High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

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

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

fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 176	87.08 231	65.21 200	89.09 113	90.21 158	79.67 18	89.98 18	95.02 18	73.17 38	91.71 236	91.30 2	91.60 89	92.34 138

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

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

test_fmvsmconf_n		85.92 54	86.04 55	85.57 76	85.03 275	69.51 93	89.62 89	90.58 142	73.42 158	87.75 42	94.02 52	72.85 43	93.24 167	90.37 6	90.75 104	93.96 61

test_fmvsmconf0.1_n		85.61 62	85.65 62	85.50 77	82.99 324	69.39 100	89.65 86	90.29 156	73.31 161	87.77 41	94.15 46	71.72 54	93.23 168	90.31 7	90.67 106	93.89 67

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 80	80.25 365	69.03 103	89.47 92	89.65 176	73.24 165	86.98 54	94.27 39	66.62 116	93.23 168	90.26 8	89.95 119	93.78 74

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 151	86.17 248	65.00 208	86.96 186	87.28 244	74.35 132	88.25 31	94.23 42	61.82 173	92.60 197	89.85 9	88.09 148	93.84 70

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 125	86.26 245	67.40 153	89.18 105	89.31 187	72.50 174	88.31 29	93.86 61	69.66 81	91.96 224	89.81 10	91.05 99	93.38 93

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 150	85.62 259	64.94 210	87.03 184	86.62 260	74.32 133	87.97 39	94.33 36	60.67 197	92.60 197	89.72 11	87.79 150	93.96 61

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

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

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 155	87.32 224	65.13 203	88.86 120	91.63 112	75.41 102	88.23 32	93.45 72	68.56 97	92.47 204	89.52 15	92.78 73	93.20 104

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

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

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

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

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

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

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 277	92.39 6				88.94 24	96.63 4	94.85 20

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 122	85.42 263	68.81 109	88.49 136	87.26 246	68.08 267	88.03 36	93.49 68	72.04 50	91.77 232	88.90 25	89.14 130	92.24 145

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 135	86.69 240	67.31 156	89.46 93	83.07 312	71.09 200	86.96 55	93.70 66	69.02 93	91.47 248	88.79 26	84.62 193	93.44 92

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 112	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 110	84.12 293	69.37 101	88.15 151	87.96 228	70.01 224	83.95 96	93.23 77	68.80 95	91.51 246	88.61 28	89.96 118	92.57 128

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 123	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 129	84.86 277	67.28 157	89.40 98	83.01 313	70.67 208	87.08 52	93.96 58	68.38 99	91.45 249	88.56 30	84.50 194	93.56 87

test_fmvsm_n_1920		85.29 70	85.34 68	85.13 88	86.12 250	69.93 86	88.65 132	90.78 138	69.97 226	88.27 30	93.98 57	71.39 60	91.54 243	88.49 31	90.45 109	93.91 64

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

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 137	85.38 264	68.40 126	88.34 143	86.85 256	67.48 274	87.48 47	93.40 73	70.89 66	91.61 237	88.38 33	89.22 128	92.16 149

fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 120	86.14 249	68.12 133	89.43 94	82.87 317	70.27 219	87.27 51	93.80 64	69.09 88	91.58 239	88.21 34	83.65 213	93.14 108

fmvsm_s_conf0.1_n_a		83.32 104	82.99 102	84.28 120	83.79 301	68.07 135	89.34 101	82.85 318	69.80 230	87.36 50	94.06 50	68.34 100	91.56 241	87.95 35	83.46 219	93.21 103

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

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 142	87.63 38	94.27 59	93.65 81
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 113

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

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 144	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

MTAPA		87.23 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 201	92.02 94	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 100

fmvsm_s_conf0.5_n_7		83.34 103	84.03 86	81.28 226	85.73 256	65.13 203	85.40 235	89.90 168	74.96 117	82.13 119	93.89 60	66.65 115	87.92 315	86.56 45	91.05 99	90.80 186

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

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

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

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

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

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

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 90	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 126	91.89 102	68.69 258	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 115

test9_res														84.90 55	95.70 26	92.87 120

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

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 167	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 41

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 61	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 128	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 121

MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 172	84.64 81	91.71 110	71.85 51	96.03 50	84.77 60	94.45 54	94.49 37

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 203	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

PC_three_1452									68.21 266	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

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

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

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

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

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 169	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS		86.69 39	86.95 37	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

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

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

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

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

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

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 161	88.69 130	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 112	83.49 74	91.14 98	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
dcpmvs_2		85.63 61	86.15 52	84.06 137	91.71 78	64.94 210	86.47 204	91.87 104	73.63 150	86.60 58	93.02 84	76.57 15	91.87 230	83.36 75	92.15 81	95.35 3

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

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 175	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 44

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

X-MVStestdata		80.37 163	77.83 199	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 436	67.45 109	96.60 33	83.06 78	94.50 51	94.07 56

mamv4		76.81 243	78.23 191	72.54 359	86.12 250	65.75 189	78.76 348	82.07 326	64.12 316	72.97 288	91.02 138	67.97 103	68.08 424	83.04 80	78.02 283	83.80 370

APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 154	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 90

agg_prior2														82.91 82	95.45 29	92.70 123

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

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 155	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 132

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 155	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 132

h-mvs33		83.15 106	82.19 114	86.02 69	90.56 98	70.85 73	88.15 151	89.16 195	76.02 91	84.67 78	91.39 123	61.54 178	95.50 66	82.71 86	75.48 320	91.72 158

hse-mvs2		81.72 128	80.94 134	84.07 135	88.72 163	67.68 144	85.87 221	87.26 246	76.02 91	84.67 78	88.22 205	61.54 178	93.48 157	82.71 86	73.44 348	91.06 177

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

ACMMP		85.89 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 71	80.73 139	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 100
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
diffmvs		82.10 120	81.88 122	82.76 197	83.00 322	63.78 234	83.68 273	89.76 172	72.94 170	82.02 121	89.85 158	65.96 129	90.79 267	82.38 90	87.30 158	93.71 76
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
patch_mono-2		83.65 92	84.54 79	80.99 235	90.06 113	65.83 184	84.21 265	88.74 214	71.60 190	85.01 70	92.44 96	74.51 25	83.50 359	82.15 91	92.15 81	93.64 83

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

TSAR-MVS + GP.		85.71 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 244	76.41 80	85.80 62	90.22 153	74.15 31	95.37 78	81.82 93	91.88 84	92.65 127

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

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

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

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

MGCFI-Net		85.06 75	85.51 65	83.70 153	89.42 131	63.01 252	89.43 94	92.62 73	76.43 79	87.53 45	91.34 124	72.82 44	93.42 162	81.28 98	88.74 137	94.66 31

casdiffmvs		85.11 73	85.14 73	85.01 91	87.20 227	65.77 188	87.75 163	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 135	81.27 99	90.48 108	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_HR		85.14 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 217	90.33 153	76.11 89	82.08 120	91.61 116	71.36 61	94.17 125	81.02 100	92.58 76	92.08 151

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 113	73.89 145	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 86

CPTT-MVS		83.73 90	83.33 97	84.92 96	93.28 49	70.86 72	92.09 36	90.38 149	68.75 257	79.57 151	92.83 88	60.60 201	93.04 186	80.92 102	91.56 92	90.86 185

ETV-MVS		84.90 78	84.67 78	85.59 75	89.39 134	68.66 120	88.74 128	92.64 72	79.97 15	84.10 92	85.71 271	69.32 85	95.38 75	80.82 103	91.37 95	92.72 122

DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 77	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		83.88 86	83.53 92	84.96 93	86.77 237	69.28 102	90.46 67	92.67 67	74.79 122	82.95 109	91.33 125	72.70 45	93.09 181	80.79 105	79.28 271	92.50 132

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 81	88.18 182	67.85 139	87.66 165	89.73 174	80.05 14	82.95 109	89.59 166	70.74 69	94.82 101	80.66 106	84.72 191	93.28 99

MSLP-MVS++		85.43 66	85.76 60	84.45 111	91.93 75	70.24 79	90.71 59	92.86 58	77.46 51	84.22 89	92.81 90	67.16 113	92.94 188	80.36 107	94.35 57	90.16 215

MVS_111021_LR		82.61 115	82.11 115	84.11 128	88.82 157	71.58 55	85.15 238	86.16 268	74.69 124	80.47 141	91.04 135	62.29 166	90.55 271	80.33 108	90.08 116	90.20 214

DELS-MVS		85.41 67	85.30 71	85.77 72	88.49 170	67.93 138	85.52 234	93.44 27	78.70 31	83.63 104	89.03 181	74.57 24	95.71 61	80.26 109	94.04 61	93.66 77
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 173	83.71 100	91.86 108	55.69 234	95.35 79	80.03 110	89.74 122	94.69 27

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 89	87.87 199	67.53 149	87.44 173	89.66 175	79.74 17	82.23 118	89.41 175	70.24 75	94.74 104	79.95 111	83.92 205	92.99 118

CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 142	83.16 108	91.07 134	75.94 18	95.19 82	79.94 112	94.38 56	93.55 88

RRT-MVS		82.60 117	82.10 116	84.10 129	87.98 195	62.94 257	87.45 172	91.27 123	77.42 52	79.85 147	90.28 149	56.62 231	94.70 107	79.87 113	88.15 147	94.67 28

OPM-MVS		83.50 98	82.95 103	85.14 85	88.79 160	70.95 69	89.13 111	91.52 116	77.55 48	80.96 137	91.75 109	60.71 195	94.50 112	79.67 114	86.51 170	89.97 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 134	92.42 80	68.32 265	84.61 82	93.48 69	72.32 46	96.15 48	79.00 115	95.43 30	94.28 48

MVSFormer		82.85 112	82.05 118	85.24 83	87.35 218	70.21 80	90.50 64	90.38 149	68.55 260	81.32 130	89.47 169	61.68 175	93.46 159	78.98 116	90.26 112	92.05 152

test_djsdf		80.30 164	79.32 165	83.27 167	83.98 297	65.37 198	90.50 64	90.38 149	68.55 260	76.19 225	88.70 188	56.44 232	93.46 159	78.98 116	80.14 261	90.97 182

test_vis1_n_1920		75.52 265	75.78 241	74.75 338	79.84 371	57.44 326	83.26 284	85.52 275	62.83 333	79.34 155	86.17 264	45.10 344	79.71 379	78.75 118	81.21 245	87.10 316

HQP_MVS		83.64 93	83.14 98	85.14 85	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 160	91.00 139	60.42 203	95.38 75	78.71 119	86.32 172	91.33 169

plane_prior5								92.44 77					95.38 75	78.71 119	86.32 172	91.33 169

LPG-MVS_test		82.08 121	81.27 127	84.50 108	89.23 143	68.76 112	90.22 73	91.94 100	75.37 104	76.64 213	91.51 118	54.29 247	94.91 95	78.44 121	83.78 206	89.83 236

LGP-MVS_train				84.50 108	89.23 143	68.76 112		91.94 100	75.37 104	76.64 213	91.51 118	54.29 247	94.91 95	78.44 121	83.78 206	89.83 236

lupinMVS		81.39 137	80.27 146	84.76 102	87.35 218	70.21 80	85.55 230	86.41 262	62.85 332	81.32 130	88.61 192	61.68 175	92.24 216	78.41 123	90.26 112	91.83 155

jason		81.39 137	80.29 145	84.70 104	86.63 242	69.90 88	85.95 218	86.77 257	63.24 325	81.07 136	89.47 169	61.08 191	92.15 218	78.33 124	90.07 117	92.05 152
jason: jason.
xiu_mvs_v1_base_debu		80.80 149	79.72 155	84.03 142	87.35 218	70.19 82	85.56 227	88.77 210	69.06 250	81.83 122	88.16 206	50.91 287	92.85 190	78.29 125	87.56 152	89.06 255

xiu_mvs_v1_base		80.80 149	79.72 155	84.03 142	87.35 218	70.19 82	85.56 227	88.77 210	69.06 250	81.83 122	88.16 206	50.91 287	92.85 190	78.29 125	87.56 152	89.06 255

xiu_mvs_v1_base_debi		80.80 149	79.72 155	84.03 142	87.35 218	70.19 82	85.56 227	88.77 210	69.06 250	81.83 122	88.16 206	50.91 287	92.85 190	78.29 125	87.56 152	89.06 255

Effi-MVS+		83.62 95	83.08 99	85.24 83	88.38 176	67.45 150	88.89 119	89.15 196	75.50 100	82.27 117	88.28 202	69.61 82	94.45 114	77.81 128	87.84 149	93.84 70

PS-MVSNAJss		82.07 122	81.31 126	84.34 116	86.51 243	67.27 158	89.27 102	91.51 117	71.75 185	79.37 153	90.22 153	63.15 153	94.27 118	77.69 129	82.36 233	91.49 165

ACMP	74.13 6	81.51 136	80.57 138	84.36 114	89.42 131	68.69 119	89.97 77	91.50 120	74.46 130	75.04 260	90.41 148	53.82 252	94.54 109	77.56 130	82.91 225	89.86 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 131

HQP-MVS		82.61 115	82.02 119	84.37 113	89.33 136	66.98 165	89.17 106	92.19 91	76.41 80	77.23 198	90.23 152	60.17 206	95.11 87	77.47 131	85.99 180	91.03 179

MVS_Test		83.15 106	83.06 100	83.41 163	86.86 233	63.21 248	86.11 215	92.00 96	74.31 134	82.87 111	89.44 174	70.03 76	93.21 170	77.39 133	88.50 142	93.81 72

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 212	93.37 74	60.40 205	96.75 26	77.20 134	93.73 64	95.29 5

anonymousdsp		78.60 203	77.15 217	82.98 184	80.51 363	67.08 163	87.24 179	89.53 180	65.66 297	75.16 255	87.19 233	52.52 261	92.25 215	77.17 135	79.34 270	89.61 243

mmtdpeth		74.16 280	73.01 284	77.60 306	83.72 304	61.13 276	85.10 240	85.10 279	72.06 183	77.21 202	80.33 366	43.84 353	85.75 337	77.14 136	52.61 414	85.91 339

VDD-MVS		83.01 111	82.36 112	84.96 93	91.02 88	66.40 172	88.91 118	88.11 223	77.57 45	84.39 87	93.29 76	52.19 267	93.91 136	77.05 137	88.70 138	94.57 35

XVG-OURS-SEG-HR		80.81 147	79.76 154	83.96 147	85.60 260	68.78 111	83.54 280	90.50 145	70.66 211	76.71 211	91.66 111	60.69 196	91.26 254	76.94 138	81.58 241	91.83 155

jajsoiax		79.29 186	77.96 194	83.27 167	84.68 282	66.57 171	89.25 103	90.16 160	69.20 246	75.46 240	89.49 168	45.75 339	93.13 179	76.84 139	80.80 251	90.11 219

SDMVSNet		80.38 161	80.18 147	80.99 235	89.03 152	64.94 210	80.45 324	89.40 183	75.19 110	76.61 215	89.98 155	60.61 200	87.69 319	76.83 140	83.55 215	90.33 209

mvs_tets		79.13 190	77.77 203	83.22 171	84.70 281	66.37 173	89.17 106	90.19 159	69.38 239	75.40 243	89.46 171	44.17 351	93.15 177	76.78 141	80.70 253	90.14 216

DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 181	93.04 41	69.80 230	82.85 112	91.22 128	73.06 40	96.02 52	76.72 142	94.63 48	91.46 168

test_cas_vis1_n_1920		73.76 286	73.74 275	73.81 347	75.90 393	59.77 296	80.51 322	82.40 322	58.30 374	81.62 128	85.69 272	44.35 350	76.41 397	76.29 143	78.61 274	85.23 349

ET-MVSNet_ETH3D		78.63 202	76.63 232	84.64 105	86.73 238	69.47 95	85.01 242	84.61 285	69.54 236	66.51 365	86.59 251	50.16 296	91.75 233	76.26 144	84.24 202	92.69 125

v2v482		80.23 165	79.29 166	83.05 180	83.62 305	64.14 227	87.04 183	89.97 165	73.61 151	78.18 178	87.22 231	61.10 190	93.82 140	76.11 145	76.78 300	91.18 173

test_fmvs1_n		70.86 319	70.24 316	72.73 357	72.51 415	55.28 358	81.27 310	79.71 356	51.49 404	78.73 162	84.87 293	27.54 411	77.02 391	76.06 146	79.97 263	85.88 340

CLD-MVS		82.31 118	81.65 124	84.29 119	88.47 171	67.73 143	85.81 225	92.35 82	75.78 94	78.33 174	86.58 253	64.01 143	94.35 115	76.05 147	87.48 155	90.79 187
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		83.72 91	82.92 104	86.14 65	84.22 291	69.48 94	91.05 56	85.27 277	81.30 6	76.83 207	91.65 112	66.09 125	95.56 63	76.00 148	93.85 62	93.38 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs1		70.93 318	70.52 311	72.16 361	73.71 404	55.05 360	80.82 313	78.77 365	51.21 405	78.58 167	84.41 301	31.20 406	76.94 392	75.88 149	80.12 262	84.47 361

XVG-OURS		80.41 160	79.23 168	83.97 146	85.64 258	69.02 105	83.03 292	90.39 148	71.09 200	77.63 189	91.49 120	54.62 246	91.35 252	75.71 150	83.47 218	91.54 162

V42		79.38 185	78.24 189	82.83 189	81.10 357	65.50 194	85.55 230	89.82 169	71.57 191	78.21 176	86.12 265	60.66 198	93.18 176	75.64 151	75.46 322	89.81 238

PS-MVSNAJ		81.69 130	81.02 132	83.70 153	89.51 127	68.21 132	84.28 264	90.09 162	70.79 205	81.26 134	85.62 276	63.15 153	94.29 116	75.62 152	88.87 133	88.59 279

xiu_mvs_v2_base		81.69 130	81.05 131	83.60 155	89.15 146	68.03 137	84.46 258	90.02 163	70.67 208	81.30 133	86.53 256	63.17 152	94.19 124	75.60 153	88.54 140	88.57 280

EIA-MVS		83.31 105	82.80 106	84.82 99	89.59 123	65.59 192	88.21 147	92.68 66	74.66 126	78.96 158	86.42 258	69.06 90	95.26 80	75.54 154	90.09 115	93.62 84

AUN-MVS		79.21 188	77.60 209	84.05 140	88.71 164	67.61 146	85.84 223	87.26 246	69.08 249	77.23 198	88.14 210	53.20 259	93.47 158	75.50 155	73.45 347	91.06 177

mvsmamba		80.60 155	79.38 162	84.27 122	89.74 121	67.24 160	87.47 170	86.95 252	70.02 223	75.38 244	88.93 182	51.24 284	92.56 200	75.47 156	89.22 128	93.00 117

reproduce_monomvs		75.40 269	74.38 266	78.46 289	83.92 299	57.80 320	83.78 271	86.94 253	73.47 157	72.25 299	84.47 299	38.74 382	89.27 292	75.32 157	70.53 367	88.31 285

OMC-MVS		82.69 113	81.97 121	84.85 98	88.75 162	67.42 151	87.98 154	90.87 136	74.92 118	79.72 149	91.65 112	62.19 169	93.96 129	75.26 158	86.42 171	93.16 106

v1144		80.03 169	79.03 172	83.01 182	83.78 302	64.51 218	87.11 182	90.57 144	71.96 184	78.08 181	86.20 263	61.41 182	93.94 132	74.93 159	77.23 291	90.60 197

MVSTER		79.01 193	77.88 198	82.38 203	83.07 319	64.80 214	84.08 269	88.95 206	69.01 253	78.69 163	87.17 234	54.70 244	92.43 206	74.69 160	80.57 255	89.89 234

test_vis1_n		69.85 333	69.21 322	71.77 363	72.66 414	55.27 359	81.48 306	76.21 384	52.03 401	75.30 251	83.20 331	28.97 409	76.22 399	74.60 161	78.41 280	83.81 369

test_fmvs2		68.35 346	67.48 345	70.98 372	69.50 418	51.95 382	80.05 330	76.38 383	49.33 407	74.65 267	84.38 302	23.30 420	75.40 408	74.51 162	75.17 331	85.60 343

PVSNet_Blended_VisFu		82.62 114	81.83 123	84.96 93	90.80 94	69.76 90	88.74 128	91.70 111	69.39 238	78.96 158	88.46 197	65.47 132	94.87 100	74.42 163	88.57 139	90.24 213

v8		79.97 171	79.02 173	82.80 192	84.09 294	64.50 220	87.96 155	90.29 156	74.13 141	75.24 253	86.81 240	62.88 158	93.89 139	74.39 164	75.40 325	90.00 227

v144192		79.47 179	78.37 185	82.78 195	83.35 310	63.96 230	86.96 186	90.36 152	69.99 225	77.50 190	85.67 274	60.66 198	93.77 144	74.27 165	76.58 301	90.62 195

ACMM	73.20 8	80.78 152	79.84 153	83.58 157	89.31 139	68.37 127	89.99 76	91.60 114	70.28 218	77.25 196	89.66 162	53.37 257	93.53 155	74.24 166	82.85 226	88.85 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 202		58.10 377	87.04 53			88.98 299	74.07 167

v1192		79.59 176	78.43 184	83.07 179	83.55 307	64.52 217	86.93 189	90.58 142	70.83 204	77.78 186	85.90 267	59.15 209	93.94 132	73.96 168	77.19 293	90.76 189

v10		79.74 173	78.67 177	82.97 185	84.06 295	64.95 209	87.88 161	90.62 141	73.11 166	75.11 257	86.56 254	61.46 181	94.05 128	73.68 169	75.55 318	89.90 233

v1921920		79.22 187	78.03 193	82.80 192	83.30 312	63.94 231	86.80 193	90.33 153	69.91 228	77.48 191	85.53 278	58.44 213	93.75 146	73.60 170	76.85 298	90.71 193

cl22		78.07 216	77.01 219	81.23 228	82.37 338	61.83 270	83.55 278	87.98 227	68.96 254	75.06 259	83.87 313	61.40 183	91.88 229	73.53 171	76.39 305	89.98 230

Effi-MVS+-dtu		80.03 169	78.57 180	84.42 112	85.13 273	68.74 114	88.77 124	88.10 224	74.99 114	74.97 261	83.49 326	57.27 225	93.36 163	73.53 171	80.88 249	91.18 173

c3_l		78.75 198	77.91 196	81.26 227	82.89 326	61.56 273	84.09 268	89.13 198	69.97 226	75.56 236	84.29 305	66.36 121	92.09 220	73.47 173	75.48 320	90.12 218

VDDNet		81.52 134	80.67 137	84.05 140	90.44 101	64.13 228	89.73 84	85.91 271	71.11 199	83.18 107	93.48 69	50.54 293	93.49 156	73.40 174	88.25 145	94.54 36

CANet_DTU		80.61 154	79.87 152	82.83 189	85.60 260	63.17 251	87.36 174	88.65 216	76.37 84	75.88 231	88.44 198	53.51 255	93.07 182	73.30 175	89.74 122	92.25 143

miper_ehance_all_eth		78.59 204	77.76 204	81.08 233	82.66 331	61.56 273	83.65 274	89.15 196	68.87 255	75.55 237	83.79 317	66.49 119	92.03 221	73.25 176	76.39 305	89.64 242

3Dnovator	76.31 5	83.38 102	82.31 113	86.59 55	87.94 196	72.94 28	90.64 60	92.14 93	77.21 58	75.47 238	92.83 88	58.56 212	94.72 105	73.24 177	92.71 75	92.13 150

v1240		78.99 194	77.78 202	82.64 198	83.21 314	63.54 239	86.62 200	90.30 155	69.74 235	77.33 194	85.68 273	57.04 227	93.76 145	73.13 178	76.92 295	90.62 195

miper_enhance_ethall		77.87 223	76.86 223	80.92 238	81.65 345	61.38 275	82.68 293	88.98 203	65.52 299	75.47 238	82.30 346	65.76 131	92.00 223	72.95 179	76.39 305	89.39 248

MG-MVS		83.41 100	83.45 93	83.28 166	92.74 65	62.28 264	88.17 149	89.50 181	75.22 107	81.49 129	92.74 94	66.75 114	95.11 87	72.85 180	91.58 91	92.45 135

EPP-MVSNet		83.40 101	83.02 101	84.57 106	90.13 107	64.47 221	92.32 30	90.73 139	74.45 131	79.35 154	91.10 132	69.05 91	95.12 85	72.78 181	87.22 159	94.13 53

test_fmvs3		63.36 370	61.82 373	67.98 387	62.51 427	46.96 408	77.37 366	74.03 394	45.24 412	67.50 347	78.79 383	12.16 432	72.98 416	72.77 182	66.02 384	83.99 367

IterMVS-LS		80.06 168	79.38 162	82.11 206	85.89 253	63.20 249	86.79 194	89.34 185	74.19 138	75.45 241	86.72 243	66.62 116	92.39 208	72.58 183	76.86 297	90.75 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		78.73 199	77.83 199	81.43 220	85.17 269	60.30 291	89.41 97	90.90 134	71.21 197	77.17 203	88.73 187	46.38 328	93.21 170	72.57 184	78.96 273	90.79 187

EI-MVSNet		80.52 159	79.98 149	82.12 205	84.28 289	63.19 250	86.41 205	88.95 206	74.18 139	78.69 163	87.54 223	66.62 116	92.43 206	72.57 184	80.57 255	90.74 191

Vis-MVSNet		83.46 99	82.80 106	85.43 79	90.25 105	68.74 114	90.30 72	90.13 161	76.33 86	80.87 138	92.89 86	61.00 192	94.20 122	72.45 186	90.97 101	93.35 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS		81.82 127	81.23 128	83.57 158	91.89 76	63.43 244	89.84 78	81.85 329	77.04 64	83.21 106	93.10 79	52.26 266	93.43 161	71.98 187	89.95 119	93.85 68

v148		78.72 200	77.80 201	81.47 219	82.73 329	61.96 268	86.30 210	88.08 225	73.26 163	76.18 226	85.47 280	62.46 163	92.36 210	71.92 188	73.82 344	90.09 221

PVSNet_BlendedMVS		80.60 155	80.02 148	82.36 204	88.85 154	65.40 195	86.16 214	92.00 96	69.34 240	78.11 179	86.09 266	66.02 127	94.27 118	71.52 189	82.06 236	87.39 303

PVSNet_Blended		80.98 142	80.34 143	82.90 187	88.85 154	65.40 195	84.43 260	92.00 96	67.62 271	78.11 179	85.05 291	66.02 127	94.27 118	71.52 189	89.50 124	89.01 260

eth_miper_zixun_eth		77.92 221	76.69 230	81.61 217	83.00 322	61.98 267	83.15 286	89.20 194	69.52 237	74.86 263	84.35 304	61.76 174	92.56 200	71.50 191	72.89 352	90.28 212

UA-Net		85.08 74	84.96 75	85.45 78	92.07 73	68.07 135	89.78 82	90.86 137	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 192	90.88 103	93.07 110

FA-MVS(test-final)		80.96 143	79.91 151	84.10 129	88.30 179	65.01 207	84.55 255	90.01 164	73.25 164	79.61 150	87.57 220	58.35 214	94.72 105	71.29 193	86.25 174	92.56 129

cl____		77.72 226	76.76 227	80.58 244	82.49 335	60.48 288	83.09 288	87.87 231	69.22 244	74.38 272	85.22 286	62.10 170	91.53 244	71.09 194	75.41 324	89.73 241

DIV-MVS_self_test		77.72 226	76.76 227	80.58 244	82.48 336	60.48 288	83.09 288	87.86 232	69.22 244	74.38 272	85.24 284	62.10 170	91.53 244	71.09 194	75.40 325	89.74 240

MonoMVSNet		76.49 251	75.80 240	78.58 283	81.55 348	58.45 307	86.36 208	86.22 266	74.87 121	74.73 265	83.73 319	51.79 279	88.73 304	70.78 196	72.15 357	88.55 281

test_yl		81.17 139	80.47 141	83.24 169	89.13 147	63.62 235	86.21 212	89.95 166	72.43 178	81.78 126	89.61 164	57.50 222	93.58 150	70.75 197	86.90 163	92.52 130

DCV-MVSNet		81.17 139	80.47 141	83.24 169	89.13 147	63.62 235	86.21 212	89.95 166	72.43 178	81.78 126	89.61 164	57.50 222	93.58 150	70.75 197	86.90 163	92.52 130

VNet		82.21 119	82.41 110	81.62 215	90.82 93	60.93 280	84.47 256	89.78 170	76.36 85	84.07 93	91.88 106	64.71 139	90.26 273	70.68 199	88.89 132	93.66 77

mvs_anonymous		79.42 182	79.11 171	80.34 249	84.45 288	57.97 315	82.59 294	87.62 237	67.40 275	76.17 228	88.56 195	68.47 98	89.59 286	70.65 200	86.05 178	93.47 91

VPA-MVSNet		80.60 155	80.55 139	80.76 241	88.07 190	60.80 283	86.86 191	91.58 115	75.67 98	80.24 143	89.45 173	63.34 147	90.25 274	70.51 201	79.22 272	91.23 172

PAPM_NR		83.02 110	82.41 110	84.82 99	92.47 70	66.37 173	87.93 158	91.80 107	73.82 146	77.32 195	90.66 144	67.90 105	94.90 97	70.37 202	89.48 125	93.19 105

thisisatest0530		79.40 183	77.76 204	84.31 117	87.69 211	65.10 206	87.36 174	84.26 292	70.04 222	77.42 192	88.26 204	49.94 299	94.79 103	70.20 203	84.70 192	93.03 114

tttt0517		79.40 183	77.91 196	83.90 149	88.10 188	63.84 232	88.37 142	84.05 294	71.45 193	76.78 209	89.12 178	49.93 301	94.89 98	70.18 204	83.18 223	92.96 119

UniMVSNet_NR-MVSNet		81.88 125	81.54 125	82.92 186	88.46 172	63.46 242	87.13 180	92.37 81	80.19 12	78.38 172	89.14 177	71.66 57	93.05 184	70.05 205	76.46 303	92.25 143

DU-MVS		81.12 141	80.52 140	82.90 187	87.80 203	63.46 242	87.02 185	91.87 104	79.01 28	78.38 172	89.07 179	65.02 136	93.05 184	70.05 205	76.46 303	92.20 146

XVG-ACMP-BASELINE		76.11 257	74.27 268	81.62 215	83.20 315	64.67 216	83.60 277	89.75 173	69.75 233	71.85 303	87.09 236	32.78 401	92.11 219	69.99 207	80.43 257	88.09 289

GeoE		81.71 129	81.01 133	83.80 152	89.51 127	64.45 222	88.97 116	88.73 215	71.27 196	78.63 166	89.76 160	66.32 122	93.20 173	69.89 208	86.02 179	93.74 75

FIs		82.07 122	82.42 109	81.04 234	88.80 159	58.34 309	88.26 146	93.49 26	76.93 66	78.47 171	91.04 135	69.92 78	92.34 212	69.87 209	84.97 188	92.44 136

114514_t		80.68 153	79.51 159	84.20 126	94.09 38	67.27 158	89.64 87	91.11 130	58.75 372	74.08 274	90.72 143	58.10 215	95.04 92	69.70 210	89.42 126	90.30 211

Anonymous20231211		78.97 195	77.69 207	82.81 191	90.54 99	64.29 225	90.11 75	91.51 117	65.01 306	76.16 229	88.13 211	50.56 292	93.03 187	69.68 211	77.56 290	91.11 175

Patchmatch-RL test		70.24 327	67.78 340	77.61 304	77.43 388	59.57 300	71.16 396	70.33 402	62.94 331	68.65 338	72.77 408	50.62 291	85.49 342	69.58 212	66.58 382	87.77 295

UniMVSNet (Re)		81.60 133	81.11 130	83.09 176	88.38 176	64.41 223	87.60 166	93.02 45	78.42 34	78.56 168	88.16 206	69.78 79	93.26 166	69.58 212	76.49 302	91.60 159

IterMVS-SCA-FT		75.43 267	73.87 273	80.11 255	82.69 330	64.85 213	81.57 305	83.47 303	69.16 247	70.49 315	84.15 311	51.95 274	88.15 312	69.23 214	72.14 358	87.34 305

v7n		78.97 195	77.58 210	83.14 174	83.45 309	65.51 193	88.32 144	91.21 125	73.69 149	72.41 296	86.32 261	57.93 216	93.81 141	69.18 215	75.65 316	90.11 219

Anonymous20240529		80.19 167	78.89 175	84.10 129	90.60 97	64.75 215	88.95 117	90.90 134	65.97 294	80.59 140	91.17 131	49.97 298	93.73 148	69.16 216	82.70 230	93.81 72

miper_lstm_enhance		74.11 281	73.11 283	77.13 312	80.11 367	59.62 298	72.23 392	86.92 255	66.76 279	70.40 316	82.92 336	56.93 228	82.92 363	69.06 217	72.63 353	88.87 267

testdata				79.97 257	90.90 91	64.21 226		84.71 283	59.27 365	85.40 66	92.91 85	62.02 172	89.08 297	68.95 218	91.37 95	86.63 326

test1111		79.43 181	79.18 170	80.15 254	89.99 114	53.31 376	87.33 176	77.05 379	75.04 113	80.23 144	92.77 93	48.97 313	92.33 213	68.87 219	92.40 80	94.81 21

GA-MVS		76.87 242	75.17 256	81.97 210	82.75 328	62.58 259	81.44 308	86.35 265	72.16 182	74.74 264	82.89 337	46.20 333	92.02 222	68.85 220	81.09 246	91.30 171

test2506		77.30 236	76.49 233	79.74 262	90.08 109	52.02 380	87.86 162	63.10 422	74.88 119	80.16 145	92.79 91	38.29 386	92.35 211	68.74 221	92.50 78	94.86 18

ECVR-MVS		79.61 174	79.26 167	80.67 243	90.08 109	54.69 363	87.89 160	77.44 375	74.88 119	80.27 142	92.79 91	48.96 314	92.45 205	68.55 222	92.50 78	94.86 18

UGNet		80.83 146	79.59 158	84.54 107	88.04 191	68.09 134	89.42 96	88.16 222	76.95 65	76.22 224	89.46 171	49.30 308	93.94 132	68.48 223	90.31 110	91.60 159
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
FC-MVSNet-test		81.52 134	82.02 119	80.03 256	88.42 175	55.97 348	87.95 156	93.42 29	77.10 62	77.38 193	90.98 141	69.96 77	91.79 231	68.46 224	84.50 194	92.33 139

DP-MVS Recon		83.11 109	82.09 117	86.15 63	94.44 19	70.92 71	88.79 123	92.20 90	70.53 213	79.17 156	91.03 137	64.12 142	96.03 50	68.39 225	90.14 114	91.50 164

UniMVSNet_ETH3D		79.10 191	78.24 189	81.70 214	86.85 234	60.24 292	87.28 178	88.79 209	74.25 137	76.84 206	90.53 147	49.48 304	91.56 241	67.98 226	82.15 234	93.29 98

D2MVS		74.82 274	73.21 281	79.64 266	79.81 372	62.56 260	80.34 326	87.35 243	64.37 313	68.86 336	82.66 341	46.37 329	90.10 276	67.91 227	81.24 244	86.25 329

IS-MVSNet		83.15 106	82.81 105	84.18 127	89.94 116	63.30 246	91.59 43	88.46 220	79.04 27	79.49 152	92.16 100	65.10 135	94.28 117	67.71 228	91.86 87	94.95 11

Fast-Effi-MVS+-dtu		78.02 218	76.49 233	82.62 199	83.16 318	66.96 167	86.94 188	87.45 242	72.45 175	71.49 308	84.17 310	54.79 243	91.58 239	67.61 229	80.31 258	89.30 251

PAPR		81.66 132	80.89 135	83.99 145	90.27 104	64.00 229	86.76 197	91.77 110	68.84 256	77.13 205	89.50 167	67.63 107	94.88 99	67.55 230	88.52 141	93.09 109

cascas		76.72 245	74.64 260	82.99 183	85.78 255	65.88 183	82.33 296	89.21 193	60.85 351	72.74 290	81.02 357	47.28 321	93.75 146	67.48 231	85.02 187	89.34 250

1314		76.53 247	75.30 254	80.21 253	83.93 298	62.32 263	84.66 250	88.81 208	60.23 356	70.16 321	84.07 312	55.30 237	90.73 269	67.37 232	83.21 222	87.59 300

无先验							87.48 169	88.98 203	60.00 358				94.12 126	67.28 233		88.97 263

thisisatest0515		77.33 235	75.38 251	83.18 172	85.27 268	63.80 233	82.11 299	83.27 306	65.06 304	75.91 230	83.84 315	49.54 303	94.27 118	67.24 234	86.19 175	91.48 166

原ACMM1				84.35 115	93.01 60	68.79 110		92.44 77	63.96 322	81.09 135	91.57 117	66.06 126	95.45 68	67.19 235	94.82 46	88.81 270

Baseline_NR-MVSNet		78.15 214	78.33 187	77.61 304	85.79 254	56.21 346	86.78 195	85.76 273	73.60 152	77.93 184	87.57 220	65.02 136	88.99 298	67.14 236	75.33 327	87.63 297

TranMVSNet+NR-MVSNet		80.84 145	80.31 144	82.42 202	87.85 200	62.33 262	87.74 164	91.33 122	80.55 9	77.99 183	89.86 157	65.23 134	92.62 195	67.05 237	75.24 330	92.30 141

Fast-Effi-MVS+		80.81 147	79.92 150	83.47 159	88.85 154	64.51 218	85.53 232	89.39 184	70.79 205	78.49 170	85.06 290	67.54 108	93.58 150	67.03 238	86.58 168	92.32 140

VPNet		78.69 201	78.66 178	78.76 279	88.31 178	55.72 352	84.45 259	86.63 259	76.79 70	78.26 175	90.55 146	59.30 208	89.70 285	66.63 239	77.05 294	90.88 184

PM-MVS		66.41 358	64.14 361	73.20 353	73.92 403	56.45 339	78.97 345	64.96 419	63.88 323	64.72 376	80.24 368	19.84 424	83.44 360	66.24 240	64.52 389	79.71 400

test-LLR		72.94 301	72.43 290	74.48 339	81.35 353	58.04 313	78.38 353	77.46 373	66.66 281	69.95 325	79.00 380	48.06 317	79.24 380	66.13 241	84.83 189	86.15 332

test-mter		71.41 313	70.39 315	74.48 339	81.35 353	58.04 313	78.38 353	77.46 373	60.32 355	69.95 325	79.00 380	36.08 395	79.24 380	66.13 241	84.83 189	86.15 332

MVS		78.19 213	76.99 221	81.78 212	85.66 257	66.99 164	84.66 250	90.47 146	55.08 393	72.02 302	85.27 283	63.83 145	94.11 127	66.10 243	89.80 121	84.24 363

NR-MVSNet		80.23 165	79.38 162	82.78 195	87.80 203	63.34 245	86.31 209	91.09 131	79.01 28	72.17 300	89.07 179	67.20 112	92.81 193	66.08 244	75.65 316	92.20 146

CVMVSNet		72.99 300	72.58 289	74.25 342	84.28 289	50.85 394	86.41 205	83.45 304	44.56 413	73.23 285	87.54 223	49.38 306	85.70 338	65.90 245	78.44 278	86.19 331

IterMVS		74.29 277	72.94 285	78.35 290	81.53 349	63.49 241	81.58 304	82.49 321	68.06 268	69.99 324	83.69 321	51.66 281	85.54 341	65.85 246	71.64 361	86.01 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 278	72.42 291	79.80 261	83.76 303	59.59 299	85.92 220	86.64 258	66.39 288	66.96 355	87.58 219	39.46 377	91.60 238	65.76 247	69.27 372	88.22 286

tpmrst		72.39 303	72.13 294	73.18 354	80.54 362	49.91 398	79.91 333	79.08 363	63.11 327	71.69 305	79.95 371	55.32 236	82.77 364	65.66 248	73.89 342	86.87 319

MAR-MVS		81.84 126	80.70 136	85.27 82	91.32 82	71.53 56	89.82 79	90.92 133	69.77 232	78.50 169	86.21 262	62.36 165	94.52 111	65.36 249	92.05 83	89.77 239
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		78.25 209	77.01 219	81.99 209	91.03 87	60.67 285	84.77 247	83.90 296	70.65 212	80.00 146	91.20 129	41.08 371	91.43 250	65.21 250	85.26 186	93.85 68

ab-mvs		79.51 177	78.97 174	81.14 231	88.46 172	60.91 281	83.84 270	89.24 192	70.36 215	79.03 157	88.87 185	63.23 151	90.21 275	65.12 251	82.57 231	92.28 142

IB-MVS	68.01 15	75.85 261	73.36 280	83.31 165	84.76 280	66.03 177	83.38 281	85.06 280	70.21 221	69.40 331	81.05 356	45.76 338	94.66 108	65.10 252	75.49 319	89.25 252
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		79.49 178	79.22 169	80.27 251	88.79 160	58.35 308	85.06 241	88.61 218	78.56 32	77.65 188	88.34 200	63.81 146	90.66 270	64.98 253	77.22 292	91.80 157

CostFormer		75.24 271	73.90 272	79.27 271	82.65 332	58.27 310	80.80 314	82.73 320	61.57 346	75.33 250	83.13 332	55.52 235	91.07 263	64.98 253	78.34 281	88.45 282

API-MVS		81.99 124	81.23 128	84.26 124	90.94 90	70.18 85	91.10 55	89.32 186	71.51 192	78.66 165	88.28 202	65.26 133	95.10 90	64.74 255	91.23 97	87.51 301

新几何1				83.42 161	93.13 54	70.71 74		85.48 276	57.43 383	81.80 125	91.98 103	63.28 148	92.27 214	64.60 256	92.99 70	87.27 308

testing91		76.54 246	75.66 245	79.18 274	88.43 174	55.89 349	81.08 311	83.00 314	73.76 148	75.34 246	84.29 305	46.20 333	90.07 277	64.33 257	84.50 194	91.58 161

testing99		76.09 258	75.12 257	79.00 275	88.16 183	55.50 355	80.79 315	81.40 334	73.30 162	75.17 254	84.27 308	44.48 348	90.02 278	64.28 258	84.22 203	91.48 166

pm-mvs1		77.25 237	76.68 231	78.93 277	84.22 291	58.62 306	86.41 205	88.36 221	71.37 194	73.31 283	88.01 212	61.22 188	89.15 296	64.24 259	73.01 351	89.03 259

TESTMET0.1,1		69.89 332	69.00 324	72.55 358	79.27 381	56.85 332	78.38 353	74.71 392	57.64 380	68.09 342	77.19 393	37.75 388	76.70 393	63.92 260	84.09 204	84.10 366

QAPM		80.88 144	79.50 160	85.03 90	88.01 194	68.97 107	91.59 43	92.00 96	66.63 286	75.15 256	92.16 100	57.70 219	95.45 68	63.52 261	88.76 136	90.66 194

baseline2		75.70 262	73.83 274	81.30 225	83.26 313	61.79 271	82.57 295	80.65 341	66.81 277	66.88 356	83.42 327	57.86 218	92.19 217	63.47 262	79.57 265	89.91 232

LCM-MVSNet-Re		77.05 238	76.94 222	77.36 308	87.20 227	51.60 387	80.06 329	80.46 345	75.20 109	67.69 345	86.72 243	62.48 162	88.98 299	63.44 263	89.25 127	91.51 163

gm-plane-assit					81.40 351	53.83 371			62.72 336		80.94 359		92.39 208	63.40 264

baseline1		76.98 240	76.75 229	77.66 302	88.13 186	55.66 353	85.12 239	81.89 327	73.04 168	76.79 208	88.90 183	62.43 164	87.78 318	63.30 265	71.18 364	89.55 245

AdaColmap		80.58 158	79.42 161	84.06 137	93.09 57	68.91 108	89.36 100	88.97 205	69.27 241	75.70 234	89.69 161	57.20 226	95.77 59	63.06 266	88.41 144	87.50 302

test_vis1_rt		60.28 375	58.42 378	65.84 392	67.25 421	55.60 354	70.44 401	60.94 425	44.33 414	59.00 400	66.64 415	24.91 415	68.67 422	62.80 267	69.48 370	73.25 411

GBi-Net		78.40 206	77.40 212	81.40 222	87.60 213	63.01 252	88.39 139	89.28 188	71.63 187	75.34 246	87.28 227	54.80 240	91.11 257	62.72 268	79.57 265	90.09 221

test1		78.40 206	77.40 212	81.40 222	87.60 213	63.01 252	88.39 139	89.28 188	71.63 187	75.34 246	87.28 227	54.80 240	91.11 257	62.72 268	79.57 265	90.09 221

FMVSNet3		77.88 222	76.85 224	80.97 237	86.84 235	62.36 261	86.52 203	88.77 210	71.13 198	75.34 246	86.66 249	54.07 250	91.10 260	62.72 268	79.57 265	89.45 247

CMPMVS	51.72 21	70.19 328	68.16 330	76.28 317	73.15 411	57.55 324	79.47 336	83.92 295	48.02 409	56.48 409	84.81 295	43.13 357	86.42 332	62.67 271	81.81 240	84.89 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 228	77.40 212	78.60 282	89.03 152	60.02 294	79.00 344	85.83 272	75.19 110	76.61 215	89.98 155	54.81 239	85.46 343	62.63 272	83.55 215	90.33 209

FMVSNet2		78.20 212	77.21 216	81.20 229	87.60 213	62.89 258	87.47 170	89.02 201	71.63 187	75.29 252	87.28 227	54.80 240	91.10 260	62.38 273	79.38 269	89.61 243

testdata2													91.01 264	62.37 274

testing11		75.14 272	74.01 269	78.53 286	88.16 183	56.38 342	80.74 318	80.42 347	70.67 208	72.69 293	83.72 320	43.61 355	89.86 280	62.29 275	83.76 208	89.36 249

CP-MVSNet		78.22 210	78.34 186	77.84 299	87.83 202	54.54 365	87.94 157	91.17 127	77.65 42	73.48 282	88.49 196	62.24 168	88.43 309	62.19 276	74.07 339	90.55 199

XXY-MVS		75.41 268	75.56 246	74.96 333	83.59 306	57.82 319	80.59 321	83.87 297	66.54 287	74.93 262	88.31 201	63.24 150	80.09 378	62.16 277	76.85 298	86.97 318

pmmvs6		74.69 275	73.39 278	78.61 281	81.38 352	57.48 325	86.64 199	87.95 229	64.99 307	70.18 319	86.61 250	50.43 294	89.52 287	62.12 278	70.18 369	88.83 269

1112_ss		77.40 234	76.43 235	80.32 250	89.11 151	60.41 290	83.65 274	87.72 236	62.13 342	73.05 287	86.72 243	62.58 161	89.97 279	62.11 279	80.80 251	90.59 198

PS-CasMVS		78.01 219	78.09 192	77.77 301	87.71 209	54.39 367	88.02 153	91.22 124	77.50 50	73.26 284	88.64 191	60.73 194	88.41 310	61.88 280	73.88 343	90.53 200

CDS-MVSNet		79.07 192	77.70 206	83.17 173	87.60 213	68.23 131	84.40 262	86.20 267	67.49 273	76.36 221	86.54 255	61.54 178	90.79 267	61.86 281	87.33 157	90.49 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 172	78.33 187	84.09 133	85.17 269	69.91 87	90.57 61	90.97 132	66.70 280	72.17 300	91.91 104	54.70 244	93.96 129	61.81 282	90.95 102	88.41 284

K. test v3		71.19 314	68.51 326	79.21 273	83.04 321	57.78 321	84.35 263	76.91 380	72.90 171	62.99 387	82.86 338	39.27 378	91.09 262	61.65 283	52.66 413	88.75 273

CHOSEN 1792x2688		77.63 230	75.69 242	83.44 160	89.98 115	68.58 122	78.70 349	87.50 240	56.38 388	75.80 233	86.84 239	58.67 211	91.40 251	61.58 284	85.75 184	90.34 208

PCF-MVS	73.52 7	80.38 161	78.84 176	85.01 91	87.71 209	68.99 106	83.65 274	91.46 121	63.00 329	77.77 187	90.28 149	66.10 124	95.09 91	61.40 285	88.22 146	90.94 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 220	77.15 217	80.36 248	87.57 217	60.21 293	83.37 282	87.78 235	66.11 290	75.37 245	87.06 238	63.27 149	90.48 272	61.38 286	82.43 232	90.40 206

HyFIR lowres test		77.53 231	75.40 250	83.94 148	89.59 123	66.62 169	80.36 325	88.64 217	56.29 389	76.45 218	85.17 287	57.64 220	93.28 165	61.34 287	83.10 224	91.91 154

PMMVS		69.34 336	68.67 325	71.35 368	75.67 395	62.03 266	75.17 378	73.46 395	50.00 406	68.68 337	79.05 378	52.07 272	78.13 385	61.16 288	82.77 227	73.90 410

FMVSNet1		77.44 232	76.12 239	81.40 222	86.81 236	63.01 252	88.39 139	89.28 188	70.49 214	74.39 271	87.28 227	49.06 312	91.11 257	60.91 289	78.52 276	90.09 221

sss		73.60 288	73.64 276	73.51 349	82.80 327	55.01 361	76.12 370	81.69 330	62.47 338	74.68 266	85.85 270	57.32 224	78.11 386	60.86 290	80.93 247	87.39 303

Test_1112_low_res		76.40 253	75.44 248	79.27 271	89.28 141	58.09 311	81.69 303	87.07 250	59.53 363	72.48 295	86.67 248	61.30 185	89.33 290	60.81 291	80.15 260	90.41 205

sc_t1		72.19 308	69.51 319	80.23 252	84.81 278	61.09 278	84.68 249	80.22 351	60.70 352	71.27 309	83.58 324	36.59 392	89.24 293	60.41 292	63.31 392	90.37 207

BH-untuned		79.47 179	78.60 179	82.05 207	89.19 145	65.91 182	86.07 216	88.52 219	72.18 180	75.42 242	87.69 217	61.15 189	93.54 154	60.38 293	86.83 165	86.70 324

WTY-MVS		75.65 263	75.68 243	75.57 324	86.40 244	56.82 333	77.92 362	82.40 322	65.10 303	76.18 226	87.72 215	63.13 156	80.90 375	60.31 294	81.96 237	89.00 262

pmmvs4		74.03 284	71.91 295	80.39 247	81.96 341	68.32 128	81.45 307	82.14 324	59.32 364	69.87 327	85.13 288	52.40 264	88.13 313	60.21 295	74.74 335	84.73 359

PEN-MVS		77.73 225	77.69 207	77.84 299	87.07 232	53.91 370	87.91 159	91.18 126	77.56 47	73.14 286	88.82 186	61.23 187	89.17 295	59.95 296	72.37 354	90.43 204

CR-MVSNet		73.37 291	71.27 304	79.67 265	81.32 355	65.19 201	75.92 372	80.30 349	59.92 359	72.73 291	81.19 354	52.50 262	86.69 327	59.84 297	77.71 286	87.11 314

mvs5depth		69.45 335	67.45 346	75.46 328	73.93 402	55.83 350	79.19 341	83.23 307	66.89 276	71.63 306	83.32 328	33.69 400	85.09 346	59.81 298	55.34 410	85.46 345

lessismore_v0				78.97 276	81.01 358	57.15 329		65.99 415		61.16 393	82.82 339	39.12 380	91.34 253	59.67 299	46.92 420	88.43 283

CNLPA		78.08 215	76.79 226	81.97 210	90.40 102	71.07 65	87.59 167	84.55 286	66.03 293	72.38 297	89.64 163	57.56 221	86.04 335	59.61 300	83.35 220	88.79 271

BH-RMVSNet		79.61 174	78.44 183	83.14 174	89.38 135	65.93 181	84.95 244	87.15 249	73.56 153	78.19 177	89.79 159	56.67 230	93.36 163	59.53 301	86.74 166	90.13 217

MS-PatchMatch		73.83 285	72.67 287	77.30 310	83.87 300	66.02 178	81.82 300	84.66 284	61.37 349	68.61 339	82.82 339	47.29 320	88.21 311	59.27 302	84.32 201	77.68 404

test_post1							78.90 347				5.43 438	48.81 316	85.44 344	59.25 303

SCA		74.22 279	72.33 292	79.91 258	84.05 296	62.17 265	79.96 332	79.29 361	66.30 289	72.38 297	80.13 369	51.95 274	88.60 307	59.25 303	77.67 289	88.96 264

FE-MVS		77.78 224	75.68 243	84.08 134	88.09 189	66.00 179	83.13 287	87.79 234	68.42 264	78.01 182	85.23 285	45.50 342	95.12 85	59.11 305	85.83 183	91.11 175

SixPastTwentyTwo		73.37 291	71.26 305	79.70 263	85.08 274	57.89 317	85.57 226	83.56 301	71.03 202	65.66 369	85.88 268	42.10 365	92.57 199	59.11 305	63.34 391	88.65 277

WR-MVS_H		78.51 205	78.49 181	78.56 284	88.02 192	56.38 342	88.43 137	92.67 67	77.14 60	73.89 276	87.55 222	66.25 123	89.24 293	58.92 307	73.55 346	90.06 225

PLC	70.83 11	78.05 217	76.37 237	83.08 178	91.88 77	67.80 141	88.19 148	89.46 182	64.33 314	69.87 327	88.38 199	53.66 253	93.58 150	58.86 308	82.73 228	87.86 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 296	71.46 300	78.54 285	82.50 334	59.85 295	82.18 298	82.84 319	58.96 368	71.15 312	89.41 175	45.48 343	84.77 350	58.82 309	71.83 360	91.02 181

EU-MVSNet		68.53 344	67.61 343	71.31 369	78.51 385	47.01 407	84.47 256	84.27 291	42.27 416	66.44 366	84.79 296	40.44 374	83.76 355	58.76 310	68.54 377	83.17 375

pmmvs-eth3d		70.50 324	67.83 338	78.52 287	77.37 389	66.18 176	81.82 300	81.51 332	58.90 369	63.90 383	80.42 364	42.69 360	86.28 333	58.56 311	65.30 387	83.11 377

TAMVS		78.89 197	77.51 211	83.03 181	87.80 203	67.79 142	84.72 248	85.05 281	67.63 270	76.75 210	87.70 216	62.25 167	90.82 266	58.53 312	87.13 160	90.49 202

WBMVS		73.43 290	72.81 286	75.28 330	87.91 197	50.99 393	78.59 352	81.31 336	65.51 301	74.47 270	84.83 294	46.39 327	86.68 328	58.41 313	77.86 284	88.17 288

ACMH+	68.96 14	76.01 259	74.01 269	82.03 208	88.60 167	65.31 199	88.86 120	87.55 238	70.25 220	67.75 344	87.47 225	41.27 369	93.19 175	58.37 314	75.94 313	87.60 298

tpm		72.37 305	71.71 297	74.35 341	82.19 339	52.00 381	79.22 340	77.29 377	64.56 310	72.95 289	83.68 322	51.35 282	83.26 362	58.33 315	75.80 314	87.81 294

BH-w/o		78.21 211	77.33 215	80.84 239	88.81 158	65.13 203	84.87 245	87.85 233	69.75 233	74.52 269	84.74 297	61.34 184	93.11 180	58.24 316	85.84 182	84.27 362

Vis-MVSNet (Re-imp)		78.36 208	78.45 182	78.07 295	88.64 166	51.78 386	86.70 198	79.63 357	74.14 140	75.11 257	90.83 142	61.29 186	89.75 283	58.10 317	91.60 89	92.69 125

MVP-Stereo		76.12 256	74.46 265	81.13 232	85.37 265	69.79 89	84.42 261	87.95 229	65.03 305	67.46 348	85.33 282	53.28 258	91.73 235	58.01 318	83.27 221	81.85 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 331	73.16 410	50.51 396	63.05 424	87.47 241		64.28 378	77.81 390	17.80 426	89.73 284	57.88 319	60.64 399	85.49 344

TR-MVS		77.44 232	76.18 238	81.20 229	88.24 180	63.24 247	84.61 253	86.40 263	67.55 272	77.81 185	86.48 257	54.10 249	93.15 177	57.75 320	82.72 229	87.20 309

F-COLMAP		76.38 254	74.33 267	82.50 201	89.28 141	66.95 168	88.41 138	89.03 200	64.05 319	66.83 357	88.61 192	46.78 325	92.89 189	57.48 321	78.55 275	87.67 296

EG-PatchMatch MVS		74.04 282	71.82 296	80.71 242	84.92 276	67.42 151	85.86 222	88.08 225	66.04 292	64.22 379	83.85 314	35.10 397	92.56 200	57.44 322	80.83 250	82.16 388

PatchmatchNet		73.12 297	71.33 303	78.49 288	83.18 316	60.85 282	79.63 334	78.57 366	64.13 315	71.73 304	79.81 374	51.20 285	85.97 336	57.40 323	76.36 310	88.66 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 239	76.80 225	77.54 307	86.24 246	53.06 379	87.52 168	90.66 140	77.08 63	72.50 294	88.67 190	60.48 202	89.52 287	57.33 324	70.74 366	90.05 226

UnsupCasMVSNet_eth		67.33 351	65.99 355	71.37 366	73.48 407	51.47 389	75.16 379	85.19 278	65.20 302	60.78 394	80.93 361	42.35 361	77.20 390	57.12 325	53.69 412	85.44 346

pmmvs5		71.55 312	70.20 317	75.61 323	77.83 386	56.39 341	81.74 302	80.89 337	57.76 379	67.46 348	84.49 298	49.26 309	85.32 345	57.08 326	75.29 328	85.11 353

testing3-2		75.12 273	75.19 255	74.91 334	90.40 102	45.09 415	80.29 327	78.42 367	78.37 37	76.54 217	87.75 214	44.36 349	87.28 324	57.04 327	83.49 217	92.37 137

Anonymous20240521		68.80 340	67.22 349	73.55 348	74.33 400	54.11 368	83.18 285	85.61 274	58.15 375	61.68 391	80.94 359	30.71 407	81.27 373	57.00 328	73.34 350	85.28 348

mvsany_test1		62.30 372	61.26 376	65.41 393	69.52 417	54.86 362	66.86 413	49.78 433	46.65 410	68.50 341	83.21 330	49.15 310	66.28 425	56.93 329	60.77 398	75.11 409

TransMVSNet (Re)		75.39 270	74.56 262	77.86 298	85.50 262	57.10 330	86.78 195	86.09 270	72.17 181	71.53 307	87.34 226	63.01 157	89.31 291	56.84 330	61.83 395	87.17 310

tt0320-xc		70.11 329	67.45 346	78.07 295	85.33 266	59.51 301	83.28 283	78.96 364	58.77 370	67.10 354	80.28 367	36.73 391	87.42 322	56.83 331	59.77 402	87.29 307

test_vis3_rt		49.26 392	47.02 394	56.00 404	54.30 433	45.27 414	66.76 415	48.08 434	36.83 423	44.38 422	53.20 427	7.17 439	64.07 427	56.77 332	55.66 407	58.65 423

EPMVS		69.02 338	68.16 330	71.59 364	79.61 376	49.80 400	77.40 365	66.93 413	62.82 334	70.01 322	79.05 378	45.79 337	77.86 388	56.58 333	75.26 329	87.13 313

KD-MVS_self_test		68.81 339	67.59 344	72.46 360	74.29 401	45.45 410	77.93 361	87.00 251	63.12 326	63.99 382	78.99 382	42.32 362	84.77 350	56.55 334	64.09 390	87.16 312

tpm2		73.26 295	71.46 300	78.63 280	83.34 311	56.71 336	80.65 320	80.40 348	56.63 387	73.55 281	82.02 351	51.80 278	91.24 255	56.35 335	78.42 279	87.95 290

LTVRE_ROB	69.57 13	76.25 255	74.54 263	81.41 221	88.60 167	64.38 224	79.24 339	89.12 199	70.76 207	69.79 329	87.86 213	49.09 311	93.20 173	56.21 336	80.16 259	86.65 325
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	75.89 260	73.93 271	81.77 213	88.71 164	66.61 170	88.62 133	89.01 202	69.81 229	66.78 358	86.70 247	41.95 367	91.51 246	55.64 337	78.14 282	87.17 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 357	64.71 359	71.90 362	81.45 350	63.52 240	57.98 426	68.95 409	53.57 396	62.59 389	76.70 394	46.22 332	75.29 409	55.25 338	79.68 264	76.88 406

tt0320		70.49 325	68.03 333	77.89 297	84.78 279	59.12 303	83.55 278	80.44 346	58.13 376	67.43 350	80.41 365	39.26 379	87.54 321	55.12 339	63.18 393	86.99 317

UBG		73.08 298	72.27 293	75.51 326	88.02 192	51.29 391	78.35 356	77.38 376	65.52 299	73.87 277	82.36 344	45.55 340	86.48 331	55.02 340	84.39 200	88.75 273

EPNet_dtu		75.46 266	74.86 258	77.23 311	82.57 333	54.60 364	86.89 190	83.09 311	71.64 186	66.25 367	85.86 269	55.99 233	88.04 314	54.92 341	86.55 169	89.05 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 383	51.45 388	61.61 398	55.51 432	44.74 417	63.52 422	45.41 437	43.69 415	58.11 404	76.45 396	17.99 425	63.76 428	54.77 342	47.59 419	76.34 407

PVSNet	64.34 18	72.08 310	70.87 309	75.69 322	86.21 247	56.44 340	74.37 386	80.73 340	62.06 343	70.17 320	82.23 348	42.86 359	83.31 361	54.77 342	84.45 198	87.32 306

ITE_SJBPF				78.22 291	81.77 344	60.57 286		83.30 305	69.25 243	67.54 346	87.20 232	36.33 394	87.28 324	54.34 344	74.62 336	86.80 321

SSC-MVS3.2		73.35 294	73.39 278	73.23 350	85.30 267	49.01 401	74.58 385	81.57 331	75.21 108	73.68 279	85.58 277	52.53 260	82.05 368	54.33 345	77.69 288	88.63 278

MDTV_nov1_ep13_2view						37.79 429	75.16 379		55.10 392	66.53 362		49.34 307		53.98 346		87.94 291

gg-mvs-nofinetune		69.95 331	67.96 334	75.94 319	83.07 319	54.51 366	77.23 367	70.29 403	63.11 327	70.32 317	62.33 417	43.62 354	88.69 305	53.88 347	87.76 151	84.62 360

PatchMatch-RL		72.38 304	70.90 308	76.80 315	88.60 167	67.38 154	79.53 335	76.17 385	62.75 335	69.36 332	82.00 352	45.51 341	84.89 349	53.62 348	80.58 254	78.12 403

test_f		52.09 388	50.82 389	55.90 405	53.82 435	42.31 424	59.42 425	58.31 429	36.45 424	56.12 411	70.96 412	12.18 431	57.79 431	53.51 349	56.57 406	67.60 416

Patchmtry		70.74 320	69.16 323	75.49 327	80.72 359	54.07 369	74.94 383	80.30 349	58.34 373	70.01 322	81.19 354	52.50 262	86.54 329	53.37 350	71.09 365	85.87 341

USDC		70.33 326	68.37 327	76.21 318	80.60 361	56.23 345	79.19 341	86.49 261	60.89 350	61.29 392	85.47 280	31.78 404	89.47 289	53.37 350	76.21 311	82.94 381

LF4IMVS		64.02 368	62.19 372	69.50 377	70.90 416	53.29 377	76.13 369	77.18 378	52.65 399	58.59 401	80.98 358	23.55 419	76.52 395	53.06 352	66.66 381	78.68 402

PAPM		77.68 229	76.40 236	81.51 218	87.29 226	61.85 269	83.78 271	89.59 178	64.74 308	71.23 310	88.70 188	62.59 160	93.66 149	52.66 353	87.03 162	89.01 260

dmvs_re		71.14 315	70.58 310	72.80 356	81.96 341	59.68 297	75.60 376	79.34 360	68.55 260	69.27 334	80.72 362	49.42 305	76.54 394	52.56 354	77.79 285	82.19 387

CL-MVSNet_self_test		72.37 305	71.46 300	75.09 332	79.49 378	53.53 372	80.76 317	85.01 282	69.12 248	70.51 314	82.05 350	57.92 217	84.13 353	52.27 355	66.00 385	87.60 298

tpm cat1		70.57 322	68.31 328	77.35 309	82.41 337	57.95 316	78.08 358	80.22 351	52.04 400	68.54 340	77.66 391	52.00 273	87.84 317	51.77 356	72.07 359	86.25 329

our_test_3		69.14 337	67.00 350	75.57 324	79.80 373	58.80 304	77.96 360	77.81 370	59.55 362	62.90 388	78.25 387	47.43 319	83.97 354	51.71 357	67.58 379	83.93 368

MDTV_nov1_ep13			69.97 318		83.18 316	53.48 373	77.10 368	80.18 353	60.45 353	69.33 333	80.44 363	48.89 315	86.90 326	51.60 358	78.51 277

myMVS_eth3d28		73.62 287	73.53 277	73.90 346	88.20 181	47.41 405	78.06 359	79.37 359	74.29 136	73.98 275	84.29 305	44.67 345	83.54 358	51.47 359	87.39 156	90.74 191

JIA-IIPM		66.32 359	62.82 371	76.82 314	77.09 390	61.72 272	65.34 419	75.38 386	58.04 378	64.51 377	62.32 418	42.05 366	86.51 330	51.45 360	69.22 373	82.21 386

testing222		74.04 282	72.66 288	78.19 292	87.89 198	55.36 356	81.06 312	79.20 362	71.30 195	74.65 267	83.57 325	39.11 381	88.67 306	51.43 361	85.75 184	90.53 200

MSDG		73.36 293	70.99 307	80.49 246	84.51 287	65.80 186	80.71 319	86.13 269	65.70 296	65.46 370	83.74 318	44.60 346	90.91 265	51.13 362	76.89 296	84.74 358

PatchT		68.46 345	67.85 336	70.29 374	80.70 360	43.93 418	72.47 391	74.88 389	60.15 357	70.55 313	76.57 395	49.94 299	81.59 370	50.58 363	74.83 334	85.34 347

GG-mvs-BLEND				75.38 329	81.59 347	55.80 351	79.32 338	69.63 405		67.19 352	73.67 406	43.24 356	88.90 303	50.41 364	84.50 194	81.45 391

KD-MVS_2432*1600		66.22 360	63.89 363	73.21 351	75.47 398	53.42 374	70.76 399	84.35 288	64.10 317	66.52 363	78.52 384	34.55 398	84.98 347	50.40 365	50.33 417	81.23 392

miper_refine_blended		66.22 360	63.89 363	73.21 351	75.47 398	53.42 374	70.76 399	84.35 288	64.10 317	66.52 363	78.52 384	34.55 398	84.98 347	50.40 365	50.33 417	81.23 392

AllTest		70.96 317	68.09 332	79.58 267	85.15 271	63.62 235	84.58 254	79.83 354	62.31 339	60.32 396	86.73 241	32.02 402	88.96 301	50.28 367	71.57 362	86.15 332

TestCases				79.58 267	85.15 271	63.62 235		79.83 354	62.31 339	60.32 396	86.73 241	32.02 402	88.96 301	50.28 367	71.57 362	86.15 332

TAPA-MVS	73.13 9	79.15 189	77.94 195	82.79 194	89.59 123	62.99 256	88.16 150	91.51 117	65.77 295	77.14 204	91.09 133	60.91 193	93.21 170	50.26 369	87.05 161	92.17 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 364	62.91 369	71.38 365	75.85 394	56.60 338	69.12 407	74.66 393	57.28 384	54.12 412	77.87 389	45.85 336	74.48 411	49.95 370	61.52 397	83.05 378

MDA-MVSNet_test_wron		65.03 364	62.92 368	71.37 366	75.93 392	56.73 334	69.09 408	74.73 391	57.28 384	54.03 413	77.89 388	45.88 335	74.39 412	49.89 371	61.55 396	82.99 380

tpmvs		71.09 316	69.29 321	76.49 316	82.04 340	56.04 347	78.92 346	81.37 335	64.05 319	67.18 353	78.28 386	49.74 302	89.77 282	49.67 372	72.37 354	83.67 371

ppachtmachnet_test		70.04 330	67.34 348	78.14 293	79.80 373	61.13 276	79.19 341	80.59 342	59.16 366	65.27 372	79.29 377	46.75 326	87.29 323	49.33 373	66.72 380	86.00 338

UnsupCasMVSNet_bld		63.70 369	61.53 375	70.21 375	73.69 405	51.39 390	72.82 390	81.89 327	55.63 391	57.81 405	71.80 410	38.67 383	78.61 383	49.26 374	52.21 415	80.63 396

UWE-MVS		72.13 309	71.49 299	74.03 344	86.66 241	47.70 403	81.40 309	76.89 381	63.60 324	75.59 235	84.22 309	39.94 376	85.62 340	48.98 375	86.13 177	88.77 272

dp		66.80 354	65.43 356	70.90 373	79.74 375	48.82 402	75.12 381	74.77 390	59.61 361	64.08 381	77.23 392	42.89 358	80.72 376	48.86 376	66.58 382	83.16 376

FMVSNet5		69.50 334	67.96 334	74.15 343	82.97 325	55.35 357	80.01 331	82.12 325	62.56 337	63.02 385	81.53 353	36.92 390	81.92 369	48.42 377	74.06 340	85.17 352

thres100view900		76.50 248	75.55 247	79.33 270	89.52 126	56.99 331	85.83 224	83.23 307	73.94 143	76.32 222	87.12 235	51.89 276	91.95 225	48.33 378	83.75 209	89.07 253

tfpn200view9		76.42 252	75.37 252	79.55 269	89.13 147	57.65 322	85.17 236	83.60 299	73.41 159	76.45 218	86.39 259	52.12 268	91.95 225	48.33 378	83.75 209	89.07 253

thres400		76.50 248	75.37 252	79.86 259	89.13 147	57.65 322	85.17 236	83.60 299	73.41 159	76.45 218	86.39 259	52.12 268	91.95 225	48.33 378	83.75 209	90.00 227

LCM-MVSNet		54.25 382	49.68 392	67.97 388	53.73 436	45.28 413	66.85 414	80.78 339	35.96 425	39.45 426	62.23 419	8.70 436	78.06 387	48.24 381	51.20 416	80.57 397

RPMNet		73.51 289	70.49 312	82.58 200	81.32 355	65.19 201	75.92 372	92.27 84	57.60 381	72.73 291	76.45 396	52.30 265	95.43 70	48.14 382	77.71 286	87.11 314

thres600view7		76.50 248	75.44 248	79.68 264	89.40 133	57.16 328	85.53 232	83.23 307	73.79 147	76.26 223	87.09 236	51.89 276	91.89 228	48.05 383	83.72 212	90.00 227

TDRefinement		67.49 349	64.34 360	76.92 313	73.47 408	61.07 279	84.86 246	82.98 315	59.77 360	58.30 403	85.13 288	26.06 412	87.89 316	47.92 384	60.59 400	81.81 390

thres200		75.55 264	74.47 264	78.82 278	87.78 206	57.85 318	83.07 290	83.51 302	72.44 177	75.84 232	84.42 300	52.08 271	91.75 233	47.41 385	83.64 214	86.86 320

PVSNet_0	57.27 20	61.67 374	59.27 377	68.85 381	79.61 376	57.44 326	68.01 409	73.44 396	55.93 390	58.54 402	70.41 413	44.58 347	77.55 389	47.01 386	35.91 425	71.55 413

DP-MVS		76.78 244	74.57 261	83.42 161	93.29 48	69.46 97	88.55 135	83.70 298	63.98 321	70.20 318	88.89 184	54.01 251	94.80 102	46.66 387	81.88 239	86.01 336

COLMAP_ROB	66.92 17	73.01 299	70.41 314	80.81 240	87.13 230	65.63 190	88.30 145	84.19 293	62.96 330	63.80 384	87.69 217	38.04 387	92.56 200	46.66 387	74.91 333	84.24 363
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 321	69.30 320	74.88 335	84.52 286	56.35 344	75.87 374	79.42 358	64.59 309	67.76 343	82.41 343	41.10 370	81.54 371	46.64 389	81.34 242	86.75 323

LS3D		76.95 241	74.82 259	83.37 164	90.45 100	67.36 155	89.15 110	86.94 253	61.87 345	69.52 330	90.61 145	51.71 280	94.53 110	46.38 390	86.71 167	88.21 287

ETVMVS		72.25 307	71.05 306	75.84 320	87.77 207	51.91 383	79.39 337	74.98 388	69.26 242	73.71 278	82.95 335	40.82 373	86.14 334	46.17 391	84.43 199	89.47 246

MDA-MVSNet-bldmvs		66.68 355	63.66 365	75.75 321	79.28 380	60.56 287	73.92 388	78.35 368	64.43 311	50.13 418	79.87 373	44.02 352	83.67 356	46.10 392	56.86 404	83.03 379

new-patchmatchnet		61.73 373	61.73 374	61.70 397	72.74 413	24.50 440	69.16 406	78.03 369	61.40 347	56.72 408	75.53 402	38.42 384	76.48 396	45.95 393	57.67 403	84.13 365

WB-MVSnew		71.96 311	71.65 298	72.89 355	84.67 285	51.88 384	82.29 297	77.57 372	62.31 339	73.67 280	83.00 334	53.49 256	81.10 374	45.75 394	82.13 235	85.70 342

TinyColmap		67.30 352	64.81 358	74.76 337	81.92 343	56.68 337	80.29 327	81.49 333	60.33 354	56.27 410	83.22 329	24.77 416	87.66 320	45.52 395	69.47 371	79.95 399

pmmvs3		57.79 378	54.26 383	68.37 384	64.02 426	56.72 335	75.12 381	65.17 417	40.20 418	52.93 414	69.86 414	20.36 423	75.48 406	45.45 396	55.25 411	72.90 412

OpenMVS_ROB	64.09 19	70.56 323	68.19 329	77.65 303	80.26 364	59.41 302	85.01 242	82.96 316	58.76 371	65.43 371	82.33 345	37.63 389	91.23 256	45.34 397	76.03 312	82.32 385

test0.0.03 1		68.00 348	67.69 341	68.90 380	77.55 387	47.43 404	75.70 375	72.95 399	66.66 281	66.56 361	82.29 347	48.06 317	75.87 403	44.97 398	74.51 337	83.41 373

testgi		66.67 356	66.53 353	67.08 390	75.62 396	41.69 425	75.93 371	76.50 382	66.11 290	65.20 375	86.59 251	35.72 396	74.71 410	43.71 399	73.38 349	84.84 357

Anonymous20231206		68.60 341	67.80 339	71.02 371	80.23 366	50.75 395	78.30 357	80.47 344	56.79 386	66.11 368	82.63 342	46.35 330	78.95 382	43.62 400	75.70 315	83.36 374

tfpnnormal		74.39 276	73.16 282	78.08 294	86.10 252	58.05 312	84.65 252	87.53 239	70.32 217	71.22 311	85.63 275	54.97 238	89.86 280	43.03 401	75.02 332	86.32 328

MIMVSNet1		68.58 342	66.78 352	73.98 345	80.07 368	51.82 385	80.77 316	84.37 287	64.40 312	59.75 399	82.16 349	36.47 393	83.63 357	42.73 402	70.33 368	86.48 327

ttmdpeth		59.91 376	57.10 380	68.34 385	67.13 422	46.65 409	74.64 384	67.41 412	48.30 408	62.52 390	85.04 292	20.40 422	75.93 402	42.55 403	45.90 423	82.44 384

test20.03		67.45 350	66.95 351	68.94 379	75.48 397	44.84 416	77.50 364	77.67 371	66.66 281	63.01 386	83.80 316	47.02 323	78.40 384	42.53 404	68.86 376	83.58 372

ADS-MVSNet2		66.20 362	63.33 366	74.82 336	79.92 369	58.75 305	67.55 411	75.19 387	53.37 397	65.25 373	75.86 399	42.32 362	80.53 377	41.57 405	68.91 374	85.18 350

ADS-MVSNet		64.36 367	62.88 370	68.78 382	79.92 369	47.17 406	67.55 411	71.18 401	53.37 397	65.25 373	75.86 399	42.32 362	73.99 413	41.57 405	68.91 374	85.18 350

Patchmatch-test		64.82 366	63.24 367	69.57 376	79.42 379	49.82 399	63.49 423	69.05 408	51.98 402	59.95 398	80.13 369	50.91 287	70.98 417	40.66 407	73.57 345	87.90 292

MVS-HIRNet		59.14 377	57.67 379	63.57 395	81.65 345	43.50 419	71.73 393	65.06 418	39.59 420	51.43 415	57.73 423	38.34 385	82.58 365	39.53 408	73.95 341	64.62 419

WAC-MVS						42.58 421								39.46 409

myMVS_eth3d		67.02 353	66.29 354	69.21 378	84.68 282	42.58 421	78.62 350	73.08 397	66.65 284	66.74 359	79.46 375	31.53 405	82.30 366	39.43 410	76.38 308	82.75 382

DSMNet-mixed		57.77 379	56.90 381	60.38 399	67.70 420	35.61 430	69.18 405	53.97 431	32.30 429	57.49 406	79.88 372	40.39 375	68.57 423	38.78 411	72.37 354	76.97 405

N_pmnet		52.79 387	53.26 385	51.40 411	78.99 382	7.68 445	69.52 403	3.89 444	51.63 403	57.01 407	74.98 403	40.83 372	65.96 426	37.78 412	64.67 388	80.56 398

testing3		68.56 343	67.67 342	71.22 370	87.33 223	42.87 420	83.06 291	71.54 400	70.36 215	69.08 335	84.38 302	30.33 408	85.69 339	37.50 413	75.45 323	85.09 354

MVStest1		56.63 380	52.76 386	68.25 386	61.67 428	53.25 378	71.67 394	68.90 410	38.59 421	50.59 417	83.05 333	25.08 414	70.66 418	36.76 414	38.56 424	80.83 395

test_0402		72.79 302	70.44 313	79.84 260	88.13 186	65.99 180	85.93 219	84.29 290	65.57 298	67.40 351	85.49 279	46.92 324	92.61 196	35.88 415	74.38 338	80.94 394

new_pmnet		50.91 390	50.29 390	52.78 410	68.58 419	34.94 432	63.71 421	56.63 430	39.73 419	44.95 421	65.47 416	21.93 421	58.48 430	34.98 416	56.62 405	64.92 418

APD_test1		53.31 386	49.93 391	63.42 396	65.68 423	50.13 397	71.59 395	66.90 414	34.43 426	40.58 425	71.56 411	8.65 437	76.27 398	34.64 417	55.36 409	63.86 420

Syy-MVS		68.05 347	67.85 336	68.67 383	84.68 282	40.97 426	78.62 350	73.08 397	66.65 284	66.74 359	79.46 375	52.11 270	82.30 366	32.89 418	76.38 308	82.75 382

dmvs_testset		62.63 371	64.11 362	58.19 401	78.55 384	24.76 439	75.28 377	65.94 416	67.91 269	60.34 395	76.01 398	53.56 254	73.94 414	31.79 419	67.65 378	75.88 408

UWE-MVS-28		65.32 363	64.93 357	66.49 391	78.70 383	38.55 428	77.86 363	64.39 420	62.00 344	64.13 380	83.60 323	41.44 368	76.00 401	31.39 420	80.89 248	84.92 355

ANet_high		50.57 391	46.10 395	63.99 394	48.67 439	39.13 427	70.99 398	80.85 338	61.39 348	31.18 428	57.70 424	17.02 427	73.65 415	31.22 421	15.89 436	79.18 401

EGC-MVSNET		52.07 389	47.05 393	67.14 389	83.51 308	60.71 284	80.50 323	67.75 411	0.07 439	0.43 440	75.85 401	24.26 417	81.54 371	28.82 422	62.25 394	59.16 422

PMMVS2		40.82 398	38.86 402	46.69 412	53.84 434	16.45 443	48.61 429	49.92 432	37.49 422	31.67 427	60.97 420	8.14 438	56.42 432	28.42 423	30.72 429	67.19 417

tmp_tt		18.61 405	21.40 408	10.23 421	4.82 444	10.11 444	34.70 431	30.74 442	1.48 438	23.91 434	26.07 435	28.42 410	13.41 440	27.12 424	15.35 437	7.17 435

test_method		31.52 401	29.28 405	38.23 415	27.03 443	6.50 446	20.94 434	62.21 423	4.05 437	22.35 435	52.50 428	13.33 429	47.58 435	27.04 425	34.04 427	60.62 421

testf1		45.72 393	41.96 397	57.00 402	56.90 430	45.32 411	66.14 416	59.26 427	26.19 430	30.89 429	60.96 421	4.14 440	70.64 419	26.39 426	46.73 421	55.04 425

APD_test2		45.72 393	41.96 397	57.00 402	56.90 430	45.32 411	66.14 416	59.26 427	26.19 430	30.89 429	60.96 421	4.14 440	70.64 419	26.39 426	46.73 421	55.04 425

FPMVS		53.68 385	51.64 387	59.81 400	65.08 424	51.03 392	69.48 404	69.58 406	41.46 417	40.67 424	72.32 409	16.46 428	70.00 421	24.24 428	65.42 386	58.40 424

Gipuma		45.18 396	41.86 399	55.16 408	77.03 391	51.52 388	32.50 432	80.52 343	32.46 428	27.12 431	35.02 432	9.52 435	75.50 405	22.31 429	60.21 401	38.45 431
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 395	45.38 396	45.55 413	73.36 409	26.85 437	67.72 410	34.19 439	54.15 395	49.65 419	56.41 426	25.43 413	62.94 429	19.45 430	28.09 430	46.86 429

DeepMVS_CX				27.40 419	40.17 442	26.90 436		24.59 443	17.44 435	23.95 433	48.61 430	9.77 434	26.48 438	18.06 431	24.47 432	28.83 432

WB-MVS		54.94 381	54.72 382	55.60 407	73.50 406	20.90 441	74.27 387	61.19 424	59.16 366	50.61 416	74.15 404	47.19 322	75.78 404	17.31 432	35.07 426	70.12 414

PMVS	37.38 22	44.16 397	40.28 401	55.82 406	40.82 441	42.54 423	65.12 420	63.99 421	34.43 426	24.48 432	57.12 425	3.92 442	76.17 400	17.10 433	55.52 408	48.75 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 403	25.89 407	43.81 414	44.55 440	35.46 431	28.87 433	39.07 438	18.20 434	18.58 436	40.18 431	2.68 443	47.37 436	17.07 434	23.78 433	48.60 428
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 384	53.59 384	54.75 409	72.87 412	19.59 442	73.84 389	60.53 426	57.58 382	49.18 420	73.45 407	46.34 331	75.47 407	16.20 435	32.28 428	69.20 415

E-PMN		31.77 400	30.64 403	35.15 417	52.87 437	27.67 434	57.09 427	47.86 435	24.64 432	16.40 437	33.05 433	11.23 433	54.90 433	14.46 436	18.15 434	22.87 433

EMVS		30.81 402	29.65 404	34.27 418	50.96 438	25.95 438	56.58 428	46.80 436	24.01 433	15.53 438	30.68 434	12.47 430	54.43 434	12.81 437	17.05 435	22.43 434

kuosan		39.70 399	40.40 400	37.58 416	64.52 425	26.98 435	65.62 418	33.02 440	46.12 411	42.79 423	48.99 429	24.10 418	46.56 437	12.16 438	26.30 431	39.20 430

wuyk23d		16.82 406	15.94 409	19.46 420	58.74 429	31.45 433	39.22 430	3.74 445	6.84 436	6.04 439	2.70 439	1.27 444	24.29 439	10.54 439	14.40 438	2.63 436

testmvs		6.04 409	8.02 412	0.10 423	0.08 445	0.03 448	69.74 402	0.04 446	0.05 440	0.31 441	1.68 440	0.02 446	0.04 441	0.24 440	0.02 439	0.25 438

test123		6.12 408	8.11 411	0.14 422	0.06 446	0.09 447	71.05 397	0.03 447	0.04 441	0.25 442	1.30 441	0.05 445	0.03 442	0.21 441	0.01 440	0.29 437

mmdepth		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

monomultidepth		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

test_blank		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

uanet_test		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

DCPMVS		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

cdsmvs_eth3d_5k		19.96 404	26.61 406	0.00 424	0.00 447	0.00 449	0.00 435	89.26 191	0.00 442	0.00 443	88.61 192	61.62 177	0.00 443	0.00 442	0.00 441	0.00 439

pcd_1.5k_mvsjas		5.26 410	7.02 413	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	63.15 153	0.00 443	0.00 442	0.00 441	0.00 439

sosnet-low-res		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

sosnet		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

uncertanet		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

Regformer		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

ab-mvs-re		7.23 407	9.64 410	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	86.72 243	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

uanet		0.00 411	0.00 414	0.00 424	0.00 447	0.00 449	0.00 435	0.00 448	0.00 442	0.00 443	0.00 442	0.00 447	0.00 443	0.00 442	0.00 441	0.00 439

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

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

eth-test2					0.00 447

eth-test					0.00 447

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

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

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

GSMVS																88.96 264

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 283				88.96 264

sam_mvs												50.01 297

MTGPA								92.02 94

test_post											5.46 437	50.36 295	84.24 352

patchmatchnet-post											74.00 405	51.12 286	88.60 307

MTMP							92.18 34	32.83 441

TEST9					93.26 52	72.96 25	88.75 126	91.89 102	68.44 263	85.00 71	93.10 79	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 131	91.84 106	68.69 258	84.87 75	93.10 79	74.43 26	95.16 83

agg_prior					92.85 62	71.94 50		91.78 109		84.41 86			94.93 94

test_prior4						72.60 34	89.01 115

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

新几何2							86.29 211

旧先验1					91.96 74	65.79 187		86.37 264			93.08 83	69.31 86			92.74 74	88.74 275

原ACMM2							86.86 191

test222					91.50 80	68.26 130	84.16 266	83.20 310	54.63 394	79.74 148	91.63 114	58.97 210			91.42 93	86.77 322

segment_acmp												73.08 39

testdata1							84.14 267		75.71 95

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

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 203

plane_prior4											91.00 139

plane_prior3						68.60 121			78.44 33	78.92 160

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 117

plane_prior						68.71 116	90.38 70		77.62 43						86.16 176

n2								0.00 448

nn								0.00 448

door-mid								69.98 404

test11								92.23 87

door								69.44 407

HQP5-MVS						66.98 165

HQP-NCC					89.33 136		89.17 106		76.41 80	77.23 198

ACMP_Plane					89.33 136		89.17 106		76.41 80	77.23 198

HQP4-MVS										77.24 197			95.11 87			91.03 179

HQP3-MVS								92.19 91							85.99 180

HQP2-MVS												60.17 206

NP-MVS					89.62 122	68.32 128					90.24 151

ACMMP++_ref															81.95 238

ACMMP++															81.25 243

Test By Simon												64.33 140

High-resolution multi-view benchmark