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.

MM		80.20 7	80.28 8	79.99 2	82.19 79	60.01 46	86.19 17	83.93 54	73.19 1	77.08 31	91.21 15	57.23 33	90.73 10	83.35 1	88.12 35	89.22 6

MVS_0304		78.73 16	78.75 15	78.66 30	80.82 103	57.62 83	85.31 30	81.31 117	70.51 2	74.17 60	91.24 14	54.99 48	89.56 17	82.29 2	88.13 34	88.80 8

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 23	63.71 12	89.23 20	81.51 3	88.44 27	88.09 26
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
MP-MVS-pluss		78.35 20	78.46 18	78.03 40	84.96 52	59.52 53	82.93 59	85.39 26	62.15 67	76.41 34	91.51 11	52.47 83	86.78 68	80.66 4	89.64 19	87.80 35
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 37	89.70 16	79.85 5	91.48 1	88.19 23
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
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 60	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 14

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 26

ACMMP_NAP		78.77 15	78.78 14	78.74 29	85.44 45	61.04 31	83.84 49	85.16 31	62.88 53	78.10 24	91.26 13	52.51 81	88.39 31	79.34 8	90.52 13	86.78 67

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 32

No_MVS				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 32

IU-MVS					87.77 4	59.15 60		85.53 25	53.93 226	84.64 3				79.07 11	90.87 5	88.37 17

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 19	58.63 25	87.24 54	79.00 12	90.37 14	85.26 129

APDe-MVS		80.16 8	80.59 6	78.86 28	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 19	89.76 15	78.70 13	88.32 31	86.79 66
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 35	66.96 5	77.58 27	90.06 36	59.47 21	89.13 22	78.67 14	89.73 16	87.03 58

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 35	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 118
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 42

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 21

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 40

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 16	54.26 57	90.06 13	78.42 19	89.02 23	87.69 38
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsmconf0.1_n		72.81 76	72.33 79	74.24 107	69.89 323	55.81 115	78.22 129	75.40 222	54.17 223	75.00 44	88.03 68	53.82 66	80.23 216	78.08 20	78.34 145	86.69 69

test_fmvsmconf0.01_n		72.17 89	71.50 87	74.16 108	67.96 340	55.58 123	78.06 135	74.67 237	54.19 222	74.54 54	88.23 61	50.35 112	80.24 215	78.07 21	77.46 154	86.65 72

test_fmvsmconf_n		73.01 74	72.59 75	74.27 106	71.28 303	55.88 114	78.21 130	75.56 219	54.31 221	74.86 48	87.80 72	54.72 52	80.23 216	78.07 21	78.48 142	86.70 68

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 107	78.57 23	90.36 27	57.51 32	86.86 66	77.39 23	89.52 21

MTAPA		76.90 34	76.42 35	78.35 35	86.08 37	63.57 2	74.92 209	80.97 129	65.13 15	75.77 36	90.88 17	48.63 128	86.66 71	77.23 24	88.17 33	84.81 143

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 67	90.60 20	54.85 51	86.72 69	77.20 25	88.06 37	85.74 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SF-MVS		78.82 13	79.22 12	77.60 44	82.88 74	57.83 80	84.99 32	88.13 2	61.86 75	79.16 20	90.75 18	57.96 26	87.09 62	77.08 26	90.18 15	87.87 31

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 26	84.96 52	61.41 26	84.03 45	83.82 63	59.34 120	79.37 19	89.76 45	59.84 16	87.62 50	76.69 27	86.74 53	87.68 39
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		78.82 13	78.67 17	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 42	90.47 26	53.96 62	88.68 27	76.48 28	89.63 20	87.16 56

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n		70.99 109	70.82 103	71.48 177	71.45 296	54.40 139	77.18 159	70.46 274	48.67 286	75.17 40	86.86 83	53.77 67	76.86 269	76.33 30	77.51 153	83.17 197

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 34	83.87 60	60.37 96	79.89 18	89.38 49	54.97 49	85.58 98	76.12 31	84.94 66	86.33 83
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
test_fmvsmvis_n_1920		70.84 111	70.38 111	72.22 162	71.16 304	55.39 127	75.86 188	72.21 261	49.03 282	73.28 72	86.17 109	51.83 94	77.29 261	75.80 32	78.05 147	83.98 165

fmvsm_s_conf0.5_n		69.58 141	68.84 137	71.79 169	72.31 286	52.90 164	77.90 137	62.43 335	49.97 272	72.85 85	85.90 120	52.21 87	76.49 277	75.75 33	70.26 248	85.97 95

fmvsm_s_conf0.1_n		69.41 148	68.60 143	71.83 167	71.07 305	52.88 165	77.85 140	62.44 334	49.58 276	72.97 81	86.22 106	51.68 97	76.48 278	75.53 34	70.10 251	86.14 90

fmvsm_l_conf0.5_n_a		70.50 119	70.27 113	71.18 189	71.30 302	54.09 141	76.89 167	69.87 277	47.90 299	74.37 57	86.49 100	53.07 77	76.69 274	75.41 35	77.11 161	82.76 204

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 32	62.57 60	73.09 79	89.97 41	50.90 108	87.48 52	75.30 36	86.85 51	87.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test9_res														75.28 37	88.31 32	83.81 172

train_agg		76.27 39	76.15 37	76.64 55	85.58 43	61.59 24	81.62 81	81.26 120	55.86 183	74.93 45	88.81 56	53.70 69	84.68 119	75.24 38	88.33 30	83.65 183

fmvsm_s_conf0.5_n_a		69.54 143	68.74 140	71.93 164	72.47 281	53.82 145	78.25 127	62.26 337	49.78 274	73.12 78	86.21 107	52.66 79	76.79 271	75.02 39	68.88 273	85.18 130

test_fmvsm_n_1920		71.73 98	71.14 98	73.50 132	72.52 279	56.53 101	75.60 192	76.16 209	48.11 295	77.22 28	85.56 128	53.10 76	77.43 258	74.86 40	77.14 160	86.55 75

fmvsm_s_conf0.1_n_a		69.32 149	68.44 149	71.96 163	70.91 307	53.78 146	78.12 133	62.30 336	49.35 278	73.20 74	86.55 99	51.99 91	76.79 271	74.83 41	68.68 278	85.32 125

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 29	63.56 41	74.29 59	90.03 38	52.56 80	88.53 30	74.79 42	88.34 29	86.63 73

DeepC-MVS	69.38 2	78.56 18	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 65	90.25 32	57.68 29	89.96 14	74.62 43	89.03 22	87.89 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PC_three_1452									55.09 204	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 14

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 44	83.03 57	85.33 27	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 25

NCCC		78.58 17	78.31 19	79.39 12	87.51 12	62.61 13	85.20 31	84.42 45	66.73 8	74.67 53	89.38 49	55.30 45	89.18 21	74.19 46	87.34 44	86.38 77

ZD-MVS					86.64 21	60.38 43		82.70 91	57.95 147	78.10 24	90.06 36	56.12 41	88.84 26	74.05 47	87.00 49

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 55	73.96 63	90.50 24	53.20 74	88.35 32	74.02 48	87.05 45	86.13 91

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 55	73.55 68	90.56 22	49.80 115	88.24 34	74.02 48	87.03 46	86.32 85

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 57	73.30 70	90.58 21	49.90 113	88.21 35	73.78 50	87.03 46	86.29 88

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 162	74.91 47	88.19 62	59.15 23	87.68 49	73.67 51	87.45 43	86.57 74

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 64	72.68 89	90.50 24	48.18 133	87.34 53	73.59 52	85.71 60	84.76 146

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 89	79.05 21	90.30 30	55.54 44	88.32 33	73.48 53	87.03 46	84.83 141
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 21

agg_prior2														73.09 55	87.93 40	84.33 153

casdiffmvs_mvg		76.14 41	76.30 36	75.66 74	76.46 220	51.83 188	79.67 109	85.08 32	65.02 19	75.84 35	88.58 60	59.42 22	85.08 109	72.75 56	83.93 76	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet		76.46 37	75.93 40	78.06 39	81.29 93	57.53 85	82.35 69	83.31 79	67.78 3	70.09 114	86.34 104	54.92 50	88.90 25	72.68 57	84.55 69	87.76 37

PGM-MVS		76.77 35	76.06 38	78.88 27	86.14 35	62.73 9	82.55 67	83.74 64	61.71 76	72.45 95	90.34 29	48.48 131	88.13 38	72.32 58	86.85 51	85.78 102

test_prior2							81.75 79		60.37 96	75.01 43	89.06 52	56.22 40		72.19 59	88.96 24

iter_conf05		75.83 47	75.63 45	76.43 58	80.84 102	51.87 187	78.13 132	84.81 40	59.65 112	72.86 84	87.47 75	56.92 34	88.17 37	72.18 60	87.79 42	89.24 5

ACMMP		76.02 43	75.33 47	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 49	69.80 124	89.74 46	45.43 173	87.16 59	72.01 61	82.87 87	85.14 131
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
EC-MVSNet		75.84 45	75.87 42	75.74 72	78.86 145	52.65 168	83.73 50	86.08 17	63.47 42	72.77 88	87.25 81	53.13 75	87.93 43	71.97 62	85.57 62	86.66 71

CS-MVS		76.25 40	75.98 39	77.06 50	80.15 118	55.63 120	84.51 35	83.90 57	63.24 45	73.30 70	87.27 80	55.06 47	86.30 84	71.78 63	84.58 68	89.25 4

mPP-MVS		76.54 36	75.93 40	78.34 36	86.47 26	63.50 3	85.74 25	82.28 95	62.90 52	71.77 99	90.26 31	46.61 160	86.55 75	71.71 64	85.66 61	84.97 138

SR-MVS		76.13 42	75.70 43	77.40 48	85.87 40	61.20 29	85.52 27	82.19 96	59.99 106	75.10 41	90.35 28	47.66 140	86.52 76	71.64 65	82.99 82	84.47 152

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 96	90.01 40	47.95 135	88.01 41	71.55 66	86.74 53	86.37 79

X-MVStestdata		70.21 125	67.28 174	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 96	6.49 408	47.95 135	88.01 41	71.55 66	86.74 53	86.37 79

dcpmvs_2		74.55 60	75.23 49	72.48 155	82.34 77	53.34 156	77.87 138	81.46 108	57.80 151	75.49 37	86.81 85	62.22 13	77.75 254	71.09 68	82.02 96	86.34 81

PHI-MVS		75.87 44	75.36 46	77.41 46	80.62 109	55.91 113	84.28 39	85.78 20	56.08 181	73.41 69	86.58 96	50.94 107	88.54 29	70.79 69	89.71 17	87.79 36

diffmvs		70.69 115	70.43 109	71.46 178	69.45 328	48.95 229	72.93 241	78.46 174	57.27 156	71.69 100	83.97 159	51.48 99	77.92 251	70.70 70	77.95 149	87.53 45
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
h-mvs33		72.71 79	71.49 88	76.40 59	81.99 82	59.58 52	76.92 166	76.74 205	60.40 93	74.81 49	85.95 118	45.54 169	85.76 94	70.41 71	70.61 240	83.86 171

hse-mvs2		71.04 107	69.86 119	74.60 96	79.58 127	57.12 96	73.96 225	75.25 225	60.40 93	74.81 49	81.95 201	45.54 169	82.90 152	70.41 71	66.83 291	83.77 176

APD-MVS_3200maxsize		74.96 50	74.39 57	76.67 54	82.20 78	58.24 77	83.67 51	83.29 80	58.41 136	73.71 66	90.14 33	45.62 166	85.99 88	69.64 73	82.85 88	85.78 102

baseline		74.61 58	74.70 54	74.34 103	75.70 228	49.99 214	77.54 148	84.63 43	62.73 59	73.98 62	87.79 73	57.67 30	83.82 135	69.49 74	82.74 90	89.20 7

OPM-MVS		74.73 54	74.25 58	76.19 64	80.81 104	59.01 67	82.60 66	83.64 66	63.74 39	72.52 92	87.49 74	47.18 151	85.88 91	69.47 75	80.78 105	83.66 182
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
casdiffmvs		74.80 52	74.89 53	74.53 99	75.59 232	50.37 206	78.17 131	85.06 34	62.80 58	74.40 56	87.86 70	57.88 27	83.61 139	69.46 76	82.79 89	89.59 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDPH-MVS		76.31 38	75.67 44	78.22 37	85.35 48	59.14 62	81.31 86	84.02 51	56.32 175	74.05 61	88.98 54	53.34 73	87.92 44	69.23 77	88.42 28	87.59 43

CPTT-MVS		72.78 77	72.08 82	74.87 88	84.88 57	61.41 26	84.15 43	77.86 185	55.27 198	67.51 166	88.08 65	41.93 206	81.85 178	69.04 78	80.01 117	81.35 231

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 33	84.85 39	61.98 74	73.06 80	88.88 55	53.72 68	89.06 23	68.27 79	88.04 38	87.42 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		74.57 59	73.90 62	76.58 56	83.49 65	59.87 49	84.29 37	81.36 112	58.07 142	73.14 76	90.07 34	44.74 180	85.84 92	68.20 80	81.76 100	84.03 162

RE-MVS-def			73.71 66		83.49 65	59.87 49	84.29 37	81.36 112	58.07 142	73.14 76	90.07 34	43.06 195		68.20 80	81.76 100	84.03 162

HQP_MVS		74.31 62	73.73 65	76.06 65	81.41 90	56.31 102	84.22 40	84.01 52	64.52 25	69.27 132	86.10 111	45.26 177	87.21 56	68.16 82	80.58 109	84.65 147

plane_prior5								84.01 52					87.21 56	68.16 82	80.58 109	84.65 147

mvsmamba		71.15 105	69.54 124	75.99 66	77.61 192	53.46 153	81.95 77	75.11 230	57.73 152	66.95 176	85.96 117	37.14 259	87.56 51	67.94 84	75.49 180	86.97 59

MVSMamba_pp		74.64 57	74.07 60	76.35 61	79.76 123	53.09 162	79.97 101	85.21 29	55.21 201	72.81 86	85.37 135	53.93 63	87.17 58	67.93 85	86.46 57	88.80 8

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 87	75.27 39	84.83 138	60.76 15	86.56 74	67.86 86	87.87 41	86.06 93

CS-MVS-test		75.62 48	75.31 48	76.56 57	80.63 108	55.13 130	83.88 48	85.22 28	62.05 71	71.49 104	86.03 114	53.83 65	86.36 82	67.74 87	86.91 50	88.19 23

LPG-MVS_test		72.74 78	71.74 84	75.76 70	80.22 113	57.51 86	82.55 67	83.40 75	61.32 79	66.67 182	87.33 78	39.15 236	86.59 72	67.70 88	77.30 158	83.19 194

LGP-MVS_train				75.76 70	80.22 113	57.51 86		83.40 75	61.32 79	66.67 182	87.33 78	39.15 236	86.59 72	67.70 88	77.30 158	83.19 194

HPM-MVS_fast		74.30 63	73.46 68	76.80 52	84.45 60	59.04 66	83.65 52	81.05 126	60.15 103	70.43 110	89.84 43	41.09 220	85.59 97	67.61 90	82.90 86	85.77 105

MVS_111021_HR		74.02 64	73.46 68	75.69 73	83.01 72	60.63 40	77.29 156	78.40 179	61.18 82	70.58 109	85.97 116	54.18 59	84.00 132	67.52 91	82.98 84	82.45 210

ETV-MVS		74.46 61	73.84 64	76.33 62	79.27 134	55.24 129	79.22 114	85.00 37	64.97 21	72.65 90	79.46 250	53.65 72	87.87 45	67.45 92	82.91 85	85.89 99

DELS-MVS		74.76 53	74.46 56	75.65 75	77.84 179	52.25 177	75.59 193	84.17 49	63.76 38	73.15 75	82.79 176	59.58 20	86.80 67	67.24 93	86.04 59	87.89 29
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
TSAR-MVS + GP.		74.90 51	74.15 59	77.17 49	82.00 81	58.77 72	81.80 78	78.57 168	58.58 133	74.32 58	84.51 148	55.94 42	87.22 55	67.11 94	84.48 71	85.52 114

BP-MVS														67.04 95

HQP-MVS		73.45 69	72.80 73	75.40 79	80.66 105	54.94 131	82.31 71	83.90 57	62.10 68	67.85 155	85.54 131	45.46 171	86.93 64	67.04 95	80.35 113	84.32 154

ACMP	63.53 6	72.30 86	71.20 96	75.59 78	80.28 111	57.54 84	82.74 63	82.84 90	60.58 90	65.24 212	86.18 108	39.25 234	86.03 87	66.95 97	76.79 165	83.22 192
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EI-MVSNet-Vis-set		72.42 85	71.59 85	74.91 86	78.47 156	54.02 142	77.05 162	79.33 153	65.03 18	71.68 101	79.35 254	52.75 78	84.89 115	66.46 98	74.23 188	85.83 101

DPM-MVS		75.47 49	75.00 50	76.88 51	81.38 92	59.16 59	79.94 102	85.71 22	56.59 170	72.46 93	86.76 86	56.89 35	87.86 46	66.36 99	88.91 25	83.64 184

patch_mono-2		69.85 132	71.09 99	66.16 263	79.11 140	54.80 135	71.97 257	74.31 242	53.50 231	70.90 107	84.17 152	57.63 31	63.31 342	66.17 100	82.02 96	80.38 249

MVSFormer		71.50 102	70.38 111	74.88 87	78.76 148	57.15 94	82.79 61	78.48 172	51.26 257	69.49 127	83.22 171	43.99 188	83.24 145	66.06 101	79.37 125	84.23 157

test_djsdf		69.45 147	67.74 157	74.58 97	74.57 251	54.92 133	82.79 61	78.48 172	51.26 257	65.41 205	83.49 169	38.37 243	83.24 145	66.06 101	69.25 268	85.56 113

sasdasda		74.67 55	74.98 51	73.71 122	78.94 143	50.56 203	80.23 95	83.87 60	60.30 100	77.15 29	86.56 97	59.65 17	82.00 175	66.01 103	82.12 93	88.58 12

canonicalmvs		74.67 55	74.98 51	73.71 122	78.94 143	50.56 203	80.23 95	83.87 60	60.30 100	77.15 29	86.56 97	59.65 17	82.00 175	66.01 103	82.12 93	88.58 12

MVS_Test		72.45 83	72.46 78	72.42 159	74.88 241	48.50 235	76.28 178	83.14 85	59.40 118	72.46 93	84.68 140	55.66 43	81.12 193	65.98 105	79.66 121	87.63 41

alignmvs		73.86 66	73.99 61	73.45 135	78.20 166	50.50 205	78.57 123	82.43 93	59.40 118	76.57 32	86.71 90	56.42 39	81.23 192	65.84 106	81.79 99	88.62 10

nrg030		72.96 75	73.01 71	72.84 148	75.41 235	50.24 207	80.02 99	82.89 89	58.36 138	74.44 55	86.73 88	58.90 24	80.83 202	65.84 106	74.46 184	87.44 47

MVS_111021_LR		69.50 145	68.78 139	71.65 174	78.38 159	59.33 56	74.82 211	70.11 276	58.08 141	67.83 159	84.68 140	41.96 205	76.34 281	65.62 108	77.54 151	79.30 266

EI-MVSNet-UG-set		71.92 94	71.06 100	74.52 100	77.98 175	53.56 150	76.62 171	79.16 154	64.40 27	71.18 105	78.95 259	52.19 88	84.66 121	65.47 109	73.57 199	85.32 125

PS-MVSNAJss		72.24 87	71.21 95	75.31 81	78.50 154	55.93 112	81.63 80	82.12 97	56.24 178	70.02 118	85.68 127	47.05 153	84.34 125	65.27 110	74.41 187	85.67 109

MSLP-MVS++		73.77 67	73.47 67	74.66 92	83.02 71	59.29 58	82.30 74	81.88 100	59.34 120	71.59 102	86.83 84	45.94 164	83.65 138	65.09 111	85.22 65	81.06 238

v2v482		70.50 119	69.45 128	73.66 125	72.62 276	50.03 213	77.58 145	80.51 136	59.90 107	69.52 126	82.14 197	47.53 144	84.88 117	65.07 112	70.17 249	86.09 92

jason		69.65 139	68.39 151	73.43 137	78.27 165	56.88 98	77.12 160	73.71 250	46.53 314	69.34 131	83.22 171	43.37 192	79.18 229	64.77 113	79.20 130	84.23 157
jason: jason.
anonymousdsp		67.00 199	64.82 216	73.57 131	70.09 319	56.13 107	76.35 176	77.35 196	48.43 291	64.99 221	80.84 226	33.01 299	80.34 211	64.66 114	67.64 285	84.23 157

lupinMVS		69.57 142	68.28 152	73.44 136	78.76 148	57.15 94	76.57 172	73.29 253	46.19 317	69.49 127	82.18 193	43.99 188	79.23 228	64.66 114	79.37 125	83.93 166

CLD-MVS		73.33 70	72.68 74	75.29 83	78.82 147	53.33 157	78.23 128	84.79 41	61.30 81	70.41 111	81.04 218	52.41 84	87.12 60	64.61 116	82.49 92	85.41 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
bld_raw_dy_0_64		72.13 93	71.18 97	74.96 85	77.70 182	51.88 186	71.67 261	84.69 42	51.27 256	65.06 217	85.80 126	54.50 56	88.19 36	64.51 117	85.45 64	84.82 142

V42		68.65 161	67.35 172	72.56 153	68.93 334	50.18 209	72.90 242	79.47 150	56.92 161	69.45 129	80.26 234	46.29 162	82.99 149	64.07 118	67.82 283	84.53 149

3Dnovator+	66.72 4	75.84 45	74.57 55	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 160	89.24 51	42.03 204	89.38 19	64.07 118	86.50 56	89.69 2

v1144		70.42 121	69.31 129	73.76 118	73.22 264	50.64 200	77.83 141	81.43 109	58.58 133	69.40 130	81.16 215	47.53 144	85.29 108	64.01 120	70.64 238	85.34 124

Effi-MVS+		73.31 71	72.54 77	75.62 76	77.87 177	53.64 148	79.62 111	79.61 147	61.63 77	72.02 98	82.61 181	56.44 38	85.97 89	63.99 121	79.07 133	87.25 55

MGCFI-Net		72.45 83	73.34 70	69.81 216	77.77 181	43.21 289	75.84 190	81.18 123	59.59 116	75.45 38	86.64 91	57.74 28	77.94 249	63.92 122	81.90 98	88.30 18

SDMVSNet		68.03 175	68.10 155	67.84 241	77.13 204	48.72 233	65.32 320	79.10 155	58.02 144	65.08 215	82.55 183	47.83 137	73.40 293	63.92 122	73.92 192	81.41 226

xiu_mvs_v1_base_debu		68.58 163	67.28 174	72.48 155	78.19 167	57.19 91	75.28 198	75.09 231	51.61 247	70.04 115	81.41 212	32.79 302	79.02 236	63.81 124	77.31 155	81.22 233

xiu_mvs_v1_base		68.58 163	67.28 174	72.48 155	78.19 167	57.19 91	75.28 198	75.09 231	51.61 247	70.04 115	81.41 212	32.79 302	79.02 236	63.81 124	77.31 155	81.22 233

xiu_mvs_v1_base_debi		68.58 163	67.28 174	72.48 155	78.19 167	57.19 91	75.28 198	75.09 231	51.61 247	70.04 115	81.41 212	32.79 302	79.02 236	63.81 124	77.31 155	81.22 233

v8		70.33 123	69.28 130	73.49 133	73.15 266	50.22 208	78.62 121	80.78 132	60.79 86	66.45 186	82.11 199	49.35 118	84.98 112	63.58 127	68.71 276	85.28 127

jajsoiax		68.25 171	66.45 187	73.66 125	75.62 230	55.49 125	80.82 90	78.51 171	52.33 241	64.33 228	84.11 154	28.28 340	81.81 180	63.48 128	70.62 239	83.67 180

mvs_tets		68.18 173	66.36 193	73.63 128	75.61 231	55.35 128	80.77 91	78.56 169	52.48 240	64.27 230	84.10 155	27.45 346	81.84 179	63.45 129	70.56 241	83.69 179

v144192		69.71 135	68.51 144	73.33 140	73.10 267	50.13 210	77.54 148	80.64 133	56.65 164	68.57 142	80.55 228	46.87 158	84.96 114	62.98 130	69.66 262	84.89 140

v1192		69.97 130	68.68 141	73.85 113	73.19 265	50.94 193	77.68 144	81.36 112	57.51 154	68.95 138	80.85 225	45.28 176	85.33 107	62.97 131	70.37 244	85.27 128

v10		70.21 125	69.02 134	73.81 115	73.51 263	50.92 195	78.74 118	81.39 110	60.05 105	66.39 187	81.83 204	47.58 142	85.41 106	62.80 132	68.86 275	85.09 134

OMC-MVS		71.40 104	70.60 106	73.78 116	76.60 216	53.15 159	79.74 108	79.78 143	58.37 137	68.75 139	86.45 102	45.43 173	80.60 206	62.58 133	77.73 150	87.58 44

XVG-OURS-SEG-HR		68.81 157	67.47 167	72.82 150	74.40 255	56.87 99	70.59 276	79.04 156	54.77 212	66.99 174	86.01 115	39.57 230	78.21 246	62.54 134	73.33 205	83.37 188

EPNet		73.09 73	72.16 80	75.90 68	75.95 226	56.28 104	83.05 56	72.39 259	66.53 10	65.27 208	87.00 82	50.40 110	85.47 103	62.48 135	86.32 58	85.94 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		69.47 146	68.17 153	73.36 139	73.06 268	50.10 211	77.39 151	80.56 134	56.58 171	68.59 140	80.37 230	44.72 181	84.98 112	62.47 136	69.82 257	85.00 136

c3_l		68.33 169	67.56 161	70.62 200	70.87 308	46.21 259	74.47 218	78.80 162	56.22 179	66.19 190	78.53 266	51.88 92	81.40 186	62.08 137	69.04 271	84.25 156

AUN-MVS		68.45 168	66.41 191	74.57 98	79.53 129	57.08 97	73.93 228	75.23 226	54.44 219	66.69 181	81.85 203	37.10 261	82.89 153	62.07 138	66.84 290	83.75 177

XVG-OURS		68.76 160	67.37 170	72.90 147	74.32 257	57.22 89	70.09 283	78.81 161	55.24 199	67.79 161	85.81 125	36.54 266	78.28 245	62.04 139	75.74 176	83.19 194

v1240		69.24 152	67.91 156	73.25 143	73.02 270	49.82 215	77.21 158	80.54 135	56.43 173	68.34 146	80.51 229	43.33 193	84.99 110	62.03 140	69.77 260	84.95 139

ET-MVSNet_ETH3D		67.96 178	65.72 205	74.68 91	76.67 214	55.62 122	75.11 203	74.74 235	52.91 235	60.03 278	80.12 236	33.68 292	82.64 164	61.86 141	76.34 169	85.78 102

VDD-MVS		72.50 81	72.09 81	73.75 120	81.58 86	49.69 219	77.76 143	77.63 190	63.21 47	73.21 73	89.02 53	42.14 203	83.32 143	61.72 142	82.50 91	88.25 20

PS-MVSNAJ		70.51 118	69.70 122	72.93 146	81.52 87	55.79 116	74.92 209	79.00 157	55.04 209	69.88 122	78.66 261	47.05 153	82.19 172	61.61 143	79.58 122	80.83 242

xiu_mvs_v2_base		70.52 117	69.75 120	72.84 148	81.21 96	55.63 120	75.11 203	78.92 159	54.92 210	69.96 121	79.68 245	47.00 157	82.09 174	61.60 144	79.37 125	80.81 243

cl22		67.47 187	66.45 187	70.54 202	69.85 324	46.49 255	73.85 231	77.35 196	55.07 207	65.51 203	77.92 273	47.64 141	81.10 194	61.58 145	69.32 265	84.01 164

miper_ehance_all_eth		68.03 175	67.24 178	70.40 204	70.54 311	46.21 259	73.98 224	78.68 166	55.07 207	66.05 192	77.80 277	52.16 89	81.31 189	61.53 146	69.32 265	83.67 180

iter_conf05_11		73.52 68	72.59 75	76.30 63	80.93 101	51.97 184	78.62 121	83.48 70	52.20 243	71.53 103	85.93 119	54.01 60	88.55 28	61.08 147	85.56 63	88.39 16

MG-MVS		73.96 65	73.89 63	74.16 108	85.65 42	49.69 219	81.59 83	81.29 119	61.45 78	71.05 106	88.11 63	51.77 95	87.73 48	61.05 148	83.09 80	85.05 135

mamv4		56.85 298	58.00 286	53.43 351	72.46 282	54.47 137	57.56 362	54.74 366	38.81 368	57.42 308	79.45 251	47.57 143	38.70 401	60.88 149	53.07 366	67.11 371

miper_enhance_ethall		67.11 196	66.09 200	70.17 208	69.21 331	45.98 261	72.85 243	78.41 178	51.38 253	65.65 201	75.98 306	51.17 103	81.25 190	60.82 150	69.32 265	83.29 191

ACMM	61.98 7	70.80 114	69.73 121	74.02 110	80.59 110	58.59 74	82.68 64	82.02 99	55.46 195	67.18 171	84.39 150	38.51 241	83.17 147	60.65 151	76.10 172	80.30 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		69.64 140	67.53 164	75.95 67	76.10 224	62.29 15	80.20 98	76.06 213	59.83 111	65.26 211	77.09 287	41.56 212	84.02 131	60.60 152	71.09 236	81.53 224

PVSNet_Blended_VisFu		71.45 103	70.39 110	74.65 93	82.01 80	58.82 71	79.93 103	80.35 139	55.09 204	65.82 200	82.16 196	49.17 122	82.64 164	60.34 153	78.62 141	82.50 209

MVSTER		67.16 195	65.58 208	71.88 166	70.37 315	49.70 217	70.25 282	78.45 175	51.52 250	69.16 136	80.37 230	38.45 242	82.50 167	60.19 154	71.46 231	83.44 187

EIA-MVS		71.78 96	70.60 106	75.30 82	79.85 122	53.54 151	77.27 157	83.26 82	57.92 148	66.49 184	79.39 252	52.07 90	86.69 70	60.05 155	79.14 132	85.66 110

v148		68.24 172	67.19 180	71.40 182	70.43 313	47.77 244	75.76 191	77.03 200	58.91 125	67.36 167	80.10 237	48.60 130	81.89 177	60.01 156	66.52 294	84.53 149

test_vis1_n_1920		58.86 283	59.06 274	58.25 322	63.76 363	43.14 290	67.49 303	66.36 306	40.22 362	65.89 197	71.95 336	31.04 318	59.75 356	59.94 157	64.90 304	71.85 345

CANet_DTU		68.18 173	67.71 160	69.59 219	74.83 243	46.24 258	78.66 120	76.85 202	59.60 113	63.45 239	82.09 200	35.25 274	77.41 259	59.88 158	78.76 138	85.14 131

IterMVS-LS		69.22 153	68.48 145	71.43 181	74.44 254	49.40 223	76.23 179	77.55 191	59.60 113	65.85 199	81.59 210	51.28 101	81.58 184	59.87 159	69.90 256	83.30 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 151	68.44 149	71.73 171	74.47 252	49.39 224	75.20 201	78.45 175	59.60 113	69.16 136	76.51 298	51.29 100	82.50 167	59.86 160	71.45 232	83.30 189

3Dnovator	64.47 5	72.49 82	71.39 91	75.79 69	77.70 182	58.99 68	80.66 93	83.15 84	62.24 66	65.46 204	86.59 95	42.38 202	85.52 99	59.59 161	84.72 67	82.85 203

eth_miper_zixun_eth		67.63 184	66.28 197	71.67 173	71.60 294	48.33 237	73.68 234	77.88 184	55.80 187	65.91 195	78.62 264	47.35 150	82.88 154	59.45 162	66.25 295	83.81 172

DIV-MVS_self_test		67.18 193	66.26 198	69.94 211	70.20 316	45.74 263	73.29 237	76.83 203	55.10 202	65.27 208	79.58 246	47.38 149	80.53 207	59.43 163	69.22 269	83.54 185

cl____		67.18 193	66.26 198	69.94 211	70.20 316	45.74 263	73.30 236	76.83 203	55.10 202	65.27 208	79.57 247	47.39 148	80.53 207	59.41 164	69.22 269	83.53 186

旧先验2							76.08 182		45.32 325	76.55 33			65.56 336	58.75 165

VDDNet		71.81 95	71.33 93	73.26 142	82.80 75	47.60 247	78.74 118	75.27 224	59.59 116	72.94 82	89.40 48	41.51 214	83.91 133	58.75 165	82.99 82	88.26 19

114514_t		70.83 112	69.56 123	74.64 94	86.21 31	54.63 136	82.34 70	81.81 102	48.22 293	63.01 246	85.83 123	40.92 221	87.10 61	57.91 167	79.79 118	82.18 215

Vis-MVSNet		72.18 88	71.37 92	74.61 95	81.29 93	55.41 126	80.90 89	78.28 181	60.73 88	69.23 135	88.09 64	44.36 185	82.65 163	57.68 168	81.75 102	85.77 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_cas_vis1_n_1920		56.91 297	56.71 295	57.51 330	59.13 384	45.40 269	63.58 330	61.29 342	36.24 372	67.14 172	71.85 337	29.89 327	56.69 370	57.65 169	63.58 317	70.46 358

PAPM_NR		72.63 80	71.80 83	75.13 84	81.72 85	53.42 155	79.91 104	83.28 81	59.14 122	66.31 189	85.90 120	51.86 93	86.06 85	57.45 170	80.62 107	85.91 98

LFMVS		71.78 96	71.59 85	72.32 160	83.40 67	46.38 256	79.75 107	71.08 268	64.18 32	72.80 87	88.64 59	42.58 199	83.72 136	57.41 171	84.49 70	86.86 63

v7n		69.01 155	67.36 171	73.98 111	72.51 280	52.65 168	78.54 125	81.30 118	60.26 102	62.67 250	81.62 207	43.61 190	84.49 122	57.01 172	68.70 277	84.79 144

GeoE		71.01 108	70.15 116	73.60 130	79.57 128	52.17 178	78.93 116	78.12 182	58.02 144	67.76 163	83.87 160	52.36 85	82.72 161	56.90 173	75.79 175	85.92 97

FA-MVS(test-final)		69.82 133	68.48 145	73.84 114	78.44 157	50.04 212	75.58 195	78.99 158	58.16 140	67.59 164	82.14 197	42.66 197	85.63 95	56.60 174	76.19 171	85.84 100

mvs_anonymous		68.03 175	67.51 165	69.59 219	72.08 288	44.57 277	71.99 256	75.23 226	51.67 246	67.06 173	82.57 182	54.68 53	77.94 249	56.56 175	75.71 177	86.26 89

Patchmatch-RL test		58.16 288	55.49 305	66.15 264	67.92 341	48.89 230	60.66 348	51.07 378	47.86 300	59.36 288	62.71 380	34.02 288	72.27 299	56.41 176	59.40 344	77.30 287

miper_lstm_enhance		62.03 261	60.88 264	65.49 276	66.71 348	46.25 257	56.29 367	75.70 216	50.68 263	61.27 269	75.48 312	40.21 224	68.03 323	56.31 177	65.25 302	82.18 215

thisisatest0530		67.92 179	65.78 204	74.33 104	76.29 221	51.03 192	76.89 167	74.25 244	53.67 229	65.59 202	81.76 205	35.15 275	85.50 101	55.94 178	72.47 218	86.47 76

EPP-MVSNet		72.16 91	71.31 94	74.71 89	78.68 151	49.70 217	82.10 75	81.65 104	60.40 93	65.94 194	85.84 122	51.74 96	86.37 81	55.93 179	79.55 124	88.07 28

PVSNet_BlendedMVS		68.56 166	67.72 158	71.07 193	77.03 208	50.57 201	74.50 217	81.52 105	53.66 230	64.22 233	79.72 244	49.13 123	82.87 155	55.82 180	73.92 192	79.77 261

PVSNet_Blended		68.59 162	67.72 158	71.19 188	77.03 208	50.57 201	72.51 249	81.52 105	51.91 245	64.22 233	77.77 280	49.13 123	82.87 155	55.82 180	79.58 122	80.14 253

PAPR		71.72 99	70.82 103	74.41 102	81.20 97	51.17 191	79.55 112	83.33 78	55.81 186	66.93 177	84.61 144	50.95 106	86.06 85	55.79 182	79.20 130	86.00 94

tttt0517		67.83 181	65.66 206	74.33 104	76.69 213	50.82 197	77.86 139	73.99 247	54.54 217	64.64 225	82.53 186	35.06 276	85.50 101	55.71 183	69.91 255	86.67 70

IterMVS-SCA-FT		62.49 253	61.52 254	65.40 277	71.99 290	50.80 198	71.15 270	69.63 280	45.71 323	60.61 273	77.93 272	37.45 252	65.99 334	55.67 184	63.50 318	79.42 264

tt0805		67.77 182	67.24 178	69.34 224	74.87 242	40.08 313	77.36 152	81.37 111	55.31 197	66.33 188	84.65 142	37.35 254	82.55 166	55.65 185	72.28 223	85.39 123

XVG-ACMP-BASELINE		64.36 236	62.23 246	70.74 198	72.35 284	52.45 175	70.80 275	78.45 175	53.84 227	59.87 281	81.10 217	16.24 382	79.32 227	55.64 186	71.76 227	80.47 246

Anonymous20231211		69.28 150	68.47 147	71.73 171	80.28 111	47.18 251	79.98 100	82.37 94	54.61 214	67.24 169	84.01 157	39.43 231	82.41 170	55.45 187	72.83 213	85.62 112

GA-MVS		65.53 220	63.70 227	71.02 194	70.87 308	48.10 239	70.48 278	74.40 240	56.69 163	64.70 224	76.77 292	33.66 293	81.10 194	55.42 188	70.32 246	83.87 170

test_yl		69.69 136	69.13 131	71.36 183	78.37 161	45.74 263	74.71 213	80.20 140	57.91 149	70.01 119	83.83 161	42.44 200	82.87 155	54.97 189	79.72 119	85.48 116

DCV-MVSNet		69.69 136	69.13 131	71.36 183	78.37 161	45.74 263	74.71 213	80.20 140	57.91 149	70.01 119	83.83 161	42.44 200	82.87 155	54.97 189	79.72 119	85.48 116

1314		64.61 232	63.21 235	68.80 231	71.87 292	47.46 248	73.95 226	78.39 180	42.88 347	59.97 279	76.60 297	38.11 247	79.39 226	54.84 191	72.32 221	79.55 262

Fast-Effi-MVS+-dtu		67.37 188	65.33 211	73.48 134	72.94 271	57.78 82	77.47 150	76.88 201	57.60 153	61.97 261	76.85 291	39.31 232	80.49 210	54.72 192	70.28 247	82.17 217

UniMVSNet_NR-MVSNet		71.11 106	71.00 101	71.44 179	79.20 136	44.13 279	76.02 186	82.60 92	66.48 11	68.20 147	84.60 145	56.82 36	82.82 159	54.62 193	70.43 242	87.36 53

DU-MVS		70.01 128	69.53 125	71.44 179	78.05 173	44.13 279	75.01 206	81.51 107	64.37 28	68.20 147	84.52 146	49.12 125	82.82 159	54.62 193	70.43 242	87.37 51

FIs		70.82 113	71.43 89	68.98 229	78.33 163	38.14 332	76.96 164	83.59 68	61.02 83	67.33 168	86.73 88	55.07 46	81.64 181	54.61 195	79.22 129	87.14 57

VPA-MVSNet		69.02 154	69.47 127	67.69 243	77.42 198	41.00 310	74.04 223	79.68 145	60.06 104	69.26 134	84.81 139	51.06 105	77.58 256	54.44 196	74.43 186	84.48 151

Anonymous20240529		69.91 131	69.02 134	72.56 153	80.19 116	47.65 245	77.56 147	80.99 128	55.45 196	69.88 122	86.76 86	39.24 235	82.18 173	54.04 197	77.10 162	87.85 32

UniMVSNet (Re)		70.63 116	70.20 114	71.89 165	78.55 153	45.29 270	75.94 187	82.92 87	63.68 40	68.16 149	83.59 166	53.89 64	83.49 142	53.97 198	71.12 235	86.89 62

D2MVS		62.30 257	60.29 267	68.34 238	66.46 351	48.42 236	65.70 312	73.42 251	47.71 301	58.16 302	75.02 315	30.51 321	77.71 255	53.96 199	71.68 229	78.90 271

原ACMM1				74.69 90	85.39 47	59.40 54		83.42 74	51.47 252	70.27 113	86.61 94	48.61 129	86.51 77	53.85 200	87.96 39	78.16 275

无先验							79.66 110	74.30 243	48.40 292				80.78 204	53.62 201		79.03 269

UA-Net		73.13 72	72.93 72	73.76 118	83.58 64	51.66 189	78.75 117	77.66 189	67.75 4	72.61 91	89.42 47	49.82 114	83.29 144	53.61 202	83.14 79	86.32 85

VNet		69.68 138	70.19 115	68.16 239	79.73 125	41.63 305	70.53 277	77.38 195	60.37 96	70.69 108	86.63 93	51.08 104	77.09 264	53.61 202	81.69 104	85.75 107

Fast-Effi-MVS+		70.28 124	69.12 133	73.73 121	78.50 154	51.50 190	75.01 206	79.46 151	56.16 180	68.59 140	79.55 248	53.97 61	84.05 128	53.34 204	77.53 152	85.65 111

testdata				64.66 283	81.52 87	52.93 163		65.29 313	46.09 318	73.88 64	87.46 76	38.08 248	66.26 333	53.31 205	78.48 142	74.78 317

thisisatest0515		65.83 216	63.50 230	72.82 150	73.75 261	49.50 222	71.32 265	73.12 255	49.39 277	63.82 235	76.50 300	34.95 278	84.84 118	53.20 206	75.49 180	84.13 161

MVS		67.37 188	66.33 194	70.51 203	75.46 234	50.94 193	73.95 226	81.85 101	41.57 354	62.54 254	78.57 265	47.98 134	85.47 103	52.97 207	82.05 95	75.14 309

IterMVS		62.79 252	61.27 258	67.35 248	69.37 329	52.04 182	71.17 268	68.24 293	52.63 239	59.82 282	76.91 290	37.32 255	72.36 297	52.80 208	63.19 321	77.66 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		69.80 134	70.58 108	67.46 245	77.61 192	34.73 363	76.05 184	83.19 83	60.84 85	65.88 198	86.46 101	54.52 55	80.76 205	52.52 209	78.12 146	86.91 61

TranMVSNet+NR-MVSNet		70.36 122	70.10 118	71.17 190	78.64 152	42.97 292	76.53 173	81.16 125	66.95 6	68.53 143	85.42 133	51.61 98	83.07 148	52.32 210	69.70 261	87.46 46

Baseline_NR-MVSNet		67.05 197	67.56 161	65.50 275	75.65 229	37.70 338	75.42 196	74.65 238	59.90 107	68.14 150	83.15 174	49.12 125	77.20 262	52.23 211	69.78 258	81.60 223

UniMVSNet_ETH3D		67.60 185	67.07 182	69.18 228	77.39 199	42.29 296	74.18 222	75.59 218	60.37 96	66.77 179	86.06 113	37.64 250	78.93 241	52.16 212	73.49 201	86.32 85

ECVR-MVS		67.72 183	67.51 165	68.35 237	79.46 130	36.29 355	74.79 212	66.93 301	58.72 128	67.19 170	88.05 66	36.10 267	81.38 187	52.07 213	84.25 72	87.39 49

test1111		67.21 190	67.14 181	67.42 246	79.24 135	34.76 362	73.89 230	65.65 310	58.71 130	66.96 175	87.95 69	36.09 268	80.53 207	52.03 214	83.79 77	86.97 59

test2506		65.33 224	64.61 217	67.50 244	79.46 130	34.19 367	74.43 219	51.92 374	58.72 128	66.75 180	88.05 66	25.99 357	80.92 200	51.94 215	84.25 72	87.39 49

API-MVS		72.17 89	71.41 90	74.45 101	81.95 83	57.22 89	84.03 45	80.38 138	59.89 110	68.40 144	82.33 190	49.64 116	87.83 47	51.87 216	84.16 75	78.30 273

PCF-MVS	61.88 8	70.95 110	69.49 126	75.35 80	77.63 187	55.71 117	76.04 185	81.81 102	50.30 268	69.66 125	85.40 134	52.51 81	84.89 115	51.82 217	80.24 115	85.45 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 92	70.73 105	76.40 59	86.57 24	57.99 79	81.15 88	82.96 86	57.03 159	66.78 178	85.56 128	44.50 183	88.11 39	51.77 218	80.23 116	83.10 198

UGNet		68.81 157	67.39 169	73.06 144	78.33 163	54.47 137	79.77 106	75.40 222	60.45 92	63.22 240	84.40 149	32.71 306	80.91 201	51.71 219	80.56 111	83.81 172
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
MAR-MVS		71.51 101	70.15 116	75.60 77	81.84 84	59.39 55	81.38 85	82.90 88	54.90 211	68.08 152	78.70 260	47.73 138	85.51 100	51.68 220	84.17 74	81.88 221
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
VPNet		67.52 186	68.11 154	65.74 272	79.18 137	36.80 347	72.17 254	72.83 256	62.04 72	67.79 161	85.83 123	48.88 127	76.60 276	51.30 221	72.97 212	83.81 172

test_fmvs1_n		51.37 332	50.35 335	54.42 345	52.85 391	37.71 337	61.16 345	51.93 373	28.15 383	63.81 236	69.73 354	13.72 386	53.95 380	51.16 222	60.65 340	71.59 347

test_fmvs1		51.32 334	50.48 334	53.81 347	53.57 389	37.51 339	60.63 349	51.16 376	28.02 385	63.62 237	69.23 357	16.41 381	53.93 381	51.01 223	60.70 339	69.99 362

QAPM		70.05 127	68.81 138	73.78 116	76.54 218	53.43 154	83.23 54	83.48 70	52.89 236	65.90 196	86.29 105	41.55 213	86.49 78	51.01 223	78.40 144	81.42 225

NR-MVSNet		69.54 143	68.85 136	71.59 176	78.05 173	43.81 283	74.20 221	80.86 131	65.18 14	62.76 248	84.52 146	52.35 86	83.59 140	50.96 225	70.78 237	87.37 51

IB-MVS	56.42 12	65.40 223	62.73 241	73.40 138	74.89 240	52.78 167	73.09 240	75.13 229	55.69 189	58.48 300	73.73 324	32.86 301	86.32 83	50.63 226	70.11 250	81.10 237
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
PM-MVS		52.33 328	50.19 336	58.75 319	62.10 372	45.14 271	65.75 311	40.38 397	43.60 339	53.52 345	72.65 328	9.16 399	65.87 335	50.41 227	54.18 363	65.24 374

cascas		65.98 214	63.42 231	73.64 127	77.26 202	52.58 171	72.26 253	77.21 198	48.56 287	61.21 270	74.60 319	32.57 311	85.82 93	50.38 228	76.75 166	82.52 208

IS-MVSNet		71.57 100	71.00 101	73.27 141	78.86 145	45.63 267	80.22 97	78.69 165	64.14 35	66.46 185	87.36 77	49.30 119	85.60 96	50.26 229	83.71 78	88.59 11

WR-MVS		68.47 167	68.47 147	68.44 236	80.20 115	39.84 316	73.75 233	76.07 212	64.68 22	68.11 151	83.63 165	50.39 111	79.14 234	49.78 230	69.66 262	86.34 81

CVMVSNet		59.63 280	59.14 273	61.08 309	74.47 252	38.84 326	75.20 201	68.74 290	31.15 379	58.24 301	76.51 298	32.39 313	68.58 319	49.77 231	65.84 298	75.81 302

CostFormer		64.04 238	62.51 242	68.61 234	71.88 291	45.77 262	71.30 266	70.60 273	47.55 303	64.31 229	76.61 296	41.63 210	79.62 223	49.74 232	69.00 272	80.42 247

新几何1				70.76 197	85.66 41	61.13 30		66.43 305	44.68 329	70.29 112	86.64 91	41.29 216	75.23 286	49.72 233	81.75 102	75.93 301

test-LLR		58.15 289	58.13 285	58.22 323	68.57 335	44.80 273	65.46 317	57.92 353	50.08 270	55.44 322	69.82 352	32.62 308	57.44 366	49.66 234	73.62 197	72.41 338

test-mter		56.42 303	55.82 303	58.22 323	68.57 335	44.80 273	65.46 317	57.92 353	39.94 365	55.44 322	69.82 352	21.92 370	57.44 366	49.66 234	73.62 197	72.41 338

Anonymous202405211		66.84 202	65.99 201	69.40 223	80.19 116	42.21 298	71.11 271	71.31 267	58.80 127	67.90 153	86.39 103	29.83 328	79.65 221	49.60 236	78.78 137	86.33 83

test_fmvs2		48.69 341	47.49 346	52.29 358	48.63 397	33.06 375	57.76 359	48.05 385	25.71 389	59.76 284	69.60 355	11.57 392	52.23 386	49.45 237	56.86 353	71.58 348

tpmrst		58.24 287	58.70 278	56.84 331	66.97 345	34.32 365	69.57 288	61.14 343	47.17 310	58.58 299	71.60 338	41.28 217	60.41 352	49.20 238	62.84 323	75.78 303

test_vis1_n		49.89 339	48.69 341	53.50 350	53.97 388	37.38 340	61.53 339	47.33 387	28.54 382	59.62 286	67.10 368	13.52 387	52.27 385	49.07 239	57.52 350	70.84 356

pm-mvs1		65.24 225	64.97 215	66.04 267	72.38 283	39.40 322	72.62 246	75.63 217	55.53 193	62.35 260	83.18 173	47.45 146	76.47 279	49.06 240	66.54 293	82.24 214

gm-plane-assit					71.40 300	41.72 304			48.85 285		73.31 326		82.48 169	48.90 241

CMPMVS	42.80 21	57.81 292	55.97 301	63.32 291	60.98 379	47.38 249	64.66 326	69.50 283	32.06 378	46.83 370	77.80 277	29.50 331	71.36 304	48.68 242	73.75 195	71.21 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 206	66.42 190	67.37 247	76.17 223	41.73 302	70.41 280	76.14 211	53.99 225	65.98 193	83.51 168	49.48 117	76.24 282	48.60 243	73.46 203	84.14 160

OurMVSNet-221017-0		61.37 269	58.63 279	69.61 218	72.05 289	48.06 240	73.93 228	72.51 258	47.23 309	54.74 331	80.92 222	21.49 374	81.24 191	48.57 244	56.22 357	79.53 263

OpenMVS	61.03 9	68.85 156	67.56 161	72.70 152	74.26 258	53.99 143	81.21 87	81.34 116	52.70 237	62.75 249	85.55 130	38.86 239	84.14 127	48.41 245	83.01 81	79.97 255

testing91		64.46 234	63.80 225	66.47 256	78.43 158	40.06 314	67.63 300	69.59 281	59.06 123	63.18 242	78.05 269	34.05 286	76.99 266	48.30 246	75.87 174	82.37 212

testing99		64.05 237	63.29 234	66.34 258	78.17 170	39.76 318	67.33 305	68.00 294	58.60 132	63.03 245	78.10 268	32.57 311	76.94 268	48.22 247	75.58 178	82.34 213

baseline2		63.42 243	61.26 259	69.89 215	72.55 278	47.62 246	71.54 262	68.38 292	50.11 269	54.82 330	75.55 311	43.06 195	80.96 197	48.13 248	67.16 289	81.11 236

TESTMET0.1,1		55.28 312	54.90 309	56.42 333	66.56 349	43.67 284	65.46 317	56.27 363	39.18 367	53.83 340	67.44 364	24.21 365	55.46 377	48.04 249	73.11 210	70.13 361

test_fmvs3		44.30 348	42.55 351	49.55 363	42.83 401	27.15 394	53.03 374	44.93 391	22.03 396	53.69 343	64.94 375	4.21 406	49.63 388	47.47 250	49.82 375	71.88 344

K. test v3		60.47 273	57.11 289	70.56 201	73.74 262	48.22 238	75.10 205	62.55 332	58.27 139	53.62 344	76.31 301	27.81 343	81.59 183	47.42 251	39.18 389	81.88 221

pmmvs6		63.69 241	62.82 240	66.27 261	70.63 310	39.27 323	73.13 239	75.47 221	52.69 238	59.75 285	82.30 191	39.71 229	77.03 265	47.40 252	64.35 311	82.53 207

sd_testset		64.46 234	64.45 218	64.51 285	77.13 204	42.25 297	62.67 334	72.11 262	58.02 144	65.08 215	82.55 183	41.22 219	69.88 314	47.32 253	73.92 192	81.41 226

baseline1		63.81 240	63.87 224	63.62 289	76.29 221	36.36 350	71.78 260	67.29 298	56.05 182	64.23 232	82.95 175	47.11 152	74.41 290	47.30 254	61.85 331	80.10 254

GBi-Net		67.21 190	66.55 185	69.19 225	77.63 187	43.33 286	77.31 153	77.83 186	56.62 167	65.04 218	82.70 177	41.85 207	80.33 212	47.18 255	72.76 214	83.92 167

test1		67.21 190	66.55 185	69.19 225	77.63 187	43.33 286	77.31 153	77.83 186	56.62 167	65.04 218	82.70 177	41.85 207	80.33 212	47.18 255	72.76 214	83.92 167

FMVSNet3		66.32 212	65.61 207	68.46 235	76.48 219	42.34 295	74.98 208	77.15 199	55.83 185	65.04 218	81.16 215	39.91 225	80.14 219	47.18 255	72.76 214	82.90 202

FMVSNet2		66.93 200	66.31 196	68.79 232	77.63 187	42.98 291	76.11 181	77.47 192	56.62 167	65.22 214	82.17 195	41.85 207	80.18 218	47.05 258	72.72 217	83.20 193

testdata2													72.18 301	46.95 259

BH-RMVSNet		68.81 157	67.42 168	72.97 145	80.11 119	52.53 172	74.26 220	76.29 208	58.48 135	68.38 145	84.20 151	42.59 198	83.83 134	46.53 260	75.91 173	82.56 205

AdaColmap		69.99 129	68.66 142	73.97 112	84.94 54	57.83 80	82.63 65	78.71 164	56.28 177	64.34 227	84.14 153	41.57 211	87.06 63	46.45 261	78.88 134	77.02 292

EG-PatchMatch MVS		64.71 230	62.87 238	70.22 205	77.68 184	53.48 152	77.99 136	78.82 160	53.37 232	56.03 318	77.41 285	24.75 364	84.04 129	46.37 262	73.42 204	73.14 328

1112_ss		64.00 239	63.36 232	65.93 269	79.28 133	42.58 294	71.35 264	72.36 260	46.41 315	60.55 274	77.89 275	46.27 163	73.28 294	46.18 263	69.97 253	81.92 220

FMVSNet1		66.70 205	65.87 202	69.19 225	77.49 196	43.33 286	77.31 153	77.83 186	56.45 172	64.60 226	82.70 177	38.08 248	80.33 212	46.08 264	72.31 222	83.92 167

HyFIR lowres test		65.67 218	63.01 237	73.67 124	79.97 121	55.65 119	69.07 292	75.52 220	42.68 348	63.53 238	77.95 271	40.43 223	81.64 181	46.01 265	71.91 226	83.73 178

lessismore_v0				69.91 213	71.42 299	47.80 242		50.90 379		50.39 360	75.56 310	27.43 347	81.33 188	45.91 266	34.10 395	80.59 245

CHOSEN 1792x2688		65.08 228	62.84 239	71.82 168	81.49 89	56.26 105	66.32 309	74.20 245	40.53 360	63.16 243	78.65 262	41.30 215	77.80 253	45.80 267	74.09 189	81.40 228

LCM-MVSNet-Re		61.88 263	61.35 256	63.46 290	74.58 250	31.48 380	61.42 341	58.14 352	58.71 130	53.02 348	79.55 248	43.07 194	76.80 270	45.69 268	77.96 148	82.11 218

ambc				65.13 281	63.72 365	37.07 344	47.66 387	78.78 163		54.37 337	71.42 339	11.24 394	80.94 198	45.64 269	53.85 365	77.38 286

MS-PatchMatch		62.42 255	61.46 255	65.31 279	75.21 238	52.10 179	72.05 255	74.05 246	46.41 315	57.42 308	74.36 320	34.35 284	77.57 257	45.62 270	73.67 196	66.26 372

ACMH+	57.40 11	66.12 213	64.06 220	72.30 161	77.79 180	52.83 166	80.39 94	78.03 183	57.30 155	57.47 306	82.55 183	27.68 344	84.17 126	45.54 271	69.78 258	79.90 256

testing11		62.81 251	61.90 250	65.54 274	78.38 159	40.76 311	67.59 302	66.78 303	55.48 194	60.13 276	77.11 286	31.67 317	76.79 271	45.53 272	74.45 185	79.06 267

CR-MVSNet		59.91 276	57.90 287	65.96 268	69.96 321	52.07 180	65.31 321	63.15 329	42.48 349	59.36 288	74.84 316	35.83 270	70.75 307	45.50 273	64.65 307	75.06 310

CDS-MVSNet		66.80 203	65.37 209	71.10 192	78.98 142	53.13 161	73.27 238	71.07 269	52.15 244	64.72 223	80.23 235	43.56 191	77.10 263	45.48 274	78.88 134	83.05 199
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		66.49 210	66.41 191	66.72 252	77.67 185	36.33 352	76.83 170	79.52 149	62.45 63	62.54 254	83.47 170	46.32 161	78.37 243	45.47 275	63.43 319	85.45 118

BH-untuned		68.27 170	67.29 173	71.21 187	79.74 124	53.22 158	76.06 183	77.46 194	57.19 157	66.10 191	81.61 208	45.37 175	83.50 141	45.42 276	76.68 167	76.91 296

PS-CasMVS		66.42 211	66.32 195	66.70 254	77.60 194	36.30 354	76.94 165	79.61 147	62.36 65	62.43 258	83.66 164	45.69 165	78.37 243	45.35 277	63.26 320	85.42 121

XXY-MVS		60.68 271	61.67 252	57.70 329	70.43 313	38.45 330	64.19 328	66.47 304	48.05 297	63.22 240	80.86 224	49.28 120	60.47 351	45.25 278	67.28 288	74.19 323

HY-MVS	56.14 13	64.55 233	63.89 222	66.55 255	74.73 246	41.02 307	69.96 284	74.43 239	49.29 279	61.66 266	80.92 222	47.43 147	76.68 275	44.91 279	71.69 228	81.94 219

PEN-MVS		66.60 207	66.45 187	67.04 250	77.11 206	36.56 349	77.03 163	80.42 137	62.95 50	62.51 256	84.03 156	46.69 159	79.07 235	44.22 280	63.08 322	85.51 115

test_post1							68.67 294				3.64 409	32.39 313	69.49 315	44.17 281

SCA		60.49 272	58.38 281	66.80 251	74.14 260	48.06 240	63.35 331	63.23 328	49.13 281	59.33 291	72.10 333	37.45 252	74.27 291	44.17 281	62.57 325	78.05 277

PMMVS		53.96 318	53.26 324	56.04 334	62.60 370	50.92 195	61.17 344	56.09 364	32.81 377	53.51 346	66.84 369	34.04 287	59.93 355	44.14 283	68.18 280	57.27 384

MVP-Stereo		65.41 222	63.80 225	70.22 205	77.62 191	55.53 124	76.30 177	78.53 170	50.59 266	56.47 316	78.65 262	39.84 227	82.68 162	44.10 284	72.12 225	72.44 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FE-MVS		65.91 215	63.33 233	73.63 128	77.36 200	51.95 185	72.62 246	75.81 214	53.70 228	65.31 206	78.96 258	28.81 337	86.39 80	43.93 285	73.48 202	82.55 206

CNLPA		65.43 221	64.02 221	69.68 217	78.73 150	58.07 78	77.82 142	70.71 272	51.49 251	61.57 268	83.58 167	38.23 246	70.82 306	43.90 286	70.10 251	80.16 252

pmmvs4		61.48 268	59.39 271	67.76 242	71.57 295	53.86 144	71.42 263	65.34 312	44.20 334	59.46 287	77.92 273	35.90 269	74.71 288	43.87 287	64.87 305	74.71 318

Test_1112_low_res		62.32 256	61.77 251	64.00 288	79.08 141	39.53 321	68.17 296	70.17 275	43.25 343	59.03 293	79.90 239	44.08 186	71.24 305	43.79 288	68.42 279	81.25 232

TransMVSNet (Re)		64.72 229	64.33 219	65.87 271	75.22 237	38.56 328	74.66 215	75.08 234	58.90 126	61.79 264	82.63 180	51.18 102	78.07 248	43.63 289	55.87 358	80.99 240

pmmvs-eth3d		58.81 284	56.31 299	66.30 260	67.61 342	52.42 176	72.30 252	64.76 316	43.55 340	54.94 329	74.19 322	28.95 334	72.60 296	43.31 290	57.21 352	73.88 326

SixPastTwentyTwo		61.65 265	58.80 277	70.20 207	75.80 227	47.22 250	75.59 193	69.68 279	54.61 214	54.11 338	79.26 255	27.07 350	82.96 150	43.27 291	49.79 376	80.41 248

BH-w/o		66.85 201	65.83 203	69.90 214	79.29 132	52.46 174	74.66 215	76.65 206	54.51 218	64.85 222	78.12 267	45.59 168	82.95 151	43.26 292	75.54 179	74.27 322

TR-MVS		66.59 209	65.07 214	71.17 190	79.18 137	49.63 221	73.48 235	75.20 228	52.95 234	67.90 153	80.33 233	39.81 228	83.68 137	43.20 293	73.56 200	80.20 251

EU-MVSNet		55.61 310	54.41 314	59.19 316	65.41 357	33.42 372	72.44 250	71.91 264	28.81 381	51.27 352	73.87 323	24.76 363	69.08 317	43.04 294	58.20 348	75.06 310

PatchmatchNet		59.84 277	58.24 282	64.65 284	73.05 269	46.70 254	69.42 289	62.18 338	47.55 303	58.88 294	71.96 335	34.49 282	69.16 316	42.99 295	63.60 316	78.07 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		67.02 198	66.92 183	67.33 249	77.95 176	37.75 336	77.57 146	82.11 98	62.03 73	62.65 251	82.48 187	50.57 109	79.46 224	42.91 296	64.01 312	84.79 144

ACMH	55.70 15	65.20 226	63.57 229	70.07 209	78.07 172	52.01 183	79.48 113	79.69 144	55.75 188	56.59 313	80.98 220	27.12 349	80.94 198	42.90 297	71.58 230	77.25 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240521		55.30 311	54.41 314	57.96 326	60.92 381	41.73 302	71.09 272	71.06 270	41.18 355	48.65 364	73.31 326	16.93 379	59.25 358	42.54 298	64.01 312	72.90 330

WTY-MVS		59.75 278	60.39 266	57.85 327	72.32 285	37.83 335	61.05 346	64.18 321	45.95 322	61.91 262	79.11 257	47.01 156	60.88 350	42.50 299	69.49 264	74.83 315

TAMVS		66.78 204	65.27 212	71.33 186	79.16 139	53.67 147	73.84 232	69.59 281	52.32 242	65.28 207	81.72 206	44.49 184	77.40 260	42.32 300	78.66 140	82.92 200

LTVRE_ROB	55.42 16	63.15 249	61.23 260	68.92 230	76.57 217	47.80 242	59.92 350	76.39 207	54.35 220	58.67 296	82.46 188	29.44 332	81.49 185	42.12 301	71.14 234	77.46 285
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
sss		56.17 306	56.57 296	54.96 340	66.93 346	36.32 353	57.94 358	61.69 340	41.67 352	58.64 297	75.32 314	38.72 240	56.25 373	42.04 302	66.19 296	72.31 341

UnsupCasMVSNet_eth		53.16 327	52.47 325	55.23 339	59.45 383	33.39 373	59.43 352	69.13 287	45.98 319	50.35 361	72.32 330	29.30 333	58.26 364	42.02 303	44.30 382	74.05 324

tpm2		62.07 260	60.10 268	67.99 240	72.79 273	43.86 282	71.05 273	66.85 302	43.14 345	62.77 247	75.39 313	38.32 244	80.80 203	41.69 304	68.88 273	79.32 265

PLC	56.13 14	65.09 227	63.21 235	70.72 199	81.04 99	54.87 134	78.57 123	77.47 192	48.51 289	55.71 319	81.89 202	33.71 291	79.71 220	41.66 305	70.37 244	77.58 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 318	53.69 321	54.79 342	66.12 354	31.96 379	62.34 337	49.05 381	44.42 333	55.54 320	71.33 341	30.22 324	56.70 369	41.65 306	62.54 326	75.71 304

DTE-MVSNet		65.58 219	65.34 210	66.31 259	76.06 225	34.79 360	76.43 175	79.38 152	62.55 61	61.66 266	83.83 161	45.60 167	79.15 233	41.64 307	60.88 337	85.00 136

PAPM		67.92 179	66.69 184	71.63 175	78.09 171	49.02 227	77.09 161	81.24 122	51.04 260	60.91 272	83.98 158	47.71 139	84.99 110	40.81 308	79.32 128	80.90 241

tpm		57.34 294	58.16 283	54.86 341	71.80 293	34.77 361	67.47 304	56.04 365	48.20 294	60.10 277	76.92 289	37.17 258	53.41 382	40.76 309	65.01 303	76.40 299

KD-MVS_self_test		55.22 313	53.89 320	59.21 315	57.80 387	27.47 391	57.75 360	74.32 241	47.38 305	50.90 355	70.00 351	28.45 339	70.30 312	40.44 310	57.92 349	79.87 257

F-COLMAP		63.05 250	60.87 265	69.58 221	76.99 210	53.63 149	78.12 133	76.16 209	47.97 298	52.41 349	81.61 208	27.87 342	78.11 247	40.07 311	66.66 292	77.00 293

Patchmtry		57.16 295	56.47 297	59.23 314	69.17 332	34.58 364	62.98 332	63.15 329	44.53 330	56.83 311	74.84 316	35.83 270	68.71 318	40.03 312	60.91 336	74.39 321

pmmvs5		56.47 302	55.68 304	58.86 318	61.41 375	36.71 348	66.37 308	62.75 331	40.38 361	53.70 341	76.62 295	34.56 280	67.05 327	40.02 313	65.27 301	72.83 331

EPNet_dtu		61.90 262	61.97 249	61.68 302	72.89 272	39.78 317	75.85 189	65.62 311	55.09 204	54.56 334	79.36 253	37.59 251	67.02 328	39.80 314	76.95 163	78.25 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CL-MVSNet_self_test		61.53 266	60.94 263	63.30 292	68.95 333	36.93 346	67.60 301	72.80 257	55.67 190	59.95 280	76.63 294	45.01 179	72.22 300	39.74 315	62.09 330	80.74 244

test_vis1_rt		41.35 355	39.45 357	47.03 366	46.65 400	37.86 334	47.76 385	38.65 398	23.10 392	44.21 379	51.22 392	11.20 395	44.08 395	39.27 316	53.02 367	59.14 379

Vis-MVSNet (Re-imp)		63.69 241	63.88 223	63.14 294	74.75 245	31.04 381	71.16 269	63.64 325	56.32 175	59.80 283	84.99 136	44.51 182	75.46 285	39.12 317	80.62 107	82.92 200

PVSNet	50.76 19	58.40 286	57.39 288	61.42 305	75.53 233	44.04 281	61.43 340	63.45 326	47.04 311	56.91 310	73.61 325	27.00 351	64.76 338	39.12 317	72.40 219	75.47 307

MDTV_nov1_ep13_2view						25.89 397	61.22 343		40.10 363	51.10 353		32.97 300		38.49 319		78.61 272

our_test_3		56.49 301	54.42 313	62.68 298	69.51 326	45.48 268	66.08 310	61.49 341	44.11 337	50.73 358	69.60 355	33.05 298	68.15 320	38.38 320	56.86 353	74.40 320

tpm cat1		59.25 282	56.95 292	66.15 264	72.19 287	46.96 252	68.09 297	65.76 309	40.03 364	57.81 304	70.56 345	38.32 244	74.51 289	38.26 321	61.50 334	77.00 293

USDC		56.35 304	54.24 317	62.69 297	64.74 359	40.31 312	65.05 323	73.83 248	43.93 338	47.58 366	77.71 281	15.36 385	75.05 287	38.19 322	61.81 332	72.70 332

MSDG		61.81 264	59.23 272	69.55 222	72.64 275	52.63 170	70.45 279	75.81 214	51.38 253	53.70 341	76.11 302	29.52 330	81.08 196	37.70 323	65.79 299	74.93 314

MDTV_nov1_ep13			57.00 291		72.73 274	38.26 331	65.02 324	64.73 317	44.74 328	55.46 321	72.48 329	32.61 310	70.47 308	37.47 324	67.75 284

gg-mvs-nofinetune		57.86 291	56.43 298	62.18 300	72.62 276	35.35 358	66.57 306	56.33 362	50.65 264	57.64 305	57.10 386	30.65 320	76.36 280	37.38 325	78.88 134	74.82 316

dmvs_re		56.77 299	56.83 294	56.61 332	69.23 330	41.02 307	58.37 355	64.18 321	50.59 266	57.45 307	71.42 339	35.54 272	58.94 360	37.23 326	67.45 286	69.87 363

RPSCF		55.80 309	54.22 318	60.53 310	65.13 358	42.91 293	64.30 327	57.62 355	36.84 371	58.05 303	82.28 192	28.01 341	56.24 374	37.14 327	58.61 347	82.44 211

testing222		62.29 258	61.31 257	65.25 280	77.87 177	38.53 329	68.34 295	66.31 307	56.37 174	63.15 244	77.58 283	28.47 338	76.18 284	37.04 328	76.65 168	81.05 239

PatchT		53.17 326	53.44 323	52.33 357	68.29 339	25.34 399	58.21 356	54.41 369	44.46 332	54.56 334	69.05 358	33.32 296	60.94 349	36.93 329	61.76 333	70.73 357

YYNet1		50.73 335	48.96 337	56.03 335	61.10 377	41.78 301	51.94 377	56.44 360	40.94 358	44.84 375	67.80 362	30.08 325	55.08 378	36.77 330	50.71 372	71.22 352

TAPA-MVS	59.36 10	66.60 207	65.20 213	70.81 196	76.63 215	48.75 231	76.52 174	80.04 142	50.64 265	65.24 212	84.93 137	39.15 236	78.54 242	36.77 330	76.88 164	85.14 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 336	48.95 338	56.00 336	61.17 376	41.84 300	51.90 378	56.45 359	40.96 357	44.79 376	67.84 361	30.04 326	55.07 379	36.71 332	50.69 373	71.11 355

ppachtmachnet_test		58.06 290	55.38 306	66.10 266	69.51 326	48.99 228	68.01 298	66.13 308	44.50 331	54.05 339	70.74 344	32.09 315	72.34 298	36.68 333	56.71 356	76.99 295

tpmvs		58.47 285	56.95 292	63.03 296	70.20 316	41.21 306	67.90 299	67.23 299	49.62 275	54.73 332	70.84 343	34.14 285	76.24 282	36.64 334	61.29 335	71.64 346

CHOSEN 280x420		47.83 343	46.36 347	52.24 359	67.37 344	49.78 216	38.91 398	43.11 395	35.00 374	43.27 381	63.30 379	28.95 334	49.19 389	36.53 335	60.80 338	57.76 383

PatchMatch-RL		56.25 305	54.55 312	61.32 308	77.06 207	56.07 109	65.57 314	54.10 371	44.13 336	53.49 347	71.27 342	25.20 361	66.78 329	36.52 336	63.66 315	61.12 376

RPMNet		61.53 266	58.42 280	70.86 195	69.96 321	52.07 180	65.31 321	81.36 112	43.20 344	59.36 288	70.15 350	35.37 273	85.47 103	36.42 337	64.65 307	75.06 310

ITE_SJBPF				62.09 301	66.16 353	44.55 278		64.32 319	47.36 306	55.31 324	80.34 232	19.27 376	62.68 345	36.29 338	62.39 327	79.04 268

JIA-IIPM		51.56 331	47.68 345	63.21 293	64.61 360	50.73 199	47.71 386	58.77 350	42.90 346	48.46 365	51.72 390	24.97 362	70.24 313	36.06 339	53.89 364	68.64 369

KD-MVS_2432*1600		53.45 322	51.50 330	59.30 312	62.82 367	37.14 342	55.33 368	71.79 265	47.34 307	55.09 327	70.52 346	21.91 371	70.45 309	35.72 340	42.97 384	70.31 359

miper_refine_blended		53.45 322	51.50 330	59.30 312	62.82 367	37.14 342	55.33 368	71.79 265	47.34 307	55.09 327	70.52 346	21.91 371	70.45 309	35.72 340	42.97 384	70.31 359

OpenMVS_ROB	52.78 18	60.03 275	58.14 284	65.69 273	70.47 312	44.82 272	75.33 197	70.86 271	45.04 326	56.06 317	76.00 303	26.89 352	79.65 221	35.36 342	67.29 287	72.60 333

GG-mvs-BLEND				62.34 299	71.36 301	37.04 345	69.20 291	57.33 358		54.73 332	65.48 374	30.37 322	77.82 252	34.82 343	74.93 183	72.17 342

UnsupCasMVSNet_bld		50.07 338	48.87 339	53.66 348	60.97 380	33.67 371	57.62 361	64.56 318	39.47 366	47.38 367	64.02 378	27.47 345	59.32 357	34.69 344	43.68 383	67.98 370

MDA-MVSNet-bldmvs		53.87 320	50.81 332	63.05 295	66.25 352	48.58 234	56.93 365	63.82 323	48.09 296	41.22 383	70.48 348	30.34 323	68.00 324	34.24 345	45.92 381	72.57 334

dp		51.89 330	51.60 329	52.77 355	68.44 338	32.45 377	62.36 336	54.57 368	44.16 335	49.31 363	67.91 360	28.87 336	56.61 371	33.89 346	54.89 360	69.24 368

AllTest		57.08 296	54.65 310	64.39 286	71.44 297	49.03 225	69.92 285	67.30 296	45.97 320	47.16 368	79.77 242	17.47 377	67.56 325	33.65 347	59.16 345	76.57 297

TestCases				64.39 286	71.44 297	49.03 225		67.30 296	45.97 320	47.16 368	79.77 242	17.47 377	67.56 325	33.65 347	59.16 345	76.57 297

test_vis3_rt		32.09 366	30.20 371	37.76 380	35.36 411	27.48 390	40.60 397	28.29 407	16.69 401	32.52 395	40.53 400	1.96 412	37.40 403	33.64 349	42.21 386	48.39 390

UWE-MVS		60.18 274	59.78 269	61.39 307	77.67 185	33.92 370	69.04 293	63.82 323	48.56 287	64.27 230	77.64 282	27.20 348	70.40 311	33.56 350	76.24 170	79.83 258

FMVSNet5		55.86 308	54.93 308	58.66 320	71.05 306	36.35 351	64.18 329	62.48 333	46.76 313	50.66 359	74.73 318	25.80 358	64.04 340	33.11 351	65.57 300	75.59 305

mvsany_test1		39.38 357	38.16 360	43.02 373	49.05 395	34.28 366	44.16 394	25.94 408	22.74 394	46.57 372	62.21 381	23.85 366	41.16 400	33.01 352	35.91 392	53.63 387

DP-MVS		65.68 217	63.66 228	71.75 170	84.93 55	56.87 99	80.74 92	73.16 254	53.06 233	59.09 292	82.35 189	36.79 265	85.94 90	32.82 353	69.96 254	72.45 336

PVSNet_0	43.31 20	47.46 345	45.64 348	52.92 354	67.60 343	44.65 275	54.06 372	54.64 367	41.59 353	46.15 373	58.75 383	30.99 319	58.66 361	32.18 354	24.81 398	55.46 386

ETVMVS		59.51 281	58.81 275	61.58 304	77.46 197	34.87 359	64.94 325	59.35 347	54.06 224	61.08 271	76.67 293	29.54 329	71.87 302	32.16 355	74.07 190	78.01 281

WB-MVSnew		59.66 279	59.69 270	59.56 311	75.19 239	35.78 357	69.34 290	64.28 320	46.88 312	61.76 265	75.79 307	40.61 222	65.20 337	32.16 355	71.21 233	77.70 282

TinyColmap		54.14 317	51.72 328	61.40 306	66.84 347	41.97 299	66.52 307	68.51 291	44.81 327	42.69 382	75.77 308	11.66 391	72.94 295	31.96 357	56.77 355	69.27 367

MIMVSNet		57.35 293	57.07 290	58.22 323	74.21 259	37.18 341	62.46 335	60.88 344	48.88 284	55.29 325	75.99 305	31.68 316	62.04 347	31.87 358	72.35 220	75.43 308

thres100view900		63.28 246	62.41 244	65.89 270	77.31 201	38.66 327	72.65 244	69.11 288	57.07 158	62.45 257	81.03 219	37.01 263	79.17 230	31.84 359	73.25 207	79.83 258

tfpn200view9		63.18 248	62.18 247	66.21 262	76.85 211	39.62 319	71.96 258	69.44 284	56.63 165	62.61 252	79.83 240	37.18 256	79.17 230	31.84 359	73.25 207	79.83 258

thres400		63.31 244	62.18 247	66.72 252	76.85 211	39.62 319	71.96 258	69.44 284	56.63 165	62.61 252	79.83 240	37.18 256	79.17 230	31.84 359	73.25 207	81.36 229

pmmvs3		44.92 347	41.95 354	53.86 346	52.58 393	43.55 285	62.11 338	46.90 389	26.05 388	40.63 384	60.19 382	11.08 396	57.91 365	31.83 362	46.15 380	60.11 377

LF4IMVS		42.95 350	42.26 352	45.04 368	48.30 398	32.50 376	54.80 370	48.49 383	28.03 384	40.51 385	70.16 349	9.24 398	43.89 396	31.63 363	49.18 378	58.72 380

COLMAP_ROB	52.97 17	61.27 270	58.81 275	68.64 233	74.63 249	52.51 173	78.42 126	73.30 252	49.92 273	50.96 354	81.51 211	23.06 367	79.40 225	31.63 363	65.85 297	74.01 325
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 344	47.73 344	47.06 365	58.81 385	9.37 413	48.78 384	59.21 348	43.28 342	44.22 378	68.66 359	25.67 359	57.20 368	31.57 365	49.35 377	74.62 319

thres600view7		63.30 245	62.27 245	66.41 257	77.18 203	38.87 325	72.35 251	69.11 288	56.98 160	62.37 259	80.96 221	37.01 263	79.00 239	31.43 366	73.05 211	81.36 229

thres200		62.20 259	61.16 261	65.34 278	75.38 236	39.99 315	69.60 287	69.29 286	55.64 192	61.87 263	76.99 288	37.07 262	78.96 240	31.28 367	73.28 206	77.06 291

LCM-MVSNet		40.30 356	35.88 362	53.57 349	42.24 402	29.15 385	45.21 392	60.53 345	22.23 395	28.02 397	50.98 393	3.72 408	61.78 348	31.22 368	38.76 390	69.78 364

test_f		31.86 367	31.05 368	34.28 382	32.33 413	21.86 403	32.34 400	30.46 405	16.02 402	39.78 389	55.45 387	4.80 404	32.36 407	30.61 369	37.66 391	48.64 389

test0.0.03 1		53.32 325	53.59 322	52.50 356	62.81 369	29.45 384	59.51 351	54.11 370	50.08 270	54.40 336	74.31 321	32.62 308	55.92 375	30.50 370	63.95 314	72.15 343

Anonymous20231206		55.10 315	55.30 307	54.48 343	69.81 325	33.94 369	62.91 333	62.13 339	41.08 356	55.18 326	75.65 309	32.75 305	56.59 372	30.32 371	67.86 282	72.91 329

tfpnnormal		62.47 254	61.63 253	64.99 282	74.81 244	39.01 324	71.22 267	73.72 249	55.22 200	60.21 275	80.09 238	41.26 218	76.98 267	30.02 372	68.09 281	78.97 270

test20.03		53.87 320	54.02 319	53.41 352	61.47 374	28.11 388	61.30 342	59.21 348	51.34 255	52.09 350	77.43 284	33.29 297	58.55 362	29.76 373	60.27 342	73.58 327

LS3D		64.71 230	62.50 243	71.34 185	79.72 126	55.71 117	79.82 105	74.72 236	48.50 290	56.62 312	84.62 143	33.59 294	82.34 171	29.65 374	75.23 182	75.97 300

mvsany_test3		32.62 365	30.57 370	38.77 379	36.16 410	24.20 401	38.10 399	20.63 412	19.14 398	40.36 387	57.43 385	5.06 403	36.63 404	29.59 375	28.66 396	55.49 385

testgi		51.90 329	52.37 326	50.51 362	60.39 382	23.55 402	58.42 354	58.15 351	49.03 282	51.83 351	79.21 256	22.39 368	55.59 376	29.24 376	62.64 324	72.40 340

MIMVSNet1		55.17 314	54.31 316	57.77 328	70.03 320	32.01 378	65.68 313	64.81 315	49.19 280	46.75 371	76.00 303	25.53 360	64.04 340	28.65 377	62.13 329	77.26 289

TDRefinement		53.44 324	50.72 333	61.60 303	64.31 362	46.96 252	70.89 274	65.27 314	41.78 350	44.61 377	77.98 270	11.52 393	66.36 332	28.57 378	51.59 370	71.49 349

WAC-MVS						27.31 392								27.77 379

myMVS_eth3d		54.86 316	54.61 311	55.61 337	74.69 247	27.31 392	65.52 315	57.49 356	50.97 261	56.52 314	72.18 331	21.87 373	68.09 321	27.70 380	64.59 309	71.44 350

ADS-MVSNet2		51.33 333	48.76 340	59.07 317	66.02 355	44.60 276	50.90 380	59.76 346	36.90 369	50.74 356	66.18 372	26.38 353	63.11 343	27.17 381	54.76 361	69.50 365

ADS-MVSNet		48.48 342	47.77 343	50.63 361	66.02 355	29.92 383	50.90 380	50.87 380	36.90 369	50.74 356	66.18 372	26.38 353	52.47 384	27.17 381	54.76 361	69.50 365

Patchmatch-test		49.08 340	48.28 342	51.50 360	64.40 361	30.85 382	45.68 390	48.46 384	35.60 373	46.10 374	72.10 333	34.47 283	46.37 393	27.08 383	60.65 340	77.27 288

MVS-HIRNet		45.52 346	44.48 349	48.65 364	68.49 337	34.05 368	59.41 353	44.50 392	27.03 386	37.96 392	50.47 394	26.16 356	64.10 339	26.74 384	59.52 343	47.82 393

test_0402		63.25 247	61.01 262	69.96 210	80.00 120	54.37 140	76.86 169	72.02 263	54.58 216	58.71 295	80.79 227	35.00 277	84.36 124	26.41 385	64.71 306	71.15 354

N_pmnet		39.35 358	40.28 356	36.54 381	63.76 363	1.62 418	49.37 383	0.76 417	34.62 375	43.61 380	66.38 371	26.25 355	42.57 397	26.02 386	51.77 369	65.44 373

testing3		56.54 300	55.92 302	58.41 321	77.52 195	27.93 389	69.72 286	56.36 361	54.75 213	58.63 298	77.80 277	20.88 375	71.75 303	25.31 387	62.25 328	75.53 306

Syy-MVS		56.00 307	56.23 300	55.32 338	74.69 247	26.44 395	65.52 315	57.49 356	50.97 261	56.52 314	72.18 331	39.89 226	68.09 321	24.20 388	64.59 309	71.44 350

DSMNet-mixed		39.30 359	38.72 358	41.03 376	51.22 394	19.66 405	45.53 391	31.35 404	15.83 403	39.80 388	67.42 366	22.19 369	45.13 394	22.43 389	52.69 368	58.31 381

dmvs_testset		50.16 337	51.90 327	44.94 370	66.49 350	11.78 410	61.01 347	51.50 375	51.17 259	50.30 362	67.44 364	39.28 233	60.29 353	22.38 390	57.49 351	62.76 375

ANet_high		41.38 354	37.47 361	53.11 353	39.73 407	24.45 400	56.94 364	69.69 278	47.65 302	26.04 399	52.32 389	12.44 389	62.38 346	21.80 391	10.61 408	72.49 335

new_pmnet		34.13 364	34.29 365	33.64 383	52.63 392	18.23 407	44.43 393	33.90 403	22.81 393	30.89 396	53.18 388	10.48 397	35.72 405	20.77 392	39.51 388	46.98 394

APD_test1		37.39 360	34.94 363	44.72 371	48.88 396	33.19 374	52.95 375	44.00 394	19.49 397	27.28 398	58.59 384	3.18 410	52.84 383	18.92 393	41.17 387	48.14 392

EGC-MVSNET		42.47 351	38.48 359	54.46 344	74.33 256	48.73 232	70.33 281	51.10 377	0.03 411	0.18 412	67.78 363	13.28 388	66.49 331	18.91 394	50.36 374	48.15 391

PMMVS2		27.40 371	25.91 374	31.87 386	39.46 408	6.57 415	31.17 401	28.52 406	23.96 390	20.45 403	48.94 397	4.20 407	37.94 402	16.51 395	19.97 401	51.09 388

test_method		19.68 375	18.10 378	24.41 390	13.68 415	3.11 417	12.06 406	42.37 396	2.00 409	11.97 407	36.38 401	5.77 402	29.35 409	15.06 396	23.65 399	40.76 398

Gipuma		34.77 362	31.91 367	43.33 372	62.05 373	37.87 333	20.39 403	67.03 300	23.23 391	18.41 404	25.84 404	4.24 405	62.73 344	14.71 397	51.32 371	29.38 402
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 352	41.11 355	45.39 367	58.03 386	41.01 309	49.50 382	53.81 372	30.07 380	33.71 394	64.03 376	11.69 390	52.08 387	14.01 398	55.11 359	43.09 395

testf1		31.46 368	28.89 372	39.16 377	41.99 404	28.78 386	46.45 388	37.56 399	14.28 404	21.10 400	48.96 395	1.48 414	47.11 391	13.63 399	34.56 393	41.60 396

APD_test2		31.46 368	28.89 372	39.16 377	41.99 404	28.78 386	46.45 388	37.56 399	14.28 404	21.10 400	48.96 395	1.48 414	47.11 391	13.63 399	34.56 393	41.60 396

tmp_tt		9.43 378	11.14 381	4.30 393	2.38 416	4.40 416	13.62 405	16.08 414	0.39 410	15.89 405	13.06 407	15.80 384	5.54 412	12.63 401	10.46 409	2.95 407

dongtai		34.52 363	34.94 363	33.26 384	61.06 378	16.00 409	52.79 376	23.78 410	40.71 359	39.33 390	48.65 398	16.91 380	48.34 390	12.18 402	19.05 402	35.44 401

WB-MVS		43.26 349	43.41 350	42.83 374	63.32 366	10.32 412	58.17 357	45.20 390	45.42 324	40.44 386	67.26 367	34.01 289	58.98 359	11.96 403	24.88 397	59.20 378

SSC-MVS		41.96 353	41.99 353	41.90 375	62.46 371	9.28 414	57.41 363	44.32 393	43.38 341	38.30 391	66.45 370	32.67 307	58.42 363	10.98 404	21.91 400	57.99 382

MVE	17.77 23	21.41 374	17.77 379	32.34 385	34.34 412	25.44 398	16.11 404	24.11 409	11.19 406	13.22 406	31.92 402	1.58 413	30.95 408	10.47 405	17.03 404	40.62 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 372	22.73 376	26.90 387	42.02 403	20.67 404	42.66 395	35.70 401	17.43 399	10.28 409	25.05 405	6.42 401	42.39 398	10.28 406	14.71 405	17.63 404

PMVS	28.69 22	36.22 361	33.29 366	45.02 369	36.82 409	35.98 356	54.68 371	48.74 382	26.31 387	21.02 402	51.61 391	2.88 411	60.10 354	9.99 407	47.58 379	38.99 400
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 373	21.84 377	26.36 388	40.20 406	19.53 406	41.95 396	34.64 402	17.09 400	9.73 410	22.83 406	7.29 400	42.22 399	9.18 408	13.66 406	17.32 405

DeepMVS_CX				12.03 392	17.97 414	10.91 411		10.60 415	7.46 407	11.07 408	28.36 403	3.28 409	11.29 411	8.01 409	9.74 410	13.89 406

kuosan		29.62 370	30.82 369	26.02 389	52.99 390	16.22 408	51.09 379	22.71 411	33.91 376	33.99 393	40.85 399	15.89 383	33.11 406	7.59 410	18.37 403	28.72 403

wuyk23d		13.32 377	12.52 380	15.71 391	47.54 399	26.27 396	31.06 402	1.98 416	4.93 408	5.18 411	1.94 411	0.45 416	18.54 410	6.81 411	12.83 407	2.33 408

testmvs		4.52 381	6.03 384	0.01 395	0.01 417	0.00 420	53.86 373	0.00 418	0.01 412	0.04 413	0.27 412	0.00 418	0.00 413	0.04 412	0.00 411	0.03 410

test123		4.73 380	6.30 383	0.02 394	0.01 417	0.01 419	56.36 366	0.00 418	0.01 412	0.04 413	0.21 413	0.01 417	0.00 413	0.03 413	0.00 411	0.04 409

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

cdsmvs_eth3d_5k		17.50 376	23.34 375	0.00 396	0.00 419	0.00 420	0.00 407	78.63 167	0.00 414	0.00 415	82.18 193	49.25 121	0.00 413	0.00 414	0.00 411	0.00 411

pcd_1.5k_mvsjas		3.92 382	5.23 385	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	47.05 153	0.00 413	0.00 414	0.00 411	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

ab-mvs-re		6.49 379	8.65 382	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	77.89 275	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 420	0.00 407	0.00 418	0.00 414	0.00 415	0.00 414	0.00 418	0.00 413	0.00 414	0.00 411	0.00 411

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 84	81.50 16

test_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 419

eth-test					0.00 419

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

save fliter					86.17 33	61.30 28	83.98 47	79.66 146	59.00 124

test0726					87.75 7	59.07 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

GSMVS																78.05 277

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 279				78.05 277

sam_mvs												33.43 295

MTGPA								80.97 129

test_post											3.55 410	33.90 290	66.52 330

patchmatchnet-post											64.03 376	34.50 281	74.27 291

MTMP							86.03 19	17.08 413

TEST9					85.58 43	61.59 24	81.62 81	81.26 120	55.65 191	74.93 45	88.81 56	53.70 69	84.68 119

test_8					85.40 46	60.96 34	81.54 84	81.18 123	55.86 183	74.81 49	88.80 58	53.70 69	84.45 123

agg_prior					85.04 50	59.96 47		81.04 127		74.68 52			84.04 129

test_prior4						62.51 14	82.08 76

test_prior				76.69 53	84.20 61	57.27 88		84.88 38					86.43 79			86.38 77

新几何2							76.12 180

旧先验1					83.04 70	53.15 159		67.52 295			87.85 71	44.08 186			80.76 106	78.03 280

原ACMM2							79.02 115

test222					83.14 68	58.68 73	72.57 248	63.45 326	41.78 350	67.56 165	86.12 110	37.13 260			78.73 139	74.98 313

segment_acmp												54.23 58

testdata1							72.65 244		60.50 91

test12				77.76 43	84.52 58	58.41 75		83.36 77		72.93 83		54.61 54	88.05 40		88.12 35	86.81 65

plane_prior7					81.41 90	55.96 111

plane_prior6					81.20 97	56.24 106						45.26 177

plane_prior4											86.10 111

plane_prior3						56.09 108			63.92 36	69.27 132

plane_prior2							84.22 40		64.52 25

plane_prior1					81.27 95

plane_prior						56.31 102	83.58 53		63.19 48						80.48 112

n2								0.00 418

nn								0.00 418

door-mid								47.19 388

test11								83.47 72

door								47.60 386

HQP5-MVS						54.94 131

HQP-NCC					80.66 105		82.31 71		62.10 68	67.85 155

ACMP_Plane					80.66 105		82.31 71		62.10 68	67.85 155

HQP4-MVS										67.85 155			86.93 64			84.32 154

HQP3-MVS								83.90 57							80.35 113

HQP2-MVS												45.46 171

NP-MVS					80.98 100	56.05 110					85.54 131

ACMMP++_ref															74.07 190

ACMMP++															72.16 224

Test By Simon												48.33 132

High-resolution multi-view benchmark