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.

CP-MVS		94.34 18	94.21 19	94.74 26	98.39 16	86.64 25	97.60 1	97.24 19	88.53 60	92.73 43	97.23 16	85.20 40	99.32 27	92.15 34	98.83 10	98.25 34

APDe-MVS		95.46 1	95.64 1	94.91 12	98.26 20	86.29 38	97.46 2	97.40 9	89.03 47	96.20 4	98.10 1	89.39 7	99.34 23	95.88 1	99.03 1	99.10 1

SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 16	96.99 55	86.33 34	97.33 3	97.30 17	91.38 12	95.39 8	97.46 9	88.98 10	99.40 21	94.12 7	98.89 7	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
EPP-MVSNet		91.70 64	91.56 59	92.13 103	95.88 90	80.50 162	97.33 3	95.25 160	86.15 112	89.76 86	95.60 80	83.42 54	98.32 100	87.37 92	93.25 126	97.56 70

HPM-MVS		94.02 27	93.88 27	94.43 37	98.39 16	85.78 49	97.25 5	97.07 30	86.90 100	92.62 47	96.80 35	84.85 46	99.17 35	92.43 26	98.65 30	98.33 24

3Dnovator	86.66 5	91.73 63	90.82 71	94.44 35	94.59 139	86.37 32	97.18 6	97.02 31	89.20 42	84.31 206	96.66 41	73.74 169	99.17 35	86.74 100	97.96 50	97.79 63

HPM-MVS_fast		93.40 41	93.22 39	93.94 48	98.36 18	84.83 57	97.15 7	96.80 49	85.77 117	92.47 51	97.13 24	82.38 61	99.07 44	90.51 62	98.40 39	97.92 57

3Dnovator+	87.14 4	92.42 56	91.37 60	95.55 2	95.63 98	88.73 2	97.07 8	96.77 52	90.84 17	84.02 210	96.62 43	75.95 135	99.34 23	87.77 85	97.68 55	98.59 9

IS-MVSNet		91.43 67	91.09 66	92.46 89	95.87 92	81.38 137	96.95 9	93.69 222	89.72 34	89.50 89	95.98 68	78.57 102	97.77 137	83.02 139	96.50 76	98.22 35

HFP-MVS		94.52 11	94.40 12	94.86 14	98.61 3	86.81 16	96.94 10	97.34 11	88.63 56	93.65 23	97.21 18	86.10 29	99.49 16	92.35 29	98.77 14	98.30 26

ACMMPR		94.43 15	94.28 15	94.91 12	98.63 2	86.69 21	96.94 10	97.32 16	88.63 56	93.53 30	97.26 15	85.04 42	99.54 10	92.35 29	98.78 13	98.50 11

XVS		94.45 13	94.32 13	94.85 16	98.54 7	86.60 26	96.93 12	97.19 22	90.66 22	92.85 36	97.16 23	85.02 43	99.49 16	91.99 38	98.56 35	98.47 14

X-MVStestdata		88.31 136	86.13 182	94.85 16	98.54 7	86.60 26	96.93 12	97.19 22	90.66 22	92.85 36	23.41 349	85.02 43	99.49 16	91.99 38	98.56 35	98.47 14

region2R		94.43 15	94.27 16	94.92 11	98.65 1	86.67 23	96.92 14	97.23 21	88.60 58	93.58 27	97.27 13	85.22 39	99.54 10	92.21 31	98.74 18	98.56 10

HSP-MVS		95.30 4	95.48 2	94.76 24	98.49 10	86.52 28	96.91 15	96.73 54	91.73 9	96.10 5	96.69 38	89.90 3	99.30 29	94.70 3	98.04 49	98.45 18

mPP-MVS		93.99 28	93.78 30	94.63 29	98.50 9	85.90 47	96.87 16	96.91 41	88.70 54	91.83 64	97.17 22	83.96 52	99.55 7	91.44 51	98.64 31	98.43 20

ACMMP		93.24 47	92.88 48	94.30 41	98.09 27	85.33 53	96.86 17	97.45 6	88.33 63	90.15 83	97.03 26	81.44 74	99.51 14	90.85 59	95.74 83	98.04 48
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
QAPM		89.51 104	88.15 124	93.59 57	94.92 126	84.58 61	96.82 18	96.70 58	78.43 256	83.41 224	96.19 62	73.18 176	99.30 29	77.11 225	96.54 75	96.89 92

CPTT-MVS		91.99 58	91.80 57	92.55 85	98.24 23	81.98 126	96.76 19	96.49 71	81.89 212	90.24 81	96.44 51	78.59 101	98.61 85	89.68 66	97.85 53	97.06 86

MP-MVS		94.25 21	94.07 24	94.77 23	98.47 11	86.31 36	96.71 20	96.98 33	89.04 46	91.98 60	97.19 20	85.43 37	99.56 3	92.06 37	98.79 11	98.44 19

PHI-MVS		93.89 31	93.65 33	94.62 30	96.84 58	86.43 31	96.69 21	97.49 4	85.15 131	93.56 29	96.28 55	85.60 35	99.31 28	92.45 25	98.79 11	98.12 42

OpenMVS	83.78 11	88.74 128	87.29 139	93.08 67	92.70 198	85.39 52	96.57 22	96.43 74	78.74 253	80.85 254	96.07 66	69.64 220	99.01 55	78.01 216	96.65 72	94.83 161

#test#		94.32 20	94.14 21	94.86 14	98.61 3	86.81 16	96.43 23	97.34 11	87.51 84	93.65 23	97.21 18	86.10 29	99.49 16	91.68 47	98.77 14	98.30 26

nrg030		91.08 74	90.39 74	93.17 64	93.07 188	86.91 14	96.41 24	96.26 82	88.30 64	88.37 100	94.85 99	82.19 66	97.64 145	91.09 53	82.95 244	94.96 150

canonicalmvs		93.27 44	92.75 49	94.85 16	95.70 96	87.66 5	96.33 25	96.41 75	90.00 28	94.09 17	94.60 106	82.33 62	98.62 84	92.40 28	92.86 134	98.27 31

VDDNet		89.56 103	88.49 114	92.76 80	95.07 120	82.09 123	96.30 26	93.19 228	81.05 232	91.88 61	96.86 30	61.16 292	98.33 99	88.43 77	92.49 137	97.84 60

APD-MVS_3200maxsize		93.78 32	93.77 31	93.80 54	97.92 31	84.19 75	96.30 26	96.87 45	86.96 96	93.92 21	97.47 8	83.88 53	98.96 64	92.71 24	97.87 52	98.26 33

CSCG		93.23 48	93.05 42	93.76 55	98.04 29	84.07 77	96.22 28	97.37 10	84.15 151	90.05 84	95.66 79	87.77 13	99.15 38	89.91 65	98.27 42	98.07 45

SD-MVS		94.96 7	95.33 5	93.88 49	97.25 52	86.69 21	96.19 29	97.11 28	90.42 24	96.95 1	97.27 13	89.53 5	96.91 215	94.38 5	98.85 8	98.03 49

Vis-MVSNet		91.75 62	91.23 63	93.29 59	95.32 109	83.78 82	96.14 30	95.98 101	89.89 29	90.45 79	96.58 45	75.09 150	98.31 101	84.75 118	96.90 66	97.78 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + MP.		94.85 8	94.94 7	94.58 31	98.25 21	86.33 34	96.11 31	96.62 65	88.14 70	96.10 5	96.96 28	89.09 9	98.94 65	94.48 4	98.68 24	98.48 13

LFMVS		90.08 90	89.13 99	92.95 73	96.71 60	82.32 121	96.08 32	89.91 303	86.79 101	92.15 58	96.81 33	62.60 280	98.34 98	87.18 94	93.90 112	98.19 36

API-MVS		90.66 79	90.07 81	92.45 90	96.36 69	84.57 62	96.06 33	95.22 166	82.39 198	89.13 91	94.27 116	80.32 82	98.46 92	80.16 186	96.71 70	94.33 186

EPNet		91.79 60	91.02 67	94.10 45	90.10 287	85.25 54	96.03 34	92.05 250	92.83 1	87.39 121	95.78 75	79.39 95	99.01 55	88.13 81	97.48 59	98.05 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part3							95.99 35		88.25 66		97.60 4		99.62 1	93.18 18

ESAPD		95.32 3	95.38 3	95.17 6	98.55 5	87.22 10	95.99 35	97.45 6	88.25 66	96.40 2	97.60 4	91.93 1	99.62 1	93.18 18	99.02 2	98.67 4

alignmvs		93.08 50	92.50 53	94.81 21	95.62 99	87.61 6	95.99 35	96.07 96	89.77 32	94.12 16	94.87 96	80.56 80	98.66 80	92.42 27	93.10 129	98.15 39

MVSFormer		91.68 65	91.30 61	92.80 78	93.86 166	83.88 80	95.96 38	95.90 108	84.66 140	91.76 65	94.91 94	77.92 109	97.30 182	89.64 67	97.11 63	97.24 76

test_djsdf		89.03 121	88.64 109	90.21 173	90.74 271	79.28 202	95.96 38	95.90 108	84.66 140	85.33 180	92.94 161	74.02 164	97.30 182	89.64 67	88.53 191	94.05 198

HPM-MVS++		95.14 6	94.91 8	95.83 1	98.25 21	89.65 1	95.92 40	96.96 37	91.75 8	94.02 19	96.83 32	88.12 11	99.55 7	93.41 15	98.94 5	98.28 28

APD-MVS		94.24 22	94.07 24	94.75 25	98.06 28	86.90 15	95.88 41	96.94 39	85.68 120	95.05 11	97.18 21	87.31 19	99.07 44	91.90 45	98.61 33	98.28 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HQP_MVS		90.60 83	90.19 78	91.82 116	94.70 135	82.73 112	95.85 42	96.22 86	90.81 18	86.91 128	94.86 97	74.23 158	98.12 108	88.15 79	89.99 164	94.63 168

plane_prior2							95.85 42		90.81 18

MSLP-MVS++		93.72 33	94.08 23	92.65 82	97.31 46	83.43 91	95.79 44	97.33 14	90.03 27	93.58 27	96.96 28	84.87 45	97.76 138	92.19 33	98.66 28	96.76 94

FC-MVSNet-test		90.27 87	90.18 79	90.53 153	93.71 172	79.85 176	95.77 45	97.59 2	89.31 40	86.27 141	94.67 103	81.93 72	97.01 207	84.26 126	88.09 201	94.71 165

FIs		90.51 84	90.35 75	90.99 144	93.99 162	80.98 148	95.73 46	97.54 3	89.15 44	86.72 132	94.68 102	81.83 73	97.24 190	85.18 111	88.31 198	94.76 164

VDD-MVS		90.74 77	89.92 85	93.20 62	96.27 71	83.02 102	95.73 46	93.86 218	88.42 62	92.53 48	96.84 31	62.09 283	98.64 82	90.95 57	92.62 136	97.93 56

UGNet		89.95 94	88.95 103	92.95 73	94.51 142	83.31 94	95.70 48	95.23 164	89.37 39	87.58 118	93.94 126	64.00 276	98.78 77	83.92 131	96.31 79	96.74 96
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
ACMMP_Plus		94.74 10	94.56 11	95.28 4	98.02 30	87.70 4	95.68 49	97.34 11	88.28 65	95.30 10	97.67 3	85.90 33	99.54 10	93.91 9	98.95 4	98.60 8

MAR-MVS		90.30 86	89.37 93	93.07 69	96.61 62	84.48 66	95.68 49	95.67 123	82.36 200	87.85 108	92.85 162	76.63 120	98.80 76	80.01 187	96.68 71	95.91 119
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
UA-Net		92.83 52	92.54 52	93.68 56	96.10 82	84.71 59	95.66 51	96.39 77	91.92 4	93.22 32	96.49 49	83.16 55	98.87 67	84.47 121	95.47 88	97.45 73

NCCC		94.81 9	94.69 10	95.17 6	97.83 32	87.46 9	95.66 51	96.93 40	92.34 2	93.94 20	96.58 45	87.74 14	99.44 20	92.83 22	98.40 39	98.62 7

DeepC-MVS_fast	89.43 2	94.04 26	93.79 29	94.80 22	97.48 41	86.78 18	95.65 53	96.89 42	89.40 38	92.81 39	96.97 27	85.37 38	99.24 31	90.87 58	98.69 21	98.38 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		87.80 155	87.37 137	89.10 228	93.23 184	78.12 235	95.61 54	97.30 17	87.90 74	83.72 216	92.01 194	79.65 94	96.01 257	76.36 229	80.54 280	93.16 247

Vis-MVSNet (Re-imp)		89.59 102	89.44 91	90.03 190	95.74 94	75.85 269	95.61 54	90.80 287	87.66 83	87.83 113	95.40 84	76.79 117	96.46 240	78.37 210	96.73 69	97.80 62

abl_6		93.18 49	93.05 42	93.57 58	97.52 38	84.27 74	95.53 56	96.67 61	87.85 76	93.20 33	97.22 17	80.35 81	99.18 34	91.91 42	97.21 62	97.26 75

VPA-MVSNet		89.62 100	88.96 102	91.60 123	93.86 166	82.89 107	95.46 57	97.33 14	87.91 73	88.43 99	93.31 143	74.17 161	97.40 175	87.32 93	82.86 246	94.52 177

PS-CasMVS		87.32 178	86.88 153	88.63 236	92.99 193	76.33 266	95.33 58	96.61 66	88.22 68	83.30 226	93.07 154	73.03 178	95.79 267	78.36 211	81.00 274	93.75 218

jajsoiax		88.24 138	87.50 133	90.48 162	90.89 266	80.14 166	95.31 59	95.65 127	84.97 134	84.24 208	94.02 122	65.31 270	97.42 168	88.56 75	88.52 192	93.89 204

ACMM	84.12 9	89.14 116	88.48 115	91.12 135	94.65 138	81.22 141	95.31 59	96.12 93	85.31 127	85.92 146	94.34 109	70.19 215	98.06 124	85.65 108	88.86 188	94.08 197
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PGM-MVS		93.96 29	93.72 32	94.68 27	98.43 13	86.22 39	95.30 61	97.78 1	87.45 85	93.26 31	97.33 11	84.62 47	99.51 14	90.75 60	98.57 34	98.32 25

LPG-MVS_test		89.45 107	88.90 105	91.12 135	94.47 143	81.49 132	95.30 61	96.14 90	86.73 102	85.45 168	95.16 89	69.89 216	98.10 114	87.70 86	89.23 178	93.77 216

view600		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 63	91.09 274	87.08 91	87.84 109	93.03 156	68.86 234	98.11 110	69.44 277	91.02 151	94.96 150

view800		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 63	91.09 274	87.08 91	87.84 109	93.03 156	68.86 234	98.11 110	69.44 277	91.02 151	94.96 150

conf0.05thres1000		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 63	91.09 274	87.08 91	87.84 109	93.03 156	68.86 234	98.11 110	69.44 277	91.02 151	94.96 150

tfpn		87.62 165	86.65 166	90.53 153	96.19 73	78.52 220	95.29 63	91.09 274	87.08 91	87.84 109	93.03 156	68.86 234	98.11 110	69.44 277	91.02 151	94.96 150

CP-MVSNet		87.63 163	87.26 141	88.74 233	93.12 187	76.59 263	95.29 63	96.58 69	88.43 61	83.49 223	92.98 160	75.28 147	95.83 264	78.97 206	81.15 269	93.79 212

CNVR-MVS		95.40 2	95.37 4	95.50 3	98.11 25	88.51 3	95.29 63	96.96 37	92.09 3	95.32 9	97.08 25	89.49 6	99.33 26	95.10 2	98.85 8	98.66 6

pm-mvs1		86.61 196	85.54 194	89.82 197	91.44 224	80.18 164	95.28 69	94.85 183	83.84 155	81.66 245	92.62 171	72.45 188	96.48 238	79.67 197	78.06 296	92.82 259

PS-MVSNAJss		89.97 93	89.62 87	91.02 142	91.90 210	80.85 153	95.26 70	95.98 101	86.26 110	86.21 142	94.29 113	79.70 90	97.65 143	88.87 73	88.10 199	94.57 174

LS3D		87.89 149	86.32 178	92.59 84	96.07 84	82.92 106	95.23 71	94.92 180	75.66 278	82.89 229	95.98 68	72.48 186	99.21 32	68.43 286	95.23 94	95.64 131

mvs_tets		88.06 144	87.28 140	90.38 168	90.94 262	79.88 174	95.22 72	95.66 125	85.10 132	84.21 209	93.94 126	63.53 278	97.40 175	88.50 76	88.40 197	93.87 207

plane_prior						82.73 112	95.21 73		89.66 35						89.88 167

PEN-MVS		86.80 191	86.27 180	88.40 249	92.32 204	75.71 270	95.18 74	96.38 78	87.97 71	82.82 230	93.15 150	73.39 174	95.92 260	76.15 233	79.03 294	93.59 233

TransMVSNet (Re)		84.43 249	83.06 250	88.54 245	91.72 216	78.44 226	95.18 74	92.82 234	82.73 194	79.67 268	92.12 186	73.49 171	95.96 259	71.10 266	68.73 328	91.21 291

114514_t		89.51 104	88.50 112	92.54 86	98.11 25	81.99 125	95.16 76	96.36 79	70.19 317	85.81 147	95.25 87	76.70 118	98.63 83	82.07 154	96.86 68	97.00 87

GBi-Net		87.26 179	85.98 187	91.08 138	94.01 158	83.10 98	95.14 77	94.94 176	83.57 163	84.37 201	91.64 203	66.59 260	96.34 246	78.23 213	85.36 221	93.79 212

test1		87.26 179	85.98 187	91.08 138	94.01 158	83.10 98	95.14 77	94.94 176	83.57 163	84.37 201	91.64 203	66.59 260	96.34 246	78.23 213	85.36 221	93.79 212

FMVSNet1		85.85 211	84.11 226	91.08 138	92.81 196	83.10 98	95.14 77	94.94 176	81.64 222	82.68 231	91.64 203	59.01 302	96.34 246	75.37 238	83.78 234	93.79 212

v7n		86.81 190	85.76 192	89.95 194	90.72 272	79.25 204	95.07 80	95.92 105	84.45 146	82.29 234	90.86 238	72.60 184	97.53 150	79.42 203	80.52 282	93.08 252

ACMP	84.23 8	89.01 123	88.35 116	90.99 144	94.73 132	81.27 138	95.07 80	95.89 110	86.48 105	83.67 218	94.30 112	69.33 223	97.99 128	87.10 99	88.55 190	93.72 220
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MPTG		94.47 12	94.30 14	95.00 9	98.42 14	86.95 12	95.06 82	96.97 34	91.07 14	93.14 34	97.56 6	84.30 49	99.56 3	93.43 13	98.75 16	98.47 14

conf200view11		87.65 159	86.71 162	90.46 164	96.12 78	78.55 217	95.03 83	91.58 263	87.15 87	88.06 104	92.29 181	68.91 231	98.10 114	70.13 271	91.10 144	94.71 165

thres100view900		87.63 163	86.71 162	90.38 168	96.12 78	78.55 217	95.03 83	91.58 263	87.15 87	88.06 104	92.29 181	68.91 231	98.10 114	70.13 271	91.10 144	94.48 182

MCST-MVS		94.45 13	94.20 20	95.19 5	98.46 12	87.50 8	95.00 85	97.12 26	87.13 89	92.51 50	96.30 54	89.24 8	99.34 23	93.46 12	98.62 32	98.73 3

pmmvs6		83.42 257	81.60 260	88.87 230	88.01 311	77.87 242	94.96 86	94.24 201	74.67 288	78.80 273	91.09 235	60.17 297	96.49 237	77.06 227	75.40 304	92.23 275

mvs-test1		89.45 107	89.14 98	90.38 168	93.33 180	77.63 250	94.95 87	94.36 197	87.70 79	87.10 125	92.81 166	73.45 172	98.03 126	85.57 109	93.04 130	95.48 134

CANet		93.54 37	93.20 40	94.55 32	95.65 97	85.73 50	94.94 88	96.69 60	91.89 5	90.69 77	95.88 72	81.99 71	99.54 10	93.14 20	97.95 51	98.39 21

DTE-MVSNet		86.11 204	85.48 198	87.98 259	91.65 220	74.92 273	94.93 89	95.75 119	87.36 86	82.26 235	93.04 155	72.85 179	95.82 265	74.04 249	77.46 299	93.20 245

TranMVSNet+NR-MVSNet		88.84 125	87.95 128	91.49 125	92.68 199	83.01 103	94.92 90	96.31 80	89.88 30	85.53 162	93.85 133	76.63 120	96.96 211	81.91 158	79.87 291	94.50 179

v748		86.27 202	85.28 202	89.25 223	90.26 284	77.58 256	94.89 91	95.50 139	84.28 150	81.41 248	90.46 249	72.57 185	97.32 181	79.81 195	78.36 295	92.84 257

DeepC-MVS	88.79 3	93.31 42	92.99 44	94.26 42	96.07 84	85.83 48	94.89 91	96.99 32	89.02 48	89.56 87	97.37 10	82.51 60	99.38 22	92.20 32	98.30 41	97.57 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres600view7		87.65 159	86.67 165	90.59 150	96.08 83	78.72 213	94.88 93	91.58 263	87.06 95	88.08 103	92.30 180	68.91 231	98.10 114	70.05 275	91.10 144	94.96 150

PVSNet_Blended_VisFu		91.38 68	90.91 69	92.80 78	96.39 68	83.17 97	94.87 94	96.66 62	83.29 173	89.27 90	94.46 108	80.29 83	99.17 35	87.57 88	95.37 90	96.05 116

VNet		92.24 57	91.91 56	93.24 61	96.59 63	83.43 91	94.84 95	96.44 72	89.19 43	94.08 18	95.90 71	77.85 112	98.17 105	88.90 72	93.38 123	98.13 41

MP-MVS-pluss		94.21 24	94.00 26	94.85 16	98.17 24	86.65 24	94.82 96	97.17 24	86.26 110	92.83 38	97.87 2	85.57 36	99.56 3	94.37 6	98.92 6	98.34 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS		87.25 181	85.36 201	92.90 75	97.65 34	83.24 95	94.81 97	92.00 252	74.99 284	81.92 243	95.00 93	72.66 182	99.05 46	66.92 295	92.33 138	96.40 101

FMVSNet2		87.19 185	85.82 191	91.30 131	94.01 158	83.67 85	94.79 98	94.94 176	83.57 163	83.88 212	92.05 193	66.59 260	96.51 236	77.56 220	85.01 225	93.73 219

UniMVSNet (Re)		89.80 98	89.07 100	92.01 104	93.60 175	84.52 63	94.78 99	97.47 5	89.26 41	86.44 138	92.32 179	82.10 67	97.39 178	84.81 117	80.84 276	94.12 193

NR-MVSNet		88.58 131	87.47 135	91.93 110	93.04 190	84.16 76	94.77 100	96.25 84	89.05 45	80.04 266	93.29 145	79.02 96	97.05 205	81.71 162	80.05 286	94.59 172

V4		86.50 198	85.54 194	89.39 215	89.13 298	78.99 211	94.73 101	95.54 134	83.59 161	82.10 238	90.61 243	71.60 192	97.45 157	82.52 145	80.01 287	91.74 281

F-COLMAP		87.95 148	86.80 158	91.40 128	96.35 70	80.88 152	94.73 101	95.45 145	79.65 243	82.04 241	94.61 105	71.13 198	98.50 90	76.24 232	91.05 149	94.80 163

ACMH	80.38 17	85.36 224	83.68 236	90.39 166	94.45 145	80.63 157	94.73 101	94.85 183	82.09 203	77.24 283	92.65 170	60.01 298	97.58 146	72.25 259	84.87 226	92.96 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn1000		86.06 206	84.92 210	89.49 212	95.54 100	77.79 244	94.72 104	89.07 317	82.05 204	85.36 179	91.94 196	68.32 252	96.65 228	67.04 292	90.24 161	94.02 200

v52		86.50 198	85.53 197	89.39 215	89.17 297	78.99 211	94.72 104	95.54 134	83.59 161	82.10 238	90.60 244	71.59 193	97.45 157	82.52 145	79.99 288	91.73 282

MVS_0304		93.25 46	92.62 50	95.14 8	95.72 95	87.58 7	94.71 106	96.59 67	91.78 7	91.46 69	96.18 63	75.45 146	99.55 7	93.53 10	98.19 44	98.28 28

Regformer-3		93.68 34	93.64 34	93.81 53	95.36 106	84.61 60	94.68 107	95.83 113	91.27 13	93.60 26	96.71 36	85.75 34	98.86 70	92.87 21	96.65 72	97.96 52

Regformer-4		93.91 30	93.81 28	94.19 44	95.36 106	85.47 51	94.68 107	96.41 75	91.60 11	93.75 22	96.71 36	85.95 32	99.10 43	93.21 17	96.65 72	98.01 51

anonymousdsp		87.84 151	87.09 146	90.12 181	89.13 298	80.54 160	94.67 109	95.55 132	82.05 204	83.82 214	92.12 186	71.47 196	97.15 196	87.15 95	87.80 204	92.67 261

DP-MVS Recon		91.95 59	91.28 62	93.96 47	98.33 19	85.92 44	94.66 110	96.66 62	82.69 196	90.03 85	95.82 74	82.30 63	99.03 50	84.57 120	96.48 77	96.91 90

tfpn_ndepth		86.10 205	84.98 206	89.43 214	95.52 103	78.29 231	94.62 111	89.60 309	81.88 218	85.43 171	90.54 245	68.47 244	96.85 219	68.46 285	90.34 160	93.15 249

Effi-MVS+		91.59 66	91.11 64	93.01 71	94.35 150	83.39 93	94.60 112	95.10 169	87.10 90	90.57 78	93.10 153	81.43 75	98.07 123	89.29 69	94.48 104	97.59 68

tfpn200view9		87.58 171	86.64 170	90.41 165	95.99 87	78.64 215	94.58 113	91.98 254	86.94 98	88.09 101	91.77 200	69.18 228	98.10 114	70.13 271	91.10 144	94.48 182

thres400		87.62 165	86.64 170	90.57 151	95.99 87	78.64 215	94.58 113	91.98 254	86.94 98	88.09 101	91.77 200	69.18 228	98.10 114	70.13 271	91.10 144	94.96 150

Regformer-1		94.22 23	94.13 22	94.51 34	95.54 100	86.36 33	94.57 115	96.44 72	91.69 10	94.32 14	96.56 47	87.05 22	99.03 50	93.35 16	97.65 57	98.15 39

Regformer-2		94.33 19	94.22 17	94.68 27	95.54 100	86.75 20	94.57 115	96.70 58	91.84 6	94.41 12	96.56 47	87.19 20	99.13 40	93.50 11	97.65 57	98.16 38

v8		87.50 174	86.71 162	89.89 195	91.37 233	79.40 194	94.50 117	95.38 151	84.81 137	83.60 220	91.33 221	76.05 128	97.42 168	82.84 142	80.51 283	92.84 257

tfpnnormal		84.72 244	83.23 248	89.20 225	92.79 197	80.05 169	94.48 118	95.81 114	82.38 199	81.08 252	91.21 228	69.01 230	96.95 212	61.69 316	80.59 279	90.58 308

EI-MVSNet-Vis-set		93.01 51	92.92 46	93.29 59	95.01 121	83.51 90	94.48 118	95.77 117	90.87 16	92.52 49	96.67 40	84.50 48	99.00 58	91.99 38	94.44 107	97.36 74

v10		87.25 181	86.38 175	89.85 196	91.19 251	79.50 181	94.48 118	95.45 145	83.79 158	83.62 219	91.19 229	75.13 148	97.42 168	81.94 157	80.60 278	92.63 263

Effi-MVS+-dtu		88.65 129	88.35 116	89.54 208	93.33 180	76.39 264	94.47 121	94.36 197	87.70 79	85.43 171	89.56 263	73.45 172	97.26 188	85.57 109	91.28 143	94.97 147

DU-MVS		89.34 114	88.50 112	91.85 114	93.04 190	83.72 83	94.47 121	96.59 67	89.50 36	86.46 135	93.29 145	77.25 113	97.23 192	84.92 114	81.02 272	94.59 172

ACMH+	81.04 14	85.05 231	83.46 243	89.82 197	94.66 137	79.37 196	94.44 123	94.12 206	82.19 202	78.04 277	92.82 165	58.23 304	97.54 149	73.77 252	82.90 245	92.54 264

UniMVSNet_NR-MVSNet		89.92 96	89.29 95	91.81 118	93.39 179	83.72 83	94.43 124	97.12 26	89.80 31	86.46 135	93.32 142	83.16 55	97.23 192	84.92 114	81.02 272	94.49 181

AdaColmap		89.89 97	89.07 100	92.37 94	97.41 42	83.03 101	94.42 125	95.92 105	82.81 192	86.34 140	94.65 104	73.89 165	99.02 53	80.69 174	95.51 86	95.05 144

EI-MVSNet-UG-set		92.74 54	92.62 50	93.12 65	94.86 129	83.20 96	94.40 126	95.74 120	90.71 21	92.05 59	96.60 44	84.00 51	98.99 59	91.55 48	93.63 116	97.17 81

TSAR-MVS + GP.		93.66 35	93.41 36	94.41 38	96.59 63	86.78 18	94.40 126	93.93 217	89.77 32	94.21 15	95.59 81	87.35 18	98.61 85	92.72 23	96.15 80	97.83 61

HQP-NCC					94.17 152		94.39 128		88.81 50	85.43 171

ACMP_Plane					94.17 152		94.39 128		88.81 50	85.43 171

HQP-MVS		89.80 98	89.28 96	91.34 129	94.17 152	81.56 129	94.39 128	96.04 99	88.81 50	85.43 171	93.97 125	73.83 167	97.96 129	87.11 97	89.77 169	94.50 179

TAPA-MVS	84.62 6	88.16 140	87.01 151	91.62 122	96.64 61	80.65 156	94.39 128	96.21 89	76.38 271	86.19 143	95.44 82	79.75 88	98.08 122	62.75 314	95.29 92	96.13 109
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1neww		87.98 145	87.25 142	90.16 175	91.38 231	79.41 190	94.37 132	95.28 156	84.48 143	85.77 149	91.53 212	76.12 124	97.45 157	84.45 123	81.89 257	93.61 231

v7new		87.98 145	87.25 142	90.16 175	91.38 231	79.41 190	94.37 132	95.28 156	84.48 143	85.77 149	91.53 212	76.12 124	97.45 157	84.45 123	81.89 257	93.61 231

v6		87.98 145	87.25 142	90.16 175	91.36 234	79.39 195	94.37 132	95.27 159	84.48 143	85.78 148	91.51 214	76.15 123	97.46 155	84.46 122	81.88 259	93.62 230

PAPM_NR		91.22 71	90.78 72	92.52 87	97.60 35	81.46 134	94.37 132	96.24 85	86.39 108	87.41 119	94.80 101	82.06 69	98.48 91	82.80 143	95.37 90	97.61 67

MTAPA		94.42 17	94.22 17	95.00 9	98.42 14	86.95 12	94.36 136	96.97 34	91.07 14	93.14 34	97.56 6	84.30 49	99.56 3	93.43 13	98.75 16	98.47 14

PLC	84.53 7	89.06 120	88.03 126	92.15 101	97.27 50	82.69 115	94.29 137	95.44 147	79.71 242	84.01 211	94.18 118	76.68 119	98.75 78	77.28 222	93.41 122	95.02 145
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		87.75 156	86.96 152	90.12 181	91.20 249	79.50 181	94.28 138	95.46 141	83.45 168	85.75 151	91.56 211	75.13 148	97.43 166	83.60 134	82.18 252	93.42 240

COLMAP_ROB	80.39 16	83.96 252	82.04 258	89.74 201	95.28 110	79.75 178	94.25 139	92.28 244	75.17 282	78.02 278	93.77 135	58.60 303	97.84 135	65.06 307	85.92 216	91.63 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V42		87.68 158	86.86 154	90.15 179	90.58 276	80.14 166	94.24 140	95.28 156	83.66 160	85.67 157	91.33 221	74.73 153	97.41 173	84.43 125	81.83 260	92.89 255

Baseline_NR-MVSNet		87.07 187	86.63 172	88.40 249	91.44 224	77.87 242	94.23 141	92.57 240	84.12 152	85.74 153	92.08 190	77.25 113	96.04 254	82.29 152	79.94 289	91.30 290

conf0.002		85.83 213	84.54 219	89.71 203	95.26 112	77.63 250	94.21 142	89.33 311	81.89 212	84.94 186	91.51 214	68.43 246	96.80 220	66.05 298	89.23 178	94.71 165

thresconf0.02		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 142	89.33 311	81.89 212	84.94 186	91.51 214	68.43 246	96.80 220	66.05 298	89.23 178	93.70 221

tfpn_n400		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 142	89.33 311	81.89 212	84.94 186	91.51 214	68.43 246	96.80 220	66.05 298	89.23 178	93.70 221

tfpnconf		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 142	89.33 311	81.89 212	84.94 186	91.51 214	68.43 246	96.80 220	66.05 298	89.23 178	93.70 221

tfpnview11		85.75 216	84.54 219	89.38 217	95.26 112	77.63 250	94.21 142	89.33 311	81.89 212	84.94 186	91.51 214	68.43 246	96.80 220	66.05 298	89.23 178	93.70 221

FMVSNet3		87.40 177	86.11 183	91.30 131	93.79 171	83.64 86	94.20 147	94.81 186	83.89 154	84.37 201	91.87 199	68.45 245	96.56 233	78.23 213	85.36 221	93.70 221

OPM-MVS		90.12 89	89.56 88	91.82 116	93.14 186	83.90 79	94.16 148	95.74 120	88.96 49	87.86 107	95.43 83	72.48 186	97.91 133	88.10 82	90.18 163	93.65 226

test_prior3		93.60 36	93.53 35	93.82 51	97.29 48	84.49 64	94.12 149	96.88 43	87.67 81	92.63 45	96.39 52	86.62 25	98.87 67	91.50 49	98.67 26	98.11 43

test_prior2							94.12 149		87.67 81	92.63 45	96.39 52	86.62 25		91.50 49	98.67 26

test_prior4						85.96 43	94.11 151

EPNet_dtu		86.49 200	85.94 189	88.14 257	90.24 285	72.82 287	94.11 151	92.20 246	86.66 104	79.42 271	92.36 178	73.52 170	95.81 266	71.26 262	93.66 115	95.80 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
divwei89l23v2f112		87.84 151	87.09 146	90.10 188	91.23 246	79.24 206	94.09 153	95.24 161	84.44 147	85.70 154	91.31 224	75.91 136	97.44 164	84.17 128	81.73 264	93.64 227

v1		87.85 150	87.10 145	90.11 186	91.21 248	79.24 206	94.09 153	95.24 161	84.44 147	85.70 154	91.31 224	75.96 134	97.45 157	84.18 127	81.73 264	93.64 227

v1141		87.84 151	87.09 146	90.11 186	91.23 246	79.25 204	94.08 155	95.24 161	84.44 147	85.69 156	91.31 224	75.91 136	97.44 164	84.17 128	81.74 263	93.63 229

CNLPA		89.07 118	87.98 127	92.34 95	96.87 57	84.78 58	94.08 155	93.24 227	81.41 228	84.46 198	95.13 91	75.57 143	96.62 230	77.21 223	93.84 114	95.61 132

TEST9					97.53 36	86.49 29	94.07 157	96.78 50	81.61 224	92.77 40	96.20 59	87.71 15	99.12 41

train_agg		93.44 39	93.08 41	94.52 33	97.53 36	86.49 29	94.07 157	96.78 50	81.86 219	92.77 40	96.20 59	87.63 16	99.12 41	92.14 35	98.69 21	97.94 53

agg_prior3		93.27 44	92.89 47	94.40 39	97.49 39	86.12 41	94.07 157	96.73 54	81.46 227	92.46 52	96.05 67	86.90 23	99.15 38	92.14 35	98.69 21	97.94 53

CDPH-MVS		92.83 52	92.30 54	94.44 35	97.79 33	86.11 42	94.06 160	96.66 62	80.09 238	92.77 40	96.63 42	86.62 25	99.04 49	87.40 90	98.66 28	98.17 37

VPNet		88.20 139	87.47 135	90.39 166	93.56 176	79.46 186	94.04 161	95.54 134	88.67 55	86.96 126	94.58 107	69.33 223	97.15 196	84.05 130	80.53 281	94.56 175

Fast-Effi-MVS+-dtu		87.44 175	86.72 161	89.63 206	92.04 209	77.68 249	94.03 162	93.94 216	85.81 115	82.42 233	91.32 223	70.33 213	97.06 204	80.33 183	90.23 162	94.14 192

test_8					97.49 39	86.30 37	94.02 163	96.76 53	81.86 219	92.70 44	96.20 59	87.63 16	99.02 53

OurMVSNet-221017-0		85.35 225	84.64 218	87.49 269	90.77 269	72.59 293	94.01 164	94.40 196	84.72 139	79.62 270	93.17 149	61.91 285	96.72 225	81.99 156	81.16 267	93.16 247

v2v482		87.84 151	87.06 149	90.17 174	90.99 258	79.23 208	94.00 165	95.13 168	84.87 135	85.53 162	92.07 192	74.45 155	97.45 157	84.71 119	81.75 262	93.85 210

DeepPCF-MVS	89.96 1	94.20 25	94.77 9	92.49 88	96.52 66	80.00 172	94.00 165	97.08 29	90.05 26	95.65 7	97.29 12	89.66 4	98.97 61	93.95 8	98.71 19	98.50 11

v1144		87.61 170	86.79 159	90.06 189	91.01 257	79.34 198	93.95 167	95.42 150	83.36 172	85.66 158	91.31 224	74.98 152	97.42 168	83.37 135	82.06 253	93.42 240

agg_prior1		93.29 43	92.97 45	94.26 42	97.38 43	85.92 44	93.92 168	96.72 56	81.96 207	92.16 56	96.23 57	87.85 12	98.97 61	91.95 41	98.55 37	97.90 58

v144192		87.19 185	86.35 176	89.74 201	90.64 275	78.24 233	93.92 168	95.43 148	81.93 209	85.51 164	91.05 236	74.21 160	97.45 157	82.86 141	81.56 266	93.53 235

PVSNet_BlendedMVS		89.98 92	89.70 86	90.82 147	96.12 78	81.25 139	93.92 168	96.83 46	83.49 167	89.10 92	92.26 183	81.04 78	98.85 73	86.72 103	87.86 203	92.35 272

v17		84.93 236	83.70 235	88.62 237	91.36 234	79.48 184	93.83 171	94.03 209	83.04 182	76.51 288	86.57 297	76.05 128	95.42 281	80.31 185	71.65 314	90.96 295

v16		84.96 234	83.74 233	88.62 237	91.40 229	79.48 184	93.83 171	94.04 207	83.03 183	76.54 287	86.59 296	76.11 127	95.42 281	80.33 183	71.80 312	90.95 297

v18		84.97 233	83.76 231	88.60 239	91.36 234	79.41 190	93.82 173	94.04 207	83.00 186	76.61 286	86.60 295	76.19 122	95.43 280	80.39 180	71.79 313	90.96 295

v1921920		86.97 189	86.06 186	89.69 205	90.53 280	78.11 236	93.80 174	95.43 148	81.90 211	85.33 180	91.05 236	72.66 182	97.41 173	82.05 155	81.80 261	93.53 235

v1192		87.25 181	86.33 177	90.00 193	90.76 270	79.04 210	93.80 174	95.48 140	82.57 197	85.48 166	91.18 230	73.38 175	97.42 168	82.30 151	82.06 253	93.53 235

v13		84.72 244	83.44 245	88.58 240	91.31 244	79.52 180	93.77 176	94.00 213	83.03 183	75.85 298	86.38 303	75.84 138	95.35 287	79.83 194	70.95 319	90.87 302

XXY-MVS		87.65 159	86.85 155	90.03 190	92.14 206	80.60 159	93.76 177	95.23 164	82.94 188	84.60 194	94.02 122	74.27 157	95.49 278	81.04 167	83.68 237	94.01 201

v12		84.74 242	83.46 243	88.58 240	91.32 241	79.50 181	93.75 178	94.01 210	83.06 179	75.98 297	86.41 302	75.82 139	95.36 286	79.87 193	70.89 321	90.89 301

V9		84.77 241	83.50 242	88.58 240	91.33 239	79.46 186	93.75 178	94.00 213	83.07 178	76.07 295	86.43 298	75.97 133	95.37 284	79.91 192	70.93 320	90.91 299

MVSTER		88.84 125	88.29 121	90.51 160	92.95 194	80.44 163	93.73 180	95.01 172	84.66 140	87.15 123	93.12 152	72.79 180	97.21 194	87.86 84	87.36 207	93.87 207

IterMVS-LS		88.36 135	87.91 130	89.70 204	93.80 169	78.29 231	93.73 180	95.08 171	85.73 118	84.75 192	91.90 198	79.88 86	96.92 214	83.83 132	82.51 248	93.89 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V14		84.79 239	83.52 241	88.57 243	91.32 241	79.43 189	93.72 182	94.01 210	83.06 179	76.22 290	86.43 298	76.01 132	95.37 284	79.96 189	70.99 318	90.91 299

v148		87.04 188	86.32 178	89.21 224	90.94 262	77.26 257	93.71 183	94.43 195	84.84 136	84.36 204	90.80 239	76.04 130	97.05 205	82.12 153	79.60 292	93.31 242

v15		84.79 239	83.53 240	88.57 243	91.30 245	79.41 190	93.70 184	94.01 210	83.06 179	76.27 289	86.42 301	76.03 131	95.38 283	80.01 187	71.00 317	90.92 298

EI-MVSNet		89.10 117	88.86 107	89.80 200	91.84 212	78.30 230	93.70 184	95.01 172	85.73 118	87.15 123	95.28 85	79.87 87	97.21 194	83.81 133	87.36 207	93.88 206

CVMVSNet		84.69 246	84.79 214	84.37 302	91.84 212	64.92 326	93.70 184	91.47 269	66.19 327	86.16 144	95.28 85	67.18 257	93.33 310	80.89 172	90.42 158	94.88 159

v11		84.67 247	83.41 246	88.44 248	91.32 241	79.13 209	93.69 187	93.99 215	82.81 192	76.20 291	86.24 305	75.48 144	95.35 287	79.53 198	71.48 316	90.85 303

v1240		86.78 192	85.85 190	89.56 207	90.45 281	77.79 244	93.61 188	95.37 153	81.65 221	85.43 171	91.15 232	71.50 195	97.43 166	81.47 164	82.05 255	93.47 239

MG-MVS		91.77 61	91.70 58	92.00 106	97.08 54	80.03 171	93.60 189	95.18 167	87.85 76	90.89 76	96.47 50	82.06 69	98.36 95	85.07 112	97.04 65	97.62 66

Fast-Effi-MVS+		89.41 110	88.64 109	91.71 120	94.74 131	80.81 154	93.54 190	95.10 169	83.11 176	86.82 131	90.67 241	79.74 89	97.75 141	80.51 179	93.55 117	96.57 99

OMC-MVS		91.23 70	90.62 73	93.08 67	96.27 71	84.07 77	93.52 191	95.93 104	86.95 97	89.51 88	96.13 65	78.50 103	98.35 97	85.84 107	92.90 133	96.83 93

CANet_DTU		90.26 88	89.41 92	92.81 77	93.46 178	83.01 103	93.48 192	94.47 194	89.43 37	87.76 116	94.23 117	70.54 211	99.03 50	84.97 113	96.39 78	96.38 102

SixPastTwentyTwo		83.91 253	82.90 252	86.92 281	90.99 258	70.67 307	93.48 192	91.99 253	85.54 122	77.62 281	92.11 188	60.59 294	96.87 217	76.05 234	77.75 297	93.20 245

MVS_Test		91.31 69	91.11 64	91.93 110	94.37 147	80.14 166	93.46 194	95.80 115	86.46 106	91.35 71	93.77 135	82.21 65	98.09 121	87.57 88	94.95 95	97.55 71

旧先验2							93.36 195		71.25 312	94.37 13			97.13 199	86.74 100

xiu_mvs_v1_base_debu		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 196	95.46 141	85.17 128	92.25 53	94.03 119	70.59 207	98.57 87	90.97 54	94.67 97	94.18 189

xiu_mvs_v1_base		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 196	95.46 141	85.17 128	92.25 53	94.03 119	70.59 207	98.57 87	90.97 54	94.67 97	94.18 189

xiu_mvs_v1_base_debi		90.64 80	90.05 82	92.40 91	93.97 163	84.46 67	93.32 196	95.46 141	85.17 128	92.25 53	94.03 119	70.59 207	98.57 87	90.97 54	94.67 97	94.18 189

EU-MVSNet		81.32 277	80.95 265	82.42 310	88.50 305	63.67 327	93.32 196	91.33 271	64.02 331	80.57 259	92.83 164	61.21 291	92.27 318	76.34 230	80.38 284	91.32 289

TAMVS		89.21 115	88.29 121	91.96 108	93.71 172	82.62 117	93.30 200	94.19 202	82.22 201	87.78 115	93.94 126	78.83 97	96.95 212	77.70 218	92.98 131	96.32 103

BH-untuned		88.60 130	88.13 125	90.01 192	95.24 118	78.50 225	93.29 201	94.15 204	84.75 138	84.46 198	93.40 139	75.76 140	97.40 175	77.59 219	94.52 103	94.12 193

无先验							93.28 202	96.26 82	73.95 292				99.05 46	80.56 177		96.59 98

thres200		87.21 184	86.24 181	90.12 181	95.36 106	78.53 219	93.26 203	92.10 247	86.42 107	88.00 106	91.11 234	69.24 227	98.00 127	69.58 276	91.04 150	93.83 211

WR-MVS		88.38 133	87.67 132	90.52 159	93.30 182	80.18 164	93.26 203	95.96 103	88.57 59	85.47 167	92.81 166	76.12 124	96.91 215	81.24 165	82.29 250	94.47 184

MVS_111021_HR		93.45 38	93.31 37	93.84 50	96.99 55	84.84 56	93.24 205	97.24 19	88.76 53	91.60 68	95.85 73	86.07 31	98.66 80	91.91 42	98.16 45	98.03 49

LCM-MVSNet-Re		88.30 137	88.32 119	88.27 252	94.71 134	72.41 295	93.15 206	90.98 282	87.77 78	79.25 272	91.96 195	78.35 105	95.75 268	83.04 138	95.62 84	96.65 97

AllTest		83.42 257	81.39 261	89.52 209	95.01 121	77.79 244	93.12 207	90.89 285	77.41 264	76.12 293	93.34 140	54.08 317	97.51 151	68.31 287	84.27 231	93.26 243

TDRefinement		79.81 287	77.34 289	87.22 277	79.24 336	75.48 272	93.12 207	92.03 251	76.45 270	75.01 301	91.58 208	49.19 326	96.44 241	70.22 270	69.18 325	89.75 311

新几何2							93.11 209

jason		90.80 76	90.10 80	92.90 75	93.04 190	83.53 89	93.08 210	94.15 204	80.22 236	91.41 70	94.91 94	76.87 115	97.93 132	90.28 64	96.90 66	97.24 76
jason: jason.
MVS_111021_LR		92.47 55	92.29 55	92.98 72	95.99 87	84.43 71	93.08 210	96.09 94	88.20 69	91.12 74	95.72 78	81.33 76	97.76 138	91.74 46	97.37 61	96.75 95

DELS-MVS		93.43 40	93.25 38	93.97 46	95.42 105	85.04 55	93.06 212	97.13 25	90.74 20	91.84 62	95.09 92	86.32 28	99.21 32	91.22 52	98.45 38	97.65 65
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
DI_MVS_plusplus_test		88.15 141	86.82 156	92.14 102	90.67 274	81.07 145	93.01 213	94.59 191	83.83 157	77.78 279	90.63 242	68.51 242	98.16 106	88.02 83	94.37 108	97.17 81

CDS-MVSNet		89.45 107	88.51 111	92.29 97	93.62 174	83.61 88	93.01 213	94.68 189	81.95 208	87.82 114	93.24 147	78.69 99	96.99 208	80.34 182	93.23 127	96.28 104
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		81.30 278	79.17 282	87.67 264	93.19 185	78.17 234	92.98 215	91.71 259	75.25 281	76.02 296	90.31 251	59.23 301	96.37 244	50.22 332	83.63 238	88.47 326

1112_ss		88.42 132	87.33 138	91.72 119	94.92 126	80.98 148	92.97 216	94.54 192	78.16 261	83.82 214	93.88 131	78.78 98	97.91 133	79.45 200	89.41 173	96.26 105

原ACMM2							92.94 217

BH-RMVSNet		88.37 134	87.48 134	91.02 142	95.28 110	79.45 188	92.89 218	93.07 230	85.45 124	86.91 128	94.84 100	70.35 212	97.76 138	73.97 250	94.59 101	95.85 122

lupinMVS		90.92 75	90.21 77	93.03 70	93.86 166	83.88 80	92.81 219	93.86 218	79.84 240	91.76 65	94.29 113	77.92 109	98.04 125	90.48 63	97.11 63	97.17 81

EG-PatchMatch MVS		82.37 267	80.34 269	88.46 247	90.27 283	79.35 197	92.80 220	94.33 199	77.14 268	73.26 311	90.18 253	47.47 329	96.72 225	70.25 268	87.32 209	89.30 313

test_normal		88.13 142	86.78 160	92.18 100	90.55 279	81.19 143	92.74 221	94.64 190	83.84 155	77.49 282	90.51 248	68.49 243	98.16 106	88.22 78	94.55 102	97.21 79

Anonymous20231211		72.97 303	69.63 308	83.00 308	83.05 328	66.91 321	92.69 222	89.45 310	61.06 334	67.50 325	83.46 316	34.34 341	93.61 307	51.11 329	63.97 333	88.48 325

PAPR		90.02 91	89.27 97	92.29 97	95.78 93	80.95 150	92.68 223	96.22 86	81.91 210	86.66 133	93.75 137	82.23 64	98.44 94	79.40 204	94.79 96	97.48 72

1314		87.51 173	86.57 173	90.34 171	92.42 202	79.74 179	92.63 224	95.35 155	78.35 257	80.14 264	91.62 207	74.05 163	97.15 196	81.05 166	93.53 118	94.12 193

1121		90.42 85	89.49 89	93.20 62	97.27 50	84.46 67	92.63 224	95.51 138	71.01 315	91.20 73	96.21 58	82.92 57	99.05 46	80.56 177	98.07 48	96.10 112

MVS		87.44 175	86.10 184	91.44 127	92.61 200	83.62 87	92.63 224	95.66 125	67.26 325	81.47 246	92.15 185	77.95 108	98.22 103	79.71 196	95.48 87	92.47 267

K. test v3		81.59 272	80.15 273	85.91 291	89.89 293	69.42 314	92.57 227	87.71 326	85.56 121	73.44 309	89.71 260	55.58 310	95.52 274	77.17 224	69.76 324	92.78 260

PVSNet_Blended		90.73 78	90.32 76	91.98 107	96.12 78	81.25 139	92.55 228	96.83 46	82.04 206	89.10 92	92.56 172	81.04 78	98.85 73	86.72 103	95.91 81	95.84 123

Test4		85.75 216	83.72 234	91.83 115	88.08 310	81.03 147	92.48 229	95.54 134	83.38 171	73.40 310	88.57 274	50.99 322	97.37 179	86.61 105	94.47 105	97.09 85

TR-MVS		86.78 192	85.76 192	89.82 197	94.37 147	78.41 227	92.47 230	92.83 233	81.11 231	86.36 139	92.40 176	68.73 239	97.48 153	73.75 253	89.85 168	93.57 234

pmmvs5		84.21 250	82.84 254	88.34 251	88.95 301	76.94 260	92.41 231	91.91 258	75.63 279	80.28 261	91.18 230	64.59 274	95.57 272	77.09 226	83.47 240	92.53 265

BH-w/o		87.57 172	87.05 150	89.12 226	94.90 128	77.90 240	92.41 231	93.51 224	82.89 191	83.70 217	91.34 220	75.75 141	97.07 203	75.49 236	93.49 119	92.39 270

WTY-MVS		89.60 101	88.92 104	91.67 121	95.47 104	81.15 144	92.38 233	94.78 187	83.11 176	89.06 94	94.32 111	78.67 100	96.61 232	81.57 163	90.89 155	97.24 76

OpenMVS_ROB	74.94 19	79.51 289	77.03 293	86.93 280	87.00 315	76.23 267	92.33 234	90.74 289	68.93 320	74.52 304	88.23 280	49.58 324	96.62 230	57.64 324	84.29 230	87.94 328

LTVRE_ROB	82.13 13	86.26 203	84.90 211	90.34 171	94.44 146	81.50 131	92.31 235	94.89 181	83.03 183	79.63 269	92.67 169	69.69 219	97.79 136	71.20 263	86.26 215	91.72 283
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
diffmvs		89.07 118	88.32 119	91.34 129	93.24 183	79.79 177	92.29 236	94.98 175	80.24 235	87.38 122	92.45 174	78.02 107	97.33 180	83.29 136	92.93 132	96.91 90

xiu_mvs_v2_base		91.13 73	90.89 70	91.86 113	94.97 124	82.42 119	92.24 237	95.64 128	86.11 114	91.74 67	93.14 151	79.67 93	98.89 66	89.06 71	95.46 89	94.28 188

test222					96.55 65	81.70 128	92.22 238	95.01 172	68.36 321	90.20 82	96.14 64	80.26 84			97.80 54	96.05 116

ab-mvs		89.41 110	88.35 116	92.60 83	95.15 119	82.65 116	92.20 239	95.60 129	83.97 153	88.55 97	93.70 138	74.16 162	98.21 104	82.46 149	89.37 174	96.94 89

testdata1							92.15 240		87.94 72

CLD-MVS		89.47 106	88.90 105	91.18 134	94.22 151	82.07 124	92.13 241	96.09 94	87.90 74	85.37 178	92.45 174	74.38 156	97.56 148	87.15 95	90.43 157	93.93 202
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVP-Stereo		85.97 209	84.86 212	89.32 221	90.92 264	82.19 122	92.11 242	94.19 202	78.76 252	78.77 274	91.63 206	68.38 251	96.56 233	75.01 243	93.95 111	89.20 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-MVSNAJ		91.18 72	90.92 68	91.96 108	95.26 112	82.60 118	92.09 243	95.70 122	86.27 109	91.84 62	92.46 173	79.70 90	98.99 59	89.08 70	95.86 82	94.29 187

HY-MVS	83.01 12	89.03 121	87.94 129	92.29 97	94.86 129	82.77 108	92.08 244	94.49 193	81.52 226	86.93 127	92.79 168	78.32 106	98.23 102	79.93 190	90.55 156	95.88 121

XVG-OURS-SEG-HR		89.95 94	89.45 90	91.47 126	94.00 161	81.21 142	91.87 245	96.06 98	85.78 116	88.55 97	95.73 77	74.67 154	97.27 186	88.71 74	89.64 171	95.91 119

Test_1112_low_res		87.65 159	86.51 174	91.08 138	94.94 125	79.28 202	91.77 246	94.30 200	76.04 276	83.51 222	92.37 177	77.86 111	97.73 142	78.69 209	89.13 185	96.22 106

IB-MVS	80.51 15	85.24 228	83.26 247	91.19 133	92.13 207	79.86 175	91.75 247	91.29 273	83.28 174	80.66 257	88.49 276	61.28 288	98.46 92	80.99 170	79.46 293	95.25 141
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
DWT-MVSNet_test		84.95 235	83.68 236	88.77 231	91.43 227	73.75 281	91.74 248	90.98 282	80.66 234	83.84 213	87.36 290	62.44 281	97.11 200	78.84 208	85.81 217	95.46 135

sss		88.93 124	88.26 123	90.94 146	94.05 156	80.78 155	91.71 249	95.38 151	81.55 225	88.63 96	93.91 130	75.04 151	95.47 279	82.47 148	91.61 141	96.57 99

XVG-ACMP-BASELINE		86.00 208	84.84 213	89.45 213	91.20 249	78.00 237	91.70 250	95.55 132	85.05 133	82.97 228	92.25 184	54.49 315	97.48 153	82.93 140	87.45 206	92.89 255

RPSCF		85.07 230	84.27 224	87.48 270	92.91 195	70.62 308	91.69 251	92.46 241	76.20 275	82.67 232	95.22 88	63.94 277	97.29 185	77.51 221	85.80 218	94.53 176

mvs_anonymous		89.37 113	89.32 94	89.51 211	93.47 177	74.22 275	91.65 252	94.83 185	82.91 190	85.45 168	93.79 134	81.23 77	96.36 245	86.47 106	94.09 110	97.94 53

MIMVSNet1		79.38 290	77.28 290	85.69 292	86.35 317	73.67 282	91.61 253	92.75 236	78.11 262	72.64 314	88.12 281	48.16 327	91.97 321	60.32 319	77.49 298	91.43 288

FMVSNet5		81.52 274	79.60 278	87.27 272	91.17 252	77.95 238	91.49 254	92.26 245	76.87 269	76.16 292	87.91 285	51.67 320	92.34 317	67.74 291	81.16 267	91.52 285

Anonymous20231206		81.03 280	79.77 276	84.82 299	87.85 314	70.26 310	91.42 255	92.08 249	73.67 293	77.75 280	89.25 265	62.43 282	93.08 314	61.50 317	82.00 256	91.12 293

testing_2		83.40 259	81.02 264	90.56 152	85.06 321	80.51 161	91.37 256	95.57 130	82.92 189	67.06 326	85.54 309	49.47 325	97.24 190	86.74 100	85.44 220	93.93 202

testgi		80.94 282	80.20 272	83.18 306	87.96 312	66.29 322	91.28 257	90.70 290	83.70 159	78.12 276	92.84 163	51.37 321	90.82 325	63.34 311	82.46 249	92.43 268

XVG-OURS		89.40 112	88.70 108	91.52 124	94.06 155	81.46 134	91.27 258	96.07 96	86.14 113	88.89 95	95.77 76	68.73 239	97.26 188	87.39 91	89.96 166	95.83 124

MS-PatchMatch		85.05 231	84.16 225	87.73 263	91.42 228	78.51 224	91.25 259	93.53 223	77.50 263	80.15 263	91.58 208	61.99 284	95.51 275	75.69 235	94.35 109	89.16 316

PatchFormer-LS_test		86.02 207	85.13 204	88.70 234	91.52 221	74.12 278	91.19 260	92.09 248	82.71 195	84.30 207	87.24 292	70.87 202	96.98 209	81.04 167	85.17 224	95.00 146

Patchmatch-test1		85.81 214	84.71 215	89.12 226	92.15 205	76.60 262	91.12 261	91.69 261	83.53 166	85.50 165	88.56 275	66.79 258	95.00 295	72.69 257	90.35 159	95.76 127

test20.03		79.95 286	79.08 283	82.55 309	85.79 318	67.74 319	91.09 262	91.08 278	81.23 230	74.48 305	89.96 257	61.63 286	90.15 326	60.08 320	76.38 301	89.76 310

PatchMatch-RL		86.77 194	85.54 194	90.47 163	95.88 90	82.71 114	90.54 263	92.31 243	79.82 241	84.32 205	91.57 210	68.77 238	96.39 243	73.16 255	93.48 121	92.32 273

GA-MVS		86.61 196	85.27 203	90.66 149	91.33 239	78.71 214	90.40 264	93.81 221	85.34 126	85.12 182	89.57 262	61.25 289	97.11 200	80.99 170	89.59 172	96.15 107

pmmvs4		85.43 223	83.86 230	90.16 175	90.02 290	82.97 105	90.27 265	92.67 238	75.93 277	80.73 255	91.74 202	71.05 199	95.73 269	78.85 207	83.46 241	91.78 280

test0.0.03 1		82.41 266	81.69 259	84.59 300	88.23 307	72.89 286	90.24 266	87.83 325	83.41 169	79.86 267	89.78 259	67.25 255	88.99 328	65.18 306	83.42 242	91.90 279

cascas		86.43 201	84.98 206	90.80 148	92.10 208	80.92 151	90.24 266	95.91 107	73.10 298	83.57 221	88.39 277	65.15 271	97.46 155	84.90 116	91.43 142	94.03 199

IterMVS		84.88 237	83.98 229	87.60 265	91.44 224	76.03 268	90.18 268	92.41 242	83.24 175	81.06 253	90.42 250	66.60 259	94.28 300	79.46 199	80.98 275	92.48 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs-eth3d		80.97 281	78.72 286	87.74 262	84.99 322	79.97 173	90.11 269	91.65 262	75.36 280	73.51 308	86.03 306	59.45 300	93.96 303	75.17 240	72.21 310	89.29 314

CHOSEN 1792x2688		88.84 125	87.69 131	92.30 96	96.14 77	81.42 136	90.01 270	95.86 112	74.52 289	87.41 119	93.94 126	75.46 145	98.36 95	80.36 181	95.53 85	97.12 84

HyFIR lowres test		88.09 143	86.81 157	91.93 110	96.00 86	80.63 157	90.01 270	95.79 116	73.42 295	87.68 117	92.10 189	73.86 166	97.96 129	80.75 173	91.70 140	97.19 80

CMPMVS	59.16 21	80.52 283	79.20 281	84.48 301	83.98 324	67.63 320	89.95 272	93.84 220	64.79 330	66.81 327	91.14 233	57.93 306	95.17 290	76.25 231	88.10 199	90.65 304
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmp4_e23		83.87 255	82.33 256	88.48 246	91.46 223	72.82 287	89.82 273	91.57 266	73.02 300	81.86 244	89.05 266	66.20 265	96.97 210	71.57 261	86.39 214	95.66 130

PAPM		86.68 195	85.39 200	90.53 153	93.05 189	79.33 201	89.79 274	94.77 188	78.82 250	81.95 242	93.24 147	76.81 116	97.30 182	66.94 293	93.16 128	94.95 157

test-LLR		85.87 210	85.41 199	87.25 274	90.95 260	71.67 298	89.55 275	89.88 304	83.41 169	84.54 196	87.95 283	67.25 255	95.11 292	81.82 159	93.37 124	94.97 147

TESTMET0.1,1		83.74 256	82.85 253	86.42 288	89.96 291	71.21 302	89.55 275	87.88 324	77.41 264	83.37 225	87.31 291	56.71 308	93.65 306	80.62 176	92.85 135	94.40 185

test-mter		84.54 248	83.64 238	87.25 274	90.95 260	71.67 298	89.55 275	89.88 304	79.17 245	84.54 196	87.95 283	55.56 311	95.11 292	81.82 159	93.37 124	94.97 147

TinyColmap		79.76 288	77.69 288	85.97 290	91.71 217	73.12 284	89.55 275	90.36 293	75.03 283	72.03 316	90.19 252	46.22 331	96.19 251	63.11 312	81.03 271	88.59 322

CostFormer		85.77 215	84.94 209	88.26 253	91.16 254	72.58 294	89.47 279	91.04 281	76.26 274	86.45 137	89.97 256	70.74 205	96.86 218	82.35 150	87.07 212	95.34 140

LF4IMVS		80.37 284	79.07 284	84.27 304	86.64 316	69.87 313	89.39 280	91.05 280	76.38 271	74.97 302	90.00 255	47.85 328	94.25 301	74.55 247	80.82 277	88.69 321

USDC		82.76 262	81.26 263	87.26 273	91.17 252	74.55 274	89.27 281	93.39 226	78.26 259	75.30 300	92.08 190	54.43 316	96.63 229	71.64 260	85.79 219	90.61 305

PCF-MVS	84.11 10	87.74 157	86.08 185	92.70 81	94.02 157	84.43 71	89.27 281	95.87 111	73.62 294	84.43 200	94.33 110	78.48 104	98.86 70	70.27 267	94.45 106	94.81 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tpm2		84.08 251	82.94 251	87.48 270	91.39 230	71.27 300	89.23 283	90.37 292	71.95 308	84.64 193	89.33 264	67.30 254	96.55 235	75.17 240	87.09 211	94.63 168

MSDG		84.86 238	83.09 249	90.14 180	93.80 169	80.05 169	89.18 284	93.09 229	78.89 248	78.19 275	91.91 197	65.86 269	97.27 186	68.47 284	88.45 194	93.11 250

tpm		84.73 243	84.02 227	86.87 284	90.33 282	68.90 315	89.06 285	89.94 302	80.85 233	85.75 151	89.86 258	68.54 241	95.97 258	77.76 217	84.05 233	95.75 128

PM-MVS		78.11 294	76.12 296	84.09 305	83.54 326	70.08 311	88.97 286	85.27 334	79.93 239	74.73 303	86.43 298	34.70 340	93.48 308	79.43 202	72.06 311	88.72 320

MDA-MVSNet-bldmvs		78.85 293	76.31 294	86.46 286	89.76 294	73.88 280	88.79 287	90.42 291	79.16 246	59.18 334	88.33 279	60.20 296	94.04 302	62.00 315	68.96 326	91.48 287

tpmrst		85.35 225	84.99 205	86.43 287	90.88 267	67.88 318	88.71 288	91.43 270	80.13 237	86.08 145	88.80 270	73.05 177	96.02 256	82.48 147	83.40 243	95.40 137

PMMVS		85.71 221	84.96 208	87.95 260	88.90 302	77.09 258	88.68 289	90.06 299	72.32 305	86.47 134	90.76 240	72.15 189	94.40 299	81.78 161	93.49 119	92.36 271

EPMVS		83.90 254	82.70 255	87.51 267	90.23 286	72.67 290	88.62 290	81.96 340	81.37 229	85.01 184	88.34 278	66.31 263	94.45 298	75.30 239	87.12 210	95.43 136

PatchmatchNet		85.85 211	84.70 216	89.29 222	91.76 215	75.54 271	88.49 291	91.30 272	81.63 223	85.05 183	88.70 272	71.71 190	96.24 249	74.61 246	89.05 186	96.08 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UnsupCasMVSNet_eth		80.07 285	78.27 287	85.46 294	85.24 320	72.63 292	88.45 292	94.87 182	82.99 187	71.64 318	88.07 282	56.34 309	91.75 322	73.48 254	63.36 335	92.01 278

tpmvs		83.35 260	82.07 257	87.20 278	91.07 256	71.00 305	88.31 293	91.70 260	78.91 247	80.49 260	87.18 293	69.30 226	97.08 202	68.12 290	83.56 239	93.51 238

N_pmnet		68.89 309	68.44 310	70.23 325	89.07 300	28.79 354	88.06 294	19.50 355	69.47 319	71.86 317	84.93 310	61.24 290	91.75 322	54.70 326	77.15 300	90.15 309

test_post1							88.00 295				9.81 351	69.31 225	95.53 273	76.65 228

GG-mvs-BLEND				87.94 261	89.73 295	77.91 239	87.80 296	78.23 346		80.58 258	83.86 313	59.88 299	95.33 289	71.20 263	92.22 139	90.60 307

DSMNet-mixed		76.94 296	76.29 295	78.89 313	83.10 327	56.11 339	87.78 297	79.77 343	60.65 335	75.64 299	88.71 271	61.56 287	88.34 330	60.07 321	89.29 177	92.21 276

MDTV_nov1_ep13			83.56 239		91.69 219	69.93 312	87.75 298	91.54 267	78.60 254	84.86 191	88.90 268	69.54 221	96.03 255	70.25 268	88.93 187

new-patchmatchnet		76.41 297	75.17 297	80.13 312	82.65 330	59.61 332	87.66 299	91.08 278	78.23 260	69.85 320	83.22 317	54.76 314	91.63 324	64.14 310	64.89 331	89.16 316

MDTV_nov1_ep13_2view						55.91 340	87.62 300		73.32 296	84.59 195		70.33 213		74.65 245		95.50 133

tpm cat1		81.96 268	80.27 270	87.01 279	91.09 255	71.02 304	87.38 301	91.53 268	66.25 326	80.17 262	86.35 304	68.22 253	96.15 252	69.16 281	82.29 250	93.86 209

PVSNet	78.82 18	85.55 222	84.65 217	88.23 255	94.72 133	71.93 296	87.12 302	92.75 236	78.80 251	84.95 185	90.53 247	64.43 275	96.71 227	74.74 244	93.86 113	96.06 115

pmmvs3		71.81 306	68.71 309	81.11 311	75.86 338	70.42 309	86.74 303	83.66 336	58.95 336	68.64 324	80.89 326	36.93 339	89.52 327	63.10 313	63.59 334	83.39 332

dp		81.47 275	80.23 271	85.17 297	89.92 292	65.49 325	86.74 303	90.10 298	76.30 273	81.10 251	87.12 294	62.81 279	95.92 260	68.13 289	79.88 290	94.09 196

MIMVSNet		82.59 265	80.53 268	88.76 232	91.51 222	78.32 229	86.57 305	90.13 297	79.32 244	80.70 256	88.69 273	52.98 319	93.07 315	66.03 303	88.86 188	94.90 158

gg-mvs-nofinetune		81.77 269	79.37 279	88.99 229	90.85 268	77.73 248	86.29 306	79.63 344	74.88 287	83.19 227	69.05 337	60.34 295	96.11 253	75.46 237	94.64 100	93.11 250

testmvs		8.92 329	11.52 330	1.12 342	1.06 355	0.46 357	86.02 307	0.65 357	0.62 351	2.74 352	9.52 352	0.31 360	0.45 355	2.38 351	0.39 351	2.46 352

testus		74.41 301	73.35 299	77.59 318	82.49 331	57.08 335	86.02 307	90.21 295	72.28 306	72.89 313	84.32 312	37.08 338	86.96 334	52.24 328	82.65 247	88.73 319

YYNet1		79.22 291	77.20 291	85.28 296	88.20 309	72.66 291	85.87 309	90.05 301	74.33 291	62.70 332	87.61 288	66.09 267	92.03 319	66.94 293	72.97 308	91.15 292

MDA-MVSNet_test_wron		79.21 292	77.19 292	85.29 295	88.22 308	72.77 289	85.87 309	90.06 299	74.34 290	62.62 333	87.56 289	66.14 266	91.99 320	66.90 296	73.01 307	91.10 294

test123		8.76 330	11.22 331	1.39 341	0.85 356	0.97 356	85.76 311	0.35 358	0.54 352	2.45 353	8.14 353	0.60 359	0.48 354	2.16 352	0.17 353	2.71 351

test1235678		72.22 304	70.31 305	77.93 317	78.04 337	58.04 334	85.76 311	89.80 306	70.15 318	63.43 331	80.20 328	42.24 335	87.24 333	48.68 334	74.50 305	88.50 323

UnsupCasMVSNet_bld		76.23 298	73.27 300	85.09 298	83.79 325	72.92 285	85.65 313	93.47 225	71.52 309	68.84 322	79.08 330	49.77 323	93.21 311	66.81 297	60.52 337	89.13 318

CR-MVSNet		85.35 225	83.76 231	90.12 181	90.58 276	79.34 198	85.24 314	91.96 256	78.27 258	85.55 160	87.87 286	71.03 200	95.61 270	73.96 251	89.36 175	95.40 137

RPMNet		83.18 261	80.87 267	90.12 181	90.58 276	79.34 198	85.24 314	90.78 288	71.44 310	85.55 160	82.97 319	70.87 202	95.61 270	61.01 318	89.36 175	95.40 137

Patchmtry		82.71 263	80.93 266	88.06 258	90.05 289	76.37 265	84.74 316	91.96 256	72.28 306	81.32 250	87.87 286	71.03 200	95.50 277	68.97 282	80.15 285	92.32 273

testmv		65.49 311	62.66 312	73.96 321	68.78 343	53.14 342	84.70 317	88.56 320	65.94 328	52.35 337	74.65 333	25.02 346	85.14 339	43.54 340	60.40 338	83.60 331

FPMVS		64.63 313	62.55 313	70.88 324	70.80 341	56.71 336	84.42 318	84.42 335	51.78 339	49.57 338	81.61 324	23.49 347	81.48 343	40.61 343	76.25 302	74.46 340

1111		70.54 308	69.71 307	73.04 322	79.30 334	44.83 347	84.23 319	88.96 318	67.33 323	65.42 328	82.28 321	41.11 336	88.11 331	47.12 336	71.60 315	86.19 330

.test1245		57.63 318	61.79 315	45.14 336	79.30 334	44.83 347	84.23 319	88.96 318	67.33 323	65.42 328	82.28 321	41.11 336	88.11 331	47.12 336	0.39 351	2.46 352

PatchT		82.68 264	81.27 262	86.89 283	90.09 288	70.94 306	84.06 321	90.15 296	74.91 285	85.63 159	83.57 315	69.37 222	94.87 297	65.19 305	88.50 193	94.84 160

new_pmnet		72.15 305	70.13 306	78.20 314	82.95 329	65.68 323	83.91 322	82.40 339	62.94 333	64.47 330	79.82 329	42.85 334	86.26 336	57.41 325	74.44 306	82.65 334

LCM-MVSNet		66.00 310	62.16 314	77.51 319	64.51 348	58.29 333	83.87 323	90.90 284	48.17 340	54.69 336	73.31 335	16.83 353	86.75 335	65.47 304	61.67 336	87.48 329

test2356		74.50 300	73.27 300	78.20 314	80.81 332	59.84 330	83.76 324	88.33 323	71.43 311	72.37 315	81.84 323	45.60 332	86.26 336	50.97 330	84.32 229	88.50 323

ADS-MVSNet2		81.66 271	79.71 277	87.50 268	91.35 237	74.19 276	83.33 325	88.48 321	72.90 301	82.24 236	85.77 307	64.98 272	93.20 312	64.57 308	83.74 235	95.12 142

ADS-MVSNet		81.56 273	79.78 275	86.90 282	91.35 237	71.82 297	83.33 325	89.16 316	72.90 301	82.24 236	85.77 307	64.98 272	93.76 304	64.57 308	83.74 235	95.12 142

PVSNet_0	73.20 20	77.22 295	74.83 298	84.37 302	90.70 273	71.10 303	83.09 327	89.67 307	72.81 303	73.93 307	83.13 318	60.79 293	93.70 305	68.54 283	50.84 340	88.30 327

MVS-HIRNet		73.70 302	72.20 302	78.18 316	91.81 214	56.42 338	82.94 328	82.58 338	55.24 337	68.88 321	66.48 338	55.32 313	95.13 291	58.12 323	88.42 196	83.01 333

Patchmatch-RL test		81.67 270	79.96 274	86.81 285	85.42 319	71.23 301	82.17 329	87.50 329	78.47 255	77.19 284	82.50 320	70.81 204	93.48 308	82.66 144	72.89 309	95.71 129

JIA-IIPM		81.04 279	78.98 285	87.25 274	88.64 303	73.48 283	81.75 330	89.61 308	73.19 297	82.05 240	73.71 334	66.07 268	95.87 263	71.18 265	84.60 228	92.41 269

Patchmatch-test		81.37 276	79.30 280	87.58 266	90.92 264	74.16 277	80.99 331	87.68 327	70.52 316	76.63 285	88.81 269	71.21 197	92.76 316	60.01 322	86.93 213	95.83 124

ANet_high		58.88 316	54.22 319	72.86 323	56.50 352	56.67 337	80.75 332	86.00 331	73.09 299	37.39 343	64.63 341	22.17 348	79.49 346	43.51 341	23.96 347	82.43 335

LP		75.51 299	72.15 303	85.61 293	87.86 313	73.93 279	80.20 333	88.43 322	67.39 322	70.05 319	80.56 327	58.18 305	93.18 313	46.28 338	70.36 323	89.71 312

CHOSEN 280x420		85.15 229	83.99 228	88.65 235	92.47 201	78.40 228	79.68 334	92.76 235	74.90 286	81.41 248	89.59 261	69.85 218	95.51 275	79.92 191	95.29 92	92.03 277

test12356		64.99 312	63.78 311	68.61 329	72.69 340	39.14 350	78.46 335	87.61 328	64.91 329	55.77 335	77.48 331	28.10 343	85.59 338	44.69 339	64.35 332	81.12 336

no-one		61.56 314	56.58 316	76.49 320	67.80 346	62.76 329	78.13 336	86.11 330	63.16 332	43.24 341	64.70 340	26.12 345	88.95 329	50.84 331	29.15 343	77.77 338

ambc				83.06 307	79.99 333	63.51 328	77.47 337	92.86 232		74.34 306	84.45 311	28.74 342	95.06 294	73.06 256	68.89 327	90.61 305

EMVS		42.07 324	41.12 324	44.92 337	63.45 349	35.56 353	73.65 338	63.48 350	33.05 347	26.88 349	45.45 348	21.27 349	67.14 349	19.80 349	23.02 348	32.06 348

E-PMN		43.23 323	42.29 323	46.03 335	65.58 347	37.41 351	73.51 339	64.62 349	33.99 346	28.47 348	47.87 346	19.90 351	67.91 348	22.23 348	24.45 346	32.77 347

PMVS	47.18 22	52.22 319	48.46 320	63.48 331	45.72 353	46.20 346	73.41 340	78.31 345	41.03 344	30.06 346	65.68 339	6.05 355	83.43 342	30.04 346	65.86 329	60.80 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		50.55 320	44.13 322	69.81 326	56.77 350	54.58 341	73.22 341	80.78 341	39.79 345	22.08 350	46.69 347	4.03 357	79.71 345	47.65 335	26.13 345	75.14 339

PMMVS2		59.60 315	56.40 317	69.21 328	68.83 342	46.58 345	73.02 342	77.48 347	55.07 338	49.21 339	72.95 336	17.43 352	80.04 344	49.32 333	44.33 341	80.99 337

PNet_i23d		50.48 321	47.18 321	60.36 332	68.59 344	44.56 349	72.75 343	72.61 348	43.92 342	33.91 345	60.19 343	6.16 354	73.52 347	38.50 344	28.04 344	63.01 342

testpf		71.41 307	72.11 304	69.30 327	84.53 323	59.79 331	62.74 344	83.14 337	71.11 313	68.83 323	81.57 325	46.70 330	84.83 341	74.51 248	75.86 303	63.30 341

tmp_tt		35.64 326	39.24 325	24.84 339	14.87 354	23.90 355	62.71 345	51.51 354	6.58 350	36.66 344	62.08 342	44.37 333	30.34 353	52.40 327	22.00 349	20.27 349

MVE	39.65 23	43.39 322	38.59 327	57.77 333	56.52 351	48.77 344	55.38 346	58.64 352	29.33 348	28.96 347	52.65 344	4.68 356	64.62 350	28.11 347	33.07 342	59.93 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		57.99 317	54.91 318	67.24 330	88.51 304	65.59 324	52.21 347	90.33 294	43.58 343	42.84 342	51.18 345	20.29 350	85.07 340	34.77 345	70.45 322	51.05 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		21.27 328	20.48 329	23.63 340	68.59 344	36.41 352	49.57 348	6.85 356	9.37 349	7.89 351	4.46 354	4.03 357	31.37 352	17.47 350	16.07 350	3.12 350

cdsmvs_eth3d_5k		22.14 327	29.52 328	0.00 343	0.00 357	0.00 358	0.00 349	95.76 118	0.00 353	0.00 354	94.29 113	75.66 142	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		6.64 332	8.86 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	79.70 90	0.00 356	0.00 353	0.00 354	0.00 354

pcd1.5k->3k		37.02 325	38.84 326	31.53 338	92.33 203	0.00 358	0.00 349	96.13 92	0.00 353	0.00 354	0.00 355	72.70 181	0.00 356	0.00 353	88.43 195	94.60 171

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		7.82 331	10.43 332	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	93.88 131	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																96.12 110

test_part2					98.55 5	87.22 10				96.40 2

test_part1								97.45 6				91.93 1			99.02 2	98.67 4

sam_mvs1												71.70 191				96.12 110

sam_mvs												70.60 206

semantic-postprocess				88.18 256	91.71 217	76.87 261		92.65 239	85.40 125	81.44 247	90.54 245	66.21 264	95.00 295	81.04 167	81.05 270	92.66 262

MTGPA								96.97 34

test_post											10.29 350	70.57 210	95.91 262

patchmatchnet-post											83.76 314	71.53 194	96.48 238

MTMP								60.64 351

gm-plane-assit					89.60 296	68.00 317			77.28 267		88.99 267		97.57 147	79.44 201

test9_res														91.91 42	98.71 19	98.07 45

agg_prior2														90.54 61	98.68 24	98.27 31

agg_prior					97.38 43	85.92 44		96.72 56		92.16 56			98.97 61

TestCases				89.52 209	95.01 121	77.79 244		90.89 285	77.41 264	76.12 293	93.34 140	54.08 317	97.51 151	68.31 287	84.27 231	93.26 243

test_prior				93.82 51	97.29 48	84.49 64		96.88 43					98.87 67			98.11 43

新几何1				93.10 66	97.30 47	84.35 73		95.56 131	71.09 314	91.26 72	96.24 56	82.87 58	98.86 70	79.19 205	98.10 47	96.07 114

旧先验1					96.79 59	81.81 127		95.67 123			96.81 33	86.69 24			97.66 56	96.97 88

原ACMM1				92.01 104	97.34 45	81.05 146		96.81 48	78.89 248	90.45 79	95.92 70	82.65 59	98.84 75	80.68 175	98.26 43	96.14 108

testdata2													98.75 78	78.30 212

segment_acmp												87.16 21

testdata				90.49 161	96.40 67	77.89 241		95.37 153	72.51 304	93.63 25	96.69 38	82.08 68	97.65 143	83.08 137	97.39 60	95.94 118

test12				94.34 40	97.13 53	86.15 40		96.29 81		91.04 75		85.08 41	99.01 55		98.13 46	97.86 59

plane_prior7					94.70 135	82.74 111

plane_prior6					94.52 141	82.75 109						74.23 158

plane_prior5								96.22 86					98.12 108	88.15 79	89.99 164	94.63 168

plane_prior4											94.86 97

plane_prior3						82.75 109			90.26 25	86.91 128

plane_prior1					94.59 139

n2								0.00 359

nn								0.00 359

door-mid								85.49 332

lessismore_v0				86.04 289	88.46 306	68.78 316		80.59 342		73.01 312	90.11 254	55.39 312	96.43 242	75.06 242	65.06 330	92.90 254

LGP-MVS_train				91.12 135	94.47 143	81.49 132		96.14 90	86.73 102	85.45 168	95.16 89	69.89 216	98.10 114	87.70 86	89.23 178	93.77 216

test11								96.57 70

door								85.33 333

HQP5-MVS						81.56 129

BP-MVS														87.11 97

HQP4-MVS										85.43 171			97.96 129			94.51 178

HQP3-MVS								96.04 99							89.77 169

HQP2-MVS												73.83 167

NP-MVS					94.37 147	82.42 119					93.98 124

ACMMP++_ref															87.47 205

ACMMP++															88.01 202

Test By Simon												80.02 85

ITE_SJBPF				88.24 254	91.88 211	77.05 259		92.92 231	85.54 122	80.13 265	93.30 144	57.29 307	96.20 250	72.46 258	84.71 227	91.49 286

DeepMVS_CX				56.31 334	74.23 339	51.81 343		56.67 353	44.85 341	48.54 340	75.16 332	27.87 344	58.74 351	40.92 342	52.22 339	58.39 345

High-resolution multi-view benchmark