High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 10	75.95 3	77.10 38	93.09 27	54.15 36	95.57 12	85.80 10	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 21	84.38 82	55.40 58	92.16 9	89.85 20	75.28 4	82.41 10	93.86 8	54.30 33	93.98 24	90.29 1	87.13 21	93.30 12

DVP-MVS++		82.44 3	82.38 5	82.62 4	91.77 4	57.49 17	84.98 136	88.88 32	58.00 214	83.60 6	93.39 18	67.21 2	96.39 4	81.64 30	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 5	80.12 191	59.50 5	92.24 8	90.72 14	69.37 31	83.22 8	94.47 2	63.81 5	93.18 33	74.02 84	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 4	81.79 11	86.80 46	56.89 29	92.77 2	86.30 85	77.83 1	77.88 34	92.13 41	60.24 6	94.78 19	78.97 44	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
MSP-MVS		82.30 6	83.47 1	78.80 57	82.99 118	52.71 132	85.04 133	88.63 43	66.08 70	86.77 3	92.75 32	72.05 1	91.46 70	83.35 19	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
SED-MVS		81.92 7	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 30	57.50 228	84.61 4	94.09 3	58.81 11	96.37 6	82.28 25	87.60 18	94.06 3

CNVR-MVS		81.76 8	81.90 8	81.33 18	90.04 10	57.70 14	91.71 10	88.87 34	70.31 24	77.64 37	93.87 7	52.58 44	93.91 27	84.17 14	87.92 16	92.39 32

MVS_0304		81.58 9	82.05 7	80.20 30	82.36 136	54.70 82	91.13 19	88.95 29	74.49 5	80.04 24	93.64 11	52.40 45	93.27 32	88.85 4	86.56 32	92.61 28

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 18	57.71 222	81.91 13	93.64 11	55.17 26	96.44 2	81.68 28	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
CANet		80.90 11	81.17 12	80.09 36	87.62 40	54.21 95	91.60 13	86.47 81	73.13 8	79.89 26	93.10 25	49.88 68	92.98 34	84.09 16	84.75 50	93.08 18

patch_mono-2		80.84 12	81.59 10	78.62 64	90.34 9	53.77 102	88.08 54	88.36 50	76.17 2	79.40 28	91.09 64	55.43 24	90.09 109	85.01 12	80.40 84	91.99 46

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 82	85.46 63	49.56 201	90.99 21	86.66 79	70.58 22	80.07 23	95.30 1	56.18 21	90.97 86	82.57 24	86.22 36	93.28 13

MVSMamba_pp		80.57 14	80.23 15	81.61 14	88.58 26	58.30 9	89.94 29	87.64 65	68.99 33	79.95 25	86.50 161	54.36 32	94.71 20	79.53 38	86.58 31	91.80 52

HPM-MVS++		80.50 15	80.71 14	79.88 38	87.34 42	55.20 66	89.93 30	87.55 66	66.04 73	79.46 27	93.00 30	53.10 41	91.76 64	80.40 36	89.56 8	92.68 27

CSCG		80.41 16	79.72 16	82.49 5	89.12 25	57.67 15	89.29 41	91.54 5	59.19 190	71.82 82	90.05 92	59.72 9	96.04 10	78.37 50	88.40 13	93.75 7

PS-MVSNAJ		80.06 17	79.52 18	81.68 13	85.58 59	60.97 3	91.69 11	87.02 71	70.62 21	80.75 20	93.22 24	37.77 202	92.50 47	82.75 22	86.25 35	91.57 58

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 72	60.73 4	91.65 12	86.86 74	70.30 25	80.77 19	93.07 29	37.63 207	92.28 53	82.73 23	85.71 39	91.57 58

DPE-MVS		79.82 19	79.66 17	80.29 28	89.27 24	55.08 71	88.70 47	87.92 56	55.55 258	81.21 18	93.69 10	56.51 19	94.27 23	78.36 51	85.70 40	91.51 61
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		79.57 20	79.23 20	80.59 23	89.50 15	56.99 26	91.38 15	88.17 52	67.71 46	73.81 57	92.75 32	46.88 89	93.28 31	78.79 47	84.07 55	91.50 62

dcpmvs_2		79.33 21	78.94 21	80.49 24	89.75 12	56.54 36	84.83 143	83.68 152	67.85 43	69.36 104	90.24 84	60.20 7	92.10 58	84.14 15	80.40 84	92.82 23

testing11		79.18 22	78.85 22	80.16 32	88.33 31	56.99 26	88.31 52	92.06 1	72.82 9	70.62 100	88.37 123	57.69 14	92.30 51	75.25 74	76.24 127	91.20 71

SMA-MVS		79.10 23	78.76 23	80.12 34	84.42 80	55.87 49	87.58 68	86.76 76	61.48 148	80.26 22	93.10 25	46.53 94	92.41 49	79.97 37	88.77 10	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
LFMVS		78.52 24	77.14 42	82.67 3	89.58 13	58.90 7	91.27 18	88.05 54	63.22 118	74.63 49	90.83 73	41.38 168	94.40 21	75.42 72	79.90 93	94.72 2

testing99		78.45 25	77.78 33	80.45 26	88.28 34	56.81 32	87.95 59	91.49 6	71.72 13	70.84 95	88.09 131	57.29 16	92.63 45	69.24 108	75.13 140	91.91 47

APDe-MVS		78.44 26	78.20 26	79.19 45	88.56 27	54.55 88	89.76 34	87.77 60	55.91 253	78.56 31	92.49 37	48.20 75	92.65 43	79.49 39	83.04 59	90.39 88
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 27	76.99 44	82.73 2	93.17 1	64.46 1	89.93 30	88.51 48	64.83 89	73.52 60	88.09 131	48.07 76	92.19 54	62.24 155	84.53 52	91.53 60

lupinMVS		78.38 28	78.11 28	79.19 45	83.02 116	55.24 63	91.57 14	84.82 123	69.12 32	76.67 40	92.02 46	44.82 121	90.23 106	80.83 35	80.09 88	92.08 40

EPNet		78.36 29	78.49 24	77.97 80	85.49 62	52.04 144	89.36 39	84.07 144	73.22 7	77.03 39	91.72 54	49.32 72	90.17 108	73.46 89	82.77 61	91.69 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 30	78.26 25	78.48 68	81.33 165	56.31 42	81.59 234	86.41 82	69.61 29	81.72 15	88.16 130	55.09 28	88.04 178	74.12 83	86.31 34	91.09 74
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testing91		78.30 31	77.54 36	80.61 22	88.16 36	57.12 25	87.94 60	91.07 13	71.43 16	70.75 96	88.04 135	55.82 23	92.65 43	69.61 105	75.00 144	92.05 42

sasdasda		78.17 32	77.86 31	79.12 50	84.30 83	54.22 93	87.71 62	84.57 132	67.70 47	77.70 35	92.11 44	50.90 56	89.95 112	78.18 54	77.54 111	93.20 15

canonicalmvs		78.17 32	77.86 31	79.12 50	84.30 83	54.22 93	87.71 62	84.57 132	67.70 47	77.70 35	92.11 44	50.90 56	89.95 112	78.18 54	77.54 111	93.20 15

alignmvs		78.08 34	77.98 29	78.39 72	83.53 99	53.22 120	89.77 33	85.45 99	66.11 68	76.59 42	91.99 48	54.07 37	89.05 136	77.34 60	77.00 116	92.89 21

DeepC-MVS_fast	67.50 3	78.00 35	77.63 34	79.13 49	88.52 28	55.12 68	89.95 28	85.98 90	68.31 35	71.33 89	92.75 32	45.52 107	90.37 99	71.15 99	85.14 46	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
VNet		77.99 36	77.92 30	78.19 76	87.43 41	50.12 188	90.93 22	91.41 8	67.48 50	75.12 44	90.15 90	46.77 91	91.00 83	73.52 88	78.46 105	93.44 9

TSAR-MVS + GP.		77.82 37	77.59 35	78.49 67	85.25 68	50.27 187	90.02 26	90.57 15	56.58 247	74.26 54	91.60 59	54.26 34	92.16 55	75.87 66	79.91 92	93.05 19

casdiffmvs_mvg		77.75 38	77.28 39	79.16 47	80.42 187	54.44 90	87.76 61	85.46 98	71.67 14	71.38 88	88.35 125	51.58 49	91.22 76	79.02 43	79.89 94	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 39	77.22 41	79.14 48	86.95 44	54.89 77	87.18 78	91.96 2	72.29 11	71.17 93	88.70 117	55.19 25	91.24 75	65.18 140	76.32 126	91.29 69

SF-MVS		77.64 40	77.42 38	78.32 74	83.75 96	52.47 137	86.63 91	87.80 57	58.78 202	74.63 49	92.38 38	47.75 81	91.35 72	78.18 54	86.85 26	91.15 73

PHI-MVS		77.49 41	77.00 43	78.95 52	85.33 66	50.69 170	88.57 49	88.59 46	58.14 211	73.60 58	93.31 21	43.14 145	93.79 28	73.81 86	88.53 12	92.37 33

WTY-MVS		77.47 42	77.52 37	77.30 93	88.33 31	46.25 279	88.46 50	90.32 16	71.40 17	72.32 78	91.72 54	53.44 39	92.37 50	66.28 127	75.42 134	93.28 13

casdiffmvs		77.36 43	76.85 45	78.88 55	80.40 188	54.66 86	87.06 81	85.88 91	72.11 12	71.57 85	88.63 122	50.89 59	90.35 100	76.00 65	79.11 100	91.63 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS-test		77.20 44	77.25 40	77.05 100	84.60 77	49.04 214	89.42 37	85.83 93	65.90 74	72.85 69	91.98 50	45.10 112	91.27 73	75.02 76	84.56 51	90.84 80

ETV-MVS		77.17 45	76.74 46	78.48 68	81.80 145	54.55 88	86.13 99	85.33 104	68.20 37	73.10 65	90.52 78	45.23 111	90.66 92	79.37 40	80.95 76	90.22 94

SteuartSystems-ACMMP		77.08 46	76.33 51	79.34 43	80.98 169	55.31 61	89.76 34	86.91 73	62.94 123	71.65 83	91.56 60	42.33 152	92.56 46	77.14 61	83.69 57	90.15 98
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 47	76.45 49	78.69 61	79.69 196	54.74 79	90.56 24	83.99 147	68.26 36	74.10 55	90.91 70	42.14 156	89.99 111	79.30 41	79.12 99	91.36 66
jason: jason.
train_agg		76.91 48	76.40 50	78.45 70	85.68 55	55.42 55	87.59 66	84.00 145	57.84 219	72.99 66	90.98 67	44.99 115	88.58 155	78.19 52	85.32 44	91.34 68

MVS		76.91 48	75.48 61	81.23 19	84.56 78	55.21 65	80.23 259	91.64 4	58.65 204	65.37 140	91.48 62	45.72 104	95.05 16	72.11 96	89.52 9	93.44 9

DeepC-MVS	67.15 4	76.90 50	76.27 52	78.80 57	80.70 180	55.02 72	86.39 93	86.71 77	66.96 55	67.91 114	89.97 94	48.03 77	91.41 71	75.60 69	84.14 54	89.96 104
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		76.86 51	76.24 53	78.71 60	80.47 186	54.20 97	83.90 169	84.88 122	71.38 18	71.51 86	89.15 110	50.51 60	90.55 96	75.71 67	78.65 103	91.39 64

CS-MVS		76.77 52	76.70 47	76.99 105	83.55 98	48.75 223	88.60 48	85.18 112	66.38 63	72.47 76	91.62 58	45.53 106	90.99 85	74.48 79	82.51 63	91.23 70

PAPM		76.76 53	76.07 55	78.81 56	80.20 189	59.11 6	86.86 87	86.23 86	68.60 34	70.18 103	88.84 115	51.57 50	87.16 207	65.48 133	86.68 29	90.15 98

MAR-MVS		76.76 53	75.60 59	80.21 29	90.87 7	54.68 84	89.14 42	89.11 26	62.95 122	70.54 101	92.33 39	41.05 169	94.95 17	57.90 200	86.55 33	91.00 77
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
PVSNet_Blended		76.53 55	76.54 48	76.50 115	85.91 52	51.83 150	88.89 45	84.24 141	67.82 44	69.09 106	89.33 107	46.70 92	88.13 174	75.43 70	81.48 75	89.55 112

ACMMP_NAP		76.43 56	75.66 58	78.73 59	81.92 142	54.67 85	84.06 165	85.35 103	61.10 154	72.99 66	91.50 61	40.25 178	91.00 83	76.84 62	86.98 24	90.51 87

MVS_111021_HR		76.39 57	75.38 64	79.42 42	85.33 66	56.47 38	88.15 53	84.97 119	65.15 87	66.06 131	89.88 95	43.79 132	92.16 55	75.03 75	80.03 91	89.64 110

CHOSEN 1792x2688		76.24 58	74.03 83	82.88 1	83.09 113	62.84 2	85.73 110	85.39 101	69.79 27	64.87 148	83.49 196	41.52 167	93.69 29	70.55 101	81.82 71	92.12 39

SD-MVS		76.18 59	74.85 72	80.18 31	85.39 64	56.90 28	85.75 108	82.45 177	56.79 242	74.48 52	91.81 52	43.72 135	90.75 90	74.61 78	78.65 103	92.91 20
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
APD-MVS		76.15 60	75.68 57	77.54 88	88.52 28	53.44 111	87.26 77	85.03 118	53.79 274	74.91 47	91.68 56	43.80 131	90.31 102	74.36 80	81.82 71	88.87 129
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDD-MVS		76.08 61	74.97 70	79.44 41	84.27 86	53.33 117	91.13 19	85.88 91	65.33 84	72.37 77	89.34 105	32.52 272	92.76 41	77.90 57	75.96 128	92.22 38

CDPH-MVS		76.05 62	75.19 66	78.62 64	86.51 48	54.98 74	87.32 72	84.59 131	58.62 205	70.75 96	90.85 72	43.10 147	90.63 94	70.50 102	84.51 53	90.24 93

fmvsm_l_conf0.5_n		75.95 63	76.16 54	75.31 148	76.01 262	48.44 234	84.98 136	71.08 335	63.50 112	81.70 16	93.52 15	50.00 64	87.18 206	87.80 5	76.87 118	90.32 91

EIA-MVS		75.92 64	75.18 67	78.13 77	85.14 69	51.60 155	87.17 79	85.32 105	64.69 90	68.56 110	90.53 77	45.79 103	91.58 67	67.21 120	82.18 67	91.20 71

fmvsm_l_conf0.5_n_a		75.88 65	76.07 55	75.31 148	76.08 258	48.34 237	85.24 124	70.62 338	63.13 120	81.45 17	93.62 14	49.98 66	87.40 202	87.76 6	76.77 119	90.20 96

test_yl		75.85 66	74.83 73	78.91 53	88.08 38	51.94 146	91.30 16	89.28 23	57.91 216	71.19 91	89.20 108	42.03 159	92.77 39	69.41 106	75.07 142	92.01 44

DCV-MVSNet		75.85 66	74.83 73	78.91 53	88.08 38	51.94 146	91.30 16	89.28 23	57.91 216	71.19 91	89.20 108	42.03 159	92.77 39	69.41 106	75.07 142	92.01 44

MVS_Test		75.85 66	74.93 71	78.62 64	84.08 88	55.20 66	83.99 167	85.17 113	68.07 40	73.38 62	82.76 207	50.44 61	89.00 139	65.90 129	80.61 80	91.64 54

ZNCC-MVS		75.82 69	75.02 69	78.23 75	83.88 94	53.80 101	86.91 86	86.05 89	59.71 176	67.85 115	90.55 76	42.23 154	91.02 82	72.66 94	85.29 45	89.87 107

ETVMVS		75.80 70	75.44 62	76.89 109	86.23 50	50.38 180	85.55 117	91.42 7	71.30 19	68.80 108	87.94 137	56.42 20	89.24 129	56.54 212	74.75 146	91.07 75

CLD-MVS		75.60 71	75.39 63	76.24 119	80.69 181	52.40 138	90.69 23	86.20 87	74.40 6	65.01 146	88.93 112	42.05 158	90.58 95	76.57 63	73.96 150	85.73 197
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvsm_n_1920		75.56 72	75.54 60	75.61 136	74.60 281	49.51 204	81.82 226	74.08 309	66.52 61	80.40 21	93.46 17	46.95 88	89.72 119	86.69 7	75.30 135	87.61 160

MP-MVS-pluss		75.54 73	75.03 68	77.04 101	81.37 164	52.65 134	84.34 156	84.46 134	61.16 152	69.14 105	91.76 53	39.98 185	88.99 141	78.19 52	84.89 49	89.48 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 74	75.20 65	75.62 135	80.98 169	49.00 215	87.43 69	84.68 129	63.49 113	70.97 94	90.15 90	42.86 149	91.14 80	74.33 81	81.90 70	86.71 179

Effi-MVS+		75.24 75	73.61 85	80.16 32	81.92 142	57.42 21	85.21 125	76.71 287	60.68 165	73.32 63	89.34 105	47.30 84	91.63 66	68.28 114	79.72 95	91.42 63

ET-MVSNet_ETH3D		75.23 76	74.08 81	78.67 62	84.52 79	55.59 51	88.92 44	89.21 25	68.06 41	53.13 296	90.22 86	49.71 69	87.62 196	72.12 95	70.82 178	92.82 23

PAPR		75.20 77	74.13 79	78.41 71	88.31 33	55.10 70	84.31 157	85.66 95	63.76 105	67.55 116	90.73 74	43.48 140	89.40 126	66.36 126	77.03 115	90.73 82

baseline2		75.15 78	74.54 77	76.98 106	81.67 152	51.74 152	83.84 171	91.94 3	69.97 26	58.98 223	86.02 164	59.73 8	91.73 65	68.37 113	70.40 183	87.48 162

diffmvs		75.11 79	74.65 75	76.46 116	78.52 221	53.35 115	83.28 191	79.94 221	70.51 23	71.64 84	88.72 116	46.02 100	86.08 241	77.52 58	75.75 132	89.96 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MP-MVS		74.99 80	74.33 78	76.95 107	82.89 123	53.05 126	85.63 113	83.50 157	57.86 218	67.25 118	90.24 84	43.38 142	88.85 149	76.03 64	82.23 66	88.96 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		74.87 81	73.90 84	77.77 83	83.30 106	53.45 110	85.75 108	85.29 107	59.22 189	66.50 127	89.85 96	40.94 171	90.76 89	70.94 100	83.35 58	89.10 124

iter_conf05		74.57 82	72.83 94	79.80 39	80.85 176	58.10 10	74.55 300	82.97 169	50.53 298	63.52 173	83.41 198	57.38 15	92.83 37	73.92 85	88.34 14	88.48 143

fmvsm_s_conf0.5_n		74.48 83	74.12 80	75.56 138	76.96 247	47.85 254	85.32 122	69.80 345	64.16 96	78.74 29	93.48 16	45.51 108	89.29 128	86.48 8	66.62 209	89.55 112

3Dnovator	64.70 6	74.46 84	72.48 98	80.41 27	82.84 125	55.40 58	83.08 196	88.61 45	67.61 49	59.85 206	88.66 118	34.57 253	93.97 25	58.42 190	88.70 11	91.85 50

test_fmvsmconf_n		74.41 85	74.05 82	75.49 142	74.16 288	48.38 235	82.66 204	72.57 322	67.05 54	75.11 45	92.88 31	46.35 95	87.81 183	83.93 17	71.71 169	90.28 92

HFP-MVS		74.37 86	73.13 92	78.10 78	84.30 83	53.68 104	85.58 114	84.36 136	56.82 240	65.78 136	90.56 75	40.70 176	90.90 87	69.18 109	80.88 77	89.71 108

VDDNet		74.37 86	72.13 108	81.09 20	79.58 197	56.52 37	90.02 26	86.70 78	52.61 284	71.23 90	87.20 149	31.75 282	93.96 26	74.30 82	75.77 131	92.79 25

MSLP-MVS++		74.21 88	72.25 104	80.11 35	81.45 162	56.47 38	86.32 95	79.65 229	58.19 210	66.36 128	92.29 40	36.11 235	90.66 92	67.39 118	82.49 64	93.18 17

API-MVS		74.17 89	72.07 110	80.49 24	90.02 11	58.55 8	87.30 74	84.27 138	57.51 227	65.77 137	87.77 140	41.61 165	95.97 11	51.71 247	82.63 62	86.94 170

MGCFI-Net		74.07 90	74.64 76	72.34 222	82.90 122	43.33 312	80.04 262	79.96 220	65.61 76	74.93 46	91.85 51	48.01 78	80.86 296	71.41 97	77.10 114	92.84 22

IB-MVS	68.87 2	74.01 91	72.03 113	79.94 37	83.04 115	55.50 53	90.24 25	88.65 41	67.14 52	61.38 194	81.74 230	53.21 40	94.28 22	60.45 174	62.41 251	90.03 102
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
h-mvs33		73.95 92	72.89 93	77.15 99	80.17 190	50.37 181	84.68 147	83.33 158	68.08 38	71.97 80	88.65 121	42.50 150	91.15 79	78.82 45	57.78 291	89.91 106

HY-MVS	67.03 5	73.90 93	73.14 90	76.18 124	84.70 76	47.36 261	75.56 290	86.36 84	66.27 65	70.66 99	83.91 188	51.05 54	89.31 127	67.10 121	72.61 162	91.88 49

CostFormer		73.89 94	72.30 103	78.66 63	82.36 136	56.58 33	75.56 290	85.30 106	66.06 71	70.50 102	76.88 282	57.02 17	89.06 135	68.27 115	68.74 194	90.33 90

fmvsm_s_conf0.1_n		73.80 95	73.26 87	75.43 143	73.28 296	47.80 255	84.57 152	69.43 347	63.34 115	78.40 32	93.29 22	44.73 124	89.22 131	85.99 9	66.28 216	89.26 117

ACMMPR		73.76 96	72.61 95	77.24 97	83.92 92	52.96 129	85.58 114	84.29 137	56.82 240	65.12 142	90.45 79	37.24 218	90.18 107	69.18 109	80.84 78	88.58 138

region2R		73.75 97	72.55 97	77.33 92	83.90 93	52.98 128	85.54 118	84.09 143	56.83 239	65.10 143	90.45 79	37.34 216	90.24 105	68.89 111	80.83 79	88.77 133

CANet_DTU		73.71 98	73.14 90	75.40 144	82.61 132	50.05 189	84.67 149	79.36 237	69.72 28	75.39 43	90.03 93	29.41 296	85.93 247	67.99 116	79.11 100	90.22 94

test_fmvsmconf0.1_n		73.69 99	73.15 88	75.34 146	70.71 325	48.26 240	82.15 216	71.83 327	66.75 57	74.47 53	92.59 36	44.89 118	87.78 188	83.59 18	71.35 173	89.97 103

fmvsm_s_conf0.5_n_a		73.68 100	73.15 88	75.29 151	75.45 269	48.05 247	83.88 170	68.84 350	63.43 114	78.60 30	93.37 20	45.32 109	88.92 146	85.39 11	64.04 229	88.89 128

thisisatest0515		73.64 101	72.20 105	77.97 80	81.63 153	53.01 127	86.69 90	88.81 37	62.53 130	64.06 161	85.65 168	52.15 48	92.50 47	58.43 188	69.84 186	88.39 144

MVSFormer		73.53 102	72.19 106	77.57 87	83.02 116	55.24 63	81.63 231	81.44 194	50.28 299	76.67 40	90.91 70	44.82 121	86.11 236	60.83 166	80.09 88	91.36 66

PVSNet_BlendedMVS		73.42 103	73.30 86	73.76 190	85.91 52	51.83 150	86.18 98	84.24 141	65.40 81	69.09 106	80.86 239	46.70 92	88.13 174	75.43 70	65.92 218	81.33 272

PVSNet_Blended_VisFu		73.40 104	72.44 99	76.30 117	81.32 166	54.70 82	85.81 104	78.82 247	63.70 106	64.53 154	85.38 173	47.11 87	87.38 203	67.75 117	77.55 110	86.81 178

MVSTER		73.25 105	72.33 101	76.01 129	85.54 61	53.76 103	83.52 176	87.16 69	67.06 53	63.88 166	81.66 231	52.77 42	90.44 97	64.66 142	64.69 225	83.84 231

EI-MVSNet-Vis-set		73.19 106	72.60 96	74.99 160	82.56 133	49.80 196	82.55 209	89.00 28	66.17 67	65.89 134	88.98 111	43.83 130	92.29 52	65.38 139	69.01 192	82.87 249

PMMVS		72.98 107	72.05 111	75.78 133	83.57 97	48.60 226	84.08 163	82.85 172	61.62 144	68.24 112	90.33 83	28.35 300	87.78 188	72.71 93	76.69 120	90.95 78

XVS		72.92 108	71.62 115	76.81 110	83.41 101	52.48 135	84.88 141	83.20 164	58.03 212	63.91 164	89.63 100	35.50 242	89.78 116	65.50 131	80.50 82	88.16 145

test2506		72.91 109	72.43 100	74.32 173	80.12 191	44.18 303	83.19 193	84.77 126	64.02 98	65.97 132	87.43 146	47.67 82	88.72 150	59.08 181	79.66 96	90.08 100

TESTMET0.1,1		72.86 110	72.33 101	74.46 167	81.98 141	50.77 168	85.13 128	85.47 97	66.09 69	67.30 117	83.69 193	37.27 217	83.57 277	65.06 141	78.97 102	89.05 125

fmvsm_s_conf0.1_n_a		72.82 111	72.05 111	75.12 156	70.95 324	47.97 250	82.72 203	68.43 352	62.52 131	78.17 33	93.08 28	44.21 127	88.86 147	84.82 13	63.54 235	88.54 140

Fast-Effi-MVS+		72.73 112	71.15 124	77.48 89	82.75 127	54.76 78	86.77 89	80.64 208	63.05 121	65.93 133	84.01 186	44.42 126	89.03 137	56.45 216	76.36 125	88.64 135

MTAPA		72.73 112	71.22 122	77.27 95	81.54 159	53.57 106	67.06 344	81.31 196	59.41 183	68.39 111	90.96 69	36.07 237	89.01 138	73.80 87	82.45 65	89.23 119

PGM-MVS		72.60 114	71.20 123	76.80 112	82.95 119	52.82 131	83.07 197	82.14 179	56.51 248	63.18 174	89.81 97	35.68 241	89.76 118	67.30 119	80.19 87	87.83 154

HPM-MVS		72.60 114	71.50 117	75.89 131	82.02 140	51.42 160	80.70 252	83.05 166	56.12 252	64.03 162	89.53 101	37.55 210	88.37 163	70.48 103	80.04 90	87.88 153
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 116	71.46 118	76.00 130	82.93 121	52.32 141	86.93 85	82.48 176	55.15 262	63.65 168	90.44 82	35.03 249	88.53 159	68.69 112	77.83 109	87.15 168

baseline1		72.51 117	72.12 109	73.69 193	85.05 70	44.46 296	83.51 180	86.13 88	71.61 15	64.64 150	87.97 136	55.00 30	89.48 124	59.07 182	56.05 304	87.13 169

EI-MVSNet-UG-set		72.37 118	71.73 114	74.29 174	81.60 155	49.29 209	81.85 224	88.64 42	65.29 86	65.05 144	88.29 128	43.18 143	91.83 63	63.74 145	67.97 199	81.75 260

MS-PatchMatch		72.34 119	71.26 121	75.61 136	82.38 135	55.55 52	88.00 55	89.95 19	65.38 82	56.51 268	80.74 241	32.28 275	92.89 35	57.95 199	88.10 15	78.39 307

HQP-MVS		72.34 119	71.44 119	75.03 158	79.02 208	51.56 156	88.00 55	83.68 152	65.45 78	64.48 155	85.13 174	37.35 214	88.62 153	66.70 122	73.12 156	84.91 210

mvs_anonymous		72.29 121	70.74 127	76.94 108	82.85 124	54.72 81	78.43 277	81.54 192	63.77 104	61.69 191	79.32 252	51.11 53	85.31 254	62.15 157	75.79 130	90.79 81

3Dnovator+	62.71 7	72.29 121	70.50 131	77.65 86	83.40 104	51.29 164	87.32 72	86.40 83	59.01 197	58.49 236	88.32 127	32.40 273	91.27 73	57.04 209	82.15 69	90.38 89

nrg030		72.27 123	71.56 116	74.42 169	75.93 263	50.60 172	86.97 83	83.21 163	62.75 125	67.15 119	84.38 182	50.07 63	86.66 222	71.19 98	62.37 252	85.99 191

UWE-MVS		72.17 124	72.15 107	72.21 224	82.26 138	44.29 300	86.83 88	89.58 21	65.58 77	65.82 135	85.06 176	45.02 114	84.35 269	54.07 229	75.18 137	87.99 152

iter_conf05_11		72.16 125	70.47 132	77.23 98	85.57 60	49.68 197	74.47 301	80.89 204	49.28 307	65.24 141	85.42 172	55.08 29	93.40 30	55.42 221	82.84 60	88.60 137

VPNet		72.07 126	71.42 120	74.04 180	78.64 219	47.17 266	89.91 32	87.97 55	72.56 10	64.66 149	85.04 177	41.83 163	88.33 167	61.17 164	60.97 258	86.62 180

DP-MVS Recon		71.99 127	70.31 137	77.01 103	90.65 8	53.44 111	89.37 38	82.97 169	56.33 250	63.56 171	89.47 102	34.02 258	92.15 57	54.05 230	72.41 163	85.43 204

test_fmvsmconf0.01_n		71.97 128	70.95 126	75.04 157	66.21 350	47.87 253	80.35 256	70.08 342	65.85 75	72.69 71	91.68 56	39.99 184	87.67 192	82.03 27	69.66 188	89.58 111

SDMVSNet		71.89 129	70.62 130	75.70 134	81.70 149	51.61 154	73.89 304	88.72 40	66.58 58	61.64 192	82.38 220	37.63 207	89.48 124	77.44 59	65.60 219	86.01 189

QAPM		71.88 130	69.33 153	79.52 40	82.20 139	54.30 92	86.30 96	88.77 38	56.61 246	59.72 208	87.48 144	33.90 260	95.36 13	47.48 275	81.49 74	88.90 127

ECVR-MVS		71.81 131	71.00 125	74.26 175	80.12 191	43.49 308	84.69 146	82.16 178	64.02 98	64.64 150	87.43 146	35.04 248	89.21 132	61.24 163	79.66 96	90.08 100

PAPM_NR		71.80 132	69.98 144	77.26 96	81.54 159	53.34 116	78.60 276	85.25 110	53.46 277	60.53 202	88.66 118	45.69 105	89.24 129	56.49 213	79.62 98	89.19 121

mPP-MVS		71.79 133	70.38 135	76.04 128	82.65 131	52.06 143	84.45 153	81.78 189	55.59 257	62.05 189	89.68 99	33.48 264	88.28 171	65.45 136	78.24 108	87.77 156

xiu_mvs_v1_base_debu		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 119	79.37 234	55.83 254	72.54 72	90.19 87	22.38 343	86.66 222	73.28 90	76.39 122	86.85 174

xiu_mvs_v1_base		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 119	79.37 234	55.83 254	72.54 72	90.19 87	22.38 343	86.66 222	73.28 90	76.39 122	86.85 174

xiu_mvs_v1_base_debi		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 119	79.37 234	55.83 254	72.54 72	90.19 87	22.38 343	86.66 222	73.28 90	76.39 122	86.85 174

hse-mvs2		71.44 137	70.68 128	73.73 192	76.34 252	47.44 260	79.45 269	79.47 233	68.08 38	71.97 80	86.01 166	42.50 150	86.93 215	78.82 45	53.46 327	86.83 177

test_fmvsmvis_n_1920		71.29 138	70.38 135	74.00 182	71.04 323	48.79 222	79.19 272	64.62 360	62.75 125	66.73 120	91.99 48	40.94 171	88.35 165	83.00 20	73.18 155	84.85 212

EPP-MVSNet		71.14 139	70.07 143	74.33 172	79.18 205	46.52 272	83.81 172	86.49 80	56.32 251	57.95 242	84.90 180	54.23 35	89.14 134	58.14 195	69.65 189	87.33 165

VPA-MVSNet		71.12 140	70.66 129	72.49 217	78.75 214	44.43 298	87.64 64	90.02 17	63.97 101	65.02 145	81.58 233	42.14 156	87.42 201	63.42 147	63.38 240	85.63 201

1314		71.11 141	69.41 150	76.22 120	79.32 201	50.49 175	80.23 259	85.14 116	59.44 182	58.93 225	88.89 114	33.83 262	89.60 123	61.49 161	77.42 113	88.57 139

test1111		71.06 142	70.42 134	72.97 206	79.48 198	41.49 330	84.82 144	82.74 173	64.20 95	62.98 177	87.43 146	35.20 245	87.92 180	58.54 187	78.42 106	89.49 114

tpmrst		71.04 143	69.77 146	74.86 162	83.19 110	55.86 50	75.64 289	78.73 251	67.88 42	64.99 147	73.73 311	49.96 67	79.56 315	65.92 128	67.85 201	89.14 123

MVP-Stereo		70.97 144	70.44 133	72.59 214	76.03 261	51.36 161	85.02 135	86.99 72	60.31 169	56.53 267	78.92 257	40.11 182	90.00 110	60.00 179	90.01 6	76.41 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 145	69.91 145	74.12 178	77.95 229	49.57 199	85.76 106	82.59 174	63.60 109	62.15 187	83.28 201	36.04 238	88.30 169	65.46 134	72.34 164	84.49 214

SR-MVS		70.92 146	69.73 147	74.50 166	83.38 105	50.48 176	84.27 158	79.35 238	48.96 310	66.57 126	90.45 79	33.65 263	87.11 208	66.42 124	74.56 147	85.91 194

tpm2		70.82 147	68.44 162	77.98 79	80.78 178	56.11 44	74.21 303	81.28 198	60.24 170	68.04 113	75.27 300	52.26 47	88.50 160	55.82 220	68.03 198	89.33 116

ACMMP		70.81 148	69.29 154	75.39 145	81.52 161	51.92 148	83.43 183	83.03 167	56.67 245	58.80 230	88.91 113	31.92 280	88.58 155	65.89 130	73.39 154	85.67 198
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
OPM-MVS		70.75 149	69.58 148	74.26 175	75.55 268	51.34 162	86.05 101	83.29 162	61.94 140	62.95 178	85.77 167	34.15 257	88.44 161	65.44 137	71.07 175	82.99 246
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ab-mvs		70.65 150	69.11 156	75.29 151	80.87 175	46.23 280	73.48 308	85.24 111	59.99 172	66.65 122	80.94 238	43.13 146	88.69 151	63.58 146	68.07 197	90.95 78

Vis-MVSNet		70.61 151	69.34 152	74.42 169	80.95 174	48.49 231	86.03 102	77.51 272	58.74 203	65.55 139	87.78 139	34.37 255	85.95 246	52.53 245	80.61 80	88.80 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 152	70.13 142	71.58 243	81.59 156	39.02 341	80.78 251	84.71 128	59.34 185	66.61 124	88.09 131	37.17 219	85.52 250	61.82 160	71.02 176	90.20 96

CDS-MVSNet		70.48 153	69.43 149	73.64 194	77.56 236	48.83 221	83.51 180	77.45 273	63.27 117	62.33 184	85.54 171	43.85 129	83.29 281	57.38 208	74.00 149	88.79 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 154	68.56 160	76.20 122	79.78 195	51.52 158	83.49 182	88.58 47	57.62 225	58.60 232	82.79 206	51.03 55	91.48 69	52.84 239	62.36 253	85.59 202

XXY-MVS		70.18 155	69.28 155	72.89 209	77.64 233	42.88 317	85.06 132	87.50 67	62.58 129	62.66 182	82.34 223	43.64 137	89.83 115	58.42 190	63.70 234	85.96 193

Anonymous202405211		70.11 156	67.88 172	76.79 113	87.20 43	47.24 265	89.49 36	77.38 275	54.88 267	66.14 129	86.84 154	20.93 352	91.54 68	56.45 216	71.62 170	91.59 56

PCF-MVS	61.03 10	70.10 157	68.40 163	75.22 155	77.15 245	51.99 145	79.30 271	82.12 180	56.47 249	61.88 190	86.48 162	43.98 128	87.24 205	55.37 222	72.79 161	86.43 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 158	68.01 169	76.27 118	84.21 87	51.22 166	87.29 75	79.33 240	58.96 199	63.63 169	86.77 155	33.29 266	90.30 104	44.63 293	73.96 150	87.30 167

1112_ss		70.05 159	69.37 151	72.10 226	80.77 179	42.78 318	85.12 131	76.75 285	59.69 177	61.19 196	92.12 42	47.48 83	83.84 272	53.04 237	68.21 196	89.66 109

BH-w/o		70.02 160	68.51 161	74.56 165	82.77 126	50.39 179	86.60 92	78.14 262	59.77 175	59.65 209	85.57 170	39.27 190	87.30 204	49.86 258	74.94 145	85.99 191

FIs		70.00 161	70.24 141	69.30 275	77.93 231	38.55 344	83.99 167	87.72 62	66.86 56	57.66 249	84.17 185	52.28 46	85.31 254	52.72 244	68.80 193	84.02 222

OpenMVS	61.00 11	69.99 162	67.55 181	77.30 93	78.37 225	54.07 99	84.36 155	85.76 94	57.22 233	56.71 264	87.67 142	30.79 288	92.83 37	43.04 300	84.06 56	85.01 208

GeoE		69.96 163	67.88 172	76.22 120	81.11 168	51.71 153	84.15 161	76.74 286	59.83 174	60.91 197	84.38 182	41.56 166	88.10 176	51.67 248	70.57 181	88.84 130

HyFIR lowres test		69.94 164	67.58 179	77.04 101	77.11 246	57.29 22	81.49 239	79.11 243	58.27 209	58.86 228	80.41 242	42.33 152	86.96 213	61.91 158	68.68 195	86.87 172

114514_t		69.87 165	67.88 172	75.85 132	88.38 30	52.35 140	86.94 84	83.68 152	53.70 275	55.68 274	85.60 169	30.07 293	91.20 77	55.84 219	71.02 176	83.99 224

miper_enhance_ethall		69.77 166	68.90 158	72.38 220	78.93 211	49.91 192	83.29 190	78.85 245	64.90 88	59.37 216	79.46 250	52.77 42	85.16 259	63.78 144	58.72 273	82.08 255

Anonymous20240529		69.71 167	67.28 186	77.00 104	83.78 95	50.36 182	88.87 46	85.10 117	47.22 319	64.03 162	83.37 199	27.93 304	92.10 58	57.78 203	67.44 203	88.53 141

TR-MVS		69.71 167	67.85 175	75.27 153	82.94 120	48.48 232	87.40 71	80.86 205	57.15 235	64.61 152	87.08 151	32.67 271	89.64 122	46.38 284	71.55 172	87.68 159

EI-MVSNet		69.70 169	68.70 159	72.68 212	75.00 275	48.90 219	79.54 266	87.16 69	61.05 155	63.88 166	83.74 191	45.87 101	90.44 97	57.42 207	64.68 226	78.70 300

test-LLR		69.65 170	69.01 157	71.60 241	78.67 216	48.17 242	85.13 128	79.72 226	59.18 192	63.13 175	82.58 214	36.91 224	80.24 306	60.56 170	75.17 138	86.39 185

APD-MVS_3200maxsize		69.62 171	68.23 167	73.80 189	81.58 157	48.22 241	81.91 222	79.50 232	48.21 314	64.24 160	89.75 98	31.91 281	87.55 198	63.08 149	73.85 152	85.64 200

v2v482		69.55 172	67.64 178	75.26 154	72.32 310	53.83 100	84.93 140	81.94 183	65.37 83	60.80 199	79.25 253	41.62 164	88.98 142	63.03 150	59.51 266	82.98 247

TAMVS		69.51 173	68.16 168	73.56 197	76.30 255	48.71 225	82.57 207	77.17 278	62.10 136	61.32 195	84.23 184	41.90 161	83.46 279	54.80 226	73.09 158	88.50 142

WB-MVSnew		69.36 174	68.24 166	72.72 211	79.26 203	49.40 206	85.72 111	88.85 35	61.33 149	64.59 153	82.38 220	34.57 253	87.53 199	46.82 281	70.63 179	81.22 276

PVSNet	62.49 8	69.27 175	67.81 176	73.64 194	84.41 81	51.85 149	84.63 150	77.80 266	66.42 62	59.80 207	84.95 179	22.14 347	80.44 304	55.03 223	75.11 141	88.62 136

MVS_111021_LR		69.07 176	67.91 170	72.54 215	77.27 240	49.56 201	79.77 264	73.96 312	59.33 187	60.73 200	87.82 138	30.19 292	81.53 289	69.94 104	72.19 166	86.53 181

GA-MVS		69.04 177	66.70 195	76.06 127	75.11 271	52.36 139	83.12 195	80.23 215	63.32 116	60.65 201	79.22 254	30.98 287	88.37 163	61.25 162	66.41 212	87.46 163

cascas		69.01 178	66.13 207	77.66 85	79.36 199	55.41 57	86.99 82	83.75 150	56.69 244	58.92 226	81.35 235	24.31 332	92.10 58	53.23 234	70.61 180	85.46 203

FA-MVS(test-final)		69.00 179	66.60 198	76.19 123	83.48 100	47.96 252	74.73 297	82.07 181	57.27 232	62.18 186	78.47 261	36.09 236	92.89 35	53.76 233	71.32 174	87.73 157

cl22		68.85 180	67.69 177	72.35 221	78.07 228	49.98 191	82.45 212	78.48 257	62.50 132	58.46 237	77.95 263	49.99 65	85.17 258	62.55 152	58.72 273	81.90 258

FMVSNet3		68.84 181	67.40 184	73.19 202	85.05 70	48.53 229	85.71 112	85.36 102	60.90 161	57.58 251	79.15 255	42.16 155	86.77 218	47.25 277	63.40 237	84.27 218

UniMVSNet_NR-MVSNet		68.82 182	68.29 165	70.40 261	75.71 266	42.59 320	84.23 159	86.78 75	66.31 64	58.51 233	82.45 217	51.57 50	84.64 267	53.11 235	55.96 305	83.96 228

v1144		68.81 183	66.82 191	74.80 163	72.34 309	53.46 108	84.68 147	81.77 190	64.25 94	60.28 203	77.91 264	40.23 179	88.95 143	60.37 175	59.52 265	81.97 256

IS-MVSNet		68.80 184	67.55 181	72.54 215	78.50 222	43.43 310	81.03 244	79.35 238	59.12 195	57.27 259	86.71 156	46.05 99	87.70 191	44.32 295	75.60 133	86.49 182

PS-MVSNAJss		68.78 185	67.17 188	73.62 196	73.01 300	48.33 239	84.95 139	84.81 124	59.30 188	58.91 227	79.84 247	37.77 202	88.86 147	62.83 151	63.12 246	83.67 234

thres200		68.71 186	67.27 187	73.02 204	84.73 75	46.76 269	85.03 134	87.73 61	62.34 134	59.87 205	83.45 197	43.15 144	88.32 168	31.25 348	67.91 200	83.98 226

UGNet		68.71 186	67.11 189	73.50 198	80.55 185	47.61 257	84.08 163	78.51 256	59.45 181	65.68 138	82.73 210	23.78 334	85.08 261	52.80 240	76.40 121	87.80 155
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
miper_ehance_all_eth		68.70 188	67.58 179	72.08 227	76.91 248	49.48 205	82.47 211	78.45 258	62.68 127	58.28 241	77.88 265	50.90 56	85.01 262	61.91 158	58.72 273	81.75 260

test_vis1_n_1920		68.59 189	68.31 164	69.44 274	69.16 336	41.51 329	84.63 150	68.58 351	58.80 201	73.26 64	88.37 123	25.30 323	80.60 301	79.10 42	67.55 202	86.23 187

EPMVS		68.45 190	65.44 226	77.47 90	84.91 73	56.17 43	71.89 324	81.91 186	61.72 143	60.85 198	72.49 325	36.21 234	87.06 210	47.32 276	71.62 170	89.17 122

test-mter		68.36 191	67.29 185	71.60 241	78.67 216	48.17 242	85.13 128	79.72 226	53.38 278	63.13 175	82.58 214	27.23 310	80.24 306	60.56 170	75.17 138	86.39 185

tpm		68.36 191	67.48 183	70.97 253	79.93 194	51.34 162	76.58 287	78.75 250	67.73 45	63.54 172	74.86 302	48.33 74	72.36 359	53.93 231	63.71 233	89.21 120

tttt0517		68.33 193	66.29 203	74.46 167	78.08 227	49.06 211	80.88 249	89.08 27	54.40 272	54.75 282	80.77 240	51.31 52	90.33 101	49.35 262	58.01 285	83.99 224

BH-untuned		68.28 194	66.40 200	73.91 184	81.62 154	50.01 190	85.56 116	77.39 274	57.63 224	57.47 256	83.69 193	36.36 233	87.08 209	44.81 291	73.08 159	84.65 213

SR-MVS-dyc-post		68.27 195	66.87 190	72.48 218	80.96 171	48.14 244	81.54 235	76.98 281	46.42 326	62.75 180	89.42 103	31.17 286	86.09 240	60.52 172	72.06 167	83.19 242

v148		68.24 196	66.35 201	73.88 185	71.76 313	51.47 159	84.23 159	81.90 187	63.69 107	58.94 224	76.44 287	43.72 135	87.78 188	60.63 168	55.86 307	82.39 253

AUN-MVS		68.20 197	66.35 201	73.76 190	76.37 251	47.45 259	79.52 268	79.52 231	60.98 157	62.34 183	86.02 164	36.59 232	86.94 214	62.32 154	53.47 326	86.89 171

c3_l		67.97 198	66.66 196	71.91 238	76.20 257	49.31 208	82.13 218	78.00 264	61.99 138	57.64 250	76.94 279	49.41 70	84.93 263	60.62 169	57.01 295	81.49 264

v1192		67.96 199	65.74 218	74.63 164	71.79 312	53.43 113	84.06 165	80.99 203	63.19 119	59.56 212	77.46 271	37.50 213	88.65 152	58.20 194	58.93 272	81.79 259

v144192		67.86 200	65.76 217	74.16 177	71.68 314	53.09 124	84.14 162	80.83 206	62.85 124	59.21 221	77.28 274	39.30 189	88.00 179	58.67 186	57.88 289	81.40 269

HPM-MVS_fast		67.86 200	66.28 204	72.61 213	80.67 182	48.34 237	81.18 242	75.95 295	50.81 297	59.55 213	88.05 134	27.86 305	85.98 243	58.83 184	73.58 153	83.51 235

AdaColmap		67.86 200	65.48 223	75.00 159	88.15 37	54.99 73	86.10 100	76.63 289	49.30 306	57.80 245	86.65 158	29.39 297	88.94 145	45.10 290	70.21 184	81.06 277

sd_testset		67.79 203	65.95 212	73.32 199	81.70 149	46.33 277	68.99 336	80.30 214	66.58 58	61.64 192	82.38 220	30.45 290	87.63 194	55.86 218	65.60 219	86.01 189

bld_raw_dy_0_64		67.71 204	65.31 229	74.91 161	74.46 282	55.35 60	62.16 360	83.72 151	39.92 354	52.13 304	79.59 249	53.58 38	92.03 61	60.05 178	82.18 67	77.37 319

UniMVSNet (Re)		67.71 204	66.80 192	70.45 259	74.44 283	42.93 316	82.42 213	84.90 121	63.69 107	59.63 210	80.99 237	47.18 85	85.23 257	51.17 252	56.75 296	83.19 242

V42		67.66 206	65.60 222	73.86 186	70.69 327	53.63 105	81.50 237	78.61 254	63.85 103	59.49 215	77.49 270	37.98 199	87.65 193	62.33 153	58.43 276	80.29 287

dmvs_re		67.61 207	66.00 210	72.42 219	81.86 144	43.45 309	64.67 350	80.00 218	69.56 30	60.07 204	85.00 178	34.71 251	87.63 194	51.48 249	66.68 207	86.17 188

WR-MVS		67.58 208	66.76 193	70.04 268	75.92 264	45.06 294	86.23 97	85.28 108	64.31 93	58.50 235	81.00 236	44.80 123	82.00 288	49.21 264	55.57 310	83.06 245

tfpn200view9		67.57 209	66.13 207	71.89 239	84.05 89	45.07 291	83.40 185	87.71 63	60.79 162	57.79 246	82.76 207	43.53 138	87.80 185	28.80 355	66.36 213	82.78 251

FMVSNet2		67.57 209	65.79 216	72.90 207	82.71 128	47.97 250	85.15 127	84.93 120	58.55 206	56.71 264	78.26 262	36.72 229	86.67 221	46.15 286	62.94 248	84.07 221

FC-MVSNet-test		67.49 211	67.91 170	66.21 306	76.06 259	33.06 364	80.82 250	87.18 68	64.44 92	54.81 280	82.87 204	50.40 62	82.60 283	48.05 272	66.55 211	82.98 247

v1921920		67.45 212	65.23 231	74.10 179	71.51 317	52.90 130	83.75 174	80.44 211	62.48 133	59.12 222	77.13 275	36.98 222	87.90 181	57.53 205	58.14 283	81.49 264

cl____		67.43 213	65.93 213	71.95 235	76.33 253	48.02 248	82.58 206	79.12 242	61.30 151	56.72 263	76.92 280	46.12 97	86.44 229	57.98 197	56.31 299	81.38 271

DIV-MVS_self_test		67.43 213	65.93 213	71.94 236	76.33 253	48.01 249	82.57 207	79.11 243	61.31 150	56.73 262	76.92 280	46.09 98	86.43 230	57.98 197	56.31 299	81.39 270

gg-mvs-nofinetune		67.43 213	64.53 239	76.13 125	85.95 51	47.79 256	64.38 351	88.28 51	39.34 356	66.62 123	41.27 391	58.69 13	89.00 139	49.64 260	86.62 30	91.59 56

thres400		67.40 216	66.13 207	71.19 249	84.05 89	45.07 291	83.40 185	87.71 63	60.79 162	57.79 246	82.76 207	43.53 138	87.80 185	28.80 355	66.36 213	80.71 282

UA-Net		67.32 217	66.23 205	70.59 257	78.85 212	41.23 333	73.60 306	75.45 299	61.54 146	66.61 124	84.53 181	38.73 195	86.57 227	42.48 305	74.24 148	83.98 226

v8		67.25 218	64.99 234	74.04 180	72.89 303	53.31 118	82.37 214	80.11 217	61.54 146	54.29 287	76.02 296	42.89 148	88.41 162	58.43 188	56.36 297	80.39 286

NR-MVSNet		67.25 218	65.99 211	71.04 252	73.27 297	43.91 304	85.32 122	84.75 127	66.05 72	53.65 294	82.11 226	45.05 113	85.97 245	47.55 274	56.18 302	83.24 240

Test_1112_low_res		67.18 220	66.23 205	70.02 269	78.75 214	41.02 334	83.43 183	73.69 314	57.29 231	58.45 238	82.39 219	45.30 110	80.88 295	50.50 254	66.26 217	88.16 145

CPTT-MVS		67.15 221	65.84 215	71.07 251	80.96 171	50.32 184	81.94 221	74.10 308	46.18 329	57.91 243	87.64 143	29.57 295	81.31 291	64.10 143	70.18 185	81.56 263

test_cas_vis1_n_1920		67.10 222	66.60 198	68.59 287	65.17 358	43.23 313	83.23 192	69.84 344	55.34 261	70.67 98	87.71 141	24.70 330	76.66 339	78.57 49	64.20 228	85.89 195

GBi-Net		67.09 223	65.47 224	71.96 232	82.71 128	46.36 274	83.52 176	83.31 159	58.55 206	57.58 251	76.23 291	36.72 229	86.20 232	47.25 277	63.40 237	83.32 237

test1		67.09 223	65.47 224	71.96 232	82.71 128	46.36 274	83.52 176	83.31 159	58.55 206	57.58 251	76.23 291	36.72 229	86.20 232	47.25 277	63.40 237	83.32 237

PatchmatchNet		67.07 225	63.63 245	77.40 91	83.10 111	58.03 11	72.11 322	77.77 267	58.85 200	59.37 216	70.83 338	37.84 201	84.93 263	42.96 301	69.83 187	89.26 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 226	64.68 237	73.93 183	71.38 320	52.66 133	83.39 187	79.98 219	61.97 139	58.44 239	77.11 276	35.25 244	87.81 183	56.46 215	58.15 281	81.33 272

eth_miper_zixun_eth		66.98 227	65.28 230	72.06 228	75.61 267	50.40 178	81.00 245	76.97 284	62.00 137	56.99 261	76.97 278	44.84 120	85.58 249	58.75 185	54.42 318	80.21 288

TranMVSNet+NR-MVSNet		66.94 228	65.61 221	70.93 254	73.45 293	43.38 311	83.02 199	84.25 139	65.31 85	58.33 240	81.90 229	39.92 186	85.52 250	49.43 261	54.89 314	83.89 230

mvsmamba		66.93 229	64.88 236	73.09 203	75.06 273	47.26 263	83.36 189	69.21 348	62.64 128	55.68 274	81.43 234	29.72 294	89.20 133	63.35 148	63.50 236	82.79 250

thres100view900		66.87 230	65.42 227	71.24 247	83.29 107	43.15 314	81.67 230	87.78 58	59.04 196	55.92 272	82.18 225	43.73 133	87.80 185	28.80 355	66.36 213	82.78 251

DU-MVS		66.84 231	65.74 218	70.16 264	73.27 297	42.59 320	81.50 237	82.92 171	63.53 111	58.51 233	82.11 226	40.75 173	84.64 267	53.11 235	55.96 305	83.24 240

IterMVS-LS		66.63 232	65.36 228	70.42 260	75.10 272	48.90 219	81.45 240	76.69 288	61.05 155	55.71 273	77.10 277	45.86 102	83.65 276	57.44 206	57.88 289	78.70 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 233	64.20 242	73.83 188	72.59 306	53.37 114	81.88 223	79.91 223	61.11 153	54.09 289	75.60 298	40.06 183	88.26 172	56.47 214	56.10 303	79.86 292

Fast-Effi-MVS+-dtu		66.53 234	64.10 243	73.84 187	72.41 308	52.30 142	84.73 145	75.66 296	59.51 180	56.34 269	79.11 256	28.11 302	85.85 248	57.74 204	63.29 241	83.35 236

thres600view7		66.46 235	65.12 232	70.47 258	83.41 101	43.80 306	82.15 216	87.78 58	59.37 184	56.02 271	82.21 224	43.73 133	86.90 216	26.51 367	64.94 222	80.71 282

LPG-MVS_test		66.44 236	64.58 238	72.02 229	74.42 284	48.60 226	83.07 197	80.64 208	54.69 269	53.75 292	83.83 189	25.73 321	86.98 211	60.33 176	64.71 223	80.48 284

tpm cat1		66.28 237	62.78 247	76.77 114	81.40 163	57.14 24	70.03 331	77.19 277	53.00 281	58.76 231	70.73 341	46.17 96	86.73 220	43.27 299	64.46 227	86.44 183

EPNet_dtu		66.25 238	66.71 194	64.87 316	78.66 218	34.12 359	82.80 202	75.51 297	61.75 142	64.47 158	86.90 153	37.06 220	72.46 358	43.65 298	69.63 190	88.02 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 239	64.96 235	70.08 266	75.17 270	49.64 198	82.01 219	74.48 306	62.15 135	57.83 244	76.08 295	30.59 289	83.79 273	65.40 138	60.93 259	76.81 323

ACMP	61.11 9	66.24 239	64.33 240	72.00 231	74.89 277	49.12 210	83.18 194	79.83 224	55.41 260	52.29 301	82.68 211	25.83 319	86.10 238	60.89 165	63.94 232	80.78 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 241	63.67 244	73.31 200	83.07 114	48.75 223	86.01 103	84.67 130	45.27 333	56.54 266	76.67 285	28.06 303	88.95 143	52.78 241	59.95 261	82.23 254

OMC-MVS		65.97 242	65.06 233	68.71 284	72.97 301	42.58 322	78.61 275	75.35 300	54.72 268	59.31 218	86.25 163	33.30 265	77.88 328	57.99 196	67.05 205	85.66 199

X-MVStestdata		65.85 243	62.20 251	76.81 110	83.41 101	52.48 135	84.88 141	83.20 164	58.03 212	63.91 164	4.82 410	35.50 242	89.78 116	65.50 131	80.50 82	88.16 145

Vis-MVSNet (Re-imp)		65.52 244	65.63 220	65.17 314	77.49 237	30.54 371	75.49 293	77.73 268	59.34 185	52.26 303	86.69 157	49.38 71	80.53 303	37.07 319	75.28 136	84.42 216

Baseline_NR-MVSNet		65.49 245	64.27 241	69.13 276	74.37 286	41.65 327	83.39 187	78.85 245	59.56 179	59.62 211	76.88 282	40.75 173	87.44 200	49.99 256	55.05 312	78.28 309

FMVSNet1		64.57 246	62.11 252	71.96 232	77.32 239	46.36 274	83.52 176	83.31 159	52.43 286	54.42 285	76.23 291	27.80 306	86.20 232	42.59 304	61.34 257	83.32 237

dp		64.41 247	61.58 255	72.90 207	82.40 134	54.09 98	72.53 314	76.59 290	60.39 168	55.68 274	70.39 342	35.18 246	76.90 337	39.34 311	61.71 255	87.73 157

ACMM	58.35 12	64.35 248	62.01 253	71.38 245	74.21 287	48.51 230	82.25 215	79.66 228	47.61 317	54.54 284	80.11 243	25.26 324	86.00 242	51.26 250	63.16 244	79.64 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 249	60.43 269	75.30 150	80.85 176	49.86 194	68.28 340	78.37 259	50.26 302	59.31 218	73.79 310	26.19 317	91.92 62	40.19 308	66.67 208	84.12 219

pm-mvs1		64.12 250	62.56 248	68.78 282	71.68 314	38.87 342	82.89 201	81.57 191	55.54 259	53.89 291	77.82 266	37.73 205	86.74 219	48.46 270	53.49 325	80.72 281

miper_lstm_enhance		63.91 251	62.30 250	68.75 283	75.06 273	46.78 268	69.02 335	81.14 199	59.68 178	52.76 298	72.39 328	40.71 175	77.99 326	56.81 211	53.09 328	81.48 266

SCA		63.84 252	60.01 273	75.32 147	78.58 220	57.92 12	61.61 363	77.53 271	56.71 243	57.75 248	70.77 339	31.97 278	79.91 312	48.80 266	56.36 297	88.13 148

test_djsdf		63.84 252	61.56 256	70.70 256	68.78 338	44.69 295	81.63 231	81.44 194	50.28 299	52.27 302	76.26 290	26.72 313	86.11 236	60.83 166	55.84 308	81.29 275

IterMVS		63.77 254	61.67 254	70.08 266	72.68 305	51.24 165	80.44 254	75.51 297	60.51 167	51.41 307	73.70 314	32.08 277	78.91 316	54.30 228	54.35 319	80.08 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 255	63.56 246	63.40 323	81.73 147	34.28 357	80.97 246	81.02 201	60.93 159	55.06 278	82.64 212	48.00 80	80.81 297	23.42 377	58.32 277	75.10 340

D2MVS		63.49 256	61.39 258	69.77 270	69.29 335	48.93 218	78.89 274	77.71 269	60.64 166	49.70 317	72.10 333	27.08 311	83.48 278	54.48 227	62.65 249	76.90 322

tt0805		63.39 257	61.31 260	69.64 271	69.36 334	38.87 342	78.00 278	85.48 96	48.82 311	55.66 277	81.66 231	24.38 331	86.37 231	49.04 265	59.36 269	83.68 233

pmmvs4		63.34 258	61.07 263	70.16 264	70.14 329	50.53 174	79.97 263	71.41 334	55.08 263	54.12 288	78.58 259	32.79 270	82.09 287	50.33 255	57.22 294	77.86 313

jajsoiax		63.21 259	60.84 264	70.32 262	68.33 343	44.45 297	81.23 241	81.05 200	53.37 279	50.96 312	77.81 267	17.49 366	85.49 252	59.31 180	58.05 284	81.02 278

MIMVSNet		63.12 260	60.29 270	71.61 240	75.92 264	46.65 270	65.15 347	81.94 183	59.14 194	54.65 283	69.47 345	25.74 320	80.63 300	41.03 307	69.56 191	87.55 161

CL-MVSNet_self_test		62.98 261	61.14 262	68.50 289	65.86 353	42.96 315	84.37 154	82.98 168	60.98 157	53.95 290	72.70 324	40.43 177	83.71 275	41.10 306	47.93 342	78.83 299

mvs_tets		62.96 262	60.55 266	70.19 263	68.22 346	44.24 302	80.90 248	80.74 207	52.99 282	50.82 314	77.56 268	16.74 369	85.44 253	59.04 183	57.94 286	80.89 279

TransMVSNet (Re)		62.82 263	60.76 265	69.02 277	73.98 290	41.61 328	86.36 94	79.30 241	56.90 237	52.53 299	76.44 287	41.85 162	87.60 197	38.83 312	40.61 366	77.86 313

pmmvs5		62.80 264	61.18 261	67.66 293	69.53 333	42.37 325	82.65 205	75.19 301	54.30 273	52.03 305	78.51 260	31.64 283	80.67 299	48.60 268	58.15 281	79.95 291

test0.0.03 1		62.54 265	62.44 249	62.86 327	72.28 311	29.51 379	82.93 200	78.78 248	59.18 192	53.07 297	82.41 218	36.91 224	77.39 332	37.45 315	58.96 271	81.66 262

UniMVSNet_ETH3D		62.51 266	60.49 267	68.57 288	68.30 344	40.88 336	73.89 304	79.93 222	51.81 292	54.77 281	79.61 248	24.80 328	81.10 292	49.93 257	61.35 256	83.73 232

v7n		62.50 267	59.27 278	72.20 225	67.25 349	49.83 195	77.87 280	80.12 216	52.50 285	48.80 322	73.07 319	32.10 276	87.90 181	46.83 280	54.92 313	78.86 298

CR-MVSNet		62.47 268	59.04 280	72.77 210	73.97 291	56.57 34	60.52 366	71.72 329	60.04 171	57.49 254	65.86 356	38.94 192	80.31 305	42.86 302	59.93 262	81.42 267

tpmvs		62.45 269	59.42 276	71.53 244	83.93 91	54.32 91	70.03 331	77.61 270	51.91 289	53.48 295	68.29 350	37.91 200	86.66 222	33.36 338	58.27 279	73.62 350

EG-PatchMatch MVS		62.40 270	59.59 274	70.81 255	73.29 295	49.05 212	85.81 104	84.78 125	51.85 291	44.19 341	73.48 317	15.52 374	89.85 114	40.16 309	67.24 204	73.54 351

XVG-OURS-SEG-HR		62.02 271	59.54 275	69.46 273	65.30 356	45.88 282	65.06 348	73.57 316	46.45 325	57.42 257	83.35 200	26.95 312	78.09 322	53.77 232	64.03 230	84.42 216

XVG-OURS		61.88 272	59.34 277	69.49 272	65.37 355	46.27 278	64.80 349	73.49 317	47.04 321	57.41 258	82.85 205	25.15 325	78.18 320	53.00 238	64.98 221	84.01 223

TAPA-MVS	56.12 14	61.82 273	60.18 272	66.71 302	78.48 223	37.97 348	75.19 295	76.41 292	46.82 322	57.04 260	86.52 160	27.67 308	77.03 334	26.50 368	67.02 206	85.14 206
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 274	61.35 259	62.00 330	81.73 147	30.09 374	80.97 246	81.02 201	60.93 159	55.06 278	82.64 212	35.09 247	80.81 297	16.40 393	58.32 277	75.10 340

tfpnnormal		61.47 275	59.09 279	68.62 286	76.29 256	41.69 326	81.14 243	85.16 114	54.48 271	51.32 308	73.63 315	32.32 274	86.89 217	21.78 381	55.71 309	77.29 320

PVSNet_0	57.04 13	61.19 276	57.24 289	73.02 204	77.45 238	50.31 185	79.43 270	77.36 276	63.96 102	47.51 331	72.45 327	25.03 326	83.78 274	52.76 243	19.22 399	84.96 209

PLC	52.38 18	60.89 277	58.97 281	66.68 304	81.77 146	45.70 286	78.96 273	74.04 311	43.66 344	47.63 328	83.19 203	23.52 337	77.78 331	37.47 314	60.46 260	76.55 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 278	60.44 268	62.07 328	75.00 275	32.73 366	79.54 266	73.49 317	36.98 364	56.28 270	83.74 191	29.28 298	69.53 367	46.48 283	63.23 242	83.94 229

CNLPA		60.59 279	58.44 283	67.05 299	79.21 204	47.26 263	79.75 265	64.34 362	42.46 350	51.90 306	83.94 187	27.79 307	75.41 344	37.12 317	59.49 267	78.47 304

anonymousdsp		60.46 280	57.65 286	68.88 278	63.63 367	45.09 290	72.93 312	78.63 253	46.52 324	51.12 309	72.80 323	21.46 350	83.07 282	57.79 202	53.97 320	78.47 304

testing3		59.97 281	60.19 271	59.32 342	77.60 234	30.01 376	81.75 228	81.79 188	53.54 276	50.34 315	79.94 244	48.99 73	76.91 335	17.19 391	50.59 335	71.03 365

ACMH	53.70 16	59.78 282	55.94 300	71.28 246	76.59 250	48.35 236	80.15 261	76.11 293	49.74 304	41.91 352	73.45 318	16.50 371	90.31 102	31.42 346	57.63 292	75.17 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 283	57.15 290	67.09 297	66.01 351	36.86 352	80.50 253	78.64 252	45.05 335	49.05 320	73.94 309	27.28 309	86.10 238	43.96 297	49.94 337	78.31 308

MSDG		59.44 284	55.14 304	72.32 223	74.69 278	50.71 169	74.39 302	73.58 315	44.44 339	43.40 346	77.52 269	19.45 356	90.87 88	31.31 347	57.49 293	75.38 336

RPMNet		59.29 285	54.25 308	74.42 169	73.97 291	56.57 34	60.52 366	76.98 281	35.72 368	57.49 254	58.87 376	37.73 205	85.26 256	27.01 366	59.93 262	81.42 267

DP-MVS		59.24 286	56.12 298	68.63 285	88.24 35	50.35 183	82.51 210	64.43 361	41.10 352	46.70 335	78.77 258	24.75 329	88.57 158	22.26 379	56.29 301	66.96 371

OpenMVS_ROB	53.19 17	59.20 287	56.00 299	68.83 280	71.13 322	44.30 299	83.64 175	75.02 302	46.42 326	46.48 337	73.03 320	18.69 360	88.14 173	27.74 363	61.80 254	74.05 347

IterMVS-SCA-FT		59.12 288	58.81 282	60.08 340	70.68 328	45.07 291	80.42 255	74.25 307	43.54 345	50.02 316	73.73 311	31.97 278	56.74 384	51.06 253	53.60 324	78.42 306

our_test_3		59.11 289	55.08 305	71.18 250	71.42 318	53.29 119	81.96 220	74.52 305	48.32 312	42.08 350	69.28 347	28.14 301	82.15 285	34.35 335	45.68 356	78.11 312

Anonymous20231206		59.08 290	57.59 287	63.55 321	68.77 339	32.14 369	80.26 258	79.78 225	50.00 303	49.39 318	72.39 328	26.64 314	78.36 319	33.12 341	57.94 286	80.14 289

KD-MVS_2432*1600		59.04 291	56.44 295	66.86 300	79.07 206	45.87 283	72.13 320	80.42 212	55.03 264	48.15 324	71.01 336	36.73 227	78.05 324	35.21 329	30.18 386	76.67 324

miper_refine_blended		59.04 291	56.44 295	66.86 300	79.07 206	45.87 283	72.13 320	80.42 212	55.03 264	48.15 324	71.01 336	36.73 227	78.05 324	35.21 329	30.18 386	76.67 324

WR-MVS_H		58.91 293	58.04 285	61.54 334	69.07 337	33.83 361	76.91 284	81.99 182	51.40 294	48.17 323	74.67 303	40.23 179	74.15 347	31.78 345	48.10 340	76.64 327

LCM-MVSNet-Re		58.82 294	56.54 293	65.68 308	79.31 202	29.09 382	61.39 365	45.79 381	60.73 164	37.65 369	72.47 326	31.42 284	81.08 293	49.66 259	70.41 182	86.87 172

Patchmatch-RL test		58.72 295	54.32 307	71.92 237	63.91 365	44.25 301	61.73 362	55.19 373	57.38 230	49.31 319	54.24 382	37.60 209	80.89 294	62.19 156	47.28 347	90.63 83

FMVSNet5		58.61 296	56.45 294	65.10 315	77.20 244	39.74 338	74.77 296	77.12 279	50.27 301	43.28 347	67.71 351	26.15 318	76.90 337	36.78 322	54.78 315	78.65 302

ppachtmachnet_test		58.56 297	54.34 306	71.24 247	71.42 318	54.74 79	81.84 225	72.27 324	49.02 309	45.86 340	68.99 348	26.27 315	83.30 280	30.12 350	43.23 361	75.69 333

ACMH+	54.58 15	58.55 298	55.24 302	68.50 289	74.68 279	45.80 285	80.27 257	70.21 341	47.15 320	42.77 349	75.48 299	16.73 370	85.98 243	35.10 333	54.78 315	73.72 349

CP-MVSNet		58.54 299	57.57 288	61.46 335	68.50 341	33.96 360	76.90 285	78.60 255	51.67 293	47.83 326	76.60 286	34.99 250	72.79 356	35.45 326	47.58 344	77.64 317

PEN-MVS		58.35 300	57.15 290	61.94 331	67.55 348	34.39 356	77.01 283	78.35 260	51.87 290	47.72 327	76.73 284	33.91 259	73.75 351	34.03 336	47.17 348	77.68 315

PS-CasMVS		58.12 301	57.03 292	61.37 336	68.24 345	33.80 362	76.73 286	78.01 263	51.20 295	47.54 330	76.20 294	32.85 268	72.76 357	35.17 331	47.37 346	77.55 318

dmvs_testset		57.65 302	58.21 284	55.97 353	74.62 280	9.82 412	63.75 353	63.34 364	67.23 51	48.89 321	83.68 195	39.12 191	76.14 340	23.43 376	59.80 264	81.96 257

UnsupCasMVSNet_eth		57.56 303	55.15 303	64.79 317	64.57 363	33.12 363	73.17 311	83.87 149	58.98 198	41.75 353	70.03 343	22.54 342	79.92 310	46.12 287	35.31 375	81.32 274

CHOSEN 280x420		57.53 304	56.38 297	60.97 338	74.01 289	48.10 246	46.30 384	54.31 375	48.18 315	50.88 313	77.43 272	38.37 198	59.16 381	54.83 224	63.14 245	75.66 334

DTE-MVSNet		57.03 305	55.73 301	60.95 339	65.94 352	32.57 367	75.71 288	77.09 280	51.16 296	46.65 336	76.34 289	32.84 269	73.22 355	30.94 349	44.87 357	77.06 321

PatchMatch-RL		56.66 306	53.75 311	65.37 313	77.91 232	45.28 289	69.78 333	60.38 368	41.35 351	47.57 329	73.73 311	16.83 368	76.91 335	36.99 320	59.21 270	73.92 348

PatchT		56.60 307	52.97 314	67.48 294	72.94 302	46.16 281	57.30 374	73.78 313	38.77 358	54.37 286	57.26 379	37.52 211	78.06 323	32.02 343	52.79 329	78.23 311

Patchmtry		56.56 308	52.95 315	67.42 295	72.53 307	50.59 173	59.05 370	71.72 329	37.86 362	46.92 333	65.86 356	38.94 192	80.06 309	36.94 321	46.72 352	71.60 361

test_0402		56.45 309	53.03 313	66.69 303	76.78 249	50.31 185	81.76 227	69.61 346	42.79 348	43.88 342	72.13 331	22.82 341	86.46 228	16.57 392	50.94 334	63.31 379

LS3D		56.40 310	53.82 310	64.12 318	81.12 167	45.69 287	73.42 309	66.14 356	35.30 372	43.24 348	79.88 245	22.18 346	79.62 314	19.10 387	64.00 231	67.05 370

ADS-MVSNet		56.17 311	51.95 321	68.84 279	80.60 183	53.07 125	55.03 377	70.02 343	44.72 336	51.00 310	61.19 368	22.83 339	78.88 317	28.54 358	53.63 322	74.57 344

XVG-ACMP-BASELINE		56.03 312	52.85 316	65.58 309	61.91 372	40.95 335	63.36 354	72.43 323	45.20 334	46.02 338	74.09 307	9.20 385	78.12 321	45.13 289	58.27 279	77.66 316

pmmvs-eth3d		55.97 313	52.78 317	65.54 310	61.02 374	46.44 273	75.36 294	67.72 354	49.61 305	43.65 344	67.58 352	21.63 349	77.04 333	44.11 296	44.33 358	73.15 355

F-COLMAP		55.96 314	53.65 312	62.87 326	72.76 304	42.77 319	74.70 299	70.37 340	40.03 353	41.11 358	79.36 251	17.77 365	73.70 352	32.80 342	53.96 321	72.15 357

CMPMVS	40.41 21	55.34 315	52.64 318	63.46 322	60.88 375	43.84 305	61.58 364	71.06 336	30.43 380	36.33 371	74.63 304	24.14 333	75.44 343	48.05 272	66.62 209	71.12 364
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 316	54.07 309	58.68 345	63.14 369	25.00 388	77.69 281	74.78 304	52.64 283	43.43 345	72.39 328	26.21 316	74.76 346	29.31 353	47.05 350	76.28 331

ADS-MVSNet2		55.21 317	51.44 322	66.51 305	80.60 183	49.56 201	55.03 377	65.44 357	44.72 336	51.00 310	61.19 368	22.83 339	75.41 344	28.54 358	53.63 322	74.57 344

SixPastTwentyTwo		54.37 318	50.10 327	67.21 296	70.70 326	41.46 331	74.73 297	64.69 359	47.56 318	39.12 364	69.49 344	18.49 363	84.69 266	31.87 344	34.20 381	75.48 335

USDC		54.36 319	51.23 323	63.76 320	64.29 364	37.71 349	62.84 359	73.48 319	56.85 238	35.47 374	71.94 334	9.23 384	78.43 318	38.43 313	48.57 339	75.13 339

testgi		54.25 320	52.57 319	59.29 343	62.76 370	21.65 396	72.21 319	70.47 339	53.25 280	41.94 351	77.33 273	14.28 375	77.95 327	29.18 354	51.72 333	78.28 309

K. test v3		54.04 321	49.42 333	67.92 292	68.55 340	42.57 323	75.51 292	63.07 365	52.07 287	39.21 363	64.59 360	19.34 357	82.21 284	37.11 318	25.31 391	78.97 297

UnsupCasMVSNet_bld		53.86 322	50.53 326	63.84 319	63.52 368	34.75 355	71.38 325	81.92 185	46.53 323	38.95 365	57.93 377	20.55 353	80.20 308	39.91 310	34.09 382	76.57 328

YYNet1		53.82 323	49.96 329	65.41 312	70.09 331	48.95 216	72.30 317	71.66 331	44.25 341	31.89 384	63.07 364	23.73 335	73.95 349	33.26 339	39.40 368	73.34 352

MDA-MVSNet_test_wron		53.82 323	49.95 330	65.43 311	70.13 330	49.05 212	72.30 317	71.65 332	44.23 342	31.85 385	63.13 363	23.68 336	74.01 348	33.25 340	39.35 369	73.23 354

test_fmvs1		53.60 325	52.54 320	56.78 349	58.07 378	30.26 372	68.95 337	42.19 387	32.46 375	63.59 170	82.56 216	11.55 378	60.81 375	58.25 193	55.27 311	79.28 294

Patchmatch-test		53.33 326	48.17 336	68.81 281	73.31 294	42.38 324	42.98 388	58.23 370	32.53 374	38.79 366	70.77 339	39.66 187	73.51 353	25.18 370	52.06 332	90.55 84

LTVRE_ROB	45.45 19	52.73 327	49.74 331	61.69 333	69.78 332	34.99 354	44.52 385	67.60 355	43.11 347	43.79 343	74.03 308	18.54 362	81.45 290	28.39 360	57.94 286	68.62 368
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
EU-MVSNet		52.63 328	50.72 325	58.37 346	62.69 371	28.13 385	72.60 313	75.97 294	30.94 379	40.76 360	72.11 332	20.16 354	70.80 363	35.11 332	46.11 354	76.19 332

test_fmvs1_n		52.55 329	51.19 324	56.65 350	51.90 388	30.14 373	67.66 341	42.84 386	32.27 376	62.30 185	82.02 228	9.12 386	60.84 374	57.82 201	54.75 317	78.99 296

OurMVSNet-221017-0		52.39 330	48.73 334	63.35 324	65.21 357	38.42 345	68.54 339	64.95 358	38.19 359	39.57 362	71.43 335	13.23 377	79.92 310	37.16 316	40.32 367	71.72 360

JIA-IIPM		52.33 331	47.77 339	66.03 307	71.20 321	46.92 267	40.00 393	76.48 291	37.10 363	46.73 334	37.02 393	32.96 267	77.88 328	35.97 324	52.45 331	73.29 353

Anonymous20240521		51.65 332	48.42 335	61.34 337	56.43 382	39.65 340	73.57 307	73.47 320	36.64 366	36.59 370	63.98 361	10.75 381	72.25 360	35.35 327	49.01 338	72.11 358

MDA-MVSNet-bldmvs		51.56 333	47.75 340	63.00 325	71.60 316	47.32 262	69.70 334	72.12 325	43.81 343	27.65 392	63.38 362	21.97 348	75.96 341	27.30 365	32.19 383	65.70 376

test_vis1_n		51.19 334	49.66 332	55.76 354	51.26 389	29.85 377	67.20 343	38.86 392	32.12 377	59.50 214	79.86 246	8.78 387	58.23 382	56.95 210	52.46 330	79.19 295

COLMAP_ROB	43.60 20	50.90 335	48.05 337	59.47 341	67.81 347	40.57 337	71.25 326	62.72 367	36.49 367	36.19 372	73.51 316	13.48 376	73.92 350	20.71 383	50.26 336	63.92 378
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 336	47.81 338	57.96 347	61.53 373	27.80 386	67.40 342	74.06 310	43.25 346	33.31 383	65.38 359	16.03 372	71.34 361	21.80 380	47.55 345	74.75 342

kuosan		50.20 337	50.09 328	50.52 361	73.09 299	29.09 382	65.25 346	74.89 303	48.27 313	41.34 355	60.85 370	43.45 141	67.48 369	18.59 389	25.07 392	55.01 384

KD-MVS_self_test		49.24 338	46.85 341	56.44 351	54.32 383	22.87 391	57.39 373	73.36 321	44.36 340	37.98 368	59.30 375	18.97 359	71.17 362	33.48 337	42.44 362	75.26 337

MVS-HIRNet		49.01 339	44.71 343	61.92 332	76.06 259	46.61 271	63.23 356	54.90 374	24.77 387	33.56 379	36.60 395	21.28 351	75.88 342	29.49 352	62.54 250	63.26 380

new-patchmatchnet		48.21 340	46.55 342	53.18 357	57.73 380	18.19 404	70.24 329	71.02 337	45.70 330	33.70 378	60.23 371	18.00 364	69.86 366	27.97 362	34.35 379	71.49 363

TinyColmap		48.15 341	44.49 345	59.13 344	65.73 354	38.04 346	63.34 355	62.86 366	38.78 357	29.48 387	67.23 354	6.46 395	73.30 354	24.59 372	41.90 364	66.04 374

AllTest		47.32 342	44.66 344	55.32 355	65.08 359	37.50 350	62.96 358	54.25 376	35.45 370	33.42 380	72.82 321	9.98 382	59.33 378	24.13 373	43.84 359	69.13 366

PM-MVS		46.92 343	43.76 350	56.41 352	52.18 387	32.26 368	63.21 357	38.18 393	37.99 361	40.78 359	66.20 355	5.09 398	65.42 371	48.19 271	41.99 363	71.54 362

test_fmvs2		45.89 344	44.32 346	50.62 360	45.85 397	24.70 389	58.87 372	37.84 395	25.22 385	52.46 300	74.56 305	7.07 390	54.69 385	49.28 263	47.70 343	72.48 356

RPSCF		45.77 345	44.13 347	50.68 359	57.67 381	29.66 378	54.92 379	45.25 383	26.69 384	45.92 339	75.92 297	17.43 367	45.70 395	27.44 364	45.95 355	76.67 324

pmmvs3		45.53 346	41.55 352	57.44 348	48.97 393	39.68 339	70.06 330	57.66 371	28.32 382	34.06 377	57.29 378	8.50 388	66.85 370	34.86 334	34.26 380	65.80 375

dongtai		43.51 347	44.07 348	41.82 370	63.75 366	21.90 394	63.80 352	72.05 326	39.59 355	33.35 382	54.54 381	41.04 170	57.30 383	10.75 399	17.77 400	46.26 393

mvsany_test1		43.38 348	42.57 351	45.82 365	50.96 390	26.10 387	55.80 375	27.74 405	27.15 383	47.41 332	74.39 306	18.67 361	44.95 396	44.66 292	36.31 373	66.40 373

mamv4		42.60 349	44.05 349	38.26 375	59.21 377	38.00 347	44.14 387	39.03 391	25.03 386	40.61 361	68.39 349	37.01 221	24.28 409	46.62 282	36.43 372	52.50 387

N_pmnet		41.25 350	39.77 353	45.66 366	68.50 341	0.82 418	72.51 315	0.38 417	35.61 369	35.26 375	61.51 367	20.07 355	67.74 368	23.51 375	40.63 365	68.42 369

TDRefinement		40.91 351	38.37 355	48.55 363	50.45 391	33.03 365	58.98 371	50.97 379	28.50 381	29.89 386	67.39 353	6.21 397	54.51 386	17.67 390	35.25 376	58.11 381

test_vis1_rt		40.29 352	38.64 354	45.25 367	48.91 394	30.09 374	59.44 369	27.07 406	24.52 388	38.48 367	51.67 387	6.71 393	49.44 390	44.33 294	46.59 353	56.23 382

DSMNet-mixed		38.35 353	35.36 358	47.33 364	48.11 395	14.91 408	37.87 394	36.60 396	19.18 392	34.37 376	59.56 374	15.53 373	53.01 388	20.14 385	46.89 351	74.07 346

test_fmvs3		37.95 354	35.75 357	44.55 368	35.50 403	18.92 400	48.32 381	34.00 400	18.36 394	41.31 357	61.58 366	2.29 405	48.06 394	42.72 303	37.71 371	66.66 372

WB-MVS		37.41 355	36.37 356	40.54 373	54.23 384	10.43 411	65.29 345	43.75 384	34.86 373	27.81 391	54.63 380	24.94 327	63.21 372	6.81 406	15.00 401	47.98 392

FPMVS		35.40 356	33.67 360	40.57 372	46.34 396	28.74 384	41.05 390	57.05 372	20.37 391	22.27 395	53.38 384	6.87 392	44.94 397	8.62 400	47.11 349	48.01 391

SSC-MVS		35.20 357	34.30 359	37.90 376	52.58 386	8.65 414	61.86 361	41.64 388	31.81 378	25.54 393	52.94 386	23.39 338	59.28 380	6.10 407	12.86 402	45.78 395

ANet_high		34.39 358	29.59 364	48.78 362	30.34 407	22.28 392	55.53 376	63.79 363	38.11 360	15.47 399	36.56 396	6.94 391	59.98 377	13.93 395	5.64 410	64.08 377

EGC-MVSNET		33.75 359	30.42 363	43.75 369	64.94 361	36.21 353	60.47 368	40.70 390	0.02 411	0.10 412	53.79 383	7.39 389	60.26 376	11.09 398	35.23 377	34.79 397

new_pmnet		33.56 360	31.89 362	38.59 374	49.01 392	20.42 397	51.01 380	37.92 394	20.58 389	23.45 394	46.79 389	6.66 394	49.28 392	20.00 386	31.57 385	46.09 394

LF4IMVS		33.04 361	32.55 361	34.52 379	40.96 398	22.03 393	44.45 386	35.62 397	20.42 390	28.12 390	62.35 365	5.03 399	31.88 408	21.61 382	34.42 378	49.63 390

LCM-MVSNet		28.07 362	23.85 370	40.71 371	27.46 412	18.93 399	30.82 400	46.19 380	12.76 399	16.40 397	34.70 398	1.90 408	48.69 393	20.25 384	24.22 393	54.51 385

mvsany_test3		28.00 363	25.98 365	34.05 380	28.97 408	15.31 406	34.54 397	18.17 411	16.24 395	29.30 388	53.37 385	2.79 403	33.38 407	30.01 351	20.41 398	53.45 386

Gipuma		27.47 364	24.26 369	37.12 378	60.55 376	29.17 381	11.68 405	60.00 369	14.18 397	10.52 406	15.12 407	2.20 407	63.01 373	8.39 401	35.65 374	19.18 403
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 365	24.85 366	33.93 381	26.17 413	15.25 407	30.24 401	22.38 410	12.53 400	28.23 389	49.43 388	2.59 404	34.34 406	25.12 371	26.99 389	52.20 388

PMMVS2		26.71 366	22.98 371	37.87 377	36.89 401	8.51 415	42.51 389	29.32 404	19.09 393	13.01 401	37.54 392	2.23 406	53.11 387	14.54 394	11.71 403	51.99 389

APD_test1		26.46 367	24.41 368	32.62 384	37.58 400	21.74 395	40.50 392	30.39 402	11.45 401	16.33 398	43.76 390	1.63 410	41.62 398	11.24 397	26.82 390	34.51 398

PMVS	19.57 22	25.07 368	22.43 373	32.99 383	23.12 414	22.98 390	40.98 391	35.19 398	15.99 396	11.95 405	35.87 397	1.47 411	49.29 391	5.41 409	31.90 384	26.70 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 369	22.95 372	30.31 385	28.59 409	18.92 400	37.43 395	17.27 413	12.90 398	21.28 396	29.92 402	1.02 412	36.35 401	28.28 361	29.82 388	35.65 396

test_method		24.09 370	21.07 374	33.16 382	27.67 411	8.35 416	26.63 402	35.11 399	3.40 408	14.35 400	36.98 394	3.46 402	35.31 403	19.08 388	22.95 394	55.81 383

testf1		21.11 371	19.08 375	27.18 387	30.56 405	18.28 402	33.43 398	24.48 407	8.02 405	12.02 403	33.50 399	0.75 414	35.09 404	7.68 402	21.32 395	28.17 400

APD_test2		21.11 371	19.08 375	27.18 387	30.56 405	18.28 402	33.43 398	24.48 407	8.02 405	12.02 403	33.50 399	0.75 414	35.09 404	7.68 402	21.32 395	28.17 400

E-PMN		19.16 373	18.40 377	21.44 389	36.19 402	13.63 409	47.59 382	30.89 401	10.73 402	5.91 409	16.59 405	3.66 401	39.77 399	5.95 408	8.14 405	10.92 405

EMVS		18.42 374	17.66 378	20.71 390	34.13 404	12.64 410	46.94 383	29.94 403	10.46 404	5.58 410	14.93 408	4.23 400	38.83 400	5.24 410	7.51 407	10.67 406

cdsmvs_eth3d_5k		18.33 375	24.44 367	0.00 396	0.00 418	0.00 420	0.00 407	89.40 22	0.00 412	0.00 415	92.02 46	38.55 196	0.00 413	0.00 414	0.00 411	0.00 411

MVE	16.60 23	17.34 376	13.39 379	29.16 386	28.43 410	19.72 398	13.73 404	23.63 409	7.23 407	7.96 407	21.41 403	0.80 413	36.08 402	6.97 404	10.39 404	31.69 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 377	10.68 380	5.73 393	2.49 416	4.21 417	10.48 406	18.04 412	0.34 410	12.59 402	20.49 404	11.39 379	7.03 412	13.84 396	6.46 409	5.95 407

wuyk23d		9.11 378	8.77 382	10.15 392	40.18 399	16.76 405	20.28 403	1.01 416	2.58 409	2.66 411	0.98 411	0.23 416	12.49 411	4.08 411	6.90 408	1.19 408

ab-mvs-re		7.68 379	10.24 381	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	92.12 42	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

testmvs		6.14 380	8.18 383	0.01 394	0.01 417	0.00 420	73.40 310	0.00 418	0.00 412	0.02 413	0.15 412	0.00 417	0.00 413	0.02 412	0.00 411	0.02 409

test123		6.01 381	8.01 384	0.01 394	0.00 418	0.01 419	71.93 323	0.00 418	0.00 412	0.02 413	0.11 413	0.00 417	0.00 413	0.02 412	0.00 411	0.02 409

pcd_1.5k_mvsjas		3.15 382	4.20 385	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	37.77 202	0.00 413	0.00 414	0.00 411	0.00 411

test_blank		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 418	0.00 420	0.00 407	0.00 418	0.00 412	0.00 415	0.00 414	0.00 417	0.00 413	0.00 414	0.00 411	0.00 411

WAC-MVS						34.28 357								22.56 378

FOURS1					83.24 108	49.90 193	84.98 136	78.76 249	47.71 316	73.42 61

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 10					96.22 8	81.46 32	86.80 27	92.34 34

PC_three_1452									66.58 58	87.27 2	93.70 9	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

No_MVS				81.53 15	91.77 4	56.03 46		91.10 10					96.22 8	81.46 32	86.80 27	92.34 34

test_one_0601					89.39 22	57.29 22		88.09 53	57.21 234	82.06 12	93.39 18	54.94 31

eth-test2					0.00 418

eth-test					0.00 418

ZD-MVS					89.55 14	53.46 108		84.38 135	57.02 236	73.97 56	91.03 65	44.57 125	91.17 78	75.41 73	81.78 73

RE-MVS-def			66.66 196		80.96 171	48.14 244	81.54 235	76.98 281	46.42 326	62.75 180	89.42 103	29.28 298		60.52 172	72.06 167	83.19 242

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 66	88.26 1				82.83 21	87.60 18	92.44 31

OPU-MVS				81.71 12	92.05 3	55.97 48	92.48 3				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

test_241102_TWO								88.76 39	57.50 228	83.60 6	94.09 3	56.14 22	96.37 6	82.28 25	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 22	58.81 11	96.45 1

9.14			78.19 27		85.67 57		88.32 51	88.84 36	59.89 173	74.58 51	92.62 35	46.80 90	92.66 42	81.40 34	85.62 41

save fliter					85.35 65	56.34 41	89.31 40	81.46 193	61.55 145

test_0728_THIRD									58.00 214	81.91 13	93.64 11	56.54 18	96.44 2	81.64 30	86.86 25	92.23 36

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 32					96.39 4	81.68 28	87.13 21	92.47 30

test0726					89.40 20	57.45 19	92.32 7	88.63 43	57.71 222	83.14 9	93.96 6	55.17 26

GSMVS																88.13 148

test_part2					89.33 23	55.48 54				82.27 11

sam_mvs1												38.86 194				88.13 148

sam_mvs												35.99 240

ambc				62.06 329	53.98 385	29.38 380	35.08 396	79.65 229		41.37 354	59.96 372	6.27 396	82.15 285	35.34 328	38.22 370	74.65 343

MTGPA								81.31 196

test_post1							70.84 328				14.72 409	34.33 256	83.86 271	48.80 266

test_post											16.22 406	37.52 211	84.72 265

patchmatchnet-post											59.74 373	38.41 197	79.91 312

GG-mvs-BLEND				77.77 83	86.68 47	50.61 171	68.67 338	88.45 49		68.73 109	87.45 145	59.15 10	90.67 91	54.83 224	87.67 17	92.03 43

MTMP							87.27 76	15.34 414

gm-plane-assit					83.24 108	54.21 95			70.91 20		88.23 129		95.25 14	66.37 125

test9_res														78.72 48	85.44 43	91.39 64

TEST9					85.68 55	55.42 55	87.59 66	84.00 145	57.72 221	72.99 66	90.98 67	44.87 119	88.58 155

test_8					85.72 54	55.31 61	87.60 65	83.88 148	57.84 219	72.84 70	90.99 66	44.99 115	88.34 166

agg_prior2														75.65 68	85.11 47	91.01 76

agg_prior					85.64 58	54.92 75		83.61 156		72.53 75			88.10 176

TestCases				55.32 355	65.08 359	37.50 350		54.25 376	35.45 370	33.42 380	72.82 321	9.98 382	59.33 378	24.13 373	43.84 359	69.13 366

test_prior4						56.39 40	87.15 80

test_prior2							89.04 43		61.88 141	73.55 59	91.46 63	48.01 78		74.73 77	85.46 42

test_prior				78.39 72	86.35 49	54.91 76		85.45 99					89.70 120			90.55 84

旧先验2							81.73 229		45.53 332	74.66 48			70.48 365	58.31 192

新几何2							81.61 233

新几何1				73.30 201	83.10 111	53.48 107		71.43 333	45.55 331	66.14 129	87.17 150	33.88 261	80.54 302	48.50 269	80.33 86	85.88 196

旧先验1					81.57 158	47.48 258		71.83 327			88.66 118	36.94 223			78.34 107	88.67 134

无先验							85.19 126	78.00 264	49.08 308				85.13 260	52.78 241		87.45 164

原ACMM2							83.77 173

原ACMM1				76.13 125	84.89 74	54.59 87		85.26 109	51.98 288	66.70 121	87.07 152	40.15 181	89.70 120	51.23 251	85.06 48	84.10 220

test222					79.36 199	50.97 167	77.99 279	67.84 353	42.54 349	62.84 179	86.53 159	30.26 291			76.91 117	85.23 205

testdata2													77.81 330	45.64 288

segment_acmp												44.97 117

testdata				67.08 298	77.59 235	45.46 288		69.20 349	44.47 338	71.50 87	88.34 126	31.21 285	70.76 364	52.20 246	75.88 129	85.03 207

testdata1							77.55 282		64.14 97

test12				79.24 44	86.89 45	56.08 45		85.16 114		72.27 79		47.15 86	91.10 81		85.93 37	90.54 86

plane_prior7					77.95 229	48.46 233

plane_prior6					78.42 224	49.39 207						36.04 238

plane_prior5								82.59 174					88.30 169	65.46 134	72.34 164	84.49 214

plane_prior4											83.28 201

plane_prior3						48.95 216			64.01 100	62.15 187

plane_prior2							85.76 106		63.60 109

plane_prior1					78.31 226

plane_prior						49.57 199	87.43 69		64.57 91						72.84 160

n2								0.00 418

nn								0.00 418

door-mid								41.31 389

lessismore_v0				67.98 291	64.76 362	41.25 332		45.75 382		36.03 373	65.63 358	19.29 358	84.11 270	35.67 325	21.24 397	78.59 303

LGP-MVS_train				72.02 229	74.42 284	48.60 226		80.64 208	54.69 269	53.75 292	83.83 189	25.73 321	86.98 211	60.33 176	64.71 223	80.48 284

test11								84.25 139

door								43.27 385

HQP5-MVS						51.56 156

HQP-NCC					79.02 208		88.00 55		65.45 78	64.48 155

ACMP_Plane					79.02 208		88.00 55		65.45 78	64.48 155

BP-MVS														66.70 122

HQP4-MVS										64.47 158			88.61 154			84.91 210

HQP3-MVS								83.68 152							73.12 156

HQP2-MVS												37.35 214

NP-MVS					78.76 213	50.43 177					85.12 175

MDTV_nov1_ep13_2view						43.62 307	71.13 327		54.95 266	59.29 220		36.76 226		46.33 285		87.32 166

MDTV_nov1_ep13			61.56 256		81.68 151	55.12 68	72.41 316	78.18 261	59.19 190	58.85 229	69.29 346	34.69 252	86.16 235	36.76 323	62.96 247

ACMMP++_ref															63.20 243

ACMMP++															59.38 268

Test By Simon												39.38 188

ITE_SJBPF				51.84 358	58.03 379	31.94 370		53.57 378	36.67 365	41.32 356	75.23 301	11.17 380	51.57 389	25.81 369	48.04 341	72.02 359

DeepMVS_CX				13.10 391	21.34 415	8.99 413		10.02 415	10.59 403	7.53 408	30.55 401	1.82 409	14.55 410	6.83 405	7.52 406	15.75 404

High-resolution multi-view benchmark