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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 64	96.26 39	72.84 30	99.38 1	92.64 30	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 17	96.19 41	70.12 47	98.91 18	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 165	76.68 2	97.29 1	95.35 17	82.87 34	91.58 16	97.22 6	79.93 5	99.10 9	83.12 115	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 81	94.37 57	72.48 213	92.07 10	96.85 20	83.82 2	99.15 2	91.53 40	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 93	92.83 80	64.03 207	93.06 125	94.33 59	82.19 42	93.65 3	96.15 43	85.89 1	97.19 91	91.02 44	97.75 1	96.43 31
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
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 78	87.30 4	92.15 7	96.15 43	66.38 70	98.94 17	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 33	84.83 14	89.07 37	96.80 23	70.86 43	99.06 15	92.64 30	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 55	88.32 3	85.71 65	94.91 84	74.11 21	98.91 18	87.26 72	95.94 8	97.03 12
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
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 56	96.89 6	94.44 51	71.65 243	92.11 8	97.21 7	76.79 9	99.11 6	92.34 32	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 146	93.00 76	58.16 342	96.72 9	94.41 53	86.50 9	90.25 28	97.83 1	75.46 14	98.67 25	92.78 29	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 96	96.04 24	63.70 219	95.04 41	95.19 22	86.74 8	91.53 18	95.15 77	73.86 22	97.58 64	93.38 24	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 51	96.53 13	93.78 71	86.89 7	89.68 34	95.78 50	65.94 75	99.10 9	92.99 27	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 47	71.92 229	90.55 24	96.93 14	73.77 23	99.08 11	91.91 38	94.90 22	96.29 35
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
HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 87	95.24 33	94.49 49	82.43 39	88.90 38	96.35 34	71.89 40	98.63 26	88.76 58	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 19	89.81 6	93.66 54	75.15 5	90.61 245	93.43 93	84.06 21	86.20 59	90.17 202	72.42 35	96.98 108	93.09 26	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 64	96.38 15	94.64 42	84.42 18	86.74 54	96.20 40	66.56 69	98.76 24	89.03 57	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 104	94.17 67	94.15 64	68.77 292	90.74 22	97.27 4	76.09 12	98.49 29	90.58 48	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.5_n_9		88.14 18	89.21 16	84.92 132	89.29 176	61.41 284	92.97 130	88.36 322	86.96 6	91.49 19	97.49 3	69.48 51	97.46 71	97.00 1	89.88 104	95.89 47

SMA-MVS		88.14 18	88.29 25	87.67 33	93.21 68	68.72 73	93.85 88	94.03 67	74.18 176	91.74 13	96.67 26	65.61 80	98.42 33	89.24 54	96.08 7	95.88 48
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
PS-MVSNAJ		88.14 18	87.61 35	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 22	89.55 35	96.06 45	53.55 241	97.89 46	91.10 42	93.31 53	94.54 115

TSAR-MVS + MP.		88.11 21	88.64 21	86.54 73	91.73 119	68.04 91	90.36 251	93.55 85	82.89 32	91.29 20	92.89 139	72.27 37	96.03 162	87.99 62	94.77 26	95.54 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_8		87.96 22	88.93 18	85.07 127	88.43 204	61.78 271	94.73 54	91.74 172	85.87 10	91.66 15	97.50 2	64.03 101	98.33 34	96.28 4	90.08 100	95.10 84

TSAR-MVS + GP.		87.96 22	88.37 24	86.70 66	93.51 62	65.32 166	95.15 36	93.84 70	78.17 113	85.93 63	94.80 87	75.80 13	98.21 36	89.38 51	88.78 116	96.59 19

DeepC-MVS_fast	79.48 2	87.95 24	88.00 29	87.79 31	95.86 27	68.32 81	95.74 21	94.11 65	83.82 23	83.49 90	96.19 41	64.53 96	98.44 31	83.42 114	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 25	87.38 39	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 26	89.55 35	95.95 48	53.45 245	97.68 54	91.07 43	92.62 60	94.54 115

EPNet		87.84 26	88.38 23	86.23 83	93.30 65	66.05 145	95.26 32	94.84 32	87.09 5	88.06 41	94.53 93	66.79 66	97.34 80	83.89 107	91.68 75	95.29 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 27	87.77 32	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 36	87.13 49	95.27 70	64.99 86	95.80 168	89.34 52	91.80 73	95.93 45

test_fmvsm_n_1920		87.69 28	88.50 22	85.27 120	87.05 247	63.55 226	93.69 98	91.08 208	84.18 20	90.17 30	97.04 11	67.58 61	97.99 41	95.72 7	90.03 101	94.26 130

fmvsm_l_conf0.5_n_3		87.54 29	88.29 25	85.30 117	86.92 253	62.63 252	95.02 43	90.28 245	84.95 13	90.27 27	96.86 18	65.36 82	97.52 69	94.93 12	90.03 101	95.76 51

APDe-MVS		87.54 29	87.84 31	86.65 67	96.07 23	66.30 141	94.84 49	93.78 71	69.35 283	88.39 40	96.34 35	67.74 60	97.66 59	90.62 47	93.44 51	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 31	88.72 20	83.84 181	86.89 255	60.04 318	95.05 39	92.17 151	84.80 15	92.27 6	96.37 32	64.62 93	96.54 134	94.43 16	91.86 71	94.94 93

fmvsm_l_conf0.5_n		87.49 32	88.19 27	85.39 112	86.95 248	64.37 195	94.30 64	88.45 320	80.51 64	92.70 4	96.86 18	69.98 48	97.15 96	95.83 6	88.08 124	94.65 109

SD-MVS		87.49 32	87.49 37	87.50 42	93.60 56	68.82 70	93.90 85	92.63 131	76.86 135	87.90 43	95.76 51	66.17 72	97.63 61	89.06 56	91.48 79	96.05 42
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
fmvsm_l_conf0.5_n_a		87.44 34	88.15 28	85.30 117	87.10 245	64.19 202	94.41 59	88.14 330	80.24 72	92.54 5	96.97 13	69.52 50	97.17 92	95.89 5	88.51 119	94.56 112

dcpmvs_2		87.37 35	87.55 36	86.85 58	95.04 32	68.20 88	90.36 251	90.66 226	79.37 88	81.20 112	93.67 123	74.73 16	96.55 133	90.88 45	92.00 69	95.82 49

alignmvs		87.28 36	86.97 43	88.24 27	91.30 133	71.14 26	95.61 25	93.56 84	79.30 89	87.07 51	95.25 72	68.43 53	96.93 116	87.87 63	84.33 167	96.65 17

train_agg		87.21 37	87.42 38	86.60 69	94.18 41	67.28 111	94.16 68	93.51 87	71.87 234	85.52 68	95.33 64	68.19 55	97.27 87	89.09 55	94.90 22	95.25 79

MG-MVS		87.11 38	86.27 56	89.62 8	97.79 1	76.27 4	94.96 45	94.49 49	78.74 104	83.87 86	92.94 137	64.34 97	96.94 114	75.19 181	94.09 38	95.66 54

SF-MVS		87.03 39	87.09 41	86.84 59	92.70 86	67.45 109	93.64 101	93.76 74	70.78 267	86.25 57	96.44 31	66.98 64	97.79 50	88.68 59	94.56 34	95.28 75

fmvsm_s_conf0.5_n_3		86.88 40	87.99 30	83.58 193	87.26 240	60.74 298	93.21 122	87.94 337	84.22 19	91.70 14	97.27 4	65.91 77	95.02 206	93.95 21	90.42 96	94.99 90

CSCG		86.87 41	86.26 57	88.72 17	95.05 31	70.79 29	93.83 93	95.33 18	68.48 296	77.63 159	94.35 102	73.04 28	98.45 30	84.92 96	93.71 47	96.92 14

sasdasda		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

canonicalmvs		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

UBG		86.83 44	86.70 49	87.20 48	93.07 74	69.81 47	93.43 114	95.56 13	81.52 49	81.50 108	92.12 158	73.58 26	96.28 146	84.37 102	85.20 157	95.51 60

PHI-MVS		86.83 44	86.85 48	86.78 63	93.47 63	65.55 161	95.39 30	95.10 25	71.77 239	85.69 66	96.52 28	62.07 136	98.77 23	86.06 84	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 46	86.90 46	86.58 71	90.42 150	66.38 138	96.09 17	93.87 69	77.73 122	84.01 85	95.66 53	63.39 115	97.94 42	87.40 70	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 47	87.63 33	84.27 169	86.15 270	61.48 281	94.69 55	91.16 200	83.79 25	90.51 26	96.28 37	64.24 98	98.22 35	95.00 11	86.88 136	93.11 177

PVSNet_Blended		86.73 48	86.86 47	86.31 82	93.76 50	67.53 106	96.33 16	93.61 82	82.34 41	81.00 117	93.08 133	63.19 119	97.29 83	87.08 75	91.38 81	94.13 139

testing11		86.71 49	86.44 54	87.55 40	93.54 60	71.35 21	93.65 100	95.58 11	81.36 56	80.69 120	92.21 157	72.30 36	96.46 139	85.18 92	83.43 177	94.82 101

test_fmvsmconf_n		86.58 50	87.17 40	84.82 138	85.28 286	62.55 253	94.26 66	89.78 264	83.81 24	87.78 45	96.33 36	65.33 83	96.98 108	94.40 17	87.55 130	94.95 92

BP-MVS1		86.54 51	86.68 51	86.13 86	87.80 228	67.18 115	92.97 130	95.62 10	79.92 75	82.84 97	94.14 111	74.95 15	96.46 139	82.91 118	88.96 115	94.74 103

jason		86.40 52	86.17 60	87.11 51	86.16 269	70.54 32	95.71 24	92.19 148	82.00 44	84.58 78	94.34 103	61.86 138	95.53 189	87.76 64	90.89 89	95.27 76
jason: jason.
NormalMVS		86.39 53	86.66 52	85.60 106	92.12 101	65.95 150	94.88 46	90.83 215	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	91.15 85	93.93 150

fmvsm_s_conf0.5_n		86.39 53	86.91 45	84.82 138	87.36 239	63.54 227	94.74 51	90.02 257	82.52 37	90.14 31	96.92 16	62.93 126	97.84 49	95.28 10	82.26 187	93.07 180

fmvsm_s_conf0.5_n_5		86.38 55	86.94 44	84.71 148	84.67 298	63.29 232	94.04 77	89.99 259	82.88 33	87.85 44	96.03 46	62.89 128	96.36 143	94.15 18	89.95 103	94.48 121

SymmetryMVS		86.32 56	86.39 55	86.12 87	90.52 148	65.95 150	94.88 46	94.58 46	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	86.59 145	95.51 60

WTY-MVS		86.32 56	85.81 68	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 35	81.91 105	94.73 88	67.93 59	97.63 61	79.55 148	82.25 188	96.54 22

myMVS_eth3d28		86.31 58	86.15 61	86.78 63	93.56 58	70.49 33	92.94 133	95.28 19	82.47 38	78.70 150	92.07 160	72.45 34	95.41 191	82.11 124	85.78 153	94.44 123

MSLP-MVS++		86.27 59	85.91 67	87.35 45	92.01 108	68.97 67	95.04 41	92.70 122	79.04 99	81.50 108	96.50 30	58.98 175	96.78 124	83.49 113	93.93 41	96.29 35

VNet		86.20 60	85.65 72	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 109	86.00 62	93.07 134	58.22 182	97.00 104	85.22 90	84.33 167	96.52 23

MVS_111021_HR		86.19 61	85.80 69	87.37 44	93.17 70	69.79 48	93.99 80	93.76 74	79.08 96	78.88 146	93.99 117	62.25 135	98.15 38	85.93 85	91.15 85	94.15 138

SPE-MVS-test		86.14 62	87.01 42	83.52 194	92.63 88	59.36 330	95.49 27	91.92 161	80.09 73	85.46 70	95.53 59	61.82 140	95.77 171	86.77 79	93.37 52	95.41 63

ACMMP_NAP		86.05 63	85.80 69	86.80 62	91.58 123	67.53 106	91.79 190	93.49 90	74.93 166	84.61 77	95.30 66	59.42 166	97.92 43	86.13 82	94.92 20	94.94 93

testing99		86.01 64	85.47 74	87.63 38	93.62 55	71.25 23	93.47 112	95.23 21	80.42 67	80.60 122	91.95 163	71.73 41	96.50 137	80.02 145	82.22 189	95.13 82

ETV-MVS		86.01 64	86.11 62	85.70 103	90.21 155	67.02 122	93.43 114	91.92 161	81.21 58	84.13 84	94.07 116	60.93 148	95.63 179	89.28 53	89.81 105	94.46 122

testing91		85.93 66	85.31 78	87.78 32	93.59 57	71.47 19	93.50 109	95.08 28	80.26 69	80.53 123	91.93 164	70.43 45	96.51 136	80.32 143	82.13 191	95.37 66

APD-MVS		85.93 66	85.99 65	85.76 100	95.98 26	65.21 169	93.59 104	92.58 133	66.54 314	86.17 60	95.88 49	63.83 105	97.00 104	86.39 81	92.94 57	95.06 86
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 68	85.46 75	87.18 49	88.20 216	72.42 15	92.41 161	92.77 120	82.11 43	80.34 126	93.07 134	68.27 54	95.02 206	78.39 162	93.59 49	94.09 141

CS-MVS		85.80 69	86.65 53	83.27 206	92.00 109	58.92 334	95.31 31	91.86 166	79.97 74	84.82 76	95.40 62	62.26 134	95.51 190	86.11 83	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 70	86.09 63	84.72 146	85.73 279	63.58 224	93.79 94	89.32 282	81.42 54	90.21 29	96.91 17	62.41 133	97.67 56	94.48 15	80.56 212	92.90 186

test_fmvsmconf0.1_n		85.71 71	86.08 64	84.62 155	80.83 345	62.33 258	93.84 91	88.81 308	83.50 27	87.00 52	96.01 47	63.36 116	96.93 116	94.04 20	87.29 133	94.61 111

CDPH-MVS		85.71 71	85.46 75	86.46 75	94.75 34	67.19 113	93.89 86	92.83 118	70.90 263	83.09 95	95.28 68	63.62 110	97.36 78	80.63 139	94.18 37	94.84 98

casdiffmvs_mvg		85.66 73	85.18 80	87.09 52	88.22 215	69.35 59	93.74 97	91.89 164	81.47 50	80.10 128	91.45 173	64.80 91	96.35 144	87.23 73	87.69 128	95.58 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 74	85.93 66	84.68 150	82.95 328	63.48 229	94.03 79	89.46 276	81.69 47	89.86 32	96.74 24	61.85 139	97.75 52	94.74 14	82.01 193	92.81 188

MGCFI-Net		85.59 75	85.73 71	85.17 124	91.41 131	62.44 254	92.87 138	91.31 192	79.65 81	86.99 53	95.14 78	62.90 127	96.12 154	87.13 74	84.13 172	96.96 13

GDP-MVS		85.54 76	85.32 77	86.18 84	87.64 231	67.95 95	92.91 136	92.36 138	77.81 119	83.69 87	94.31 105	72.84 30	96.41 141	80.39 142	85.95 151	94.19 134

DeepC-MVS	77.85 3	85.52 77	85.24 79	86.37 79	88.80 191	66.64 132	92.15 169	93.68 80	81.07 59	76.91 170	93.64 124	62.59 130	98.44 31	85.50 86	92.84 59	94.03 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 78	84.87 86	86.84 59	88.25 213	69.07 63	93.04 127	91.76 171	81.27 57	80.84 119	92.07 160	64.23 99	96.06 160	84.98 95	87.43 132	95.39 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 79	85.08 82	86.06 88	93.09 73	65.65 157	93.89 86	93.41 95	73.75 187	79.94 130	94.68 90	60.61 151	98.03 40	82.63 121	93.72 46	94.52 117

fmvsm_s_conf0.5_n_7		85.24 80	86.69 50	80.91 274	84.52 303	60.10 316	93.35 117	90.35 238	83.41 28	86.54 56	96.27 38	60.50 152	90.02 358	94.84 13	90.38 97	92.61 192

MP-MVS-pluss		85.24 80	85.13 81	85.56 107	91.42 128	65.59 159	91.54 200	92.51 135	74.56 169	80.62 121	95.64 54	59.15 170	97.00 104	86.94 77	93.80 43	94.07 143
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 82	84.69 90	86.63 68	92.91 78	69.91 43	92.61 151	95.80 9	80.31 68	80.38 125	92.27 154	68.73 52	95.19 203	75.94 175	83.27 179	94.81 102

PAPR		85.15 83	84.47 91	87.18 49	96.02 25	68.29 82	91.85 188	93.00 113	76.59 145	79.03 142	95.00 79	61.59 141	97.61 63	78.16 163	89.00 114	95.63 55

fmvsm_s_conf0.5_n_2		85.06 84	85.60 73	83.44 200	86.92 253	60.53 305	94.41 59	87.31 343	83.30 29	88.72 39	96.72 25	54.28 233	97.75 52	94.07 19	84.68 164	92.04 215

MP-MVS		85.02 85	84.97 84	85.17 124	92.60 89	64.27 200	93.24 119	92.27 141	73.13 198	79.63 134	94.43 96	61.90 137	97.17 92	85.00 94	92.56 61	94.06 144
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 86	84.44 92	86.71 65	88.33 210	68.73 72	90.24 256	91.82 170	81.05 60	81.18 113	92.50 146	63.69 108	96.08 159	84.45 101	86.71 143	95.32 71

CHOSEN 1792x2688		84.98 87	83.45 106	89.57 11	89.94 160	75.14 6	92.07 175	92.32 139	81.87 45	75.68 179	88.27 232	60.18 155	98.60 27	80.46 141	90.27 99	94.96 91

MVSMamba_PlusPlus		84.97 88	83.65 100	88.93 14	90.17 156	74.04 8	87.84 312	92.69 125	62.18 352	81.47 110	87.64 246	71.47 42	96.28 146	84.69 98	94.74 31	96.47 28

EIA-MVS		84.84 89	84.88 85	84.69 149	91.30 133	62.36 257	93.85 88	92.04 154	79.45 84	79.33 139	94.28 107	62.42 132	96.35 144	80.05 144	91.25 84	95.38 65

lecture		84.77 90	84.81 88	84.65 151	92.12 101	62.27 261	94.74 51	92.64 130	68.35 297	85.53 67	95.30 66	59.77 162	97.91 44	83.73 109	91.15 85	93.77 158

fmvsm_s_conf0.1_n_a		84.76 91	84.84 87	84.53 157	80.23 358	63.50 228	92.79 140	88.73 311	80.46 65	89.84 33	96.65 27	60.96 147	97.57 66	93.80 22	80.14 214	92.53 197

HFP-MVS		84.73 92	84.40 93	85.72 102	93.75 52	65.01 175	93.50 109	93.19 103	72.19 223	79.22 140	94.93 82	59.04 173	97.67 56	81.55 128	92.21 64	94.49 120

MVS		84.66 93	82.86 125	90.06 2	90.93 140	74.56 7	87.91 310	95.54 14	68.55 294	72.35 231	94.71 89	59.78 161	98.90 20	81.29 134	94.69 32	96.74 16

GST-MVS		84.63 94	84.29 94	85.66 104	92.82 82	65.27 167	93.04 127	93.13 106	73.20 196	78.89 143	94.18 110	59.41 167	97.85 48	81.45 130	92.48 63	93.86 155

EC-MVSNet		84.53 95	85.04 83	83.01 212	89.34 172	61.37 285	94.42 58	91.09 206	77.91 117	83.24 91	94.20 109	58.37 180	95.40 192	85.35 87	91.41 80	92.27 209

fmvsm_s_conf0.1_n_2		84.40 96	84.78 89	83.27 206	85.25 287	60.41 308	94.13 71	85.69 365	83.05 31	87.99 42	96.37 32	52.75 250	97.68 54	93.75 23	84.05 173	91.71 221

ACMMPR		84.37 97	84.06 95	85.28 119	93.56 58	64.37 195	93.50 109	93.15 105	72.19 223	78.85 148	94.86 85	56.69 202	97.45 72	81.55 128	92.20 65	94.02 146

region2R		84.36 98	84.03 96	85.36 115	93.54 60	64.31 198	93.43 114	92.95 114	72.16 226	78.86 147	94.84 86	56.97 197	97.53 68	81.38 132	92.11 67	94.24 132

LFMVS		84.34 99	82.73 127	89.18 13	94.76 33	73.25 11	94.99 44	91.89 164	71.90 231	82.16 104	93.49 128	47.98 300	97.05 99	82.55 122	84.82 160	97.25 8

test_yl		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 286	81.09 114	92.88 140	57.00 195	97.44 73	81.11 136	81.76 196	96.23 38

DCV-MVSNet		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 286	81.09 114	92.88 140	57.00 195	97.44 73	81.11 136	81.76 196	96.23 38

diffmvs		84.28 100	83.83 97	85.61 105	87.40 237	68.02 92	90.88 231	89.24 285	80.54 63	81.64 107	92.52 145	59.83 160	94.52 233	87.32 71	85.11 158	94.29 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 100	83.36 112	87.02 55	92.22 96	67.74 99	84.65 338	94.50 48	79.15 93	82.23 103	87.93 241	66.88 65	96.94 114	80.53 140	82.20 190	96.39 33

ETVMVS		84.22 104	83.71 98	85.76 100	92.58 90	68.25 86	92.45 160	95.53 15	79.54 83	79.46 136	91.64 171	70.29 46	94.18 245	69.16 243	82.76 185	94.84 98

MAR-MVS		84.18 105	83.43 107	86.44 76	96.25 21	65.93 152	94.28 65	94.27 61	74.41 171	79.16 141	95.61 55	53.99 236	98.88 22	69.62 237	93.26 54	94.50 119
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
MVS_Test		84.16 106	83.20 115	87.05 54	91.56 124	69.82 46	89.99 265	92.05 153	77.77 121	82.84 97	86.57 263	63.93 104	96.09 156	74.91 186	89.18 111	95.25 79

CANet_DTU		84.09 107	83.52 101	85.81 97	90.30 153	66.82 127	91.87 186	89.01 300	85.27 11	86.09 61	93.74 121	47.71 306	96.98 108	77.90 165	89.78 107	93.65 161

ET-MVSNet_ETH3D		84.01 108	83.15 118	86.58 71	90.78 145	70.89 28	94.74 51	94.62 43	81.44 53	58.19 370	93.64 124	73.64 25	92.35 313	82.66 120	78.66 232	96.50 27

PVSNet_Blended_VisFu		83.97 109	83.50 103	85.39 112	90.02 158	66.59 135	93.77 95	91.73 173	77.43 130	77.08 169	89.81 210	63.77 107	96.97 111	79.67 147	88.21 122	92.60 193

MTAPA		83.91 110	83.38 111	85.50 108	91.89 115	65.16 171	81.75 368	92.23 142	75.32 161	80.53 123	95.21 75	56.06 211	97.16 95	84.86 97	92.55 62	94.18 135

XVS		83.87 111	83.47 105	85.05 128	93.22 66	63.78 212	92.92 134	92.66 127	73.99 179	78.18 153	94.31 105	55.25 217	97.41 75	79.16 152	91.58 77	93.95 148

Effi-MVS+		83.82 112	82.76 126	86.99 56	89.56 168	69.40 54	91.35 211	86.12 359	72.59 210	83.22 94	92.81 143	59.60 164	96.01 164	81.76 127	87.80 127	95.56 58

test_fmvsmvis_n_1920		83.80 113	83.48 104	84.77 142	82.51 331	63.72 217	91.37 209	83.99 383	81.42 54	77.68 158	95.74 52	58.37 180	97.58 64	93.38 24	86.87 137	93.00 183

EI-MVSNet-Vis-set		83.77 114	83.67 99	84.06 173	92.79 85	63.56 225	91.76 193	94.81 34	79.65 81	77.87 156	94.09 114	63.35 117	97.90 45	79.35 150	79.36 222	90.74 242

MVSFormer		83.75 115	82.88 124	86.37 79	89.24 181	71.18 24	89.07 288	90.69 223	65.80 319	87.13 49	94.34 103	64.99 86	92.67 300	72.83 202	91.80 73	95.27 76

CP-MVS		83.71 116	83.40 110	84.65 151	93.14 71	63.84 210	94.59 56	92.28 140	71.03 261	77.41 162	94.92 83	55.21 220	96.19 151	81.32 133	90.70 91	93.91 152

test_fmvsmconf0.01_n		83.70 117	83.52 101	84.25 170	75.26 404	61.72 275	92.17 168	87.24 345	82.36 40	84.91 75	95.41 61	55.60 215	96.83 123	92.85 28	85.87 152	94.21 133

baseline2		83.68 118	83.42 109	84.48 160	87.37 238	66.00 147	90.06 260	95.93 8	79.71 80	69.08 269	90.39 191	77.92 6	96.28 146	78.91 157	81.38 200	91.16 235

reproduce-ours		83.51 119	83.33 113	84.06 173	92.18 99	60.49 306	90.74 237	92.04 154	64.35 329	83.24 91	95.59 57	59.05 171	97.27 87	83.61 110	89.17 112	94.41 127

our_new_method		83.51 119	83.33 113	84.06 173	92.18 99	60.49 306	90.74 237	92.04 154	64.35 329	83.24 91	95.59 57	59.05 171	97.27 87	83.61 110	89.17 112	94.41 127

thisisatest0515		83.41 121	82.49 131	86.16 85	89.46 171	68.26 84	93.54 106	94.70 39	74.31 174	75.75 177	90.92 181	72.62 32	96.52 135	69.64 235	81.50 199	93.71 159

PVSNet_BlendedMVS		83.38 122	83.43 107	83.22 208	93.76 50	67.53 106	94.06 73	93.61 82	79.13 94	81.00 117	85.14 281	63.19 119	97.29 83	87.08 75	73.91 270	84.83 342

test2506		83.29 123	82.92 123	84.37 164	88.39 207	63.18 238	92.01 178	91.35 191	77.66 124	78.49 152	91.42 174	64.58 95	95.09 205	73.19 198	89.23 109	94.85 95

PGM-MVS		83.25 124	82.70 128	84.92 132	92.81 84	64.07 206	90.44 246	92.20 146	71.28 255	77.23 166	94.43 96	55.17 221	97.31 82	79.33 151	91.38 81	93.37 167

HPM-MVS		83.25 124	82.95 122	84.17 171	92.25 95	62.88 247	90.91 228	91.86 166	70.30 272	77.12 167	93.96 118	56.75 200	96.28 146	82.04 125	91.34 83	93.34 168
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 126	82.96 120	83.73 185	92.02 105	59.74 322	90.37 250	92.08 152	63.70 336	82.86 96	95.48 60	58.62 177	97.17 92	83.06 116	88.42 120	94.26 130

EI-MVSNet-UG-set		83.14 127	82.96 120	83.67 190	92.28 94	63.19 237	91.38 208	94.68 40	79.22 91	76.60 172	93.75 120	62.64 129	97.76 51	78.07 164	78.01 235	90.05 251

testing3-2		83.11 128	83.15 118	82.98 213	91.92 112	64.01 208	94.39 62	95.37 16	78.32 110	75.53 184	90.06 208	73.18 27	93.18 279	74.34 191	75.27 259	91.77 220

VDD-MVS		83.06 129	81.81 140	86.81 61	90.86 143	67.70 100	95.40 29	91.50 186	75.46 157	81.78 106	92.34 153	40.09 348	97.13 97	86.85 78	82.04 192	95.60 56

h-mvs33		83.01 130	82.56 130	84.35 165	89.34 172	62.02 265	92.72 143	93.76 74	81.45 51	82.73 100	92.25 156	60.11 156	97.13 97	87.69 65	62.96 351	93.91 152

PAPM_NR		82.97 131	81.84 139	86.37 79	94.10 44	66.76 130	87.66 316	92.84 117	69.96 276	74.07 205	93.57 126	63.10 124	97.50 70	70.66 230	90.58 93	94.85 95

mPP-MVS		82.96 132	82.44 132	84.52 158	92.83 80	62.92 245	92.76 141	91.85 168	71.52 251	75.61 182	94.24 108	53.48 244	96.99 107	78.97 155	90.73 90	93.64 162

SR-MVS		82.81 133	82.58 129	83.50 197	93.35 64	61.16 288	92.23 166	91.28 197	64.48 328	81.27 111	95.28 68	53.71 240	95.86 166	82.87 119	88.77 117	93.49 165

DP-MVS Recon		82.73 134	81.65 141	85.98 90	97.31 4	67.06 118	95.15 36	91.99 158	69.08 289	76.50 174	93.89 119	54.48 229	98.20 37	70.76 228	85.66 155	92.69 189

CLD-MVS		82.73 134	82.35 134	83.86 180	87.90 223	67.65 102	95.45 28	92.18 149	85.06 12	72.58 222	92.27 154	52.46 253	95.78 169	84.18 103	79.06 227	88.16 279
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 136	82.38 133	83.73 185	89.25 178	59.58 325	92.24 165	94.89 31	77.96 115	79.86 131	92.38 151	56.70 201	97.05 99	77.26 168	80.86 207	94.55 113

3Dnovator	73.91 6	82.69 137	80.82 154	88.31 26	89.57 167	71.26 22	92.60 152	94.39 56	78.84 101	67.89 290	92.48 149	48.42 295	98.52 28	68.80 248	94.40 36	95.15 81

RRT-MVS		82.61 138	81.16 145	86.96 57	91.10 137	68.75 71	87.70 315	92.20 146	76.97 133	72.68 218	87.10 257	51.30 266	96.41 141	83.56 112	87.84 126	95.74 52

MVSTER		82.47 139	82.05 135	83.74 183	92.68 87	69.01 65	91.90 185	93.21 100	79.83 76	72.14 232	85.71 276	74.72 17	94.72 220	75.72 177	72.49 280	87.50 286

TESTMET0.1,1		82.41 140	81.98 138	83.72 187	88.08 217	63.74 214	92.70 145	93.77 73	79.30 89	77.61 160	87.57 248	58.19 183	94.08 250	73.91 193	86.68 144	93.33 170

CostFormer		82.33 141	81.15 146	85.86 95	89.01 186	68.46 78	82.39 365	93.01 111	75.59 155	80.25 127	81.57 326	72.03 39	94.96 210	79.06 154	77.48 243	94.16 137

API-MVS		82.28 142	80.53 163	87.54 41	96.13 22	70.59 31	93.63 102	91.04 212	65.72 321	75.45 185	92.83 142	56.11 210	98.89 21	64.10 294	89.75 108	93.15 175

IB-MVS	77.80 4	82.18 143	80.46 165	87.35 45	89.14 183	70.28 36	95.59 26	95.17 24	78.85 100	70.19 257	85.82 274	70.66 44	97.67 56	72.19 214	66.52 321	94.09 141
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
xiu_mvs_v1_base_debu		82.16 144	81.12 147	85.26 121	86.42 262	68.72 73	92.59 154	90.44 235	73.12 199	84.20 81	94.36 98	38.04 361	95.73 173	84.12 104	86.81 138	91.33 228

xiu_mvs_v1_base		82.16 144	81.12 147	85.26 121	86.42 262	68.72 73	92.59 154	90.44 235	73.12 199	84.20 81	94.36 98	38.04 361	95.73 173	84.12 104	86.81 138	91.33 228

xiu_mvs_v1_base_debi		82.16 144	81.12 147	85.26 121	86.42 262	68.72 73	92.59 154	90.44 235	73.12 199	84.20 81	94.36 98	38.04 361	95.73 173	84.12 104	86.81 138	91.33 228

3Dnovator+	73.60 7	82.10 147	80.60 161	86.60 69	90.89 142	66.80 129	95.20 34	93.44 92	74.05 178	67.42 297	92.49 148	49.46 285	97.65 60	70.80 227	91.68 75	95.33 69

MVS_111021_LR		82.02 148	81.52 142	83.51 196	88.42 205	62.88 247	89.77 268	88.93 304	76.78 138	75.55 183	93.10 131	50.31 274	95.38 194	83.82 108	87.02 135	92.26 210

PMMVS		81.98 149	82.04 136	81.78 248	89.76 164	56.17 362	91.13 224	90.69 223	77.96 115	80.09 129	93.57 126	46.33 317	94.99 209	81.41 131	87.46 131	94.17 136

baseline1		81.84 150	81.03 151	84.28 168	91.60 122	66.62 133	91.08 225	91.66 180	81.87 45	74.86 193	91.67 170	69.98 48	94.92 213	71.76 217	64.75 338	91.29 233

EPP-MVSNet		81.79 151	81.52 142	82.61 223	88.77 192	60.21 314	93.02 129	93.66 81	68.52 295	72.90 216	90.39 191	72.19 38	94.96 210	74.93 185	79.29 225	92.67 190

WBMVS		81.67 152	80.98 153	83.72 187	93.07 74	69.40 54	94.33 63	93.05 109	76.84 136	72.05 234	84.14 292	74.49 19	93.88 264	72.76 205	68.09 309	87.88 281

test_vis1_n_1920		81.66 153	82.01 137	80.64 277	82.24 333	55.09 371	94.76 50	86.87 349	81.67 48	84.40 80	94.63 91	38.17 358	94.67 224	91.98 37	83.34 178	92.16 213

APD-MVS_3200maxsize		81.64 154	81.32 144	82.59 225	92.36 92	58.74 336	91.39 206	91.01 213	63.35 340	79.72 133	94.62 92	51.82 256	96.14 153	79.71 146	87.93 125	92.89 187

mvsmamba		81.55 155	80.72 156	84.03 177	91.42 128	66.93 125	83.08 357	89.13 293	78.55 108	67.50 295	87.02 258	51.79 258	90.07 357	87.48 68	90.49 95	95.10 84

ACMMP		81.49 156	80.67 158	83.93 179	91.71 120	62.90 246	92.13 170	92.22 145	71.79 238	71.68 240	93.49 128	50.32 273	96.96 112	78.47 161	84.22 171	91.93 218
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
KinetiMVS		81.43 157	80.11 167	85.38 114	86.60 258	65.47 165	92.90 137	93.54 86	75.33 160	77.31 164	90.39 191	46.81 309	96.75 125	71.65 220	86.46 148	93.93 150

CDS-MVSNet		81.43 157	80.74 155	83.52 194	86.26 266	64.45 189	92.09 173	90.65 227	75.83 153	73.95 207	89.81 210	63.97 103	92.91 290	71.27 221	82.82 182	93.20 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 159	79.99 171	85.46 109	90.39 152	68.40 79	86.88 327	90.61 228	74.41 171	70.31 256	84.67 286	63.79 106	92.32 315	73.13 199	85.70 154	95.67 53

ECVR-MVS		81.29 160	80.38 166	84.01 178	88.39 207	61.96 267	92.56 157	86.79 351	77.66 124	76.63 171	91.42 174	46.34 316	95.24 202	74.36 190	89.23 109	94.85 95

guyue		81.23 161	80.57 162	83.21 210	86.64 256	61.85 269	92.52 158	92.78 119	78.69 105	74.92 192	89.42 214	50.07 277	95.35 195	80.79 138	79.31 224	92.42 199

icg_test_0403		81.19 162	79.88 173	85.13 126	88.54 195	64.75 180	88.84 293	90.80 218	76.73 141	75.21 188	90.18 197	54.22 234	96.21 150	73.47 195	80.95 203	94.43 124

thisisatest0530		81.15 163	80.07 168	84.39 163	88.26 212	65.63 158	91.40 204	94.62 43	71.27 256	70.93 247	89.18 219	72.47 33	96.04 161	65.62 283	76.89 250	91.49 224

Fast-Effi-MVS+		81.14 164	80.01 170	84.51 159	90.24 154	65.86 153	94.12 72	89.15 291	73.81 186	75.37 187	88.26 233	57.26 190	94.53 232	66.97 268	84.92 159	93.15 175

HQP-MVS		81.14 164	80.64 159	82.64 222	87.54 233	63.66 222	94.06 73	91.70 178	79.80 77	74.18 200	90.30 194	51.63 261	95.61 181	77.63 166	78.90 228	88.63 270

hse-mvs2		81.12 166	81.11 150	81.16 262	86.52 261	57.48 351	89.40 279	91.16 200	81.45 51	82.73 100	90.49 189	60.11 156	94.58 225	87.69 65	60.41 378	91.41 227

SR-MVS-dyc-post		81.06 167	80.70 157	82.15 239	92.02 105	58.56 339	90.90 229	90.45 231	62.76 347	78.89 143	94.46 94	51.26 267	95.61 181	78.77 159	86.77 141	92.28 206

HyFIR lowres test		81.03 168	79.56 180	85.43 110	87.81 227	68.11 90	90.18 257	90.01 258	70.65 269	72.95 215	86.06 270	63.61 111	94.50 234	75.01 184	79.75 218	93.67 160

nrg030		80.93 169	79.86 174	84.13 172	83.69 317	68.83 69	93.23 120	91.20 198	75.55 156	75.06 190	88.22 236	63.04 125	94.74 219	81.88 126	66.88 318	88.82 268

Vis-MVSNet		80.92 170	79.98 172	83.74 183	88.48 201	61.80 270	93.44 113	88.26 329	73.96 182	77.73 157	91.76 167	49.94 279	94.76 217	65.84 280	90.37 98	94.65 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 171	80.02 169	83.33 201	87.87 224	60.76 296	92.62 150	86.86 350	77.86 118	75.73 178	91.39 176	46.35 315	94.70 223	72.79 204	88.68 118	94.52 117

UWE-MVS		80.81 172	81.01 152	80.20 287	89.33 174	57.05 356	91.91 184	94.71 38	75.67 154	75.01 191	89.37 215	63.13 123	91.44 340	67.19 265	82.80 184	92.12 214

icg_test_0407		80.80 173	79.39 186	85.00 131	88.54 195	64.75 180	88.40 301	90.80 218	76.73 141	73.95 207	90.18 197	51.55 263	95.81 167	73.47 195	80.95 203	94.43 124

1314		80.70 174	78.95 193	85.94 92	87.77 230	67.56 104	87.91 310	92.55 134	72.17 225	67.44 296	93.09 132	50.27 275	97.04 102	71.68 219	87.64 129	93.23 172

AstraMVS		80.66 175	79.79 176	83.28 205	85.07 293	61.64 277	92.19 167	90.58 229	79.40 86	74.77 195	90.18 197	45.93 321	95.61 181	83.04 117	76.96 249	92.60 193

tpmrst		80.57 176	79.14 191	84.84 137	90.10 157	68.28 83	81.70 369	89.72 271	77.63 126	75.96 176	79.54 358	64.94 88	92.71 297	75.43 179	77.28 246	93.55 163

1112_ss		80.56 177	79.83 175	82.77 217	88.65 193	60.78 294	92.29 163	88.36 322	72.58 211	72.46 228	94.95 80	65.09 85	93.42 276	66.38 274	77.71 237	94.10 140

VDDNet		80.50 178	78.26 201	87.21 47	86.19 267	69.79 48	94.48 57	91.31 192	60.42 368	79.34 138	90.91 182	38.48 356	96.56 132	82.16 123	81.05 202	95.27 76

BH-w/o		80.49 179	79.30 188	84.05 176	90.83 144	64.36 197	93.60 103	89.42 279	74.35 173	69.09 268	90.15 204	55.23 219	95.61 181	64.61 291	86.43 149	92.17 212

test_cas_vis1_n_1920		80.45 180	80.61 160	79.97 296	78.25 385	57.01 358	94.04 77	88.33 324	79.06 98	82.81 99	93.70 122	38.65 353	91.63 331	90.82 46	79.81 216	91.27 234

TAMVS		80.37 181	79.45 183	83.13 211	85.14 290	63.37 230	91.23 218	90.76 222	74.81 168	72.65 220	88.49 226	60.63 150	92.95 285	69.41 239	81.95 194	93.08 179

HQP_MVS		80.34 182	79.75 177	82.12 241	86.94 249	62.42 255	93.13 123	91.31 192	78.81 102	72.53 223	89.14 221	50.66 270	95.55 187	76.74 169	78.53 233	88.39 276

SDMVSNet		80.26 183	78.88 194	84.40 162	89.25 178	67.63 103	85.35 334	93.02 110	76.77 139	70.84 248	87.12 255	47.95 303	96.09 156	85.04 93	74.55 261	89.48 261

HPM-MVS_fast		80.25 184	79.55 182	82.33 231	91.55 125	59.95 319	91.32 213	89.16 290	65.23 325	74.71 197	93.07 134	47.81 305	95.74 172	74.87 188	88.23 121	91.31 232

ab-mvs		80.18 185	78.31 200	85.80 98	88.44 203	65.49 164	83.00 360	92.67 126	71.82 237	77.36 163	85.01 282	54.50 226	96.59 129	76.35 174	75.63 257	95.32 71

IS-MVSNet		80.14 186	79.41 184	82.33 231	87.91 222	60.08 317	91.97 182	88.27 327	72.90 206	71.44 244	91.73 169	61.44 142	93.66 271	62.47 308	86.53 146	93.24 171

test-LLR		80.10 187	79.56 180	81.72 250	86.93 251	61.17 286	92.70 145	91.54 183	71.51 252	75.62 180	86.94 259	53.83 237	92.38 310	72.21 212	84.76 162	91.60 222

PVSNet	73.49 8	80.05 188	78.63 196	84.31 166	90.92 141	64.97 176	92.47 159	91.05 211	79.18 92	72.43 229	90.51 188	37.05 373	94.06 252	68.06 254	86.00 150	93.90 154

UA-Net		80.02 189	79.65 178	81.11 265	89.33 174	57.72 346	86.33 331	89.00 303	77.44 129	81.01 116	89.15 220	59.33 168	95.90 165	61.01 315	84.28 169	89.73 257

test-mter		79.96 190	79.38 187	81.72 250	86.93 251	61.17 286	92.70 145	91.54 183	73.85 184	75.62 180	86.94 259	49.84 281	92.38 310	72.21 212	84.76 162	91.60 222

QAPM		79.95 191	77.39 220	87.64 34	89.63 166	71.41 20	93.30 118	93.70 79	65.34 324	67.39 299	91.75 168	47.83 304	98.96 16	57.71 331	89.81 105	92.54 196

UGNet		79.87 192	78.68 195	83.45 199	89.96 159	61.51 279	92.13 170	90.79 221	76.83 137	78.85 148	86.33 267	38.16 359	96.17 152	67.93 257	87.17 134	92.67 190
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
tpm2		79.80 193	77.95 207	85.34 116	88.28 211	68.26 84	81.56 371	91.42 189	70.11 274	77.59 161	80.50 344	67.40 62	94.26 243	67.34 262	77.35 244	93.51 164

thres200		79.66 194	78.33 199	83.66 191	92.54 91	65.82 155	93.06 125	96.31 3	74.90 167	73.30 212	88.66 224	59.67 163	95.61 181	47.84 374	78.67 231	89.56 260

CPTT-MVS		79.59 195	79.16 190	80.89 275	91.54 126	59.80 321	92.10 172	88.54 319	60.42 368	72.96 214	93.28 130	48.27 296	92.80 294	78.89 158	86.50 147	90.06 250

Test_1112_low_res		79.56 196	78.60 197	82.43 227	88.24 214	60.39 310	92.09 173	87.99 334	72.10 227	71.84 236	87.42 250	64.62 93	93.04 281	65.80 281	77.30 245	93.85 156

tttt0517		79.50 197	78.53 198	82.41 230	87.22 242	61.43 283	89.75 269	94.76 35	69.29 284	67.91 288	88.06 240	72.92 29	95.63 179	62.91 304	73.90 271	90.16 249

reproduce_monomvs		79.49 198	79.11 192	80.64 277	92.91 78	61.47 282	91.17 223	93.28 98	83.09 30	64.04 329	82.38 312	66.19 71	94.57 227	81.19 135	57.71 386	85.88 325

FIs		79.47 199	79.41 184	79.67 304	85.95 273	59.40 327	91.68 197	93.94 68	78.06 114	68.96 274	88.28 231	66.61 68	91.77 327	66.20 277	74.99 260	87.82 282

mamba_0404		79.46 200	77.65 210	84.91 134	88.37 209	67.04 120	89.59 270	87.03 346	67.99 300	75.45 185	89.32 216	47.98 300	95.34 196	71.23 222	81.90 195	92.34 202

BH-RMVSNet		79.46 200	77.65 210	84.89 135	91.68 121	65.66 156	93.55 105	88.09 332	72.93 203	73.37 211	91.12 180	46.20 319	96.12 154	56.28 337	85.61 156	92.91 185

PCF-MVS	73.15 9	79.29 202	77.63 212	84.29 167	86.06 271	65.96 149	87.03 323	91.10 205	69.86 278	69.79 264	90.64 184	57.54 189	96.59 129	64.37 293	82.29 186	90.32 247
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 203	79.57 179	78.24 324	88.46 202	52.29 382	90.41 248	89.12 294	74.24 175	69.13 267	91.91 165	65.77 78	90.09 356	59.00 327	88.09 123	92.33 203

114514_t		79.17 204	77.67 209	83.68 189	95.32 29	65.53 162	92.85 139	91.60 182	63.49 338	67.92 287	90.63 186	46.65 312	95.72 177	67.01 267	83.54 176	89.79 255

FA-MVS(test-final)		79.12 205	77.23 222	84.81 141	90.54 147	63.98 209	81.35 374	91.71 175	71.09 260	74.85 194	82.94 305	52.85 248	97.05 99	67.97 255	81.73 198	93.41 166

mamba_test_0407		79.09 206	77.21 223	84.75 144	88.50 199	66.98 123	89.21 284	87.03 346	67.99 300	74.12 204	89.32 216	47.98 300	95.29 201	71.23 222	79.52 219	91.98 217

VPA-MVSNet		79.03 207	78.00 205	82.11 244	85.95 273	64.48 188	93.22 121	94.66 41	75.05 165	74.04 206	84.95 283	52.17 255	93.52 273	74.90 187	67.04 317	88.32 278

OPM-MVS		79.00 208	78.09 203	81.73 249	83.52 320	63.83 211	91.64 199	90.30 243	76.36 149	71.97 235	89.93 209	46.30 318	95.17 204	75.10 182	77.70 238	86.19 313
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 209	78.22 202	81.25 259	85.33 283	62.73 250	89.53 276	93.21 100	72.39 218	72.14 232	90.13 205	60.99 145	94.72 220	67.73 259	72.49 280	86.29 310

AdaColmap		78.94 210	77.00 227	84.76 143	96.34 17	65.86 153	92.66 149	87.97 336	62.18 352	70.56 250	92.37 152	43.53 333	97.35 79	64.50 292	82.86 181	91.05 237

GeoE		78.90 211	77.43 216	83.29 204	88.95 187	62.02 265	92.31 162	86.23 357	70.24 273	71.34 245	89.27 218	54.43 230	94.04 255	63.31 300	80.81 209	93.81 157

miper_enhance_ethall		78.86 212	77.97 206	81.54 254	88.00 221	65.17 170	91.41 202	89.15 291	75.19 163	68.79 277	83.98 295	67.17 63	92.82 292	72.73 206	65.30 328	86.62 307

VPNet		78.82 213	77.53 215	82.70 220	84.52 303	66.44 137	93.93 83	92.23 142	80.46 65	72.60 221	88.38 230	49.18 289	93.13 280	72.47 210	63.97 348	88.55 273

EPNet_dtu		78.80 214	79.26 189	77.43 332	88.06 218	49.71 398	91.96 183	91.95 160	77.67 123	76.56 173	91.28 178	58.51 178	90.20 354	56.37 336	80.95 203	92.39 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 215	77.43 216	82.88 215	92.21 97	64.49 186	92.05 176	96.28 4	73.48 193	71.75 238	88.26 233	60.07 158	95.32 197	45.16 387	77.58 240	88.83 266

TR-MVS		78.77 216	77.37 221	82.95 214	90.49 149	60.88 292	93.67 99	90.07 253	70.08 275	74.51 198	91.37 177	45.69 322	95.70 178	60.12 321	80.32 213	92.29 205

thres400		78.68 217	77.43 216	82.43 227	92.21 97	64.49 186	92.05 176	96.28 4	73.48 193	71.75 238	88.26 233	60.07 158	95.32 197	45.16 387	77.58 240	87.48 287

BH-untuned		78.68 217	77.08 224	83.48 198	89.84 161	63.74 214	92.70 145	88.59 317	71.57 249	66.83 306	88.65 225	51.75 259	95.39 193	59.03 326	84.77 161	91.32 231

OMC-MVS		78.67 219	77.91 208	80.95 272	85.76 278	57.40 353	88.49 299	88.67 314	73.85 184	72.43 229	92.10 159	49.29 288	94.55 231	72.73 206	77.89 236	90.91 241

tpm		78.58 220	77.03 225	83.22 208	85.94 275	64.56 184	83.21 356	91.14 204	78.31 111	73.67 209	79.68 356	64.01 102	92.09 321	66.07 278	71.26 290	93.03 181

OpenMVS	70.45 11	78.54 221	75.92 244	86.41 78	85.93 276	71.68 18	92.74 142	92.51 135	66.49 315	64.56 323	91.96 162	43.88 332	98.10 39	54.61 342	90.65 92	89.44 263

EPMVS		78.49 222	75.98 243	86.02 89	91.21 135	69.68 52	80.23 383	91.20 198	75.25 162	72.48 227	78.11 367	54.65 225	93.69 270	57.66 332	83.04 180	94.69 105

AUN-MVS		78.37 223	77.43 216	81.17 261	86.60 258	57.45 352	89.46 278	91.16 200	74.11 177	74.40 199	90.49 189	55.52 216	94.57 227	74.73 189	60.43 377	91.48 225

thres100view900		78.37 223	77.01 226	82.46 226	91.89 115	63.21 236	91.19 222	96.33 1	72.28 221	70.45 253	87.89 242	60.31 153	95.32 197	45.16 387	77.58 240	88.83 266

GA-MVS		78.33 225	76.23 239	84.65 151	83.65 318	66.30 141	91.44 201	90.14 251	76.01 151	70.32 255	84.02 294	42.50 337	94.72 220	70.98 225	77.00 248	92.94 184

cascas		78.18 226	75.77 246	85.41 111	87.14 244	69.11 62	92.96 132	91.15 203	66.71 313	70.47 251	86.07 269	37.49 367	96.48 138	70.15 233	79.80 217	90.65 243

UniMVSNet_NR-MVSNet		78.15 227	77.55 214	79.98 294	84.46 306	60.26 312	92.25 164	93.20 102	77.50 128	68.88 275	86.61 262	66.10 73	92.13 319	66.38 274	62.55 355	87.54 285

LuminaMVS		78.14 228	76.66 231	82.60 224	80.82 346	64.64 183	89.33 280	90.45 231	68.25 298	74.73 196	85.51 278	41.15 343	94.14 246	78.96 156	80.69 211	89.04 264

ICG_test_0404		78.11 229	76.29 238	83.59 192	88.54 195	64.75 180	84.63 339	90.80 218	76.73 141	61.16 351	90.18 197	40.17 347	91.58 333	73.47 195	80.95 203	94.43 124

thres600view7		78.00 230	76.66 231	82.03 246	91.93 111	63.69 220	91.30 214	96.33 1	72.43 216	70.46 252	87.89 242	60.31 153	94.92 213	42.64 399	76.64 251	87.48 287

FC-MVSNet-test		77.99 231	78.08 204	77.70 327	84.89 296	55.51 368	90.27 254	93.75 77	76.87 134	66.80 307	87.59 247	65.71 79	90.23 353	62.89 305	73.94 269	87.37 290

Anonymous202405211		77.96 232	75.33 252	85.87 94	93.73 53	64.52 185	94.85 48	85.36 368	62.52 350	76.11 175	90.18 197	29.43 404	97.29 83	68.51 250	77.24 247	95.81 50

cl22		77.94 233	76.78 229	81.42 256	87.57 232	64.93 178	90.67 240	88.86 307	72.45 215	67.63 294	82.68 309	64.07 100	92.91 290	71.79 215	65.30 328	86.44 308

XXY-MVS		77.94 233	76.44 234	82.43 227	82.60 330	64.44 190	92.01 178	91.83 169	73.59 192	70.00 260	85.82 274	54.43 230	94.76 217	69.63 236	68.02 311	88.10 280

MS-PatchMatch		77.90 235	76.50 233	82.12 241	85.99 272	69.95 42	91.75 195	92.70 122	73.97 181	62.58 346	84.44 290	41.11 344	95.78 169	63.76 297	92.17 66	80.62 390

FMVSNet3		77.73 236	76.04 242	82.80 216	91.20 136	68.99 66	91.87 186	91.99 158	73.35 195	67.04 302	83.19 304	56.62 203	92.14 318	59.80 323	69.34 297	87.28 293

VortexMVS		77.62 237	76.44 234	81.13 263	88.58 194	63.73 216	91.24 217	91.30 196	77.81 119	65.76 312	81.97 318	49.69 283	93.72 268	76.40 173	65.26 331	85.94 323

miper_ehance_all_eth		77.60 238	76.44 234	81.09 269	85.70 280	64.41 193	90.65 241	88.64 316	72.31 219	67.37 300	82.52 310	64.77 92	92.64 303	70.67 229	65.30 328	86.24 312

UniMVSNet (Re)		77.58 239	76.78 229	79.98 294	84.11 312	60.80 293	91.76 193	93.17 104	76.56 146	69.93 263	84.78 285	63.32 118	92.36 312	64.89 290	62.51 357	86.78 301

PatchmatchNet		77.46 240	74.63 259	85.96 91	89.55 169	70.35 35	79.97 388	89.55 274	72.23 222	70.94 246	76.91 379	57.03 193	92.79 295	54.27 344	81.17 201	94.74 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 241	75.65 248	82.73 218	80.38 354	67.13 117	91.85 188	90.23 248	75.09 164	69.37 265	83.39 301	53.79 239	94.44 235	71.77 216	65.00 335	86.63 306

CHOSEN 280x420		77.35 242	76.95 228	78.55 319	87.07 246	62.68 251	69.71 420	82.95 390	68.80 291	71.48 243	87.27 254	66.03 74	84.00 404	76.47 172	82.81 183	88.95 265

PS-MVSNAJss		77.26 243	76.31 237	80.13 289	80.64 350	59.16 332	90.63 244	91.06 210	72.80 207	68.58 281	84.57 288	53.55 241	93.96 260	72.97 200	71.96 284	87.27 294

gg-mvs-nofinetune		77.18 244	74.31 266	85.80 98	91.42 128	68.36 80	71.78 414	94.72 37	49.61 411	77.12 167	45.92 440	77.41 8	93.98 259	67.62 260	93.16 55	95.05 87

WB-MVSnew		77.14 245	76.18 241	80.01 293	86.18 268	63.24 234	91.26 215	94.11 65	71.72 241	73.52 210	87.29 253	45.14 327	93.00 283	56.98 334	79.42 220	83.80 351

MVP-Stereo		77.12 246	76.23 239	79.79 301	81.72 338	66.34 140	89.29 281	90.88 214	70.56 270	62.01 349	82.88 306	49.34 286	94.13 247	65.55 285	93.80 43	78.88 405
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 247	75.37 250	82.20 237	89.25 178	62.11 264	82.06 366	89.09 296	76.77 139	70.84 248	87.12 255	41.43 342	95.01 208	67.23 264	74.55 261	89.48 261

MonoMVSNet		76.99 248	75.08 255	82.73 218	83.32 322	63.24 234	86.47 330	86.37 353	79.08 96	66.31 310	79.30 360	49.80 282	91.72 328	79.37 149	65.70 326	93.23 172

dmvs_re		76.93 249	75.36 251	81.61 252	87.78 229	60.71 300	80.00 387	87.99 334	79.42 85	69.02 271	89.47 213	46.77 310	94.32 237	63.38 299	74.45 264	89.81 254

X-MVStestdata		76.86 250	74.13 272	85.05 128	93.22 66	63.78 212	92.92 134	92.66 127	73.99 179	78.18 153	10.19 455	55.25 217	97.41 75	79.16 152	91.58 77	93.95 148

DU-MVS		76.86 250	75.84 245	79.91 297	82.96 326	60.26 312	91.26 215	91.54 183	76.46 148	68.88 275	86.35 265	56.16 208	92.13 319	66.38 274	62.55 355	87.35 291

Anonymous20240529		76.84 252	74.15 271	84.88 136	91.02 138	64.95 177	93.84 91	91.09 206	53.57 399	73.00 213	87.42 250	35.91 377	97.32 81	69.14 244	72.41 282	92.36 201

UWE-MVS-28		76.83 253	77.60 213	74.51 361	84.58 302	50.34 394	88.22 304	94.60 45	74.46 170	66.66 308	88.98 223	62.53 131	85.50 396	57.55 333	80.80 210	87.69 284

c3_l		76.83 253	75.47 249	80.93 273	85.02 294	64.18 203	90.39 249	88.11 331	71.66 242	66.65 309	81.64 324	63.58 114	92.56 304	69.31 241	62.86 352	86.04 318

WR-MVS		76.76 255	75.74 247	79.82 300	84.60 300	62.27 261	92.60 152	92.51 135	76.06 150	67.87 291	85.34 279	56.76 199	90.24 352	62.20 309	63.69 350	86.94 299

v1144		76.73 256	74.88 256	82.27 233	80.23 358	66.60 134	91.68 197	90.21 250	73.69 189	69.06 270	81.89 319	52.73 251	94.40 236	69.21 242	65.23 332	85.80 326

IterMVS-LS		76.49 257	75.18 254	80.43 281	84.49 305	62.74 249	90.64 242	88.80 309	72.40 217	65.16 318	81.72 322	60.98 146	92.27 316	67.74 258	64.65 340	86.29 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 258	74.55 262	82.19 238	79.14 372	67.82 97	90.26 255	89.42 279	73.75 187	68.63 280	81.89 319	51.31 265	94.09 249	71.69 218	64.84 336	84.66 343

Elysia		76.45 259	74.17 269	83.30 202	80.43 352	64.12 204	89.58 271	90.83 215	61.78 360	72.53 223	85.92 272	34.30 384	94.81 215	68.10 252	84.01 174	90.97 238

StellarMVS		76.45 259	74.17 269	83.30 202	80.43 352	64.12 204	89.58 271	90.83 215	61.78 360	72.53 223	85.92 272	34.30 384	94.81 215	68.10 252	84.01 174	90.97 238

v148		76.19 261	74.47 264	81.36 257	80.05 360	64.44 190	91.75 195	90.23 248	73.68 190	67.13 301	80.84 339	55.92 213	93.86 267	68.95 246	61.73 366	85.76 329

Effi-MVS+-dtu		76.14 262	75.28 253	78.72 318	83.22 323	55.17 370	89.87 266	87.78 338	75.42 158	67.98 286	81.43 328	45.08 328	92.52 306	75.08 183	71.63 285	88.48 274

cl____		76.07 263	74.67 257	80.28 284	85.15 289	61.76 273	90.12 258	88.73 311	71.16 257	65.43 315	81.57 326	61.15 143	92.95 285	66.54 271	62.17 359	86.13 316

DIV-MVS_self_test		76.07 263	74.67 257	80.28 284	85.14 290	61.75 274	90.12 258	88.73 311	71.16 257	65.42 316	81.60 325	61.15 143	92.94 289	66.54 271	62.16 361	86.14 314

FMVSNet2		76.07 263	74.01 274	82.26 235	88.85 188	67.66 101	91.33 212	91.61 181	70.84 264	65.98 311	82.25 314	48.03 297	92.00 323	58.46 328	68.73 305	87.10 296

v144192		76.05 266	74.03 273	82.12 241	79.50 366	66.55 136	91.39 206	89.71 272	72.30 220	68.17 284	81.33 331	51.75 259	94.03 257	67.94 256	64.19 343	85.77 327

NR-MVSNet		76.05 266	74.59 260	80.44 280	82.96 326	62.18 263	90.83 233	91.73 173	77.12 132	60.96 353	86.35 265	59.28 169	91.80 326	60.74 316	61.34 370	87.35 291

v1192		75.98 268	73.92 275	82.15 239	79.73 362	66.24 143	91.22 219	89.75 266	72.67 209	68.49 282	81.42 329	49.86 280	94.27 241	67.08 266	65.02 334	85.95 321

FE-MVS		75.97 269	73.02 287	84.82 138	89.78 162	65.56 160	77.44 399	91.07 209	64.55 327	72.66 219	79.85 354	46.05 320	96.69 127	54.97 341	80.82 208	92.21 211

eth_miper_zixun_eth		75.96 270	74.40 265	80.66 276	84.66 299	63.02 240	89.28 282	88.27 327	71.88 233	65.73 313	81.65 323	59.45 165	92.81 293	68.13 251	60.53 375	86.14 314

TranMVSNet+NR-MVSNet		75.86 271	74.52 263	79.89 298	82.44 332	60.64 303	91.37 209	91.37 190	76.63 144	67.65 293	86.21 268	52.37 254	91.55 334	61.84 311	60.81 373	87.48 287

SCA		75.82 272	72.76 290	85.01 130	86.63 257	70.08 38	81.06 376	89.19 288	71.60 248	70.01 259	77.09 377	45.53 323	90.25 349	60.43 318	73.27 273	94.68 106

LPG-MVS_test		75.82 272	74.58 261	79.56 308	84.31 309	59.37 328	90.44 246	89.73 269	69.49 281	64.86 319	88.42 228	38.65 353	94.30 239	72.56 208	72.76 277	85.01 340

GBi-Net		75.65 274	73.83 276	81.10 266	88.85 188	65.11 172	90.01 262	90.32 239	70.84 264	67.04 302	80.25 349	48.03 297	91.54 335	59.80 323	69.34 297	86.64 303

test1		75.65 274	73.83 276	81.10 266	88.85 188	65.11 172	90.01 262	90.32 239	70.84 264	67.04 302	80.25 349	48.03 297	91.54 335	59.80 323	69.34 297	86.64 303

v1921920		75.63 276	73.49 281	82.06 245	79.38 367	66.35 139	91.07 227	89.48 275	71.98 228	67.99 285	81.22 334	49.16 291	93.90 263	66.56 270	64.56 341	85.92 324

ACMP	71.68 10	75.58 277	74.23 268	79.62 306	84.97 295	59.64 323	90.80 234	89.07 298	70.39 271	62.95 342	87.30 252	38.28 357	93.87 265	72.89 201	71.45 288	85.36 336
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 278	73.26 285	81.61 252	80.67 349	66.82 127	89.54 275	89.27 284	71.65 243	63.30 337	80.30 348	54.99 223	94.06 252	67.33 263	62.33 358	83.94 349

tpm cat1		75.30 279	72.21 299	84.58 156	88.52 198	67.77 98	78.16 397	88.02 333	61.88 358	68.45 283	76.37 383	60.65 149	94.03 257	53.77 347	74.11 267	91.93 218

PLC	68.80 14	75.23 280	73.68 279	79.86 299	92.93 77	58.68 337	90.64 242	88.30 325	60.90 365	64.43 327	90.53 187	42.38 338	94.57 227	56.52 335	76.54 252	86.33 309
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 281	72.98 288	81.88 247	79.20 369	66.00 147	90.75 236	89.11 295	71.63 247	67.41 298	81.22 334	47.36 307	93.87 265	65.46 286	64.72 339	85.77 327

Fast-Effi-MVS+-dtu		75.04 282	73.37 283	80.07 290	80.86 344	59.52 326	91.20 221	85.38 367	71.90 231	65.20 317	84.84 284	41.46 341	92.97 284	66.50 273	72.96 276	87.73 283

dp		75.01 283	72.09 300	83.76 182	89.28 177	66.22 144	79.96 389	89.75 266	71.16 257	67.80 292	77.19 376	51.81 257	92.54 305	50.39 357	71.44 289	92.51 198

TAPA-MVS	70.22 12	74.94 284	73.53 280	79.17 313	90.40 151	52.07 383	89.19 286	89.61 273	62.69 349	70.07 258	92.67 144	48.89 294	94.32 237	38.26 413	79.97 215	91.12 236
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 285	73.32 284	79.74 303	86.53 260	60.31 311	89.03 291	92.70 122	78.61 107	68.98 273	83.34 302	41.93 340	92.23 317	52.77 351	65.97 324	86.69 302

v10		74.77 286	72.54 296	81.46 255	80.33 356	66.71 131	89.15 287	89.08 297	70.94 262	63.08 340	79.86 353	52.52 252	94.04 255	65.70 282	62.17 359	83.64 352

XVG-OURS-SEG-HR		74.70 287	73.08 286	79.57 307	78.25 385	57.33 354	80.49 379	87.32 341	63.22 342	68.76 278	90.12 207	44.89 329	91.59 332	70.55 231	74.09 268	89.79 255

ACMM	69.62 13	74.34 288	72.73 292	79.17 313	84.25 311	57.87 344	90.36 251	89.93 260	63.17 344	65.64 314	86.04 271	37.79 365	94.10 248	65.89 279	71.52 287	85.55 332
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 289	72.30 298	80.32 282	91.49 127	61.66 276	90.85 232	80.72 396	56.67 391	63.85 332	90.64 184	46.75 311	90.84 343	53.79 346	75.99 256	88.47 275

XVG-OURS		74.25 290	72.46 297	79.63 305	78.45 383	57.59 350	80.33 381	87.39 340	63.86 334	68.76 278	89.62 212	40.50 346	91.72 328	69.00 245	74.25 266	89.58 258

test_fmvs1		74.07 291	73.69 278	75.22 352	78.91 376	47.34 411	89.06 290	74.69 412	63.68 337	79.41 137	91.59 172	24.36 415	87.77 379	85.22 90	76.26 254	90.55 246

CVMVSNet		74.04 292	74.27 267	73.33 371	85.33 283	43.94 425	89.53 276	88.39 321	54.33 398	70.37 254	90.13 205	49.17 290	84.05 402	61.83 312	79.36 222	91.99 216

Baseline_NR-MVSNet		73.99 293	72.83 289	77.48 331	80.78 347	59.29 331	91.79 190	84.55 376	68.85 290	68.99 272	80.70 340	56.16 208	92.04 322	62.67 306	60.98 372	81.11 384

pmmvs4		73.92 294	71.81 304	80.25 286	79.17 370	65.24 168	87.43 319	87.26 344	67.64 306	63.46 335	83.91 296	48.96 293	91.53 338	62.94 303	65.49 327	83.96 348

D2MVS		73.80 295	72.02 301	79.15 315	79.15 371	62.97 241	88.58 298	90.07 253	72.94 202	59.22 363	78.30 364	42.31 339	92.70 299	65.59 284	72.00 283	81.79 379

SD_0403		73.79 296	73.48 282	74.69 358	85.33 283	45.56 421	83.80 346	85.57 366	76.55 147	62.96 341	88.45 227	50.62 272	87.59 383	48.80 367	79.28 226	90.92 240

CR-MVSNet		73.79 296	70.82 312	82.70 220	83.15 324	67.96 93	70.25 417	84.00 381	73.67 191	69.97 261	72.41 400	57.82 186	89.48 362	52.99 350	73.13 274	90.64 244

test_djsdf		73.76 298	72.56 295	77.39 333	77.00 397	53.93 376	89.07 288	90.69 223	65.80 319	63.92 330	82.03 317	43.14 336	92.67 300	72.83 202	68.53 306	85.57 331

pmmvs5		73.35 299	71.52 306	78.86 317	78.64 380	60.61 304	91.08 225	86.90 348	67.69 303	63.32 336	83.64 297	44.33 331	90.53 346	62.04 310	66.02 323	85.46 334

Anonymous20231211		73.08 300	70.39 316	81.13 263	90.62 146	63.33 231	91.40 204	90.06 255	51.84 404	64.46 326	80.67 342	36.49 375	94.07 251	63.83 296	64.17 344	85.98 320

tt0805		73.07 301	70.73 313	80.07 290	78.37 384	57.05 356	87.78 313	92.18 149	61.23 364	67.04 302	86.49 264	31.35 397	94.58 225	65.06 289	67.12 316	88.57 272

miper_lstm_enhance		73.05 302	71.73 305	77.03 338	83.80 315	58.32 341	81.76 367	88.88 305	69.80 279	61.01 352	78.23 366	57.19 191	87.51 384	65.34 287	59.53 380	85.27 339

jajsoiax		73.05 302	71.51 307	77.67 328	77.46 394	54.83 372	88.81 294	90.04 256	69.13 288	62.85 344	83.51 299	31.16 398	92.75 296	70.83 226	69.80 293	85.43 335

LCM-MVSNet-Re		72.93 304	71.84 303	76.18 347	88.49 200	48.02 406	80.07 386	70.17 427	73.96 182	52.25 397	80.09 352	49.98 278	88.24 373	67.35 261	84.23 170	92.28 206

pm-mvs1		72.89 305	71.09 309	78.26 323	79.10 373	57.62 348	90.80 234	89.30 283	67.66 304	62.91 343	81.78 321	49.11 292	92.95 285	60.29 320	58.89 383	84.22 347

tpmvs		72.88 306	69.76 322	82.22 236	90.98 139	67.05 119	78.22 396	88.30 325	63.10 345	64.35 328	74.98 390	55.09 222	94.27 241	43.25 393	69.57 296	85.34 337

test0.0.03 1		72.76 307	72.71 293	72.88 375	80.25 357	47.99 407	91.22 219	89.45 277	71.51 252	62.51 347	87.66 245	53.83 237	85.06 398	50.16 359	67.84 314	85.58 330

UniMVSNet_ETH3D		72.74 308	70.53 315	79.36 310	78.62 381	56.64 360	85.01 336	89.20 287	63.77 335	64.84 321	84.44 290	34.05 386	91.86 325	63.94 295	70.89 292	89.57 259

mvs_tets		72.71 309	71.11 308	77.52 329	77.41 395	54.52 374	88.45 300	89.76 265	68.76 293	62.70 345	83.26 303	29.49 403	92.71 297	70.51 232	69.62 295	85.34 337

FMVSNet1		72.71 309	69.91 320	81.10 266	83.60 319	65.11 172	90.01 262	90.32 239	63.92 333	63.56 334	80.25 349	36.35 376	91.54 335	54.46 343	66.75 319	86.64 303

test_fmvs1_n		72.69 311	71.92 302	74.99 356	71.15 417	47.08 413	87.34 321	75.67 407	63.48 339	78.08 155	91.17 179	20.16 429	87.87 376	84.65 99	75.57 258	90.01 252

IterMVS		72.65 312	70.83 310	78.09 325	82.17 334	62.96 242	87.64 317	86.28 355	71.56 250	60.44 356	78.85 362	45.42 325	86.66 388	63.30 301	61.83 363	84.65 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 313	72.74 291	72.10 383	87.87 224	49.45 400	88.07 306	89.01 300	72.91 204	63.11 338	88.10 237	63.63 109	85.54 393	32.73 428	69.23 300	81.32 382

PatchMatch-RL		72.06 314	69.98 317	78.28 322	89.51 170	55.70 367	83.49 349	83.39 388	61.24 363	63.72 333	82.76 307	34.77 381	93.03 282	53.37 349	77.59 239	86.12 317

PVSNet_0	68.08 15	71.81 315	68.32 331	82.27 233	84.68 297	62.31 260	88.68 296	90.31 242	75.84 152	57.93 375	80.65 343	37.85 364	94.19 244	69.94 234	29.05 443	90.31 248

MIMVSNet		71.64 316	68.44 329	81.23 260	81.97 337	64.44 190	73.05 411	88.80 309	69.67 280	64.59 322	74.79 392	32.79 389	87.82 377	53.99 345	76.35 253	91.42 226

test_vis1_n		71.63 317	70.73 313	74.31 365	69.63 423	47.29 412	86.91 325	72.11 420	63.21 343	75.18 189	90.17 202	20.40 427	85.76 392	84.59 100	74.42 265	89.87 253

IterMVS-SCA-FT		71.55 318	69.97 318	76.32 345	81.48 340	60.67 302	87.64 317	85.99 360	66.17 317	59.50 361	78.88 361	45.53 323	83.65 406	62.58 307	61.93 362	84.63 346

v7n		71.31 319	68.65 326	79.28 311	76.40 399	60.77 295	86.71 328	89.45 277	64.17 332	58.77 368	78.24 365	44.59 330	93.54 272	57.76 330	61.75 365	83.52 355

anonymousdsp		71.14 320	69.37 324	76.45 344	72.95 412	54.71 373	84.19 343	88.88 305	61.92 357	62.15 348	79.77 355	38.14 360	91.44 340	68.90 247	67.45 315	83.21 361

F-COLMAP		70.66 321	68.44 329	77.32 334	86.37 265	55.91 365	88.00 308	86.32 354	56.94 389	57.28 379	88.07 239	33.58 387	92.49 307	51.02 354	68.37 307	83.55 353

WR-MVS_H		70.59 322	69.94 319	72.53 377	81.03 343	51.43 387	87.35 320	92.03 157	67.38 307	60.23 358	80.70 340	55.84 214	83.45 408	46.33 382	58.58 385	82.72 368

CP-MVSNet		70.50 323	69.91 320	72.26 380	80.71 348	51.00 391	87.23 322	90.30 243	67.84 302	59.64 360	82.69 308	50.23 276	82.30 416	51.28 353	59.28 381	83.46 357

RPMNet		70.42 324	65.68 345	84.63 154	83.15 324	67.96 93	70.25 417	90.45 231	46.83 420	69.97 261	65.10 423	56.48 207	95.30 200	35.79 418	73.13 274	90.64 244

testing3		70.38 325	70.83 310	69.03 395	85.82 277	43.93 426	90.72 239	90.56 230	68.06 299	60.24 357	86.82 261	64.83 90	84.12 400	26.33 436	64.10 345	79.04 403

tfpnnormal		70.10 326	67.36 335	78.32 321	83.45 321	60.97 291	88.85 292	92.77 120	64.85 326	60.83 354	78.53 363	43.52 334	93.48 274	31.73 431	61.70 367	80.52 391

TransMVSNet (Re)		70.07 327	67.66 333	77.31 335	80.62 351	59.13 333	91.78 192	84.94 372	65.97 318	60.08 359	80.44 345	50.78 269	91.87 324	48.84 366	45.46 416	80.94 386

CL-MVSNet_self_test		69.92 328	68.09 332	75.41 350	73.25 411	55.90 366	90.05 261	89.90 261	69.96 276	61.96 350	76.54 380	51.05 268	87.64 380	49.51 363	50.59 406	82.70 370

DP-MVS		69.90 329	66.48 337	80.14 288	95.36 28	62.93 243	89.56 273	76.11 405	50.27 410	57.69 377	85.23 280	39.68 349	95.73 173	33.35 423	71.05 291	81.78 380

PS-CasMVS		69.86 330	69.13 325	72.07 384	80.35 355	50.57 393	87.02 324	89.75 266	67.27 308	59.19 364	82.28 313	46.58 313	82.24 417	50.69 356	59.02 382	83.39 359

Syy-MVS		69.65 331	69.52 323	70.03 391	87.87 224	43.21 427	88.07 306	89.01 300	72.91 204	63.11 338	88.10 237	45.28 326	85.54 393	22.07 441	69.23 300	81.32 382

MSDG		69.54 332	65.73 344	80.96 271	85.11 292	63.71 218	84.19 343	83.28 389	56.95 388	54.50 386	84.03 293	31.50 395	96.03 162	42.87 397	69.13 302	83.14 363

PEN-MVS		69.46 333	68.56 327	72.17 382	79.27 368	49.71 398	86.90 326	89.24 285	67.24 311	59.08 365	82.51 311	47.23 308	83.54 407	48.42 369	57.12 387	83.25 360

LS3D		69.17 334	66.40 339	77.50 330	91.92 112	56.12 363	85.12 335	80.37 398	46.96 418	56.50 381	87.51 249	37.25 368	93.71 269	32.52 430	79.40 221	82.68 371

PatchT		69.11 335	65.37 349	80.32 282	82.07 336	63.68 221	67.96 427	87.62 339	50.86 408	69.37 265	65.18 422	57.09 192	88.53 369	41.59 402	66.60 320	88.74 269

KD-MVS_2432*1600		69.03 336	66.37 340	77.01 339	85.56 281	61.06 289	81.44 372	90.25 246	67.27 308	58.00 373	76.53 381	54.49 227	87.63 381	48.04 371	35.77 434	82.34 374

miper_refine_blended		69.03 336	66.37 340	77.01 339	85.56 281	61.06 289	81.44 372	90.25 246	67.27 308	58.00 373	76.53 381	54.49 227	87.63 381	48.04 371	35.77 434	82.34 374

mvsany_test1		68.77 338	68.56 327	69.39 393	73.57 410	45.88 420	80.93 377	60.88 441	59.65 374	71.56 241	90.26 196	43.22 335	75.05 428	74.26 192	62.70 354	87.25 295

ACMH	63.93 17	68.62 339	64.81 351	80.03 292	85.22 288	63.25 233	87.72 314	84.66 374	60.83 366	51.57 401	79.43 359	27.29 410	94.96 210	41.76 400	64.84 336	81.88 378
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 340	65.41 348	77.96 326	78.69 379	62.93 243	89.86 267	89.17 289	60.55 367	50.27 406	77.73 371	22.60 423	94.06 252	47.18 378	72.65 279	76.88 416

ADS-MVSNet		68.54 341	64.38 358	81.03 270	88.06 218	66.90 126	68.01 425	84.02 380	57.57 382	64.48 324	69.87 410	38.68 351	89.21 364	40.87 404	67.89 312	86.97 297

DTE-MVSNet		68.46 342	67.33 336	71.87 386	77.94 389	49.00 404	86.16 332	88.58 318	66.36 316	58.19 370	82.21 315	46.36 314	83.87 405	44.97 390	55.17 394	82.73 367

mmtdpeth		68.33 343	66.37 340	74.21 366	82.81 329	51.73 384	84.34 341	80.42 397	67.01 312	71.56 241	68.58 414	30.52 401	92.35 313	75.89 176	36.21 432	78.56 410

our_test_3		68.29 344	64.69 353	79.11 316	78.92 374	64.85 179	88.40 301	85.06 370	60.32 370	52.68 395	76.12 385	40.81 345	89.80 361	44.25 392	55.65 392	82.67 372

Patchmatch-RL test		68.17 345	64.49 356	79.19 312	71.22 416	53.93 376	70.07 419	71.54 424	69.22 285	56.79 380	62.89 427	56.58 204	88.61 366	69.53 238	52.61 401	95.03 89

XVG-ACMP-BASELINE		68.04 346	65.53 347	75.56 349	74.06 409	52.37 381	78.43 393	85.88 361	62.03 355	58.91 367	81.21 336	20.38 428	91.15 342	60.69 317	68.18 308	83.16 362

FMVSNet5		68.04 346	65.66 346	75.18 354	84.43 307	57.89 343	83.54 348	86.26 356	61.83 359	53.64 392	73.30 395	37.15 371	85.08 397	48.99 365	61.77 364	82.56 373

ppachtmachnet_test		67.72 348	63.70 360	79.77 302	78.92 374	66.04 146	88.68 296	82.90 391	60.11 372	55.45 383	75.96 386	39.19 350	90.55 345	39.53 408	52.55 402	82.71 369

ACMH+	65.35 16	67.65 349	64.55 354	76.96 341	84.59 301	57.10 355	88.08 305	80.79 395	58.59 380	53.00 394	81.09 338	26.63 412	92.95 285	46.51 380	61.69 368	80.82 387

pmmvs6		67.57 350	64.76 352	76.00 348	72.82 414	53.37 378	88.71 295	86.78 352	53.19 400	57.58 378	78.03 368	35.33 380	92.41 309	55.56 339	54.88 396	82.21 376

Anonymous20231206		67.53 351	65.78 343	72.79 376	74.95 405	47.59 409	88.23 303	87.32 341	61.75 362	58.07 372	77.29 374	37.79 365	87.29 386	42.91 395	63.71 349	83.48 356

Patchmtry		67.53 351	63.93 359	78.34 320	82.12 335	64.38 194	68.72 422	84.00 381	48.23 417	59.24 362	72.41 400	57.82 186	89.27 363	46.10 383	56.68 391	81.36 381

USDC		67.43 353	64.51 355	76.19 346	77.94 389	55.29 369	78.38 394	85.00 371	73.17 197	48.36 414	80.37 346	21.23 425	92.48 308	52.15 352	64.02 347	80.81 388

ADS-MVSNet2		66.90 354	63.44 362	77.26 336	88.06 218	60.70 301	68.01 425	75.56 409	57.57 382	64.48 324	69.87 410	38.68 351	84.10 401	40.87 404	67.89 312	86.97 297

CMPMVS	48.56 21	66.77 355	64.41 357	73.84 368	70.65 420	50.31 395	77.79 398	85.73 364	45.54 423	44.76 424	82.14 316	35.40 379	90.14 355	63.18 302	74.54 263	81.07 385
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 356	62.92 365	76.80 343	76.51 398	57.77 345	89.22 283	83.41 387	55.48 395	53.86 390	77.84 369	26.28 413	93.95 261	34.90 420	68.76 304	78.68 408

LTVRE_ROB	59.60 19	66.27 357	63.54 361	74.45 362	84.00 314	51.55 386	67.08 429	83.53 385	58.78 378	54.94 385	80.31 347	34.54 382	93.23 278	40.64 406	68.03 310	78.58 409
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
JIA-IIPM		66.06 358	62.45 368	76.88 342	81.42 342	54.45 375	57.49 441	88.67 314	49.36 412	63.86 331	46.86 439	56.06 211	90.25 349	49.53 362	68.83 303	85.95 321

Patchmatch-test		65.86 359	60.94 374	80.62 279	83.75 316	58.83 335	58.91 440	75.26 411	44.50 426	50.95 405	77.09 377	58.81 176	87.90 375	35.13 419	64.03 346	95.12 83

UnsupCasMVSNet_eth		65.79 360	63.10 363	73.88 367	70.71 419	50.29 396	81.09 375	89.88 262	72.58 211	49.25 411	74.77 393	32.57 391	87.43 385	55.96 338	41.04 424	83.90 350

test_fmvs2		65.78 361	64.84 350	68.60 397	66.54 429	41.71 429	83.27 353	69.81 428	54.38 397	67.91 288	84.54 289	15.35 434	81.22 421	75.65 178	66.16 322	82.88 364

dmvs_testset		65.55 362	66.45 338	62.86 409	79.87 361	22.35 455	76.55 401	71.74 422	77.42 131	55.85 382	87.77 244	51.39 264	80.69 422	31.51 434	65.92 325	85.55 332

pmmvs-eth3d		65.53 363	62.32 369	75.19 353	69.39 424	59.59 324	82.80 361	83.43 386	62.52 350	51.30 403	72.49 398	32.86 388	87.16 387	55.32 340	50.73 405	78.83 406

mamv4		65.18 364	67.43 334	58.44 413	77.88 391	49.36 403	69.40 421	70.99 426	48.31 416	57.78 376	85.53 277	59.01 174	51.88 451	73.67 194	64.32 342	74.07 421

SixPastTwentyTwo		64.92 365	61.78 372	74.34 364	78.74 378	49.76 397	83.42 352	79.51 401	62.86 346	50.27 406	77.35 372	30.92 400	90.49 347	45.89 384	47.06 411	82.78 365

OurMVSNet-221017-0		64.68 366	62.17 370	72.21 381	76.08 402	47.35 410	80.67 378	81.02 394	56.19 392	51.60 400	79.66 357	27.05 411	88.56 368	53.60 348	53.63 399	80.71 389

test_0402		64.54 367	61.09 373	74.92 357	84.10 313	60.75 297	87.95 309	79.71 400	52.03 402	52.41 396	77.20 375	32.21 393	91.64 330	23.14 439	61.03 371	72.36 427

testgi		64.48 368	62.87 366	69.31 394	71.24 415	40.62 432	85.49 333	79.92 399	65.36 323	54.18 388	83.49 300	23.74 418	84.55 399	41.60 401	60.79 374	82.77 366

RPSCF		64.24 369	61.98 371	71.01 389	76.10 401	45.00 422	75.83 406	75.94 406	46.94 419	58.96 366	84.59 287	31.40 396	82.00 418	47.76 376	60.33 379	86.04 318

EU-MVSNet		64.01 370	63.01 364	67.02 403	74.40 408	38.86 438	83.27 353	86.19 358	45.11 424	54.27 387	81.15 337	36.91 374	80.01 424	48.79 368	57.02 388	82.19 377

test20.03		63.83 371	62.65 367	67.38 402	70.58 421	39.94 434	86.57 329	84.17 378	63.29 341	51.86 399	77.30 373	37.09 372	82.47 414	38.87 412	54.13 398	79.73 397

sc_t1		63.81 372	59.39 380	77.10 337	77.62 392	56.03 364	84.32 342	73.56 416	46.66 421	58.22 369	73.06 396	23.28 421	90.62 344	50.93 355	46.84 412	84.64 345

MDA-MVSNet_test_wron		63.78 373	60.16 376	74.64 359	78.15 387	60.41 308	83.49 349	84.03 379	56.17 394	39.17 434	71.59 406	37.22 369	83.24 411	42.87 397	48.73 408	80.26 394

YYNet1		63.76 374	60.14 377	74.62 360	78.06 388	60.19 315	83.46 351	83.99 383	56.18 393	39.25 433	71.56 407	37.18 370	83.34 409	42.90 396	48.70 409	80.32 393

K. test v3		63.09 375	59.61 379	73.53 370	76.26 400	49.38 402	83.27 353	77.15 404	64.35 329	47.77 416	72.32 402	28.73 405	87.79 378	49.93 361	36.69 431	83.41 358

COLMAP_ROB	57.96 20	62.98 376	59.65 378	72.98 374	81.44 341	53.00 380	83.75 347	75.53 410	48.34 415	48.81 413	81.40 330	24.14 416	90.30 348	32.95 425	60.52 376	75.65 419
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 377	59.08 381	71.10 388	67.19 427	48.72 405	83.91 345	85.23 369	50.38 409	47.84 415	71.22 409	20.74 426	85.51 395	46.47 381	58.75 384	79.06 402

tt0320		61.85 378	57.45 387	75.03 355	77.49 393	57.60 349	82.74 362	73.65 415	43.65 430	53.65 391	68.18 416	25.47 414	88.66 365	45.56 386	46.68 413	78.81 407

AllTest		61.66 379	58.06 383	72.46 378	79.57 363	51.42 388	80.17 384	68.61 430	51.25 406	45.88 418	81.23 332	19.86 430	86.58 389	38.98 410	57.01 389	79.39 399

UnsupCasMVSNet_bld		61.60 380	57.71 384	73.29 372	68.73 425	51.64 385	78.61 392	89.05 299	57.20 387	46.11 417	61.96 430	28.70 406	88.60 367	50.08 360	38.90 429	79.63 398

MDA-MVSNet-bldmvs		61.54 381	57.70 385	73.05 373	79.53 365	57.00 359	83.08 357	81.23 393	57.57 382	34.91 438	72.45 399	32.79 389	86.26 391	35.81 417	41.95 422	75.89 418

tt0320-xc		61.51 382	56.89 390	75.37 351	78.50 382	58.61 338	82.61 363	71.27 425	44.31 427	53.17 393	68.03 418	23.38 419	88.46 370	47.77 375	43.00 421	79.03 404

mvs5depth		61.03 383	57.65 386	71.18 387	67.16 428	47.04 415	72.74 412	77.49 402	57.47 385	60.52 355	72.53 397	22.84 422	88.38 371	49.15 364	38.94 428	78.11 413

KD-MVS_self_test		60.87 384	58.60 382	67.68 400	66.13 430	39.93 435	75.63 408	84.70 373	57.32 386	49.57 409	68.45 415	29.55 402	82.87 412	48.09 370	47.94 410	80.25 395

kuosan		60.86 385	60.24 375	62.71 410	81.57 339	46.43 417	75.70 407	85.88 361	57.98 381	48.95 412	69.53 412	58.42 179	76.53 426	28.25 435	35.87 433	65.15 434

TinyColmap		60.32 386	56.42 393	72.00 385	78.78 377	53.18 379	78.36 395	75.64 408	52.30 401	41.59 432	75.82 388	14.76 437	88.35 372	35.84 416	54.71 397	74.46 420

MVS-HIRNet		60.25 387	55.55 394	74.35 363	84.37 308	56.57 361	71.64 415	74.11 413	34.44 437	45.54 422	42.24 445	31.11 399	89.81 359	40.36 407	76.10 255	76.67 417

MIMVSNet1		60.16 388	57.33 388	68.67 396	69.71 422	44.13 424	78.92 391	84.21 377	55.05 396	44.63 425	71.85 404	23.91 417	81.54 420	32.63 429	55.03 395	80.35 392

PM-MVS		59.40 389	56.59 391	67.84 398	63.63 433	41.86 428	76.76 400	63.22 438	59.01 377	51.07 404	72.27 403	11.72 441	83.25 410	61.34 313	50.28 407	78.39 411

new-patchmatchnet		59.30 390	56.48 392	67.79 399	65.86 431	44.19 423	82.47 364	81.77 392	59.94 373	43.65 428	66.20 421	27.67 409	81.68 419	39.34 409	41.40 423	77.50 415

test_vis1_rt		59.09 391	57.31 389	64.43 406	68.44 426	46.02 419	83.05 359	48.63 450	51.96 403	49.57 409	63.86 426	16.30 432	80.20 423	71.21 224	62.79 353	67.07 433

test_fmvs3		56.82 392	54.86 396	62.69 411	53.59 444	35.47 441	75.87 405	65.64 435	43.91 428	55.10 384	71.43 408	6.91 449	74.40 431	68.64 249	52.63 400	78.20 412

DSMNet-mixed		56.78 393	54.44 397	63.79 407	63.21 434	29.44 450	64.43 432	64.10 437	42.12 434	51.32 402	71.60 405	31.76 394	75.04 429	36.23 415	65.20 333	86.87 300

pmmvs3		55.51 394	51.50 400	67.53 401	57.90 442	50.93 392	80.37 380	73.66 414	40.63 435	44.15 427	64.75 424	16.30 432	78.97 425	44.77 391	40.98 426	72.69 425

TDRefinement		55.28 395	51.58 399	66.39 404	59.53 441	46.15 418	76.23 403	72.80 417	44.60 425	42.49 430	76.28 384	15.29 435	82.39 415	33.20 424	43.75 418	70.62 429

dongtai		55.18 396	55.46 395	54.34 421	76.03 403	36.88 439	76.07 404	84.61 375	51.28 405	43.41 429	64.61 425	56.56 205	67.81 439	18.09 444	28.50 444	58.32 437

LF4IMVS		54.01 397	52.12 398	59.69 412	62.41 436	39.91 436	68.59 423	68.28 432	42.96 432	44.55 426	75.18 389	14.09 439	68.39 438	41.36 403	51.68 403	70.78 428

ttmdpeth		53.34 398	49.96 401	63.45 408	62.07 438	40.04 433	72.06 413	65.64 435	42.54 433	51.88 398	77.79 370	13.94 440	76.48 427	32.93 426	30.82 442	73.84 422

MVStest1		51.35 399	46.89 403	64.74 405	65.06 432	51.10 390	67.33 428	72.58 418	30.20 441	35.30 436	74.82 391	27.70 408	69.89 436	24.44 438	24.57 445	73.22 423

N_pmnet		50.55 400	49.11 402	54.88 419	77.17 396	4.02 463	84.36 340	2.00 461	48.59 413	45.86 420	68.82 413	32.22 392	82.80 413	31.58 432	51.38 404	77.81 414

new_pmnet		49.31 401	46.44 404	57.93 414	62.84 435	40.74 431	68.47 424	62.96 439	36.48 436	35.09 437	57.81 434	14.97 436	72.18 433	32.86 427	46.44 414	60.88 436

mvsany_test3		48.86 402	46.35 405	56.41 415	46.00 450	31.67 446	62.26 434	47.25 451	43.71 429	45.54 422	68.15 417	10.84 442	64.44 447	57.95 329	35.44 436	73.13 424

test_f		46.58 403	43.45 407	55.96 416	45.18 451	32.05 445	61.18 435	49.49 449	33.39 438	42.05 431	62.48 429	7.00 448	65.56 443	47.08 379	43.21 420	70.27 430

WB-MVS		46.23 404	44.94 406	50.11 424	62.13 437	21.23 457	76.48 402	55.49 443	45.89 422	35.78 435	61.44 432	35.54 378	72.83 432	9.96 451	21.75 446	56.27 439

FPMVS		45.64 405	43.10 409	53.23 422	51.42 447	36.46 440	64.97 431	71.91 421	29.13 442	27.53 442	61.55 431	9.83 444	65.01 445	16.00 448	55.58 393	58.22 438

SSC-MVS		44.51 406	43.35 408	47.99 428	61.01 440	18.90 459	74.12 410	54.36 444	43.42 431	34.10 439	60.02 433	34.42 383	70.39 435	9.14 453	19.57 447	54.68 440

EGC-MVSNET		42.35 407	38.09 410	55.11 418	74.57 406	46.62 416	71.63 416	55.77 442	0.04 456	0.24 457	62.70 428	14.24 438	74.91 430	17.59 445	46.06 415	43.80 442

LCM-MVSNet		40.54 408	35.79 413	54.76 420	36.92 457	30.81 447	51.41 444	69.02 429	22.07 444	24.63 444	45.37 441	4.56 453	65.81 442	33.67 422	34.50 437	67.67 431

APD_test1		40.50 409	37.31 412	50.09 425	51.88 445	35.27 442	59.45 439	52.59 446	21.64 445	26.12 443	57.80 435	4.56 453	66.56 441	22.64 440	39.09 427	48.43 441

test_vis3_rt		40.46 410	37.79 411	48.47 427	44.49 452	33.35 444	66.56 430	32.84 458	32.39 439	29.65 440	39.13 448	3.91 456	68.65 437	50.17 358	40.99 425	43.40 443

ANet_high		40.27 411	35.20 414	55.47 417	34.74 458	34.47 443	63.84 433	71.56 423	48.42 414	18.80 447	41.08 446	9.52 445	64.45 446	20.18 442	8.66 454	67.49 432

test_method		38.59 412	35.16 415	48.89 426	54.33 443	21.35 456	45.32 447	53.71 445	7.41 453	28.74 441	51.62 437	8.70 446	52.87 450	33.73 421	32.89 438	72.47 426

PMMVS2		37.93 413	33.61 416	50.92 423	46.31 449	24.76 453	60.55 438	50.05 447	28.94 443	20.93 445	47.59 438	4.41 455	65.13 444	25.14 437	18.55 449	62.87 435

Gipuma		34.91 414	31.44 417	45.30 429	70.99 418	39.64 437	19.85 451	72.56 419	20.10 447	16.16 451	21.47 452	5.08 452	71.16 434	13.07 449	43.70 419	25.08 449
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 415	29.47 418	42.67 431	41.89 454	30.81 447	52.07 442	43.45 452	15.45 448	18.52 448	44.82 442	2.12 457	58.38 448	16.05 446	30.87 440	38.83 444

APD_test2		32.77 415	29.47 418	42.67 431	41.89 454	30.81 447	52.07 442	43.45 452	15.45 448	18.52 448	44.82 442	2.12 457	58.38 448	16.05 446	30.87 440	38.83 444

PMVS	26.43 22	31.84 417	28.16 420	42.89 430	25.87 460	27.58 451	50.92 445	49.78 448	21.37 446	14.17 452	40.81 447	2.01 459	66.62 440	9.61 452	38.88 430	34.49 448
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 418	24.00 422	26.45 435	43.74 453	18.44 460	60.86 436	39.66 454	15.11 450	9.53 454	22.10 451	6.52 450	46.94 453	8.31 454	10.14 451	13.98 451

MVE	24.84 23	24.35 419	19.77 425	38.09 433	34.56 459	26.92 452	26.57 449	38.87 456	11.73 452	11.37 453	27.44 449	1.37 460	50.42 452	11.41 450	14.60 450	36.93 446
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 420	23.20 424	25.46 436	41.52 456	16.90 461	60.56 437	38.79 457	14.62 451	8.99 455	20.24 454	7.35 447	45.82 454	7.25 455	9.46 452	13.64 452

tmp_tt		22.26 421	23.75 423	17.80 437	5.23 461	12.06 462	35.26 448	39.48 455	2.82 455	18.94 446	44.20 444	22.23 424	24.64 456	36.30 414	9.31 453	16.69 450

cdsmvs_eth3d_5k		19.86 422	26.47 421	0.00 441	0.00 464	0.00 466	0.00 452	93.45 91	0.00 459	0.00 460	95.27 70	49.56 284	0.00 460	0.00 459	0.00 457	0.00 456

wuyk23d		11.30 423	10.95 426	12.33 438	48.05 448	19.89 458	25.89 450	1.92 462	3.58 454	3.12 456	1.37 456	0.64 461	15.77 457	6.23 456	7.77 455	1.35 453

ab-mvs-re		7.91 424	10.55 427	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	94.95 80	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

testmvs		7.23 425	9.62 428	0.06 440	0.04 462	0.02 465	84.98 337	0.02 463	0.03 457	0.18 458	1.21 457	0.01 463	0.02 458	0.14 457	0.01 456	0.13 455

test123		6.92 426	9.21 429	0.08 439	0.03 463	0.05 464	81.65 370	0.01 464	0.02 458	0.14 459	0.85 458	0.03 462	0.02 458	0.12 458	0.00 457	0.16 454

pcd_1.5k_mvsjas		4.46 427	5.95 430	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	53.55 241	0.00 460	0.00 459	0.00 457	0.00 456

mmdepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

monomultidepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

test_blank		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

uanet_test		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

DCPMVS		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

sosnet-low-res		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

sosnet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

uncertanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

Regformer		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

uanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 457	0.00 456

WAC-MVS						49.45 400								31.56 433

FOURS1					93.95 46	61.77 272	93.96 81	91.92 161	62.14 354	86.57 55

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

PC_three_1452									80.91 61	94.07 2	96.83 22	83.57 4	99.12 5	95.70 9	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 62	71.42 254	90.67 23	96.85 20	74.45 20

eth-test2					0.00 464

eth-test					0.00 464

ZD-MVS					96.63 9	65.50 163		93.50 89	70.74 268	85.26 73	95.19 76	64.92 89	97.29 83	87.51 67	93.01 56

RE-MVS-def			80.48 164		92.02 105	58.56 339	90.90 229	90.45 231	62.76 347	78.89 143	94.46 94	49.30 287		78.77 159	86.77 141	92.28 206

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 62	95.28 1				92.34 32	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 12	83.82 2	99.15 2	95.72 7	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 243	92.07 10	97.21 7	74.58 18	99.11 6	92.34 32	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 51	71.65 243	92.11 8	97.05 10	76.79 9	99.11 6

9.14			87.63 33		93.86 48		94.41 59	94.18 62	72.76 208	86.21 58	96.51 29	66.64 67	97.88 47	90.08 49	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 127	78.19 112

test_0728_THIRD									72.48 213	90.55 24	96.93 14	76.24 11	99.08 11	91.53 40	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 57					99.15 2	91.91 38	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 229	91.89 12	97.11 9	73.77 23

GSMVS																94.68 106

test_part2					96.29 19	68.16 89				90.78 21

sam_mvs1												57.85 185				94.68 106

sam_mvs												54.91 224

ambc				69.61 392	61.38 439	41.35 430	49.07 446	85.86 363		50.18 408	66.40 420	10.16 443	88.14 374	45.73 385	44.20 417	79.32 401

MTGPA								92.23 142

test_post1							78.95 390				20.70 453	53.05 246	91.50 339	60.43 318

test_post											23.01 450	56.49 206	92.67 300

patchmatchnet-post											67.62 419	57.62 188	90.25 349

GG-mvs-BLEND				86.53 74	91.91 114	69.67 53	75.02 409	94.75 36		78.67 151	90.85 183	77.91 7	94.56 230	72.25 211	93.74 45	95.36 68

MTMP							93.77 95	32.52 459

gm-plane-assit					88.42 205	67.04 120			78.62 106		91.83 166		97.37 77	76.57 171

test9_res														89.41 50	94.96 19	95.29 73

TEST9					94.18 41	67.28 111	94.16 68	93.51 87	71.75 240	85.52 68	95.33 64	68.01 57	97.27 87

test_8					94.19 40	67.19 113	94.15 70	93.42 94	71.87 234	85.38 71	95.35 63	68.19 55	96.95 113

agg_prior2														86.41 80	94.75 30	95.33 69

agg_prior					94.16 43	66.97 124		93.31 97		84.49 79			96.75 125

TestCases				72.46 378	79.57 363	51.42 388		68.61 430	51.25 406	45.88 418	81.23 332	19.86 430	86.58 389	38.98 410	57.01 389	79.39 399

test_prior4						67.18 115	93.92 84

test_prior2							95.10 38		75.40 159	85.25 74	95.61 55	67.94 58		87.47 69	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 108					96.84 122			95.05 87

旧先验2							92.00 181		59.37 376	87.54 48			93.47 275	75.39 180

新几何2							91.41 202

新几何1				84.73 145	92.32 93	64.28 199		91.46 188	59.56 375	79.77 132	92.90 138	56.95 198	96.57 131	63.40 298	92.91 58	93.34 168

旧先验1					91.94 110	60.74 298		91.50 186			94.36 98	65.23 84			91.84 72	94.55 113

无先验							92.71 144	92.61 132	62.03 355				97.01 103	66.63 269		93.97 147

原ACMM2							92.01 178

原ACMM1				84.42 161	93.21 68	64.27 200		93.40 96	65.39 322	79.51 135	92.50 146	58.11 184	96.69 127	65.27 288	93.96 40	92.32 204

test222					89.77 163	61.60 278	89.55 274	89.42 279	56.83 390	77.28 165	92.43 150	52.76 249			91.14 88	93.09 178

testdata2													96.09 156	61.26 314

segment_acmp												65.94 75

testdata				81.34 258	89.02 185	57.72 346		89.84 263	58.65 379	85.32 72	94.09 114	57.03 193	93.28 277	69.34 240	90.56 94	93.03 181

testdata1							89.21 284		77.55 127

test12				87.09 52	94.60 36	68.86 68		92.91 115		82.67 102		65.44 81	97.55 67		93.69 48	94.84 98

plane_prior7					86.94 249	61.51 279

plane_prior6					87.23 241	62.32 259						50.66 270

plane_prior5								91.31 192					95.55 187	76.74 169	78.53 233	88.39 276

plane_prior4											89.14 221

plane_prior3						61.95 268			79.09 95	72.53 223

plane_prior2							93.13 123		78.81 102

plane_prior1					87.15 243

plane_prior						62.42 255	93.85 88		79.38 87						78.80 230

n2								0.00 465

nn								0.00 465

door-mid								66.01 434

lessismore_v0				73.72 369	72.93 413	47.83 408		61.72 440		45.86 420	73.76 394	28.63 407	89.81 359	47.75 377	31.37 439	83.53 354

LGP-MVS_train				79.56 308	84.31 309	59.37 328		89.73 269	69.49 281	64.86 319	88.42 228	38.65 353	94.30 239	72.56 208	72.76 277	85.01 340

test11								93.01 111

door								66.57 433

HQP5-MVS						63.66 222

HQP-NCC					87.54 233		94.06 73		79.80 77	74.18 200

ACMP_Plane					87.54 233		94.06 73		79.80 77	74.18 200

BP-MVS														77.63 166

HQP4-MVS										74.18 200			95.61 181			88.63 270

HQP3-MVS								91.70 178							78.90 228

HQP2-MVS												51.63 261

NP-MVS					87.41 236	63.04 239					90.30 194

MDTV_nov1_ep13_2view						59.90 320	80.13 385		67.65 305	72.79 217		54.33 232		59.83 322		92.58 195

MDTV_nov1_ep13			72.61 294		89.06 184	68.48 77	80.33 381	90.11 252	71.84 236	71.81 237	75.92 387	53.01 247	93.92 262	48.04 371	73.38 272

ACMMP++_ref															71.63 285

ACMMP++															69.72 294

Test By Simon												54.21 235

ITE_SJBPF				70.43 390	74.44 407	47.06 414		77.32 403	60.16 371	54.04 389	83.53 298	23.30 420	84.01 403	43.07 394	61.58 369	80.21 396

DeepMVS_CX				34.71 434	51.45 446	24.73 454		28.48 460	31.46 440	17.49 450	52.75 436	5.80 451	42.60 455	18.18 443	19.42 448	36.81 447

High-resolution multi-view benchmark