.51 115	71.75 179	79.37 148	90.22 148	63.15 148	94.27 118	77.69 124	82.36 228	91.49 161

ACMP	74.13 6	81.51 131	80.57 133	84.36 113	89.42 131	68.69 119	89.97 77	91.50 118	74.46 125	75.04 255	90.41 143	53.82 247	94.54 109	77.56 125	82.91 220	89.86 229
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	89.33 136	66.98 164	89.17 104	92.19 90	76.41 79	77.23 193	90.23 147	60.17 201	95.11 87	77.47 126	85.99 175	91.03 175

MVS_Test		83.15 101	83.06 95	83.41 159	86.86 231	63.21 243	86.11 211	92.00 95	74.31 129	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	80.51 354	67.08 162	87.24 175	89.53 176	65.66 291	75.16 250	87.19 228	52.52 255	92.25 212	77.17 130	79.34 265	89.61 237

mmtdpeth		74.16 275	73.01 278	77.60 298	83.72 295	61.13 271	85.10 235	85.10 274	72.06 177	77.21 197	80.33 358	43.84 347	85.75 329	77.14 131	52.61 405	85.91 330

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 44	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	85.60 255	68.78 111	83.54 273	90.50 143	70.66 205	76.71 206	91.66 106	60.69 191	91.26 250	76.94 133	81.58 236	91.83 151

jajsoiax		79.29 181	77.96 189	83.27 163	84.68 273	66.57 170	89.25 103	90.16 158	69.20 240	75.46 235	89.49 163	45.75 333	93.13 178	76.84 134	80.80 246	90.11 213

SDMVSNet		80.38 156	80.18 142	80.99 230	89.03 152	64.94 205	80.45 316	89.40 179	75.19 106	76.61 210	89.98 150	60.61 195	87.69 313	76.83 135	83.55 210	90.33 203

mvs_tets		79.13 185	77.77 198	83.22 167	84.70 272	66.37 172	89.17 104	90.19 157	69.38 233	75.40 238	89.46 166	44.17 345	93.15 176	76.78 136	80.70 248	90.14 210

DPM-MVS		84.93 72	84.29 79	86.84 50	90.20 106	73.04 23	87.12 177	93.04 41	69.80 224	82.85 108	91.22 123	73.06 40	96.02 52	76.72 137	94.63 48	91.46 164

test_cas_vis1_n_1920		73.76 281	73.74 270	73.81 339	75.90 384	59.77 290	80.51 314	82.40 317	58.30 366	81.62 123	85.69 267	44.35 344	76.41 388	76.29 138	78.61 269	85.23 340

ET-MVSNet_ETH3D		78.63 197	76.63 227	84.64 104	86.73 236	69.47 95	85.01 237	84.61 280	69.54 230	66.51 356	86.59 246	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	83.62 296	64.14 222	87.04 179	89.97 163	73.61 146	78.18 173	87.22 226	61.10 185	93.82 139	76.11 140	76.78 294	91.18 169

test_fmvs1_n		70.86 312	70.24 310	72.73 348	72.51 406	55.28 350	81.27 302	79.71 348	51.49 395	78.73 157	84.87 287	27.54 402	77.02 382	76.06 141	79.97 258	85.88 331

CLD-MVS		82.31 113	81.65 119	84.29 117	88.47 171	67.73 143	85.81 221	92.35 82	75.78 93	78.33 169	86.58 248	64.01 138	94.35 115	76.05 142	87.48 150	90.79 182
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	84.22 282	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		70.93 311	70.52 305	72.16 352	73.71 395	55.05 352	80.82 305	78.77 356	51.21 396	78.58 162	84.41 295	31.20 397	76.94 383	75.88 144	80.12 257	84.47 352

XVG-OURS		80.41 155	79.23 163	83.97 143	85.64 253	69.02 105	83.03 284	90.39 146	71.09 194	77.63 184	91.49 115	54.62 241	91.35 248	75.71 145	83.47 213	91.54 158

V42		79.38 180	78.24 184	82.83 185	81.10 348	65.50 191	85.55 226	89.82 166	71.57 185	78.21 171	86.12 260	60.66 193	93.18 175	75.64 146	75.46 316	89.81 232

PS-MVSNAJ		81.69 125	81.02 127	83.70 150	89.51 127	68.21 132	84.28 258	90.09 160	70.79 199	81.26 129	85.62 271	63.15 148	94.29 116	75.62 147	88.87 129	88.59 272

xiu_mvs_v2_base		81.69 125	81.05 126	83.60 152	89.15 146	68.03 137	84.46 252	90.02 161	70.67 202	81.30 128	86.53 251	63.17 147	94.19 123	75.60 148	88.54 136	88.57 273

EIA-MVS		83.31 100	82.80 101	84.82 99	89.59 123	65.59 189	88.21 143	92.68 66	74.66 121	78.96 153	86.42 253	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	88.71 164	67.61 146	85.84 219	87.26 241	69.08 243	77.23 193	88.14 205	53.20 254	93.47 157	75.50 150	73.45 341	91.06 173

mvsmamba		80.60 150	79.38 157	84.27 120	89.74 121	67.24 159	87.47 166	86.95 247	70.02 217	75.38 239	88.93 177	51.24 278	92.56 198	75.47 151	89.22 124	93.00 113

reproduce_monomvs		75.40 264	74.38 261	78.46 283	83.92 290	57.80 312	83.78 265	86.94 248	73.47 152	72.25 293	84.47 293	38.74 375	89.27 288	75.32 152	70.53 361	88.31 278

OMC-MVS		82.69 108	81.97 116	84.85 98	88.75 162	67.42 151	87.98 150	90.87 134	74.92 113	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	83.78 293	64.51 213	87.11 178	90.57 142	71.96 178	78.08 176	86.20 258	61.41 177	93.94 131	74.93 154	77.23 285	90.60 192

MVSTER		79.01 188	77.88 193	82.38 199	83.07 310	64.80 209	84.08 263	88.95 201	69.01 247	78.69 158	87.17 229	54.70 239	92.43 203	74.69 155	80.57 250	89.89 228

test_vis1_n		69.85 324	69.21 315	71.77 354	72.66 405	55.27 351	81.48 298	76.21 375	52.03 392	75.30 246	83.20 324	28.97 400	76.22 390	74.60 156	78.41 275	83.81 360

test_fmvs2		68.35 337	67.48 337	70.98 363	69.50 409	51.95 374	80.05 322	76.38 374	49.33 398	74.65 262	84.38 296	23.30 411	75.40 399	74.51 157	75.17 325	85.60 334

PVSNet_Blended_VisFu		82.62 109	81.83 118	84.96 93	90.80 94	69.76 90	88.74 124	91.70 110	69.39 232	78.96 153	88.46 192	65.47 127	94.87 100	74.42 158	88.57 135	90.24 207

v8		79.97 166	79.02 168	82.80 188	84.09 285	64.50 215	87.96 151	90.29 154	74.13 136	75.24 248	86.81 235	62.88 153	93.89 138	74.39 159	75.40 319	90.00 221

v144192		79.47 174	78.37 180	82.78 191	83.35 301	63.96 225	86.96 182	90.36 150	69.99 219	77.50 185	85.67 269	60.66 193	93.77 143	74.27 160	76.58 295	90.62 190

ACMM	73.20 8	80.78 147	79.84 148	83.58 153	89.31 139	68.37 127	89.99 76	91.60 112	70.28 212	77.25 191	89.66 157	53.37 252	93.53 154	74.24 161	82.85 221	88.85 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 198		58.10 368	87.04 49			88.98 294	74.07 162

v1192		79.59 171	78.43 179	83.07 175	83.55 298	64.52 212	86.93 185	90.58 140	70.83 198	77.78 181	85.90 262	59.15 204	93.94 131	73.96 163	77.19 287	90.76 184

v10		79.74 168	78.67 172	82.97 181	84.06 286	64.95 204	87.88 157	90.62 139	73.11 161	75.11 252	86.56 249	61.46 176	94.05 127	73.68 164	75.55 312	89.90 227

v1921920		79.22 182	78.03 188	82.80 188	83.30 303	63.94 226	86.80 189	90.33 151	69.91 222	77.48 186	85.53 272	58.44 208	93.75 145	73.60 165	76.85 292	90.71 188

cl22		78.07 211	77.01 214	81.23 223	82.37 329	61.83 265	83.55 272	87.98 222	68.96 248	75.06 254	83.87 307	61.40 178	91.88 225	73.53 166	76.39 299	89.98 224

Effi-MVS+-dtu		80.03 164	78.57 175	84.42 111	85.13 266	68.74 114	88.77 121	88.10 219	74.99 110	74.97 256	83.49 319	57.27 220	93.36 162	73.53 166	80.88 244	91.18 169

c3_l		78.75 193	77.91 191	81.26 222	82.89 317	61.56 268	84.09 262	89.13 193	69.97 220	75.56 231	84.29 299	66.36 116	92.09 217	73.47 168	75.48 314	90.12 212

VDDNet		81.52 129	80.67 132	84.05 137	90.44 101	64.13 223	89.73 84	85.91 266	71.11 193	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	85.60 255	63.17 246	87.36 170	88.65 211	76.37 83	75.88 226	88.44 193	53.51 250	93.07 181	73.30 170	89.74 118	92.25 139

miper_ehance_all_eth		78.59 199	77.76 199	81.08 228	82.66 322	61.56 268	83.65 268	89.15 191	68.87 249	75.55 232	83.79 311	66.49 114	92.03 218	73.25 171	76.39 299	89.64 236

3Dnovator	76.31 5	83.38 98	82.31 108	86.59 55	87.94 196	72.94 28	90.64 60	92.14 92	77.21 57	75.47 233	92.83 83	58.56 207	94.72 105	73.24 172	92.71 74	92.13 146

v1240		78.99 189	77.78 197	82.64 194	83.21 305	63.54 234	86.62 196	90.30 153	69.74 229	77.33 189	85.68 268	57.04 222	93.76 144	73.13 173	76.92 289	90.62 190

miper_enhance_ethall		77.87 218	76.86 218	80.92 233	81.65 336	61.38 270	82.68 285	88.98 198	65.52 293	75.47 233	82.30 339	65.76 126	92.00 220	72.95 174	76.39 299	89.39 242

MG-MVS		83.41 96	83.45 88	83.28 162	92.74 65	62.28 259	88.17 145	89.50 177	75.22 104	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	90.13 107	64.47 216	92.32 30	90.73 137	74.45 126	79.35 149	91.10 127	69.05 89	95.12 85	72.78 176	87.22 154	94.13 52

test_fmvs3		63.36 361	61.82 364	67.98 378	62.51 418	46.96 399	77.37 358	74.03 385	45.24 403	67.50 340	78.79 374	12.16 423	72.98 407	72.77 177	66.02 378	83.99 358

IterMVS-LS		80.06 163	79.38 157	82.11 202	85.89 249	63.20 244	86.79 190	89.34 181	74.19 133	75.45 236	86.72 238	66.62 111	92.39 205	72.58 178	76.86 291	90.75 185
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	85.17 262	60.30 285	89.41 97	90.90 132	71.21 191	77.17 198	88.73 182	46.38 322	93.21 169	72.57 179	78.96 268	90.79 182

EI-MVSNet		80.52 154	79.98 144	82.12 201	84.28 280	63.19 245	86.41 201	88.95 201	74.18 134	78.69 158	87.54 218	66.62 111	92.43 203	72.57 179	80.57 250	90.74 186

Vis-MVSNet		83.46 95	82.80 101	85.43 79	90.25 105	68.74 114	90.30 72	90.13 159	76.33 85	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 63	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	82.73 320	61.96 263	86.30 206	88.08 220	73.26 158	76.18 221	85.47 274	62.46 158	92.36 207	71.92 183	73.82 338	90.09 215

PVSNet_BlendedMVS		80.60 150	80.02 143	82.36 200	88.85 154	65.40 192	86.16 210	92.00 95	69.34 234	78.11 174	86.09 261	66.02 122	94.27 118	71.52 184	82.06 231	87.39 296

PVSNet_Blended		80.98 137	80.34 138	82.90 183	88.85 154	65.40 192	84.43 254	92.00 95	67.62 265	78.11 174	85.05 285	66.02 122	94.27 118	71.52 184	89.50 120	89.01 254

eth_miper_zixun_eth		77.92 216	76.69 225	81.61 213	83.00 313	61.98 262	83.15 278	89.20 189	69.52 231	74.86 258	84.35 298	61.76 169	92.56 198	71.50 186	72.89 346	90.28 206

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	88.30 179	65.01 202	84.55 249	90.01 162	73.25 159	79.61 145	87.57 215	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	82.49 326	60.48 282	83.09 280	87.87 226	69.22 238	74.38 267	85.22 280	62.10 165	91.53 240	71.09 189	75.41 318	89.73 235

DIV-MVS_self_test		77.72 221	76.76 222	80.58 239	82.48 327	60.48 282	83.09 280	87.86 227	69.22 238	74.38 267	85.24 278	62.10 165	91.53 240	71.09 189	75.40 319	89.74 234

MonoMVSNet		76.49 246	75.80 235	78.58 277	81.55 339	58.45 299	86.36 204	86.22 261	74.87 116	74.73 260	83.73 313	51.79 273	88.73 299	70.78 191	72.15 351	88.55 274

test_yl		81.17 134	80.47 136	83.24 165	89.13 147	63.62 230	86.21 208	89.95 164	72.43 172	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	89.13 147	63.62 230	86.21 208	89.95 164	72.43 172	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 84	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	84.45 279	57.97 307	82.59 286	87.62 232	67.40 269	76.17 223	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	88.07 190	60.80 277	86.86 187	91.58 113	75.67 97	80.24 138	89.45 168	63.34 142	90.25 270	70.51 196	79.22 267	91.23 168

PAPM_NR		83.02 105	82.41 105	84.82 99	92.47 70	66.37 172	87.93 154	91.80 106	73.82 141	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	87.69 210	65.10 201	87.36 170	84.26 287	70.04 216	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	88.10 188	63.84 227	88.37 138	84.05 289	71.45 187	76.78 204	89.12 173	49.93 295	94.89 98	70.18 199	83.18 218	92.96 115

UniMVSNet_NR-MVSNet		81.88 120	81.54 120	82.92 182	88.46 172	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	76.46 297	92.25 139

DU-MVS		81.12 136	80.52 135	82.90 183	87.80 203	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	76.46 297	92.20 142

XVG-ACMP-BASELINE		76.11 252	74.27 263	81.62 211	83.20 306	64.67 211	83.60 271	89.75 169	69.75 227	71.85 297	87.09 231	32.78 392	92.11 216	69.99 202	80.43 252	88.09 282

GeoE		81.71 124	81.01 128	83.80 149	89.51 127	64.45 217	88.97 114	88.73 210	71.27 190	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	88.80 159	58.34 301	88.26 142	93.49 26	76.93 65	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	58.75 364	74.08 269	90.72 138	58.10 210	95.04 92	69.70 205	89.42 122	90.30 205

Anonymous20231211		78.97 190	77.69 202	82.81 187	90.54 99	64.29 220	90.11 75	91.51 115	65.01 300	76.16 224	88.13 206	50.56 286	93.03 185	69.68 206	77.56 284	91.11 171

Patchmatch-RL test		70.24 319	67.78 332	77.61 296	77.43 379	59.57 294	71.16 387	70.33 393	62.94 325	68.65 331	72.77 399	50.62 285	85.49 334	69.58 207	66.58 376	87.77 288

UniMVSNet (Re)		81.60 128	81.11 125	83.09 172	88.38 176	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	76.49 296	91.60 155

IterMVS-SCA-FT		75.43 262	73.87 268	80.11 249	82.69 321	64.85 208	81.57 297	83.47 298	69.16 241	70.49 308	84.15 305	51.95 268	88.15 307	69.23 209	72.14 352	87.34 298

v7n		78.97 190	77.58 205	83.14 170	83.45 300	65.51 190	88.32 140	91.21 123	73.69 144	72.41 290	86.32 256	57.93 211	93.81 140	69.18 210	75.65 310	90.11 213

Anonymous20240529		80.19 162	78.89 170	84.10 126	90.60 97	64.75 210	88.95 115	90.90 132	65.97 288	80.59 135	91.17 126	49.97 292	93.73 147	69.16 211	82.70 225	93.81 70

miper_lstm_enhance		74.11 276	73.11 277	77.13 304	80.11 358	59.62 292	72.23 383	86.92 250	66.76 273	70.40 309	82.92 329	56.93 223	82.92 355	69.06 212	72.63 347	88.87 261

testdata				79.97 251	90.90 91	64.21 221		84.71 278	59.27 358	85.40 62	92.91 80	62.02 167	89.08 292	68.95 213	91.37 93	86.63 317

test1111		79.43 176	79.18 165	80.15 248	89.99 114	53.31 368	87.33 172	77.05 370	75.04 109	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	75.17 251	81.97 206	82.75 319	62.58 254	81.44 300	86.35 260	72.16 176	74.74 259	82.89 330	46.20 327	92.02 219	68.85 215	81.09 241	91.30 167

test2506		77.30 231	76.49 228	79.74 256	90.08 109	52.02 372	87.86 158	63.10 413	74.88 114	80.16 140	92.79 86	38.29 379	92.35 208	68.74 216	92.50 77	94.86 18

ECVR-MVS		79.61 169	79.26 162	80.67 238	90.08 109	54.69 355	87.89 156	77.44 366	74.88 114	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	88.04 191	68.09 134	89.42 96	88.16 217	76.95 64	76.22 219	89.46 166	49.30 302	93.94 131	68.48 218	90.31 106	91.60 155
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	88.42 175	55.97 340	87.95 152	93.42 29	77.10 61	77.38 188	90.98 136	69.96 77	91.79 227	68.46 219	84.50 189	92.33 135

DP-MVS Recon		83.11 104	82.09 112	86.15 63	94.44 19	70.92 71	88.79 120	92.20 89	70.53 207	79.17 151	91.03 132	64.12 137	96.03 50	68.39 220	90.14 110	91.50 160

UniMVSNet_ETH3D		79.10 186	78.24 184	81.70 210	86.85 232	60.24 286	87.28 174	88.79 204	74.25 132	76.84 201	90.53 142	49.48 298	91.56 237	67.98 221	82.15 229	93.29 95

D2MVS		74.82 269	73.21 275	79.64 260	79.81 363	62.56 255	80.34 318	87.35 238	64.37 307	68.86 329	82.66 334	46.37 323	90.10 272	67.91 222	81.24 239	86.25 320

IS-MVSNet		83.15 101	82.81 100	84.18 124	89.94 116	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	83.16 309	66.96 166	86.94 184	87.45 237	72.45 169	71.49 302	84.17 304	54.79 238	91.58 235	67.61 224	80.31 253	89.30 245

PAPR		81.66 127	80.89 130	83.99 142	90.27 104	64.00 224	86.76 193	91.77 109	68.84 250	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	74.64 255	82.99 179	85.78 251	65.88 182	82.33 288	89.21 188	60.85 345	72.74 284	81.02 350	47.28 315	93.75 145	67.48 226	85.02 182	89.34 244

1314		76.53 242	75.30 249	80.21 247	83.93 289	62.32 258	84.66 244	88.81 203	60.23 349	70.16 314	84.07 306	55.30 232	90.73 265	67.37 227	83.21 217	87.59 293

无先验							87.48 165	88.98 198	60.00 351				94.12 125	67.28 228		88.97 257

thisisatest0515		77.33 230	75.38 246	83.18 168	85.27 261	63.80 228	82.11 291	83.27 301	65.06 298	75.91 225	83.84 309	49.54 297	94.27 118	67.24 229	86.19 170	91.48 162

原ACMM1				84.35 114	93.01 60	68.79 110		92.44 77	63.96 316	81.09 130	91.57 112	66.06 121	95.45 68	67.19 230	94.82 46	88.81 264

Baseline_NR-MVSNet		78.15 209	78.33 182	77.61 296	85.79 250	56.21 338	86.78 191	85.76 268	73.60 147	77.93 179	87.57 215	65.02 131	88.99 293	67.14 231	75.33 321	87.63 290

TranMVSNet+NR-MVSNet		80.84 140	80.31 139	82.42 198	87.85 200	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	75.24 324	92.30 137

Fast-Effi-MVS+		80.81 142	79.92 145	83.47 155	88.85 154	64.51 213	85.53 228	89.39 180	70.79 199	78.49 165	85.06 284	67.54 104	93.58 149	67.03 233	86.58 163	92.32 136

VPNet		78.69 196	78.66 173	78.76 273	88.31 178	55.72 344	84.45 253	86.63 254	76.79 69	78.26 170	90.55 141	59.30 203	89.70 281	66.63 234	77.05 288	90.88 180

PM-MVS		66.41 349	64.14 352	73.20 344	73.92 394	56.45 331	78.97 337	64.96 410	63.88 317	64.72 367	80.24 359	19.84 415	83.44 352	66.24 235	64.52 383	79.71 391

test-LLR		72.94 295	72.43 284	74.48 331	81.35 344	58.04 305	78.38 345	77.46 364	66.66 275	69.95 318	79.00 371	48.06 311	79.24 371	66.13 236	84.83 184	86.15 323

test-mter		71.41 306	70.39 309	74.48 331	81.35 344	58.04 305	78.38 345	77.46 364	60.32 348	69.95 318	79.00 371	36.08 386	79.24 371	66.13 236	84.83 184	86.15 323

MVS		78.19 208	76.99 216	81.78 208	85.66 252	66.99 163	84.66 244	90.47 144	55.08 384	72.02 296	85.27 277	63.83 140	94.11 126	66.10 238	89.80 117	84.24 354

NR-MVSNet		80.23 160	79.38 157	82.78 191	87.80 203	63.34 240	86.31 205	91.09 129	79.01 27	72.17 294	89.07 174	67.20 108	92.81 191	66.08 239	75.65 310	92.20 142

CVMVSNet		72.99 294	72.58 283	74.25 334	84.28 280	50.85 386	86.41 201	83.45 299	44.56 404	73.23 279	87.54 218	49.38 300	85.70 330	65.90 240	78.44 273	86.19 322

IterMVS		74.29 272	72.94 279	78.35 284	81.53 340	63.49 236	81.58 296	82.49 316	68.06 262	69.99 317	83.69 315	51.66 275	85.54 333	65.85 241	71.64 355	86.01 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 273	72.42 285	79.80 255	83.76 294	59.59 293	85.92 216	86.64 253	66.39 282	66.96 346	87.58 214	39.46 371	91.60 234	65.76 242	69.27 366	88.22 279

tpmrst		72.39 297	72.13 288	73.18 345	80.54 353	49.91 390	79.91 325	79.08 355	63.11 321	71.69 299	79.95 362	55.32 231	82.77 356	65.66 243	73.89 336	86.87 310

MAR-MVS		81.84 121	80.70 131	85.27 82	91.32 82	71.53 56	89.82 79	90.92 131	69.77 226	78.50 164	86.21 257	62.36 160	94.52 111	65.36 244	92.05 82	89.77 233
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	70.65 206	80.00 141	91.20 124	41.08 365	91.43 246	65.21 245	85.26 181	93.85 66

ab-mvs		79.51 172	78.97 169	81.14 226	88.46 172	60.91 275	83.84 264	89.24 187	70.36 209	79.03 152	88.87 180	63.23 146	90.21 271	65.12 246	82.57 226	92.28 138

IB-MVS	68.01 15	75.85 256	73.36 274	83.31 161	84.76 271	66.03 176	83.38 274	85.06 275	70.21 215	69.40 324	81.05 349	45.76 332	94.66 108	65.10 247	75.49 313	89.25 246
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	88.79 160	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	77.22 286	91.80 153

CostFormer		75.24 266	73.90 267	79.27 265	82.65 323	58.27 302	80.80 306	82.73 315	61.57 340	75.33 245	83.13 325	55.52 230	91.07 259	64.98 248	78.34 276	88.45 275

API-MVS		81.99 119	81.23 123	84.26 122	90.94 90	70.18 85	91.10 55	89.32 182	71.51 186	78.66 160	88.28 197	65.26 128	95.10 90	64.74 250	91.23 95	87.51 294

新几何1				83.42 157	93.13 54	70.71 74		85.48 271	57.43 374	81.80 120	91.98 98	63.28 143	92.27 211	64.60 251	92.99 70	87.27 300

testing91		76.54 241	75.66 240	79.18 268	88.43 174	55.89 341	81.08 303	83.00 309	73.76 143	75.34 241	84.29 299	46.20 327	90.07 273	64.33 252	84.50 189	91.58 157

testing99		76.09 253	75.12 252	79.00 269	88.16 183	55.50 347	80.79 307	81.40 328	73.30 157	75.17 249	84.27 302	44.48 342	90.02 274	64.28 253	84.22 198	91.48 162

pm-mvs1		77.25 232	76.68 226	78.93 271	84.22 282	58.62 298	86.41 201	88.36 216	71.37 188	73.31 277	88.01 207	61.22 183	89.15 291	64.24 254	73.01 345	89.03 253

TESTMET0.1,1		69.89 323	69.00 317	72.55 349	79.27 372	56.85 324	78.38 345	74.71 383	57.64 371	68.09 335	77.19 384	37.75 381	76.70 384	63.92 255	84.09 199	84.10 357

QAPM		80.88 139	79.50 155	85.03 90	88.01 194	68.97 107	91.59 43	92.00 95	66.63 280	75.15 251	92.16 95	57.70 214	95.45 68	63.52 256	88.76 132	90.66 189

baseline2		75.70 257	73.83 269	81.30 221	83.26 304	61.79 266	82.57 287	80.65 335	66.81 271	66.88 347	83.42 320	57.86 213	92.19 214	63.47 257	79.57 260	89.91 226

LCM-MVSNet-Re		77.05 233	76.94 217	77.36 300	87.20 225	51.60 379	80.06 321	80.46 339	75.20 105	67.69 338	86.72 238	62.48 157	88.98 294	63.44 258	89.25 123	91.51 159

gm-plane-assit					81.40 342	53.83 363			62.72 330		80.94 352		92.39 205	63.40 259

baseline1		76.98 235	76.75 224	77.66 294	88.13 186	55.66 345	85.12 234	81.89 322	73.04 163	76.79 203	88.90 178	62.43 159	87.78 312	63.30 260	71.18 358	89.55 239

AdaColmap		80.58 153	79.42 156	84.06 134	93.09 57	68.91 108	89.36 100	88.97 200	69.27 235	75.70 229	89.69 156	57.20 221	95.77 59	63.06 261	88.41 139	87.50 295

test_vis1_rt		60.28 366	58.42 369	65.84 383	67.25 412	55.60 346	70.44 392	60.94 416	44.33 405	59.00 391	66.64 406	24.91 406	68.67 413	62.80 262	69.48 364	73.25 402

GBi-Net		78.40 201	77.40 207	81.40 218	87.60 212	63.01 247	88.39 135	89.28 183	71.63 181	75.34 241	87.28 222	54.80 235	91.11 253	62.72 263	79.57 260	90.09 215

test1		78.40 201	77.40 207	81.40 218	87.60 212	63.01 247	88.39 135	89.28 183	71.63 181	75.34 241	87.28 222	54.80 235	91.11 253	62.72 263	79.57 260	90.09 215

FMVSNet3		77.88 217	76.85 219	80.97 232	86.84 233	62.36 256	86.52 199	88.77 205	71.13 192	75.34 241	86.66 244	54.07 245	91.10 256	62.72 263	79.57 260	89.45 241

CMPMVS	51.72 21	70.19 320	68.16 323	76.28 309	73.15 402	57.55 316	79.47 328	83.92 290	48.02 400	56.48 400	84.81 289	43.13 351	86.42 324	62.67 266	81.81 235	84.89 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 223	77.40 207	78.60 276	89.03 152	60.02 288	79.00 336	85.83 267	75.19 106	76.61 210	89.98 150	54.81 234	85.46 335	62.63 267	83.55 210	90.33 203

FMVSNet2		78.20 207	77.21 211	81.20 224	87.60 212	62.89 253	87.47 166	89.02 196	71.63 181	75.29 247	87.28 222	54.80 235	91.10 256	62.38 268	79.38 264	89.61 237

testdata2													91.01 260	62.37 269

testing11		75.14 267	74.01 264	78.53 280	88.16 183	56.38 334	80.74 310	80.42 340	70.67 202	72.69 287	83.72 314	43.61 349	89.86 276	62.29 270	83.76 203	89.36 243

CP-MVSNet		78.22 205	78.34 181	77.84 291	87.83 202	54.54 357	87.94 153	91.17 125	77.65 41	73.48 276	88.49 191	62.24 163	88.43 304	62.19 271	74.07 333	90.55 194

XXY-MVS		75.41 263	75.56 241	74.96 325	83.59 297	57.82 311	80.59 313	83.87 292	66.54 281	74.93 257	88.31 196	63.24 145	80.09 369	62.16 272	76.85 292	86.97 309

pmmvs6		74.69 270	73.39 273	78.61 275	81.38 343	57.48 317	86.64 195	87.95 224	64.99 301	70.18 312	86.61 245	50.43 288	89.52 283	62.12 273	70.18 363	88.83 263

1112_ss		77.40 229	76.43 230	80.32 245	89.11 151	60.41 284	83.65 268	87.72 231	62.13 336	73.05 281	86.72 238	62.58 156	89.97 275	62.11 274	80.80 246	90.59 193

PS-CasMVS		78.01 214	78.09 187	77.77 293	87.71 208	54.39 359	88.02 149	91.22 122	77.50 49	73.26 278	88.64 186	60.73 189	88.41 305	61.88 275	73.88 337	90.53 195

CDS-MVSNet		79.07 187	77.70 201	83.17 169	87.60 212	68.23 131	84.40 256	86.20 262	67.49 267	76.36 216	86.54 250	61.54 173	90.79 263	61.86 276	87.33 152	90.49 197
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	85.17 262	69.91 87	90.57 61	90.97 130	66.70 274	72.17 294	91.91 99	54.70 239	93.96 128	61.81 277	90.95 98	88.41 277

K. test v3		71.19 307	68.51 319	79.21 267	83.04 312	57.78 313	84.35 257	76.91 371	72.90 166	62.99 378	82.86 331	39.27 372	91.09 258	61.65 278	52.66 404	88.75 267

CHOSEN 1792x2688		77.63 225	75.69 237	83.44 156	89.98 115	68.58 122	78.70 341	87.50 235	56.38 379	75.80 228	86.84 234	58.67 206	91.40 247	61.58 279	85.75 179	90.34 202

PCF-MVS	73.52 7	80.38 156	78.84 171	85.01 91	87.71 208	68.99 106	83.65 268	91.46 119	63.00 323	77.77 182	90.28 144	66.10 119	95.09 91	61.40 280	88.22 141	90.94 179
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	87.57 216	60.21 287	83.37 275	87.78 230	66.11 284	75.37 240	87.06 233	63.27 144	90.48 268	61.38 281	82.43 227	90.40 201

HyFIR lowres test		77.53 226	75.40 245	83.94 145	89.59 123	66.62 168	80.36 317	88.64 212	56.29 380	76.45 213	85.17 281	57.64 215	93.28 164	61.34 282	83.10 219	91.91 150

PMMVS		69.34 327	68.67 318	71.35 359	75.67 386	62.03 261	75.17 370	73.46 386	50.00 397	68.68 330	79.05 369	52.07 266	78.13 376	61.16 283	82.77 222	73.90 401

FMVSNet1		77.44 227	76.12 234	81.40 218	86.81 234	63.01 247	88.39 135	89.28 183	70.49 208	74.39 266	87.28 222	49.06 306	91.11 253	60.91 284	78.52 271	90.09 215

sss		73.60 283	73.64 271	73.51 341	82.80 318	55.01 353	76.12 362	81.69 325	62.47 332	74.68 261	85.85 265	57.32 219	78.11 377	60.86 285	80.93 242	87.39 296

Test_1112_low_res		76.40 248	75.44 243	79.27 265	89.28 141	58.09 303	81.69 295	87.07 245	59.53 356	72.48 289	86.67 243	61.30 180	89.33 286	60.81 286	80.15 255	90.41 200

BH-untuned		79.47 174	78.60 174	82.05 203	89.19 145	65.91 181	86.07 212	88.52 214	72.18 174	75.42 237	87.69 212	61.15 184	93.54 153	60.38 287	86.83 160	86.70 315

WTY-MVS		75.65 258	75.68 238	75.57 316	86.40 241	56.82 325	77.92 354	82.40 317	65.10 297	76.18 221	87.72 210	63.13 151	80.90 366	60.31 288	81.96 232	89.00 256

pmmvs4		74.03 279	71.91 289	80.39 242	81.96 332	68.32 128	81.45 299	82.14 319	59.32 357	69.87 320	85.13 282	52.40 258	88.13 308	60.21 289	74.74 329	84.73 350

PEN-MVS		77.73 220	77.69 202	77.84 291	87.07 230	53.91 362	87.91 155	91.18 124	77.56 46	73.14 280	88.82 181	61.23 182	89.17 290	59.95 290	72.37 348	90.43 199

CR-MVSNet		73.37 286	71.27 298	79.67 259	81.32 346	65.19 198	75.92 364	80.30 342	59.92 352	72.73 285	81.19 347	52.50 256	86.69 319	59.84 291	77.71 281	87.11 306

mvs5depth		69.45 326	67.45 338	75.46 320	73.93 393	55.83 342	79.19 333	83.23 302	66.89 270	71.63 300	83.32 321	33.69 391	85.09 338	59.81 292	55.34 401	85.46 336

lessismore_v0				78.97 270	81.01 349	57.15 321		65.99 406		61.16 384	82.82 332	39.12 373	91.34 249	59.67 293	46.92 411	88.43 276

CNLPA		78.08 210	76.79 221	81.97 206	90.40 102	71.07 65	87.59 163	84.55 281	66.03 287	72.38 291	89.64 158	57.56 216	86.04 327	59.61 294	83.35 215	88.79 265

BH-RMVSNet		79.61 169	78.44 178	83.14 170	89.38 135	65.93 180	84.95 239	87.15 244	73.56 148	78.19 172	89.79 154	56.67 225	93.36 162	59.53 295	86.74 161	90.13 211

MS-PatchMatch		73.83 280	72.67 281	77.30 302	83.87 291	66.02 177	81.82 292	84.66 279	61.37 343	68.61 332	82.82 332	47.29 314	88.21 306	59.27 296	84.32 196	77.68 395

test_post1							78.90 339				5.43 429	48.81 310	85.44 336	59.25 297

SCA		74.22 274	72.33 286	79.91 252	84.05 287	62.17 260	79.96 324	79.29 353	66.30 283	72.38 291	80.13 360	51.95 268	88.60 302	59.25 297	77.67 283	88.96 258

FE-MVS		77.78 219	75.68 238	84.08 131	88.09 189	66.00 178	83.13 279	87.79 229	68.42 258	78.01 177	85.23 279	45.50 336	95.12 85	59.11 299	85.83 178	91.11 171

SixPastTwentyTwo		73.37 286	71.26 299	79.70 257	85.08 267	57.89 309	85.57 222	83.56 296	71.03 196	65.66 360	85.88 263	42.10 359	92.57 197	59.11 299	63.34 385	88.65 271

WR-MVS_H		78.51 200	78.49 176	78.56 278	88.02 192	56.38 334	88.43 133	92.67 67	77.14 59	73.89 271	87.55 217	66.25 118	89.24 289	58.92 301	73.55 340	90.06 219

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	64.33 308	69.87 320	88.38 194	53.66 248	93.58 149	58.86 302	82.73 223	87.86 286
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 290	71.46 294	78.54 279	82.50 325	59.85 289	82.18 290	82.84 314	58.96 361	71.15 305	89.41 170	45.48 337	84.77 342	58.82 303	71.83 354	91.02 177

EU-MVSNet		68.53 335	67.61 335	71.31 360	78.51 376	47.01 398	84.47 250	84.27 286	42.27 407	66.44 357	84.79 290	40.44 368	83.76 347	58.76 304	68.54 371	83.17 366

pmmvs-eth3d		70.50 317	67.83 330	78.52 281	77.37 380	66.18 175	81.82 292	81.51 326	58.90 362	63.90 374	80.42 357	42.69 354	86.28 325	58.56 305	65.30 381	83.11 368

TAMVS		78.89 192	77.51 206	83.03 177	87.80 203	67.79 142	84.72 243	85.05 276	67.63 264	76.75 205	87.70 211	62.25 162	90.82 262	58.53 306	87.13 155	90.49 197

WBMVS		73.43 285	72.81 280	75.28 322	87.91 197	50.99 385	78.59 344	81.31 330	65.51 295	74.47 265	84.83 288	46.39 321	86.68 320	58.41 307	77.86 279	88.17 281

ACMH+	68.96 14	76.01 254	74.01 264	82.03 204	88.60 167	65.31 196	88.86 118	87.55 233	70.25 214	67.75 337	87.47 220	41.27 363	93.19 174	58.37 308	75.94 307	87.60 291

tpm		72.37 299	71.71 291	74.35 333	82.19 330	52.00 373	79.22 332	77.29 368	64.56 304	72.95 283	83.68 316	51.35 276	83.26 354	58.33 309	75.80 308	87.81 287

BH-w/o		78.21 206	77.33 210	80.84 234	88.81 158	65.13 200	84.87 240	87.85 228	69.75 227	74.52 264	84.74 291	61.34 179	93.11 179	58.24 310	85.84 177	84.27 353

Vis-MVSNet (Re-imp)		78.36 203	78.45 177	78.07 289	88.64 166	51.78 378	86.70 194	79.63 349	74.14 135	75.11 252	90.83 137	61.29 181	89.75 279	58.10 311	91.60 88	92.69 121

MVP-Stereo		76.12 251	74.46 260	81.13 227	85.37 260	69.79 89	84.42 255	87.95 224	65.03 299	67.46 341	85.33 276	53.28 253	91.73 231	58.01 312	83.27 216	81.85 380
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 323	73.16 401	50.51 388	63.05 415	87.47 236		64.28 369	77.81 381	17.80 417	89.73 280	57.88 313	60.64 391	85.49 335

TR-MVS		77.44 227	76.18 233	81.20 224	88.24 180	63.24 242	84.61 247	86.40 258	67.55 266	77.81 180	86.48 252	54.10 244	93.15 176	57.75 314	82.72 224	87.20 301

F-COLMAP		76.38 249	74.33 262	82.50 197	89.28 141	66.95 167	88.41 134	89.03 195	64.05 313	66.83 348	88.61 187	46.78 319	92.89 187	57.48 315	78.55 270	87.67 289

EG-PatchMatch MVS		74.04 277	71.82 290	80.71 237	84.92 269	67.42 151	85.86 218	88.08 220	66.04 286	64.22 370	83.85 308	35.10 388	92.56 198	57.44 316	80.83 245	82.16 379

PatchmatchNet		73.12 291	71.33 297	78.49 282	83.18 307	60.85 276	79.63 326	78.57 357	64.13 309	71.73 298	79.81 365	51.20 279	85.97 328	57.40 317	76.36 304	88.66 270
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	86.24 242	53.06 371	87.52 164	90.66 138	77.08 62	72.50 288	88.67 185	60.48 197	89.52 283	57.33 318	70.74 360	90.05 220

UnsupCasMVSNet_eth		67.33 342	65.99 346	71.37 357	73.48 398	51.47 381	75.16 371	85.19 273	65.20 296	60.78 385	80.93 354	42.35 355	77.20 381	57.12 319	53.69 403	85.44 337

pmmvs5		71.55 305	70.20 311	75.61 315	77.83 377	56.39 333	81.74 294	80.89 331	57.76 370	67.46 341	84.49 292	49.26 303	85.32 337	57.08 320	75.29 322	85.11 344

testing3-2		75.12 268	75.19 250	74.91 326	90.40 102	45.09 406	80.29 319	78.42 358	78.37 36	76.54 212	87.75 209	44.36 343	87.28 316	57.04 321	83.49 212	92.37 133

Anonymous20240521		68.80 331	67.22 340	73.55 340	74.33 391	54.11 360	83.18 277	85.61 269	58.15 367	61.68 382	80.94 352	30.71 398	81.27 364	57.00 322	73.34 344	85.28 339

mvsany_test1		62.30 363	61.26 367	65.41 384	69.52 408	54.86 354	66.86 404	49.78 424	46.65 401	68.50 334	83.21 323	49.15 304	66.28 416	56.93 323	60.77 390	75.11 400

TransMVSNet (Re)		75.39 265	74.56 257	77.86 290	85.50 257	57.10 322	86.78 191	86.09 265	72.17 175	71.53 301	87.34 221	63.01 152	89.31 287	56.84 324	61.83 387	87.17 302

test_vis3_rt		49.26 383	47.02 385	56.00 395	54.30 424	45.27 405	66.76 406	48.08 425	36.83 414	44.38 413	53.20 418	7.17 430	64.07 418	56.77 325	55.66 398	58.65 414

EPMVS		69.02 329	68.16 323	71.59 355	79.61 367	49.80 392	77.40 357	66.93 404	62.82 328	70.01 315	79.05 369	45.79 331	77.86 379	56.58 326	75.26 323	87.13 305

KD-MVS_self_test		68.81 330	67.59 336	72.46 351	74.29 392	45.45 401	77.93 353	87.00 246	63.12 320	63.99 373	78.99 373	42.32 356	84.77 342	56.55 327	64.09 384	87.16 304

tpm2		73.26 289	71.46 294	78.63 274	83.34 302	56.71 328	80.65 312	80.40 341	56.63 378	73.55 275	82.02 344	51.80 272	91.24 251	56.35 328	78.42 274	87.95 283

LTVRE_ROB	69.57 13	76.25 250	74.54 258	81.41 217	88.60 167	64.38 219	79.24 331	89.12 194	70.76 201	69.79 322	87.86 208	49.09 305	93.20 172	56.21 329	80.16 254	86.65 316
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	73.93 266	81.77 209	88.71 164	66.61 169	88.62 129	89.01 197	69.81 223	66.78 349	86.70 242	41.95 361	91.51 242	55.64 330	78.14 277	87.17 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 348	64.71 350	71.90 353	81.45 341	63.52 235	57.98 417	68.95 400	53.57 387	62.59 380	76.70 385	46.22 326	75.29 400	55.25 331	79.68 259	76.88 397

UBG		73.08 292	72.27 287	75.51 318	88.02 192	51.29 383	78.35 348	77.38 367	65.52 293	73.87 272	82.36 337	45.55 334	86.48 323	55.02 332	84.39 195	88.75 267

EPNet_dtu		75.46 261	74.86 253	77.23 303	82.57 324	54.60 356	86.89 186	83.09 306	71.64 180	66.25 358	85.86 264	55.99 228	88.04 309	54.92 333	86.55 164	89.05 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 374	51.45 379	61.61 389	55.51 423	44.74 408	63.52 413	45.41 428	43.69 406	58.11 395	76.45 387	17.99 416	63.76 419	54.77 334	47.59 410	76.34 398

PVSNet	64.34 18	72.08 303	70.87 303	75.69 314	86.21 243	56.44 332	74.37 377	80.73 334	62.06 337	70.17 313	82.23 341	42.86 353	83.31 353	54.77 334	84.45 193	87.32 299

ITE_SJBPF				78.22 285	81.77 335	60.57 280		83.30 300	69.25 237	67.54 339	87.20 227	36.33 385	87.28 316	54.34 336	74.62 330	86.80 312

MDTV_nov1_ep13_2view						37.79 420	75.16 371		55.10 383	66.53 353		49.34 301		53.98 337		87.94 284

gg-mvs-nofinetune		69.95 322	67.96 326	75.94 311	83.07 310	54.51 358	77.23 359	70.29 394	63.11 321	70.32 310	62.33 408	43.62 348	88.69 300	53.88 338	87.76 146	84.62 351

PatchMatch-RL		72.38 298	70.90 302	76.80 307	88.60 167	67.38 153	79.53 327	76.17 376	62.75 329	69.36 325	82.00 345	45.51 335	84.89 341	53.62 339	80.58 249	78.12 394

test_f		52.09 379	50.82 380	55.90 396	53.82 426	42.31 415	59.42 416	58.31 420	36.45 415	56.12 402	70.96 403	12.18 422	57.79 422	53.51 340	56.57 397	67.60 407

Patchmtry		70.74 313	69.16 316	75.49 319	80.72 350	54.07 361	74.94 375	80.30 342	58.34 365	70.01 315	81.19 347	52.50 256	86.54 321	53.37 341	71.09 359	85.87 332

USDC		70.33 318	68.37 320	76.21 310	80.60 352	56.23 337	79.19 333	86.49 256	60.89 344	61.29 383	85.47 274	31.78 395	89.47 285	53.37 341	76.21 305	82.94 372

LF4IMVS		64.02 359	62.19 363	69.50 368	70.90 407	53.29 369	76.13 361	77.18 369	52.65 390	58.59 392	80.98 351	23.55 410	76.52 386	53.06 343	66.66 375	78.68 393

PAPM		77.68 224	76.40 231	81.51 214	87.29 224	61.85 264	83.78 265	89.59 174	64.74 302	71.23 303	88.70 183	62.59 155	93.66 148	52.66 344	87.03 157	89.01 254

dmvs_re		71.14 308	70.58 304	72.80 347	81.96 332	59.68 291	75.60 368	79.34 352	68.55 254	69.27 327	80.72 355	49.42 299	76.54 385	52.56 345	77.79 280	82.19 378

CL-MVSNet_self_test		72.37 299	71.46 294	75.09 324	79.49 369	53.53 364	80.76 309	85.01 277	69.12 242	70.51 307	82.05 343	57.92 212	84.13 345	52.27 346	66.00 379	87.60 291

tpm cat1		70.57 315	68.31 321	77.35 301	82.41 328	57.95 308	78.08 350	80.22 344	52.04 391	68.54 333	77.66 382	52.00 267	87.84 311	51.77 347	72.07 353	86.25 320

our_test_3		69.14 328	67.00 341	75.57 316	79.80 364	58.80 296	77.96 352	77.81 361	59.55 355	62.90 379	78.25 378	47.43 313	83.97 346	51.71 348	67.58 373	83.93 359

MDTV_nov1_ep13			69.97 312		83.18 307	53.48 365	77.10 360	80.18 345	60.45 346	69.33 326	80.44 356	48.89 309	86.90 318	51.60 349	78.51 272

myMVS_eth3d28		73.62 282	73.53 272	73.90 338	88.20 181	47.41 396	78.06 351	79.37 351	74.29 131	73.98 270	84.29 299	44.67 339	83.54 350	51.47 350	87.39 151	90.74 186

JIA-IIPM		66.32 350	62.82 362	76.82 306	77.09 381	61.72 267	65.34 410	75.38 377	58.04 369	64.51 368	62.32 409	42.05 360	86.51 322	51.45 351	69.22 367	82.21 377

testing222		74.04 277	72.66 282	78.19 286	87.89 198	55.36 348	81.06 304	79.20 354	71.30 189	74.65 262	83.57 318	39.11 374	88.67 301	51.43 352	85.75 179	90.53 195

MSDG		73.36 288	70.99 301	80.49 241	84.51 278	65.80 184	80.71 311	86.13 264	65.70 290	65.46 361	83.74 312	44.60 340	90.91 261	51.13 353	76.89 290	84.74 349

PatchT		68.46 336	67.85 328	70.29 365	80.70 351	43.93 409	72.47 382	74.88 380	60.15 350	70.55 306	76.57 386	49.94 293	81.59 361	50.58 354	74.83 328	85.34 338

GG-mvs-BLEND				75.38 321	81.59 338	55.80 343	79.32 330	69.63 396		67.19 344	73.67 397	43.24 350	88.90 298	50.41 355	84.50 189	81.45 382

KD-MVS_2432*1600		66.22 351	63.89 354	73.21 342	75.47 389	53.42 366	70.76 390	84.35 283	64.10 311	66.52 354	78.52 375	34.55 389	84.98 339	50.40 356	50.33 408	81.23 383

miper_refine_blended		66.22 351	63.89 354	73.21 342	75.47 389	53.42 366	70.76 390	84.35 283	64.10 311	66.52 354	78.52 375	34.55 389	84.98 339	50.40 356	50.33 408	81.23 383

AllTest		70.96 310	68.09 325	79.58 261	85.15 264	63.62 230	84.58 248	79.83 346	62.31 333	60.32 387	86.73 236	32.02 393	88.96 296	50.28 358	71.57 356	86.15 323

TestCases				79.58 261	85.15 264	63.62 230		79.83 346	62.31 333	60.32 387	86.73 236	32.02 393	88.96 296	50.28 358	71.57 356	86.15 323

TAPA-MVS	73.13 9	79.15 184	77.94 190	82.79 190	89.59 123	62.99 251	88.16 146	91.51 115	65.77 289	77.14 199	91.09 128	60.91 188	93.21 169	50.26 360	87.05 156	92.17 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 355	62.91 360	71.38 356	75.85 385	56.60 330	69.12 398	74.66 384	57.28 375	54.12 403	77.87 380	45.85 330	74.48 402	49.95 361	61.52 389	83.05 369

MDA-MVSNet_test_wron		65.03 355	62.92 359	71.37 357	75.93 383	56.73 326	69.09 399	74.73 382	57.28 375	54.03 404	77.89 379	45.88 329	74.39 403	49.89 362	61.55 388	82.99 371

tpmvs		71.09 309	69.29 314	76.49 308	82.04 331	56.04 339	78.92 338	81.37 329	64.05 313	67.18 345	78.28 377	49.74 296	89.77 278	49.67 363	72.37 348	83.67 362

ppachtmachnet_test		70.04 321	67.34 339	78.14 287	79.80 364	61.13 271	79.19 333	80.59 336	59.16 359	65.27 363	79.29 368	46.75 320	87.29 315	49.33 364	66.72 374	86.00 329

UnsupCasMVSNet_bld		63.70 360	61.53 366	70.21 366	73.69 396	51.39 382	72.82 381	81.89 322	55.63 382	57.81 396	71.80 401	38.67 376	78.61 374	49.26 365	52.21 406	80.63 387

UWE-MVS		72.13 302	71.49 293	74.03 336	86.66 238	47.70 394	81.40 301	76.89 372	63.60 318	75.59 230	84.22 303	39.94 370	85.62 332	48.98 366	86.13 172	88.77 266

dp		66.80 345	65.43 347	70.90 364	79.74 366	48.82 393	75.12 373	74.77 381	59.61 354	64.08 372	77.23 383	42.89 352	80.72 367	48.86 367	66.58 376	83.16 367

FMVSNet5		69.50 325	67.96 326	74.15 335	82.97 316	55.35 349	80.01 323	82.12 320	62.56 331	63.02 376	81.53 346	36.92 383	81.92 360	48.42 368	74.06 334	85.17 343

thres100view900		76.50 243	75.55 242	79.33 264	89.52 126	56.99 323	85.83 220	83.23 302	73.94 138	76.32 217	87.12 230	51.89 270	91.95 221	48.33 369	83.75 204	89.07 247

tfpn200view9		76.42 247	75.37 247	79.55 263	89.13 147	57.65 314	85.17 231	83.60 294	73.41 154	76.45 213	86.39 254	52.12 262	91.95 221	48.33 369	83.75 204	89.07 247

thres400		76.50 243	75.37 247	79.86 253	89.13 147	57.65 314	85.17 231	83.60 294	73.41 154	76.45 213	86.39 254	52.12 262	91.95 221	48.33 369	83.75 204	90.00 221

LCM-MVSNet		54.25 373	49.68 383	67.97 379	53.73 427	45.28 404	66.85 405	80.78 333	35.96 416	39.45 417	62.23 410	8.70 427	78.06 378	48.24 372	51.20 407	80.57 388

RPMNet		73.51 284	70.49 306	82.58 196	81.32 346	65.19 198	75.92 364	92.27 84	57.60 372	72.73 285	76.45 387	52.30 259	95.43 70	48.14 373	77.71 281	87.11 306

thres600view7		76.50 243	75.44 243	79.68 258	89.40 133	57.16 320	85.53 228	83.23 302	73.79 142	76.26 218	87.09 231	51.89 270	91.89 224	48.05 374	83.72 207	90.00 221

TDRefinement		67.49 340	64.34 351	76.92 305	73.47 399	61.07 273	84.86 241	82.98 310	59.77 353	58.30 394	85.13 282	26.06 403	87.89 310	47.92 375	60.59 392	81.81 381

thres200		75.55 259	74.47 259	78.82 272	87.78 206	57.85 310	83.07 282	83.51 297	72.44 171	75.84 227	84.42 294	52.08 265	91.75 229	47.41 376	83.64 209	86.86 311

PVSNet_0	57.27 20	61.67 365	59.27 368	68.85 372	79.61 367	57.44 318	68.01 400	73.44 387	55.93 381	58.54 393	70.41 404	44.58 341	77.55 380	47.01 377	35.91 416	71.55 404

DP-MVS		76.78 239	74.57 256	83.42 157	93.29 48	69.46 97	88.55 131	83.70 293	63.98 315	70.20 311	88.89 179	54.01 246	94.80 102	46.66 378	81.88 234	86.01 327

COLMAP_ROB	66.92 17	73.01 293	70.41 308	80.81 235	87.13 228	65.63 188	88.30 141	84.19 288	62.96 324	63.80 375	87.69 212	38.04 380	92.56 198	46.66 378	74.91 327	84.24 354
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 314	69.30 313	74.88 327	84.52 277	56.35 336	75.87 366	79.42 350	64.59 303	67.76 336	82.41 336	41.10 364	81.54 362	46.64 380	81.34 237	86.75 314

LS3D		76.95 236	74.82 254	83.37 160	90.45 100	67.36 154	89.15 108	86.94 248	61.87 339	69.52 323	90.61 140	51.71 274	94.53 110	46.38 381	86.71 162	88.21 280

ETVMVS		72.25 301	71.05 300	75.84 312	87.77 207	51.91 375	79.39 329	74.98 379	69.26 236	73.71 273	82.95 328	40.82 367	86.14 326	46.17 382	84.43 194	89.47 240

MDA-MVSNet-bldmvs		66.68 346	63.66 356	75.75 313	79.28 371	60.56 281	73.92 379	78.35 359	64.43 305	50.13 409	79.87 364	44.02 346	83.67 348	46.10 383	56.86 395	83.03 370

new-patchmatchnet		61.73 364	61.73 365	61.70 388	72.74 404	24.50 431	69.16 397	78.03 360	61.40 341	56.72 399	75.53 393	38.42 377	76.48 387	45.95 384	57.67 394	84.13 356

WB-MVSnew		71.96 304	71.65 292	72.89 346	84.67 276	51.88 376	82.29 289	77.57 363	62.31 333	73.67 274	83.00 327	53.49 251	81.10 365	45.75 385	82.13 230	85.70 333

TinyColmap		67.30 343	64.81 349	74.76 329	81.92 334	56.68 329	80.29 319	81.49 327	60.33 347	56.27 401	83.22 322	24.77 407	87.66 314	45.52 386	69.47 365	79.95 390

pmmvs3		57.79 369	54.26 374	68.37 375	64.02 417	56.72 327	75.12 373	65.17 408	40.20 409	52.93 405	69.86 405	20.36 414	75.48 397	45.45 387	55.25 402	72.90 403

OpenMVS_ROB	64.09 19	70.56 316	68.19 322	77.65 295	80.26 355	59.41 295	85.01 237	82.96 311	58.76 363	65.43 362	82.33 338	37.63 382	91.23 252	45.34 388	76.03 306	82.32 376

test0.0.03 1		68.00 339	67.69 333	68.90 371	77.55 378	47.43 395	75.70 367	72.95 390	66.66 275	66.56 352	82.29 340	48.06 311	75.87 394	44.97 389	74.51 331	83.41 364

testgi		66.67 347	66.53 344	67.08 381	75.62 387	41.69 416	75.93 363	76.50 373	66.11 284	65.20 366	86.59 246	35.72 387	74.71 401	43.71 390	73.38 343	84.84 348

Anonymous20231206		68.60 332	67.80 331	71.02 362	80.23 357	50.75 387	78.30 349	80.47 338	56.79 377	66.11 359	82.63 335	46.35 324	78.95 373	43.62 391	75.70 309	83.36 365

tfpnnormal		74.39 271	73.16 276	78.08 288	86.10 248	58.05 304	84.65 246	87.53 234	70.32 211	71.22 304	85.63 270	54.97 233	89.86 276	43.03 392	75.02 326	86.32 319

MIMVSNet1		68.58 333	66.78 343	73.98 337	80.07 359	51.82 377	80.77 308	84.37 282	64.40 306	59.75 390	82.16 342	36.47 384	83.63 349	42.73 393	70.33 362	86.48 318

ttmdpeth		59.91 367	57.10 371	68.34 376	67.13 413	46.65 400	74.64 376	67.41 403	48.30 399	62.52 381	85.04 286	20.40 413	75.93 393	42.55 394	45.90 414	82.44 375

test20.03		67.45 341	66.95 342	68.94 370	75.48 388	44.84 407	77.50 356	77.67 362	66.66 275	63.01 377	83.80 310	47.02 317	78.40 375	42.53 395	68.86 370	83.58 363

ADS-MVSNet2		66.20 353	63.33 357	74.82 328	79.92 360	58.75 297	67.55 402	75.19 378	53.37 388	65.25 364	75.86 390	42.32 356	80.53 368	41.57 396	68.91 368	85.18 341

ADS-MVSNet		64.36 358	62.88 361	68.78 373	79.92 360	47.17 397	67.55 402	71.18 392	53.37 388	65.25 364	75.86 390	42.32 356	73.99 404	41.57 396	68.91 368	85.18 341

Patchmatch-test		64.82 357	63.24 358	69.57 367	79.42 370	49.82 391	63.49 414	69.05 399	51.98 393	59.95 389	80.13 360	50.91 281	70.98 408	40.66 398	73.57 339	87.90 285

MVS-HIRNet		59.14 368	57.67 370	63.57 386	81.65 336	43.50 410	71.73 384	65.06 409	39.59 411	51.43 406	57.73 414	38.34 378	82.58 357	39.53 399	73.95 335	64.62 410

WAC-MVS						42.58 412								39.46 400

myMVS_eth3d		67.02 344	66.29 345	69.21 369	84.68 273	42.58 412	78.62 342	73.08 388	66.65 278	66.74 350	79.46 366	31.53 396	82.30 358	39.43 401	76.38 302	82.75 373

DSMNet-mixed		57.77 370	56.90 372	60.38 390	67.70 411	35.61 421	69.18 396	53.97 422	32.30 420	57.49 397	79.88 363	40.39 369	68.57 414	38.78 402	72.37 348	76.97 396

N_pmnet		52.79 378	53.26 376	51.40 402	78.99 373	7.68 436	69.52 394	3.89 435	51.63 394	57.01 398	74.98 394	40.83 366	65.96 417	37.78 403	64.67 382	80.56 389

testing3		68.56 334	67.67 334	71.22 361	87.33 222	42.87 411	83.06 283	71.54 391	70.36 209	69.08 328	84.38 296	30.33 399	85.69 331	37.50 404	75.45 317	85.09 345

MVStest1		56.63 371	52.76 377	68.25 377	61.67 419	53.25 370	71.67 385	68.90 401	38.59 412	50.59 408	83.05 326	25.08 405	70.66 409	36.76 405	38.56 415	80.83 386

test_0402		72.79 296	70.44 307	79.84 254	88.13 186	65.99 179	85.93 215	84.29 285	65.57 292	67.40 343	85.49 273	46.92 318	92.61 194	35.88 406	74.38 332	80.94 385

new_pmnet		50.91 381	50.29 381	52.78 401	68.58 410	34.94 423	63.71 412	56.63 421	39.73 410	44.95 412	65.47 407	21.93 412	58.48 421	34.98 407	56.62 396	64.92 409

APD_test1		53.31 377	49.93 382	63.42 387	65.68 414	50.13 389	71.59 386	66.90 405	34.43 417	40.58 416	71.56 402	8.65 428	76.27 389	34.64 408	55.36 400	63.86 411

Syy-MVS		68.05 338	67.85 328	68.67 374	84.68 273	40.97 417	78.62 342	73.08 388	66.65 278	66.74 350	79.46 366	52.11 264	82.30 358	32.89 409	76.38 302	82.75 373

dmvs_testset		62.63 362	64.11 353	58.19 392	78.55 375	24.76 430	75.28 369	65.94 407	67.91 263	60.34 386	76.01 389	53.56 249	73.94 405	31.79 410	67.65 372	75.88 399

UWE-MVS-28		65.32 354	64.93 348	66.49 382	78.70 374	38.55 419	77.86 355	64.39 411	62.00 338	64.13 371	83.60 317	41.44 362	76.00 392	31.39 411	80.89 243	84.92 346

ANet_high		50.57 382	46.10 386	63.99 385	48.67 430	39.13 418	70.99 389	80.85 332	61.39 342	31.18 419	57.70 415	17.02 418	73.65 406	31.22 412	15.89 427	79.18 392

EGC-MVSNET		52.07 380	47.05 384	67.14 380	83.51 299	60.71 278	80.50 315	67.75 402	0.07 430	0.43 431	75.85 392	24.26 408	81.54 362	28.82 413	62.25 386	59.16 413

PMMVS2		40.82 389	38.86 393	46.69 403	53.84 425	16.45 434	48.61 420	49.92 423	37.49 413	31.67 418	60.97 411	8.14 429	56.42 423	28.42 414	30.72 420	67.19 408

tmp_tt		18.61 396	21.40 399	10.23 412	4.82 435	10.11 435	34.70 422	30.74 433	1.48 429	23.91 425	26.07 426	28.42 401	13.41 431	27.12 415	15.35 428	7.17 426

test_method		31.52 392	29.28 396	38.23 406	27.03 434	6.50 437	20.94 425	62.21 414	4.05 428	22.35 426	52.50 419	13.33 420	47.58 426	27.04 416	34.04 418	60.62 412

testf1		45.72 384	41.96 388	57.00 393	56.90 421	45.32 402	66.14 407	59.26 418	26.19 421	30.89 420	60.96 412	4.14 431	70.64 410	26.39 417	46.73 412	55.04 416

APD_test2		45.72 384	41.96 388	57.00 393	56.90 421	45.32 402	66.14 407	59.26 418	26.19 421	30.89 420	60.96 412	4.14 431	70.64 410	26.39 417	46.73 412	55.04 416

FPMVS		53.68 376	51.64 378	59.81 391	65.08 415	51.03 384	69.48 395	69.58 397	41.46 408	40.67 415	72.32 400	16.46 419	70.00 412	24.24 419	65.42 380	58.40 415

Gipuma		45.18 387	41.86 390	55.16 399	77.03 382	51.52 380	32.50 423	80.52 337	32.46 419	27.12 422	35.02 423	9.52 426	75.50 396	22.31 420	60.21 393	38.45 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 386	45.38 387	45.55 404	73.36 400	26.85 428	67.72 401	34.19 430	54.15 386	49.65 410	56.41 417	25.43 404	62.94 420	19.45 421	28.09 421	46.86 420

DeepMVS_CX				27.40 410	40.17 433	26.90 427		24.59 434	17.44 426	23.95 424	48.61 421	9.77 425	26.48 429	18.06 422	24.47 423	28.83 423

WB-MVS		54.94 372	54.72 373	55.60 398	73.50 397	20.90 432	74.27 378	61.19 415	59.16 359	50.61 407	74.15 395	47.19 316	75.78 395	17.31 423	35.07 417	70.12 405

PMVS	37.38 22	44.16 388	40.28 392	55.82 397	40.82 432	42.54 414	65.12 411	63.99 412	34.43 417	24.48 423	57.12 416	3.92 433	76.17 391	17.10 424	55.52 399	48.75 418
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 394	25.89 398	43.81 405	44.55 431	35.46 422	28.87 424	39.07 429	18.20 425	18.58 427	40.18 422	2.68 434	47.37 427	17.07 425	23.78 424	48.60 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 375	53.59 375	54.75 400	72.87 403	19.59 433	73.84 380	60.53 417	57.58 373	49.18 411	73.45 398	46.34 325	75.47 398	16.20 426	32.28 419	69.20 406

E-PMN		31.77 391	30.64 394	35.15 408	52.87 428	27.67 425	57.09 418	47.86 426	24.64 423	16.40 428	33.05 424	11.23 424	54.90 424	14.46 427	18.15 425	22.87 424

EMVS		30.81 393	29.65 395	34.27 409	50.96 429	25.95 429	56.58 419	46.80 427	24.01 424	15.53 429	30.68 425	12.47 421	54.43 425	12.81 428	17.05 426	22.43 425

kuosan		39.70 390	40.40 391	37.58 407	64.52 416	26.98 426	65.62 409	33.02 431	46.12 402	42.79 414	48.99 420	24.10 409	46.56 428	12.16 429	26.30 422	39.20 421

wuyk23d		16.82 397	15.94 400	19.46 411	58.74 420	31.45 424	39.22 421	3.74 436	6.84 427	6.04 430	2.70 430	1.27 435	24.29 430	10.54 430	14.40 429	2.63 427

testmvs		6.04 400	8.02 403	0.10 414	0.08 436	0.03 439	69.74 393	0.04 437	0.05 431	0.31 432	1.68 431	0.02 437	0.04 432	0.24 431	0.02 430	0.25 429

test123		6.12 399	8.11 402	0.14 413	0.06 437	0.09 438	71.05 388	0.03 438	0.04 432	0.25 433	1.30 432	0.05 436	0.03 433	0.21 432	0.01 431	0.29 428

mmdepth		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

monomultidepth		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

test_blank		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uanet_test		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

DCPMVS		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

cdsmvs_eth3d_5k		19.96 395	26.61 397	0.00 415	0.00 438	0.00 440	0.00 426	89.26 186	0.00 433	0.00 434	88.61 187	61.62 172	0.00 434	0.00 433	0.00 432	0.00 430

pcd_1.5k_mvsjas		5.26 401	7.02 404	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	63.15 148	0.00 434	0.00 433	0.00 432	0.00 430

sosnet-low-res		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

sosnet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uncertanet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

Regformer		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

ab-mvs-re		7.23 398	9.64 401	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	86.72 238	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uanet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.00 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 124	91.30 15

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 37	92.05 11	95.74 6	80.83 11

eth-test2					0.00 438

eth-test					0.00 438

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 58	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	74.31 129

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 52	92.81 3	95.79 3	80.98 9

GSMVS																88.96 258

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 277				88.96 258

sam_mvs												50.01 291

MTGPA								92.02 93

test_post											5.46 428	50.36 289	84.24 344

patchmatchnet-post											74.00 396	51.12 280	88.60 302

MTMP							92.18 34	32.83 432

TEST9					93.26 52	72.96 25	88.75 122	91.89 101	68.44 257	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	68.69 252	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	88.74 269

原ACMM2							86.86 187

test222					91.50 80	68.26 130	84.16 260	83.20 305	54.63 385	79.74 143	91.63 109	58.97 205			91.42 92	86.77 313

segment_acmp												73.08 39

testdata1							84.14 261		75.71 94

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					90.08 109	68.51 124

plane_prior6					89.84 118	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					89.90 117

plane_prior						68.71 116	90.38 70		77.62 42						86.16 171

n2								0.00 439

nn								0.00 439

door-mid								69.98 395

test11								92.23 87

door								69.44 398

HQP5-MVS						66.98 164

HQP-NCC					89.33 136		89.17 104		76.41 79	77.23 193

ACMP_Plane					89.33 136		89.17 104		76.41 79	77.23 193

HQP4-MVS										77.24 192			95.11 87			91.03 175

HQP3-MVS								92.19 90							85.99 175

HQP2-MVS												60.17 201

NP-MVS					89.62 122	68.32 128					90.24 146

ACMMP++_ref															81.95 233

ACMMP++															81.25 238

Test By Simon												64.33 135

High-resolution multi-view benchmark