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		82.69 2	83.29 3	80.89 23	84.38 83	55.40 60	92.16 10	89.85 20	75.28 4	82.41 11	93.86 8	54.30 34	93.98 23	90.29 1	87.13 22	93.30 12

MVS_0304		82.10 7	82.64 4	80.47 28	86.63 47	54.69 84	92.20 9	86.66 80	74.48 5	82.63 10	93.80 9	50.83 59	93.70 28	90.11 2	86.44 35	93.01 21

OPU-MVS				81.71 13	92.05 3	55.97 50	92.48 3				94.01 5	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

PC_three_1452									66.58 58	87.27 2	93.70 10	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

fmvsm_l_conf0.5_n		75.95 64	76.16 55	75.31 149	76.01 264	48.44 235	84.98 135	71.08 336	63.50 113	81.70 17	93.52 15	50.00 64	87.18 205	87.80 5	76.87 119	90.32 93

fmvsm_l_conf0.5_n_a		75.88 66	76.07 56	75.31 149	76.08 260	48.34 238	85.24 123	70.62 339	63.13 121	81.45 18	93.62 14	49.98 66	87.40 201	87.76 6	76.77 120	90.20 98

test_fmvsm_n_1920		75.56 73	75.54 61	75.61 137	74.60 282	49.51 205	81.82 224	74.08 310	66.52 61	80.40 22	93.46 17	46.95 88	89.72 119	86.69 7	75.30 136	87.61 162

fmvsm_s_conf0.5_n		74.48 85	74.12 81	75.56 139	76.96 249	47.85 255	85.32 121	69.80 346	64.16 97	78.74 28	93.48 16	45.51 108	89.29 128	86.48 8	66.62 210	89.55 114

fmvsm_s_conf0.1_n		73.80 97	73.26 89	75.43 144	73.28 296	47.80 256	84.57 151	69.43 348	63.34 116	78.40 31	93.29 22	44.73 124	89.22 131	85.99 9	66.28 217	89.26 119

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 18	93.77 1	91.10 10	75.95 3	77.10 37	93.09 27	54.15 37	95.57 12	85.80 10	85.87 40	93.31 11

fmvsm_s_conf0.5_n_a		73.68 102	73.15 90	75.29 152	75.45 271	48.05 248	83.88 169	68.84 350	63.43 115	78.60 29	93.37 20	45.32 109	88.92 145	85.39 11	64.04 230	88.89 130

patch_mono-2		80.84 12	81.59 10	78.62 66	90.34 9	53.77 103	88.08 53	88.36 50	76.17 2	79.40 27	91.09 64	55.43 26	90.09 109	85.01 12	80.40 84	91.99 47

fmvsm_s_conf0.1_n_a		72.82 113	72.05 113	75.12 157	70.95 324	47.97 251	82.72 201	68.43 352	62.52 131	78.17 32	93.08 28	44.21 127	88.86 146	84.82 13	63.54 236	88.54 141

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 49	59.34 6	80.45 252	89.51 22	69.76 28	71.05 93	86.66 158	58.68 14	93.24 31	84.64 14	90.40 6	93.14 18

CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 16	91.71 11	88.87 34	70.31 24	77.64 36	93.87 7	52.58 44	93.91 26	84.17 15	87.92 17	92.39 33

dcpmvs_2		79.33 21	78.94 21	80.49 26	89.75 12	56.54 38	84.83 142	83.68 153	67.85 43	69.36 104	90.24 84	60.20 7	92.10 59	84.14 16	80.40 84	92.82 25

CANet		80.90 11	81.17 12	80.09 38	87.62 39	54.21 96	91.60 14	86.47 83	73.13 8	79.89 25	93.10 25	49.88 68	92.98 33	84.09 17	84.75 52	93.08 19

test_fmvsmconf_n		74.41 87	74.05 83	75.49 143	74.16 288	48.38 236	82.66 202	72.57 323	67.05 54	75.11 44	92.88 31	46.35 95	87.81 182	83.93 18	71.71 170	90.28 94

test_fmvsmconf0.1_n		73.69 101	73.15 90	75.34 147	70.71 325	48.26 241	82.15 214	71.83 328	66.75 57	74.47 52	92.59 36	44.89 118	87.78 187	83.59 19	71.35 174	89.97 105

MSP-MVS		82.30 6	83.47 1	78.80 59	82.99 119	52.71 133	85.04 132	88.63 43	66.08 70	86.77 3	92.75 32	72.05 1	91.46 70	83.35 20	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
test_fmvsmvis_n_1920		71.29 139	70.38 136	74.00 183	71.04 323	48.79 223	79.19 271	64.62 360	62.75 126	66.73 120	91.99 48	40.94 171	88.35 164	83.00 21	73.18 156	84.85 214

IU-MVS					89.48 17	57.49 19		91.38 9	66.22 66	88.26 1				82.83 22	87.60 19	92.44 32

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 60	60.97 3	91.69 12	87.02 72	70.62 21	80.75 21	93.22 24	37.77 202	92.50 48	82.75 23	86.25 37	91.57 60

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 73	60.73 4	91.65 13	86.86 75	70.30 25	80.77 20	93.07 29	37.63 207	92.28 54	82.73 24	85.71 41	91.57 60

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 84	85.46 64	49.56 202	90.99 21	86.66 80	70.58 22	80.07 24	95.30 1	56.18 23	90.97 86	82.57 25	86.22 38	93.28 13

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 31	92.48 3	88.94 30	57.50 229	84.61 4	94.09 3	58.81 11	96.37 6	82.28 26	87.60 19	94.06 3

test_241102_TWO								88.76 39	57.50 229	83.60 6	94.09 3	56.14 24	96.37 6	82.28 26	87.43 21	92.55 30

test_fmvsmconf0.01_n		71.97 129	70.95 128	75.04 158	66.21 350	47.87 254	80.35 255	70.08 343	65.85 75	72.69 70	91.68 56	39.99 184	87.67 191	82.03 28	69.66 189	89.58 113

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 21	92.34 5	89.99 18	57.71 223	81.91 14	93.64 12	55.17 29	96.44 2	81.68 29	87.13 22	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
test_0728_SECOND				82.20 8	89.50 15	57.73 15	92.34 5	88.88 32					96.39 4	81.68 29	87.13 22	92.47 31

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 19	84.98 135	88.88 32	58.00 215	83.60 6	93.39 18	67.21 2	96.39 4	81.64 31	91.98 4	93.98 5

test_0728_THIRD									58.00 215	81.91 14	93.64 12	56.54 20	96.44 2	81.64 31	86.86 28	92.23 37

MSC_two_6792asdad				81.53 15	91.77 4	56.03 48		91.10 10					96.22 8	81.46 33	86.80 30	92.34 35

No_MVS				81.53 15	91.77 4	56.03 48		91.10 10					96.22 8	81.46 33	86.80 30	92.34 35

9.14			78.19 27		85.67 58		88.32 50	88.84 36	59.89 174	74.58 50	92.62 35	46.80 90	92.66 43	81.40 35	85.62 43

lupinMVS		78.38 28	78.11 28	79.19 47	83.02 117	55.24 64	91.57 15	84.82 125	69.12 33	76.67 39	92.02 46	44.82 121	90.23 106	80.83 36	80.09 88	92.08 41

HPM-MVS++		80.50 14	80.71 14	79.88 40	87.34 41	55.20 67	89.93 29	87.55 67	66.04 73	79.46 26	93.00 30	53.10 41	91.76 64	80.40 37	89.56 9	92.68 29

SMA-MVS		79.10 23	78.76 23	80.12 36	84.42 81	55.87 51	87.58 67	86.76 77	61.48 148	80.26 23	93.10 25	46.53 94	92.41 50	79.97 38	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
bld_raw_conf03		77.39 43	76.21 54	80.94 22	85.57 61	58.25 10	74.47 300	87.61 65	65.51 78	65.24 141	85.42 172	55.43 26	92.75 42	79.53 39	87.13 22	91.80 53

APDe-MVS		78.44 26	78.20 26	79.19 47	88.56 26	54.55 89	89.76 33	87.77 60	55.91 254	78.56 30	92.49 37	48.20 75	92.65 44	79.49 40	83.04 61	90.39 90
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ETV-MVS		77.17 46	76.74 46	78.48 70	81.80 146	54.55 89	86.13 98	85.33 106	68.20 37	73.10 64	90.52 78	45.23 111	90.66 92	79.37 41	80.95 76	90.22 96

jason		77.01 48	76.45 49	78.69 63	79.69 198	54.74 80	90.56 24	83.99 149	68.26 36	74.10 54	90.91 70	42.14 156	89.99 111	79.30 42	79.12 99	91.36 68
jason: jason.
test_vis1_n_1920		68.59 191	68.31 165	69.44 274	69.16 336	41.51 329	84.63 149	68.58 351	58.80 202	73.26 63	88.37 123	25.30 323	80.60 301	79.10 43	67.55 203	86.23 189

casdiffmvs_mvg		77.75 38	77.28 39	79.16 49	80.42 189	54.44 91	87.76 60	85.46 100	71.67 14	71.38 87	88.35 125	51.58 48	91.22 76	79.02 44	79.89 94	91.83 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 45	56.89 31	92.77 2	86.30 87	77.83 1	77.88 33	92.13 41	60.24 6	94.78 19	78.97 45	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
h-mvs33		73.95 94	72.89 95	77.15 100	80.17 192	50.37 183	84.68 146	83.33 159	68.08 38	71.97 79	88.65 121	42.50 150	91.15 79	78.82 46	57.78 291	89.91 108

hse-mvs2		71.44 138	70.68 130	73.73 193	76.34 254	47.44 261	79.45 268	79.47 233	68.08 38	71.97 79	86.01 166	42.50 150	86.93 214	78.82 46	53.46 327	86.83 179

NCCC		79.57 20	79.23 20	80.59 25	89.50 15	56.99 28	91.38 16	88.17 52	67.71 46	73.81 56	92.75 32	46.88 89	93.28 30	78.79 48	84.07 57	91.50 64

test9_res														78.72 49	85.44 45	91.39 66

test_cas_vis1_n_1920		67.10 223	66.60 200	68.59 287	65.17 358	43.23 313	83.23 190	69.84 345	55.34 262	70.67 98	87.71 141	24.70 330	76.66 339	78.57 50	64.20 229	85.89 197

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 17	89.29 40	91.54 5	59.19 191	71.82 81	90.05 92	59.72 9	96.04 10	78.37 51	88.40 14	93.75 7

DPE-MVS		79.82 19	79.66 17	80.29 31	89.27 24	55.08 72	88.70 46	87.92 56	55.55 259	81.21 19	93.69 11	56.51 21	94.27 22	78.36 52	85.70 42	91.51 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		75.54 74	75.03 69	77.04 102	81.37 165	52.65 135	84.34 155	84.46 136	61.16 152	69.14 105	91.76 53	39.98 185	88.99 140	78.19 53	84.89 51	89.48 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 49	76.40 50	78.45 72	85.68 56	55.42 57	87.59 65	84.00 147	57.84 220	72.99 65	90.98 67	44.99 115	88.58 154	78.19 53	85.32 46	91.34 70

sasdasda		78.17 32	77.86 31	79.12 52	84.30 84	54.22 94	87.71 61	84.57 134	67.70 47	77.70 34	92.11 44	50.90 55	89.95 112	78.18 55	77.54 111	93.20 15

SF-MVS		77.64 40	77.42 38	78.32 76	83.75 97	52.47 138	86.63 90	87.80 57	58.78 203	74.63 48	92.38 38	47.75 81	91.35 72	78.18 55	86.85 29	91.15 75

canonicalmvs		78.17 32	77.86 31	79.12 52	84.30 84	54.22 94	87.71 61	84.57 134	67.70 47	77.70 34	92.11 44	50.90 55	89.95 112	78.18 55	77.54 111	93.20 15

VDD-MVS		76.08 62	74.97 71	79.44 43	84.27 87	53.33 118	91.13 20	85.88 93	65.33 85	72.37 76	89.34 105	32.52 273	92.76 41	77.90 58	75.96 129	92.22 39

diffmvs		75.11 81	74.65 76	76.46 117	78.52 223	53.35 116	83.28 189	79.94 221	70.51 23	71.64 83	88.72 116	46.02 100	86.08 240	77.52 59	75.75 133	89.96 106
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SDMVSNet		71.89 130	70.62 132	75.70 135	81.70 150	51.61 156	73.89 304	88.72 40	66.58 58	61.64 193	82.38 221	37.63 207	89.48 124	77.44 60	65.60 220	86.01 191

alignmvs		78.08 34	77.98 29	78.39 74	83.53 100	53.22 121	89.77 32	85.45 101	66.11 68	76.59 41	91.99 48	54.07 38	89.05 135	77.34 61	77.00 116	92.89 23

SteuartSystems-ACMMP		77.08 47	76.33 51	79.34 45	80.98 170	55.31 62	89.76 33	86.91 74	62.94 124	71.65 82	91.56 60	42.33 152	92.56 47	77.14 62	83.69 59	90.15 100
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 57	75.66 59	78.73 61	81.92 143	54.67 86	84.06 164	85.35 105	61.10 155	72.99 65	91.50 61	40.25 178	91.00 83	76.84 63	86.98 27	90.51 89

CLD-MVS		75.60 72	75.39 64	76.24 120	80.69 183	52.40 139	90.69 23	86.20 89	74.40 6	65.01 147	88.93 112	42.05 158	90.58 95	76.57 64	73.96 151	85.73 199
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS		74.99 82	74.33 79	76.95 108	82.89 124	53.05 127	85.63 112	83.50 158	57.86 219	67.25 118	90.24 84	43.38 142	88.85 148	76.03 65	82.23 67	88.96 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		77.36 44	76.85 45	78.88 57	80.40 190	54.66 87	87.06 80	85.88 93	72.11 12	71.57 84	88.63 122	50.89 58	90.35 100	76.00 66	79.11 100	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
TSAR-MVS + GP.		77.82 37	77.59 35	78.49 69	85.25 69	50.27 189	90.02 26	90.57 15	56.58 248	74.26 53	91.60 59	54.26 35	92.16 56	75.87 67	79.91 92	93.05 20

baseline		76.86 52	76.24 53	78.71 62	80.47 188	54.20 98	83.90 168	84.88 124	71.38 18	71.51 85	89.15 110	50.51 60	90.55 96	75.71 68	78.65 103	91.39 66

agg_prior2														75.65 69	85.11 49	91.01 78

DeepC-MVS	67.15 4	76.90 51	76.27 52	78.80 59	80.70 182	55.02 73	86.39 92	86.71 78	66.96 55	67.91 114	89.97 94	48.03 77	91.41 71	75.60 70	84.14 56	89.96 106
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_BlendedMVS		73.42 105	73.30 88	73.76 191	85.91 53	51.83 152	86.18 97	84.24 143	65.40 82	69.09 106	80.86 240	46.70 92	88.13 173	75.43 71	65.92 219	81.33 273

PVSNet_Blended		76.53 56	76.54 48	76.50 116	85.91 53	51.83 152	88.89 44	84.24 143	67.82 44	69.09 106	89.33 107	46.70 92	88.13 173	75.43 71	81.48 75	89.55 114

LFMVS		78.52 24	77.14 42	82.67 3	89.58 13	58.90 8	91.27 19	88.05 54	63.22 119	74.63 48	90.83 73	41.38 168	94.40 20	75.42 73	79.90 93	94.72 2

ZD-MVS					89.55 14	53.46 109		84.38 137	57.02 237	73.97 55	91.03 65	44.57 125	91.17 78	75.41 74	81.78 73

testing11		79.18 22	78.85 22	80.16 34	88.33 30	56.99 28	88.31 51	92.06 1	72.82 9	70.62 100	88.37 123	57.69 15	92.30 52	75.25 75	76.24 128	91.20 73

MVS_111021_HR		76.39 58	75.38 65	79.42 44	85.33 67	56.47 40	88.15 52	84.97 121	65.15 88	66.06 131	89.88 95	43.79 132	92.16 56	75.03 76	80.03 91	89.64 112

CS-MVS-test		77.20 45	77.25 40	77.05 101	84.60 78	49.04 215	89.42 36	85.83 95	65.90 74	72.85 68	91.98 50	45.10 112	91.27 73	75.02 77	84.56 53	90.84 82

test_prior2							89.04 42		61.88 141	73.55 58	91.46 63	48.01 78		74.73 78	85.46 44

SD-MVS		76.18 60	74.85 73	80.18 33	85.39 65	56.90 30	85.75 107	82.45 178	56.79 243	74.48 51	91.81 52	43.72 135	90.75 90	74.61 79	78.65 103	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
CS-MVS		76.77 53	76.70 47	76.99 106	83.55 99	48.75 224	88.60 47	85.18 114	66.38 63	72.47 75	91.62 58	45.53 106	90.99 85	74.48 80	82.51 64	91.23 72

APD-MVS		76.15 61	75.68 58	77.54 90	88.52 27	53.44 112	87.26 76	85.03 120	53.79 275	74.91 46	91.68 56	43.80 131	90.31 102	74.36 81	81.82 71	88.87 131
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EC-MVSNet		75.30 75	75.20 66	75.62 136	80.98 170	49.00 216	87.43 68	84.68 131	63.49 114	70.97 94	90.15 90	42.86 149	91.14 80	74.33 82	81.90 70	86.71 181

VDDNet		74.37 88	72.13 110	81.09 20	79.58 199	56.52 39	90.02 26	86.70 79	52.61 285	71.23 89	87.20 149	31.75 283	93.96 25	74.30 83	75.77 132	92.79 27

TSAR-MVS + MP.		78.31 30	78.26 25	78.48 70	81.33 166	56.31 44	81.59 232	86.41 84	69.61 30	81.72 16	88.16 130	55.09 31	88.04 177	74.12 84	86.31 36	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
DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 193	59.50 5	92.24 8	90.72 14	69.37 32	83.22 8	94.47 2	63.81 5	93.18 32	74.02 85	93.25 2	94.80 1

mvsmamba		69.38 175	67.52 184	74.95 162	82.86 125	52.22 144	67.36 343	76.75 285	61.14 153	49.43 317	82.04 229	37.26 218	84.14 269	73.93 86	76.91 117	88.50 143

MVSMamba_PlusPlus		75.28 76	73.39 87	80.96 21	80.85 177	58.25 10	74.47 300	87.61 65	50.53 299	65.24 141	83.41 198	57.38 16	92.83 36	73.92 87	87.13 22	91.80 53

iter_conf05		74.57 84	72.83 96	79.80 41	80.85 177	58.10 12	74.55 299	82.97 170	50.53 299	63.52 174	83.41 198	57.38 16	92.83 36	73.92 87	88.34 15	88.48 145

PHI-MVS		77.49 41	77.00 43	78.95 54	85.33 67	50.69 172	88.57 48	88.59 46	58.14 212	73.60 57	93.31 21	43.14 145	93.79 27	73.81 89	88.53 13	92.37 34

MTAPA		72.73 114	71.22 124	77.27 97	81.54 160	53.57 107	67.06 345	81.31 197	59.41 184	68.39 111	90.96 69	36.07 238	89.01 137	73.80 90	82.45 66	89.23 121

VNet		77.99 36	77.92 30	78.19 78	87.43 40	50.12 190	90.93 22	91.41 8	67.48 50	75.12 43	90.15 90	46.77 91	91.00 83	73.52 91	78.46 105	93.44 9

EPNet		78.36 29	78.49 24	77.97 82	85.49 63	52.04 146	89.36 38	84.07 146	73.22 7	77.03 38	91.72 54	49.32 72	90.17 108	73.46 92	82.77 62	91.69 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		71.60 135	70.29 139	75.55 140	77.26 243	53.15 122	85.34 118	79.37 234	55.83 255	72.54 71	90.19 87	22.38 343	86.66 221	73.28 93	76.39 123	86.85 176

xiu_mvs_v1_base		71.60 135	70.29 139	75.55 140	77.26 243	53.15 122	85.34 118	79.37 234	55.83 255	72.54 71	90.19 87	22.38 343	86.66 221	73.28 93	76.39 123	86.85 176

xiu_mvs_v1_base_debi		71.60 135	70.29 139	75.55 140	77.26 243	53.15 122	85.34 118	79.37 234	55.83 255	72.54 71	90.19 87	22.38 343	86.66 221	73.28 93	76.39 123	86.85 176

PMMVS		72.98 109	72.05 113	75.78 134	83.57 98	48.60 227	84.08 162	82.85 173	61.62 144	68.24 112	90.33 83	28.35 300	87.78 187	72.71 96	76.69 121	90.95 80

ZNCC-MVS		75.82 70	75.02 70	78.23 77	83.88 95	53.80 102	86.91 85	86.05 91	59.71 177	67.85 115	90.55 76	42.23 154	91.02 82	72.66 97	85.29 47	89.87 109

ET-MVSNet_ETH3D		75.23 78	74.08 82	78.67 64	84.52 80	55.59 53	88.92 43	89.21 26	68.06 41	53.13 296	90.22 86	49.71 69	87.62 195	72.12 98	70.82 179	92.82 25

MVS		76.91 49	75.48 62	81.23 19	84.56 79	55.21 66	80.23 258	91.64 4	58.65 205	65.37 140	91.48 62	45.72 104	95.05 16	72.11 99	89.52 10	93.44 9

MGCFI-Net		74.07 92	74.64 77	72.34 222	82.90 123	43.33 312	80.04 261	79.96 220	65.61 76	74.93 45	91.85 51	48.01 78	80.86 296	71.41 100	77.10 114	92.84 24

nrg030		72.27 125	71.56 118	74.42 170	75.93 265	50.60 174	86.97 82	83.21 164	62.75 126	67.15 119	84.38 182	50.07 63	86.66 221	71.19 101	62.37 252	85.99 193

DeepC-MVS_fast	67.50 3	78.00 35	77.63 34	79.13 51	88.52 27	55.12 69	89.95 28	85.98 92	68.31 35	71.33 88	92.75 32	45.52 107	90.37 99	71.15 102	85.14 48	91.91 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		74.87 83	73.90 85	77.77 85	83.30 107	53.45 111	85.75 107	85.29 109	59.22 190	66.50 127	89.85 96	40.94 171	90.76 89	70.94 103	83.35 60	89.10 126

CHOSEN 1792x2688		76.24 59	74.03 84	82.88 1	83.09 114	62.84 2	85.73 109	85.39 103	69.79 27	64.87 149	83.49 196	41.52 167	93.69 29	70.55 104	81.82 71	92.12 40

CDPH-MVS		76.05 63	75.19 67	78.62 66	86.51 48	54.98 75	87.32 71	84.59 133	58.62 206	70.75 96	90.85 72	43.10 147	90.63 94	70.50 105	84.51 55	90.24 95

HPM-MVS		72.60 116	71.50 119	75.89 132	82.02 141	51.42 162	80.70 250	83.05 167	56.12 253	64.03 163	89.53 101	37.55 210	88.37 162	70.48 106	80.04 90	87.88 155
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		69.07 178	67.91 171	72.54 215	77.27 242	49.56 202	79.77 263	73.96 313	59.33 188	60.73 201	87.82 138	30.19 293	81.53 289	69.94 107	72.19 167	86.53 183

testing91		78.30 31	77.54 36	80.61 24	88.16 35	57.12 27	87.94 59	91.07 13	71.43 16	70.75 96	88.04 135	55.82 25	92.65 44	69.61 108	75.00 145	92.05 43

test_yl		75.85 67	74.83 74	78.91 55	88.08 37	51.94 148	91.30 17	89.28 24	57.91 217	71.19 90	89.20 108	42.03 159	92.77 39	69.41 109	75.07 143	92.01 45

DCV-MVSNet		75.85 67	74.83 74	78.91 55	88.08 37	51.94 148	91.30 17	89.28 24	57.91 217	71.19 90	89.20 108	42.03 159	92.77 39	69.41 109	75.07 143	92.01 45

testing99		78.45 25	77.78 33	80.45 29	88.28 33	56.81 34	87.95 58	91.49 6	71.72 13	70.84 95	88.09 131	57.29 18	92.63 46	69.24 111	75.13 141	91.91 48

HFP-MVS		74.37 88	73.13 94	78.10 80	84.30 84	53.68 105	85.58 113	84.36 138	56.82 241	65.78 136	90.56 75	40.70 176	90.90 87	69.18 112	80.88 77	89.71 110

ACMMPR		73.76 98	72.61 97	77.24 99	83.92 93	52.96 130	85.58 113	84.29 139	56.82 241	65.12 143	90.45 79	37.24 219	90.18 107	69.18 112	80.84 78	88.58 139

region2R		73.75 99	72.55 99	77.33 94	83.90 94	52.98 129	85.54 117	84.09 145	56.83 240	65.10 144	90.45 79	37.34 216	90.24 105	68.89 114	80.83 79	88.77 135

CP-MVS		72.59 118	71.46 120	76.00 131	82.93 122	52.32 142	86.93 84	82.48 177	55.15 263	63.65 169	90.44 82	35.03 250	88.53 158	68.69 115	77.83 109	87.15 170

baseline2		75.15 80	74.54 78	76.98 107	81.67 153	51.74 154	83.84 170	91.94 3	69.97 26	58.98 224	86.02 164	59.73 8	91.73 65	68.37 116	70.40 184	87.48 164

Effi-MVS+		75.24 77	73.61 86	80.16 34	81.92 143	57.42 23	85.21 124	76.71 288	60.68 166	73.32 62	89.34 105	47.30 84	91.63 66	68.28 117	79.72 95	91.42 65

CostFormer		73.89 96	72.30 105	78.66 65	82.36 138	56.58 35	75.56 289	85.30 108	66.06 71	70.50 102	76.88 282	57.02 19	89.06 134	68.27 118	68.74 195	90.33 92

CANet_DTU		73.71 100	73.14 92	75.40 145	82.61 134	50.05 191	84.67 148	79.36 237	69.72 29	75.39 42	90.03 93	29.41 296	85.93 246	67.99 119	79.11 100	90.22 96

PVSNet_Blended_VisFu		73.40 106	72.44 101	76.30 118	81.32 167	54.70 83	85.81 103	78.82 247	63.70 107	64.53 155	85.38 173	47.11 87	87.38 202	67.75 120	77.55 110	86.81 180

MSLP-MVS++		74.21 90	72.25 106	80.11 37	81.45 163	56.47 40	86.32 94	79.65 229	58.19 211	66.36 128	92.29 40	36.11 236	90.66 92	67.39 121	82.49 65	93.18 17

PGM-MVS		72.60 116	71.20 125	76.80 113	82.95 120	52.82 132	83.07 195	82.14 180	56.51 249	63.18 175	89.81 97	35.68 242	89.76 118	67.30 122	80.19 87	87.83 156

EIA-MVS		75.92 65	75.18 68	78.13 79	85.14 70	51.60 157	87.17 78	85.32 107	64.69 91	68.56 110	90.53 77	45.79 103	91.58 67	67.21 123	82.18 68	91.20 73

HY-MVS	67.03 5	73.90 95	73.14 92	76.18 125	84.70 77	47.36 262	75.56 289	86.36 86	66.27 65	70.66 99	83.91 188	51.05 53	89.31 127	67.10 124	72.61 163	91.88 50

BP-MVS														66.70 125

HQP-MVS		72.34 121	71.44 121	75.03 159	79.02 210	51.56 158	88.00 54	83.68 153	65.45 79	64.48 156	85.13 174	37.35 214	88.62 152	66.70 125	73.12 157	84.91 212

SR-MVS		70.92 147	69.73 148	74.50 167	83.38 106	50.48 178	84.27 157	79.35 238	48.96 311	66.57 126	90.45 79	33.65 264	87.11 207	66.42 127	74.56 148	85.91 196

gm-plane-assit					83.24 109	54.21 96			70.91 20		88.23 129		95.25 14	66.37 128

PAPR		75.20 79	74.13 80	78.41 73	88.31 32	55.10 71	84.31 156	85.66 97	63.76 106	67.55 116	90.73 74	43.48 140	89.40 126	66.36 129	77.03 115	90.73 84

WTY-MVS		77.47 42	77.52 37	77.30 95	88.33 30	46.25 279	88.46 49	90.32 16	71.40 17	72.32 77	91.72 54	53.44 39	92.37 51	66.28 130	75.42 135	93.28 13

tpmrst		71.04 144	69.77 147	74.86 163	83.19 111	55.86 52	75.64 288	78.73 251	67.88 42	64.99 148	73.73 311	49.96 67	79.56 315	65.92 131	67.85 202	89.14 125

MVS_Test		75.85 67	74.93 72	78.62 66	84.08 89	55.20 67	83.99 166	85.17 115	68.07 40	73.38 61	82.76 208	50.44 61	89.00 138	65.90 132	80.61 80	91.64 56

ACMMP		70.81 149	69.29 155	75.39 146	81.52 162	51.92 150	83.43 182	83.03 168	56.67 246	58.80 231	88.91 113	31.92 281	88.58 154	65.89 133	73.39 155	85.67 200
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
XVS		72.92 110	71.62 117	76.81 111	83.41 102	52.48 136	84.88 140	83.20 165	58.03 213	63.91 165	89.63 100	35.50 243	89.78 116	65.50 134	80.50 82	88.16 147

X-MVStestdata		65.85 243	62.20 251	76.81 111	83.41 102	52.48 136	84.88 140	83.20 165	58.03 213	63.91 165	4.82 410	35.50 243	89.78 116	65.50 134	80.50 82	88.16 147

PAPM		76.76 54	76.07 56	78.81 58	80.20 191	59.11 7	86.86 86	86.23 88	68.60 34	70.18 103	88.84 115	51.57 49	87.16 206	65.48 136	86.68 32	90.15 100

HQP_MVS		70.96 146	69.91 146	74.12 179	77.95 231	49.57 200	85.76 105	82.59 175	63.60 110	62.15 188	83.28 202	36.04 239	88.30 168	65.46 137	72.34 165	84.49 216

plane_prior5								82.59 175					88.30 168	65.46 137	72.34 165	84.49 216

mPP-MVS		71.79 134	70.38 136	76.04 129	82.65 133	52.06 145	84.45 152	81.78 190	55.59 258	62.05 190	89.68 99	33.48 265	88.28 170	65.45 139	78.24 108	87.77 158

OPM-MVS		70.75 150	69.58 149	74.26 176	75.55 270	51.34 164	86.05 100	83.29 163	61.94 140	62.95 179	85.77 167	34.15 258	88.44 160	65.44 140	71.07 176	82.99 248
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Effi-MVS+-dtu		66.24 239	64.96 236	70.08 266	75.17 272	49.64 199	82.01 217	74.48 307	62.15 135	57.83 245	76.08 295	30.59 290	83.79 273	65.40 141	60.93 259	76.81 323

EI-MVSNet-Vis-set		73.19 108	72.60 98	74.99 161	82.56 135	49.80 198	82.55 207	89.00 29	66.17 67	65.89 134	88.98 111	43.83 130	92.29 53	65.38 142	69.01 193	82.87 251

testing222		77.70 39	77.22 41	79.14 50	86.95 43	54.89 78	87.18 77	91.96 2	72.29 11	71.17 92	88.70 117	55.19 28	91.24 75	65.18 143	76.32 127	91.29 71

TESTMET0.1,1		72.86 112	72.33 103	74.46 168	81.98 142	50.77 170	85.13 127	85.47 99	66.09 69	67.30 117	83.69 193	37.27 217	83.57 277	65.06 144	78.97 102	89.05 127

MVSTER		73.25 107	72.33 103	76.01 130	85.54 62	53.76 104	83.52 175	87.16 70	67.06 53	63.88 167	81.66 233	52.77 42	90.44 97	64.66 145	64.69 226	83.84 233

CPTT-MVS		67.15 222	65.84 217	71.07 251	80.96 172	50.32 186	81.94 219	74.10 309	46.18 330	57.91 244	87.64 143	29.57 295	81.31 291	64.10 146	70.18 186	81.56 264

miper_enhance_ethall		69.77 167	68.90 159	72.38 220	78.93 213	49.91 194	83.29 188	78.85 245	64.90 89	59.37 217	79.46 250	52.77 42	85.16 258	63.78 147	58.72 273	82.08 256

EI-MVSNet-UG-set		72.37 120	71.73 116	74.29 175	81.60 156	49.29 210	81.85 222	88.64 42	65.29 87	65.05 145	88.29 128	43.18 143	91.83 63	63.74 148	67.97 200	81.75 261

ab-mvs		70.65 151	69.11 157	75.29 152	80.87 176	46.23 280	73.48 308	85.24 113	59.99 173	66.65 122	80.94 239	43.13 146	88.69 150	63.58 149	68.07 198	90.95 80

VPA-MVSNet		71.12 141	70.66 131	72.49 217	78.75 216	44.43 298	87.64 63	90.02 17	63.97 102	65.02 146	81.58 235	42.14 156	87.42 200	63.42 150	63.38 240	85.63 203

APD-MVS_3200maxsize		69.62 172	68.23 168	73.80 190	81.58 158	48.22 242	81.91 220	79.50 232	48.21 315	64.24 161	89.75 98	31.91 282	87.55 197	63.08 151	73.85 153	85.64 202

v2v482		69.55 173	67.64 179	75.26 155	72.32 310	53.83 101	84.93 139	81.94 184	65.37 84	60.80 200	79.25 253	41.62 164	88.98 141	63.03 152	59.51 266	82.98 249

PS-MVSNAJss		68.78 187	67.17 190	73.62 197	73.01 300	48.33 240	84.95 138	84.81 126	59.30 189	58.91 228	79.84 248	37.77 202	88.86 146	62.83 153	63.12 246	83.67 236

cl22		68.85 182	67.69 178	72.35 221	78.07 230	49.98 193	82.45 210	78.48 257	62.50 132	58.46 238	77.95 263	49.99 65	85.17 257	62.55 154	58.72 273	81.90 259

V42		67.66 207	65.60 224	73.86 187	70.69 327	53.63 106	81.50 235	78.61 254	63.85 104	59.49 216	77.49 270	37.98 199	87.65 192	62.33 155	58.43 276	80.29 288

AUN-MVS		68.20 199	66.35 203	73.76 191	76.37 253	47.45 260	79.52 267	79.52 231	60.98 158	62.34 184	86.02 164	36.59 233	86.94 213	62.32 156	53.47 326	86.89 173

MG-MVS		78.42 27	76.99 44	82.73 2	93.17 1	64.46 1	89.93 29	88.51 48	64.83 90	73.52 59	88.09 131	48.07 76	92.19 55	62.24 157	84.53 54	91.53 62

Patchmatch-RL test		58.72 295	54.32 307	71.92 237	63.91 365	44.25 301	61.73 362	55.19 373	57.38 231	49.31 319	54.24 382	37.60 209	80.89 294	62.19 158	47.28 347	90.63 85

mvs_anonymous		72.29 123	70.74 129	76.94 109	82.85 126	54.72 82	78.43 276	81.54 193	63.77 105	61.69 192	79.32 252	51.11 52	85.31 253	62.15 159	75.79 131	90.79 83

miper_ehance_all_eth		68.70 190	67.58 180	72.08 227	76.91 250	49.48 206	82.47 209	78.45 258	62.68 128	58.28 242	77.88 265	50.90 55	85.01 261	61.91 160	58.72 273	81.75 261

HyFIR lowres test		69.94 165	67.58 180	77.04 102	77.11 248	57.29 24	81.49 237	79.11 243	58.27 210	58.86 229	80.41 243	42.33 152	86.96 212	61.91 160	68.68 196	86.87 174

sss		70.49 153	70.13 143	71.58 243	81.59 157	39.02 341	80.78 249	84.71 130	59.34 186	66.61 124	88.09 131	37.17 220	85.52 249	61.82 162	71.02 177	90.20 98

1314		71.11 142	69.41 151	76.22 121	79.32 203	50.49 177	80.23 258	85.14 118	59.44 183	58.93 226	88.89 114	33.83 263	89.60 123	61.49 163	77.42 113	88.57 140

GA-MVS		69.04 179	66.70 197	76.06 128	75.11 273	52.36 140	83.12 193	80.23 215	63.32 117	60.65 202	79.22 254	30.98 288	88.37 162	61.25 164	66.41 213	87.46 165

ECVR-MVS		71.81 132	71.00 127	74.26 176	80.12 193	43.49 308	84.69 145	82.16 179	64.02 99	64.64 151	87.43 146	35.04 249	89.21 132	61.24 165	79.66 96	90.08 102

VPNet		72.07 127	71.42 122	74.04 181	78.64 221	47.17 266	89.91 31	87.97 55	72.56 10	64.66 150	85.04 177	41.83 163	88.33 166	61.17 166	60.97 258	86.62 182

ACMP	61.11 9	66.24 239	64.33 240	72.00 231	74.89 278	49.12 211	83.18 192	79.83 224	55.41 261	52.29 301	82.68 212	25.83 319	86.10 237	60.89 167	63.94 233	80.78 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 104	72.19 108	77.57 89	83.02 117	55.24 64	81.63 229	81.44 195	50.28 301	76.67 39	90.91 70	44.82 121	86.11 235	60.83 168	80.09 88	91.36 68

test_djsdf		63.84 252	61.56 256	70.70 256	68.78 338	44.69 295	81.63 229	81.44 195	50.28 301	52.27 302	76.26 290	26.72 313	86.11 235	60.83 168	55.84 308	81.29 276

v148		68.24 198	66.35 203	73.88 186	71.76 313	51.47 161	84.23 158	81.90 188	63.69 108	58.94 225	76.44 287	43.72 135	87.78 187	60.63 170	55.86 307	82.39 254

c3_l		67.97 200	66.66 198	71.91 238	76.20 259	49.31 209	82.13 216	78.00 264	61.99 138	57.64 251	76.94 279	49.41 70	84.93 262	60.62 171	57.01 295	81.49 265

test-LLR		69.65 171	69.01 158	71.60 241	78.67 218	48.17 243	85.13 127	79.72 226	59.18 193	63.13 176	82.58 215	36.91 225	80.24 306	60.56 172	75.17 139	86.39 187

test-mter		68.36 193	67.29 187	71.60 241	78.67 218	48.17 243	85.13 127	79.72 226	53.38 279	63.13 176	82.58 215	27.23 310	80.24 306	60.56 172	75.17 139	86.39 187

SR-MVS-dyc-post		68.27 197	66.87 192	72.48 218	80.96 172	48.14 245	81.54 233	76.98 281	46.42 327	62.75 181	89.42 103	31.17 287	86.09 239	60.52 174	72.06 168	83.19 244

RE-MVS-def			66.66 198		80.96 172	48.14 245	81.54 233	76.98 281	46.42 327	62.75 181	89.42 103	29.28 298		60.52 174	72.06 168	83.19 244

IB-MVS	68.87 2	74.01 93	72.03 115	79.94 39	83.04 116	55.50 55	90.24 25	88.65 41	67.14 52	61.38 195	81.74 232	53.21 40	94.28 21	60.45 176	62.41 251	90.03 104
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
v1144		68.81 185	66.82 193	74.80 164	72.34 309	53.46 109	84.68 146	81.77 191	64.25 95	60.28 204	77.91 264	40.23 179	88.95 142	60.37 177	59.52 265	81.97 257

LPG-MVS_test		66.44 236	64.58 238	72.02 229	74.42 284	48.60 227	83.07 195	80.64 208	54.69 270	53.75 292	83.83 189	25.73 321	86.98 210	60.33 178	64.71 224	80.48 285

LGP-MVS_train				72.02 229	74.42 284	48.60 227		80.64 208	54.69 270	53.75 292	83.83 189	25.73 321	86.98 210	60.33 178	64.71 224	80.48 285

MVP-Stereo		70.97 145	70.44 134	72.59 214	76.03 263	51.36 163	85.02 134	86.99 73	60.31 170	56.53 268	78.92 257	40.11 182	90.00 110	60.00 180	90.01 7	76.41 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 259	60.84 264	70.32 262	68.33 343	44.45 297	81.23 239	81.05 201	53.37 280	50.96 311	77.81 267	17.49 366	85.49 251	59.31 181	58.05 284	81.02 279

test2506		72.91 111	72.43 102	74.32 174	80.12 193	44.18 303	83.19 191	84.77 128	64.02 99	65.97 132	87.43 146	47.67 82	88.72 149	59.08 182	79.66 96	90.08 102

baseline1		72.51 119	72.12 111	73.69 194	85.05 71	44.46 296	83.51 179	86.13 90	71.61 15	64.64 151	87.97 136	55.00 32	89.48 124	59.07 183	56.05 304	87.13 171

mvs_tets		62.96 262	60.55 266	70.19 263	68.22 346	44.24 302	80.90 246	80.74 207	52.99 283	50.82 313	77.56 268	16.74 369	85.44 252	59.04 184	57.94 286	80.89 280

HPM-MVS_fast		67.86 202	66.28 206	72.61 213	80.67 184	48.34 238	81.18 240	75.95 296	50.81 298	59.55 214	88.05 134	27.86 305	85.98 242	58.83 185	73.58 154	83.51 237

eth_miper_zixun_eth		66.98 228	65.28 231	72.06 228	75.61 269	50.40 180	81.00 243	76.97 284	62.00 137	56.99 262	76.97 278	44.84 120	85.58 248	58.75 186	54.42 318	80.21 289

v144192		67.86 202	65.76 219	74.16 178	71.68 314	53.09 125	84.14 161	80.83 206	62.85 125	59.21 222	77.28 274	39.30 189	88.00 178	58.67 187	57.88 289	81.40 270

test1111		71.06 143	70.42 135	72.97 206	79.48 200	41.49 330	84.82 143	82.74 174	64.20 96	62.98 178	87.43 146	35.20 246	87.92 179	58.54 188	78.42 106	89.49 116

thisisatest0515		73.64 103	72.20 107	77.97 82	81.63 154	53.01 128	86.69 89	88.81 37	62.53 130	64.06 162	85.65 168	52.15 47	92.50 48	58.43 189	69.84 187	88.39 146

v8		67.25 219	64.99 235	74.04 181	72.89 303	53.31 119	82.37 212	80.11 217	61.54 146	54.29 287	76.02 296	42.89 148	88.41 161	58.43 189	56.36 297	80.39 287

XXY-MVS		70.18 156	69.28 156	72.89 209	77.64 235	42.88 317	85.06 131	87.50 68	62.58 129	62.66 183	82.34 224	43.64 137	89.83 115	58.42 191	63.70 235	85.96 195

3Dnovator	64.70 6	74.46 86	72.48 100	80.41 30	82.84 127	55.40 60	83.08 194	88.61 45	67.61 49	59.85 207	88.66 118	34.57 254	93.97 24	58.42 191	88.70 12	91.85 51

旧先验2							81.73 227		45.53 333	74.66 47			70.48 365	58.31 193

test_fmvs1		53.60 325	52.54 320	56.78 349	58.07 378	30.26 372	68.95 337	42.19 387	32.46 375	63.59 171	82.56 217	11.55 378	60.81 375	58.25 194	55.27 311	79.28 295

v1192		67.96 201	65.74 220	74.63 165	71.79 312	53.43 114	84.06 164	80.99 204	63.19 120	59.56 213	77.46 271	37.50 213	88.65 151	58.20 195	58.93 272	81.79 260

EPP-MVSNet		71.14 140	70.07 144	74.33 173	79.18 207	46.52 272	83.81 171	86.49 82	56.32 252	57.95 243	84.90 180	54.23 36	89.14 133	58.14 196	69.65 190	87.33 167

OMC-MVS		65.97 242	65.06 234	68.71 284	72.97 301	42.58 322	78.61 274	75.35 301	54.72 269	59.31 219	86.25 163	33.30 266	77.88 328	57.99 197	67.05 206	85.66 201

cl____		67.43 214	65.93 215	71.95 235	76.33 255	48.02 249	82.58 204	79.12 242	61.30 151	56.72 264	76.92 280	46.12 97	86.44 228	57.98 198	56.31 299	81.38 272

DIV-MVS_self_test		67.43 214	65.93 215	71.94 236	76.33 255	48.01 250	82.57 205	79.11 243	61.31 150	56.73 263	76.92 280	46.09 98	86.43 229	57.98 198	56.31 299	81.39 271

MS-PatchMatch		72.34 121	71.26 123	75.61 137	82.38 137	55.55 54	88.00 54	89.95 19	65.38 83	56.51 269	80.74 242	32.28 276	92.89 34	57.95 200	88.10 16	78.39 308

MAR-MVS		76.76 54	75.60 60	80.21 32	90.87 7	54.68 85	89.14 41	89.11 27	62.95 123	70.54 101	92.33 39	41.05 169	94.95 17	57.90 201	86.55 34	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
test_fmvs1_n		52.55 329	51.19 324	56.65 350	51.90 388	30.14 373	67.66 341	42.84 386	32.27 376	62.30 186	82.02 230	9.12 386	60.84 374	57.82 202	54.75 317	78.99 297

anonymousdsp		60.46 280	57.65 286	68.88 278	63.63 367	45.09 290	72.93 312	78.63 253	46.52 325	51.12 308	72.80 323	21.46 350	83.07 282	57.79 203	53.97 320	78.47 305

Anonymous20240529		69.71 168	67.28 188	77.00 105	83.78 96	50.36 184	88.87 45	85.10 119	47.22 320	64.03 163	83.37 200	27.93 304	92.10 59	57.78 204	67.44 204	88.53 142

Fast-Effi-MVS+-dtu		66.53 234	64.10 243	73.84 188	72.41 308	52.30 143	84.73 144	75.66 297	59.51 181	56.34 270	79.11 256	28.11 302	85.85 247	57.74 205	63.29 241	83.35 238

v1921920		67.45 213	65.23 232	74.10 180	71.51 317	52.90 131	83.75 173	80.44 211	62.48 133	59.12 223	77.13 275	36.98 223	87.90 180	57.53 206	58.14 283	81.49 265

IterMVS-LS		66.63 232	65.36 230	70.42 260	75.10 274	48.90 220	81.45 238	76.69 289	61.05 156	55.71 274	77.10 277	45.86 102	83.65 276	57.44 207	57.88 289	78.70 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 170	68.70 160	72.68 212	75.00 276	48.90 220	79.54 265	87.16 70	61.05 156	63.88 167	83.74 191	45.87 101	90.44 97	57.42 208	64.68 227	78.70 301

CDS-MVSNet		70.48 154	69.43 150	73.64 195	77.56 238	48.83 222	83.51 179	77.45 273	63.27 118	62.33 185	85.54 171	43.85 129	83.29 281	57.38 209	74.00 150	88.79 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 123	70.50 133	77.65 88	83.40 105	51.29 166	87.32 71	86.40 85	59.01 198	58.49 237	88.32 127	32.40 274	91.27 73	57.04 210	82.15 69	90.38 91

test_vis1_n		51.19 334	49.66 332	55.76 354	51.26 389	29.85 377	67.20 344	38.86 392	32.12 377	59.50 215	79.86 247	8.78 387	58.23 382	56.95 211	52.46 330	79.19 296

miper_lstm_enhance		63.91 251	62.30 250	68.75 283	75.06 275	46.78 268	69.02 335	81.14 200	59.68 179	52.76 298	72.39 328	40.71 175	77.99 326	56.81 212	53.09 328	81.48 267

ETVMVS		75.80 71	75.44 63	76.89 110	86.23 51	50.38 182	85.55 116	91.42 7	71.30 19	68.80 108	87.94 137	56.42 22	89.24 129	56.54 213	74.75 147	91.07 77

PAPM_NR		71.80 133	69.98 145	77.26 98	81.54 160	53.34 117	78.60 275	85.25 112	53.46 278	60.53 203	88.66 118	45.69 105	89.24 129	56.49 214	79.62 98	89.19 123

v10		66.61 233	64.20 242	73.83 189	72.59 306	53.37 115	81.88 221	79.91 223	61.11 154	54.09 289	75.60 298	40.06 183	88.26 171	56.47 215	56.10 303	79.86 293

v1240		66.99 227	64.68 237	73.93 184	71.38 320	52.66 134	83.39 186	79.98 219	61.97 139	58.44 240	77.11 276	35.25 245	87.81 182	56.46 216	58.15 281	81.33 273

Anonymous202405211		70.11 157	67.88 173	76.79 114	87.20 42	47.24 265	89.49 35	77.38 275	54.88 268	66.14 129	86.84 154	20.93 352	91.54 68	56.45 217	71.62 171	91.59 58

Fast-Effi-MVS+		72.73 114	71.15 126	77.48 91	82.75 129	54.76 79	86.77 88	80.64 208	63.05 122	65.93 133	84.01 186	44.42 126	89.03 136	56.45 217	76.36 126	88.64 137

sd_testset		67.79 205	65.95 214	73.32 200	81.70 150	46.33 277	68.99 336	80.30 214	66.58 58	61.64 193	82.38 221	30.45 291	87.63 193	55.86 219	65.60 220	86.01 191

114514_t		69.87 166	67.88 173	75.85 133	88.38 29	52.35 141	86.94 83	83.68 153	53.70 276	55.68 275	85.60 169	30.07 294	91.20 77	55.84 220	71.02 177	83.99 226

tpm2		70.82 148	68.44 163	77.98 81	80.78 180	56.11 46	74.21 303	81.28 199	60.24 171	68.04 113	75.27 300	52.26 46	88.50 159	55.82 221	68.03 199	89.33 118

PCF-MVS	61.03 10	70.10 158	68.40 164	75.22 156	77.15 247	51.99 147	79.30 270	82.12 181	56.47 250	61.88 191	86.48 162	43.98 128	87.24 204	55.37 222	72.79 162	86.43 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	62.49 8	69.27 177	67.81 177	73.64 195	84.41 82	51.85 151	84.63 149	77.80 266	66.42 62	59.80 208	84.95 179	22.14 347	80.44 304	55.03 223	75.11 142	88.62 138

CHOSEN 280x420		57.53 304	56.38 297	60.97 338	74.01 289	48.10 247	46.30 384	54.31 375	48.18 316	50.88 312	77.43 272	38.37 198	59.16 381	54.83 224	63.14 245	75.66 334

GG-mvs-BLEND				77.77 85	86.68 46	50.61 173	68.67 338	88.45 49		68.73 109	87.45 145	59.15 10	90.67 91	54.83 224	87.67 18	92.03 44

TAMVS		69.51 174	68.16 169	73.56 198	76.30 257	48.71 226	82.57 205	77.17 278	62.10 136	61.32 196	84.23 184	41.90 161	83.46 279	54.80 226	73.09 159	88.50 143

D2MVS		63.49 256	61.39 258	69.77 270	69.29 335	48.93 219	78.89 273	77.71 269	60.64 167	49.70 316	72.10 333	27.08 311	83.48 278	54.48 227	62.65 249	76.90 322

IterMVS		63.77 254	61.67 254	70.08 266	72.68 305	51.24 167	80.44 253	75.51 298	60.51 168	51.41 306	73.70 314	32.08 278	78.91 316	54.30 228	54.35 319	80.08 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		72.17 126	72.15 109	72.21 224	82.26 139	44.29 300	86.83 87	89.58 21	65.58 77	65.82 135	85.06 176	45.02 114	84.35 268	54.07 229	75.18 138	87.99 154

DP-MVS Recon		71.99 128	70.31 138	77.01 104	90.65 8	53.44 112	89.37 37	82.97 170	56.33 251	63.56 172	89.47 102	34.02 259	92.15 58	54.05 230	72.41 164	85.43 206

tpm		68.36 193	67.48 185	70.97 253	79.93 196	51.34 164	76.58 286	78.75 250	67.73 45	63.54 173	74.86 302	48.33 74	72.36 359	53.93 231	63.71 234	89.21 122

XVG-OURS-SEG-HR		62.02 271	59.54 275	69.46 273	65.30 356	45.88 282	65.06 349	73.57 317	46.45 326	57.42 258	83.35 201	26.95 312	78.09 322	53.77 232	64.03 231	84.42 218

FA-MVS(test-final)		69.00 181	66.60 200	76.19 124	83.48 101	47.96 253	74.73 296	82.07 182	57.27 233	62.18 187	78.47 261	36.09 237	92.89 34	53.76 233	71.32 175	87.73 159

cascas		69.01 180	66.13 209	77.66 87	79.36 201	55.41 59	86.99 81	83.75 152	56.69 245	58.92 227	81.35 236	24.31 332	92.10 59	53.23 234	70.61 181	85.46 205

UniMVSNet_NR-MVSNet		68.82 184	68.29 166	70.40 261	75.71 268	42.59 320	84.23 158	86.78 76	66.31 64	58.51 234	82.45 218	51.57 49	84.64 266	53.11 235	55.96 305	83.96 230

DU-MVS		66.84 231	65.74 220	70.16 264	73.27 297	42.59 320	81.50 235	82.92 172	63.53 112	58.51 234	82.11 227	40.75 173	84.64 266	53.11 235	55.96 305	83.24 242

1112_ss		70.05 160	69.37 152	72.10 226	80.77 181	42.78 318	85.12 130	76.75 285	59.69 178	61.19 197	92.12 42	47.48 83	83.84 272	53.04 237	68.21 197	89.66 111

XVG-OURS		61.88 272	59.34 277	69.49 272	65.37 355	46.27 278	64.80 350	73.49 318	47.04 322	57.41 259	82.85 206	25.15 325	78.18 320	53.00 238	64.98 222	84.01 225

thisisatest0530		70.47 155	68.56 161	76.20 123	79.78 197	51.52 160	83.49 181	88.58 47	57.62 226	58.60 233	82.79 207	51.03 54	91.48 69	52.84 239	62.36 253	85.59 204

UGNet		68.71 188	67.11 191	73.50 199	80.55 187	47.61 258	84.08 162	78.51 256	59.45 182	65.68 138	82.73 211	23.78 334	85.08 260	52.80 240	76.40 122	87.80 157
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
Anonymous20231211		66.08 241	63.67 244	73.31 201	83.07 115	48.75 224	86.01 102	84.67 132	45.27 334	56.54 267	76.67 285	28.06 303	88.95 142	52.78 241	59.95 261	82.23 255

无先验							85.19 125	78.00 264	49.08 309				85.13 259	52.78 241		87.45 166

PVSNet_0	57.04 13	61.19 276	57.24 289	73.02 204	77.45 240	50.31 187	79.43 269	77.36 276	63.96 103	47.51 331	72.45 327	25.03 326	83.78 274	52.76 243	19.22 399	84.96 211

FIs		70.00 162	70.24 142	69.30 275	77.93 233	38.55 344	83.99 166	87.72 62	66.86 56	57.66 250	84.17 185	52.28 45	85.31 253	52.72 244	68.80 194	84.02 224

Vis-MVSNet		70.61 152	69.34 153	74.42 170	80.95 175	48.49 232	86.03 101	77.51 272	58.74 204	65.55 139	87.78 139	34.37 256	85.95 245	52.53 245	80.61 80	88.80 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 298	77.59 237	45.46 288		69.20 349	44.47 339	71.50 86	88.34 126	31.21 286	70.76 364	52.20 246	75.88 130	85.03 209

API-MVS		74.17 91	72.07 112	80.49 26	90.02 11	58.55 9	87.30 73	84.27 140	57.51 228	65.77 137	87.77 140	41.61 165	95.97 11	51.71 247	82.63 63	86.94 172

GeoE		69.96 164	67.88 173	76.22 121	81.11 169	51.71 155	84.15 160	76.74 287	59.83 175	60.91 198	84.38 182	41.56 166	88.10 175	51.67 248	70.57 182	88.84 132

dmvs_re		67.61 208	66.00 212	72.42 219	81.86 145	43.45 309	64.67 351	80.00 218	69.56 31	60.07 205	85.00 178	34.71 252	87.63 193	51.48 249	66.68 208	86.17 190

ACMM	58.35 12	64.35 248	62.01 253	71.38 245	74.21 287	48.51 231	82.25 213	79.66 228	47.61 318	54.54 284	80.11 244	25.26 324	86.00 241	51.26 250	63.16 244	79.64 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 126	84.89 75	54.59 88		85.26 111	51.98 289	66.70 121	87.07 152	40.15 181	89.70 120	51.23 251	85.06 50	84.10 222

UniMVSNet (Re)		67.71 206	66.80 194	70.45 259	74.44 283	42.93 316	82.42 211	84.90 123	63.69 108	59.63 211	80.99 238	47.18 85	85.23 256	51.17 252	56.75 296	83.19 244

IterMVS-SCA-FT		59.12 288	58.81 282	60.08 340	70.68 328	45.07 291	80.42 254	74.25 308	43.54 346	50.02 315	73.73 311	31.97 279	56.74 384	51.06 253	53.60 324	78.42 307

Test_1112_low_res		67.18 221	66.23 207	70.02 269	78.75 216	41.02 334	83.43 182	73.69 315	57.29 232	58.45 239	82.39 220	45.30 110	80.88 295	50.50 254	66.26 218	88.16 147

pmmvs4		63.34 258	61.07 263	70.16 264	70.14 329	50.53 176	79.97 262	71.41 335	55.08 264	54.12 288	78.58 259	32.79 271	82.09 287	50.33 255	57.22 294	77.86 314

Baseline_NR-MVSNet		65.49 245	64.27 241	69.13 276	74.37 286	41.65 327	83.39 186	78.85 245	59.56 180	59.62 212	76.88 282	40.75 173	87.44 199	49.99 256	55.05 312	78.28 310

UniMVSNet_ETH3D		62.51 266	60.49 267	68.57 288	68.30 344	40.88 336	73.89 304	79.93 222	51.81 293	54.77 281	79.61 249	24.80 328	81.10 292	49.93 257	61.35 256	83.73 234

BH-w/o		70.02 161	68.51 162	74.56 166	82.77 128	50.39 181	86.60 91	78.14 262	59.77 176	59.65 210	85.57 170	39.27 190	87.30 203	49.86 258	74.94 146	85.99 193

LCM-MVSNet-Re		58.82 294	56.54 293	65.68 308	79.31 204	29.09 382	61.39 365	45.79 381	60.73 165	37.65 369	72.47 326	31.42 285	81.08 293	49.66 259	70.41 183	86.87 174

gg-mvs-nofinetune		67.43 214	64.53 239	76.13 126	85.95 52	47.79 257	64.38 352	88.28 51	39.34 356	66.62 123	41.27 391	58.69 13	89.00 138	49.64 260	86.62 33	91.59 58

TranMVSNet+NR-MVSNet		66.94 229	65.61 223	70.93 254	73.45 293	43.38 311	83.02 197	84.25 141	65.31 86	58.33 241	81.90 231	39.92 186	85.52 249	49.43 261	54.89 314	83.89 232

tttt0517		68.33 195	66.29 205	74.46 168	78.08 229	49.06 212	80.88 247	89.08 28	54.40 273	54.75 282	80.77 241	51.31 51	90.33 101	49.35 262	58.01 285	83.99 226

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

WR-MVS		67.58 209	66.76 195	70.04 268	75.92 266	45.06 294	86.23 96	85.28 110	64.31 94	58.50 236	81.00 237	44.80 123	82.00 288	49.21 264	55.57 310	83.06 247

tt0805		63.39 257	61.31 260	69.64 271	69.36 334	38.87 342	78.00 277	85.48 98	48.82 312	55.66 277	81.66 233	24.38 331	86.37 230	49.04 265	59.36 269	83.68 235

test_post1							70.84 328				14.72 409	34.33 257	83.86 271	48.80 266

SCA		63.84 252	60.01 273	75.32 148	78.58 222	57.92 14	61.61 363	77.53 271	56.71 244	57.75 249	70.77 339	31.97 279	79.91 312	48.80 266	56.36 297	88.13 150

pmmvs5		62.80 264	61.18 261	67.66 293	69.53 333	42.37 325	82.65 203	75.19 302	54.30 274	52.03 304	78.51 260	31.64 284	80.67 299	48.60 268	58.15 281	79.95 292

新几何1				73.30 202	83.10 112	53.48 108		71.43 334	45.55 332	66.14 129	87.17 150	33.88 262	80.54 302	48.50 269	80.33 86	85.88 198

pm-mvs1		64.12 250	62.56 248	68.78 282	71.68 314	38.87 342	82.89 199	81.57 192	55.54 260	53.89 291	77.82 266	37.73 205	86.74 218	48.46 270	53.49 325	80.72 282

PM-MVS		46.92 343	43.76 350	56.41 352	52.18 387	32.26 368	63.21 358	38.18 393	37.99 361	40.78 359	66.20 355	5.09 398	65.42 371	48.19 271	41.99 363	71.54 362

FC-MVSNet-test		67.49 212	67.91 171	66.21 306	76.06 261	33.06 364	80.82 248	87.18 69	64.44 93	54.81 280	82.87 205	50.40 62	82.60 283	48.05 272	66.55 212	82.98 249

CMPMVS	40.41 21	55.34 315	52.64 318	63.46 322	60.88 375	43.84 305	61.58 364	71.06 337	30.43 380	36.33 371	74.63 304	24.14 333	75.44 343	48.05 272	66.62 210	71.12 364
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 219	65.99 213	71.04 252	73.27 297	43.91 304	85.32 121	84.75 129	66.05 72	53.65 294	82.11 227	45.05 113	85.97 244	47.55 274	56.18 302	83.24 242

QAPM		71.88 131	69.33 154	79.52 42	82.20 140	54.30 93	86.30 95	88.77 38	56.61 247	59.72 209	87.48 144	33.90 261	95.36 13	47.48 275	81.49 74	88.90 129

EPMVS		68.45 192	65.44 228	77.47 92	84.91 74	56.17 45	71.89 324	81.91 187	61.72 143	60.85 199	72.49 325	36.21 235	87.06 209	47.32 276	71.62 171	89.17 124

GBi-Net		67.09 224	65.47 226	71.96 232	82.71 130	46.36 274	83.52 175	83.31 160	58.55 207	57.58 252	76.23 291	36.72 230	86.20 231	47.25 277	63.40 237	83.32 239

test1		67.09 224	65.47 226	71.96 232	82.71 130	46.36 274	83.52 175	83.31 160	58.55 207	57.58 252	76.23 291	36.72 230	86.20 231	47.25 277	63.40 237	83.32 239

FMVSNet3		68.84 183	67.40 186	73.19 203	85.05 71	48.53 230	85.71 111	85.36 104	60.90 162	57.58 252	79.15 255	42.16 155	86.77 217	47.25 277	63.40 237	84.27 220

v7n		62.50 267	59.27 278	72.20 225	67.25 349	49.83 197	77.87 279	80.12 216	52.50 286	48.80 322	73.07 319	32.10 277	87.90 180	46.83 280	54.92 313	78.86 299

WB-MVSnew		69.36 176	68.24 167	72.72 211	79.26 205	49.40 207	85.72 110	88.85 35	61.33 149	64.59 154	82.38 221	34.57 254	87.53 198	46.82 281	70.63 180	81.22 277

mamv4		42.60 349	44.05 349	38.26 375	59.21 377	38.00 347	44.14 387	39.03 391	25.03 386	40.61 361	68.39 349	37.01 222	24.28 409	46.62 282	36.43 372	52.50 387

CVMVSNet		60.85 278	60.44 268	62.07 328	75.00 276	32.73 366	79.54 265	73.49 318	36.98 364	56.28 271	83.74 191	29.28 298	69.53 367	46.48 283	63.23 242	83.94 231

TR-MVS		69.71 168	67.85 176	75.27 154	82.94 121	48.48 233	87.40 70	80.86 205	57.15 236	64.61 153	87.08 151	32.67 272	89.64 122	46.38 284	71.55 173	87.68 161

MDTV_nov1_ep13_2view						43.62 307	71.13 327		54.95 267	59.29 221		36.76 227		46.33 285		87.32 168

FMVSNet2		67.57 210	65.79 218	72.90 207	82.71 130	47.97 251	85.15 126	84.93 122	58.55 207	56.71 265	78.26 262	36.72 230	86.67 220	46.15 286	62.94 248	84.07 223

UnsupCasMVSNet_eth		57.56 303	55.15 303	64.79 317	64.57 363	33.12 363	73.17 311	83.87 151	58.98 199	41.75 353	70.03 343	22.54 342	79.92 310	46.12 287	35.31 375	81.32 275

testdata2													77.81 330	45.64 288

XVG-ACMP-BASELINE		56.03 312	52.85 316	65.58 309	61.91 372	40.95 335	63.36 355	72.43 324	45.20 335	46.02 338	74.09 307	9.20 385	78.12 321	45.13 289	58.27 279	77.66 317

AdaColmap		67.86 202	65.48 225	75.00 160	88.15 36	54.99 74	86.10 99	76.63 290	49.30 308	57.80 246	86.65 159	29.39 297	88.94 144	45.10 290	70.21 185	81.06 278

BH-untuned		68.28 196	66.40 202	73.91 185	81.62 155	50.01 192	85.56 115	77.39 274	57.63 225	57.47 257	83.69 193	36.36 234	87.08 208	44.81 291	73.08 160	84.65 215

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

BH-RMVSNet		70.08 159	68.01 170	76.27 119	84.21 88	51.22 168	87.29 74	79.33 240	58.96 200	63.63 170	86.77 155	33.29 267	90.30 104	44.63 293	73.96 151	87.30 169

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

IS-MVSNet		68.80 186	67.55 182	72.54 215	78.50 224	43.43 310	81.03 242	79.35 238	59.12 196	57.27 260	86.71 156	46.05 99	87.70 190	44.32 295	75.60 134	86.49 184

pmmvs-eth3d		55.97 313	52.78 317	65.54 310	61.02 374	46.44 273	75.36 293	67.72 354	49.61 307	43.65 344	67.58 352	21.63 349	77.04 333	44.11 296	44.33 358	73.15 355

pmmvs6		59.64 283	57.15 290	67.09 297	66.01 351	36.86 352	80.50 251	78.64 252	45.05 336	49.05 320	73.94 309	27.28 309	86.10 237	43.96 297	49.94 337	78.31 309

EPNet_dtu		66.25 238	66.71 196	64.87 316	78.66 220	34.12 359	82.80 200	75.51 298	61.75 142	64.47 159	86.90 153	37.06 221	72.46 358	43.65 298	69.63 191	88.02 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 237	62.78 247	76.77 115	81.40 164	57.14 26	70.03 331	77.19 277	53.00 282	58.76 232	70.73 341	46.17 96	86.73 219	43.27 299	64.46 228	86.44 185

OpenMVS	61.00 11	69.99 163	67.55 182	77.30 95	78.37 227	54.07 100	84.36 154	85.76 96	57.22 234	56.71 265	87.67 142	30.79 289	92.83 36	43.04 300	84.06 58	85.01 210

PatchmatchNet		67.07 226	63.63 245	77.40 93	83.10 112	58.03 13	72.11 322	77.77 267	58.85 201	59.37 217	70.83 338	37.84 201	84.93 262	42.96 301	69.83 188	89.26 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 268	59.04 280	72.77 210	73.97 291	56.57 36	60.52 366	71.72 330	60.04 172	57.49 255	65.86 356	38.94 192	80.31 305	42.86 302	59.93 262	81.42 268

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

FMVSNet1		64.57 246	62.11 252	71.96 232	77.32 241	46.36 274	83.52 175	83.31 160	52.43 287	54.42 285	76.23 291	27.80 306	86.20 231	42.59 304	61.34 257	83.32 239

UA-Net		67.32 218	66.23 207	70.59 257	78.85 214	41.23 333	73.60 306	75.45 300	61.54 146	66.61 124	84.53 181	38.73 195	86.57 226	42.48 305	74.24 149	83.98 228

CL-MVSNet_self_test		62.98 261	61.14 262	68.50 289	65.86 353	42.96 315	84.37 153	82.98 169	60.98 158	53.95 290	72.70 324	40.43 177	83.71 275	41.10 306	47.93 342	78.83 300

MIMVSNet		63.12 260	60.29 270	71.61 240	75.92 266	46.65 270	65.15 348	81.94 184	59.14 195	54.65 283	69.47 345	25.74 320	80.63 300	41.03 307	69.56 192	87.55 163

FE-MVS		64.15 249	60.43 269	75.30 151	80.85 177	49.86 196	68.28 340	78.37 259	50.26 304	59.31 219	73.79 310	26.19 317	91.92 62	40.19 308	66.67 209	84.12 221

EG-PatchMatch MVS		62.40 270	59.59 274	70.81 255	73.29 295	49.05 213	85.81 103	84.78 127	51.85 292	44.19 341	73.48 317	15.52 374	89.85 114	40.16 309	67.24 205	73.54 351

UnsupCasMVSNet_bld		53.86 322	50.53 326	63.84 319	63.52 368	34.75 355	71.38 325	81.92 186	46.53 324	38.95 365	57.93 377	20.55 353	80.20 308	39.91 310	34.09 382	76.57 328

dp		64.41 247	61.58 255	72.90 207	82.40 136	54.09 99	72.53 314	76.59 291	60.39 169	55.68 275	70.39 342	35.18 247	76.90 337	39.34 311	61.71 255	87.73 159

TransMVSNet (Re)		62.82 263	60.76 265	69.02 277	73.98 290	41.61 328	86.36 93	79.30 241	56.90 238	52.53 299	76.44 287	41.85 162	87.60 196	38.83 312	40.61 366	77.86 314

USDC		54.36 319	51.23 323	63.76 320	64.29 364	37.71 349	62.84 360	73.48 320	56.85 239	35.47 374	71.94 334	9.23 384	78.43 318	38.43 313	48.57 339	75.13 339

PLC	52.38 18	60.89 277	58.97 281	66.68 304	81.77 147	45.70 286	78.96 272	74.04 312	43.66 345	47.63 328	83.19 204	23.52 337	77.78 331	37.47 314	60.46 260	76.55 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 265	62.44 249	62.86 327	72.28 311	29.51 379	82.93 198	78.78 248	59.18 193	53.07 297	82.41 219	36.91 225	77.39 332	37.45 315	58.96 271	81.66 263

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

CNLPA		60.59 279	58.44 283	67.05 299	79.21 206	47.26 264	79.75 264	64.34 362	42.46 351	51.90 305	83.94 187	27.79 307	75.41 344	37.12 317	59.49 267	78.47 305

K. test v3		54.04 321	49.42 333	67.92 292	68.55 340	42.57 323	75.51 291	63.07 365	52.07 288	39.21 363	64.59 360	19.34 357	82.21 284	37.11 318	25.31 391	78.97 298

Vis-MVSNet (Re-imp)		65.52 244	65.63 222	65.17 314	77.49 239	30.54 371	75.49 292	77.73 268	59.34 186	52.26 303	86.69 157	49.38 71	80.53 303	37.07 319	75.28 137	84.42 218

PatchMatch-RL		56.66 306	53.75 311	65.37 313	77.91 234	45.28 289	69.78 333	60.38 368	41.35 352	47.57 329	73.73 311	16.83 368	76.91 335	36.99 320	59.21 270	73.92 348

Patchmtry		56.56 308	52.95 315	67.42 295	72.53 307	50.59 175	59.05 370	71.72 330	37.86 362	46.92 333	65.86 356	38.94 192	80.06 309	36.94 321	46.72 352	71.60 361

FMVSNet5		58.61 296	56.45 294	65.10 315	77.20 246	39.74 338	74.77 295	77.12 279	50.27 303	43.28 347	67.71 351	26.15 318	76.90 337	36.78 322	54.78 315	78.65 303

MDTV_nov1_ep13			61.56 256		81.68 152	55.12 69	72.41 316	78.18 261	59.19 191	58.85 230	69.29 346	34.69 253	86.16 234	36.76 323	62.96 247

JIA-IIPM		52.33 331	47.77 339	66.03 307	71.20 321	46.92 267	40.00 393	76.48 292	37.10 363	46.73 334	37.02 393	32.96 268	77.88 328	35.97 324	52.45 331	73.29 353

lessismore_v0				67.98 291	64.76 362	41.25 332		45.75 382		36.03 373	65.63 358	19.29 358	84.11 270	35.67 325	21.24 397	78.59 304

CP-MVSNet		58.54 299	57.57 288	61.46 335	68.50 341	33.96 360	76.90 284	78.60 255	51.67 294	47.83 326	76.60 286	34.99 251	72.79 356	35.45 326	47.58 344	77.64 318

Anonymous20240521		51.65 332	48.42 335	61.34 337	56.43 382	39.65 340	73.57 307	73.47 321	36.64 366	36.59 370	63.98 361	10.75 381	72.25 360	35.35 327	49.01 338	72.11 358

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

KD-MVS_2432*1600		59.04 291	56.44 295	66.86 300	79.07 208	45.87 283	72.13 320	80.42 212	55.03 265	48.15 324	71.01 336	36.73 228	78.05 324	35.21 329	30.18 386	76.67 324

miper_refine_blended		59.04 291	56.44 295	66.86 300	79.07 208	45.87 283	72.13 320	80.42 212	55.03 265	48.15 324	71.01 336	36.73 228	78.05 324	35.21 329	30.18 386	76.67 324

PS-CasMVS		58.12 301	57.03 292	61.37 336	68.24 345	33.80 362	76.73 285	78.01 263	51.20 296	47.54 330	76.20 294	32.85 269	72.76 357	35.17 331	47.37 346	77.55 319

EU-MVSNet		52.63 328	50.72 325	58.37 346	62.69 371	28.13 385	72.60 313	75.97 295	30.94 379	40.76 360	72.11 332	20.16 354	70.80 363	35.11 332	46.11 354	76.19 332

ACMH+	54.58 15	58.55 298	55.24 302	68.50 289	74.68 280	45.80 285	80.27 256	70.21 342	47.15 321	42.77 349	75.48 299	16.73 370	85.98 242	35.10 333	54.78 315	73.72 349

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

our_test_3		59.11 289	55.08 305	71.18 250	71.42 318	53.29 120	81.96 218	74.52 306	48.32 313	42.08 350	69.28 347	28.14 301	82.15 285	34.35 335	45.68 356	78.11 313

PEN-MVS		58.35 300	57.15 290	61.94 331	67.55 348	34.39 356	77.01 282	78.35 260	51.87 291	47.72 327	76.73 284	33.91 260	73.75 351	34.03 336	47.17 348	77.68 316

KD-MVS_self_test		49.24 338	46.85 341	56.44 351	54.32 383	22.87 391	57.39 373	73.36 322	44.36 341	37.98 368	59.30 375	18.97 359	71.17 362	33.48 337	42.44 362	75.26 337

tpmvs		62.45 269	59.42 276	71.53 244	83.93 92	54.32 92	70.03 331	77.61 270	51.91 290	53.48 295	68.29 350	37.91 200	86.66 221	33.36 338	58.27 279	73.62 350

YYNet1		53.82 323	49.96 329	65.41 312	70.09 331	48.95 217	72.30 317	71.66 332	44.25 342	31.89 384	63.07 364	23.73 335	73.95 349	33.26 339	39.40 368	73.34 352

MDA-MVSNet_test_wron		53.82 323	49.95 330	65.43 311	70.13 330	49.05 213	72.30 317	71.65 333	44.23 343	31.85 385	63.13 363	23.68 336	74.01 348	33.25 340	39.35 369	73.23 354

Anonymous20231206		59.08 290	57.59 287	63.55 321	68.77 339	32.14 369	80.26 257	79.78 225	50.00 305	49.39 318	72.39 328	26.64 314	78.36 319	33.12 341	57.94 286	80.14 290

F-COLMAP		55.96 314	53.65 312	62.87 326	72.76 304	42.77 319	74.70 298	70.37 341	40.03 354	41.11 358	79.36 251	17.77 365	73.70 352	32.80 342	53.96 321	72.15 357

PatchT		56.60 307	52.97 314	67.48 294	72.94 302	46.16 281	57.30 374	73.78 314	38.77 358	54.37 286	57.26 379	37.52 211	78.06 323	32.02 343	52.79 329	78.23 312

SixPastTwentyTwo		54.37 318	50.10 327	67.21 296	70.70 326	41.46 331	74.73 296	64.69 359	47.56 319	39.12 364	69.49 344	18.49 363	84.69 265	31.87 344	34.20 381	75.48 335

WR-MVS_H		58.91 293	58.04 285	61.54 334	69.07 337	33.83 361	76.91 283	81.99 183	51.40 295	48.17 323	74.67 303	40.23 179	74.15 347	31.78 345	48.10 340	76.64 327

ACMH	53.70 16	59.78 282	55.94 300	71.28 246	76.59 252	48.35 237	80.15 260	76.11 294	49.74 306	41.91 352	73.45 318	16.50 371	90.31 102	31.42 346	57.63 292	75.17 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 284	55.14 304	72.32 223	74.69 279	50.71 171	74.39 302	73.58 316	44.44 340	43.40 346	77.52 269	19.45 356	90.87 88	31.31 347	57.49 293	75.38 336

thres200		68.71 188	67.27 189	73.02 204	84.73 76	46.76 269	85.03 133	87.73 61	62.34 134	59.87 206	83.45 197	43.15 144	88.32 167	31.25 348	67.91 201	83.98 228

DTE-MVSNet		57.03 305	55.73 301	60.95 339	65.94 352	32.57 367	75.71 287	77.09 280	51.16 297	46.65 336	76.34 289	32.84 270	73.22 355	30.94 349	44.87 357	77.06 321

ppachtmachnet_test		58.56 297	54.34 306	71.24 247	71.42 318	54.74 80	81.84 223	72.27 325	49.02 310	45.86 340	68.99 348	26.27 315	83.30 280	30.12 350	43.23 361	75.69 333

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

MVS-HIRNet		49.01 339	44.71 343	61.92 332	76.06 261	46.61 271	63.23 357	54.90 374	24.77 387	33.56 379	36.60 395	21.28 351	75.88 342	29.49 352	62.54 250	63.26 380

test20.03		55.22 316	54.07 309	58.68 345	63.14 369	25.00 388	77.69 280	74.78 305	52.64 284	43.43 345	72.39 328	26.21 316	74.76 346	29.31 353	47.05 350	76.28 331

testgi		54.25 320	52.57 319	59.29 343	62.76 370	21.65 396	72.21 319	70.47 340	53.25 281	41.94 351	77.33 273	14.28 375	77.95 327	29.18 354	51.72 333	78.28 310

thres100view900		66.87 230	65.42 229	71.24 247	83.29 108	43.15 314	81.67 228	87.78 58	59.04 197	55.92 273	82.18 226	43.73 133	87.80 184	28.80 355	66.36 214	82.78 252

tfpn200view9		67.57 210	66.13 209	71.89 239	84.05 90	45.07 291	83.40 184	87.71 63	60.79 163	57.79 247	82.76 208	43.53 138	87.80 184	28.80 355	66.36 214	82.78 252

thres400		67.40 217	66.13 209	71.19 249	84.05 90	45.07 291	83.40 184	87.71 63	60.79 163	57.79 247	82.76 208	43.53 138	87.80 184	28.80 355	66.36 214	80.71 283

ADS-MVSNet2		55.21 317	51.44 322	66.51 305	80.60 185	49.56 202	55.03 377	65.44 357	44.72 337	51.00 309	61.19 368	22.83 339	75.41 344	28.54 358	53.63 322	74.57 344

ADS-MVSNet		56.17 311	51.95 321	68.84 279	80.60 185	53.07 126	55.03 377	70.02 344	44.72 337	51.00 309	61.19 368	22.83 339	78.88 317	28.54 358	53.63 322	74.57 344

LTVRE_ROB	45.45 19	52.73 327	49.74 331	61.69 333	69.78 332	34.99 354	44.52 385	67.60 355	43.11 348	43.79 343	74.03 308	18.54 362	81.45 290	28.39 360	57.94 286	68.62 368
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_vis3_rt		24.79 369	22.95 372	30.31 385	28.59 409	18.92 400	37.43 395	17.27 413	12.90 398	21.28 396	29.92 402	1.02 412	36.35 401	28.28 361	29.82 388	35.65 396

new-patchmatchnet		48.21 340	46.55 342	53.18 357	57.73 380	18.19 404	70.24 329	71.02 338	45.70 331	33.70 378	60.23 371	18.00 364	69.86 366	27.97 362	34.35 379	71.49 363

OpenMVS_ROB	53.19 17	59.20 287	56.00 299	68.83 280	71.13 322	44.30 299	83.64 174	75.02 303	46.42 327	46.48 337	73.03 320	18.69 360	88.14 172	27.74 363	61.80 254	74.05 347

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

MDA-MVSNet-bldmvs		51.56 333	47.75 340	63.00 325	71.60 316	47.32 263	69.70 334	72.12 326	43.81 344	27.65 392	63.38 362	21.97 348	75.96 341	27.30 365	32.19 383	65.70 376

RPMNet		59.29 285	54.25 308	74.42 170	73.97 291	56.57 36	60.52 366	76.98 281	35.72 368	57.49 255	58.87 376	37.73 205	85.26 255	27.01 366	59.93 262	81.42 268

thres600view7		66.46 235	65.12 233	70.47 258	83.41 102	43.80 306	82.15 214	87.78 58	59.37 185	56.02 272	82.21 225	43.73 133	86.90 215	26.51 367	64.94 223	80.71 283

TAPA-MVS	56.12 14	61.82 273	60.18 272	66.71 302	78.48 225	37.97 348	75.19 294	76.41 293	46.82 323	57.04 261	86.52 161	27.67 308	77.03 334	26.50 368	67.02 207	85.14 208
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 358	58.03 379	31.94 370		53.57 378	36.67 365	41.32 356	75.23 301	11.17 380	51.57 389	25.81 369	48.04 341	72.02 359

Patchmatch-test		53.33 326	48.17 336	68.81 281	73.31 294	42.38 324	42.98 388	58.23 370	32.53 374	38.79 366	70.77 339	39.66 187	73.51 353	25.18 370	52.06 332	90.55 86

test_f		27.12 365	24.85 366	33.93 381	26.17 413	15.25 407	30.24 401	22.38 410	12.53 400	28.23 389	49.43 388	2.59 404	34.34 406	25.12 371	26.99 389	52.20 388

TinyColmap		48.15 341	44.49 345	59.13 344	65.73 354	38.04 346	63.34 356	62.86 366	38.78 357	29.48 387	67.23 354	6.46 395	73.30 354	24.59 372	41.90 364	66.04 374

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

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

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

dmvs_testset		57.65 302	58.21 284	55.97 353	74.62 281	9.82 412	63.75 354	63.34 364	67.23 51	48.89 321	83.68 195	39.12 191	76.14 340	23.43 376	59.80 264	81.96 258

myMVS_eth3d		63.52 255	63.56 246	63.40 323	81.73 148	34.28 357	80.97 244	81.02 202	60.93 160	55.06 278	82.64 213	48.00 80	80.81 297	23.42 377	58.32 277	75.10 340

WAC-MVS						34.28 357								22.56 378

DP-MVS		59.24 286	56.12 298	68.63 285	88.24 34	50.35 185	82.51 208	64.43 361	41.10 353	46.70 335	78.77 258	24.75 329	88.57 157	22.26 379	56.29 301	66.96 371

MIMVSNet1		50.35 336	47.81 338	57.96 347	61.53 373	27.80 386	67.40 342	74.06 311	43.25 347	33.31 383	65.38 359	16.03 372	71.34 361	21.80 380	47.55 345	74.75 342

tfpnnormal		61.47 275	59.09 279	68.62 286	76.29 258	41.69 326	81.14 241	85.16 116	54.48 272	51.32 307	73.63 315	32.32 275	86.89 216	21.78 381	55.71 309	77.29 320

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

COLMAP_ROB	43.60 20	50.90 335	48.05 337	59.47 341	67.81 347	40.57 337	71.25 326	62.72 367	36.49 367	36.19 372	73.51 316	13.48 376	73.92 350	20.71 383	50.26 336	63.92 378
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 362	23.85 370	40.71 371	27.46 412	18.93 399	30.82 400	46.19 380	12.76 399	16.40 397	34.70 398	1.90 408	48.69 393	20.25 384	24.22 393	54.51 385

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

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

LS3D		56.40 310	53.82 310	64.12 318	81.12 168	45.69 287	73.42 309	66.14 356	35.30 372	43.24 348	79.88 246	22.18 346	79.62 314	19.10 387	64.00 232	67.05 370

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

kuosan		50.20 337	50.09 328	50.52 361	73.09 299	29.09 382	65.25 347	74.89 304	48.27 314	41.34 355	60.85 370	43.45 141	67.48 369	18.59 389	25.07 392	55.01 384

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

testing3		59.97 281	60.19 271	59.32 342	77.60 236	30.01 376	81.75 226	81.79 189	53.54 277	50.34 314	79.94 245	48.99 73	76.91 335	17.19 391	50.59 335	71.03 365

test_0402		56.45 309	53.03 313	66.69 303	76.78 251	50.31 187	81.76 225	69.61 347	42.79 349	43.88 342	72.13 331	22.82 341	86.46 227	16.57 392	50.94 334	63.31 379

Syy-MVS		61.51 274	61.35 259	62.00 330	81.73 148	30.09 374	80.97 244	81.02 202	60.93 160	55.06 278	82.64 213	35.09 248	80.81 297	16.40 393	58.32 277	75.10 340

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

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

tmp_tt		9.44 377	10.68 380	5.73 393	2.49 416	4.21 417	10.48 406	18.04 412	0.34 410	12.59 402	20.49 404	11.39 379	7.03 412	13.84 396	6.46 409	5.95 407

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

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

dongtai		43.51 347	44.07 348	41.82 370	63.75 366	21.90 394	63.80 353	72.05 327	39.59 355	33.35 382	54.54 381	41.04 170	57.30 383	10.75 399	17.77 400	46.26 393

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

Gipuma		27.47 364	24.26 369	37.12 378	60.55 376	29.17 381	11.68 405	60.00 369	14.18 397	10.52 406	15.12 407	2.20 407	63.01 373	8.39 401	35.65 374	19.18 403
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		21.11 371	19.08 375	27.18 387	30.56 405	18.28 402	33.43 398	24.48 407	8.02 405	12.02 403	33.50 399	0.75 414	35.09 404	7.68 402	21.32 395	28.17 400

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

MVE	16.60 23	17.34 376	13.39 379	29.16 386	28.43 410	19.72 398	13.73 404	23.63 409	7.23 407	7.96 407	21.41 403	0.80 413	36.08 402	6.97 404	10.39 404	31.69 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 391	21.34 415	8.99 413		10.02 415	10.59 403	7.53 408	30.55 401	1.82 409	14.55 410	6.83 405	7.52 406	15.75 404

WB-MVS		37.41 355	36.37 356	40.54 373	54.23 384	10.43 411	65.29 346	43.75 384	34.86 373	27.81 391	54.63 380	24.94 327	63.21 372	6.81 406	15.00 401	47.98 392

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

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

PMVS	19.57 22	25.07 368	22.43 373	32.99 383	23.12 414	22.98 390	40.98 391	35.19 398	15.99 396	11.95 405	35.87 397	1.47 411	49.29 391	5.41 409	31.90 384	26.70 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 374	17.66 378	20.71 390	34.13 404	12.64 410	46.94 383	29.94 403	10.46 404	5.58 410	14.93 408	4.23 400	38.83 400	5.24 410	7.51 407	10.67 406

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

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

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

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

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

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

cdsmvs_eth3d_5k		18.33 375	24.44 367	0.00 396	0.00 418	0.00 420	0.00 407	89.40 23	0.00 412	0.00 415	92.02 46	38.55 196	0.00 413	0.00 414	0.00 411	0.00 411

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

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

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

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

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

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

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

FOURS1					83.24 109	49.90 195	84.98 135	78.76 249	47.71 317	73.42 60

test_one_0601					89.39 22	57.29 24		88.09 53	57.21 235	82.06 13	93.39 18	54.94 33

eth-test2					0.00 418

eth-test					0.00 418

test_241102_ONE					89.48 17	56.89 31		88.94 30	57.53 227	84.61 4	93.29 22	58.81 11	96.45 1

save fliter					85.35 66	56.34 43	89.31 39	81.46 194	61.55 145

test0726					89.40 20	57.45 21	92.32 7	88.63 43	57.71 223	83.14 9	93.96 6	55.17 29

GSMVS																88.13 150

test_part2					89.33 23	55.48 56				82.27 12

sam_mvs1												38.86 194				88.13 150

sam_mvs												35.99 241

MTGPA								81.31 197

test_post											16.22 406	37.52 211	84.72 264

patchmatchnet-post											59.74 373	38.41 197	79.91 312

MTMP							87.27 75	15.34 414

TEST9					85.68 56	55.42 57	87.59 65	84.00 147	57.72 222	72.99 65	90.98 67	44.87 119	88.58 154

test_8					85.72 55	55.31 62	87.60 64	83.88 150	57.84 220	72.84 69	90.99 66	44.99 115	88.34 165

agg_prior					85.64 59	54.92 76		83.61 157		72.53 74			88.10 175

test_prior4						56.39 42	87.15 79

test_prior				78.39 74	86.35 50	54.91 77		85.45 101					89.70 120			90.55 86

新几何2							81.61 231

旧先验1					81.57 159	47.48 259		71.83 328			88.66 118	36.94 224			78.34 107	88.67 136

原ACMM2							83.77 172

test222					79.36 201	50.97 169	77.99 278	67.84 353	42.54 350	62.84 180	86.53 160	30.26 292			76.91 117	85.23 207

segment_acmp												44.97 117

testdata1							77.55 281		64.14 98

test12				79.24 46	86.89 44	56.08 47		85.16 116		72.27 78		47.15 86	91.10 81		85.93 39	90.54 88

plane_prior7					77.95 231	48.46 234

plane_prior6					78.42 226	49.39 208						36.04 239

plane_prior4											83.28 202

plane_prior3						48.95 217			64.01 101	62.15 188

plane_prior2							85.76 105		63.60 110

plane_prior1					78.31 228

plane_prior						49.57 200	87.43 68		64.57 92						72.84 161

n2								0.00 418

nn								0.00 418

door-mid								41.31 389

test11								84.25 141

door								43.27 385

HQP5-MVS						51.56 158

HQP-NCC					79.02 210		88.00 54		65.45 79	64.48 156

ACMP_Plane					79.02 210		88.00 54		65.45 79	64.48 156

HQP4-MVS										64.47 159			88.61 153			84.91 212

HQP3-MVS								83.68 153							73.12 157

HQP2-MVS												37.35 214

NP-MVS					78.76 215	50.43 179					85.12 175

ACMMP++_ref															63.20 243

ACMMP++															59.38 268

Test By Simon												39.38 188

High-resolution multi-view benchmark