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 57	96.26 33	72.84 29	99.38 1	92.64 23	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 100	71.10 27	97.02 3	96.04 6	88.70 2	91.57 15	96.19 35	70.12 45	98.91 18	96.83 1	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 160	76.68 2	97.29 1	95.35 16	82.87 25	91.58 14	97.22 4	79.93 5	99.10 9	83.12 105	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 42	93.96 72	94.37 53	72.48 192	92.07 9	96.85 17	83.82 2	99.15 2	91.53 33	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 91	92.83 79	64.03 195	93.06 115	94.33 55	82.19 32	93.65 3	96.15 37	85.89 1	97.19 86	91.02 37	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 36	97.00 5	93.73 74	87.30 4	92.15 6	96.15 37	66.38 67	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 31	84.83 11	89.07 32	96.80 20	70.86 41	99.06 15	92.64 23	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 70	73.88 9	97.01 4	94.40 51	88.32 3	85.71 58	94.91 76	74.11 21	98.91 18	87.26 65	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 55	96.89 6	94.44 47	71.65 222	92.11 7	97.21 5	76.79 9	99.11 6	92.34 25	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 135	93.00 75	58.16 316	96.72 9	94.41 49	86.50 8	90.25 23	97.83 1	75.46 14	98.67 25	92.78 22	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 94	96.04 24	63.70 205	95.04 40	95.19 20	86.74 7	91.53 16	95.15 69	73.86 22	97.58 61	93.38 17	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 50	96.53 13	93.78 67	86.89 6	89.68 29	95.78 43	65.94 72	99.10 9	92.99 20	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 38	96.64 10	94.52 43	71.92 208	90.55 21	96.93 12	73.77 23	99.08 11	91.91 31	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 86	95.24 33	94.49 45	82.43 29	88.90 33	96.35 30	71.89 38	98.63 26	88.76 51	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 229	93.43 88	84.06 15	86.20 52	90.17 186	72.42 33	96.98 103	93.09 19	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 63	96.38 15	94.64 40	84.42 12	86.74 48	96.20 34	66.56 66	98.76 24	89.03 50	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 103	94.17 59	94.15 60	68.77 271	90.74 19	97.27 2	76.09 12	98.49 29	90.58 41	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 22	87.67 33	93.21 67	68.72 72	93.85 79	94.03 63	74.18 155	91.74 12	96.67 23	65.61 77	98.42 33	89.24 47	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 30	89.71 7	92.06 101	76.72 1	95.75 20	93.26 94	83.86 16	89.55 30	96.06 39	53.55 227	97.89 43	91.10 35	93.31 53	94.54 110

TSAR-MVS + MP.		88.11 20	88.64 18	86.54 72	91.73 115	68.04 90	90.36 235	93.55 81	82.89 24	91.29 17	92.89 129	72.27 35	96.03 151	87.99 55	94.77 26	95.54 57
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 21	86.70 65	93.51 61	65.32 160	95.15 36	93.84 66	78.17 99	85.93 56	94.80 79	75.80 13	98.21 34	89.38 44	88.78 108	96.59 19

DeepC-MVS_fast	79.48 2	87.95 22	88.00 25	87.79 31	95.86 27	68.32 80	95.74 21	94.11 61	83.82 17	83.49 80	96.19 35	64.53 91	98.44 31	83.42 104	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 34	89.55 12	91.41 127	76.43 3	95.74 21	93.12 102	83.53 19	89.55 30	95.95 41	53.45 231	97.68 51	91.07 36	92.62 60	94.54 110

EPNet		87.84 24	88.38 20	86.23 82	93.30 64	66.05 142	95.26 32	94.84 30	87.09 5	88.06 36	94.53 85	66.79 63	97.34 75	83.89 98	91.68 74	95.29 70
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 25	87.77 28	87.63 38	89.24 175	71.18 24	96.57 12	92.90 111	82.70 27	87.13 43	95.27 62	64.99 82	95.80 156	89.34 45	91.80 72	95.93 45

test_fmvsm_n_1920		87.69 26	88.50 19	85.27 115	87.05 234	63.55 212	93.69 89	91.08 196	84.18 14	90.17 25	97.04 9	67.58 58	97.99 39	95.72 5	90.03 96	94.26 120

APDe-MVS		87.54 27	87.84 27	86.65 66	96.07 23	66.30 138	94.84 45	93.78 67	69.35 262	88.39 35	96.34 31	67.74 57	97.66 56	90.62 40	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_l_conf0.5_n		87.49 28	88.19 23	85.39 109	86.95 235	64.37 185	94.30 56	88.45 297	80.51 54	92.70 4	96.86 16	69.98 46	97.15 91	95.83 4	88.08 116	94.65 104

SD-MVS		87.49 28	87.49 32	87.50 42	93.60 56	68.82 69	93.90 76	92.63 123	76.86 120	87.90 38	95.76 44	66.17 69	97.63 58	89.06 49	91.48 78	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 30	88.15 24	85.30 113	87.10 232	64.19 192	94.41 52	88.14 306	80.24 62	92.54 5	96.97 11	69.52 48	97.17 87	95.89 3	88.51 111	94.56 107

dcpmvs_2		87.37 31	87.55 31	86.85 58	95.04 32	68.20 87	90.36 235	90.66 208	79.37 77	81.20 102	93.67 113	74.73 16	96.55 125	90.88 38	92.00 69	95.82 48

alignmvs		87.28 32	86.97 38	88.24 27	91.30 129	71.14 26	95.61 25	93.56 80	79.30 78	87.07 45	95.25 64	68.43 50	96.93 111	87.87 56	84.33 155	96.65 17

train_agg		87.21 33	87.42 33	86.60 68	94.18 41	67.28 110	94.16 60	93.51 82	71.87 213	85.52 60	95.33 57	68.19 52	97.27 82	89.09 48	94.90 22	95.25 76

MG-MVS		87.11 34	86.27 47	89.62 8	97.79 1	76.27 4	94.96 43	94.49 45	78.74 93	83.87 78	92.94 127	64.34 92	96.94 109	75.19 166	94.09 38	95.66 52

SF-MVS		87.03 35	87.09 36	86.84 59	92.70 85	67.45 108	93.64 92	93.76 70	70.78 246	86.25 50	96.44 28	66.98 61	97.79 47	88.68 52	94.56 34	95.28 72

fmvsm_s_conf0.5_n_3		86.88 36	87.99 26	83.58 177	87.26 227	60.74 276	93.21 112	87.94 313	84.22 13	91.70 13	97.27 2	65.91 74	95.02 189	93.95 14	90.42 93	94.99 86

CSCG		86.87 37	86.26 48	88.72 17	95.05 31	70.79 29	93.83 84	95.33 17	68.48 275	77.63 148	94.35 94	73.04 27	98.45 30	84.92 87	93.71 47	96.92 14

sasdasda		86.85 38	86.25 49	88.66 20	91.80 113	71.92 16	93.54 97	91.71 165	80.26 59	87.55 40	95.25 64	63.59 105	96.93 111	88.18 53	84.34 153	97.11 9

canonicalmvs		86.85 38	86.25 49	88.66 20	91.80 113	71.92 16	93.54 97	91.71 165	80.26 59	87.55 40	95.25 64	63.59 105	96.93 111	88.18 53	84.34 153	97.11 9

UBG		86.83 40	86.70 43	87.20 48	93.07 73	69.81 46	93.43 105	95.56 13	81.52 39	81.50 98	92.12 148	73.58 26	96.28 136	84.37 93	85.20 145	95.51 58

PHI-MVS		86.83 40	86.85 42	86.78 63	93.47 62	65.55 156	95.39 30	95.10 23	71.77 218	85.69 59	96.52 25	62.07 125	98.77 23	86.06 77	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 42	86.90 40	86.58 70	90.42 145	66.38 135	96.09 17	93.87 65	77.73 107	84.01 77	95.66 46	63.39 108	97.94 40	87.40 63	93.55 50	95.42 59
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 43	86.86 41	86.31 81	93.76 50	67.53 105	96.33 16	93.61 78	82.34 31	81.00 107	93.08 123	63.19 112	97.29 78	87.08 68	91.38 80	94.13 129

testing11		86.71 44	86.44 46	87.55 40	93.54 59	71.35 21	93.65 91	95.58 11	81.36 46	80.69 110	92.21 147	72.30 34	96.46 130	85.18 83	83.43 163	94.82 96

test_fmvsmconf_n		86.58 45	87.17 35	84.82 128	85.28 266	62.55 237	94.26 58	89.78 241	83.81 18	87.78 39	96.33 32	65.33 79	96.98 103	94.40 11	87.55 122	94.95 88

BP-MVS1		86.54 46	86.68 44	86.13 85	87.80 215	67.18 114	92.97 120	95.62 10	79.92 65	82.84 87	94.14 103	74.95 15	96.46 130	82.91 107	88.96 107	94.74 98

jason		86.40 47	86.17 51	87.11 51	86.16 251	70.54 32	95.71 24	92.19 140	82.00 34	84.58 70	94.34 95	61.86 127	95.53 176	87.76 57	90.89 86	95.27 73
jason: jason.
fmvsm_s_conf0.5_n		86.39 48	86.91 39	84.82 128	87.36 226	63.54 213	94.74 47	90.02 235	82.52 28	90.14 26	96.92 14	62.93 117	97.84 46	95.28 8	82.26 173	93.07 166

WTY-MVS		86.32 49	85.81 58	87.85 29	92.82 81	69.37 57	95.20 34	95.25 18	82.71 26	81.91 95	94.73 80	67.93 56	97.63 58	79.55 135	82.25 174	96.54 22

MSLP-MVS++		86.27 50	85.91 57	87.35 45	92.01 105	68.97 66	95.04 40	92.70 116	79.04 88	81.50 98	96.50 27	58.98 162	96.78 117	83.49 103	93.93 41	96.29 35

VNet		86.20 51	85.65 62	87.84 30	93.92 47	69.99 38	95.73 23	95.94 7	78.43 96	86.00 55	93.07 124	58.22 169	97.00 99	85.22 81	84.33 155	96.52 23

MVS_111021_HR		86.19 52	85.80 59	87.37 44	93.17 69	69.79 47	93.99 71	93.76 70	79.08 85	78.88 136	93.99 107	62.25 124	98.15 36	85.93 78	91.15 84	94.15 128

SPE-MVS-test		86.14 53	87.01 37	83.52 178	92.63 87	59.36 305	95.49 27	91.92 152	80.09 63	85.46 62	95.53 52	61.82 129	95.77 159	86.77 72	93.37 52	95.41 60

ACMMP_NAP		86.05 54	85.80 59	86.80 62	91.58 119	67.53 105	91.79 175	93.49 85	74.93 146	84.61 69	95.30 59	59.42 153	97.92 41	86.13 75	94.92 20	94.94 89

testing99		86.01 55	85.47 64	87.63 38	93.62 55	71.25 23	93.47 103	95.23 19	80.42 57	80.60 112	91.95 152	71.73 39	96.50 128	80.02 132	82.22 175	95.13 79

ETV-MVS		86.01 55	86.11 52	85.70 101	90.21 150	67.02 120	93.43 105	91.92 152	81.21 48	84.13 76	94.07 106	60.93 137	95.63 167	89.28 46	89.81 97	94.46 116

testing91		85.93 57	85.31 68	87.78 32	93.59 57	71.47 19	93.50 100	95.08 26	80.26 59	80.53 113	91.93 153	70.43 43	96.51 127	80.32 130	82.13 177	95.37 63

APD-MVS		85.93 57	85.99 55	85.76 98	95.98 26	65.21 163	93.59 95	92.58 125	66.54 289	86.17 53	95.88 42	63.83 98	97.00 99	86.39 74	92.94 57	95.06 82
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 59	85.46 65	87.18 49	88.20 203	72.42 15	92.41 147	92.77 114	82.11 33	80.34 116	93.07 124	68.27 51	95.02 189	78.39 148	93.59 49	94.09 131

CS-MVS		85.80 60	86.65 45	83.27 187	92.00 106	58.92 309	95.31 31	91.86 157	79.97 64	84.82 68	95.40 55	62.26 123	95.51 177	86.11 76	92.08 68	95.37 63

fmvsm_s_conf0.5_n_a		85.75 61	86.09 53	84.72 135	85.73 260	63.58 210	93.79 85	89.32 259	81.42 44	90.21 24	96.91 15	62.41 122	97.67 53	94.48 10	80.56 192	92.90 172

test_fmvsmconf0.1_n		85.71 62	86.08 54	84.62 142	80.83 321	62.33 242	93.84 82	88.81 285	83.50 20	87.00 46	96.01 40	63.36 109	96.93 111	94.04 13	87.29 125	94.61 106

CDPH-MVS		85.71 62	85.46 65	86.46 74	94.75 34	67.19 112	93.89 77	92.83 113	70.90 242	83.09 85	95.28 60	63.62 103	97.36 73	80.63 126	94.18 37	94.84 93

casdiffmvs_mvg		85.66 64	85.18 70	87.09 52	88.22 202	69.35 58	93.74 88	91.89 155	81.47 40	80.10 118	91.45 162	64.80 87	96.35 134	87.23 66	87.69 120	95.58 55
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 65	85.93 56	84.68 138	82.95 304	63.48 215	94.03 70	89.46 253	81.69 37	89.86 27	96.74 21	61.85 128	97.75 49	94.74 9	82.01 179	92.81 174

MGCFI-Net		85.59 66	85.73 61	85.17 119	91.41 127	62.44 238	92.87 125	91.31 182	79.65 71	86.99 47	95.14 70	62.90 118	96.12 143	87.13 67	84.13 160	96.96 13

GDP-MVS		85.54 67	85.32 67	86.18 83	87.64 218	67.95 94	92.91 124	92.36 130	77.81 105	83.69 79	94.31 97	72.84 29	96.41 132	80.39 129	85.95 140	94.19 124

DeepC-MVS	77.85 3	85.52 68	85.24 69	86.37 78	88.80 185	66.64 129	92.15 154	93.68 76	81.07 49	76.91 158	93.64 114	62.59 120	98.44 31	85.50 79	92.84 59	94.03 135
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 69	84.87 76	86.84 59	88.25 200	69.07 62	93.04 117	91.76 162	81.27 47	80.84 109	92.07 150	64.23 93	96.06 149	84.98 86	87.43 124	95.39 61
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 70	85.08 72	86.06 86	93.09 72	65.65 152	93.89 77	93.41 90	73.75 166	79.94 120	94.68 82	60.61 140	98.03 38	82.63 110	93.72 46	94.52 112

MP-MVS-pluss		85.24 71	85.13 71	85.56 104	91.42 124	65.59 154	91.54 185	92.51 127	74.56 149	80.62 111	95.64 47	59.15 157	97.00 99	86.94 70	93.80 43	94.07 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 72	84.69 79	86.63 67	92.91 77	69.91 42	92.61 138	95.80 9	80.31 58	80.38 115	92.27 144	68.73 49	95.19 186	75.94 160	83.27 165	94.81 97

PAPR		85.15 73	84.47 80	87.18 49	96.02 25	68.29 81	91.85 173	93.00 108	76.59 127	79.03 132	95.00 71	61.59 130	97.61 60	78.16 149	89.00 106	95.63 53

fmvsm_s_conf0.5_n_2		85.06 74	85.60 63	83.44 184	86.92 240	60.53 283	94.41 52	87.31 319	83.30 21	88.72 34	96.72 22	54.28 220	97.75 49	94.07 12	84.68 152	92.04 197

MP-MVS		85.02 75	84.97 74	85.17 119	92.60 88	64.27 190	93.24 109	92.27 133	73.13 177	79.63 124	94.43 88	61.90 126	97.17 87	85.00 85	92.56 61	94.06 134
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 76	84.44 81	86.71 64	88.33 197	68.73 71	90.24 240	91.82 161	81.05 50	81.18 103	92.50 136	63.69 101	96.08 148	84.45 92	86.71 134	95.32 68

CHOSEN 1792x2688		84.98 77	83.45 95	89.57 11	89.94 155	75.14 6	92.07 160	92.32 131	81.87 35	75.68 167	88.27 210	60.18 143	98.60 27	80.46 128	90.27 95	94.96 87

MVSMamba_PlusPlus		84.97 78	83.65 89	88.93 14	90.17 151	74.04 8	87.84 287	92.69 118	62.18 327	81.47 100	87.64 224	71.47 40	96.28 136	84.69 89	94.74 31	96.47 28

EIA-MVS		84.84 79	84.88 75	84.69 137	91.30 129	62.36 241	93.85 79	92.04 145	79.45 74	79.33 129	94.28 99	62.42 121	96.35 134	80.05 131	91.25 83	95.38 62

fmvsm_s_conf0.1_n_a		84.76 80	84.84 77	84.53 144	80.23 331	63.50 214	92.79 127	88.73 288	80.46 55	89.84 28	96.65 24	60.96 136	97.57 63	93.80 15	80.14 194	92.53 181

HFP-MVS		84.73 81	84.40 82	85.72 100	93.75 52	65.01 169	93.50 100	93.19 98	72.19 202	79.22 130	94.93 74	59.04 160	97.67 53	81.55 116	92.21 64	94.49 115

MVS		84.66 82	82.86 113	90.06 2	90.93 136	74.56 7	87.91 285	95.54 14	68.55 273	72.35 209	94.71 81	59.78 149	98.90 20	81.29 122	94.69 32	96.74 16

GST-MVS		84.63 83	84.29 83	85.66 102	92.82 81	65.27 161	93.04 117	93.13 101	73.20 175	78.89 133	94.18 102	59.41 154	97.85 45	81.45 118	92.48 63	93.86 143

EC-MVSNet		84.53 84	85.04 73	83.01 192	89.34 167	61.37 263	94.42 51	91.09 194	77.91 103	83.24 81	94.20 101	58.37 167	95.40 178	85.35 80	91.41 79	92.27 191

fmvsm_s_conf0.1_n_2		84.40 85	84.78 78	83.27 187	85.25 267	60.41 286	94.13 63	85.69 339	83.05 23	87.99 37	96.37 29	52.75 236	97.68 51	93.75 16	84.05 161	91.71 201

ACMMPR		84.37 86	84.06 84	85.28 114	93.56 58	64.37 185	93.50 100	93.15 100	72.19 202	78.85 138	94.86 77	56.69 189	97.45 67	81.55 116	92.20 65	94.02 136

region2R		84.36 87	84.03 85	85.36 111	93.54 59	64.31 188	93.43 105	92.95 109	72.16 205	78.86 137	94.84 78	56.97 184	97.53 65	81.38 120	92.11 67	94.24 122

LFMVS		84.34 88	82.73 115	89.18 13	94.76 33	73.25 11	94.99 42	91.89 155	71.90 210	82.16 94	93.49 118	47.98 282	97.05 94	82.55 111	84.82 148	97.25 8

test_yl		84.28 89	83.16 105	87.64 34	94.52 37	69.24 59	95.78 18	95.09 24	69.19 265	81.09 104	92.88 130	57.00 182	97.44 68	81.11 124	81.76 181	96.23 38

DCV-MVSNet		84.28 89	83.16 105	87.64 34	94.52 37	69.24 59	95.78 18	95.09 24	69.19 265	81.09 104	92.88 130	57.00 182	97.44 68	81.11 124	81.76 181	96.23 38

diffmvs		84.28 89	83.83 86	85.61 103	87.40 224	68.02 91	90.88 215	89.24 262	80.54 53	81.64 97	92.52 135	59.83 148	94.52 214	87.32 64	85.11 146	94.29 119
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 89	83.36 101	87.02 55	92.22 95	67.74 98	84.65 313	94.50 44	79.15 82	82.23 93	87.93 219	66.88 62	96.94 109	80.53 127	82.20 176	96.39 33

ETVMVS		84.22 93	83.71 87	85.76 98	92.58 89	68.25 85	92.45 146	95.53 15	79.54 73	79.46 126	91.64 160	70.29 44	94.18 226	69.16 221	82.76 171	94.84 93

MAR-MVS		84.18 94	83.43 96	86.44 75	96.25 21	65.93 147	94.28 57	94.27 57	74.41 150	79.16 131	95.61 48	53.99 222	98.88 22	69.62 215	93.26 54	94.50 114
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 95	83.20 104	87.05 54	91.56 120	69.82 45	89.99 249	92.05 144	77.77 106	82.84 87	86.57 241	63.93 97	96.09 145	74.91 171	89.18 103	95.25 76

CANet_DTU		84.09 96	83.52 90	85.81 95	90.30 148	66.82 124	91.87 171	89.01 277	85.27 9	86.09 54	93.74 111	47.71 286	96.98 103	77.90 151	89.78 99	93.65 148

ET-MVSNet_ETH3D		84.01 97	83.15 107	86.58 70	90.78 141	70.89 28	94.74 47	94.62 41	81.44 43	58.19 342	93.64 114	73.64 25	92.35 291	82.66 109	78.66 209	96.50 27

PVSNet_Blended_VisFu		83.97 98	83.50 92	85.39 109	90.02 153	66.59 132	93.77 86	91.73 163	77.43 115	77.08 157	89.81 193	63.77 100	96.97 106	79.67 134	88.21 114	92.60 178

MTAPA		83.91 99	83.38 100	85.50 105	91.89 111	65.16 165	81.75 338	92.23 134	75.32 141	80.53 113	95.21 67	56.06 198	97.16 90	84.86 88	92.55 62	94.18 125

XVS		83.87 100	83.47 94	85.05 121	93.22 65	63.78 199	92.92 122	92.66 120	73.99 158	78.18 142	94.31 97	55.25 204	97.41 70	79.16 139	91.58 76	93.95 138

Effi-MVS+		83.82 101	82.76 114	86.99 56	89.56 163	69.40 53	91.35 196	86.12 333	72.59 189	83.22 84	92.81 133	59.60 151	96.01 153	81.76 115	87.80 119	95.56 56

test_fmvsmvis_n_1920		83.80 102	83.48 93	84.77 132	82.51 307	63.72 203	91.37 194	83.99 356	81.42 44	77.68 147	95.74 45	58.37 167	97.58 61	93.38 17	86.87 128	93.00 169

EI-MVSNet-Vis-set		83.77 103	83.67 88	84.06 159	92.79 84	63.56 211	91.76 178	94.81 32	79.65 71	77.87 145	94.09 104	63.35 110	97.90 42	79.35 137	79.36 201	90.74 219

MVSFormer		83.75 104	82.88 112	86.37 78	89.24 175	71.18 24	89.07 267	90.69 205	65.80 294	87.13 43	94.34 95	64.99 82	92.67 278	72.83 183	91.80 72	95.27 73

CP-MVS		83.71 105	83.40 99	84.65 139	93.14 70	63.84 197	94.59 49	92.28 132	71.03 240	77.41 151	94.92 75	55.21 207	96.19 140	81.32 121	90.70 88	93.91 140

test_fmvsmconf0.01_n		83.70 106	83.52 90	84.25 156	75.26 374	61.72 256	92.17 153	87.24 321	82.36 30	84.91 67	95.41 54	55.60 202	96.83 116	92.85 21	85.87 141	94.21 123

baseline2		83.68 107	83.42 98	84.48 147	87.37 225	66.00 144	90.06 244	95.93 8	79.71 70	69.08 247	90.39 180	77.92 6	96.28 136	78.91 143	81.38 185	91.16 215

reproduce-ours		83.51 108	83.33 102	84.06 159	92.18 98	60.49 284	90.74 221	92.04 145	64.35 304	83.24 81	95.59 50	59.05 158	97.27 82	83.61 100	89.17 104	94.41 117

our_new_method		83.51 108	83.33 102	84.06 159	92.18 98	60.49 284	90.74 221	92.04 145	64.35 304	83.24 81	95.59 50	59.05 158	97.27 82	83.61 100	89.17 104	94.41 117

thisisatest0515		83.41 110	82.49 119	86.16 84	89.46 166	68.26 83	93.54 97	94.70 37	74.31 153	75.75 165	90.92 170	72.62 31	96.52 126	69.64 213	81.50 184	93.71 146

PVSNet_BlendedMVS		83.38 111	83.43 96	83.22 189	93.76 50	67.53 105	94.06 65	93.61 78	79.13 83	81.00 107	85.14 256	63.19 112	97.29 78	87.08 68	73.91 245	84.83 315

test2506		83.29 112	82.92 111	84.37 151	88.39 195	63.18 223	92.01 163	91.35 181	77.66 109	78.49 141	91.42 163	64.58 90	95.09 188	73.19 179	89.23 101	94.85 90

PGM-MVS		83.25 113	82.70 116	84.92 124	92.81 83	64.07 194	90.44 230	92.20 138	71.28 234	77.23 154	94.43 88	55.17 208	97.31 77	79.33 138	91.38 80	93.37 154

HPM-MVS		83.25 113	82.95 110	84.17 157	92.25 94	62.88 232	90.91 212	91.86 157	70.30 251	77.12 155	93.96 108	56.75 187	96.28 136	82.04 113	91.34 82	93.34 155
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 115	82.96 108	83.73 170	92.02 102	59.74 297	90.37 234	92.08 143	63.70 311	82.86 86	95.48 53	58.62 164	97.17 87	83.06 106	88.42 112	94.26 120

EI-MVSNet-UG-set		83.14 116	82.96 108	83.67 175	92.28 93	63.19 222	91.38 193	94.68 38	79.22 80	76.60 160	93.75 110	62.64 119	97.76 48	78.07 150	78.01 212	90.05 228

VDD-MVS		83.06 117	81.81 128	86.81 61	90.86 139	67.70 99	95.40 29	91.50 176	75.46 138	81.78 96	92.34 143	40.09 323	97.13 92	86.85 71	82.04 178	95.60 54

h-mvs33		83.01 118	82.56 118	84.35 152	89.34 167	62.02 248	92.72 130	93.76 70	81.45 41	82.73 90	92.25 146	60.11 144	97.13 92	87.69 58	62.96 324	93.91 140

PAPM_NR		82.97 119	81.84 127	86.37 78	94.10 44	66.76 127	87.66 291	92.84 112	69.96 255	74.07 186	93.57 116	63.10 115	97.50 66	70.66 208	90.58 90	94.85 90

mPP-MVS		82.96 120	82.44 120	84.52 145	92.83 79	62.92 230	92.76 128	91.85 159	71.52 230	75.61 170	94.24 100	53.48 230	96.99 102	78.97 142	90.73 87	93.64 149

SR-MVS		82.81 121	82.58 117	83.50 181	93.35 63	61.16 266	92.23 152	91.28 186	64.48 303	81.27 101	95.28 60	53.71 226	95.86 155	82.87 108	88.77 109	93.49 152

DP-MVS Recon		82.73 122	81.65 129	85.98 88	97.31 4	67.06 117	95.15 36	91.99 149	69.08 268	76.50 162	93.89 109	54.48 216	98.20 35	70.76 206	85.66 143	92.69 175

CLD-MVS		82.73 122	82.35 122	83.86 166	87.90 210	67.65 101	95.45 28	92.18 141	85.06 10	72.58 202	92.27 144	52.46 239	95.78 157	84.18 94	79.06 204	88.16 255
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 124	82.38 121	83.73 170	89.25 172	59.58 300	92.24 151	94.89 29	77.96 101	79.86 121	92.38 141	56.70 188	97.05 94	77.26 154	80.86 189	94.55 108

3Dnovator	73.91 6	82.69 125	80.82 142	88.31 26	89.57 162	71.26 22	92.60 139	94.39 52	78.84 90	67.89 267	92.48 139	48.42 277	98.52 28	68.80 226	94.40 36	95.15 78

RRT-MVS		82.61 126	81.16 133	86.96 57	91.10 133	68.75 70	87.70 290	92.20 138	76.97 118	72.68 198	87.10 235	51.30 251	96.41 132	83.56 102	87.84 118	95.74 50

MVSTER		82.47 127	82.05 123	83.74 168	92.68 86	69.01 64	91.90 170	93.21 95	79.83 66	72.14 210	85.71 252	74.72 17	94.72 201	75.72 162	72.49 255	87.50 261

TESTMET0.1,1		82.41 128	81.98 126	83.72 172	88.08 204	63.74 201	92.70 132	93.77 69	79.30 78	77.61 149	87.57 226	58.19 170	94.08 230	73.91 177	86.68 135	93.33 157

CostFormer		82.33 129	81.15 134	85.86 93	89.01 180	68.46 77	82.39 335	93.01 106	75.59 136	80.25 117	81.57 299	72.03 37	94.96 193	79.06 141	77.48 220	94.16 127

API-MVS		82.28 130	80.53 150	87.54 41	96.13 22	70.59 31	93.63 93	91.04 200	65.72 296	75.45 172	92.83 132	56.11 197	98.89 21	64.10 270	89.75 100	93.15 162

IB-MVS	77.80 4	82.18 131	80.46 152	87.35 45	89.14 177	70.28 35	95.59 26	95.17 22	78.85 89	70.19 235	85.82 250	70.66 42	97.67 53	72.19 195	66.52 296	94.09 131
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		82.16 132	81.12 135	85.26 116	86.42 244	68.72 72	92.59 141	90.44 215	73.12 178	84.20 73	94.36 90	38.04 336	95.73 161	84.12 95	86.81 129	91.33 208

xiu_mvs_v1_base		82.16 132	81.12 135	85.26 116	86.42 244	68.72 72	92.59 141	90.44 215	73.12 178	84.20 73	94.36 90	38.04 336	95.73 161	84.12 95	86.81 129	91.33 208

xiu_mvs_v1_base_debi		82.16 132	81.12 135	85.26 116	86.42 244	68.72 72	92.59 141	90.44 215	73.12 178	84.20 73	94.36 90	38.04 336	95.73 161	84.12 95	86.81 129	91.33 208

3Dnovator+	73.60 7	82.10 135	80.60 149	86.60 68	90.89 138	66.80 126	95.20 34	93.44 87	74.05 157	67.42 274	92.49 138	49.46 267	97.65 57	70.80 205	91.68 74	95.33 66

MVS_111021_LR		82.02 136	81.52 130	83.51 180	88.42 193	62.88 232	89.77 252	88.93 281	76.78 123	75.55 171	93.10 121	50.31 258	95.38 180	83.82 99	87.02 127	92.26 192

PMMVS		81.98 137	82.04 124	81.78 226	89.76 159	56.17 335	91.13 208	90.69 205	77.96 101	80.09 119	93.57 116	46.33 296	94.99 192	81.41 119	87.46 123	94.17 126

baseline1		81.84 138	81.03 139	84.28 155	91.60 118	66.62 130	91.08 209	91.66 170	81.87 35	74.86 177	91.67 159	69.98 46	94.92 196	71.76 198	64.75 311	91.29 213

EPP-MVSNet		81.79 139	81.52 130	82.61 202	88.77 186	60.21 291	93.02 119	93.66 77	68.52 274	72.90 196	90.39 180	72.19 36	94.96 193	74.93 170	79.29 203	92.67 176

WBMVS		81.67 140	80.98 141	83.72 172	93.07 73	69.40 53	94.33 55	93.05 104	76.84 121	72.05 212	84.14 267	74.49 19	93.88 244	72.76 186	68.09 284	87.88 257

test_vis1_n_1920		81.66 141	82.01 125	80.64 253	82.24 309	55.09 343	94.76 46	86.87 323	81.67 38	84.40 72	94.63 83	38.17 333	94.67 205	91.98 30	83.34 164	92.16 195

APD-MVS_3200maxsize		81.64 142	81.32 132	82.59 203	92.36 91	58.74 311	91.39 191	91.01 201	63.35 315	79.72 123	94.62 84	51.82 242	96.14 142	79.71 133	87.93 117	92.89 173

mvsmamba		81.55 143	80.72 144	84.03 163	91.42 124	66.93 122	83.08 329	89.13 270	78.55 95	67.50 272	87.02 236	51.79 244	90.07 332	87.48 61	90.49 92	95.10 81

ACMMP		81.49 144	80.67 146	83.93 165	91.71 116	62.90 231	92.13 155	92.22 137	71.79 217	71.68 218	93.49 118	50.32 257	96.96 107	78.47 147	84.22 159	91.93 199
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		81.43 145	80.74 143	83.52 178	86.26 248	64.45 179	92.09 158	90.65 209	75.83 134	73.95 188	89.81 193	63.97 96	92.91 268	71.27 201	82.82 168	93.20 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 146	79.99 157	85.46 106	90.39 147	68.40 78	86.88 302	90.61 210	74.41 150	70.31 234	84.67 261	63.79 99	92.32 293	73.13 180	85.70 142	95.67 51

ECVR-MVS		81.29 147	80.38 153	84.01 164	88.39 195	61.96 250	92.56 144	86.79 325	77.66 109	76.63 159	91.42 163	46.34 295	95.24 185	74.36 175	89.23 101	94.85 90

thisisatest0530		81.15 148	80.07 154	84.39 150	88.26 199	65.63 153	91.40 189	94.62 41	71.27 235	70.93 225	89.18 199	72.47 32	96.04 150	65.62 259	76.89 226	91.49 204

Fast-Effi-MVS+		81.14 149	80.01 156	84.51 146	90.24 149	65.86 148	94.12 64	89.15 268	73.81 165	75.37 173	88.26 211	57.26 177	94.53 213	66.97 244	84.92 147	93.15 162

HQP-MVS		81.14 149	80.64 147	82.64 201	87.54 220	63.66 208	94.06 65	91.70 168	79.80 67	74.18 182	90.30 182	51.63 247	95.61 169	77.63 152	78.90 205	88.63 246

hse-mvs2		81.12 151	81.11 138	81.16 240	86.52 243	57.48 324	89.40 260	91.16 189	81.45 41	82.73 90	90.49 178	60.11 144	94.58 206	87.69 58	60.41 351	91.41 207

SR-MVS-dyc-post		81.06 152	80.70 145	82.15 217	92.02 102	58.56 313	90.90 213	90.45 212	62.76 322	78.89 133	94.46 86	51.26 252	95.61 169	78.77 145	86.77 132	92.28 188

HyFIR lowres test		81.03 153	79.56 164	85.43 107	87.81 214	68.11 89	90.18 241	90.01 236	70.65 248	72.95 195	86.06 248	63.61 104	94.50 215	75.01 169	79.75 198	93.67 147

nrg030		80.93 154	79.86 159	84.13 158	83.69 293	68.83 68	93.23 110	91.20 187	75.55 137	75.06 175	88.22 214	63.04 116	94.74 200	81.88 114	66.88 293	88.82 244

Vis-MVSNet		80.92 155	79.98 158	83.74 168	88.48 190	61.80 252	93.44 104	88.26 305	73.96 161	77.73 146	91.76 156	49.94 262	94.76 198	65.84 256	90.37 94	94.65 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 156	80.02 155	83.33 185	87.87 211	60.76 274	92.62 137	86.86 324	77.86 104	75.73 166	91.39 165	46.35 294	94.70 204	72.79 185	88.68 110	94.52 112

UWE-MVS		80.81 157	81.01 140	80.20 263	89.33 169	57.05 329	91.91 169	94.71 36	75.67 135	75.01 176	89.37 197	63.13 114	91.44 316	67.19 241	82.80 170	92.12 196

1314		80.70 158	78.95 176	85.94 90	87.77 217	67.56 103	87.91 285	92.55 126	72.17 204	67.44 273	93.09 122	50.27 259	97.04 97	71.68 200	87.64 121	93.23 159

tpmrst		80.57 159	79.14 174	84.84 127	90.10 152	68.28 82	81.70 339	89.72 248	77.63 111	75.96 164	79.54 331	64.94 84	92.71 275	75.43 164	77.28 223	93.55 150

1112_ss		80.56 160	79.83 160	82.77 196	88.65 187	60.78 272	92.29 149	88.36 299	72.58 190	72.46 206	94.95 72	65.09 81	93.42 255	66.38 250	77.71 214	94.10 130

VDDNet		80.50 161	78.26 184	87.21 47	86.19 249	69.79 47	94.48 50	91.31 182	60.42 341	79.34 128	90.91 171	38.48 331	96.56 124	82.16 112	81.05 187	95.27 73

BH-w/o		80.49 162	79.30 171	84.05 162	90.83 140	64.36 187	93.60 94	89.42 256	74.35 152	69.09 246	90.15 188	55.23 206	95.61 169	64.61 267	86.43 138	92.17 194

test_cas_vis1_n_1920		80.45 163	80.61 148	79.97 272	78.25 357	57.01 331	94.04 69	88.33 300	79.06 87	82.81 89	93.70 112	38.65 328	91.63 308	90.82 39	79.81 196	91.27 214

TAMVS		80.37 164	79.45 167	83.13 191	85.14 270	63.37 216	91.23 202	90.76 204	74.81 148	72.65 200	88.49 205	60.63 139	92.95 263	69.41 217	81.95 180	93.08 165

HQP_MVS		80.34 165	79.75 161	82.12 219	86.94 236	62.42 239	93.13 113	91.31 182	78.81 91	72.53 203	89.14 201	50.66 255	95.55 174	76.74 155	78.53 210	88.39 252

SDMVSNet		80.26 166	78.88 177	84.40 149	89.25 172	67.63 102	85.35 309	93.02 105	76.77 124	70.84 226	87.12 233	47.95 283	96.09 145	85.04 84	74.55 236	89.48 238

HPM-MVS_fast		80.25 167	79.55 166	82.33 209	91.55 121	59.95 294	91.32 198	89.16 267	65.23 300	74.71 179	93.07 124	47.81 285	95.74 160	74.87 173	88.23 113	91.31 212

ab-mvs		80.18 168	78.31 183	85.80 96	88.44 192	65.49 159	83.00 332	92.67 119	71.82 216	77.36 152	85.01 257	54.50 213	96.59 121	76.35 159	75.63 233	95.32 68

IS-MVSNet		80.14 169	79.41 168	82.33 209	87.91 209	60.08 293	91.97 167	88.27 303	72.90 185	71.44 222	91.73 158	61.44 131	93.66 250	62.47 284	86.53 136	93.24 158

test-LLR		80.10 170	79.56 164	81.72 228	86.93 238	61.17 264	92.70 132	91.54 173	71.51 231	75.62 168	86.94 237	53.83 223	92.38 288	72.21 193	84.76 150	91.60 202

PVSNet	73.49 8	80.05 171	78.63 179	84.31 153	90.92 137	64.97 170	92.47 145	91.05 199	79.18 81	72.43 207	90.51 177	37.05 348	94.06 232	68.06 230	86.00 139	93.90 142

UA-Net		80.02 172	79.65 162	81.11 242	89.33 169	57.72 320	86.33 306	89.00 280	77.44 114	81.01 106	89.15 200	59.33 155	95.90 154	61.01 291	84.28 157	89.73 234

test-mter		79.96 173	79.38 170	81.72 228	86.93 238	61.17 264	92.70 132	91.54 173	73.85 163	75.62 168	86.94 237	49.84 264	92.38 288	72.21 193	84.76 150	91.60 202

QAPM		79.95 174	77.39 201	87.64 34	89.63 161	71.41 20	93.30 108	93.70 75	65.34 299	67.39 276	91.75 157	47.83 284	98.96 16	57.71 307	89.81 97	92.54 180

UGNet		79.87 175	78.68 178	83.45 183	89.96 154	61.51 259	92.13 155	90.79 203	76.83 122	78.85 138	86.33 245	38.16 334	96.17 141	67.93 233	87.17 126	92.67 176
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		79.80 176	77.95 190	85.34 112	88.28 198	68.26 83	81.56 341	91.42 179	70.11 253	77.59 150	80.50 317	67.40 59	94.26 224	67.34 238	77.35 221	93.51 151

thres200		79.66 177	78.33 182	83.66 176	92.54 90	65.82 150	93.06 115	96.31 3	74.90 147	73.30 192	88.66 203	59.67 150	95.61 169	47.84 346	78.67 208	89.56 237

CPTT-MVS		79.59 178	79.16 173	80.89 251	91.54 122	59.80 296	92.10 157	88.54 296	60.42 341	72.96 194	93.28 120	48.27 278	92.80 272	78.89 144	86.50 137	90.06 227

Test_1112_low_res		79.56 179	78.60 180	82.43 205	88.24 201	60.39 288	92.09 158	87.99 310	72.10 206	71.84 214	87.42 228	64.62 89	93.04 259	65.80 257	77.30 222	93.85 144

tttt0517		79.50 180	78.53 181	82.41 208	87.22 229	61.43 262	89.75 253	94.76 33	69.29 263	67.91 265	88.06 218	72.92 28	95.63 167	62.91 280	73.90 246	90.16 226

reproduce_monomvs		79.49 181	79.11 175	80.64 253	92.91 77	61.47 261	91.17 207	93.28 93	83.09 22	64.04 304	82.38 286	66.19 68	94.57 208	81.19 123	57.71 359	85.88 298

FIs		79.47 182	79.41 168	79.67 279	85.95 254	59.40 302	91.68 182	93.94 64	78.06 100	68.96 251	88.28 209	66.61 65	91.77 304	66.20 253	74.99 235	87.82 258

BH-RMVSNet		79.46 183	77.65 193	84.89 125	91.68 117	65.66 151	93.55 96	88.09 308	72.93 182	73.37 191	91.12 169	46.20 298	96.12 143	56.28 312	85.61 144	92.91 171

PCF-MVS	73.15 9	79.29 184	77.63 194	84.29 154	86.06 252	65.96 146	87.03 298	91.10 193	69.86 257	69.79 242	90.64 173	57.54 176	96.59 121	64.37 269	82.29 172	90.32 224
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 185	79.57 163	78.24 299	88.46 191	52.29 354	90.41 232	89.12 271	74.24 154	69.13 245	91.91 154	65.77 75	90.09 331	59.00 303	88.09 115	92.33 185

114514_t		79.17 186	77.67 192	83.68 174	95.32 29	65.53 157	92.85 126	91.60 172	63.49 313	67.92 264	90.63 175	46.65 291	95.72 165	67.01 243	83.54 162	89.79 232

FA-MVS(test-final)		79.12 187	77.23 203	84.81 131	90.54 143	63.98 196	81.35 344	91.71 165	71.09 239	74.85 178	82.94 279	52.85 234	97.05 94	67.97 231	81.73 183	93.41 153

VPA-MVSNet		79.03 188	78.00 188	82.11 222	85.95 254	64.48 178	93.22 111	94.66 39	75.05 145	74.04 187	84.95 258	52.17 241	93.52 252	74.90 172	67.04 292	88.32 254

OPM-MVS		79.00 189	78.09 186	81.73 227	83.52 296	63.83 198	91.64 184	90.30 222	76.36 130	71.97 213	89.93 192	46.30 297	95.17 187	75.10 167	77.70 215	86.19 287
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 190	78.22 185	81.25 237	85.33 264	62.73 235	89.53 257	93.21 95	72.39 197	72.14 210	90.13 189	60.99 134	94.72 201	67.73 235	72.49 255	86.29 284

AdaColmap		78.94 191	77.00 207	84.76 133	96.34 17	65.86 148	92.66 136	87.97 312	62.18 327	70.56 228	92.37 142	43.53 311	97.35 74	64.50 268	82.86 167	91.05 217

GeoE		78.90 192	77.43 197	83.29 186	88.95 181	62.02 248	92.31 148	86.23 331	70.24 252	71.34 223	89.27 198	54.43 217	94.04 235	63.31 276	80.81 191	93.81 145

miper_enhance_ethall		78.86 193	77.97 189	81.54 232	88.00 208	65.17 164	91.41 187	89.15 268	75.19 143	68.79 254	83.98 270	67.17 60	92.82 270	72.73 187	65.30 302	86.62 281

VPNet		78.82 194	77.53 196	82.70 199	84.52 280	66.44 134	93.93 74	92.23 134	80.46 55	72.60 201	88.38 208	49.18 271	93.13 258	72.47 191	63.97 321	88.55 249

EPNet_dtu		78.80 195	79.26 172	77.43 307	88.06 205	49.71 369	91.96 168	91.95 151	77.67 108	76.56 161	91.28 167	58.51 165	90.20 329	56.37 311	80.95 188	92.39 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 196	77.43 197	82.88 194	92.21 96	64.49 176	92.05 161	96.28 4	73.48 172	71.75 216	88.26 211	60.07 146	95.32 181	45.16 357	77.58 217	88.83 242

TR-MVS		78.77 197	77.37 202	82.95 193	90.49 144	60.88 270	93.67 90	90.07 231	70.08 254	74.51 180	91.37 166	45.69 300	95.70 166	60.12 297	80.32 193	92.29 187

thres400		78.68 198	77.43 197	82.43 205	92.21 96	64.49 176	92.05 161	96.28 4	73.48 172	71.75 216	88.26 211	60.07 146	95.32 181	45.16 357	77.58 217	87.48 262

BH-untuned		78.68 198	77.08 204	83.48 182	89.84 156	63.74 201	92.70 132	88.59 294	71.57 228	66.83 283	88.65 204	51.75 245	95.39 179	59.03 302	84.77 149	91.32 211

OMC-MVS		78.67 200	77.91 191	80.95 249	85.76 259	57.40 326	88.49 276	88.67 291	73.85 163	72.43 207	92.10 149	49.29 270	94.55 212	72.73 187	77.89 213	90.91 218

tpm		78.58 201	77.03 205	83.22 189	85.94 256	64.56 174	83.21 328	91.14 192	78.31 97	73.67 189	79.68 329	64.01 95	92.09 298	66.07 254	71.26 265	93.03 167

OpenMVS	70.45 11	78.54 202	75.92 221	86.41 77	85.93 257	71.68 18	92.74 129	92.51 127	66.49 290	64.56 298	91.96 151	43.88 310	98.10 37	54.61 317	90.65 89	89.44 240

EPMVS		78.49 203	75.98 220	86.02 87	91.21 131	69.68 51	80.23 353	91.20 187	75.25 142	72.48 205	78.11 340	54.65 212	93.69 249	57.66 308	83.04 166	94.69 100

AUN-MVS		78.37 204	77.43 197	81.17 239	86.60 242	57.45 325	89.46 259	91.16 189	74.11 156	74.40 181	90.49 178	55.52 203	94.57 208	74.73 174	60.43 350	91.48 205

thres100view900		78.37 204	77.01 206	82.46 204	91.89 111	63.21 221	91.19 206	96.33 1	72.28 200	70.45 231	87.89 220	60.31 141	95.32 181	45.16 357	77.58 217	88.83 242

GA-MVS		78.33 206	76.23 216	84.65 139	83.65 294	66.30 138	91.44 186	90.14 229	76.01 132	70.32 233	84.02 269	42.50 315	94.72 201	70.98 203	77.00 225	92.94 170

cascas		78.18 207	75.77 223	85.41 108	87.14 231	69.11 61	92.96 121	91.15 191	66.71 288	70.47 229	86.07 247	37.49 342	96.48 129	70.15 211	79.80 197	90.65 220

UniMVSNet_NR-MVSNet		78.15 208	77.55 195	79.98 270	84.46 282	60.26 289	92.25 150	93.20 97	77.50 113	68.88 252	86.61 240	66.10 70	92.13 296	66.38 250	62.55 328	87.54 260

thres600view7		78.00 209	76.66 211	82.03 224	91.93 108	63.69 206	91.30 199	96.33 1	72.43 195	70.46 230	87.89 220	60.31 141	94.92 196	42.64 369	76.64 227	87.48 262

FC-MVSNet-test		77.99 210	78.08 187	77.70 302	84.89 275	55.51 340	90.27 238	93.75 73	76.87 119	66.80 284	87.59 225	65.71 76	90.23 328	62.89 281	73.94 244	87.37 265

Anonymous202405211		77.96 211	75.33 229	85.87 92	93.73 53	64.52 175	94.85 44	85.36 341	62.52 325	76.11 163	90.18 185	29.43 377	97.29 78	68.51 228	77.24 224	95.81 49

cl22		77.94 212	76.78 209	81.42 234	87.57 219	64.93 172	90.67 224	88.86 284	72.45 194	67.63 271	82.68 283	64.07 94	92.91 268	71.79 196	65.30 302	86.44 282

XXY-MVS		77.94 212	76.44 213	82.43 205	82.60 306	64.44 180	92.01 163	91.83 160	73.59 171	70.00 238	85.82 250	54.43 217	94.76 198	69.63 214	68.02 286	88.10 256

MS-PatchMatch		77.90 214	76.50 212	82.12 219	85.99 253	69.95 41	91.75 180	92.70 116	73.97 160	62.58 320	84.44 265	41.11 320	95.78 157	63.76 273	92.17 66	80.62 362

FMVSNet3		77.73 215	76.04 219	82.80 195	91.20 132	68.99 65	91.87 171	91.99 149	73.35 174	67.04 279	83.19 278	56.62 190	92.14 295	59.80 299	69.34 272	87.28 268

miper_ehance_all_eth		77.60 216	76.44 213	81.09 246	85.70 261	64.41 183	90.65 225	88.64 293	72.31 198	67.37 277	82.52 284	64.77 88	92.64 281	70.67 207	65.30 302	86.24 286

UniMVSNet (Re)		77.58 217	76.78 209	79.98 270	84.11 288	60.80 271	91.76 178	93.17 99	76.56 128	69.93 241	84.78 260	63.32 111	92.36 290	64.89 266	62.51 330	86.78 276

PatchmatchNet		77.46 218	74.63 236	85.96 89	89.55 164	70.35 34	79.97 358	89.55 251	72.23 201	70.94 224	76.91 352	57.03 180	92.79 273	54.27 319	81.17 186	94.74 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 219	75.65 225	82.73 197	80.38 327	67.13 116	91.85 173	90.23 226	75.09 144	69.37 243	83.39 276	53.79 225	94.44 216	71.77 197	65.00 308	86.63 280

CHOSEN 280x420		77.35 220	76.95 208	78.55 294	87.07 233	62.68 236	69.71 390	82.95 363	68.80 270	71.48 221	87.27 232	66.03 71	84.00 374	76.47 158	82.81 169	88.95 241

PS-MVSNAJss		77.26 221	76.31 215	80.13 265	80.64 325	59.16 307	90.63 228	91.06 198	72.80 186	68.58 258	84.57 263	53.55 227	93.96 240	72.97 181	71.96 259	87.27 269

gg-mvs-nofinetune		77.18 222	74.31 243	85.80 96	91.42 124	68.36 79	71.78 384	94.72 35	49.61 384	77.12 155	45.92 410	77.41 8	93.98 239	67.62 236	93.16 55	95.05 83

WB-MVSnew		77.14 223	76.18 218	80.01 269	86.18 250	63.24 219	91.26 200	94.11 61	71.72 220	73.52 190	87.29 231	45.14 305	93.00 261	56.98 309	79.42 199	83.80 323

MVP-Stereo		77.12 224	76.23 216	79.79 277	81.72 314	66.34 137	89.29 261	90.88 202	70.56 249	62.01 323	82.88 280	49.34 268	94.13 227	65.55 261	93.80 43	78.88 376
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 225	75.37 227	82.20 215	89.25 172	62.11 247	82.06 336	89.09 273	76.77 124	70.84 226	87.12 233	41.43 319	95.01 191	67.23 240	74.55 236	89.48 238

MonoMVSNet		76.99 226	75.08 232	82.73 197	83.32 298	63.24 219	86.47 305	86.37 327	79.08 85	66.31 286	79.30 333	49.80 265	91.72 305	79.37 136	65.70 300	93.23 159

dmvs_re		76.93 227	75.36 228	81.61 230	87.78 216	60.71 278	80.00 357	87.99 310	79.42 75	69.02 249	89.47 196	46.77 289	94.32 218	63.38 275	74.45 239	89.81 231

X-MVStestdata		76.86 228	74.13 247	85.05 121	93.22 65	63.78 199	92.92 122	92.66 120	73.99 158	78.18 142	10.19 425	55.25 204	97.41 70	79.16 139	91.58 76	93.95 138

DU-MVS		76.86 228	75.84 222	79.91 273	82.96 302	60.26 289	91.26 200	91.54 173	76.46 129	68.88 252	86.35 243	56.16 195	92.13 296	66.38 250	62.55 328	87.35 266

Anonymous20240529		76.84 230	74.15 246	84.88 126	91.02 134	64.95 171	93.84 82	91.09 194	53.57 372	73.00 193	87.42 228	35.91 352	97.32 76	69.14 222	72.41 257	92.36 184

c3_l		76.83 231	75.47 226	80.93 250	85.02 273	64.18 193	90.39 233	88.11 307	71.66 221	66.65 285	81.64 297	63.58 107	92.56 282	69.31 219	62.86 325	86.04 292

WR-MVS		76.76 232	75.74 224	79.82 276	84.60 278	62.27 245	92.60 139	92.51 127	76.06 131	67.87 268	85.34 254	56.76 186	90.24 327	62.20 285	63.69 323	86.94 274

v1144		76.73 233	74.88 233	82.27 211	80.23 331	66.60 131	91.68 182	90.21 228	73.69 168	69.06 248	81.89 292	52.73 237	94.40 217	69.21 220	65.23 305	85.80 299

IterMVS-LS		76.49 234	75.18 231	80.43 257	84.49 281	62.74 234	90.64 226	88.80 286	72.40 196	65.16 293	81.72 295	60.98 135	92.27 294	67.74 234	64.65 313	86.29 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 235	74.55 239	82.19 216	79.14 345	67.82 96	90.26 239	89.42 256	73.75 166	68.63 257	81.89 292	51.31 250	94.09 229	71.69 199	64.84 309	84.66 316

v148		76.19 236	74.47 241	81.36 235	80.05 333	64.44 180	91.75 180	90.23 226	73.68 169	67.13 278	80.84 312	55.92 200	93.86 247	68.95 224	61.73 339	85.76 302

Effi-MVS+-dtu		76.14 237	75.28 230	78.72 293	83.22 299	55.17 342	89.87 250	87.78 314	75.42 139	67.98 263	81.43 301	45.08 306	92.52 284	75.08 168	71.63 260	88.48 250

cl____		76.07 238	74.67 234	80.28 260	85.15 269	61.76 254	90.12 242	88.73 288	71.16 236	65.43 290	81.57 299	61.15 132	92.95 263	66.54 247	62.17 332	86.13 290

DIV-MVS_self_test		76.07 238	74.67 234	80.28 260	85.14 270	61.75 255	90.12 242	88.73 288	71.16 236	65.42 291	81.60 298	61.15 132	92.94 267	66.54 247	62.16 334	86.14 288

FMVSNet2		76.07 238	74.01 249	82.26 213	88.85 182	67.66 100	91.33 197	91.61 171	70.84 243	65.98 287	82.25 288	48.03 279	92.00 300	58.46 304	68.73 280	87.10 271

v144192		76.05 241	74.03 248	82.12 219	79.50 339	66.55 133	91.39 191	89.71 249	72.30 199	68.17 261	81.33 304	51.75 245	94.03 237	67.94 232	64.19 316	85.77 300

NR-MVSNet		76.05 241	74.59 237	80.44 256	82.96 302	62.18 246	90.83 217	91.73 163	77.12 117	60.96 326	86.35 243	59.28 156	91.80 303	60.74 292	61.34 343	87.35 266

v1192		75.98 243	73.92 250	82.15 217	79.73 335	66.24 140	91.22 203	89.75 243	72.67 188	68.49 259	81.42 302	49.86 263	94.27 222	67.08 242	65.02 307	85.95 295

FE-MVS		75.97 244	73.02 260	84.82 128	89.78 157	65.56 155	77.44 369	91.07 197	64.55 302	72.66 199	79.85 327	46.05 299	96.69 119	54.97 316	80.82 190	92.21 193

eth_miper_zixun_eth		75.96 245	74.40 242	80.66 252	84.66 277	63.02 225	89.28 262	88.27 303	71.88 212	65.73 288	81.65 296	59.45 152	92.81 271	68.13 229	60.53 348	86.14 288

TranMVSNet+NR-MVSNet		75.86 246	74.52 240	79.89 274	82.44 308	60.64 281	91.37 194	91.37 180	76.63 126	67.65 270	86.21 246	52.37 240	91.55 310	61.84 287	60.81 346	87.48 262

SCA		75.82 247	72.76 263	85.01 123	86.63 241	70.08 37	81.06 346	89.19 265	71.60 227	70.01 237	77.09 350	45.53 301	90.25 324	60.43 294	73.27 248	94.68 101

LPG-MVS_test		75.82 247	74.58 238	79.56 283	84.31 285	59.37 303	90.44 230	89.73 246	69.49 260	64.86 294	88.42 206	38.65 328	94.30 220	72.56 189	72.76 252	85.01 313

GBi-Net		75.65 249	73.83 251	81.10 243	88.85 182	65.11 166	90.01 246	90.32 218	70.84 243	67.04 279	80.25 322	48.03 279	91.54 311	59.80 299	69.34 272	86.64 277

test1		75.65 249	73.83 251	81.10 243	88.85 182	65.11 166	90.01 246	90.32 218	70.84 243	67.04 279	80.25 322	48.03 279	91.54 311	59.80 299	69.34 272	86.64 277

v1921920		75.63 251	73.49 256	82.06 223	79.38 340	66.35 136	91.07 211	89.48 252	71.98 207	67.99 262	81.22 307	49.16 273	93.90 243	66.56 246	64.56 314	85.92 297

ACMP	71.68 10	75.58 252	74.23 245	79.62 281	84.97 274	59.64 298	90.80 218	89.07 275	70.39 250	62.95 316	87.30 230	38.28 332	93.87 245	72.89 182	71.45 263	85.36 309
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 253	73.26 258	81.61 230	80.67 324	66.82 124	89.54 256	89.27 261	71.65 222	63.30 312	80.30 321	54.99 210	94.06 232	67.33 239	62.33 331	83.94 321

tpm cat1		75.30 254	72.21 272	84.58 143	88.52 188	67.77 97	78.16 367	88.02 309	61.88 333	68.45 260	76.37 356	60.65 138	94.03 237	53.77 322	74.11 242	91.93 199

PLC	68.80 14	75.23 255	73.68 254	79.86 275	92.93 76	58.68 312	90.64 226	88.30 301	60.90 338	64.43 302	90.53 176	42.38 316	94.57 208	56.52 310	76.54 228	86.33 283
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 256	72.98 261	81.88 225	79.20 342	66.00 144	90.75 220	89.11 272	71.63 226	67.41 275	81.22 307	47.36 287	93.87 245	65.46 262	64.72 312	85.77 300

Fast-Effi-MVS+-dtu		75.04 257	73.37 257	80.07 266	80.86 320	59.52 301	91.20 205	85.38 340	71.90 210	65.20 292	84.84 259	41.46 318	92.97 262	66.50 249	72.96 251	87.73 259

dp		75.01 258	72.09 273	83.76 167	89.28 171	66.22 141	79.96 359	89.75 243	71.16 236	67.80 269	77.19 349	51.81 243	92.54 283	50.39 330	71.44 264	92.51 182

TAPA-MVS	70.22 12	74.94 259	73.53 255	79.17 288	90.40 146	52.07 355	89.19 265	89.61 250	62.69 324	70.07 236	92.67 134	48.89 276	94.32 218	38.26 383	79.97 195	91.12 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 260	72.54 269	81.46 233	80.33 329	66.71 128	89.15 266	89.08 274	70.94 241	63.08 315	79.86 326	52.52 238	94.04 235	65.70 258	62.17 332	83.64 324

XVG-OURS-SEG-HR		74.70 261	73.08 259	79.57 282	78.25 357	57.33 327	80.49 349	87.32 317	63.22 317	68.76 255	90.12 191	44.89 307	91.59 309	70.55 209	74.09 243	89.79 232

ACMM	69.62 13	74.34 262	72.73 265	79.17 288	84.25 287	57.87 318	90.36 235	89.93 237	63.17 319	65.64 289	86.04 249	37.79 340	94.10 228	65.89 255	71.52 262	85.55 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 263	72.30 271	80.32 258	91.49 123	61.66 257	90.85 216	80.72 369	56.67 364	63.85 307	90.64 173	46.75 290	90.84 319	53.79 321	75.99 232	88.47 251

XVG-OURS		74.25 264	72.46 270	79.63 280	78.45 355	57.59 323	80.33 351	87.39 316	63.86 309	68.76 255	89.62 195	40.50 322	91.72 305	69.00 223	74.25 241	89.58 235

test_fmvs1		74.07 265	73.69 253	75.22 325	78.91 349	47.34 382	89.06 269	74.69 385	63.68 312	79.41 127	91.59 161	24.36 387	87.77 351	85.22 81	76.26 230	90.55 223

CVMVSNet		74.04 266	74.27 244	73.33 341	85.33 264	43.94 395	89.53 257	88.39 298	54.33 371	70.37 232	90.13 189	49.17 272	84.05 372	61.83 288	79.36 201	91.99 198

Baseline_NR-MVSNet		73.99 267	72.83 262	77.48 306	80.78 322	59.29 306	91.79 175	84.55 349	68.85 269	68.99 250	80.70 313	56.16 195	92.04 299	62.67 282	60.98 345	81.11 356

pmmvs4		73.92 268	71.81 277	80.25 262	79.17 343	65.24 162	87.43 294	87.26 320	67.64 281	63.46 310	83.91 271	48.96 275	91.53 314	62.94 279	65.49 301	83.96 320

D2MVS		73.80 269	72.02 274	79.15 290	79.15 344	62.97 226	88.58 275	90.07 231	72.94 181	59.22 336	78.30 337	42.31 317	92.70 277	65.59 260	72.00 258	81.79 351

CR-MVSNet		73.79 270	70.82 285	82.70 199	83.15 300	67.96 92	70.25 387	84.00 354	73.67 170	69.97 239	72.41 372	57.82 173	89.48 336	52.99 325	73.13 249	90.64 221

test_djsdf		73.76 271	72.56 268	77.39 308	77.00 367	53.93 348	89.07 267	90.69 205	65.80 294	63.92 305	82.03 291	43.14 314	92.67 278	72.83 183	68.53 281	85.57 304

pmmvs5		73.35 272	71.52 279	78.86 292	78.64 353	60.61 282	91.08 209	86.90 322	67.69 278	63.32 311	83.64 272	44.33 309	90.53 321	62.04 286	66.02 298	85.46 307

Anonymous20231211		73.08 273	70.39 289	81.13 241	90.62 142	63.33 217	91.40 189	90.06 233	51.84 377	64.46 301	80.67 315	36.49 350	94.07 231	63.83 272	64.17 317	85.98 294

tt0805		73.07 274	70.73 286	80.07 266	78.37 356	57.05 329	87.78 288	92.18 141	61.23 337	67.04 279	86.49 242	31.35 370	94.58 206	65.06 265	67.12 291	88.57 248

miper_lstm_enhance		73.05 275	71.73 278	77.03 312	83.80 291	58.32 315	81.76 337	88.88 282	69.80 258	61.01 325	78.23 339	57.19 178	87.51 355	65.34 263	59.53 353	85.27 312

jajsoiax		73.05 275	71.51 280	77.67 303	77.46 364	54.83 344	88.81 271	90.04 234	69.13 267	62.85 318	83.51 274	31.16 371	92.75 274	70.83 204	69.80 268	85.43 308

LCM-MVSNet-Re		72.93 277	71.84 276	76.18 321	88.49 189	48.02 377	80.07 356	70.17 397	73.96 161	52.25 367	80.09 325	49.98 261	88.24 345	67.35 237	84.23 158	92.28 188

pm-mvs1		72.89 278	71.09 282	78.26 298	79.10 346	57.62 322	90.80 218	89.30 260	67.66 279	62.91 317	81.78 294	49.11 274	92.95 263	60.29 296	58.89 356	84.22 319

tpmvs		72.88 279	69.76 295	82.22 214	90.98 135	67.05 118	78.22 366	88.30 301	63.10 320	64.35 303	74.98 363	55.09 209	94.27 222	43.25 363	69.57 271	85.34 310

test0.0.03 1		72.76 280	72.71 266	72.88 345	80.25 330	47.99 378	91.22 203	89.45 254	71.51 231	62.51 321	87.66 223	53.83 223	85.06 368	50.16 332	67.84 289	85.58 303

UniMVSNet_ETH3D		72.74 281	70.53 288	79.36 285	78.62 354	56.64 333	85.01 311	89.20 264	63.77 310	64.84 296	84.44 265	34.05 359	91.86 302	63.94 271	70.89 267	89.57 236

mvs_tets		72.71 282	71.11 281	77.52 304	77.41 365	54.52 346	88.45 277	89.76 242	68.76 272	62.70 319	83.26 277	29.49 376	92.71 275	70.51 210	69.62 270	85.34 310

FMVSNet1		72.71 282	69.91 293	81.10 243	83.60 295	65.11 166	90.01 246	90.32 218	63.92 308	63.56 309	80.25 322	36.35 351	91.54 311	54.46 318	66.75 294	86.64 277

test_fmvs1_n		72.69 284	71.92 275	74.99 328	71.15 387	47.08 384	87.34 296	75.67 380	63.48 314	78.08 144	91.17 168	20.16 399	87.87 348	84.65 90	75.57 234	90.01 229

IterMVS		72.65 285	70.83 283	78.09 300	82.17 310	62.96 227	87.64 292	86.28 329	71.56 229	60.44 329	78.85 335	45.42 303	86.66 359	63.30 277	61.83 336	84.65 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 286	72.74 264	72.10 353	87.87 211	49.45 371	88.07 281	89.01 277	72.91 183	63.11 313	88.10 215	63.63 102	85.54 364	32.73 398	69.23 275	81.32 354

PatchMatch-RL		72.06 287	69.98 290	78.28 297	89.51 165	55.70 339	83.49 321	83.39 361	61.24 336	63.72 308	82.76 281	34.77 356	93.03 260	53.37 324	77.59 216	86.12 291

PVSNet_0	68.08 15	71.81 288	68.32 304	82.27 211	84.68 276	62.31 244	88.68 273	90.31 221	75.84 133	57.93 347	80.65 316	37.85 339	94.19 225	69.94 212	29.05 413	90.31 225

MIMVSNet		71.64 289	68.44 302	81.23 238	81.97 313	64.44 180	73.05 381	88.80 286	69.67 259	64.59 297	74.79 365	32.79 362	87.82 349	53.99 320	76.35 229	91.42 206

test_vis1_n		71.63 290	70.73 286	74.31 335	69.63 393	47.29 383	86.91 300	72.11 391	63.21 318	75.18 174	90.17 186	20.40 397	85.76 363	84.59 91	74.42 240	89.87 230

IterMVS-SCA-FT		71.55 291	69.97 291	76.32 319	81.48 316	60.67 280	87.64 292	85.99 334	66.17 292	59.50 334	78.88 334	45.53 301	83.65 376	62.58 283	61.93 335	84.63 318

v7n		71.31 292	68.65 299	79.28 286	76.40 369	60.77 273	86.71 303	89.45 254	64.17 307	58.77 341	78.24 338	44.59 308	93.54 251	57.76 306	61.75 338	83.52 327

anonymousdsp		71.14 293	69.37 297	76.45 318	72.95 382	54.71 345	84.19 316	88.88 282	61.92 332	62.15 322	79.77 328	38.14 335	91.44 316	68.90 225	67.45 290	83.21 333

F-COLMAP		70.66 294	68.44 302	77.32 309	86.37 247	55.91 337	88.00 283	86.32 328	56.94 362	57.28 351	88.07 217	33.58 360	92.49 285	51.02 328	68.37 282	83.55 325

WR-MVS_H		70.59 295	69.94 292	72.53 347	81.03 319	51.43 359	87.35 295	92.03 148	67.38 282	60.23 331	80.70 313	55.84 201	83.45 378	46.33 353	58.58 358	82.72 340

CP-MVSNet		70.50 296	69.91 293	72.26 350	80.71 323	51.00 363	87.23 297	90.30 222	67.84 277	59.64 333	82.69 282	50.23 260	82.30 386	51.28 327	59.28 354	83.46 329

RPMNet		70.42 297	65.68 318	84.63 141	83.15 300	67.96 92	70.25 387	90.45 212	46.83 393	69.97 239	65.10 393	56.48 194	95.30 184	35.79 388	73.13 249	90.64 221

testing3		70.38 298	70.83 283	69.03 365	85.82 258	43.93 396	90.72 223	90.56 211	68.06 276	60.24 330	86.82 239	64.83 86	84.12 370	26.33 406	64.10 318	79.04 375

tfpnnormal		70.10 299	67.36 308	78.32 296	83.45 297	60.97 269	88.85 270	92.77 114	64.85 301	60.83 327	78.53 336	43.52 312	93.48 253	31.73 401	61.70 340	80.52 363

TransMVSNet (Re)		70.07 300	67.66 306	77.31 310	80.62 326	59.13 308	91.78 177	84.94 345	65.97 293	60.08 332	80.44 318	50.78 254	91.87 301	48.84 339	45.46 387	80.94 358

CL-MVSNet_self_test		69.92 301	68.09 305	75.41 324	73.25 381	55.90 338	90.05 245	89.90 238	69.96 255	61.96 324	76.54 353	51.05 253	87.64 352	49.51 336	50.59 379	82.70 342

DP-MVS		69.90 302	66.48 310	80.14 264	95.36 28	62.93 228	89.56 254	76.11 378	50.27 383	57.69 349	85.23 255	39.68 324	95.73 161	33.35 393	71.05 266	81.78 352

PS-CasMVS		69.86 303	69.13 298	72.07 354	80.35 328	50.57 365	87.02 299	89.75 243	67.27 283	59.19 337	82.28 287	46.58 292	82.24 387	50.69 329	59.02 355	83.39 331

Syy-MVS		69.65 304	69.52 296	70.03 361	87.87 211	43.21 397	88.07 281	89.01 277	72.91 183	63.11 313	88.10 215	45.28 304	85.54 364	22.07 411	69.23 275	81.32 354

MSDG		69.54 305	65.73 317	80.96 248	85.11 272	63.71 204	84.19 316	83.28 362	56.95 361	54.50 358	84.03 268	31.50 368	96.03 151	42.87 367	69.13 277	83.14 335

PEN-MVS		69.46 306	68.56 300	72.17 352	79.27 341	49.71 369	86.90 301	89.24 262	67.24 286	59.08 338	82.51 285	47.23 288	83.54 377	48.42 341	57.12 360	83.25 332

LS3D		69.17 307	66.40 312	77.50 305	91.92 109	56.12 336	85.12 310	80.37 371	46.96 391	56.50 353	87.51 227	37.25 343	93.71 248	32.52 400	79.40 200	82.68 343

PatchT		69.11 308	65.37 322	80.32 258	82.07 312	63.68 207	67.96 397	87.62 315	50.86 381	69.37 243	65.18 392	57.09 179	88.53 342	41.59 372	66.60 295	88.74 245

KD-MVS_2432*1600		69.03 309	66.37 313	77.01 313	85.56 262	61.06 267	81.44 342	90.25 224	67.27 283	58.00 345	76.53 354	54.49 214	87.63 353	48.04 343	35.77 404	82.34 346

miper_refine_blended		69.03 309	66.37 313	77.01 313	85.56 262	61.06 267	81.44 342	90.25 224	67.27 283	58.00 345	76.53 354	54.49 214	87.63 353	48.04 343	35.77 404	82.34 346

mvsany_test1		68.77 311	68.56 300	69.39 363	73.57 380	45.88 391	80.93 347	60.88 411	59.65 347	71.56 219	90.26 184	43.22 313	75.05 398	74.26 176	62.70 327	87.25 270

ACMH	63.93 17	68.62 312	64.81 324	80.03 268	85.22 268	63.25 218	87.72 289	84.66 347	60.83 339	51.57 371	79.43 332	27.29 383	94.96 193	41.76 370	64.84 309	81.88 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 313	65.41 321	77.96 301	78.69 352	62.93 228	89.86 251	89.17 266	60.55 340	50.27 376	77.73 344	22.60 393	94.06 232	47.18 349	72.65 254	76.88 386

ADS-MVSNet		68.54 314	64.38 331	81.03 247	88.06 205	66.90 123	68.01 395	84.02 353	57.57 355	64.48 299	69.87 382	38.68 326	89.21 338	40.87 374	67.89 287	86.97 272

DTE-MVSNet		68.46 315	67.33 309	71.87 356	77.94 361	49.00 375	86.16 307	88.58 295	66.36 291	58.19 342	82.21 289	46.36 293	83.87 375	44.97 360	55.17 367	82.73 339

mmtdpeth		68.33 316	66.37 313	74.21 336	82.81 305	51.73 356	84.34 315	80.42 370	67.01 287	71.56 219	68.58 386	30.52 374	92.35 291	75.89 161	36.21 402	78.56 380

our_test_3		68.29 317	64.69 326	79.11 291	78.92 347	64.85 173	88.40 278	85.06 343	60.32 343	52.68 365	76.12 358	40.81 321	89.80 335	44.25 362	55.65 365	82.67 344

Patchmatch-RL test		68.17 318	64.49 329	79.19 287	71.22 386	53.93 348	70.07 389	71.54 395	69.22 264	56.79 352	62.89 397	56.58 191	88.61 339	69.53 216	52.61 374	95.03 85

XVG-ACMP-BASELINE		68.04 319	65.53 320	75.56 323	74.06 379	52.37 353	78.43 363	85.88 335	62.03 330	58.91 340	81.21 309	20.38 398	91.15 318	60.69 293	68.18 283	83.16 334

FMVSNet5		68.04 319	65.66 319	75.18 327	84.43 283	57.89 317	83.54 320	86.26 330	61.83 334	53.64 363	73.30 368	37.15 346	85.08 367	48.99 338	61.77 337	82.56 345

ppachtmachnet_test		67.72 321	63.70 333	79.77 278	78.92 347	66.04 143	88.68 273	82.90 364	60.11 345	55.45 355	75.96 359	39.19 325	90.55 320	39.53 378	52.55 375	82.71 341

ACMH+	65.35 16	67.65 322	64.55 327	76.96 315	84.59 279	57.10 328	88.08 280	80.79 368	58.59 353	53.00 364	81.09 311	26.63 385	92.95 263	46.51 351	61.69 341	80.82 359

pmmvs6		67.57 323	64.76 325	76.00 322	72.82 384	53.37 350	88.71 272	86.78 326	53.19 373	57.58 350	78.03 341	35.33 355	92.41 287	55.56 314	54.88 369	82.21 348

Anonymous20231206		67.53 324	65.78 316	72.79 346	74.95 375	47.59 380	88.23 279	87.32 317	61.75 335	58.07 344	77.29 347	37.79 340	87.29 357	42.91 365	63.71 322	83.48 328

Patchmtry		67.53 324	63.93 332	78.34 295	82.12 311	64.38 184	68.72 392	84.00 354	48.23 390	59.24 335	72.41 372	57.82 173	89.27 337	46.10 354	56.68 364	81.36 353

USDC		67.43 326	64.51 328	76.19 320	77.94 361	55.29 341	78.38 364	85.00 344	73.17 176	48.36 384	80.37 319	21.23 395	92.48 286	52.15 326	64.02 320	80.81 360

ADS-MVSNet2		66.90 327	63.44 335	77.26 311	88.06 205	60.70 279	68.01 395	75.56 382	57.57 355	64.48 299	69.87 382	38.68 326	84.10 371	40.87 374	67.89 287	86.97 272

CMPMVS	48.56 21	66.77 328	64.41 330	73.84 338	70.65 390	50.31 366	77.79 368	85.73 338	45.54 395	44.76 394	82.14 290	35.40 354	90.14 330	63.18 278	74.54 238	81.07 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 329	62.92 338	76.80 317	76.51 368	57.77 319	89.22 263	83.41 360	55.48 368	53.86 362	77.84 342	26.28 386	93.95 241	34.90 390	68.76 279	78.68 378

LTVRE_ROB	59.60 19	66.27 330	63.54 334	74.45 332	84.00 290	51.55 358	67.08 399	83.53 358	58.78 351	54.94 357	80.31 320	34.54 357	93.23 257	40.64 376	68.03 285	78.58 379
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		66.06 331	62.45 341	76.88 316	81.42 318	54.45 347	57.49 411	88.67 291	49.36 385	63.86 306	46.86 409	56.06 198	90.25 324	49.53 335	68.83 278	85.95 295

Patchmatch-test		65.86 332	60.94 347	80.62 255	83.75 292	58.83 310	58.91 410	75.26 384	44.50 398	50.95 375	77.09 350	58.81 163	87.90 347	35.13 389	64.03 319	95.12 80

UnsupCasMVSNet_eth		65.79 333	63.10 336	73.88 337	70.71 389	50.29 367	81.09 345	89.88 239	72.58 190	49.25 381	74.77 366	32.57 364	87.43 356	55.96 313	41.04 394	83.90 322

test_fmvs2		65.78 334	64.84 323	68.60 367	66.54 399	41.71 399	83.27 325	69.81 398	54.38 370	67.91 265	84.54 264	15.35 404	81.22 391	75.65 163	66.16 297	82.88 336

dmvs_testset		65.55 335	66.45 311	62.86 379	79.87 334	22.35 425	76.55 371	71.74 393	77.42 116	55.85 354	87.77 222	51.39 249	80.69 392	31.51 404	65.92 299	85.55 305

pmmvs-eth3d		65.53 336	62.32 342	75.19 326	69.39 394	59.59 299	82.80 333	83.43 359	62.52 325	51.30 373	72.49 370	32.86 361	87.16 358	55.32 315	50.73 378	78.83 377

mamv4		65.18 337	67.43 307	58.44 383	77.88 363	49.36 374	69.40 391	70.99 396	48.31 389	57.78 348	85.53 253	59.01 161	51.88 421	73.67 178	64.32 315	74.07 391

SixPastTwentyTwo		64.92 338	61.78 345	74.34 334	78.74 351	49.76 368	83.42 324	79.51 374	62.86 321	50.27 376	77.35 345	30.92 373	90.49 322	45.89 355	47.06 384	82.78 337

OurMVSNet-221017-0		64.68 339	62.17 343	72.21 351	76.08 372	47.35 381	80.67 348	81.02 367	56.19 365	51.60 370	79.66 330	27.05 384	88.56 341	53.60 323	53.63 372	80.71 361

test_0402		64.54 340	61.09 346	74.92 329	84.10 289	60.75 275	87.95 284	79.71 373	52.03 375	52.41 366	77.20 348	32.21 366	91.64 307	23.14 409	61.03 344	72.36 397

testgi		64.48 341	62.87 339	69.31 364	71.24 385	40.62 402	85.49 308	79.92 372	65.36 298	54.18 360	83.49 275	23.74 390	84.55 369	41.60 371	60.79 347	82.77 338

RPSCF		64.24 342	61.98 344	71.01 359	76.10 371	45.00 392	75.83 376	75.94 379	46.94 392	58.96 339	84.59 262	31.40 369	82.00 388	47.76 347	60.33 352	86.04 292

EU-MVSNet		64.01 343	63.01 337	67.02 373	74.40 378	38.86 408	83.27 325	86.19 332	45.11 396	54.27 359	81.15 310	36.91 349	80.01 394	48.79 340	57.02 361	82.19 349

test20.03		63.83 344	62.65 340	67.38 372	70.58 391	39.94 404	86.57 304	84.17 351	63.29 316	51.86 369	77.30 346	37.09 347	82.47 384	38.87 382	54.13 371	79.73 369

MDA-MVSNet_test_wron		63.78 345	60.16 349	74.64 330	78.15 359	60.41 286	83.49 321	84.03 352	56.17 367	39.17 404	71.59 378	37.22 344	83.24 381	42.87 367	48.73 381	80.26 366

YYNet1		63.76 346	60.14 350	74.62 331	78.06 360	60.19 292	83.46 323	83.99 356	56.18 366	39.25 403	71.56 379	37.18 345	83.34 379	42.90 366	48.70 382	80.32 365

K. test v3		63.09 347	59.61 352	73.53 340	76.26 370	49.38 373	83.27 325	77.15 377	64.35 304	47.77 386	72.32 374	28.73 378	87.79 350	49.93 334	36.69 401	83.41 330

COLMAP_ROB	57.96 20	62.98 348	59.65 351	72.98 344	81.44 317	53.00 352	83.75 319	75.53 383	48.34 388	48.81 383	81.40 303	24.14 388	90.30 323	32.95 395	60.52 349	75.65 389
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 349	59.08 353	71.10 358	67.19 397	48.72 376	83.91 318	85.23 342	50.38 382	47.84 385	71.22 381	20.74 396	85.51 366	46.47 352	58.75 357	79.06 374

AllTest		61.66 350	58.06 355	72.46 348	79.57 336	51.42 360	80.17 354	68.61 400	51.25 379	45.88 388	81.23 305	19.86 400	86.58 360	38.98 380	57.01 362	79.39 371

UnsupCasMVSNet_bld		61.60 351	57.71 356	73.29 342	68.73 395	51.64 357	78.61 362	89.05 276	57.20 360	46.11 387	61.96 400	28.70 379	88.60 340	50.08 333	38.90 399	79.63 370

MDA-MVSNet-bldmvs		61.54 352	57.70 357	73.05 343	79.53 338	57.00 332	83.08 329	81.23 366	57.57 355	34.91 408	72.45 371	32.79 362	86.26 362	35.81 387	41.95 392	75.89 388

mvs5depth		61.03 353	57.65 358	71.18 357	67.16 398	47.04 386	72.74 382	77.49 375	57.47 358	60.52 328	72.53 369	22.84 392	88.38 343	49.15 337	38.94 398	78.11 383

KD-MVS_self_test		60.87 354	58.60 354	67.68 370	66.13 400	39.93 405	75.63 378	84.70 346	57.32 359	49.57 379	68.45 387	29.55 375	82.87 382	48.09 342	47.94 383	80.25 367

kuosan		60.86 355	60.24 348	62.71 380	81.57 315	46.43 388	75.70 377	85.88 335	57.98 354	48.95 382	69.53 384	58.42 166	76.53 396	28.25 405	35.87 403	65.15 404

TinyColmap		60.32 356	56.42 363	72.00 355	78.78 350	53.18 351	78.36 365	75.64 381	52.30 374	41.59 402	75.82 361	14.76 407	88.35 344	35.84 386	54.71 370	74.46 390

MVS-HIRNet		60.25 357	55.55 364	74.35 333	84.37 284	56.57 334	71.64 385	74.11 386	34.44 407	45.54 392	42.24 415	31.11 372	89.81 333	40.36 377	76.10 231	76.67 387

MIMVSNet1		60.16 358	57.33 359	68.67 366	69.71 392	44.13 394	78.92 361	84.21 350	55.05 369	44.63 395	71.85 376	23.91 389	81.54 390	32.63 399	55.03 368	80.35 364

PM-MVS		59.40 359	56.59 361	67.84 368	63.63 403	41.86 398	76.76 370	63.22 408	59.01 350	51.07 374	72.27 375	11.72 411	83.25 380	61.34 289	50.28 380	78.39 381

new-patchmatchnet		59.30 360	56.48 362	67.79 369	65.86 401	44.19 393	82.47 334	81.77 365	59.94 346	43.65 398	66.20 391	27.67 382	81.68 389	39.34 379	41.40 393	77.50 385

test_vis1_rt		59.09 361	57.31 360	64.43 376	68.44 396	46.02 390	83.05 331	48.63 420	51.96 376	49.57 379	63.86 396	16.30 402	80.20 393	71.21 202	62.79 326	67.07 403

test_fmvs3		56.82 362	54.86 366	62.69 381	53.59 414	35.47 411	75.87 375	65.64 405	43.91 399	55.10 356	71.43 380	6.91 419	74.40 401	68.64 227	52.63 373	78.20 382

DSMNet-mixed		56.78 363	54.44 367	63.79 377	63.21 404	29.44 420	64.43 402	64.10 407	42.12 404	51.32 372	71.60 377	31.76 367	75.04 399	36.23 385	65.20 306	86.87 275

pmmvs3		55.51 364	51.50 370	67.53 371	57.90 412	50.93 364	80.37 350	73.66 387	40.63 405	44.15 397	64.75 394	16.30 402	78.97 395	44.77 361	40.98 396	72.69 395

TDRefinement		55.28 365	51.58 369	66.39 374	59.53 411	46.15 389	76.23 373	72.80 388	44.60 397	42.49 400	76.28 357	15.29 405	82.39 385	33.20 394	43.75 389	70.62 399

dongtai		55.18 366	55.46 365	54.34 391	76.03 373	36.88 409	76.07 374	84.61 348	51.28 378	43.41 399	64.61 395	56.56 192	67.81 409	18.09 414	28.50 414	58.32 407

LF4IMVS		54.01 367	52.12 368	59.69 382	62.41 406	39.91 406	68.59 393	68.28 402	42.96 402	44.55 396	75.18 362	14.09 409	68.39 408	41.36 373	51.68 376	70.78 398

ttmdpeth		53.34 368	49.96 371	63.45 378	62.07 408	40.04 403	72.06 383	65.64 405	42.54 403	51.88 368	77.79 343	13.94 410	76.48 397	32.93 396	30.82 412	73.84 392

MVStest1		51.35 369	46.89 373	64.74 375	65.06 402	51.10 362	67.33 398	72.58 389	30.20 411	35.30 406	74.82 364	27.70 381	69.89 406	24.44 408	24.57 415	73.22 393

N_pmnet		50.55 370	49.11 372	54.88 389	77.17 366	4.02 433	84.36 314	2.00 431	48.59 386	45.86 390	68.82 385	32.22 365	82.80 383	31.58 402	51.38 377	77.81 384

new_pmnet		49.31 371	46.44 374	57.93 384	62.84 405	40.74 401	68.47 394	62.96 409	36.48 406	35.09 407	57.81 404	14.97 406	72.18 403	32.86 397	46.44 385	60.88 406

mvsany_test3		48.86 372	46.35 375	56.41 385	46.00 420	31.67 416	62.26 404	47.25 421	43.71 400	45.54 392	68.15 388	10.84 412	64.44 417	57.95 305	35.44 406	73.13 394

test_f		46.58 373	43.45 377	55.96 386	45.18 421	32.05 415	61.18 405	49.49 419	33.39 408	42.05 401	62.48 399	7.00 418	65.56 413	47.08 350	43.21 391	70.27 400

WB-MVS		46.23 374	44.94 376	50.11 394	62.13 407	21.23 427	76.48 372	55.49 413	45.89 394	35.78 405	61.44 402	35.54 353	72.83 402	9.96 421	21.75 416	56.27 409

FPMVS		45.64 375	43.10 379	53.23 392	51.42 417	36.46 410	64.97 401	71.91 392	29.13 412	27.53 412	61.55 401	9.83 414	65.01 415	16.00 418	55.58 366	58.22 408

SSC-MVS		44.51 376	43.35 378	47.99 398	61.01 410	18.90 429	74.12 380	54.36 414	43.42 401	34.10 409	60.02 403	34.42 358	70.39 405	9.14 423	19.57 417	54.68 410

EGC-MVSNET		42.35 377	38.09 380	55.11 388	74.57 376	46.62 387	71.63 386	55.77 412	0.04 426	0.24 427	62.70 398	14.24 408	74.91 400	17.59 415	46.06 386	43.80 412

LCM-MVSNet		40.54 378	35.79 383	54.76 390	36.92 427	30.81 417	51.41 414	69.02 399	22.07 414	24.63 414	45.37 411	4.56 423	65.81 412	33.67 392	34.50 407	67.67 401

APD_test1		40.50 379	37.31 382	50.09 395	51.88 415	35.27 412	59.45 409	52.59 416	21.64 415	26.12 413	57.80 405	4.56 423	66.56 411	22.64 410	39.09 397	48.43 411

test_vis3_rt		40.46 380	37.79 381	48.47 397	44.49 422	33.35 414	66.56 400	32.84 428	32.39 409	29.65 410	39.13 418	3.91 426	68.65 407	50.17 331	40.99 395	43.40 413

ANet_high		40.27 381	35.20 384	55.47 387	34.74 428	34.47 413	63.84 403	71.56 394	48.42 387	18.80 417	41.08 416	9.52 415	64.45 416	20.18 412	8.66 424	67.49 402

test_method		38.59 382	35.16 385	48.89 396	54.33 413	21.35 426	45.32 417	53.71 415	7.41 423	28.74 411	51.62 407	8.70 416	52.87 420	33.73 391	32.89 408	72.47 396

PMMVS2		37.93 383	33.61 386	50.92 393	46.31 419	24.76 423	60.55 408	50.05 417	28.94 413	20.93 415	47.59 408	4.41 425	65.13 414	25.14 407	18.55 419	62.87 405

Gipuma		34.91 384	31.44 387	45.30 399	70.99 388	39.64 407	19.85 421	72.56 390	20.10 417	16.16 421	21.47 422	5.08 422	71.16 404	13.07 419	43.70 390	25.08 419
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 385	29.47 388	42.67 401	41.89 424	30.81 417	52.07 412	43.45 422	15.45 418	18.52 418	44.82 412	2.12 427	58.38 418	16.05 416	30.87 410	38.83 414

APD_test2		32.77 385	29.47 388	42.67 401	41.89 424	30.81 417	52.07 412	43.45 422	15.45 418	18.52 418	44.82 412	2.12 427	58.38 418	16.05 416	30.87 410	38.83 414

PMVS	26.43 22	31.84 387	28.16 390	42.89 400	25.87 430	27.58 421	50.92 415	49.78 418	21.37 416	14.17 422	40.81 417	2.01 429	66.62 410	9.61 422	38.88 400	34.49 418
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 388	24.00 392	26.45 405	43.74 423	18.44 430	60.86 406	39.66 424	15.11 420	9.53 424	22.10 421	6.52 420	46.94 423	8.31 424	10.14 421	13.98 421

MVE	24.84 23	24.35 389	19.77 395	38.09 403	34.56 429	26.92 422	26.57 419	38.87 426	11.73 422	11.37 423	27.44 419	1.37 430	50.42 422	11.41 420	14.60 420	36.93 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 390	23.20 394	25.46 406	41.52 426	16.90 431	60.56 407	38.79 427	14.62 421	8.99 425	20.24 424	7.35 417	45.82 424	7.25 425	9.46 422	13.64 422

tmp_tt		22.26 391	23.75 393	17.80 407	5.23 431	12.06 432	35.26 418	39.48 425	2.82 425	18.94 416	44.20 414	22.23 394	24.64 426	36.30 384	9.31 423	16.69 420

cdsmvs_eth3d_5k		19.86 392	26.47 391	0.00 411	0.00 434	0.00 436	0.00 422	93.45 86	0.00 429	0.00 430	95.27 62	49.56 266	0.00 430	0.00 429	0.00 427	0.00 426

wuyk23d		11.30 393	10.95 396	12.33 408	48.05 418	19.89 428	25.89 420	1.92 432	3.58 424	3.12 426	1.37 426	0.64 431	15.77 427	6.23 426	7.77 425	1.35 423

ab-mvs-re		7.91 394	10.55 397	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	94.95 72	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

testmvs		7.23 395	9.62 398	0.06 410	0.04 432	0.02 435	84.98 312	0.02 433	0.03 427	0.18 428	1.21 427	0.01 433	0.02 428	0.14 427	0.01 426	0.13 425

test123		6.92 396	9.21 399	0.08 409	0.03 433	0.05 434	81.65 340	0.01 434	0.02 428	0.14 429	0.85 428	0.03 432	0.02 428	0.12 428	0.00 427	0.16 424

pcd_1.5k_mvsjas		4.46 397	5.95 400	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	53.55 227	0.00 430	0.00 429	0.00 427	0.00 426

mmdepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

monomultidepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

test_blank		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

uanet_test		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

DCPMVS		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

sosnet-low-res		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

sosnet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

uncertanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

Regformer		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

uanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 427	0.00 426

WAC-MVS						49.45 371								31.56 403

FOURS1					93.95 46	61.77 253	93.96 72	91.92 152	62.14 329	86.57 49

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 27					99.07 13	92.01 28	94.77 26	96.51 24

PC_three_1452									80.91 51	94.07 2	96.83 19	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 27					99.07 13	92.01 28	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 49		94.18 58	71.42 233	90.67 20	96.85 17	74.45 20

eth-test2					0.00 434

eth-test					0.00 434

ZD-MVS					96.63 9	65.50 158		93.50 84	70.74 247	85.26 65	95.19 68	64.92 85	97.29 78	87.51 60	93.01 56

RE-MVS-def			80.48 151		92.02 102	58.56 313	90.90 213	90.45 212	62.76 322	78.89 133	94.46 86	49.30 269		78.77 145	86.77 132	92.28 188

IU-MVS					96.46 11	69.91 42		95.18 21	80.75 52	95.28 1				92.34 25	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 49	71.65 222	92.07 9	97.21 5	74.58 18	99.11 6	92.34 25	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 55		94.44 47	71.65 222	92.11 7	97.05 8	76.79 9	99.11 6

9.14			87.63 29		93.86 48		94.41 52	94.18 58	72.76 187	86.21 51	96.51 26	66.64 64	97.88 44	90.08 42	94.04 39

save fliter					93.84 49	67.89 95	95.05 39	92.66 120	78.19 98

test_0728_THIRD									72.48 192	90.55 21	96.93 12	76.24 11	99.08 11	91.53 33	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 33	96.64 10	94.37 53					99.15 2	91.91 31	94.90 22	96.51 24

test0726					96.40 15	69.99 38	96.76 8	94.33 55	71.92 208	91.89 11	97.11 7	73.77 23

GSMVS																94.68 101

test_part2					96.29 19	68.16 88				90.78 18

sam_mvs1												57.85 172				94.68 101

sam_mvs												54.91 211

ambc				69.61 362	61.38 409	41.35 400	49.07 416	85.86 337		50.18 378	66.40 390	10.16 413	88.14 346	45.73 356	44.20 388	79.32 373

MTGPA								92.23 134

test_post1							78.95 360				20.70 423	53.05 232	91.50 315	60.43 294

test_post											23.01 420	56.49 193	92.67 278

patchmatchnet-post											67.62 389	57.62 175	90.25 324

GG-mvs-BLEND				86.53 73	91.91 110	69.67 52	75.02 379	94.75 34		78.67 140	90.85 172	77.91 7	94.56 211	72.25 192	93.74 45	95.36 65

MTMP							93.77 86	32.52 429

gm-plane-assit					88.42 193	67.04 119			78.62 94		91.83 155		97.37 72	76.57 157

test9_res														89.41 43	94.96 19	95.29 70

TEST9					94.18 41	67.28 110	94.16 60	93.51 82	71.75 219	85.52 60	95.33 57	68.01 54	97.27 82

test_8					94.19 40	67.19 112	94.15 62	93.42 89	71.87 213	85.38 63	95.35 56	68.19 52	96.95 108

agg_prior2														86.41 73	94.75 30	95.33 66

agg_prior					94.16 43	66.97 121		93.31 92		84.49 71			96.75 118

TestCases				72.46 348	79.57 336	51.42 360		68.61 400	51.25 379	45.88 388	81.23 305	19.86 400	86.58 360	38.98 380	57.01 362	79.39 371

test_prior4						67.18 114	93.92 75

test_prior2							95.10 38		75.40 140	85.25 66	95.61 48	67.94 55		87.47 62	94.77 26

test_prior				86.42 76	94.71 35	67.35 109		93.10 103					96.84 115			95.05 83

旧先验2							92.00 166		59.37 349	87.54 42			93.47 254	75.39 165

新几何2							91.41 187

新几何1				84.73 134	92.32 92	64.28 189		91.46 178	59.56 348	79.77 122	92.90 128	56.95 185	96.57 123	63.40 274	92.91 58	93.34 155

旧先验1					91.94 107	60.74 276		91.50 176			94.36 90	65.23 80			91.84 71	94.55 108

无先验							92.71 131	92.61 124	62.03 330				97.01 98	66.63 245		93.97 137

原ACMM2							92.01 163

原ACMM1				84.42 148	93.21 67	64.27 190		93.40 91	65.39 297	79.51 125	92.50 136	58.11 171	96.69 119	65.27 264	93.96 40	92.32 186

test222					89.77 158	61.60 258	89.55 255	89.42 256	56.83 363	77.28 153	92.43 140	52.76 235			91.14 85	93.09 164

testdata2													96.09 145	61.26 290

segment_acmp												65.94 72

testdata				81.34 236	89.02 179	57.72 320		89.84 240	58.65 352	85.32 64	94.09 104	57.03 180	93.28 256	69.34 218	90.56 91	93.03 167

testdata1							89.21 264		77.55 112

test12				87.09 52	94.60 36	68.86 67		92.91 110		82.67 92		65.44 78	97.55 64		93.69 48	94.84 93

plane_prior7					86.94 236	61.51 259

plane_prior6					87.23 228	62.32 243						50.66 255

plane_prior5								91.31 182					95.55 174	76.74 155	78.53 210	88.39 252

plane_prior4											89.14 201

plane_prior3						61.95 251			79.09 84	72.53 203

plane_prior2							93.13 113		78.81 91

plane_prior1					87.15 230

plane_prior						62.42 239	93.85 79		79.38 76						78.80 207

n2								0.00 435

nn								0.00 435

door-mid								66.01 404

lessismore_v0				73.72 339	72.93 383	47.83 379		61.72 410		45.86 390	73.76 367	28.63 380	89.81 333	47.75 348	31.37 409	83.53 326

LGP-MVS_train				79.56 283	84.31 285	59.37 303		89.73 246	69.49 260	64.86 294	88.42 206	38.65 328	94.30 220	72.56 189	72.76 252	85.01 313

test11								93.01 106

door								66.57 403

HQP5-MVS						63.66 208

HQP-NCC					87.54 220		94.06 65		79.80 67	74.18 182

ACMP_Plane					87.54 220		94.06 65		79.80 67	74.18 182

BP-MVS														77.63 152

HQP4-MVS										74.18 182			95.61 169			88.63 246

HQP3-MVS								91.70 168							78.90 205

HQP2-MVS												51.63 247

NP-MVS					87.41 223	63.04 224					90.30 182

MDTV_nov1_ep13_2view						59.90 295	80.13 355		67.65 280	72.79 197		54.33 219		59.83 298		92.58 179

MDTV_nov1_ep13			72.61 267		89.06 178	68.48 76	80.33 351	90.11 230	71.84 215	71.81 215	75.92 360	53.01 233	93.92 242	48.04 343	73.38 247

ACMMP++_ref															71.63 260

ACMMP++															69.72 269

Test By Simon												54.21 221

ITE_SJBPF				70.43 360	74.44 377	47.06 385		77.32 376	60.16 344	54.04 361	83.53 273	23.30 391	84.01 373	43.07 364	61.58 342	80.21 368

DeepMVS_CX				34.71 404	51.45 416	24.73 424		28.48 430	31.46 410	17.49 420	52.75 406	5.80 421	42.60 425	18.18 413	19.42 418	36.81 417

High-resolution multi-view benchmark