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 32	96.45 34	97.40 39	99.36 12	93.11 53	98.87 1	98.06 56	91.17 121	96.40 43	97.99 48	90.99 44	99.58 53	95.61 41	99.61 6	99.49 24

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 23	94.82 10	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 6	99.68 35	97.93 1	99.69 1	99.75 1

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 42	98.80 3	98.28 23	92.99 67	96.45 42	98.30 33	91.90 31	99.85 9	95.61 41	99.68 2	99.54 17

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 61	98.74 4	98.06 56	90.57 141	96.77 28	98.35 22	90.21 54	99.53 68	94.80 61	99.63 4	99.38 37

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 109	89.56 150	98.67 5	97.00 174	90.69 131	94.24 90	97.62 75	89.79 59	98.81 135	93.39 87	96.49 124	98.92 74

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 138	93.36 49	98.65 6	98.36 16	94.12 37	89.25 213	98.06 43	82.20 171	99.77 20	93.41 86	99.32 39	99.18 50

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	98.29 34	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

X-MVStestdata		91.71 170	89.67 223	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	32.69 340	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 38	98.53 9	97.85 87	95.55 5	98.56 3	97.81 59	93.90 4	99.65 39	96.62 13	99.21 48	99.48 26

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 25	98.52 10	98.32 19	93.21 58	97.18 18	98.29 34	92.08 26	99.83 13	95.63 39	99.59 7	99.54 17

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 35	98.52 10	98.24 27	93.19 61	97.14 21	98.34 25	91.59 37	99.87 5	95.46 44	99.59 7	99.64 4

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 31	98.52 10	98.31 21	93.21 58	97.15 20	98.33 28	91.35 39	99.86 6	95.63 39	99.59 7	99.62 5

mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 45	98.50 13	98.09 48	93.27 57	95.95 58	98.33 28	91.04 43	99.88 3	95.20 46	99.57 11	99.60 8

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 124	95.34 4	98.48 14	97.87 84	94.65 28	88.53 223	98.02 45	83.69 125	99.71 27	93.18 89	98.96 64	99.44 30

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 100	90.56 125	98.44 15	96.22 218	93.21 58	93.99 93	97.74 64	85.55 106	98.45 162	89.98 132	97.86 88	99.14 54

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 57	94.25 20	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 1	99.71 27	96.57 16	99.62 5	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 34	98.41 17	98.06 56	93.37 53	95.54 73	98.34 25	90.59 50	99.88 3	94.83 59	99.54 13	99.49 24

QAPM		93.45 113	92.27 131	96.98 58	96.77 130	92.62 65	98.39 18	98.12 41	84.50 269	88.27 229	97.77 62	82.39 167	99.81 17	85.40 221	98.81 67	98.51 98

nrg030		94.05 94	93.31 102	96.27 88	95.22 197	94.59 12	98.34 19	97.46 123	92.93 74	91.21 156	96.64 115	87.23 89	98.22 175	94.99 56	85.80 249	95.98 190

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 17	91.48 95	98.33 20	98.11 44	87.79 213	95.17 77	98.03 44	87.09 90	99.61 45	93.51 81	99.42 28	99.02 62

CSCG		96.05 49	95.91 45	96.46 77	99.24 19	90.47 128	98.30 21	98.57 11	89.01 175	93.97 95	97.57 79	92.62 16	99.76 21	94.66 64	99.27 43	99.15 53

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 106	95.15 7	98.28 22	97.60 107	94.52 29	96.27 45	96.12 141	87.65 81	99.18 99	96.20 26	94.82 147	98.91 75

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 187	92.73 62	98.27 23	98.12 41	84.86 264	85.78 262	97.75 63	78.89 228	99.74 22	87.50 186	98.65 71	96.73 168

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 114	91.58 94	98.26 24	98.12 41	94.38 33	94.90 79	98.15 39	82.28 168	98.92 125	91.45 121	98.58 74	99.01 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP		96.27 44	95.93 44	97.28 45	99.24 19	92.62 65	98.25 25	98.81 3	92.99 67	94.56 84	98.39 20	88.96 63	99.85 9	94.57 65	97.63 94	99.36 39
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 60	95.16 60	95.99 98	96.34 150	91.21 103	98.22 26	97.57 110	91.42 114	96.22 46	97.32 87	86.20 99	97.92 227	94.07 68	99.05 60	98.85 80

test_djsdf		93.07 124	92.76 112	94.00 179	93.49 275	88.70 181	98.22 26	97.57 110	91.42 114	90.08 180	95.55 172	82.85 154	97.92 227	94.07 68	91.58 194	95.40 218

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 55	94.07 27	98.21 28	98.45 15	89.86 150	97.11 24	98.01 46	92.52 19	99.69 33	96.03 31	99.53 14	99.36 39

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 25	98.15 29	98.32 19	92.57 81	97.18 18	98.29 34	92.08 26	99.83 13	95.12 49	99.59 7	99.54 17

FC-MVSNet-test		93.94 98	93.57 90	95.04 139	95.48 181	91.45 98	98.12 30	98.71 5	93.37 53	90.23 169	96.70 110	87.66 80	97.85 233	91.49 119	90.39 213	95.83 196

FIs		94.09 92	93.70 86	95.27 127	95.70 175	92.03 81	98.10 31	98.68 7	93.36 55	90.39 166	96.70 110	87.63 82	97.94 223	92.25 97	90.50 212	95.84 195

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 146	97.61 104	87.92 215	98.10 31	95.80 239	92.22 86	93.02 116	97.45 86	84.53 119	97.91 230	88.24 166	97.97 86	99.02 62

VDDNet		93.05 125	92.07 133	96.02 96	96.84 126	90.39 130	98.08 33	95.85 236	86.22 248	95.79 64	98.46 12	67.59 301	99.19 97	94.92 57	94.85 145	98.47 105

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 18	94.24 21	98.07 34	97.85 87	93.72 45	98.57 2	98.35 22	93.69 7	99.40 85	97.06 3	99.46 23	99.44 30

WR-MVS_H		92.00 166	91.35 161	93.95 184	95.09 205	89.47 155	98.04 35	98.68 7	91.46 112	88.34 225	94.68 207	85.86 103	97.56 255	85.77 215	84.24 273	94.82 255

view600		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 287	92.09 93	93.17 111	95.52 174	78.14 239	99.11 106	81.61 265	94.04 160	96.98 155

view800		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 287	92.09 93	93.17 111	95.52 174	78.14 239	99.11 106	81.61 265	94.04 160	96.98 155

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 287	92.09 93	93.17 111	95.52 174	78.14 239	99.11 106	81.61 265	94.04 160	96.98 155

tfpn		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 287	92.09 93	93.17 111	95.52 174	78.14 239	99.11 106	81.61 265	94.04 160	96.98 155

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 28	92.31 71	97.98 40	98.06 56	93.11 64	97.44 13	98.55 9	90.93 45	99.55 63	96.06 29	99.25 44	99.51 21

LFMVS		93.60 108	92.63 119	96.52 69	98.13 77	91.27 102	97.94 41	93.39 313	90.57 141	96.29 44	98.31 31	69.00 294	99.16 101	94.18 67	95.87 133	99.12 57

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 49	94.46 14	97.92 42	98.14 39	94.82 21	99.01 1	98.55 9	94.18 3	97.41 266	96.94 5	99.64 3	99.32 41

abl_6		96.40 40	96.21 40	96.98 58	98.89 31	92.20 76	97.89 43	98.03 65	93.34 56	97.22 17	98.42 16	87.93 77	99.72 26	95.10 50	99.07 59	99.02 62

UGNet		94.04 95	93.28 103	96.31 84	96.85 125	91.19 106	97.88 44	97.68 101	94.40 31	93.00 117	96.18 138	73.39 277	99.61 45	91.72 112	98.46 75	98.13 119
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 55	95.23 58	97.78 19	97.56 108	95.19 5	97.86 45	97.17 151	94.39 32	96.47 40	96.40 131	85.89 102	99.20 96	96.21 25	95.11 143	98.95 71

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 175	92.39 70	97.86 45	98.66 9	92.30 85	92.09 136	95.37 181	80.49 201	98.40 164	93.95 71	85.86 248	95.75 203

EPNet		95.20 66	94.56 71	97.14 53	92.80 294	92.68 63	97.85 47	94.87 281	96.64 1	92.46 125	97.80 61	86.23 97	99.65 39	93.72 78	98.62 72	99.10 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PS-CasMVS		91.55 183	90.84 182	93.69 203	94.96 210	88.28 189	97.84 48	98.24 27	91.46 112	88.04 232	95.80 155	79.67 214	97.48 260	87.02 196	84.54 271	95.31 224

CP-MVSNet		91.89 168	91.24 167	93.82 190	95.05 206	88.57 183	97.82 49	98.19 32	91.70 106	88.21 230	95.76 160	81.96 175	97.52 258	87.86 173	84.65 269	95.37 221

API-MVS		94.84 78	94.49 75	95.90 100	97.90 92	92.00 83	97.80 50	97.48 118	89.19 165	94.81 81	96.71 108	88.84 65	99.17 100	88.91 157	98.76 69	96.53 172

pm-mvs1		90.72 215	89.65 225	93.96 183	94.29 237	89.63 146	97.79 51	96.82 192	89.07 173	86.12 261	95.48 179	78.61 230	97.78 240	86.97 197	81.67 293	94.46 269

PEN-MVS		91.20 198	90.44 193	93.48 214	94.49 229	87.91 217	97.76 52	98.18 34	91.29 117	87.78 235	95.74 162	80.35 204	97.33 271	85.46 220	82.96 287	95.19 233

PS-MVSNAJss		93.74 104	93.51 94	94.44 163	93.91 262	89.28 171	97.75 53	97.56 113	92.50 82	89.94 182	96.54 125	88.65 68	98.18 179	93.83 77	90.90 205	95.86 192

HQP_MVS		93.78 103	93.43 98	94.82 149	96.21 154	89.99 135	97.74 54	97.51 116	94.85 17	91.34 148	96.64 115	81.32 185	98.60 150	93.02 90	92.23 181	95.86 192

plane_prior2							97.74 54		94.85 17

jajsoiax		92.42 149	91.89 140	94.03 178	93.33 281	88.50 185	97.73 56	97.53 114	92.00 101	88.85 217	96.50 127	75.62 260	98.11 185	93.88 75	91.56 195	95.48 209

TransMVSNet (Re)		88.94 245	87.56 248	93.08 230	94.35 234	88.45 187	97.73 56	95.23 262	87.47 220	84.26 273	95.29 184	79.86 211	97.33 271	79.44 287	74.44 321	93.45 285

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 93	89.96 140	97.72 58	95.85 236	92.43 83	95.86 60	98.44 14	68.42 298	99.39 86	96.31 19	94.85 145	98.71 88

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 27	94.93 9	97.72 58	98.10 46	91.50 110	98.01 6	98.32 30	92.33 21	99.58 53	94.85 58	99.51 17	99.53 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
conf200view11		92.45 147	91.58 153	95.05 138	97.92 89	89.37 164	97.71 60	94.66 283	92.20 88	93.31 105	94.90 196	78.06 243	99.08 116	81.40 272	94.08 155	96.70 170

thres100view900		92.43 148	91.58 153	94.98 143	97.92 89	89.37 164	97.71 60	94.66 283	92.20 88	93.31 105	94.90 196	78.06 243	99.08 116	81.40 272	94.08 155	96.48 175

v7n		90.76 211	89.86 215	93.45 217	93.54 272	87.60 223	97.70 62	97.37 138	88.85 181	87.65 239	94.08 239	81.08 187	98.10 186	84.68 230	83.79 281	94.66 264

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 35	91.86 86	97.67 63	98.49 12	94.66 27	97.24 16	98.41 19	92.31 24	98.94 124	96.61 14	99.46 23	98.96 69

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 80	92.19 77	97.67 63	97.47 121	88.13 208	93.00 117	95.84 152	84.86 115	99.51 72	87.99 171	98.17 82	97.83 133
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		93.57 110	92.61 121	96.47 75	97.59 106	91.61 91	97.67 63	97.72 96	85.17 259	90.29 168	98.34 25	84.60 117	99.73 23	83.85 247	98.27 79	98.06 124

UA-Net		95.95 53	95.53 50	97.20 52	97.67 100	92.98 57	97.65 66	98.13 40	94.81 22	96.61 33	98.35 22	88.87 64	99.51 72	90.36 131	97.35 104	99.11 58

thres600view7		92.49 146	91.60 152	95.18 129	97.91 91	89.47 155	97.65 66	94.66 283	92.18 92	93.33 104	94.91 195	78.06 243	99.10 111	81.61 265	94.06 159	96.98 155

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 43	97.65 66	98.98 1	92.22 86	97.14 21	98.44 14	91.17 41	99.85 9	94.35 66	99.46 23	99.57 11

LPG-MVS_test		92.94 129	92.56 122	94.10 174	96.16 159	88.26 190	97.65 66	97.46 123	91.29 117	90.12 176	97.16 94	79.05 222	98.73 142	92.25 97	91.89 189	95.31 224

DTE-MVSNet		90.56 220	89.75 221	93.01 231	93.95 260	87.25 227	97.64 70	97.65 104	90.74 129	87.12 249	95.68 166	79.97 210	97.00 282	83.33 251	81.66 294	94.78 260

mvs_tets		92.31 154	91.76 142	93.94 187	93.41 277	88.29 188	97.63 71	97.53 114	92.04 99	88.76 218	96.45 129	74.62 267	98.09 188	93.91 73	91.48 196	95.45 213

v748		90.34 224	89.54 226	92.75 239	93.25 282	85.71 251	97.61 72	97.17 151	88.54 194	87.20 248	93.54 254	81.02 188	98.01 210	85.73 217	81.80 291	94.52 267

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 24	95.16 6	97.60 73	98.19 32	92.82 76	97.93 8	98.74 3	91.60 36	99.86 6	96.26 20	99.52 15	99.67 2

v52		90.70 217	90.00 210	92.82 234	93.24 283	87.03 233	97.60 73	97.14 155	88.21 202	87.69 237	93.94 242	80.91 193	98.07 193	87.39 187	83.87 280	93.36 288

V4		90.71 216	90.00 210	92.82 234	93.21 286	87.03 233	97.59 75	97.16 154	88.21 202	87.69 237	93.92 244	80.93 192	98.06 198	87.39 187	83.90 279	93.39 286

ACMM	89.79 8	92.96 128	92.50 127	94.35 167	96.30 152	88.71 180	97.58 76	97.36 140	91.40 116	90.53 162	96.65 114	79.77 212	98.75 141	91.24 125	91.64 192	95.59 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		89.70 238	88.40 242	93.60 207	95.15 201	90.10 131	97.56 77	98.16 36	87.28 226	86.16 260	94.63 210	77.57 249	98.05 201	74.48 301	84.59 270	92.65 294

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 25	96.16 1	97.55 78	97.97 77	95.59 4	96.61 33	97.89 50	92.57 17	99.84 12	95.95 32	99.51 17	99.40 33

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 220	92.07 79	97.53 79	98.11 44	92.90 75	89.56 201	96.12 141	83.16 131	97.60 254	89.30 145	83.20 286	95.75 203

anonymousdsp		92.16 161	91.55 155	93.97 182	92.58 298	89.55 151	97.51 80	97.42 133	89.42 160	88.40 224	94.84 200	80.66 198	97.88 232	91.87 109	91.28 200	94.48 268

VNet		95.89 54	95.45 51	97.21 51	98.07 79	92.94 58	97.50 81	98.15 37	93.87 41	97.52 10	97.61 76	85.29 108	99.53 68	95.81 36	95.27 141	99.16 51

GBi-Net		91.35 193	90.27 199	94.59 156	96.51 141	91.18 107	97.50 81	96.93 184	88.82 184	89.35 207	94.51 213	73.87 271	97.29 273	86.12 208	88.82 225	95.31 224

test1		91.35 193	90.27 199	94.59 156	96.51 141	91.18 107	97.50 81	96.93 184	88.82 184	89.35 207	94.51 213	73.87 271	97.29 273	86.12 208	88.82 225	95.31 224

FMVSNet1		89.88 235	88.31 243	94.59 156	95.41 183	91.18 107	97.50 81	96.93 184	86.62 243	87.41 243	94.51 213	65.94 308	97.29 273	83.04 254	87.43 239	95.31 224

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 221	90.94 115	97.47 85	97.43 132	89.14 172	88.90 215	96.43 130	79.71 213	98.24 174	89.56 141	87.68 236	95.67 207

114514_t		93.95 97	93.06 106	96.63 64	99.07 26	91.61 91	97.46 86	97.96 78	77.99 312	93.00 117	97.57 79	86.14 101	99.33 90	89.22 148	99.15 52	98.94 72

tfpn200view9		92.38 151	91.52 157	94.95 146	97.85 94	89.29 169	97.41 87	94.88 278	92.19 90	93.27 108	94.46 217	78.17 236	99.08 116	81.40 272	94.08 155	96.48 175

thres400		92.42 149	91.52 157	95.12 137	97.85 94	89.29 169	97.41 87	94.88 278	92.19 90	93.27 108	94.46 217	78.17 236	99.08 116	81.40 272	94.08 155	96.98 155

FMVSNet2		91.31 195	90.08 206	94.99 141	96.51 141	92.21 74	97.41 87	96.95 182	88.82 184	88.62 220	94.75 205	73.87 271	97.42 265	85.20 224	88.55 231	95.35 222

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 44	94.30 18	97.41 87	98.04 63	94.81 22	96.59 35	98.37 21	91.24 40	99.64 44	95.16 47	99.52 15	99.42 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 184	93.34 50	97.39 91	98.71 5	93.14 63	90.10 178	94.83 201	87.71 79	98.03 206	91.67 117	83.99 275	95.46 212

NR-MVSNet		92.34 152	91.27 166	95.53 116	94.95 211	93.05 54	97.39 91	98.07 54	92.65 80	84.46 270	95.71 163	85.00 112	97.77 242	89.71 137	83.52 283	95.78 199

DP-MVS		92.76 137	91.51 159	96.52 69	98.77 33	90.99 112	97.38 93	96.08 223	82.38 287	89.29 210	97.87 53	83.77 124	99.69 33	81.37 276	96.69 120	98.89 78

ACMP	89.59 10	92.62 139	92.14 132	94.05 177	96.40 148	88.20 196	97.36 94	97.25 148	91.52 109	88.30 227	96.64 115	78.46 232	98.72 144	91.86 110	91.48 196	95.23 231
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs6		87.81 267	86.19 269	92.69 241	91.32 305	86.30 245	97.34 95	96.41 209	80.59 303	84.05 277	94.37 224	67.37 303	97.67 248	84.75 228	79.51 301	94.09 278

v8		91.29 196	90.53 192	93.57 211	94.15 243	88.12 203	97.34 95	97.06 166	88.99 176	88.32 226	94.26 235	83.08 138	98.01 210	87.62 183	83.92 278	94.57 266

NCCC		97.30 8	97.03 9	98.11 6	98.77 33	95.06 8	97.34 95	98.04 63	95.96 2	97.09 25	97.88 52	93.18 9	99.71 27	95.84 35	99.17 51	99.56 13

v10		91.04 204	90.23 202	93.49 213	94.12 247	88.16 199	97.32 98	97.08 163	88.26 201	88.29 228	94.22 236	82.17 172	97.97 217	86.45 203	84.12 274	94.33 273

V42		91.58 181	90.87 178	93.73 199	94.05 256	88.50 185	97.32 98	96.97 178	88.80 187	89.71 194	94.33 226	82.54 161	98.05 201	89.01 155	85.07 261	94.64 265

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 85	93.17 52	97.30 100	98.06 56	93.92 40	93.38 103	98.66 4	86.83 92	99.73 23	95.60 43	99.22 47	98.96 69
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 2	98.90 30	95.86 2	97.27 101	98.08 49	95.81 3	97.87 9	98.31 31	94.26 2	99.68 35	97.02 4	99.49 21	99.57 11

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 73	90.86 118	97.27 101	98.25 25	90.21 144	94.18 91	97.27 89	87.48 85	99.73 23	93.53 80	97.77 92	98.55 93

mvs-test1		93.63 107	93.69 87	93.46 216	96.02 165	84.61 265	97.24 103	96.72 195	93.85 42	92.30 131	95.76 160	83.08 138	98.89 129	91.69 115	96.54 123	96.87 165

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 16	97.24 103	98.08 49	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

plane_prior						89.99 135	97.24 103		94.06 38						92.16 185

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 31	90.68 123	97.24 103	97.73 93	91.80 104	92.93 122	96.62 122	89.13 62	99.14 104	89.21 149	97.78 91	98.97 68

ACMH	87.59 16	90.53 221	89.42 228	93.87 189	96.21 154	87.92 215	97.24 103	96.94 183	88.45 195	83.91 278	96.27 136	71.92 279	98.62 149	84.43 235	89.43 221	95.05 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		92.23 159	91.31 164	94.99 141	95.56 178	90.96 114	97.22 108	97.86 86	92.96 73	90.96 158	96.62 122	75.06 263	98.20 176	91.90 107	83.65 282	95.80 198

v1neww		91.70 173	91.01 172	93.75 196	94.19 239	88.14 201	97.20 109	96.98 175	89.18 167	89.87 186	94.44 219	83.10 136	98.06 198	89.06 153	85.09 259	95.06 240

v7new		91.70 173	91.01 172	93.75 196	94.19 239	88.14 201	97.20 109	96.98 175	89.18 167	89.87 186	94.44 219	83.10 136	98.06 198	89.06 153	85.09 259	95.06 240

v6		91.69 175	91.00 174	93.75 196	94.14 244	88.12 203	97.20 109	96.98 175	89.19 165	89.90 183	94.42 221	83.04 142	98.07 193	89.07 152	85.10 258	95.07 237

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 55	88.98 177	97.18 112	97.29 145	87.75 215	90.49 163	97.10 98	85.21 109	99.50 74	86.70 199	96.72 119	97.63 139

UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 187	92.83 59	97.17 113	98.58 10	92.98 72	90.13 174	95.80 155	88.37 73	97.85 233	91.71 113	83.93 276	95.73 205

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 211	92.83 59	97.16 114	98.24 27	93.02 66	90.13 174	95.71 163	83.47 127	97.85 233	91.71 113	83.93 276	95.78 199

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 16	97.15 115	98.08 49	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 242	96.02 165	83.25 277	97.14 116	96.72 195	93.85 42	91.20 157	93.44 260	83.08 138	98.30 172	91.69 115	95.73 136	96.50 174

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 3	97.12 117	98.07 54	93.54 51	96.08 51	97.69 66	93.86 5	99.71 27	96.50 17	99.39 32	99.55 15

v7		91.47 187	90.73 186	93.68 204	94.13 245	88.16 199	97.09 118	97.05 167	88.38 197	89.80 189	94.52 212	82.21 170	98.01 210	88.00 170	85.42 252	94.87 249

MVSTER		93.20 120	92.81 111	94.37 166	96.56 138	89.59 149	97.06 119	97.12 158	91.24 120	91.30 151	95.96 146	82.02 174	98.05 201	93.48 83	90.55 210	95.47 211

Fast-Effi-MVS+-dtu		92.29 156	91.99 137	93.21 227	95.27 192	85.52 254	97.03 120	96.63 205	92.09 93	89.11 214	95.14 190	80.33 205	98.08 189	87.54 185	94.74 150	96.03 189

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 22	94.19 22	97.03 120	98.08 49	88.35 199	95.09 78	97.65 70	89.97 57	99.48 75	92.08 104	98.59 73	98.44 107

CANet		96.39 41	96.02 43	97.50 37	97.62 103	93.38 47	97.02 122	97.96 78	95.42 8	94.86 80	97.81 59	87.38 87	99.82 16	96.88 7	99.20 49	99.29 43

FMVSNet3		91.78 169	90.69 188	95.03 140	96.53 140	92.27 73	97.02 122	96.93 184	89.79 155	89.35 207	94.65 209	77.01 251	97.47 261	86.12 208	88.82 225	95.35 222

Baseline_NR-MVSNet		91.20 198	90.62 189	92.95 233	93.83 265	88.03 209	97.01 124	95.12 267	88.42 196	89.70 195	95.13 191	83.47 127	97.44 263	89.66 139	83.24 285	93.37 287

ACMH+	87.92 14	90.20 228	89.18 232	93.25 224	96.48 144	86.45 244	96.99 125	96.68 200	88.83 183	84.79 269	96.22 137	70.16 292	98.53 156	84.42 236	88.04 233	94.77 261

OurMVSNet-221017-0		90.51 222	90.19 205	91.44 275	93.41 277	81.25 289	96.98 126	96.28 213	91.68 107	86.55 257	96.30 134	74.20 270	97.98 214	88.96 156	87.40 241	95.09 234

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 21	94.71 11	96.96 127	98.06 56	90.67 132	95.55 72	98.78 2	91.07 42	99.86 6	96.58 15	99.55 12	99.38 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1141		91.61 177	90.89 175	93.78 193	94.01 257	88.24 192	96.96 127	96.96 179	89.17 169	89.75 192	94.29 229	82.99 146	98.03 206	88.85 159	85.00 264	95.07 237

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 60	92.02 82	96.96 127	97.76 90	95.01 16	97.08 26	98.42 16	91.71 33	99.54 65	96.80 9	99.13 54	99.48 26

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 60	92.66 64	96.96 127	98.01 68	95.12 13	97.14 21	98.42 16	91.82 32	99.61 45	96.90 6	99.13 54	99.50 22

divwei89l23v2f112		91.61 177	90.89 175	93.78 193	94.01 257	88.22 194	96.96 127	96.96 179	89.17 169	89.75 192	94.28 231	83.02 144	98.03 206	88.86 158	84.98 266	95.08 235

v1		91.61 177	90.89 175	93.78 193	94.01 257	88.21 195	96.96 127	96.96 179	89.17 169	89.78 191	94.29 229	82.97 148	98.05 201	88.85 159	84.99 265	95.08 235

v2v482		91.59 180	90.85 180	93.80 191	93.87 264	88.17 198	96.94 133	96.88 189	89.54 156	89.53 202	94.90 196	81.70 181	98.02 209	89.25 147	85.04 263	95.20 232

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 245	96.82 129	81.89 285	96.92 134	93.71 308	92.41 84	84.30 272	94.60 211	85.08 111	97.03 279	91.51 118	97.36 103	98.40 110

COLMAP_ROB	87.81 15	90.40 223	89.28 230	93.79 192	97.95 86	87.13 232	96.92 134	95.89 235	82.83 284	86.88 256	97.18 93	73.77 274	99.29 92	78.44 291	93.62 168	94.95 243
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 37	96.47 32	96.63 64	98.24 69	91.20 105	96.89 136	97.73 93	94.74 25	96.49 39	98.49 11	90.88 47	99.58 53	96.44 18	98.32 78	99.13 55

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 98	94.50 13	96.87 137	97.95 80	95.46 6	95.60 70	98.01 46	80.96 189	99.83 13	97.23 2	99.25 44	99.23 47

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 73	90.93 116	96.86 138	97.72 96	94.67 26	96.16 48	98.46 12	90.43 51	99.58 53	96.23 21	97.96 87	98.90 76

v1144		91.37 192	90.60 190	93.68 204	93.89 263	88.23 193	96.84 139	97.03 172	88.37 198	89.69 196	94.39 222	82.04 173	97.98 214	87.80 175	85.37 253	94.84 251

v144192		91.06 203	90.28 198	93.39 218	93.66 270	87.23 229	96.83 140	97.07 164	87.43 221	89.69 196	94.28 231	81.48 182	98.00 213	87.18 194	84.92 267	94.93 247

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 63	93.48 44	96.83 140	97.99 75	95.20 12	97.46 12	98.25 37	92.48 20	99.58 53	96.79 11	99.29 41	99.55 15

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 63	93.84 33	96.83 140	98.10 46	95.24 10	97.49 11	98.25 37	92.57 17	99.61 45	96.80 9	99.29 41	99.56 13

v18		88.71 250	87.52 249	92.27 247	94.16 242	88.11 205	96.82 143	95.96 225	87.03 230	80.76 294	89.81 295	83.15 132	96.22 290	84.69 229	75.31 312	92.49 298

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 130	90.03 132	96.81 144	97.13 157	88.19 204	91.30 151	94.27 233	86.21 98	98.63 147	87.66 181	96.46 126	98.12 120

v17		88.67 252	87.47 252	92.26 249	94.13 245	88.09 207	96.81 144	95.95 226	87.02 231	80.72 295	89.75 297	83.11 135	96.20 291	84.61 232	75.15 314	92.49 298

v16		88.69 251	87.50 250	92.26 249	94.19 239	88.11 205	96.81 144	95.95 226	87.01 232	80.71 296	89.80 296	83.08 138	96.20 291	84.61 232	75.34 311	92.48 300

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 65	93.39 46	96.79 147	96.72 195	94.17 36	97.44 13	97.66 69	92.76 11	99.33 90	96.86 8	97.76 93	99.08 60

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 62	89.60 148	96.79 147	97.65 104	81.83 291	91.52 143	97.23 92	87.94 76	98.91 126	71.31 312	98.37 77	98.17 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v148		90.99 205	90.38 195	92.81 237	93.83 265	85.80 249	96.78 149	96.68 200	89.45 159	88.75 219	93.93 243	82.96 150	97.82 237	87.83 174	83.25 284	94.80 257

v11		88.41 261	87.19 263	92.08 259	94.08 253	87.77 219	96.75 150	95.85 236	86.74 242	80.50 300	89.50 304	82.49 163	96.08 298	83.55 248	75.20 313	92.38 307

v1921920		90.85 209	90.03 209	93.29 223	93.55 271	86.96 237	96.74 151	97.04 170	87.36 223	89.52 203	94.34 225	80.23 207	97.97 217	86.27 204	85.21 256	94.94 245

v1192		91.07 202	90.23 202	93.58 210	93.70 268	87.82 218	96.73 152	97.07 164	87.77 214	89.58 199	94.32 227	80.90 196	97.97 217	86.52 201	85.48 250	94.95 243

V9		88.49 258	87.26 256	92.18 253	94.12 247	87.97 213	96.73 152	95.90 230	86.95 236	80.40 302	89.61 299	82.98 147	96.13 293	84.14 239	74.55 318	92.44 302

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 162	98.27 66	89.46 157	96.73 152	98.36 16	90.17 145	94.36 87	95.24 187	88.02 74	99.58 53	93.44 84	90.72 208	94.36 272

v15		88.53 254	87.31 254	92.20 252	94.09 251	88.05 208	96.72 155	95.90 230	87.01 232	80.53 299	89.60 301	83.02 144	96.13 293	84.29 237	74.64 315	92.41 304

V14		88.52 255	87.30 255	92.17 254	94.12 247	87.99 210	96.72 155	95.91 229	86.98 234	80.50 300	89.63 298	83.03 143	96.12 295	84.23 238	74.60 317	92.40 305

v13		88.45 260	87.22 260	92.16 256	94.08 253	87.95 214	96.71 157	95.90 230	86.86 241	80.27 306	89.55 303	82.92 151	96.12 295	84.02 242	74.63 316	92.40 305

v12		88.46 259	87.23 259	92.17 254	94.10 250	87.99 210	96.71 157	95.90 230	86.91 237	80.34 304	89.58 302	82.92 151	96.11 297	84.09 240	74.50 320	92.42 303

TAMVS		94.01 96	93.46 96	95.64 111	96.16 159	90.45 129	96.71 157	96.89 188	89.27 163	93.46 102	96.92 102	87.29 88	97.94 223	88.70 163	95.74 135	98.53 95

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 128	89.55 151	96.70 160	97.17 151	91.17 121	95.60 70	96.11 143	87.87 78	98.76 140	93.01 92	97.17 108	98.72 86

SixPastTwentyTwo		89.15 244	88.54 241	90.98 279	93.49 275	80.28 299	96.70 160	94.70 282	90.78 128	84.15 275	95.57 170	71.78 281	97.71 246	84.63 231	85.07 261	94.94 245

EPNet_dtu		91.71 170	91.28 165	92.99 232	93.76 267	83.71 272	96.69 162	95.28 258	93.15 62	87.02 253	95.95 147	83.37 129	97.38 269	79.46 286	96.84 113	97.88 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 48	91.15 110	96.69 162	97.39 135	87.29 225	91.37 146	96.71 108	88.39 72	99.52 71	87.33 190	97.13 109	97.73 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testgi		87.97 264	87.21 261	90.24 292	92.86 292	80.76 291	96.67 164	94.97 274	91.74 105	85.52 264	95.83 153	62.66 314	94.47 314	76.25 298	88.36 232	95.48 209

OPM-MVS		93.28 118	92.76 112	94.82 149	94.63 225	90.77 122	96.65 165	97.18 149	93.72 45	91.68 141	97.26 90	79.33 219	98.63 147	92.13 101	92.28 180	95.07 237

HQP-NCC					95.86 168		96.65 165		93.55 48	90.14 170

ACMP_Plane					95.86 168		96.65 165		93.55 48	90.14 170

HQP-MVS		93.19 121	92.74 116	94.54 161	95.86 168	89.33 166	96.65 165	97.39 135	93.55 48	90.14 170	95.87 150	80.95 190	98.50 159	92.13 101	92.10 186	95.78 199

EU-MVSNet		88.72 249	88.90 235	88.20 299	93.15 289	74.21 316	96.63 169	94.22 301	85.18 258	87.32 246	95.97 145	76.16 255	94.98 312	85.27 222	86.17 245	95.41 214

v1240		90.70 217	89.85 216	93.23 225	93.51 274	86.80 238	96.61 170	97.02 173	87.16 228	89.58 199	94.31 228	79.55 216	97.98 214	85.52 219	85.44 251	94.90 248

K. test v3		87.64 268	86.75 266	90.32 291	93.02 291	79.48 305	96.61 170	92.08 321	90.66 134	80.25 307	94.09 238	67.21 304	96.65 285	85.96 213	80.83 298	94.83 253

thres200		92.23 159	91.39 160	94.75 155	97.61 104	89.03 176	96.60 172	95.09 268	92.08 98	93.28 107	94.00 240	78.39 234	99.04 121	81.26 277	94.18 154	96.19 180

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 99	92.05 80	96.59 173	97.35 141	90.61 138	94.64 83	96.93 101	86.41 96	99.39 86	91.20 126	94.71 151	98.94 72

CNLPA		94.28 85	93.53 93	96.52 69	98.38 58	92.55 67	96.59 173	96.88 189	90.13 146	91.91 138	97.24 91	85.21 109	99.09 114	87.64 182	97.83 89	97.92 127

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 46	91.68 90	96.59 173	97.81 89	89.87 149	92.15 134	97.06 99	83.62 126	99.54 65	89.34 144	98.07 84	97.70 138

IterMVS-LS		92.29 156	91.94 139	93.34 221	96.25 153	86.97 236	96.57 176	97.05 167	90.67 132	89.50 204	94.80 203	86.59 93	97.64 251	89.91 133	86.11 247	95.40 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		90.23 227	88.98 234	93.98 180	97.94 87	86.64 240	96.51 177	95.54 247	85.38 255	85.49 265	96.77 106	70.28 290	99.15 102	80.02 282	92.87 173	96.15 183

EI-MVSNet		93.03 126	92.88 110	93.48 214	95.77 173	86.98 235	96.44 178	97.12 158	90.66 134	91.30 151	97.64 73	86.56 94	98.05 201	89.91 133	90.55 210	95.41 214

CVMVSNet		91.23 197	91.75 143	89.67 296	95.77 173	74.69 315	96.44 178	94.88 278	85.81 252	92.18 133	97.64 73	79.07 221	95.58 307	88.06 169	95.86 134	98.74 84

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 51	91.28 101	96.43 180	97.57 110	92.04 99	94.77 82	97.96 49	87.01 91	99.09 114	91.31 123	96.77 116	98.36 114

test_prior4						93.66 39	96.42 181

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 133	91.47 96	96.41 182	97.41 134	91.02 126	94.50 85	95.92 148	87.53 84	98.78 137	93.89 74	96.81 115	98.84 82

TEST9					98.70 36	94.19 22	96.41 182	98.02 66	88.17 206	96.03 52	97.56 81	92.74 12	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 36	94.19 22	96.41 182	98.02 66	88.58 191	96.03 52	97.56 81	92.73 13	99.59 50	95.04 51	99.37 37	99.39 34

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 38	93.88 31	96.41 182	98.00 70	87.93 210	95.81 62	97.47 85	92.33 21	99.59 50	95.04 51	99.37 37	99.39 34

WR-MVS		92.34 152	91.53 156	94.77 154	95.13 203	90.83 119	96.40 186	97.98 76	91.88 103	89.29 210	95.54 173	82.50 162	97.80 238	89.79 136	85.27 255	95.69 206

BH-untuned		92.94 129	92.62 120	93.92 188	97.22 112	86.16 247	96.40 186	96.25 216	90.06 147	89.79 190	96.17 140	83.19 130	98.35 168	87.19 193	97.27 106	97.24 152

TDRefinement		86.53 275	84.76 280	91.85 264	82.23 330	84.25 266	96.38 188	95.35 254	84.97 263	84.09 276	94.94 193	65.76 309	98.34 170	84.60 234	74.52 319	92.97 289

test_8					98.67 38	94.06 28	96.37 189	98.01 68	88.58 191	95.98 57	97.55 83	92.73 13	99.58 53

test_prior3		96.46 39	96.20 41	97.23 48	98.67 38	92.99 55	96.35 190	98.00 70	92.80 77	96.03 52	97.59 77	92.01 28	99.41 83	95.01 53	99.38 33	99.29 43

test_prior2							96.35 190		92.80 77	96.03 52	97.59 77	92.01 28		95.01 53	99.38 33

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 29	94.44 15	96.35 190	97.88 82	86.98 234	96.65 32	97.89 50	91.99 30	99.47 76	92.26 95	99.46 23	99.39 34

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 157	91.46 97	96.33 193	97.04 170	88.97 178	93.56 98	96.51 126	87.55 83	97.89 231	89.80 135	95.95 131	98.44 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sss		94.51 82	93.80 84	96.64 62	97.07 118	91.97 84	96.32 194	98.06 56	88.94 179	94.50 85	96.78 105	84.60 117	99.27 93	91.90 107	96.02 129	98.68 90

1112_ss		93.37 115	92.42 129	96.21 91	97.05 121	90.99 112	96.31 195	96.72 195	86.87 240	89.83 188	96.69 112	86.51 95	99.14 104	88.12 168	93.67 166	98.50 100

LTVRE_ROB	88.41 13	90.99 205	89.92 213	94.19 171	96.18 157	89.55 151	96.31 195	97.09 161	87.88 212	85.67 263	95.91 149	78.79 229	98.57 153	81.50 270	89.98 216	94.44 270
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		86.46 276	84.79 279	91.45 274	95.02 208	85.55 253	96.29 197	94.89 277	80.90 298	82.21 282	93.97 241	68.21 299	97.29 273	62.98 322	88.68 230	91.51 315

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 38	93.79 35	96.28 198	98.00 70	88.76 188	95.68 66	97.55 83	92.70 15	99.57 61	95.01 53	99.32 39	99.32 41

pmmvs5		89.86 236	88.87 236	92.82 234	92.86 292	86.23 246	96.26 199	95.39 251	84.24 270	87.12 249	94.51 213	74.27 269	97.36 270	87.61 184	87.57 237	94.86 250

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 135	91.71 87	96.25 200	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 186

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 135	91.71 87	96.25 200	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 186

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 135	91.71 87	96.25 200	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 186

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 72	91.35 100	96.24 203	98.79 4	93.99 39	95.80 63	97.65 70	89.92 58	99.24 95	95.87 33	99.20 49	98.58 92

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 146	92.13 78	96.21 204	96.67 202	94.38 33	93.53 100	97.03 100	79.34 218	99.71 27	90.76 127	98.45 76	97.82 134

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 51	92.31 71	96.20 205	98.90 2	94.30 35	95.86 60	97.74 64	92.33 21	99.38 88	96.04 30	99.42 28	99.28 46

BH-RMVSNet		92.72 138	91.97 138	94.97 144	97.16 115	87.99 210	96.15 206	95.60 244	90.62 136	91.87 139	97.15 96	78.41 233	98.57 153	83.16 252	97.60 95	98.36 114

DI_MVS_plusplus_test		92.01 164	90.77 183	95.73 109	93.34 279	89.78 144	96.14 207	96.18 220	90.58 140	81.80 287	93.50 256	74.95 265	98.90 127	93.51 81	96.94 112	98.51 98

Anonymous20231206		87.09 272	86.14 270	89.93 295	91.22 306	80.35 296	96.11 208	95.35 254	83.57 279	84.16 274	93.02 265	73.54 276	95.61 305	72.16 309	86.14 246	93.84 281

diffmvs		93.43 114	92.75 114	95.48 121	96.47 145	89.61 147	96.09 209	97.14 155	85.97 251	93.09 115	95.35 182	84.87 114	98.55 155	89.51 142	96.26 128	98.28 116

jason		94.84 78	94.39 79	96.18 92	95.52 179	90.93 116	96.09 209	96.52 207	89.28 162	96.01 56	97.32 87	84.70 116	98.77 139	95.15 48	98.91 66	98.85 80
jason: jason.
EG-PatchMatch MVS		87.02 273	85.44 274	91.76 270	92.67 296	85.00 259	96.08 211	96.45 208	83.41 281	79.52 309	93.49 257	57.10 322	97.72 245	79.34 288	90.87 206	92.56 296

1314		92.81 136	92.03 135	95.14 135	95.33 190	89.52 154	96.04 212	97.44 130	87.72 216	86.25 259	95.33 183	83.84 123	98.79 136	89.26 146	97.05 110	97.11 153

1121		94.71 80	93.83 83	97.34 41	98.57 49	93.64 40	96.04 212	97.73 93	81.56 296	95.68 66	97.85 56	90.23 53	99.65 39	87.68 179	99.12 57	98.73 85

MVS		91.71 170	90.44 193	95.51 117	95.20 199	91.59 93	96.04 212	97.45 127	73.44 324	87.36 245	95.60 169	85.42 107	99.10 111	85.97 212	97.46 97	95.83 196

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 54	90.53 126	96.04 212	97.48 118	93.47 52	95.67 69	98.10 40	89.17 61	99.25 94	91.27 124	98.77 68	99.13 55

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 78	86.63 243	96.00 216	98.15 37	95.43 7	97.95 7	98.56 7	93.40 8	99.36 89	96.77 12	99.48 22	99.45 28

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 96	93.19 51	95.96 217	98.18 34	95.23 11	95.87 59	97.65 70	91.45 38	99.70 32	95.87 33	99.44 27	99.00 67
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							95.94 218		81.66 292	97.34 15			98.82 134	92.26 95

test_normal		92.01 164	90.75 185	95.80 104	93.24 283	89.97 138	95.93 219	96.24 217	90.62 136	81.63 288	93.45 259	74.98 264	98.89 129	93.61 79	97.04 111	98.55 93

test20.03		86.14 279	85.40 275	88.35 297	90.12 309	80.06 301	95.90 220	95.20 263	88.59 190	81.29 290	93.62 252	71.43 283	92.65 321	71.26 313	81.17 296	92.34 308

MVP-Stereo		90.74 214	90.08 206	92.71 240	93.19 288	88.20 196	95.86 221	96.27 214	86.07 250	84.86 268	94.76 204	77.84 247	97.75 243	83.88 246	98.01 85	92.17 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		90.76 211	89.89 214	93.38 219	95.04 207	83.70 273	95.85 222	94.30 298	88.19 204	90.46 164	92.80 267	73.61 275	98.50 159	88.16 167	90.58 209	97.95 126

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 150	91.21 103	95.83 223	96.27 214	88.93 180	96.22 46	96.88 103	86.20 99	98.85 132	95.27 45	99.05 60	98.82 83

mvs_anonymous		93.82 101	93.74 85	94.06 176	96.44 147	85.41 255	95.81 224	97.05 167	89.85 152	90.09 179	96.36 133	87.44 86	97.75 243	93.97 70	96.69 120	99.02 62

新几何2							95.79 225

无先验							95.79 225	97.87 84	83.87 276				99.65 39	87.68 179		98.89 78

Test4		89.48 240	87.50 250	95.44 124	90.76 308	89.72 145	95.78 227	97.09 161	90.28 143	77.67 313	91.74 287	55.42 326	98.08 189	91.92 106	96.83 114	98.52 96

OpenMVS_ROB	81.14 20	84.42 287	82.28 288	90.83 282	90.06 310	84.05 270	95.73 228	94.04 304	73.89 323	80.17 308	91.53 289	59.15 319	97.64 251	66.92 318	89.05 224	90.80 318

原ACMM2							95.67 229

BH-w/o		92.14 163	91.75 143	93.31 222	96.99 123	85.73 250	95.67 229	95.69 241	88.73 189	89.26 212	94.82 202	82.97 148	98.07 193	85.26 223	96.32 127	96.13 185

TR-MVS		91.48 186	90.59 191	94.16 173	96.40 148	87.33 224	95.67 229	95.34 257	87.68 217	91.46 144	95.52 174	76.77 252	98.35 168	82.85 256	93.61 169	96.79 167

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 117	92.49 69	95.64 232	96.64 203	89.05 174	93.00 117	95.79 158	85.77 105	99.45 79	89.16 151	94.35 152	97.96 125

Anonymous20231211		78.22 301	75.30 302	86.99 305	86.14 323	74.16 317	95.62 233	93.88 307	66.43 327	74.44 317	87.86 316	41.39 334	95.11 311	62.49 323	69.46 327	91.71 312

RPSCF		90.75 213	90.86 179	90.42 290	96.84 126	76.29 313	95.61 234	96.34 211	83.89 274	91.38 145	97.87 53	76.45 253	98.78 137	87.16 195	92.23 181	96.20 179

MS-PatchMatch		90.27 225	89.77 219	91.78 268	94.33 235	84.72 264	95.55 235	96.73 194	86.17 249	86.36 258	95.28 186	71.28 284	97.80 238	84.09 240	98.14 83	92.81 293

PAPR		94.18 87	93.42 100	96.48 74	97.64 102	91.42 99	95.55 235	97.71 99	88.99 176	92.34 130	95.82 154	89.19 60	99.11 106	86.14 207	97.38 102	98.90 76

PatchFormer-LS_test		91.68 176	91.18 171	93.19 228	95.24 196	83.63 275	95.53 237	95.44 250	89.82 153	91.37 146	92.58 272	80.85 197	98.52 157	89.65 140	90.16 215	97.42 150

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 122	89.97 138	95.53 237	96.64 203	85.38 255	89.65 198	95.18 188	85.86 103	99.10 111	87.70 177	93.58 171	98.49 102

FMVSNet5		87.29 271	85.79 272	91.78 268	94.80 219	87.28 225	95.49 239	95.28 258	84.09 272	83.85 279	91.82 284	62.95 313	94.17 315	78.48 290	85.34 254	93.91 280

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 66	89.46 157	95.47 240	98.36 16	88.84 182	94.36 87	96.09 144	88.02 74	99.58 53	93.44 84	98.18 81	98.40 110

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 112	90.50 127	95.44 241	97.44 130	93.70 47	96.46 41	96.18 138	88.59 71	99.53 68	94.79 63	97.81 90	96.17 181

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 111	91.96 85	95.40 242	97.45 127	89.81 154	93.22 110	96.28 135	79.62 215	99.46 77	90.74 128	93.11 172	98.50 100

MIMVSNet1		84.93 286	83.05 286	90.56 288	89.56 314	84.84 263	95.40 242	95.35 254	83.91 273	80.38 303	92.21 282	57.23 321	93.34 319	70.69 315	82.75 290	93.50 283

test222					98.24 69	92.21 74	95.33 244	97.60 107	79.22 307	95.25 75	97.84 58	88.80 66			99.15 52	98.72 86

XVG-ACMP-BASELINE		90.93 207	90.21 204	93.09 229	94.31 236	85.89 248	95.33 244	97.26 146	91.06 125	89.38 206	95.44 180	68.61 296	98.60 150	89.46 143	91.05 203	94.79 259

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 110	90.66 124	95.31 246	97.48 118	93.85 42	96.51 38	95.70 165	88.65 68	99.65 39	94.80 61	98.27 79	96.17 181

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 151	97.06 120	88.53 184	95.28 247	97.45 127	91.68 107	94.08 92	97.68 67	82.41 166	98.90 127	93.84 76	92.47 178	96.98 155

CLD-MVS		92.98 127	92.53 125	94.32 169	96.12 163	89.20 173	95.28 247	97.47 121	92.66 79	89.90 183	95.62 168	80.58 199	98.40 164	92.73 93	92.40 179	95.38 220
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 75	90.80 120	95.27 249	97.18 149	87.96 209	91.86 140	95.68 166	80.44 202	98.99 122	84.01 243	97.54 96	96.89 164

testdata1							95.26 250		93.10 65

test0.0.03 1		89.37 243	88.70 237	91.41 276	92.47 299	85.63 252	95.22 251	92.70 318	91.11 123	86.91 255	93.65 251	79.02 224	93.19 320	78.00 292	89.18 223	95.41 214

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 66	89.38 163	95.18 252	98.48 14	85.60 254	93.76 97	97.11 97	83.15 132	99.61 45	91.33 122	98.72 70	99.19 49

IB-MVS	87.33 17	89.91 233	88.28 244	94.79 153	95.26 195	87.70 221	95.12 253	93.95 306	89.35 161	87.03 252	92.49 273	70.74 288	99.19 97	89.18 150	81.37 295	97.49 148
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		87.33 270	85.03 277	94.22 170	87.77 320	89.32 168	94.97 254	97.11 160	89.22 164	71.64 322	88.73 308	55.16 327	97.94 223	91.95 105	88.73 229	95.41 214

DSMNet-mixed		86.34 277	86.12 271	87.00 304	89.88 312	70.43 321	94.93 255	90.08 329	77.97 313	85.42 267	92.78 268	74.44 268	93.96 316	74.43 302	95.14 142	96.62 171

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 118	88.61 182	94.79 256	97.46 123	91.97 102	93.99 93	97.86 55	81.74 180	98.88 131	92.64 94	92.67 177	96.92 163

Patchmatch-test1		91.54 184	90.85 180	93.59 208	95.59 177	84.95 261	94.72 257	95.58 246	90.82 127	92.25 132	93.58 253	75.80 257	97.41 266	83.35 249	95.98 130	98.40 110

pmmvs4		90.93 207	89.85 216	94.17 172	93.34 279	90.79 121	94.60 258	96.02 224	84.62 267	87.45 241	95.15 189	81.88 178	97.45 262	87.70 177	87.87 235	94.27 276

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 69	89.88 141	94.58 259	98.49 12	85.06 261	93.78 96	95.78 159	82.86 153	98.67 145	91.77 111	95.71 137	99.07 61

MDA-MVSNet-bldmvs		85.00 285	82.95 287	91.17 278	93.13 290	83.33 276	94.56 260	95.00 272	84.57 268	65.13 328	92.65 269	70.45 289	95.85 301	73.57 306	77.49 304	94.33 273

PMMVS		92.86 133	92.34 130	94.42 165	94.92 213	86.73 239	94.53 261	96.38 210	84.78 266	94.27 89	95.12 192	83.13 134	98.40 164	91.47 120	96.49 124	98.12 120

pmmvs-eth3d		86.22 278	84.45 281	91.53 273	88.34 317	87.25 227	94.47 262	95.01 271	83.47 280	79.51 310	89.61 299	69.75 293	95.71 304	83.13 253	76.73 307	91.64 313

LF4IMVS		87.94 265	87.25 257	89.98 294	92.38 300	80.05 302	94.38 263	95.25 261	87.59 219	84.34 271	94.74 206	64.31 311	97.66 250	84.83 226	87.45 238	92.23 309

GA-MVS		91.38 191	90.31 196	94.59 156	94.65 224	87.62 222	94.34 264	96.19 219	90.73 130	90.35 167	93.83 245	71.84 280	97.96 221	87.22 192	93.61 169	98.21 117

IterMVS		90.15 230	89.67 223	91.61 272	95.48 181	83.72 271	94.33 265	96.12 222	89.99 148	87.31 247	94.15 237	75.78 258	96.27 289	86.97 197	86.89 243	94.83 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		91.42 189	91.19 170	92.12 257	94.59 226	80.66 292	94.29 266	92.98 315	91.11 123	90.76 160	92.37 275	79.02 224	98.07 193	88.81 161	96.74 117	97.63 139

TESTMET0.1,1		90.06 231	89.42 228	91.97 261	94.41 233	80.62 294	94.29 266	91.97 322	87.28 226	90.44 165	92.47 274	68.79 295	97.67 248	88.50 165	96.60 122	97.61 143

test-mter		90.19 229	89.54 226	92.12 257	94.59 226	80.66 292	94.29 266	92.98 315	87.68 217	90.76 160	92.37 275	67.67 300	98.07 193	88.81 161	96.74 117	97.63 139

CMPMVS	62.92 21	85.62 283	84.92 278	87.74 301	89.14 315	73.12 319	94.17 269	96.80 193	73.98 322	73.65 318	94.93 194	66.36 305	97.61 253	83.95 245	91.28 200	92.48 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		78.73 299	78.71 298	78.79 317	92.80 294	46.50 343	94.14 270	43.71 347	78.61 310	80.83 291	91.66 288	74.94 266	96.36 287	67.24 317	84.45 272	93.50 283

tpmp4_e23		89.58 239	88.59 239	92.54 244	95.16 200	81.53 287	94.11 271	95.09 268	81.66 292	88.60 221	93.44 260	75.11 262	98.33 171	82.45 261	91.72 191	97.75 135

CostFormer		91.18 201	90.70 187	92.62 243	94.84 217	81.76 286	94.09 272	94.43 292	84.15 271	92.72 124	93.77 247	79.43 217	98.20 176	90.70 129	92.18 184	97.90 128

tpm		90.25 226	89.74 222	91.76 270	93.92 261	79.73 303	93.98 273	93.54 312	88.28 200	91.99 137	93.25 263	77.51 250	97.44 263	87.30 191	87.94 234	98.12 120

TinyColmap		86.82 274	85.35 276	91.21 277	94.91 215	82.99 278	93.94 274	94.02 305	83.58 278	81.56 289	94.68 207	62.34 315	98.13 182	75.78 299	87.35 242	92.52 297

USDC		88.94 245	87.83 247	92.27 247	94.66 223	84.96 260	93.86 275	95.90 230	87.34 224	83.40 280	95.56 171	67.43 302	98.19 178	82.64 260	89.67 220	93.66 282

tpm2		89.96 232	89.21 231	92.23 251	94.91 215	81.25 289	93.78 276	94.42 293	80.62 302	91.56 142	93.44 260	76.44 254	97.94 223	85.60 218	92.08 188	97.49 148

new-patchmatchnet		83.18 290	81.87 291	87.11 303	86.88 322	75.99 314	93.70 277	95.18 264	85.02 262	77.30 314	88.40 311	65.99 307	93.88 317	74.19 305	70.18 325	91.47 317

MSDG		91.42 189	90.24 201	94.96 145	97.15 116	88.91 178	93.69 278	96.32 212	85.72 253	86.93 254	96.47 128	80.24 206	98.98 123	80.57 279	95.05 144	96.98 155

EPMVS		90.70 217	89.81 218	93.37 220	94.73 222	84.21 267	93.67 279	88.02 332	89.50 158	92.38 128	93.49 257	77.82 248	97.78 240	86.03 211	92.68 176	98.11 123

cascas		91.20 198	90.08 206	94.58 160	94.97 209	89.16 175	93.65 280	97.59 109	79.90 304	89.40 205	92.92 266	75.36 261	98.36 167	92.14 100	94.75 149	96.23 178

UnsupCasMVSNet_eth		85.99 280	84.45 281	90.62 287	89.97 311	82.40 282	93.62 281	97.37 138	89.86 150	78.59 312	92.37 275	65.25 310	95.35 310	82.27 263	70.75 324	94.10 277

PM-MVS		83.48 289	81.86 292	88.31 298	87.83 319	77.59 311	93.43 282	91.75 323	86.91 237	80.63 297	89.91 293	44.42 333	95.84 302	85.17 225	76.73 307	91.50 316

tpmrst		91.44 188	91.32 163	91.79 267	95.15 201	79.20 307	93.42 283	95.37 253	88.55 193	93.49 101	93.67 250	82.49 163	98.27 173	90.41 130	89.34 222	97.90 128

PAPM		91.52 185	90.30 197	95.20 128	95.30 191	89.83 142	93.38 284	96.85 191	86.26 247	88.59 222	95.80 155	84.88 113	98.15 181	75.67 300	95.93 132	97.63 139

testmvs		13.36 321	16.33 322	4.48 334	5.04 346	2.26 348	93.18 285	3.28 349	2.70 342	8.24 343	21.66 341	2.29 351	2.19 345	7.58 342	2.96 342	9.00 340

testus		82.63 293	82.15 289	84.07 309	87.31 321	67.67 327	93.18 285	94.29 299	82.47 286	82.14 284	90.69 290	53.01 328	91.94 324	66.30 319	89.96 217	92.62 295

YYNet1		85.87 281	84.23 283	90.78 286	92.38 300	82.46 281	93.17 287	95.14 266	82.12 289	67.69 323	92.36 278	78.16 238	95.50 309	77.31 295	79.73 300	94.39 271

MDA-MVSNet_test_wron		85.87 281	84.23 283	90.80 285	92.38 300	82.57 279	93.17 287	95.15 265	82.15 288	67.65 324	92.33 281	78.20 235	95.51 308	77.33 294	79.74 299	94.31 275

PatchmatchNet		91.91 167	91.35 161	93.59 208	95.38 185	84.11 269	93.15 289	95.39 251	89.54 156	92.10 135	93.68 249	82.82 155	98.13 182	84.81 227	95.32 140	98.52 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		89.83 237	89.15 233	91.89 263	94.92 213	80.30 298	93.11 290	95.46 249	86.28 246	88.08 231	92.65 269	80.44 202	98.52 157	81.47 271	89.92 218	96.84 166

MDTV_nov1_ep13_2view						70.35 323	93.10 291		83.88 275	93.55 99		82.47 165		86.25 205		98.38 113

MDTV_nov1_ep13			90.76 184		95.22 197	80.33 297	93.03 292	95.28 258	88.14 207	92.84 123	93.83 245	81.34 184	98.08 189	82.86 255	94.34 153

PVSNet	86.66 18	92.24 158	91.74 145	93.73 199	97.77 97	83.69 274	92.88 293	96.72 195	87.91 211	93.00 117	94.86 199	78.51 231	99.05 120	86.53 200	97.45 101	98.47 105

dp		88.90 247	88.26 245	90.81 283	94.58 228	76.62 312	92.85 294	94.93 276	85.12 260	90.07 181	93.07 264	75.81 256	98.12 184	80.53 280	87.42 240	97.71 137

test_post1							92.81 295				16.58 344	80.53 200	97.68 247	86.20 206

pmmvs3		79.97 297	77.50 301	87.39 302	82.80 328	79.38 306	92.70 296	90.75 327	70.69 326	78.66 311	87.47 320	51.34 330	93.40 318	73.39 307	69.65 326	89.38 321

tpm cat1		88.36 262	87.21 261	91.81 266	95.13 203	80.55 295	92.58 297	95.70 240	74.97 320	87.45 241	91.96 283	78.01 246	98.17 180	80.39 281	88.74 228	96.72 169

PCF-MVS	89.48 11	91.56 182	89.95 212	96.36 82	96.60 134	92.52 68	92.51 298	97.26 146	79.41 305	88.90 215	96.56 124	84.04 122	99.55 63	77.01 297	97.30 105	97.01 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test123		13.04 322	15.66 323	5.18 333	4.51 347	3.45 347	92.50 299	1.81 350	2.50 343	7.58 344	20.15 342	3.67 350	2.18 346	7.13 343	1.07 344	9.90 339

test1235678		79.82 298	78.53 299	83.69 310	82.55 329	67.55 328	92.50 299	94.13 302	79.28 306	72.10 321	86.45 322	57.27 320	90.68 328	61.60 325	80.90 297	92.82 291

GG-mvs-BLEND				93.62 206	93.69 269	89.20 173	92.39 301	83.33 339		87.98 234	89.84 294	71.00 286	96.87 283	82.08 264	95.40 139	94.80 257

1111		78.29 300	77.55 300	80.50 313	83.89 325	59.98 335	91.89 302	93.71 308	75.06 318	73.60 319	87.67 317	55.66 324	92.60 322	58.54 329	77.92 303	88.93 322

.test1245		65.38 309	69.22 307	53.86 329	83.89 325	59.98 335	91.89 302	93.71 308	75.06 318	73.60 319	87.67 317	55.66 324	92.60 322	58.54 329	2.96 342	9.00 340

new_pmnet		82.89 291	81.12 296	88.18 300	89.63 313	80.18 300	91.77 304	92.57 319	76.79 316	75.56 316	88.23 313	61.22 317	94.48 313	71.43 311	82.92 288	89.87 320

MIMVSNet		88.50 257	86.76 265	93.72 201	94.84 217	87.77 219	91.39 305	94.05 303	86.41 245	87.99 233	92.59 271	63.27 312	95.82 303	77.44 293	92.84 175	97.57 146

FPMVS		71.27 305	69.85 305	75.50 320	74.64 333	59.03 337	91.30 306	91.50 324	58.80 331	57.92 331	88.28 312	29.98 340	85.53 335	53.43 333	82.84 289	81.95 327

testmv		72.22 304	70.02 304	78.82 316	73.06 338	61.75 333	91.24 307	92.31 320	74.45 321	61.06 330	80.51 327	34.21 336	88.63 332	55.31 332	68.07 329	86.06 324

test2356		82.77 292	82.14 290	84.65 308	85.77 324	70.36 322	91.22 308	93.69 311	81.58 294	81.82 286	89.00 307	60.63 318	90.77 327	64.74 320	90.80 207	92.82 291

gg-mvs-nofinetune		87.82 266	85.61 273	94.44 163	94.46 230	89.27 172	91.21 309	84.61 338	80.88 299	89.89 185	74.98 329	71.50 282	97.53 257	85.75 216	97.21 107	96.51 173

ADS-MVSNet2		89.45 241	88.59 239	92.03 260	95.86 168	82.26 283	90.93 310	94.32 297	83.23 282	91.28 154	91.81 285	79.01 226	95.99 299	79.52 284	91.39 198	97.84 131

ADS-MVSNet		89.89 234	88.68 238	93.53 212	95.86 168	84.89 262	90.93 310	95.07 270	83.23 282	91.28 154	91.81 285	79.01 226	97.85 233	79.52 284	91.39 198	97.84 131

UnsupCasMVSNet_bld		82.13 295	79.46 297	90.14 293	88.00 318	82.47 280	90.89 312	96.62 206	78.94 308	75.61 315	84.40 324	56.63 323	96.31 288	77.30 296	66.77 330	91.63 314

PVSNet_0	82.17 19	85.46 284	83.64 285	90.92 281	95.27 192	79.49 304	90.55 313	95.60 244	83.76 277	83.00 281	89.95 292	71.09 285	97.97 217	82.75 258	60.79 331	95.31 224

CHOSEN 280x420		93.12 122	92.72 117	94.34 168	96.71 132	87.27 226	90.29 314	97.72 96	86.61 244	91.34 148	95.29 184	84.29 121	98.41 163	93.25 88	98.94 65	97.35 151

CR-MVSNet		90.82 210	89.77 219	93.95 184	94.45 231	87.19 230	90.23 315	95.68 242	86.89 239	92.40 126	92.36 278	80.91 193	97.05 277	81.09 278	93.95 164	97.60 144

RPMNet		88.52 255	86.72 267	93.95 184	94.45 231	87.19 230	90.23 315	94.99 273	77.87 314	92.40 126	87.55 319	80.17 208	97.05 277	68.84 316	93.95 164	97.60 144

LCM-MVSNet		72.55 303	69.39 306	82.03 311	70.81 340	65.42 331	90.12 317	94.36 296	55.02 332	65.88 327	81.72 325	24.16 344	89.96 329	74.32 304	68.10 328	90.71 319

Patchmtry		88.64 253	87.25 257	92.78 238	94.09 251	86.64 240	89.82 318	95.68 242	80.81 301	87.63 240	92.36 278	80.91 193	97.03 279	78.86 289	85.12 257	94.67 263

PatchT		88.87 248	87.42 253	93.22 226	94.08 253	85.10 258	89.51 319	94.64 286	81.92 290	92.36 129	88.15 314	80.05 209	97.01 281	72.43 308	93.65 167	97.54 147

JIA-IIPM		88.26 263	87.04 264	91.91 262	93.52 273	81.42 288	89.38 320	94.38 294	80.84 300	90.93 159	80.74 326	79.22 220	97.92 227	82.76 257	91.62 193	96.38 177

Patchmatch-test		89.42 242	87.99 246	93.70 202	95.27 192	85.11 257	88.98 321	94.37 295	81.11 297	87.10 251	93.69 248	82.28 168	97.50 259	74.37 303	94.76 148	98.48 104

MVS-HIRNet		82.47 294	81.21 295	86.26 307	95.38 185	69.21 326	88.96 322	89.49 331	66.28 328	80.79 293	74.08 331	68.48 297	97.39 268	71.93 310	95.47 138	92.18 310

test12356		74.97 302	74.13 303	77.49 318	78.81 331	56.23 339	88.53 323	92.75 317	75.14 317	67.50 325	85.07 323	44.88 332	89.96 329	58.71 328	75.75 309	86.26 323

Patchmatch-RL test		87.38 269	86.24 268	90.81 283	88.74 316	78.40 310	88.12 324	93.17 314	87.11 229	82.17 283	89.29 305	81.95 176	95.60 306	88.64 164	77.02 305	98.41 109

LP		84.13 288	81.85 293	90.97 280	93.20 287	82.12 284	87.68 325	94.27 300	76.80 315	81.93 285	88.52 309	72.97 278	95.95 300	59.53 327	81.73 292	94.84 251

no-one		68.12 307	63.78 310	81.13 312	74.01 335	70.22 324	87.61 326	90.71 328	72.63 325	53.13 333	71.89 332	30.29 338	91.45 325	61.53 326	32.21 336	81.72 328

PMMVS2		70.19 306	66.92 308	80.01 314	76.35 332	65.67 330	86.22 327	87.58 334	64.83 330	62.38 329	80.29 328	26.78 342	88.49 333	63.79 321	54.07 332	85.88 325

ambc				86.56 306	83.60 327	70.00 325	85.69 328	94.97 274		80.60 298	88.45 310	37.42 335	96.84 284	82.69 259	75.44 310	92.86 290

ANet_high		63.94 310	59.58 311	77.02 319	61.24 343	66.06 329	85.66 329	87.93 333	78.53 311	42.94 335	71.04 333	25.42 343	80.71 337	52.60 334	30.83 338	84.28 326

EMVS		52.08 316	51.31 316	54.39 328	72.62 339	45.39 344	83.84 330	75.51 344	41.13 338	40.77 338	59.65 338	30.08 339	73.60 341	28.31 340	29.90 339	44.18 337

E-PMN		53.28 314	52.56 315	55.43 327	74.43 334	47.13 342	83.63 331	76.30 343	42.23 337	42.59 336	62.22 337	28.57 341	74.40 340	31.53 339	31.51 337	44.78 336

PNet_i23d		59.01 311	55.87 312	68.44 324	73.98 336	51.37 340	81.36 332	82.41 340	52.37 334	42.49 337	70.39 334	11.39 345	79.99 339	49.77 335	38.71 334	73.97 332

wuykxyi23d		56.92 313	51.11 317	74.38 323	62.30 342	61.47 334	80.09 333	84.87 337	49.62 335	30.80 341	57.20 339	7.03 347	82.94 336	55.69 331	32.36 335	78.72 330

PMVS	53.92 22	58.58 312	55.40 313	68.12 325	51.00 344	48.64 341	78.86 334	87.10 336	46.77 336	35.84 340	74.28 330	8.76 346	86.34 334	42.07 337	73.91 322	69.38 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 317	53.82 314	46.29 330	33.73 345	45.30 345	78.32 335	67.24 346	18.02 340	50.93 334	87.05 321	52.99 329	53.11 343	70.76 314	25.29 340	40.46 338

testpf		80.97 296	81.40 294	79.65 315	91.53 304	72.43 320	73.47 336	89.55 330	78.63 309	80.81 292	89.06 306	61.36 316	91.36 326	83.34 250	84.89 268	75.15 331

MVE	50.73 23	53.25 315	48.81 318	66.58 326	65.34 341	57.50 338	72.49 337	70.94 345	40.15 339	39.28 339	63.51 336	6.89 349	73.48 342	38.29 338	42.38 333	68.76 334
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		67.86 308	65.41 309	75.18 321	92.66 297	73.45 318	66.50 338	94.52 291	53.33 333	57.80 332	66.07 335	30.81 337	89.20 331	48.15 336	78.88 302	62.90 335
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		25.11 319	24.57 321	26.74 332	73.98 336	39.89 346	57.88 339	9.80 348	12.27 341	10.39 342	6.97 345	7.03 347	36.44 344	25.43 341	17.39 341	3.89 342

cdsmvs_eth3d_5k		23.24 320	30.99 320	0.00 335	0.00 348	0.00 349	0.00 340	97.63 106	0.00 344	0.00 345	96.88 103	84.38 120	0.00 347	0.00 344	0.00 345	0.00 343

pcd_1.5k_mvsjas		7.39 324	9.85 325	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	88.65 68	0.00 347	0.00 344	0.00 345	0.00 343

pcd1.5k->3k		38.37 318	40.51 319	31.96 331	94.29 237	0.00 349	0.00 340	97.69 100	0.00 344	0.00 345	0.00 346	81.45 183	0.00 347	0.00 344	91.11 202	95.89 191

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ab-mvs-re		8.06 323	10.74 324	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	96.69 112	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 345	0.00 343

ESAPD								98.25 25

sam_mvs1												82.76 156

sam_mvs												81.94 177

semantic-postprocess				91.82 265	95.52 179	84.20 268		96.15 221	90.61 138	87.39 244	94.27 233	75.63 259	96.44 286	87.34 189	86.88 244	94.82 255

MTGPA								98.08 49

test_post											17.58 343	81.76 179	98.08 189

patchmatchnet-post											90.45 291	82.65 160	98.10 186

MTMP								82.03 341

gm-plane-assit					93.22 285	78.89 309			84.82 265		93.52 255		98.64 146	87.72 176

test9_res														94.81 60	99.38 33	99.45 28

agg_prior2														93.94 72	99.38 33	99.50 22

agg_prior					98.67 38	93.79 35		98.00 70		95.68 66			99.57 61

TestCases				93.98 180	97.94 87	86.64 240		95.54 247	85.38 255	85.49 265	96.77 106	70.28 290	99.15 102	80.02 282	92.87 173	96.15 183

test_prior				97.23 48	98.67 38	92.99 55		98.00 70					99.41 83			99.29 43

新几何1				97.32 42	98.60 45	93.59 41		97.75 91	81.58 294	95.75 65	97.85 56	90.04 56	99.67 37	86.50 202	99.13 54	98.69 89

旧先验1					98.38 58	93.38 47		97.75 91			98.09 41	92.30 25			99.01 62	99.16 51

原ACMM1				96.38 80	98.59 46	91.09 111		97.89 81	87.41 222	95.22 76	97.68 67	90.25 52	99.54 65	87.95 172	99.12 57	98.49 102

testdata2													99.67 37	85.96 213

segment_acmp												92.89 10

testdata				95.46 123	98.18 76	88.90 179		97.66 102	82.73 285	97.03 27	98.07 42	90.06 55	98.85 132	89.67 138	98.98 63	98.64 91

test12				97.65 29	98.46 51	94.26 19		97.66 102		95.52 74		90.89 46	99.46 77		99.25 44	99.22 48

plane_prior7					96.21 154	89.98 137

plane_prior6					96.10 164	90.00 133						81.32 185

plane_prior5								97.51 116					98.60 150	93.02 90	92.23 181	95.86 192

plane_prior4											96.64 115

plane_prior3						90.00 133			94.46 30	91.34 148

plane_prior1					96.14 162

n2								0.00 351

nn								0.00 351

door-mid								91.06 326

lessismore_v0				90.45 289	91.96 303	79.09 308		87.19 335		80.32 305	94.39 222	66.31 306	97.55 256	84.00 244	76.84 306	94.70 262

LGP-MVS_train				94.10 174	96.16 159	88.26 190		97.46 123	91.29 117	90.12 176	97.16 94	79.05 222	98.73 142	92.25 97	91.89 189	95.31 224

test11								97.88 82

door								91.13 325

HQP5-MVS						89.33 166

BP-MVS														92.13 101

HQP4-MVS										90.14 170			98.50 159			95.78 199

HQP3-MVS								97.39 135							92.10 186

HQP2-MVS												80.95 190

NP-MVS					95.99 167	89.81 143					95.87 150

ACMMP++_ref															90.30 214

ACMMP++															91.02 204

Test By Simon												88.73 67

ITE_SJBPF				92.43 246	95.34 187	85.37 256		95.92 228	91.47 111	87.75 236	96.39 132	71.00 286	97.96 221	82.36 262	89.86 219	93.97 279

DeepMVS_CX				74.68 322	90.84 307	64.34 332		81.61 342	65.34 329	67.47 326	88.01 315	48.60 331	80.13 338	62.33 324	73.68 323	79.58 329

High-resolution multi-view benchmark