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.

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 105	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 178	93.37 57	60.40 183	96.75 22	77.20 108	93.73 63	95.29 2

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 60	93.00 42	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 48	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 62	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 37	76.73 67	84.45 64	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 64	84.66 61	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 39	94.10 8	75.90 84	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 78	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

X-MVStestdata		80.37 140	77.83 174	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 78	12.47 362	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 53	93.59 21	76.27 78	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 64	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 58

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 96	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 58

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 39	83.84 75	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 65
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 70	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 178	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 38	75.53 90	83.86 74	94.42 31	67.87 85	96.64 27	82.70 65	94.57 51	93.66 58

GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 60	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 48	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 63	89.69 161	74.31 114	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 28	93.49 24	74.75 106	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 45	93.04 38	76.62 69	84.22 69	93.36 58	71.44 54	96.76 21	80.82 78	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 88	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 140	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 44	92.35 73	74.62 109	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 61	82.81 90	94.25 36	66.44 97	96.24 39	82.88 60	94.28 58	93.38 72

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 58	94.05 13	70.80 173	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 68	93.94 15	77.12 54	82.82 89	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 69	83.68 77	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 78

SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 86	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 23	75.17 99	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		86.45 43	86.10 49	87.51 37	90.09 106	70.94 69	89.70 84	92.59 65	81.78 4	81.32 105	91.43 94	70.34 62	97.23 9	84.26 42	93.36 65	94.37 25

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 43	94.70 3	74.47 111	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 29	93.33 31	76.07 81	83.81 76	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 156	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

ACMMP		85.89 52	85.39 58	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 64	80.73 114	93.82 50	64.33 118	96.29 37	82.67 66	90.69 91	93.23 78
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
DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 46	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 27	94.07 9	72.45 145	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 36	92.83 54	73.01 142	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		85.76 54	85.29 62	87.17 45	93.49 48	71.08 63	88.58 117	92.42 71	68.32 225	84.61 62	93.48 54	72.32 46	96.15 45	79.00 88	95.43 30	94.28 30

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 109	91.89 96	68.69 220	85.00 50	93.10 62	74.43 27	95.41 72	84.97 29	95.71 26	93.02 88

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 54	93.56 22	73.95 122	83.16 83	91.07 103	75.94 15	95.19 82	79.94 86	94.38 56	93.55 68

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 126	71.24 62	88.43 119	92.05 86	82.50 1	86.88 32	90.09 123	74.45 26	95.61 59	84.38 39	90.63 92	94.01 41

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 110	86.84 33	94.65 18	67.31 90	95.77 56	84.80 34	92.85 69	92.84 94

DPM-MVS		84.93 68	84.29 72	86.84 50	90.20 104	73.04 22	87.12 160	93.04 38	69.80 193	82.85 88	91.22 98	73.06 41	96.02 48	76.72 115	94.63 49	91.46 137

TSAR-MVS + GP.		85.71 55	85.33 59	86.84 50	91.34 84	72.50 35	89.07 98	87.28 225	76.41 71	85.80 40	90.22 121	74.15 34	95.37 78	81.82 70	91.88 76	92.65 100

test12				86.80 52	92.63 68	70.70 76		91.79 101		82.71 91		71.67 52	96.16 44		94.50 52	93.54 69

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 86	93.39 30	77.53 43	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 37	93.19 35	77.87 34	90.32 13	94.00 46	74.83 24	93.78 139	87.63 14	94.27 59	93.65 63
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
Regformer-1		86.41 46	86.33 42	86.64 55	89.33 126	70.93 70	88.43 119	91.39 115	82.14 3	86.65 34	90.09 123	74.39 29	95.01 91	83.97 47	90.63 92	93.97 43

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 116	91.78 102	68.96 215	84.41 65	93.18 61	74.94 23	94.93 92	84.75 35	95.33 34	93.01 89

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 179	72.94 27	90.64 57	92.14 84	77.21 50	75.47 203	92.83 70	58.56 190	94.72 104	73.24 145	92.71 71	92.13 119

HPM-MVS_fast		85.35 61	84.95 67	86.57 58	93.69 43	70.58 80	92.15 31	91.62 106	73.89 125	82.67 92	94.09 42	62.60 140	95.54 64	80.93 76	92.93 67	93.57 67

Regformer-4		85.68 56	85.45 57	86.35 59	88.95 143	69.67 95	88.29 129	91.29 117	81.73 5	85.36 45	90.01 126	72.62 44	95.35 79	83.28 53	87.57 125	94.03 39

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 96	88.85 105	92.97 47	75.41 92	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

test_prior				86.33 60	92.61 69	69.59 96		92.97 47					95.48 66			93.91 45

MVS_111021_HR		85.14 65	84.75 69	86.32 62	91.65 81	72.70 29	85.98 193	90.33 143	76.11 80	82.08 95	91.61 88	71.36 55	94.17 122	81.02 75	92.58 73	92.08 120

SR-MVS-dyc-post		85.77 53	85.61 55	86.23 63	93.06 57	70.63 77	91.88 34	92.27 76	73.53 133	85.69 42	94.45 26	65.00 115	95.56 61	82.75 61	91.87 77	92.50 103

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 98	91.93 33	92.99 44	73.54 132	85.94 37	94.51 24	65.80 107	95.61 59	83.04 57	92.51 74	93.53 70

DP-MVS Recon		83.11 87	82.09 94	86.15 65	94.44 19	70.92 71	88.79 107	92.20 81	70.53 180	79.17 126	91.03 106	64.12 120	96.03 47	68.39 186	90.14 99	91.50 134

EPNet		83.72 75	82.92 83	86.14 66	84.22 245	69.48 99	91.05 51	85.27 250	81.30 7	76.83 173	91.65 85	66.09 102	95.56 61	76.00 120	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 91	91.79 38	92.28 75	75.07 100	86.40 35	94.58 20	65.00 115	95.56 61	84.34 41	92.60 72	92.90 92

abl_6		85.23 63	84.95 67	86.07 68	92.23 74	70.48 81	90.80 55	92.08 85	73.51 135	85.26 46	94.16 39	62.75 139	95.92 53	82.46 68	91.30 86	91.81 127

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 112	69.44 103	90.45 66	93.00 42	76.70 68	88.01 25	91.23 97	73.28 38	93.91 134	81.50 72	88.80 113	94.77 14

hse-mvs3		83.15 84	82.19 92	86.02 70	90.56 97	70.85 73	88.15 136	89.16 177	76.02 82	84.67 59	91.39 95	61.54 158	95.50 65	82.71 63	75.48 270	91.72 129

alignmvs		85.48 57	85.32 60	85.96 71	89.51 119	69.47 100	89.74 82	92.47 67	76.17 79	87.73 28	91.46 93	70.32 63	93.78 139	81.51 71	88.95 110	94.63 18

DELS-MVS		85.41 60	85.30 61	85.77 72	88.49 161	67.93 134	85.52 210	93.44 27	78.70 28	83.63 80	89.03 153	74.57 25	95.71 58	80.26 84	94.04 61	93.66 58
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
Regformer-3		85.23 63	85.07 64	85.70 73	88.95 143	69.01 107	88.29 129	89.91 155	80.95 8	85.01 49	90.01 126	72.45 45	94.19 120	82.50 67	87.57 125	93.90 47

ETV-MVS		84.90 70	84.67 70	85.59 74	89.39 124	68.66 121	88.74 111	92.64 64	79.97 17	84.10 71	85.71 240	69.32 74	95.38 75	80.82 78	91.37 84	92.72 95

UA-Net		85.08 67	84.96 66	85.45 75	92.07 76	68.07 132	89.78 81	90.86 130	82.48 2	84.60 63	93.20 60	69.35 73	95.22 81	71.39 158	90.88 90	93.07 85

Vis-MVSNet		83.46 79	82.80 85	85.43 76	90.25 103	68.74 115	90.30 69	90.13 149	76.33 77	80.87 113	92.89 68	61.00 172	94.20 119	72.45 152	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 171	67.85 135	87.66 147	89.73 160	80.05 16	82.95 85	89.59 137	70.74 60	94.82 100	80.66 81	84.72 161	93.28 77

mvs-test1		80.88 121	79.40 139	85.29 78	85.13 233	69.75 94	89.28 89	88.10 206	74.99 101	76.44 184	86.72 212	57.27 202	94.26 118	73.53 138	83.18 181	91.87 124

MAR-MVS		81.84 104	80.70 114	85.27 79	91.32 85	71.53 57	89.82 78	90.92 127	69.77 194	78.50 138	86.21 232	62.36 146	94.52 108	65.36 211	92.05 75	89.77 202
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+		83.62 77	83.08 79	85.24 80	88.38 166	67.45 142	88.89 103	89.15 178	75.50 91	82.27 93	88.28 172	69.61 71	94.45 110	77.81 102	87.84 123	93.84 51

MVSFormer		82.85 90	82.05 96	85.24 80	87.35 195	70.21 83	90.50 61	90.38 139	68.55 222	81.32 105	89.47 140	61.68 155	93.46 157	78.98 89	90.26 97	92.05 121

OPM-MVS		83.50 78	82.95 82	85.14 82	88.79 151	70.95 68	89.13 97	91.52 109	77.55 42	80.96 112	91.75 83	60.71 175	94.50 109	79.67 87	86.51 144	89.97 194

HQP_MVS		83.64 76	83.14 78	85.14 82	90.08 107	68.71 117	91.25 46	92.44 68	79.12 23	78.92 130	91.00 107	60.42 181	95.38 75	78.71 91	86.32 146	91.33 139

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 84	87.87 180	67.53 141	87.44 153	89.66 162	79.74 18	82.23 94	89.41 146	70.24 64	94.74 103	79.95 85	83.92 169	92.99 90

QAPM		80.88 121	79.50 137	85.03 85	88.01 178	68.97 109	91.59 39	92.00 90	66.63 240	75.15 217	92.16 76	57.70 196	95.45 68	63.52 221	88.76 114	90.66 160

casdiffmvs		85.11 66	85.14 63	85.01 86	87.20 202	65.77 172	87.75 145	92.83 54	77.84 35	84.36 68	92.38 74	72.15 48	93.93 133	81.27 74	90.48 94	95.33 1

PCF-MVS	73.52 7	80.38 139	78.84 151	85.01 86	87.71 187	68.99 108	83.65 246	91.46 114	63.00 280	77.77 155	90.28 118	66.10 101	95.09 89	61.40 243	88.22 122	90.94 152
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
nrg030		83.88 72	83.53 74	84.96 88	86.77 210	69.28 104	90.46 65	92.67 60	74.79 104	82.95 85	91.33 96	72.70 43	93.09 174	80.79 80	79.28 227	92.50 103

VDD-MVS		83.01 89	82.36 90	84.96 88	91.02 89	66.40 159	88.91 102	88.11 205	77.57 39	84.39 67	93.29 59	52.19 238	93.91 134	77.05 110	88.70 115	94.57 21

PVSNet_Blended_VisFu		82.62 93	81.83 101	84.96 88	90.80 94	69.76 93	88.74 111	91.70 105	69.39 200	78.96 128	88.46 167	65.47 109	94.87 99	74.42 130	88.57 116	90.24 176

CPTT-MVS		83.73 74	83.33 77	84.92 91	93.28 50	70.86 72	92.09 32	90.38 139	68.75 219	79.57 122	92.83 70	60.60 179	93.04 178	80.92 77	91.56 82	90.86 154

test_part1		82.78 91	82.08 95	84.89 92	90.66 95	66.97 153	90.96 52	92.93 50	77.19 51	80.53 116	90.04 125	63.44 125	95.39 74	76.04 119	76.90 247	92.31 110

OMC-MVS		82.69 92	81.97 99	84.85 93	88.75 153	67.42 143	87.98 138	90.87 129	74.92 103	79.72 121	91.65 85	62.19 150	93.96 127	75.26 127	86.42 145	93.16 83

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 115	65.59 174	88.21 132	92.68 59	74.66 108	78.96 128	86.42 228	69.06 77	95.26 80	75.54 125	90.09 100	93.62 65

PAPM_NR		83.02 88	82.41 88	84.82 94	92.47 72	66.37 160	87.93 142	91.80 100	73.82 126	77.32 163	90.66 112	67.90 84	94.90 96	70.37 166	89.48 107	93.19 82

baseline		84.93 68	84.98 65	84.80 96	87.30 200	65.39 179	87.30 156	92.88 51	77.62 37	84.04 73	92.26 75	71.81 50	93.96 127	81.31 73	90.30 96	95.03 4

lupinMVS		81.39 115	80.27 124	84.76 97	87.35 195	70.21 83	85.55 206	86.41 237	62.85 283	81.32 105	88.61 162	61.68 155	92.24 201	78.41 97	90.26 97	91.83 125

jason		81.39 115	80.29 123	84.70 98	86.63 212	69.90 90	85.95 194	86.77 233	63.24 276	81.07 111	89.47 140	61.08 171	92.15 204	78.33 98	90.07 102	92.05 121
jason: jason.
ET-MVSNet_ETH3D		78.63 177	76.63 206	84.64 99	86.73 211	69.47 100	85.01 216	84.61 257	69.54 198	66.51 306	86.59 221	50.16 264	91.75 216	76.26 116	84.24 167	92.69 98

EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 105	64.47 196	92.32 26	90.73 131	74.45 113	79.35 125	91.10 101	69.05 78	95.12 84	72.78 149	87.22 133	94.13 34

UGNet		80.83 125	79.59 135	84.54 101	88.04 176	68.09 131	89.42 87	88.16 204	76.95 58	76.22 188	89.46 142	49.30 275	93.94 130	68.48 184	90.31 95	91.60 130
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
LPG-MVS_test		82.08 99	81.27 105	84.50 102	89.23 134	68.76 113	90.22 71	91.94 94	75.37 94	76.64 179	91.51 90	54.29 221	94.91 94	78.44 95	83.78 170	89.83 199

LGP-MVS_train				84.50 102	89.23 134	68.76 113		91.94 94	75.37 94	76.64 179	91.51 90	54.29 221	94.91 94	78.44 95	83.78 170	89.83 199

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 82	90.71 56	92.86 52	77.46 45	84.22 69	92.81 72	67.16 92	92.94 180	80.36 82	94.35 57	90.16 178

Effi-MVS+-dtu		80.03 146	78.57 155	84.42 105	85.13 233	68.74 115	88.77 108	88.10 206	74.99 101	74.97 222	83.49 276	57.27 202	93.36 160	73.53 138	80.88 205	91.18 143

RRT_MVS		79.88 149	78.38 160	84.38 106	85.42 227	70.60 79	88.71 113	88.75 196	72.30 150	78.83 132	89.14 148	44.44 305	92.18 203	78.50 94	79.33 226	90.35 172

HQP-MVS		82.61 94	82.02 97	84.37 107	89.33 126	66.98 151	89.17 92	92.19 82	76.41 71	77.23 166	90.23 120	60.17 184	95.11 85	77.47 105	85.99 152	91.03 148

ACMP	74.13 6	81.51 114	80.57 116	84.36 108	89.42 122	68.69 120	89.97 76	91.50 113	74.46 112	75.04 221	90.41 117	53.82 226	94.54 106	77.56 104	82.91 184	89.86 198
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
原ACMM1				84.35 109	93.01 59	68.79 111		92.44 68	63.96 274	81.09 110	91.57 89	66.06 103	95.45 68	67.19 197	94.82 47	88.81 231

PS-MVSNAJss		82.07 100	81.31 104	84.34 110	86.51 213	67.27 147	89.27 90	91.51 110	71.75 157	79.37 124	90.22 121	63.15 133	94.27 114	77.69 103	82.36 192	91.49 135

thisisatest0530		79.40 159	77.76 178	84.31 111	87.69 189	65.10 186	87.36 154	84.26 264	70.04 187	77.42 160	88.26 174	49.94 267	94.79 102	70.20 167	84.70 162	93.03 87

CS-MVS		85.32 62	85.66 54	84.30 112	88.28 169	65.31 181	91.18 49	93.48 26	78.06 33	83.14 84	90.53 115	69.93 67	95.45 68	82.96 58	93.40 64	92.15 118

CLD-MVS		82.31 96	81.65 102	84.29 113	88.47 162	67.73 138	85.81 201	92.35 73	75.78 85	78.33 143	86.58 223	64.01 121	94.35 111	76.05 118	87.48 130	90.79 155
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 102	81.23 106	84.26 114	90.94 90	70.18 88	91.10 50	89.32 169	71.51 163	78.66 135	88.28 172	65.26 110	95.10 88	64.74 217	91.23 87	87.51 258

114514_t		80.68 132	79.51 136	84.20 115	94.09 36	67.27 147	89.64 85	91.11 124	58.75 317	74.08 231	90.72 111	58.10 192	95.04 90	69.70 174	89.42 108	90.30 174

IS-MVSNet		83.15 84	82.81 84	84.18 116	89.94 110	63.30 221	91.59 39	88.46 202	79.04 25	79.49 123	92.16 76	65.10 112	94.28 113	67.71 189	91.86 79	94.95 5

MVS_111021_LR		82.61 94	82.11 93	84.11 117	88.82 148	71.58 56	85.15 213	86.16 242	74.69 107	80.47 117	91.04 104	62.29 147	90.55 246	80.33 83	90.08 101	90.20 177

Anonymous20240529		80.19 144	78.89 150	84.10 118	90.60 96	64.75 190	88.95 101	90.90 128	65.97 248	80.59 115	91.17 100	49.97 266	93.73 145	69.16 180	82.70 189	93.81 53

OpenMVS	72.83 10	79.77 150	78.33 163	84.09 119	85.17 230	69.91 89	90.57 59	90.97 126	66.70 236	72.17 249	91.91 80	54.70 218	93.96 127	61.81 240	90.95 89	88.41 242

hse-mvs2		81.72 106	80.94 112	84.07 120	88.72 154	67.68 139	85.87 197	87.26 226	76.02 82	84.67 59	88.22 175	61.54 158	93.48 155	82.71 63	73.44 297	91.06 146

AdaColmap		80.58 136	79.42 138	84.06 121	93.09 56	68.91 110	89.36 88	88.97 187	69.27 203	75.70 200	89.69 132	57.20 204	95.77 56	63.06 227	88.41 120	87.50 259

AUN-MVS		79.21 164	77.60 183	84.05 122	88.71 155	67.61 140	85.84 199	87.26 226	69.08 210	77.23 166	88.14 180	53.20 231	93.47 156	75.50 126	73.45 296	91.06 146

1121		80.84 123	79.77 130	84.05 122	93.11 55	70.78 74	84.66 223	85.42 249	57.37 326	81.76 103	92.02 78	63.41 126	94.12 123	67.28 194	92.93 67	87.26 265

VDDNet		81.52 112	80.67 115	84.05 122	90.44 100	64.13 203	89.73 83	85.91 245	71.11 168	83.18 82	93.48 54	50.54 261	93.49 154	73.40 142	88.25 121	94.54 22

xiu_mvs_v1_base_debu		80.80 128	79.72 132	84.03 125	87.35 195	70.19 85	85.56 203	88.77 192	69.06 211	81.83 97	88.16 176	50.91 255	92.85 182	78.29 99	87.56 127	89.06 216

xiu_mvs_v1_base		80.80 128	79.72 132	84.03 125	87.35 195	70.19 85	85.56 203	88.77 192	69.06 211	81.83 97	88.16 176	50.91 255	92.85 182	78.29 99	87.56 127	89.06 216

xiu_mvs_v1_base_debi		80.80 128	79.72 132	84.03 125	87.35 195	70.19 85	85.56 203	88.77 192	69.06 211	81.83 97	88.16 176	50.91 255	92.85 182	78.29 99	87.56 127	89.06 216

PAPR		81.66 110	80.89 113	83.99 128	90.27 102	64.00 204	86.76 174	91.77 104	68.84 218	77.13 171	89.50 138	67.63 86	94.88 98	67.55 191	88.52 118	93.09 84

XVG-OURS		80.41 138	79.23 144	83.97 129	85.64 223	69.02 106	83.03 259	90.39 138	71.09 169	77.63 157	91.49 92	54.62 220	91.35 227	75.71 121	83.47 177	91.54 132

XVG-OURS-SEG-HR		80.81 126	79.76 131	83.96 130	85.60 224	68.78 112	83.54 251	90.50 136	70.66 178	76.71 177	91.66 84	60.69 176	91.26 229	76.94 112	81.58 199	91.83 125

HyFIR lowres test		77.53 205	75.40 220	83.94 131	89.59 115	66.62 156	80.36 282	88.64 199	56.29 332	76.45 181	85.17 253	57.64 197	93.28 162	61.34 245	83.10 183	91.91 123

tttt0517		79.40 159	77.91 171	83.90 132	88.10 174	63.84 207	88.37 126	84.05 266	71.45 164	76.78 175	89.12 150	49.93 269	94.89 97	70.18 168	83.18 181	92.96 91

GeoE		81.71 107	81.01 111	83.80 133	89.51 119	64.45 197	88.97 100	88.73 197	71.27 166	78.63 136	89.76 131	66.32 99	93.20 166	69.89 172	86.02 151	93.74 56

PS-MVSNAJ		81.69 108	81.02 110	83.70 134	89.51 119	68.21 130	84.28 237	90.09 150	70.79 174	81.26 109	85.62 244	63.15 133	94.29 112	75.62 123	88.87 112	88.59 237

xiu_mvs_v2_base		81.69 108	81.05 109	83.60 135	89.15 137	68.03 133	84.46 231	90.02 151	70.67 177	81.30 108	86.53 226	63.17 132	94.19 120	75.60 124	88.54 117	88.57 238

ACMM	73.20 8	80.78 131	79.84 129	83.58 136	89.31 131	68.37 125	89.99 75	91.60 107	70.28 184	77.25 164	89.66 133	53.37 229	93.53 153	74.24 133	82.85 185	88.85 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		81.82 105	81.23 106	83.57 137	91.89 79	63.43 219	89.84 77	81.85 296	77.04 57	83.21 81	93.10 62	52.26 237	93.43 159	71.98 153	89.95 103	93.85 49

Fast-Effi-MVS+		80.81 126	79.92 127	83.47 138	88.85 145	64.51 193	85.53 208	89.39 167	70.79 174	78.49 139	85.06 256	67.54 87	93.58 148	67.03 200	86.58 142	92.32 109

CHOSEN 1792x2688		77.63 204	75.69 213	83.44 139	89.98 109	68.58 123	78.70 300	87.50 221	56.38 331	75.80 199	86.84 208	58.67 189	91.40 226	61.58 242	85.75 155	90.34 173

新几何1				83.42 140	93.13 53	70.71 75		85.48 248	57.43 325	81.80 100	91.98 79	63.28 128	92.27 198	64.60 218	92.99 66	87.27 264

DP-MVS		76.78 217	74.57 229	83.42 140	93.29 49	69.46 102	88.55 118	83.70 270	63.98 273	70.20 266	88.89 155	54.01 225	94.80 101	46.66 329	81.88 197	86.01 291

MVS_Test		83.15 84	83.06 80	83.41 142	86.86 206	63.21 223	86.11 191	92.00 90	74.31 114	82.87 87	89.44 145	70.03 65	93.21 164	77.39 107	88.50 119	93.81 53

LS3D		76.95 215	74.82 227	83.37 143	90.45 99	67.36 146	89.15 96	86.94 231	61.87 293	69.52 278	90.61 113	51.71 249	94.53 107	46.38 332	86.71 141	88.21 244

IB-MVS	68.01 15	75.85 231	73.36 243	83.31 144	84.76 237	66.03 163	83.38 252	85.06 252	70.21 186	69.40 279	81.05 300	45.76 298	94.66 105	65.10 214	75.49 269	89.25 213
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
MG-MVS		83.41 80	83.45 75	83.28 145	92.74 66	62.28 237	88.17 134	89.50 165	75.22 97	81.49 104	92.74 73	66.75 93	95.11 85	72.85 148	91.58 81	92.45 106

jajsoiax		79.29 162	77.96 169	83.27 146	84.68 239	66.57 158	89.25 91	90.16 148	69.20 207	75.46 205	89.49 139	45.75 299	93.13 172	76.84 113	80.80 207	90.11 182

test_djsdf		80.30 141	79.32 142	83.27 146	83.98 250	65.37 180	90.50 61	90.38 139	68.55 222	76.19 189	88.70 158	56.44 208	93.46 157	78.98 89	80.14 217	90.97 151

test_yl		81.17 117	80.47 119	83.24 148	89.13 138	63.62 210	86.21 188	89.95 153	72.43 148	81.78 101	89.61 135	57.50 199	93.58 148	70.75 161	86.90 137	92.52 101

DCV-MVSNet		81.17 117	80.47 119	83.24 148	89.13 138	63.62 210	86.21 188	89.95 153	72.43 148	81.78 101	89.61 135	57.50 199	93.58 148	70.75 161	86.90 137	92.52 101

mvs_tets		79.13 166	77.77 177	83.22 150	84.70 238	66.37 160	89.17 92	90.19 147	69.38 201	75.40 208	89.46 142	44.17 307	93.15 170	76.78 114	80.70 209	90.14 179

thisisatest0515		77.33 209	75.38 221	83.18 151	85.27 229	63.80 208	82.11 266	83.27 279	65.06 257	75.91 195	83.84 270	49.54 271	94.27 114	67.24 196	86.19 148	91.48 136

CDS-MVSNet		79.07 168	77.70 180	83.17 152	87.60 190	68.23 129	84.40 235	86.20 241	67.49 230	76.36 185	86.54 225	61.54 158	90.79 242	61.86 239	87.33 131	90.49 167
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7n		78.97 171	77.58 184	83.14 153	83.45 258	65.51 175	88.32 127	91.21 120	73.69 128	72.41 246	86.32 231	57.93 193	93.81 138	69.18 179	75.65 266	90.11 182

BH-RMVSNet		79.61 152	78.44 158	83.14 153	89.38 125	65.93 167	84.95 218	87.15 228	73.56 131	78.19 146	89.79 130	56.67 207	93.36 160	59.53 257	86.74 140	90.13 180

UniMVSNet (Re)		81.60 111	81.11 108	83.09 155	88.38 166	64.41 198	87.60 148	93.02 41	78.42 31	78.56 137	88.16 176	69.78 69	93.26 163	69.58 176	76.49 254	91.60 130

PLC	70.83 11	78.05 193	76.37 210	83.08 156	91.88 80	67.80 136	88.19 133	89.46 166	64.33 267	69.87 275	88.38 169	53.66 227	93.58 148	58.86 264	82.73 187	87.86 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1192		79.59 153	78.43 159	83.07 157	83.55 257	64.52 192	86.93 166	90.58 134	70.83 172	77.78 154	85.90 236	59.15 187	93.94 130	73.96 135	77.19 244	90.76 156

v2v482		80.23 142	79.29 143	83.05 158	83.62 255	64.14 202	87.04 162	89.97 152	73.61 129	78.18 147	87.22 200	61.10 170	93.82 137	76.11 117	76.78 252	91.18 143

TAMVS		78.89 173	77.51 185	83.03 159	87.80 183	67.79 137	84.72 222	85.05 253	67.63 227	76.75 176	87.70 185	62.25 148	90.82 241	58.53 268	87.13 134	90.49 167

v1144		80.03 146	79.03 147	83.01 160	83.78 253	64.51 193	87.11 161	90.57 135	71.96 155	78.08 150	86.20 233	61.41 162	93.94 130	74.93 128	77.23 242	90.60 163

cascas		76.72 218	74.64 228	82.99 161	85.78 221	65.88 169	82.33 264	89.21 175	60.85 299	72.74 241	81.02 301	47.28 286	93.75 143	67.48 192	85.02 157	89.34 211

anonymousdsp		78.60 178	77.15 191	82.98 162	80.51 312	67.08 149	87.24 158	89.53 164	65.66 251	75.16 216	87.19 202	52.52 232	92.25 200	77.17 109	79.34 225	89.61 206

v10		79.74 151	78.67 152	82.97 163	84.06 248	64.95 187	87.88 144	90.62 133	73.11 139	75.11 218	86.56 224	61.46 161	94.05 126	73.68 136	75.55 268	89.90 196

UniMVSNet_NR-MVSNet		81.88 103	81.54 103	82.92 164	88.46 163	63.46 217	87.13 159	92.37 72	80.19 14	78.38 141	89.14 148	71.66 53	93.05 176	70.05 169	76.46 255	92.25 113

DU-MVS		81.12 119	80.52 118	82.90 165	87.80 183	63.46 217	87.02 163	91.87 98	79.01 26	78.38 141	89.07 151	65.02 113	93.05 176	70.05 169	76.46 255	92.20 115

PVSNet_Blended		80.98 120	80.34 121	82.90 165	88.85 145	65.40 177	84.43 233	92.00 90	67.62 228	78.11 148	85.05 257	66.02 104	94.27 114	71.52 155	89.50 106	89.01 221

CANet_DTU		80.61 133	79.87 128	82.83 167	85.60 224	63.17 226	87.36 154	88.65 198	76.37 75	75.88 197	88.44 168	53.51 228	93.07 175	73.30 143	89.74 105	92.25 113

V42		79.38 161	78.24 165	82.83 167	81.10 306	65.50 176	85.55 206	89.82 156	71.57 162	78.21 145	86.12 234	60.66 177	93.18 169	75.64 122	75.46 272	89.81 201

Anonymous20231211		78.97 171	77.69 181	82.81 169	90.54 98	64.29 200	90.11 73	91.51 110	65.01 259	76.16 193	88.13 181	50.56 260	93.03 179	69.68 175	77.56 240	91.11 145

v1921920		79.22 163	78.03 168	82.80 170	83.30 261	63.94 206	86.80 170	90.33 143	69.91 191	77.48 159	85.53 245	58.44 191	93.75 143	73.60 137	76.85 250	90.71 159

v8		79.97 148	79.02 148	82.80 170	84.09 247	64.50 195	87.96 139	90.29 146	74.13 121	75.24 215	86.81 209	62.88 138	93.89 136	74.39 131	75.40 274	90.00 190

TAPA-MVS	73.13 9	79.15 165	77.94 170	82.79 172	89.59 115	62.99 230	88.16 135	91.51 110	65.77 249	77.14 170	91.09 102	60.91 173	93.21 164	50.26 312	87.05 135	92.17 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v144192		79.47 156	78.37 161	82.78 173	83.35 259	63.96 205	86.96 164	90.36 142	69.99 188	77.50 158	85.67 242	60.66 177	93.77 141	74.27 132	76.58 253	90.62 161

NR-MVSNet		80.23 142	79.38 140	82.78 173	87.80 183	63.34 220	86.31 185	91.09 125	79.01 26	72.17 249	89.07 151	67.20 91	92.81 185	66.08 206	75.65 266	92.20 115

diffmvs		82.10 98	81.88 100	82.76 175	83.00 271	63.78 209	83.68 245	89.76 158	72.94 143	82.02 96	89.85 129	65.96 106	90.79 242	82.38 69	87.30 132	93.71 57

v1240		78.99 170	77.78 176	82.64 176	83.21 263	63.54 214	86.62 177	90.30 145	69.74 197	77.33 162	85.68 241	57.04 205	93.76 142	73.13 146	76.92 246	90.62 161

Fast-Effi-MVS+-dtu		78.02 194	76.49 207	82.62 177	83.16 267	66.96 154	86.94 165	87.45 223	72.45 145	71.49 256	84.17 265	54.79 217	91.58 220	67.61 190	80.31 214	89.30 212

RPMNet		73.51 252	70.49 268	82.58 178	81.32 303	65.19 183	75.92 316	92.27 76	57.60 324	72.73 242	76.45 335	52.30 236	95.43 71	48.14 324	77.71 237	87.11 271

F-COLMAP		76.38 225	74.33 234	82.50 179	89.28 132	66.95 155	88.41 122	89.03 182	64.05 271	66.83 300	88.61 162	46.78 289	92.89 181	57.48 276	78.55 229	87.67 253

TranMVSNet+NR-MVSNet		80.84 123	80.31 122	82.42 180	87.85 181	62.33 235	87.74 146	91.33 116	80.55 11	77.99 151	89.86 128	65.23 111	92.62 186	67.05 199	75.24 280	92.30 111

MVSTER		79.01 169	77.88 173	82.38 181	83.07 268	64.80 189	84.08 242	88.95 188	69.01 214	78.69 133	87.17 203	54.70 218	92.43 192	74.69 129	80.57 211	89.89 197

PVSNet_BlendedMVS		80.60 134	80.02 125	82.36 182	88.85 145	65.40 177	86.16 190	92.00 90	69.34 202	78.11 148	86.09 235	66.02 104	94.27 114	71.52 155	82.06 194	87.39 260

EI-MVSNet		80.52 137	79.98 126	82.12 183	84.28 243	63.19 225	86.41 182	88.95 188	74.18 119	78.69 133	87.54 191	66.62 94	92.43 192	72.57 151	80.57 211	90.74 158

IterMVS-LS		80.06 145	79.38 140	82.11 184	85.89 219	63.20 224	86.79 171	89.34 168	74.19 118	75.45 206	86.72 212	66.62 94	92.39 194	72.58 150	76.86 249	90.75 157
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
bset_n11_16_dypcd		77.12 211	75.47 217	82.06 185	81.12 305	65.99 165	81.37 275	83.20 282	69.94 190	76.09 194	83.38 278	47.75 283	92.26 199	78.51 93	77.91 236	87.95 246

BH-untuned		79.47 156	78.60 154	82.05 186	89.19 136	65.91 168	86.07 192	88.52 201	72.18 151	75.42 207	87.69 186	61.15 169	93.54 152	60.38 250	86.83 139	86.70 279

ACMH+	68.96 14	76.01 229	74.01 236	82.03 187	88.60 158	65.31 181	88.86 104	87.55 219	70.25 185	67.75 289	87.47 193	41.27 322	93.19 168	58.37 269	75.94 263	87.60 255

Anonymous202405211		78.25 185	77.01 193	81.99 188	91.03 88	60.67 255	84.77 221	83.90 268	70.65 179	80.00 119	91.20 99	41.08 324	91.43 225	65.21 212	85.26 156	93.85 49

GA-MVS		76.87 216	75.17 225	81.97 189	82.75 277	62.58 232	81.44 274	86.35 240	72.16 153	74.74 225	82.89 282	46.20 294	92.02 208	68.85 183	81.09 203	91.30 141

CNLPA		78.08 191	76.79 200	81.97 189	90.40 101	71.07 64	87.59 149	84.55 258	66.03 247	72.38 247	89.64 134	57.56 198	86.04 297	59.61 256	83.35 178	88.79 232

MVS		78.19 189	76.99 195	81.78 191	85.66 222	66.99 150	84.66 223	90.47 137	55.08 336	72.02 251	85.27 250	63.83 123	94.11 125	66.10 205	89.80 104	84.24 310

ACMH	67.68 16	75.89 230	73.93 237	81.77 192	88.71 155	66.61 157	88.62 115	89.01 184	69.81 192	66.78 301	86.70 217	41.95 321	91.51 224	55.64 288	78.14 235	87.17 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		79.10 167	78.24 165	81.70 193	86.85 207	60.24 261	87.28 157	88.79 191	74.25 117	76.84 172	90.53 115	49.48 272	91.56 221	67.98 187	82.15 193	93.29 76

VNet		82.21 97	82.41 88	81.62 194	90.82 93	60.93 251	84.47 229	89.78 157	76.36 76	84.07 72	91.88 82	64.71 117	90.26 248	70.68 163	88.89 111	93.66 58

XVG-ACMP-BASELINE		76.11 228	74.27 235	81.62 194	83.20 264	64.67 191	83.60 249	89.75 159	69.75 195	71.85 252	87.09 205	32.78 346	92.11 205	69.99 171	80.43 213	88.09 245

eth_miper_zixun_eth		77.92 197	76.69 204	81.61 196	83.00 271	61.98 240	83.15 255	89.20 176	69.52 199	74.86 224	84.35 263	61.76 154	92.56 189	71.50 157	72.89 301	90.28 175

PAPM		77.68 203	76.40 209	81.51 197	87.29 201	61.85 242	83.78 244	89.59 163	64.74 261	71.23 257	88.70 158	62.59 141	93.66 147	52.66 299	87.03 136	89.01 221

v148		78.72 175	77.80 175	81.47 198	82.73 278	61.96 241	86.30 186	88.08 208	73.26 138	76.18 190	85.47 247	62.46 144	92.36 196	71.92 154	73.82 293	90.09 184

LTVRE_ROB	69.57 13	76.25 226	74.54 231	81.41 199	88.60 158	64.38 199	79.24 293	89.12 181	70.76 176	69.79 277	87.86 183	49.09 277	93.20 166	56.21 287	80.16 215	86.65 280
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
GBi-Net		78.40 181	77.40 186	81.40 200	87.60 190	63.01 227	88.39 123	89.28 170	71.63 159	75.34 210	87.28 196	54.80 214	91.11 232	62.72 228	79.57 220	90.09 184

test1		78.40 181	77.40 186	81.40 200	87.60 190	63.01 227	88.39 123	89.28 170	71.63 159	75.34 210	87.28 196	54.80 214	91.11 232	62.72 228	79.57 220	90.09 184

FMVSNet1		77.44 206	76.12 212	81.40 200	86.81 209	63.01 227	88.39 123	89.28 170	70.49 181	74.39 228	87.28 196	49.06 278	91.11 232	60.91 247	78.52 230	90.09 184

RRT_test8_iter05		78.38 183	77.40 186	81.34 203	86.00 218	58.86 270	86.55 180	91.26 118	72.13 154	75.91 195	87.42 194	44.97 302	93.73 145	77.02 111	75.30 277	91.45 138

baseline2		75.70 232	73.83 240	81.30 204	83.26 262	61.79 244	82.57 262	80.65 305	66.81 233	66.88 298	83.42 277	57.86 195	92.19 202	63.47 222	79.57 220	89.91 195

cl_fuxian		78.75 174	77.91 171	81.26 205	82.89 275	61.56 246	84.09 241	89.13 180	69.97 189	75.56 201	84.29 264	66.36 98	92.09 206	73.47 141	75.48 270	90.12 181

cl-mvsnet2		78.07 192	77.01 193	81.23 206	82.37 287	61.83 243	83.55 250	87.98 210	68.96 215	75.06 220	83.87 268	61.40 163	91.88 214	73.53 138	76.39 257	89.98 193

FMVSNet2		78.20 188	77.21 190	81.20 207	87.60 190	62.89 231	87.47 152	89.02 183	71.63 159	75.29 214	87.28 196	54.80 214	91.10 235	62.38 232	79.38 224	89.61 206

TR-MVS		77.44 206	76.18 211	81.20 207	88.24 170	63.24 222	84.61 227	86.40 238	67.55 229	77.81 153	86.48 227	54.10 223	93.15 170	57.75 275	82.72 188	87.20 266

ab-mvs		79.51 154	78.97 149	81.14 209	88.46 163	60.91 252	83.84 243	89.24 174	70.36 182	79.03 127	88.87 156	63.23 131	90.21 250	65.12 213	82.57 190	92.28 112

MVP-Stereo		76.12 227	74.46 233	81.13 210	85.37 228	69.79 92	84.42 234	87.95 211	65.03 258	67.46 292	85.33 249	53.28 230	91.73 218	58.01 273	83.27 179	81.85 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
miper_ehance_all_eth		78.59 179	77.76 178	81.08 211	82.66 280	61.56 246	83.65 246	89.15 178	68.87 217	75.55 202	83.79 272	66.49 96	92.03 207	73.25 144	76.39 257	89.64 205

FIs		82.07 100	82.42 87	81.04 212	88.80 150	58.34 275	88.26 131	93.49 24	76.93 59	78.47 140	91.04 104	69.92 68	92.34 197	69.87 173	84.97 158	92.44 107

FMVSNet3		77.88 198	76.85 198	80.97 213	86.84 208	62.36 234	86.52 181	88.77 192	71.13 167	75.34 210	86.66 219	54.07 224	91.10 235	62.72 228	79.57 220	89.45 209

miper_enhance_ethall		77.87 199	76.86 197	80.92 214	81.65 294	61.38 248	82.68 260	88.98 185	65.52 253	75.47 203	82.30 290	65.76 108	92.00 209	72.95 147	76.39 257	89.39 210

BH-w/o		78.21 187	77.33 189	80.84 215	88.81 149	65.13 185	84.87 219	87.85 215	69.75 195	74.52 227	84.74 260	61.34 164	93.11 173	58.24 271	85.84 154	84.27 309

COLMAP_ROB	66.92 17	73.01 259	70.41 270	80.81 216	87.13 204	65.63 173	88.30 128	84.19 265	62.96 281	63.80 324	87.69 186	38.04 334	92.56 189	46.66 329	74.91 282	84.24 310
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		80.60 134	80.55 117	80.76 217	88.07 175	60.80 254	86.86 168	91.58 108	75.67 89	80.24 118	89.45 144	63.34 127	90.25 249	70.51 165	79.22 228	91.23 142

EG-PatchMatch MVS		74.04 247	71.82 256	80.71 218	84.92 236	67.42 143	85.86 198	88.08 208	66.04 246	64.22 320	83.85 269	35.10 342	92.56 189	57.44 277	80.83 206	82.16 328

cl-mvsnet____		77.72 201	76.76 201	80.58 219	82.49 284	60.48 258	83.09 256	87.87 213	69.22 205	74.38 229	85.22 252	62.10 151	91.53 222	71.09 159	75.41 273	89.73 204

cl-mvsnet1		77.72 201	76.76 201	80.58 219	82.48 285	60.48 258	83.09 256	87.86 214	69.22 205	74.38 229	85.24 251	62.10 151	91.53 222	71.09 159	75.40 274	89.74 203

MSDG		73.36 255	70.99 264	80.49 221	84.51 241	65.80 170	80.71 279	86.13 243	65.70 250	65.46 311	83.74 273	44.60 303	90.91 240	51.13 305	76.89 248	84.74 305

pmmvs4		74.03 248	71.91 254	80.39 222	81.96 291	68.32 126	81.45 273	82.14 292	59.32 311	69.87 275	85.13 254	52.40 235	88.13 282	60.21 252	74.74 284	84.73 306

HY-MVS	69.67 12	77.95 196	77.15 191	80.36 223	87.57 194	60.21 262	83.37 253	87.78 216	66.11 244	75.37 209	87.06 207	63.27 129	90.48 247	61.38 244	82.43 191	90.40 171

mvs_anonymous		79.42 158	79.11 146	80.34 224	84.45 242	57.97 281	82.59 261	87.62 218	67.40 231	76.17 192	88.56 165	68.47 80	89.59 259	70.65 164	86.05 150	93.47 71

1112_ss		77.40 208	76.43 208	80.32 225	89.11 142	60.41 260	83.65 246	87.72 217	62.13 291	73.05 239	86.72 212	62.58 142	89.97 253	62.11 237	80.80 207	90.59 164

WR-MVS		79.49 155	79.22 145	80.27 226	88.79 151	58.35 274	85.06 215	88.61 200	78.56 29	77.65 156	88.34 170	63.81 124	90.66 245	64.98 215	77.22 243	91.80 128

1314		76.53 219	75.30 224	80.21 227	83.93 251	62.32 236	84.66 223	88.81 190	60.23 303	70.16 269	84.07 267	55.30 212	90.73 244	67.37 193	83.21 180	87.59 257

IterMVS-SCA-FT		75.43 236	73.87 239	80.11 228	82.69 279	64.85 188	81.57 272	83.47 276	69.16 208	70.49 263	84.15 266	51.95 244	88.15 281	69.23 178	72.14 306	87.34 262

FC-MVSNet-test		81.52 112	82.02 97	80.03 229	88.42 165	55.97 311	87.95 140	93.42 29	77.10 55	77.38 161	90.98 109	69.96 66	91.79 215	68.46 185	84.50 163	92.33 108

testdata				79.97 230	90.90 91	64.21 201		84.71 255	59.27 312	85.40 44	92.91 67	62.02 153	89.08 268	68.95 182	91.37 84	86.63 281

SCA		74.22 245	72.33 252	79.91 231	84.05 249	62.17 238	79.96 287	79.29 319	66.30 243	72.38 247	80.13 310	51.95 244	88.60 276	59.25 259	77.67 239	88.96 225

thres400		76.50 220	75.37 222	79.86 232	89.13 138	57.65 287	85.17 211	83.60 271	73.41 136	76.45 181	86.39 229	52.12 239	91.95 210	48.33 320	83.75 172	90.00 190

test_0402		72.79 262	70.44 269	79.84 233	88.13 172	65.99 165	85.93 195	84.29 262	65.57 252	67.40 294	85.49 246	46.92 288	92.61 187	35.88 350	74.38 287	80.94 334

OurMVSNet-221017-0		74.26 244	72.42 251	79.80 234	83.76 254	59.59 266	85.92 196	86.64 234	66.39 242	66.96 297	87.58 188	39.46 328	91.60 219	65.76 209	69.27 318	88.22 243

SixPastTwentyTwo		73.37 253	71.26 263	79.70 235	85.08 235	57.89 283	85.57 202	83.56 273	71.03 170	65.66 310	85.88 237	42.10 319	92.57 188	59.11 261	63.34 335	88.65 236

thres600view7		76.50 220	75.44 218	79.68 236	89.40 123	57.16 292	85.53 208	83.23 280	73.79 127	76.26 187	87.09 205	51.89 246	91.89 213	48.05 325	83.72 175	90.00 190

CR-MVSNet		73.37 253	71.27 262	79.67 237	81.32 303	65.19 183	75.92 316	80.30 311	59.92 306	72.73 242	81.19 298	52.50 233	86.69 292	59.84 254	77.71 237	87.11 271

D2MVS		74.82 240	73.21 244	79.64 238	79.81 319	62.56 233	80.34 283	87.35 224	64.37 266	68.86 282	82.66 286	46.37 291	90.10 252	67.91 188	81.24 202	86.25 284

AllTest		70.96 273	68.09 285	79.58 239	85.15 231	63.62 210	84.58 228	79.83 315	62.31 289	60.32 334	86.73 210	32.02 347	88.96 272	50.28 310	71.57 310	86.15 287

TestCases				79.58 239	85.15 231	63.62 210		79.83 315	62.31 289	60.32 334	86.73 210	32.02 347	88.96 272	50.28 310	71.57 310	86.15 287

tfpn200view9		76.42 223	75.37 222	79.55 241	89.13 138	57.65 287	85.17 211	83.60 271	73.41 136	76.45 181	86.39 229	52.12 239	91.95 210	48.33 320	83.75 172	89.07 214

thres100view900		76.50 220	75.55 216	79.33 242	89.52 118	56.99 295	85.83 200	83.23 280	73.94 123	76.32 186	87.12 204	51.89 246	91.95 210	48.33 320	83.75 172	89.07 214

CostFormer		75.24 239	73.90 238	79.27 243	82.65 281	58.27 276	80.80 276	82.73 288	61.57 294	75.33 213	83.13 280	55.52 210	91.07 238	64.98 215	78.34 234	88.45 240

Test_1112_low_res		76.40 224	75.44 218	79.27 243	89.28 132	58.09 277	81.69 270	87.07 229	59.53 310	72.48 245	86.67 218	61.30 165	89.33 263	60.81 249	80.15 216	90.41 170

DWT-MVSNet_test		73.70 250	71.86 255	79.21 245	82.91 274	58.94 269	82.34 263	82.17 291	65.21 254	71.05 260	78.31 324	44.21 306	90.17 251	63.29 226	77.28 241	88.53 239

K. test v3		71.19 271	68.51 279	79.21 245	83.04 270	57.78 286	84.35 236	76.91 330	72.90 144	62.99 327	82.86 283	39.27 329	91.09 237	61.65 241	52.66 348	88.75 233

lessismore_v0				78.97 247	81.01 307	57.15 293		65.99 353		61.16 332	82.82 284	39.12 330	91.34 228	59.67 255	46.92 353	88.43 241

pm-mvs1		77.25 210	76.68 205	78.93 248	84.22 245	58.62 273	86.41 182	88.36 203	71.37 165	73.31 235	88.01 182	61.22 168	89.15 267	64.24 219	73.01 300	89.03 220

thres200		75.55 234	74.47 232	78.82 249	87.78 186	57.85 284	83.07 258	83.51 274	72.44 147	75.84 198	84.42 262	52.08 241	91.75 216	47.41 327	83.64 176	86.86 275

VPNet		78.69 176	78.66 153	78.76 250	88.31 168	55.72 313	84.45 232	86.63 235	76.79 63	78.26 144	90.55 114	59.30 186	89.70 258	66.63 201	77.05 245	90.88 153

tpm2		73.26 256	71.46 258	78.63 251	83.34 260	56.71 300	80.65 280	80.40 310	56.63 330	73.55 233	82.02 295	51.80 248	91.24 230	56.35 286	78.42 233	87.95 246

pmmvs6		74.69 241	73.39 242	78.61 252	81.38 300	57.48 290	86.64 176	87.95 211	64.99 260	70.18 267	86.61 220	50.43 262	89.52 260	62.12 236	70.18 316	88.83 230

WR-MVS_H		78.51 180	78.49 156	78.56 253	88.02 177	56.38 306	88.43 119	92.67 60	77.14 53	73.89 232	87.55 190	66.25 100	89.24 265	58.92 263	73.55 295	90.06 188

RPSCF		73.23 257	71.46 258	78.54 254	82.50 283	59.85 263	82.18 265	82.84 287	58.96 314	71.15 259	89.41 146	45.48 301	84.77 307	58.82 265	71.83 308	91.02 150

pmmvs-eth3d		70.50 278	67.83 289	78.52 255	77.37 334	66.18 162	81.82 267	81.51 298	58.90 315	63.90 323	80.42 308	42.69 314	86.28 296	58.56 267	65.30 331	83.11 321

PatchmatchNet		73.12 258	71.33 261	78.49 256	83.18 265	60.85 253	79.63 289	78.57 321	64.13 268	71.73 253	79.81 315	51.20 253	85.97 298	57.40 278	76.36 260	88.66 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS		74.29 243	72.94 247	78.35 257	81.53 297	63.49 216	81.58 271	82.49 289	68.06 226	69.99 272	83.69 274	51.66 250	85.54 300	65.85 208	71.64 309	86.01 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ITE_SJBPF				78.22 258	81.77 293	60.57 256		83.30 278	69.25 204	67.54 291	87.20 201	36.33 339	87.28 290	54.34 292	74.62 285	86.80 276

ppachtmachnet_test		70.04 282	67.34 295	78.14 259	79.80 320	61.13 249	79.19 295	80.59 306	59.16 313	65.27 313	79.29 316	46.75 290	87.29 289	49.33 316	66.72 325	86.00 293

tfpnnormal		74.39 242	73.16 245	78.08 260	86.10 217	58.05 278	84.65 226	87.53 220	70.32 183	71.22 258	85.63 243	54.97 213	89.86 254	43.03 341	75.02 281	86.32 283

Vis-MVSNet (Re-imp)		78.36 184	78.45 157	78.07 261	88.64 157	51.78 333	86.70 175	79.63 317	74.14 120	75.11 218	90.83 110	61.29 166	89.75 256	58.10 272	91.60 80	92.69 98

TransMVSNet (Re)		75.39 238	74.56 230	77.86 262	85.50 226	57.10 294	86.78 172	86.09 244	72.17 152	71.53 255	87.34 195	63.01 137	89.31 264	56.84 283	61.83 336	87.17 267

PEN-MVS		77.73 200	77.69 181	77.84 263	87.07 205	53.91 323	87.91 143	91.18 121	77.56 41	73.14 238	88.82 157	61.23 167	89.17 266	59.95 253	72.37 303	90.43 169

CP-MVSNet		78.22 186	78.34 162	77.84 263	87.83 182	54.54 318	87.94 141	91.17 122	77.65 36	73.48 234	88.49 166	62.24 149	88.43 278	62.19 234	74.07 288	90.55 165

PS-CasMVS		78.01 195	78.09 167	77.77 265	87.71 187	54.39 320	88.02 137	91.22 119	77.50 44	73.26 236	88.64 161	60.73 174	88.41 279	61.88 238	73.88 292	90.53 166

baseline1		76.98 214	76.75 203	77.66 266	88.13 172	55.66 314	85.12 214	81.89 294	73.04 141	76.79 174	88.90 154	62.43 145	87.78 286	63.30 225	71.18 312	89.55 208

OpenMVS_ROB	64.09 19	70.56 277	68.19 282	77.65 267	80.26 313	59.41 268	85.01 216	82.96 286	58.76 316	65.43 312	82.33 289	37.63 336	91.23 231	45.34 337	76.03 262	82.32 326

Patchmatch-RL test		70.24 280	67.78 291	77.61 268	77.43 333	59.57 267	71.16 332	70.33 344	62.94 282	68.65 284	72.77 342	50.62 259	85.49 301	69.58 176	66.58 327	87.77 252

Baseline_NR-MVSNet		78.15 190	78.33 163	77.61 268	85.79 220	56.21 309	86.78 172	85.76 246	73.60 130	77.93 152	87.57 189	65.02 113	88.99 269	67.14 198	75.33 276	87.63 254

DTE-MVSNet		76.99 213	76.80 199	77.54 270	86.24 215	53.06 330	87.52 150	90.66 132	77.08 56	72.50 244	88.67 160	60.48 180	89.52 260	57.33 279	70.74 314	90.05 189

LCM-MVSNet-Re		77.05 212	76.94 196	77.36 271	87.20 202	51.60 334	80.06 285	80.46 309	75.20 98	67.69 290	86.72 212	62.48 143	88.98 270	63.44 223	89.25 109	91.51 133

tpm cat1		70.57 276	68.31 281	77.35 272	82.41 286	57.95 282	78.08 305	80.22 313	52.04 342	68.54 286	77.66 330	52.00 243	87.84 285	51.77 301	72.07 307	86.25 284

MS-PatchMatch		73.83 249	72.67 248	77.30 273	83.87 252	66.02 164	81.82 267	84.66 256	61.37 297	68.61 285	82.82 284	47.29 285	88.21 280	59.27 258	84.32 166	77.68 343

MVS_0304		72.48 263	70.89 266	77.24 274	82.20 288	59.68 264	84.11 240	83.49 275	67.10 232	66.87 299	80.59 306	35.00 343	87.40 288	59.07 262	79.58 219	84.63 307

EPNet_dtu		75.46 235	74.86 226	77.23 275	82.57 282	54.60 317	86.89 167	83.09 284	71.64 158	66.25 308	85.86 238	55.99 209	88.04 283	54.92 290	86.55 143	89.05 219
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		74.11 246	73.11 246	77.13 276	80.11 315	59.62 265	72.23 330	86.92 232	66.76 235	70.40 264	82.92 281	56.93 206	82.92 318	69.06 181	72.63 302	88.87 228

TDRefinement		67.49 296	64.34 305	76.92 277	73.47 349	61.07 250	84.86 220	82.98 285	59.77 307	58.30 340	85.13 254	26.06 352	87.89 284	47.92 326	60.59 340	81.81 330

JIA-IIPM		66.32 305	62.82 315	76.82 278	77.09 335	61.72 245	65.34 348	75.38 333	58.04 321	64.51 318	62.32 349	42.05 320	86.51 294	51.45 304	69.22 319	82.21 327

PatchMatch-RL		72.38 265	70.90 265	76.80 279	88.60 158	67.38 145	79.53 290	76.17 332	62.75 285	69.36 280	82.00 296	45.51 300	84.89 306	53.62 295	80.58 210	78.12 342

tpmvs		71.09 272	69.29 275	76.49 280	82.04 290	56.04 310	78.92 298	81.37 300	64.05 271	67.18 296	78.28 325	49.74 270	89.77 255	49.67 315	72.37 303	83.67 315

CMPMVS	51.72 21	70.19 281	68.16 283	76.28 281	73.15 351	57.55 289	79.47 291	83.92 267	48.02 346	56.48 345	84.81 258	43.13 311	86.42 295	62.67 231	81.81 198	84.89 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 279	68.37 280	76.21 282	80.60 310	56.23 308	79.19 295	86.49 236	60.89 298	61.29 331	85.47 247	31.78 349	89.47 262	53.37 296	76.21 261	82.94 325

gg-mvs-nofinetune		69.95 283	67.96 286	75.94 283	83.07 268	54.51 319	77.23 311	70.29 345	63.11 278	70.32 265	62.33 348	43.62 309	88.69 275	53.88 294	87.76 124	84.62 308

MDA-MVSNet-bldmvs		66.68 301	63.66 309	75.75 284	79.28 327	60.56 257	73.92 327	78.35 322	64.43 264	50.13 351	79.87 314	44.02 308	83.67 313	46.10 333	56.86 343	83.03 323

PVSNet	64.34 18	72.08 268	70.87 267	75.69 285	86.21 216	56.44 304	74.37 326	80.73 304	62.06 292	70.17 268	82.23 292	42.86 313	83.31 316	54.77 291	84.45 165	87.32 263

pmmvs5		71.55 269	70.20 272	75.61 286	77.83 331	56.39 305	81.74 269	80.89 301	57.76 322	67.46 292	84.49 261	49.26 276	85.32 303	57.08 281	75.29 278	85.11 302

our_test_3		69.14 287	67.00 297	75.57 287	79.80 320	58.80 271	77.96 306	77.81 324	59.55 309	62.90 328	78.25 326	47.43 284	83.97 311	51.71 302	67.58 324	83.93 314

WTY-MVS		75.65 233	75.68 214	75.57 287	86.40 214	56.82 297	77.92 308	82.40 290	65.10 256	76.18 190	87.72 184	63.13 136	80.90 325	60.31 251	81.96 195	89.00 223

Patchmtry		70.74 274	69.16 276	75.49 289	80.72 308	54.07 322	74.94 325	80.30 311	58.34 318	70.01 270	81.19 298	52.50 233	86.54 293	53.37 296	71.09 313	85.87 294

GG-mvs-BLEND				75.38 290	81.59 296	55.80 312	79.32 292	69.63 347		67.19 295	73.67 341	43.24 310	88.90 274	50.41 307	84.50 163	81.45 331

ambc				75.24 291	73.16 350	50.51 341	63.05 352	87.47 222		64.28 319	77.81 329	17.80 358	89.73 257	57.88 274	60.64 339	85.49 295

CL-MVSNet_2432*1600		72.37 266	71.46 258	75.09 292	79.49 325	53.53 325	80.76 278	85.01 254	69.12 209	70.51 262	82.05 294	57.92 194	84.13 310	52.27 300	66.00 329	87.60 255

XXY-MVS		75.41 237	75.56 215	74.96 293	83.59 256	57.82 285	80.59 281	83.87 269	66.54 241	74.93 223	88.31 171	63.24 130	80.09 328	62.16 235	76.85 250	86.97 273

MIMVSNet		70.69 275	69.30 274	74.88 294	84.52 240	56.35 307	75.87 318	79.42 318	64.59 262	67.76 288	82.41 288	41.10 323	81.54 323	46.64 331	81.34 200	86.75 278

ADS-MVSNet2		66.20 308	63.33 310	74.82 295	79.92 317	58.75 272	67.55 345	75.19 334	53.37 339	65.25 314	75.86 336	42.32 316	80.53 327	41.57 344	68.91 320	85.18 299

TinyColmap		67.30 299	64.81 303	74.76 296	81.92 292	56.68 301	80.29 284	81.49 299	60.33 301	56.27 346	83.22 279	24.77 353	87.66 287	45.52 335	69.47 317	79.95 338

test-LLR		72.94 261	72.43 250	74.48 297	81.35 301	58.04 279	78.38 301	77.46 326	66.66 237	69.95 273	79.00 319	48.06 281	79.24 329	66.13 203	84.83 159	86.15 287

test-mter		71.41 270	70.39 271	74.48 297	81.35 301	58.04 279	78.38 301	77.46 326	60.32 302	69.95 273	79.00 319	36.08 340	79.24 329	66.13 203	84.83 159	86.15 287

tpm		72.37 266	71.71 257	74.35 299	82.19 289	52.00 331	79.22 294	77.29 328	64.56 263	72.95 240	83.68 275	51.35 251	83.26 317	58.33 270	75.80 264	87.81 251

CVMVSNet		72.99 260	72.58 249	74.25 300	84.28 243	50.85 339	86.41 182	83.45 277	44.56 347	73.23 237	87.54 191	49.38 273	85.70 299	65.90 207	78.44 232	86.19 286

FMVSNet5		69.50 285	67.96 286	74.15 301	82.97 273	55.35 315	80.01 286	82.12 293	62.56 287	63.02 325	81.53 297	36.92 337	81.92 321	48.42 319	74.06 289	85.17 301

MIMVSNet1		68.58 292	66.78 299	73.98 302	80.07 316	51.82 332	80.77 277	84.37 259	64.40 265	59.75 337	82.16 293	36.47 338	83.63 314	42.73 342	70.33 315	86.48 282

Anonymous20240521		68.80 290	67.22 296	73.55 303	74.33 344	54.11 321	83.18 254	85.61 247	58.15 319	61.68 330	80.94 303	30.71 350	81.27 324	57.00 282	73.34 299	85.28 298

sss		73.60 251	73.64 241	73.51 304	82.80 276	55.01 316	76.12 314	81.69 297	62.47 288	74.68 226	85.85 239	57.32 201	78.11 335	60.86 248	80.93 204	87.39 260

KD-MVS_2432*1600		66.22 306	63.89 307	73.21 305	75.47 342	53.42 327	70.76 335	84.35 260	64.10 269	66.52 304	78.52 322	34.55 344	84.98 304	50.40 308	50.33 351	81.23 332

miper_refine_blended		66.22 306	63.89 307	73.21 305	75.47 342	53.42 327	70.76 335	84.35 260	64.10 269	66.52 304	78.52 322	34.55 344	84.98 304	50.40 308	50.33 351	81.23 332

PM-MVS		66.41 304	64.14 306	73.20 307	73.92 346	56.45 303	78.97 297	64.96 356	63.88 275	64.72 317	80.24 309	19.84 357	83.44 315	66.24 202	64.52 333	79.71 339

tpmrst		72.39 264	72.13 253	73.18 308	80.54 311	49.91 342	79.91 288	79.08 320	63.11 278	71.69 254	79.95 312	55.32 211	82.77 319	65.66 210	73.89 291	86.87 274

TESTMET0.1,1		69.89 284	69.00 277	72.55 309	79.27 328	56.85 296	78.38 301	74.71 338	57.64 323	68.09 287	77.19 332	37.75 335	76.70 340	63.92 220	84.09 168	84.10 313

DIV-MVS_2432*1600		68.81 289	67.59 294	72.46 310	74.29 345	45.45 349	77.93 307	87.00 230	63.12 277	63.99 322	78.99 321	42.32 316	84.77 307	56.55 285	64.09 334	87.16 269

CHOSEN 280x420		66.51 303	64.71 304	71.90 311	81.45 298	63.52 215	57.98 353	68.95 351	53.57 338	62.59 329	76.70 333	46.22 293	75.29 347	55.25 289	79.68 218	76.88 345

EPMVS		69.02 288	68.16 283	71.59 312	79.61 323	49.80 344	77.40 310	66.93 352	62.82 284	70.01 270	79.05 317	45.79 297	77.86 337	56.58 284	75.26 279	87.13 270

YYNet1		65.03 309	62.91 313	71.38 313	75.85 338	56.60 302	69.12 342	74.66 339	57.28 327	54.12 347	77.87 328	45.85 296	74.48 349	49.95 313	61.52 338	83.05 322

MDA-MVSNet_test_wron		65.03 309	62.92 312	71.37 314	75.93 337	56.73 298	69.09 343	74.73 337	57.28 327	54.03 348	77.89 327	45.88 295	74.39 350	49.89 314	61.55 337	82.99 324

UnsupCasMVSNet_eth		67.33 298	65.99 301	71.37 314	73.48 348	51.47 336	75.16 321	85.19 251	65.20 255	60.78 333	80.93 305	42.35 315	77.20 339	57.12 280	53.69 347	85.44 296

PMMVS		69.34 286	68.67 278	71.35 316	75.67 339	62.03 239	75.17 320	73.46 340	50.00 345	68.68 283	79.05 317	52.07 242	78.13 334	61.16 246	82.77 186	73.90 346

EU-MVSNet		68.53 293	67.61 293	71.31 317	78.51 330	47.01 348	84.47 229	84.27 263	42.27 348	66.44 307	84.79 259	40.44 326	83.76 312	58.76 266	68.54 323	83.17 319

Anonymous20231206		68.60 291	67.80 290	71.02 318	80.23 314	50.75 340	78.30 304	80.47 308	56.79 329	66.11 309	82.63 287	46.35 292	78.95 331	43.62 340	75.70 265	83.36 318

dp		66.80 300	65.43 302	70.90 319	79.74 322	48.82 345	75.12 323	74.77 336	59.61 308	64.08 321	77.23 331	42.89 312	80.72 326	48.86 318	66.58 327	83.16 320

PatchT		68.46 294	67.85 288	70.29 320	80.70 309	43.93 352	72.47 329	74.88 335	60.15 304	70.55 261	76.57 334	49.94 267	81.59 322	50.58 306	74.83 283	85.34 297

UnsupCasMVSNet_bld		63.70 314	61.53 318	70.21 321	73.69 347	51.39 337	72.82 328	81.89 294	55.63 334	57.81 341	71.80 344	38.67 331	78.61 332	49.26 317	52.21 349	80.63 335

Patchmatch-test		64.82 311	63.24 311	69.57 322	79.42 326	49.82 343	63.49 351	69.05 350	51.98 343	59.95 336	80.13 310	50.91 255	70.98 353	40.66 346	73.57 294	87.90 249

LF4IMVS		64.02 313	62.19 316	69.50 323	70.90 353	53.29 329	76.13 313	77.18 329	52.65 341	58.59 338	80.98 302	23.55 354	76.52 341	53.06 298	66.66 326	78.68 341

test20.03		67.45 297	66.95 298	68.94 324	75.48 341	44.84 351	77.50 309	77.67 325	66.66 237	63.01 326	83.80 271	47.02 287	78.40 333	42.53 343	68.86 322	83.58 316

test0.0.03 1		68.00 295	67.69 292	68.90 325	77.55 332	47.43 346	75.70 319	72.95 342	66.66 237	66.56 302	82.29 291	48.06 281	75.87 344	44.97 338	74.51 286	83.41 317

PVSNet_0	57.27 20	61.67 316	59.27 319	68.85 326	79.61 323	57.44 291	68.01 344	73.44 341	55.93 333	58.54 339	70.41 345	44.58 304	77.55 338	47.01 328	35.91 354	71.55 348

ADS-MVSNet		64.36 312	62.88 314	68.78 327	79.92 317	47.17 347	67.55 345	71.18 343	53.37 339	65.25 314	75.86 336	42.32 316	73.99 351	41.57 344	68.91 320	85.18 299

pmmvs3		57.79 318	54.26 322	68.37 328	64.02 357	56.72 299	75.12 323	65.17 354	40.20 350	52.93 349	69.86 346	20.36 356	75.48 346	45.45 336	55.25 346	72.90 347

LCM-MVSNet		54.25 320	49.68 326	67.97 329	53.73 360	45.28 350	66.85 347	80.78 303	35.96 354	39.45 354	62.23 350	8.70 366	78.06 336	48.24 323	51.20 350	80.57 336

testgi		66.67 302	66.53 300	67.08 330	75.62 340	41.69 355	75.93 315	76.50 331	66.11 244	65.20 316	86.59 221	35.72 341	74.71 348	43.71 339	73.38 298	84.84 304

ANet_high		50.57 324	46.10 327	63.99 331	48.67 363	39.13 356	70.99 334	80.85 302	61.39 296	31.18 356	57.70 353	17.02 359	73.65 352	31.22 352	15.89 361	79.18 340

MVS-HIRNet		59.14 317	57.67 320	63.57 332	81.65 294	43.50 353	71.73 331	65.06 355	39.59 352	51.43 350	57.73 352	38.34 333	82.58 320	39.53 347	73.95 290	64.62 351

new-patchmatchnet		61.73 315	61.73 317	61.70 333	72.74 352	24.50 365	69.16 341	78.03 323	61.40 295	56.72 344	75.53 338	38.42 332	76.48 342	45.95 334	57.67 342	84.13 312

DSMNet-mixed		57.77 319	56.90 321	60.38 334	67.70 355	35.61 358	69.18 340	53.97 360	32.30 357	57.49 342	79.88 313	40.39 327	68.57 355	38.78 348	72.37 303	76.97 344

FPMVS		53.68 321	51.64 324	59.81 335	65.08 356	51.03 338	69.48 339	69.58 348	41.46 349	40.67 353	72.32 343	16.46 360	70.00 354	24.24 356	65.42 330	58.40 353

PMVS	37.38 22	44.16 326	40.28 329	55.82 336	40.82 365	42.54 354	65.12 349	63.99 357	34.43 355	24.48 358	57.12 354	3.92 368	76.17 343	17.10 359	55.52 345	48.75 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 325	41.86 328	55.16 337	77.03 336	51.52 335	32.50 359	80.52 307	32.46 356	27.12 357	35.02 358	9.52 365	75.50 345	22.31 357	60.21 341	38.45 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		50.91 323	50.29 325	52.78 338	68.58 354	34.94 360	63.71 350	56.63 359	39.73 351	44.95 352	65.47 347	21.93 355	58.48 357	34.98 351	56.62 344	64.92 350

N_pmnet		52.79 322	53.26 323	51.40 339	78.99 329	7.68 368	69.52 338	3.89 368	51.63 344	57.01 343	74.98 339	40.83 325	65.96 356	37.78 349	64.67 332	80.56 337

PMMVS2		40.82 327	38.86 330	46.69 340	53.84 359	16.45 366	48.61 356	49.92 361	37.49 353	31.67 355	60.97 351	8.14 367	56.42 358	28.42 353	30.72 356	67.19 349

MVE	26.22 23	30.37 331	25.89 335	43.81 341	44.55 364	35.46 359	28.87 360	39.07 364	18.20 360	18.58 362	40.18 357	2.68 369	47.37 362	17.07 360	23.78 358	48.60 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 329	29.28 333	38.23 342	27.03 367	6.50 369	20.94 361	62.21 358	4.05 363	22.35 361	52.50 355	13.33 361	47.58 361	27.04 355	34.04 355	60.62 352

E-PMN		31.77 328	30.64 331	35.15 343	52.87 361	27.67 362	57.09 354	47.86 362	24.64 358	16.40 363	33.05 359	11.23 363	54.90 359	14.46 361	18.15 359	22.87 358

EMVS		30.81 330	29.65 332	34.27 344	50.96 362	25.95 364	56.58 355	46.80 363	24.01 359	15.53 364	30.68 360	12.47 362	54.43 360	12.81 362	17.05 360	22.43 359

DeepMVS_CX				27.40 345	40.17 366	26.90 363		24.59 367	17.44 361	23.95 359	48.61 356	9.77 364	26.48 363	18.06 358	24.47 357	28.83 357

wuyk23d		16.82 334	15.94 337	19.46 346	58.74 358	31.45 361	39.22 357	3.74 369	6.84 362	6.04 365	2.70 365	1.27 370	24.29 364	10.54 363	14.40 363	2.63 361

tmp_tt		18.61 333	21.40 336	10.23 347	4.82 368	10.11 367	34.70 358	30.74 366	1.48 364	23.91 360	26.07 361	28.42 351	13.41 365	27.12 354	15.35 362	7.17 360

test123		6.12 336	8.11 339	0.14 348	0.06 370	0.09 370	71.05 333	0.03 371	0.04 366	0.25 367	1.30 367	0.05 371	0.03 367	0.21 365	0.01 365	0.29 362

testmvs		6.04 337	8.02 340	0.10 349	0.08 369	0.03 371	69.74 337	0.04 370	0.05 365	0.31 366	1.68 366	0.02 372	0.04 366	0.24 364	0.02 364	0.25 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		19.96 332	26.61 334	0.00 350	0.00 371	0.00 372	0.00 362	89.26 173	0.00 367	0.00 368	88.61 162	61.62 157	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		5.26 338	7.02 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	63.15 133	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		7.23 335	9.64 338	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	86.72 212	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 171	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

RE-MVS-def			85.48 56		93.06 57	70.63 77	91.88 34	92.27 76	73.53 133	85.69 42	94.45 26	63.87 122		82.75 61	91.87 77	92.50 103

IU-MVS					95.30 2	71.25 59		92.95 49	66.81 233	92.39 5				88.94 8	96.63 2	94.85 10

test_241102_TWO								94.06 10	77.24 48	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 52	93.10 1	95.39 9	82.99 1	97.27 7

9.14			88.26 14		92.84 63		91.52 42	94.75 1	73.93 124	88.57 20	94.67 17	75.57 20	95.79 55	86.77 20	95.76 24

save fliter					93.80 39	72.35 42	90.47 63	91.17 122	74.31 114

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 46	92.81 3	95.79 3	80.98 7

GSMVS																88.96 225

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 252				88.96 225

sam_mvs												50.01 265

MTGPA								92.02 87

test_post1							78.90 299				5.43 364	48.81 280	85.44 302	59.25 259

test_post											5.46 363	50.36 263	84.24 309

patchmatchnet-post											74.00 340	51.12 254	88.60 276

MTMP							92.18 30	32.83 365

gm-plane-assit					81.40 299	53.83 324			62.72 286		80.94 303		92.39 194	63.40 224

test9_res														84.90 30	95.70 27	92.87 93

TEST9					93.26 51	72.96 24	88.75 109	91.89 96	68.44 224	85.00 50	93.10 62	74.36 30	95.41 72

test_8					93.13 53	72.57 34	88.68 114	91.84 99	68.69 220	84.87 56	93.10 62	74.43 27	95.16 83

agg_prior2														82.91 59	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 102		84.41 65			94.93 92

test_prior4						72.60 33	89.01 99

test_prior2							88.85 105		75.41 92	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

旧先验2							86.56 179		58.10 320	87.04 31			88.98 270	74.07 134

新几何2							86.29 187

旧先验1					91.96 77	65.79 171		86.37 239			93.08 66	69.31 75			92.74 70	88.74 234

无先验							87.48 151	88.98 185	60.00 305				94.12 123	67.28 194		88.97 224

原ACMM2							86.86 168

test222					91.50 83	68.26 128	84.16 238	83.20 282	54.63 337	79.74 120	91.63 87	58.97 188			91.42 83	86.77 277

testdata2													91.01 239	62.37 233

segment_acmp												73.08 40

testdata1							84.14 239		75.71 86

plane_prior7					90.08 107	68.51 124

plane_prior6					89.84 112	68.70 119						60.42 181

plane_prior5								92.44 68					95.38 75	78.71 91	86.32 146	91.33 139

plane_prior4											91.00 107

plane_prior3						68.60 122			78.44 30	78.92 130

plane_prior2							91.25 46		79.12 23

plane_prior1					89.90 111

plane_prior						68.71 117	90.38 67		77.62 37						86.16 149

n2								0.00 372

nn								0.00 372

door-mid								69.98 346

test11								92.23 79

door								69.44 349

HQP5-MVS						66.98 151

HQP-NCC					89.33 126		89.17 92		76.41 71	77.23 166

ACMP_Plane					89.33 126		89.17 92		76.41 71	77.23 166

BP-MVS														77.47 105

HQP4-MVS										77.24 165			95.11 85			91.03 148

HQP3-MVS								92.19 82							85.99 152

HQP2-MVS												60.17 184

NP-MVS					89.62 114	68.32 126					90.24 119

MDTV_nov1_ep13_2view						37.79 357	75.16 321		55.10 335	66.53 303		49.34 274		53.98 293		87.94 248

MDTV_nov1_ep13			69.97 273		83.18 265	53.48 326	77.10 312	80.18 314	60.45 300	69.33 281	80.44 307	48.89 279	86.90 291	51.60 303	78.51 231

ACMMP++_ref															81.95 196

ACMMP++															81.25 201

Test By Simon												64.33 118

High-resolution multi-view benchmark