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 58		96.86 50	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 22	98.96 4	99.37 1	99.70 3

fmvsm_l_conf0.5_n		94.89 16	95.24 17	93.86 53	94.42 165	84.61 79	99.13 11	96.15 133	92.06 25	97.92 3	98.52 23	84.52 35	99.74 38	98.76 6	95.67 115	97.22 146

SMA-MVS		94.70 21	94.68 21	94.76 30	98.02 59	85.94 45	97.47 95	96.77 60	85.32 139	97.92 3	98.70 15	83.09 53	99.84 13	95.79 43	99.08 10	98.49 57
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 16	93.72 62	94.50 163	84.30 84	99.14 10	96.00 144	91.94 28	97.91 5	98.60 18	84.78 33	99.77 29	98.84 5	96.03 109	97.08 154

SED-MVS		95.88 5	96.22 4	94.87 26	99.03 15	85.03 71	99.12 12	96.78 54	88.72 66	97.79 6	98.91 2	88.48 16	99.82 19	98.15 11	98.97 17	99.74 1

test_241102_ONE					99.03 15	85.03 71		96.78 54	88.72 66	97.79 6	98.90 5	88.48 16	99.82 19

DVP-MVS++		96.05 4	96.41 3	94.96 25	99.05 9	85.34 58	98.13 49	96.77 60	88.38 74	97.70 8	98.77 10	92.06 3	99.84 13	97.47 24	99.37 1	99.70 3

test_241102_TWO								96.78 54	88.72 66	97.70 8	98.91 2	87.86 20	99.82 19	98.15 11	99.00 15	99.47 9

patch_mono-2		95.14 13	96.08 7	92.33 121	98.44 43	77.84 245	98.43 36	97.21 22	92.58 19	97.68 10	97.65 76	86.88 24	99.83 17	98.25 9	97.60 70	99.33 18

test0726					99.05 9	85.18 63	99.11 15	96.78 54	88.75 64	97.65 11	98.91 2	87.69 21

TSAR-MVS + MP.		94.79 20	95.17 18	93.64 65	97.66 69	84.10 87	95.85 212	96.42 107	91.26 33	97.49 12	96.80 118	86.50 26	98.49 132	95.54 49	99.03 13	98.33 65
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	92.45 114	95.29 134	80.96 153	99.29 2	97.21 22	94.50 7	97.29 13	98.44 27	82.15 58	99.78 28	98.56 7	97.68 68	96.61 172

MSP-MVS		95.62 8	96.54 1	92.86 98	98.31 48	80.10 179	97.42 102	96.78 54	92.20 22	97.11 14	98.29 33	93.46 1	99.10 101	96.01 39	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
MVS_0304		95.58 9	95.44 15	96.01 10	97.63 70	89.26 12	99.27 3	96.59 87	94.71 4	97.08 15	97.99 52	78.69 95	99.86 10	99.15 2	97.85 63	98.91 36

fmvsm_s_conf0.5_n_a		93.34 43	93.71 36	92.22 128	93.38 198	81.71 137	98.86 25	96.98 37	91.64 29	96.85 16	98.55 19	75.58 149	99.77 29	97.88 19	93.68 138	95.18 212

CNVR-MVS		96.30 1	96.54 1	95.55 15	99.31 5	87.69 25	99.06 17	97.12 29	94.66 5	96.79 17	98.78 9	86.42 27	99.95 3	97.59 23	99.18 7	99.00 32

DVP-MVS		95.58 9	95.91 9	94.57 35	99.05 9	85.18 63	99.06 17	96.46 102	88.75 64	96.69 18	98.76 12	87.69 21	99.76 31	97.90 17	98.85 21	98.77 41
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 74	96.69 18	98.76 12	89.64 12	99.76 31	97.47 24	98.84 23	99.38 14

SD-MVS		94.84 18	95.02 19	94.29 41	97.87 64	84.61 79	97.76 74	96.19 131	89.59 56	96.66 20	98.17 41	84.33 37	99.60 59	96.09 38	98.50 39	98.66 49
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 98	89.50 9	99.18 6	98.10 8	95.68 1	96.64 21	97.92 58	80.72 66	99.80 25	99.16 1	97.96 59	99.15 27

fmvsm_s_conf0.1_n_a		92.38 68	92.49 60	92.06 136	88.08 315	81.62 140	97.97 61	96.01 143	90.62 42	96.58 22	98.33 32	74.09 179	99.71 45	97.23 28	93.46 143	94.86 217

test_one_0601					98.91 18	84.56 81		96.70 70	88.06 82	96.57 23	98.77 10	88.04 19

DPE-MVS		95.32 11	95.55 12	94.64 34	98.79 23	84.87 76	97.77 72	96.74 65	86.11 122	96.54 24	98.89 6	88.39 18	99.74 38	97.67 22	99.05 12	99.31 20
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	96.26 101	95.09 1	99.15 8	96.98 37	93.39 14	96.45 25	98.79 8	90.17 9	99.99 1	89.33 131	99.25 6	99.70 3

fmvsm_s_conf0.5_n		93.69 37	94.13 33	92.34 119	94.56 156	82.01 122	99.07 16	97.13 27	92.09 23	96.25 26	98.53 22	76.47 131	99.80 25	98.39 8	94.71 124	95.22 211

PS-MVSNAJ		94.17 30	93.52 41	96.10 9	95.65 123	92.35 2	98.21 44	95.79 158	92.42 21	96.24 27	98.18 38	71.04 216	99.17 95	96.77 34	97.39 79	96.79 165

旧先验2							96.97 140		74.06 328	96.10 28			97.76 167	88.38 142

test_part2					98.90 19	85.14 69				96.07 29

fmvsm_s_conf0.1_n		92.93 50	93.16 48	92.24 126	90.52 278	81.92 126	98.42 37	96.24 125	91.17 34	96.02 30	98.35 31	75.34 160	99.74 38	97.84 20	94.58 126	95.05 213

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 11	95.07 142	92.02 6	98.19 45	95.68 164	92.06 25	96.01 31	98.14 42	70.83 220	98.96 109	96.74 36	96.57 99	96.76 168

balanced_conf03		94.60 23	94.30 29	95.48 16	96.45 96	88.82 14	96.33 186	95.58 168	91.12 35	95.84 32	93.87 196	83.47 48	98.37 141	97.26 27	98.81 24	99.24 23

HPM-MVS++		95.32 11	95.48 14	94.85 27	98.62 34	86.04 41	97.81 70	96.93 43	92.45 20	95.69 33	98.50 24	85.38 29	99.85 11	94.75 57	99.18 7	98.65 50

NCCC		95.63 7	95.94 8	94.69 33	99.21 6	85.15 68	99.16 7	96.96 40	94.11 9	95.59 34	98.64 17	85.07 31	99.91 4	95.61 46	99.10 9	99.00 32

EPNet		94.06 33	94.15 32	93.76 58	97.27 88	84.35 82	98.29 41	97.64 14	94.57 6	95.36 35	96.88 113	79.96 78	99.12 100	91.30 101	96.11 106	97.82 105
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 76	88.12 18	99.06 17	96.39 112	94.07 10	95.34 36	97.80 67	76.83 126	99.87 8	97.08 31	97.64 69	98.89 37

test_fmvsmconf_n		93.99 34	94.36 28	92.86 98	92.82 216	81.12 147	99.26 4	96.37 116	93.47 13	95.16 37	98.21 36	79.00 88	99.64 55	98.21 10	96.73 97	97.83 103

TEST9					98.64 31	83.71 93	97.82 68	96.65 77	84.29 172	95.16 37	98.09 45	84.39 36	99.36 81

train_agg		94.28 27	94.45 25	93.74 59	98.64 31	83.71 93	97.82 68	96.65 77	84.50 163	95.16 37	98.09 45	84.33 37	99.36 81	95.91 42	98.96 19	98.16 77

test_8					98.63 33	83.64 96	97.81 70	96.63 82	84.50 163	95.10 40	98.11 44	84.33 37	99.23 86

DeepPCF-MVS	89.82 1	94.61 22	96.17 5	89.91 204	97.09 91	70.21 335	98.99 23	96.69 72	95.57 2	95.08 41	99.23 1	86.40 28	99.87 8	97.84 20	98.66 32	99.65 6

SF-MVS		94.17 30	94.05 34	94.55 36	97.56 75	85.95 43	97.73 76	96.43 106	84.02 177	95.07 42	98.74 14	82.93 54	99.38 78	95.42 51	98.51 37	98.32 66

APDe-MVS		94.56 24	94.75 20	93.96 51	98.84 22	83.40 101	98.04 57	96.41 108	85.79 130	95.00 43	98.28 34	84.32 40	99.18 94	97.35 26	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVSFormer		91.36 93	90.57 99	93.73 61	93.00 209	88.08 19	94.80 256	94.48 233	80.74 240	94.90 44	97.13 103	78.84 91	95.10 306	83.77 179	97.46 74	98.02 85

lupinMVS		93.87 36	93.58 40	94.75 31	93.00 209	88.08 19	99.15 8	95.50 174	91.03 38	94.90 44	97.66 72	78.84 91	97.56 178	94.64 60	97.46 74	98.62 52

CS-MVS-test		92.98 48	93.67 37	90.90 175	96.52 95	76.87 264	98.68 28	94.73 216	90.36 49	94.84 46	97.89 62	77.94 105	97.15 209	94.28 66	97.80 65	98.70 48

9.14			94.26 31		98.10 57		98.14 46	96.52 95	84.74 155	94.83 47	98.80 7	82.80 56	99.37 80	95.95 41	98.42 43

testdata				90.13 196	95.92 114	74.17 299		96.49 101	73.49 333	94.82 48	97.99 52	78.80 93	97.93 156	83.53 187	97.52 73	98.29 70

APD-MVS		93.61 38	93.59 39	93.69 63	98.76 24	83.26 104	97.21 112	96.09 137	82.41 216	94.65 49	98.21 36	81.96 61	98.81 119	94.65 59	98.36 48	99.01 31
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior2							98.37 39		86.08 124	94.57 50	98.02 51	83.14 50		95.05 53	98.79 27

CS-MVS		92.73 55	93.48 42	90.48 187	96.27 100	75.93 284	98.55 34	94.93 203	89.32 59	94.54 51	97.67 71	78.91 90	97.02 213	93.80 69	97.32 81	98.49 57

FOURS1					98.51 39	78.01 237	98.13 49	96.21 128	83.04 200	94.39 52

ACMMP_NAP		93.46 41	93.23 46	94.17 46	97.16 89	84.28 85	96.82 153	96.65 77	86.24 120	94.27 53	97.99 52	77.94 105	99.83 17	93.39 74	98.57 34	98.39 63

agg_prior					98.59 35	83.13 106		96.56 92		94.19 54			99.16 96

SteuartSystems-ACMMP		94.13 32	94.44 26	93.20 84	95.41 128	81.35 144	99.02 21	96.59 87	89.50 58	94.18 55	98.36 30	83.68 47	99.45 75	94.77 56	98.45 42	98.81 40
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		93.59 39	93.63 38	93.48 76	98.05 58	81.76 134	98.64 31	97.13 27	82.60 212	94.09 56	98.49 25	80.35 70	99.85 11	94.74 58	98.62 33	98.83 39

test_fmvsmconf0.1_n		93.08 47	93.22 47	92.65 107	88.45 311	80.81 158	99.00 22	95.11 196	93.21 15	94.00 57	97.91 60	76.84 124	99.59 60	97.91 16	96.55 100	97.54 124

MVSMamba_PlusPlus		92.37 69	91.55 80	94.83 28	95.37 130	87.69 25	95.60 223	95.42 183	74.65 322	93.95 58	92.81 214	83.11 51	97.70 169	94.49 61	98.53 35	99.11 28

bld_raw_conf03		93.57 40	93.09 49	94.98 24	95.96 112	87.69 25	95.60 223	95.42 183	89.51 57	93.95 58	93.63 203	79.64 80	98.15 151	95.61 46	98.53 35	99.11 28

TSAR-MVS + GP.		94.35 26	94.50 23	93.89 52	97.38 85	83.04 108	98.10 51	95.29 191	91.57 30	93.81 60	97.45 85	86.64 25	99.43 76	96.28 37	94.01 133	99.20 25

CANet_DTU		90.98 104	90.04 115	93.83 54	94.76 152	86.23 39	96.32 187	93.12 311	93.11 16	93.71 61	96.82 117	63.08 263	99.48 73	84.29 172	95.12 120	95.77 195

VNet		92.11 74	91.22 86	94.79 29	96.91 92	86.98 32	97.91 63	97.96 10	86.38 119	93.65 62	95.74 138	70.16 225	98.95 111	93.39 74	88.87 184	98.43 61

test_vis1_n_1920		89.95 125	90.59 98	88.03 243	92.36 226	68.98 344	99.12 12	94.34 245	93.86 11	93.64 63	97.01 109	51.54 335	99.59 60	96.76 35	96.71 98	95.53 202

ZD-MVS					99.09 8	83.22 105		96.60 86	82.88 205	93.61 64	98.06 50	82.93 54	99.14 97	95.51 50	98.49 40

xiu_mvs_v1_base_debu		90.54 114	89.54 125	93.55 71	92.31 227	87.58 28	96.99 135	94.87 207	87.23 104	93.27 65	97.56 81	57.43 305	98.32 143	92.72 87	93.46 143	94.74 221

xiu_mvs_v1_base		90.54 114	89.54 125	93.55 71	92.31 227	87.58 28	96.99 135	94.87 207	87.23 104	93.27 65	97.56 81	57.43 305	98.32 143	92.72 87	93.46 143	94.74 221

xiu_mvs_v1_base_debi		90.54 114	89.54 125	93.55 71	92.31 227	87.58 28	96.99 135	94.87 207	87.23 104	93.27 65	97.56 81	57.43 305	98.32 143	92.72 87	93.46 143	94.74 221

CDPH-MVS		93.12 45	92.91 51	93.74 59	98.65 30	83.88 89	97.67 80	96.26 123	83.00 202	93.22 68	98.24 35	81.31 63	99.21 88	89.12 132	98.74 30	98.14 79

ETV-MVS		92.72 57	92.87 52	92.28 125	94.54 158	81.89 128	97.98 59	95.21 194	89.77 55	93.11 69	96.83 115	77.23 120	97.50 186	95.74 44	95.38 118	97.44 133

MSLP-MVS++		94.28 27	94.39 27	93.97 50	98.30 49	84.06 88	98.64 31	96.93 43	90.71 41	93.08 70	98.70 15	79.98 77	99.21 88	94.12 67	99.07 11	98.63 51

alignmvs		92.97 49	92.26 65	95.12 21	95.54 125	87.77 23	98.67 29	96.38 113	88.04 83	93.01 71	97.45 85	79.20 86	98.60 125	93.25 80	88.76 185	98.99 34

sasdasda		92.27 70	91.22 86	95.41 17	95.80 118	88.31 15	97.09 130	94.64 224	88.49 71	92.99 72	97.31 92	72.68 194	98.57 127	93.38 76	88.58 188	99.36 16

canonicalmvs		92.27 70	91.22 86	95.41 17	95.80 118	88.31 15	97.09 130	94.64 224	88.49 71	92.99 72	97.31 92	72.68 194	98.57 127	93.38 76	88.58 188	99.36 16

EC-MVSNet		91.73 82	92.11 69	90.58 184	93.54 190	77.77 248	98.07 54	94.40 242	87.44 98	92.99 72	97.11 105	74.59 173	96.87 223	93.75 70	97.08 85	97.11 152

MGCFI-Net		91.95 76	91.03 92	94.72 32	95.68 122	86.38 37	96.93 145	94.48 233	88.25 78	92.78 75	97.24 98	72.34 199	98.46 135	93.13 83	88.43 192	99.32 19

jason		92.73 55	92.23 66	94.21 45	90.50 279	87.30 31	98.65 30	95.09 197	90.61 43	92.76 76	97.13 103	75.28 161	97.30 198	93.32 78	96.75 96	98.02 85
jason: jason.
test_cas_vis1_n_1920		89.90 126	90.02 116	89.54 212	90.14 287	74.63 294	98.71 27	94.43 240	93.04 17	92.40 77	96.35 127	53.41 331	99.08 103	95.59 48	96.16 104	94.90 215

test12				94.25 42	98.34 46	85.55 55		96.35 117		92.36 78		80.84 65	99.22 87		98.31 50	97.98 92

MG-MVS		94.25 29	93.72 35	95.85 12	99.38 3	89.35 11	97.98 59	98.09 9	89.99 52	92.34 79	96.97 110	81.30 64	98.99 107	88.54 138	98.88 20	99.20 25

test_fmvs1		87.79 174	88.52 141	85.62 292	92.98 213	64.31 361	97.88 65	92.42 321	87.95 85	92.24 80	95.82 137	47.94 350	98.44 139	95.31 52	94.09 130	94.09 232

h-mvs33		89.30 136	88.95 134	90.36 190	95.07 142	76.04 278	96.96 142	97.11 30	90.39 47	92.22 81	95.10 166	74.70 169	98.86 116	93.14 81	65.89 353	96.16 185

hse-mvs2		88.22 166	88.21 145	88.25 237	93.54 190	73.41 302	95.41 232	95.89 152	90.39 47	92.22 81	94.22 186	74.70 169	96.66 234	93.14 81	64.37 358	94.69 225

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 24	97.10 31	95.17 3	92.11 83	98.46 26	87.33 23	99.97 2	97.21 29	99.31 4	99.63 7

test_fmvsmconf0.01_n		91.08 101	90.68 97	92.29 124	82.43 369	80.12 178	97.94 62	93.93 264	92.07 24	91.97 84	97.60 79	67.56 234	99.53 68	97.09 30	95.56 117	97.21 148

SR-MVS		92.16 72	92.27 64	91.83 148	98.37 45	78.41 223	96.67 164	95.76 159	82.19 220	91.97 84	98.07 49	76.44 132	98.64 123	93.71 71	97.27 82	98.45 60

region2R		92.72 57	92.70 55	92.79 101	98.68 26	80.53 168	97.53 90	96.51 96	85.22 142	91.94 86	97.98 55	77.26 116	99.67 53	90.83 108	98.37 47	98.18 75

Effi-MVS+		90.70 110	89.90 121	93.09 89	93.61 187	83.48 99	95.20 240	92.79 317	83.22 195	91.82 87	95.70 140	71.82 207	97.48 188	91.25 102	93.67 139	98.32 66

HFP-MVS		92.89 51	92.86 53	92.98 93	98.71 25	81.12 147	97.58 85	96.70 70	85.20 144	91.75 88	97.97 57	78.47 97	99.71 45	90.95 104	98.41 44	98.12 81

DeepC-MVS_fast	89.06 2	94.48 25	94.30 29	95.02 22	98.86 21	85.68 51	98.06 55	96.64 80	93.64 12	91.74 89	98.54 20	80.17 75	99.90 5	92.28 91	98.75 29	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 59	92.67 56	92.75 102	98.66 28	80.57 164	97.58 85	96.69 72	85.20 144	91.57 90	97.92 58	77.01 121	99.67 53	90.95 104	98.41 44	98.00 90

DELS-MVS		94.98 14	94.49 24	96.44 6	96.42 97	90.59 7	99.21 5	97.02 35	94.40 8	91.46 91	97.08 106	83.32 49	99.69 49	92.83 86	98.70 31	99.04 30
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 59	92.71 54	92.63 109	98.52 37	80.29 171	97.37 106	96.44 104	87.04 109	91.38 92	97.83 66	77.24 118	99.59 60	90.46 114	98.07 55	98.02 85

X-MVStestdata		86.26 197	84.14 216	92.63 109	98.52 37	80.29 171	97.37 106	96.44 104	87.04 109	91.38 92	20.73 410	77.24 118	99.59 60	90.46 114	98.07 55	98.02 85

PMMVS		89.46 133	89.92 120	88.06 241	94.64 153	69.57 341	96.22 192	94.95 202	87.27 103	91.37 94	96.54 125	65.88 246	97.39 193	88.54 138	93.89 135	97.23 145

test_fmvs1_n		86.34 195	86.72 180	85.17 299	87.54 322	63.64 366	96.91 147	92.37 323	87.49 97	91.33 95	95.58 146	40.81 375	98.46 135	95.00 54	93.49 141	93.41 246

dcpmvs_2		93.10 46	93.46 43	92.02 139	97.77 65	79.73 189	94.82 254	93.86 271	86.91 111	91.33 95	96.76 119	85.20 30	98.06 152	96.90 33	97.60 70	98.27 72

原ACMM1				91.22 167	97.77 65	78.10 235		96.61 83	81.05 234	91.28 97	97.42 89	77.92 107	98.98 108	79.85 216	98.51 37	96.59 173

新几何1				93.12 87	97.44 79	81.60 141		96.71 69	74.54 324	91.22 98	97.57 80	79.13 87	99.51 71	77.40 242	98.46 41	98.26 73

UA-Net		88.92 143	88.48 142	90.24 193	94.06 178	77.18 261	93.04 298	94.66 221	87.39 100	91.09 99	93.89 195	74.92 166	98.18 150	75.83 258	91.43 165	95.35 207

ZNCC-MVS		92.75 53	92.60 58	93.23 83	98.24 51	81.82 132	97.63 81	96.50 98	85.00 150	91.05 100	97.74 69	78.38 98	99.80 25	90.48 113	98.34 49	98.07 83

APD-MVS_3200maxsize		91.23 97	91.35 83	90.89 176	97.89 62	76.35 274	96.30 188	95.52 173	79.82 263	91.03 101	97.88 63	74.70 169	98.54 129	92.11 94	96.89 90	97.77 108

test_vis1_n		85.60 208	85.70 188	85.33 296	84.79 353	64.98 359	96.83 151	91.61 334	87.36 101	91.00 102	94.84 174	36.14 381	97.18 205	95.66 45	93.03 148	93.82 237

GST-MVS		92.43 67	92.22 67	93.04 91	98.17 54	81.64 139	97.40 104	96.38 113	84.71 157	90.90 103	97.40 90	77.55 113	99.76 31	89.75 125	97.74 66	97.72 111

PGM-MVS		91.93 77	91.80 75	92.32 123	98.27 50	79.74 188	95.28 234	97.27 20	83.83 185	90.89 104	97.78 68	76.12 139	99.56 66	88.82 135	97.93 62	97.66 116

SR-MVS-dyc-post		91.29 95	91.45 82	90.80 178	97.76 67	76.03 279	96.20 194	95.44 179	80.56 245	90.72 105	97.84 64	75.76 145	98.61 124	91.99 96	96.79 94	97.75 109

RE-MVS-def			91.18 90		97.76 67	76.03 279	96.20 194	95.44 179	80.56 245	90.72 105	97.84 64	73.36 189		91.99 96	96.79 94	97.75 109

MP-MVS		92.61 62	92.67 56	92.42 117	98.13 56	79.73 189	97.33 108	96.20 129	85.63 132	90.53 107	97.66 72	78.14 103	99.70 48	92.12 93	98.30 51	97.85 101
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 86	90.37 106	95.39 19	96.12 105	88.25 17	90.22 329	97.58 15	88.33 76	90.50 108	91.96 230	79.26 84	99.06 104	90.29 119	89.07 180	98.88 38

CP-MVS		92.54 64	92.60 58	92.34 119	98.50 40	79.90 182	98.40 38	96.40 110	84.75 154	90.48 109	98.09 45	77.40 115	99.21 88	91.15 103	98.23 53	97.92 96

diffmvs		91.17 98	90.74 96	92.44 116	93.11 208	82.50 116	96.25 191	93.62 286	87.79 89	90.40 110	95.93 134	73.44 188	97.42 190	93.62 73	92.55 153	97.41 135
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		90.29 121	89.18 129	93.62 67	95.23 135	84.93 74	94.41 261	94.66 221	84.31 168	90.37 111	91.02 243	75.13 163	97.82 165	83.11 192	94.42 128	98.12 81

MTAPA		92.45 66	92.31 63	92.86 98	97.90 61	80.85 157	92.88 301	96.33 118	87.92 86	90.20 112	98.18 38	76.71 129	99.76 31	92.57 90	98.09 54	97.96 95

test_yl		91.46 90	90.53 100	94.24 43	97.41 81	85.18 63	98.08 52	97.72 11	80.94 235	89.85 113	96.14 130	75.61 146	98.81 119	90.42 117	88.56 190	98.74 42

DCV-MVSNet		91.46 90	90.53 100	94.24 43	97.41 81	85.18 63	98.08 52	97.72 11	80.94 235	89.85 113	96.14 130	75.61 146	98.81 119	90.42 117	88.56 190	98.74 42

WTY-MVS		92.65 61	91.68 77	95.56 14	96.00 108	88.90 13	98.23 43	97.65 13	88.57 69	89.82 115	97.22 100	79.29 83	99.06 104	89.57 127	88.73 186	98.73 46

MVS_111021_HR		93.41 42	93.39 44	93.47 78	97.34 86	82.83 110	97.56 87	98.27 6	89.16 62	89.71 116	97.14 102	79.77 79	99.56 66	93.65 72	97.94 60	98.02 85

sss		90.87 108	89.96 118	93.60 68	94.15 173	83.84 92	97.14 123	98.13 7	85.93 128	89.68 117	96.09 132	71.67 208	99.30 83	87.69 148	89.16 179	97.66 116

test222					96.15 104	78.41 223	95.87 210	96.46 102	71.97 344	89.66 118	97.45 85	76.33 136			98.24 52	98.30 69

LFMVS		89.27 137	87.64 156	94.16 48	97.16 89	85.52 56	97.18 116	94.66 221	79.17 277	89.63 119	96.57 124	55.35 322	98.22 147	89.52 129	89.54 175	98.74 42

CostFormer		89.08 139	88.39 143	91.15 168	93.13 206	79.15 204	88.61 341	96.11 136	83.14 197	89.58 120	86.93 301	83.83 46	96.87 223	88.22 144	85.92 218	97.42 134

PVSNet_BlendedMVS		90.05 123	89.96 118	90.33 191	97.47 77	83.86 90	98.02 58	96.73 66	87.98 84	89.53 121	89.61 264	76.42 133	99.57 64	94.29 64	79.59 263	87.57 328

PVSNet_Blended		93.13 44	92.98 50	93.57 70	97.47 77	83.86 90	99.32 1	96.73 66	91.02 39	89.53 121	96.21 129	76.42 133	99.57 64	94.29 64	95.81 114	97.29 144

HPM-MVS		91.62 87	91.53 81	91.89 143	97.88 63	79.22 201	96.99 135	95.73 162	82.07 222	89.50 123	97.19 101	75.59 148	98.93 114	90.91 106	97.94 60	97.54 124
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testing11		92.48 65	92.04 72	93.78 57	95.94 113	86.00 42	97.56 87	97.08 32	87.52 96	89.32 124	95.40 150	84.60 34	98.02 153	91.93 98	89.04 181	97.32 140

EI-MVSNet-Vis-set		91.84 81	91.77 76	92.04 138	97.60 72	81.17 146	96.61 165	96.87 48	88.20 80	89.19 125	97.55 84	78.69 95	99.14 97	90.29 119	90.94 168	95.80 194

testing222		91.09 100	90.49 102	92.87 97	95.82 116	85.04 70	96.51 172	97.28 19	86.05 125	89.13 126	95.34 152	80.16 76	96.62 235	85.82 160	88.31 194	96.96 157

MP-MVS-pluss		92.58 63	92.35 62	93.29 80	97.30 87	82.53 114	96.44 177	96.04 142	84.68 158	89.12 127	98.37 29	77.48 114	99.74 38	93.31 79	98.38 46	97.59 122
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 164	87.02 175	92.06 136	95.09 140	80.18 177	97.55 89	94.45 238	83.09 198	89.10 128	95.92 136	47.97 349	98.49 132	93.08 85	86.91 207	97.52 129

baseline		90.76 109	90.10 113	92.74 103	92.90 215	82.56 113	94.60 258	94.56 230	87.69 92	89.06 129	95.67 142	73.76 183	97.51 185	90.43 116	92.23 159	98.16 77

testing99		91.91 78	91.35 83	93.60 68	95.98 110	85.70 49	97.31 109	96.92 45	86.82 113	88.91 130	95.25 153	84.26 41	97.89 163	88.80 136	87.94 198	97.21 148

EIA-MVS		91.73 82	92.05 71	90.78 180	94.52 159	76.40 273	98.06 55	95.34 189	89.19 61	88.90 131	97.28 97	77.56 112	97.73 168	90.77 109	96.86 93	98.20 74

testing91		91.90 79	91.31 85	93.66 64	95.99 109	85.68 51	97.39 105	96.89 46	86.75 117	88.85 132	95.23 156	83.93 44	97.90 162	88.91 133	87.89 199	97.41 135

mvsany_test1		87.58 178	88.22 144	85.67 290	89.78 291	67.18 351	95.25 237	87.93 369	83.96 180	88.79 133	97.06 108	72.52 196	94.53 321	92.21 92	86.45 211	95.30 209

HPM-MVS_fast		90.38 120	90.17 112	91.03 171	97.61 71	77.35 257	97.15 122	95.48 175	79.51 269	88.79 133	96.90 111	71.64 210	98.81 119	87.01 156	97.44 76	96.94 158

ETVMVS		90.99 103	90.26 107	93.19 85	95.81 117	85.64 53	96.97 140	97.18 25	85.43 136	88.77 135	94.86 173	82.00 60	96.37 242	82.70 195	88.60 187	97.57 123

PAPM		92.87 52	92.40 61	94.30 40	92.25 234	87.85 22	96.40 181	96.38 113	91.07 37	88.72 136	96.90 111	82.11 59	97.37 195	90.05 122	97.70 67	97.67 115

MVS_111021_LR		91.60 88	91.64 79	91.47 159	95.74 120	78.79 214	96.15 196	96.77 60	88.49 71	88.64 137	97.07 107	72.33 200	99.19 93	93.13 83	96.48 101	96.43 177

casdiffmvs		90.95 106	90.39 104	92.63 109	92.82 216	82.53 114	96.83 151	94.47 236	87.69 92	88.47 138	95.56 147	74.04 180	97.54 182	90.90 107	92.74 151	97.83 103
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 80	91.82 74	92.07 135	98.38 44	78.63 217	97.29 110	96.09 137	85.12 146	88.45 139	97.66 72	75.53 150	99.68 51	89.83 123	98.02 58	97.88 97

PAPR		92.74 54	92.17 68	94.45 37	98.89 20	84.87 76	97.20 114	96.20 129	87.73 91	88.40 140	98.12 43	78.71 94	99.76 31	87.99 145	96.28 102	98.74 42

tpmrst		88.36 161	87.38 166	91.31 161	94.36 167	79.92 181	87.32 352	95.26 193	85.32 139	88.34 141	86.13 317	80.60 69	96.70 231	83.78 178	85.34 226	97.30 143

GG-mvs-BLEND				93.49 75	94.94 146	86.26 38	81.62 379	97.00 36		88.32 142	94.30 184	91.23 5	96.21 249	88.49 140	97.43 77	98.00 90

EI-MVSNet-UG-set		91.35 94	91.22 86	91.73 150	97.39 83	80.68 161	96.47 174	96.83 51	87.92 86	88.30 143	97.36 91	77.84 108	99.13 99	89.43 130	89.45 176	95.37 206

MAR-MVS		90.63 112	90.22 109	91.86 145	98.47 42	78.20 233	97.18 116	96.61 83	83.87 184	88.18 144	98.18 38	68.71 229	99.75 36	83.66 184	97.15 84	97.63 119
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 84	90.85 93	94.34 39	99.50 1	85.00 73	98.51 35	95.96 148	80.57 244	88.08 145	97.63 78	76.84 124	99.89 7	85.67 162	94.88 121	98.13 80

VDDNet		86.44 193	84.51 207	92.22 128	91.56 255	81.83 131	97.10 129	94.64 224	69.50 357	87.84 146	95.19 160	48.01 348	97.92 161	89.82 124	86.92 206	96.89 162

UGNet		87.73 175	86.55 182	91.27 164	95.16 139	79.11 205	96.35 184	96.23 126	88.14 81	87.83 147	90.48 251	50.65 338	99.09 102	80.13 213	94.03 131	95.60 199
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		90.96 105	90.39 104	92.65 107	93.54 190	82.46 117	96.37 182	97.35 17	86.78 115	87.55 148	95.25 153	77.83 109	97.50 186	84.07 174	94.80 122	97.98 92

tpm2		87.35 181	86.26 183	90.62 183	92.93 214	78.67 216	88.06 347	95.99 145	79.33 272	87.40 149	86.43 312	80.28 72	96.40 240	80.23 211	85.73 222	96.79 165

CPTT-MVS		89.72 129	89.87 122	89.29 215	98.33 47	73.30 305	97.70 78	95.35 188	75.68 313	87.40 149	97.44 88	70.43 222	98.25 146	89.56 128	96.90 89	96.33 182

gg-mvs-nofinetune		85.48 212	82.90 234	93.24 82	94.51 162	85.82 47	79.22 383	96.97 39	61.19 381	87.33 151	53.01 399	90.58 6	96.07 252	86.07 159	97.23 83	97.81 106

CHOSEN 280x420		91.71 85	91.85 73	91.29 163	94.94 146	82.69 111	87.89 348	96.17 132	85.94 127	87.27 152	94.31 183	90.27 8	95.65 278	94.04 68	95.86 112	95.53 202

test_fmvsmvis_n_1920		92.12 73	92.10 70	92.17 131	90.87 271	81.04 149	98.34 40	93.90 268	92.71 18	87.24 153	97.90 61	74.83 167	99.72 43	96.96 32	96.20 103	95.76 196

casdiffmvs_mvg		91.13 99	90.45 103	93.17 86	92.99 212	83.58 97	97.46 97	94.56 230	87.69 92	87.19 154	94.98 171	74.50 174	97.60 175	91.88 99	92.79 150	98.34 64
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		87.65 177	87.89 150	86.93 270	94.57 155	71.37 329	96.72 159	96.50 98	88.56 70	87.12 155	95.02 168	75.91 143	94.01 331	66.62 318	90.00 172	95.42 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 151	87.82 152	91.24 165	92.68 218	78.82 211	96.95 143	93.85 272	87.55 95	87.07 156	95.13 164	63.43 261	97.21 203	77.58 238	96.15 105	97.70 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvsmamba		90.53 117	90.08 114	91.88 144	94.81 150	80.93 154	93.94 276	94.45 238	88.24 79	87.02 157	92.35 221	68.04 231	95.80 267	94.86 55	97.03 87	98.92 35

thisisatest0515		90.95 106	90.26 107	93.01 92	94.03 181	84.27 86	97.91 63	96.67 74	83.18 196	86.87 158	95.51 148	88.66 15	97.85 164	80.46 207	89.01 182	96.92 161

TESTMET0.1,1		89.83 127	89.34 128	91.31 161	92.54 224	80.19 176	97.11 126	96.57 90	86.15 121	86.85 159	91.83 234	79.32 82	96.95 217	81.30 202	92.35 157	96.77 167

PVSNet_Blended_VisFu		91.24 96	90.77 95	92.66 106	95.09 140	82.40 118	97.77 72	95.87 155	88.26 77	86.39 160	93.94 194	76.77 127	99.27 84	88.80 136	94.00 134	96.31 183

API-MVS		90.18 122	88.97 132	93.80 56	98.66 28	82.95 109	97.50 94	95.63 167	75.16 317	86.31 161	97.69 70	72.49 197	99.90 5	81.26 203	96.07 107	98.56 54

test-LLR		88.48 157	87.98 149	89.98 200	92.26 232	77.23 259	97.11 126	95.96 148	83.76 187	86.30 162	91.38 237	72.30 201	96.78 229	80.82 204	91.92 161	95.94 190

test-mter		88.95 141	88.60 139	89.98 200	92.26 232	77.23 259	97.11 126	95.96 148	85.32 139	86.30 162	91.38 237	76.37 135	96.78 229	80.82 204	91.92 161	95.94 190

PAPM_NR		91.46 90	90.82 94	93.37 79	98.50 40	81.81 133	95.03 250	96.13 134	84.65 159	86.10 164	97.65 76	79.24 85	99.75 36	83.20 190	96.88 91	98.56 54

FA-MVS(test-final)		87.71 176	86.23 184	92.17 131	94.19 171	80.55 165	87.16 354	96.07 140	82.12 221	85.98 165	88.35 279	72.04 205	98.49 132	80.26 210	89.87 173	97.48 132

MDTV_nov1_ep13_2view						81.74 135	86.80 356		80.65 242	85.65 166		74.26 176		76.52 250		96.98 156

ECVR-MVS		88.35 162	87.25 168	91.65 152	93.54 190	79.40 196	96.56 169	90.78 348	86.78 115	85.57 167	95.25 153	57.25 309	97.56 178	84.73 170	94.80 122	97.98 92

AUN-MVS		86.25 198	85.57 190	88.26 236	93.57 189	73.38 303	95.45 230	95.88 153	83.94 181	85.47 168	94.21 187	73.70 186	96.67 233	83.54 186	64.41 357	94.73 224

PVSNet	82.34 9	89.02 140	87.79 153	92.71 105	95.49 126	81.50 142	97.70 78	97.29 18	87.76 90	85.47 168	95.12 165	56.90 311	98.90 115	80.33 208	94.02 132	97.71 113

EPP-MVSNet		89.76 128	89.72 123	89.87 205	93.78 183	76.02 281	97.22 111	96.51 96	79.35 271	85.11 170	95.01 169	84.82 32	97.10 211	87.46 151	88.21 196	96.50 175

test1111		88.11 167	87.04 174	91.35 160	93.15 204	78.79 214	96.57 167	90.78 348	86.88 112	85.04 171	95.20 159	57.23 310	97.39 193	83.88 176	94.59 125	97.87 99

FE-MVS		86.06 200	84.15 215	91.78 149	94.33 168	79.81 183	84.58 371	96.61 83	76.69 307	85.00 172	87.38 292	70.71 221	98.37 141	70.39 301	91.70 164	97.17 151

OMC-MVS		88.80 148	88.16 147	90.72 181	95.30 133	77.92 242	94.81 255	94.51 232	86.80 114	84.97 173	96.85 114	67.53 235	98.60 125	85.08 166	87.62 201	95.63 198

CHOSEN 1792x2688		91.07 102	90.21 110	93.64 65	95.18 138	83.53 98	96.26 190	96.13 134	88.92 63	84.90 174	93.10 212	72.86 192	99.62 58	88.86 134	95.67 115	97.79 107

thres200		88.92 143	87.65 155	92.73 104	96.30 99	85.62 54	97.85 66	98.86 1	84.38 167	84.82 175	93.99 193	75.12 164	98.01 154	70.86 298	86.67 208	94.56 226

UWE-MVS		88.56 156	88.91 136	87.50 257	94.17 172	72.19 316	95.82 214	97.05 34	84.96 151	84.78 176	93.51 206	81.33 62	94.75 314	79.43 219	89.17 178	95.57 200

MDTV_nov1_ep13			83.69 219		94.09 177	81.01 150	86.78 357	96.09 137	83.81 186	84.75 177	84.32 340	74.44 175	96.54 236	63.88 332	85.07 227

CDS-MVSNet		89.50 132	88.96 133	91.14 169	91.94 251	80.93 154	97.09 130	95.81 157	84.26 173	84.72 178	94.20 188	80.31 71	95.64 279	83.37 189	88.96 183	96.85 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 118	89.97 117	91.64 153	97.58 74	78.21 232	96.78 156	96.72 68	84.73 156	84.72 178	97.23 99	71.22 213	99.63 57	88.37 143	92.41 156	97.08 154
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 75	91.65 78	93.12 87	98.53 36	80.59 163	97.47 95	97.18 25	77.06 305	84.64 180	97.98 55	83.98 43	99.52 69	90.72 110	97.33 80	99.23 24

ab-mvs		87.08 182	84.94 203	93.48 76	93.34 199	83.67 95	88.82 338	95.70 163	81.18 232	84.55 181	90.14 259	62.72 264	98.94 113	85.49 164	82.54 247	97.85 101

EPMVS		87.47 180	85.90 187	92.18 130	95.41 128	82.26 121	87.00 355	96.28 121	85.88 129	84.23 182	85.57 323	75.07 165	96.26 246	71.14 296	92.50 154	98.03 84

Anonymous202405211		84.41 228	81.93 249	91.85 147	96.78 94	78.41 223	97.44 98	91.34 338	70.29 352	84.06 183	94.26 185	41.09 373	98.96 109	79.46 218	82.65 246	98.17 76

HyFIR lowres test		89.36 134	88.60 139	91.63 155	94.91 148	80.76 160	95.60 223	95.53 171	82.56 213	84.03 184	91.24 240	78.03 104	96.81 227	87.07 155	88.41 193	97.32 140

tfpn200view9		88.48 157	87.15 170	92.47 113	96.21 102	85.30 61	97.44 98	98.85 2	83.37 193	83.99 185	93.82 197	75.36 157	97.93 156	69.04 306	86.24 215	94.17 228

thres400		88.42 160	87.15 170	92.23 127	96.21 102	85.30 61	97.44 98	98.85 2	83.37 193	83.99 185	93.82 197	75.36 157	97.93 156	69.04 306	86.24 215	93.45 244

tpm		85.55 209	84.47 210	88.80 225	90.19 284	75.39 289	88.79 339	94.69 217	84.83 153	83.96 187	85.21 329	78.22 101	94.68 318	76.32 254	78.02 280	96.34 180

Fast-Effi-MVS+		87.93 172	86.94 177	90.92 174	94.04 179	79.16 203	98.26 42	93.72 282	81.29 231	83.94 188	92.90 213	69.83 226	96.68 232	76.70 248	91.74 163	96.93 159

XVG-OURS-SEG-HR		85.74 206	85.16 199	87.49 259	90.22 283	71.45 328	91.29 321	94.09 259	81.37 230	83.90 189	95.22 157	60.30 280	97.53 184	85.58 163	84.42 230	93.50 242

thisisatest0530		89.65 130	89.02 131	91.53 157	93.46 196	80.78 159	96.52 170	96.67 74	81.69 228	83.79 190	94.90 172	88.85 14	97.68 171	77.80 231	87.49 204	96.14 186

DeepC-MVS	86.58 3	91.53 89	91.06 91	92.94 95	94.52 159	81.89 128	95.95 204	95.98 146	90.76 40	83.76 191	96.76 119	73.24 190	99.71 45	91.67 100	96.96 88	97.22 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 151	88.16 147	90.20 195	93.61 187	76.86 265	96.77 158	93.07 312	84.02 177	83.62 192	95.60 145	74.69 172	96.24 248	78.43 230	93.66 140	97.49 131

iter_conf05		90.65 111	89.59 124	93.82 55	95.37 130	87.90 21	91.32 320	93.55 290	74.65 322	83.45 193	92.81 214	83.11 51	97.70 169	94.49 61	97.57 72	95.85 193

thres100view900		88.30 163	86.95 176	92.33 121	96.10 106	84.90 75	97.14 123	98.85 2	82.69 210	83.41 194	93.66 201	75.43 154	97.93 156	69.04 306	86.24 215	94.17 228

thres600view7		88.06 168	86.70 181	92.15 133	96.10 106	85.17 67	97.14 123	98.85 2	82.70 209	83.41 194	93.66 201	75.43 154	97.82 165	67.13 315	85.88 219	93.45 244

XVG-OURS		85.18 215	84.38 211	87.59 253	90.42 281	71.73 325	91.06 324	94.07 260	82.00 224	83.29 196	95.08 167	56.42 316	97.55 180	83.70 183	83.42 235	93.49 243

Vis-MVSNet (Re-imp)		88.88 145	88.87 137	88.91 222	93.89 182	74.43 297	96.93 145	94.19 253	84.39 166	83.22 197	95.67 142	78.24 100	94.70 316	78.88 226	94.40 129	97.61 121

TAMVS		88.48 157	87.79 153	90.56 185	91.09 266	79.18 202	96.45 176	95.88 153	83.64 190	83.12 198	93.33 207	75.94 142	95.74 274	82.40 196	88.27 195	96.75 169

baseline1		88.85 146	87.49 162	92.93 96	95.21 137	86.85 33	95.47 229	94.61 227	87.29 102	83.11 199	94.99 170	80.70 67	96.89 221	82.28 197	73.72 295	95.05 213

AdaColmap		88.81 147	87.61 159	92.39 118	99.33 4	79.95 180	96.70 163	95.58 168	77.51 297	83.05 200	96.69 123	61.90 273	99.72 43	84.29 172	93.47 142	97.50 130

PatchmatchNet		86.83 188	85.12 200	91.95 141	94.12 176	82.27 120	86.55 359	95.64 166	84.59 161	82.98 201	84.99 335	77.26 116	95.96 259	68.61 309	91.34 166	97.64 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 207	83.64 222	91.60 156	92.30 230	81.86 130	92.88 301	95.56 170	84.85 152	82.52 202	85.12 333	58.04 298	95.39 289	73.89 276	87.58 203	97.54 124

114514_t		88.79 149	87.57 160	92.45 114	98.21 53	81.74 135	96.99 135	95.45 178	75.16 317	82.48 203	95.69 141	68.59 230	98.50 131	80.33 208	95.18 119	97.10 153

PatchT		79.75 291	76.85 303	88.42 230	89.55 298	75.49 288	77.37 389	94.61 227	63.07 372	82.46 204	73.32 385	75.52 151	93.41 342	51.36 374	84.43 229	96.36 178

TR-MVS		86.30 196	84.93 204	90.42 188	94.63 154	77.58 252	96.57 167	93.82 273	80.30 253	82.42 205	95.16 162	58.74 291	97.55 180	74.88 266	87.82 200	96.13 187

HQP-NCC					92.08 243		97.63 81		90.52 44	82.30 206

ACMP_Plane					92.08 243		97.63 81		90.52 44	82.30 206

HQP4-MVS										82.30 206			97.32 196			91.13 255

HQP-MVS		87.91 173	87.55 161	88.98 221	92.08 243	78.48 219	97.63 81	94.80 212	90.52 44	82.30 206	94.56 179	65.40 250	97.32 196	87.67 149	83.01 239	91.13 255

CR-MVSNet		83.53 241	81.36 258	90.06 197	90.16 285	79.75 186	79.02 385	91.12 340	84.24 174	82.27 210	80.35 363	75.45 152	93.67 337	63.37 336	86.25 213	96.75 169

RPMNet		79.85 290	75.92 309	91.64 153	90.16 285	79.75 186	79.02 385	95.44 179	58.43 391	82.27 210	72.55 388	73.03 191	98.41 140	46.10 387	86.25 213	96.75 169

CVMVSNet		84.83 220	85.57 190	82.63 331	91.55 256	60.38 377	95.13 244	95.03 200	80.60 243	82.10 212	94.71 176	66.40 245	90.19 371	74.30 273	90.32 171	97.31 142

PLC	83.97 7	88.00 170	87.38 166	89.83 207	98.02 59	76.46 271	97.16 120	94.43 240	79.26 276	81.98 213	96.28 128	69.36 227	99.27 84	77.71 235	92.25 158	93.77 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 300	77.20 299	84.40 313	89.74 294	64.06 364	75.30 393	95.44 179	62.15 375	81.90 214	59.08 397	78.92 89	95.59 283	66.51 321	85.78 221	93.54 241

Anonymous20240529		83.15 248	80.60 268	90.80 178	95.74 120	78.27 227	96.81 154	94.92 204	60.10 386	81.89 215	92.54 219	45.82 357	98.82 118	79.25 222	78.32 278	95.31 208

tttt0517		88.57 155	88.19 146	89.71 211	93.00 209	75.99 282	95.67 218	96.67 74	80.78 239	81.82 216	94.40 182	88.97 13	97.58 177	76.05 256	86.31 212	95.57 200

WB-MVSnew		84.08 233	83.51 226	85.80 286	91.34 261	76.69 269	95.62 222	96.27 122	81.77 226	81.81 217	92.81 214	58.23 295	94.70 316	66.66 317	87.06 205	85.99 352

BH-RMVSNet		86.84 187	85.28 195	91.49 158	95.35 132	80.26 174	96.95 143	92.21 324	82.86 206	81.77 218	95.46 149	59.34 287	97.64 173	69.79 304	93.81 137	96.57 174

HQP_MVS		87.50 179	87.09 173	88.74 226	91.86 252	77.96 239	97.18 116	94.69 217	89.89 53	81.33 219	94.15 189	64.77 255	97.30 198	87.08 153	82.82 243	90.96 257

plane_prior3						77.75 249			90.17 51	81.33 219

VPA-MVSNet		85.32 213	83.83 218	89.77 210	90.25 282	82.63 112	96.36 183	97.07 33	83.03 201	81.21 221	89.02 269	61.58 274	96.31 245	85.02 168	70.95 310	90.36 263

GeoE		86.36 194	85.20 196	89.83 207	93.17 203	76.13 276	97.53 90	92.11 325	79.58 268	80.99 222	94.01 192	66.60 244	96.17 251	73.48 280	89.30 177	97.20 150

GA-MVS		85.79 205	84.04 217	91.02 172	89.47 300	80.27 173	96.90 148	94.84 210	85.57 133	80.88 223	89.08 267	56.56 315	96.47 239	77.72 234	85.35 225	96.34 180

1112_ss		88.60 154	87.47 164	92.00 140	93.21 201	80.97 152	96.47 174	92.46 320	83.64 190	80.86 224	97.30 95	80.24 73	97.62 174	77.60 237	85.49 223	97.40 137

dp		84.30 230	82.31 243	90.28 192	94.24 170	77.97 238	86.57 358	95.53 171	79.94 262	80.75 225	85.16 331	71.49 212	96.39 241	63.73 333	83.36 236	96.48 176

Test_1112_low_res		88.03 169	86.73 179	91.94 142	93.15 204	80.88 156	96.44 177	92.41 322	83.59 192	80.74 226	91.16 241	80.18 74	97.59 176	77.48 240	85.40 224	97.36 139

cascas		86.50 192	84.48 209	92.55 112	92.64 222	85.95 43	97.04 134	95.07 199	75.32 315	80.50 227	91.02 243	54.33 329	97.98 155	86.79 157	87.62 201	93.71 239

TAPA-MVS	81.61 12	85.02 217	83.67 220	89.06 218	96.79 93	73.27 308	95.92 206	94.79 214	74.81 320	80.47 228	96.83 115	71.07 215	98.19 149	49.82 380	92.57 152	95.71 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 203	85.10 201	88.06 241	88.34 312	77.83 246	95.72 216	94.20 252	87.89 88	80.45 229	94.05 191	58.57 292	97.26 202	83.88 176	82.76 245	89.09 291
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		86.79 189	85.43 192	90.87 177	88.76 305	85.34 58	97.06 133	94.33 246	84.31 168	80.45 229	91.98 229	72.36 198	96.36 243	88.48 141	71.13 308	90.93 259

EI-MVSNet		85.80 204	85.20 196	87.59 253	91.55 256	77.41 255	95.13 244	95.36 186	80.43 250	80.33 231	94.71 176	73.72 184	95.97 256	76.96 246	78.64 272	89.39 279

MVSTER		89.25 138	88.92 135	90.24 193	95.98 110	84.66 78	96.79 155	95.36 186	87.19 107	80.33 231	90.61 250	90.02 11	95.97 256	85.38 165	78.64 272	90.09 271

ADS-MVSNet2		79.57 294	77.53 297	85.71 289	93.78 183	72.13 317	79.48 381	86.11 378	73.09 336	80.14 233	79.99 366	62.15 268	90.14 372	59.49 348	83.52 233	94.85 218

ADS-MVSNet		81.26 277	78.36 290	89.96 202	93.78 183	79.78 184	79.48 381	93.60 287	73.09 336	80.14 233	79.99 366	62.15 268	95.24 298	59.49 348	83.52 233	94.85 218

test_fmvs2		79.59 293	79.90 280	78.67 352	82.86 368	55.82 388	95.20 240	89.55 356	81.09 233	80.12 235	89.80 261	34.31 386	93.51 340	87.82 146	78.36 277	86.69 341

baseline2		90.39 118	90.21 110	90.93 173	90.86 272	80.99 151	95.20 240	97.41 16	86.03 126	80.07 236	94.61 178	90.58 6	97.47 189	87.29 152	89.86 174	94.35 227

Effi-MVS+-dtu		84.61 224	84.90 205	83.72 321	91.96 249	63.14 369	94.95 251	93.34 301	85.57 133	79.79 237	87.12 298	61.99 271	95.61 282	83.55 185	85.83 220	92.41 251

VPNet		84.69 222	82.92 233	90.01 198	89.01 304	83.45 100	96.71 161	95.46 177	85.71 131	79.65 238	92.18 225	56.66 314	96.01 255	83.05 193	67.84 341	90.56 261

SDMVSNet		87.02 183	85.61 189	91.24 165	94.14 174	83.30 103	93.88 278	95.98 146	84.30 170	79.63 239	92.01 226	58.23 295	97.68 171	90.28 121	82.02 251	92.75 247

sd_testset		84.62 223	83.11 231	89.17 216	94.14 174	77.78 247	91.54 319	94.38 243	84.30 170	79.63 239	92.01 226	52.28 333	96.98 215	77.67 236	82.02 251	92.75 247

CLD-MVS		87.97 171	87.48 163	89.44 213	92.16 239	80.54 167	98.14 46	94.92 204	91.41 31	79.43 241	95.40 150	62.34 266	97.27 201	90.60 112	82.90 242	90.50 262
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	88.67 151	87.14 172	93.26 81	93.12 207	84.32 83	98.76 26	97.27 20	87.19 107	79.36 242	90.45 252	83.92 45	98.53 130	84.41 171	69.79 321	96.93 159
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		85.00 218	83.66 221	89.02 220	95.86 115	74.55 296	92.49 305	93.60 287	79.30 274	79.29 243	91.47 235	58.53 293	98.45 137	70.22 302	92.17 160	94.07 233

mamv4		85.50 210	86.76 178	81.72 337	93.23 200	54.93 391	89.95 331	92.94 314	69.96 354	79.00 244	92.20 224	80.69 68	94.22 327	92.06 95	90.77 169	96.01 188

CNLPA		86.96 184	85.37 194	91.72 151	97.59 73	79.34 199	97.21 112	91.05 343	74.22 325	78.90 245	96.75 121	67.21 239	98.95 111	74.68 268	90.77 169	96.88 163

MVS		90.60 113	88.64 138	96.50 5	94.25 169	90.53 8	93.33 290	97.21 22	77.59 296	78.88 246	97.31 92	71.52 211	99.69 49	89.60 126	98.03 57	99.27 22

mvs_anonymous		88.68 150	87.62 158	91.86 145	94.80 151	81.69 138	93.53 286	94.92 204	82.03 223	78.87 247	90.43 253	75.77 144	95.34 292	85.04 167	93.16 147	98.55 56

tpm cat1		83.63 240	81.38 257	90.39 189	93.53 195	78.19 234	85.56 366	95.09 197	70.78 350	78.51 248	83.28 349	74.80 168	97.03 212	66.77 316	84.05 231	95.95 189

UniMVSNet (Re)		85.31 214	84.23 213	88.55 229	89.75 292	80.55 165	96.72 159	96.89 46	85.42 137	78.40 249	88.93 270	75.38 156	95.52 286	78.58 228	68.02 338	89.57 278

FIs		86.73 191	86.10 185	88.61 228	90.05 288	80.21 175	96.14 197	96.95 41	85.56 135	78.37 250	92.30 222	76.73 128	95.28 296	79.51 217	79.27 266	90.35 264

BH-w/o		88.24 165	87.47 164	90.54 186	95.03 145	78.54 218	97.41 103	93.82 273	84.08 175	78.23 251	94.51 181	69.34 228	97.21 203	80.21 212	94.58 126	95.87 192

UniMVSNet_NR-MVSNet		85.49 211	84.59 206	88.21 239	89.44 301	79.36 197	96.71 161	96.41 108	85.22 142	78.11 252	90.98 245	76.97 123	95.14 303	79.14 223	68.30 335	90.12 269

DU-MVS		84.57 225	83.33 229	88.28 235	88.76 305	79.36 197	96.43 179	95.41 185	85.42 137	78.11 252	90.82 246	67.61 232	95.14 303	79.14 223	68.30 335	90.33 265

dmvs_re		84.10 232	82.90 234	87.70 248	91.41 260	73.28 306	90.59 327	93.19 305	85.02 148	77.96 254	93.68 200	57.92 303	96.18 250	75.50 261	80.87 255	93.63 240

miper_enhance_ethall		85.95 202	85.20 196	88.19 240	94.85 149	79.76 185	96.00 201	94.06 261	82.98 203	77.74 255	88.76 272	79.42 81	95.46 288	80.58 206	72.42 302	89.36 284

v1144		82.90 254	81.27 259	87.78 247	86.29 333	79.07 208	96.14 197	93.93 264	80.05 259	77.38 256	86.80 303	65.50 248	95.93 261	75.21 264	70.13 316	88.33 314

FC-MVSNet-test		85.96 201	85.39 193	87.66 250	89.38 302	78.02 236	95.65 220	96.87 48	85.12 146	77.34 257	91.94 232	76.28 137	94.74 315	77.09 243	78.82 270	90.21 267

v2v482		83.46 242	81.86 250	88.25 237	86.19 335	79.65 191	96.34 185	94.02 262	81.56 229	77.32 258	88.23 281	65.62 247	96.03 253	77.77 232	69.72 323	89.09 291

Baseline_NR-MVSNet		81.22 278	80.07 276	84.68 305	85.32 349	75.12 291	96.48 173	88.80 364	76.24 311	77.28 259	86.40 313	67.61 232	94.39 324	75.73 260	66.73 351	84.54 362

V42		83.04 251	81.53 255	87.57 255	86.27 334	79.09 207	95.87 210	94.11 258	80.35 252	77.22 260	86.79 304	65.32 252	96.02 254	77.74 233	70.14 315	87.61 327

v144192		82.43 260	80.73 265	87.54 256	85.81 342	78.22 229	95.98 202	93.78 278	79.09 279	77.11 261	86.49 308	64.66 257	95.91 262	74.20 274	69.42 324	88.49 308

ACMM	80.70 13	83.72 239	82.85 236	86.31 280	91.19 263	72.12 318	95.88 209	94.29 247	80.44 248	77.02 262	91.96 230	55.24 323	97.14 210	79.30 221	80.38 258	89.67 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 264	80.55 269	87.60 252	85.94 339	78.47 222	95.85 212	93.80 276	79.33 272	76.97 263	86.51 307	63.33 262	95.87 263	73.11 281	70.13 316	88.46 310

PCF-MVS	84.09 5	86.77 190	85.00 202	92.08 134	92.06 246	83.07 107	92.14 309	94.47 236	79.63 267	76.90 264	94.78 175	71.15 214	99.20 92	72.87 282	91.05 167	93.98 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		85.11 216	84.17 214	87.92 244	95.06 144	78.82 211	95.51 227	94.22 251	79.74 265	76.77 265	87.92 286	75.96 141	95.68 275	79.93 215	72.42 302	89.27 286

v1921920		82.02 267	80.23 273	87.41 260	85.62 343	77.92 242	95.79 215	93.69 283	78.86 283	76.67 266	86.44 310	62.50 265	95.83 265	72.69 283	69.77 322	88.47 309

WR-MVS		84.32 229	82.96 232	88.41 231	89.38 302	80.32 170	96.59 166	96.25 124	83.97 179	76.63 267	90.36 254	67.53 235	94.86 312	75.82 259	70.09 319	90.06 273

BH-untuned		86.95 185	85.94 186	89.99 199	94.52 159	77.46 254	96.78 156	93.37 300	81.80 225	76.62 268	93.81 199	66.64 243	97.02 213	76.06 255	93.88 136	95.48 204

v1240		81.70 271	79.83 281	87.30 264	85.50 344	77.70 251	95.48 228	93.44 293	78.46 288	76.53 269	86.44 310	60.85 278	95.84 264	71.59 290	70.17 314	88.35 313

PS-MVSNAJss		84.91 219	84.30 212	86.74 271	85.89 341	74.40 298	94.95 251	94.16 255	83.93 182	76.45 270	90.11 260	71.04 216	95.77 269	83.16 191	79.02 269	90.06 273

miper_ehance_all_eth		84.57 225	83.60 224	87.50 257	92.64 222	78.25 228	95.40 233	93.47 292	79.28 275	76.41 271	87.64 289	76.53 130	95.24 298	78.58 228	72.42 302	89.01 296

LPG-MVS_test		84.20 231	83.49 227	86.33 277	90.88 269	73.06 309	95.28 234	94.13 256	82.20 218	76.31 272	93.20 208	54.83 327	96.95 217	83.72 181	80.83 256	88.98 297

LGP-MVS_train				86.33 277	90.88 269	73.06 309		94.13 256	82.20 218	76.31 272	93.20 208	54.83 327	96.95 217	83.72 181	80.83 256	88.98 297

F-COLMAP		84.50 227	83.44 228	87.67 249	95.22 136	72.22 314	95.95 204	93.78 278	75.74 312	76.30 274	95.18 161	59.50 285	98.45 137	72.67 284	86.59 210	92.35 252

tpmvs		83.04 251	80.77 264	89.84 206	95.43 127	77.96 239	85.59 365	95.32 190	75.31 316	76.27 275	83.70 345	73.89 181	97.41 191	59.53 347	81.93 253	94.14 230

tt0805		81.20 279	79.06 287	87.61 251	86.50 329	72.97 311	93.66 281	95.48 175	74.11 326	76.23 276	91.99 228	41.36 372	97.40 192	77.44 241	74.78 291	92.45 250

3Dnovator	82.32 10	89.33 135	87.64 156	94.42 38	93.73 186	85.70 49	97.73 76	96.75 64	86.73 118	76.21 277	95.93 134	62.17 267	99.68 51	81.67 201	97.81 64	97.88 97

TranMVSNet+NR-MVSNet		83.24 247	81.71 252	87.83 245	87.71 319	78.81 213	96.13 199	94.82 211	84.52 162	76.18 278	90.78 248	64.07 258	94.60 319	74.60 271	66.59 352	90.09 271

c3_l		83.80 237	82.65 239	87.25 265	92.10 242	77.74 250	95.25 237	93.04 313	78.58 286	76.01 279	87.21 297	75.25 162	95.11 305	77.54 239	68.89 329	88.91 302

1314		88.94 142	87.20 169	94.17 46	93.21 201	85.73 48	93.33 290	96.64 80	82.89 204	75.98 280	96.36 126	66.83 242	99.39 77	83.52 188	96.02 110	97.39 138

Fast-Effi-MVS+-dtu		83.33 244	82.60 240	85.50 294	89.55 298	69.38 342	96.09 200	91.38 335	82.30 217	75.96 281	91.41 236	56.71 312	95.58 284	75.13 265	84.90 228	91.54 253

XXY-MVS		83.84 236	82.00 248	89.35 214	87.13 324	81.38 143	95.72 216	94.26 248	80.15 257	75.92 282	90.63 249	61.96 272	96.52 237	78.98 225	73.28 300	90.14 268

GBi-Net		82.42 261	80.43 271	88.39 232	92.66 219	81.95 123	94.30 266	93.38 297	79.06 280	75.82 283	85.66 319	56.38 317	93.84 333	71.23 293	75.38 288	89.38 281

test1		82.42 261	80.43 271	88.39 232	92.66 219	81.95 123	94.30 266	93.38 297	79.06 280	75.82 283	85.66 319	56.38 317	93.84 333	71.23 293	75.38 288	89.38 281

FMVSNet3		84.71 221	82.71 238	90.70 182	94.55 157	87.71 24	95.92 206	94.67 220	81.73 227	75.82 283	88.08 284	66.99 240	94.47 322	71.23 293	75.38 288	89.91 275

eth_miper_zixun_eth		83.12 249	82.01 247	86.47 276	91.85 254	74.80 292	94.33 264	93.18 307	79.11 278	75.74 286	87.25 296	72.71 193	95.32 294	76.78 247	67.13 347	89.27 286

IterMVS-LS		83.93 235	82.80 237	87.31 263	91.46 259	77.39 256	95.66 219	93.43 295	80.44 248	75.51 287	87.26 295	73.72 184	95.16 302	76.99 244	70.72 312	89.39 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 131	87.85 151	94.99 23	94.49 164	86.76 35	97.84 67	95.74 161	86.10 123	75.47 288	96.02 133	65.00 254	99.51 71	82.91 194	97.07 86	98.72 47

test_djsdf		83.00 253	82.45 242	84.64 307	84.07 361	69.78 338	94.80 256	94.48 233	80.74 240	75.41 289	87.70 288	61.32 277	95.10 306	83.77 179	79.76 259	89.04 294

v148		82.41 263	80.89 262	86.99 269	86.18 336	76.81 266	96.27 189	93.82 273	80.49 247	75.28 290	86.11 318	67.32 238	95.75 271	75.48 262	67.03 349	88.42 312

QAPM		86.88 186	84.51 207	93.98 49	94.04 179	85.89 46	97.19 115	96.05 141	73.62 330	75.12 291	95.62 144	62.02 270	99.74 38	70.88 297	96.06 108	96.30 184

UniMVSNet_ETH3D		80.86 283	78.75 289	87.22 266	86.31 332	72.02 319	91.95 310	93.76 281	73.51 331	75.06 292	90.16 258	43.04 366	95.66 276	76.37 253	78.55 275	93.98 234

cl____		83.27 245	82.12 245	86.74 271	92.20 235	75.95 283	95.11 246	93.27 303	78.44 289	74.82 293	87.02 300	74.19 177	95.19 300	74.67 269	69.32 325	89.09 291

DIV-MVS_self_test		83.27 245	82.12 245	86.74 271	92.19 236	75.92 285	95.11 246	93.26 304	78.44 289	74.81 294	87.08 299	74.19 177	95.19 300	74.66 270	69.30 326	89.11 290

FMVSNet2		82.79 255	80.44 270	89.83 207	92.66 219	85.43 57	95.42 231	94.35 244	79.06 280	74.46 295	87.28 293	56.38 317	94.31 325	69.72 305	74.68 292	89.76 276

MIMVSNet		79.18 299	75.99 308	88.72 227	87.37 323	80.66 162	79.96 380	91.82 329	77.38 299	74.33 296	81.87 355	41.78 369	90.74 367	66.36 323	83.10 238	94.76 220

RPSCF		77.73 309	76.63 304	81.06 341	88.66 309	55.76 389	87.77 349	87.88 370	64.82 370	74.14 297	92.79 217	49.22 345	96.81 227	67.47 313	76.88 282	90.62 260

ACMP	81.66 11	84.00 234	83.22 230	86.33 277	91.53 258	72.95 312	95.91 208	93.79 277	83.70 189	73.79 298	92.22 223	54.31 330	96.89 221	83.98 175	79.74 261	89.16 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 276	79.54 282	86.73 274	85.02 351	76.91 263	96.22 192	91.65 332	77.65 295	73.55 299	88.61 274	55.70 320	94.43 323	74.12 275	73.35 299	88.86 303

jajsoiax		82.12 266	81.15 261	85.03 301	84.19 359	70.70 331	94.22 270	93.95 263	83.07 199	73.48 300	89.75 262	49.66 344	95.37 291	82.24 198	79.76 259	89.02 295

Syy-MVS		77.97 307	78.05 293	77.74 356	92.13 240	56.85 384	93.97 274	94.23 249	82.43 214	73.39 301	93.57 204	57.95 301	87.86 378	32.40 398	82.34 248	88.51 306

myMVS_eth3d		81.93 268	82.18 244	81.18 340	92.13 240	67.18 351	93.97 274	94.23 249	82.43 214	73.39 301	93.57 204	76.98 122	87.86 378	50.53 378	82.34 248	88.51 306

mvs_tets		81.74 270	80.71 266	84.84 302	84.22 358	70.29 334	93.91 277	93.78 278	82.77 208	73.37 303	89.46 265	47.36 354	95.31 295	81.99 199	79.55 265	88.92 301

pmmvs4		82.54 259	80.79 263	87.79 246	86.11 337	80.49 169	93.55 285	93.18 307	77.29 300	73.35 304	89.40 266	65.26 253	95.05 309	75.32 263	73.61 296	87.83 322

LS3D		82.22 265	79.94 279	89.06 218	97.43 80	74.06 301	93.20 296	92.05 326	61.90 376	73.33 305	95.21 158	59.35 286	99.21 88	54.54 367	92.48 155	93.90 236

v10		81.43 275	79.53 283	87.11 267	86.38 330	78.87 210	94.31 265	93.43 295	77.88 292	73.24 306	85.26 327	65.44 249	95.75 271	72.14 287	67.71 342	86.72 340

v8		81.88 269	80.06 277	87.32 262	86.63 328	79.04 209	94.41 261	93.65 285	78.77 284	73.19 307	85.57 323	66.87 241	95.81 266	73.84 278	67.61 343	87.11 336

test0.0.03 1		82.79 255	82.48 241	83.74 320	86.81 327	72.22 314	96.52 170	95.03 200	83.76 187	73.00 308	93.20 208	72.30 201	88.88 374	64.15 331	77.52 281	90.12 269

anonymousdsp		80.98 282	79.97 278	84.01 315	81.73 371	70.44 333	92.49 305	93.58 289	77.10 304	72.98 309	86.31 314	57.58 304	94.90 310	79.32 220	78.63 274	86.69 341

XVG-ACMP-BASELINE		79.38 297	77.90 295	83.81 317	84.98 352	67.14 355	89.03 337	93.18 307	80.26 256	72.87 310	88.15 283	38.55 377	96.26 246	76.05 256	78.05 279	88.02 319

WR-MVS_H		81.02 280	80.09 274	83.79 318	88.08 315	71.26 330	94.46 259	96.54 93	80.08 258	72.81 311	86.82 302	70.36 223	92.65 346	64.18 330	67.50 344	87.46 333

OpenMVS	79.58 14	86.09 199	83.62 223	93.50 74	90.95 268	86.71 36	97.44 98	95.83 156	75.35 314	72.64 312	95.72 139	57.42 308	99.64 55	71.41 291	95.85 113	94.13 231

Anonymous20231211		79.72 292	77.19 300	87.33 261	95.59 124	77.16 262	95.18 243	94.18 254	59.31 389	72.57 313	86.20 316	47.89 351	95.66 276	74.53 272	69.24 327	89.18 288

CP-MVSNet		81.01 281	80.08 275	83.79 318	87.91 317	70.51 332	94.29 269	95.65 165	80.83 237	72.54 314	88.84 271	63.71 259	92.32 349	68.58 310	68.36 334	88.55 305

miper_lstm_enhance		81.66 273	80.66 267	84.67 306	91.19 263	71.97 321	91.94 311	93.19 305	77.86 293	72.27 315	85.26 327	73.46 187	93.42 341	73.71 279	67.05 348	88.61 304

PS-CasMVS		80.27 288	79.18 284	83.52 324	87.56 321	69.88 337	94.08 272	95.29 191	80.27 255	72.08 316	88.51 278	59.22 289	92.23 351	67.49 312	68.15 337	88.45 311

FMVSNet1		79.50 295	76.54 305	88.39 232	88.47 310	81.95 123	94.30 266	93.38 297	73.14 335	72.04 317	85.66 319	43.86 360	93.84 333	65.48 325	72.53 301	89.38 281

PEN-MVS		79.47 296	78.26 292	83.08 327	86.36 331	68.58 345	93.85 279	94.77 215	79.76 264	71.37 318	88.55 275	59.79 281	92.46 347	64.50 329	65.40 354	88.19 316

testing3		80.74 284	81.17 260	79.44 349	91.15 265	63.48 367	97.16 120	95.76 159	80.83 237	71.36 319	93.15 211	78.22 101	87.30 383	43.19 391	79.67 262	87.55 331

Patchmtry		77.36 313	74.59 318	85.67 290	89.75 292	75.75 287	77.85 388	91.12 340	60.28 384	71.23 320	80.35 363	75.45 152	93.56 339	57.94 353	67.34 346	87.68 325

IterMVS		80.67 285	79.16 285	85.20 298	89.79 290	76.08 277	92.97 300	91.86 328	80.28 254	71.20 321	85.14 332	57.93 302	91.34 361	72.52 285	70.74 311	88.18 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 274	78.28 291	91.04 170	98.14 55	78.48 219	95.09 249	86.97 372	61.14 382	71.12 322	92.78 218	59.59 283	99.38 78	53.11 371	86.61 209	95.27 210

IterMVS-SCA-FT		80.51 287	79.10 286	84.73 304	89.63 297	74.66 293	92.98 299	91.81 330	80.05 259	71.06 323	85.18 330	58.04 298	91.40 360	72.48 286	70.70 313	88.12 318

v7n		79.32 298	77.34 298	85.28 297	84.05 362	72.89 313	93.38 288	93.87 270	75.02 319	70.68 324	84.37 339	59.58 284	95.62 281	67.60 311	67.50 344	87.32 335

MS-PatchMatch		83.05 250	81.82 251	86.72 275	89.64 296	79.10 206	94.88 253	94.59 229	79.70 266	70.67 325	89.65 263	50.43 340	96.82 226	70.82 300	95.99 111	84.25 365

DTE-MVSNet		78.37 302	77.06 301	82.32 334	85.22 350	67.17 354	93.40 287	93.66 284	78.71 285	70.53 326	88.29 280	59.06 290	92.23 351	61.38 343	63.28 363	87.56 329

pm-mvs1		80.05 289	78.02 294	86.15 282	85.42 345	75.81 286	95.11 246	92.69 319	77.13 302	70.36 327	87.43 291	58.44 294	95.27 297	71.36 292	64.25 359	87.36 334

D2MVS		82.67 257	81.55 254	86.04 284	87.77 318	76.47 270	95.21 239	96.58 89	82.66 211	70.26 328	85.46 326	60.39 279	95.80 267	76.40 252	79.18 267	85.83 355

PVSNet_0	77.72 15	81.70 271	78.95 288	89.94 203	90.77 275	76.72 268	95.96 203	96.95 41	85.01 149	70.24 329	88.53 277	52.32 332	98.20 148	86.68 158	44.08 396	94.89 216

CL-MVSNet_self_test		75.81 322	74.14 324	80.83 343	78.33 381	67.79 348	94.22 270	93.52 291	77.28 301	69.82 330	81.54 357	61.47 276	89.22 373	57.59 356	53.51 380	85.48 357

tfpnnormal		78.14 304	75.42 311	86.31 280	88.33 313	79.24 200	94.41 261	96.22 127	73.51 331	69.81 331	85.52 325	55.43 321	95.75 271	47.65 385	67.86 340	83.95 368

EU-MVSNet		76.92 317	76.95 302	76.83 360	84.10 360	54.73 392	91.77 314	92.71 318	72.74 339	69.57 332	88.69 273	58.03 300	87.43 382	64.91 328	70.00 320	88.33 314

ITE_SJBPF				82.38 332	87.00 325	65.59 358		89.55 356	79.99 261	69.37 333	91.30 239	41.60 371	95.33 293	62.86 338	74.63 293	86.24 347

DSMNet-mixed		73.13 336	72.45 331	75.19 366	77.51 384	46.82 397	85.09 369	82.01 390	67.61 365	69.27 334	81.33 358	50.89 337	86.28 385	54.54 367	83.80 232	92.46 249

MVP-Stereo		82.65 258	81.67 253	85.59 293	86.10 338	78.29 226	93.33 290	92.82 316	77.75 294	69.17 335	87.98 285	59.28 288	95.76 270	71.77 288	96.88 91	82.73 373
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 286	77.77 296	89.14 217	93.43 197	77.24 258	91.89 312	90.18 352	69.86 356	68.02 336	91.94 232	52.21 334	98.84 117	59.32 350	83.12 237	91.35 254

NR-MVSNet		83.35 243	81.52 256	88.84 223	88.76 305	81.31 145	94.45 260	95.16 195	84.65 159	67.81 337	90.82 246	70.36 223	94.87 311	74.75 267	66.89 350	90.33 265

TransMVSNet (Re)		76.94 316	74.38 320	84.62 308	85.92 340	75.25 290	95.28 234	89.18 361	73.88 329	67.22 338	86.46 309	59.64 282	94.10 329	59.24 351	52.57 384	84.50 363

Anonymous20231206		75.29 325	73.64 326	80.22 345	80.75 372	63.38 368	93.36 289	90.71 350	73.09 336	67.12 339	83.70 345	50.33 341	90.85 366	53.63 370	70.10 318	86.44 344

ppachtmachnet_test		77.19 314	74.22 322	86.13 283	85.39 346	78.22 229	93.98 273	91.36 337	71.74 346	67.11 340	84.87 336	56.67 313	93.37 343	52.21 372	64.59 356	86.80 339

KD-MVS_2432*1600		77.63 310	74.92 315	85.77 287	90.86 272	79.44 194	88.08 345	93.92 266	76.26 309	67.05 341	82.78 351	72.15 203	91.92 354	61.53 340	41.62 399	85.94 353

miper_refine_blended		77.63 310	74.92 315	85.77 287	90.86 272	79.44 194	88.08 345	93.92 266	76.26 309	67.05 341	82.78 351	72.15 203	91.92 354	61.53 340	41.62 399	85.94 353

Patchmatch-test		78.25 303	74.72 317	88.83 224	91.20 262	74.10 300	73.91 396	88.70 367	59.89 387	66.82 343	85.12 333	78.38 98	94.54 320	48.84 383	79.58 264	97.86 100

test_fmvs3		69.56 347	69.19 347	70.67 370	69.01 396	47.05 396	90.87 325	86.81 374	71.31 349	66.79 344	77.15 374	16.40 401	83.17 392	81.84 200	62.51 365	81.79 381

LTVRE_ROB	73.68 18	77.99 305	75.74 310	84.74 303	90.45 280	72.02 319	86.41 360	91.12 340	72.57 341	66.63 345	87.27 294	54.95 326	96.98 215	56.29 362	75.98 283	85.21 359
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		77.18 315	76.06 307	80.55 344	83.78 365	60.00 379	90.35 328	91.05 343	77.01 306	66.62 346	87.92 286	47.73 352	94.03 330	71.63 289	68.44 333	87.62 326

testgi		74.88 327	73.40 327	79.32 350	80.13 376	61.75 372	93.21 295	86.64 376	79.49 270	66.56 347	91.06 242	35.51 384	88.67 375	56.79 361	71.25 307	87.56 329

LCM-MVSNet-Re		83.75 238	83.54 225	84.39 314	93.54 190	64.14 363	92.51 304	84.03 385	83.90 183	66.14 348	86.59 306	67.36 237	92.68 345	84.89 169	92.87 149	96.35 179

pmmvs6		74.65 328	71.67 335	83.60 323	79.13 379	69.94 336	93.31 293	90.88 347	61.05 383	65.83 349	84.15 342	43.43 362	94.83 313	66.62 318	60.63 368	86.02 351

our_test_3		77.90 308	75.37 312	85.48 295	85.39 346	76.74 267	93.63 282	91.67 331	73.39 334	65.72 350	84.65 338	58.20 297	93.13 344	57.82 354	67.87 339	86.57 343

COLMAP_ROB	73.24 19	75.74 323	73.00 330	83.94 316	92.38 225	69.08 343	91.85 313	86.93 373	61.48 379	65.32 351	90.27 255	42.27 368	96.93 220	50.91 376	75.63 287	85.80 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 319	74.16 323	83.35 326	90.05 288	76.17 275	89.58 333	89.85 354	71.39 348	65.29 352	80.42 362	50.61 339	87.70 381	61.05 345	69.24 327	86.18 348

ACMH+	76.62 16	77.47 312	74.94 314	85.05 300	91.07 267	71.58 327	93.26 294	90.01 353	71.80 345	64.76 353	88.55 275	41.62 370	96.48 238	62.35 339	71.00 309	87.09 337

Patchmatch-RL test		76.65 318	74.01 325	84.55 309	77.37 385	64.23 362	78.49 387	82.84 389	78.48 287	64.63 354	73.40 384	76.05 140	91.70 359	76.99 244	57.84 372	97.72 111

SixPastTwentyTwo		76.04 320	74.32 321	81.22 339	84.54 355	61.43 375	91.16 322	89.30 360	77.89 291	64.04 355	86.31 314	48.23 346	94.29 326	63.54 335	63.84 361	87.93 321

AllTest		75.92 321	73.06 329	84.47 310	92.18 237	67.29 349	91.07 323	84.43 383	67.63 361	63.48 356	90.18 256	38.20 378	97.16 206	57.04 358	73.37 297	88.97 299

TestCases				84.47 310	92.18 237	67.29 349		84.43 383	67.63 361	63.48 356	90.18 256	38.20 378	97.16 206	57.04 358	73.37 297	88.97 299

ACMH	75.40 17	77.99 305	74.96 313	87.10 268	90.67 276	76.41 272	93.19 297	91.64 333	72.47 342	63.44 358	87.61 290	43.34 363	97.16 206	58.34 352	73.94 294	87.72 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 124	89.03 130	92.95 94	94.38 166	86.77 34	98.14 46	96.31 120	89.30 60	63.33 359	96.72 122	90.09 10	93.63 338	90.70 111	82.29 250	98.46 59

USDC		78.65 301	76.25 306	85.85 285	87.58 320	74.60 295	89.58 333	90.58 351	84.05 176	63.13 360	88.23 281	40.69 376	96.86 225	66.57 320	75.81 286	86.09 350

LF4IMVS		72.36 340	70.82 338	76.95 359	79.18 378	56.33 385	86.12 362	86.11 378	69.30 358	63.06 361	86.66 305	33.03 388	92.25 350	65.33 326	68.64 331	82.28 377

dmvs_testset		72.00 343	73.36 328	67.91 372	83.83 364	31.90 412	85.30 367	77.12 397	82.80 207	63.05 362	92.46 220	61.54 275	82.55 394	42.22 393	71.89 306	89.29 285

KD-MVS_self_test		70.97 346	69.31 346	75.95 365	76.24 391	55.39 390	87.45 350	90.94 346	70.20 353	62.96 363	77.48 373	44.01 359	88.09 376	61.25 344	53.26 381	84.37 364

Anonymous20240521		72.06 342	69.91 343	78.50 354	77.11 386	61.67 374	91.62 318	90.97 345	65.52 368	62.37 364	79.05 369	36.32 380	90.96 365	57.75 355	68.52 332	82.87 370

test_0402		72.68 338	69.54 345	82.09 335	88.67 308	71.81 324	92.72 303	86.77 375	61.52 378	62.21 365	83.91 343	43.22 364	93.76 336	34.60 396	72.23 305	80.72 383

OpenMVS_ROB	68.52 20	73.02 337	69.57 344	83.37 325	80.54 375	71.82 323	93.60 284	88.22 368	62.37 374	61.98 366	83.15 350	35.31 385	95.47 287	45.08 389	75.88 285	82.82 371

MVS-HIRNet		71.36 345	67.00 351	84.46 312	90.58 277	69.74 339	79.15 384	87.74 371	46.09 396	61.96 367	50.50 400	45.14 358	95.64 279	53.74 369	88.11 197	88.00 320

test20.03		72.36 340	71.15 337	75.98 364	77.79 382	59.16 381	92.40 307	89.35 359	74.09 327	61.50 368	84.32 340	48.09 347	85.54 388	50.63 377	62.15 366	83.24 369

mvsany_test3		67.19 354	65.34 356	72.72 368	63.08 402	48.57 395	83.12 376	78.09 396	72.07 343	61.21 369	77.11 375	22.94 396	87.78 380	78.59 227	51.88 385	81.80 380

PM-MVS		69.32 350	66.93 352	76.49 361	73.60 393	55.84 387	85.91 363	79.32 395	74.72 321	61.09 370	78.18 371	21.76 397	91.10 364	70.86 298	56.90 374	82.51 374

TDRefinement		69.20 351	65.78 355	79.48 348	66.04 401	62.21 371	88.21 343	86.12 377	62.92 373	61.03 371	85.61 322	33.23 387	94.16 328	55.82 365	53.02 382	82.08 378

ambc				76.02 363	68.11 398	51.43 393	64.97 401	89.59 355		60.49 372	74.49 381	17.17 400	92.46 347	61.50 342	52.85 383	84.17 366

pmmvs-eth3d		73.59 331	70.66 339	82.38 332	76.40 389	73.38 303	89.39 336	89.43 358	72.69 340	60.34 373	77.79 372	46.43 356	91.26 363	66.42 322	57.06 373	82.51 374

test_vis1_rt		73.96 329	72.40 332	78.64 353	83.91 363	61.16 376	95.63 221	68.18 405	76.32 308	60.09 374	74.77 379	29.01 394	97.54 182	87.74 147	75.94 284	77.22 388

kuosan		73.55 332	72.39 333	77.01 358	89.68 295	66.72 356	85.24 368	93.44 293	67.76 360	60.04 375	83.40 348	71.90 206	84.25 390	45.34 388	54.75 375	80.06 384

K. test v3		73.62 330	71.59 336	79.69 347	82.98 367	59.85 380	90.85 326	88.83 363	77.13 302	58.90 376	82.11 353	43.62 361	91.72 358	65.83 324	54.10 379	87.50 332

EG-PatchMatch MVS		74.92 326	72.02 334	83.62 322	83.76 366	73.28 306	93.62 283	92.04 327	68.57 359	58.88 377	83.80 344	31.87 390	95.57 285	56.97 360	78.67 271	82.00 379

lessismore_v0				79.98 346	80.59 374	58.34 383		80.87 391		58.49 378	83.46 347	43.10 365	93.89 332	63.11 337	48.68 388	87.72 323

N_pmnet		61.30 358	60.20 361	64.60 377	84.32 357	17.00 418	91.67 317	10.98 416	61.77 377	58.45 379	78.55 370	49.89 343	91.83 357	42.27 392	63.94 360	84.97 360

TinyColmap		72.41 339	68.99 348	82.68 330	88.11 314	69.59 340	88.41 342	85.20 380	65.55 367	57.91 380	84.82 337	30.80 392	95.94 260	51.38 373	68.70 330	82.49 376

UnsupCasMVSNet_eth		73.25 335	70.57 340	81.30 338	77.53 383	66.33 357	87.24 353	93.89 269	80.38 251	57.90 381	81.59 356	42.91 367	90.56 368	65.18 327	48.51 389	87.01 338

MIMVSNet1		69.44 349	66.65 353	77.84 355	76.48 388	62.84 370	87.42 351	88.97 362	66.96 366	57.75 382	79.72 368	32.77 389	85.83 387	46.32 386	63.42 362	84.85 361

pmmvs3		65.75 356	62.18 359	76.45 362	67.12 400	64.54 360	88.68 340	85.05 381	54.77 395	57.54 383	73.79 382	29.40 393	86.21 386	55.49 366	47.77 392	78.62 386

dongtai		69.47 348	68.98 349	70.93 369	86.87 326	58.45 382	88.19 344	93.18 307	63.98 371	56.04 384	80.17 365	70.97 219	79.24 396	33.46 397	47.94 391	75.09 390

test_f		64.01 357	62.13 360	69.65 371	63.00 403	45.30 402	83.66 375	80.68 392	61.30 380	55.70 385	72.62 387	14.23 403	84.64 389	69.84 303	58.11 371	79.00 385

new-patchmatchnet		68.85 352	65.93 354	77.61 357	73.57 394	63.94 365	90.11 330	88.73 366	71.62 347	55.08 386	73.60 383	40.84 374	87.22 384	51.35 375	48.49 390	81.67 382

UnsupCasMVSNet_bld		68.60 353	64.50 357	80.92 342	74.63 392	67.80 347	83.97 373	92.94 314	65.12 369	54.63 387	68.23 393	35.97 382	92.17 353	60.13 346	44.83 394	82.78 372

CMPMVS	54.94 21	75.71 324	74.56 319	79.17 351	79.69 377	55.98 386	89.59 332	93.30 302	60.28 384	53.85 388	89.07 268	47.68 353	96.33 244	76.55 249	81.02 254	85.22 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 355	63.18 358	75.18 367	76.27 390	61.74 373	83.79 374	84.66 382	56.64 393	51.57 389	71.85 391	31.29 391	87.93 377	49.98 379	62.55 364	75.86 389

test_method		56.77 360	54.53 364	63.49 379	76.49 387	40.70 405	75.68 392	74.24 399	19.47 407	48.73 390	71.89 390	19.31 398	65.80 407	57.46 357	47.51 393	83.97 367

YYNet1		73.53 334	70.43 341	82.85 329	84.52 356	71.73 325	91.69 316	91.37 336	67.63 361	46.79 391	81.21 359	55.04 325	90.43 369	55.93 363	59.70 370	86.38 345

MDA-MVSNet_test_wron		73.54 333	70.43 341	82.86 328	84.55 354	71.85 322	91.74 315	91.32 339	67.63 361	46.73 392	81.09 360	55.11 324	90.42 370	55.91 364	59.76 369	86.31 346

WB-MVS		57.26 359	56.22 362	60.39 383	69.29 395	35.91 410	86.39 361	70.06 403	59.84 388	46.46 393	72.71 386	51.18 336	78.11 397	15.19 407	34.89 402	67.14 396

SSC-MVS		56.01 362	54.96 363	59.17 384	68.42 397	34.13 411	84.98 370	69.23 404	58.08 392	45.36 394	71.67 392	50.30 342	77.46 398	14.28 408	32.33 403	65.91 397

MDA-MVSNet-bldmvs		71.45 344	67.94 350	81.98 336	85.33 348	68.50 346	92.35 308	88.76 365	70.40 351	42.99 395	81.96 354	46.57 355	91.31 362	48.75 384	54.39 378	86.11 349

APD_test1		56.56 361	53.58 365	65.50 374	67.93 399	46.51 399	77.24 391	72.95 400	38.09 398	42.75 396	75.17 378	13.38 404	82.78 393	40.19 394	54.53 377	67.23 395

DeepMVS_CX				64.06 378	78.53 380	43.26 403		68.11 407	69.94 355	38.55 397	76.14 377	18.53 399	79.34 395	43.72 390	41.62 399	69.57 393

LCM-MVSNet		52.52 365	48.24 368	65.35 375	47.63 412	41.45 404	72.55 397	83.62 387	31.75 400	37.66 398	57.92 398	9.19 410	76.76 400	49.26 381	44.60 395	77.84 387

test_vis3_rt		54.10 364	51.04 367	63.27 380	58.16 404	46.08 401	84.17 372	49.32 415	56.48 394	36.56 399	49.48 402	8.03 411	91.91 356	67.29 314	49.87 386	51.82 401

FPMVS		55.09 363	52.93 366	61.57 381	55.98 405	40.51 406	83.11 377	83.41 388	37.61 399	34.95 400	71.95 389	14.40 402	76.95 399	29.81 399	65.16 355	67.25 394

PMMVS2		50.90 367	46.31 370	64.67 376	55.53 406	46.67 398	77.30 390	71.02 402	40.89 397	34.16 401	59.32 396	9.83 409	76.14 402	40.09 395	28.63 404	71.21 391

testf1		45.70 369	42.41 371	55.58 385	53.29 409	40.02 407	68.96 399	62.67 409	27.45 402	29.85 402	61.58 394	5.98 412	73.83 404	28.49 402	43.46 397	52.90 399

APD_test2		45.70 369	42.41 371	55.58 385	53.29 409	40.02 407	68.96 399	62.67 409	27.45 402	29.85 402	61.58 394	5.98 412	73.83 404	28.49 402	43.46 397	52.90 399

tmp_tt		41.54 372	41.93 374	40.38 390	20.10 416	26.84 414	61.93 402	59.09 411	14.81 409	28.51 404	80.58 361	35.53 383	48.33 411	63.70 334	13.11 408	45.96 404

Gipuma		45.11 371	42.05 373	54.30 387	80.69 373	51.30 394	35.80 405	83.81 386	28.13 401	27.94 405	34.53 405	11.41 408	76.70 401	21.45 404	54.65 376	34.90 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 368	41.28 375	61.04 382	39.91 414	46.25 400	70.59 398	76.18 398	58.87 390	23.09 406	48.00 403	12.58 406	66.54 406	28.65 401	13.62 407	70.35 392

MVE	35.65 22	33.85 374	29.49 379	46.92 389	41.86 413	36.28 409	50.45 404	56.52 412	18.75 408	18.28 407	37.84 404	2.41 415	58.41 408	18.71 405	20.62 405	46.06 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 373	35.53 376	50.18 388	29.72 415	30.30 413	59.60 403	66.20 408	26.06 404	17.91 408	49.53 401	3.12 414	74.09 403	18.19 406	49.40 387	46.14 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 375	32.39 377	33.65 391	53.35 408	25.70 415	74.07 395	53.33 413	21.08 405	17.17 409	33.63 407	11.85 407	54.84 409	12.98 409	14.04 406	20.42 406

EMVS		31.70 376	31.45 378	32.48 392	50.72 411	23.95 416	74.78 394	52.30 414	20.36 406	16.08 410	31.48 408	12.80 405	53.60 410	11.39 410	13.10 409	19.88 407

wuyk23d		14.10 378	13.89 381	14.72 393	55.23 407	22.91 417	33.83 406	3.56 417	4.94 410	4.11 411	2.28 413	2.06 416	19.66 412	10.23 411	8.74 410	1.59 410

testmvs		9.92 379	12.94 382	0.84 395	0.65 417	0.29 420	93.78 280	0.39 418	0.42 411	2.85 412	15.84 411	0.17 418	0.30 414	2.18 412	0.21 411	1.91 409

test123		9.07 380	11.73 383	1.11 394	0.50 418	0.77 419	89.44 335	0.20 419	0.34 412	2.15 413	10.72 412	0.34 417	0.32 413	1.79 413	0.08 412	2.23 408

EGC-MVSNET		52.46 366	47.56 369	67.15 373	81.98 370	60.11 378	82.54 378	72.44 401	0.11 413	0.70 414	74.59 380	25.11 395	83.26 391	29.04 400	61.51 367	58.09 398

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		21.43 377	28.57 380	0.00 396	0.00 419	0.00 421	0.00 407	95.93 151	0.00 414	0.00 415	97.66 72	63.57 260	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		5.92 382	7.89 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	71.04 216	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.11 381	10.81 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	97.30 95	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						67.18 351								49.00 382

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 51					99.81 22	98.08 14	98.81 24	99.43 11

No_MVS				97.14 3	99.05 9	92.19 4		96.83 51					99.81 22	98.08 14	98.81 24	99.43 11

eth-test2					0.00 419

eth-test					0.00 419

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 12				98.54 20	92.06 3	99.84 13	99.11 3	99.37 1	99.74 1

save fliter					98.24 51	83.34 102	98.61 33	96.57 90	91.32 32

test_0728_SECOND				95.14 20	99.04 14	86.14 40	99.06 17	96.77 60					99.84 13	97.90 17	98.85 21	99.45 10

GSMVS																97.54 124

sam_mvs1												77.59 111				97.54 124

sam_mvs												75.35 159

MTGPA								96.33 118

test_post1							85.88 364				30.24 409	73.77 182	95.07 308	73.89 276

test_post											33.80 406	76.17 138	95.97 256

patchmatchnet-post											77.09 376	77.78 110	95.39 289

MTMP							97.53 90	68.16 406

gm-plane-assit					92.27 231	79.64 192			84.47 165		95.15 163		97.93 156	85.81 161

test9_res														96.00 40	99.03 13	98.31 68

agg_prior2														94.30 63	99.00 15	98.57 53

test_prior4						82.34 119	97.75 75

test_prior				93.09 89	98.68 26	81.91 127		96.40 110					99.06 104			98.29 70

新几何2							96.42 180

旧先验1					97.39 83	79.58 193		96.54 93			98.08 48	84.00 42			97.42 78	97.62 120

无先验							96.87 149	96.78 54	77.39 298				99.52 69	79.95 214		98.43 61

原ACMM2							96.84 150

testdata2													99.48 73	76.45 251

segment_acmp												82.69 57

testdata1							95.57 226		87.44 98

plane_prior7					91.86 252	77.55 253

plane_prior6					91.98 248	77.92 242						64.77 255

plane_prior5								94.69 217					97.30 198	87.08 153	82.82 243	90.96 257

plane_prior4											94.15 189

plane_prior2							97.18 116		89.89 53

plane_prior1					91.95 250

plane_prior						77.96 239	97.52 93		90.36 49						82.96 241

n2								0.00 420

nn								0.00 420

door-mid								79.75 394

test11								96.50 98

door								80.13 393

HQP5-MVS						78.48 219

BP-MVS														87.67 149

HQP3-MVS								94.80 212							83.01 239

HQP2-MVS												65.40 250

NP-MVS					92.04 247	78.22 229					94.56 179

ACMMP++_ref															78.45 276

ACMMP++															79.05 268

Test By Simon												71.65 209

High-resolution multi-view benchmark