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	72.48 220	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	71.65 250	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	58.16 351	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	63.70 227	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	71.92 236	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	68.77 301	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	61.94 277	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	61.41 293	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	74.18 183	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	61.78 280	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	63.55 234	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	62.63 260	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	69.35 290	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	60.04 327	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	76.86 140	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	71.87 241	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	75.19 187	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	70.78 274	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	83.58 201	87.26 245	60.74 307	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	68.48 305	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	71.77 246	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	61.48 290	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	62.55 261	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	63.54 235	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	63.29 240	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	83.52 202	92.63 88	59.36 339	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	74.93 173	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	66.54 323	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	78.39 168	93.59 49	94.09 145

CS-MVS		85.80 70	86.65 54	83.27 214	92.00 109	58.92 343	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	63.58 232	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	80.83 354	62.33 266	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	70.90 270	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	82.95 337	63.48 237	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	92.81 195

MGCFI-Net		85.59 76	85.73 72	85.17 127	91.41 131	62.44 262	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	73.75 194	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	80.91 282	84.52 311	60.10 325	93.35 118	90.35 241	83.41 29	86.54 57	96.27 39	60.50 153	90.02 366	94.84 14	90.38 98	92.61 199

MP-MVS-pluss		85.24 81	85.13 82	85.56 109	91.42 128	65.59 165	91.54 203	92.51 135	74.56 176	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	75.94 181	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	76.59 151	79.03 147	95.00 80	61.59 142	97.61 63	78.16 169	89.00 115	95.63 55

fmvsm_s_conf0.5_n_2		85.06 85	85.60 74	83.44 208	86.92 258	60.53 314	94.41 60	87.31 350	83.30 30	88.72 40	96.72 26	54.28 238	97.75 52	94.07 20	84.68 168	92.04 222

MP-MVS		85.02 86	84.97 85	85.17 127	92.60 89	64.27 207	93.24 120	92.27 141	73.13 205	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	88.27 241	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	87.84 318	92.69 125	62.18 361	81.47 112	87.64 255	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	62.36 265	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	62.27 269	94.74 52	92.64 130	68.35 306	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	80.23 367	63.50 236	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	92.53 204

HFP-MVS		84.73 94	84.40 94	85.72 104	93.75 52	65.01 181	93.50 110	93.19 103	72.19 230	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	87.91 316	95.54 14	68.55 303	72.35 240	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	73.20 203	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	83.01 220	89.34 172	61.37 294	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	92.27 216

fmvsm_s_conf0.1_n_2		84.40 98	84.78 90	83.27 214	85.25 295	60.41 317	94.13 72	85.69 374	83.05 32	87.99 43	96.37 33	52.75 255	97.68 54	93.75 24	84.05 177	91.71 230

ACMMPR		84.37 99	84.06 97	85.28 122	93.56 58	64.37 202	93.50 110	93.15 105	72.19 230	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	72.16 233	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	71.90 238	82.16 105	93.49 129	47.98 307	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	69.19 293	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	69.19 293	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	94.52 238	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	84.65 345	94.50 48	79.15 97	82.23 104	87.93 250	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	94.18 252	69.16 250	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	74.41 178	79.16 146	95.61 56	53.99 241	98.88 22	69.62 244	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	86.57 272	63.93 105	96.09 157	74.91 192	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	47.71 313	96.98 109	77.90 171	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	58.19 379	93.64 125	73.64 25	92.35 321	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	94.42 241	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	89.81 217	63.77 108	96.97 112	79.67 152	88.21 123	92.60 200

MTAPA		83.91 114	83.38 114	85.50 110	91.89 115	65.16 177	81.75 375	92.23 142	75.32 168	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	63.78 220	92.92 135	92.66 127	73.99 186	78.18 158	94.31 106	55.25 222	97.41 75	79.16 158	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	86.12 368	72.59 217	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	82.51 340	63.72 225	91.37 212	83.99 392	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	63.56 233	91.76 196	94.81 34	79.65 85	77.87 161	94.09 115	63.35 118	97.90 45	79.35 156	79.36 230	90.74 251

MVSFormer		83.75 119	82.88 128	86.37 81	89.24 181	71.18 24	89.07 294	90.69 226	65.80 328	87.13 50	94.34 104	64.99 87	92.67 307	72.83 209	91.80 74	95.27 77

CP-MVS		83.71 120	83.40 113	84.65 157	93.14 71	63.84 218	94.59 57	92.28 140	71.03 268	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	75.26 413	61.72 284	92.17 171	87.24 352	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	69.08 278	90.39 196	77.92 6	96.28 147	78.91 163	81.38 205	91.16 244

reproduce-ours		83.51 123	83.33 116	84.06 179	92.18 99	60.49 315	90.74 241	92.04 154	64.35 338	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	60.49 315	90.74 241	92.04 154	64.35 338	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	74.31 181	75.75 182	90.92 186	72.62 32	96.52 136	69.64 242	81.50 204	93.71 164

PVSNet_BlendedMVS		83.38 126	83.43 110	83.22 216	93.76 50	67.53 109	94.06 74	93.61 82	79.13 98	81.00 120	85.14 290	63.19 120	97.29 83	87.08 77	73.91 278	84.83 351

test2506		83.29 127	82.92 127	84.37 170	88.39 211	63.18 246	92.01 181	91.35 191	77.66 128	78.49 157	91.42 178	64.58 96	95.09 209	73.19 205	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	71.28 262	77.23 171	94.43 97	55.17 226	97.31 82	79.33 157	91.38 82	93.37 173

HPM-MVS		83.25 128	82.95 126	84.17 177	92.25 95	62.88 255	90.91 231	91.86 166	70.30 279	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	83.73 193	92.02 105	59.74 331	90.37 255	92.08 152	63.70 345	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	83.67 198	92.28 94	63.19 245	91.38 211	94.68 40	79.22 95	76.60 177	93.75 121	62.64 130	97.76 51	78.07 170	78.01 243	90.05 260

testing3-2		83.11 132	83.15 122	82.98 221	91.92 112	64.01 215	94.39 63	95.37 16	78.32 114	75.53 189	90.06 214	73.18 27	93.18 286	74.34 197	75.27 267	91.77 229

VDD-MVS		83.06 133	81.81 145	86.81 61	90.86 143	67.70 103	95.40 30	91.50 186	75.46 163	81.78 107	92.34 154	40.09 357	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	62.02 273	92.72 144	93.76 74	81.45 52	82.73 101	92.25 157	60.11 158	97.13 98	87.69 66	62.96 360	93.91 157

PAPM_NR		82.97 135	81.84 144	86.37 81	94.10 44	66.76 135	87.66 322	92.84 117	69.96 283	74.07 213	93.57 127	63.10 125	97.50 70	70.66 237	90.58 94	94.85 96

mPP-MVS		82.96 136	82.44 136	84.52 164	92.83 80	62.92 253	92.76 142	91.85 168	71.52 258	75.61 187	94.24 109	53.48 249	96.99 108	78.97 161	90.73 91	93.64 167

SR-MVS		82.81 137	82.58 133	83.50 205	93.35 64	61.16 297	92.23 169	91.28 197	64.48 337	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	69.08 298	76.50 179	93.89 120	54.48 234	98.20 37	70.76 235	85.66 157	92.69 196

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	72.58 231	92.27 155	52.46 258	95.78 170	84.18 106	79.06 235	88.16 288
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	83.73 193	89.25 178	59.58 334	92.24 168	94.89 31	77.96 119	79.86 135	92.38 152	56.70 206	97.05 100	77.26 174	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	67.89 299	92.48 150	48.42 302	98.52 28	68.80 255	94.40 36	95.15 82

RRT-MVS		82.61 142	81.16 150	86.96 57	91.10 137	68.75 73	87.70 321	92.20 146	76.97 138	72.68 227	87.10 266	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	75.46 163	79.64 138	92.01 164	59.51 168	94.38 243	82.99 121	82.26 191	93.54 169

MVSTER		82.47 144	82.05 139	83.74 191	92.68 87	69.01 67	91.90 188	93.21 100	79.83 80	72.14 241	85.71 285	74.72 17	94.72 224	75.72 183	72.49 288	87.50 295

TESTMET0.1,1		82.41 145	81.98 142	83.72 195	88.08 222	63.74 222	92.70 146	93.77 73	79.30 93	77.61 165	87.57 257	58.19 188	94.08 257	73.91 199	86.68 145	93.33 176

CostFormer		82.33 146	81.15 151	85.86 97	89.01 186	68.46 80	82.39 372	93.01 111	75.59 161	80.25 131	81.57 335	72.03 39	94.96 214	79.06 160	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	65.72 330	75.45 190	92.83 143	56.11 215	98.89 21	64.10 303	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	70.19 266	85.82 283	70.66 44	97.67 56	72.19 221	66.52 330	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	73.12 206	84.20 82	94.36 99	38.04 370	95.73 174	84.12 107	86.81 139	91.33 237

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	73.12 206	84.20 82	94.36 99	38.04 370	95.73 174	84.12 107	86.81 139	91.33 237

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	73.12 206	84.20 82	94.36 99	38.04 370	95.73 174	84.12 107	86.81 139	91.33 237

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	74.05 185	67.42 306	92.49 149	49.46 292	97.65 60	70.80 234	91.68 76	95.33 70

MVS_111021_LR		82.02 153	81.52 147	83.51 204	88.42 209	62.88 255	89.77 273	88.93 310	76.78 143	75.55 188	93.10 132	50.31 281	95.38 197	83.82 111	87.02 136	92.26 217

PMMVS		81.98 154	82.04 140	81.78 256	89.76 164	56.17 371	91.13 227	90.69 226	77.96 119	80.09 133	93.57 127	46.33 326	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	74.86 199	91.67 174	69.98 48	94.92 217	71.76 224	64.75 347	91.29 242

EPP-MVSNet		81.79 156	81.52 147	82.61 231	88.77 192	60.21 323	93.02 130	93.66 81	68.52 304	72.90 225	90.39 196	72.19 38	94.96 214	74.93 191	79.29 233	92.67 197

WBMVS		81.67 157	80.98 158	83.72 195	93.07 74	69.40 56	94.33 64	93.05 109	76.84 141	72.05 243	84.14 301	74.49 19	93.88 271	72.76 212	68.09 317	87.88 290

test_vis1_n_1920		81.66 158	82.01 141	80.64 285	82.24 342	55.09 380	94.76 51	86.87 356	81.67 49	84.40 81	94.63 92	38.17 367	94.67 229	91.98 38	83.34 182	92.16 220

APD-MVS_3200maxsize		81.64 159	81.32 149	82.59 233	92.36 92	58.74 345	91.39 209	91.01 214	63.35 349	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	83.08 364	89.13 297	78.55 112	67.50 304	87.02 267	51.79 263	90.07 365	87.48 69	90.49 96	95.10 85

ACMMP		81.49 161	80.67 163	83.93 185	91.71 120	62.90 254	92.13 173	92.22 145	71.79 245	71.68 249	93.49 129	50.32 280	96.96 113	78.47 167	84.22 175	91.93 227
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	75.33 167	77.31 169	90.39 196	46.81 318	96.75 126	71.65 227	86.46 150	93.93 154

CDS-MVSNet		81.43 162	80.74 160	83.52 202	86.26 272	64.45 196	92.09 176	90.65 230	75.83 159	73.95 215	89.81 217	63.97 104	92.91 297	71.27 228	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	86.88 333	90.61 231	74.41 178	70.31 265	84.67 295	63.79 107	92.32 323	73.13 206	85.70 156	95.67 53

ECVR-MVS		81.29 165	80.38 171	84.01 184	88.39 211	61.96 275	92.56 159	86.79 358	77.66 128	76.63 176	91.42 178	46.34 325	95.24 206	74.36 196	89.23 110	94.85 96

guyue		81.23 166	80.57 167	83.21 218	86.64 262	61.85 278	92.52 160	92.78 119	78.69 109	74.92 198	89.42 221	50.07 284	95.35 198	80.79 143	79.31 232	92.42 206

IMVS_0403		81.19 167	79.88 178	85.13 129	88.54 195	64.75 186	88.84 299	90.80 220	76.73 146	75.21 193	90.18 202	54.22 239	96.21 151	73.47 201	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	71.27 263	70.93 256	89.18 226	72.47 33	96.04 162	65.62 292	76.89 258	91.49 233

Fast-Effi-MVS+		81.14 169	80.01 175	84.51 165	90.24 154	65.86 159	94.12 73	89.15 295	73.81 193	75.37 192	88.26 242	57.26 195	94.53 237	66.97 277	84.92 163	93.15 181

HQP-MVS		81.14 169	80.64 164	82.64 230	87.54 238	63.66 230	94.06 74	91.70 178	79.80 81	74.18 206	90.30 199	51.63 266	95.61 184	77.63 172	78.90 236	88.63 279

hse-mvs2		81.12 171	81.11 155	81.16 270	86.52 267	57.48 360	89.40 285	91.16 200	81.45 52	82.73 101	90.49 194	60.11 158	94.58 230	87.69 66	60.41 387	91.41 236

SR-MVS-dyc-post		81.06 172	80.70 162	82.15 247	92.02 105	58.56 348	90.90 232	90.45 234	62.76 356	78.89 148	94.46 95	51.26 273	95.61 184	78.77 165	86.77 142	92.28 213

HyFIR lowres test		81.03 173	79.56 185	85.43 112	87.81 232	68.11 92	90.18 262	90.01 261	70.65 276	72.95 224	86.06 279	63.61 112	94.50 239	75.01 190	79.75 224	93.67 165

nrg030		80.93 174	79.86 179	84.13 178	83.69 326	68.83 71	93.23 121	91.20 198	75.55 162	75.06 195	88.22 245	63.04 126	94.74 223	81.88 131	66.88 327	88.82 277

Vis-MVSNet		80.92 175	79.98 177	83.74 191	88.48 205	61.80 279	93.44 114	88.26 335	73.96 189	77.73 162	91.76 171	49.94 286	94.76 221	65.84 289	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	83.33 209	87.87 229	60.76 305	92.62 152	86.86 357	77.86 122	75.73 183	91.39 180	46.35 324	94.70 228	72.79 211	88.68 119	94.52 120

UWE-MVS		80.81 177	81.01 157	80.20 295	89.33 174	57.05 365	91.91 187	94.71 38	75.67 160	75.01 196	89.37 222	63.13 124	91.44 348	67.19 274	82.80 188	92.12 221

IMVS_0407		80.80 178	79.39 191	85.00 134	88.54 195	64.75 186	88.40 307	90.80 220	76.73 146	73.95 215	90.18 202	51.55 268	95.81 168	73.47 201	80.95 208	94.43 127

1314		80.70 179	78.95 199	85.94 94	87.77 235	67.56 107	87.91 316	92.55 134	72.17 232	67.44 305	93.09 133	50.27 282	97.04 103	71.68 226	87.64 130	93.23 178

AstraMVS		80.66 180	79.79 181	83.28 213	85.07 301	61.64 286	92.19 170	90.58 232	79.40 90	74.77 201	90.18 202	45.93 330	95.61 184	83.04 120	76.96 257	92.60 200

tpmrst		80.57 181	79.14 197	84.84 141	90.10 157	68.28 85	81.70 376	89.72 274	77.63 130	75.96 181	79.54 367	64.94 89	92.71 304	75.43 185	77.28 254	93.55 168

1112_ss		80.56 182	79.83 180	82.77 225	88.65 193	60.78 303	92.29 166	88.36 328	72.58 218	72.46 237	94.95 81	65.09 86	93.42 283	66.38 283	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	60.42 377	79.34 143	90.91 187	38.48 365	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	74.35 180	69.09 277	90.15 210	55.23 224	95.61 184	64.61 300	86.43 151	92.17 219

test_cas_vis1_n_1920		80.45 185	80.61 165	79.97 304	78.25 394	57.01 367	94.04 78	88.33 330	79.06 102	82.81 100	93.70 123	38.65 362	91.63 339	90.82 47	79.81 222	91.27 243

icg_test_0407_2		80.38 186	79.22 195	83.88 186	88.54 195	64.75 186	86.79 334	90.80 220	76.73 146	73.95 215	90.18 202	51.55 268	92.45 316	73.47 201	80.95 208	94.43 127

TAMVS		80.37 187	79.45 188	83.13 219	85.14 298	63.37 238	91.23 221	90.76 225	74.81 175	72.65 229	88.49 235	60.63 151	92.95 292	69.41 246	81.95 199	93.08 185

HQP_MVS		80.34 188	79.75 182	82.12 249	86.94 254	62.42 263	93.13 124	91.31 192	78.81 106	72.53 232	89.14 228	50.66 277	95.55 190	76.74 175	78.53 241	88.39 285

SDMVSNet		80.26 189	78.88 200	84.40 168	89.25 178	67.63 106	85.35 341	93.02 110	76.77 144	70.84 257	87.12 264	47.95 310	96.09 157	85.04 95	74.55 269	89.48 270

HPM-MVS_fast		80.25 190	79.55 187	82.33 239	91.55 125	59.95 328	91.32 216	89.16 294	65.23 334	74.71 203	93.07 135	47.81 312	95.74 173	74.87 194	88.23 122	91.31 241

ab-mvs		80.18 191	78.31 206	85.80 100	88.44 207	65.49 170	83.00 367	92.67 126	71.82 244	77.36 168	85.01 291	54.50 231	96.59 130	76.35 180	75.63 265	95.32 72

IS-MVSNet		80.14 192	79.41 189	82.33 239	87.91 227	60.08 326	91.97 185	88.27 333	72.90 213	71.44 253	91.73 173	61.44 143	93.66 278	62.47 317	86.53 148	93.24 177

test-LLR		80.10 193	79.56 185	81.72 258	86.93 256	61.17 295	92.70 146	91.54 183	71.51 259	75.62 185	86.94 268	53.83 242	92.38 318	72.21 219	84.76 166	91.60 231

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	72.43 238	90.51 193	37.05 382	94.06 259	68.06 261	86.00 152	93.90 159

UA-Net		80.02 195	79.65 183	81.11 273	89.33 174	57.72 355	86.33 338	89.00 309	77.44 133	81.01 119	89.15 227	59.33 172	95.90 166	61.01 324	84.28 173	89.73 266

test-mter		79.96 196	79.38 192	81.72 258	86.93 256	61.17 295	92.70 146	91.54 183	73.85 191	75.62 185	86.94 268	49.84 288	92.38 318	72.21 219	84.76 166	91.60 231

QAPM		79.95 197	77.39 227	87.64 34	89.63 166	71.41 20	93.30 119	93.70 79	65.34 333	67.39 308	91.75 172	47.83 311	98.96 16	57.71 340	89.81 106	92.54 203

UGNet		79.87 198	78.68 201	83.45 207	89.96 159	61.51 288	92.13 173	90.79 224	76.83 142	78.85 153	86.33 276	38.16 368	96.17 153	67.93 264	87.17 135	92.67 197
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	81.56 378	91.42 189	70.11 281	77.59 166	80.50 353	67.40 63	94.26 250	67.34 271	77.35 252	93.51 170

thres200		79.66 200	78.33 205	83.66 199	92.54 91	65.82 161	93.06 126	96.31 3	74.90 174	73.30 221	88.66 233	59.67 165	95.61 184	47.84 383	78.67 239	89.56 269

CPTT-MVS		79.59 201	79.16 196	80.89 283	91.54 126	59.80 330	92.10 175	88.54 325	60.42 377	72.96 223	93.28 131	48.27 303	92.80 301	78.89 164	86.50 149	90.06 259

Test_1112_low_res		79.56 202	78.60 203	82.43 235	88.24 218	60.39 319	92.09 176	87.99 340	72.10 234	71.84 245	87.42 259	64.62 94	93.04 288	65.80 290	77.30 253	93.85 161

tttt0517		79.50 203	78.53 204	82.41 238	87.22 247	61.43 292	89.75 274	94.76 35	69.29 291	67.91 297	88.06 249	72.92 29	95.63 181	62.91 313	73.90 279	90.16 258

reproduce_monomvs		79.49 204	79.11 198	80.64 285	92.91 78	61.47 291	91.17 226	93.28 98	83.09 31	64.04 338	82.38 321	66.19 72	94.57 232	81.19 140	57.71 395	85.88 334

FIs		79.47 205	79.41 189	79.67 312	85.95 280	59.40 336	91.68 200	93.94 68	78.06 118	68.96 283	88.28 240	66.61 69	91.77 335	66.20 286	74.99 268	87.82 291

SSM_0404		79.46 206	77.65 217	84.91 137	88.37 213	67.04 123	89.59 275	87.03 353	67.99 309	75.45 190	89.32 223	47.98 307	95.34 200	71.23 229	81.90 200	92.34 209

BH-RMVSNet		79.46 206	77.65 217	84.89 138	91.68 121	65.66 162	93.55 106	88.09 338	72.93 210	73.37 220	91.12 185	46.20 328	96.12 155	56.28 346	85.61 158	92.91 191

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	79.29 209	77.63 219	84.29 173	86.06 278	65.96 155	87.03 329	91.10 206	69.86 285	69.79 273	90.64 189	57.54 194	96.59 130	64.37 302	82.29 190	90.32 256
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 210	79.57 184	78.24 333	88.46 206	52.29 391	90.41 253	89.12 298	74.24 182	69.13 276	91.91 169	65.77 79	90.09 364	59.00 336	88.09 124	92.33 210

114514_t		79.17 211	77.67 216	83.68 197	95.32 29	65.53 168	92.85 140	91.60 182	63.49 347	67.92 296	90.63 191	46.65 321	95.72 179	67.01 276	83.54 180	89.79 264

FA-MVS(test-final)		79.12 212	77.23 229	84.81 145	90.54 147	63.98 216	81.35 381	91.71 175	71.09 267	74.85 200	82.94 314	52.85 253	97.05 100	67.97 262	81.73 203	93.41 172

SSM_0407		79.09 213	77.21 230	84.75 149	88.50 200	66.98 126	89.21 290	87.03 353	67.99 309	74.12 210	89.32 223	47.98 307	95.29 205	71.23 229	79.52 225	91.98 224

VPA-MVSNet		79.03 214	78.00 211	82.11 252	85.95 280	64.48 195	93.22 122	94.66 41	75.05 172	74.04 214	84.95 292	52.17 260	93.52 280	74.90 193	67.04 326	88.32 287

OPM-MVS		79.00 215	78.09 209	81.73 257	83.52 329	63.83 219	91.64 202	90.30 246	76.36 155	71.97 244	89.93 216	46.30 327	95.17 208	75.10 188	77.70 246	86.19 322
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 216	78.22 208	81.25 267	85.33 291	62.73 258	89.53 282	93.21 100	72.39 225	72.14 241	90.13 211	60.99 146	94.72 224	67.73 266	72.49 288	86.29 319

AdaColmap		78.94 217	77.00 234	84.76 148	96.34 17	65.86 159	92.66 151	87.97 342	62.18 361	70.56 259	92.37 153	43.53 342	97.35 79	64.50 301	82.86 185	91.05 246

GeoE		78.90 218	77.43 223	83.29 212	88.95 187	62.02 273	92.31 165	86.23 364	70.24 280	71.34 254	89.27 225	54.43 235	94.04 262	63.31 309	80.81 215	93.81 162

miper_enhance_ethall		78.86 219	77.97 212	81.54 262	88.00 226	65.17 176	91.41 205	89.15 295	75.19 170	68.79 286	83.98 304	67.17 64	92.82 299	72.73 213	65.30 337	86.62 316

VPNet		78.82 220	77.53 222	82.70 228	84.52 311	66.44 142	93.93 84	92.23 142	80.46 67	72.60 230	88.38 239	49.18 296	93.13 287	72.47 217	63.97 357	88.55 282

EPNet_dtu		78.80 221	79.26 194	77.43 341	88.06 223	49.71 407	91.96 186	91.95 160	77.67 127	76.56 178	91.28 183	58.51 183	90.20 362	56.37 345	80.95 208	92.39 207
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 222	77.43 223	82.88 223	92.21 97	64.49 193	92.05 179	96.28 4	73.48 200	71.75 247	88.26 242	60.07 160	95.32 201	45.16 396	77.58 248	88.83 275

TR-MVS		78.77 223	77.37 228	82.95 222	90.49 149	60.88 301	93.67 100	90.07 256	70.08 282	74.51 204	91.37 181	45.69 331	95.70 180	60.12 330	80.32 219	92.29 212

thres400		78.68 224	77.43 223	82.43 235	92.21 97	64.49 193	92.05 179	96.28 4	73.48 200	71.75 247	88.26 242	60.07 160	95.32 201	45.16 396	77.58 248	87.48 296

BH-untuned		78.68 224	77.08 231	83.48 206	89.84 161	63.74 222	92.70 146	88.59 323	71.57 256	66.83 315	88.65 234	51.75 264	95.39 196	59.03 335	84.77 165	91.32 240

OMC-MVS		78.67 226	77.91 215	80.95 280	85.76 286	57.40 362	88.49 305	88.67 320	73.85 191	72.43 238	92.10 161	49.29 295	94.55 236	72.73 213	77.89 244	90.91 250

tpm		78.58 227	77.03 232	83.22 216	85.94 282	64.56 191	83.21 363	91.14 204	78.31 115	73.67 218	79.68 365	64.01 103	92.09 329	66.07 287	71.26 298	93.03 187

OpenMVS	70.45 11	78.54 228	75.92 251	86.41 80	85.93 283	71.68 18	92.74 143	92.51 135	66.49 324	64.56 332	91.96 166	43.88 341	98.10 39	54.61 351	90.65 93	89.44 272

EPMVS		78.49 229	75.98 250	86.02 91	91.21 135	69.68 53	80.23 390	91.20 198	75.25 169	72.48 236	78.11 376	54.65 230	93.69 277	57.66 341	83.04 184	94.69 108

AUN-MVS		78.37 230	77.43 223	81.17 269	86.60 264	57.45 361	89.46 284	91.16 200	74.11 184	74.40 205	90.49 194	55.52 221	94.57 232	74.73 195	60.43 386	91.48 234

thres100view900		78.37 230	77.01 233	82.46 234	91.89 115	63.21 244	91.19 225	96.33 1	72.28 228	70.45 262	87.89 251	60.31 155	95.32 201	45.16 396	77.58 248	88.83 275

GA-MVS		78.33 232	76.23 246	84.65 157	83.65 327	66.30 146	91.44 204	90.14 254	76.01 157	70.32 264	84.02 303	42.50 346	94.72 224	70.98 232	77.00 256	92.94 190

cascas		78.18 233	75.77 253	85.41 113	87.14 249	69.11 64	92.96 133	91.15 203	66.71 322	70.47 260	86.07 278	37.49 376	96.48 139	70.15 240	79.80 223	90.65 252

UniMVSNet_NR-MVSNet		78.15 234	77.55 221	79.98 302	84.46 314	60.26 321	92.25 167	93.20 102	77.50 132	68.88 284	86.61 271	66.10 74	92.13 327	66.38 283	62.55 364	87.54 294

LuminaMVS		78.14 235	76.66 238	82.60 232	80.82 355	64.64 190	89.33 286	90.45 234	68.25 307	74.73 202	85.51 287	41.15 352	94.14 253	78.96 162	80.69 217	89.04 273

IMVS_0404		78.11 236	76.29 245	83.59 200	88.54 195	64.75 186	84.63 346	90.80 220	76.73 146	61.16 360	90.18 202	40.17 356	91.58 341	73.47 201	80.95 208	94.43 127

thres600view7		78.00 237	76.66 238	82.03 254	91.93 111	63.69 228	91.30 217	96.33 1	72.43 223	70.46 261	87.89 251	60.31 155	94.92 217	42.64 408	76.64 259	87.48 296

FC-MVSNet-test		77.99 238	78.08 210	77.70 336	84.89 304	55.51 377	90.27 259	93.75 77	76.87 139	66.80 316	87.59 256	65.71 80	90.23 361	62.89 314	73.94 277	87.37 299

Anonymous202405211		77.96 239	75.33 259	85.87 96	93.73 53	64.52 192	94.85 49	85.36 377	62.52 359	76.11 180	90.18 202	29.43 413	97.29 83	68.51 257	77.24 255	95.81 50

cl22		77.94 240	76.78 236	81.42 264	87.57 237	64.93 184	90.67 244	88.86 313	72.45 222	67.63 303	82.68 318	64.07 101	92.91 297	71.79 222	65.30 337	86.44 317

XXY-MVS		77.94 240	76.44 241	82.43 235	82.60 339	64.44 197	92.01 181	91.83 169	73.59 199	70.00 269	85.82 283	54.43 235	94.76 221	69.63 243	68.02 319	88.10 289

MS-PatchMatch		77.90 242	76.50 240	82.12 249	85.99 279	69.95 42	91.75 198	92.70 122	73.97 188	62.58 355	84.44 299	41.11 353	95.78 170	63.76 306	92.17 66	80.62 399

FMVSNet3		77.73 243	76.04 249	82.80 224	91.20 136	68.99 68	91.87 189	91.99 158	73.35 202	67.04 311	83.19 313	56.62 208	92.14 326	59.80 332	69.34 305	87.28 302

VortexMVS		77.62 244	76.44 241	81.13 271	88.58 194	63.73 224	91.24 220	91.30 196	77.81 123	65.76 321	81.97 327	49.69 290	93.72 275	76.40 179	65.26 340	85.94 332

miper_ehance_all_eth		77.60 245	76.44 241	81.09 277	85.70 288	64.41 200	90.65 245	88.64 322	72.31 226	67.37 309	82.52 319	64.77 93	92.64 310	70.67 236	65.30 337	86.24 321

UniMVSNet (Re)		77.58 246	76.78 236	79.98 302	84.11 320	60.80 302	91.76 196	93.17 104	76.56 152	69.93 272	84.78 294	63.32 119	92.36 320	64.89 299	62.51 366	86.78 310

PatchmatchNet		77.46 247	74.63 266	85.96 93	89.55 169	70.35 35	79.97 395	89.55 278	72.23 229	70.94 255	76.91 388	57.03 198	92.79 302	54.27 353	81.17 206	94.74 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 248	75.65 255	82.73 226	80.38 363	67.13 120	91.85 191	90.23 251	75.09 171	69.37 274	83.39 310	53.79 244	94.44 240	71.77 223	65.00 344	86.63 315

CHOSEN 280x420		77.35 249	76.95 235	78.55 328	87.07 251	62.68 259	69.71 427	82.95 399	68.80 300	71.48 252	87.27 263	66.03 75	84.00 412	76.47 178	82.81 187	88.95 274

PS-MVSNAJss		77.26 250	76.31 244	80.13 297	80.64 359	59.16 341	90.63 248	91.06 211	72.80 214	68.58 290	84.57 297	53.55 246	93.96 267	72.97 207	71.96 292	87.27 303

gg-mvs-nofinetune		77.18 251	74.31 273	85.80 100	91.42 128	68.36 82	71.78 421	94.72 37	49.61 420	77.12 172	45.92 449	77.41 8	93.98 266	67.62 267	93.16 55	95.05 88

WB-MVSnew		77.14 252	76.18 248	80.01 301	86.18 274	63.24 242	91.26 218	94.11 65	71.72 248	73.52 219	87.29 262	45.14 336	93.00 290	56.98 343	79.42 228	83.80 360

MVP-Stereo		77.12 253	76.23 246	79.79 309	81.72 347	66.34 145	89.29 287	90.88 215	70.56 277	62.01 358	82.88 315	49.34 293	94.13 254	65.55 294	93.80 43	78.88 414
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 254	75.37 257	82.20 245	89.25 178	62.11 272	82.06 373	89.09 300	76.77 144	70.84 257	87.12 264	41.43 351	95.01 212	67.23 273	74.55 269	89.48 270

MonoMVSNet		76.99 255	75.08 262	82.73 226	83.32 331	63.24 242	86.47 337	86.37 360	79.08 100	66.31 319	79.30 369	49.80 289	91.72 336	79.37 155	65.70 335	93.23 178

dmvs_re		76.93 256	75.36 258	81.61 260	87.78 234	60.71 309	80.00 394	87.99 340	79.42 89	69.02 280	89.47 220	46.77 319	94.32 244	63.38 308	74.45 272	89.81 263

X-MVStestdata		76.86 257	74.13 279	85.05 131	93.22 66	63.78 220	92.92 135	92.66 127	73.99 186	78.18 158	10.19 464	55.25 222	97.41 75	79.16 158	91.58 78	93.95 152

DU-MVS		76.86 257	75.84 252	79.91 305	82.96 335	60.26 321	91.26 218	91.54 183	76.46 154	68.88 284	86.35 274	56.16 213	92.13 327	66.38 283	62.55 364	87.35 300

Anonymous20240529		76.84 259	74.15 278	84.88 139	91.02 138	64.95 183	93.84 92	91.09 207	53.57 408	73.00 222	87.42 259	35.91 386	97.32 81	69.14 251	72.41 290	92.36 208

UWE-MVS-28		76.83 260	77.60 220	74.51 370	84.58 310	50.34 403	88.22 310	94.60 45	74.46 177	66.66 317	88.98 232	62.53 132	85.50 404	57.55 342	80.80 216	87.69 293

c3_l		76.83 260	75.47 256	80.93 281	85.02 302	64.18 210	90.39 254	88.11 337	71.66 249	66.65 318	81.64 333	63.58 115	92.56 311	69.31 248	62.86 361	86.04 327

WR-MVS		76.76 262	75.74 254	79.82 308	84.60 308	62.27 269	92.60 154	92.51 135	76.06 156	67.87 300	85.34 288	56.76 204	90.24 360	62.20 318	63.69 359	86.94 308

v1144		76.73 263	74.88 263	82.27 241	80.23 367	66.60 139	91.68 200	90.21 253	73.69 196	69.06 279	81.89 328	52.73 256	94.40 242	69.21 249	65.23 341	85.80 335

IterMVS-LS		76.49 264	75.18 261	80.43 289	84.49 313	62.74 257	90.64 246	88.80 315	72.40 224	65.16 327	81.72 331	60.98 147	92.27 324	67.74 265	64.65 349	86.29 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 265	74.55 269	82.19 246	79.14 381	67.82 100	90.26 260	89.42 283	73.75 194	68.63 289	81.89 328	51.31 271	94.09 256	71.69 225	64.84 345	84.66 352

Elysia		76.45 266	74.17 276	83.30 210	80.43 361	64.12 211	89.58 276	90.83 217	61.78 369	72.53 232	85.92 281	34.30 393	94.81 219	68.10 259	84.01 178	90.97 247

StellarMVS		76.45 266	74.17 276	83.30 210	80.43 361	64.12 211	89.58 276	90.83 217	61.78 369	72.53 232	85.92 281	34.30 393	94.81 219	68.10 259	84.01 178	90.97 247

mamba_0408		76.22 268	73.37 290	84.77 146	88.50 200	66.98 126	58.80 448	86.18 366	69.12 296	74.12 210	89.01 230	47.50 314	95.35 198	67.57 268	79.52 225	91.98 224

v148		76.19 269	74.47 271	81.36 265	80.05 369	64.44 197	91.75 198	90.23 251	73.68 197	67.13 310	80.84 348	55.92 218	93.86 274	68.95 253	61.73 375	85.76 338

Effi-MVS+-dtu		76.14 270	75.28 260	78.72 327	83.22 332	55.17 379	89.87 271	87.78 344	75.42 165	67.98 295	81.43 337	45.08 337	92.52 313	75.08 189	71.63 293	88.48 283

cl____		76.07 271	74.67 264	80.28 292	85.15 297	61.76 282	90.12 263	88.73 317	71.16 264	65.43 324	81.57 335	61.15 144	92.95 292	66.54 280	62.17 368	86.13 325

DIV-MVS_self_test		76.07 271	74.67 264	80.28 292	85.14 298	61.75 283	90.12 263	88.73 317	71.16 264	65.42 325	81.60 334	61.15 144	92.94 296	66.54 280	62.16 370	86.14 323

FMVSNet2		76.07 271	74.01 281	82.26 243	88.85 188	67.66 104	91.33 215	91.61 181	70.84 271	65.98 320	82.25 323	48.03 304	92.00 331	58.46 337	68.73 313	87.10 305

v144192		76.05 274	74.03 280	82.12 249	79.50 375	66.55 141	91.39 209	89.71 275	72.30 227	68.17 293	81.33 340	51.75 264	94.03 264	67.94 263	64.19 352	85.77 336

NR-MVSNet		76.05 274	74.59 267	80.44 288	82.96 335	62.18 271	90.83 236	91.73 173	77.12 137	60.96 362	86.35 274	59.28 173	91.80 334	60.74 325	61.34 379	87.35 300

v1192		75.98 276	73.92 282	82.15 247	79.73 371	66.24 148	91.22 222	89.75 269	72.67 216	68.49 291	81.42 338	49.86 287	94.27 248	67.08 275	65.02 343	85.95 330

FE-MVS		75.97 277	73.02 296	84.82 142	89.78 162	65.56 166	77.44 406	91.07 210	64.55 336	72.66 228	79.85 363	46.05 329	96.69 128	54.97 350	80.82 214	92.21 218

eth_miper_zixun_eth		75.96 278	74.40 272	80.66 284	84.66 307	63.02 248	89.28 288	88.27 333	71.88 240	65.73 322	81.65 332	59.45 169	92.81 300	68.13 258	60.53 384	86.14 323

TranMVSNet+NR-MVSNet		75.86 279	74.52 270	79.89 306	82.44 341	60.64 312	91.37 212	91.37 190	76.63 150	67.65 302	86.21 277	52.37 259	91.55 342	61.84 320	60.81 382	87.48 296

SCA		75.82 280	72.76 299	85.01 133	86.63 263	70.08 38	81.06 383	89.19 292	71.60 255	70.01 268	77.09 386	45.53 332	90.25 357	60.43 327	73.27 281	94.68 109

LPG-MVS_test		75.82 280	74.58 268	79.56 316	84.31 317	59.37 337	90.44 251	89.73 272	69.49 288	64.86 328	88.42 237	38.65 362	94.30 246	72.56 215	72.76 285	85.01 349

GBi-Net		75.65 282	73.83 283	81.10 274	88.85 188	65.11 178	90.01 267	90.32 242	70.84 271	67.04 311	80.25 358	48.03 304	91.54 343	59.80 332	69.34 305	86.64 312

test1		75.65 282	73.83 283	81.10 274	88.85 188	65.11 178	90.01 267	90.32 242	70.84 271	67.04 311	80.25 358	48.03 304	91.54 343	59.80 332	69.34 305	86.64 312

v1921920		75.63 284	73.49 288	82.06 253	79.38 376	66.35 144	91.07 230	89.48 279	71.98 235	67.99 294	81.22 343	49.16 298	93.90 270	66.56 279	64.56 350	85.92 333

ACMP	71.68 10	75.58 285	74.23 275	79.62 314	84.97 303	59.64 332	90.80 237	89.07 302	70.39 278	62.95 351	87.30 261	38.28 366	93.87 272	72.89 208	71.45 296	85.36 345
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 286	73.26 294	81.61 260	80.67 358	66.82 132	89.54 280	89.27 288	71.65 250	63.30 346	80.30 357	54.99 228	94.06 259	67.33 272	62.33 367	83.94 358

tpm cat1		75.30 287	72.21 308	84.58 162	88.52 199	67.77 101	78.16 404	88.02 339	61.88 367	68.45 292	76.37 392	60.65 150	94.03 264	53.77 356	74.11 275	91.93 227

PLC	68.80 14	75.23 288	73.68 286	79.86 307	92.93 77	58.68 346	90.64 246	88.30 331	60.90 374	64.43 336	90.53 192	42.38 347	94.57 232	56.52 344	76.54 260	86.33 318
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 289	72.98 297	81.88 255	79.20 378	66.00 153	90.75 240	89.11 299	71.63 254	67.41 307	81.22 343	47.36 316	93.87 272	65.46 295	64.72 348	85.77 336

Fast-Effi-MVS+-dtu		75.04 290	73.37 290	80.07 298	80.86 353	59.52 335	91.20 224	85.38 376	71.90 238	65.20 326	84.84 293	41.46 350	92.97 291	66.50 282	72.96 284	87.73 292

dp		75.01 291	72.09 309	83.76 190	89.28 177	66.22 149	79.96 396	89.75 269	71.16 264	67.80 301	77.19 385	51.81 262	92.54 312	50.39 366	71.44 297	92.51 205

TAPA-MVS	70.22 12	74.94 292	73.53 287	79.17 322	90.40 151	52.07 392	89.19 292	89.61 277	62.69 358	70.07 267	92.67 145	48.89 301	94.32 244	38.26 422	79.97 221	91.12 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 293	73.32 293	79.74 311	86.53 266	60.31 320	89.03 297	92.70 122	78.61 111	68.98 282	83.34 311	41.93 349	92.23 325	52.77 360	65.97 333	86.69 311

SSM_04072		74.86 294	73.37 290	79.35 319	88.50 200	66.98 126	58.80 448	86.18 366	69.12 296	74.12 210	89.01 230	47.50 314	79.09 433	67.57 268	79.52 225	91.98 224

v10		74.77 295	72.54 305	81.46 263	80.33 365	66.71 136	89.15 293	89.08 301	70.94 269	63.08 349	79.86 362	52.52 257	94.04 262	65.70 291	62.17 368	83.64 361

XVG-OURS-SEG-HR		74.70 296	73.08 295	79.57 315	78.25 394	57.33 363	80.49 386	87.32 348	63.22 351	68.76 287	90.12 213	44.89 338	91.59 340	70.55 238	74.09 276	89.79 264

ACMM	69.62 13	74.34 297	72.73 301	79.17 322	84.25 319	57.87 353	90.36 256	89.93 263	63.17 353	65.64 323	86.04 280	37.79 374	94.10 255	65.89 288	71.52 295	85.55 341
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 298	72.30 307	80.32 290	91.49 127	61.66 285	90.85 235	80.72 405	56.67 400	63.85 341	90.64 189	46.75 320	90.84 351	53.79 355	75.99 264	88.47 284

XVG-OURS		74.25 299	72.46 306	79.63 313	78.45 392	57.59 359	80.33 388	87.39 346	63.86 343	68.76 287	89.62 219	40.50 355	91.72 336	69.00 252	74.25 274	89.58 267

test_fmvs1		74.07 300	73.69 285	75.22 361	78.91 385	47.34 420	89.06 296	74.69 421	63.68 346	79.41 142	91.59 176	24.36 424	87.77 387	85.22 92	76.26 262	90.55 255

CVMVSNet		74.04 301	74.27 274	73.33 380	85.33 291	43.94 434	89.53 282	88.39 327	54.33 407	70.37 263	90.13 211	49.17 297	84.05 410	61.83 321	79.36 230	91.99 223

Baseline_NR-MVSNet		73.99 302	72.83 298	77.48 340	80.78 356	59.29 340	91.79 193	84.55 385	68.85 299	68.99 281	80.70 349	56.16 213	92.04 330	62.67 315	60.98 381	81.11 393

pmmvs4		73.92 303	71.81 313	80.25 294	79.17 379	65.24 174	87.43 325	87.26 351	67.64 315	63.46 344	83.91 305	48.96 300	91.53 346	62.94 312	65.49 336	83.96 357

D2MVS		73.80 304	72.02 310	79.15 324	79.15 380	62.97 249	88.58 304	90.07 256	72.94 209	59.22 372	78.30 373	42.31 348	92.70 306	65.59 293	72.00 291	81.79 388

SD_0403		73.79 305	73.48 289	74.69 367	85.33 291	45.56 430	83.80 353	85.57 375	76.55 153	62.96 350	88.45 236	50.62 279	87.59 391	48.80 376	79.28 234	90.92 249

CR-MVSNet		73.79 305	70.82 321	82.70 228	83.15 333	67.96 95	70.25 424	84.00 390	73.67 198	69.97 270	72.41 409	57.82 191	89.48 370	52.99 359	73.13 282	90.64 253

test_djsdf		73.76 307	72.56 304	77.39 342	77.00 406	53.93 385	89.07 294	90.69 226	65.80 328	63.92 339	82.03 326	43.14 345	92.67 307	72.83 209	68.53 314	85.57 340

pmmvs5		73.35 308	71.52 315	78.86 326	78.64 389	60.61 313	91.08 228	86.90 355	67.69 312	63.32 345	83.64 306	44.33 340	90.53 354	62.04 319	66.02 332	85.46 343

Anonymous20231211		73.08 309	70.39 325	81.13 271	90.62 146	63.33 239	91.40 207	90.06 258	51.84 413	64.46 335	80.67 351	36.49 384	94.07 258	63.83 305	64.17 353	85.98 329

tt0805		73.07 310	70.73 322	80.07 298	78.37 393	57.05 365	87.78 319	92.18 149	61.23 373	67.04 311	86.49 273	31.35 406	94.58 230	65.06 298	67.12 325	88.57 281

miper_lstm_enhance		73.05 311	71.73 314	77.03 347	83.80 324	58.32 350	81.76 374	88.88 311	69.80 286	61.01 361	78.23 375	57.19 196	87.51 392	65.34 296	59.53 389	85.27 348

jajsoiax		73.05 311	71.51 316	77.67 337	77.46 403	54.83 381	88.81 300	90.04 259	69.13 295	62.85 353	83.51 308	31.16 407	92.75 303	70.83 233	69.80 301	85.43 344

LCM-MVSNet-Re		72.93 313	71.84 312	76.18 356	88.49 204	48.02 415	80.07 393	70.17 436	73.96 189	52.25 406	80.09 361	49.98 285	88.24 381	67.35 270	84.23 174	92.28 213

pm-mvs1		72.89 314	71.09 318	78.26 332	79.10 382	57.62 357	90.80 237	89.30 287	67.66 313	62.91 352	81.78 330	49.11 299	92.95 292	60.29 329	58.89 392	84.22 356

tpmvs		72.88 315	69.76 331	82.22 244	90.98 139	67.05 122	78.22 403	88.30 331	63.10 354	64.35 337	74.98 399	55.09 227	94.27 248	43.25 402	69.57 304	85.34 346

test0.0.03 1		72.76 316	72.71 302	72.88 384	80.25 366	47.99 416	91.22 222	89.45 281	71.51 259	62.51 356	87.66 254	53.83 242	85.06 406	50.16 368	67.84 323	85.58 339

UniMVSNet_ETH3D		72.74 317	70.53 324	79.36 318	78.62 390	56.64 369	85.01 343	89.20 291	63.77 344	64.84 330	84.44 299	34.05 395	91.86 333	63.94 304	70.89 300	89.57 268

mvs_tets		72.71 318	71.11 317	77.52 338	77.41 404	54.52 383	88.45 306	89.76 268	68.76 302	62.70 354	83.26 312	29.49 412	92.71 304	70.51 239	69.62 303	85.34 346

FMVSNet1		72.71 318	69.91 329	81.10 274	83.60 328	65.11 178	90.01 267	90.32 242	63.92 342	63.56 343	80.25 358	36.35 385	91.54 343	54.46 352	66.75 328	86.64 312

test_fmvs1_n		72.69 320	71.92 311	74.99 365	71.15 426	47.08 422	87.34 327	75.67 416	63.48 348	78.08 160	91.17 184	20.16 438	87.87 384	84.65 101	75.57 266	90.01 261

IterMVS		72.65 321	70.83 319	78.09 334	82.17 343	62.96 250	87.64 323	86.28 362	71.56 257	60.44 365	78.85 371	45.42 334	86.66 396	63.30 310	61.83 372	84.65 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 322	72.74 300	72.10 392	87.87 229	49.45 409	88.07 312	89.01 305	72.91 211	63.11 347	88.10 246	63.63 110	85.54 401	32.73 437	69.23 308	81.32 391

PatchMatch-RL		72.06 323	69.98 326	78.28 331	89.51 170	55.70 376	83.49 356	83.39 397	61.24 372	63.72 342	82.76 316	34.77 390	93.03 289	53.37 358	77.59 247	86.12 326

PVSNet_0	68.08 15	71.81 324	68.32 340	82.27 241	84.68 305	62.31 268	88.68 302	90.31 245	75.84 158	57.93 384	80.65 352	37.85 373	94.19 251	69.94 241	29.05 452	90.31 257

MIMVSNet		71.64 325	68.44 338	81.23 268	81.97 346	64.44 197	73.05 418	88.80 315	69.67 287	64.59 331	74.79 401	32.79 398	87.82 385	53.99 354	76.35 261	91.42 235

test_vis1_n		71.63 326	70.73 322	74.31 374	69.63 432	47.29 421	86.91 331	72.11 429	63.21 352	75.18 194	90.17 208	20.40 436	85.76 400	84.59 102	74.42 273	89.87 262

IterMVS-SCA-FT		71.55 327	69.97 327	76.32 354	81.48 349	60.67 311	87.64 323	85.99 369	66.17 326	59.50 370	78.88 370	45.53 332	83.65 414	62.58 316	61.93 371	84.63 355

v7n		71.31 328	68.65 335	79.28 320	76.40 408	60.77 304	86.71 335	89.45 281	64.17 341	58.77 377	78.24 374	44.59 339	93.54 279	57.76 339	61.75 374	83.52 364

anonymousdsp		71.14 329	69.37 333	76.45 353	72.95 421	54.71 382	84.19 350	88.88 311	61.92 366	62.15 357	79.77 364	38.14 369	91.44 348	68.90 254	67.45 324	83.21 370

F-COLMAP		70.66 330	68.44 338	77.32 343	86.37 271	55.91 374	88.00 314	86.32 361	56.94 398	57.28 388	88.07 248	33.58 396	92.49 314	51.02 363	68.37 315	83.55 362

WR-MVS_H		70.59 331	69.94 328	72.53 386	81.03 352	51.43 396	87.35 326	92.03 157	67.38 316	60.23 367	80.70 349	55.84 219	83.45 416	46.33 391	58.58 394	82.72 377

CP-MVSNet		70.50 332	69.91 329	72.26 389	80.71 357	51.00 400	87.23 328	90.30 246	67.84 311	59.64 369	82.69 317	50.23 283	82.30 424	51.28 362	59.28 390	83.46 366

RPMNet		70.42 333	65.68 354	84.63 160	83.15 333	67.96 95	70.25 424	90.45 234	46.83 429	69.97 270	65.10 432	56.48 212	95.30 204	35.79 427	73.13 282	90.64 253

testing3		70.38 334	70.83 319	69.03 404	85.82 285	43.93 435	90.72 243	90.56 233	68.06 308	60.24 366	86.82 270	64.83 91	84.12 408	26.33 445	64.10 354	79.04 412

tfpnnormal		70.10 335	67.36 344	78.32 330	83.45 330	60.97 300	88.85 298	92.77 120	64.85 335	60.83 363	78.53 372	43.52 343	93.48 281	31.73 440	61.70 376	80.52 400

TransMVSNet (Re)		70.07 336	67.66 342	77.31 344	80.62 360	59.13 342	91.78 195	84.94 381	65.97 327	60.08 368	80.44 354	50.78 276	91.87 332	48.84 375	45.46 425	80.94 395

CL-MVSNet_self_test		69.92 337	68.09 341	75.41 359	73.25 420	55.90 375	90.05 266	89.90 264	69.96 283	61.96 359	76.54 389	51.05 275	87.64 388	49.51 372	50.59 415	82.70 379

DP-MVS		69.90 338	66.48 346	80.14 296	95.36 28	62.93 251	89.56 278	76.11 414	50.27 419	57.69 386	85.23 289	39.68 358	95.73 174	33.35 432	71.05 299	81.78 389

PS-CasMVS		69.86 339	69.13 334	72.07 393	80.35 364	50.57 402	87.02 330	89.75 269	67.27 317	59.19 373	82.28 322	46.58 322	82.24 425	50.69 365	59.02 391	83.39 368

Syy-MVS		69.65 340	69.52 332	70.03 400	87.87 229	43.21 436	88.07 312	89.01 305	72.91 211	63.11 347	88.10 246	45.28 335	85.54 401	22.07 450	69.23 308	81.32 391

MSDG		69.54 341	65.73 353	80.96 279	85.11 300	63.71 226	84.19 350	83.28 398	56.95 397	54.50 395	84.03 302	31.50 404	96.03 163	42.87 406	69.13 310	83.14 372

PEN-MVS		69.46 342	68.56 336	72.17 391	79.27 377	49.71 407	86.90 332	89.24 289	67.24 320	59.08 374	82.51 320	47.23 317	83.54 415	48.42 378	57.12 396	83.25 369

LS3D		69.17 343	66.40 348	77.50 339	91.92 112	56.12 372	85.12 342	80.37 407	46.96 427	56.50 390	87.51 258	37.25 377	93.71 276	32.52 439	79.40 229	82.68 380

PatchT		69.11 344	65.37 358	80.32 290	82.07 345	63.68 229	67.96 434	87.62 345	50.86 417	69.37 274	65.18 431	57.09 197	88.53 377	41.59 411	66.60 329	88.74 278

KD-MVS_2432*1600		69.03 345	66.37 349	77.01 348	85.56 289	61.06 298	81.44 379	90.25 249	67.27 317	58.00 382	76.53 390	54.49 232	87.63 389	48.04 380	35.77 443	82.34 383

miper_refine_blended		69.03 345	66.37 349	77.01 348	85.56 289	61.06 298	81.44 379	90.25 249	67.27 317	58.00 382	76.53 390	54.49 232	87.63 389	48.04 380	35.77 443	82.34 383

mvsany_test1		68.77 347	68.56 336	69.39 402	73.57 419	45.88 429	80.93 384	60.88 450	59.65 383	71.56 250	90.26 201	43.22 344	75.05 437	74.26 198	62.70 363	87.25 304

ACMH	63.93 17	68.62 348	64.81 360	80.03 300	85.22 296	63.25 241	87.72 320	84.66 383	60.83 375	51.57 410	79.43 368	27.29 419	94.96 214	41.76 409	64.84 345	81.88 387
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 349	65.41 357	77.96 335	78.69 388	62.93 251	89.86 272	89.17 293	60.55 376	50.27 415	77.73 380	22.60 432	94.06 259	47.18 387	72.65 287	76.88 425

ADS-MVSNet		68.54 350	64.38 367	81.03 278	88.06 223	66.90 131	68.01 432	84.02 389	57.57 391	64.48 333	69.87 419	38.68 360	89.21 372	40.87 413	67.89 321	86.97 306

DTE-MVSNet		68.46 351	67.33 345	71.87 395	77.94 398	49.00 413	86.16 339	88.58 324	66.36 325	58.19 379	82.21 324	46.36 323	83.87 413	44.97 399	55.17 403	82.73 376

mmtdpeth		68.33 352	66.37 349	74.21 375	82.81 338	51.73 393	84.34 348	80.42 406	67.01 321	71.56 250	68.58 423	30.52 410	92.35 321	75.89 182	36.21 441	78.56 419

our_test_3		68.29 353	64.69 362	79.11 325	78.92 383	64.85 185	88.40 307	85.06 379	60.32 379	52.68 404	76.12 394	40.81 354	89.80 369	44.25 401	55.65 401	82.67 381

Patchmatch-RL test		68.17 354	64.49 365	79.19 321	71.22 425	53.93 385	70.07 426	71.54 433	69.22 292	56.79 389	62.89 436	56.58 209	88.61 374	69.53 245	52.61 410	95.03 90

XVG-ACMP-BASELINE		68.04 355	65.53 356	75.56 358	74.06 418	52.37 390	78.43 400	85.88 370	62.03 364	58.91 376	81.21 345	20.38 437	91.15 350	60.69 326	68.18 316	83.16 371

FMVSNet5		68.04 355	65.66 355	75.18 363	84.43 315	57.89 352	83.54 355	86.26 363	61.83 368	53.64 401	73.30 404	37.15 380	85.08 405	48.99 374	61.77 373	82.56 382

ppachtmachnet_test		67.72 357	63.70 369	79.77 310	78.92 383	66.04 152	88.68 302	82.90 400	60.11 381	55.45 392	75.96 395	39.19 359	90.55 353	39.53 417	52.55 411	82.71 378

ACMH+	65.35 16	67.65 358	64.55 363	76.96 350	84.59 309	57.10 364	88.08 311	80.79 404	58.59 389	53.00 403	81.09 347	26.63 421	92.95 292	46.51 389	61.69 377	80.82 396

pmmvs6		67.57 359	64.76 361	76.00 357	72.82 423	53.37 387	88.71 301	86.78 359	53.19 409	57.58 387	78.03 377	35.33 389	92.41 317	55.56 348	54.88 405	82.21 385

Anonymous20231206		67.53 360	65.78 352	72.79 385	74.95 414	47.59 418	88.23 309	87.32 348	61.75 371	58.07 381	77.29 383	37.79 374	87.29 394	42.91 404	63.71 358	83.48 365

Patchmtry		67.53 360	63.93 368	78.34 329	82.12 344	64.38 201	68.72 429	84.00 390	48.23 426	59.24 371	72.41 409	57.82 191	89.27 371	46.10 392	56.68 400	81.36 390

USDC		67.43 362	64.51 364	76.19 355	77.94 398	55.29 378	78.38 401	85.00 380	73.17 204	48.36 423	80.37 355	21.23 434	92.48 315	52.15 361	64.02 356	80.81 397

ADS-MVSNet2		66.90 363	63.44 371	77.26 345	88.06 223	60.70 310	68.01 432	75.56 418	57.57 391	64.48 333	69.87 419	38.68 360	84.10 409	40.87 413	67.89 321	86.97 306

CMPMVS	48.56 21	66.77 364	64.41 366	73.84 377	70.65 429	50.31 404	77.79 405	85.73 373	45.54 432	44.76 433	82.14 325	35.40 388	90.14 363	63.18 311	74.54 271	81.07 394
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 365	62.92 374	76.80 352	76.51 407	57.77 354	89.22 289	83.41 396	55.48 404	53.86 399	77.84 378	26.28 422	93.95 268	34.90 429	68.76 312	78.68 417

LTVRE_ROB	59.60 19	66.27 366	63.54 370	74.45 371	84.00 322	51.55 395	67.08 436	83.53 394	58.78 387	54.94 394	80.31 356	34.54 391	93.23 285	40.64 415	68.03 318	78.58 418
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		66.06 367	62.45 377	76.88 351	81.42 351	54.45 384	57.49 450	88.67 320	49.36 421	63.86 340	46.86 448	56.06 216	90.25 357	49.53 371	68.83 311	85.95 330

Patchmatch-test		65.86 368	60.94 383	80.62 287	83.75 325	58.83 344	58.91 447	75.26 420	44.50 435	50.95 414	77.09 386	58.81 180	87.90 383	35.13 428	64.03 355	95.12 84

UnsupCasMVSNet_eth		65.79 369	63.10 372	73.88 376	70.71 428	50.29 405	81.09 382	89.88 265	72.58 218	49.25 420	74.77 402	32.57 400	87.43 393	55.96 347	41.04 433	83.90 359

test_fmvs2		65.78 370	64.84 359	68.60 406	66.54 438	41.71 438	83.27 360	69.81 437	54.38 406	67.91 297	84.54 298	15.35 443	81.22 429	75.65 184	66.16 331	82.88 373

dmvs_testset		65.55 371	66.45 347	62.86 418	79.87 370	22.35 464	76.55 408	71.74 431	77.42 135	55.85 391	87.77 253	51.39 270	80.69 430	31.51 443	65.92 334	85.55 341

pmmvs-eth3d		65.53 372	62.32 378	75.19 362	69.39 433	59.59 333	82.80 368	83.43 395	62.52 359	51.30 412	72.49 407	32.86 397	87.16 395	55.32 349	50.73 414	78.83 415

mamv4		65.18 373	67.43 343	58.44 422	77.88 400	49.36 412	69.40 428	70.99 435	48.31 425	57.78 385	85.53 286	59.01 178	51.88 460	73.67 200	64.32 351	74.07 430

SixPastTwentyTwo		64.92 374	61.78 381	74.34 373	78.74 387	49.76 406	83.42 359	79.51 410	62.86 355	50.27 415	77.35 381	30.92 409	90.49 355	45.89 393	47.06 420	82.78 374

OurMVSNet-221017-0		64.68 375	62.17 379	72.21 390	76.08 411	47.35 419	80.67 385	81.02 403	56.19 401	51.60 409	79.66 366	27.05 420	88.56 376	53.60 357	53.63 408	80.71 398

test_0402		64.54 376	61.09 382	74.92 366	84.10 321	60.75 306	87.95 315	79.71 409	52.03 411	52.41 405	77.20 384	32.21 402	91.64 338	23.14 448	61.03 380	72.36 436

testgi		64.48 377	62.87 375	69.31 403	71.24 424	40.62 441	85.49 340	79.92 408	65.36 332	54.18 397	83.49 309	23.74 427	84.55 407	41.60 410	60.79 383	82.77 375

RPSCF		64.24 378	61.98 380	71.01 398	76.10 410	45.00 431	75.83 413	75.94 415	46.94 428	58.96 375	84.59 296	31.40 405	82.00 426	47.76 385	60.33 388	86.04 327

EU-MVSNet		64.01 379	63.01 373	67.02 412	74.40 417	38.86 447	83.27 360	86.19 365	45.11 433	54.27 396	81.15 346	36.91 383	80.01 432	48.79 377	57.02 397	82.19 386

test20.03		63.83 380	62.65 376	67.38 411	70.58 430	39.94 443	86.57 336	84.17 387	63.29 350	51.86 408	77.30 382	37.09 381	82.47 422	38.87 421	54.13 407	79.73 406

sc_t1		63.81 381	59.39 389	77.10 346	77.62 401	56.03 373	84.32 349	73.56 425	46.66 430	58.22 378	73.06 405	23.28 430	90.62 352	50.93 364	46.84 421	84.64 354

MDA-MVSNet_test_wron		63.78 382	60.16 385	74.64 368	78.15 396	60.41 317	83.49 356	84.03 388	56.17 403	39.17 443	71.59 415	37.22 378	83.24 419	42.87 406	48.73 417	80.26 403

YYNet1		63.76 383	60.14 386	74.62 369	78.06 397	60.19 324	83.46 358	83.99 392	56.18 402	39.25 442	71.56 416	37.18 379	83.34 417	42.90 405	48.70 418	80.32 402

K. test v3		63.09 384	59.61 388	73.53 379	76.26 409	49.38 411	83.27 360	77.15 413	64.35 338	47.77 425	72.32 411	28.73 414	87.79 386	49.93 370	36.69 440	83.41 367

COLMAP_ROB	57.96 20	62.98 385	59.65 387	72.98 383	81.44 350	53.00 389	83.75 354	75.53 419	48.34 424	48.81 422	81.40 339	24.14 425	90.30 356	32.95 434	60.52 385	75.65 428
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 386	59.08 390	71.10 397	67.19 436	48.72 414	83.91 352	85.23 378	50.38 418	47.84 424	71.22 418	20.74 435	85.51 403	46.47 390	58.75 393	79.06 411

tt0320		61.85 387	57.45 396	75.03 364	77.49 402	57.60 358	82.74 369	73.65 424	43.65 439	53.65 400	68.18 425	25.47 423	88.66 373	45.56 395	46.68 422	78.81 416

AllTest		61.66 388	58.06 392	72.46 387	79.57 372	51.42 397	80.17 391	68.61 439	51.25 415	45.88 427	81.23 341	19.86 439	86.58 397	38.98 419	57.01 398	79.39 408

UnsupCasMVSNet_bld		61.60 389	57.71 393	73.29 381	68.73 434	51.64 394	78.61 399	89.05 303	57.20 396	46.11 426	61.96 439	28.70 415	88.60 375	50.08 369	38.90 438	79.63 407

MDA-MVSNet-bldmvs		61.54 390	57.70 394	73.05 382	79.53 374	57.00 368	83.08 364	81.23 402	57.57 391	34.91 447	72.45 408	32.79 398	86.26 399	35.81 426	41.95 431	75.89 427

tt0320-xc		61.51 391	56.89 399	75.37 360	78.50 391	58.61 347	82.61 370	71.27 434	44.31 436	53.17 402	68.03 427	23.38 428	88.46 378	47.77 384	43.00 430	79.03 413

mvs5depth		61.03 392	57.65 395	71.18 396	67.16 437	47.04 424	72.74 419	77.49 411	57.47 394	60.52 364	72.53 406	22.84 431	88.38 379	49.15 373	38.94 437	78.11 422

KD-MVS_self_test		60.87 393	58.60 391	67.68 409	66.13 439	39.93 444	75.63 415	84.70 382	57.32 395	49.57 418	68.45 424	29.55 411	82.87 420	48.09 379	47.94 419	80.25 404

kuosan		60.86 394	60.24 384	62.71 419	81.57 348	46.43 426	75.70 414	85.88 370	57.98 390	48.95 421	69.53 421	58.42 184	76.53 435	28.25 444	35.87 442	65.15 443

TinyColmap		60.32 395	56.42 402	72.00 394	78.78 386	53.18 388	78.36 402	75.64 417	52.30 410	41.59 441	75.82 397	14.76 446	88.35 380	35.84 425	54.71 406	74.46 429

MVS-HIRNet		60.25 396	55.55 403	74.35 372	84.37 316	56.57 370	71.64 422	74.11 422	34.44 446	45.54 431	42.24 454	31.11 408	89.81 367	40.36 416	76.10 263	76.67 426

MIMVSNet1		60.16 397	57.33 397	68.67 405	69.71 431	44.13 433	78.92 398	84.21 386	55.05 405	44.63 434	71.85 413	23.91 426	81.54 428	32.63 438	55.03 404	80.35 401

PM-MVS		59.40 398	56.59 400	67.84 407	63.63 442	41.86 437	76.76 407	63.22 447	59.01 386	51.07 413	72.27 412	11.72 450	83.25 418	61.34 322	50.28 416	78.39 420

new-patchmatchnet		59.30 399	56.48 401	67.79 408	65.86 440	44.19 432	82.47 371	81.77 401	59.94 382	43.65 437	66.20 430	27.67 418	81.68 427	39.34 418	41.40 432	77.50 424

test_vis1_rt		59.09 400	57.31 398	64.43 415	68.44 435	46.02 428	83.05 366	48.63 459	51.96 412	49.57 418	63.86 435	16.30 441	80.20 431	71.21 231	62.79 362	67.07 442

test_fmvs3		56.82 401	54.86 405	62.69 420	53.59 453	35.47 450	75.87 412	65.64 444	43.91 437	55.10 393	71.43 417	6.91 458	74.40 440	68.64 256	52.63 409	78.20 421

DSMNet-mixed		56.78 402	54.44 406	63.79 416	63.21 443	29.44 459	64.43 439	64.10 446	42.12 443	51.32 411	71.60 414	31.76 403	75.04 438	36.23 424	65.20 342	86.87 309

pmmvs3		55.51 403	51.50 409	67.53 410	57.90 451	50.93 401	80.37 387	73.66 423	40.63 444	44.15 436	64.75 433	16.30 441	78.97 434	44.77 400	40.98 435	72.69 434

TDRefinement		55.28 404	51.58 408	66.39 413	59.53 450	46.15 427	76.23 410	72.80 426	44.60 434	42.49 439	76.28 393	15.29 444	82.39 423	33.20 433	43.75 427	70.62 438

dongtai		55.18 405	55.46 404	54.34 430	76.03 412	36.88 448	76.07 411	84.61 384	51.28 414	43.41 438	64.61 434	56.56 210	67.81 448	18.09 453	28.50 453	58.32 446

LF4IMVS		54.01 406	52.12 407	59.69 421	62.41 445	39.91 445	68.59 430	68.28 441	42.96 441	44.55 435	75.18 398	14.09 448	68.39 447	41.36 412	51.68 412	70.78 437

ttmdpeth		53.34 407	49.96 410	63.45 417	62.07 447	40.04 442	72.06 420	65.64 444	42.54 442	51.88 407	77.79 379	13.94 449	76.48 436	32.93 435	30.82 451	73.84 431

MVStest1		51.35 408	46.89 412	64.74 414	65.06 441	51.10 399	67.33 435	72.58 427	30.20 450	35.30 445	74.82 400	27.70 417	69.89 445	24.44 447	24.57 454	73.22 432

N_pmnet		50.55 409	49.11 411	54.88 428	77.17 405	4.02 472	84.36 347	2.00 470	48.59 422	45.86 429	68.82 422	32.22 401	82.80 421	31.58 441	51.38 413	77.81 423

new_pmnet		49.31 410	46.44 413	57.93 423	62.84 444	40.74 440	68.47 431	62.96 448	36.48 445	35.09 446	57.81 443	14.97 445	72.18 442	32.86 436	46.44 423	60.88 445

mvsany_test3		48.86 411	46.35 414	56.41 424	46.00 459	31.67 455	62.26 441	47.25 460	43.71 438	45.54 431	68.15 426	10.84 451	64.44 456	57.95 338	35.44 445	73.13 433

test_f		46.58 412	43.45 416	55.96 425	45.18 460	32.05 454	61.18 442	49.49 458	33.39 447	42.05 440	62.48 438	7.00 457	65.56 452	47.08 388	43.21 429	70.27 439

WB-MVS		46.23 413	44.94 415	50.11 433	62.13 446	21.23 466	76.48 409	55.49 452	45.89 431	35.78 444	61.44 441	35.54 387	72.83 441	9.96 460	21.75 455	56.27 448

FPMVS		45.64 414	43.10 418	53.23 431	51.42 456	36.46 449	64.97 438	71.91 430	29.13 451	27.53 451	61.55 440	9.83 453	65.01 454	16.00 457	55.58 402	58.22 447

SSC-MVS		44.51 415	43.35 417	47.99 437	61.01 449	18.90 468	74.12 417	54.36 453	43.42 440	34.10 448	60.02 442	34.42 392	70.39 444	9.14 462	19.57 456	54.68 449

EGC-MVSNET		42.35 416	38.09 419	55.11 427	74.57 415	46.62 425	71.63 423	55.77 451	0.04 465	0.24 466	62.70 437	14.24 447	74.91 439	17.59 454	46.06 424	43.80 451

LCM-MVSNet		40.54 417	35.79 422	54.76 429	36.92 466	30.81 456	51.41 453	69.02 438	22.07 453	24.63 453	45.37 450	4.56 462	65.81 451	33.67 431	34.50 446	67.67 440

APD_test1		40.50 418	37.31 421	50.09 434	51.88 454	35.27 451	59.45 446	52.59 455	21.64 454	26.12 452	57.80 444	4.56 462	66.56 450	22.64 449	39.09 436	48.43 450

test_vis3_rt		40.46 419	37.79 420	48.47 436	44.49 461	33.35 453	66.56 437	32.84 467	32.39 448	29.65 449	39.13 457	3.91 465	68.65 446	50.17 367	40.99 434	43.40 452

ANet_high		40.27 420	35.20 423	55.47 426	34.74 467	34.47 452	63.84 440	71.56 432	48.42 423	18.80 456	41.08 455	9.52 454	64.45 455	20.18 451	8.66 463	67.49 441

test_method		38.59 421	35.16 424	48.89 435	54.33 452	21.35 465	45.32 456	53.71 454	7.41 462	28.74 450	51.62 446	8.70 455	52.87 459	33.73 430	32.89 447	72.47 435

PMMVS2		37.93 422	33.61 425	50.92 432	46.31 458	24.76 462	60.55 445	50.05 456	28.94 452	20.93 454	47.59 447	4.41 464	65.13 453	25.14 446	18.55 458	62.87 444

Gipuma		34.91 423	31.44 426	45.30 438	70.99 427	39.64 446	19.85 460	72.56 428	20.10 456	16.16 460	21.47 461	5.08 461	71.16 443	13.07 458	43.70 428	25.08 458
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 424	29.47 427	42.67 440	41.89 463	30.81 456	52.07 451	43.45 461	15.45 457	18.52 457	44.82 451	2.12 466	58.38 457	16.05 455	30.87 449	38.83 453

APD_test2		32.77 424	29.47 427	42.67 440	41.89 463	30.81 456	52.07 451	43.45 461	15.45 457	18.52 457	44.82 451	2.12 466	58.38 457	16.05 455	30.87 449	38.83 453

PMVS	26.43 22	31.84 426	28.16 429	42.89 439	25.87 469	27.58 460	50.92 454	49.78 457	21.37 455	14.17 461	40.81 456	2.01 468	66.62 449	9.61 461	38.88 439	34.49 457
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 427	24.00 431	26.45 444	43.74 462	18.44 469	60.86 443	39.66 463	15.11 459	9.53 463	22.10 460	6.52 459	46.94 462	8.31 463	10.14 460	13.98 460

MVE	24.84 23	24.35 428	19.77 434	38.09 442	34.56 468	26.92 461	26.57 458	38.87 465	11.73 461	11.37 462	27.44 458	1.37 469	50.42 461	11.41 459	14.60 459	36.93 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 429	23.20 433	25.46 445	41.52 465	16.90 470	60.56 444	38.79 466	14.62 460	8.99 464	20.24 463	7.35 456	45.82 463	7.25 464	9.46 461	13.64 461

tmp_tt		22.26 430	23.75 432	17.80 446	5.23 470	12.06 471	35.26 457	39.48 464	2.82 464	18.94 455	44.20 453	22.23 433	24.64 465	36.30 423	9.31 462	16.69 459

cdsmvs_eth3d_5k		19.86 431	26.47 430	0.00 450	0.00 473	0.00 475	0.00 461	93.45 91	0.00 468	0.00 469	95.27 71	49.56 291	0.00 469	0.00 468	0.00 466	0.00 465

wuyk23d		11.30 432	10.95 435	12.33 447	48.05 457	19.89 467	25.89 459	1.92 471	3.58 463	3.12 465	1.37 465	0.64 470	15.77 466	6.23 465	7.77 464	1.35 462

ab-mvs-re		7.91 433	10.55 436	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	94.95 81	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

testmvs		7.23 434	9.62 437	0.06 449	0.04 471	0.02 474	84.98 344	0.02 472	0.03 466	0.18 467	1.21 466	0.01 472	0.02 467	0.14 466	0.01 465	0.13 464

test123		6.92 435	9.21 438	0.08 448	0.03 472	0.05 473	81.65 377	0.01 473	0.02 467	0.14 468	0.85 467	0.03 471	0.02 467	0.12 467	0.00 466	0.16 463

pcd_1.5k_mvsjas		4.46 436	5.95 439	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	53.55 246	0.00 469	0.00 468	0.00 466	0.00 465

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 466	0.00 465

WAC-MVS						49.45 409								31.56 442

FOURS1					93.95 46	61.77 281	93.96 82	91.92 161	62.14 363	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	71.42 261	90.67 24	96.85 21	74.45 20

eth-test2					0.00 473

eth-test					0.00 473

ZD-MVS					96.63 9	65.50 169		93.50 89	70.74 275	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	58.56 348	90.90 232	90.45 234	62.76 356	78.89 148	94.46 95	49.30 294		78.77 165	86.77 142	92.28 213

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	71.65 250	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	71.65 250	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	72.76 215	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									72.48 220	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	71.92 236	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				69.61 401	61.38 448	41.35 439	49.07 455	85.86 372		50.18 417	66.40 429	10.16 452	88.14 382	45.73 394	44.20 426	79.32 410

MTGPA								92.23 142

test_post1							78.95 397				20.70 462	53.05 251	91.50 347	60.43 327

test_post											23.01 459	56.49 211	92.67 307

patchmatchnet-post											67.62 428	57.62 193	90.25 357

GG-mvs-BLEND				86.53 76	91.91 114	69.67 54	75.02 416	94.75 36		78.67 156	90.85 188	77.91 7	94.56 235	72.25 218	93.74 45	95.36 68

MTMP							93.77 96	32.52 468

gm-plane-assit					88.42 209	67.04 123			78.62 110		91.83 170		97.37 77	76.57 177

test9_res														89.41 51	94.96 19	95.29 74

TEST9					94.18 41	67.28 114	94.16 69	93.51 87	71.75 247	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	71.87 241	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				72.46 387	79.57 372	51.42 397		68.61 439	51.25 415	45.88 427	81.23 341	19.86 439	86.58 397	38.98 419	57.01 398	79.39 408

test_prior4						67.18 118	93.92 85

test_prior2							95.10 39		75.40 166	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		59.37 385	87.54 49			93.47 282	75.39 186

新几何2							91.41 205

新几何1				84.73 150	92.32 93	64.28 206		91.46 188	59.56 384	79.77 136	92.90 139	56.95 203	96.57 132	63.40 307	92.91 58	93.34 174

旧先验1					91.94 110	60.74 307		91.50 186			94.36 99	65.23 85			91.84 73	94.55 116

无先验							92.71 145	92.61 132	62.03 364				97.01 104	66.63 278		93.97 151

原ACMM2							92.01 181

原ACMM1				84.42 167	93.21 68	64.27 207		93.40 96	65.39 331	79.51 140	92.50 147	58.11 189	96.69 128	65.27 297	93.96 40	92.32 211

test222					89.77 163	61.60 287	89.55 279	89.42 283	56.83 399	77.28 170	92.43 151	52.76 254			91.14 89	93.09 184

testdata2													96.09 157	61.26 323

segment_acmp												65.94 76

testdata				81.34 266	89.02 185	57.72 355		89.84 266	58.65 388	85.32 73	94.09 115	57.03 198	93.28 284	69.34 247	90.56 95	93.03 187

testdata1							89.21 290		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	61.51 288

plane_prior6					87.23 246	62.32 267						50.66 277

plane_prior5								91.31 192					95.55 190	76.74 175	78.53 241	88.39 285

plane_prior4											89.14 228

plane_prior3						61.95 276			79.09 99	72.53 232

plane_prior2							93.13 124		78.81 106

plane_prior1					87.15 248

plane_prior						62.42 263	93.85 89		79.38 91						78.80 238

n2								0.00 474

nn								0.00 474

door-mid								66.01 443

lessismore_v0				73.72 378	72.93 422	47.83 417		61.72 449		45.86 429	73.76 403	28.63 416	89.81 367	47.75 386	31.37 448	83.53 363

LGP-MVS_train				79.56 316	84.31 317	59.37 337		89.73 272	69.49 288	64.86 328	88.42 237	38.65 362	94.30 246	72.56 215	72.76 285	85.01 349

test11								93.01 111

door								66.57 442

HQP5-MVS						63.66 230

HQP-NCC					87.54 238		94.06 74		79.80 81	74.18 206

ACMP_Plane					87.54 238		94.06 74		79.80 81	74.18 206

BP-MVS														77.63 172

HQP4-MVS										74.18 206			95.61 184			88.63 279

HQP3-MVS								91.70 178							78.90 236

HQP2-MVS												51.63 266

NP-MVS					87.41 241	63.04 247					90.30 199

MDTV_nov1_ep13_2view						59.90 329	80.13 392		67.65 314	72.79 226		54.33 237		59.83 331		92.58 202

MDTV_nov1_ep13			72.61 303		89.06 184	68.48 79	80.33 388	90.11 255	71.84 243	71.81 246	75.92 396	53.01 252	93.92 269	48.04 380	73.38 280

ACMMP++_ref															71.63 293

ACMMP++															69.72 302

Test By Simon												54.21 240

ITE_SJBPF				70.43 399	74.44 416	47.06 423		77.32 412	60.16 380	54.04 398	83.53 307	23.30 429	84.01 411	43.07 403	61.58 378	80.21 405

DeepMVS_CX				34.71 443	51.45 455	24.73 463		28.48 469	31.46 449	17.49 459	52.75 445	5.80 460	42.60 464	18.18 452	19.42 457	36.81 456

High-resolution multi-view benchmark