High-res multi-view results

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

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

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

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

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

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

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

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 12	75.95 3	77.10 40	93.09 30	54.15 40	95.57 12	85.80 10	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 22	84.38 87	55.40 59	92.16 10	89.85 23	75.28 4	82.41 11	93.86 8	54.30 37	93.98 23	90.29 1	87.13 21	93.30 12

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 142	88.88 37	58.00 231	83.60 6	93.39 21	67.21 2	96.39 4	81.64 35	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 201	59.50 5	92.24 8	90.72 16	69.37 35	83.22 8	94.47 2	63.81 5	93.18 32	74.02 90	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 48	56.89 29	92.77 2	86.30 94	77.83 1	77.88 36	92.13 47	60.24 7	94.78 19	78.97 50	89.61 8	93.69 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MSP-MVS		82.30 6	83.47 1	78.80 59	82.99 124	52.71 136	85.04 139	88.63 48	66.08 80	86.77 3	92.75 36	72.05 1	91.46 70	83.35 22	93.53 1	92.23 37
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MVS_0304		82.10 7	82.64 4	80.47 27	86.63 50	54.69 84	92.20 9	86.66 86	74.48 5	82.63 10	93.80 10	50.83 63	93.70 28	90.11 2	86.44 33	93.01 21

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 35	57.50 245	84.61 4	94.09 3	58.81 13	96.37 6	82.28 29	87.60 18	94.06 3

CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 14	91.71 11	88.87 39	70.31 27	77.64 39	93.87 7	52.58 48	93.91 26	84.17 17	87.92 16	92.39 33

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 21	57.71 239	81.91 15	93.64 14	55.17 31	96.44 2	81.68 33	87.13 21	92.72 28
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
CANet		80.90 11	81.17 12	80.09 37	87.62 41	54.21 96	91.60 14	86.47 90	73.13 9	79.89 26	93.10 28	49.88 72	92.98 33	84.09 19	84.75 50	93.08 19

patch_mono-2		80.84 12	81.59 10	78.62 66	90.34 9	53.77 104	88.08 54	88.36 55	76.17 2	79.40 29	91.09 70	55.43 29	90.09 110	85.01 13	80.40 82	91.99 48

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 85	85.46 67	49.56 206	90.99 21	86.66 86	70.58 25	80.07 25	95.30 1	56.18 26	90.97 87	82.57 28	86.22 36	93.28 13

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 67	89.93 29	87.55 72	66.04 83	79.46 27	93.00 34	53.10 45	91.76 63	80.40 43	89.56 9	92.68 29

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 41	91.54 5	59.19 207	71.82 88	90.05 103	59.72 10	96.04 10	78.37 56	88.40 14	93.75 7

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 269	89.51 26	69.76 31	71.05 100	86.66 171	58.68 16	93.24 31	84.64 16	90.40 6	93.14 18

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 64	60.97 3	91.69 12	87.02 78	70.62 24	80.75 22	93.22 27	37.77 213	92.50 46	82.75 26	86.25 35	91.57 60

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 76	60.73 4	91.65 13	86.86 81	70.30 28	80.77 21	93.07 32	37.63 218	92.28 52	82.73 27	85.71 39	91.57 60

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 72	88.70 47	87.92 62	55.55 275	81.21 20	93.69 13	56.51 24	94.27 22	78.36 57	85.70 40	91.51 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		79.57 20	79.23 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 57	67.71 50	73.81 62	92.75 36	46.88 94	93.28 30	78.79 53	84.07 55	91.50 64

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 149	83.68 162	67.85 47	69.36 113	90.24 95	60.20 8	92.10 58	84.14 18	80.40 82	92.82 25

testing11		79.18 22	78.85 23	80.16 33	88.33 30	56.99 26	88.31 52	92.06 1	72.82 11	70.62 108	88.37 135	57.69 19	92.30 50	75.25 80	76.24 131	91.20 73

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 85	55.87 49	87.58 69	86.76 83	61.48 164	80.26 24	93.10 28	46.53 99	92.41 48	79.97 44	88.77 11	92.08 41
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
UBG		78.86 24	78.86 22	78.86 57	87.80 40	55.43 55	87.67 64	91.21 11	72.83 10	72.10 85	88.40 134	58.53 17	89.08 137	73.21 100	77.98 108	92.08 41

LFMVS		78.52 25	77.14 44	82.67 3	89.58 13	58.90 8	91.27 19	88.05 60	63.22 133	74.63 54	90.83 81	41.38 178	94.40 20	75.42 78	79.90 91	94.72 2

testing99		78.45 26	77.78 35	80.45 28	88.28 33	56.81 32	87.95 59	91.49 6	71.72 16	70.84 102	88.09 144	57.29 21	92.63 44	69.24 120	75.13 148	91.91 49

APDe-MVS		78.44 27	78.20 27	79.19 45	88.56 26	54.55 89	89.76 33	87.77 66	55.91 270	78.56 32	92.49 42	48.20 79	92.65 42	79.49 45	83.04 59	90.39 94
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 28	76.99 46	82.73 2	93.17 1	64.46 1	89.93 29	88.51 53	64.83 99	73.52 65	88.09 144	48.07 80	92.19 54	62.24 171	84.53 52	91.53 62

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 122	55.24 63	91.57 15	84.82 134	69.12 36	76.67 42	92.02 52	44.82 130	90.23 107	80.83 42	80.09 86	92.08 41

EPNet		78.36 30	78.49 25	77.97 82	85.49 66	52.04 150	89.36 39	84.07 155	73.22 8	77.03 41	91.72 60	49.32 76	90.17 109	73.46 96	82.77 60	91.69 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 31	78.26 26	78.48 70	81.33 175	56.31 42	81.59 249	86.41 91	69.61 33	81.72 17	88.16 143	55.09 33	88.04 184	74.12 89	86.31 34	91.09 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testing91		78.30 32	77.54 38	80.61 23	88.16 35	57.12 25	87.94 60	91.07 15	71.43 19	70.75 103	88.04 148	55.82 28	92.65 42	69.61 116	75.00 152	92.05 44

sasdasda		78.17 33	77.86 33	79.12 50	84.30 88	54.22 94	87.71 62	84.57 143	67.70 51	77.70 37	92.11 50	50.90 59	89.95 113	78.18 60	77.54 113	93.20 15

canonicalmvs		78.17 33	77.86 33	79.12 50	84.30 88	54.22 94	87.71 62	84.57 143	67.70 51	77.70 37	92.11 50	50.90 59	89.95 113	78.18 60	77.54 113	93.20 15

alignmvs		78.08 35	77.98 30	78.39 74	83.53 104	53.22 122	89.77 32	85.45 110	66.11 78	76.59 44	91.99 54	54.07 41	89.05 139	77.34 66	77.00 118	92.89 23

DeepC-MVS_fast	67.50 3	78.00 36	77.63 36	79.13 49	88.52 27	55.12 69	89.95 28	85.98 101	68.31 38	71.33 95	92.75 36	45.52 115	90.37 100	71.15 107	85.14 46	91.91 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		77.99 37	77.92 32	78.19 78	87.43 43	50.12 194	90.93 22	91.41 8	67.48 54	75.12 49	90.15 101	46.77 96	91.00 84	73.52 95	78.46 104	93.44 9

TSAR-MVS + GP.		77.82 38	77.59 37	78.49 69	85.25 72	50.27 193	90.02 26	90.57 17	56.58 264	74.26 59	91.60 65	54.26 38	92.16 55	75.87 72	79.91 90	93.05 20

myMVS_eth3d28		77.77 39	77.94 31	77.27 99	87.58 42	52.89 133	86.06 102	91.33 10	74.15 7	68.16 125	88.24 141	58.17 18	88.31 174	69.88 115	77.87 109	90.61 88

casdiffmvs_mvg		77.75 40	77.28 41	79.16 47	80.42 197	54.44 91	87.76 61	85.46 109	71.67 17	71.38 94	88.35 137	51.58 52	91.22 77	79.02 49	79.89 92	91.83 53
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		77.70 41	77.22 43	79.14 48	86.95 46	54.89 78	87.18 79	91.96 2	72.29 13	71.17 99	88.70 128	55.19 30	91.24 76	65.18 155	76.32 129	91.29 71

SF-MVS		77.64 42	77.42 40	78.32 76	83.75 101	52.47 141	86.63 92	87.80 63	58.78 219	74.63 54	92.38 44	47.75 85	91.35 72	78.18 60	86.85 27	91.15 75

PHI-MVS		77.49 43	77.00 45	78.95 53	85.33 70	50.69 176	88.57 49	88.59 51	58.14 228	73.60 63	93.31 24	43.14 154	93.79 27	73.81 93	88.53 13	92.37 34

WTY-MVS		77.47 44	77.52 39	77.30 97	88.33 30	46.25 293	88.46 50	90.32 19	71.40 20	72.32 83	91.72 60	53.44 43	92.37 49	66.28 140	75.42 142	93.28 13

casdiffmvs		77.36 45	76.85 47	78.88 56	80.40 198	54.66 87	87.06 82	85.88 102	72.11 14	71.57 91	88.63 133	50.89 62	90.35 101	76.00 71	79.11 99	91.63 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		77.20 46	77.25 42	77.05 104	84.60 82	49.04 221	89.42 36	85.83 104	65.90 84	72.85 74	91.98 56	45.10 121	91.27 74	75.02 82	84.56 51	90.84 83

ETV-MVS		77.17 47	76.74 48	78.48 70	81.80 154	54.55 89	86.13 100	85.33 115	68.20 40	73.10 70	90.52 87	45.23 120	90.66 93	79.37 46	80.95 74	90.22 100

SteuartSystems-ACMMP		77.08 48	76.33 53	79.34 43	80.98 179	55.31 61	89.76 33	86.91 80	62.94 138	71.65 89	91.56 66	42.33 162	92.56 45	77.14 67	83.69 57	90.15 105
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 49	76.45 51	78.69 63	79.69 206	54.74 80	90.56 24	83.99 158	68.26 39	74.10 60	90.91 78	42.14 166	89.99 112	79.30 47	79.12 98	91.36 68
jason: jason.
train_agg		76.91 50	76.40 52	78.45 72	85.68 60	55.42 56	87.59 67	84.00 156	57.84 236	72.99 71	90.98 73	44.99 124	88.58 160	78.19 58	85.32 44	91.34 70

MVS		76.91 50	75.48 65	81.23 19	84.56 83	55.21 65	80.23 275	91.64 4	58.65 221	65.37 153	91.48 68	45.72 111	95.05 16	72.11 104	89.52 10	93.44 9

DeepC-MVS	67.15 4	76.90 52	76.27 54	78.80 59	80.70 190	55.02 73	86.39 94	86.71 84	66.96 63	67.91 127	89.97 105	48.03 81	91.41 71	75.60 75	84.14 54	89.96 111
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 53	76.24 55	78.71 62	80.47 196	54.20 98	83.90 180	84.88 133	71.38 21	71.51 92	89.15 121	50.51 64	90.55 97	75.71 73	78.65 102	91.39 66

CS-MVS		76.77 54	76.70 49	76.99 109	83.55 103	48.75 231	88.60 48	85.18 123	66.38 71	72.47 81	91.62 64	45.53 114	90.99 86	74.48 85	82.51 62	91.23 72

PAPM		76.76 55	76.07 57	78.81 58	80.20 199	59.11 7	86.86 88	86.23 95	68.60 37	70.18 111	88.84 126	51.57 53	87.16 216	65.48 148	86.68 30	90.15 105

MAR-MVS		76.76 55	75.60 62	80.21 31	90.87 7	54.68 85	89.14 42	89.11 32	62.95 137	70.54 109	92.33 45	41.05 179	94.95 17	57.90 217	86.55 32	91.00 79
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 57	76.54 50	76.50 119	85.91 57	51.83 156	88.89 45	84.24 152	67.82 48	69.09 117	89.33 118	46.70 97	88.13 180	75.43 76	81.48 73	89.55 119

ACMMP_NAP		76.43 58	75.66 61	78.73 61	81.92 151	54.67 86	84.06 174	85.35 114	61.10 171	72.99 71	91.50 67	40.25 189	91.00 84	76.84 68	86.98 25	90.51 92

MVS_111021_HR		76.39 59	75.38 69	79.42 42	85.33 70	56.47 38	88.15 53	84.97 130	65.15 97	66.06 144	89.88 106	43.79 141	92.16 55	75.03 81	80.03 89	89.64 117

CHOSEN 1792x2688		76.24 60	74.03 91	82.88 1	83.09 118	62.84 2	85.73 113	85.39 112	69.79 30	64.87 161	83.49 210	41.52 177	93.69 29	70.55 109	81.82 69	92.12 40

SD-MVS		76.18 61	74.85 79	80.18 32	85.39 68	56.90 28	85.75 111	82.45 186	56.79 259	74.48 57	91.81 58	43.72 144	90.75 91	74.61 84	78.65 102	92.91 22
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS		76.15 62	75.68 60	77.54 92	88.52 27	53.44 113	87.26 78	85.03 129	53.79 292	74.91 52	91.68 62	43.80 140	90.31 103	74.36 86	81.82 69	88.87 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS1		76.09 63	75.55 63	77.71 88	79.49 208	52.27 147	84.70 152	90.49 18	64.44 102	69.86 112	90.31 94	55.05 34	91.35 72	70.07 113	75.58 141	89.53 121

VDD-MVS		76.08 64	74.97 76	79.44 41	84.27 91	53.33 119	91.13 20	85.88 102	65.33 94	72.37 82	89.34 116	32.52 291	92.76 40	77.90 63	75.96 135	92.22 39

CDPH-MVS		76.05 65	75.19 71	78.62 66	86.51 51	54.98 75	87.32 73	84.59 142	58.62 222	70.75 103	90.85 80	43.10 156	90.63 95	70.50 110	84.51 53	90.24 99

fmvsm_l_conf0.5_n		75.95 66	76.16 56	75.31 156	76.01 278	48.44 242	84.98 142	71.08 352	63.50 127	81.70 18	93.52 17	50.00 68	87.18 215	87.80 5	76.87 121	90.32 97

EIA-MVS		75.92 67	75.18 72	78.13 79	85.14 73	51.60 161	87.17 80	85.32 116	64.69 100	68.56 121	90.53 86	45.79 110	91.58 67	67.21 133	82.18 66	91.20 73

fmvsm_l_conf0.5_n_a		75.88 68	76.07 57	75.31 156	76.08 273	48.34 245	85.24 129	70.62 355	63.13 135	81.45 19	93.62 16	49.98 70	87.40 211	87.76 6	76.77 122	90.20 102

test_yl		75.85 69	74.83 80	78.91 54	88.08 37	51.94 152	91.30 17	89.28 29	57.91 233	71.19 97	89.20 119	42.03 169	92.77 38	69.41 117	75.07 150	92.01 46

DCV-MVSNet		75.85 69	74.83 80	78.91 54	88.08 37	51.94 152	91.30 17	89.28 29	57.91 233	71.19 97	89.20 119	42.03 169	92.77 38	69.41 117	75.07 150	92.01 46

MVS_Test		75.85 69	74.93 77	78.62 66	84.08 93	55.20 67	83.99 176	85.17 124	68.07 43	73.38 67	82.76 221	50.44 65	89.00 142	65.90 144	80.61 78	91.64 56

ZNCC-MVS		75.82 72	75.02 75	78.23 77	83.88 99	53.80 103	86.91 87	86.05 100	59.71 193	67.85 128	90.55 85	42.23 164	91.02 83	72.66 102	85.29 45	89.87 114

ETVMVS		75.80 73	75.44 66	76.89 113	86.23 55	50.38 186	85.55 120	91.42 7	71.30 22	68.80 119	87.94 150	56.42 25	89.24 132	56.54 229	74.75 155	91.07 77

fmvsm_l_conf0.5_n_3		75.73 74	75.78 59	75.61 142	76.03 276	48.33 247	85.34 123	72.92 337	67.16 57	78.55 33	93.85 9	46.22 101	87.53 206	85.61 11	76.30 130	90.98 80

CLD-MVS		75.60 75	75.39 68	76.24 123	80.69 191	52.40 142	90.69 23	86.20 96	74.40 6	65.01 159	88.93 123	42.05 168	90.58 96	76.57 69	73.96 159	85.73 213
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 76	75.54 64	75.61 142	74.60 299	49.51 211	81.82 240	74.08 322	66.52 69	80.40 23	93.46 19	46.95 93	89.72 120	86.69 7	75.30 143	87.61 172

MP-MVS-pluss		75.54 77	75.03 74	77.04 105	81.37 174	52.65 138	84.34 164	84.46 145	61.16 168	69.14 116	91.76 59	39.98 196	88.99 144	78.19 58	84.89 49	89.48 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 78	75.20 70	75.62 141	80.98 179	49.00 222	87.43 70	84.68 140	63.49 128	70.97 101	90.15 101	42.86 159	91.14 81	74.33 87	81.90 68	86.71 193

MVSMamba_PlusPlus		75.28 79	73.39 94	80.96 21	80.85 186	58.25 10	74.47 319	87.61 71	50.53 316	65.24 154	83.41 212	57.38 20	92.83 36	73.92 92	87.13 21	91.80 54

GDP-MVS		75.27 80	74.38 85	77.95 84	79.04 219	52.86 134	85.22 130	86.19 97	62.43 148	70.66 106	90.40 92	53.51 42	91.60 66	69.25 119	72.68 171	89.39 125

Effi-MVS+		75.24 81	73.61 93	80.16 33	81.92 151	57.42 21	85.21 131	76.71 300	60.68 182	73.32 68	89.34 116	47.30 89	91.63 65	68.28 127	79.72 93	91.42 65

ET-MVSNet_ETH3D		75.23 82	74.08 89	78.67 64	84.52 84	55.59 51	88.92 44	89.21 31	68.06 44	53.13 315	90.22 97	49.71 73	87.62 203	72.12 103	70.82 189	92.82 25

PAPR		75.20 83	74.13 87	78.41 73	88.31 32	55.10 71	84.31 165	85.66 106	63.76 120	67.55 129	90.73 83	43.48 149	89.40 127	66.36 139	77.03 117	90.73 86

baseline2		75.15 84	74.54 84	76.98 110	81.67 161	51.74 158	83.84 182	91.94 3	69.97 29	58.98 239	86.02 177	59.73 9	91.73 64	68.37 126	70.40 194	87.48 174

diffmvs		75.11 85	74.65 82	76.46 120	78.52 233	53.35 117	83.28 201	79.94 232	70.51 26	71.64 90	88.72 127	46.02 107	86.08 253	77.52 64	75.75 139	89.96 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		75.02 86	75.07 73	74.88 172	74.33 304	47.83 266	83.99 176	73.54 330	67.10 59	76.32 45	92.43 43	45.42 117	86.35 243	82.98 24	79.50 97	90.47 93

MP-MVS		74.99 87	74.33 86	76.95 111	82.89 129	53.05 128	85.63 116	83.50 167	57.86 235	67.25 131	90.24 95	43.38 151	88.85 153	76.03 70	82.23 65	88.96 136
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_3		74.97 88	75.42 67	73.62 208	76.99 259	46.67 283	83.13 205	71.14 351	66.20 75	82.13 13	93.76 11	47.49 87	84.00 286	81.95 32	76.02 132	90.19 104

fmvsm_s_conf0.5_n_4		74.92 89	74.88 78	75.03 167	75.96 279	47.53 271	85.84 106	73.19 336	67.07 60	79.43 28	92.60 40	46.12 103	88.03 185	84.70 15	69.01 203	89.53 121

GST-MVS		74.87 90	73.90 92	77.77 86	83.30 111	53.45 112	85.75 111	85.29 118	59.22 206	66.50 140	89.85 107	40.94 181	90.76 90	70.94 108	83.35 58	89.10 134

fmvsm_s_conf0.5_n		74.48 91	74.12 88	75.56 145	76.96 260	47.85 265	85.32 127	69.80 362	64.16 110	78.74 30	93.48 18	45.51 116	89.29 131	86.48 8	66.62 221	89.55 119

3Dnovator	64.70 6	74.46 92	72.48 107	80.41 29	82.84 132	55.40 59	83.08 207	88.61 50	67.61 53	59.85 222	88.66 129	34.57 272	93.97 24	58.42 206	88.70 12	91.85 52

test_fmvsmconf_n		74.41 93	74.05 90	75.49 150	74.16 306	48.38 243	82.66 215	72.57 338	67.05 62	75.11 50	92.88 35	46.35 100	87.81 190	83.93 20	71.71 180	90.28 98

HFP-MVS		74.37 94	73.13 102	78.10 80	84.30 88	53.68 106	85.58 117	84.36 147	56.82 257	65.78 149	90.56 84	40.70 186	90.90 88	69.18 121	80.88 75	89.71 115

VDDNet		74.37 94	72.13 118	81.09 20	79.58 207	56.52 37	90.02 26	86.70 85	52.61 302	71.23 96	87.20 162	31.75 301	93.96 25	74.30 88	75.77 138	92.79 27

MSLP-MVS++		74.21 96	72.25 114	80.11 36	81.45 172	56.47 38	86.32 96	79.65 240	58.19 227	66.36 141	92.29 46	36.11 252	90.66 93	67.39 131	82.49 63	93.18 17

API-MVS		74.17 97	72.07 120	80.49 25	90.02 11	58.55 9	87.30 75	84.27 149	57.51 244	65.77 150	87.77 153	41.61 175	95.97 11	51.71 264	82.63 61	86.94 183

MGCFI-Net		74.07 98	74.64 83	72.34 237	82.90 128	43.33 330	80.04 278	79.96 231	65.61 86	74.93 51	91.85 57	48.01 82	80.86 314	71.41 105	77.10 116	92.84 24

IB-MVS	68.87 2	74.01 99	72.03 123	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 58	61.38 208	81.74 246	53.21 44	94.28 21	60.45 191	62.41 266	90.03 109
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		73.95 100	72.89 103	77.15 103	80.17 200	50.37 187	84.68 154	83.33 168	68.08 41	71.97 86	88.65 132	42.50 160	91.15 80	78.82 51	57.78 307	89.91 113

WBMVS		73.93 101	73.39 94	75.55 146	87.82 39	55.21 65	89.37 37	87.29 74	67.27 55	63.70 181	80.30 258	60.32 6	86.47 237	61.58 177	62.85 263	84.97 225

HY-MVS	67.03 5	73.90 102	73.14 100	76.18 128	84.70 80	47.36 275	75.56 309	86.36 93	66.27 73	70.66 106	83.91 202	51.05 57	89.31 130	67.10 134	72.61 172	91.88 51

CostFormer		73.89 103	72.30 113	78.66 65	82.36 143	56.58 33	75.56 309	85.30 117	66.06 81	70.50 110	76.88 299	57.02 22	89.06 138	68.27 128	68.74 206	90.33 96

fmvsm_s_conf0.1_n		73.80 104	73.26 97	75.43 151	73.28 314	47.80 267	84.57 159	69.43 364	63.34 130	78.40 34	93.29 25	44.73 133	89.22 134	85.99 9	66.28 229	89.26 127

ACMMPR		73.76 105	72.61 104	77.24 102	83.92 97	52.96 131	85.58 117	84.29 148	56.82 257	65.12 155	90.45 88	37.24 230	90.18 108	69.18 121	80.84 76	88.58 147

region2R		73.75 106	72.55 106	77.33 96	83.90 98	52.98 130	85.54 121	84.09 154	56.83 256	65.10 156	90.45 88	37.34 227	90.24 106	68.89 123	80.83 77	88.77 143

CANet_DTU		73.71 107	73.14 100	75.40 152	82.61 139	50.05 195	84.67 156	79.36 248	69.72 32	75.39 48	90.03 104	29.41 314	85.93 261	67.99 129	79.11 99	90.22 100

test_fmvsmconf0.1_n		73.69 108	73.15 98	75.34 154	70.71 344	48.26 249	82.15 229	71.83 343	66.75 65	74.47 58	92.59 41	44.89 127	87.78 195	83.59 21	71.35 184	89.97 110

fmvsm_s_conf0.5_n_a		73.68 109	73.15 98	75.29 159	75.45 287	48.05 258	83.88 181	68.84 367	63.43 129	78.60 31	93.37 23	45.32 118	88.92 149	85.39 12	64.04 244	88.89 138

thisisatest0515		73.64 110	72.20 115	77.97 82	81.63 162	53.01 129	86.69 91	88.81 42	62.53 144	64.06 174	85.65 181	52.15 51	92.50 46	58.43 204	69.84 197	88.39 154

MVSFormer		73.53 111	72.19 116	77.57 91	83.02 122	55.24 63	81.63 246	81.44 203	50.28 317	76.67 42	90.91 78	44.82 130	86.11 248	60.83 183	80.09 86	91.36 68

PVSNet_BlendedMVS		73.42 112	73.30 96	73.76 202	85.91 57	51.83 156	86.18 99	84.24 152	65.40 91	69.09 117	80.86 254	46.70 97	88.13 180	75.43 76	65.92 232	81.33 291

PVSNet_Blended_VisFu		73.40 113	72.44 108	76.30 121	81.32 176	54.70 83	85.81 107	78.82 258	63.70 121	64.53 167	85.38 185	47.11 92	87.38 212	67.75 130	77.55 112	86.81 192

RRT-MVS		73.29 114	71.37 132	79.07 52	84.63 81	54.16 99	78.16 295	86.64 88	61.67 159	60.17 219	82.35 237	40.63 187	92.26 53	70.19 112	77.87 109	90.81 84

MVSTER		73.25 115	72.33 111	76.01 133	85.54 65	53.76 105	83.52 187	87.16 76	67.06 61	63.88 179	81.66 247	52.77 46	90.44 98	64.66 159	64.69 240	83.84 249

EI-MVSNet-Vis-set		73.19 116	72.60 105	74.99 170	82.56 140	49.80 202	82.55 220	89.00 34	66.17 76	65.89 147	88.98 122	43.83 139	92.29 51	65.38 154	69.01 203	82.87 268

PMMVS		72.98 117	72.05 121	75.78 137	83.57 102	48.60 234	84.08 172	82.85 181	61.62 160	68.24 124	90.33 93	28.35 318	87.78 195	72.71 101	76.69 123	90.95 81

XVS		72.92 118	71.62 126	76.81 114	83.41 106	52.48 139	84.88 147	83.20 174	58.03 229	63.91 177	89.63 111	35.50 259	89.78 117	65.50 146	80.50 80	88.16 157

test2506		72.91 119	72.43 109	74.32 184	80.12 201	44.18 319	83.19 203	84.77 137	64.02 112	65.97 145	87.43 159	47.67 86	88.72 154	59.08 197	79.66 94	90.08 107

TESTMET0.1,1		72.86 120	72.33 111	74.46 178	81.98 148	50.77 174	85.13 134	85.47 108	66.09 79	67.30 130	83.69 207	37.27 228	83.57 293	65.06 157	78.97 101	89.05 135

fmvsm_s_conf0.1_n_a		72.82 121	72.05 121	75.12 165	70.95 343	47.97 261	82.72 214	68.43 369	62.52 145	78.17 35	93.08 31	44.21 136	88.86 150	84.82 14	63.54 250	88.54 149

Fast-Effi-MVS+		72.73 122	71.15 136	77.48 93	82.75 134	54.76 79	86.77 90	80.64 218	63.05 136	65.93 146	84.01 200	44.42 135	89.03 140	56.45 233	76.36 128	88.64 145

MTAPA		72.73 122	71.22 134	77.27 99	81.54 168	53.57 108	67.06 364	81.31 205	59.41 200	68.39 122	90.96 75	36.07 254	89.01 141	73.80 94	82.45 64	89.23 129

PGM-MVS		72.60 124	71.20 135	76.80 116	82.95 125	52.82 135	83.07 208	82.14 188	56.51 265	63.18 187	89.81 108	35.68 258	89.76 119	67.30 132	80.19 85	87.83 166

HPM-MVS		72.60 124	71.50 128	75.89 135	82.02 147	51.42 166	80.70 267	83.05 176	56.12 269	64.03 175	89.53 112	37.55 221	88.37 168	70.48 111	80.04 88	87.88 165
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 126	71.46 129	76.00 134	82.93 127	52.32 145	86.93 86	82.48 185	55.15 279	63.65 182	90.44 91	35.03 266	88.53 164	68.69 124	77.83 111	87.15 181

baseline1		72.51 127	72.12 119	73.69 205	85.05 74	44.46 312	83.51 191	86.13 99	71.61 18	64.64 163	87.97 149	55.00 35	89.48 125	59.07 198	56.05 320	87.13 182

EI-MVSNet-UG-set		72.37 128	71.73 124	74.29 185	81.60 164	49.29 216	81.85 238	88.64 47	65.29 96	65.05 157	88.29 140	43.18 152	91.83 62	63.74 162	67.97 211	81.75 279

MS-PatchMatch		72.34 129	71.26 133	75.61 142	82.38 142	55.55 52	88.00 55	89.95 22	65.38 92	56.51 286	80.74 256	32.28 294	92.89 34	57.95 215	88.10 15	78.39 326

HQP-MVS		72.34 129	71.44 130	75.03 167	79.02 220	51.56 162	88.00 55	83.68 162	65.45 88	64.48 168	85.13 186	37.35 225	88.62 157	66.70 135	73.12 165	84.91 227

testing3-2		72.30 131	72.35 110	72.15 241	83.07 119	47.64 269	85.46 122	89.81 24	66.17 76	61.96 203	84.88 193	58.93 12	82.27 301	55.87 235	64.97 236	86.54 195

mvs_anonymous		72.29 132	70.74 140	76.94 112	82.85 131	54.72 82	78.43 294	81.54 201	63.77 119	61.69 205	79.32 268	51.11 56	85.31 268	62.15 173	75.79 137	90.79 85

3Dnovator+	62.71 7	72.29 132	70.50 144	77.65 90	83.40 109	51.29 170	87.32 73	86.40 92	59.01 214	58.49 252	88.32 139	32.40 292	91.27 74	57.04 226	82.15 67	90.38 95

nrg030		72.27 134	71.56 127	74.42 180	75.93 280	50.60 178	86.97 84	83.21 173	62.75 140	67.15 132	84.38 195	50.07 67	86.66 231	71.19 106	62.37 267	85.99 207

UWE-MVS		72.17 135	72.15 117	72.21 239	82.26 144	44.29 316	86.83 89	89.58 25	65.58 87	65.82 148	85.06 188	45.02 123	84.35 283	54.07 246	75.18 145	87.99 164

VPNet		72.07 136	71.42 131	74.04 191	78.64 231	47.17 279	89.91 31	87.97 61	72.56 12	64.66 162	85.04 189	41.83 173	88.33 172	61.17 181	60.97 273	86.62 194

fmvsm_s_conf0.5_n_2		72.02 137	71.72 125	72.92 220	76.79 262	45.90 296	84.48 160	66.11 375	64.26 106	76.12 46	93.40 20	36.26 250	86.04 254	81.47 37	66.54 224	86.82 191

DP-MVS Recon		71.99 138	70.31 151	77.01 107	90.65 8	53.44 113	89.37 37	82.97 179	56.33 267	63.56 185	89.47 113	34.02 277	92.15 57	54.05 247	72.41 173	85.43 220

test_fmvsmconf0.01_n		71.97 139	70.95 139	75.04 166	66.21 369	47.87 264	80.35 272	70.08 359	65.85 85	72.69 76	91.68 62	39.99 195	87.67 199	82.03 31	69.66 199	89.58 118

SDMVSNet		71.89 140	70.62 143	75.70 140	81.70 158	51.61 160	73.89 322	88.72 45	66.58 66	61.64 206	82.38 234	37.63 218	89.48 125	77.44 65	65.60 233	86.01 205

QAPM		71.88 141	69.33 168	79.52 40	82.20 146	54.30 93	86.30 97	88.77 43	56.61 263	59.72 224	87.48 157	33.90 279	95.36 13	47.48 292	81.49 72	88.90 137

ECVR-MVS		71.81 142	71.00 138	74.26 186	80.12 201	43.49 325	84.69 153	82.16 187	64.02 112	64.64 163	87.43 159	35.04 265	89.21 135	61.24 180	79.66 94	90.08 107

PAPM_NR		71.80 143	69.98 158	77.26 101	81.54 168	53.34 118	78.60 293	85.25 121	53.46 295	60.53 217	88.66 129	45.69 112	89.24 132	56.49 230	79.62 96	89.19 131

mPP-MVS		71.79 144	70.38 149	76.04 132	82.65 138	52.06 149	84.45 161	81.78 198	55.59 274	62.05 202	89.68 110	33.48 283	88.28 177	65.45 151	78.24 107	87.77 168

reproduce-ours		71.77 145	70.43 146	75.78 137	81.96 149	49.54 209	82.54 221	81.01 212	48.77 329	69.21 114	90.96 75	37.13 233	89.40 127	66.28 140	76.01 133	88.39 154

our_new_method		71.77 145	70.43 146	75.78 137	81.96 149	49.54 209	82.54 221	81.01 212	48.77 329	69.21 114	90.96 75	37.13 233	89.40 127	66.28 140	76.01 133	88.39 154

xiu_mvs_v1_base_debu		71.60 147	70.29 152	75.55 146	77.26 253	53.15 123	85.34 123	79.37 245	55.83 271	72.54 77	90.19 98	22.38 361	86.66 231	73.28 97	76.39 125	86.85 187

xiu_mvs_v1_base		71.60 147	70.29 152	75.55 146	77.26 253	53.15 123	85.34 123	79.37 245	55.83 271	72.54 77	90.19 98	22.38 361	86.66 231	73.28 97	76.39 125	86.85 187

xiu_mvs_v1_base_debi		71.60 147	70.29 152	75.55 146	77.26 253	53.15 123	85.34 123	79.37 245	55.83 271	72.54 77	90.19 98	22.38 361	86.66 231	73.28 97	76.39 125	86.85 187

fmvsm_s_conf0.1_n_2		71.45 150	71.01 137	72.78 224	75.37 288	45.82 300	84.18 169	64.59 380	64.02 112	75.67 47	93.02 33	34.99 267	85.99 256	81.18 41	66.04 231	86.52 197

hse-mvs2		71.44 151	70.68 141	73.73 204	76.34 266	47.44 274	79.45 286	79.47 244	68.08 41	71.97 86	86.01 179	42.50 160	86.93 224	78.82 51	53.46 344	86.83 190

test_fmvsmvis_n_1920		71.29 152	70.38 149	74.00 193	71.04 342	48.79 230	79.19 289	64.62 379	62.75 140	66.73 133	91.99 54	40.94 181	88.35 170	83.00 23	73.18 164	84.85 229

EPP-MVSNet		71.14 153	70.07 157	74.33 183	79.18 216	46.52 286	83.81 183	86.49 89	56.32 268	57.95 258	84.90 192	54.23 39	89.14 136	58.14 211	69.65 200	87.33 178

VPA-MVSNet		71.12 154	70.66 142	72.49 232	78.75 226	44.43 314	87.64 65	90.02 20	63.97 116	65.02 158	81.58 249	42.14 166	87.42 210	63.42 164	63.38 254	85.63 217

1314		71.11 155	69.41 165	76.22 124	79.32 212	50.49 181	80.23 275	85.14 127	59.44 199	58.93 241	88.89 125	33.83 281	89.60 124	61.49 178	77.42 115	88.57 148

reproduce_model		71.07 156	69.67 162	75.28 161	81.51 171	48.82 229	81.73 243	80.57 221	47.81 335	68.26 123	90.78 82	36.49 248	88.60 159	65.12 156	74.76 154	88.42 153

test1111		71.06 157	70.42 148	72.97 219	79.48 209	41.49 348	84.82 150	82.74 182	64.20 109	62.98 190	87.43 159	35.20 262	87.92 187	58.54 203	78.42 105	89.49 123

tpmrst		71.04 158	69.77 160	74.86 173	83.19 115	55.86 50	75.64 308	78.73 262	67.88 46	64.99 160	73.73 329	49.96 71	79.56 334	65.92 143	67.85 213	89.14 133

MVP-Stereo		70.97 159	70.44 145	72.59 229	76.03 276	51.36 167	85.02 141	86.99 79	60.31 186	56.53 285	78.92 273	40.11 193	90.00 111	60.00 195	90.01 7	76.41 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 160	69.91 159	74.12 189	77.95 241	49.57 204	85.76 109	82.59 183	63.60 124	62.15 200	83.28 215	36.04 255	88.30 175	65.46 149	72.34 175	84.49 231

SR-MVS		70.92 161	69.73 161	74.50 177	83.38 110	50.48 182	84.27 166	79.35 249	48.96 327	66.57 139	90.45 88	33.65 282	87.11 217	66.42 137	74.56 156	85.91 210

tpm2		70.82 162	68.44 178	77.98 81	80.78 188	56.11 44	74.21 321	81.28 207	60.24 187	68.04 126	75.27 317	52.26 50	88.50 165	55.82 238	68.03 210	89.33 126

ACMMP		70.81 163	69.29 169	75.39 153	81.52 170	51.92 154	83.43 194	83.03 177	56.67 262	58.80 246	88.91 124	31.92 299	88.58 160	65.89 145	73.39 163	85.67 214
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		70.75 164	69.58 163	74.26 186	75.55 286	51.34 168	86.05 103	83.29 172	61.94 155	62.95 191	85.77 180	34.15 276	88.44 166	65.44 152	71.07 186	82.99 265
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ab-mvs		70.65 165	69.11 171	75.29 159	80.87 185	46.23 294	73.48 326	85.24 122	59.99 189	66.65 135	80.94 253	43.13 155	88.69 155	63.58 163	68.07 209	90.95 81

Vis-MVSNet		70.61 166	69.34 167	74.42 180	80.95 184	48.49 239	86.03 104	77.51 284	58.74 220	65.55 152	87.78 152	34.37 274	85.95 260	52.53 262	80.61 78	88.80 141
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 167	70.13 156	71.58 260	81.59 165	39.02 359	80.78 266	84.71 139	59.34 202	66.61 137	88.09 144	37.17 232	85.52 264	61.82 176	71.02 187	90.20 102

CDS-MVSNet		70.48 168	69.43 164	73.64 206	77.56 248	48.83 228	83.51 191	77.45 285	63.27 132	62.33 197	85.54 184	43.85 138	83.29 298	57.38 225	74.00 158	88.79 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 169	68.56 175	76.20 126	79.78 205	51.52 164	83.49 193	88.58 52	57.62 242	58.60 248	82.79 220	51.03 58	91.48 69	52.84 256	62.36 268	85.59 218

XXY-MVS		70.18 170	69.28 170	72.89 223	77.64 245	42.88 335	85.06 138	87.50 73	62.58 143	62.66 195	82.34 238	43.64 146	89.83 116	58.42 206	63.70 249	85.96 209

Anonymous202405211		70.11 171	67.88 189	76.79 117	87.20 45	47.24 278	89.49 35	77.38 287	54.88 284	66.14 142	86.84 167	20.93 370	91.54 68	56.45 233	71.62 181	91.59 58

PCF-MVS	61.03 10	70.10 172	68.40 179	75.22 164	77.15 257	51.99 151	79.30 288	82.12 189	56.47 266	61.88 204	86.48 175	43.98 137	87.24 214	55.37 239	72.79 170	86.43 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 173	68.01 185	76.27 122	84.21 92	51.22 172	87.29 76	79.33 251	58.96 216	63.63 183	86.77 168	33.29 285	90.30 105	44.63 310	73.96 159	87.30 180

1112_ss		70.05 174	69.37 166	72.10 242	80.77 189	42.78 336	85.12 137	76.75 297	59.69 194	61.19 210	92.12 48	47.48 88	83.84 288	53.04 254	68.21 208	89.66 116

BH-w/o		70.02 175	68.51 177	74.56 176	82.77 133	50.39 185	86.60 93	78.14 274	59.77 192	59.65 225	85.57 183	39.27 201	87.30 213	49.86 275	74.94 153	85.99 207

FIs		70.00 176	70.24 155	69.30 292	77.93 243	38.55 362	83.99 176	87.72 68	66.86 64	57.66 265	84.17 198	52.28 49	85.31 268	52.72 261	68.80 205	84.02 240

OpenMVS	61.00 11	69.99 177	67.55 198	77.30 97	78.37 237	54.07 101	84.36 163	85.76 105	57.22 250	56.71 282	87.67 155	30.79 307	92.83 36	43.04 318	84.06 56	85.01 224

GeoE		69.96 178	67.88 189	76.22 124	81.11 178	51.71 159	84.15 170	76.74 299	59.83 191	60.91 212	84.38 195	41.56 176	88.10 182	51.67 265	70.57 192	88.84 140

HyFIR lowres test		69.94 179	67.58 196	77.04 105	77.11 258	57.29 22	81.49 254	79.11 254	58.27 226	58.86 244	80.41 257	42.33 162	86.96 222	61.91 174	68.68 207	86.87 185

114514_t		69.87 180	67.88 189	75.85 136	88.38 29	52.35 144	86.94 85	83.68 162	53.70 293	55.68 292	85.60 182	30.07 312	91.20 78	55.84 237	71.02 187	83.99 242

miper_enhance_ethall		69.77 181	68.90 173	72.38 235	78.93 223	49.91 198	83.29 200	78.85 256	64.90 98	59.37 232	79.46 266	52.77 46	85.16 273	63.78 161	58.72 289	82.08 274

reproduce_monomvs		69.71 182	68.52 176	73.29 215	86.43 53	48.21 251	83.91 179	86.17 98	68.02 45	54.91 297	77.46 287	42.96 157	88.86 150	68.44 125	48.38 357	82.80 269

Anonymous20240529		69.71 182	67.28 204	77.00 108	83.78 100	50.36 188	88.87 46	85.10 128	47.22 339	64.03 175	83.37 213	27.93 322	92.10 58	57.78 220	67.44 215	88.53 150

TR-MVS		69.71 182	67.85 192	75.27 162	82.94 126	48.48 240	87.40 72	80.86 215	57.15 252	64.61 165	87.08 164	32.67 290	89.64 123	46.38 301	71.55 183	87.68 171

EI-MVSNet		69.70 185	68.70 174	72.68 227	75.00 293	48.90 226	79.54 283	87.16 76	61.05 172	63.88 179	83.74 205	45.87 108	90.44 98	57.42 224	64.68 241	78.70 319

test-LLR		69.65 186	69.01 172	71.60 258	78.67 228	48.17 252	85.13 134	79.72 237	59.18 209	63.13 188	82.58 228	36.91 239	80.24 324	60.56 187	75.17 146	86.39 201

APD-MVS_3200maxsize		69.62 187	68.23 183	73.80 201	81.58 166	48.22 250	81.91 236	79.50 243	48.21 333	64.24 173	89.75 109	31.91 300	87.55 205	63.08 165	73.85 161	85.64 216

v2v482		69.55 188	67.64 195	75.26 163	72.32 328	53.83 102	84.93 146	81.94 192	65.37 93	60.80 214	79.25 269	41.62 174	88.98 145	63.03 166	59.51 282	82.98 266

TAMVS		69.51 189	68.16 184	73.56 210	76.30 269	48.71 233	82.57 218	77.17 290	62.10 151	61.32 209	84.23 197	41.90 171	83.46 295	54.80 243	73.09 167	88.50 151

mvsmamba		69.38 190	67.52 200	74.95 171	82.86 130	52.22 148	67.36 362	76.75 297	61.14 169	49.43 336	82.04 243	37.26 229	84.14 284	73.93 91	76.91 119	88.50 151

WB-MVSnew		69.36 191	68.24 182	72.72 226	79.26 214	49.40 213	85.72 114	88.85 40	61.33 165	64.59 166	82.38 234	34.57 272	87.53 206	46.82 298	70.63 190	81.22 295

PVSNet	62.49 8	69.27 192	67.81 193	73.64 206	84.41 86	51.85 155	84.63 157	77.80 278	66.42 70	59.80 223	84.95 191	22.14 365	80.44 322	55.03 240	75.11 149	88.62 146

MVS_111021_LR		69.07 193	67.91 187	72.54 230	77.27 252	49.56 206	79.77 281	73.96 325	59.33 204	60.73 215	87.82 151	30.19 311	81.53 307	69.94 114	72.19 177	86.53 196

GA-MVS		69.04 194	66.70 214	76.06 131	75.11 290	52.36 143	83.12 206	80.23 226	63.32 131	60.65 216	79.22 270	30.98 306	88.37 168	61.25 179	66.41 225	87.46 175

cascas		69.01 195	66.13 226	77.66 89	79.36 210	55.41 58	86.99 83	83.75 161	56.69 261	58.92 242	81.35 250	24.31 350	92.10 58	53.23 251	70.61 191	85.46 219

FA-MVS(test-final)		69.00 196	66.60 217	76.19 127	83.48 105	47.96 263	74.73 316	82.07 190	57.27 249	62.18 199	78.47 277	36.09 253	92.89 34	53.76 250	71.32 185	87.73 169

cl22		68.85 197	67.69 194	72.35 236	78.07 240	49.98 197	82.45 225	78.48 268	62.50 146	58.46 253	77.95 279	49.99 69	85.17 272	62.55 168	58.72 289	81.90 277

FMVSNet3		68.84 198	67.40 202	73.19 216	85.05 74	48.53 237	85.71 115	85.36 113	60.90 178	57.58 267	79.15 271	42.16 165	86.77 227	47.25 294	63.40 251	84.27 235

UniMVSNet_NR-MVSNet		68.82 199	68.29 181	70.40 278	75.71 283	42.59 338	84.23 167	86.78 82	66.31 72	58.51 249	82.45 231	51.57 53	84.64 281	53.11 252	55.96 321	83.96 246

v1144		68.81 200	66.82 210	74.80 174	72.34 327	53.46 110	84.68 154	81.77 199	64.25 107	60.28 218	77.91 280	40.23 190	88.95 146	60.37 192	59.52 281	81.97 275

IS-MVSNet		68.80 201	67.55 198	72.54 230	78.50 234	43.43 327	81.03 259	79.35 249	59.12 212	57.27 275	86.71 169	46.05 106	87.70 198	44.32 313	75.60 140	86.49 198

PS-MVSNAJss		68.78 202	67.17 206	73.62 208	73.01 318	48.33 247	84.95 145	84.81 135	59.30 205	58.91 243	79.84 263	37.77 213	88.86 150	62.83 167	63.12 260	83.67 253

thres200		68.71 203	67.27 205	73.02 217	84.73 79	46.76 282	85.03 140	87.73 67	62.34 149	59.87 221	83.45 211	43.15 153	88.32 173	31.25 368	67.91 212	83.98 244

UGNet		68.71 203	67.11 207	73.50 211	80.55 195	47.61 270	84.08 172	78.51 267	59.45 198	65.68 151	82.73 224	23.78 352	85.08 275	52.80 257	76.40 124	87.80 167
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		68.70 205	67.58 196	72.08 243	76.91 261	49.48 212	82.47 224	78.45 269	62.68 142	58.28 257	77.88 281	50.90 59	85.01 276	61.91 174	58.72 289	81.75 279

test_vis1_n_1920		68.59 206	68.31 180	69.44 291	69.16 355	41.51 347	84.63 157	68.58 368	58.80 218	73.26 69	88.37 135	25.30 341	80.60 319	79.10 48	67.55 214	86.23 203

EPMVS		68.45 207	65.44 245	77.47 94	84.91 77	56.17 43	71.89 343	81.91 195	61.72 158	60.85 213	72.49 343	36.21 251	87.06 219	47.32 293	71.62 181	89.17 132

test-mter		68.36 208	67.29 203	71.60 258	78.67 228	48.17 252	85.13 134	79.72 237	53.38 296	63.13 188	82.58 228	27.23 328	80.24 324	60.56 187	75.17 146	86.39 201

tpm		68.36 208	67.48 201	70.97 270	79.93 204	51.34 168	76.58 305	78.75 261	67.73 49	63.54 186	74.86 319	48.33 78	72.36 379	53.93 248	63.71 248	89.21 130

tttt0517		68.33 210	66.29 222	74.46 178	78.08 239	49.06 218	80.88 264	89.08 33	54.40 290	54.75 300	80.77 255	51.31 55	90.33 102	49.35 279	58.01 301	83.99 242

BH-untuned		68.28 211	66.40 219	73.91 196	81.62 163	50.01 196	85.56 119	77.39 286	57.63 241	57.47 272	83.69 207	36.36 249	87.08 218	44.81 308	73.08 168	84.65 230

SR-MVS-dyc-post		68.27 212	66.87 209	72.48 233	80.96 181	48.14 254	81.54 250	76.98 293	46.42 346	62.75 193	89.42 114	31.17 305	86.09 252	60.52 189	72.06 178	83.19 261

v148		68.24 213	66.35 220	73.88 197	71.76 332	51.47 165	84.23 167	81.90 196	63.69 122	58.94 240	76.44 304	43.72 144	87.78 195	60.63 185	55.86 323	82.39 272

AUN-MVS		68.20 214	66.35 220	73.76 202	76.37 265	47.45 273	79.52 285	79.52 242	60.98 174	62.34 196	86.02 177	36.59 247	86.94 223	62.32 170	53.47 343	86.89 184

SSC-MVS3.2		68.13 215	66.89 208	71.85 256	82.26 144	43.97 320	82.09 232	89.29 28	71.74 15	61.12 211	79.83 264	34.60 271	87.45 208	41.23 324	59.85 279	84.14 236

c3_l		67.97 216	66.66 215	71.91 254	76.20 272	49.31 215	82.13 231	78.00 276	61.99 153	57.64 266	76.94 296	49.41 74	84.93 277	60.62 186	57.01 311	81.49 283

v1192		67.96 217	65.74 237	74.63 175	71.79 331	53.43 115	84.06 174	80.99 214	63.19 134	59.56 228	77.46 287	37.50 224	88.65 156	58.20 210	58.93 288	81.79 278

v144192		67.86 218	65.76 236	74.16 188	71.68 333	53.09 126	84.14 171	80.83 216	62.85 139	59.21 237	77.28 291	39.30 200	88.00 186	58.67 202	57.88 305	81.40 288

HPM-MVS_fast		67.86 218	66.28 223	72.61 228	80.67 192	48.34 245	81.18 257	75.95 308	50.81 315	59.55 229	88.05 147	27.86 323	85.98 257	58.83 200	73.58 162	83.51 254

AdaColmap		67.86 218	65.48 242	75.00 169	88.15 36	54.99 74	86.10 101	76.63 302	49.30 324	57.80 261	86.65 172	29.39 315	88.94 148	45.10 307	70.21 195	81.06 296

sd_testset		67.79 221	65.95 231	73.32 212	81.70 158	46.33 291	68.99 355	80.30 225	66.58 66	61.64 206	82.38 234	30.45 309	87.63 201	55.86 236	65.60 233	86.01 205

UniMVSNet (Re)		67.71 222	66.80 211	70.45 276	74.44 300	42.93 334	82.42 226	84.90 132	63.69 122	59.63 226	80.99 252	47.18 90	85.23 271	51.17 269	56.75 312	83.19 261

V42		67.66 223	65.60 241	73.86 198	70.69 346	53.63 107	81.50 252	78.61 265	63.85 118	59.49 231	77.49 286	37.98 210	87.65 200	62.33 169	58.43 292	80.29 306

dmvs_re		67.61 224	66.00 229	72.42 234	81.86 153	43.45 326	64.67 370	80.00 229	69.56 34	60.07 220	85.00 190	34.71 269	87.63 201	51.48 266	66.68 219	86.17 204

WR-MVS		67.58 225	66.76 212	70.04 285	75.92 281	45.06 310	86.23 98	85.28 119	64.31 105	58.50 251	81.00 251	44.80 132	82.00 306	49.21 281	55.57 326	83.06 264

tfpn200view9		67.57 226	66.13 226	71.89 255	84.05 94	45.07 307	83.40 196	87.71 69	60.79 179	57.79 262	82.76 221	43.53 147	87.80 192	28.80 375	66.36 226	82.78 270

FMVSNet2		67.57 226	65.79 235	72.90 221	82.71 135	47.97 261	85.15 133	84.93 131	58.55 223	56.71 282	78.26 278	36.72 244	86.67 230	46.15 303	62.94 262	84.07 239

FC-MVSNet-test		67.49 228	67.91 187	66.21 324	76.06 274	33.06 384	80.82 265	87.18 75	64.44 102	54.81 298	82.87 218	50.40 66	82.60 300	48.05 289	66.55 223	82.98 266

v1921920		67.45 229	65.23 249	74.10 190	71.51 336	52.90 132	83.75 185	80.44 222	62.48 147	59.12 238	77.13 292	36.98 237	87.90 188	57.53 222	58.14 299	81.49 283

UWE-MVS-28		67.43 230	67.98 186	65.75 326	75.66 284	34.74 374	80.00 279	88.17 57	64.21 108	57.27 275	84.14 199	45.68 113	78.82 337	44.33 311	72.40 174	83.70 251

cl____		67.43 230	65.93 232	71.95 251	76.33 267	48.02 259	82.58 217	79.12 253	61.30 167	56.72 281	76.92 297	46.12 103	86.44 239	57.98 213	56.31 315	81.38 290

DIV-MVS_self_test		67.43 230	65.93 232	71.94 252	76.33 267	48.01 260	82.57 218	79.11 254	61.31 166	56.73 280	76.92 297	46.09 105	86.43 240	57.98 213	56.31 315	81.39 289

gg-mvs-nofinetune		67.43 230	64.53 256	76.13 129	85.95 56	47.79 268	64.38 371	88.28 56	39.34 376	66.62 136	41.27 413	58.69 15	89.00 142	49.64 277	86.62 31	91.59 58

thres400		67.40 234	66.13 226	71.19 266	84.05 94	45.07 307	83.40 196	87.71 69	60.79 179	57.79 262	82.76 221	43.53 147	87.80 192	28.80 375	66.36 226	80.71 301

UA-Net		67.32 235	66.23 224	70.59 274	78.85 224	41.23 351	73.60 324	75.45 312	61.54 162	66.61 137	84.53 194	38.73 206	86.57 236	42.48 323	74.24 157	83.98 244

v8		67.25 236	64.99 252	74.04 191	72.89 321	53.31 120	82.37 227	80.11 228	61.54 162	54.29 306	76.02 313	42.89 158	88.41 167	58.43 204	56.36 313	80.39 305

NR-MVSNet		67.25 236	65.99 230	71.04 269	73.27 315	43.91 321	85.32 127	84.75 138	66.05 82	53.65 313	82.11 241	45.05 122	85.97 259	47.55 291	56.18 318	83.24 259

Test_1112_low_res		67.18 238	66.23 224	70.02 286	78.75 226	41.02 352	83.43 194	73.69 327	57.29 248	58.45 254	82.39 233	45.30 119	80.88 313	50.50 271	66.26 230	88.16 157

CPTT-MVS		67.15 239	65.84 234	71.07 268	80.96 181	50.32 190	81.94 235	74.10 321	46.18 349	57.91 259	87.64 156	29.57 313	81.31 309	64.10 160	70.18 196	81.56 282

test_cas_vis1_n_1920		67.10 240	66.60 217	68.59 304	65.17 377	43.23 331	83.23 202	69.84 361	55.34 278	70.67 105	87.71 154	24.70 348	76.66 359	78.57 55	64.20 243	85.89 211

GBi-Net		67.09 241	65.47 243	71.96 248	82.71 135	46.36 288	83.52 187	83.31 169	58.55 223	57.58 267	76.23 308	36.72 244	86.20 244	47.25 294	63.40 251	83.32 256

test1		67.09 241	65.47 243	71.96 248	82.71 135	46.36 288	83.52 187	83.31 169	58.55 223	57.58 267	76.23 308	36.72 244	86.20 244	47.25 294	63.40 251	83.32 256

PatchmatchNet		67.07 243	63.63 263	77.40 95	83.10 116	58.03 11	72.11 341	77.77 279	58.85 217	59.37 232	70.83 356	37.84 212	84.93 277	42.96 319	69.83 198	89.26 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 244	64.68 254	73.93 195	71.38 339	52.66 137	83.39 198	79.98 230	61.97 154	58.44 255	77.11 293	35.25 261	87.81 190	56.46 232	58.15 297	81.33 291

eth_miper_zixun_eth		66.98 245	65.28 248	72.06 244	75.61 285	50.40 184	81.00 260	76.97 296	62.00 152	56.99 278	76.97 295	44.84 129	85.58 263	58.75 201	54.42 334	80.21 307

TranMVSNet+NR-MVSNet		66.94 246	65.61 240	70.93 271	73.45 311	43.38 328	83.02 210	84.25 150	65.31 95	58.33 256	81.90 245	39.92 197	85.52 264	49.43 278	54.89 330	83.89 248

thres100view900		66.87 247	65.42 246	71.24 264	83.29 112	43.15 332	81.67 245	87.78 64	59.04 213	55.92 290	82.18 240	43.73 142	87.80 192	28.80 375	66.36 226	82.78 270

DU-MVS		66.84 248	65.74 237	70.16 281	73.27 315	42.59 338	81.50 252	82.92 180	63.53 126	58.51 249	82.11 241	40.75 183	84.64 281	53.11 252	55.96 321	83.24 259

MonoMVSNet		66.80 249	64.41 257	73.96 194	76.21 271	48.07 257	76.56 306	78.26 272	64.34 104	54.32 305	74.02 326	37.21 231	86.36 242	64.85 158	53.96 337	87.45 176

IterMVS-LS		66.63 250	65.36 247	70.42 277	75.10 291	48.90 226	81.45 255	76.69 301	61.05 172	55.71 291	77.10 294	45.86 109	83.65 292	57.44 223	57.88 305	78.70 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 251	64.20 260	73.83 200	72.59 324	53.37 116	81.88 237	79.91 234	61.11 170	54.09 308	75.60 315	40.06 194	88.26 178	56.47 231	56.10 319	79.86 311

Fast-Effi-MVS+-dtu		66.53 252	64.10 261	73.84 199	72.41 326	52.30 146	84.73 151	75.66 309	59.51 197	56.34 287	79.11 272	28.11 320	85.85 262	57.74 221	63.29 255	83.35 255

thres600view7		66.46 253	65.12 250	70.47 275	83.41 106	43.80 323	82.15 229	87.78 64	59.37 201	56.02 289	82.21 239	43.73 142	86.90 225	26.51 387	64.94 237	80.71 301

LPG-MVS_test		66.44 254	64.58 255	72.02 245	74.42 301	48.60 234	83.07 208	80.64 218	54.69 286	53.75 311	83.83 203	25.73 339	86.98 220	60.33 193	64.71 238	80.48 303

tpm cat1		66.28 255	62.78 265	76.77 118	81.40 173	57.14 24	70.03 350	77.19 289	53.00 299	58.76 247	70.73 359	46.17 102	86.73 229	43.27 317	64.46 242	86.44 199

EPNet_dtu		66.25 256	66.71 213	64.87 335	78.66 230	34.12 379	82.80 213	75.51 310	61.75 157	64.47 171	86.90 166	37.06 235	72.46 378	43.65 316	69.63 201	88.02 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 257	64.96 253	70.08 283	75.17 289	49.64 203	82.01 233	74.48 319	62.15 150	57.83 260	76.08 312	30.59 308	83.79 289	65.40 153	60.93 274	76.81 341

ACMP	61.11 9	66.24 257	64.33 258	72.00 247	74.89 295	49.12 217	83.18 204	79.83 235	55.41 277	52.29 320	82.68 225	25.83 337	86.10 250	60.89 182	63.94 247	80.78 299
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 259	63.67 262	73.31 213	83.07 119	48.75 231	86.01 105	84.67 141	45.27 353	56.54 284	76.67 302	28.06 321	88.95 146	52.78 258	59.95 276	82.23 273

OMC-MVS		65.97 260	65.06 251	68.71 301	72.97 319	42.58 340	78.61 292	75.35 313	54.72 285	59.31 234	86.25 176	33.30 284	77.88 348	57.99 212	67.05 217	85.66 215

X-MVStestdata		65.85 261	62.20 269	76.81 114	83.41 106	52.48 139	84.88 147	83.20 174	58.03 229	63.91 177	4.82 432	35.50 259	89.78 117	65.50 146	80.50 80	88.16 157

Vis-MVSNet (Re-imp)		65.52 262	65.63 239	65.17 333	77.49 249	30.54 391	75.49 312	77.73 280	59.34 202	52.26 322	86.69 170	49.38 75	80.53 321	37.07 338	75.28 144	84.42 233

Baseline_NR-MVSNet		65.49 263	64.27 259	69.13 293	74.37 303	41.65 345	83.39 198	78.85 256	59.56 196	59.62 227	76.88 299	40.75 183	87.44 209	49.99 273	55.05 328	78.28 328

FMVSNet1		64.57 264	62.11 270	71.96 248	77.32 251	46.36 288	83.52 187	83.31 169	52.43 304	54.42 303	76.23 308	27.80 324	86.20 244	42.59 322	61.34 272	83.32 256

dp		64.41 265	61.58 273	72.90 221	82.40 141	54.09 100	72.53 333	76.59 303	60.39 185	55.68 292	70.39 360	35.18 263	76.90 357	39.34 330	61.71 270	87.73 169

ACMM	58.35 12	64.35 266	62.01 271	71.38 262	74.21 305	48.51 238	82.25 228	79.66 239	47.61 337	54.54 302	80.11 259	25.26 342	86.00 255	51.26 267	63.16 258	79.64 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 267	60.43 287	75.30 158	80.85 186	49.86 200	68.28 359	78.37 270	50.26 320	59.31 234	73.79 328	26.19 335	91.92 61	40.19 327	66.67 220	84.12 237

pm-mvs1		64.12 268	62.56 266	68.78 299	71.68 333	38.87 360	82.89 212	81.57 200	55.54 276	53.89 310	77.82 282	37.73 216	86.74 228	48.46 287	53.49 342	80.72 300

miper_lstm_enhance		63.91 269	62.30 268	68.75 300	75.06 292	46.78 281	69.02 354	81.14 208	59.68 195	52.76 317	72.39 346	40.71 185	77.99 346	56.81 228	53.09 345	81.48 285

SCA		63.84 270	60.01 291	75.32 155	78.58 232	57.92 12	61.61 383	77.53 283	56.71 260	57.75 264	70.77 357	31.97 297	79.91 330	48.80 283	56.36 313	88.13 160

test_djsdf		63.84 270	61.56 274	70.70 273	68.78 357	44.69 311	81.63 246	81.44 203	50.28 317	52.27 321	76.26 307	26.72 331	86.11 248	60.83 183	55.84 324	81.29 294

IterMVS		63.77 272	61.67 272	70.08 283	72.68 323	51.24 171	80.44 270	75.51 310	60.51 184	51.41 325	73.70 332	32.08 296	78.91 335	54.30 245	54.35 335	80.08 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 273	63.56 264	63.40 342	81.73 156	34.28 376	80.97 261	81.02 210	60.93 176	55.06 295	82.64 226	48.00 84	80.81 315	23.42 397	58.32 293	75.10 359

D2MVS		63.49 274	61.39 276	69.77 287	69.29 354	48.93 225	78.89 291	77.71 281	60.64 183	49.70 335	72.10 351	27.08 329	83.48 294	54.48 244	62.65 264	76.90 340

tt0805		63.39 275	61.31 278	69.64 288	69.36 353	38.87 360	78.00 296	85.48 107	48.82 328	55.66 294	81.66 247	24.38 349	86.37 241	49.04 282	59.36 285	83.68 252

pmmvs4		63.34 276	61.07 281	70.16 281	70.14 348	50.53 180	79.97 280	71.41 350	55.08 280	54.12 307	78.58 275	32.79 289	82.09 305	50.33 272	57.22 310	77.86 332

jajsoiax		63.21 277	60.84 282	70.32 279	68.33 362	44.45 313	81.23 256	81.05 209	53.37 297	50.96 330	77.81 283	17.49 384	85.49 266	59.31 196	58.05 300	81.02 297

MIMVSNet		63.12 278	60.29 288	71.61 257	75.92 281	46.65 284	65.15 367	81.94 192	59.14 211	54.65 301	69.47 363	25.74 338	80.63 318	41.03 326	69.56 202	87.55 173

CL-MVSNet_self_test		62.98 279	61.14 280	68.50 306	65.86 372	42.96 333	84.37 162	82.98 178	60.98 174	53.95 309	72.70 342	40.43 188	83.71 291	41.10 325	47.93 360	78.83 318

mvs_tets		62.96 280	60.55 284	70.19 280	68.22 365	44.24 318	80.90 263	80.74 217	52.99 300	50.82 332	77.56 284	16.74 388	85.44 267	59.04 199	57.94 302	80.89 298

TransMVSNet (Re)		62.82 281	60.76 283	69.02 294	73.98 308	41.61 346	86.36 95	79.30 252	56.90 254	52.53 318	76.44 304	41.85 172	87.60 204	38.83 331	40.61 384	77.86 332

pmmvs5		62.80 282	61.18 279	67.66 310	69.53 352	42.37 343	82.65 216	75.19 314	54.30 291	52.03 323	78.51 276	31.64 302	80.67 317	48.60 285	58.15 297	79.95 310

test0.0.03 1		62.54 283	62.44 267	62.86 347	72.28 330	29.51 400	82.93 211	78.78 259	59.18 209	53.07 316	82.41 232	36.91 239	77.39 352	37.45 334	58.96 287	81.66 281

UniMVSNet_ETH3D		62.51 284	60.49 285	68.57 305	68.30 363	40.88 354	73.89 322	79.93 233	51.81 310	54.77 299	79.61 265	24.80 346	81.10 310	49.93 274	61.35 271	83.73 250

v7n		62.50 285	59.27 296	72.20 240	67.25 368	49.83 201	77.87 298	80.12 227	52.50 303	48.80 341	73.07 337	32.10 295	87.90 188	46.83 297	54.92 329	78.86 317

CR-MVSNet		62.47 286	59.04 298	72.77 225	73.97 309	56.57 34	60.52 386	71.72 345	60.04 188	57.49 270	65.86 375	38.94 203	80.31 323	42.86 320	59.93 277	81.42 286

tpmvs		62.45 287	59.42 294	71.53 261	83.93 96	54.32 92	70.03 350	77.61 282	51.91 307	53.48 314	68.29 369	37.91 211	86.66 231	33.36 358	58.27 295	73.62 370

EG-PatchMatch MVS		62.40 288	59.59 292	70.81 272	73.29 313	49.05 219	85.81 107	84.78 136	51.85 309	44.19 361	73.48 335	15.52 393	89.85 115	40.16 328	67.24 216	73.54 371

XVG-OURS-SEG-HR		62.02 289	59.54 293	69.46 290	65.30 375	45.88 297	65.06 368	73.57 329	46.45 345	57.42 273	83.35 214	26.95 330	78.09 342	53.77 249	64.03 245	84.42 233

XVG-OURS		61.88 290	59.34 295	69.49 289	65.37 374	46.27 292	64.80 369	73.49 331	47.04 341	57.41 274	82.85 219	25.15 343	78.18 340	53.00 255	64.98 235	84.01 241

TAPA-MVS	56.12 14	61.82 291	60.18 290	66.71 320	78.48 235	37.97 366	75.19 314	76.41 305	46.82 342	57.04 277	86.52 174	27.67 326	77.03 354	26.50 388	67.02 218	85.14 222
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 292	61.35 277	62.00 350	81.73 156	30.09 395	80.97 261	81.02 210	60.93 176	55.06 295	82.64 226	35.09 264	80.81 315	16.40 414	58.32 293	75.10 359

tfpnnormal		61.47 293	59.09 297	68.62 303	76.29 270	41.69 344	81.14 258	85.16 125	54.48 288	51.32 326	73.63 333	32.32 293	86.89 226	21.78 401	55.71 325	77.29 338

PVSNet_0	57.04 13	61.19 294	57.24 307	73.02 217	77.45 250	50.31 191	79.43 287	77.36 288	63.96 117	47.51 351	72.45 345	25.03 344	83.78 290	52.76 260	19.22 420	84.96 226

PLC	52.38 18	60.89 295	58.97 299	66.68 322	81.77 155	45.70 302	78.96 290	74.04 324	43.66 365	47.63 348	83.19 217	23.52 355	77.78 351	37.47 333	60.46 275	76.55 347
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 296	60.44 286	62.07 348	75.00 293	32.73 386	79.54 283	73.49 331	36.98 384	56.28 288	83.74 205	29.28 316	69.53 387	46.48 300	63.23 256	83.94 247

CNLPA		60.59 297	58.44 301	67.05 317	79.21 215	47.26 277	79.75 282	64.34 382	42.46 371	51.90 324	83.94 201	27.79 325	75.41 364	37.12 336	59.49 283	78.47 323

anonymousdsp		60.46 298	57.65 304	68.88 295	63.63 386	45.09 306	72.93 330	78.63 264	46.52 344	51.12 327	72.80 341	21.46 368	83.07 299	57.79 219	53.97 336	78.47 323

testing3		59.97 299	60.19 289	59.32 362	77.60 246	30.01 397	81.75 242	81.79 197	53.54 294	50.34 333	79.94 260	48.99 77	76.91 355	17.19 412	50.59 352	71.03 385

ACMH	53.70 16	59.78 300	55.94 318	71.28 263	76.59 264	48.35 244	80.15 277	76.11 306	49.74 322	41.91 372	73.45 336	16.50 390	90.31 103	31.42 366	57.63 308	75.17 357
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 301	57.15 308	67.09 315	66.01 370	36.86 370	80.50 268	78.64 263	45.05 355	49.05 339	73.94 327	27.28 327	86.10 250	43.96 315	49.94 354	78.31 327

MSDG		59.44 302	55.14 322	72.32 238	74.69 296	50.71 175	74.39 320	73.58 328	44.44 360	43.40 366	77.52 285	19.45 374	90.87 89	31.31 367	57.49 309	75.38 354

RPMNet		59.29 303	54.25 327	74.42 180	73.97 309	56.57 34	60.52 386	76.98 293	35.72 388	57.49 270	58.87 398	37.73 216	85.26 270	27.01 386	59.93 277	81.42 286

DP-MVS		59.24 304	56.12 316	68.63 302	88.24 34	50.35 189	82.51 223	64.43 381	41.10 373	46.70 355	78.77 274	24.75 347	88.57 163	22.26 399	56.29 317	66.96 391

OpenMVS_ROB	53.19 17	59.20 305	56.00 317	68.83 297	71.13 341	44.30 315	83.64 186	75.02 315	46.42 346	46.48 357	73.03 338	18.69 378	88.14 179	27.74 383	61.80 269	74.05 367

IterMVS-SCA-FT		59.12 306	58.81 300	60.08 360	70.68 347	45.07 307	80.42 271	74.25 320	43.54 366	50.02 334	73.73 329	31.97 297	56.74 406	51.06 270	53.60 341	78.42 325

our_test_3		59.11 307	55.08 323	71.18 267	71.42 337	53.29 121	81.96 234	74.52 318	48.32 331	42.08 370	69.28 366	28.14 319	82.15 303	34.35 355	45.68 374	78.11 331

Anonymous20231206		59.08 308	57.59 305	63.55 340	68.77 358	32.14 389	80.26 274	79.78 236	50.00 321	49.39 337	72.39 346	26.64 332	78.36 339	33.12 361	57.94 302	80.14 308

KD-MVS_2432*1600		59.04 309	56.44 313	66.86 318	79.07 217	45.87 298	72.13 339	80.42 223	55.03 281	48.15 343	71.01 354	36.73 242	78.05 344	35.21 349	30.18 406	76.67 342

miper_refine_blended		59.04 309	56.44 313	66.86 318	79.07 217	45.87 298	72.13 339	80.42 223	55.03 281	48.15 343	71.01 354	36.73 242	78.05 344	35.21 349	30.18 406	76.67 342

WR-MVS_H		58.91 311	58.04 303	61.54 354	69.07 356	33.83 381	76.91 302	81.99 191	51.40 312	48.17 342	74.67 320	40.23 190	74.15 367	31.78 365	48.10 358	76.64 345

LCM-MVSNet-Re		58.82 312	56.54 311	65.68 327	79.31 213	29.09 403	61.39 385	45.79 403	60.73 181	37.65 390	72.47 344	31.42 303	81.08 311	49.66 276	70.41 193	86.87 185

Patchmatch-RL test		58.72 313	54.32 326	71.92 253	63.91 384	44.25 317	61.73 382	55.19 394	57.38 247	49.31 338	54.24 404	37.60 220	80.89 312	62.19 172	47.28 365	90.63 87

FMVSNet5		58.61 314	56.45 312	65.10 334	77.20 256	39.74 356	74.77 315	77.12 291	50.27 319	43.28 367	67.71 370	26.15 336	76.90 357	36.78 341	54.78 331	78.65 321

ppachtmachnet_test		58.56 315	54.34 325	71.24 264	71.42 337	54.74 80	81.84 239	72.27 340	49.02 326	45.86 360	68.99 367	26.27 333	83.30 297	30.12 370	43.23 379	75.69 351

ACMH+	54.58 15	58.55 316	55.24 320	68.50 306	74.68 297	45.80 301	80.27 273	70.21 358	47.15 340	42.77 369	75.48 316	16.73 389	85.98 257	35.10 353	54.78 331	73.72 369

CP-MVSNet		58.54 317	57.57 306	61.46 355	68.50 360	33.96 380	76.90 303	78.60 266	51.67 311	47.83 346	76.60 303	34.99 267	72.79 376	35.45 346	47.58 362	77.64 336

PEN-MVS		58.35 318	57.15 308	61.94 351	67.55 367	34.39 375	77.01 301	78.35 271	51.87 308	47.72 347	76.73 301	33.91 278	73.75 371	34.03 356	47.17 366	77.68 334

PS-CasMVS		58.12 319	57.03 310	61.37 356	68.24 364	33.80 382	76.73 304	78.01 275	51.20 313	47.54 350	76.20 311	32.85 287	72.76 377	35.17 351	47.37 364	77.55 337

mmtdpeth		57.93 320	54.78 324	67.39 313	72.32 328	43.38 328	72.72 331	68.93 366	54.45 289	56.85 279	62.43 386	17.02 386	83.46 295	57.95 215	30.31 405	75.31 355

dmvs_testset		57.65 321	58.21 302	55.97 373	74.62 298	9.82 434	63.75 373	63.34 384	67.23 56	48.89 340	83.68 209	39.12 202	76.14 360	23.43 396	59.80 280	81.96 276

UnsupCasMVSNet_eth		57.56 322	55.15 321	64.79 336	64.57 382	33.12 383	73.17 329	83.87 160	58.98 215	41.75 373	70.03 361	22.54 360	79.92 328	46.12 304	35.31 393	81.32 293

CHOSEN 280x420		57.53 323	56.38 315	60.97 358	74.01 307	48.10 256	46.30 406	54.31 396	48.18 334	50.88 331	77.43 289	38.37 209	59.16 402	54.83 241	63.14 259	75.66 352

DTE-MVSNet		57.03 324	55.73 319	60.95 359	65.94 371	32.57 387	75.71 307	77.09 292	51.16 314	46.65 356	76.34 306	32.84 288	73.22 375	30.94 369	44.87 375	77.06 339

PatchMatch-RL		56.66 325	53.75 330	65.37 332	77.91 244	45.28 305	69.78 352	60.38 388	41.35 372	47.57 349	73.73 329	16.83 387	76.91 355	36.99 339	59.21 286	73.92 368

PatchT		56.60 326	52.97 333	67.48 311	72.94 320	46.16 295	57.30 394	73.78 326	38.77 378	54.37 304	57.26 401	37.52 222	78.06 343	32.02 363	52.79 346	78.23 330

Patchmtry		56.56 327	52.95 334	67.42 312	72.53 325	50.59 179	59.05 390	71.72 345	37.86 382	46.92 353	65.86 375	38.94 203	80.06 327	36.94 340	46.72 370	71.60 381

test_0402		56.45 328	53.03 332	66.69 321	76.78 263	50.31 191	81.76 241	69.61 363	42.79 369	43.88 362	72.13 349	22.82 359	86.46 238	16.57 413	50.94 351	63.31 400

LS3D		56.40 329	53.82 329	64.12 337	81.12 177	45.69 303	73.42 327	66.14 374	35.30 392	43.24 368	79.88 261	22.18 364	79.62 333	19.10 408	64.00 246	67.05 390

ADS-MVSNet		56.17 330	51.95 340	68.84 296	80.60 193	53.07 127	55.03 398	70.02 360	44.72 357	51.00 328	61.19 390	22.83 357	78.88 336	28.54 378	53.63 339	74.57 364

XVG-ACMP-BASELINE		56.03 331	52.85 335	65.58 328	61.91 391	40.95 353	63.36 374	72.43 339	45.20 354	46.02 358	74.09 324	9.20 406	78.12 341	45.13 306	58.27 295	77.66 335

pmmvs-eth3d		55.97 332	52.78 336	65.54 329	61.02 393	46.44 287	75.36 313	67.72 371	49.61 323	43.65 364	67.58 371	21.63 367	77.04 353	44.11 314	44.33 376	73.15 375

F-COLMAP		55.96 333	53.65 331	62.87 346	72.76 322	42.77 337	74.70 318	70.37 357	40.03 374	41.11 378	79.36 267	17.77 383	73.70 372	32.80 362	53.96 337	72.15 377

CMPMVS	40.41 21	55.34 334	52.64 337	63.46 341	60.88 394	43.84 322	61.58 384	71.06 353	30.43 400	36.33 392	74.63 321	24.14 351	75.44 363	48.05 289	66.62 221	71.12 384
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 335	54.07 328	58.68 365	63.14 388	25.00 409	77.69 299	74.78 317	52.64 301	43.43 365	72.39 346	26.21 334	74.76 366	29.31 373	47.05 368	76.28 349

ADS-MVSNet2		55.21 336	51.44 341	66.51 323	80.60 193	49.56 206	55.03 398	65.44 376	44.72 357	51.00 328	61.19 390	22.83 357	75.41 364	28.54 378	53.63 339	74.57 364

SixPastTwentyTwo		54.37 337	50.10 346	67.21 314	70.70 345	41.46 349	74.73 316	64.69 378	47.56 338	39.12 385	69.49 362	18.49 381	84.69 280	31.87 364	34.20 399	75.48 353

USDC		54.36 338	51.23 342	63.76 339	64.29 383	37.71 367	62.84 379	73.48 333	56.85 255	35.47 395	71.94 352	9.23 405	78.43 338	38.43 332	48.57 356	75.13 358

testgi		54.25 339	52.57 338	59.29 363	62.76 389	21.65 418	72.21 338	70.47 356	53.25 298	41.94 371	77.33 290	14.28 394	77.95 347	29.18 374	51.72 350	78.28 328

K. test v3		54.04 340	49.42 352	67.92 309	68.55 359	42.57 341	75.51 311	63.07 385	52.07 305	39.21 384	64.59 381	19.34 375	82.21 302	37.11 337	25.31 411	78.97 316

UnsupCasMVSNet_bld		53.86 341	50.53 345	63.84 338	63.52 387	34.75 373	71.38 344	81.92 194	46.53 343	38.95 386	57.93 399	20.55 371	80.20 326	39.91 329	34.09 400	76.57 346

YYNet1		53.82 342	49.96 348	65.41 331	70.09 350	48.95 223	72.30 336	71.66 347	44.25 362	31.89 405	63.07 385	23.73 353	73.95 369	33.26 359	39.40 386	73.34 372

MDA-MVSNet_test_wron		53.82 342	49.95 349	65.43 330	70.13 349	49.05 219	72.30 336	71.65 348	44.23 363	31.85 406	63.13 384	23.68 354	74.01 368	33.25 360	39.35 387	73.23 374

test_fmvs1		53.60 344	52.54 339	56.78 369	58.07 397	30.26 393	68.95 356	42.19 409	32.46 395	63.59 184	82.56 230	11.55 398	60.81 396	58.25 209	55.27 327	79.28 313

Patchmatch-test		53.33 345	48.17 355	68.81 298	73.31 312	42.38 342	42.98 410	58.23 390	32.53 394	38.79 387	70.77 357	39.66 198	73.51 373	25.18 390	52.06 349	90.55 89

LTVRE_ROB	45.45 19	52.73 346	49.74 350	61.69 353	69.78 351	34.99 372	44.52 407	67.60 372	43.11 368	43.79 363	74.03 325	18.54 380	81.45 308	28.39 380	57.94 302	68.62 388
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		52.63 347	50.72 344	58.37 366	62.69 390	28.13 406	72.60 332	75.97 307	30.94 399	40.76 380	72.11 350	20.16 372	70.80 383	35.11 352	46.11 372	76.19 350

test_fmvs1_n		52.55 348	51.19 343	56.65 370	51.90 408	30.14 394	67.66 360	42.84 408	32.27 396	62.30 198	82.02 244	9.12 407	60.84 395	57.82 218	54.75 333	78.99 315

OurMVSNet-221017-0		52.39 349	48.73 353	63.35 343	65.21 376	38.42 363	68.54 358	64.95 377	38.19 379	39.57 383	71.43 353	13.23 396	79.92 328	37.16 335	40.32 385	71.72 380

JIA-IIPM		52.33 350	47.77 358	66.03 325	71.20 340	46.92 280	40.00 415	76.48 304	37.10 383	46.73 354	37.02 415	32.96 286	77.88 348	35.97 344	52.45 348	73.29 373

Anonymous20240521		51.65 351	48.42 354	61.34 357	56.43 402	39.65 358	73.57 325	73.47 334	36.64 386	36.59 391	63.98 382	10.75 401	72.25 380	35.35 347	49.01 355	72.11 378

MDA-MVSNet-bldmvs		51.56 352	47.75 359	63.00 344	71.60 335	47.32 276	69.70 353	72.12 341	43.81 364	27.65 413	63.38 383	21.97 366	75.96 361	27.30 385	32.19 401	65.70 396

test_vis1_n		51.19 353	49.66 351	55.76 374	51.26 410	29.85 398	67.20 363	38.86 414	32.12 397	59.50 230	79.86 262	8.78 408	58.23 403	56.95 227	52.46 347	79.19 314

mvs5depth		50.97 354	46.98 360	62.95 345	56.63 401	34.23 378	62.73 380	67.35 373	45.03 356	48.00 345	65.41 379	10.40 402	79.88 332	36.00 343	31.27 404	74.73 362

COLMAP_ROB	43.60 20	50.90 355	48.05 356	59.47 361	67.81 366	40.57 355	71.25 345	62.72 387	36.49 387	36.19 393	73.51 334	13.48 395	73.92 370	20.71 403	50.26 353	63.92 399
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 356	47.81 357	57.96 367	61.53 392	27.80 407	67.40 361	74.06 323	43.25 367	33.31 404	65.38 380	16.03 391	71.34 381	21.80 400	47.55 363	74.75 361

kuosan		50.20 357	50.09 347	50.52 381	73.09 317	29.09 403	65.25 366	74.89 316	48.27 332	41.34 375	60.85 392	43.45 150	67.48 389	18.59 410	25.07 412	55.01 406

KD-MVS_self_test		49.24 358	46.85 361	56.44 371	54.32 403	22.87 412	57.39 393	73.36 335	44.36 361	37.98 389	59.30 397	18.97 377	71.17 382	33.48 357	42.44 380	75.26 356

MVS-HIRNet		49.01 359	44.71 363	61.92 352	76.06 274	46.61 285	63.23 376	54.90 395	24.77 408	33.56 400	36.60 417	21.28 369	75.88 362	29.49 372	62.54 265	63.26 401

new-patchmatchnet		48.21 360	46.55 362	53.18 377	57.73 399	18.19 426	70.24 348	71.02 354	45.70 350	33.70 399	60.23 393	18.00 382	69.86 386	27.97 382	34.35 397	71.49 383

TinyColmap		48.15 361	44.49 365	59.13 364	65.73 373	38.04 364	63.34 375	62.86 386	38.78 377	29.48 408	67.23 373	6.46 416	73.30 374	24.59 392	41.90 382	66.04 394

AllTest		47.32 362	44.66 364	55.32 375	65.08 378	37.50 368	62.96 378	54.25 397	35.45 390	33.42 401	72.82 339	9.98 403	59.33 399	24.13 393	43.84 377	69.13 386

PM-MVS		46.92 363	43.76 370	56.41 372	52.18 407	32.26 388	63.21 377	38.18 415	37.99 381	40.78 379	66.20 374	5.09 420	65.42 391	48.19 288	41.99 381	71.54 382

test_fmvs2		45.89 364	44.32 366	50.62 380	45.85 419	24.70 410	58.87 392	37.84 417	25.22 406	52.46 319	74.56 322	7.07 411	54.69 407	49.28 280	47.70 361	72.48 376

RPSCF		45.77 365	44.13 367	50.68 379	57.67 400	29.66 399	54.92 400	45.25 405	26.69 405	45.92 359	75.92 314	17.43 385	45.70 417	27.44 384	45.95 373	76.67 342

pmmvs3		45.53 366	41.55 372	57.44 368	48.97 415	39.68 357	70.06 349	57.66 391	28.32 403	34.06 398	57.29 400	8.50 409	66.85 390	34.86 354	34.26 398	65.80 395

dongtai		43.51 367	44.07 368	41.82 392	63.75 385	21.90 416	63.80 372	72.05 342	39.59 375	33.35 403	54.54 403	41.04 180	57.30 404	10.75 421	17.77 421	46.26 415

mvsany_test1		43.38 368	42.57 371	45.82 387	50.96 411	26.10 408	55.80 396	27.74 427	27.15 404	47.41 352	74.39 323	18.67 379	44.95 418	44.66 309	36.31 391	66.40 393

mamv4		42.60 369	44.05 369	38.26 397	59.21 396	38.00 365	44.14 409	39.03 413	25.03 407	40.61 381	68.39 368	37.01 236	24.28 431	46.62 299	36.43 390	52.50 409

N_pmnet		41.25 370	39.77 373	45.66 388	68.50 360	0.82 440	72.51 334	0.38 439	35.61 389	35.26 396	61.51 389	20.07 373	67.74 388	23.51 395	40.63 383	68.42 389

TDRefinement		40.91 371	38.37 375	48.55 385	50.45 412	33.03 385	58.98 391	50.97 400	28.50 401	29.89 407	67.39 372	6.21 418	54.51 408	17.67 411	35.25 394	58.11 403

ttmdpeth		40.58 372	37.50 376	49.85 382	49.40 413	22.71 413	56.65 395	46.78 401	28.35 402	40.29 382	69.42 364	5.35 419	61.86 394	20.16 405	21.06 418	64.96 397

test_vis1_rt		40.29 373	38.64 374	45.25 389	48.91 416	30.09 395	59.44 389	27.07 428	24.52 409	38.48 388	51.67 409	6.71 414	49.44 412	44.33 311	46.59 371	56.23 404

MVStest1		38.35 374	34.53 380	49.82 383	51.43 409	30.41 392	50.39 402	55.25 393	17.56 416	26.45 414	65.85 377	11.72 397	57.00 405	14.79 415	17.31 422	62.05 402

DSMNet-mixed		38.35 374	35.36 379	47.33 386	48.11 417	14.91 430	37.87 416	36.60 418	19.18 413	34.37 397	59.56 396	15.53 392	53.01 410	20.14 406	46.89 369	74.07 366

test_fmvs3		37.95 376	35.75 378	44.55 390	35.50 425	18.92 422	48.32 403	34.00 422	18.36 415	41.31 377	61.58 388	2.29 427	48.06 416	42.72 321	37.71 389	66.66 392

WB-MVS		37.41 377	36.37 377	40.54 395	54.23 404	10.43 433	65.29 365	43.75 406	34.86 393	27.81 412	54.63 402	24.94 345	63.21 392	6.81 428	15.00 423	47.98 414

FPMVS		35.40 378	33.67 382	40.57 394	46.34 418	28.74 405	41.05 412	57.05 392	20.37 412	22.27 417	53.38 406	6.87 413	44.94 419	8.62 422	47.11 367	48.01 413

SSC-MVS		35.20 379	34.30 381	37.90 398	52.58 406	8.65 436	61.86 381	41.64 410	31.81 398	25.54 415	52.94 408	23.39 356	59.28 401	6.10 429	12.86 424	45.78 417

ANet_high		34.39 380	29.59 386	48.78 384	30.34 429	22.28 414	55.53 397	63.79 383	38.11 380	15.47 421	36.56 418	6.94 412	59.98 398	13.93 417	5.64 432	64.08 398

EGC-MVSNET		33.75 381	30.42 385	43.75 391	64.94 380	36.21 371	60.47 388	40.70 412	0.02 433	0.10 434	53.79 405	7.39 410	60.26 397	11.09 420	35.23 395	34.79 419

new_pmnet		33.56 382	31.89 384	38.59 396	49.01 414	20.42 419	51.01 401	37.92 416	20.58 410	23.45 416	46.79 411	6.66 415	49.28 414	20.00 407	31.57 403	46.09 416

LF4IMVS		33.04 383	32.55 383	34.52 401	40.96 420	22.03 415	44.45 408	35.62 419	20.42 411	28.12 411	62.35 387	5.03 421	31.88 430	21.61 402	34.42 396	49.63 412

LCM-MVSNet		28.07 384	23.85 392	40.71 393	27.46 434	18.93 421	30.82 422	46.19 402	12.76 421	16.40 419	34.70 420	1.90 430	48.69 415	20.25 404	24.22 413	54.51 407

mvsany_test3		28.00 385	25.98 387	34.05 402	28.97 430	15.31 428	34.54 419	18.17 433	16.24 417	29.30 409	53.37 407	2.79 425	33.38 429	30.01 371	20.41 419	53.45 408

Gipuma		27.47 386	24.26 391	37.12 400	60.55 395	29.17 402	11.68 427	60.00 389	14.18 419	10.52 428	15.12 429	2.20 429	63.01 393	8.39 423	35.65 392	19.18 425
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 387	24.85 388	33.93 403	26.17 435	15.25 429	30.24 423	22.38 432	12.53 422	28.23 410	49.43 410	2.59 426	34.34 428	25.12 391	26.99 409	52.20 410

PMMVS2		26.71 388	22.98 393	37.87 399	36.89 423	8.51 437	42.51 411	29.32 426	19.09 414	13.01 423	37.54 414	2.23 428	53.11 409	14.54 416	11.71 425	51.99 411

APD_test1		26.46 389	24.41 390	32.62 406	37.58 422	21.74 417	40.50 414	30.39 424	11.45 423	16.33 420	43.76 412	1.63 432	41.62 420	11.24 419	26.82 410	34.51 420

PMVS	19.57 22	25.07 390	22.43 395	32.99 405	23.12 436	22.98 411	40.98 413	35.19 420	15.99 418	11.95 427	35.87 419	1.47 433	49.29 413	5.41 431	31.90 402	26.70 424
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 391	22.95 394	30.31 407	28.59 431	18.92 422	37.43 417	17.27 435	12.90 420	21.28 418	29.92 424	1.02 434	36.35 423	28.28 381	29.82 408	35.65 418

test_method		24.09 392	21.07 396	33.16 404	27.67 433	8.35 438	26.63 424	35.11 421	3.40 430	14.35 422	36.98 416	3.46 424	35.31 425	19.08 409	22.95 414	55.81 405

testf1		21.11 393	19.08 397	27.18 409	30.56 427	18.28 424	33.43 420	24.48 429	8.02 427	12.02 425	33.50 421	0.75 436	35.09 426	7.68 424	21.32 415	28.17 422

APD_test2		21.11 393	19.08 397	27.18 409	30.56 427	18.28 424	33.43 420	24.48 429	8.02 427	12.02 425	33.50 421	0.75 436	35.09 426	7.68 424	21.32 415	28.17 422

E-PMN		19.16 395	18.40 399	21.44 411	36.19 424	13.63 431	47.59 404	30.89 423	10.73 424	5.91 431	16.59 427	3.66 423	39.77 421	5.95 430	8.14 427	10.92 427

EMVS		18.42 396	17.66 400	20.71 412	34.13 426	12.64 432	46.94 405	29.94 425	10.46 426	5.58 432	14.93 430	4.23 422	38.83 422	5.24 432	7.51 429	10.67 428

cdsmvs_eth3d_5k		18.33 397	24.44 389	0.00 418	0.00 440	0.00 442	0.00 429	89.40 27	0.00 434	0.00 437	92.02 52	38.55 207	0.00 435	0.00 436	0.00 433	0.00 433

MVE	16.60 23	17.34 398	13.39 401	29.16 408	28.43 432	19.72 420	13.73 426	23.63 431	7.23 429	7.96 429	21.41 425	0.80 435	36.08 424	6.97 426	10.39 426	31.69 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 399	10.68 402	5.73 415	2.49 438	4.21 439	10.48 428	18.04 434	0.34 432	12.59 424	20.49 426	11.39 399	7.03 434	13.84 418	6.46 431	5.95 429

wuyk23d		9.11 400	8.77 404	10.15 414	40.18 421	16.76 427	20.28 425	1.01 438	2.58 431	2.66 433	0.98 433	0.23 438	12.49 433	4.08 433	6.90 430	1.19 430

ab-mvs-re		7.68 401	10.24 403	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	92.12 48	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

testmvs		6.14 402	8.18 405	0.01 416	0.01 439	0.00 442	73.40 328	0.00 440	0.00 434	0.02 435	0.15 434	0.00 439	0.00 435	0.02 434	0.00 433	0.02 431

test123		6.01 403	8.01 406	0.01 416	0.00 440	0.01 441	71.93 342	0.00 440	0.00 434	0.02 435	0.11 435	0.00 439	0.00 435	0.02 434	0.00 433	0.02 431

pcd_1.5k_mvsjas		3.15 404	4.20 407	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	37.77 213	0.00 435	0.00 436	0.00 433	0.00 433

mmdepth		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

monomultidepth		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

test_blank		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

uanet_test		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

DCPMVS		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

sosnet-low-res		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

sosnet		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

uncertanet		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

Regformer		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

uanet		0.00 405	0.00 408	0.00 418	0.00 440	0.00 442	0.00 429	0.00 440	0.00 434	0.00 437	0.00 436	0.00 439	0.00 435	0.00 436	0.00 433	0.00 433

WAC-MVS						34.28 376								22.56 398

FOURS1					83.24 113	49.90 199	84.98 142	78.76 260	47.71 336	73.42 66

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 38	86.80 28	92.34 35

PC_three_1452									66.58 66	87.27 2	93.70 12	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

No_MVS				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 38	86.80 28	92.34 35

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 251	82.06 14	93.39 21	54.94 36

eth-test2					0.00 440

eth-test					0.00 440

ZD-MVS					89.55 14	53.46 110		84.38 146	57.02 253	73.97 61	91.03 71	44.57 134	91.17 79	75.41 79	81.78 71

RE-MVS-def			66.66 215		80.96 181	48.14 254	81.54 250	76.98 293	46.42 346	62.75 193	89.42 114	29.28 316		60.52 189	72.06 178	83.19 261

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 74	88.26 1				82.83 25	87.60 18	92.44 32

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 5	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

test_241102_TWO								88.76 44	57.50 245	83.60 6	94.09 3	56.14 27	96.37 6	82.28 29	87.43 20	92.55 30

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 243	84.61 4	93.29 25	58.81 13	96.45 1

9.14			78.19 28		85.67 62		88.32 51	88.84 41	59.89 190	74.58 56	92.62 39	46.80 95	92.66 41	81.40 40	85.62 41

save fliter					85.35 69	56.34 41	89.31 40	81.46 202	61.55 161

test_0728_THIRD									58.00 231	81.91 15	93.64 14	56.54 23	96.44 2	81.64 35	86.86 26	92.23 37

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 37					96.39 4	81.68 33	87.13 21	92.47 31

test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 239	83.14 9	93.96 6	55.17 31

GSMVS																88.13 160

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 205				88.13 160

sam_mvs												35.99 257

ambc				62.06 349	53.98 405	29.38 401	35.08 418	79.65 240		41.37 374	59.96 394	6.27 417	82.15 303	35.34 348	38.22 388	74.65 363

MTGPA								81.31 205

test_post1							70.84 347				14.72 431	34.33 275	83.86 287	48.80 283

test_post											16.22 428	37.52 222	84.72 279

patchmatchnet-post											59.74 395	38.41 208	79.91 330

GG-mvs-BLEND				77.77 86	86.68 49	50.61 177	68.67 357	88.45 54		68.73 120	87.45 158	59.15 11	90.67 92	54.83 241	87.67 17	92.03 45

MTMP							87.27 77	15.34 436

gm-plane-assit					83.24 113	54.21 96			70.91 23		88.23 142		95.25 14	66.37 138

test9_res														78.72 54	85.44 43	91.39 66

TEST9					85.68 60	55.42 56	87.59 67	84.00 156	57.72 238	72.99 71	90.98 73	44.87 128	88.58 160

test_8					85.72 59	55.31 61	87.60 66	83.88 159	57.84 236	72.84 75	90.99 72	44.99 124	88.34 171

agg_prior2														75.65 74	85.11 47	91.01 78

agg_prior					85.64 63	54.92 76		83.61 166		72.53 80			88.10 182

TestCases				55.32 375	65.08 378	37.50 368		54.25 397	35.45 390	33.42 401	72.82 339	9.98 403	59.33 399	24.13 393	43.84 377	69.13 386

test_prior4						56.39 40	87.15 81

test_prior2							89.04 43		61.88 156	73.55 64	91.46 69	48.01 82		74.73 83	85.46 42

test_prior				78.39 74	86.35 54	54.91 77		85.45 110					89.70 121			90.55 89

旧先验2							81.73 243		45.53 352	74.66 53			70.48 385	58.31 208

新几何2							81.61 248

新几何1				73.30 214	83.10 116	53.48 109		71.43 349	45.55 351	66.14 142	87.17 163	33.88 280	80.54 320	48.50 286	80.33 84	85.88 212

旧先验1					81.57 167	47.48 272		71.83 343			88.66 129	36.94 238			78.34 106	88.67 144

无先验							85.19 132	78.00 276	49.08 325				85.13 274	52.78 258		87.45 176

原ACMM2							83.77 184

原ACMM1				76.13 129	84.89 78	54.59 88		85.26 120	51.98 306	66.70 134	87.07 165	40.15 192	89.70 121	51.23 268	85.06 48	84.10 238

test222					79.36 210	50.97 173	77.99 297	67.84 370	42.54 370	62.84 192	86.53 173	30.26 310			76.91 119	85.23 221

testdata2													77.81 350	45.64 305

segment_acmp												44.97 126

testdata				67.08 316	77.59 247	45.46 304		69.20 365	44.47 359	71.50 93	88.34 138	31.21 304	70.76 384	52.20 263	75.88 136	85.03 223

testdata1							77.55 300		64.14 111

test12				79.24 44	86.89 47	56.08 45		85.16 125		72.27 84		47.15 91	91.10 82		85.93 37	90.54 91

plane_prior7					77.95 241	48.46 241

plane_prior6					78.42 236	49.39 214						36.04 255

plane_prior5								82.59 183					88.30 175	65.46 149	72.34 175	84.49 231

plane_prior4											83.28 215

plane_prior3						48.95 223			64.01 115	62.15 200

plane_prior2							85.76 109		63.60 124

plane_prior1					78.31 238

plane_prior						49.57 204	87.43 70		64.57 101						72.84 169

n2								0.00 440

nn								0.00 440

door-mid								41.31 411

lessismore_v0				67.98 308	64.76 381	41.25 350		45.75 404		36.03 394	65.63 378	19.29 376	84.11 285	35.67 345	21.24 417	78.59 322

LGP-MVS_train				72.02 245	74.42 301	48.60 234		80.64 218	54.69 286	53.75 311	83.83 203	25.73 339	86.98 220	60.33 193	64.71 238	80.48 303

test11								84.25 150

door								43.27 407

HQP5-MVS						51.56 162

HQP-NCC					79.02 220		88.00 55		65.45 88	64.48 168

ACMP_Plane					79.02 220		88.00 55		65.45 88	64.48 168

BP-MVS														66.70 135

HQP4-MVS										64.47 171			88.61 158			84.91 227

HQP3-MVS								83.68 162							73.12 165

HQP2-MVS												37.35 225

NP-MVS					78.76 225	50.43 183					85.12 187

MDTV_nov1_ep13_2view						43.62 324	71.13 346		54.95 283	59.29 236		36.76 241		46.33 302		87.32 179

MDTV_nov1_ep13			61.56 274		81.68 160	55.12 69	72.41 335	78.18 273	59.19 207	58.85 245	69.29 365	34.69 270	86.16 247	36.76 342	62.96 261

ACMMP++_ref															63.20 257

ACMMP++															59.38 284

Test By Simon												39.38 199

ITE_SJBPF				51.84 378	58.03 398	31.94 390		53.57 399	36.67 385	41.32 376	75.23 318	11.17 400	51.57 411	25.81 389	48.04 359	72.02 379

DeepMVS_CX				13.10 413	21.34 437	8.99 435		10.02 437	10.59 425	7.53 430	30.55 423	1.82 431	14.55 432	6.83 427	7.52 428	15.75 426

High-resolution multi-view benchmark