High-res multi-view results

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

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

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

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

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

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

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

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 65	96.26 40	72.84 30	99.38 1	92.64 31	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 18	96.19 42	70.12 47	98.91 18	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 165	76.68 2	97.29 1	95.35 17	82.87 35	91.58 17	97.22 7	79.93 5	99.10 9	83.12 118	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 82	94.37 57	72.48 221	92.07 10	96.85 21	83.82 2	99.15 2	91.53 41	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 95	92.83 80	64.03 214	93.06 126	94.33 59	82.19 43	93.65 3	96.15 44	85.89 1	97.19 92	91.02 45	97.75 1	96.43 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 78	87.30 4	92.15 7	96.15 44	66.38 71	98.94 17	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 33	84.83 15	89.07 38	96.80 24	70.86 43	99.06 15	92.64 31	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 55	88.32 3	85.71 66	94.91 85	74.11 21	98.91 18	87.26 73	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 58	96.89 6	94.44 51	71.65 251	92.11 8	97.21 8	76.79 9	99.11 6	92.34 33	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 151	93.00 76	58.16 352	96.72 9	94.41 53	86.50 9	90.25 29	97.83 1	75.46 14	98.67 25	92.78 30	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 98	96.04 24	63.70 228	95.04 42	95.19 22	86.74 8	91.53 19	95.15 78	73.86 22	97.58 64	93.38 25	92.00 70	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 52	96.53 13	93.78 71	86.89 7	89.68 35	95.78 51	65.94 76	99.10 9	92.99 28	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 47	71.92 237	90.55 25	96.93 15	73.77 23	99.08 11	91.91 39	94.90 22	96.29 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 89	95.24 34	94.49 49	82.43 40	88.90 39	96.35 35	71.89 40	98.63 26	88.76 59	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 20	89.81 6	93.66 54	75.15 5	90.61 249	93.43 93	84.06 22	86.20 60	90.17 208	72.42 35	96.98 109	93.09 27	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 66	96.38 15	94.64 42	84.42 19	86.74 55	96.20 41	66.56 70	98.76 24	89.03 58	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 17	87.45 43	96.26 20	67.56 107	94.17 68	94.15 64	68.77 302	90.74 23	97.27 5	76.09 12	98.49 29	90.58 49	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_9		88.24 18	89.36 16	84.85 140	88.15 221	61.94 278	95.65 25	89.70 276	85.54 11	92.07 10	97.33 4	67.51 62	97.27 87	96.23 5	92.07 69	95.35 69

fmvsm_s_conf0.5_n_9		88.14 19	89.21 17	84.92 135	89.29 176	61.41 294	92.97 131	88.36 328	86.96 6	91.49 20	97.49 3	69.48 51	97.46 71	97.00 1	89.88 105	95.89 47

SMA-MVS		88.14 19	88.29 26	87.67 33	93.21 68	68.72 75	93.85 89	94.03 67	74.18 184	91.74 14	96.67 27	65.61 81	98.42 33	89.24 55	96.08 7	95.88 48
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PS-MVSNAJ		88.14 19	87.61 36	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 23	89.55 36	96.06 46	53.55 246	97.89 46	91.10 43	93.31 53	94.54 118

TSAR-MVS + MP.		88.11 22	88.64 22	86.54 75	91.73 119	68.04 93	90.36 256	93.55 85	82.89 33	91.29 21	92.89 140	72.27 37	96.03 163	87.99 63	94.77 26	95.54 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_8		87.96 23	88.93 19	85.07 130	88.43 208	61.78 281	94.73 55	91.74 172	85.87 10	91.66 16	97.50 2	64.03 102	98.33 34	96.28 4	90.08 101	95.10 85

TSAR-MVS + GP.		87.96 23	88.37 25	86.70 67	93.51 62	65.32 172	95.15 37	93.84 70	78.17 117	85.93 64	94.80 88	75.80 13	98.21 36	89.38 52	88.78 117	96.59 19

DeepC-MVS_fast	79.48 2	87.95 25	88.00 30	87.79 31	95.86 27	68.32 83	95.74 21	94.11 65	83.82 24	83.49 91	96.19 42	64.53 97	98.44 31	83.42 117	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 26	87.38 40	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 27	89.55 36	95.95 49	53.45 250	97.68 54	91.07 44	92.62 60	94.54 118

EPNet		87.84 27	88.38 24	86.23 85	93.30 65	66.05 151	95.26 33	94.84 32	87.09 5	88.06 42	94.53 94	66.79 67	97.34 80	83.89 110	91.68 76	95.29 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 28	87.77 33	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 37	87.13 50	95.27 71	64.99 87	95.80 169	89.34 53	91.80 74	95.93 45

test_fmvsm_n_1920		87.69 29	88.50 23	85.27 123	87.05 252	63.55 235	93.69 99	91.08 209	84.18 21	90.17 31	97.04 12	67.58 61	97.99 41	95.72 8	90.03 102	94.26 134

fmvsm_l_conf0.5_n_3		87.54 30	88.29 26	85.30 120	86.92 258	62.63 261	95.02 44	90.28 248	84.95 14	90.27 28	96.86 19	65.36 83	97.52 69	94.93 13	90.03 102	95.76 51

APDe-MVS		87.54 30	87.84 32	86.65 68	96.07 23	66.30 146	94.84 50	93.78 71	69.35 291	88.39 41	96.34 36	67.74 60	97.66 59	90.62 48	93.44 51	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 32	88.72 21	83.84 188	86.89 260	60.04 328	95.05 40	92.17 151	84.80 16	92.27 6	96.37 33	64.62 94	96.54 135	94.43 17	91.86 72	94.94 94

fmvsm_l_conf0.5_n		87.49 33	88.19 28	85.39 114	86.95 253	64.37 202	94.30 65	88.45 326	80.51 66	92.70 4	96.86 19	69.98 48	97.15 97	95.83 7	88.08 125	94.65 112

SD-MVS		87.49 33	87.49 38	87.50 42	93.60 56	68.82 72	93.90 86	92.63 131	76.86 141	87.90 44	95.76 52	66.17 73	97.63 61	89.06 57	91.48 80	96.05 42
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_l_conf0.5_n_a		87.44 35	88.15 29	85.30 120	87.10 250	64.19 209	94.41 60	88.14 336	80.24 75	92.54 5	96.97 14	69.52 50	97.17 93	95.89 6	88.51 120	94.56 115

dcpmvs_2		87.37 36	87.55 37	86.85 58	95.04 32	68.20 90	90.36 256	90.66 229	79.37 92	81.20 115	93.67 124	74.73 16	96.55 134	90.88 46	92.00 70	95.82 49

alignmvs		87.28 37	86.97 44	88.24 27	91.30 133	71.14 26	95.61 26	93.56 84	79.30 93	87.07 52	95.25 73	68.43 53	96.93 117	87.87 64	84.33 171	96.65 17

train_agg		87.21 38	87.42 39	86.60 70	94.18 41	67.28 114	94.16 69	93.51 87	71.87 242	85.52 69	95.33 65	68.19 55	97.27 87	89.09 56	94.90 22	95.25 80

MG-MVS		87.11 39	86.27 57	89.62 8	97.79 1	76.27 4	94.96 46	94.49 49	78.74 108	83.87 87	92.94 138	64.34 98	96.94 115	75.19 188	94.09 38	95.66 54

SF-MVS		87.03 40	87.09 42	86.84 59	92.70 86	67.45 112	93.64 102	93.76 74	70.78 275	86.25 58	96.44 32	66.98 65	97.79 50	88.68 60	94.56 34	95.28 76

fmvsm_s_conf0.5_n_3		86.88 41	87.99 31	83.58 202	87.26 245	60.74 308	93.21 123	87.94 343	84.22 20	91.70 15	97.27 5	65.91 78	95.02 210	93.95 22	90.42 97	94.99 91

CSCG		86.87 42	86.26 58	88.72 17	95.05 31	70.79 29	93.83 94	95.33 18	68.48 306	77.63 164	94.35 103	73.04 28	98.45 30	84.92 98	93.71 47	96.92 14

sasdasda		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 72	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 169	97.11 9

canonicalmvs		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 72	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 169	97.11 9

UBG		86.83 45	86.70 50	87.20 48	93.07 74	69.81 47	93.43 115	95.56 13	81.52 50	81.50 110	92.12 160	73.58 26	96.28 147	84.37 105	85.20 161	95.51 60

PHI-MVS		86.83 45	86.85 49	86.78 63	93.47 63	65.55 167	95.39 31	95.10 25	71.77 247	85.69 67	96.52 29	62.07 137	98.77 23	86.06 86	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 47	86.90 47	86.58 73	90.42 150	66.38 143	96.09 17	93.87 69	77.73 126	84.01 86	95.66 54	63.39 116	97.94 42	87.40 71	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 48	87.63 34	84.27 175	86.15 276	61.48 291	94.69 56	91.16 200	83.79 26	90.51 27	96.28 38	64.24 99	98.22 35	95.00 12	86.88 137	93.11 183

PVSNet_Blended		86.73 49	86.86 48	86.31 84	93.76 50	67.53 109	96.33 16	93.61 82	82.34 42	81.00 120	93.08 134	63.19 120	97.29 83	87.08 77	91.38 82	94.13 143

testing11		86.71 50	86.44 55	87.55 40	93.54 60	71.35 21	93.65 101	95.58 11	81.36 57	80.69 124	92.21 159	72.30 36	96.46 140	85.18 94	83.43 181	94.82 103

test_fmvsmconf_n		86.58 51	87.17 41	84.82 142	85.28 294	62.55 262	94.26 67	89.78 267	83.81 25	87.78 46	96.33 37	65.33 84	96.98 109	94.40 18	87.55 131	94.95 93

BP-MVS1		86.54 52	86.68 52	86.13 88	87.80 233	67.18 118	92.97 131	95.62 10	79.92 79	82.84 98	94.14 112	74.95 15	96.46 140	82.91 122	88.96 116	94.74 105

jason		86.40 53	86.17 61	87.11 51	86.16 275	70.54 32	95.71 24	92.19 148	82.00 45	84.58 79	94.34 104	61.86 139	95.53 192	87.76 65	90.89 90	95.27 77
jason: jason.
NormalMVS		86.39 54	86.66 53	85.60 108	92.12 101	65.95 156	94.88 47	90.83 217	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 90	91.15 86	93.93 154

fmvsm_s_conf0.5_n		86.39 54	86.91 46	84.82 142	87.36 244	63.54 236	94.74 52	90.02 260	82.52 38	90.14 32	96.92 17	62.93 127	97.84 49	95.28 11	82.26 191	93.07 186

fmvsm_s_conf0.5_n_5		86.38 56	86.94 45	84.71 153	84.67 306	63.29 241	94.04 78	89.99 262	82.88 34	87.85 45	96.03 47	62.89 129	96.36 144	94.15 19	89.95 104	94.48 124

SymmetryMVS		86.32 57	86.39 56	86.12 89	90.52 148	65.95 156	94.88 47	94.58 46	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 90	86.59 146	95.51 60

WTY-MVS		86.32 57	85.81 69	87.85 29	92.82 82	69.37 60	95.20 35	95.25 20	82.71 36	81.91 106	94.73 89	67.93 59	97.63 61	79.55 153	82.25 193	96.54 22

myMVS_eth3d28		86.31 59	86.15 62	86.78 63	93.56 58	70.49 33	92.94 134	95.28 19	82.47 39	78.70 155	92.07 162	72.45 34	95.41 194	82.11 129	85.78 155	94.44 126

MSLP-MVS++		86.27 60	85.91 68	87.35 45	92.01 108	68.97 69	95.04 42	92.70 122	79.04 103	81.50 110	96.50 31	58.98 179	96.78 125	83.49 116	93.93 41	96.29 35

VNet		86.20 61	85.65 73	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 113	86.00 63	93.07 135	58.22 187	97.00 105	85.22 92	84.33 171	96.52 23

MVS_111021_HR		86.19 62	85.80 70	87.37 44	93.17 70	69.79 48	93.99 81	93.76 74	79.08 100	78.88 151	93.99 118	62.25 136	98.15 38	85.93 87	91.15 86	94.15 142

SPE-MVS-test		86.14 63	87.01 43	83.52 203	92.63 88	59.36 340	95.49 28	91.92 161	80.09 76	85.46 71	95.53 60	61.82 141	95.77 172	86.77 81	93.37 52	95.41 63

ACMMP_NAP		86.05 64	85.80 70	86.80 62	91.58 123	67.53 109	91.79 193	93.49 90	74.93 174	84.61 78	95.30 67	59.42 170	97.92 43	86.13 84	94.92 20	94.94 94

testing99		86.01 65	85.47 75	87.63 38	93.62 55	71.25 23	93.47 113	95.23 21	80.42 69	80.60 126	91.95 167	71.73 41	96.50 138	80.02 150	82.22 194	95.13 83

ETV-MVS		86.01 65	86.11 63	85.70 105	90.21 155	67.02 125	93.43 115	91.92 161	81.21 59	84.13 85	94.07 117	60.93 149	95.63 181	89.28 54	89.81 106	94.46 125

testing91		85.93 67	85.31 79	87.78 32	93.59 57	71.47 19	93.50 110	95.08 28	80.26 72	80.53 127	91.93 168	70.43 45	96.51 137	80.32 148	82.13 196	95.37 66

APD-MVS		85.93 67	85.99 66	85.76 102	95.98 26	65.21 175	93.59 105	92.58 133	66.54 324	86.17 61	95.88 50	63.83 106	97.00 105	86.39 83	92.94 57	95.06 87
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 69	85.46 76	87.18 49	88.20 220	72.42 15	92.41 164	92.77 120	82.11 44	80.34 130	93.07 135	68.27 54	95.02 210	78.39 169	93.59 49	94.09 145

CS-MVS		85.80 70	86.65 54	83.27 215	92.00 109	58.92 344	95.31 32	91.86 166	79.97 77	84.82 77	95.40 63	62.26 135	95.51 193	86.11 85	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 71	86.09 64	84.72 151	85.73 287	63.58 233	93.79 95	89.32 286	81.42 55	90.21 30	96.91 18	62.41 134	97.67 56	94.48 16	80.56 218	92.90 192

test_fmvsmconf0.1_n		85.71 72	86.08 65	84.62 161	80.83 355	62.33 267	93.84 92	88.81 314	83.50 28	87.00 53	96.01 48	63.36 117	96.93 117	94.04 21	87.29 134	94.61 114

CDPH-MVS		85.71 72	85.46 76	86.46 77	94.75 34	67.19 116	93.89 87	92.83 118	70.90 271	83.09 96	95.28 69	63.62 111	97.36 78	80.63 144	94.18 37	94.84 99

casdiffmvs_mvg		85.66 74	85.18 81	87.09 52	88.22 219	69.35 61	93.74 98	91.89 164	81.47 51	80.10 132	91.45 177	64.80 92	96.35 145	87.23 74	87.69 129	95.58 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 75	85.93 67	84.68 155	82.95 338	63.48 238	94.03 80	89.46 280	81.69 48	89.86 33	96.74 25	61.85 140	97.75 52	94.74 15	82.01 198	92.81 196

MGCFI-Net		85.59 76	85.73 72	85.17 127	91.41 131	62.44 263	92.87 139	91.31 192	79.65 85	86.99 54	95.14 79	62.90 128	96.12 155	87.13 76	84.13 176	96.96 13

GDP-MVS		85.54 77	85.32 78	86.18 86	87.64 236	67.95 97	92.91 137	92.36 138	77.81 123	83.69 88	94.31 106	72.84 30	96.41 142	80.39 147	85.95 153	94.19 138

DeepC-MVS	77.85 3	85.52 78	85.24 80	86.37 81	88.80 191	66.64 137	92.15 172	93.68 80	81.07 61	76.91 175	93.64 125	62.59 131	98.44 31	85.50 88	92.84 59	94.03 149
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 79	84.87 87	86.84 59	88.25 217	69.07 65	93.04 128	91.76 171	81.27 58	80.84 122	92.07 162	64.23 100	96.06 161	84.98 97	87.43 133	95.39 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 80	85.08 83	86.06 90	93.09 73	65.65 163	93.89 87	93.41 95	73.75 195	79.94 134	94.68 91	60.61 152	98.03 40	82.63 125	93.72 46	94.52 120

fmvsm_s_conf0.5_n_7		85.24 81	86.69 51	80.91 283	84.52 311	60.10 326	93.35 118	90.35 241	83.41 29	86.54 57	96.27 39	60.50 153	90.02 367	94.84 14	90.38 98	92.61 200

MP-MVS-pluss		85.24 81	85.13 82	85.56 109	91.42 128	65.59 165	91.54 203	92.51 135	74.56 177	80.62 125	95.64 55	59.15 174	97.00 105	86.94 79	93.80 43	94.07 147
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 83	84.69 91	86.63 69	92.91 78	69.91 43	92.61 153	95.80 9	80.31 71	80.38 129	92.27 155	68.73 52	95.19 207	75.94 182	83.27 183	94.81 104

PAPR		85.15 84	84.47 92	87.18 49	96.02 25	68.29 84	91.85 191	93.00 113	76.59 152	79.03 147	95.00 80	61.59 142	97.61 63	78.16 170	89.00 115	95.63 55

fmvsm_s_conf0.5_n_2		85.06 85	85.60 74	83.44 209	86.92 258	60.53 315	94.41 60	87.31 351	83.30 30	88.72 40	96.72 26	54.28 238	97.75 52	94.07 20	84.68 168	92.04 223

MP-MVS		85.02 86	84.97 85	85.17 127	92.60 89	64.27 207	93.24 120	92.27 141	73.13 206	79.63 139	94.43 97	61.90 138	97.17 93	85.00 96	92.56 61	94.06 148
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 87	84.44 93	86.71 66	88.33 214	68.73 74	90.24 261	91.82 170	81.05 62	81.18 116	92.50 147	63.69 109	96.08 160	84.45 104	86.71 144	95.32 72

CHOSEN 1792x2688		84.98 88	83.45 109	89.57 11	89.94 160	75.14 6	92.07 178	92.32 139	81.87 46	75.68 184	88.27 242	60.18 157	98.60 27	80.46 146	90.27 100	94.96 92

MVSMamba_PlusPlus		84.97 89	83.65 102	88.93 14	90.17 156	74.04 8	87.84 319	92.69 125	62.18 362	81.47 112	87.64 256	71.47 42	96.28 147	84.69 100	94.74 31	96.47 28

viewmanbaseed2359cas		84.89 90	84.26 96	86.78 63	88.50 200	69.77 50	92.69 150	91.13 205	81.11 60	81.54 109	91.98 165	60.35 154	95.73 174	84.47 103	86.56 147	94.84 99

EIA-MVS		84.84 91	84.88 86	84.69 154	91.30 133	62.36 266	93.85 89	92.04 154	79.45 88	79.33 144	94.28 108	62.42 133	96.35 145	80.05 149	91.25 85	95.38 65

lecture		84.77 92	84.81 89	84.65 157	92.12 101	62.27 270	94.74 52	92.64 130	68.35 307	85.53 68	95.30 67	59.77 164	97.91 44	83.73 112	91.15 86	93.77 163

fmvsm_s_conf0.1_n_a		84.76 93	84.84 88	84.53 163	80.23 368	63.50 237	92.79 141	88.73 317	80.46 67	89.84 34	96.65 28	60.96 148	97.57 66	93.80 23	80.14 220	92.53 205

HFP-MVS		84.73 94	84.40 94	85.72 104	93.75 52	65.01 181	93.50 110	93.19 103	72.19 231	79.22 145	94.93 83	59.04 177	97.67 56	81.55 133	92.21 64	94.49 123

MVS		84.66 95	82.86 129	90.06 2	90.93 140	74.56 7	87.91 317	95.54 14	68.55 304	72.35 241	94.71 90	59.78 163	98.90 20	81.29 139	94.69 32	96.74 16

GST-MVS		84.63 96	84.29 95	85.66 106	92.82 82	65.27 173	93.04 128	93.13 106	73.20 204	78.89 148	94.18 111	59.41 171	97.85 48	81.45 135	92.48 63	93.86 160

EC-MVSNet		84.53 97	85.04 84	83.01 221	89.34 172	61.37 295	94.42 59	91.09 207	77.91 121	83.24 92	94.20 110	58.37 185	95.40 195	85.35 89	91.41 81	92.27 217

fmvsm_s_conf0.1_n_2		84.40 98	84.78 90	83.27 215	85.25 295	60.41 318	94.13 72	85.69 375	83.05 32	87.99 43	96.37 33	52.75 255	97.68 54	93.75 24	84.05 177	91.71 231

ACMMPR		84.37 99	84.06 97	85.28 122	93.56 58	64.37 202	93.50 110	93.15 105	72.19 231	78.85 153	94.86 86	56.69 207	97.45 72	81.55 133	92.20 65	94.02 150

region2R		84.36 100	84.03 98	85.36 118	93.54 60	64.31 205	93.43 115	92.95 114	72.16 234	78.86 152	94.84 87	56.97 202	97.53 68	81.38 137	92.11 67	94.24 136

LFMVS		84.34 101	82.73 131	89.18 13	94.76 33	73.25 11	94.99 45	91.89 164	71.90 239	82.16 105	93.49 129	47.98 308	97.05 100	82.55 126	84.82 164	97.25 8

test_yl		84.28 102	83.16 120	87.64 34	94.52 37	69.24 62	95.78 18	95.09 26	69.19 294	81.09 117	92.88 141	57.00 200	97.44 73	81.11 141	81.76 201	96.23 38

DCV-MVSNet		84.28 102	83.16 120	87.64 34	94.52 37	69.24 62	95.78 18	95.09 26	69.19 294	81.09 117	92.88 141	57.00 200	97.44 73	81.11 141	81.76 201	96.23 38

diffmvs		84.28 102	83.83 99	85.61 107	87.40 242	68.02 94	90.88 234	89.24 289	80.54 65	81.64 108	92.52 146	59.83 162	94.52 239	87.32 72	85.11 162	94.29 133
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	84.28 102	83.36 115	87.02 55	92.22 96	67.74 102	84.65 346	94.50 48	79.15 97	82.23 104	87.93 251	66.88 66	96.94 115	80.53 145	82.20 195	96.39 33

ETVMVS		84.22 106	83.71 100	85.76 102	92.58 90	68.25 88	92.45 162	95.53 15	79.54 87	79.46 141	91.64 175	70.29 46	94.18 253	69.16 251	82.76 189	94.84 99

MAR-MVS		84.18 107	83.43 110	86.44 78	96.25 21	65.93 158	94.28 66	94.27 61	74.41 179	79.16 146	95.61 56	53.99 241	98.88 22	69.62 245	93.26 54	94.50 122
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		84.16 108	83.20 118	87.05 54	91.56 124	69.82 46	89.99 270	92.05 153	77.77 125	82.84 98	86.57 273	63.93 105	96.09 157	74.91 193	89.18 112	95.25 80

CANet_DTU		84.09 109	83.52 103	85.81 99	90.30 153	66.82 132	91.87 189	89.01 305	85.27 12	86.09 62	93.74 122	47.71 314	96.98 109	77.90 172	89.78 108	93.65 166

viewmacassd2359aftdt		84.03 110	83.18 119	86.59 72	86.76 261	69.44 55	92.44 163	90.85 216	80.38 70	80.78 123	91.33 182	58.54 182	95.62 183	82.15 128	85.41 159	94.72 107

ET-MVSNet_ETH3D		84.01 111	83.15 122	86.58 73	90.78 145	70.89 28	94.74 52	94.62 43	81.44 54	58.19 380	93.64 125	73.64 25	92.35 322	82.66 124	78.66 240	96.50 27

diffmvs_AUTHOR		83.97 112	83.49 106	85.39 114	86.09 277	67.83 99	90.76 239	89.05 303	79.94 78	81.43 113	92.23 158	59.53 167	94.42 242	87.18 75	85.22 160	93.92 156

PVSNet_Blended_VisFu		83.97 112	83.50 105	85.39 114	90.02 158	66.59 140	93.77 96	91.73 173	77.43 134	77.08 174	89.81 218	63.77 108	96.97 112	79.67 152	88.21 123	92.60 201

MTAPA		83.91 114	83.38 114	85.50 110	91.89 115	65.16 177	81.75 376	92.23 142	75.32 169	80.53 127	95.21 76	56.06 216	97.16 96	84.86 99	92.55 62	94.18 139

XVS		83.87 115	83.47 108	85.05 131	93.22 66	63.78 221	92.92 135	92.66 127	73.99 187	78.18 158	94.31 106	55.25 222	97.41 75	79.16 159	91.58 78	93.95 152

Effi-MVS+		83.82 116	82.76 130	86.99 56	89.56 168	69.40 56	91.35 214	86.12 369	72.59 218	83.22 95	92.81 144	59.60 166	96.01 165	81.76 132	87.80 128	95.56 58

test_fmvsmvis_n_1920		83.80 117	83.48 107	84.77 146	82.51 341	63.72 226	91.37 212	83.99 393	81.42 55	77.68 163	95.74 53	58.37 185	97.58 64	93.38 25	86.87 138	93.00 189

EI-MVSNet-Vis-set		83.77 118	83.67 101	84.06 179	92.79 85	63.56 234	91.76 196	94.81 34	79.65 85	77.87 161	94.09 115	63.35 118	97.90 45	79.35 157	79.36 230	90.74 252

MVSFormer		83.75 119	82.88 128	86.37 81	89.24 181	71.18 24	89.07 295	90.69 226	65.80 329	87.13 50	94.34 104	64.99 87	92.67 308	72.83 210	91.80 74	95.27 77

CP-MVS		83.71 120	83.40 113	84.65 157	93.14 71	63.84 219	94.59 57	92.28 140	71.03 269	77.41 167	94.92 84	55.21 225	96.19 152	81.32 138	90.70 92	93.91 157

test_fmvsmconf0.01_n		83.70 121	83.52 103	84.25 176	75.26 414	61.72 285	92.17 171	87.24 353	82.36 41	84.91 76	95.41 62	55.60 220	96.83 124	92.85 29	85.87 154	94.21 137

baseline2		83.68 122	83.42 112	84.48 166	87.37 243	66.00 153	90.06 265	95.93 8	79.71 84	69.08 279	90.39 196	77.92 6	96.28 147	78.91 164	81.38 205	91.16 245

reproduce-ours		83.51 123	83.33 116	84.06 179	92.18 99	60.49 316	90.74 241	92.04 154	64.35 339	83.24 92	95.59 58	59.05 175	97.27 87	83.61 113	89.17 113	94.41 131

our_new_method		83.51 123	83.33 116	84.06 179	92.18 99	60.49 316	90.74 241	92.04 154	64.35 339	83.24 92	95.59 58	59.05 175	97.27 87	83.61 113	89.17 113	94.41 131

thisisatest0515		83.41 125	82.49 135	86.16 87	89.46 171	68.26 86	93.54 107	94.70 39	74.31 182	75.75 182	90.92 186	72.62 32	96.52 136	69.64 243	81.50 204	93.71 164

PVSNet_BlendedMVS		83.38 126	83.43 110	83.22 217	93.76 50	67.53 109	94.06 74	93.61 82	79.13 98	81.00 120	85.14 291	63.19 120	97.29 83	87.08 77	73.91 278	84.83 352

test2506		83.29 127	82.92 127	84.37 170	88.39 211	63.18 247	92.01 181	91.35 191	77.66 128	78.49 157	91.42 178	64.58 96	95.09 209	73.19 206	89.23 110	94.85 96

PGM-MVS		83.25 128	82.70 132	84.92 135	92.81 84	64.07 213	90.44 251	92.20 146	71.28 263	77.23 171	94.43 97	55.17 226	97.31 82	79.33 158	91.38 82	93.37 173

HPM-MVS		83.25 128	82.95 126	84.17 177	92.25 95	62.88 256	90.91 231	91.86 166	70.30 280	77.12 172	93.96 119	56.75 205	96.28 147	82.04 130	91.34 84	93.34 174
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 130	82.96 124	83.73 194	92.02 105	59.74 332	90.37 255	92.08 152	63.70 346	82.86 97	95.48 61	58.62 181	97.17 93	83.06 119	88.42 121	94.26 134

EI-MVSNet-UG-set		83.14 131	82.96 124	83.67 199	92.28 94	63.19 246	91.38 211	94.68 40	79.22 95	76.60 177	93.75 121	62.64 130	97.76 51	78.07 171	78.01 243	90.05 261

testing3-2		83.11 132	83.15 122	82.98 222	91.92 112	64.01 215	94.39 63	95.37 16	78.32 114	75.53 189	90.06 214	73.18 27	93.18 287	74.34 198	75.27 267	91.77 230

VDD-MVS		83.06 133	81.81 145	86.81 61	90.86 143	67.70 103	95.40 30	91.50 186	75.46 164	81.78 107	92.34 154	40.09 358	97.13 98	86.85 80	82.04 197	95.60 56

h-mvs33		83.01 134	82.56 134	84.35 171	89.34 172	62.02 274	92.72 144	93.76 74	81.45 52	82.73 101	92.25 157	60.11 158	97.13 98	87.69 66	62.96 361	93.91 157

PAPM_NR		82.97 135	81.84 144	86.37 81	94.10 44	66.76 135	87.66 323	92.84 117	69.96 284	74.07 214	93.57 127	63.10 125	97.50 70	70.66 238	90.58 94	94.85 96

mPP-MVS		82.96 136	82.44 136	84.52 164	92.83 80	62.92 254	92.76 142	91.85 168	71.52 259	75.61 187	94.24 109	53.48 249	96.99 108	78.97 162	90.73 91	93.64 167

SR-MVS		82.81 137	82.58 133	83.50 206	93.35 64	61.16 298	92.23 169	91.28 197	64.48 338	81.27 114	95.28 69	53.71 245	95.86 167	82.87 123	88.77 118	93.49 171

DP-MVS Recon		82.73 138	81.65 146	85.98 92	97.31 4	67.06 121	95.15 37	91.99 158	69.08 299	76.50 179	93.89 120	54.48 234	98.20 37	70.76 236	85.66 157	92.69 197

CLD-MVS		82.73 138	82.35 138	83.86 187	87.90 228	67.65 105	95.45 29	92.18 149	85.06 13	72.58 232	92.27 155	52.46 258	95.78 170	84.18 106	79.06 235	88.16 289
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 140	82.38 137	83.73 194	89.25 178	59.58 335	92.24 168	94.89 31	77.96 119	79.86 135	92.38 152	56.70 206	97.05 100	77.26 175	80.86 213	94.55 116

3Dnovator	73.91 6	82.69 141	80.82 159	88.31 26	89.57 167	71.26 22	92.60 154	94.39 56	78.84 105	67.89 300	92.48 150	48.42 303	98.52 28	68.80 256	94.40 36	95.15 82

RRT-MVS		82.61 142	81.16 150	86.96 57	91.10 137	68.75 73	87.70 322	92.20 146	76.97 139	72.68 228	87.10 267	51.30 272	96.41 142	83.56 115	87.84 127	95.74 52

viewmambaseed2359dif		82.60 143	81.91 143	84.67 156	85.83 284	66.09 150	90.50 250	89.01 305	75.46 164	79.64 138	92.01 164	59.51 168	94.38 244	82.99 121	82.26 191	93.54 169

MVSTER		82.47 144	82.05 139	83.74 192	92.68 87	69.01 67	91.90 188	93.21 100	79.83 80	72.14 242	85.71 286	74.72 17	94.72 224	75.72 184	72.49 288	87.50 296

TESTMET0.1,1		82.41 145	81.98 142	83.72 196	88.08 222	63.74 223	92.70 146	93.77 73	79.30 93	77.61 165	87.57 258	58.19 188	94.08 258	73.91 200	86.68 145	93.33 176

CostFormer		82.33 146	81.15 151	85.86 97	89.01 186	68.46 80	82.39 373	93.01 111	75.59 162	80.25 131	81.57 336	72.03 39	94.96 214	79.06 161	77.48 251	94.16 141

API-MVS		82.28 147	80.53 168	87.54 41	96.13 22	70.59 31	93.63 103	91.04 213	65.72 331	75.45 190	92.83 143	56.11 215	98.89 21	64.10 304	89.75 109	93.15 181

IB-MVS	77.80 4	82.18 148	80.46 170	87.35 45	89.14 183	70.28 36	95.59 27	95.17 24	78.85 104	70.19 267	85.82 284	70.66 44	97.67 56	72.19 222	66.52 331	94.09 145
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
xiu_mvs_v1_base_debu		82.16 149	81.12 152	85.26 124	86.42 268	68.72 75	92.59 156	90.44 238	73.12 207	84.20 82	94.36 99	38.04 371	95.73 174	84.12 107	86.81 139	91.33 238

xiu_mvs_v1_base		82.16 149	81.12 152	85.26 124	86.42 268	68.72 75	92.59 156	90.44 238	73.12 207	84.20 82	94.36 99	38.04 371	95.73 174	84.12 107	86.81 139	91.33 238

xiu_mvs_v1_base_debi		82.16 149	81.12 152	85.26 124	86.42 268	68.72 75	92.59 156	90.44 238	73.12 207	84.20 82	94.36 99	38.04 371	95.73 174	84.12 107	86.81 139	91.33 238

3Dnovator+	73.60 7	82.10 152	80.60 166	86.60 70	90.89 142	66.80 134	95.20 35	93.44 92	74.05 186	67.42 307	92.49 149	49.46 293	97.65 60	70.80 235	91.68 76	95.33 70

MVS_111021_LR		82.02 153	81.52 147	83.51 205	88.42 209	62.88 256	89.77 273	88.93 310	76.78 144	75.55 188	93.10 132	50.31 282	95.38 197	83.82 111	87.02 136	92.26 218

PMMVS		81.98 154	82.04 140	81.78 257	89.76 164	56.17 372	91.13 227	90.69 226	77.96 119	80.09 133	93.57 127	46.33 327	94.99 213	81.41 136	87.46 132	94.17 140

baseline1		81.84 155	81.03 156	84.28 174	91.60 122	66.62 138	91.08 228	91.66 180	81.87 46	74.86 200	91.67 174	69.98 48	94.92 217	71.76 225	64.75 348	91.29 243

EPP-MVSNet		81.79 156	81.52 147	82.61 232	88.77 192	60.21 324	93.02 130	93.66 81	68.52 305	72.90 226	90.39 196	72.19 38	94.96 214	74.93 192	79.29 233	92.67 198

WBMVS		81.67 157	80.98 158	83.72 196	93.07 74	69.40 56	94.33 64	93.05 109	76.84 142	72.05 244	84.14 302	74.49 19	93.88 272	72.76 213	68.09 317	87.88 291

test_vis1_n_1920		81.66 158	82.01 141	80.64 286	82.24 343	55.09 381	94.76 51	86.87 357	81.67 49	84.40 81	94.63 92	38.17 368	94.67 230	91.98 38	83.34 182	92.16 221

APD-MVS_3200maxsize		81.64 159	81.32 149	82.59 234	92.36 92	58.74 346	91.39 209	91.01 214	63.35 350	79.72 137	94.62 93	51.82 261	96.14 154	79.71 151	87.93 126	92.89 193

mvsmamba		81.55 160	80.72 161	84.03 183	91.42 128	66.93 130	83.08 365	89.13 297	78.55 112	67.50 305	87.02 268	51.79 263	90.07 366	87.48 69	90.49 96	95.10 85

ACMMP		81.49 161	80.67 163	83.93 185	91.71 120	62.90 255	92.13 173	92.22 145	71.79 246	71.68 250	93.49 129	50.32 281	96.96 113	78.47 168	84.22 175	91.93 228
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		81.43 162	80.11 172	85.38 117	86.60 264	65.47 171	92.90 138	93.54 86	75.33 168	77.31 169	90.39 196	46.81 319	96.75 126	71.65 228	86.46 150	93.93 154

CDS-MVSNet		81.43 162	80.74 160	83.52 203	86.26 272	64.45 196	92.09 176	90.65 230	75.83 160	73.95 216	89.81 218	63.97 104	92.91 298	71.27 229	82.82 186	93.20 180
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 164	79.99 176	85.46 111	90.39 152	68.40 81	86.88 334	90.61 231	74.41 179	70.31 266	84.67 296	63.79 107	92.32 324	73.13 207	85.70 156	95.67 53

ECVR-MVS		81.29 165	80.38 171	84.01 184	88.39 211	61.96 276	92.56 159	86.79 359	77.66 128	76.63 176	91.42 178	46.34 326	95.24 206	74.36 197	89.23 110	94.85 96

guyue		81.23 166	80.57 167	83.21 219	86.64 262	61.85 279	92.52 160	92.78 119	78.69 109	74.92 199	89.42 222	50.07 285	95.35 198	80.79 143	79.31 232	92.42 207

IMVS_0403		81.19 167	79.88 178	85.13 129	88.54 195	64.75 186	88.84 300	90.80 220	76.73 147	75.21 193	90.18 202	54.22 239	96.21 151	73.47 202	80.95 208	94.43 127

thisisatest0530		81.15 168	80.07 173	84.39 169	88.26 216	65.63 164	91.40 207	94.62 43	71.27 264	70.93 257	89.18 227	72.47 33	96.04 162	65.62 293	76.89 258	91.49 234

Fast-Effi-MVS+		81.14 169	80.01 175	84.51 165	90.24 154	65.86 159	94.12 73	89.15 295	73.81 194	75.37 192	88.26 243	57.26 195	94.53 238	66.97 278	84.92 163	93.15 181

HQP-MVS		81.14 169	80.64 164	82.64 231	87.54 238	63.66 231	94.06 74	91.70 178	79.80 81	74.18 207	90.30 199	51.63 266	95.61 184	77.63 173	78.90 236	88.63 280

hse-mvs2		81.12 171	81.11 155	81.16 271	86.52 267	57.48 361	89.40 286	91.16 200	81.45 52	82.73 101	90.49 194	60.11 158	94.58 231	87.69 66	60.41 388	91.41 237

SR-MVS-dyc-post		81.06 172	80.70 162	82.15 248	92.02 105	58.56 349	90.90 232	90.45 234	62.76 357	78.89 148	94.46 95	51.26 273	95.61 184	78.77 166	86.77 142	92.28 214

HyFIR lowres test		81.03 173	79.56 185	85.43 112	87.81 232	68.11 92	90.18 262	90.01 261	70.65 277	72.95 225	86.06 280	63.61 112	94.50 240	75.01 191	79.75 224	93.67 165

nrg030		80.93 174	79.86 179	84.13 178	83.69 327	68.83 71	93.23 121	91.20 198	75.55 163	75.06 195	88.22 246	63.04 126	94.74 223	81.88 131	66.88 328	88.82 278

Vis-MVSNet		80.92 175	79.98 177	83.74 192	88.48 205	61.80 280	93.44 114	88.26 335	73.96 190	77.73 162	91.76 171	49.94 287	94.76 221	65.84 290	90.37 99	94.65 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 176	80.02 174	83.33 210	87.87 229	60.76 306	92.62 152	86.86 358	77.86 122	75.73 183	91.39 180	46.35 325	94.70 229	72.79 212	88.68 119	94.52 120

UWE-MVS		80.81 177	81.01 157	80.20 296	89.33 174	57.05 366	91.91 187	94.71 38	75.67 161	75.01 196	89.37 223	63.13 124	91.44 349	67.19 275	82.80 188	92.12 222

IMVS_0407		80.80 178	79.39 191	85.00 134	88.54 195	64.75 186	88.40 308	90.80 220	76.73 147	73.95 216	90.18 202	51.55 268	95.81 168	73.47 202	80.95 208	94.43 127

1314		80.70 179	78.95 199	85.94 94	87.77 235	67.56 107	87.91 317	92.55 134	72.17 233	67.44 306	93.09 133	50.27 283	97.04 103	71.68 227	87.64 130	93.23 178

AstraMVS		80.66 180	79.79 181	83.28 214	85.07 301	61.64 287	92.19 170	90.58 232	79.40 90	74.77 202	90.18 202	45.93 331	95.61 184	83.04 120	76.96 257	92.60 201

tpmrst		80.57 181	79.14 197	84.84 141	90.10 157	68.28 85	81.70 377	89.72 274	77.63 130	75.96 181	79.54 368	64.94 89	92.71 305	75.43 186	77.28 254	93.55 168

1112_ss		80.56 182	79.83 180	82.77 226	88.65 193	60.78 304	92.29 166	88.36 328	72.58 219	72.46 238	94.95 81	65.09 86	93.42 284	66.38 284	77.71 245	94.10 144

VDDNet		80.50 183	78.26 207	87.21 47	86.19 273	69.79 48	94.48 58	91.31 192	60.42 378	79.34 143	90.91 187	38.48 366	96.56 133	82.16 127	81.05 207	95.27 77

BH-w/o		80.49 184	79.30 193	84.05 182	90.83 144	64.36 204	93.60 104	89.42 283	74.35 181	69.09 278	90.15 210	55.23 224	95.61 184	64.61 301	86.43 151	92.17 220

test_cas_vis1_n_1920		80.45 185	80.61 165	79.97 305	78.25 395	57.01 368	94.04 78	88.33 330	79.06 102	82.81 100	93.70 123	38.65 363	91.63 340	90.82 47	79.81 222	91.27 244

icg_test_0407_2		80.38 186	79.22 195	83.88 186	88.54 195	64.75 186	86.79 335	90.80 220	76.73 147	73.95 216	90.18 202	51.55 268	92.45 317	73.47 202	80.95 208	94.43 127

TAMVS		80.37 187	79.45 188	83.13 220	85.14 298	63.37 239	91.23 221	90.76 225	74.81 176	72.65 230	88.49 236	60.63 151	92.95 293	69.41 247	81.95 199	93.08 185

HQP_MVS		80.34 188	79.75 182	82.12 250	86.94 254	62.42 264	93.13 124	91.31 192	78.81 106	72.53 233	89.14 229	50.66 278	95.55 190	76.74 176	78.53 241	88.39 286

SDMVSNet		80.26 189	78.88 200	84.40 168	89.25 178	67.63 106	85.35 342	93.02 110	76.77 145	70.84 258	87.12 265	47.95 311	96.09 157	85.04 95	74.55 269	89.48 271

HPM-MVS_fast		80.25 190	79.55 187	82.33 240	91.55 125	59.95 329	91.32 216	89.16 294	65.23 335	74.71 204	93.07 135	47.81 313	95.74 173	74.87 195	88.23 122	91.31 242

ab-mvs		80.18 191	78.31 206	85.80 100	88.44 207	65.49 170	83.00 368	92.67 126	71.82 245	77.36 168	85.01 292	54.50 231	96.59 130	76.35 181	75.63 265	95.32 72

IS-MVSNet		80.14 192	79.41 189	82.33 240	87.91 227	60.08 327	91.97 185	88.27 333	72.90 214	71.44 254	91.73 173	61.44 143	93.66 279	62.47 318	86.53 148	93.24 177

test-LLR		80.10 193	79.56 185	81.72 259	86.93 256	61.17 296	92.70 146	91.54 183	71.51 260	75.62 185	86.94 269	53.83 242	92.38 319	72.21 220	84.76 166	91.60 232

PVSNet	73.49 8	80.05 194	78.63 202	84.31 172	90.92 141	64.97 182	92.47 161	91.05 212	79.18 96	72.43 239	90.51 193	37.05 383	94.06 260	68.06 262	86.00 152	93.90 159

UA-Net		80.02 195	79.65 183	81.11 274	89.33 174	57.72 356	86.33 339	89.00 309	77.44 133	81.01 119	89.15 228	59.33 172	95.90 166	61.01 325	84.28 173	89.73 267

test-mter		79.96 196	79.38 192	81.72 259	86.93 256	61.17 296	92.70 146	91.54 183	73.85 192	75.62 185	86.94 269	49.84 289	92.38 319	72.21 220	84.76 166	91.60 232

QAPM		79.95 197	77.39 228	87.64 34	89.63 166	71.41 20	93.30 119	93.70 79	65.34 334	67.39 309	91.75 172	47.83 312	98.96 16	57.71 341	89.81 106	92.54 204

UGNet		79.87 198	78.68 201	83.45 208	89.96 159	61.51 289	92.13 173	90.79 224	76.83 143	78.85 153	86.33 277	38.16 369	96.17 153	67.93 265	87.17 135	92.67 198
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		79.80 199	77.95 214	85.34 119	88.28 215	68.26 86	81.56 379	91.42 189	70.11 282	77.59 166	80.50 354	67.40 63	94.26 251	67.34 272	77.35 252	93.51 170

thres200		79.66 200	78.33 205	83.66 200	92.54 91	65.82 161	93.06 126	96.31 3	74.90 175	73.30 222	88.66 234	59.67 165	95.61 184	47.84 384	78.67 239	89.56 270

CPTT-MVS		79.59 201	79.16 196	80.89 284	91.54 126	59.80 331	92.10 175	88.54 325	60.42 378	72.96 224	93.28 131	48.27 304	92.80 302	78.89 165	86.50 149	90.06 260

Test_1112_low_res		79.56 202	78.60 203	82.43 236	88.24 218	60.39 320	92.09 176	87.99 340	72.10 235	71.84 246	87.42 260	64.62 94	93.04 289	65.80 291	77.30 253	93.85 161

tttt0517		79.50 203	78.53 204	82.41 239	87.22 247	61.43 293	89.75 274	94.76 35	69.29 292	67.91 298	88.06 250	72.92 29	95.63 181	62.91 314	73.90 279	90.16 259

reproduce_monomvs		79.49 204	79.11 198	80.64 286	92.91 78	61.47 292	91.17 226	93.28 98	83.09 31	64.04 339	82.38 322	66.19 72	94.57 233	81.19 140	57.71 396	85.88 335

FIs		79.47 205	79.41 189	79.67 313	85.95 280	59.40 337	91.68 200	93.94 68	78.06 118	68.96 284	88.28 241	66.61 69	91.77 336	66.20 287	74.99 268	87.82 292

SSM_0404		79.46 206	77.65 218	84.91 137	88.37 213	67.04 123	89.59 275	87.03 354	67.99 310	75.45 190	89.32 224	47.98 308	95.34 200	71.23 230	81.90 200	92.34 210

BH-RMVSNet		79.46 206	77.65 218	84.89 138	91.68 121	65.66 162	93.55 106	88.09 338	72.93 211	73.37 221	91.12 185	46.20 329	96.12 155	56.28 347	85.61 158	92.91 191

viewdifsd2359ckpt11		79.42 208	77.95 214	83.81 189	83.87 324	63.85 217	89.54 280	87.38 347	77.39 136	74.94 197	89.95 215	51.11 274	94.72 224	79.52 154	67.90 320	92.88 194

viewmsd2359difaftdt		79.42 208	77.96 213	83.81 189	83.88 323	63.85 217	89.54 280	87.38 347	77.39 136	74.94 197	89.95 215	51.11 274	94.72 224	79.52 154	67.90 320	92.88 194

PCF-MVS	73.15 9	79.29 210	77.63 220	84.29 173	86.06 278	65.96 155	87.03 330	91.10 206	69.86 286	69.79 274	90.64 189	57.54 194	96.59 130	64.37 303	82.29 190	90.32 257
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 211	79.57 184	78.24 334	88.46 206	52.29 392	90.41 253	89.12 298	74.24 183	69.13 277	91.91 169	65.77 79	90.09 365	59.00 337	88.09 124	92.33 211

114514_t		79.17 212	77.67 217	83.68 198	95.32 29	65.53 168	92.85 140	91.60 182	63.49 348	67.92 297	90.63 191	46.65 322	95.72 179	67.01 277	83.54 180	89.79 265

FA-MVS(test-final)		79.12 213	77.23 230	84.81 145	90.54 147	63.98 216	81.35 382	91.71 175	71.09 268	74.85 201	82.94 315	52.85 253	97.05 100	67.97 263	81.73 203	93.41 172

SSM_0407		79.09 214	77.21 231	84.75 149	88.50 200	66.98 126	89.21 291	87.03 354	67.99 310	74.12 211	89.32 224	47.98 308	95.29 205	71.23 230	79.52 225	91.98 225

VPA-MVSNet		79.03 215	78.00 211	82.11 253	85.95 280	64.48 195	93.22 122	94.66 41	75.05 173	74.04 215	84.95 293	52.17 260	93.52 281	74.90 194	67.04 327	88.32 288

OPM-MVS		79.00 216	78.09 209	81.73 258	83.52 330	63.83 220	91.64 202	90.30 246	76.36 156	71.97 245	89.93 217	46.30 328	95.17 208	75.10 189	77.70 246	86.19 323
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 217	78.22 208	81.25 268	85.33 291	62.73 259	89.53 283	93.21 100	72.39 226	72.14 242	90.13 211	60.99 146	94.72 224	67.73 267	72.49 288	86.29 320

AdaColmap		78.94 218	77.00 235	84.76 148	96.34 17	65.86 159	92.66 151	87.97 342	62.18 362	70.56 260	92.37 153	43.53 343	97.35 79	64.50 302	82.86 185	91.05 247

GeoE		78.90 219	77.43 224	83.29 213	88.95 187	62.02 274	92.31 165	86.23 365	70.24 281	71.34 255	89.27 226	54.43 235	94.04 263	63.31 310	80.81 215	93.81 162

miper_enhance_ethall		78.86 220	77.97 212	81.54 263	88.00 226	65.17 176	91.41 205	89.15 295	75.19 171	68.79 287	83.98 305	67.17 64	92.82 300	72.73 214	65.30 338	86.62 317

VPNet		78.82 221	77.53 223	82.70 229	84.52 311	66.44 142	93.93 84	92.23 142	80.46 67	72.60 231	88.38 240	49.18 297	93.13 288	72.47 218	63.97 358	88.55 283

EPNet_dtu		78.80 222	79.26 194	77.43 342	88.06 223	49.71 408	91.96 186	91.95 160	77.67 127	76.56 178	91.28 183	58.51 183	90.20 363	56.37 346	80.95 208	92.39 208
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 223	77.43 224	82.88 224	92.21 97	64.49 193	92.05 179	96.28 4	73.48 201	71.75 248	88.26 243	60.07 160	95.32 201	45.16 397	77.58 248	88.83 276

TR-MVS		78.77 224	77.37 229	82.95 223	90.49 149	60.88 302	93.67 100	90.07 256	70.08 283	74.51 205	91.37 181	45.69 332	95.70 180	60.12 331	80.32 219	92.29 213

thres400		78.68 225	77.43 224	82.43 236	92.21 97	64.49 193	92.05 179	96.28 4	73.48 201	71.75 248	88.26 243	60.07 160	95.32 201	45.16 397	77.58 248	87.48 297

BH-untuned		78.68 225	77.08 232	83.48 207	89.84 161	63.74 223	92.70 146	88.59 323	71.57 257	66.83 316	88.65 235	51.75 264	95.39 196	59.03 336	84.77 165	91.32 241

OMC-MVS		78.67 227	77.91 216	80.95 281	85.76 286	57.40 363	88.49 306	88.67 320	73.85 192	72.43 239	92.10 161	49.29 296	94.55 237	72.73 214	77.89 244	90.91 251

tpm		78.58 228	77.03 233	83.22 217	85.94 282	64.56 191	83.21 364	91.14 204	78.31 115	73.67 219	79.68 366	64.01 103	92.09 330	66.07 288	71.26 298	93.03 187

OpenMVS	70.45 11	78.54 229	75.92 252	86.41 80	85.93 283	71.68 18	92.74 143	92.51 135	66.49 325	64.56 333	91.96 166	43.88 342	98.10 39	54.61 352	90.65 93	89.44 273

EPMVS		78.49 230	75.98 251	86.02 91	91.21 135	69.68 53	80.23 391	91.20 198	75.25 170	72.48 237	78.11 377	54.65 230	93.69 278	57.66 342	83.04 184	94.69 108

AUN-MVS		78.37 231	77.43 224	81.17 270	86.60 264	57.45 362	89.46 285	91.16 200	74.11 185	74.40 206	90.49 194	55.52 221	94.57 233	74.73 196	60.43 387	91.48 235

thres100view900		78.37 231	77.01 234	82.46 235	91.89 115	63.21 245	91.19 225	96.33 1	72.28 229	70.45 263	87.89 252	60.31 155	95.32 201	45.16 397	77.58 248	88.83 276

GA-MVS		78.33 233	76.23 247	84.65 157	83.65 328	66.30 146	91.44 204	90.14 254	76.01 158	70.32 265	84.02 304	42.50 347	94.72 224	70.98 233	77.00 256	92.94 190

cascas		78.18 234	75.77 254	85.41 113	87.14 249	69.11 64	92.96 133	91.15 203	66.71 323	70.47 261	86.07 279	37.49 377	96.48 139	70.15 241	79.80 223	90.65 253

UniMVSNet_NR-MVSNet		78.15 235	77.55 222	79.98 303	84.46 314	60.26 322	92.25 167	93.20 102	77.50 132	68.88 285	86.61 272	66.10 74	92.13 328	66.38 284	62.55 365	87.54 295

LuminaMVS		78.14 236	76.66 239	82.60 233	80.82 356	64.64 190	89.33 287	90.45 234	68.25 308	74.73 203	85.51 288	41.15 353	94.14 254	78.96 163	80.69 217	89.04 274

IMVS_0404		78.11 237	76.29 246	83.59 201	88.54 195	64.75 186	84.63 347	90.80 220	76.73 147	61.16 361	90.18 202	40.17 357	91.58 342	73.47 202	80.95 208	94.43 127

thres600view7		78.00 238	76.66 239	82.03 255	91.93 111	63.69 229	91.30 217	96.33 1	72.43 224	70.46 262	87.89 252	60.31 155	94.92 217	42.64 409	76.64 259	87.48 297

FC-MVSNet-test		77.99 239	78.08 210	77.70 337	84.89 304	55.51 378	90.27 259	93.75 77	76.87 140	66.80 317	87.59 257	65.71 80	90.23 362	62.89 315	73.94 277	87.37 300

Anonymous202405211		77.96 240	75.33 260	85.87 96	93.73 53	64.52 192	94.85 49	85.36 378	62.52 360	76.11 180	90.18 202	29.43 414	97.29 83	68.51 258	77.24 255	95.81 50

cl22		77.94 241	76.78 237	81.42 265	87.57 237	64.93 184	90.67 244	88.86 313	72.45 223	67.63 304	82.68 319	64.07 101	92.91 298	71.79 223	65.30 338	86.44 318

XXY-MVS		77.94 241	76.44 242	82.43 236	82.60 340	64.44 197	92.01 181	91.83 169	73.59 200	70.00 270	85.82 284	54.43 235	94.76 221	69.63 244	68.02 319	88.10 290

MS-PatchMatch		77.90 243	76.50 241	82.12 250	85.99 279	69.95 42	91.75 198	92.70 122	73.97 189	62.58 356	84.44 300	41.11 354	95.78 170	63.76 307	92.17 66	80.62 400

FMVSNet3		77.73 244	76.04 250	82.80 225	91.20 136	68.99 68	91.87 189	91.99 158	73.35 203	67.04 312	83.19 314	56.62 208	92.14 327	59.80 333	69.34 305	87.28 303

VortexMVS		77.62 245	76.44 242	81.13 272	88.58 194	63.73 225	91.24 220	91.30 196	77.81 123	65.76 322	81.97 328	49.69 291	93.72 276	76.40 180	65.26 341	85.94 333

miper_ehance_all_eth		77.60 246	76.44 242	81.09 278	85.70 288	64.41 200	90.65 245	88.64 322	72.31 227	67.37 310	82.52 320	64.77 93	92.64 311	70.67 237	65.30 338	86.24 322

UniMVSNet (Re)		77.58 247	76.78 237	79.98 303	84.11 320	60.80 303	91.76 196	93.17 104	76.56 153	69.93 273	84.78 295	63.32 119	92.36 321	64.89 300	62.51 367	86.78 311

PatchmatchNet		77.46 248	74.63 267	85.96 93	89.55 169	70.35 35	79.97 396	89.55 278	72.23 230	70.94 256	76.91 389	57.03 198	92.79 303	54.27 354	81.17 206	94.74 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 249	75.65 256	82.73 227	80.38 364	67.13 120	91.85 191	90.23 251	75.09 172	69.37 275	83.39 311	53.79 244	94.44 241	71.77 224	65.00 345	86.63 316

CHOSEN 280x420		77.35 250	76.95 236	78.55 329	87.07 251	62.68 260	69.71 428	82.95 400	68.80 301	71.48 253	87.27 264	66.03 75	84.00 413	76.47 179	82.81 187	88.95 275

PS-MVSNAJss		77.26 251	76.31 245	80.13 298	80.64 360	59.16 342	90.63 248	91.06 211	72.80 215	68.58 291	84.57 298	53.55 246	93.96 268	72.97 208	71.96 292	87.27 304

gg-mvs-nofinetune		77.18 252	74.31 274	85.80 100	91.42 128	68.36 82	71.78 422	94.72 37	49.61 421	77.12 172	45.92 450	77.41 8	93.98 267	67.62 268	93.16 55	95.05 88

WB-MVSnew		77.14 253	76.18 249	80.01 302	86.18 274	63.24 243	91.26 218	94.11 65	71.72 249	73.52 220	87.29 263	45.14 337	93.00 291	56.98 344	79.42 228	83.80 361

MVP-Stereo		77.12 254	76.23 247	79.79 310	81.72 348	66.34 145	89.29 288	90.88 215	70.56 278	62.01 359	82.88 316	49.34 294	94.13 255	65.55 295	93.80 43	78.88 415
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 255	75.37 258	82.20 246	89.25 178	62.11 273	82.06 374	89.09 300	76.77 145	70.84 258	87.12 265	41.43 352	95.01 212	67.23 274	74.55 269	89.48 271

MonoMVSNet		76.99 256	75.08 263	82.73 227	83.32 332	63.24 243	86.47 338	86.37 361	79.08 100	66.31 320	79.30 370	49.80 290	91.72 337	79.37 156	65.70 336	93.23 178

dmvs_re		76.93 257	75.36 259	81.61 261	87.78 234	60.71 310	80.00 395	87.99 340	79.42 89	69.02 281	89.47 221	46.77 320	94.32 245	63.38 309	74.45 272	89.81 264

X-MVStestdata		76.86 258	74.13 280	85.05 131	93.22 66	63.78 221	92.92 135	92.66 127	73.99 187	78.18 158	10.19 465	55.25 222	97.41 75	79.16 159	91.58 78	93.95 152

DU-MVS		76.86 258	75.84 253	79.91 306	82.96 336	60.26 322	91.26 218	91.54 183	76.46 155	68.88 285	86.35 275	56.16 213	92.13 328	66.38 284	62.55 365	87.35 301

Anonymous20240529		76.84 260	74.15 279	84.88 139	91.02 138	64.95 183	93.84 92	91.09 207	53.57 409	73.00 223	87.42 260	35.91 387	97.32 81	69.14 252	72.41 290	92.36 209

UWE-MVS-28		76.83 261	77.60 221	74.51 371	84.58 310	50.34 404	88.22 311	94.60 45	74.46 178	66.66 318	88.98 233	62.53 132	85.50 405	57.55 343	80.80 216	87.69 294

c3_l		76.83 261	75.47 257	80.93 282	85.02 302	64.18 210	90.39 254	88.11 337	71.66 250	66.65 319	81.64 334	63.58 115	92.56 312	69.31 249	62.86 362	86.04 328

WR-MVS		76.76 263	75.74 255	79.82 309	84.60 308	62.27 270	92.60 154	92.51 135	76.06 157	67.87 301	85.34 289	56.76 204	90.24 361	62.20 319	63.69 360	86.94 309

v1144		76.73 264	74.88 264	82.27 242	80.23 368	66.60 139	91.68 200	90.21 253	73.69 197	69.06 280	81.89 329	52.73 256	94.40 243	69.21 250	65.23 342	85.80 336

IterMVS-LS		76.49 265	75.18 262	80.43 290	84.49 313	62.74 258	90.64 246	88.80 315	72.40 225	65.16 328	81.72 332	60.98 147	92.27 325	67.74 266	64.65 350	86.29 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 266	74.55 270	82.19 247	79.14 382	67.82 100	90.26 260	89.42 283	73.75 195	68.63 290	81.89 329	51.31 271	94.09 257	71.69 226	64.84 346	84.66 353

Elysia		76.45 267	74.17 277	83.30 211	80.43 362	64.12 211	89.58 276	90.83 217	61.78 370	72.53 233	85.92 282	34.30 394	94.81 219	68.10 260	84.01 178	90.97 248

StellarMVS		76.45 267	74.17 277	83.30 211	80.43 362	64.12 211	89.58 276	90.83 217	61.78 370	72.53 233	85.92 282	34.30 394	94.81 219	68.10 260	84.01 178	90.97 248

mamba_0408		76.22 269	73.37 291	84.77 146	88.50 200	66.98 126	58.80 449	86.18 367	69.12 297	74.12 211	89.01 231	47.50 315	95.35 198	67.57 269	79.52 225	91.98 225

v148		76.19 270	74.47 272	81.36 266	80.05 370	64.44 197	91.75 198	90.23 251	73.68 198	67.13 311	80.84 349	55.92 218	93.86 275	68.95 254	61.73 376	85.76 339

Effi-MVS+-dtu		76.14 271	75.28 261	78.72 328	83.22 333	55.17 380	89.87 271	87.78 344	75.42 166	67.98 296	81.43 338	45.08 338	92.52 314	75.08 190	71.63 293	88.48 284

cl____		76.07 272	74.67 265	80.28 293	85.15 297	61.76 283	90.12 263	88.73 317	71.16 265	65.43 325	81.57 336	61.15 144	92.95 293	66.54 281	62.17 369	86.13 326

DIV-MVS_self_test		76.07 272	74.67 265	80.28 293	85.14 298	61.75 284	90.12 263	88.73 317	71.16 265	65.42 326	81.60 335	61.15 144	92.94 297	66.54 281	62.16 371	86.14 324

FMVSNet2		76.07 272	74.01 282	82.26 244	88.85 188	67.66 104	91.33 215	91.61 181	70.84 272	65.98 321	82.25 324	48.03 305	92.00 332	58.46 338	68.73 313	87.10 306

v144192		76.05 275	74.03 281	82.12 250	79.50 376	66.55 141	91.39 209	89.71 275	72.30 228	68.17 294	81.33 341	51.75 264	94.03 265	67.94 264	64.19 353	85.77 337

NR-MVSNet		76.05 275	74.59 268	80.44 289	82.96 336	62.18 272	90.83 236	91.73 173	77.12 138	60.96 363	86.35 275	59.28 173	91.80 335	60.74 326	61.34 380	87.35 301

v1192		75.98 277	73.92 283	82.15 248	79.73 372	66.24 148	91.22 222	89.75 269	72.67 217	68.49 292	81.42 339	49.86 288	94.27 249	67.08 276	65.02 344	85.95 331

FE-MVS		75.97 278	73.02 297	84.82 142	89.78 162	65.56 166	77.44 407	91.07 210	64.55 337	72.66 229	79.85 364	46.05 330	96.69 128	54.97 351	80.82 214	92.21 219

eth_miper_zixun_eth		75.96 279	74.40 273	80.66 285	84.66 307	63.02 249	89.28 289	88.27 333	71.88 241	65.73 323	81.65 333	59.45 169	92.81 301	68.13 259	60.53 385	86.14 324

TranMVSNet+NR-MVSNet		75.86 280	74.52 271	79.89 307	82.44 342	60.64 313	91.37 212	91.37 190	76.63 151	67.65 303	86.21 278	52.37 259	91.55 343	61.84 321	60.81 383	87.48 297

SCA		75.82 281	72.76 300	85.01 133	86.63 263	70.08 38	81.06 384	89.19 292	71.60 256	70.01 269	77.09 387	45.53 333	90.25 358	60.43 328	73.27 281	94.68 109

LPG-MVS_test		75.82 281	74.58 269	79.56 317	84.31 317	59.37 338	90.44 251	89.73 272	69.49 289	64.86 329	88.42 238	38.65 363	94.30 247	72.56 216	72.76 285	85.01 350

GBi-Net		75.65 283	73.83 284	81.10 275	88.85 188	65.11 178	90.01 267	90.32 242	70.84 272	67.04 312	80.25 359	48.03 305	91.54 344	59.80 333	69.34 305	86.64 313

test1		75.65 283	73.83 284	81.10 275	88.85 188	65.11 178	90.01 267	90.32 242	70.84 272	67.04 312	80.25 359	48.03 305	91.54 344	59.80 333	69.34 305	86.64 313

v1921920		75.63 285	73.49 289	82.06 254	79.38 377	66.35 144	91.07 230	89.48 279	71.98 236	67.99 295	81.22 344	49.16 299	93.90 271	66.56 280	64.56 351	85.92 334

ACMP	71.68 10	75.58 286	74.23 276	79.62 315	84.97 303	59.64 333	90.80 237	89.07 302	70.39 279	62.95 352	87.30 262	38.28 367	93.87 273	72.89 209	71.45 296	85.36 346
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 287	73.26 295	81.61 261	80.67 359	66.82 132	89.54 280	89.27 288	71.65 251	63.30 347	80.30 358	54.99 228	94.06 260	67.33 273	62.33 368	83.94 359

tpm cat1		75.30 288	72.21 309	84.58 162	88.52 199	67.77 101	78.16 405	88.02 339	61.88 368	68.45 293	76.37 393	60.65 150	94.03 265	53.77 357	74.11 275	91.93 228

PLC	68.80 14	75.23 289	73.68 287	79.86 308	92.93 77	58.68 347	90.64 246	88.30 331	60.90 375	64.43 337	90.53 192	42.38 348	94.57 233	56.52 345	76.54 260	86.33 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 290	72.98 298	81.88 256	79.20 379	66.00 153	90.75 240	89.11 299	71.63 255	67.41 308	81.22 344	47.36 317	93.87 273	65.46 296	64.72 349	85.77 337

Fast-Effi-MVS+-dtu		75.04 291	73.37 291	80.07 299	80.86 354	59.52 336	91.20 224	85.38 377	71.90 239	65.20 327	84.84 294	41.46 351	92.97 292	66.50 283	72.96 284	87.73 293

dp		75.01 292	72.09 310	83.76 191	89.28 177	66.22 149	79.96 397	89.75 269	71.16 265	67.80 302	77.19 386	51.81 262	92.54 313	50.39 367	71.44 297	92.51 206

TAPA-MVS	70.22 12	74.94 293	73.53 288	79.17 323	90.40 151	52.07 393	89.19 293	89.61 277	62.69 359	70.07 268	92.67 145	48.89 302	94.32 245	38.26 423	79.97 221	91.12 246
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 294	73.32 294	79.74 312	86.53 266	60.31 321	89.03 298	92.70 122	78.61 111	68.98 283	83.34 312	41.93 350	92.23 326	52.77 361	65.97 334	86.69 312

SSM_04072		74.86 295	73.37 291	79.35 320	88.50 200	66.98 126	58.80 449	86.18 367	69.12 297	74.12 211	89.01 231	47.50 315	79.09 434	67.57 269	79.52 225	91.98 225

v10		74.77 296	72.54 306	81.46 264	80.33 366	66.71 136	89.15 294	89.08 301	70.94 270	63.08 350	79.86 363	52.52 257	94.04 263	65.70 292	62.17 369	83.64 362

XVG-OURS-SEG-HR		74.70 297	73.08 296	79.57 316	78.25 395	57.33 364	80.49 387	87.32 349	63.22 352	68.76 288	90.12 213	44.89 339	91.59 341	70.55 239	74.09 276	89.79 265

ACMM	69.62 13	74.34 298	72.73 302	79.17 323	84.25 319	57.87 354	90.36 256	89.93 263	63.17 354	65.64 324	86.04 281	37.79 375	94.10 256	65.89 289	71.52 295	85.55 342
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 299	72.30 308	80.32 291	91.49 127	61.66 286	90.85 235	80.72 406	56.67 401	63.85 342	90.64 189	46.75 321	90.84 352	53.79 356	75.99 264	88.47 285

XVG-OURS		74.25 300	72.46 307	79.63 314	78.45 393	57.59 360	80.33 389	87.39 346	63.86 344	68.76 288	89.62 220	40.50 356	91.72 337	69.00 253	74.25 274	89.58 268

test_fmvs1		74.07 301	73.69 286	75.22 362	78.91 386	47.34 421	89.06 297	74.69 422	63.68 347	79.41 142	91.59 176	24.36 425	87.77 388	85.22 92	76.26 262	90.55 256

CVMVSNet		74.04 302	74.27 275	73.33 381	85.33 291	43.94 435	89.53 283	88.39 327	54.33 408	70.37 264	90.13 211	49.17 298	84.05 411	61.83 322	79.36 230	91.99 224

Baseline_NR-MVSNet		73.99 303	72.83 299	77.48 341	80.78 357	59.29 341	91.79 193	84.55 386	68.85 300	68.99 282	80.70 350	56.16 213	92.04 331	62.67 316	60.98 382	81.11 394

pmmvs4		73.92 304	71.81 314	80.25 295	79.17 380	65.24 174	87.43 326	87.26 352	67.64 316	63.46 345	83.91 306	48.96 301	91.53 347	62.94 313	65.49 337	83.96 358

D2MVS		73.80 305	72.02 311	79.15 325	79.15 381	62.97 250	88.58 305	90.07 256	72.94 210	59.22 373	78.30 374	42.31 349	92.70 307	65.59 294	72.00 291	81.79 389

SD_0403		73.79 306	73.48 290	74.69 368	85.33 291	45.56 431	83.80 354	85.57 376	76.55 154	62.96 351	88.45 237	50.62 280	87.59 392	48.80 377	79.28 234	90.92 250

CR-MVSNet		73.79 306	70.82 322	82.70 229	83.15 334	67.96 95	70.25 425	84.00 391	73.67 199	69.97 271	72.41 410	57.82 191	89.48 371	52.99 360	73.13 282	90.64 254

test_djsdf		73.76 308	72.56 305	77.39 343	77.00 407	53.93 386	89.07 295	90.69 226	65.80 329	63.92 340	82.03 327	43.14 346	92.67 308	72.83 210	68.53 314	85.57 341

pmmvs5		73.35 309	71.52 316	78.86 327	78.64 390	60.61 314	91.08 228	86.90 356	67.69 313	63.32 346	83.64 307	44.33 341	90.53 355	62.04 320	66.02 333	85.46 344

Anonymous20231211		73.08 310	70.39 326	81.13 272	90.62 146	63.33 240	91.40 207	90.06 258	51.84 414	64.46 336	80.67 352	36.49 385	94.07 259	63.83 306	64.17 354	85.98 330

tt0805		73.07 311	70.73 323	80.07 299	78.37 394	57.05 366	87.78 320	92.18 149	61.23 374	67.04 312	86.49 274	31.35 407	94.58 231	65.06 299	67.12 326	88.57 282

miper_lstm_enhance		73.05 312	71.73 315	77.03 348	83.80 325	58.32 351	81.76 375	88.88 311	69.80 287	61.01 362	78.23 376	57.19 196	87.51 393	65.34 297	59.53 390	85.27 349

jajsoiax		73.05 312	71.51 317	77.67 338	77.46 404	54.83 382	88.81 301	90.04 259	69.13 296	62.85 354	83.51 309	31.16 408	92.75 304	70.83 234	69.80 301	85.43 345

LCM-MVSNet-Re		72.93 314	71.84 313	76.18 357	88.49 204	48.02 416	80.07 394	70.17 437	73.96 190	52.25 407	80.09 362	49.98 286	88.24 382	67.35 271	84.23 174	92.28 214

pm-mvs1		72.89 315	71.09 319	78.26 333	79.10 383	57.62 358	90.80 237	89.30 287	67.66 314	62.91 353	81.78 331	49.11 300	92.95 293	60.29 330	58.89 393	84.22 357

tpmvs		72.88 316	69.76 332	82.22 245	90.98 139	67.05 122	78.22 404	88.30 331	63.10 355	64.35 338	74.98 400	55.09 227	94.27 249	43.25 403	69.57 304	85.34 347

test0.0.03 1		72.76 317	72.71 303	72.88 385	80.25 367	47.99 417	91.22 222	89.45 281	71.51 260	62.51 357	87.66 255	53.83 242	85.06 407	50.16 369	67.84 324	85.58 340

UniMVSNet_ETH3D		72.74 318	70.53 325	79.36 319	78.62 391	56.64 370	85.01 344	89.20 291	63.77 345	64.84 331	84.44 300	34.05 396	91.86 334	63.94 305	70.89 300	89.57 269

mvs_tets		72.71 319	71.11 318	77.52 339	77.41 405	54.52 384	88.45 307	89.76 268	68.76 303	62.70 355	83.26 313	29.49 413	92.71 305	70.51 240	69.62 303	85.34 347

FMVSNet1		72.71 319	69.91 330	81.10 275	83.60 329	65.11 178	90.01 267	90.32 242	63.92 343	63.56 344	80.25 359	36.35 386	91.54 344	54.46 353	66.75 329	86.64 313

test_fmvs1_n		72.69 321	71.92 312	74.99 366	71.15 427	47.08 423	87.34 328	75.67 417	63.48 349	78.08 160	91.17 184	20.16 439	87.87 385	84.65 101	75.57 266	90.01 262

IterMVS		72.65 322	70.83 320	78.09 335	82.17 344	62.96 251	87.64 324	86.28 363	71.56 258	60.44 366	78.85 372	45.42 335	86.66 397	63.30 311	61.83 373	84.65 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 323	72.74 301	72.10 393	87.87 229	49.45 410	88.07 313	89.01 305	72.91 212	63.11 348	88.10 247	63.63 110	85.54 402	32.73 438	69.23 308	81.32 392

PatchMatch-RL		72.06 324	69.98 327	78.28 332	89.51 170	55.70 377	83.49 357	83.39 398	61.24 373	63.72 343	82.76 317	34.77 391	93.03 290	53.37 359	77.59 247	86.12 327

PVSNet_0	68.08 15	71.81 325	68.32 341	82.27 242	84.68 305	62.31 269	88.68 303	90.31 245	75.84 159	57.93 385	80.65 353	37.85 374	94.19 252	69.94 242	29.05 453	90.31 258

MIMVSNet		71.64 326	68.44 339	81.23 269	81.97 347	64.44 197	73.05 419	88.80 315	69.67 288	64.59 332	74.79 402	32.79 399	87.82 386	53.99 355	76.35 261	91.42 236

test_vis1_n		71.63 327	70.73 323	74.31 375	69.63 433	47.29 422	86.91 332	72.11 430	63.21 353	75.18 194	90.17 208	20.40 437	85.76 401	84.59 102	74.42 273	89.87 263

IterMVS-SCA-FT		71.55 328	69.97 328	76.32 355	81.48 350	60.67 312	87.64 324	85.99 370	66.17 327	59.50 371	78.88 371	45.53 333	83.65 415	62.58 317	61.93 372	84.63 356

v7n		71.31 329	68.65 336	79.28 321	76.40 409	60.77 305	86.71 336	89.45 281	64.17 342	58.77 378	78.24 375	44.59 340	93.54 280	57.76 340	61.75 375	83.52 365

anonymousdsp		71.14 330	69.37 334	76.45 354	72.95 422	54.71 383	84.19 351	88.88 311	61.92 367	62.15 358	79.77 365	38.14 370	91.44 349	68.90 255	67.45 325	83.21 371

F-COLMAP		70.66 331	68.44 339	77.32 344	86.37 271	55.91 375	88.00 315	86.32 362	56.94 399	57.28 389	88.07 249	33.58 397	92.49 315	51.02 364	68.37 315	83.55 363

WR-MVS_H		70.59 332	69.94 329	72.53 387	81.03 353	51.43 397	87.35 327	92.03 157	67.38 317	60.23 368	80.70 350	55.84 219	83.45 417	46.33 392	58.58 395	82.72 378

CP-MVSNet		70.50 333	69.91 330	72.26 390	80.71 358	51.00 401	87.23 329	90.30 246	67.84 312	59.64 370	82.69 318	50.23 284	82.30 425	51.28 363	59.28 391	83.46 367

RPMNet		70.42 334	65.68 355	84.63 160	83.15 334	67.96 95	70.25 425	90.45 234	46.83 430	69.97 271	65.10 433	56.48 212	95.30 204	35.79 428	73.13 282	90.64 254

testing3		70.38 335	70.83 320	69.03 405	85.82 285	43.93 436	90.72 243	90.56 233	68.06 309	60.24 367	86.82 271	64.83 91	84.12 409	26.33 446	64.10 355	79.04 413

tfpnnormal		70.10 336	67.36 345	78.32 331	83.45 331	60.97 301	88.85 299	92.77 120	64.85 336	60.83 364	78.53 373	43.52 344	93.48 282	31.73 441	61.70 377	80.52 401

TransMVSNet (Re)		70.07 337	67.66 343	77.31 345	80.62 361	59.13 343	91.78 195	84.94 382	65.97 328	60.08 369	80.44 355	50.78 277	91.87 333	48.84 376	45.46 426	80.94 396

CL-MVSNet_self_test		69.92 338	68.09 342	75.41 360	73.25 421	55.90 376	90.05 266	89.90 264	69.96 284	61.96 360	76.54 390	51.05 276	87.64 389	49.51 373	50.59 416	82.70 380

DP-MVS		69.90 339	66.48 347	80.14 297	95.36 28	62.93 252	89.56 278	76.11 415	50.27 420	57.69 387	85.23 290	39.68 359	95.73 174	33.35 433	71.05 299	81.78 390

PS-CasMVS		69.86 340	69.13 335	72.07 394	80.35 365	50.57 403	87.02 331	89.75 269	67.27 318	59.19 374	82.28 323	46.58 323	82.24 426	50.69 366	59.02 392	83.39 369

Syy-MVS		69.65 341	69.52 333	70.03 401	87.87 229	43.21 437	88.07 313	89.01 305	72.91 212	63.11 348	88.10 247	45.28 336	85.54 402	22.07 451	69.23 308	81.32 392

MSDG		69.54 342	65.73 354	80.96 280	85.11 300	63.71 227	84.19 351	83.28 399	56.95 398	54.50 396	84.03 303	31.50 405	96.03 163	42.87 407	69.13 310	83.14 373

PEN-MVS		69.46 343	68.56 337	72.17 392	79.27 378	49.71 408	86.90 333	89.24 289	67.24 321	59.08 375	82.51 321	47.23 318	83.54 416	48.42 379	57.12 397	83.25 370

LS3D		69.17 344	66.40 349	77.50 340	91.92 112	56.12 373	85.12 343	80.37 408	46.96 428	56.50 391	87.51 259	37.25 378	93.71 277	32.52 440	79.40 229	82.68 381

PatchT		69.11 345	65.37 359	80.32 291	82.07 346	63.68 230	67.96 435	87.62 345	50.86 418	69.37 275	65.18 432	57.09 197	88.53 378	41.59 412	66.60 330	88.74 279

KD-MVS_2432*1600		69.03 346	66.37 350	77.01 349	85.56 289	61.06 299	81.44 380	90.25 249	67.27 318	58.00 383	76.53 391	54.49 232	87.63 390	48.04 381	35.77 444	82.34 384

miper_refine_blended		69.03 346	66.37 350	77.01 349	85.56 289	61.06 299	81.44 380	90.25 249	67.27 318	58.00 383	76.53 391	54.49 232	87.63 390	48.04 381	35.77 444	82.34 384

mvsany_test1		68.77 348	68.56 337	69.39 403	73.57 420	45.88 430	80.93 385	60.88 451	59.65 384	71.56 251	90.26 201	43.22 345	75.05 438	74.26 199	62.70 364	87.25 305

ACMH	63.93 17	68.62 349	64.81 361	80.03 301	85.22 296	63.25 242	87.72 321	84.66 384	60.83 376	51.57 411	79.43 369	27.29 420	94.96 214	41.76 410	64.84 346	81.88 388
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 350	65.41 358	77.96 336	78.69 389	62.93 252	89.86 272	89.17 293	60.55 377	50.27 416	77.73 381	22.60 433	94.06 260	47.18 388	72.65 287	76.88 426

ADS-MVSNet		68.54 351	64.38 368	81.03 279	88.06 223	66.90 131	68.01 433	84.02 390	57.57 392	64.48 334	69.87 420	38.68 361	89.21 373	40.87 414	67.89 322	86.97 307

DTE-MVSNet		68.46 352	67.33 346	71.87 396	77.94 399	49.00 414	86.16 340	88.58 324	66.36 326	58.19 380	82.21 325	46.36 324	83.87 414	44.97 400	55.17 404	82.73 377

mmtdpeth		68.33 353	66.37 350	74.21 376	82.81 339	51.73 394	84.34 349	80.42 407	67.01 322	71.56 251	68.58 424	30.52 411	92.35 322	75.89 183	36.21 442	78.56 420

our_test_3		68.29 354	64.69 363	79.11 326	78.92 384	64.85 185	88.40 308	85.06 380	60.32 380	52.68 405	76.12 395	40.81 355	89.80 370	44.25 402	55.65 402	82.67 382

Patchmatch-RL test		68.17 355	64.49 366	79.19 322	71.22 426	53.93 386	70.07 427	71.54 434	69.22 293	56.79 390	62.89 437	56.58 209	88.61 375	69.53 246	52.61 411	95.03 90

XVG-ACMP-BASELINE		68.04 356	65.53 357	75.56 359	74.06 419	52.37 391	78.43 401	85.88 371	62.03 365	58.91 377	81.21 346	20.38 438	91.15 351	60.69 327	68.18 316	83.16 372

FMVSNet5		68.04 356	65.66 356	75.18 364	84.43 315	57.89 353	83.54 356	86.26 364	61.83 369	53.64 402	73.30 405	37.15 381	85.08 406	48.99 375	61.77 374	82.56 383

ppachtmachnet_test		67.72 358	63.70 370	79.77 311	78.92 384	66.04 152	88.68 303	82.90 401	60.11 382	55.45 393	75.96 396	39.19 360	90.55 354	39.53 418	52.55 412	82.71 379

ACMH+	65.35 16	67.65 359	64.55 364	76.96 351	84.59 309	57.10 365	88.08 312	80.79 405	58.59 390	53.00 404	81.09 348	26.63 422	92.95 293	46.51 390	61.69 378	80.82 397

pmmvs6		67.57 360	64.76 362	76.00 358	72.82 424	53.37 388	88.71 302	86.78 360	53.19 410	57.58 388	78.03 378	35.33 390	92.41 318	55.56 349	54.88 406	82.21 386

Anonymous20231206		67.53 361	65.78 353	72.79 386	74.95 415	47.59 419	88.23 310	87.32 349	61.75 372	58.07 382	77.29 384	37.79 375	87.29 395	42.91 405	63.71 359	83.48 366

Patchmtry		67.53 361	63.93 369	78.34 330	82.12 345	64.38 201	68.72 430	84.00 391	48.23 427	59.24 372	72.41 410	57.82 191	89.27 372	46.10 393	56.68 401	81.36 391

USDC		67.43 363	64.51 365	76.19 356	77.94 399	55.29 379	78.38 402	85.00 381	73.17 205	48.36 424	80.37 356	21.23 435	92.48 316	52.15 362	64.02 357	80.81 398

ADS-MVSNet2		66.90 364	63.44 372	77.26 346	88.06 223	60.70 311	68.01 433	75.56 419	57.57 392	64.48 334	69.87 420	38.68 361	84.10 410	40.87 414	67.89 322	86.97 307

CMPMVS	48.56 21	66.77 365	64.41 367	73.84 378	70.65 430	50.31 405	77.79 406	85.73 374	45.54 433	44.76 434	82.14 326	35.40 389	90.14 364	63.18 312	74.54 271	81.07 395
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 366	62.92 375	76.80 353	76.51 408	57.77 355	89.22 290	83.41 397	55.48 405	53.86 400	77.84 379	26.28 423	93.95 269	34.90 430	68.76 312	78.68 418

LTVRE_ROB	59.60 19	66.27 367	63.54 371	74.45 372	84.00 322	51.55 396	67.08 437	83.53 395	58.78 388	54.94 395	80.31 357	34.54 392	93.23 286	40.64 416	68.03 318	78.58 419
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		66.06 368	62.45 378	76.88 352	81.42 352	54.45 385	57.49 451	88.67 320	49.36 422	63.86 341	46.86 449	56.06 216	90.25 358	49.53 372	68.83 311	85.95 331

Patchmatch-test		65.86 369	60.94 384	80.62 288	83.75 326	58.83 345	58.91 448	75.26 421	44.50 436	50.95 415	77.09 387	58.81 180	87.90 384	35.13 429	64.03 356	95.12 84

UnsupCasMVSNet_eth		65.79 370	63.10 373	73.88 377	70.71 429	50.29 406	81.09 383	89.88 265	72.58 219	49.25 421	74.77 403	32.57 401	87.43 394	55.96 348	41.04 434	83.90 360

test_fmvs2		65.78 371	64.84 360	68.60 407	66.54 439	41.71 439	83.27 361	69.81 438	54.38 407	67.91 298	84.54 299	15.35 444	81.22 430	75.65 185	66.16 332	82.88 374

dmvs_testset		65.55 372	66.45 348	62.86 419	79.87 371	22.35 465	76.55 409	71.74 432	77.42 135	55.85 392	87.77 254	51.39 270	80.69 431	31.51 444	65.92 335	85.55 342

pmmvs-eth3d		65.53 373	62.32 379	75.19 363	69.39 434	59.59 334	82.80 369	83.43 396	62.52 360	51.30 413	72.49 408	32.86 398	87.16 396	55.32 350	50.73 415	78.83 416

mamv4		65.18 374	67.43 344	58.44 423	77.88 401	49.36 413	69.40 429	70.99 436	48.31 426	57.78 386	85.53 287	59.01 178	51.88 461	73.67 201	64.32 352	74.07 431

SixPastTwentyTwo		64.92 375	61.78 382	74.34 374	78.74 388	49.76 407	83.42 360	79.51 411	62.86 356	50.27 416	77.35 382	30.92 410	90.49 356	45.89 394	47.06 421	82.78 375

OurMVSNet-221017-0		64.68 376	62.17 380	72.21 391	76.08 412	47.35 420	80.67 386	81.02 404	56.19 402	51.60 410	79.66 367	27.05 421	88.56 377	53.60 358	53.63 409	80.71 399

test_0402		64.54 377	61.09 383	74.92 367	84.10 321	60.75 307	87.95 316	79.71 410	52.03 412	52.41 406	77.20 385	32.21 403	91.64 339	23.14 449	61.03 381	72.36 437

testgi		64.48 378	62.87 376	69.31 404	71.24 425	40.62 442	85.49 341	79.92 409	65.36 333	54.18 398	83.49 310	23.74 428	84.55 408	41.60 411	60.79 384	82.77 376

RPSCF		64.24 379	61.98 381	71.01 399	76.10 411	45.00 432	75.83 414	75.94 416	46.94 429	58.96 376	84.59 297	31.40 406	82.00 427	47.76 386	60.33 389	86.04 328

EU-MVSNet		64.01 380	63.01 374	67.02 413	74.40 418	38.86 448	83.27 361	86.19 366	45.11 434	54.27 397	81.15 347	36.91 384	80.01 433	48.79 378	57.02 398	82.19 387

test20.03		63.83 381	62.65 377	67.38 412	70.58 431	39.94 444	86.57 337	84.17 388	63.29 351	51.86 409	77.30 383	37.09 382	82.47 423	38.87 422	54.13 408	79.73 407

sc_t1		63.81 382	59.39 390	77.10 347	77.62 402	56.03 374	84.32 350	73.56 426	46.66 431	58.22 379	73.06 406	23.28 431	90.62 353	50.93 365	46.84 422	84.64 355

MDA-MVSNet_test_wron		63.78 383	60.16 386	74.64 369	78.15 397	60.41 318	83.49 357	84.03 389	56.17 404	39.17 444	71.59 416	37.22 379	83.24 420	42.87 407	48.73 418	80.26 404

YYNet1		63.76 384	60.14 387	74.62 370	78.06 398	60.19 325	83.46 359	83.99 393	56.18 403	39.25 443	71.56 417	37.18 380	83.34 418	42.90 406	48.70 419	80.32 403

K. test v3		63.09 385	59.61 389	73.53 380	76.26 410	49.38 412	83.27 361	77.15 414	64.35 339	47.77 426	72.32 412	28.73 415	87.79 387	49.93 371	36.69 441	83.41 368

COLMAP_ROB	57.96 20	62.98 386	59.65 388	72.98 384	81.44 351	53.00 390	83.75 355	75.53 420	48.34 425	48.81 423	81.40 340	24.14 426	90.30 357	32.95 435	60.52 386	75.65 429
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 387	59.08 391	71.10 398	67.19 437	48.72 415	83.91 353	85.23 379	50.38 419	47.84 425	71.22 419	20.74 436	85.51 404	46.47 391	58.75 394	79.06 412

tt0320		61.85 388	57.45 397	75.03 365	77.49 403	57.60 359	82.74 370	73.65 425	43.65 440	53.65 401	68.18 426	25.47 424	88.66 374	45.56 396	46.68 423	78.81 417

AllTest		61.66 389	58.06 393	72.46 388	79.57 373	51.42 398	80.17 392	68.61 440	51.25 416	45.88 428	81.23 342	19.86 440	86.58 398	38.98 420	57.01 399	79.39 409

UnsupCasMVSNet_bld		61.60 390	57.71 394	73.29 382	68.73 435	51.64 395	78.61 400	89.05 303	57.20 397	46.11 427	61.96 440	28.70 416	88.60 376	50.08 370	38.90 439	79.63 408

MDA-MVSNet-bldmvs		61.54 391	57.70 395	73.05 383	79.53 375	57.00 369	83.08 365	81.23 403	57.57 392	34.91 448	72.45 409	32.79 399	86.26 400	35.81 427	41.95 432	75.89 428

tt0320-xc		61.51 392	56.89 400	75.37 361	78.50 392	58.61 348	82.61 371	71.27 435	44.31 437	53.17 403	68.03 428	23.38 429	88.46 379	47.77 385	43.00 431	79.03 414

mvs5depth		61.03 393	57.65 396	71.18 397	67.16 438	47.04 425	72.74 420	77.49 412	57.47 395	60.52 365	72.53 407	22.84 432	88.38 380	49.15 374	38.94 438	78.11 423

KD-MVS_self_test		60.87 394	58.60 392	67.68 410	66.13 440	39.93 445	75.63 416	84.70 383	57.32 396	49.57 419	68.45 425	29.55 412	82.87 421	48.09 380	47.94 420	80.25 405

kuosan		60.86 395	60.24 385	62.71 420	81.57 349	46.43 427	75.70 415	85.88 371	57.98 391	48.95 422	69.53 422	58.42 184	76.53 436	28.25 445	35.87 443	65.15 444

TinyColmap		60.32 396	56.42 403	72.00 395	78.78 387	53.18 389	78.36 403	75.64 418	52.30 411	41.59 442	75.82 398	14.76 447	88.35 381	35.84 426	54.71 407	74.46 430

MVS-HIRNet		60.25 397	55.55 404	74.35 373	84.37 316	56.57 371	71.64 423	74.11 423	34.44 447	45.54 432	42.24 455	31.11 409	89.81 368	40.36 417	76.10 263	76.67 427

MIMVSNet1		60.16 398	57.33 398	68.67 406	69.71 432	44.13 434	78.92 399	84.21 387	55.05 406	44.63 435	71.85 414	23.91 427	81.54 429	32.63 439	55.03 405	80.35 402

PM-MVS		59.40 399	56.59 401	67.84 408	63.63 443	41.86 438	76.76 408	63.22 448	59.01 387	51.07 414	72.27 413	11.72 451	83.25 419	61.34 323	50.28 417	78.39 421

new-patchmatchnet		59.30 400	56.48 402	67.79 409	65.86 441	44.19 433	82.47 372	81.77 402	59.94 383	43.65 438	66.20 431	27.67 419	81.68 428	39.34 419	41.40 433	77.50 425

test_vis1_rt		59.09 401	57.31 399	64.43 416	68.44 436	46.02 429	83.05 367	48.63 460	51.96 413	49.57 419	63.86 436	16.30 442	80.20 432	71.21 232	62.79 363	67.07 443

test_fmvs3		56.82 402	54.86 406	62.69 421	53.59 454	35.47 451	75.87 413	65.64 445	43.91 438	55.10 394	71.43 418	6.91 459	74.40 441	68.64 257	52.63 410	78.20 422

DSMNet-mixed		56.78 403	54.44 407	63.79 417	63.21 444	29.44 460	64.43 440	64.10 447	42.12 444	51.32 412	71.60 415	31.76 404	75.04 439	36.23 425	65.20 343	86.87 310

pmmvs3		55.51 404	51.50 410	67.53 411	57.90 452	50.93 402	80.37 388	73.66 424	40.63 445	44.15 437	64.75 434	16.30 442	78.97 435	44.77 401	40.98 436	72.69 435

TDRefinement		55.28 405	51.58 409	66.39 414	59.53 451	46.15 428	76.23 411	72.80 427	44.60 435	42.49 440	76.28 394	15.29 445	82.39 424	33.20 434	43.75 428	70.62 439

dongtai		55.18 406	55.46 405	54.34 431	76.03 413	36.88 449	76.07 412	84.61 385	51.28 415	43.41 439	64.61 435	56.56 210	67.81 449	18.09 454	28.50 454	58.32 447

LF4IMVS		54.01 407	52.12 408	59.69 422	62.41 446	39.91 446	68.59 431	68.28 442	42.96 442	44.55 436	75.18 399	14.09 449	68.39 448	41.36 413	51.68 413	70.78 438

ttmdpeth		53.34 408	49.96 411	63.45 418	62.07 448	40.04 443	72.06 421	65.64 445	42.54 443	51.88 408	77.79 380	13.94 450	76.48 437	32.93 436	30.82 452	73.84 432

MVStest1		51.35 409	46.89 413	64.74 415	65.06 442	51.10 400	67.33 436	72.58 428	30.20 451	35.30 446	74.82 401	27.70 418	69.89 446	24.44 448	24.57 455	73.22 433

N_pmnet		50.55 410	49.11 412	54.88 429	77.17 406	4.02 473	84.36 348	2.00 471	48.59 423	45.86 430	68.82 423	32.22 402	82.80 422	31.58 442	51.38 414	77.81 424

new_pmnet		49.31 411	46.44 414	57.93 424	62.84 445	40.74 441	68.47 432	62.96 449	36.48 446	35.09 447	57.81 444	14.97 446	72.18 443	32.86 437	46.44 424	60.88 446

mvsany_test3		48.86 412	46.35 415	56.41 425	46.00 460	31.67 456	62.26 442	47.25 461	43.71 439	45.54 432	68.15 427	10.84 452	64.44 457	57.95 339	35.44 446	73.13 434

test_f		46.58 413	43.45 417	55.96 426	45.18 461	32.05 455	61.18 443	49.49 459	33.39 448	42.05 441	62.48 439	7.00 458	65.56 453	47.08 389	43.21 430	70.27 440

WB-MVS		46.23 414	44.94 416	50.11 434	62.13 447	21.23 467	76.48 410	55.49 453	45.89 432	35.78 445	61.44 442	35.54 388	72.83 442	9.96 461	21.75 456	56.27 449

FPMVS		45.64 415	43.10 419	53.23 432	51.42 457	36.46 450	64.97 439	71.91 431	29.13 452	27.53 452	61.55 441	9.83 454	65.01 455	16.00 458	55.58 403	58.22 448

SSC-MVS		44.51 416	43.35 418	47.99 438	61.01 450	18.90 469	74.12 418	54.36 454	43.42 441	34.10 449	60.02 443	34.42 393	70.39 445	9.14 463	19.57 457	54.68 450

EGC-MVSNET		42.35 417	38.09 420	55.11 428	74.57 416	46.62 426	71.63 424	55.77 452	0.04 466	0.24 467	62.70 438	14.24 448	74.91 440	17.59 455	46.06 425	43.80 452

LCM-MVSNet		40.54 418	35.79 423	54.76 430	36.92 467	30.81 457	51.41 454	69.02 439	22.07 454	24.63 454	45.37 451	4.56 463	65.81 452	33.67 432	34.50 447	67.67 441

APD_test1		40.50 419	37.31 422	50.09 435	51.88 455	35.27 452	59.45 447	52.59 456	21.64 455	26.12 453	57.80 445	4.56 463	66.56 451	22.64 450	39.09 437	48.43 451

test_vis3_rt		40.46 420	37.79 421	48.47 437	44.49 462	33.35 454	66.56 438	32.84 468	32.39 449	29.65 450	39.13 458	3.91 466	68.65 447	50.17 368	40.99 435	43.40 453

ANet_high		40.27 421	35.20 424	55.47 427	34.74 468	34.47 453	63.84 441	71.56 433	48.42 424	18.80 457	41.08 456	9.52 455	64.45 456	20.18 452	8.66 464	67.49 442

test_method		38.59 422	35.16 425	48.89 436	54.33 453	21.35 466	45.32 457	53.71 455	7.41 463	28.74 451	51.62 447	8.70 456	52.87 460	33.73 431	32.89 448	72.47 436

PMMVS2		37.93 423	33.61 426	50.92 433	46.31 459	24.76 463	60.55 446	50.05 457	28.94 453	20.93 455	47.59 448	4.41 465	65.13 454	25.14 447	18.55 459	62.87 445

Gipuma		34.91 424	31.44 427	45.30 439	70.99 428	39.64 447	19.85 461	72.56 429	20.10 457	16.16 461	21.47 462	5.08 462	71.16 444	13.07 459	43.70 429	25.08 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 425	29.47 428	42.67 441	41.89 464	30.81 457	52.07 452	43.45 462	15.45 458	18.52 458	44.82 452	2.12 467	58.38 458	16.05 456	30.87 450	38.83 454

APD_test2		32.77 425	29.47 428	42.67 441	41.89 464	30.81 457	52.07 452	43.45 462	15.45 458	18.52 458	44.82 452	2.12 467	58.38 458	16.05 456	30.87 450	38.83 454

PMVS	26.43 22	31.84 427	28.16 430	42.89 440	25.87 470	27.58 461	50.92 455	49.78 458	21.37 456	14.17 462	40.81 457	2.01 469	66.62 450	9.61 462	38.88 440	34.49 458
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 428	24.00 432	26.45 445	43.74 463	18.44 470	60.86 444	39.66 464	15.11 460	9.53 464	22.10 461	6.52 460	46.94 463	8.31 464	10.14 461	13.98 461

MVE	24.84 23	24.35 429	19.77 435	38.09 443	34.56 469	26.92 462	26.57 459	38.87 466	11.73 462	11.37 463	27.44 459	1.37 470	50.42 462	11.41 460	14.60 460	36.93 456
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 430	23.20 434	25.46 446	41.52 466	16.90 471	60.56 445	38.79 467	14.62 461	8.99 465	20.24 464	7.35 457	45.82 464	7.25 465	9.46 462	13.64 462

tmp_tt		22.26 431	23.75 433	17.80 447	5.23 471	12.06 472	35.26 458	39.48 465	2.82 465	18.94 456	44.20 454	22.23 434	24.64 466	36.30 424	9.31 463	16.69 460

cdsmvs_eth3d_5k		19.86 432	26.47 431	0.00 451	0.00 474	0.00 476	0.00 462	93.45 91	0.00 469	0.00 470	95.27 71	49.56 292	0.00 470	0.00 469	0.00 467	0.00 466

wuyk23d		11.30 433	10.95 436	12.33 448	48.05 458	19.89 468	25.89 460	1.92 472	3.58 464	3.12 466	1.37 466	0.64 471	15.77 467	6.23 466	7.77 465	1.35 463

ab-mvs-re		7.91 434	10.55 437	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	94.95 81	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

testmvs		7.23 435	9.62 438	0.06 450	0.04 472	0.02 475	84.98 345	0.02 473	0.03 467	0.18 468	1.21 467	0.01 473	0.02 468	0.14 467	0.01 466	0.13 465

test123		6.92 436	9.21 439	0.08 449	0.03 473	0.05 474	81.65 378	0.01 474	0.02 468	0.14 469	0.85 468	0.03 472	0.02 468	0.12 468	0.00 467	0.16 464

pcd_1.5k_mvsjas		4.46 437	5.95 440	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	53.55 246	0.00 470	0.00 469	0.00 467	0.00 466

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 467	0.00 466

WAC-MVS						49.45 410								31.56 443

FOURS1					93.95 46	61.77 282	93.96 82	91.92 161	62.14 364	86.57 56

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

PC_three_1452									80.91 63	94.07 2	96.83 23	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 51		94.18 62	71.42 262	90.67 24	96.85 21	74.45 20

eth-test2					0.00 474

eth-test					0.00 474

ZD-MVS					96.63 9	65.50 169		93.50 89	70.74 276	85.26 74	95.19 77	64.92 90	97.29 83	87.51 68	93.01 56

RE-MVS-def			80.48 169		92.02 105	58.56 349	90.90 232	90.45 234	62.76 357	78.89 148	94.46 95	49.30 295		78.77 166	86.77 142	92.28 214

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 64	95.28 1				92.34 33	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 13	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 251	92.07 10	97.21 8	74.58 18	99.11 6	92.34 33	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 58		94.44 51	71.65 251	92.11 8	97.05 11	76.79 9	99.11 6

9.14			87.63 34		93.86 48		94.41 60	94.18 62	72.76 216	86.21 59	96.51 30	66.64 68	97.88 47	90.08 50	94.04 39

save fliter					93.84 49	67.89 98	95.05 40	92.66 127	78.19 116

test_0728_THIRD									72.48 221	90.55 25	96.93 15	76.24 11	99.08 11	91.53 41	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 57					99.15 2	91.91 39	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 237	91.89 13	97.11 10	73.77 23

GSMVS																94.68 109

test_part2					96.29 19	68.16 91				90.78 22

sam_mvs1												57.85 190				94.68 109

sam_mvs												54.91 229

ambc				69.61 402	61.38 449	41.35 440	49.07 456	85.86 373		50.18 418	66.40 430	10.16 453	88.14 383	45.73 395	44.20 427	79.32 411

MTGPA								92.23 142

test_post1							78.95 398				20.70 463	53.05 251	91.50 348	60.43 328

test_post											23.01 460	56.49 211	92.67 308

patchmatchnet-post											67.62 429	57.62 193	90.25 358

GG-mvs-BLEND				86.53 76	91.91 114	69.67 54	75.02 417	94.75 36		78.67 156	90.85 188	77.91 7	94.56 236	72.25 219	93.74 45	95.36 68

MTMP							93.77 96	32.52 469

gm-plane-assit					88.42 209	67.04 123			78.62 110		91.83 170		97.37 77	76.57 178

test9_res														89.41 51	94.96 19	95.29 74

TEST9					94.18 41	67.28 114	94.16 69	93.51 87	71.75 248	85.52 69	95.33 65	68.01 57	97.27 87

test_8					94.19 40	67.19 116	94.15 71	93.42 94	71.87 242	85.38 72	95.35 64	68.19 55	96.95 114

agg_prior2														86.41 82	94.75 30	95.33 70

agg_prior					94.16 43	66.97 129		93.31 97		84.49 80			96.75 126

TestCases				72.46 388	79.57 373	51.42 398		68.61 440	51.25 416	45.88 428	81.23 342	19.86 440	86.58 398	38.98 420	57.01 399	79.39 409

test_prior4						67.18 118	93.92 85

test_prior2							95.10 39		75.40 167	85.25 75	95.61 56	67.94 58		87.47 70	94.77 26

test_prior				86.42 79	94.71 35	67.35 113		93.10 108					96.84 123			95.05 88

旧先验2							92.00 184		59.37 386	87.54 49			93.47 283	75.39 187

新几何2							91.41 205

新几何1				84.73 150	92.32 93	64.28 206		91.46 188	59.56 385	79.77 136	92.90 139	56.95 203	96.57 132	63.40 308	92.91 58	93.34 174

旧先验1					91.94 110	60.74 308		91.50 186			94.36 99	65.23 85			91.84 73	94.55 116

无先验							92.71 145	92.61 132	62.03 365				97.01 104	66.63 279		93.97 151

原ACMM2							92.01 181

原ACMM1				84.42 167	93.21 68	64.27 207		93.40 96	65.39 332	79.51 140	92.50 147	58.11 189	96.69 128	65.27 298	93.96 40	92.32 212

test222					89.77 163	61.60 288	89.55 279	89.42 283	56.83 400	77.28 170	92.43 151	52.76 254			91.14 89	93.09 184

testdata2													96.09 157	61.26 324

segment_acmp												65.94 76

testdata				81.34 267	89.02 185	57.72 356		89.84 266	58.65 389	85.32 73	94.09 115	57.03 198	93.28 285	69.34 248	90.56 95	93.03 187

testdata1							89.21 291		77.55 131

test12				87.09 52	94.60 36	68.86 70		92.91 115		82.67 103		65.44 82	97.55 67		93.69 48	94.84 99

plane_prior7					86.94 254	61.51 289

plane_prior6					87.23 246	62.32 268						50.66 278

plane_prior5								91.31 192					95.55 190	76.74 176	78.53 241	88.39 286

plane_prior4											89.14 229

plane_prior3						61.95 277			79.09 99	72.53 233

plane_prior2							93.13 124		78.81 106

plane_prior1					87.15 248

plane_prior						62.42 264	93.85 89		79.38 91						78.80 238

n2								0.00 475

nn								0.00 475

door-mid								66.01 444

lessismore_v0				73.72 379	72.93 423	47.83 418		61.72 450		45.86 430	73.76 404	28.63 417	89.81 368	47.75 387	31.37 449	83.53 364

LGP-MVS_train				79.56 317	84.31 317	59.37 338		89.73 272	69.49 289	64.86 329	88.42 238	38.65 363	94.30 247	72.56 216	72.76 285	85.01 350

test11								93.01 111

door								66.57 443

HQP5-MVS						63.66 231

HQP-NCC					87.54 238		94.06 74		79.80 81	74.18 207

ACMP_Plane					87.54 238		94.06 74		79.80 81	74.18 207

BP-MVS														77.63 173

HQP4-MVS										74.18 207			95.61 184			88.63 280

HQP3-MVS								91.70 178							78.90 236

HQP2-MVS												51.63 266

NP-MVS					87.41 241	63.04 248					90.30 199

MDTV_nov1_ep13_2view						59.90 330	80.13 393		67.65 315	72.79 227		54.33 237		59.83 332		92.58 203

MDTV_nov1_ep13			72.61 304		89.06 184	68.48 79	80.33 389	90.11 255	71.84 244	71.81 247	75.92 397	53.01 252	93.92 270	48.04 381	73.38 280

ACMMP++_ref															71.63 293

ACMMP++															69.72 302

Test By Simon												54.21 240

ITE_SJBPF				70.43 400	74.44 417	47.06 424		77.32 413	60.16 381	54.04 399	83.53 308	23.30 430	84.01 412	43.07 404	61.58 379	80.21 406

DeepMVS_CX				34.71 444	51.45 456	24.73 464		28.48 470	31.46 450	17.49 460	52.75 446	5.80 461	42.60 465	18.18 453	19.42 458	36.81 457

High-resolution multi-view benchmark