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.

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 175	97.09 77	70.21 300	98.99 8	96.69 66	95.57 1	95.08 19	99.23 1	86.40 12	99.87 8	97.84 1	98.66 22	99.65 1

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 33	95.17 2	92.11 50	98.46 11	87.33 7	99.97 1	97.21 5	99.31 1	99.63 2

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 26	94.66 3	96.79 4	98.78 4	86.42 11	99.95 2	97.59 3	99.18 3	99.00 13

EPNet		94.06 23	94.15 22	93.76 40	97.27 74	84.35 59	98.29 20	97.64 18	94.57 4	95.36 14	96.88 85	79.96 60	99.12 78	91.30 57	96.11 76	97.82 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 83	90.59 3	99.21 2	97.02 38	94.40 5	91.46 58	97.08 79	83.32 31	99.69 27	92.83 43	98.70 21	99.04 11
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
NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 44	99.16 3	96.96 43	94.11 6	95.59 13	98.64 7	85.07 14	99.91 3	95.61 18	99.10 5	99.00 13

CANet		94.89 8	94.64 12	95.63 8	97.55 62	88.12 11	99.06 5	96.39 100	94.07 7	95.34 15	97.80 48	76.83 97	99.87 8	97.08 6	97.64 51	98.89 16

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 34	98.06 28	96.64 72	93.64 8	91.74 55	98.54 8	80.17 57	99.90 4	92.28 50	98.75 18	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		93.82 29	93.11 35	95.95 5	96.79 79	89.15 7	98.56 15	95.30 162	93.61 9	94.82 24	98.02 34	66.60 199	99.88 7	96.94 7	97.39 58	98.81 19

CANet_DTU		90.98 73	90.04 76	93.83 38	94.76 130	86.23 26	96.32 150	93.12 267	93.11 10	93.71 36	96.82 88	63.08 227	99.48 48	84.29 118	95.12 89	95.77 154

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 25	86.04 27	97.81 38	96.93 46	92.45 11	95.69 12	98.50 9	85.38 13	99.85 10	94.75 23	99.18 3	98.65 28

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 106	92.35 1	98.21 23	95.79 136	92.42 12	96.24 7	98.18 18	71.04 165	99.17 73	96.77 8	97.39 58	96.79 129

HSP-MVS		95.55 4	96.51 2	92.66 88	98.31 40	80.10 149	97.42 70	96.46 90	92.20 13	97.11 3	98.29 14	93.46 1	99.10 79	96.01 12	99.30 2	98.77 21

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 123	92.02 2	98.19 24	95.68 140	92.06 14	96.01 11	98.14 23	70.83 168	98.96 87	96.74 9	96.57 72	96.76 132

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 36	97.38 71	83.04 85	98.10 26	95.29 163	91.57 15	93.81 35	97.45 63	86.64 8	99.43 51	96.28 10	94.01 97	99.20 9

CLD-MVS		87.97 130	87.48 118	89.44 183	92.16 193	80.54 137	98.14 25	94.92 175	91.41 16	79.43 193	95.40 111	62.34 230	97.27 158	90.60 65	82.90 202	90.50 215
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TSAR-MVS + MP.		94.79 10	95.17 8	93.64 46	97.66 57	84.10 64	95.85 176	96.42 94	91.26 17	97.49 2	96.80 89	86.50 10	98.49 106	95.54 19	99.03 9	98.33 41

PAPM		92.87 41	92.40 46	94.30 25	92.25 190	87.85 14	96.40 146	96.38 101	91.07 18	88.72 91	96.90 83	82.11 38	97.37 152	90.05 71	97.70 50	97.67 89

lupinMVS		93.87 28	93.58 28	94.75 19	93.00 173	88.08 12	99.15 4	95.50 149	91.03 19	94.90 22	97.66 51	78.84 71	97.56 142	94.64 26	97.46 53	98.62 30

PVSNet_Blended		93.13 35	92.98 37	93.57 50	97.47 63	83.86 67	99.32 1	96.73 60	91.02 20	89.53 83	96.21 97	76.42 103	99.57 39	94.29 28	95.81 83	97.29 115

DeepC-MVS	86.58 3	91.53 65	91.06 65	92.94 77	94.52 142	81.89 106	95.95 167	95.98 126	90.76 21	83.76 138	96.76 90	73.24 146	99.71 23	91.67 54	96.96 66	97.22 119
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		94.28 16	94.39 17	93.97 34	98.30 41	84.06 65	98.64 13	96.93 46	90.71 22	93.08 42	98.70 5	79.98 59	99.21 65	94.12 30	99.07 7	98.63 29

jason		92.73 44	92.23 50	94.21 29	90.50 221	87.30 21	98.65 12	95.09 167	90.61 23	92.76 45	97.13 77	75.28 132	97.30 155	93.32 38	96.75 71	98.02 63
jason: jason.
HQP-NCC					92.08 194		97.63 51		90.52 24	82.30 153

ACMP_Plane					92.08 194		97.63 51		90.52 24	82.30 153

HQP-MVS		87.91 132	87.55 117	88.98 190	92.08 194	78.48 204	97.63 51	94.80 183	90.52 24	82.30 153	94.56 137	65.40 212	97.32 153	87.67 96	83.01 193	91.13 208

plane_prior						77.96 223	97.52 60		90.36 27						82.96 195

plane_prior3						77.75 230			90.17 28	81.33 169

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 31	98.09 14	89.99 29	92.34 49	96.97 82	81.30 47	98.99 85	88.54 85	98.88 14	99.20 9

HQP_MVS		87.50 135	87.09 128	88.74 195	91.86 204	77.96 223	97.18 81	94.69 188	89.89 30	81.33 169	94.15 144	64.77 217	97.30 155	87.08 99	82.82 203	90.96 210

plane_prior2							97.18 81		89.89 30

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 55	84.61 54	97.76 45	96.19 115	89.59 32	96.66 5	98.17 22	84.33 21	99.60 36	96.09 11	98.50 26	98.66 27

Regformer-1		94.00 25	94.04 23	93.87 37	98.41 35	84.29 61	97.43 68	97.04 37	89.50 33	92.75 46	98.13 24	82.60 36	99.26 60	93.55 33	96.99 64	98.06 60

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 66	95.41 113	81.35 120	99.02 7	96.59 78	89.50 33	94.18 33	98.36 13	83.68 29	99.45 50	94.77 22	98.45 28	98.81 19
Skip Steuart: Steuart Systems R&D Blog.
Regformer-2		93.92 26	94.01 24	93.67 45	98.41 35	83.75 70	97.43 68	97.00 39	89.43 35	92.69 47	98.13 24	82.48 37	99.22 63	93.51 34	96.99 64	98.04 61

MVS_111021_HR		93.41 33	93.39 30	93.47 60	97.34 72	82.83 90	97.56 56	98.27 12	89.16 36	89.71 78	97.14 76	79.77 61	99.56 41	93.65 32	97.94 46	98.02 63

Regformer-3		93.19 34	93.19 33	93.19 67	98.10 46	83.01 87	97.08 98	96.98 41	88.98 37	91.35 63	97.89 44	80.80 49	99.23 61	92.30 49	95.20 86	97.32 110

CHOSEN 1792x2688		91.07 72	90.21 74	93.64 46	95.18 119	83.53 75	96.26 156	96.13 117	88.92 38	84.90 121	93.10 165	72.86 149	99.62 35	88.86 82	95.67 84	97.79 83

Regformer-4		93.06 37	93.12 34	92.89 78	98.10 46	82.20 99	97.08 98	96.92 48	88.87 39	91.23 65	97.89 44	80.57 51	99.19 70	92.21 51	95.20 86	97.29 115

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 93	88.90 8	98.23 22	97.65 17	88.57 40	89.82 77	97.22 74	79.29 63	99.06 81	89.57 77	88.73 140	98.73 25

PatchFormer-LS_test		90.14 88	89.30 91	92.65 90	95.43 111	82.46 94	93.46 240	96.35 103	88.56 41	84.82 122	95.22 119	84.63 19	97.55 143	78.40 167	86.81 154	97.94 73

EPNet_dtu		87.65 134	87.89 106	86.93 236	94.57 134	71.37 291	96.72 120	96.50 87	88.56 41	87.12 107	95.02 131	75.91 112	94.01 285	66.62 261	90.00 132	95.42 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		92.27 53	91.22 62	95.41 11	95.80 102	88.31 9	97.09 96	94.64 195	88.49 43	92.99 44	97.31 69	72.68 150	98.57 102	93.38 37	88.58 142	99.36 4

MVS_111021_LR		91.60 64	91.64 58	91.47 129	95.74 103	78.79 196	96.15 159	96.77 54	88.49 43	88.64 92	97.07 80	72.33 153	99.19 70	93.13 41	96.48 73	96.43 140

DWT-MVSNet_test		90.52 83	89.80 84	92.70 87	95.73 105	82.20 99	93.69 234	96.55 82	88.34 45	87.04 109	95.34 112	86.53 9	97.55 143	76.32 190	88.66 141	98.34 40

HY-MVS	84.06 6	91.63 62	90.37 71	95.39 12	96.12 88	88.25 10	90.22 289	97.58 19	88.33 46	90.50 71	91.96 174	79.26 65	99.06 81	90.29 69	89.07 136	98.88 17

PVSNet_Blended_VisFu		91.24 70	90.77 68	92.66 88	95.09 121	82.40 95	97.77 41	95.87 133	88.26 47	86.39 111	93.94 148	76.77 98	99.27 58	88.80 84	94.00 98	96.31 146

EI-MVSNet-Vis-set		91.84 59	91.77 55	92.04 110	97.60 59	81.17 122	96.61 129	96.87 50	88.20 48	89.19 87	97.55 61	78.69 75	99.14 76	90.29 69	90.94 128	95.80 153

UGNet		87.73 133	86.55 136	91.27 133	95.16 120	79.11 180	96.35 148	96.23 111	88.14 49	87.83 102	90.48 197	50.65 299	99.09 80	80.13 152	94.03 95	95.60 159
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
alignmvs		92.97 39	92.26 49	95.12 13	95.54 109	87.77 15	98.67 11	96.38 101	88.04 50	93.01 43	97.45 63	79.20 67	98.60 100	93.25 40	88.76 139	98.99 15

PVSNet_BlendedMVS		90.05 89	89.96 78	90.33 156	97.47 63	83.86 67	98.02 30	96.73 60	87.98 51	89.53 83	89.61 209	76.42 103	99.57 39	94.29 28	79.59 216	87.57 279

zzz-MVS		92.74 42	92.71 40	92.86 79	97.90 51	80.85 129	96.47 135	96.33 105	87.92 52	90.20 74	98.18 18	76.71 101	99.76 14	92.57 47	98.09 40	97.96 71

MTAPA		92.45 52	92.31 48	92.86 79	97.90 51	80.85 129	92.88 258	96.33 105	87.92 52	90.20 74	98.18 18	76.71 101	99.76 14	92.57 47	98.09 40	97.96 71

EI-MVSNet-UG-set		91.35 69	91.22 62	91.73 123	97.39 68	80.68 132	96.47 135	96.83 52	87.92 52	88.30 98	97.36 68	77.84 85	99.13 77	89.43 80	89.45 134	95.37 163

OPM-MVS		85.84 162	85.10 154	88.06 208	88.34 251	77.83 229	95.72 179	94.20 212	87.89 55	80.45 178	94.05 146	58.57 256	97.26 159	83.88 121	82.76 205	89.09 241

PVSNet	82.34 9	89.02 103	87.79 109	92.71 86	95.49 110	81.50 117	97.70 48	97.29 20	87.76 56	85.47 117	95.12 128	56.90 274	98.90 94	80.33 148	94.02 96	97.71 87

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 79	96.20 113	87.73 57	88.40 94	98.12 27	78.71 74	99.76 14	87.99 93	96.28 74	98.74 22

Vis-MVSNet		88.67 113	87.82 108	91.24 135	92.68 176	78.82 193	96.95 108	93.85 235	87.55 58	87.07 108	95.13 127	63.43 225	97.21 160	77.58 176	96.15 75	97.70 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata1							95.57 184		87.44 59

UA-Net		88.92 106	88.48 100	90.24 158	94.06 152	77.18 241	93.04 255	94.66 192	87.39 60	91.09 67	93.89 149	74.92 136	98.18 117	75.83 194	91.43 125	95.35 164

PMMVS		89.46 96	89.92 80	88.06 208	94.64 131	69.57 306	96.22 157	94.95 174	87.27 61	91.37 62	96.54 94	65.88 204	97.39 151	88.54 85	93.89 99	97.23 118

xiu_mvs_v1_base_debu		90.54 80	89.54 87	93.55 51	92.31 183	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 269	98.32 110	92.72 44	93.46 105	94.74 177

xiu_mvs_v1_base		90.54 80	89.54 87	93.55 51	92.31 183	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 269	98.32 110	92.72 44	93.46 105	94.74 177

xiu_mvs_v1_base_debi		90.54 80	89.54 87	93.55 51	92.31 183	87.58 18	96.99 102	94.87 178	87.23 62	93.27 38	97.56 58	57.43 269	98.32 110	92.72 44	93.46 105	94.74 177

MVSTER		89.25 100	88.92 95	90.24 158	95.98 94	84.66 53	96.79 116	95.36 158	87.19 65	80.33 180	90.61 196	90.02 5	95.97 210	85.38 110	78.64 225	90.09 225

IB-MVS	85.34 4	88.67 113	87.14 127	93.26 64	93.12 172	84.32 60	98.76 10	97.27 21	87.19 65	79.36 194	90.45 198	83.92 27	98.53 104	84.41 117	69.79 273	96.93 124
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
XVS		92.69 46	92.71 40	92.63 91	98.52 30	80.29 142	97.37 72	96.44 92	87.04 67	91.38 59	97.83 47	77.24 92	99.59 37	90.46 66	98.07 42	98.02 63

X-MVStestdata		86.26 158	84.14 175	92.63 91	98.52 30	80.29 142	97.37 72	96.44 92	87.04 67	91.38 59	20.73 362	77.24 92	99.59 37	90.46 66	98.07 42	98.02 63

OMC-MVS		88.80 110	88.16 103	90.72 148	95.30 116	77.92 226	94.81 210	94.51 199	86.80 69	84.97 120	96.85 86	67.53 181	98.60 100	85.08 111	87.62 149	95.63 158

3Dnovator	82.32 10	89.33 98	87.64 112	94.42 23	93.73 161	85.70 33	97.73 47	96.75 58	86.73 70	76.21 230	95.93 101	62.17 231	99.68 29	81.67 143	97.81 49	97.88 75

casdiffmvs1		91.94 56	91.49 60	93.28 63	95.02 125	83.53 75	95.37 189	95.49 150	86.52 71	94.24 30	91.65 179	79.04 69	97.74 133	91.67 54	94.45 92	98.57 31

VNet		92.11 54	91.22 62	94.79 17	96.91 78	86.98 22	97.91 32	97.96 15	86.38 72	93.65 37	95.74 103	70.16 172	98.95 90	93.39 35	88.87 138	98.43 37

ACMMP_Plus		93.46 32	93.23 32	94.17 30	97.16 75	84.28 62	96.82 114	96.65 69	86.24 73	94.27 29	97.99 37	77.94 83	99.83 13	93.39 35	98.57 24	98.39 39

TESTMET0.1,1		89.83 90	89.34 90	91.31 130	92.54 181	80.19 147	97.11 92	96.57 79	86.15 74	86.85 110	91.83 178	79.32 62	96.95 172	81.30 144	92.35 115	96.77 131

ESAPD		95.32 5	95.55 6	94.64 21	98.79 12	84.87 51	97.77 41	96.74 59	86.11 75	96.54 6	98.89 2	88.39 6	99.74 19	97.67 2	99.05 8	99.31 5

3Dnovator+	82.88 8	89.63 94	87.85 107	94.99 15	94.49 145	86.76 23	97.84 35	95.74 137	86.10 76	75.47 239	96.02 100	65.00 216	99.51 46	82.91 138	97.07 63	98.72 26

test_prior3		94.03 24	94.34 18	93.09 72	98.68 16	81.91 104	98.37 18	96.40 97	86.08 77	94.57 27	98.02 34	83.14 32	99.06 81	95.05 20	98.79 15	98.29 45

test_prior2							98.37 18		86.08 77	94.57 27	98.02 34	83.14 32		95.05 20	98.79 15

CHOSEN 280x420		91.71 61	91.85 52	91.29 132	94.94 126	82.69 91	87.89 307	96.17 116	85.94 79	87.27 106	94.31 140	90.27 4	95.65 237	94.04 31	95.86 81	95.53 160

sss		90.87 75	89.96 78	93.60 49	94.15 149	83.84 69	97.14 87	98.13 13	85.93 80	89.68 79	96.09 99	71.67 157	99.30 57	87.69 95	89.16 135	97.66 90

EPMVS		87.47 136	85.90 145	92.18 105	95.41 113	82.26 98	87.00 314	96.28 108	85.88 81	84.23 130	85.57 270	75.07 135	96.26 196	71.14 230	92.50 112	98.03 62

APDe-MVS		94.56 13	94.75 10	93.96 35	98.84 11	83.40 79	98.04 29	96.41 95	85.79 82	95.00 21	98.28 15	84.32 24	99.18 72	97.35 4	98.77 17	99.28 6

#test#		92.99 38	92.99 36	92.98 75	98.71 14	81.12 123	97.77 41	96.70 64	85.75 83	91.75 53	97.97 41	78.47 76	99.71 23	91.36 56	98.41 30	98.12 57

VPNet		84.69 185	82.92 190	90.01 169	89.01 243	83.45 78	96.71 122	95.46 152	85.71 84	79.65 186	92.18 172	56.66 278	96.01 209	83.05 137	67.84 293	90.56 214

MP-MVS		92.61 49	92.67 43	92.42 96	98.13 45	79.73 157	97.33 74	96.20 113	85.63 85	90.53 70	97.66 51	78.14 81	99.70 26	92.12 52	98.30 37	97.85 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Effi-MVS+-dtu		84.61 186	84.90 160	83.72 289	91.96 200	63.14 325	94.95 207	93.34 261	85.57 86	79.79 185	87.12 239	61.99 235	95.61 241	83.55 129	85.83 167	92.41 204

mvs-test1		86.83 148	87.17 124	85.81 248	91.96 200	65.24 318	97.90 33	93.34 261	85.57 86	84.51 129	95.14 126	61.99 235	97.19 162	83.55 129	90.55 130	95.00 172

GA-MVS		85.79 169	84.04 176	91.02 141	89.47 239	80.27 144	96.90 111	94.84 181	85.57 86	80.88 172	89.08 212	56.56 280	96.47 189	77.72 173	85.35 176	96.34 143

FIs		86.73 152	86.10 138	88.61 197	90.05 229	80.21 146	96.14 160	96.95 44	85.56 89	78.37 201	92.30 170	76.73 99	95.28 256	79.51 156	79.27 220	90.35 217

DU-MVS		84.57 187	83.33 187	88.28 205	88.76 245	79.36 169	96.43 144	95.41 157	85.42 90	78.11 203	90.82 192	67.61 179	95.14 260	79.14 161	68.30 287	90.33 218

UniMVSNet (Re)		85.31 177	84.23 174	88.55 198	89.75 232	80.55 136	96.72 120	96.89 49	85.42 90	78.40 200	88.93 215	75.38 128	95.52 245	78.58 165	68.02 290	89.57 232

SMA-MVS		94.70 11	94.68 11	94.76 18	98.02 49	85.94 29	97.47 62	96.77 54	85.32 92	97.92 1	98.70 5	83.09 34	99.84 12	95.79 16	99.08 6	98.49 36

test-mter		88.95 104	88.60 98	89.98 171	92.26 188	77.23 239	97.11 92	95.96 127	85.32 92	86.30 113	91.38 182	76.37 105	96.78 182	80.82 146	91.92 121	95.94 150

tpmrst		88.36 121	87.38 121	91.31 130	94.36 146	79.92 151	87.32 310	95.26 165	85.32 92	88.34 95	86.13 263	80.60 50	96.70 184	83.78 122	85.34 177	97.30 113

region2R		92.72 45	92.70 42	92.79 82	98.68 16	80.53 138	97.53 58	96.51 85	85.22 95	91.94 51	97.98 39	77.26 90	99.67 31	90.83 63	98.37 34	98.18 50

UniMVSNet_NR-MVSNet		85.49 174	84.59 161	88.21 207	89.44 240	79.36 169	96.71 122	96.41 95	85.22 95	78.11 203	90.98 191	76.97 95	95.14 260	79.14 161	68.30 287	90.12 223

HFP-MVS		92.89 40	92.86 39	92.98 75	98.71 14	81.12 123	97.58 54	96.70 64	85.20 97	91.75 53	97.97 41	78.47 76	99.71 23	90.95 60	98.41 30	98.12 57

ACMMPR		92.69 46	92.67 43	92.75 84	98.66 19	80.57 135	97.58 54	96.69 66	85.20 97	91.57 56	97.92 43	77.01 94	99.67 31	90.95 60	98.41 30	98.00 68

FC-MVSNet-test		85.96 160	85.39 148	87.66 221	89.38 241	78.02 221	95.65 182	96.87 50	85.12 99	77.34 213	91.94 176	76.28 107	94.74 271	77.09 182	78.82 223	90.21 220

mPP-MVS		91.88 58	91.82 53	92.07 108	98.38 37	78.63 199	97.29 75	96.09 120	85.12 99	88.45 93	97.66 51	75.53 116	99.68 29	89.83 73	98.02 45	97.88 75

test_normal		85.83 163	83.51 184	92.78 83	86.33 281	83.01 87	95.56 186	95.46 152	85.11 101	65.73 294	86.63 251	56.62 279	97.86 128	87.87 94	92.29 117	97.47 105

DI_MVS_plusplus_test		85.92 161	83.61 182	92.86 79	86.43 276	83.20 81	95.57 184	95.46 152	85.10 102	65.99 292	86.84 246	56.70 276	97.89 127	88.10 92	92.33 116	97.48 104

PVSNet_0	77.72 15	81.70 232	78.95 242	89.94 174	90.77 218	76.72 247	95.96 166	96.95 44	85.01 103	70.24 275	88.53 221	52.32 296	98.20 115	86.68 104	44.08 348	94.89 173

casdiffmvs		90.98 73	90.24 72	93.19 67	94.60 133	84.15 63	95.01 206	94.98 173	84.98 104	91.53 57	91.14 187	76.72 100	97.62 139	89.78 75	93.42 108	97.81 81

v1.0		39.63 332	52.84 325	0.00 354	98.90 7	0.00 369	0.00 360	96.77 54	84.95 105	96.07 9	98.83 3	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Test4		82.30 226	79.15 241	91.78 122	81.84 320	81.74 111	94.04 227	94.20 212	84.86 106	59.75 326	83.88 289	37.14 336	96.28 195	84.60 116	92.00 120	97.30 113

Patchmatch-test1		84.89 182	82.76 194	91.27 133	92.30 186	81.86 107	92.88 258	95.56 145	84.85 107	82.52 149	85.19 275	58.04 263	94.21 281	65.93 267	87.58 151	97.74 85

tpm		85.55 173	84.47 165	88.80 194	90.19 226	75.39 258	88.79 300	94.69 188	84.83 108	83.96 135	85.21 274	78.22 80	94.68 273	76.32 190	78.02 231	96.34 143

CP-MVS		92.54 51	92.60 45	92.34 98	98.50 32	79.90 152	98.40 17	96.40 97	84.75 109	90.48 72	98.09 29	77.40 89	99.21 65	91.15 59	98.23 39	97.92 74

ACMMP		90.39 84	89.97 77	91.64 125	97.58 61	78.21 217	96.78 117	96.72 62	84.73 110	84.72 125	97.23 73	71.22 162	99.63 34	88.37 90	92.41 114	97.08 121
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
MP-MVS-pluss		92.58 50	92.35 47	93.29 62	97.30 73	82.53 93	96.44 140	96.04 124	84.68 111	89.12 88	98.37 12	77.48 88	99.74 19	93.31 39	98.38 33	97.59 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NR-MVSNet		83.35 207	81.52 213	88.84 192	88.76 245	81.31 121	94.45 216	95.16 166	84.65 112	67.81 283	90.82 192	70.36 170	94.87 267	74.75 203	66.89 300	90.33 218

PAPM_NR		91.46 66	90.82 67	93.37 61	98.50 32	81.81 109	95.03 205	96.13 117	84.65 112	86.10 115	97.65 55	79.24 66	99.75 17	83.20 134	96.88 69	98.56 33

PatchmatchNet		86.83 148	85.12 153	91.95 112	94.12 150	82.27 97	86.55 318	95.64 142	84.59 114	82.98 148	84.99 280	77.26 90	95.96 214	68.61 250	91.34 126	97.64 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TranMVSNet+NR-MVSNet		83.24 209	81.71 210	87.83 216	87.71 258	78.81 195	96.13 162	94.82 182	84.52 115	76.18 231	90.78 194	64.07 220	94.60 274	74.60 205	66.59 304	90.09 225

train_agg		94.28 16	94.45 15	93.74 41	98.64 22	83.71 71	97.82 36	96.65 69	84.50 116	95.16 16	98.09 29	84.33 21	99.36 55	95.91 14	98.96 12	98.16 52

test_8					98.63 24	83.64 74	97.81 38	96.63 75	84.50 116	95.10 18	98.11 28	84.33 21	99.23 61

gm-plane-assit					92.27 187	79.64 166			84.47 118		95.15 125		97.93 120	85.81 106

diffmvs		89.05 102	88.22 102	91.55 127	93.88 156	79.73 157	93.18 254	94.40 205	84.43 119	88.32 96	90.40 200	72.91 148	97.41 149	84.71 115	91.74 123	97.51 101

Vis-MVSNet (Re-imp)		88.88 108	88.87 96	88.91 191	93.89 155	74.43 264	96.93 110	94.19 214	84.39 120	83.22 145	95.67 107	78.24 79	94.70 272	78.88 164	94.40 94	97.61 95

agg_prior1		94.10 21	94.31 19	93.48 57	98.59 27	83.13 82	97.77 41	96.56 80	84.38 121	94.19 31	98.13 24	84.66 18	99.16 74	95.74 17	98.74 19	98.15 54

thres200		88.92 106	87.65 111	92.73 85	96.30 84	85.62 35	97.85 34	98.86 1	84.38 121	84.82 122	93.99 147	75.12 134	98.01 118	70.86 232	86.67 155	94.56 180

nrg030		86.79 150	85.43 147	90.87 146	88.76 245	85.34 39	97.06 100	94.33 208	84.31 123	80.45 178	91.98 173	72.36 152	96.36 192	88.48 88	71.13 256	90.93 212

MVS_Test		90.29 86	89.18 92	93.62 48	95.23 117	84.93 47	94.41 217	94.66 192	84.31 123	90.37 73	91.02 189	75.13 133	97.82 129	83.11 136	94.42 93	98.12 57

TEST9					98.64 22	83.71 71	97.82 36	96.65 69	84.29 125	95.16 16	98.09 29	84.39 20	99.36 55

agg_prior3		94.10 21	94.29 21	93.53 54	98.62 25	83.03 86	97.80 40	96.64 72	84.28 126	95.01 20	98.03 33	83.40 30	99.41 52	95.91 14	98.96 12	98.16 52

CDS-MVSNet		89.50 95	88.96 94	91.14 138	91.94 203	80.93 127	97.09 96	95.81 135	84.26 127	84.72 125	94.20 143	80.31 53	95.64 238	83.37 133	88.96 137	96.85 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CR-MVSNet		83.53 199	81.36 216	90.06 167	90.16 227	79.75 154	79.02 337	91.12 291	84.24 128	82.27 161	80.35 310	75.45 118	93.67 291	63.37 285	86.25 158	96.75 133

BH-w/o		88.24 125	87.47 119	90.54 152	95.03 124	78.54 202	97.41 71	93.82 236	84.08 129	78.23 202	94.51 139	69.34 175	97.21 160	80.21 151	94.58 91	95.87 152

USDC		78.65 260	76.25 262	85.85 247	87.58 259	74.60 262	89.58 293	90.58 307	84.05 130	63.13 306	88.23 224	40.69 332	96.86 178	66.57 263	75.81 237	86.09 301

IS-MVSNet		88.67 113	88.16 103	90.20 160	93.61 162	76.86 244	96.77 119	93.07 268	84.02 131	83.62 139	95.60 109	74.69 139	96.24 198	78.43 166	93.66 103	97.49 103

WR-MVS		84.32 190	82.96 189	88.41 200	89.38 241	80.32 141	96.59 130	96.25 110	83.97 132	76.63 222	90.36 201	67.53 181	94.86 268	75.82 195	70.09 268	90.06 227

PS-MVSNAJss		84.91 181	84.30 173	86.74 237	85.89 295	74.40 265	94.95 207	94.16 219	83.93 133	76.45 225	90.11 206	71.04 165	95.77 224	83.16 135	79.02 222	90.06 227

LCM-MVSNet-Re		83.75 196	83.54 183	84.39 279	93.54 164	64.14 321	92.51 264	84.03 344	83.90 134	66.14 291	86.59 252	67.36 183	92.68 297	84.89 114	92.87 110	96.35 142

MAR-MVS		90.63 78	90.22 73	91.86 119	98.47 34	78.20 218	97.18 81	96.61 76	83.87 135	88.18 99	98.18 18	68.71 177	99.75 17	83.66 128	97.15 62	97.63 93
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
PGM-MVS		91.93 57	91.80 54	92.32 100	98.27 42	79.74 156	95.28 190	97.27 21	83.83 136	90.89 69	97.78 49	76.12 109	99.56 41	88.82 83	97.93 48	97.66 90

MDTV_nov1_ep13			83.69 178		94.09 151	81.01 125	86.78 316	96.09 120	83.81 137	84.75 124	84.32 285	74.44 140	96.54 186	63.88 281	85.07 179

test-LLR		88.48 117	87.98 105	89.98 171	92.26 188	77.23 239	97.11 92	95.96 127	83.76 138	86.30 113	91.38 182	72.30 154	96.78 182	80.82 146	91.92 121	95.94 150

test0.0.03 1		82.79 217	82.48 197	83.74 288	86.81 264	72.22 277	96.52 133	95.03 171	83.76 138	73.00 254	93.20 162	72.30 154	88.88 331	64.15 276	77.52 233	90.12 223

ACMP	81.66 11	84.00 193	83.22 188	86.33 240	91.53 209	72.95 275	95.91 171	93.79 240	83.70 140	73.79 246	92.22 171	54.31 295	96.89 176	83.98 120	79.74 215	89.16 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
1112_ss		88.60 116	87.47 119	92.00 111	93.21 168	80.97 126	96.47 135	92.46 274	83.64 141	80.86 173	97.30 71	80.24 55	97.62 139	77.60 175	85.49 170	97.40 107

TAMVS		88.48 117	87.79 109	90.56 151	91.09 213	79.18 177	96.45 138	95.88 132	83.64 141	83.12 146	93.33 161	75.94 111	95.74 229	82.40 139	88.27 145	96.75 133

Test_1112_low_res		88.03 128	86.73 133	91.94 113	93.15 170	80.88 128	96.44 140	92.41 275	83.59 143	80.74 175	91.16 186	80.18 56	97.59 141	77.48 177	85.40 171	97.36 109

tfpn200view9		88.48 117	87.15 125	92.47 94	96.21 85	85.30 40	97.44 64	98.85 2	83.37 144	83.99 133	93.82 150	75.36 129	97.93 120	69.04 242	86.24 160	94.17 181

thres400		88.42 120	87.15 125	92.23 102	96.21 85	85.30 40	97.44 64	98.85 2	83.37 144	83.99 133	93.82 150	75.36 129	97.93 120	69.04 242	86.24 160	93.45 197

Effi-MVS+		90.70 76	89.90 81	93.09 72	93.61 162	83.48 77	95.20 193	92.79 271	83.22 146	91.82 52	95.70 105	71.82 156	97.48 148	91.25 58	93.67 102	98.32 42

CostFormer		89.08 101	88.39 101	91.15 137	93.13 171	79.15 179	88.61 302	96.11 119	83.14 147	89.58 82	86.93 242	83.83 28	96.87 177	88.22 91	85.92 164	97.42 106

VDD-MVS		88.28 123	87.02 129	92.06 109	95.09 121	80.18 148	97.55 57	94.45 203	83.09 148	89.10 89	95.92 102	47.97 310	98.49 106	93.08 42	86.91 153	97.52 100

jajsoiax		82.12 228	81.15 218	85.03 257	84.19 312	70.70 296	94.22 225	93.95 230	83.07 149	73.48 248	89.75 207	49.66 304	95.37 252	82.24 141	79.76 213	89.02 244

VPA-MVSNet		85.32 176	83.83 177	89.77 180	90.25 224	82.63 92	96.36 147	97.07 36	83.03 150	81.21 171	89.02 214	61.58 239	96.31 194	85.02 113	70.95 258	90.36 216

CDPH-MVS		93.12 36	92.91 38	93.74 41	98.65 21	83.88 66	97.67 50	96.26 109	83.00 151	93.22 41	98.24 16	81.31 46	99.21 65	89.12 81	98.74 19	98.14 55

1314		88.94 105	87.20 123	94.17 30	93.21 168	85.73 32	93.33 244	96.64 72	82.89 152	75.98 232	96.36 95	66.83 195	99.39 53	83.52 132	96.02 79	97.39 108

BH-RMVSNet		86.84 147	85.28 150	91.49 128	95.35 115	80.26 145	96.95 108	92.21 276	82.86 153	81.77 167	95.46 110	59.34 250	97.64 138	69.79 239	93.81 101	96.57 137

mvs_tets		81.74 231	80.71 223	84.84 261	84.22 311	70.29 299	93.91 229	93.78 241	82.77 154	73.37 249	89.46 210	47.36 314	95.31 255	81.99 142	79.55 219	88.92 249

thres600view7		88.06 127	86.70 134	92.15 106	96.10 89	85.17 43	97.14 87	98.85 2	82.70 155	83.41 140	93.66 153	75.43 124	97.82 129	67.13 257	85.88 165	93.45 197

tfpn111		88.08 126	86.70 134	92.20 104	96.10 89	84.90 48	97.14 87	98.85 2	82.69 156	83.41 140	93.66 153	75.43 124	97.82 129	67.13 257	85.88 165	93.89 188

conf200view11		88.27 124	86.95 130	92.24 101	96.10 89	84.90 48	97.14 87	98.85 2	82.69 156	83.41 140	93.66 153	75.43 124	97.93 120	69.04 242	86.24 160	93.89 188

thres100view900		88.30 122	86.95 130	92.33 99	96.10 89	84.90 48	97.14 87	98.85 2	82.69 156	83.41 140	93.66 153	75.43 124	97.93 120	69.04 242	86.24 160	94.17 181

PHI-MVS		93.59 31	93.63 26	93.48 57	98.05 48	81.76 110	98.64 13	97.13 25	82.60 159	94.09 34	98.49 10	80.35 52	99.85 10	94.74 24	98.62 23	98.83 18

HyFIR lowres test		89.36 97	88.60 98	91.63 126	94.91 128	80.76 131	95.60 183	95.53 146	82.56 160	84.03 132	91.24 185	78.03 82	96.81 180	87.07 101	88.41 144	97.32 110

APD-MVS		93.61 30	93.59 27	93.69 44	98.76 13	83.26 80	97.21 77	96.09 120	82.41 161	94.65 26	98.21 17	81.96 39	98.81 97	94.65 25	98.36 35	99.01 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Fast-Effi-MVS+-dtu		83.33 208	82.60 196	85.50 252	89.55 237	69.38 307	96.09 164	91.38 286	82.30 162	75.96 233	91.41 181	56.71 275	95.58 243	75.13 201	84.90 180	91.54 206

LPG-MVS_test		84.20 192	83.49 185	86.33 240	90.88 216	73.06 273	95.28 190	94.13 220	82.20 163	76.31 226	93.20 162	54.83 292	96.95 172	83.72 125	80.83 210	88.98 245

LGP-MVS_train				86.33 240	90.88 216	73.06 273		94.13 220	82.20 163	76.31 226	93.20 162	54.83 292	96.95 172	83.72 125	80.83 210	88.98 245

HPM-MVS		91.62 63	91.53 59	91.89 114	97.88 54	79.22 176	96.99 102	95.73 138	82.07 165	89.50 85	97.19 75	75.59 115	98.93 93	90.91 62	97.94 46	97.54 97
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvs_anonymous		88.68 112	87.62 114	91.86 119	94.80 129	81.69 115	93.53 239	94.92 175	82.03 166	78.87 198	90.43 199	75.77 113	95.34 253	85.04 112	93.16 109	98.55 35

XVG-OURS		85.18 178	84.38 166	87.59 223	90.42 223	71.73 286	91.06 286	94.07 227	82.00 167	83.29 144	95.08 129	56.42 281	97.55 143	83.70 127	83.42 189	93.49 196

view600		87.45 137	85.98 140	91.88 115	95.90 96	84.52 55	96.68 125	98.85 2	81.85 168	82.30 153	93.39 157	75.44 120	97.66 134	64.02 277	85.36 172	93.45 197

view800		87.45 137	85.98 140	91.88 115	95.90 96	84.52 55	96.68 125	98.85 2	81.85 168	82.30 153	93.39 157	75.44 120	97.66 134	64.02 277	85.36 172	93.45 197

conf0.05thres1000		87.45 137	85.98 140	91.88 115	95.90 96	84.52 55	96.68 125	98.85 2	81.85 168	82.30 153	93.39 157	75.44 120	97.66 134	64.02 277	85.36 172	93.45 197

tfpn		87.45 137	85.98 140	91.88 115	95.90 96	84.52 55	96.68 125	98.85 2	81.85 168	82.30 153	93.39 157	75.44 120	97.66 134	64.02 277	85.36 172	93.45 197

BH-untuned		86.95 145	85.94 144	89.99 170	94.52 142	77.46 234	96.78 117	93.37 260	81.80 172	76.62 223	93.81 152	66.64 198	97.02 170	76.06 192	93.88 100	95.48 161

FMVSNet3		84.71 184	82.71 195	90.70 149	94.55 135	87.71 16	95.92 169	94.67 191	81.73 173	75.82 235	88.08 227	66.99 193	94.47 276	71.23 227	75.38 239	89.91 229

v2v482		83.46 200	81.86 205	88.25 206	86.19 287	79.65 165	96.34 149	94.02 228	81.56 174	77.32 214	88.23 224	65.62 207	96.03 205	77.77 168	69.72 275	89.09 241

XVG-OURS-SEG-HR		85.74 170	85.16 152	87.49 227	90.22 225	71.45 290	91.29 283	94.09 226	81.37 175	83.90 137	95.22 119	60.30 242	97.53 147	85.58 108	84.42 183	93.50 195

Fast-Effi-MVS+		87.93 131	86.94 132	90.92 144	94.04 153	79.16 178	98.26 21	93.72 244	81.29 176	83.94 136	92.90 166	69.83 173	96.68 185	76.70 186	91.74 123	96.93 124

ab-mvs		87.08 143	84.94 158	93.48 57	93.34 167	83.67 73	88.82 299	95.70 139	81.18 177	84.55 128	90.14 205	62.72 228	98.94 92	85.49 109	82.54 207	97.85 78

abl_6		89.80 91	89.71 86	90.07 166	96.53 82	75.52 256	94.48 214	95.04 170	81.12 178	89.22 86	97.00 81	68.83 176	98.96 87	89.86 72	95.27 85	95.73 155

原ACMM1				91.22 136	97.77 56	78.10 220		96.61 76	81.05 179	91.28 64	97.42 67	77.92 84	98.98 86	79.85 155	98.51 25	96.59 136

0601test		91.46 66	90.53 70	94.24 28	97.41 67	85.18 42	98.08 27	97.72 16	80.94 180	89.85 76	96.14 98	75.61 114	98.81 97	90.42 68	88.56 143	98.74 22

v1141		83.36 205	81.81 208	88.01 210	86.61 272	79.26 172	96.44 140	94.12 223	80.88 181	77.48 210	86.87 244	67.08 188	96.03 205	77.14 180	69.69 276	88.75 253

divwei89l23v2f112		83.36 205	81.81 208	88.01 210	86.60 273	79.23 175	96.44 140	94.12 223	80.88 181	77.49 208	86.87 244	67.08 188	96.03 205	77.14 180	69.67 277	88.76 251

v1		83.37 204	81.82 206	88.01 210	86.58 274	79.24 173	96.45 138	94.13 220	80.88 181	77.48 210	86.88 243	67.15 186	96.04 204	77.15 179	69.67 277	88.76 251

CP-MVSNet		81.01 240	80.08 231	83.79 286	87.91 256	70.51 297	94.29 224	95.65 141	80.83 184	72.54 260	88.84 216	63.71 221	92.32 301	68.58 251	68.36 286	88.55 257

v1neww		83.45 201	81.95 201	87.95 213	86.66 266	79.04 184	96.32 150	94.17 216	80.76 185	77.56 206	87.25 236	67.02 191	96.08 201	77.73 170	70.07 269	88.74 255

v7new		83.45 201	81.95 201	87.95 213	86.66 266	79.04 184	96.32 150	94.17 216	80.76 185	77.56 206	87.25 236	67.02 191	96.08 201	77.73 170	70.07 269	88.74 255

v6		83.45 201	81.94 203	87.95 213	86.62 270	79.03 187	96.32 150	94.17 216	80.76 185	77.57 205	87.23 238	67.03 190	96.09 200	77.73 170	70.06 271	88.75 253

MVSFormer		91.36 68	90.57 69	93.73 43	93.00 173	88.08 12	94.80 211	94.48 200	80.74 188	94.90 22	97.13 77	78.84 71	95.10 262	83.77 123	97.46 53	98.02 63

test_djsdf		83.00 214	82.45 198	84.64 269	84.07 314	69.78 303	94.80 211	94.48 200	80.74 188	75.41 240	87.70 230	61.32 240	95.10 262	83.77 123	79.76 213	89.04 243

MDTV_nov1_ep13_2view						81.74 111	86.80 315		80.65 190	85.65 116		74.26 141		76.52 188		96.98 122

CVMVSNet		84.83 183	85.57 146	82.63 299	91.55 207	60.38 331	95.13 201	95.03 171	80.60 191	82.10 163	94.71 135	66.40 201	90.19 328	74.30 207	90.32 131	97.31 112

DP-MVS Recon		91.72 60	90.85 66	94.34 24	99.50 1	85.00 46	98.51 16	95.96 127	80.57 192	88.08 100	97.63 56	76.84 96	99.89 6	85.67 107	94.88 90	98.13 56

v148		82.41 224	80.89 219	86.99 235	86.18 288	76.81 245	96.27 155	93.82 236	80.49 193	75.28 241	86.11 264	67.32 184	95.75 226	75.48 198	67.03 299	88.42 262

IterMVS-LS		83.93 194	82.80 193	87.31 231	91.46 210	77.39 236	95.66 181	93.43 254	80.44 194	75.51 238	87.26 235	73.72 144	95.16 259	76.99 183	70.72 260	89.39 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMM	80.70 13	83.72 197	82.85 192	86.31 243	91.19 212	72.12 280	95.88 173	94.29 209	80.44 194	77.02 218	91.96 174	55.24 288	97.14 166	79.30 159	80.38 212	89.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EI-MVSNet		85.80 164	85.20 151	87.59 223	91.55 207	77.41 235	95.13 201	95.36 158	80.43 196	80.33 180	94.71 135	73.72 144	95.97 210	76.96 185	78.64 225	89.39 234

UnsupCasMVSNet_eth		73.25 299	70.57 301	81.30 306	77.53 334	66.33 316	87.24 311	93.89 233	80.38 197	57.90 333	81.59 304	42.91 325	90.56 325	65.18 271	48.51 343	87.01 289

V42		83.04 212	81.53 212	87.57 225	86.27 285	79.09 182	95.87 174	94.11 225	80.35 198	77.22 216	86.79 249	65.32 214	96.02 208	77.74 169	70.14 264	87.61 278

TR-MVS		86.30 157	84.93 159	90.42 153	94.63 132	77.58 232	96.57 131	93.82 236	80.30 199	82.42 152	95.16 124	58.74 255	97.55 143	74.88 202	87.82 148	96.13 148

IterMVS		80.67 242	79.16 240	85.20 255	89.79 231	76.08 252	92.97 257	91.86 280	80.28 200	71.20 265	85.14 278	57.93 267	91.34 319	72.52 216	70.74 259	88.18 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		80.27 244	79.18 239	83.52 292	87.56 260	69.88 302	94.08 226	95.29 163	80.27 201	72.08 261	88.51 222	59.22 252	92.23 303	67.49 255	68.15 289	88.45 261

XVG-ACMP-BASELINE		79.38 252	77.90 248	83.81 285	84.98 306	67.14 315	89.03 298	93.18 264	80.26 202	72.87 256	88.15 226	38.55 333	96.26 196	76.05 193	78.05 230	88.02 270

testing_2		76.96 281	73.18 291	88.30 204	75.87 341	79.64 166	89.92 291	94.21 211	80.16 203	51.23 340	75.94 330	33.94 341	95.81 222	82.28 140	75.12 242	89.46 233

XXY-MVS		83.84 195	82.00 200	89.35 184	87.13 262	81.38 119	95.72 179	94.26 210	80.15 204	75.92 234	90.63 195	61.96 237	96.52 187	78.98 163	73.28 250	90.14 221

WR-MVS_H		81.02 239	80.09 230	83.79 286	88.08 254	71.26 294	94.46 215	96.54 83	80.08 205	72.81 257	86.82 247	70.36 170	92.65 298	64.18 275	67.50 296	87.46 284

semantic-postprocess				84.73 265	89.63 236	74.66 261		91.81 282	80.05 206	71.06 267	85.18 276	57.98 266	91.40 318	72.48 217	70.70 261	88.12 269

v1144		82.90 216	81.27 217	87.78 218	86.29 283	79.07 183	96.14 160	93.93 231	80.05 206	77.38 212	86.80 248	65.50 208	95.93 216	75.21 200	70.13 265	88.33 265

ITE_SJBPF				82.38 301	87.00 263	65.59 317		89.55 312	79.99 208	69.37 279	91.30 184	41.60 329	95.33 254	62.86 287	74.63 244	86.24 298

dp		84.30 191	82.31 199	90.28 157	94.24 148	77.97 222	86.57 317	95.53 146	79.94 209	80.75 174	85.16 277	71.49 161	96.39 191	63.73 282	83.36 190	96.48 139

v7		82.99 215	81.41 214	87.73 219	86.41 277	78.86 192	96.10 163	93.98 229	79.88 210	77.49 208	87.11 240	65.44 210	95.97 210	75.69 197	70.59 262	88.36 263

APD-MVS_3200maxsize		91.23 71	91.35 61	90.89 145	97.89 53	76.35 250	96.30 154	95.52 148	79.82 211	91.03 68	97.88 46	74.70 138	98.54 103	92.11 53	96.89 68	97.77 84

PEN-MVS		79.47 251	78.26 245	83.08 295	86.36 280	68.58 309	93.85 231	94.77 186	79.76 212	71.37 263	88.55 219	59.79 243	92.46 299	64.50 273	65.40 305	88.19 267

MS-PatchMatch		83.05 211	81.82 206	86.72 239	89.64 235	79.10 181	94.88 209	94.59 198	79.70 213	70.67 269	89.65 208	50.43 301	96.82 179	70.82 234	95.99 80	84.25 313

tpmp4_e23		86.46 154	84.95 157	90.98 143	93.74 160	78.60 201	88.13 305	95.90 131	79.65 214	85.42 118	85.67 265	80.08 58	97.06 168	71.71 222	84.26 184	97.28 117

PCF-MVS	84.09 5	86.77 151	85.00 156	92.08 107	92.06 197	83.07 84	92.14 273	94.47 202	79.63 215	76.90 220	94.78 134	71.15 163	99.20 69	72.87 213	91.05 127	93.98 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HPM-MVS_fast		90.38 85	90.17 75	91.03 140	97.61 58	77.35 237	97.15 86	95.48 151	79.51 216	88.79 90	96.90 83	71.64 159	98.81 97	87.01 102	97.44 55	96.94 123

testgi		74.88 292	73.40 290	79.32 315	80.13 328	61.75 328	93.21 251	86.64 333	79.49 217	66.56 290	91.06 188	35.51 339	88.67 332	56.79 306	71.25 255	87.56 280

EPP-MVSNet		89.76 92	89.72 85	89.87 176	93.78 157	76.02 253	97.22 76	96.51 85	79.35 218	85.11 119	95.01 132	84.82 15	97.10 167	87.46 98	88.21 146	96.50 138

v1192		82.31 225	80.55 225	87.60 222	85.94 293	78.47 207	95.85 176	93.80 239	79.33 219	76.97 219	86.51 253	63.33 226	95.87 218	73.11 212	70.13 265	88.46 260

tpm2		87.35 141	86.26 137	90.62 150	92.93 175	78.67 197	88.06 306	95.99 125	79.33 219	87.40 103	86.43 258	80.28 54	96.40 190	80.23 150	85.73 169	96.79 129

PatchMatch-RL		85.00 180	83.66 180	89.02 189	95.86 100	74.55 263	92.49 265	93.60 249	79.30 221	79.29 195	91.47 180	58.53 257	98.45 108	70.22 235	92.17 119	94.07 185

PLC	83.97 7	88.00 129	87.38 121	89.83 178	98.02 49	76.46 248	97.16 85	94.43 204	79.26 222	81.98 164	96.28 96	69.36 174	99.27 58	77.71 174	92.25 118	93.77 192
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LFMVS		89.27 99	87.64 112	94.16 32	97.16 75	85.52 37	97.18 81	94.66 192	79.17 223	89.63 81	96.57 93	55.35 287	98.22 114	89.52 79	89.54 133	98.74 22

v144192		82.43 221	80.73 222	87.54 226	85.81 296	78.22 214	95.98 165	93.78 241	79.09 224	77.11 217	86.49 254	64.66 219	95.91 217	74.20 208	69.42 280	88.49 258

GBi-Net		82.42 222	80.43 227	88.39 201	92.66 177	81.95 101	94.30 221	93.38 257	79.06 225	75.82 235	85.66 266	56.38 282	93.84 287	71.23 227	75.38 239	89.38 236

test1		82.42 222	80.43 227	88.39 201	92.66 177	81.95 101	94.30 221	93.38 257	79.06 225	75.82 235	85.66 266	56.38 282	93.84 287	71.23 227	75.38 239	89.38 236

FMVSNet2		82.79 217	80.44 226	89.83 178	92.66 177	85.43 38	95.42 188	94.35 207	79.06 225	74.46 243	87.28 233	56.38 282	94.31 279	69.72 240	74.68 243	89.76 230

v1921920		82.02 229	80.23 229	87.41 228	85.62 297	77.92 226	95.79 178	93.69 245	78.86 228	76.67 221	86.44 256	62.50 229	95.83 221	72.69 214	69.77 274	88.47 259

v8		81.88 230	80.06 233	87.32 230	86.63 269	79.04 184	94.41 217	93.65 247	78.77 229	73.19 253	85.57 270	66.87 194	95.81 222	73.84 211	67.61 295	87.11 287

DTE-MVSNet		78.37 261	77.06 254	82.32 303	85.22 304	67.17 314	93.40 241	93.66 246	78.71 230	70.53 270	88.29 223	59.06 253	92.23 303	61.38 290	63.28 313	87.56 280

Patchmatch-RL test		76.65 284	74.01 288	84.55 273	77.37 336	64.23 320	78.49 339	82.84 349	78.48 231	64.63 300	73.40 336	76.05 110	91.70 317	76.99 183	57.84 321	97.72 86

v1240		81.70 232	79.83 236	87.30 232	85.50 298	77.70 231	95.48 187	93.44 252	78.46 232	76.53 224	86.44 256	60.85 241	95.84 220	71.59 224	70.17 263	88.35 264

SixPastTwentyTwo		76.04 286	74.32 285	81.22 307	84.54 308	61.43 330	91.16 284	89.30 315	77.89 233	64.04 301	86.31 260	48.23 307	94.29 280	63.54 284	63.84 311	87.93 272

v10		81.43 235	79.53 238	87.11 233	86.38 278	78.87 191	94.31 220	93.43 254	77.88 234	73.24 252	85.26 273	65.44 210	95.75 226	72.14 218	67.71 294	86.72 292

MVP-Stereo		82.65 219	81.67 211	85.59 251	86.10 291	78.29 211	93.33 244	92.82 270	77.75 235	69.17 281	87.98 228	59.28 251	95.76 225	71.77 221	96.88 69	82.73 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		81.34 236	79.54 237	86.73 238	85.02 305	76.91 243	96.22 157	91.65 284	77.65 236	73.55 247	88.61 218	55.70 285	94.43 277	74.12 209	73.35 249	88.86 250

MVS		90.60 79	88.64 97	96.50 1	94.25 147	90.53 4	93.33 244	97.21 23	77.59 237	78.88 197	97.31 69	71.52 160	99.69 27	89.60 76	98.03 44	99.27 7

AdaColmap		88.81 109	87.61 115	92.39 97	99.33 4	79.95 150	96.70 124	95.58 144	77.51 238	83.05 147	96.69 92	61.90 238	99.72 22	84.29 118	93.47 104	97.50 102

无先验							96.87 112	96.78 53	77.39 239				99.52 43	79.95 153		98.43 37

MIMVSNet		79.18 255	75.99 264	88.72 196	87.37 261	80.66 133	79.96 332	91.82 281	77.38 240	74.33 244	81.87 303	41.78 327	90.74 324	66.36 266	83.10 192	94.76 176

tfpn_ndepth		87.25 142	86.00 139	91.01 142	95.86 100	81.46 118	96.53 132	97.09 34	77.35 241	81.36 168	95.07 130	84.74 17	95.86 219	60.88 292	85.14 178	95.72 156

pmmvs4		82.54 220	80.79 220	87.79 217	86.11 290	80.49 140	93.55 238	93.18 264	77.29 242	73.35 250	89.40 211	65.26 215	95.05 265	75.32 199	73.61 246	87.83 273

pm-mvs1		80.05 245	78.02 247	86.15 245	85.42 299	75.81 254	95.11 203	92.69 273	77.13 243	70.36 271	87.43 232	58.44 258	95.27 257	71.36 226	64.25 309	87.36 285

K. test v3		73.62 295	71.59 297	79.69 313	82.98 319	59.85 333	90.85 287	88.83 318	77.13 243	58.90 327	82.11 301	43.62 320	91.72 316	65.83 268	54.10 336	87.50 283

anonymousdsp		80.98 241	79.97 234	84.01 281	81.73 321	70.44 298	92.49 265	93.58 251	77.10 245	72.98 255	86.31 260	57.58 268	94.90 266	79.32 158	78.63 227	86.69 293

CSCG		92.02 55	91.65 57	93.12 70	98.53 29	80.59 134	97.47 62	97.18 24	77.06 246	84.64 127	97.98 39	83.98 26	99.52 43	90.72 64	97.33 60	99.23 8

OurMVSNet-221017-0		77.18 279	76.06 263	80.55 310	83.78 316	60.00 332	90.35 288	91.05 294	77.01 247	66.62 289	87.92 229	47.73 312	94.03 284	71.63 223	68.44 285	87.62 277

Anonymous20240521		79.73 247	78.10 246	84.63 270	87.90 257	71.58 288	93.91 229	94.39 206	76.69 248	70.27 274	87.00 241	58.97 254	94.76 270	64.38 274	69.43 279	87.54 282

Baseline_NR-MVSNet		81.22 238	80.07 232	84.68 267	85.32 303	75.12 260	96.48 134	88.80 319	76.24 249	77.28 215	86.40 259	67.61 179	94.39 278	75.73 196	66.73 303	84.54 311

conf0.01		85.70 171	84.35 167	89.77 180	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	93.89 188

conf0.002		85.70 171	84.35 167	89.77 180	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	93.89 188

thresconf0.02		85.80 164	84.35 167	90.17 161	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	95.09 168

tfpn_n400		85.80 164	84.35 167	90.17 161	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	95.09 168

tfpnconf		85.80 164	84.35 167	90.17 161	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	95.09 168

tfpnview11		85.80 164	84.35 167	90.17 161	94.53 136	79.70 159	95.17 195	97.11 27	75.97 250	79.44 187	95.31 113	81.90 40	95.73 230	56.78 307	82.91 196	95.09 168

tfpn1000		86.43 156	85.10 154	90.41 154	95.56 108	80.51 139	95.90 172	97.09 34	75.91 256	80.02 184	94.82 133	84.78 16	95.47 248	57.36 302	84.46 181	95.26 167

F-COLMAP		84.50 188	83.44 186	87.67 220	95.22 118	72.22 277	95.95 167	93.78 241	75.74 257	76.30 228	95.18 123	59.50 247	98.45 108	72.67 215	86.59 157	92.35 205

v18		77.96 266	75.61 267	84.98 258	86.66 266	79.01 188	93.02 256	90.94 296	75.69 258	63.19 305	77.62 319	67.11 187	92.07 306	70.05 236	56.24 325	83.87 318

CPTT-MVS		89.72 93	89.87 82	89.29 185	98.33 39	73.30 270	97.70 48	95.35 160	75.68 259	87.40 103	97.44 66	70.43 169	98.25 113	89.56 78	96.90 67	96.33 145

v17		77.79 269	75.41 270	84.94 259	86.53 275	78.94 189	92.83 261	90.88 298	75.51 260	62.97 310	77.50 321	66.69 197	92.03 308	69.80 238	56.01 327	83.83 319

v16		77.84 268	75.47 268	84.93 260	86.62 270	78.93 190	92.84 260	90.89 297	75.50 261	63.03 309	77.54 320	66.82 196	92.04 307	69.82 237	56.22 326	83.82 320

v15		77.52 271	75.09 272	84.82 262	86.37 279	78.82 193	92.58 263	90.78 300	75.47 262	62.53 312	77.17 322	66.58 200	91.92 309	69.18 241	55.16 329	83.73 321

OpenMVS	79.58 14	86.09 159	83.62 181	93.50 55	90.95 215	86.71 24	97.44 64	95.83 134	75.35 263	72.64 258	95.72 104	57.42 272	99.64 33	71.41 225	95.85 82	94.13 184

cascas		86.50 153	84.48 164	92.55 93	92.64 180	85.95 28	97.04 101	95.07 169	75.32 264	80.50 176	91.02 189	54.33 294	97.98 119	86.79 103	87.62 149	93.71 193

tpmvs		83.04 212	80.77 221	89.84 177	95.43 111	77.96 223	85.59 323	95.32 161	75.31 265	76.27 229	83.70 294	73.89 142	97.41 149	59.53 294	81.93 208	94.14 183

V14		77.43 273	74.99 273	84.75 263	86.32 282	78.67 197	92.44 267	90.77 301	75.28 266	62.42 313	77.13 323	66.40 201	91.88 310	69.01 246	55.14 330	83.70 322

114514_t		88.79 111	87.57 116	92.45 95	98.21 43	81.74 111	96.99 102	95.45 155	75.16 267	82.48 150	95.69 106	68.59 178	98.50 105	80.33 148	95.18 88	97.10 120

API-MVS		90.18 87	88.97 93	93.80 39	98.66 19	82.95 89	97.50 61	95.63 143	75.16 267	86.31 112	97.69 50	72.49 151	99.90 4	81.26 145	96.07 77	98.56 33

V9		77.32 275	74.87 276	84.69 266	86.26 286	78.52 203	92.33 270	90.72 302	75.11 269	62.21 315	77.08 325	66.19 203	91.87 311	68.84 247	55.06 332	83.69 323

v7n		79.32 253	77.34 251	85.28 254	84.05 315	72.89 276	93.38 242	93.87 234	75.02 270	70.68 268	84.37 284	59.58 246	95.62 240	67.60 254	67.50 296	87.32 286

v748		78.69 259	76.79 259	84.39 279	83.40 318	70.78 295	93.25 250	93.62 248	74.96 271	69.40 278	83.74 291	59.40 248	95.39 250	68.74 248	64.59 307	86.99 290

v12		77.20 277	74.73 277	84.63 270	86.15 289	78.41 208	92.17 272	90.71 303	74.92 272	62.05 317	77.00 326	65.83 205	91.83 312	68.69 249	55.01 333	83.64 324

v11		77.21 276	74.72 278	84.68 267	86.29 283	78.62 200	92.30 271	90.63 306	74.86 273	62.32 314	76.73 328	65.49 209	91.83 312	68.17 253	55.72 328	83.59 325

TAPA-MVS	81.61 12	85.02 179	83.67 179	89.06 187	96.79 79	73.27 272	95.92 169	94.79 185	74.81 274	80.47 177	96.83 87	71.07 164	98.19 116	49.82 332	92.57 111	95.71 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v13		77.11 280	74.63 280	84.55 273	86.08 292	78.27 212	92.06 274	90.68 305	74.73 275	61.86 320	76.93 327	65.73 206	91.81 315	68.55 252	55.07 331	83.59 325

PM-MVS		69.32 311	66.93 311	76.49 321	73.60 344	55.84 339	85.91 321	79.32 355	74.72 276	61.09 322	78.18 317	21.76 351	91.10 322	70.86 232	56.90 323	82.51 332

v52		78.70 257	76.95 255	83.95 282	81.71 322	71.34 292	91.93 276	93.43 254	74.69 277	70.36 271	83.71 293	58.04 263	95.50 246	71.84 219	66.82 302	85.00 308

V4		78.70 257	76.95 255	83.95 282	81.66 323	71.34 292	91.94 275	93.44 252	74.69 277	70.35 273	83.73 292	58.07 262	95.50 246	71.84 219	66.86 301	85.02 307

新几何1				93.12 70	97.44 65	81.60 116		96.71 63	74.54 279	91.22 66	97.57 57	79.13 68	99.51 46	77.40 178	98.46 27	98.26 48

1121		90.66 77	89.82 83	93.16 69	97.39 68	81.71 114	93.33 244	96.66 68	74.45 280	91.38 59	97.55 61	79.27 64	99.52 43	79.95 153	98.43 29	98.26 48

CNLPA		86.96 144	85.37 149	91.72 124	97.59 60	79.34 171	97.21 77	91.05 294	74.22 281	78.90 196	96.75 91	67.21 185	98.95 90	74.68 204	90.77 129	96.88 127

test20.03		72.36 304	71.15 298	75.98 324	77.79 333	59.16 335	92.40 268	89.35 314	74.09 282	61.50 321	84.32 285	48.09 308	85.54 343	50.63 330	62.15 315	83.24 327

旧先验2							96.97 107		74.06 283	96.10 8			97.76 132	88.38 89

TransMVSNet (Re)		76.94 282	74.38 284	84.62 272	85.92 294	75.25 259	95.28 190	89.18 316	73.88 284	67.22 284	86.46 255	59.64 244	94.10 283	59.24 298	52.57 339	84.50 312

QAPM		86.88 146	84.51 162	93.98 33	94.04 153	85.89 30	97.19 80	96.05 123	73.62 285	75.12 242	95.62 108	62.02 234	99.74 19	70.88 231	96.06 78	96.30 147

tfpnnormal		78.14 263	75.42 269	86.31 243	88.33 252	79.24 173	94.41 217	96.22 112	73.51 286	69.81 276	85.52 272	55.43 286	95.75 226	47.65 336	67.86 292	83.95 317

testdata				90.13 165	95.92 95	74.17 266		96.49 89	73.49 287	94.82 24	97.99 37	78.80 73	97.93 120	83.53 131	97.52 52	98.29 45

our_test_3		77.90 267	75.37 271	85.48 253	85.39 300	76.74 246	93.63 235	91.67 283	73.39 288	65.72 295	84.65 283	58.20 260	93.13 296	57.82 301	67.87 291	86.57 294

FMVSNet1		79.50 250	76.54 261	88.39 201	88.47 250	81.95 101	94.30 221	93.38 257	73.14 289	72.04 262	85.66 266	43.86 319	93.84 287	65.48 269	72.53 253	89.38 236

Anonymous20231206		75.29 290	73.64 289	80.22 311	80.75 324	63.38 324	93.36 243	90.71 303	73.09 290	67.12 285	83.70 294	50.33 302	90.85 323	53.63 321	70.10 267	86.44 295

ADS-MVSNet2		79.57 249	77.53 250	85.71 249	93.78 157	72.13 279	79.48 333	86.11 335	73.09 290	80.14 182	79.99 312	62.15 232	90.14 329	59.49 295	83.52 187	94.85 174

ADS-MVSNet		81.26 237	78.36 243	89.96 173	93.78 157	79.78 153	79.48 333	93.60 249	73.09 290	80.14 182	79.99 312	62.15 232	95.24 258	59.49 295	83.52 187	94.85 174

EU-MVSNet		76.92 283	76.95 255	76.83 320	84.10 313	54.73 341	91.77 279	92.71 272	72.74 293	69.57 277	88.69 217	58.03 265	87.43 335	64.91 272	70.00 272	88.33 265

pmmvs-eth3d		73.59 296	70.66 300	82.38 301	76.40 338	73.38 269	89.39 297	89.43 313	72.69 294	60.34 325	77.79 318	46.43 316	91.26 321	66.42 265	57.06 322	82.51 332

LTVRE_ROB	73.68 18	77.99 264	75.74 266	84.74 264	90.45 222	72.02 281	86.41 319	91.12 291	72.57 295	66.63 288	87.27 234	54.95 291	96.98 171	56.29 313	75.98 235	85.21 306
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
ACMH	75.40 17	77.99 264	74.96 274	87.10 234	90.67 219	76.41 249	93.19 253	91.64 285	72.47 296	63.44 304	87.61 231	43.34 322	97.16 163	58.34 299	73.94 245	87.72 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					96.15 87	78.41 208	95.87 174	96.46 90	71.97 297	89.66 80	97.45 63	76.33 106			98.24 38	98.30 44

test2356		74.41 294	74.53 283	74.07 327	76.13 340	54.45 342	94.74 213	92.08 277	71.96 298	65.51 296	83.05 300	56.96 273	83.71 345	52.74 323	77.58 232	84.06 315

ACMH+	76.62 16	77.47 272	74.94 275	85.05 256	91.07 214	71.58 288	93.26 249	90.01 309	71.80 299	64.76 299	88.55 219	41.62 328	96.48 188	62.35 288	71.00 257	87.09 288

ppachtmachnet_test		77.19 278	74.22 286	86.13 246	85.39 300	78.22 214	93.98 228	91.36 288	71.74 300	67.11 286	84.87 281	56.67 277	93.37 295	52.21 324	64.59 307	86.80 291

new-patchmatchnet		68.85 313	65.93 313	77.61 318	73.57 345	63.94 323	90.11 290	88.73 321	71.62 301	55.08 336	73.60 333	40.84 331	87.22 336	51.35 327	48.49 344	81.67 338

testus		70.06 309	69.09 307	72.98 328	74.54 343	51.28 346	93.78 232	87.34 327	71.49 302	62.69 311	83.46 296	24.44 350	84.77 344	51.22 328	72.85 251	82.90 328

FMVSNet5		76.46 285	74.16 287	83.35 294	90.05 229	76.17 251	89.58 293	89.85 310	71.39 303	65.29 298	80.42 309	50.61 300	87.70 334	61.05 291	69.24 281	86.18 299

tpm cat1		83.63 198	81.38 215	90.39 155	93.53 165	78.19 219	85.56 324	95.09 167	70.78 304	78.51 199	83.28 298	74.80 137	97.03 169	66.77 260	84.05 185	95.95 149

MDA-MVSNet-bldmvs		71.45 306	67.94 309	81.98 305	85.33 302	68.50 310	92.35 269	88.76 320	70.40 305	42.99 346	81.96 302	46.57 315	91.31 320	48.75 335	54.39 335	86.11 300

Anonymous202405211		84.41 189	81.93 204	91.85 121	96.78 81	78.41 208	97.44 64	91.34 289	70.29 306	84.06 131	94.26 142	41.09 330	98.96 87	79.46 157	82.65 206	98.17 51

DeepMVS_CX				64.06 337	78.53 332	43.26 353		68.11 360	69.94 307	38.55 348	76.14 329	18.53 353	79.34 349	43.72 340	41.62 350	69.57 349

MSDG		80.62 243	77.77 249	89.14 186	93.43 166	77.24 238	91.89 277	90.18 308	69.86 308	68.02 282	91.94 176	52.21 297	98.84 95	59.32 297	83.12 191	91.35 207

VDDNet		86.44 155	84.51 162	92.22 103	91.56 206	81.83 108	97.10 95	94.64 195	69.50 309	87.84 101	95.19 122	48.01 309	97.92 126	89.82 74	86.92 152	96.89 126

LF4IMVS		72.36 304	70.82 299	76.95 319	79.18 330	56.33 337	86.12 320	86.11 335	69.30 310	63.06 308	86.66 250	33.03 343	92.25 302	65.33 270	68.64 284	82.28 335

EG-PatchMatch MVS		74.92 291	72.02 295	83.62 290	83.76 317	73.28 271	93.62 236	92.04 279	68.57 311	58.88 328	83.80 290	31.87 345	95.57 244	56.97 305	78.67 224	82.00 337

AllTest		75.92 287	73.06 292	84.47 275	92.18 191	67.29 312	91.07 285	84.43 341	67.63 312	63.48 302	90.18 203	38.20 334	97.16 163	57.04 303	73.37 247	88.97 247

TestCases				84.47 275	92.18 191	67.29 312		84.43 341	67.63 312	63.48 302	90.18 203	38.20 334	97.16 163	57.04 303	73.37 247	88.97 247

YYNet1		73.53 298	70.43 303	82.85 297	84.52 309	71.73 286	91.69 281	91.37 287	67.63 312	46.79 344	81.21 306	55.04 290	90.43 326	55.93 314	59.70 320	86.38 296

MDA-MVSNet_test_wron		73.54 297	70.43 303	82.86 296	84.55 307	71.85 282	91.74 280	91.32 290	67.63 312	46.73 345	81.09 307	55.11 289	90.42 327	55.91 315	59.76 319	86.31 297

DSMNet-mixed		73.13 300	72.45 294	75.19 325	77.51 335	46.82 348	85.09 325	82.01 350	67.61 316	69.27 280	81.33 305	50.89 298	86.28 338	54.54 318	83.80 186	92.46 203

MIMVSNet1		69.44 310	66.65 312	77.84 317	76.48 337	62.84 326	87.42 309	88.97 317	66.96 317	57.75 334	79.72 314	32.77 344	85.83 340	46.32 337	63.42 312	84.85 310

TinyColmap		72.41 303	68.99 308	82.68 298	88.11 253	69.59 305	88.41 303	85.20 338	65.55 318	57.91 332	84.82 282	30.80 347	95.94 215	51.38 325	68.70 283	82.49 334

UnsupCasMVSNet_bld		68.60 314	64.50 315	80.92 309	74.63 342	67.80 311	83.97 326	92.94 269	65.12 319	54.63 337	68.23 344	35.97 337	92.17 305	60.13 293	44.83 346	82.78 330

RPSCF		77.73 270	76.63 260	81.06 308	88.66 249	55.76 340	87.77 308	87.88 325	64.82 320	74.14 245	92.79 167	49.22 306	96.81 180	67.47 256	76.88 234	90.62 213

PatchT		79.75 246	76.85 258	88.42 199	89.55 237	75.49 257	77.37 341	94.61 197	63.07 321	82.46 151	73.32 337	75.52 117	93.41 294	51.36 326	84.43 182	96.36 141

testpf		70.88 308	70.47 302	72.08 329	88.92 244	59.57 334	48.62 357	93.15 266	63.05 322	63.07 307	79.51 315	58.33 259	86.63 337	66.93 259	72.69 252	70.05 348

TDRefinement		69.20 312	65.78 314	79.48 314	66.04 351	62.21 327	88.21 304	86.12 334	62.92 323	61.03 323	85.61 269	33.23 342	94.16 282	55.82 316	53.02 337	82.08 336

test1235678		64.50 319	62.19 319	71.42 330	66.82 350	48.00 347	89.44 295	87.90 324	62.82 324	49.12 343	71.31 342	30.14 348	82.19 347	41.88 342	60.32 318	84.06 315

OpenMVS_ROB	68.52 20	73.02 301	69.57 305	83.37 293	80.54 327	71.82 283	93.60 237	88.22 323	62.37 325	61.98 318	83.15 299	35.31 340	95.47 248	45.08 339	75.88 236	82.82 329

JIA-IIPM		79.00 256	77.20 252	84.40 278	89.74 234	64.06 322	75.30 343	95.44 156	62.15 326	81.90 165	59.08 347	78.92 70	95.59 242	66.51 264	85.78 168	93.54 194

LS3D		82.22 227	79.94 235	89.06 187	97.43 66	74.06 268	93.20 252	92.05 278	61.90 327	73.33 251	95.21 121	59.35 249	99.21 65	54.54 318	92.48 113	93.90 187

N_pmnet		61.30 320	60.20 321	64.60 336	84.32 310	17.00 366	91.67 282	10.98 367	61.77 328	58.45 330	78.55 316	49.89 303	91.83 312	42.27 341	63.94 310	84.97 309

test_0402		72.68 302	69.54 306	82.09 304	88.67 248	71.81 284	92.72 262	86.77 332	61.52 329	62.21 315	83.91 288	43.22 323	93.76 290	34.60 348	72.23 254	80.72 339

COLMAP_ROB	73.24 19	75.74 288	73.00 293	83.94 284	92.38 182	69.08 308	91.85 278	86.93 331	61.48 330	65.32 297	90.27 202	42.27 326	96.93 175	50.91 329	75.63 238	85.80 303
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
gg-mvs-nofinetune		85.48 175	82.90 191	93.24 65	94.51 144	85.82 31	79.22 335	96.97 42	61.19 331	87.33 105	53.01 349	90.58 3	96.07 203	86.07 105	97.23 61	97.81 81

DP-MVS		81.47 234	78.28 244	91.04 139	98.14 44	78.48 204	95.09 204	86.97 330	61.14 332	71.12 266	92.78 168	59.59 245	99.38 54	53.11 322	86.61 156	95.27 166

pmmvs6		74.65 293	71.67 296	83.60 291	79.13 331	69.94 301	93.31 248	90.88 298	61.05 333	65.83 293	84.15 287	43.43 321	94.83 269	66.62 261	60.63 317	86.02 302

Patchmtry		77.36 274	74.59 281	85.67 250	89.75 232	75.75 255	77.85 340	91.12 291	60.28 334	71.23 264	80.35 310	75.45 118	93.56 293	57.94 300	67.34 298	87.68 276

CMPMVS	54.94 21	75.71 289	74.56 282	79.17 316	79.69 329	55.98 338	89.59 292	93.30 263	60.28 334	53.85 338	89.07 213	47.68 313	96.33 193	76.55 187	81.02 209	85.22 305
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240529		83.15 210	80.60 224	90.80 147	95.74 103	78.27 212	96.81 115	94.92 175	60.10 336	81.89 166	92.54 169	45.82 317	98.82 96	79.25 160	78.32 229	95.31 165

Patchmatch-test		78.25 262	74.72 278	88.83 193	91.20 211	74.10 267	73.91 347	88.70 322	59.89 337	66.82 287	85.12 279	78.38 78	94.54 275	48.84 334	79.58 217	97.86 77

Anonymous20231211		79.72 248	77.19 253	87.33 229	95.59 107	77.16 242	95.18 194	94.18 215	59.31 338	72.57 259	86.20 262	47.89 311	95.66 236	74.53 206	69.24 281	89.18 239

ANet_high		46.22 328	41.28 332	61.04 340	39.91 364	46.25 350	70.59 351	76.18 356	58.87 339	23.09 356	48.00 353	12.58 359	66.54 357	28.65 353	13.62 357	70.35 347

RPMNet		79.32 253	75.75 265	90.06 167	90.16 227	79.75 154	79.02 337	93.92 232	58.43 340	82.27 161	72.55 338	73.03 147	93.67 291	46.10 338	86.25 158	96.75 133

test12356		58.24 321	56.06 323	64.77 334	60.65 352	39.42 357	82.78 330	84.34 343	57.47 341	42.65 347	69.10 343	19.21 352	81.18 348	38.97 345	49.40 340	73.69 344

new_pmnet		66.18 316	63.18 318	75.18 326	76.27 339	61.74 329	83.79 327	84.66 340	56.64 342	51.57 339	71.85 340	31.29 346	87.93 333	49.98 331	62.55 314	75.86 343

LP		68.54 315	63.92 317	82.39 300	87.93 255	71.79 285	72.37 350	86.01 337	55.89 343	58.33 331	71.46 341	49.58 305	90.10 330	32.25 350	61.48 316	85.27 304

1111		65.60 318	64.33 316	69.41 331	68.26 346	45.11 351	87.06 312	87.32 328	54.99 344	51.20 341	73.45 334	63.57 222	85.70 341	36.53 346	56.59 324	77.42 342

.test1245		54.61 323	58.07 322	44.24 346	68.26 346	45.11 351	87.06 312	87.32 328	54.99 344	51.20 341	73.45 334	63.57 222	85.70 341	36.53 346	0.21 361	1.91 361

no-one		51.12 326	45.81 329	67.03 332	53.16 359	52.22 343	75.21 344	80.40 352	54.89 346	28.26 354	48.50 352	15.54 355	82.81 346	39.29 344	17.06 354	66.07 351

pmmvs3		65.75 317	62.18 320	76.45 322	67.12 349	64.54 319	88.68 301	85.05 339	54.77 347	57.54 335	73.79 332	29.40 349	86.21 339	55.49 317	47.77 345	78.62 340

testmv		54.58 324	51.53 326	63.74 338	53.58 357	40.82 355	83.26 328	83.92 345	54.07 348	36.35 350	61.26 345	14.80 356	77.07 350	33.00 349	43.53 349	73.33 345

MVS-HIRNet		71.36 307	67.00 310	84.46 277	90.58 220	69.74 304	79.15 336	87.74 326	46.09 349	61.96 319	50.50 350	45.14 318	95.64 238	53.74 320	88.11 147	88.00 271

PMMVS2		50.90 327	46.31 328	64.67 335	55.53 354	46.67 349	77.30 342	71.02 357	40.89 350	34.16 352	59.32 346	9.83 362	76.14 354	40.09 343	28.63 351	71.21 346

FPMVS		55.09 322	52.93 324	61.57 339	55.98 353	40.51 356	83.11 329	83.41 348	37.61 351	34.95 351	71.95 339	14.40 357	76.95 351	29.81 352	65.16 306	67.25 350

LCM-MVSNet		52.52 325	48.24 327	65.35 333	47.63 361	41.45 354	72.55 349	83.62 347	31.75 352	37.66 349	57.92 348	9.19 363	76.76 352	49.26 333	44.60 347	77.84 341

PNet_i23d		41.20 331	38.13 333	50.41 343	55.23 355	36.24 360	73.80 348	65.45 362	29.75 353	21.36 357	47.05 354	3.43 364	63.23 358	28.17 354	18.92 353	51.76 353

Gipuma		45.11 329	42.05 330	54.30 342	80.69 325	51.30 345	35.80 358	83.81 346	28.13 354	27.94 355	34.53 356	11.41 361	76.70 353	21.45 355	54.65 334	34.90 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	34.80 23	39.19 333	35.53 334	50.18 344	29.72 365	30.30 361	59.60 355	66.20 361	26.06 355	17.91 359	49.53 351	3.12 365	74.09 355	18.19 357	49.40 340	46.14 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		37.75 334	31.85 337	55.46 341	40.00 363	38.01 358	59.81 354	69.47 358	25.46 356	12.42 362	30.55 360	2.06 367	67.08 356	31.81 351	15.03 355	61.29 352

E-PMN		32.70 337	32.39 336	33.65 348	53.35 358	25.70 363	74.07 346	53.33 365	21.08 357	17.17 360	33.63 358	11.85 360	54.84 360	12.98 358	14.04 356	20.42 358

EMVS		31.70 338	31.45 338	32.48 349	50.72 360	23.95 364	74.78 345	52.30 366	20.36 358	16.08 361	31.48 359	12.80 358	53.60 361	11.39 359	13.10 359	19.88 359

MVE	35.65 22	33.85 336	29.49 339	46.92 345	41.86 362	36.28 359	50.45 356	56.52 364	18.75 359	18.28 358	37.84 355	2.41 366	58.41 359	18.71 356	20.62 352	46.06 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		41.54 330	41.93 331	40.38 347	20.10 366	26.84 362	61.93 353	59.09 363	14.81 360	28.51 353	80.58 308	35.53 338	48.33 362	63.70 283	13.11 358	45.96 356

wuyk23d		14.10 340	13.89 341	14.72 351	55.23 355	22.91 365	33.83 359	3.56 368	4.94 361	4.11 363	2.28 365	2.06 367	19.66 363	10.23 360	8.74 360	1.59 363

testmvs		9.92 341	12.94 342	0.84 353	0.65 367	0.29 368	93.78 232	0.39 369	0.42 362	2.85 364	15.84 363	0.17 370	0.30 365	2.18 361	0.21 361	1.91 361

test123		9.07 342	11.73 343	1.11 352	0.50 368	0.77 367	89.44 295	0.20 370	0.34 363	2.15 365	10.72 364	0.34 369	0.32 364	1.79 362	0.08 363	2.23 360

cdsmvs_eth3d_5k		21.43 339	28.57 340	0.00 354	0.00 369	0.00 369	0.00 360	95.93 130	0.00 364	0.00 366	97.66 51	63.57 222	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		5.92 344	7.89 345	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	71.04 165	0.00 366	0.00 363	0.00 364	0.00 364

pcd1.5k->3k		34.11 335	35.46 335	30.05 350	86.70 265	0.00 369	0.00 360	94.74 187	0.00 364	0.00 366	0.00 366	58.13 261	0.00 366	0.00 363	79.56 218	90.14 221

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		8.11 343	10.81 344	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	97.30 71	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																97.54 97

test_part2					98.90 7	85.14 45				96.07 9

test_part1				0.00 354		0.00 369	0.00 360	96.77 54				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												77.59 87				97.54 97

sam_mvs												75.35 131

ambc				76.02 323	68.11 348	51.43 344	64.97 352	89.59 311		60.49 324	74.49 331	17.17 354	92.46 299	61.50 289	52.85 338	84.17 314

MTGPA								96.33 105

test_post1							85.88 322				30.24 361	73.77 143	95.07 264	73.89 210

test_post											33.80 357	76.17 108	95.97 210

patchmatchnet-post											77.09 324	77.78 86	95.39 250

GG-mvs-BLEND				93.49 56	94.94 126	86.26 25	81.62 331	97.00 39		88.32 96	94.30 141	91.23 2	96.21 199	88.49 87	97.43 56	98.00 68

MTMP							97.53 58	68.16 359

test9_res														96.00 13	99.03 9	98.31 43

agg_prior2														94.30 27	99.00 11	98.57 31

agg_prior					98.59 27	83.13 82		96.56 80		94.19 31			99.16 74

test_prior4						82.34 96	97.75 46

test_prior				93.09 72	98.68 16	81.91 104		96.40 97					99.06 81			98.29 45

新几何2							96.42 145

旧先验1					97.39 68	79.58 168		96.54 83			98.08 32	84.00 25			97.42 57	97.62 94

原ACMM2							96.84 113

testdata2													99.48 48	76.45 189

segment_acmp												82.69 35

test12				94.25 27	98.34 38	85.55 36		96.35 103		92.36 48		80.84 48	99.22 63		98.31 36	97.98 70

plane_prior7					91.86 204	77.55 233

plane_prior6					91.98 199	77.92 226						64.77 217

plane_prior5								94.69 188					97.30 155	87.08 99	82.82 203	90.96 210

plane_prior4											94.15 144

plane_prior1					91.95 202

n2								0.00 371

nn								0.00 371

door-mid								79.75 354

lessismore_v0				79.98 312	80.59 326	58.34 336		80.87 351		58.49 329	83.46 296	43.10 324	93.89 286	63.11 286	48.68 342	87.72 274

test11								96.50 87

door								80.13 353

HQP5-MVS						78.48 204

BP-MVS														87.67 96

HQP4-MVS										82.30 153			97.32 153			91.13 208

HQP3-MVS								94.80 183							83.01 193

HQP2-MVS												65.40 212

NP-MVS					92.04 198	78.22 214					94.56 137

ACMMP++_ref															78.45 228

ACMMP++															79.05 221

Test By Simon												71.65 158

High-resolution multi-view benchmark