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++		95.14 5	94.91 7	95.83 1	98.25 19	89.65 1	95.92 38	96.96 36	91.75 8	94.02 17	96.83 30	88.12 9	99.55 5	93.41 15	98.94 3	98.28 26

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 94	88.73 2	97.07 8	96.77 51	90.84 17	84.02 199	96.62 41	75.95 133	99.34 21	87.77 83	97.68 53	98.59 7

CNVR-MVS		95.40 2	95.37 3	95.50 3	98.11 23	88.51 3	95.29 61	96.96 36	92.09 3	95.32 7	97.08 23	89.49 4	99.33 24	95.10 2	98.85 6	98.66 4

ACMMP_Plus		94.74 9	94.56 10	95.28 4	98.02 28	87.70 4	95.68 47	97.34 10	88.28 65	95.30 8	97.67 3	85.90 31	99.54 8	93.91 9	98.95 2	98.60 6

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 92	87.66 5	96.33 25	96.41 74	90.00 28	94.09 15	94.60 104	82.33 60	98.62 82	92.40 26	92.86 132	98.27 29

alignmvs		93.08 49	92.50 52	94.81 20	95.62 95	87.61 6	95.99 35	96.07 95	89.77 32	94.12 14	94.87 94	80.56 78	98.66 78	92.42 25	93.10 127	98.15 37

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 91	87.58 7	94.71 101	96.59 66	91.78 7	91.46 67	96.18 61	75.45 144	99.55 5	93.53 10	98.19 42	98.28 26

MCST-MVS		94.45 12	94.20 19	95.19 5	98.46 10	87.50 8	95.00 81	97.12 25	87.13 85	92.51 48	96.30 52	89.24 6	99.34 21	93.46 12	98.62 30	98.73 3

NCCC		94.81 8	94.69 9	95.17 6	97.83 30	87.46 9	95.66 49	96.93 39	92.34 2	93.94 18	96.58 43	87.74 12	99.44 18	92.83 20	98.40 37	98.62 5

MPTG		94.47 11	94.30 13	95.00 8	98.42 12	86.95 10	95.06 80	96.97 33	91.07 14	93.14 32	97.56 4	84.30 47	99.56 1	93.43 13	98.75 14	98.47 12

MTAPA		94.42 16	94.22 16	95.00 8	98.42 12	86.95 10	94.36 130	96.97 33	91.07 14	93.14 32	97.56 4	84.30 47	99.56 1	93.43 13	98.75 14	98.47 12

nrg030		91.08 73	90.39 73	93.17 63	93.07 177	86.91 12	96.41 24	96.26 81	88.30 64	88.37 98	94.85 97	82.19 64	97.64 141	91.09 51	82.95 233	94.96 146

APD-MVS		94.24 21	94.07 23	94.75 24	98.06 26	86.90 13	95.88 39	96.94 38	85.68 116	95.05 9	97.18 19	87.31 17	99.07 42	91.90 43	98.61 31	98.28 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		94.52 10	94.40 11	94.86 13	98.61 3	86.81 14	96.94 10	97.34 10	88.63 56	93.65 21	97.21 16	86.10 27	99.49 14	92.35 27	98.77 12	98.30 24

#test#		94.32 19	94.14 20	94.86 13	98.61 3	86.81 14	96.43 23	97.34 10	87.51 82	93.65 21	97.21 16	86.10 27	99.49 14	91.68 45	98.77 12	98.30 24

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 61	86.78 16	94.40 120	93.93 216	89.77 32	94.21 13	95.59 79	87.35 16	98.61 83	92.72 21	96.15 78	97.83 59

DeepC-MVS_fast	89.43 2	94.04 25	93.79 28	94.80 21	97.48 39	86.78 16	95.65 51	96.89 41	89.40 38	92.81 37	96.97 25	85.37 36	99.24 29	90.87 56	98.69 19	98.38 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		94.33 18	94.22 16	94.68 26	95.54 96	86.75 18	94.57 109	96.70 57	91.84 6	94.41 10	96.56 45	87.19 18	99.13 38	93.50 11	97.65 55	98.16 36

SD-MVS		94.96 6	95.33 4	93.88 48	97.25 50	86.69 19	96.19 29	97.11 27	90.42 24	96.95 1	97.27 11	89.53 3	96.91 211	94.38 5	98.85 6	98.03 47

ACMMPR		94.43 14	94.28 14	94.91 11	98.63 2	86.69 19	96.94 10	97.32 15	88.63 56	93.53 28	97.26 13	85.04 40	99.54 8	92.35 27	98.78 11	98.50 9

region2R		94.43 14	94.27 15	94.92 10	98.65 1	86.67 21	96.92 14	97.23 20	88.60 58	93.58 25	97.27 11	85.22 37	99.54 8	92.21 29	98.74 16	98.56 8

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 22	86.65 22	94.82 92	97.17 23	86.26 106	92.83 36	97.87 2	85.57 34	99.56 1	94.37 6	98.92 4	98.34 21
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		94.34 17	94.21 18	94.74 25	98.39 14	86.64 23	97.60 1	97.24 18	88.53 60	92.73 41	97.23 14	85.20 38	99.32 25	92.15 32	98.83 8	98.25 32

XVS		94.45 12	94.32 12	94.85 15	98.54 5	86.60 24	96.93 12	97.19 21	90.66 22	92.85 34	97.16 21	85.02 41	99.49 14	91.99 36	98.56 33	98.47 12

X-MVStestdata		88.31 135	86.13 179	94.85 15	98.54 5	86.60 24	96.93 12	97.19 21	90.66 22	92.85 34	23.41 338	85.02 41	99.49 14	91.99 36	98.56 33	98.47 12

HSP-MVS		95.30 3	95.48 2	94.76 23	98.49 8	86.52 26	96.91 15	96.73 53	91.73 9	96.10 3	96.69 36	89.90 1	99.30 27	94.70 3	98.04 47	98.45 16

TEST9					97.53 34	86.49 27	94.07 146	96.78 49	81.61 213	92.77 38	96.20 57	87.71 13	99.12 39

train_agg		93.44 38	93.08 40	94.52 32	97.53 34	86.49 27	94.07 146	96.78 49	81.86 208	92.77 38	96.20 57	87.63 14	99.12 39	92.14 33	98.69 19	97.94 51

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 56	86.43 29	96.69 21	97.49 4	85.15 127	93.56 27	96.28 53	85.60 33	99.31 26	92.45 23	98.79 9	98.12 40

3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 128	86.37 30	97.18 6	97.02 30	89.20 42	84.31 195	96.66 39	73.74 167	99.17 33	86.74 98	97.96 48	97.79 61

Regformer-1		94.22 22	94.13 21	94.51 33	95.54 96	86.36 31	94.57 109	96.44 71	91.69 10	94.32 12	96.56 45	87.05 20	99.03 48	93.35 16	97.65 55	98.15 37

TSAR-MVS + MP.		94.85 7	94.94 6	94.58 30	98.25 19	86.33 32	96.11 31	96.62 64	88.14 68	96.10 3	96.96 26	89.09 7	98.94 63	94.48 4	98.68 22	98.48 11

SteuartSystems-ACMMP		95.20 4	95.32 5	94.85 15	96.99 53	86.33 32	97.33 3	97.30 16	91.38 12	95.39 6	97.46 7	88.98 8	99.40 19	94.12 7	98.89 5	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		94.25 20	94.07 23	94.77 22	98.47 9	86.31 34	96.71 20	96.98 32	89.04 46	91.98 58	97.19 18	85.43 35	99.56 1	92.06 35	98.79 9	98.44 17

test_8					97.49 37	86.30 35	94.02 152	96.76 52	81.86 208	92.70 42	96.20 57	87.63 14	99.02 51

APDe-MVS		95.46 1	95.64 1	94.91 11	98.26 18	86.29 36	97.46 2	97.40 8	89.03 47	96.20 2	98.10 1	89.39 5	99.34 21	95.88 1	99.03 1	99.10 1

PGM-MVS		93.96 28	93.72 31	94.68 26	98.43 11	86.22 37	95.30 59	97.78 1	87.45 83	93.26 29	97.33 9	84.62 45	99.51 12	90.75 58	98.57 32	98.32 23

test12				94.34 39	97.13 51	86.15 38		96.29 80		91.04 73		85.08 39	99.01 53		98.13 44	97.86 57

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 37	86.12 39	94.07 146	96.73 53	81.46 216	92.46 50	96.05 65	86.90 21	99.15 36	92.14 33	98.69 19	97.94 51

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 31	86.11 40	94.06 149	96.66 61	80.09 227	92.77 38	96.63 40	86.62 23	99.04 47	87.40 88	98.66 26	98.17 35

test_prior4						85.96 41	94.11 140

agg_prior1		93.29 42	92.97 44	94.26 41	97.38 41	85.92 42	93.92 157	96.72 55	81.96 202	92.16 54	96.23 55	87.85 10	98.97 59	91.95 39	98.55 35	97.90 56

agg_prior					97.38 41	85.92 42		96.72 55		92.16 54			98.97 59

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 17	85.92 42	94.66 105	96.66 61	82.69 192	90.03 83	95.82 72	82.30 61	99.03 48	84.57 118	96.48 75	96.91 88

mPP-MVS		93.99 27	93.78 29	94.63 28	98.50 7	85.90 45	96.87 16	96.91 40	88.70 54	91.83 62	97.17 20	83.96 50	99.55 5	91.44 49	98.64 29	98.43 18

DeepC-MVS	88.79 3	93.31 41	92.99 43	94.26 41	96.07 80	85.83 46	94.89 87	96.99 31	89.02 48	89.56 85	97.37 8	82.51 58	99.38 20	92.20 30	98.30 39	97.57 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		94.02 26	93.88 26	94.43 36	98.39 14	85.78 47	97.25 5	97.07 29	86.90 96	92.62 45	96.80 33	84.85 44	99.17 33	92.43 24	98.65 28	98.33 22

CANet		93.54 36	93.20 39	94.55 31	95.65 93	85.73 48	94.94 84	96.69 59	91.89 5	90.69 75	95.88 70	81.99 69	99.54 8	93.14 18	97.95 49	98.39 19

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 100	85.47 49	94.68 102	96.41 74	91.60 11	93.75 20	96.71 34	85.95 30	99.10 41	93.21 17	96.65 70	98.01 49

OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 187	85.39 50	96.57 22	96.43 73	78.74 242	80.85 243	96.07 64	69.64 218	99.01 53	78.01 214	96.65 70	94.83 157

ACMMP		93.24 46	92.88 47	94.30 40	98.09 25	85.33 51	96.86 17	97.45 7	88.33 63	90.15 81	97.03 24	81.44 72	99.51 12	90.85 57	95.74 81	98.04 46
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
EPNet		91.79 59	91.02 66	94.10 44	90.10 276	85.25 52	96.03 34	92.05 249	92.83 1	87.39 117	95.78 73	79.39 93	99.01 53	88.13 79	97.48 57	98.05 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 99	85.04 53	93.06 201	97.13 24	90.74 20	91.84 60	95.09 90	86.32 26	99.21 30	91.22 50	98.45 36	97.65 63
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
MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 53	84.84 54	93.24 194	97.24 18	88.76 53	91.60 66	95.85 71	86.07 29	98.66 78	91.91 40	98.16 43	98.03 47

HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 16	84.83 55	97.15 7	96.80 48	85.77 113	92.47 49	97.13 22	82.38 59	99.07 42	90.51 60	98.40 37	97.92 55

CNLPA		89.07 117	87.98 126	92.34 94	96.87 55	84.78 56	94.08 144	93.24 226	81.41 217	84.46 187	95.13 89	75.57 141	96.62 219	77.21 221	93.84 112	95.61 128

UA-Net		92.83 51	92.54 51	93.68 55	96.10 78	84.71 57	95.66 49	96.39 76	91.92 4	93.22 30	96.49 47	83.16 53	98.87 65	84.47 119	95.47 86	97.45 71

Regformer-3		93.68 33	93.64 33	93.81 52	95.36 100	84.61 58	94.68 102	95.83 112	91.27 13	93.60 24	96.71 34	85.75 32	98.86 68	92.87 19	96.65 70	97.96 50

QAPM		89.51 103	88.15 123	93.59 56	94.92 115	84.58 59	96.82 18	96.70 57	78.43 245	83.41 213	96.19 60	73.18 174	99.30 27	77.11 223	96.54 73	96.89 90

API-MVS		90.66 78	90.07 80	92.45 89	96.36 67	84.57 60	96.06 33	95.22 165	82.39 194	89.13 89	94.27 114	80.32 80	98.46 90	80.16 184	96.71 68	94.33 179

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 164	84.52 61	94.78 95	97.47 5	89.26 41	86.44 134	92.32 177	82.10 65	97.39 174	84.81 115	80.84 265	94.12 186

test_prior3		93.60 35	93.53 34	93.82 50	97.29 46	84.49 62	94.12 138	96.88 42	87.67 79	92.63 43	96.39 50	86.62 23	98.87 65	91.50 47	98.67 24	98.11 41

test_prior				93.82 50	97.29 46	84.49 62		96.88 42					98.87 65			98.11 41

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 60	84.48 64	95.68 47	95.67 122	82.36 196	87.85 104	92.85 160	76.63 118	98.80 74	80.01 185	96.68 69	95.91 115
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
xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 185	95.46 140	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 185	95.46 140	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 152	84.46 65	93.32 185	95.46 140	85.17 124	92.25 51	94.03 117	70.59 205	98.57 85	90.97 52	94.67 95	94.18 182

1121		90.42 84	89.49 88	93.20 61	97.27 48	84.46 65	92.63 213	95.51 137	71.01 304	91.20 71	96.21 56	82.92 55	99.05 44	80.56 175	98.07 46	96.10 108

MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 83	84.43 69	93.08 199	96.09 93	88.20 67	91.12 72	95.72 76	81.33 74	97.76 134	91.74 44	97.37 59	96.75 93

PCF-MVS	84.11 10	87.74 156	86.08 182	92.70 80	94.02 146	84.43 69	89.27 270	95.87 110	73.62 283	84.43 189	94.33 108	78.48 102	98.86 68	70.27 265	94.45 104	94.81 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
新几何1				93.10 65	97.30 45	84.35 71		95.56 130	71.09 303	91.26 70	96.24 54	82.87 56	98.86 68	79.19 203	98.10 45	96.07 110

abl_6		93.18 48	93.05 41	93.57 57	97.52 36	84.27 72	95.53 54	96.67 60	87.85 74	93.20 31	97.22 15	80.35 79	99.18 32	91.91 40	97.21 60	97.26 73

APD-MVS_3200maxsize		93.78 31	93.77 30	93.80 53	97.92 29	84.19 73	96.30 26	96.87 44	86.96 92	93.92 19	97.47 6	83.88 51	98.96 62	92.71 22	97.87 50	98.26 31

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 179	84.16 74	94.77 96	96.25 83	89.05 45	80.04 255	93.29 143	79.02 94	97.05 201	81.71 160	80.05 275	94.59 166

CSCG		93.23 47	93.05 41	93.76 54	98.04 27	84.07 75	96.22 28	97.37 9	84.15 147	90.05 82	95.66 77	87.77 11	99.15 36	89.91 63	98.27 40	98.07 43

OMC-MVS		91.23 69	90.62 72	93.08 66	96.27 69	84.07 75	93.52 180	95.93 103	86.95 93	89.51 86	96.13 63	78.50 101	98.35 95	85.84 105	92.90 131	96.83 91

OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 175	83.90 77	94.16 137	95.74 119	88.96 49	87.86 103	95.43 81	72.48 184	97.91 129	88.10 80	90.18 157	93.65 214

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 155	83.88 78	95.96 36	95.90 107	84.66 136	91.76 63	94.91 92	77.92 107	97.30 178	89.64 65	97.11 61	97.24 74

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 155	83.88 78	92.81 208	93.86 217	79.84 229	91.76 63	94.29 111	77.92 107	98.04 121	90.48 61	97.11 61	97.17 79

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 103	83.78 80	96.14 30	95.98 100	89.89 29	90.45 77	96.58 43	75.09 148	98.31 99	84.75 116	96.90 64	97.78 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 168	83.72 81	94.43 118	97.12 25	89.80 31	86.46 131	93.32 140	83.16 53	97.23 188	84.92 112	81.02 261	94.49 175

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 179	83.72 81	94.47 115	96.59 66	89.50 36	86.46 131	93.29 143	77.25 111	97.23 188	84.92 112	81.02 261	94.59 166

FMVSNet2		87.19 182	85.82 188	91.30 130	94.01 147	83.67 83	94.79 94	94.94 175	83.57 159	83.88 201	92.05 189	66.59 249	96.51 225	77.56 218	85.01 214	93.73 211

FMVSNet3		87.40 174	86.11 180	91.30 130	93.79 160	83.64 84	94.20 136	94.81 185	83.89 150	84.37 190	91.87 194	68.45 240	96.56 222	78.23 211	85.36 210	93.70 213

MVS		87.44 172	86.10 181	91.44 126	92.61 189	83.62 85	92.63 213	95.66 124	67.26 314	81.47 235	92.15 181	77.95 106	98.22 101	79.71 194	95.48 85	92.47 254

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 163	83.61 86	93.01 202	94.68 188	81.95 203	87.82 110	93.24 145	78.69 97	96.99 204	80.34 180	93.23 125	96.28 102
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		90.80 75	90.10 79	92.90 74	93.04 179	83.53 87	93.08 199	94.15 203	80.22 225	91.41 68	94.91 92	76.87 113	97.93 128	90.28 62	96.90 64	97.24 74
jason: jason.
EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 110	83.51 88	94.48 112	95.77 116	90.87 16	92.52 47	96.67 38	84.50 46	99.00 56	91.99 36	94.44 105	97.36 72

MSLP-MVS++		93.72 32	94.08 22	92.65 81	97.31 44	83.43 89	95.79 42	97.33 13	90.03 27	93.58 25	96.96 26	84.87 43	97.76 134	92.19 31	98.66 26	96.76 92

VNet		92.24 56	91.91 55	93.24 60	96.59 61	83.43 89	94.84 91	96.44 71	89.19 43	94.08 16	95.90 69	77.85 110	98.17 103	88.90 70	93.38 121	98.13 39

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 139	83.39 91	94.60 106	95.10 168	87.10 86	90.57 76	93.10 151	81.43 73	98.07 119	89.29 67	94.48 102	97.59 66

UGNet		89.95 93	88.95 102	92.95 72	94.51 131	83.31 92	95.70 46	95.23 163	89.37 39	87.58 114	93.94 124	64.00 265	98.78 75	83.92 129	96.31 77	96.74 94
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
DP-MVS		87.25 178	85.36 198	92.90 74	97.65 32	83.24 93	94.81 93	92.00 251	74.99 273	81.92 232	95.00 91	72.66 180	99.05 44	66.92 289	92.33 136	96.40 99

EI-MVSNet-UG-set		92.74 53	92.62 49	93.12 64	94.86 118	83.20 94	94.40 120	95.74 119	90.71 21	92.05 57	96.60 42	84.00 49	98.99 57	91.55 46	93.63 114	97.17 79

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 66	83.17 95	94.87 90	96.66 61	83.29 169	89.27 88	94.46 106	80.29 81	99.17 33	87.57 86	95.37 88	96.05 112

GBi-Net		87.26 176	85.98 184	91.08 137	94.01 147	83.10 96	95.14 75	94.94 175	83.57 159	84.37 190	91.64 198	66.59 249	96.34 235	78.23 211	85.36 210	93.79 204

test1		87.26 176	85.98 184	91.08 137	94.01 147	83.10 96	95.14 75	94.94 175	83.57 159	84.37 190	91.64 198	66.59 249	96.34 235	78.23 211	85.36 210	93.79 204

FMVSNet1		85.85 206	84.11 216	91.08 137	92.81 185	83.10 96	95.14 75	94.94 175	81.64 211	82.68 220	91.64 198	59.01 291	96.34 235	75.37 236	83.78 223	93.79 204

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 40	83.03 99	94.42 119	95.92 104	82.81 188	86.34 136	94.65 102	73.89 163	99.02 51	80.69 172	95.51 84	95.05 140

VDD-MVS		90.74 76	89.92 84	93.20 61	96.27 69	83.02 100	95.73 44	93.86 217	88.42 62	92.53 46	96.84 29	62.09 272	98.64 80	90.95 55	92.62 134	97.93 54

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 167	83.01 101	93.48 181	94.47 193	89.43 37	87.76 112	94.23 115	70.54 209	99.03 48	84.97 111	96.39 76	96.38 100

TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 188	83.01 101	94.92 86	96.31 79	89.88 30	85.53 158	93.85 131	76.63 118	96.96 207	81.91 156	79.87 280	94.50 173

pmmvs4		85.43 213	83.86 220	90.16 172	90.02 279	82.97 103	90.27 254	92.67 237	75.93 266	80.73 244	91.74 197	71.05 197	95.73 258	78.85 205	83.46 230	91.78 267

LS3D		87.89 148	86.32 175	92.59 83	96.07 80	82.92 104	95.23 69	94.92 179	75.66 267	82.89 218	95.98 66	72.48 184	99.21 30	68.43 281	95.23 92	95.64 127

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 155	82.89 105	95.46 55	97.33 13	87.91 71	88.43 97	93.31 141	74.17 159	97.40 171	87.32 91	82.86 235	94.52 171

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 118	82.77 106	92.08 233	94.49 192	81.52 215	86.93 123	92.79 166	78.32 104	98.23 100	79.93 188	90.55 152	95.88 117

plane_prior6					94.52 130	82.75 107						74.23 156

plane_prior3						82.75 107			90.26 25	86.91 124

plane_prior7					94.70 124	82.74 109

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 124	82.73 110	95.85 40	96.22 85	90.81 18	86.91 124	94.86 95	74.23 156	98.12 106	88.15 77	89.99 158	94.63 162

plane_prior						82.73 110	95.21 71		89.66 35						89.88 161

PatchMatch-RL		86.77 191	85.54 191	90.47 162	95.88 86	82.71 112	90.54 252	92.31 242	79.82 230	84.32 194	91.57 205	68.77 234	96.39 232	73.16 253	93.48 119	92.32 260

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 48	82.69 113	94.29 131	95.44 146	79.71 231	84.01 200	94.18 116	76.68 117	98.75 76	77.28 220	93.41 120	95.02 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ab-mvs		89.41 109	88.35 115	92.60 82	95.15 108	82.65 114	92.20 228	95.60 128	83.97 149	88.55 95	93.70 136	74.16 160	98.21 102	82.46 147	89.37 168	96.94 87

TAMVS		89.21 114	88.29 120	91.96 107	93.71 161	82.62 115	93.30 189	94.19 201	82.22 197	87.78 111	93.94 124	78.83 95	96.95 208	77.70 216	92.98 129	96.32 101

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 106	82.60 116	92.09 232	95.70 121	86.27 105	91.84 60	92.46 171	79.70 88	98.99 57	89.08 68	95.86 80	94.29 180

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 113	82.42 117	92.24 226	95.64 127	86.11 110	91.74 65	93.14 149	79.67 91	98.89 64	89.06 69	95.46 87	94.28 181

NP-MVS					94.37 136	82.42 117					93.98 122

LFMVS		90.08 89	89.13 98	92.95 72	96.71 58	82.32 119	96.08 32	89.91 300	86.79 97	92.15 56	96.81 31	62.60 269	98.34 96	87.18 92	93.90 110	98.19 34

MVP-Stereo		85.97 204	84.86 207	89.32 211	90.92 253	82.19 120	92.11 231	94.19 201	78.76 241	78.77 263	91.63 201	68.38 241	96.56 222	75.01 241	93.95 109	89.20 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
VDDNet		89.56 102	88.49 113	92.76 79	95.07 109	82.09 121	96.30 26	93.19 227	81.05 221	91.88 59	96.86 28	61.16 281	98.33 97	88.43 75	92.49 135	97.84 58

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 140	82.07 122	92.13 230	96.09 93	87.90 72	85.37 173	92.45 172	74.38 154	97.56 144	87.15 93	90.43 153	93.93 194
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
114514_t		89.51 103	88.50 111	92.54 85	98.11 23	81.99 123	95.16 74	96.36 78	70.19 306	85.81 143	95.25 85	76.70 116	98.63 81	82.07 152	96.86 66	97.00 85

CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 21	81.98 124	96.76 19	96.49 70	81.89 207	90.24 79	96.44 49	78.59 99	98.61 83	89.68 64	97.85 51	97.06 84

旧先验1					96.79 57	81.81 125		95.67 122			96.81 31	86.69 22			97.66 54	96.97 86

test222					96.55 63	81.70 126	92.22 227	95.01 171	68.36 310	90.20 80	96.14 62	80.26 82			97.80 52	96.05 112

HQP5-MVS						81.56 127

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 141	81.56 127	94.39 122	96.04 98	88.81 50	85.43 167	93.97 123	73.83 165	97.96 125	87.11 95	89.77 163	94.50 173

LTVRE_ROB	82.13 13	86.26 200	84.90 206	90.34 168	94.44 135	81.50 129	92.31 224	94.89 180	83.03 179	79.63 258	92.67 167	69.69 217	97.79 132	71.20 261	86.26 204	91.72 270
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
LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 132	81.49 130	95.30 59	96.14 89	86.73 98	85.45 164	95.16 87	69.89 214	98.10 112	87.70 84	89.23 172	93.77 208

LGP-MVS_train				91.12 134	94.47 132	81.49 130		96.14 89	86.73 98	85.45 164	95.16 87	69.89 214	98.10 112	87.70 84	89.23 172	93.77 208

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 144	81.46 132	91.27 247	96.07 95	86.14 109	88.89 93	95.77 74	68.73 235	97.26 184	87.39 89	89.96 160	95.83 120

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 33	81.46 132	94.37 126	96.24 84	86.39 104	87.41 115	94.80 99	82.06 67	98.48 89	82.80 141	95.37 88	97.61 65

CHOSEN 1792x2688		88.84 124	87.69 130	92.30 95	96.14 75	81.42 134	90.01 259	95.86 111	74.52 278	87.41 115	93.94 124	75.46 143	98.36 93	80.36 179	95.53 83	97.12 82

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 88	81.38 135	96.95 9	93.69 221	89.72 34	89.50 87	95.98 66	78.57 100	97.77 133	83.02 137	96.50 74	98.22 33

ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 121	81.27 136	95.07 78	95.89 109	86.48 101	83.67 207	94.30 110	69.33 221	97.99 124	87.10 97	88.55 179	93.72 212
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 76	81.25 137	93.92 157	96.83 45	83.49 163	89.10 90	92.26 179	81.04 76	98.85 71	86.72 101	87.86 192	92.35 259

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 76	81.25 137	92.55 217	96.83 45	82.04 201	89.10 90	92.56 170	81.04 76	98.85 71	86.72 101	95.91 79	95.84 119

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 127	81.22 139	95.31 57	96.12 92	85.31 123	85.92 142	94.34 107	70.19 213	98.06 120	85.65 106	88.86 177	94.08 190
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 150	81.21 140	91.87 234	96.06 97	85.78 112	88.55 95	95.73 75	74.67 152	97.27 182	88.71 72	89.64 165	95.91 115

test_normal		88.13 141	86.78 159	92.18 99	90.55 268	81.19 141	92.74 210	94.64 189	83.84 151	77.49 271	90.51 237	68.49 239	98.16 104	88.22 76	94.55 100	97.21 77

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 98	81.15 142	92.38 222	94.78 186	83.11 172	89.06 92	94.32 109	78.67 98	96.61 221	81.57 161	90.89 151	97.24 74

DI_MVS_plusplus_test		88.15 140	86.82 155	92.14 101	90.67 263	81.07 143	93.01 202	94.59 190	83.83 153	77.78 268	90.63 232	68.51 238	98.16 104	88.02 81	94.37 106	97.17 79

原ACMM1				92.01 103	97.34 43	81.05 144		96.81 47	78.89 237	90.45 77	95.92 68	82.65 57	98.84 73	80.68 173	98.26 41	96.14 106

Test4		85.75 210	83.72 224	91.83 114	88.08 299	81.03 145	92.48 218	95.54 133	83.38 167	73.40 299	88.57 263	50.99 311	97.37 175	86.61 103	94.47 103	97.09 83

FIs		90.51 83	90.35 74	90.99 143	93.99 151	80.98 146	95.73 44	97.54 3	89.15 44	86.72 128	94.68 100	81.83 71	97.24 186	85.18 109	88.31 187	94.76 160

1112_ss		88.42 131	87.33 137	91.72 118	94.92 115	80.98 146	92.97 205	94.54 191	78.16 250	83.82 203	93.88 129	78.78 96	97.91 129	79.45 198	89.41 167	96.26 103

PAPR		90.02 90	89.27 96	92.29 96	95.78 89	80.95 148	92.68 212	96.22 85	81.91 205	86.66 129	93.75 135	82.23 62	98.44 92	79.40 202	94.79 94	97.48 70

cascas		86.43 198	84.98 203	90.80 147	92.10 197	80.92 149	90.24 255	95.91 106	73.10 287	83.57 210	88.39 266	65.15 260	97.46 151	84.90 114	91.43 140	94.03 192

F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 68	80.88 150	94.73 97	95.45 144	79.65 232	82.04 230	94.61 103	71.13 196	98.50 88	76.24 230	91.05 145	94.80 159

PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 199	80.85 151	95.26 68	95.98 100	86.26 106	86.21 138	94.29 111	79.70 88	97.65 139	88.87 71	88.10 188	94.57 168

Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 120	80.81 152	93.54 179	95.10 168	83.11 172	86.82 127	90.67 231	79.74 87	97.75 137	80.51 177	93.55 115	96.57 97

sss		88.93 123	88.26 122	90.94 145	94.05 145	80.78 153	91.71 238	95.38 150	81.55 214	88.63 94	93.91 128	75.04 149	95.47 268	82.47 146	91.61 139	96.57 97

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 59	80.65 154	94.39 122	96.21 88	76.38 260	86.19 139	95.44 80	79.75 86	98.08 118	62.75 303	95.29 90	96.13 107
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 82	80.63 155	90.01 259	95.79 115	73.42 284	87.68 113	92.10 185	73.86 164	97.96 125	80.75 171	91.70 138	97.19 78

ACMH	80.38 17	85.36 214	83.68 226	90.39 164	94.45 134	80.63 155	94.73 97	94.85 182	82.09 199	77.24 272	92.65 168	60.01 287	97.58 142	72.25 257	84.87 215	92.96 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		87.65 158	86.85 154	90.03 187	92.14 195	80.60 157	93.76 166	95.23 163	82.94 184	84.60 183	94.02 120	74.27 155	95.49 267	81.04 165	83.68 226	94.01 193

anonymousdsp		87.84 150	87.09 145	90.12 178	89.13 287	80.54 158	94.67 104	95.55 131	82.05 200	83.82 203	92.12 182	71.47 194	97.15 192	87.15 93	87.80 193	92.67 248

testing_2		83.40 249	81.02 254	90.56 151	85.06 310	80.51 159	91.37 245	95.57 129	82.92 185	67.06 315	85.54 298	49.47 314	97.24 186	86.74 98	85.44 209	93.93 194

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 86	80.50 160	97.33 3	95.25 159	86.15 108	89.76 84	95.60 78	83.42 52	98.32 98	87.37 90	93.25 124	97.56 68

MVSTER		88.84 124	88.29 120	90.51 159	92.95 183	80.44 161	93.73 169	95.01 171	84.66 136	87.15 119	93.12 150	72.79 178	97.21 190	87.86 82	87.36 196	93.87 199

pm-mvs1		86.61 193	85.54 191	89.82 194	91.44 213	80.18 162	95.28 67	94.85 182	83.84 151	81.66 234	92.62 169	72.45 186	96.48 227	79.67 195	78.06 285	92.82 246

WR-MVS		88.38 132	87.67 131	90.52 158	93.30 171	80.18 162	93.26 192	95.96 102	88.57 59	85.47 163	92.81 164	76.12 122	96.91 211	81.24 163	82.29 239	94.47 177

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 255	80.14 164	95.31 57	95.65 126	84.97 130	84.24 197	94.02 120	65.31 259	97.42 164	88.56 73	88.52 181	93.89 196

V42		87.68 157	86.86 153	90.15 176	90.58 265	80.14 164	94.24 134	95.28 155	83.66 156	85.67 153	91.33 211	74.73 151	97.41 169	84.43 123	81.83 249	92.89 242

MVS_Test		91.31 68	91.11 63	91.93 109	94.37 136	80.14 164	93.46 183	95.80 114	86.46 102	91.35 69	93.77 133	82.21 63	98.09 117	87.57 86	94.95 93	97.55 69

tfpnnormal		84.72 234	83.23 238	89.20 215	92.79 186	80.05 167	94.48 112	95.81 113	82.38 195	81.08 241	91.21 218	69.01 228	96.95 208	61.69 305	80.59 268	90.58 295

MSDG		84.86 228	83.09 239	90.14 177	93.80 158	80.05 167	89.18 273	93.09 228	78.89 237	78.19 264	91.91 192	65.86 258	97.27 182	68.47 280	88.45 183	93.11 237

MG-MVS		91.77 60	91.70 57	92.00 105	97.08 52	80.03 169	93.60 178	95.18 166	87.85 74	90.89 74	96.47 48	82.06 67	98.36 93	85.07 110	97.04 63	97.62 64

DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 64	80.00 170	94.00 154	97.08 28	90.05 26	95.65 5	97.29 10	89.66 2	98.97 59	93.95 8	98.71 17	98.50 9

pmmvs-eth3d		80.97 271	78.72 276	87.74 252	84.99 311	79.97 171	90.11 258	91.65 261	75.36 269	73.51 297	86.03 295	59.45 289	93.96 292	75.17 238	72.21 299	89.29 301

mvs_tets		88.06 143	87.28 139	90.38 166	90.94 251	79.88 172	95.22 70	95.66 124	85.10 128	84.21 198	93.94 124	63.53 267	97.40 171	88.50 74	88.40 186	93.87 199

IB-MVS	80.51 15	85.24 218	83.26 237	91.19 132	92.13 196	79.86 173	91.75 236	91.29 270	83.28 170	80.66 246	88.49 265	61.28 277	98.46 90	80.99 168	79.46 282	95.25 137
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
FC-MVSNet-test		90.27 86	90.18 78	90.53 152	93.71 161	79.85 174	95.77 43	97.59 2	89.31 40	86.27 137	94.67 101	81.93 70	97.01 203	84.26 124	88.09 190	94.71 161

diffmvs		89.07 117	88.32 118	91.34 128	93.24 172	79.79 175	92.29 225	94.98 174	80.24 224	87.38 118	92.45 172	78.02 105	97.33 176	83.29 134	92.93 130	96.91 88

COLMAP_ROB	80.39 16	83.96 242	82.04 248	89.74 198	95.28 104	79.75 176	94.25 133	92.28 243	75.17 271	78.02 267	93.77 133	58.60 292	97.84 131	65.06 296	85.92 205	91.63 271
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
1314		87.51 170	86.57 170	90.34 168	92.42 191	79.74 177	92.63 213	95.35 154	78.35 246	80.14 253	91.62 202	74.05 161	97.15 192	81.05 164	93.53 116	94.12 186

v13		84.72 234	83.44 235	88.58 230	91.31 233	79.52 178	93.77 165	94.00 212	83.03 179	75.85 287	86.38 292	75.84 136	95.35 276	79.83 192	70.95 308	90.87 289

v12		84.74 232	83.46 233	88.58 230	91.32 230	79.50 179	93.75 167	94.01 209	83.06 175	75.98 286	86.41 291	75.82 137	95.36 275	79.87 191	70.89 310	90.89 288

v7		87.75 155	86.96 151	90.12 178	91.20 238	79.50 179	94.28 132	95.46 140	83.45 164	85.75 147	91.56 206	75.13 146	97.43 162	83.60 132	82.18 241	93.42 228

v10		87.25 178	86.38 172	89.85 193	91.19 240	79.50 179	94.48 112	95.45 144	83.79 154	83.62 208	91.19 219	75.13 146	97.42 164	81.94 155	80.60 267	92.63 250

v17		84.93 226	83.70 225	88.62 227	91.36 223	79.48 182	93.83 160	94.03 208	83.04 178	76.51 277	86.57 286	76.05 126	95.42 270	80.31 183	71.65 303	90.96 282

v16		84.96 224	83.74 223	88.62 227	91.40 218	79.48 182	93.83 160	94.04 206	83.03 179	76.54 276	86.59 285	76.11 125	95.42 270	80.33 181	71.80 301	90.95 284

VPNet		88.20 138	87.47 134	90.39 164	93.56 165	79.46 184	94.04 150	95.54 133	88.67 55	86.96 122	94.58 105	69.33 221	97.15 192	84.05 128	80.53 270	94.56 169

V9		84.77 231	83.50 232	88.58 230	91.33 228	79.46 184	93.75 167	94.00 212	83.07 174	76.07 284	86.43 287	75.97 131	95.37 273	79.91 190	70.93 309	90.91 286

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 104	79.45 186	92.89 207	93.07 229	85.45 120	86.91 124	94.84 98	70.35 210	97.76 134	73.97 248	94.59 99	95.85 118

V14		84.79 229	83.52 231	88.57 233	91.32 230	79.43 187	93.72 171	94.01 209	83.06 175	76.22 279	86.43 287	76.01 130	95.37 273	79.96 187	70.99 307	90.91 286

v1neww		87.98 144	87.25 141	90.16 172	91.38 220	79.41 188	94.37 126	95.28 155	84.48 139	85.77 145	91.53 207	76.12 122	97.45 153	84.45 121	81.89 246	93.61 219

v7new		87.98 144	87.25 141	90.16 172	91.38 220	79.41 188	94.37 126	95.28 155	84.48 139	85.77 145	91.53 207	76.12 122	97.45 153	84.45 121	81.89 246	93.61 219

v18		84.97 223	83.76 221	88.60 229	91.36 223	79.41 188	93.82 162	94.04 206	83.00 182	76.61 275	86.60 284	76.19 120	95.43 269	80.39 178	71.79 302	90.96 282

v15		84.79 229	83.53 230	88.57 233	91.30 234	79.41 188	93.70 173	94.01 209	83.06 175	76.27 278	86.42 290	76.03 129	95.38 272	80.01 185	71.00 306	90.92 285

v8		87.50 171	86.71 161	89.89 192	91.37 222	79.40 192	94.50 111	95.38 150	84.81 133	83.60 209	91.33 211	76.05 126	97.42 164	82.84 140	80.51 272	92.84 244

v6		87.98 144	87.25 141	90.16 172	91.36 223	79.39 193	94.37 126	95.27 158	84.48 139	85.78 144	91.51 209	76.15 121	97.46 151	84.46 120	81.88 248	93.62 218

ACMH+	81.04 14	85.05 221	83.46 233	89.82 194	94.66 126	79.37 194	94.44 117	94.12 205	82.19 198	78.04 266	92.82 163	58.23 293	97.54 145	73.77 250	82.90 234	92.54 251

EG-PatchMatch MVS		82.37 257	80.34 259	88.46 237	90.27 272	79.35 195	92.80 209	94.33 198	77.14 257	73.26 300	90.18 242	47.47 318	96.72 215	70.25 266	87.32 198	89.30 300

v1144		87.61 167	86.79 158	90.06 186	91.01 246	79.34 196	93.95 156	95.42 149	83.36 168	85.66 154	91.31 214	74.98 150	97.42 164	83.37 133	82.06 242	93.42 228

CR-MVSNet		85.35 215	83.76 221	90.12 178	90.58 265	79.34 196	85.24 303	91.96 255	78.27 247	85.55 156	87.87 275	71.03 198	95.61 259	73.96 249	89.36 169	95.40 133

RPMNet		83.18 251	80.87 257	90.12 178	90.58 265	79.34 196	85.24 303	90.78 285	71.44 299	85.55 156	82.97 308	70.87 200	95.61 259	61.01 307	89.36 169	95.40 133

PAPM		86.68 192	85.39 197	90.53 152	93.05 178	79.33 199	89.79 263	94.77 187	78.82 239	81.95 231	93.24 145	76.81 114	97.30 178	66.94 287	93.16 126	94.95 153

test_djsdf		89.03 120	88.64 108	90.21 170	90.74 260	79.28 200	95.96 36	95.90 107	84.66 136	85.33 174	92.94 159	74.02 162	97.30 178	89.64 65	88.53 180	94.05 191

Test_1112_low_res		87.65 158	86.51 171	91.08 137	94.94 114	79.28 200	91.77 235	94.30 199	76.04 265	83.51 211	92.37 175	77.86 109	97.73 138	78.69 207	89.13 174	96.22 104

v7n		86.81 187	85.76 189	89.95 191	90.72 261	79.25 202	95.07 78	95.92 104	84.45 142	82.29 223	90.86 228	72.60 182	97.53 146	79.42 201	80.52 271	93.08 239

v1141		87.84 150	87.09 145	90.11 183	91.23 235	79.25 202	94.08 144	95.24 160	84.44 143	85.69 152	91.31 214	75.91 134	97.44 160	84.17 126	81.74 252	93.63 217

divwei89l23v2f112		87.84 150	87.09 145	90.10 185	91.23 235	79.24 204	94.09 142	95.24 160	84.44 143	85.70 150	91.31 214	75.91 134	97.44 160	84.17 126	81.73 253	93.64 215

v1		87.85 149	87.10 144	90.11 183	91.21 237	79.24 204	94.09 142	95.24 160	84.44 143	85.70 150	91.31 214	75.96 132	97.45 153	84.18 125	81.73 253	93.64 215

v2v482		87.84 150	87.06 148	90.17 171	90.99 247	79.23 206	94.00 154	95.13 167	84.87 131	85.53 158	92.07 188	74.45 153	97.45 153	84.71 117	81.75 251	93.85 202

v11		84.67 237	83.41 236	88.44 238	91.32 230	79.13 207	93.69 176	93.99 214	82.81 188	76.20 280	86.24 294	75.48 142	95.35 276	79.53 196	71.48 305	90.85 290

v1192		87.25 178	86.33 174	90.00 190	90.76 259	79.04 208	93.80 163	95.48 139	82.57 193	85.48 162	91.18 220	73.38 173	97.42 164	82.30 149	82.06 242	93.53 223

v52		86.50 195	85.53 194	89.39 209	89.17 286	78.99 209	94.72 100	95.54 133	83.59 157	82.10 227	90.60 234	71.59 191	97.45 153	82.52 143	79.99 277	91.73 269

V4		86.50 195	85.54 191	89.39 209	89.13 287	78.99 209	94.73 97	95.54 133	83.59 157	82.10 227	90.61 233	71.60 190	97.45 153	82.52 143	80.01 276	91.74 268

thres600view7		87.65 158	86.67 162	90.59 149	96.08 79	78.72 211	94.88 89	91.58 262	87.06 91	88.08 101	92.30 178	68.91 229	98.10 112	70.05 271	91.10 142	94.96 146

GA-MVS		86.61 193	85.27 200	90.66 148	91.33 228	78.71 212	90.40 253	93.81 220	85.34 122	85.12 176	89.57 251	61.25 278	97.11 196	80.99 168	89.59 166	96.15 105

tfpn200view9		87.58 168	86.64 167	90.41 163	95.99 83	78.64 213	94.58 107	91.98 253	86.94 94	88.09 99	91.77 195	69.18 226	98.10 112	70.13 269	91.10 142	94.48 176

thres400		87.62 162	86.64 167	90.57 150	95.99 83	78.64 213	94.58 107	91.98 253	86.94 94	88.09 99	91.77 195	69.18 226	98.10 112	70.13 269	91.10 142	94.96 146

thres200		87.21 181	86.24 178	90.12 178	95.36 100	78.53 215	93.26 192	92.10 246	86.42 103	88.00 102	91.11 224	69.24 225	98.00 123	69.58 272	91.04 146	93.83 203

view600		87.62 162	86.65 163	90.53 152	96.19 71	78.52 216	95.29 61	91.09 271	87.08 87	87.84 105	93.03 154	68.86 230	98.11 108	69.44 273	91.02 147	94.96 146

view800		87.62 162	86.65 163	90.53 152	96.19 71	78.52 216	95.29 61	91.09 271	87.08 87	87.84 105	93.03 154	68.86 230	98.11 108	69.44 273	91.02 147	94.96 146

conf0.05thres1000		87.62 162	86.65 163	90.53 152	96.19 71	78.52 216	95.29 61	91.09 271	87.08 87	87.84 105	93.03 154	68.86 230	98.11 108	69.44 273	91.02 147	94.96 146

tfpn		87.62 162	86.65 163	90.53 152	96.19 71	78.52 216	95.29 61	91.09 271	87.08 87	87.84 105	93.03 154	68.86 230	98.11 108	69.44 273	91.02 147	94.96 146

MS-PatchMatch		85.05 221	84.16 215	87.73 253	91.42 217	78.51 220	91.25 248	93.53 222	77.50 252	80.15 252	91.58 203	61.99 273	95.51 264	75.69 233	94.35 107	89.16 303

BH-untuned		88.60 129	88.13 124	90.01 189	95.24 107	78.50 221	93.29 190	94.15 203	84.75 134	84.46 187	93.40 137	75.76 138	97.40 171	77.59 217	94.52 101	94.12 186

TransMVSNet (Re)		84.43 239	83.06 240	88.54 235	91.72 205	78.44 222	95.18 72	92.82 233	82.73 190	79.67 257	92.12 182	73.49 169	95.96 248	71.10 264	68.73 317	91.21 278

TR-MVS		86.78 189	85.76 189	89.82 194	94.37 136	78.41 223	92.47 219	92.83 232	81.11 220	86.36 135	92.40 174	68.73 235	97.48 149	73.75 251	89.85 162	93.57 222

CHOSEN 280x420		85.15 219	83.99 218	88.65 225	92.47 190	78.40 224	79.68 323	92.76 234	74.90 275	81.41 237	89.59 250	69.85 216	95.51 264	79.92 189	95.29 90	92.03 264

MIMVSNet		82.59 255	80.53 258	88.76 222	91.51 211	78.32 225	86.57 294	90.13 294	79.32 233	80.70 245	88.69 262	52.98 308	93.07 304	66.03 292	88.86 177	94.90 154

EI-MVSNet		89.10 116	88.86 106	89.80 197	91.84 201	78.30 226	93.70 173	95.01 171	85.73 114	87.15 119	95.28 83	79.87 85	97.21 190	83.81 131	87.36 196	93.88 198

IterMVS-LS		88.36 134	87.91 129	89.70 200	93.80 158	78.29 227	93.73 169	95.08 170	85.73 114	84.75 181	91.90 193	79.88 84	96.92 210	83.83 130	82.51 237	93.89 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		87.19 182	86.35 173	89.74 198	90.64 264	78.24 228	93.92 157	95.43 147	81.93 204	85.51 160	91.05 226	74.21 158	97.45 153	82.86 139	81.56 255	93.53 223

test_0402		81.30 268	79.17 272	87.67 254	93.19 174	78.17 229	92.98 204	91.71 258	75.25 270	76.02 285	90.31 240	59.23 290	96.37 233	50.22 321	83.63 227	88.47 313

WR-MVS_H		87.80 154	87.37 136	89.10 218	93.23 173	78.12 230	95.61 52	97.30 16	87.90 72	83.72 205	92.01 190	79.65 92	96.01 246	76.36 227	80.54 269	93.16 235

v1921920		86.97 186	86.06 183	89.69 201	90.53 269	78.11 231	93.80 163	95.43 147	81.90 206	85.33 174	91.05 226	72.66 180	97.41 169	82.05 153	81.80 250	93.53 223

XVG-ACMP-BASELINE		86.00 203	84.84 208	89.45 208	91.20 238	78.00 232	91.70 239	95.55 131	85.05 129	82.97 217	92.25 180	54.49 304	97.48 149	82.93 138	87.45 195	92.89 242

FMVSNet5		81.52 264	79.60 268	87.27 262	91.17 241	77.95 233	91.49 243	92.26 244	76.87 258	76.16 281	87.91 274	51.67 309	92.34 306	67.74 286	81.16 256	91.52 272

GG-mvs-BLEND				87.94 251	89.73 284	77.91 234	87.80 285	78.23 336		80.58 247	83.86 302	59.88 288	95.33 278	71.20 261	92.22 137	90.60 294

BH-w/o		87.57 169	87.05 149	89.12 216	94.90 117	77.90 235	92.41 220	93.51 223	82.89 187	83.70 206	91.34 210	75.75 139	97.07 199	75.49 234	93.49 117	92.39 257

testdata				90.49 160	96.40 65	77.89 236		95.37 152	72.51 293	93.63 23	96.69 36	82.08 66	97.65 139	83.08 135	97.39 58	95.94 114

pmmvs6		83.42 247	81.60 250	88.87 220	88.01 300	77.87 237	94.96 82	94.24 200	74.67 277	78.80 262	91.09 225	60.17 286	96.49 226	77.06 225	75.40 293	92.23 262

Baseline_NR-MVSNet		87.07 184	86.63 169	88.40 239	91.44 213	77.87 237	94.23 135	92.57 239	84.12 148	85.74 149	92.08 186	77.25 111	96.04 243	82.29 150	79.94 278	91.30 277

AllTest		83.42 247	81.39 251	89.52 205	95.01 110	77.79 239	93.12 196	90.89 282	77.41 253	76.12 282	93.34 138	54.08 306	97.51 147	68.31 282	84.27 220	93.26 231

TestCases				89.52 205	95.01 110	77.79 239		90.89 282	77.41 253	76.12 282	93.34 138	54.08 306	97.51 147	68.31 282	84.27 220	93.26 231

v1240		86.78 189	85.85 187	89.56 203	90.45 270	77.79 239	93.61 177	95.37 152	81.65 210	85.43 167	91.15 222	71.50 193	97.43 162	81.47 162	82.05 244	93.47 227

gg-mvs-nofinetune		81.77 259	79.37 269	88.99 219	90.85 257	77.73 242	86.29 295	79.63 334	74.88 276	83.19 216	69.05 326	60.34 284	96.11 242	75.46 235	94.64 98	93.11 237

Fast-Effi-MVS+-dtu		87.44 172	86.72 160	89.63 202	92.04 198	77.68 243	94.03 151	93.94 215	85.81 111	82.42 222	91.32 213	70.33 211	97.06 200	80.33 181	90.23 156	94.14 185

mvs-test1		89.45 106	89.14 97	90.38 166	93.33 169	77.63 244	94.95 83	94.36 196	87.70 77	87.10 121	92.81 164	73.45 170	98.03 122	85.57 107	93.04 128	95.48 130

v748		86.27 199	85.28 199	89.25 213	90.26 273	77.58 245	94.89 87	95.50 138	84.28 146	81.41 237	90.46 238	72.57 183	97.32 177	79.81 193	78.36 284	92.84 244

v148		87.04 185	86.32 175	89.21 214	90.94 251	77.26 246	93.71 172	94.43 194	84.84 132	84.36 193	90.80 229	76.04 128	97.05 201	82.12 151	79.60 281	93.31 230

PMMVS		85.71 211	84.96 204	87.95 250	88.90 291	77.09 247	88.68 278	90.06 296	72.32 294	86.47 130	90.76 230	72.15 187	94.40 288	81.78 159	93.49 117	92.36 258

ITE_SJBPF				88.24 244	91.88 200	77.05 248		92.92 230	85.54 118	80.13 254	93.30 142	57.29 296	96.20 239	72.46 256	84.71 216	91.49 273

pmmvs5		84.21 240	82.84 244	88.34 241	88.95 290	76.94 249	92.41 220	91.91 257	75.63 268	80.28 250	91.18 220	64.59 263	95.57 261	77.09 224	83.47 229	92.53 252

semantic-postprocess				88.18 246	91.71 206	76.87 250		92.65 238	85.40 121	81.44 236	90.54 235	66.21 253	95.00 284	81.04 165	81.05 259	92.66 249

Patchmatch-test1		85.81 208	84.71 210	89.12 216	92.15 194	76.60 251	91.12 250	91.69 260	83.53 162	85.50 161	88.56 264	66.79 247	95.00 284	72.69 255	90.35 155	95.76 123

CP-MVSNet		87.63 161	87.26 140	88.74 223	93.12 176	76.59 252	95.29 61	96.58 68	88.43 61	83.49 212	92.98 158	75.28 145	95.83 253	78.97 204	81.15 258	93.79 204

Effi-MVS+-dtu		88.65 128	88.35 115	89.54 204	93.33 169	76.39 253	94.47 115	94.36 196	87.70 77	85.43 167	89.56 252	73.45 170	97.26 184	85.57 107	91.28 141	94.97 143

Patchmtry		82.71 253	80.93 256	88.06 248	90.05 278	76.37 254	84.74 305	91.96 255	72.28 295	81.32 239	87.87 275	71.03 198	95.50 266	68.97 278	80.15 274	92.32 260

PS-CasMVS		87.32 175	86.88 152	88.63 226	92.99 182	76.33 255	95.33 56	96.61 65	88.22 66	83.30 215	93.07 152	73.03 176	95.79 256	78.36 209	81.00 263	93.75 210

OpenMVS_ROB	74.94 19	79.51 279	77.03 283	86.93 270	87.00 304	76.23 256	92.33 223	90.74 286	68.93 309	74.52 293	88.23 269	49.58 313	96.62 219	57.64 313	84.29 219	87.94 315

IterMVS		84.88 227	83.98 219	87.60 255	91.44 213	76.03 257	90.18 257	92.41 241	83.24 171	81.06 242	90.42 239	66.60 248	94.28 289	79.46 197	80.98 264	92.48 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 187	95.74 90	75.85 258	95.61 52	90.80 284	87.66 81	87.83 109	95.40 82	76.79 115	96.46 229	78.37 208	96.73 67	97.80 60

PEN-MVS		86.80 188	86.27 177	88.40 239	92.32 193	75.71 259	95.18 72	96.38 77	87.97 69	82.82 219	93.15 148	73.39 172	95.92 249	76.15 231	79.03 283	93.59 221

PatchmatchNet		85.85 206	84.70 211	89.29 212	91.76 204	75.54 260	88.49 280	91.30 269	81.63 212	85.05 177	88.70 261	71.71 188	96.24 238	74.61 244	89.05 175	96.08 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		79.81 277	77.34 279	87.22 267	79.24 325	75.48 261	93.12 196	92.03 250	76.45 259	75.01 290	91.58 203	49.19 315	96.44 230	70.22 268	69.18 314	89.75 298

DTE-MVSNet		86.11 201	85.48 195	87.98 249	91.65 209	74.92 262	94.93 85	95.75 118	87.36 84	82.26 224	93.04 153	72.85 177	95.82 254	74.04 247	77.46 288	93.20 233

USDC		82.76 252	81.26 253	87.26 263	91.17 241	74.55 263	89.27 270	93.39 225	78.26 248	75.30 289	92.08 186	54.43 305	96.63 218	71.64 258	85.79 208	90.61 292

mvs_anonymous		89.37 112	89.32 93	89.51 207	93.47 166	74.22 264	91.65 241	94.83 184	82.91 186	85.45 164	93.79 132	81.23 75	96.36 234	86.47 104	94.09 108	97.94 51

ADS-MVSNet2		81.66 261	79.71 267	87.50 258	91.35 226	74.19 265	83.33 314	88.48 311	72.90 290	82.24 225	85.77 296	64.98 261	93.20 301	64.57 297	83.74 224	95.12 138

Patchmatch-test		81.37 266	79.30 270	87.58 256	90.92 253	74.16 266	80.99 320	87.68 317	70.52 305	76.63 274	88.81 258	71.21 195	92.76 305	60.01 311	86.93 202	95.83 120

PatchFormer-LS_test		86.02 202	85.13 201	88.70 224	91.52 210	74.12 267	91.19 249	92.09 247	82.71 191	84.30 196	87.24 281	70.87 200	96.98 205	81.04 165	85.17 213	95.00 142

LP		75.51 289	72.15 293	85.61 283	87.86 302	73.93 268	80.20 322	88.43 312	67.39 311	70.05 308	80.56 316	58.18 294	93.18 302	46.28 327	70.36 312	89.71 299

MDA-MVSNet-bldmvs		78.85 283	76.31 284	86.46 276	89.76 283	73.88 269	88.79 276	90.42 288	79.16 235	59.18 323	88.33 268	60.20 285	94.04 291	62.00 304	68.96 315	91.48 274

DWT-MVSNet_test		84.95 225	83.68 226	88.77 221	91.43 216	73.75 270	91.74 237	90.98 279	80.66 223	83.84 202	87.36 279	62.44 270	97.11 196	78.84 206	85.81 206	95.46 131

MIMVSNet1		79.38 280	77.28 280	85.69 282	86.35 306	73.67 271	91.61 242	92.75 235	78.11 251	72.64 303	88.12 270	48.16 316	91.97 310	60.32 308	77.49 287	91.43 275

JIA-IIPM		81.04 269	78.98 275	87.25 264	88.64 292	73.48 272	81.75 319	89.61 305	73.19 286	82.05 229	73.71 323	66.07 257	95.87 252	71.18 263	84.60 217	92.41 256

TinyColmap		79.76 278	77.69 278	85.97 280	91.71 206	73.12 273	89.55 264	90.36 290	75.03 272	72.03 305	90.19 241	46.22 320	96.19 240	63.11 301	81.03 260	88.59 309

UnsupCasMVSNet_bld		76.23 288	73.27 290	85.09 288	83.79 314	72.92 274	85.65 302	93.47 224	71.52 298	68.84 311	79.08 319	49.77 312	93.21 300	66.81 291	60.52 326	89.13 305

test0.0.03 1		82.41 256	81.69 249	84.59 290	88.23 296	72.89 275	90.24 255	87.83 315	83.41 165	79.86 256	89.78 248	67.25 244	88.99 317	65.18 295	83.42 231	91.90 266

EPNet_dtu		86.49 197	85.94 186	88.14 247	90.24 274	72.82 276	94.11 140	92.20 245	86.66 100	79.42 260	92.36 176	73.52 168	95.81 255	71.26 260	93.66 113	95.80 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpmp4_e23		83.87 245	82.33 246	88.48 236	91.46 212	72.82 276	89.82 262	91.57 263	73.02 289	81.86 233	89.05 255	66.20 254	96.97 206	71.57 259	86.39 203	95.66 126

MDA-MVSNet_test_wron		79.21 282	77.19 282	85.29 285	88.22 297	72.77 278	85.87 298	90.06 296	74.34 279	62.62 322	87.56 278	66.14 255	91.99 309	66.90 290	73.01 296	91.10 281

EPMVS		83.90 244	82.70 245	87.51 257	90.23 275	72.67 279	88.62 279	81.96 330	81.37 218	85.01 178	88.34 267	66.31 252	94.45 287	75.30 237	87.12 199	95.43 132

YYNet1		79.22 281	77.20 281	85.28 286	88.20 298	72.66 280	85.87 298	90.05 298	74.33 280	62.70 321	87.61 277	66.09 256	92.03 308	66.94 287	72.97 297	91.15 279

UnsupCasMVSNet_eth		80.07 275	78.27 277	85.46 284	85.24 309	72.63 281	88.45 281	94.87 181	82.99 183	71.64 307	88.07 271	56.34 298	91.75 311	73.48 252	63.36 324	92.01 265

OurMVSNet-221017-0		85.35 215	84.64 213	87.49 259	90.77 258	72.59 282	94.01 153	94.40 195	84.72 135	79.62 259	93.17 147	61.91 274	96.72 215	81.99 154	81.16 256	93.16 235

CostFormer		85.77 209	84.94 205	88.26 243	91.16 243	72.58 283	89.47 268	91.04 278	76.26 263	86.45 133	89.97 245	70.74 203	96.86 214	82.35 148	87.07 201	95.34 136

LCM-MVSNet-Re		88.30 136	88.32 118	88.27 242	94.71 123	72.41 284	93.15 195	90.98 279	87.77 76	79.25 261	91.96 191	78.35 103	95.75 257	83.04 136	95.62 82	96.65 95

PVSNet	78.82 18	85.55 212	84.65 212	88.23 245	94.72 122	71.93 285	87.12 291	92.75 235	78.80 240	84.95 179	90.53 236	64.43 264	96.71 217	74.74 242	93.86 111	96.06 111

ADS-MVSNet		81.56 263	79.78 265	86.90 272	91.35 226	71.82 286	83.33 314	89.16 307	72.90 290	82.24 225	85.77 296	64.98 261	93.76 293	64.57 297	83.74 224	95.12 138

test-LLR		85.87 205	85.41 196	87.25 264	90.95 249	71.67 287	89.55 264	89.88 301	83.41 165	84.54 185	87.95 272	67.25 244	95.11 281	81.82 157	93.37 122	94.97 143

test-mter		84.54 238	83.64 228	87.25 264	90.95 249	71.67 287	89.55 264	89.88 301	79.17 234	84.54 185	87.95 272	55.56 300	95.11 281	81.82 157	93.37 122	94.97 143

tpm2		84.08 241	82.94 241	87.48 260	91.39 219	71.27 289	89.23 272	90.37 289	71.95 297	84.64 182	89.33 253	67.30 243	96.55 224	75.17 238	87.09 200	94.63 162

Patchmatch-RL test		81.67 260	79.96 264	86.81 275	85.42 308	71.23 290	82.17 318	87.50 319	78.47 244	77.19 273	82.50 309	70.81 202	93.48 297	82.66 142	72.89 298	95.71 125

TESTMET0.1,1		83.74 246	82.85 243	86.42 278	89.96 280	71.21 291	89.55 264	87.88 314	77.41 253	83.37 214	87.31 280	56.71 297	93.65 295	80.62 174	92.85 133	94.40 178

PVSNet_0	73.20 20	77.22 285	74.83 288	84.37 292	90.70 262	71.10 292	83.09 316	89.67 304	72.81 292	73.93 296	83.13 307	60.79 282	93.70 294	68.54 279	50.84 329	88.30 314

tpm cat1		81.96 258	80.27 260	87.01 269	91.09 244	71.02 293	87.38 290	91.53 265	66.25 315	80.17 251	86.35 293	68.22 242	96.15 241	69.16 277	82.29 239	93.86 201

tpmvs		83.35 250	82.07 247	87.20 268	91.07 245	71.00 294	88.31 282	91.70 259	78.91 236	80.49 249	87.18 282	69.30 224	97.08 198	68.12 285	83.56 228	93.51 226

PatchT		82.68 254	81.27 252	86.89 273	90.09 277	70.94 295	84.06 310	90.15 293	74.91 274	85.63 155	83.57 304	69.37 220	94.87 286	65.19 294	88.50 182	94.84 156

SixPastTwentyTwo		83.91 243	82.90 242	86.92 271	90.99 247	70.67 296	93.48 181	91.99 252	85.54 118	77.62 270	92.11 184	60.59 283	96.87 213	76.05 232	77.75 286	93.20 233

RPSCF		85.07 220	84.27 214	87.48 260	92.91 184	70.62 297	91.69 240	92.46 240	76.20 264	82.67 221	95.22 86	63.94 266	97.29 181	77.51 219	85.80 207	94.53 170

pmmvs3		71.81 296	68.71 299	81.11 301	75.86 327	70.42 298	86.74 292	83.66 326	58.95 325	68.64 313	80.89 315	36.93 328	89.52 316	63.10 302	63.59 323	83.39 319

Anonymous20231206		81.03 270	79.77 266	84.82 289	87.85 303	70.26 299	91.42 244	92.08 248	73.67 282	77.75 269	89.25 254	62.43 271	93.08 303	61.50 306	82.00 245	91.12 280

PM-MVS		78.11 284	76.12 286	84.09 295	83.54 315	70.08 300	88.97 275	85.27 324	79.93 228	74.73 292	86.43 287	34.70 329	93.48 297	79.43 200	72.06 300	88.72 307

MDTV_nov1_ep13			83.56 229		91.69 208	69.93 301	87.75 287	91.54 264	78.60 243	84.86 180	88.90 257	69.54 219	96.03 244	70.25 266	88.93 176

LF4IMVS		80.37 274	79.07 274	84.27 294	86.64 305	69.87 302	89.39 269	91.05 277	76.38 260	74.97 291	90.00 244	47.85 317	94.25 290	74.55 245	80.82 266	88.69 308

K. test v3		81.59 262	80.15 263	85.91 281	89.89 282	69.42 303	92.57 216	87.71 316	85.56 117	73.44 298	89.71 249	55.58 299	95.52 263	77.17 222	69.76 313	92.78 247

tpm		84.73 233	84.02 217	86.87 274	90.33 271	68.90 304	89.06 274	89.94 299	80.85 222	85.75 147	89.86 247	68.54 237	95.97 247	77.76 215	84.05 222	95.75 124

lessismore_v0				86.04 279	88.46 295	68.78 305		80.59 332		73.01 301	90.11 243	55.39 301	96.43 231	75.06 240	65.06 319	92.90 241

gm-plane-assit					89.60 285	68.00 306			77.28 256		88.99 256		97.57 143	79.44 199

tpmrst		85.35 215	84.99 202	86.43 277	90.88 256	67.88 307	88.71 277	91.43 267	80.13 226	86.08 141	88.80 259	73.05 175	96.02 245	82.48 145	83.40 232	95.40 133

test20.03		79.95 276	79.08 273	82.55 299	85.79 307	67.74 308	91.09 251	91.08 275	81.23 219	74.48 294	89.96 246	61.63 275	90.15 315	60.08 309	76.38 290	89.76 297

CMPMVS	59.16 21	80.52 273	79.20 271	84.48 291	83.98 313	67.63 309	89.95 261	93.84 219	64.79 319	66.81 316	91.14 223	57.93 295	95.17 279	76.25 229	88.10 188	90.65 291
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231211		72.97 293	69.63 298	83.00 298	83.05 317	66.91 310	92.69 211	89.45 306	61.06 323	67.50 314	83.46 305	34.34 330	93.61 296	51.11 318	63.97 322	88.48 312

testgi		80.94 272	80.20 262	83.18 296	87.96 301	66.29 311	91.28 246	90.70 287	83.70 155	78.12 265	92.84 161	51.37 310	90.82 314	63.34 300	82.46 238	92.43 255

new_pmnet		72.15 295	70.13 296	78.20 304	82.95 318	65.68 312	83.91 311	82.40 329	62.94 322	64.47 319	79.82 318	42.85 323	86.26 325	57.41 314	74.44 295	82.65 321

Gipuma		57.99 307	54.91 308	67.24 320	88.51 293	65.59 313	52.21 336	90.33 291	43.58 332	42.84 331	51.18 334	20.29 339	85.07 329	34.77 334	70.45 311	51.05 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dp		81.47 265	80.23 261	85.17 287	89.92 281	65.49 314	86.74 292	90.10 295	76.30 262	81.10 240	87.12 283	62.81 268	95.92 249	68.13 284	79.88 279	94.09 189

CVMVSNet		84.69 236	84.79 209	84.37 292	91.84 201	64.92 315	93.70 173	91.47 266	66.19 316	86.16 140	95.28 83	67.18 246	93.33 299	80.89 170	90.42 154	94.88 155

EU-MVSNet		81.32 267	80.95 255	82.42 300	88.50 294	63.67 316	93.32 185	91.33 268	64.02 320	80.57 248	92.83 162	61.21 280	92.27 307	76.34 228	80.38 273	91.32 276

ambc				83.06 297	79.99 322	63.51 317	77.47 326	92.86 231		74.34 295	84.45 300	28.74 331	95.06 283	73.06 254	68.89 316	90.61 292

no-one		61.56 304	56.58 306	76.49 310	67.80 335	62.76 318	78.13 325	86.11 320	63.16 321	43.24 330	64.70 329	26.12 334	88.95 318	50.84 320	29.15 332	77.77 325

test2356		74.50 290	73.27 290	78.20 304	80.81 321	59.84 319	83.76 313	88.33 313	71.43 300	72.37 304	81.84 312	45.60 321	86.26 325	50.97 319	84.32 218	88.50 310

testpf		71.41 297	72.11 294	69.30 317	84.53 312	59.79 320	62.74 333	83.14 327	71.11 302	68.83 312	81.57 314	46.70 319	84.83 330	74.51 246	75.86 292	63.30 328

new-patchmatchnet		76.41 287	75.17 287	80.13 302	82.65 319	59.61 321	87.66 288	91.08 275	78.23 249	69.85 309	83.22 306	54.76 303	91.63 313	64.14 299	64.89 320	89.16 303

LCM-MVSNet		66.00 300	62.16 304	77.51 309	64.51 337	58.29 322	83.87 312	90.90 281	48.17 329	54.69 325	73.31 324	16.83 342	86.75 324	65.47 293	61.67 325	87.48 316

test1235678		72.22 294	70.31 295	77.93 307	78.04 326	58.04 323	85.76 300	89.80 303	70.15 307	63.43 320	80.20 317	42.24 324	87.24 322	48.68 323	74.50 294	88.50 310

testus		74.41 291	73.35 289	77.59 308	82.49 320	57.08 324	86.02 296	90.21 292	72.28 295	72.89 302	84.32 301	37.08 327	86.96 323	52.24 317	82.65 236	88.73 306

FPMVS		64.63 303	62.55 303	70.88 314	70.80 330	56.71 325	84.42 307	84.42 325	51.78 328	49.57 327	81.61 313	23.49 336	81.48 332	40.61 332	76.25 291	74.46 327

ANet_high		58.88 306	54.22 309	72.86 313	56.50 341	56.67 326	80.75 321	86.00 321	73.09 288	37.39 332	64.63 330	22.17 337	79.49 335	43.51 330	23.96 336	82.43 322

MVS-HIRNet		73.70 292	72.20 292	78.18 306	91.81 203	56.42 327	82.94 317	82.58 328	55.24 326	68.88 310	66.48 327	55.32 302	95.13 280	58.12 312	88.42 185	83.01 320

DSMNet-mixed		76.94 286	76.29 285	78.89 303	83.10 316	56.11 328	87.78 286	79.77 333	60.65 324	75.64 288	88.71 260	61.56 276	88.34 319	60.07 310	89.29 171	92.21 263

MDTV_nov1_ep13_2view						55.91 329	87.62 289		73.32 285	84.59 184		70.33 211		74.65 243		95.50 129

wuykxyi23d		50.55 310	44.13 312	69.81 316	56.77 339	54.58 330	73.22 330	80.78 331	39.79 334	22.08 339	46.69 336	4.03 346	79.71 334	47.65 324	26.13 334	75.14 326

testmv		65.49 301	62.66 302	73.96 311	68.78 332	53.14 331	84.70 306	88.56 310	65.94 317	52.35 326	74.65 322	25.02 335	85.14 328	43.54 329	60.40 327	83.60 318

DeepMVS_CX				56.31 324	74.23 328	51.81 332		56.67 343	44.85 330	48.54 329	75.16 321	27.87 333	58.74 340	40.92 331	52.22 328	58.39 332

MVE	39.65 23	43.39 312	38.59 317	57.77 323	56.52 340	48.77 333	55.38 335	58.64 342	29.33 337	28.96 336	52.65 333	4.68 345	64.62 339	28.11 336	33.07 331	59.93 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		59.60 305	56.40 307	69.21 318	68.83 331	46.58 334	73.02 331	77.48 337	55.07 327	49.21 328	72.95 325	17.43 341	80.04 333	49.32 322	44.33 330	80.99 324

PMVS	47.18 22	52.22 309	48.46 310	63.48 321	45.72 342	46.20 335	73.41 329	78.31 335	41.03 333	30.06 335	65.68 328	6.05 344	83.43 331	30.04 335	65.86 318	60.80 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		70.54 298	69.71 297	73.04 312	79.30 323	44.83 336	84.23 308	88.96 308	67.33 312	65.42 317	82.28 310	41.11 325	88.11 320	47.12 325	71.60 304	86.19 317

.test1245		57.63 308	61.79 305	45.14 326	79.30 323	44.83 336	84.23 308	88.96 308	67.33 312	65.42 317	82.28 310	41.11 325	88.11 320	47.12 325	0.39 340	2.46 339

PNet_i23d		50.48 311	47.18 311	60.36 322	68.59 333	44.56 338	72.75 332	72.61 338	43.92 331	33.91 334	60.19 332	6.16 343	73.52 336	38.50 333	28.04 333	63.01 329

test12356		64.99 302	63.78 301	68.61 319	72.69 329	39.14 339	78.46 324	87.61 318	64.91 318	55.77 324	77.48 320	28.10 332	85.59 327	44.69 328	64.35 321	81.12 323

E-PMN		43.23 313	42.29 313	46.03 325	65.58 336	37.41 340	73.51 328	64.62 339	33.99 335	28.47 337	47.87 335	19.90 340	67.91 337	22.23 337	24.45 335	32.77 334

wuyk23d		21.27 318	20.48 319	23.63 330	68.59 333	36.41 341	49.57 337	6.85 346	9.37 338	7.89 340	4.46 343	4.03 346	31.37 341	17.47 339	16.07 339	3.12 337

EMVS		42.07 314	41.12 314	44.92 327	63.45 338	35.56 342	73.65 327	63.48 340	33.05 336	26.88 338	45.45 337	21.27 338	67.14 338	19.80 338	23.02 337	32.06 335

N_pmnet		68.89 299	68.44 300	70.23 315	89.07 289	28.79 343	88.06 283	19.50 345	69.47 308	71.86 306	84.93 299	61.24 279	91.75 311	54.70 315	77.15 289	90.15 296

tmp_tt		35.64 316	39.24 315	24.84 329	14.87 343	23.90 344	62.71 334	51.51 344	6.58 339	36.66 333	62.08 331	44.37 322	30.34 342	52.40 316	22.00 338	20.27 336

test123		8.76 320	11.22 321	1.39 331	0.85 345	0.97 345	85.76 300	0.35 348	0.54 341	2.45 342	8.14 342	0.60 348	0.48 343	2.16 341	0.17 342	2.71 338

testmvs		8.92 319	11.52 320	1.12 332	1.06 344	0.46 346	86.02 296	0.65 347	0.62 340	2.74 341	9.52 341	0.31 349	0.45 344	2.38 340	0.39 340	2.46 339

cdsmvs_eth3d_5k		22.14 317	29.52 318	0.00 333	0.00 346	0.00 347	0.00 338	95.76 117	0.00 342	0.00 343	94.29 111	75.66 140	0.00 345	0.00 342	0.00 343	0.00 341

pcd_1.5k_mvsjas		6.64 322	8.86 323	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	79.70 88	0.00 345	0.00 342	0.00 343	0.00 341

pcd1.5k->3k		37.02 315	38.84 316	31.53 328	92.33 192	0.00 347	0.00 338	96.13 91	0.00 342	0.00 343	0.00 344	72.70 179	0.00 345	0.00 342	88.43 184	94.60 165

sosnet-low-res		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

sosnet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uncertanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

Regformer		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ab-mvs-re		7.82 321	10.43 322	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	93.88 129	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ESAPD								97.46 6

sam_mvs1												71.70 189

sam_mvs												70.60 204

MTGPA								96.97 33

test_post1							88.00 284				9.81 340	69.31 223	95.53 262	76.65 226

test_post											10.29 339	70.57 208	95.91 251

patchmatchnet-post											83.76 303	71.53 192	96.48 227

MTMP								60.64 341

test9_res														91.91 40	98.71 17	98.07 43

agg_prior2														90.54 59	98.68 22	98.27 29

test_prior2							94.12 138		87.67 79	92.63 43	96.39 50	86.62 23		91.50 47	98.67 24

旧先验2							93.36 184		71.25 301	94.37 11			97.13 195	86.74 98

新几何2							93.11 198

无先验							93.28 191	96.26 81	73.95 281				99.05 44	80.56 175		96.59 96

原ACMM2							92.94 206

testdata2													98.75 76	78.30 210

segment_acmp												87.16 19

testdata1							92.15 229		87.94 70

plane_prior5								96.22 85					98.12 106	88.15 77	89.99 158	94.63 162

plane_prior4											94.86 95

plane_prior2							95.85 40		90.81 18

plane_prior1					94.59 128

n2								0.00 349

nn								0.00 349

door-mid								85.49 322

test11								96.57 69

door								85.33 323

HQP-NCC					94.17 141		94.39 122		88.81 50	85.43 167

ACMP_Plane					94.17 141		94.39 122		88.81 50	85.43 167

BP-MVS														87.11 95

HQP4-MVS										85.43 167			97.96 125			94.51 172

HQP3-MVS								96.04 98							89.77 163

HQP2-MVS												73.83 165

ACMMP++_ref															87.47 194

ACMMP++															88.01 191

Test By Simon												80.02 83

High-resolution multi-view benchmark