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 62	96.26 37	72.84 30	99.38 1	92.64 28	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 16	96.19 39	70.12 47	98.91 18	96.83 1	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 162	76.68 2	97.29 1	95.35 17	82.87 30	91.58 15	97.22 4	79.93 5	99.10 9	83.12 110	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 77	94.37 56	72.48 201	92.07 10	96.85 18	83.82 2	99.15 2	91.53 38	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 92	92.83 80	64.03 196	93.06 121	94.33 58	82.19 38	93.65 3	96.15 41	85.89 1	97.19 88	91.02 42	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 77	87.30 4	92.15 7	96.15 41	66.38 69	98.94 17	96.71 2	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 12	89.07 35	96.80 21	70.86 43	99.06 15	92.64 28	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 54	88.32 3	85.71 63	94.91 81	74.11 21	98.91 18	87.26 70	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 56	96.89 6	94.44 50	71.65 231	92.11 8	97.21 5	76.79 9	99.11 6	92.34 30	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 137	93.00 76	58.16 324	96.72 9	94.41 52	86.50 8	90.25 26	97.83 1	75.46 14	98.67 25	92.78 27	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 95	96.04 24	63.70 207	95.04 41	95.19 22	86.74 7	91.53 17	95.15 74	73.86 22	97.58 62	93.38 22	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 51	96.53 13	93.78 70	86.89 6	89.68 32	95.78 48	65.94 74	99.10 9	92.99 25	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 46	71.92 217	90.55 22	96.93 12	73.77 23	99.08 11	91.91 36	94.90 22	96.29 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 87	95.24 33	94.49 48	82.43 35	88.90 36	96.35 32	71.89 40	98.63 26	88.76 56	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 236	93.43 91	84.06 17	86.20 57	90.17 192	72.42 35	96.98 105	93.09 24	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 64	96.38 15	94.64 42	84.42 14	86.74 52	96.20 38	66.56 68	98.76 24	89.03 55	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 104	94.17 63	94.15 63	68.77 280	90.74 20	97.27 2	76.09 12	98.49 29	90.58 46	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
SMA-MVS		88.14 18	88.29 23	87.67 33	93.21 68	68.72 73	93.85 84	94.03 66	74.18 164	91.74 13	96.67 24	65.61 79	98.42 33	89.24 52	96.08 7	95.88 47
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PS-MVSNAJ		88.14 18	87.61 33	89.71 7	92.06 102	76.72 1	95.75 20	93.26 97	83.86 18	89.55 33	96.06 43	53.55 235	97.89 44	91.10 40	93.31 53	94.54 112

TSAR-MVS + MP.		88.11 20	88.64 19	86.54 73	91.73 117	68.04 91	90.36 242	93.55 84	82.89 28	91.29 18	92.89 134	72.27 37	96.03 155	87.99 60	94.77 26	95.54 58
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		87.96 21	88.37 22	86.70 66	93.51 62	65.32 161	95.15 36	93.84 69	78.17 107	85.93 61	94.80 84	75.80 13	98.21 35	89.38 49	88.78 112	96.59 19

DeepC-MVS_fast	79.48 2	87.95 22	88.00 27	87.79 31	95.86 27	68.32 81	95.74 21	94.11 64	83.82 19	83.49 85	96.19 39	64.53 95	98.44 31	83.42 109	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 23	87.38 37	89.55 12	91.41 129	76.43 3	95.74 21	93.12 105	83.53 22	89.55 33	95.95 46	53.45 239	97.68 52	91.07 41	92.62 60	94.54 112

EPNet		87.84 24	88.38 21	86.23 83	93.30 65	66.05 143	95.26 32	94.84 32	87.09 5	88.06 39	94.53 90	66.79 65	97.34 77	83.89 103	91.68 75	95.29 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 25	87.77 30	87.63 38	89.24 177	71.18 24	96.57 12	92.90 114	82.70 32	87.13 47	95.27 67	64.99 85	95.80 160	89.34 50	91.80 73	95.93 45

test_fmvsm_n_1920		87.69 26	88.50 20	85.27 117	87.05 236	63.55 214	93.69 94	91.08 202	84.18 16	90.17 28	97.04 9	67.58 60	97.99 40	95.72 5	90.03 98	94.26 124

fmvsm_l_conf0.5_n_3		87.54 27	88.29 23	85.30 114	86.92 242	62.63 240	95.02 43	90.28 231	84.95 11	90.27 25	96.86 16	65.36 81	97.52 67	94.93 10	90.03 98	95.76 50

APDe-MVS		87.54 27	87.84 29	86.65 67	96.07 23	66.30 139	94.84 47	93.78 70	69.35 271	88.39 38	96.34 33	67.74 59	97.66 57	90.62 45	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 29	88.72 18	83.84 171	86.89 244	60.04 301	95.05 39	92.17 147	84.80 13	92.27 6	96.37 30	64.62 92	96.54 128	94.43 14	91.86 71	94.94 90

fmvsm_l_conf0.5_n		87.49 30	88.19 25	85.39 110	86.95 237	64.37 186	94.30 60	88.45 306	80.51 60	92.70 4	96.86 16	69.98 48	97.15 93	95.83 4	88.08 120	94.65 106

SD-MVS		87.49 30	87.49 35	87.50 42	93.60 56	68.82 70	93.90 81	92.63 127	76.86 128	87.90 41	95.76 49	66.17 71	97.63 59	89.06 54	91.48 79	96.05 42
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_l_conf0.5_n_a		87.44 32	88.15 26	85.30 114	87.10 234	64.19 193	94.41 55	88.14 315	80.24 68	92.54 5	96.97 11	69.52 50	97.17 89	95.89 3	88.51 115	94.56 109

dcpmvs_2		87.37 33	87.55 34	86.85 58	95.04 32	68.20 88	90.36 242	90.66 214	79.37 83	81.20 107	93.67 118	74.73 16	96.55 127	90.88 43	92.00 69	95.82 48

alignmvs		87.28 34	86.97 41	88.24 27	91.30 131	71.14 26	95.61 25	93.56 83	79.30 84	87.07 49	95.25 69	68.43 52	96.93 113	87.87 61	84.33 161	96.65 17

train_agg		87.21 35	87.42 36	86.60 69	94.18 41	67.28 111	94.16 64	93.51 85	71.87 222	85.52 65	95.33 62	68.19 54	97.27 84	89.09 53	94.90 22	95.25 77

MG-MVS		87.11 36	86.27 52	89.62 8	97.79 1	76.27 4	94.96 45	94.49 48	78.74 99	83.87 83	92.94 132	64.34 96	96.94 111	75.19 172	94.09 38	95.66 53

SF-MVS		87.03 37	87.09 39	86.84 59	92.70 86	67.45 109	93.64 97	93.76 73	70.78 255	86.25 55	96.44 29	66.98 63	97.79 48	88.68 57	94.56 34	95.28 73

fmvsm_s_conf0.5_n_3		86.88 38	87.99 28	83.58 182	87.26 229	60.74 281	93.21 118	87.94 322	84.22 15	91.70 14	97.27 2	65.91 76	95.02 194	93.95 19	90.42 94	94.99 87

CSCG		86.87 39	86.26 53	88.72 17	95.05 31	70.79 29	93.83 89	95.33 18	68.48 284	77.63 154	94.35 99	73.04 28	98.45 30	84.92 92	93.71 47	96.92 14

sasdasda		86.85 40	86.25 54	88.66 20	91.80 115	71.92 16	93.54 102	91.71 170	80.26 65	87.55 44	95.25 69	63.59 110	96.93 113	88.18 58	84.34 159	97.11 9

canonicalmvs		86.85 40	86.25 54	88.66 20	91.80 115	71.92 16	93.54 102	91.71 170	80.26 65	87.55 44	95.25 69	63.59 110	96.93 113	88.18 58	84.34 159	97.11 9

UBG		86.83 42	86.70 47	87.20 48	93.07 74	69.81 47	93.43 110	95.56 13	81.52 45	81.50 103	92.12 153	73.58 26	96.28 140	84.37 98	85.20 151	95.51 59

PHI-MVS		86.83 42	86.85 46	86.78 63	93.47 63	65.55 157	95.39 30	95.10 25	71.77 227	85.69 64	96.52 26	62.07 132	98.77 23	86.06 82	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 44	86.90 44	86.58 71	90.42 147	66.38 136	96.09 17	93.87 68	77.73 115	84.01 82	95.66 51	63.39 113	97.94 41	87.40 68	93.55 50	95.42 60
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 45	87.63 31	84.27 159	86.15 257	61.48 265	94.69 51	91.16 194	83.79 21	90.51 24	96.28 35	64.24 97	98.22 34	95.00 9	86.88 132	93.11 168

PVSNet_Blended		86.73 46	86.86 45	86.31 82	93.76 50	67.53 106	96.33 16	93.61 81	82.34 37	81.00 112	93.08 128	63.19 117	97.29 80	87.08 73	91.38 81	94.13 133

testing11		86.71 47	86.44 51	87.55 40	93.54 60	71.35 21	93.65 96	95.58 11	81.36 52	80.69 115	92.21 152	72.30 36	96.46 133	85.18 88	83.43 169	94.82 98

test_fmvsmconf_n		86.58 48	87.17 38	84.82 130	85.28 272	62.55 241	94.26 62	89.78 250	83.81 20	87.78 43	96.33 34	65.33 82	96.98 105	94.40 15	87.55 126	94.95 89

BP-MVS1		86.54 49	86.68 49	86.13 86	87.80 217	67.18 115	92.97 126	95.62 10	79.92 71	82.84 92	94.14 108	74.95 15	96.46 133	82.91 112	88.96 111	94.74 100

jason		86.40 50	86.17 56	87.11 51	86.16 256	70.54 32	95.71 24	92.19 144	82.00 40	84.58 75	94.34 100	61.86 134	95.53 180	87.76 62	90.89 87	95.27 74
jason: jason.
fmvsm_s_conf0.5_n		86.39 51	86.91 43	84.82 130	87.36 228	63.54 215	94.74 49	90.02 243	82.52 33	90.14 29	96.92 14	62.93 122	97.84 47	95.28 8	82.26 179	93.07 171

fmvsm_s_conf0.5_n_5		86.38 52	86.94 42	84.71 139	84.67 283	63.29 220	94.04 73	89.99 245	82.88 29	87.85 42	96.03 44	62.89 124	96.36 137	94.15 16	89.95 100	94.48 118

WTY-MVS		86.32 53	85.81 64	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 31	81.91 100	94.73 85	67.93 58	97.63 59	79.55 141	82.25 180	96.54 22

myMVS_eth3d28		86.31 54	86.15 57	86.78 63	93.56 58	70.49 33	92.94 128	95.28 19	82.47 34	78.70 145	92.07 155	72.45 34	95.41 182	82.11 118	85.78 147	94.44 120

MSLP-MVS++		86.27 55	85.91 63	87.35 45	92.01 106	68.97 67	95.04 41	92.70 119	79.04 94	81.50 103	96.50 28	58.98 170	96.78 119	83.49 108	93.93 41	96.29 35

VNet		86.20 56	85.65 68	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 103	86.00 60	93.07 129	58.22 177	97.00 101	85.22 86	84.33 161	96.52 23

MVS_111021_HR		86.19 57	85.80 65	87.37 44	93.17 70	69.79 48	93.99 76	93.76 73	79.08 91	78.88 141	93.99 112	62.25 131	98.15 37	85.93 83	91.15 85	94.15 132

SPE-MVS-test		86.14 58	87.01 40	83.52 183	92.63 88	59.36 313	95.49 27	91.92 157	80.09 69	85.46 67	95.53 57	61.82 136	95.77 163	86.77 77	93.37 52	95.41 61

ACMMP_NAP		86.05 59	85.80 65	86.80 62	91.58 121	67.53 106	91.79 182	93.49 88	74.93 154	84.61 74	95.30 64	59.42 161	97.92 42	86.13 80	94.92 20	94.94 90

testing99		86.01 60	85.47 70	87.63 38	93.62 55	71.25 23	93.47 108	95.23 21	80.42 63	80.60 117	91.95 158	71.73 41	96.50 131	80.02 138	82.22 181	95.13 80

ETV-MVS		86.01 60	86.11 58	85.70 102	90.21 152	67.02 121	93.43 110	91.92 157	81.21 54	84.13 81	94.07 111	60.93 144	95.63 171	89.28 51	89.81 101	94.46 119

testing91		85.93 62	85.31 74	87.78 32	93.59 57	71.47 19	93.50 105	95.08 28	80.26 65	80.53 118	91.93 159	70.43 45	96.51 130	80.32 136	82.13 183	95.37 64

APD-MVS		85.93 62	85.99 61	85.76 99	95.98 26	65.21 164	93.59 100	92.58 129	66.54 298	86.17 58	95.88 47	63.83 103	97.00 101	86.39 79	92.94 57	95.06 83
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 64	85.46 71	87.18 49	88.20 205	72.42 15	92.41 154	92.77 117	82.11 39	80.34 121	93.07 129	68.27 53	95.02 194	78.39 154	93.59 49	94.09 135

CS-MVS		85.80 65	86.65 50	83.27 192	92.00 107	58.92 317	95.31 31	91.86 162	79.97 70	84.82 73	95.40 60	62.26 130	95.51 181	86.11 81	92.08 68	95.37 64

fmvsm_s_conf0.5_n_a		85.75 66	86.09 59	84.72 137	85.73 266	63.58 212	93.79 90	89.32 268	81.42 50	90.21 27	96.91 15	62.41 129	97.67 54	94.48 13	80.56 199	92.90 177

test_fmvsmconf0.1_n		85.71 67	86.08 60	84.62 145	80.83 330	62.33 246	93.84 87	88.81 294	83.50 23	87.00 50	96.01 45	63.36 114	96.93 113	94.04 18	87.29 129	94.61 108

CDPH-MVS		85.71 67	85.46 71	86.46 75	94.75 34	67.19 113	93.89 82	92.83 116	70.90 251	83.09 90	95.28 65	63.62 108	97.36 75	80.63 132	94.18 37	94.84 95

casdiffmvs_mvg		85.66 69	85.18 76	87.09 52	88.22 204	69.35 59	93.74 93	91.89 160	81.47 46	80.10 123	91.45 168	64.80 90	96.35 138	87.23 71	87.69 124	95.58 56
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 70	85.93 62	84.68 141	82.95 313	63.48 217	94.03 75	89.46 262	81.69 43	89.86 30	96.74 22	61.85 135	97.75 50	94.74 12	82.01 185	92.81 179

MGCFI-Net		85.59 71	85.73 67	85.17 121	91.41 129	62.44 242	92.87 132	91.31 187	79.65 77	86.99 51	95.14 75	62.90 123	96.12 147	87.13 72	84.13 166	96.96 13

GDP-MVS		85.54 72	85.32 73	86.18 84	87.64 220	67.95 95	92.91 131	92.36 134	77.81 113	83.69 84	94.31 102	72.84 30	96.41 135	80.39 135	85.95 145	94.19 128

DeepC-MVS	77.85 3	85.52 73	85.24 75	86.37 79	88.80 187	66.64 130	92.15 161	93.68 79	81.07 55	76.91 164	93.64 119	62.59 126	98.44 31	85.50 84	92.84 59	94.03 139
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 74	84.87 82	86.84 59	88.25 202	69.07 63	93.04 123	91.76 167	81.27 53	80.84 114	92.07 155	64.23 98	96.06 153	84.98 91	87.43 128	95.39 62
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 75	85.08 78	86.06 87	93.09 73	65.65 153	93.89 82	93.41 93	73.75 175	79.94 125	94.68 87	60.61 147	98.03 39	82.63 115	93.72 46	94.52 114

fmvsm_s_conf0.5_n_7		85.24 76	86.69 48	80.91 257	84.52 288	60.10 299	93.35 113	90.35 224	83.41 24	86.54 54	96.27 36	60.50 148	90.02 340	94.84 11	90.38 95	92.61 183

MP-MVS-pluss		85.24 76	85.13 77	85.56 105	91.42 126	65.59 155	91.54 192	92.51 131	74.56 157	80.62 116	95.64 52	59.15 165	97.00 101	86.94 75	93.80 43	94.07 137
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 78	84.69 85	86.63 68	92.91 78	69.91 43	92.61 145	95.80 9	80.31 64	80.38 120	92.27 149	68.73 51	95.19 191	75.94 166	83.27 171	94.81 99

PAPR		85.15 79	84.47 86	87.18 49	96.02 25	68.29 82	91.85 180	93.00 111	76.59 135	79.03 137	95.00 76	61.59 137	97.61 61	78.16 155	89.00 110	95.63 54

fmvsm_s_conf0.5_n_2		85.06 80	85.60 69	83.44 189	86.92 242	60.53 288	94.41 55	87.31 328	83.30 25	88.72 37	96.72 23	54.28 228	97.75 50	94.07 17	84.68 158	92.04 203

MP-MVS		85.02 81	84.97 80	85.17 121	92.60 89	64.27 191	93.24 115	92.27 137	73.13 186	79.63 129	94.43 93	61.90 133	97.17 89	85.00 90	92.56 61	94.06 138
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 82	84.44 87	86.71 65	88.33 199	68.73 72	90.24 247	91.82 166	81.05 56	81.18 108	92.50 141	63.69 106	96.08 152	84.45 97	86.71 139	95.32 69

CHOSEN 1792x2688		84.98 83	83.45 101	89.57 11	89.94 157	75.14 6	92.07 167	92.32 135	81.87 41	75.68 173	88.27 218	60.18 151	98.60 27	80.46 134	90.27 97	94.96 88

MVSMamba_PlusPlus		84.97 84	83.65 95	88.93 14	90.17 153	74.04 8	87.84 296	92.69 122	62.18 336	81.47 105	87.64 232	71.47 42	96.28 140	84.69 94	94.74 31	96.47 28

EIA-MVS		84.84 85	84.88 81	84.69 140	91.30 131	62.36 245	93.85 84	92.04 150	79.45 80	79.33 134	94.28 104	62.42 128	96.35 138	80.05 137	91.25 84	95.38 63

fmvsm_s_conf0.1_n_a		84.76 86	84.84 83	84.53 147	80.23 340	63.50 216	92.79 134	88.73 297	80.46 61	89.84 31	96.65 25	60.96 143	97.57 64	93.80 20	80.14 201	92.53 187

HFP-MVS		84.73 87	84.40 88	85.72 101	93.75 52	65.01 170	93.50 105	93.19 101	72.19 211	79.22 135	94.93 79	59.04 168	97.67 54	81.55 122	92.21 64	94.49 117

MVS		84.66 88	82.86 120	90.06 2	90.93 138	74.56 7	87.91 294	95.54 14	68.55 282	72.35 216	94.71 86	59.78 157	98.90 20	81.29 128	94.69 32	96.74 16

GST-MVS		84.63 89	84.29 89	85.66 103	92.82 82	65.27 162	93.04 123	93.13 104	73.20 184	78.89 138	94.18 107	59.41 162	97.85 46	81.45 124	92.48 63	93.86 147

EC-MVSNet		84.53 90	85.04 79	83.01 197	89.34 169	61.37 268	94.42 54	91.09 200	77.91 111	83.24 86	94.20 106	58.37 175	95.40 183	85.35 85	91.41 80	92.27 197

fmvsm_s_conf0.1_n_2		84.40 91	84.78 84	83.27 192	85.25 273	60.41 291	94.13 67	85.69 348	83.05 27	87.99 40	96.37 30	52.75 244	97.68 52	93.75 21	84.05 167	91.71 208

ACMMPR		84.37 92	84.06 90	85.28 116	93.56 58	64.37 186	93.50 105	93.15 103	72.19 211	78.85 143	94.86 82	56.69 197	97.45 69	81.55 122	92.20 65	94.02 140

region2R		84.36 93	84.03 91	85.36 112	93.54 60	64.31 189	93.43 110	92.95 112	72.16 214	78.86 142	94.84 83	56.97 192	97.53 66	81.38 126	92.11 67	94.24 126

LFMVS		84.34 94	82.73 122	89.18 13	94.76 33	73.25 11	94.99 44	91.89 160	71.90 219	82.16 99	93.49 123	47.98 290	97.05 96	82.55 116	84.82 154	97.25 8

test_yl		84.28 95	83.16 111	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 274	81.09 109	92.88 135	57.00 190	97.44 70	81.11 130	81.76 187	96.23 38

DCV-MVSNet		84.28 95	83.16 111	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 274	81.09 109	92.88 135	57.00 190	97.44 70	81.11 130	81.76 187	96.23 38

diffmvs		84.28 95	83.83 92	85.61 104	87.40 226	68.02 92	90.88 222	89.24 271	80.54 59	81.64 102	92.52 140	59.83 156	94.52 219	87.32 69	85.11 152	94.29 123
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 95	83.36 107	87.02 55	92.22 96	67.74 99	84.65 322	94.50 47	79.15 88	82.23 98	87.93 227	66.88 64	96.94 111	80.53 133	82.20 182	96.39 33

ETVMVS		84.22 99	83.71 93	85.76 99	92.58 90	68.25 86	92.45 153	95.53 15	79.54 79	79.46 131	91.64 166	70.29 46	94.18 231	69.16 228	82.76 177	94.84 95

MAR-MVS		84.18 100	83.43 102	86.44 76	96.25 21	65.93 148	94.28 61	94.27 60	74.41 159	79.16 136	95.61 53	53.99 230	98.88 22	69.62 222	93.26 54	94.50 116
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		84.16 101	83.20 110	87.05 54	91.56 122	69.82 46	89.99 256	92.05 149	77.77 114	82.84 92	86.57 249	63.93 102	96.09 149	74.91 177	89.18 107	95.25 77

CANet_DTU		84.09 102	83.52 96	85.81 96	90.30 150	66.82 125	91.87 178	89.01 286	85.27 9	86.09 59	93.74 116	47.71 294	96.98 105	77.90 157	89.78 103	93.65 152

ET-MVSNet_ETH3D		84.01 103	83.15 113	86.58 71	90.78 143	70.89 28	94.74 49	94.62 43	81.44 49	58.19 351	93.64 119	73.64 25	92.35 297	82.66 114	78.66 216	96.50 27

PVSNet_Blended_VisFu		83.97 104	83.50 98	85.39 110	90.02 155	66.59 133	93.77 91	91.73 168	77.43 123	77.08 163	89.81 200	63.77 105	96.97 108	79.67 140	88.21 118	92.60 184

MTAPA		83.91 105	83.38 106	85.50 106	91.89 113	65.16 166	81.75 347	92.23 138	75.32 149	80.53 118	95.21 72	56.06 206	97.16 92	84.86 93	92.55 62	94.18 129

XVS		83.87 106	83.47 100	85.05 123	93.22 66	63.78 201	92.92 129	92.66 124	73.99 167	78.18 148	94.31 102	55.25 212	97.41 72	79.16 145	91.58 77	93.95 142

Effi-MVS+		83.82 107	82.76 121	86.99 56	89.56 165	69.40 54	91.35 203	86.12 342	72.59 198	83.22 89	92.81 138	59.60 159	96.01 157	81.76 121	87.80 123	95.56 57

test_fmvsmvis_n_1920		83.80 108	83.48 99	84.77 134	82.51 316	63.72 205	91.37 201	83.99 365	81.42 50	77.68 153	95.74 50	58.37 175	97.58 62	93.38 22	86.87 133	93.00 174

EI-MVSNet-Vis-set		83.77 109	83.67 94	84.06 163	92.79 85	63.56 213	91.76 185	94.81 34	79.65 77	77.87 151	94.09 109	63.35 115	97.90 43	79.35 143	79.36 208	90.74 226

MVSFormer		83.75 110	82.88 119	86.37 79	89.24 177	71.18 24	89.07 274	90.69 211	65.80 303	87.13 47	94.34 100	64.99 85	92.67 284	72.83 190	91.80 73	95.27 74

CP-MVS		83.71 111	83.40 105	84.65 142	93.14 71	63.84 199	94.59 52	92.28 136	71.03 249	77.41 157	94.92 80	55.21 215	96.19 144	81.32 127	90.70 89	93.91 144

test_fmvsmconf0.01_n		83.70 112	83.52 96	84.25 160	75.26 383	61.72 260	92.17 160	87.24 330	82.36 36	84.91 72	95.41 59	55.60 210	96.83 118	92.85 26	85.87 146	94.21 127

baseline2		83.68 113	83.42 104	84.48 150	87.37 227	66.00 145	90.06 251	95.93 8	79.71 76	69.08 254	90.39 186	77.92 6	96.28 140	78.91 149	81.38 191	91.16 222

reproduce-ours		83.51 114	83.33 108	84.06 163	92.18 99	60.49 289	90.74 228	92.04 150	64.35 313	83.24 86	95.59 55	59.05 166	97.27 84	83.61 105	89.17 108	94.41 121

our_new_method		83.51 114	83.33 108	84.06 163	92.18 99	60.49 289	90.74 228	92.04 150	64.35 313	83.24 86	95.59 55	59.05 166	97.27 84	83.61 105	89.17 108	94.41 121

thisisatest0515		83.41 116	82.49 126	86.16 85	89.46 168	68.26 84	93.54 102	94.70 39	74.31 162	75.75 171	90.92 176	72.62 32	96.52 129	69.64 220	81.50 190	93.71 150

PVSNet_BlendedMVS		83.38 117	83.43 102	83.22 194	93.76 50	67.53 106	94.06 69	93.61 81	79.13 89	81.00 112	85.14 264	63.19 117	97.29 80	87.08 73	73.91 253	84.83 324

test2506		83.29 118	82.92 118	84.37 154	88.39 197	63.18 226	92.01 170	91.35 186	77.66 117	78.49 147	91.42 169	64.58 94	95.09 193	73.19 186	89.23 105	94.85 92

PGM-MVS		83.25 119	82.70 123	84.92 126	92.81 84	64.07 195	90.44 237	92.20 142	71.28 243	77.23 160	94.43 93	55.17 216	97.31 79	79.33 144	91.38 81	93.37 158

HPM-MVS		83.25 119	82.95 117	84.17 161	92.25 95	62.88 235	90.91 219	91.86 162	70.30 260	77.12 161	93.96 113	56.75 195	96.28 140	82.04 119	91.34 83	93.34 159
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 121	82.96 115	83.73 175	92.02 103	59.74 305	90.37 241	92.08 148	63.70 320	82.86 91	95.48 58	58.62 172	97.17 89	83.06 111	88.42 116	94.26 124

EI-MVSNet-UG-set		83.14 122	82.96 115	83.67 180	92.28 94	63.19 225	91.38 200	94.68 40	79.22 86	76.60 166	93.75 115	62.64 125	97.76 49	78.07 156	78.01 219	90.05 235

testing3-2		83.11 123	83.15 113	82.98 198	91.92 110	64.01 197	94.39 58	95.37 16	78.32 104	75.53 178	90.06 198	73.18 27	93.18 263	74.34 182	75.27 242	91.77 207

VDD-MVS		83.06 124	81.81 135	86.81 61	90.86 141	67.70 100	95.40 29	91.50 181	75.46 146	81.78 101	92.34 148	40.09 332	97.13 94	86.85 76	82.04 184	95.60 55

h-mvs33		83.01 125	82.56 125	84.35 155	89.34 169	62.02 252	92.72 137	93.76 73	81.45 47	82.73 95	92.25 151	60.11 152	97.13 94	87.69 63	62.96 333	93.91 144

PAPM_NR		82.97 126	81.84 134	86.37 79	94.10 44	66.76 128	87.66 300	92.84 115	69.96 264	74.07 193	93.57 121	63.10 120	97.50 68	70.66 215	90.58 91	94.85 92

mPP-MVS		82.96 127	82.44 127	84.52 148	92.83 80	62.92 233	92.76 135	91.85 164	71.52 239	75.61 176	94.24 105	53.48 238	96.99 104	78.97 148	90.73 88	93.64 153

SR-MVS		82.81 128	82.58 124	83.50 186	93.35 64	61.16 271	92.23 159	91.28 191	64.48 312	81.27 106	95.28 65	53.71 234	95.86 159	82.87 113	88.77 113	93.49 156

DP-MVS Recon		82.73 129	81.65 136	85.98 89	97.31 4	67.06 118	95.15 36	91.99 154	69.08 277	76.50 168	93.89 114	54.48 224	98.20 36	70.76 213	85.66 149	92.69 180

CLD-MVS		82.73 129	82.35 129	83.86 170	87.90 212	67.65 102	95.45 28	92.18 145	85.06 10	72.58 209	92.27 149	52.46 247	95.78 161	84.18 99	79.06 211	88.16 262
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 131	82.38 128	83.73 175	89.25 174	59.58 308	92.24 158	94.89 31	77.96 109	79.86 126	92.38 146	56.70 196	97.05 96	77.26 160	80.86 195	94.55 110

3Dnovator	73.91 6	82.69 132	80.82 149	88.31 26	89.57 164	71.26 22	92.60 146	94.39 55	78.84 96	67.89 275	92.48 144	48.42 285	98.52 28	68.80 233	94.40 36	95.15 79

RRT-MVS		82.61 133	81.16 140	86.96 57	91.10 135	68.75 71	87.70 299	92.20 142	76.97 126	72.68 205	87.10 243	51.30 259	96.41 135	83.56 107	87.84 122	95.74 51

MVSTER		82.47 134	82.05 130	83.74 173	92.68 87	69.01 65	91.90 177	93.21 98	79.83 72	72.14 217	85.71 260	74.72 17	94.72 206	75.72 168	72.49 263	87.50 269

TESTMET0.1,1		82.41 135	81.98 133	83.72 177	88.08 206	63.74 203	92.70 139	93.77 72	79.30 84	77.61 155	87.57 234	58.19 178	94.08 235	73.91 184	86.68 140	93.33 161

CostFormer		82.33 136	81.15 141	85.86 94	89.01 182	68.46 78	82.39 344	93.01 109	75.59 144	80.25 122	81.57 308	72.03 39	94.96 198	79.06 147	77.48 227	94.16 131

API-MVS		82.28 137	80.53 157	87.54 41	96.13 22	70.59 31	93.63 98	91.04 206	65.72 305	75.45 179	92.83 137	56.11 205	98.89 21	64.10 277	89.75 104	93.15 166

IB-MVS	77.80 4	82.18 138	80.46 159	87.35 45	89.14 179	70.28 36	95.59 26	95.17 24	78.85 95	70.19 242	85.82 258	70.66 44	97.67 54	72.19 202	66.52 304	94.09 135
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		82.16 139	81.12 142	85.26 118	86.42 249	68.72 73	92.59 148	90.44 221	73.12 187	84.20 78	94.36 95	38.04 345	95.73 165	84.12 100	86.81 134	91.33 215

xiu_mvs_v1_base		82.16 139	81.12 142	85.26 118	86.42 249	68.72 73	92.59 148	90.44 221	73.12 187	84.20 78	94.36 95	38.04 345	95.73 165	84.12 100	86.81 134	91.33 215

xiu_mvs_v1_base_debi		82.16 139	81.12 142	85.26 118	86.42 249	68.72 73	92.59 148	90.44 221	73.12 187	84.20 78	94.36 95	38.04 345	95.73 165	84.12 100	86.81 134	91.33 215

3Dnovator+	73.60 7	82.10 142	80.60 156	86.60 69	90.89 140	66.80 127	95.20 34	93.44 90	74.05 166	67.42 282	92.49 143	49.46 275	97.65 58	70.80 212	91.68 75	95.33 67

MVS_111021_LR		82.02 143	81.52 137	83.51 185	88.42 195	62.88 235	89.77 259	88.93 290	76.78 131	75.55 177	93.10 126	50.31 266	95.38 185	83.82 104	87.02 131	92.26 198

PMMVS		81.98 144	82.04 131	81.78 232	89.76 161	56.17 343	91.13 215	90.69 211	77.96 109	80.09 124	93.57 121	46.33 304	94.99 197	81.41 125	87.46 127	94.17 130

baseline1		81.84 145	81.03 146	84.28 158	91.60 120	66.62 131	91.08 216	91.66 175	81.87 41	74.86 184	91.67 165	69.98 48	94.92 201	71.76 205	64.75 320	91.29 220

EPP-MVSNet		81.79 146	81.52 137	82.61 208	88.77 188	60.21 297	93.02 125	93.66 80	68.52 283	72.90 203	90.39 186	72.19 38	94.96 198	74.93 176	79.29 210	92.67 181

WBMVS		81.67 147	80.98 148	83.72 177	93.07 74	69.40 54	94.33 59	93.05 107	76.84 129	72.05 219	84.14 275	74.49 19	93.88 249	72.76 193	68.09 292	87.88 264

test_vis1_n_1920		81.66 148	82.01 132	80.64 260	82.24 318	55.09 351	94.76 48	86.87 332	81.67 44	84.40 77	94.63 88	38.17 342	94.67 210	91.98 35	83.34 170	92.16 201

APD-MVS_3200maxsize		81.64 149	81.32 139	82.59 209	92.36 92	58.74 319	91.39 198	91.01 207	63.35 324	79.72 128	94.62 89	51.82 250	96.14 146	79.71 139	87.93 121	92.89 178

mvsmamba		81.55 150	80.72 151	84.03 167	91.42 126	66.93 123	83.08 338	89.13 279	78.55 102	67.50 280	87.02 244	51.79 252	90.07 339	87.48 66	90.49 93	95.10 82

ACMMP		81.49 151	80.67 153	83.93 169	91.71 118	62.90 234	92.13 162	92.22 141	71.79 226	71.68 225	93.49 123	50.32 265	96.96 109	78.47 153	84.22 165	91.93 205
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
CDS-MVSNet		81.43 152	80.74 150	83.52 183	86.26 253	64.45 180	92.09 165	90.65 215	75.83 142	73.95 195	89.81 200	63.97 101	92.91 274	71.27 208	82.82 174	93.20 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 153	79.99 164	85.46 107	90.39 149	68.40 79	86.88 311	90.61 216	74.41 159	70.31 241	84.67 269	63.79 104	92.32 299	73.13 187	85.70 148	95.67 52

ECVR-MVS		81.29 154	80.38 160	84.01 168	88.39 197	61.96 254	92.56 151	86.79 334	77.66 117	76.63 165	91.42 169	46.34 303	95.24 190	74.36 181	89.23 105	94.85 92

thisisatest0530		81.15 155	80.07 161	84.39 153	88.26 201	65.63 154	91.40 196	94.62 43	71.27 244	70.93 232	89.18 206	72.47 33	96.04 154	65.62 266	76.89 233	91.49 211

Fast-Effi-MVS+		81.14 156	80.01 163	84.51 149	90.24 151	65.86 149	94.12 68	89.15 277	73.81 174	75.37 180	88.26 219	57.26 185	94.53 218	66.97 251	84.92 153	93.15 166

HQP-MVS		81.14 156	80.64 154	82.64 207	87.54 222	63.66 210	94.06 69	91.70 173	79.80 73	74.18 189	90.30 188	51.63 255	95.61 173	77.63 158	78.90 212	88.63 253

hse-mvs2		81.12 158	81.11 145	81.16 246	86.52 248	57.48 332	89.40 267	91.16 194	81.45 47	82.73 95	90.49 184	60.11 152	94.58 211	87.69 63	60.41 360	91.41 214

SR-MVS-dyc-post		81.06 159	80.70 152	82.15 223	92.02 103	58.56 321	90.90 220	90.45 218	62.76 331	78.89 138	94.46 91	51.26 260	95.61 173	78.77 151	86.77 137	92.28 194

HyFIR lowres test		81.03 160	79.56 171	85.43 108	87.81 216	68.11 90	90.18 248	90.01 244	70.65 257	72.95 202	86.06 256	63.61 109	94.50 220	75.01 175	79.75 205	93.67 151

nrg030		80.93 161	79.86 166	84.13 162	83.69 302	68.83 69	93.23 116	91.20 192	75.55 145	75.06 182	88.22 222	63.04 121	94.74 205	81.88 120	66.88 301	88.82 251

Vis-MVSNet		80.92 162	79.98 165	83.74 173	88.48 192	61.80 256	93.44 109	88.26 314	73.96 170	77.73 152	91.76 162	49.94 270	94.76 203	65.84 263	90.37 96	94.65 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 163	80.02 162	83.33 190	87.87 213	60.76 279	92.62 144	86.86 333	77.86 112	75.73 172	91.39 171	46.35 302	94.70 209	72.79 192	88.68 114	94.52 114

UWE-MVS		80.81 164	81.01 147	80.20 270	89.33 171	57.05 337	91.91 176	94.71 38	75.67 143	75.01 183	89.37 204	63.13 119	91.44 323	67.19 248	82.80 176	92.12 202

1314		80.70 165	78.95 183	85.94 91	87.77 219	67.56 104	87.91 294	92.55 130	72.17 213	67.44 281	93.09 127	50.27 267	97.04 99	71.68 207	87.64 125	93.23 163

tpmrst		80.57 166	79.14 181	84.84 129	90.10 154	68.28 83	81.70 348	89.72 257	77.63 119	75.96 170	79.54 340	64.94 87	92.71 281	75.43 170	77.28 230	93.55 154

1112_ss		80.56 167	79.83 167	82.77 202	88.65 189	60.78 277	92.29 156	88.36 308	72.58 199	72.46 213	94.95 77	65.09 84	93.42 260	66.38 257	77.71 221	94.10 134

VDDNet		80.50 168	78.26 191	87.21 47	86.19 254	69.79 48	94.48 53	91.31 187	60.42 350	79.34 133	90.91 177	38.48 340	96.56 126	82.16 117	81.05 193	95.27 74

BH-w/o		80.49 169	79.30 178	84.05 166	90.83 142	64.36 188	93.60 99	89.42 265	74.35 161	69.09 253	90.15 194	55.23 214	95.61 173	64.61 274	86.43 143	92.17 200

test_cas_vis1_n_1920		80.45 170	80.61 155	79.97 279	78.25 366	57.01 339	94.04 73	88.33 309	79.06 93	82.81 94	93.70 117	38.65 337	91.63 315	90.82 44	79.81 203	91.27 221

TAMVS		80.37 171	79.45 174	83.13 196	85.14 276	63.37 218	91.23 209	90.76 210	74.81 156	72.65 207	88.49 213	60.63 146	92.95 269	69.41 224	81.95 186	93.08 170

HQP_MVS		80.34 172	79.75 168	82.12 225	86.94 238	62.42 243	93.13 119	91.31 187	78.81 97	72.53 210	89.14 208	50.66 263	95.55 178	76.74 161	78.53 217	88.39 259

SDMVSNet		80.26 173	78.88 184	84.40 152	89.25 174	67.63 103	85.35 318	93.02 108	76.77 132	70.84 233	87.12 241	47.95 291	96.09 149	85.04 89	74.55 244	89.48 245

HPM-MVS_fast		80.25 174	79.55 173	82.33 215	91.55 123	59.95 302	91.32 205	89.16 276	65.23 309	74.71 186	93.07 129	47.81 293	95.74 164	74.87 179	88.23 117	91.31 219

ab-mvs		80.18 175	78.31 190	85.80 97	88.44 194	65.49 160	83.00 341	92.67 123	71.82 225	77.36 158	85.01 265	54.50 221	96.59 123	76.35 165	75.63 240	95.32 69

IS-MVSNet		80.14 176	79.41 175	82.33 215	87.91 211	60.08 300	91.97 174	88.27 312	72.90 194	71.44 229	91.73 164	61.44 138	93.66 255	62.47 291	86.53 141	93.24 162

test-LLR		80.10 177	79.56 171	81.72 234	86.93 240	61.17 269	92.70 139	91.54 178	71.51 240	75.62 174	86.94 245	53.83 231	92.38 294	72.21 200	84.76 156	91.60 209

PVSNet	73.49 8	80.05 178	78.63 186	84.31 156	90.92 139	64.97 171	92.47 152	91.05 205	79.18 87	72.43 214	90.51 183	37.05 357	94.06 237	68.06 237	86.00 144	93.90 146

UA-Net		80.02 179	79.65 169	81.11 248	89.33 171	57.72 328	86.33 315	89.00 289	77.44 122	81.01 111	89.15 207	59.33 163	95.90 158	61.01 298	84.28 163	89.73 241

test-mter		79.96 180	79.38 177	81.72 234	86.93 240	61.17 269	92.70 139	91.54 178	73.85 172	75.62 174	86.94 245	49.84 272	92.38 294	72.21 200	84.76 156	91.60 209

QAPM		79.95 181	77.39 209	87.64 34	89.63 163	71.41 20	93.30 114	93.70 78	65.34 308	67.39 284	91.75 163	47.83 292	98.96 16	57.71 314	89.81 101	92.54 186

UGNet		79.87 182	78.68 185	83.45 188	89.96 156	61.51 263	92.13 162	90.79 209	76.83 130	78.85 143	86.33 253	38.16 343	96.17 145	67.93 240	87.17 130	92.67 181
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		79.80 183	77.95 197	85.34 113	88.28 200	68.26 84	81.56 350	91.42 184	70.11 262	77.59 156	80.50 326	67.40 61	94.26 229	67.34 245	77.35 228	93.51 155

thres200		79.66 184	78.33 189	83.66 181	92.54 91	65.82 151	93.06 121	96.31 3	74.90 155	73.30 199	88.66 211	59.67 158	95.61 173	47.84 355	78.67 215	89.56 244

CPTT-MVS		79.59 185	79.16 180	80.89 258	91.54 124	59.80 304	92.10 164	88.54 305	60.42 350	72.96 201	93.28 125	48.27 286	92.80 278	78.89 150	86.50 142	90.06 234

Test_1112_low_res		79.56 186	78.60 187	82.43 211	88.24 203	60.39 293	92.09 165	87.99 319	72.10 215	71.84 221	87.42 236	64.62 92	93.04 265	65.80 264	77.30 229	93.85 148

tttt0517		79.50 187	78.53 188	82.41 214	87.22 231	61.43 267	89.75 260	94.76 35	69.29 272	67.91 273	88.06 226	72.92 29	95.63 171	62.91 287	73.90 254	90.16 233

reproduce_monomvs		79.49 188	79.11 182	80.64 260	92.91 78	61.47 266	91.17 214	93.28 96	83.09 26	64.04 313	82.38 295	66.19 70	94.57 213	81.19 129	57.71 368	85.88 307

FIs		79.47 189	79.41 175	79.67 287	85.95 260	59.40 310	91.68 189	93.94 67	78.06 108	68.96 259	88.28 217	66.61 67	91.77 311	66.20 260	74.99 243	87.82 265

BH-RMVSNet		79.46 190	77.65 200	84.89 127	91.68 119	65.66 152	93.55 101	88.09 317	72.93 191	73.37 198	91.12 175	46.20 306	96.12 147	56.28 320	85.61 150	92.91 176

PCF-MVS	73.15 9	79.29 191	77.63 201	84.29 157	86.06 258	65.96 147	87.03 307	91.10 199	69.86 266	69.79 249	90.64 179	57.54 184	96.59 123	64.37 276	82.29 178	90.32 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 192	79.57 170	78.24 307	88.46 193	52.29 362	90.41 239	89.12 280	74.24 163	69.13 252	91.91 160	65.77 77	90.09 338	59.00 310	88.09 119	92.33 191

114514_t		79.17 193	77.67 199	83.68 179	95.32 29	65.53 158	92.85 133	91.60 177	63.49 322	67.92 272	90.63 181	46.65 299	95.72 169	67.01 250	83.54 168	89.79 239

FA-MVS(test-final)		79.12 194	77.23 211	84.81 133	90.54 145	63.98 198	81.35 353	91.71 170	71.09 248	74.85 185	82.94 288	52.85 242	97.05 96	67.97 238	81.73 189	93.41 157

VPA-MVSNet		79.03 195	78.00 195	82.11 228	85.95 260	64.48 179	93.22 117	94.66 41	75.05 153	74.04 194	84.95 266	52.17 249	93.52 257	74.90 178	67.04 300	88.32 261

OPM-MVS		79.00 196	78.09 193	81.73 233	83.52 305	63.83 200	91.64 191	90.30 229	76.36 138	71.97 220	89.93 199	46.30 305	95.17 192	75.10 173	77.70 222	86.19 296
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 197	78.22 192	81.25 243	85.33 270	62.73 238	89.53 264	93.21 98	72.39 206	72.14 217	90.13 195	60.99 141	94.72 206	67.73 242	72.49 263	86.29 293

AdaColmap		78.94 198	77.00 215	84.76 135	96.34 17	65.86 149	92.66 143	87.97 321	62.18 336	70.56 235	92.37 147	43.53 319	97.35 76	64.50 275	82.86 173	91.05 224

GeoE		78.90 199	77.43 205	83.29 191	88.95 183	62.02 252	92.31 155	86.23 340	70.24 261	71.34 230	89.27 205	54.43 225	94.04 240	63.31 283	80.81 197	93.81 149

miper_enhance_ethall		78.86 200	77.97 196	81.54 238	88.00 210	65.17 165	91.41 194	89.15 277	75.19 151	68.79 262	83.98 278	67.17 62	92.82 276	72.73 194	65.30 311	86.62 290

VPNet		78.82 201	77.53 204	82.70 205	84.52 288	66.44 135	93.93 79	92.23 138	80.46 61	72.60 208	88.38 216	49.18 279	93.13 264	72.47 198	63.97 330	88.55 256

EPNet_dtu		78.80 202	79.26 179	77.43 315	88.06 207	49.71 378	91.96 175	91.95 156	77.67 116	76.56 167	91.28 173	58.51 173	90.20 336	56.37 319	80.95 194	92.39 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 203	77.43 205	82.88 200	92.21 97	64.49 177	92.05 168	96.28 4	73.48 181	71.75 223	88.26 219	60.07 154	95.32 186	45.16 366	77.58 224	88.83 249

TR-MVS		78.77 204	77.37 210	82.95 199	90.49 146	60.88 275	93.67 95	90.07 239	70.08 263	74.51 187	91.37 172	45.69 308	95.70 170	60.12 304	80.32 200	92.29 193

thres400		78.68 205	77.43 205	82.43 211	92.21 97	64.49 177	92.05 168	96.28 4	73.48 181	71.75 223	88.26 219	60.07 154	95.32 186	45.16 366	77.58 224	87.48 270

BH-untuned		78.68 205	77.08 212	83.48 187	89.84 158	63.74 203	92.70 139	88.59 303	71.57 237	66.83 291	88.65 212	51.75 253	95.39 184	59.03 309	84.77 155	91.32 218

OMC-MVS		78.67 207	77.91 198	80.95 255	85.76 265	57.40 334	88.49 284	88.67 300	73.85 172	72.43 214	92.10 154	49.29 278	94.55 217	72.73 194	77.89 220	90.91 225

tpm		78.58 208	77.03 213	83.22 194	85.94 262	64.56 175	83.21 337	91.14 198	78.31 105	73.67 196	79.68 338	64.01 100	92.09 305	66.07 261	71.26 273	93.03 172

OpenMVS	70.45 11	78.54 209	75.92 229	86.41 78	85.93 263	71.68 18	92.74 136	92.51 131	66.49 299	64.56 307	91.96 157	43.88 318	98.10 38	54.61 325	90.65 90	89.44 247

EPMVS		78.49 210	75.98 228	86.02 88	91.21 133	69.68 52	80.23 362	91.20 192	75.25 150	72.48 212	78.11 349	54.65 220	93.69 254	57.66 315	83.04 172	94.69 102

AUN-MVS		78.37 211	77.43 205	81.17 245	86.60 246	57.45 333	89.46 266	91.16 194	74.11 165	74.40 188	90.49 184	55.52 211	94.57 213	74.73 180	60.43 359	91.48 212

thres100view900		78.37 211	77.01 214	82.46 210	91.89 113	63.21 224	91.19 213	96.33 1	72.28 209	70.45 238	87.89 228	60.31 149	95.32 186	45.16 366	77.58 224	88.83 249

GA-MVS		78.33 213	76.23 224	84.65 142	83.65 303	66.30 139	91.44 193	90.14 237	76.01 140	70.32 240	84.02 277	42.50 323	94.72 206	70.98 210	77.00 232	92.94 175

cascas		78.18 214	75.77 231	85.41 109	87.14 233	69.11 62	92.96 127	91.15 197	66.71 297	70.47 236	86.07 255	37.49 351	96.48 132	70.15 218	79.80 204	90.65 227

UniMVSNet_NR-MVSNet		78.15 215	77.55 203	79.98 277	84.46 291	60.26 295	92.25 157	93.20 100	77.50 121	68.88 260	86.61 248	66.10 72	92.13 303	66.38 257	62.55 337	87.54 268

thres600view7		78.00 216	76.66 219	82.03 230	91.93 109	63.69 208	91.30 206	96.33 1	72.43 204	70.46 237	87.89 228	60.31 149	94.92 201	42.64 378	76.64 234	87.48 270

FC-MVSNet-test		77.99 217	78.08 194	77.70 310	84.89 281	55.51 348	90.27 245	93.75 76	76.87 127	66.80 292	87.59 233	65.71 78	90.23 335	62.89 288	73.94 252	87.37 273

Anonymous202405211		77.96 218	75.33 237	85.87 93	93.73 53	64.52 176	94.85 46	85.36 350	62.52 334	76.11 169	90.18 191	29.43 386	97.29 80	68.51 235	77.24 231	95.81 49

cl22		77.94 219	76.78 217	81.42 240	87.57 221	64.93 173	90.67 231	88.86 293	72.45 203	67.63 279	82.68 292	64.07 99	92.91 274	71.79 203	65.30 311	86.44 291

XXY-MVS		77.94 219	76.44 221	82.43 211	82.60 315	64.44 181	92.01 170	91.83 165	73.59 180	70.00 245	85.82 258	54.43 225	94.76 203	69.63 221	68.02 294	88.10 263

MS-PatchMatch		77.90 221	76.50 220	82.12 225	85.99 259	69.95 42	91.75 187	92.70 119	73.97 169	62.58 329	84.44 273	41.11 329	95.78 161	63.76 280	92.17 66	80.62 371

FMVSNet3		77.73 222	76.04 227	82.80 201	91.20 134	68.99 66	91.87 178	91.99 154	73.35 183	67.04 287	83.19 287	56.62 198	92.14 302	59.80 306	69.34 280	87.28 276

miper_ehance_all_eth		77.60 223	76.44 221	81.09 252	85.70 267	64.41 184	90.65 232	88.64 302	72.31 207	67.37 285	82.52 293	64.77 91	92.64 287	70.67 214	65.30 311	86.24 295

UniMVSNet (Re)		77.58 224	76.78 217	79.98 277	84.11 297	60.80 276	91.76 185	93.17 102	76.56 136	69.93 248	84.78 268	63.32 116	92.36 296	64.89 273	62.51 339	86.78 284

PatchmatchNet		77.46 225	74.63 244	85.96 90	89.55 166	70.35 35	79.97 367	89.55 260	72.23 210	70.94 231	76.91 361	57.03 188	92.79 279	54.27 327	81.17 192	94.74 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 226	75.65 233	82.73 203	80.38 336	67.13 117	91.85 180	90.23 234	75.09 152	69.37 250	83.39 284	53.79 233	94.44 221	71.77 204	65.00 317	86.63 289

CHOSEN 280x420		77.35 227	76.95 216	78.55 302	87.07 235	62.68 239	69.71 399	82.95 372	68.80 279	71.48 228	87.27 240	66.03 73	84.00 383	76.47 164	82.81 175	88.95 248

PS-MVSNAJss		77.26 228	76.31 223	80.13 272	80.64 334	59.16 315	90.63 235	91.06 204	72.80 195	68.58 266	84.57 271	53.55 235	93.96 245	72.97 188	71.96 267	87.27 277

gg-mvs-nofinetune		77.18 229	74.31 251	85.80 97	91.42 126	68.36 80	71.78 393	94.72 37	49.61 393	77.12 161	45.92 419	77.41 8	93.98 244	67.62 243	93.16 55	95.05 84

WB-MVSnew		77.14 230	76.18 226	80.01 276	86.18 255	63.24 222	91.26 207	94.11 64	71.72 229	73.52 197	87.29 239	45.14 313	93.00 267	56.98 317	79.42 206	83.80 332

MVP-Stereo		77.12 231	76.23 224	79.79 284	81.72 323	66.34 138	89.29 268	90.88 208	70.56 258	62.01 332	82.88 289	49.34 276	94.13 232	65.55 268	93.80 43	78.88 385
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 232	75.37 235	82.20 221	89.25 174	62.11 251	82.06 345	89.09 282	76.77 132	70.84 233	87.12 241	41.43 328	95.01 196	67.23 247	74.55 244	89.48 245

MonoMVSNet		76.99 233	75.08 240	82.73 203	83.32 307	63.24 222	86.47 314	86.37 336	79.08 91	66.31 295	79.30 342	49.80 273	91.72 312	79.37 142	65.70 309	93.23 163

dmvs_re		76.93 234	75.36 236	81.61 236	87.78 218	60.71 283	80.00 366	87.99 319	79.42 81	69.02 256	89.47 203	46.77 297	94.32 223	63.38 282	74.45 247	89.81 238

X-MVStestdata		76.86 235	74.13 255	85.05 123	93.22 66	63.78 201	92.92 129	92.66 124	73.99 167	78.18 148	10.19 434	55.25 212	97.41 72	79.16 145	91.58 77	93.95 142

DU-MVS		76.86 235	75.84 230	79.91 280	82.96 311	60.26 295	91.26 207	91.54 178	76.46 137	68.88 260	86.35 251	56.16 203	92.13 303	66.38 257	62.55 337	87.35 274

Anonymous20240529		76.84 237	74.15 254	84.88 128	91.02 136	64.95 172	93.84 87	91.09 200	53.57 381	73.00 200	87.42 236	35.91 361	97.32 78	69.14 229	72.41 265	92.36 190

UWE-MVS-28		76.83 238	77.60 202	74.51 340	84.58 287	50.34 374	88.22 288	94.60 45	74.46 158	66.66 293	88.98 210	62.53 127	85.50 375	57.55 316	80.80 198	87.69 267

c3_l		76.83 238	75.47 234	80.93 256	85.02 279	64.18 194	90.39 240	88.11 316	71.66 230	66.65 294	81.64 306	63.58 112	92.56 288	69.31 226	62.86 334	86.04 301

WR-MVS		76.76 240	75.74 232	79.82 283	84.60 285	62.27 249	92.60 146	92.51 131	76.06 139	67.87 276	85.34 262	56.76 194	90.24 334	62.20 292	63.69 332	86.94 282

v1144		76.73 241	74.88 241	82.27 217	80.23 340	66.60 132	91.68 189	90.21 236	73.69 177	69.06 255	81.89 301	52.73 245	94.40 222	69.21 227	65.23 314	85.80 308

IterMVS-LS		76.49 242	75.18 239	80.43 264	84.49 290	62.74 237	90.64 233	88.80 295	72.40 205	65.16 302	81.72 304	60.98 142	92.27 300	67.74 241	64.65 322	86.29 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 243	74.55 247	82.19 222	79.14 354	67.82 97	90.26 246	89.42 265	73.75 175	68.63 265	81.89 301	51.31 258	94.09 234	71.69 206	64.84 318	84.66 325

v148		76.19 244	74.47 249	81.36 241	80.05 342	64.44 181	91.75 187	90.23 234	73.68 178	67.13 286	80.84 321	55.92 208	93.86 252	68.95 231	61.73 348	85.76 311

Effi-MVS+-dtu		76.14 245	75.28 238	78.72 301	83.22 308	55.17 350	89.87 257	87.78 323	75.42 147	67.98 271	81.43 310	45.08 314	92.52 290	75.08 174	71.63 268	88.48 257

cl____		76.07 246	74.67 242	80.28 267	85.15 275	61.76 258	90.12 249	88.73 297	71.16 245	65.43 299	81.57 308	61.15 139	92.95 269	66.54 254	62.17 341	86.13 299

DIV-MVS_self_test		76.07 246	74.67 242	80.28 267	85.14 276	61.75 259	90.12 249	88.73 297	71.16 245	65.42 300	81.60 307	61.15 139	92.94 273	66.54 254	62.16 343	86.14 297

FMVSNet2		76.07 246	74.01 257	82.26 219	88.85 184	67.66 101	91.33 204	91.61 176	70.84 252	65.98 296	82.25 297	48.03 287	92.00 307	58.46 311	68.73 288	87.10 279

v144192		76.05 249	74.03 256	82.12 225	79.50 348	66.55 134	91.39 198	89.71 258	72.30 208	68.17 269	81.33 313	51.75 253	94.03 242	67.94 239	64.19 325	85.77 309

NR-MVSNet		76.05 249	74.59 245	80.44 263	82.96 311	62.18 250	90.83 224	91.73 168	77.12 125	60.96 335	86.35 251	59.28 164	91.80 310	60.74 299	61.34 352	87.35 274

v1192		75.98 251	73.92 258	82.15 223	79.73 344	66.24 141	91.22 210	89.75 252	72.67 197	68.49 267	81.42 311	49.86 271	94.27 227	67.08 249	65.02 316	85.95 304

FE-MVS		75.97 252	73.02 269	84.82 130	89.78 159	65.56 156	77.44 378	91.07 203	64.55 311	72.66 206	79.85 336	46.05 307	96.69 121	54.97 324	80.82 196	92.21 199

eth_miper_zixun_eth		75.96 253	74.40 250	80.66 259	84.66 284	63.02 228	89.28 269	88.27 312	71.88 221	65.73 297	81.65 305	59.45 160	92.81 277	68.13 236	60.53 357	86.14 297

TranMVSNet+NR-MVSNet		75.86 254	74.52 248	79.89 281	82.44 317	60.64 286	91.37 201	91.37 185	76.63 134	67.65 278	86.21 254	52.37 248	91.55 317	61.84 294	60.81 355	87.48 270

SCA		75.82 255	72.76 272	85.01 125	86.63 245	70.08 38	81.06 355	89.19 274	71.60 236	70.01 244	77.09 359	45.53 309	90.25 331	60.43 301	73.27 256	94.68 103

LPG-MVS_test		75.82 255	74.58 246	79.56 291	84.31 294	59.37 311	90.44 237	89.73 255	69.49 269	64.86 303	88.42 214	38.65 337	94.30 225	72.56 196	72.76 260	85.01 322

GBi-Net		75.65 257	73.83 259	81.10 249	88.85 184	65.11 167	90.01 253	90.32 225	70.84 252	67.04 287	80.25 331	48.03 287	91.54 318	59.80 306	69.34 280	86.64 286

test1		75.65 257	73.83 259	81.10 249	88.85 184	65.11 167	90.01 253	90.32 225	70.84 252	67.04 287	80.25 331	48.03 287	91.54 318	59.80 306	69.34 280	86.64 286

v1921920		75.63 259	73.49 264	82.06 229	79.38 349	66.35 137	91.07 218	89.48 261	71.98 216	67.99 270	81.22 316	49.16 281	93.90 248	66.56 253	64.56 323	85.92 306

ACMP	71.68 10	75.58 260	74.23 253	79.62 289	84.97 280	59.64 306	90.80 225	89.07 284	70.39 259	62.95 325	87.30 238	38.28 341	93.87 250	72.89 189	71.45 271	85.36 318
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 261	73.26 267	81.61 236	80.67 333	66.82 125	89.54 263	89.27 270	71.65 231	63.30 321	80.30 330	54.99 218	94.06 237	67.33 246	62.33 340	83.94 330

tpm cat1		75.30 262	72.21 281	84.58 146	88.52 190	67.77 98	78.16 376	88.02 318	61.88 342	68.45 268	76.37 365	60.65 145	94.03 242	53.77 330	74.11 250	91.93 205

PLC	68.80 14	75.23 263	73.68 262	79.86 282	92.93 77	58.68 320	90.64 233	88.30 310	60.90 347	64.43 311	90.53 182	42.38 324	94.57 213	56.52 318	76.54 235	86.33 292
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 264	72.98 270	81.88 231	79.20 351	66.00 145	90.75 227	89.11 281	71.63 235	67.41 283	81.22 316	47.36 295	93.87 250	65.46 269	64.72 321	85.77 309

Fast-Effi-MVS+-dtu		75.04 265	73.37 265	80.07 273	80.86 329	59.52 309	91.20 212	85.38 349	71.90 219	65.20 301	84.84 267	41.46 327	92.97 268	66.50 256	72.96 259	87.73 266

dp		75.01 266	72.09 282	83.76 172	89.28 173	66.22 142	79.96 368	89.75 252	71.16 245	67.80 277	77.19 358	51.81 251	92.54 289	50.39 339	71.44 272	92.51 188

TAPA-MVS	70.22 12	74.94 267	73.53 263	79.17 296	90.40 148	52.07 363	89.19 272	89.61 259	62.69 333	70.07 243	92.67 139	48.89 284	94.32 223	38.26 392	79.97 202	91.12 223
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 268	73.32 266	79.74 286	86.53 247	60.31 294	89.03 277	92.70 119	78.61 101	68.98 258	83.34 285	41.93 326	92.23 301	52.77 334	65.97 307	86.69 285

v10		74.77 269	72.54 278	81.46 239	80.33 338	66.71 129	89.15 273	89.08 283	70.94 250	63.08 324	79.86 335	52.52 246	94.04 240	65.70 265	62.17 341	83.64 333

XVG-OURS-SEG-HR		74.70 270	73.08 268	79.57 290	78.25 366	57.33 335	80.49 358	87.32 326	63.22 326	68.76 263	90.12 197	44.89 315	91.59 316	70.55 216	74.09 251	89.79 239

ACMM	69.62 13	74.34 271	72.73 274	79.17 296	84.25 296	57.87 326	90.36 242	89.93 246	63.17 328	65.64 298	86.04 257	37.79 349	94.10 233	65.89 262	71.52 270	85.55 314
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 272	72.30 280	80.32 265	91.49 125	61.66 261	90.85 223	80.72 378	56.67 373	63.85 316	90.64 179	46.75 298	90.84 326	53.79 329	75.99 239	88.47 258

XVG-OURS		74.25 273	72.46 279	79.63 288	78.45 364	57.59 331	80.33 360	87.39 325	63.86 318	68.76 263	89.62 202	40.50 331	91.72 312	69.00 230	74.25 249	89.58 242

test_fmvs1		74.07 274	73.69 261	75.22 333	78.91 358	47.34 391	89.06 276	74.69 394	63.68 321	79.41 132	91.59 167	24.36 396	87.77 359	85.22 86	76.26 237	90.55 230

CVMVSNet		74.04 275	74.27 252	73.33 350	85.33 270	43.94 404	89.53 264	88.39 307	54.33 380	70.37 239	90.13 195	49.17 280	84.05 381	61.83 295	79.36 208	91.99 204

Baseline_NR-MVSNet		73.99 276	72.83 271	77.48 314	80.78 331	59.29 314	91.79 182	84.55 358	68.85 278	68.99 257	80.70 322	56.16 203	92.04 306	62.67 289	60.98 354	81.11 365

pmmvs4		73.92 277	71.81 286	80.25 269	79.17 352	65.24 163	87.43 303	87.26 329	67.64 290	63.46 319	83.91 279	48.96 283	91.53 321	62.94 286	65.49 310	83.96 329

D2MVS		73.80 278	72.02 283	79.15 298	79.15 353	62.97 229	88.58 283	90.07 239	72.94 190	59.22 345	78.30 346	42.31 325	92.70 283	65.59 267	72.00 266	81.79 360

CR-MVSNet		73.79 279	70.82 294	82.70 205	83.15 309	67.96 93	70.25 396	84.00 363	73.67 179	69.97 246	72.41 381	57.82 181	89.48 344	52.99 333	73.13 257	90.64 228

test_djsdf		73.76 280	72.56 277	77.39 316	77.00 376	53.93 356	89.07 274	90.69 211	65.80 303	63.92 314	82.03 300	43.14 322	92.67 284	72.83 190	68.53 289	85.57 313

pmmvs5		73.35 281	71.52 288	78.86 300	78.64 362	60.61 287	91.08 216	86.90 331	67.69 287	63.32 320	83.64 280	44.33 317	90.53 328	62.04 293	66.02 306	85.46 316

Anonymous20231211		73.08 282	70.39 298	81.13 247	90.62 144	63.33 219	91.40 196	90.06 241	51.84 386	64.46 310	80.67 324	36.49 359	94.07 236	63.83 279	64.17 326	85.98 303

tt0805		73.07 283	70.73 295	80.07 273	78.37 365	57.05 337	87.78 297	92.18 145	61.23 346	67.04 287	86.49 250	31.35 379	94.58 211	65.06 272	67.12 299	88.57 255

miper_lstm_enhance		73.05 284	71.73 287	77.03 320	83.80 300	58.32 323	81.76 346	88.88 291	69.80 267	61.01 334	78.23 348	57.19 186	87.51 363	65.34 270	59.53 362	85.27 321

jajsoiax		73.05 284	71.51 289	77.67 311	77.46 373	54.83 352	88.81 279	90.04 242	69.13 276	62.85 327	83.51 282	31.16 380	92.75 280	70.83 211	69.80 276	85.43 317

LCM-MVSNet-Re		72.93 286	71.84 285	76.18 329	88.49 191	48.02 386	80.07 365	70.17 406	73.96 170	52.25 376	80.09 334	49.98 269	88.24 353	67.35 244	84.23 164	92.28 194

pm-mvs1		72.89 287	71.09 291	78.26 306	79.10 355	57.62 330	90.80 225	89.30 269	67.66 288	62.91 326	81.78 303	49.11 282	92.95 269	60.29 303	58.89 365	84.22 328

tpmvs		72.88 288	69.76 304	82.22 220	90.98 137	67.05 119	78.22 375	88.30 310	63.10 329	64.35 312	74.98 372	55.09 217	94.27 227	43.25 372	69.57 279	85.34 319

test0.0.03 1		72.76 289	72.71 275	72.88 354	80.25 339	47.99 387	91.22 210	89.45 263	71.51 240	62.51 330	87.66 231	53.83 231	85.06 377	50.16 341	67.84 297	85.58 312

UniMVSNet_ETH3D		72.74 290	70.53 297	79.36 293	78.62 363	56.64 341	85.01 320	89.20 273	63.77 319	64.84 305	84.44 273	34.05 368	91.86 309	63.94 278	70.89 275	89.57 243

mvs_tets		72.71 291	71.11 290	77.52 312	77.41 374	54.52 354	88.45 285	89.76 251	68.76 281	62.70 328	83.26 286	29.49 385	92.71 281	70.51 217	69.62 278	85.34 319

FMVSNet1		72.71 291	69.91 302	81.10 249	83.60 304	65.11 167	90.01 253	90.32 225	63.92 317	63.56 318	80.25 331	36.35 360	91.54 318	54.46 326	66.75 302	86.64 286

test_fmvs1_n		72.69 293	71.92 284	74.99 336	71.15 396	47.08 393	87.34 305	75.67 389	63.48 323	78.08 150	91.17 174	20.16 408	87.87 356	84.65 95	75.57 241	90.01 236

IterMVS		72.65 294	70.83 292	78.09 308	82.17 319	62.96 230	87.64 301	86.28 338	71.56 238	60.44 338	78.85 344	45.42 311	86.66 367	63.30 284	61.83 345	84.65 326
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 295	72.74 273	72.10 362	87.87 213	49.45 380	88.07 290	89.01 286	72.91 192	63.11 322	88.10 223	63.63 107	85.54 372	32.73 407	69.23 283	81.32 363

PatchMatch-RL		72.06 296	69.98 299	78.28 305	89.51 167	55.70 347	83.49 330	83.39 370	61.24 345	63.72 317	82.76 290	34.77 365	93.03 266	53.37 332	77.59 223	86.12 300

PVSNet_0	68.08 15	71.81 297	68.32 313	82.27 217	84.68 282	62.31 248	88.68 281	90.31 228	75.84 141	57.93 356	80.65 325	37.85 348	94.19 230	69.94 219	29.05 422	90.31 232

MIMVSNet		71.64 298	68.44 311	81.23 244	81.97 322	64.44 181	73.05 390	88.80 295	69.67 268	64.59 306	74.79 374	32.79 371	87.82 357	53.99 328	76.35 236	91.42 213

test_vis1_n		71.63 299	70.73 295	74.31 344	69.63 402	47.29 392	86.91 309	72.11 400	63.21 327	75.18 181	90.17 192	20.40 406	85.76 371	84.59 96	74.42 248	89.87 237

IterMVS-SCA-FT		71.55 300	69.97 300	76.32 327	81.48 325	60.67 285	87.64 301	85.99 343	66.17 301	59.50 343	78.88 343	45.53 309	83.65 385	62.58 290	61.93 344	84.63 327

v7n		71.31 301	68.65 308	79.28 294	76.40 378	60.77 278	86.71 312	89.45 263	64.17 316	58.77 350	78.24 347	44.59 316	93.54 256	57.76 313	61.75 347	83.52 336

anonymousdsp		71.14 302	69.37 306	76.45 326	72.95 391	54.71 353	84.19 325	88.88 291	61.92 341	62.15 331	79.77 337	38.14 344	91.44 323	68.90 232	67.45 298	83.21 342

F-COLMAP		70.66 303	68.44 311	77.32 317	86.37 252	55.91 345	88.00 292	86.32 337	56.94 371	57.28 360	88.07 225	33.58 369	92.49 291	51.02 337	68.37 290	83.55 334

WR-MVS_H		70.59 304	69.94 301	72.53 356	81.03 328	51.43 367	87.35 304	92.03 153	67.38 291	60.23 340	80.70 322	55.84 209	83.45 387	46.33 362	58.58 367	82.72 349

CP-MVSNet		70.50 305	69.91 302	72.26 359	80.71 332	51.00 371	87.23 306	90.30 229	67.84 286	59.64 342	82.69 291	50.23 268	82.30 395	51.28 336	59.28 363	83.46 338

RPMNet		70.42 306	65.68 327	84.63 144	83.15 309	67.96 93	70.25 396	90.45 218	46.83 402	69.97 246	65.10 402	56.48 202	95.30 189	35.79 397	73.13 257	90.64 228

testing3		70.38 307	70.83 292	69.03 374	85.82 264	43.93 405	90.72 230	90.56 217	68.06 285	60.24 339	86.82 247	64.83 89	84.12 379	26.33 415	64.10 327	79.04 384

tfpnnormal		70.10 308	67.36 317	78.32 304	83.45 306	60.97 274	88.85 278	92.77 117	64.85 310	60.83 336	78.53 345	43.52 320	93.48 258	31.73 410	61.70 349	80.52 372

TransMVSNet (Re)		70.07 309	67.66 315	77.31 318	80.62 335	59.13 316	91.78 184	84.94 354	65.97 302	60.08 341	80.44 327	50.78 262	91.87 308	48.84 348	45.46 396	80.94 367

CL-MVSNet_self_test		69.92 310	68.09 314	75.41 332	73.25 390	55.90 346	90.05 252	89.90 247	69.96 264	61.96 333	76.54 362	51.05 261	87.64 360	49.51 345	50.59 388	82.70 351

DP-MVS		69.90 311	66.48 319	80.14 271	95.36 28	62.93 231	89.56 261	76.11 387	50.27 392	57.69 358	85.23 263	39.68 333	95.73 165	33.35 402	71.05 274	81.78 361

PS-CasMVS		69.86 312	69.13 307	72.07 363	80.35 337	50.57 373	87.02 308	89.75 252	67.27 292	59.19 346	82.28 296	46.58 300	82.24 396	50.69 338	59.02 364	83.39 340

Syy-MVS		69.65 313	69.52 305	70.03 370	87.87 213	43.21 406	88.07 290	89.01 286	72.91 192	63.11 322	88.10 223	45.28 312	85.54 372	22.07 420	69.23 283	81.32 363

MSDG		69.54 314	65.73 326	80.96 254	85.11 278	63.71 206	84.19 325	83.28 371	56.95 370	54.50 367	84.03 276	31.50 377	96.03 155	42.87 376	69.13 285	83.14 344

PEN-MVS		69.46 315	68.56 309	72.17 361	79.27 350	49.71 378	86.90 310	89.24 271	67.24 295	59.08 347	82.51 294	47.23 296	83.54 386	48.42 350	57.12 369	83.25 341

LS3D		69.17 316	66.40 321	77.50 313	91.92 110	56.12 344	85.12 319	80.37 380	46.96 400	56.50 362	87.51 235	37.25 352	93.71 253	32.52 409	79.40 207	82.68 352

PatchT		69.11 317	65.37 331	80.32 265	82.07 321	63.68 209	67.96 406	87.62 324	50.86 390	69.37 250	65.18 401	57.09 187	88.53 350	41.59 381	66.60 303	88.74 252

KD-MVS_2432*1600		69.03 318	66.37 322	77.01 321	85.56 268	61.06 272	81.44 351	90.25 232	67.27 292	58.00 354	76.53 363	54.49 222	87.63 361	48.04 352	35.77 413	82.34 355

miper_refine_blended		69.03 318	66.37 322	77.01 321	85.56 268	61.06 272	81.44 351	90.25 232	67.27 292	58.00 354	76.53 363	54.49 222	87.63 361	48.04 352	35.77 413	82.34 355

mvsany_test1		68.77 320	68.56 309	69.39 372	73.57 389	45.88 400	80.93 356	60.88 420	59.65 356	71.56 226	90.26 190	43.22 321	75.05 407	74.26 183	62.70 336	87.25 278

ACMH	63.93 17	68.62 321	64.81 333	80.03 275	85.22 274	63.25 221	87.72 298	84.66 356	60.83 348	51.57 380	79.43 341	27.29 392	94.96 198	41.76 379	64.84 318	81.88 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 322	65.41 330	77.96 309	78.69 361	62.93 231	89.86 258	89.17 275	60.55 349	50.27 385	77.73 353	22.60 402	94.06 237	47.18 358	72.65 262	76.88 395

ADS-MVSNet		68.54 323	64.38 340	81.03 253	88.06 207	66.90 124	68.01 404	84.02 362	57.57 364	64.48 308	69.87 391	38.68 335	89.21 346	40.87 383	67.89 295	86.97 280

DTE-MVSNet		68.46 324	67.33 318	71.87 365	77.94 370	49.00 384	86.16 316	88.58 304	66.36 300	58.19 351	82.21 298	46.36 301	83.87 384	44.97 369	55.17 376	82.73 348

mmtdpeth		68.33 325	66.37 322	74.21 345	82.81 314	51.73 364	84.34 324	80.42 379	67.01 296	71.56 226	68.58 395	30.52 383	92.35 297	75.89 167	36.21 411	78.56 389

our_test_3		68.29 326	64.69 335	79.11 299	78.92 356	64.85 174	88.40 286	85.06 352	60.32 352	52.68 374	76.12 367	40.81 330	89.80 343	44.25 371	55.65 374	82.67 353

Patchmatch-RL test		68.17 327	64.49 338	79.19 295	71.22 395	53.93 356	70.07 398	71.54 404	69.22 273	56.79 361	62.89 406	56.58 199	88.61 347	69.53 223	52.61 383	95.03 86

XVG-ACMP-BASELINE		68.04 328	65.53 329	75.56 331	74.06 388	52.37 361	78.43 372	85.88 344	62.03 339	58.91 349	81.21 318	20.38 407	91.15 325	60.69 300	68.18 291	83.16 343

FMVSNet5		68.04 328	65.66 328	75.18 335	84.43 292	57.89 325	83.54 329	86.26 339	61.83 343	53.64 372	73.30 377	37.15 355	85.08 376	48.99 347	61.77 346	82.56 354

ppachtmachnet_test		67.72 330	63.70 342	79.77 285	78.92 356	66.04 144	88.68 281	82.90 373	60.11 354	55.45 364	75.96 368	39.19 334	90.55 327	39.53 387	52.55 384	82.71 350

ACMH+	65.35 16	67.65 331	64.55 336	76.96 323	84.59 286	57.10 336	88.08 289	80.79 377	58.59 362	53.00 373	81.09 320	26.63 394	92.95 269	46.51 360	61.69 350	80.82 368

pmmvs6		67.57 332	64.76 334	76.00 330	72.82 393	53.37 358	88.71 280	86.78 335	53.19 382	57.58 359	78.03 350	35.33 364	92.41 293	55.56 322	54.88 378	82.21 357

Anonymous20231206		67.53 333	65.78 325	72.79 355	74.95 384	47.59 389	88.23 287	87.32 326	61.75 344	58.07 353	77.29 356	37.79 349	87.29 365	42.91 374	63.71 331	83.48 337

Patchmtry		67.53 333	63.93 341	78.34 303	82.12 320	64.38 185	68.72 401	84.00 363	48.23 399	59.24 344	72.41 381	57.82 181	89.27 345	46.10 363	56.68 373	81.36 362

USDC		67.43 335	64.51 337	76.19 328	77.94 370	55.29 349	78.38 373	85.00 353	73.17 185	48.36 393	80.37 328	21.23 404	92.48 292	52.15 335	64.02 329	80.81 369

ADS-MVSNet2		66.90 336	63.44 344	77.26 319	88.06 207	60.70 284	68.01 404	75.56 391	57.57 364	64.48 308	69.87 391	38.68 335	84.10 380	40.87 383	67.89 295	86.97 280

CMPMVS	48.56 21	66.77 337	64.41 339	73.84 347	70.65 399	50.31 375	77.79 377	85.73 347	45.54 404	44.76 403	82.14 299	35.40 363	90.14 337	63.18 285	74.54 246	81.07 366
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 338	62.92 347	76.80 325	76.51 377	57.77 327	89.22 270	83.41 369	55.48 377	53.86 371	77.84 351	26.28 395	93.95 246	34.90 399	68.76 287	78.68 387

LTVRE_ROB	59.60 19	66.27 339	63.54 343	74.45 341	84.00 299	51.55 366	67.08 408	83.53 367	58.78 360	54.94 366	80.31 329	34.54 366	93.23 262	40.64 385	68.03 293	78.58 388
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		66.06 340	62.45 350	76.88 324	81.42 327	54.45 355	57.49 420	88.67 300	49.36 394	63.86 315	46.86 418	56.06 206	90.25 331	49.53 344	68.83 286	85.95 304

Patchmatch-test		65.86 341	60.94 356	80.62 262	83.75 301	58.83 318	58.91 419	75.26 393	44.50 407	50.95 384	77.09 359	58.81 171	87.90 355	35.13 398	64.03 328	95.12 81

UnsupCasMVSNet_eth		65.79 342	63.10 345	73.88 346	70.71 398	50.29 376	81.09 354	89.88 248	72.58 199	49.25 390	74.77 375	32.57 373	87.43 364	55.96 321	41.04 403	83.90 331

test_fmvs2		65.78 343	64.84 332	68.60 376	66.54 408	41.71 408	83.27 334	69.81 407	54.38 379	67.91 273	84.54 272	15.35 413	81.22 400	75.65 169	66.16 305	82.88 345

dmvs_testset		65.55 344	66.45 320	62.86 388	79.87 343	22.35 434	76.55 380	71.74 402	77.42 124	55.85 363	87.77 230	51.39 257	80.69 401	31.51 413	65.92 308	85.55 314

pmmvs-eth3d		65.53 345	62.32 351	75.19 334	69.39 403	59.59 307	82.80 342	83.43 368	62.52 334	51.30 382	72.49 379	32.86 370	87.16 366	55.32 323	50.73 387	78.83 386

mamv4		65.18 346	67.43 316	58.44 392	77.88 372	49.36 383	69.40 400	70.99 405	48.31 398	57.78 357	85.53 261	59.01 169	51.88 430	73.67 185	64.32 324	74.07 400

SixPastTwentyTwo		64.92 347	61.78 354	74.34 343	78.74 360	49.76 377	83.42 333	79.51 383	62.86 330	50.27 385	77.35 354	30.92 382	90.49 329	45.89 364	47.06 393	82.78 346

OurMVSNet-221017-0		64.68 348	62.17 352	72.21 360	76.08 381	47.35 390	80.67 357	81.02 376	56.19 374	51.60 379	79.66 339	27.05 393	88.56 349	53.60 331	53.63 381	80.71 370

test_0402		64.54 349	61.09 355	74.92 337	84.10 298	60.75 280	87.95 293	79.71 382	52.03 384	52.41 375	77.20 357	32.21 375	91.64 314	23.14 418	61.03 353	72.36 406

testgi		64.48 350	62.87 348	69.31 373	71.24 394	40.62 411	85.49 317	79.92 381	65.36 307	54.18 369	83.49 283	23.74 399	84.55 378	41.60 380	60.79 356	82.77 347

RPSCF		64.24 351	61.98 353	71.01 368	76.10 380	45.00 401	75.83 385	75.94 388	46.94 401	58.96 348	84.59 270	31.40 378	82.00 397	47.76 356	60.33 361	86.04 301

EU-MVSNet		64.01 352	63.01 346	67.02 382	74.40 387	38.86 417	83.27 334	86.19 341	45.11 405	54.27 368	81.15 319	36.91 358	80.01 403	48.79 349	57.02 370	82.19 358

test20.03		63.83 353	62.65 349	67.38 381	70.58 400	39.94 413	86.57 313	84.17 360	63.29 325	51.86 378	77.30 355	37.09 356	82.47 393	38.87 391	54.13 380	79.73 378

MDA-MVSNet_test_wron		63.78 354	60.16 358	74.64 338	78.15 368	60.41 291	83.49 330	84.03 361	56.17 376	39.17 413	71.59 387	37.22 353	83.24 390	42.87 376	48.73 390	80.26 375

YYNet1		63.76 355	60.14 359	74.62 339	78.06 369	60.19 298	83.46 332	83.99 365	56.18 375	39.25 412	71.56 388	37.18 354	83.34 388	42.90 375	48.70 391	80.32 374

K. test v3		63.09 356	59.61 361	73.53 349	76.26 379	49.38 382	83.27 334	77.15 386	64.35 313	47.77 395	72.32 383	28.73 387	87.79 358	49.93 343	36.69 410	83.41 339

COLMAP_ROB	57.96 20	62.98 357	59.65 360	72.98 353	81.44 326	53.00 360	83.75 328	75.53 392	48.34 397	48.81 392	81.40 312	24.14 397	90.30 330	32.95 404	60.52 358	75.65 398
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 358	59.08 362	71.10 367	67.19 406	48.72 385	83.91 327	85.23 351	50.38 391	47.84 394	71.22 390	20.74 405	85.51 374	46.47 361	58.75 366	79.06 383

AllTest		61.66 359	58.06 364	72.46 357	79.57 345	51.42 368	80.17 363	68.61 409	51.25 388	45.88 397	81.23 314	19.86 409	86.58 368	38.98 389	57.01 371	79.39 380

UnsupCasMVSNet_bld		61.60 360	57.71 365	73.29 351	68.73 404	51.64 365	78.61 371	89.05 285	57.20 369	46.11 396	61.96 409	28.70 388	88.60 348	50.08 342	38.90 408	79.63 379

MDA-MVSNet-bldmvs		61.54 361	57.70 366	73.05 352	79.53 347	57.00 340	83.08 338	81.23 375	57.57 364	34.91 417	72.45 380	32.79 371	86.26 370	35.81 396	41.95 401	75.89 397

mvs5depth		61.03 362	57.65 367	71.18 366	67.16 407	47.04 395	72.74 391	77.49 384	57.47 367	60.52 337	72.53 378	22.84 401	88.38 351	49.15 346	38.94 407	78.11 392

KD-MVS_self_test		60.87 363	58.60 363	67.68 379	66.13 409	39.93 414	75.63 387	84.70 355	57.32 368	49.57 388	68.45 396	29.55 384	82.87 391	48.09 351	47.94 392	80.25 376

kuosan		60.86 364	60.24 357	62.71 389	81.57 324	46.43 397	75.70 386	85.88 344	57.98 363	48.95 391	69.53 393	58.42 174	76.53 405	28.25 414	35.87 412	65.15 413

TinyColmap		60.32 365	56.42 372	72.00 364	78.78 359	53.18 359	78.36 374	75.64 390	52.30 383	41.59 411	75.82 370	14.76 416	88.35 352	35.84 395	54.71 379	74.46 399

MVS-HIRNet		60.25 366	55.55 373	74.35 342	84.37 293	56.57 342	71.64 394	74.11 395	34.44 416	45.54 401	42.24 424	31.11 381	89.81 341	40.36 386	76.10 238	76.67 396

MIMVSNet1		60.16 367	57.33 368	68.67 375	69.71 401	44.13 403	78.92 370	84.21 359	55.05 378	44.63 404	71.85 385	23.91 398	81.54 399	32.63 408	55.03 377	80.35 373

PM-MVS		59.40 368	56.59 370	67.84 377	63.63 412	41.86 407	76.76 379	63.22 417	59.01 359	51.07 383	72.27 384	11.72 420	83.25 389	61.34 296	50.28 389	78.39 390

new-patchmatchnet		59.30 369	56.48 371	67.79 378	65.86 410	44.19 402	82.47 343	81.77 374	59.94 355	43.65 407	66.20 400	27.67 391	81.68 398	39.34 388	41.40 402	77.50 394

test_vis1_rt		59.09 370	57.31 369	64.43 385	68.44 405	46.02 399	83.05 340	48.63 429	51.96 385	49.57 388	63.86 405	16.30 411	80.20 402	71.21 209	62.79 335	67.07 412

test_fmvs3		56.82 371	54.86 375	62.69 390	53.59 423	35.47 420	75.87 384	65.64 414	43.91 408	55.10 365	71.43 389	6.91 428	74.40 410	68.64 234	52.63 382	78.20 391

DSMNet-mixed		56.78 372	54.44 376	63.79 386	63.21 413	29.44 429	64.43 411	64.10 416	42.12 413	51.32 381	71.60 386	31.76 376	75.04 408	36.23 394	65.20 315	86.87 283

pmmvs3		55.51 373	51.50 379	67.53 380	57.90 421	50.93 372	80.37 359	73.66 396	40.63 414	44.15 406	64.75 403	16.30 411	78.97 404	44.77 370	40.98 405	72.69 404

TDRefinement		55.28 374	51.58 378	66.39 383	59.53 420	46.15 398	76.23 382	72.80 397	44.60 406	42.49 409	76.28 366	15.29 414	82.39 394	33.20 403	43.75 398	70.62 408

dongtai		55.18 375	55.46 374	54.34 400	76.03 382	36.88 418	76.07 383	84.61 357	51.28 387	43.41 408	64.61 404	56.56 200	67.81 418	18.09 423	28.50 423	58.32 416

LF4IMVS		54.01 376	52.12 377	59.69 391	62.41 415	39.91 415	68.59 402	68.28 411	42.96 411	44.55 405	75.18 371	14.09 418	68.39 417	41.36 382	51.68 385	70.78 407

ttmdpeth		53.34 377	49.96 380	63.45 387	62.07 417	40.04 412	72.06 392	65.64 414	42.54 412	51.88 377	77.79 352	13.94 419	76.48 406	32.93 405	30.82 421	73.84 401

MVStest1		51.35 378	46.89 382	64.74 384	65.06 411	51.10 370	67.33 407	72.58 398	30.20 420	35.30 415	74.82 373	27.70 390	69.89 415	24.44 417	24.57 424	73.22 402

N_pmnet		50.55 379	49.11 381	54.88 398	77.17 375	4.02 442	84.36 323	2.00 440	48.59 395	45.86 399	68.82 394	32.22 374	82.80 392	31.58 411	51.38 386	77.81 393

new_pmnet		49.31 380	46.44 383	57.93 393	62.84 414	40.74 410	68.47 403	62.96 418	36.48 415	35.09 416	57.81 413	14.97 415	72.18 412	32.86 406	46.44 394	60.88 415

mvsany_test3		48.86 381	46.35 384	56.41 394	46.00 429	31.67 425	62.26 413	47.25 430	43.71 409	45.54 401	68.15 397	10.84 421	64.44 426	57.95 312	35.44 415	73.13 403

test_f		46.58 382	43.45 386	55.96 395	45.18 430	32.05 424	61.18 414	49.49 428	33.39 417	42.05 410	62.48 408	7.00 427	65.56 422	47.08 359	43.21 400	70.27 409

WB-MVS		46.23 383	44.94 385	50.11 403	62.13 416	21.23 436	76.48 381	55.49 422	45.89 403	35.78 414	61.44 411	35.54 362	72.83 411	9.96 430	21.75 425	56.27 418

FPMVS		45.64 384	43.10 388	53.23 401	51.42 426	36.46 419	64.97 410	71.91 401	29.13 421	27.53 421	61.55 410	9.83 423	65.01 424	16.00 427	55.58 375	58.22 417

SSC-MVS		44.51 385	43.35 387	47.99 407	61.01 419	18.90 438	74.12 389	54.36 423	43.42 410	34.10 418	60.02 412	34.42 367	70.39 414	9.14 432	19.57 426	54.68 419

EGC-MVSNET		42.35 386	38.09 389	55.11 397	74.57 385	46.62 396	71.63 395	55.77 421	0.04 435	0.24 436	62.70 407	14.24 417	74.91 409	17.59 424	46.06 395	43.80 421

LCM-MVSNet		40.54 387	35.79 392	54.76 399	36.92 436	30.81 426	51.41 423	69.02 408	22.07 423	24.63 423	45.37 420	4.56 432	65.81 421	33.67 401	34.50 416	67.67 410

APD_test1		40.50 388	37.31 391	50.09 404	51.88 424	35.27 421	59.45 418	52.59 425	21.64 424	26.12 422	57.80 414	4.56 432	66.56 420	22.64 419	39.09 406	48.43 420

test_vis3_rt		40.46 389	37.79 390	48.47 406	44.49 431	33.35 423	66.56 409	32.84 437	32.39 418	29.65 419	39.13 427	3.91 435	68.65 416	50.17 340	40.99 404	43.40 422

ANet_high		40.27 390	35.20 393	55.47 396	34.74 437	34.47 422	63.84 412	71.56 403	48.42 396	18.80 426	41.08 425	9.52 424	64.45 425	20.18 421	8.66 433	67.49 411

test_method		38.59 391	35.16 394	48.89 405	54.33 422	21.35 435	45.32 426	53.71 424	7.41 432	28.74 420	51.62 416	8.70 425	52.87 429	33.73 400	32.89 417	72.47 405

PMMVS2		37.93 392	33.61 395	50.92 402	46.31 428	24.76 432	60.55 417	50.05 426	28.94 422	20.93 424	47.59 417	4.41 434	65.13 423	25.14 416	18.55 428	62.87 414

Gipuma		34.91 393	31.44 396	45.30 408	70.99 397	39.64 416	19.85 430	72.56 399	20.10 426	16.16 430	21.47 431	5.08 431	71.16 413	13.07 428	43.70 399	25.08 428
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 394	29.47 397	42.67 410	41.89 433	30.81 426	52.07 421	43.45 431	15.45 427	18.52 427	44.82 421	2.12 436	58.38 427	16.05 425	30.87 419	38.83 423

APD_test2		32.77 394	29.47 397	42.67 410	41.89 433	30.81 426	52.07 421	43.45 431	15.45 427	18.52 427	44.82 421	2.12 436	58.38 427	16.05 425	30.87 419	38.83 423

PMVS	26.43 22	31.84 396	28.16 399	42.89 409	25.87 439	27.58 430	50.92 424	49.78 427	21.37 425	14.17 431	40.81 426	2.01 438	66.62 419	9.61 431	38.88 409	34.49 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 397	24.00 401	26.45 414	43.74 432	18.44 439	60.86 415	39.66 433	15.11 429	9.53 433	22.10 430	6.52 429	46.94 432	8.31 433	10.14 430	13.98 430

MVE	24.84 23	24.35 398	19.77 404	38.09 412	34.56 438	26.92 431	26.57 428	38.87 435	11.73 431	11.37 432	27.44 428	1.37 439	50.42 431	11.41 429	14.60 429	36.93 425
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 399	23.20 403	25.46 415	41.52 435	16.90 440	60.56 416	38.79 436	14.62 430	8.99 434	20.24 433	7.35 426	45.82 433	7.25 434	9.46 431	13.64 431

tmp_tt		22.26 400	23.75 402	17.80 416	5.23 440	12.06 441	35.26 427	39.48 434	2.82 434	18.94 425	44.20 423	22.23 403	24.64 435	36.30 393	9.31 432	16.69 429

cdsmvs_eth3d_5k		19.86 401	26.47 400	0.00 420	0.00 443	0.00 445	0.00 431	93.45 89	0.00 438	0.00 439	95.27 67	49.56 274	0.00 439	0.00 438	0.00 436	0.00 435

wuyk23d		11.30 402	10.95 405	12.33 417	48.05 427	19.89 437	25.89 429	1.92 441	3.58 433	3.12 435	1.37 435	0.64 440	15.77 436	6.23 435	7.77 434	1.35 432

ab-mvs-re		7.91 403	10.55 406	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	94.95 77	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

testmvs		7.23 404	9.62 407	0.06 419	0.04 441	0.02 444	84.98 321	0.02 442	0.03 436	0.18 437	1.21 436	0.01 442	0.02 437	0.14 436	0.01 435	0.13 434

test123		6.92 405	9.21 408	0.08 418	0.03 442	0.05 443	81.65 349	0.01 443	0.02 437	0.14 438	0.85 437	0.03 441	0.02 437	0.12 437	0.00 436	0.16 433

pcd_1.5k_mvsjas		4.46 406	5.95 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	53.55 235	0.00 439	0.00 438	0.00 436	0.00 435

mmdepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

monomultidepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

test_blank		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

uanet_test		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

DCPMVS		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

sosnet-low-res		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

sosnet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

uncertanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

Regformer		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

uanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 436	0.00 435

WAC-MVS						49.45 380								31.56 412

FOURS1					93.95 46	61.77 257	93.96 77	91.92 157	62.14 338	86.57 53

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 33	94.77 26	96.51 24

PC_three_1452									80.91 57	94.07 2	96.83 20	83.57 4	99.12 5	95.70 7	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 33	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 61	71.42 242	90.67 21	96.85 18	74.45 20

eth-test2					0.00 443

eth-test					0.00 443

ZD-MVS					96.63 9	65.50 159		93.50 87	70.74 256	85.26 70	95.19 73	64.92 88	97.29 80	87.51 65	93.01 56

RE-MVS-def			80.48 158		92.02 103	58.56 321	90.90 220	90.45 218	62.76 331	78.89 138	94.46 91	49.30 277		78.77 151	86.77 137	92.28 194

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 58	95.28 1				92.34 30	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 10	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

test_241102_TWO								94.41 52	71.65 231	92.07 10	97.21 5	74.58 18	99.11 6	92.34 30	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 50	71.65 231	92.11 8	97.05 8	76.79 9	99.11 6

9.14			87.63 31		93.86 48		94.41 55	94.18 61	72.76 196	86.21 56	96.51 27	66.64 66	97.88 45	90.08 47	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 124	78.19 106

test_0728_THIRD									72.48 201	90.55 22	96.93 12	76.24 11	99.08 11	91.53 38	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 56					99.15 2	91.91 36	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 58	71.92 217	91.89 12	97.11 7	73.77 23

GSMVS																94.68 103

test_part2					96.29 19	68.16 89				90.78 19

sam_mvs1												57.85 180				94.68 103

sam_mvs												54.91 219

ambc				69.61 371	61.38 418	41.35 409	49.07 425	85.86 346		50.18 387	66.40 399	10.16 422	88.14 354	45.73 365	44.20 397	79.32 382

MTGPA								92.23 138

test_post1							78.95 369				20.70 432	53.05 240	91.50 322	60.43 301

test_post											23.01 429	56.49 201	92.67 284

patchmatchnet-post											67.62 398	57.62 183	90.25 331

GG-mvs-BLEND				86.53 74	91.91 112	69.67 53	75.02 388	94.75 36		78.67 146	90.85 178	77.91 7	94.56 216	72.25 199	93.74 45	95.36 66

MTMP							93.77 91	32.52 438

gm-plane-assit					88.42 195	67.04 120			78.62 100		91.83 161		97.37 74	76.57 163

test9_res														89.41 48	94.96 19	95.29 71

TEST9					94.18 41	67.28 111	94.16 64	93.51 85	71.75 228	85.52 65	95.33 62	68.01 56	97.27 84

test_8					94.19 40	67.19 113	94.15 66	93.42 92	71.87 222	85.38 68	95.35 61	68.19 54	96.95 110

agg_prior2														86.41 78	94.75 30	95.33 67

agg_prior					94.16 43	66.97 122		93.31 95		84.49 76			96.75 120

TestCases				72.46 357	79.57 345	51.42 368		68.61 409	51.25 388	45.88 397	81.23 314	19.86 409	86.58 368	38.98 389	57.01 371	79.39 380

test_prior4						67.18 115	93.92 80

test_prior2							95.10 38		75.40 148	85.25 71	95.61 53	67.94 57		87.47 67	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 106					96.84 117			95.05 84

旧先验2							92.00 173		59.37 358	87.54 46			93.47 259	75.39 171

新几何2							91.41 194

新几何1				84.73 136	92.32 93	64.28 190		91.46 183	59.56 357	79.77 127	92.90 133	56.95 193	96.57 125	63.40 281	92.91 58	93.34 159

旧先验1					91.94 108	60.74 281		91.50 181			94.36 95	65.23 83			91.84 72	94.55 110

无先验							92.71 138	92.61 128	62.03 339				97.01 100	66.63 252		93.97 141

原ACMM2							92.01 170

原ACMM1				84.42 151	93.21 68	64.27 191		93.40 94	65.39 306	79.51 130	92.50 141	58.11 179	96.69 121	65.27 271	93.96 40	92.32 192

test222					89.77 160	61.60 262	89.55 262	89.42 265	56.83 372	77.28 159	92.43 145	52.76 243			91.14 86	93.09 169

testdata2													96.09 149	61.26 297

segment_acmp												65.94 74

testdata				81.34 242	89.02 181	57.72 328		89.84 249	58.65 361	85.32 69	94.09 109	57.03 188	93.28 261	69.34 225	90.56 92	93.03 172

testdata1							89.21 271		77.55 120

test12				87.09 52	94.60 36	68.86 68		92.91 113		82.67 97		65.44 80	97.55 65		93.69 48	94.84 95

plane_prior7					86.94 238	61.51 263

plane_prior6					87.23 230	62.32 247						50.66 263

plane_prior5								91.31 187					95.55 178	76.74 161	78.53 217	88.39 259

plane_prior4											89.14 208

plane_prior3						61.95 255			79.09 90	72.53 210

plane_prior2							93.13 119		78.81 97

plane_prior1					87.15 232

plane_prior						62.42 243	93.85 84		79.38 82						78.80 214

n2								0.00 444

nn								0.00 444

door-mid								66.01 413

lessismore_v0				73.72 348	72.93 392	47.83 388		61.72 419		45.86 399	73.76 376	28.63 389	89.81 341	47.75 357	31.37 418	83.53 335

LGP-MVS_train				79.56 291	84.31 294	59.37 311		89.73 255	69.49 269	64.86 303	88.42 214	38.65 337	94.30 225	72.56 196	72.76 260	85.01 322

test11								93.01 109

door								66.57 412

HQP5-MVS						63.66 210

HQP-NCC					87.54 222		94.06 69		79.80 73	74.18 189

ACMP_Plane					87.54 222		94.06 69		79.80 73	74.18 189

BP-MVS														77.63 158

HQP4-MVS										74.18 189			95.61 173			88.63 253

HQP3-MVS								91.70 173							78.90 212

HQP2-MVS												51.63 255

NP-MVS					87.41 225	63.04 227					90.30 188

MDTV_nov1_ep13_2view						59.90 303	80.13 364		67.65 289	72.79 204		54.33 227		59.83 305		92.58 185

MDTV_nov1_ep13			72.61 276		89.06 180	68.48 77	80.33 360	90.11 238	71.84 224	71.81 222	75.92 369	53.01 241	93.92 247	48.04 352	73.38 255

ACMMP++_ref															71.63 268

ACMMP++															69.72 277

Test By Simon												54.21 229

ITE_SJBPF				70.43 369	74.44 386	47.06 394		77.32 385	60.16 353	54.04 370	83.53 281	23.30 400	84.01 382	43.07 373	61.58 351	80.21 377

DeepMVS_CX				34.71 413	51.45 425	24.73 433		28.48 439	31.46 419	17.49 429	52.75 415	5.80 430	42.60 434	18.18 422	19.42 427	36.81 426

High-resolution multi-view benchmark