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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 34	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 54	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 48	96.29 16	88.16 35	94.17 99	86.07 43	98.48 18	97.22 19

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 66	85.72 33	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 143	91.10 1	97.53 75	96.58 31
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
abl_6		93.02 4	93.16 4	92.60 4	94.73 44	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 38	91.86 3	95.09 64	87.54 18	98.02 41	93.71 117

HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 32	92.99 13	94.23 26	85.21 34	92.51 56	95.13 44	90.65 10	95.34 54	88.06 9	98.15 36	95.95 42

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 67	88.83 24	95.51 45	87.16 28	97.60 68	92.73 153

test1172		92.40 7	92.41 7	92.37 6	94.68 45	89.04 6	91.98 32	93.62 57	90.14 11	93.63 36	94.16 85	88.83 24	95.51 45	87.11 30	97.54 74	92.54 163

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 42	94.02 91	90.15 17	95.67 34	86.82 32	97.34 81	92.19 182

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 43	93.51 8	94.85 16	82.88 57	91.77 71	93.94 100	90.55 13	95.73 31	88.50 7	98.23 29	95.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 83	85.17 36	92.47 26	95.05 15	87.65 24	93.21 43	94.39 73	90.09 18	95.08 65	86.67 33	97.60 68	94.18 94

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 68	86.15 23	93.37 10	95.10 14	90.28 9	92.11 63	95.03 46	89.75 21	94.93 69	79.95 110	98.27 27	95.04 65
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 27	93.35 11	94.16 31	82.52 61	92.39 59	94.14 86	89.15 23	95.62 35	87.35 23	98.24 28	94.56 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
mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 100	83.09 54	91.54 73	94.25 79	87.67 43	95.51 45	87.21 27	98.11 37	93.12 139

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 83	94.00 92	88.26 32	95.71 32	87.28 26	98.39 21	92.55 162

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 101	94.03 90	86.57 56	95.80 25	87.35 23	97.62 66	94.20 92

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 19	92.02 31	91.81 121	84.07 40	92.00 66	94.40 71	86.63 54	95.28 57	88.59 5	98.31 24	92.30 174

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 56	93.52 7	92.79 95	88.22 20	88.53 134	97.64 2	83.45 84	94.55 84	86.02 46	98.60 13	96.67 28

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 29	92.58 22	93.25 75	81.99 66	91.40 76	94.17 84	87.51 44	95.87 19	87.74 11	97.76 58	93.99 101

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 42	81.69 61	90.00 58	94.27 23	82.35 63	93.67 34	94.82 52	91.18 5	95.52 43	85.36 51	98.73 7	95.23 60

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 28	92.63 21	93.30 72	81.91 68	90.88 88	94.21 80	87.75 41	95.87 19	87.60 16	97.71 62	93.83 109

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 44	92.58 22	93.29 73	81.99 66	91.47 74	93.96 96	88.35 30	95.56 39	87.74 11	97.74 60	92.85 148

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 19	88.94 85	91.81 121	84.07 40	92.00 66	94.40 71	86.63 54	95.28 57	88.59 5	98.31 24	92.30 174

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 54	92.18 29	94.22 27	80.14 89	91.29 79	93.97 93	87.93 40	95.87 19	88.65 4	97.96 48	94.12 98

APDe-MVS		91.22 24	91.92 13	89.14 67	92.97 85	78.04 92	92.84 16	94.14 35	83.33 51	93.90 25	95.73 27	88.77 26	96.41 1	87.60 16	97.98 45	92.98 144

PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 30	90.00 58	93.90 46	80.32 86	91.74 72	94.41 70	88.17 34	95.98 11	86.37 36	97.99 43	93.96 104

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 61	80.97 68	91.49 39	93.48 63	82.82 58	92.60 55	93.97 93	88.19 33	96.29 4	87.61 15	98.20 33	94.39 87
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 16	92.20 28	93.03 86	82.59 60	88.52 135	94.37 74	86.74 53	95.41 52	86.32 37	98.21 31	93.19 137
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 57	91.75 37	93.74 52	80.98 79	91.38 77	93.80 103	87.20 48	95.80 25	87.10 31	97.69 63	93.93 105

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 74	88.13 99	94.51 21	75.79 143	92.94 45	94.96 47	88.36 29	95.01 67	90.70 2	98.40 20	95.09 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 30	91.50 23	88.44 83	93.00 84	76.26 122	89.65 70	95.55 7	87.72 23	93.89 27	94.94 48	91.62 4	93.44 133	78.35 128	98.76 4	95.61 49

ACMMP_NAP		90.65 31	91.07 38	89.42 62	95.93 16	79.54 79	89.95 61	93.68 56	77.65 119	91.97 68	94.89 49	88.38 28	95.45 50	89.27 3	97.87 53	93.27 133

ACMM	79.39 9	90.65 31	90.99 40	89.63 57	95.03 35	83.53 49	89.62 71	93.35 66	79.20 101	93.83 28	93.60 110	90.81 8	92.96 150	85.02 56	98.45 19	92.41 168
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 33	90.34 50	91.38 27	89.03 190	84.23 47	93.58 6	94.68 19	90.65 7	90.33 95	93.95 99	84.50 72	95.37 53	80.87 100	95.50 150	94.53 81

ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 47	80.98 67	89.16 81	94.05 40	79.03 104	92.87 47	93.74 107	90.60 12	95.21 61	82.87 78	98.76 4	94.87 68
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 35	91.08 36	88.88 69	93.38 75	78.65 88	89.15 82	94.05 40	84.68 38	93.90 25	94.11 88	88.13 36	96.30 3	84.51 63	97.81 55	91.70 198
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 44	90.65 46	93.29 73	77.00 127	91.47 74	93.96 96	88.35 30	95.56 39	84.88 58	97.74 60	92.85 148

SED-MVS		90.46 37	91.64 19	86.93 102	94.18 50	72.65 143	90.47 51	93.69 54	83.77 44	94.11 23	94.27 75	90.28 15	95.84 23	86.03 44	97.92 49	92.29 176

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 80	90.98 44	93.24 76	75.37 151	92.84 49	95.28 37	85.58 66	96.09 7	87.92 10	97.76 58	93.88 107
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
SF-MVS		90.27 39	90.80 45	88.68 78	92.86 89	77.09 110	91.19 42	95.74 5	81.38 74	92.28 60	93.80 103	86.89 51	94.64 78	85.52 49	97.51 76	94.30 90

v7n		90.13 40	90.96 41	87.65 95	91.95 116	71.06 170	89.99 60	93.05 83	86.53 28	94.29 19	96.27 17	82.69 91	94.08 103	86.25 40	97.63 65	97.82 8

PMVS	80.48 6	90.08 41	90.66 47	88.34 86	96.71 3	92.97 1	90.31 54	89.57 182	88.51 19	90.11 97	95.12 45	90.98 7	88.92 255	77.55 142	97.07 89	83.13 322
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 42	91.09 35	87.00 101	91.55 135	72.64 145	96.19 2	94.10 38	85.33 32	93.49 39	94.64 60	81.12 120	95.88 17	87.41 21	95.94 133	92.48 165

DVP-MVS		90.06 43	91.32 30	86.29 115	94.16 53	72.56 149	90.54 48	91.01 142	83.61 47	93.75 31	94.65 57	89.76 19	95.78 28	86.42 34	97.97 46	90.55 226
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
PS-CasMVS		90.06 43	91.92 13	84.47 154	96.56 7	58.83 291	89.04 83	92.74 97	91.40 5	96.12 4	96.06 22	87.23 47	95.57 38	79.42 120	98.74 6	99.00 2

PEN-MVS		90.03 45	91.88 16	84.48 153	96.57 6	58.88 288	88.95 84	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 36	78.69 125	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 81	80.37 71	91.91 35	93.11 79	81.10 77	95.32 10	97.24 5	72.94 205	94.85 72	85.07 54	97.78 56	97.26 16

DTE-MVSNet		89.98 47	91.91 15	84.21 161	96.51 8	57.84 296	88.93 86	92.84 94	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 122	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 46	89.49 76	93.98 42	79.68 93	92.09 64	93.89 101	83.80 80	93.10 147	82.67 80	98.04 38	93.64 122

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 144	81.66 64	91.25 40	94.13 36	88.89 13	88.83 129	94.26 78	77.55 153	95.86 22	84.88 58	95.87 137	95.24 59

WR-MVS_H		89.91 50	91.31 31	85.71 132	96.32 10	62.39 247	89.54 74	93.31 70	90.21 10	95.57 9	95.66 30	81.42 117	95.90 15	80.94 99	98.80 3	98.84 5

OPM-MVS		89.80 51	89.97 52	89.27 64	94.76 41	79.86 75	86.76 123	92.78 96	78.78 107	92.51 56	93.64 109	88.13 36	93.84 113	84.83 60	97.55 71	94.10 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 52	89.27 63	91.30 28	93.51 71	84.79 41	89.89 63	90.63 152	70.00 222	94.55 15	96.67 11	87.94 39	93.59 125	84.27 64	95.97 130	95.52 50

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 178	86.67 17	90.51 50	90.20 169	69.87 223	95.06 11	96.14 21	84.28 75	93.07 148	87.68 13	96.34 116	97.09 21

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 100	82.98 55	91.98 32	90.08 172	71.54 202	94.28 21	96.54 13	81.57 115	94.27 90	86.26 38	96.49 109	97.09 21

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 46	85.95 26	86.84 119	93.91 45	80.07 90	86.75 165	93.26 113	93.64 2	90.93 206	84.60 62	90.75 265	93.97 103

APD-MVS		89.54 56	89.63 57	89.26 65	92.57 94	81.34 66	90.19 56	93.08 82	80.87 81	91.13 81	93.19 114	86.22 61	95.97 12	82.23 86	97.18 86	90.45 228
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		89.51 57	89.48 60	89.59 59	92.26 105	80.80 69	90.14 57	93.54 61	83.37 50	90.57 92	92.55 137	84.99 68	96.15 5	81.26 94	96.61 104	91.83 193

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 75	84.72 42	89.70 66	90.29 166	69.27 226	94.39 17	96.38 15	86.02 64	93.52 129	83.96 66	95.92 135	95.34 54

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 15	92.09 30	92.30 106	79.74 92	87.50 151	92.38 141	81.42 117	93.28 138	83.07 75	97.24 84	91.67 199

ACMH	76.49 14	89.34 60	91.14 34	83.96 167	92.50 97	70.36 175	89.55 72	93.84 50	81.89 69	94.70 13	95.44 35	90.69 9	88.31 265	83.33 73	98.30 26	93.20 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 61	90.91 43	84.37 155	96.34 9	58.61 293	88.66 93	92.06 111	90.78 6	95.67 7	95.17 43	81.80 113	95.54 42	79.00 123	98.69 10	98.95 4

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 48	86.11 25	85.91 133	93.60 60	80.16 88	89.13 125	93.44 111	83.82 79	90.98 204	83.86 68	95.30 158	93.60 124

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 63	93.64 69	79.07 83	88.54 95	94.20 28	73.53 169	89.71 110	94.82 52	85.09 67	95.77 30	84.17 65	98.03 40	93.26 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 159	97.00 2	64.33 225	89.67 69	88.38 199	88.84 15	94.29 19	97.57 3	90.48 14	91.26 196	72.57 198	97.65 64	97.34 15

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 24	92.75 17	93.90 46	78.43 112	89.16 124	92.25 148	72.03 217	96.36 2	88.21 8	90.93 260	92.98 144
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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 73	93.75 64	77.44 103	86.31 130	95.27 12	70.80 210	92.28 60	93.80 103	86.89 51	94.64 78	85.52 49	97.51 76	94.30 90

SD-MVS		88.96 67	89.88 53	86.22 118	91.63 128	77.07 111	89.82 64	93.77 51	78.90 105	92.88 46	92.29 146	86.11 62	90.22 228	86.24 41	97.24 84	91.36 206
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
HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 30	89.70 66	91.27 135	78.20 114	86.69 168	92.28 147	80.36 130	95.06 66	86.17 42	96.49 109	90.22 231

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 79	91.94 118	77.27 109	88.71 91	95.26 13	76.08 134	90.66 91	92.69 132	84.48 73	93.83 114	83.38 72	97.48 78	94.47 82

test_0402		88.65 70	89.58 59	85.88 128	92.55 95	72.22 157	84.01 165	89.44 184	88.63 18	94.38 18	95.77 26	86.38 60	93.59 125	79.84 111	95.21 159	91.82 194

DP-MVS		88.60 71	89.01 66	87.36 98	91.30 142	77.50 101	87.55 107	92.97 89	87.95 22	89.62 114	92.87 125	84.56 71	93.89 110	77.65 140	96.62 103	90.70 220

Anonymous20231211		88.40 72	89.62 58	84.73 149	90.46 166	65.27 216	88.86 87	93.02 87	87.15 25	93.05 44	97.10 6	82.28 100	92.02 176	76.70 151	97.99 43	96.88 25

PS-MVSNAJss		88.31 73	87.90 79	89.56 60	93.31 77	77.96 95	87.94 103	91.97 114	70.73 212	94.19 22	96.67 11	76.94 162	94.57 82	83.07 75	96.28 118	96.15 34

RRT_MVS		88.30 74	87.83 80	89.70 54	93.62 70	75.70 125	92.36 27	89.06 190	77.34 122	93.63 36	95.83 25	65.40 249	95.90 15	85.01 57	98.23 29	97.49 13

OMC-MVS		88.19 75	87.52 86	90.19 49	91.94 118	81.68 63	87.49 109	93.17 78	76.02 137	88.64 132	91.22 172	84.24 76	93.37 136	77.97 138	97.03 90	95.52 50

CS-MVS		88.14 76	87.67 84	89.54 61	89.56 180	79.18 82	90.47 51	94.77 18	79.37 99	84.32 212	89.33 220	83.87 77	94.53 85	82.45 82	94.89 172	94.90 66

TSAR-MVS + MP.		88.14 76	87.82 81	89.09 68	95.72 22	76.74 115	92.49 25	91.19 138	67.85 246	86.63 169	94.84 51	79.58 136	95.96 13	87.62 14	94.50 182	94.56 78
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DROMVSNet		88.01 78	88.32 76	87.09 100	89.28 185	72.03 159	90.31 54	96.31 3	80.88 80	85.12 196	89.67 214	84.47 74	95.46 49	82.56 81	96.26 121	93.77 115

RPSCF		88.00 79	86.93 97	91.22 31	90.08 172	89.30 5	89.68 68	91.11 139	79.26 100	89.68 111	94.81 55	82.44 94	87.74 269	76.54 154	88.74 287	96.61 30

AllTest		87.97 80	87.40 89	89.68 55	91.59 129	83.40 50	89.50 75	95.44 9	79.47 95	88.00 144	93.03 117	82.66 92	91.47 188	70.81 207	96.14 124	94.16 95

mvsmamba		87.87 81	87.23 90	89.78 53	92.31 104	76.51 119	91.09 43	91.87 117	72.61 188	92.16 62	95.23 41	66.01 245	95.59 37	86.02 46	97.78 56	97.24 17

TranMVSNet+NR-MVSNet		87.86 82	88.76 73	85.18 140	94.02 58	64.13 226	84.38 158	91.29 134	84.88 37	92.06 65	93.84 102	86.45 58	93.73 116	73.22 189	98.66 11	97.69 9

nrg030		87.85 83	88.49 74	85.91 126	90.07 174	69.73 178	87.86 104	94.20 28	74.04 163	92.70 54	94.66 56	85.88 65	91.50 187	79.72 114	97.32 82	96.50 32

ETH3D cwj APD-0.16		87.83 84	87.62 85	88.47 81	91.21 145	78.20 90	87.26 111	94.54 20	72.05 198	88.89 126	92.31 145	83.86 78	94.24 93	81.59 93	96.87 94	92.97 147

CNVR-MVS		87.81 85	87.68 83	88.21 88	92.87 87	77.30 108	85.25 143	91.23 136	77.31 124	87.07 159	91.47 167	82.94 89	94.71 75	84.67 61	96.27 120	92.62 160

HQP_MVS		87.75 86	87.43 88	88.70 77	93.45 72	76.42 120	89.45 77	93.61 58	79.44 97	86.55 170	92.95 122	74.84 180	95.22 59	80.78 102	95.83 138	94.46 83

NCCC		87.36 87	86.87 98	88.83 70	92.32 103	78.84 86	86.58 127	91.09 140	78.77 108	84.85 203	90.89 185	80.85 123	95.29 55	81.14 96	95.32 155	92.34 172

DeepPCF-MVS	81.24 5	87.28 88	86.21 108	90.49 43	91.48 139	84.90 39	83.41 186	92.38 105	70.25 219	89.35 122	90.68 192	82.85 90	94.57 82	79.55 116	95.95 132	92.00 187

SixPastTwentyTwo		87.20 89	87.45 87	86.45 111	92.52 96	69.19 187	87.84 105	88.05 207	81.66 71	94.64 14	96.53 14	65.94 246	94.75 74	83.02 77	96.83 97	95.41 52

test_part1		87.15 90	87.82 81	85.15 141	88.88 194	63.04 237	87.98 101	94.85 16	82.52 61	93.61 38	95.73 27	67.51 236	95.71 32	80.48 107	98.83 2	96.69 27

CS-MVS-test		87.00 91	86.43 103	88.71 76	89.46 181	77.46 102	89.42 79	95.73 6	77.87 117	81.64 259	87.25 254	82.43 95	94.53 85	77.65 140	96.46 111	94.14 97

UniMVSNet (Re)		86.87 92	86.98 96	86.55 109	93.11 82	68.48 191	83.80 174	92.87 91	80.37 84	89.61 116	91.81 159	77.72 150	94.18 97	75.00 171	98.53 16	96.99 24

Vis-MVSNet		86.86 93	86.58 101	87.72 93	92.09 112	77.43 105	87.35 110	92.09 110	78.87 106	84.27 218	94.05 89	78.35 145	93.65 118	80.54 106	91.58 246	92.08 184
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 94	87.06 93	86.17 122	92.86 89	67.02 202	82.55 211	91.56 125	83.08 55	90.92 85	91.82 158	78.25 146	93.99 105	74.16 176	98.35 22	97.49 13

DU-MVS		86.80 95	86.99 95	86.21 120	93.24 79	67.02 202	83.16 195	92.21 107	81.73 70	90.92 85	91.97 152	77.20 156	93.99 105	74.16 176	98.35 22	97.61 10

Regformer-2		86.74 96	86.08 110	88.73 74	84.18 281	79.20 81	83.52 181	89.33 186	83.33 51	89.92 105	85.07 291	83.23 87	93.16 143	83.39 71	92.72 223	93.83 109

IS-MVSNet		86.66 97	86.82 100	86.17 122	92.05 114	66.87 204	91.21 41	88.64 195	86.30 30	89.60 117	92.59 134	69.22 228	94.91 70	73.89 180	97.89 52	96.72 26

v10		86.54 98	87.10 92	84.84 145	88.16 209	63.28 234	86.64 126	92.20 108	75.42 150	92.81 51	94.50 63	74.05 190	94.06 104	83.88 67	96.28 118	97.17 20

pmmvs6		86.52 99	88.06 78	81.90 209	92.22 108	62.28 250	84.66 151	89.15 188	83.54 49	89.85 106	97.32 4	88.08 38	86.80 282	70.43 215	97.30 83	96.62 29

Regformer-4		86.41 100	85.71 118	88.52 79	84.27 277	77.57 100	84.07 162	88.00 209	82.82 58	89.84 107	85.48 279	82.06 104	92.77 156	83.83 69	91.04 254	95.22 62

PHI-MVS		86.38 101	85.81 115	88.08 89	88.44 203	77.34 106	89.35 80	93.05 83	73.15 180	84.76 204	87.70 245	78.87 140	94.18 97	80.67 104	96.29 117	92.73 153

test_prior3		86.31 102	86.31 105	86.32 113	90.59 163	71.99 160	83.37 187	92.85 92	75.43 148	84.58 206	91.57 163	81.92 110	94.17 99	79.54 117	96.97 91	92.80 150

CSCG		86.26 103	86.47 102	85.60 134	90.87 156	74.26 133	87.98 101	91.85 118	80.35 85	89.54 120	88.01 239	79.09 138	92.13 172	75.51 164	95.06 166	90.41 229

DeepC-MVS_fast	80.27 8	86.23 104	85.65 120	87.96 92	91.30 142	76.92 112	87.19 112	91.99 113	70.56 213	84.96 199	90.69 191	80.01 133	95.14 62	78.37 127	95.78 143	91.82 194
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 105	86.83 99	84.36 157	87.82 213	62.35 249	86.42 129	91.33 133	76.78 129	92.73 53	94.48 65	73.41 199	93.72 117	83.10 74	95.41 151	97.01 23

Anonymous20240529		86.20 106	87.13 91	83.42 180	90.19 170	64.55 223	84.55 153	90.71 149	85.85 31	89.94 104	95.24 40	82.13 102	90.40 223	69.19 226	96.40 114	95.31 56

CDPH-MVS		86.17 107	85.54 121	88.05 91	92.25 106	75.45 126	83.85 171	92.01 112	65.91 259	86.19 177	91.75 161	83.77 81	94.98 68	77.43 145	96.71 101	93.73 116

Regformer-1		86.00 108	85.50 122	87.49 96	84.18 281	76.90 113	83.52 181	87.94 211	82.18 65	89.19 123	85.07 291	82.28 100	91.89 180	82.40 84	92.72 223	93.69 118

NR-MVSNet		86.00 108	86.22 107	85.34 138	93.24 79	64.56 222	82.21 223	90.46 155	80.99 78	88.42 137	91.97 152	77.56 152	93.85 111	72.46 199	98.65 12	97.61 10

train_agg		85.98 110	85.28 125	88.07 90	92.34 101	79.70 77	83.94 167	90.32 160	65.79 260	84.49 208	90.97 181	81.93 108	93.63 120	81.21 95	96.54 107	90.88 215

FC-MVSNet-test		85.93 111	87.05 94	82.58 200	92.25 106	56.44 307	85.75 137	93.09 81	77.33 123	91.94 69	94.65 57	74.78 182	93.41 135	75.11 170	98.58 14	97.88 7

Effi-MVS+-dtu		85.82 112	83.38 160	93.14 3	87.13 228	91.15 2	87.70 106	88.42 197	74.57 158	83.56 229	85.65 276	78.49 143	94.21 95	72.04 201	92.88 218	94.05 100

agg_prior1		85.72 113	85.20 126	87.28 99	91.58 132	77.69 98	83.69 177	90.30 163	66.29 257	84.32 212	91.07 178	82.13 102	93.18 141	81.02 97	96.36 115	90.98 211

TAPA-MVS	77.73 12	85.71 114	84.83 133	88.37 85	88.78 196	79.72 76	87.15 114	93.50 62	69.17 227	85.80 187	89.56 215	80.76 124	92.13 172	73.21 194	95.51 149	93.25 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 115	86.14 109	83.58 176	87.97 210	67.13 200	87.55 107	94.32 22	73.44 171	88.47 136	87.54 248	86.45 58	91.06 203	75.76 163	93.76 197	92.54 163

EPP-MVSNet		85.47 116	85.04 129	86.77 106	91.52 138	69.37 181	91.63 38	87.98 210	81.51 73	87.05 160	91.83 157	66.18 244	95.29 55	70.75 210	96.89 93	95.64 47

GeoE		85.45 117	85.81 115	84.37 155	90.08 172	67.07 201	85.86 136	91.39 132	72.33 194	87.59 149	90.25 202	84.85 69	92.37 166	78.00 136	91.94 240	93.66 119

FIs		85.35 118	86.27 106	82.60 199	91.86 121	57.31 300	85.10 145	93.05 83	75.83 142	91.02 84	93.97 93	73.57 195	92.91 154	73.97 179	98.02 41	97.58 12

casdiffmvs		85.21 119	85.85 114	83.31 183	86.17 253	62.77 241	83.03 197	93.93 44	74.69 157	88.21 142	92.68 133	82.29 99	91.89 180	77.87 139	93.75 199	95.27 58

baseline		85.20 120	85.93 112	83.02 189	86.30 247	62.37 248	84.55 153	93.96 43	74.48 160	87.12 155	92.03 151	82.30 98	91.94 177	78.39 126	94.21 189	94.74 75

K. test v3		85.14 121	84.73 134	86.37 112	91.13 150	69.63 180	85.45 141	76.68 308	84.06 42	92.44 58	96.99 8	62.03 267	94.65 77	80.58 105	93.24 208	94.83 74

EI-MVSNet-Vis-set		85.12 122	84.53 142	86.88 103	84.01 284	72.76 142	83.91 170	85.18 250	80.44 83	88.75 130	85.49 278	80.08 132	91.92 178	82.02 87	90.85 263	95.97 40

ETH3 D test6400		85.09 123	84.87 132	85.75 131	90.80 158	69.34 182	85.90 134	93.31 70	65.43 266	86.11 180	89.95 208	80.92 122	94.86 71	75.90 161	95.57 148	93.05 141

Regformer-3		85.06 124	84.67 139	86.22 118	84.27 277	73.43 137	84.07 162	85.26 248	80.77 82	88.62 133	85.48 279	80.56 128	90.39 224	81.99 88	91.04 254	94.85 72

EI-MVSNet-UG-set		85.04 125	84.44 144	86.85 104	83.87 287	72.52 151	83.82 172	85.15 251	80.27 87	88.75 130	85.45 282	79.95 134	91.90 179	81.92 90	90.80 264	96.13 35

X-MVStestdata		85.04 125	82.70 171	92.08 10	95.64 24	86.25 21	92.64 19	93.33 67	85.07 35	89.99 101	16.05 377	86.57 56	95.80 25	87.35 23	97.62 66	94.20 92

MSLP-MVS++		85.00 127	86.03 111	81.90 209	91.84 124	71.56 168	86.75 124	93.02 87	75.95 140	87.12 155	89.39 218	77.98 147	89.40 250	77.46 143	94.78 175	84.75 299

F-COLMAP		84.97 128	83.42 159	89.63 57	92.39 99	83.40 50	88.83 88	91.92 116	73.19 179	80.18 279	89.15 224	77.04 160	93.28 138	65.82 253	92.28 231	92.21 181

bld_raw_dy_0_64		84.85 129	84.44 144	86.07 124	93.73 66	74.93 129	88.57 94	81.90 279	70.44 214	91.28 80	95.18 42	56.62 301	89.28 251	85.15 53	97.09 88	93.99 101

3Dnovator	80.37 7	84.80 130	84.71 137	85.06 143	86.36 245	74.71 130	88.77 90	90.00 174	75.65 145	84.96 199	93.17 115	74.06 189	91.19 198	78.28 130	91.09 252	89.29 247

IterMVS-LS		84.73 131	84.98 130	83.96 167	87.35 223	63.66 229	83.25 191	89.88 176	76.06 135	89.62 114	92.37 144	73.40 201	92.52 161	78.16 133	94.77 177	95.69 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 132	84.34 149	85.49 137	90.18 171	75.86 124	79.23 266	87.13 221	73.35 172	85.56 191	89.34 219	83.60 83	90.50 221	76.64 152	94.05 193	90.09 236

HQP-MVS		84.61 133	84.06 152	86.27 116	91.19 146	70.66 172	84.77 147	92.68 98	73.30 175	80.55 273	90.17 206	72.10 213	94.61 80	77.30 146	94.47 183	93.56 126

v1192		84.57 134	84.69 138	84.21 161	87.75 215	62.88 239	83.02 198	91.43 129	69.08 229	89.98 103	90.89 185	72.70 209	93.62 123	82.41 83	94.97 169	96.13 35

mvs-test1		84.55 135	82.12 180	91.84 20	87.13 228	89.54 4	85.05 146	88.42 197	74.57 158	80.60 270	82.98 312	78.49 143	93.98 107	72.04 201	89.77 274	92.00 187

FMVSNet1		84.55 135	85.45 123	81.85 211	90.27 169	61.05 262	86.83 120	88.27 204	78.57 111	89.66 113	95.64 31	75.43 173	90.68 216	69.09 227	95.33 154	93.82 111

v1144		84.54 137	84.72 136	84.00 165	87.67 217	62.55 245	82.97 199	90.93 145	70.32 218	89.80 108	90.99 180	73.50 196	93.48 131	81.69 92	94.65 180	95.97 40

Gipuma		84.44 138	86.33 104	78.78 256	84.20 280	73.57 136	89.55 72	90.44 156	84.24 39	84.38 210	94.89 49	76.35 171	80.40 329	76.14 158	96.80 99	82.36 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 139	83.93 155	85.63 133	91.59 129	71.58 167	83.52 181	92.13 109	61.82 288	83.96 223	89.75 213	79.93 135	93.46 132	78.33 129	94.34 186	91.87 192

VDDNet		84.35 140	85.39 124	81.25 220	95.13 33	59.32 281	85.42 142	81.11 283	86.41 29	87.41 152	96.21 19	73.61 194	90.61 219	66.33 247	96.85 95	93.81 114

ETV-MVS		84.31 141	83.91 156	85.52 135	88.58 199	70.40 174	84.50 157	93.37 64	78.76 109	84.07 222	78.72 348	80.39 129	95.13 63	73.82 182	92.98 216	91.04 210

v1240		84.30 142	84.51 143	83.65 174	87.65 218	61.26 259	82.85 203	91.54 126	67.94 244	90.68 90	90.65 194	71.71 219	93.64 119	82.84 79	94.78 175	96.07 37

MVS_111021_LR		84.28 143	83.76 157	85.83 130	89.23 187	83.07 53	80.99 241	83.56 266	72.71 186	86.07 181	89.07 226	81.75 114	86.19 292	77.11 148	93.36 204	88.24 259

h-mvs33		84.25 144	82.76 170	88.72 75	91.82 126	82.60 58	84.00 166	84.98 257	71.27 204	86.70 166	90.55 196	63.04 263	93.92 109	78.26 131	94.20 190	89.63 239

v144192		84.24 145	84.41 146	83.71 173	87.59 220	61.57 255	82.95 200	91.03 141	67.82 247	89.80 108	90.49 197	73.28 202	93.51 130	81.88 91	94.89 172	96.04 39

dcpmvs_2		84.23 146	85.14 127	81.50 217	88.61 198	61.98 253	82.90 202	93.11 79	68.66 235	92.77 52	92.39 140	78.50 142	87.63 271	76.99 150	92.30 228	94.90 66

v1921920		84.23 146	84.37 148	83.79 170	87.64 219	61.71 254	82.91 201	91.20 137	67.94 244	90.06 98	90.34 199	72.04 216	93.59 125	82.32 85	94.91 170	96.07 37

VDD-MVS		84.23 146	84.58 141	83.20 186	91.17 149	65.16 218	83.25 191	84.97 258	79.79 91	87.18 154	94.27 75	74.77 183	90.89 209	69.24 223	96.54 107	93.55 128

v2v482		84.09 149	84.24 150	83.62 175	87.13 228	61.40 256	82.71 206	89.71 178	72.19 197	89.55 118	91.41 168	70.70 224	93.20 140	81.02 97	93.76 197	96.25 33

EG-PatchMatch MVS		84.08 150	84.11 151	83.98 166	92.22 108	72.61 148	82.20 225	87.02 226	72.63 187	88.86 127	91.02 179	78.52 141	91.11 201	73.41 187	91.09 252	88.21 260

DP-MVS Recon		84.05 151	83.22 162	86.52 110	91.73 127	75.27 127	83.23 193	92.40 103	72.04 199	82.04 249	88.33 235	77.91 149	93.95 108	66.17 248	95.12 164	90.34 230

TransMVSNet (Re)		84.02 152	85.74 117	78.85 255	91.00 153	55.20 317	82.29 219	87.26 217	79.65 94	88.38 139	95.52 34	83.00 88	86.88 280	67.97 238	96.60 105	94.45 85

Baseline_NR-MVSNet		84.00 153	85.90 113	78.29 267	91.47 140	53.44 326	82.29 219	87.00 229	79.06 103	89.55 118	95.72 29	77.20 156	86.14 293	72.30 200	98.51 17	95.28 57

TSAR-MVS + GP.		83.95 154	82.69 172	87.72 93	89.27 186	81.45 65	83.72 176	81.58 282	74.73 156	85.66 188	86.06 271	72.56 211	92.69 158	75.44 166	95.21 159	89.01 255

alignmvs		83.94 155	83.98 154	83.80 169	87.80 214	67.88 197	84.54 155	91.42 131	73.27 178	88.41 138	87.96 240	72.33 212	90.83 211	76.02 160	94.11 191	92.69 157

Effi-MVS+		83.90 156	84.01 153	83.57 177	87.22 226	65.61 215	86.55 128	92.40 103	78.64 110	81.34 264	84.18 302	83.65 82	92.93 152	74.22 175	87.87 297	92.17 183

CANet		83.79 157	82.85 169	86.63 107	86.17 253	72.21 158	83.76 175	91.43 129	77.24 125	74.39 323	87.45 250	75.36 174	95.42 51	77.03 149	92.83 219	92.25 180

pm-mvs1		83.69 158	84.95 131	79.91 242	90.04 176	59.66 278	82.43 215	87.44 214	75.52 147	87.85 146	95.26 39	81.25 119	85.65 299	68.74 231	96.04 127	94.42 86

AdaColmap		83.66 159	83.69 158	83.57 177	90.05 175	72.26 156	86.29 132	90.00 174	78.19 115	81.65 258	87.16 256	83.40 85	94.24 93	61.69 282	94.76 178	84.21 304

MIMVSNet1		83.63 160	84.59 140	80.74 230	94.06 57	62.77 241	82.72 205	84.53 262	77.57 121	90.34 94	95.92 24	76.88 168	85.83 297	61.88 280	97.42 79	93.62 123

WR-MVS		83.56 161	84.40 147	81.06 225	93.43 74	54.88 318	78.67 273	85.02 255	81.24 75	90.74 89	91.56 165	72.85 206	91.08 202	68.00 237	98.04 38	97.23 18

CNLPA		83.55 162	83.10 166	84.90 144	89.34 184	83.87 48	84.54 155	88.77 192	79.09 102	83.54 230	88.66 232	74.87 179	81.73 323	66.84 244	92.29 230	89.11 249

LCM-MVSNet-Re		83.48 163	85.06 128	78.75 257	85.94 257	55.75 312	80.05 250	94.27 23	76.47 130	96.09 5	94.54 62	83.31 86	89.75 244	59.95 293	94.89 172	90.75 218

hse-mvs2		83.47 164	81.81 185	88.47 81	91.03 152	82.27 59	82.61 207	83.69 264	71.27 204	86.70 166	86.05 272	63.04 263	92.41 164	78.26 131	93.62 203	90.71 219

V42		83.47 164	83.37 161	83.75 172	83.16 293	63.33 233	81.31 235	90.23 168	69.51 225	90.91 87	90.81 188	74.16 188	92.29 170	80.06 108	90.22 271	95.62 48

VPA-MVSNet		83.47 164	84.73 134	79.69 246	90.29 168	57.52 299	81.30 237	88.69 194	76.29 131	87.58 150	94.44 66	80.60 127	87.20 275	66.60 246	96.82 98	94.34 89

PAPM_NR		83.23 167	83.19 164	83.33 182	90.90 155	65.98 212	88.19 98	90.78 148	78.13 116	80.87 268	87.92 243	73.49 198	92.42 163	70.07 217	88.40 288	91.60 201

CLD-MVS		83.18 168	82.64 173	84.79 146	89.05 189	67.82 198	77.93 281	92.52 101	68.33 237	85.07 197	81.54 329	82.06 104	92.96 150	69.35 222	97.91 51	93.57 125
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 169	85.68 119	75.65 298	81.24 309	45.26 365	79.94 252	92.91 90	83.83 43	91.33 78	96.88 10	80.25 131	85.92 295	68.89 229	95.89 136	95.76 44

FA-MVS(test-final)		83.13 170	83.02 167	83.43 179	86.16 255	66.08 211	88.00 100	88.36 200	75.55 146	85.02 198	92.75 130	65.12 250	92.50 162	74.94 172	91.30 250	91.72 196

114514_t		83.10 171	82.54 176	84.77 148	92.90 86	69.10 189	86.65 125	90.62 153	54.66 330	81.46 261	90.81 188	76.98 161	94.38 89	72.62 197	96.18 122	90.82 217

UGNet		82.78 172	81.64 187	86.21 120	86.20 252	76.24 123	86.86 118	85.68 242	77.07 126	73.76 326	92.82 126	69.64 225	91.82 183	69.04 228	93.69 200	90.56 225
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
LF4IMVS		82.75 173	81.93 184	85.19 139	82.08 300	80.15 73	85.53 140	88.76 193	68.01 241	85.58 190	87.75 244	71.80 218	86.85 281	74.02 178	93.87 196	88.58 258

EI-MVSNet		82.61 174	82.42 178	83.20 186	83.25 291	63.66 229	83.50 184	85.07 252	76.06 135	86.55 170	85.10 288	73.41 199	90.25 225	78.15 135	90.67 267	95.68 46

QAPM		82.59 175	82.59 175	82.58 200	86.44 240	66.69 205	89.94 62	90.36 159	67.97 243	84.94 201	92.58 136	72.71 208	92.18 171	70.63 213	87.73 299	88.85 256

Fast-Effi-MVS+-dtu		82.54 176	81.41 191	85.90 127	85.60 258	76.53 118	83.07 196	89.62 181	73.02 182	79.11 288	83.51 307	80.74 125	90.24 227	68.76 230	89.29 278	90.94 213

MVS_Test		82.47 177	83.22 162	80.22 239	82.62 299	57.75 298	82.54 212	91.96 115	71.16 208	82.89 237	92.52 139	77.41 154	90.50 221	80.04 109	87.84 298	92.40 169

v148		82.31 178	82.48 177	81.81 214	85.59 259	59.66 278	81.47 233	86.02 238	72.85 183	88.05 143	90.65 194	70.73 223	90.91 208	75.15 169	91.79 241	94.87 68

API-MVS		82.28 179	82.61 174	81.30 219	86.29 248	69.79 176	88.71 91	87.67 213	78.42 113	82.15 247	84.15 303	77.98 147	91.59 186	65.39 255	92.75 220	82.51 329

MVSFormer		82.23 180	81.57 190	84.19 163	85.54 260	69.26 184	91.98 32	90.08 172	71.54 202	76.23 307	85.07 291	58.69 288	94.27 90	86.26 38	88.77 285	89.03 253

EIA-MVS		82.19 181	81.23 194	85.10 142	87.95 211	69.17 188	83.22 194	93.33 67	70.42 215	78.58 291	79.77 345	77.29 155	94.20 96	71.51 204	88.96 283	91.93 191

PCF-MVS	74.62 15	82.15 182	80.92 198	85.84 129	89.43 182	72.30 155	80.53 245	91.82 120	57.36 319	87.81 147	89.92 210	77.67 151	93.63 120	58.69 298	95.08 165	91.58 202
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 183	80.31 204	87.45 97	90.86 157	80.29 72	85.88 135	90.65 151	68.17 239	76.32 306	86.33 266	73.12 204	92.61 160	61.40 285	90.02 273	89.44 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 184	82.07 181	81.85 211	86.38 242	61.05 262	86.83 120	88.27 204	72.43 189	86.00 182	95.64 31	63.78 257	90.68 216	65.95 249	93.34 205	93.82 111

test1		82.02 184	82.07 181	81.85 211	86.38 242	61.05 262	86.83 120	88.27 204	72.43 189	86.00 182	95.64 31	63.78 257	90.68 216	65.95 249	93.34 205	93.82 111

OpenMVS	76.72 13	81.98 186	82.00 183	81.93 208	84.42 273	68.22 193	88.50 96	89.48 183	66.92 252	81.80 256	91.86 154	72.59 210	90.16 230	71.19 206	91.25 251	87.40 272

KD-MVS_self_test		81.93 187	83.14 165	78.30 266	84.75 268	52.75 330	80.37 247	89.42 185	70.24 220	90.26 96	93.39 112	74.55 187	86.77 283	68.61 233	96.64 102	95.38 53

tfpnnormal		81.79 188	82.95 168	78.31 265	88.93 193	55.40 313	80.83 244	82.85 271	76.81 128	85.90 186	94.14 86	74.58 186	86.51 287	66.82 245	95.68 147	93.01 143

c3_l		81.64 189	81.59 189	81.79 215	80.86 315	59.15 285	78.61 274	90.18 170	68.36 236	87.20 153	87.11 258	69.39 226	91.62 185	78.16 133	94.43 185	94.60 77

PVSNet_Blended_VisFu		81.55 190	80.49 202	84.70 151	91.58 132	73.24 140	84.21 159	91.67 124	62.86 281	80.94 266	87.16 256	67.27 238	92.87 155	69.82 219	88.94 284	87.99 264

DELS-MVS		81.44 191	81.25 192	82.03 207	84.27 277	62.87 240	76.47 302	92.49 102	70.97 209	81.64 259	83.83 304	75.03 177	92.70 157	74.29 174	92.22 234	90.51 227
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
FMVSNet2		81.31 192	81.61 188	80.41 236	86.38 242	58.75 292	83.93 169	86.58 231	72.43 189	87.65 148	92.98 119	63.78 257	90.22 228	66.86 242	93.92 195	92.27 178

TinyColmap		81.25 193	82.34 179	77.99 272	85.33 262	60.68 270	82.32 218	88.33 202	71.26 206	86.97 161	92.22 150	77.10 159	86.98 279	62.37 275	95.17 161	86.31 283

AUN-MVS		81.18 194	78.78 225	88.39 84	90.93 154	82.14 60	82.51 213	83.67 265	64.69 274	80.29 276	85.91 275	51.07 320	92.38 165	76.29 157	93.63 202	90.65 223

tttt0517		81.07 195	79.58 216	85.52 135	88.99 192	66.45 207	87.03 116	75.51 316	73.76 167	88.32 141	90.20 203	37.96 366	94.16 102	79.36 121	95.13 162	95.93 43

Fast-Effi-MVS+		81.04 196	80.57 199	82.46 204	87.50 221	63.22 235	78.37 277	89.63 180	68.01 241	81.87 252	82.08 324	82.31 97	92.65 159	67.10 241	88.30 293	91.51 204

BH-untuned		80.96 197	80.99 196	80.84 229	88.55 200	68.23 192	80.33 248	88.46 196	72.79 185	86.55 170	86.76 261	74.72 184	91.77 184	61.79 281	88.99 282	82.52 328

1121		80.86 198	79.81 215	84.02 164	93.93 60	78.70 87	81.64 230	80.18 290	55.43 327	83.67 226	91.15 175	71.29 221	91.41 193	67.95 239	93.06 213	81.96 333

eth_miper_zixun_eth		80.84 199	80.22 208	82.71 197	81.41 307	60.98 265	77.81 283	90.14 171	67.31 250	86.95 162	87.24 255	64.26 253	92.31 168	75.23 168	91.61 244	94.85 72

xiu_mvs_v1_base_debu		80.84 199	80.14 210	82.93 192	88.31 204	71.73 163	79.53 257	87.17 218	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 189	88.33 289	86.90 278

xiu_mvs_v1_base		80.84 199	80.14 210	82.93 192	88.31 204	71.73 163	79.53 257	87.17 218	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 189	88.33 289	86.90 278

xiu_mvs_v1_base_debi		80.84 199	80.14 210	82.93 192	88.31 204	71.73 163	79.53 257	87.17 218	65.43 266	79.59 281	82.73 319	76.94 162	90.14 233	73.22 189	88.33 289	86.90 278

IterMVS-SCA-FT		80.64 203	79.41 217	84.34 158	83.93 285	69.66 179	76.28 304	81.09 284	72.43 189	86.47 176	90.19 204	60.46 273	93.15 145	77.45 144	86.39 310	90.22 231

BH-RMVSNet		80.53 204	80.22 208	81.49 218	87.19 227	66.21 210	77.79 284	86.23 235	74.21 162	83.69 225	88.50 233	73.25 203	90.75 213	63.18 272	87.90 296	87.52 270

Anonymous202405211		80.51 205	81.19 195	78.49 262	88.48 201	57.26 301	76.63 299	82.49 273	81.21 76	84.30 216	92.24 149	67.99 234	86.24 291	62.22 276	95.13 162	91.98 190

DIV-MVS_self_test		80.43 206	80.23 206	81.02 226	79.99 323	59.25 282	77.07 294	87.02 226	67.38 248	86.19 177	89.22 221	63.09 261	90.16 230	76.32 155	95.80 141	93.66 119

cl____		80.42 207	80.23 206	81.02 226	79.99 323	59.25 282	77.07 294	87.02 226	67.37 249	86.18 179	89.21 222	63.08 262	90.16 230	76.31 156	95.80 141	93.65 121

diffmvs		80.40 208	80.48 203	80.17 240	79.02 335	60.04 274	77.54 288	90.28 167	66.65 255	82.40 242	87.33 253	73.50 196	87.35 274	77.98 137	89.62 276	93.13 138

EPNet		80.37 209	78.41 232	86.23 117	76.75 346	73.28 138	87.18 113	77.45 303	76.24 133	68.14 346	88.93 228	65.41 248	93.85 111	69.47 221	96.12 126	91.55 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
iter_conf_final		80.36 210	78.88 222	84.79 146	86.29 248	66.36 209	86.95 117	86.25 234	68.16 240	82.09 248	89.48 216	36.59 369	94.51 87	79.83 112	94.30 187	93.50 129

miper_ehance_all_eth		80.34 211	80.04 213	81.24 222	79.82 325	58.95 287	77.66 285	89.66 179	65.75 263	85.99 185	85.11 287	68.29 233	91.42 192	76.03 159	92.03 236	93.33 130

MG-MVS		80.32 212	80.94 197	78.47 263	88.18 207	52.62 333	82.29 219	85.01 256	72.01 200	79.24 287	92.54 138	69.36 227	93.36 137	70.65 212	89.19 281	89.45 241

VPNet		80.25 213	81.68 186	75.94 297	92.46 98	47.98 356	76.70 298	81.67 281	73.45 170	84.87 202	92.82 126	74.66 185	86.51 287	61.66 283	96.85 95	93.33 130

MAR-MVS		80.24 214	78.74 227	84.73 149	86.87 238	78.18 91	85.75 137	87.81 212	65.67 265	77.84 296	78.50 349	73.79 193	90.53 220	61.59 284	90.87 262	85.49 292
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
PM-MVS		80.20 215	79.00 221	83.78 171	88.17 208	86.66 18	81.31 235	66.81 360	69.64 224	88.33 140	90.19 204	64.58 251	83.63 315	71.99 203	90.03 272	81.06 347

Anonymous20240521		80.18 216	81.25 192	76.95 284	83.15 294	60.84 267	82.46 214	85.99 239	68.76 233	86.78 163	93.73 108	59.13 285	77.44 336	73.71 183	97.55 71	92.56 161

LFMVS		80.15 217	80.56 200	78.89 254	89.19 188	55.93 309	85.22 144	73.78 328	82.96 56	84.28 217	92.72 131	57.38 297	90.07 237	63.80 266	95.75 144	90.68 221

DPM-MVS		80.10 218	79.18 220	82.88 195	90.71 161	69.74 177	78.87 270	90.84 146	60.29 303	75.64 315	85.92 274	67.28 237	93.11 146	71.24 205	91.79 241	85.77 289

MSDG		80.06 219	79.99 214	80.25 238	83.91 286	68.04 196	77.51 289	89.19 187	77.65 119	81.94 250	83.45 309	76.37 170	86.31 290	63.31 271	86.59 307	86.41 281

FE-MVS		79.98 220	78.86 223	83.36 181	86.47 239	66.45 207	89.73 65	84.74 261	72.80 184	84.22 221	91.38 169	44.95 350	93.60 124	63.93 265	91.50 247	90.04 237

ab-mvs		79.67 221	80.56 200	76.99 283	88.48 201	56.93 303	84.70 150	86.06 237	68.95 231	80.78 269	93.08 116	75.30 175	84.62 307	56.78 307	90.90 261	89.43 243

VNet		79.31 222	80.27 205	76.44 291	87.92 212	53.95 322	75.58 311	84.35 263	74.39 161	82.23 245	90.72 190	72.84 207	84.39 309	60.38 292	93.98 194	90.97 212

thisisatest0530		79.07 223	77.33 242	84.26 160	87.13 228	64.58 221	83.66 179	75.95 311	68.86 232	85.22 195	87.36 252	38.10 364	93.57 128	75.47 165	94.28 188	94.62 76

cl22		78.97 224	78.21 234	81.24 222	77.74 339	59.01 286	77.46 291	87.13 221	65.79 260	84.32 212	85.10 288	58.96 287	90.88 210	75.36 167	92.03 236	93.84 108

patch_mono-2		78.89 225	79.39 218	77.41 281	84.78 267	68.11 194	75.60 309	83.11 268	60.96 297	79.36 284	89.89 211	75.18 176	72.97 347	73.32 188	92.30 228	91.15 208

RPMNet		78.88 226	78.28 233	80.68 233	79.58 326	62.64 243	82.58 209	94.16 31	74.80 155	75.72 313	92.59 134	48.69 326	95.56 39	73.48 186	82.91 337	83.85 309

PAPR		78.84 227	78.10 235	81.07 224	85.17 263	60.22 273	82.21 223	90.57 154	62.51 283	75.32 318	84.61 298	74.99 178	92.30 169	59.48 296	88.04 295	90.68 221

iter_conf05		78.81 228	77.35 241	83.21 185	82.98 297	60.75 269	84.09 161	88.34 201	63.12 279	84.25 220	89.48 216	31.41 374	94.51 87	76.64 152	95.83 138	94.38 88

PVSNet_BlendedMVS		78.80 229	77.84 236	81.65 216	84.43 271	63.41 231	79.49 260	90.44 156	61.70 291	75.43 316	87.07 259	69.11 229	91.44 190	60.68 290	92.24 232	90.11 235

FMVSNet3		78.80 229	78.55 229	79.57 248	82.89 298	56.89 305	81.76 227	85.77 241	69.04 230	86.00 182	90.44 198	51.75 318	90.09 236	65.95 249	93.34 205	91.72 196

test_yl		78.71 231	78.51 230	79.32 251	84.32 275	58.84 289	78.38 275	85.33 246	75.99 138	82.49 240	86.57 262	58.01 291	90.02 239	62.74 273	92.73 221	89.10 250

DCV-MVSNet		78.71 231	78.51 230	79.32 251	84.32 275	58.84 289	78.38 275	85.33 246	75.99 138	82.49 240	86.57 262	58.01 291	90.02 239	62.74 273	92.73 221	89.10 250

test1111		78.53 233	78.85 224	77.56 278	92.22 108	47.49 358	82.61 207	69.24 352	72.43 189	85.28 194	94.20 81	51.91 316	90.07 237	65.36 256	96.45 112	95.11 63

ECVR-MVS		78.44 234	78.63 228	77.88 274	91.85 122	48.95 352	83.68 178	69.91 350	72.30 195	84.26 219	94.20 81	51.89 317	89.82 241	63.58 267	96.02 128	94.87 68

pmmvs-eth3d		78.42 235	77.04 245	82.57 202	87.44 222	74.41 132	80.86 243	79.67 293	55.68 325	84.69 205	90.31 201	60.91 271	85.42 300	62.20 277	91.59 245	87.88 267

MVS_0304		78.17 236	77.23 243	80.99 228	84.13 283	69.07 190	81.39 234	80.81 286	76.28 132	67.53 351	89.11 225	62.87 265	86.77 283	60.90 289	92.01 239	87.13 275

mvs_anonymous		78.13 237	78.76 226	76.23 296	79.24 332	50.31 349	78.69 272	84.82 259	61.60 292	83.09 236	92.82 126	73.89 192	87.01 276	68.33 236	86.41 309	91.37 205

TAMVS		78.08 238	76.36 251	83.23 184	90.62 162	72.87 141	79.08 267	80.01 292	61.72 290	81.35 263	86.92 260	63.96 256	88.78 259	50.61 340	93.01 215	88.04 263

miper_enhance_ethall		77.83 239	76.93 246	80.51 234	76.15 352	58.01 295	75.47 313	88.82 191	58.05 313	83.59 228	80.69 333	64.41 252	91.20 197	73.16 195	92.03 236	92.33 173

Vis-MVSNet (Re-imp)		77.82 240	77.79 237	77.92 273	88.82 195	51.29 343	83.28 189	71.97 340	74.04 163	82.23 245	89.78 212	57.38 297	89.41 249	57.22 306	95.41 151	93.05 141

CANet_DTU		77.81 241	77.05 244	80.09 241	81.37 308	59.90 276	83.26 190	88.29 203	69.16 228	67.83 349	83.72 305	60.93 270	89.47 246	69.22 225	89.70 275	90.88 215

OpenMVS_ROB	70.19 17	77.77 242	77.46 238	78.71 258	84.39 274	61.15 260	81.18 239	82.52 272	62.45 285	83.34 231	87.37 251	66.20 243	88.66 261	64.69 261	85.02 321	86.32 282

MDA-MVSNet-bldmvs		77.47 243	76.90 247	79.16 253	79.03 334	64.59 220	66.58 351	75.67 314	73.15 180	88.86 127	88.99 227	66.94 239	81.23 325	64.71 260	88.22 294	91.64 200

jason		77.42 244	75.75 257	82.43 205	87.10 232	69.27 183	77.99 280	81.94 278	51.47 348	77.84 296	85.07 291	60.32 275	89.00 253	70.74 211	89.27 280	89.03 253
jason: jason.
CDS-MVSNet		77.32 245	75.40 260	83.06 188	89.00 191	72.48 152	77.90 282	82.17 276	60.81 298	78.94 289	83.49 308	59.30 283	88.76 260	54.64 324	92.37 227	87.93 266
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 246	76.75 248	78.52 261	87.01 234	61.30 258	75.55 312	87.12 224	61.24 294	74.45 322	78.79 347	77.20 156	90.93 206	64.62 263	84.80 327	83.32 318

MVSTER		77.09 247	75.70 258	81.25 220	75.27 359	61.08 261	77.49 290	85.07 252	60.78 299	86.55 170	88.68 231	43.14 356	90.25 225	73.69 184	90.67 267	92.42 167

PS-MVSNAJ		77.04 248	76.53 250	78.56 260	87.09 233	61.40 256	75.26 314	87.13 221	61.25 293	74.38 324	77.22 356	76.94 162	90.94 205	64.63 262	84.83 326	83.35 317

IterMVS		76.91 249	76.34 252	78.64 259	80.91 313	64.03 227	76.30 303	79.03 296	64.88 273	83.11 234	89.16 223	59.90 279	84.46 308	68.61 233	85.15 320	87.42 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 250	75.67 259	80.34 237	80.48 321	62.16 252	73.50 325	84.80 260	57.61 317	82.24 244	87.54 248	51.31 319	87.65 270	70.40 216	93.19 210	91.23 207

CL-MVSNet_self_test		76.81 251	77.38 240	75.12 301	86.90 236	51.34 341	73.20 328	80.63 288	68.30 238	81.80 256	88.40 234	66.92 240	80.90 326	55.35 318	94.90 171	93.12 139

TR-MVS		76.77 252	75.79 256	79.72 245	86.10 256	65.79 214	77.14 292	83.02 269	65.20 271	81.40 262	82.10 323	66.30 242	90.73 215	55.57 315	85.27 318	82.65 324

USDC		76.63 253	76.73 249	76.34 293	83.46 289	57.20 302	80.02 251	88.04 208	52.14 344	83.65 227	91.25 171	63.24 260	86.65 286	54.66 323	94.11 191	85.17 294

BH-w/o		76.57 254	76.07 255	78.10 270	86.88 237	65.92 213	77.63 286	86.33 232	65.69 264	80.89 267	79.95 342	68.97 231	90.74 214	53.01 332	85.25 319	77.62 353

Patchmtry		76.56 255	77.46 238	73.83 307	79.37 331	46.60 362	82.41 216	76.90 305	73.81 166	85.56 191	92.38 141	48.07 328	83.98 312	63.36 270	95.31 157	90.92 214

PVSNet_Blended		76.49 256	75.40 260	79.76 244	84.43 271	63.41 231	75.14 315	90.44 156	57.36 319	75.43 316	78.30 350	69.11 229	91.44 190	60.68 290	87.70 300	84.42 302

miper_lstm_enhance		76.45 257	76.10 254	77.51 279	76.72 347	60.97 266	64.69 354	85.04 254	63.98 276	83.20 233	88.22 236	56.67 300	78.79 334	73.22 189	93.12 211	92.78 152

lupinMVS		76.37 258	74.46 268	82.09 206	85.54 260	69.26 184	76.79 296	80.77 287	50.68 354	76.23 307	82.82 317	58.69 288	88.94 254	69.85 218	88.77 285	88.07 261

cascas		76.29 259	74.81 264	80.72 232	84.47 270	62.94 238	73.89 323	87.34 215	55.94 324	75.16 320	76.53 359	63.97 255	91.16 199	65.00 257	90.97 259	88.06 262

thres600view7		75.97 260	75.35 262	77.85 276	87.01 234	51.84 339	80.45 246	73.26 332	75.20 152	83.10 235	86.31 268	45.54 341	89.05 252	55.03 321	92.24 232	92.66 158

GA-MVS		75.83 261	74.61 265	79.48 250	81.87 302	59.25 282	73.42 326	82.88 270	68.68 234	79.75 280	81.80 326	50.62 322	89.46 247	66.85 243	85.64 315	89.72 238

MVP-Stereo		75.81 262	73.51 277	82.71 197	89.35 183	73.62 135	80.06 249	85.20 249	60.30 302	73.96 325	87.94 241	57.89 295	89.45 248	52.02 335	74.87 363	85.06 296
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres100view900		75.45 263	75.05 263	76.66 290	87.27 224	51.88 338	81.07 240	73.26 332	75.68 144	83.25 232	86.37 265	45.54 341	88.80 256	51.98 336	90.99 256	89.31 245

ET-MVSNet_ETH3D		75.28 264	72.77 284	82.81 196	83.03 296	68.11 194	77.09 293	76.51 309	60.67 301	77.60 300	80.52 337	38.04 365	91.15 200	70.78 209	90.68 266	89.17 248

thres400		75.14 265	74.23 270	77.86 275	86.24 250	52.12 335	79.24 264	73.87 326	73.34 173	81.82 254	84.60 299	46.02 335	88.80 256	51.98 336	90.99 256	92.66 158

wuyk23d		75.13 266	79.30 219	62.63 345	75.56 355	75.18 128	80.89 242	73.10 334	75.06 154	94.76 12	95.32 36	87.73 42	52.85 374	34.16 373	97.11 87	59.85 370

EU-MVSNet		75.12 267	74.43 269	77.18 282	83.11 295	59.48 280	85.71 139	82.43 274	39.76 372	85.64 189	88.76 229	44.71 352	87.88 268	73.86 181	85.88 314	84.16 305

HyFIR lowres test		75.12 267	72.66 286	82.50 203	91.44 141	65.19 217	72.47 330	87.31 216	46.79 360	80.29 276	84.30 301	52.70 315	92.10 175	51.88 339	86.73 306	90.22 231

CMPMVS	59.41 20	75.12 267	73.57 275	79.77 243	75.84 354	67.22 199	81.21 238	82.18 275	50.78 352	76.50 303	87.66 246	55.20 310	82.99 317	62.17 279	90.64 270	89.09 252
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 270	72.98 283	80.73 231	84.95 264	71.71 166	76.23 305	77.59 302	52.83 338	77.73 299	86.38 264	56.35 304	84.97 304	57.72 305	87.05 304	85.51 291

tfpn200view9		74.86 271	74.23 270	76.74 289	86.24 250	52.12 335	79.24 264	73.87 326	73.34 173	81.82 254	84.60 299	46.02 335	88.80 256	51.98 336	90.99 256	89.31 245

1112_ss		74.82 272	73.74 273	78.04 271	89.57 179	60.04 274	76.49 301	87.09 225	54.31 331	73.66 327	79.80 343	60.25 276	86.76 285	58.37 299	84.15 330	87.32 273

EGC-MVSNET		74.79 273	69.99 307	89.19 66	94.89 39	87.00 14	91.89 36	86.28 233	1.09 378	2.23 380	95.98 23	81.87 112	89.48 245	79.76 113	95.96 131	91.10 209

ppachtmachnet_test		74.73 274	74.00 272	76.90 286	80.71 318	56.89 305	71.53 334	78.42 298	58.24 311	79.32 286	82.92 316	57.91 294	84.26 310	65.60 254	91.36 249	89.56 240

Patchmatch-RL test		74.48 275	73.68 274	76.89 287	84.83 266	66.54 206	72.29 331	69.16 353	57.70 315	86.76 164	86.33 266	45.79 340	82.59 318	69.63 220	90.65 269	81.54 338

PatchMatch-RL		74.48 275	73.22 280	78.27 268	87.70 216	85.26 35	75.92 307	70.09 348	64.34 275	76.09 309	81.25 331	65.87 247	78.07 335	53.86 326	83.82 331	71.48 361

XXY-MVS		74.44 277	76.19 253	69.21 326	84.61 269	52.43 334	71.70 333	77.18 304	60.73 300	80.60 270	90.96 183	75.44 172	69.35 355	56.13 311	88.33 289	85.86 288

test2506		74.12 278	73.39 278	76.28 294	91.85 122	44.20 368	84.06 164	48.20 380	72.30 195	81.90 251	94.20 81	27.22 381	89.77 242	64.81 259	96.02 128	94.87 68

CR-MVSNet		74.00 279	73.04 282	76.85 288	79.58 326	62.64 243	82.58 209	76.90 305	50.50 355	75.72 313	92.38 141	48.07 328	84.07 311	68.72 232	82.91 337	83.85 309

Test_1112_low_res		73.90 280	73.08 281	76.35 292	90.35 167	55.95 308	73.40 327	86.17 236	50.70 353	73.14 328	85.94 273	58.31 290	85.90 296	56.51 309	83.22 334	87.20 274

test20.03		73.75 281	74.59 267	71.22 319	81.11 311	51.12 345	70.15 339	72.10 339	70.42 215	80.28 278	91.50 166	64.21 254	74.72 346	46.96 356	94.58 181	87.82 269

SCA		73.32 282	72.57 288	75.58 299	81.62 304	55.86 310	78.89 269	71.37 345	61.73 289	74.93 321	83.42 310	60.46 273	87.01 276	58.11 303	82.63 341	83.88 306

baseline1		73.26 283	73.54 276	72.43 315	84.92 265	47.79 357	79.89 253	74.00 324	65.93 258	78.81 290	86.28 269	56.36 303	81.63 324	56.63 308	79.04 354	87.87 268

1314		73.22 284	72.56 289	75.20 300	80.41 322	57.84 296	81.64 230	85.36 245	51.68 347	73.10 329	76.65 358	61.45 269	85.19 302	63.54 268	79.21 353	82.59 325

MVS		73.21 285	72.59 287	75.06 302	80.97 312	60.81 268	81.64 230	85.92 240	46.03 363	71.68 335	77.54 352	68.47 232	89.77 242	55.70 314	85.39 316	74.60 358

HY-MVS	64.64 18	73.03 286	72.47 290	74.71 303	83.36 290	54.19 320	82.14 226	81.96 277	56.76 323	69.57 343	86.21 270	60.03 277	84.83 306	49.58 345	82.65 339	85.11 295

thisisatest0515		73.00 287	70.52 301	80.46 235	81.45 306	59.90 276	73.16 329	74.31 323	57.86 314	76.08 310	77.78 351	37.60 367	92.12 174	65.00 257	91.45 248	89.35 244

EPNet_dtu		72.87 288	71.33 299	77.49 280	77.72 340	60.55 271	82.35 217	75.79 312	66.49 256	58.39 373	81.06 332	53.68 313	85.98 294	53.55 327	92.97 217	85.95 286
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 289	71.41 298	76.28 294	83.25 291	60.34 272	83.50 184	79.02 297	37.77 373	76.33 305	85.10 288	49.60 325	87.41 273	70.54 214	77.54 359	81.08 345

CHOSEN 1792x2688		72.45 290	70.56 300	78.13 269	90.02 177	63.08 236	68.72 343	83.16 267	42.99 369	75.92 311	85.46 281	57.22 299	85.18 303	49.87 344	81.67 342	86.14 284

testgi		72.36 291	74.61 265	65.59 339	80.56 320	42.82 372	68.29 344	73.35 331	66.87 253	81.84 253	89.93 209	72.08 215	66.92 363	46.05 358	92.54 225	87.01 277

thres200		72.34 292	71.55 297	74.70 304	83.48 288	51.60 340	75.02 316	73.71 329	70.14 221	78.56 292	80.57 336	46.20 333	88.20 266	46.99 355	89.29 278	84.32 303

FPMVS		72.29 293	72.00 292	73.14 310	88.63 197	85.00 37	74.65 319	67.39 354	71.94 201	77.80 298	87.66 246	50.48 323	75.83 342	49.95 342	79.51 349	58.58 372

FMVSNet5		72.10 294	71.69 294	73.32 308	81.57 305	53.02 329	76.77 297	78.37 299	63.31 277	76.37 304	91.85 155	36.68 368	78.98 332	47.87 352	92.45 226	87.95 265

our_test_3		71.85 295	71.59 295	72.62 313	80.71 318	53.78 323	69.72 341	71.71 344	58.80 308	78.03 293	80.51 338	56.61 302	78.84 333	62.20 277	86.04 313	85.23 293

PAPM		71.77 296	70.06 306	76.92 285	86.39 241	53.97 321	76.62 300	86.62 230	53.44 335	63.97 363	84.73 297	57.79 296	92.34 167	39.65 368	81.33 345	84.45 301

IB-MVS	62.13 19	71.64 297	68.97 311	79.66 247	80.80 317	62.26 251	73.94 322	76.90 305	63.27 278	68.63 345	76.79 357	33.83 372	91.84 182	59.28 297	87.26 302	84.88 297
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
UnsupCasMVSNet_eth		71.63 298	72.30 291	69.62 324	76.47 349	52.70 332	70.03 340	80.97 285	59.18 306	79.36 284	88.21 237	60.50 272	69.12 356	58.33 301	77.62 358	87.04 276

Anonymous20231206		71.38 299	71.88 293	69.88 322	86.31 246	54.37 319	70.39 338	74.62 319	52.57 340	76.73 302	88.76 229	59.94 278	72.06 349	44.35 361	93.23 209	83.23 320

MIMVSNet		71.09 300	71.59 295	69.57 325	87.23 225	50.07 350	78.91 268	71.83 341	60.20 304	71.26 336	91.76 160	55.08 311	76.09 340	41.06 366	87.02 305	82.54 327

MS-PatchMatch		70.93 301	70.22 304	73.06 311	81.85 303	62.50 246	73.82 324	77.90 300	52.44 341	75.92 311	81.27 330	55.67 307	81.75 322	55.37 317	77.70 357	74.94 357

pmmvs5		70.73 302	70.07 305	72.72 312	77.03 345	52.73 331	74.14 320	75.65 315	50.36 356	72.17 333	85.37 285	55.42 309	80.67 328	52.86 333	87.59 301	84.77 298

PatchT		70.52 303	72.76 285	63.79 344	79.38 330	33.53 378	77.63 286	65.37 362	73.61 168	71.77 334	92.79 129	44.38 353	75.65 343	64.53 264	85.37 317	82.18 331

N_pmnet		70.20 304	68.80 313	74.38 305	80.91 313	84.81 40	59.12 364	76.45 310	55.06 328	75.31 319	82.36 322	55.74 306	54.82 373	47.02 354	87.24 303	83.52 313

tpmvs		70.16 305	69.56 309	71.96 317	74.71 362	48.13 354	79.63 255	75.45 317	65.02 272	70.26 340	81.88 325	45.34 346	85.68 298	58.34 300	75.39 362	82.08 332

new-patchmatchnet		70.10 306	73.37 279	60.29 352	81.23 310	16.95 382	59.54 362	74.62 319	62.93 280	80.97 265	87.93 242	62.83 266	71.90 350	55.24 319	95.01 168	92.00 187

YYNet1		70.06 307	70.44 302	68.90 327	73.76 364	53.42 327	58.99 365	67.20 356	58.42 310	87.10 157	85.39 284	59.82 280	67.32 360	59.79 294	83.50 333	85.96 285

MDA-MVSNet_test_wron		70.05 308	70.44 302	68.88 328	73.84 363	53.47 325	58.93 366	67.28 355	58.43 309	87.09 158	85.40 283	59.80 281	67.25 361	59.66 295	83.54 332	85.92 287

CostFormer		69.98 309	68.68 314	73.87 306	77.14 343	50.72 347	79.26 263	74.51 321	51.94 346	70.97 339	84.75 296	45.16 349	87.49 272	55.16 320	79.23 352	83.40 316

baseline2		69.77 310	66.89 320	78.41 264	79.51 328	58.09 294	76.23 305	69.57 351	57.50 318	64.82 361	77.45 354	46.02 335	88.44 262	53.08 329	77.83 356	88.70 257

PatchmatchNet		69.71 311	68.83 312	72.33 316	77.66 341	53.60 324	79.29 262	69.99 349	57.66 316	72.53 331	82.93 315	46.45 332	80.08 331	60.91 288	72.09 365	83.31 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
JIA-IIPM		69.41 312	66.64 324	77.70 277	73.19 366	71.24 169	75.67 308	65.56 361	70.42 215	65.18 357	92.97 121	33.64 373	83.06 316	53.52 328	69.61 371	78.79 352

UnsupCasMVSNet_bld		69.21 313	69.68 308	67.82 333	79.42 329	51.15 344	67.82 348	75.79 312	54.15 332	77.47 301	85.36 286	59.26 284	70.64 352	48.46 349	79.35 351	81.66 336

gg-mvs-nofinetune		68.96 314	69.11 310	68.52 332	76.12 353	45.32 364	83.59 180	55.88 375	86.68 26	64.62 362	97.01 7	30.36 376	83.97 313	44.78 360	82.94 336	76.26 355

tpm2		68.45 315	66.83 321	73.30 309	78.93 336	48.50 353	79.76 254	71.76 342	47.50 359	69.92 342	83.60 306	42.07 358	88.40 263	48.44 350	79.51 349	83.01 323

tpm		67.95 316	68.08 317	67.55 334	78.74 337	43.53 370	75.60 309	67.10 359	54.92 329	72.23 332	88.10 238	42.87 357	75.97 341	52.21 334	80.95 348	83.15 321

WTY-MVS		67.91 317	68.35 315	66.58 337	80.82 316	48.12 355	65.96 352	72.60 335	53.67 334	71.20 337	81.68 328	58.97 286	69.06 357	48.57 348	81.67 342	82.55 326

test-LLR		67.21 318	66.74 322	68.63 330	76.45 350	55.21 315	67.89 345	67.14 357	62.43 286	65.08 358	72.39 364	43.41 354	69.37 353	61.00 286	84.89 324	81.31 340

sss		66.92 319	67.26 319	65.90 338	77.23 342	51.10 346	64.79 353	71.72 343	52.12 345	70.13 341	80.18 340	57.96 293	65.36 368	50.21 341	81.01 347	81.25 342

KD-MVS_2432*1600		66.87 320	65.81 325	70.04 320	67.50 375	47.49 358	62.56 358	79.16 294	61.21 295	77.98 294	80.61 334	25.29 383	82.48 319	53.02 330	84.92 322	80.16 350

miper_refine_blended		66.87 320	65.81 325	70.04 320	67.50 375	47.49 358	62.56 358	79.16 294	61.21 295	77.98 294	80.61 334	25.29 383	82.48 319	53.02 330	84.92 322	80.16 350

tpm cat1		66.76 322	65.21 328	71.42 318	77.09 344	50.62 348	78.01 279	73.68 330	44.89 365	68.64 344	79.00 346	45.51 343	82.42 321	49.91 343	70.15 368	81.23 344

PVSNet	58.17 21	66.41 323	65.63 327	68.75 329	81.96 301	49.88 351	62.19 360	72.51 337	51.03 350	68.04 347	75.34 362	50.84 321	74.77 344	45.82 359	82.96 335	81.60 337

tpmrst		66.28 324	66.69 323	65.05 342	72.82 370	39.33 373	78.20 278	70.69 347	53.16 337	67.88 348	80.36 339	48.18 327	74.75 345	58.13 302	70.79 367	81.08 345

Patchmatch-test		65.91 325	67.38 318	61.48 350	75.51 356	43.21 371	68.84 342	63.79 364	62.48 284	72.80 330	83.42 310	44.89 351	59.52 372	48.27 351	86.45 308	81.70 335

ADS-MVSNet2		65.87 326	63.64 331	72.55 314	73.16 367	56.92 304	67.10 349	74.81 318	49.74 357	66.04 354	82.97 313	46.71 330	77.26 337	42.29 363	69.96 369	83.46 314

test-mter		65.00 327	63.79 330	68.63 330	76.45 350	55.21 315	67.89 345	67.14 357	50.98 351	65.08 358	72.39 364	28.27 379	69.37 353	61.00 286	84.89 324	81.31 340

test0.0.03 1		64.66 328	64.36 329	65.57 340	75.03 361	46.89 361	64.69 354	61.58 369	62.43 286	71.18 338	77.54 352	43.41 354	68.47 358	40.75 367	82.65 339	81.35 339

pmmvs3		62.47 329	60.02 341	69.80 323	71.58 373	64.00 228	70.52 337	58.44 373	39.77 371	66.05 353	75.84 360	27.10 382	72.28 348	46.15 357	84.77 328	73.11 359

EPMVS		62.47 329	62.63 333	62.01 346	70.63 374	38.74 374	74.76 317	52.86 377	53.91 333	67.71 350	80.01 341	39.40 362	66.60 364	55.54 316	68.81 372	80.68 349

ADS-MVSNet		61.90 331	62.19 334	61.03 351	73.16 367	36.42 376	67.10 349	61.75 367	49.74 357	66.04 354	82.97 313	46.71 330	63.21 370	42.29 363	69.96 369	83.46 314

PMMVS		61.65 332	60.38 338	65.47 341	65.40 379	69.26 184	63.97 356	61.73 368	36.80 374	60.11 368	68.43 368	59.42 282	66.35 365	48.97 347	78.57 355	60.81 369

E-PMN		61.59 333	61.62 335	61.49 349	66.81 377	55.40 313	53.77 368	60.34 370	66.80 254	58.90 371	65.50 370	40.48 361	66.12 366	55.72 313	86.25 311	62.95 368

TESTMET0.1,1		61.29 334	60.32 339	64.19 343	72.06 371	51.30 342	67.89 345	62.09 365	45.27 364	60.65 367	69.01 367	27.93 380	64.74 369	56.31 310	81.65 344	76.53 354

MVS-HIRNet		61.16 335	62.92 332	55.87 355	79.09 333	35.34 377	71.83 332	57.98 374	46.56 361	59.05 370	91.14 176	49.95 324	76.43 339	38.74 369	71.92 366	55.84 373

EMVS		61.10 336	60.81 337	61.99 347	65.96 378	55.86 310	53.10 369	58.97 372	67.06 251	56.89 374	63.33 371	40.98 359	67.03 362	54.79 322	86.18 312	63.08 367

DSMNet-mixed		60.98 337	61.61 336	59.09 354	72.88 369	45.05 366	74.70 318	46.61 381	26.20 375	65.34 356	90.32 200	55.46 308	63.12 371	41.72 365	81.30 346	69.09 365

dp		60.70 338	60.29 340	61.92 348	72.04 372	38.67 375	70.83 335	64.08 363	51.28 349	60.75 366	77.28 355	36.59 369	71.58 351	47.41 353	62.34 374	75.52 356

CHOSEN 280x420		59.08 339	56.52 344	66.76 336	76.51 348	64.39 224	49.62 370	59.00 371	43.86 367	55.66 375	68.41 369	35.55 371	68.21 359	43.25 362	76.78 361	67.69 366

PVSNet_0	51.08 22	56.10 340	54.97 345	59.48 353	75.12 360	53.28 328	55.16 367	61.89 366	44.30 366	59.16 369	62.48 372	54.22 312	65.91 367	35.40 372	47.01 375	59.25 371

new_pmnet		55.69 341	57.66 343	49.76 357	75.47 357	30.59 379	59.56 361	51.45 378	43.62 368	62.49 364	75.48 361	40.96 360	49.15 376	37.39 371	72.52 364	69.55 364

PMMVS2		55.64 342	59.27 342	44.74 358	64.30 380	12.32 383	40.60 371	49.79 379	53.19 336	65.06 360	84.81 295	53.60 314	49.76 375	32.68 375	89.41 277	72.15 360

MVE	40.22 23	51.82 343	50.47 346	55.87 355	62.66 381	51.91 337	31.61 373	39.28 382	40.65 370	50.76 376	74.98 363	56.24 305	44.67 377	33.94 374	64.11 373	71.04 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 344	29.60 347	33.06 359	17.99 383	3.84 385	13.62 374	73.92 325	2.79 377	18.29 379	53.41 374	28.53 378	43.25 378	22.56 376	35.27 377	52.11 374

cdsmvs_eth3d_5k		20.81 345	27.75 348	0.00 364	0.00 387	0.00 388	0.00 375	85.44 244	0.00 382	0.00 383	82.82 317	81.46 116	0.00 383	0.00 381	0.00 381	0.00 379

tmp_tt		20.25 346	24.50 349	7.49 361	4.47 384	8.70 384	34.17 372	25.16 384	1.00 379	32.43 378	18.49 376	39.37 363	9.21 380	21.64 377	43.75 376	4.57 376

ab-mvs-re		6.65 347	8.87 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	79.80 343	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		6.41 348	8.55 351	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	76.94 162	0.00 383	0.00 381	0.00 381	0.00 379

test123		6.27 349	8.08 352	0.84 362	1.11 386	0.57 386	62.90 357	0.82 386	0.54 380	1.07 382	2.75 381	1.26 385	0.30 381	1.04 379	1.26 380	1.66 377

testmvs		5.91 350	7.65 353	0.72 363	1.20 385	0.37 387	59.14 363	0.67 387	0.49 381	1.11 381	2.76 380	0.94 386	0.24 382	1.02 380	1.47 379	1.55 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 71	91.55 135	77.99 93		91.01 142					96.05 8	87.45 19	98.17 34	92.40 169

PC_three_1452									58.96 307	90.06 98	91.33 170	80.66 126	93.03 149	75.78 162	95.94 133	92.48 165

No_MVS				88.81 71	91.55 135	77.99 93		91.01 142					96.05 8	87.45 19	98.17 34	92.40 169

test_one_0601					93.85 63	73.27 139		94.11 37	86.57 27	93.47 41	94.64 60	88.42 27

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					92.22 108	80.48 70		91.85 118	71.22 207	90.38 93	92.98 119	86.06 63	96.11 6	81.99 88	96.75 100

RE-MVS-def			92.61 5		94.13 55	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 67	90.64 11		87.16 28	97.60 68	92.73 153

IU-MVS					94.18 50	72.64 145		90.82 147	56.98 321	89.67 112				85.78 48	97.92 49	93.28 132

OPU-MVS				88.27 87	91.89 120	77.83 96	90.47 51				91.22 172	81.12 120	94.68 76	74.48 173	95.35 153	92.29 176

test_241102_TWO								93.71 53	83.77 44	93.49 39	94.27 75	89.27 22	95.84 23	86.03 44	97.82 54	92.04 185

test_241102_ONE					94.18 50	72.65 143		93.69 54	83.62 46	94.11 23	93.78 106	90.28 15	95.50 48

9.14			89.29 62		91.84 124		88.80 89	95.32 11	75.14 153	91.07 82	92.89 124	87.27 46	93.78 115	83.69 70	97.55 71

save fliter					93.75 64	77.44 103	86.31 130	89.72 177	70.80 210

test_0728_THIRD									85.33 32	93.75 31	94.65 57	87.44 45	95.78 28	87.41 21	98.21 31	92.98 144

test_0728_SECOND				86.79 105	94.25 49	72.45 153	90.54 48	94.10 38					95.88 17	86.42 34	97.97 46	92.02 186

test0726					94.16 53	72.56 149	90.63 47	93.90 46	83.61 47	93.75 31	94.49 64	89.76 19

GSMVS																83.88 306

test_part2					93.86 62	77.77 97				92.84 49

sam_mvs1												46.11 334				83.88 306

sam_mvs												45.92 339

ambc				82.98 190	90.55 165	64.86 219	88.20 97	89.15 188		89.40 121	93.96 96	71.67 220	91.38 195	78.83 124	96.55 106	92.71 156

MTGPA								91.81 121

test_post1							78.85 271				3.13 378	45.19 348	80.13 330	58.11 303

test_post											3.10 379	45.43 344	77.22 338

patchmatchnet-post											81.71 327	45.93 338	87.01 276

GG-mvs-BLEND				67.16 335	73.36 365	46.54 363	84.15 160	55.04 376		58.64 372	61.95 373	29.93 377	83.87 314	38.71 370	76.92 360	71.07 362

MTMP							90.66 45	33.14 383

gm-plane-assit					75.42 358	44.97 367			52.17 342		72.36 366		87.90 267	54.10 325

test9_res														80.83 101	96.45 112	90.57 224

TEST9					92.34 101	79.70 77	83.94 167	90.32 160	65.41 270	84.49 208	90.97 181	82.03 106	93.63 120

test_8					92.09 112	78.87 85	83.82 172	90.31 162	65.79 260	84.36 211	90.96 183	81.93 108	93.44 133

agg_prior2														79.68 115	96.16 123	90.22 231

agg_prior					91.58 132	77.69 98		90.30 163		84.32 212			93.18 141

TestCases				89.68 55	91.59 129	83.40 50		95.44 9	79.47 95	88.00 144	93.03 117	82.66 92	91.47 188	70.81 207	96.14 124	94.16 95

test_prior4						78.97 84	84.59 152

test_prior2							83.37 187		75.43 148	84.58 206	91.57 163	81.92 110		79.54 117	96.97 91

test_prior				86.32 113	90.59 163	71.99 160		92.85 92					94.17 99			92.80 150

旧先验2							81.73 228		56.88 322	86.54 175			84.90 305	72.81 196

新几何2							81.72 229

新几何1				82.95 191	93.96 59	78.56 89		80.24 289	55.45 326	83.93 224	91.08 177	71.19 222	88.33 264	65.84 252	93.07 212	81.95 334

旧先验1					91.97 115	71.77 162		81.78 280			91.84 156	73.92 191			93.65 201	83.61 312

无先验							82.81 204	85.62 243	58.09 312				91.41 193	67.95 239		84.48 300

原ACMM2							82.26 222

原ACMM1				84.60 152	92.81 92	74.01 134		91.50 127	62.59 282	82.73 239	90.67 193	76.53 169	94.25 92	69.24 223	95.69 146	85.55 290

test222					93.31 77	76.54 116	79.38 261	77.79 301	52.59 339	82.36 243	90.84 187	66.83 241			91.69 243	81.25 342

testdata2													86.43 289	63.52 269

segment_acmp												81.94 107

testdata				79.54 249	92.87 87	72.34 154		80.14 291	59.91 305	85.47 193	91.75 161	67.96 235	85.24 301	68.57 235	92.18 235	81.06 347

testdata1							79.62 256		73.95 165

test12				86.57 108	90.74 159	72.63 147		90.69 150		82.76 238		79.20 137	94.80 73		95.32 155	92.27 178

plane_prior7					93.45 72	77.31 107

plane_prior6					92.61 93	76.54 116						74.84 180

plane_prior5								93.61 58					95.22 59	80.78 102	95.83 138	94.46 83

plane_prior4											92.95 122

plane_prior3						76.85 114			77.79 118	86.55 170

plane_prior2							89.45 77		79.44 97

plane_prior1					92.83 91

plane_prior						76.42 120	87.15 114		75.94 141						95.03 167

n2								0.00 388

nn								0.00 388

door-mid								74.45 322

lessismore_v0				85.95 125	91.10 151	70.99 171		70.91 346		91.79 70	94.42 69	61.76 268	92.93 152	79.52 119	93.03 214	93.93 105

LGP-MVS_train				90.82 38	94.75 42	81.69 61		94.27 23	82.35 63	93.67 34	94.82 52	91.18 5	95.52 43	85.36 51	98.73 7	95.23 60

test11								91.46 128

door								72.57 336

HQP5-MVS						70.66 172

HQP-NCC					91.19 146		84.77 147		73.30 175	80.55 273

ACMP_Plane					91.19 146		84.77 147		73.30 175	80.55 273

BP-MVS														77.30 146

HQP4-MVS										80.56 272			94.61 80			93.56 126

HQP3-MVS								92.68 98							94.47 183

HQP2-MVS												72.10 213

NP-MVS					91.95 116	74.55 131					90.17 206

MDTV_nov1_ep13_2view						27.60 381	70.76 336		46.47 362	61.27 365		45.20 347		49.18 346		83.75 311

MDTV_nov1_ep13			68.29 316		78.03 338	43.87 369	74.12 321	72.22 338	52.17 342	67.02 352	85.54 277	45.36 345	80.85 327	55.73 312	84.42 329

ACMMP++_ref															95.74 145

ACMMP++															97.35 80

Test By Simon												79.09 138

ITE_SJBPF				90.11 50	90.72 160	84.97 38		90.30 163	81.56 72	90.02 100	91.20 174	82.40 96	90.81 212	73.58 185	94.66 179	94.56 78

DeepMVS_CX				24.13 360	32.95 382	29.49 380		21.63 385	12.07 376	37.95 377	45.07 375	30.84 375	19.21 379	17.94 378	33.06 378	23.69 375

High-resolution multi-view benchmark