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.

IU-MVS					99.03 15	85.34 53		96.86 51	92.05 27	98.74 1				98.15 11	98.97 17	99.42 13

PC_three_1452									91.12 35	98.33 2	98.42 28	92.51 2	99.81 21	98.96 3	99.37 1	99.70 3

fmvsm_l_conf0.5_n		94.89 16	95.24 16	93.86 48	94.42 160	84.61 74	99.13 10	96.15 133	92.06 25	97.92 3	98.52 23	84.52 37	99.74 38	98.76 5	95.67 110	97.22 140

SMA-MVS		94.70 21	94.68 21	94.76 26	98.02 59	85.94 40	97.47 96	96.77 61	85.32 137	97.92 3	98.70 15	83.09 52	99.84 12	95.79 42	99.08 10	98.49 51
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
fmvsm_l_conf0.5_n_a		94.91 15	95.30 15	93.72 56	94.50 158	84.30 80	99.14 9	96.00 144	91.94 28	97.91 5	98.60 18	84.78 35	99.77 29	98.84 4	96.03 104	97.08 148

SED-MVS		95.88 5	96.22 4	94.87 23	99.03 15	85.03 66	99.12 11	96.78 55	88.72 66	97.79 6	98.91 2	88.48 17	99.82 18	98.15 11	98.97 17	99.74 1

test_241102_ONE					99.03 15	85.03 66		96.78 55	88.72 66	97.79 6	98.90 5	88.48 17	99.82 18

DVP-MVS++		96.05 4	96.41 3	94.96 22	99.05 9	85.34 53	98.13 49	96.77 61	88.38 73	97.70 8	98.77 10	92.06 3	99.84 12	97.47 24	99.37 1	99.70 3

test_241102_TWO								96.78 55	88.72 66	97.70 8	98.91 2	87.86 21	99.82 18	98.15 11	99.00 15	99.47 9

patch_mono-2		95.14 13	96.08 7	92.33 115	98.44 43	77.84 240	98.43 36	97.21 23	92.58 19	97.68 10	97.65 76	86.88 26	99.83 16	98.25 9	97.60 67	99.33 17

test0726					99.05 9	85.18 58	99.11 14	96.78 55	88.75 64	97.65 11	98.91 2	87.69 22

TSAR-MVS + MP.		94.79 20	95.17 18	93.64 59	97.66 69	84.10 83	95.85 212	96.42 107	91.26 33	97.49 12	96.80 116	86.50 28	98.49 131	95.54 47	99.03 13	98.33 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvsm_n_1920		94.81 19	95.60 11	92.45 108	95.29 129	80.96 149	99.29 2	97.21 23	94.50 7	97.29 13	98.44 27	82.15 57	99.78 28	98.56 7	97.68 65	96.61 166

MSP-MVS		95.62 8	96.54 1	92.86 92	98.31 48	80.10 174	97.42 103	96.78 55	92.20 22	97.11 14	98.29 33	93.46 1	99.10 101	96.01 38	99.30 5	99.38 14
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
fmvsm_s_conf0.5_n_a		93.34 41	93.71 35	92.22 122	93.38 193	81.71 133	98.86 24	96.98 38	91.64 29	96.85 15	98.55 19	75.58 146	99.77 29	97.88 19	93.68 133	95.18 204

CNVR-MVS		96.30 1	96.54 1	95.55 15	99.31 5	87.69 22	99.06 16	97.12 30	94.66 5	96.79 16	98.78 9	86.42 29	99.95 3	97.59 23	99.18 7	99.00 27

DVP-MVS		95.58 9	95.91 9	94.57 30	99.05 9	85.18 58	99.06 16	96.46 102	88.75 64	96.69 17	98.76 12	87.69 22	99.76 31	97.90 17	98.85 21	98.77 34
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_THIRD									88.38 73	96.69 17	98.76 12	89.64 13	99.76 31	97.47 24	98.84 23	99.38 14

SD-MVS		94.84 18	95.02 19	94.29 36	97.87 64	84.61 74	97.76 74	96.19 131	89.59 56	96.66 19	98.17 41	84.33 39	99.60 59	96.09 37	98.50 36	98.66 42
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
MM		95.85 6	95.74 10	96.15 8	96.34 96	89.50 9	99.18 5	98.10 8	95.68 1	96.64 20	97.92 58	80.72 66	99.80 25	99.16 1	97.96 56	99.15 24

fmvsm_s_conf0.1_n_a		92.38 66	92.49 58	92.06 130	88.08 308	81.62 136	97.97 61	96.01 143	90.62 41	96.58 21	98.33 32	74.09 176	99.71 45	97.23 27	93.46 138	94.86 209

test_one_0601					98.91 18	84.56 76		96.70 71	88.06 79	96.57 22	98.77 10	88.04 20

DPE-MVS		95.32 11	95.55 12	94.64 29	98.79 23	84.87 71	97.77 72	96.74 66	86.11 120	96.54 23	98.89 6	88.39 19	99.74 38	97.67 22	99.05 12	99.31 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		96.21 2	95.53 13	98.26 1	96.26 99	95.09 1	99.15 7	96.98 38	93.39 14	96.45 24	98.79 8	90.17 10	99.99 1	89.33 124	99.25 6	99.70 3

fmvsm_s_conf0.5_n		93.69 36	94.13 32	92.34 113	94.56 151	82.01 118	99.07 15	97.13 28	92.09 23	96.25 25	98.53 22	76.47 128	99.80 25	98.39 8	94.71 119	95.22 203

PS-MVSNAJ		94.17 29	93.52 40	96.10 9	95.65 119	92.35 2	98.21 44	95.79 158	92.42 21	96.24 26	98.18 38	71.04 210	99.17 95	96.77 33	97.39 75	96.79 159

旧先验2							96.97 141		74.06 326	96.10 27			97.76 163	88.38 136

test_part2					98.90 19	85.14 64				96.07 28

fmvsm_s_conf0.1_n		92.93 48	93.16 47	92.24 120	90.52 272	81.92 122	98.42 37	96.24 125	91.17 34	96.02 29	98.35 31	75.34 157	99.74 38	97.84 20	94.58 121	95.05 205

xiu_mvs_v2_base		93.92 34	93.26 44	95.91 10	95.07 137	92.02 6	98.19 45	95.68 164	92.06 25	96.01 30	98.14 42	70.83 213	98.96 109	96.74 35	96.57 94	96.76 162

HPM-MVS++		95.32 11	95.48 14	94.85 24	98.62 34	86.04 36	97.81 70	96.93 44	92.45 20	95.69 31	98.50 24	85.38 31	99.85 10	94.75 54	99.18 7	98.65 43

NCCC		95.63 7	95.94 8	94.69 28	99.21 6	85.15 63	99.16 6	96.96 41	94.11 9	95.59 32	98.64 17	85.07 33	99.91 4	95.61 45	99.10 9	99.00 27

EPNet		94.06 32	94.15 31	93.76 52	97.27 87	84.35 78	98.29 41	97.64 15	94.57 6	95.36 33	96.88 111	79.96 77	99.12 100	91.30 93	96.11 101	97.82 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		94.89 16	94.64 22	95.63 13	97.55 75	88.12 16	99.06 16	96.39 112	94.07 10	95.34 34	97.80 67	76.83 123	99.87 8	97.08 30	97.64 66	98.89 30

test_fmvsmconf_n		93.99 33	94.36 28	92.86 92	92.82 210	81.12 143	99.26 3	96.37 116	93.47 13	95.16 35	98.21 36	79.00 86	99.64 55	98.21 10	96.73 92	97.83 97

TEST9					98.64 31	83.71 89	97.82 68	96.65 78	84.29 170	95.16 35	98.09 45	84.39 38	99.36 81

train_agg		94.28 26	94.45 25	93.74 53	98.64 31	83.71 89	97.82 68	96.65 78	84.50 161	95.16 35	98.09 45	84.33 39	99.36 81	95.91 41	98.96 19	98.16 71

MVS_0304		95.36 10	95.20 17	95.85 11	94.89 144	89.22 12	98.83 25	97.88 11	94.68 4	95.14 38	97.99 52	80.80 65	99.81 21	98.60 6	97.95 57	98.50 50

test_8					98.63 33	83.64 92	97.81 70	96.63 83	84.50 161	95.10 39	98.11 44	84.33 39	99.23 86

DeepPCF-MVS	89.82 1	94.61 22	96.17 5	89.91 199	97.09 90	70.21 333	98.99 22	96.69 73	95.57 2	95.08 40	99.23 1	86.40 30	99.87 8	97.84 20	98.66 31	99.65 6

SF-MVS		94.17 29	94.05 33	94.55 31	97.56 74	85.95 38	97.73 76	96.43 106	84.02 175	95.07 41	98.74 14	82.93 53	99.38 78	95.42 49	98.51 34	98.32 60

APDe-MVS		94.56 23	94.75 20	93.96 46	98.84 22	83.40 97	98.04 57	96.41 108	85.79 128	95.00 42	98.28 34	84.32 42	99.18 94	97.35 26	98.77 27	99.28 19
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVSFormer		91.36 88	90.57 94	93.73 55	93.00 203	88.08 17	94.80 255	94.48 229	80.74 240	94.90 43	97.13 101	78.84 89	95.10 302	83.77 175	97.46 70	98.02 79

lupinMVS		93.87 35	93.58 39	94.75 27	93.00 203	88.08 17	99.15 7	95.50 173	91.03 37	94.90 43	97.66 72	78.84 89	97.56 172	94.64 57	97.46 70	98.62 45

CS-MVS-test		92.98 46	93.67 36	90.90 169	96.52 94	76.87 259	98.68 28	94.73 213	90.36 48	94.84 45	97.89 62	77.94 102	97.15 203	94.28 61	97.80 62	98.70 41

9.14			94.26 30		98.10 57		98.14 46	96.52 95	84.74 153	94.83 46	98.80 7	82.80 55	99.37 80	95.95 40	98.42 40

testdata				90.13 191	95.92 111	74.17 296		96.49 101	73.49 331	94.82 47	97.99 52	78.80 91	97.93 152	83.53 183	97.52 69	98.29 64

APD-MVS		93.61 37	93.59 38	93.69 57	98.76 24	83.26 100	97.21 114	96.09 137	82.41 216	94.65 48	98.21 36	81.96 60	98.81 119	94.65 56	98.36 45	99.01 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior2							98.37 39		86.08 122	94.57 49	98.02 51	83.14 51		95.05 51	98.79 26

CS-MVS		92.73 53	93.48 41	90.48 181	96.27 98	75.93 279	98.55 34	94.93 200	89.32 58	94.54 50	97.67 71	78.91 88	97.02 207	93.80 64	97.32 77	98.49 51

FOURS1					98.51 39	78.01 232	98.13 49	96.21 128	83.04 200	94.39 51

ACMMP_NAP		93.46 39	93.23 45	94.17 41	97.16 88	84.28 81	96.82 153	96.65 78	86.24 118	94.27 52	97.99 52	77.94 102	99.83 16	93.39 69	98.57 33	98.39 57

agg_prior					98.59 35	83.13 102		96.56 92		94.19 53			99.16 96

SteuartSystems-ACMMP		94.13 31	94.44 26	93.20 78	95.41 125	81.35 140	99.02 20	96.59 88	89.50 57	94.18 54	98.36 30	83.68 49	99.45 75	94.77 53	98.45 39	98.81 33
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		93.59 38	93.63 37	93.48 70	98.05 58	81.76 130	98.64 31	97.13 28	82.60 212	94.09 55	98.49 25	80.35 69	99.85 10	94.74 55	98.62 32	98.83 32

test_fmvsmconf0.1_n		93.08 45	93.22 46	92.65 101	88.45 304	80.81 153	99.00 21	95.11 193	93.21 15	94.00 56	97.91 60	76.84 121	99.59 60	97.91 16	96.55 95	97.54 118

TSAR-MVS + GP.		94.35 25	94.50 23	93.89 47	97.38 84	83.04 104	98.10 51	95.29 188	91.57 30	93.81 57	97.45 85	86.64 27	99.43 76	96.28 36	94.01 128	99.20 22

CANet_DTU		90.98 99	90.04 109	93.83 49	94.76 147	86.23 34	96.32 186	93.12 303	93.11 16	93.71 58	96.82 115	63.08 256	99.48 73	84.29 167	95.12 115	95.77 187

VNet		92.11 70	91.22 83	94.79 25	96.91 91	86.98 27	97.91 63	97.96 10	86.38 117	93.65 59	95.74 136	70.16 218	98.95 111	93.39 69	88.87 178	98.43 55

test_vis1_n_1920		89.95 119	90.59 93	88.03 238	92.36 220	68.98 342	99.12 11	94.34 239	93.86 11	93.64 60	97.01 107	51.54 330	99.59 60	96.76 34	96.71 93	95.53 194

ZD-MVS					99.09 8	83.22 101		96.60 87	82.88 205	93.61 61	98.06 50	82.93 53	99.14 97	95.51 48	98.49 37

xiu_mvs_v1_base_debu		90.54 108	89.54 119	93.55 65	92.31 221	87.58 23	96.99 136	94.87 204	87.23 101	93.27 62	97.56 81	57.43 299	98.32 140	92.72 80	93.46 138	94.74 213

xiu_mvs_v1_base		90.54 108	89.54 119	93.55 65	92.31 221	87.58 23	96.99 136	94.87 204	87.23 101	93.27 62	97.56 81	57.43 299	98.32 140	92.72 80	93.46 138	94.74 213

xiu_mvs_v1_base_debi		90.54 108	89.54 119	93.55 65	92.31 221	87.58 23	96.99 136	94.87 204	87.23 101	93.27 62	97.56 81	57.43 299	98.32 140	92.72 80	93.46 138	94.74 213

CDPH-MVS		93.12 43	92.91 49	93.74 53	98.65 30	83.88 85	97.67 81	96.26 123	83.00 202	93.22 65	98.24 35	81.31 62	99.21 88	89.12 125	98.74 29	98.14 73

ETV-MVS		92.72 55	92.87 50	92.28 119	94.54 153	81.89 124	97.98 59	95.21 191	89.77 55	93.11 66	96.83 113	77.23 117	97.50 180	95.74 43	95.38 113	97.44 127

MSLP-MVS++		94.28 26	94.39 27	93.97 45	98.30 49	84.06 84	98.64 31	96.93 44	90.71 40	93.08 67	98.70 15	79.98 76	99.21 88	94.12 62	99.07 11	98.63 44

alignmvs		92.97 47	92.26 63	95.12 19	95.54 122	87.77 20	98.67 29	96.38 113	88.04 80	93.01 68	97.45 85	79.20 84	98.60 125	93.25 74	88.76 179	98.99 29

canonicalmvs		92.27 67	91.22 83	95.41 16	95.80 116	88.31 14	97.09 132	94.64 221	88.49 71	92.99 69	97.31 92	72.68 191	98.57 127	93.38 71	88.58 182	99.36 16

EC-MVSNet		91.73 77	92.11 67	90.58 178	93.54 185	77.77 243	98.07 54	94.40 236	87.44 95	92.99 69	97.11 103	74.59 170	96.87 217	93.75 65	97.08 81	97.11 146

jason		92.73 53	92.23 64	94.21 40	90.50 273	87.30 26	98.65 30	95.09 194	90.61 42	92.76 71	97.13 101	75.28 158	97.30 192	93.32 72	96.75 91	98.02 79
jason: jason.
test_cas_vis1_n_1920		89.90 120	90.02 110	89.54 207	90.14 281	74.63 291	98.71 27	94.43 234	93.04 17	92.40 72	96.35 125	53.41 326	99.08 103	95.59 46	96.16 99	94.90 207

test12				94.25 37	98.34 46	85.55 50		96.35 117		92.36 73		80.84 64	99.22 87		98.31 47	97.98 86

MG-MVS		94.25 28	93.72 34	95.85 11	99.38 3	89.35 11	97.98 59	98.09 9	89.99 51	92.34 74	96.97 108	81.30 63	98.99 107	88.54 132	98.88 20	99.20 22

test_fmvs1		87.79 169	88.52 136	85.62 290	92.98 207	64.31 358	97.88 65	92.42 312	87.95 82	92.24 75	95.82 135	47.94 345	98.44 137	95.31 50	94.09 125	94.09 224

h-mvs33		89.30 131	88.95 129	90.36 185	95.07 137	76.04 273	96.96 143	97.11 31	90.39 46	92.22 76	95.10 164	74.70 166	98.86 116	93.14 75	65.89 350	96.16 179

hse-mvs2		88.22 161	88.21 140	88.25 232	93.54 185	73.41 299	95.41 230	95.89 152	90.39 46	92.22 76	94.22 184	74.70 166	96.66 228	93.14 75	64.37 355	94.69 217

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 23	97.10 32	95.17 3	92.11 78	98.46 26	87.33 24	99.97 2	97.21 28	99.31 4	99.63 7

test_fmvsmconf0.01_n		91.08 96	90.68 92	92.29 118	82.43 364	80.12 173	97.94 62	93.93 259	92.07 24	91.97 79	97.60 79	67.56 226	99.53 68	97.09 29	95.56 112	97.21 142

SR-MVS		92.16 68	92.27 62	91.83 141	98.37 45	78.41 218	96.67 164	95.76 159	82.19 220	91.97 79	98.07 49	76.44 129	98.64 123	93.71 66	97.27 78	98.45 54

region2R		92.72 55	92.70 53	92.79 95	98.68 26	80.53 163	97.53 91	96.51 96	85.22 140	91.94 81	97.98 55	77.26 113	99.67 53	90.83 100	98.37 44	98.18 69

Effi-MVS+		90.70 105	89.90 115	93.09 83	93.61 182	83.48 95	95.20 239	92.79 308	83.22 194	91.82 82	95.70 138	71.82 201	97.48 182	91.25 94	93.67 134	98.32 60

HFP-MVS		92.89 49	92.86 51	92.98 87	98.71 25	81.12 143	97.58 86	96.70 71	85.20 142	91.75 83	97.97 57	78.47 94	99.71 45	90.95 96	98.41 41	98.12 75

DeepC-MVS_fast	89.06 2	94.48 24	94.30 29	95.02 20	98.86 21	85.68 46	98.06 55	96.64 81	93.64 12	91.74 84	98.54 20	80.17 74	99.90 5	92.28 84	98.75 28	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		92.69 57	92.67 54	92.75 96	98.66 28	80.57 159	97.58 86	96.69 73	85.20 142	91.57 85	97.92 58	77.01 118	99.67 53	90.95 96	98.41 41	98.00 84

DELS-MVS		94.98 14	94.49 24	96.44 6	96.42 95	90.59 7	99.21 4	97.02 36	94.40 8	91.46 86	97.08 104	83.32 50	99.69 49	92.83 79	98.70 30	99.04 25
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
XVS		92.69 57	92.71 52	92.63 103	98.52 37	80.29 166	97.37 107	96.44 104	87.04 106	91.38 87	97.83 66	77.24 115	99.59 60	90.46 106	98.07 52	98.02 79

X-MVStestdata		86.26 192	84.14 211	92.63 103	98.52 37	80.29 166	97.37 107	96.44 104	87.04 106	91.38 87	20.73 405	77.24 115	99.59 60	90.46 106	98.07 52	98.02 79

PMMVS		89.46 128	89.92 114	88.06 236	94.64 148	69.57 339	96.22 191	94.95 199	87.27 100	91.37 89	96.54 123	65.88 238	97.39 187	88.54 132	93.89 130	97.23 139

test_fmvs1_n		86.34 190	86.72 174	85.17 297	87.54 316	63.64 363	96.91 147	92.37 314	87.49 94	91.33 90	95.58 144	40.81 370	98.46 134	95.00 52	93.49 136	93.41 238

dcpmvs_2		93.10 44	93.46 42	92.02 133	97.77 65	79.73 184	94.82 253	93.86 266	86.91 108	91.33 90	96.76 117	85.20 32	98.06 148	96.90 32	97.60 67	98.27 66

原ACMM1				91.22 160	97.77 65	78.10 230		96.61 84	81.05 234	91.28 92	97.42 89	77.92 104	98.98 108	79.85 213	98.51 34	96.59 167

新几何1				93.12 81	97.44 78	81.60 137		96.71 70	74.54 322	91.22 93	97.57 80	79.13 85	99.51 71	77.40 239	98.46 38	98.26 67

UA-Net		88.92 138	88.48 137	90.24 188	94.06 173	77.18 256	93.04 297	94.66 218	87.39 97	91.09 94	93.89 193	74.92 163	98.18 147	75.83 255	91.43 160	95.35 199

ZNCC-MVS		92.75 51	92.60 56	93.23 77	98.24 51	81.82 128	97.63 82	96.50 98	85.00 148	91.05 95	97.74 69	78.38 95	99.80 25	90.48 105	98.34 46	98.07 77

APD-MVS_3200maxsize		91.23 92	91.35 80	90.89 170	97.89 62	76.35 269	96.30 187	95.52 172	79.82 263	91.03 96	97.88 63	74.70 166	98.54 128	92.11 87	96.89 85	97.77 102

test_vis1_n		85.60 203	85.70 182	85.33 294	84.79 348	64.98 356	96.83 151	91.61 325	87.36 98	91.00 97	94.84 172	36.14 376	97.18 199	95.66 44	93.03 143	93.82 229

GST-MVS		92.43 65	92.22 65	93.04 85	98.17 54	81.64 135	97.40 105	96.38 113	84.71 155	90.90 98	97.40 90	77.55 110	99.76 31	89.75 118	97.74 63	97.72 105

PGM-MVS		91.93 72	91.80 73	92.32 117	98.27 50	79.74 183	95.28 233	97.27 21	83.83 183	90.89 99	97.78 68	76.12 136	99.56 66	88.82 129	97.93 60	97.66 110

SR-MVS-dyc-post		91.29 90	91.45 79	90.80 172	97.76 67	76.03 274	96.20 194	95.44 178	80.56 245	90.72 100	97.84 64	75.76 142	98.61 124	91.99 88	96.79 89	97.75 103

RE-MVS-def			91.18 86		97.76 67	76.03 274	96.20 194	95.44 178	80.56 245	90.72 100	97.84 64	73.36 186		91.99 88	96.79 89	97.75 103

MP-MVS		92.61 60	92.67 54	92.42 111	98.13 56	79.73 184	97.33 109	96.20 129	85.63 130	90.53 102	97.66 72	78.14 100	99.70 48	92.12 86	98.30 48	97.85 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HY-MVS	84.06 6	91.63 81	90.37 101	95.39 17	96.12 103	88.25 15	90.22 327	97.58 16	88.33 75	90.50 103	91.96 224	79.26 82	99.06 104	90.29 112	89.07 174	98.88 31

CP-MVS		92.54 62	92.60 56	92.34 113	98.50 40	79.90 177	98.40 38	96.40 110	84.75 152	90.48 104	98.09 45	77.40 112	99.21 88	91.15 95	98.23 50	97.92 90

diffmvs		91.17 93	90.74 91	92.44 110	93.11 202	82.50 112	96.25 190	93.62 281	87.79 86	90.40 105	95.93 132	73.44 185	97.42 184	93.62 68	92.55 148	97.41 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		90.29 114	89.18 124	93.62 61	95.23 130	84.93 69	94.41 260	94.66 218	84.31 166	90.37 106	91.02 239	75.13 160	97.82 161	83.11 188	94.42 123	98.12 75

MTAPA		92.45 64	92.31 61	92.86 92	97.90 61	80.85 152	92.88 300	96.33 118	87.92 83	90.20 107	98.18 38	76.71 126	99.76 31	92.57 83	98.09 51	97.96 89

test_yl		91.46 85	90.53 95	94.24 38	97.41 80	85.18 58	98.08 52	97.72 12	80.94 235	89.85 108	96.14 128	75.61 143	98.81 119	90.42 110	88.56 183	98.74 35

DCV-MVSNet		91.46 85	90.53 95	94.24 38	97.41 80	85.18 58	98.08 52	97.72 12	80.94 235	89.85 108	96.14 128	75.61 143	98.81 119	90.42 110	88.56 183	98.74 35

WTY-MVS		92.65 59	91.68 75	95.56 14	96.00 106	88.90 13	98.23 43	97.65 14	88.57 69	89.82 110	97.22 98	79.29 81	99.06 104	89.57 120	88.73 180	98.73 39

MVS_111021_HR		93.41 40	93.39 43	93.47 72	97.34 85	82.83 106	97.56 88	98.27 6	89.16 61	89.71 111	97.14 100	79.77 78	99.56 66	93.65 67	97.94 58	98.02 79

sss		90.87 103	89.96 112	93.60 62	94.15 168	83.84 88	97.14 125	98.13 7	85.93 126	89.68 112	96.09 130	71.67 202	99.30 83	87.69 142	89.16 173	97.66 110

test222					96.15 102	78.41 218	95.87 210	96.46 102	71.97 342	89.66 113	97.45 85	76.33 133			98.24 49	98.30 63

LFMVS		89.27 132	87.64 151	94.16 43	97.16 88	85.52 51	97.18 118	94.66 218	79.17 277	89.63 114	96.57 122	55.35 316	98.22 144	89.52 122	89.54 169	98.74 35

CostFormer		89.08 134	88.39 138	91.15 162	93.13 200	79.15 199	88.61 338	96.11 136	83.14 196	89.58 115	86.93 298	83.83 48	96.87 217	88.22 138	85.92 210	97.42 128

PVSNet_BlendedMVS		90.05 117	89.96 112	90.33 186	97.47 76	83.86 86	98.02 58	96.73 67	87.98 81	89.53 116	89.61 261	76.42 130	99.57 64	94.29 59	79.59 258	87.57 325

PVSNet_Blended		93.13 42	92.98 48	93.57 64	97.47 76	83.86 86	99.32 1	96.73 67	91.02 38	89.53 116	96.21 127	76.42 130	99.57 64	94.29 59	95.81 109	97.29 138

HPM-MVS		91.62 82	91.53 78	91.89 137	97.88 63	79.22 196	96.99 136	95.73 162	82.07 222	89.50 118	97.19 99	75.59 145	98.93 114	90.91 98	97.94 58	97.54 118
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testing11		92.48 63	92.04 70	93.78 51	95.94 110	86.00 37	97.56 88	97.08 33	87.52 93	89.32 119	95.40 148	84.60 36	98.02 149	91.93 90	89.04 175	97.32 134

EI-MVSNet-Vis-set		91.84 76	91.77 74	92.04 132	97.60 71	81.17 142	96.61 165	96.87 49	88.20 77	89.19 120	97.55 84	78.69 93	99.14 97	90.29 112	90.94 163	95.80 186

testing222		91.09 95	90.49 97	92.87 91	95.82 114	85.04 65	96.51 172	97.28 20	86.05 123	89.13 121	95.34 150	80.16 75	96.62 229	85.82 155	88.31 186	96.96 151

MP-MVS-pluss		92.58 61	92.35 60	93.29 74	97.30 86	82.53 110	96.44 177	96.04 142	84.68 156	89.12 122	98.37 29	77.48 111	99.74 38	93.31 73	98.38 43	97.59 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 159	87.02 170	92.06 130	95.09 135	80.18 172	97.55 90	94.45 233	83.09 198	89.10 123	95.92 134	47.97 344	98.49 131	93.08 78	86.91 199	97.52 123

baseline		90.76 104	90.10 108	92.74 97	92.90 209	82.56 109	94.60 257	94.56 226	87.69 89	89.06 124	95.67 140	73.76 180	97.51 179	90.43 109	92.23 154	98.16 71

testing99		91.91 73	91.35 80	93.60 62	95.98 108	85.70 44	97.31 110	96.92 46	86.82 111	88.91 125	95.25 151	84.26 43	97.89 159	88.80 130	87.94 190	97.21 142

EIA-MVS		91.73 77	92.05 69	90.78 174	94.52 154	76.40 268	98.06 55	95.34 186	89.19 60	88.90 126	97.28 96	77.56 109	97.73 164	90.77 101	96.86 88	98.20 68

testing91		91.90 74	91.31 82	93.66 58	95.99 107	85.68 46	97.39 106	96.89 47	86.75 115	88.85 127	95.23 154	83.93 46	97.90 158	88.91 127	87.89 191	97.41 129

mvsany_test1		87.58 173	88.22 139	85.67 288	89.78 285	67.18 349	95.25 236	87.93 362	83.96 178	88.79 128	97.06 106	72.52 192	94.53 319	92.21 85	86.45 203	95.30 201

HPM-MVS_fast		90.38 113	90.17 107	91.03 165	97.61 70	77.35 252	97.15 124	95.48 174	79.51 269	88.79 128	96.90 109	71.64 204	98.81 119	87.01 150	97.44 72	96.94 152

ETVMVS		90.99 98	90.26 102	93.19 79	95.81 115	85.64 48	96.97 141	97.18 26	85.43 134	88.77 130	94.86 171	82.00 59	96.37 236	82.70 191	88.60 181	97.57 117

PAPM		92.87 50	92.40 59	94.30 35	92.25 228	87.85 19	96.40 181	96.38 113	91.07 36	88.72 131	96.90 109	82.11 58	97.37 189	90.05 115	97.70 64	97.67 109

MVS_111021_LR		91.60 83	91.64 77	91.47 152	95.74 117	78.79 209	96.15 196	96.77 61	88.49 71	88.64 132	97.07 105	72.33 195	99.19 93	93.13 77	96.48 96	96.43 171

casdiffmvs		90.95 101	90.39 99	92.63 103	92.82 210	82.53 110	96.83 151	94.47 231	87.69 89	88.47 133	95.56 145	74.04 177	97.54 176	90.90 99	92.74 146	97.83 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mPP-MVS		91.88 75	91.82 72	92.07 129	98.38 44	78.63 212	97.29 111	96.09 137	85.12 144	88.45 134	97.66 72	75.53 147	99.68 51	89.83 116	98.02 55	97.88 91

PAPR		92.74 52	92.17 66	94.45 32	98.89 20	84.87 71	97.20 116	96.20 129	87.73 88	88.40 135	98.12 43	78.71 92	99.76 31	87.99 139	96.28 97	98.74 35

tpmrst		88.36 156	87.38 161	91.31 154	94.36 162	79.92 176	87.32 348	95.26 190	85.32 137	88.34 136	86.13 314	80.60 68	96.70 225	83.78 174	85.34 218	97.30 137

GG-mvs-BLEND				93.49 69	94.94 141	86.26 33	81.62 374	97.00 37		88.32 137	94.30 182	91.23 5	96.21 243	88.49 134	97.43 73	98.00 84

EI-MVSNet-UG-set		91.35 89	91.22 83	91.73 143	97.39 82	80.68 156	96.47 174	96.83 52	87.92 83	88.30 138	97.36 91	77.84 105	99.13 99	89.43 123	89.45 170	95.37 198

MAR-MVS		90.63 106	90.22 104	91.86 138	98.47 42	78.20 228	97.18 118	96.61 84	83.87 182	88.18 139	98.18 38	68.71 222	99.75 36	83.66 180	97.15 80	97.63 113
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
DP-MVS Recon		91.72 79	90.85 88	94.34 34	99.50 1	85.00 68	98.51 35	95.96 148	80.57 244	88.08 140	97.63 78	76.84 121	99.89 7	85.67 157	94.88 116	98.13 74

VDDNet		86.44 188	84.51 202	92.22 122	91.56 249	81.83 127	97.10 131	94.64 221	69.50 354	87.84 141	95.19 158	48.01 343	97.92 157	89.82 117	86.92 198	96.89 156

UGNet		87.73 170	86.55 176	91.27 157	95.16 134	79.11 200	96.35 184	96.23 126	88.14 78	87.83 142	90.48 248	50.65 333	99.09 102	80.13 210	94.03 126	95.60 191
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
test2506		90.96 100	90.39 99	92.65 101	93.54 185	82.46 113	96.37 182	97.35 18	86.78 113	87.55 143	95.25 151	77.83 106	97.50 180	84.07 169	94.80 117	97.98 86

tpm2		87.35 176	86.26 177	90.62 177	92.93 208	78.67 211	88.06 343	95.99 145	79.33 272	87.40 144	86.43 309	80.28 71	96.40 234	80.23 208	85.73 214	96.79 159

CPTT-MVS		89.72 123	89.87 116	89.29 210	98.33 47	73.30 302	97.70 78	95.35 185	75.68 313	87.40 144	97.44 88	70.43 215	98.25 143	89.56 121	96.90 84	96.33 176

gg-mvs-nofinetune		85.48 206	82.90 230	93.24 76	94.51 157	85.82 42	79.22 378	96.97 40	61.19 376	87.33 146	53.01 394	90.58 6	96.07 246	86.07 154	97.23 79	97.81 100

CHOSEN 280x420		91.71 80	91.85 71	91.29 156	94.94 141	82.69 107	87.89 344	96.17 132	85.94 125	87.27 147	94.31 181	90.27 9	95.65 274	94.04 63	95.86 107	95.53 194

test_fmvsmvis_n_1920		92.12 69	92.10 68	92.17 125	90.87 265	81.04 145	98.34 40	93.90 263	92.71 18	87.24 148	97.90 61	74.83 164	99.72 43	96.96 31	96.20 98	95.76 188

casdiffmvs_mvg		91.13 94	90.45 98	93.17 80	92.99 206	83.58 93	97.46 98	94.56 226	87.69 89	87.19 149	94.98 169	74.50 171	97.60 169	91.88 91	92.79 145	98.34 58
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet_dtu		87.65 172	87.89 145	86.93 266	94.57 150	71.37 327	96.72 159	96.50 98	88.56 70	87.12 150	95.02 166	75.91 140	94.01 328	66.62 315	90.00 166	95.42 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 146	87.82 147	91.24 158	92.68 212	78.82 206	96.95 144	93.85 267	87.55 92	87.07 151	95.13 162	63.43 254	97.21 197	77.58 235	96.15 100	97.70 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		90.95 101	90.26 102	93.01 86	94.03 176	84.27 82	97.91 63	96.67 75	83.18 195	86.87 152	95.51 146	88.66 16	97.85 160	80.46 204	89.01 176	96.92 155

TESTMET0.1,1		89.83 121	89.34 122	91.31 154	92.54 218	80.19 171	97.11 128	96.57 90	86.15 119	86.85 153	91.83 228	79.32 80	96.95 211	81.30 198	92.35 152	96.77 161

PVSNet_Blended_VisFu		91.24 91	90.77 90	92.66 100	95.09 135	82.40 114	97.77 72	95.87 155	88.26 76	86.39 154	93.94 192	76.77 124	99.27 84	88.80 130	94.00 129	96.31 177

API-MVS		90.18 115	88.97 127	93.80 50	98.66 28	82.95 105	97.50 95	95.63 167	75.16 317	86.31 155	97.69 70	72.49 193	99.90 5	81.26 199	96.07 102	98.56 47

test-LLR		88.48 152	87.98 144	89.98 195	92.26 226	77.23 254	97.11 128	95.96 148	83.76 186	86.30 156	91.38 232	72.30 196	96.78 223	80.82 201	91.92 156	95.94 183

test-mter		88.95 136	88.60 134	89.98 195	92.26 226	77.23 254	97.11 128	95.96 148	85.32 137	86.30 156	91.38 232	76.37 132	96.78 223	80.82 201	91.92 156	95.94 183

PAPM_NR		91.46 85	90.82 89	93.37 73	98.50 40	81.81 129	95.03 249	96.13 134	84.65 157	86.10 158	97.65 76	79.24 83	99.75 36	83.20 186	96.88 86	98.56 47

FA-MVS(test-final)		87.71 171	86.23 178	92.17 125	94.19 166	80.55 160	87.16 350	96.07 140	82.12 221	85.98 159	88.35 276	72.04 200	98.49 131	80.26 207	89.87 167	97.48 126

MDTV_nov1_ep13_2view						81.74 131	86.80 352		80.65 242	85.65 160		74.26 173		76.52 247		96.98 150

ECVR-MVS		88.35 157	87.25 163	91.65 145	93.54 185	79.40 191	96.56 169	90.78 339	86.78 113	85.57 161	95.25 151	57.25 303	97.56 172	84.73 165	94.80 117	97.98 86

AUN-MVS		86.25 193	85.57 184	88.26 231	93.57 184	73.38 300	95.45 228	95.88 153	83.94 179	85.47 162	94.21 185	73.70 183	96.67 227	83.54 182	64.41 354	94.73 216

PVSNet	82.34 9	89.02 135	87.79 148	92.71 99	95.49 123	81.50 138	97.70 78	97.29 19	87.76 87	85.47 162	95.12 163	56.90 305	98.90 115	80.33 205	94.02 127	97.71 107

EPP-MVSNet		89.76 122	89.72 118	89.87 200	93.78 178	76.02 276	97.22 112	96.51 96	79.35 271	85.11 164	95.01 167	84.82 34	97.10 205	87.46 145	88.21 188	96.50 169

test1111		88.11 162	87.04 169	91.35 153	93.15 198	78.79 209	96.57 167	90.78 339	86.88 110	85.04 165	95.20 157	57.23 304	97.39 187	83.88 172	94.59 120	97.87 93

FE-MVS		86.06 195	84.15 210	91.78 142	94.33 163	79.81 178	84.58 366	96.61 84	76.69 307	85.00 166	87.38 289	70.71 214	98.37 139	70.39 298	91.70 159	97.17 145

OMC-MVS		88.80 143	88.16 142	90.72 175	95.30 128	77.92 237	94.81 254	94.51 228	86.80 112	84.97 167	96.85 112	67.53 227	98.60 125	85.08 161	87.62 193	95.63 190

CHOSEN 1792x2688		91.07 97	90.21 105	93.64 59	95.18 133	83.53 94	96.26 189	96.13 134	88.92 63	84.90 168	93.10 208	72.86 189	99.62 58	88.86 128	95.67 110	97.79 101

thres200		88.92 138	87.65 150	92.73 98	96.30 97	85.62 49	97.85 66	98.86 1	84.38 165	84.82 169	93.99 191	75.12 161	98.01 150	70.86 295	86.67 200	94.56 218

UWE-MVS		88.56 151	88.91 131	87.50 252	94.17 167	72.19 313	95.82 214	97.05 35	84.96 149	84.78 170	93.51 202	81.33 61	94.75 311	79.43 216	89.17 172	95.57 192

MDTV_nov1_ep13			83.69 214		94.09 172	81.01 146	86.78 353	96.09 137	83.81 184	84.75 171	84.32 337	74.44 172	96.54 230	63.88 329	85.07 219

CDS-MVSNet		89.50 127	88.96 128	91.14 163	91.94 245	80.93 150	97.09 132	95.81 157	84.26 171	84.72 172	94.20 186	80.31 70	95.64 275	83.37 185	88.96 177	96.85 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 111	89.97 111	91.64 146	97.58 73	78.21 227	96.78 156	96.72 69	84.73 154	84.72 172	97.23 97	71.22 207	99.63 57	88.37 137	92.41 151	97.08 148
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
CSCG		92.02 71	91.65 76	93.12 81	98.53 36	80.59 158	97.47 96	97.18 26	77.06 305	84.64 174	97.98 55	83.98 45	99.52 69	90.72 102	97.33 76	99.23 21

ab-mvs		87.08 177	84.94 197	93.48 70	93.34 194	83.67 91	88.82 335	95.70 163	81.18 232	84.55 175	90.14 256	62.72 257	98.94 113	85.49 159	82.54 239	97.85 95

EPMVS		87.47 175	85.90 181	92.18 124	95.41 125	82.26 117	87.00 351	96.28 121	85.88 127	84.23 176	85.57 320	75.07 162	96.26 240	71.14 293	92.50 149	98.03 78

Anonymous202405211		84.41 223	81.93 245	91.85 140	96.78 93	78.41 218	97.44 99	91.34 329	70.29 350	84.06 177	94.26 183	41.09 368	98.96 109	79.46 215	82.65 238	98.17 70

HyFIR lowres test		89.36 129	88.60 134	91.63 148	94.91 143	80.76 155	95.60 223	95.53 170	82.56 213	84.03 178	91.24 236	78.03 101	96.81 221	87.07 149	88.41 185	97.32 134

tfpn200view9		88.48 152	87.15 165	92.47 107	96.21 100	85.30 56	97.44 99	98.85 2	83.37 192	83.99 179	93.82 194	75.36 154	97.93 152	69.04 303	86.24 207	94.17 220

thres400		88.42 155	87.15 165	92.23 121	96.21 100	85.30 56	97.44 99	98.85 2	83.37 192	83.99 179	93.82 194	75.36 154	97.93 152	69.04 303	86.24 207	93.45 236

tpm		85.55 204	84.47 205	88.80 220	90.19 278	75.39 284	88.79 336	94.69 214	84.83 151	83.96 181	85.21 326	78.22 98	94.68 315	76.32 251	78.02 276	96.34 174

Fast-Effi-MVS+		87.93 167	86.94 172	90.92 168	94.04 174	79.16 198	98.26 42	93.72 277	81.29 231	83.94 182	92.90 209	69.83 219	96.68 226	76.70 245	91.74 158	96.93 153

XVG-OURS-SEG-HR		85.74 201	85.16 193	87.49 254	90.22 277	71.45 326	91.29 319	94.09 254	81.37 230	83.90 183	95.22 155	60.30 274	97.53 178	85.58 158	84.42 222	93.50 234

thisisatest0530		89.65 124	89.02 126	91.53 150	93.46 191	80.78 154	96.52 170	96.67 75	81.69 228	83.79 184	94.90 170	88.85 15	97.68 165	77.80 228	87.49 196	96.14 180

DeepC-MVS	86.58 3	91.53 84	91.06 87	92.94 89	94.52 154	81.89 124	95.95 204	95.98 146	90.76 39	83.76 185	96.76 117	73.24 187	99.71 45	91.67 92	96.96 83	97.22 140
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 146	88.16 142	90.20 190	93.61 182	76.86 260	96.77 158	93.07 304	84.02 175	83.62 186	95.60 143	74.69 169	96.24 242	78.43 227	93.66 135	97.49 125

thres100view900		88.30 158	86.95 171	92.33 115	96.10 104	84.90 70	97.14 125	98.85 2	82.69 210	83.41 187	93.66 198	75.43 151	97.93 152	69.04 303	86.24 207	94.17 220

thres600view7		88.06 163	86.70 175	92.15 127	96.10 104	85.17 62	97.14 125	98.85 2	82.70 209	83.41 187	93.66 198	75.43 151	97.82 161	67.13 312	85.88 211	93.45 236

XVG-OURS		85.18 209	84.38 206	87.59 248	90.42 275	71.73 323	91.06 322	94.07 255	82.00 224	83.29 189	95.08 165	56.42 310	97.55 174	83.70 179	83.42 227	93.49 235

Vis-MVSNet (Re-imp)		88.88 140	88.87 132	88.91 217	93.89 177	74.43 294	96.93 146	94.19 248	84.39 164	83.22 190	95.67 140	78.24 97	94.70 313	78.88 223	94.40 124	97.61 115

TAMVS		88.48 152	87.79 148	90.56 179	91.09 260	79.18 197	96.45 176	95.88 153	83.64 189	83.12 191	93.33 203	75.94 139	95.74 270	82.40 192	88.27 187	96.75 163

baseline1		88.85 141	87.49 157	92.93 90	95.21 132	86.85 29	95.47 227	94.61 223	87.29 99	83.11 192	94.99 168	80.70 67	96.89 215	82.28 193	73.72 292	95.05 205

AdaColmap		88.81 142	87.61 154	92.39 112	99.33 4	79.95 175	96.70 163	95.58 168	77.51 297	83.05 193	96.69 121	61.90 266	99.72 43	84.29 167	93.47 137	97.50 124

PatchmatchNet		86.83 183	85.12 194	91.95 135	94.12 171	82.27 116	86.55 355	95.64 166	84.59 159	82.98 194	84.99 332	77.26 113	95.96 255	68.61 306	91.34 161	97.64 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 202	83.64 217	91.60 149	92.30 224	81.86 126	92.88 300	95.56 169	84.85 150	82.52 195	85.12 330	58.04 292	95.39 285	73.89 273	87.58 195	97.54 118

114514_t		88.79 144	87.57 155	92.45 108	98.21 53	81.74 131	96.99 136	95.45 177	75.16 317	82.48 196	95.69 139	68.59 223	98.50 130	80.33 205	95.18 114	97.10 147

PatchT		79.75 288	76.85 300	88.42 225	89.55 291	75.49 283	77.37 384	94.61 223	63.07 367	82.46 197	73.32 380	75.52 148	93.41 339	51.36 371	84.43 221	96.36 172

TR-MVS		86.30 191	84.93 198	90.42 182	94.63 149	77.58 247	96.57 167	93.82 268	80.30 253	82.42 198	95.16 160	58.74 285	97.55 174	74.88 263	87.82 192	96.13 181

HQP-NCC					92.08 237		97.63 82		90.52 43	82.30 199

ACMP_Plane					92.08 237		97.63 82		90.52 43	82.30 199

HQP4-MVS										82.30 199			97.32 190			91.13 247

HQP-MVS		87.91 168	87.55 156	88.98 216	92.08 237	78.48 214	97.63 82	94.80 209	90.52 43	82.30 199	94.56 177	65.40 242	97.32 190	87.67 143	83.01 231	91.13 247

CR-MVSNet		83.53 237	81.36 254	90.06 192	90.16 279	79.75 181	79.02 380	91.12 331	84.24 172	82.27 203	80.35 359	75.45 149	93.67 334	63.37 333	86.25 205	96.75 163

RPMNet		79.85 287	75.92 306	91.64 146	90.16 279	79.75 181	79.02 380	95.44 178	58.43 386	82.27 203	72.55 383	73.03 188	98.41 138	46.10 384	86.25 205	96.75 163

CVMVSNet		84.83 215	85.57 184	82.63 329	91.55 250	60.38 374	95.13 243	95.03 197	80.60 243	82.10 205	94.71 174	66.40 237	90.19 368	74.30 270	90.32 165	97.31 136

iter_conf_final		89.51 126	89.21 123	90.39 183	95.60 120	84.44 77	97.22 112	89.09 353	89.11 62	82.07 206	92.80 211	87.03 25	96.03 247	89.10 126	80.89 247	90.70 252

PLC	83.97 7	88.00 165	87.38 161	89.83 202	98.02 59	76.46 266	97.16 122	94.43 234	79.26 276	81.98 207	96.28 126	69.36 220	99.27 84	77.71 232	92.25 153	93.77 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 297	77.20 296	84.40 311	89.74 288	64.06 361	75.30 388	95.44 178	62.15 370	81.90 208	59.08 392	78.92 87	95.59 279	66.51 318	85.78 213	93.54 233

Anonymous20240529		83.15 244	80.60 265	90.80 172	95.74 117	78.27 222	96.81 154	94.92 201	60.10 381	81.89 209	92.54 215	45.82 352	98.82 118	79.25 219	78.32 274	95.31 200

tttt0517		88.57 150	88.19 141	89.71 206	93.00 203	75.99 277	95.67 218	96.67 75	80.78 239	81.82 210	94.40 180	88.97 14	97.58 171	76.05 253	86.31 204	95.57 192

WB-MVSnew		84.08 228	83.51 221	85.80 283	91.34 255	76.69 264	95.62 222	96.27 122	81.77 226	81.81 211	92.81 210	58.23 289	94.70 313	66.66 314	87.06 197	85.99 349

BH-RMVSNet		86.84 182	85.28 189	91.49 151	95.35 127	80.26 169	96.95 144	92.21 315	82.86 206	81.77 212	95.46 147	59.34 281	97.64 167	69.79 301	93.81 132	96.57 168

iter_conf05		90.14 116	89.79 117	91.17 161	95.85 113	86.93 28	97.68 80	88.67 360	89.93 52	81.73 213	92.80 211	90.37 8	96.03 247	90.44 108	80.65 251	90.56 254

HQP_MVS		87.50 174	87.09 168	88.74 221	91.86 246	77.96 234	97.18 118	94.69 214	89.89 53	81.33 214	94.15 187	64.77 248	97.30 192	87.08 147	82.82 235	90.96 249

plane_prior3						77.75 244			90.17 50	81.33 214

VPA-MVSNet		85.32 207	83.83 213	89.77 205	90.25 276	82.63 108	96.36 183	97.07 34	83.03 201	81.21 216	89.02 266	61.58 267	96.31 239	85.02 163	70.95 307	90.36 257

GeoE		86.36 189	85.20 190	89.83 202	93.17 197	76.13 271	97.53 91	92.11 316	79.58 268	80.99 217	94.01 190	66.60 236	96.17 245	73.48 277	89.30 171	97.20 144

GA-MVS		85.79 200	84.04 212	91.02 166	89.47 293	80.27 168	96.90 148	94.84 207	85.57 131	80.88 218	89.08 264	56.56 309	96.47 233	77.72 231	85.35 217	96.34 174

1112_ss		88.60 149	87.47 159	92.00 134	93.21 195	80.97 148	96.47 174	92.46 311	83.64 189	80.86 219	97.30 94	80.24 72	97.62 168	77.60 234	85.49 215	97.40 131

dp		84.30 225	82.31 239	90.28 187	94.24 165	77.97 233	86.57 354	95.53 170	79.94 262	80.75 220	85.16 328	71.49 206	96.39 235	63.73 330	83.36 228	96.48 170

Test_1112_low_res		88.03 164	86.73 173	91.94 136	93.15 198	80.88 151	96.44 177	92.41 313	83.59 191	80.74 221	91.16 237	80.18 73	97.59 170	77.48 237	85.40 216	97.36 133

cascas		86.50 187	84.48 204	92.55 106	92.64 216	85.95 38	97.04 135	95.07 196	75.32 315	80.50 222	91.02 239	54.33 323	97.98 151	86.79 152	87.62 193	93.71 231

TAPA-MVS	81.61 12	85.02 212	83.67 215	89.06 213	96.79 92	73.27 305	95.92 206	94.79 211	74.81 320	80.47 223	96.83 113	71.07 209	98.19 146	49.82 377	92.57 147	95.71 189
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 198	85.10 195	88.06 236	88.34 305	77.83 241	95.72 216	94.20 247	87.89 85	80.45 224	94.05 189	58.57 286	97.26 196	83.88 172	82.76 237	89.09 288
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		86.79 184	85.43 186	90.87 171	88.76 298	85.34 53	97.06 134	94.33 240	84.31 166	80.45 224	91.98 223	72.36 194	96.36 237	88.48 135	71.13 305	90.93 251

EI-MVSNet		85.80 199	85.20 190	87.59 248	91.55 250	77.41 250	95.13 243	95.36 183	80.43 250	80.33 226	94.71 174	73.72 181	95.97 252	76.96 243	78.64 267	89.39 275

MVSTER		89.25 133	88.92 130	90.24 188	95.98 108	84.66 73	96.79 155	95.36 183	87.19 104	80.33 226	90.61 247	90.02 12	95.97 252	85.38 160	78.64 267	90.09 266

ADS-MVSNet2		79.57 291	77.53 294	85.71 286	93.78 178	72.13 314	79.48 376	86.11 372	73.09 334	80.14 228	79.99 361	62.15 261	90.14 369	59.49 345	83.52 225	94.85 210

ADS-MVSNet		81.26 274	78.36 287	89.96 197	93.78 178	79.78 179	79.48 376	93.60 282	73.09 334	80.14 228	79.99 361	62.15 261	95.24 294	59.49 345	83.52 225	94.85 210

test_fmvs2		79.59 290	79.90 277	78.67 349	82.86 363	55.82 384	95.20 239	89.55 347	81.09 233	80.12 230	89.80 258	34.31 381	93.51 337	87.82 140	78.36 273	86.69 338

baseline2		90.39 111	90.21 105	90.93 167	90.86 266	80.99 147	95.20 239	97.41 17	86.03 124	80.07 231	94.61 176	90.58 6	97.47 183	87.29 146	89.86 168	94.35 219

Effi-MVS+-dtu		84.61 219	84.90 199	83.72 319	91.96 243	63.14 366	94.95 250	93.34 294	85.57 131	79.79 232	87.12 295	61.99 264	95.61 278	83.55 181	85.83 212	92.41 243

VPNet		84.69 217	82.92 229	90.01 193	89.01 297	83.45 96	96.71 161	95.46 176	85.71 129	79.65 233	92.18 219	56.66 308	96.01 251	83.05 189	67.84 338	90.56 254

SDMVSNet		87.02 178	85.61 183	91.24 158	94.14 169	83.30 99	93.88 277	95.98 146	84.30 168	79.63 234	92.01 220	58.23 289	97.68 165	90.28 114	82.02 243	92.75 239

sd_testset		84.62 218	83.11 227	89.17 211	94.14 169	77.78 242	91.54 318	94.38 237	84.30 168	79.63 234	92.01 220	52.28 328	96.98 209	77.67 233	82.02 243	92.75 239

CLD-MVS		87.97 166	87.48 158	89.44 208	92.16 233	80.54 162	98.14 46	94.92 201	91.41 31	79.43 236	95.40 148	62.34 259	97.27 195	90.60 104	82.90 234	90.50 256
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IB-MVS	85.34 4	88.67 146	87.14 167	93.26 75	93.12 201	84.32 79	98.76 26	97.27 21	87.19 104	79.36 237	90.45 249	83.92 47	98.53 129	84.41 166	69.79 318	96.93 153
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
PatchMatch-RL		85.00 213	83.66 216	89.02 215	95.86 112	74.55 293	92.49 304	93.60 282	79.30 274	79.29 238	91.47 230	58.53 287	98.45 135	70.22 299	92.17 155	94.07 225

CNLPA		86.96 179	85.37 188	91.72 144	97.59 72	79.34 194	97.21 114	91.05 334	74.22 323	78.90 239	96.75 119	67.21 231	98.95 111	74.68 265	90.77 164	96.88 157

MVS		90.60 107	88.64 133	96.50 5	94.25 164	90.53 8	93.33 289	97.21 23	77.59 296	78.88 240	97.31 92	71.52 205	99.69 49	89.60 119	98.03 54	99.27 20

mvs_anonymous		88.68 145	87.62 153	91.86 138	94.80 146	81.69 134	93.53 285	94.92 201	82.03 223	78.87 241	90.43 250	75.77 141	95.34 288	85.04 162	93.16 142	98.55 49

mvsmamba		85.17 210	84.54 201	87.05 264	87.94 310	75.11 287	96.22 191	87.79 364	86.91 108	78.55 242	91.77 229	64.93 247	95.91 258	86.94 151	79.80 253	90.12 263

tpm cat1		83.63 236	81.38 253	90.39 183	93.53 190	78.19 229	85.56 362	95.09 194	70.78 348	78.51 243	83.28 345	74.80 165	97.03 206	66.77 313	84.05 223	95.95 182

UniMVSNet (Re)		85.31 208	84.23 208	88.55 224	89.75 286	80.55 160	96.72 159	96.89 47	85.42 135	78.40 244	88.93 267	75.38 153	95.52 282	78.58 225	68.02 335	89.57 274

FIs		86.73 186	86.10 179	88.61 223	90.05 282	80.21 170	96.14 197	96.95 42	85.56 133	78.37 245	92.30 217	76.73 125	95.28 292	79.51 214	79.27 261	90.35 258

BH-w/o		88.24 160	87.47 159	90.54 180	95.03 140	78.54 213	97.41 104	93.82 268	84.08 173	78.23 246	94.51 179	69.34 221	97.21 197	80.21 209	94.58 121	95.87 185

UniMVSNet_NR-MVSNet		85.49 205	84.59 200	88.21 234	89.44 294	79.36 192	96.71 161	96.41 108	85.22 140	78.11 247	90.98 241	76.97 120	95.14 299	79.14 220	68.30 332	90.12 263

DU-MVS		84.57 220	83.33 224	88.28 230	88.76 298	79.36 192	96.43 179	95.41 182	85.42 135	78.11 247	90.82 243	67.61 224	95.14 299	79.14 220	68.30 332	90.33 259

dmvs_re		84.10 227	82.90 230	87.70 243	91.41 254	73.28 303	90.59 325	93.19 298	85.02 146	77.96 249	93.68 197	57.92 297	96.18 244	75.50 258	80.87 248	93.63 232

miper_enhance_ethall		85.95 197	85.20 190	88.19 235	94.85 145	79.76 180	96.00 201	94.06 256	82.98 203	77.74 250	88.76 269	79.42 79	95.46 284	80.58 203	72.42 299	89.36 280

v1144		82.90 250	81.27 255	87.78 242	86.29 327	79.07 203	96.14 197	93.93 259	80.05 259	77.38 251	86.80 300	65.50 240	95.93 257	75.21 261	70.13 313	88.33 311

FC-MVSNet-test		85.96 196	85.39 187	87.66 245	89.38 295	78.02 231	95.65 220	96.87 49	85.12 144	77.34 252	91.94 226	76.28 134	94.74 312	77.09 240	78.82 265	90.21 261

v2v482		83.46 238	81.86 246	88.25 232	86.19 329	79.65 186	96.34 185	94.02 257	81.56 229	77.32 253	88.23 278	65.62 239	96.03 247	77.77 229	69.72 320	89.09 288

Baseline_NR-MVSNet		81.22 275	80.07 273	84.68 303	85.32 344	75.12 286	96.48 173	88.80 356	76.24 311	77.28 254	86.40 310	67.61 224	94.39 322	75.73 257	66.73 348	84.54 359

V42		83.04 247	81.53 251	87.57 250	86.27 328	79.09 202	95.87 210	94.11 253	80.35 252	77.22 255	86.79 301	65.32 244	96.02 250	77.74 230	70.14 312	87.61 324

v144192		82.43 256	80.73 262	87.54 251	85.81 336	78.22 224	95.98 202	93.78 273	79.09 279	77.11 256	86.49 305	64.66 250	95.91 258	74.20 271	69.42 321	88.49 305

ACMM	80.70 13	83.72 235	82.85 232	86.31 276	91.19 257	72.12 315	95.88 209	94.29 242	80.44 248	77.02 257	91.96 224	55.24 317	97.14 204	79.30 218	80.38 252	89.67 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 260	80.55 266	87.60 247	85.94 333	78.47 217	95.85 212	93.80 271	79.33 272	76.97 258	86.51 304	63.33 255	95.87 260	73.11 278	70.13 313	88.46 307

PCF-MVS	84.09 5	86.77 185	85.00 196	92.08 128	92.06 240	83.07 103	92.14 308	94.47 231	79.63 267	76.90 259	94.78 173	71.15 208	99.20 92	72.87 279	91.05 162	93.98 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		85.11 211	84.17 209	87.92 239	95.06 139	78.82 206	95.51 225	94.22 246	79.74 265	76.77 260	87.92 283	75.96 138	95.68 271	79.93 212	72.42 299	89.27 282

v1921920		82.02 264	80.23 270	87.41 255	85.62 338	77.92 237	95.79 215	93.69 278	78.86 283	76.67 261	86.44 307	62.50 258	95.83 262	72.69 280	69.77 319	88.47 306

WR-MVS		84.32 224	82.96 228	88.41 226	89.38 295	80.32 165	96.59 166	96.25 124	83.97 177	76.63 262	90.36 251	67.53 227	94.86 309	75.82 256	70.09 316	90.06 268

BH-untuned		86.95 180	85.94 180	89.99 194	94.52 154	77.46 249	96.78 156	93.37 293	81.80 225	76.62 263	93.81 196	66.64 235	97.02 207	76.06 252	93.88 131	95.48 196

v1240		81.70 268	79.83 278	87.30 259	85.50 339	77.70 246	95.48 226	93.44 287	78.46 288	76.53 264	86.44 307	60.85 271	95.84 261	71.59 287	70.17 311	88.35 310

bld_raw_dy_0_64		82.13 262	80.76 261	86.24 278	85.78 337	75.03 288	94.40 263	82.62 384	83.12 197	76.46 265	90.96 242	53.83 325	94.55 317	81.04 200	78.60 270	89.14 286

PS-MVSNAJss		84.91 214	84.30 207	86.74 267	85.89 335	74.40 295	94.95 250	94.16 250	83.93 180	76.45 266	90.11 257	71.04 210	95.77 265	83.16 187	79.02 264	90.06 268

miper_ehance_all_eth		84.57 220	83.60 219	87.50 252	92.64 216	78.25 223	95.40 231	93.47 286	79.28 275	76.41 267	87.64 286	76.53 127	95.24 294	78.58 225	72.42 299	89.01 293

LPG-MVS_test		84.20 226	83.49 222	86.33 273	90.88 263	73.06 306	95.28 233	94.13 251	82.20 218	76.31 268	93.20 204	54.83 321	96.95 211	83.72 177	80.83 249	88.98 294

LGP-MVS_train				86.33 273	90.88 263	73.06 306		94.13 251	82.20 218	76.31 268	93.20 204	54.83 321	96.95 211	83.72 177	80.83 249	88.98 294

F-COLMAP		84.50 222	83.44 223	87.67 244	95.22 131	72.22 311	95.95 204	93.78 273	75.74 312	76.30 270	95.18 159	59.50 279	98.45 135	72.67 281	86.59 202	92.35 244

tpmvs		83.04 247	80.77 260	89.84 201	95.43 124	77.96 234	85.59 361	95.32 187	75.31 316	76.27 271	83.70 342	73.89 178	97.41 185	59.53 344	81.93 245	94.14 222

tt0805		81.20 276	79.06 284	87.61 246	86.50 323	72.97 308	93.66 280	95.48 174	74.11 324	76.23 272	91.99 222	41.36 367	97.40 186	77.44 238	74.78 288	92.45 242

3Dnovator	82.32 10	89.33 130	87.64 151	94.42 33	93.73 181	85.70 44	97.73 76	96.75 65	86.73 116	76.21 273	95.93 132	62.17 260	99.68 51	81.67 197	97.81 61	97.88 91

TranMVSNet+NR-MVSNet		83.24 243	81.71 248	87.83 240	87.71 313	78.81 208	96.13 199	94.82 208	84.52 160	76.18 274	90.78 245	64.07 251	94.60 316	74.60 268	66.59 349	90.09 266

c3_l		83.80 233	82.65 235	87.25 260	92.10 236	77.74 245	95.25 236	93.04 305	78.58 286	76.01 275	87.21 294	75.25 159	95.11 301	77.54 236	68.89 326	88.91 299

1314		88.94 137	87.20 164	94.17 41	93.21 195	85.73 43	93.33 289	96.64 81	82.89 204	75.98 276	96.36 124	66.83 234	99.39 77	83.52 184	96.02 105	97.39 132

Fast-Effi-MVS+-dtu		83.33 240	82.60 236	85.50 292	89.55 291	69.38 340	96.09 200	91.38 326	82.30 217	75.96 277	91.41 231	56.71 306	95.58 280	75.13 262	84.90 220	91.54 245

XXY-MVS		83.84 232	82.00 244	89.35 209	87.13 319	81.38 139	95.72 216	94.26 243	80.15 257	75.92 278	90.63 246	61.96 265	96.52 231	78.98 222	73.28 297	90.14 262

RRT_MVS		83.88 231	83.27 225	85.71 286	87.53 317	72.12 315	95.35 232	94.33 240	83.81 184	75.86 279	91.28 235	60.55 272	95.09 304	83.93 171	76.76 279	89.90 271

GBi-Net		82.42 257	80.43 268	88.39 227	92.66 213	81.95 119	94.30 266	93.38 290	79.06 280	75.82 280	85.66 316	56.38 311	93.84 330	71.23 290	75.38 285	89.38 277

test1		82.42 257	80.43 268	88.39 227	92.66 213	81.95 119	94.30 266	93.38 290	79.06 280	75.82 280	85.66 316	56.38 311	93.84 330	71.23 290	75.38 285	89.38 277

FMVSNet3		84.71 216	82.71 234	90.70 176	94.55 152	87.71 21	95.92 206	94.67 217	81.73 227	75.82 280	88.08 281	66.99 232	94.47 320	71.23 290	75.38 285	89.91 270

eth_miper_zixun_eth		83.12 245	82.01 243	86.47 272	91.85 248	74.80 289	94.33 264	93.18 300	79.11 278	75.74 283	87.25 293	72.71 190	95.32 290	76.78 244	67.13 344	89.27 282

IterMVS-LS		83.93 230	82.80 233	87.31 258	91.46 253	77.39 251	95.66 219	93.43 288	80.44 248	75.51 284	87.26 292	73.72 181	95.16 298	76.99 241	70.72 309	89.39 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 125	87.85 146	94.99 21	94.49 159	86.76 31	97.84 67	95.74 161	86.10 121	75.47 285	96.02 131	65.00 246	99.51 71	82.91 190	97.07 82	98.72 40

test_djsdf		83.00 249	82.45 238	84.64 305	84.07 356	69.78 336	94.80 255	94.48 229	80.74 240	75.41 286	87.70 285	61.32 270	95.10 302	83.77 175	79.76 254	89.04 291

v148		82.41 259	80.89 258	86.99 265	86.18 330	76.81 261	96.27 188	93.82 268	80.49 247	75.28 287	86.11 315	67.32 230	95.75 267	75.48 259	67.03 346	88.42 309

QAPM		86.88 181	84.51 202	93.98 44	94.04 174	85.89 41	97.19 117	96.05 141	73.62 328	75.12 288	95.62 142	62.02 263	99.74 38	70.88 294	96.06 103	96.30 178

UniMVSNet_ETH3D		80.86 280	78.75 286	87.22 261	86.31 326	72.02 317	91.95 309	93.76 276	73.51 329	75.06 289	90.16 255	43.04 361	95.66 272	76.37 250	78.55 271	93.98 226

cl____		83.27 241	82.12 241	86.74 267	92.20 229	75.95 278	95.11 245	93.27 296	78.44 289	74.82 290	87.02 297	74.19 174	95.19 296	74.67 266	69.32 322	89.09 288

DIV-MVS_self_test		83.27 241	82.12 241	86.74 267	92.19 230	75.92 280	95.11 245	93.26 297	78.44 289	74.81 291	87.08 296	74.19 174	95.19 296	74.66 267	69.30 323	89.11 287

FMVSNet2		82.79 251	80.44 267	89.83 202	92.66 213	85.43 52	95.42 229	94.35 238	79.06 280	74.46 292	87.28 290	56.38 311	94.31 323	69.72 302	74.68 289	89.76 272

MIMVSNet		79.18 296	75.99 305	88.72 222	87.37 318	80.66 157	79.96 375	91.82 320	77.38 299	74.33 293	81.87 351	41.78 364	90.74 364	66.36 320	83.10 230	94.76 212

RPSCF		77.73 306	76.63 301	81.06 338	88.66 302	55.76 385	87.77 345	87.88 363	64.82 366	74.14 294	92.79 213	49.22 340	96.81 221	67.47 310	76.88 278	90.62 253

ACMP	81.66 11	84.00 229	83.22 226	86.33 273	91.53 252	72.95 309	95.91 208	93.79 272	83.70 188	73.79 295	92.22 218	54.31 324	96.89 215	83.98 170	79.74 256	89.16 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 273	79.54 279	86.73 270	85.02 346	76.91 258	96.22 191	91.65 323	77.65 295	73.55 296	88.61 271	55.70 314	94.43 321	74.12 272	73.35 296	88.86 300

jajsoiax		82.12 263	81.15 257	85.03 299	84.19 354	70.70 329	94.22 270	93.95 258	83.07 199	73.48 297	89.75 259	49.66 339	95.37 287	82.24 194	79.76 254	89.02 292

Syy-MVS		77.97 304	78.05 290	77.74 353	92.13 234	56.85 380	93.97 274	94.23 244	82.43 214	73.39 298	93.57 200	57.95 295	87.86 375	32.40 393	82.34 240	88.51 303

myMVS_eth3d		81.93 265	82.18 240	81.18 337	92.13 234	67.18 349	93.97 274	94.23 244	82.43 214	73.39 298	93.57 200	76.98 119	87.86 375	50.53 375	82.34 240	88.51 303

mvs_tets		81.74 267	80.71 263	84.84 300	84.22 353	70.29 332	93.91 276	93.78 273	82.77 208	73.37 300	89.46 262	47.36 349	95.31 291	81.99 195	79.55 260	88.92 298

pmmvs4		82.54 255	80.79 259	87.79 241	86.11 331	80.49 164	93.55 284	93.18 300	77.29 300	73.35 301	89.40 263	65.26 245	95.05 306	75.32 260	73.61 293	87.83 319

LS3D		82.22 261	79.94 276	89.06 213	97.43 79	74.06 298	93.20 295	92.05 317	61.90 371	73.33 302	95.21 156	59.35 280	99.21 88	54.54 364	92.48 150	93.90 228

v10		81.43 272	79.53 280	87.11 262	86.38 324	78.87 205	94.31 265	93.43 288	77.88 292	73.24 303	85.26 324	65.44 241	95.75 267	72.14 284	67.71 339	86.72 337

v8		81.88 266	80.06 274	87.32 257	86.63 322	79.04 204	94.41 260	93.65 280	78.77 284	73.19 304	85.57 320	66.87 233	95.81 263	73.84 275	67.61 340	87.11 333

test0.0.03 1		82.79 251	82.48 237	83.74 318	86.81 321	72.22 311	96.52 170	95.03 197	83.76 186	73.00 305	93.20 204	72.30 196	88.88 371	64.15 328	77.52 277	90.12 263

anonymousdsp		80.98 279	79.97 275	84.01 313	81.73 366	70.44 331	92.49 304	93.58 284	77.10 304	72.98 306	86.31 311	57.58 298	94.90 307	79.32 217	78.63 269	86.69 338

XVG-ACMP-BASELINE		79.38 294	77.90 292	83.81 315	84.98 347	67.14 353	89.03 334	93.18 300	80.26 256	72.87 307	88.15 280	38.55 372	96.26 240	76.05 253	78.05 275	88.02 316

WR-MVS_H		81.02 277	80.09 271	83.79 316	88.08 308	71.26 328	94.46 258	96.54 93	80.08 258	72.81 308	86.82 299	70.36 216	92.65 343	64.18 327	67.50 341	87.46 330

OpenMVS	79.58 14	86.09 194	83.62 218	93.50 68	90.95 262	86.71 32	97.44 99	95.83 156	75.35 314	72.64 309	95.72 137	57.42 302	99.64 55	71.41 288	95.85 108	94.13 223

Anonymous20231211		79.72 289	77.19 297	87.33 256	95.59 121	77.16 257	95.18 242	94.18 249	59.31 384	72.57 310	86.20 313	47.89 346	95.66 272	74.53 269	69.24 324	89.18 284

CP-MVSNet		81.01 278	80.08 272	83.79 316	87.91 311	70.51 330	94.29 269	95.65 165	80.83 237	72.54 311	88.84 268	63.71 252	92.32 346	68.58 307	68.36 331	88.55 302

miper_lstm_enhance		81.66 270	80.66 264	84.67 304	91.19 257	71.97 319	91.94 310	93.19 298	77.86 293	72.27 312	85.26 324	73.46 184	93.42 338	73.71 276	67.05 345	88.61 301

PS-CasMVS		80.27 285	79.18 281	83.52 322	87.56 315	69.88 335	94.08 272	95.29 188	80.27 255	72.08 313	88.51 275	59.22 283	92.23 348	67.49 309	68.15 334	88.45 308

FMVSNet1		79.50 292	76.54 302	88.39 227	88.47 303	81.95 119	94.30 266	93.38 290	73.14 333	72.04 314	85.66 316	43.86 355	93.84 330	65.48 322	72.53 298	89.38 277

PEN-MVS		79.47 293	78.26 289	83.08 325	86.36 325	68.58 343	93.85 278	94.77 212	79.76 264	71.37 315	88.55 272	59.79 275	92.46 344	64.50 326	65.40 351	88.19 313

testing3		80.74 281	81.17 256	79.44 346	91.15 259	63.48 364	97.16 122	95.76 159	80.83 237	71.36 316	93.15 207	78.22 98	87.30 380	43.19 387	79.67 257	87.55 328

Patchmtry		77.36 310	74.59 315	85.67 288	89.75 286	75.75 282	77.85 383	91.12 331	60.28 379	71.23 317	80.35 359	75.45 149	93.56 336	57.94 350	67.34 343	87.68 322

IterMVS		80.67 282	79.16 282	85.20 296	89.79 284	76.08 272	92.97 299	91.86 319	80.28 254	71.20 318	85.14 329	57.93 296	91.34 358	72.52 282	70.74 308	88.18 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 271	78.28 288	91.04 164	98.14 55	78.48 214	95.09 248	86.97 366	61.14 377	71.12 319	92.78 214	59.59 277	99.38 78	53.11 368	86.61 201	95.27 202

IterMVS-SCA-FT		80.51 284	79.10 283	84.73 302	89.63 290	74.66 290	92.98 298	91.81 321	80.05 259	71.06 320	85.18 327	58.04 292	91.40 357	72.48 283	70.70 310	88.12 315

v7n		79.32 295	77.34 295	85.28 295	84.05 357	72.89 310	93.38 287	93.87 265	75.02 319	70.68 321	84.37 336	59.58 278	95.62 277	67.60 308	67.50 341	87.32 332

MS-PatchMatch		83.05 246	81.82 247	86.72 271	89.64 289	79.10 201	94.88 252	94.59 225	79.70 266	70.67 322	89.65 260	50.43 335	96.82 220	70.82 297	95.99 106	84.25 362

DTE-MVSNet		78.37 299	77.06 298	82.32 332	85.22 345	67.17 352	93.40 286	93.66 279	78.71 285	70.53 323	88.29 277	59.06 284	92.23 348	61.38 340	63.28 360	87.56 326

pm-mvs1		80.05 286	78.02 291	86.15 279	85.42 340	75.81 281	95.11 245	92.69 310	77.13 302	70.36 324	87.43 288	58.44 288	95.27 293	71.36 289	64.25 356	87.36 331

D2MVS		82.67 253	81.55 250	86.04 281	87.77 312	76.47 265	95.21 238	96.58 89	82.66 211	70.26 325	85.46 323	60.39 273	95.80 264	76.40 249	79.18 262	85.83 352

PVSNet_0	77.72 15	81.70 268	78.95 285	89.94 198	90.77 269	76.72 263	95.96 203	96.95 42	85.01 147	70.24 326	88.53 274	52.32 327	98.20 145	86.68 153	44.08 391	94.89 208

CL-MVSNet_self_test		75.81 319	74.14 321	80.83 340	78.33 376	67.79 346	94.22 270	93.52 285	77.28 301	69.82 327	81.54 353	61.47 269	89.22 370	57.59 353	53.51 376	85.48 354

tfpnnormal		78.14 301	75.42 308	86.31 276	88.33 306	79.24 195	94.41 260	96.22 127	73.51 329	69.81 328	85.52 322	55.43 315	95.75 267	47.65 382	67.86 337	83.95 365

EU-MVSNet		76.92 314	76.95 299	76.83 356	84.10 355	54.73 387	91.77 313	92.71 309	72.74 337	69.57 329	88.69 270	58.03 294	87.43 379	64.91 325	70.00 317	88.33 311

ITE_SJBPF				82.38 330	87.00 320	65.59 355		89.55 347	79.99 261	69.37 330	91.30 234	41.60 366	95.33 289	62.86 335	74.63 290	86.24 344

DSMNet-mixed		73.13 332	72.45 328	75.19 362	77.51 379	46.82 392	85.09 364	82.01 385	67.61 361	69.27 331	81.33 354	50.89 332	86.28 382	54.54 364	83.80 224	92.46 241

MVP-Stereo		82.65 254	81.67 249	85.59 291	86.10 332	78.29 221	93.33 289	92.82 307	77.75 294	69.17 332	87.98 282	59.28 282	95.76 266	71.77 285	96.88 86	82.73 370
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 283	77.77 293	89.14 212	93.43 192	77.24 253	91.89 311	90.18 343	69.86 353	68.02 333	91.94 226	52.21 329	98.84 117	59.32 347	83.12 229	91.35 246

NR-MVSNet		83.35 239	81.52 252	88.84 218	88.76 298	81.31 141	94.45 259	95.16 192	84.65 157	67.81 334	90.82 243	70.36 216	94.87 308	74.75 264	66.89 347	90.33 259

TransMVSNet (Re)		76.94 313	74.38 317	84.62 306	85.92 334	75.25 285	95.28 233	89.18 352	73.88 327	67.22 335	86.46 306	59.64 276	94.10 326	59.24 348	52.57 380	84.50 360

Anonymous20231206		75.29 322	73.64 323	80.22 342	80.75 367	63.38 365	93.36 288	90.71 341	73.09 334	67.12 336	83.70 342	50.33 336	90.85 363	53.63 367	70.10 315	86.44 341

ppachtmachnet_test		77.19 311	74.22 319	86.13 280	85.39 341	78.22 224	93.98 273	91.36 328	71.74 344	67.11 337	84.87 333	56.67 307	93.37 340	52.21 369	64.59 353	86.80 336

KD-MVS_2432*1600		77.63 307	74.92 312	85.77 284	90.86 266	79.44 189	88.08 341	93.92 261	76.26 309	67.05 338	82.78 347	72.15 198	91.92 351	61.53 337	41.62 394	85.94 350

miper_refine_blended		77.63 307	74.92 312	85.77 284	90.86 266	79.44 189	88.08 341	93.92 261	76.26 309	67.05 338	82.78 347	72.15 198	91.92 351	61.53 337	41.62 394	85.94 350

Patchmatch-test		78.25 300	74.72 314	88.83 219	91.20 256	74.10 297	73.91 391	88.70 359	59.89 382	66.82 340	85.12 330	78.38 95	94.54 318	48.84 380	79.58 259	97.86 94

test_fmvs3		69.56 343	69.19 343	70.67 365	69.01 391	47.05 391	90.87 323	86.81 368	71.31 347	66.79 341	77.15 369	16.40 396	83.17 388	81.84 196	62.51 362	81.79 378

LTVRE_ROB	73.68 18	77.99 302	75.74 307	84.74 301	90.45 274	72.02 317	86.41 356	91.12 331	72.57 339	66.63 342	87.27 291	54.95 320	96.98 209	56.29 359	75.98 280	85.21 356
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
OurMVSNet-221017-0		77.18 312	76.06 304	80.55 341	83.78 360	60.00 376	90.35 326	91.05 334	77.01 306	66.62 343	87.92 283	47.73 347	94.03 327	71.63 286	68.44 330	87.62 323

testgi		74.88 324	73.40 324	79.32 347	80.13 371	61.75 369	93.21 294	86.64 370	79.49 270	66.56 344	91.06 238	35.51 379	88.67 372	56.79 358	71.25 304	87.56 326

LCM-MVSNet-Re		83.75 234	83.54 220	84.39 312	93.54 185	64.14 360	92.51 303	84.03 379	83.90 181	66.14 345	86.59 303	67.36 229	92.68 342	84.89 164	92.87 144	96.35 173

pmmvs6		74.65 325	71.67 331	83.60 321	79.13 374	69.94 334	93.31 292	90.88 338	61.05 378	65.83 346	84.15 339	43.43 357	94.83 310	66.62 315	60.63 365	86.02 348

our_test_3		77.90 305	75.37 309	85.48 293	85.39 341	76.74 262	93.63 281	91.67 322	73.39 332	65.72 347	84.65 335	58.20 291	93.13 341	57.82 351	67.87 336	86.57 340

COLMAP_ROB	73.24 19	75.74 320	73.00 327	83.94 314	92.38 219	69.08 341	91.85 312	86.93 367	61.48 374	65.32 348	90.27 252	42.27 363	96.93 214	50.91 373	75.63 284	85.80 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 316	74.16 320	83.35 324	90.05 282	76.17 270	89.58 330	89.85 345	71.39 346	65.29 349	80.42 358	50.61 334	87.70 378	61.05 342	69.24 324	86.18 345

ACMH+	76.62 16	77.47 309	74.94 311	85.05 298	91.07 261	71.58 325	93.26 293	90.01 344	71.80 343	64.76 350	88.55 272	41.62 365	96.48 232	62.35 336	71.00 306	87.09 334

Patchmatch-RL test		76.65 315	74.01 322	84.55 307	77.37 380	64.23 359	78.49 382	82.84 383	78.48 287	64.63 351	73.40 379	76.05 137	91.70 356	76.99 241	57.84 369	97.72 105

SixPastTwentyTwo		76.04 317	74.32 318	81.22 336	84.54 350	61.43 372	91.16 320	89.30 351	77.89 291	64.04 352	86.31 311	48.23 341	94.29 324	63.54 332	63.84 358	87.93 318

AllTest		75.92 318	73.06 326	84.47 308	92.18 231	67.29 347	91.07 321	84.43 377	67.63 357	63.48 353	90.18 253	38.20 373	97.16 200	57.04 355	73.37 294	88.97 296

TestCases				84.47 308	92.18 231	67.29 347		84.43 377	67.63 357	63.48 353	90.18 253	38.20 373	97.16 200	57.04 355	73.37 294	88.97 296

ACMH	75.40 17	77.99 302	74.96 310	87.10 263	90.67 270	76.41 267	93.19 296	91.64 324	72.47 340	63.44 355	87.61 287	43.34 358	97.16 200	58.34 349	73.94 291	87.72 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 118	89.03 125	92.95 88	94.38 161	86.77 30	98.14 46	96.31 120	89.30 59	63.33 356	96.72 120	90.09 11	93.63 335	90.70 103	82.29 242	98.46 53

USDC		78.65 298	76.25 303	85.85 282	87.58 314	74.60 292	89.58 330	90.58 342	84.05 174	63.13 357	88.23 278	40.69 371	96.86 219	66.57 317	75.81 283	86.09 347

LF4IMVS		72.36 336	70.82 334	76.95 355	79.18 373	56.33 381	86.12 358	86.11 372	69.30 355	63.06 358	86.66 302	33.03 383	92.25 347	65.33 323	68.64 328	82.28 374

dmvs_testset		72.00 339	73.36 325	67.91 367	83.83 359	31.90 407	85.30 363	77.12 392	82.80 207	63.05 359	92.46 216	61.54 268	82.55 390	42.22 389	71.89 303	89.29 281

KD-MVS_self_test		70.97 342	69.31 342	75.95 361	76.24 386	55.39 386	87.45 346	90.94 337	70.20 351	62.96 360	77.48 368	44.01 354	88.09 373	61.25 341	53.26 377	84.37 361

Anonymous20240521		72.06 338	69.91 339	78.50 351	77.11 381	61.67 371	91.62 317	90.97 336	65.52 364	62.37 361	79.05 364	36.32 375	90.96 362	57.75 352	68.52 329	82.87 367

test_0402		72.68 334	69.54 341	82.09 333	88.67 301	71.81 322	92.72 302	86.77 369	61.52 373	62.21 362	83.91 340	43.22 359	93.76 333	34.60 392	72.23 302	80.72 380

OpenMVS_ROB	68.52 20	73.02 333	69.57 340	83.37 323	80.54 370	71.82 321	93.60 283	88.22 361	62.37 369	61.98 363	83.15 346	35.31 380	95.47 283	45.08 385	75.88 282	82.82 368

MVS-HIRNet		71.36 341	67.00 346	84.46 310	90.58 271	69.74 337	79.15 379	87.74 365	46.09 391	61.96 364	50.50 395	45.14 353	95.64 275	53.74 366	88.11 189	88.00 317

test20.03		72.36 336	71.15 333	75.98 360	77.79 377	59.16 378	92.40 306	89.35 350	74.09 325	61.50 365	84.32 337	48.09 342	85.54 385	50.63 374	62.15 363	83.24 366

mvsany_test3		67.19 349	65.34 351	72.72 364	63.08 397	48.57 390	83.12 371	78.09 391	72.07 341	61.21 366	77.11 370	22.94 391	87.78 377	78.59 224	51.88 381	81.80 377

PM-MVS		69.32 345	66.93 347	76.49 357	73.60 388	55.84 383	85.91 359	79.32 390	74.72 321	61.09 367	78.18 366	21.76 392	91.10 361	70.86 295	56.90 371	82.51 371

TDRefinement		69.20 346	65.78 350	79.48 345	66.04 396	62.21 368	88.21 340	86.12 371	62.92 368	61.03 368	85.61 319	33.23 382	94.16 325	55.82 362	53.02 378	82.08 375

ambc				76.02 359	68.11 393	51.43 388	64.97 396	89.59 346		60.49 369	74.49 376	17.17 395	92.46 344	61.50 339	52.85 379	84.17 363

pmmvs-eth3d		73.59 328	70.66 335	82.38 330	76.40 384	73.38 300	89.39 333	89.43 349	72.69 338	60.34 370	77.79 367	46.43 351	91.26 360	66.42 319	57.06 370	82.51 371

test_vis1_rt		73.96 326	72.40 329	78.64 350	83.91 358	61.16 373	95.63 221	68.18 400	76.32 308	60.09 371	74.77 374	29.01 389	97.54 176	87.74 141	75.94 281	77.22 384

K. test v3		73.62 327	71.59 332	79.69 344	82.98 362	59.85 377	90.85 324	88.83 355	77.13 302	58.90 372	82.11 349	43.62 356	91.72 355	65.83 321	54.10 375	87.50 329

EG-PatchMatch MVS		74.92 323	72.02 330	83.62 320	83.76 361	73.28 303	93.62 282	92.04 318	68.57 356	58.88 373	83.80 341	31.87 385	95.57 281	56.97 357	78.67 266	82.00 376

lessismore_v0				79.98 343	80.59 369	58.34 379		80.87 386		58.49 374	83.46 344	43.10 360	93.89 329	63.11 334	48.68 384	87.72 320

N_pmnet		61.30 353	60.20 356	64.60 372	84.32 352	17.00 413	91.67 316	10.98 411	61.77 372	58.45 375	78.55 365	49.89 338	91.83 354	42.27 388	63.94 357	84.97 357

TinyColmap		72.41 335	68.99 344	82.68 328	88.11 307	69.59 338	88.41 339	85.20 374	65.55 363	57.91 376	84.82 334	30.80 387	95.94 256	51.38 370	68.70 327	82.49 373

UnsupCasMVSNet_eth		73.25 331	70.57 336	81.30 335	77.53 378	66.33 354	87.24 349	93.89 264	80.38 251	57.90 377	81.59 352	42.91 362	90.56 365	65.18 324	48.51 385	87.01 335

MIMVSNet1		69.44 344	66.65 348	77.84 352	76.48 383	62.84 367	87.42 347	88.97 354	66.96 362	57.75 378	79.72 363	32.77 384	85.83 384	46.32 383	63.42 359	84.85 358

pmmvs3		65.75 351	62.18 354	76.45 358	67.12 395	64.54 357	88.68 337	85.05 375	54.77 390	57.54 379	73.79 377	29.40 388	86.21 383	55.49 363	47.77 387	78.62 382

test_f		64.01 352	62.13 355	69.65 366	63.00 398	45.30 397	83.66 370	80.68 387	61.30 375	55.70 380	72.62 382	14.23 398	84.64 386	69.84 300	58.11 368	79.00 381

new-patchmatchnet		68.85 347	65.93 349	77.61 354	73.57 389	63.94 362	90.11 328	88.73 358	71.62 345	55.08 381	73.60 378	40.84 369	87.22 381	51.35 372	48.49 386	81.67 379

UnsupCasMVSNet_bld		68.60 348	64.50 352	80.92 339	74.63 387	67.80 345	83.97 368	92.94 306	65.12 365	54.63 382	68.23 388	35.97 377	92.17 350	60.13 343	44.83 389	82.78 369

CMPMVS	54.94 21	75.71 321	74.56 316	79.17 348	79.69 372	55.98 382	89.59 329	93.30 295	60.28 379	53.85 383	89.07 265	47.68 348	96.33 238	76.55 246	81.02 246	85.22 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 350	63.18 353	75.18 363	76.27 385	61.74 370	83.79 369	84.66 376	56.64 388	51.57 384	71.85 386	31.29 386	87.93 374	49.98 376	62.55 361	75.86 385

test_method		56.77 355	54.53 359	63.49 374	76.49 382	40.70 400	75.68 387	74.24 394	19.47 402	48.73 385	71.89 385	19.31 393	65.80 402	57.46 354	47.51 388	83.97 364

YYNet1		73.53 330	70.43 337	82.85 327	84.52 351	71.73 323	91.69 315	91.37 327	67.63 357	46.79 386	81.21 355	55.04 319	90.43 366	55.93 360	59.70 367	86.38 342

MDA-MVSNet_test_wron		73.54 329	70.43 337	82.86 326	84.55 349	71.85 320	91.74 314	91.32 330	67.63 357	46.73 387	81.09 356	55.11 318	90.42 367	55.91 361	59.76 366	86.31 343

WB-MVS		57.26 354	56.22 357	60.39 378	69.29 390	35.91 405	86.39 357	70.06 398	59.84 383	46.46 388	72.71 381	51.18 331	78.11 392	15.19 402	34.89 397	67.14 391

SSC-MVS		56.01 357	54.96 358	59.17 379	68.42 392	34.13 406	84.98 365	69.23 399	58.08 387	45.36 389	71.67 387	50.30 337	77.46 393	14.28 403	32.33 398	65.91 392

MDA-MVSNet-bldmvs		71.45 340	67.94 345	81.98 334	85.33 343	68.50 344	92.35 307	88.76 357	70.40 349	42.99 390	81.96 350	46.57 350	91.31 359	48.75 381	54.39 374	86.11 346

APD_test1		56.56 356	53.58 360	65.50 369	67.93 394	46.51 394	77.24 386	72.95 395	38.09 393	42.75 391	75.17 373	13.38 399	82.78 389	40.19 390	54.53 373	67.23 390

DeepMVS_CX				64.06 373	78.53 375	43.26 398		68.11 402	69.94 352	38.55 392	76.14 372	18.53 394	79.34 391	43.72 386	41.62 394	69.57 388

LCM-MVSNet		52.52 360	48.24 363	65.35 370	47.63 407	41.45 399	72.55 392	83.62 381	31.75 395	37.66 393	57.92 393	9.19 405	76.76 395	49.26 378	44.60 390	77.84 383

test_vis3_rt		54.10 359	51.04 362	63.27 375	58.16 399	46.08 396	84.17 367	49.32 410	56.48 389	36.56 394	49.48 397	8.03 406	91.91 353	67.29 311	49.87 382	51.82 396

FPMVS		55.09 358	52.93 361	61.57 376	55.98 400	40.51 401	83.11 372	83.41 382	37.61 394	34.95 395	71.95 384	14.40 397	76.95 394	29.81 394	65.16 352	67.25 389

PMMVS2		50.90 362	46.31 365	64.67 371	55.53 401	46.67 393	77.30 385	71.02 397	40.89 392	34.16 396	59.32 391	9.83 404	76.14 397	40.09 391	28.63 399	71.21 386

testf1		45.70 364	42.41 366	55.58 380	53.29 404	40.02 402	68.96 394	62.67 404	27.45 397	29.85 397	61.58 389	5.98 407	73.83 399	28.49 397	43.46 392	52.90 394

APD_test2		45.70 364	42.41 366	55.58 380	53.29 404	40.02 402	68.96 394	62.67 404	27.45 397	29.85 397	61.58 389	5.98 407	73.83 399	28.49 397	43.46 392	52.90 394

tmp_tt		41.54 367	41.93 369	40.38 385	20.10 411	26.84 409	61.93 397	59.09 406	14.81 404	28.51 399	80.58 357	35.53 378	48.33 406	63.70 331	13.11 403	45.96 399

Gipuma		45.11 366	42.05 368	54.30 382	80.69 368	51.30 389	35.80 400	83.81 380	28.13 396	27.94 400	34.53 400	11.41 403	76.70 396	21.45 399	54.65 372	34.90 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 363	41.28 370	61.04 377	39.91 409	46.25 395	70.59 393	76.18 393	58.87 385	23.09 401	48.00 398	12.58 401	66.54 401	28.65 396	13.62 402	70.35 387

MVE	35.65 22	33.85 369	29.49 374	46.92 384	41.86 408	36.28 404	50.45 399	56.52 407	18.75 403	18.28 402	37.84 399	2.41 410	58.41 403	18.71 400	20.62 400	46.06 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 368	35.53 371	50.18 383	29.72 410	30.30 408	59.60 398	66.20 403	26.06 399	17.91 403	49.53 396	3.12 409	74.09 398	18.19 401	49.40 383	46.14 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 370	32.39 372	33.65 386	53.35 403	25.70 410	74.07 390	53.33 408	21.08 400	17.17 404	33.63 402	11.85 402	54.84 404	12.98 404	14.04 401	20.42 401

EMVS		31.70 371	31.45 373	32.48 387	50.72 406	23.95 411	74.78 389	52.30 409	20.36 401	16.08 405	31.48 403	12.80 400	53.60 405	11.39 405	13.10 404	19.88 402

wuyk23d		14.10 373	13.89 376	14.72 388	55.23 402	22.91 412	33.83 401	3.56 412	4.94 405	4.11 406	2.28 408	2.06 411	19.66 407	10.23 406	8.74 405	1.59 405

testmvs		9.92 374	12.94 377	0.84 390	0.65 412	0.29 415	93.78 279	0.39 413	0.42 406	2.85 407	15.84 406	0.17 413	0.30 409	2.18 407	0.21 406	1.91 404

test123		9.07 375	11.73 378	1.11 389	0.50 413	0.77 414	89.44 332	0.20 414	0.34 407	2.15 408	10.72 407	0.34 412	0.32 408	1.79 408	0.08 407	2.23 403

EGC-MVSNET		52.46 361	47.56 364	67.15 368	81.98 365	60.11 375	82.54 373	72.44 396	0.11 408	0.70 409	74.59 375	25.11 390	83.26 387	29.04 395	61.51 364	58.09 393

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		21.43 372	28.57 375	0.00 391	0.00 414	0.00 416	0.00 402	95.93 151	0.00 409	0.00 410	97.66 72	63.57 253	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		5.92 377	7.89 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	71.04 210	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.11 376	10.81 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	97.30 94	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						67.18 349								49.00 379

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 52					99.81 21	98.08 14	98.81 24	99.43 11

No_MVS				97.14 3	99.05 9	92.19 4		96.83 52					99.81 21	98.08 14	98.81 24	99.43 11

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 11				98.54 20	92.06 3	99.84 12	99.11 2	99.37 1	99.74 1

save fliter					98.24 51	83.34 98	98.61 33	96.57 90	91.32 32

test_0728_SECOND				95.14 18	99.04 14	86.14 35	99.06 16	96.77 61					99.84 12	97.90 17	98.85 21	99.45 10

GSMVS																97.54 118

sam_mvs1												77.59 108				97.54 118

sam_mvs												75.35 156

MTGPA								96.33 118

test_post1							85.88 360				30.24 404	73.77 179	95.07 305	73.89 273

test_post											33.80 401	76.17 135	95.97 252

patchmatchnet-post											77.09 371	77.78 107	95.39 285

MTMP							97.53 91	68.16 401

gm-plane-assit					92.27 225	79.64 187			84.47 163		95.15 161		97.93 152	85.81 156

test9_res														96.00 39	99.03 13	98.31 62

agg_prior2														94.30 58	99.00 15	98.57 46

test_prior4						82.34 115	97.75 75

test_prior				93.09 83	98.68 26	81.91 123		96.40 110					99.06 104			98.29 64

新几何2							96.42 180

旧先验1					97.39 82	79.58 188		96.54 93			98.08 48	84.00 44			97.42 74	97.62 114

无先验							96.87 149	96.78 55	77.39 298				99.52 69	79.95 211		98.43 55

原ACMM2							96.84 150

testdata2													99.48 73	76.45 248

segment_acmp												82.69 56

testdata1							95.57 224		87.44 95

plane_prior7					91.86 246	77.55 248

plane_prior6					91.98 242	77.92 237						64.77 248

plane_prior5								94.69 214					97.30 192	87.08 147	82.82 235	90.96 249

plane_prior4											94.15 187

plane_prior2							97.18 118		89.89 53

plane_prior1					91.95 244

plane_prior						77.96 234	97.52 94		90.36 48						82.96 233

n2								0.00 415

nn								0.00 415

door-mid								79.75 389

test11								96.50 98

door								80.13 388

HQP5-MVS						78.48 214

BP-MVS														87.67 143

HQP3-MVS								94.80 209							83.01 231

HQP2-MVS												65.40 242

NP-MVS					92.04 241	78.22 224					94.56 177

ACMMP++_ref															78.45 272

ACMMP++															79.05 263

Test By Simon												71.65 203

High-resolution multi-view benchmark