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 57	74.03 81	82.88 1	83.09 113	62.84 2	85.73 111	85.39 100	69.79 29	64.87 146	83.49 195	41.52 161	93.69 30	70.55 100	81.82 68	92.12 39

MG-MVS		78.42 27	76.99 43	82.73 2	93.17 1	64.46 1	89.93 30	88.51 48	64.83 89	73.52 58	88.09 131	48.07 71	92.19 53	62.24 154	84.53 51	91.53 59

LFMVS		78.52 24	77.14 41	82.67 3	89.58 13	58.90 7	91.27 19	88.05 54	63.22 118	74.63 47	90.83 73	41.38 162	94.40 22	75.42 72	79.90 90	94.72 2

DVP-MVS++		82.44 3	82.38 5	82.62 4	91.77 4	57.49 15	84.98 137	88.88 32	58.00 214	83.60 6	93.39 20	67.21 2	96.39 4	81.64 32	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 5	80.12 189	59.50 5	92.24 9	90.72 14	69.37 33	83.22 9	94.47 2	63.81 5	93.18 33	74.02 84	93.25 2	94.80 1

CSCG		80.41 15	79.72 15	82.49 5	89.12 25	57.67 13	89.29 41	91.54 5	59.19 190	71.82 80	90.05 92	59.72 9	96.04 10	78.37 51	88.40 14	93.75 9

SED-MVS		81.92 7	81.75 9	82.44 7	89.48 17	56.89 29	92.48 4	88.94 30	57.50 228	84.61 4	94.09 3	58.81 11	96.37 6	82.28 27	87.60 18	94.06 3

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 17	92.34 6	89.99 18	57.71 222	81.91 14	93.64 13	55.17 25	96.44 2	81.68 30	87.13 21	92.72 26
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 6	88.88 32					96.39 4	81.68 30	87.13 21	92.47 30

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 10	75.95 4	77.10 37	93.09 29	54.15 33	95.57 12	85.80 10	85.87 37	93.31 13

bld_raw_dy_0_64		75.36 73	73.18 86	81.89 11	87.91 40	57.01 24	86.77 89	67.69 350	78.56 1	65.01 143	93.99 7	22.18 339	94.84 19	84.07 17	72.45 158	93.82 7

iter_conf05_11		79.47 20	78.68 23	81.84 12	87.91 40	57.01 24	93.27 2	79.49 227	74.74 6	83.40 8	94.00 6	21.51 344	94.70 21	84.07 17	89.68 7	93.82 7

DELS-MVS		82.32 5	82.50 4	81.79 13	86.80 48	56.89 29	92.77 3	86.30 84	77.83 2	77.88 34	92.13 43	60.24 6	94.78 20	78.97 45	89.61 8	93.69 10
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 14	92.05 3	55.97 48	92.48 4				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

PS-MVSNAJ		80.06 16	79.52 17	81.68 15	85.58 61	60.97 3	91.69 12	87.02 70	70.62 23	80.75 21	93.22 26	37.77 195	92.50 46	82.75 24	86.25 34	91.57 57

MSC_two_6792asdad				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 34

No_MVS				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 34

xiu_mvs_v2_base		79.86 17	79.31 18	81.53 16	85.03 73	60.73 4	91.65 13	86.86 73	70.30 27	80.77 20	93.07 31	37.63 200	92.28 52	82.73 25	85.71 38	91.57 57

CNVR-MVS		81.76 8	81.90 8	81.33 19	90.04 10	57.70 12	91.71 11	88.87 34	70.31 26	77.64 36	93.87 9	52.58 40	93.91 28	84.17 14	87.92 16	92.39 32

MVS		76.91 47	75.48 60	81.23 20	84.56 79	55.21 65	80.23 261	91.64 4	58.65 204	65.37 138	91.48 62	45.72 99	95.05 16	72.11 95	89.52 10	93.44 11

VDDNet		74.37 85	72.13 107	81.09 21	79.58 195	56.52 37	90.02 27	86.70 77	52.61 285	71.23 88	87.20 149	31.75 274	93.96 27	74.30 82	75.77 126	92.79 25

MM		82.69 2	83.29 3	80.89 22	84.38 83	55.40 59	92.16 10	89.85 20	75.28 5	82.41 11	93.86 10	54.30 30	93.98 25	90.29 1	87.13 21	93.30 14

testing91		78.30 31	77.54 35	80.61 23	88.16 35	57.12 23	87.94 60	91.07 13	71.43 18	70.75 94	88.04 135	55.82 22	92.65 42	69.61 104	75.00 139	92.05 42

NCCC		79.57 19	79.23 19	80.59 24	89.50 15	56.99 26	91.38 16	88.17 52	67.71 48	73.81 55	92.75 34	46.88 84	93.28 31	78.79 48	84.07 54	91.50 61

dcpmvs_2		79.33 21	78.94 20	80.49 25	89.75 12	56.54 36	84.83 144	83.68 149	67.85 45	69.36 102	90.24 84	60.20 7	92.10 57	84.14 15	80.40 81	92.82 23

API-MVS		74.17 88	72.07 109	80.49 25	90.02 11	58.55 8	87.30 74	84.27 136	57.51 227	65.77 135	87.77 140	41.61 159	95.97 11	51.71 245	82.63 60	86.94 167

testing99		78.45 25	77.78 32	80.45 27	88.28 33	56.81 32	87.95 59	91.49 6	71.72 15	70.84 93	88.09 131	57.29 15	92.63 44	69.24 107	75.13 135	91.91 47

3Dnovator	64.70 6	74.46 83	72.48 96	80.41 28	82.84 124	55.40 59	83.08 197	88.61 45	67.61 50	59.85 204	88.66 118	34.57 245	93.97 26	58.42 188	88.70 12	91.85 50

DPE-MVS		79.82 18	79.66 16	80.29 29	89.27 24	55.08 71	88.70 47	87.92 56	55.55 258	81.21 19	93.69 12	56.51 18	94.27 24	78.36 52	85.70 39	91.51 60
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		76.76 52	75.60 58	80.21 30	90.87 7	54.68 84	89.14 42	89.11 26	62.95 122	70.54 99	92.33 41	41.05 163	94.95 17	57.90 198	86.55 32	91.00 76
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 9	82.05 7	80.20 31	82.36 135	54.70 82	91.13 20	88.95 29	74.49 7	80.04 25	93.64 13	52.40 41	93.27 32	88.85 4	86.56 31	92.61 28

SD-MVS		76.18 58	74.85 71	80.18 32	85.39 65	56.90 28	85.75 109	82.45 173	56.79 242	74.48 50	91.81 52	43.72 130	90.75 89	74.61 78	78.65 100	92.91 21
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
testing11		79.18 22	78.85 21	80.16 33	88.33 30	56.99 26	88.31 52	92.06 1	72.82 11	70.62 98	88.37 123	57.69 14	92.30 50	75.25 74	76.24 122	91.20 70

Effi-MVS+		75.24 75	73.61 83	80.16 33	81.92 141	57.42 19	85.21 126	76.71 282	60.68 165	73.32 61	89.34 105	47.30 79	91.63 64	68.28 113	79.72 92	91.42 62

SMA-MVS		79.10 23	78.76 22	80.12 35	84.42 81	55.87 50	87.58 68	86.76 75	61.48 148	80.26 23	93.10 27	46.53 89	92.41 48	79.97 39	88.77 11	92.08 40
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 87	72.25 102	80.11 36	81.45 161	56.47 38	86.32 96	79.65 223	58.19 210	66.36 126	92.29 42	36.11 227	90.66 91	67.39 117	82.49 62	93.18 18

CANet		80.90 11	81.17 12	80.09 37	87.62 42	54.21 94	91.60 14	86.47 80	73.13 10	79.89 26	93.10 27	49.88 63	92.98 34	84.09 16	84.75 49	93.08 19

IB-MVS	68.87 2	74.01 89	72.03 112	79.94 38	83.04 115	55.50 54	90.24 26	88.65 41	67.14 53	61.38 192	81.74 228	53.21 36	94.28 23	60.45 173	62.41 248	90.03 101
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 14	80.71 14	79.88 39	87.34 44	55.20 66	89.93 30	87.55 65	66.04 74	79.46 27	93.00 32	53.10 37	91.76 62	80.40 38	89.56 9	92.68 27

QAPM		71.88 128	69.33 151	79.52 40	82.20 138	54.30 92	86.30 97	88.77 38	56.61 246	59.72 206	87.48 144	33.90 252	95.36 13	47.48 273	81.49 71	88.90 126

VDD-MVS		76.08 60	74.97 69	79.44 41	84.27 86	53.33 116	91.13 20	85.88 90	65.33 84	72.37 75	89.34 105	32.52 264	92.76 40	77.90 57	75.96 123	92.22 38

MVS_111021_HR		76.39 56	75.38 63	79.42 42	85.33 67	56.47 38	88.15 53	84.97 118	65.15 87	66.06 129	89.88 95	43.79 127	92.16 54	75.03 75	80.03 88	89.64 109

SteuartSystems-ACMMP		77.08 45	76.33 50	79.34 43	80.98 168	55.31 61	89.76 34	86.91 72	62.94 123	71.65 81	91.56 60	42.33 146	92.56 45	77.14 61	83.69 56	90.15 97
Skip Steuart: Steuart Systems R&D Blog.
test12				79.24 44	86.89 47	56.08 45		85.16 113		72.27 77		47.15 81	91.10 79		85.93 36	90.54 85

APDe-MVS		78.44 26	78.20 26	79.19 45	88.56 26	54.55 88	89.76 34	87.77 60	55.91 253	78.56 31	92.49 39	48.20 70	92.65 42	79.49 40	83.04 58	90.39 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lupinMVS		78.38 28	78.11 28	79.19 45	83.02 116	55.24 63	91.57 15	84.82 122	69.12 34	76.67 39	92.02 47	44.82 116	90.23 105	80.83 37	80.09 85	92.08 40

casdiffmvs_mvg		77.75 37	77.28 38	79.16 47	80.42 185	54.44 90	87.76 62	85.46 97	71.67 16	71.38 86	88.35 125	51.58 45	91.22 74	79.02 44	79.89 91	91.83 51
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 38	77.22 40	79.14 48	86.95 46	54.89 77	87.18 78	91.96 2	72.29 13	71.17 91	88.70 117	55.19 24	91.24 73	65.18 139	76.32 121	91.29 68

DeepC-MVS_fast	67.50 3	78.00 34	77.63 33	79.13 49	88.52 27	55.12 68	89.95 29	85.98 89	68.31 37	71.33 87	92.75 34	45.52 102	90.37 98	71.15 97	85.14 45	91.91 47
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 32	77.86 31	79.12 50	84.30 84	54.22 93	87.71 63	84.57 131	67.70 49	77.70 35	92.11 46	50.90 52	89.95 111	78.18 55	77.54 108	93.20 17

PHI-MVS		77.49 40	77.00 42	78.95 51	85.33 67	50.69 169	88.57 49	88.59 46	58.14 211	73.60 56	93.31 23	43.14 139	93.79 29	73.81 85	88.53 13	92.37 33

test_yl		75.85 65	74.83 72	78.91 52	88.08 37	51.94 145	91.30 17	89.28 23	57.91 216	71.19 89	89.20 108	42.03 153	92.77 38	69.41 105	75.07 137	92.01 44

DCV-MVSNet		75.85 65	74.83 72	78.91 52	88.08 37	51.94 145	91.30 17	89.28 23	57.91 216	71.19 89	89.20 108	42.03 153	92.77 38	69.41 105	75.07 137	92.01 44

casdiffmvs		77.36 42	76.85 44	78.88 54	80.40 186	54.66 86	87.06 81	85.88 90	72.11 14	71.57 83	88.63 122	50.89 54	90.35 99	76.00 65	79.11 97	91.63 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
PAPM		76.76 52	76.07 54	78.81 55	80.20 187	59.11 6	86.86 87	86.23 85	68.60 36	70.18 101	88.84 115	51.57 46	87.16 206	65.48 132	86.68 29	90.15 97

MSP-MVS		82.30 6	83.47 1	78.80 56	82.99 118	52.71 131	85.04 134	88.63 43	66.08 71	86.77 3	92.75 34	72.05 1	91.46 68	83.35 21	93.53 1	92.23 36
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 49	76.27 51	78.80 56	80.70 178	55.02 72	86.39 94	86.71 76	66.96 56	67.91 112	89.97 94	48.03 72	91.41 69	75.60 69	84.14 53	89.96 103
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 55	75.66 57	78.73 58	81.92 141	54.67 85	84.06 166	85.35 102	61.10 154	72.99 64	91.50 61	40.25 171	91.00 81	76.84 62	86.98 24	90.51 86

baseline		76.86 50	76.24 52	78.71 59	80.47 184	54.20 96	83.90 170	84.88 121	71.38 20	71.51 84	89.15 110	50.51 55	90.55 95	75.71 67	78.65 100	91.39 63

jason		77.01 46	76.45 48	78.69 60	79.69 194	54.74 79	90.56 25	83.99 145	68.26 38	74.10 53	90.91 70	42.14 150	89.99 110	79.30 42	79.12 96	91.36 65
jason: jason.
ET-MVSNet_ETH3D		75.23 76	74.08 79	78.67 61	84.52 80	55.59 52	88.92 44	89.21 25	68.06 43	53.13 294	90.22 86	49.71 64	87.62 195	72.12 94	70.82 174	92.82 23

CostFormer		73.89 92	72.30 101	78.66 62	82.36 135	56.58 33	75.56 291	85.30 105	66.06 72	70.50 100	76.88 280	57.02 16	89.06 133	68.27 114	68.74 190	90.33 89

patch_mono-2		80.84 12	81.59 10	78.62 63	90.34 9	53.77 101	88.08 54	88.36 50	76.17 3	79.40 28	91.09 64	55.43 23	90.09 108	85.01 12	80.40 81	91.99 46

MVS_Test		75.85 65	74.93 70	78.62 63	84.08 88	55.20 66	83.99 168	85.17 112	68.07 42	73.38 60	82.76 205	50.44 56	89.00 137	65.90 128	80.61 77	91.64 53

CDPH-MVS		76.05 61	75.19 65	78.62 63	86.51 50	54.98 74	87.32 72	84.59 130	58.62 205	70.75 94	90.85 72	43.10 141	90.63 93	70.50 101	84.51 52	90.24 92

TSAR-MVS + GP.		77.82 36	77.59 34	78.49 66	85.25 69	50.27 186	90.02 27	90.57 15	56.58 247	74.26 52	91.60 59	54.26 31	92.16 54	75.87 66	79.91 89	93.05 20

ETV-MVS		77.17 44	76.74 45	78.48 67	81.80 144	54.55 88	86.13 100	85.33 103	68.20 39	73.10 63	90.52 78	45.23 106	90.66 91	79.37 41	80.95 73	90.22 93

TSAR-MVS + MP.		78.31 30	78.26 25	78.48 67	81.33 164	56.31 42	81.59 235	86.41 81	69.61 31	81.72 16	88.16 130	55.09 27	88.04 176	74.12 83	86.31 33	91.09 73
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 47	76.40 49	78.45 69	85.68 57	55.42 56	87.59 66	84.00 143	57.84 219	72.99 64	90.98 67	44.99 110	88.58 153	78.19 53	85.32 43	91.34 67

PAPR		75.20 77	74.13 77	78.41 70	88.31 32	55.10 70	84.31 158	85.66 94	63.76 105	67.55 114	90.73 74	43.48 135	89.40 124	66.36 125	77.03 110	90.73 81

alignmvs		78.08 33	77.98 29	78.39 71	83.53 99	53.22 119	89.77 33	85.45 98	66.11 69	76.59 41	91.99 49	54.07 34	89.05 134	77.34 60	77.00 111	92.89 22

test_prior				78.39 71	86.35 51	54.91 76		85.45 98					89.70 118			90.55 83

SF-MVS		77.64 39	77.42 37	78.32 73	83.75 96	52.47 136	86.63 92	87.80 57	58.78 202	74.63 47	92.38 40	47.75 75	91.35 70	78.18 55	86.85 26	91.15 72

ZNCC-MVS		75.82 68	75.02 68	78.23 74	83.88 94	53.80 100	86.91 86	86.05 88	59.71 176	67.85 113	90.55 76	42.23 148	91.02 80	72.66 93	85.29 44	89.87 106

VNet		77.99 35	77.92 30	78.19 75	87.43 43	50.12 187	90.93 23	91.41 8	67.48 51	75.12 43	90.15 90	46.77 86	91.00 81	73.52 87	78.46 102	93.44 11

EIA-MVS		75.92 63	75.18 66	78.13 76	85.14 70	51.60 154	87.17 79	85.32 104	64.69 90	68.56 108	90.53 77	45.79 98	91.58 65	67.21 119	82.18 65	91.20 70

HFP-MVS		74.37 85	73.13 91	78.10 77	84.30 84	53.68 103	85.58 115	84.36 134	56.82 240	65.78 134	90.56 75	40.70 169	90.90 85	69.18 108	80.88 74	89.71 107

tpm2		70.82 145	68.44 160	77.98 78	80.78 176	56.11 44	74.21 302	81.28 194	60.24 170	68.04 111	75.27 298	52.26 43	88.50 158	55.82 218	68.03 194	89.33 115

thisisatest0515		73.64 99	72.20 104	77.97 79	81.63 152	53.01 126	86.69 91	88.81 37	62.53 130	64.06 159	85.65 167	52.15 44	92.50 46	58.43 186	69.84 182	88.39 141

EPNet		78.36 29	78.49 24	77.97 79	85.49 63	52.04 143	89.36 39	84.07 142	73.22 9	77.03 38	91.72 54	49.32 67	90.17 107	73.46 88	82.77 59	91.69 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 81	85.46 64	49.56 199	90.99 22	86.66 78	70.58 24	80.07 24	95.30 1	56.18 20	90.97 84	82.57 26	86.22 35	93.28 15

GST-MVS		74.87 81	73.90 82	77.77 82	83.30 106	53.45 109	85.75 109	85.29 106	59.22 189	66.50 125	89.85 96	40.94 164	90.76 88	70.94 99	83.35 57	89.10 123

GG-mvs-BLEND				77.77 82	86.68 49	50.61 170	68.67 337	88.45 49		68.73 107	87.45 145	59.15 10	90.67 90	54.83 221	87.67 17	92.03 43

cascas		69.01 176	66.13 205	77.66 84	79.36 197	55.41 58	86.99 82	83.75 148	56.69 244	58.92 224	81.35 233	24.31 324	92.10 57	53.23 232	70.61 176	85.46 200

3Dnovator+	62.71 7	72.29 120	70.50 130	77.65 85	83.40 104	51.29 163	87.32 72	86.40 82	59.01 197	58.49 234	88.32 127	32.40 265	91.27 71	57.04 207	82.15 66	90.38 88

MVSFormer		73.53 100	72.19 105	77.57 86	83.02 116	55.24 63	81.63 232	81.44 190	50.28 299	76.67 39	90.91 70	44.82 116	86.11 235	60.83 165	80.09 85	91.36 65

APD-MVS		76.15 59	75.68 56	77.54 87	88.52 27	53.44 110	87.26 77	85.03 117	53.79 275	74.91 45	91.68 56	43.80 126	90.31 101	74.36 80	81.82 68	88.87 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Fast-Effi-MVS+		72.73 111	71.15 123	77.48 88	82.75 126	54.76 78	86.77 89	80.64 203	63.05 121	65.93 131	84.01 185	44.42 121	89.03 135	56.45 214	76.36 120	88.64 134

EPMVS		68.45 188	65.44 224	77.47 89	84.91 74	56.17 43	71.89 323	81.91 182	61.72 143	60.85 196	72.49 323	36.21 226	87.06 209	47.32 274	71.62 166	89.17 121

PatchmatchNet		67.07 222	63.63 242	77.40 90	83.10 111	58.03 9	72.11 321	77.77 262	58.85 200	59.37 214	70.83 336	37.84 194	84.93 262	42.96 298	69.83 183	89.26 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
region2R		73.75 95	72.55 95	77.33 91	83.90 93	52.98 127	85.54 119	84.09 141	56.83 239	65.10 140	90.45 79	37.34 209	90.24 104	68.89 110	80.83 76	88.77 132

iter_conf05		73.51 101	72.24 103	77.33 91	87.93 39	55.97 48	87.90 61	70.81 332	68.72 35	64.04 160	84.36 182	47.54 77	90.87 86	71.11 98	67.75 198	85.13 204

WTY-MVS		77.47 41	77.52 36	77.30 93	88.33 30	46.25 277	88.46 50	90.32 16	71.40 19	72.32 76	91.72 54	53.44 35	92.37 49	66.28 126	75.42 129	93.28 15

OpenMVS	61.00 11	69.99 160	67.55 179	77.30 93	78.37 223	54.07 98	84.36 156	85.76 93	57.22 233	56.71 262	87.67 142	30.79 280	92.83 37	43.04 297	84.06 55	85.01 206

MTAPA		72.73 111	71.22 121	77.27 95	81.54 158	53.57 105	67.06 343	81.31 192	59.41 183	68.39 109	90.96 69	36.07 229	89.01 136	73.80 86	82.45 63	89.23 118

PAPM_NR		71.80 130	69.98 142	77.26 96	81.54 158	53.34 115	78.60 277	85.25 109	53.46 278	60.53 200	88.66 118	45.69 100	89.24 127	56.49 211	79.62 95	89.19 120

ACMMPR		73.76 94	72.61 93	77.24 97	83.92 92	52.96 128	85.58 115	84.29 135	56.82 240	65.12 139	90.45 79	37.24 211	90.18 106	69.18 108	80.84 75	88.58 136

h-mvs33		73.95 90	72.89 92	77.15 98	80.17 188	50.37 180	84.68 148	83.33 155	68.08 40	71.97 78	88.65 121	42.50 144	91.15 77	78.82 46	57.78 288	89.91 105

CS-MVS-test		77.20 43	77.25 39	77.05 99	84.60 78	49.04 212	89.42 37	85.83 92	65.90 75	72.85 67	91.98 51	45.10 107	91.27 71	75.02 76	84.56 50	90.84 79

MP-MVS-pluss		75.54 72	75.03 67	77.04 100	81.37 163	52.65 133	84.34 157	84.46 132	61.16 152	69.14 103	91.76 53	39.98 178	88.99 139	78.19 53	84.89 48	89.48 114
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HyFIR lowres test		69.94 162	67.58 177	77.04 100	77.11 244	57.29 20	81.49 240	79.11 238	58.27 209	58.86 226	80.41 240	42.33 146	86.96 212	61.91 157	68.68 191	86.87 169

DP-MVS Recon		71.99 125	70.31 135	77.01 102	90.65 8	53.44 110	89.37 38	82.97 166	56.33 250	63.56 170	89.47 102	34.02 250	92.15 56	54.05 228	72.41 159	85.43 201

Anonymous20240529		69.71 165	67.28 184	77.00 103	83.78 95	50.36 181	88.87 46	85.10 116	47.22 317	64.03 161	83.37 197	27.93 296	92.10 57	57.78 201	67.44 200	88.53 139

CS-MVS		76.77 51	76.70 46	76.99 104	83.55 98	48.75 221	88.60 48	85.18 111	66.38 64	72.47 74	91.62 58	45.53 101	90.99 83	74.48 79	82.51 61	91.23 69

baseline2		75.15 78	74.54 75	76.98 105	81.67 151	51.74 151	83.84 172	91.94 3	69.97 28	58.98 221	86.02 163	59.73 8	91.73 63	68.37 112	70.40 179	87.48 159

MP-MVS		74.99 80	74.33 76	76.95 106	82.89 122	53.05 125	85.63 114	83.50 154	57.86 218	67.25 116	90.24 84	43.38 136	88.85 147	76.03 64	82.23 64	88.96 125
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 120	70.74 126	76.94 107	82.85 123	54.72 81	78.43 278	81.54 188	63.77 104	61.69 189	79.32 249	51.11 49	85.31 253	62.15 156	75.79 125	90.79 80

ETVMVS		75.80 69	75.44 61	76.89 108	86.23 52	50.38 179	85.55 118	91.42 7	71.30 21	68.80 106	87.94 137	56.42 19	89.24 127	56.54 210	74.75 141	91.07 74

XVS		72.92 107	71.62 114	76.81 109	83.41 101	52.48 134	84.88 142	83.20 161	58.03 212	63.91 163	89.63 100	35.50 234	89.78 114	65.50 130	80.50 79	88.16 142

X-MVStestdata		65.85 240	62.20 248	76.81 109	83.41 101	52.48 134	84.88 142	83.20 161	58.03 212	63.91 163	4.82 405	35.50 234	89.78 114	65.50 130	80.50 79	88.16 142

PGM-MVS		72.60 113	71.20 122	76.80 111	82.95 119	52.82 130	83.07 198	82.14 175	56.51 248	63.18 172	89.81 97	35.68 233	89.76 116	67.30 118	80.19 84	87.83 151

Anonymous202405211		70.11 154	67.88 170	76.79 112	87.20 45	47.24 263	89.49 36	77.38 270	54.88 267	66.14 127	86.84 154	20.93 347	91.54 66	56.45 214	71.62 166	91.59 55

tpm cat1		66.28 234	62.78 244	76.77 113	81.40 162	57.14 22	70.03 330	77.19 272	53.00 282	58.76 229	70.73 339	46.17 91	86.73 219	43.27 296	64.46 224	86.44 180

PVSNet_Blended		76.53 54	76.54 47	76.50 114	85.91 54	51.83 149	88.89 45	84.24 139	67.82 46	69.09 104	89.33 107	46.70 87	88.13 172	75.43 70	81.48 72	89.55 111

diffmvs		75.11 79	74.65 74	76.46 115	78.52 219	53.35 114	83.28 192	79.94 215	70.51 25	71.64 82	88.72 116	46.02 95	86.08 240	77.52 58	75.75 127	89.96 103
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		73.40 103	72.44 97	76.30 116	81.32 165	54.70 82	85.81 105	78.82 242	63.70 106	64.53 152	85.38 171	47.11 82	87.38 202	67.75 116	77.55 107	86.81 175

BH-RMVSNet		70.08 156	68.01 167	76.27 117	84.21 87	51.22 165	87.29 75	79.33 235	58.96 199	63.63 168	86.77 155	33.29 258	90.30 103	44.63 290	73.96 145	87.30 164

CLD-MVS		75.60 70	75.39 62	76.24 118	80.69 179	52.40 137	90.69 24	86.20 86	74.40 8	65.01 143	88.93 112	42.05 152	90.58 94	76.57 63	73.96 145	85.73 194
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		69.96 161	67.88 170	76.22 119	81.11 167	51.71 152	84.15 162	76.74 281	59.83 174	60.91 195	84.38 180	41.56 160	88.10 174	51.67 246	70.57 177	88.84 129

1314		71.11 139	69.41 148	76.22 119	79.32 199	50.49 174	80.23 261	85.14 115	59.44 182	58.93 223	88.89 114	33.83 254	89.60 121	61.49 160	77.42 109	88.57 137

thisisatest0530		70.47 152	68.56 158	76.20 121	79.78 193	51.52 157	83.49 183	88.58 47	57.62 225	58.60 230	82.79 204	51.03 51	91.48 67	52.84 237	62.36 250	85.59 199

FA-MVS(test-final)		69.00 177	66.60 196	76.19 122	83.48 100	47.96 250	74.73 298	82.07 177	57.27 232	62.18 184	78.47 259	36.09 228	92.89 35	53.76 231	71.32 170	87.73 154

HY-MVS	67.03 5	73.90 91	73.14 89	76.18 123	84.70 77	47.36 259	75.56 291	86.36 83	66.27 66	70.66 97	83.91 187	51.05 50	89.31 125	67.10 120	72.61 157	91.88 49

gg-mvs-nofinetune		67.43 210	64.53 236	76.13 124	85.95 53	47.79 254	64.38 349	88.28 51	39.34 352	66.62 121	41.27 386	58.69 13	89.00 137	49.64 258	86.62 30	91.59 55

原ACMM1				76.13 124	84.89 75	54.59 87		85.26 108	51.98 289	66.70 119	87.07 152	40.15 174	89.70 118	51.23 249	85.06 47	84.10 218

GA-MVS		69.04 175	66.70 193	76.06 126	75.11 269	52.36 138	83.12 196	80.23 210	63.32 116	60.65 199	79.22 252	30.98 279	88.37 161	61.25 161	66.41 209	87.46 160

mPP-MVS		71.79 131	70.38 133	76.04 127	82.65 130	52.06 142	84.45 154	81.78 185	55.59 257	62.05 187	89.68 99	33.48 256	88.28 169	65.45 135	78.24 105	87.77 153

MVSTER		73.25 104	72.33 99	76.01 128	85.54 62	53.76 102	83.52 177	87.16 68	67.06 54	63.88 165	81.66 229	52.77 38	90.44 96	64.66 141	64.69 222	83.84 229

CP-MVS		72.59 115	71.46 117	76.00 129	82.93 121	52.32 140	86.93 85	82.48 172	55.15 262	63.65 167	90.44 82	35.03 241	88.53 157	68.69 111	77.83 106	87.15 165

HPM-MVS		72.60 113	71.50 116	75.89 130	82.02 139	51.42 159	80.70 254	83.05 163	56.12 252	64.03 161	89.53 101	37.55 203	88.37 161	70.48 102	80.04 87	87.88 150
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		69.87 163	67.88 170	75.85 131	88.38 29	52.35 139	86.94 84	83.68 149	53.70 276	55.68 272	85.60 168	30.07 285	91.20 75	55.84 217	71.02 172	83.99 222

PMMVS		72.98 106	72.05 110	75.78 132	83.57 97	48.60 224	84.08 164	82.85 168	61.62 144	68.24 110	90.33 83	28.35 292	87.78 186	72.71 92	76.69 115	90.95 77

SDMVSNet		71.89 127	70.62 129	75.70 133	81.70 148	51.61 153	73.89 303	88.72 40	66.58 59	61.64 190	82.38 218	37.63 200	89.48 122	77.44 59	65.60 216	86.01 186

EC-MVSNet		75.30 74	75.20 64	75.62 134	80.98 168	49.00 213	87.43 69	84.68 128	63.49 113	70.97 92	90.15 90	42.86 143	91.14 78	74.33 81	81.90 67	86.71 176

test_fmvsm_n_1920		75.56 71	75.54 59	75.61 135	74.60 279	49.51 202	81.82 227	74.08 304	66.52 62	80.40 22	93.46 19	46.95 83	89.72 117	86.69 7	75.30 130	87.61 157

MS-PatchMatch		72.34 118	71.26 120	75.61 135	82.38 134	55.55 53	88.00 55	89.95 19	65.38 82	56.51 266	80.74 239	32.28 267	92.89 35	57.95 197	88.10 15	78.39 306

fmvsm_s_conf0.5_n		74.48 82	74.12 78	75.56 137	76.96 245	47.85 252	85.32 123	69.80 340	64.16 96	78.74 29	93.48 18	45.51 103	89.29 126	86.48 8	66.62 206	89.55 111

xiu_mvs_v1_base_debu		71.60 132	70.29 136	75.55 138	77.26 239	53.15 120	85.34 120	79.37 229	55.83 254	72.54 70	90.19 87	22.38 335	86.66 221	73.28 89	76.39 117	86.85 171

xiu_mvs_v1_base		71.60 132	70.29 136	75.55 138	77.26 239	53.15 120	85.34 120	79.37 229	55.83 254	72.54 70	90.19 87	22.38 335	86.66 221	73.28 89	76.39 117	86.85 171

xiu_mvs_v1_base_debi		71.60 132	70.29 136	75.55 138	77.26 239	53.15 120	85.34 120	79.37 229	55.83 254	72.54 70	90.19 87	22.38 335	86.66 221	73.28 89	76.39 117	86.85 171

test_fmvsmconf_n		74.41 84	74.05 80	75.49 141	74.16 285	48.38 233	82.66 205	72.57 317	67.05 55	75.11 44	92.88 33	46.35 90	87.81 181	83.93 19	71.71 165	90.28 91

fmvsm_s_conf0.1_n		73.80 93	73.26 85	75.43 142	73.28 294	47.80 253	84.57 153	69.43 342	63.34 115	78.40 32	93.29 24	44.73 119	89.22 129	85.99 9	66.28 213	89.26 116

CANet_DTU		73.71 96	73.14 89	75.40 143	82.61 131	50.05 188	84.67 150	79.36 232	69.72 30	75.39 42	90.03 93	29.41 288	85.93 246	67.99 115	79.11 97	90.22 93

ACMMP		70.81 146	69.29 152	75.39 144	81.52 160	51.92 147	83.43 184	83.03 164	56.67 245	58.80 228	88.91 113	31.92 272	88.58 153	65.89 129	73.39 149	85.67 195
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 97	73.15 87	75.34 145	70.71 322	48.26 238	82.15 217	71.83 321	66.75 58	74.47 51	92.59 38	44.89 113	87.78 186	83.59 20	71.35 169	89.97 102

SCA		63.84 249	60.01 271	75.32 146	78.58 218	57.92 10	61.61 359	77.53 266	56.71 243	57.75 246	70.77 337	31.97 270	79.91 310	48.80 264	56.36 294	88.13 145

fmvsm_l_conf0.5_n_a		75.88 64	76.07 54	75.31 147	76.08 256	48.34 235	85.24 125	70.62 333	63.13 120	81.45 18	93.62 16	49.98 61	87.40 201	87.76 6	76.77 114	90.20 95

fmvsm_l_conf0.5_n		75.95 62	76.16 53	75.31 147	76.01 260	48.44 232	84.98 137	71.08 329	63.50 112	81.70 17	93.52 17	50.00 59	87.18 205	87.80 5	76.87 113	90.32 90

FE-MVS		64.15 246	60.43 267	75.30 149	80.85 175	49.86 193	68.28 339	78.37 254	50.26 302	59.31 216	73.79 308	26.19 309	91.92 60	40.19 305	66.67 205	84.12 217

fmvsm_s_conf0.5_n_a		73.68 98	73.15 87	75.29 150	75.45 267	48.05 245	83.88 171	68.84 345	63.43 114	78.60 30	93.37 22	45.32 104	88.92 144	85.39 11	64.04 226	88.89 127

ab-mvs		70.65 148	69.11 154	75.29 150	80.87 174	46.23 278	73.48 307	85.24 110	59.99 172	66.65 120	80.94 236	43.13 140	88.69 149	63.58 145	68.07 193	90.95 77

TR-MVS		69.71 165	67.85 173	75.27 152	82.94 120	48.48 230	87.40 71	80.86 200	57.15 235	64.61 150	87.08 151	32.67 263	89.64 120	46.38 281	71.55 168	87.68 156

v2v482		69.55 170	67.64 176	75.26 153	72.32 307	53.83 99	84.93 141	81.94 179	65.37 83	60.80 197	79.25 251	41.62 158	88.98 140	63.03 149	59.51 263	82.98 245

PCF-MVS	61.03 10	70.10 155	68.40 161	75.22 154	77.15 243	51.99 144	79.30 272	82.12 176	56.47 249	61.88 188	86.48 161	43.98 123	87.24 204	55.37 219	72.79 156	86.43 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.1_n_a		72.82 110	72.05 110	75.12 155	70.95 321	47.97 248	82.72 204	68.43 347	62.52 131	78.17 33	93.08 30	44.21 122	88.86 145	84.82 13	63.54 232	88.54 138

test_fmvsmconf0.01_n		71.97 126	70.95 125	75.04 156	66.21 347	47.87 251	80.35 258	70.08 337	65.85 76	72.69 69	91.68 56	39.99 177	87.67 190	82.03 29	69.66 184	89.58 110

HQP-MVS		72.34 118	71.44 118	75.03 157	79.02 206	51.56 155	88.00 55	83.68 149	65.45 78	64.48 153	85.13 172	37.35 207	88.62 151	66.70 121	73.12 151	84.91 208

AdaColmap		67.86 198	65.48 221	75.00 158	88.15 36	54.99 73	86.10 101	76.63 284	49.30 306	57.80 243	86.65 158	29.39 289	88.94 143	45.10 287	70.21 180	81.06 276

EI-MVSNet-Vis-set		73.19 105	72.60 94	74.99 159	82.56 132	49.80 195	82.55 210	89.00 28	66.17 68	65.89 132	88.98 111	43.83 125	92.29 51	65.38 138	69.01 188	82.87 247

tpmrst		71.04 141	69.77 144	74.86 160	83.19 110	55.86 51	75.64 290	78.73 246	67.88 44	64.99 145	73.73 309	49.96 62	79.56 313	65.92 127	67.85 197	89.14 122

v1144		68.81 181	66.82 189	74.80 161	72.34 306	53.46 107	84.68 148	81.77 186	64.25 94	60.28 201	77.91 262	40.23 172	88.95 141	60.37 174	59.52 262	81.97 254

v1192		67.96 197	65.74 216	74.63 162	71.79 309	53.43 112	84.06 166	80.99 199	63.19 119	59.56 210	77.46 269	37.50 206	88.65 150	58.20 192	58.93 269	81.79 257

BH-w/o		70.02 158	68.51 159	74.56 163	82.77 125	50.39 178	86.60 93	78.14 257	59.77 175	59.65 207	85.57 169	39.27 183	87.30 203	49.86 256	74.94 140	85.99 188

SR-MVS		70.92 144	69.73 145	74.50 164	83.38 105	50.48 175	84.27 159	79.35 233	48.96 309	66.57 124	90.45 79	33.65 255	87.11 207	66.42 123	74.56 142	85.91 191

tttt0517		68.33 191	66.29 201	74.46 165	78.08 225	49.06 209	80.88 251	89.08 27	54.40 272	54.75 280	80.77 238	51.31 48	90.33 100	49.35 260	58.01 282	83.99 222

TESTMET0.1,1		72.86 109	72.33 99	74.46 165	81.98 140	50.77 167	85.13 129	85.47 96	66.09 70	67.30 115	83.69 192	37.27 210	83.57 276	65.06 140	78.97 99	89.05 124

nrg030		72.27 122	71.56 115	74.42 167	75.93 261	50.60 171	86.97 83	83.21 160	62.75 125	67.15 117	84.38 180	50.07 58	86.66 221	71.19 96	62.37 249	85.99 188

RPMNet		59.29 283	54.25 306	74.42 167	73.97 288	56.57 34	60.52 362	76.98 276	35.72 364	57.49 252	58.87 372	37.73 198	85.26 255	27.01 363	59.93 259	81.42 266

Vis-MVSNet		70.61 149	69.34 150	74.42 167	80.95 173	48.49 229	86.03 103	77.51 267	58.74 203	65.55 137	87.78 139	34.37 247	85.95 245	52.53 243	80.61 77	88.80 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		71.14 137	70.07 141	74.33 170	79.18 203	46.52 270	83.81 173	86.49 79	56.32 251	57.95 240	84.90 178	54.23 32	89.14 132	58.14 193	69.65 185	87.33 162

test2506		72.91 108	72.43 98	74.32 171	80.12 189	44.18 302	83.19 194	84.77 125	64.02 98	65.97 130	87.43 146	47.67 76	88.72 148	59.08 179	79.66 93	90.08 99

EI-MVSNet-UG-set		72.37 117	71.73 113	74.29 172	81.60 154	49.29 207	81.85 225	88.64 42	65.29 86	65.05 141	88.29 128	43.18 137	91.83 61	63.74 144	67.97 195	81.75 258

ECVR-MVS		71.81 129	71.00 124	74.26 173	80.12 189	43.49 307	84.69 147	82.16 174	64.02 98	64.64 148	87.43 146	35.04 240	89.21 130	61.24 162	79.66 93	90.08 99

OPM-MVS		70.75 147	69.58 146	74.26 173	75.55 266	51.34 161	86.05 102	83.29 159	61.94 140	62.95 176	85.77 166	34.15 249	88.44 159	65.44 136	71.07 171	82.99 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		67.86 198	65.76 215	74.16 175	71.68 311	53.09 123	84.14 163	80.83 201	62.85 124	59.21 219	77.28 272	39.30 182	88.00 177	58.67 184	57.88 286	81.40 268

HQP_MVS		70.96 143	69.91 143	74.12 176	77.95 227	49.57 197	85.76 107	82.59 170	63.60 109	62.15 185	83.28 199	36.04 230	88.30 167	65.46 133	72.34 160	84.49 212

v1921920		67.45 209	65.23 228	74.10 177	71.51 314	52.90 129	83.75 175	80.44 206	62.48 133	59.12 220	77.13 273	36.98 214	87.90 179	57.53 203	58.14 280	81.49 262

v8		67.25 215	64.99 231	74.04 178	72.89 300	53.31 117	82.37 215	80.11 212	61.54 146	54.29 285	76.02 294	42.89 142	88.41 160	58.43 186	56.36 294	80.39 285

VPNet		72.07 124	71.42 119	74.04 178	78.64 217	47.17 264	89.91 32	87.97 55	72.56 12	64.66 147	85.04 175	41.83 157	88.33 165	61.17 163	60.97 255	86.62 177

test_fmvsmvis_n_1920		71.29 136	70.38 133	74.00 180	71.04 320	48.79 220	79.19 273	64.62 356	62.75 125	66.73 118	91.99 49	40.94 164	88.35 163	83.00 22	73.18 150	84.85 210

v1240		66.99 223	64.68 234	73.93 181	71.38 317	52.66 132	83.39 188	79.98 214	61.97 139	58.44 237	77.11 274	35.25 236	87.81 181	56.46 213	58.15 278	81.33 271

BH-untuned		68.28 192	66.40 198	73.91 182	81.62 153	50.01 189	85.56 117	77.39 269	57.63 224	57.47 254	83.69 192	36.36 225	87.08 208	44.81 288	73.08 154	84.65 211

v148		68.24 194	66.35 199	73.88 183	71.76 310	51.47 158	84.23 160	81.90 183	63.69 107	58.94 222	76.44 285	43.72 130	87.78 186	60.63 167	55.86 304	82.39 251

V42		67.66 203	65.60 220	73.86 184	70.69 324	53.63 104	81.50 238	78.61 249	63.85 103	59.49 213	77.49 268	37.98 192	87.65 191	62.33 152	58.43 273	80.29 286

Fast-Effi-MVS+-dtu		66.53 231	64.10 240	73.84 185	72.41 305	52.30 141	84.73 146	75.66 292	59.51 180	56.34 267	79.11 254	28.11 294	85.85 247	57.74 202	63.29 238	83.35 234

v10		66.61 230	64.20 239	73.83 186	72.59 303	53.37 113	81.88 224	79.91 217	61.11 153	54.09 287	75.60 296	40.06 176	88.26 170	56.47 212	56.10 300	79.86 291

APD-MVS_3200maxsize		69.62 169	68.23 165	73.80 187	81.58 156	48.22 239	81.91 223	79.50 226	48.21 312	64.24 158	89.75 98	31.91 273	87.55 197	63.08 148	73.85 147	85.64 197

AUN-MVS		68.20 195	66.35 199	73.76 188	76.37 249	47.45 257	79.52 269	79.52 225	60.98 157	62.34 181	86.02 163	36.59 224	86.94 213	62.32 153	53.47 324	86.89 168

PVSNet_BlendedMVS		73.42 102	73.30 84	73.76 188	85.91 54	51.83 149	86.18 99	84.24 139	65.40 81	69.09 104	80.86 237	46.70 87	88.13 172	75.43 70	65.92 215	81.33 271

hse-mvs2		71.44 135	70.68 127	73.73 190	76.34 250	47.44 258	79.45 270	79.47 228	68.08 40	71.97 78	86.01 165	42.50 144	86.93 214	78.82 46	53.46 325	86.83 174

baseline1		72.51 116	72.12 108	73.69 191	85.05 71	44.46 295	83.51 181	86.13 87	71.61 17	64.64 148	87.97 136	55.00 28	89.48 122	59.07 180	56.05 301	87.13 166

CDS-MVSNet		70.48 151	69.43 147	73.64 192	77.56 234	48.83 219	83.51 181	77.45 268	63.27 117	62.33 182	85.54 170	43.85 124	83.29 280	57.38 206	74.00 144	88.79 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet	62.49 8	69.27 173	67.81 174	73.64 192	84.41 82	51.85 148	84.63 151	77.80 261	66.42 63	59.80 205	84.95 177	22.14 341	80.44 302	55.03 220	75.11 136	88.62 135

PS-MVSNAJss		68.78 183	67.17 186	73.62 194	73.01 297	48.33 237	84.95 140	84.81 123	59.30 188	58.91 225	79.84 245	37.77 195	88.86 145	62.83 150	63.12 243	83.67 232

TAMVS		69.51 171	68.16 166	73.56 195	76.30 253	48.71 223	82.57 208	77.17 273	62.10 136	61.32 193	84.23 183	41.90 155	83.46 278	54.80 223	73.09 153	88.50 140

UGNet		68.71 184	67.11 187	73.50 196	80.55 183	47.61 255	84.08 164	78.51 251	59.45 181	65.68 136	82.73 208	23.78 326	85.08 260	52.80 238	76.40 116	87.80 152
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		67.79 201	65.95 210	73.32 197	81.70 148	46.33 275	68.99 335	80.30 209	66.58 59	61.64 190	82.38 218	30.45 282	87.63 193	55.86 216	65.60 216	86.01 186

Anonymous20231211		66.08 238	63.67 241	73.31 198	83.07 114	48.75 221	86.01 104	84.67 129	45.27 331	56.54 264	76.67 283	28.06 295	88.95 141	52.78 239	59.95 258	82.23 252

新几何1				73.30 199	83.10 111	53.48 106		71.43 327	45.55 329	66.14 127	87.17 150	33.88 253	80.54 300	48.50 267	80.33 83	85.88 193

FMVSNet3		68.84 179	67.40 182	73.19 200	85.05 71	48.53 227	85.71 113	85.36 101	60.90 161	57.58 249	79.15 253	42.16 149	86.77 217	47.25 275	63.40 234	84.27 216

mvsmamba		66.93 226	64.88 233	73.09 201	75.06 271	47.26 261	83.36 190	69.21 343	62.64 128	55.68 272	81.43 232	29.72 286	89.20 131	63.35 147	63.50 233	82.79 248

thres200		68.71 184	67.27 185	73.02 202	84.73 76	46.76 267	85.03 135	87.73 61	62.34 134	59.87 203	83.45 196	43.15 138	88.32 166	31.25 345	67.91 196	83.98 224

PVSNet_0	57.04 13	61.19 274	57.24 287	73.02 202	77.45 236	50.31 184	79.43 271	77.36 271	63.96 102	47.51 329	72.45 325	25.03 318	83.78 273	52.76 241	19.22 395	84.96 207

test1111		71.06 140	70.42 132	72.97 204	79.48 196	41.49 328	84.82 145	82.74 169	64.20 95	62.98 175	87.43 146	35.20 237	87.92 178	58.54 185	78.42 103	89.49 113

dp		64.41 244	61.58 252	72.90 205	82.40 133	54.09 97	72.53 313	76.59 285	60.39 168	55.68 272	70.39 340	35.18 238	76.90 335	39.34 308	61.71 252	87.73 154

FMVSNet2		67.57 206	65.79 214	72.90 205	82.71 127	47.97 248	85.15 128	84.93 119	58.55 206	56.71 262	78.26 260	36.72 221	86.67 220	46.15 283	62.94 245	84.07 219

XXY-MVS		70.18 153	69.28 153	72.89 207	77.64 231	42.88 315	85.06 133	87.50 66	62.58 129	62.66 180	82.34 221	43.64 132	89.83 113	58.42 188	63.70 231	85.96 190

CR-MVSNet		62.47 266	59.04 278	72.77 208	73.97 288	56.57 34	60.52 362	71.72 323	60.04 171	57.49 252	65.86 353	38.94 185	80.31 303	42.86 299	59.93 259	81.42 266

WB-MVSnew		69.36 172	68.24 164	72.72 209	79.26 201	49.40 204	85.72 112	88.85 35	61.33 149	64.59 151	82.38 218	34.57 245	87.53 198	46.82 279	70.63 175	81.22 275

EI-MVSNet		69.70 167	68.70 157	72.68 210	75.00 273	48.90 217	79.54 267	87.16 68	61.05 155	63.88 165	83.74 190	45.87 96	90.44 96	57.42 205	64.68 223	78.70 299

HPM-MVS_fast		67.86 198	66.28 202	72.61 211	80.67 180	48.34 235	81.18 244	75.95 291	50.81 298	59.55 211	88.05 134	27.86 297	85.98 242	58.83 182	73.58 148	83.51 233

MVP-Stereo		70.97 142	70.44 131	72.59 212	76.03 259	51.36 160	85.02 136	86.99 71	60.31 169	56.53 265	78.92 255	40.11 175	90.00 109	60.00 177	90.01 6	76.41 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		69.07 174	67.91 168	72.54 213	77.27 238	49.56 199	79.77 265	73.96 307	59.33 187	60.73 198	87.82 138	30.19 284	81.53 288	69.94 103	72.19 162	86.53 178

IS-MVSNet		68.80 182	67.55 179	72.54 213	78.50 220	43.43 309	81.03 246	79.35 233	59.12 195	57.27 257	86.71 156	46.05 94	87.70 189	44.32 292	75.60 128	86.49 179

VPA-MVSNet		71.12 138	70.66 128	72.49 215	78.75 212	44.43 297	87.64 64	90.02 17	63.97 101	65.02 142	81.58 231	42.14 150	87.42 200	63.42 146	63.38 237	85.63 198

SR-MVS-dyc-post		68.27 193	66.87 188	72.48 216	80.96 170	48.14 242	81.54 236	76.98 276	46.42 324	62.75 178	89.42 103	31.17 278	86.09 239	60.52 171	72.06 163	83.19 240

dmvs_re		67.61 204	66.00 208	72.42 217	81.86 143	43.45 308	64.67 348	80.00 213	69.56 32	60.07 202	85.00 176	34.71 243	87.63 193	51.48 247	66.68 204	86.17 185

miper_enhance_ethall		69.77 164	68.90 156	72.38 218	78.93 209	49.91 191	83.29 191	78.85 240	64.90 88	59.37 214	79.46 247	52.77 38	85.16 258	63.78 143	58.72 270	82.08 253

cl22		68.85 178	67.69 175	72.35 219	78.07 226	49.98 190	82.45 213	78.48 252	62.50 132	58.46 235	77.95 261	49.99 60	85.17 257	62.55 151	58.72 270	81.90 256

MSDG		59.44 282	55.14 302	72.32 220	74.69 276	50.71 168	74.39 301	73.58 310	44.44 337	43.40 344	77.52 267	19.45 351	90.87 86	31.31 344	57.49 290	75.38 334

UWE-MVS		72.17 123	72.15 106	72.21 221	82.26 137	44.29 299	86.83 88	89.58 21	65.58 77	65.82 133	85.06 174	45.02 109	84.35 268	54.07 227	75.18 132	87.99 149

v7n		62.50 265	59.27 276	72.20 222	67.25 346	49.83 194	77.87 281	80.12 211	52.50 286	48.80 320	73.07 317	32.10 268	87.90 179	46.83 278	54.92 311	78.86 297

1112_ss		70.05 157	69.37 149	72.10 223	80.77 177	42.78 316	85.12 132	76.75 280	59.69 177	61.19 194	92.12 44	47.48 78	83.84 271	53.04 235	68.21 192	89.66 108

miper_ehance_all_eth		68.70 186	67.58 177	72.08 224	76.91 246	49.48 203	82.47 212	78.45 253	62.68 127	58.28 239	77.88 263	50.90 52	85.01 261	61.91 157	58.72 270	81.75 258

eth_miper_zixun_eth		66.98 224	65.28 227	72.06 225	75.61 265	50.40 177	81.00 247	76.97 279	62.00 137	56.99 259	76.97 276	44.84 115	85.58 248	58.75 183	54.42 316	80.21 287

LPG-MVS_test		66.44 233	64.58 235	72.02 226	74.42 281	48.60 224	83.07 198	80.64 203	54.69 269	53.75 290	83.83 188	25.73 313	86.98 210	60.33 175	64.71 220	80.48 283

LGP-MVS_train				72.02 226	74.42 281	48.60 224		80.64 203	54.69 269	53.75 290	83.83 188	25.73 313	86.98 210	60.33 175	64.71 220	80.48 283

ACMP	61.11 9	66.24 236	64.33 237	72.00 228	74.89 275	49.12 208	83.18 195	79.83 218	55.41 260	52.29 300	82.68 209	25.83 311	86.10 237	60.89 164	63.94 229	80.78 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GBi-Net		67.09 220	65.47 222	71.96 229	82.71 127	46.36 272	83.52 177	83.31 156	58.55 206	57.58 249	76.23 289	36.72 221	86.20 231	47.25 275	63.40 234	83.32 235

test1		67.09 220	65.47 222	71.96 229	82.71 127	46.36 272	83.52 177	83.31 156	58.55 206	57.58 249	76.23 289	36.72 221	86.20 231	47.25 275	63.40 234	83.32 235

FMVSNet1		64.57 243	62.11 249	71.96 229	77.32 237	46.36 272	83.52 177	83.31 156	52.43 287	54.42 283	76.23 289	27.80 298	86.20 231	42.59 301	61.34 254	83.32 235

cl____		67.43 210	65.93 211	71.95 232	76.33 251	48.02 246	82.58 207	79.12 237	61.30 151	56.72 261	76.92 278	46.12 92	86.44 228	57.98 195	56.31 296	81.38 270

DIV-MVS_self_test		67.43 210	65.93 211	71.94 233	76.33 251	48.01 247	82.57 208	79.11 238	61.31 150	56.73 260	76.92 278	46.09 93	86.43 229	57.98 195	56.31 296	81.39 269

Patchmatch-RL test		58.72 293	54.32 305	71.92 234	63.91 362	44.25 300	61.73 358	55.19 369	57.38 230	49.31 317	54.24 377	37.60 202	80.89 293	62.19 155	47.28 345	90.63 82

c3_l		67.97 196	66.66 194	71.91 235	76.20 255	49.31 206	82.13 219	78.00 259	61.99 138	57.64 248	76.94 277	49.41 65	84.93 262	60.62 168	57.01 292	81.49 262

tfpn200view9		67.57 206	66.13 205	71.89 236	84.05 89	45.07 290	83.40 186	87.71 63	60.79 162	57.79 244	82.76 205	43.53 133	87.80 183	28.80 352	66.36 210	82.78 249

RRT_MVS		63.68 252	61.01 261	71.70 237	73.48 290	45.98 280	81.19 243	76.08 289	54.33 273	52.84 296	79.27 250	22.21 338	87.65 191	54.13 226	55.54 308	81.46 265

MIMVSNet		63.12 258	60.29 268	71.61 238	75.92 262	46.65 268	65.15 345	81.94 179	59.14 194	54.65 281	69.47 343	25.74 312	80.63 298	41.03 304	69.56 187	87.55 158

test-LLR		69.65 168	69.01 155	71.60 239	78.67 214	48.17 240	85.13 129	79.72 220	59.18 192	63.13 173	82.58 212	36.91 216	80.24 304	60.56 169	75.17 133	86.39 182

test-mter		68.36 189	67.29 183	71.60 239	78.67 214	48.17 240	85.13 129	79.72 220	53.38 279	63.13 173	82.58 212	27.23 302	80.24 304	60.56 169	75.17 133	86.39 182

sss		70.49 150	70.13 140	71.58 241	81.59 155	39.02 339	80.78 253	84.71 127	59.34 185	66.61 122	88.09 131	37.17 212	85.52 249	61.82 159	71.02 172	90.20 95

tpmvs		62.45 267	59.42 274	71.53 242	83.93 91	54.32 91	70.03 330	77.61 265	51.91 290	53.48 293	68.29 347	37.91 193	86.66 221	33.36 335	58.27 276	73.62 348

ACMM	58.35 12	64.35 245	62.01 250	71.38 243	74.21 284	48.51 228	82.25 216	79.66 222	47.61 315	54.54 282	80.11 241	25.26 316	86.00 241	51.26 248	63.16 241	79.64 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	53.70 16	59.78 280	55.94 298	71.28 244	76.59 248	48.35 234	80.15 263	76.11 288	49.74 304	41.91 350	73.45 316	16.50 366	90.31 101	31.42 343	57.63 289	75.17 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ppachtmachnet_test		58.56 295	54.34 304	71.24 245	71.42 315	54.74 79	81.84 226	72.27 319	49.02 308	45.86 338	68.99 346	26.27 307	83.30 279	30.12 347	43.23 359	75.69 331

thres100view900		66.87 227	65.42 225	71.24 245	83.29 107	43.15 312	81.67 231	87.78 58	59.04 196	55.92 270	82.18 223	43.73 128	87.80 183	28.80 352	66.36 210	82.78 249

thres400		67.40 213	66.13 205	71.19 247	84.05 89	45.07 290	83.40 186	87.71 63	60.79 162	57.79 244	82.76 205	43.53 133	87.80 183	28.80 352	66.36 210	80.71 281

our_test_3		59.11 287	55.08 303	71.18 248	71.42 315	53.29 118	81.96 221	74.52 300	48.32 311	42.08 348	69.28 345	28.14 293	82.15 284	34.35 332	45.68 354	78.11 311

CPTT-MVS		67.15 218	65.84 213	71.07 249	80.96 170	50.32 183	81.94 222	74.10 303	46.18 327	57.91 241	87.64 143	29.57 287	81.31 290	64.10 142	70.18 181	81.56 261

NR-MVSNet		67.25 215	65.99 209	71.04 250	73.27 295	43.91 303	85.32 123	84.75 126	66.05 73	53.65 292	82.11 224	45.05 108	85.97 244	47.55 272	56.18 299	83.24 238

tpm		68.36 189	67.48 181	70.97 251	79.93 192	51.34 161	76.58 288	78.75 245	67.73 47	63.54 171	74.86 300	48.33 69	72.36 357	53.93 229	63.71 230	89.21 119

TranMVSNet+NR-MVSNet		66.94 225	65.61 219	70.93 252	73.45 291	43.38 310	83.02 200	84.25 137	65.31 85	58.33 238	81.90 227	39.92 179	85.52 249	49.43 259	54.89 312	83.89 228

EG-PatchMatch MVS		62.40 268	59.59 272	70.81 253	73.29 293	49.05 210	85.81 105	84.78 124	51.85 292	44.19 339	73.48 315	15.52 369	89.85 112	40.16 306	67.24 201	73.54 349

test_djsdf		63.84 249	61.56 253	70.70 254	68.78 335	44.69 294	81.63 232	81.44 190	50.28 299	52.27 301	76.26 288	26.72 305	86.11 235	60.83 165	55.84 305	81.29 274

UA-Net		67.32 214	66.23 203	70.59 255	78.85 210	41.23 331	73.60 305	75.45 295	61.54 146	66.61 122	84.53 179	38.73 188	86.57 226	42.48 302	74.24 143	83.98 224

thres600view7		66.46 232	65.12 229	70.47 256	83.41 101	43.80 305	82.15 217	87.78 58	59.37 184	56.02 269	82.21 222	43.73 128	86.90 215	26.51 364	64.94 219	80.71 281

UniMVSNet (Re)		67.71 202	66.80 190	70.45 257	74.44 280	42.93 314	82.42 214	84.90 120	63.69 107	59.63 208	80.99 235	47.18 80	85.23 256	51.17 250	56.75 293	83.19 240

IterMVS-LS		66.63 229	65.36 226	70.42 258	75.10 270	48.90 217	81.45 241	76.69 283	61.05 155	55.71 271	77.10 275	45.86 97	83.65 275	57.44 204	57.88 286	78.70 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		68.82 180	68.29 163	70.40 259	75.71 264	42.59 318	84.23 160	86.78 74	66.31 65	58.51 231	82.45 215	51.57 46	84.64 266	53.11 233	55.96 302	83.96 226

jajsoiax		63.21 257	60.84 262	70.32 260	68.33 340	44.45 296	81.23 242	81.05 196	53.37 280	50.96 310	77.81 265	17.49 361	85.49 251	59.31 178	58.05 281	81.02 277

mvs_tets		62.96 260	60.55 264	70.19 261	68.22 343	44.24 301	80.90 250	80.74 202	52.99 283	50.82 312	77.56 266	16.74 364	85.44 252	59.04 181	57.94 283	80.89 278

pmmvs4		63.34 256	61.07 260	70.16 262	70.14 326	50.53 173	79.97 264	71.41 328	55.08 263	54.12 286	78.58 257	32.79 262	82.09 286	50.33 253	57.22 291	77.86 312

DU-MVS		66.84 228	65.74 216	70.16 262	73.27 295	42.59 318	81.50 238	82.92 167	63.53 111	58.51 231	82.11 224	40.75 166	84.64 266	53.11 233	55.96 302	83.24 238

Effi-MVS+-dtu		66.24 236	64.96 232	70.08 264	75.17 268	49.64 196	82.01 220	74.48 301	62.15 135	57.83 242	76.08 293	30.59 281	83.79 272	65.40 137	60.93 256	76.81 321

IterMVS		63.77 251	61.67 251	70.08 264	72.68 302	51.24 164	80.44 256	75.51 293	60.51 167	51.41 305	73.70 312	32.08 269	78.91 314	54.30 225	54.35 317	80.08 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		67.58 205	66.76 191	70.04 266	75.92 262	45.06 293	86.23 98	85.28 107	64.31 93	58.50 233	81.00 234	44.80 118	82.00 287	49.21 262	55.57 307	83.06 243

Test_1112_low_res		67.18 217	66.23 203	70.02 267	78.75 212	41.02 332	83.43 184	73.69 309	57.29 231	58.45 236	82.39 217	45.30 105	80.88 294	50.50 252	66.26 214	88.16 142

D2MVS		63.49 254	61.39 255	69.77 268	69.29 332	48.93 216	78.89 275	77.71 264	60.64 166	49.70 315	72.10 331	27.08 303	83.48 277	54.48 224	62.65 246	76.90 320

tt0805		63.39 255	61.31 257	69.64 269	69.36 331	38.87 340	78.00 279	85.48 95	48.82 310	55.66 275	81.66 229	24.38 323	86.37 230	49.04 263	59.36 266	83.68 231

XVG-OURS		61.88 270	59.34 275	69.49 270	65.37 352	46.27 276	64.80 347	73.49 312	47.04 319	57.41 256	82.85 203	25.15 317	78.18 318	53.00 236	64.98 218	84.01 221

XVG-OURS-SEG-HR		62.02 269	59.54 273	69.46 271	65.30 353	45.88 281	65.06 346	73.57 311	46.45 323	57.42 255	83.35 198	26.95 304	78.09 320	53.77 230	64.03 227	84.42 214

test_vis1_n_1920		68.59 187	68.31 162	69.44 272	69.16 333	41.51 327	84.63 151	68.58 346	58.80 201	73.26 62	88.37 123	25.30 315	80.60 299	79.10 43	67.55 199	86.23 184

FIs		70.00 159	70.24 139	69.30 273	77.93 229	38.55 342	83.99 168	87.72 62	66.86 57	57.66 247	84.17 184	52.28 42	85.31 253	52.72 242	68.80 189	84.02 220

Baseline_NR-MVSNet		65.49 242	64.27 238	69.13 274	74.37 283	41.65 325	83.39 188	78.85 240	59.56 179	59.62 209	76.88 280	40.75 166	87.44 199	49.99 254	55.05 310	78.28 308

TransMVSNet (Re)		62.82 261	60.76 263	69.02 275	73.98 287	41.61 326	86.36 95	79.30 236	56.90 237	52.53 298	76.44 285	41.85 156	87.60 196	38.83 309	40.61 364	77.86 312

anonymousdsp		60.46 278	57.65 284	68.88 276	63.63 363	45.09 289	72.93 311	78.63 248	46.52 322	51.12 307	72.80 321	21.46 345	83.07 281	57.79 200	53.97 318	78.47 303

ADS-MVSNet		56.17 309	51.95 319	68.84 277	80.60 181	53.07 124	55.03 373	70.02 338	44.72 334	51.00 308	61.19 365	22.83 331	78.88 315	28.54 355	53.63 320	74.57 342

OpenMVS_ROB	53.19 17	59.20 285	56.00 297	68.83 278	71.13 319	44.30 298	83.64 176	75.02 298	46.42 324	46.48 335	73.03 318	18.69 355	88.14 171	27.74 360	61.80 251	74.05 345

Patchmatch-test		53.33 324	48.17 333	68.81 279	73.31 292	42.38 322	42.98 383	58.23 366	32.53 370	38.79 362	70.77 337	39.66 180	73.51 351	25.18 367	52.06 330	90.55 83

pm-mvs1		64.12 247	62.56 245	68.78 280	71.68 311	38.87 340	82.89 202	81.57 187	55.54 259	53.89 289	77.82 264	37.73 198	86.74 218	48.46 268	53.49 323	80.72 280

miper_lstm_enhance		63.91 248	62.30 247	68.75 281	75.06 271	46.78 266	69.02 334	81.14 195	59.68 178	52.76 297	72.39 326	40.71 168	77.99 324	56.81 209	53.09 326	81.48 264

OMC-MVS		65.97 239	65.06 230	68.71 282	72.97 298	42.58 320	78.61 276	75.35 296	54.72 268	59.31 216	86.25 162	33.30 257	77.88 326	57.99 194	67.05 202	85.66 196

DP-MVS		59.24 284	56.12 296	68.63 283	88.24 34	50.35 182	82.51 211	64.43 357	41.10 350	46.70 333	78.77 256	24.75 321	88.57 156	22.26 376	56.29 298	66.96 369

tfpnnormal		61.47 273	59.09 277	68.62 284	76.29 254	41.69 324	81.14 245	85.16 113	54.48 271	51.32 306	73.63 313	32.32 266	86.89 216	21.78 378	55.71 306	77.29 318

test_cas_vis1_n_1920		67.10 219	66.60 196	68.59 285	65.17 355	43.23 311	83.23 193	69.84 339	55.34 261	70.67 96	87.71 141	24.70 322	76.66 337	78.57 50	64.20 225	85.89 192

UniMVSNet_ETH3D		62.51 264	60.49 265	68.57 286	68.30 341	40.88 334	73.89 303	79.93 216	51.81 293	54.77 279	79.61 246	24.80 320	81.10 291	49.93 255	61.35 253	83.73 230

CL-MVSNet_self_test		62.98 259	61.14 259	68.50 287	65.86 350	42.96 313	84.37 155	82.98 165	60.98 157	53.95 288	72.70 322	40.43 170	83.71 274	41.10 303	47.93 340	78.83 298

ACMH+	54.58 15	58.55 296	55.24 300	68.50 287	74.68 277	45.80 284	80.27 259	70.21 336	47.15 318	42.77 347	75.48 297	16.73 365	85.98 242	35.10 330	54.78 313	73.72 347

lessismore_v0				67.98 289	64.76 359	41.25 330		45.75 378		36.03 369	65.63 355	19.29 353	84.11 269	35.67 322	21.24 393	78.59 302

K. test v3		54.04 319	49.42 330	67.92 290	68.55 337	42.57 321	75.51 293	63.07 361	52.07 288	39.21 359	64.59 357	19.34 352	82.21 283	37.11 315	25.31 388	78.97 296

pmmvs5		62.80 262	61.18 258	67.66 291	69.53 330	42.37 323	82.65 206	75.19 297	54.30 274	52.03 303	78.51 258	31.64 275	80.67 297	48.60 266	58.15 278	79.95 290

PatchT		56.60 305	52.97 312	67.48 292	72.94 299	46.16 279	57.30 370	73.78 308	38.77 354	54.37 284	57.26 375	37.52 204	78.06 321	32.02 340	52.79 327	78.23 310

Patchmtry		56.56 306	52.95 313	67.42 293	72.53 304	50.59 172	59.05 366	71.72 323	37.86 358	46.92 331	65.86 353	38.94 185	80.06 307	36.94 318	46.72 350	71.60 359

SixPastTwentyTwo		54.37 316	50.10 325	67.21 294	70.70 323	41.46 329	74.73 298	64.69 355	47.56 316	39.12 360	69.49 342	18.49 358	84.69 265	31.87 341	34.20 378	75.48 333

pmmvs6		59.64 281	57.15 288	67.09 295	66.01 348	36.86 349	80.50 255	78.64 247	45.05 333	49.05 318	73.94 307	27.28 301	86.10 237	43.96 294	49.94 335	78.31 307

testdata				67.08 296	77.59 233	45.46 287		69.20 344	44.47 336	71.50 85	88.34 126	31.21 277	70.76 362	52.20 244	75.88 124	85.03 205

CNLPA		60.59 277	58.44 281	67.05 297	79.21 202	47.26 261	79.75 266	64.34 358	42.46 348	51.90 304	83.94 186	27.79 299	75.41 342	37.12 314	59.49 264	78.47 303

KD-MVS_2432*1600		59.04 289	56.44 293	66.86 298	79.07 204	45.87 282	72.13 319	80.42 207	55.03 264	48.15 322	71.01 334	36.73 219	78.05 322	35.21 326	30.18 383	76.67 322

miper_refine_blended		59.04 289	56.44 293	66.86 298	79.07 204	45.87 282	72.13 319	80.42 207	55.03 264	48.15 322	71.01 334	36.73 219	78.05 322	35.21 326	30.18 383	76.67 322

TAPA-MVS	56.12 14	61.82 271	60.18 270	66.71 300	78.48 221	37.97 345	75.19 296	76.41 287	46.82 320	57.04 258	86.52 160	27.67 300	77.03 332	26.50 365	67.02 203	85.14 203
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_0402		56.45 307	53.03 311	66.69 301	76.78 247	50.31 184	81.76 228	69.61 341	42.79 346	43.88 340	72.13 329	22.82 333	86.46 227	16.57 388	50.94 332	63.31 377

PLC	52.38 18	60.89 275	58.97 279	66.68 302	81.77 145	45.70 285	78.96 274	74.04 306	43.66 342	47.63 326	83.19 201	23.52 329	77.78 329	37.47 311	60.46 257	76.55 327
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 315	51.44 320	66.51 303	80.60 181	49.56 199	55.03 373	65.44 353	44.72 334	51.00 308	61.19 365	22.83 331	75.41 342	28.54 355	53.63 320	74.57 342

FC-MVSNet-test		67.49 208	67.91 168	66.21 304	76.06 257	33.06 361	80.82 252	87.18 67	64.44 92	54.81 278	82.87 202	50.40 57	82.60 282	48.05 270	66.55 208	82.98 245

JIA-IIPM		52.33 329	47.77 336	66.03 305	71.20 318	46.92 265	40.00 388	76.48 286	37.10 359	46.73 332	37.02 388	32.96 259	77.88 326	35.97 321	52.45 329	73.29 351

LCM-MVSNet-Re		58.82 292	56.54 291	65.68 306	79.31 200	29.09 379	61.39 361	45.79 377	60.73 164	37.65 365	72.47 324	31.42 276	81.08 292	49.66 257	70.41 178	86.87 169

XVG-ACMP-BASELINE		56.03 310	52.85 314	65.58 307	61.91 368	40.95 333	63.36 351	72.43 318	45.20 332	46.02 336	74.09 305	9.20 380	78.12 319	45.13 286	58.27 276	77.66 315

pmmvs-eth3d		55.97 311	52.78 315	65.54 308	61.02 370	46.44 271	75.36 295	67.72 349	49.61 305	43.65 342	67.58 349	21.63 343	77.04 331	44.11 293	44.33 356	73.15 353

MDA-MVSNet_test_wron		53.82 321	49.95 327	65.43 309	70.13 327	49.05 210	72.30 316	71.65 326	44.23 340	31.85 380	63.13 360	23.68 328	74.01 346	33.25 337	39.35 367	73.23 352

YYNet1		53.82 321	49.96 326	65.41 310	70.09 328	48.95 214	72.30 316	71.66 325	44.25 339	31.89 379	63.07 361	23.73 327	73.95 347	33.26 336	39.40 366	73.34 350

PatchMatch-RL		56.66 304	53.75 309	65.37 311	77.91 230	45.28 288	69.78 332	60.38 364	41.35 349	47.57 327	73.73 309	16.83 363	76.91 333	36.99 317	59.21 267	73.92 346

Vis-MVSNet (Re-imp)		65.52 241	65.63 218	65.17 312	77.49 235	30.54 368	75.49 294	77.73 263	59.34 185	52.26 302	86.69 157	49.38 66	80.53 301	37.07 316	75.28 131	84.42 214

FMVSNet5		58.61 294	56.45 292	65.10 313	77.20 242	39.74 336	74.77 297	77.12 274	50.27 301	43.28 345	67.71 348	26.15 310	76.90 335	36.78 319	54.78 313	78.65 301

EPNet_dtu		66.25 235	66.71 192	64.87 314	78.66 216	34.12 356	82.80 203	75.51 293	61.75 142	64.47 156	86.90 153	37.06 213	72.46 356	43.65 295	69.63 186	88.02 148
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		57.56 301	55.15 301	64.79 315	64.57 360	33.12 360	73.17 310	83.87 147	58.98 198	41.75 351	70.03 341	22.54 334	79.92 308	46.12 284	35.31 372	81.32 273

LS3D		56.40 308	53.82 308	64.12 316	81.12 166	45.69 286	73.42 308	66.14 352	35.30 368	43.24 346	79.88 243	22.18 339	79.62 312	19.10 384	64.00 228	67.05 368

UnsupCasMVSNet_bld		53.86 320	50.53 324	63.84 317	63.52 364	34.75 352	71.38 324	81.92 181	46.53 321	38.95 361	57.93 373	20.55 348	80.20 306	39.91 307	34.09 379	76.57 326

USDC		54.36 317	51.23 321	63.76 318	64.29 361	37.71 346	62.84 356	73.48 314	56.85 238	35.47 370	71.94 332	9.23 379	78.43 316	38.43 310	48.57 337	75.13 337

Anonymous20231206		59.08 288	57.59 285	63.55 319	68.77 336	32.14 366	80.26 260	79.78 219	50.00 303	49.39 316	72.39 326	26.64 306	78.36 317	33.12 338	57.94 283	80.14 288

CMPMVS	40.41 21	55.34 313	52.64 316	63.46 320	60.88 371	43.84 304	61.58 360	71.06 330	30.43 376	36.33 367	74.63 302	24.14 325	75.44 341	48.05 270	66.62 206	71.12 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
myMVS_eth3d		63.52 253	63.56 243	63.40 321	81.73 146	34.28 354	80.97 248	81.02 197	60.93 159	55.06 276	82.64 210	48.00 74	80.81 295	23.42 374	58.32 274	75.10 338

OurMVSNet-221017-0		52.39 328	48.73 331	63.35 322	65.21 354	38.42 343	68.54 338	64.95 354	38.19 355	39.57 358	71.43 333	13.23 372	79.92 308	37.16 313	40.32 365	71.72 358

MDA-MVSNet-bldmvs		51.56 331	47.75 337	63.00 323	71.60 313	47.32 260	69.70 333	72.12 320	43.81 341	27.65 387	63.38 359	21.97 342	75.96 339	27.30 362	32.19 380	65.70 374

F-COLMAP		55.96 312	53.65 310	62.87 324	72.76 301	42.77 317	74.70 300	70.37 335	40.03 351	41.11 355	79.36 248	17.77 360	73.70 350	32.80 339	53.96 319	72.15 355

test0.0.03 1		62.54 263	62.44 246	62.86 325	72.28 308	29.51 376	82.93 201	78.78 243	59.18 192	53.07 295	82.41 216	36.91 216	77.39 330	37.45 312	58.96 268	81.66 260

CVMVSNet		60.85 276	60.44 266	62.07 326	75.00 273	32.73 363	79.54 267	73.49 312	36.98 360	56.28 268	83.74 190	29.28 290	69.53 365	46.48 280	63.23 239	83.94 227

ambc				62.06 327	53.98 380	29.38 377	35.08 391	79.65 223		41.37 352	59.96 368	6.27 391	82.15 284	35.34 325	38.22 368	74.65 341

Syy-MVS		61.51 272	61.35 256	62.00 328	81.73 146	30.09 371	80.97 248	81.02 197	60.93 159	55.06 276	82.64 210	35.09 239	80.81 295	16.40 389	58.32 274	75.10 338

PEN-MVS		58.35 298	57.15 288	61.94 329	67.55 345	34.39 353	77.01 284	78.35 255	51.87 291	47.72 325	76.73 282	33.91 251	73.75 349	34.03 333	47.17 346	77.68 314

MVS-HIRNet		49.01 336	44.71 340	61.92 330	76.06 257	46.61 269	63.23 353	54.90 370	24.77 382	33.56 375	36.60 390	21.28 346	75.88 340	29.49 349	62.54 247	63.26 378

LTVRE_ROB	45.45 19	52.73 325	49.74 328	61.69 331	69.78 329	34.99 351	44.52 381	67.60 351	43.11 345	43.79 341	74.03 306	18.54 357	81.45 289	28.39 357	57.94 283	68.62 366
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		58.91 291	58.04 283	61.54 332	69.07 334	33.83 358	76.91 285	81.99 178	51.40 295	48.17 321	74.67 301	40.23 172	74.15 345	31.78 342	48.10 338	76.64 325

CP-MVSNet		58.54 297	57.57 286	61.46 333	68.50 338	33.96 357	76.90 286	78.60 250	51.67 294	47.83 324	76.60 284	34.99 242	72.79 354	35.45 323	47.58 342	77.64 316

PS-CasMVS		58.12 299	57.03 290	61.37 334	68.24 342	33.80 359	76.73 287	78.01 258	51.20 296	47.54 328	76.20 292	32.85 260	72.76 355	35.17 328	47.37 344	77.55 317

Anonymous20240521		51.65 330	48.42 332	61.34 335	56.43 377	39.65 338	73.57 306	73.47 315	36.64 362	36.59 366	63.98 358	10.75 376	72.25 358	35.35 324	49.01 336	72.11 356

CHOSEN 280x420		57.53 302	56.38 295	60.97 336	74.01 286	48.10 244	46.30 380	54.31 371	48.18 313	50.88 311	77.43 270	38.37 191	59.16 378	54.83 221	63.14 242	75.66 332

DTE-MVSNet		57.03 303	55.73 299	60.95 337	65.94 349	32.57 364	75.71 289	77.09 275	51.16 297	46.65 334	76.34 287	32.84 261	73.22 353	30.94 346	44.87 355	77.06 319

IterMVS-SCA-FT		59.12 286	58.81 280	60.08 338	70.68 325	45.07 290	80.42 257	74.25 302	43.54 343	50.02 314	73.73 309	31.97 270	56.74 380	51.06 251	53.60 322	78.42 305

COLMAP_ROB	43.60 20	50.90 333	48.05 334	59.47 339	67.81 344	40.57 335	71.25 325	62.72 363	36.49 363	36.19 368	73.51 314	13.48 371	73.92 348	20.71 380	50.26 334	63.92 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing3		59.97 279	60.19 269	59.32 340	77.60 232	30.01 373	81.75 229	81.79 184	53.54 277	50.34 313	79.94 242	48.99 68	76.91 333	17.19 387	50.59 333	71.03 363

testgi		54.25 318	52.57 317	59.29 341	62.76 366	21.65 391	72.21 318	70.47 334	53.25 281	41.94 349	77.33 271	14.28 370	77.95 325	29.18 351	51.72 331	78.28 308

TinyColmap		48.15 338	44.49 342	59.13 342	65.73 351	38.04 344	63.34 352	62.86 362	38.78 353	29.48 382	67.23 351	6.46 390	73.30 352	24.59 369	41.90 362	66.04 372

test20.03		55.22 314	54.07 307	58.68 343	63.14 365	25.00 384	77.69 282	74.78 299	52.64 284	43.43 343	72.39 326	26.21 308	74.76 344	29.31 350	47.05 348	76.28 329

EU-MVSNet		52.63 326	50.72 323	58.37 344	62.69 367	28.13 381	72.60 312	75.97 290	30.94 375	40.76 357	72.11 330	20.16 349	70.80 361	35.11 329	46.11 352	76.19 330

MIMVSNet1		50.35 334	47.81 335	57.96 345	61.53 369	27.80 382	67.40 341	74.06 305	43.25 344	33.31 378	65.38 356	16.03 367	71.34 359	21.80 377	47.55 343	74.75 340

pmmvs3		45.53 343	41.55 347	57.44 346	48.97 388	39.68 337	70.06 329	57.66 367	28.32 378	34.06 373	57.29 374	8.50 383	66.85 367	34.86 331	34.26 377	65.80 373

test_fmvs1		53.60 323	52.54 318	56.78 347	58.07 373	30.26 369	68.95 336	42.19 383	32.46 371	63.59 169	82.56 214	11.55 373	60.81 372	58.25 191	55.27 309	79.28 293

test_fmvs1_n		52.55 327	51.19 322	56.65 348	51.90 383	30.14 370	67.66 340	42.84 382	32.27 372	62.30 183	82.02 226	9.12 381	60.84 371	57.82 199	54.75 315	78.99 295

KD-MVS_self_test		49.24 335	46.85 338	56.44 349	54.32 378	22.87 387	57.39 369	73.36 316	44.36 338	37.98 364	59.30 371	18.97 354	71.17 360	33.48 334	42.44 360	75.26 335

PM-MVS		46.92 340	43.76 345	56.41 350	52.18 382	32.26 365	63.21 354	38.18 388	37.99 357	40.78 356	66.20 352	5.09 393	65.42 368	48.19 269	41.99 361	71.54 360

dmvs_testset		57.65 300	58.21 282	55.97 351	74.62 278	9.82 407	63.75 350	63.34 360	67.23 52	48.89 319	83.68 194	39.12 184	76.14 338	23.43 373	59.80 261	81.96 255

test_vis1_n		51.19 332	49.66 329	55.76 352	51.26 384	29.85 374	67.20 342	38.86 387	32.12 373	59.50 212	79.86 244	8.78 382	58.23 379	56.95 208	52.46 328	79.19 294

AllTest		47.32 339	44.66 341	55.32 353	65.08 356	37.50 347	62.96 355	54.25 372	35.45 366	33.42 376	72.82 319	9.98 377	59.33 375	24.13 370	43.84 357	69.13 364

TestCases				55.32 353	65.08 356	37.50 347		54.25 372	35.45 366	33.42 376	72.82 319	9.98 377	59.33 375	24.13 370	43.84 357	69.13 364

new-patchmatchnet		48.21 337	46.55 339	53.18 355	57.73 375	18.19 399	70.24 328	71.02 331	45.70 328	33.70 374	60.23 367	18.00 359	69.86 364	27.97 359	34.35 376	71.49 361

ITE_SJBPF				51.84 356	58.03 374	31.94 367		53.57 374	36.67 361	41.32 353	75.23 299	11.17 375	51.57 385	25.81 366	48.04 339	72.02 357

RPSCF		45.77 342	44.13 344	50.68 357	57.67 376	29.66 375	54.92 375	45.25 379	26.69 380	45.92 337	75.92 295	17.43 362	45.70 391	27.44 361	45.95 353	76.67 322

test_fmvs2		45.89 341	44.32 343	50.62 358	45.85 392	24.70 385	58.87 368	37.84 390	25.22 381	52.46 299	74.56 303	7.07 385	54.69 381	49.28 261	47.70 341	72.48 354

ANet_high		34.39 353	29.59 359	48.78 359	30.34 402	22.28 388	55.53 372	63.79 359	38.11 356	15.47 394	36.56 391	6.94 386	59.98 374	13.93 391	5.64 405	64.08 375

TDRefinement		40.91 346	38.37 350	48.55 360	50.45 386	33.03 362	58.98 367	50.97 375	28.50 377	29.89 381	67.39 350	6.21 392	54.51 382	17.67 386	35.25 373	58.11 379

DSMNet-mixed		38.35 348	35.36 353	47.33 361	48.11 390	14.91 403	37.87 389	36.60 391	19.18 387	34.37 372	59.56 370	15.53 368	53.01 384	20.14 382	46.89 349	74.07 344

mvsany_test1		43.38 344	42.57 346	45.82 362	50.96 385	26.10 383	55.80 371	27.74 400	27.15 379	47.41 330	74.39 304	18.67 356	44.95 392	44.66 289	36.31 370	66.40 371

N_pmnet		41.25 345	39.77 348	45.66 363	68.50 338	0.82 413	72.51 314	0.38 412	35.61 365	35.26 371	61.51 364	20.07 350	67.74 366	23.51 372	40.63 363	68.42 367

test_vis1_rt		40.29 347	38.64 349	45.25 364	48.91 389	30.09 371	59.44 365	27.07 401	24.52 383	38.48 363	51.67 382	6.71 388	49.44 386	44.33 291	46.59 351	56.23 380

test_fmvs3		37.95 349	35.75 352	44.55 365	35.50 398	18.92 395	48.32 377	34.00 395	18.36 389	41.31 354	61.58 363	2.29 400	48.06 390	42.72 300	37.71 369	66.66 370

EGC-MVSNET		33.75 354	30.42 358	43.75 366	64.94 358	36.21 350	60.47 364	40.70 386	0.02 406	0.10 407	53.79 378	7.39 384	60.26 373	11.09 394	35.23 374	34.79 392

LCM-MVSNet		28.07 357	23.85 365	40.71 367	27.46 407	18.93 394	30.82 395	46.19 376	12.76 394	16.40 392	34.70 393	1.90 403	48.69 389	20.25 381	24.22 389	54.51 382

FPMVS		35.40 351	33.67 355	40.57 368	46.34 391	28.74 380	41.05 385	57.05 368	20.37 386	22.27 390	53.38 379	6.87 387	44.94 393	8.62 395	47.11 347	48.01 387

WB-MVS		37.41 350	36.37 351	40.54 369	54.23 379	10.43 406	65.29 344	43.75 380	34.86 369	27.81 386	54.63 376	24.94 319	63.21 369	6.81 401	15.00 396	47.98 388

new_pmnet		33.56 355	31.89 357	38.59 370	49.01 387	20.42 392	51.01 376	37.92 389	20.58 384	23.45 389	46.79 384	6.66 389	49.28 388	20.00 383	31.57 382	46.09 389

SSC-MVS		35.20 352	34.30 354	37.90 371	52.58 381	8.65 409	61.86 357	41.64 384	31.81 374	25.54 388	52.94 381	23.39 330	59.28 377	6.10 402	12.86 397	45.78 390

PMMVS2		26.71 361	22.98 366	37.87 372	36.89 396	8.51 410	42.51 384	29.32 399	19.09 388	13.01 396	37.54 387	2.23 401	53.11 383	14.54 390	11.71 398	51.99 385

Gipuma		27.47 359	24.26 364	37.12 373	60.55 372	29.17 378	11.68 400	60.00 365	14.18 392	10.52 401	15.12 402	2.20 402	63.01 370	8.39 396	35.65 371	19.18 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		33.04 356	32.55 356	34.52 374	40.96 393	22.03 389	44.45 382	35.62 392	20.42 385	28.12 385	62.35 362	5.03 394	31.88 404	21.61 379	34.42 375	49.63 386

mvsany_test3		28.00 358	25.98 360	34.05 375	28.97 403	15.31 401	34.54 392	18.17 406	16.24 390	29.30 383	53.37 380	2.79 398	33.38 403	30.01 348	20.41 394	53.45 383

test_f		27.12 360	24.85 361	33.93 376	26.17 408	15.25 402	30.24 396	22.38 405	12.53 395	28.23 384	49.43 383	2.59 399	34.34 402	25.12 368	26.99 386	52.20 384

test_method		24.09 365	21.07 369	33.16 377	27.67 406	8.35 411	26.63 397	35.11 394	3.40 403	14.35 395	36.98 389	3.46 397	35.31 399	19.08 385	22.95 390	55.81 381

PMVS	19.57 22	25.07 363	22.43 368	32.99 378	23.12 409	22.98 386	40.98 386	35.19 393	15.99 391	11.95 400	35.87 392	1.47 406	49.29 387	5.41 404	31.90 381	26.70 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		26.46 362	24.41 363	32.62 379	37.58 395	21.74 390	40.50 387	30.39 397	11.45 396	16.33 393	43.76 385	1.63 405	41.62 394	11.24 393	26.82 387	34.51 393

test_vis3_rt		24.79 364	22.95 367	30.31 380	28.59 404	18.92 395	37.43 390	17.27 408	12.90 393	21.28 391	29.92 397	1.02 407	36.35 397	28.28 358	29.82 385	35.65 391

MVE	16.60 23	17.34 371	13.39 374	29.16 381	28.43 405	19.72 393	13.73 399	23.63 404	7.23 402	7.96 402	21.41 398	0.80 408	36.08 398	6.97 399	10.39 399	31.69 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		21.11 366	19.08 370	27.18 382	30.56 400	18.28 397	33.43 393	24.48 402	8.02 400	12.02 398	33.50 394	0.75 409	35.09 400	7.68 397	21.32 391	28.17 395

APD_test2		21.11 366	19.08 370	27.18 382	30.56 400	18.28 397	33.43 393	24.48 402	8.02 400	12.02 398	33.50 394	0.75 409	35.09 400	7.68 397	21.32 391	28.17 395

E-PMN		19.16 368	18.40 372	21.44 384	36.19 397	13.63 404	47.59 378	30.89 396	10.73 397	5.91 404	16.59 400	3.66 396	39.77 395	5.95 403	8.14 400	10.92 400

EMVS		18.42 369	17.66 373	20.71 385	34.13 399	12.64 405	46.94 379	29.94 398	10.46 399	5.58 405	14.93 403	4.23 395	38.83 396	5.24 405	7.51 402	10.67 401

DeepMVS_CX				13.10 386	21.34 410	8.99 408		10.02 410	10.59 398	7.53 403	30.55 396	1.82 404	14.55 405	6.83 400	7.52 401	15.75 399

wuyk23d		9.11 373	8.77 377	10.15 387	40.18 394	16.76 400	20.28 398	1.01 411	2.58 404	2.66 406	0.98 406	0.23 411	12.49 406	4.08 406	6.90 403	1.19 403

tmp_tt		9.44 372	10.68 375	5.73 388	2.49 411	4.21 412	10.48 401	18.04 407	0.34 405	12.59 397	20.49 399	11.39 374	7.03 407	13.84 392	6.46 404	5.95 402

testmvs		6.14 375	8.18 378	0.01 389	0.01 412	0.00 415	73.40 309	0.00 413	0.00 407	0.02 408	0.15 407	0.00 412	0.00 408	0.02 407	0.00 406	0.02 404

test123		6.01 376	8.01 379	0.01 389	0.00 413	0.01 414	71.93 322	0.00 413	0.00 407	0.02 408	0.11 408	0.00 412	0.00 408	0.02 407	0.00 406	0.02 404

test_blank		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

cdsmvs_eth3d_5k		18.33 370	24.44 362	0.00 391	0.00 413	0.00 415	0.00 402	89.40 22	0.00 407	0.00 410	92.02 47	38.55 189	0.00 408	0.00 409	0.00 406	0.00 406

pcd_1.5k_mvsjas		3.15 377	4.20 380	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	37.77 195	0.00 408	0.00 409	0.00 406	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

ab-mvs-re		7.68 374	10.24 376	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	92.12 44	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

WAC-MVS						34.28 354								22.56 375

FOURS1					83.24 108	49.90 192	84.98 137	78.76 244	47.71 314	73.42 59

PC_three_1452									66.58 59	87.27 2	93.70 11	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 53	57.21 234	82.06 13	93.39 20	54.94 29

eth-test2					0.00 413

eth-test					0.00 413

ZD-MVS					89.55 14	53.46 107		84.38 133	57.02 236	73.97 54	91.03 65	44.57 120	91.17 76	75.41 73	81.78 70

RE-MVS-def			66.66 194		80.96 170	48.14 242	81.54 236	76.98 276	46.42 324	62.75 178	89.42 103	29.28 290		60.52 171	72.06 163	83.19 240

IU-MVS					89.48 17	57.49 15		91.38 9	66.22 67	88.26 1				82.83 23	87.60 18	92.44 31

test_241102_TWO								88.76 39	57.50 228	83.60 6	94.09 3	56.14 21	96.37 6	82.28 27	87.43 20	92.55 29

test_241102_ONE					89.48 17	56.89 29		88.94 30	57.53 226	84.61 4	93.29 24	58.81 11	96.45 1

9.14			78.19 27		85.67 59		88.32 51	88.84 36	59.89 173	74.58 49	92.62 37	46.80 85	92.66 41	81.40 36	85.62 40

save fliter					85.35 66	56.34 41	89.31 40	81.46 189	61.55 145

test_0728_THIRD									58.00 214	81.91 14	93.64 13	56.54 17	96.44 2	81.64 32	86.86 25	92.23 36

test0726					89.40 20	57.45 17	92.32 8	88.63 43	57.71 222	83.14 10	93.96 8	55.17 25

GSMVS																88.13 145

test_part2					89.33 23	55.48 55				82.27 12

sam_mvs1												38.86 187				88.13 145

sam_mvs												35.99 232

MTGPA								81.31 192

test_post1							70.84 327				14.72 404	34.33 248	83.86 270	48.80 264

test_post											16.22 401	37.52 204	84.72 264

patchmatchnet-post											59.74 369	38.41 190	79.91 310

MTMP							87.27 76	15.34 409

gm-plane-assit					83.24 108	54.21 94			70.91 22		88.23 129		95.25 14	66.37 124

test9_res														78.72 49	85.44 42	91.39 63

TEST9					85.68 57	55.42 56	87.59 66	84.00 143	57.72 221	72.99 64	90.98 67	44.87 114	88.58 153

test_8					85.72 56	55.31 61	87.60 65	83.88 146	57.84 219	72.84 68	90.99 66	44.99 110	88.34 164

agg_prior2														75.65 68	85.11 46	91.01 75

agg_prior					85.64 60	54.92 75		83.61 153		72.53 73			88.10 174

test_prior4						56.39 40	87.15 80

test_prior2							89.04 43		61.88 141	73.55 57	91.46 63	48.01 73		74.73 77	85.46 41

旧先验2							81.73 230		45.53 330	74.66 46			70.48 363	58.31 190

新几何2							81.61 234

旧先验1					81.57 157	47.48 256		71.83 321			88.66 118	36.94 215			78.34 104	88.67 133

无先验							85.19 127	78.00 259	49.08 307				85.13 259	52.78 239		87.45 161

原ACMM2							83.77 174

test222					79.36 197	50.97 166	77.99 280	67.84 348	42.54 347	62.84 177	86.53 159	30.26 283			76.91 112	85.23 202

testdata2													77.81 328	45.64 285

segment_acmp												44.97 112

testdata1							77.55 283		64.14 97

plane_prior7					77.95 227	48.46 231

plane_prior6					78.42 222	49.39 205						36.04 230

plane_prior5								82.59 170					88.30 167	65.46 133	72.34 160	84.49 212

plane_prior4											83.28 199

plane_prior3						48.95 214			64.01 100	62.15 185

plane_prior2							85.76 107		63.60 109

plane_prior1					78.31 224

plane_prior						49.57 197	87.43 69		64.57 91						72.84 155

n2								0.00 413

nn								0.00 413

door-mid								41.31 385

test11								84.25 137

door								43.27 381

HQP5-MVS						51.56 155

HQP-NCC					79.02 206		88.00 55		65.45 78	64.48 153

ACMP_Plane					79.02 206		88.00 55		65.45 78	64.48 153

BP-MVS														66.70 121

HQP4-MVS										64.47 156			88.61 152			84.91 208

HQP3-MVS								83.68 149							73.12 151

HQP2-MVS												37.35 207

NP-MVS					78.76 211	50.43 176					85.12 173

MDTV_nov1_ep13_2view						43.62 306	71.13 326		54.95 266	59.29 218		36.76 218		46.33 282		87.32 163

MDTV_nov1_ep13			61.56 253		81.68 150	55.12 68	72.41 315	78.18 256	59.19 190	58.85 227	69.29 344	34.69 244	86.16 234	36.76 320	62.96 244

ACMMP++_ref															63.20 240

ACMMP++															59.38 265

Test By Simon												39.38 181

High-resolution multi-view benchmark