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 5	97.66 2	73.37 12	97.13 2	95.58 12	89.33 1	85.77 73	96.26 47	72.84 32	99.38 2	92.64 33	95.93 9	97.08 12

MM		90.87 2	91.52 2	88.92 16	92.12 108	71.10 29	97.02 3	96.04 6	88.70 2	91.57 20	96.19 49	70.12 50	98.91 22	96.83 2	95.06 17	96.76 16

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 174	76.68 2	97.29 1	95.35 18	82.87 37	91.58 19	97.22 9	79.93 6	99.10 10	83.12 137	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 17	97.31 4	69.91 45	93.96 91	94.37 65	72.48 252	92.07 12	96.85 28	83.82 2	99.15 3	91.53 49	97.42 4	97.55 5

MSP-MVS		90.38 5	91.87 1	85.88 118	92.83 87	64.03 251	93.06 136	94.33 67	82.19 45	93.65 4	96.15 51	85.89 1	97.19 100	91.02 53	97.75 1	96.43 32
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
MGCNet		90.32 6	90.90 7	88.55 24	94.05 51	70.23 39	97.00 5	93.73 87	87.30 4	92.15 9	96.15 51	66.38 78	98.94 21	96.71 3	94.67 35	96.47 29

CNVR-MVS		90.32 6	90.89 8	88.61 23	96.76 9	70.65 32	96.47 14	94.83 36	84.83 17	89.07 44	96.80 31	70.86 46	99.06 16	92.64 33	95.71 11	96.12 42

DELS-MVS		90.05 8	90.09 11	89.94 5	93.14 78	73.88 9	97.01 4	94.40 63	88.32 3	85.71 74	94.91 92	74.11 23	98.91 22	87.26 82	95.94 8	97.03 13
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 19	96.45 13	69.38 63	96.89 6	94.44 56	71.65 282	92.11 10	97.21 10	76.79 10	99.11 7	92.34 36	95.36 14	97.62 3

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 180	93.00 83	58.16 394	96.72 9	94.41 61	86.50 9	90.25 34	97.83 2	75.46 16	98.67 31	92.78 32	95.49 13	97.32 7

patch_mono-2		89.71 11	90.99 6	85.85 121	96.04 26	63.70 268	95.04 43	95.19 23	86.74 8	91.53 21	95.15 85	73.86 24	97.58 71	93.38 27	92.00 77	96.28 39

CANet		89.61 12	89.99 12	88.46 25	94.39 45	69.71 54	96.53 13	93.78 80	86.89 7	89.68 40	95.78 58	65.94 83	99.10 10	92.99 30	93.91 46	96.58 22

DVP-MVS		89.41 13	89.73 14	88.45 26	96.40 16	69.99 41	96.64 10	94.52 52	71.92 268	90.55 30	96.93 20	73.77 25	99.08 12	91.91 42	94.90 22	96.29 37
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 36	95.10 33	68.23 107	95.24 34	94.49 54	82.43 42	88.90 46	96.35 42	71.89 43	98.63 32	88.76 67	96.40 6	96.06 43

BridgeMVS		89.08 15	88.84 22	89.81 7	93.66 60	75.15 5	90.61 286	93.43 103	84.06 24	86.20 68	90.17 234	72.42 37	96.98 117	93.09 29	95.92 10	97.29 8

NCCC		89.07 16	89.46 16	87.91 30	96.60 11	69.05 79	96.38 15	94.64 46	84.42 21	86.74 63	96.20 48	66.56 77	98.76 29	89.03 66	94.56 36	95.92 51

MED-MVS		89.02 17	89.57 15	87.38 47	94.76 36	67.28 137	94.47 64	94.87 33	70.68 309	91.27 24	96.93 20	76.77 12	98.98 17	91.55 45	94.82 26	95.88 54

DPE-MVS		88.77 18	89.21 19	87.45 45	96.26 22	67.56 128	94.17 77	94.15 72	68.77 338	90.74 28	97.27 7	76.09 14	98.49 35	90.58 57	94.91 21	96.30 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
aaEdge-Enhanced		88.25 19	88.55 26	87.33 51	96.33 19	67.28 137	93.93 93	94.81 37	70.09 317	88.91 45	96.95 18	70.12 50	98.73 30	91.55 45	94.28 39	95.99 48

fmvsm_l_conf0.5_n_9		88.24 20	89.36 17	84.85 168	88.15 235	61.94 319	95.65 25	89.70 311	85.54 12	92.07 12	97.33 6	67.51 68	97.27 95	96.23 5	92.07 76	95.35 78

fmvsm_s_conf0.5_n_9		88.14 21	89.21 19	84.92 163	89.29 185	61.41 336	92.97 141	88.36 370	86.96 6	91.49 22	97.49 4	69.48 55	97.46 78	97.00 1	89.88 114	95.89 53

SMA-MVS		88.14 21	88.29 30	87.67 35	93.21 75	68.72 91	93.85 99	94.03 76	74.18 213	91.74 16	96.67 34	65.61 88	98.42 39	89.24 63	96.08 7	95.88 54
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 21	87.61 40	89.71 8	92.06 111	76.72 1	95.75 20	93.26 109	83.86 25	89.55 41	96.06 53	53.55 281	97.89 53	91.10 51	93.31 57	94.54 139

TSAR-MVS + MP.		88.11 24	88.64 25	86.54 94	91.73 126	68.04 112	90.36 294	93.55 95	82.89 35	91.29 23	92.89 147	72.27 39	96.03 173	87.99 72	94.77 28	95.54 68
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_11		87.99 25	89.25 18	84.23 208	89.07 193	61.60 329	94.87 51	89.06 341	85.65 11	91.09 26	97.41 5	68.26 60	97.43 82	95.07 13	92.74 65	93.66 196

fmvsm_s_conf0.5_n_8		87.96 26	88.93 21	85.07 157	88.43 222	61.78 322	94.73 59	91.74 185	85.87 10	91.66 18	97.50 3	64.03 109	98.33 40	96.28 4	90.08 110	95.10 97

TSAR-MVS + GP.		87.96 26	88.37 29	86.70 76	93.51 68	65.32 205	95.15 37	93.84 79	78.17 137	85.93 72	94.80 95	75.80 15	98.21 42	89.38 60	88.78 127	96.59 20

DeepC-MVS_fast	79.48 2	87.95 28	88.00 34	87.79 33	95.86 29	68.32 101	95.74 21	94.11 73	83.82 26	83.49 99	96.19 49	64.53 104	98.44 37	83.42 135	94.88 25	96.61 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		87.93 29	88.67 24	85.71 128	88.69 204	63.71 266	94.56 62	90.22 287	85.04 15	92.27 7	97.05 13	63.67 117	98.15 44	95.09 12	91.39 89	95.27 87

xiu_mvs_v2_base		87.92 30	87.38 44	89.55 13	91.41 138	76.43 3	95.74 21	93.12 117	83.53 29	89.55 41	95.95 56	53.45 285	97.68 61	91.07 52	92.62 66	94.54 139

EPNet		87.84 31	88.38 28	86.23 108	93.30 72	66.05 182	95.26 33	94.84 35	87.09 5	88.06 50	94.53 101	66.79 73	97.34 88	83.89 126	91.68 83	95.29 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 32	87.77 37	87.63 40	89.24 190	71.18 26	96.57 12	92.90 128	82.70 39	87.13 58	95.27 78	64.99 94	95.80 189	89.34 61	91.80 81	95.93 50

test_fmvsm_n_1920		87.69 33	88.50 27	85.27 150	87.05 273	63.55 275	93.69 109	91.08 231	84.18 23	90.17 36	97.04 15	67.58 67	97.99 48	95.72 8	90.03 111	94.26 160

fmvsm_l_conf0.5_n_3		87.54 34	88.29 30	85.30 147	86.92 284	62.63 302	95.02 45	90.28 282	84.95 16	90.27 33	96.86 26	65.36 90	97.52 76	94.93 15	90.03 111	95.76 59

APDe-MVS		87.54 34	87.84 36	86.65 79	96.07 25	66.30 176	94.84 53	93.78 80	69.35 327	88.39 49	96.34 43	67.74 66	97.66 66	90.62 56	93.44 55	96.01 46
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 36	88.72 23	83.84 220	86.89 286	60.04 370	95.05 41	92.17 164	84.80 18	92.27 7	96.37 40	64.62 101	96.54 143	94.43 19	91.86 79	94.94 106

fmvsm_l_conf0.5_n		87.49 37	88.19 32	85.39 139	86.95 279	64.37 237	94.30 74	88.45 368	80.51 72	92.70 5	96.86 26	69.98 52	97.15 105	95.83 7	88.08 135	94.65 132

SD-MVS		87.49 37	87.49 42	87.50 44	93.60 62	68.82 86	93.90 96	92.63 143	76.86 167	87.90 52	95.76 59	66.17 80	97.63 68	89.06 65	91.48 87	96.05 44
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 39	88.15 33	85.30 147	87.10 271	64.19 246	94.41 69	88.14 379	80.24 84	92.54 6	96.97 17	69.52 54	97.17 101	95.89 6	88.51 130	94.56 136

dcpmvs_2		87.37 40	87.55 41	86.85 64	95.04 35	68.20 109	90.36 294	90.66 261	79.37 111	81.20 123	93.67 131	74.73 18	96.55 142	90.88 54	92.00 77	95.82 57

alignmvs		87.28 41	86.97 48	88.24 29	91.30 140	71.14 28	95.61 26	93.56 94	79.30 112	87.07 60	95.25 80	68.43 58	96.93 125	87.87 73	84.33 188	96.65 18

train_agg		87.21 42	87.42 43	86.60 82	94.18 47	67.28 137	94.16 78	93.51 97	71.87 273	85.52 77	95.33 72	68.19 61	97.27 95	89.09 64	94.90 22	95.25 91

MG-MVS		87.11 43	86.27 62	89.62 9	97.79 1	76.27 4	94.96 48	94.49 54	78.74 127	83.87 95	92.94 145	64.34 105	96.94 123	75.19 220	94.09 42	95.66 63

SF-MVS		87.03 44	87.09 46	86.84 65	92.70 93	67.45 134	93.64 112	93.76 83	70.78 307	86.25 66	96.44 39	66.98 71	97.79 57	88.68 68	94.56 36	95.28 86

TestfortrainingZip a		86.96 45	86.88 52	87.23 52	94.76 36	67.02 151	94.47 64	94.08 75	70.68 309	88.57 48	96.93 20	69.03 56	98.78 27	84.41 119	88.95 126	95.88 54

fmvsm_s_conf0.5_n_3		86.88 46	87.99 35	83.58 234	87.26 262	60.74 350	93.21 133	87.94 386	84.22 22	91.70 17	97.27 7	65.91 85	95.02 238	93.95 24	90.42 105	94.99 103

CSCG		86.87 47	86.26 63	88.72 18	95.05 34	70.79 31	93.83 104	95.33 19	68.48 342	77.63 191	94.35 110	73.04 30	98.45 36	84.92 110	93.71 51	96.92 15

sasdasda		86.85 48	86.25 64	88.66 21	91.80 124	71.92 18	93.54 117	91.71 188	80.26 81	87.55 55	95.25 80	63.59 121	96.93 125	88.18 70	84.34 186	97.11 10

canonicalmvs		86.85 48	86.25 64	88.66 21	91.80 124	71.92 18	93.54 117	91.71 188	80.26 81	87.55 55	95.25 80	63.59 121	96.93 125	88.18 70	84.34 186	97.11 10

UBG		86.83 50	86.70 55	87.20 54	93.07 81	69.81 49	93.43 125	95.56 14	81.52 53	81.50 118	92.12 169	73.58 28	96.28 156	84.37 120	85.20 175	95.51 69

PHI-MVS		86.83 50	86.85 54	86.78 70	93.47 69	65.55 199	95.39 31	95.10 26	71.77 278	85.69 75	96.52 36	62.07 151	98.77 28	86.06 97	95.60 12	96.03 45

SteuartSystems-ACMMP		86.82 52	86.90 51	86.58 85	90.42 159	66.38 173	96.09 17	93.87 78	77.73 148	84.01 94	95.66 61	63.39 124	97.94 49	87.40 80	93.55 54	95.42 71
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 38	84.27 206	86.15 306	61.48 333	94.69 60	91.16 217	83.79 28	90.51 32	96.28 45	64.24 106	98.22 41	95.00 14	86.88 148	93.11 215

PVSNet_Blended		86.73 54	86.86 53	86.31 107	93.76 56	67.53 130	96.33 16	93.61 92	82.34 44	81.00 130	93.08 141	63.19 129	97.29 91	87.08 88	91.38 90	94.13 170

testing11		86.71 55	86.44 60	87.55 42	93.54 66	71.35 23	93.65 111	95.58 12	81.36 60	80.69 135	92.21 166	72.30 38	96.46 148	85.18 106	83.43 206	94.82 117

test_fmvsmconf_n		86.58 56	87.17 45	84.82 170	85.28 328	62.55 303	94.26 76	89.78 302	83.81 27	87.78 54	96.33 44	65.33 91	96.98 117	94.40 20	87.55 141	94.95 105

BP-MVS1		86.54 57	86.68 57	86.13 111	87.80 250	67.18 144	92.97 141	95.62 11	79.92 89	82.84 106	94.14 119	74.95 17	96.46 148	82.91 141	88.96 125	94.74 122

jason		86.40 58	86.17 66	87.11 57	86.16 305	70.54 34	95.71 24	92.19 161	82.00 47	84.58 87	94.34 111	61.86 154	95.53 218	87.76 74	90.89 98	95.27 87
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 133	92.12 108	65.95 188	94.88 49	90.83 248	84.69 19	83.67 97	94.10 120	63.16 131	96.91 129	85.31 102	91.15 94	93.93 184

fmvsm_s_conf0.5_n		86.39 59	86.91 50	84.82 170	87.36 261	63.54 276	94.74 56	90.02 295	82.52 40	90.14 37	96.92 24	62.93 136	97.84 56	95.28 11	82.26 219	93.07 218

fmvsm_s_conf0.5_n_5		86.38 61	86.94 49	84.71 182	84.67 340	63.29 282	94.04 87	89.99 297	82.88 36	87.85 53	96.03 54	62.89 138	96.36 152	94.15 21	89.95 113	94.48 149

SymmetryMVS		86.32 62	86.39 61	86.12 112	90.52 157	65.95 188	94.88 49	94.58 51	84.69 19	83.67 97	94.10 120	63.16 131	96.91 129	85.31 102	86.59 157	95.51 69

WTY-MVS		86.32 62	85.81 74	87.85 31	92.82 89	69.37 65	95.20 35	95.25 21	82.71 38	81.91 114	94.73 96	67.93 65	97.63 68	79.55 182	82.25 221	96.54 23

myMVS_eth3d28		86.31 64	86.15 67	86.78 70	93.56 64	70.49 35	92.94 144	95.28 20	82.47 41	78.70 179	92.07 172	72.45 36	95.41 220	82.11 150	85.78 168	94.44 151

MSLP-MVS++		86.27 65	85.91 73	87.35 49	92.01 115	68.97 82	95.04 43	92.70 134	79.04 122	81.50 118	96.50 38	58.98 200	96.78 133	83.49 134	93.93 45	96.29 37

VNet		86.20 66	85.65 78	87.84 32	93.92 53	69.99 41	95.73 23	95.94 7	78.43 133	86.00 71	93.07 142	58.22 214	97.00 113	85.22 104	84.33 188	96.52 24

MVS_111021_HR		86.19 67	85.80 75	87.37 48	93.17 77	69.79 50	93.99 90	93.76 83	79.08 119	78.88 175	93.99 125	62.25 147	98.15 44	85.93 98	91.15 94	94.15 168

SPE-MVS-test		86.14 68	87.01 47	83.52 235	92.63 95	59.36 382	95.49 28	91.92 174	80.09 85	85.46 79	95.53 67	61.82 156	95.77 194	86.77 92	93.37 56	95.41 72

ACMMP_NAP		86.05 69	85.80 75	86.80 69	91.58 130	67.53 130	91.79 214	93.49 100	74.93 201	84.61 86	95.30 74	59.42 189	97.92 50	86.13 95	94.92 20	94.94 106

testing99		86.01 70	85.47 80	87.63 40	93.62 61	71.25 25	93.47 123	95.23 22	80.42 76	80.60 137	91.95 181	71.73 44	96.50 146	80.02 179	82.22 222	95.13 95

ETV-MVS		86.01 70	86.11 68	85.70 129	90.21 164	67.02 151	93.43 125	91.92 174	81.21 62	84.13 93	94.07 124	60.93 165	95.63 206	89.28 62	89.81 115	94.46 150

testing91		85.93 72	85.31 84	87.78 34	93.59 63	71.47 21	93.50 120	95.08 29	80.26 81	80.53 141	91.93 182	70.43 48	96.51 145	80.32 177	82.13 225	95.37 75

APD-MVS		85.93 72	85.99 71	85.76 125	95.98 28	65.21 208	93.59 115	92.58 145	66.54 361	86.17 69	95.88 57	63.83 113	97.00 113	86.39 94	92.94 62	95.06 99
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 74	85.46 81	87.18 55	88.20 234	72.42 17	92.41 180	92.77 132	82.11 46	80.34 145	93.07 142	68.27 59	95.02 238	78.39 198	93.59 53	94.09 174

CS-MVS		85.80 75	86.65 59	83.27 247	92.00 116	58.92 386	95.31 32	91.86 179	79.97 86	84.82 85	95.40 70	62.26 146	95.51 219	86.11 96	92.08 75	95.37 75

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 180	85.73 319	63.58 273	93.79 105	89.32 322	81.42 58	90.21 35	96.91 25	62.41 143	97.67 63	94.48 18	80.56 248	92.90 224

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 191	80.83 391	62.33 308	93.84 102	88.81 354	83.50 30	87.00 61	96.01 55	63.36 125	96.93 125	94.04 23	87.29 145	94.61 134

CDPH-MVS		85.71 77	85.46 81	86.46 98	94.75 40	67.19 142	93.89 97	92.83 130	70.90 303	83.09 104	95.28 76	63.62 119	97.36 86	80.63 173	94.18 41	94.84 112

casdiffmvs_mvg		85.66 79	85.18 86	87.09 58	88.22 233	69.35 66	93.74 108	91.89 177	81.47 54	80.10 148	91.45 197	64.80 99	96.35 153	87.23 83	87.69 139	95.58 66
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 80	85.93 72	84.68 185	82.95 372	63.48 278	94.03 89	89.46 316	81.69 51	89.86 38	96.74 32	61.85 155	97.75 59	94.74 17	82.01 227	92.81 228

MGCFI-Net		85.59 81	85.73 77	85.17 154	91.41 138	62.44 304	92.87 150	91.31 207	79.65 98	86.99 62	95.14 86	62.90 137	96.12 165	87.13 85	84.13 194	96.96 14

GDP-MVS		85.54 82	85.32 83	86.18 109	87.64 253	67.95 116	92.91 148	92.36 151	77.81 145	83.69 96	94.31 113	72.84 32	96.41 150	80.39 176	85.95 164	94.19 164

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 103	88.80 202	66.64 167	92.15 190	93.68 89	81.07 64	76.91 205	93.64 132	62.59 140	98.44 37	85.50 100	92.84 64	94.03 179
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 84	84.87 92	86.84 65	88.25 231	69.07 76	93.04 138	91.76 184	81.27 61	80.84 133	92.07 172	64.23 107	96.06 171	84.98 109	87.43 143	95.39 73
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 85	85.08 88	86.06 113	93.09 80	65.65 195	93.89 97	93.41 105	73.75 224	79.94 150	94.68 98	60.61 170	98.03 47	82.63 145	93.72 50	94.52 141

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 323	84.52 345	60.10 368	93.35 128	90.35 275	83.41 31	86.54 65	96.27 46	60.50 171	90.02 410	94.84 16	90.38 106	92.61 232

MP-MVS-pluss		85.24 86	85.13 87	85.56 134	91.42 135	65.59 197	91.54 234	92.51 147	74.56 204	80.62 136	95.64 62	59.15 196	97.00 113	86.94 90	93.80 47	94.07 176
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 88	84.69 96	86.63 81	92.91 85	69.91 45	92.61 166	95.80 9	80.31 80	80.38 143	92.27 162	68.73 57	95.19 235	75.94 214	83.27 209	94.81 119

PAPR		85.15 89	84.47 97	87.18 55	96.02 27	68.29 102	91.85 212	93.00 123	76.59 178	79.03 171	95.00 87	61.59 157	97.61 70	78.16 199	89.00 124	95.63 64

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 241	86.92 284	60.53 357	94.41 69	87.31 394	83.30 32	88.72 47	96.72 33	54.28 273	97.75 59	94.07 22	84.68 185	92.04 255

MP-MVS		85.02 91	84.97 90	85.17 154	92.60 96	64.27 242	93.24 130	92.27 154	73.13 236	79.63 160	94.43 104	61.90 152	97.17 101	85.00 108	92.56 67	94.06 177
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 92	84.44 98	86.71 75	88.33 228	68.73 90	90.24 299	91.82 183	81.05 65	81.18 124	92.50 154	63.69 116	96.08 170	84.45 118	86.71 155	95.32 81

CHOSEN 1792x2688		84.98 93	83.45 121	89.57 12	89.94 169	75.14 6	92.07 196	92.32 152	81.87 49	75.68 215	88.27 271	60.18 175	98.60 33	80.46 175	90.27 109	94.96 104

MVSMamba_PlusPlus		84.97 94	83.65 114	88.93 15	90.17 165	74.04 8	87.84 357	92.69 137	62.18 406	81.47 120	87.64 286	71.47 45	96.28 156	84.69 112	94.74 33	96.47 29

balanced_ft_v1		84.95 95	83.81 109	88.38 27	93.31 71	73.59 11	85.95 381	92.51 147	77.25 161	73.97 247	89.14 257	59.30 192	95.25 233	92.50 35	90.34 108	96.31 35

E3new		84.94 96	84.36 100	86.69 78	89.06 194	69.31 67	92.68 163	91.29 212	80.72 69	81.03 127	92.14 168	61.89 153	95.91 177	84.59 115	85.85 167	94.86 108

viewmanbaseed2359cas		84.89 97	84.26 102	86.78 70	88.50 213	69.77 52	92.69 162	91.13 223	81.11 63	81.54 117	91.98 178	60.35 172	95.73 196	84.47 117	86.56 158	94.84 112

EIA-MVS		84.84 98	84.88 91	84.69 184	91.30 140	62.36 307	93.85 99	92.04 167	79.45 107	79.33 165	94.28 115	62.42 142	96.35 153	80.05 178	91.25 93	95.38 74

lecture		84.77 99	84.81 94	84.65 187	92.12 108	62.27 311	94.74 56	92.64 142	68.35 343	85.53 76	95.30 74	59.77 182	97.91 51	83.73 130	91.15 94	93.77 193

fmvsm_s_conf0.1_n_a		84.76 100	84.84 93	84.53 193	80.23 404	63.50 277	92.79 152	88.73 358	80.46 74	89.84 39	96.65 35	60.96 164	97.57 73	93.80 25	80.14 250	92.53 237

viewcassd2359sk11		84.74 101	84.11 103	86.64 80	88.57 207	69.20 74	92.61 166	91.23 214	80.58 70	80.85 132	91.96 179	61.39 159	95.89 179	84.28 121	85.49 172	94.82 117

HFP-MVS		84.73 102	84.40 99	85.72 127	93.75 58	65.01 214	93.50 120	93.19 113	72.19 262	79.22 168	94.93 90	59.04 199	97.67 63	81.55 160	92.21 71	94.49 148

MVS		84.66 103	82.86 146	90.06 3	90.93 149	74.56 7	87.91 355	95.54 15	68.55 340	72.35 274	94.71 97	59.78 181	98.90 24	81.29 166	94.69 34	96.74 17

hybridcas		84.65 104	83.95 106	86.74 74	87.18 267	68.78 88	92.94 144	91.36 205	80.47 73	79.32 166	91.67 193	62.13 150	96.19 161	83.15 136	87.36 144	95.25 91

GST-MVS		84.63 105	84.29 101	85.66 130	92.82 89	65.27 206	93.04 138	93.13 116	73.20 234	78.89 172	94.18 118	59.41 190	97.85 55	81.45 162	92.48 69	93.86 190

Casviewmamba		84.58 106	83.95 106	86.47 97	87.22 264	67.76 122	92.71 156	90.96 241	80.81 67	79.29 167	91.85 184	62.20 148	96.33 155	84.60 114	85.91 165	95.32 81

EC-MVSNet		84.53 107	85.04 89	83.01 253	89.34 181	61.37 337	94.42 68	91.09 227	77.91 143	83.24 100	94.20 117	58.37 212	95.40 221	85.35 101	91.41 88	92.27 249

E2		84.45 108	83.74 110	86.56 87	87.90 243	69.06 77	92.53 174	91.13 223	80.35 78	80.58 139	91.69 191	60.70 166	95.84 182	83.80 128	84.99 177	94.79 120

E3		84.45 108	83.74 110	86.56 87	87.90 243	69.06 77	92.53 174	91.13 223	80.35 78	80.58 139	91.69 191	60.70 166	95.84 182	83.80 128	84.99 177	94.79 120

fmvsm_s_conf0.1_n_2		84.40 110	84.78 95	83.27 247	85.25 329	60.41 360	94.13 81	85.69 419	83.05 34	87.99 51	96.37 40	52.75 290	97.68 61	93.75 26	84.05 195	91.71 263

ACMMPR		84.37 111	84.06 104	85.28 149	93.56 64	64.37 237	93.50 120	93.15 115	72.19 262	78.85 177	94.86 93	56.69 239	97.45 79	81.55 160	92.20 72	94.02 180

region2R		84.36 112	84.03 105	85.36 144	93.54 66	64.31 240	93.43 125	92.95 126	72.16 265	78.86 176	94.84 94	56.97 234	97.53 75	81.38 164	92.11 74	94.24 162

LFMVS		84.34 113	82.73 148	89.18 14	94.76 36	73.25 13	94.99 47	91.89 177	71.90 270	82.16 113	93.49 136	47.98 343	97.05 108	82.55 146	84.82 181	97.25 9

test_yl		84.28 114	83.16 136	87.64 36	94.52 43	69.24 72	95.78 18	95.09 27	69.19 330	81.09 125	92.88 148	57.00 232	97.44 80	81.11 169	81.76 231	96.23 40

DCV-MVSNet		84.28 114	83.16 136	87.64 36	94.52 43	69.24 72	95.78 18	95.09 27	69.19 330	81.09 125	92.88 148	57.00 232	97.44 80	81.11 169	81.76 231	96.23 40

diffmvs		84.28 114	83.83 108	85.61 132	87.40 259	68.02 113	90.88 269	89.24 326	80.54 71	81.64 116	92.52 153	59.83 180	94.52 271	87.32 81	85.11 176	94.29 158
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	84.28 114	83.36 127	87.02 61	92.22 103	67.74 123	84.65 390	94.50 53	79.15 116	82.23 112	87.93 280	66.88 72	96.94 123	80.53 174	82.20 223	96.39 34

ETVMVS		84.22 118	83.71 112	85.76 125	92.58 97	68.25 106	92.45 178	95.53 16	79.54 105	79.46 162	91.64 195	70.29 49	94.18 286	69.16 283	82.76 215	94.84 112

MAR-MVS		84.18 119	83.43 122	86.44 100	96.25 23	65.93 190	94.28 75	94.27 69	74.41 207	79.16 170	95.61 63	53.99 276	98.88 26	69.62 277	93.26 58	94.50 147
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		84.16 120	83.20 133	87.05 60	91.56 131	69.82 48	89.99 308	92.05 166	77.77 147	82.84 106	86.57 303	63.93 112	96.09 167	74.91 225	89.18 121	95.25 91

CANet_DTU		84.09 121	83.52 115	85.81 122	90.30 162	66.82 161	91.87 210	89.01 344	85.27 13	86.09 70	93.74 129	47.71 349	96.98 117	77.90 201	89.78 117	93.65 197

viewdifsd2359ckpt13		84.08 122	83.21 131	86.70 76	88.49 217	69.55 58	92.25 184	91.14 221	79.71 96	79.73 157	91.72 190	58.83 203	95.89 179	82.06 151	84.99 177	94.66 131

viewmacassd2359aftdt		84.03 123	83.18 135	86.59 84	86.76 287	69.44 60	92.44 179	90.85 247	80.38 77	80.78 134	91.33 203	58.54 209	95.62 208	82.15 149	85.41 173	94.72 125

ET-MVSNet_ETH3D		84.01 124	83.15 138	86.58 85	90.78 154	70.89 30	94.74 56	94.62 48	81.44 57	58.19 424	93.64 132	73.64 27	92.35 364	82.66 144	78.66 271	96.50 28

E4		84.00 125	83.19 134	86.46 98	86.99 274	68.85 84	92.39 181	90.99 240	79.94 87	80.17 147	91.36 202	59.73 183	95.79 191	82.87 142	84.22 192	94.74 122

diffmvs_AUTHOR		83.97 126	83.49 118	85.39 139	86.09 307	67.83 119	90.76 274	89.05 342	79.94 87	81.43 121	92.23 165	59.53 186	94.42 275	87.18 84	85.22 174	93.92 186

PVSNet_Blended_VisFu		83.97 126	83.50 117	85.39 139	90.02 167	66.59 170	93.77 106	91.73 186	77.43 157	77.08 204	89.81 245	63.77 115	96.97 120	79.67 181	88.21 133	92.60 233

MTAPA		83.91 128	83.38 126	85.50 135	91.89 122	65.16 210	81.75 424	92.23 155	75.32 195	80.53 141	95.21 83	56.06 248	97.16 104	84.86 111	92.55 68	94.18 165

XVS		83.87 129	83.47 120	85.05 158	93.22 73	63.78 260	92.92 146	92.66 139	73.99 216	78.18 185	94.31 113	55.25 255	97.41 83	79.16 188	91.58 85	93.95 182

Effi-MVS+		83.82 130	82.76 147	86.99 62	89.56 177	69.40 61	91.35 247	86.12 413	72.59 249	83.22 103	92.81 151	59.60 185	96.01 175	81.76 159	87.80 138	95.56 67

test_fmvsmvis_n_1920		83.80 131	83.48 119	84.77 175	82.51 375	63.72 265	91.37 243	83.99 437	81.42 58	77.68 190	95.74 60	58.37 212	97.58 71	93.38 27	86.87 149	93.00 221

EI-MVSNet-Vis-set		83.77 132	83.67 113	84.06 211	92.79 92	63.56 274	91.76 220	94.81 37	79.65 98	77.87 188	94.09 122	63.35 126	97.90 52	79.35 186	79.36 261	90.74 285

hybridnocas07		83.76 133	83.21 131	85.39 139	86.64 288	67.40 135	91.08 261	88.77 357	79.78 95	80.35 144	92.15 167	59.24 195	94.67 260	87.11 87	83.79 199	94.11 172

MVSFormer		83.75 134	82.88 145	86.37 103	89.24 190	71.18 26	89.07 333	90.69 258	65.80 371	87.13 58	94.34 111	64.99 94	92.67 350	72.83 241	91.80 81	95.27 87

CP-MVS		83.71 135	83.40 125	84.65 187	93.14 78	63.84 258	94.59 61	92.28 153	71.03 301	77.41 195	94.92 91	55.21 258	96.19 161	81.32 165	90.70 100	93.91 187

test_fmvsmconf0.01_n		83.70 136	83.52 115	84.25 207	75.26 455	61.72 326	92.17 189	87.24 396	82.36 43	84.91 84	95.41 69	55.60 253	96.83 132	92.85 31	85.87 166	94.21 163

onestephybrid01		83.68 137	83.31 130	84.81 173	86.53 293	65.38 204	90.54 287	89.14 334	79.52 106	81.01 128	92.02 174	58.91 201	94.91 247	88.26 69	83.86 198	94.14 169

baseline2		83.68 137	83.42 124	84.48 196	87.37 260	66.00 185	90.06 303	95.93 8	79.71 96	69.08 312	90.39 222	77.92 7	96.28 156	78.91 193	81.38 235	91.16 278

E5new		83.62 139	82.65 150	86.55 89	86.98 275	69.28 70	91.69 224	90.96 241	79.61 100	79.80 152	91.25 205	58.04 218	95.84 182	81.83 157	83.66 203	94.52 141

E6new		83.62 139	82.65 150	86.55 89	86.98 275	69.29 68	91.69 224	90.95 244	79.60 103	79.80 152	91.25 205	58.04 218	95.84 182	81.84 155	83.67 201	94.52 141

E6		83.62 139	82.65 150	86.55 89	86.98 275	69.29 68	91.69 224	90.95 244	79.60 103	79.80 152	91.25 205	58.04 218	95.84 182	81.84 155	83.67 201	94.52 141

E5		83.62 139	82.65 150	86.55 89	86.98 275	69.28 70	91.69 224	90.96 241	79.61 100	79.80 152	91.25 205	58.04 218	95.84 182	81.83 157	83.66 203	94.52 141

hybrid		83.58 143	83.00 140	85.34 145	86.38 300	67.51 133	90.92 265	88.87 352	78.49 132	80.59 138	92.09 171	58.77 206	94.46 273	87.12 86	83.74 200	94.06 177

viewdifsd2359ckpt09		83.52 144	82.57 156	86.37 103	88.02 240	68.47 97	91.78 217	89.63 312	79.61 100	78.56 182	92.00 177	59.28 193	95.96 176	81.94 153	82.35 216	94.69 126

reproduce-ours		83.51 145	83.33 128	84.06 211	92.18 106	60.49 358	90.74 276	92.04 167	64.35 383	83.24 100	95.59 65	59.05 197	97.27 95	83.61 131	89.17 122	94.41 156

our_new_method		83.51 145	83.33 128	84.06 211	92.18 106	60.49 358	90.74 276	92.04 167	64.35 383	83.24 100	95.59 65	59.05 197	97.27 95	83.61 131	89.17 122	94.41 156

thisisatest0515		83.41 147	82.49 158	86.16 110	89.46 180	68.26 104	93.54 117	94.70 43	74.31 210	75.75 213	90.92 212	72.62 34	96.52 144	69.64 275	81.50 234	93.71 194

PVSNet_BlendedMVS		83.38 148	83.43 122	83.22 249	93.76 56	67.53 130	94.06 83	93.61 92	79.13 117	81.00 130	85.14 324	63.19 129	97.29 91	87.08 88	73.91 310	84.83 395

test2506		83.29 149	82.92 144	84.37 200	88.39 225	63.18 288	92.01 199	91.35 206	77.66 150	78.49 184	91.42 198	64.58 103	95.09 237	73.19 237	89.23 119	94.85 109

PGM-MVS		83.25 150	82.70 149	84.92 163	92.81 91	64.07 250	90.44 289	92.20 159	71.28 295	77.23 199	94.43 104	55.17 259	97.31 90	79.33 187	91.38 90	93.37 205

HPM-MVS		83.25 150	82.95 143	84.17 209	92.25 102	62.88 297	90.91 266	91.86 179	70.30 314	77.12 201	93.96 126	56.75 237	96.28 156	82.04 152	91.34 92	93.34 206
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
viewmamba		83.23 152	82.64 154	85.00 161	86.40 299	66.16 180	90.68 279	88.35 372	79.92 89	78.68 180	92.02 174	58.86 202	94.72 253	85.55 99	83.31 208	94.12 171

reproduce_model		83.15 153	82.96 141	83.73 226	92.02 112	59.74 374	90.37 293	92.08 165	63.70 390	82.86 105	95.48 68	58.62 207	97.17 101	83.06 138	88.42 131	94.26 160

EI-MVSNet-UG-set		83.14 154	82.96 141	83.67 231	92.28 101	63.19 287	91.38 242	94.68 44	79.22 114	76.60 207	93.75 128	62.64 139	97.76 58	78.07 200	78.01 274	90.05 294

testing3-2		83.11 155	83.15 138	82.98 254	91.92 119	64.01 253	94.39 72	95.37 17	78.32 134	75.53 220	90.06 241	73.18 29	93.18 328	74.34 230	75.27 299	91.77 262

VDD-MVS		83.06 156	81.81 170	86.81 68	90.86 152	67.70 124	95.40 30	91.50 199	75.46 190	81.78 115	92.34 161	40.09 400	97.13 106	86.85 91	82.04 226	95.60 65

h-mvs33		83.01 157	82.56 157	84.35 201	89.34 181	62.02 315	92.72 155	93.76 83	81.45 55	82.73 109	92.25 164	60.11 176	97.13 106	87.69 75	62.96 398	93.91 187

PAPM_NR		82.97 158	81.84 169	86.37 103	94.10 50	66.76 164	87.66 361	92.84 129	69.96 319	74.07 245	93.57 134	63.10 134	97.50 77	70.66 270	90.58 102	94.85 109

mPP-MVS		82.96 159	82.44 159	84.52 194	92.83 87	62.92 295	92.76 153	91.85 181	71.52 290	75.61 218	94.24 116	53.48 284	96.99 116	78.97 191	90.73 99	93.64 198

viewdifsd2359ckpt07		82.95 160	82.04 164	85.66 130	87.19 266	66.73 165	91.56 233	90.39 274	77.58 153	77.58 194	91.19 209	58.57 208	95.65 205	82.32 147	82.01 227	94.60 135

SR-MVS		82.81 161	82.58 155	83.50 238	93.35 70	61.16 340	92.23 187	91.28 213	64.48 382	81.27 122	95.28 76	53.71 280	95.86 181	82.87 142	88.77 128	93.49 203

DP-MVS Recon		82.73 162	81.65 171	85.98 115	97.31 4	67.06 147	95.15 37	91.99 171	69.08 335	76.50 210	93.89 127	54.48 269	98.20 43	70.76 268	85.66 170	92.69 229

CLD-MVS		82.73 162	82.35 161	83.86 219	87.90 243	67.65 126	95.45 29	92.18 162	85.06 14	72.58 265	92.27 162	52.46 293	95.78 192	84.18 122	79.06 266	88.16 323
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 164	82.38 160	83.73 226	89.25 187	59.58 377	92.24 186	94.89 32	77.96 140	79.86 151	92.38 159	56.70 238	97.05 108	77.26 204	80.86 243	94.55 137

3Dnovator	73.91 6	82.69 165	80.82 186	88.31 28	89.57 176	71.26 24	92.60 168	94.39 64	78.84 124	67.89 335	92.48 157	48.42 338	98.52 34	68.80 288	94.40 38	95.15 94

RRT-MVS		82.61 166	81.16 177	86.96 63	91.10 144	68.75 89	87.70 360	92.20 159	76.97 165	72.68 261	87.10 297	51.30 307	96.41 150	83.56 133	87.84 137	95.74 60

viewmambaseed2359dif		82.60 167	81.91 168	84.67 186	85.83 314	66.09 181	90.50 288	89.01 344	75.46 190	79.64 159	92.01 176	59.51 187	94.38 277	82.99 140	82.26 219	93.54 200

MVSTER		82.47 168	82.05 163	83.74 224	92.68 94	69.01 80	91.90 209	93.21 110	79.83 91	72.14 275	85.71 317	74.72 19	94.72 253	75.72 216	72.49 320	87.50 330

TESTMET0.1,1		82.41 169	81.98 167	83.72 228	88.08 236	63.74 262	92.70 158	93.77 82	79.30 112	77.61 192	87.57 288	58.19 215	94.08 291	73.91 232	86.68 156	93.33 208

CostFormer		82.33 170	81.15 178	85.86 120	89.01 197	68.46 98	82.39 420	93.01 121	75.59 188	80.25 146	81.57 371	72.03 41	94.96 242	79.06 190	77.48 283	94.16 167

API-MVS		82.28 171	80.53 196	87.54 43	96.13 24	70.59 33	93.63 113	91.04 237	65.72 373	75.45 221	92.83 150	56.11 247	98.89 25	64.10 345	89.75 118	93.15 213

dtuplus		82.25 172	81.42 175	84.71 182	85.38 324	66.05 182	90.62 285	89.27 324	75.16 198	79.22 168	91.76 186	58.05 217	94.56 267	81.18 168	82.19 224	93.52 201

casdiffseed414692147		82.20 173	80.75 187	86.55 89	87.13 270	69.57 57	91.79 214	90.48 266	78.12 138	78.52 183	90.10 240	55.92 250	95.80 189	72.42 250	82.28 218	94.28 159

IB-MVS	77.80 4	82.18 174	80.46 198	87.35 49	89.14 192	70.28 38	95.59 27	95.17 25	78.85 123	70.19 300	85.82 314	70.66 47	97.67 63	72.19 254	66.52 365	94.09 174
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
nomal-1		82.17 175	81.45 174	84.34 202	90.99 147	69.47 59	83.86 398	93.64 91	77.94 142	73.62 252	85.72 316	66.65 74	91.90 375	80.76 172	79.90 252	91.64 264

xiu_mvs_v1_base_debu		82.16 176	81.12 179	85.26 151	86.42 296	68.72 91	92.59 170	90.44 271	73.12 237	84.20 90	94.36 106	38.04 413	95.73 196	84.12 123	86.81 150	91.33 271

xiu_mvs_v1_base		82.16 176	81.12 179	85.26 151	86.42 296	68.72 91	92.59 170	90.44 271	73.12 237	84.20 90	94.36 106	38.04 413	95.73 196	84.12 123	86.81 150	91.33 271

xiu_mvs_v1_base_debi		82.16 176	81.12 179	85.26 151	86.42 296	68.72 91	92.59 170	90.44 271	73.12 237	84.20 90	94.36 106	38.04 413	95.73 196	84.12 123	86.81 150	91.33 271

3Dnovator+	73.60 7	82.10 179	80.60 194	86.60 82	90.89 151	66.80 163	95.20 35	93.44 102	74.05 215	67.42 343	92.49 156	49.46 328	97.65 67	70.80 267	91.68 83	95.33 79

MVS_111021_LR		82.02 180	81.52 172	83.51 237	88.42 223	62.88 297	89.77 311	88.93 349	76.78 170	75.55 219	93.10 139	50.31 317	95.38 223	83.82 127	87.02 147	92.26 250

PMMVS		81.98 181	82.04 164	81.78 291	89.76 173	56.17 417	91.13 260	90.69 258	77.96 140	80.09 149	93.57 134	46.33 368	94.99 241	81.41 163	87.46 142	94.17 166

baseline1		81.84 182	81.03 183	84.28 205	91.60 129	66.62 168	91.08 261	91.66 193	81.87 49	74.86 231	91.67 193	69.98 52	94.92 245	71.76 257	64.75 382	91.29 276

EPP-MVSNet		81.79 183	81.52 172	82.61 264	88.77 203	60.21 366	93.02 140	93.66 90	68.52 341	72.90 259	90.39 222	72.19 40	94.96 242	74.93 224	79.29 264	92.67 230

WBMVS		81.67 184	80.98 185	83.72 228	93.07 81	69.40 61	94.33 73	93.05 119	76.84 168	72.05 277	84.14 337	74.49 21	93.88 305	72.76 244	68.09 351	87.88 325

test_vis1_n_1920		81.66 185	82.01 166	80.64 327	82.24 377	55.09 426	94.76 55	86.87 400	81.67 52	84.40 89	94.63 99	38.17 410	94.67 260	91.98 41	83.34 207	92.16 253

APD-MVS_3200maxsize		81.64 186	81.32 176	82.59 266	92.36 99	58.74 388	91.39 240	91.01 239	63.35 394	79.72 158	94.62 100	51.82 296	96.14 164	79.71 180	87.93 136	92.89 225

PRO-TEST		81.59 187	82.22 162	79.70 354	91.09 145	48.99 461	81.78 422	90.76 256	81.94 48	63.52 381	87.90 281	58.82 204	95.28 232	91.87 44	92.28 70	94.83 116

mvsmamba		81.55 188	80.72 189	84.03 215	91.42 135	66.93 159	83.08 411	89.13 335	78.55 131	67.50 341	87.02 298	51.79 298	90.07 409	87.48 78	90.49 104	95.10 97

ACMMP		81.49 189	80.67 191	83.93 217	91.71 127	62.90 296	92.13 191	92.22 158	71.79 277	71.68 283	93.49 136	50.32 316	96.96 121	78.47 197	84.22 192	91.93 260
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		81.43 190	80.11 200	85.38 143	86.60 291	65.47 203	92.90 149	93.54 96	75.33 194	77.31 197	90.39 222	46.81 358	96.75 134	71.65 260	86.46 161	93.93 184

CDS-MVSNet		81.43 190	80.74 188	83.52 235	86.26 302	64.45 231	92.09 194	90.65 262	75.83 186	73.95 248	89.81 245	63.97 111	92.91 339	71.27 261	82.82 212	93.20 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 192	79.99 204	85.46 136	90.39 161	68.40 99	86.88 372	90.61 263	74.41 207	70.31 299	84.67 329	63.79 114	92.32 366	73.13 238	85.70 169	95.67 62

0.3-1-1-0.015		81.31 193	79.49 216	86.77 73	85.74 318	68.70 95	95.01 46	94.42 59	74.29 211	77.09 203	85.61 318	63.31 128	95.69 204	76.63 208	63.30 395	95.91 52

ECVR-MVS		81.29 194	80.38 199	84.01 216	88.39 225	61.96 317	92.56 173	86.79 402	77.66 150	76.63 206	91.42 198	46.34 367	95.24 234	74.36 229	89.23 119	94.85 109

0.4-1-1-0.2		81.28 195	79.42 218	86.84 65	85.80 316	68.82 86	95.10 39	94.43 58	74.45 206	77.18 200	85.54 319	62.27 145	95.70 202	76.72 207	63.30 395	96.01 46

guyue		81.23 196	80.57 195	83.21 251	86.64 288	61.85 320	92.52 176	92.78 131	78.69 128	74.92 230	89.42 250	50.07 320	95.35 224	80.79 171	79.31 263	92.42 239

IMVS_0403		81.19 197	79.88 206	85.13 156	88.54 208	64.75 219	88.84 338	90.80 251	76.73 173	75.21 224	90.18 228	54.22 274	96.21 160	73.47 233	80.95 238	94.43 152

thisisatest0530		81.15 198	80.07 201	84.39 199	88.26 230	65.63 196	91.40 238	94.62 48	71.27 296	70.93 290	89.18 255	72.47 35	96.04 172	65.62 330	76.89 290	91.49 267

Fast-Effi-MVS+		81.14 199	80.01 203	84.51 195	90.24 163	65.86 191	94.12 82	89.15 332	73.81 223	75.37 223	88.26 272	57.26 227	94.53 270	66.97 313	84.92 180	93.15 213

HQP-MVS		81.14 199	80.64 192	82.64 263	87.54 255	63.66 271	94.06 83	91.70 191	79.80 92	74.18 238	90.30 225	51.63 301	95.61 210	77.63 202	78.90 267	88.63 313

hse-mvs2		81.12 201	81.11 182	81.16 311	86.52 295	57.48 403	89.40 324	91.16 217	81.45 55	82.73 109	90.49 220	60.11 176	94.58 262	87.69 75	60.41 425	91.41 270

SR-MVS-dyc-post		81.06 202	80.70 190	82.15 282	92.02 112	58.56 391	90.90 267	90.45 267	62.76 401	78.89 172	94.46 102	51.26 308	95.61 210	78.77 195	86.77 153	92.28 246

HyFIR lowres test		81.03 203	79.56 213	85.43 137	87.81 249	68.11 111	90.18 300	90.01 296	70.65 311	72.95 258	86.06 310	63.61 120	94.50 272	75.01 223	79.75 255	93.67 195

0.4-1-1-0.1		80.99 204	79.16 226	86.51 96	85.55 323	68.21 108	94.77 54	94.42 59	73.75 224	76.57 208	85.41 321	62.35 144	95.62 208	76.30 213	63.28 397	95.71 61

nrg030		80.93 205	79.86 207	84.13 210	83.69 361	68.83 85	93.23 131	91.20 215	75.55 189	75.06 226	88.22 275	63.04 135	94.74 252	81.88 154	66.88 362	88.82 311

Vis-MVSNet		80.92 206	79.98 205	83.74 224	88.48 219	61.80 321	93.44 124	88.26 378	73.96 219	77.73 189	91.76 186	49.94 322	94.76 250	65.84 325	90.37 107	94.65 132
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 207	80.02 202	83.33 242	87.87 246	60.76 348	92.62 165	86.86 401	77.86 144	75.73 214	91.39 200	46.35 366	94.70 259	72.79 243	88.68 129	94.52 141

UWE-MVS		80.81 208	81.01 184	80.20 337	89.33 183	57.05 410	91.91 208	94.71 42	75.67 187	75.01 227	89.37 251	63.13 133	91.44 392	67.19 310	82.80 214	92.12 254

IMVS_0407		80.80 209	79.39 221	85.00 161	88.54 208	64.75 219	88.40 346	90.80 251	76.73 173	73.95 248	90.18 228	51.55 303	95.81 188	73.47 233	80.95 238	94.43 152

1314		80.70 210	78.95 230	85.94 117	87.77 252	67.56 128	87.91 355	92.55 146	72.17 264	67.44 342	93.09 140	50.27 318	97.04 111	71.68 259	87.64 140	93.23 210

AstraMVS		80.66 211	79.79 209	83.28 246	85.07 335	61.64 328	92.19 188	90.58 264	79.40 109	74.77 233	90.18 228	45.93 372	95.61 210	83.04 139	76.96 289	92.60 233

tpmrst		80.57 212	79.14 228	84.84 169	90.10 166	68.28 103	81.70 425	89.72 309	77.63 152	75.96 212	79.54 403	64.94 96	92.71 347	75.43 218	77.28 286	93.55 199

1112_ss		80.56 213	79.83 208	82.77 258	88.65 205	60.78 346	92.29 183	88.36 370	72.58 250	72.46 271	94.95 88	65.09 93	93.42 323	66.38 319	77.71 276	94.10 173

VDDNet		80.50 214	78.26 238	87.21 53	86.19 303	69.79 50	94.48 63	91.31 207	60.42 422	79.34 164	90.91 213	38.48 408	96.56 141	82.16 148	81.05 237	95.27 87

BH-w/o		80.49 215	79.30 223	84.05 214	90.83 153	64.36 239	93.60 114	89.42 319	74.35 209	69.09 311	90.15 236	55.23 257	95.61 210	64.61 340	86.43 162	92.17 252

test_cas_vis1_n_1920		80.45 216	80.61 193	79.97 346	78.25 431	57.01 412	94.04 87	88.33 373	79.06 121	82.81 108	93.70 130	38.65 405	91.63 383	90.82 55	79.81 253	91.27 277

icg_test_0407_2		80.38 217	79.22 225	83.88 218	88.54 208	64.75 219	86.79 373	90.80 251	76.73 173	73.95 248	90.18 228	51.55 303	92.45 359	73.47 233	80.95 238	94.43 152

TAMVS		80.37 218	79.45 217	83.13 252	85.14 332	63.37 279	91.23 254	90.76 256	74.81 203	72.65 263	88.49 265	60.63 169	92.95 334	69.41 279	81.95 229	93.08 217

HQP_MVS		80.34 219	79.75 210	82.12 284	86.94 280	62.42 305	93.13 134	91.31 207	78.81 125	72.53 266	89.14 257	50.66 313	95.55 216	76.74 205	78.53 272	88.39 319

SDMVSNet		80.26 220	78.88 231	84.40 198	89.25 187	67.63 127	85.35 384	93.02 120	76.77 171	70.84 291	87.12 295	47.95 346	96.09 167	85.04 107	74.55 301	89.48 304

HPM-MVS_fast		80.25 221	79.55 215	82.33 274	91.55 132	59.95 371	91.32 249	89.16 331	65.23 379	74.71 235	93.07 142	47.81 348	95.74 195	74.87 227	88.23 132	91.31 275

ab-mvs		80.18 222	78.31 237	85.80 123	88.44 221	65.49 202	83.00 414	92.67 138	71.82 276	77.36 196	85.01 325	54.50 266	96.59 138	76.35 212	75.63 297	95.32 81

IS-MVSNet		80.14 223	79.41 219	82.33 274	87.91 242	60.08 369	91.97 203	88.27 376	72.90 245	71.44 287	91.73 189	61.44 158	93.66 314	62.47 359	86.53 159	93.24 209

test-LLR		80.10 224	79.56 213	81.72 293	86.93 282	61.17 338	92.70 158	91.54 196	71.51 291	75.62 216	86.94 299	53.83 277	92.38 361	72.21 252	84.76 183	91.60 265

PVSNet	73.49 8	80.05 225	78.63 233	84.31 203	90.92 150	64.97 215	92.47 177	91.05 236	79.18 115	72.43 272	90.51 219	37.05 425	94.06 293	68.06 297	86.00 163	93.90 189

UA-Net		80.02 226	79.65 211	81.11 314	89.33 183	57.72 398	86.33 378	89.00 348	77.44 156	81.01 128	89.15 256	59.33 191	95.90 178	61.01 366	84.28 190	89.73 300

test-mter		79.96 227	79.38 222	81.72 293	86.93 282	61.17 338	92.70 158	91.54 196	73.85 221	75.62 216	86.94 299	49.84 324	92.38 361	72.21 252	84.76 183	91.60 265

QAPM		79.95 228	77.39 259	87.64 36	89.63 175	71.41 22	93.30 129	93.70 88	65.34 378	67.39 345	91.75 188	47.83 347	98.96 19	57.71 383	89.81 115	92.54 236

UGNet		79.87 229	78.68 232	83.45 240	89.96 168	61.51 331	92.13 191	90.79 255	76.83 169	78.85 177	86.33 307	38.16 411	96.17 163	67.93 300	87.17 146	92.67 230
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		79.80 230	77.95 245	85.34 145	88.28 229	68.26 104	81.56 427	91.42 202	70.11 316	77.59 193	80.50 389	67.40 69	94.26 284	67.34 307	77.35 284	93.51 202

thres200		79.66 231	78.33 236	83.66 232	92.54 98	65.82 193	93.06 136	96.31 3	74.90 202	73.30 255	88.66 263	59.67 184	95.61 210	47.84 428	78.67 270	89.56 303

CPTT-MVS		79.59 232	79.16 226	80.89 325	91.54 133	59.80 373	92.10 193	88.54 367	60.42 422	72.96 257	93.28 138	48.27 339	92.80 344	78.89 194	86.50 160	90.06 293

Test_1112_low_res		79.56 233	78.60 234	82.43 268	88.24 232	60.39 362	92.09 194	87.99 383	72.10 266	71.84 279	87.42 290	64.62 101	93.04 330	65.80 326	77.30 285	93.85 191

tttt0517		79.50 234	78.53 235	82.41 271	87.22 264	61.43 335	89.75 312	94.76 39	69.29 328	67.91 333	88.06 279	72.92 31	95.63 206	62.91 355	73.90 311	90.16 292

reproduce_monomvs		79.49 235	79.11 229	80.64 327	92.91 85	61.47 334	91.17 259	93.28 108	83.09 33	64.04 375	82.38 357	66.19 79	94.57 264	81.19 167	57.71 433	85.88 378

FIs		79.47 236	79.41 219	79.67 355	85.95 310	59.40 379	91.68 228	93.94 77	78.06 139	68.96 317	88.28 270	66.61 76	91.77 379	66.20 322	74.99 300	87.82 326

SSM_0404		79.46 237	77.65 249	84.91 165	88.37 227	67.04 149	89.59 313	87.03 397	67.99 346	75.45 221	89.32 252	47.98 343	95.34 226	71.23 262	81.90 230	92.34 242

BH-RMVSNet		79.46 237	77.65 249	84.89 166	91.68 128	65.66 194	93.55 116	88.09 381	72.93 242	73.37 254	91.12 211	46.20 370	96.12 165	56.28 389	85.61 171	92.91 223

viewdifsd2359ckpt11		79.42 239	77.95 245	83.81 221	83.87 358	63.85 256	89.54 318	87.38 390	77.39 159	74.94 228	89.95 242	51.11 309	94.72 253	79.52 183	67.90 354	92.88 226

viewmsd2359difaftdt		79.42 239	77.96 244	83.81 221	83.88 357	63.85 256	89.54 318	87.38 390	77.39 159	74.94 228	89.95 242	51.11 309	94.72 253	79.52 183	67.90 354	92.88 226

PCF-MVS	73.15 9	79.29 241	77.63 251	84.29 204	86.06 308	65.96 187	87.03 368	91.10 226	69.86 321	69.79 307	90.64 215	57.54 226	96.59 138	64.37 344	82.29 217	90.32 290
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 242	79.57 212	78.24 376	88.46 220	52.29 438	90.41 291	89.12 336	74.24 212	69.13 310	91.91 183	65.77 86	90.09 408	59.00 379	88.09 134	92.33 243

114514_t		79.17 243	77.67 248	83.68 230	95.32 32	65.53 200	92.85 151	91.60 195	63.49 392	67.92 332	90.63 217	46.65 363	95.72 201	67.01 312	83.54 205	89.79 298

FA-MVS(test-final)		79.12 244	77.23 261	84.81 173	90.54 156	63.98 255	81.35 430	91.71 188	71.09 300	74.85 232	82.94 350	52.85 288	97.05 108	67.97 298	81.73 233	93.41 204

SSM_0407		79.09 245	77.21 262	84.75 178	88.50 213	66.98 155	89.21 329	87.03 397	67.99 346	74.12 242	89.32 252	47.98 343	95.29 231	71.23 262	79.52 256	91.98 257

VPA-MVSNet		79.03 246	78.00 242	82.11 287	85.95 310	64.48 230	93.22 132	94.66 45	75.05 200	74.04 246	84.95 326	52.17 295	93.52 316	74.90 226	67.04 361	88.32 322

OPM-MVS		79.00 247	78.09 240	81.73 292	83.52 364	63.83 259	91.64 230	90.30 280	76.36 182	71.97 278	89.93 244	46.30 369	95.17 236	75.10 221	77.70 277	86.19 366
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 248	78.22 239	81.25 308	85.33 325	62.73 300	89.53 321	93.21 110	72.39 257	72.14 275	90.13 237	60.99 162	94.72 253	67.73 302	72.49 320	86.29 363

AdaColmap		78.94 249	77.00 266	84.76 177	96.34 18	65.86 191	92.66 164	87.97 385	62.18 406	70.56 293	92.37 160	43.53 385	97.35 87	64.50 343	82.86 211	91.05 280

GeoE		78.90 250	77.43 255	83.29 245	88.95 198	62.02 315	92.31 182	86.23 409	70.24 315	71.34 288	89.27 254	54.43 270	94.04 296	63.31 351	80.81 245	93.81 192

miper_enhance_ethall		78.86 251	77.97 243	81.54 299	88.00 241	65.17 209	91.41 236	89.15 332	75.19 197	68.79 320	83.98 340	67.17 70	92.82 342	72.73 245	65.30 372	86.62 353

VPNet		78.82 252	77.53 254	82.70 261	84.52 345	66.44 172	93.93 93	92.23 155	80.46 74	72.60 264	88.38 269	49.18 332	93.13 329	72.47 249	63.97 391	88.55 316

EPNet_dtu		78.80 253	79.26 224	77.43 384	88.06 237	49.71 455	91.96 204	91.95 173	77.67 149	76.56 209	91.28 204	58.51 210	90.20 406	56.37 388	80.95 238	92.39 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 254	77.43 255	82.88 256	92.21 104	64.49 228	92.05 197	96.28 4	73.48 231	71.75 281	88.26 272	60.07 178	95.32 227	45.16 441	77.58 280	88.83 309

TR-MVS		78.77 255	77.37 260	82.95 255	90.49 158	60.88 344	93.67 110	90.07 291	70.08 318	74.51 236	91.37 201	45.69 373	95.70 202	60.12 373	80.32 249	92.29 245

thres400		78.68 256	77.43 255	82.43 268	92.21 104	64.49 228	92.05 197	96.28 4	73.48 231	71.75 281	88.26 272	60.07 178	95.32 227	45.16 441	77.58 280	87.48 331

BH-untuned		78.68 256	77.08 263	83.48 239	89.84 170	63.74 262	92.70 158	88.59 364	71.57 288	66.83 352	88.65 264	51.75 299	95.39 222	59.03 378	84.77 182	91.32 274

OMC-MVS		78.67 258	77.91 247	80.95 321	85.76 317	57.40 405	88.49 344	88.67 361	73.85 221	72.43 272	92.10 170	49.29 331	94.55 269	72.73 245	77.89 275	90.91 284

tpm		78.58 259	77.03 264	83.22 249	85.94 312	64.56 226	83.21 410	91.14 221	78.31 135	73.67 251	79.68 401	64.01 110	92.09 372	66.07 323	71.26 330	93.03 219

OpenMVS	70.45 11	78.54 260	75.92 285	86.41 102	85.93 313	71.68 20	92.74 154	92.51 147	66.49 362	64.56 369	91.96 179	43.88 384	98.10 46	54.61 394	90.65 101	89.44 306

EPMVS		78.49 261	75.98 284	86.02 114	91.21 142	69.68 55	80.23 439	91.20 215	75.25 196	72.48 270	78.11 412	54.65 265	93.69 313	57.66 384	83.04 210	94.69 126

AUN-MVS		78.37 262	77.43 255	81.17 310	86.60 291	57.45 404	89.46 323	91.16 217	74.11 214	74.40 237	90.49 220	55.52 254	94.57 264	74.73 228	60.43 424	91.48 268

thres100view900		78.37 262	77.01 265	82.46 267	91.89 122	63.21 286	91.19 258	96.33 1	72.28 260	70.45 296	87.89 282	60.31 173	95.32 227	45.16 441	77.58 280	88.83 309

GA-MVS		78.33 264	76.23 280	84.65 187	83.65 362	66.30 176	91.44 235	90.14 289	76.01 184	70.32 298	84.02 339	42.50 389	94.72 253	70.98 265	77.00 288	92.94 222

cascas		78.18 265	75.77 287	85.41 138	87.14 269	69.11 75	92.96 143	91.15 220	66.71 360	70.47 294	86.07 309	37.49 419	96.48 147	70.15 273	79.80 254	90.65 286

UniMVSNet_NR-MVSNet		78.15 266	77.55 253	79.98 344	84.46 348	60.26 364	92.25 184	93.20 112	77.50 155	68.88 318	86.61 302	66.10 81	92.13 370	66.38 319	62.55 402	87.54 329

LuminaMVS		78.14 267	76.66 270	82.60 265	80.82 392	64.64 225	89.33 325	90.45 267	68.25 344	74.73 234	85.51 320	41.15 395	94.14 287	78.96 192	80.69 247	89.04 307

IMVS_0404		78.11 268	76.29 279	83.59 233	88.54 208	64.75 219	84.63 391	90.80 251	76.73 173	61.16 400	90.18 228	40.17 399	91.58 385	73.47 233	80.95 238	94.43 152

thres600view7		78.00 269	76.66 270	82.03 289	91.93 118	63.69 269	91.30 250	96.33 1	72.43 255	70.46 295	87.89 282	60.31 173	94.92 245	42.64 453	76.64 291	87.48 331

FC-MVSNet-test		77.99 270	78.08 241	77.70 379	84.89 338	55.51 423	90.27 297	93.75 86	76.87 166	66.80 353	87.59 287	65.71 87	90.23 405	62.89 356	73.94 309	87.37 334

Anonymous202405211		77.96 271	75.33 293	85.87 119	93.73 59	64.52 227	94.85 52	85.36 422	62.52 404	76.11 211	90.18 228	29.43 459	97.29 91	68.51 291	77.24 287	95.81 58

cl22		77.94 272	76.78 268	81.42 301	87.57 254	64.93 217	90.67 280	88.86 353	72.45 254	67.63 339	82.68 354	64.07 108	92.91 339	71.79 255	65.30 372	86.44 356

XXY-MVS		77.94 272	76.44 273	82.43 268	82.60 374	64.44 232	92.01 199	91.83 182	73.59 230	70.00 303	85.82 314	54.43 270	94.76 250	69.63 276	68.02 353	88.10 324

MS-PatchMatch		77.90 274	76.50 272	82.12 284	85.99 309	69.95 44	91.75 222	92.70 134	73.97 218	62.58 393	84.44 333	41.11 396	95.78 192	63.76 348	92.17 73	80.62 444

usedtu_dtu_shiyan1		77.89 275	76.39 276	82.40 272	81.92 382	67.01 153	91.94 206	93.00 123	77.01 163	68.44 327	84.15 335	54.78 263	93.25 325	65.76 327	70.53 333	86.94 343

FE-MVSNET3		77.89 275	76.39 276	82.40 272	81.92 382	67.01 153	91.94 206	93.00 123	77.01 163	68.44 327	84.15 335	54.78 263	93.25 325	65.76 327	70.53 333	86.94 343

FMVSNet3		77.73 277	76.04 283	82.80 257	91.20 143	68.99 81	91.87 210	91.99 171	73.35 233	67.04 348	83.19 349	56.62 240	92.14 369	59.80 375	69.34 339	87.28 337

VortexMVS		77.62 278	76.44 273	81.13 312	88.58 206	63.73 264	91.24 253	91.30 211	77.81 145	65.76 358	81.97 363	49.69 326	93.72 309	76.40 211	65.26 375	85.94 376

miper_ehance_all_eth		77.60 279	76.44 273	81.09 318	85.70 320	64.41 235	90.65 281	88.64 363	72.31 258	67.37 346	82.52 355	64.77 100	92.64 353	70.67 269	65.30 372	86.24 365

UniMVSNet (Re)		77.58 280	76.78 268	79.98 344	84.11 354	60.80 345	91.76 220	93.17 114	76.56 179	69.93 306	84.78 328	63.32 127	92.36 363	64.89 337	62.51 404	86.78 347

PatchmatchNet		77.46 281	74.63 300	85.96 116	89.55 178	70.35 37	79.97 444	89.55 314	72.23 261	70.94 289	76.91 426	57.03 230	92.79 345	54.27 396	81.17 236	94.74 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 282	75.65 289	82.73 259	80.38 400	67.13 146	91.85 212	90.23 285	75.09 199	69.37 308	83.39 346	53.79 279	94.44 274	71.77 256	65.00 379	86.63 352

CHOSEN 280x420		77.35 283	76.95 267	78.55 371	87.07 272	62.68 301	69.71 476	82.95 445	68.80 337	71.48 286	87.27 294	66.03 82	84.00 458	76.47 210	82.81 213	88.95 308

PS-MVSNAJss		77.26 284	76.31 278	80.13 339	80.64 396	59.16 384	90.63 284	91.06 233	72.80 246	68.58 324	84.57 331	53.55 281	93.96 301	72.97 239	71.96 324	87.27 338

gg-mvs-nofinetune		77.18 285	74.31 307	85.80 123	91.42 135	68.36 100	71.78 470	94.72 41	49.61 467	77.12 201	45.92 498	77.41 9	93.98 300	67.62 303	93.16 60	95.05 100

WB-MVSnew		77.14 286	76.18 282	80.01 343	86.18 304	63.24 284	91.26 251	94.11 73	71.72 280	73.52 253	87.29 293	45.14 378	93.00 332	56.98 386	79.42 259	83.80 404

MVP-Stereo		77.12 287	76.23 280	79.79 351	81.72 384	66.34 175	89.29 326	90.88 246	70.56 312	62.01 396	82.88 351	49.34 329	94.13 288	65.55 332	93.80 47	78.88 460
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 288	75.37 291	82.20 280	89.25 187	62.11 314	82.06 421	89.09 338	76.77 171	70.84 291	87.12 295	41.43 394	95.01 240	67.23 309	74.55 301	89.48 304

MonoMVSNet		76.99 289	75.08 296	82.73 259	83.32 366	63.24 284	86.47 377	86.37 405	79.08 119	66.31 356	79.30 405	49.80 325	91.72 380	79.37 185	65.70 370	93.23 210

dmvs_re		76.93 290	75.36 292	81.61 297	87.78 251	60.71 352	80.00 443	87.99 383	79.42 108	69.02 314	89.47 249	46.77 360	94.32 278	63.38 350	74.45 304	89.81 297

X-MVStestdata		76.86 291	74.13 313	85.05 158	93.22 73	63.78 260	92.92 146	92.66 139	73.99 216	78.18 185	10.19 531	55.25 255	97.41 83	79.16 188	91.58 85	93.95 182

DU-MVS		76.86 291	75.84 286	79.91 347	82.96 370	60.26 364	91.26 251	91.54 196	76.46 181	68.88 318	86.35 305	56.16 245	92.13 370	66.38 319	62.55 402	87.35 335

Anonymous20240529		76.84 293	74.15 312	84.88 167	91.02 146	64.95 216	93.84 102	91.09 227	53.57 455	73.00 256	87.42 290	35.91 430	97.32 89	69.14 284	72.41 322	92.36 241

UWE-MVS-28		76.83 294	77.60 252	74.51 415	84.58 344	50.34 451	88.22 349	94.60 50	74.46 205	66.66 354	88.98 262	62.53 141	85.50 450	57.55 385	80.80 246	87.69 328

c3_l		76.83 294	75.47 290	80.93 322	85.02 336	64.18 247	90.39 292	88.11 380	71.66 281	66.65 355	81.64 369	63.58 123	92.56 354	69.31 281	62.86 399	86.04 371

WR-MVS		76.76 296	75.74 288	79.82 350	84.60 342	62.27 311	92.60 168	92.51 147	76.06 183	67.87 336	85.34 322	56.76 236	90.24 404	62.20 360	63.69 393	86.94 343

v1144		76.73 297	74.88 297	82.27 276	80.23 404	66.60 169	91.68 228	90.21 288	73.69 227	69.06 313	81.89 364	52.73 291	94.40 276	69.21 282	65.23 376	85.80 379

IterMVS-LS		76.49 298	75.18 295	80.43 331	84.49 347	62.74 299	90.64 282	88.80 355	72.40 256	65.16 364	81.72 367	60.98 163	92.27 367	67.74 301	64.65 384	86.29 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 299	74.55 303	82.19 281	79.14 418	67.82 120	90.26 298	89.42 319	73.75 224	68.63 323	81.89 364	51.31 306	94.09 290	71.69 258	64.84 380	84.66 396

Elysia		76.45 300	74.17 310	83.30 243	80.43 398	64.12 248	89.58 314	90.83 248	61.78 414	72.53 266	85.92 312	34.30 437	94.81 248	68.10 295	84.01 196	90.97 281

StellarMVS		76.45 300	74.17 310	83.30 243	80.43 398	64.12 248	89.58 314	90.83 248	61.78 414	72.53 266	85.92 312	34.30 437	94.81 248	68.10 295	84.01 196	90.97 281

mamba_0408		76.22 302	73.37 325	84.77 175	88.50 213	66.98 155	58.80 496	86.18 411	69.12 333	74.12 242	89.01 260	47.50 350	95.35 224	67.57 304	79.52 256	91.98 257

v148		76.19 303	74.47 305	81.36 304	80.05 406	64.44 232	91.75 222	90.23 285	73.68 228	67.13 347	80.84 384	55.92 250	93.86 308	68.95 286	61.73 413	85.76 382

Effi-MVS+-dtu		76.14 304	75.28 294	78.72 370	83.22 367	55.17 425	89.87 309	87.78 387	75.42 192	67.98 331	81.43 373	45.08 379	92.52 356	75.08 222	71.63 325	88.48 317

cl____		76.07 305	74.67 298	80.28 334	85.15 331	61.76 324	90.12 301	88.73 358	71.16 297	65.43 361	81.57 371	61.15 160	92.95 334	66.54 316	62.17 406	86.13 369

DIV-MVS_self_test		76.07 305	74.67 298	80.28 334	85.14 332	61.75 325	90.12 301	88.73 358	71.16 297	65.42 362	81.60 370	61.15 160	92.94 338	66.54 316	62.16 408	86.14 367

FMVSNet2		76.07 305	74.01 315	82.26 278	88.85 199	67.66 125	91.33 248	91.61 194	70.84 304	65.98 357	82.25 359	48.03 340	92.00 374	58.46 380	68.73 347	87.10 340

v144192		76.05 308	74.03 314	82.12 284	79.50 412	66.55 171	91.39 240	89.71 310	72.30 259	68.17 329	81.33 376	51.75 299	94.03 298	67.94 299	64.19 386	85.77 380

NR-MVSNet		76.05 308	74.59 301	80.44 330	82.96 370	62.18 313	90.83 271	91.73 186	77.12 162	60.96 402	86.35 305	59.28 193	91.80 378	60.74 368	61.34 417	87.35 335

v1192		75.98 310	73.92 316	82.15 282	79.73 408	66.24 178	91.22 255	89.75 304	72.67 248	68.49 325	81.42 374	49.86 323	94.27 282	67.08 311	65.02 378	85.95 374

FE-MVS		75.97 311	73.02 331	84.82 170	89.78 171	65.56 198	77.44 455	91.07 232	64.55 381	72.66 262	79.85 399	46.05 371	96.69 136	54.97 393	80.82 244	92.21 251

eth_miper_zixun_eth		75.96 312	74.40 306	80.66 326	84.66 341	63.02 290	89.28 327	88.27 376	71.88 272	65.73 359	81.65 368	59.45 188	92.81 343	68.13 294	60.53 422	86.14 367

TranMVSNet+NR-MVSNet		75.86 313	74.52 304	79.89 348	82.44 376	60.64 355	91.37 243	91.37 204	76.63 177	67.65 338	86.21 308	52.37 294	91.55 386	61.84 362	60.81 420	87.48 331

SCA		75.82 314	72.76 335	85.01 160	86.63 290	70.08 40	81.06 432	89.19 329	71.60 287	70.01 302	77.09 424	45.53 374	90.25 401	60.43 370	73.27 313	94.68 128

LPG-MVS_test		75.82 314	74.58 302	79.56 359	84.31 351	59.37 380	90.44 289	89.73 307	69.49 325	64.86 365	88.42 267	38.65 405	94.30 280	72.56 247	72.76 317	85.01 393

GBi-Net		75.65 316	73.83 318	81.10 315	88.85 199	65.11 211	90.01 305	90.32 276	70.84 304	67.04 348	80.25 394	48.03 340	91.54 387	59.80 375	69.34 339	86.64 349

test1		75.65 316	73.83 318	81.10 315	88.85 199	65.11 211	90.01 305	90.32 276	70.84 304	67.04 348	80.25 394	48.03 340	91.54 387	59.80 375	69.34 339	86.64 349

v1921920		75.63 318	73.49 323	82.06 288	79.38 413	66.35 174	91.07 264	89.48 315	71.98 267	67.99 330	81.22 379	49.16 334	93.90 304	66.56 315	64.56 385	85.92 377

ACMP	71.68 10	75.58 319	74.23 309	79.62 357	84.97 337	59.64 375	90.80 272	89.07 340	70.39 313	62.95 389	87.30 292	38.28 409	93.87 306	72.89 240	71.45 328	85.36 389
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 320	73.26 329	81.61 297	80.67 395	66.82 161	89.54 318	89.27 324	71.65 282	63.30 384	80.30 393	54.99 261	94.06 293	67.33 308	62.33 405	83.94 402

tpm cat1		75.30 321	72.21 344	84.58 192	88.52 212	67.77 121	78.16 453	88.02 382	61.88 412	68.45 326	76.37 435	60.65 168	94.03 298	53.77 400	74.11 307	91.93 260

PLC	68.80 14	75.23 322	73.68 321	79.86 349	92.93 84	58.68 389	90.64 282	88.30 374	60.90 419	64.43 373	90.53 218	42.38 390	94.57 264	56.52 387	76.54 292	86.33 362
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 323	72.98 333	81.88 290	79.20 415	66.00 185	90.75 275	89.11 337	71.63 286	67.41 344	81.22 379	47.36 352	93.87 306	65.46 333	64.72 383	85.77 380

blend_shiyan4		75.18 324	73.00 332	81.69 295	75.62 451	64.75 219	91.78 217	91.06 233	65.89 370	61.35 399	77.39 417	62.16 149	93.71 310	68.18 292	63.60 394	86.61 354

Fast-Effi-MVS+-dtu		75.04 325	73.37 325	80.07 340	80.86 390	59.52 378	91.20 257	85.38 421	71.90 270	65.20 363	84.84 327	41.46 393	92.97 333	66.50 318	72.96 316	87.73 327

dp		75.01 326	72.09 345	83.76 223	89.28 186	66.22 179	79.96 445	89.75 304	71.16 297	67.80 337	77.19 423	51.81 297	92.54 355	50.39 411	71.44 329	92.51 238

TAPA-MVS	70.22 12	74.94 327	73.53 322	79.17 365	90.40 160	52.07 439	89.19 331	89.61 313	62.69 403	70.07 301	92.67 152	48.89 337	94.32 278	38.26 468	79.97 251	91.12 279
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 328	73.32 328	79.74 353	86.53 293	60.31 363	89.03 336	92.70 134	78.61 130	68.98 316	83.34 347	41.93 392	92.23 368	52.77 405	65.97 368	86.69 348

SSM_04072		74.86 329	73.37 325	79.35 362	88.50 213	66.98 155	58.80 496	86.18 411	69.12 333	74.12 242	89.01 260	47.50 350	79.09 483	67.57 304	79.52 256	91.98 257

v10		74.77 330	72.54 341	81.46 300	80.33 402	66.71 166	89.15 332	89.08 339	70.94 302	63.08 387	79.86 398	52.52 292	94.04 296	65.70 329	62.17 406	83.64 405

XVG-OURS-SEG-HR		74.70 331	73.08 330	79.57 358	78.25 431	57.33 406	80.49 435	87.32 392	63.22 396	68.76 321	90.12 239	44.89 380	91.59 384	70.55 271	74.09 308	89.79 298

dtuonly		74.56 332	73.92 316	76.48 396	77.15 442	57.27 407	85.09 387	81.23 448	71.37 294	67.61 340	89.65 247	46.68 362	83.84 460	68.79 289	77.69 278	88.33 321

ACMM	69.62 13	74.34 333	72.73 337	79.17 365	84.25 353	57.87 396	90.36 294	89.93 298	63.17 398	65.64 360	86.04 311	37.79 417	94.10 289	65.89 324	71.52 327	85.55 385
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 334	72.30 343	80.32 332	91.49 134	61.66 327	90.85 270	80.72 452	56.67 446	63.85 378	90.64 215	46.75 361	90.84 395	53.79 399	75.99 296	88.47 318

XVG-OURS		74.25 335	72.46 342	79.63 356	78.45 429	57.59 402	80.33 437	87.39 389	63.86 388	68.76 321	89.62 248	40.50 398	91.72 380	69.00 285	74.25 306	89.58 301

test_fmvs1		74.07 336	73.69 320	75.22 405	78.91 422	47.34 468	89.06 335	74.69 471	63.68 391	79.41 163	91.59 196	24.36 470	87.77 431	85.22 104	76.26 294	90.55 289

CVMVSNet		74.04 337	74.27 308	73.33 425	85.33 325	43.94 482	89.53 321	88.39 369	54.33 454	70.37 297	90.13 237	49.17 333	84.05 456	61.83 363	79.36 261	91.99 256

Baseline_NR-MVSNet		73.99 338	72.83 334	77.48 383	80.78 393	59.29 383	91.79 214	84.55 430	68.85 336	68.99 315	80.70 385	56.16 245	92.04 373	62.67 357	60.98 419	81.11 438

pmmvs4		73.92 339	71.81 349	80.25 336	79.17 416	65.24 207	87.43 364	87.26 395	67.64 353	63.46 382	83.91 341	48.96 336	91.53 390	62.94 354	65.49 371	83.96 401

D2MVS		73.80 340	72.02 346	79.15 367	79.15 417	62.97 291	88.58 343	90.07 291	72.94 241	59.22 417	78.30 409	42.31 391	92.70 349	65.59 331	72.00 323	81.79 433

SD_0403		73.79 341	73.48 324	74.69 412	85.33 325	45.56 478	83.80 399	85.57 420	76.55 180	62.96 388	88.45 266	50.62 315	87.59 435	48.80 421	79.28 265	90.92 283

CR-MVSNet		73.79 341	70.82 357	82.70 261	83.15 368	67.96 114	70.25 473	84.00 435	73.67 229	69.97 304	72.41 452	57.82 223	89.48 414	52.99 404	73.13 314	90.64 287

test_djsdf		73.76 343	72.56 340	77.39 385	77.00 443	53.93 431	89.07 333	90.69 258	65.80 371	63.92 376	82.03 362	43.14 388	92.67 350	72.83 241	68.53 348	85.57 384

pmmvs5		73.35 344	71.52 351	78.86 369	78.64 426	60.61 356	91.08 261	86.90 399	67.69 350	63.32 383	83.64 342	44.33 383	90.53 398	62.04 361	66.02 367	85.46 387

Anonymous20231211		73.08 345	70.39 361	81.13 312	90.62 155	63.33 280	91.40 238	90.06 293	51.84 460	64.46 372	80.67 387	36.49 428	94.07 292	63.83 347	64.17 387	85.98 373

tt0805		73.07 346	70.73 358	80.07 340	78.37 430	57.05 410	87.78 358	92.18 162	61.23 418	67.04 348	86.49 304	31.35 451	94.58 262	65.06 336	67.12 360	88.57 315

miper_lstm_enhance		73.05 347	71.73 350	77.03 390	83.80 359	58.32 393	81.76 423	88.88 350	69.80 322	61.01 401	78.23 411	57.19 228	87.51 437	65.34 334	59.53 427	85.27 392

jajsoiax		73.05 347	71.51 352	77.67 380	77.46 439	54.83 427	88.81 339	90.04 294	69.13 332	62.85 391	83.51 344	31.16 452	92.75 346	70.83 266	69.80 335	85.43 388

LCM-MVSNet-Re		72.93 349	71.84 348	76.18 400	88.49 217	48.02 463	80.07 442	70.17 485	73.96 219	52.25 450	80.09 397	49.98 321	88.24 425	67.35 306	84.23 191	92.28 246

pm-mvs1		72.89 350	71.09 354	78.26 375	79.10 419	57.62 400	90.80 272	89.30 323	67.66 351	62.91 390	81.78 366	49.11 335	92.95 334	60.29 372	58.89 430	84.22 400

tpmvs		72.88 351	69.76 367	82.22 279	90.98 148	67.05 148	78.22 452	88.30 374	63.10 399	64.35 374	74.98 442	55.09 260	94.27 282	43.25 447	69.57 338	85.34 390

test0.0.03 1		72.76 352	72.71 338	72.88 429	80.25 403	47.99 464	91.22 255	89.45 317	71.51 291	62.51 394	87.66 285	53.83 277	85.06 452	50.16 413	67.84 358	85.58 383

UniMVSNet_ETH3D		72.74 353	70.53 360	79.36 361	78.62 427	56.64 414	85.01 388	89.20 328	63.77 389	64.84 367	84.44 333	34.05 439	91.86 377	63.94 346	70.89 332	89.57 302

mvs_tets		72.71 354	71.11 353	77.52 381	77.41 440	54.52 429	88.45 345	89.76 303	68.76 339	62.70 392	83.26 348	29.49 458	92.71 347	70.51 272	69.62 337	85.34 390

FMVSNet1		72.71 354	69.91 365	81.10 315	83.60 363	65.11 211	90.01 305	90.32 276	63.92 387	63.56 380	80.25 394	36.35 429	91.54 387	54.46 395	66.75 363	86.64 349

test_fmvs1_n		72.69 356	71.92 347	74.99 410	71.15 471	47.08 470	87.34 366	75.67 466	63.48 393	78.08 187	91.17 210	20.16 484	87.87 428	84.65 113	75.57 298	90.01 295

IterMVS		72.65 357	70.83 355	78.09 377	82.17 378	62.96 292	87.64 362	86.28 407	71.56 289	60.44 408	78.85 407	45.42 376	86.66 441	63.30 352	61.83 410	84.65 397
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 358	72.74 336	72.10 437	87.87 246	49.45 457	88.07 351	89.01 344	72.91 243	63.11 385	88.10 276	63.63 118	85.54 447	32.73 486	69.23 342	81.32 436

wanda-best-256-512		72.42 359	69.43 369	81.37 302	75.39 452	64.24 244	91.58 231	91.09 227	66.36 363	60.64 404	76.86 427	47.20 354	93.47 318	64.80 338	50.98 455	86.40 357

FE-blended-shiyan7		72.42 359	69.43 369	81.37 302	75.39 452	64.24 244	91.58 231	91.09 227	66.36 363	60.64 404	76.86 427	47.20 354	93.47 318	64.80 338	50.98 455	86.40 357

blended_shiyan8		72.26 361	69.25 373	81.29 306	75.23 457	64.03 251	91.36 246	91.04 237	66.11 368	60.42 409	76.73 431	46.79 359	93.45 321	64.58 342	51.00 454	86.37 360

blended_shiyan6		72.26 361	69.26 372	81.27 307	75.24 456	64.00 254	91.37 243	91.06 233	66.12 367	60.34 410	76.75 430	46.82 357	93.45 321	64.61 340	50.98 455	86.37 360

PatchMatch-RL		72.06 363	69.98 362	78.28 374	89.51 179	55.70 422	83.49 403	83.39 443	61.24 417	63.72 379	82.76 352	34.77 434	93.03 331	53.37 403	77.59 279	86.12 370

gbinet_0.2-2-1-0.02		71.92 364	68.92 375	80.91 323	75.87 450	63.30 281	91.95 205	91.40 203	65.62 374	61.57 398	77.27 421	44.71 381	92.88 341	61.00 367	50.87 459	86.54 355

PVSNet_0	68.08 15	71.81 365	68.32 381	82.27 276	84.68 339	62.31 310	88.68 341	90.31 279	75.84 185	57.93 429	80.65 388	37.85 416	94.19 285	69.94 274	29.05 501	90.31 291

MIMVSNet		71.64 366	68.44 379	81.23 309	81.97 381	64.44 232	73.05 467	88.80 355	69.67 324	64.59 368	74.79 444	32.79 443	87.82 429	53.99 397	76.35 293	91.42 269

test_vis1_n		71.63 367	70.73 358	74.31 419	69.63 478	47.29 469	86.91 370	72.11 479	63.21 397	75.18 225	90.17 234	20.40 482	85.76 446	84.59 115	74.42 305	89.87 296

IterMVS-SCA-FT		71.55 368	69.97 363	76.32 398	81.48 386	60.67 354	87.64 362	85.99 414	66.17 366	59.50 415	78.88 406	45.53 374	83.65 461	62.58 358	61.93 409	84.63 399

v7n		71.31 369	68.65 376	79.28 363	76.40 445	60.77 347	86.71 374	89.45 317	64.17 386	58.77 422	78.24 410	44.59 382	93.54 315	57.76 382	61.75 412	83.52 408

anonymousdsp		71.14 370	69.37 371	76.45 397	72.95 466	54.71 428	84.19 395	88.88 350	61.92 411	62.15 395	79.77 400	38.14 412	91.44 392	68.90 287	67.45 359	83.21 414

usedtu_blend_shiyan5		71.06 371	67.54 384	81.62 296	75.39 452	64.75 219	85.67 382	86.47 404	56.48 447	60.64 404	76.85 429	47.20 354	93.71 310	68.18 292	50.98 455	86.40 357

F-COLMAP		70.66 372	68.44 379	77.32 386	86.37 301	55.91 420	88.00 353	86.32 406	56.94 444	57.28 432	88.07 278	33.58 441	92.49 357	51.02 408	68.37 349	83.55 406

WR-MVS_H		70.59 373	69.94 364	72.53 431	81.03 389	51.43 443	87.35 365	92.03 170	67.38 354	60.23 412	80.70 385	55.84 252	83.45 464	46.33 436	58.58 432	82.72 421

CP-MVSNet		70.50 374	69.91 365	72.26 434	80.71 394	51.00 447	87.23 367	90.30 280	67.84 349	59.64 414	82.69 353	50.23 319	82.30 474	51.28 407	59.28 428	83.46 410

RPMNet		70.42 375	65.68 395	84.63 190	83.15 368	67.96 114	70.25 473	90.45 267	46.83 476	69.97 304	65.10 478	56.48 244	95.30 230	35.79 473	73.13 314	90.64 287

testing3		70.38 376	70.83 355	69.03 451	85.82 315	43.93 483	90.72 278	90.56 265	68.06 345	60.24 411	86.82 301	64.83 98	84.12 454	26.33 495	64.10 388	79.04 458

tfpnnormal		70.10 377	67.36 385	78.32 373	83.45 365	60.97 343	88.85 337	92.77 132	64.85 380	60.83 403	78.53 408	43.52 386	93.48 317	31.73 489	61.70 414	80.52 445

TransMVSNet (Re)		70.07 378	67.66 383	77.31 387	80.62 397	59.13 385	91.78 217	84.94 426	65.97 369	60.08 413	80.44 390	50.78 312	91.87 376	48.84 420	45.46 474	80.94 440

CL-MVSNet_self_test		69.92 379	68.09 382	75.41 403	73.25 464	55.90 421	90.05 304	89.90 299	69.96 319	61.96 397	76.54 432	51.05 311	87.64 432	49.51 417	50.59 461	82.70 423

DP-MVS		69.90 380	66.48 387	80.14 338	95.36 31	62.93 293	89.56 316	76.11 464	50.27 466	57.69 430	85.23 323	39.68 401	95.73 196	33.35 480	71.05 331	81.78 434

PS-CasMVS		69.86 381	69.13 374	72.07 438	80.35 401	50.57 450	87.02 369	89.75 304	67.27 355	59.19 418	82.28 358	46.58 364	82.24 475	50.69 410	59.02 429	83.39 412

Syy-MVS		69.65 382	69.52 368	70.03 446	87.87 246	43.21 484	88.07 351	89.01 344	72.91 243	63.11 385	88.10 276	45.28 377	85.54 447	22.07 500	69.23 342	81.32 436

MSDG		69.54 383	65.73 394	80.96 320	85.11 334	63.71 266	84.19 395	83.28 444	56.95 443	54.50 439	84.03 338	31.50 449	96.03 173	42.87 451	69.13 344	83.14 416

PEN-MVS		69.46 384	68.56 377	72.17 436	79.27 414	49.71 455	86.90 371	89.24 326	67.24 358	59.08 419	82.51 356	47.23 353	83.54 463	48.42 423	57.12 434	83.25 413

LS3D		69.17 385	66.40 389	77.50 382	91.92 119	56.12 418	85.12 386	80.37 454	46.96 474	56.50 434	87.51 289	37.25 420	93.71 310	32.52 488	79.40 260	82.68 424

PatchT		69.11 386	65.37 399	80.32 332	82.07 380	63.68 270	67.96 482	87.62 388	50.86 464	69.37 308	65.18 477	57.09 229	88.53 421	41.59 457	66.60 364	88.74 312

KD-MVS_2432*1600		69.03 387	66.37 390	77.01 391	85.56 321	61.06 341	81.44 428	90.25 283	67.27 355	58.00 427	76.53 433	54.49 267	87.63 433	48.04 425	35.77 492	82.34 427

miper_refine_blended		69.03 387	66.37 390	77.01 391	85.56 321	61.06 341	81.44 428	90.25 283	67.27 355	58.00 427	76.53 433	54.49 267	87.63 433	48.04 425	35.77 492	82.34 427

mvsany_test1		68.77 389	68.56 377	69.39 449	73.57 463	45.88 477	80.93 433	60.88 499	59.65 428	71.56 284	90.26 227	43.22 387	75.05 487	74.26 231	62.70 401	87.25 339

ACMH	63.93 17	68.62 390	64.81 401	80.03 342	85.22 330	63.25 283	87.72 359	84.66 428	60.83 420	51.57 454	79.43 404	27.29 465	94.96 242	41.76 455	64.84 380	81.88 432
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 391	65.41 398	77.96 378	78.69 425	62.93 293	89.86 310	89.17 330	60.55 421	50.27 460	77.73 416	22.60 478	94.06 293	47.18 432	72.65 319	76.88 473

ADS-MVSNet		68.54 392	64.38 408	81.03 319	88.06 237	66.90 160	68.01 480	84.02 434	57.57 437	64.48 370	69.87 464	38.68 403	89.21 416	40.87 459	67.89 356	86.97 341

DTE-MVSNet		68.46 393	67.33 386	71.87 440	77.94 435	49.00 460	86.16 380	88.58 365	66.36 363	58.19 424	82.21 360	46.36 365	83.87 459	44.97 444	55.17 441	82.73 420

mmtdpeth		68.33 394	66.37 390	74.21 420	82.81 373	51.73 440	84.34 393	80.42 453	67.01 359	71.56 284	68.58 468	30.52 456	92.35 364	75.89 215	36.21 490	78.56 465

our_test_3		68.29 395	64.69 403	79.11 368	78.92 420	64.85 218	88.40 346	85.06 424	60.32 424	52.68 448	76.12 437	40.81 397	89.80 413	44.25 446	55.65 439	82.67 425

Patchmatch-RL test		68.17 396	64.49 406	79.19 364	71.22 470	53.93 431	70.07 475	71.54 483	69.22 329	56.79 433	62.89 482	56.58 241	88.61 418	69.53 278	52.61 449	95.03 102

XVG-ACMP-BASELINE		68.04 397	65.53 397	75.56 402	74.06 462	52.37 437	78.43 449	85.88 415	62.03 409	58.91 421	81.21 381	20.38 483	91.15 394	60.69 369	68.18 350	83.16 415

FMVSNet5		68.04 397	65.66 396	75.18 407	84.43 349	57.89 395	83.54 401	86.26 408	61.83 413	53.64 445	73.30 447	37.15 423	85.08 451	48.99 419	61.77 411	82.56 426

ppachtmachnet_test		67.72 399	63.70 411	79.77 352	78.92 420	66.04 184	88.68 341	82.90 446	60.11 426	55.45 436	75.96 438	39.19 402	90.55 397	39.53 463	52.55 450	82.71 422

ACMH+	65.35 16	67.65 400	64.55 404	76.96 393	84.59 343	57.10 409	88.08 350	80.79 451	58.59 435	53.00 447	81.09 383	26.63 467	92.95 334	46.51 434	61.69 415	80.82 441

pmmvs6		67.57 401	64.76 402	76.00 401	72.82 468	53.37 433	88.71 340	86.78 403	53.19 456	57.58 431	78.03 413	35.33 433	92.41 360	55.56 391	54.88 443	82.21 429

Anonymous20231206		67.53 402	65.78 393	72.79 430	74.95 458	47.59 466	88.23 348	87.32 392	61.75 416	58.07 426	77.29 420	37.79 417	87.29 439	42.91 449	63.71 392	83.48 409

Patchmtry		67.53 402	63.93 410	78.34 372	82.12 379	64.38 236	68.72 477	84.00 435	48.23 473	59.24 416	72.41 452	57.82 223	89.27 415	46.10 437	56.68 438	81.36 435

USDC		67.43 404	64.51 405	76.19 399	77.94 435	55.29 424	78.38 450	85.00 425	73.17 235	48.36 469	80.37 391	21.23 480	92.48 358	52.15 406	64.02 390	80.81 442

ADS-MVSNet2		66.90 405	63.44 413	77.26 388	88.06 237	60.70 353	68.01 480	75.56 468	57.57 437	64.48 370	69.87 464	38.68 403	84.10 455	40.87 459	67.89 356	86.97 341

FE-MVSNET2		66.80 406	64.06 409	75.03 408	69.84 476	57.11 408	86.57 375	88.57 366	67.94 348	50.97 458	72.16 456	33.79 440	87.55 436	53.94 398	52.74 447	80.45 446

CMPMVS	48.56 21	66.77 407	64.41 407	73.84 422	70.65 474	50.31 452	77.79 454	85.73 418	45.54 479	44.76 480	82.14 361	35.40 432	90.14 407	63.18 353	74.54 303	81.07 439
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 408	62.92 416	76.80 395	76.51 444	57.77 397	89.22 328	83.41 442	55.48 451	53.86 443	77.84 414	26.28 468	93.95 302	34.90 475	68.76 346	78.68 463

LTVRE_ROB	59.60 19	66.27 409	63.54 412	74.45 416	84.00 356	51.55 442	67.08 484	83.53 440	58.78 433	54.94 438	80.31 392	34.54 435	93.23 327	40.64 461	68.03 352	78.58 464
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		66.06 410	62.45 419	76.88 394	81.42 388	54.45 430	57.49 498	88.67 361	49.36 469	63.86 377	46.86 497	56.06 248	90.25 401	49.53 416	68.83 345	85.95 374

Patchmatch-test		65.86 411	60.94 426	80.62 329	83.75 360	58.83 387	58.91 495	75.26 470	44.50 483	50.95 459	77.09 424	58.81 205	87.90 427	35.13 474	64.03 389	95.12 96

UnsupCasMVSNet_eth		65.79 412	63.10 414	73.88 421	70.71 473	50.29 453	81.09 431	89.88 300	72.58 250	49.25 466	74.77 445	32.57 445	87.43 438	55.96 390	41.04 482	83.90 403

test_fmvs2		65.78 413	64.84 400	68.60 453	66.54 485	41.71 487	83.27 407	69.81 486	54.38 453	67.91 333	84.54 332	15.35 490	81.22 479	75.65 217	66.16 366	82.88 417

dmvs_testset		65.55 414	66.45 388	62.86 466	79.87 407	22.35 515	76.55 457	71.74 481	77.42 158	55.85 435	87.77 284	51.39 305	80.69 480	31.51 492	65.92 369	85.55 385

pmmvs-eth3d		65.53 415	62.32 420	75.19 406	69.39 479	59.59 376	82.80 415	83.43 441	62.52 404	51.30 456	72.49 450	32.86 442	87.16 440	55.32 392	50.73 460	78.83 461

SixPastTwentyTwo		64.92 416	61.78 424	74.34 418	78.74 424	49.76 454	83.42 406	79.51 457	62.86 400	50.27 460	77.35 418	30.92 454	90.49 399	45.89 438	47.06 468	82.78 418

OurMVSNet-221017-0		64.68 417	62.17 421	72.21 435	76.08 448	47.35 467	80.67 434	81.02 450	56.19 448	51.60 453	79.66 402	27.05 466	88.56 420	53.60 401	53.63 446	80.71 443

test_0402		64.54 418	61.09 425	74.92 411	84.10 355	60.75 349	87.95 354	79.71 456	52.03 458	52.41 449	77.20 422	32.21 447	91.64 382	23.14 498	61.03 418	72.36 483

testgi		64.48 419	62.87 417	69.31 450	71.24 469	40.62 490	85.49 383	79.92 455	65.36 377	54.18 441	83.49 345	23.74 473	84.55 453	41.60 456	60.79 421	82.77 419

RPSCF		64.24 420	61.98 423	71.01 443	76.10 447	45.00 479	75.83 462	75.94 465	46.94 475	58.96 420	84.59 330	31.40 450	82.00 476	47.76 430	60.33 426	86.04 371

EU-MVSNet		64.01 421	63.01 415	67.02 460	74.40 461	38.86 496	83.27 407	86.19 410	45.11 481	54.27 440	81.15 382	36.91 426	80.01 482	48.79 422	57.02 435	82.19 430

test20.03		63.83 422	62.65 418	67.38 459	70.58 475	39.94 492	86.57 375	84.17 432	63.29 395	51.86 452	77.30 419	37.09 424	82.47 471	38.87 467	54.13 445	79.73 452

sc_t1		63.81 423	59.39 432	77.10 389	77.62 437	56.03 419	84.32 394	73.56 475	46.66 477	58.22 423	73.06 448	23.28 476	90.62 396	50.93 409	46.84 469	84.64 398

MDA-MVSNet_test_wron		63.78 424	60.16 428	74.64 413	78.15 433	60.41 360	83.49 403	84.03 433	56.17 450	39.17 491	71.59 459	37.22 421	83.24 467	42.87 451	48.73 463	80.26 449

YYNet1		63.76 425	60.14 429	74.62 414	78.06 434	60.19 367	83.46 405	83.99 437	56.18 449	39.25 490	71.56 460	37.18 422	83.34 465	42.90 450	48.70 464	80.32 448

dtuonlycased		63.47 426	62.08 422	67.64 457	73.22 465	52.55 436	86.25 379	79.10 458	65.40 375	49.47 465	67.33 474	36.80 427	82.37 473	53.47 402	47.68 466	68.01 487

K. test v3		63.09 427	59.61 431	73.53 424	76.26 446	49.38 459	83.27 407	77.15 462	64.35 383	47.77 471	72.32 454	28.73 460	87.79 430	49.93 415	36.69 489	83.41 411

COLMAP_ROB	57.96 20	62.98 428	59.65 430	72.98 428	81.44 387	53.00 435	83.75 400	75.53 469	48.34 472	48.81 468	81.40 375	24.14 471	90.30 400	32.95 483	60.52 423	75.65 476
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 429	59.08 433	71.10 442	67.19 483	48.72 462	83.91 397	85.23 423	50.38 465	47.84 470	71.22 462	20.74 481	85.51 449	46.47 435	58.75 431	79.06 457

tt0320		61.85 430	57.45 439	75.03 408	77.49 438	57.60 401	82.74 416	73.65 474	43.65 487	53.65 444	68.18 470	25.47 469	88.66 417	45.56 440	46.68 470	78.81 462

AllTest		61.66 431	58.06 435	72.46 432	79.57 409	51.42 444	80.17 440	68.61 488	51.25 462	45.88 474	81.23 377	19.86 485	86.58 442	38.98 465	57.01 436	79.39 454

UnsupCasMVSNet_bld		61.60 432	57.71 436	73.29 426	68.73 480	51.64 441	78.61 448	89.05 342	57.20 442	46.11 473	61.96 486	28.70 461	88.60 419	50.08 414	38.90 487	79.63 453

MDA-MVSNet-bldmvs		61.54 433	57.70 437	73.05 427	79.53 411	57.00 413	83.08 411	81.23 448	57.57 437	34.91 495	72.45 451	32.79 443	86.26 444	35.81 472	41.95 480	75.89 475

tt0320-xc		61.51 434	56.89 443	75.37 404	78.50 428	58.61 390	82.61 418	71.27 484	44.31 484	53.17 446	68.03 472	23.38 474	88.46 422	47.77 429	43.00 479	79.03 459

mvs5depth		61.03 435	57.65 438	71.18 441	67.16 484	47.04 472	72.74 468	77.49 460	57.47 440	60.52 407	72.53 449	22.84 477	88.38 423	49.15 418	38.94 486	78.11 468

KD-MVS_self_test		60.87 436	58.60 434	67.68 456	66.13 486	39.93 493	75.63 464	84.70 427	57.32 441	49.57 463	68.45 469	29.55 457	82.87 468	48.09 424	47.94 465	80.25 450

kuosan		60.86 437	60.24 427	62.71 467	81.57 385	46.43 474	75.70 463	85.88 415	57.98 436	48.95 467	69.53 466	58.42 211	76.53 485	28.25 494	35.87 491	65.15 492

FE-MVSNET		60.52 438	57.18 442	70.53 444	67.53 482	50.68 449	82.62 417	76.28 463	59.33 431	46.71 472	71.10 463	30.54 455	83.61 462	33.15 482	47.37 467	77.29 472

TinyColmap		60.32 439	56.42 446	72.00 439	78.78 423	53.18 434	78.36 451	75.64 467	52.30 457	41.59 489	75.82 440	14.76 493	88.35 424	35.84 471	54.71 444	74.46 477

MVS-HIRNet		60.25 440	55.55 447	74.35 417	84.37 350	56.57 416	71.64 471	74.11 472	34.44 494	45.54 478	42.24 507	31.11 453	89.81 411	40.36 462	76.10 295	76.67 474

MIMVSNet1		60.16 441	57.33 440	68.67 452	69.71 477	44.13 481	78.92 447	84.21 431	55.05 452	44.63 481	71.85 457	23.91 472	81.54 478	32.63 487	55.03 442	80.35 447

PM-MVS		59.40 442	56.59 444	67.84 454	63.63 489	41.86 485	76.76 456	63.22 496	59.01 432	51.07 457	72.27 455	11.72 497	83.25 466	61.34 364	50.28 462	78.39 466

new-patchmatchnet		59.30 443	56.48 445	67.79 455	65.86 487	44.19 480	82.47 419	81.77 447	59.94 427	43.65 485	66.20 476	27.67 464	81.68 477	39.34 464	41.40 481	77.50 471

test_vis1_rt		59.09 444	57.31 441	64.43 463	68.44 481	46.02 476	83.05 413	48.63 508	51.96 459	49.57 463	63.86 481	16.30 488	80.20 481	71.21 264	62.79 400	67.07 490

usedtu_dtu_shiyan2		57.76 445	53.69 451	69.95 447	57.60 499	41.80 486	83.50 402	83.67 439	45.26 480	43.79 484	62.82 483	17.63 487	85.93 445	42.56 454	46.40 472	82.12 431

test_fmvs3		56.82 446	54.86 449	62.69 468	53.59 501	35.47 499	75.87 461	65.64 493	43.91 485	55.10 437	71.43 461	6.91 505	74.40 490	68.64 290	52.63 448	78.20 467

DSMNet-mixed		56.78 447	54.44 450	63.79 464	63.21 490	29.44 508	64.43 487	64.10 495	42.12 491	51.32 455	71.60 458	31.76 448	75.04 488	36.23 470	65.20 377	86.87 346

pmmvs3		55.51 448	51.50 454	67.53 458	57.90 498	50.93 448	80.37 436	73.66 473	40.63 492	44.15 483	64.75 479	16.30 488	78.97 484	44.77 445	40.98 484	72.69 481

TDRefinement		55.28 449	51.58 453	66.39 461	59.53 497	46.15 475	76.23 459	72.80 476	44.60 482	42.49 487	76.28 436	15.29 491	82.39 472	33.20 481	43.75 476	70.62 485

dongtai		55.18 450	55.46 448	54.34 477	76.03 449	36.88 497	76.07 460	84.61 429	51.28 461	43.41 486	64.61 480	56.56 242	67.81 498	18.09 505	28.50 502	58.32 496

LF4IMVS		54.01 451	52.12 452	59.69 469	62.41 492	39.91 494	68.59 478	68.28 490	42.96 489	44.55 482	75.18 441	14.09 495	68.39 497	41.36 458	51.68 451	70.78 484

ttmdpeth		53.34 452	49.96 455	63.45 465	62.07 494	40.04 491	72.06 469	65.64 493	42.54 490	51.88 451	77.79 415	13.94 496	76.48 486	32.93 484	30.82 500	73.84 478

MVStest1		51.35 453	46.89 457	64.74 462	65.06 488	51.10 446	67.33 483	72.58 477	30.20 498	35.30 493	74.82 443	27.70 463	69.89 495	24.44 497	24.57 503	73.22 479

N_pmnet		50.55 454	49.11 456	54.88 475	77.17 441	4.02 537	84.36 392	2.00 534	48.59 470	45.86 476	68.82 467	32.22 446	82.80 470	31.58 490	51.38 453	77.81 470

new_pmnet		49.31 455	46.44 458	57.93 470	62.84 491	40.74 489	68.47 479	62.96 497	36.48 493	35.09 494	57.81 492	14.97 492	72.18 492	32.86 485	46.44 471	60.88 495

mvsany_test3		48.86 456	46.35 459	56.41 471	46.00 507	31.67 504	62.26 489	47.25 509	43.71 486	45.54 478	68.15 471	10.84 498	64.44 506	57.95 381	35.44 494	73.13 480

test_f		46.58 457	43.45 461	55.96 472	45.18 508	32.05 503	61.18 490	49.49 507	33.39 495	42.05 488	62.48 485	7.00 504	65.56 502	47.08 433	43.21 478	70.27 486

WB-MVS		46.23 458	44.94 460	50.11 480	62.13 493	21.23 517	76.48 458	55.49 501	45.89 478	35.78 492	61.44 488	35.54 431	72.83 491	9.96 519	21.75 505	56.27 498

FPMVS		45.64 459	43.10 463	53.23 478	51.42 504	36.46 498	64.97 486	71.91 480	29.13 499	27.53 501	61.55 487	9.83 500	65.01 504	16.00 511	55.58 440	58.22 497

SSC-MVS		44.51 460	43.35 462	47.99 484	61.01 496	18.90 519	74.12 466	54.36 502	43.42 488	34.10 496	60.02 491	34.42 436	70.39 494	9.14 521	19.57 506	54.68 499

EGC-MVSNET		42.35 461	38.09 464	55.11 474	74.57 459	46.62 473	71.63 472	55.77 500	0.04 555	0.24 557	62.70 484	14.24 494	74.91 489	17.59 506	46.06 473	43.80 501

LCM-MVSNet		40.54 462	35.79 467	54.76 476	36.92 515	30.81 505	51.41 501	69.02 487	22.07 503	24.63 503	45.37 500	4.56 509	65.81 501	33.67 479	34.50 495	67.67 488

APD_test1		40.50 463	37.31 466	50.09 481	51.88 502	35.27 500	59.45 494	52.59 504	21.64 504	26.12 502	57.80 493	4.56 509	66.56 500	22.64 499	39.09 485	48.43 500

test_vis3_rt		40.46 464	37.79 465	48.47 483	44.49 509	33.35 502	66.56 485	32.84 516	32.39 496	29.65 497	39.13 513	3.91 513	68.65 496	50.17 412	40.99 483	43.40 502

ANet_high		40.27 465	35.20 468	55.47 473	34.74 517	34.47 501	63.84 488	71.56 482	48.42 471	18.80 507	41.08 509	9.52 501	64.45 505	20.18 501	8.66 519	67.49 489

test_method		38.59 466	35.16 469	48.89 482	54.33 500	21.35 516	45.32 506	53.71 503	7.41 518	28.74 499	51.62 495	8.70 502	52.87 509	33.73 478	32.89 496	72.47 482

PMMVS2		37.93 467	33.61 470	50.92 479	46.31 506	24.76 511	60.55 493	50.05 505	28.94 500	20.93 505	47.59 496	4.41 511	65.13 503	25.14 496	18.55 508	62.87 493

Gipuma		34.91 468	31.44 471	45.30 485	70.99 472	39.64 495	19.85 517	72.56 478	20.10 506	16.16 513	21.47 526	5.08 508	71.16 493	13.07 513	43.70 477	25.08 518
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ArgMatch-SfM		33.21 469	29.25 475	45.06 486	35.86 516	22.89 514	48.07 505	16.80 520	23.93 502	27.57 500	61.10 490	1.59 520	47.14 511	34.29 476	14.08 510	65.16 491

ArgMatch-Sym		33.10 470	29.80 472	43.01 487	37.34 514	24.00 513	51.27 502	13.51 521	26.37 501	28.91 498	61.40 489	1.65 519	43.37 514	34.16 477	13.61 511	61.66 494

testf1		32.77 471	29.47 473	42.67 489	41.89 511	30.81 505	52.07 499	43.45 510	15.45 507	18.52 508	44.82 501	2.12 515	58.38 507	16.05 509	30.87 498	38.83 505

APD_test2		32.77 471	29.47 473	42.67 489	41.89 511	30.81 505	52.07 499	43.45 510	15.45 507	18.52 508	44.82 501	2.12 515	58.38 507	16.05 509	30.87 498	38.83 505

PMVS	26.43 22	31.84 473	28.16 476	42.89 488	25.87 521	27.58 509	50.92 503	49.78 506	21.37 505	14.17 516	40.81 510	2.01 517	66.62 499	9.61 520	38.88 488	34.49 510
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 474	24.00 478	26.45 494	43.74 510	18.44 520	60.86 491	39.66 512	15.11 510	9.53 524	22.10 525	6.52 506	46.94 512	8.31 522	10.14 516	13.98 523

MVE	24.84 23	24.35 475	19.77 481	38.09 491	34.56 518	26.92 510	26.57 509	38.87 514	11.73 514	11.37 520	27.44 520	1.37 521	50.42 510	11.41 518	14.60 509	36.93 507
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 476	23.20 480	25.46 497	41.52 513	16.90 521	60.56 492	38.79 515	14.62 511	8.99 526	20.24 528	7.35 503	45.82 513	7.25 525	9.46 517	13.64 525

tmp_tt		22.26 477	23.75 479	17.80 502	5.23 542	12.06 524	35.26 507	39.48 513	2.82 525	18.94 506	44.20 506	22.23 479	24.64 520	36.30 469	9.31 518	16.69 522

DenseAffine		21.45 478	18.65 483	29.86 493	28.31 519	16.04 522	32.25 508	6.12 524	15.38 509	16.38 512	44.57 505	0.55 524	32.44 516	16.82 507	7.46 521	41.09 503

cdsmvs_eth3d_5k		19.86 479	26.47 477	0.00 540	0.00 564	0.00 567	0.00 552	93.45 101	0.00 559	0.00 560	95.27 78	49.56 327	0.00 560	0.00 559	0.00 558	0.00 556

VLMVS_CLIP		19.60 480	19.74 482	19.17 501	13.13 528	5.80 531	23.18 513	23.62 519	3.86 521	24.51 504	44.74 503	2.91 514	29.01 517	19.90 502	21.84 504	22.70 520

RoMa-SfM		18.71 481	16.37 484	25.74 496	19.88 523	12.86 523	26.27 510	3.78 529	13.07 512	15.56 514	45.71 499	0.48 525	28.39 518	16.22 508	6.37 522	35.97 509

LoFTR		18.06 482	15.31 486	26.33 495	21.95 522	10.94 525	21.35 515	12.80 522	6.90 519	12.24 518	41.28 508	0.46 526	27.67 519	7.81 523	12.96 512	40.38 504

PDCNetPlus		17.19 483	15.58 485	22.00 498	25.94 520	10.36 527	23.05 514	5.04 526	12.02 513	10.87 522	39.50 512	0.88 522	23.24 521	18.38 503	4.57 527	32.39 512

DKM		16.33 484	14.55 487	21.65 499	19.49 524	10.79 526	24.23 512	2.86 531	10.86 515	13.52 517	40.31 511	0.32 531	21.73 523	14.27 512	5.12 524	32.43 511

MatchFormer		14.02 485	12.22 489	19.42 500	17.64 525	8.79 528	19.96 516	10.04 523	4.23 520	10.54 523	32.75 518	0.31 533	22.88 522	4.03 530	10.48 515	26.57 515

RoMa-HiRes		13.29 486	12.09 490	16.86 503	12.76 529	7.74 529	17.91 519	2.10 533	8.64 516	11.87 519	39.11 514	0.36 529	17.55 524	12.17 515	3.91 530	25.30 517

VLMVS		13.23 487	13.55 488	12.28 508	12.68 530	2.77 541	12.60 520	3.80 528	0.44 537	17.98 510	44.70 504	4.14 512	6.39 530	12.99 514	12.66 513	27.68 514

DKM-HiRes		12.72 488	11.70 491	15.79 505	14.70 526	7.68 530	18.04 518	1.85 538	8.12 517	11.31 521	35.19 516	0.24 539	14.23 528	12.15 516	3.71 531	25.48 516

wuyk23d		11.30 489	10.95 493	12.33 507	48.05 505	19.89 518	25.89 511	1.92 537	3.58 522	3.12 532	1.37 555	0.64 523	15.77 526	6.23 527	7.77 520	1.35 539

MVS_clip		10.33 490	11.48 492	6.89 512	13.99 527	4.67 534	11.14 521	0.96 546	1.27 529	14.61 515	35.92 515	1.90 518	2.27 537	11.90 517	11.60 514	13.74 524

GLUNet-SfM		8.91 491	6.39 500	16.47 504	9.50 534	4.77 532	5.87 529	5.53 525	2.45 526	6.66 528	22.23 524	0.25 537	15.78 525	2.84 531	2.14 541	28.86 513

ELoFTR		8.49 492	6.65 499	14.00 506	5.91 536	3.43 539	7.42 526	4.01 527	2.94 524	6.41 529	25.06 521	0.11 544	15.41 527	5.10 529	2.92 534	23.17 519

PMatch-SfM		8.29 493	7.44 498	10.83 509	6.92 535	3.67 538	9.75 522	1.15 540	3.49 523	6.97 527	28.70 519	0.04 556	8.89 529	7.67 524	2.24 540	19.92 521

MASt3R-SfM		8.20 494	8.57 497	7.11 511	5.75 539	3.12 540	9.54 523	3.21 530	2.39 528	9.18 525	34.80 517	0.37 528	5.21 532	6.46 526	5.41 523	12.99 527

ab-mvs-re		7.91 495	10.55 494	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	94.95 88	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

testmvs		7.23 496	9.62 495	0.06 539	0.04 562	0.02 566	84.98 389	0.02 564	0.03 556	0.18 558	1.21 556	0.01 562	0.02 558	0.14 543	0.01 557	0.13 555

test123		6.92 497	9.21 496	0.08 538	0.03 563	0.05 564	81.65 426	0.01 565	0.02 557	0.14 559	0.85 557	0.03 560	0.02 558	0.12 546	0.00 558	0.16 554

PMatch-Up-SfM		6.11 498	5.72 502	7.28 510	5.02 543	2.48 542	7.03 528	0.71 548	2.41 527	5.37 530	23.67 522	0.03 560	5.84 531	5.77 528	1.48 551	13.50 526

ALIKED-LG		4.67 499	4.76 503	4.39 513	11.74 531	4.58 535	8.52 524	2.37 532	1.12 530	3.02 533	10.43 530	0.40 527	4.25 533	0.52 540	4.70 526	4.35 529

pcd_1.5k_mvsjas		4.46 500	5.95 501	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	53.55 281	0.00 560	0.00 559	0.00 558	0.00 556

ALIKED-MNN		4.24 501	4.26 504	4.20 514	10.96 532	4.68 533	7.92 525	2.00 534	0.81 531	2.44 538	9.09 532	0.30 534	4.03 534	0.46 541	4.36 529	3.88 532

ALIKED-NN		4.04 502	4.13 505	3.78 515	10.26 533	4.26 536	7.33 527	1.98 536	0.76 532	2.52 535	9.08 533	0.32 531	3.67 535	0.44 542	4.45 528	3.40 536

MVS_baseline		3.15 503	3.66 506	1.62 523	2.62 558	0.05 564	0.90 551	0.14 563	0.02 557	4.44 531	18.48 529	0.16 543	0.00 560	1.30 532	4.85 525	4.80 528

XFeat-MNN		2.31 504	2.37 507	2.13 516	1.47 560	0.97 555	3.08 535	1.31 539	0.53 534	2.60 534	7.72 534	0.22 541	2.31 536	1.02 534	3.40 532	3.10 537

SP-DiffGlue		2.24 505	2.34 508	1.94 520	1.88 559	1.08 549	3.10 534	1.13 541	0.55 533	2.52 535	7.60 535	0.33 530	0.99 543	1.25 533	2.70 535	3.76 534

SP-LightGlue		2.23 506	2.31 509	1.99 517	5.90 537	1.01 551	4.31 530	1.04 543	0.50 535	1.20 540	4.36 537	0.28 535	1.06 540	0.64 536	2.57 536	3.91 530

SP-SuperGlue		2.21 507	2.29 510	1.97 518	5.76 538	1.01 551	4.31 530	1.06 542	0.50 535	1.22 539	4.35 538	0.28 535	1.04 542	0.64 536	2.52 537	3.86 533

SP-MNN		2.16 508	2.22 511	1.97 518	5.52 540	0.92 556	4.28 532	1.01 544	0.41 539	1.13 541	4.35 538	0.23 540	1.09 539	0.61 538	2.45 538	3.91 530

SP-NN		2.08 509	2.16 512	1.87 521	5.30 541	0.91 557	4.18 533	0.96 546	0.43 538	1.09 542	4.20 540	0.25 537	1.06 540	0.60 539	2.38 539	3.63 535

XFeat-NN		1.98 510	2.09 513	1.67 522	1.35 561	0.77 560	2.62 536	0.97 545	0.41 539	2.46 537	6.79 536	0.19 542	1.75 538	0.84 535	3.18 533	2.48 538

SIFT-NN		1.43 511	1.51 514	1.19 524	4.60 544	1.57 543	2.30 537	0.51 549	0.34 541	0.74 543	2.84 541	0.08 545	0.84 544	0.13 544	2.07 542	1.15 540

SIFT-MNN		1.35 512	1.42 515	1.14 525	4.26 545	1.44 544	2.10 538	0.51 549	0.34 541	0.64 544	2.76 542	0.07 546	0.83 545	0.13 544	1.98 544	1.15 540

SIFT-NN-NCMNet		1.29 513	1.36 516	1.08 526	3.95 547	1.39 545	2.05 539	0.49 551	0.33 543	0.63 546	2.62 545	0.07 546	0.81 546	0.12 546	2.02 543	1.05 544

SIFT-NCM-Cal		1.23 514	1.30 517	1.04 527	4.06 546	1.29 546	1.92 541	0.42 552	0.33 543	0.45 551	2.46 548	0.06 551	0.81 546	0.10 553	1.89 545	1.02 546

SIFT-NN-CMatch		1.18 515	1.24 518	1.01 528	3.44 551	1.19 548	1.78 542	0.42 552	0.33 543	0.64 544	2.63 543	0.07 546	0.77 548	0.12 546	1.73 547	1.08 542

SIFT-NN-UMatch		1.16 516	1.23 519	0.96 529	3.23 553	1.06 550	1.93 540	0.42 552	0.33 543	0.53 548	2.63 543	0.07 546	0.77 548	0.11 549	1.79 546	1.05 544

SIFT-ConvMatch		1.15 517	1.22 520	0.96 529	3.82 548	1.20 547	1.64 545	0.38 555	0.33 543	0.52 549	2.53 546	0.06 551	0.76 550	0.11 549	1.59 549	0.91 547

SIFT-UMatch		1.11 518	1.18 521	0.87 532	3.66 549	1.00 554	1.70 543	0.35 557	0.32 548	0.46 550	2.50 547	0.06 551	0.75 551	0.11 549	1.51 550	0.87 549

SIFT-NN-PointCN		1.06 519	1.12 522	0.88 531	2.98 554	0.84 559	1.67 544	0.37 556	0.30 551	0.54 547	2.38 549	0.07 546	0.72 552	0.11 549	1.64 548	1.07 543

SIFT-CM-Cal		1.03 520	1.10 523	0.85 533	3.54 550	1.01 551	1.42 547	0.32 558	0.32 548	0.44 552	2.30 551	0.06 551	0.71 553	0.09 555	1.37 552	0.82 550

SIFT-UM-Cal		1.01 521	1.09 524	0.77 534	3.43 552	0.85 558	1.49 546	0.29 560	0.31 550	0.42 553	2.34 550	0.06 551	0.69 554	0.10 553	1.37 552	0.77 552

SIFT-PCN-Cal		0.88 522	0.93 526	0.70 535	2.93 555	0.60 562	1.22 549	0.27 561	0.28 552	0.36 554	2.00 552	0.04 556	0.61 556	0.09 555	1.23 555	0.89 548

SIFT-PointCN		0.88 522	0.94 525	0.69 536	2.88 556	0.61 561	1.32 548	0.30 559	0.28 552	0.36 554	1.93 553	0.04 556	0.62 555	0.09 555	1.26 554	0.82 550

SIFT-NCMNet		0.73 524	0.80 527	0.54 537	2.66 557	0.54 563	1.00 550	0.16 562	0.28 552	0.32 556	1.65 554	0.04 556	0.51 557	0.07 558	0.98 556	0.58 553

mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 558	0.00 556

PatchmatchNet2					0.00 564	56.61 415	85.20 385	78.52 459	49.54 468
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														31.49 493	51.52 452	77.88 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													82.83 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					95.84 30	67.84 118		94.64 46		89.45 43		71.94 42	98.96 19	91.55 45	94.82 26

aaatest				87.42 46	94.76 36	67.28 137	94.47 64	94.87 33	73.09 240	91.27 24	96.95 18		98.98 17	91.55 45	94.28 39	95.99 48

TestfortrainingZip				90.29 2	97.24 8	73.67 10	94.47 64	95.75 10	69.78 323	95.97 1	98.23 1	80.55 5	99.42 1		93.26 58	97.76 2

WAC-MVS						49.45 457								31.56 491

FOURS1					93.95 52	61.77 323	93.96 91	91.92 174	62.14 408	86.57 64

MSC_two_6792asdad				89.60 10	97.31 4	73.22 14		95.05 30					99.07 14	92.01 39	94.77 28	96.51 25

PC_three_1452									80.91 66	94.07 3	96.83 30	83.57 4	99.12 6	95.70 10	97.42 4	97.55 5

No_MVS				89.60 10	97.31 4	73.22 14		95.05 30					99.07 14	92.01 39	94.77 28	96.51 25

test_one_0601					96.32 20	69.74 53		94.18 70	71.42 293	90.67 29	96.85 28	74.45 22

eth-test2					0.00 564

eth-test					0.00 564

ZD-MVS					96.63 10	65.50 201		93.50 99	70.74 308	85.26 82	95.19 84	64.92 97	97.29 91	87.51 77	93.01 61

RE-MVS-def			80.48 197		92.02 112	58.56 391	90.90 267	90.45 267	62.76 401	78.89 172	94.46 102	49.30 330		78.77 195	86.77 153	92.28 246

IU-MVS					96.46 12	69.91 45		95.18 24	80.75 68	95.28 2				92.34 36	95.36 14	96.47 29

OPU-MVS				89.97 4	97.52 3	73.15 16	96.89 6				97.00 16	83.82 2	99.15 3	95.72 8	97.63 3	97.62 3

test_241102_TWO								94.41 61	71.65 282	92.07 12	97.21 10	74.58 20	99.11 7	92.34 36	95.36 14	96.59 20

test_241102_ONE					96.45 13	69.38 63		94.44 56	71.65 282	92.11 10	97.05 13	76.79 10	99.11 7

9.14			87.63 38		93.86 54		94.41 69	94.18 70	72.76 247	86.21 67	96.51 37	66.64 75	97.88 54	90.08 58	94.04 43

save fliter					93.84 55	67.89 117	95.05 41	92.66 139	78.19 136

test_0728_THIRD									72.48 252	90.55 30	96.93 20	76.24 13	99.08 12	91.53 49	94.99 18	96.43 32

test_0728_SECOND				88.70 19	96.45 13	70.43 36	96.64 10	94.37 65					99.15 3	91.91 42	94.90 22	96.51 25

test0726					96.40 16	69.99 41	96.76 8	94.33 67	71.92 268	91.89 15	97.11 12	73.77 25

GSMVS																94.68 128

test_part2					96.29 21	68.16 110				90.78 27

sam_mvs1												57.85 222				94.68 128

sam_mvs												54.91 262

ambc				69.61 448	61.38 495	41.35 488	49.07 504	85.86 417		50.18 462	66.40 475	10.16 499	88.14 426	45.73 439	44.20 475	79.32 456

MTGPA								92.23 155

test_post1							78.95 446				20.70 527	53.05 286	91.50 391	60.43 370

test_post											23.01 523	56.49 243	92.67 350

patchmatchnet-post											67.62 473	57.62 225	90.25 401

GG-mvs-BLEND				86.53 95	91.91 121	69.67 56	75.02 465	94.75 40		78.67 181	90.85 214	77.91 8	94.56 267	72.25 251	93.74 49	95.36 77

MTMP							93.77 106	32.52 517

gm-plane-assit					88.42 223	67.04 149			78.62 129		91.83 185		97.37 85	76.57 209

test9_res														89.41 59	94.96 19	95.29 84

TEST9					94.18 47	67.28 137	94.16 78	93.51 97	71.75 279	85.52 77	95.33 72	68.01 63	97.27 95

test_8					94.19 46	67.19 142	94.15 80	93.42 104	71.87 273	85.38 80	95.35 71	68.19 61	96.95 122

agg_prior2														86.41 93	94.75 32	95.33 79

agg_prior					94.16 49	66.97 158		93.31 107		84.49 88			96.75 134

TestCases				72.46 432	79.57 409	51.42 444		68.61 488	51.25 462	45.88 474	81.23 377	19.86 485	86.58 442	38.98 465	57.01 436	79.39 454

test_prior4						67.18 144	93.92 95

test_prior2							95.10 39		75.40 193	85.25 83	95.61 63	67.94 64		87.47 79	94.77 28

test_prior				86.42 101	94.71 41	67.35 136		93.10 118					96.84 131			95.05 100

旧先验2							92.00 202		59.37 430	87.54 57			93.47 318	75.39 219

新几何2							91.41 236

新几何1				84.73 179	92.32 100	64.28 241		91.46 201	59.56 429	79.77 156	92.90 146	56.95 235	96.57 140	63.40 349	92.91 63	93.34 206

旧先验1					91.94 117	60.74 350		91.50 199			94.36 106	65.23 92			91.84 80	94.55 137

无先验							92.71 156	92.61 144	62.03 409				97.01 112	66.63 314		93.97 181

原ACMM2							92.01 199

原ACMM1				84.42 197	93.21 75	64.27 242		93.40 106	65.39 376	79.51 161	92.50 154	58.11 216	96.69 136	65.27 335	93.96 44	92.32 244

test222					89.77 172	61.60 329	89.55 317	89.42 319	56.83 445	77.28 198	92.43 158	52.76 289			91.14 97	93.09 216

testdata2													96.09 167	61.26 365

segment_acmp												65.94 83

testdata				81.34 305	89.02 196	57.72 398		89.84 301	58.65 434	85.32 81	94.09 122	57.03 230	93.28 324	69.34 280	90.56 103	93.03 219

testdata1							89.21 329		77.55 154

test12				87.09 58	94.60 42	68.86 83		92.91 127		82.67 111		65.44 89	97.55 74		93.69 52	94.84 112

plane_prior7					86.94 280	61.51 331

plane_prior6					87.23 263	62.32 309						50.66 313

plane_prior5								91.31 207					95.55 216	76.74 205	78.53 272	88.39 319

plane_prior4											89.14 257

plane_prior3						61.95 318			79.09 118	72.53 266

plane_prior2							93.13 134		78.81 125

plane_prior1					87.15 268

plane_prior						62.42 305	93.85 99		79.38 110						78.80 269

n2								0.00 566

nn								0.00 566

door-mid								66.01 492

lessismore_v0				73.72 423	72.93 467	47.83 465		61.72 498		45.86 476	73.76 446	28.63 462	89.81 411	47.75 431	31.37 497	83.53 407

LGP-MVS_train				79.56 359	84.31 351	59.37 380		89.73 307	69.49 325	64.86 365	88.42 267	38.65 405	94.30 280	72.56 247	72.76 317	85.01 393

test11								93.01 121

door								66.57 491

HQP5-MVS						63.66 271

HQP-NCC					87.54 255		94.06 83		79.80 92	74.18 238

ACMP_Plane					87.54 255		94.06 83		79.80 92	74.18 238

BP-MVS														77.63 202

HQP4-MVS										74.18 238			95.61 210			88.63 313

HQP3-MVS								91.70 191							78.90 267

HQP2-MVS												51.63 301

NP-MVS					87.41 258	63.04 289					90.30 225

MDTV_nov1_ep13_2view						59.90 372	80.13 441		67.65 352	72.79 260		54.33 272		59.83 374		92.58 235

MDTV_nov1_ep13			72.61 339		89.06 194	68.48 96	80.33 437	90.11 290	71.84 275	71.81 280	75.92 439	53.01 287	93.92 303	48.04 425	73.38 312

ACMMP++_ref															71.63 325

ACMMP++															69.72 336

Test By Simon												54.21 275

ITE_SJBPF				70.43 445	74.44 460	47.06 471		77.32 461	60.16 425	54.04 442	83.53 343	23.30 475	84.01 457	43.07 448	61.58 416	80.21 451

DeepMVS_CX				34.71 492	51.45 503	24.73 512		28.48 518	31.46 497	17.49 511	52.75 494	5.80 507	42.60 515	18.18 504	19.42 507	36.81 508

High-resolution multi-view benchmark