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.

v1.0		87.46 43	81.44 80	94.48 2	97.96 3	98.62 2	96.45 2	92.82 2	96.24 4	90.25 6	96.16 3	93.09 1	93.32 4	93.93 13	92.02 20	96.07 19	0.00 242

HPM-MVS++		94.04 6	94.96 3	92.96 9	97.93 4	97.71 14	94.65 10	91.01 9	95.91 5	87.43 14	93.52 9	92.63 2	92.29 9	94.22 12	92.34 16	94.47 49	98.37 22

ESAPD		95.10 1	95.53 1	94.60 1	97.77 6	98.64 1	96.60 1	92.45 4	96.34 3	91.41 2	96.70 2	92.26 3	93.56 3	93.68 15	91.73 28	95.79 28	99.37 4

abl_6				89.54 28	95.55 34	97.59 16	89.01 52	85.00 35	94.67 12	83.04 29	84.70 32	91.47 4	89.46 21			95.20 38	98.63 16

SMA-MVS		93.47 8	94.29 8	92.52 11	97.72 7	97.77 13	94.46 13	90.19 13	94.96 9	87.15 15	90.15 22	90.99 5	91.49 13	94.31 10	93.33 9	94.10 54	98.53 20

MCST-MVS		94.10 5	94.77 5	93.31 7	98.31 1	98.34 4	95.43 5	92.54 3	94.41 14	83.05 28	91.38 16	90.97 6	92.24 10	95.05 5	94.02 5	98.31 1	99.20 7

CNVR-MVS		94.53 3	94.85 4	94.15 4	98.03 2	98.59 3	95.56 4	92.91 1	94.86 10	88.46 12	91.32 18	90.83 7	94.03 2	95.20 3	94.16 4	95.89 25	99.01 12

SD-MVS		93.36 10	94.33 6	92.22 12	94.68 39	97.89 12	94.56 11	90.89 11	94.80 11	90.04 7	93.53 8	90.14 8	89.78 18	92.74 23	92.17 17	93.35 100	99.07 10

TSAR-MVS + GP.		91.29 18	93.11 16	89.18 30	87.81 88	96.21 42	92.51 29	83.83 41	94.24 15	83.77 22	91.87 15	89.62 9	90.07 16	90.40 45	90.31 42	97.09 6	99.10 9

HSP-MVS		94.69 2	95.39 2	93.88 5	96.78 15	98.11 6	94.75 8	90.91 10	96.89 2	89.12 11	96.98 1	89.47 10	94.76 1	95.24 2	93.29 10	96.98 7	97.73 30

NCCC		93.59 7	94.00 10	93.10 8	97.90 5	97.93 10	95.40 6	92.39 5	94.47 13	84.94 19	91.21 19	89.32 11	92.53 7	93.90 14	92.98 12	95.44 33	98.22 24

DeepPCF-MVS	86.71 1	91.00 20	94.05 9	87.43 41	95.58 33	98.17 5	86.22 69	88.59 20	97.01 1	76.77 48	85.11 31	88.90 12	87.29 37	95.02 6	94.69 3	90.15 189	99.48 3

train_agg		91.99 15	93.71 11	89.98 24	96.42 24	97.03 25	94.31 15	89.05 19	93.33 18	77.75 43	95.06 5	88.27 13	88.38 31	92.02 29	91.41 32	94.00 58	98.84 15

TSAR-MVS + MP.		93.07 11	93.53 12	92.53 10	94.23 42	97.54 18	94.75 8	89.87 14	95.26 8	89.20 10	93.16 10	88.19 14	92.15 11	91.79 32	89.65 51	94.99 42	99.16 8

APDe-MVS		94.31 4	94.30 7	94.33 3	97.57 8	98.06 8	95.79 3	91.98 6	95.50 7	92.19 1	95.25 4	87.97 15	92.93 5	93.01 21	91.02 38	95.52 31	99.29 5

PHI-MVS		89.88 27	92.75 18	86.52 51	94.97 36	97.57 17	89.99 46	84.56 37	92.52 24	69.72 80	90.35 21	87.11 16	84.89 53	91.82 31	92.37 15	95.02 40	97.51 33

ACMMP_Plus		92.16 13	92.91 17	91.28 18	96.95 12	97.36 19	93.66 18	89.23 18	93.33 18	83.71 23	90.53 20	86.84 17	90.39 15	93.30 19	91.56 30	93.74 68	97.43 37

TSAR-MVS + ACMM		90.98 21	93.18 15	88.42 35	95.69 31	96.73 30	94.52 12	86.97 27	92.99 22	76.32 50	92.31 13	86.64 18	84.40 61	92.97 22	92.02 20	92.62 141	98.59 18

CSCG		89.81 28	89.69 30	89.96 25	96.55 20	97.90 11	92.89 24	87.06 25	88.74 46	86.17 16	78.24 39	86.53 19	84.75 56	87.82 79	90.59 41	92.32 147	98.01 26

APD-MVS		93.47 8	93.44 13	93.50 6	97.06 11	97.09 23	95.27 7	91.47 7	95.71 6	89.57 8	93.66 7	86.28 20	92.81 6	92.06 28	90.70 40	94.83 46	98.60 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MTAPA										91.14 3		85.84 21

SteuartSystems-ACMMP		92.31 12	93.31 14	91.15 19	96.88 13	97.36 19	93.95 17	89.44 16	92.62 23	83.20 25	94.34 6	85.55 22	88.95 25	93.07 20	91.90 24	94.51 48	98.30 23
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	81.14 5	88.59 33	87.49 40	89.88 26	95.83 30	96.45 38	91.94 34	82.41 53	87.09 52	85.94 18	62.80 92	85.37 23	89.46 21	91.51 35	91.89 26	93.72 70	97.30 40

zzz-MVS		91.59 17	91.12 23	92.13 13	96.76 16	96.68 31	93.39 20	88.00 21	93.63 17	90.76 5	83.97 33	85.33 24	89.89 17	91.60 34	89.65 51	94.00 58	96.97 52

MVSTER		87.68 41	89.12 32	86.01 54	88.11 85	90.05 111	89.28 50	77.05 83	91.37 27	79.97 38	76.70 43	85.25 25	84.89 53	93.53 16	91.41 32	96.73 10	95.55 82

MP-MVS		90.81 22	91.45 20	90.06 23	96.59 19	96.33 39	92.46 30	87.19 23	90.27 35	82.54 32	91.38 16	84.88 26	88.27 32	90.58 44	89.30 56	93.30 102	97.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		89.98 25	90.42 29	89.47 29	94.13 43	98.05 9	91.76 35	83.27 44	90.87 32	81.90 35	72.32 53	84.82 27	88.42 29	94.52 9	93.78 7	97.34 4	98.58 19

QAPM		87.06 44	86.46 47	87.75 38	96.63 17	97.09 23	91.71 36	82.62 50	80.58 72	71.28 72	66.04 75	84.24 28	87.01 39	89.93 50	89.91 45	97.26 5	97.44 35

DeepC-MVS_fast	86.59 2	91.69 16	91.39 22	92.05 15	97.43 9	96.92 28	94.05 16	90.23 12	93.31 21	83.19 26	77.91 40	84.23 29	92.42 8	94.62 8	94.83 2	95.00 41	97.88 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MTMP										90.95 4		84.13 30

UA-Net		78.30 101	80.92 81	75.25 118	87.42 93	92.48 87	79.54 132	75.49 97	60.47 153	60.52 106	68.44 67	84.08 31	57.54 195	88.54 67	88.45 60	90.96 176	83.97 194

CHOSEN 280x420		82.15 68	85.87 52	77.80 104	86.54 102	93.42 72	81.74 107	59.96 203	78.99 77	63.99 90	74.50 48	83.95 32	80.99 79	89.53 55	85.01 93	93.56 82	95.71 81

HFP-MVS		92.02 14	92.13 19	91.89 16	97.16 10	96.46 36	93.57 19	87.60 22	93.79 16	88.17 13	93.15 11	83.94 33	91.19 14	90.81 42	89.83 46	93.66 73	96.94 54

GG-mvs-BLEND		62.08 210	88.31 37	31.46 234	0.16 244	98.10 7	91.57 37	0.09 241	85.07 59	0.21 245	73.90 50	83.74 34	0.19 243	88.98 60	89.39 54	96.58 12	99.02 11

MVS_0304		88.43 36	89.46 31	87.21 42	91.85 55	97.60 15	92.62 27	81.10 59	87.16 51	73.80 58	72.19 55	83.36 35	87.03 38	94.64 7	93.67 8	96.88 8	97.64 32

MSLP-MVS++		90.33 24	88.82 34	92.10 14	96.52 22	95.93 43	94.35 14	86.26 29	88.37 48	89.24 9	75.94 45	82.60 36	89.71 19	89.45 56	92.17 17	96.51 14	97.24 42

CP-MVS		90.57 23	90.68 25	90.44 21	96.13 26	95.90 47	92.77 26	86.86 28	92.12 26	84.19 21	89.18 25	82.37 37	89.43 23	89.65 54	88.43 61	93.27 104	97.13 46

3Dnovator	80.58 8	88.20 37	86.53 46	90.15 22	96.86 14	96.46 36	91.97 33	83.06 47	85.16 58	83.66 24	62.28 95	82.15 38	88.98 24	90.99 40	92.65 14	96.38 18	96.03 73

ACMMPR		91.15 19	91.44 21	90.81 20	96.61 18	96.25 40	93.09 21	87.08 24	93.32 20	84.78 20	92.08 14	82.10 39	89.71 19	90.24 46	89.82 47	93.61 78	96.30 69

XVS					89.65 65	95.93 43	85.97 74			76.32 50		82.05 40				93.51 86

X-MVStestdata					89.65 65	95.93 43	85.97 74			76.32 50		82.05 40				93.51 86

X-MVS		89.73 29	90.65 26	88.66 33	96.44 23	95.93 43	92.26 32	86.98 26	90.73 33	76.32 50	89.56 24	82.05 40	86.51 43	89.98 49	89.60 53	93.43 95	96.72 63

CPTT-MVS		88.17 38	87.84 39	88.55 34	93.33 45	93.75 67	92.33 31	84.75 36	89.87 39	81.72 36	83.93 34	81.12 43	88.45 28	85.42 107	84.07 109	90.72 181	96.72 63

PGM-MVS		89.97 26	90.64 27	89.18 30	96.53 21	95.90 47	93.06 22	82.48 52	90.04 37	80.37 37	92.75 12	80.96 44	88.93 26	89.88 51	89.08 57	93.69 72	95.86 76

CDPH-MVS		88.76 32	90.43 28	86.81 47	96.04 28	96.53 35	92.95 23	85.95 31	90.36 34	67.93 85	85.80 30	80.69 45	83.82 63	90.81 42	91.85 27	94.18 52	96.99 51

EPP-MVSNet		80.82 74	82.79 68	78.52 99	86.31 107	92.37 88	79.83 120	74.51 105	73.79 103	64.46 88	67.01 70	80.63 46	74.33 115	85.63 104	84.35 106	91.68 161	95.79 79

EPNet		89.30 30	90.89 24	87.44 40	95.67 32	96.81 29	91.13 38	83.12 46	91.14 29	76.31 54	87.60 27	80.40 47	84.45 59	92.13 27	91.12 37	93.96 61	97.01 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mPP-MVS					95.90 29							80.22 48

OpenMVS	77.91 11	85.09 54	83.42 63	87.03 43	96.12 27	96.55 34	89.36 49	81.59 57	79.19 75	75.20 55	55.84 123	79.04 49	84.45 59	88.47 68	89.35 55	95.48 32	95.48 83

PCF-MVS	82.38 4	85.52 52	84.41 59	86.81 47	91.51 57	96.23 41	90.27 43	89.81 15	77.87 80	70.67 74	69.20 65	77.86 50	85.55 48	85.92 103	86.38 79	93.03 122	97.43 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OMC-MVS		86.38 47	86.21 50	86.57 50	92.30 51	94.35 63	87.60 59	83.51 43	92.32 25	77.37 46	72.27 54	77.83 51	86.59 42	87.62 82	85.95 85	92.08 151	93.11 125

gg-mvs-nofinetune		72.10 146	74.79 130	68.97 183	83.31 126	95.22 58	85.66 76	48.77 227	35.68 228	22.17 235	30.49 221	77.73 52	76.37 108	94.30 11	93.03 11	97.55 2	97.05 47

test-LLR		79.52 87	83.42 63	74.97 119	81.79 131	91.26 97	76.17 173	70.57 139	77.71 82	52.14 138	66.26 72	77.47 53	73.10 121	87.02 86	87.16 69	96.05 22	97.02 48

TESTMET0.1,1		79.15 89	83.42 63	74.18 129	79.81 144	91.26 97	76.17 173	67.83 161	77.71 82	52.14 138	66.26 72	77.47 53	73.10 121	87.02 86	87.16 69	96.05 22	97.02 48

PVSNet_Blended_VisFu		82.55 64	83.70 62	81.21 78	89.66 64	95.15 59	82.41 104	77.36 80	72.53 108	73.64 59	61.15 100	77.19 55	70.35 154	91.31 38	89.72 50	93.84 64	98.85 14

DeepC-MVS	84.14 3	88.80 31	88.03 38	89.71 27	94.83 37	96.56 32	92.57 28	89.38 17	89.25 43	79.59 40	70.02 63	77.05 56	88.24 33	92.44 25	92.79 13	93.65 76	98.10 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		87.82 39	88.84 33	86.62 49	94.42 41	97.36 19	88.21 56	83.26 45	83.42 62	72.52 67	82.63 35	76.93 57	84.95 52	91.93 30	91.15 36	96.39 17	98.49 21

IS_MVSNet		80.92 72	84.14 61	77.16 109	87.43 92	93.90 65	80.44 112	74.64 103	75.05 94	61.10 100	65.59 77	76.89 58	67.39 165	90.88 41	90.05 44	91.95 155	96.62 66

Vis-MVSNet (Re-imp)		78.28 102	82.68 69	73.16 149	86.64 99	92.68 79	78.07 154	74.48 106	74.05 99	53.47 129	64.22 86	76.52 59	54.28 200	88.96 61	88.29 63	92.03 153	94.00 106

PatchmatchNet		76.85 117	80.03 88	73.15 150	84.08 124	91.04 101	77.76 158	55.85 213	79.43 73	52.74 134	62.08 96	76.02 60	74.56 113	79.92 156	81.41 158	93.92 63	90.29 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-mter		77.90 106	82.44 72	72.60 154	78.52 148	90.24 107	73.85 185	65.31 179	76.37 89	51.29 142	65.58 78	75.94 61	71.36 134	85.98 101	86.26 81	95.26 36	96.71 65

DELS-MVS		87.75 40	86.92 44	88.71 32	94.69 38	97.34 22	92.78 25	84.50 38	77.87 80	81.94 34	67.17 69	75.49 62	82.84 69	95.38 1	95.93 1	95.55 30	99.27 6
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
TAPA-MVS	80.99 7	84.83 57	84.42 58	85.31 56	91.89 54	93.73 68	88.53 55	82.80 48	89.99 38	69.78 79	71.53 59	75.03 63	85.47 50	86.26 98	84.54 104	93.39 98	89.90 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		85.65 51	86.30 48	84.88 58	95.51 35	95.89 49	86.50 68	76.71 84	89.23 44	68.59 82	70.93 61	74.49 64	88.55 27	89.40 57	90.30 43	93.42 96	93.88 115
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
DI_MVS_plusplus_trai		83.32 62	82.53 71	84.25 61	86.26 108	93.66 69	90.23 44	77.16 82	77.05 87	74.06 57	53.74 132	74.33 65	83.61 65	91.40 37	89.82 47	94.17 53	97.73 30

MVS_111021_LR		87.58 42	88.67 36	86.31 52	92.58 49	95.89 49	86.20 71	82.49 51	89.08 45	77.47 45	86.20 29	74.22 66	85.49 49	90.03 48	88.52 59	93.66 73	96.74 62

ACMMP		88.48 34	88.71 35	88.22 37	94.61 40	95.53 51	90.64 42	85.60 33	90.97 30	78.62 42	89.88 23	74.20 67	86.29 44	88.16 76	86.37 80	93.57 80	95.86 76
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
CostFormer		80.72 76	81.81 78	79.44 94	86.50 103	91.65 96	84.31 91	59.84 204	80.86 71	72.69 61	62.46 94	73.74 68	79.93 89	82.58 130	84.50 105	93.37 99	96.90 58

UGNet		80.71 79	83.09 67	77.93 103	87.02 96	92.71 78	80.28 116	76.53 86	73.83 102	71.35 71	70.07 62	73.71 69	58.93 192	87.39 83	86.97 72	93.48 92	96.94 54
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
AdaColmap		88.46 35	85.75 54	91.62 17	96.25 25	95.35 56	90.71 40	91.08 8	90.22 36	86.17 16	74.33 49	73.67 70	92.00 12	86.31 97	85.82 88	93.52 83	94.53 95

GBi-Net		80.72 76	80.49 83	81.00 83	78.18 150	86.19 152	86.73 62	72.57 116	83.02 64	72.63 64	56.55 115	73.48 71	80.99 79	86.57 91	86.83 75	94.89 43	90.77 142

test1		80.72 76	80.49 83	81.00 83	78.18 150	86.19 152	86.73 62	72.57 116	83.02 64	72.63 64	56.55 115	73.48 71	80.99 79	86.57 91	86.83 75	94.89 43	90.77 142

FMVSNet3		81.93 70	81.98 75	81.88 71	79.49 146	87.02 134	88.15 58	72.57 116	83.02 64	72.63 64	56.55 115	73.48 71	82.34 72	91.49 36	91.20 35	96.07 19	91.13 140

MDTV_nov1_ep13		77.20 114	80.04 86	73.90 135	82.22 129	90.14 109	79.25 138	61.52 197	78.63 79	56.98 117	65.52 79	72.80 74	73.05 123	80.93 148	83.20 122	90.36 185	89.05 163

RPMNet		73.46 134	77.85 103	68.34 184	81.71 134	85.52 161	73.83 186	50.54 225	74.05 99	46.10 187	53.03 137	71.91 75	66.31 171	83.55 122	82.18 137	91.55 165	94.71 91

CNLPA		84.72 59	82.14 74	87.73 39	92.85 48	93.83 66	84.70 88	85.07 34	90.90 31	83.16 27	56.28 119	71.53 76	88.14 34	84.19 115	84.00 113	92.48 144	94.26 101

FMVSNet5		72.83 138	73.89 137	71.59 170	67.42 218	76.28 214	75.88 177	63.74 189	77.27 85	54.59 126	53.32 134	71.48 77	73.85 117	81.95 140	81.69 143	94.06 57	75.20 215

DWT-MVSNet_training		82.66 63	83.34 66	81.87 72	88.71 72	92.63 80	82.07 106	72.21 122	86.37 55	72.64 62	64.51 83	71.44 78	80.35 87	84.43 113	87.73 67	95.27 34	96.25 70

CR-MVSNet		74.84 124	77.91 102	71.26 176	81.77 133	85.52 161	78.32 147	54.14 217	74.05 99	51.09 146	50.00 145	71.38 79	70.77 144	86.48 94	84.03 111	91.46 167	93.92 111

PLC	81.02 6	84.81 58	81.81 78	88.31 36	93.77 44	90.35 105	88.80 53	84.47 39	86.76 53	82.17 33	66.56 71	71.01 80	88.41 30	85.48 105	84.28 107	92.26 149	88.21 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PMMVS		82.26 65	85.48 55	78.51 101	85.92 113	91.92 92	78.30 149	70.77 136	86.30 56	61.11 99	82.46 36	70.88 81	84.70 57	88.05 77	84.78 100	90.24 188	93.98 107

EPMVS		77.16 115	79.08 93	74.92 120	86.73 97	91.98 91	78.62 145	55.44 214	79.43 73	56.59 120	61.24 99	70.73 82	76.97 102	80.59 151	81.43 157	95.15 39	88.17 174

MS-PatchMatch		77.47 109	76.48 113	78.63 98	89.89 63	90.42 104	85.42 77	69.53 145	70.79 114	60.43 107	50.05 144	70.62 83	70.66 147	86.71 90	82.54 129	95.86 27	84.23 190

casdiffmvs1		86.12 49	86.10 51	86.15 53	88.98 69	95.46 52	89.62 47	75.02 100	86.42 54	79.82 39	73.81 51	70.05 84	87.88 35	87.97 78	92.04 19	95.60 29	96.94 54

CANet_DTU		83.33 61	86.59 45	79.53 92	88.88 71	94.87 60	86.63 65	68.85 150	85.45 57	50.54 160	77.86 41	69.94 85	85.62 47	92.63 24	90.88 39	96.63 11	94.46 96

thresconf0.02		78.87 92	80.50 82	76.96 110	87.88 87	91.71 95	82.90 103	78.51 65	67.91 125	50.85 153	74.56 46	69.93 86	67.32 166	86.86 89	85.65 89	94.32 51	86.89 181

Vis-MVSNet		77.24 112	79.99 89	74.02 133	84.62 121	93.92 64	80.33 115	72.55 119	62.58 146	55.25 123	64.45 84	69.49 87	57.00 196	88.78 63	88.21 64	94.36 50	92.54 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP-MVS		86.17 48	87.35 41	84.80 59	91.41 58	92.37 88	91.05 39	84.35 40	88.52 47	64.21 89	87.05 28	68.91 88	84.80 55	89.12 59	88.16 65	92.96 125	97.31 39

MVS_Test		84.60 60	85.13 56	83.99 62	88.17 83	95.27 57	88.21 56	73.15 113	84.31 60	70.55 76	68.67 66	68.78 89	86.99 40	91.71 33	91.90 24	96.84 9	95.27 87

canonicalmvs		85.93 50	86.26 49	85.54 55	88.94 70	95.44 53	89.56 48	76.01 90	87.83 49	77.70 44	76.43 44	68.66 90	87.80 36	87.02 86	91.51 31	93.25 108	96.95 53

EPNet_dtu		78.49 99	81.96 76	74.45 126	92.57 50	88.74 123	82.98 98	78.83 62	83.28 63	44.64 198	77.40 42	67.73 91	53.98 204	85.44 106	84.91 94	93.71 71	86.22 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		74.27 128	73.24 139	75.48 117	81.01 137	80.18 198	76.24 172	72.37 120	74.84 96	68.24 83	72.47 52	67.39 92	73.89 116	71.05 212	69.38 221	81.14 228	77.37 210

PatchT		72.66 139	76.58 112	68.09 186	79.02 147	86.09 156	59.81 216	51.78 223	72.00 109	51.09 146	46.84 155	66.70 93	70.77 144	86.48 94	84.03 111	96.07 19	93.92 111

Anonymous20240521		79.76 85	79.17 91	80.44 88	84.65 120	84.51 173	84.20 92	72.36 121	75.17 93	70.81 73	66.21 74	66.56 94	80.99 79	82.89 126	84.56 103	89.65 195	94.30 100

FMVSNet2		79.24 88	78.14 101	80.53 87	78.18 150	86.19 152	86.73 62	71.91 126	72.97 105	70.48 77	50.63 142	66.56 94	80.99 79	90.10 47	89.77 49	94.89 43	90.77 142

Anonymous20231211		78.61 96	75.57 124	82.15 68	84.43 123	90.26 106	84.08 93	77.68 76	71.09 111	72.90 60	39.24 207	66.21 96	84.23 62	82.15 134	84.04 110	89.61 196	96.03 73

PVSNet_BlendedMVS		86.98 45	87.05 42	86.90 44	93.03 46	96.98 26	86.57 66	81.82 55	89.78 40	82.78 30	71.54 57	66.07 97	80.73 84	93.46 17	91.97 22	96.45 15	99.53 1

PVSNet_Blended		86.98 45	87.05 42	86.90 44	93.03 46	96.98 26	86.57 66	81.82 55	89.78 40	82.78 30	71.54 57	66.07 97	80.73 84	93.46 17	91.97 22	96.45 15	99.53 1

CLD-MVS		85.43 53	84.24 60	86.83 46	87.69 91	93.16 75	90.01 45	82.72 49	87.17 50	79.28 41	71.43 60	65.81 99	86.02 45	87.33 84	86.96 73	95.25 37	97.83 29
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmp4_e23		78.57 97	78.48 97	78.68 97	85.38 116	89.14 122	84.69 89	60.32 202	78.81 78	70.65 75	57.89 107	65.54 100	79.63 90	80.09 155	83.24 121	91.41 168	94.63 94

casdiffmvs		84.93 55	85.04 57	84.79 60	88.47 76	95.36 55	87.59 60	74.52 104	84.05 61	76.42 49	72.09 56	65.20 101	85.78 46	91.10 39	91.33 34	95.95 24	96.17 72

HyFIR lowres test		78.08 105	76.81 108	79.56 91	90.77 61	94.64 61	82.97 99	69.85 143	69.81 121	59.53 110	33.52 217	64.66 102	78.97 94	88.77 64	88.38 62	95.27 34	97.86 28

tpmrst		76.27 120	77.65 105	74.66 122	86.13 110	89.53 119	79.31 137	54.91 215	77.19 86	56.27 121	55.87 122	64.58 103	77.25 99	80.85 149	80.21 177	94.07 56	95.32 85

tpm cat1		76.93 116	76.19 118	77.79 105	85.08 119	88.58 125	82.96 100	59.33 205	75.72 92	72.64 62	51.25 140	64.41 104	75.74 110	77.90 184	80.10 180	90.97 175	95.35 84

OPM-MVS		81.34 71	78.18 100	85.02 57	91.27 59	91.78 94	90.66 41	83.62 42	62.39 147	65.91 86	63.35 89	64.33 105	85.03 51	87.77 80	85.88 87	93.66 73	91.75 138

IterMVS-LS		76.80 118	76.33 116	77.35 108	84.07 125	84.11 174	81.54 108	68.52 152	66.17 129	61.74 93	57.84 108	64.31 106	74.88 111	83.48 124	86.21 82	93.34 101	92.16 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211			75.59 123		85.13 118	91.06 100	84.62 90	77.96 69	69.47 122		40.79 200	63.84 107	84.57 58	83.55 122	84.69 101	89.69 194	95.75 80

CDS-MVSNet		76.57 119	76.78 109	76.32 113	80.94 138	89.75 116	82.94 101	72.64 115	59.01 164	62.95 92	58.60 106	62.67 108	66.91 169	86.26 98	87.20 68	91.57 163	93.97 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
conf0.002		80.80 75	80.30 85	81.38 76	88.59 73	93.19 74	85.12 82	78.10 66	70.15 115	61.55 96	63.30 90	62.66 109	81.11 74	88.74 65	86.94 74	93.79 66	97.15 44

diffmvs		82.25 66	82.33 73	82.15 68	86.10 111	94.52 62	86.22 69	73.32 112	82.19 68	70.14 78	67.88 68	62.49 110	83.02 67	85.97 102	88.53 58	94.10 54	94.77 90

LGP-MVS_train		82.12 69	82.57 70	81.59 73	89.26 68	90.23 108	88.76 54	78.05 68	81.26 70	61.64 95	79.52 37	62.11 111	79.59 91	85.20 108	84.68 102	92.27 148	95.02 89

ADS-MVSNet		72.11 145	73.72 138	70.24 181	81.24 136	86.59 144	74.75 182	50.56 224	72.58 107	49.17 167	55.40 124	61.46 112	73.80 118	76.01 194	78.14 190	91.93 156	85.86 184

tpm		73.50 133	74.85 129	71.93 166	83.19 127	86.84 138	78.61 146	55.91 212	65.64 131	48.90 169	56.30 118	61.09 113	72.31 125	79.10 174	80.61 176	92.68 139	94.35 99

CHOSEN 1792x2688		80.23 81	79.16 92	81.48 74	91.97 52	96.56 32	86.18 72	75.40 98	76.17 90	61.32 98	37.43 212	61.08 114	76.52 105	92.35 26	91.64 29	97.46 3	98.86 13

ACMM	78.09 10	80.91 73	78.39 98	83.86 64	89.61 67	87.71 128	85.16 81	80.67 60	79.04 76	74.18 56	63.82 87	60.84 115	82.59 70	84.33 114	83.59 116	90.96 176	89.39 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	79.58 9	82.23 67	81.82 77	82.71 66	88.15 84	90.95 102	85.23 80	78.52 64	81.70 69	72.52 67	78.41 38	60.63 116	80.48 86	82.88 127	83.44 118	91.37 169	94.70 92
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+		79.80 84	80.04 86	79.52 93	85.53 114	93.31 73	85.28 78	70.68 138	74.15 98	58.79 113	62.03 97	60.51 117	83.37 66	88.41 69	86.09 84	93.49 91	95.80 78

FC-MVSNet-test		67.04 187	72.47 142	60.70 213	76.92 158	81.41 190	61.52 212	69.45 146	65.58 133	26.74 231	61.79 98	60.40 118	41.17 223	77.60 187	77.78 192	88.41 204	82.70 201

test0.0.03 1		71.70 152	74.68 131	68.23 185	81.79 131	83.81 177	68.64 198	70.57 139	68.81 123	43.47 199	62.77 93	60.09 119	51.77 210	82.48 131	81.67 144	93.16 114	83.13 197

tfpn_ndepth		78.22 103	78.84 95	77.49 106	88.32 82	90.95 102	80.79 111	76.31 88	74.24 97	59.50 111	69.52 64	60.02 120	67.11 167	85.06 109	82.95 127	92.94 130	89.18 161

TSAR-MVS + COLMAP		84.93 55	85.79 53	83.92 63	90.90 60	93.57 70	89.25 51	82.00 54	91.29 28	61.66 94	88.25 26	59.46 121	86.71 41	89.79 52	87.09 71	93.01 123	91.09 141

CVMVSNet		68.95 182	70.79 151	66.79 192	79.69 145	83.75 178	72.05 191	70.90 135	56.20 178	36.30 213	54.94 128	59.22 122	54.03 203	78.33 180	78.65 186	87.77 209	84.44 188

IterMVS		72.43 141	74.05 136	70.55 180	80.34 141	81.17 193	77.44 161	61.00 199	63.57 144	46.82 183	55.88 121	59.09 123	65.03 173	83.15 125	83.83 115	92.67 140	91.65 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.01		80.10 82	79.04 94	81.34 77	88.56 74	93.09 76	85.12 82	78.08 67	70.15 115	61.43 97	60.90 101	58.54 124	81.11 74	88.66 66	84.80 96	93.74 68	97.14 45

LS3D		78.72 94	75.79 120	82.15 68	91.91 53	89.39 120	83.66 96	85.88 32	76.81 88	59.22 112	57.67 109	58.53 125	83.72 64	82.07 136	81.63 145	88.50 203	84.39 189

tfpn1000		75.39 122	76.18 119	74.47 125	86.71 98	90.10 110	77.57 159	74.78 101	68.76 124	53.33 130	63.57 88	58.37 126	60.84 188	83.80 119	81.24 162	93.58 79	87.42 177

dps		75.76 121	75.02 128	76.63 112	84.51 122	88.12 126	77.51 160	58.33 207	75.91 91	71.98 69	57.37 110	57.85 127	76.81 104	77.89 185	78.40 189	90.63 182	89.63 151

Effi-MVS+-dtu		74.57 125	74.60 132	74.53 124	81.38 135	86.74 141	80.39 114	67.70 162	67.36 127	53.06 131	59.86 103	57.50 128	75.84 109	80.19 153	78.62 187	88.79 202	91.95 137

FMVSNet1		74.26 129	71.95 145	76.95 111	74.28 198	83.94 176	83.61 97	69.99 141	57.08 169	65.08 87	42.39 191	57.41 129	76.98 101	86.57 91	86.83 75	91.77 160	89.42 154

tfpn		77.45 110	76.23 117	78.87 96	87.15 95	91.90 93	82.17 105	76.59 85	62.98 145	56.93 118	53.08 136	57.31 130	76.41 107	87.26 85	85.20 92	93.95 62	95.89 75

gm-plane-assit		64.86 196	68.15 178	61.02 212	76.44 170	68.29 224	41.60 232	53.37 220	34.68 230	26.19 233	33.22 218	57.09 131	71.97 126	95.12 4	93.97 6	96.54 13	94.66 93

TAMVS		72.06 147	71.76 146	72.41 159	76.68 162	88.12 126	74.82 181	68.09 157	53.52 192	56.91 119	52.94 138	56.93 132	66.91 169	81.37 145	82.44 131	91.07 173	86.99 179

FC-MVSNet-train		79.54 86	78.20 99	81.09 82	86.55 101	88.63 124	79.96 118	78.53 63	70.90 113	68.24 83	65.87 76	56.45 133	80.29 88	86.20 100	84.08 108	92.97 124	95.31 86

IB-MVS	74.10 12	78.52 98	78.51 96	78.52 99	90.15 62	95.39 54	71.95 192	77.53 78	74.95 95	77.25 47	58.93 105	55.92 134	58.37 194	79.01 175	87.89 66	95.88 26	97.47 34
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
tfpn_n400		74.36 126	74.39 134	74.32 127	86.37 105	89.86 113	79.71 122	75.69 94	60.00 155	47.47 177	64.85 80	54.72 135	63.70 179	83.80 119	83.35 119	92.96 125	84.16 191

tfpnconf		74.36 126	74.39 134	74.32 127	86.37 105	89.86 113	79.71 122	75.69 94	60.00 155	47.47 177	64.85 80	54.72 135	63.70 179	83.80 119	83.35 119	92.96 125	84.16 191

tfpnview11		74.85 123	75.06 127	74.61 123	86.58 100	89.54 118	79.98 117	75.81 92	64.95 138	47.47 177	64.85 80	54.72 135	63.86 176	84.54 112	82.20 136	93.97 60	84.64 186

Fast-Effi-MVS+-dtu		73.56 132	75.32 126	71.50 172	80.35 140	86.83 139	79.72 121	58.07 208	67.64 126	44.83 195	60.28 102	54.07 138	73.59 120	81.90 142	82.30 133	92.46 145	94.18 102

MSDG		78.11 104	73.17 140	83.86 64	91.78 56	86.83 139	85.25 79	86.02 30	72.84 106	69.69 81	51.43 139	54.00 139	77.61 97	81.95 140	82.27 134	92.83 137	82.91 199

Fast-Effi-MVS+		77.37 111	76.68 111	78.17 102	82.84 128	89.94 112	81.47 109	68.01 158	72.99 104	60.26 108	55.07 126	53.20 140	82.99 68	86.47 96	86.12 83	93.46 93	92.98 128

testpf		59.38 214	64.51 199	53.40 221	76.71 161	66.40 226	50.18 226	38.98 238	64.13 141	35.10 217	47.91 153	51.41 141	43.16 217	66.37 222	71.23 214	76.25 230	84.14 193

tfpn111		80.42 80	79.77 90	81.18 79	88.42 77	92.55 84	85.12 82	77.94 71	70.15 115	61.00 103	74.56 46	51.22 142	81.11 74	88.23 70	84.80 96	93.50 88	96.90 58

conf200view11		79.04 91	77.21 106	81.18 79	88.42 77	92.55 84	85.12 82	77.94 71	70.15 115	61.00 103	56.65 112	51.22 142	81.11 74	88.23 70	84.80 96	93.50 88	96.90 58

thres100view900		79.83 83	77.79 104	82.21 67	88.42 77	93.54 71	87.07 61	81.11 58	70.15 115	61.01 101	56.65 112	51.22 142	81.78 73	89.77 53	85.95 85	93.84 64	97.26 41

tfpn200view9		79.05 90	77.21 106	81.18 79	88.42 77	92.55 84	85.12 82	77.94 71	70.15 115	61.01 101	56.65 112	51.22 142	81.11 74	88.23 70	84.80 96	93.50 88	96.90 58

thres200		78.69 95	76.71 110	80.99 85	88.35 81	92.56 82	86.03 73	77.94 71	66.27 128	60.66 105	56.08 120	51.11 146	79.45 92	88.23 70	85.54 91	93.52 83	97.20 43

thres400		78.39 100	76.39 115	80.73 86	88.02 86	92.94 77	84.77 87	78.88 61	65.20 136	59.70 109	55.20 125	50.85 147	79.45 92	88.81 62	84.81 95	93.57 80	96.91 57

view600		77.68 107	75.68 121	80.01 89	87.72 89	92.57 81	83.79 94	77.95 70	64.41 139	58.72 114	54.32 130	50.54 148	78.25 95	88.23 70	83.13 123	93.64 77	96.59 67

thres600view7		77.66 108	75.67 122	79.98 90	87.71 90	92.56 82	83.79 94	77.94 71	64.41 139	58.69 115	54.32 130	50.54 148	78.23 96	88.23 70	83.06 125	93.52 83	96.55 68

view800		77.22 113	75.35 125	79.41 95	87.42 93	92.21 90	82.94 101	77.19 81	63.67 143	57.78 116	53.68 133	50.19 150	77.32 98	87.70 81	83.84 114	93.79 66	96.19 71

conf0.05thres1000		74.20 130	71.44 148	77.43 107	86.09 112	89.85 115	80.82 110	75.79 93	53.51 193	54.71 124	44.37 168	49.78 151	74.67 112	85.02 110	83.47 117	92.49 143	94.10 104

MIMVSNet		68.66 183	69.43 165	67.76 187	64.92 223	84.68 171	74.16 183	54.10 219	60.85 150	51.27 143	39.47 204	49.48 152	67.48 164	84.86 111	85.57 90	94.63 47	81.10 205

COLMAP_ROB	66.31 15	69.91 172	66.61 187	73.76 136	86.44 104	82.76 181	76.59 169	76.46 87	63.82 142	50.92 152	45.60 157	49.13 153	65.87 172	74.96 198	74.45 209	86.30 215	75.57 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GA-MVS		73.62 131	74.52 133	72.58 155	79.93 142	89.29 121	78.02 155	71.67 132	60.79 152	42.68 202	54.41 129	49.07 154	70.07 157	89.39 58	86.55 78	93.13 119	92.12 134

ACMH+	72.14 13	72.38 142	69.34 167	75.93 114	85.21 117	84.89 168	76.96 167	76.04 89	59.76 157	51.63 141	50.37 143	48.69 155	76.90 103	76.06 193	78.69 185	88.85 201	86.90 180

UniMVSNet_NR-MVSNet		73.11 137	72.59 141	73.71 137	76.90 159	86.58 145	77.01 164	75.82 91	65.59 132	48.82 170	50.97 141	48.42 156	71.61 131	79.19 172	83.03 126	92.11 150	94.37 97

pm-mvs1		69.62 178	68.07 179	71.44 173	77.21 157	85.32 164	76.11 175	71.05 134	46.55 216	51.17 145	41.83 196	48.20 157	61.81 185	84.00 117	81.14 169	91.28 170	89.42 154

PatchMatch-RL		78.75 93	76.47 114	81.41 75	88.53 75	91.10 99	78.09 153	77.51 79	77.33 84	71.98 69	64.38 85	48.10 158	82.55 71	84.06 116	82.35 132	89.78 191	87.97 175

tmp_tt				39.78 230	56.31 231	31.71 242	35.84 237	15.08 240	82.57 67	50.83 154	63.07 91	47.51 159	15.28 237	52.23 231	44.24 234	65.35 235

ACMH	71.22 14	72.65 140	70.13 156	75.59 115	86.19 109	86.14 155	75.76 178	77.63 77	54.79 186	46.16 186	53.28 135	47.28 160	77.24 100	78.91 177	81.18 166	90.57 183	89.33 157
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		72.12 144	72.28 144	71.93 166	76.77 160	87.38 130	75.73 179	73.51 111	65.76 130	50.24 162	48.65 151	46.49 161	63.85 177	80.10 154	82.47 130	91.49 166	95.13 88

v18		71.13 160	68.98 169	73.63 141	76.66 163	79.78 200	79.95 119	65.98 173	61.34 149	54.71 124	44.75 160	46.06 162	71.27 135	79.59 161	81.51 151	93.21 110	89.81 149

v17		70.82 165	68.69 174	73.31 145	76.53 165	79.67 201	79.45 134	65.80 177	60.32 154	53.75 128	44.51 166	45.92 163	71.09 138	79.49 167	81.38 160	93.26 107	89.54 152

v16		70.93 163	68.76 173	73.47 143	76.60 164	79.66 202	79.57 131	65.81 176	60.85 150	54.44 127	44.50 167	45.90 164	71.15 136	79.50 166	81.39 159	93.27 104	89.51 153

v8		71.42 159	69.69 161	73.43 144	76.45 167	85.12 167	79.53 133	67.47 165	59.34 162	52.90 132	44.60 162	45.82 165	71.05 141	79.56 165	81.45 155	93.17 112	91.96 136

v6		72.04 148	70.26 152	74.11 130	76.46 166	87.06 131	79.60 124	71.75 129	59.48 159	52.69 135	44.61 161	45.79 166	71.01 142	79.57 162	81.45 155	93.16 114	93.85 118

v1neww		72.02 149	70.23 154	74.10 131	76.45 167	87.06 131	79.59 127	71.75 129	59.35 160	52.60 136	44.59 163	45.74 167	71.06 139	79.57 162	81.46 153	93.16 114	93.84 119

v7new		72.02 149	70.23 154	74.10 131	76.45 167	87.06 131	79.59 127	71.75 129	59.35 160	52.60 136	44.59 163	45.74 167	71.06 139	79.57 162	81.46 153	93.16 114	93.84 119

V42		71.58 153	70.11 157	73.30 146	75.66 191	86.68 142	79.17 140	69.92 142	59.29 163	52.80 133	44.36 169	45.66 169	68.83 159	79.48 168	81.49 152	93.44 94	93.82 121

anonymousdsp		67.61 185	68.94 170	66.04 196	71.44 212	83.97 175	66.45 203	63.53 191	50.54 203	42.42 203	49.39 146	45.63 170	62.84 182	77.99 183	81.34 161	89.59 197	93.75 122

EG-PatchMatch MVS		66.23 191	65.20 192	67.43 189	77.74 154	86.20 151	72.51 190	63.68 190	43.95 220	43.44 200	36.22 214	45.43 171	54.04 202	81.00 147	80.95 174	93.15 118	82.67 202

v1		71.54 154	69.71 160	73.66 140	76.08 172	86.88 135	79.60 124	72.06 125	57.00 171	50.75 157	44.23 172	44.79 172	70.61 149	79.62 158	81.52 148	92.88 134	93.93 109

v15		70.00 171	67.82 181	72.55 156	76.06 174	79.37 205	79.10 141	65.30 180	56.89 173	51.18 144	43.96 178	44.76 173	70.52 151	79.40 169	81.22 164	93.13 119	89.14 162

V14		69.91 172	67.71 183	72.47 158	76.01 176	79.30 206	78.92 142	65.17 181	56.74 174	51.08 149	43.82 181	44.73 174	70.44 153	79.31 170	81.14 169	93.20 111	88.91 166

v1141		71.53 155	69.69 161	73.68 138	76.08 172	86.86 136	79.59 127	72.07 124	57.01 170	50.78 155	44.23 172	44.70 175	70.68 146	79.61 160	81.52 148	92.89 131	93.92 111

divwei89l23v2f112		71.53 155	69.69 161	73.68 138	76.09 171	86.86 136	79.60 124	72.08 123	56.96 172	50.78 155	44.24 171	44.70 175	70.65 148	79.62 158	81.53 146	92.89 131	93.93 109

V9		69.79 176	67.57 184	72.38 160	75.95 178	79.21 207	78.72 144	65.06 182	56.51 176	51.06 150	43.66 182	44.70 175	70.28 155	79.22 171	81.06 172	93.24 109	88.67 170

v12		69.66 177	67.45 185	72.23 161	75.89 183	79.13 209	78.29 150	64.96 185	56.40 177	50.75 157	43.53 184	44.60 178	70.21 156	79.11 173	80.99 173	93.27 104	88.41 171

v13		69.55 179	67.33 186	72.14 164	75.83 186	79.04 210	78.22 151	64.85 186	56.16 179	50.60 159	43.43 186	44.56 179	70.05 158	79.01 175	80.92 175	93.28 103	88.22 172

CMPMVS	50.59 17	66.74 189	62.72 209	71.42 174	85.40 115	89.72 117	72.69 189	70.72 137	51.24 199	51.75 140	38.91 208	44.40 180	63.74 178	70.84 213	71.52 213	84.19 220	72.45 220
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WR-MVS		64.98 195	66.59 188	63.09 205	74.34 197	82.68 182	64.98 209	69.17 149	54.42 189	36.18 214	44.32 170	44.35 181	44.65 213	73.60 200	77.83 191	89.21 200	88.96 165

Baseline_NR-MVSNet		70.61 166	68.87 171	72.65 153	75.95 178	80.49 196	75.92 176	74.75 102	65.10 137	48.78 172	41.28 199	44.28 182	68.45 160	78.67 178	79.64 181	92.04 152	92.62 129

pmmvs4		73.38 136	71.53 147	75.55 116	75.95 178	85.24 165	77.25 163	71.59 133	71.03 112	63.10 91	49.09 150	44.22 183	73.73 119	82.04 137	80.18 178	91.68 161	88.89 167

v10		70.97 162	69.44 164	72.75 151	75.90 182	84.58 172	79.43 136	66.45 170	58.07 166	49.93 164	43.87 179	43.68 184	71.91 128	82.04 137	81.70 141	92.89 131	92.11 135

v7		71.49 157	69.98 158	73.25 147	75.89 183	86.45 146	79.44 135	69.29 148	58.07 166	50.08 163	43.87 179	43.67 185	71.94 127	82.03 139	81.70 141	92.88 134	94.04 105

v148		70.34 167	68.46 176	72.54 157	76.04 175	86.38 147	74.83 180	72.73 114	55.88 182	55.26 122	43.32 188	43.49 186	64.52 174	76.93 191	80.11 179	91.85 158	93.11 125

MVS-HIRNet		64.63 199	64.03 203	65.33 198	75.01 193	82.84 180	58.54 220	52.10 222	55.42 184	49.29 166	29.83 224	43.48 187	66.97 168	78.28 181	78.81 184	90.07 190	79.52 207

pmmvs6		64.24 202	61.77 213	67.12 190	72.39 205	81.39 191	71.33 193	65.95 175	36.05 227	48.48 173	30.55 220	43.45 188	58.75 193	77.88 186	76.36 203	85.83 216	86.70 182

v11		69.84 175	67.85 180	72.17 162	75.78 189	79.15 208	78.20 152	64.76 187	56.10 180	49.50 165	43.54 183	43.36 189	71.62 130	82.21 133	81.52 148	93.17 112	89.05 163

MDTV_nov1_ep13_2view		64.72 198	64.94 194	64.46 201	71.14 213	81.94 188	67.53 199	54.54 216	55.92 181	43.29 201	44.02 176	43.27 190	59.87 191	71.85 209	74.77 207	90.36 185	82.82 200

v2v482		71.73 151	69.80 159	73.99 134	75.88 185	86.66 143	79.58 130	71.90 127	57.58 168	50.41 161	45.35 158	43.24 191	73.05 123	79.69 157	82.18 137	93.08 121	93.87 116

TranMVSNet+NR-MVSNet		71.12 161	70.24 153	72.15 163	76.01 176	84.80 170	76.55 170	75.65 96	61.99 148	45.29 191	48.42 152	43.07 192	67.55 163	78.28 181	82.83 128	91.85 158	92.29 131

DU-MVS		72.19 143	71.35 149	73.17 148	75.95 178	86.02 157	77.01 164	74.42 107	65.39 134	48.82 170	49.10 148	42.81 193	71.61 131	78.67 178	83.10 124	91.22 171	94.37 97

WR-MVS_H		64.14 205	65.36 191	62.71 207	72.47 204	82.33 186	65.13 206	66.99 168	51.81 198	36.47 212	43.33 187	42.77 194	43.99 215	72.41 206	75.99 204	91.20 172	88.86 168

USDC		73.43 135	72.31 143	74.73 121	80.86 139	86.21 150	80.42 113	71.83 128	71.69 110	46.94 181	59.60 104	42.58 195	76.47 106	82.66 129	81.22 164	91.88 157	82.24 204

v1144		70.93 163	69.42 166	72.70 152	75.48 192	86.26 148	79.22 139	69.39 147	55.61 183	48.05 175	43.47 185	42.55 196	71.51 133	82.11 135	81.74 140	92.56 142	94.17 103

TDRefinement		67.82 184	64.91 195	71.22 177	82.08 130	81.45 189	77.42 162	73.79 110	59.62 158	48.35 174	42.35 193	42.40 197	60.87 187	74.69 199	74.64 208	84.83 219	79.20 208

TransMVSNet (Re)		66.87 188	64.30 200	69.88 182	78.32 149	81.35 192	73.88 184	74.34 109	43.19 222	45.20 193	40.12 201	42.37 198	55.97 198	80.85 149	79.15 182	91.56 164	83.06 198

v52		65.34 192	64.59 197	66.21 194	69.63 216	82.41 185	69.22 196	62.80 193	49.63 207	45.15 194	39.31 206	41.85 199	60.68 189	72.61 203	77.02 199	89.75 193	89.33 157

V4		65.34 192	64.59 197	66.21 194	69.64 215	82.42 184	69.22 196	62.80 193	49.60 208	45.21 192	39.33 205	41.82 200	60.66 190	72.61 203	77.03 198	89.76 192	89.32 159

pmmvs5		70.01 170	69.31 168	70.82 179	75.80 188	86.26 148	72.94 187	67.91 159	53.84 191	47.22 180	47.31 154	41.47 201	67.61 162	83.93 118	81.93 139	93.42 96	90.42 146

v144192		70.10 169	68.55 175	71.90 168	74.55 195	85.67 159	77.81 156	68.22 156	54.65 187	46.91 182	42.76 189	41.27 202	70.95 143	80.48 152	81.11 171	92.96 125	93.90 114

v1192		70.32 168	68.77 172	72.12 165	74.76 194	85.62 160	78.73 143	68.53 151	55.08 185	46.34 185	42.39 191	40.67 203	71.90 129	82.27 132	81.53 146	92.43 146	93.86 117

testgi		63.11 209	64.88 196	61.05 211	75.83 186	78.51 212	60.42 215	66.20 172	48.77 211	34.56 218	56.96 111	40.35 204	40.95 224	77.46 189	77.22 196	88.37 206	74.86 217

EU-MVSNet		58.73 215	60.92 214	56.17 217	66.17 221	72.39 221	58.85 219	61.24 198	48.47 213	27.91 228	46.70 156	40.06 205	39.07 225	68.27 218	70.34 218	83.77 221	80.23 206

v1921920		69.85 174	68.38 177	71.58 171	74.35 196	85.39 163	77.78 157	67.88 160	54.64 188	45.39 190	42.11 194	39.97 206	71.10 137	81.68 143	81.17 168	92.96 125	93.69 124

NR-MVSNet		71.47 158	71.11 150	71.90 168	77.73 155	86.02 157	76.88 168	74.42 107	65.39 134	46.09 188	49.10 148	39.87 207	64.27 175	81.40 144	82.24 135	91.99 154	93.75 122

v1240		69.28 181	67.82 181	71.00 178	74.09 199	85.13 166	76.54 171	67.28 167	53.17 194	44.70 196	41.55 198	39.38 208	70.51 152	81.29 146	81.18 166	92.88 134	93.02 127

v748		65.00 194	63.86 204	66.33 193	71.85 209	82.15 187	66.80 201	65.64 178	48.50 212	47.98 176	39.62 202	39.20 209	56.44 197	71.25 210	77.53 194	89.29 198	88.74 169

v7n		66.43 190	65.51 190	67.51 188	71.63 211	83.10 179	70.89 195	65.02 183	50.13 206	44.68 197	39.59 203	38.77 210	62.57 183	77.59 188	78.91 183	90.29 187	90.44 145

PEN-MVS		64.35 201	64.29 201	64.42 202	72.67 202	79.83 199	66.97 200	68.24 155	51.21 200	35.29 216	44.09 174	38.51 211	52.36 208	71.06 211	77.65 193	90.99 174	87.68 176

CP-MVSNet		64.84 197	64.97 193	64.69 200	72.09 206	81.04 194	66.66 202	67.53 164	52.45 196	37.40 209	44.00 177	38.37 212	53.54 205	72.26 207	76.93 200	90.94 178	89.75 150

test20.03		57.93 217	59.22 216	56.44 216	71.84 210	73.78 220	53.55 224	65.96 174	43.02 223	28.46 227	37.50 211	38.17 213	30.41 231	75.25 196	74.42 210	88.41 204	72.37 221

DTE-MVSNet		63.26 208	63.41 207	63.08 206	72.59 203	78.56 211	65.03 208	68.28 154	50.53 204	32.38 220	44.03 175	37.79 214	49.48 211	70.83 214	76.73 201	90.73 180	85.42 185

PS-CasMVS		64.22 204	64.19 202	64.25 203	71.86 208	80.67 195	66.42 204	67.43 166	50.64 202	36.48 211	42.60 190	37.46 215	52.56 207	71.98 208	76.69 202	90.76 179	89.29 160

tfpnnormal		69.29 180	65.58 189	73.62 142	79.87 143	84.82 169	76.97 166	75.12 99	45.29 218	49.03 168	35.57 215	37.20 216	68.02 161	82.70 128	81.24 162	92.69 138	92.20 132

SixPastTwentyTwo		63.75 206	63.42 206	64.13 204	72.91 201	80.34 197	61.29 213	63.90 188	49.58 209	40.42 206	54.99 127	37.13 217	60.90 186	68.46 217	70.80 216	85.37 218	82.65 203

Anonymous20231206		62.05 211	61.83 212	62.30 209	72.09 206	77.84 213	63.10 211	67.62 163	50.20 205	36.68 210	29.59 225	37.05 218	43.90 216	77.33 190	77.31 195	90.41 184	83.49 195

LP		59.72 213	58.23 218	61.44 210	75.67 190	74.97 218	61.05 214	48.34 228	54.02 190	40.82 205	31.61 219	36.92 219	54.69 199	67.52 219	71.18 215	88.08 207	71.42 223

N_pmnet		60.52 212	58.83 217	62.50 208	68.97 217	75.61 217	59.72 218	66.47 169	51.90 197	41.26 204	35.42 216	35.63 220	52.25 209	67.07 221	70.08 219	86.35 214	76.10 212

LTVRE_ROB	63.07 16	64.49 200	63.16 208	66.04 196	77.47 156	82.64 183	70.98 194	65.02 183	34.01 231	29.61 223	49.12 147	35.58 221	70.57 150	75.10 197	78.45 188	82.60 223	87.24 178
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
1111		48.34 225	47.93 226	48.83 225	58.14 229	59.33 232	37.54 233	43.85 232	31.76 232	29.36 224	23.26 231	34.58 222	42.20 218	65.15 223	68.72 222	81.86 225	52.66 232

.test1245		33.05 231	31.21 234	35.20 232	58.14 229	59.33 232	37.54 233	43.85 232	31.76 232	29.36 224	23.26 231	34.58 222	42.20 218	65.15 223	0.77 239	0.11 243	3.62 240

test2356		58.43 216	59.52 215	57.16 215	66.71 219	68.00 225	54.69 222	60.91 201	49.22 210	28.63 226	41.86 195	33.68 224	44.36 214	72.98 201	75.47 206	87.69 210	75.40 214

TinyColmap		67.16 186	63.51 205	71.42 174	77.94 153	79.54 204	72.80 188	69.78 144	56.58 175	45.52 189	44.53 165	33.53 225	74.45 114	76.91 192	77.06 197	88.03 208	76.41 211

MIMVSNet1		52.76 221	53.95 220	51.38 223	41.96 239	70.79 223	53.56 223	63.03 192	39.36 225	27.83 229	22.73 234	33.07 226	34.47 228	70.49 215	72.69 212	87.41 211	68.51 224

pmmvs-eth3d		64.24 202	61.96 211	66.90 191	66.35 220	76.04 216	66.09 205	66.31 171	52.59 195	50.94 151	37.61 210	32.79 227	62.43 184	75.78 195	75.48 205	89.27 199	83.39 196

PM-MVS		63.52 207	62.51 210	64.70 199	64.79 225	76.08 215	65.07 207	62.08 195	58.13 165	46.56 184	44.98 159	31.31 228	62.89 181	72.58 205	69.93 220	86.81 213	84.55 187

new_pmnet		50.32 223	51.36 224	49.11 224	49.19 236	64.89 228	48.66 230	47.99 230	47.55 214	26.27 232	29.51 226	28.66 229	44.89 212	61.12 228	62.74 229	77.66 229	65.03 227

new-patchmatchnet		53.91 220	52.69 221	55.33 220	64.83 224	70.90 222	52.24 225	61.75 196	41.09 224	30.82 221	29.90 223	28.22 230	36.69 226	61.52 227	65.08 226	85.64 217	72.14 222

FPMVS		50.25 224	45.67 229	55.58 218	70.48 214	60.12 230	59.78 217	59.33 205	46.66 215	37.94 207	30.22 222	27.51 231	35.94 227	50.98 232	47.90 232	70.02 233	56.31 229

pmmvs3		52.59 222	52.43 223	52.78 222	54.53 233	64.49 229	50.07 227	46.89 231	35.31 229	30.19 222	27.27 227	26.96 232	53.02 206	67.28 220	70.54 217	81.96 224	75.20 215

PMVS	36.83 18	40.62 229	36.39 231	45.56 228	58.40 228	33.20 240	32.62 239	56.02 211	28.25 234	37.92 208	22.29 235	26.15 233	25.29 233	48.49 234	43.82 235	63.13 236	52.53 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testus		55.91 218	56.38 219	55.37 219	65.15 222	65.88 227	50.07 227	60.92 200	45.62 217	26.99 230	41.74 197	24.43 234	42.08 220	69.50 216	73.60 211	86.97 212	73.91 218

MDA-MVSNet-bldmvs		54.99 219	52.66 222	57.71 214	52.74 235	74.87 219	55.61 221	68.41 153	43.65 221	32.54 219	37.93 209	22.11 235	54.11 201	48.85 233	67.34 223	82.85 222	73.88 219

DeepMVS_CX						48.96 237	43.77 231	40.58 236	50.93 201	24.67 234	36.95 213	20.18 236	41.60 221	38.92 236		52.37 239	53.31 231

testmv		46.89 226	46.37 227	47.48 226	60.96 226	58.36 234	36.71 235	56.94 209	27.16 235	17.93 237	23.94 229	18.84 237	31.06 229	61.55 225	66.72 224	81.28 226	68.05 225

test1235678		46.88 227	46.36 228	47.48 226	60.96 226	58.35 235	36.71 235	56.94 209	27.15 236	17.93 237	23.93 230	18.82 238	31.06 229	61.55 225	66.71 225	81.27 227	68.04 226

test12356		41.15 228	41.46 230	40.78 229	53.10 234	49.87 236	33.37 238	52.25 221	25.12 237	15.64 239	22.76 233	15.01 239	15.81 236	52.97 230	64.54 227	74.50 232	59.96 228

PMMVS2		32.52 232	33.92 233	30.88 235	34.15 242	44.70 239	27.79 240	39.69 237	22.21 238	4.31 244	15.73 236	14.13 240	12.45 240	40.11 235	47.00 233	66.88 234	53.54 230

no-one		32.08 233	31.09 235	33.23 233	46.10 237	46.90 238	20.80 242	49.13 226	16.27 239	7.85 241	10.62 237	10.68 241	13.65 239	31.50 237	51.31 231	61.83 237	50.38 234

ambc			50.35 225		55.61 232	59.93 231	48.73 229		44.08 219	35.81 215	24.01 228	10.64 242	41.57 222	72.83 202	63.35 228	74.99 231	77.61 209

Gipuma		35.20 230	33.96 232	36.65 231	43.30 238	32.51 241	26.96 241	48.31 229	38.87 226	20.08 236	8.08 238	7.41 243	26.44 232	53.60 229	58.43 230	54.81 238	38.79 236
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		20.61 236	16.32 238	25.62 237	36.41 240	18.93 245	11.51 244	43.75 234	15.65 240	6.53 243	7.56 241	4.68 244	22.03 234	14.56 240	23.10 238	33.51 241	29.77 238

E-PMN		21.42 234	17.56 237	25.94 236	36.25 241	19.02 244	11.56 243	43.72 235	15.25 241	6.99 242	8.04 239	4.53 245	21.77 235	16.13 239	26.16 237	35.34 240	33.77 237

MVE	25.07 19	21.25 235	23.51 236	18.62 238	15.07 243	29.77 243	10.67 245	34.60 239	12.51 242	9.46 240	7.84 240	3.82 246	14.38 238	27.45 238	42.42 236	27.56 242	40.74 235
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.76 237	1.23 239	0.21 239	0.05 245	0.21 246	0.38 247	0.09 241	0.94 243	0.05 246	2.13 243	0.08 247	0.60 242	0.82 241	0.77 239	0.11 243	3.62 240

test123		0.67 238	1.11 240	0.16 240	0.01 246	0.14 247	0.20 248	0.04 243	0.77 244	0.02 247	2.15 242	0.02 248	0.61 241	0.23 242	0.72 241	0.07 245	3.76 239

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					73.80 200	79.57 203	64.47 210

Patchmatch-RL test							8.17 246

NP-MVS									89.55 42

Patchmtry						87.41 129	78.32 147	54.14 217		51.09 146

High-resolution multi-view benchmark