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 61	96.26 36	72.84 30	99.38 1	92.64 27	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 38	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 29	91.58 15	97.22 4	79.93 5	99.10 9	83.12 109	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	72.48 200	92.07 10	96.85 18	83.82 2	99.15 2	91.53 37	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 92	92.83 80	64.03 196	93.06 120	94.33 58	82.19 37	93.65 3	96.15 40	85.89 1	97.19 88	91.02 41	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 40	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 27	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 62	94.91 80	74.11 21	98.91 18	87.26 69	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	71.65 230	92.11 8	97.21 5	76.79 9	99.11 6	92.34 29	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 137	93.00 76	58.16 323	96.72 9	94.41 52	86.50 8	90.25 26	97.83 1	75.46 14	98.67 25	92.78 26	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 73	73.86 22	97.58 62	93.38 21	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 47	65.94 74	99.10 9	92.99 24	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	71.92 216	90.55 22	96.93 12	73.77 23	99.08 11	91.91 35	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 34	88.90 36	96.35 32	71.89 40	98.63 26	88.76 55	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 235	93.43 91	84.06 17	86.20 56	90.17 191	72.42 35	96.98 105	93.09 23	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 37	66.56 68	98.76 24	89.03 54	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	68.77 279	90.74 20	97.27 2	76.09 12	98.49 29	90.58 45	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	74.18 163	91.74 13	96.67 24	65.61 79	98.42 33	89.24 51	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 42	53.55 234	97.89 44	91.10 39	93.31 53	94.54 112

TSAR-MVS + MP.		88.11 20	88.64 19	86.54 73	91.73 117	68.04 91	90.36 241	93.55 84	82.89 27	91.29 18	92.89 133	72.27 37	96.03 155	87.99 59	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	78.17 106	85.93 60	94.80 83	75.80 13	98.21 35	89.38 48	88.78 111	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 84	96.19 38	64.53 95	98.44 31	83.42 108	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 45	53.45 238	97.68 52	91.07 40	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 89	66.79 65	97.34 77	83.89 102	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 31	87.13 47	95.27 66	64.99 85	95.80 160	89.34 49	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 97	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	90.28 230	84.95 11	90.27 25	96.86 16	65.36 81	97.52 67	94.93 10	90.03 97	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	69.35 270	88.39 38	96.34 33	67.74 59	97.66 57	90.62 44	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	60.04 300	95.05 39	92.17 147	84.80 13	92.27 6	96.37 30	64.62 92	96.54 128	94.43 13	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	88.45 305	80.51 59	92.70 4	96.86 16	69.98 48	97.15 93	95.83 4	88.08 119	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	76.86 127	87.90 41	95.76 48	66.17 71	97.63 59	89.06 53	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	88.14 314	80.24 67	92.54 5	96.97 11	69.52 50	97.17 89	95.89 3	88.51 114	94.56 109

dcpmvs_2		87.37 33	87.55 34	86.85 58	95.04 32	68.20 88	90.36 241	90.66 214	79.37 82	81.20 106	93.67 117	74.73 16	96.55 127	90.88 42	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 83	87.07 49	95.25 68	68.43 52	96.93 113	87.87 60	84.33 160	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	71.87 221	85.52 64	95.33 61	68.19 54	97.27 84	89.09 52	94.90 22	95.25 77

MG-MVS		87.11 36	86.27 51	89.62 8	97.79 1	76.27 4	94.96 45	94.49 48	78.74 98	83.87 82	92.94 131	64.34 96	96.94 111	75.19 171	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	70.78 254	86.25 54	96.44 29	66.98 63	97.79 48	88.68 56	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	93.21 117	87.94 321	84.22 15	91.70 14	97.27 2	65.91 76	95.02 194	93.95 18	90.42 94	94.99 87

CSCG		86.87 39	86.26 52	88.72 17	95.05 31	70.79 29	93.83 89	95.33 18	68.48 283	77.63 153	94.35 98	73.04 28	98.45 30	84.92 91	93.71 47	96.92 14

sasdasda		86.85 40	86.25 53	88.66 20	91.80 115	71.92 16	93.54 102	91.71 170	80.26 64	87.55 44	95.25 68	63.59 110	96.93 113	88.18 57	84.34 158	97.11 9

canonicalmvs		86.85 40	86.25 53	88.66 20	91.80 115	71.92 16	93.54 102	91.71 170	80.26 64	87.55 44	95.25 68	63.59 110	96.93 113	88.18 57	84.34 158	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 44	81.50 102	92.12 152	73.58 26	96.28 140	84.37 97	85.20 150	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	71.77 226	85.69 63	96.52 26	62.07 132	98.77 23	86.06 81	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	77.73 114	84.01 81	95.66 50	63.39 113	97.94 41	87.40 67	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	86.88 131	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 36	81.00 111	93.08 127	63.19 117	97.29 80	87.08 72	91.38 81	94.13 133

testing11		86.71 47	86.44 50	87.55 40	93.54 60	71.35 21	93.65 96	95.58 11	81.36 51	80.69 114	92.21 151	72.30 36	96.46 133	85.18 87	83.43 168	94.82 98

test_fmvsmconf_n		86.58 48	87.17 38	84.82 130	85.28 272	62.55 241	94.26 62	89.78 249	83.81 20	87.78 43	96.33 34	65.33 82	96.98 105	94.40 14	87.55 125	94.95 89

BP-MVS1		86.54 49	86.68 48	86.13 86	87.80 217	67.18 115	92.97 125	95.62 10	79.92 70	82.84 91	94.14 107	74.95 15	96.46 133	82.91 111	88.96 110	94.74 100

jason		86.40 50	86.17 55	87.11 51	86.16 256	70.54 32	95.71 24	92.19 144	82.00 39	84.58 74	94.34 99	61.86 134	95.53 180	87.76 61	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	90.02 242	82.52 32	90.14 29	96.92 14	62.93 122	97.84 47	95.28 8	82.26 178	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	89.99 244	82.88 28	87.85 42	96.03 43	62.89 124	96.36 137	94.15 15	89.95 99	94.48 118

WTY-MVS		86.32 53	85.81 63	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 30	81.91 99	94.73 84	67.93 58	97.63 59	79.55 140	82.25 179	96.54 22

myMVS_eth3d28		86.31 54	86.15 56	86.78 63	93.56 58	70.49 33	92.94 127	95.28 19	82.47 33	78.70 144	92.07 154	72.45 34	95.41 182	82.11 117	85.78 146	94.44 120

MSLP-MVS++		86.27 55	85.91 62	87.35 45	92.01 106	68.97 67	95.04 41	92.70 119	79.04 93	81.50 102	96.50 28	58.98 169	96.78 119	83.49 107	93.93 41	96.29 35

VNet		86.20 56	85.65 67	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 102	86.00 59	93.07 128	58.22 176	97.00 101	85.22 85	84.33 160	96.52 23

MVS_111021_HR		86.19 57	85.80 64	87.37 44	93.17 70	69.79 48	93.99 76	93.76 73	79.08 90	78.88 140	93.99 111	62.25 131	98.15 37	85.93 82	91.15 85	94.15 132

SPE-MVS-test		86.14 58	87.01 40	83.52 183	92.63 88	59.36 312	95.49 27	91.92 157	80.09 68	85.46 66	95.53 56	61.82 136	95.77 163	86.77 76	93.37 52	95.41 61

ACMMP_NAP		86.05 59	85.80 64	86.80 62	91.58 121	67.53 106	91.79 181	93.49 88	74.93 153	84.61 73	95.30 63	59.42 160	97.92 42	86.13 79	94.92 20	94.94 90

testing99		86.01 60	85.47 69	87.63 38	93.62 55	71.25 23	93.47 108	95.23 21	80.42 62	80.60 116	91.95 157	71.73 41	96.50 131	80.02 137	82.22 180	95.13 80

ETV-MVS		86.01 60	86.11 57	85.70 102	90.21 152	67.02 121	93.43 110	91.92 157	81.21 53	84.13 80	94.07 110	60.93 144	95.63 171	89.28 50	89.81 100	94.46 119

testing91		85.93 62	85.31 73	87.78 32	93.59 57	71.47 19	93.50 105	95.08 28	80.26 64	80.53 117	91.93 158	70.43 45	96.51 130	80.32 135	82.13 182	95.37 64

APD-MVS		85.93 62	85.99 60	85.76 99	95.98 26	65.21 164	93.59 100	92.58 129	66.54 297	86.17 57	95.88 46	63.83 103	97.00 101	86.39 78	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 70	87.18 49	88.20 205	72.42 15	92.41 153	92.77 117	82.11 38	80.34 120	93.07 128	68.27 53	95.02 194	78.39 153	93.59 49	94.09 135

CS-MVS		85.80 65	86.65 49	83.27 192	92.00 107	58.92 316	95.31 31	91.86 162	79.97 69	84.82 72	95.40 59	62.26 130	95.51 181	86.11 80	92.08 68	95.37 64

fmvsm_s_conf0.5_n_a		85.75 66	86.09 58	84.72 137	85.73 266	63.58 212	93.79 90	89.32 267	81.42 49	90.21 27	96.91 15	62.41 129	97.67 54	94.48 12	80.56 198	92.90 177

test_fmvsmconf0.1_n		85.71 67	86.08 59	84.62 145	80.83 329	62.33 246	93.84 87	88.81 293	83.50 23	87.00 50	96.01 44	63.36 114	96.93 113	94.04 17	87.29 128	94.61 108

CDPH-MVS		85.71 67	85.46 70	86.46 75	94.75 34	67.19 113	93.89 82	92.83 116	70.90 250	83.09 89	95.28 64	63.62 108	97.36 75	80.63 131	94.18 37	94.84 95

casdiffmvs_mvg		85.66 69	85.18 75	87.09 52	88.22 204	69.35 59	93.74 93	91.89 160	81.47 45	80.10 122	91.45 167	64.80 90	96.35 138	87.23 70	87.69 123	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 61	84.68 141	82.95 312	63.48 217	94.03 75	89.46 261	81.69 42	89.86 30	96.74 22	61.85 135	97.75 50	94.74 11	82.01 184	92.81 179

MGCFI-Net		85.59 71	85.73 66	85.17 121	91.41 129	62.44 242	92.87 131	91.31 187	79.65 76	86.99 51	95.14 74	62.90 123	96.12 147	87.13 71	84.13 165	96.96 13

GDP-MVS		85.54 72	85.32 72	86.18 84	87.64 220	67.95 95	92.91 130	92.36 134	77.81 112	83.69 83	94.31 101	72.84 30	96.41 135	80.39 134	85.95 144	94.19 128

DeepC-MVS	77.85 3	85.52 73	85.24 74	86.37 79	88.80 187	66.64 130	92.15 160	93.68 79	81.07 54	76.91 163	93.64 118	62.59 126	98.44 31	85.50 83	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 81	86.84 59	88.25 202	69.07 63	93.04 122	91.76 167	81.27 52	80.84 113	92.07 154	64.23 98	96.06 153	84.98 90	87.43 127	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 77	86.06 87	93.09 73	65.65 153	93.89 82	93.41 93	73.75 174	79.94 124	94.68 86	60.61 147	98.03 39	82.63 114	93.72 46	94.52 114

MP-MVS-pluss		85.24 76	85.13 76	85.56 105	91.42 126	65.59 155	91.54 191	92.51 131	74.56 156	80.62 115	95.64 51	59.15 164	97.00 101	86.94 74	93.80 43	94.07 137
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 77	84.69 84	86.63 68	92.91 78	69.91 43	92.61 144	95.80 9	80.31 63	80.38 119	92.27 148	68.73 51	95.19 191	75.94 165	83.27 170	94.81 99

PAPR		85.15 78	84.47 85	87.18 49	96.02 25	68.29 82	91.85 179	93.00 111	76.59 134	79.03 136	95.00 75	61.59 137	97.61 61	78.16 154	89.00 109	95.63 54

fmvsm_s_conf0.5_n_2		85.06 79	85.60 68	83.44 189	86.92 242	60.53 288	94.41 55	87.31 327	83.30 24	88.72 37	96.72 23	54.28 227	97.75 50	94.07 16	84.68 157	92.04 202

MP-MVS		85.02 80	84.97 79	85.17 121	92.60 89	64.27 191	93.24 114	92.27 137	73.13 185	79.63 128	94.43 92	61.90 133	97.17 89	85.00 89	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 81	84.44 86	86.71 65	88.33 199	68.73 72	90.24 246	91.82 166	81.05 55	81.18 107	92.50 140	63.69 106	96.08 152	84.45 96	86.71 138	95.32 69

CHOSEN 1792x2688		84.98 82	83.45 100	89.57 11	89.94 157	75.14 6	92.07 166	92.32 135	81.87 40	75.68 172	88.27 217	60.18 150	98.60 27	80.46 133	90.27 96	94.96 88

MVSMamba_PlusPlus		84.97 83	83.65 94	88.93 14	90.17 153	74.04 8	87.84 295	92.69 122	62.18 335	81.47 104	87.64 231	71.47 42	96.28 140	84.69 93	94.74 31	96.47 28

EIA-MVS		84.84 84	84.88 80	84.69 140	91.30 131	62.36 245	93.85 84	92.04 150	79.45 79	79.33 133	94.28 103	62.42 128	96.35 138	80.05 136	91.25 84	95.38 63

fmvsm_s_conf0.1_n_a		84.76 85	84.84 82	84.53 147	80.23 339	63.50 216	92.79 133	88.73 296	80.46 60	89.84 31	96.65 25	60.96 143	97.57 64	93.80 19	80.14 200	92.53 186

HFP-MVS		84.73 86	84.40 87	85.72 101	93.75 52	65.01 170	93.50 105	93.19 101	72.19 210	79.22 134	94.93 78	59.04 167	97.67 54	81.55 121	92.21 64	94.49 117

MVS		84.66 87	82.86 119	90.06 2	90.93 138	74.56 7	87.91 293	95.54 14	68.55 281	72.35 215	94.71 85	59.78 156	98.90 20	81.29 127	94.69 32	96.74 16

GST-MVS		84.63 88	84.29 88	85.66 103	92.82 82	65.27 162	93.04 122	93.13 104	73.20 183	78.89 137	94.18 106	59.41 161	97.85 46	81.45 123	92.48 63	93.86 147

EC-MVSNet		84.53 89	85.04 78	83.01 197	89.34 169	61.37 268	94.42 54	91.09 200	77.91 110	83.24 85	94.20 105	58.37 174	95.40 183	85.35 84	91.41 80	92.27 196

fmvsm_s_conf0.1_n_2		84.40 90	84.78 83	83.27 192	85.25 273	60.41 291	94.13 67	85.69 347	83.05 26	87.99 40	96.37 30	52.75 243	97.68 52	93.75 20	84.05 166	91.71 207

ACMMPR		84.37 91	84.06 89	85.28 116	93.56 58	64.37 186	93.50 105	93.15 103	72.19 210	78.85 142	94.86 81	56.69 196	97.45 69	81.55 121	92.20 65	94.02 140

region2R		84.36 92	84.03 90	85.36 112	93.54 60	64.31 189	93.43 110	92.95 112	72.16 213	78.86 141	94.84 82	56.97 191	97.53 66	81.38 125	92.11 67	94.24 126

LFMVS		84.34 93	82.73 121	89.18 13	94.76 33	73.25 11	94.99 44	91.89 160	71.90 218	82.16 98	93.49 122	47.98 289	97.05 96	82.55 115	84.82 153	97.25 8

test_yl		84.28 94	83.16 110	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 273	81.09 108	92.88 134	57.00 189	97.44 70	81.11 129	81.76 186	96.23 38

DCV-MVSNet		84.28 94	83.16 110	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 273	81.09 108	92.88 134	57.00 189	97.44 70	81.11 129	81.76 186	96.23 38

diffmvs		84.28 94	83.83 91	85.61 104	87.40 226	68.02 92	90.88 221	89.24 270	80.54 58	81.64 101	92.52 139	59.83 155	94.52 219	87.32 68	85.11 151	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 94	83.36 106	87.02 55	92.22 96	67.74 99	84.65 321	94.50 47	79.15 87	82.23 97	87.93 226	66.88 64	96.94 111	80.53 132	82.20 181	96.39 33

ETVMVS		84.22 98	83.71 92	85.76 99	92.58 90	68.25 86	92.45 152	95.53 15	79.54 78	79.46 130	91.64 165	70.29 46	94.18 231	69.16 227	82.76 176	94.84 95

MAR-MVS		84.18 99	83.43 101	86.44 76	96.25 21	65.93 148	94.28 61	94.27 60	74.41 158	79.16 135	95.61 52	53.99 229	98.88 22	69.62 221	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		84.16 100	83.20 109	87.05 54	91.56 122	69.82 46	89.99 255	92.05 149	77.77 113	82.84 91	86.57 248	63.93 102	96.09 149	74.91 176	89.18 106	95.25 77

CANet_DTU		84.09 101	83.52 95	85.81 96	90.30 150	66.82 125	91.87 177	89.01 285	85.27 9	86.09 58	93.74 115	47.71 293	96.98 105	77.90 156	89.78 102	93.65 152

ET-MVSNet_ETH3D		84.01 102	83.15 112	86.58 71	90.78 143	70.89 28	94.74 49	94.62 43	81.44 48	58.19 350	93.64 118	73.64 25	92.35 297	82.66 113	78.66 215	96.50 27

PVSNet_Blended_VisFu		83.97 103	83.50 97	85.39 110	90.02 155	66.59 133	93.77 91	91.73 168	77.43 122	77.08 162	89.81 199	63.77 105	96.97 108	79.67 139	88.21 117	92.60 183

MTAPA		83.91 104	83.38 105	85.50 106	91.89 113	65.16 166	81.75 346	92.23 138	75.32 148	80.53 117	95.21 71	56.06 205	97.16 92	84.86 92	92.55 62	94.18 129

XVS		83.87 105	83.47 99	85.05 123	93.22 66	63.78 201	92.92 128	92.66 124	73.99 166	78.18 147	94.31 101	55.25 211	97.41 72	79.16 144	91.58 77	93.95 142

Effi-MVS+		83.82 106	82.76 120	86.99 56	89.56 165	69.40 54	91.35 202	86.12 341	72.59 197	83.22 88	92.81 137	59.60 158	96.01 157	81.76 120	87.80 122	95.56 57

test_fmvsmvis_n_1920		83.80 107	83.48 98	84.77 134	82.51 315	63.72 205	91.37 200	83.99 364	81.42 49	77.68 152	95.74 49	58.37 174	97.58 62	93.38 21	86.87 132	93.00 174

EI-MVSNet-Vis-set		83.77 108	83.67 93	84.06 163	92.79 85	63.56 213	91.76 184	94.81 34	79.65 76	77.87 150	94.09 108	63.35 115	97.90 43	79.35 142	79.36 207	90.74 225

MVSFormer		83.75 109	82.88 118	86.37 79	89.24 177	71.18 24	89.07 273	90.69 211	65.80 302	87.13 47	94.34 99	64.99 85	92.67 284	72.83 189	91.80 73	95.27 74

CP-MVS		83.71 110	83.40 104	84.65 142	93.14 71	63.84 199	94.59 52	92.28 136	71.03 248	77.41 156	94.92 79	55.21 214	96.19 144	81.32 126	90.70 89	93.91 144

test_fmvsmconf0.01_n		83.70 111	83.52 95	84.25 160	75.26 382	61.72 260	92.17 159	87.24 329	82.36 35	84.91 71	95.41 58	55.60 209	96.83 118	92.85 25	85.87 145	94.21 127

baseline2		83.68 112	83.42 103	84.48 150	87.37 227	66.00 145	90.06 250	95.93 8	79.71 75	69.08 253	90.39 185	77.92 6	96.28 140	78.91 148	81.38 190	91.16 221

reproduce-ours		83.51 113	83.33 107	84.06 163	92.18 99	60.49 289	90.74 227	92.04 150	64.35 312	83.24 85	95.59 54	59.05 165	97.27 84	83.61 104	89.17 107	94.41 121

our_new_method		83.51 113	83.33 107	84.06 163	92.18 99	60.49 289	90.74 227	92.04 150	64.35 312	83.24 85	95.59 54	59.05 165	97.27 84	83.61 104	89.17 107	94.41 121

thisisatest0515		83.41 115	82.49 125	86.16 85	89.46 168	68.26 84	93.54 102	94.70 39	74.31 161	75.75 170	90.92 175	72.62 32	96.52 129	69.64 219	81.50 189	93.71 150

PVSNet_BlendedMVS		83.38 116	83.43 101	83.22 194	93.76 50	67.53 106	94.06 69	93.61 81	79.13 88	81.00 111	85.14 263	63.19 117	97.29 80	87.08 72	73.91 252	84.83 323

test2506		83.29 117	82.92 117	84.37 154	88.39 197	63.18 226	92.01 169	91.35 186	77.66 116	78.49 146	91.42 168	64.58 94	95.09 193	73.19 185	89.23 104	94.85 92

PGM-MVS		83.25 118	82.70 122	84.92 126	92.81 84	64.07 195	90.44 236	92.20 142	71.28 242	77.23 159	94.43 92	55.17 215	97.31 79	79.33 143	91.38 81	93.37 158

HPM-MVS		83.25 118	82.95 116	84.17 161	92.25 95	62.88 235	90.91 218	91.86 162	70.30 259	77.12 160	93.96 112	56.75 194	96.28 140	82.04 118	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		83.15 120	82.96 114	83.73 175	92.02 103	59.74 304	90.37 240	92.08 148	63.70 319	82.86 90	95.48 57	58.62 171	97.17 89	83.06 110	88.42 115	94.26 124

EI-MVSNet-UG-set		83.14 121	82.96 114	83.67 180	92.28 94	63.19 225	91.38 199	94.68 40	79.22 85	76.60 165	93.75 114	62.64 125	97.76 49	78.07 155	78.01 218	90.05 234

testing3-2		83.11 122	83.15 112	82.98 198	91.92 110	64.01 197	94.39 58	95.37 16	78.32 103	75.53 177	90.06 197	73.18 27	93.18 263	74.34 181	75.27 241	91.77 206

VDD-MVS		83.06 123	81.81 134	86.81 61	90.86 141	67.70 100	95.40 29	91.50 181	75.46 145	81.78 100	92.34 147	40.09 331	97.13 94	86.85 75	82.04 183	95.60 55

h-mvs33		83.01 124	82.56 124	84.35 155	89.34 169	62.02 252	92.72 136	93.76 73	81.45 46	82.73 94	92.25 150	60.11 151	97.13 94	87.69 62	62.96 332	93.91 144

PAPM_NR		82.97 125	81.84 133	86.37 79	94.10 44	66.76 128	87.66 299	92.84 115	69.96 263	74.07 192	93.57 120	63.10 120	97.50 68	70.66 214	90.58 91	94.85 92

mPP-MVS		82.96 126	82.44 126	84.52 148	92.83 80	62.92 233	92.76 134	91.85 164	71.52 238	75.61 175	94.24 104	53.48 237	96.99 104	78.97 147	90.73 88	93.64 153

SR-MVS		82.81 127	82.58 123	83.50 186	93.35 64	61.16 271	92.23 158	91.28 191	64.48 311	81.27 105	95.28 64	53.71 233	95.86 159	82.87 112	88.77 112	93.49 156

DP-MVS Recon		82.73 128	81.65 135	85.98 89	97.31 4	67.06 118	95.15 36	91.99 154	69.08 276	76.50 167	93.89 113	54.48 223	98.20 36	70.76 212	85.66 148	92.69 180

CLD-MVS		82.73 128	82.35 128	83.86 170	87.90 212	67.65 102	95.45 28	92.18 145	85.06 10	72.58 208	92.27 148	52.46 246	95.78 161	84.18 98	79.06 210	88.16 261
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 130	82.38 127	83.73 175	89.25 174	59.58 307	92.24 157	94.89 31	77.96 108	79.86 125	92.38 145	56.70 195	97.05 96	77.26 159	80.86 194	94.55 110

3Dnovator	73.91 6	82.69 131	80.82 148	88.31 26	89.57 164	71.26 22	92.60 145	94.39 55	78.84 95	67.89 274	92.48 143	48.42 284	98.52 28	68.80 232	94.40 36	95.15 79

RRT-MVS		82.61 132	81.16 139	86.96 57	91.10 135	68.75 71	87.70 298	92.20 142	76.97 125	72.68 204	87.10 242	51.30 258	96.41 135	83.56 106	87.84 121	95.74 51

MVSTER		82.47 133	82.05 129	83.74 173	92.68 87	69.01 65	91.90 176	93.21 98	79.83 71	72.14 216	85.71 259	74.72 17	94.72 206	75.72 167	72.49 262	87.50 268

TESTMET0.1,1		82.41 134	81.98 132	83.72 177	88.08 206	63.74 203	92.70 138	93.77 72	79.30 83	77.61 154	87.57 233	58.19 177	94.08 235	73.91 183	86.68 139	93.33 161

CostFormer		82.33 135	81.15 140	85.86 94	89.01 182	68.46 78	82.39 343	93.01 109	75.59 143	80.25 121	81.57 307	72.03 39	94.96 198	79.06 146	77.48 226	94.16 131

API-MVS		82.28 136	80.53 156	87.54 41	96.13 22	70.59 31	93.63 98	91.04 206	65.72 304	75.45 178	92.83 136	56.11 204	98.89 21	64.10 276	89.75 103	93.15 166

IB-MVS	77.80 4	82.18 137	80.46 158	87.35 45	89.14 179	70.28 36	95.59 26	95.17 24	78.85 94	70.19 241	85.82 257	70.66 44	97.67 54	72.19 201	66.52 303	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		82.16 138	81.12 141	85.26 118	86.42 249	68.72 73	92.59 147	90.44 221	73.12 186	84.20 77	94.36 94	38.04 344	95.73 165	84.12 99	86.81 133	91.33 214

xiu_mvs_v1_base		82.16 138	81.12 141	85.26 118	86.42 249	68.72 73	92.59 147	90.44 221	73.12 186	84.20 77	94.36 94	38.04 344	95.73 165	84.12 99	86.81 133	91.33 214

xiu_mvs_v1_base_debi		82.16 138	81.12 141	85.26 118	86.42 249	68.72 73	92.59 147	90.44 221	73.12 186	84.20 77	94.36 94	38.04 344	95.73 165	84.12 99	86.81 133	91.33 214

3Dnovator+	73.60 7	82.10 141	80.60 155	86.60 69	90.89 140	66.80 127	95.20 34	93.44 90	74.05 165	67.42 281	92.49 142	49.46 274	97.65 58	70.80 211	91.68 75	95.33 67

MVS_111021_LR		82.02 142	81.52 136	83.51 185	88.42 195	62.88 235	89.77 258	88.93 289	76.78 130	75.55 176	93.10 125	50.31 265	95.38 185	83.82 103	87.02 130	92.26 197

PMMVS		81.98 143	82.04 130	81.78 232	89.76 161	56.17 342	91.13 214	90.69 211	77.96 108	80.09 123	93.57 120	46.33 303	94.99 197	81.41 124	87.46 126	94.17 130

baseline1		81.84 144	81.03 145	84.28 158	91.60 120	66.62 131	91.08 215	91.66 175	81.87 40	74.86 183	91.67 164	69.98 48	94.92 201	71.76 204	64.75 319	91.29 219

EPP-MVSNet		81.79 145	81.52 136	82.61 208	88.77 188	60.21 297	93.02 124	93.66 80	68.52 282	72.90 202	90.39 185	72.19 38	94.96 198	74.93 175	79.29 209	92.67 181

WBMVS		81.67 146	80.98 147	83.72 177	93.07 74	69.40 54	94.33 59	93.05 107	76.84 128	72.05 218	84.14 274	74.49 19	93.88 249	72.76 192	68.09 291	87.88 263

test_vis1_n_1920		81.66 147	82.01 131	80.64 259	82.24 317	55.09 350	94.76 48	86.87 331	81.67 43	84.40 76	94.63 87	38.17 341	94.67 210	91.98 34	83.34 169	92.16 200

APD-MVS_3200maxsize		81.64 148	81.32 138	82.59 209	92.36 92	58.74 318	91.39 197	91.01 207	63.35 323	79.72 127	94.62 88	51.82 249	96.14 146	79.71 138	87.93 120	92.89 178

mvsmamba		81.55 149	80.72 150	84.03 167	91.42 126	66.93 123	83.08 337	89.13 278	78.55 101	67.50 279	87.02 243	51.79 251	90.07 339	87.48 65	90.49 93	95.10 82

ACMMP		81.49 150	80.67 152	83.93 169	91.71 118	62.90 234	92.13 161	92.22 141	71.79 225	71.68 224	93.49 122	50.32 264	96.96 109	78.47 152	84.22 164	91.93 204
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		81.43 151	80.74 149	83.52 183	86.26 253	64.45 180	92.09 164	90.65 215	75.83 141	73.95 194	89.81 199	63.97 101	92.91 274	71.27 207	82.82 173	93.20 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 152	79.99 163	85.46 107	90.39 149	68.40 79	86.88 310	90.61 216	74.41 158	70.31 240	84.67 268	63.79 104	92.32 299	73.13 186	85.70 147	95.67 52

ECVR-MVS		81.29 153	80.38 159	84.01 168	88.39 197	61.96 254	92.56 150	86.79 333	77.66 116	76.63 164	91.42 168	46.34 302	95.24 190	74.36 180	89.23 104	94.85 92

thisisatest0530		81.15 154	80.07 160	84.39 153	88.26 201	65.63 154	91.40 195	94.62 43	71.27 243	70.93 231	89.18 205	72.47 33	96.04 154	65.62 265	76.89 232	91.49 210

Fast-Effi-MVS+		81.14 155	80.01 162	84.51 149	90.24 151	65.86 149	94.12 68	89.15 276	73.81 173	75.37 179	88.26 218	57.26 184	94.53 218	66.97 250	84.92 152	93.15 166

HQP-MVS		81.14 155	80.64 153	82.64 207	87.54 222	63.66 210	94.06 69	91.70 173	79.80 72	74.18 188	90.30 187	51.63 254	95.61 173	77.63 157	78.90 211	88.63 252

hse-mvs2		81.12 157	81.11 144	81.16 246	86.52 248	57.48 331	89.40 266	91.16 194	81.45 46	82.73 94	90.49 183	60.11 151	94.58 211	87.69 62	60.41 359	91.41 213

SR-MVS-dyc-post		81.06 158	80.70 151	82.15 223	92.02 103	58.56 320	90.90 219	90.45 218	62.76 330	78.89 137	94.46 90	51.26 259	95.61 173	78.77 150	86.77 136	92.28 193

HyFIR lowres test		81.03 159	79.56 170	85.43 108	87.81 216	68.11 90	90.18 247	90.01 243	70.65 256	72.95 201	86.06 255	63.61 109	94.50 220	75.01 174	79.75 204	93.67 151

nrg030		80.93 160	79.86 165	84.13 162	83.69 301	68.83 69	93.23 115	91.20 192	75.55 144	75.06 181	88.22 221	63.04 121	94.74 205	81.88 119	66.88 300	88.82 250

Vis-MVSNet		80.92 161	79.98 164	83.74 173	88.48 192	61.80 256	93.44 109	88.26 313	73.96 169	77.73 151	91.76 161	49.94 269	94.76 203	65.84 262	90.37 95	94.65 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 162	80.02 161	83.33 190	87.87 213	60.76 279	92.62 143	86.86 332	77.86 111	75.73 171	91.39 170	46.35 301	94.70 209	72.79 191	88.68 113	94.52 114

UWE-MVS		80.81 163	81.01 146	80.20 269	89.33 171	57.05 336	91.91 175	94.71 38	75.67 142	75.01 182	89.37 203	63.13 119	91.44 323	67.19 247	82.80 175	92.12 201

1314		80.70 164	78.95 182	85.94 91	87.77 219	67.56 104	87.91 293	92.55 130	72.17 212	67.44 280	93.09 126	50.27 266	97.04 99	71.68 206	87.64 124	93.23 163

tpmrst		80.57 165	79.14 180	84.84 129	90.10 154	68.28 83	81.70 347	89.72 256	77.63 118	75.96 169	79.54 339	64.94 87	92.71 281	75.43 169	77.28 229	93.55 154

1112_ss		80.56 166	79.83 166	82.77 202	88.65 189	60.78 277	92.29 155	88.36 307	72.58 198	72.46 212	94.95 76	65.09 84	93.42 260	66.38 256	77.71 220	94.10 134

VDDNet		80.50 167	78.26 190	87.21 47	86.19 254	69.79 48	94.48 53	91.31 187	60.42 349	79.34 132	90.91 176	38.48 339	96.56 126	82.16 116	81.05 192	95.27 74

BH-w/o		80.49 168	79.30 177	84.05 166	90.83 142	64.36 188	93.60 99	89.42 264	74.35 160	69.09 252	90.15 193	55.23 213	95.61 173	64.61 273	86.43 142	92.17 199

test_cas_vis1_n_1920		80.45 169	80.61 154	79.97 278	78.25 365	57.01 338	94.04 73	88.33 308	79.06 92	82.81 93	93.70 116	38.65 336	91.63 315	90.82 43	79.81 202	91.27 220

TAMVS		80.37 170	79.45 173	83.13 196	85.14 276	63.37 218	91.23 208	90.76 210	74.81 155	72.65 206	88.49 212	60.63 146	92.95 269	69.41 223	81.95 185	93.08 170

HQP_MVS		80.34 171	79.75 167	82.12 225	86.94 238	62.42 243	93.13 118	91.31 187	78.81 96	72.53 209	89.14 207	50.66 262	95.55 178	76.74 160	78.53 216	88.39 258

SDMVSNet		80.26 172	78.88 183	84.40 152	89.25 174	67.63 103	85.35 317	93.02 108	76.77 131	70.84 232	87.12 240	47.95 290	96.09 149	85.04 88	74.55 243	89.48 244

HPM-MVS_fast		80.25 173	79.55 172	82.33 215	91.55 123	59.95 301	91.32 204	89.16 275	65.23 308	74.71 185	93.07 128	47.81 292	95.74 164	74.87 178	88.23 116	91.31 218

ab-mvs		80.18 174	78.31 189	85.80 97	88.44 194	65.49 160	83.00 340	92.67 123	71.82 224	77.36 157	85.01 264	54.50 220	96.59 123	76.35 164	75.63 239	95.32 69

IS-MVSNet		80.14 175	79.41 174	82.33 215	87.91 211	60.08 299	91.97 173	88.27 311	72.90 193	71.44 228	91.73 163	61.44 138	93.66 255	62.47 290	86.53 140	93.24 162

test-LLR		80.10 176	79.56 170	81.72 234	86.93 240	61.17 269	92.70 138	91.54 178	71.51 239	75.62 173	86.94 244	53.83 230	92.38 294	72.21 199	84.76 155	91.60 208

PVSNet	73.49 8	80.05 177	78.63 185	84.31 156	90.92 139	64.97 171	92.47 151	91.05 205	79.18 86	72.43 213	90.51 182	37.05 356	94.06 237	68.06 236	86.00 143	93.90 146

UA-Net		80.02 178	79.65 168	81.11 248	89.33 171	57.72 327	86.33 314	89.00 288	77.44 121	81.01 110	89.15 206	59.33 162	95.90 158	61.01 297	84.28 162	89.73 240

test-mter		79.96 179	79.38 176	81.72 234	86.93 240	61.17 269	92.70 138	91.54 178	73.85 171	75.62 173	86.94 244	49.84 271	92.38 294	72.21 199	84.76 155	91.60 208

QAPM		79.95 180	77.39 208	87.64 34	89.63 163	71.41 20	93.30 113	93.70 78	65.34 307	67.39 283	91.75 162	47.83 291	98.96 16	57.71 313	89.81 100	92.54 185

UGNet		79.87 181	78.68 184	83.45 188	89.96 156	61.51 263	92.13 161	90.79 209	76.83 129	78.85 142	86.33 252	38.16 342	96.17 145	67.93 239	87.17 129	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		79.80 182	77.95 196	85.34 113	88.28 200	68.26 84	81.56 349	91.42 184	70.11 261	77.59 155	80.50 325	67.40 61	94.26 229	67.34 244	77.35 227	93.51 155

thres200		79.66 183	78.33 188	83.66 181	92.54 91	65.82 151	93.06 120	96.31 3	74.90 154	73.30 198	88.66 210	59.67 157	95.61 173	47.84 354	78.67 214	89.56 243

CPTT-MVS		79.59 184	79.16 179	80.89 257	91.54 124	59.80 303	92.10 163	88.54 304	60.42 349	72.96 200	93.28 124	48.27 285	92.80 278	78.89 149	86.50 141	90.06 233

Test_1112_low_res		79.56 185	78.60 186	82.43 211	88.24 203	60.39 293	92.09 164	87.99 318	72.10 214	71.84 220	87.42 235	64.62 92	93.04 265	65.80 263	77.30 228	93.85 148

tttt0517		79.50 186	78.53 187	82.41 214	87.22 231	61.43 267	89.75 259	94.76 35	69.29 271	67.91 272	88.06 225	72.92 29	95.63 171	62.91 286	73.90 253	90.16 232

reproduce_monomvs		79.49 187	79.11 181	80.64 259	92.91 78	61.47 266	91.17 213	93.28 96	83.09 25	64.04 312	82.38 294	66.19 70	94.57 213	81.19 128	57.71 367	85.88 306

FIs		79.47 188	79.41 174	79.67 286	85.95 260	59.40 309	91.68 188	93.94 67	78.06 107	68.96 258	88.28 216	66.61 67	91.77 311	66.20 259	74.99 242	87.82 264

BH-RMVSNet		79.46 189	77.65 199	84.89 127	91.68 119	65.66 152	93.55 101	88.09 316	72.93 190	73.37 197	91.12 174	46.20 305	96.12 147	56.28 319	85.61 149	92.91 176

PCF-MVS	73.15 9	79.29 190	77.63 200	84.29 157	86.06 258	65.96 147	87.03 306	91.10 199	69.86 265	69.79 248	90.64 178	57.54 183	96.59 123	64.37 275	82.29 177	90.32 230
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 191	79.57 169	78.24 306	88.46 193	52.29 361	90.41 238	89.12 279	74.24 162	69.13 251	91.91 159	65.77 77	90.09 338	59.00 309	88.09 118	92.33 190

114514_t		79.17 192	77.67 198	83.68 179	95.32 29	65.53 158	92.85 132	91.60 177	63.49 321	67.92 271	90.63 180	46.65 298	95.72 169	67.01 249	83.54 167	89.79 238

FA-MVS(test-final)		79.12 193	77.23 210	84.81 133	90.54 145	63.98 198	81.35 352	91.71 170	71.09 247	74.85 184	82.94 287	52.85 241	97.05 96	67.97 237	81.73 188	93.41 157

VPA-MVSNet		79.03 194	78.00 194	82.11 228	85.95 260	64.48 179	93.22 116	94.66 41	75.05 152	74.04 193	84.95 265	52.17 248	93.52 257	74.90 177	67.04 299	88.32 260

OPM-MVS		79.00 195	78.09 192	81.73 233	83.52 304	63.83 200	91.64 190	90.30 228	76.36 137	71.97 219	89.93 198	46.30 304	95.17 192	75.10 172	77.70 221	86.19 295
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 196	78.22 191	81.25 243	85.33 270	62.73 238	89.53 263	93.21 98	72.39 205	72.14 216	90.13 194	60.99 141	94.72 206	67.73 241	72.49 262	86.29 292

AdaColmap		78.94 197	77.00 214	84.76 135	96.34 17	65.86 149	92.66 142	87.97 320	62.18 335	70.56 234	92.37 146	43.53 318	97.35 76	64.50 274	82.86 172	91.05 223

GeoE		78.90 198	77.43 204	83.29 191	88.95 183	62.02 252	92.31 154	86.23 339	70.24 260	71.34 229	89.27 204	54.43 224	94.04 240	63.31 282	80.81 196	93.81 149

miper_enhance_ethall		78.86 199	77.97 195	81.54 238	88.00 210	65.17 165	91.41 193	89.15 276	75.19 150	68.79 261	83.98 277	67.17 62	92.82 276	72.73 193	65.30 310	86.62 289

VPNet		78.82 200	77.53 203	82.70 205	84.52 288	66.44 135	93.93 79	92.23 138	80.46 60	72.60 207	88.38 215	49.18 278	93.13 264	72.47 197	63.97 329	88.55 255

EPNet_dtu		78.80 201	79.26 178	77.43 314	88.06 207	49.71 377	91.96 174	91.95 156	77.67 115	76.56 166	91.28 172	58.51 172	90.20 336	56.37 318	80.95 193	92.39 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 202	77.43 204	82.88 200	92.21 97	64.49 177	92.05 167	96.28 4	73.48 180	71.75 222	88.26 218	60.07 153	95.32 186	45.16 365	77.58 223	88.83 248

TR-MVS		78.77 203	77.37 209	82.95 199	90.49 146	60.88 275	93.67 95	90.07 238	70.08 262	74.51 186	91.37 171	45.69 307	95.70 170	60.12 303	80.32 199	92.29 192

thres400		78.68 204	77.43 204	82.43 211	92.21 97	64.49 177	92.05 167	96.28 4	73.48 180	71.75 222	88.26 218	60.07 153	95.32 186	45.16 365	77.58 223	87.48 269

BH-untuned		78.68 204	77.08 211	83.48 187	89.84 158	63.74 203	92.70 138	88.59 302	71.57 236	66.83 290	88.65 211	51.75 252	95.39 184	59.03 308	84.77 154	91.32 217

OMC-MVS		78.67 206	77.91 197	80.95 255	85.76 265	57.40 333	88.49 283	88.67 299	73.85 171	72.43 213	92.10 153	49.29 277	94.55 217	72.73 193	77.89 219	90.91 224

tpm		78.58 207	77.03 212	83.22 194	85.94 262	64.56 175	83.21 336	91.14 198	78.31 104	73.67 195	79.68 337	64.01 100	92.09 305	66.07 260	71.26 272	93.03 172

OpenMVS	70.45 11	78.54 208	75.92 228	86.41 78	85.93 263	71.68 18	92.74 135	92.51 131	66.49 298	64.56 306	91.96 156	43.88 317	98.10 38	54.61 324	90.65 90	89.44 246

EPMVS		78.49 209	75.98 227	86.02 88	91.21 133	69.68 52	80.23 361	91.20 192	75.25 149	72.48 211	78.11 348	54.65 219	93.69 254	57.66 314	83.04 171	94.69 102

AUN-MVS		78.37 210	77.43 204	81.17 245	86.60 246	57.45 332	89.46 265	91.16 194	74.11 164	74.40 187	90.49 183	55.52 210	94.57 213	74.73 179	60.43 358	91.48 211

thres100view900		78.37 210	77.01 213	82.46 210	91.89 113	63.21 224	91.19 212	96.33 1	72.28 208	70.45 237	87.89 227	60.31 148	95.32 186	45.16 365	77.58 223	88.83 248

GA-MVS		78.33 212	76.23 223	84.65 142	83.65 302	66.30 139	91.44 192	90.14 236	76.01 139	70.32 239	84.02 276	42.50 322	94.72 206	70.98 209	77.00 231	92.94 175

cascas		78.18 213	75.77 230	85.41 109	87.14 233	69.11 62	92.96 126	91.15 197	66.71 296	70.47 235	86.07 254	37.49 350	96.48 132	70.15 217	79.80 203	90.65 226

UniMVSNet_NR-MVSNet		78.15 214	77.55 202	79.98 276	84.46 290	60.26 295	92.25 156	93.20 100	77.50 120	68.88 259	86.61 247	66.10 72	92.13 303	66.38 256	62.55 336	87.54 267

thres600view7		78.00 215	76.66 218	82.03 230	91.93 109	63.69 208	91.30 205	96.33 1	72.43 203	70.46 236	87.89 227	60.31 148	94.92 201	42.64 377	76.64 233	87.48 269

FC-MVSNet-test		77.99 216	78.08 193	77.70 309	84.89 281	55.51 347	90.27 244	93.75 76	76.87 126	66.80 291	87.59 232	65.71 78	90.23 335	62.89 287	73.94 251	87.37 272

Anonymous202405211		77.96 217	75.33 236	85.87 93	93.73 53	64.52 176	94.85 46	85.36 349	62.52 333	76.11 168	90.18 190	29.43 385	97.29 80	68.51 234	77.24 230	95.81 49

cl22		77.94 218	76.78 216	81.42 240	87.57 221	64.93 173	90.67 230	88.86 292	72.45 202	67.63 278	82.68 291	64.07 99	92.91 274	71.79 202	65.30 310	86.44 290

XXY-MVS		77.94 218	76.44 220	82.43 211	82.60 314	64.44 181	92.01 169	91.83 165	73.59 179	70.00 244	85.82 257	54.43 224	94.76 203	69.63 220	68.02 293	88.10 262

MS-PatchMatch		77.90 220	76.50 219	82.12 225	85.99 259	69.95 42	91.75 186	92.70 119	73.97 168	62.58 328	84.44 272	41.11 328	95.78 161	63.76 279	92.17 66	80.62 370

FMVSNet3		77.73 221	76.04 226	82.80 201	91.20 134	68.99 66	91.87 177	91.99 154	73.35 182	67.04 286	83.19 286	56.62 197	92.14 302	59.80 305	69.34 279	87.28 275

miper_ehance_all_eth		77.60 222	76.44 220	81.09 252	85.70 267	64.41 184	90.65 231	88.64 301	72.31 206	67.37 284	82.52 292	64.77 91	92.64 287	70.67 213	65.30 310	86.24 294

UniMVSNet (Re)		77.58 223	76.78 216	79.98 276	84.11 296	60.80 276	91.76 184	93.17 102	76.56 135	69.93 247	84.78 267	63.32 116	92.36 296	64.89 272	62.51 338	86.78 283

PatchmatchNet		77.46 224	74.63 243	85.96 90	89.55 166	70.35 35	79.97 366	89.55 259	72.23 209	70.94 230	76.91 360	57.03 187	92.79 279	54.27 326	81.17 191	94.74 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 225	75.65 232	82.73 203	80.38 335	67.13 117	91.85 179	90.23 233	75.09 151	69.37 249	83.39 283	53.79 232	94.44 221	71.77 203	65.00 316	86.63 288

CHOSEN 280x420		77.35 226	76.95 215	78.55 301	87.07 235	62.68 239	69.71 398	82.95 371	68.80 278	71.48 227	87.27 239	66.03 73	84.00 382	76.47 163	82.81 174	88.95 247

PS-MVSNAJss		77.26 227	76.31 222	80.13 271	80.64 333	59.16 314	90.63 234	91.06 204	72.80 194	68.58 265	84.57 270	53.55 234	93.96 245	72.97 187	71.96 266	87.27 276

gg-mvs-nofinetune		77.18 228	74.31 250	85.80 97	91.42 126	68.36 80	71.78 392	94.72 37	49.61 392	77.12 160	45.92 418	77.41 8	93.98 244	67.62 242	93.16 55	95.05 84

WB-MVSnew		77.14 229	76.18 225	80.01 275	86.18 255	63.24 222	91.26 206	94.11 64	71.72 228	73.52 196	87.29 238	45.14 312	93.00 267	56.98 316	79.42 205	83.80 331

MVP-Stereo		77.12 230	76.23 223	79.79 283	81.72 322	66.34 138	89.29 267	90.88 208	70.56 257	62.01 331	82.88 288	49.34 275	94.13 232	65.55 267	93.80 43	78.88 384
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 231	75.37 234	82.20 221	89.25 174	62.11 251	82.06 344	89.09 281	76.77 131	70.84 232	87.12 240	41.43 327	95.01 196	67.23 246	74.55 243	89.48 244

MonoMVSNet		76.99 232	75.08 239	82.73 203	83.32 306	63.24 222	86.47 313	86.37 335	79.08 90	66.31 294	79.30 341	49.80 272	91.72 312	79.37 141	65.70 308	93.23 163

dmvs_re		76.93 233	75.36 235	81.61 236	87.78 218	60.71 283	80.00 365	87.99 318	79.42 80	69.02 255	89.47 202	46.77 296	94.32 223	63.38 281	74.45 246	89.81 237

X-MVStestdata		76.86 234	74.13 254	85.05 123	93.22 66	63.78 201	92.92 128	92.66 124	73.99 166	78.18 147	10.19 433	55.25 211	97.41 72	79.16 144	91.58 77	93.95 142

DU-MVS		76.86 234	75.84 229	79.91 279	82.96 310	60.26 295	91.26 206	91.54 178	76.46 136	68.88 259	86.35 250	56.16 202	92.13 303	66.38 256	62.55 336	87.35 273

Anonymous20240529		76.84 236	74.15 253	84.88 128	91.02 136	64.95 172	93.84 87	91.09 200	53.57 380	73.00 199	87.42 235	35.91 360	97.32 78	69.14 228	72.41 264	92.36 189

UWE-MVS-28		76.83 237	77.60 201	74.51 339	84.58 287	50.34 373	88.22 287	94.60 45	74.46 157	66.66 292	88.98 209	62.53 127	85.50 374	57.55 315	80.80 197	87.69 266

c3_l		76.83 237	75.47 233	80.93 256	85.02 279	64.18 194	90.39 239	88.11 315	71.66 229	66.65 293	81.64 305	63.58 112	92.56 288	69.31 225	62.86 333	86.04 300

WR-MVS		76.76 239	75.74 231	79.82 282	84.60 285	62.27 249	92.60 145	92.51 131	76.06 138	67.87 275	85.34 261	56.76 193	90.24 334	62.20 291	63.69 331	86.94 281

v1144		76.73 240	74.88 240	82.27 217	80.23 339	66.60 132	91.68 188	90.21 235	73.69 176	69.06 254	81.89 300	52.73 244	94.40 222	69.21 226	65.23 313	85.80 307

IterMVS-LS		76.49 241	75.18 238	80.43 263	84.49 289	62.74 237	90.64 232	88.80 294	72.40 204	65.16 301	81.72 303	60.98 142	92.27 300	67.74 240	64.65 321	86.29 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 242	74.55 246	82.19 222	79.14 353	67.82 97	90.26 245	89.42 264	73.75 174	68.63 264	81.89 300	51.31 257	94.09 234	71.69 205	64.84 317	84.66 324

v148		76.19 243	74.47 248	81.36 241	80.05 341	64.44 181	91.75 186	90.23 233	73.68 177	67.13 285	80.84 320	55.92 207	93.86 252	68.95 230	61.73 347	85.76 310

Effi-MVS+-dtu		76.14 244	75.28 237	78.72 300	83.22 307	55.17 349	89.87 256	87.78 322	75.42 146	67.98 270	81.43 309	45.08 313	92.52 290	75.08 173	71.63 267	88.48 256

cl____		76.07 245	74.67 241	80.28 266	85.15 275	61.76 258	90.12 248	88.73 296	71.16 244	65.43 298	81.57 307	61.15 139	92.95 269	66.54 253	62.17 340	86.13 298

DIV-MVS_self_test		76.07 245	74.67 241	80.28 266	85.14 276	61.75 259	90.12 248	88.73 296	71.16 244	65.42 299	81.60 306	61.15 139	92.94 273	66.54 253	62.16 342	86.14 296

FMVSNet2		76.07 245	74.01 256	82.26 219	88.85 184	67.66 101	91.33 203	91.61 176	70.84 251	65.98 295	82.25 296	48.03 286	92.00 307	58.46 310	68.73 287	87.10 278

v144192		76.05 248	74.03 255	82.12 225	79.50 347	66.55 134	91.39 197	89.71 257	72.30 207	68.17 268	81.33 312	51.75 252	94.03 242	67.94 238	64.19 324	85.77 308

NR-MVSNet		76.05 248	74.59 244	80.44 262	82.96 310	62.18 250	90.83 223	91.73 168	77.12 124	60.96 334	86.35 250	59.28 163	91.80 310	60.74 298	61.34 351	87.35 273

v1192		75.98 250	73.92 257	82.15 223	79.73 343	66.24 141	91.22 209	89.75 251	72.67 196	68.49 266	81.42 310	49.86 270	94.27 227	67.08 248	65.02 315	85.95 303

FE-MVS		75.97 251	73.02 268	84.82 130	89.78 159	65.56 156	77.44 377	91.07 203	64.55 310	72.66 205	79.85 335	46.05 306	96.69 121	54.97 323	80.82 195	92.21 198

eth_miper_zixun_eth		75.96 252	74.40 249	80.66 258	84.66 284	63.02 228	89.28 268	88.27 311	71.88 220	65.73 296	81.65 304	59.45 159	92.81 277	68.13 235	60.53 356	86.14 296

TranMVSNet+NR-MVSNet		75.86 253	74.52 247	79.89 280	82.44 316	60.64 286	91.37 200	91.37 185	76.63 133	67.65 277	86.21 253	52.37 247	91.55 317	61.84 293	60.81 354	87.48 269

SCA		75.82 254	72.76 271	85.01 125	86.63 245	70.08 38	81.06 354	89.19 273	71.60 235	70.01 243	77.09 358	45.53 308	90.25 331	60.43 300	73.27 255	94.68 103

LPG-MVS_test		75.82 254	74.58 245	79.56 290	84.31 293	59.37 310	90.44 236	89.73 254	69.49 268	64.86 302	88.42 213	38.65 336	94.30 225	72.56 195	72.76 259	85.01 321

GBi-Net		75.65 256	73.83 258	81.10 249	88.85 184	65.11 167	90.01 252	90.32 224	70.84 251	67.04 286	80.25 330	48.03 286	91.54 318	59.80 305	69.34 279	86.64 285

test1		75.65 256	73.83 258	81.10 249	88.85 184	65.11 167	90.01 252	90.32 224	70.84 251	67.04 286	80.25 330	48.03 286	91.54 318	59.80 305	69.34 279	86.64 285

v1921920		75.63 258	73.49 263	82.06 229	79.38 348	66.35 137	91.07 217	89.48 260	71.98 215	67.99 269	81.22 315	49.16 280	93.90 248	66.56 252	64.56 322	85.92 305

ACMP	71.68 10	75.58 259	74.23 252	79.62 288	84.97 280	59.64 305	90.80 224	89.07 283	70.39 258	62.95 324	87.30 237	38.28 340	93.87 250	72.89 188	71.45 270	85.36 317
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 260	73.26 266	81.61 236	80.67 332	66.82 125	89.54 262	89.27 269	71.65 230	63.30 320	80.30 329	54.99 217	94.06 237	67.33 245	62.33 339	83.94 329

tpm cat1		75.30 261	72.21 280	84.58 146	88.52 190	67.77 98	78.16 375	88.02 317	61.88 341	68.45 267	76.37 364	60.65 145	94.03 242	53.77 329	74.11 249	91.93 204

PLC	68.80 14	75.23 262	73.68 261	79.86 281	92.93 77	58.68 319	90.64 232	88.30 309	60.90 346	64.43 310	90.53 181	42.38 323	94.57 213	56.52 317	76.54 234	86.33 291
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 263	72.98 269	81.88 231	79.20 350	66.00 145	90.75 226	89.11 280	71.63 234	67.41 282	81.22 315	47.36 294	93.87 250	65.46 268	64.72 320	85.77 308

Fast-Effi-MVS+-dtu		75.04 264	73.37 264	80.07 272	80.86 328	59.52 308	91.20 211	85.38 348	71.90 218	65.20 300	84.84 266	41.46 326	92.97 268	66.50 255	72.96 258	87.73 265

dp		75.01 265	72.09 281	83.76 172	89.28 173	66.22 142	79.96 367	89.75 251	71.16 244	67.80 276	77.19 357	51.81 250	92.54 289	50.39 338	71.44 271	92.51 187

TAPA-MVS	70.22 12	74.94 266	73.53 262	79.17 295	90.40 148	52.07 362	89.19 271	89.61 258	62.69 332	70.07 242	92.67 138	48.89 283	94.32 223	38.26 391	79.97 201	91.12 222
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 267	73.32 265	79.74 285	86.53 247	60.31 294	89.03 276	92.70 119	78.61 100	68.98 257	83.34 284	41.93 325	92.23 301	52.77 333	65.97 306	86.69 284

v10		74.77 268	72.54 277	81.46 239	80.33 337	66.71 129	89.15 272	89.08 282	70.94 249	63.08 323	79.86 334	52.52 245	94.04 240	65.70 264	62.17 340	83.64 332

XVG-OURS-SEG-HR		74.70 269	73.08 267	79.57 289	78.25 365	57.33 334	80.49 357	87.32 325	63.22 325	68.76 262	90.12 196	44.89 314	91.59 316	70.55 215	74.09 250	89.79 238

ACMM	69.62 13	74.34 270	72.73 273	79.17 295	84.25 295	57.87 325	90.36 241	89.93 245	63.17 327	65.64 297	86.04 256	37.79 348	94.10 233	65.89 261	71.52 269	85.55 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 271	72.30 279	80.32 264	91.49 125	61.66 261	90.85 222	80.72 377	56.67 372	63.85 315	90.64 178	46.75 297	90.84 326	53.79 328	75.99 238	88.47 257

XVG-OURS		74.25 272	72.46 278	79.63 287	78.45 363	57.59 330	80.33 359	87.39 324	63.86 317	68.76 262	89.62 201	40.50 330	91.72 312	69.00 229	74.25 248	89.58 241

test_fmvs1		74.07 273	73.69 260	75.22 332	78.91 357	47.34 390	89.06 275	74.69 393	63.68 320	79.41 131	91.59 166	24.36 395	87.77 358	85.22 85	76.26 236	90.55 229

CVMVSNet		74.04 274	74.27 251	73.33 349	85.33 270	43.94 403	89.53 263	88.39 306	54.33 379	70.37 238	90.13 194	49.17 279	84.05 380	61.83 294	79.36 207	91.99 203

Baseline_NR-MVSNet		73.99 275	72.83 270	77.48 313	80.78 330	59.29 313	91.79 181	84.55 357	68.85 277	68.99 256	80.70 321	56.16 202	92.04 306	62.67 288	60.98 353	81.11 364

pmmvs4		73.92 276	71.81 285	80.25 268	79.17 351	65.24 163	87.43 302	87.26 328	67.64 289	63.46 318	83.91 278	48.96 282	91.53 321	62.94 285	65.49 309	83.96 328

D2MVS		73.80 277	72.02 282	79.15 297	79.15 352	62.97 229	88.58 282	90.07 238	72.94 189	59.22 344	78.30 345	42.31 324	92.70 283	65.59 266	72.00 265	81.79 359

CR-MVSNet		73.79 278	70.82 293	82.70 205	83.15 308	67.96 93	70.25 395	84.00 362	73.67 178	69.97 245	72.41 380	57.82 180	89.48 343	52.99 332	73.13 256	90.64 227

test_djsdf		73.76 279	72.56 276	77.39 315	77.00 375	53.93 355	89.07 273	90.69 211	65.80 302	63.92 313	82.03 299	43.14 321	92.67 284	72.83 189	68.53 288	85.57 312

pmmvs5		73.35 280	71.52 287	78.86 299	78.64 361	60.61 287	91.08 215	86.90 330	67.69 286	63.32 319	83.64 279	44.33 316	90.53 328	62.04 292	66.02 305	85.46 315

Anonymous20231211		73.08 281	70.39 297	81.13 247	90.62 144	63.33 219	91.40 195	90.06 240	51.84 385	64.46 309	80.67 323	36.49 358	94.07 236	63.83 278	64.17 325	85.98 302

tt0805		73.07 282	70.73 294	80.07 272	78.37 364	57.05 336	87.78 296	92.18 145	61.23 345	67.04 286	86.49 249	31.35 378	94.58 211	65.06 271	67.12 298	88.57 254

miper_lstm_enhance		73.05 283	71.73 286	77.03 319	83.80 299	58.32 322	81.76 345	88.88 290	69.80 266	61.01 333	78.23 347	57.19 185	87.51 362	65.34 269	59.53 361	85.27 320

jajsoiax		73.05 283	71.51 288	77.67 310	77.46 372	54.83 351	88.81 278	90.04 241	69.13 275	62.85 326	83.51 281	31.16 379	92.75 280	70.83 210	69.80 275	85.43 316

LCM-MVSNet-Re		72.93 285	71.84 284	76.18 328	88.49 191	48.02 385	80.07 364	70.17 405	73.96 169	52.25 375	80.09 333	49.98 268	88.24 352	67.35 243	84.23 163	92.28 193

pm-mvs1		72.89 286	71.09 290	78.26 305	79.10 354	57.62 329	90.80 224	89.30 268	67.66 287	62.91 325	81.78 302	49.11 281	92.95 269	60.29 302	58.89 364	84.22 327

tpmvs		72.88 287	69.76 303	82.22 220	90.98 137	67.05 119	78.22 374	88.30 309	63.10 328	64.35 311	74.98 371	55.09 216	94.27 227	43.25 371	69.57 278	85.34 318

test0.0.03 1		72.76 288	72.71 274	72.88 353	80.25 338	47.99 386	91.22 209	89.45 262	71.51 239	62.51 329	87.66 230	53.83 230	85.06 376	50.16 340	67.84 296	85.58 311

UniMVSNet_ETH3D		72.74 289	70.53 296	79.36 292	78.62 362	56.64 340	85.01 319	89.20 272	63.77 318	64.84 304	84.44 272	34.05 367	91.86 309	63.94 277	70.89 274	89.57 242

mvs_tets		72.71 290	71.11 289	77.52 311	77.41 373	54.52 353	88.45 284	89.76 250	68.76 280	62.70 327	83.26 285	29.49 384	92.71 281	70.51 216	69.62 277	85.34 318

FMVSNet1		72.71 290	69.91 301	81.10 249	83.60 303	65.11 167	90.01 252	90.32 224	63.92 316	63.56 317	80.25 330	36.35 359	91.54 318	54.46 325	66.75 301	86.64 285

test_fmvs1_n		72.69 292	71.92 283	74.99 335	71.15 395	47.08 392	87.34 304	75.67 388	63.48 322	78.08 149	91.17 173	20.16 407	87.87 355	84.65 94	75.57 240	90.01 235

IterMVS		72.65 293	70.83 291	78.09 307	82.17 318	62.96 230	87.64 300	86.28 337	71.56 237	60.44 337	78.85 343	45.42 310	86.66 366	63.30 283	61.83 344	84.65 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 294	72.74 272	72.10 361	87.87 213	49.45 379	88.07 289	89.01 285	72.91 191	63.11 321	88.10 222	63.63 107	85.54 371	32.73 406	69.23 282	81.32 362

PatchMatch-RL		72.06 295	69.98 298	78.28 304	89.51 167	55.70 346	83.49 329	83.39 369	61.24 344	63.72 316	82.76 289	34.77 364	93.03 266	53.37 331	77.59 222	86.12 299

PVSNet_0	68.08 15	71.81 296	68.32 312	82.27 217	84.68 282	62.31 248	88.68 280	90.31 227	75.84 140	57.93 355	80.65 324	37.85 347	94.19 230	69.94 218	29.05 421	90.31 231

MIMVSNet		71.64 297	68.44 310	81.23 244	81.97 321	64.44 181	73.05 389	88.80 294	69.67 267	64.59 305	74.79 373	32.79 370	87.82 356	53.99 327	76.35 235	91.42 212

test_vis1_n		71.63 298	70.73 294	74.31 343	69.63 401	47.29 391	86.91 308	72.11 399	63.21 326	75.18 180	90.17 191	20.40 405	85.76 370	84.59 95	74.42 247	89.87 236

IterMVS-SCA-FT		71.55 299	69.97 299	76.32 326	81.48 324	60.67 285	87.64 300	85.99 342	66.17 300	59.50 342	78.88 342	45.53 308	83.65 384	62.58 289	61.93 343	84.63 326

v7n		71.31 300	68.65 307	79.28 293	76.40 377	60.77 278	86.71 311	89.45 262	64.17 315	58.77 349	78.24 346	44.59 315	93.54 256	57.76 312	61.75 346	83.52 335

anonymousdsp		71.14 301	69.37 305	76.45 325	72.95 390	54.71 352	84.19 324	88.88 290	61.92 340	62.15 330	79.77 336	38.14 343	91.44 323	68.90 231	67.45 297	83.21 341

F-COLMAP		70.66 302	68.44 310	77.32 316	86.37 252	55.91 344	88.00 291	86.32 336	56.94 370	57.28 359	88.07 224	33.58 368	92.49 291	51.02 336	68.37 289	83.55 333

WR-MVS_H		70.59 303	69.94 300	72.53 355	81.03 327	51.43 366	87.35 303	92.03 153	67.38 290	60.23 339	80.70 321	55.84 208	83.45 386	46.33 361	58.58 366	82.72 348

CP-MVSNet		70.50 304	69.91 301	72.26 358	80.71 331	51.00 370	87.23 305	90.30 228	67.84 285	59.64 341	82.69 290	50.23 267	82.30 394	51.28 335	59.28 362	83.46 337

RPMNet		70.42 305	65.68 326	84.63 144	83.15 308	67.96 93	70.25 395	90.45 218	46.83 401	69.97 245	65.10 401	56.48 201	95.30 189	35.79 396	73.13 256	90.64 227

testing3		70.38 306	70.83 291	69.03 373	85.82 264	43.93 404	90.72 229	90.56 217	68.06 284	60.24 338	86.82 246	64.83 89	84.12 378	26.33 414	64.10 326	79.04 383

tfpnnormal		70.10 307	67.36 316	78.32 303	83.45 305	60.97 274	88.85 277	92.77 117	64.85 309	60.83 335	78.53 344	43.52 319	93.48 258	31.73 409	61.70 348	80.52 371

TransMVSNet (Re)		70.07 308	67.66 314	77.31 317	80.62 334	59.13 315	91.78 183	84.94 353	65.97 301	60.08 340	80.44 326	50.78 261	91.87 308	48.84 347	45.46 395	80.94 366

CL-MVSNet_self_test		69.92 309	68.09 313	75.41 331	73.25 389	55.90 345	90.05 251	89.90 246	69.96 263	61.96 332	76.54 361	51.05 260	87.64 359	49.51 344	50.59 387	82.70 350

DP-MVS		69.90 310	66.48 318	80.14 270	95.36 28	62.93 231	89.56 260	76.11 386	50.27 391	57.69 357	85.23 262	39.68 332	95.73 165	33.35 401	71.05 273	81.78 360

PS-CasMVS		69.86 311	69.13 306	72.07 362	80.35 336	50.57 372	87.02 307	89.75 251	67.27 291	59.19 345	82.28 295	46.58 299	82.24 395	50.69 337	59.02 363	83.39 339

Syy-MVS		69.65 312	69.52 304	70.03 369	87.87 213	43.21 405	88.07 289	89.01 285	72.91 191	63.11 321	88.10 222	45.28 311	85.54 371	22.07 419	69.23 282	81.32 362

MSDG		69.54 313	65.73 325	80.96 254	85.11 278	63.71 206	84.19 324	83.28 370	56.95 369	54.50 366	84.03 275	31.50 376	96.03 155	42.87 375	69.13 284	83.14 343

PEN-MVS		69.46 314	68.56 308	72.17 360	79.27 349	49.71 377	86.90 309	89.24 270	67.24 294	59.08 346	82.51 293	47.23 295	83.54 385	48.42 349	57.12 368	83.25 340

LS3D		69.17 315	66.40 320	77.50 312	91.92 110	56.12 343	85.12 318	80.37 379	46.96 399	56.50 361	87.51 234	37.25 351	93.71 253	32.52 408	79.40 206	82.68 351

PatchT		69.11 316	65.37 330	80.32 264	82.07 320	63.68 209	67.96 405	87.62 323	50.86 389	69.37 249	65.18 400	57.09 186	88.53 349	41.59 380	66.60 302	88.74 251

KD-MVS_2432*1600		69.03 317	66.37 321	77.01 320	85.56 268	61.06 272	81.44 350	90.25 231	67.27 291	58.00 353	76.53 362	54.49 221	87.63 360	48.04 351	35.77 412	82.34 354

miper_refine_blended		69.03 317	66.37 321	77.01 320	85.56 268	61.06 272	81.44 350	90.25 231	67.27 291	58.00 353	76.53 362	54.49 221	87.63 360	48.04 351	35.77 412	82.34 354

mvsany_test1		68.77 319	68.56 308	69.39 371	73.57 388	45.88 399	80.93 355	60.88 419	59.65 355	71.56 225	90.26 189	43.22 320	75.05 406	74.26 182	62.70 335	87.25 277

ACMH	63.93 17	68.62 320	64.81 332	80.03 274	85.22 274	63.25 221	87.72 297	84.66 355	60.83 347	51.57 379	79.43 340	27.29 391	94.96 198	41.76 378	64.84 317	81.88 358
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 321	65.41 329	77.96 308	78.69 360	62.93 231	89.86 257	89.17 274	60.55 348	50.27 384	77.73 352	22.60 401	94.06 237	47.18 357	72.65 261	76.88 394

ADS-MVSNet		68.54 322	64.38 339	81.03 253	88.06 207	66.90 124	68.01 403	84.02 361	57.57 363	64.48 307	69.87 390	38.68 334	89.21 345	40.87 382	67.89 294	86.97 279

DTE-MVSNet		68.46 323	67.33 317	71.87 364	77.94 369	49.00 383	86.16 315	88.58 303	66.36 299	58.19 350	82.21 297	46.36 300	83.87 383	44.97 368	55.17 375	82.73 347

mmtdpeth		68.33 324	66.37 321	74.21 344	82.81 313	51.73 363	84.34 323	80.42 378	67.01 295	71.56 225	68.58 394	30.52 382	92.35 297	75.89 166	36.21 410	78.56 388

our_test_3		68.29 325	64.69 334	79.11 298	78.92 355	64.85 174	88.40 285	85.06 351	60.32 351	52.68 373	76.12 366	40.81 329	89.80 342	44.25 370	55.65 373	82.67 352

Patchmatch-RL test		68.17 326	64.49 337	79.19 294	71.22 394	53.93 355	70.07 397	71.54 403	69.22 272	56.79 360	62.89 405	56.58 198	88.61 346	69.53 222	52.61 382	95.03 86

XVG-ACMP-BASELINE		68.04 327	65.53 328	75.56 330	74.06 387	52.37 360	78.43 371	85.88 343	62.03 338	58.91 348	81.21 317	20.38 406	91.15 325	60.69 299	68.18 290	83.16 342

FMVSNet5		68.04 327	65.66 327	75.18 334	84.43 291	57.89 324	83.54 328	86.26 338	61.83 342	53.64 371	73.30 376	37.15 354	85.08 375	48.99 346	61.77 345	82.56 353

ppachtmachnet_test		67.72 329	63.70 341	79.77 284	78.92 355	66.04 144	88.68 280	82.90 372	60.11 353	55.45 363	75.96 367	39.19 333	90.55 327	39.53 386	52.55 383	82.71 349

ACMH+	65.35 16	67.65 330	64.55 335	76.96 322	84.59 286	57.10 335	88.08 288	80.79 376	58.59 361	53.00 372	81.09 319	26.63 393	92.95 269	46.51 359	61.69 349	80.82 367

pmmvs6		67.57 331	64.76 333	76.00 329	72.82 392	53.37 357	88.71 279	86.78 334	53.19 381	57.58 358	78.03 349	35.33 363	92.41 293	55.56 321	54.88 377	82.21 356

Anonymous20231206		67.53 332	65.78 324	72.79 354	74.95 383	47.59 388	88.23 286	87.32 325	61.75 343	58.07 352	77.29 355	37.79 348	87.29 364	42.91 373	63.71 330	83.48 336

Patchmtry		67.53 332	63.93 340	78.34 302	82.12 319	64.38 185	68.72 400	84.00 362	48.23 398	59.24 343	72.41 380	57.82 180	89.27 344	46.10 362	56.68 372	81.36 361

USDC		67.43 334	64.51 336	76.19 327	77.94 369	55.29 348	78.38 372	85.00 352	73.17 184	48.36 392	80.37 327	21.23 403	92.48 292	52.15 334	64.02 328	80.81 368

ADS-MVSNet2		66.90 335	63.44 343	77.26 318	88.06 207	60.70 284	68.01 403	75.56 390	57.57 363	64.48 307	69.87 390	38.68 334	84.10 379	40.87 382	67.89 294	86.97 279

CMPMVS	48.56 21	66.77 336	64.41 338	73.84 346	70.65 398	50.31 374	77.79 376	85.73 346	45.54 403	44.76 402	82.14 298	35.40 362	90.14 337	63.18 284	74.54 245	81.07 365
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 337	62.92 346	76.80 324	76.51 376	57.77 326	89.22 269	83.41 368	55.48 376	53.86 370	77.84 350	26.28 394	93.95 246	34.90 398	68.76 286	78.68 386

LTVRE_ROB	59.60 19	66.27 338	63.54 342	74.45 340	84.00 298	51.55 365	67.08 407	83.53 366	58.78 359	54.94 365	80.31 328	34.54 365	93.23 262	40.64 384	68.03 292	78.58 387
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		66.06 339	62.45 349	76.88 323	81.42 326	54.45 354	57.49 419	88.67 299	49.36 393	63.86 314	46.86 417	56.06 205	90.25 331	49.53 343	68.83 285	85.95 303

Patchmatch-test		65.86 340	60.94 355	80.62 261	83.75 300	58.83 317	58.91 418	75.26 392	44.50 406	50.95 383	77.09 358	58.81 170	87.90 354	35.13 397	64.03 327	95.12 81

UnsupCasMVSNet_eth		65.79 341	63.10 344	73.88 345	70.71 397	50.29 375	81.09 353	89.88 247	72.58 198	49.25 389	74.77 374	32.57 372	87.43 363	55.96 320	41.04 402	83.90 330

test_fmvs2		65.78 342	64.84 331	68.60 375	66.54 407	41.71 407	83.27 333	69.81 406	54.38 378	67.91 272	84.54 271	15.35 412	81.22 399	75.65 168	66.16 304	82.88 344

dmvs_testset		65.55 343	66.45 319	62.86 387	79.87 342	22.35 433	76.55 379	71.74 401	77.42 123	55.85 362	87.77 229	51.39 256	80.69 400	31.51 412	65.92 307	85.55 313

pmmvs-eth3d		65.53 344	62.32 350	75.19 333	69.39 402	59.59 306	82.80 341	83.43 367	62.52 333	51.30 381	72.49 378	32.86 369	87.16 365	55.32 322	50.73 386	78.83 385

mamv4		65.18 345	67.43 315	58.44 391	77.88 371	49.36 382	69.40 399	70.99 404	48.31 397	57.78 356	85.53 260	59.01 168	51.88 429	73.67 184	64.32 323	74.07 399

SixPastTwentyTwo		64.92 346	61.78 353	74.34 342	78.74 359	49.76 376	83.42 332	79.51 382	62.86 329	50.27 384	77.35 353	30.92 381	90.49 329	45.89 363	47.06 392	82.78 345

OurMVSNet-221017-0		64.68 347	62.17 351	72.21 359	76.08 380	47.35 389	80.67 356	81.02 375	56.19 373	51.60 378	79.66 338	27.05 392	88.56 348	53.60 330	53.63 380	80.71 369

test_0402		64.54 348	61.09 354	74.92 336	84.10 297	60.75 280	87.95 292	79.71 381	52.03 383	52.41 374	77.20 356	32.21 374	91.64 314	23.14 417	61.03 352	72.36 405

testgi		64.48 349	62.87 347	69.31 372	71.24 393	40.62 410	85.49 316	79.92 380	65.36 306	54.18 368	83.49 282	23.74 398	84.55 377	41.60 379	60.79 355	82.77 346

RPSCF		64.24 350	61.98 352	71.01 367	76.10 379	45.00 400	75.83 384	75.94 387	46.94 400	58.96 347	84.59 269	31.40 377	82.00 396	47.76 355	60.33 360	86.04 300

EU-MVSNet		64.01 351	63.01 345	67.02 381	74.40 386	38.86 416	83.27 333	86.19 340	45.11 404	54.27 367	81.15 318	36.91 357	80.01 402	48.79 348	57.02 369	82.19 357

test20.03		63.83 352	62.65 348	67.38 380	70.58 399	39.94 412	86.57 312	84.17 359	63.29 324	51.86 377	77.30 354	37.09 355	82.47 392	38.87 390	54.13 379	79.73 377

MDA-MVSNet_test_wron		63.78 353	60.16 357	74.64 337	78.15 367	60.41 291	83.49 329	84.03 360	56.17 375	39.17 412	71.59 386	37.22 352	83.24 389	42.87 375	48.73 389	80.26 374

YYNet1		63.76 354	60.14 358	74.62 338	78.06 368	60.19 298	83.46 331	83.99 364	56.18 374	39.25 411	71.56 387	37.18 353	83.34 387	42.90 374	48.70 390	80.32 373

K. test v3		63.09 355	59.61 360	73.53 348	76.26 378	49.38 381	83.27 333	77.15 385	64.35 312	47.77 394	72.32 382	28.73 386	87.79 357	49.93 342	36.69 409	83.41 338

COLMAP_ROB	57.96 20	62.98 356	59.65 359	72.98 352	81.44 325	53.00 359	83.75 327	75.53 391	48.34 396	48.81 391	81.40 311	24.14 396	90.30 330	32.95 403	60.52 357	75.65 397
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 357	59.08 361	71.10 366	67.19 405	48.72 384	83.91 326	85.23 350	50.38 390	47.84 393	71.22 389	20.74 404	85.51 373	46.47 360	58.75 365	79.06 382

AllTest		61.66 358	58.06 363	72.46 356	79.57 344	51.42 367	80.17 362	68.61 408	51.25 387	45.88 396	81.23 313	19.86 408	86.58 367	38.98 388	57.01 370	79.39 379

UnsupCasMVSNet_bld		61.60 359	57.71 364	73.29 350	68.73 403	51.64 364	78.61 370	89.05 284	57.20 368	46.11 395	61.96 408	28.70 387	88.60 347	50.08 341	38.90 407	79.63 378

MDA-MVSNet-bldmvs		61.54 360	57.70 365	73.05 351	79.53 346	57.00 339	83.08 337	81.23 374	57.57 363	34.91 416	72.45 379	32.79 370	86.26 369	35.81 395	41.95 400	75.89 396

mvs5depth		61.03 361	57.65 366	71.18 365	67.16 406	47.04 394	72.74 390	77.49 383	57.47 366	60.52 336	72.53 377	22.84 400	88.38 350	49.15 345	38.94 406	78.11 391

KD-MVS_self_test		60.87 362	58.60 362	67.68 378	66.13 408	39.93 413	75.63 386	84.70 354	57.32 367	49.57 387	68.45 395	29.55 383	82.87 390	48.09 350	47.94 391	80.25 375

kuosan		60.86 363	60.24 356	62.71 388	81.57 323	46.43 396	75.70 385	85.88 343	57.98 362	48.95 390	69.53 392	58.42 173	76.53 404	28.25 413	35.87 411	65.15 412

TinyColmap		60.32 364	56.42 371	72.00 363	78.78 358	53.18 358	78.36 373	75.64 389	52.30 382	41.59 410	75.82 369	14.76 415	88.35 351	35.84 394	54.71 378	74.46 398

MVS-HIRNet		60.25 365	55.55 372	74.35 341	84.37 292	56.57 341	71.64 393	74.11 394	34.44 415	45.54 400	42.24 423	31.11 380	89.81 340	40.36 385	76.10 237	76.67 395

MIMVSNet1		60.16 366	57.33 367	68.67 374	69.71 400	44.13 402	78.92 369	84.21 358	55.05 377	44.63 403	71.85 384	23.91 397	81.54 398	32.63 407	55.03 376	80.35 372

PM-MVS		59.40 367	56.59 369	67.84 376	63.63 411	41.86 406	76.76 378	63.22 416	59.01 358	51.07 382	72.27 383	11.72 419	83.25 388	61.34 295	50.28 388	78.39 389

new-patchmatchnet		59.30 368	56.48 370	67.79 377	65.86 409	44.19 401	82.47 342	81.77 373	59.94 354	43.65 406	66.20 399	27.67 390	81.68 397	39.34 387	41.40 401	77.50 393

test_vis1_rt		59.09 369	57.31 368	64.43 384	68.44 404	46.02 398	83.05 339	48.63 428	51.96 384	49.57 387	63.86 404	16.30 410	80.20 401	71.21 208	62.79 334	67.07 411

test_fmvs3		56.82 370	54.86 374	62.69 389	53.59 422	35.47 419	75.87 383	65.64 413	43.91 407	55.10 364	71.43 388	6.91 427	74.40 409	68.64 233	52.63 381	78.20 390

DSMNet-mixed		56.78 371	54.44 375	63.79 385	63.21 412	29.44 428	64.43 410	64.10 415	42.12 412	51.32 380	71.60 385	31.76 375	75.04 407	36.23 393	65.20 314	86.87 282

pmmvs3		55.51 372	51.50 378	67.53 379	57.90 420	50.93 371	80.37 358	73.66 395	40.63 413	44.15 405	64.75 402	16.30 410	78.97 403	44.77 369	40.98 404	72.69 403

TDRefinement		55.28 373	51.58 377	66.39 382	59.53 419	46.15 397	76.23 381	72.80 396	44.60 405	42.49 408	76.28 365	15.29 413	82.39 393	33.20 402	43.75 397	70.62 407

dongtai		55.18 374	55.46 373	54.34 399	76.03 381	36.88 417	76.07 382	84.61 356	51.28 386	43.41 407	64.61 403	56.56 199	67.81 417	18.09 422	28.50 422	58.32 415

LF4IMVS		54.01 375	52.12 376	59.69 390	62.41 414	39.91 414	68.59 401	68.28 410	42.96 410	44.55 404	75.18 370	14.09 417	68.39 416	41.36 381	51.68 384	70.78 406

ttmdpeth		53.34 376	49.96 379	63.45 386	62.07 416	40.04 411	72.06 391	65.64 413	42.54 411	51.88 376	77.79 351	13.94 418	76.48 405	32.93 404	30.82 420	73.84 400

MVStest1		51.35 377	46.89 381	64.74 383	65.06 410	51.10 369	67.33 406	72.58 397	30.20 419	35.30 414	74.82 372	27.70 389	69.89 414	24.44 416	24.57 423	73.22 401

N_pmnet		50.55 378	49.11 380	54.88 397	77.17 374	4.02 441	84.36 322	2.00 439	48.59 394	45.86 398	68.82 393	32.22 373	82.80 391	31.58 410	51.38 385	77.81 392

new_pmnet		49.31 379	46.44 382	57.93 392	62.84 413	40.74 409	68.47 402	62.96 417	36.48 414	35.09 415	57.81 412	14.97 414	72.18 411	32.86 405	46.44 393	60.88 414

mvsany_test3		48.86 380	46.35 383	56.41 393	46.00 428	31.67 424	62.26 412	47.25 429	43.71 408	45.54 400	68.15 396	10.84 420	64.44 425	57.95 311	35.44 414	73.13 402

test_f		46.58 381	43.45 385	55.96 394	45.18 429	32.05 423	61.18 413	49.49 427	33.39 416	42.05 409	62.48 407	7.00 426	65.56 421	47.08 358	43.21 399	70.27 408

WB-MVS		46.23 382	44.94 384	50.11 402	62.13 415	21.23 435	76.48 380	55.49 421	45.89 402	35.78 413	61.44 410	35.54 361	72.83 410	9.96 429	21.75 424	56.27 417

FPMVS		45.64 383	43.10 387	53.23 400	51.42 425	36.46 418	64.97 409	71.91 400	29.13 420	27.53 420	61.55 409	9.83 422	65.01 423	16.00 426	55.58 374	58.22 416

SSC-MVS		44.51 384	43.35 386	47.99 406	61.01 418	18.90 437	74.12 388	54.36 422	43.42 409	34.10 417	60.02 411	34.42 366	70.39 413	9.14 431	19.57 425	54.68 418

EGC-MVSNET		42.35 385	38.09 388	55.11 396	74.57 384	46.62 395	71.63 394	55.77 420	0.04 434	0.24 435	62.70 406	14.24 416	74.91 408	17.59 423	46.06 394	43.80 420

LCM-MVSNet		40.54 386	35.79 391	54.76 398	36.92 435	30.81 425	51.41 422	69.02 407	22.07 422	24.63 422	45.37 419	4.56 431	65.81 420	33.67 400	34.50 415	67.67 409

APD_test1		40.50 387	37.31 390	50.09 403	51.88 423	35.27 420	59.45 417	52.59 424	21.64 423	26.12 421	57.80 413	4.56 431	66.56 419	22.64 418	39.09 405	48.43 419

test_vis3_rt		40.46 388	37.79 389	48.47 405	44.49 430	33.35 422	66.56 408	32.84 436	32.39 417	29.65 418	39.13 426	3.91 434	68.65 415	50.17 339	40.99 403	43.40 421

ANet_high		40.27 389	35.20 392	55.47 395	34.74 436	34.47 421	63.84 411	71.56 402	48.42 395	18.80 425	41.08 424	9.52 423	64.45 424	20.18 420	8.66 432	67.49 410

test_method		38.59 390	35.16 393	48.89 404	54.33 421	21.35 434	45.32 425	53.71 423	7.41 431	28.74 419	51.62 415	8.70 424	52.87 428	33.73 399	32.89 416	72.47 404

PMMVS2		37.93 391	33.61 394	50.92 401	46.31 427	24.76 431	60.55 416	50.05 425	28.94 421	20.93 423	47.59 416	4.41 433	65.13 422	25.14 415	18.55 427	62.87 413

Gipuma		34.91 392	31.44 395	45.30 407	70.99 396	39.64 415	19.85 429	72.56 398	20.10 425	16.16 429	21.47 430	5.08 430	71.16 412	13.07 427	43.70 398	25.08 427
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 393	29.47 396	42.67 409	41.89 432	30.81 425	52.07 420	43.45 430	15.45 426	18.52 426	44.82 420	2.12 435	58.38 426	16.05 424	30.87 418	38.83 422

APD_test2		32.77 393	29.47 396	42.67 409	41.89 432	30.81 425	52.07 420	43.45 430	15.45 426	18.52 426	44.82 420	2.12 435	58.38 426	16.05 424	30.87 418	38.83 422

PMVS	26.43 22	31.84 395	28.16 398	42.89 408	25.87 438	27.58 429	50.92 423	49.78 426	21.37 424	14.17 430	40.81 425	2.01 437	66.62 418	9.61 430	38.88 408	34.49 426
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 396	24.00 400	26.45 413	43.74 431	18.44 438	60.86 414	39.66 432	15.11 428	9.53 432	22.10 429	6.52 428	46.94 431	8.31 432	10.14 429	13.98 429

MVE	24.84 23	24.35 397	19.77 403	38.09 411	34.56 437	26.92 430	26.57 427	38.87 434	11.73 430	11.37 431	27.44 427	1.37 438	50.42 430	11.41 428	14.60 428	36.93 424
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 398	23.20 402	25.46 414	41.52 434	16.90 439	60.56 415	38.79 435	14.62 429	8.99 433	20.24 432	7.35 425	45.82 432	7.25 433	9.46 430	13.64 430

tmp_tt		22.26 399	23.75 401	17.80 415	5.23 439	12.06 440	35.26 426	39.48 433	2.82 433	18.94 424	44.20 422	22.23 402	24.64 434	36.30 392	9.31 431	16.69 428

cdsmvs_eth3d_5k		19.86 400	26.47 399	0.00 419	0.00 442	0.00 444	0.00 430	93.45 89	0.00 437	0.00 438	95.27 66	49.56 273	0.00 438	0.00 437	0.00 435	0.00 434

wuyk23d		11.30 401	10.95 404	12.33 416	48.05 426	19.89 436	25.89 428	1.92 440	3.58 432	3.12 434	1.37 434	0.64 439	15.77 435	6.23 434	7.77 433	1.35 431

ab-mvs-re		7.91 402	10.55 405	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	94.95 76	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

testmvs		7.23 403	9.62 406	0.06 418	0.04 440	0.02 443	84.98 320	0.02 441	0.03 435	0.18 436	1.21 435	0.01 441	0.02 436	0.14 435	0.01 434	0.13 433

test123		6.92 404	9.21 407	0.08 417	0.03 441	0.05 442	81.65 348	0.01 442	0.02 436	0.14 437	0.85 436	0.03 440	0.02 436	0.12 436	0.00 435	0.16 432

pcd_1.5k_mvsjas		4.46 405	5.95 408	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	53.55 234	0.00 438	0.00 437	0.00 435	0.00 434

mmdepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

monomultidepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

test_blank		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

uanet_test		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

DCPMVS		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

sosnet-low-res		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

sosnet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

uncertanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

Regformer		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

uanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 435	0.00 434

WAC-MVS						49.45 379								31.56 411

FOURS1					93.95 46	61.77 257	93.96 77	91.92 157	62.14 337	86.57 53

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 32	94.77 26	96.51 24

PC_three_1452									80.91 56	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 32	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 61	71.42 241	90.67 21	96.85 18	74.45 20

eth-test2					0.00 442

eth-test					0.00 442

ZD-MVS					96.63 9	65.50 159		93.50 87	70.74 255	85.26 69	95.19 72	64.92 88	97.29 80	87.51 64	93.01 56

RE-MVS-def			80.48 157		92.02 103	58.56 320	90.90 219	90.45 218	62.76 330	78.89 137	94.46 90	49.30 276		78.77 150	86.77 136	92.28 193

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 57	95.28 1				92.34 29	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	71.65 230	92.07 10	97.21 5	74.58 18	99.11 6	92.34 29	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 50	71.65 230	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	72.76 195	86.21 55	96.51 27	66.64 66	97.88 45	90.08 46	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 124	78.19 105

test_0728_THIRD									72.48 200	90.55 22	96.93 12	76.24 11	99.08 11	91.53 37	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 35	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 58	71.92 216	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												57.85 179				94.68 103

sam_mvs												54.91 218

ambc				69.61 370	61.38 417	41.35 408	49.07 424	85.86 345		50.18 386	66.40 398	10.16 421	88.14 353	45.73 364	44.20 396	79.32 381

MTGPA								92.23 138

test_post1							78.95 368				20.70 431	53.05 239	91.50 322	60.43 300

test_post											23.01 428	56.49 200	92.67 284

patchmatchnet-post											67.62 397	57.62 182	90.25 331

GG-mvs-BLEND				86.53 74	91.91 112	69.67 53	75.02 387	94.75 36		78.67 145	90.85 177	77.91 7	94.56 216	72.25 198	93.74 45	95.36 66

MTMP							93.77 91	32.52 437

gm-plane-assit					88.42 195	67.04 120			78.62 99		91.83 160		97.37 74	76.57 162

test9_res														89.41 47	94.96 19	95.29 71

TEST9					94.18 41	67.28 111	94.16 64	93.51 85	71.75 227	85.52 64	95.33 61	68.01 56	97.27 84

test_8					94.19 40	67.19 113	94.15 66	93.42 92	71.87 221	85.38 67	95.35 60	68.19 54	96.95 110

agg_prior2														86.41 77	94.75 30	95.33 67

agg_prior					94.16 43	66.97 122		93.31 95		84.49 75			96.75 120

TestCases				72.46 356	79.57 344	51.42 367		68.61 408	51.25 387	45.88 396	81.23 313	19.86 408	86.58 367	38.98 388	57.01 370	79.39 379

test_prior4						67.18 115	93.92 80

test_prior2							95.10 38		75.40 147	85.25 70	95.61 52	67.94 57		87.47 66	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							92.00 172		59.37 357	87.54 46			93.47 259	75.39 170

新几何2							91.41 193

新几何1				84.73 136	92.32 93	64.28 190		91.46 183	59.56 356	79.77 126	92.90 132	56.95 192	96.57 125	63.40 280	92.91 58	93.34 159

旧先验1					91.94 108	60.74 281		91.50 181			94.36 94	65.23 83			91.84 72	94.55 110

无先验							92.71 137	92.61 128	62.03 338				97.01 100	66.63 251		93.97 141

原ACMM2							92.01 169

原ACMM1				84.42 151	93.21 68	64.27 191		93.40 94	65.39 305	79.51 129	92.50 140	58.11 178	96.69 121	65.27 270	93.96 40	92.32 191

test222					89.77 160	61.60 262	89.55 261	89.42 264	56.83 371	77.28 158	92.43 144	52.76 242			91.14 86	93.09 169

testdata2													96.09 149	61.26 296

segment_acmp												65.94 74

testdata				81.34 242	89.02 181	57.72 327		89.84 248	58.65 360	85.32 68	94.09 108	57.03 187	93.28 261	69.34 224	90.56 92	93.03 172

testdata1							89.21 270		77.55 119

test12				87.09 52	94.60 36	68.86 68		92.91 113		82.67 96		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						50.66 262

plane_prior5								91.31 187					95.55 178	76.74 160	78.53 216	88.39 258

plane_prior4											89.14 207

plane_prior3						61.95 255			79.09 89	72.53 209

plane_prior2							93.13 118		78.81 96

plane_prior1					87.15 232

plane_prior						62.42 243	93.85 84		79.38 81						78.80 213

n2								0.00 443

nn								0.00 443

door-mid								66.01 412

lessismore_v0				73.72 347	72.93 391	47.83 387		61.72 418		45.86 398	73.76 375	28.63 388	89.81 340	47.75 356	31.37 417	83.53 334

LGP-MVS_train				79.56 290	84.31 293	59.37 310		89.73 254	69.49 268	64.86 302	88.42 213	38.65 336	94.30 225	72.56 195	72.76 259	85.01 321

test11								93.01 109

door								66.57 411

HQP5-MVS						63.66 210

HQP-NCC					87.54 222		94.06 69		79.80 72	74.18 188

ACMP_Plane					87.54 222		94.06 69		79.80 72	74.18 188

BP-MVS														77.63 157

HQP4-MVS										74.18 188			95.61 173			88.63 252

HQP3-MVS								91.70 173							78.90 211

HQP2-MVS												51.63 254

NP-MVS					87.41 225	63.04 227					90.30 187

MDTV_nov1_ep13_2view						59.90 302	80.13 363		67.65 288	72.79 203		54.33 226		59.83 304		92.58 184

MDTV_nov1_ep13			72.61 275		89.06 180	68.48 77	80.33 359	90.11 237	71.84 223	71.81 221	75.92 368	53.01 240	93.92 247	48.04 351	73.38 254

ACMMP++_ref															71.63 267

ACMMP++															69.72 276

Test By Simon												54.21 228

ITE_SJBPF				70.43 368	74.44 385	47.06 393		77.32 384	60.16 352	54.04 369	83.53 280	23.30 399	84.01 381	43.07 372	61.58 350	80.21 376

DeepMVS_CX				34.71 412	51.45 424	24.73 432		28.48 438	31.46 418	17.49 428	52.75 414	5.80 429	42.60 433	18.18 421	19.42 426	36.81 425

High-resolution multi-view benchmark