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	93.56 1	96.55 35	97.84 7	92.68 195	98.71 79	78.11 293	99.70 15	97.71 72	98.18 1	97.36 43	99.76 1	90.37 38	99.94 29	99.27 4	99.54 47	99.99 1

MCST-MVS		98.18 2	97.95 6	98.86 2	99.85 3	96.60 6	99.70 15	97.98 43	97.18 2	95.96 70	99.33 14	92.62 18	100.00 1	98.99 8	99.93 1	99.98 2

CNVR-MVS		98.46 1	98.38 1	98.72 5	99.80 4	96.19 10	99.80 8	97.99 42	97.05 3	99.41 1	99.59 2	92.89 17	100.00 1	98.99 8	99.90 4	99.96 4

DPM-MVS		97.86 6	97.25 14	99.68 1	98.25 88	99.10 1	99.76 10	97.78 62	96.61 4	98.15 23	99.53 5	93.62 13	100.00 1	91.79 125	99.80 20	99.94 10

EPNet		96.82 28	96.68 29	97.25 48	98.65 80	93.10 64	99.48 34	98.76 12	96.54 5	97.84 36	98.22 104	87.49 76	99.66 71	95.35 74	97.78 100	99.00 105
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		98.12 3	98.11 3	98.13 17	99.76 6	94.46 37	99.81 6	97.88 48	96.54 5	98.84 11	99.46 8	92.55 19	99.98 11	98.25 25	99.93 1	99.94 10

DeepC-MVS_fast	93.52 2	97.16 18	96.84 23	98.13 17	99.61 21	94.45 38	98.85 105	97.64 84	96.51 7	95.88 73	99.39 13	87.35 82	99.99 5	96.61 48	99.69 32	99.96 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		97.12 19	96.60 30	98.68 6	98.03 96	96.57 7	99.84 3	97.84 52	96.36 8	95.20 86	98.24 103	88.17 64	99.83 53	96.11 60	99.60 43	99.64 58
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
CANet		97.00 22	96.49 31	98.55 7	98.86 75	96.10 11	99.83 5	97.52 108	95.90 9	97.21 44	98.90 63	82.66 146	99.93 31	98.71 11	98.80 81	99.63 60

PS-MVSNAJ		96.87 27	96.40 33	98.29 12	97.35 112	97.29 2	99.03 88	97.11 151	95.83 10	98.97 8	99.14 34	82.48 149	99.60 82	98.60 14	99.08 67	98.00 165

save fliter					99.34 45	93.85 50	99.65 21	97.63 88	95.69 11

HPM-MVS++		97.72 8	97.59 8	98.14 16	99.53 35	94.76 31	99.19 63	97.75 65	95.66 12	98.21 22	99.29 15	91.10 26	99.99 5	97.68 34	99.87 6	99.68 53

CANet_DTU		94.31 93	93.35 102	97.20 50	97.03 124	94.71 33	98.62 132	95.54 245	95.61 13	97.21 44	98.47 96	71.88 224	99.84 51	88.38 159	97.46 107	97.04 188

xiu_mvs_v2_base		96.66 31	96.17 41	98.11 19	97.11 120	96.96 3	99.01 91	97.04 158	95.51 14	98.86 10	99.11 40	82.19 155	99.36 110	98.59 16	98.14 94	98.00 165

MSP-MVS		97.77 7	98.18 2	96.53 89	99.54 30	90.14 125	99.41 47	97.70 73	95.46 15	98.60 14	99.19 25	95.71 4	99.49 94	98.15 27	99.85 10	99.95 7

TSAR-MVS + GP.		96.95 24	96.91 20	97.07 52	98.88 73	91.62 88	99.58 25	96.54 184	95.09 16	96.84 56	98.63 85	91.16 24	99.77 63	99.04 7	96.42 118	99.81 26

MSLP-MVS++		97.50 11	97.45 11	97.63 31	99.65 17	93.21 60	99.70 15	98.13 36	94.61 17	97.78 37	99.46 8	89.85 42	99.81 58	97.97 29	99.91 3	99.88 19

DPE-MVS		98.11 4	98.00 4	98.44 11	99.50 37	95.39 16	99.29 59	97.72 71	94.50 18	98.64 13	99.54 3	93.32 14	99.97 18	99.58 3	99.90 4	99.95 7

TSAR-MVS + MP.		97.44 12	97.46 10	97.39 42	99.12 60	93.49 57	98.52 143	97.50 113	94.46 19	98.99 7	98.64 83	91.58 22	99.08 125	98.49 19	99.83 12	99.60 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MG-MVS		97.24 14	96.83 24	98.47 10	99.79 5	95.71 13	99.07 83	99.06 9	94.45 20	96.42 65	98.70 79	88.81 54	99.74 66	95.35 74	99.86 9	99.97 3

PAPM		96.35 41	95.94 48	97.58 33	94.10 205	95.25 17	98.93 97	98.17 31	94.26 21	93.94 106	98.72 76	89.68 46	97.88 170	96.36 55	99.29 61	99.62 62

CLD-MVS		91.06 157	90.71 154	92.10 202	94.05 208	86.10 210	99.55 29	96.29 198	94.16 22	84.70 199	97.17 143	69.62 238	97.82 174	94.74 86	86.08 210	92.39 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SteuartSystems-ACMMP		97.25 13	97.34 13	97.01 55	97.38 111	91.46 92	99.75 11	97.66 78	94.14 23	98.13 24	99.26 17	92.16 20	99.66 71	97.91 31	99.64 35	99.90 15
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS		98.07 5	98.00 4	98.29 12	99.66 13	95.20 21	99.72 12	97.47 118	93.95 24	99.07 5	99.46 8	93.18 15	99.97 18	99.64 1	99.82 15	99.69 52

test0726					99.66 13	95.20 21	99.77 9	97.70 73	93.95 24	99.35 2	99.54 3	93.18 15

HQP-NCC					93.95 209		99.16 68		93.92 26	87.57 177

ACMP_Plane					93.95 209		99.16 68		93.92 26	87.57 177

HQP-MVS		91.50 149	91.23 143	92.29 199	93.95 209	86.39 201	99.16 68	96.37 191	93.92 26	87.57 177	96.67 161	73.34 210	97.77 178	93.82 102	86.29 205	92.72 213

DeepC-MVS	91.02 4	94.56 89	93.92 93	96.46 91	97.16 118	90.76 112	98.39 161	97.11 151	93.92 26	88.66 170	98.33 99	78.14 183	99.85 50	95.02 80	98.57 87	98.78 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		96.69 30	96.69 28	96.72 79	98.58 83	91.00 107	99.14 77	99.45 1	93.86 30	95.15 87	98.73 74	88.48 59	99.76 64	97.23 38	99.56 46	99.40 78

lupinMVS		96.32 43	95.94 48	97.44 38	95.05 189	94.87 25	99.86 2	96.50 185	93.82 31	98.04 29	98.77 70	85.52 111	98.09 156	96.98 44	98.97 72	99.37 79

plane_prior						86.07 212	99.14 77		93.81 32						86.26 207

SD-MVS		97.51 10	97.40 12	97.81 26	99.01 66	93.79 51	99.33 57	97.38 130	93.73 33	98.83 12	99.02 47	90.87 31	99.88 40	98.69 12	99.74 24	99.77 38
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
plane_prior3						85.91 215			93.65 34	86.99 183

PVSNet_Blended		95.94 54	95.66 56	96.75 75	98.77 77	91.61 89	99.88 1	98.04 39	93.64 35	94.21 101	97.76 115	83.50 133	99.87 43	97.41 36	97.75 101	98.79 128

APDe-MVS		97.53 9	97.47 9	97.70 29	99.58 23	93.63 52	99.56 28	97.52 108	93.59 36	98.01 31	99.12 37	90.80 33	99.55 84	99.26 5	99.79 21	99.93 13

jason		95.40 67	94.86 69	97.03 54	92.91 236	94.23 43	99.70 15	96.30 195	93.56 37	96.73 62	98.52 90	81.46 161	97.91 167	96.08 61	98.47 90	98.96 110
jason: jason.
MVS_111021_LR		95.78 60	95.94 48	95.28 132	98.19 92	87.69 174	98.80 110	99.26 7	93.39 38	95.04 89	98.69 81	84.09 129	99.76 64	96.96 45	99.06 68	98.38 150

HQP_MVS		91.26 153	90.95 147	92.16 201	93.84 216	86.07 212	99.02 89	96.30 195	93.38 39	86.99 183	96.52 163	72.92 214	97.75 183	93.46 107	86.17 208	92.67 215

plane_prior2							99.02 89		93.38 39

EIA-MVS		96.00 50	96.00 46	96.00 108	96.56 137	91.05 105	99.63 22	96.61 175	93.26 41	97.39 42	98.30 101	86.62 95	98.13 153	98.07 28	97.57 102	98.82 125

test_0728_THIRD									93.01 42	99.07 5	99.46 8	94.66 9	99.97 18	99.25 6	99.82 15	99.95 7

xiu_mvs_v1_base_debu		94.73 80	93.98 87	96.99 58	95.19 177	95.24 18	98.62 132	96.50 185	92.99 43	97.52 39	98.83 67	72.37 219	99.15 119	97.03 40	96.74 113	96.58 194

xiu_mvs_v1_base		94.73 80	93.98 87	96.99 58	95.19 177	95.24 18	98.62 132	96.50 185	92.99 43	97.52 39	98.83 67	72.37 219	99.15 119	97.03 40	96.74 113	96.58 194

xiu_mvs_v1_base_debi		94.73 80	93.98 87	96.99 58	95.19 177	95.24 18	98.62 132	96.50 185	92.99 43	97.52 39	98.83 67	72.37 219	99.15 119	97.03 40	96.74 113	96.58 194

EPNet_dtu		92.28 139	92.15 128	92.70 194	97.29 114	84.84 232	98.64 130	97.82 54	92.91 46	93.02 119	97.02 149	85.48 116	95.70 267	72.25 288	94.89 140	97.55 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-1		96.97 23	96.80 25	97.47 37	99.46 41	93.11 63	98.89 102	97.94 44	92.89 47	96.90 49	99.02 47	89.78 43	99.53 87	97.06 39	99.26 63	99.75 40

OPM-MVS		89.76 178	89.15 171	91.57 213	90.53 265	85.58 223	98.11 182	95.93 219	92.88 48	86.05 191	96.47 166	67.06 256	97.87 171	89.29 153	86.08 210	91.26 252

Regformer-2		96.94 26	96.78 26	97.42 39	99.46 41	92.97 70	98.89 102	97.93 45	92.86 49	96.88 50	99.02 47	89.74 45	99.53 87	97.03 40	99.26 63	99.75 40

CS-MVS		95.85 56	95.86 51	95.82 115	96.80 131	89.78 139	99.84 3	96.60 176	92.60 50	96.81 60	98.70 79	85.04 119	98.25 149	97.90 32	98.43 91	99.42 77

zzz-MVS		96.21 47	95.96 47	96.96 63	99.29 52	91.19 97	98.69 122	97.45 120	92.58 51	94.39 98	99.24 20	86.43 101	99.99 5	96.22 56	99.40 57	99.71 48

MTAPA		96.09 49	95.80 54	96.96 63	99.29 52	91.19 97	97.23 216	97.45 120	92.58 51	94.39 98	99.24 20	86.43 101	99.99 5	96.22 56	99.40 57	99.71 48

ETV-MVS		95.11 72	95.27 63	94.64 150	96.34 144	86.51 196	99.59 24	96.62 174	92.51 53	94.08 104	98.64 83	86.05 107	98.24 150	95.07 79	98.50 89	99.18 96

CHOSEN 280x420		96.80 29	96.85 22	96.66 83	97.85 98	94.42 40	94.76 274	98.36 23	92.50 54	95.62 80	97.52 127	97.92 1	97.38 200	98.31 24	98.80 81	98.20 161

Regformer-3		96.50 36	96.36 35	96.91 65	99.34 45	91.72 86	98.71 117	97.90 47	92.48 55	96.00 67	98.95 58	88.60 56	99.52 90	96.44 53	98.83 78	99.49 71

Regformer-4		96.45 39	96.33 37	96.81 72	99.34 45	91.44 93	98.71 117	97.88 48	92.43 56	95.97 69	98.95 58	88.42 60	99.51 91	96.40 54	98.83 78	99.49 71

testdata1							97.89 192		92.43 56

PAPR		96.35 41	95.82 52	97.94 23	99.63 18	94.19 45	99.42 46	97.55 104	92.43 56	93.82 110	99.12 37	87.30 83	99.91 34	94.02 96	99.06 68	99.74 43

HY-MVS	88.56 7	95.29 68	94.23 79	98.48 9	97.72 100	96.41 8	94.03 281	98.74 13	92.42 59	95.65 79	94.76 190	86.52 98	99.49 94	95.29 76	92.97 154	99.53 66

XVS		96.47 38	96.37 34	96.77 73	99.62 19	90.66 117	99.43 44	97.58 98	92.41 60	96.86 53	98.96 56	87.37 78	99.87 43	95.65 65	99.43 54	99.78 33

X-MVStestdata		90.69 165	88.66 180	96.77 73	99.62 19	90.66 117	99.43 44	97.58 98	92.41 60	96.86 53	29.59 336	87.37 78	99.87 43	95.65 65	99.43 54	99.78 33

UGNet		91.91 145	90.85 149	95.10 134	97.06 122	88.69 159	98.01 189	98.24 27	92.41 60	92.39 125	93.61 211	60.52 283	99.68 69	88.14 162	97.25 109	96.92 190
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
WTY-MVS		95.97 52	95.11 66	98.54 8	97.62 104	96.65 5	99.44 42	98.74 13	92.25 63	95.21 85	98.46 98	86.56 97	99.46 101	95.00 81	92.69 158	99.50 70

OMC-MVS		93.90 102	93.62 98	94.73 147	98.63 81	87.00 190	98.04 188	96.56 182	92.19 64	92.46 123	98.73 74	79.49 173	99.14 122	92.16 123	94.34 145	98.03 164

ET-MVSNet_ETH3D		92.56 136	91.45 141	95.88 113	96.39 142	94.13 46	99.46 39	96.97 164	92.18 65	66.94 311	98.29 102	94.65 10	94.28 297	94.34 94	83.82 226	99.24 91

CHOSEN 1792x2688		94.35 92	93.82 95	95.95 111	97.40 110	88.74 158	98.41 157	98.27 25	92.18 65	91.43 136	96.40 167	78.88 176	99.81 58	93.59 105	97.81 97	99.30 85

PVSNet_Blended_VisFu		94.67 84	94.11 83	96.34 98	97.14 119	91.10 102	99.32 58	97.43 125	92.10 67	91.53 135	96.38 170	83.29 139	99.68 69	93.42 109	96.37 119	98.25 157

Effi-MVS+-dtu		89.97 176	90.68 155	87.81 275	95.15 181	71.98 311	97.87 195	95.40 253	91.92 68	87.57 177	91.44 242	74.27 203	96.84 215	89.45 146	93.10 153	94.60 206

mvs-test1		91.57 148	92.20 126	89.70 248	95.15 181	74.34 302	99.51 32	95.40 253	91.92 68	91.02 142	97.25 135	74.27 203	98.08 159	89.45 146	95.83 131	96.67 191

EI-MVSNet-Vis-set		95.76 62	95.63 60	96.17 103	99.14 59	90.33 120	98.49 149	97.82 54	91.92 68	94.75 92	98.88 65	87.06 86	99.48 99	95.40 73	97.17 111	98.70 135

canonicalmvs		95.02 74	93.96 90	98.20 14	97.53 109	95.92 12	98.71 117	96.19 205	91.78 71	95.86 75	98.49 94	79.53 172	99.03 126	96.12 59	91.42 182	99.66 56

EI-MVSNet-UG-set		95.43 64	95.29 62	95.86 114	99.07 64	89.87 136	98.43 154	97.80 59	91.78 71	94.11 103	98.77 70	86.25 105	99.48 99	94.95 83	96.45 117	98.22 159

diffmvs		94.59 88	94.19 80	95.81 116	95.54 168	90.69 115	98.70 121	95.68 239	91.61 73	95.96 70	97.81 112	80.11 167	98.06 160	96.52 51	95.76 132	98.67 136

Vis-MVSNet		92.64 132	91.85 133	95.03 139	95.12 183	88.23 165	98.48 150	96.81 168	91.61 73	92.16 128	97.22 138	71.58 229	98.00 166	85.85 188	97.81 97	98.88 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	87.35 11	93.17 125	91.77 136	97.37 44	95.41 172	93.07 65	98.82 108	97.85 51	91.53 75	82.56 220	97.58 126	71.97 223	99.82 56	91.01 131	99.23 65	99.22 94

alignmvs		95.77 61	95.00 68	98.06 20	97.35 112	95.68 14	99.71 14	97.50 113	91.50 76	96.16 66	98.61 86	86.28 104	99.00 127	96.19 58	91.74 176	99.51 69

PVSNet_BlendedMVS		93.36 118	93.20 106	93.84 174	98.77 77	91.61 89	99.47 35	98.04 39	91.44 77	94.21 101	92.63 229	83.50 133	99.87 43	97.41 36	83.37 230	90.05 284

test_prior3		97.07 21	97.09 15	97.01 55	99.58 23	91.77 83	99.57 26	97.57 101	91.43 78	98.12 26	98.97 53	90.43 36	99.49 94	98.33 21	99.81 18	99.79 29

test_prior2							99.57 26		91.43 78	98.12 26	98.97 53	90.43 36		98.33 21	99.81 18

PVSNet	87.13 12	93.69 107	92.83 114	96.28 99	97.99 97	90.22 124	99.38 49	98.93 10	91.42 80	93.66 111	97.68 120	71.29 231	99.64 77	87.94 164	97.20 110	98.98 108

3Dnovator+	87.72 8	93.43 115	91.84 134	98.17 15	95.73 162	95.08 23	98.92 99	97.04 158	91.42 80	81.48 239	97.60 124	74.60 198	99.79 61	90.84 134	98.97 72	99.64 58

PatchFormer-LS_test		94.08 95	93.60 99	95.53 125	96.92 126	89.57 144	96.51 241	97.34 134	91.29 82	92.22 127	97.18 141	91.66 21	98.02 163	87.05 172	92.21 168	99.00 105

testtj		97.23 16	97.05 17	97.75 28	99.75 7	93.34 59	99.16 68	97.74 67	91.28 83	98.40 18	99.29 15	89.95 41	99.98 11	98.20 26	99.70 31	99.94 10

PMMVS		93.62 112	93.90 94	92.79 191	96.79 132	81.40 267	98.85 105	96.81 168	91.25 84	96.82 59	98.15 108	77.02 189	98.13 153	93.15 113	96.30 122	98.83 124

IB-MVS	89.43 6	92.12 142	90.83 152	95.98 110	95.40 173	90.78 111	99.81 6	98.06 37	91.23 85	85.63 194	93.66 210	90.63 34	98.78 131	91.22 128	71.85 296	98.36 153
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
baseline		93.91 101	93.30 103	95.72 119	95.10 187	90.07 129	97.48 206	95.91 224	91.03 86	93.54 112	97.68 120	79.58 170	98.02 163	94.27 95	95.14 138	99.08 102

casdiffmvs		93.98 99	93.43 101	95.61 123	95.07 188	89.86 137	98.80 110	95.84 230	90.98 87	92.74 121	97.66 122	79.71 169	98.10 155	94.72 87	95.37 137	98.87 120

DWT-MVSNet_test		94.36 91	93.95 91	95.62 121	96.99 125	89.47 146	96.62 238	97.38 130	90.96 88	93.07 118	97.27 134	93.73 12	98.09 156	85.86 187	93.65 149	99.29 86

#test#		96.48 37	96.34 36	96.90 66	99.69 9	90.96 108	99.53 31	97.81 57	90.94 89	96.88 50	99.05 44	87.57 73	99.96 23	95.87 64	99.72 26	99.78 33

UA-Net		93.30 120	92.62 118	95.34 130	96.27 146	88.53 163	95.88 262	96.97 164	90.90 90	95.37 83	97.07 147	82.38 152	99.10 124	83.91 207	94.86 141	98.38 150

ACMMP_NAP		96.59 33	96.18 39	97.81 26	98.82 76	93.55 54	98.88 104	97.59 96	90.66 91	97.98 32	99.14 34	86.59 96	100.00 1	96.47 52	99.46 50	99.89 18

mPP-MVS		95.90 55	95.75 55	96.38 96	99.58 23	89.41 148	99.26 60	97.41 127	90.66 91	94.82 91	98.95 58	86.15 106	99.98 11	95.24 77	99.64 35	99.74 43

PAPM_NR		95.43 64	95.05 67	96.57 87	99.42 43	90.14 125	98.58 140	97.51 110	90.65 93	92.44 124	98.90 63	87.77 72	99.90 36	90.88 133	99.32 60	99.68 53

MP-MVS		96.00 50	95.82 52	96.54 88	99.47 40	90.13 127	99.36 53	97.41 127	90.64 94	95.49 81	98.95 58	85.51 113	99.98 11	96.00 63	99.59 45	99.52 67
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
region2R		96.30 44	96.17 41	96.70 80	99.70 8	90.31 121	99.46 39	97.66 78	90.55 95	97.07 47	99.07 41	86.85 90	99.97 18	95.43 72	99.74 24	99.81 26

HFP-MVS		96.42 40	96.26 38	96.90 66	99.69 9	90.96 108	99.47 35	97.81 57	90.54 96	96.88 50	99.05 44	87.57 73	99.96 23	95.65 65	99.72 26	99.78 33

ACMMPR		96.28 45	96.14 45	96.73 77	99.68 11	90.47 119	99.47 35	97.80 59	90.54 96	96.83 58	99.03 46	86.51 99	99.95 26	95.65 65	99.72 26	99.75 40

SR-MVS		96.13 48	96.16 43	96.07 106	99.42 43	89.04 151	98.59 138	97.33 135	90.44 98	96.84 56	99.12 37	86.75 92	99.41 106	97.47 35	99.44 53	99.76 39

EPMVS		92.59 135	91.59 139	95.59 124	97.22 116	90.03 133	91.78 297	98.04 39	90.42 99	91.66 131	90.65 258	86.49 100	97.46 197	81.78 227	96.31 121	99.28 88

agg_prior1		97.12 19	97.03 18	97.38 43	99.54 30	92.66 73	99.35 54	97.64 84	90.38 100	97.98 32	99.17 29	90.84 32	99.61 80	98.57 18	99.78 23	99.87 22

ACMMP		94.67 84	94.30 77	95.79 117	99.25 55	88.13 167	98.41 157	98.67 19	90.38 100	91.43 136	98.72 76	82.22 154	99.95 26	93.83 101	95.76 132	99.29 86
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
VNet		95.08 73	94.26 78	97.55 36	98.07 95	93.88 49	98.68 124	98.73 15	90.33 102	97.16 46	97.43 131	79.19 175	99.53 87	96.91 46	91.85 174	99.24 91

test-LLR		93.11 126	92.68 116	94.40 155	94.94 193	87.27 187	99.15 74	97.25 137	90.21 103	91.57 132	94.04 196	84.89 122	97.58 192	85.94 184	96.13 124	98.36 153

test0.0.03 1		88.96 187	88.61 181	90.03 241	91.09 259	84.43 237	98.97 95	97.02 161	90.21 103	80.29 246	96.31 171	84.89 122	91.93 317	72.98 285	85.70 213	93.73 208

train_agg		97.20 17	97.08 16	97.57 35	99.57 27	93.17 61	99.38 49	97.66 78	90.18 105	98.39 19	99.18 27	90.94 28	99.66 71	98.58 17	99.85 10	99.88 19

test_8					99.55 29	93.07 65	99.37 52	97.64 84	90.18 105	98.36 21	99.19 25	90.94 28	99.64 77

1314		93.44 114	91.98 132	97.84 24	95.24 174	94.38 41	96.22 252	97.92 46	90.18 105	82.28 225	97.71 119	77.63 186	99.80 60	91.94 124	98.67 85	99.34 82

CVMVSNet		90.30 168	90.91 148	88.46 271	94.32 203	73.58 306	97.61 203	97.59 96	90.16 108	88.43 173	97.10 145	76.83 190	92.86 304	82.64 218	93.54 150	98.93 115

MVSTER		92.71 130	92.32 122	93.86 173	97.29 114	92.95 71	99.01 91	96.59 178	90.09 109	85.51 195	94.00 200	94.61 11	96.56 225	90.77 136	83.03 232	92.08 230

APD-MVS		96.95 24	96.72 27	97.63 31	99.51 36	93.58 53	99.16 68	97.44 123	90.08 110	98.59 15	99.07 41	89.06 50	99.42 104	97.92 30	99.66 33	99.88 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		96.22 46	96.15 44	96.42 94	99.67 12	89.62 143	99.70 15	97.61 91	90.07 111	96.00 67	99.16 31	87.43 77	99.92 32	96.03 62	99.72 26	99.70 50

SCA		90.64 166	89.25 169	94.83 143	94.95 192	88.83 154	96.26 249	97.21 142	90.06 112	90.03 158	90.62 260	66.61 258	96.81 217	83.16 212	94.36 144	98.84 121

baseline2		94.04 97	93.80 96	94.74 146	93.07 234	90.25 122	98.12 181	98.16 33	89.86 113	86.53 190	96.95 152	95.56 5	98.05 161	91.44 127	94.53 142	95.93 201

baseline1		92.61 134	91.28 142	96.58 86	97.05 123	94.63 35	97.72 199	96.20 203	89.82 114	88.56 171	96.85 157	86.85 90	97.82 174	88.42 158	80.10 246	97.30 179

PVSNet_0	83.28 16	87.31 212	85.16 225	93.74 177	94.78 196	84.59 235	98.91 100	98.69 18	89.81 115	78.59 267	93.23 219	61.95 278	99.34 113	94.75 85	55.72 322	97.30 179

GST-MVS		95.97 52	95.66 56	96.90 66	99.49 39	91.22 95	99.45 41	97.48 116	89.69 116	95.89 72	98.72 76	86.37 103	99.95 26	94.62 90	99.22 66	99.52 67

GA-MVS		90.10 173	88.69 179	94.33 157	92.44 240	87.97 171	99.08 82	96.26 200	89.65 117	86.92 185	93.11 222	68.09 246	96.96 211	82.54 220	90.15 192	98.05 163

MDTV_nov1_ep13			90.47 159		96.14 152	88.55 161	91.34 300	97.51 110	89.58 118	92.24 126	90.50 268	86.99 89	97.61 191	77.64 251	92.34 164

TEST9					99.57 27	93.17 61	99.38 49	97.66 78	89.57 119	98.39 19	99.18 27	90.88 30	99.66 71

PatchmatchNet		92.05 144	91.04 145	95.06 137	96.17 150	89.04 151	91.26 301	97.26 136	89.56 120	90.64 148	90.56 264	88.35 62	97.11 206	79.53 236	96.07 128	99.03 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SMA-MVS		97.24 14	96.99 19	98.00 21	99.30 51	94.20 44	99.16 68	97.65 83	89.55 121	99.22 4	99.52 6	90.34 39	99.99 5	98.32 23	99.83 12	99.82 25

sss		94.85 77	93.94 92	97.58 33	96.43 141	94.09 47	98.93 97	99.16 8	89.50 122	95.27 84	97.85 110	81.50 160	99.65 75	92.79 118	94.02 147	98.99 107

ACMP	87.39 10	88.71 197	88.24 188	90.12 238	93.91 214	81.06 275	98.50 147	95.67 240	89.43 123	80.37 245	95.55 179	65.67 263	97.83 173	90.55 137	84.51 218	91.47 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
9.14			96.87 21		99.34 45		99.50 33	97.49 115	89.41 124	98.59 15	99.43 12	89.78 43	99.69 68	98.69 12	99.62 39

abl_6		94.63 86	94.48 74	95.09 135	98.61 82	86.96 191	98.06 187	96.97 164	89.31 125	95.86 75	98.56 88	79.82 168	99.64 77	94.53 92	98.65 86	98.66 138

DI_MVS_plusplus_test		89.41 183	87.24 200	95.92 112	89.06 283	90.75 114	98.18 175	96.63 173	89.29 126	70.54 299	90.31 270	63.50 273	98.40 143	92.25 122	95.44 136	98.60 139

thres200		93.69 107	92.59 119	96.97 62	97.76 99	94.74 32	99.35 54	99.36 2	89.23 127	91.21 141	96.97 151	83.42 136	98.77 132	85.08 191	90.96 185	97.39 177

PGM-MVS		95.85 56	95.65 58	96.45 92	99.50 37	89.77 140	98.22 171	98.90 11	89.19 128	96.74 61	98.95 58	85.91 109	99.92 32	93.94 97	99.46 50	99.66 56

TESTMET0.1,1		93.82 104	93.26 105	95.49 126	95.21 176	90.25 122	99.15 74	97.54 107	89.18 129	91.79 129	94.87 188	89.13 49	97.63 189	86.21 181	96.29 123	98.60 139

UniMVSNet (Re)		89.50 182	88.32 187	93.03 186	92.21 243	90.96 108	98.90 101	98.39 22	89.13 130	83.22 210	92.03 232	81.69 158	96.34 244	86.79 178	72.53 289	91.81 235

FIs		90.70 164	89.87 162	93.18 183	92.29 241	91.12 100	98.17 178	98.25 26	89.11 131	83.44 209	94.82 189	82.26 153	96.17 252	87.76 165	82.76 234	92.25 222

tpmrst		92.78 129	92.16 127	94.65 149	96.27 146	87.45 181	91.83 296	97.10 154	89.10 132	94.68 94	90.69 255	88.22 63	97.73 185	89.78 143	91.80 175	98.77 131

CDPH-MVS		96.56 34	96.18 39	97.70 29	99.59 22	93.92 48	99.13 80	97.44 123	89.02 133	97.90 35	99.22 22	88.90 53	99.49 94	94.63 89	99.79 21	99.68 53

原ACMM1				96.18 101	99.03 65	90.08 128		97.63 88	88.98 134	97.00 48	98.97 53	88.14 66	99.71 67	88.23 161	99.62 39	98.76 132

XVG-OURS		90.83 161	90.49 158	91.86 205	95.23 175	81.25 271	95.79 267	95.92 220	88.96 135	90.02 159	98.03 109	71.60 228	99.35 112	91.06 130	87.78 200	94.98 204

MP-MVS-pluss		95.80 59	95.30 61	97.29 45	98.95 70	92.66 73	98.59 138	97.14 147	88.95 136	93.12 116	99.25 18	85.62 110	99.94 29	96.56 50	99.48 49	99.28 88
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test-mter		93.27 122	92.89 113	94.40 155	94.94 193	87.27 187	99.15 74	97.25 137	88.95 136	91.57 132	94.04 196	88.03 68	97.58 192	85.94 184	96.13 124	98.36 153

APD-MVS_3200maxsize		95.64 63	95.65 58	95.62 121	99.24 56	87.80 173	98.42 155	97.22 141	88.93 138	96.64 64	98.98 52	85.49 114	99.36 110	96.68 47	99.27 62	99.70 50

CR-MVSNet		88.83 192	87.38 197	93.16 184	93.47 224	86.24 205	84.97 317	94.20 284	88.92 139	90.76 146	86.88 301	84.43 126	94.82 288	70.64 292	92.17 170	98.41 147

DU-MVS		88.83 192	87.51 194	92.79 191	91.46 255	90.07 129	98.71 117	97.62 90	88.87 140	83.21 211	93.68 208	74.63 196	95.93 262	86.95 175	72.47 290	92.36 219

FC-MVSNet-test		90.22 170	89.40 166	92.67 196	91.78 251	89.86 137	97.89 192	98.22 28	88.81 141	82.96 215	94.66 191	81.90 157	95.96 260	85.89 186	82.52 237	92.20 226

USDC		84.74 246	82.93 250	90.16 237	91.73 252	83.54 247	95.00 272	93.30 295	88.77 142	73.19 291	93.30 217	53.62 304	97.65 188	75.88 265	81.54 241	89.30 293

testgi		82.29 266	81.00 270	86.17 286	87.24 303	74.84 301	97.39 207	91.62 313	88.63 143	75.85 281	95.42 183	46.07 318	91.55 318	66.87 302	79.94 247	92.12 228

VPNet		88.30 201	86.57 205	93.49 178	91.95 247	91.35 94	98.18 175	97.20 143	88.61 144	84.52 202	94.89 187	62.21 277	96.76 220	89.34 150	72.26 293	92.36 219

IS-MVSNet		93.00 127	92.51 120	94.49 152	96.14 152	87.36 184	98.31 166	95.70 237	88.58 145	90.17 156	97.50 128	83.02 142	97.22 203	87.06 171	96.07 128	98.90 117

PS-MVSNAJss		89.54 181	89.05 172	91.00 220	88.77 287	84.36 238	97.39 207	95.97 213	88.47 146	81.88 234	93.80 206	82.48 149	96.50 229	89.34 150	83.34 231	92.15 227

jajsoiax		87.35 211	86.51 207	89.87 242	87.75 301	81.74 264	97.03 223	95.98 212	88.47 146	80.15 248	93.80 206	61.47 279	96.36 238	89.44 148	84.47 220	91.50 244

Fast-Effi-MVS+-dtu		88.84 190	88.59 183	89.58 252	93.44 227	78.18 291	98.65 128	94.62 274	88.46 148	84.12 205	95.37 184	68.91 240	96.52 228	82.06 224	91.70 178	94.06 207

tfpn200view9		93.43 115	92.27 124	96.90 66	97.68 102	94.84 27	99.18 65	99.36 2	88.45 149	90.79 144	96.90 154	83.31 137	98.75 134	84.11 204	90.69 187	97.12 183

thres400		93.39 117	92.27 124	96.73 77	97.68 102	94.84 27	99.18 65	99.36 2	88.45 149	90.79 144	96.90 154	83.31 137	98.75 134	84.11 204	90.69 187	96.61 192

LCM-MVSNet-Re		88.59 198	88.61 181	88.51 270	95.53 169	72.68 309	96.85 228	88.43 325	88.45 149	73.14 292	90.63 259	75.82 191	94.38 296	92.95 114	95.71 134	98.48 145

PLC	91.07 3	94.23 94	94.01 86	94.87 141	99.17 58	87.49 179	99.25 61	96.55 183	88.43 152	91.26 139	98.21 106	85.92 108	99.86 48	89.77 144	97.57 102	97.24 181
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
XVG-OURS-SEG-HR		90.95 159	90.66 156	91.83 206	95.18 180	81.14 274	95.92 259	95.92 220	88.40 153	90.33 155	97.85 110	70.66 234	99.38 108	92.83 117	88.83 196	94.98 204

UniMVSNet_NR-MVSNet		89.60 180	88.55 184	92.75 193	92.17 244	90.07 129	98.74 116	98.15 34	88.37 154	83.21 211	93.98 201	82.86 144	95.93 262	86.95 175	72.47 290	92.25 222

MAR-MVS		94.43 90	94.09 84	95.45 127	99.10 62	87.47 180	98.39 161	97.79 61	88.37 154	94.02 105	99.17 29	78.64 181	99.91 34	92.48 119	98.85 77	98.96 110
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
Vis-MVSNet (Re-imp)		93.26 123	93.00 112	94.06 167	96.14 152	86.71 195	98.68 124	96.70 171	88.30 156	89.71 164	97.64 123	85.43 117	96.39 236	88.06 163	96.32 120	99.08 102

1112_ss		92.71 130	91.55 140	96.20 100	95.56 167	91.12 100	98.48 150	94.69 272	88.29 157	86.89 186	98.50 92	87.02 87	98.66 139	84.75 195	89.77 194	98.81 126

Test_1112_low_res		92.27 140	90.97 146	96.18 101	95.53 169	91.10 102	98.47 152	94.66 273	88.28 158	86.83 188	93.50 215	87.00 88	98.65 140	84.69 196	89.74 195	98.80 127

gm-plane-assit					94.69 198	88.14 166			88.22 159		97.20 139		98.29 147	90.79 135

mvs_tets		87.09 214	86.22 210	89.71 247	87.87 297	81.39 268	96.73 235	95.90 225	88.19 160	79.99 250	93.61 211	59.96 285	96.31 246	89.40 149	84.34 221	91.43 248

BH-w/o		92.32 138	91.79 135	93.91 172	96.85 128	86.18 208	99.11 81	95.74 234	88.13 161	84.81 198	97.00 150	77.26 188	97.91 167	89.16 155	98.03 95	97.64 170

nrg030		90.23 169	88.87 175	94.32 158	91.53 254	93.54 55	98.79 114	95.89 227	88.12 162	84.55 201	94.61 192	78.80 179	96.88 214	92.35 121	75.21 266	92.53 217

D2MVS		87.96 204	87.39 196	89.70 248	91.84 250	83.40 248	98.31 166	98.49 20	88.04 163	78.23 271	90.26 271	73.57 208	96.79 219	84.21 201	83.53 228	88.90 298

NR-MVSNet		87.74 207	86.00 214	92.96 188	91.46 255	90.68 116	96.65 237	97.42 126	88.02 164	73.42 290	93.68 208	77.31 187	95.83 265	84.26 200	71.82 297	92.36 219

thres100view900		93.34 119	92.15 128	96.90 66	97.62 104	94.84 27	99.06 85	99.36 2	87.96 165	90.47 152	96.78 158	83.29 139	98.75 134	84.11 204	90.69 187	97.12 183

thres600view7		93.18 124	92.00 131	96.75 75	97.62 104	94.92 24	99.07 83	99.36 2	87.96 165	90.47 152	96.78 158	83.29 139	98.71 138	82.93 216	90.47 191	96.61 192

CDS-MVSNet		93.47 113	93.04 110	94.76 144	94.75 197	89.45 147	98.82 108	97.03 160	87.91 167	90.97 143	96.48 165	89.06 50	96.36 238	89.50 145	92.81 157	98.49 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMM	86.95 13	88.77 195	88.22 189	90.43 232	93.61 221	81.34 269	98.50 147	95.92 220	87.88 168	83.85 207	95.20 185	67.20 254	97.89 169	86.90 177	84.90 216	92.06 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm		89.67 179	88.95 174	91.82 207	92.54 239	81.43 266	92.95 290	95.92 220	87.81 169	90.50 151	89.44 284	84.99 120	95.65 268	83.67 210	82.71 235	98.38 150

TranMVSNet+NR-MVSNet		87.75 205	86.31 209	92.07 203	90.81 262	88.56 160	98.33 163	97.18 144	87.76 170	81.87 235	93.90 203	72.45 218	95.43 273	83.13 214	71.30 300	92.23 224

PatchMatch-RL		91.47 150	90.54 157	94.26 160	98.20 90	86.36 203	96.94 225	97.14 147	87.75 171	88.98 168	95.75 177	71.80 226	99.40 107	80.92 230	97.39 108	97.02 189

BH-RMVSNet		91.25 155	89.99 161	95.03 139	96.75 133	88.55 161	98.65 128	94.95 265	87.74 172	87.74 176	97.80 113	68.27 245	98.14 152	80.53 234	97.49 106	98.41 147

LPG-MVS_test		88.86 189	88.47 186	90.06 239	93.35 229	80.95 276	98.22 171	95.94 217	87.73 173	83.17 213	96.11 172	66.28 261	97.77 178	90.19 139	85.19 214	91.46 246

LGP-MVS_train				90.06 239	93.35 229	80.95 276		95.94 217	87.73 173	83.17 213	96.11 172	66.28 261	97.77 178	90.19 139	85.19 214	91.46 246

MVS_Test		93.67 110	92.67 117	96.69 81	96.72 134	92.66 73	97.22 217	96.03 211	87.69 175	95.12 88	94.03 198	81.55 159	98.28 148	89.17 154	96.46 116	99.14 98

ITE_SJBPF				87.93 273	92.26 242	76.44 297		93.47 294	87.67 176	79.95 251	95.49 182	56.50 293	97.38 200	75.24 268	82.33 238	89.98 286

HyFIR lowres test		93.68 109	93.29 104	94.87 141	97.57 108	88.04 169	98.18 175	98.47 21	87.57 177	91.24 140	95.05 186	85.49 114	97.46 197	93.22 111	92.82 155	99.10 100

thisisatest0515		94.75 79	94.19 80	96.43 93	96.13 155	92.64 77	99.47 35	97.60 92	87.55 178	93.17 115	97.59 125	94.71 8	98.42 142	88.28 160	93.20 151	98.24 158

TAMVS		92.62 133	92.09 130	94.20 162	94.10 205	87.68 175	98.41 157	96.97 164	87.53 179	89.74 162	96.04 174	84.77 125	96.49 230	88.97 156	92.31 165	98.42 146

MDTV_nov1_ep13_2view						91.17 99	91.38 299		87.45 180	93.08 117		86.67 94		87.02 173		98.95 114

XVG-ACMP-BASELINE		85.86 234	84.95 229	88.57 268	89.90 271	77.12 296	94.30 277	95.60 243	87.40 181	82.12 228	92.99 225	53.42 305	97.66 187	85.02 193	83.83 224	90.92 260

HPM-MVS		95.41 66	95.22 64	95.99 109	99.29 52	89.14 149	99.17 67	97.09 155	87.28 182	95.40 82	98.48 95	84.93 121	99.38 108	95.64 69	99.65 34	99.47 74
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
无先验							98.52 143	97.82 54	87.20 183				99.90 36	87.64 167		99.85 24

VDD-MVS		91.24 156	90.18 160	94.45 154	97.08 121	85.84 219	98.40 160	96.10 209	86.99 184	93.36 113	98.16 107	54.27 302	99.20 116	96.59 49	90.63 190	98.31 156

WR-MVS		88.54 199	87.22 201	92.52 197	91.93 249	89.50 145	98.56 141	97.84 52	86.99 184	81.87 235	93.81 205	74.25 205	95.92 264	85.29 189	74.43 272	92.12 228

Effi-MVS+		93.87 103	93.15 107	96.02 107	95.79 159	90.76 112	96.70 236	95.78 231	86.98 186	95.71 77	97.17 143	79.58 170	98.01 165	94.57 91	96.09 126	99.31 84

CostFormer		92.89 128	92.48 121	94.12 165	94.99 191	85.89 216	92.89 291	97.00 163	86.98 186	95.00 90	90.78 251	90.05 40	97.51 196	92.92 116	91.73 177	98.96 110

VPA-MVSNet		89.10 185	87.66 193	93.45 179	92.56 238	91.02 106	97.97 190	98.32 24	86.92 188	86.03 192	92.01 234	68.84 242	97.10 208	90.92 132	75.34 265	92.23 224

MVSFormer		94.71 83	94.08 85	96.61 84	95.05 189	94.87 25	97.77 197	96.17 206	86.84 189	98.04 29	98.52 90	85.52 111	95.99 258	89.83 141	98.97 72	98.96 110

test_djsdf		88.26 203	87.73 191	89.84 244	88.05 296	82.21 261	97.77 197	96.17 206	86.84 189	82.41 224	91.95 237	72.07 222	95.99 258	89.83 141	84.50 219	91.32 250

AdaColmap		93.82 104	93.06 108	96.10 105	99.88 1	89.07 150	98.33 163	97.55 104	86.81 191	90.39 154	98.65 82	75.09 195	99.98 11	93.32 110	97.53 105	99.26 90

test_yl		95.27 69	94.60 72	97.28 46	98.53 84	92.98 68	99.05 86	98.70 16	86.76 192	94.65 95	97.74 117	87.78 70	99.44 102	95.57 70	92.61 159	99.44 75

DCV-MVSNet		95.27 69	94.60 72	97.28 46	98.53 84	92.98 68	99.05 86	98.70 16	86.76 192	94.65 95	97.74 117	87.78 70	99.44 102	95.57 70	92.61 159	99.44 75

mvs_anonymous		92.50 137	91.65 138	95.06 137	96.60 136	89.64 142	97.06 222	96.44 189	86.64 194	84.14 204	93.93 202	82.49 148	96.17 252	91.47 126	96.08 127	99.35 80

thisisatest0530		94.00 98	93.52 100	95.43 128	95.76 161	90.02 134	98.99 93	97.60 92	86.58 195	91.74 130	97.36 133	94.78 7	98.34 144	86.37 180	92.48 162	97.94 167

DP-MVS Recon		95.85 56	95.15 65	97.95 22	99.87 2	94.38 41	99.60 23	97.48 116	86.58 195	94.42 97	99.13 36	87.36 81	99.98 11	93.64 104	98.33 93	99.48 73

F-COLMAP		92.07 143	91.75 137	93.02 187	98.16 93	82.89 256	98.79 114	95.97 213	86.54 197	87.92 175	97.80 113	78.69 180	99.65 75	85.97 183	95.93 130	96.53 197

PHI-MVS		96.65 32	96.46 32	97.21 49	99.34 45	91.77 83	99.70 15	98.05 38	86.48 198	98.05 28	99.20 24	89.33 48	99.96 23	98.38 20	99.62 39	99.90 15

DeepMVS_CX				76.08 308	90.74 264	51.65 329		90.84 318	86.47 199	57.89 319	87.98 291	35.88 327	92.60 308	65.77 305	65.06 311	83.97 319

BH-untuned		91.46 151	90.84 150	93.33 181	96.51 140	84.83 233	98.84 107	95.50 247	86.44 200	83.50 208	96.70 160	75.49 194	97.77 178	86.78 179	97.81 97	97.40 176

CNLPA		93.64 111	92.74 115	96.36 97	98.96 69	90.01 135	99.19 63	95.89 227	86.22 201	89.40 165	98.85 66	80.66 166	99.84 51	88.57 157	96.92 112	99.24 91

OurMVSNet-221017-0		84.13 259	83.59 247	85.77 289	87.81 298	70.24 314	94.89 273	93.65 292	86.08 202	76.53 275	93.28 218	61.41 280	96.14 254	80.95 229	77.69 258	90.93 259

tttt0517		93.30 120	93.01 111	94.17 163	95.57 166	86.47 198	98.51 146	97.60 92	85.99 203	90.55 149	97.19 140	94.80 6	98.31 145	85.06 192	91.86 173	97.74 169

FMVSNet3		88.81 194	87.08 202	93.99 170	96.52 139	94.59 36	98.08 185	96.20 203	85.85 204	82.12 228	91.60 241	74.05 206	95.40 275	79.04 240	80.24 243	91.99 233

HPM-MVS_fast		94.89 75	94.62 71	95.70 120	99.11 61	88.44 164	99.14 77	97.11 151	85.82 205	95.69 78	98.47 96	83.46 135	99.32 114	93.16 112	99.63 38	99.35 80

旧先验2							98.67 126		85.75 206	98.96 9			98.97 128	93.84 100

ab-mvs		91.05 158	89.17 170	96.69 81	95.96 156	91.72 86	92.62 292	97.23 140	85.61 207	89.74 162	93.89 204	68.55 243	99.42 104	91.09 129	87.84 199	98.92 116

新几何1				97.40 41	98.92 71	92.51 80		97.77 64	85.52 208	96.69 63	99.06 43	88.08 67	99.89 39	84.88 194	99.62 39	99.79 29

TR-MVS		90.77 162	89.44 165	94.76 144	96.31 145	88.02 170	97.92 191	95.96 215	85.52 208	88.22 174	97.23 137	66.80 257	98.09 156	84.58 197	92.38 163	98.17 162

1121		95.19 71	94.45 75	97.42 39	98.88 73	92.58 78	96.22 252	97.75 65	85.50 210	96.86 53	99.01 51	88.59 58	99.90 36	87.64 167	99.60 43	99.79 29

CP-MVSNet		86.54 224	85.45 223	89.79 246	91.02 261	82.78 259	97.38 209	97.56 103	85.37 211	79.53 257	93.03 223	71.86 225	95.25 279	79.92 235	73.43 284	91.34 249

EU-MVSNet		84.19 257	84.42 240	83.52 298	88.64 290	67.37 319	96.04 258	95.76 233	85.29 212	78.44 268	93.18 220	70.67 233	91.48 319	75.79 266	75.98 262	91.70 237

testdata				95.26 133	98.20 90	87.28 186		97.60 92	85.21 213	98.48 17	99.15 32	88.15 65	98.72 137	90.29 138	99.45 52	99.78 33

IterMVS-LS		88.34 200	87.44 195	91.04 219	94.10 205	85.85 218	98.10 183	95.48 248	85.12 214	82.03 232	91.21 244	81.35 162	95.63 269	83.86 208	75.73 264	91.63 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 177	89.38 167	91.36 215	94.32 203	85.87 217	97.61 203	96.59 178	85.10 215	85.51 195	97.10 145	81.30 163	96.56 225	83.85 209	83.03 232	91.64 238

IterMVS		85.81 236	84.67 235	89.22 259	93.51 223	83.67 246	96.32 246	94.80 268	85.09 216	78.69 263	90.17 278	66.57 260	93.17 303	79.48 238	77.42 259	90.81 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	89.78 5	91.26 153	89.63 163	96.16 104	95.44 171	91.58 91	95.29 270	96.10 209	85.07 217	82.75 216	97.45 130	78.28 182	99.78 62	80.60 233	95.65 135	97.12 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IterMVS-SCA-FT		85.73 239	84.64 236	89.00 264	93.46 226	82.90 255	96.27 247	94.70 271	85.02 218	78.62 265	90.35 269	66.61 258	93.33 302	79.38 239	77.36 260	90.76 266

Fast-Effi-MVS+		91.72 147	90.79 153	94.49 152	95.89 157	87.40 183	99.54 30	95.70 237	85.01 219	89.28 167	95.68 178	77.75 185	97.57 195	83.22 211	95.06 139	98.51 143

WR-MVS_H		86.53 225	85.49 222	89.66 251	91.04 260	83.31 250	97.53 205	98.20 30	84.95 220	79.64 254	90.90 249	78.01 184	95.33 276	76.29 262	72.81 286	90.35 276

MVS		93.92 100	92.28 123	98.83 3	95.69 163	96.82 4	96.22 252	98.17 31	84.89 221	84.34 203	98.61 86	79.32 174	99.83 53	93.88 99	99.43 54	99.86 23

PS-CasMVS		85.81 236	84.58 237	89.49 256	90.77 263	82.11 262	97.20 218	97.36 132	84.83 222	79.12 262	92.84 226	67.42 253	95.16 281	78.39 247	73.25 285	91.21 253

dp		90.16 172	88.83 177	94.14 164	96.38 143	86.42 199	91.57 298	97.06 157	84.76 223	88.81 169	90.19 277	84.29 128	97.43 199	75.05 269	91.35 184	98.56 141

UnsupCasMVSNet_eth		78.90 282	76.67 284	85.58 290	82.81 316	74.94 300	91.98 295	96.31 194	84.64 224	65.84 314	87.71 293	51.33 309	92.23 313	72.89 286	56.50 321	89.56 292

v2v482		87.27 213	85.76 217	91.78 212	89.59 275	87.58 177	98.56 141	95.54 245	84.53 225	82.51 221	91.78 238	73.11 213	96.47 231	82.07 223	74.14 278	91.30 251

EPP-MVSNet		93.75 106	93.67 97	94.01 169	95.86 158	85.70 221	98.67 126	97.66 78	84.46 226	91.36 138	97.18 141	91.16 24	97.79 176	92.93 115	93.75 148	98.53 142

PEN-MVS		85.21 244	83.93 245	89.07 263	89.89 272	81.31 270	97.09 221	97.24 139	84.45 227	78.66 264	92.68 228	68.44 244	94.87 286	75.98 264	70.92 301	91.04 257

SixPastTwentyTwo		82.63 265	81.58 264	85.79 288	88.12 295	71.01 313	95.17 271	92.54 301	84.33 228	72.93 295	92.08 231	60.41 284	95.61 270	74.47 274	74.15 277	90.75 267

testing_2		80.92 273	77.24 281	91.98 204	78.88 322	87.83 172	93.96 282	95.72 235	84.27 229	56.20 320	80.42 314	38.64 326	96.40 235	87.20 170	79.85 248	91.72 236

XXY-MVS		87.75 205	86.02 213	92.95 189	90.46 266	89.70 141	97.71 200	95.90 225	84.02 230	80.95 241	94.05 195	67.51 252	97.10 208	85.16 190	78.41 253	92.04 232

tpm2		91.77 146	91.09 144	93.82 175	94.83 195	85.56 224	92.51 293	97.16 146	84.00 231	93.83 109	90.66 257	87.54 75	97.17 204	87.73 166	91.55 180	98.72 133

anonymousdsp		86.69 220	85.75 218	89.53 253	86.46 307	82.94 253	96.39 243	95.71 236	83.97 232	79.63 255	90.70 254	68.85 241	95.94 261	86.01 182	84.02 223	89.72 289

v148		86.38 227	85.06 226	90.37 234	89.47 280	84.10 241	98.52 143	95.48 248	83.80 233	80.93 242	90.22 275	74.60 198	96.31 246	80.92 230	71.55 298	90.69 270

MS-PatchMatch		86.75 219	85.92 215	89.22 259	91.97 246	82.47 260	96.91 226	96.14 208	83.74 234	77.73 272	93.53 214	58.19 288	97.37 202	76.75 258	98.35 92	87.84 302

test222					98.32 87	91.21 96	98.08 185	97.58 98	83.74 234	95.87 74	99.02 47	86.74 93			99.64 35	99.81 26

K. test v3		81.04 272	79.77 274	84.83 293	87.41 302	70.23 315	95.60 268	93.93 287	83.70 236	67.51 309	89.35 286	55.76 294	93.58 301	76.67 259	68.03 306	90.67 271

V42		87.00 215	85.68 219	90.98 221	89.91 270	86.08 211	98.32 165	95.61 242	83.67 237	82.72 217	90.67 256	74.00 207	96.53 227	81.94 226	74.28 275	90.32 277

API-MVS		94.78 78	94.18 82	96.59 85	99.21 57	90.06 132	98.80 110	97.78 62	83.59 238	93.85 108	99.21 23	83.79 131	99.97 18	92.37 120	99.00 71	99.74 43

DTE-MVSNet		84.14 258	82.80 252	88.14 272	88.95 286	79.87 282	96.81 229	96.24 201	83.50 239	77.60 273	92.52 230	67.89 250	94.24 298	72.64 287	69.05 304	90.32 277

LFMVS		92.23 141	90.84 150	96.42 94	98.24 89	91.08 104	98.24 170	96.22 202	83.39 240	94.74 93	98.31 100	61.12 282	98.85 129	94.45 93	92.82 155	99.32 83

LF4IMVS		81.94 269	81.17 269	84.25 296	87.23 304	68.87 318	93.35 288	91.93 310	83.35 241	75.40 283	93.00 224	49.25 315	96.65 222	78.88 243	78.11 255	87.22 308

v1144		86.83 218	85.31 224	91.40 214	89.75 273	87.21 189	98.31 166	95.45 250	83.22 242	82.70 218	90.78 251	73.36 209	96.36 238	79.49 237	74.69 270	90.63 272

CPTT-MVS		94.60 87	94.43 76	95.09 135	99.66 13	86.85 192	99.44 42	97.47 118	83.22 242	94.34 100	98.96 56	82.50 147	99.55 84	94.81 84	99.50 48	98.88 118

Patchmatch-RL test		81.90 270	80.13 272	87.23 280	80.71 318	70.12 316	84.07 321	88.19 326	83.16 244	70.57 298	82.18 310	87.18 84	92.59 309	82.28 222	62.78 313	98.98 108

ADS-MVSNet2		87.62 209	86.88 204	89.86 243	96.21 148	79.14 284	87.15 310	92.99 296	83.01 245	89.91 160	87.27 297	78.87 177	92.80 307	74.20 276	92.27 166	97.64 170

ADS-MVSNet		88.99 186	87.30 198	94.07 166	96.21 148	87.56 178	87.15 310	96.78 170	83.01 245	89.91 160	87.27 297	78.87 177	97.01 210	74.20 276	92.27 166	97.64 170

GBi-Net		86.67 221	84.96 227	91.80 208	95.11 184	88.81 155	96.77 230	95.25 258	82.94 247	82.12 228	90.25 272	62.89 274	94.97 283	79.04 240	80.24 243	91.62 240

test1		86.67 221	84.96 227	91.80 208	95.11 184	88.81 155	96.77 230	95.25 258	82.94 247	82.12 228	90.25 272	62.89 274	94.97 283	79.04 240	80.24 243	91.62 240

FMVSNet2		86.90 216	84.79 233	93.24 182	95.11 184	92.54 79	97.67 201	95.86 229	82.94 247	80.55 243	91.17 245	62.89 274	95.29 278	77.23 252	79.71 250	91.90 234

CSCG		94.87 76	94.71 70	95.36 129	99.54 30	86.49 197	99.34 56	98.15 34	82.71 250	90.15 157	99.25 18	89.48 47	99.86 48	94.97 82	98.82 80	99.72 47

OpenMVS	85.28 14	90.75 163	88.84 176	96.48 90	93.58 222	93.51 56	98.80 110	97.41 127	82.59 251	78.62 265	97.49 129	68.00 248	99.82 56	84.52 198	98.55 88	96.11 200

114514_t		94.06 96	93.05 109	97.06 53	99.08 63	92.26 81	98.97 95	97.01 162	82.58 252	92.57 122	98.22 104	80.68 165	99.30 115	89.34 150	99.02 70	99.63 60

pmmvs4		87.58 210	86.17 212	91.80 208	89.58 276	88.92 153	97.25 214	95.28 257	82.54 253	80.49 244	93.17 221	75.62 193	96.05 257	82.75 217	78.90 251	90.42 275

v1192		86.32 228	84.71 234	91.17 217	89.53 278	86.40 200	98.13 179	95.44 251	82.52 254	82.42 223	90.62 260	71.58 229	96.33 245	77.23 252	74.88 267	90.79 264

v144192		86.40 226	84.89 230	90.91 222	89.48 279	85.59 222	98.21 173	95.43 252	82.45 255	82.62 219	90.58 263	72.79 217	96.36 238	78.45 246	74.04 279	90.79 264

TAPA-MVS	87.50 9	90.35 167	89.05 172	94.25 161	98.48 86	85.17 228	98.42 155	96.58 181	82.44 256	87.24 182	98.53 89	82.77 145	98.84 130	59.09 317	97.88 96	98.72 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
miper_lstm_enhance		86.90 216	86.20 211	89.00 264	94.53 200	81.19 272	96.74 234	95.24 261	82.33 257	80.15 248	90.51 267	81.99 156	94.68 293	80.71 232	73.58 280	91.12 255

v1921920		86.02 231	84.44 239	90.77 225	89.32 281	85.20 226	98.10 183	95.35 256	82.19 258	82.25 226	90.71 253	70.73 232	96.30 249	76.85 257	74.49 271	90.80 263

MVP-Stereo		86.61 223	85.83 216	88.93 266	88.70 289	83.85 245	96.07 257	94.41 280	82.15 259	75.64 282	91.96 236	67.65 251	96.45 233	77.20 254	98.72 83	86.51 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v8		86.11 230	84.45 238	91.10 218	89.99 269	86.85 192	97.24 215	95.36 255	81.99 260	79.89 252	89.86 280	74.53 200	96.39 236	78.83 244	72.32 292	90.05 284

tpmvs		89.16 184	87.76 190	93.35 180	97.19 117	84.75 234	90.58 307	97.36 132	81.99 260	84.56 200	89.31 287	83.98 130	98.17 151	74.85 272	90.00 193	97.12 183

test_normal		68.74 296	64.03 298	82.86 300	73.54 325	64.63 321	41.77 332	94.81 267	81.96 262	42.22 327	60.30 324	30.12 328	95.33 276	77.97 249	73.56 281	88.61 299

pm-mvs1		84.68 248	82.78 254	90.40 233	89.58 276	85.18 227	97.31 210	94.73 270	81.93 263	76.05 278	92.01 234	65.48 265	96.11 255	78.75 245	69.14 303	89.91 287

v1240		85.77 238	84.11 242	90.73 226	89.26 282	85.15 229	97.88 194	95.23 263	81.89 264	82.16 227	90.55 265	69.60 239	96.31 246	75.59 267	74.87 268	90.72 269

test20.03		78.51 285	77.48 279	81.62 304	83.07 315	71.03 312	96.11 256	92.83 299	81.66 265	69.31 302	89.68 282	57.53 289	87.29 325	58.65 318	68.47 305	86.53 311

pmmvs5		85.87 233	84.40 241	90.30 235	88.53 291	84.23 239	98.60 136	93.71 290	81.53 266	80.29 246	92.02 233	64.51 268	95.52 271	82.04 225	78.34 254	91.15 254

MIMVSNet		84.48 253	81.83 261	92.42 198	91.73 252	87.36 184	85.52 313	94.42 279	81.40 267	81.91 233	87.58 294	51.92 308	92.81 306	73.84 279	88.15 198	97.08 187

our_test_3		84.47 254	82.80 252	89.50 254	89.01 284	83.90 244	97.03 223	94.56 275	81.33 268	75.36 284	90.52 266	71.69 227	94.54 295	68.81 295	76.84 261	90.07 282

v10		85.73 239	84.01 244	90.87 224	90.03 268	86.73 194	97.20 218	95.22 264	81.25 269	79.85 253	89.75 281	73.30 212	96.28 250	76.87 256	72.64 288	89.61 291

ACMH+	83.78 15	84.21 256	82.56 260	89.15 261	93.73 220	79.16 283	96.43 242	94.28 282	81.09 270	74.00 289	94.03 198	54.58 301	97.67 186	76.10 263	78.81 252	90.63 272

ACMH	83.09 17	84.60 250	82.61 259	90.57 228	93.18 232	82.94 253	96.27 247	94.92 266	81.01 271	72.61 297	93.61 211	56.54 292	97.79 176	74.31 275	81.07 242	90.99 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PM-MVS		74.88 291	72.85 293	80.98 306	78.98 321	64.75 320	90.81 304	85.77 328	80.95 272	68.23 306	82.81 308	29.08 329	92.84 305	76.54 260	62.46 315	85.36 316

QAPM		91.41 152	89.49 164	97.17 51	95.66 165	93.42 58	98.60 136	97.51 110	80.92 273	81.39 240	97.41 132	72.89 216	99.87 43	82.33 221	98.68 84	98.21 160

v7n		84.42 255	82.75 255	89.43 257	88.15 294	81.86 263	96.75 233	95.67 240	80.53 274	78.38 269	89.43 285	69.89 235	96.35 243	73.83 280	72.13 294	90.07 282

cascas		90.93 160	89.33 168	95.76 118	95.69 163	93.03 67	98.99 93	96.59 178	80.49 275	86.79 189	94.45 193	65.23 266	98.60 141	93.52 106	92.18 169	95.66 203

Baseline_NR-MVSNet		85.83 235	84.82 232	88.87 267	88.73 288	83.34 249	98.63 131	91.66 312	80.41 276	82.44 222	91.35 243	74.63 196	95.42 274	84.13 203	71.39 299	87.84 302

Anonymous20231206		80.76 274	79.42 276	84.79 294	84.78 310	72.98 307	96.53 239	92.97 297	79.56 277	74.33 286	88.83 288	61.27 281	92.15 314	60.59 314	75.92 263	89.24 295

MVS_0304		84.13 259	82.66 257	88.52 269	93.07 234	80.15 279	95.81 266	98.21 29	79.27 278	86.85 187	86.40 304	41.33 323	94.69 292	76.36 261	86.69 204	90.73 268

DSMNet-mixed		81.60 271	81.43 266	82.10 302	84.36 311	60.79 323	93.63 286	86.74 327	79.00 279	79.32 259	87.15 299	63.87 271	89.78 320	66.89 301	91.92 172	95.73 202

LTVRE_ROB	81.71 19	84.59 251	82.72 256	90.18 236	92.89 237	83.18 251	93.15 289	94.74 269	78.99 280	75.14 285	92.69 227	65.64 264	97.63 189	69.46 293	81.82 240	89.74 288
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
ppachtmachnet_test		83.63 263	81.57 265	89.80 245	89.01 284	85.09 230	97.13 220	94.50 276	78.84 281	76.14 277	91.00 247	69.78 236	94.61 294	63.40 308	74.36 273	89.71 290

TransMVSNet (Re)		81.97 268	79.61 275	89.08 262	89.70 274	84.01 242	97.26 213	91.85 311	78.84 281	73.07 294	91.62 240	67.17 255	95.21 280	67.50 298	59.46 319	88.02 301

UniMVSNet_ETH3D		85.65 241	83.79 246	91.21 216	90.41 267	80.75 278	95.36 269	95.78 231	78.76 283	81.83 237	94.33 194	49.86 313	96.66 221	84.30 199	83.52 229	96.22 199

tfpnnormal		83.65 262	81.35 267	90.56 229	91.37 257	88.06 168	97.29 211	97.87 50	78.51 284	76.20 276	90.91 248	64.78 267	96.47 231	61.71 312	73.50 282	87.13 309

FMVSNet1		83.94 261	81.32 268	91.80 208	91.94 248	88.81 155	96.77 230	95.25 258	77.98 285	78.25 270	90.25 272	50.37 312	94.97 283	73.27 283	77.81 257	91.62 240

pmmvs-eth3d		78.71 284	76.16 286	86.38 284	80.25 319	81.19 272	94.17 279	92.13 307	77.97 286	66.90 312	82.31 309	55.76 294	92.56 310	73.63 282	62.31 316	85.38 315

AllTest		84.97 245	83.12 249	90.52 230	96.82 129	78.84 286	95.89 260	92.17 305	77.96 287	75.94 279	95.50 180	55.48 296	99.18 117	71.15 289	87.14 201	93.55 210

TestCases				90.52 230	96.82 129	78.84 286		92.17 305	77.96 287	75.94 279	95.50 180	55.48 296	99.18 117	71.15 289	87.14 201	93.55 210

MSDG		88.29 202	86.37 208	94.04 168	96.90 127	86.15 209	96.52 240	94.36 281	77.89 289	79.22 260	96.95 152	69.72 237	99.59 83	73.20 284	92.58 161	96.37 198

new-patchmatchnet		74.80 292	72.40 294	81.99 303	78.36 323	72.20 310	94.44 275	92.36 303	77.06 290	63.47 315	79.98 316	51.04 310	88.85 322	60.53 315	54.35 323	84.92 318

FMVSNet5		82.29 266	80.54 271	87.52 277	93.79 219	84.01 242	93.73 284	92.47 302	76.92 291	74.27 287	86.15 306	63.69 272	89.24 321	69.07 294	74.79 269	89.29 294

Anonymous202405211		88.84 190	87.03 203	94.27 159	98.14 94	84.18 240	98.44 153	95.58 244	76.79 292	89.34 166	96.88 156	53.42 305	99.54 86	87.53 169	87.12 203	99.09 101

VDDNet		90.08 174	88.54 185	94.69 148	94.41 202	87.68 175	98.21 173	96.40 190	76.21 293	93.33 114	97.75 116	54.93 300	98.77 132	94.71 88	90.96 185	97.61 174

tpm cat1		88.89 188	87.27 199	93.76 176	95.79 159	85.32 225	90.76 305	97.09 155	76.14 294	85.72 193	88.59 290	82.92 143	98.04 162	76.96 255	91.43 181	97.90 168

MDA-MVSNet-bldmvs		77.82 288	74.75 290	87.03 281	88.33 292	78.52 289	96.34 245	92.85 298	75.57 295	48.87 324	87.89 292	57.32 291	92.49 311	60.79 313	64.80 312	90.08 281

TinyColmap		80.42 276	77.94 277	87.85 274	92.09 245	78.58 288	93.74 283	89.94 321	74.99 296	69.77 301	91.78 238	46.09 317	97.58 192	65.17 306	77.89 256	87.38 305

LS3D		90.19 171	88.72 178	94.59 151	98.97 67	86.33 204	96.90 227	96.60 176	74.96 297	84.06 206	98.74 73	75.78 192	99.83 53	74.93 270	97.57 102	97.62 173

EG-PatchMatch MVS		79.92 277	77.59 278	86.90 282	87.06 305	77.90 295	96.20 255	94.06 286	74.61 298	66.53 313	88.76 289	40.40 325	96.20 251	67.02 300	83.66 227	86.61 310

TDRefinement		78.01 286	75.31 287	86.10 287	70.06 327	73.84 304	93.59 287	91.58 314	74.51 299	73.08 293	91.04 246	49.63 314	97.12 205	74.88 271	59.47 318	87.33 306

RPSCF		85.33 243	85.55 221	84.67 295	94.63 199	62.28 322	93.73 284	93.76 288	74.38 300	85.23 197	97.06 148	64.09 269	98.31 145	80.98 228	86.08 210	93.41 212

MDA-MVSNet_test_wron		79.65 279	77.05 282	87.45 278	87.79 300	80.13 280	96.25 250	94.44 277	73.87 301	51.80 322	87.47 296	68.04 247	92.12 315	66.02 303	67.79 307	90.09 280

YYNet1		79.64 280	77.04 283	87.43 279	87.80 299	79.98 281	96.23 251	94.44 277	73.83 302	51.83 321	87.53 295	67.96 249	92.07 316	66.00 304	67.75 308	90.23 279

MIMVSNet1		75.92 290	73.30 292	83.81 297	81.29 317	75.57 299	92.26 294	92.05 308	73.09 303	67.48 310	86.18 305	40.87 324	87.64 324	55.78 320	70.68 302	88.21 300

Patchmatch-test		86.25 229	84.06 243	92.82 190	94.42 201	82.88 257	82.88 324	94.23 283	71.58 304	79.39 258	90.62 260	89.00 52	96.42 234	63.03 309	91.37 183	99.16 97

COLMAP_ROB	82.69 18	84.54 252	82.82 251	89.70 248	96.72 134	78.85 285	95.89 260	92.83 299	71.55 305	77.54 274	95.89 176	59.40 286	99.14 122	67.26 299	88.26 197	91.11 256
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		85.44 242	83.19 248	92.22 200	93.13 233	83.00 252	83.80 323	96.37 191	70.62 306	90.55 149	79.63 317	84.81 124	94.87 286	58.18 319	91.59 179	98.79 128

DP-MVS		88.75 196	86.56 206	95.34 130	98.92 71	87.45 181	97.64 202	93.52 293	70.55 307	81.49 238	97.25 135	74.43 201	99.88 40	71.14 291	94.09 146	98.67 136

new_pmnet		76.02 289	73.71 291	82.95 299	83.88 313	72.85 308	91.26 301	92.26 304	70.44 308	62.60 316	81.37 311	47.64 316	92.32 312	61.85 311	72.10 295	83.68 320

N_pmnet		70.19 295	69.87 296	71.12 310	88.24 293	30.63 337	95.85 265	28.70 338	70.18 309	68.73 303	86.55 303	64.04 270	93.81 299	53.12 323	73.46 283	88.94 297

UnsupCasMVSNet_bld		73.85 293	70.14 295	84.99 292	79.44 320	75.73 298	88.53 308	95.24 261	70.12 310	61.94 317	74.81 319	41.41 322	93.62 300	68.65 296	51.13 326	85.62 314

JIA-IIPM		85.97 232	84.85 231	89.33 258	93.23 231	73.68 305	85.05 316	97.13 149	69.62 311	91.56 134	68.03 322	88.03 68	96.96 211	77.89 250	93.12 152	97.34 178

Patchmtry		83.61 264	81.64 263	89.50 254	93.36 228	82.84 258	84.10 320	94.20 284	69.47 312	79.57 256	86.88 301	84.43 126	94.78 290	68.48 297	74.30 274	90.88 261

test_0402		78.81 283	76.33 285	86.26 285	91.18 258	78.44 290	95.88 262	91.34 316	68.55 313	70.51 300	89.91 279	52.65 307	94.99 282	47.14 325	79.78 249	85.34 317

CMPMVS	58.40 21	80.48 275	80.11 273	81.59 305	85.10 309	59.56 324	94.14 280	95.95 216	68.54 314	60.71 318	93.31 216	55.35 298	97.87 171	83.06 215	84.85 217	87.33 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
gg-mvs-nofinetune		90.00 175	87.71 192	96.89 71	96.15 151	94.69 34	85.15 315	97.74 67	68.32 315	92.97 120	60.16 325	96.10 3	96.84 215	93.89 98	98.87 76	99.14 98

pmmvs6		79.90 278	77.31 280	87.67 276	84.17 312	78.13 292	95.86 264	93.68 291	67.94 316	72.67 296	89.62 283	50.98 311	95.75 266	74.80 273	66.04 309	89.14 296

OpenMVS_ROB	73.86 20	77.99 287	75.06 289	86.77 283	83.81 314	77.94 294	96.38 244	91.53 315	67.54 317	68.38 304	87.13 300	43.94 319	96.08 256	55.03 321	81.83 239	86.29 313

Anonymous20231211		84.72 247	82.65 258	90.91 222	97.71 101	84.55 236	97.28 212	96.67 172	66.88 318	79.18 261	90.87 250	58.47 287	96.60 223	82.61 219	74.20 276	91.59 243

Anonymous20240529		87.66 208	85.58 220	93.92 171	97.59 107	85.01 231	98.13 179	97.13 149	66.69 319	88.47 172	96.01 175	55.09 299	99.51 91	87.00 174	84.12 222	97.23 182

ANet_high		50.71 302	46.17 304	64.33 313	44.27 335	52.30 328	76.13 327	78.73 332	64.95 320	27.37 331	55.23 328	14.61 336	67.74 331	36.01 327	18.23 330	72.95 325

RPMNet		84.62 249	81.78 262	93.16 184	93.47 224	86.24 205	84.97 317	96.28 199	64.85 321	90.76 146	78.80 318	80.95 164	94.82 288	53.76 322	92.17 170	98.41 147

pmmvs3		72.86 294	69.76 297	82.17 301	73.86 324	74.19 303	94.20 278	89.01 324	64.23 322	67.72 307	80.91 313	41.48 321	88.65 323	62.40 310	54.02 324	83.68 320

MVS-HIRNet		79.01 281	75.13 288	90.66 227	93.82 218	81.69 265	85.16 314	93.75 289	54.54 323	74.17 288	59.15 327	57.46 290	96.58 224	63.74 307	94.38 143	93.72 209

PMMVS2		58.97 299	55.07 301	70.69 311	62.72 328	55.37 327	85.97 312	80.52 331	49.48 324	45.94 325	68.31 321	15.73 335	80.78 328	49.79 324	37.12 327	75.91 323

FPMVS		61.57 297	60.32 299	65.34 312	60.14 331	42.44 332	91.02 303	89.72 322	44.15 325	42.63 326	80.93 312	19.02 331	80.59 329	42.50 326	72.76 287	73.00 324

LCM-MVSNet		60.07 298	56.37 300	71.18 309	54.81 333	48.67 330	82.17 325	89.48 323	37.95 326	49.13 323	69.12 320	13.75 337	81.76 326	59.28 316	51.63 325	83.10 322

Gipuma		54.77 300	52.22 303	62.40 314	86.50 306	59.37 325	50.20 331	90.35 320	36.52 327	41.20 328	49.49 329	18.33 333	81.29 327	32.10 328	65.34 310	46.54 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	41.42 23	45.67 303	42.50 305	55.17 316	34.28 336	32.37 335	66.24 329	78.71 333	30.72 328	22.04 334	59.59 326	4.59 338	77.85 330	27.49 329	58.84 320	55.29 327
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		41.02 305	40.93 306	41.29 318	61.97 329	33.83 334	84.00 322	65.17 336	27.17 329	27.56 330	46.72 331	17.63 334	60.41 334	19.32 331	18.82 329	29.61 330

EMVS		39.96 306	39.88 307	40.18 319	59.57 332	32.12 336	84.79 319	64.57 337	26.27 330	26.14 332	44.18 334	18.73 332	59.29 335	17.03 332	17.67 331	29.12 331

MVE	44.00 22	41.70 304	37.64 308	53.90 317	49.46 334	43.37 331	65.09 330	66.66 335	26.19 331	25.77 333	48.53 330	3.58 340	63.35 333	26.15 330	27.28 328	54.97 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		53.66 301	52.86 302	56.05 315	32.75 337	41.97 333	73.42 328	76.12 334	21.91 332	39.68 329	96.39 169	42.59 320	65.10 332	78.00 248	14.92 332	61.08 326

wuyk23d		16.71 309	16.73 312	16.65 320	60.15 330	25.22 338	41.24 333	5.17 339	6.56 333	5.48 337	3.61 337	3.64 339	22.72 336	15.20 333	9.52 333	1.99 334

testmvs		18.81 308	23.05 310	6.10 322	4.48 338	2.29 340	97.78 196	3.00 340	3.27 334	18.60 335	62.71 323	1.53 342	2.49 338	14.26 334	1.80 334	13.50 333

test123		16.58 310	19.47 311	7.91 321	3.59 339	5.37 339	94.32 276	1.39 341	2.49 335	13.98 336	44.60 333	2.91 341	2.65 337	11.35 335	0.57 335	15.70 332

cdsmvs_eth3d_5k		22.52 307	30.03 309	0.00 323	0.00 340	0.00 341	0.00 334	97.17 145	0.00 336	0.00 338	98.77 70	74.35 202	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		6.87 312	9.16 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	82.48 149	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		8.21 311	10.94 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	98.50 92	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save filter2										99.29 3	99.48 7	91.45 23	99.99 5	98.94 10	99.83 12	99.74 43

test_0728_SECOND				98.77 4	99.66 13	96.37 9	99.72 12	97.68 76					99.98 11	99.64 1	99.82 15	99.96 4

GSMVS																98.84 121

test_part2					99.54 30	95.42 15				98.13 24

test_part1				0.00 323		0.00 341	0.00 334	97.69 75				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												88.39 61				98.84 121

sam_mvs												87.08 85

ambc				79.60 307	72.76 326	56.61 326	76.20 326	92.01 309		68.25 305	80.23 315	23.34 330	94.73 291	73.78 281	60.81 317	87.48 304

MTGPA								97.45 120

test_post1							90.74 306				41.37 335	85.38 118	96.36 238	83.16 212

test_post											46.00 332	87.37 78	97.11 206

patchmatchnet-post											84.86 307	88.73 55	96.81 217

GG-mvs-BLEND				96.98 61	96.53 138	94.81 30	87.20 309	97.74 67		93.91 107	96.40 167	96.56 2	96.94 213	95.08 78	98.95 75	99.20 95

MTMP							99.21 62	91.09 317

test9_res														98.60 14	99.87 6	99.90 15

agg_prior2														97.84 33	99.87 6	99.91 14

agg_prior					99.54 30	92.66 73		97.64 84		97.98 32			99.61 80

test_prior4						92.00 82	99.41 47

test_prior				97.01 55	99.58 23	91.77 83		97.57 101					99.49 94			99.79 29

新几何2							98.26 169

旧先验1					98.97 67	92.90 72		97.74 67			99.15 32	91.05 27			99.33 59	99.60 63

原ACMM2							98.69 122

testdata2													99.88 40	84.16 202

segment_acmp												90.56 35

test12				97.83 25	99.33 50	94.45 38		97.55 104		97.56 38		88.60 56	99.50 93		99.71 30	99.55 65

plane_prior7					93.84 216	85.73 220

plane_prior6					93.92 213	86.02 214						72.92 214

plane_prior5								96.30 195					97.75 183	93.46 107	86.17 208	92.67 215

plane_prior4											96.52 163

plane_prior1					93.90 215

n2								0.00 342

nn								0.00 342

door-mid								84.90 330

lessismore_v0				85.08 291	85.59 308	69.28 317		90.56 319		67.68 308	90.21 276	54.21 303	95.46 272	73.88 278	62.64 314	90.50 274

test11								97.68 76

door								85.30 329

HQP5-MVS						86.39 201

BP-MVS														93.82 102

HQP4-MVS										87.57 177			97.77 178			92.72 213

HQP3-MVS								96.37 191							86.29 205

HQP2-MVS												73.34 210

NP-MVS					93.94 212	86.22 207					96.67 161

ACMMP++_ref															82.64 236

ACMMP++															83.83 224

Test By Simon												83.62 132

High-resolution multi-view benchmark