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 39	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	84.98 141	88.88 37	58.00 229	83.60 6	93.39 21	67.21 2	96.39 4	81.64 33	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 88	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 35	92.13 45	60.24 7	94.78 19	78.97 48	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	85.04 138	88.63 48	66.08 78	86.77 3	92.75 36	72.05 1	91.46 70	83.35 21	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	57.50 243	84.61 4	94.09 3	58.81 13	96.37 6	82.28 27	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 38	93.87 7	52.58 48	93.91 26	84.17 16	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	57.71 237	81.91 15	93.64 14	55.17 31	96.44 2	81.68 31	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 18	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 28	91.09 68	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 26	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 81	79.46 27	93.00 34	53.10 45	91.76 63	80.40 41	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	59.19 205	71.82 86	90.05 101	59.72 10	96.04 10	78.37 54	88.40 14	93.75 7

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 267	89.51 26	69.76 31	71.05 98	86.66 169	58.68 16	93.24 31	84.64 15	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	37.77 211	92.50 46	82.75 24	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	37.63 216	92.28 52	82.73 25	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	55.55 273	81.21 20	93.69 13	56.51 24	94.27 22	78.36 55	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 60	92.75 36	46.88 94	93.28 30	78.79 51	84.07 55	91.50 64

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 148	83.68 162	67.85 47	69.36 111	90.24 93	60.20 8	92.10 58	84.14 17	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	70.62 106	88.37 133	57.69 19	92.30 50	75.25 78	76.24 130	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	61.48 162	80.26 24	93.10 28	46.53 99	92.41 48	79.97 42	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 83	88.40 132	58.53 17	89.08 137	73.21 98	77.98 107	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	63.22 131	74.63 52	90.83 79	41.38 176	94.40 20	75.42 76	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	70.84 100	88.09 142	57.29 21	92.63 44	69.24 118	75.13 147	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	55.91 268	78.56 31	92.49 41	48.20 79	92.65 42	79.49 43	83.04 59	90.39 93
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 97	73.52 63	88.09 142	48.07 80	92.19 54	62.24 169	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 41	92.02 50	44.82 128	90.23 107	80.83 40	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 40	91.72 58	49.32 76	90.17 109	73.46 94	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	81.59 247	86.41 91	69.61 33	81.72 17	88.16 141	55.09 33	88.04 184	74.12 87	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	70.75 101	88.04 146	55.82 28	92.65 42	69.61 114	75.00 151	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 36	92.11 48	50.90 59	89.95 113	78.18 58	77.54 112	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 36	92.11 48	50.90 59	89.95 113	78.18 58	77.54 112	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 76	76.59 43	91.99 52	54.07 41	89.05 139	77.34 64	77.00 117	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 93	92.75 36	45.52 114	90.37 100	71.15 105	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 47	90.15 99	46.77 96	91.00 84	73.52 93	78.46 103	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	56.58 262	74.26 57	91.60 63	54.26 38	92.16 55	75.87 70	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	68.16 123	88.24 139	58.17 18	88.31 174	69.88 113	77.87 108	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 92	88.35 135	51.58 52	91.22 77	79.02 47	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 97	88.70 126	55.19 30	91.24 76	65.18 153	76.32 128	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	58.78 217	74.63 52	92.38 42	47.75 85	91.35 72	78.18 58	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	58.14 226	73.60 61	93.31 24	43.14 152	93.79 27	73.81 91	88.53 13	92.37 34

WTY-MVS		77.47 44	77.52 39	77.30 97	88.33 30	46.25 291	88.46 50	90.32 19	71.40 20	72.32 81	91.72 58	53.44 43	92.37 49	66.28 138	75.42 141	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 89	88.63 131	50.89 62	90.35 101	76.00 69	79.11 98	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 82	72.85 72	91.98 54	45.10 119	91.27 74	75.02 80	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 68	90.52 85	45.23 118	90.66 93	79.37 44	80.95 74	90.22 99

SteuartSystems-ACMMP		77.08 48	76.33 53	79.34 43	80.98 179	55.31 61	89.76 33	86.91 80	62.94 136	71.65 87	91.56 64	42.33 160	92.56 45	77.14 65	83.69 57	90.15 104
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 58	90.91 76	42.14 164	89.99 112	79.30 45	79.12 97	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	57.84 234	72.99 69	90.98 71	44.99 122	88.58 160	78.19 56	85.32 44	91.34 70

MVS		76.91 50	75.48 65	81.23 19	84.56 83	55.21 65	80.23 273	91.64 4	58.65 219	65.37 151	91.48 66	45.72 110	95.05 16	72.11 102	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 61	67.91 125	89.97 103	48.03 81	91.41 71	75.60 73	84.14 54	89.96 110
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	83.90 178	84.88 133	71.38 21	71.51 90	89.15 119	50.51 64	90.55 97	75.71 71	78.65 101	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 69	72.47 79	91.62 62	45.53 113	90.99 86	74.48 83	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	70.18 109	88.84 124	51.57 53	87.16 215	65.48 146	86.68 30	90.15 104

MAR-MVS		76.76 55	75.60 62	80.21 31	90.87 7	54.68 85	89.14 42	89.11 32	62.95 135	70.54 107	92.33 43	41.05 177	94.95 17	57.90 215	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	69.09 115	89.33 116	46.70 97	88.13 180	75.43 74	81.48 73	89.55 118

ACMMP_NAP		76.43 58	75.66 61	78.73 61	81.92 151	54.67 86	84.06 173	85.35 114	61.10 169	72.99 69	91.50 65	40.25 187	91.00 84	76.84 66	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 95	66.06 142	89.88 104	43.79 139	92.16 55	75.03 79	80.03 89	89.64 116

CHOSEN 1792x2688		76.24 60	74.03 89	82.88 1	83.09 118	62.84 2	85.73 112	85.39 112	69.79 30	64.87 159	83.49 208	41.52 175	93.69 29	70.55 107	81.82 69	92.12 40

SD-MVS		76.18 61	74.85 77	80.18 32	85.39 68	56.90 28	85.75 110	82.45 186	56.79 257	74.48 55	91.81 56	43.72 142	90.75 91	74.61 82	78.65 101	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	53.79 290	74.91 50	91.68 60	43.80 138	90.31 103	74.36 84	81.82 69	88.87 137
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	84.70 151	90.49 18	64.44 100	69.86 110	90.31 92	55.05 34	91.35 72	70.07 111	75.58 140	89.53 120

VDD-MVS		76.08 64	74.97 75	79.44 41	84.27 91	53.33 119	91.13 20	85.88 102	65.33 92	72.37 80	89.34 114	32.52 289	92.76 40	77.90 61	75.96 134	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	58.62 220	70.75 101	90.85 78	43.10 154	90.63 95	70.50 108	84.51 53	90.24 98

fmvsm_l_conf0.5_n		75.95 66	76.16 56	75.31 156	76.01 278	48.44 242	84.98 141	71.08 350	63.50 125	81.70 18	93.52 17	50.00 68	87.18 214	87.80 5	76.87 120	90.32 96

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 98	68.56 119	90.53 84	45.79 109	91.58 67	67.21 131	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	85.24 128	70.62 353	63.13 133	81.45 19	93.62 16	49.98 70	87.40 210	87.76 6	76.77 121	90.20 101

test_yl		75.85 69	74.83 78	78.91 54	88.08 37	51.94 152	91.30 17	89.28 29	57.91 231	71.19 95	89.20 117	42.03 167	92.77 38	69.41 115	75.07 149	92.01 46

DCV-MVSNet		75.85 69	74.83 78	78.91 54	88.08 37	51.94 152	91.30 17	89.28 29	57.91 231	71.19 95	89.20 117	42.03 167	92.77 38	69.41 115	75.07 149	92.01 46

MVS_Test		75.85 69	74.93 76	78.62 66	84.08 93	55.20 67	83.99 175	85.17 124	68.07 43	73.38 65	82.76 219	50.44 65	89.00 142	65.90 142	80.61 78	91.64 56

ZNCC-MVS		75.82 72	75.02 74	78.23 77	83.88 99	53.80 103	86.91 87	86.05 100	59.71 191	67.85 126	90.55 83	42.23 162	91.02 83	72.66 100	85.29 45	89.87 113

ETVMVS		75.80 73	75.44 66	76.89 113	86.23 55	50.38 186	85.55 119	91.42 7	71.30 22	68.80 117	87.94 148	56.42 25	89.24 132	56.54 227	74.75 154	91.07 77

fmvsm_l_conf0.5_n_3		75.73 74	75.78 59	75.61 142	76.03 276	48.33 247	85.34 122	72.92 335	67.16 57	78.55 32	93.85 9	46.22 101	87.53 205	85.61 11	76.30 129	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	65.01 157	88.93 121	42.05 166	90.58 96	76.57 67	73.96 158	85.73 211
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	74.60 298	49.51 211	81.82 238	74.08 322	66.52 67	80.40 23	93.46 19	46.95 93	89.72 120	86.69 7	75.30 142	87.61 170

MP-MVS-pluss		75.54 77	75.03 73	77.04 105	81.37 174	52.65 138	84.34 163	84.46 145	61.16 166	69.14 114	91.76 57	39.98 194	88.99 144	78.19 56	84.89 49	89.48 122
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	63.49 126	70.97 99	90.15 99	42.86 157	91.14 81	74.33 85	81.90 68	86.71 191

MVSMamba_PlusPlus		75.28 79	73.39 92	80.96 21	80.85 186	58.25 10	74.47 317	87.61 71	50.53 314	65.24 152	83.41 210	57.38 20	92.83 36	73.92 90	87.13 21	91.80 54

GDP-MVS		75.27 80	74.38 83	77.95 84	79.04 219	52.86 134	85.22 129	86.19 97	62.43 146	70.66 104	90.40 90	53.51 42	91.60 66	69.25 117	72.68 170	89.39 123

Effi-MVS+		75.24 81	73.61 91	80.16 33	81.92 151	57.42 21	85.21 130	76.71 300	60.68 180	73.32 66	89.34 114	47.30 89	91.63 65	68.28 125	79.72 93	91.42 65

ET-MVSNet_ETH3D		75.23 82	74.08 87	78.67 64	84.52 84	55.59 51	88.92 44	89.21 31	68.06 44	53.13 313	90.22 95	49.71 73	87.62 202	72.12 101	70.82 188	92.82 25

PAPR		75.20 83	74.13 85	78.41 73	88.31 32	55.10 71	84.31 164	85.66 106	63.76 118	67.55 127	90.73 81	43.48 147	89.40 127	66.36 137	77.03 116	90.73 86

baseline2		75.15 84	74.54 82	76.98 110	81.67 161	51.74 158	83.84 180	91.94 3	69.97 29	58.98 237	86.02 175	59.73 9	91.73 64	68.37 124	70.40 193	87.48 172

diffmvs		75.11 85	74.65 80	76.46 120	78.52 233	53.35 117	83.28 199	79.94 232	70.51 26	71.64 88	88.72 125	46.02 106	86.08 251	77.52 62	75.75 138	89.96 110
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 86	74.33 84	76.95 111	82.89 129	53.05 128	85.63 115	83.50 167	57.86 233	67.25 129	90.24 93	43.38 149	88.85 153	76.03 68	82.23 65	88.96 134
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 87	75.42 67	73.62 206	76.99 259	46.67 281	83.13 203	71.14 349	66.20 73	82.13 13	93.76 11	47.49 87	84.00 284	81.95 30	76.02 131	90.19 103

GST-MVS		74.87 88	73.90 90	77.77 86	83.30 111	53.45 112	85.75 110	85.29 118	59.22 204	66.50 138	89.85 105	40.94 179	90.76 90	70.94 106	83.35 58	89.10 132

fmvsm_s_conf0.5_n		74.48 89	74.12 86	75.56 145	76.96 260	47.85 265	85.32 126	69.80 360	64.16 108	78.74 29	93.48 18	45.51 115	89.29 131	86.48 8	66.62 219	89.55 118

3Dnovator	64.70 6	74.46 90	72.48 105	80.41 29	82.84 132	55.40 59	83.08 205	88.61 50	67.61 53	59.85 220	88.66 127	34.57 270	93.97 24	58.42 204	88.70 12	91.85 52

test_fmvsmconf_n		74.41 91	74.05 88	75.49 150	74.16 304	48.38 243	82.66 213	72.57 336	67.05 60	75.11 48	92.88 35	46.35 100	87.81 189	83.93 19	71.71 179	90.28 97

HFP-MVS		74.37 92	73.13 100	78.10 80	84.30 88	53.68 106	85.58 116	84.36 147	56.82 255	65.78 147	90.56 82	40.70 184	90.90 88	69.18 119	80.88 75	89.71 114

VDDNet		74.37 92	72.13 116	81.09 20	79.58 207	56.52 37	90.02 26	86.70 85	52.61 300	71.23 94	87.20 160	31.75 299	93.96 25	74.30 86	75.77 137	92.79 27

MSLP-MVS++		74.21 94	72.25 112	80.11 36	81.45 172	56.47 38	86.32 96	79.65 240	58.19 225	66.36 139	92.29 44	36.11 250	90.66 93	67.39 129	82.49 63	93.18 17

API-MVS		74.17 95	72.07 118	80.49 25	90.02 11	58.55 9	87.30 75	84.27 149	57.51 242	65.77 148	87.77 151	41.61 173	95.97 11	51.71 262	82.63 61	86.94 181

MGCFI-Net		74.07 96	74.64 81	72.34 235	82.90 128	43.33 328	80.04 276	79.96 231	65.61 84	74.93 49	91.85 55	48.01 82	80.86 312	71.41 103	77.10 115	92.84 24

IB-MVS	68.87 2	74.01 97	72.03 121	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 58	61.38 206	81.74 244	53.21 44	94.28 21	60.45 189	62.41 264	90.03 108
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 98	72.89 101	77.15 103	80.17 200	50.37 187	84.68 153	83.33 168	68.08 41	71.97 84	88.65 130	42.50 158	91.15 80	78.82 49	57.78 305	89.91 112

WBMVS		73.93 99	73.39 92	75.55 146	87.82 39	55.21 65	89.37 37	87.29 74	67.27 55	63.70 179	80.30 256	60.32 6	86.47 236	61.58 175	62.85 261	84.97 223

HY-MVS	67.03 5	73.90 100	73.14 98	76.18 128	84.70 80	47.36 273	75.56 307	86.36 93	66.27 71	70.66 104	83.91 200	51.05 57	89.31 130	67.10 132	72.61 171	91.88 51

CostFormer		73.89 101	72.30 111	78.66 65	82.36 143	56.58 33	75.56 307	85.30 117	66.06 79	70.50 108	76.88 297	57.02 22	89.06 138	68.27 126	68.74 204	90.33 95

fmvsm_s_conf0.1_n		73.80 102	73.26 95	75.43 151	73.28 312	47.80 266	84.57 158	69.43 362	63.34 128	78.40 33	93.29 25	44.73 131	89.22 134	85.99 9	66.28 227	89.26 125

ACMMPR		73.76 103	72.61 102	77.24 102	83.92 97	52.96 131	85.58 116	84.29 148	56.82 255	65.12 153	90.45 86	37.24 228	90.18 108	69.18 119	80.84 76	88.58 145

region2R		73.75 104	72.55 104	77.33 96	83.90 98	52.98 130	85.54 120	84.09 154	56.83 254	65.10 154	90.45 86	37.34 225	90.24 106	68.89 121	80.83 77	88.77 141

CANet_DTU		73.71 105	73.14 98	75.40 152	82.61 139	50.05 195	84.67 155	79.36 248	69.72 32	75.39 46	90.03 102	29.41 312	85.93 259	67.99 127	79.11 98	90.22 99

test_fmvsmconf0.1_n		73.69 106	73.15 96	75.34 154	70.71 342	48.26 249	82.15 227	71.83 341	66.75 63	74.47 56	92.59 40	44.89 125	87.78 194	83.59 20	71.35 183	89.97 109

fmvsm_s_conf0.5_n_a		73.68 107	73.15 96	75.29 159	75.45 286	48.05 258	83.88 179	68.84 365	63.43 127	78.60 30	93.37 23	45.32 116	88.92 149	85.39 12	64.04 242	88.89 136

thisisatest0515		73.64 108	72.20 113	77.97 82	81.63 162	53.01 129	86.69 91	88.81 42	62.53 142	64.06 172	85.65 179	52.15 51	92.50 46	58.43 202	69.84 196	88.39 152

MVSFormer		73.53 109	72.19 114	77.57 91	83.02 122	55.24 63	81.63 244	81.44 203	50.28 315	76.67 41	90.91 76	44.82 128	86.11 246	60.83 181	80.09 86	91.36 68

PVSNet_BlendedMVS		73.42 110	73.30 94	73.76 200	85.91 57	51.83 156	86.18 99	84.24 152	65.40 89	69.09 115	80.86 252	46.70 97	88.13 180	75.43 74	65.92 230	81.33 289

PVSNet_Blended_VisFu		73.40 111	72.44 106	76.30 121	81.32 176	54.70 83	85.81 106	78.82 258	63.70 119	64.53 165	85.38 183	47.11 92	87.38 211	67.75 128	77.55 111	86.81 190

RRT-MVS		73.29 112	71.37 130	79.07 52	84.63 81	54.16 99	78.16 293	86.64 88	61.67 157	60.17 217	82.35 235	40.63 185	92.26 53	70.19 110	77.87 108	90.81 84

MVSTER		73.25 113	72.33 109	76.01 133	85.54 65	53.76 105	83.52 185	87.16 76	67.06 59	63.88 177	81.66 245	52.77 46	90.44 98	64.66 157	64.69 238	83.84 247

EI-MVSNet-Vis-set		73.19 114	72.60 103	74.99 169	82.56 140	49.80 202	82.55 218	89.00 34	66.17 74	65.89 145	88.98 120	43.83 137	92.29 51	65.38 152	69.01 202	82.87 266

PMMVS		72.98 115	72.05 119	75.78 137	83.57 102	48.60 234	84.08 171	82.85 181	61.62 158	68.24 122	90.33 91	28.35 316	87.78 194	72.71 99	76.69 122	90.95 81

XVS		72.92 116	71.62 124	76.81 114	83.41 106	52.48 139	84.88 146	83.20 174	58.03 227	63.91 175	89.63 109	35.50 257	89.78 117	65.50 144	80.50 80	88.16 155

test2506		72.91 117	72.43 107	74.32 182	80.12 201	44.18 317	83.19 201	84.77 137	64.02 110	65.97 143	87.43 157	47.67 86	88.72 154	59.08 195	79.66 94	90.08 106

TESTMET0.1,1		72.86 118	72.33 109	74.46 176	81.98 148	50.77 174	85.13 133	85.47 108	66.09 77	67.30 128	83.69 205	37.27 226	83.57 291	65.06 155	78.97 100	89.05 133

fmvsm_s_conf0.1_n_a		72.82 119	72.05 119	75.12 165	70.95 341	47.97 261	82.72 212	68.43 367	62.52 143	78.17 34	93.08 31	44.21 134	88.86 150	84.82 14	63.54 248	88.54 147

Fast-Effi-MVS+		72.73 120	71.15 134	77.48 93	82.75 134	54.76 79	86.77 90	80.64 218	63.05 134	65.93 144	84.01 198	44.42 133	89.03 140	56.45 231	76.36 127	88.64 143

MTAPA		72.73 120	71.22 132	77.27 99	81.54 168	53.57 108	67.06 362	81.31 205	59.41 198	68.39 120	90.96 73	36.07 252	89.01 141	73.80 92	82.45 64	89.23 127

PGM-MVS		72.60 122	71.20 133	76.80 116	82.95 125	52.82 135	83.07 206	82.14 188	56.51 263	63.18 185	89.81 106	35.68 256	89.76 119	67.30 130	80.19 85	87.83 164

HPM-MVS		72.60 122	71.50 126	75.89 135	82.02 147	51.42 166	80.70 265	83.05 176	56.12 267	64.03 173	89.53 110	37.55 219	88.37 168	70.48 109	80.04 88	87.88 163
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 124	71.46 127	76.00 134	82.93 127	52.32 145	86.93 86	82.48 185	55.15 277	63.65 180	90.44 89	35.03 264	88.53 164	68.69 122	77.83 110	87.15 179

baseline1		72.51 125	72.12 117	73.69 203	85.05 74	44.46 310	83.51 189	86.13 99	71.61 18	64.64 161	87.97 147	55.00 35	89.48 125	59.07 196	56.05 318	87.13 180

EI-MVSNet-UG-set		72.37 126	71.73 122	74.29 183	81.60 164	49.29 216	81.85 236	88.64 47	65.29 94	65.05 155	88.29 138	43.18 150	91.83 62	63.74 160	67.97 209	81.75 277

MS-PatchMatch		72.34 127	71.26 131	75.61 142	82.38 142	55.55 52	88.00 55	89.95 22	65.38 90	56.51 284	80.74 254	32.28 292	92.89 34	57.95 213	88.10 15	78.39 324

HQP-MVS		72.34 127	71.44 128	75.03 167	79.02 220	51.56 162	88.00 55	83.68 162	65.45 86	64.48 166	85.13 184	37.35 223	88.62 157	66.70 133	73.12 164	84.91 225

testing3-2		72.30 129	72.35 108	72.15 239	83.07 119	47.64 268	85.46 121	89.81 24	66.17 74	61.96 201	84.88 191	58.93 12	82.27 299	55.87 233	64.97 234	86.54 193

mvs_anonymous		72.29 130	70.74 138	76.94 112	82.85 131	54.72 82	78.43 292	81.54 201	63.77 117	61.69 203	79.32 266	51.11 56	85.31 266	62.15 171	75.79 136	90.79 85

3Dnovator+	62.71 7	72.29 130	70.50 142	77.65 90	83.40 109	51.29 170	87.32 73	86.40 92	59.01 212	58.49 250	88.32 137	32.40 290	91.27 74	57.04 224	82.15 67	90.38 94

nrg030		72.27 132	71.56 125	74.42 178	75.93 279	50.60 178	86.97 84	83.21 173	62.75 138	67.15 130	84.38 193	50.07 67	86.66 230	71.19 104	62.37 265	85.99 205

UWE-MVS		72.17 133	72.15 115	72.21 237	82.26 144	44.29 314	86.83 89	89.58 25	65.58 85	65.82 146	85.06 186	45.02 121	84.35 281	54.07 244	75.18 144	87.99 162

VPNet		72.07 134	71.42 129	74.04 189	78.64 231	47.17 277	89.91 31	87.97 61	72.56 12	64.66 160	85.04 187	41.83 171	88.33 172	61.17 179	60.97 271	86.62 192

fmvsm_s_conf0.5_n_2		72.02 135	71.72 123	72.92 218	76.79 262	45.90 294	84.48 159	66.11 373	64.26 104	76.12 44	93.40 20	36.26 248	86.04 252	81.47 35	66.54 222	86.82 189

DP-MVS Recon		71.99 136	70.31 149	77.01 107	90.65 8	53.44 113	89.37 37	82.97 179	56.33 265	63.56 183	89.47 111	34.02 275	92.15 57	54.05 245	72.41 172	85.43 218

test_fmvsmconf0.01_n		71.97 137	70.95 137	75.04 166	66.21 367	47.87 264	80.35 270	70.08 357	65.85 83	72.69 74	91.68 60	39.99 193	87.67 198	82.03 29	69.66 198	89.58 117

SDMVSNet		71.89 138	70.62 141	75.70 140	81.70 158	51.61 160	73.89 320	88.72 45	66.58 64	61.64 204	82.38 232	37.63 216	89.48 125	77.44 63	65.60 231	86.01 203

QAPM		71.88 139	69.33 166	79.52 40	82.20 146	54.30 93	86.30 97	88.77 43	56.61 261	59.72 222	87.48 155	33.90 277	95.36 13	47.48 290	81.49 72	88.90 135

ECVR-MVS		71.81 140	71.00 136	74.26 184	80.12 201	43.49 323	84.69 152	82.16 187	64.02 110	64.64 161	87.43 157	35.04 263	89.21 135	61.24 178	79.66 94	90.08 106

PAPM_NR		71.80 141	69.98 156	77.26 101	81.54 168	53.34 118	78.60 291	85.25 121	53.46 293	60.53 215	88.66 127	45.69 111	89.24 132	56.49 228	79.62 96	89.19 129

mPP-MVS		71.79 142	70.38 147	76.04 132	82.65 138	52.06 149	84.45 160	81.78 198	55.59 272	62.05 200	89.68 108	33.48 281	88.28 177	65.45 149	78.24 106	87.77 166

reproduce-ours		71.77 143	70.43 144	75.78 137	81.96 149	49.54 209	82.54 219	81.01 212	48.77 327	69.21 112	90.96 73	37.13 231	89.40 127	66.28 138	76.01 132	88.39 152

our_new_method		71.77 143	70.43 144	75.78 137	81.96 149	49.54 209	82.54 219	81.01 212	48.77 327	69.21 112	90.96 73	37.13 231	89.40 127	66.28 138	76.01 132	88.39 152

xiu_mvs_v1_base_debu		71.60 145	70.29 150	75.55 146	77.26 253	53.15 123	85.34 122	79.37 245	55.83 269	72.54 75	90.19 96	22.38 359	86.66 230	73.28 95	76.39 124	86.85 185

xiu_mvs_v1_base		71.60 145	70.29 150	75.55 146	77.26 253	53.15 123	85.34 122	79.37 245	55.83 269	72.54 75	90.19 96	22.38 359	86.66 230	73.28 95	76.39 124	86.85 185

xiu_mvs_v1_base_debi		71.60 145	70.29 150	75.55 146	77.26 253	53.15 123	85.34 122	79.37 245	55.83 269	72.54 75	90.19 96	22.38 359	86.66 230	73.28 95	76.39 124	86.85 185

fmvsm_s_conf0.1_n_2		71.45 148	71.01 135	72.78 222	75.37 287	45.82 298	84.18 168	64.59 378	64.02 110	75.67 45	93.02 33	34.99 265	85.99 254	81.18 39	66.04 229	86.52 195

hse-mvs2		71.44 149	70.68 139	73.73 202	76.34 266	47.44 272	79.45 284	79.47 244	68.08 41	71.97 84	86.01 177	42.50 158	86.93 223	78.82 49	53.46 342	86.83 188

test_fmvsmvis_n_1920		71.29 150	70.38 147	74.00 191	71.04 340	48.79 230	79.19 287	64.62 377	62.75 138	66.73 131	91.99 52	40.94 179	88.35 170	83.00 22	73.18 163	84.85 227

EPP-MVSNet		71.14 151	70.07 155	74.33 181	79.18 216	46.52 284	83.81 181	86.49 89	56.32 266	57.95 256	84.90 190	54.23 39	89.14 136	58.14 209	69.65 199	87.33 176

VPA-MVSNet		71.12 152	70.66 140	72.49 230	78.75 226	44.43 312	87.64 65	90.02 20	63.97 114	65.02 156	81.58 247	42.14 164	87.42 209	63.42 162	63.38 252	85.63 215

1314		71.11 153	69.41 163	76.22 124	79.32 212	50.49 181	80.23 273	85.14 127	59.44 197	58.93 239	88.89 123	33.83 279	89.60 124	61.49 176	77.42 114	88.57 146

reproduce_model		71.07 154	69.67 160	75.28 161	81.51 171	48.82 229	81.73 241	80.57 221	47.81 333	68.26 121	90.78 80	36.49 246	88.60 159	65.12 154	74.76 153	88.42 151

test1111		71.06 155	70.42 146	72.97 217	79.48 209	41.49 346	84.82 149	82.74 182	64.20 107	62.98 188	87.43 157	35.20 260	87.92 186	58.54 201	78.42 104	89.49 121

tpmrst		71.04 156	69.77 158	74.86 171	83.19 115	55.86 50	75.64 306	78.73 262	67.88 46	64.99 158	73.73 327	49.96 71	79.56 332	65.92 141	67.85 211	89.14 131

MVP-Stereo		70.97 157	70.44 143	72.59 227	76.03 276	51.36 167	85.02 140	86.99 79	60.31 184	56.53 283	78.92 271	40.11 191	90.00 111	60.00 193	90.01 7	76.41 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 158	69.91 157	74.12 187	77.95 241	49.57 204	85.76 108	82.59 183	63.60 122	62.15 198	83.28 213	36.04 253	88.30 175	65.46 147	72.34 174	84.49 229

SR-MVS		70.92 159	69.73 159	74.50 175	83.38 110	50.48 182	84.27 165	79.35 249	48.96 325	66.57 137	90.45 86	33.65 280	87.11 216	66.42 135	74.56 155	85.91 208

tpm2		70.82 160	68.44 176	77.98 81	80.78 188	56.11 44	74.21 319	81.28 207	60.24 185	68.04 124	75.27 315	52.26 50	88.50 165	55.82 236	68.03 208	89.33 124

ACMMP		70.81 161	69.29 167	75.39 153	81.52 170	51.92 154	83.43 192	83.03 177	56.67 260	58.80 244	88.91 122	31.92 297	88.58 160	65.89 143	73.39 162	85.67 212
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		70.75 162	69.58 161	74.26 184	75.55 285	51.34 168	86.05 103	83.29 172	61.94 153	62.95 189	85.77 178	34.15 274	88.44 166	65.44 150	71.07 185	82.99 263
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ab-mvs		70.65 163	69.11 169	75.29 159	80.87 185	46.23 292	73.48 324	85.24 122	59.99 187	66.65 133	80.94 251	43.13 153	88.69 155	63.58 161	68.07 207	90.95 81

Vis-MVSNet		70.61 164	69.34 165	74.42 178	80.95 184	48.49 239	86.03 104	77.51 284	58.74 218	65.55 150	87.78 150	34.37 272	85.95 258	52.53 260	80.61 78	88.80 139
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 165	70.13 154	71.58 258	81.59 165	39.02 357	80.78 264	84.71 139	59.34 200	66.61 135	88.09 142	37.17 230	85.52 262	61.82 174	71.02 186	90.20 101

CDS-MVSNet		70.48 166	69.43 162	73.64 204	77.56 248	48.83 228	83.51 189	77.45 285	63.27 130	62.33 195	85.54 182	43.85 136	83.29 296	57.38 223	74.00 157	88.79 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 167	68.56 173	76.20 126	79.78 205	51.52 164	83.49 191	88.58 52	57.62 240	58.60 246	82.79 218	51.03 58	91.48 69	52.84 254	62.36 266	85.59 216

XXY-MVS		70.18 168	69.28 168	72.89 221	77.64 245	42.88 333	85.06 137	87.50 73	62.58 141	62.66 193	82.34 236	43.64 144	89.83 116	58.42 204	63.70 247	85.96 207

Anonymous202405211		70.11 169	67.88 187	76.79 117	87.20 45	47.24 276	89.49 35	77.38 287	54.88 282	66.14 140	86.84 165	20.93 368	91.54 68	56.45 231	71.62 180	91.59 58

PCF-MVS	61.03 10	70.10 170	68.40 177	75.22 164	77.15 257	51.99 151	79.30 286	82.12 189	56.47 264	61.88 202	86.48 173	43.98 135	87.24 213	55.37 237	72.79 169	86.43 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 171	68.01 183	76.27 122	84.21 92	51.22 172	87.29 76	79.33 251	58.96 214	63.63 181	86.77 166	33.29 283	90.30 105	44.63 308	73.96 158	87.30 178

1112_ss		70.05 172	69.37 164	72.10 240	80.77 189	42.78 334	85.12 136	76.75 297	59.69 192	61.19 208	92.12 46	47.48 88	83.84 286	53.04 252	68.21 206	89.66 115

BH-w/o		70.02 173	68.51 175	74.56 174	82.77 133	50.39 185	86.60 93	78.14 274	59.77 190	59.65 223	85.57 181	39.27 199	87.30 212	49.86 273	74.94 152	85.99 205

FIs		70.00 174	70.24 153	69.30 290	77.93 243	38.55 360	83.99 175	87.72 68	66.86 62	57.66 263	84.17 196	52.28 49	85.31 266	52.72 259	68.80 203	84.02 238

OpenMVS	61.00 11	69.99 175	67.55 196	77.30 97	78.37 237	54.07 101	84.36 162	85.76 105	57.22 248	56.71 280	87.67 153	30.79 305	92.83 36	43.04 316	84.06 56	85.01 222

GeoE		69.96 176	67.88 187	76.22 124	81.11 178	51.71 159	84.15 169	76.74 299	59.83 189	60.91 210	84.38 193	41.56 174	88.10 182	51.67 263	70.57 191	88.84 138

HyFIR lowres test		69.94 177	67.58 194	77.04 105	77.11 258	57.29 22	81.49 252	79.11 254	58.27 224	58.86 242	80.41 255	42.33 160	86.96 221	61.91 172	68.68 205	86.87 183

114514_t		69.87 178	67.88 187	75.85 136	88.38 29	52.35 144	86.94 85	83.68 162	53.70 291	55.68 290	85.60 180	30.07 310	91.20 78	55.84 235	71.02 186	83.99 240

miper_enhance_ethall		69.77 179	68.90 171	72.38 233	78.93 223	49.91 198	83.29 198	78.85 256	64.90 96	59.37 230	79.46 264	52.77 46	85.16 271	63.78 159	58.72 287	82.08 272

reproduce_monomvs		69.71 180	68.52 174	73.29 213	86.43 53	48.21 251	83.91 177	86.17 98	68.02 45	54.91 295	77.46 285	42.96 155	88.86 150	68.44 123	48.38 355	82.80 267

Anonymous20240529		69.71 180	67.28 202	77.00 108	83.78 100	50.36 188	88.87 46	85.10 128	47.22 337	64.03 173	83.37 211	27.93 320	92.10 58	57.78 218	67.44 213	88.53 148

TR-MVS		69.71 180	67.85 190	75.27 162	82.94 126	48.48 240	87.40 72	80.86 215	57.15 250	64.61 163	87.08 162	32.67 288	89.64 123	46.38 299	71.55 182	87.68 169

EI-MVSNet		69.70 183	68.70 172	72.68 225	75.00 292	48.90 226	79.54 281	87.16 76	61.05 170	63.88 177	83.74 203	45.87 107	90.44 98	57.42 222	64.68 239	78.70 317

test-LLR		69.65 184	69.01 170	71.60 256	78.67 228	48.17 252	85.13 133	79.72 237	59.18 207	63.13 186	82.58 226	36.91 237	80.24 322	60.56 185	75.17 145	86.39 199

APD-MVS_3200maxsize		69.62 185	68.23 181	73.80 199	81.58 166	48.22 250	81.91 234	79.50 243	48.21 331	64.24 171	89.75 107	31.91 298	87.55 204	63.08 163	73.85 160	85.64 214

v2v482		69.55 186	67.64 193	75.26 163	72.32 326	53.83 102	84.93 145	81.94 192	65.37 91	60.80 212	79.25 267	41.62 172	88.98 145	63.03 164	59.51 280	82.98 264

TAMVS		69.51 187	68.16 182	73.56 208	76.30 269	48.71 233	82.57 216	77.17 290	62.10 149	61.32 207	84.23 195	41.90 169	83.46 293	54.80 241	73.09 166	88.50 149

mvsmamba		69.38 188	67.52 198	74.95 170	82.86 130	52.22 148	67.36 360	76.75 297	61.14 167	49.43 334	82.04 241	37.26 227	84.14 282	73.93 89	76.91 118	88.50 149

WB-MVSnew		69.36 189	68.24 180	72.72 224	79.26 214	49.40 213	85.72 113	88.85 40	61.33 163	64.59 164	82.38 232	34.57 270	87.53 205	46.82 296	70.63 189	81.22 293

PVSNet	62.49 8	69.27 190	67.81 191	73.64 204	84.41 86	51.85 155	84.63 156	77.80 278	66.42 68	59.80 221	84.95 189	22.14 363	80.44 320	55.03 238	75.11 148	88.62 144

MVS_111021_LR		69.07 191	67.91 185	72.54 228	77.27 252	49.56 206	79.77 279	73.96 325	59.33 202	60.73 213	87.82 149	30.19 309	81.53 305	69.94 112	72.19 176	86.53 194

GA-MVS		69.04 192	66.70 212	76.06 131	75.11 289	52.36 143	83.12 204	80.23 226	63.32 129	60.65 214	79.22 268	30.98 304	88.37 168	61.25 177	66.41 223	87.46 173

cascas		69.01 193	66.13 224	77.66 89	79.36 210	55.41 58	86.99 83	83.75 161	56.69 259	58.92 240	81.35 248	24.31 348	92.10 58	53.23 249	70.61 190	85.46 217

FA-MVS(test-final)		69.00 194	66.60 215	76.19 127	83.48 105	47.96 263	74.73 314	82.07 190	57.27 247	62.18 197	78.47 275	36.09 251	92.89 34	53.76 248	71.32 184	87.73 167

cl22		68.85 195	67.69 192	72.35 234	78.07 240	49.98 197	82.45 223	78.48 268	62.50 144	58.46 251	77.95 277	49.99 69	85.17 270	62.55 166	58.72 287	81.90 275

FMVSNet3		68.84 196	67.40 200	73.19 214	85.05 74	48.53 237	85.71 114	85.36 113	60.90 176	57.58 265	79.15 269	42.16 163	86.77 226	47.25 292	63.40 249	84.27 233

UniMVSNet_NR-MVSNet		68.82 197	68.29 179	70.40 276	75.71 282	42.59 336	84.23 166	86.78 82	66.31 70	58.51 247	82.45 229	51.57 53	84.64 279	53.11 250	55.96 319	83.96 244

v1144		68.81 198	66.82 208	74.80 172	72.34 325	53.46 110	84.68 153	81.77 199	64.25 105	60.28 216	77.91 278	40.23 188	88.95 146	60.37 190	59.52 279	81.97 273

IS-MVSNet		68.80 199	67.55 196	72.54 228	78.50 234	43.43 325	81.03 257	79.35 249	59.12 210	57.27 273	86.71 167	46.05 105	87.70 197	44.32 311	75.60 139	86.49 196

PS-MVSNAJss		68.78 200	67.17 204	73.62 206	73.01 316	48.33 247	84.95 144	84.81 135	59.30 203	58.91 241	79.84 261	37.77 211	88.86 150	62.83 165	63.12 258	83.67 251

thres200		68.71 201	67.27 203	73.02 215	84.73 79	46.76 280	85.03 139	87.73 67	62.34 147	59.87 219	83.45 209	43.15 151	88.32 173	31.25 366	67.91 210	83.98 242

UGNet		68.71 201	67.11 205	73.50 209	80.55 195	47.61 269	84.08 171	78.51 267	59.45 196	65.68 149	82.73 222	23.78 350	85.08 273	52.80 255	76.40 123	87.80 165
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		68.70 203	67.58 194	72.08 241	76.91 261	49.48 212	82.47 222	78.45 269	62.68 140	58.28 255	77.88 279	50.90 59	85.01 274	61.91 172	58.72 287	81.75 277

test_vis1_n_1920		68.59 204	68.31 178	69.44 289	69.16 353	41.51 345	84.63 156	68.58 366	58.80 216	73.26 67	88.37 133	25.30 339	80.60 317	79.10 46	67.55 212	86.23 201

EPMVS		68.45 205	65.44 243	77.47 94	84.91 77	56.17 43	71.89 341	81.91 195	61.72 156	60.85 211	72.49 341	36.21 249	87.06 218	47.32 291	71.62 180	89.17 130

test-mter		68.36 206	67.29 201	71.60 256	78.67 228	48.17 252	85.13 133	79.72 237	53.38 294	63.13 186	82.58 226	27.23 326	80.24 322	60.56 185	75.17 145	86.39 199

tpm		68.36 206	67.48 199	70.97 268	79.93 204	51.34 168	76.58 303	78.75 261	67.73 49	63.54 184	74.86 317	48.33 78	72.36 377	53.93 246	63.71 246	89.21 128

tttt0517		68.33 208	66.29 220	74.46 176	78.08 239	49.06 218	80.88 262	89.08 33	54.40 288	54.75 298	80.77 253	51.31 55	90.33 102	49.35 277	58.01 299	83.99 240

BH-untuned		68.28 209	66.40 217	73.91 194	81.62 163	50.01 196	85.56 118	77.39 286	57.63 239	57.47 270	83.69 205	36.36 247	87.08 217	44.81 306	73.08 167	84.65 228

SR-MVS-dyc-post		68.27 210	66.87 207	72.48 231	80.96 181	48.14 254	81.54 248	76.98 293	46.42 344	62.75 191	89.42 112	31.17 303	86.09 250	60.52 187	72.06 177	83.19 259

v148		68.24 211	66.35 218	73.88 195	71.76 330	51.47 165	84.23 166	81.90 196	63.69 120	58.94 238	76.44 302	43.72 142	87.78 194	60.63 183	55.86 321	82.39 270

AUN-MVS		68.20 212	66.35 218	73.76 200	76.37 265	47.45 271	79.52 283	79.52 242	60.98 172	62.34 194	86.02 175	36.59 245	86.94 222	62.32 168	53.47 341	86.89 182

SSC-MVS3.2		68.13 213	66.89 206	71.85 254	82.26 144	43.97 318	82.09 230	89.29 28	71.74 15	61.12 209	79.83 262	34.60 269	87.45 207	41.23 322	59.85 277	84.14 234

c3_l		67.97 214	66.66 213	71.91 252	76.20 272	49.31 215	82.13 229	78.00 276	61.99 151	57.64 264	76.94 294	49.41 74	84.93 275	60.62 184	57.01 309	81.49 281

v1192		67.96 215	65.74 235	74.63 173	71.79 329	53.43 115	84.06 173	80.99 214	63.19 132	59.56 226	77.46 285	37.50 222	88.65 156	58.20 208	58.93 286	81.79 276

v144192		67.86 216	65.76 234	74.16 186	71.68 331	53.09 126	84.14 170	80.83 216	62.85 137	59.21 235	77.28 289	39.30 198	88.00 185	58.67 200	57.88 303	81.40 286

HPM-MVS_fast		67.86 216	66.28 221	72.61 226	80.67 192	48.34 245	81.18 255	75.95 308	50.81 313	59.55 227	88.05 145	27.86 321	85.98 255	58.83 198	73.58 161	83.51 252

AdaColmap		67.86 216	65.48 240	75.00 168	88.15 36	54.99 74	86.10 101	76.63 302	49.30 322	57.80 259	86.65 170	29.39 313	88.94 148	45.10 305	70.21 194	81.06 294

sd_testset		67.79 219	65.95 229	73.32 210	81.70 158	46.33 289	68.99 353	80.30 225	66.58 64	61.64 204	82.38 232	30.45 307	87.63 200	55.86 234	65.60 231	86.01 203

UniMVSNet (Re)		67.71 220	66.80 209	70.45 274	74.44 299	42.93 332	82.42 224	84.90 132	63.69 120	59.63 224	80.99 250	47.18 90	85.23 269	51.17 267	56.75 310	83.19 259

V42		67.66 221	65.60 239	73.86 196	70.69 344	53.63 107	81.50 250	78.61 265	63.85 116	59.49 229	77.49 284	37.98 208	87.65 199	62.33 167	58.43 290	80.29 304

dmvs_re		67.61 222	66.00 227	72.42 232	81.86 153	43.45 324	64.67 368	80.00 229	69.56 34	60.07 218	85.00 188	34.71 267	87.63 200	51.48 264	66.68 217	86.17 202

WR-MVS		67.58 223	66.76 210	70.04 283	75.92 280	45.06 308	86.23 98	85.28 119	64.31 103	58.50 249	81.00 249	44.80 130	82.00 304	49.21 279	55.57 324	83.06 262

tfpn200view9		67.57 224	66.13 224	71.89 253	84.05 94	45.07 305	83.40 194	87.71 69	60.79 177	57.79 260	82.76 219	43.53 145	87.80 191	28.80 373	66.36 224	82.78 268

FMVSNet2		67.57 224	65.79 233	72.90 219	82.71 135	47.97 261	85.15 132	84.93 131	58.55 221	56.71 280	78.26 276	36.72 242	86.67 229	46.15 301	62.94 260	84.07 237

FC-MVSNet-test		67.49 226	67.91 185	66.21 322	76.06 274	33.06 382	80.82 263	87.18 75	64.44 100	54.81 296	82.87 216	50.40 66	82.60 298	48.05 287	66.55 221	82.98 264

v1921920		67.45 227	65.23 247	74.10 188	71.51 334	52.90 132	83.75 183	80.44 222	62.48 145	59.12 236	77.13 290	36.98 235	87.90 187	57.53 220	58.14 297	81.49 281

UWE-MVS-28		67.43 228	67.98 184	65.75 324	75.66 283	34.74 372	80.00 277	88.17 57	64.21 106	57.27 273	84.14 197	45.68 112	78.82 335	44.33 309	72.40 173	83.70 249

cl____		67.43 228	65.93 230	71.95 249	76.33 267	48.02 259	82.58 215	79.12 253	61.30 165	56.72 279	76.92 295	46.12 103	86.44 238	57.98 211	56.31 313	81.38 288

DIV-MVS_self_test		67.43 228	65.93 230	71.94 250	76.33 267	48.01 260	82.57 216	79.11 254	61.31 164	56.73 278	76.92 295	46.09 104	86.43 239	57.98 211	56.31 313	81.39 287

gg-mvs-nofinetune		67.43 228	64.53 254	76.13 129	85.95 56	47.79 267	64.38 369	88.28 56	39.34 374	66.62 134	41.27 411	58.69 15	89.00 142	49.64 275	86.62 31	91.59 58

thres400		67.40 232	66.13 224	71.19 264	84.05 94	45.07 305	83.40 194	87.71 69	60.79 177	57.79 260	82.76 219	43.53 145	87.80 191	28.80 373	66.36 224	80.71 299

UA-Net		67.32 233	66.23 222	70.59 272	78.85 224	41.23 349	73.60 322	75.45 312	61.54 160	66.61 135	84.53 192	38.73 204	86.57 235	42.48 321	74.24 156	83.98 242

v8		67.25 234	64.99 250	74.04 189	72.89 319	53.31 120	82.37 225	80.11 228	61.54 160	54.29 304	76.02 311	42.89 156	88.41 167	58.43 202	56.36 311	80.39 303

NR-MVSNet		67.25 234	65.99 228	71.04 267	73.27 313	43.91 319	85.32 126	84.75 138	66.05 80	53.65 311	82.11 239	45.05 120	85.97 257	47.55 289	56.18 316	83.24 257

Test_1112_low_res		67.18 236	66.23 222	70.02 284	78.75 226	41.02 350	83.43 192	73.69 327	57.29 246	58.45 252	82.39 231	45.30 117	80.88 311	50.50 269	66.26 228	88.16 155

CPTT-MVS		67.15 237	65.84 232	71.07 266	80.96 181	50.32 190	81.94 233	74.10 321	46.18 347	57.91 257	87.64 154	29.57 311	81.31 307	64.10 158	70.18 195	81.56 280

test_cas_vis1_n_1920		67.10 238	66.60 215	68.59 302	65.17 375	43.23 329	83.23 200	69.84 359	55.34 276	70.67 103	87.71 152	24.70 346	76.66 357	78.57 53	64.20 241	85.89 209

GBi-Net		67.09 239	65.47 241	71.96 246	82.71 135	46.36 286	83.52 185	83.31 169	58.55 221	57.58 265	76.23 306	36.72 242	86.20 242	47.25 292	63.40 249	83.32 254

test1		67.09 239	65.47 241	71.96 246	82.71 135	46.36 286	83.52 185	83.31 169	58.55 221	57.58 265	76.23 306	36.72 242	86.20 242	47.25 292	63.40 249	83.32 254

PatchmatchNet		67.07 241	63.63 261	77.40 95	83.10 116	58.03 11	72.11 339	77.77 279	58.85 215	59.37 230	70.83 354	37.84 210	84.93 275	42.96 317	69.83 197	89.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 242	64.68 252	73.93 193	71.38 337	52.66 137	83.39 196	79.98 230	61.97 152	58.44 253	77.11 291	35.25 259	87.81 189	56.46 230	58.15 295	81.33 289

eth_miper_zixun_eth		66.98 243	65.28 246	72.06 242	75.61 284	50.40 184	81.00 258	76.97 296	62.00 150	56.99 276	76.97 293	44.84 127	85.58 261	58.75 199	54.42 332	80.21 305

TranMVSNet+NR-MVSNet		66.94 244	65.61 238	70.93 269	73.45 309	43.38 326	83.02 208	84.25 150	65.31 93	58.33 254	81.90 243	39.92 195	85.52 262	49.43 276	54.89 328	83.89 246

thres100view900		66.87 245	65.42 244	71.24 262	83.29 112	43.15 330	81.67 243	87.78 64	59.04 211	55.92 288	82.18 238	43.73 140	87.80 191	28.80 373	66.36 224	82.78 268

DU-MVS		66.84 246	65.74 235	70.16 279	73.27 313	42.59 336	81.50 250	82.92 180	63.53 124	58.51 247	82.11 239	40.75 181	84.64 279	53.11 250	55.96 319	83.24 257

MonoMVSNet		66.80 247	64.41 255	73.96 192	76.21 271	48.07 257	76.56 304	78.26 272	64.34 102	54.32 303	74.02 324	37.21 229	86.36 241	64.85 156	53.96 335	87.45 174

IterMVS-LS		66.63 248	65.36 245	70.42 275	75.10 290	48.90 226	81.45 253	76.69 301	61.05 170	55.71 289	77.10 292	45.86 108	83.65 290	57.44 221	57.88 303	78.70 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 249	64.20 258	73.83 198	72.59 322	53.37 116	81.88 235	79.91 234	61.11 168	54.09 306	75.60 313	40.06 192	88.26 178	56.47 229	56.10 317	79.86 309

Fast-Effi-MVS+-dtu		66.53 250	64.10 259	73.84 197	72.41 324	52.30 146	84.73 150	75.66 309	59.51 195	56.34 285	79.11 270	28.11 318	85.85 260	57.74 219	63.29 253	83.35 253

thres600view7		66.46 251	65.12 248	70.47 273	83.41 106	43.80 321	82.15 227	87.78 64	59.37 199	56.02 287	82.21 237	43.73 140	86.90 224	26.51 385	64.94 235	80.71 299

LPG-MVS_test		66.44 252	64.58 253	72.02 243	74.42 300	48.60 234	83.07 206	80.64 218	54.69 284	53.75 309	83.83 201	25.73 337	86.98 219	60.33 191	64.71 236	80.48 301

tpm cat1		66.28 253	62.78 263	76.77 118	81.40 173	57.14 24	70.03 348	77.19 289	53.00 297	58.76 245	70.73 357	46.17 102	86.73 228	43.27 315	64.46 240	86.44 197

EPNet_dtu		66.25 254	66.71 211	64.87 333	78.66 230	34.12 377	82.80 211	75.51 310	61.75 155	64.47 169	86.90 164	37.06 233	72.46 376	43.65 314	69.63 200	88.02 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 255	64.96 251	70.08 281	75.17 288	49.64 203	82.01 231	74.48 319	62.15 148	57.83 258	76.08 310	30.59 306	83.79 287	65.40 151	60.93 272	76.81 339

ACMP	61.11 9	66.24 255	64.33 256	72.00 245	74.89 294	49.12 217	83.18 202	79.83 235	55.41 275	52.29 318	82.68 223	25.83 335	86.10 248	60.89 180	63.94 245	80.78 297
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 257	63.67 260	73.31 211	83.07 119	48.75 231	86.01 105	84.67 141	45.27 351	56.54 282	76.67 300	28.06 319	88.95 146	52.78 256	59.95 274	82.23 271

OMC-MVS		65.97 258	65.06 249	68.71 299	72.97 317	42.58 338	78.61 290	75.35 313	54.72 283	59.31 232	86.25 174	33.30 282	77.88 346	57.99 210	67.05 215	85.66 213

X-MVStestdata		65.85 259	62.20 267	76.81 114	83.41 106	52.48 139	84.88 146	83.20 174	58.03 227	63.91 175	4.82 430	35.50 257	89.78 117	65.50 144	80.50 80	88.16 155

Vis-MVSNet (Re-imp)		65.52 260	65.63 237	65.17 331	77.49 249	30.54 389	75.49 310	77.73 280	59.34 200	52.26 320	86.69 168	49.38 75	80.53 319	37.07 336	75.28 143	84.42 231

Baseline_NR-MVSNet		65.49 261	64.27 257	69.13 291	74.37 302	41.65 343	83.39 196	78.85 256	59.56 194	59.62 225	76.88 297	40.75 181	87.44 208	49.99 271	55.05 326	78.28 326

FMVSNet1		64.57 262	62.11 268	71.96 246	77.32 251	46.36 286	83.52 185	83.31 169	52.43 302	54.42 301	76.23 306	27.80 322	86.20 242	42.59 320	61.34 270	83.32 254

dp		64.41 263	61.58 271	72.90 219	82.40 141	54.09 100	72.53 331	76.59 303	60.39 183	55.68 290	70.39 358	35.18 261	76.90 355	39.34 328	61.71 268	87.73 167

ACMM	58.35 12	64.35 264	62.01 269	71.38 260	74.21 303	48.51 238	82.25 226	79.66 239	47.61 335	54.54 300	80.11 257	25.26 340	86.00 253	51.26 265	63.16 256	79.64 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 265	60.43 285	75.30 158	80.85 186	49.86 200	68.28 357	78.37 270	50.26 318	59.31 232	73.79 326	26.19 333	91.92 61	40.19 325	66.67 218	84.12 235

pm-mvs1		64.12 266	62.56 264	68.78 297	71.68 331	38.87 358	82.89 210	81.57 200	55.54 274	53.89 308	77.82 280	37.73 214	86.74 227	48.46 285	53.49 340	80.72 298

miper_lstm_enhance		63.91 267	62.30 266	68.75 298	75.06 291	46.78 279	69.02 352	81.14 208	59.68 193	52.76 315	72.39 344	40.71 183	77.99 344	56.81 226	53.09 343	81.48 283

SCA		63.84 268	60.01 289	75.32 155	78.58 232	57.92 12	61.61 381	77.53 283	56.71 258	57.75 262	70.77 355	31.97 295	79.91 328	48.80 281	56.36 311	88.13 158

test_djsdf		63.84 268	61.56 272	70.70 271	68.78 355	44.69 309	81.63 244	81.44 203	50.28 315	52.27 319	76.26 305	26.72 329	86.11 246	60.83 181	55.84 322	81.29 292

IterMVS		63.77 270	61.67 270	70.08 281	72.68 321	51.24 171	80.44 268	75.51 310	60.51 182	51.41 323	73.70 330	32.08 294	78.91 333	54.30 243	54.35 333	80.08 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 271	63.56 262	63.40 340	81.73 156	34.28 374	80.97 259	81.02 210	60.93 174	55.06 293	82.64 224	48.00 84	80.81 313	23.42 395	58.32 291	75.10 357

D2MVS		63.49 272	61.39 274	69.77 285	69.29 352	48.93 225	78.89 289	77.71 281	60.64 181	49.70 333	72.10 349	27.08 327	83.48 292	54.48 242	62.65 262	76.90 338

tt0805		63.39 273	61.31 276	69.64 286	69.36 351	38.87 358	78.00 294	85.48 107	48.82 326	55.66 292	81.66 245	24.38 347	86.37 240	49.04 280	59.36 283	83.68 250

pmmvs4		63.34 274	61.07 279	70.16 279	70.14 346	50.53 180	79.97 278	71.41 348	55.08 278	54.12 305	78.58 273	32.79 287	82.09 303	50.33 270	57.22 308	77.86 330

jajsoiax		63.21 275	60.84 280	70.32 277	68.33 360	44.45 311	81.23 254	81.05 209	53.37 295	50.96 328	77.81 281	17.49 382	85.49 264	59.31 194	58.05 298	81.02 295

MIMVSNet		63.12 276	60.29 286	71.61 255	75.92 280	46.65 282	65.15 365	81.94 192	59.14 209	54.65 299	69.47 361	25.74 336	80.63 316	41.03 324	69.56 201	87.55 171

CL-MVSNet_self_test		62.98 277	61.14 278	68.50 304	65.86 370	42.96 331	84.37 161	82.98 178	60.98 172	53.95 307	72.70 340	40.43 186	83.71 289	41.10 323	47.93 358	78.83 316

mvs_tets		62.96 278	60.55 282	70.19 278	68.22 363	44.24 316	80.90 261	80.74 217	52.99 298	50.82 330	77.56 282	16.74 386	85.44 265	59.04 197	57.94 300	80.89 296

TransMVSNet (Re)		62.82 279	60.76 281	69.02 292	73.98 306	41.61 344	86.36 95	79.30 252	56.90 252	52.53 316	76.44 302	41.85 170	87.60 203	38.83 329	40.61 382	77.86 330

pmmvs5		62.80 280	61.18 277	67.66 308	69.53 350	42.37 341	82.65 214	75.19 314	54.30 289	52.03 321	78.51 274	31.64 300	80.67 315	48.60 283	58.15 295	79.95 308

test0.0.03 1		62.54 281	62.44 265	62.86 345	72.28 328	29.51 398	82.93 209	78.78 259	59.18 207	53.07 314	82.41 230	36.91 237	77.39 350	37.45 332	58.96 285	81.66 279

UniMVSNet_ETH3D		62.51 282	60.49 283	68.57 303	68.30 361	40.88 352	73.89 320	79.93 233	51.81 308	54.77 297	79.61 263	24.80 344	81.10 308	49.93 272	61.35 269	83.73 248

v7n		62.50 283	59.27 294	72.20 238	67.25 366	49.83 201	77.87 296	80.12 227	52.50 301	48.80 339	73.07 335	32.10 293	87.90 187	46.83 295	54.92 327	78.86 315

CR-MVSNet		62.47 284	59.04 296	72.77 223	73.97 307	56.57 34	60.52 384	71.72 343	60.04 186	57.49 268	65.86 373	38.94 201	80.31 321	42.86 318	59.93 275	81.42 284

tpmvs		62.45 285	59.42 292	71.53 259	83.93 96	54.32 92	70.03 348	77.61 282	51.91 305	53.48 312	68.29 367	37.91 209	86.66 230	33.36 356	58.27 293	73.62 368

EG-PatchMatch MVS		62.40 286	59.59 290	70.81 270	73.29 311	49.05 219	85.81 106	84.78 136	51.85 307	44.19 359	73.48 333	15.52 391	89.85 115	40.16 326	67.24 214	73.54 369

XVG-OURS-SEG-HR		62.02 287	59.54 291	69.46 288	65.30 373	45.88 295	65.06 366	73.57 329	46.45 343	57.42 271	83.35 212	26.95 328	78.09 340	53.77 247	64.03 243	84.42 231

XVG-OURS		61.88 288	59.34 293	69.49 287	65.37 372	46.27 290	64.80 367	73.49 330	47.04 339	57.41 272	82.85 217	25.15 341	78.18 338	53.00 253	64.98 233	84.01 239

TAPA-MVS	56.12 14	61.82 289	60.18 288	66.71 318	78.48 235	37.97 364	75.19 312	76.41 305	46.82 340	57.04 275	86.52 172	27.67 324	77.03 352	26.50 386	67.02 216	85.14 220
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 290	61.35 275	62.00 348	81.73 156	30.09 393	80.97 259	81.02 210	60.93 174	55.06 293	82.64 224	35.09 262	80.81 313	16.40 412	58.32 291	75.10 357

tfpnnormal		61.47 291	59.09 295	68.62 301	76.29 270	41.69 342	81.14 256	85.16 125	54.48 286	51.32 324	73.63 331	32.32 291	86.89 225	21.78 399	55.71 323	77.29 336

PVSNet_0	57.04 13	61.19 292	57.24 305	73.02 215	77.45 250	50.31 191	79.43 285	77.36 288	63.96 115	47.51 349	72.45 343	25.03 342	83.78 288	52.76 258	19.22 418	84.96 224

PLC	52.38 18	60.89 293	58.97 297	66.68 320	81.77 155	45.70 300	78.96 288	74.04 324	43.66 363	47.63 346	83.19 215	23.52 353	77.78 349	37.47 331	60.46 273	76.55 345
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 294	60.44 284	62.07 346	75.00 292	32.73 384	79.54 281	73.49 330	36.98 382	56.28 286	83.74 203	29.28 314	69.53 385	46.48 298	63.23 254	83.94 245

CNLPA		60.59 295	58.44 299	67.05 315	79.21 215	47.26 275	79.75 280	64.34 380	42.46 369	51.90 322	83.94 199	27.79 323	75.41 362	37.12 334	59.49 281	78.47 321

anonymousdsp		60.46 296	57.65 302	68.88 293	63.63 384	45.09 304	72.93 328	78.63 264	46.52 342	51.12 325	72.80 339	21.46 366	83.07 297	57.79 217	53.97 334	78.47 321

testing3		59.97 297	60.19 287	59.32 360	77.60 246	30.01 395	81.75 240	81.79 197	53.54 292	50.34 331	79.94 258	48.99 77	76.91 353	17.19 410	50.59 350	71.03 383

ACMH	53.70 16	59.78 298	55.94 316	71.28 261	76.59 264	48.35 244	80.15 275	76.11 306	49.74 320	41.91 370	73.45 334	16.50 388	90.31 103	31.42 364	57.63 306	75.17 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 299	57.15 306	67.09 313	66.01 368	36.86 368	80.50 266	78.64 263	45.05 353	49.05 337	73.94 325	27.28 325	86.10 248	43.96 313	49.94 352	78.31 325

MSDG		59.44 300	55.14 320	72.32 236	74.69 295	50.71 175	74.39 318	73.58 328	44.44 358	43.40 364	77.52 283	19.45 372	90.87 89	31.31 365	57.49 307	75.38 352

RPMNet		59.29 301	54.25 325	74.42 178	73.97 307	56.57 34	60.52 384	76.98 293	35.72 386	57.49 268	58.87 396	37.73 214	85.26 268	27.01 384	59.93 275	81.42 284

DP-MVS		59.24 302	56.12 314	68.63 300	88.24 34	50.35 189	82.51 221	64.43 379	41.10 371	46.70 353	78.77 272	24.75 345	88.57 163	22.26 397	56.29 315	66.96 389

OpenMVS_ROB	53.19 17	59.20 303	56.00 315	68.83 295	71.13 339	44.30 313	83.64 184	75.02 315	46.42 344	46.48 355	73.03 336	18.69 376	88.14 179	27.74 381	61.80 267	74.05 365

IterMVS-SCA-FT		59.12 304	58.81 298	60.08 358	70.68 345	45.07 305	80.42 269	74.25 320	43.54 364	50.02 332	73.73 327	31.97 295	56.74 404	51.06 268	53.60 339	78.42 323

our_test_3		59.11 305	55.08 321	71.18 265	71.42 335	53.29 121	81.96 232	74.52 318	48.32 329	42.08 368	69.28 364	28.14 317	82.15 301	34.35 353	45.68 372	78.11 329

Anonymous20231206		59.08 306	57.59 303	63.55 338	68.77 356	32.14 387	80.26 272	79.78 236	50.00 319	49.39 335	72.39 344	26.64 330	78.36 337	33.12 359	57.94 300	80.14 306

KD-MVS_2432*1600		59.04 307	56.44 311	66.86 316	79.07 217	45.87 296	72.13 337	80.42 223	55.03 279	48.15 341	71.01 352	36.73 240	78.05 342	35.21 347	30.18 404	76.67 340

miper_refine_blended		59.04 307	56.44 311	66.86 316	79.07 217	45.87 296	72.13 337	80.42 223	55.03 279	48.15 341	71.01 352	36.73 240	78.05 342	35.21 347	30.18 404	76.67 340

WR-MVS_H		58.91 309	58.04 301	61.54 352	69.07 354	33.83 379	76.91 300	81.99 191	51.40 310	48.17 340	74.67 318	40.23 188	74.15 365	31.78 363	48.10 356	76.64 343

LCM-MVSNet-Re		58.82 310	56.54 309	65.68 325	79.31 213	29.09 401	61.39 383	45.79 401	60.73 179	37.65 388	72.47 342	31.42 301	81.08 309	49.66 274	70.41 192	86.87 183

Patchmatch-RL test		58.72 311	54.32 324	71.92 251	63.91 382	44.25 315	61.73 380	55.19 392	57.38 245	49.31 336	54.24 402	37.60 218	80.89 310	62.19 170	47.28 363	90.63 87

FMVSNet5		58.61 312	56.45 310	65.10 332	77.20 256	39.74 354	74.77 313	77.12 291	50.27 317	43.28 365	67.71 368	26.15 334	76.90 355	36.78 339	54.78 329	78.65 319

ppachtmachnet_test		58.56 313	54.34 323	71.24 262	71.42 335	54.74 80	81.84 237	72.27 338	49.02 324	45.86 358	68.99 365	26.27 331	83.30 295	30.12 368	43.23 377	75.69 349

ACMH+	54.58 15	58.55 314	55.24 318	68.50 304	74.68 296	45.80 299	80.27 271	70.21 356	47.15 338	42.77 367	75.48 314	16.73 387	85.98 255	35.10 351	54.78 329	73.72 367

CP-MVSNet		58.54 315	57.57 304	61.46 353	68.50 358	33.96 378	76.90 301	78.60 266	51.67 309	47.83 344	76.60 301	34.99 265	72.79 374	35.45 344	47.58 360	77.64 334

PEN-MVS		58.35 316	57.15 306	61.94 349	67.55 365	34.39 373	77.01 299	78.35 271	51.87 306	47.72 345	76.73 299	33.91 276	73.75 369	34.03 354	47.17 364	77.68 332

PS-CasMVS		58.12 317	57.03 308	61.37 354	68.24 362	33.80 380	76.73 302	78.01 275	51.20 311	47.54 348	76.20 309	32.85 285	72.76 375	35.17 349	47.37 362	77.55 335

mmtdpeth		57.93 318	54.78 322	67.39 311	72.32 326	43.38 326	72.72 329	68.93 364	54.45 287	56.85 277	62.43 384	17.02 384	83.46 293	57.95 213	30.31 403	75.31 353

dmvs_testset		57.65 319	58.21 300	55.97 371	74.62 297	9.82 432	63.75 371	63.34 382	67.23 56	48.89 338	83.68 207	39.12 200	76.14 358	23.43 394	59.80 278	81.96 274

UnsupCasMVSNet_eth		57.56 320	55.15 319	64.79 334	64.57 380	33.12 381	73.17 327	83.87 160	58.98 213	41.75 371	70.03 359	22.54 358	79.92 326	46.12 302	35.31 391	81.32 291

CHOSEN 280x420		57.53 321	56.38 313	60.97 356	74.01 305	48.10 256	46.30 404	54.31 394	48.18 332	50.88 329	77.43 287	38.37 207	59.16 400	54.83 239	63.14 257	75.66 350

DTE-MVSNet		57.03 322	55.73 317	60.95 357	65.94 369	32.57 385	75.71 305	77.09 292	51.16 312	46.65 354	76.34 304	32.84 286	73.22 373	30.94 367	44.87 373	77.06 337

PatchMatch-RL		56.66 323	53.75 328	65.37 330	77.91 244	45.28 303	69.78 350	60.38 386	41.35 370	47.57 347	73.73 327	16.83 385	76.91 353	36.99 337	59.21 284	73.92 366

PatchT		56.60 324	52.97 331	67.48 309	72.94 318	46.16 293	57.30 392	73.78 326	38.77 376	54.37 302	57.26 399	37.52 220	78.06 341	32.02 361	52.79 344	78.23 328

Patchmtry		56.56 325	52.95 332	67.42 310	72.53 323	50.59 179	59.05 388	71.72 343	37.86 380	46.92 351	65.86 373	38.94 201	80.06 325	36.94 338	46.72 368	71.60 379

test_0402		56.45 326	53.03 330	66.69 319	76.78 263	50.31 191	81.76 239	69.61 361	42.79 367	43.88 360	72.13 347	22.82 357	86.46 237	16.57 411	50.94 349	63.31 398

LS3D		56.40 327	53.82 327	64.12 335	81.12 177	45.69 301	73.42 325	66.14 372	35.30 390	43.24 366	79.88 259	22.18 362	79.62 331	19.10 406	64.00 244	67.05 388

ADS-MVSNet		56.17 328	51.95 338	68.84 294	80.60 193	53.07 127	55.03 396	70.02 358	44.72 355	51.00 326	61.19 388	22.83 355	78.88 334	28.54 376	53.63 337	74.57 362

XVG-ACMP-BASELINE		56.03 329	52.85 333	65.58 326	61.91 389	40.95 351	63.36 372	72.43 337	45.20 352	46.02 356	74.09 322	9.20 404	78.12 339	45.13 304	58.27 293	77.66 333

pmmvs-eth3d		55.97 330	52.78 334	65.54 327	61.02 391	46.44 285	75.36 311	67.72 369	49.61 321	43.65 362	67.58 369	21.63 365	77.04 351	44.11 312	44.33 374	73.15 373

F-COLMAP		55.96 331	53.65 329	62.87 344	72.76 320	42.77 335	74.70 316	70.37 355	40.03 372	41.11 376	79.36 265	17.77 381	73.70 370	32.80 360	53.96 335	72.15 375

CMPMVS	40.41 21	55.34 332	52.64 335	63.46 339	60.88 392	43.84 320	61.58 382	71.06 351	30.43 398	36.33 390	74.63 319	24.14 349	75.44 361	48.05 287	66.62 219	71.12 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 333	54.07 326	58.68 363	63.14 386	25.00 407	77.69 297	74.78 317	52.64 299	43.43 363	72.39 344	26.21 332	74.76 364	29.31 371	47.05 366	76.28 347

ADS-MVSNet2		55.21 334	51.44 339	66.51 321	80.60 193	49.56 206	55.03 396	65.44 374	44.72 355	51.00 326	61.19 388	22.83 355	75.41 362	28.54 376	53.63 337	74.57 362

SixPastTwentyTwo		54.37 335	50.10 344	67.21 312	70.70 343	41.46 347	74.73 314	64.69 376	47.56 336	39.12 383	69.49 360	18.49 379	84.69 278	31.87 362	34.20 397	75.48 351

USDC		54.36 336	51.23 340	63.76 337	64.29 381	37.71 365	62.84 377	73.48 332	56.85 253	35.47 393	71.94 350	9.23 403	78.43 336	38.43 330	48.57 354	75.13 356

testgi		54.25 337	52.57 336	59.29 361	62.76 387	21.65 416	72.21 336	70.47 354	53.25 296	41.94 369	77.33 288	14.28 392	77.95 345	29.18 372	51.72 348	78.28 326

K. test v3		54.04 338	49.42 350	67.92 307	68.55 357	42.57 339	75.51 309	63.07 383	52.07 303	39.21 382	64.59 379	19.34 373	82.21 300	37.11 335	25.31 409	78.97 314

UnsupCasMVSNet_bld		53.86 339	50.53 343	63.84 336	63.52 385	34.75 371	71.38 342	81.92 194	46.53 341	38.95 384	57.93 397	20.55 369	80.20 324	39.91 327	34.09 398	76.57 344

YYNet1		53.82 340	49.96 346	65.41 329	70.09 348	48.95 223	72.30 334	71.66 345	44.25 360	31.89 403	63.07 383	23.73 351	73.95 367	33.26 357	39.40 384	73.34 370

MDA-MVSNet_test_wron		53.82 340	49.95 347	65.43 328	70.13 347	49.05 219	72.30 334	71.65 346	44.23 361	31.85 404	63.13 382	23.68 352	74.01 366	33.25 358	39.35 385	73.23 372

test_fmvs1		53.60 342	52.54 337	56.78 367	58.07 395	30.26 391	68.95 354	42.19 407	32.46 393	63.59 182	82.56 228	11.55 396	60.81 394	58.25 207	55.27 325	79.28 311

Patchmatch-test		53.33 343	48.17 353	68.81 296	73.31 310	42.38 340	42.98 408	58.23 388	32.53 392	38.79 385	70.77 355	39.66 196	73.51 371	25.18 388	52.06 347	90.55 89

LTVRE_ROB	45.45 19	52.73 344	49.74 348	61.69 351	69.78 349	34.99 370	44.52 405	67.60 370	43.11 366	43.79 361	74.03 323	18.54 378	81.45 306	28.39 378	57.94 300	68.62 386
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		52.63 345	50.72 342	58.37 364	62.69 388	28.13 404	72.60 330	75.97 307	30.94 397	40.76 378	72.11 348	20.16 370	70.80 381	35.11 350	46.11 370	76.19 348

test_fmvs1_n		52.55 346	51.19 341	56.65 368	51.90 406	30.14 392	67.66 358	42.84 406	32.27 394	62.30 196	82.02 242	9.12 405	60.84 393	57.82 216	54.75 331	78.99 313

OurMVSNet-221017-0		52.39 347	48.73 351	63.35 341	65.21 374	38.42 361	68.54 356	64.95 375	38.19 377	39.57 381	71.43 351	13.23 394	79.92 326	37.16 333	40.32 383	71.72 378

JIA-IIPM		52.33 348	47.77 356	66.03 323	71.20 338	46.92 278	40.00 413	76.48 304	37.10 381	46.73 352	37.02 413	32.96 284	77.88 346	35.97 342	52.45 346	73.29 371

Anonymous20240521		51.65 349	48.42 352	61.34 355	56.43 400	39.65 356	73.57 323	73.47 333	36.64 384	36.59 389	63.98 380	10.75 399	72.25 378	35.35 345	49.01 353	72.11 376

MDA-MVSNet-bldmvs		51.56 350	47.75 357	63.00 342	71.60 333	47.32 274	69.70 351	72.12 339	43.81 362	27.65 411	63.38 381	21.97 364	75.96 359	27.30 383	32.19 399	65.70 394

test_vis1_n		51.19 351	49.66 349	55.76 372	51.26 408	29.85 396	67.20 361	38.86 412	32.12 395	59.50 228	79.86 260	8.78 406	58.23 401	56.95 225	52.46 345	79.19 312

mvs5depth		50.97 352	46.98 358	62.95 343	56.63 399	34.23 376	62.73 378	67.35 371	45.03 354	48.00 343	65.41 377	10.40 400	79.88 330	36.00 341	31.27 402	74.73 360

COLMAP_ROB	43.60 20	50.90 353	48.05 354	59.47 359	67.81 364	40.57 353	71.25 343	62.72 385	36.49 385	36.19 391	73.51 332	13.48 393	73.92 368	20.71 401	50.26 351	63.92 397
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 354	47.81 355	57.96 365	61.53 390	27.80 405	67.40 359	74.06 323	43.25 365	33.31 402	65.38 378	16.03 389	71.34 379	21.80 398	47.55 361	74.75 359

kuosan		50.20 355	50.09 345	50.52 379	73.09 315	29.09 401	65.25 364	74.89 316	48.27 330	41.34 373	60.85 390	43.45 148	67.48 387	18.59 408	25.07 410	55.01 404

KD-MVS_self_test		49.24 356	46.85 359	56.44 369	54.32 401	22.87 410	57.39 391	73.36 334	44.36 359	37.98 387	59.30 395	18.97 375	71.17 380	33.48 355	42.44 378	75.26 354

MVS-HIRNet		49.01 357	44.71 361	61.92 350	76.06 274	46.61 283	63.23 374	54.90 393	24.77 406	33.56 398	36.60 415	21.28 367	75.88 360	29.49 370	62.54 263	63.26 399

new-patchmatchnet		48.21 358	46.55 360	53.18 375	57.73 397	18.19 424	70.24 346	71.02 352	45.70 348	33.70 397	60.23 391	18.00 380	69.86 384	27.97 380	34.35 395	71.49 381

TinyColmap		48.15 359	44.49 363	59.13 362	65.73 371	38.04 362	63.34 373	62.86 384	38.78 375	29.48 406	67.23 371	6.46 414	73.30 372	24.59 390	41.90 380	66.04 392

AllTest		47.32 360	44.66 362	55.32 373	65.08 376	37.50 366	62.96 376	54.25 395	35.45 388	33.42 399	72.82 337	9.98 401	59.33 397	24.13 391	43.84 375	69.13 384

PM-MVS		46.92 361	43.76 368	56.41 370	52.18 405	32.26 386	63.21 375	38.18 413	37.99 379	40.78 377	66.20 372	5.09 418	65.42 389	48.19 286	41.99 379	71.54 380

test_fmvs2		45.89 362	44.32 364	50.62 378	45.85 417	24.70 408	58.87 390	37.84 415	25.22 404	52.46 317	74.56 320	7.07 409	54.69 405	49.28 278	47.70 359	72.48 374

RPSCF		45.77 363	44.13 365	50.68 377	57.67 398	29.66 397	54.92 398	45.25 403	26.69 403	45.92 357	75.92 312	17.43 383	45.70 415	27.44 382	45.95 371	76.67 340

pmmvs3		45.53 364	41.55 370	57.44 366	48.97 413	39.68 355	70.06 347	57.66 389	28.32 401	34.06 396	57.29 398	8.50 407	66.85 388	34.86 352	34.26 396	65.80 393

dongtai		43.51 365	44.07 366	41.82 390	63.75 383	21.90 414	63.80 370	72.05 340	39.59 373	33.35 401	54.54 401	41.04 178	57.30 402	10.75 419	17.77 419	46.26 413

mvsany_test1		43.38 366	42.57 369	45.82 385	50.96 409	26.10 406	55.80 394	27.74 425	27.15 402	47.41 350	74.39 321	18.67 377	44.95 416	44.66 307	36.31 389	66.40 391

mamv4		42.60 367	44.05 367	38.26 395	59.21 394	38.00 363	44.14 407	39.03 411	25.03 405	40.61 379	68.39 366	37.01 234	24.28 429	46.62 297	36.43 388	52.50 407

N_pmnet		41.25 368	39.77 371	45.66 386	68.50 358	0.82 438	72.51 332	0.38 437	35.61 387	35.26 394	61.51 387	20.07 371	67.74 386	23.51 393	40.63 381	68.42 387

TDRefinement		40.91 369	38.37 373	48.55 383	50.45 410	33.03 383	58.98 389	50.97 398	28.50 399	29.89 405	67.39 370	6.21 416	54.51 406	17.67 409	35.25 392	58.11 401

ttmdpeth		40.58 370	37.50 374	49.85 380	49.40 411	22.71 411	56.65 393	46.78 399	28.35 400	40.29 380	69.42 362	5.35 417	61.86 392	20.16 403	21.06 416	64.96 395

test_vis1_rt		40.29 371	38.64 372	45.25 387	48.91 414	30.09 393	59.44 387	27.07 426	24.52 407	38.48 386	51.67 407	6.71 412	49.44 410	44.33 309	46.59 369	56.23 402

MVStest1		38.35 372	34.53 378	49.82 381	51.43 407	30.41 390	50.39 400	55.25 391	17.56 414	26.45 412	65.85 375	11.72 395	57.00 403	14.79 413	17.31 420	62.05 400

DSMNet-mixed		38.35 372	35.36 377	47.33 384	48.11 415	14.91 428	37.87 414	36.60 416	19.18 411	34.37 395	59.56 394	15.53 390	53.01 408	20.14 404	46.89 367	74.07 364

test_fmvs3		37.95 374	35.75 376	44.55 388	35.50 423	18.92 420	48.32 401	34.00 420	18.36 413	41.31 375	61.58 386	2.29 425	48.06 414	42.72 319	37.71 387	66.66 390

WB-MVS		37.41 375	36.37 375	40.54 393	54.23 402	10.43 431	65.29 363	43.75 404	34.86 391	27.81 410	54.63 400	24.94 343	63.21 390	6.81 426	15.00 421	47.98 412

FPMVS		35.40 376	33.67 380	40.57 392	46.34 416	28.74 403	41.05 410	57.05 390	20.37 410	22.27 415	53.38 404	6.87 411	44.94 417	8.62 420	47.11 365	48.01 411

SSC-MVS		35.20 377	34.30 379	37.90 396	52.58 404	8.65 434	61.86 379	41.64 408	31.81 396	25.54 413	52.94 406	23.39 354	59.28 399	6.10 427	12.86 422	45.78 415

ANet_high		34.39 378	29.59 384	48.78 382	30.34 427	22.28 412	55.53 395	63.79 381	38.11 378	15.47 419	36.56 416	6.94 410	59.98 396	13.93 415	5.64 430	64.08 396

EGC-MVSNET		33.75 379	30.42 383	43.75 389	64.94 378	36.21 369	60.47 386	40.70 410	0.02 431	0.10 432	53.79 403	7.39 408	60.26 395	11.09 418	35.23 393	34.79 417

new_pmnet		33.56 380	31.89 382	38.59 394	49.01 412	20.42 417	51.01 399	37.92 414	20.58 408	23.45 414	46.79 409	6.66 413	49.28 412	20.00 405	31.57 401	46.09 414

LF4IMVS		33.04 381	32.55 381	34.52 399	40.96 418	22.03 413	44.45 406	35.62 417	20.42 409	28.12 409	62.35 385	5.03 419	31.88 428	21.61 400	34.42 394	49.63 410

LCM-MVSNet		28.07 382	23.85 390	40.71 391	27.46 432	18.93 419	30.82 420	46.19 400	12.76 419	16.40 417	34.70 418	1.90 428	48.69 413	20.25 402	24.22 411	54.51 405

mvsany_test3		28.00 383	25.98 385	34.05 400	28.97 428	15.31 426	34.54 417	18.17 431	16.24 415	29.30 407	53.37 405	2.79 423	33.38 427	30.01 369	20.41 417	53.45 406

Gipuma		27.47 384	24.26 389	37.12 398	60.55 393	29.17 400	11.68 425	60.00 387	14.18 417	10.52 426	15.12 427	2.20 427	63.01 391	8.39 421	35.65 390	19.18 423
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 385	24.85 386	33.93 401	26.17 433	15.25 427	30.24 421	22.38 430	12.53 420	28.23 408	49.43 408	2.59 424	34.34 426	25.12 389	26.99 407	52.20 408

PMMVS2		26.71 386	22.98 391	37.87 397	36.89 421	8.51 435	42.51 409	29.32 424	19.09 412	13.01 421	37.54 412	2.23 426	53.11 407	14.54 414	11.71 423	51.99 409

APD_test1		26.46 387	24.41 388	32.62 404	37.58 420	21.74 415	40.50 412	30.39 422	11.45 421	16.33 418	43.76 410	1.63 430	41.62 418	11.24 417	26.82 408	34.51 418

PMVS	19.57 22	25.07 388	22.43 393	32.99 403	23.12 434	22.98 409	40.98 411	35.19 418	15.99 416	11.95 425	35.87 417	1.47 431	49.29 411	5.41 429	31.90 400	26.70 422
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 389	22.95 392	30.31 405	28.59 429	18.92 420	37.43 415	17.27 433	12.90 418	21.28 416	29.92 422	1.02 432	36.35 421	28.28 379	29.82 406	35.65 416

test_method		24.09 390	21.07 394	33.16 402	27.67 431	8.35 436	26.63 422	35.11 419	3.40 428	14.35 420	36.98 414	3.46 422	35.31 423	19.08 407	22.95 412	55.81 403

testf1		21.11 391	19.08 395	27.18 407	30.56 425	18.28 422	33.43 418	24.48 427	8.02 425	12.02 423	33.50 419	0.75 434	35.09 424	7.68 422	21.32 413	28.17 420

APD_test2		21.11 391	19.08 395	27.18 407	30.56 425	18.28 422	33.43 418	24.48 427	8.02 425	12.02 423	33.50 419	0.75 434	35.09 424	7.68 422	21.32 413	28.17 420

E-PMN		19.16 393	18.40 397	21.44 409	36.19 422	13.63 429	47.59 402	30.89 421	10.73 422	5.91 429	16.59 425	3.66 421	39.77 419	5.95 428	8.14 425	10.92 425

EMVS		18.42 394	17.66 398	20.71 410	34.13 424	12.64 430	46.94 403	29.94 423	10.46 424	5.58 430	14.93 428	4.23 420	38.83 420	5.24 430	7.51 427	10.67 426

cdsmvs_eth3d_5k		18.33 395	24.44 387	0.00 416	0.00 438	0.00 440	0.00 427	89.40 27	0.00 432	0.00 435	92.02 50	38.55 205	0.00 433	0.00 434	0.00 431	0.00 431

MVE	16.60 23	17.34 396	13.39 399	29.16 406	28.43 430	19.72 418	13.73 424	23.63 429	7.23 427	7.96 427	21.41 423	0.80 433	36.08 422	6.97 424	10.39 424	31.69 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 397	10.68 400	5.73 413	2.49 436	4.21 437	10.48 426	18.04 432	0.34 430	12.59 422	20.49 424	11.39 397	7.03 432	13.84 416	6.46 429	5.95 427

wuyk23d		9.11 398	8.77 402	10.15 412	40.18 419	16.76 425	20.28 423	1.01 436	2.58 429	2.66 431	0.98 431	0.23 436	12.49 431	4.08 431	6.90 428	1.19 428

ab-mvs-re		7.68 399	10.24 401	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	92.12 46	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

testmvs		6.14 400	8.18 403	0.01 414	0.01 437	0.00 440	73.40 326	0.00 438	0.00 432	0.02 433	0.15 432	0.00 437	0.00 433	0.02 432	0.00 431	0.02 429

test123		6.01 401	8.01 404	0.01 414	0.00 438	0.01 439	71.93 340	0.00 438	0.00 432	0.02 433	0.11 433	0.00 437	0.00 433	0.02 432	0.00 431	0.02 429

pcd_1.5k_mvsjas		3.15 402	4.20 405	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	37.77 211	0.00 433	0.00 434	0.00 431	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 438	0.00 440	0.00 427	0.00 438	0.00 432	0.00 435	0.00 434	0.00 437	0.00 433	0.00 434	0.00 431	0.00 431

WAC-MVS						34.28 374								22.56 396

FOURS1					83.24 113	49.90 199	84.98 141	78.76 260	47.71 334	73.42 64

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 36	86.80 28	92.34 35

PC_three_1452									66.58 64	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 36	86.80 28	92.34 35

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 249	82.06 14	93.39 21	54.94 36

eth-test2					0.00 438

eth-test					0.00 438

ZD-MVS					89.55 14	53.46 110		84.38 146	57.02 251	73.97 59	91.03 69	44.57 132	91.17 79	75.41 77	81.78 71

RE-MVS-def			66.66 213		80.96 181	48.14 254	81.54 248	76.98 293	46.42 344	62.75 191	89.42 112	29.28 314		60.52 187	72.06 177	83.19 259

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 72	88.26 1				82.83 23	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	57.50 243	83.60 6	94.09 3	56.14 27	96.37 6	82.28 27	87.43 20	92.55 30

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 241	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	59.89 188	74.58 54	92.62 39	46.80 95	92.66 41	81.40 38	85.62 41

save fliter					85.35 69	56.34 41	89.31 40	81.46 202	61.55 159

test_0728_THIRD									58.00 229	81.91 15	93.64 14	56.54 23	96.44 2	81.64 33	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 31	87.13 21	92.47 31

test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 237	83.14 9	93.96 6	55.17 31

GSMVS																88.13 158

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 203				88.13 158

sam_mvs												35.99 255

ambc				62.06 347	53.98 403	29.38 399	35.08 416	79.65 240		41.37 372	59.96 392	6.27 415	82.15 301	35.34 346	38.22 386	74.65 361

MTGPA								81.31 205

test_post1							70.84 345				14.72 429	34.33 273	83.86 285	48.80 281

test_post											16.22 426	37.52 220	84.72 277

patchmatchnet-post											59.74 393	38.41 206	79.91 328

GG-mvs-BLEND				77.77 86	86.68 49	50.61 177	68.67 355	88.45 54		68.73 118	87.45 156	59.15 11	90.67 92	54.83 239	87.67 17	92.03 45

MTMP							87.27 77	15.34 434

gm-plane-assit					83.24 113	54.21 96			70.91 23		88.23 140		95.25 14	66.37 136

test9_res														78.72 52	85.44 43	91.39 66

TEST9					85.68 60	55.42 56	87.59 67	84.00 156	57.72 236	72.99 69	90.98 71	44.87 126	88.58 160

test_8					85.72 59	55.31 61	87.60 66	83.88 159	57.84 234	72.84 73	90.99 70	44.99 122	88.34 171

agg_prior2														75.65 72	85.11 47	91.01 78

agg_prior					85.64 63	54.92 76		83.61 166		72.53 78			88.10 182

TestCases				55.32 373	65.08 376	37.50 366		54.25 395	35.45 388	33.42 399	72.82 337	9.98 401	59.33 397	24.13 391	43.84 375	69.13 384

test_prior4						56.39 40	87.15 81

test_prior2							89.04 43		61.88 154	73.55 62	91.46 67	48.01 82		74.73 81	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							81.73 241		45.53 350	74.66 51			70.48 383	58.31 206

新几何2							81.61 246

新几何1				73.30 212	83.10 116	53.48 109		71.43 347	45.55 349	66.14 140	87.17 161	33.88 278	80.54 318	48.50 284	80.33 84	85.88 210

旧先验1					81.57 167	47.48 270		71.83 341			88.66 127	36.94 236			78.34 105	88.67 142

无先验							85.19 131	78.00 276	49.08 323				85.13 272	52.78 256		87.45 174

原ACMM2							83.77 182

原ACMM1				76.13 129	84.89 78	54.59 88		85.26 120	51.98 304	66.70 132	87.07 163	40.15 190	89.70 121	51.23 266	85.06 48	84.10 236

test222					79.36 210	50.97 173	77.99 295	67.84 368	42.54 368	62.84 190	86.53 171	30.26 308			76.91 118	85.23 219

testdata2													77.81 348	45.64 303

segment_acmp												44.97 124

testdata				67.08 314	77.59 247	45.46 302		69.20 363	44.47 357	71.50 91	88.34 136	31.21 302	70.76 382	52.20 261	75.88 135	85.03 221

testdata1							77.55 298		64.14 109

test12				79.24 44	86.89 47	56.08 45		85.16 125		72.27 82		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						36.04 253

plane_prior5								82.59 183					88.30 175	65.46 147	72.34 174	84.49 229

plane_prior4											83.28 213

plane_prior3						48.95 223			64.01 113	62.15 198

plane_prior2							85.76 108		63.60 122

plane_prior1					78.31 238

plane_prior						49.57 204	87.43 70		64.57 99						72.84 168

n2								0.00 438

nn								0.00 438

door-mid								41.31 409

lessismore_v0				67.98 306	64.76 379	41.25 348		45.75 402		36.03 392	65.63 376	19.29 374	84.11 283	35.67 343	21.24 415	78.59 320

LGP-MVS_train				72.02 243	74.42 300	48.60 234		80.64 218	54.69 284	53.75 309	83.83 201	25.73 337	86.98 219	60.33 191	64.71 236	80.48 301

test11								84.25 150

door								43.27 405

HQP5-MVS						51.56 162

HQP-NCC					79.02 220		88.00 55		65.45 86	64.48 166

ACMP_Plane					79.02 220		88.00 55		65.45 86	64.48 166

BP-MVS														66.70 133

HQP4-MVS										64.47 169			88.61 158			84.91 225

HQP3-MVS								83.68 162							73.12 164

HQP2-MVS												37.35 223

NP-MVS					78.76 225	50.43 183					85.12 185

MDTV_nov1_ep13_2view						43.62 322	71.13 344		54.95 281	59.29 234		36.76 239		46.33 300		87.32 177

MDTV_nov1_ep13			61.56 272		81.68 160	55.12 69	72.41 333	78.18 273	59.19 205	58.85 243	69.29 363	34.69 268	86.16 245	36.76 340	62.96 259

ACMMP++_ref															63.20 255

ACMMP++															59.38 282

Test By Simon												39.38 197

ITE_SJBPF				51.84 376	58.03 396	31.94 388		53.57 397	36.67 383	41.32 374	75.23 316	11.17 398	51.57 409	25.81 387	48.04 357	72.02 377

DeepMVS_CX				13.10 411	21.34 435	8.99 433		10.02 435	10.59 423	7.53 428	30.55 421	1.82 429	14.55 430	6.83 425	7.52 426	15.75 424

High-resolution multi-view benchmark