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		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 12	75.95 3	77.10 44	93.09 33	54.15 40	95.57 12	85.80 13	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 22	84.38 87	55.40 59	92.16 10	89.85 23	75.28 4	82.41 11	93.86 11	54.30 37	93.98 23	90.29 1	87.13 21	93.30 12

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 154	88.88 37	58.00 256	83.60 6	93.39 24	67.21 2	96.39 4	81.64 40	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 216	59.50 5	92.24 8	90.72 16	69.37 44	83.22 8	94.47 3	63.81 5	93.18 33	74.02 100	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 48	56.89 29	92.77 2	86.30 95	77.83 1	77.88 40	92.13 51	60.24 7	94.78 19	78.97 55	89.61 8	93.69 8
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
MSP-MVS		82.30 6	83.47 1	78.80 59	82.99 124	52.71 142	85.04 151	88.63 48	66.08 96	86.77 3	92.75 40	72.05 1	91.46 73	83.35 27	93.53 1	92.23 37
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		82.10 7	82.64 4	80.47 27	86.63 50	54.69 88	92.20 9	86.66 86	74.48 5	82.63 10	93.80 13	50.83 63	93.70 28	90.11 2	86.44 33	93.01 21

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 35	57.50 270	84.61 4	94.09 6	58.81 13	96.37 6	82.28 34	87.60 18	94.06 3

CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 14	91.71 11	88.87 39	70.31 32	77.64 43	93.87 10	52.58 48	93.91 26	84.17 21	87.92 16	92.39 33

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 21	57.71 264	81.91 15	93.64 17	55.17 31	96.44 2	81.68 38	87.13 21	92.72 28
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
CANet		80.90 11	81.17 12	80.09 37	87.62 41	54.21 100	91.60 14	86.47 91	73.13 9	79.89 29	93.10 31	49.88 73	92.98 34	84.09 23	84.75 50	93.08 19

patch_mono-2		80.84 12	81.59 10	78.62 67	90.34 9	53.77 108	88.08 56	88.36 55	76.17 2	79.40 33	91.09 75	55.43 29	90.09 119	85.01 16	80.40 84	91.99 49

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 90	85.46 67	49.56 228	90.99 21	86.66 86	70.58 30	80.07 28	95.30 1	56.18 26	90.97 96	82.57 33	86.22 36	93.28 13

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 68	89.93 29	87.55 72	66.04 99	79.46 31	93.00 37	53.10 45	91.76 65	80.40 48	89.56 9	92.68 29

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 43	91.54 5	59.19 232	71.82 97	90.05 111	59.72 10	96.04 10	78.37 61	88.40 14	93.75 7

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 295	89.51 26	69.76 40	71.05 112	86.66 186	58.68 16	93.24 31	84.64 20	90.40 6	93.14 18

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 64	60.97 3	91.69 12	87.02 78	70.62 29	80.75 23	93.22 30	37.77 231	92.50 47	82.75 31	86.25 35	91.57 62

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 76	60.73 4	91.65 13	86.86 81	70.30 33	80.77 22	93.07 35	37.63 237	92.28 54	82.73 32	85.71 39	91.57 62

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 73	88.70 49	87.92 62	55.55 300	81.21 20	93.69 16	56.51 24	94.27 22	78.36 62	85.70 40	91.51 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		79.57 20	79.23 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 57	67.71 62	73.81 67	92.75 40	46.88 98	93.28 30	78.79 58	84.07 55	91.50 66

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 162	83.68 176	67.85 59	69.36 127	90.24 103	60.20 8	92.10 60	84.14 22	80.40 84	92.82 25

testing11		79.18 22	78.85 23	80.16 33	88.33 30	56.99 26	88.31 54	92.06 1	72.82 11	70.62 120	88.37 146	57.69 19	92.30 52	75.25 88	76.24 139	91.20 77

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 85	55.87 49	87.58 71	86.76 83	61.48 188	80.26 27	93.10 31	46.53 105	92.41 49	79.97 49	88.77 11	92.08 41
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
UBG		78.86 24	78.86 22	78.86 57	87.80 40	55.43 55	87.67 66	91.21 11	72.83 10	72.10 93	88.40 145	58.53 17	89.08 152	73.21 112	77.98 113	92.08 41

LFMVS		78.52 25	77.14 45	82.67 3	89.58 13	58.90 8	91.27 19	88.05 60	63.22 151	74.63 58	90.83 89	41.38 192	94.40 20	75.42 86	79.90 93	94.72 2

testing99		78.45 26	77.78 35	80.45 28	88.28 33	56.81 32	87.95 61	91.49 6	71.72 18	70.84 114	88.09 156	57.29 21	92.63 45	69.24 135	75.13 158	91.91 50

APDe-MVS		78.44 27	78.20 27	79.19 45	88.56 26	54.55 93	89.76 33	87.77 66	55.91 295	78.56 36	92.49 46	48.20 81	92.65 43	79.49 50	83.04 59	90.39 104
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 28	76.99 49	82.73 2	93.17 1	64.46 1	89.93 29	88.51 53	64.83 116	73.52 70	88.09 156	48.07 82	92.19 56	62.24 196	84.53 52	91.53 64

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 122	55.24 63	91.57 15	84.82 144	69.12 45	76.67 46	92.02 56	44.82 143	90.23 116	80.83 47	80.09 88	92.08 41

EPNet		78.36 30	78.49 25	77.97 87	85.49 66	52.04 158	89.36 39	84.07 168	73.22 8	77.03 45	91.72 65	49.32 77	90.17 118	73.46 107	82.77 60	91.69 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 31	78.26 26	78.48 72	81.33 182	56.31 42	81.59 270	86.41 92	69.61 42	81.72 17	88.16 154	55.09 33	88.04 204	74.12 99	86.31 34	91.09 81
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testing91		78.30 32	77.54 38	80.61 23	88.16 35	57.12 25	87.94 62	91.07 15	71.43 22	70.75 115	88.04 161	55.82 28	92.65 43	69.61 131	75.00 162	92.05 44

sasdasda		78.17 33	77.86 33	79.12 50	84.30 88	54.22 98	87.71 64	84.57 154	67.70 63	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

canonicalmvs		78.17 33	77.86 33	79.12 50	84.30 88	54.22 98	87.71 64	84.57 154	67.70 63	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

alignmvs		78.08 35	77.98 30	78.39 77	83.53 104	53.22 126	89.77 32	85.45 111	66.11 94	76.59 48	91.99 58	54.07 41	89.05 154	77.34 71	77.00 125	92.89 23

DeepC-MVS_fast	67.50 3	78.00 36	77.63 36	79.13 49	88.52 27	55.12 70	89.95 28	85.98 102	68.31 48	71.33 107	92.75 40	45.52 127	90.37 109	71.15 121	85.14 46	91.91 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		77.99 37	77.92 32	78.19 83	87.43 43	50.12 215	90.93 22	91.41 8	67.48 66	75.12 53	90.15 109	46.77 102	91.00 91	73.52 105	78.46 108	93.44 9

TSAR-MVS + GP.		77.82 38	77.59 37	78.49 71	85.25 72	50.27 214	90.02 26	90.57 17	56.58 289	74.26 63	91.60 70	54.26 38	92.16 57	75.87 80	79.91 92	93.05 20

myMVS_eth3d28		77.77 39	77.94 31	77.27 108	87.58 42	52.89 139	86.06 110	91.33 10	74.15 7	68.16 139	88.24 152	58.17 18	88.31 194	69.88 130	77.87 114	90.61 97

casdiffmvs_mvg		77.75 40	77.28 42	79.16 47	80.42 211	54.44 95	87.76 63	85.46 110	71.67 20	71.38 106	88.35 148	51.58 52	91.22 81	79.02 54	79.89 94	91.83 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		77.70 41	77.22 44	79.14 48	86.95 46	54.89 81	87.18 82	91.96 2	72.29 13	71.17 111	88.70 136	55.19 30	91.24 80	65.18 173	76.32 137	91.29 74

SF-MVS		77.64 42	77.42 41	78.32 80	83.75 101	52.47 147	86.63 98	87.80 63	58.78 244	74.63 58	92.38 48	47.75 88	91.35 75	78.18 65	86.85 27	91.15 80

PHI-MVS		77.49 43	77.00 48	78.95 53	85.33 70	50.69 195	88.57 51	88.59 51	58.14 253	73.60 68	93.31 27	43.14 168	93.79 27	73.81 103	88.53 13	92.37 34

WTY-MVS		77.47 44	77.52 39	77.30 106	88.33 30	46.25 324	88.46 52	90.32 19	71.40 23	72.32 90	91.72 65	53.44 43	92.37 51	66.28 158	75.42 152	93.28 13

SymmetryMVS		77.43 45	77.09 46	78.44 75	82.56 140	52.32 151	89.31 40	84.15 166	72.20 14	73.23 75	91.05 76	46.52 106	91.00 91	76.23 76	78.55 107	92.00 48

casdiffmvs		77.36 46	76.85 51	78.88 56	80.40 212	54.66 91	87.06 85	85.88 103	72.11 16	71.57 101	88.63 141	50.89 62	90.35 110	76.00 79	79.11 102	91.63 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		77.20 47	77.25 43	77.05 114	84.60 82	49.04 245	89.42 36	85.83 105	65.90 100	72.85 81	91.98 60	45.10 134	91.27 78	75.02 90	84.56 51	90.84 91

ETV-MVS		77.17 48	76.74 53	78.48 72	81.80 159	54.55 93	86.13 108	85.33 116	68.20 50	73.10 77	90.52 95	45.23 133	90.66 102	79.37 51	80.95 74	90.22 110

fmvsm_l_conf0.5_n_9		77.10 49	77.48 40	75.98 150	77.54 271	47.77 296	86.35 102	73.46 366	68.69 46	81.07 21	94.40 4	49.06 78	88.89 166	87.39 8	79.32 100	91.27 75

NormalMVS		77.09 50	77.02 47	77.32 105	81.66 167	52.32 151	89.31 40	82.11 205	72.20 14	73.23 75	91.05 76	46.52 106	91.00 91	76.23 76	80.83 77	88.64 162

SteuartSystems-ACMMP		77.08 51	76.33 59	79.34 43	80.98 190	55.31 61	89.76 33	86.91 80	62.94 156	71.65 99	91.56 71	42.33 176	92.56 46	77.14 73	83.69 57	90.15 115
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 52	76.45 57	78.69 63	79.69 221	54.74 84	90.56 24	83.99 171	68.26 49	74.10 64	90.91 86	42.14 180	89.99 121	79.30 52	79.12 101	91.36 70
jason: jason.
train_agg		76.91 53	76.40 58	78.45 74	85.68 60	55.42 56	87.59 69	84.00 169	57.84 261	72.99 78	90.98 80	44.99 137	88.58 178	78.19 63	85.32 44	91.34 72

MVS		76.91 53	75.48 73	81.23 19	84.56 83	55.21 65	80.23 301	91.64 4	58.65 246	65.37 168	91.48 73	45.72 122	95.05 16	72.11 118	89.52 10	93.44 9

DeepC-MVS	67.15 4	76.90 55	76.27 60	78.80 59	80.70 201	55.02 75	86.39 100	86.71 84	66.96 78	67.91 142	89.97 113	48.03 83	91.41 74	75.60 83	84.14 54	89.96 125
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		76.86 56	76.24 61	78.71 62	80.47 207	54.20 102	83.90 195	84.88 143	71.38 24	71.51 104	89.15 129	50.51 65	90.55 106	75.71 81	78.65 105	91.39 68

CS-MVS		76.77 57	76.70 54	76.99 119	83.55 103	48.75 255	88.60 50	85.18 125	66.38 87	72.47 88	91.62 69	45.53 126	90.99 95	74.48 94	82.51 62	91.23 76

PAPM		76.76 58	76.07 64	78.81 58	80.20 214	59.11 7	86.86 93	86.23 96	68.60 47	70.18 125	88.84 134	51.57 53	87.16 239	65.48 166	86.68 30	90.15 115

MAR-MVS		76.76 58	75.60 70	80.21 31	90.87 7	54.68 89	89.14 44	89.11 32	62.95 155	70.54 121	92.33 49	41.05 193	94.95 17	57.90 245	86.55 32	91.00 85
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
viewmanbaseed2359cas		76.71 60	76.16 62	78.37 79	81.16 184	55.05 74	86.96 88	85.32 117	71.71 19	72.25 92	88.50 143	46.86 99	88.96 161	74.55 93	78.08 112	91.08 82

viewcassd2359sk11		76.66 61	76.01 66	78.62 67	81.14 185	54.95 78	86.88 92	85.04 136	71.37 25	71.76 98	88.44 144	48.02 84	89.57 137	74.17 98	77.23 121	91.33 73

fmvsm_s_conf0.5_n_9		76.66 61	76.94 50	75.85 153	79.54 224	48.30 274	82.63 235	71.84 375	70.25 34	80.63 25	94.53 2	50.78 64	87.42 231	88.32 5	73.92 172	91.82 55

PVSNet_Blended		76.53 63	76.54 56	76.50 133	85.91 57	51.83 166	88.89 47	84.24 163	67.82 60	69.09 131	89.33 126	46.70 103	88.13 200	75.43 84	81.48 73	89.55 134

fmvsm_s_conf0.5_n_8		76.50 64	76.68 55	75.94 151	78.67 246	47.92 290	85.18 143	74.71 347	68.09 52	80.67 24	94.26 5	47.09 96	89.26 145	86.62 10	74.85 164	90.65 95

ACMMP_NAP		76.43 65	75.66 69	78.73 61	81.92 156	54.67 90	84.06 189	85.35 115	61.10 195	72.99 78	91.50 72	40.25 203	91.00 91	76.84 74	86.98 25	90.51 102

MVS_111021_HR		76.39 66	75.38 77	79.42 42	85.33 70	56.47 38	88.15 55	84.97 139	65.15 114	66.06 159	89.88 114	43.79 154	92.16 57	75.03 89	80.03 91	89.64 132

CHOSEN 1792x2688		76.24 67	74.03 102	82.88 1	83.09 118	62.84 2	85.73 121	85.39 113	69.79 38	64.87 179	83.49 239	41.52 191	93.69 29	70.55 123	81.82 69	92.12 40

SD-MVS		76.18 68	74.85 89	80.18 32	85.39 68	56.90 28	85.75 119	82.45 201	56.79 284	74.48 61	91.81 62	43.72 157	90.75 100	74.61 92	78.65 105	92.91 22
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_s_conf0.5_n_6		76.17 69	76.84 52	74.15 216	77.42 274	46.46 317	85.53 131	77.86 306	69.78 39	79.78 30	92.90 38	46.80 100	84.81 306	84.67 19	76.86 129	91.17 79

APD-MVS		76.15 70	75.68 68	77.54 99	88.52 27	53.44 117	87.26 81	85.03 137	53.79 321	74.91 56	91.68 67	43.80 153	90.31 112	74.36 95	81.82 69	88.87 155
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS1		76.09 71	75.55 71	77.71 94	79.49 225	52.27 155	84.70 165	90.49 18	64.44 119	69.86 126	90.31 102	55.05 34	91.35 75	70.07 128	75.58 151	89.53 136

VDD-MVS		76.08 72	74.97 86	79.44 41	84.27 91	53.33 123	91.13 20	85.88 103	65.33 111	72.37 89	89.34 124	32.52 316	92.76 41	77.90 68	75.96 144	92.22 39

CDPH-MVS		76.05 73	75.19 79	78.62 67	86.51 51	54.98 77	87.32 76	84.59 153	58.62 247	70.75 115	90.85 88	43.10 170	90.63 104	70.50 125	84.51 53	90.24 109

viewdifsd2359ckpt13		75.96 74	75.07 82	78.65 66	81.14 185	55.21 65	86.15 107	84.95 140	69.98 35	70.49 123	88.16 154	46.10 114	89.86 125	72.39 115	76.23 140	90.89 90

fmvsm_l_conf0.5_n		75.95 75	76.16 62	75.31 177	76.01 305	48.44 267	84.98 154	71.08 385	63.50 145	81.70 18	93.52 20	50.00 69	87.18 238	87.80 6	76.87 128	90.32 107

EIA-MVS		75.92 76	75.18 80	78.13 84	85.14 73	51.60 174	87.17 83	85.32 117	64.69 117	68.56 135	90.53 94	45.79 121	91.58 70	67.21 151	82.18 66	91.20 77

viewmacassd2359aftdt		75.91 77	75.14 81	78.21 82	79.40 227	54.82 82	86.71 96	84.98 138	70.89 28	71.52 103	87.89 164	45.43 129	88.85 170	72.35 116	77.08 123	90.97 87

fmvsm_l_conf0.5_n_a		75.88 78	76.07 64	75.31 177	76.08 300	48.34 270	85.24 139	70.62 388	63.13 153	81.45 19	93.62 19	49.98 71	87.40 233	87.76 7	76.77 130	90.20 112

test_yl		75.85 79	74.83 90	78.91 54	88.08 37	51.94 161	91.30 17	89.28 29	57.91 258	71.19 109	89.20 127	42.03 183	92.77 39	69.41 132	75.07 160	92.01 46

DCV-MVSNet		75.85 79	74.83 90	78.91 54	88.08 37	51.94 161	91.30 17	89.28 29	57.91 258	71.19 109	89.20 127	42.03 183	92.77 39	69.41 132	75.07 160	92.01 46

MVS_Test		75.85 79	74.93 87	78.62 67	84.08 93	55.20 68	83.99 191	85.17 126	68.07 55	73.38 72	82.76 250	50.44 66	89.00 157	65.90 162	80.61 80	91.64 58

ZNCC-MVS		75.82 82	75.02 85	78.23 81	83.88 99	53.80 107	86.91 91	86.05 101	59.71 218	67.85 143	90.55 93	42.23 178	91.02 89	72.66 114	85.29 45	89.87 128

ETVMVS		75.80 83	75.44 74	76.89 123	86.23 55	50.38 207	85.55 129	91.42 7	71.30 26	68.80 133	87.94 163	56.42 25	89.24 146	56.54 257	74.75 166	91.07 83

fmvsm_l_conf0.5_n_3		75.73 84	75.78 67	75.61 161	76.03 303	48.33 272	85.34 133	72.92 369	67.16 69	78.55 37	93.85 12	46.22 110	87.53 227	85.61 14	76.30 138	90.98 86

CLD-MVS		75.60 85	75.39 76	76.24 138	80.69 202	52.40 148	90.69 23	86.20 97	74.40 6	65.01 175	88.93 131	42.05 182	90.58 105	76.57 75	73.96 170	85.73 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvsm_n_1920		75.56 86	75.54 72	75.61 161	74.60 328	49.51 233	81.82 259	74.08 353	66.52 84	80.40 26	93.46 22	46.95 97	89.72 132	86.69 9	75.30 153	87.61 195

MP-MVS-pluss		75.54 87	75.03 84	77.04 115	81.37 181	52.65 144	84.34 179	84.46 156	61.16 192	69.14 130	91.76 63	39.98 210	88.99 159	78.19 63	84.89 49	89.48 139
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 88	75.20 78	75.62 160	80.98 190	49.00 246	87.43 72	84.68 151	63.49 146	70.97 113	90.15 109	42.86 173	91.14 85	74.33 96	81.90 68	86.71 222

MVSMamba_PlusPlus		75.28 89	73.39 107	80.96 21	80.85 197	58.25 10	74.47 350	87.61 71	50.53 347	65.24 170	83.41 241	57.38 20	92.83 37	73.92 102	87.13 21	91.80 56

GDP-MVS		75.27 90	74.38 95	77.95 89	79.04 237	52.86 140	85.22 140	86.19 98	62.43 171	70.66 118	90.40 100	53.51 42	91.60 69	69.25 134	72.68 187	89.39 140

Effi-MVS+		75.24 91	73.61 106	80.16 33	81.92 156	57.42 21	85.21 141	76.71 329	60.68 206	73.32 73	89.34 124	47.30 92	91.63 68	68.28 144	79.72 95	91.42 67

ET-MVSNet_ETH3D		75.23 92	74.08 100	78.67 64	84.52 84	55.59 51	88.92 46	89.21 31	68.06 56	53.13 347	90.22 105	49.71 74	87.62 224	72.12 117	70.82 209	92.82 25

PAPR		75.20 93	74.13 98	78.41 76	88.31 32	55.10 72	84.31 180	85.66 107	63.76 138	67.55 144	90.73 91	43.48 162	89.40 140	66.36 157	77.03 124	90.73 94

baseline2		75.15 94	74.54 94	76.98 120	81.67 166	51.74 171	83.84 197	91.94 3	69.97 36	58.98 267	86.02 196	59.73 9	91.73 67	68.37 143	70.40 218	87.48 197

diffmvs		75.11 95	74.65 92	76.46 134	78.52 252	53.35 121	83.28 216	79.94 253	70.51 31	71.64 100	88.72 135	46.02 117	86.08 278	77.52 69	75.75 148	89.96 125
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_conf0.5_n_5		75.02 96	75.07 82	74.88 194	74.33 333	47.83 293	83.99 191	73.54 361	67.10 71	76.32 49	92.43 47	45.42 130	86.35 268	82.98 29	79.50 99	90.47 103

MP-MVS		74.99 97	74.33 96	76.95 121	82.89 129	53.05 134	85.63 125	83.50 181	57.86 260	67.25 146	90.24 103	43.38 165	88.85 170	76.03 78	82.23 65	88.96 152
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_3		74.97 98	75.42 75	73.62 236	76.99 284	46.67 313	83.13 222	71.14 384	66.20 91	82.13 13	93.76 14	47.49 90	84.00 315	81.95 37	76.02 141	90.19 114

fmvsm_s_conf0.5_n_4		74.92 99	74.88 88	75.03 189	75.96 306	47.53 299	85.84 114	73.19 368	67.07 73	79.43 32	92.60 44	46.12 112	88.03 205	84.70 18	69.01 227	89.53 136

GST-MVS		74.87 100	73.90 104	77.77 92	83.30 111	53.45 116	85.75 119	85.29 120	59.22 231	66.50 155	89.85 115	40.94 195	90.76 99	70.94 122	83.35 58	89.10 150

viewdifsd2359ckpt07		74.81 101	74.01 103	77.21 112	79.62 222	53.13 131	85.70 124	83.75 174	68.12 51	68.14 140	87.33 176	46.51 108	87.92 207	73.32 108	73.63 174	90.57 98

diffmvs_AUTHOR		74.80 102	74.30 97	76.29 136	77.34 275	53.19 127	83.17 221	79.50 265	69.93 37	71.55 102	88.57 142	45.85 120	86.03 280	77.17 72	75.64 149	89.67 130

fmvsm_s_conf0.5_n		74.48 103	74.12 99	75.56 164	76.96 285	47.85 292	85.32 137	69.80 395	64.16 127	78.74 34	93.48 21	45.51 128	89.29 144	86.48 11	66.62 249	89.55 134

3Dnovator	64.70 6	74.46 104	72.48 122	80.41 29	82.84 132	55.40 59	83.08 224	88.61 50	67.61 65	59.85 250	88.66 137	34.57 294	93.97 24	58.42 234	88.70 12	91.85 53

test_fmvsmconf_n		74.41 105	74.05 101	75.49 169	74.16 336	48.38 268	82.66 233	72.57 370	67.05 75	75.11 54	92.88 39	46.35 109	87.81 211	83.93 24	71.71 198	90.28 108

HFP-MVS		74.37 106	73.13 115	78.10 85	84.30 88	53.68 110	85.58 126	84.36 158	56.82 282	65.78 164	90.56 92	40.70 200	90.90 97	69.18 136	80.88 75	89.71 129

VDDNet		74.37 106	72.13 133	81.09 20	79.58 223	56.52 37	90.02 26	86.70 85	52.61 331	71.23 108	87.20 177	31.75 329	93.96 25	74.30 97	75.77 147	92.79 27

MSLP-MVS++		74.21 108	72.25 129	80.11 36	81.45 179	56.47 38	86.32 103	79.65 262	58.19 252	66.36 156	92.29 50	36.11 271	90.66 102	67.39 149	82.49 63	93.18 17

API-MVS		74.17 109	72.07 135	80.49 25	90.02 11	58.55 9	87.30 78	84.27 160	57.51 269	65.77 165	87.77 167	41.61 189	95.97 11	51.71 296	82.63 61	86.94 211

lecture		74.14 110	73.05 116	77.44 102	81.66 167	50.39 205	87.43 72	84.22 165	51.38 342	72.10 93	90.95 85	38.31 226	93.23 32	70.51 124	80.83 77	88.69 160

MGCFI-Net		74.07 111	74.64 93	72.34 267	82.90 128	43.33 362	80.04 304	79.96 252	65.61 102	74.93 55	91.85 61	48.01 85	80.86 345	71.41 119	77.10 122	92.84 24

IB-MVS	68.87 2	74.01 112	72.03 138	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 70	61.38 235	81.74 275	53.21 44	94.28 21	60.45 216	62.41 296	90.03 123
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
h-mvs33		73.95 113	72.89 117	77.15 113	80.17 215	50.37 208	84.68 167	83.33 182	68.08 53	71.97 95	88.65 140	42.50 174	91.15 84	78.82 56	57.78 339	89.91 127

WBMVS		73.93 114	73.39 107	75.55 165	87.82 39	55.21 65	89.37 37	87.29 74	67.27 67	63.70 204	80.30 287	60.32 6	86.47 262	61.58 202	62.85 293	84.97 256

HY-MVS	67.03 5	73.90 115	73.14 113	76.18 143	84.70 80	47.36 305	75.56 339	86.36 94	66.27 89	70.66 118	83.91 230	51.05 57	89.31 143	67.10 152	72.61 188	91.88 52

CostFormer		73.89 116	72.30 128	78.66 65	82.36 144	56.58 33	75.56 339	85.30 119	66.06 97	70.50 122	76.88 332	57.02 22	89.06 153	68.27 145	68.74 233	90.33 106

fmvsm_s_conf0.1_n		73.80 117	73.26 110	75.43 170	73.28 344	47.80 294	84.57 173	69.43 397	63.34 148	78.40 38	93.29 28	44.73 146	89.22 148	85.99 12	66.28 258	89.26 143

ACMMPR		73.76 118	72.61 119	77.24 111	83.92 97	52.96 137	85.58 126	84.29 159	56.82 282	65.12 171	90.45 96	37.24 249	90.18 117	69.18 136	80.84 76	88.58 166

region2R		73.75 119	72.55 121	77.33 104	83.90 98	52.98 136	85.54 130	84.09 167	56.83 281	65.10 172	90.45 96	37.34 246	90.24 115	68.89 138	80.83 77	88.77 159

CANet_DTU		73.71 120	73.14 113	75.40 171	82.61 139	50.05 216	84.67 169	79.36 271	69.72 41	75.39 52	90.03 112	29.41 343	85.93 287	67.99 147	79.11 102	90.22 110

test_fmvsmconf0.1_n		73.69 121	73.15 111	75.34 175	70.71 376	48.26 275	82.15 248	71.83 376	66.75 80	74.47 62	92.59 45	44.89 140	87.78 216	83.59 26	71.35 204	89.97 124

fmvsm_s_conf0.5_n_a		73.68 122	73.15 111	75.29 180	75.45 314	48.05 284	83.88 196	68.84 400	63.43 147	78.60 35	93.37 26	45.32 131	88.92 165	85.39 15	64.04 273	88.89 154

thisisatest0515		73.64 123	72.20 130	77.97 87	81.63 169	53.01 135	86.69 97	88.81 42	62.53 167	64.06 194	85.65 200	52.15 51	92.50 47	58.43 232	69.84 221	88.39 176

MVSFormer		73.53 124	72.19 131	77.57 97	83.02 122	55.24 63	81.63 267	81.44 222	50.28 348	76.67 46	90.91 86	44.82 143	86.11 273	60.83 208	80.09 88	91.36 70

viewmambaseed2359dif		73.51 125	72.78 118	75.71 158	76.93 286	51.89 164	82.81 230	79.66 260	65.46 104	70.29 124	88.05 159	45.55 125	85.85 288	73.49 106	72.76 186	89.39 140

PVSNet_BlendedMVS		73.42 126	73.30 109	73.76 230	85.91 57	51.83 166	86.18 106	84.24 163	65.40 108	69.09 131	80.86 283	46.70 103	88.13 200	75.43 84	65.92 261	81.33 324

PVSNet_Blended_VisFu		73.40 127	72.44 123	76.30 135	81.32 183	54.70 87	85.81 115	78.82 283	63.70 139	64.53 186	85.38 206	47.11 95	87.38 234	67.75 148	77.55 117	86.81 221

RRT-MVS		73.29 128	71.37 147	79.07 52	84.63 81	54.16 103	78.16 324	86.64 88	61.67 183	60.17 247	82.35 266	40.63 201	92.26 55	70.19 127	77.87 114	90.81 92

MVSTER		73.25 129	72.33 126	76.01 148	85.54 65	53.76 109	83.52 202	87.16 76	67.06 74	63.88 199	81.66 276	52.77 46	90.44 107	64.66 178	64.69 269	83.84 280

EI-MVSNet-Vis-set		73.19 130	72.60 120	74.99 192	82.56 140	49.80 223	82.55 239	89.00 34	66.17 92	65.89 162	88.98 130	43.83 152	92.29 53	65.38 172	69.01 227	82.87 300

fmvsm_s_conf0.5_n_7		73.10 131	73.89 105	70.72 303	74.17 335	46.03 327	83.28 216	74.19 351	67.10 71	73.94 66	91.73 64	43.42 164	77.61 383	83.92 25	73.26 178	88.53 171

PMMVS		72.98 132	72.05 136	75.78 155	83.57 102	48.60 259	84.08 187	82.85 195	61.62 184	68.24 138	90.33 101	28.35 347	87.78 216	72.71 113	76.69 131	90.95 88

XVS		72.92 133	71.62 141	76.81 126	83.41 106	52.48 145	84.88 159	83.20 188	58.03 254	63.91 197	89.63 119	35.50 280	89.78 129	65.50 164	80.50 82	88.16 179

test2506		72.91 134	72.43 124	74.32 211	80.12 216	44.18 351	83.19 219	84.77 147	64.02 129	65.97 160	87.43 173	47.67 89	88.72 172	59.08 224	79.66 96	90.08 121

TESTMET0.1,1		72.86 135	72.33 126	74.46 203	81.98 153	50.77 193	85.13 145	85.47 109	66.09 95	67.30 145	83.69 236	37.27 247	83.57 322	65.06 175	78.97 104	89.05 151

fmvsm_s_conf0.1_n_a		72.82 136	72.05 136	75.12 186	70.95 375	47.97 287	82.72 232	68.43 402	62.52 168	78.17 39	93.08 34	44.21 149	88.86 167	84.82 17	63.54 280	88.54 170

Fast-Effi-MVS+		72.73 137	71.15 151	77.48 100	82.75 134	54.76 83	86.77 95	80.64 237	63.05 154	65.93 161	84.01 228	44.42 148	89.03 155	56.45 261	76.36 136	88.64 162

MTAPA		72.73 137	71.22 149	77.27 108	81.54 175	53.57 112	67.06 399	81.31 224	59.41 225	68.39 136	90.96 82	36.07 273	89.01 156	73.80 104	82.45 64	89.23 145

PGM-MVS		72.60 139	71.20 150	76.80 128	82.95 125	52.82 141	83.07 225	82.14 203	56.51 290	63.18 212	89.81 116	35.68 277	89.76 131	67.30 150	80.19 87	87.83 188

HPM-MVS		72.60 139	71.50 143	75.89 152	82.02 152	51.42 179	80.70 292	83.05 190	56.12 294	64.03 195	89.53 120	37.55 240	88.37 188	70.48 126	80.04 90	87.88 187
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 141	71.46 144	76.00 149	82.93 127	52.32 151	86.93 90	82.48 200	55.15 306	63.65 207	90.44 99	35.03 287	88.53 184	68.69 141	77.83 116	87.15 207

baseline1		72.51 142	72.12 134	73.69 233	85.05 74	44.46 344	83.51 206	86.13 100	71.61 21	64.64 182	87.97 162	55.00 35	89.48 138	59.07 225	56.05 353	87.13 208

IMVS_0403		72.39 143	70.59 159	77.79 91	82.26 145	50.87 187	81.76 260	85.16 128	62.91 157	64.87 179	86.07 192	37.71 236	92.40 50	64.03 181	70.55 213	90.09 117

EI-MVSNet-UG-set		72.37 144	71.73 139	74.29 212	81.60 171	49.29 240	81.85 257	88.64 47	65.29 113	65.05 173	88.29 151	43.18 166	91.83 64	63.74 186	67.97 239	81.75 312

MS-PatchMatch		72.34 145	71.26 148	75.61 161	82.38 143	55.55 52	88.00 57	89.95 22	65.38 109	56.51 317	80.74 285	32.28 319	92.89 35	57.95 243	88.10 15	78.39 359

HQP-MVS		72.34 145	71.44 145	75.03 189	79.02 238	51.56 175	88.00 57	83.68 176	65.45 105	64.48 187	85.13 208	37.35 244	88.62 175	66.70 153	73.12 180	84.91 258

testing3-2		72.30 147	72.35 125	72.15 271	83.07 119	47.64 297	85.46 132	89.81 24	66.17 92	61.96 230	84.88 217	58.93 12	82.27 332	55.87 263	64.97 265	86.54 224

mvs_anonymous		72.29 148	70.74 155	76.94 122	82.85 131	54.72 86	78.43 323	81.54 220	63.77 137	61.69 232	79.32 299	51.11 56	85.31 295	62.15 198	75.79 146	90.79 93

3Dnovator+	62.71 7	72.29 148	70.50 160	77.65 96	83.40 109	51.29 183	87.32 76	86.40 93	59.01 239	58.49 282	88.32 150	32.40 317	91.27 78	57.04 254	82.15 67	90.38 105

nrg030		72.27 150	71.56 142	74.42 205	75.93 307	50.60 197	86.97 87	83.21 187	62.75 162	67.15 147	84.38 222	50.07 68	86.66 256	71.19 120	62.37 297	85.99 236

UWE-MVS		72.17 151	72.15 132	72.21 269	82.26 145	44.29 348	86.83 94	89.58 25	65.58 103	65.82 163	85.06 210	45.02 136	84.35 311	54.07 278	75.18 155	87.99 186

VPNet		72.07 152	71.42 146	74.04 219	78.64 250	47.17 309	89.91 31	87.97 61	72.56 12	64.66 181	85.04 213	41.83 187	88.33 192	61.17 206	60.97 305	86.62 223

fmvsm_s_conf0.5_n_2		72.02 153	71.72 140	72.92 250	76.79 288	45.90 328	84.48 174	66.11 408	64.26 123	76.12 50	93.40 23	36.26 269	86.04 279	81.47 42	66.54 252	86.82 220

DP-MVS Recon		71.99 154	70.31 167	77.01 117	90.65 8	53.44 117	89.37 37	82.97 193	56.33 292	63.56 210	89.47 121	34.02 300	92.15 59	54.05 279	72.41 189	85.43 249

IMVS_0407		71.97 155	70.10 173	77.57 97	82.26 145	50.87 187	80.69 293	85.16 128	62.91 157	63.68 205	86.07 192	35.56 278	91.75 66	64.03 181	70.55 213	90.09 117

test_fmvsmconf0.01_n		71.97 155	70.95 154	75.04 188	66.21 405	47.87 291	80.35 298	70.08 392	65.85 101	72.69 83	91.68 67	39.99 209	87.67 220	82.03 36	69.66 223	89.58 133

SDMVSNet		71.89 157	70.62 158	75.70 159	81.70 163	51.61 173	73.89 353	88.72 45	66.58 81	61.64 233	82.38 263	37.63 237	89.48 138	77.44 70	65.60 262	86.01 234

QAPM		71.88 158	69.33 186	79.52 40	82.20 151	54.30 97	86.30 104	88.77 43	56.61 288	59.72 252	87.48 171	33.90 302	95.36 13	47.48 324	81.49 72	88.90 153

ECVR-MVS		71.81 159	71.00 153	74.26 213	80.12 216	43.49 357	84.69 166	82.16 202	64.02 129	64.64 182	87.43 173	35.04 286	89.21 149	61.24 205	79.66 96	90.08 121

PAPM_NR		71.80 160	69.98 176	77.26 110	81.54 175	53.34 122	78.60 322	85.25 123	53.46 324	60.53 245	88.66 137	45.69 123	89.24 146	56.49 258	79.62 98	89.19 147

mPP-MVS		71.79 161	70.38 165	76.04 147	82.65 138	52.06 157	84.45 175	81.78 216	55.59 299	62.05 229	89.68 118	33.48 306	88.28 197	65.45 169	78.24 111	87.77 190

reproduce-ours		71.77 162	70.43 162	75.78 155	81.96 154	49.54 231	82.54 240	81.01 231	48.77 360	69.21 128	90.96 82	37.13 252	89.40 140	66.28 158	76.01 142	88.39 176

our_new_method		71.77 162	70.43 162	75.78 155	81.96 154	49.54 231	82.54 240	81.01 231	48.77 360	69.21 128	90.96 82	37.13 252	89.40 140	66.28 158	76.01 142	88.39 176

xiu_mvs_v1_base_debu		71.60 164	70.29 168	75.55 165	77.26 278	53.15 128	85.34 133	79.37 268	55.83 296	72.54 84	90.19 106	22.38 392	86.66 256	73.28 109	76.39 133	86.85 216

xiu_mvs_v1_base		71.60 164	70.29 168	75.55 165	77.26 278	53.15 128	85.34 133	79.37 268	55.83 296	72.54 84	90.19 106	22.38 392	86.66 256	73.28 109	76.39 133	86.85 216

xiu_mvs_v1_base_debi		71.60 164	70.29 168	75.55 165	77.26 278	53.15 128	85.34 133	79.37 268	55.83 296	72.54 84	90.19 106	22.38 392	86.66 256	73.28 109	76.39 133	86.85 216

fmvsm_s_conf0.1_n_2		71.45 167	71.01 152	72.78 254	75.37 315	45.82 332	84.18 184	64.59 414	64.02 129	75.67 51	93.02 36	34.99 288	85.99 282	81.18 46	66.04 260	86.52 226

hse-mvs2		71.44 168	70.68 156	73.73 232	76.34 293	47.44 304	79.45 315	79.47 267	68.08 53	71.97 95	86.01 198	42.50 174	86.93 247	78.82 56	53.46 377	86.83 219

test_fmvsmvis_n_1920		71.29 169	70.38 165	74.00 221	71.04 374	48.79 254	79.19 318	64.62 412	62.75 162	66.73 148	91.99 58	40.94 195	88.35 190	83.00 28	73.18 179	84.85 260

icg_test_0407_2		71.26 170	69.99 175	75.09 187	82.26 145	50.87 187	79.65 311	85.16 128	62.91 157	63.68 205	86.07 192	35.56 278	84.32 312	64.03 181	70.55 213	90.09 117

KinetiMVS		71.15 171	69.25 189	76.82 125	77.99 261	50.49 200	85.05 150	86.51 89	59.78 216	64.10 193	85.34 207	32.16 320	91.33 77	58.82 228	73.54 176	88.64 162

EPP-MVSNet		71.14 172	70.07 174	74.33 210	79.18 234	46.52 316	83.81 198	86.49 90	56.32 293	57.95 288	84.90 216	54.23 39	89.14 151	58.14 239	69.65 224	87.33 201

VPA-MVSNet		71.12 173	70.66 157	72.49 262	78.75 244	44.43 346	87.64 67	90.02 20	63.97 133	65.02 174	81.58 278	42.14 180	87.42 231	63.42 188	63.38 284	85.63 246

1314		71.11 174	69.41 183	76.22 139	79.32 230	50.49 200	80.23 301	85.14 134	59.44 224	58.93 269	88.89 133	33.83 304	89.60 136	61.49 203	77.42 120	88.57 167

reproduce_model		71.07 175	69.67 180	75.28 182	81.51 178	48.82 253	81.73 263	80.57 240	47.81 366	68.26 137	90.78 90	36.49 267	88.60 177	65.12 174	74.76 165	88.42 175

test1111		71.06 176	70.42 164	72.97 249	79.48 226	41.49 382	84.82 163	82.74 196	64.20 126	62.98 215	87.43 173	35.20 283	87.92 207	58.54 231	78.42 109	89.49 138

tpmrst		71.04 177	69.77 178	74.86 195	83.19 115	55.86 50	75.64 338	78.73 287	67.88 58	64.99 176	73.73 362	49.96 72	79.56 365	65.92 161	67.85 241	89.14 149

MVP-Stereo		70.97 178	70.44 161	72.59 259	76.03 303	51.36 180	85.02 153	86.99 79	60.31 210	56.53 316	78.92 304	40.11 207	90.00 120	60.00 220	90.01 7	76.41 382
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 179	69.91 177	74.12 217	77.95 262	49.57 225	85.76 117	82.59 197	63.60 142	62.15 226	83.28 244	36.04 274	88.30 195	65.46 167	72.34 191	84.49 262

SR-MVS		70.92 180	69.73 179	74.50 202	83.38 110	50.48 202	84.27 181	79.35 272	48.96 358	66.57 154	90.45 96	33.65 305	87.11 240	66.42 155	74.56 167	85.91 239

tpm2		70.82 181	68.44 201	77.98 86	80.78 199	56.11 44	74.21 352	81.28 226	60.24 211	68.04 141	75.27 350	52.26 50	88.50 185	55.82 266	68.03 238	89.33 142

ACMMP		70.81 182	69.29 187	75.39 174	81.52 177	51.92 163	83.43 209	83.03 191	56.67 287	58.80 274	88.91 132	31.92 325	88.58 178	65.89 163	73.39 177	85.67 243
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
OPM-MVS		70.75 183	69.58 181	74.26 213	75.55 313	51.34 181	86.05 111	83.29 186	61.94 179	62.95 216	85.77 199	34.15 299	88.44 186	65.44 170	71.07 206	82.99 296
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt11		70.68 184	69.10 192	75.40 171	75.33 316	50.85 191	81.57 271	78.00 302	66.99 76	64.96 177	85.52 204	39.52 213	86.81 250	68.86 139	61.15 304	88.56 168

viewmsd2359difaftdt		70.68 184	69.10 192	75.40 171	75.33 316	50.85 191	81.57 271	78.00 302	66.99 76	64.96 177	85.52 204	39.52 213	86.81 250	68.86 139	61.16 303	88.56 168

ab-mvs		70.65 186	69.11 191	75.29 180	80.87 196	46.23 325	73.48 358	85.24 124	59.99 213	66.65 150	80.94 282	43.13 169	88.69 173	63.58 187	68.07 237	90.95 88

Vis-MVSNet		70.61 187	69.34 185	74.42 205	80.95 195	48.49 264	86.03 112	77.51 313	58.74 245	65.55 167	87.78 166	34.37 297	85.95 286	52.53 294	80.61 80	88.80 157
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
guyue		70.53 188	69.12 190	74.76 198	77.61 267	47.53 299	84.86 161	85.17 126	62.70 164	62.18 224	83.74 233	34.72 290	89.86 125	64.69 177	66.38 254	86.87 213

sss		70.49 189	70.13 172	71.58 290	81.59 172	39.02 394	80.78 290	84.71 150	59.34 227	66.61 152	88.09 156	37.17 251	85.52 291	61.82 201	71.02 207	90.20 112

CDS-MVSNet		70.48 190	69.43 182	73.64 234	77.56 270	48.83 252	83.51 206	77.45 314	63.27 150	62.33 222	85.54 203	43.85 151	83.29 327	57.38 253	74.00 169	88.79 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 191	68.56 197	76.20 141	79.78 220	51.52 177	83.49 208	88.58 52	57.62 267	58.60 278	82.79 249	51.03 58	91.48 72	52.84 288	62.36 298	85.59 247

XXY-MVS		70.18 192	69.28 188	72.89 253	77.64 266	42.88 367	85.06 149	87.50 73	62.58 166	62.66 220	82.34 267	43.64 159	89.83 128	58.42 234	63.70 278	85.96 238

SSM_0404		70.13 193	67.87 215	76.88 124	80.22 213	52.00 159	81.71 265	80.18 246	54.07 319	65.36 169	85.05 211	33.09 309	91.03 87	59.40 221	71.80 197	87.63 194

AstraMVS		70.12 194	68.56 197	74.81 196	76.48 291	47.48 301	84.35 178	82.58 199	63.80 136	62.09 228	84.54 218	31.39 332	89.96 122	68.24 146	63.58 279	87.00 210

Anonymous202405211		70.11 195	67.88 212	76.79 129	87.20 45	47.24 308	89.49 35	77.38 316	54.88 311	66.14 157	86.84 182	20.93 401	91.54 71	56.45 261	71.62 199	91.59 60

PCF-MVS	61.03 10	70.10 196	68.40 202	75.22 185	77.15 282	51.99 160	79.30 317	82.12 204	56.47 291	61.88 231	86.48 190	43.98 150	87.24 237	55.37 271	72.79 185	86.43 229
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 197	68.01 208	76.27 137	84.21 92	51.22 185	87.29 79	79.33 274	58.96 241	63.63 208	86.77 183	33.29 308	90.30 114	44.63 342	73.96 170	87.30 203

1112_ss		70.05 198	69.37 184	72.10 272	80.77 200	42.78 368	85.12 148	76.75 326	59.69 219	61.19 237	92.12 52	47.48 91	83.84 317	53.04 286	68.21 236	89.66 131

BH-w/o		70.02 199	68.51 200	74.56 201	82.77 133	50.39 205	86.60 99	78.14 300	59.77 217	59.65 253	85.57 202	39.27 217	87.30 235	49.86 307	74.94 163	85.99 236

FIs		70.00 200	70.24 171	69.30 323	77.93 264	38.55 397	83.99 191	87.72 68	66.86 79	57.66 295	84.17 226	52.28 49	85.31 295	52.72 293	68.80 232	84.02 271

OpenMVS	61.00 11	69.99 201	67.55 222	77.30 106	78.37 256	54.07 105	84.36 177	85.76 106	57.22 275	56.71 313	87.67 169	30.79 336	92.83 37	43.04 350	84.06 56	85.01 255

GeoE		69.96 202	67.88 212	76.22 139	81.11 188	51.71 172	84.15 185	76.74 328	59.83 215	60.91 239	84.38 222	41.56 190	88.10 202	51.67 297	70.57 212	88.84 156

HyFIR lowres test		69.94 203	67.58 220	77.04 115	77.11 283	57.29 22	81.49 277	79.11 277	58.27 251	58.86 272	80.41 286	42.33 176	86.96 245	61.91 199	68.68 234	86.87 213

114514_t		69.87 204	67.88 212	75.85 153	88.38 29	52.35 150	86.94 89	83.68 176	53.70 322	55.68 323	85.60 201	30.07 341	91.20 82	55.84 265	71.02 207	83.99 273

miper_enhance_ethall		69.77 205	68.90 195	72.38 265	78.93 241	49.91 219	83.29 215	78.85 281	64.90 115	59.37 260	79.46 297	52.77 46	85.16 300	63.78 185	58.72 321	82.08 307

SSM_0407		69.71 206	67.38 227	76.69 132	80.45 208	51.81 168	81.36 279	80.18 246	54.07 319	63.82 201	85.05 211	33.09 309	91.01 90	59.40 221	68.97 229	87.25 204

reproduce_monomvs		69.71 206	68.52 199	73.29 244	86.43 53	48.21 277	83.91 194	86.17 99	68.02 57	54.91 328	77.46 319	42.96 171	88.86 167	68.44 142	48.38 390	82.80 301

Anonymous20240529		69.71 206	67.28 229	77.00 118	83.78 100	50.36 209	88.87 48	85.10 135	47.22 370	64.03 195	83.37 242	27.93 351	92.10 60	57.78 248	67.44 243	88.53 171

TR-MVS		69.71 206	67.85 216	75.27 183	82.94 126	48.48 265	87.40 75	80.86 234	57.15 277	64.61 184	87.08 179	32.67 315	89.64 135	46.38 333	71.55 201	87.68 193

EI-MVSNet		69.70 210	68.70 196	72.68 257	75.00 322	48.90 250	79.54 312	87.16 76	61.05 196	63.88 199	83.74 233	45.87 118	90.44 107	57.42 252	64.68 270	78.70 352

test-LLR		69.65 211	69.01 194	71.60 288	78.67 246	48.17 278	85.13 145	79.72 258	59.18 234	63.13 213	82.58 257	36.91 258	80.24 355	60.56 212	75.17 156	86.39 230

APD-MVS_3200maxsize		69.62 212	68.23 206	73.80 229	81.58 173	48.22 276	81.91 255	79.50 265	48.21 364	64.24 192	89.75 117	31.91 326	87.55 226	63.08 189	73.85 173	85.64 245

v2v482		69.55 213	67.64 219	75.26 184	72.32 358	53.83 106	84.93 158	81.94 210	65.37 110	60.80 241	79.25 300	41.62 188	88.98 160	63.03 191	59.51 314	82.98 298

TAMVS		69.51 214	68.16 207	73.56 238	76.30 296	48.71 258	82.57 237	77.17 319	62.10 174	61.32 236	84.23 225	41.90 185	83.46 324	54.80 275	73.09 182	88.50 173

mvsmamba		69.38 215	67.52 224	74.95 193	82.86 130	52.22 156	67.36 397	76.75 326	61.14 193	49.43 369	82.04 272	37.26 248	84.14 313	73.93 101	76.91 126	88.50 173

WB-MVSnew		69.36 216	68.24 205	72.72 256	79.26 232	49.40 237	85.72 122	88.85 40	61.33 189	64.59 185	82.38 263	34.57 294	87.53 227	46.82 330	70.63 210	81.22 328

PVSNet	62.49 8	69.27 217	67.81 217	73.64 234	84.41 86	51.85 165	84.63 170	77.80 307	66.42 86	59.80 251	84.95 215	22.14 396	80.44 353	55.03 272	75.11 159	88.62 165

IMVS_0404		69.11 218	67.25 231	74.68 199	82.26 145	50.87 187	76.74 333	85.16 128	62.91 157	50.76 365	86.07 192	26.76 360	83.06 329	64.03 181	70.55 213	90.09 117

MVS_111021_LR		69.07 219	67.91 210	72.54 260	77.27 277	49.56 228	79.77 309	73.96 356	59.33 229	60.73 242	87.82 165	30.19 340	81.53 338	69.94 129	72.19 194	86.53 225

GA-MVS		69.04 220	66.70 241	76.06 146	75.11 319	52.36 149	83.12 223	80.23 245	63.32 149	60.65 243	79.22 301	30.98 335	88.37 188	61.25 204	66.41 253	87.46 198

cascas		69.01 221	66.13 253	77.66 95	79.36 228	55.41 58	86.99 86	83.75 174	56.69 286	58.92 270	81.35 279	24.31 381	92.10 60	53.23 283	70.61 211	85.46 248

FA-MVS(test-final)		69.00 222	66.60 244	76.19 142	83.48 105	47.96 289	74.73 346	82.07 208	57.27 274	62.18 224	78.47 308	36.09 272	92.89 35	53.76 282	71.32 205	87.73 191

cl22		68.85 223	67.69 218	72.35 266	78.07 260	49.98 218	82.45 244	78.48 294	62.50 169	58.46 283	77.95 311	49.99 70	85.17 299	62.55 193	58.72 321	81.90 310

FMVSNet3		68.84 224	67.40 226	73.19 246	85.05 74	48.53 262	85.71 123	85.36 114	60.90 202	57.58 297	79.15 302	42.16 179	86.77 252	47.25 326	63.40 281	84.27 266

UniMVSNet_NR-MVSNet		68.82 225	68.29 204	70.40 309	75.71 310	42.59 370	84.23 182	86.78 82	66.31 88	58.51 279	82.45 260	51.57 53	84.64 309	53.11 284	55.96 354	83.96 277

v1144		68.81 226	66.82 237	74.80 197	72.34 357	53.46 114	84.68 167	81.77 217	64.25 124	60.28 246	77.91 312	40.23 204	88.95 162	60.37 217	59.52 313	81.97 308

IS-MVSNet		68.80 227	67.55 222	72.54 260	78.50 253	43.43 359	81.03 283	79.35 272	59.12 237	57.27 305	86.71 184	46.05 116	87.70 219	44.32 345	75.60 150	86.49 227

PS-MVSNAJss		68.78 228	67.17 232	73.62 236	73.01 348	48.33 272	84.95 157	84.81 145	59.30 230	58.91 271	79.84 292	37.77 231	88.86 167	62.83 192	63.12 290	83.67 284

thres200		68.71 229	67.27 230	73.02 247	84.73 79	46.76 312	85.03 152	87.73 67	62.34 172	59.87 249	83.45 240	43.15 167	88.32 193	31.25 404	67.91 240	83.98 275

UGNet		68.71 229	67.11 233	73.50 239	80.55 206	47.61 298	84.08 187	78.51 293	59.45 223	65.68 166	82.73 253	23.78 383	85.08 302	52.80 289	76.40 132	87.80 189
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
miper_ehance_all_eth		68.70 231	67.58 220	72.08 273	76.91 287	49.48 234	82.47 243	78.45 295	62.68 165	58.28 287	77.88 313	50.90 59	85.01 303	61.91 199	58.72 321	81.75 312

test_vis1_n_1920		68.59 232	68.31 203	69.44 322	69.16 391	41.51 381	84.63 170	68.58 401	58.80 243	73.26 74	88.37 146	25.30 371	80.60 350	79.10 53	67.55 242	86.23 232

VortexMVS		68.49 233	66.84 236	73.46 240	81.10 189	48.75 255	84.63 170	84.73 149	62.05 175	57.22 307	77.08 327	34.54 296	89.20 150	63.08 189	57.12 343	82.43 304

EPMVS		68.45 234	65.44 272	77.47 101	84.91 77	56.17 43	71.89 378	81.91 213	61.72 182	60.85 240	72.49 376	36.21 270	87.06 242	47.32 325	71.62 199	89.17 148

test-mter		68.36 235	67.29 228	71.60 288	78.67 246	48.17 278	85.13 145	79.72 258	53.38 325	63.13 213	82.58 257	27.23 357	80.24 355	60.56 212	75.17 156	86.39 230

tpm		68.36 235	67.48 225	70.97 300	79.93 219	51.34 181	76.58 335	78.75 286	67.73 61	63.54 211	74.86 352	48.33 80	72.36 415	53.93 280	63.71 277	89.21 146

tttt0517		68.33 237	66.29 249	74.46 203	78.08 259	49.06 242	80.88 288	89.08 33	54.40 317	54.75 332	80.77 284	51.31 55	90.33 111	49.35 311	58.01 333	83.99 273

BH-untuned		68.28 238	66.40 246	73.91 224	81.62 170	50.01 217	85.56 128	77.39 315	57.63 266	57.47 302	83.69 236	36.36 268	87.08 241	44.81 340	73.08 183	84.65 261

SR-MVS-dyc-post		68.27 239	66.87 235	72.48 263	80.96 192	48.14 280	81.54 273	76.98 322	46.42 377	62.75 218	89.42 122	31.17 334	86.09 277	60.52 214	72.06 195	83.19 292

v148		68.24 240	66.35 247	73.88 225	71.76 363	51.47 178	84.23 182	81.90 214	63.69 140	58.94 268	76.44 337	43.72 157	87.78 216	60.63 210	55.86 356	82.39 305

AUN-MVS		68.20 241	66.35 247	73.76 230	76.37 292	47.45 303	79.52 314	79.52 264	60.98 198	62.34 221	86.02 196	36.59 266	86.94 246	62.32 195	53.47 376	86.89 212

SSC-MVS3.2		68.13 242	66.89 234	71.85 286	82.26 145	43.97 352	82.09 251	89.29 28	71.74 17	61.12 238	79.83 293	34.60 293	87.45 229	41.23 356	59.85 311	84.14 267

c3_l		67.97 243	66.66 242	71.91 284	76.20 299	49.31 239	82.13 250	78.00 302	61.99 177	57.64 296	76.94 329	49.41 75	84.93 304	60.62 211	57.01 344	81.49 316

v1192		67.96 244	65.74 264	74.63 200	71.79 362	53.43 119	84.06 189	80.99 233	63.19 152	59.56 256	77.46 319	37.50 243	88.65 174	58.20 238	58.93 320	81.79 311

v144192		67.86 245	65.76 263	74.16 215	71.68 364	53.09 132	84.14 186	80.83 235	62.85 161	59.21 265	77.28 323	39.30 216	88.00 206	58.67 230	57.88 337	81.40 321

HPM-MVS_fast		67.86 245	66.28 250	72.61 258	80.67 203	48.34 270	81.18 281	75.95 337	50.81 345	59.55 257	88.05 159	27.86 352	85.98 283	58.83 227	73.58 175	83.51 285

AdaColmap		67.86 245	65.48 269	75.00 191	88.15 36	54.99 76	86.10 109	76.63 331	49.30 355	57.80 291	86.65 187	29.39 344	88.94 164	45.10 339	70.21 219	81.06 329

sd_testset		67.79 248	65.95 258	73.32 241	81.70 163	46.33 322	68.99 390	80.30 244	66.58 81	61.64 233	82.38 263	30.45 338	87.63 222	55.86 264	65.60 262	86.01 234

UniMVSNet (Re)		67.71 249	66.80 238	70.45 307	74.44 329	42.93 366	82.42 245	84.90 142	63.69 140	59.63 254	80.99 281	47.18 93	85.23 298	51.17 301	56.75 345	83.19 292

V42		67.66 250	65.60 268	73.86 226	70.69 378	53.63 111	81.50 275	78.61 290	63.85 135	59.49 259	77.49 318	37.98 228	87.65 221	62.33 194	58.43 324	80.29 339

dmvs_re		67.61 251	66.00 256	72.42 264	81.86 158	43.45 358	64.67 405	80.00 250	69.56 43	60.07 248	85.00 214	34.71 291	87.63 222	51.48 298	66.68 247	86.17 233

WR-MVS		67.58 252	66.76 239	70.04 316	75.92 308	45.06 342	86.23 105	85.28 121	64.31 122	58.50 281	81.00 280	44.80 145	82.00 337	49.21 313	55.57 359	83.06 295

tfpn200view9		67.57 253	66.13 253	71.89 285	84.05 94	45.07 339	83.40 211	87.71 69	60.79 203	57.79 292	82.76 250	43.53 160	87.80 213	28.80 411	66.36 255	82.78 302

FMVSNet2		67.57 253	65.79 262	72.90 251	82.71 135	47.97 287	85.15 144	84.93 141	58.55 248	56.71 313	78.26 310	36.72 263	86.67 255	46.15 335	62.94 292	84.07 270

FC-MVSNet-test		67.49 255	67.91 210	66.21 357	76.06 301	33.06 419	80.82 289	87.18 75	64.44 119	54.81 330	82.87 247	50.40 67	82.60 330	48.05 321	66.55 251	82.98 298

v1921920		67.45 256	65.23 276	74.10 218	71.51 367	52.90 138	83.75 200	80.44 241	62.48 170	59.12 266	77.13 324	36.98 256	87.90 209	57.53 250	58.14 331	81.49 316

UWE-MVS-28		67.43 257	67.98 209	65.75 359	75.66 311	34.74 409	80.00 307	88.17 57	64.21 125	57.27 305	84.14 227	45.68 124	78.82 368	44.33 343	72.40 190	83.70 282

cl____		67.43 257	65.93 259	71.95 281	76.33 294	48.02 285	82.58 236	79.12 276	61.30 191	56.72 312	76.92 330	46.12 112	86.44 264	57.98 241	56.31 348	81.38 323

DIV-MVS_self_test		67.43 257	65.93 259	71.94 282	76.33 294	48.01 286	82.57 237	79.11 277	61.31 190	56.73 311	76.92 330	46.09 115	86.43 265	57.98 241	56.31 348	81.39 322

gg-mvs-nofinetune		67.43 257	64.53 284	76.13 144	85.95 56	47.79 295	64.38 406	88.28 56	39.34 410	66.62 151	41.27 450	58.69 15	89.00 157	49.64 309	86.62 31	91.59 60

thres400		67.40 261	66.13 253	71.19 296	84.05 94	45.07 339	83.40 211	87.71 69	60.79 203	57.79 292	82.76 250	43.53 160	87.80 213	28.80 411	66.36 255	80.71 334

UA-Net		67.32 262	66.23 251	70.59 305	78.85 242	41.23 385	73.60 356	75.45 341	61.54 186	66.61 152	84.53 221	38.73 222	86.57 261	42.48 355	74.24 168	83.98 275

v8		67.25 263	64.99 280	74.04 219	72.89 351	53.31 124	82.37 246	80.11 249	61.54 186	54.29 338	76.02 346	42.89 172	88.41 187	58.43 232	56.36 346	80.39 338

NR-MVSNet		67.25 263	65.99 257	71.04 299	73.27 345	43.91 353	85.32 137	84.75 148	66.05 98	53.65 345	82.11 270	45.05 135	85.97 285	47.55 323	56.18 351	83.24 290

Test_1112_low_res		67.18 265	66.23 251	70.02 317	78.75 244	41.02 386	83.43 209	73.69 358	57.29 273	58.45 284	82.39 262	45.30 132	80.88 344	50.50 303	66.26 259	88.16 179

CPTT-MVS		67.15 266	65.84 261	71.07 298	80.96 192	50.32 211	81.94 254	74.10 352	46.18 383	57.91 289	87.64 170	29.57 342	81.31 340	64.10 180	70.18 220	81.56 315

test_cas_vis1_n_1920		67.10 267	66.60 244	68.59 335	65.17 413	43.23 363	83.23 218	69.84 394	55.34 305	70.67 117	87.71 168	24.70 378	76.66 392	78.57 60	64.20 272	85.89 240

GBi-Net		67.09 268	65.47 270	71.96 278	82.71 135	46.36 319	83.52 202	83.31 183	58.55 248	57.58 297	76.23 341	36.72 263	86.20 269	47.25 326	63.40 281	83.32 287

test1		67.09 268	65.47 270	71.96 278	82.71 135	46.36 319	83.52 202	83.31 183	58.55 248	57.58 297	76.23 341	36.72 263	86.20 269	47.25 326	63.40 281	83.32 287

PatchmatchNet		67.07 270	63.63 292	77.40 103	83.10 116	58.03 11	72.11 376	77.77 308	58.85 242	59.37 260	70.83 389	37.84 230	84.93 304	42.96 351	69.83 222	89.26 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 271	64.68 282	73.93 223	71.38 371	52.66 143	83.39 213	79.98 251	61.97 178	58.44 285	77.11 325	35.25 282	87.81 211	56.46 260	58.15 329	81.33 324

eth_miper_zixun_eth		66.98 272	65.28 275	72.06 274	75.61 312	50.40 204	81.00 284	76.97 325	62.00 176	56.99 309	76.97 328	44.84 142	85.58 290	58.75 229	54.42 367	80.21 340

TranMVSNet+NR-MVSNet		66.94 273	65.61 267	70.93 301	73.45 341	43.38 360	83.02 227	84.25 161	65.31 112	58.33 286	81.90 274	39.92 211	85.52 291	49.43 310	54.89 363	83.89 279

thres100view900		66.87 274	65.42 273	71.24 294	83.29 112	43.15 364	81.67 266	87.78 64	59.04 238	55.92 321	82.18 269	43.73 155	87.80 213	28.80 411	66.36 255	82.78 302

DU-MVS		66.84 275	65.74 264	70.16 312	73.27 345	42.59 370	81.50 275	82.92 194	63.53 144	58.51 279	82.11 270	40.75 197	84.64 309	53.11 284	55.96 354	83.24 290

MonoMVSNet		66.80 276	64.41 285	73.96 222	76.21 298	48.07 283	76.56 336	78.26 298	64.34 121	54.32 337	74.02 359	37.21 250	86.36 267	64.85 176	53.96 370	87.45 199

IterMVS-LS		66.63 277	65.36 274	70.42 308	75.10 320	48.90 250	81.45 278	76.69 330	61.05 196	55.71 322	77.10 326	45.86 119	83.65 321	57.44 251	57.88 337	78.70 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 278	64.20 289	73.83 228	72.59 354	53.37 120	81.88 256	79.91 255	61.11 194	54.09 340	75.60 348	40.06 208	88.26 198	56.47 259	56.10 352	79.86 344

LuminaMVS		66.60 279	64.37 286	73.27 245	70.06 384	49.57 225	80.77 291	81.76 218	50.81 345	60.56 244	78.41 309	24.50 379	87.26 236	64.24 179	68.25 235	82.99 296

Fast-Effi-MVS+-dtu		66.53 280	64.10 290	73.84 227	72.41 356	52.30 154	84.73 164	75.66 338	59.51 222	56.34 318	79.11 303	28.11 349	85.85 288	57.74 249	63.29 285	83.35 286

thres600view7		66.46 281	65.12 278	70.47 306	83.41 106	43.80 355	82.15 248	87.78 64	59.37 226	56.02 320	82.21 268	43.73 155	86.90 248	26.51 423	64.94 266	80.71 334

LPG-MVS_test		66.44 282	64.58 283	72.02 275	74.42 330	48.60 259	83.07 225	80.64 237	54.69 313	53.75 343	83.83 231	25.73 369	86.98 243	60.33 218	64.71 267	80.48 336

mamba_0408		66.33 283	62.87 294	76.70 131	80.45 208	51.81 168	46.11 442	78.90 279	55.46 302	63.82 201	84.54 218	31.91 326	91.03 87	55.68 267	68.97 229	87.25 204

tpm cat1		66.28 284	62.78 296	76.77 130	81.40 180	57.14 24	70.03 385	77.19 318	53.00 328	58.76 275	70.73 392	46.17 111	86.73 254	43.27 349	64.46 271	86.44 228

EPNet_dtu		66.25 285	66.71 240	64.87 368	78.66 249	34.12 414	82.80 231	75.51 339	61.75 181	64.47 190	86.90 181	37.06 254	72.46 414	43.65 348	69.63 225	88.02 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 286	64.96 281	70.08 314	75.17 318	49.64 224	82.01 252	74.48 349	62.15 173	57.83 290	76.08 345	30.59 337	83.79 318	65.40 171	60.93 306	76.81 375

ACMP	61.11 9	66.24 286	64.33 287	72.00 277	74.89 324	49.12 241	83.18 220	79.83 256	55.41 304	52.29 352	82.68 254	25.83 367	86.10 275	60.89 207	63.94 276	80.78 332
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 288	63.67 291	73.31 242	83.07 119	48.75 255	86.01 113	84.67 152	45.27 387	56.54 315	76.67 335	28.06 350	88.95 162	52.78 290	59.95 308	82.23 306

OMC-MVS		65.97 289	65.06 279	68.71 332	72.97 349	42.58 372	78.61 321	75.35 342	54.72 312	59.31 262	86.25 191	33.30 307	77.88 379	57.99 240	67.05 245	85.66 244

X-MVStestdata		65.85 290	62.20 302	76.81 126	83.41 106	52.48 145	84.88 159	83.20 188	58.03 254	63.91 197	4.82 469	35.50 280	89.78 129	65.50 164	80.50 82	88.16 179

Elysia		65.59 291	62.65 297	74.42 205	69.85 385	49.46 235	80.04 304	82.11 205	46.32 380	58.74 276	79.64 294	20.30 404	88.57 181	55.48 269	71.37 202	85.22 251

StellarMVS		65.59 291	62.65 297	74.42 205	69.85 385	49.46 235	80.04 304	82.11 205	46.32 380	58.74 276	79.64 294	20.30 404	88.57 181	55.48 269	71.37 202	85.22 251

Vis-MVSNet (Re-imp)		65.52 293	65.63 266	65.17 366	77.49 272	30.54 427	75.49 342	77.73 309	59.34 227	52.26 354	86.69 185	49.38 76	80.53 352	37.07 371	75.28 154	84.42 264

SD_0403		65.51 294	65.18 277	66.48 356	78.37 256	29.94 434	74.64 349	78.55 292	66.47 85	54.87 329	84.35 224	38.20 227	82.47 331	38.90 363	72.30 193	87.05 209

Baseline_NR-MVSNet		65.49 295	64.27 288	69.13 324	74.37 332	41.65 379	83.39 213	78.85 281	59.56 221	59.62 255	76.88 332	40.75 197	87.44 230	49.99 305	55.05 361	78.28 361

FMVSNet1		64.57 296	62.11 303	71.96 278	77.32 276	46.36 319	83.52 202	83.31 183	52.43 333	54.42 335	76.23 341	27.80 353	86.20 269	42.59 354	61.34 302	83.32 287

dp		64.41 297	61.58 306	72.90 251	82.40 142	54.09 104	72.53 366	76.59 332	60.39 209	55.68 323	70.39 393	35.18 284	76.90 390	39.34 362	61.71 300	87.73 191

ACMM	58.35 12	64.35 298	62.01 304	71.38 292	74.21 334	48.51 263	82.25 247	79.66 260	47.61 368	54.54 334	80.11 288	25.26 372	86.00 281	51.26 299	63.16 288	79.64 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 299	60.43 320	75.30 179	80.85 197	49.86 221	68.28 394	78.37 296	50.26 351	59.31 262	73.79 361	26.19 365	91.92 63	40.19 359	66.67 248	84.12 268

pm-mvs1		64.12 300	62.56 299	68.78 330	71.68 364	38.87 395	82.89 229	81.57 219	55.54 301	53.89 342	77.82 314	37.73 234	86.74 253	48.46 319	53.49 375	80.72 333

SSM_04072		64.04 301	62.87 294	67.56 342	80.45 208	51.81 168	46.11 442	78.90 279	55.46 302	63.82 201	84.54 218	31.91 326	63.62 428	55.68 267	68.97 229	87.25 204

miper_lstm_enhance		63.91 302	62.30 301	68.75 331	75.06 321	46.78 311	69.02 389	81.14 227	59.68 220	52.76 349	72.39 379	40.71 199	77.99 377	56.81 256	53.09 378	81.48 318

SCA		63.84 303	60.01 324	75.32 176	78.58 251	57.92 12	61.61 418	77.53 312	56.71 285	57.75 294	70.77 390	31.97 323	79.91 361	48.80 315	56.36 346	88.13 182

test_djsdf		63.84 303	61.56 307	70.70 304	68.78 393	44.69 343	81.63 267	81.44 222	50.28 348	52.27 353	76.26 340	26.72 361	86.11 273	60.83 208	55.84 357	81.29 327

IterMVS		63.77 305	61.67 305	70.08 314	72.68 353	51.24 184	80.44 296	75.51 339	60.51 208	51.41 357	73.70 365	32.08 322	78.91 366	54.30 277	54.35 368	80.08 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 306	63.56 293	63.40 378	81.73 161	34.28 411	80.97 285	81.02 229	60.93 200	55.06 326	82.64 255	48.00 87	80.81 346	23.42 434	58.32 325	75.10 393

D2MVS		63.49 307	61.39 309	69.77 318	69.29 390	48.93 249	78.89 320	77.71 310	60.64 207	49.70 368	72.10 384	27.08 358	83.48 323	54.48 276	62.65 294	76.90 373

tt0805		63.39 308	61.31 311	69.64 319	69.36 389	38.87 395	78.00 325	85.48 108	48.82 359	55.66 325	81.66 276	24.38 380	86.37 266	49.04 314	59.36 317	83.68 283

pmmvs4		63.34 309	61.07 314	70.16 312	70.14 381	50.53 199	79.97 308	71.41 383	55.08 307	54.12 339	78.58 306	32.79 314	82.09 336	50.33 304	57.22 342	77.86 365

jajsoiax		63.21 310	60.84 315	70.32 310	68.33 398	44.45 345	81.23 280	81.05 228	53.37 326	50.96 362	77.81 315	17.49 421	85.49 293	59.31 223	58.05 332	81.02 330

MIMVSNet		63.12 311	60.29 321	71.61 287	75.92 308	46.65 314	65.15 402	81.94 210	59.14 236	54.65 333	69.47 396	25.74 368	80.63 349	41.03 358	69.56 226	87.55 196

CL-MVSNet_self_test		62.98 312	61.14 313	68.50 337	65.86 408	42.96 365	84.37 176	82.98 192	60.98 198	53.95 341	72.70 375	40.43 202	83.71 320	41.10 357	47.93 393	78.83 351

mvs_tets		62.96 313	60.55 317	70.19 311	68.22 401	44.24 350	80.90 287	80.74 236	52.99 329	50.82 364	77.56 316	16.74 425	85.44 294	59.04 226	57.94 334	80.89 331

TransMVSNet (Re)		62.82 314	60.76 316	69.02 325	73.98 338	41.61 380	86.36 101	79.30 275	56.90 279	52.53 350	76.44 337	41.85 186	87.60 225	38.83 364	40.61 417	77.86 365

pmmvs5		62.80 315	61.18 312	67.66 341	69.53 388	42.37 375	82.65 234	75.19 343	54.30 318	52.03 355	78.51 307	31.64 330	80.67 348	48.60 317	58.15 329	79.95 343

test0.0.03 1		62.54 316	62.44 300	62.86 383	72.28 360	29.51 437	82.93 228	78.78 284	59.18 234	53.07 348	82.41 261	36.91 258	77.39 384	37.45 367	58.96 319	81.66 314

UniMVSNet_ETH3D		62.51 317	60.49 318	68.57 336	68.30 399	40.88 388	73.89 353	79.93 254	51.81 339	54.77 331	79.61 296	24.80 376	81.10 341	49.93 306	61.35 301	83.73 281

v7n		62.50 318	59.27 329	72.20 270	67.25 404	49.83 222	77.87 327	80.12 248	52.50 332	48.80 374	73.07 370	32.10 321	87.90 209	46.83 329	54.92 362	78.86 350

CR-MVSNet		62.47 319	59.04 331	72.77 255	73.97 339	56.57 34	60.52 421	71.72 378	60.04 212	57.49 300	65.86 410	38.94 219	80.31 354	42.86 352	59.93 309	81.42 319

tpmvs		62.45 320	59.42 327	71.53 291	83.93 96	54.32 96	70.03 385	77.61 311	51.91 336	53.48 346	68.29 402	37.91 229	86.66 256	33.36 394	58.27 327	73.62 404

EG-PatchMatch MVS		62.40 321	59.59 325	70.81 302	73.29 343	49.05 243	85.81 115	84.78 146	51.85 338	44.19 397	73.48 368	15.52 430	89.85 127	40.16 360	67.24 244	73.54 405

XVG-OURS-SEG-HR		62.02 322	59.54 326	69.46 321	65.30 411	45.88 329	65.06 403	73.57 360	46.45 376	57.42 303	83.35 243	26.95 359	78.09 373	53.77 281	64.03 274	84.42 264

XVG-OURS		61.88 323	59.34 328	69.49 320	65.37 410	46.27 323	64.80 404	73.49 362	47.04 372	57.41 304	82.85 248	25.15 373	78.18 371	53.00 287	64.98 264	84.01 272

TAPA-MVS	56.12 14	61.82 324	60.18 323	66.71 352	78.48 254	37.97 401	75.19 344	76.41 334	46.82 373	57.04 308	86.52 189	27.67 355	77.03 387	26.50 424	67.02 246	85.14 253
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 325	61.35 310	62.00 386	81.73 161	30.09 431	80.97 285	81.02 229	60.93 200	55.06 326	82.64 255	35.09 285	80.81 346	16.40 451	58.32 325	75.10 393

tfpnnormal		61.47 326	59.09 330	68.62 334	76.29 297	41.69 378	81.14 282	85.16 128	54.48 315	51.32 358	73.63 366	32.32 318	86.89 249	21.78 438	55.71 358	77.29 371

PVSNet_0	57.04 13	61.19 327	57.24 340	73.02 247	77.45 273	50.31 212	79.43 316	77.36 317	63.96 134	47.51 384	72.45 378	25.03 374	83.78 319	52.76 292	19.22 457	84.96 257

PLC	52.38 18	60.89 328	58.97 332	66.68 354	81.77 160	45.70 334	78.96 319	74.04 355	43.66 399	47.63 381	83.19 246	23.52 386	77.78 382	37.47 366	60.46 307	76.55 381
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 329	60.44 319	62.07 384	75.00 322	32.73 421	79.54 312	73.49 362	36.98 420	56.28 319	83.74 233	29.28 345	69.53 423	46.48 332	63.23 286	83.94 278

CNLPA		60.59 330	58.44 334	67.05 349	79.21 233	47.26 307	79.75 310	64.34 416	42.46 405	51.90 356	83.94 229	27.79 354	75.41 400	37.12 369	59.49 315	78.47 356

anonymousdsp		60.46 331	57.65 337	68.88 326	63.63 422	45.09 338	72.93 362	78.63 289	46.52 375	51.12 359	72.80 374	21.46 399	83.07 328	57.79 247	53.97 369	78.47 356

testing3		59.97 332	60.19 322	59.32 399	77.60 268	30.01 433	81.75 262	81.79 215	53.54 323	50.34 366	79.94 289	48.99 79	76.91 388	17.19 449	50.59 385	71.03 422

ACMH	53.70 16	59.78 333	55.94 351	71.28 293	76.59 290	48.35 269	80.15 303	76.11 335	49.74 353	41.91 408	73.45 369	16.50 427	90.31 112	31.42 402	57.63 340	75.17 391
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 334	57.15 341	67.09 347	66.01 406	36.86 405	80.50 294	78.64 288	45.05 389	49.05 372	73.94 360	27.28 356	86.10 275	43.96 347	49.94 387	78.31 360

MSDG		59.44 335	55.14 355	72.32 268	74.69 325	50.71 194	74.39 351	73.58 359	44.44 394	43.40 402	77.52 317	19.45 408	90.87 98	31.31 403	57.49 341	75.38 388

RPMNet		59.29 336	54.25 360	74.42 205	73.97 339	56.57 34	60.52 421	76.98 322	35.72 425	57.49 300	58.87 435	37.73 234	85.26 297	27.01 422	59.93 309	81.42 319

DP-MVS		59.24 337	56.12 349	68.63 333	88.24 34	50.35 210	82.51 242	64.43 415	41.10 407	46.70 389	78.77 305	24.75 377	88.57 181	22.26 436	56.29 350	66.96 428

OpenMVS_ROB	53.19 17	59.20 338	56.00 350	68.83 328	71.13 373	44.30 347	83.64 201	75.02 344	46.42 377	46.48 391	73.03 371	18.69 413	88.14 199	27.74 419	61.80 299	74.05 401

IterMVS-SCA-FT		59.12 339	58.81 333	60.08 397	70.68 379	45.07 339	80.42 297	74.25 350	43.54 400	50.02 367	73.73 362	31.97 323	56.74 443	51.06 302	53.60 374	78.42 358

our_test_3		59.11 340	55.08 356	71.18 297	71.42 369	53.29 125	81.96 253	74.52 348	48.32 362	42.08 406	69.28 399	28.14 348	82.15 334	34.35 390	45.68 407	78.11 364

Anonymous20231206		59.08 341	57.59 338	63.55 375	68.77 394	32.14 425	80.26 300	79.78 257	50.00 352	49.39 370	72.39 379	26.64 362	78.36 370	33.12 397	57.94 334	80.14 341

KD-MVS_2432*1600		59.04 342	56.44 346	66.86 350	79.07 235	45.87 330	72.13 374	80.42 242	55.03 308	48.15 376	71.01 387	36.73 261	78.05 375	35.21 384	30.18 443	76.67 376

miper_refine_blended		59.04 342	56.44 346	66.86 350	79.07 235	45.87 330	72.13 374	80.42 242	55.03 308	48.15 376	71.01 387	36.73 261	78.05 375	35.21 384	30.18 443	76.67 376

WR-MVS_H		58.91 344	58.04 336	61.54 390	69.07 392	33.83 416	76.91 331	81.99 209	51.40 341	48.17 375	74.67 353	40.23 204	74.15 403	31.78 401	48.10 391	76.64 379

LCM-MVSNet-Re		58.82 345	56.54 344	65.68 360	79.31 231	29.09 440	61.39 420	45.79 440	60.73 205	37.65 426	72.47 377	31.42 331	81.08 342	49.66 308	70.41 217	86.87 213

Patchmatch-RL test		58.72 346	54.32 359	71.92 283	63.91 420	44.25 349	61.73 417	55.19 431	57.38 272	49.31 371	54.24 441	37.60 239	80.89 343	62.19 197	47.28 398	90.63 96

FMVSNet5		58.61 347	56.45 345	65.10 367	77.20 281	39.74 390	74.77 345	77.12 320	50.27 350	43.28 403	67.71 403	26.15 366	76.90 390	36.78 375	54.78 364	78.65 354

ppachtmachnet_test		58.56 348	54.34 358	71.24 294	71.42 369	54.74 84	81.84 258	72.27 372	49.02 357	45.86 394	68.99 400	26.27 363	83.30 326	30.12 406	43.23 412	75.69 385

ACMH+	54.58 15	58.55 349	55.24 353	68.50 337	74.68 326	45.80 333	80.27 299	70.21 391	47.15 371	42.77 405	75.48 349	16.73 426	85.98 283	35.10 388	54.78 364	73.72 403

CP-MVSNet		58.54 350	57.57 339	61.46 391	68.50 396	33.96 415	76.90 332	78.60 291	51.67 340	47.83 379	76.60 336	34.99 288	72.79 412	35.45 381	47.58 395	77.64 369

PEN-MVS		58.35 351	57.15 341	61.94 387	67.55 403	34.39 410	77.01 330	78.35 297	51.87 337	47.72 380	76.73 334	33.91 301	73.75 407	34.03 391	47.17 399	77.68 367

PS-CasMVS		58.12 352	57.03 343	61.37 392	68.24 400	33.80 417	76.73 334	78.01 301	51.20 343	47.54 383	76.20 344	32.85 312	72.76 413	35.17 386	47.37 397	77.55 370

mmtdpeth		57.93 353	54.78 357	67.39 345	72.32 358	43.38 360	72.72 364	68.93 399	54.45 316	56.85 310	62.43 421	17.02 423	83.46 324	57.95 243	30.31 442	75.31 389

dmvs_testset		57.65 354	58.21 335	55.97 410	74.62 327	9.82 471	63.75 408	63.34 418	67.23 68	48.89 373	83.68 238	39.12 218	76.14 395	23.43 432	59.80 312	81.96 309

UnsupCasMVSNet_eth		57.56 355	55.15 354	64.79 369	64.57 418	33.12 418	73.17 361	83.87 173	58.98 240	41.75 409	70.03 394	22.54 391	79.92 359	46.12 336	35.31 430	81.32 326

CHOSEN 280x420		57.53 356	56.38 348	60.97 395	74.01 337	48.10 282	46.30 441	54.31 433	48.18 365	50.88 363	77.43 321	38.37 225	59.16 439	54.83 273	63.14 289	75.66 386

DTE-MVSNet		57.03 357	55.73 352	60.95 396	65.94 407	32.57 422	75.71 337	77.09 321	51.16 344	46.65 390	76.34 339	32.84 313	73.22 411	30.94 405	44.87 408	77.06 372

PatchMatch-RL		56.66 358	53.75 363	65.37 365	77.91 265	45.28 337	69.78 387	60.38 422	41.35 406	47.57 382	73.73 362	16.83 424	76.91 388	36.99 372	59.21 318	73.92 402

PatchT		56.60 359	52.97 366	67.48 343	72.94 350	46.16 326	57.30 429	73.78 357	38.77 412	54.37 336	57.26 438	37.52 241	78.06 374	32.02 399	52.79 379	78.23 363

Patchmtry		56.56 360	52.95 367	67.42 344	72.53 355	50.59 198	59.05 425	71.72 378	37.86 417	46.92 387	65.86 410	38.94 219	80.06 358	36.94 373	46.72 403	71.60 418

test_0402		56.45 361	53.03 365	66.69 353	76.78 289	50.31 212	81.76 260	69.61 396	42.79 403	43.88 398	72.13 382	22.82 390	86.46 263	16.57 450	50.94 384	63.31 437

LS3D		56.40 362	53.82 362	64.12 371	81.12 187	45.69 335	73.42 359	66.14 407	35.30 429	43.24 404	79.88 290	22.18 395	79.62 364	19.10 445	64.00 275	67.05 427

ADS-MVSNet		56.17 363	51.95 373	68.84 327	80.60 204	53.07 133	55.03 433	70.02 393	44.72 391	51.00 360	61.19 427	22.83 388	78.88 367	28.54 414	53.63 372	74.57 398

XVG-ACMP-BASELINE		56.03 364	52.85 368	65.58 361	61.91 427	40.95 387	63.36 409	72.43 371	45.20 388	46.02 392	74.09 357	9.20 443	78.12 372	45.13 338	58.27 327	77.66 368

pmmvs-eth3d		55.97 365	52.78 369	65.54 362	61.02 429	46.44 318	75.36 343	67.72 404	49.61 354	43.65 400	67.58 404	21.63 398	77.04 386	44.11 346	44.33 409	73.15 410

F-COLMAP		55.96 366	53.65 364	62.87 382	72.76 352	42.77 369	74.70 348	70.37 390	40.03 408	41.11 414	79.36 298	17.77 419	73.70 408	32.80 398	53.96 370	72.15 414

CMPMVS	40.41 21	55.34 367	52.64 370	63.46 377	60.88 430	43.84 354	61.58 419	71.06 386	30.43 437	36.33 429	74.63 354	24.14 382	75.44 399	48.05 321	66.62 249	71.12 421
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 368	54.07 361	58.68 402	63.14 424	25.00 446	77.69 328	74.78 346	52.64 330	43.43 401	72.39 379	26.21 364	74.76 402	29.31 409	47.05 401	76.28 383

ADS-MVSNet2		55.21 369	51.44 374	66.51 355	80.60 204	49.56 228	55.03 433	65.44 409	44.72 391	51.00 360	61.19 427	22.83 388	75.41 400	28.54 414	53.63 372	74.57 398

SixPastTwentyTwo		54.37 370	50.10 379	67.21 346	70.70 377	41.46 383	74.73 346	64.69 411	47.56 369	39.12 421	69.49 395	18.49 416	84.69 308	31.87 400	34.20 436	75.48 387

USDC		54.36 371	51.23 375	63.76 373	64.29 419	37.71 402	62.84 414	73.48 364	56.85 280	35.47 432	71.94 385	9.23 442	78.43 369	38.43 365	48.57 389	75.13 392

testgi		54.25 372	52.57 371	59.29 400	62.76 425	21.65 455	72.21 372	70.47 389	53.25 327	41.94 407	77.33 322	14.28 431	77.95 378	29.18 410	51.72 383	78.28 361

K. test v3		54.04 373	49.42 386	67.92 340	68.55 395	42.57 373	75.51 341	63.07 419	52.07 334	39.21 420	64.59 416	19.34 409	82.21 333	37.11 370	25.31 448	78.97 349

UnsupCasMVSNet_bld		53.86 374	50.53 378	63.84 372	63.52 423	34.75 408	71.38 379	81.92 212	46.53 374	38.95 422	57.93 436	20.55 403	80.20 357	39.91 361	34.09 437	76.57 380

YYNet1		53.82 375	49.96 381	65.41 364	70.09 383	48.95 247	72.30 370	71.66 380	44.25 396	31.89 442	63.07 420	23.73 384	73.95 405	33.26 395	39.40 422	73.34 406

MDA-MVSNet_test_wron		53.82 375	49.95 382	65.43 363	70.13 382	49.05 243	72.30 370	71.65 381	44.23 397	31.85 443	63.13 419	23.68 385	74.01 404	33.25 396	39.35 423	73.23 409

test_fmvs1		53.60 377	52.54 372	56.78 406	58.07 434	30.26 429	68.95 391	42.19 446	32.46 432	63.59 209	82.56 259	11.55 435	60.81 433	58.25 237	55.27 360	79.28 346

sc_t1		53.51 378	49.92 383	64.29 370	70.33 380	39.55 393	72.93 362	59.60 425	38.74 413	47.16 386	66.47 407	17.59 420	76.50 393	36.83 374	39.62 421	76.82 374

Patchmatch-test		53.33 379	48.17 392	68.81 329	73.31 342	42.38 374	42.98 447	58.23 426	32.53 431	38.79 423	70.77 390	39.66 212	73.51 409	25.18 426	52.06 382	90.55 99

LTVRE_ROB	45.45 19	52.73 380	49.74 384	61.69 389	69.78 387	34.99 407	44.52 444	67.60 405	43.11 402	43.79 399	74.03 358	18.54 415	81.45 339	28.39 416	57.94 334	68.62 425
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
EU-MVSNet		52.63 381	50.72 377	58.37 403	62.69 426	28.13 443	72.60 365	75.97 336	30.94 436	40.76 416	72.11 383	20.16 406	70.80 419	35.11 387	46.11 405	76.19 384

test_fmvs1_n		52.55 382	51.19 376	56.65 407	51.90 445	30.14 430	67.66 395	42.84 445	32.27 433	62.30 223	82.02 273	9.12 444	60.84 432	57.82 246	54.75 366	78.99 348

tt0320		52.45 383	48.75 387	63.55 375	71.47 368	41.85 377	72.42 368	59.73 424	36.33 424	44.52 395	61.55 425	19.34 409	76.45 394	33.53 392	39.85 420	72.36 413

OurMVSNet-221017-0		52.39 384	48.73 388	63.35 379	65.21 412	38.42 398	68.54 393	64.95 410	38.19 414	39.57 419	71.43 386	13.23 433	79.92 359	37.16 368	40.32 419	71.72 417

JIA-IIPM		52.33 385	47.77 395	66.03 358	71.20 372	46.92 310	40.00 452	76.48 333	37.10 419	46.73 388	37.02 452	32.96 311	77.88 379	35.97 378	52.45 381	73.29 408

tt0320-xc		52.22 386	48.38 390	63.75 374	72.19 361	42.25 376	72.19 373	57.59 428	37.24 418	44.41 396	61.56 424	17.90 418	75.89 397	35.60 380	36.73 426	73.12 411

Anonymous20240521		51.65 387	48.42 389	61.34 393	56.43 439	39.65 392	73.57 357	73.47 365	36.64 422	36.59 428	63.98 417	10.75 438	72.25 416	35.35 382	49.01 388	72.11 415

MDA-MVSNet-bldmvs		51.56 388	47.75 396	63.00 380	71.60 366	47.32 306	69.70 388	72.12 373	43.81 398	27.65 450	63.38 418	21.97 397	75.96 396	27.30 421	32.19 438	65.70 433

FE-MVSNET		51.43 389	48.22 391	61.06 394	60.78 431	32.48 423	73.85 355	64.62 412	46.30 382	37.47 427	66.27 408	20.80 402	77.38 385	23.43 432	40.48 418	73.31 407

test_vis1_n		51.19 390	49.66 385	55.76 411	51.26 447	29.85 435	67.20 398	38.86 451	32.12 434	59.50 258	79.86 291	8.78 445	58.23 440	56.95 255	52.46 380	79.19 347

mvs5depth		50.97 391	46.98 397	62.95 381	56.63 438	34.23 413	62.73 415	67.35 406	45.03 390	48.00 378	65.41 414	10.40 439	79.88 363	36.00 377	31.27 441	74.73 396

COLMAP_ROB	43.60 20	50.90 392	48.05 393	59.47 398	67.81 402	40.57 389	71.25 380	62.72 421	36.49 423	36.19 430	73.51 367	13.48 432	73.92 406	20.71 440	50.26 386	63.92 436
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 393	47.81 394	57.96 404	61.53 428	27.80 444	67.40 396	74.06 354	43.25 401	33.31 441	65.38 415	16.03 428	71.34 417	21.80 437	47.55 396	74.75 395

kuosan		50.20 394	50.09 380	50.52 418	73.09 347	29.09 440	65.25 401	74.89 345	48.27 363	41.34 411	60.85 429	43.45 163	67.48 425	18.59 447	25.07 449	55.01 443

KD-MVS_self_test		49.24 395	46.85 398	56.44 408	54.32 440	22.87 449	57.39 428	73.36 367	44.36 395	37.98 425	59.30 434	18.97 412	71.17 418	33.48 393	42.44 413	75.26 390

MVS-HIRNet		49.01 396	44.71 400	61.92 388	76.06 301	46.61 315	63.23 411	54.90 432	24.77 445	33.56 437	36.60 454	21.28 400	75.88 398	29.49 408	62.54 295	63.26 438

new-patchmatchnet		48.21 397	46.55 399	53.18 414	57.73 436	18.19 463	70.24 383	71.02 387	45.70 384	33.70 436	60.23 430	18.00 417	69.86 422	27.97 418	34.35 434	71.49 420

TinyColmap		48.15 398	44.49 402	59.13 401	65.73 409	38.04 399	63.34 410	62.86 420	38.78 411	29.48 445	67.23 406	6.46 453	73.30 410	24.59 428	41.90 415	66.04 431

AllTest		47.32 399	44.66 401	55.32 412	65.08 414	37.50 403	62.96 413	54.25 434	35.45 427	33.42 438	72.82 372	9.98 440	59.33 436	24.13 429	43.84 410	69.13 423

PM-MVS		46.92 400	43.76 407	56.41 409	52.18 444	32.26 424	63.21 412	38.18 452	37.99 416	40.78 415	66.20 409	5.09 457	65.42 427	48.19 320	41.99 414	71.54 419

test_fmvs2		45.89 401	44.32 403	50.62 417	45.85 456	24.70 447	58.87 427	37.84 454	25.22 443	52.46 351	74.56 355	7.07 448	54.69 444	49.28 312	47.70 394	72.48 412

RPSCF		45.77 402	44.13 404	50.68 416	57.67 437	29.66 436	54.92 435	45.25 442	26.69 442	45.92 393	75.92 347	17.43 422	45.70 454	27.44 420	45.95 406	76.67 376

pmmvs3		45.53 403	41.55 409	57.44 405	48.97 452	39.68 391	70.06 384	57.66 427	28.32 440	34.06 435	57.29 437	8.50 446	66.85 426	34.86 389	34.26 435	65.80 432

dongtai		43.51 404	44.07 405	41.82 429	63.75 421	21.90 453	63.80 407	72.05 374	39.59 409	33.35 440	54.54 440	41.04 194	57.30 441	10.75 458	17.77 458	46.26 452

mvsany_test1		43.38 405	42.57 408	45.82 424	50.96 448	26.10 445	55.80 431	27.74 464	27.15 441	47.41 385	74.39 356	18.67 414	44.95 455	44.66 341	36.31 428	66.40 430

mamv4		42.60 406	44.05 406	38.26 434	59.21 433	38.00 400	44.14 446	39.03 450	25.03 444	40.61 417	68.39 401	37.01 255	24.28 468	46.62 331	36.43 427	52.50 446

N_pmnet		41.25 407	39.77 410	45.66 425	68.50 396	0.82 477	72.51 367	0.38 476	35.61 426	35.26 433	61.51 426	20.07 407	67.74 424	23.51 431	40.63 416	68.42 426

TDRefinement		40.91 408	38.37 412	48.55 422	50.45 449	33.03 420	58.98 426	50.97 437	28.50 438	29.89 444	67.39 405	6.21 455	54.51 445	17.67 448	35.25 431	58.11 440

ttmdpeth		40.58 409	37.50 413	49.85 419	49.40 450	22.71 450	56.65 430	46.78 438	28.35 439	40.29 418	69.42 397	5.35 456	61.86 431	20.16 442	21.06 455	64.96 434

test_vis1_rt		40.29 410	38.64 411	45.25 426	48.91 453	30.09 431	59.44 424	27.07 465	24.52 446	38.48 424	51.67 446	6.71 451	49.44 449	44.33 343	46.59 404	56.23 441

MVStest1		38.35 411	34.53 417	49.82 420	51.43 446	30.41 428	50.39 437	55.25 430	17.56 453	26.45 451	65.85 412	11.72 434	57.00 442	14.79 452	17.31 459	62.05 439

DSMNet-mixed		38.35 411	35.36 416	47.33 423	48.11 454	14.91 467	37.87 453	36.60 455	19.18 450	34.37 434	59.56 433	15.53 429	53.01 447	20.14 443	46.89 402	74.07 400

test_fmvs3		37.95 413	35.75 415	44.55 427	35.50 462	18.92 459	48.32 438	34.00 459	18.36 452	41.31 413	61.58 423	2.29 464	48.06 453	42.72 353	37.71 425	66.66 429

WB-MVS		37.41 414	36.37 414	40.54 432	54.23 441	10.43 470	65.29 400	43.75 443	34.86 430	27.81 449	54.63 439	24.94 375	63.21 429	6.81 465	15.00 460	47.98 451

FPMVS		35.40 415	33.67 419	40.57 431	46.34 455	28.74 442	41.05 449	57.05 429	20.37 449	22.27 454	53.38 443	6.87 450	44.94 456	8.62 459	47.11 400	48.01 450

SSC-MVS		35.20 416	34.30 418	37.90 435	52.58 443	8.65 473	61.86 416	41.64 447	31.81 435	25.54 452	52.94 445	23.39 387	59.28 438	6.10 466	12.86 461	45.78 454

ANet_high		34.39 417	29.59 423	48.78 421	30.34 466	22.28 451	55.53 432	63.79 417	38.11 415	15.47 458	36.56 455	6.94 449	59.98 435	13.93 454	5.64 469	64.08 435

EGC-MVSNET		33.75 418	30.42 422	43.75 428	64.94 416	36.21 406	60.47 423	40.70 449	0.02 470	0.10 471	53.79 442	7.39 447	60.26 434	11.09 457	35.23 432	34.79 456

new_pmnet		33.56 419	31.89 421	38.59 433	49.01 451	20.42 456	51.01 436	37.92 453	20.58 447	23.45 453	46.79 448	6.66 452	49.28 451	20.00 444	31.57 440	46.09 453

LF4IMVS		33.04 420	32.55 420	34.52 438	40.96 457	22.03 452	44.45 445	35.62 456	20.42 448	28.12 448	62.35 422	5.03 458	31.88 467	21.61 439	34.42 433	49.63 449

LCM-MVSNet		28.07 421	23.85 429	40.71 430	27.46 471	18.93 458	30.82 459	46.19 439	12.76 458	16.40 456	34.70 457	1.90 467	48.69 452	20.25 441	24.22 450	54.51 444

mvsany_test3		28.00 422	25.98 424	34.05 439	28.97 467	15.31 465	34.54 456	18.17 470	16.24 454	29.30 446	53.37 444	2.79 462	33.38 466	30.01 407	20.41 456	53.45 445

Gipuma		27.47 423	24.26 428	37.12 437	60.55 432	29.17 439	11.68 464	60.00 423	14.18 456	10.52 465	15.12 466	2.20 466	63.01 430	8.39 460	35.65 429	19.18 462
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 424	24.85 425	33.93 440	26.17 472	15.25 466	30.24 460	22.38 469	12.53 459	28.23 447	49.43 447	2.59 463	34.34 465	25.12 427	26.99 446	52.20 447

PMMVS2		26.71 425	22.98 430	37.87 436	36.89 460	8.51 474	42.51 448	29.32 463	19.09 451	13.01 460	37.54 451	2.23 465	53.11 446	14.54 453	11.71 462	51.99 448

APD_test1		26.46 426	24.41 427	32.62 443	37.58 459	21.74 454	40.50 451	30.39 461	11.45 460	16.33 457	43.76 449	1.63 469	41.62 457	11.24 456	26.82 447	34.51 457

PMVS	19.57 22	25.07 427	22.43 432	32.99 442	23.12 473	22.98 448	40.98 450	35.19 457	15.99 455	11.95 464	35.87 456	1.47 470	49.29 450	5.41 468	31.90 439	26.70 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 428	22.95 431	30.31 444	28.59 468	18.92 459	37.43 454	17.27 472	12.90 457	21.28 455	29.92 461	1.02 471	36.35 460	28.28 417	29.82 445	35.65 455

test_method		24.09 429	21.07 433	33.16 441	27.67 470	8.35 475	26.63 461	35.11 458	3.40 467	14.35 459	36.98 453	3.46 461	35.31 462	19.08 446	22.95 451	55.81 442

testf1		21.11 430	19.08 434	27.18 446	30.56 464	18.28 461	33.43 457	24.48 466	8.02 464	12.02 462	33.50 458	0.75 473	35.09 463	7.68 461	21.32 452	28.17 459

APD_test2		21.11 430	19.08 434	27.18 446	30.56 464	18.28 461	33.43 457	24.48 466	8.02 464	12.02 462	33.50 458	0.75 473	35.09 463	7.68 461	21.32 452	28.17 459

E-PMN		19.16 432	18.40 436	21.44 448	36.19 461	13.63 468	47.59 439	30.89 460	10.73 461	5.91 468	16.59 464	3.66 460	39.77 458	5.95 467	8.14 464	10.92 464

EMVS		18.42 433	17.66 437	20.71 449	34.13 463	12.64 469	46.94 440	29.94 462	10.46 463	5.58 469	14.93 467	4.23 459	38.83 459	5.24 469	7.51 466	10.67 465

cdsmvs_eth3d_5k		18.33 434	24.44 426	0.00 455	0.00 477	0.00 479	0.00 466	89.40 27	0.00 471	0.00 474	92.02 56	38.55 223	0.00 472	0.00 473	0.00 470	0.00 470

MVE	16.60 23	17.34 435	13.39 438	29.16 445	28.43 469	19.72 457	13.73 463	23.63 468	7.23 466	7.96 466	21.41 462	0.80 472	36.08 461	6.97 463	10.39 463	31.69 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 436	10.68 439	5.73 452	2.49 475	4.21 476	10.48 465	18.04 471	0.34 469	12.59 461	20.49 463	11.39 436	7.03 471	13.84 455	6.46 468	5.95 466

wuyk23d		9.11 437	8.77 441	10.15 451	40.18 458	16.76 464	20.28 462	1.01 475	2.58 468	2.66 470	0.98 470	0.23 475	12.49 470	4.08 470	6.90 467	1.19 467

ab-mvs-re		7.68 438	10.24 440	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	92.12 52	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

testmvs		6.14 439	8.18 442	0.01 453	0.01 476	0.00 479	73.40 360	0.00 477	0.00 471	0.02 472	0.15 471	0.00 476	0.00 472	0.02 471	0.00 470	0.02 468

test123		6.01 440	8.01 443	0.01 453	0.00 477	0.01 478	71.93 377	0.00 477	0.00 471	0.02 472	0.11 472	0.00 476	0.00 472	0.02 471	0.00 470	0.02 468

pcd_1.5k_mvsjas		3.15 441	4.20 444	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	37.77 231	0.00 472	0.00 473	0.00 470	0.00 470

mmdepth		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

monomultidepth		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

test_blank		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

uanet_test		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

DCPMVS		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

sosnet-low-res		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

sosnet		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

uncertanet		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

Regformer		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

uanet		0.00 442	0.00 445	0.00 455	0.00 477	0.00 479	0.00 466	0.00 477	0.00 471	0.00 474	0.00 473	0.00 476	0.00 472	0.00 473	0.00 470	0.00 470

WAC-MVS						34.28 411								22.56 435

FOURS1					83.24 113	49.90 220	84.98 154	78.76 285	47.71 367	73.42 71

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 43	86.80 28	92.34 35

PC_three_1452									66.58 81	87.27 2	93.70 15	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

No_MVS				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 43	86.80 28	92.34 35

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 276	82.06 14	93.39 24	54.94 36

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					89.55 14	53.46 114		84.38 157	57.02 278	73.97 65	91.03 78	44.57 147	91.17 83	75.41 87	81.78 71

RE-MVS-def			66.66 242		80.96 192	48.14 280	81.54 273	76.98 322	46.42 377	62.75 218	89.42 122	29.28 345		60.52 214	72.06 195	83.19 292

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 90	88.26 1				82.83 30	87.60 18	92.44 32

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 8	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

test_241102_TWO								88.76 44	57.50 270	83.60 6	94.09 6	56.14 27	96.37 6	82.28 34	87.43 20	92.55 30

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 268	84.61 4	93.29 28	58.81 13	96.45 1

9.14			78.19 28		85.67 62		88.32 53	88.84 41	59.89 214	74.58 60	92.62 43	46.80 100	92.66 42	81.40 45	85.62 41

save fliter					85.35 69	56.34 41	89.31 40	81.46 221	61.55 185

test_0728_THIRD									58.00 256	81.91 15	93.64 17	56.54 23	96.44 2	81.64 40	86.86 26	92.23 37

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 37					96.39 4	81.68 38	87.13 21	92.47 31

test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 264	83.14 9	93.96 9	55.17 31

GSMVS																88.13 182

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 221				88.13 182

sam_mvs												35.99 276

ambc				62.06 385	53.98 442	29.38 438	35.08 455	79.65 262		41.37 410	59.96 431	6.27 454	82.15 334	35.34 383	38.22 424	74.65 397

MTGPA								81.31 224

test_post1							70.84 382				14.72 468	34.33 298	83.86 316	48.80 315

test_post											16.22 465	37.52 241	84.72 307

patchmatchnet-post											59.74 432	38.41 224	79.91 361

GG-mvs-BLEND				77.77 92	86.68 49	50.61 196	68.67 392	88.45 54		68.73 134	87.45 172	59.15 11	90.67 101	54.83 273	87.67 17	92.03 45

MTMP							87.27 80	15.34 473

gm-plane-assit					83.24 113	54.21 100			70.91 27		88.23 153		95.25 14	66.37 156

test9_res														78.72 59	85.44 43	91.39 68

TEST9					85.68 60	55.42 56	87.59 69	84.00 169	57.72 263	72.99 78	90.98 80	44.87 141	88.58 178

test_8					85.72 59	55.31 61	87.60 68	83.88 172	57.84 261	72.84 82	90.99 79	44.99 137	88.34 191

agg_prior2														75.65 82	85.11 47	91.01 84

agg_prior					85.64 63	54.92 79		83.61 180		72.53 87			88.10 202

TestCases				55.32 412	65.08 414	37.50 403		54.25 434	35.45 427	33.42 438	72.82 372	9.98 440	59.33 436	24.13 429	43.84 410	69.13 423

test_prior4						56.39 40	87.15 84

test_prior2							89.04 45		61.88 180	73.55 69	91.46 74	48.01 85		74.73 91	85.46 42

test_prior				78.39 77	86.35 54	54.91 80		85.45 111					89.70 133			90.55 99

旧先验2							81.73 263		45.53 386	74.66 57			70.48 421	58.31 236

新几何2							81.61 269

新几何1				73.30 243	83.10 116	53.48 113		71.43 382	45.55 385	66.14 157	87.17 178	33.88 303	80.54 351	48.50 318	80.33 86	85.88 241

旧先验1					81.57 174	47.48 301		71.83 376			88.66 137	36.94 257			78.34 110	88.67 161

无先验							85.19 142	78.00 302	49.08 356				85.13 301	52.78 290		87.45 199

原ACMM2							83.77 199

原ACMM1				76.13 144	84.89 78	54.59 92		85.26 122	51.98 335	66.70 149	87.07 180	40.15 206	89.70 133	51.23 300	85.06 48	84.10 269

test222					79.36 228	50.97 186	77.99 326	67.84 403	42.54 404	62.84 217	86.53 188	30.26 339			76.91 126	85.23 250

testdata2													77.81 381	45.64 337

segment_acmp												44.97 139

testdata				67.08 348	77.59 269	45.46 336		69.20 398	44.47 393	71.50 105	88.34 149	31.21 333	70.76 420	52.20 295	75.88 145	85.03 254

testdata1							77.55 329		64.14 128

test12				79.24 44	86.89 47	56.08 45		85.16 128		72.27 91		47.15 94	91.10 86		85.93 37	90.54 101

plane_prior7					77.95 262	48.46 266

plane_prior6					78.42 255	49.39 238						36.04 274

plane_prior5								82.59 197					88.30 195	65.46 167	72.34 191	84.49 262

plane_prior4											83.28 244

plane_prior3						48.95 247			64.01 132	62.15 226

plane_prior2							85.76 117		63.60 142

plane_prior1					78.31 258

plane_prior						49.57 225	87.43 72		64.57 118						72.84 184

n2								0.00 477

nn								0.00 477

door-mid								41.31 448

lessismore_v0				67.98 339	64.76 417	41.25 384		45.75 441		36.03 431	65.63 413	19.29 411	84.11 314	35.67 379	21.24 454	78.59 355

LGP-MVS_train				72.02 275	74.42 330	48.60 259		80.64 237	54.69 313	53.75 343	83.83 231	25.73 369	86.98 243	60.33 218	64.71 267	80.48 336

test11								84.25 161

door								43.27 444

HQP5-MVS						51.56 175

HQP-NCC					79.02 238		88.00 57		65.45 105	64.48 187

ACMP_Plane					79.02 238		88.00 57		65.45 105	64.48 187

BP-MVS														66.70 153

HQP4-MVS										64.47 190			88.61 176			84.91 258

HQP3-MVS								83.68 176							73.12 180

HQP2-MVS												37.35 244

NP-MVS					78.76 243	50.43 203					85.12 209

MDTV_nov1_ep13_2view						43.62 356	71.13 381		54.95 310	59.29 264		36.76 260		46.33 334		87.32 202

MDTV_nov1_ep13			61.56 307		81.68 165	55.12 70	72.41 369	78.18 299	59.19 232	58.85 273	69.29 398	34.69 292	86.16 272	36.76 376	62.96 291

ACMMP++_ref															63.20 287

ACMMP++															59.38 316

Test By Simon												39.38 215

ITE_SJBPF				51.84 415	58.03 435	31.94 426		53.57 436	36.67 421	41.32 412	75.23 351	11.17 437	51.57 448	25.81 425	48.04 392	72.02 416

DeepMVS_CX				13.10 450	21.34 474	8.99 472		10.02 474	10.59 462	7.53 467	30.55 460	1.82 468	14.55 469	6.83 464	7.52 465	15.75 463

High-resolution multi-view benchmark