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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SD-MVS		98.52 5	98.77 4	98.23 12	98.15 45	99.26 19	98.79 24	97.59 11	98.52 1	96.25 11	97.99 11	99.75 3	99.01 3	98.27 23	97.97 24	99.59 4	99.63 1

CSCG		97.44 29	97.18 36	97.75 24	99.47 6	99.52 3	98.55 29	95.41 35	97.69 19	95.72 15	94.29 47	95.53 54	98.10 27	96.20 100	97.38 44	99.24 78	99.62 2

TSAR-MVS + MP.		98.49 6	98.78 3	98.15 16	98.14 46	99.17 26	99.34 3	97.18 24	98.44 3	95.72 15	97.84 13	99.28 9	98.87 6	99.05 1	98.05 21	99.66 1	99.60 3

TSAR-MVS + ACMM		97.71 25	98.60 9	96.66 36	98.64 36	99.05 30	98.85 23	97.23 23	98.45 2	89.40 74	97.51 20	99.27 10	96.88 57	98.53 12	97.81 31	98.96 114	99.59 4

APDe-MVS		98.87 1	98.96 1	98.77 1	99.58 1	99.53 2	99.44 1	97.81 1	98.22 7	97.33 2	98.70 2	99.33 7	98.86 7	98.96 3	98.40 11	99.63 3	99.57 5

SteuartSystems-ACMMP		98.38 11	98.71 6	97.99 20	99.34 17	99.46 5	99.34 3	97.33 20	97.31 30	94.25 26	98.06 9	99.17 13	98.13 26	98.98 2	98.46 9	99.55 9	99.54 6
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		98.58 4	98.88 2	98.24 10	99.58 1	99.44 6	99.05 16	97.63 8	97.76 14	94.92 23	97.94 12	99.84 1	98.85 9	98.95 4	98.70 5	99.44 40	99.50 7

canonicalmvs		95.25 56	95.45 59	95.00 60	95.27 92	98.72 66	96.89 56	89.82 100	96.51 47	90.84 54	93.72 48	86.01 98	97.66 35	95.78 112	97.94 26	99.54 11	99.50 7

EPP-MVSNet		95.27 55	96.18 51	94.20 77	94.88 105	98.64 70	94.97 106	90.70 90	95.34 77	89.67 70	91.66 70	93.84 59	95.42 78	97.32 50	97.00 51	99.58 6	99.47 9

HSP-MVS		98.59 2	98.65 7	98.52 3	99.44 10	99.57 1	99.34 3	97.65 6	97.36 29	96.62 8	98.49 5	99.65 5	98.67 17	98.60 11	97.44 41	99.40 50	99.46 10

ESAPD		98.59 2	98.77 4	98.39 6	99.46 8	99.50 4	99.11 13	97.80 2	97.20 33	96.06 13	98.56 3	99.83 2	98.43 23	98.84 7	98.03 23	99.45 30	99.45 11

PVSNet_Blended_VisFu		94.77 64	95.54 58	93.87 83	96.48 65	98.97 41	94.33 119	91.84 80	94.93 87	90.37 61	85.04 127	94.99 55	90.87 146	98.12 31	97.30 47	99.30 69	99.45 11

MVS_0304		96.31 44	96.91 42	95.62 49	97.21 58	99.20 24	98.55 29	93.10 55	97.04 39	89.73 68	90.30 84	96.35 46	95.71 71	98.14 29	97.93 28	99.38 54	99.40 13

MSLP-MVS++		98.04 20	97.93 29	98.18 13	99.10 23	99.09 29	98.34 33	96.99 27	97.54 25	96.60 9	94.82 43	98.45 30	98.89 5	97.46 48	98.77 4	99.17 90	99.37 14

DeepC-MVS	94.87 4	96.76 42	96.50 46	97.05 32	98.21 44	99.28 17	98.67 25	97.38 16	97.31 30	90.36 62	89.19 92	93.58 62	98.19 25	98.31 21	98.50 7	99.51 17	99.36 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		96.84 39	97.20 34	96.42 37	97.92 48	99.24 23	98.60 27	93.51 47	97.11 36	93.07 34	91.16 74	97.24 39	96.21 67	98.24 26	98.05 21	99.22 84	99.35 16

3Dnovator+	93.91 7	97.23 32	97.22 33	97.24 29	98.89 31	98.85 59	98.26 34	93.25 52	97.99 11	95.56 18	90.01 88	98.03 35	98.05 28	97.91 37	98.43 10	99.44 40	99.35 16

TSAR-MVS + GP.		97.45 28	98.36 15	96.39 38	95.56 77	98.93 49	97.74 44	93.31 49	97.61 23	94.24 27	98.44 7	99.19 11	98.03 29	97.60 44	97.41 43	99.44 40	99.33 18

UGNet		94.92 57	96.63 44	92.93 97	96.03 71	98.63 72	94.53 116	91.52 85	96.23 54	90.03 64	92.87 57	96.10 51	86.28 194	96.68 74	96.60 60	99.16 93	99.32 19
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
MCST-MVS		98.20 15	98.36 15	98.01 19	99.40 12	99.05 30	99.00 19	97.62 9	97.59 24	93.70 30	97.42 23	99.30 8	98.77 13	98.39 20	97.48 39	99.59 4	99.31 20

FC-MVSNet-train		93.85 79	93.91 84	93.78 85	94.94 104	96.79 115	94.29 120	91.13 87	93.84 102	88.26 85	90.40 83	85.23 105	94.65 86	96.54 84	95.31 104	99.38 54	99.28 21

X-MVS		97.84 21	98.19 24	97.42 27	99.40 12	99.35 10	99.06 15	97.25 21	97.38 28	90.85 51	96.06 32	98.72 24	98.53 21	98.41 19	98.15 17	99.46 26	99.28 21

EPNet		96.27 45	96.97 39	95.46 52	98.47 39	98.28 81	97.41 49	93.67 45	95.86 67	92.86 39	97.51 20	93.79 60	91.76 130	97.03 58	97.03 50	98.61 161	99.28 21
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		98.32 14	98.34 18	98.29 8	99.34 17	99.30 15	99.15 11	97.35 17	97.49 26	95.58 17	97.72 15	98.62 28	98.82 11	98.29 22	97.67 34	99.51 17	99.28 21

DELS-MVS		96.06 46	96.04 52	96.07 45	97.77 50	99.25 21	98.10 37	93.26 50	94.42 92	92.79 40	88.52 99	93.48 63	95.06 82	98.51 13	98.83 1	99.45 30	99.28 21
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
HFP-MVS		98.48 7	98.62 8	98.32 7	99.39 15	99.33 14	99.27 8	97.42 14	98.27 5	95.25 19	98.34 8	98.83 21	99.08 1	98.26 24	98.08 20	99.48 21	99.26 26

HPM-MVS++		98.34 13	98.47 12	98.18 13	99.46 8	99.15 27	99.10 14	97.69 5	97.67 20	94.93 22	97.62 16	99.70 4	98.60 18	98.45 16	97.46 40	99.31 67	99.26 26

3Dnovator	93.79 8	97.08 34	97.20 34	96.95 34	99.09 24	99.03 35	98.20 35	93.33 48	97.99 11	93.82 29	90.61 82	96.80 43	97.82 31	97.90 38	98.78 3	99.47 24	99.26 26

MP-MVS		98.09 19	98.30 21	97.84 23	99.34 17	99.19 25	99.23 10	97.40 15	97.09 37	93.03 37	97.58 18	98.85 20	98.57 20	98.44 18	97.69 33	99.48 21	99.23 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
IS_MVSNet		95.28 54	96.43 48	93.94 80	95.30 90	99.01 39	95.90 84	91.12 88	94.13 98	87.50 94	91.23 73	94.45 58	94.17 93	98.45 16	98.50 7	99.65 2	99.23 29

Anonymous20240521		88.75 154	89.28 149	88.12 160	88.65 203	96.11 132	90.55 190	85.89 147	88.36 181	77.73 136	76.40 165	75.83 154	86.56 192	95.15 124	93.92 138	99.32 63	99.22 31

ACMMPR		98.40 10	98.49 10	98.28 9	99.41 11	99.40 7	99.36 2	97.35 17	98.30 4	95.02 21	97.79 14	98.39 31	99.04 2	98.26 24	98.10 18	99.50 19	99.22 31

APD-MVS		98.36 12	98.32 19	98.41 5	99.47 6	99.26 19	99.12 12	97.77 4	96.73 43	96.12 12	97.27 24	98.88 19	98.46 22	98.47 15	98.39 12	99.52 13	99.22 31
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet (Re)		90.03 136	89.61 144	90.51 122	89.97 161	96.12 131	92.32 151	89.26 107	90.99 145	80.95 126	78.25 159	75.08 159	91.14 137	93.78 145	93.87 140	99.41 48	99.21 34

DeepPCF-MVS	95.28 2	97.00 36	98.35 17	95.42 53	97.30 56	98.94 43	94.82 111	96.03 33	98.24 6	92.11 45	95.80 34	98.64 27	95.51 76	98.95 4	98.66 6	96.78 196	99.20 35

conf0.05thres1000		92.47 106	91.39 129	93.73 86	95.21 95	98.52 75	95.66 92	91.56 83	90.87 147	84.27 105	82.79 142	76.12 150	96.29 65	96.59 79	95.68 94	99.39 52	99.19 36

ACMMP_Plus		98.20 15	98.49 10	97.85 22	99.50 4	99.40 7	99.26 9	97.64 7	97.47 27	92.62 43	97.59 17	99.09 16	98.71 15	98.82 9	97.86 30	99.40 50	99.19 36

train_agg		97.65 26	98.06 26	97.18 30	98.94 28	98.91 55	98.98 22	97.07 26	96.71 44	90.66 56	97.43 22	99.08 18	98.20 24	97.96 36	97.14 49	99.22 84	99.19 36

QAPM		96.78 41	97.14 37	96.36 39	99.05 25	99.14 28	98.02 38	93.26 50	97.27 32	90.84 54	91.16 74	97.31 38	97.64 36	97.70 42	98.20 15	99.33 61	99.18 39

zzz-MVS		98.43 9	98.31 20	98.57 2	99.48 5	99.40 7	99.32 6	97.62 9	97.70 17	96.67 6	96.59 28	99.09 16	98.86 7	98.65 10	97.56 37	99.45 30	99.17 40

anonymousdsp		88.90 151	91.00 133	86.44 195	88.74 202	95.97 136	90.40 192	82.86 178	88.77 174	67.33 209	81.18 149	81.44 126	90.22 171	96.23 98	94.27 130	99.12 99	99.16 41

ACMMP		97.37 30	97.48 32	97.25 28	98.88 32	99.28 17	98.47 31	96.86 29	97.04 39	92.15 44	97.57 19	96.05 52	97.67 34	97.27 51	95.99 79	99.46 26	99.14 42
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
CHOSEN 1792x2688		92.66 104	92.49 108	92.85 98	97.13 59	98.89 58	95.90 84	88.50 115	95.32 78	83.31 111	71.99 203	88.96 85	94.10 95	96.69 73	96.49 61	98.15 178	99.10 43

DeepC-MVS_fast	96.13 1	98.13 17	98.27 22	97.97 21	99.16 22	99.03 35	99.05 16	97.24 22	98.22 7	94.17 28	95.82 33	98.07 33	98.69 16	98.83 8	98.80 2	99.52 13	99.10 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		97.81 22	98.11 25	97.46 26	99.55 3	99.34 13	99.32 6	94.51 40	96.21 55	93.07 34	98.05 10	97.95 36	98.82 11	98.22 27	97.89 29	99.48 21	99.09 45

CNVR-MVS		98.47 8	98.46 13	98.48 4	99.40 12	99.05 30	99.02 18	97.54 12	97.73 15	96.65 7	97.20 25	99.13 14	98.85 9	98.91 6	98.10 18	99.41 48	99.08 46

PVSNet_BlendedMVS		95.41 52	95.28 60	95.57 50	97.42 54	99.02 37	95.89 86	93.10 55	96.16 56	93.12 32	91.99 66	85.27 103	94.66 84	98.09 33	97.34 45	99.24 78	99.08 46

PVSNet_Blended		95.41 52	95.28 60	95.57 50	97.42 54	99.02 37	95.89 86	93.10 55	96.16 56	93.12 32	91.99 66	85.27 103	94.66 84	98.09 33	97.34 45	99.24 78	99.08 46

IB-MVS	89.56 15	91.71 113	92.50 107	90.79 119	95.94 73	98.44 78	87.05 206	91.38 86	93.15 110	92.98 38	84.78 128	85.14 106	78.27 214	92.47 167	94.44 128	99.10 101	99.08 46
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
ACMP	92.88 9	94.43 68	94.38 76	94.50 74	96.01 72	97.69 94	95.85 89	92.09 74	95.74 71	89.12 77	95.14 40	82.62 122	94.77 83	95.73 113	94.67 116	99.14 96	99.06 50
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		94.79 62	94.36 77	95.30 55	95.21 95	97.46 97	97.23 51	92.24 71	96.43 48	91.77 47	92.69 58	84.31 109	96.06 68	95.52 117	95.03 109	99.31 67	99.06 50
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		90.35 131	89.96 140	90.80 118	89.66 165	95.83 144	92.48 143	90.53 93	90.96 146	79.57 130	79.33 156	77.14 144	93.21 111	92.91 161	94.50 127	99.37 57	99.05 52

DU-MVS		89.67 140	88.84 151	90.63 121	89.26 189	95.61 149	92.48 143	89.91 98	91.22 142	79.57 130	77.72 160	71.18 196	93.21 111	92.53 165	94.57 121	99.35 58	99.05 52

CPTT-MVS		97.78 23	97.54 30	98.05 18	98.91 30	99.05 30	99.00 19	96.96 28	97.14 35	95.92 14	95.50 36	98.78 23	98.99 4	97.20 53	96.07 75	98.54 165	99.04 54

tfpnnormal		88.50 155	87.01 191	90.23 125	91.36 144	95.78 146	92.74 138	90.09 96	83.65 214	76.33 147	71.46 208	69.58 205	91.84 128	95.54 116	94.02 135	99.06 108	99.03 55

LGP-MVS_train		94.12 73	94.62 70	93.53 89	96.44 66	97.54 95	97.40 50	91.84 80	94.66 88	81.09 125	95.70 35	83.36 118	95.10 81	96.36 94	95.71 93	99.32 63	99.03 55

PHI-MVS		97.78 23	98.44 14	97.02 33	98.73 33	99.25 21	98.11 36	95.54 34	96.66 46	92.79 40	98.52 4	99.38 6	97.50 38	97.84 39	98.39 12	99.45 30	99.03 55

MVS_111021_HR		97.04 35	98.20 23	95.69 48	98.44 41	99.29 16	96.59 72	93.20 53	97.70 17	89.94 66	98.46 6	96.89 41	96.71 60	98.11 32	97.95 25	99.27 73	99.01 58

HQP-MVS		94.43 68	94.57 71	94.27 76	96.41 67	97.23 102	96.89 56	93.98 42	95.94 64	83.68 109	95.01 42	84.46 108	95.58 74	95.47 118	94.85 115	99.07 105	99.00 59

NR-MVSNet		89.34 143	88.66 152	90.13 130	90.40 154	95.61 149	93.04 136	89.91 98	91.22 142	78.96 133	77.72 160	68.90 209	89.16 177	94.24 141	93.95 136	99.32 63	98.99 60

MAR-MVS		95.50 48	95.60 56	95.39 54	98.67 35	98.18 87	95.89 86	89.81 101	94.55 91	91.97 46	92.99 54	90.21 77	97.30 40	96.79 67	97.49 38	98.72 152	98.99 60
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
Effi-MVS+		92.93 100	93.86 86	91.86 105	94.07 118	98.09 91	95.59 93	85.98 145	94.27 95	79.54 132	91.12 77	81.81 124	96.71 60	96.67 75	96.06 76	99.27 73	98.98 62

CP-MVSNet		87.89 173	87.27 182	88.62 150	89.30 185	95.06 174	90.60 189	85.78 148	87.43 195	75.98 149	74.60 177	68.14 211	90.76 147	93.07 159	93.60 145	99.30 69	98.98 62

NCCC		98.10 18	98.05 27	98.17 15	99.38 16	99.05 30	99.00 19	97.53 13	98.04 10	95.12 20	94.80 44	99.18 12	98.58 19	98.49 14	97.78 32	99.39 52	98.98 62

ACMH	90.77 13	91.51 118	91.63 124	91.38 111	95.62 76	96.87 109	91.76 176	89.66 103	91.58 139	78.67 134	86.73 111	78.12 136	93.77 99	94.59 131	94.54 124	98.78 143	98.98 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HyFIR lowres test		92.03 108	91.55 126	92.58 102	97.13 59	98.72 66	94.65 114	86.54 137	93.58 106	82.56 114	67.75 218	90.47 76	95.67 72	95.87 108	95.54 100	98.91 118	98.93 66

CDPH-MVS		96.84 39	97.49 31	96.09 43	98.92 29	98.85 59	98.61 26	95.09 36	96.00 62	87.29 97	95.45 38	97.42 37	97.16 43	97.83 40	97.94 26	99.44 40	98.92 67

TranMVSNet+NR-MVSNet		89.23 146	88.48 155	90.11 131	89.07 195	95.25 170	92.91 137	90.43 94	90.31 154	77.10 141	76.62 163	71.57 194	91.83 129	92.12 178	94.59 120	99.32 63	98.92 67

PS-CasMVS		87.33 188	86.68 196	88.10 161	89.22 194	94.93 179	90.35 193	85.70 149	86.44 201	74.01 171	73.43 196	66.59 217	90.04 172	92.92 160	93.52 146	99.28 71	98.91 69

Vis-MVSNet		92.77 102	95.00 67	90.16 127	94.10 117	98.79 61	94.76 113	88.26 116	92.37 131	79.95 128	88.19 101	91.58 69	84.38 204	97.59 45	97.58 36	99.52 13	98.91 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Baseline_NR-MVSNet		89.27 145	88.01 161	90.73 120	89.26 189	93.71 199	92.71 140	89.78 102	90.73 149	81.28 124	73.53 195	72.85 175	92.30 121	92.53 165	93.84 142	99.07 105	98.88 71

OpenMVS	92.33 11	95.50 48	95.22 62	95.82 47	98.98 26	98.97 41	97.67 46	93.04 58	94.64 89	89.18 76	84.44 131	94.79 56	96.79 58	97.23 52	97.61 35	99.24 78	98.88 71

LTVRE_ROB	87.32 16	87.55 179	88.25 157	86.73 190	90.66 151	95.80 145	93.05 135	84.77 162	83.35 215	60.32 221	83.12 140	67.39 212	93.32 108	94.36 138	94.86 114	98.28 176	98.87 73
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
MVS_111021_LR		97.16 33	98.01 28	96.16 42	98.47 39	98.98 40	96.94 55	93.89 43	97.64 22	91.44 48	98.89 1	96.41 45	97.20 42	98.02 35	97.29 48	99.04 110	98.85 74

WR-MVS_H		87.93 169	87.85 169	88.03 167	89.62 167	95.58 153	90.47 191	85.55 151	87.20 197	76.83 143	74.42 181	72.67 186	86.37 193	93.22 156	93.04 153	99.33 61	98.83 75

v748		85.88 200	85.66 203	86.14 198	88.03 206	94.63 189	87.02 207	84.59 166	84.30 211	74.56 165	70.94 210	67.27 213	83.94 208	90.96 197	92.74 175	98.71 153	98.81 76

WR-MVS		87.93 169	88.09 159	87.75 173	89.26 189	95.28 167	90.81 187	86.69 136	88.90 171	75.29 156	74.31 183	73.72 165	85.19 200	92.26 168	93.32 150	99.27 73	98.81 76

DI_MVS_plusplus_trai		94.01 76	93.63 92	94.44 75	94.54 111	98.26 84	97.51 48	90.63 91	95.88 66	89.34 75	80.54 152	89.36 81	95.48 77	96.33 95	96.27 68	99.17 90	98.78 78

v7n		86.43 196	86.52 199	86.33 196	87.91 208	94.93 179	90.15 194	83.05 176	86.57 199	70.21 200	71.48 207	66.78 215	87.72 184	94.19 143	92.96 156	98.92 117	98.76 79

tfpn		92.91 101	91.44 128	94.63 73	95.42 78	98.92 53	96.41 78	92.10 73	93.19 109	87.34 96	86.85 109	69.20 207	97.01 54	96.88 59	96.28 67	99.47 24	98.75 80

view800		93.45 95	92.37 115	94.71 71	95.42 78	98.92 53	96.51 75	92.19 72	93.14 111	87.62 92	86.72 112	76.54 149	97.08 53	96.86 60	95.74 92	99.45 30	98.70 81

diffmvs		94.83 59	95.64 55	93.89 82	94.73 107	97.96 93	96.49 76	89.13 110	96.82 42	89.47 72	91.66 70	93.63 61	95.15 80	94.76 129	95.93 80	98.36 175	98.69 82

Effi-MVS+-dtu		91.78 112	93.59 94	89.68 135	92.44 137	97.11 104	94.40 118	84.94 161	92.43 127	75.48 152	91.09 78	83.75 113	93.55 104	96.61 76	95.47 101	97.24 192	98.67 83

thres600view7		93.49 94	92.37 115	94.79 70	95.42 78	98.93 49	96.58 73	92.31 64	93.04 113	87.88 90	86.62 115	76.94 146	97.09 52	96.82 62	95.63 96	99.45 30	98.63 84

view600		93.50 93	92.39 114	94.80 69	95.41 81	98.93 49	96.60 71	92.30 69	93.09 112	87.96 89	86.67 114	76.97 145	97.12 46	96.83 61	95.64 95	99.43 46	98.62 85

abl_6				96.82 35	98.60 37	98.74 63	97.74 44	93.73 44	96.25 53	94.37 25	94.55 46	98.60 29	97.25 41			99.27 73	98.61 86

IterMVS-LS		92.56 105	93.18 100	91.84 106	93.90 120	94.97 177	94.99 105	86.20 141	94.18 97	82.68 113	85.81 124	87.36 92	94.43 88	95.31 120	96.02 78	98.87 120	98.60 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn111		94.05 74	93.34 99	94.88 64	95.33 84	98.94 43	96.82 59	92.31 64	92.63 118	88.26 85	92.61 59	78.01 138	97.12 46	96.82 62	95.85 87	99.45 30	98.56 88

conf200view11		93.64 83	92.57 103	94.88 64	95.33 84	98.94 43	96.82 59	92.31 64	92.63 118	88.26 85	87.21 105	78.01 138	97.12 46	96.82 62	95.85 87	99.45 30	98.56 88

tfpn200view9		93.64 83	92.57 103	94.89 63	95.33 84	98.94 43	96.82 59	92.31 64	92.63 118	88.29 82	87.21 105	78.01 138	97.12 46	96.82 62	95.85 87	99.45 30	98.56 88

thres400		93.56 89	92.43 111	94.87 67	95.40 82	98.91 55	96.70 68	92.38 63	92.93 115	88.19 88	86.69 113	77.35 143	97.13 44	96.75 71	95.85 87	99.42 47	98.56 88

MVS_Test		94.82 60	95.66 54	93.84 84	94.79 106	98.35 80	96.49 76	89.10 111	96.12 58	87.09 98	92.58 61	90.61 75	96.48 63	96.51 88	96.89 54	99.11 100	98.54 92

Vis-MVSNet (Re-imp)		94.46 67	96.24 50	92.40 103	95.23 94	98.64 70	95.56 96	90.99 89	94.42 92	85.02 103	90.88 80	94.65 57	88.01 183	98.17 28	98.37 14	99.57 8	98.53 93

v52		86.57 194	86.63 197	86.50 193	87.47 212	94.89 182	89.90 195	83.39 173	86.36 202	71.17 195	71.53 206	71.65 191	88.34 181	91.14 195	92.32 185	98.74 151	98.52 94

V4		86.56 195	86.61 198	86.50 193	87.49 211	94.90 181	89.87 196	83.39 173	86.25 203	71.20 194	71.57 205	71.58 193	88.30 182	91.14 195	92.31 186	98.75 150	98.52 94

Fast-Effi-MVS+		91.87 110	92.08 118	91.62 110	92.91 133	97.21 103	94.93 107	84.60 165	93.61 104	81.49 123	83.50 137	78.95 133	96.62 62	96.55 83	96.22 72	99.16 93	98.51 96

MVSTER		94.89 58	95.07 65	94.68 72	94.71 108	96.68 118	97.00 53	90.57 92	95.18 84	93.05 36	95.21 39	86.41 95	93.72 100	97.59 45	95.88 85	99.00 111	98.50 97

thres200		93.62 86	92.54 105	94.88 64	95.36 83	98.93 49	96.75 67	92.31 64	92.84 116	88.28 84	86.99 108	77.81 142	97.13 44	96.82 62	95.92 81	99.45 30	98.49 98

v1921920		87.31 189	87.13 188	87.52 183	88.87 199	94.72 185	91.96 174	84.59 166	88.28 186	69.86 203	72.50 201	70.03 204	91.10 138	93.33 154	92.61 181	98.71 153	98.44 99

v144192		87.40 185	87.20 184	87.64 178	88.89 197	94.88 183	91.65 177	84.70 164	87.80 190	71.17 195	73.20 198	70.91 197	90.75 148	92.69 163	92.49 182	98.71 153	98.43 100

v1192		87.51 180	87.31 181	87.74 174	89.04 196	94.87 184	92.07 167	85.03 159	88.49 177	70.32 198	72.65 200	70.35 201	91.21 136	93.59 146	92.80 163	98.78 143	98.42 101

v7		88.18 163	88.01 161	88.39 152	89.45 174	95.14 173	92.36 147	85.37 154	89.29 165	72.94 185	73.98 190	72.77 178	91.38 134	93.59 146	92.87 159	98.82 123	98.42 101

v10		88.00 167	87.96 166	88.05 165	89.44 175	94.68 186	92.36 147	83.35 175	89.37 164	72.96 183	73.98 190	72.79 177	91.35 135	93.59 146	92.88 158	98.81 127	98.42 101

thres100view900		93.55 92	92.47 110	94.81 68	95.33 84	98.74 63	96.78 66	92.30 69	92.63 118	88.29 82	87.21 105	78.01 138	96.78 59	96.38 92	95.92 81	99.38 54	98.40 104

AdaColmap		97.53 27	96.93 40	98.24 10	99.21 20	98.77 62	98.47 31	97.34 19	96.68 45	96.52 10	95.11 41	96.12 50	98.72 14	97.19 55	96.24 71	99.17 90	98.39 105

PCF-MVS	93.95 6	95.65 47	95.14 63	96.25 40	97.73 52	98.73 65	97.59 47	97.13 25	92.50 126	89.09 78	89.85 89	96.65 44	96.90 56	94.97 128	94.89 113	99.08 103	98.38 106
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+-dtu		91.19 121	93.64 91	88.33 154	92.19 140	96.46 124	93.99 123	81.52 194	92.59 124	71.82 189	92.17 65	85.54 101	91.68 131	95.73 113	94.64 118	98.80 130	98.34 107

v1144		87.92 172	87.79 170	88.07 162	89.27 188	95.15 172	92.17 165	85.62 150	88.52 176	71.52 190	73.80 193	72.40 189	91.06 140	93.54 151	92.80 163	98.81 127	98.33 108

V42		88.31 160	87.95 167	88.73 149	89.44 175	95.34 166	92.23 162	87.21 131	88.83 172	74.49 167	74.89 176	73.43 171	90.41 165	92.08 185	92.77 169	98.60 163	98.33 108

CDS-MVSNet		92.77 102	93.60 93	91.80 107	92.63 135	96.80 112	95.24 102	89.14 109	90.30 155	84.58 104	86.76 110	90.65 74	90.42 162	95.89 107	96.49 61	98.79 136	98.32 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PEN-MVS		87.22 190	86.50 200	88.07 162	88.88 198	94.44 192	90.99 186	86.21 139	86.53 200	73.66 177	74.97 175	66.56 218	89.42 176	91.20 194	93.48 147	99.24 78	98.31 111

conf0.01		93.33 96	91.89 121	95.00 60	95.32 88	98.94 43	96.82 59	92.41 62	92.63 118	88.91 80	88.02 103	72.75 180	97.12 46	96.78 68	95.85 87	99.44 40	98.27 112

v1240		86.89 191	86.75 195	87.06 188	88.75 201	94.65 188	91.30 183	84.05 169	87.49 194	68.94 207	71.96 204	68.86 210	90.65 156	93.33 154	92.72 177	98.67 157	98.24 113

v8		88.21 162	87.94 168	88.51 151	89.62 167	95.01 176	92.31 152	84.99 160	88.94 170	74.70 164	75.03 171	73.51 169	90.67 155	92.11 181	92.74 175	98.80 130	98.24 113

v1neww		88.41 157	88.00 164	88.89 142	89.61 169	95.44 159	92.31 152	87.65 126	89.09 166	74.30 169	75.02 172	73.42 172	90.68 153	92.12 178	92.77 169	98.79 136	98.18 115

v7new		88.41 157	88.00 164	88.89 142	89.61 169	95.44 159	92.31 152	87.65 126	89.09 166	74.30 169	75.02 172	73.42 172	90.68 153	92.12 178	92.77 169	98.79 136	98.18 115

v6		88.43 156	88.01 161	88.92 141	89.60 171	95.43 161	92.36 147	87.66 125	89.07 168	74.50 166	75.06 170	73.47 170	90.59 158	92.11 181	92.76 173	98.79 136	98.18 115

conf0.002		93.20 99	91.63 124	95.02 58	95.31 89	98.94 43	96.82 59	92.43 61	92.63 118	88.99 79	88.16 102	70.49 199	97.12 46	96.77 69	96.30 63	99.44 40	98.16 118

CNLPA		96.90 38	96.28 49	97.64 25	98.56 38	98.63 72	96.85 58	96.60 31	97.73 15	97.08 4	89.78 90	96.28 49	97.80 33	96.73 72	96.63 59	98.94 115	98.14 119

v1141		88.17 164	87.69 173	88.74 147	89.44 175	95.41 162	92.25 160	87.98 120	88.38 180	73.54 180	74.43 180	72.71 184	90.45 160	92.08 185	92.72 177	98.79 136	98.09 120

divwei89l23v2f112		88.17 164	87.69 173	88.74 147	89.44 175	95.41 162	92.26 158	87.97 122	88.29 185	73.57 179	74.45 179	72.75 180	90.42 162	92.08 185	92.72 177	98.81 127	98.09 120

v1		88.17 164	87.66 175	88.77 146	89.44 175	95.40 164	92.29 155	87.98 120	88.21 188	73.75 174	74.41 182	72.75 180	90.36 168	92.07 188	92.71 180	98.80 130	98.09 120

PLC	94.95 3	97.37 30	96.77 43	98.07 17	98.97 27	98.21 86	97.94 41	96.85 30	97.66 21	97.58 1	93.33 52	96.84 42	98.01 30	97.13 57	96.20 74	99.09 102	98.01 123
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v2v482		88.25 161	87.71 172	88.88 144	89.23 193	95.28 167	92.10 166	87.89 124	88.69 175	73.31 182	75.32 167	71.64 192	91.89 127	92.10 184	92.92 157	98.86 122	97.99 124

ACMH+	90.88 12	91.41 119	91.13 131	91.74 108	95.11 99	96.95 106	93.13 134	89.48 106	92.42 128	79.93 129	85.13 126	78.02 137	93.82 98	93.49 152	93.88 139	98.94 115	97.99 124

v148		87.51 180	86.79 193	88.36 153	89.39 181	95.21 171	89.84 197	88.20 118	87.61 193	77.56 138	73.38 197	70.32 202	86.80 190	90.70 198	92.31 186	98.37 174	97.98 126

v11		87.58 178	87.50 180	87.67 177	89.34 183	91.91 212	92.22 164	81.63 191	89.01 169	72.95 184	74.11 188	72.51 188	91.08 139	94.01 144	93.00 155	98.77 147	97.93 127

v12		87.38 186	87.13 188	87.68 176	89.30 185	91.92 211	92.01 173	81.94 189	88.35 182	73.69 176	74.10 189	72.57 187	90.33 170	92.23 170	92.82 161	98.80 130	97.91 128

v16		87.87 174	87.60 178	88.19 157	89.70 164	92.01 207	92.37 146	82.54 183	89.67 160	75.00 162	75.02 172	73.65 166	90.73 151	92.14 177	92.80 163	98.77 147	97.90 129

v17		87.83 175	87.56 179	88.13 159	89.65 166	92.02 206	92.34 150	82.55 182	89.38 163	74.76 163	75.14 169	73.59 167	90.70 152	92.15 176	92.78 167	98.78 143	97.89 130

v13		87.34 187	87.11 190	87.62 179	89.30 185	91.91 212	92.04 169	81.86 190	88.35 182	73.36 181	73.88 192	72.69 185	90.34 169	92.23 170	92.82 161	98.80 130	97.88 131

v18		87.93 169	87.61 177	88.31 155	89.74 163	92.04 205	92.59 142	82.71 180	89.70 158	75.32 155	75.23 168	73.55 168	90.74 149	92.11 181	92.77 169	98.78 143	97.87 132

V9		87.41 184	87.15 187	87.72 175	89.33 184	91.93 210	92.23 162	82.02 188	88.35 182	73.59 178	74.13 187	72.77 178	90.37 167	92.21 172	92.80 163	98.79 136	97.86 133

V14		87.47 182	87.19 185	87.80 172	89.37 182	91.95 209	92.25 160	82.12 187	88.39 179	73.83 173	74.31 183	72.84 176	90.44 161	92.20 173	92.78 167	98.80 130	97.84 134

v15		87.46 183	87.16 186	87.81 171	89.41 180	91.96 208	92.26 158	82.28 186	88.42 178	73.72 175	74.29 185	72.73 183	90.41 165	92.17 175	92.76 173	98.79 136	97.83 135

tfpn1000		94.14 72	94.54 72	93.67 87	95.27 92	98.50 77	95.36 100	91.84 80	96.31 51	87.38 95	92.98 55	84.04 110	92.60 117	96.49 89	95.62 97	99.55 9	97.82 136

TAPA-MVS	94.18 5	96.38 43	96.49 47	96.25 40	98.26 43	98.66 68	98.00 39	94.96 38	97.17 34	89.48 71	92.91 56	96.35 46	97.53 37	96.59 79	95.90 83	99.28 71	97.82 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn_ndepth		94.36 71	94.64 69	94.04 79	95.16 97	98.51 76	95.58 94	92.09 74	95.78 70	88.52 81	92.38 63	85.74 100	93.34 107	96.39 90	95.90 83	99.54 11	97.79 138

CANet_DTU		93.92 78	96.57 45	90.83 117	95.63 75	98.39 79	96.99 54	87.38 129	96.26 52	71.97 188	96.31 30	93.02 64	94.53 87	97.38 49	96.83 56	98.49 168	97.79 138

GBi-Net		93.81 80	94.18 80	93.38 91	91.34 145	95.86 141	96.22 80	88.68 112	95.23 81	90.40 58	86.39 118	91.16 70	94.40 90	96.52 85	96.30 63	99.21 87	97.79 138

test1		93.81 80	94.18 80	93.38 91	91.34 145	95.86 141	96.22 80	88.68 112	95.23 81	90.40 58	86.39 118	91.16 70	94.40 90	96.52 85	96.30 63	99.21 87	97.79 138

FMVSNet3		93.79 82	94.17 82	93.35 93	91.21 148	95.99 134	96.62 69	88.68 112	95.23 81	90.40 58	86.39 118	91.16 70	94.11 94	95.96 105	96.67 58	99.07 105	97.79 138

FMVSNet2		93.30 97	93.36 98	93.22 95	91.34 145	95.86 141	96.22 80	88.24 117	95.15 85	89.92 67	81.64 146	89.36 81	94.40 90	96.77 69	96.98 52	99.21 87	97.79 138

pm-mvs1		89.19 147	89.02 150	89.38 138	90.40 154	95.74 147	92.05 168	88.10 119	86.13 205	77.70 137	73.72 194	79.44 132	88.97 178	95.81 111	94.51 126	99.08 103	97.78 144

FMVSNet1		91.54 117	90.93 134	92.26 104	90.35 156	95.27 169	95.22 103	87.16 132	91.37 141	87.62 92	75.45 166	83.84 112	94.43 88	96.52 85	96.30 63	98.82 123	97.74 145

LS3D		95.46 50	95.14 63	95.84 46	97.91 49	98.90 57	98.58 28	97.79 3	97.07 38	83.65 110	88.71 95	88.64 87	97.82 31	97.49 47	97.42 42	99.26 77	97.72 146

OMC-MVS		97.00 36	96.92 41	97.09 31	98.69 34	98.66 68	97.85 42	95.02 37	98.09 9	94.47 24	93.15 53	96.90 40	97.38 39	97.16 56	96.82 57	99.13 97	97.65 147

DTE-MVSNet		86.67 193	86.09 201	87.35 185	88.45 205	94.08 196	90.65 188	86.05 144	86.13 205	72.19 187	74.58 178	66.77 216	87.61 186	90.31 200	93.12 152	99.13 97	97.62 148

CHOSEN 280x420		95.46 50	97.01 38	93.66 88	97.28 57	97.98 92	96.40 79	85.39 153	96.10 60	91.07 50	96.53 29	96.34 48	95.61 73	97.65 43	96.95 53	96.21 200	97.49 149

IterMVS		90.20 132	92.43 111	87.61 180	92.82 134	94.31 195	94.11 121	81.54 193	92.97 114	69.90 202	84.71 129	88.16 91	89.96 173	95.25 121	94.17 131	97.31 191	97.46 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		91.63 114	93.82 89	89.08 140	92.02 141	96.40 127	93.26 132	87.26 130	93.72 103	77.26 140	88.61 98	89.86 79	85.50 197	95.72 115	95.02 110	99.16 93	97.44 151

gg-mvs-nofinetune		86.17 198	88.57 154	83.36 207	93.44 126	98.15 89	96.58 73	72.05 226	74.12 226	49.23 233	64.81 221	90.85 73	89.90 174	97.83 40	96.84 55	98.97 113	97.41 152

test-mter		90.95 123	93.54 97	87.93 170	90.28 157	96.80 112	91.44 178	82.68 181	92.15 136	74.37 168	89.57 91	88.23 90	90.88 145	96.37 93	94.31 129	97.93 185	97.37 153

ACMM	92.75 10	94.41 70	93.84 87	95.09 57	96.41 67	96.80 112	94.88 110	93.54 46	96.41 49	90.16 63	92.31 64	83.11 119	96.32 64	96.22 99	94.65 117	99.22 84	97.35 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		89.28 144	90.75 137	87.57 181	91.77 142	96.48 123	92.29 155	87.58 128	90.61 152	65.77 212	84.48 130	76.84 147	89.46 175	95.84 109	93.68 144	98.52 166	97.34 155

pmmvs6		85.98 199	84.89 209	87.25 186	88.83 200	94.35 194	89.36 199	85.30 157	78.51 223	75.44 153	62.71 224	75.41 156	87.65 185	93.58 149	92.40 184	96.89 194	97.29 156

thresconf0.02		93.57 88	93.84 87	93.25 94	95.03 103	98.16 88	95.80 91	92.46 60	96.12 58	83.88 107	92.61 59	80.39 128	92.83 115	96.11 104	96.21 73	99.49 20	97.28 157

OPM-MVS		93.61 87	92.43 111	95.00 60	96.94 61	97.34 100	97.78 43	94.23 41	89.64 161	85.53 101	88.70 96	82.81 120	96.28 66	96.28 97	95.00 112	99.24 78	97.22 158

SixPastTwentyTwo		88.37 159	89.47 145	87.08 187	90.01 160	95.93 140	87.41 203	85.32 155	90.26 156	70.26 199	86.34 121	71.95 190	90.93 142	92.89 162	91.72 193	98.55 164	97.22 158

pmmvs5		87.83 175	88.09 159	87.51 184	89.59 172	95.48 154	89.75 198	84.73 163	86.07 207	71.44 191	80.57 151	70.09 203	90.74 149	94.47 134	92.87 159	98.82 123	97.10 160

CVMVSNet		89.77 139	91.66 123	87.56 182	93.21 131	95.45 156	91.94 175	89.22 108	89.62 162	69.34 206	83.99 134	85.90 99	84.81 202	94.30 139	95.28 105	96.85 195	97.09 161

PMMVS		94.61 66	95.56 57	93.50 90	94.30 114	96.74 116	94.91 109	89.56 105	95.58 73	87.72 91	96.15 31	92.86 65	96.06 68	95.47 118	95.02 110	98.43 173	97.09 161

CR-MVSNet		90.16 134	91.96 120	88.06 164	93.32 128	95.95 138	93.36 130	75.99 215	92.40 129	75.19 157	83.18 139	85.37 102	92.05 123	95.21 122	94.56 122	98.47 170	97.08 163

PatchT		89.13 148	91.71 122	86.11 199	92.92 132	95.59 151	83.64 214	75.09 219	91.87 138	75.19 157	82.63 143	85.06 107	92.05 123	95.21 122	94.56 122	97.76 187	97.08 163

tfpnview11		93.63 85	94.42 74	92.71 99	95.08 100	98.26 84	95.58 94	92.06 76	96.32 50	81.88 116	93.44 49	83.43 115	92.14 122	96.58 81	95.88 85	99.52 13	97.07 165

tfpn_n400		93.56 89	94.36 77	92.63 100	95.07 101	98.28 81	95.50 98	91.98 78	95.48 74	81.88 116	93.44 49	83.43 115	92.01 125	96.60 77	96.27 68	99.34 59	97.04 166

tfpnconf		93.56 89	94.36 77	92.63 100	95.07 101	98.28 81	95.50 98	91.98 78	95.48 74	81.88 116	93.44 49	83.43 115	92.01 125	96.60 77	96.27 68	99.34 59	97.04 166

UA-Net		93.96 77	95.95 53	91.64 109	96.06 70	98.59 74	95.29 101	90.00 97	91.06 144	82.87 112	90.64 81	98.06 34	86.06 195	98.14 29	98.20 15	99.58 6	96.96 168

tpm		87.95 168	89.44 146	86.21 197	92.53 136	94.62 190	91.40 179	76.36 213	91.46 140	69.80 204	87.43 104	75.14 157	91.55 132	89.85 206	90.60 199	95.61 206	96.96 168

RPMNet		90.19 133	92.03 119	88.05 165	93.46 125	95.95 138	93.41 129	74.59 221	92.40 129	75.91 150	84.22 132	86.41 95	92.49 118	94.42 136	93.85 141	98.44 171	96.96 168

test-LLR		91.62 115	93.56 95	89.35 139	93.31 129	96.57 121	92.02 171	87.06 133	92.34 132	75.05 160	90.20 85	88.64 87	90.93 142	96.19 101	94.07 133	97.75 188	96.90 171

TESTMET0.1,1		91.07 122	93.56 95	88.17 158	90.43 153	96.57 121	92.02 171	82.83 179	92.34 132	75.05 160	90.20 85	88.64 87	90.93 142	96.19 101	94.07 133	97.75 188	96.90 171

pmmvs4		90.55 128	89.91 141	91.30 112	90.26 158	94.95 178	92.73 139	87.94 123	93.44 108	85.35 102	82.28 145	76.09 152	93.02 114	93.56 150	92.26 189	98.51 167	96.77 173

TSAR-MVS + COLMAP		94.79 62	94.51 73	95.11 56	96.50 64	97.54 95	97.99 40	94.54 39	97.81 13	85.88 100	96.73 27	81.28 127	96.99 55	96.29 96	95.21 107	98.76 149	96.73 174

PatchMatch-RL		94.69 65	94.41 75	95.02 58	97.63 53	98.15 89	94.50 117	91.99 77	95.32 78	91.31 49	95.47 37	83.44 114	96.02 70	96.56 82	95.23 106	98.69 156	96.67 175

PM-MVS		84.72 205	84.47 210	85.03 202	84.67 217	91.57 215	86.27 209	82.31 185	87.65 192	70.62 197	76.54 164	56.41 229	88.75 180	92.59 164	89.85 203	97.54 190	96.66 176

test0.0.03 1		91.97 109	93.91 84	89.72 132	93.31 129	96.40 127	91.34 181	87.06 133	93.86 100	81.67 121	91.15 76	89.16 83	86.02 196	95.08 125	95.09 108	98.91 118	96.64 177

testgi		89.42 141	91.50 127	87.00 189	92.40 138	95.59 151	89.15 200	85.27 158	92.78 117	72.42 186	91.75 69	76.00 153	84.09 206	94.38 137	93.82 143	98.65 159	96.15 178

CostFormer		90.69 125	90.48 139	90.93 115	94.18 115	96.08 133	94.03 122	78.20 204	93.47 107	89.96 65	90.97 79	80.30 129	93.72 100	87.66 214	88.75 206	95.51 208	96.12 179

EU-MVSNet		85.62 201	87.65 176	83.24 208	88.54 204	92.77 203	87.12 205	85.32 155	86.71 198	64.54 214	78.52 158	75.11 158	78.35 213	92.25 169	92.28 188	95.58 207	95.93 180

tpmp4_e23		89.82 137	89.31 148	90.42 123	94.01 119	95.45 156	94.63 115	78.37 201	93.59 105	87.09 98	86.62 115	76.59 148	93.06 113	88.50 208	88.52 207	95.36 209	95.88 181

DWT-MVSNet_training		91.30 120	89.73 142	93.13 96	94.64 110	96.87 109	94.93 107	86.17 142	94.22 96	93.18 31	89.11 93	73.28 174	93.59 103	88.00 211	90.73 198	96.26 199	95.87 182

TransMVSNet (Re)		87.73 177	86.79 193	88.83 145	90.76 150	94.40 193	91.33 182	89.62 104	84.73 210	75.41 154	72.73 199	71.41 195	86.80 190	94.53 133	93.93 137	99.06 108	95.83 183

EPNet_dtu		92.45 107	95.02 66	89.46 136	98.02 47	95.47 155	94.79 112	92.62 59	94.97 86	70.11 201	94.76 45	92.61 67	84.07 207	95.94 106	95.56 99	97.15 193	95.82 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		94.82 60	93.73 90	96.09 43	98.34 42	97.43 99	97.06 52	96.05 32	95.84 68	90.56 57	86.30 122	89.10 84	95.55 75	96.13 103	95.61 98	99.00 111	95.73 185

pmmvs-eth3d		84.33 207	82.94 214	85.96 201	84.16 220	90.94 216	86.55 208	83.79 170	84.25 212	75.85 151	70.64 212	56.43 228	87.44 188	92.20 173	90.41 201	97.97 184	95.68 186

GG-mvs-BLEND		66.17 227	94.91 68	32.63 235	1.32 241	96.64 119	91.40 179	0.85 239	94.39 94	2.20 243	90.15 87	95.70 53	2.27 239	96.39 90	95.44 102	97.78 186	95.68 186

gm-plane-assit		83.26 210	85.29 205	80.89 211	89.52 173	89.89 219	70.26 228	78.24 203	77.11 224	58.01 226	74.16 186	66.90 214	90.63 157	97.20 53	96.05 77	98.66 158	95.68 186

TAMVS		90.54 129	90.87 136	90.16 127	91.48 143	96.61 120	93.26 132	86.08 143	87.71 191	81.66 122	83.11 141	84.04 110	90.42 162	94.54 132	94.60 119	98.04 183	95.48 189

EG-PatchMatch MVS		86.68 192	87.24 183	86.02 200	90.58 152	96.26 129	91.08 185	81.59 192	84.96 209	69.80 204	71.35 209	75.08 159	84.23 205	94.24 141	93.35 149	98.82 123	95.46 190

COLMAP_ROB	90.49 14	93.27 98	92.71 102	93.93 81	97.75 51	97.44 98	96.07 83	93.17 54	95.40 76	83.86 108	83.76 136	88.72 86	93.87 96	94.25 140	94.11 132	98.87 120	95.28 191
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231211		75.89 219	74.18 224	77.88 220	81.42 223	87.72 222	79.33 224	81.05 195	66.49 233	60.00 224	45.74 232	51.46 232	71.22 223	85.70 219	86.91 219	94.25 222	95.25 192

ambc			73.83 225		76.23 232	85.13 227	82.27 218		84.16 213	65.58 213	52.82 230	23.31 241	73.55 221	91.41 193	85.26 225	92.97 226	94.70 193

MS-PatchMatch		91.82 111	92.51 106	91.02 113	95.83 74	96.88 107	95.05 104	84.55 168	93.85 101	82.01 115	82.51 144	91.71 68	90.52 159	95.07 126	93.03 154	98.13 179	94.52 194

TDRefinement		89.07 149	88.15 158	90.14 129	95.16 97	96.88 107	95.55 97	90.20 95	89.68 159	76.42 146	76.67 162	74.30 162	84.85 201	93.11 157	91.91 191	98.64 160	94.47 195

MDTV_nov1_ep13		91.57 116	93.18 100	89.70 133	93.39 127	96.97 105	93.53 127	80.91 196	95.70 72	81.86 119	92.40 62	89.93 78	93.25 110	91.97 190	90.80 197	95.25 213	94.46 196

dps		90.11 135	89.37 147	90.98 114	93.89 121	96.21 130	93.49 128	77.61 206	91.95 137	92.74 42	88.85 94	78.77 135	92.37 120	87.71 213	87.71 212	95.80 203	94.38 197

Anonymous20231206		83.84 208	85.19 206	82.26 209	87.38 213	92.87 201	85.49 211	83.65 171	86.07 207	63.44 217	68.42 215	69.01 208	75.45 217	93.34 153	92.44 183	98.12 181	94.20 198

USDC		90.69 125	90.52 138	90.88 116	94.17 116	96.43 125	95.82 90	86.76 135	93.92 99	76.27 148	86.49 117	74.30 162	93.67 102	95.04 127	93.36 148	98.61 161	94.13 199

tpm cat1		88.90 151	87.78 171	90.22 126	93.88 122	95.39 165	93.79 125	78.11 205	92.55 125	89.43 73	81.31 148	79.84 131	91.40 133	84.95 220	86.34 222	94.68 220	94.09 200

RPSCF		94.05 74	94.00 83	94.12 78	96.20 69	96.41 126	96.61 70	91.54 84	95.83 69	89.73 68	96.94 26	92.80 66	95.35 79	91.63 192	90.44 200	95.27 212	93.94 201

tpmrst		88.86 153	89.62 143	87.97 169	94.33 113	95.98 135	92.62 141	76.36 213	94.62 90	76.94 142	85.98 123	82.80 121	92.80 116	86.90 215	87.15 216	94.77 217	93.93 202

MDTV_nov1_ep13_2view		86.30 197	88.27 156	84.01 204	87.71 210	94.67 187	88.08 202	76.78 209	90.59 153	68.66 208	80.46 153	80.12 130	87.58 187	89.95 205	88.20 209	95.25 213	93.90 203

ADS-MVSNet		89.80 138	91.33 130	88.00 168	94.43 112	96.71 117	92.29 155	74.95 220	96.07 61	77.39 139	88.67 97	86.09 97	93.26 109	88.44 209	89.57 204	95.68 205	93.81 204

PatchmatchNet		90.56 127	92.49 108	88.31 155	93.83 123	96.86 111	92.42 145	76.50 212	95.96 63	78.31 135	91.96 68	89.66 80	93.48 105	90.04 203	89.20 205	95.32 210	93.73 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		88.99 150	91.07 132	86.57 192	86.78 215	95.62 148	91.20 184	75.40 218	90.65 151	76.57 144	84.05 133	82.44 123	91.01 141	95.84 109	95.38 103	98.48 169	93.50 206

EPMVS		90.88 124	92.12 117	89.44 137	94.71 108	97.24 101	93.55 126	76.81 208	95.89 65	81.77 120	91.49 72	86.47 94	93.87 96	90.21 201	90.07 202	95.92 202	93.49 207

testpf		83.57 209	85.70 202	81.08 210	90.99 149	88.96 221	82.71 217	65.32 234	90.22 157	73.86 172	81.58 147	76.10 151	81.19 211	84.14 224	85.41 224	92.43 227	93.45 208

TinyColmap		89.42 141	88.58 153	90.40 124	93.80 124	95.45 156	93.96 124	86.54 137	92.24 134	76.49 145	80.83 150	70.44 200	93.37 106	94.45 135	93.30 151	98.26 177	93.37 209

MDA-MVSNet-bldmvs		80.11 215	80.24 217	79.94 213	77.01 231	93.21 200	78.86 225	85.94 146	82.71 218	60.86 218	79.71 155	51.77 231	83.71 209	75.60 229	86.37 221	93.28 225	92.35 210

N_pmnet		84.80 203	85.10 207	84.45 203	89.25 192	92.86 202	84.04 213	86.21 139	88.78 173	66.73 211	72.41 202	74.87 161	85.21 199	88.32 210	86.45 220	95.30 211	92.04 211

test20.03		82.92 211	85.52 204	79.90 214	87.75 209	91.84 214	82.80 216	82.99 177	82.65 219	60.32 221	78.90 157	70.50 198	67.10 225	92.05 189	90.89 196	98.44 171	91.80 212

pmmvs3		79.16 217	80.12 218	78.05 218	79.36 226	86.59 226	78.13 226	73.87 222	76.42 225	57.51 227	70.59 213	57.02 226	84.66 203	90.10 202	88.32 208	94.75 218	91.77 213

testus		81.33 213	84.13 211	78.06 217	84.54 218	87.72 222	79.66 221	80.42 197	87.36 196	54.13 232	83.83 135	56.63 227	73.21 222	90.51 199	91.74 192	96.40 197	91.11 214

LP		84.43 206	85.10 207	83.66 205	92.31 139	93.89 197	87.13 204	72.88 223	90.81 148	67.08 210	70.65 211	75.76 155	86.87 189	86.43 218	87.15 216	95.70 204	90.98 215

FMVSNet5		90.36 130	90.93 134	89.70 133	87.99 207	92.25 204	92.03 170	83.51 172	92.20 135	84.13 106	85.59 125	86.48 93	92.43 119	94.61 130	94.52 125	98.13 179	90.85 216

new-patchmatchnet		78.49 218	78.19 219	78.84 216	84.13 221	90.06 218	77.11 227	80.39 198	79.57 222	59.64 225	66.01 219	55.65 230	75.62 216	84.55 223	80.70 226	96.14 201	90.77 217

test2356		81.26 214	84.10 212	77.95 219	84.35 219	87.38 224	79.56 222	79.53 200	86.17 204	54.14 231	83.24 138	60.71 221	73.77 218	90.01 204	91.18 195	96.33 198	90.01 218

MIMVSNet1		80.03 216	80.93 216	78.97 215	72.46 234	90.73 217	80.81 220	82.44 184	80.39 220	63.64 216	57.57 226	64.93 219	76.37 215	91.66 191	91.55 194	98.07 182	89.70 219

MVS-HIRNet		85.36 202	86.89 192	83.57 206	90.13 159	94.51 191	83.57 215	72.61 224	88.27 187	71.22 193	68.97 214	81.81 124	88.91 179	93.08 158	91.94 190	94.97 216	89.64 220

CMPMVS	65.18 17	84.76 204	83.10 213	86.69 191	95.29 91	95.05 175	88.37 201	85.51 152	80.27 221	71.31 192	68.37 216	73.85 164	85.25 198	87.72 212	87.75 211	94.38 221	88.70 221
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		81.53 212	82.68 215	80.20 212	83.47 222	89.47 220	82.21 219	78.36 202	87.86 189	60.14 223	67.90 217	69.43 206	82.03 210	89.22 207	87.47 213	94.99 215	87.39 222

testmv		72.66 222	74.40 221	70.62 223	80.64 224	81.51 231	64.99 233	76.60 210	68.76 229	44.81 234	63.78 222	48.00 233	62.52 227	84.74 221	87.17 214	94.19 223	86.86 223

test1235678		72.65 223	74.40 221	70.62 223	80.64 224	81.50 232	64.99 233	76.59 211	68.74 230	44.81 234	63.78 222	47.99 234	62.51 228	84.73 222	87.17 214	94.19 223	86.85 224

DeepMVS_CX						86.86 225	79.50 223	70.43 228	90.73 149	63.66 215	80.36 154	60.83 220	79.68 212	76.23 228		89.46 230	86.53 225

test12356		69.55 224	71.53 226	67.24 227	77.70 230	78.48 233	65.92 230	75.55 217	68.39 231	44.26 236	61.80 225	40.70 236	47.92 235	81.45 227	87.01 218	92.09 228	82.89 226

PMMVS2		64.36 228	65.94 229	62.52 229	67.37 236	77.44 234	64.39 235	69.32 231	61.47 234	34.59 238	46.09 231	41.03 235	48.02 234	74.56 231	78.23 227	91.43 229	82.76 227

FPMVS		75.84 220	74.59 220	77.29 221	86.92 214	83.89 228	85.01 212	80.05 199	82.91 217	60.61 220	65.25 220	60.41 222	63.86 226	75.60 229	73.60 231	87.29 232	80.47 228

Gipuma		68.35 225	66.71 227	70.27 225	74.16 233	68.78 237	63.93 236	71.77 227	83.34 216	54.57 230	34.37 233	31.88 237	68.69 224	83.30 225	85.53 223	88.48 231	79.78 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
1111		73.35 221	74.40 221	72.12 222	78.22 227	82.24 229	65.06 231	65.61 232	70.28 227	55.42 228	56.30 227	57.35 224	73.66 219	86.73 216	88.16 210	94.75 218	79.76 230

PMVS	63.12 18	67.27 226	66.39 228	68.30 226	77.98 229	60.24 238	59.53 237	76.82 207	66.65 232	60.74 219	54.39 229	59.82 223	51.24 231	73.92 232	70.52 232	83.48 234	79.17 231
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		55.96 230	55.63 231	56.35 231	68.48 235	73.29 236	43.03 238	72.52 225	44.01 237	34.80 237	32.83 234	29.11 238	35.21 236	56.63 234	75.72 229	84.04 233	77.79 232

MVE	50.86 19	49.54 233	51.43 232	47.33 234	44.14 239	59.20 239	36.45 241	60.59 235	41.47 238	31.14 239	29.58 235	17.06 242	48.52 233	62.22 233	74.63 230	63.12 238	75.87 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		50.67 231	47.85 233	53.96 232	64.13 238	50.98 241	38.06 239	69.51 229	51.40 236	24.60 240	29.46 237	24.39 240	56.07 230	48.17 235	59.70 233	71.40 236	70.84 234

EMVS		49.98 232	46.76 234	53.74 233	64.96 237	51.29 240	37.81 240	69.35 230	51.83 235	22.69 241	29.57 236	25.06 239	57.28 229	44.81 236	56.11 234	70.32 237	68.64 235

.test1245		56.65 229	56.09 230	57.30 230	78.22 227	82.24 229	65.06 231	65.61 232	70.28 227	55.42 228	56.30 227	57.35 224	73.66 219	86.73 216	15.01 235	5.84 239	24.75 236

testmvs		12.09 234	16.94 235	6.42 236	3.15 240	6.08 242	9.51 243	3.84 237	21.46 239	5.31 242	27.49 238	6.76 243	10.89 237	17.06 237	15.01 235	5.84 239	24.75 236

test123		9.58 235	13.53 236	4.97 237	1.31 242	5.47 243	8.32 244	2.95 238	18.14 240	2.03 244	20.82 239	2.34 244	10.60 238	10.00 238	14.16 237	4.60 241	23.77 238

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					89.78 162	93.84 198	85.59 210

MTAPA										96.83 5		99.12 15

MTMP										97.18 3		98.83 21

Patchmatch-RL test							34.61 242

tmp_tt				66.88 228	86.07 216	73.86 235	68.22 229	33.38 236	96.88 41	80.67 127	88.23 100	78.82 134	49.78 232	82.68 226	77.47 228	83.19 235

XVS					96.60 62	99.35 10	96.82 59			90.85 51		98.72 24				99.46 26

X-MVStestdata					96.60 62	99.35 10	96.82 59			90.85 51		98.72 24				99.46 26

mPP-MVS					99.21 20							98.29 32

NP-MVS									95.32 78

Patchmtry						95.96 137	93.36 130	75.99 215		75.19 157

High-resolution multi-view benchmark