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.

CHOSEN 1792x2688		76.24 51	74.03 72	82.88 1	83.09 106	62.84 2	85.73 104	85.39 93	69.79 22	64.87 137	83.49 186	41.52 153	93.69 28	70.55 95	81.82 67	92.12 37

MG-MVS		78.42 23	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 41	64.83 81	73.52 55	88.09 125	48.07 64	92.19 48	62.24 147	84.53 50	91.53 55

LFMVS		78.52 21	77.14 35	82.67 3	89.58 13	58.90 7	91.27 18	88.05 47	63.22 109	74.63 44	90.83 69	41.38 154	94.40 20	75.42 68	79.90 89	94.72 2

DVP-MVS++		82.44 2	82.38 4	82.62 4	91.77 4	57.49 15	84.98 127	88.88 26	58.00 204	83.60 6	93.39 16	67.21 2	96.39 4	81.64 28	91.98 4	93.98 5

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 181	59.50 5	92.24 8	90.72 9	69.37 26	83.22 8	94.47 2	63.81 5	93.18 31	74.02 79	93.25 2	94.80 1

CSCG		80.41 14	79.72 14	82.49 5	89.12 25	57.67 13	89.29 40	91.54 3	59.19 180	71.82 77	90.05 88	59.72 9	96.04 10	78.37 47	88.40 13	93.75 7

SED-MVS		81.92 6	81.75 8	82.44 7	89.48 17	56.89 25	92.48 3	88.94 24	57.50 218	84.61 4	94.09 3	58.81 11	96.37 6	82.28 23	87.60 17	94.06 3

DVP-MVS		81.30 9	81.00 12	82.20 8	89.40 20	57.45 17	92.34 5	89.99 13	57.71 212	81.91 13	93.64 11	55.17 20	96.44 2	81.68 26	87.13 20	92.72 24
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
test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 5	88.88 26					96.39 4	81.68 26	87.13 20	92.47 28

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 6	75.95 3	77.10 34	93.09 25	54.15 28	95.57 12	85.80 8	85.87 36	93.31 11

DELS-MVS		82.32 4	82.50 3	81.79 11	86.80 42	56.89 25	92.77 2	86.30 77	77.83 1	77.88 31	92.13 39	60.24 6	94.78 19	78.97 41	89.61 7	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
OPU-MVS				81.71 12	92.05 3	55.97 43	92.48 3				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

PS-MVSNAJ		80.06 15	79.52 16	81.68 13	85.58 55	60.97 3	91.69 11	87.02 63	70.62 16	80.75 18	93.22 22	37.77 187	92.50 42	82.75 20	86.25 33	91.57 53

MSC_two_6792asdad				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 30	86.80 26	92.34 32

No_MVS				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 30	86.80 26	92.34 32

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 14	85.03 67	60.73 4	91.65 12	86.86 66	70.30 20	80.77 17	93.07 27	37.63 192	92.28 47	82.73 21	85.71 37	91.57 53

CNVR-MVS		81.76 7	81.90 7	81.33 17	90.04 10	57.70 12	91.71 10	88.87 28	70.31 19	77.64 33	93.87 7	52.58 35	93.91 26	84.17 12	87.92 15	92.39 30

MVS		76.91 41	75.48 52	81.23 18	84.56 73	55.21 60	80.23 251	91.64 2	58.65 194	65.37 130	91.48 58	45.72 92	95.05 16	72.11 90	89.52 9	93.44 9

VDDNet		74.37 75	72.13 96	81.09 19	79.58 187	56.52 32	90.02 26	86.70 70	52.61 275	71.23 85	87.20 140	31.75 266	93.96 25	74.30 77	75.77 121	92.79 23

MM				80.89 20		55.40 54	92.16 9	89.85 15	75.28 4	82.41 10	93.86 8	54.30 25	93.98 23	90.29 1	87.13 20	93.30 12

NCCC		79.57 18	79.23 18	80.59 21	89.50 15	56.99 23	91.38 15	88.17 45	67.71 41	73.81 52	92.75 30	46.88 77	93.28 29	78.79 44	84.07 53	91.50 57

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 12	56.54 31	84.83 133	83.68 142	67.85 38	69.36 95	90.24 80	60.20 7	92.10 52	84.14 13	80.40 80	92.82 21

API-MVS		74.17 78	72.07 98	80.49 22	90.02 11	58.55 8	87.30 70	84.27 129	57.51 217	65.77 127	87.77 131	41.61 151	95.97 11	51.71 237	82.63 59	86.94 157

3Dnovator	64.70 6	74.46 73	72.48 86	80.41 24	82.84 117	55.40 54	83.08 187	88.61 38	67.61 43	59.85 194	88.66 113	34.57 238	93.97 24	58.42 181	88.70 11	91.85 46

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 24	55.08 66	88.70 46	87.92 49	55.55 248	81.21 16	93.69 10	56.51 16	94.27 22	78.36 48	85.70 38	91.51 56
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		76.76 46	75.60 50	80.21 26	90.87 7	54.68 78	89.14 41	89.11 20	62.95 112	70.54 92	92.33 37	41.05 155	94.95 17	57.90 191	86.55 31	91.00 69
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_0304		81.58 8	82.05 6	80.20 27	82.36 128	54.70 76	91.13 19	88.95 23	74.49 5	80.04 22	93.64 11	52.40 36	93.27 30	88.85 4	86.56 30	92.61 26

SD-MVS		76.18 52	74.85 62	80.18 28	85.39 59	56.90 24	85.75 102	82.45 166	56.79 232	74.48 47	91.81 48	43.72 122	90.75 84	74.61 73	78.65 99	92.91 19
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
Effi-MVS+		75.24 65	73.61 74	80.16 29	81.92 133	57.42 19	85.21 116	76.71 274	60.68 154	73.32 58	89.34 101	47.30 72	91.63 59	68.28 106	79.72 91	91.42 58

SMA-MVS		79.10 20	78.76 20	80.12 30	84.42 75	55.87 45	87.58 64	86.76 68	61.48 138	80.26 20	93.10 23	46.53 82	92.41 44	79.97 35	88.77 10	92.08 38
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
MSLP-MVS++		74.21 77	72.25 92	80.11 31	81.45 153	56.47 33	86.32 89	79.65 216	58.19 200	66.36 118	92.29 38	36.11 219	90.66 86	67.39 110	82.49 61	93.18 16

CANet		80.90 10	81.17 11	80.09 32	87.62 37	54.21 88	91.60 13	86.47 73	73.13 8	79.89 23	93.10 23	49.88 56	92.98 32	84.09 14	84.75 48	93.08 17

IB-MVS	68.87 2	74.01 79	72.03 101	79.94 33	83.04 108	55.50 49	90.24 25	88.65 34	67.14 46	61.38 182	81.74 218	53.21 31	94.28 21	60.45 166	62.41 238	90.03 92
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
HPM-MVS++		80.50 13	80.71 13	79.88 34	87.34 39	55.20 61	89.93 29	87.55 58	66.04 67	79.46 24	93.00 28	53.10 32	91.76 57	80.40 34	89.56 8	92.68 25

QAPM		71.88 117	69.33 141	79.52 35	82.20 130	54.30 86	86.30 90	88.77 31	56.61 236	59.72 196	87.48 135	33.90 244	95.36 13	47.48 265	81.49 70	88.90 117

VDD-MVS		76.08 54	74.97 60	79.44 36	84.27 79	53.33 111	91.13 19	85.88 83	65.33 76	72.37 72	89.34 101	32.52 256	92.76 38	77.90 53	75.96 118	92.22 36

MVS_111021_HR		76.39 50	75.38 54	79.42 37	85.33 61	56.47 33	88.15 51	84.97 111	65.15 79	66.06 122	89.88 91	43.79 119	92.16 49	75.03 70	80.03 87	89.64 100

SteuartSystems-ACMMP		77.08 39	76.33 44	79.34 38	80.98 160	55.31 56	89.76 33	86.91 65	62.94 113	71.65 78	91.56 56	42.33 138	92.56 41	77.14 57	83.69 55	90.15 88
Skip Steuart: Steuart Systems R&D Blog.
test12				79.24 39	86.89 41	56.08 40		85.16 106		72.27 74		47.15 74	91.10 73		85.93 35	90.54 78

APDe-MVS		78.44 22	78.20 23	79.19 40	88.56 26	54.55 82	89.76 33	87.77 53	55.91 243	78.56 28	92.49 35	48.20 63	92.65 40	79.49 36	83.04 57	90.39 80
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lupinMVS		78.38 24	78.11 25	79.19 40	83.02 109	55.24 58	91.57 14	84.82 115	69.12 27	76.67 36	92.02 43	44.82 108	90.23 100	80.83 33	80.09 84	92.08 38

casdiffmvs_mvg		77.75 32	77.28 33	79.16 42	80.42 177	54.44 84	87.76 58	85.46 90	71.67 11	71.38 83	88.35 119	51.58 40	91.22 68	79.02 40	79.89 90	91.83 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 43	88.52 27	55.12 63	89.95 28	85.98 82	68.31 30	71.33 84	92.75 30	45.52 95	90.37 93	71.15 92	85.14 44	91.91 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		78.17 27	77.86 28	79.12 44	84.30 77	54.22 87	87.71 59	84.57 124	67.70 42	77.70 32	92.11 42	50.90 47	89.95 106	78.18 51	77.54 107	93.20 15

PHI-MVS		77.49 34	77.00 36	78.95 45	85.33 61	50.69 164	88.57 48	88.59 39	58.14 201	73.60 53	93.31 19	43.14 131	93.79 27	73.81 80	88.53 12	92.37 31

test_yl		75.85 57	74.83 63	78.91 46	88.08 34	51.94 140	91.30 16	89.28 17	57.91 206	71.19 86	89.20 104	42.03 145	92.77 36	69.41 99	75.07 130	92.01 41

DCV-MVSNet		75.85 57	74.83 63	78.91 46	88.08 34	51.94 140	91.30 16	89.28 17	57.91 206	71.19 86	89.20 104	42.03 145	92.77 36	69.41 99	75.07 130	92.01 41

casdiffmvs		77.36 36	76.85 38	78.88 48	80.40 178	54.66 80	87.06 76	85.88 83	72.11 10	71.57 80	88.63 117	50.89 49	90.35 94	76.00 61	79.11 96	91.63 50
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM		76.76 46	76.07 47	78.81 49	80.20 179	59.11 6	86.86 82	86.23 78	68.60 29	70.18 94	88.84 111	51.57 41	87.16 197	65.48 125	86.68 28	90.15 88

MSP-MVS		82.30 5	83.47 1	78.80 50	82.99 111	52.71 126	85.04 124	88.63 36	66.08 64	86.77 3	92.75 30	72.05 1	91.46 63	83.35 17	93.53 1	92.23 34
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
DeepC-MVS	67.15 4	76.90 43	76.27 45	78.80 50	80.70 170	55.02 67	86.39 87	86.71 69	66.96 49	67.91 104	89.97 90	48.03 65	91.41 64	75.60 65	84.14 52	89.96 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		76.43 49	75.66 49	78.73 52	81.92 133	54.67 79	84.06 156	85.35 95	61.10 143	72.99 61	91.50 57	40.25 163	91.00 75	76.84 58	86.98 23	90.51 79

baseline		76.86 44	76.24 46	78.71 53	80.47 176	54.20 90	83.90 160	84.88 114	71.38 14	71.51 81	89.15 106	50.51 50	90.55 90	75.71 63	78.65 99	91.39 59

jason		77.01 40	76.45 42	78.69 54	79.69 186	54.74 73	90.56 24	83.99 138	68.26 31	74.10 50	90.91 66	42.14 142	89.99 105	79.30 38	79.12 95	91.36 61
jason: jason.
ET-MVSNet_ETH3D		75.23 66	74.08 70	78.67 55	84.52 74	55.59 47	88.92 43	89.21 19	68.06 36	53.13 284	90.22 82	49.71 57	87.62 189	72.12 89	70.82 164	92.82 21

CostFormer		73.89 82	72.30 91	78.66 56	82.36 128	56.58 28	75.56 282	85.30 98	66.06 65	70.50 93	76.88 271	57.02 14	89.06 127	68.27 107	68.74 180	90.33 82

patch_mono-2		80.84 11	81.59 9	78.62 57	90.34 9	53.77 95	88.08 52	88.36 43	76.17 2	79.40 25	91.09 60	55.43 19	90.09 103	85.01 10	80.40 80	91.99 43

MVS_Test		75.85 57	74.93 61	78.62 57	84.08 81	55.20 61	83.99 158	85.17 105	68.07 35	73.38 57	82.76 196	50.44 51	89.00 131	65.90 121	80.61 76	91.64 49

CDPH-MVS		76.05 55	75.19 56	78.62 57	86.51 44	54.98 69	87.32 68	84.59 123	58.62 195	70.75 89	90.85 68	43.10 133	90.63 88	70.50 96	84.51 51	90.24 84

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 60	85.25 63	50.27 180	90.02 26	90.57 10	56.58 237	74.26 49	91.60 55	54.26 26	92.16 49	75.87 62	79.91 88	93.05 18

ETV-MVS		77.17 38	76.74 39	78.48 61	81.80 136	54.55 82	86.13 93	85.33 96	68.20 32	73.10 60	90.52 74	45.23 99	90.66 86	79.37 37	80.95 72	90.22 85

TSAR-MVS + MP.		78.31 26	78.26 22	78.48 61	81.33 156	56.31 37	81.59 225	86.41 74	69.61 24	81.72 15	88.16 124	55.09 22	88.04 170	74.12 78	86.31 32	91.09 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
train_agg		76.91 41	76.40 43	78.45 63	85.68 51	55.42 51	87.59 62	84.00 136	57.84 209	72.99 61	90.98 63	44.99 102	88.58 147	78.19 49	85.32 42	91.34 63

PAPR		75.20 67	74.13 68	78.41 64	88.31 31	55.10 65	84.31 148	85.66 87	63.76 97	67.55 106	90.73 70	43.48 127	89.40 119	66.36 118	77.03 109	90.73 74

alignmvs		78.08 28	77.98 26	78.39 65	83.53 92	53.22 114	89.77 32	85.45 91	66.11 62	76.59 38	91.99 45	54.07 29	89.05 128	77.34 56	77.00 110	92.89 20

test_prior				78.39 65	86.35 45	54.91 71		85.45 91					89.70 113			90.55 76

SF-MVS		77.64 33	77.42 32	78.32 67	83.75 89	52.47 131	86.63 85	87.80 50	58.78 192	74.63 44	92.38 36	47.75 68	91.35 65	78.18 51	86.85 25	91.15 66

ZNCC-MVS		75.82 60	75.02 59	78.23 68	83.88 87	53.80 94	86.91 81	86.05 81	59.71 166	67.85 105	90.55 72	42.23 140	91.02 74	72.66 88	85.29 43	89.87 97

VNet		77.99 30	77.92 27	78.19 69	87.43 38	50.12 181	90.93 22	91.41 4	67.48 44	75.12 40	90.15 86	46.77 79	91.00 75	73.52 82	78.46 101	93.44 9

EIA-MVS		75.92 56	75.18 57	78.13 70	85.14 64	51.60 149	87.17 74	85.32 97	64.69 82	68.56 100	90.53 73	45.79 91	91.58 60	67.21 112	82.18 64	91.20 65

HFP-MVS		74.37 75	73.13 81	78.10 71	84.30 77	53.68 97	85.58 107	84.36 127	56.82 230	65.78 126	90.56 71	40.70 161	90.90 79	69.18 101	80.88 73	89.71 98

tpm2		70.82 135	68.44 150	77.98 72	80.78 168	56.11 39	74.21 293	81.28 187	60.24 160	68.04 103	75.27 289	52.26 38	88.50 152	55.82 211	68.03 184	89.33 106

thisisatest0515		73.64 89	72.20 94	77.97 73	81.63 144	53.01 121	86.69 84	88.81 30	62.53 120	64.06 149	85.65 158	52.15 39	92.50 42	58.43 179	69.84 172	88.39 132

EPNet		78.36 25	78.49 21	77.97 73	85.49 57	52.04 138	89.36 38	84.07 135	73.22 7	77.03 35	91.72 50	49.32 60	90.17 102	73.46 83	82.77 58	91.69 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 75	85.46 58	49.56 193	90.99 21	86.66 71	70.58 17	80.07 21	95.30 1	56.18 17	90.97 78	82.57 22	86.22 34	93.28 13

GST-MVS		74.87 71	73.90 73	77.77 76	83.30 99	53.45 104	85.75 102	85.29 99	59.22 179	66.50 117	89.85 92	40.94 156	90.76 83	70.94 94	83.35 56	89.10 114

GG-mvs-BLEND				77.77 76	86.68 43	50.61 165	68.67 328	88.45 42		68.73 99	87.45 136	59.15 10	90.67 85	54.83 214	87.67 16	92.03 40

cascas		69.01 165	66.13 194	77.66 78	79.36 189	55.41 53	86.99 77	83.75 141	56.69 234	58.92 214	81.35 223	24.31 316	92.10 52	53.23 224	70.61 165	85.46 191

3Dnovator+	62.71 7	72.29 110	70.50 119	77.65 79	83.40 97	51.29 158	87.32 68	86.40 75	59.01 187	58.49 224	88.32 121	32.40 257	91.27 66	57.04 200	82.15 65	90.38 81

MVSFormer		73.53 90	72.19 95	77.57 80	83.02 109	55.24 58	81.63 222	81.44 183	50.28 290	76.67 36	90.91 66	44.82 108	86.11 226	60.83 158	80.09 84	91.36 61

APD-MVS		76.15 53	75.68 48	77.54 81	88.52 27	53.44 105	87.26 73	85.03 110	53.79 265	74.91 42	91.68 52	43.80 118	90.31 96	74.36 75	81.82 67	88.87 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Fast-Effi-MVS+		72.73 101	71.15 112	77.48 82	82.75 119	54.76 72	86.77 83	80.64 196	63.05 111	65.93 124	84.01 176	44.42 113	89.03 129	56.45 207	76.36 117	88.64 125

EPMVS		68.45 177	65.44 213	77.47 83	84.91 68	56.17 38	71.89 314	81.91 175	61.72 133	60.85 186	72.49 314	36.21 218	87.06 200	47.32 266	71.62 156	89.17 112

PatchmatchNet		67.07 211	63.63 231	77.40 84	83.10 104	58.03 9	72.11 312	77.77 254	58.85 190	59.37 204	70.83 327	37.84 186	84.93 253	42.96 289	69.83 173	89.26 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
region2R		73.75 85	72.55 85	77.33 85	83.90 86	52.98 122	85.54 110	84.09 134	56.83 229	65.10 132	90.45 75	37.34 201	90.24 99	68.89 103	80.83 75	88.77 123

iter_conf05		73.51 91	72.24 93	77.33 85	87.93 36	55.97 43	87.90 57	70.81 324	68.72 28	64.04 150	84.36 172	47.54 70	90.87 80	71.11 93	67.75 188	85.13 195

WTY-MVS		77.47 35	77.52 31	77.30 87	88.33 30	46.25 268	88.46 49	90.32 11	71.40 13	72.32 73	91.72 50	53.44 30	92.37 45	66.28 119	75.42 124	93.28 13

OpenMVS	61.00 11	69.99 150	67.55 168	77.30 87	78.37 214	54.07 92	84.36 146	85.76 86	57.22 223	56.71 252	87.67 133	30.79 272	92.83 35	43.04 288	84.06 54	85.01 197

MTAPA		72.73 101	71.22 110	77.27 89	81.54 150	53.57 99	67.06 334	81.31 185	59.41 173	68.39 101	90.96 65	36.07 221	89.01 130	73.80 81	82.45 62	89.23 109

PAPM_NR		71.80 119	69.98 131	77.26 90	81.54 150	53.34 110	78.60 267	85.25 102	53.46 268	60.53 190	88.66 113	45.69 93	89.24 122	56.49 204	79.62 94	89.19 111

ACMMPR		73.76 84	72.61 83	77.24 91	83.92 85	52.96 123	85.58 107	84.29 128	56.82 230	65.12 131	90.45 75	37.24 203	90.18 101	69.18 101	80.84 74	88.58 127

h-mvs33		73.95 80	72.89 82	77.15 92	80.17 180	50.37 174	84.68 138	83.33 148	68.08 33	71.97 75	88.65 116	42.50 136	91.15 71	78.82 42	57.78 279	89.91 96

CS-MVS-test		77.20 37	77.25 34	77.05 93	84.60 72	49.04 205	89.42 36	85.83 85	65.90 68	72.85 64	91.98 47	45.10 100	91.27 66	75.02 71	84.56 49	90.84 72

MP-MVS-pluss		75.54 63	75.03 58	77.04 94	81.37 155	52.65 128	84.34 147	84.46 125	61.16 141	69.14 96	91.76 49	39.98 170	88.99 133	78.19 49	84.89 47	89.48 105
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HyFIR lowres test		69.94 152	67.58 166	77.04 94	77.11 235	57.29 20	81.49 230	79.11 230	58.27 199	58.86 216	80.41 230	42.33 138	86.96 203	61.91 150	68.68 181	86.87 159

DP-MVS Recon		71.99 114	70.31 124	77.01 96	90.65 8	53.44 105	89.37 37	82.97 159	56.33 240	63.56 160	89.47 98	34.02 242	92.15 51	54.05 220	72.41 149	85.43 192

Anonymous20240529		69.71 155	67.28 173	77.00 97	83.78 88	50.36 175	88.87 45	85.10 109	47.22 308	64.03 151	83.37 188	27.93 288	92.10 52	57.78 194	67.44 190	88.53 130

CS-MVS		76.77 45	76.70 40	76.99 98	83.55 91	48.75 214	88.60 47	85.18 104	66.38 57	72.47 71	91.62 54	45.53 94	90.99 77	74.48 74	82.51 60	91.23 64

baseline2		75.15 68	74.54 66	76.98 99	81.67 143	51.74 146	83.84 162	91.94 1	69.97 21	58.98 211	86.02 154	59.73 8	91.73 58	68.37 105	70.40 169	87.48 149

MP-MVS		74.99 70	74.33 67	76.95 100	82.89 115	53.05 120	85.63 106	83.50 147	57.86 208	67.25 108	90.24 80	43.38 128	88.85 141	76.03 60	82.23 63	88.96 116
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 110	70.74 115	76.94 101	82.85 116	54.72 75	78.43 268	81.54 181	63.77 96	61.69 179	79.32 239	51.11 44	85.31 244	62.15 149	75.79 120	90.79 73

iter_conf_final		71.46 124	69.68 135	76.81 102	86.03 46	53.49 100	84.73 135	74.37 294	60.27 159	66.28 119	84.36 172	35.14 231	90.87 80	65.41 130	70.51 167	86.05 176

XVS		72.92 97	71.62 103	76.81 102	83.41 94	52.48 129	84.88 131	83.20 154	58.03 202	63.91 153	89.63 96	35.50 226	89.78 109	65.50 123	80.50 78	88.16 133

X-MVStestdata		65.85 229	62.20 237	76.81 102	83.41 94	52.48 129	84.88 131	83.20 154	58.03 202	63.91 153	4.82 396	35.50 226	89.78 109	65.50 123	80.50 78	88.16 133

PGM-MVS		72.60 103	71.20 111	76.80 105	82.95 112	52.82 125	83.07 188	82.14 168	56.51 238	63.18 162	89.81 93	35.68 225	89.76 111	67.30 111	80.19 83	87.83 141

Anonymous202405211		70.11 144	67.88 159	76.79 106	87.20 40	47.24 254	89.49 35	77.38 262	54.88 257	66.14 120	86.84 145	20.93 337	91.54 61	56.45 207	71.62 156	91.59 51

tpm cat1		66.28 223	62.78 233	76.77 107	81.40 154	57.14 22	70.03 321	77.19 264	53.00 272	58.76 219	70.73 330	46.17 84	86.73 210	43.27 287	64.46 214	86.44 170

PVSNet_Blended		76.53 48	76.54 41	76.50 108	85.91 48	51.83 144	88.89 44	84.24 132	67.82 39	69.09 97	89.33 103	46.70 80	88.13 166	75.43 66	81.48 71	89.55 102

diffmvs		75.11 69	74.65 65	76.46 109	78.52 210	53.35 109	83.28 182	79.94 208	70.51 18	71.64 79	88.72 112	46.02 88	86.08 231	77.52 54	75.75 122	89.96 94
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		73.40 93	72.44 87	76.30 110	81.32 157	54.70 76	85.81 98	78.82 234	63.70 98	64.53 142	85.38 162	47.11 75	87.38 194	67.75 109	77.55 106	86.81 165

BH-RMVSNet		70.08 146	68.01 156	76.27 111	84.21 80	51.22 160	87.29 71	79.33 227	58.96 189	63.63 158	86.77 146	33.29 250	90.30 98	44.63 281	73.96 136	87.30 154

CLD-MVS		75.60 61	75.39 53	76.24 112	80.69 171	52.40 132	90.69 23	86.20 79	74.40 6	65.01 135	88.93 108	42.05 144	90.58 89	76.57 59	73.96 136	85.73 185
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		69.96 151	67.88 159	76.22 113	81.11 159	51.71 147	84.15 152	76.74 273	59.83 164	60.91 185	84.38 170	41.56 152	88.10 168	51.67 238	70.57 166	88.84 120

1314		71.11 129	69.41 138	76.22 113	79.32 191	50.49 169	80.23 251	85.14 108	59.44 172	58.93 213	88.89 110	33.83 246	89.60 116	61.49 153	77.42 108	88.57 128

thisisatest0530		70.47 142	68.56 148	76.20 115	79.78 185	51.52 152	83.49 173	88.58 40	57.62 215	58.60 220	82.79 195	51.03 46	91.48 62	52.84 229	62.36 240	85.59 190

FA-MVS(test-final)		69.00 166	66.60 185	76.19 116	83.48 93	47.96 241	74.73 289	82.07 170	57.27 222	62.18 174	78.47 249	36.09 220	92.89 33	53.76 223	71.32 160	87.73 144

HY-MVS	67.03 5	73.90 81	73.14 79	76.18 117	84.70 71	47.36 250	75.56 282	86.36 76	66.27 59	70.66 91	83.91 178	51.05 45	89.31 120	67.10 113	72.61 148	91.88 45

gg-mvs-nofinetune		67.43 199	64.53 225	76.13 118	85.95 47	47.79 245	64.38 340	88.28 44	39.34 343	66.62 113	41.27 377	58.69 13	89.00 131	49.64 250	86.62 29	91.59 51

原ACMM1				76.13 118	84.89 69	54.59 81		85.26 101	51.98 279	66.70 111	87.07 143	40.15 166	89.70 113	51.23 241	85.06 46	84.10 209

GA-MVS		69.04 164	66.70 182	76.06 120	75.11 258	52.36 133	83.12 186	80.23 203	63.32 107	60.65 189	79.22 242	30.98 271	88.37 155	61.25 154	66.41 199	87.46 150

mPP-MVS		71.79 120	70.38 122	76.04 121	82.65 123	52.06 137	84.45 144	81.78 178	55.59 247	62.05 177	89.68 95	33.48 248	88.28 163	65.45 128	78.24 104	87.77 143

MVSTER		73.25 94	72.33 89	76.01 122	85.54 56	53.76 96	83.52 167	87.16 61	67.06 47	63.88 155	81.66 219	52.77 33	90.44 91	64.66 134	64.69 212	83.84 220

CP-MVS		72.59 105	71.46 106	76.00 123	82.93 114	52.32 135	86.93 80	82.48 165	55.15 252	63.65 157	90.44 78	35.03 234	88.53 151	68.69 104	77.83 105	87.15 155

HPM-MVS		72.60 103	71.50 105	75.89 124	82.02 131	51.42 154	80.70 244	83.05 156	56.12 242	64.03 151	89.53 97	37.55 195	88.37 155	70.48 97	80.04 86	87.88 140
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		69.87 153	67.88 159	75.85 125	88.38 29	52.35 134	86.94 79	83.68 142	53.70 266	55.68 262	85.60 159	30.07 277	91.20 69	55.84 210	71.02 162	83.99 213

PMMVS		72.98 96	72.05 99	75.78 126	83.57 90	48.60 217	84.08 154	82.85 161	61.62 134	68.24 102	90.33 79	28.35 284	87.78 180	72.71 87	76.69 112	90.95 70

SDMVSNet		71.89 116	70.62 118	75.70 127	81.70 140	51.61 148	73.89 294	88.72 33	66.58 52	61.64 180	82.38 209	37.63 192	89.48 117	77.44 55	65.60 206	86.01 177

EC-MVSNet		75.30 64	75.20 55	75.62 128	80.98 160	49.00 206	87.43 65	84.68 121	63.49 104	70.97 88	90.15 86	42.86 135	91.14 72	74.33 76	81.90 66	86.71 166

test_fmvsm_n_1920		75.56 62	75.54 51	75.61 129	74.60 268	49.51 196	81.82 217	74.08 297	66.52 55	80.40 19	93.46 15	46.95 76	89.72 112	86.69 5	75.30 125	87.61 147

MS-PatchMatch		72.34 108	71.26 109	75.61 129	82.38 127	55.55 48	88.00 53	89.95 14	65.38 74	56.51 256	80.74 229	32.28 259	92.89 33	57.95 190	88.10 14	78.39 297

fmvsm_s_conf0.5_n		74.48 72	74.12 69	75.56 131	76.96 236	47.85 243	85.32 114	69.80 331	64.16 88	78.74 26	93.48 14	45.51 96	89.29 121	86.48 6	66.62 196	89.55 102

xiu_mvs_v1_base_debu		71.60 121	70.29 125	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 244	72.54 67	90.19 83	22.38 327	86.66 212	73.28 84	76.39 114	86.85 161

xiu_mvs_v1_base		71.60 121	70.29 125	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 244	72.54 67	90.19 83	22.38 327	86.66 212	73.28 84	76.39 114	86.85 161

xiu_mvs_v1_base_debi		71.60 121	70.29 125	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 244	72.54 67	90.19 83	22.38 327	86.66 212	73.28 84	76.39 114	86.85 161

test_fmvsmconf_n		74.41 74	74.05 71	75.49 135	74.16 274	48.38 225	82.66 195	72.57 310	67.05 48	75.11 41	92.88 29	46.35 83	87.81 175	83.93 15	71.71 155	90.28 83

fmvsm_s_conf0.1_n		73.80 83	73.26 76	75.43 136	73.28 283	47.80 244	84.57 143	69.43 333	63.34 106	78.40 29	93.29 20	44.73 111	89.22 123	85.99 7	66.28 203	89.26 107

CANet_DTU		73.71 86	73.14 79	75.40 137	82.61 124	50.05 182	84.67 140	79.36 224	69.72 23	75.39 39	90.03 89	29.41 280	85.93 237	67.99 108	79.11 96	90.22 85

ACMMP		70.81 136	69.29 142	75.39 138	81.52 152	51.92 142	83.43 174	83.03 157	56.67 235	58.80 218	88.91 109	31.92 264	88.58 147	65.89 122	73.39 140	85.67 186
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
test_fmvsmconf0.1_n		73.69 87	73.15 77	75.34 139	70.71 311	48.26 229	82.15 207	71.83 314	66.75 51	74.47 48	92.59 34	44.89 105	87.78 180	83.59 16	71.35 159	89.97 93

SCA		63.84 238	60.01 260	75.32 140	78.58 209	57.92 10	61.61 350	77.53 258	56.71 233	57.75 236	70.77 328	31.97 262	79.91 300	48.80 256	56.36 285	88.13 136

FE-MVS		64.15 235	60.43 256	75.30 141	80.85 167	49.86 187	68.28 330	78.37 246	50.26 293	59.31 206	73.79 299	26.19 301	91.92 55	40.19 296	66.67 195	84.12 208

fmvsm_s_conf0.5_n_a		73.68 88	73.15 77	75.29 142	75.45 256	48.05 236	83.88 161	68.84 336	63.43 105	78.60 27	93.37 18	45.32 97	88.92 138	85.39 9	64.04 216	88.89 118

ab-mvs		70.65 138	69.11 144	75.29 142	80.87 166	46.23 269	73.48 298	85.24 103	59.99 162	66.65 112	80.94 226	43.13 132	88.69 143	63.58 138	68.07 183	90.95 70

TR-MVS		69.71 155	67.85 162	75.27 144	82.94 113	48.48 223	87.40 67	80.86 193	57.15 225	64.61 141	87.08 142	32.67 255	89.64 115	46.38 272	71.55 158	87.68 146

v2v482		69.55 160	67.64 165	75.26 145	72.32 296	53.83 93	84.93 130	81.94 172	65.37 75	60.80 187	79.25 241	41.62 150	88.98 134	63.03 142	59.51 254	82.98 236

PCF-MVS	61.03 10	70.10 145	68.40 151	75.22 146	77.15 234	51.99 139	79.30 262	82.12 169	56.47 239	61.88 178	86.48 152	43.98 115	87.24 196	55.37 212	72.79 147	86.43 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.1_n_a		72.82 100	72.05 99	75.12 147	70.95 310	47.97 239	82.72 194	68.43 338	62.52 121	78.17 30	93.08 26	44.21 114	88.86 139	84.82 11	63.54 222	88.54 129

test_fmvsmconf0.01_n		71.97 115	70.95 114	75.04 148	66.21 337	47.87 242	80.35 248	70.08 328	65.85 69	72.69 66	91.68 52	39.99 169	87.67 184	82.03 25	69.66 174	89.58 101

HQP-MVS		72.34 108	71.44 107	75.03 149	79.02 197	51.56 150	88.00 53	83.68 142	65.45 70	64.48 143	85.13 163	37.35 199	88.62 145	66.70 114	73.12 142	84.91 199

AdaColmap		67.86 187	65.48 210	75.00 150	88.15 33	54.99 68	86.10 94	76.63 276	49.30 297	57.80 233	86.65 149	29.39 281	88.94 137	45.10 278	70.21 170	81.06 266

EI-MVSNet-Vis-set		73.19 95	72.60 84	74.99 151	82.56 125	49.80 189	82.55 200	89.00 22	66.17 61	65.89 125	88.98 107	43.83 117	92.29 46	65.38 132	69.01 178	82.87 238

tpmrst		71.04 131	69.77 133	74.86 152	83.19 103	55.86 46	75.64 281	78.73 238	67.88 37	64.99 136	73.73 300	49.96 55	79.56 303	65.92 120	67.85 187	89.14 113

v1144		68.81 170	66.82 178	74.80 153	72.34 295	53.46 102	84.68 138	81.77 179	64.25 86	60.28 191	77.91 252	40.23 164	88.95 135	60.37 167	59.52 253	81.97 245

v1192		67.96 186	65.74 205	74.63 154	71.79 298	53.43 107	84.06 156	80.99 192	63.19 110	59.56 200	77.46 259	37.50 198	88.65 144	58.20 185	58.93 260	81.79 248

BH-w/o		70.02 148	68.51 149	74.56 155	82.77 118	50.39 173	86.60 86	78.14 249	59.77 165	59.65 197	85.57 160	39.27 175	87.30 195	49.86 248	74.94 132	85.99 179

SR-MVS		70.92 134	69.73 134	74.50 156	83.38 98	50.48 170	84.27 149	79.35 225	48.96 300	66.57 116	90.45 75	33.65 247	87.11 198	66.42 116	74.56 133	85.91 182

tttt0517		68.33 180	66.29 190	74.46 157	78.08 216	49.06 202	80.88 241	89.08 21	54.40 262	54.75 270	80.77 228	51.31 43	90.33 95	49.35 252	58.01 273	83.99 213

TESTMET0.1,1		72.86 99	72.33 89	74.46 157	81.98 132	50.77 162	85.13 119	85.47 89	66.09 63	67.30 107	83.69 183	37.27 202	83.57 266	65.06 133	78.97 98	89.05 115

nrg030		72.27 112	71.56 104	74.42 159	75.93 250	50.60 166	86.97 78	83.21 153	62.75 115	67.15 109	84.38 170	50.07 53	86.66 212	71.19 91	62.37 239	85.99 179

RPMNet		59.29 273	54.25 296	74.42 159	73.97 277	56.57 29	60.52 353	76.98 268	35.72 355	57.49 242	58.87 363	37.73 190	85.26 246	27.01 354	59.93 249	81.42 257

Vis-MVSNet		70.61 139	69.34 140	74.42 159	80.95 165	48.49 222	86.03 96	77.51 259	58.74 193	65.55 129	87.78 130	34.37 239	85.95 236	52.53 235	80.61 76	88.80 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		71.14 127	70.07 130	74.33 162	79.18 194	46.52 261	83.81 163	86.49 72	56.32 241	57.95 230	84.90 168	54.23 27	89.14 126	58.14 186	69.65 175	87.33 152

test2506		72.91 98	72.43 88	74.32 163	80.12 181	44.18 293	83.19 184	84.77 118	64.02 90	65.97 123	87.43 137	47.67 69	88.72 142	59.08 172	79.66 92	90.08 90

EI-MVSNet-UG-set		72.37 107	71.73 102	74.29 164	81.60 146	49.29 200	81.85 215	88.64 35	65.29 78	65.05 133	88.29 122	43.18 129	91.83 56	63.74 137	67.97 185	81.75 249

ECVR-MVS		71.81 118	71.00 113	74.26 165	80.12 181	43.49 298	84.69 137	82.16 167	64.02 90	64.64 139	87.43 137	35.04 233	89.21 124	61.24 155	79.66 92	90.08 90

OPM-MVS		70.75 137	69.58 136	74.26 165	75.55 255	51.34 156	86.05 95	83.29 152	61.94 130	62.95 166	85.77 157	34.15 241	88.44 153	65.44 129	71.07 161	82.99 235
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		67.86 187	65.76 204	74.16 167	71.68 300	53.09 118	84.14 153	80.83 194	62.85 114	59.21 209	77.28 262	39.30 174	88.00 171	58.67 177	57.88 277	81.40 259

HQP_MVS		70.96 133	69.91 132	74.12 168	77.95 218	49.57 191	85.76 100	82.59 163	63.60 101	62.15 175	83.28 190	36.04 222	88.30 161	65.46 126	72.34 150	84.49 203

v1921920		67.45 198	65.23 217	74.10 169	71.51 303	52.90 124	83.75 165	80.44 199	62.48 123	59.12 210	77.13 263	36.98 206	87.90 173	57.53 196	58.14 271	81.49 253

v8		67.25 204	64.99 220	74.04 170	72.89 289	53.31 112	82.37 205	80.11 205	61.54 136	54.29 275	76.02 285	42.89 134	88.41 154	58.43 179	56.36 285	80.39 275

VPNet		72.07 113	71.42 108	74.04 170	78.64 208	47.17 255	89.91 31	87.97 48	72.56 9	64.66 138	85.04 165	41.83 149	88.33 159	61.17 156	60.97 245	86.62 167

test_fmvsmvis_n_1920		71.29 126	70.38 122	74.00 172	71.04 309	48.79 213	79.19 263	64.62 346	62.75 115	66.73 110	91.99 45	40.94 156	88.35 157	83.00 18	73.18 141	84.85 201

v1240		66.99 212	64.68 223	73.93 173	71.38 306	52.66 127	83.39 178	79.98 207	61.97 129	58.44 227	77.11 264	35.25 228	87.81 175	56.46 206	58.15 269	81.33 262

BH-untuned		68.28 181	66.40 187	73.91 174	81.62 145	50.01 183	85.56 109	77.39 261	57.63 214	57.47 244	83.69 183	36.36 217	87.08 199	44.81 279	73.08 145	84.65 202

v148		68.24 183	66.35 188	73.88 175	71.76 299	51.47 153	84.23 150	81.90 176	63.69 99	58.94 212	76.44 276	43.72 122	87.78 180	60.63 160	55.86 295	82.39 242

V42		67.66 192	65.60 209	73.86 176	70.69 313	53.63 98	81.50 228	78.61 241	63.85 95	59.49 203	77.49 258	37.98 184	87.65 185	62.33 145	58.43 264	80.29 276

Fast-Effi-MVS+-dtu		66.53 220	64.10 229	73.84 177	72.41 294	52.30 136	84.73 135	75.66 284	59.51 170	56.34 257	79.11 244	28.11 286	85.85 238	57.74 195	63.29 228	83.35 225

v10		66.61 219	64.20 228	73.83 178	72.59 292	53.37 108	81.88 214	79.91 210	61.11 142	54.09 277	75.60 287	40.06 168	88.26 164	56.47 205	56.10 291	79.86 281

APD-MVS_3200maxsize		69.62 159	68.23 154	73.80 179	81.58 148	48.22 230	81.91 213	79.50 219	48.21 303	64.24 148	89.75 94	31.91 265	87.55 191	63.08 141	73.85 138	85.64 188

AUN-MVS		68.20 184	66.35 188	73.76 180	76.37 240	47.45 248	79.52 259	79.52 218	60.98 146	62.34 171	86.02 154	36.59 216	86.94 204	62.32 146	53.47 315	86.89 158

PVSNet_BlendedMVS		73.42 92	73.30 75	73.76 180	85.91 48	51.83 144	86.18 92	84.24 132	65.40 73	69.09 97	80.86 227	46.70 80	88.13 166	75.43 66	65.92 205	81.33 262

hse-mvs2		71.44 125	70.68 116	73.73 182	76.34 241	47.44 249	79.45 260	79.47 220	68.08 33	71.97 75	86.01 156	42.50 136	86.93 205	78.82 42	53.46 316	86.83 164

baseline1		72.51 106	72.12 97	73.69 183	85.05 65	44.46 287	83.51 171	86.13 80	71.61 12	64.64 139	87.97 128	55.00 23	89.48 117	59.07 173	56.05 292	87.13 156

CDS-MVSNet		70.48 141	69.43 137	73.64 184	77.56 225	48.83 212	83.51 171	77.45 260	63.27 108	62.33 172	85.54 161	43.85 116	83.29 270	57.38 199	74.00 135	88.79 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet	62.49 8	69.27 162	67.81 163	73.64 184	84.41 76	51.85 143	84.63 141	77.80 253	66.42 56	59.80 195	84.95 167	22.14 332	80.44 292	55.03 213	75.11 129	88.62 126

PS-MVSNAJss		68.78 172	67.17 175	73.62 186	73.01 286	48.33 228	84.95 129	84.81 116	59.30 178	58.91 215	79.84 235	37.77 187	88.86 139	62.83 143	63.12 233	83.67 223

TAMVS		69.51 161	68.16 155	73.56 187	76.30 244	48.71 216	82.57 198	77.17 265	62.10 126	61.32 183	84.23 174	41.90 147	83.46 268	54.80 216	73.09 144	88.50 131

UGNet		68.71 173	67.11 176	73.50 188	80.55 175	47.61 246	84.08 154	78.51 243	59.45 171	65.68 128	82.73 199	23.78 318	85.08 251	52.80 230	76.40 113	87.80 142
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
sd_testset		67.79 190	65.95 199	73.32 189	81.70 140	46.33 266	68.99 326	80.30 202	66.58 52	61.64 180	82.38 209	30.45 274	87.63 187	55.86 209	65.60 206	86.01 177

Anonymous20231211		66.08 227	63.67 230	73.31 190	83.07 107	48.75 214	86.01 97	84.67 122	45.27 322	56.54 254	76.67 274	28.06 287	88.95 135	52.78 231	59.95 248	82.23 243

新几何1				73.30 191	83.10 104	53.48 101		71.43 320	45.55 320	66.14 120	87.17 141	33.88 245	80.54 290	48.50 259	80.33 82	85.88 184

FMVSNet3		68.84 168	67.40 171	73.19 192	85.05 65	48.53 220	85.71 105	85.36 94	60.90 150	57.58 239	79.15 243	42.16 141	86.77 208	47.25 267	63.40 224	84.27 207

mvsmamba		66.93 215	64.88 222	73.09 193	75.06 260	47.26 252	83.36 180	69.21 334	62.64 118	55.68 262	81.43 222	29.72 278	89.20 125	63.35 140	63.50 223	82.79 239

thres200		68.71 173	67.27 174	73.02 194	84.73 70	46.76 258	85.03 125	87.73 54	62.34 124	59.87 193	83.45 187	43.15 130	88.32 160	31.25 336	67.91 186	83.98 215

PVSNet_0	57.04 13	61.19 263	57.24 276	73.02 194	77.45 227	50.31 178	79.43 261	77.36 263	63.96 94	47.51 319	72.45 316	25.03 310	83.78 263	52.76 233	19.22 386	84.96 198

test1111		71.06 130	70.42 121	72.97 196	79.48 188	41.49 319	84.82 134	82.74 162	64.20 87	62.98 165	87.43 137	35.20 229	87.92 172	58.54 178	78.42 102	89.49 104

dp		64.41 233	61.58 241	72.90 197	82.40 126	54.09 91	72.53 304	76.59 277	60.39 157	55.68 262	70.39 331	35.18 230	76.90 326	39.34 299	61.71 242	87.73 144

FMVSNet2		67.57 195	65.79 203	72.90 197	82.71 120	47.97 239	85.15 118	84.93 112	58.55 196	56.71 252	78.26 250	36.72 213	86.67 211	46.15 274	62.94 235	84.07 210

XXY-MVS		70.18 143	69.28 143	72.89 199	77.64 222	42.88 306	85.06 123	87.50 59	62.58 119	62.66 170	82.34 211	43.64 124	89.83 108	58.42 181	63.70 221	85.96 181

CR-MVSNet		62.47 255	59.04 267	72.77 200	73.97 277	56.57 29	60.52 353	71.72 316	60.04 161	57.49 242	65.86 344	38.94 177	80.31 293	42.86 290	59.93 249	81.42 257

EI-MVSNet		69.70 157	68.70 147	72.68 201	75.00 262	48.90 210	79.54 257	87.16 61	61.05 144	63.88 155	83.74 181	45.87 89	90.44 91	57.42 198	64.68 213	78.70 290

HPM-MVS_fast		67.86 187	66.28 191	72.61 202	80.67 172	48.34 227	81.18 234	75.95 283	50.81 288	59.55 201	88.05 127	27.86 289	85.98 233	58.83 175	73.58 139	83.51 224

MVP-Stereo		70.97 132	70.44 120	72.59 203	76.03 249	51.36 155	85.02 126	86.99 64	60.31 158	56.53 255	78.92 245	40.11 167	90.00 104	60.00 170	90.01 6	76.41 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		69.07 163	67.91 157	72.54 204	77.27 229	49.56 193	79.77 255	73.96 300	59.33 177	60.73 188	87.82 129	30.19 276	81.53 278	69.94 98	72.19 152	86.53 168

IS-MVSNet		68.80 171	67.55 168	72.54 204	78.50 211	43.43 300	81.03 236	79.35 225	59.12 185	57.27 247	86.71 147	46.05 87	87.70 183	44.32 283	75.60 123	86.49 169

VPA-MVSNet		71.12 128	70.66 117	72.49 206	78.75 203	44.43 289	87.64 60	90.02 12	63.97 93	65.02 134	81.58 221	42.14 142	87.42 193	63.42 139	63.38 227	85.63 189

SR-MVS-dyc-post		68.27 182	66.87 177	72.48 207	80.96 162	48.14 233	81.54 226	76.98 268	46.42 315	62.75 168	89.42 99	31.17 270	86.09 230	60.52 164	72.06 153	83.19 231

dmvs_re		67.61 193	66.00 197	72.42 208	81.86 135	43.45 299	64.67 339	80.00 206	69.56 25	60.07 192	85.00 166	34.71 236	87.63 187	51.48 239	66.68 194	86.17 175

miper_enhance_ethall		69.77 154	68.90 146	72.38 209	78.93 200	49.91 185	83.29 181	78.85 232	64.90 80	59.37 204	79.46 237	52.77 33	85.16 249	63.78 136	58.72 261	82.08 244

cl22		68.85 167	67.69 164	72.35 210	78.07 217	49.98 184	82.45 203	78.48 244	62.50 122	58.46 225	77.95 251	49.99 54	85.17 248	62.55 144	58.72 261	81.90 247

MSDG		59.44 272	55.14 292	72.32 211	74.69 265	50.71 163	74.39 292	73.58 303	44.44 328	43.40 335	77.52 257	19.45 341	90.87 80	31.31 335	57.49 281	75.38 325

v7n		62.50 254	59.27 265	72.20 212	67.25 335	49.83 188	77.87 271	80.12 204	52.50 276	48.80 310	73.07 308	32.10 260	87.90 173	46.83 270	54.92 302	78.86 288

1112_ss		70.05 147	69.37 139	72.10 213	80.77 169	42.78 307	85.12 122	76.75 272	59.69 167	61.19 184	92.12 40	47.48 71	83.84 261	53.04 227	68.21 182	89.66 99

miper_ehance_all_eth		68.70 175	67.58 166	72.08 214	76.91 237	49.48 197	82.47 202	78.45 245	62.68 117	58.28 229	77.88 253	50.90 47	85.01 252	61.91 150	58.72 261	81.75 249

eth_miper_zixun_eth		66.98 213	65.28 216	72.06 215	75.61 254	50.40 172	81.00 237	76.97 271	62.00 127	56.99 249	76.97 267	44.84 107	85.58 239	58.75 176	54.42 307	80.21 277

LPG-MVS_test		66.44 222	64.58 224	72.02 216	74.42 270	48.60 217	83.07 188	80.64 196	54.69 259	53.75 280	83.83 179	25.73 305	86.98 201	60.33 168	64.71 210	80.48 273

LGP-MVS_train				72.02 216	74.42 270	48.60 217		80.64 196	54.69 259	53.75 280	83.83 179	25.73 305	86.98 201	60.33 168	64.71 210	80.48 273

ACMP	61.11 9	66.24 225	64.33 226	72.00 218	74.89 264	49.12 201	83.18 185	79.83 211	55.41 250	52.29 290	82.68 200	25.83 303	86.10 228	60.89 157	63.94 219	80.78 269
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GBi-Net		67.09 209	65.47 211	71.96 219	82.71 120	46.36 263	83.52 167	83.31 149	58.55 196	57.58 239	76.23 280	36.72 213	86.20 222	47.25 267	63.40 224	83.32 226

test1		67.09 209	65.47 211	71.96 219	82.71 120	46.36 263	83.52 167	83.31 149	58.55 196	57.58 239	76.23 280	36.72 213	86.20 222	47.25 267	63.40 224	83.32 226

FMVSNet1		64.57 232	62.11 238	71.96 219	77.32 228	46.36 263	83.52 167	83.31 149	52.43 277	54.42 273	76.23 280	27.80 290	86.20 222	42.59 292	61.34 244	83.32 226

cl____		67.43 199	65.93 200	71.95 222	76.33 242	48.02 237	82.58 197	79.12 229	61.30 140	56.72 251	76.92 269	46.12 85	86.44 219	57.98 188	56.31 287	81.38 261

DIV-MVS_self_test		67.43 199	65.93 200	71.94 223	76.33 242	48.01 238	82.57 198	79.11 230	61.31 139	56.73 250	76.92 269	46.09 86	86.43 220	57.98 188	56.31 287	81.39 260

Patchmatch-RL test		58.72 283	54.32 295	71.92 224	63.91 352	44.25 291	61.73 349	55.19 360	57.38 220	49.31 307	54.24 368	37.60 194	80.89 283	62.19 148	47.28 336	90.63 75

c3_l		67.97 185	66.66 183	71.91 225	76.20 246	49.31 199	82.13 209	78.00 251	61.99 128	57.64 238	76.94 268	49.41 58	84.93 253	60.62 161	57.01 283	81.49 253

tfpn200view9		67.57 195	66.13 194	71.89 226	84.05 82	45.07 282	83.40 176	87.71 56	60.79 151	57.79 234	82.76 196	43.53 125	87.80 177	28.80 343	66.36 200	82.78 240

RRT_MVS		63.68 241	61.01 250	71.70 227	73.48 279	45.98 271	81.19 233	76.08 281	54.33 263	52.84 286	79.27 240	22.21 330	87.65 185	54.13 219	55.54 299	81.46 256

MIMVSNet		63.12 247	60.29 257	71.61 228	75.92 251	46.65 259	65.15 336	81.94 172	59.14 184	54.65 271	69.47 334	25.74 304	80.63 288	41.03 295	69.56 177	87.55 148

test-LLR		69.65 158	69.01 145	71.60 229	78.67 205	48.17 231	85.13 119	79.72 213	59.18 182	63.13 163	82.58 203	36.91 208	80.24 294	60.56 162	75.17 127	86.39 172

test-mter		68.36 178	67.29 172	71.60 229	78.67 205	48.17 231	85.13 119	79.72 213	53.38 269	63.13 163	82.58 203	27.23 294	80.24 294	60.56 162	75.17 127	86.39 172

sss		70.49 140	70.13 129	71.58 231	81.59 147	39.02 330	80.78 243	84.71 120	59.34 175	66.61 114	88.09 125	37.17 204	85.52 240	61.82 152	71.02 162	90.20 87

tpmvs		62.45 256	59.42 263	71.53 232	83.93 84	54.32 85	70.03 321	77.61 257	51.91 280	53.48 283	68.29 338	37.91 185	86.66 212	33.36 326	58.27 267	73.62 339

ACMM	58.35 12	64.35 234	62.01 239	71.38 233	74.21 273	48.51 221	82.25 206	79.66 215	47.61 306	54.54 272	80.11 231	25.26 308	86.00 232	51.26 240	63.16 231	79.64 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	53.70 16	59.78 269	55.94 288	71.28 234	76.59 239	48.35 226	80.15 253	76.11 280	49.74 295	41.91 341	73.45 307	16.50 357	90.31 96	31.42 334	57.63 280	75.17 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ppachtmachnet_test		58.56 285	54.34 294	71.24 235	71.42 304	54.74 73	81.84 216	72.27 312	49.02 299	45.86 329	68.99 337	26.27 299	83.30 269	30.12 338	43.23 350	75.69 322

thres100view900		66.87 216	65.42 214	71.24 235	83.29 100	43.15 303	81.67 221	87.78 51	59.04 186	55.92 260	82.18 213	43.73 120	87.80 177	28.80 343	66.36 200	82.78 240

thres400		67.40 202	66.13 194	71.19 237	84.05 82	45.07 282	83.40 176	87.71 56	60.79 151	57.79 234	82.76 196	43.53 125	87.80 177	28.80 343	66.36 200	80.71 271

our_test_3		59.11 277	55.08 293	71.18 238	71.42 304	53.29 113	81.96 211	74.52 292	48.32 302	42.08 339	69.28 336	28.14 285	82.15 274	34.35 323	45.68 345	78.11 302

CPTT-MVS		67.15 207	65.84 202	71.07 239	80.96 162	50.32 177	81.94 212	74.10 296	46.18 318	57.91 231	87.64 134	29.57 279	81.31 280	64.10 135	70.18 171	81.56 252

NR-MVSNet		67.25 204	65.99 198	71.04 240	73.27 284	43.91 294	85.32 114	84.75 119	66.05 66	53.65 282	82.11 214	45.05 101	85.97 235	47.55 264	56.18 290	83.24 229

tpm		68.36 178	67.48 170	70.97 241	79.93 184	51.34 156	76.58 279	78.75 237	67.73 40	63.54 161	74.86 291	48.33 62	72.36 348	53.93 221	63.71 220	89.21 110

TranMVSNet+NR-MVSNet		66.94 214	65.61 208	70.93 242	73.45 280	43.38 301	83.02 190	84.25 130	65.31 77	58.33 228	81.90 217	39.92 171	85.52 240	49.43 251	54.89 303	83.89 219

EG-PatchMatch MVS		62.40 257	59.59 261	70.81 243	73.29 282	49.05 203	85.81 98	84.78 117	51.85 282	44.19 330	73.48 306	15.52 360	89.85 107	40.16 297	67.24 191	73.54 340

test_djsdf		63.84 238	61.56 242	70.70 244	68.78 324	44.69 286	81.63 222	81.44 183	50.28 290	52.27 291	76.26 279	26.72 297	86.11 226	60.83 158	55.84 296	81.29 265

UA-Net		67.32 203	66.23 192	70.59 245	78.85 201	41.23 322	73.60 296	75.45 287	61.54 136	66.61 114	84.53 169	38.73 180	86.57 217	42.48 293	74.24 134	83.98 215

thres600view7		66.46 221	65.12 218	70.47 246	83.41 94	43.80 296	82.15 207	87.78 51	59.37 174	56.02 259	82.21 212	43.73 120	86.90 206	26.51 355	64.94 209	80.71 271

UniMVSNet (Re)		67.71 191	66.80 179	70.45 247	74.44 269	42.93 305	82.42 204	84.90 113	63.69 99	59.63 198	80.99 225	47.18 73	85.23 247	51.17 242	56.75 284	83.19 231

IterMVS-LS		66.63 218	65.36 215	70.42 248	75.10 259	48.90 210	81.45 231	76.69 275	61.05 144	55.71 261	77.10 265	45.86 90	83.65 265	57.44 197	57.88 277	78.70 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		68.82 169	68.29 153	70.40 249	75.71 253	42.59 309	84.23 150	86.78 67	66.31 58	58.51 221	82.45 206	51.57 41	84.64 257	53.11 225	55.96 293	83.96 217

jajsoiax		63.21 246	60.84 251	70.32 250	68.33 329	44.45 288	81.23 232	81.05 189	53.37 270	50.96 300	77.81 255	17.49 351	85.49 242	59.31 171	58.05 272	81.02 267

mvs_tets		62.96 249	60.55 253	70.19 251	68.22 332	44.24 292	80.90 240	80.74 195	52.99 273	50.82 302	77.56 256	16.74 355	85.44 243	59.04 174	57.94 274	80.89 268

pmmvs4		63.34 245	61.07 249	70.16 252	70.14 315	50.53 168	79.97 254	71.41 321	55.08 253	54.12 276	78.58 247	32.79 254	82.09 276	50.33 245	57.22 282	77.86 303

DU-MVS		66.84 217	65.74 205	70.16 252	73.27 284	42.59 309	81.50 228	82.92 160	63.53 103	58.51 221	82.11 214	40.75 158	84.64 257	53.11 225	55.96 293	83.24 229

Effi-MVS+-dtu		66.24 225	64.96 221	70.08 254	75.17 257	49.64 190	82.01 210	74.48 293	62.15 125	57.83 232	76.08 284	30.59 273	83.79 262	65.40 131	60.93 246	76.81 312

IterMVS		63.77 240	61.67 240	70.08 254	72.68 291	51.24 159	80.44 246	75.51 285	60.51 156	51.41 295	73.70 303	32.08 261	78.91 305	54.30 218	54.35 308	80.08 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		67.58 194	66.76 180	70.04 256	75.92 251	45.06 285	86.23 91	85.28 100	64.31 85	58.50 223	81.00 224	44.80 110	82.00 277	49.21 254	55.57 298	83.06 234

Test_1112_low_res		67.18 206	66.23 192	70.02 257	78.75 203	41.02 323	83.43 174	73.69 302	57.29 221	58.45 226	82.39 208	45.30 98	80.88 284	50.50 244	66.26 204	88.16 133

D2MVS		63.49 243	61.39 244	69.77 258	69.29 321	48.93 209	78.89 265	77.71 256	60.64 155	49.70 305	72.10 322	27.08 295	83.48 267	54.48 217	62.65 236	76.90 311

tt0805		63.39 244	61.31 246	69.64 259	69.36 320	38.87 331	78.00 269	85.48 88	48.82 301	55.66 265	81.66 219	24.38 315	86.37 221	49.04 255	59.36 257	83.68 222

XVG-OURS		61.88 259	59.34 264	69.49 260	65.37 342	46.27 267	64.80 338	73.49 305	47.04 310	57.41 246	82.85 194	25.15 309	78.18 309	53.00 228	64.98 208	84.01 212

XVG-OURS-SEG-HR		62.02 258	59.54 262	69.46 261	65.30 343	45.88 272	65.06 337	73.57 304	46.45 314	57.42 245	83.35 189	26.95 296	78.09 311	53.77 222	64.03 217	84.42 205

test_vis1_n_1920		68.59 176	68.31 152	69.44 262	69.16 322	41.51 318	84.63 141	68.58 337	58.80 191	73.26 59	88.37 118	25.30 307	80.60 289	79.10 39	67.55 189	86.23 174

FIs		70.00 149	70.24 128	69.30 263	77.93 220	38.55 333	83.99 158	87.72 55	66.86 50	57.66 237	84.17 175	52.28 37	85.31 244	52.72 234	68.80 179	84.02 211

Baseline_NR-MVSNet		65.49 231	64.27 227	69.13 264	74.37 272	41.65 316	83.39 178	78.85 232	59.56 169	59.62 199	76.88 271	40.75 158	87.44 192	49.99 246	55.05 301	78.28 299

TransMVSNet (Re)		62.82 250	60.76 252	69.02 265	73.98 276	41.61 317	86.36 88	79.30 228	56.90 227	52.53 288	76.44 276	41.85 148	87.60 190	38.83 300	40.61 355	77.86 303

anonymousdsp		60.46 267	57.65 273	68.88 266	63.63 353	45.09 281	72.93 302	78.63 240	46.52 313	51.12 297	72.80 312	21.46 335	83.07 271	57.79 193	53.97 309	78.47 294

ADS-MVSNet		56.17 299	51.95 309	68.84 267	80.60 173	53.07 119	55.03 364	70.02 329	44.72 325	51.00 298	61.19 356	22.83 323	78.88 306	28.54 346	53.63 311	74.57 333

OpenMVS_ROB	53.19 17	59.20 275	56.00 287	68.83 268	71.13 308	44.30 290	83.64 166	75.02 290	46.42 315	46.48 326	73.03 309	18.69 345	88.14 165	27.74 351	61.80 241	74.05 336

Patchmatch-test		53.33 314	48.17 323	68.81 269	73.31 281	42.38 313	42.98 374	58.23 357	32.53 361	38.79 353	70.77 328	39.66 172	73.51 342	25.18 358	52.06 321	90.55 76

pm-mvs1		64.12 236	62.56 234	68.78 270	71.68 300	38.87 331	82.89 192	81.57 180	55.54 249	53.89 279	77.82 254	37.73 190	86.74 209	48.46 260	53.49 314	80.72 270

miper_lstm_enhance		63.91 237	62.30 236	68.75 271	75.06 260	46.78 257	69.02 325	81.14 188	59.68 168	52.76 287	72.39 317	40.71 160	77.99 315	56.81 203	53.09 317	81.48 255

OMC-MVS		65.97 228	65.06 219	68.71 272	72.97 287	42.58 311	78.61 266	75.35 288	54.72 258	59.31 206	86.25 153	33.30 249	77.88 317	57.99 187	67.05 192	85.66 187

DP-MVS		59.24 274	56.12 286	68.63 273	88.24 32	50.35 176	82.51 201	64.43 347	41.10 341	46.70 324	78.77 246	24.75 313	88.57 150	22.26 367	56.29 289	66.96 360

tfpnnormal		61.47 262	59.09 266	68.62 274	76.29 245	41.69 315	81.14 235	85.16 106	54.48 261	51.32 296	73.63 304	32.32 258	86.89 207	21.78 369	55.71 297	77.29 309

test_cas_vis1_n_1920		67.10 208	66.60 185	68.59 275	65.17 345	43.23 302	83.23 183	69.84 330	55.34 251	70.67 90	87.71 132	24.70 314	76.66 328	78.57 46	64.20 215	85.89 183

UniMVSNet_ETH3D		62.51 253	60.49 254	68.57 276	68.30 330	40.88 325	73.89 294	79.93 209	51.81 283	54.77 269	79.61 236	24.80 312	81.10 281	49.93 247	61.35 243	83.73 221

CL-MVSNet_self_test		62.98 248	61.14 248	68.50 277	65.86 340	42.96 304	84.37 145	82.98 158	60.98 146	53.95 278	72.70 313	40.43 162	83.71 264	41.10 294	47.93 331	78.83 289

ACMH+	54.58 15	58.55 286	55.24 290	68.50 277	74.68 266	45.80 275	80.27 249	70.21 327	47.15 309	42.77 338	75.48 288	16.73 356	85.98 233	35.10 321	54.78 304	73.72 338

lessismore_v0				67.98 279	64.76 349	41.25 321		45.75 369		36.03 360	65.63 346	19.29 343	84.11 259	35.67 313	21.24 384	78.59 293

bld_raw_dy_0_64		59.75 270	57.01 280	67.96 280	66.73 336	45.30 279	77.59 273	59.97 356	50.49 289	47.15 321	77.03 266	17.45 352	79.06 304	56.92 202	59.76 252	79.51 283

K. test v3		54.04 309	49.42 320	67.92 281	68.55 326	42.57 312	75.51 284	63.07 351	52.07 278	39.21 350	64.59 348	19.34 342	82.21 273	37.11 306	25.31 379	78.97 287

pmmvs5		62.80 251	61.18 247	67.66 282	69.53 319	42.37 314	82.65 196	75.19 289	54.30 264	52.03 293	78.51 248	31.64 267	80.67 287	48.60 258	58.15 269	79.95 280

PatchT		56.60 295	52.97 302	67.48 283	72.94 288	46.16 270	57.30 361	73.78 301	38.77 345	54.37 274	57.26 366	37.52 196	78.06 312	32.02 331	52.79 318	78.23 301

Patchmtry		56.56 296	52.95 303	67.42 284	72.53 293	50.59 167	59.05 357	71.72 316	37.86 349	46.92 322	65.86 344	38.94 177	80.06 297	36.94 309	46.72 341	71.60 350

SixPastTwentyTwo		54.37 306	50.10 315	67.21 285	70.70 312	41.46 320	74.73 289	64.69 345	47.56 307	39.12 351	69.49 333	18.49 348	84.69 256	31.87 332	34.20 369	75.48 324

pmmvs6		59.64 271	57.15 277	67.09 286	66.01 338	36.86 340	80.50 245	78.64 239	45.05 324	49.05 308	73.94 298	27.28 293	86.10 228	43.96 285	49.94 326	78.31 298

testdata				67.08 287	77.59 224	45.46 278		69.20 335	44.47 327	71.50 82	88.34 120	31.21 269	70.76 353	52.20 236	75.88 119	85.03 196

CNLPA		60.59 266	58.44 270	67.05 288	79.21 193	47.26 252	79.75 256	64.34 348	42.46 339	51.90 294	83.94 177	27.79 291	75.41 333	37.12 305	59.49 255	78.47 294

KD-MVS_2432*1600		59.04 279	56.44 283	66.86 289	79.07 195	45.87 273	72.13 310	80.42 200	55.03 254	48.15 312	71.01 325	36.73 211	78.05 313	35.21 317	30.18 374	76.67 313

miper_refine_blended		59.04 279	56.44 283	66.86 289	79.07 195	45.87 273	72.13 310	80.42 200	55.03 254	48.15 312	71.01 325	36.73 211	78.05 313	35.21 317	30.18 374	76.67 313

TAPA-MVS	56.12 14	61.82 260	60.18 259	66.71 291	78.48 212	37.97 336	75.19 287	76.41 279	46.82 311	57.04 248	86.52 151	27.67 292	77.03 323	26.50 356	67.02 193	85.14 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_0402		56.45 297	53.03 301	66.69 292	76.78 238	50.31 178	81.76 218	69.61 332	42.79 337	43.88 331	72.13 320	22.82 325	86.46 218	16.57 379	50.94 323	63.31 368

PLC	52.38 18	60.89 264	58.97 268	66.68 293	81.77 137	45.70 276	78.96 264	74.04 299	43.66 333	47.63 316	83.19 192	23.52 321	77.78 320	37.47 302	60.46 247	76.55 318
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 305	51.44 310	66.51 294	80.60 173	49.56 193	55.03 364	65.44 343	44.72 325	51.00 298	61.19 356	22.83 323	75.41 333	28.54 346	53.63 311	74.57 333

FC-MVSNet-test		67.49 197	67.91 157	66.21 295	76.06 247	33.06 352	80.82 242	87.18 60	64.44 84	54.81 268	82.87 193	50.40 52	82.60 272	48.05 262	66.55 198	82.98 236

JIA-IIPM		52.33 319	47.77 326	66.03 296	71.20 307	46.92 256	40.00 379	76.48 278	37.10 350	46.73 323	37.02 379	32.96 251	77.88 317	35.97 312	52.45 320	73.29 342

LCM-MVSNet-Re		58.82 282	56.54 281	65.68 297	79.31 192	29.09 370	61.39 352	45.79 368	60.73 153	37.65 356	72.47 315	31.42 268	81.08 282	49.66 249	70.41 168	86.87 159

XVG-ACMP-BASELINE		56.03 300	52.85 304	65.58 298	61.91 358	40.95 324	63.36 342	72.43 311	45.20 323	46.02 327	74.09 296	9.20 371	78.12 310	45.13 277	58.27 267	77.66 306

pmmvs-eth3d		55.97 301	52.78 305	65.54 299	61.02 360	46.44 262	75.36 286	67.72 340	49.61 296	43.65 333	67.58 340	21.63 334	77.04 322	44.11 284	44.33 347	73.15 344

MDA-MVSNet_test_wron		53.82 311	49.95 317	65.43 300	70.13 316	49.05 203	72.30 307	71.65 319	44.23 331	31.85 371	63.13 351	23.68 320	74.01 337	33.25 328	39.35 358	73.23 343

YYNet1		53.82 311	49.96 316	65.41 301	70.09 317	48.95 207	72.30 307	71.66 318	44.25 330	31.89 370	63.07 352	23.73 319	73.95 338	33.26 327	39.40 357	73.34 341

PatchMatch-RL		56.66 294	53.75 299	65.37 302	77.91 221	45.28 280	69.78 323	60.38 354	41.35 340	47.57 317	73.73 300	16.83 354	76.91 324	36.99 308	59.21 258	73.92 337

Vis-MVSNet (Re-imp)		65.52 230	65.63 207	65.17 303	77.49 226	30.54 359	75.49 285	77.73 255	59.34 175	52.26 292	86.69 148	49.38 59	80.53 291	37.07 307	75.28 126	84.42 205

FMVSNet5		58.61 284	56.45 282	65.10 304	77.20 233	39.74 327	74.77 288	77.12 266	50.27 292	43.28 336	67.71 339	26.15 302	76.90 326	36.78 310	54.78 304	78.65 292

EPNet_dtu		66.25 224	66.71 181	64.87 305	78.66 207	34.12 347	82.80 193	75.51 285	61.75 132	64.47 146	86.90 144	37.06 205	72.46 347	43.65 286	69.63 176	88.02 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		57.56 291	55.15 291	64.79 306	64.57 350	33.12 351	73.17 301	83.87 140	58.98 188	41.75 342	70.03 332	22.54 326	79.92 298	46.12 275	35.31 363	81.32 264

LS3D		56.40 298	53.82 298	64.12 307	81.12 158	45.69 277	73.42 299	66.14 342	35.30 359	43.24 337	79.88 233	22.18 331	79.62 302	19.10 375	64.00 218	67.05 359

UnsupCasMVSNet_bld		53.86 310	50.53 314	63.84 308	63.52 354	34.75 343	71.38 315	81.92 174	46.53 312	38.95 352	57.93 364	20.55 338	80.20 296	39.91 298	34.09 370	76.57 317

USDC		54.36 307	51.23 311	63.76 309	64.29 351	37.71 337	62.84 347	73.48 307	56.85 228	35.47 361	71.94 323	9.23 370	78.43 307	38.43 301	48.57 328	75.13 328

Anonymous20231206		59.08 278	57.59 274	63.55 310	68.77 325	32.14 357	80.26 250	79.78 212	50.00 294	49.39 306	72.39 317	26.64 298	78.36 308	33.12 329	57.94 274	80.14 278

CMPMVS	40.41 21	55.34 303	52.64 306	63.46 311	60.88 361	43.84 295	61.58 351	71.06 322	30.43 367	36.33 358	74.63 293	24.14 317	75.44 332	48.05 262	66.62 196	71.12 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
myMVS_eth3d		63.52 242	63.56 232	63.40 312	81.73 138	34.28 345	80.97 238	81.02 190	60.93 148	55.06 266	82.64 201	48.00 67	80.81 285	23.42 365	58.32 265	75.10 329

OurMVSNet-221017-0		52.39 318	48.73 321	63.35 313	65.21 344	38.42 334	68.54 329	64.95 344	38.19 346	39.57 349	71.43 324	13.23 363	79.92 298	37.16 304	40.32 356	71.72 349

MDA-MVSNet-bldmvs		51.56 321	47.75 327	63.00 314	71.60 302	47.32 251	69.70 324	72.12 313	43.81 332	27.65 378	63.38 350	21.97 333	75.96 330	27.30 353	32.19 371	65.70 365

F-COLMAP		55.96 302	53.65 300	62.87 315	72.76 290	42.77 308	74.70 291	70.37 326	40.03 342	41.11 346	79.36 238	17.77 350	73.70 341	32.80 330	53.96 310	72.15 346

test0.0.03 1		62.54 252	62.44 235	62.86 316	72.28 297	29.51 367	82.93 191	78.78 235	59.18 182	53.07 285	82.41 207	36.91 208	77.39 321	37.45 303	58.96 259	81.66 251

CVMVSNet		60.85 265	60.44 255	62.07 317	75.00 262	32.73 354	79.54 257	73.49 305	36.98 351	56.28 258	83.74 181	29.28 282	69.53 356	46.48 271	63.23 229	83.94 218

ambc				62.06 318	53.98 370	29.38 368	35.08 382	79.65 216		41.37 343	59.96 359	6.27 382	82.15 274	35.34 316	38.22 359	74.65 332

Syy-MVS		61.51 261	61.35 245	62.00 319	81.73 138	30.09 362	80.97 238	81.02 190	60.93 148	55.06 266	82.64 201	35.09 232	80.81 285	16.40 380	58.32 265	75.10 329

PEN-MVS		58.35 288	57.15 277	61.94 320	67.55 334	34.39 344	77.01 275	78.35 247	51.87 281	47.72 315	76.73 273	33.91 243	73.75 340	34.03 324	47.17 337	77.68 305

MVS-HIRNet		49.01 326	44.71 330	61.92 321	76.06 247	46.61 260	63.23 344	54.90 361	24.77 373	33.56 366	36.60 381	21.28 336	75.88 331	29.49 340	62.54 237	63.26 369

LTVRE_ROB	45.45 19	52.73 315	49.74 318	61.69 322	69.78 318	34.99 342	44.52 372	67.60 341	43.11 336	43.79 332	74.03 297	18.54 347	81.45 279	28.39 348	57.94 274	68.62 357
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
WR-MVS_H		58.91 281	58.04 272	61.54 323	69.07 323	33.83 349	76.91 276	81.99 171	51.40 285	48.17 311	74.67 292	40.23 164	74.15 336	31.78 333	48.10 329	76.64 316

CP-MVSNet		58.54 287	57.57 275	61.46 324	68.50 327	33.96 348	76.90 277	78.60 242	51.67 284	47.83 314	76.60 275	34.99 235	72.79 345	35.45 314	47.58 333	77.64 307

PS-CasMVS		58.12 289	57.03 279	61.37 325	68.24 331	33.80 350	76.73 278	78.01 250	51.20 286	47.54 318	76.20 283	32.85 252	72.76 346	35.17 319	47.37 335	77.55 308

Anonymous20240521		51.65 320	48.42 322	61.34 326	56.43 367	39.65 329	73.57 297	73.47 308	36.64 353	36.59 357	63.98 349	10.75 367	72.25 349	35.35 315	49.01 327	72.11 347

CHOSEN 280x420		57.53 292	56.38 285	60.97 327	74.01 275	48.10 235	46.30 371	54.31 362	48.18 304	50.88 301	77.43 260	38.37 183	59.16 369	54.83 214	63.14 232	75.66 323

DTE-MVSNet		57.03 293	55.73 289	60.95 328	65.94 339	32.57 355	75.71 280	77.09 267	51.16 287	46.65 325	76.34 278	32.84 253	73.22 344	30.94 337	44.87 346	77.06 310

IterMVS-SCA-FT		59.12 276	58.81 269	60.08 329	70.68 314	45.07 282	80.42 247	74.25 295	43.54 334	50.02 304	73.73 300	31.97 262	56.74 371	51.06 243	53.60 313	78.42 296

COLMAP_ROB	43.60 20	50.90 323	48.05 324	59.47 330	67.81 333	40.57 326	71.25 316	62.72 353	36.49 354	36.19 359	73.51 305	13.48 362	73.92 339	20.71 371	50.26 325	63.92 367
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing3		59.97 268	60.19 258	59.32 331	77.60 223	30.01 364	81.75 219	81.79 177	53.54 267	50.34 303	79.94 232	48.99 61	76.91 324	17.19 378	50.59 324	71.03 354

testgi		54.25 308	52.57 307	59.29 332	62.76 356	21.65 382	72.21 309	70.47 325	53.25 271	41.94 340	77.33 261	14.28 361	77.95 316	29.18 342	51.72 322	78.28 299

TinyColmap		48.15 328	44.49 332	59.13 333	65.73 341	38.04 335	63.34 343	62.86 352	38.78 344	29.48 373	67.23 342	6.46 381	73.30 343	24.59 360	41.90 353	66.04 363

test20.03		55.22 304	54.07 297	58.68 334	63.14 355	25.00 375	77.69 272	74.78 291	52.64 274	43.43 334	72.39 317	26.21 300	74.76 335	29.31 341	47.05 339	76.28 320

EU-MVSNet		52.63 316	50.72 313	58.37 335	62.69 357	28.13 372	72.60 303	75.97 282	30.94 366	40.76 348	72.11 321	20.16 339	70.80 352	35.11 320	46.11 343	76.19 321

MIMVSNet1		50.35 324	47.81 325	57.96 336	61.53 359	27.80 373	67.40 332	74.06 298	43.25 335	33.31 369	65.38 347	16.03 358	71.34 350	21.80 368	47.55 334	74.75 331

pmmvs3		45.53 333	41.55 337	57.44 337	48.97 378	39.68 328	70.06 320	57.66 358	28.32 369	34.06 364	57.29 365	8.50 374	66.85 358	34.86 322	34.26 368	65.80 364

test_fmvs1		53.60 313	52.54 308	56.78 338	58.07 363	30.26 360	68.95 327	42.19 374	32.46 362	63.59 159	82.56 205	11.55 364	60.81 363	58.25 184	55.27 300	79.28 284

test_fmvs1_n		52.55 317	51.19 312	56.65 339	51.90 373	30.14 361	67.66 331	42.84 373	32.27 363	62.30 173	82.02 216	9.12 372	60.84 362	57.82 192	54.75 306	78.99 286

KD-MVS_self_test		49.24 325	46.85 328	56.44 340	54.32 368	22.87 378	57.39 360	73.36 309	44.36 329	37.98 355	59.30 362	18.97 344	71.17 351	33.48 325	42.44 351	75.26 326

PM-MVS		46.92 330	43.76 335	56.41 341	52.18 372	32.26 356	63.21 345	38.18 379	37.99 348	40.78 347	66.20 343	5.09 384	65.42 359	48.19 261	41.99 352	71.54 351

dmvs_testset		57.65 290	58.21 271	55.97 342	74.62 267	9.82 398	63.75 341	63.34 350	67.23 45	48.89 309	83.68 185	39.12 176	76.14 329	23.43 364	59.80 251	81.96 246

test_vis1_n		51.19 322	49.66 319	55.76 343	51.26 374	29.85 365	67.20 333	38.86 378	32.12 364	59.50 202	79.86 234	8.78 373	58.23 370	56.95 201	52.46 319	79.19 285

AllTest		47.32 329	44.66 331	55.32 344	65.08 346	37.50 338	62.96 346	54.25 363	35.45 357	33.42 367	72.82 310	9.98 368	59.33 366	24.13 361	43.84 348	69.13 355

TestCases				55.32 344	65.08 346	37.50 338		54.25 363	35.45 357	33.42 367	72.82 310	9.98 368	59.33 366	24.13 361	43.84 348	69.13 355

new-patchmatchnet		48.21 327	46.55 329	53.18 346	57.73 365	18.19 390	70.24 319	71.02 323	45.70 319	33.70 365	60.23 358	18.00 349	69.86 355	27.97 350	34.35 367	71.49 352

ITE_SJBPF				51.84 347	58.03 364	31.94 358		53.57 365	36.67 352	41.32 344	75.23 290	11.17 366	51.57 376	25.81 357	48.04 330	72.02 348

RPSCF		45.77 332	44.13 334	50.68 348	57.67 366	29.66 366	54.92 366	45.25 370	26.69 371	45.92 328	75.92 286	17.43 353	45.70 382	27.44 352	45.95 344	76.67 313

test_fmvs2		45.89 331	44.32 333	50.62 349	45.85 382	24.70 376	58.87 359	37.84 381	25.22 372	52.46 289	74.56 294	7.07 376	54.69 372	49.28 253	47.70 332	72.48 345

ANet_high		34.39 343	29.59 349	48.78 350	30.34 392	22.28 379	55.53 363	63.79 349	38.11 347	15.47 385	36.56 382	6.94 377	59.98 365	13.93 382	5.64 396	64.08 366

TDRefinement		40.91 336	38.37 340	48.55 351	50.45 376	33.03 353	58.98 358	50.97 366	28.50 368	29.89 372	67.39 341	6.21 383	54.51 373	17.67 377	35.25 364	58.11 370

DSMNet-mixed		38.35 338	35.36 343	47.33 352	48.11 380	14.91 394	37.87 380	36.60 382	19.18 378	34.37 363	59.56 361	15.53 359	53.01 375	20.14 373	46.89 340	74.07 335

mvsany_test1		43.38 334	42.57 336	45.82 353	50.96 375	26.10 374	55.80 362	27.74 391	27.15 370	47.41 320	74.39 295	18.67 346	44.95 383	44.66 280	36.31 361	66.40 362

N_pmnet		41.25 335	39.77 338	45.66 354	68.50 327	0.82 404	72.51 305	0.38 403	35.61 356	35.26 362	61.51 355	20.07 340	67.74 357	23.51 363	40.63 354	68.42 358

test_vis1_rt		40.29 337	38.64 339	45.25 355	48.91 379	30.09 362	59.44 356	27.07 392	24.52 374	38.48 354	51.67 373	6.71 379	49.44 377	44.33 282	46.59 342	56.23 371

test_fmvs3		37.95 339	35.75 342	44.55 356	35.50 388	18.92 386	48.32 368	34.00 386	18.36 380	41.31 345	61.58 354	2.29 391	48.06 381	42.72 291	37.71 360	66.66 361

EGC-MVSNET		33.75 344	30.42 348	43.75 357	64.94 348	36.21 341	60.47 355	40.70 377	0.02 397	0.10 398	53.79 369	7.39 375	60.26 364	11.09 385	35.23 365	34.79 383

LCM-MVSNet		28.07 347	23.85 355	40.71 358	27.46 397	18.93 385	30.82 386	46.19 367	12.76 385	16.40 383	34.70 384	1.90 394	48.69 380	20.25 372	24.22 380	54.51 373

FPMVS		35.40 341	33.67 345	40.57 359	46.34 381	28.74 371	41.05 376	57.05 359	20.37 377	22.27 381	53.38 370	6.87 378	44.94 384	8.62 386	47.11 338	48.01 378

WB-MVS		37.41 340	36.37 341	40.54 360	54.23 369	10.43 397	65.29 335	43.75 371	34.86 360	27.81 377	54.63 367	24.94 311	63.21 360	6.81 392	15.00 387	47.98 379

new_pmnet		33.56 345	31.89 347	38.59 361	49.01 377	20.42 383	51.01 367	37.92 380	20.58 375	23.45 380	46.79 375	6.66 380	49.28 379	20.00 374	31.57 373	46.09 380

SSC-MVS		35.20 342	34.30 344	37.90 362	52.58 371	8.65 400	61.86 348	41.64 375	31.81 365	25.54 379	52.94 372	23.39 322	59.28 368	6.10 393	12.86 388	45.78 381

PMMVS2		26.71 351	22.98 356	37.87 363	36.89 386	8.51 401	42.51 375	29.32 390	19.09 379	13.01 387	37.54 378	2.23 392	53.11 374	14.54 381	11.71 389	51.99 376

Gipuma		27.47 349	24.26 354	37.12 364	60.55 362	29.17 369	11.68 391	60.00 355	14.18 383	10.52 392	15.12 393	2.20 393	63.01 361	8.39 387	35.65 362	19.18 389
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		33.04 346	32.55 346	34.52 365	40.96 383	22.03 380	44.45 373	35.62 383	20.42 376	28.12 376	62.35 353	5.03 385	31.88 395	21.61 370	34.42 366	49.63 377

mvsany_test3		28.00 348	25.98 350	34.05 366	28.97 393	15.31 392	34.54 383	18.17 397	16.24 381	29.30 374	53.37 371	2.79 389	33.38 394	30.01 339	20.41 385	53.45 374

test_f		27.12 350	24.85 351	33.93 367	26.17 398	15.25 393	30.24 387	22.38 396	12.53 386	28.23 375	49.43 374	2.59 390	34.34 393	25.12 359	26.99 377	52.20 375

test_method		24.09 355	21.07 359	33.16 368	27.67 396	8.35 402	26.63 388	35.11 385	3.40 394	14.35 386	36.98 380	3.46 388	35.31 390	19.08 376	22.95 381	55.81 372

PMVS	19.57 22	25.07 353	22.43 358	32.99 369	23.12 399	22.98 377	40.98 377	35.19 384	15.99 382	11.95 391	35.87 383	1.47 397	49.29 378	5.41 395	31.90 372	26.70 388
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		26.46 352	24.41 353	32.62 370	37.58 385	21.74 381	40.50 378	30.39 388	11.45 387	16.33 384	43.76 376	1.63 396	41.62 385	11.24 384	26.82 378	34.51 384

test_vis3_rt		24.79 354	22.95 357	30.31 371	28.59 394	18.92 386	37.43 381	17.27 399	12.90 384	21.28 382	29.92 388	1.02 398	36.35 388	28.28 349	29.82 376	35.65 382

MVE	16.60 23	17.34 361	13.39 364	29.16 372	28.43 395	19.72 384	13.73 390	23.63 395	7.23 393	7.96 393	21.41 389	0.80 399	36.08 389	6.97 390	10.39 390	31.69 385
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		21.11 356	19.08 360	27.18 373	30.56 390	18.28 388	33.43 384	24.48 393	8.02 391	12.02 389	33.50 385	0.75 400	35.09 391	7.68 388	21.32 382	28.17 386

APD_test2		21.11 356	19.08 360	27.18 373	30.56 390	18.28 388	33.43 384	24.48 393	8.02 391	12.02 389	33.50 385	0.75 400	35.09 391	7.68 388	21.32 382	28.17 386

E-PMN		19.16 358	18.40 362	21.44 375	36.19 387	13.63 395	47.59 369	30.89 387	10.73 388	5.91 395	16.59 391	3.66 387	39.77 386	5.95 394	8.14 391	10.92 391

EMVS		18.42 359	17.66 363	20.71 376	34.13 389	12.64 396	46.94 370	29.94 389	10.46 390	5.58 396	14.93 394	4.23 386	38.83 387	5.24 396	7.51 393	10.67 392

DeepMVS_CX				13.10 377	21.34 400	8.99 399		10.02 401	10.59 389	7.53 394	30.55 387	1.82 395	14.55 396	6.83 391	7.52 392	15.75 390

wuyk23d		9.11 363	8.77 367	10.15 378	40.18 384	16.76 391	20.28 389	1.01 402	2.58 395	2.66 397	0.98 397	0.23 402	12.49 397	4.08 397	6.90 394	1.19 394

tmp_tt		9.44 362	10.68 365	5.73 379	2.49 401	4.21 403	10.48 392	18.04 398	0.34 396	12.59 388	20.49 390	11.39 365	7.03 398	13.84 383	6.46 395	5.95 393

testmvs		6.14 365	8.18 368	0.01 380	0.01 402	0.00 406	73.40 300	0.00 404	0.00 398	0.02 399	0.15 398	0.00 403	0.00 399	0.02 398	0.00 397	0.02 395

test123		6.01 366	8.01 369	0.01 380	0.00 403	0.01 405	71.93 313	0.00 404	0.00 398	0.02 399	0.11 399	0.00 403	0.00 399	0.02 398	0.00 397	0.02 395

test_blank		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

uanet_test		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

DCPMVS		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

cdsmvs_eth3d_5k		18.33 360	24.44 352	0.00 382	0.00 403	0.00 406	0.00 393	89.40 16	0.00 398	0.00 401	92.02 43	38.55 181	0.00 399	0.00 400	0.00 397	0.00 397

pcd_1.5k_mvsjas		3.15 367	4.20 370	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	37.77 187	0.00 399	0.00 400	0.00 397	0.00 397

sosnet-low-res		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

sosnet		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

uncertanet		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

Regformer		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

ab-mvs-re		7.68 364	10.24 366	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	92.12 40	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

uanet		0.00 368	0.00 371	0.00 382	0.00 403	0.00 406	0.00 393	0.00 404	0.00 398	0.00 401	0.00 400	0.00 403	0.00 399	0.00 400	0.00 397	0.00 397

WAC-MVS						34.28 345								22.56 366

FOURS1					83.24 101	49.90 186	84.98 127	78.76 236	47.71 305	73.42 56

PC_three_1452									66.58 52	87.27 2	93.70 9	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

test_one_0601					89.39 22	57.29 20		88.09 46	57.21 224	82.06 12	93.39 16	54.94 24

eth-test2					0.00 403

eth-test					0.00 403

ZD-MVS					89.55 14	53.46 102		84.38 126	57.02 226	73.97 51	91.03 61	44.57 112	91.17 70	75.41 69	81.78 69

RE-MVS-def			66.66 183		80.96 162	48.14 233	81.54 226	76.98 268	46.42 315	62.75 168	89.42 99	29.28 282		60.52 164	72.06 153	83.19 231

IU-MVS					89.48 17	57.49 15		91.38 5	66.22 60	88.26 1				82.83 19	87.60 17	92.44 29

test_241102_TWO								88.76 32	57.50 218	83.60 6	94.09 3	56.14 18	96.37 6	82.28 23	87.43 19	92.55 27

test_241102_ONE					89.48 17	56.89 25		88.94 24	57.53 216	84.61 4	93.29 20	58.81 11	96.45 1

9.14			78.19 24		85.67 53		88.32 50	88.84 29	59.89 163	74.58 46	92.62 33	46.80 78	92.66 39	81.40 32	85.62 39

save fliter					85.35 60	56.34 36	89.31 39	81.46 182	61.55 135

test_0728_THIRD									58.00 204	81.91 13	93.64 11	56.54 15	96.44 2	81.64 28	86.86 24	92.23 34

test0726					89.40 20	57.45 17	92.32 7	88.63 36	57.71 212	83.14 9	93.96 6	55.17 20

GSMVS																88.13 136

test_part2					89.33 23	55.48 50				82.27 11

sam_mvs1												38.86 179				88.13 136

sam_mvs												35.99 224

MTGPA								81.31 185

test_post1							70.84 318				14.72 395	34.33 240	83.86 260	48.80 256

test_post											16.22 392	37.52 196	84.72 255

patchmatchnet-post											59.74 360	38.41 182	79.91 300

MTMP							87.27 72	15.34 400

gm-plane-assit					83.24 101	54.21 88			70.91 15		88.23 123		95.25 14	66.37 117

test9_res														78.72 45	85.44 41	91.39 59

TEST9					85.68 51	55.42 51	87.59 62	84.00 136	57.72 211	72.99 61	90.98 63	44.87 106	88.58 147

test_8					85.72 50	55.31 56	87.60 61	83.88 139	57.84 209	72.84 65	90.99 62	44.99 102	88.34 158

agg_prior2														75.65 64	85.11 45	91.01 68

agg_prior					85.64 54	54.92 70		83.61 146		72.53 70			88.10 168

test_prior4						56.39 35	87.15 75

test_prior2							89.04 42		61.88 131	73.55 54	91.46 59	48.01 66		74.73 72	85.46 40

旧先验2							81.73 220		45.53 321	74.66 43			70.48 354	58.31 183

新几何2							81.61 224

旧先验1					81.57 149	47.48 247		71.83 314			88.66 113	36.94 207			78.34 103	88.67 124

无先验							85.19 117	78.00 251	49.08 298				85.13 250	52.78 231		87.45 151

原ACMM2							83.77 164

test222					79.36 189	50.97 161	77.99 270	67.84 339	42.54 338	62.84 167	86.53 150	30.26 275			76.91 111	85.23 193

testdata2													77.81 319	45.64 276

segment_acmp												44.97 104

testdata1							77.55 274		64.14 89

plane_prior7					77.95 218	48.46 224

plane_prior6					78.42 213	49.39 198						36.04 222

plane_prior5								82.59 163					88.30 161	65.46 126	72.34 150	84.49 203

plane_prior4											83.28 190

plane_prior3						48.95 207			64.01 92	62.15 175

plane_prior2							85.76 100		63.60 101

plane_prior1					78.31 215

plane_prior						49.57 191	87.43 65		64.57 83						72.84 146

n2								0.00 404

nn								0.00 404

door-mid								41.31 376

test11								84.25 130

door								43.27 372

HQP5-MVS						51.56 150

HQP-NCC					79.02 197		88.00 53		65.45 70	64.48 143

ACMP_Plane					79.02 197		88.00 53		65.45 70	64.48 143

BP-MVS														66.70 114

HQP4-MVS										64.47 146			88.61 146			84.91 199

HQP3-MVS								83.68 142							73.12 142

HQP2-MVS												37.35 199

NP-MVS					78.76 202	50.43 171					85.12 164

MDTV_nov1_ep13_2view						43.62 297	71.13 317		54.95 256	59.29 208		36.76 210		46.33 273		87.32 153

MDTV_nov1_ep13			61.56 242		81.68 142	55.12 63	72.41 306	78.18 248	59.19 180	58.85 217	69.29 335	34.69 237	86.16 225	36.76 311	62.96 234

ACMMP++_ref															63.20 230

ACMMP++															59.38 256

Test By Simon												39.38 173

High-resolution multi-view benchmark