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 58	96.26 34	72.84 30	99.38 1	92.64 24	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 15	96.19 36	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 26	91.58 14	97.22 4	79.93 5	99.10 9	83.12 106	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 74	94.37 56	72.48 197	92.07 9	96.85 18	83.82 2	99.15 2	91.53 34	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 92	92.83 80	64.03 196	93.06 117	94.33 58	82.19 34	93.65 3	96.15 38	85.89 1	97.19 87	91.02 38	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 6	96.15 38	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 33	96.80 21	70.86 43	99.06 15	92.64 24	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 59	94.91 77	74.11 21	98.91 18	87.26 66	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	71.65 227	92.11 7	97.21 5	76.79 9	99.11 6	92.34 26	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 137	93.00 76	58.16 320	96.72 9	94.41 52	86.50 8	90.25 24	97.83 1	75.46 14	98.67 25	92.78 23	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 40	95.19 22	86.74 7	91.53 16	95.15 70	73.86 22	97.58 61	93.38 18	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 30	95.78 44	65.94 74	99.10 9	92.99 21	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	71.92 213	90.55 21	96.93 12	73.77 23	99.08 11	91.91 32	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 31	88.90 34	96.35 31	71.89 40	98.63 26	88.76 52	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 232	93.43 91	84.06 16	86.20 53	90.17 188	72.42 35	96.98 104	93.09 20	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 13	86.74 49	96.20 35	66.56 68	98.76 24	89.03 51	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 61	94.15 63	68.77 276	90.74 19	97.27 2	76.09 12	98.49 29	90.58 42	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 68	68.72 73	93.85 81	94.03 66	74.18 160	91.74 12	96.67 24	65.61 79	98.42 33	89.24 48	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 31	89.71 7	92.06 102	76.72 1	95.75 20	93.26 97	83.86 17	89.55 31	96.06 40	53.55 231	97.89 43	91.10 36	93.31 53	94.54 111

TSAR-MVS + MP.		88.11 20	88.64 18	86.54 73	91.73 117	68.04 91	90.36 238	93.55 84	82.89 25	91.29 17	92.89 130	72.27 37	96.03 152	87.99 56	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 21	86.70 66	93.51 62	65.32 161	95.15 36	93.84 69	78.17 103	85.93 57	94.80 80	75.80 13	98.21 34	89.38 45	88.78 109	96.59 19

DeepC-MVS_fast	79.48 2	87.95 22	88.00 26	87.79 31	95.86 27	68.32 81	95.74 21	94.11 64	83.82 18	83.49 81	96.19 36	64.53 94	98.44 31	83.42 105	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 35	89.55 12	91.41 129	76.43 3	95.74 21	93.12 105	83.53 20	89.55 31	95.95 42	53.45 235	97.68 51	91.07 37	92.62 60	94.54 111

EPNet		87.84 24	88.38 20	86.23 83	93.30 65	66.05 143	95.26 32	94.84 32	87.09 5	88.06 37	94.53 86	66.79 65	97.34 76	83.89 99	91.68 74	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 29	87.63 38	89.24 177	71.18 24	96.57 12	92.90 114	82.70 28	87.13 44	95.27 63	64.99 85	95.80 157	89.34 46	91.80 72	95.93 45

test_fmvsm_n_1920		87.69 26	88.50 19	85.27 117	87.05 236	63.55 214	93.69 91	91.08 200	84.18 15	90.17 26	97.04 9	67.58 60	97.99 39	95.72 5	90.03 96	94.26 122

fmvsm_l_conf0.5_n_3		87.54 27	88.29 22	85.30 114	86.92 242	62.63 239	95.02 42	90.28 228	84.95 11	90.27 23	96.86 16	65.36 81	97.52 66	94.93 9	90.03 96	95.76 50

APDe-MVS		87.54 27	87.84 28	86.65 67	96.07 23	66.30 139	94.84 46	93.78 70	69.35 267	88.39 36	96.34 32	67.74 59	97.66 56	90.62 41	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 29	88.19 24	85.39 110	86.95 237	64.37 186	94.30 58	88.45 302	80.51 56	92.70 4	96.86 16	69.98 48	97.15 92	95.83 4	88.08 117	94.65 105

SD-MVS		87.49 29	87.49 33	87.50 42	93.60 56	68.82 70	93.90 78	92.63 127	76.86 124	87.90 39	95.76 45	66.17 71	97.63 58	89.06 50	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 31	88.15 25	85.30 114	87.10 234	64.19 193	94.41 53	88.14 311	80.24 64	92.54 5	96.97 11	69.52 50	97.17 88	95.89 3	88.51 112	94.56 108

dcpmvs_2		87.37 32	87.55 32	86.85 58	95.04 32	68.20 88	90.36 238	90.66 212	79.37 79	81.20 103	93.67 114	74.73 16	96.55 126	90.88 39	92.00 69	95.82 48

alignmvs		87.28 33	86.97 39	88.24 27	91.30 131	71.14 26	95.61 25	93.56 83	79.30 80	87.07 46	95.25 65	68.43 52	96.93 112	87.87 57	84.33 157	96.65 17

train_agg		87.21 34	87.42 34	86.60 69	94.18 41	67.28 111	94.16 62	93.51 85	71.87 218	85.52 61	95.33 58	68.19 54	97.27 83	89.09 49	94.90 22	95.25 77

MG-MVS		87.11 35	86.27 48	89.62 8	97.79 1	76.27 4	94.96 44	94.49 48	78.74 95	83.87 79	92.94 128	64.34 95	96.94 110	75.19 168	94.09 38	95.66 53

SF-MVS		87.03 36	87.09 37	86.84 59	92.70 86	67.45 109	93.64 94	93.76 73	70.78 251	86.25 51	96.44 29	66.98 63	97.79 47	88.68 53	94.56 34	95.28 73

fmvsm_s_conf0.5_n_3		86.88 37	87.99 27	83.58 179	87.26 229	60.74 279	93.21 114	87.94 318	84.22 14	91.70 13	97.27 2	65.91 76	95.02 191	93.95 15	90.42 93	94.99 87

CSCG		86.87 38	86.26 49	88.72 17	95.05 31	70.79 29	93.83 86	95.33 18	68.48 280	77.63 150	94.35 95	73.04 28	98.45 30	84.92 88	93.71 47	96.92 14

sasdasda		86.85 39	86.25 50	88.66 20	91.80 115	71.92 16	93.54 99	91.71 169	80.26 61	87.55 41	95.25 65	63.59 108	96.93 112	88.18 54	84.34 155	97.11 9

canonicalmvs		86.85 39	86.25 50	88.66 20	91.80 115	71.92 16	93.54 99	91.71 169	80.26 61	87.55 41	95.25 65	63.59 108	96.93 112	88.18 54	84.34 155	97.11 9

UBG		86.83 41	86.70 44	87.20 48	93.07 74	69.81 47	93.43 107	95.56 13	81.52 41	81.50 99	92.12 149	73.58 26	96.28 137	84.37 94	85.20 147	95.51 59

PHI-MVS		86.83 41	86.85 43	86.78 63	93.47 63	65.55 157	95.39 30	95.10 25	71.77 223	85.69 60	96.52 26	62.07 129	98.77 23	86.06 78	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 43	86.90 41	86.58 71	90.42 147	66.38 136	96.09 17	93.87 68	77.73 111	84.01 78	95.66 47	63.39 111	97.94 40	87.40 64	93.55 50	95.42 60
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 44	86.86 42	86.31 82	93.76 50	67.53 106	96.33 16	93.61 81	82.34 33	81.00 108	93.08 124	63.19 115	97.29 79	87.08 69	91.38 80	94.13 131

testing11		86.71 45	86.44 47	87.55 40	93.54 60	71.35 21	93.65 93	95.58 11	81.36 48	80.69 111	92.21 148	72.30 36	96.46 131	85.18 84	83.43 165	94.82 97

test_fmvsmconf_n		86.58 46	87.17 36	84.82 130	85.28 270	62.55 240	94.26 60	89.78 246	83.81 19	87.78 40	96.33 33	65.33 82	96.98 104	94.40 12	87.55 123	94.95 89

BP-MVS1		86.54 47	86.68 45	86.13 86	87.80 217	67.18 115	92.97 122	95.62 10	79.92 67	82.84 88	94.14 104	74.95 15	96.46 131	82.91 108	88.96 108	94.74 99

jason		86.40 48	86.17 52	87.11 51	86.16 255	70.54 32	95.71 24	92.19 144	82.00 36	84.58 71	94.34 96	61.86 131	95.53 177	87.76 58	90.89 86	95.27 74
jason: jason.
fmvsm_s_conf0.5_n		86.39 49	86.91 40	84.82 130	87.36 228	63.54 215	94.74 48	90.02 240	82.52 29	90.14 27	96.92 14	62.93 120	97.84 46	95.28 8	82.26 175	93.07 168

WTY-MVS		86.32 50	85.81 60	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 27	81.91 96	94.73 81	67.93 58	97.63 58	79.55 137	82.25 176	96.54 22

myMVS_eth3d28		86.31 51	86.15 53	86.78 63	93.56 58	70.49 33	92.94 124	95.28 19	82.47 30	78.70 141	92.07 151	72.45 34	95.41 179	82.11 114	85.78 143	94.44 118

MSLP-MVS++		86.27 52	85.91 59	87.35 45	92.01 106	68.97 67	95.04 40	92.70 119	79.04 90	81.50 99	96.50 28	58.98 166	96.78 118	83.49 104	93.93 41	96.29 35

VNet		86.20 53	85.65 64	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 99	86.00 56	93.07 125	58.22 173	97.00 100	85.22 82	84.33 157	96.52 23

MVS_111021_HR		86.19 54	85.80 61	87.37 44	93.17 70	69.79 48	93.99 73	93.76 73	79.08 87	78.88 137	93.99 108	62.25 128	98.15 36	85.93 79	91.15 84	94.15 130

SPE-MVS-test		86.14 55	87.01 38	83.52 180	92.63 88	59.36 309	95.49 27	91.92 156	80.09 65	85.46 63	95.53 53	61.82 133	95.77 160	86.77 73	93.37 52	95.41 61

ACMMP_NAP		86.05 56	85.80 61	86.80 62	91.58 121	67.53 106	91.79 178	93.49 88	74.93 150	84.61 70	95.30 60	59.42 157	97.92 41	86.13 76	94.92 20	94.94 90

testing99		86.01 57	85.47 66	87.63 38	93.62 55	71.25 23	93.47 105	95.23 21	80.42 59	80.60 113	91.95 154	71.73 41	96.50 129	80.02 134	82.22 177	95.13 80

ETV-MVS		86.01 57	86.11 54	85.70 102	90.21 152	67.02 121	93.43 107	91.92 156	81.21 50	84.13 77	94.07 107	60.93 141	95.63 168	89.28 47	89.81 98	94.46 117

testing91		85.93 59	85.31 70	87.78 32	93.59 57	71.47 19	93.50 102	95.08 28	80.26 61	80.53 114	91.93 155	70.43 45	96.51 128	80.32 132	82.13 179	95.37 64

APD-MVS		85.93 59	85.99 57	85.76 99	95.98 26	65.21 164	93.59 97	92.58 129	66.54 294	86.17 54	95.88 43	63.83 101	97.00 100	86.39 75	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 61	85.46 67	87.18 49	88.20 205	72.42 15	92.41 150	92.77 117	82.11 35	80.34 117	93.07 125	68.27 53	95.02 191	78.39 150	93.59 49	94.09 133

CS-MVS		85.80 62	86.65 46	83.27 189	92.00 107	58.92 313	95.31 31	91.86 161	79.97 66	84.82 69	95.40 56	62.26 127	95.51 178	86.11 77	92.08 68	95.37 64

fmvsm_s_conf0.5_n_a		85.75 63	86.09 55	84.72 137	85.73 264	63.58 212	93.79 87	89.32 264	81.42 46	90.21 25	96.91 15	62.41 126	97.67 53	94.48 11	80.56 195	92.90 174

test_fmvsmconf0.1_n		85.71 64	86.08 56	84.62 144	80.83 326	62.33 245	93.84 84	88.81 290	83.50 21	87.00 47	96.01 41	63.36 112	96.93 112	94.04 14	87.29 126	94.61 107

CDPH-MVS		85.71 64	85.46 67	86.46 75	94.75 34	67.19 113	93.89 79	92.83 116	70.90 247	83.09 86	95.28 61	63.62 106	97.36 74	80.63 128	94.18 37	94.84 94

casdiffmvs_mvg		85.66 66	85.18 72	87.09 52	88.22 204	69.35 59	93.74 90	91.89 159	81.47 42	80.10 119	91.45 164	64.80 90	96.35 135	87.23 67	87.69 121	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 67	85.93 58	84.68 140	82.95 309	63.48 217	94.03 72	89.46 258	81.69 39	89.86 28	96.74 22	61.85 132	97.75 49	94.74 10	82.01 181	92.81 176

MGCFI-Net		85.59 68	85.73 63	85.17 121	91.41 129	62.44 241	92.87 128	91.31 186	79.65 73	86.99 48	95.14 71	62.90 121	96.12 144	87.13 68	84.13 162	96.96 13

GDP-MVS		85.54 69	85.32 69	86.18 84	87.64 220	67.95 95	92.91 127	92.36 134	77.81 109	83.69 80	94.31 98	72.84 30	96.41 133	80.39 131	85.95 141	94.19 126

DeepC-MVS	77.85 3	85.52 70	85.24 71	86.37 79	88.80 187	66.64 130	92.15 157	93.68 79	81.07 51	76.91 160	93.64 115	62.59 123	98.44 31	85.50 80	92.84 59	94.03 137
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 71	84.87 78	86.84 59	88.25 202	69.07 63	93.04 119	91.76 166	81.27 49	80.84 110	92.07 151	64.23 96	96.06 150	84.98 87	87.43 125	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 72	85.08 74	86.06 87	93.09 73	65.65 153	93.89 79	93.41 93	73.75 171	79.94 121	94.68 83	60.61 144	98.03 38	82.63 111	93.72 46	94.52 113

MP-MVS-pluss		85.24 73	85.13 73	85.56 105	91.42 126	65.59 155	91.54 188	92.51 131	74.56 153	80.62 112	95.64 48	59.15 161	97.00 100	86.94 71	93.80 43	94.07 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 74	84.69 81	86.63 68	92.91 78	69.91 43	92.61 141	95.80 9	80.31 60	80.38 116	92.27 145	68.73 51	95.19 188	75.94 162	83.27 167	94.81 98

PAPR		85.15 75	84.47 82	87.18 49	96.02 25	68.29 82	91.85 176	93.00 111	76.59 131	79.03 133	95.00 72	61.59 134	97.61 60	78.16 151	89.00 107	95.63 54

fmvsm_s_conf0.5_n_2		85.06 76	85.60 65	83.44 186	86.92 242	60.53 286	94.41 53	87.31 324	83.30 22	88.72 35	96.72 23	54.28 224	97.75 49	94.07 13	84.68 154	92.04 199

MP-MVS		85.02 77	84.97 76	85.17 121	92.60 89	64.27 191	93.24 111	92.27 137	73.13 182	79.63 125	94.43 89	61.90 130	97.17 88	85.00 86	92.56 61	94.06 136
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 78	84.44 83	86.71 65	88.33 199	68.73 72	90.24 243	91.82 165	81.05 52	81.18 104	92.50 137	63.69 104	96.08 149	84.45 93	86.71 135	95.32 69

CHOSEN 1792x2688		84.98 79	83.45 97	89.57 11	89.94 157	75.14 6	92.07 163	92.32 135	81.87 37	75.68 169	88.27 214	60.18 147	98.60 27	80.46 130	90.27 95	94.96 88

MVSMamba_PlusPlus		84.97 80	83.65 91	88.93 14	90.17 153	74.04 8	87.84 292	92.69 122	62.18 332	81.47 101	87.64 228	71.47 42	96.28 137	84.69 90	94.74 31	96.47 28

EIA-MVS		84.84 81	84.88 77	84.69 139	91.30 131	62.36 244	93.85 81	92.04 149	79.45 76	79.33 130	94.28 100	62.42 125	96.35 135	80.05 133	91.25 83	95.38 63

fmvsm_s_conf0.1_n_a		84.76 82	84.84 79	84.53 146	80.23 336	63.50 216	92.79 130	88.73 293	80.46 57	89.84 29	96.65 25	60.96 140	97.57 63	93.80 16	80.14 197	92.53 183

HFP-MVS		84.73 83	84.40 84	85.72 101	93.75 52	65.01 170	93.50 102	93.19 101	72.19 207	79.22 131	94.93 75	59.04 164	97.67 53	81.55 118	92.21 64	94.49 116

MVS		84.66 84	82.86 116	90.06 2	90.93 138	74.56 7	87.91 290	95.54 14	68.55 278	72.35 212	94.71 82	59.78 153	98.90 20	81.29 124	94.69 32	96.74 16

GST-MVS		84.63 85	84.29 85	85.66 103	92.82 82	65.27 162	93.04 119	93.13 104	73.20 180	78.89 134	94.18 103	59.41 158	97.85 45	81.45 120	92.48 63	93.86 145

EC-MVSNet		84.53 86	85.04 75	83.01 194	89.34 169	61.37 266	94.42 52	91.09 198	77.91 107	83.24 82	94.20 102	58.37 171	95.40 180	85.35 81	91.41 79	92.27 193

fmvsm_s_conf0.1_n_2		84.40 87	84.78 80	83.27 189	85.25 271	60.41 289	94.13 65	85.69 344	83.05 24	87.99 38	96.37 30	52.75 240	97.68 51	93.75 17	84.05 163	91.71 204

ACMMPR		84.37 88	84.06 86	85.28 116	93.56 58	64.37 186	93.50 102	93.15 103	72.19 207	78.85 139	94.86 78	56.69 193	97.45 68	81.55 118	92.20 65	94.02 138

region2R		84.36 89	84.03 87	85.36 112	93.54 60	64.31 189	93.43 107	92.95 112	72.16 210	78.86 138	94.84 79	56.97 188	97.53 65	81.38 122	92.11 67	94.24 124

LFMVS		84.34 90	82.73 118	89.18 13	94.76 33	73.25 11	94.99 43	91.89 159	71.90 215	82.16 95	93.49 119	47.98 286	97.05 95	82.55 112	84.82 150	97.25 8

test_yl		84.28 91	83.16 107	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 270	81.09 105	92.88 131	57.00 186	97.44 69	81.11 126	81.76 183	96.23 38

DCV-MVSNet		84.28 91	83.16 107	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 270	81.09 105	92.88 131	57.00 186	97.44 69	81.11 126	81.76 183	96.23 38

diffmvs		84.28 91	83.83 88	85.61 104	87.40 226	68.02 92	90.88 218	89.24 267	80.54 55	81.64 98	92.52 136	59.83 152	94.52 216	87.32 65	85.11 148	94.29 121
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 91	83.36 103	87.02 55	92.22 96	67.74 99	84.65 318	94.50 47	79.15 84	82.23 94	87.93 223	66.88 64	96.94 110	80.53 129	82.20 178	96.39 33

ETVMVS		84.22 95	83.71 89	85.76 99	92.58 90	68.25 86	92.45 149	95.53 15	79.54 75	79.46 127	91.64 162	70.29 46	94.18 228	69.16 224	82.76 173	94.84 94

MAR-MVS		84.18 96	83.43 98	86.44 76	96.25 21	65.93 148	94.28 59	94.27 60	74.41 155	79.16 132	95.61 49	53.99 226	98.88 22	69.62 218	93.26 54	94.50 115
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 97	83.20 106	87.05 54	91.56 122	69.82 46	89.99 252	92.05 148	77.77 110	82.84 88	86.57 245	63.93 100	96.09 146	74.91 173	89.18 104	95.25 77

CANet_DTU		84.09 98	83.52 92	85.81 96	90.30 150	66.82 125	91.87 174	89.01 282	85.27 9	86.09 55	93.74 112	47.71 290	96.98 104	77.90 153	89.78 100	93.65 150

ET-MVSNet_ETH3D		84.01 99	83.15 109	86.58 71	90.78 143	70.89 28	94.74 48	94.62 43	81.44 45	58.19 347	93.64 115	73.64 25	92.35 294	82.66 110	78.66 212	96.50 27

PVSNet_Blended_VisFu		83.97 100	83.50 94	85.39 110	90.02 155	66.59 133	93.77 88	91.73 167	77.43 119	77.08 159	89.81 196	63.77 103	96.97 107	79.67 136	88.21 115	92.60 180

MTAPA		83.91 101	83.38 102	85.50 106	91.89 113	65.16 166	81.75 343	92.23 138	75.32 145	80.53 114	95.21 68	56.06 202	97.16 91	84.86 89	92.55 62	94.18 127

XVS		83.87 102	83.47 96	85.05 123	93.22 66	63.78 201	92.92 125	92.66 124	73.99 163	78.18 144	94.31 98	55.25 208	97.41 71	79.16 141	91.58 76	93.95 140

Effi-MVS+		83.82 103	82.76 117	86.99 56	89.56 165	69.40 54	91.35 199	86.12 338	72.59 194	83.22 85	92.81 134	59.60 155	96.01 154	81.76 117	87.80 120	95.56 57

test_fmvsmvis_n_1920		83.80 104	83.48 95	84.77 134	82.51 312	63.72 205	91.37 197	83.99 361	81.42 46	77.68 149	95.74 46	58.37 171	97.58 61	93.38 18	86.87 129	93.00 171

EI-MVSNet-Vis-set		83.77 105	83.67 90	84.06 161	92.79 85	63.56 213	91.76 181	94.81 34	79.65 73	77.87 147	94.09 105	63.35 113	97.90 42	79.35 139	79.36 204	90.74 222

MVSFormer		83.75 106	82.88 115	86.37 79	89.24 177	71.18 24	89.07 270	90.69 209	65.80 299	87.13 44	94.34 96	64.99 85	92.67 281	72.83 186	91.80 72	95.27 74

CP-MVS		83.71 107	83.40 101	84.65 141	93.14 71	63.84 199	94.59 50	92.28 136	71.03 245	77.41 153	94.92 76	55.21 211	96.19 141	81.32 123	90.70 88	93.91 142

test_fmvsmconf0.01_n		83.70 108	83.52 92	84.25 158	75.26 379	61.72 259	92.17 156	87.24 326	82.36 32	84.91 68	95.41 55	55.60 206	96.83 117	92.85 22	85.87 142	94.21 125

baseline2		83.68 109	83.42 100	84.48 149	87.37 227	66.00 145	90.06 247	95.93 8	79.71 72	69.08 250	90.39 182	77.92 6	96.28 137	78.91 145	81.38 187	91.16 218

reproduce-ours		83.51 110	83.33 104	84.06 161	92.18 99	60.49 287	90.74 224	92.04 149	64.35 309	83.24 82	95.59 51	59.05 162	97.27 83	83.61 101	89.17 105	94.41 119

our_new_method		83.51 110	83.33 104	84.06 161	92.18 99	60.49 287	90.74 224	92.04 149	64.35 309	83.24 82	95.59 51	59.05 162	97.27 83	83.61 101	89.17 105	94.41 119

thisisatest0515		83.41 112	82.49 122	86.16 85	89.46 168	68.26 84	93.54 99	94.70 39	74.31 158	75.75 167	90.92 172	72.62 32	96.52 127	69.64 216	81.50 186	93.71 148

PVSNet_BlendedMVS		83.38 113	83.43 98	83.22 191	93.76 50	67.53 106	94.06 67	93.61 81	79.13 85	81.00 108	85.14 260	63.19 115	97.29 79	87.08 69	73.91 249	84.83 320

test2506		83.29 114	82.92 114	84.37 153	88.39 197	63.18 225	92.01 166	91.35 185	77.66 113	78.49 143	91.42 165	64.58 93	95.09 190	73.19 182	89.23 102	94.85 91

PGM-MVS		83.25 115	82.70 119	84.92 126	92.81 84	64.07 195	90.44 233	92.20 142	71.28 239	77.23 156	94.43 89	55.17 212	97.31 78	79.33 140	91.38 80	93.37 156

HPM-MVS		83.25 115	82.95 113	84.17 159	92.25 95	62.88 234	90.91 215	91.86 161	70.30 256	77.12 157	93.96 109	56.75 191	96.28 137	82.04 115	91.34 82	93.34 157
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 117	82.96 111	83.73 172	92.02 103	59.74 301	90.37 237	92.08 147	63.70 316	82.86 87	95.48 54	58.62 168	97.17 88	83.06 107	88.42 113	94.26 122

EI-MVSNet-UG-set		83.14 118	82.96 111	83.67 177	92.28 94	63.19 224	91.38 196	94.68 40	79.22 82	76.60 162	93.75 111	62.64 122	97.76 48	78.07 152	78.01 215	90.05 231

testing3-2		83.11 119	83.15 109	82.98 195	91.92 110	64.01 197	94.39 56	95.37 16	78.32 100	75.53 174	90.06 194	73.18 27	93.18 260	74.34 178	75.27 238	91.77 203

VDD-MVS		83.06 120	81.81 131	86.81 61	90.86 141	67.70 100	95.40 29	91.50 180	75.46 142	81.78 97	92.34 144	40.09 328	97.13 93	86.85 72	82.04 180	95.60 55

h-mvs33		83.01 121	82.56 121	84.35 154	89.34 169	62.02 251	92.72 133	93.76 73	81.45 43	82.73 91	92.25 147	60.11 148	97.13 93	87.69 59	62.96 329	93.91 142

PAPM_NR		82.97 122	81.84 130	86.37 79	94.10 44	66.76 128	87.66 296	92.84 115	69.96 260	74.07 189	93.57 117	63.10 118	97.50 67	70.66 211	90.58 90	94.85 91

mPP-MVS		82.96 123	82.44 123	84.52 147	92.83 80	62.92 232	92.76 131	91.85 163	71.52 235	75.61 172	94.24 101	53.48 234	96.99 103	78.97 144	90.73 87	93.64 151

SR-MVS		82.81 124	82.58 120	83.50 183	93.35 64	61.16 269	92.23 155	91.28 190	64.48 308	81.27 102	95.28 61	53.71 230	95.86 156	82.87 109	88.77 110	93.49 154

DP-MVS Recon		82.73 125	81.65 132	85.98 89	97.31 4	67.06 118	95.15 36	91.99 153	69.08 273	76.50 164	93.89 110	54.48 220	98.20 35	70.76 209	85.66 145	92.69 177

CLD-MVS		82.73 125	82.35 125	83.86 168	87.90 212	67.65 102	95.45 28	92.18 145	85.06 10	72.58 205	92.27 145	52.46 243	95.78 158	84.18 95	79.06 207	88.16 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 127	82.38 124	83.73 172	89.25 174	59.58 304	92.24 154	94.89 31	77.96 105	79.86 122	92.38 142	56.70 192	97.05 95	77.26 156	80.86 191	94.55 109

3Dnovator	73.91 6	82.69 128	80.82 145	88.31 26	89.57 164	71.26 22	92.60 142	94.39 55	78.84 92	67.89 271	92.48 140	48.42 281	98.52 28	68.80 229	94.40 36	95.15 79

RRT-MVS		82.61 129	81.16 136	86.96 57	91.10 135	68.75 71	87.70 295	92.20 142	76.97 122	72.68 201	87.10 239	51.30 255	96.41 133	83.56 103	87.84 119	95.74 51

MVSTER		82.47 130	82.05 126	83.74 170	92.68 87	69.01 65	91.90 173	93.21 98	79.83 68	72.14 213	85.71 256	74.72 17	94.72 203	75.72 164	72.49 259	87.50 265

TESTMET0.1,1		82.41 131	81.98 129	83.72 174	88.08 206	63.74 203	92.70 135	93.77 72	79.30 80	77.61 151	87.57 230	58.19 174	94.08 232	73.91 180	86.68 136	93.33 159

CostFormer		82.33 132	81.15 137	85.86 94	89.01 182	68.46 78	82.39 340	93.01 109	75.59 140	80.25 118	81.57 304	72.03 39	94.96 195	79.06 143	77.48 223	94.16 129

API-MVS		82.28 133	80.53 153	87.54 41	96.13 22	70.59 31	93.63 95	91.04 204	65.72 301	75.45 175	92.83 133	56.11 201	98.89 21	64.10 273	89.75 101	93.15 164

IB-MVS	77.80 4	82.18 134	80.46 155	87.35 45	89.14 179	70.28 36	95.59 26	95.17 24	78.85 91	70.19 238	85.82 254	70.66 44	97.67 53	72.19 198	66.52 300	94.09 133
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		82.16 135	81.12 138	85.26 118	86.42 248	68.72 73	92.59 144	90.44 219	73.12 183	84.20 74	94.36 91	38.04 341	95.73 162	84.12 96	86.81 130	91.33 211

xiu_mvs_v1_base		82.16 135	81.12 138	85.26 118	86.42 248	68.72 73	92.59 144	90.44 219	73.12 183	84.20 74	94.36 91	38.04 341	95.73 162	84.12 96	86.81 130	91.33 211

xiu_mvs_v1_base_debi		82.16 135	81.12 138	85.26 118	86.42 248	68.72 73	92.59 144	90.44 219	73.12 183	84.20 74	94.36 91	38.04 341	95.73 162	84.12 96	86.81 130	91.33 211

3Dnovator+	73.60 7	82.10 138	80.60 152	86.60 69	90.89 140	66.80 127	95.20 34	93.44 90	74.05 162	67.42 278	92.49 139	49.46 271	97.65 57	70.80 208	91.68 74	95.33 67

MVS_111021_LR		82.02 139	81.52 133	83.51 182	88.42 195	62.88 234	89.77 255	88.93 286	76.78 127	75.55 173	93.10 122	50.31 262	95.38 182	83.82 100	87.02 128	92.26 194

PMMVS		81.98 140	82.04 127	81.78 229	89.76 161	56.17 339	91.13 211	90.69 209	77.96 105	80.09 120	93.57 117	46.33 300	94.99 194	81.41 121	87.46 124	94.17 128

baseline1		81.84 141	81.03 142	84.28 157	91.60 120	66.62 131	91.08 212	91.66 174	81.87 37	74.86 180	91.67 161	69.98 48	94.92 198	71.76 201	64.75 316	91.29 216

EPP-MVSNet		81.79 142	81.52 133	82.61 205	88.77 188	60.21 295	93.02 121	93.66 80	68.52 279	72.90 199	90.39 182	72.19 38	94.96 195	74.93 172	79.29 206	92.67 178

WBMVS		81.67 143	80.98 144	83.72 174	93.07 74	69.40 54	94.33 57	93.05 107	76.84 125	72.05 215	84.14 271	74.49 19	93.88 246	72.76 189	68.09 288	87.88 260

test_vis1_n_1920		81.66 144	82.01 128	80.64 256	82.24 314	55.09 347	94.76 47	86.87 328	81.67 40	84.40 73	94.63 84	38.17 338	94.67 207	91.98 31	83.34 166	92.16 197

APD-MVS_3200maxsize		81.64 145	81.32 135	82.59 206	92.36 92	58.74 315	91.39 194	91.01 205	63.35 320	79.72 124	94.62 85	51.82 246	96.14 143	79.71 135	87.93 118	92.89 175

mvsmamba		81.55 146	80.72 147	84.03 165	91.42 126	66.93 123	83.08 334	89.13 275	78.55 98	67.50 276	87.02 240	51.79 248	90.07 336	87.48 62	90.49 92	95.10 82

ACMMP		81.49 147	80.67 149	83.93 167	91.71 118	62.90 233	92.13 158	92.22 141	71.79 222	71.68 221	93.49 119	50.32 261	96.96 108	78.47 149	84.22 161	91.93 201
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		81.43 148	80.74 146	83.52 180	86.26 252	64.45 180	92.09 161	90.65 213	75.83 138	73.95 191	89.81 196	63.97 99	92.91 271	71.27 204	82.82 170	93.20 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 149	79.99 160	85.46 107	90.39 149	68.40 79	86.88 307	90.61 214	74.41 155	70.31 237	84.67 265	63.79 102	92.32 296	73.13 183	85.70 144	95.67 52

ECVR-MVS		81.29 150	80.38 156	84.01 166	88.39 197	61.96 253	92.56 147	86.79 330	77.66 113	76.63 161	91.42 165	46.34 299	95.24 187	74.36 177	89.23 102	94.85 91

thisisatest0530		81.15 151	80.07 157	84.39 152	88.26 201	65.63 154	91.40 192	94.62 43	71.27 240	70.93 228	89.18 202	72.47 33	96.04 151	65.62 262	76.89 229	91.49 207

Fast-Effi-MVS+		81.14 152	80.01 159	84.51 148	90.24 151	65.86 149	94.12 66	89.15 273	73.81 170	75.37 176	88.26 215	57.26 181	94.53 215	66.97 247	84.92 149	93.15 164

HQP-MVS		81.14 152	80.64 150	82.64 204	87.54 222	63.66 210	94.06 67	91.70 172	79.80 69	74.18 185	90.30 184	51.63 251	95.61 170	77.63 154	78.90 208	88.63 249

hse-mvs2		81.12 154	81.11 141	81.16 243	86.52 247	57.48 328	89.40 263	91.16 193	81.45 43	82.73 91	90.49 180	60.11 148	94.58 208	87.69 59	60.41 356	91.41 210

SR-MVS-dyc-post		81.06 155	80.70 148	82.15 220	92.02 103	58.56 317	90.90 216	90.45 216	62.76 327	78.89 134	94.46 87	51.26 256	95.61 170	78.77 147	86.77 133	92.28 190

HyFIR lowres test		81.03 156	79.56 167	85.43 108	87.81 216	68.11 90	90.18 244	90.01 241	70.65 253	72.95 198	86.06 252	63.61 107	94.50 217	75.01 171	79.75 201	93.67 149

nrg030		80.93 157	79.86 162	84.13 160	83.69 298	68.83 69	93.23 112	91.20 191	75.55 141	75.06 178	88.22 218	63.04 119	94.74 202	81.88 116	66.88 297	88.82 247

Vis-MVSNet		80.92 158	79.98 161	83.74 170	88.48 192	61.80 255	93.44 106	88.26 310	73.96 166	77.73 148	91.76 158	49.94 266	94.76 200	65.84 259	90.37 94	94.65 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 159	80.02 158	83.33 187	87.87 213	60.76 277	92.62 140	86.86 329	77.86 108	75.73 168	91.39 167	46.35 298	94.70 206	72.79 188	88.68 111	94.52 113

UWE-MVS		80.81 160	81.01 143	80.20 266	89.33 171	57.05 333	91.91 172	94.71 38	75.67 139	75.01 179	89.37 200	63.13 117	91.44 320	67.19 244	82.80 172	92.12 198

1314		80.70 161	78.95 179	85.94 91	87.77 219	67.56 104	87.91 290	92.55 130	72.17 209	67.44 277	93.09 123	50.27 263	97.04 98	71.68 203	87.64 122	93.23 161

tpmrst		80.57 162	79.14 177	84.84 129	90.10 154	68.28 83	81.70 344	89.72 253	77.63 115	75.96 166	79.54 336	64.94 87	92.71 278	75.43 166	77.28 226	93.55 152

1112_ss		80.56 163	79.83 163	82.77 199	88.65 189	60.78 275	92.29 152	88.36 304	72.58 195	72.46 209	94.95 73	65.09 84	93.42 257	66.38 253	77.71 217	94.10 132

VDDNet		80.50 164	78.26 187	87.21 47	86.19 253	69.79 48	94.48 51	91.31 186	60.42 346	79.34 129	90.91 173	38.48 336	96.56 125	82.16 113	81.05 189	95.27 74

BH-w/o		80.49 165	79.30 174	84.05 164	90.83 142	64.36 188	93.60 96	89.42 261	74.35 157	69.09 249	90.15 190	55.23 210	95.61 170	64.61 270	86.43 139	92.17 196

test_cas_vis1_n_1920		80.45 166	80.61 151	79.97 275	78.25 362	57.01 335	94.04 71	88.33 305	79.06 89	82.81 90	93.70 113	38.65 333	91.63 312	90.82 40	79.81 199	91.27 217

TAMVS		80.37 167	79.45 170	83.13 193	85.14 274	63.37 218	91.23 205	90.76 208	74.81 152	72.65 203	88.49 209	60.63 143	92.95 266	69.41 220	81.95 182	93.08 167

HQP_MVS		80.34 168	79.75 164	82.12 222	86.94 238	62.42 242	93.13 115	91.31 186	78.81 93	72.53 206	89.14 204	50.66 259	95.55 175	76.74 157	78.53 213	88.39 255

SDMVSNet		80.26 169	78.88 180	84.40 151	89.25 174	67.63 103	85.35 314	93.02 108	76.77 128	70.84 229	87.12 237	47.95 287	96.09 146	85.04 85	74.55 240	89.48 241

HPM-MVS_fast		80.25 170	79.55 169	82.33 212	91.55 123	59.95 298	91.32 201	89.16 272	65.23 305	74.71 182	93.07 125	47.81 289	95.74 161	74.87 175	88.23 114	91.31 215

ab-mvs		80.18 171	78.31 186	85.80 97	88.44 194	65.49 160	83.00 337	92.67 123	71.82 221	77.36 154	85.01 261	54.50 217	96.59 122	76.35 161	75.63 236	95.32 69

IS-MVSNet		80.14 172	79.41 171	82.33 212	87.91 211	60.08 297	91.97 170	88.27 308	72.90 190	71.44 225	91.73 160	61.44 135	93.66 252	62.47 287	86.53 137	93.24 160

test-LLR		80.10 173	79.56 167	81.72 231	86.93 240	61.17 267	92.70 135	91.54 177	71.51 236	75.62 170	86.94 241	53.83 227	92.38 291	72.21 196	84.76 152	91.60 205

PVSNet	73.49 8	80.05 174	78.63 182	84.31 155	90.92 139	64.97 171	92.47 148	91.05 203	79.18 83	72.43 210	90.51 179	37.05 353	94.06 234	68.06 233	86.00 140	93.90 144

UA-Net		80.02 175	79.65 165	81.11 245	89.33 171	57.72 324	86.33 311	89.00 285	77.44 118	81.01 107	89.15 203	59.33 159	95.90 155	61.01 294	84.28 159	89.73 237

test-mter		79.96 176	79.38 173	81.72 231	86.93 240	61.17 267	92.70 135	91.54 177	73.85 168	75.62 170	86.94 241	49.84 268	92.38 291	72.21 196	84.76 152	91.60 205

QAPM		79.95 177	77.39 205	87.64 34	89.63 163	71.41 20	93.30 110	93.70 78	65.34 304	67.39 280	91.75 159	47.83 288	98.96 16	57.71 310	89.81 98	92.54 182

UGNet		79.87 178	78.68 181	83.45 185	89.96 156	61.51 262	92.13 158	90.79 207	76.83 126	78.85 139	86.33 249	38.16 339	96.17 142	67.93 236	87.17 127	92.67 178
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		79.80 179	77.95 193	85.34 113	88.28 200	68.26 84	81.56 346	91.42 183	70.11 258	77.59 152	80.50 322	67.40 61	94.26 226	67.34 241	77.35 224	93.51 153

thres200		79.66 180	78.33 185	83.66 178	92.54 91	65.82 151	93.06 117	96.31 3	74.90 151	73.30 195	88.66 207	59.67 154	95.61 170	47.84 351	78.67 211	89.56 240

CPTT-MVS		79.59 181	79.16 176	80.89 254	91.54 124	59.80 300	92.10 160	88.54 301	60.42 346	72.96 197	93.28 121	48.27 282	92.80 275	78.89 146	86.50 138	90.06 230

Test_1112_low_res		79.56 182	78.60 183	82.43 208	88.24 203	60.39 291	92.09 161	87.99 315	72.10 211	71.84 217	87.42 232	64.62 92	93.04 262	65.80 260	77.30 225	93.85 146

tttt0517		79.50 183	78.53 184	82.41 211	87.22 231	61.43 265	89.75 256	94.76 35	69.29 268	67.91 269	88.06 222	72.92 29	95.63 168	62.91 283	73.90 250	90.16 229

reproduce_monomvs		79.49 184	79.11 178	80.64 256	92.91 78	61.47 264	91.17 210	93.28 96	83.09 23	64.04 309	82.38 291	66.19 70	94.57 210	81.19 125	57.71 364	85.88 303

FIs		79.47 185	79.41 171	79.67 283	85.95 258	59.40 306	91.68 185	93.94 67	78.06 104	68.96 255	88.28 213	66.61 67	91.77 308	66.20 256	74.99 239	87.82 261

BH-RMVSNet		79.46 186	77.65 196	84.89 127	91.68 119	65.66 152	93.55 98	88.09 313	72.93 187	73.37 194	91.12 171	46.20 302	96.12 144	56.28 316	85.61 146	92.91 173

PCF-MVS	73.15 9	79.29 187	77.63 197	84.29 156	86.06 256	65.96 147	87.03 303	91.10 197	69.86 262	69.79 245	90.64 175	57.54 180	96.59 122	64.37 272	82.29 174	90.32 227
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 188	79.57 166	78.24 303	88.46 193	52.29 358	90.41 235	89.12 276	74.24 159	69.13 248	91.91 156	65.77 77	90.09 335	59.00 306	88.09 116	92.33 187

114514_t		79.17 189	77.67 195	83.68 176	95.32 29	65.53 158	92.85 129	91.60 176	63.49 318	67.92 268	90.63 177	46.65 295	95.72 166	67.01 246	83.54 164	89.79 235

FA-MVS(test-final)		79.12 190	77.23 207	84.81 133	90.54 145	63.98 198	81.35 349	91.71 169	71.09 244	74.85 181	82.94 284	52.85 238	97.05 95	67.97 234	81.73 185	93.41 155

VPA-MVSNet		79.03 191	78.00 191	82.11 225	85.95 258	64.48 179	93.22 113	94.66 41	75.05 149	74.04 190	84.95 262	52.17 245	93.52 254	74.90 174	67.04 296	88.32 257

OPM-MVS		79.00 192	78.09 189	81.73 230	83.52 301	63.83 200	91.64 187	90.30 226	76.36 134	71.97 216	89.93 195	46.30 301	95.17 189	75.10 169	77.70 218	86.19 292
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 193	78.22 188	81.25 240	85.33 268	62.73 237	89.53 260	93.21 98	72.39 202	72.14 213	90.13 191	60.99 138	94.72 203	67.73 238	72.49 259	86.29 289

AdaColmap		78.94 194	77.00 211	84.76 135	96.34 17	65.86 149	92.66 139	87.97 317	62.18 332	70.56 231	92.37 143	43.53 315	97.35 75	64.50 271	82.86 169	91.05 220

GeoE		78.90 195	77.43 201	83.29 188	88.95 183	62.02 251	92.31 151	86.23 336	70.24 257	71.34 226	89.27 201	54.43 221	94.04 237	63.31 279	80.81 193	93.81 147

miper_enhance_ethall		78.86 196	77.97 192	81.54 235	88.00 210	65.17 165	91.41 190	89.15 273	75.19 147	68.79 258	83.98 274	67.17 62	92.82 273	72.73 190	65.30 307	86.62 286

VPNet		78.82 197	77.53 200	82.70 202	84.52 285	66.44 135	93.93 76	92.23 138	80.46 57	72.60 204	88.38 212	49.18 275	93.13 261	72.47 194	63.97 326	88.55 252

EPNet_dtu		78.80 198	79.26 175	77.43 311	88.06 207	49.71 374	91.96 171	91.95 155	77.67 112	76.56 163	91.28 169	58.51 169	90.20 333	56.37 315	80.95 190	92.39 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 199	77.43 201	82.88 197	92.21 97	64.49 177	92.05 164	96.28 4	73.48 177	71.75 219	88.26 215	60.07 150	95.32 183	45.16 362	77.58 220	88.83 245

TR-MVS		78.77 200	77.37 206	82.95 196	90.49 146	60.88 273	93.67 92	90.07 236	70.08 259	74.51 183	91.37 168	45.69 304	95.70 167	60.12 300	80.32 196	92.29 189

thres400		78.68 201	77.43 201	82.43 208	92.21 97	64.49 177	92.05 164	96.28 4	73.48 177	71.75 219	88.26 215	60.07 150	95.32 183	45.16 362	77.58 220	87.48 266

BH-untuned		78.68 201	77.08 208	83.48 184	89.84 158	63.74 203	92.70 135	88.59 299	71.57 233	66.83 287	88.65 208	51.75 249	95.39 181	59.03 305	84.77 151	91.32 214

OMC-MVS		78.67 203	77.91 194	80.95 252	85.76 263	57.40 330	88.49 280	88.67 296	73.85 168	72.43 210	92.10 150	49.29 274	94.55 214	72.73 190	77.89 216	90.91 221

tpm		78.58 204	77.03 209	83.22 191	85.94 260	64.56 175	83.21 333	91.14 196	78.31 101	73.67 192	79.68 334	64.01 98	92.09 302	66.07 257	71.26 269	93.03 169

OpenMVS	70.45 11	78.54 205	75.92 225	86.41 78	85.93 261	71.68 18	92.74 132	92.51 131	66.49 295	64.56 303	91.96 153	43.88 314	98.10 37	54.61 321	90.65 89	89.44 243

EPMVS		78.49 206	75.98 224	86.02 88	91.21 133	69.68 52	80.23 358	91.20 191	75.25 146	72.48 208	78.11 345	54.65 216	93.69 251	57.66 311	83.04 168	94.69 101

AUN-MVS		78.37 207	77.43 201	81.17 242	86.60 245	57.45 329	89.46 262	91.16 193	74.11 161	74.40 184	90.49 180	55.52 207	94.57 210	74.73 176	60.43 355	91.48 208

thres100view900		78.37 207	77.01 210	82.46 207	91.89 113	63.21 223	91.19 209	96.33 1	72.28 205	70.45 234	87.89 224	60.31 145	95.32 183	45.16 362	77.58 220	88.83 245

GA-MVS		78.33 209	76.23 220	84.65 141	83.65 299	66.30 139	91.44 189	90.14 234	76.01 136	70.32 236	84.02 273	42.50 319	94.72 203	70.98 206	77.00 228	92.94 172

cascas		78.18 210	75.77 227	85.41 109	87.14 233	69.11 62	92.96 123	91.15 195	66.71 293	70.47 232	86.07 251	37.49 347	96.48 130	70.15 214	79.80 200	90.65 223

UniMVSNet_NR-MVSNet		78.15 211	77.55 199	79.98 273	84.46 287	60.26 293	92.25 153	93.20 100	77.50 117	68.88 256	86.61 244	66.10 72	92.13 300	66.38 253	62.55 333	87.54 264

thres600view7		78.00 212	76.66 215	82.03 227	91.93 109	63.69 208	91.30 202	96.33 1	72.43 200	70.46 233	87.89 224	60.31 145	94.92 198	42.64 374	76.64 230	87.48 266

FC-MVSNet-test		77.99 213	78.08 190	77.70 306	84.89 279	55.51 344	90.27 241	93.75 76	76.87 123	66.80 288	87.59 229	65.71 78	90.23 332	62.89 284	73.94 248	87.37 269

Anonymous202405211		77.96 214	75.33 233	85.87 93	93.73 53	64.52 176	94.85 45	85.36 346	62.52 330	76.11 165	90.18 187	29.43 382	97.29 79	68.51 231	77.24 227	95.81 49

cl22		77.94 215	76.78 213	81.42 237	87.57 221	64.93 173	90.67 227	88.86 289	72.45 199	67.63 275	82.68 288	64.07 97	92.91 271	71.79 199	65.30 307	86.44 287

XXY-MVS		77.94 215	76.44 217	82.43 208	82.60 311	64.44 181	92.01 166	91.83 164	73.59 176	70.00 241	85.82 254	54.43 221	94.76 200	69.63 217	68.02 290	88.10 259

MS-PatchMatch		77.90 217	76.50 216	82.12 222	85.99 257	69.95 42	91.75 183	92.70 119	73.97 165	62.58 325	84.44 269	41.11 325	95.78 158	63.76 276	92.17 66	80.62 367

FMVSNet3		77.73 218	76.04 223	82.80 198	91.20 134	68.99 66	91.87 174	91.99 153	73.35 179	67.04 283	83.19 283	56.62 194	92.14 299	59.80 302	69.34 276	87.28 272

miper_ehance_all_eth		77.60 219	76.44 217	81.09 249	85.70 265	64.41 184	90.65 228	88.64 298	72.31 203	67.37 281	82.52 289	64.77 91	92.64 284	70.67 210	65.30 307	86.24 291

UniMVSNet (Re)		77.58 220	76.78 213	79.98 273	84.11 293	60.80 274	91.76 181	93.17 102	76.56 132	69.93 244	84.78 264	63.32 114	92.36 293	64.89 269	62.51 335	86.78 280

PatchmatchNet		77.46 221	74.63 240	85.96 90	89.55 166	70.35 35	79.97 363	89.55 256	72.23 206	70.94 227	76.91 357	57.03 184	92.79 276	54.27 323	81.17 188	94.74 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 222	75.65 229	82.73 200	80.38 332	67.13 117	91.85 176	90.23 231	75.09 148	69.37 246	83.39 280	53.79 229	94.44 218	71.77 200	65.00 313	86.63 285

CHOSEN 280x420		77.35 223	76.95 212	78.55 298	87.07 235	62.68 238	69.71 395	82.95 368	68.80 275	71.48 224	87.27 236	66.03 73	84.00 379	76.47 160	82.81 171	88.95 244

PS-MVSNAJss		77.26 224	76.31 219	80.13 268	80.64 330	59.16 311	90.63 231	91.06 202	72.80 191	68.58 262	84.57 267	53.55 231	93.96 242	72.97 184	71.96 263	87.27 273

gg-mvs-nofinetune		77.18 225	74.31 247	85.80 97	91.42 126	68.36 80	71.78 389	94.72 37	49.61 389	77.12 157	45.92 415	77.41 8	93.98 241	67.62 239	93.16 55	95.05 84

WB-MVSnew		77.14 226	76.18 222	80.01 272	86.18 254	63.24 221	91.26 203	94.11 64	71.72 225	73.52 193	87.29 235	45.14 309	93.00 264	56.98 313	79.42 202	83.80 328

MVP-Stereo		77.12 227	76.23 220	79.79 280	81.72 319	66.34 138	89.29 264	90.88 206	70.56 254	62.01 328	82.88 285	49.34 272	94.13 229	65.55 264	93.80 43	78.88 381
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 228	75.37 231	82.20 218	89.25 174	62.11 250	82.06 341	89.09 278	76.77 128	70.84 229	87.12 237	41.43 324	95.01 193	67.23 243	74.55 240	89.48 241

MonoMVSNet		76.99 229	75.08 236	82.73 200	83.32 303	63.24 221	86.47 310	86.37 332	79.08 87	66.31 291	79.30 338	49.80 269	91.72 309	79.37 138	65.70 305	93.23 161

dmvs_re		76.93 230	75.36 232	81.61 233	87.78 218	60.71 281	80.00 362	87.99 315	79.42 77	69.02 252	89.47 199	46.77 293	94.32 220	63.38 278	74.45 243	89.81 234

X-MVStestdata		76.86 231	74.13 251	85.05 123	93.22 66	63.78 201	92.92 125	92.66 124	73.99 163	78.18 144	10.19 430	55.25 208	97.41 71	79.16 141	91.58 76	93.95 140

DU-MVS		76.86 231	75.84 226	79.91 276	82.96 307	60.26 293	91.26 203	91.54 177	76.46 133	68.88 256	86.35 247	56.16 199	92.13 300	66.38 253	62.55 333	87.35 270

Anonymous20240529		76.84 233	74.15 250	84.88 128	91.02 136	64.95 172	93.84 84	91.09 198	53.57 377	73.00 196	87.42 232	35.91 357	97.32 77	69.14 225	72.41 261	92.36 186

UWE-MVS-28		76.83 234	77.60 198	74.51 336	84.58 284	50.34 370	88.22 284	94.60 45	74.46 154	66.66 289	88.98 206	62.53 124	85.50 371	57.55 312	80.80 194	87.69 263

c3_l		76.83 234	75.47 230	80.93 253	85.02 277	64.18 194	90.39 236	88.11 312	71.66 226	66.65 290	81.64 302	63.58 110	92.56 285	69.31 222	62.86 330	86.04 297

WR-MVS		76.76 236	75.74 228	79.82 279	84.60 282	62.27 248	92.60 142	92.51 131	76.06 135	67.87 272	85.34 258	56.76 190	90.24 331	62.20 288	63.69 328	86.94 278

v1144		76.73 237	74.88 237	82.27 214	80.23 336	66.60 132	91.68 185	90.21 233	73.69 173	69.06 251	81.89 297	52.73 241	94.40 219	69.21 223	65.23 310	85.80 304

IterMVS-LS		76.49 238	75.18 235	80.43 260	84.49 286	62.74 236	90.64 229	88.80 291	72.40 201	65.16 298	81.72 300	60.98 139	92.27 297	67.74 237	64.65 318	86.29 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 239	74.55 243	82.19 219	79.14 350	67.82 97	90.26 242	89.42 261	73.75 171	68.63 261	81.89 297	51.31 254	94.09 231	71.69 202	64.84 314	84.66 321

v148		76.19 240	74.47 245	81.36 238	80.05 338	64.44 181	91.75 183	90.23 231	73.68 174	67.13 282	80.84 317	55.92 204	93.86 249	68.95 227	61.73 344	85.76 307

Effi-MVS+-dtu		76.14 241	75.28 234	78.72 297	83.22 304	55.17 346	89.87 253	87.78 319	75.42 143	67.98 267	81.43 306	45.08 310	92.52 287	75.08 170	71.63 264	88.48 253

cl____		76.07 242	74.67 238	80.28 263	85.15 273	61.76 257	90.12 245	88.73 293	71.16 241	65.43 295	81.57 304	61.15 136	92.95 266	66.54 250	62.17 337	86.13 295

DIV-MVS_self_test		76.07 242	74.67 238	80.28 263	85.14 274	61.75 258	90.12 245	88.73 293	71.16 241	65.42 296	81.60 303	61.15 136	92.94 270	66.54 250	62.16 339	86.14 293

FMVSNet2		76.07 242	74.01 253	82.26 216	88.85 184	67.66 101	91.33 200	91.61 175	70.84 248	65.98 292	82.25 293	48.03 283	92.00 304	58.46 307	68.73 284	87.10 275

v144192		76.05 245	74.03 252	82.12 222	79.50 344	66.55 134	91.39 194	89.71 254	72.30 204	68.17 265	81.33 309	51.75 249	94.03 239	67.94 235	64.19 321	85.77 305

NR-MVSNet		76.05 245	74.59 241	80.44 259	82.96 307	62.18 249	90.83 220	91.73 167	77.12 121	60.96 331	86.35 247	59.28 160	91.80 307	60.74 295	61.34 348	87.35 270

v1192		75.98 247	73.92 254	82.15 220	79.73 340	66.24 141	91.22 206	89.75 248	72.67 193	68.49 263	81.42 307	49.86 267	94.27 224	67.08 245	65.02 312	85.95 300

FE-MVS		75.97 248	73.02 265	84.82 130	89.78 159	65.56 156	77.44 374	91.07 201	64.55 307	72.66 202	79.85 332	46.05 303	96.69 120	54.97 320	80.82 192	92.21 195

eth_miper_zixun_eth		75.96 249	74.40 246	80.66 255	84.66 281	63.02 227	89.28 265	88.27 308	71.88 217	65.73 293	81.65 301	59.45 156	92.81 274	68.13 232	60.53 353	86.14 293

TranMVSNet+NR-MVSNet		75.86 250	74.52 244	79.89 277	82.44 313	60.64 284	91.37 197	91.37 184	76.63 130	67.65 274	86.21 250	52.37 244	91.55 314	61.84 290	60.81 351	87.48 266

SCA		75.82 251	72.76 268	85.01 125	86.63 244	70.08 38	81.06 351	89.19 270	71.60 232	70.01 240	77.09 355	45.53 305	90.25 328	60.43 297	73.27 252	94.68 102

LPG-MVS_test		75.82 251	74.58 242	79.56 287	84.31 290	59.37 307	90.44 233	89.73 251	69.49 265	64.86 299	88.42 210	38.65 333	94.30 222	72.56 192	72.76 256	85.01 318

GBi-Net		75.65 253	73.83 255	81.10 246	88.85 184	65.11 167	90.01 249	90.32 222	70.84 248	67.04 283	80.25 327	48.03 283	91.54 315	59.80 302	69.34 276	86.64 282

test1		75.65 253	73.83 255	81.10 246	88.85 184	65.11 167	90.01 249	90.32 222	70.84 248	67.04 283	80.25 327	48.03 283	91.54 315	59.80 302	69.34 276	86.64 282

v1921920		75.63 255	73.49 260	82.06 226	79.38 345	66.35 137	91.07 214	89.48 257	71.98 212	67.99 266	81.22 312	49.16 277	93.90 245	66.56 249	64.56 319	85.92 302

ACMP	71.68 10	75.58 256	74.23 249	79.62 285	84.97 278	59.64 302	90.80 221	89.07 280	70.39 255	62.95 321	87.30 234	38.28 337	93.87 247	72.89 185	71.45 267	85.36 314
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 257	73.26 263	81.61 233	80.67 329	66.82 125	89.54 259	89.27 266	71.65 227	63.30 317	80.30 326	54.99 214	94.06 234	67.33 242	62.33 336	83.94 326

tpm cat1		75.30 258	72.21 277	84.58 145	88.52 190	67.77 98	78.16 372	88.02 314	61.88 338	68.45 264	76.37 361	60.65 142	94.03 239	53.77 326	74.11 246	91.93 201

PLC	68.80 14	75.23 259	73.68 258	79.86 278	92.93 77	58.68 316	90.64 229	88.30 306	60.90 343	64.43 307	90.53 178	42.38 320	94.57 210	56.52 314	76.54 231	86.33 288
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 260	72.98 266	81.88 228	79.20 347	66.00 145	90.75 223	89.11 277	71.63 231	67.41 279	81.22 312	47.36 291	93.87 247	65.46 265	64.72 317	85.77 305

Fast-Effi-MVS+-dtu		75.04 261	73.37 261	80.07 269	80.86 325	59.52 305	91.20 208	85.38 345	71.90 215	65.20 297	84.84 263	41.46 323	92.97 265	66.50 252	72.96 255	87.73 262

dp		75.01 262	72.09 278	83.76 169	89.28 173	66.22 142	79.96 364	89.75 248	71.16 241	67.80 273	77.19 354	51.81 247	92.54 286	50.39 335	71.44 268	92.51 184

TAPA-MVS	70.22 12	74.94 263	73.53 259	79.17 292	90.40 148	52.07 359	89.19 268	89.61 255	62.69 329	70.07 239	92.67 135	48.89 280	94.32 220	38.26 388	79.97 198	91.12 219
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 264	73.32 262	79.74 282	86.53 246	60.31 292	89.03 273	92.70 119	78.61 97	68.98 254	83.34 281	41.93 322	92.23 298	52.77 330	65.97 303	86.69 281

v10		74.77 265	72.54 274	81.46 236	80.33 334	66.71 129	89.15 269	89.08 279	70.94 246	63.08 320	79.86 331	52.52 242	94.04 237	65.70 261	62.17 337	83.64 329

XVG-OURS-SEG-HR		74.70 266	73.08 264	79.57 286	78.25 362	57.33 331	80.49 354	87.32 322	63.22 322	68.76 259	90.12 193	44.89 311	91.59 313	70.55 212	74.09 247	89.79 235

ACMM	69.62 13	74.34 267	72.73 270	79.17 292	84.25 292	57.87 322	90.36 238	89.93 242	63.17 324	65.64 294	86.04 253	37.79 345	94.10 230	65.89 258	71.52 266	85.55 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 268	72.30 276	80.32 261	91.49 125	61.66 260	90.85 219	80.72 374	56.67 369	63.85 312	90.64 175	46.75 294	90.84 323	53.79 325	75.99 235	88.47 254

XVG-OURS		74.25 269	72.46 275	79.63 284	78.45 360	57.59 327	80.33 356	87.39 321	63.86 314	68.76 259	89.62 198	40.50 327	91.72 309	69.00 226	74.25 245	89.58 238

test_fmvs1		74.07 270	73.69 257	75.22 329	78.91 354	47.34 387	89.06 272	74.69 390	63.68 317	79.41 128	91.59 163	24.36 392	87.77 355	85.22 82	76.26 233	90.55 226

CVMVSNet		74.04 271	74.27 248	73.33 346	85.33 268	43.94 400	89.53 260	88.39 303	54.33 376	70.37 235	90.13 191	49.17 276	84.05 377	61.83 291	79.36 204	91.99 200

Baseline_NR-MVSNet		73.99 272	72.83 267	77.48 310	80.78 327	59.29 310	91.79 178	84.55 354	68.85 274	68.99 253	80.70 318	56.16 199	92.04 303	62.67 285	60.98 350	81.11 361

pmmvs4		73.92 273	71.81 282	80.25 265	79.17 348	65.24 163	87.43 299	87.26 325	67.64 286	63.46 315	83.91 275	48.96 279	91.53 318	62.94 282	65.49 306	83.96 325

D2MVS		73.80 274	72.02 279	79.15 294	79.15 349	62.97 228	88.58 279	90.07 236	72.94 186	59.22 341	78.30 342	42.31 321	92.70 280	65.59 263	72.00 262	81.79 356

CR-MVSNet		73.79 275	70.82 290	82.70 202	83.15 305	67.96 93	70.25 392	84.00 359	73.67 175	69.97 242	72.41 377	57.82 177	89.48 340	52.99 329	73.13 253	90.64 224

test_djsdf		73.76 276	72.56 273	77.39 312	77.00 372	53.93 352	89.07 270	90.69 209	65.80 299	63.92 310	82.03 296	43.14 318	92.67 281	72.83 186	68.53 285	85.57 309

pmmvs5		73.35 277	71.52 284	78.86 296	78.64 358	60.61 285	91.08 212	86.90 327	67.69 283	63.32 316	83.64 276	44.33 313	90.53 325	62.04 289	66.02 302	85.46 312

Anonymous20231211		73.08 278	70.39 294	81.13 244	90.62 144	63.33 219	91.40 192	90.06 238	51.84 382	64.46 306	80.67 320	36.49 355	94.07 233	63.83 275	64.17 322	85.98 299

tt0805		73.07 279	70.73 291	80.07 269	78.37 361	57.05 333	87.78 293	92.18 145	61.23 342	67.04 283	86.49 246	31.35 375	94.58 208	65.06 268	67.12 295	88.57 251

miper_lstm_enhance		73.05 280	71.73 283	77.03 316	83.80 296	58.32 319	81.76 342	88.88 287	69.80 263	61.01 330	78.23 344	57.19 182	87.51 359	65.34 266	59.53 358	85.27 317

jajsoiax		73.05 280	71.51 285	77.67 307	77.46 369	54.83 348	88.81 275	90.04 239	69.13 272	62.85 323	83.51 278	31.16 376	92.75 277	70.83 207	69.80 272	85.43 313

LCM-MVSNet-Re		72.93 282	71.84 281	76.18 325	88.49 191	48.02 382	80.07 361	70.17 402	73.96 166	52.25 372	80.09 330	49.98 265	88.24 349	67.35 240	84.23 160	92.28 190

pm-mvs1		72.89 283	71.09 287	78.26 302	79.10 351	57.62 326	90.80 221	89.30 265	67.66 284	62.91 322	81.78 299	49.11 278	92.95 266	60.29 299	58.89 361	84.22 324

tpmvs		72.88 284	69.76 300	82.22 217	90.98 137	67.05 119	78.22 371	88.30 306	63.10 325	64.35 308	74.98 368	55.09 213	94.27 224	43.25 368	69.57 275	85.34 315

test0.0.03 1		72.76 285	72.71 271	72.88 350	80.25 335	47.99 383	91.22 206	89.45 259	71.51 236	62.51 326	87.66 227	53.83 227	85.06 373	50.16 337	67.84 293	85.58 308

UniMVSNet_ETH3D		72.74 286	70.53 293	79.36 289	78.62 359	56.64 337	85.01 316	89.20 269	63.77 315	64.84 301	84.44 269	34.05 364	91.86 306	63.94 274	70.89 271	89.57 239

mvs_tets		72.71 287	71.11 286	77.52 308	77.41 370	54.52 350	88.45 281	89.76 247	68.76 277	62.70 324	83.26 282	29.49 381	92.71 278	70.51 213	69.62 274	85.34 315

FMVSNet1		72.71 287	69.91 298	81.10 246	83.60 300	65.11 167	90.01 249	90.32 222	63.92 313	63.56 314	80.25 327	36.35 356	91.54 315	54.46 322	66.75 298	86.64 282

test_fmvs1_n		72.69 289	71.92 280	74.99 332	71.15 392	47.08 389	87.34 301	75.67 385	63.48 319	78.08 146	91.17 170	20.16 404	87.87 352	84.65 91	75.57 237	90.01 232

IterMVS		72.65 290	70.83 288	78.09 304	82.17 315	62.96 229	87.64 297	86.28 334	71.56 234	60.44 334	78.85 340	45.42 307	86.66 363	63.30 280	61.83 341	84.65 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 291	72.74 269	72.10 358	87.87 213	49.45 376	88.07 286	89.01 282	72.91 188	63.11 318	88.10 219	63.63 105	85.54 368	32.73 403	69.23 279	81.32 359

PatchMatch-RL		72.06 292	69.98 295	78.28 301	89.51 167	55.70 343	83.49 326	83.39 366	61.24 341	63.72 313	82.76 286	34.77 361	93.03 263	53.37 328	77.59 219	86.12 296

PVSNet_0	68.08 15	71.81 293	68.32 309	82.27 214	84.68 280	62.31 247	88.68 277	90.31 225	75.84 137	57.93 352	80.65 321	37.85 344	94.19 227	69.94 215	29.05 418	90.31 228

MIMVSNet		71.64 294	68.44 307	81.23 241	81.97 318	64.44 181	73.05 386	88.80 291	69.67 264	64.59 302	74.79 370	32.79 367	87.82 353	53.99 324	76.35 232	91.42 209

test_vis1_n		71.63 295	70.73 291	74.31 340	69.63 398	47.29 388	86.91 305	72.11 396	63.21 323	75.18 177	90.17 188	20.40 402	85.76 367	84.59 92	74.42 244	89.87 233

IterMVS-SCA-FT		71.55 296	69.97 296	76.32 323	81.48 321	60.67 283	87.64 297	85.99 339	66.17 297	59.50 339	78.88 339	45.53 305	83.65 381	62.58 286	61.93 340	84.63 323

v7n		71.31 297	68.65 304	79.28 290	76.40 374	60.77 276	86.71 308	89.45 259	64.17 312	58.77 346	78.24 343	44.59 312	93.54 253	57.76 309	61.75 343	83.52 332

anonymousdsp		71.14 298	69.37 302	76.45 322	72.95 387	54.71 349	84.19 321	88.88 287	61.92 337	62.15 327	79.77 333	38.14 340	91.44 320	68.90 228	67.45 294	83.21 338

F-COLMAP		70.66 299	68.44 307	77.32 313	86.37 251	55.91 341	88.00 288	86.32 333	56.94 367	57.28 356	88.07 221	33.58 365	92.49 288	51.02 333	68.37 286	83.55 330

WR-MVS_H		70.59 300	69.94 297	72.53 352	81.03 324	51.43 363	87.35 300	92.03 152	67.38 287	60.23 336	80.70 318	55.84 205	83.45 383	46.33 358	58.58 363	82.72 345

CP-MVSNet		70.50 301	69.91 298	72.26 355	80.71 328	51.00 367	87.23 302	90.30 226	67.84 282	59.64 338	82.69 287	50.23 264	82.30 391	51.28 332	59.28 359	83.46 334

RPMNet		70.42 302	65.68 323	84.63 143	83.15 305	67.96 93	70.25 392	90.45 216	46.83 398	69.97 242	65.10 398	56.48 198	95.30 186	35.79 393	73.13 253	90.64 224

testing3		70.38 303	70.83 288	69.03 370	85.82 262	43.93 401	90.72 226	90.56 215	68.06 281	60.24 335	86.82 243	64.83 89	84.12 375	26.33 411	64.10 323	79.04 380

tfpnnormal		70.10 304	67.36 313	78.32 300	83.45 302	60.97 272	88.85 274	92.77 117	64.85 306	60.83 332	78.53 341	43.52 316	93.48 255	31.73 406	61.70 345	80.52 368

TransMVSNet (Re)		70.07 305	67.66 311	77.31 314	80.62 331	59.13 312	91.78 180	84.94 350	65.97 298	60.08 337	80.44 323	50.78 258	91.87 305	48.84 344	45.46 392	80.94 363

CL-MVSNet_self_test		69.92 306	68.09 310	75.41 328	73.25 386	55.90 342	90.05 248	89.90 243	69.96 260	61.96 329	76.54 358	51.05 257	87.64 356	49.51 341	50.59 384	82.70 347

DP-MVS		69.90 307	66.48 315	80.14 267	95.36 28	62.93 230	89.56 257	76.11 383	50.27 388	57.69 354	85.23 259	39.68 329	95.73 162	33.35 398	71.05 270	81.78 357

PS-CasMVS		69.86 308	69.13 303	72.07 359	80.35 333	50.57 369	87.02 304	89.75 248	67.27 288	59.19 342	82.28 292	46.58 296	82.24 392	50.69 334	59.02 360	83.39 336

Syy-MVS		69.65 309	69.52 301	70.03 366	87.87 213	43.21 402	88.07 286	89.01 282	72.91 188	63.11 318	88.10 219	45.28 308	85.54 368	22.07 416	69.23 279	81.32 359

MSDG		69.54 310	65.73 322	80.96 251	85.11 276	63.71 206	84.19 321	83.28 367	56.95 366	54.50 363	84.03 272	31.50 373	96.03 152	42.87 372	69.13 281	83.14 340

PEN-MVS		69.46 311	68.56 305	72.17 357	79.27 346	49.71 374	86.90 306	89.24 267	67.24 291	59.08 343	82.51 290	47.23 292	83.54 382	48.42 346	57.12 365	83.25 337

LS3D		69.17 312	66.40 317	77.50 309	91.92 110	56.12 340	85.12 315	80.37 376	46.96 396	56.50 358	87.51 231	37.25 348	93.71 250	32.52 405	79.40 203	82.68 348

PatchT		69.11 313	65.37 327	80.32 261	82.07 317	63.68 209	67.96 402	87.62 320	50.86 386	69.37 246	65.18 397	57.09 183	88.53 346	41.59 377	66.60 299	88.74 248

KD-MVS_2432*1600		69.03 314	66.37 318	77.01 317	85.56 266	61.06 270	81.44 347	90.25 229	67.27 288	58.00 350	76.53 359	54.49 218	87.63 357	48.04 348	35.77 409	82.34 351

miper_refine_blended		69.03 314	66.37 318	77.01 317	85.56 266	61.06 270	81.44 347	90.25 229	67.27 288	58.00 350	76.53 359	54.49 218	87.63 357	48.04 348	35.77 409	82.34 351

mvsany_test1		68.77 316	68.56 305	69.39 368	73.57 385	45.88 396	80.93 352	60.88 416	59.65 352	71.56 222	90.26 186	43.22 317	75.05 403	74.26 179	62.70 332	87.25 274

ACMH	63.93 17	68.62 317	64.81 329	80.03 271	85.22 272	63.25 220	87.72 294	84.66 352	60.83 344	51.57 376	79.43 337	27.29 388	94.96 195	41.76 375	64.84 314	81.88 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 318	65.41 326	77.96 305	78.69 357	62.93 230	89.86 254	89.17 271	60.55 345	50.27 381	77.73 349	22.60 398	94.06 234	47.18 354	72.65 258	76.88 391

ADS-MVSNet		68.54 319	64.38 336	81.03 250	88.06 207	66.90 124	68.01 400	84.02 358	57.57 360	64.48 304	69.87 387	38.68 331	89.21 342	40.87 379	67.89 291	86.97 276

DTE-MVSNet		68.46 320	67.33 314	71.87 361	77.94 366	49.00 380	86.16 312	88.58 300	66.36 296	58.19 347	82.21 294	46.36 297	83.87 380	44.97 365	55.17 372	82.73 344

mmtdpeth		68.33 321	66.37 318	74.21 341	82.81 310	51.73 360	84.34 320	80.42 375	67.01 292	71.56 222	68.58 391	30.52 379	92.35 294	75.89 163	36.21 407	78.56 385

our_test_3		68.29 322	64.69 331	79.11 295	78.92 352	64.85 174	88.40 282	85.06 348	60.32 348	52.68 370	76.12 363	40.81 326	89.80 339	44.25 367	55.65 370	82.67 349

Patchmatch-RL test		68.17 323	64.49 334	79.19 291	71.22 391	53.93 352	70.07 394	71.54 400	69.22 269	56.79 357	62.89 402	56.58 195	88.61 343	69.53 219	52.61 379	95.03 86

XVG-ACMP-BASELINE		68.04 324	65.53 325	75.56 327	74.06 384	52.37 357	78.43 368	85.88 340	62.03 335	58.91 345	81.21 314	20.38 403	91.15 322	60.69 296	68.18 287	83.16 339

FMVSNet5		68.04 324	65.66 324	75.18 331	84.43 288	57.89 321	83.54 325	86.26 335	61.83 339	53.64 368	73.30 373	37.15 351	85.08 372	48.99 343	61.77 342	82.56 350

ppachtmachnet_test		67.72 326	63.70 338	79.77 281	78.92 352	66.04 144	88.68 277	82.90 369	60.11 350	55.45 360	75.96 364	39.19 330	90.55 324	39.53 383	52.55 380	82.71 346

ACMH+	65.35 16	67.65 327	64.55 332	76.96 319	84.59 283	57.10 332	88.08 285	80.79 373	58.59 358	53.00 369	81.09 316	26.63 390	92.95 266	46.51 356	61.69 346	80.82 364

pmmvs6		67.57 328	64.76 330	76.00 326	72.82 389	53.37 354	88.71 276	86.78 331	53.19 378	57.58 355	78.03 346	35.33 360	92.41 290	55.56 318	54.88 374	82.21 353

Anonymous20231206		67.53 329	65.78 321	72.79 351	74.95 380	47.59 385	88.23 283	87.32 322	61.75 340	58.07 349	77.29 352	37.79 345	87.29 361	42.91 370	63.71 327	83.48 333

Patchmtry		67.53 329	63.93 337	78.34 299	82.12 316	64.38 185	68.72 397	84.00 359	48.23 395	59.24 340	72.41 377	57.82 177	89.27 341	46.10 359	56.68 369	81.36 358

USDC		67.43 331	64.51 333	76.19 324	77.94 366	55.29 345	78.38 369	85.00 349	73.17 181	48.36 389	80.37 324	21.23 400	92.48 289	52.15 331	64.02 325	80.81 365

ADS-MVSNet2		66.90 332	63.44 340	77.26 315	88.06 207	60.70 282	68.01 400	75.56 387	57.57 360	64.48 304	69.87 387	38.68 331	84.10 376	40.87 379	67.89 291	86.97 276

CMPMVS	48.56 21	66.77 333	64.41 335	73.84 343	70.65 395	50.31 371	77.79 373	85.73 343	45.54 400	44.76 399	82.14 295	35.40 359	90.14 334	63.18 281	74.54 242	81.07 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 334	62.92 343	76.80 321	76.51 373	57.77 323	89.22 266	83.41 365	55.48 373	53.86 367	77.84 347	26.28 391	93.95 243	34.90 395	68.76 283	78.68 383

LTVRE_ROB	59.60 19	66.27 335	63.54 339	74.45 337	84.00 295	51.55 362	67.08 404	83.53 363	58.78 356	54.94 362	80.31 325	34.54 362	93.23 259	40.64 381	68.03 289	78.58 384
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		66.06 336	62.45 346	76.88 320	81.42 323	54.45 351	57.49 416	88.67 296	49.36 390	63.86 311	46.86 414	56.06 202	90.25 328	49.53 340	68.83 282	85.95 300

Patchmatch-test		65.86 337	60.94 352	80.62 258	83.75 297	58.83 314	58.91 415	75.26 389	44.50 403	50.95 380	77.09 355	58.81 167	87.90 351	35.13 394	64.03 324	95.12 81

UnsupCasMVSNet_eth		65.79 338	63.10 341	73.88 342	70.71 394	50.29 372	81.09 350	89.88 244	72.58 195	49.25 386	74.77 371	32.57 369	87.43 360	55.96 317	41.04 399	83.90 327

test_fmvs2		65.78 339	64.84 328	68.60 372	66.54 404	41.71 404	83.27 330	69.81 403	54.38 375	67.91 269	84.54 268	15.35 409	81.22 396	75.65 165	66.16 301	82.88 341

dmvs_testset		65.55 340	66.45 316	62.86 384	79.87 339	22.35 430	76.55 376	71.74 398	77.42 120	55.85 359	87.77 226	51.39 253	80.69 397	31.51 409	65.92 304	85.55 310

pmmvs-eth3d		65.53 341	62.32 347	75.19 330	69.39 399	59.59 303	82.80 338	83.43 364	62.52 330	51.30 378	72.49 375	32.86 366	87.16 362	55.32 319	50.73 383	78.83 382

mamv4		65.18 342	67.43 312	58.44 388	77.88 368	49.36 379	69.40 396	70.99 401	48.31 394	57.78 353	85.53 257	59.01 165	51.88 426	73.67 181	64.32 320	74.07 396

SixPastTwentyTwo		64.92 343	61.78 350	74.34 339	78.74 356	49.76 373	83.42 329	79.51 379	62.86 326	50.27 381	77.35 350	30.92 378	90.49 326	45.89 360	47.06 389	82.78 342

OurMVSNet-221017-0		64.68 344	62.17 348	72.21 356	76.08 377	47.35 386	80.67 353	81.02 372	56.19 370	51.60 375	79.66 335	27.05 389	88.56 345	53.60 327	53.63 377	80.71 366

test_0402		64.54 345	61.09 351	74.92 333	84.10 294	60.75 278	87.95 289	79.71 378	52.03 380	52.41 371	77.20 353	32.21 371	91.64 311	23.14 414	61.03 349	72.36 402

testgi		64.48 346	62.87 344	69.31 369	71.24 390	40.62 407	85.49 313	79.92 377	65.36 303	54.18 365	83.49 279	23.74 395	84.55 374	41.60 376	60.79 352	82.77 343

RPSCF		64.24 347	61.98 349	71.01 364	76.10 376	45.00 397	75.83 381	75.94 384	46.94 397	58.96 344	84.59 266	31.40 374	82.00 393	47.76 352	60.33 357	86.04 297

EU-MVSNet		64.01 348	63.01 342	67.02 378	74.40 383	38.86 413	83.27 330	86.19 337	45.11 401	54.27 364	81.15 315	36.91 354	80.01 399	48.79 345	57.02 366	82.19 354

test20.03		63.83 349	62.65 345	67.38 377	70.58 396	39.94 409	86.57 309	84.17 356	63.29 321	51.86 374	77.30 351	37.09 352	82.47 389	38.87 387	54.13 376	79.73 374

MDA-MVSNet_test_wron		63.78 350	60.16 354	74.64 334	78.15 364	60.41 289	83.49 326	84.03 357	56.17 372	39.17 409	71.59 383	37.22 349	83.24 386	42.87 372	48.73 386	80.26 371

YYNet1		63.76 351	60.14 355	74.62 335	78.06 365	60.19 296	83.46 328	83.99 361	56.18 371	39.25 408	71.56 384	37.18 350	83.34 384	42.90 371	48.70 387	80.32 370

K. test v3		63.09 352	59.61 357	73.53 345	76.26 375	49.38 378	83.27 330	77.15 382	64.35 309	47.77 391	72.32 379	28.73 383	87.79 354	49.93 339	36.69 406	83.41 335

COLMAP_ROB	57.96 20	62.98 353	59.65 356	72.98 349	81.44 322	53.00 356	83.75 324	75.53 388	48.34 393	48.81 388	81.40 308	24.14 393	90.30 327	32.95 400	60.52 354	75.65 394
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 354	59.08 358	71.10 363	67.19 402	48.72 381	83.91 323	85.23 347	50.38 387	47.84 390	71.22 386	20.74 401	85.51 370	46.47 357	58.75 362	79.06 379

AllTest		61.66 355	58.06 360	72.46 353	79.57 341	51.42 364	80.17 359	68.61 405	51.25 384	45.88 393	81.23 310	19.86 405	86.58 364	38.98 385	57.01 367	79.39 376

UnsupCasMVSNet_bld		61.60 356	57.71 361	73.29 347	68.73 400	51.64 361	78.61 367	89.05 281	57.20 365	46.11 392	61.96 405	28.70 384	88.60 344	50.08 338	38.90 404	79.63 375

MDA-MVSNet-bldmvs		61.54 357	57.70 362	73.05 348	79.53 343	57.00 336	83.08 334	81.23 371	57.57 360	34.91 413	72.45 376	32.79 367	86.26 366	35.81 392	41.95 397	75.89 393

mvs5depth		61.03 358	57.65 363	71.18 362	67.16 403	47.04 391	72.74 387	77.49 380	57.47 363	60.52 333	72.53 374	22.84 397	88.38 347	49.15 342	38.94 403	78.11 388

KD-MVS_self_test		60.87 359	58.60 359	67.68 375	66.13 405	39.93 410	75.63 383	84.70 351	57.32 364	49.57 384	68.45 392	29.55 380	82.87 387	48.09 347	47.94 388	80.25 372

kuosan		60.86 360	60.24 353	62.71 385	81.57 320	46.43 393	75.70 382	85.88 340	57.98 359	48.95 387	69.53 389	58.42 170	76.53 401	28.25 410	35.87 408	65.15 409

TinyColmap		60.32 361	56.42 368	72.00 360	78.78 355	53.18 355	78.36 370	75.64 386	52.30 379	41.59 407	75.82 366	14.76 412	88.35 348	35.84 391	54.71 375	74.46 395

MVS-HIRNet		60.25 362	55.55 369	74.35 338	84.37 289	56.57 338	71.64 390	74.11 391	34.44 412	45.54 397	42.24 420	31.11 377	89.81 337	40.36 382	76.10 234	76.67 392

MIMVSNet1		60.16 363	57.33 364	68.67 371	69.71 397	44.13 399	78.92 366	84.21 355	55.05 374	44.63 400	71.85 381	23.91 394	81.54 395	32.63 404	55.03 373	80.35 369

PM-MVS		59.40 364	56.59 366	67.84 373	63.63 408	41.86 403	76.76 375	63.22 413	59.01 355	51.07 379	72.27 380	11.72 416	83.25 385	61.34 292	50.28 385	78.39 386

new-patchmatchnet		59.30 365	56.48 367	67.79 374	65.86 406	44.19 398	82.47 339	81.77 370	59.94 351	43.65 403	66.20 396	27.67 387	81.68 394	39.34 384	41.40 398	77.50 390

test_vis1_rt		59.09 366	57.31 365	64.43 381	68.44 401	46.02 395	83.05 336	48.63 425	51.96 381	49.57 384	63.86 401	16.30 407	80.20 398	71.21 205	62.79 331	67.07 408

test_fmvs3		56.82 367	54.86 371	62.69 386	53.59 419	35.47 416	75.87 380	65.64 410	43.91 404	55.10 361	71.43 385	6.91 424	74.40 406	68.64 230	52.63 378	78.20 387

DSMNet-mixed		56.78 368	54.44 372	63.79 382	63.21 409	29.44 425	64.43 407	64.10 412	42.12 409	51.32 377	71.60 382	31.76 372	75.04 404	36.23 390	65.20 311	86.87 279

pmmvs3		55.51 369	51.50 375	67.53 376	57.90 417	50.93 368	80.37 355	73.66 392	40.63 410	44.15 402	64.75 399	16.30 407	78.97 400	44.77 366	40.98 401	72.69 400

TDRefinement		55.28 370	51.58 374	66.39 379	59.53 416	46.15 394	76.23 378	72.80 393	44.60 402	42.49 405	76.28 362	15.29 410	82.39 390	33.20 399	43.75 394	70.62 404

dongtai		55.18 371	55.46 370	54.34 396	76.03 378	36.88 414	76.07 379	84.61 353	51.28 383	43.41 404	64.61 400	56.56 196	67.81 414	18.09 419	28.50 419	58.32 412

LF4IMVS		54.01 372	52.12 373	59.69 387	62.41 411	39.91 411	68.59 398	68.28 407	42.96 407	44.55 401	75.18 367	14.09 414	68.39 413	41.36 378	51.68 381	70.78 403

ttmdpeth		53.34 373	49.96 376	63.45 383	62.07 413	40.04 408	72.06 388	65.64 410	42.54 408	51.88 373	77.79 348	13.94 415	76.48 402	32.93 401	30.82 417	73.84 397

MVStest1		51.35 374	46.89 378	64.74 380	65.06 407	51.10 366	67.33 403	72.58 394	30.20 416	35.30 411	74.82 369	27.70 386	69.89 411	24.44 413	24.57 420	73.22 398

N_pmnet		50.55 375	49.11 377	54.88 394	77.17 371	4.02 438	84.36 319	2.00 436	48.59 391	45.86 395	68.82 390	32.22 370	82.80 388	31.58 407	51.38 382	77.81 389

new_pmnet		49.31 376	46.44 379	57.93 389	62.84 410	40.74 406	68.47 399	62.96 414	36.48 411	35.09 412	57.81 409	14.97 411	72.18 408	32.86 402	46.44 390	60.88 411

mvsany_test3		48.86 377	46.35 380	56.41 390	46.00 425	31.67 421	62.26 409	47.25 426	43.71 405	45.54 397	68.15 393	10.84 417	64.44 422	57.95 308	35.44 411	73.13 399

test_f		46.58 378	43.45 382	55.96 391	45.18 426	32.05 420	61.18 410	49.49 424	33.39 413	42.05 406	62.48 404	7.00 423	65.56 418	47.08 355	43.21 396	70.27 405

WB-MVS		46.23 379	44.94 381	50.11 399	62.13 412	21.23 432	76.48 377	55.49 418	45.89 399	35.78 410	61.44 407	35.54 358	72.83 407	9.96 426	21.75 421	56.27 414

FPMVS		45.64 380	43.10 384	53.23 397	51.42 422	36.46 415	64.97 406	71.91 397	29.13 417	27.53 417	61.55 406	9.83 419	65.01 420	16.00 423	55.58 371	58.22 413

SSC-MVS		44.51 381	43.35 383	47.99 403	61.01 415	18.90 434	74.12 385	54.36 419	43.42 406	34.10 414	60.02 408	34.42 363	70.39 410	9.14 428	19.57 422	54.68 415

EGC-MVSNET		42.35 382	38.09 385	55.11 393	74.57 381	46.62 392	71.63 391	55.77 417	0.04 431	0.24 432	62.70 403	14.24 413	74.91 405	17.59 420	46.06 391	43.80 417

LCM-MVSNet		40.54 383	35.79 388	54.76 395	36.92 432	30.81 422	51.41 419	69.02 404	22.07 419	24.63 419	45.37 416	4.56 428	65.81 417	33.67 397	34.50 412	67.67 406

APD_test1		40.50 384	37.31 387	50.09 400	51.88 420	35.27 417	59.45 414	52.59 421	21.64 420	26.12 418	57.80 410	4.56 428	66.56 416	22.64 415	39.09 402	48.43 416

test_vis3_rt		40.46 385	37.79 386	48.47 402	44.49 427	33.35 419	66.56 405	32.84 433	32.39 414	29.65 415	39.13 423	3.91 431	68.65 412	50.17 336	40.99 400	43.40 418

ANet_high		40.27 386	35.20 389	55.47 392	34.74 433	34.47 418	63.84 408	71.56 399	48.42 392	18.80 422	41.08 421	9.52 420	64.45 421	20.18 417	8.66 429	67.49 407

test_method		38.59 387	35.16 390	48.89 401	54.33 418	21.35 431	45.32 422	53.71 420	7.41 428	28.74 416	51.62 412	8.70 421	52.87 425	33.73 396	32.89 413	72.47 401

PMMVS2		37.93 388	33.61 391	50.92 398	46.31 424	24.76 428	60.55 413	50.05 422	28.94 418	20.93 420	47.59 413	4.41 430	65.13 419	25.14 412	18.55 424	62.87 410

Gipuma		34.91 389	31.44 392	45.30 404	70.99 393	39.64 412	19.85 426	72.56 395	20.10 422	16.16 426	21.47 427	5.08 427	71.16 409	13.07 424	43.70 395	25.08 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 390	29.47 393	42.67 406	41.89 429	30.81 422	52.07 417	43.45 427	15.45 423	18.52 423	44.82 417	2.12 432	58.38 423	16.05 421	30.87 415	38.83 419

APD_test2		32.77 390	29.47 393	42.67 406	41.89 429	30.81 422	52.07 417	43.45 427	15.45 423	18.52 423	44.82 417	2.12 432	58.38 423	16.05 421	30.87 415	38.83 419

PMVS	26.43 22	31.84 392	28.16 395	42.89 405	25.87 435	27.58 426	50.92 420	49.78 423	21.37 421	14.17 427	40.81 422	2.01 434	66.62 415	9.61 427	38.88 405	34.49 423
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 393	24.00 397	26.45 410	43.74 428	18.44 435	60.86 411	39.66 429	15.11 425	9.53 429	22.10 426	6.52 425	46.94 428	8.31 429	10.14 426	13.98 426

MVE	24.84 23	24.35 394	19.77 400	38.09 408	34.56 434	26.92 427	26.57 424	38.87 431	11.73 427	11.37 428	27.44 424	1.37 435	50.42 427	11.41 425	14.60 425	36.93 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 395	23.20 399	25.46 411	41.52 431	16.90 436	60.56 412	38.79 432	14.62 426	8.99 430	20.24 429	7.35 422	45.82 429	7.25 430	9.46 427	13.64 427

tmp_tt		22.26 396	23.75 398	17.80 412	5.23 436	12.06 437	35.26 423	39.48 430	2.82 430	18.94 421	44.20 419	22.23 399	24.64 431	36.30 389	9.31 428	16.69 425

cdsmvs_eth3d_5k		19.86 397	26.47 396	0.00 416	0.00 439	0.00 441	0.00 427	93.45 89	0.00 434	0.00 435	95.27 63	49.56 270	0.00 435	0.00 434	0.00 432	0.00 431

wuyk23d		11.30 398	10.95 401	12.33 413	48.05 423	19.89 433	25.89 425	1.92 437	3.58 429	3.12 431	1.37 431	0.64 436	15.77 432	6.23 431	7.77 430	1.35 428

ab-mvs-re		7.91 399	10.55 402	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	94.95 73	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

testmvs		7.23 400	9.62 403	0.06 415	0.04 437	0.02 440	84.98 317	0.02 438	0.03 432	0.18 433	1.21 432	0.01 438	0.02 433	0.14 432	0.01 431	0.13 430

test123		6.92 401	9.21 404	0.08 414	0.03 438	0.05 439	81.65 345	0.01 439	0.02 433	0.14 434	0.85 433	0.03 437	0.02 433	0.12 433	0.00 432	0.16 429

pcd_1.5k_mvsjas		4.46 402	5.95 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	53.55 231	0.00 435	0.00 434	0.00 432	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 432	0.00 431

WAC-MVS						49.45 376								31.56 408

FOURS1					93.95 46	61.77 256	93.96 74	91.92 156	62.14 334	86.57 50

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

PC_three_1452									80.91 53	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 29	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 61	71.42 238	90.67 20	96.85 18	74.45 20

eth-test2					0.00 439

eth-test					0.00 439

ZD-MVS					96.63 9	65.50 159		93.50 87	70.74 252	85.26 66	95.19 69	64.92 88	97.29 79	87.51 61	93.01 56

RE-MVS-def			80.48 154		92.02 103	58.56 317	90.90 216	90.45 216	62.76 327	78.89 134	94.46 87	49.30 273		78.77 147	86.77 133	92.28 190

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 54	95.28 1				92.34 26	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	71.65 227	92.07 9	97.21 5	74.58 18	99.11 6	92.34 26	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 50	71.65 227	92.11 7	97.05 8	76.79 9	99.11 6

9.14			87.63 30		93.86 48		94.41 53	94.18 61	72.76 192	86.21 52	96.51 27	66.64 66	97.88 44	90.08 43	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 124	78.19 102

test_0728_THIRD									72.48 197	90.55 21	96.93 12	76.24 11	99.08 11	91.53 34	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 32	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 58	71.92 213	91.89 11	97.11 7	73.77 23

GSMVS																94.68 102

test_part2					96.29 19	68.16 89				90.78 18

sam_mvs1												57.85 176				94.68 102

sam_mvs												54.91 215

ambc				69.61 367	61.38 414	41.35 405	49.07 421	85.86 342		50.18 383	66.40 395	10.16 418	88.14 350	45.73 361	44.20 393	79.32 378

MTGPA								92.23 138

test_post1							78.95 365				20.70 428	53.05 236	91.50 319	60.43 297

test_post											23.01 425	56.49 197	92.67 281

patchmatchnet-post											67.62 394	57.62 179	90.25 328

GG-mvs-BLEND				86.53 74	91.91 112	69.67 53	75.02 384	94.75 36		78.67 142	90.85 174	77.91 7	94.56 213	72.25 195	93.74 45	95.36 66

MTMP							93.77 88	32.52 434

gm-plane-assit					88.42 195	67.04 120			78.62 96		91.83 157		97.37 73	76.57 159

test9_res														89.41 44	94.96 19	95.29 71

TEST9					94.18 41	67.28 111	94.16 62	93.51 85	71.75 224	85.52 61	95.33 58	68.01 56	97.27 83

test_8					94.19 40	67.19 113	94.15 64	93.42 92	71.87 218	85.38 64	95.35 57	68.19 54	96.95 109

agg_prior2														86.41 74	94.75 30	95.33 67

agg_prior					94.16 43	66.97 122		93.31 95		84.49 72			96.75 119

TestCases				72.46 353	79.57 341	51.42 364		68.61 405	51.25 384	45.88 393	81.23 310	19.86 405	86.58 364	38.98 385	57.01 367	79.39 376

test_prior4						67.18 115	93.92 77

test_prior2							95.10 38		75.40 144	85.25 67	95.61 49	67.94 57		87.47 63	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 106					96.84 116			95.05 84

旧先验2							92.00 169		59.37 354	87.54 43			93.47 256	75.39 167

新几何2							91.41 190

新几何1				84.73 136	92.32 93	64.28 190		91.46 182	59.56 353	79.77 123	92.90 129	56.95 189	96.57 124	63.40 277	92.91 58	93.34 157

旧先验1					91.94 108	60.74 279		91.50 180			94.36 91	65.23 83			91.84 71	94.55 109

无先验							92.71 134	92.61 128	62.03 335				97.01 99	66.63 248		93.97 139

原ACMM2							92.01 166

原ACMM1				84.42 150	93.21 68	64.27 191		93.40 94	65.39 302	79.51 126	92.50 137	58.11 175	96.69 120	65.27 267	93.96 40	92.32 188

test222					89.77 160	61.60 261	89.55 258	89.42 261	56.83 368	77.28 155	92.43 141	52.76 239			91.14 85	93.09 166

testdata2													96.09 146	61.26 293

segment_acmp												65.94 74

testdata				81.34 239	89.02 181	57.72 324		89.84 245	58.65 357	85.32 65	94.09 105	57.03 184	93.28 258	69.34 221	90.56 91	93.03 169

testdata1							89.21 267		77.55 116

test12				87.09 52	94.60 36	68.86 68		92.91 113		82.67 93		65.44 80	97.55 64		93.69 48	94.84 94

plane_prior7					86.94 238	61.51 262

plane_prior6					87.23 230	62.32 246						50.66 259

plane_prior5								91.31 186					95.55 175	76.74 157	78.53 213	88.39 255

plane_prior4											89.14 204

plane_prior3						61.95 254			79.09 86	72.53 206

plane_prior2							93.13 115		78.81 93

plane_prior1					87.15 232

plane_prior						62.42 242	93.85 81		79.38 78						78.80 210

n2								0.00 440

nn								0.00 440

door-mid								66.01 409

lessismore_v0				73.72 344	72.93 388	47.83 384		61.72 415		45.86 395	73.76 372	28.63 385	89.81 337	47.75 353	31.37 414	83.53 331

LGP-MVS_train				79.56 287	84.31 290	59.37 307		89.73 251	69.49 265	64.86 299	88.42 210	38.65 333	94.30 222	72.56 192	72.76 256	85.01 318

test11								93.01 109

door								66.57 408

HQP5-MVS						63.66 210

HQP-NCC					87.54 222		94.06 67		79.80 69	74.18 185

ACMP_Plane					87.54 222		94.06 67		79.80 69	74.18 185

BP-MVS														77.63 154

HQP4-MVS										74.18 185			95.61 170			88.63 249

HQP3-MVS								91.70 172							78.90 208

HQP2-MVS												51.63 251

NP-MVS					87.41 225	63.04 226					90.30 184

MDTV_nov1_ep13_2view						59.90 299	80.13 360		67.65 285	72.79 200		54.33 223		59.83 301		92.58 181

MDTV_nov1_ep13			72.61 272		89.06 180	68.48 77	80.33 356	90.11 235	71.84 220	71.81 218	75.92 365	53.01 237	93.92 244	48.04 348	73.38 251

ACMMP++_ref															71.63 264

ACMMP++															69.72 273

Test By Simon												54.21 225

ITE_SJBPF				70.43 365	74.44 382	47.06 390		77.32 381	60.16 349	54.04 366	83.53 277	23.30 396	84.01 378	43.07 369	61.58 347	80.21 373

DeepMVS_CX				34.71 409	51.45 421	24.73 429		28.48 435	31.46 415	17.49 425	52.75 411	5.80 426	42.60 430	18.18 418	19.42 423	36.81 422

High-resolution multi-view benchmark