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.

HPM-MVS		96.69 33	96.45 35	97.40 40	99.36 12	93.11 55	98.87 1	98.06 57	91.17 123	96.40 45	97.99 50	90.99 46	99.58 55	95.61 41	99.61 8	99.49 26

APDe-MVS		97.82 1	97.73 1	98.08 8	99.15 25	94.82 12	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 8	99.68 37	97.93 1	99.69 1	99.75 1

CP-MVS		97.02 20	96.81 21	97.64 32	99.33 14	93.54 44	98.80 3	98.28 23	92.99 69	96.45 44	98.30 35	91.90 33	99.85 11	95.61 41	99.68 2	99.54 19

HPM-MVS_fast		96.51 38	96.27 39	97.22 51	99.32 15	92.74 63	98.74 4	98.06 57	90.57 143	96.77 30	98.35 24	90.21 56	99.53 70	94.80 63	99.63 4	99.38 39

EPP-MVSNet		95.22 66	95.04 63	95.76 106	97.49 118	89.56 159	98.67 5	97.00 175	90.69 133	94.24 92	97.62 77	89.79 61	98.81 137	93.39 89	96.49 126	98.92 76

3Dnovator	91.36 5	95.19 68	94.44 79	97.44 39	96.56 147	93.36 51	98.65 6	98.36 16	94.12 37	89.25 222	98.06 45	82.20 173	99.77 22	93.41 88	99.32 41	99.18 52

XVS		97.18 11	96.96 13	97.81 17	99.38 8	94.03 31	98.59 7	98.20 31	94.85 17	96.59 37	98.29 36	91.70 36	99.80 20	95.66 37	99.40 32	99.62 7

X-MVStestdata		91.71 174	89.67 231	97.81 17	99.38 8	94.03 31	98.59 7	98.20 31	94.85 17	96.59 37	32.69 349	91.70 36	99.80 20	95.66 37	99.40 32	99.62 7

HSP-MVS		97.53 5	97.49 4	97.63 34	99.40 5	93.77 40	98.53 9	97.85 88	95.55 5	98.56 3	97.81 61	93.90 6	99.65 41	96.62 13	99.21 50	99.48 28

HFP-MVS		97.14 14	96.92 15	97.83 15	99.42 3	94.12 27	98.52 10	98.32 19	93.21 60	97.18 20	98.29 36	92.08 28	99.83 15	95.63 39	99.59 9	99.54 19

region2R		97.07 17	96.84 18	97.77 22	99.46 1	93.79 37	98.52 10	98.24 28	93.19 63	97.14 23	98.34 27	91.59 39	99.87 5	95.46 44	99.59 9	99.64 4

ACMMPR		97.07 17	96.84 18	97.79 19	99.44 2	93.88 33	98.52 10	98.31 21	93.21 60	97.15 22	98.33 30	91.35 41	99.86 8	95.63 39	99.59 9	99.62 7

mPP-MVS		96.86 26	96.60 28	97.64 32	99.40 5	93.44 47	98.50 13	98.09 49	93.27 59	95.95 60	98.33 30	91.04 45	99.88 3	95.20 46	99.57 13	99.60 10

3Dnovator+	91.43 4	95.40 60	94.48 77	98.16 6	96.90 133	95.34 6	98.48 14	97.87 85	94.65 28	88.53 232	98.02 47	83.69 127	99.71 29	93.18 91	98.96 66	99.44 32

IS-MVSNet		94.90 76	94.52 75	96.05 96	97.67 104	90.56 127	98.44 15	96.22 219	93.21 60	93.99 95	97.74 66	85.55 108	98.45 165	89.98 134	97.86 90	99.14 56

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 13	98.39 59	94.25 22	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 3	99.71 29	96.57 16	99.62 7	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		96.77 30	96.45 35	97.72 25	99.39 7	93.80 36	98.41 17	98.06 57	93.37 55	95.54 75	98.34 27	90.59 52	99.88 3	94.83 61	99.54 15	99.49 26

QAPM		93.45 114	92.27 132	96.98 59	96.77 139	92.62 67	98.39 18	98.12 42	84.50 278	88.27 238	97.77 64	82.39 169	99.81 19	85.40 223	98.81 69	98.51 100

nrg030		94.05 95	93.31 103	96.27 89	95.22 206	94.59 14	98.34 19	97.46 124	92.93 76	91.21 165	96.64 117	87.23 91	98.22 184	94.99 58	85.80 258	95.98 200

CPTT-MVS		95.57 59	95.19 60	96.70 62	99.27 19	91.48 97	98.33 20	98.11 45	87.79 222	95.17 79	98.03 46	87.09 92	99.61 47	93.51 83	99.42 30	99.02 64

CSCG		96.05 50	95.91 46	96.46 78	99.24 21	90.47 130	98.30 21	98.57 11	89.01 177	93.97 97	97.57 81	92.62 18	99.76 23	94.66 66	99.27 45	99.15 55

canonicalmvs		96.02 52	95.45 52	97.75 24	97.59 110	95.15 9	98.28 22	97.60 108	94.52 29	96.27 47	96.12 143	87.65 83	99.18 101	96.20 26	94.82 149	98.91 77

OpenMVS	89.19 12	92.86 134	91.68 147	96.40 79	95.34 196	92.73 64	98.27 23	98.12 42	84.86 273	85.78 271	97.75 65	78.89 237	99.74 24	87.50 188	98.65 73	96.73 172

Vis-MVSNet		95.23 65	94.81 65	96.51 73	97.18 123	91.58 96	98.26 24	98.12 42	94.38 33	94.90 81	98.15 41	82.28 170	98.92 127	91.45 123	98.58 76	99.01 68
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP		96.27 45	95.93 45	97.28 46	99.24 21	92.62 67	98.25 25	98.81 3	92.99 69	94.56 86	98.39 22	88.96 65	99.85 11	94.57 67	97.63 96	99.36 41
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
MVSFormer		95.37 61	95.16 61	95.99 99	96.34 159	91.21 105	98.22 26	97.57 111	91.42 116	96.22 48	97.32 89	86.20 101	97.92 236	94.07 70	99.05 62	98.85 82

test_djsdf		93.07 125	92.76 113	94.00 187	93.49 284	88.70 190	98.22 26	97.57 111	91.42 116	90.08 189	95.55 174	82.85 156	97.92 236	94.07 70	91.58 203	95.40 229

PHI-MVS		96.77 30	96.46 34	97.71 27	98.40 57	94.07 29	98.21 28	98.45 15	89.86 152	97.11 26	98.01 48	92.52 21	99.69 35	96.03 31	99.53 16	99.36 41

#test#		97.02 20	96.75 25	97.83 15	99.42 3	94.12 27	98.15 29	98.32 19	92.57 83	97.18 20	98.29 36	92.08 28	99.83 15	95.12 51	99.59 9	99.54 19

FC-MVSNet-test		93.94 99	93.57 91	95.04 140	95.48 190	91.45 100	98.12 30	98.71 5	93.37 55	90.23 178	96.70 112	87.66 82	97.85 242	91.49 121	90.39 222	95.83 206

FIs		94.09 93	93.70 87	95.27 128	95.70 184	92.03 83	98.10 31	98.68 7	93.36 57	90.39 175	96.70 112	87.63 84	97.94 232	92.25 99	90.50 221	95.84 205

Vis-MVSNet (Re-imp)		94.15 89	93.88 83	94.95 147	97.61 108	87.92 224	98.10 31	95.80 240	92.22 88	93.02 118	97.45 88	84.53 121	97.91 239	88.24 168	97.97 88	99.02 64

VDDNet		93.05 126	92.07 134	96.02 97	96.84 135	90.39 132	98.08 33	95.85 237	86.22 257	95.79 66	98.46 14	67.59 310	99.19 99	94.92 59	94.85 147	98.47 107

TSAR-MVS + MP.		97.42 6	97.33 6	97.69 28	99.25 20	94.24 23	98.07 34	97.85 88	93.72 47	98.57 2	98.35 24	93.69 9	99.40 87	97.06 3	99.46 25	99.44 32

WR-MVS_H		92.00 167	91.35 162	93.95 192	95.09 214	89.47 164	98.04 35	98.68 7	91.46 114	88.34 234	94.68 210	85.86 105	97.56 264	85.77 217	84.24 282	94.82 266

view600		92.55 141	91.68 147	95.18 130	97.98 83	89.44 168	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 248	99.11 108	81.61 269	94.04 162	96.98 159

view800		92.55 141	91.68 147	95.18 130	97.98 83	89.44 168	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 248	99.11 108	81.61 269	94.04 162	96.98 159

conf0.05thres1000		92.55 141	91.68 147	95.18 130	97.98 83	89.44 168	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 248	99.11 108	81.61 269	94.04 162	96.98 159

tfpn		92.55 141	91.68 147	95.18 130	97.98 83	89.44 168	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 248	99.11 108	81.61 269	94.04 162	96.98 159

APD-MVS_3200maxsize		96.81 28	96.71 26	97.12 55	99.01 30	92.31 73	97.98 40	98.06 57	93.11 66	97.44 15	98.55 9	90.93 47	99.55 65	96.06 29	99.25 46	99.51 23

LFMVS		93.60 109	92.63 120	96.52 70	98.13 79	91.27 104	97.94 41	93.39 314	90.57 143	96.29 46	98.31 33	69.00 303	99.16 103	94.18 69	95.87 135	99.12 59

SD-MVS		97.41 7	97.53 2	97.06 56	98.57 51	94.46 16	97.92 42	98.14 40	94.82 21	99.01 1	98.55 9	94.18 5	97.41 275	96.94 5	99.64 3	99.32 43

tfpn1000		91.99 168	91.05 173	94.80 154	97.78 99	89.66 154	97.91 43	92.90 324	88.99 178	91.73 143	94.84 202	78.99 230	98.33 179	82.41 265	93.91 168	96.40 182

abl_6		96.40 41	96.21 41	96.98 59	98.89 33	92.20 78	97.89 44	98.03 66	93.34 58	97.22 19	98.42 18	87.93 79	99.72 28	95.10 52	99.07 61	99.02 64

UGNet		94.04 96	93.28 104	96.31 85	96.85 134	91.19 108	97.88 45	97.68 102	94.40 31	93.00 119	96.18 140	73.39 286	99.61 47	91.72 114	98.46 77	98.13 123
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		95.87 56	95.23 59	97.78 20	97.56 112	95.19 7	97.86 46	97.17 152	94.39 32	96.47 42	96.40 133	85.89 104	99.20 98	96.21 25	95.11 145	98.95 73

VPA-MVSNet		93.24 120	92.48 129	95.51 118	95.70 184	92.39 72	97.86 46	98.66 9	92.30 87	92.09 138	95.37 183	80.49 203	98.40 172	93.95 73	85.86 257	95.75 213

EPNet		95.20 67	94.56 72	97.14 54	92.80 303	92.68 65	97.85 48	94.87 282	96.64 1	92.46 127	97.80 63	86.23 99	99.65 41	93.72 80	98.62 74	99.10 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PS-CasMVS		91.55 191	90.84 185	93.69 211	94.96 219	88.28 198	97.84 49	98.24 28	91.46 114	88.04 241	95.80 157	79.67 216	97.48 269	87.02 198	84.54 280	95.31 235

CP-MVSNet		91.89 170	91.24 168	93.82 198	95.05 215	88.57 192	97.82 50	98.19 33	91.70 108	88.21 239	95.76 162	81.96 177	97.52 267	87.86 175	84.65 278	95.37 232

API-MVS		94.84 79	94.49 76	95.90 101	97.90 94	92.00 85	97.80 51	97.48 119	89.19 167	94.81 83	96.71 110	88.84 67	99.17 102	88.91 159	98.76 71	96.53 177

pm-mvs1		90.72 223	89.65 233	93.96 191	94.29 246	89.63 155	97.79 52	96.82 193	89.07 175	86.12 270	95.48 181	78.61 239	97.78 249	86.97 199	81.67 302	94.46 280

PEN-MVS		91.20 206	90.44 201	93.48 222	94.49 238	87.91 226	97.76 53	98.18 35	91.29 119	87.78 244	95.74 164	80.35 206	97.33 280	85.46 222	82.96 296	95.19 244

tfpn_ndepth		91.88 171	90.96 177	94.62 163	97.73 102	89.93 143	97.75 54	92.92 323	88.93 183	91.73 143	93.80 255	78.91 231	98.49 164	83.02 257	93.86 169	95.45 223

PS-MVSNAJss		93.74 105	93.51 95	94.44 171	93.91 271	89.28 180	97.75 54	97.56 114	92.50 84	89.94 191	96.54 127	88.65 70	98.18 188	93.83 79	90.90 214	95.86 202

HQP_MVS		93.78 104	93.43 99	94.82 151	96.21 163	89.99 137	97.74 56	97.51 117	94.85 17	91.34 152	96.64 117	81.32 187	98.60 152	93.02 92	92.23 190	95.86 202

plane_prior2							97.74 56		94.85 17

jajsoiax		92.42 150	91.89 141	94.03 186	93.33 290	88.50 194	97.73 58	97.53 115	92.00 103	88.85 226	96.50 129	75.62 269	98.11 194	93.88 77	91.56 204	95.48 219

TransMVSNet (Re)		88.94 253	87.56 256	93.08 238	94.35 243	88.45 196	97.73 58	95.23 263	87.47 229	84.26 282	95.29 186	79.86 213	97.33 280	79.44 296	74.44 330	93.45 296

VDD-MVS		93.82 102	93.08 106	96.02 97	97.88 95	89.96 142	97.72 60	95.85 237	92.43 85	95.86 62	98.44 16	68.42 307	99.39 88	96.31 19	94.85 147	98.71 90

APD-MVS		96.95 23	96.60 28	98.01 9	99.03 29	94.93 11	97.72 60	98.10 47	91.50 112	98.01 8	98.32 32	92.33 23	99.58 55	94.85 60	99.51 19	99.53 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
conf200view11		92.45 148	91.58 154	95.05 139	97.92 91	89.37 173	97.71 62	94.66 284	92.20 90	93.31 107	94.90 198	78.06 252	99.08 118	81.40 276	94.08 157	96.70 174

thres100view900		92.43 149	91.58 154	94.98 144	97.92 91	89.37 173	97.71 62	94.66 284	92.20 90	93.31 107	94.90 198	78.06 252	99.08 118	81.40 276	94.08 157	96.48 180

v7n		90.76 219	89.86 223	93.45 225	93.54 281	87.60 232	97.70 64	97.37 139	88.85 185	87.65 248	94.08 247	81.08 189	98.10 195	84.68 232	83.79 290	94.66 275

MSLP-MVS++		96.94 24	97.06 9	96.59 68	98.72 37	91.86 88	97.67 65	98.49 12	94.66 27	97.24 18	98.41 21	92.31 26	98.94 126	96.61 14	99.46 25	98.96 71

MAR-MVS		94.22 87	93.46 97	96.51 73	98.00 82	92.19 79	97.67 65	97.47 122	88.13 217	93.00 119	95.84 154	84.86 117	99.51 74	87.99 173	98.17 84	97.83 137
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
LS3D		93.57 111	92.61 122	96.47 76	97.59 110	91.61 93	97.67 65	97.72 97	85.17 268	90.29 177	98.34 27	84.60 119	99.73 25	83.85 249	98.27 81	98.06 128

UA-Net		95.95 54	95.53 51	97.20 53	97.67 104	92.98 59	97.65 68	98.13 41	94.81 22	96.61 35	98.35 24	88.87 66	99.51 74	90.36 133	97.35 106	99.11 60

thres600view7		92.49 147	91.60 153	95.18 130	97.91 93	89.47 164	97.65 68	94.66 284	92.18 94	93.33 106	94.91 197	78.06 252	99.10 113	81.61 269	94.06 161	96.98 159

PGM-MVS		96.81 28	96.53 31	97.65 30	99.35 13	93.53 45	97.65 68	98.98 1	92.22 88	97.14 23	98.44 16	91.17 43	99.85 11	94.35 68	99.46 25	99.57 13

LPG-MVS_test		92.94 130	92.56 123	94.10 182	96.16 168	88.26 199	97.65 68	97.46 124	91.29 119	90.12 185	97.16 96	79.05 224	98.73 144	92.25 99	91.89 198	95.31 235

conf0.002		91.74 173	90.67 192	94.94 150	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.70 174

thresconf0.02		91.69 179	90.67 192	94.75 158	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 192

tfpn_n400		91.69 179	90.67 192	94.75 158	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 192

tfpnconf		91.69 179	90.67 192	94.75 158	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 192

tfpnview11		91.69 179	90.67 192	94.75 158	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 192

DTE-MVSNet		90.56 228	89.75 229	93.01 239	93.95 269	87.25 236	97.64 72	97.65 105	90.74 131	87.12 258	95.68 168	79.97 212	97.00 291	83.33 253	81.66 303	94.78 271

mvs_tets		92.31 155	91.76 143	93.94 195	93.41 286	88.29 197	97.63 78	97.53 115	92.04 101	88.76 227	96.45 131	74.62 276	98.09 197	93.91 75	91.48 205	95.45 223

v748		90.34 232	89.54 234	92.75 247	93.25 291	85.71 260	97.61 79	97.17 152	88.54 198	87.20 257	93.54 263	81.02 190	98.01 219	85.73 219	81.80 300	94.52 278

ACMMP_Plus		97.20 10	96.86 17	98.23 4	99.09 26	95.16 8	97.60 80	98.19 33	92.82 78	97.93 10	98.74 3	91.60 38	99.86 8	96.26 20	99.52 17	99.67 2

v52		90.70 225	90.00 218	92.82 242	93.24 292	87.03 242	97.60 80	97.14 156	88.21 211	87.69 246	93.94 250	80.91 195	98.07 202	87.39 189	83.87 289	93.36 299

V4		90.71 224	90.00 218	92.82 242	93.21 295	87.03 242	97.59 82	97.16 155	88.21 211	87.69 246	93.92 252	80.93 194	98.06 207	87.39 189	83.90 288	93.39 297

ACMM	89.79 8	92.96 129	92.50 128	94.35 175	96.30 161	88.71 189	97.58 83	97.36 141	91.40 118	90.53 171	96.65 116	79.77 214	98.75 143	91.24 127	91.64 201	95.59 218
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		89.70 246	88.40 250	93.60 215	95.15 210	90.10 133	97.56 84	98.16 37	87.28 235	86.16 269	94.63 213	77.57 258	98.05 210	74.48 310	84.59 279	92.65 305

HPM-MVS++		97.34 8	96.97 12	98.47 1	99.08 27	96.16 1	97.55 85	97.97 78	95.59 4	96.61 35	97.89 52	92.57 19	99.84 14	95.95 32	99.51 19	99.40 35

TranMVSNet+NR-MVSNet		92.50 145	91.63 152	95.14 136	94.76 229	92.07 81	97.53 86	98.11 45	92.90 77	89.56 210	96.12 143	83.16 133	97.60 263	89.30 147	83.20 295	95.75 213

anonymousdsp		92.16 162	91.55 156	93.97 190	92.58 307	89.55 160	97.51 87	97.42 134	89.42 162	88.40 233	94.84 202	80.66 200	97.88 241	91.87 111	91.28 209	94.48 279

test_part3							97.50 88		93.81 45		98.53 11		99.87 5	95.19 47

ESAPD		97.57 4	97.29 7	98.41 2	99.28 17	95.74 3	97.50 88	98.26 25	93.81 45	98.10 6	98.53 11	95.31 1	99.87 5	95.19 47	99.63 4	99.63 5

VNet		95.89 55	95.45 52	97.21 52	98.07 81	92.94 60	97.50 88	98.15 38	93.87 41	97.52 12	97.61 78	85.29 110	99.53 70	95.81 36	95.27 143	99.16 53

GBi-Net		91.35 201	90.27 207	94.59 164	96.51 150	91.18 109	97.50 88	96.93 185	88.82 188	89.35 216	94.51 216	73.87 280	97.29 282	86.12 210	88.82 234	95.31 235

test1		91.35 201	90.27 207	94.59 164	96.51 150	91.18 109	97.50 88	96.93 185	88.82 188	89.35 216	94.51 216	73.87 280	97.29 282	86.12 210	88.82 234	95.31 235

FMVSNet1		89.88 243	88.31 251	94.59 164	95.41 192	91.18 109	97.50 88	96.93 185	86.62 252	87.41 252	94.51 216	65.94 317	97.29 282	83.04 256	87.43 248	95.31 235

XXY-MVS		92.16 162	91.23 169	94.95 147	94.75 230	90.94 117	97.47 94	97.43 133	89.14 174	88.90 224	96.43 132	79.71 215	98.24 183	89.56 143	87.68 245	95.67 217

114514_t		93.95 98	93.06 107	96.63 65	99.07 28	91.61 93	97.46 95	97.96 79	77.99 321	93.00 119	97.57 81	86.14 103	99.33 92	89.22 150	99.15 54	98.94 74

tfpn200view9		92.38 152	91.52 158	94.95 147	97.85 96	89.29 178	97.41 96	94.88 279	92.19 92	93.27 110	94.46 220	78.17 245	99.08 118	81.40 276	94.08 157	96.48 180

thres400		92.42 150	91.52 158	95.12 138	97.85 96	89.29 178	97.41 96	94.88 279	92.19 92	93.27 110	94.46 220	78.17 245	99.08 118	81.40 276	94.08 157	96.98 159

FMVSNet2		91.31 203	90.08 214	94.99 142	96.51 150	92.21 76	97.41 96	96.95 183	88.82 188	88.62 229	94.75 208	73.87 280	97.42 274	85.20 226	88.55 240	95.35 233

DeepC-MVS_fast	93.89 2	96.93 25	96.64 27	97.78 20	98.64 46	94.30 20	97.41 96	98.04 64	94.81 22	96.59 37	98.37 23	91.24 42	99.64 46	95.16 49	99.52 17	99.42 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		93.31 118	92.55 124	95.61 113	95.39 193	93.34 52	97.39 100	98.71 5	93.14 65	90.10 187	94.83 204	87.71 81	98.03 215	91.67 119	83.99 284	95.46 222

NR-MVSNet		92.34 153	91.27 167	95.53 117	94.95 220	93.05 56	97.39 100	98.07 55	92.65 82	84.46 279	95.71 165	85.00 114	97.77 251	89.71 139	83.52 292	95.78 209

DP-MVS		92.76 138	91.51 160	96.52 70	98.77 35	90.99 114	97.38 102	96.08 224	82.38 296	89.29 219	97.87 55	83.77 126	99.69 35	81.37 280	96.69 122	98.89 80

ACMP	89.59 10	92.62 140	92.14 133	94.05 185	96.40 157	88.20 205	97.36 103	97.25 149	91.52 111	88.30 236	96.64 117	78.46 241	98.72 146	91.86 112	91.48 205	95.23 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs6		87.81 275	86.19 277	92.69 249	91.32 314	86.30 254	97.34 104	96.41 210	80.59 312	84.05 286	94.37 227	67.37 312	97.67 257	84.75 230	79.51 310	94.09 289

v8		91.29 204	90.53 200	93.57 219	94.15 252	88.12 212	97.34 104	97.06 167	88.99 178	88.32 235	94.26 243	83.08 140	98.01 219	87.62 185	83.92 287	94.57 277

NCCC		97.30 9	97.03 10	98.11 7	98.77 35	95.06 10	97.34 104	98.04 64	95.96 2	97.09 27	97.88 54	93.18 11	99.71 29	95.84 35	99.17 53	99.56 15

v10		91.04 212	90.23 210	93.49 221	94.12 256	88.16 208	97.32 107	97.08 164	88.26 210	88.29 237	94.22 244	82.17 174	97.97 226	86.45 205	84.12 283	94.33 284

V42		91.58 189	90.87 181	93.73 207	94.05 265	88.50 194	97.32 107	96.97 179	88.80 191	89.71 203	94.33 229	82.54 163	98.05 210	89.01 157	85.07 270	94.64 276

DeepC-MVS	93.07 3	96.06 49	95.66 50	97.29 45	97.96 87	93.17 54	97.30 109	98.06 57	93.92 40	93.38 105	98.66 4	86.83 94	99.73 25	95.60 43	99.22 49	98.96 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNVR-MVS		97.68 2	97.44 5	98.37 3	98.90 32	95.86 2	97.27 110	98.08 50	95.81 3	97.87 11	98.31 33	94.26 4	99.68 37	97.02 4	99.49 23	99.57 13

PVSNet_Blended_VisFu		95.27 64	94.91 64	96.38 81	98.20 75	90.86 120	97.27 110	98.25 27	90.21 146	94.18 93	97.27 91	87.48 87	99.73 25	93.53 82	97.77 94	98.55 95

mvs-test1		93.63 108	93.69 88	93.46 224	96.02 174	84.61 274	97.24 112	96.72 196	93.85 42	92.30 133	95.76 162	83.08 140	98.89 131	91.69 117	96.54 125	96.87 169

MTAPA		97.08 16	96.78 23	97.97 11	99.37 10	94.42 18	97.24 112	98.08 50	95.07 14	96.11 51	98.59 5	90.88 49	99.90 1	96.18 27	99.50 21	99.58 11

plane_prior						89.99 137	97.24 112		94.06 38						92.16 194

PAPM_NR		95.01 70	94.59 71	96.26 90	98.89 33	90.68 125	97.24 112	97.73 94	91.80 106	92.93 124	96.62 124	89.13 64	99.14 106	89.21 151	97.78 93	98.97 70

ACMH	87.59 16	90.53 229	89.42 236	93.87 197	96.21 163	87.92 224	97.24 112	96.94 184	88.45 199	83.91 287	96.27 138	71.92 288	98.62 151	84.43 237	89.43 230	95.05 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		92.23 160	91.31 165	94.99 142	95.56 187	90.96 116	97.22 117	97.86 87	92.96 75	90.96 167	96.62 124	75.06 272	98.20 185	91.90 109	83.65 291	95.80 208

v1neww		91.70 177	91.01 174	93.75 204	94.19 248	88.14 210	97.20 118	96.98 176	89.18 169	89.87 195	94.44 222	83.10 138	98.06 207	89.06 155	85.09 268	95.06 251

v7new		91.70 177	91.01 174	93.75 204	94.19 248	88.14 210	97.20 118	96.98 176	89.18 169	89.87 195	94.44 222	83.10 138	98.06 207	89.06 155	85.09 268	95.06 251

v6		91.69 179	91.00 176	93.75 204	94.14 253	88.12 212	97.20 118	96.98 176	89.19 167	89.90 192	94.42 224	83.04 144	98.07 202	89.07 154	85.10 267	95.07 248

F-COLMAP		93.58 110	92.98 108	95.37 127	98.40 57	88.98 186	97.18 121	97.29 146	87.75 224	90.49 172	97.10 100	85.21 111	99.50 76	86.70 201	96.72 121	97.63 143

UniMVSNet_NR-MVSNet		93.37 116	92.67 119	95.47 123	95.34 196	92.83 61	97.17 122	98.58 10	92.98 74	90.13 183	95.80 157	88.37 75	97.85 242	91.71 115	83.93 285	95.73 215

DU-MVS		92.90 132	92.04 135	95.49 120	94.95 220	92.83 61	97.16 123	98.24 28	93.02 68	90.13 183	95.71 165	83.47 129	97.85 242	91.71 115	83.93 285	95.78 209

MPTG		97.07 17	96.77 24	97.97 11	99.37 10	94.42 18	97.15 124	98.08 50	95.07 14	96.11 51	98.59 5	90.88 49	99.90 1	96.18 27	99.50 21	99.58 11

Effi-MVS+-dtu		93.08 124	93.21 105	92.68 250	96.02 174	83.25 286	97.14 125	96.72 196	93.85 42	91.20 166	93.44 269	83.08 140	98.30 181	91.69 117	95.73 138	96.50 179

MCST-MVS		97.18 11	96.84 18	98.20 5	99.30 16	95.35 5	97.12 126	98.07 55	93.54 53	96.08 53	97.69 68	93.86 7	99.71 29	96.50 17	99.39 34	99.55 17

v7		91.47 195	90.73 189	93.68 212	94.13 254	88.16 208	97.09 127	97.05 168	88.38 206	89.80 198	94.52 215	82.21 172	98.01 219	88.00 172	85.42 261	94.87 260

MVSTER		93.20 121	92.81 112	94.37 174	96.56 147	89.59 158	97.06 128	97.12 159	91.24 122	91.30 155	95.96 148	82.02 176	98.05 210	93.48 85	90.55 219	95.47 221

Fast-Effi-MVS+-dtu		92.29 157	91.99 138	93.21 235	95.27 201	85.52 263	97.03 129	96.63 206	92.09 95	89.11 223	95.14 192	80.33 207	98.08 198	87.54 187	94.74 152	96.03 199

DP-MVS Recon		95.68 57	95.12 62	97.37 41	99.19 24	94.19 24	97.03 129	98.08 50	88.35 208	95.09 80	97.65 72	89.97 59	99.48 77	92.08 106	98.59 75	98.44 111

CANet		96.39 42	96.02 44	97.50 38	97.62 107	93.38 49	97.02 131	97.96 79	95.42 8	94.86 82	97.81 61	87.38 89	99.82 18	96.88 7	99.20 51	99.29 45

FMVSNet3		91.78 172	90.69 191	95.03 141	96.53 149	92.27 75	97.02 131	96.93 185	89.79 157	89.35 216	94.65 212	77.01 260	97.47 270	86.12 210	88.82 234	95.35 233

Baseline_NR-MVSNet		91.20 206	90.62 197	92.95 241	93.83 274	88.03 218	97.01 133	95.12 268	88.42 205	89.70 204	95.13 193	83.47 129	97.44 272	89.66 141	83.24 294	93.37 298

ACMH+	87.92 14	90.20 236	89.18 240	93.25 232	96.48 153	86.45 253	96.99 134	96.68 201	88.83 187	84.79 278	96.22 139	70.16 301	98.53 158	84.42 238	88.04 242	94.77 272

OurMVSNet-221017-0		90.51 230	90.19 213	91.44 283	93.41 286	81.25 298	96.98 135	96.28 214	91.68 109	86.55 266	96.30 136	74.20 279	97.98 223	88.96 158	87.40 250	95.09 245

MP-MVS-pluss		96.70 32	96.27 39	97.98 10	99.23 23	94.71 13	96.96 136	98.06 57	90.67 134	95.55 74	98.78 2	91.07 44	99.86 8	96.58 15	99.55 14	99.38 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1141		91.61 185	90.89 178	93.78 201	94.01 266	88.24 201	96.96 136	96.96 180	89.17 171	89.75 201	94.29 237	82.99 148	98.03 215	88.85 161	85.00 273	95.07 248

Regformer-3		96.85 27	96.80 22	97.01 57	98.34 62	92.02 84	96.96 136	97.76 91	95.01 16	97.08 28	98.42 18	91.71 35	99.54 67	96.80 9	99.13 56	99.48 28

Regformer-4		96.97 22	96.87 16	97.25 48	98.34 62	92.66 66	96.96 136	98.01 69	95.12 13	97.14 23	98.42 18	91.82 34	99.61 47	96.90 6	99.13 56	99.50 24

divwei89l23v2f112		91.61 185	90.89 178	93.78 201	94.01 266	88.22 203	96.96 136	96.96 180	89.17 171	89.75 201	94.28 239	83.02 146	98.03 215	88.86 160	84.98 275	95.08 246

v1		91.61 185	90.89 178	93.78 201	94.01 266	88.21 204	96.96 136	96.96 180	89.17 171	89.78 200	94.29 237	82.97 150	98.05 210	88.85 161	84.99 274	95.08 246

v2v482		91.59 188	90.85 183	93.80 199	93.87 273	88.17 207	96.94 142	96.88 190	89.54 158	89.53 211	94.90 198	81.70 183	98.02 218	89.25 149	85.04 272	95.20 243

LCM-MVSNet-Re		92.50 145	92.52 127	92.44 253	96.82 138	81.89 294	96.92 143	93.71 309	92.41 86	84.30 281	94.60 214	85.08 113	97.03 288	91.51 120	97.36 105	98.40 114

COLMAP_ROB	87.81 15	90.40 231	89.28 238	93.79 200	97.95 88	87.13 241	96.92 143	95.89 236	82.83 293	86.88 265	97.18 95	73.77 283	99.29 94	78.44 300	93.62 172	94.95 254
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EI-MVSNet-Vis-set		96.51 38	96.47 33	96.63 65	98.24 71	91.20 107	96.89 145	97.73 94	94.74 25	96.49 41	98.49 13	90.88 49	99.58 55	96.44 18	98.32 80	99.13 57

MVS_0304		96.05 50	95.45 52	97.85 14	97.75 101	94.50 15	96.87 146	97.95 81	95.46 6	95.60 72	98.01 48	80.96 191	99.83 15	97.23 2	99.25 46	99.23 49

EI-MVSNet-UG-set		96.34 43	96.30 38	96.47 76	98.20 75	90.93 118	96.86 147	97.72 97	94.67 26	96.16 50	98.46 14	90.43 53	99.58 55	96.23 21	97.96 89	98.90 78

v1144		91.37 200	90.60 198	93.68 212	93.89 272	88.23 202	96.84 148	97.03 173	88.37 207	89.69 205	94.39 225	82.04 175	97.98 223	87.80 177	85.37 262	94.84 262

v144192		91.06 211	90.28 206	93.39 226	93.66 279	87.23 238	96.83 149	97.07 165	87.43 230	89.69 205	94.28 239	81.48 184	98.00 222	87.18 196	84.92 276	94.93 258

Regformer-1		97.10 15	96.96 13	97.54 37	98.32 65	93.48 46	96.83 149	97.99 76	95.20 12	97.46 14	98.25 39	92.48 22	99.58 55	96.79 11	99.29 43	99.55 17

Regformer-2		97.16 13	96.99 11	97.67 29	98.32 65	93.84 35	96.83 149	98.10 47	95.24 10	97.49 13	98.25 39	92.57 19	99.61 47	96.80 9	99.29 43	99.56 15

v18		88.71 258	87.52 257	92.27 255	94.16 251	88.11 214	96.82 152	95.96 226	87.03 239	80.76 303	89.81 304	83.15 134	96.22 299	84.69 231	75.31 321	92.49 309

Fast-Effi-MVS+		93.46 113	92.75 115	95.59 114	96.77 139	90.03 134	96.81 153	97.13 158	88.19 213	91.30 155	94.27 241	86.21 100	98.63 149	87.66 183	96.46 128	98.12 124

v17		88.67 260	87.47 260	92.26 257	94.13 254	88.09 216	96.81 153	95.95 227	87.02 240	80.72 304	89.75 306	83.11 137	96.20 300	84.61 234	75.15 323	92.49 309

v16		88.69 259	87.50 258	92.26 257	94.19 248	88.11 214	96.81 153	95.95 227	87.01 241	80.71 305	89.80 305	83.08 140	96.20 300	84.61 234	75.34 320	92.48 311

TSAR-MVS + GP.		96.69 33	96.49 32	97.27 47	98.31 67	93.39 48	96.79 156	96.72 196	94.17 36	97.44 15	97.66 71	92.76 13	99.33 92	96.86 8	97.76 95	99.08 62

TAPA-MVS	90.10 7	92.30 156	91.22 170	95.56 115	98.33 64	89.60 157	96.79 156	97.65 105	81.83 300	91.52 147	97.23 94	87.94 78	98.91 128	71.31 321	98.37 79	98.17 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v148		90.99 213	90.38 203	92.81 245	93.83 274	85.80 258	96.78 158	96.68 201	89.45 161	88.75 228	93.93 251	82.96 152	97.82 246	87.83 176	83.25 293	94.80 268

v11		88.41 269	87.19 271	92.08 267	94.08 262	87.77 228	96.75 159	95.85 237	86.74 251	80.50 309	89.50 313	82.49 165	96.08 307	83.55 250	75.20 322	92.38 318

v1921920		90.85 217	90.03 217	93.29 231	93.55 280	86.96 246	96.74 160	97.04 171	87.36 232	89.52 212	94.34 228	80.23 209	97.97 226	86.27 206	85.21 265	94.94 256

v1192		91.07 210	90.23 210	93.58 218	93.70 277	87.82 227	96.73 161	97.07 165	87.77 223	89.58 208	94.32 230	80.90 198	97.97 226	86.52 203	85.48 259	94.95 254

V9		88.49 266	87.26 264	92.18 261	94.12 256	87.97 222	96.73 161	95.90 231	86.95 245	80.40 311	89.61 308	82.98 149	96.13 302	84.14 241	74.55 327	92.44 313

PVSNet_BlendedMVS		94.06 94	93.92 82	94.47 170	98.27 68	89.46 166	96.73 161	98.36 16	90.17 147	94.36 89	95.24 189	88.02 76	99.58 55	93.44 86	90.72 217	94.36 283

v15		88.53 262	87.31 262	92.20 260	94.09 260	88.05 217	96.72 164	95.90 231	87.01 241	80.53 308	89.60 310	83.02 146	96.13 302	84.29 239	74.64 324	92.41 315

V14		88.52 263	87.30 263	92.17 262	94.12 256	87.99 219	96.72 164	95.91 230	86.98 243	80.50 309	89.63 307	83.03 145	96.12 304	84.23 240	74.60 326	92.40 316

v13		88.45 268	87.22 268	92.16 264	94.08 262	87.95 223	96.71 166	95.90 231	86.86 250	80.27 315	89.55 312	82.92 153	96.12 304	84.02 244	74.63 325	92.40 316

v12		88.46 267	87.23 267	92.17 262	94.10 259	87.99 219	96.71 166	95.90 231	86.91 246	80.34 313	89.58 311	82.92 153	96.11 306	84.09 242	74.50 329	92.42 314

TAMVS		94.01 97	93.46 97	95.64 112	96.16 168	90.45 131	96.71 166	96.89 189	89.27 165	93.46 104	96.92 104	87.29 90	97.94 232	88.70 165	95.74 137	98.53 97

MVS_Test		94.89 77	94.62 70	95.68 111	96.83 137	89.55 160	96.70 169	97.17 152	91.17 123	95.60 72	96.11 145	87.87 80	98.76 142	93.01 94	97.17 110	98.72 88

SixPastTwentyTwo		89.15 252	88.54 249	90.98 287	93.49 284	80.28 308	96.70 169	94.70 283	90.78 130	84.15 284	95.57 172	71.78 290	97.71 255	84.63 233	85.07 270	94.94 256

EPNet_dtu		91.71 174	91.28 166	92.99 240	93.76 276	83.71 281	96.69 171	95.28 259	93.15 64	87.02 262	95.95 149	83.37 131	97.38 278	79.46 295	96.84 115	97.88 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	91.00 6	94.11 92	93.43 99	96.13 94	98.58 50	91.15 112	96.69 171	97.39 136	87.29 234	91.37 150	96.71 110	88.39 74	99.52 73	87.33 192	97.13 111	97.73 140
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testgi		87.97 272	87.21 269	90.24 300	92.86 301	80.76 300	96.67 173	94.97 275	91.74 107	85.52 273	95.83 155	62.66 323	94.47 323	76.25 307	88.36 241	95.48 219

OPM-MVS		93.28 119	92.76 113	94.82 151	94.63 234	90.77 124	96.65 174	97.18 150	93.72 47	91.68 145	97.26 92	79.33 221	98.63 149	92.13 103	92.28 189	95.07 248

HQP-NCC					95.86 177		96.65 174		93.55 50	90.14 179

ACMP_Plane					95.86 177		96.65 174		93.55 50	90.14 179

HQP-MVS		93.19 122	92.74 117	94.54 169	95.86 177	89.33 175	96.65 174	97.39 136	93.55 50	90.14 179	95.87 152	80.95 192	98.50 161	92.13 103	92.10 195	95.78 209

EU-MVSNet		88.72 257	88.90 243	88.20 307	93.15 298	74.21 325	96.63 178	94.22 302	85.18 267	87.32 255	95.97 147	76.16 264	94.98 321	85.27 224	86.17 254	95.41 225

v1240		90.70 225	89.85 224	93.23 233	93.51 283	86.80 247	96.61 179	97.02 174	87.16 237	89.58 208	94.31 231	79.55 218	97.98 223	85.52 221	85.44 260	94.90 259

K. test v3		87.64 276	86.75 274	90.32 299	93.02 300	79.48 314	96.61 179	92.08 329	90.66 136	80.25 316	94.09 246	67.21 313	96.65 294	85.96 215	80.83 307	94.83 264

thres200		92.23 160	91.39 161	94.75 158	97.61 108	89.03 185	96.60 181	95.09 269	92.08 100	93.28 109	94.00 248	78.39 243	99.04 123	81.26 286	94.18 156	96.19 186

WTY-MVS		94.71 81	94.02 81	96.79 61	97.71 103	92.05 82	96.59 182	97.35 142	90.61 140	94.64 85	96.93 103	86.41 98	99.39 88	91.20 128	94.71 153	98.94 74

CNLPA		94.28 86	93.53 94	96.52 70	98.38 60	92.55 69	96.59 182	96.88 190	90.13 148	91.91 140	97.24 93	85.21 111	99.09 116	87.64 184	97.83 91	97.92 131

AdaColmap		94.34 85	93.68 89	96.31 85	98.59 48	91.68 92	96.59 182	97.81 90	89.87 151	92.15 136	97.06 101	83.62 128	99.54 67	89.34 146	98.07 86	97.70 142

IterMVS-LS		92.29 157	91.94 140	93.34 229	96.25 162	86.97 245	96.57 185	97.05 168	90.67 134	89.50 213	94.80 206	86.59 95	97.64 260	89.91 135	86.11 256	95.40 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		90.23 235	88.98 242	93.98 188	97.94 89	86.64 249	96.51 186	95.54 248	85.38 264	85.49 274	96.77 108	70.28 299	99.15 104	80.02 291	92.87 182	96.15 189

EI-MVSNet		93.03 127	92.88 111	93.48 222	95.77 182	86.98 244	96.44 187	97.12 159	90.66 136	91.30 155	97.64 75	86.56 96	98.05 210	89.91 135	90.55 219	95.41 225

CVMVSNet		91.23 205	91.75 144	89.67 304	95.77 182	74.69 324	96.44 187	94.88 279	85.81 261	92.18 135	97.64 75	79.07 223	95.58 316	88.06 171	95.86 136	98.74 86

OMC-MVS		95.09 69	94.70 69	96.25 91	98.46 53	91.28 103	96.43 189	97.57 111	92.04 101	94.77 84	97.96 51	87.01 93	99.09 116	91.31 125	96.77 118	98.36 118

test_prior4						93.66 41	96.42 190

Effi-MVS+		94.93 75	94.45 78	96.36 83	96.61 142	91.47 98	96.41 191	97.41 135	91.02 128	94.50 87	95.92 150	87.53 86	98.78 139	93.89 76	96.81 117	98.84 84

TEST9					98.70 38	94.19 24	96.41 191	98.02 67	88.17 215	96.03 54	97.56 83	92.74 14	99.59 52

train_agg		96.30 44	95.83 47	97.72 25	98.70 38	94.19 24	96.41 191	98.02 67	88.58 195	96.03 54	97.56 83	92.73 15	99.59 52	95.04 53	99.37 39	99.39 36

agg_prior3		96.16 48	95.67 49	97.62 35	98.67 40	93.88 33	96.41 191	98.00 71	87.93 219	95.81 64	97.47 87	92.33 23	99.59 52	95.04 53	99.37 39	99.39 36

WR-MVS		92.34 153	91.53 157	94.77 157	95.13 212	90.83 121	96.40 195	97.98 77	91.88 105	89.29 219	95.54 175	82.50 164	97.80 247	89.79 138	85.27 264	95.69 216

BH-untuned		92.94 130	92.62 121	93.92 196	97.22 121	86.16 256	96.40 195	96.25 217	90.06 149	89.79 199	96.17 142	83.19 132	98.35 176	87.19 195	97.27 108	97.24 156

TDRefinement		86.53 283	84.76 288	91.85 272	82.23 339	84.25 275	96.38 197	95.35 255	84.97 272	84.09 285	94.94 195	65.76 318	98.34 178	84.60 236	74.52 328	92.97 300

test_8					98.67 40	94.06 30	96.37 198	98.01 69	88.58 195	95.98 59	97.55 85	92.73 15	99.58 55

test_prior3		96.46 40	96.20 42	97.23 49	98.67 40	92.99 57	96.35 199	98.00 71	92.80 79	96.03 54	97.59 79	92.01 30	99.41 85	95.01 55	99.38 35	99.29 45

test_prior2							96.35 199		92.80 79	96.03 54	97.59 79	92.01 30		95.01 55	99.38 35

CDPH-MVS		95.97 53	95.38 55	97.77 22	98.93 31	94.44 17	96.35 199	97.88 83	86.98 243	96.65 34	97.89 52	91.99 32	99.47 78	92.26 97	99.46 25	99.39 36

CDS-MVSNet		94.14 91	93.54 93	95.93 100	96.18 166	91.46 99	96.33 202	97.04 171	88.97 181	93.56 100	96.51 128	87.55 85	97.89 240	89.80 137	95.95 133	98.44 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sss		94.51 83	93.80 85	96.64 63	97.07 127	91.97 86	96.32 203	98.06 57	88.94 182	94.50 87	96.78 107	84.60 119	99.27 95	91.90 109	96.02 131	98.68 92

1112_ss		93.37 116	92.42 130	96.21 92	97.05 130	90.99 114	96.31 204	96.72 196	86.87 249	89.83 197	96.69 114	86.51 97	99.14 106	88.12 170	93.67 170	98.50 102

LTVRE_ROB	88.41 13	90.99 213	89.92 221	94.19 179	96.18 166	89.55 160	96.31 204	97.09 162	87.88 221	85.67 272	95.91 151	78.79 238	98.57 155	81.50 274	89.98 225	94.44 281
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_0402		86.46 284	84.79 287	91.45 282	95.02 217	85.55 262	96.29 206	94.89 278	80.90 307	82.21 291	93.97 249	68.21 308	97.29 282	62.98 331	88.68 239	91.51 326

agg_prior1		96.22 47	95.77 48	97.56 36	98.67 40	93.79 37	96.28 207	98.00 71	88.76 192	95.68 68	97.55 85	92.70 17	99.57 63	95.01 55	99.32 41	99.32 43

pmmvs5		89.86 244	88.87 244	92.82 242	92.86 301	86.23 255	96.26 208	95.39 252	84.24 279	87.12 258	94.51 216	74.27 278	97.36 279	87.61 186	87.57 246	94.86 261

xiu_mvs_v1_base_debu		95.01 70	94.76 66	95.75 107	96.58 144	91.71 89	96.25 209	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 192

xiu_mvs_v1_base		95.01 70	94.76 66	95.75 107	96.58 144	91.71 89	96.25 209	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 192

xiu_mvs_v1_base_debi		95.01 70	94.76 66	95.75 107	96.58 144	91.71 89	96.25 209	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 192

MVS_111021_LR		96.24 46	96.19 43	96.39 80	98.23 74	91.35 102	96.24 212	98.79 4	93.99 39	95.80 65	97.65 72	89.92 60	99.24 97	95.87 33	99.20 51	98.58 94

CANet_DTU		94.37 84	93.65 90	96.55 69	96.46 155	92.13 80	96.21 213	96.67 203	94.38 33	93.53 102	97.03 102	79.34 220	99.71 29	90.76 129	98.45 78	97.82 138

MVS_111021_HR		96.68 35	96.58 30	96.99 58	98.46 53	92.31 73	96.20 214	98.90 2	94.30 35	95.86 62	97.74 66	92.33 23	99.38 90	96.04 30	99.42 30	99.28 48

BH-RMVSNet		92.72 139	91.97 139	94.97 145	97.16 124	87.99 219	96.15 215	95.60 245	90.62 138	91.87 141	97.15 98	78.41 242	98.57 155	83.16 254	97.60 97	98.36 118

DI_MVS_plusplus_test		92.01 165	90.77 186	95.73 110	93.34 288	89.78 147	96.14 216	96.18 221	90.58 142	81.80 296	93.50 265	74.95 274	98.90 129	93.51 83	96.94 114	98.51 100

Anonymous20231206		87.09 280	86.14 278	89.93 303	91.22 315	80.35 305	96.11 217	95.35 255	83.57 288	84.16 283	93.02 274	73.54 285	95.61 314	72.16 318	86.14 255	93.84 292

diffmvs		93.43 115	92.75 115	95.48 122	96.47 154	89.61 156	96.09 218	97.14 156	85.97 260	93.09 117	95.35 184	84.87 116	98.55 157	89.51 144	96.26 130	98.28 120

jason		94.84 79	94.39 80	96.18 93	95.52 188	90.93 118	96.09 218	96.52 208	89.28 164	96.01 58	97.32 89	84.70 118	98.77 141	95.15 50	98.91 68	98.85 82
jason: jason.
EG-PatchMatch MVS		87.02 281	85.44 282	91.76 278	92.67 305	85.00 268	96.08 220	96.45 209	83.41 290	79.52 318	93.49 266	57.10 331	97.72 254	79.34 297	90.87 215	92.56 307

1314		92.81 137	92.03 136	95.14 136	95.33 199	89.52 163	96.04 221	97.44 131	87.72 225	86.25 268	95.33 185	83.84 125	98.79 138	89.26 148	97.05 112	97.11 157

1121		94.71 81	93.83 84	97.34 42	98.57 51	93.64 42	96.04 221	97.73 94	81.56 305	95.68 68	97.85 58	90.23 55	99.65 41	87.68 181	99.12 59	98.73 87

MVS		91.71 174	90.44 201	95.51 118	95.20 208	91.59 95	96.04 221	97.45 128	73.44 333	87.36 254	95.60 171	85.42 109	99.10 113	85.97 214	97.46 99	95.83 206

MG-MVS		95.61 58	95.38 55	96.31 85	98.42 56	90.53 128	96.04 221	97.48 119	93.47 54	95.67 71	98.10 42	89.17 63	99.25 96	91.27 126	98.77 70	99.13 57

DeepPCF-MVS	93.97 1	96.61 36	97.09 8	95.15 135	98.09 80	86.63 252	96.00 225	98.15 38	95.43 7	97.95 9	98.56 7	93.40 10	99.36 91	96.77 12	99.48 24	99.45 30

DELS-MVS		96.61 36	96.38 37	97.30 44	97.79 98	93.19 53	95.96 226	98.18 35	95.23 11	95.87 61	97.65 72	91.45 40	99.70 34	95.87 33	99.44 29	99.00 69
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
旧先验2							95.94 227		81.66 301	97.34 17			98.82 136	92.26 97

test_normal		92.01 165	90.75 188	95.80 105	93.24 292	89.97 140	95.93 228	96.24 218	90.62 138	81.63 297	93.45 268	74.98 273	98.89 131	93.61 81	97.04 113	98.55 95

test20.03		86.14 287	85.40 283	88.35 305	90.12 318	80.06 310	95.90 229	95.20 264	88.59 194	81.29 299	93.62 261	71.43 292	92.65 330	71.26 322	81.17 305	92.34 319

MVP-Stereo		90.74 222	90.08 214	92.71 248	93.19 297	88.20 205	95.86 230	96.27 215	86.07 259	84.86 277	94.76 207	77.84 256	97.75 252	83.88 248	98.01 87	92.17 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		90.76 219	89.89 222	93.38 227	95.04 216	83.70 282	95.85 231	94.30 299	88.19 213	90.46 173	92.80 276	73.61 284	98.50 161	88.16 169	90.58 218	97.95 130

lupinMVS		94.99 74	94.56 72	96.29 88	96.34 159	91.21 105	95.83 232	96.27 215	88.93 183	96.22 48	96.88 105	86.20 101	98.85 134	95.27 45	99.05 62	98.82 85

mvs_anonymous		93.82 102	93.74 86	94.06 184	96.44 156	85.41 264	95.81 233	97.05 168	89.85 154	90.09 188	96.36 135	87.44 88	97.75 252	93.97 72	96.69 122	99.02 64

新几何2							95.79 234

无先验							95.79 234	97.87 85	83.87 285				99.65 41	87.68 181		98.89 80

Test4		89.48 248	87.50 258	95.44 125	90.76 317	89.72 148	95.78 236	97.09 162	90.28 145	77.67 322	91.74 296	55.42 335	98.08 198	91.92 108	96.83 116	98.52 98

OpenMVS_ROB	81.14 20	84.42 295	82.28 296	90.83 290	90.06 319	84.05 279	95.73 237	94.04 305	73.89 332	80.17 317	91.53 298	59.15 328	97.64 260	66.92 327	89.05 233	90.80 329

原ACMM2							95.67 238

BH-w/o		92.14 164	91.75 144	93.31 230	96.99 132	85.73 259	95.67 238	95.69 242	88.73 193	89.26 221	94.82 205	82.97 150	98.07 202	85.26 225	96.32 129	96.13 191

TR-MVS		91.48 194	90.59 199	94.16 181	96.40 157	87.33 233	95.67 238	95.34 258	87.68 226	91.46 148	95.52 176	76.77 261	98.35 176	82.85 259	93.61 173	96.79 171

HY-MVS	89.66 9	93.87 100	92.95 109	96.63 65	97.10 126	92.49 71	95.64 241	96.64 204	89.05 176	93.00 119	95.79 160	85.77 107	99.45 81	89.16 153	94.35 154	97.96 129

Anonymous20231211		78.22 309	75.30 310	86.99 313	86.14 332	74.16 326	95.62 242	93.88 308	66.43 336	74.44 326	87.86 325	41.39 343	95.11 320	62.49 332	69.46 336	91.71 323

RPSCF		90.75 221	90.86 182	90.42 298	96.84 135	76.29 322	95.61 243	96.34 212	83.89 283	91.38 149	97.87 55	76.45 262	98.78 139	87.16 197	92.23 190	96.20 185

MS-PatchMatch		90.27 233	89.77 227	91.78 276	94.33 244	84.72 273	95.55 244	96.73 195	86.17 258	86.36 267	95.28 188	71.28 293	97.80 247	84.09 242	98.14 85	92.81 304

PAPR		94.18 88	93.42 101	96.48 75	97.64 106	91.42 101	95.55 244	97.71 100	88.99 178	92.34 132	95.82 156	89.19 62	99.11 108	86.14 209	97.38 104	98.90 78

PatchFormer-LS_test		91.68 184	91.18 172	93.19 236	95.24 205	83.63 284	95.53 246	95.44 251	89.82 155	91.37 150	92.58 281	80.85 199	98.52 159	89.65 142	90.16 224	97.42 154

Test_1112_low_res		92.84 136	91.84 142	95.85 103	97.04 131	89.97 140	95.53 246	96.64 204	85.38 264	89.65 207	95.18 190	85.86 105	99.10 113	87.70 179	93.58 175	98.49 104

FMVSNet5		87.29 279	85.79 280	91.78 276	94.80 228	87.28 234	95.49 248	95.28 259	84.09 281	83.85 288	91.82 293	62.95 322	94.17 324	78.48 299	85.34 263	93.91 291

PVSNet_Blended		94.87 78	94.56 72	95.81 104	98.27 68	89.46 166	95.47 249	98.36 16	88.84 186	94.36 89	96.09 146	88.02 76	99.58 55	93.44 86	98.18 83	98.40 114

xiu_mvs_v2_base		95.32 63	95.29 58	95.40 126	97.22 121	90.50 129	95.44 250	97.44 131	93.70 49	96.46 43	96.18 140	88.59 73	99.53 70	94.79 65	97.81 92	96.17 187

ab-mvs		93.57 111	92.55 124	96.64 63	97.28 120	91.96 87	95.40 251	97.45 128	89.81 156	93.22 112	96.28 137	79.62 217	99.46 79	90.74 130	93.11 181	98.50 102

MIMVSNet1		84.93 294	83.05 294	90.56 296	89.56 323	84.84 272	95.40 251	95.35 255	83.91 282	80.38 312	92.21 291	57.23 330	93.34 328	70.69 324	82.75 299	93.50 294

test222					98.24 71	92.21 76	95.33 253	97.60 108	79.22 316	95.25 77	97.84 60	88.80 68			99.15 54	98.72 88

XVG-ACMP-BASELINE		90.93 215	90.21 212	93.09 237	94.31 245	85.89 257	95.33 253	97.26 147	91.06 127	89.38 215	95.44 182	68.61 305	98.60 152	89.46 145	91.05 212	94.79 270

PS-MVSNAJ		95.37 61	95.33 57	95.49 120	97.35 119	90.66 126	95.31 255	97.48 119	93.85 42	96.51 40	95.70 167	88.65 70	99.65 41	94.80 63	98.27 81	96.17 187

XVG-OURS-SEG-HR		93.86 101	93.55 92	94.81 153	97.06 129	88.53 193	95.28 256	97.45 128	91.68 109	94.08 94	97.68 69	82.41 168	98.90 129	93.84 78	92.47 187	96.98 159

CLD-MVS		92.98 128	92.53 126	94.32 177	96.12 172	89.20 182	95.28 256	97.47 122	92.66 81	89.90 192	95.62 170	80.58 201	98.40 172	92.73 95	92.40 188	95.38 231
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchMatch-RL		92.90 132	92.02 137	95.56 115	98.19 77	90.80 122	95.27 258	97.18 150	87.96 218	91.86 142	95.68 168	80.44 204	98.99 124	84.01 245	97.54 98	96.89 168

testdata1							95.26 259		93.10 67

test0.0.03 1		89.37 251	88.70 245	91.41 284	92.47 308	85.63 261	95.22 260	92.70 326	91.11 125	86.91 264	93.65 260	79.02 226	93.19 329	78.00 301	89.18 232	95.41 225

CHOSEN 1792x2688		94.15 89	93.51 95	96.06 95	98.27 68	89.38 172	95.18 261	98.48 14	85.60 263	93.76 99	97.11 99	83.15 134	99.61 47	91.33 124	98.72 72	99.19 51

IB-MVS	87.33 17	89.91 241	88.28 252	94.79 156	95.26 204	87.70 230	95.12 262	93.95 307	89.35 163	87.03 261	92.49 282	70.74 297	99.19 99	89.18 152	81.37 304	97.49 152
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
testing_2		87.33 278	85.03 285	94.22 178	87.77 329	89.32 177	94.97 263	97.11 161	89.22 166	71.64 331	88.73 317	55.16 336	97.94 232	91.95 107	88.73 238	95.41 225

DSMNet-mixed		86.34 285	86.12 279	87.00 312	89.88 321	70.43 330	94.93 264	90.08 337	77.97 322	85.42 276	92.78 277	74.44 277	93.96 325	74.43 311	95.14 144	96.62 176

XVG-OURS		93.72 106	93.35 102	94.80 154	97.07 127	88.61 191	94.79 265	97.46 124	91.97 104	93.99 95	97.86 57	81.74 182	98.88 133	92.64 96	92.67 186	96.92 167

Patchmatch-test1		91.54 192	90.85 183	93.59 216	95.59 186	84.95 270	94.72 266	95.58 247	90.82 129	92.25 134	93.58 262	75.80 266	97.41 275	83.35 251	95.98 132	98.40 114

pmmvs4		90.93 215	89.85 224	94.17 180	93.34 288	90.79 123	94.60 267	96.02 225	84.62 276	87.45 250	95.15 191	81.88 180	97.45 271	87.70 179	87.87 244	94.27 287

HyFIR lowres test		93.66 107	92.92 110	95.87 102	98.24 71	89.88 144	94.58 268	98.49 12	85.06 270	93.78 98	95.78 161	82.86 155	98.67 147	91.77 113	95.71 139	99.07 63

MDA-MVSNet-bldmvs		85.00 293	82.95 295	91.17 286	93.13 299	83.33 285	94.56 269	95.00 273	84.57 277	65.13 337	92.65 278	70.45 298	95.85 310	73.57 315	77.49 313	94.33 284

PMMVS		92.86 134	92.34 131	94.42 173	94.92 222	86.73 248	94.53 270	96.38 211	84.78 275	94.27 91	95.12 194	83.13 136	98.40 172	91.47 122	96.49 126	98.12 124

pmmvs-eth3d		86.22 286	84.45 289	91.53 281	88.34 326	87.25 236	94.47 271	95.01 272	83.47 289	79.51 319	89.61 308	69.75 302	95.71 313	83.13 255	76.73 316	91.64 324

LF4IMVS		87.94 273	87.25 265	89.98 302	92.38 309	80.05 311	94.38 272	95.25 262	87.59 228	84.34 280	94.74 209	64.31 320	97.66 259	84.83 228	87.45 247	92.23 320

GA-MVS		91.38 199	90.31 204	94.59 164	94.65 233	87.62 231	94.34 273	96.19 220	90.73 132	90.35 176	93.83 253	71.84 289	97.96 230	87.22 194	93.61 173	98.21 121

IterMVS		90.15 238	89.67 231	91.61 280	95.48 190	83.72 280	94.33 274	96.12 223	89.99 150	87.31 256	94.15 245	75.78 267	96.27 298	86.97 199	86.89 252	94.83 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		91.42 197	91.19 171	92.12 265	94.59 235	80.66 301	94.29 275	92.98 321	91.11 125	90.76 169	92.37 284	79.02 226	98.07 202	88.81 163	96.74 119	97.63 143

TESTMET0.1,1		90.06 239	89.42 236	91.97 269	94.41 242	80.62 303	94.29 275	91.97 330	87.28 235	90.44 174	92.47 283	68.79 304	97.67 257	88.50 167	96.60 124	97.61 147

test-mter		90.19 237	89.54 234	92.12 265	94.59 235	80.66 301	94.29 275	92.98 321	87.68 226	90.76 169	92.37 284	67.67 309	98.07 202	88.81 163	96.74 119	97.63 143

CMPMVS	62.92 21	85.62 291	84.92 286	87.74 309	89.14 324	73.12 328	94.17 278	96.80 194	73.98 331	73.65 327	94.93 196	66.36 314	97.61 262	83.95 247	91.28 209	92.48 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		78.73 307	78.71 306	78.79 325	92.80 303	46.50 352	94.14 279	43.71 355	78.61 319	80.83 300	91.66 297	74.94 275	96.36 296	67.24 326	84.45 281	93.50 294

tpmp4_e23		89.58 247	88.59 247	92.54 252	95.16 209	81.53 296	94.11 280	95.09 269	81.66 301	88.60 230	93.44 269	75.11 271	98.33 179	82.45 264	91.72 200	97.75 139

CostFormer		91.18 209	90.70 190	92.62 251	94.84 226	81.76 295	94.09 281	94.43 293	84.15 280	92.72 126	93.77 256	79.43 219	98.20 185	90.70 131	92.18 193	97.90 132

tpm		90.25 234	89.74 230	91.76 278	93.92 270	79.73 312	93.98 282	93.54 313	88.28 209	91.99 139	93.25 272	77.51 259	97.44 272	87.30 193	87.94 243	98.12 124

TinyColmap		86.82 282	85.35 284	91.21 285	94.91 224	82.99 287	93.94 283	94.02 306	83.58 287	81.56 298	94.68 210	62.34 324	98.13 191	75.78 308	87.35 251	92.52 308

USDC		88.94 253	87.83 255	92.27 255	94.66 232	84.96 269	93.86 284	95.90 231	87.34 233	83.40 289	95.56 173	67.43 311	98.19 187	82.64 263	89.67 229	93.66 293

tpm2		89.96 240	89.21 239	92.23 259	94.91 224	81.25 298	93.78 285	94.42 294	80.62 311	91.56 146	93.44 269	76.44 263	97.94 232	85.60 220	92.08 197	97.49 152

new-patchmatchnet		83.18 298	81.87 299	87.11 311	86.88 331	75.99 323	93.70 286	95.18 265	85.02 271	77.30 323	88.40 320	65.99 316	93.88 326	74.19 314	70.18 334	91.47 328

MSDG		91.42 197	90.24 209	94.96 146	97.15 125	88.91 187	93.69 287	96.32 213	85.72 262	86.93 263	96.47 130	80.24 208	98.98 125	80.57 288	95.05 146	96.98 159

EPMVS		90.70 225	89.81 226	93.37 228	94.73 231	84.21 276	93.67 288	88.02 340	89.50 160	92.38 130	93.49 266	77.82 257	97.78 249	86.03 213	92.68 185	98.11 127

cascas		91.20 206	90.08 214	94.58 168	94.97 218	89.16 184	93.65 289	97.59 110	79.90 313	89.40 214	92.92 275	75.36 270	98.36 175	92.14 102	94.75 151	96.23 184

UnsupCasMVSNet_eth		85.99 288	84.45 289	90.62 295	89.97 320	82.40 291	93.62 290	97.37 139	89.86 152	78.59 321	92.37 284	65.25 319	95.35 319	82.27 267	70.75 333	94.10 288

PM-MVS		83.48 297	81.86 300	88.31 306	87.83 328	77.59 320	93.43 291	91.75 331	86.91 246	80.63 306	89.91 302	44.42 342	95.84 311	85.17 227	76.73 316	91.50 327

tpmrst		91.44 196	91.32 164	91.79 275	95.15 210	79.20 316	93.42 292	95.37 254	88.55 197	93.49 103	93.67 259	82.49 165	98.27 182	90.41 132	89.34 231	97.90 132

PAPM		91.52 193	90.30 205	95.20 129	95.30 200	89.83 145	93.38 293	96.85 192	86.26 256	88.59 231	95.80 157	84.88 115	98.15 190	75.67 309	95.93 134	97.63 143

testmvs		13.36 329	16.33 330	4.48 342	5.04 355	2.26 357	93.18 294	3.28 357	2.70 351	8.24 352	21.66 350	2.29 360	2.19 354	7.58 351	2.96 351	9.00 351

testus		82.63 301	82.15 297	84.07 317	87.31 330	67.67 336	93.18 294	94.29 300	82.47 295	82.14 293	90.69 299	53.01 337	91.94 333	66.30 328	89.96 226	92.62 306

YYNet1		85.87 289	84.23 291	90.78 294	92.38 309	82.46 290	93.17 296	95.14 267	82.12 298	67.69 332	92.36 287	78.16 247	95.50 318	77.31 304	79.73 309	94.39 282

MDA-MVSNet_test_wron		85.87 289	84.23 291	90.80 293	92.38 309	82.57 288	93.17 296	95.15 266	82.15 297	67.65 333	92.33 290	78.20 244	95.51 317	77.33 303	79.74 308	94.31 286

PatchmatchNet		91.91 169	91.35 162	93.59 216	95.38 194	84.11 278	93.15 298	95.39 252	89.54 158	92.10 137	93.68 258	82.82 157	98.13 191	84.81 229	95.32 142	98.52 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		89.83 245	89.15 241	91.89 271	94.92 222	80.30 307	93.11 299	95.46 250	86.28 255	88.08 240	92.65 278	80.44 204	98.52 159	81.47 275	89.92 227	96.84 170

MDTV_nov1_ep13_2view						70.35 332	93.10 300		83.88 284	93.55 101		82.47 167		86.25 207		98.38 117

MDTV_nov1_ep13			90.76 187		95.22 206	80.33 306	93.03 301	95.28 259	88.14 216	92.84 125	93.83 253	81.34 186	98.08 198	82.86 258	94.34 155

PVSNet	86.66 18	92.24 159	91.74 146	93.73 207	97.77 100	83.69 283	92.88 302	96.72 196	87.91 220	93.00 119	94.86 201	78.51 240	99.05 122	86.53 202	97.45 103	98.47 107

dp		88.90 255	88.26 253	90.81 291	94.58 237	76.62 321	92.85 303	94.93 277	85.12 269	90.07 190	93.07 273	75.81 265	98.12 193	80.53 289	87.42 249	97.71 141

test_post1							92.81 304				16.58 353	80.53 202	97.68 256	86.20 208

pmmvs3		79.97 305	77.50 309	87.39 310	82.80 337	79.38 315	92.70 305	90.75 335	70.69 335	78.66 320	87.47 329	51.34 339	93.40 327	73.39 316	69.65 335	89.38 332

tpm cat1		88.36 270	87.21 269	91.81 274	95.13 212	80.55 304	92.58 306	95.70 241	74.97 329	87.45 250	91.96 292	78.01 255	98.17 189	80.39 290	88.74 237	96.72 173

PCF-MVS	89.48 11	91.56 190	89.95 220	96.36 83	96.60 143	92.52 70	92.51 307	97.26 147	79.41 314	88.90 224	96.56 126	84.04 124	99.55 65	77.01 306	97.30 107	97.01 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test123		13.04 330	15.66 331	5.18 341	4.51 356	3.45 356	92.50 308	1.81 358	2.50 352	7.58 353	20.15 351	3.67 359	2.18 355	7.13 352	1.07 353	9.90 350

test1235678		79.82 306	78.53 307	83.69 318	82.55 338	67.55 337	92.50 308	94.13 303	79.28 315	72.10 330	86.45 331	57.27 329	90.68 337	61.60 334	80.90 306	92.82 302

GG-mvs-BLEND				93.62 214	93.69 278	89.20 182	92.39 310	83.33 347		87.98 243	89.84 303	71.00 295	96.87 292	82.08 268	95.40 141	94.80 268

1111		78.29 308	77.55 308	80.50 321	83.89 334	59.98 344	91.89 311	93.71 309	75.06 327	73.60 328	87.67 326	55.66 333	92.60 331	58.54 338	77.92 312	88.93 333

.test1245		65.38 317	69.22 315	53.86 337	83.89 334	59.98 344	91.89 311	93.71 309	75.06 327	73.60 328	87.67 326	55.66 333	92.60 331	58.54 338	2.96 351	9.00 351

new_pmnet		82.89 299	81.12 304	88.18 308	89.63 322	80.18 309	91.77 313	92.57 327	76.79 325	75.56 325	88.23 322	61.22 326	94.48 322	71.43 320	82.92 297	89.87 331

MIMVSNet		88.50 265	86.76 273	93.72 209	94.84 226	87.77 228	91.39 314	94.05 304	86.41 254	87.99 242	92.59 280	63.27 321	95.82 312	77.44 302	92.84 184	97.57 150

FPMVS		71.27 313	69.85 313	75.50 328	74.64 342	59.03 346	91.30 315	91.50 332	58.80 340	57.92 340	88.28 321	29.98 349	85.53 344	53.43 342	82.84 298	81.95 338

testmv		72.22 312	70.02 312	78.82 324	73.06 347	61.75 342	91.24 316	92.31 328	74.45 330	61.06 339	80.51 336	34.21 345	88.63 341	55.31 341	68.07 338	86.06 335

test2356		82.77 300	82.14 298	84.65 316	85.77 333	70.36 331	91.22 317	93.69 312	81.58 303	81.82 295	89.00 316	60.63 327	90.77 336	64.74 329	90.80 216	92.82 302

gg-mvs-nofinetune		87.82 274	85.61 281	94.44 171	94.46 239	89.27 181	91.21 318	84.61 346	80.88 308	89.89 194	74.98 338	71.50 291	97.53 266	85.75 218	97.21 109	96.51 178

ADS-MVSNet2		89.45 249	88.59 247	92.03 268	95.86 177	82.26 292	90.93 319	94.32 298	83.23 291	91.28 158	91.81 294	79.01 228	95.99 308	79.52 293	91.39 207	97.84 135

ADS-MVSNet		89.89 242	88.68 246	93.53 220	95.86 177	84.89 271	90.93 319	95.07 271	83.23 291	91.28 158	91.81 294	79.01 228	97.85 242	79.52 293	91.39 207	97.84 135

UnsupCasMVSNet_bld		82.13 303	79.46 305	90.14 301	88.00 327	82.47 289	90.89 321	96.62 207	78.94 317	75.61 324	84.40 333	56.63 332	96.31 297	77.30 305	66.77 339	91.63 325

PVSNet_0	82.17 19	85.46 292	83.64 293	90.92 289	95.27 201	79.49 313	90.55 322	95.60 245	83.76 286	83.00 290	89.95 301	71.09 294	97.97 226	82.75 261	60.79 340	95.31 235

CHOSEN 280x420		93.12 123	92.72 118	94.34 176	96.71 141	87.27 235	90.29 323	97.72 97	86.61 253	91.34 152	95.29 186	84.29 123	98.41 171	93.25 90	98.94 67	97.35 155

CR-MVSNet		90.82 218	89.77 227	93.95 192	94.45 240	87.19 239	90.23 324	95.68 243	86.89 248	92.40 128	92.36 287	80.91 195	97.05 286	81.09 287	93.95 166	97.60 148

RPMNet		88.52 263	86.72 275	93.95 192	94.45 240	87.19 239	90.23 324	94.99 274	77.87 323	92.40 128	87.55 328	80.17 210	97.05 286	68.84 325	93.95 166	97.60 148

LCM-MVSNet		72.55 311	69.39 314	82.03 319	70.81 349	65.42 340	90.12 326	94.36 297	55.02 341	65.88 336	81.72 334	24.16 353	89.96 338	74.32 313	68.10 337	90.71 330

Patchmtry		88.64 261	87.25 265	92.78 246	94.09 260	86.64 249	89.82 327	95.68 243	80.81 310	87.63 249	92.36 287	80.91 195	97.03 288	78.86 298	85.12 266	94.67 274

PatchT		88.87 256	87.42 261	93.22 234	94.08 262	85.10 267	89.51 328	94.64 287	81.92 299	92.36 131	88.15 323	80.05 211	97.01 290	72.43 317	93.65 171	97.54 151

JIA-IIPM		88.26 271	87.04 272	91.91 270	93.52 282	81.42 297	89.38 329	94.38 295	80.84 309	90.93 168	80.74 335	79.22 222	97.92 236	82.76 260	91.62 202	96.38 183

Patchmatch-test		89.42 250	87.99 254	93.70 210	95.27 201	85.11 266	88.98 330	94.37 296	81.11 306	87.10 260	93.69 257	82.28 170	97.50 268	74.37 312	94.76 150	98.48 106

MVS-HIRNet		82.47 302	81.21 303	86.26 315	95.38 194	69.21 335	88.96 331	89.49 339	66.28 337	80.79 302	74.08 340	68.48 306	97.39 277	71.93 319	95.47 140	92.18 321

test12356		74.97 310	74.13 311	77.49 326	78.81 340	56.23 348	88.53 332	92.75 325	75.14 326	67.50 334	85.07 332	44.88 341	89.96 338	58.71 337	75.75 318	86.26 334

Patchmatch-RL test		87.38 277	86.24 276	90.81 291	88.74 325	78.40 319	88.12 333	93.17 315	87.11 238	82.17 292	89.29 314	81.95 178	95.60 315	88.64 166	77.02 314	98.41 113

LP		84.13 296	81.85 301	90.97 288	93.20 296	82.12 293	87.68 334	94.27 301	76.80 324	81.93 294	88.52 318	72.97 287	95.95 309	59.53 336	81.73 301	94.84 262

no-one		68.12 315	63.78 318	81.13 320	74.01 344	70.22 333	87.61 335	90.71 336	72.63 334	53.13 342	71.89 341	30.29 347	91.45 334	61.53 335	32.21 345	81.72 339

PMMVS2		70.19 314	66.92 316	80.01 322	76.35 341	65.67 339	86.22 336	87.58 342	64.83 339	62.38 338	80.29 337	26.78 351	88.49 342	63.79 330	54.07 341	85.88 336

ambc				86.56 314	83.60 336	70.00 334	85.69 337	94.97 275		80.60 307	88.45 319	37.42 344	96.84 293	82.69 262	75.44 319	92.86 301

ANet_high		63.94 318	59.58 319	77.02 327	61.24 352	66.06 338	85.66 338	87.93 341	78.53 320	42.94 344	71.04 342	25.42 352	80.71 346	52.60 343	30.83 347	84.28 337

EMVS		52.08 324	51.31 324	54.39 336	72.62 348	45.39 353	83.84 339	75.51 352	41.13 347	40.77 347	59.65 347	30.08 348	73.60 350	28.31 349	29.90 348	44.18 348

E-PMN		53.28 322	52.56 323	55.43 335	74.43 343	47.13 351	83.63 340	76.30 351	42.23 346	42.59 345	62.22 346	28.57 350	74.40 349	31.53 348	31.51 346	44.78 347

PNet_i23d		59.01 319	55.87 320	68.44 332	73.98 345	51.37 349	81.36 341	82.41 348	52.37 343	42.49 346	70.39 343	11.39 354	79.99 348	49.77 344	38.71 343	73.97 343

wuykxyi23d		56.92 321	51.11 325	74.38 331	62.30 351	61.47 343	80.09 342	84.87 345	49.62 344	30.80 350	57.20 348	7.03 356	82.94 345	55.69 340	32.36 344	78.72 341

PMVS	53.92 22	58.58 320	55.40 321	68.12 333	51.00 353	48.64 350	78.86 343	87.10 344	46.77 345	35.84 349	74.28 339	8.76 355	86.34 343	42.07 346	73.91 331	69.38 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 325	53.82 322	46.29 338	33.73 354	45.30 354	78.32 344	67.24 354	18.02 349	50.93 343	87.05 330	52.99 338	53.11 352	70.76 323	25.29 349	40.46 349

testpf		80.97 304	81.40 302	79.65 323	91.53 313	72.43 329	73.47 345	89.55 338	78.63 318	80.81 301	89.06 315	61.36 325	91.36 335	83.34 252	84.89 277	75.15 342

MVE	50.73 23	53.25 323	48.81 326	66.58 334	65.34 350	57.50 347	72.49 346	70.94 353	40.15 348	39.28 348	63.51 345	6.89 358	73.48 351	38.29 347	42.38 342	68.76 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		67.86 316	65.41 317	75.18 329	92.66 306	73.45 327	66.50 347	94.52 292	53.33 342	57.80 341	66.07 344	30.81 346	89.20 340	48.15 345	78.88 311	62.90 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		25.11 327	24.57 329	26.74 340	73.98 345	39.89 355	57.88 348	9.80 356	12.27 350	10.39 351	6.97 354	7.03 356	36.44 353	25.43 350	17.39 350	3.89 353

cdsmvs_eth3d_5k		23.24 328	30.99 328	0.00 343	0.00 357	0.00 358	0.00 349	97.63 107	0.00 353	0.00 354	96.88 105	84.38 122	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		7.39 332	9.85 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	88.65 70	0.00 356	0.00 353	0.00 354	0.00 354

pcd1.5k->3k		38.37 326	40.51 327	31.96 339	94.29 246	0.00 358	0.00 349	97.69 101	0.00 353	0.00 354	0.00 355	81.45 185	0.00 356	0.00 353	91.11 211	95.89 201

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

ab-mvs-re		8.06 331	10.74 332	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	96.69 114	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

GSMVS																98.45 109

test_part2					99.28 17	95.74 3				98.10 6

test_part1								98.26 25				95.31 1			99.63 4	99.63 5

sam_mvs1												82.76 158				98.45 109

sam_mvs												81.94 179

semantic-postprocess				91.82 273	95.52 188	84.20 277		96.15 222	90.61 140	87.39 253	94.27 241	75.63 268	96.44 295	87.34 191	86.88 253	94.82 266

MTGPA								98.08 50

test_post											17.58 352	81.76 181	98.08 198

patchmatchnet-post											90.45 300	82.65 162	98.10 195

MTMP								82.03 349

gm-plane-assit					93.22 294	78.89 318			84.82 274		93.52 264		98.64 148	87.72 178

test9_res														94.81 62	99.38 35	99.45 30

agg_prior2														93.94 74	99.38 35	99.50 24

agg_prior					98.67 40	93.79 37		98.00 71		95.68 68			99.57 63

TestCases				93.98 188	97.94 89	86.64 249		95.54 248	85.38 264	85.49 274	96.77 108	70.28 299	99.15 104	80.02 291	92.87 182	96.15 189

test_prior				97.23 49	98.67 40	92.99 57		98.00 71					99.41 85			99.29 45

新几何1				97.32 43	98.60 47	93.59 43		97.75 92	81.58 303	95.75 67	97.85 58	90.04 58	99.67 39	86.50 204	99.13 56	98.69 91

旧先验1					98.38 60	93.38 49		97.75 92			98.09 43	92.30 27			99.01 64	99.16 53

原ACMM1				96.38 81	98.59 48	91.09 113		97.89 82	87.41 231	95.22 78	97.68 69	90.25 54	99.54 67	87.95 174	99.12 59	98.49 104

testdata2													99.67 39	85.96 215

segment_acmp												92.89 12

testdata				95.46 124	98.18 78	88.90 188		97.66 103	82.73 294	97.03 29	98.07 44	90.06 57	98.85 134	89.67 140	98.98 65	98.64 93

test12				97.65 30	98.46 53	94.26 21		97.66 103		95.52 76		90.89 48	99.46 79		99.25 46	99.22 50

plane_prior7					96.21 163	89.98 139

plane_prior6					96.10 173	90.00 135						81.32 187

plane_prior5								97.51 117					98.60 152	93.02 92	92.23 190	95.86 202

plane_prior4											96.64 117

plane_prior3						90.00 135			94.46 30	91.34 152

plane_prior1					96.14 171

n2								0.00 359

nn								0.00 359

door-mid								91.06 334

lessismore_v0				90.45 297	91.96 312	79.09 317		87.19 343		80.32 314	94.39 225	66.31 315	97.55 265	84.00 246	76.84 315	94.70 273

LGP-MVS_train				94.10 182	96.16 168	88.26 199		97.46 124	91.29 119	90.12 185	97.16 96	79.05 224	98.73 144	92.25 99	91.89 198	95.31 235

test11								97.88 83

door								91.13 333

HQP5-MVS						89.33 175

BP-MVS														92.13 103

HQP4-MVS										90.14 179			98.50 161			95.78 209

HQP3-MVS								97.39 136							92.10 195

HQP2-MVS												80.95 192

NP-MVS					95.99 176	89.81 146					95.87 152

ACMMP++_ref															90.30 223

ACMMP++															91.02 213

Test By Simon												88.73 69

ITE_SJBPF				92.43 254	95.34 196	85.37 265		95.92 229	91.47 113	87.75 245	96.39 134	71.00 295	97.96 230	82.36 266	89.86 228	93.97 290

DeepMVS_CX				74.68 330	90.84 316	64.34 341		81.61 350	65.34 338	67.47 335	88.01 324	48.60 340	80.13 347	62.33 333	73.68 332	79.58 340

High-resolution multi-view benchmark