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 63	96.26 38	72.84 30	99.38 1	92.64 29	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 17	96.19 40	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 31	91.58 16	97.22 5	79.93 5	99.10 9	83.12 111	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 78	94.37 56	72.48 205	92.07 10	96.85 19	83.82 2	99.15 2	91.53 39	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 92	92.83 80	64.03 197	93.06 122	94.33 58	82.19 39	93.65 3	96.15 42	85.89 1	97.19 89	91.02 43	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 42	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 13	89.07 36	96.80 22	70.86 43	99.06 15	92.64 29	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 64	94.91 82	74.11 21	98.91 18	87.26 71	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	71.65 235	92.11 8	97.21 6	76.79 9	99.11 6	92.34 31	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 138	93.00 76	58.16 330	96.72 9	94.41 52	86.50 8	90.25 27	97.83 1	75.46 14	98.67 25	92.78 28	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 95	96.04 24	63.70 209	95.04 41	95.19 22	86.74 7	91.53 18	95.15 75	73.86 22	97.58 63	93.38 23	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 33	95.78 49	65.94 74	99.10 9	92.99 26	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	71.92 221	90.55 23	96.93 13	73.77 23	99.08 11	91.91 37	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 36	88.90 37	96.35 33	71.89 40	98.63 26	88.76 57	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 18	89.81 6	93.66 54	75.15 5	90.61 240	93.43 91	84.06 18	86.20 58	90.17 194	72.42 35	96.98 106	93.09 25	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 15	86.74 53	96.20 39	66.56 68	98.76 24	89.03 56	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 64	94.15 63	68.77 284	90.74 21	97.27 3	76.09 12	98.49 29	90.58 47	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 24	87.67 33	93.21 68	68.72 73	93.85 85	94.03 66	74.18 168	91.74 13	96.67 25	65.61 79	98.42 33	89.24 53	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 34	89.71 7	92.06 102	76.72 1	95.75 20	93.26 97	83.86 19	89.55 34	96.06 44	53.55 236	97.89 45	91.10 41	93.31 53	94.54 113

TSAR-MVS + MP.		88.11 20	88.64 20	86.54 73	91.73 117	68.04 91	90.36 246	93.55 84	82.89 29	91.29 19	92.89 135	72.27 37	96.03 156	87.99 61	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
fmvsm_s_conf0.5_n_8		87.96 21	88.93 17	85.07 123	88.43 196	61.78 260	94.73 51	91.74 169	85.87 9	91.66 15	97.50 2	64.03 100	98.33 34	96.28 3	90.08 98	95.10 82

TSAR-MVS + GP.		87.96 21	88.37 23	86.70 66	93.51 62	65.32 161	95.15 36	93.84 69	78.17 110	85.93 62	94.80 85	75.80 13	98.21 36	89.38 50	88.78 113	96.59 19

DeepC-MVS_fast	79.48 2	87.95 23	88.00 28	87.79 31	95.86 27	68.32 81	95.74 21	94.11 64	83.82 20	83.49 86	96.19 40	64.53 95	98.44 31	83.42 110	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 24	87.38 38	89.55 12	91.41 129	76.43 3	95.74 21	93.12 105	83.53 23	89.55 34	95.95 47	53.45 240	97.68 53	91.07 42	92.62 60	94.54 113

EPNet		87.84 25	88.38 22	86.23 83	93.30 65	66.05 143	95.26 32	94.84 32	87.09 5	88.06 40	94.53 91	66.79 65	97.34 78	83.89 104	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 26	87.77 31	87.63 38	89.24 177	71.18 24	96.57 12	92.90 114	82.70 33	87.13 48	95.27 68	64.99 85	95.80 161	89.34 51	91.80 73	95.93 45

test_fmvsm_n_1920		87.69 27	88.50 21	85.27 117	87.05 238	63.55 216	93.69 95	91.08 205	84.18 17	90.17 29	97.04 10	67.58 60	97.99 41	95.72 6	90.03 99	94.26 125

fmvsm_l_conf0.5_n_3		87.54 28	88.29 24	85.30 114	86.92 244	62.63 242	95.02 43	90.28 236	84.95 12	90.27 26	96.86 17	65.36 81	97.52 68	94.93 11	90.03 99	95.76 50

APDe-MVS		87.54 28	87.84 30	86.65 67	96.07 23	66.30 139	94.84 47	93.78 70	69.35 275	88.39 39	96.34 34	67.74 59	97.66 58	90.62 46	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 30	88.72 19	83.84 172	86.89 246	60.04 306	95.05 39	92.17 148	84.80 14	92.27 6	96.37 31	64.62 92	96.54 129	94.43 15	91.86 71	94.94 91

fmvsm_l_conf0.5_n		87.49 31	88.19 26	85.39 110	86.95 239	64.37 187	94.30 61	88.45 311	80.51 61	92.70 4	96.86 17	69.98 48	97.15 94	95.83 5	88.08 121	94.65 107

SD-MVS		87.49 31	87.49 36	87.50 42	93.60 56	68.82 70	93.90 82	92.63 128	76.86 132	87.90 42	95.76 50	66.17 71	97.63 60	89.06 55	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 33	88.15 27	85.30 114	87.10 236	64.19 194	94.41 56	88.14 320	80.24 69	92.54 5	96.97 12	69.52 50	97.17 90	95.89 4	88.51 116	94.56 110

dcpmvs_2		87.37 34	87.55 35	86.85 58	95.04 32	68.20 88	90.36 246	90.66 217	79.37 85	81.20 108	93.67 119	74.73 16	96.55 128	90.88 44	92.00 69	95.82 48

alignmvs		87.28 35	86.97 42	88.24 27	91.30 131	71.14 26	95.61 25	93.56 83	79.30 86	87.07 50	95.25 70	68.43 52	96.93 114	87.87 62	84.33 162	96.65 17

train_agg		87.21 36	87.42 37	86.60 69	94.18 41	67.28 111	94.16 65	93.51 85	71.87 226	85.52 66	95.33 63	68.19 54	97.27 85	89.09 54	94.90 22	95.25 77

MG-MVS		87.11 37	86.27 53	89.62 8	97.79 1	76.27 4	94.96 45	94.49 48	78.74 101	83.87 84	92.94 133	64.34 96	96.94 112	75.19 177	94.09 38	95.66 53

SF-MVS		87.03 38	87.09 40	86.84 59	92.70 86	67.45 109	93.64 98	93.76 73	70.78 259	86.25 56	96.44 30	66.98 63	97.79 49	88.68 58	94.56 34	95.28 73

fmvsm_s_conf0.5_n_3		86.88 39	87.99 29	83.58 183	87.26 231	60.74 286	93.21 119	87.94 327	84.22 16	91.70 14	97.27 3	65.91 76	95.02 197	93.95 20	90.42 94	94.99 88

CSCG		86.87 40	86.26 54	88.72 17	95.05 31	70.79 29	93.83 90	95.33 18	68.48 288	77.63 155	94.35 100	73.04 28	98.45 30	84.92 93	93.71 47	96.92 14

sasdasda		86.85 41	86.25 55	88.66 20	91.80 115	71.92 16	93.54 103	91.71 172	80.26 66	87.55 45	95.25 70	63.59 111	96.93 114	88.18 59	84.34 160	97.11 9

canonicalmvs		86.85 41	86.25 55	88.66 20	91.80 115	71.92 16	93.54 103	91.71 172	80.26 66	87.55 45	95.25 70	63.59 111	96.93 114	88.18 59	84.34 160	97.11 9

UBG		86.83 43	86.70 48	87.20 48	93.07 74	69.81 47	93.43 111	95.56 13	81.52 46	81.50 104	92.12 154	73.58 26	96.28 141	84.37 99	85.20 152	95.51 59

PHI-MVS		86.83 43	86.85 47	86.78 63	93.47 63	65.55 157	95.39 30	95.10 25	71.77 231	85.69 65	96.52 27	62.07 133	98.77 23	86.06 83	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 45	86.90 45	86.58 71	90.42 147	66.38 136	96.09 17	93.87 68	77.73 119	84.01 83	95.66 52	63.39 114	97.94 42	87.40 69	93.55 50	95.42 60
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 46	87.63 32	84.27 160	86.15 260	61.48 270	94.69 52	91.16 197	83.79 22	90.51 25	96.28 36	64.24 97	98.22 35	95.00 10	86.88 133	93.11 169

PVSNet_Blended		86.73 47	86.86 46	86.31 82	93.76 50	67.53 106	96.33 16	93.61 81	82.34 38	81.00 113	93.08 129	63.19 118	97.29 81	87.08 74	91.38 81	94.13 134

testing11		86.71 48	86.44 52	87.55 40	93.54 60	71.35 21	93.65 97	95.58 11	81.36 53	80.69 116	92.21 153	72.30 36	96.46 134	85.18 89	83.43 170	94.82 99

test_fmvsmconf_n		86.58 49	87.17 39	84.82 131	85.28 275	62.55 243	94.26 63	89.78 255	83.81 21	87.78 44	96.33 35	65.33 82	96.98 106	94.40 16	87.55 127	94.95 90

BP-MVS1		86.54 50	86.68 50	86.13 86	87.80 219	67.18 115	92.97 127	95.62 10	79.92 72	82.84 93	94.14 109	74.95 15	96.46 134	82.91 114	88.96 112	94.74 101

jason		86.40 51	86.17 57	87.11 51	86.16 259	70.54 32	95.71 24	92.19 145	82.00 41	84.58 76	94.34 101	61.86 135	95.53 182	87.76 63	90.89 87	95.27 74
jason: jason.
fmvsm_s_conf0.5_n		86.39 52	86.91 44	84.82 131	87.36 230	63.54 217	94.74 49	90.02 248	82.52 34	90.14 30	96.92 15	62.93 123	97.84 48	95.28 9	82.26 180	93.07 172

fmvsm_s_conf0.5_n_5		86.38 53	86.94 43	84.71 140	84.67 287	63.29 222	94.04 74	89.99 250	82.88 30	87.85 43	96.03 45	62.89 125	96.36 138	94.15 17	89.95 101	94.48 119

WTY-MVS		86.32 54	85.81 65	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 32	81.91 101	94.73 86	67.93 58	97.63 60	79.55 144	82.25 181	96.54 22

myMVS_eth3d28		86.31 55	86.15 58	86.78 63	93.56 58	70.49 33	92.94 129	95.28 19	82.47 35	78.70 146	92.07 156	72.45 34	95.41 184	82.11 120	85.78 148	94.44 121

MSLP-MVS++		86.27 56	85.91 64	87.35 45	92.01 106	68.97 67	95.04 41	92.70 120	79.04 96	81.50 104	96.50 29	58.98 171	96.78 120	83.49 109	93.93 41	96.29 35

VNet		86.20 57	85.65 69	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 106	86.00 61	93.07 130	58.22 178	97.00 102	85.22 87	84.33 162	96.52 23

MVS_111021_HR		86.19 58	85.80 66	87.37 44	93.17 70	69.79 48	93.99 77	93.76 73	79.08 93	78.88 142	93.99 113	62.25 132	98.15 38	85.93 84	91.15 85	94.15 133

SPE-MVS-test		86.14 59	87.01 41	83.52 184	92.63 88	59.36 318	95.49 27	91.92 158	80.09 70	85.46 68	95.53 58	61.82 137	95.77 164	86.77 78	93.37 52	95.41 61

ACMMP_NAP		86.05 60	85.80 66	86.80 62	91.58 121	67.53 106	91.79 185	93.49 88	74.93 158	84.61 75	95.30 65	59.42 162	97.92 43	86.13 81	94.92 20	94.94 91

testing99		86.01 61	85.47 71	87.63 38	93.62 55	71.25 23	93.47 109	95.23 21	80.42 64	80.60 118	91.95 159	71.73 41	96.50 132	80.02 141	82.22 182	95.13 80

ETV-MVS		86.01 61	86.11 59	85.70 102	90.21 152	67.02 121	93.43 111	91.92 158	81.21 55	84.13 82	94.07 112	60.93 145	95.63 172	89.28 52	89.81 102	94.46 120

testing91		85.93 63	85.31 75	87.78 32	93.59 57	71.47 19	93.50 106	95.08 28	80.26 66	80.53 119	91.93 160	70.43 45	96.51 131	80.32 139	82.13 184	95.37 64

APD-MVS		85.93 63	85.99 62	85.76 99	95.98 26	65.21 164	93.59 101	92.58 130	66.54 303	86.17 59	95.88 48	63.83 104	97.00 102	86.39 80	92.94 57	95.06 84
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 65	85.46 72	87.18 49	88.20 207	72.42 15	92.41 156	92.77 118	82.11 40	80.34 122	93.07 130	68.27 53	95.02 197	78.39 158	93.59 49	94.09 136

CS-MVS		85.80 66	86.65 51	83.27 194	92.00 107	58.92 322	95.31 31	91.86 163	79.97 71	84.82 74	95.40 61	62.26 131	95.51 183	86.11 82	92.08 68	95.37 64

fmvsm_s_conf0.5_n_a		85.75 67	86.09 60	84.72 138	85.73 269	63.58 214	93.79 91	89.32 273	81.42 51	90.21 28	96.91 16	62.41 130	97.67 55	94.48 14	80.56 201	92.90 178

test_fmvsmconf0.1_n		85.71 68	86.08 61	84.62 146	80.83 334	62.33 248	93.84 88	88.81 299	83.50 24	87.00 51	96.01 46	63.36 115	96.93 114	94.04 19	87.29 130	94.61 109

CDPH-MVS		85.71 68	85.46 72	86.46 75	94.75 34	67.19 113	93.89 83	92.83 116	70.90 255	83.09 91	95.28 66	63.62 109	97.36 76	80.63 135	94.18 37	94.84 96

casdiffmvs_mvg		85.66 70	85.18 77	87.09 52	88.22 206	69.35 59	93.74 94	91.89 161	81.47 47	80.10 124	91.45 169	64.80 90	96.35 139	87.23 72	87.69 125	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 71	85.93 63	84.68 142	82.95 317	63.48 219	94.03 76	89.46 267	81.69 44	89.86 31	96.74 23	61.85 136	97.75 51	94.74 13	82.01 186	92.81 180

MGCFI-Net		85.59 72	85.73 68	85.17 121	91.41 129	62.44 244	92.87 133	91.31 189	79.65 78	86.99 52	95.14 76	62.90 124	96.12 148	87.13 73	84.13 167	96.96 13

GDP-MVS		85.54 73	85.32 74	86.18 84	87.64 222	67.95 95	92.91 132	92.36 135	77.81 116	83.69 85	94.31 103	72.84 30	96.41 136	80.39 138	85.95 146	94.19 129

DeepC-MVS	77.85 3	85.52 74	85.24 76	86.37 79	88.80 187	66.64 130	92.15 164	93.68 79	81.07 56	76.91 165	93.64 120	62.59 127	98.44 31	85.50 85	92.84 59	94.03 140
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 75	84.87 83	86.84 59	88.25 204	69.07 63	93.04 124	91.76 168	81.27 54	80.84 115	92.07 156	64.23 98	96.06 154	84.98 92	87.43 129	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 76	85.08 79	86.06 87	93.09 73	65.65 153	93.89 83	93.41 93	73.75 179	79.94 126	94.68 88	60.61 148	98.03 40	82.63 117	93.72 46	94.52 115

fmvsm_s_conf0.5_n_7		85.24 77	86.69 49	80.91 262	84.52 292	60.10 304	93.35 114	90.35 229	83.41 25	86.54 55	96.27 37	60.50 149	90.02 346	94.84 12	90.38 95	92.61 184

MP-MVS-pluss		85.24 77	85.13 78	85.56 105	91.42 126	65.59 155	91.54 195	92.51 132	74.56 161	80.62 117	95.64 53	59.15 166	97.00 102	86.94 76	93.80 43	94.07 138
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 79	84.69 86	86.63 68	92.91 78	69.91 43	92.61 146	95.80 9	80.31 65	80.38 121	92.27 150	68.73 51	95.19 194	75.94 171	83.27 172	94.81 100

PAPR		85.15 80	84.47 87	87.18 49	96.02 25	68.29 82	91.85 183	93.00 111	76.59 139	79.03 138	95.00 77	61.59 138	97.61 62	78.16 159	89.00 111	95.63 54

fmvsm_s_conf0.5_n_2		85.06 81	85.60 70	83.44 190	86.92 244	60.53 293	94.41 56	87.31 333	83.30 26	88.72 38	96.72 24	54.28 229	97.75 51	94.07 18	84.68 159	92.04 206

MP-MVS		85.02 82	84.97 81	85.17 121	92.60 89	64.27 192	93.24 116	92.27 138	73.13 190	79.63 130	94.43 94	61.90 134	97.17 90	85.00 91	92.56 61	94.06 139
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 83	84.44 88	86.71 65	88.33 201	68.73 72	90.24 251	91.82 167	81.05 57	81.18 109	92.50 142	63.69 107	96.08 153	84.45 98	86.71 140	95.32 69

CHOSEN 1792x2688		84.98 84	83.45 102	89.57 11	89.94 157	75.14 6	92.07 170	92.32 136	81.87 42	75.68 174	88.27 221	60.18 152	98.60 27	80.46 137	90.27 97	94.96 89

MVSMamba_PlusPlus		84.97 85	83.65 96	88.93 14	90.17 153	74.04 8	87.84 301	92.69 123	62.18 341	81.47 106	87.64 235	71.47 42	96.28 141	84.69 95	94.74 31	96.47 28

EIA-MVS		84.84 86	84.88 82	84.69 141	91.30 131	62.36 247	93.85 85	92.04 151	79.45 81	79.33 135	94.28 105	62.42 129	96.35 139	80.05 140	91.25 84	95.38 63

fmvsm_s_conf0.1_n_a		84.76 87	84.84 84	84.53 148	80.23 345	63.50 218	92.79 135	88.73 302	80.46 62	89.84 32	96.65 26	60.96 144	97.57 65	93.80 21	80.14 203	92.53 189

HFP-MVS		84.73 88	84.40 89	85.72 101	93.75 52	65.01 170	93.50 106	93.19 101	72.19 215	79.22 136	94.93 80	59.04 169	97.67 55	81.55 124	92.21 64	94.49 118

MVS		84.66 89	82.86 121	90.06 2	90.93 138	74.56 7	87.91 299	95.54 14	68.55 286	72.35 220	94.71 87	59.78 158	98.90 20	81.29 130	94.69 32	96.74 16

GST-MVS		84.63 90	84.29 90	85.66 103	92.82 82	65.27 162	93.04 124	93.13 104	73.20 188	78.89 139	94.18 108	59.41 163	97.85 47	81.45 126	92.48 63	93.86 148

EC-MVSNet		84.53 91	85.04 80	83.01 200	89.34 169	61.37 273	94.42 55	91.09 203	77.91 114	83.24 87	94.20 107	58.37 176	95.40 185	85.35 86	91.41 80	92.27 200

fmvsm_s_conf0.1_n_2		84.40 92	84.78 85	83.27 194	85.25 276	60.41 296	94.13 68	85.69 353	83.05 28	87.99 41	96.37 31	52.75 245	97.68 53	93.75 22	84.05 168	91.71 211

ACMMPR		84.37 93	84.06 91	85.28 116	93.56 58	64.37 187	93.50 106	93.15 103	72.19 215	78.85 144	94.86 83	56.69 198	97.45 70	81.55 124	92.20 65	94.02 141

region2R		84.36 94	84.03 92	85.36 112	93.54 60	64.31 190	93.43 111	92.95 112	72.16 218	78.86 143	94.84 84	56.97 193	97.53 67	81.38 128	92.11 67	94.24 127

LFMVS		84.34 95	82.73 123	89.18 13	94.76 33	73.25 11	94.99 44	91.89 161	71.90 223	82.16 100	93.49 124	47.98 293	97.05 97	82.55 118	84.82 155	97.25 8

test_yl		84.28 96	83.16 112	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 278	81.09 110	92.88 136	57.00 191	97.44 71	81.11 132	81.76 188	96.23 38

DCV-MVSNet		84.28 96	83.16 112	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 278	81.09 110	92.88 136	57.00 191	97.44 71	81.11 132	81.76 188	96.23 38

diffmvs		84.28 96	83.83 93	85.61 104	87.40 228	68.02 92	90.88 226	89.24 276	80.54 60	81.64 103	92.52 141	59.83 157	94.52 222	87.32 70	85.11 153	94.29 124
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 96	83.36 108	87.02 55	92.22 96	67.74 99	84.65 327	94.50 47	79.15 90	82.23 99	87.93 230	66.88 64	96.94 112	80.53 136	82.20 183	96.39 33

ETVMVS		84.22 100	83.71 94	85.76 99	92.58 90	68.25 86	92.45 155	95.53 15	79.54 80	79.46 132	91.64 167	70.29 46	94.18 234	69.16 233	82.76 178	94.84 96

MAR-MVS		84.18 101	83.43 103	86.44 76	96.25 21	65.93 148	94.28 62	94.27 60	74.41 163	79.16 137	95.61 54	53.99 231	98.88 22	69.62 227	93.26 54	94.50 117
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		84.16 102	83.20 111	87.05 54	91.56 122	69.82 46	89.99 260	92.05 150	77.77 118	82.84 93	86.57 252	63.93 103	96.09 150	74.91 182	89.18 108	95.25 77

CANet_DTU		84.09 103	83.52 97	85.81 96	90.30 150	66.82 125	91.87 181	89.01 291	85.27 10	86.09 60	93.74 117	47.71 297	96.98 106	77.90 161	89.78 104	93.65 153

ET-MVSNet_ETH3D		84.01 104	83.15 114	86.58 71	90.78 143	70.89 28	94.74 49	94.62 43	81.44 50	58.19 357	93.64 120	73.64 25	92.35 302	82.66 116	78.66 219	96.50 27

PVSNet_Blended_VisFu		83.97 105	83.50 99	85.39 110	90.02 155	66.59 133	93.77 92	91.73 170	77.43 127	77.08 164	89.81 202	63.77 106	96.97 109	79.67 143	88.21 119	92.60 185

MTAPA		83.91 106	83.38 107	85.50 106	91.89 113	65.16 166	81.75 355	92.23 139	75.32 153	80.53 119	95.21 73	56.06 207	97.16 93	84.86 94	92.55 62	94.18 130

XVS		83.87 107	83.47 101	85.05 124	93.22 66	63.78 202	92.92 130	92.66 125	73.99 171	78.18 149	94.31 103	55.25 213	97.41 73	79.16 148	91.58 77	93.95 143

Effi-MVS+		83.82 108	82.76 122	86.99 56	89.56 165	69.40 54	91.35 206	86.12 347	72.59 202	83.22 90	92.81 139	59.60 160	96.01 158	81.76 123	87.80 124	95.56 57

test_fmvsmvis_n_1920		83.80 109	83.48 100	84.77 135	82.51 320	63.72 207	91.37 204	83.99 370	81.42 51	77.68 154	95.74 51	58.37 176	97.58 63	93.38 23	86.87 134	93.00 175

EI-MVSNet-Vis-set		83.77 110	83.67 95	84.06 164	92.79 85	63.56 215	91.76 188	94.81 34	79.65 78	77.87 152	94.09 110	63.35 116	97.90 44	79.35 146	79.36 210	90.74 229

MVSFormer		83.75 111	82.88 120	86.37 79	89.24 177	71.18 24	89.07 279	90.69 214	65.80 308	87.13 48	94.34 101	64.99 85	92.67 289	72.83 195	91.80 73	95.27 74

CP-MVS		83.71 112	83.40 106	84.65 143	93.14 71	63.84 200	94.59 53	92.28 137	71.03 253	77.41 158	94.92 81	55.21 216	96.19 145	81.32 129	90.70 89	93.91 145

test_fmvsmconf0.01_n		83.70 113	83.52 97	84.25 161	75.26 391	61.72 264	92.17 163	87.24 335	82.36 37	84.91 73	95.41 60	55.60 211	96.83 119	92.85 27	85.87 147	94.21 128

baseline2		83.68 114	83.42 105	84.48 151	87.37 229	66.00 145	90.06 255	95.93 8	79.71 77	69.08 258	90.39 187	77.92 6	96.28 141	78.91 153	81.38 192	91.16 225

reproduce-ours		83.51 115	83.33 109	84.06 164	92.18 99	60.49 294	90.74 232	92.04 151	64.35 318	83.24 87	95.59 56	59.05 167	97.27 85	83.61 106	89.17 109	94.41 122

our_new_method		83.51 115	83.33 109	84.06 164	92.18 99	60.49 294	90.74 232	92.04 151	64.35 318	83.24 87	95.59 56	59.05 167	97.27 85	83.61 106	89.17 109	94.41 122

thisisatest0515		83.41 117	82.49 127	86.16 85	89.46 168	68.26 84	93.54 103	94.70 39	74.31 166	75.75 172	90.92 177	72.62 32	96.52 130	69.64 225	81.50 191	93.71 151

PVSNet_BlendedMVS		83.38 118	83.43 103	83.22 196	93.76 50	67.53 106	94.06 70	93.61 81	79.13 91	81.00 113	85.14 268	63.19 118	97.29 81	87.08 74	73.91 257	84.83 329

test2506		83.29 119	82.92 119	84.37 155	88.39 199	63.18 228	92.01 173	91.35 188	77.66 121	78.49 148	91.42 170	64.58 94	95.09 196	73.19 191	89.23 106	94.85 93

PGM-MVS		83.25 120	82.70 124	84.92 127	92.81 84	64.07 196	90.44 241	92.20 143	71.28 247	77.23 161	94.43 94	55.17 217	97.31 80	79.33 147	91.38 81	93.37 159

HPM-MVS		83.25 120	82.95 118	84.17 162	92.25 95	62.88 237	90.91 223	91.86 163	70.30 264	77.12 162	93.96 114	56.75 196	96.28 141	82.04 121	91.34 83	93.34 160
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 122	82.96 116	83.73 176	92.02 103	59.74 310	90.37 245	92.08 149	63.70 325	82.86 92	95.48 59	58.62 173	97.17 90	83.06 112	88.42 117	94.26 125

EI-MVSNet-UG-set		83.14 123	82.96 116	83.67 181	92.28 94	63.19 227	91.38 203	94.68 40	79.22 88	76.60 167	93.75 116	62.64 126	97.76 50	78.07 160	78.01 222	90.05 238

testing3-2		83.11 124	83.15 114	82.98 201	91.92 110	64.01 198	94.39 59	95.37 16	78.32 107	75.53 179	90.06 200	73.18 27	93.18 268	74.34 187	75.27 246	91.77 210

VDD-MVS		83.06 125	81.81 136	86.81 61	90.86 141	67.70 100	95.40 29	91.50 183	75.46 150	81.78 102	92.34 149	40.09 337	97.13 95	86.85 77	82.04 185	95.60 55

h-mvs33		83.01 126	82.56 126	84.35 156	89.34 169	62.02 254	92.72 138	93.76 73	81.45 48	82.73 96	92.25 152	60.11 153	97.13 95	87.69 64	62.96 338	93.91 145

PAPM_NR		82.97 127	81.84 135	86.37 79	94.10 44	66.76 128	87.66 305	92.84 115	69.96 268	74.07 197	93.57 122	63.10 121	97.50 69	70.66 220	90.58 91	94.85 93

mPP-MVS		82.96 128	82.44 128	84.52 149	92.83 80	62.92 235	92.76 136	91.85 165	71.52 243	75.61 177	94.24 106	53.48 239	96.99 105	78.97 151	90.73 88	93.64 154

SR-MVS		82.81 129	82.58 125	83.50 187	93.35 64	61.16 276	92.23 161	91.28 194	64.48 317	81.27 107	95.28 66	53.71 235	95.86 160	82.87 115	88.77 114	93.49 157

DP-MVS Recon		82.73 130	81.65 137	85.98 89	97.31 4	67.06 118	95.15 36	91.99 155	69.08 281	76.50 169	93.89 115	54.48 225	98.20 37	70.76 218	85.66 150	92.69 181

CLD-MVS		82.73 130	82.35 130	83.86 171	87.90 214	67.65 102	95.45 28	92.18 146	85.06 11	72.58 213	92.27 150	52.46 248	95.78 162	84.18 100	79.06 214	88.16 266
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 132	82.38 129	83.73 176	89.25 174	59.58 313	92.24 160	94.89 31	77.96 112	79.86 127	92.38 147	56.70 197	97.05 97	77.26 164	80.86 196	94.55 111

3Dnovator	73.91 6	82.69 133	80.82 150	88.31 26	89.57 164	71.26 22	92.60 147	94.39 55	78.84 98	67.89 279	92.48 145	48.42 288	98.52 28	68.80 238	94.40 36	95.15 79

RRT-MVS		82.61 134	81.16 141	86.96 57	91.10 135	68.75 71	87.70 304	92.20 143	76.97 130	72.68 209	87.10 246	51.30 260	96.41 136	83.56 108	87.84 123	95.74 51

MVSTER		82.47 135	82.05 131	83.74 174	92.68 87	69.01 65	91.90 180	93.21 98	79.83 73	72.14 221	85.71 263	74.72 17	94.72 209	75.72 173	72.49 267	87.50 273

TESTMET0.1,1		82.41 136	81.98 134	83.72 178	88.08 208	63.74 204	92.70 140	93.77 72	79.30 86	77.61 156	87.57 237	58.19 179	94.08 239	73.91 189	86.68 141	93.33 162

CostFormer		82.33 137	81.15 142	85.86 94	89.01 182	68.46 78	82.39 352	93.01 109	75.59 148	80.25 123	81.57 313	72.03 39	94.96 201	79.06 150	77.48 230	94.16 132

API-MVS		82.28 138	80.53 159	87.54 41	96.13 22	70.59 31	93.63 99	91.04 209	65.72 310	75.45 180	92.83 138	56.11 206	98.89 21	64.10 282	89.75 105	93.15 167

IB-MVS	77.80 4	82.18 139	80.46 161	87.35 45	89.14 179	70.28 36	95.59 26	95.17 24	78.85 97	70.19 246	85.82 261	70.66 44	97.67 55	72.19 207	66.52 308	94.09 136
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		82.16 140	81.12 143	85.26 118	86.42 252	68.72 73	92.59 149	90.44 226	73.12 191	84.20 79	94.36 96	38.04 350	95.73 166	84.12 101	86.81 135	91.33 218

xiu_mvs_v1_base		82.16 140	81.12 143	85.26 118	86.42 252	68.72 73	92.59 149	90.44 226	73.12 191	84.20 79	94.36 96	38.04 350	95.73 166	84.12 101	86.81 135	91.33 218

xiu_mvs_v1_base_debi		82.16 140	81.12 143	85.26 118	86.42 252	68.72 73	92.59 149	90.44 226	73.12 191	84.20 79	94.36 96	38.04 350	95.73 166	84.12 101	86.81 135	91.33 218

3Dnovator+	73.60 7	82.10 143	80.60 157	86.60 69	90.89 140	66.80 127	95.20 34	93.44 90	74.05 170	67.42 286	92.49 144	49.46 278	97.65 59	70.80 217	91.68 75	95.33 67

MVS_111021_LR		82.02 144	81.52 138	83.51 186	88.42 197	62.88 237	89.77 263	88.93 295	76.78 135	75.55 178	93.10 127	50.31 267	95.38 187	83.82 105	87.02 132	92.26 201

PMMVS		81.98 145	82.04 132	81.78 236	89.76 161	56.17 350	91.13 219	90.69 214	77.96 112	80.09 125	93.57 122	46.33 307	94.99 200	81.41 127	87.46 128	94.17 131

baseline1		81.84 146	81.03 147	84.28 159	91.60 120	66.62 131	91.08 220	91.66 177	81.87 42	74.86 186	91.67 166	69.98 48	94.92 204	71.76 210	64.75 325	91.29 223

EPP-MVSNet		81.79 147	81.52 138	82.61 211	88.77 188	60.21 302	93.02 126	93.66 80	68.52 287	72.90 207	90.39 187	72.19 38	94.96 201	74.93 181	79.29 213	92.67 182

WBMVS		81.67 148	80.98 149	83.72 178	93.07 74	69.40 54	94.33 60	93.05 107	76.84 133	72.05 223	84.14 279	74.49 19	93.88 253	72.76 198	68.09 296	87.88 268

test_vis1_n_1920		81.66 149	82.01 133	80.64 265	82.24 322	55.09 359	94.76 48	86.87 337	81.67 45	84.40 78	94.63 89	38.17 347	94.67 213	91.98 36	83.34 171	92.16 204

APD-MVS_3200maxsize		81.64 150	81.32 140	82.59 213	92.36 92	58.74 324	91.39 201	91.01 210	63.35 329	79.72 129	94.62 90	51.82 251	96.14 147	79.71 142	87.93 122	92.89 179

mvsmamba		81.55 151	80.72 152	84.03 168	91.42 126	66.93 123	83.08 344	89.13 284	78.55 105	67.50 284	87.02 247	51.79 253	90.07 345	87.48 67	90.49 93	95.10 82

ACMMP		81.49 152	80.67 154	83.93 170	91.71 118	62.90 236	92.13 165	92.22 142	71.79 230	71.68 229	93.49 124	50.32 266	96.96 110	78.47 157	84.22 166	91.93 208
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		81.43 153	80.74 151	83.52 184	86.26 256	64.45 181	92.09 168	90.65 218	75.83 146	73.95 199	89.81 202	63.97 102	92.91 279	71.27 213	82.82 175	93.20 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 154	79.99 166	85.46 107	90.39 149	68.40 79	86.88 316	90.61 219	74.41 163	70.31 245	84.67 273	63.79 105	92.32 304	73.13 192	85.70 149	95.67 52

ECVR-MVS		81.29 155	80.38 162	84.01 169	88.39 199	61.96 256	92.56 152	86.79 339	77.66 121	76.63 166	91.42 170	46.34 306	95.24 193	74.36 186	89.23 106	94.85 93

guyue		81.23 156	80.57 158	83.21 198	86.64 247	61.85 258	92.52 153	92.78 117	78.69 102	74.92 185	89.42 206	50.07 270	95.35 188	80.79 134	79.31 212	92.42 191

thisisatest0530		81.15 157	80.07 163	84.39 154	88.26 203	65.63 154	91.40 199	94.62 43	71.27 248	70.93 236	89.18 209	72.47 33	96.04 155	65.62 271	76.89 237	91.49 214

Fast-Effi-MVS+		81.14 158	80.01 165	84.51 150	90.24 151	65.86 149	94.12 69	89.15 282	73.81 178	75.37 181	88.26 222	57.26 186	94.53 221	66.97 256	84.92 154	93.15 167

HQP-MVS		81.14 158	80.64 155	82.64 210	87.54 224	63.66 212	94.06 70	91.70 175	79.80 74	74.18 193	90.30 189	51.63 256	95.61 174	77.63 162	78.90 215	88.63 257

hse-mvs2		81.12 160	81.11 146	81.16 250	86.52 251	57.48 339	89.40 271	91.16 197	81.45 48	82.73 96	90.49 185	60.11 153	94.58 214	87.69 64	60.41 365	91.41 217

SR-MVS-dyc-post		81.06 161	80.70 153	82.15 227	92.02 103	58.56 327	90.90 224	90.45 222	62.76 336	78.89 139	94.46 92	51.26 261	95.61 174	78.77 155	86.77 138	92.28 197

HyFIR lowres test		81.03 162	79.56 174	85.43 108	87.81 218	68.11 90	90.18 252	90.01 249	70.65 261	72.95 206	86.06 259	63.61 110	94.50 223	75.01 180	79.75 207	93.67 152

nrg030		80.93 163	79.86 168	84.13 163	83.69 306	68.83 69	93.23 117	91.20 195	75.55 149	75.06 183	88.22 225	63.04 122	94.74 208	81.88 122	66.88 305	88.82 255

Vis-MVSNet		80.92 164	79.98 167	83.74 174	88.48 193	61.80 259	93.44 110	88.26 319	73.96 174	77.73 153	91.76 163	49.94 272	94.76 206	65.84 268	90.37 96	94.65 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 165	80.02 164	83.33 191	87.87 215	60.76 284	92.62 145	86.86 338	77.86 115	75.73 173	91.39 172	46.35 305	94.70 212	72.79 197	88.68 115	94.52 115

UWE-MVS		80.81 166	81.01 148	80.20 275	89.33 171	57.05 344	91.91 179	94.71 38	75.67 147	75.01 184	89.37 207	63.13 120	91.44 328	67.19 253	82.80 177	92.12 205

1314		80.70 167	78.95 186	85.94 91	87.77 221	67.56 104	87.91 299	92.55 131	72.17 217	67.44 285	93.09 128	50.27 268	97.04 100	71.68 212	87.64 126	93.23 164

AstraMVS		80.66 168	79.79 170	83.28 193	85.07 282	61.64 266	92.19 162	90.58 220	79.40 83	74.77 188	90.18 192	45.93 311	95.61 174	83.04 113	76.96 236	92.60 185

tpmrst		80.57 169	79.14 184	84.84 130	90.10 154	68.28 83	81.70 356	89.72 262	77.63 123	75.96 171	79.54 345	64.94 87	92.71 286	75.43 175	77.28 233	93.55 155

1112_ss		80.56 170	79.83 169	82.77 205	88.65 189	60.78 282	92.29 158	88.36 313	72.58 203	72.46 217	94.95 78	65.09 84	93.42 265	66.38 262	77.71 224	94.10 135

VDDNet		80.50 171	78.26 194	87.21 47	86.19 257	69.79 48	94.48 54	91.31 189	60.42 355	79.34 134	90.91 178	38.48 345	96.56 127	82.16 119	81.05 194	95.27 74

BH-w/o		80.49 172	79.30 181	84.05 167	90.83 142	64.36 189	93.60 100	89.42 270	74.35 165	69.09 257	90.15 196	55.23 215	95.61 174	64.61 279	86.43 144	92.17 203

test_cas_vis1_n_1920		80.45 173	80.61 156	79.97 284	78.25 372	57.01 346	94.04 74	88.33 314	79.06 95	82.81 95	93.70 118	38.65 342	91.63 320	90.82 45	79.81 205	91.27 224

TAMVS		80.37 174	79.45 177	83.13 199	85.14 279	63.37 220	91.23 213	90.76 213	74.81 160	72.65 211	88.49 216	60.63 147	92.95 274	69.41 229	81.95 187	93.08 171

HQP_MVS		80.34 175	79.75 171	82.12 229	86.94 240	62.42 245	93.13 120	91.31 189	78.81 99	72.53 214	89.14 211	50.66 264	95.55 180	76.74 165	78.53 220	88.39 263

SDMVSNet		80.26 176	78.88 187	84.40 153	89.25 174	67.63 103	85.35 323	93.02 108	76.77 136	70.84 237	87.12 244	47.95 294	96.09 150	85.04 90	74.55 248	89.48 248

HPM-MVS_fast		80.25 177	79.55 176	82.33 219	91.55 123	59.95 307	91.32 208	89.16 281	65.23 314	74.71 190	93.07 130	47.81 296	95.74 165	74.87 184	88.23 118	91.31 222

ab-mvs		80.18 178	78.31 193	85.80 97	88.44 195	65.49 160	83.00 347	92.67 124	71.82 229	77.36 159	85.01 269	54.50 222	96.59 124	76.35 170	75.63 244	95.32 69

IS-MVSNet		80.14 179	79.41 178	82.33 219	87.91 213	60.08 305	91.97 177	88.27 317	72.90 198	71.44 233	91.73 165	61.44 139	93.66 260	62.47 296	86.53 142	93.24 163

test-LLR		80.10 180	79.56 174	81.72 238	86.93 242	61.17 274	92.70 140	91.54 180	71.51 244	75.62 175	86.94 248	53.83 232	92.38 299	72.21 205	84.76 157	91.60 212

PVSNet	73.49 8	80.05 181	78.63 189	84.31 157	90.92 139	64.97 171	92.47 154	91.05 208	79.18 89	72.43 218	90.51 184	37.05 362	94.06 241	68.06 242	86.00 145	93.90 147

UA-Net		80.02 182	79.65 172	81.11 253	89.33 171	57.72 334	86.33 320	89.00 294	77.44 126	81.01 112	89.15 210	59.33 164	95.90 159	61.01 303	84.28 164	89.73 244

test-mter		79.96 183	79.38 180	81.72 238	86.93 242	61.17 274	92.70 140	91.54 180	73.85 176	75.62 175	86.94 248	49.84 274	92.38 299	72.21 205	84.76 157	91.60 212

QAPM		79.95 184	77.39 212	87.64 34	89.63 163	71.41 20	93.30 115	93.70 78	65.34 313	67.39 288	91.75 164	47.83 295	98.96 16	57.71 319	89.81 102	92.54 188

UGNet		79.87 185	78.68 188	83.45 189	89.96 156	61.51 268	92.13 165	90.79 212	76.83 134	78.85 144	86.33 256	38.16 348	96.17 146	67.93 245	87.17 131	92.67 182
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		79.80 186	77.95 200	85.34 113	88.28 202	68.26 84	81.56 358	91.42 186	70.11 266	77.59 157	80.50 331	67.40 61	94.26 232	67.34 250	77.35 231	93.51 156

thres200		79.66 187	78.33 192	83.66 182	92.54 91	65.82 151	93.06 122	96.31 3	74.90 159	73.30 203	88.66 214	59.67 159	95.61 174	47.84 361	78.67 218	89.56 247

CPTT-MVS		79.59 188	79.16 183	80.89 263	91.54 124	59.80 309	92.10 167	88.54 310	60.42 355	72.96 205	93.28 126	48.27 289	92.80 283	78.89 154	86.50 143	90.06 237

Test_1112_low_res		79.56 189	78.60 190	82.43 215	88.24 205	60.39 298	92.09 168	87.99 324	72.10 219	71.84 225	87.42 239	64.62 92	93.04 270	65.80 269	77.30 232	93.85 149

tttt0517		79.50 190	78.53 191	82.41 218	87.22 233	61.43 272	89.75 264	94.76 35	69.29 276	67.91 277	88.06 229	72.92 29	95.63 172	62.91 292	73.90 258	90.16 236

reproduce_monomvs		79.49 191	79.11 185	80.64 265	92.91 78	61.47 271	91.17 218	93.28 96	83.09 27	64.04 318	82.38 299	66.19 70	94.57 216	81.19 131	57.71 373	85.88 312

FIs		79.47 192	79.41 178	79.67 292	85.95 263	59.40 315	91.68 192	93.94 67	78.06 111	68.96 263	88.28 220	66.61 67	91.77 316	66.20 265	74.99 247	87.82 269

BH-RMVSNet		79.46 193	77.65 203	84.89 128	91.68 119	65.66 152	93.55 102	88.09 322	72.93 195	73.37 202	91.12 176	46.20 309	96.12 148	56.28 325	85.61 151	92.91 177

PCF-MVS	73.15 9	79.29 194	77.63 204	84.29 158	86.06 261	65.96 147	87.03 312	91.10 202	69.86 270	69.79 253	90.64 180	57.54 185	96.59 124	64.37 281	82.29 179	90.32 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 195	79.57 173	78.24 312	88.46 194	52.29 370	90.41 243	89.12 285	74.24 167	69.13 256	91.91 161	65.77 77	90.09 344	59.00 315	88.09 120	92.33 194

114514_t		79.17 196	77.67 202	83.68 180	95.32 29	65.53 158	92.85 134	91.60 179	63.49 327	67.92 276	90.63 182	46.65 302	95.72 170	67.01 255	83.54 169	89.79 242

FA-MVS(test-final)		79.12 197	77.23 214	84.81 134	90.54 145	63.98 199	81.35 361	91.71 172	71.09 252	74.85 187	82.94 292	52.85 243	97.05 97	67.97 243	81.73 190	93.41 158

VPA-MVSNet		79.03 198	78.00 198	82.11 232	85.95 263	64.48 180	93.22 118	94.66 41	75.05 157	74.04 198	84.95 270	52.17 250	93.52 262	74.90 183	67.04 304	88.32 265

OPM-MVS		79.00 199	78.09 196	81.73 237	83.52 309	63.83 201	91.64 194	90.30 234	76.36 142	71.97 224	89.93 201	46.30 308	95.17 195	75.10 178	77.70 225	86.19 300
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 200	78.22 195	81.25 247	85.33 273	62.73 240	89.53 268	93.21 98	72.39 210	72.14 221	90.13 197	60.99 142	94.72 209	67.73 247	72.49 267	86.29 297

AdaColmap		78.94 201	77.00 218	84.76 136	96.34 17	65.86 149	92.66 144	87.97 326	62.18 341	70.56 239	92.37 148	43.53 323	97.35 77	64.50 280	82.86 174	91.05 227

GeoE		78.90 202	77.43 208	83.29 192	88.95 183	62.02 254	92.31 157	86.23 345	70.24 265	71.34 234	89.27 208	54.43 226	94.04 244	63.31 288	80.81 198	93.81 150

miper_enhance_ethall		78.86 203	77.97 199	81.54 242	88.00 212	65.17 165	91.41 197	89.15 282	75.19 155	68.79 266	83.98 282	67.17 62	92.82 281	72.73 199	65.30 315	86.62 294

VPNet		78.82 204	77.53 207	82.70 208	84.52 292	66.44 135	93.93 80	92.23 139	80.46 62	72.60 212	88.38 219	49.18 282	93.13 269	72.47 203	63.97 335	88.55 260

EPNet_dtu		78.80 205	79.26 182	77.43 320	88.06 209	49.71 386	91.96 178	91.95 157	77.67 120	76.56 168	91.28 174	58.51 174	90.20 342	56.37 324	80.95 195	92.39 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 206	77.43 208	82.88 203	92.21 97	64.49 178	92.05 171	96.28 4	73.48 185	71.75 227	88.26 222	60.07 155	95.32 189	45.16 374	77.58 227	88.83 253

TR-MVS		78.77 207	77.37 213	82.95 202	90.49 146	60.88 280	93.67 96	90.07 244	70.08 267	74.51 191	91.37 173	45.69 312	95.70 171	60.12 309	80.32 202	92.29 196

thres400		78.68 208	77.43 208	82.43 215	92.21 97	64.49 178	92.05 171	96.28 4	73.48 185	71.75 227	88.26 222	60.07 155	95.32 189	45.16 374	77.58 227	87.48 274

BH-untuned		78.68 208	77.08 215	83.48 188	89.84 158	63.74 204	92.70 140	88.59 308	71.57 241	66.83 295	88.65 215	51.75 254	95.39 186	59.03 314	84.77 156	91.32 221

OMC-MVS		78.67 210	77.91 201	80.95 260	85.76 268	57.40 341	88.49 289	88.67 305	73.85 176	72.43 218	92.10 155	49.29 281	94.55 220	72.73 199	77.89 223	90.91 228

tpm		78.58 211	77.03 216	83.22 196	85.94 265	64.56 176	83.21 343	91.14 201	78.31 108	73.67 200	79.68 343	64.01 101	92.09 310	66.07 266	71.26 277	93.03 173

OpenMVS	70.45 11	78.54 212	75.92 234	86.41 78	85.93 266	71.68 18	92.74 137	92.51 132	66.49 304	64.56 312	91.96 158	43.88 322	98.10 39	54.61 330	90.65 90	89.44 250

EPMVS		78.49 213	75.98 233	86.02 88	91.21 133	69.68 52	80.23 370	91.20 195	75.25 154	72.48 216	78.11 354	54.65 221	93.69 259	57.66 320	83.04 173	94.69 103

AUN-MVS		78.37 214	77.43 208	81.17 249	86.60 249	57.45 340	89.46 270	91.16 197	74.11 169	74.40 192	90.49 185	55.52 212	94.57 216	74.73 185	60.43 364	91.48 215

thres100view900		78.37 214	77.01 217	82.46 214	91.89 113	63.21 226	91.19 217	96.33 1	72.28 213	70.45 242	87.89 231	60.31 150	95.32 189	45.16 374	77.58 227	88.83 253

GA-MVS		78.33 216	76.23 229	84.65 143	83.65 307	66.30 139	91.44 196	90.14 242	76.01 144	70.32 244	84.02 281	42.50 327	94.72 209	70.98 215	77.00 235	92.94 176

cascas		78.18 217	75.77 236	85.41 109	87.14 235	69.11 62	92.96 128	91.15 200	66.71 302	70.47 240	86.07 258	37.49 356	96.48 133	70.15 223	79.80 206	90.65 230

UniMVSNet_NR-MVSNet		78.15 218	77.55 206	79.98 282	84.46 295	60.26 300	92.25 159	93.20 100	77.50 125	68.88 264	86.61 251	66.10 72	92.13 308	66.38 262	62.55 342	87.54 272

LuminaMVS		78.14 219	76.66 222	82.60 212	80.82 335	64.64 175	89.33 272	90.45 222	68.25 289	74.73 189	85.51 265	41.15 333	94.14 235	78.96 152	80.69 200	89.04 251

thres600view7		78.00 220	76.66 222	82.03 234	91.93 109	63.69 210	91.30 209	96.33 1	72.43 208	70.46 241	87.89 231	60.31 150	94.92 204	42.64 386	76.64 238	87.48 274

FC-MVSNet-test		77.99 221	78.08 197	77.70 315	84.89 285	55.51 356	90.27 249	93.75 76	76.87 131	66.80 296	87.59 236	65.71 78	90.23 341	62.89 293	73.94 256	87.37 277

Anonymous202405211		77.96 222	75.33 242	85.87 93	93.73 53	64.52 177	94.85 46	85.36 355	62.52 339	76.11 170	90.18 192	29.43 391	97.29 81	68.51 240	77.24 234	95.81 49

cl22		77.94 223	76.78 220	81.42 244	87.57 223	64.93 173	90.67 235	88.86 298	72.45 207	67.63 283	82.68 296	64.07 99	92.91 279	71.79 208	65.30 315	86.44 295

XXY-MVS		77.94 223	76.44 225	82.43 215	82.60 319	64.44 182	92.01 173	91.83 166	73.59 184	70.00 249	85.82 261	54.43 226	94.76 206	69.63 226	68.02 298	88.10 267

MS-PatchMatch		77.90 225	76.50 224	82.12 229	85.99 262	69.95 42	91.75 190	92.70 120	73.97 173	62.58 334	84.44 277	41.11 334	95.78 162	63.76 285	92.17 66	80.62 377

FMVSNet3		77.73 226	76.04 232	82.80 204	91.20 134	68.99 66	91.87 181	91.99 155	73.35 187	67.04 291	83.19 291	56.62 199	92.14 307	59.80 311	69.34 284	87.28 280

VortexMVS		77.62 227	76.44 225	81.13 251	88.58 190	63.73 206	91.24 212	91.30 193	77.81 116	65.76 301	81.97 305	49.69 276	93.72 257	76.40 169	65.26 318	85.94 310

miper_ehance_all_eth		77.60 228	76.44 225	81.09 257	85.70 270	64.41 185	90.65 236	88.64 307	72.31 211	67.37 289	82.52 297	64.77 91	92.64 292	70.67 219	65.30 315	86.24 299

UniMVSNet (Re)		77.58 229	76.78 220	79.98 282	84.11 301	60.80 281	91.76 188	93.17 102	76.56 140	69.93 252	84.78 272	63.32 117	92.36 301	64.89 278	62.51 344	86.78 288

PatchmatchNet		77.46 230	74.63 249	85.96 90	89.55 166	70.35 35	79.97 375	89.55 265	72.23 214	70.94 235	76.91 366	57.03 189	92.79 284	54.27 332	81.17 193	94.74 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 231	75.65 238	82.73 206	80.38 341	67.13 117	91.85 183	90.23 239	75.09 156	69.37 254	83.39 288	53.79 234	94.44 224	71.77 209	65.00 322	86.63 293

CHOSEN 280x420		77.35 232	76.95 219	78.55 307	87.07 237	62.68 241	69.71 407	82.95 377	68.80 283	71.48 232	87.27 243	66.03 73	84.00 391	76.47 168	82.81 176	88.95 252

PS-MVSNAJss		77.26 233	76.31 228	80.13 277	80.64 339	59.16 320	90.63 239	91.06 207	72.80 199	68.58 270	84.57 275	53.55 236	93.96 249	72.97 193	71.96 271	87.27 281

gg-mvs-nofinetune		77.18 234	74.31 256	85.80 97	91.42 126	68.36 80	71.78 401	94.72 37	49.61 398	77.12 162	45.92 427	77.41 8	93.98 248	67.62 248	93.16 55	95.05 85

WB-MVSnew		77.14 235	76.18 231	80.01 281	86.18 258	63.24 224	91.26 210	94.11 64	71.72 233	73.52 201	87.29 242	45.14 317	93.00 272	56.98 322	79.42 208	83.80 338

MVP-Stereo		77.12 236	76.23 229	79.79 289	81.72 327	66.34 138	89.29 273	90.88 211	70.56 262	62.01 337	82.88 293	49.34 279	94.13 236	65.55 273	93.80 43	78.88 392
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 237	75.37 240	82.20 225	89.25 174	62.11 253	82.06 353	89.09 287	76.77 136	70.84 237	87.12 244	41.43 332	95.01 199	67.23 252	74.55 248	89.48 248

MonoMVSNet		76.99 238	75.08 245	82.73 206	83.32 311	63.24 224	86.47 319	86.37 341	79.08 93	66.31 299	79.30 347	49.80 275	91.72 317	79.37 145	65.70 313	93.23 164

dmvs_re		76.93 239	75.36 241	81.61 240	87.78 220	60.71 288	80.00 374	87.99 324	79.42 82	69.02 260	89.47 205	46.77 300	94.32 226	63.38 287	74.45 251	89.81 241

X-MVStestdata		76.86 240	74.13 260	85.05 124	93.22 66	63.78 202	92.92 130	92.66 125	73.99 171	78.18 149	10.19 442	55.25 213	97.41 73	79.16 148	91.58 77	93.95 143

DU-MVS		76.86 240	75.84 235	79.91 285	82.96 315	60.26 300	91.26 210	91.54 180	76.46 141	68.88 264	86.35 254	56.16 204	92.13 308	66.38 262	62.55 342	87.35 278

Anonymous20240529		76.84 242	74.15 259	84.88 129	91.02 136	64.95 172	93.84 88	91.09 203	53.57 386	73.00 204	87.42 239	35.91 366	97.32 79	69.14 234	72.41 269	92.36 193

UWE-MVS-28		76.83 243	77.60 205	74.51 348	84.58 291	50.34 382	88.22 293	94.60 45	74.46 162	66.66 297	88.98 213	62.53 128	85.50 383	57.55 321	80.80 199	87.69 271

c3_l		76.83 243	75.47 239	80.93 261	85.02 283	64.18 195	90.39 244	88.11 321	71.66 234	66.65 298	81.64 311	63.58 113	92.56 293	69.31 231	62.86 339	86.04 305

WR-MVS		76.76 245	75.74 237	79.82 288	84.60 289	62.27 251	92.60 147	92.51 132	76.06 143	67.87 280	85.34 266	56.76 195	90.24 340	62.20 297	63.69 337	86.94 286

v1144		76.73 246	74.88 246	82.27 221	80.23 345	66.60 132	91.68 192	90.21 241	73.69 181	69.06 259	81.89 306	52.73 246	94.40 225	69.21 232	65.23 319	85.80 313

IterMVS-LS		76.49 247	75.18 244	80.43 269	84.49 294	62.74 239	90.64 237	88.80 300	72.40 209	65.16 307	81.72 309	60.98 143	92.27 305	67.74 246	64.65 327	86.29 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 248	74.55 252	82.19 226	79.14 359	67.82 97	90.26 250	89.42 270	73.75 179	68.63 269	81.89 306	51.31 259	94.09 238	71.69 211	64.84 323	84.66 330

v148		76.19 249	74.47 254	81.36 245	80.05 347	64.44 182	91.75 190	90.23 239	73.68 182	67.13 290	80.84 326	55.92 209	93.86 256	68.95 236	61.73 353	85.76 316

Effi-MVS+-dtu		76.14 250	75.28 243	78.72 306	83.22 312	55.17 358	89.87 261	87.78 328	75.42 151	67.98 275	81.43 315	45.08 318	92.52 295	75.08 179	71.63 272	88.48 261

cl____		76.07 251	74.67 247	80.28 272	85.15 278	61.76 262	90.12 253	88.73 302	71.16 249	65.43 304	81.57 313	61.15 140	92.95 274	66.54 259	62.17 346	86.13 303

DIV-MVS_self_test		76.07 251	74.67 247	80.28 272	85.14 279	61.75 263	90.12 253	88.73 302	71.16 249	65.42 305	81.60 312	61.15 140	92.94 278	66.54 259	62.16 348	86.14 301

FMVSNet2		76.07 251	74.01 262	82.26 223	88.85 184	67.66 101	91.33 207	91.61 178	70.84 256	65.98 300	82.25 301	48.03 290	92.00 312	58.46 316	68.73 292	87.10 283

v144192		76.05 254	74.03 261	82.12 229	79.50 353	66.55 134	91.39 201	89.71 263	72.30 212	68.17 273	81.33 318	51.75 254	94.03 246	67.94 244	64.19 330	85.77 314

NR-MVSNet		76.05 254	74.59 250	80.44 268	82.96 315	62.18 252	90.83 228	91.73 170	77.12 129	60.96 340	86.35 254	59.28 165	91.80 315	60.74 304	61.34 357	87.35 278

v1192		75.98 256	73.92 263	82.15 227	79.73 349	66.24 141	91.22 214	89.75 257	72.67 201	68.49 271	81.42 316	49.86 273	94.27 230	67.08 254	65.02 321	85.95 308

FE-MVS		75.97 257	73.02 274	84.82 131	89.78 159	65.56 156	77.44 386	91.07 206	64.55 316	72.66 210	79.85 341	46.05 310	96.69 122	54.97 329	80.82 197	92.21 202

eth_miper_zixun_eth		75.96 258	74.40 255	80.66 264	84.66 288	63.02 230	89.28 274	88.27 317	71.88 225	65.73 302	81.65 310	59.45 161	92.81 282	68.13 241	60.53 362	86.14 301

TranMVSNet+NR-MVSNet		75.86 259	74.52 253	79.89 286	82.44 321	60.64 291	91.37 204	91.37 187	76.63 138	67.65 282	86.21 257	52.37 249	91.55 322	61.84 299	60.81 360	87.48 274

SCA		75.82 260	72.76 277	85.01 126	86.63 248	70.08 38	81.06 363	89.19 279	71.60 240	70.01 248	77.09 364	45.53 313	90.25 337	60.43 306	73.27 260	94.68 104

LPG-MVS_test		75.82 260	74.58 251	79.56 296	84.31 298	59.37 316	90.44 241	89.73 260	69.49 273	64.86 308	88.42 217	38.65 342	94.30 228	72.56 201	72.76 264	85.01 327

GBi-Net		75.65 262	73.83 264	81.10 254	88.85 184	65.11 167	90.01 257	90.32 230	70.84 256	67.04 291	80.25 336	48.03 290	91.54 323	59.80 311	69.34 284	86.64 290

test1		75.65 262	73.83 264	81.10 254	88.85 184	65.11 167	90.01 257	90.32 230	70.84 256	67.04 291	80.25 336	48.03 290	91.54 323	59.80 311	69.34 284	86.64 290

v1921920		75.63 264	73.49 269	82.06 233	79.38 354	66.35 137	91.07 222	89.48 266	71.98 220	67.99 274	81.22 321	49.16 284	93.90 252	66.56 258	64.56 328	85.92 311

ACMP	71.68 10	75.58 265	74.23 258	79.62 294	84.97 284	59.64 311	90.80 229	89.07 289	70.39 263	62.95 330	87.30 241	38.28 346	93.87 254	72.89 194	71.45 275	85.36 323
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 266	73.26 272	81.61 240	80.67 338	66.82 125	89.54 267	89.27 275	71.65 235	63.30 326	80.30 335	54.99 219	94.06 241	67.33 251	62.33 345	83.94 336

tpm cat1		75.30 267	72.21 286	84.58 147	88.52 191	67.77 98	78.16 384	88.02 323	61.88 347	68.45 272	76.37 370	60.65 146	94.03 246	53.77 335	74.11 254	91.93 208

PLC	68.80 14	75.23 268	73.68 267	79.86 287	92.93 77	58.68 325	90.64 237	88.30 315	60.90 352	64.43 316	90.53 183	42.38 328	94.57 216	56.52 323	76.54 239	86.33 296
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 269	72.98 275	81.88 235	79.20 356	66.00 145	90.75 231	89.11 286	71.63 239	67.41 287	81.22 321	47.36 298	93.87 254	65.46 274	64.72 326	85.77 314

Fast-Effi-MVS+-dtu		75.04 270	73.37 270	80.07 278	80.86 333	59.52 314	91.20 216	85.38 354	71.90 223	65.20 306	84.84 271	41.46 331	92.97 273	66.50 261	72.96 263	87.73 270

dp		75.01 271	72.09 287	83.76 173	89.28 173	66.22 142	79.96 376	89.75 257	71.16 249	67.80 281	77.19 363	51.81 252	92.54 294	50.39 345	71.44 276	92.51 190

TAPA-MVS	70.22 12	74.94 272	73.53 268	79.17 301	90.40 148	52.07 371	89.19 277	89.61 264	62.69 338	70.07 247	92.67 140	48.89 287	94.32 226	38.26 400	79.97 204	91.12 226
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 273	73.32 271	79.74 291	86.53 250	60.31 299	89.03 282	92.70 120	78.61 104	68.98 262	83.34 289	41.93 330	92.23 306	52.77 339	65.97 311	86.69 289

v10		74.77 274	72.54 283	81.46 243	80.33 343	66.71 129	89.15 278	89.08 288	70.94 254	63.08 329	79.86 340	52.52 247	94.04 244	65.70 270	62.17 346	83.64 339

XVG-OURS-SEG-HR		74.70 275	73.08 273	79.57 295	78.25 372	57.33 342	80.49 366	87.32 331	63.22 331	68.76 267	90.12 199	44.89 319	91.59 321	70.55 221	74.09 255	89.79 242

ACMM	69.62 13	74.34 276	72.73 279	79.17 301	84.25 300	57.87 332	90.36 246	89.93 251	63.17 333	65.64 303	86.04 260	37.79 354	94.10 237	65.89 267	71.52 274	85.55 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 277	72.30 285	80.32 270	91.49 125	61.66 265	90.85 227	80.72 383	56.67 378	63.85 321	90.64 180	46.75 301	90.84 331	53.79 334	75.99 243	88.47 262

XVG-OURS		74.25 278	72.46 284	79.63 293	78.45 370	57.59 338	80.33 368	87.39 330	63.86 323	68.76 267	89.62 204	40.50 336	91.72 317	69.00 235	74.25 253	89.58 245

test_fmvs1		74.07 279	73.69 266	75.22 340	78.91 363	47.34 399	89.06 281	74.69 399	63.68 326	79.41 133	91.59 168	24.36 402	87.77 367	85.22 87	76.26 241	90.55 233

CVMVSNet		74.04 280	74.27 257	73.33 358	85.33 273	43.94 412	89.53 268	88.39 312	54.33 385	70.37 243	90.13 197	49.17 283	84.05 389	61.83 300	79.36 210	91.99 207

Baseline_NR-MVSNet		73.99 281	72.83 276	77.48 319	80.78 336	59.29 319	91.79 185	84.55 363	68.85 282	68.99 261	80.70 327	56.16 204	92.04 311	62.67 294	60.98 359	81.11 371

pmmvs4		73.92 282	71.81 291	80.25 274	79.17 357	65.24 163	87.43 308	87.26 334	67.64 295	63.46 324	83.91 283	48.96 286	91.53 326	62.94 291	65.49 314	83.96 335

D2MVS		73.80 283	72.02 288	79.15 303	79.15 358	62.97 231	88.58 288	90.07 244	72.94 194	59.22 350	78.30 351	42.31 329	92.70 288	65.59 272	72.00 270	81.79 366

CR-MVSNet		73.79 284	70.82 299	82.70 208	83.15 313	67.96 93	70.25 404	84.00 368	73.67 183	69.97 250	72.41 387	57.82 182	89.48 350	52.99 338	73.13 261	90.64 231

test_djsdf		73.76 285	72.56 282	77.39 321	77.00 384	53.93 364	89.07 279	90.69 214	65.80 308	63.92 319	82.03 304	43.14 326	92.67 289	72.83 195	68.53 293	85.57 318

pmmvs5		73.35 286	71.52 293	78.86 305	78.64 367	60.61 292	91.08 220	86.90 336	67.69 292	63.32 325	83.64 284	44.33 321	90.53 334	62.04 298	66.02 310	85.46 321

Anonymous20231211		73.08 287	70.39 303	81.13 251	90.62 144	63.33 221	91.40 199	90.06 246	51.84 391	64.46 315	80.67 329	36.49 364	94.07 240	63.83 284	64.17 331	85.98 307

tt0805		73.07 288	70.73 300	80.07 278	78.37 371	57.05 344	87.78 302	92.18 146	61.23 351	67.04 291	86.49 253	31.35 384	94.58 214	65.06 277	67.12 303	88.57 259

miper_lstm_enhance		73.05 289	71.73 292	77.03 326	83.80 304	58.32 329	81.76 354	88.88 296	69.80 271	61.01 339	78.23 353	57.19 187	87.51 371	65.34 275	59.53 367	85.27 326

jajsoiax		73.05 289	71.51 294	77.67 316	77.46 381	54.83 360	88.81 284	90.04 247	69.13 280	62.85 332	83.51 286	31.16 385	92.75 285	70.83 216	69.80 280	85.43 322

LCM-MVSNet-Re		72.93 291	71.84 290	76.18 335	88.49 192	48.02 394	80.07 373	70.17 414	73.96 174	52.25 384	80.09 339	49.98 271	88.24 361	67.35 249	84.23 165	92.28 197

pm-mvs1		72.89 292	71.09 296	78.26 311	79.10 360	57.62 336	90.80 229	89.30 274	67.66 293	62.91 331	81.78 308	49.11 285	92.95 274	60.29 308	58.89 370	84.22 334

tpmvs		72.88 293	69.76 309	82.22 224	90.98 137	67.05 119	78.22 383	88.30 315	63.10 334	64.35 317	74.98 377	55.09 218	94.27 230	43.25 380	69.57 283	85.34 324

test0.0.03 1		72.76 294	72.71 280	72.88 362	80.25 344	47.99 395	91.22 214	89.45 268	71.51 244	62.51 335	87.66 234	53.83 232	85.06 385	50.16 347	67.84 301	85.58 317

UniMVSNet_ETH3D		72.74 295	70.53 302	79.36 298	78.62 368	56.64 348	85.01 325	89.20 278	63.77 324	64.84 310	84.44 277	34.05 373	91.86 314	63.94 283	70.89 279	89.57 246

mvs_tets		72.71 296	71.11 295	77.52 317	77.41 382	54.52 362	88.45 290	89.76 256	68.76 285	62.70 333	83.26 290	29.49 390	92.71 286	70.51 222	69.62 282	85.34 324

FMVSNet1		72.71 296	69.91 307	81.10 254	83.60 308	65.11 167	90.01 257	90.32 230	63.92 322	63.56 323	80.25 336	36.35 365	91.54 323	54.46 331	66.75 306	86.64 290

test_fmvs1_n		72.69 298	71.92 289	74.99 344	71.15 404	47.08 401	87.34 310	75.67 394	63.48 328	78.08 151	91.17 175	20.16 416	87.87 364	84.65 96	75.57 245	90.01 239

IterMVS		72.65 299	70.83 297	78.09 313	82.17 323	62.96 232	87.64 306	86.28 343	71.56 242	60.44 343	78.85 349	45.42 315	86.66 375	63.30 289	61.83 350	84.65 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 300	72.74 278	72.10 370	87.87 215	49.45 388	88.07 295	89.01 291	72.91 196	63.11 327	88.10 226	63.63 108	85.54 380	32.73 415	69.23 287	81.32 369

PatchMatch-RL		72.06 301	69.98 304	78.28 310	89.51 167	55.70 355	83.49 336	83.39 375	61.24 350	63.72 322	82.76 294	34.77 370	93.03 271	53.37 337	77.59 226	86.12 304

PVSNet_0	68.08 15	71.81 302	68.32 318	82.27 221	84.68 286	62.31 250	88.68 286	90.31 233	75.84 145	57.93 362	80.65 330	37.85 353	94.19 233	69.94 224	29.05 430	90.31 235

MIMVSNet		71.64 303	68.44 316	81.23 248	81.97 326	64.44 182	73.05 398	88.80 300	69.67 272	64.59 311	74.79 379	32.79 376	87.82 365	53.99 333	76.35 240	91.42 216

test_vis1_n		71.63 304	70.73 300	74.31 352	69.63 410	47.29 400	86.91 314	72.11 407	63.21 332	75.18 182	90.17 194	20.40 414	85.76 379	84.59 97	74.42 252	89.87 240

IterMVS-SCA-FT		71.55 305	69.97 305	76.32 333	81.48 329	60.67 290	87.64 306	85.99 348	66.17 306	59.50 348	78.88 348	45.53 313	83.65 393	62.58 295	61.93 349	84.63 333

v7n		71.31 306	68.65 313	79.28 299	76.40 386	60.77 283	86.71 317	89.45 268	64.17 321	58.77 355	78.24 352	44.59 320	93.54 261	57.76 318	61.75 352	83.52 342

anonymousdsp		71.14 307	69.37 311	76.45 332	72.95 399	54.71 361	84.19 331	88.88 296	61.92 346	62.15 336	79.77 342	38.14 349	91.44 328	68.90 237	67.45 302	83.21 348

F-COLMAP		70.66 308	68.44 316	77.32 322	86.37 255	55.91 353	88.00 297	86.32 342	56.94 376	57.28 366	88.07 228	33.58 374	92.49 296	51.02 342	68.37 294	83.55 340

WR-MVS_H		70.59 309	69.94 306	72.53 364	81.03 332	51.43 375	87.35 309	92.03 154	67.38 296	60.23 345	80.70 327	55.84 210	83.45 395	46.33 369	58.58 372	82.72 355

CP-MVSNet		70.50 310	69.91 307	72.26 367	80.71 337	51.00 379	87.23 311	90.30 234	67.84 291	59.64 347	82.69 295	50.23 269	82.30 403	51.28 341	59.28 368	83.46 344

RPMNet		70.42 311	65.68 332	84.63 145	83.15 313	67.96 93	70.25 404	90.45 222	46.83 407	69.97 250	65.10 410	56.48 203	95.30 192	35.79 405	73.13 261	90.64 231

testing3		70.38 312	70.83 297	69.03 382	85.82 267	43.93 413	90.72 234	90.56 221	68.06 290	60.24 344	86.82 250	64.83 89	84.12 387	26.33 423	64.10 332	79.04 390

tfpnnormal		70.10 313	67.36 322	78.32 309	83.45 310	60.97 279	88.85 283	92.77 118	64.85 315	60.83 341	78.53 350	43.52 324	93.48 263	31.73 418	61.70 354	80.52 378

TransMVSNet (Re)		70.07 314	67.66 320	77.31 323	80.62 340	59.13 321	91.78 187	84.94 359	65.97 307	60.08 346	80.44 332	50.78 263	91.87 313	48.84 354	45.46 403	80.94 373

CL-MVSNet_self_test		69.92 315	68.09 319	75.41 338	73.25 398	55.90 354	90.05 256	89.90 252	69.96 268	61.96 338	76.54 367	51.05 262	87.64 368	49.51 351	50.59 393	82.70 357

DP-MVS		69.90 316	66.48 324	80.14 276	95.36 28	62.93 233	89.56 265	76.11 392	50.27 397	57.69 364	85.23 267	39.68 338	95.73 166	33.35 410	71.05 278	81.78 367

PS-CasMVS		69.86 317	69.13 312	72.07 371	80.35 342	50.57 381	87.02 313	89.75 257	67.27 297	59.19 351	82.28 300	46.58 303	82.24 404	50.69 344	59.02 369	83.39 346

Syy-MVS		69.65 318	69.52 310	70.03 378	87.87 215	43.21 414	88.07 295	89.01 291	72.91 196	63.11 327	88.10 226	45.28 316	85.54 380	22.07 428	69.23 287	81.32 369

MSDG		69.54 319	65.73 331	80.96 259	85.11 281	63.71 208	84.19 331	83.28 376	56.95 375	54.50 373	84.03 280	31.50 382	96.03 156	42.87 384	69.13 289	83.14 350

PEN-MVS		69.46 320	68.56 314	72.17 369	79.27 355	49.71 386	86.90 315	89.24 276	67.24 300	59.08 352	82.51 298	47.23 299	83.54 394	48.42 356	57.12 374	83.25 347

LS3D		69.17 321	66.40 326	77.50 318	91.92 110	56.12 351	85.12 324	80.37 385	46.96 405	56.50 368	87.51 238	37.25 357	93.71 258	32.52 417	79.40 209	82.68 358

PatchT		69.11 322	65.37 336	80.32 270	82.07 325	63.68 211	67.96 414	87.62 329	50.86 395	69.37 254	65.18 409	57.09 188	88.53 357	41.59 389	66.60 307	88.74 256

KD-MVS_2432*1600		69.03 323	66.37 327	77.01 327	85.56 271	61.06 277	81.44 359	90.25 237	67.27 297	58.00 360	76.53 368	54.49 223	87.63 369	48.04 358	35.77 421	82.34 361

miper_refine_blended		69.03 323	66.37 327	77.01 327	85.56 271	61.06 277	81.44 359	90.25 237	67.27 297	58.00 360	76.53 368	54.49 223	87.63 369	48.04 358	35.77 421	82.34 361

mvsany_test1		68.77 325	68.56 314	69.39 380	73.57 397	45.88 408	80.93 364	60.88 428	59.65 361	71.56 230	90.26 191	43.22 325	75.05 415	74.26 188	62.70 341	87.25 282

ACMH	63.93 17	68.62 326	64.81 338	80.03 280	85.22 277	63.25 223	87.72 303	84.66 361	60.83 353	51.57 388	79.43 346	27.29 397	94.96 201	41.76 387	64.84 323	81.88 365
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 327	65.41 335	77.96 314	78.69 366	62.93 233	89.86 262	89.17 280	60.55 354	50.27 393	77.73 358	22.60 410	94.06 241	47.18 365	72.65 266	76.88 403

ADS-MVSNet		68.54 328	64.38 345	81.03 258	88.06 209	66.90 124	68.01 412	84.02 367	57.57 369	64.48 313	69.87 397	38.68 340	89.21 352	40.87 391	67.89 299	86.97 284

DTE-MVSNet		68.46 329	67.33 323	71.87 373	77.94 376	49.00 392	86.16 321	88.58 309	66.36 305	58.19 357	82.21 302	46.36 304	83.87 392	44.97 377	55.17 381	82.73 354

mmtdpeth		68.33 330	66.37 327	74.21 353	82.81 318	51.73 372	84.34 329	80.42 384	67.01 301	71.56 230	68.58 401	30.52 388	92.35 302	75.89 172	36.21 419	78.56 397

our_test_3		68.29 331	64.69 340	79.11 304	78.92 361	64.85 174	88.40 291	85.06 357	60.32 357	52.68 382	76.12 372	40.81 335	89.80 349	44.25 379	55.65 379	82.67 359

Patchmatch-RL test		68.17 332	64.49 343	79.19 300	71.22 403	53.93 364	70.07 406	71.54 411	69.22 277	56.79 367	62.89 414	56.58 200	88.61 354	69.53 228	52.61 388	95.03 87

XVG-ACMP-BASELINE		68.04 333	65.53 334	75.56 337	74.06 396	52.37 369	78.43 380	85.88 349	62.03 344	58.91 354	81.21 323	20.38 415	91.15 330	60.69 305	68.18 295	83.16 349

FMVSNet5		68.04 333	65.66 333	75.18 342	84.43 296	57.89 331	83.54 335	86.26 344	61.83 348	53.64 379	73.30 382	37.15 360	85.08 384	48.99 353	61.77 351	82.56 360

ppachtmachnet_test		67.72 335	63.70 347	79.77 290	78.92 361	66.04 144	88.68 286	82.90 378	60.11 359	55.45 370	75.96 373	39.19 339	90.55 333	39.53 395	52.55 389	82.71 356

ACMH+	65.35 16	67.65 336	64.55 341	76.96 329	84.59 290	57.10 343	88.08 294	80.79 382	58.59 367	53.00 381	81.09 325	26.63 399	92.95 274	46.51 367	61.69 355	80.82 374

pmmvs6		67.57 337	64.76 339	76.00 336	72.82 401	53.37 366	88.71 285	86.78 340	53.19 387	57.58 365	78.03 355	35.33 369	92.41 298	55.56 327	54.88 383	82.21 363

Anonymous20231206		67.53 338	65.78 330	72.79 363	74.95 392	47.59 397	88.23 292	87.32 331	61.75 349	58.07 359	77.29 361	37.79 354	87.29 373	42.91 382	63.71 336	83.48 343

Patchmtry		67.53 338	63.93 346	78.34 308	82.12 324	64.38 186	68.72 409	84.00 368	48.23 404	59.24 349	72.41 387	57.82 182	89.27 351	46.10 370	56.68 378	81.36 368

USDC		67.43 340	64.51 342	76.19 334	77.94 376	55.29 357	78.38 381	85.00 358	73.17 189	48.36 401	80.37 333	21.23 412	92.48 297	52.15 340	64.02 334	80.81 375

ADS-MVSNet2		66.90 341	63.44 349	77.26 324	88.06 209	60.70 289	68.01 412	75.56 396	57.57 369	64.48 313	69.87 397	38.68 340	84.10 388	40.87 391	67.89 299	86.97 284

CMPMVS	48.56 21	66.77 342	64.41 344	73.84 355	70.65 407	50.31 383	77.79 385	85.73 352	45.54 410	44.76 411	82.14 303	35.40 368	90.14 343	63.18 290	74.54 250	81.07 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 343	62.92 352	76.80 331	76.51 385	57.77 333	89.22 275	83.41 374	55.48 382	53.86 377	77.84 356	26.28 400	93.95 250	34.90 407	68.76 291	78.68 395

LTVRE_ROB	59.60 19	66.27 344	63.54 348	74.45 349	84.00 303	51.55 374	67.08 416	83.53 372	58.78 365	54.94 372	80.31 334	34.54 371	93.23 267	40.64 393	68.03 297	78.58 396
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		66.06 345	62.45 355	76.88 330	81.42 331	54.45 363	57.49 428	88.67 305	49.36 399	63.86 320	46.86 426	56.06 207	90.25 337	49.53 350	68.83 290	85.95 308

Patchmatch-test		65.86 346	60.94 361	80.62 267	83.75 305	58.83 323	58.91 427	75.26 398	44.50 413	50.95 392	77.09 364	58.81 172	87.90 363	35.13 406	64.03 333	95.12 81

UnsupCasMVSNet_eth		65.79 347	63.10 350	73.88 354	70.71 406	50.29 384	81.09 362	89.88 253	72.58 203	49.25 398	74.77 380	32.57 378	87.43 372	55.96 326	41.04 411	83.90 337

test_fmvs2		65.78 348	64.84 337	68.60 384	66.54 416	41.71 416	83.27 340	69.81 415	54.38 384	67.91 277	84.54 276	15.35 421	81.22 408	75.65 174	66.16 309	82.88 351

dmvs_testset		65.55 349	66.45 325	62.86 396	79.87 348	22.35 442	76.55 388	71.74 409	77.42 128	55.85 369	87.77 233	51.39 258	80.69 409	31.51 421	65.92 312	85.55 319

pmmvs-eth3d		65.53 350	62.32 356	75.19 341	69.39 411	59.59 312	82.80 348	83.43 373	62.52 339	51.30 390	72.49 385	32.86 375	87.16 374	55.32 328	50.73 392	78.83 393

mamv4		65.18 351	67.43 321	58.44 400	77.88 378	49.36 391	69.40 408	70.99 413	48.31 403	57.78 363	85.53 264	59.01 170	51.88 438	73.67 190	64.32 329	74.07 408

SixPastTwentyTwo		64.92 352	61.78 359	74.34 351	78.74 365	49.76 385	83.42 339	79.51 388	62.86 335	50.27 393	77.35 359	30.92 387	90.49 335	45.89 371	47.06 398	82.78 352

OurMVSNet-221017-0		64.68 353	62.17 357	72.21 368	76.08 389	47.35 398	80.67 365	81.02 381	56.19 379	51.60 387	79.66 344	27.05 398	88.56 356	53.60 336	53.63 386	80.71 376

test_0402		64.54 354	61.09 360	74.92 345	84.10 302	60.75 285	87.95 298	79.71 387	52.03 389	52.41 383	77.20 362	32.21 380	91.64 319	23.14 426	61.03 358	72.36 414

testgi		64.48 355	62.87 353	69.31 381	71.24 402	40.62 419	85.49 322	79.92 386	65.36 312	54.18 375	83.49 287	23.74 405	84.55 386	41.60 388	60.79 361	82.77 353

RPSCF		64.24 356	61.98 358	71.01 376	76.10 388	45.00 409	75.83 393	75.94 393	46.94 406	58.96 353	84.59 274	31.40 383	82.00 405	47.76 363	60.33 366	86.04 305

EU-MVSNet		64.01 357	63.01 351	67.02 390	74.40 395	38.86 425	83.27 340	86.19 346	45.11 411	54.27 374	81.15 324	36.91 363	80.01 411	48.79 355	57.02 375	82.19 364

test20.03		63.83 358	62.65 354	67.38 389	70.58 408	39.94 421	86.57 318	84.17 365	63.29 330	51.86 386	77.30 360	37.09 361	82.47 401	38.87 399	54.13 385	79.73 384

sc_t1		63.81 359	59.39 367	77.10 325	77.62 379	56.03 352	84.32 330	73.56 403	46.66 408	58.22 356	73.06 383	23.28 408	90.62 332	50.93 343	46.84 399	84.64 332

MDA-MVSNet_test_wron		63.78 360	60.16 363	74.64 346	78.15 374	60.41 296	83.49 336	84.03 366	56.17 381	39.17 421	71.59 393	37.22 358	83.24 398	42.87 384	48.73 395	80.26 381

YYNet1		63.76 361	60.14 364	74.62 347	78.06 375	60.19 303	83.46 338	83.99 370	56.18 380	39.25 420	71.56 394	37.18 359	83.34 396	42.90 383	48.70 396	80.32 380

K. test v3		63.09 362	59.61 366	73.53 357	76.26 387	49.38 390	83.27 340	77.15 391	64.35 318	47.77 403	72.32 389	28.73 392	87.79 366	49.93 349	36.69 418	83.41 345

COLMAP_ROB	57.96 20	62.98 363	59.65 365	72.98 361	81.44 330	53.00 368	83.75 334	75.53 397	48.34 402	48.81 400	81.40 317	24.14 403	90.30 336	32.95 412	60.52 363	75.65 406
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 364	59.08 368	71.10 375	67.19 414	48.72 393	83.91 333	85.23 356	50.38 396	47.84 402	71.22 396	20.74 413	85.51 382	46.47 368	58.75 371	79.06 389

tt0320		61.85 365	57.45 374	75.03 343	77.49 380	57.60 337	82.74 349	73.65 402	43.65 417	53.65 378	68.18 403	25.47 401	88.66 353	45.56 373	46.68 400	78.81 394

AllTest		61.66 366	58.06 370	72.46 365	79.57 350	51.42 376	80.17 371	68.61 417	51.25 393	45.88 405	81.23 319	19.86 417	86.58 376	38.98 397	57.01 376	79.39 386

UnsupCasMVSNet_bld		61.60 367	57.71 371	73.29 359	68.73 412	51.64 373	78.61 379	89.05 290	57.20 374	46.11 404	61.96 417	28.70 393	88.60 355	50.08 348	38.90 416	79.63 385

MDA-MVSNet-bldmvs		61.54 368	57.70 372	73.05 360	79.53 352	57.00 347	83.08 344	81.23 380	57.57 369	34.91 425	72.45 386	32.79 376	86.26 378	35.81 404	41.95 409	75.89 405

tt0320-xc		61.51 369	56.89 377	75.37 339	78.50 369	58.61 326	82.61 350	71.27 412	44.31 414	53.17 380	68.03 405	23.38 406	88.46 358	47.77 362	43.00 408	79.03 391

mvs5depth		61.03 370	57.65 373	71.18 374	67.16 415	47.04 403	72.74 399	77.49 389	57.47 372	60.52 342	72.53 384	22.84 409	88.38 359	49.15 352	38.94 415	78.11 400

KD-MVS_self_test		60.87 371	58.60 369	67.68 387	66.13 417	39.93 422	75.63 395	84.70 360	57.32 373	49.57 396	68.45 402	29.55 389	82.87 399	48.09 357	47.94 397	80.25 382

kuosan		60.86 372	60.24 362	62.71 397	81.57 328	46.43 405	75.70 394	85.88 349	57.98 368	48.95 399	69.53 399	58.42 175	76.53 413	28.25 422	35.87 420	65.15 421

TinyColmap		60.32 373	56.42 380	72.00 372	78.78 364	53.18 367	78.36 382	75.64 395	52.30 388	41.59 419	75.82 375	14.76 424	88.35 360	35.84 403	54.71 384	74.46 407

MVS-HIRNet		60.25 374	55.55 381	74.35 350	84.37 297	56.57 349	71.64 402	74.11 400	34.44 424	45.54 409	42.24 432	31.11 386	89.81 347	40.36 394	76.10 242	76.67 404

MIMVSNet1		60.16 375	57.33 375	68.67 383	69.71 409	44.13 411	78.92 378	84.21 364	55.05 383	44.63 412	71.85 391	23.91 404	81.54 407	32.63 416	55.03 382	80.35 379

PM-MVS		59.40 376	56.59 378	67.84 385	63.63 420	41.86 415	76.76 387	63.22 425	59.01 364	51.07 391	72.27 390	11.72 428	83.25 397	61.34 301	50.28 394	78.39 398

new-patchmatchnet		59.30 377	56.48 379	67.79 386	65.86 418	44.19 410	82.47 351	81.77 379	59.94 360	43.65 415	66.20 408	27.67 396	81.68 406	39.34 396	41.40 410	77.50 402

test_vis1_rt		59.09 378	57.31 376	64.43 393	68.44 413	46.02 407	83.05 346	48.63 437	51.96 390	49.57 396	63.86 413	16.30 419	80.20 410	71.21 214	62.79 340	67.07 420

test_fmvs3		56.82 379	54.86 383	62.69 398	53.59 431	35.47 428	75.87 392	65.64 422	43.91 415	55.10 371	71.43 395	6.91 436	74.40 418	68.64 239	52.63 387	78.20 399

DSMNet-mixed		56.78 380	54.44 384	63.79 394	63.21 421	29.44 437	64.43 419	64.10 424	42.12 421	51.32 389	71.60 392	31.76 381	75.04 416	36.23 402	65.20 320	86.87 287

pmmvs3		55.51 381	51.50 387	67.53 388	57.90 429	50.93 380	80.37 367	73.66 401	40.63 422	44.15 414	64.75 411	16.30 419	78.97 412	44.77 378	40.98 413	72.69 412

TDRefinement		55.28 382	51.58 386	66.39 391	59.53 428	46.15 406	76.23 390	72.80 404	44.60 412	42.49 417	76.28 371	15.29 422	82.39 402	33.20 411	43.75 405	70.62 416

dongtai		55.18 383	55.46 382	54.34 408	76.03 390	36.88 426	76.07 391	84.61 362	51.28 392	43.41 416	64.61 412	56.56 201	67.81 426	18.09 431	28.50 431	58.32 424

LF4IMVS		54.01 384	52.12 385	59.69 399	62.41 423	39.91 423	68.59 410	68.28 419	42.96 419	44.55 413	75.18 376	14.09 426	68.39 425	41.36 390	51.68 390	70.78 415

ttmdpeth		53.34 385	49.96 388	63.45 395	62.07 425	40.04 420	72.06 400	65.64 422	42.54 420	51.88 385	77.79 357	13.94 427	76.48 414	32.93 413	30.82 429	73.84 409

MVStest1		51.35 386	46.89 390	64.74 392	65.06 419	51.10 378	67.33 415	72.58 405	30.20 428	35.30 423	74.82 378	27.70 395	69.89 423	24.44 425	24.57 432	73.22 410

N_pmnet		50.55 387	49.11 389	54.88 406	77.17 383	4.02 450	84.36 328	2.00 448	48.59 400	45.86 407	68.82 400	32.22 379	82.80 400	31.58 419	51.38 391	77.81 401

new_pmnet		49.31 388	46.44 391	57.93 401	62.84 422	40.74 418	68.47 411	62.96 426	36.48 423	35.09 424	57.81 421	14.97 423	72.18 420	32.86 414	46.44 401	60.88 423

mvsany_test3		48.86 389	46.35 392	56.41 402	46.00 437	31.67 433	62.26 421	47.25 438	43.71 416	45.54 409	68.15 404	10.84 429	64.44 434	57.95 317	35.44 423	73.13 411

test_f		46.58 390	43.45 394	55.96 403	45.18 438	32.05 432	61.18 422	49.49 436	33.39 425	42.05 418	62.48 416	7.00 435	65.56 430	47.08 366	43.21 407	70.27 417

WB-MVS		46.23 391	44.94 393	50.11 411	62.13 424	21.23 444	76.48 389	55.49 430	45.89 409	35.78 422	61.44 419	35.54 367	72.83 419	9.96 438	21.75 433	56.27 426

FPMVS		45.64 392	43.10 396	53.23 409	51.42 434	36.46 427	64.97 418	71.91 408	29.13 429	27.53 429	61.55 418	9.83 431	65.01 432	16.00 435	55.58 380	58.22 425

SSC-MVS		44.51 393	43.35 395	47.99 415	61.01 427	18.90 446	74.12 397	54.36 431	43.42 418	34.10 426	60.02 420	34.42 372	70.39 422	9.14 440	19.57 434	54.68 427

EGC-MVSNET		42.35 394	38.09 397	55.11 405	74.57 393	46.62 404	71.63 403	55.77 429	0.04 443	0.24 444	62.70 415	14.24 425	74.91 417	17.59 432	46.06 402	43.80 429

LCM-MVSNet		40.54 395	35.79 400	54.76 407	36.92 444	30.81 434	51.41 431	69.02 416	22.07 431	24.63 431	45.37 428	4.56 440	65.81 429	33.67 409	34.50 424	67.67 418

APD_test1		40.50 396	37.31 399	50.09 412	51.88 432	35.27 429	59.45 426	52.59 433	21.64 432	26.12 430	57.80 422	4.56 440	66.56 428	22.64 427	39.09 414	48.43 428

test_vis3_rt		40.46 397	37.79 398	48.47 414	44.49 439	33.35 431	66.56 417	32.84 445	32.39 426	29.65 427	39.13 435	3.91 443	68.65 424	50.17 346	40.99 412	43.40 430

ANet_high		40.27 398	35.20 401	55.47 404	34.74 445	34.47 430	63.84 420	71.56 410	48.42 401	18.80 434	41.08 433	9.52 432	64.45 433	20.18 429	8.66 441	67.49 419

test_method		38.59 399	35.16 402	48.89 413	54.33 430	21.35 443	45.32 434	53.71 432	7.41 440	28.74 428	51.62 424	8.70 433	52.87 437	33.73 408	32.89 425	72.47 413

PMMVS2		37.93 400	33.61 403	50.92 410	46.31 436	24.76 440	60.55 425	50.05 434	28.94 430	20.93 432	47.59 425	4.41 442	65.13 431	25.14 424	18.55 436	62.87 422

Gipuma		34.91 401	31.44 404	45.30 416	70.99 405	39.64 424	19.85 438	72.56 406	20.10 434	16.16 438	21.47 439	5.08 439	71.16 421	13.07 436	43.70 406	25.08 436
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 402	29.47 405	42.67 418	41.89 441	30.81 434	52.07 429	43.45 439	15.45 435	18.52 435	44.82 429	2.12 444	58.38 435	16.05 433	30.87 427	38.83 431

APD_test2		32.77 402	29.47 405	42.67 418	41.89 441	30.81 434	52.07 429	43.45 439	15.45 435	18.52 435	44.82 429	2.12 444	58.38 435	16.05 433	30.87 427	38.83 431

PMVS	26.43 22	31.84 404	28.16 407	42.89 417	25.87 447	27.58 438	50.92 432	49.78 435	21.37 433	14.17 439	40.81 434	2.01 446	66.62 427	9.61 439	38.88 417	34.49 435
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 405	24.00 409	26.45 422	43.74 440	18.44 447	60.86 423	39.66 441	15.11 437	9.53 441	22.10 438	6.52 437	46.94 440	8.31 441	10.14 438	13.98 438

MVE	24.84 23	24.35 406	19.77 412	38.09 420	34.56 446	26.92 439	26.57 436	38.87 443	11.73 439	11.37 440	27.44 436	1.37 447	50.42 439	11.41 437	14.60 437	36.93 433
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 407	23.20 411	25.46 423	41.52 443	16.90 448	60.56 424	38.79 444	14.62 438	8.99 442	20.24 441	7.35 434	45.82 441	7.25 442	9.46 439	13.64 439

tmp_tt		22.26 408	23.75 410	17.80 424	5.23 448	12.06 449	35.26 435	39.48 442	2.82 442	18.94 433	44.20 431	22.23 411	24.64 443	36.30 401	9.31 440	16.69 437

cdsmvs_eth3d_5k		19.86 409	26.47 408	0.00 428	0.00 451	0.00 453	0.00 439	93.45 89	0.00 446	0.00 447	95.27 68	49.56 277	0.00 447	0.00 446	0.00 444	0.00 443

wuyk23d		11.30 410	10.95 413	12.33 425	48.05 435	19.89 445	25.89 437	1.92 449	3.58 441	3.12 443	1.37 443	0.64 448	15.77 444	6.23 443	7.77 442	1.35 440

ab-mvs-re		7.91 411	10.55 414	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	94.95 78	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

testmvs		7.23 412	9.62 415	0.06 427	0.04 449	0.02 452	84.98 326	0.02 450	0.03 444	0.18 445	1.21 444	0.01 450	0.02 445	0.14 444	0.01 443	0.13 442

test123		6.92 413	9.21 416	0.08 426	0.03 450	0.05 451	81.65 357	0.01 451	0.02 445	0.14 446	0.85 445	0.03 449	0.02 445	0.12 445	0.00 444	0.16 441

pcd_1.5k_mvsjas		4.46 414	5.95 417	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	53.55 236	0.00 447	0.00 446	0.00 444	0.00 443

mmdepth		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

monomultidepth		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

test_blank		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

uanet_test		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

DCPMVS		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

sosnet-low-res		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

sosnet		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

uncertanet		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

Regformer		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

uanet		0.00 415	0.00 418	0.00 428	0.00 451	0.00 453	0.00 439	0.00 452	0.00 446	0.00 447	0.00 446	0.00 451	0.00 447	0.00 446	0.00 444	0.00 443

WAC-MVS						49.45 388								31.56 420

FOURS1					93.95 46	61.77 261	93.96 78	91.92 158	62.14 343	86.57 54

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 34	94.77 26	96.51 24

PC_three_1452									80.91 58	94.07 2	96.83 21	83.57 4	99.12 5	95.70 8	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 34	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 61	71.42 246	90.67 22	96.85 19	74.45 20

eth-test2					0.00 451

eth-test					0.00 451

ZD-MVS					96.63 9	65.50 159		93.50 87	70.74 260	85.26 71	95.19 74	64.92 88	97.29 81	87.51 66	93.01 56

RE-MVS-def			80.48 160		92.02 103	58.56 327	90.90 224	90.45 222	62.76 336	78.89 139	94.46 92	49.30 280		78.77 155	86.77 138	92.28 197

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 59	95.28 1				92.34 31	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 11	83.82 2	99.15 2	95.72 6	97.63 3	97.62 2

test_241102_TWO								94.41 52	71.65 235	92.07 10	97.21 6	74.58 18	99.11 6	92.34 31	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 50	71.65 235	92.11 8	97.05 9	76.79 9	99.11 6

9.14			87.63 32		93.86 48		94.41 56	94.18 61	72.76 200	86.21 57	96.51 28	66.64 66	97.88 46	90.08 48	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 125	78.19 109

test_0728_THIRD									72.48 205	90.55 23	96.93 13	76.24 11	99.08 11	91.53 39	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 37	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 58	71.92 221	91.89 12	97.11 8	73.77 23

GSMVS																94.68 104

test_part2					96.29 19	68.16 89				90.78 20

sam_mvs1												57.85 181				94.68 104

sam_mvs												54.91 220

ambc				69.61 379	61.38 426	41.35 417	49.07 433	85.86 351		50.18 395	66.40 407	10.16 430	88.14 362	45.73 372	44.20 404	79.32 388

MTGPA								92.23 139

test_post1							78.95 377				20.70 440	53.05 241	91.50 327	60.43 306

test_post											23.01 437	56.49 202	92.67 289

patchmatchnet-post											67.62 406	57.62 184	90.25 337

GG-mvs-BLEND				86.53 74	91.91 112	69.67 53	75.02 396	94.75 36		78.67 147	90.85 179	77.91 7	94.56 219	72.25 204	93.74 45	95.36 66

MTMP							93.77 92	32.52 446

gm-plane-assit					88.42 197	67.04 120			78.62 103		91.83 162		97.37 75	76.57 167

test9_res														89.41 49	94.96 19	95.29 71

TEST9					94.18 41	67.28 111	94.16 65	93.51 85	71.75 232	85.52 66	95.33 63	68.01 56	97.27 85

test_8					94.19 40	67.19 113	94.15 67	93.42 92	71.87 226	85.38 69	95.35 62	68.19 54	96.95 111

agg_prior2														86.41 79	94.75 30	95.33 67

agg_prior					94.16 43	66.97 122		93.31 95		84.49 77			96.75 121

TestCases				72.46 365	79.57 350	51.42 376		68.61 417	51.25 393	45.88 405	81.23 319	19.86 417	86.58 376	38.98 397	57.01 376	79.39 386

test_prior4						67.18 115	93.92 81

test_prior2							95.10 38		75.40 152	85.25 72	95.61 54	67.94 57		87.47 68	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 106					96.84 118			95.05 85

旧先验2							92.00 176		59.37 363	87.54 47			93.47 264	75.39 176

新几何2							91.41 197

新几何1				84.73 137	92.32 93	64.28 191		91.46 185	59.56 362	79.77 128	92.90 134	56.95 194	96.57 126	63.40 286	92.91 58	93.34 160

旧先验1					91.94 108	60.74 286		91.50 183			94.36 96	65.23 83			91.84 72	94.55 111

无先验							92.71 139	92.61 129	62.03 344				97.01 101	66.63 257		93.97 142

原ACMM2							92.01 173

原ACMM1				84.42 152	93.21 68	64.27 192		93.40 94	65.39 311	79.51 131	92.50 142	58.11 180	96.69 122	65.27 276	93.96 40	92.32 195

test222					89.77 160	61.60 267	89.55 266	89.42 270	56.83 377	77.28 160	92.43 146	52.76 244			91.14 86	93.09 170

testdata2													96.09 150	61.26 302

segment_acmp												65.94 74

testdata				81.34 246	89.02 181	57.72 334		89.84 254	58.65 366	85.32 70	94.09 110	57.03 189	93.28 266	69.34 230	90.56 92	93.03 173

testdata1							89.21 276		77.55 124

test12				87.09 52	94.60 36	68.86 68		92.91 113		82.67 98		65.44 80	97.55 66		93.69 48	94.84 96

plane_prior7					86.94 240	61.51 268

plane_prior6					87.23 232	62.32 249						50.66 264

plane_prior5								91.31 189					95.55 180	76.74 165	78.53 220	88.39 263

plane_prior4											89.14 211

plane_prior3						61.95 257			79.09 92	72.53 214

plane_prior2							93.13 120		78.81 99

plane_prior1					87.15 234

plane_prior						62.42 245	93.85 85		79.38 84						78.80 217

n2								0.00 452

nn								0.00 452

door-mid								66.01 421

lessismore_v0				73.72 356	72.93 400	47.83 396		61.72 427		45.86 407	73.76 381	28.63 394	89.81 347	47.75 364	31.37 426	83.53 341

LGP-MVS_train				79.56 296	84.31 298	59.37 316		89.73 260	69.49 273	64.86 308	88.42 217	38.65 342	94.30 228	72.56 201	72.76 264	85.01 327

test11								93.01 109

door								66.57 420

HQP5-MVS						63.66 212

HQP-NCC					87.54 224		94.06 70		79.80 74	74.18 193

ACMP_Plane					87.54 224		94.06 70		79.80 74	74.18 193

BP-MVS														77.63 162

HQP4-MVS										74.18 193			95.61 174			88.63 257

HQP3-MVS								91.70 175							78.90 215

HQP2-MVS												51.63 256

NP-MVS					87.41 227	63.04 229					90.30 189

MDTV_nov1_ep13_2view						59.90 308	80.13 372		67.65 294	72.79 208		54.33 228		59.83 310		92.58 187

MDTV_nov1_ep13			72.61 281		89.06 180	68.48 77	80.33 368	90.11 243	71.84 228	71.81 226	75.92 374	53.01 242	93.92 251	48.04 358	73.38 259

ACMMP++_ref															71.63 272

ACMMP++															69.72 281

Test By Simon												54.21 230

ITE_SJBPF				70.43 377	74.44 394	47.06 402		77.32 390	60.16 358	54.04 376	83.53 285	23.30 407	84.01 390	43.07 381	61.58 356	80.21 383

DeepMVS_CX				34.71 421	51.45 433	24.73 441		28.48 447	31.46 427	17.49 437	52.75 423	5.80 438	42.60 442	18.18 430	19.42 435	36.81 434

High-resolution multi-view benchmark