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 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 71	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 51	97.23 2	95.32 2	99.01 2	97.26 9	80.16 150	98.99 1	95.15 1	99.14 2	96.47 35

TDRefinement		93.52 3	93.39 5	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 54	96.29 22	88.16 36	94.17 106	86.07 56	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 69	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 80	93.16 150	91.10 2	97.53 81	96.58 33
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
reproduce_model		92.89 5	93.18 8	92.01 13	94.20 54	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 43	95.72 39	89.60 5	98.27 28	92.08 236

reproduce-ours		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 29	89.13 7	98.26 30	91.76 247

our_new_method		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 29	89.13 7	98.26 30	91.76 247

HPM-MVS_fast		92.50 8	92.54 10	92.37 6	95.93 16	85.81 33	92.99 12	94.23 28	85.21 44	92.51 62	95.13 52	90.65 10	95.34 59	88.06 16	98.15 39	95.95 46

lecture		92.43 9	93.50 3	89.21 66	94.43 44	79.31 84	92.69 19	95.72 8	88.48 22	94.43 20	95.73 34	91.34 4	94.68 82	90.26 4	98.44 20	93.63 146

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 50	87.16 38	97.60 75	92.73 192

SR-MVS		92.23 11	92.34 12	91.91 17	94.89 38	87.85 10	92.51 25	93.87 52	88.20 24	93.24 43	94.02 100	90.15 17	95.67 41	86.82 43	97.34 85	92.19 231

HPM-MVS		92.13 12	92.20 14	91.91 17	95.58 26	84.67 46	93.51 8	94.85 16	82.88 72	91.77 76	93.94 108	90.55 13	95.73 38	88.50 12	98.23 33	95.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 13	92.24 13	91.48 25	93.02 88	85.17 39	92.47 27	95.05 15	87.65 28	93.21 47	94.39 81	90.09 18	95.08 70	86.67 45	97.60 75	94.18 114

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 70	86.15 24	93.37 10	95.10 14	90.28 10	92.11 68	95.03 54	89.75 21	94.93 74	79.95 134	98.27 28	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 15	91.87 20	92.03 12	95.53 27	85.91 28	93.35 11	94.16 33	82.52 75	92.39 65	94.14 93	89.15 26	95.62 42	87.35 33	98.24 32	94.56 90
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 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 117	83.09 69	91.54 78	94.25 87	87.67 46	95.51 50	87.21 37	98.11 40	93.12 175

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 74	83.16 68	91.06 88	94.00 101	88.26 33	95.71 40	87.28 36	98.39 23	92.55 206

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 78	85.07 45	89.99 110	94.03 99	86.57 59	95.80 31	87.35 33	97.62 73	94.20 111

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 38	91.81 144	84.07 55	92.00 71	94.40 80	86.63 58	95.28 62	88.59 11	98.31 26	92.30 223

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 108	88.22 23	88.53 147	97.64 6	83.45 94	94.55 90	86.02 60	98.60 13	96.67 30

ACMMPR		91.49 20	91.35 31	91.92 16	95.74 20	85.88 30	92.58 23	93.25 84	81.99 78	91.40 80	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 122

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 67	94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	98.73 7	95.23 66

region2R		91.44 23	91.30 35	91.87 19	95.75 19	85.90 29	92.63 22	93.30 82	81.91 80	90.88 95	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 131

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 83	81.99 78	91.47 79	93.96 105	88.35 32	95.56 45	87.74 22	97.74 62	92.85 189

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 101	91.29 84	93.97 102	87.93 42	95.87 20	88.65 10	97.96 51	94.12 118

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 90	78.04 96	92.84 16	94.14 37	83.33 66	93.90 29	95.73 34	88.77 28	96.41 3	87.60 27	97.98 48	92.98 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 27	90.95 45	91.93 15	95.67 23	85.85 31	90.00 67	93.90 49	80.32 98	91.74 77	94.41 79	88.17 35	95.98 13	86.37 49	97.99 46	93.96 124

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 65	80.97 70	91.49 45	93.48 72	82.82 73	92.60 61	93.97 102	88.19 34	96.29 6	87.61 26	98.20 36	94.39 105
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 46	91.83 20	96.18 11	86.88 17	92.20 31	93.03 98	82.59 74	88.52 148	94.37 82	86.74 57	95.41 57	86.32 50	98.21 34	93.19 170
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 30	91.01 42	90.82 37	95.45 28	82.73 59	91.75 43	93.74 59	80.98 91	91.38 81	93.80 112	87.20 51	95.80 31	87.10 40	97.69 65	93.93 125

MP-MVS-pluss		90.81 31	91.08 39	89.99 50	95.97 14	79.88 77	88.13 110	94.51 19	75.79 159	92.94 51	94.96 55	88.36 31	95.01 72	90.70 3	98.40 22	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 32	91.50 26	88.44 83	93.00 89	76.26 122	89.65 80	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 141	78.35 156	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 41	89.42 62	95.93 16	79.54 82	89.95 71	93.68 64	77.65 136	91.97 72	94.89 57	88.38 30	95.45 55	89.27 6	97.87 56	93.27 165

ACMM	79.39 9	90.65 33	90.99 43	89.63 58	95.03 34	83.53 51	89.62 81	93.35 77	79.20 114	93.83 32	93.60 122	90.81 8	92.96 157	85.02 74	98.45 19	92.41 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 55	91.38 28	89.03 203	84.23 49	93.58 6	94.68 18	90.65 8	90.33 104	93.95 107	84.50 82	95.37 58	80.87 124	95.50 157	94.53 94

ACMP	79.16 10	90.54 36	90.60 53	90.35 45	94.36 51	80.98 69	89.16 92	94.05 42	79.03 117	92.87 53	93.74 117	90.60 12	95.21 65	82.87 102	98.76 4	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 37	91.08 39	88.88 71	93.38 79	78.65 90	89.15 93	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 84	97.81 58	91.70 251
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		90.48 38	91.14 36	88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	83.72 60	93.24 43	93.18 131	87.06 52	95.85 24	84.99 75	97.69 65	93.54 156

SED-MVS		90.46 39	91.64 22	86.93 109	94.18 55	72.65 156	90.47 60	93.69 62	83.77 58	94.11 27	94.27 83	90.28 15	95.84 27	86.03 57	97.92 52	92.29 225

SMA-MVS		90.31 40	90.48 54	89.83 55	95.31 30	79.52 83	90.98 52	93.24 85	75.37 168	92.84 55	95.28 48	85.58 72	96.09 8	87.92 18	97.76 60	93.88 128
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 41	90.80 48	88.68 78	92.86 94	77.09 111	91.19 49	95.74 6	81.38 86	92.28 67	93.80 112	86.89 56	94.64 85	85.52 66	97.51 82	94.30 110

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 190	89.99 69	93.05 95	86.53 35	94.29 23	96.27 23	82.69 103	94.08 109	86.25 53	97.63 71	97.82 8

ME-MVS		90.09 43	90.66 51	88.38 85	92.82 97	76.12 126	89.40 90	93.70 61	83.72 60	92.39 65	93.18 131	88.02 40	95.47 53	84.99 75	97.69 65	93.54 156

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 221	88.51 21	90.11 106	95.12 53	90.98 7	88.92 280	77.55 170	97.07 92	83.13 408
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 45	91.09 38	87.00 107	91.55 139	72.64 158	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 138	95.88 18	87.41 31	95.94 137	92.48 209

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 162	90.54 57	91.01 172	83.61 63	93.75 35	94.65 65	89.76 19	95.78 35	86.42 47	97.97 49	90.55 288
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 46	91.92 16	84.47 173	96.56 6	58.83 361	89.04 94	92.74 110	91.40 6	96.12 5	96.06 29	87.23 50	95.57 44	79.42 144	98.74 6	99.00 2

PEN-MVS		90.03 48	91.88 19	84.48 172	96.57 5	58.88 358	88.95 95	93.19 87	91.62 5	96.01 7	96.16 27	87.02 54	95.60 43	78.69 152	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 49	89.74 60	90.83 36	93.16 86	80.37 74	91.91 41	93.11 91	81.10 89	95.32 14	97.24 10	72.94 254	94.85 76	85.07 71	97.78 59	97.26 16

DTE-MVSNet		89.98 50	91.91 18	84.21 182	96.51 7	57.84 369	88.93 96	92.84 106	91.92 4	96.16 4	96.23 24	86.95 55	95.99 12	79.05 148	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 50	89.84 58	90.41 43	94.91 37	84.50 48	89.49 86	93.98 44	79.68 106	92.09 69	93.89 110	83.80 89	93.10 153	82.67 106	98.04 41	93.64 145

TestfortrainingZip a		89.97 52	90.77 49	87.58 100	94.38 48	73.21 150	92.12 33	93.85 53	77.53 140	93.24 43	93.18 131	87.06 52	95.85 24	87.89 19	97.69 65	93.68 140

3Dnovator+	83.92 2	89.97 52	89.66 61	90.92 35	91.27 148	81.66 66	91.25 47	94.13 38	88.89 15	88.83 139	94.26 86	77.55 179	95.86 23	84.88 78	95.87 143	95.24 65

WR-MVS_H		89.91 54	91.31 34	85.71 138	96.32 9	62.39 301	89.54 84	93.31 81	90.21 12	95.57 11	95.66 37	81.42 135	95.90 17	80.94 123	98.80 3	98.84 5

OPM-MVS		89.80 55	89.97 56	89.27 64	94.76 40	79.86 78	86.76 137	92.78 109	78.78 120	92.51 62	93.64 121	88.13 37	93.84 120	84.83 80	97.55 78	94.10 119
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 56	89.27 67	91.30 29	93.51 73	84.79 44	89.89 73	90.63 182	70.00 257	94.55 19	96.67 17	87.94 41	93.59 132	84.27 86	95.97 133	95.52 56

anonymousdsp		89.73 57	88.88 77	92.27 8	89.82 184	86.67 18	90.51 59	90.20 203	69.87 258	95.06 15	96.14 28	84.28 85	93.07 154	87.68 24	96.34 115	97.09 20

test_djsdf		89.62 58	89.01 71	91.45 26	92.36 107	82.98 57	91.98 39	90.08 206	71.54 235	94.28 25	96.54 19	81.57 133	94.27 96	86.26 51	96.49 109	97.09 20

XVG-OURS-SEG-HR		89.59 59	89.37 65	90.28 46	94.47 43	85.95 27	86.84 133	93.91 48	80.07 102	86.75 197	93.26 128	93.64 2	90.93 218	84.60 83	90.75 316	93.97 123

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 94	80.87 93	91.13 86	93.19 130	86.22 66	95.97 14	82.23 112	97.18 90	90.45 290
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 61	88.81 80	91.19 32	93.38 79	84.72 45	89.70 76	90.29 200	69.27 265	94.39 21	96.38 21	86.02 69	93.52 137	83.96 88	95.92 139	95.34 60

CPTT-MVS		89.39 62	88.98 73	90.63 40	95.09 33	86.95 16	92.09 37	92.30 126	79.74 105	87.50 181	92.38 167	81.42 135	93.28 146	83.07 98	97.24 88	91.67 252

ACMH	76.49 14	89.34 63	91.14 36	83.96 190	92.50 103	70.36 199	89.55 82	93.84 56	81.89 81	94.70 17	95.44 44	90.69 9	88.31 298	83.33 94	98.30 27	93.20 169
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 64	89.12 68	89.84 53	88.67 214	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 46	84.94 77	89.44 272	74.12 221	96.10 128	94.45 99

APD_test2		89.30 64	89.12 68	89.84 53	88.67 214	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 46	84.94 77	89.44 272	74.12 221	96.10 128	94.45 99

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 364	88.66 103	92.06 133	90.78 7	95.67 8	95.17 51	81.80 130	95.54 47	79.00 149	98.69 10	98.95 4

XVG-OURS		89.18 67	88.83 79	90.23 47	94.28 52	86.11 26	85.91 152	93.60 67	80.16 100	89.13 135	93.44 124	83.82 88	90.98 215	83.86 90	95.30 165	93.60 149

DeepC-MVS	82.31 4	89.15 68	89.08 70	89.37 63	93.64 71	79.07 86	88.54 106	94.20 31	73.53 193	89.71 118	94.82 60	85.09 76	95.77 37	84.17 87	98.03 43	93.26 167
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 69	90.72 50	84.31 180	97.00 2	64.33 272	89.67 79	88.38 241	88.84 17	94.29 23	97.57 7	90.48 14	91.26 206	72.57 252	97.65 70	97.34 15

MSP-MVS		89.08 70	88.16 87	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 125	89.16 133	92.25 176	72.03 268	96.36 4	88.21 13	90.93 308	92.98 185
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
SD-MVS		88.96 71	89.88 57	86.22 125	91.63 133	77.07 112	89.82 74	93.77 58	78.90 118	92.88 52	92.29 174	86.11 67	90.22 246	86.24 54	97.24 88	91.36 260
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 72	88.45 83	90.38 44	94.92 36	85.85 31	89.70 76	91.27 164	78.20 128	86.69 201	92.28 175	80.36 148	95.06 71	86.17 55	96.49 109	90.22 294

Elysia		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 96	74.07 231	94.82 77	88.19 14	95.92 139	96.80 27

StellarMVS		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 96	74.07 231	94.82 77	88.19 14	95.92 139	96.80 27

test_0402		88.65 75	89.58 64	85.88 134	92.55 101	72.22 170	84.01 201	89.44 224	88.63 20	94.38 22	95.77 32	86.38 65	93.59 132	79.84 135	95.21 166	91.82 245

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 146	77.50 104	87.55 119	92.97 102	87.95 26	89.62 122	92.87 150	84.56 81	93.89 117	77.65 168	96.62 104	90.70 280

APD_test1		88.40 77	87.91 89	89.88 52	89.50 190	86.65 20	89.98 70	91.91 139	84.26 53	90.87 96	93.92 109	82.18 118	89.29 276	73.75 229	94.81 185	93.70 139

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 169	65.27 261	88.86 97	93.02 99	87.15 30	93.05 50	97.10 11	82.28 116	92.02 183	76.70 180	97.99 46	96.88 26

PS-MVSNAJss		88.31 79	87.90 90	89.56 60	93.31 81	77.96 99	87.94 115	91.97 136	70.73 247	94.19 26	96.67 17	76.94 193	94.57 88	83.07 98	96.28 117	96.15 38

OMC-MVS		88.19 80	87.52 94	90.19 48	91.94 124	81.68 65	87.49 122	93.17 88	76.02 153	88.64 144	91.22 216	84.24 86	93.37 144	77.97 166	97.03 93	95.52 56

CS-MVS		88.14 81	87.67 93	89.54 61	89.56 188	79.18 85	90.47 60	94.77 17	79.37 112	84.32 268	89.33 283	83.87 87	94.53 91	82.45 108	94.89 181	94.90 76

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 167	67.85 292	86.63 202	94.84 59	79.58 156	95.96 15	87.62 25	94.50 194	94.56 90
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 83	89.79 59	82.98 221	93.26 83	63.94 276	91.10 50	89.64 218	85.07 45	90.91 92	91.09 222	89.16 25	91.87 188	82.03 113	95.87 143	93.13 172

EC-MVSNet		88.01 84	88.32 86	87.09 104	89.28 195	72.03 173	90.31 64	96.31 4	80.88 92	85.12 243	89.67 276	84.47 83	95.46 54	82.56 107	96.26 120	93.77 137

RPSCF		88.00 85	86.93 108	91.22 31	90.08 177	89.30 5	89.68 78	91.11 168	79.26 113	89.68 119	94.81 63	82.44 107	87.74 309	76.54 185	88.74 352	96.61 32

AllTest		87.97 86	87.40 98	89.68 56	91.59 134	83.40 52	89.50 85	95.44 11	79.47 108	88.00 163	93.03 141	82.66 104	91.47 196	70.81 266	96.14 125	94.16 115

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 273	84.38 193	91.29 160	84.88 48	92.06 70	93.84 111	86.45 62	93.73 122	73.22 243	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 179	69.73 207	87.86 116	94.20 31	74.04 185	92.70 60	94.66 64	85.88 70	91.50 195	79.72 137	97.32 86	96.50 34

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 110	85.25 170	91.23 165	77.31 143	87.07 191	91.47 206	82.94 99	94.71 81	84.67 82	96.27 119	92.62 200

HQP_MVS		87.75 90	87.43 97	88.70 77	93.45 75	76.42 119	89.45 87	93.61 65	79.44 110	86.55 203	92.95 147	74.84 218	95.22 63	80.78 126	95.83 145	94.46 97

sc_t1		87.70 91	88.94 74	83.99 188	93.47 74	67.15 238	85.05 175	88.21 248	86.81 32	91.87 74	97.65 5	85.51 74	87.91 304	74.22 216	97.63 71	96.92 25

MM		87.64 92	87.15 100	89.09 69	89.51 189	76.39 121	88.68 102	86.76 279	84.54 50	83.58 287	93.78 114	73.36 249	96.48 2	87.98 17	96.21 121	94.41 104

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 207	92.03 120	62.26 305	91.49 45	92.62 114	88.07 25	88.07 160	96.17 26	72.24 263	95.79 34	84.85 79	94.16 207	92.58 204

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 170	78.77 121	84.85 255	90.89 233	80.85 141	95.29 60	81.14 121	95.32 162	92.34 221

DeepPCF-MVS	81.24 5	87.28 95	86.21 119	90.49 42	91.48 143	84.90 42	83.41 224	92.38 122	70.25 254	89.35 130	90.68 243	82.85 102	94.57 88	79.55 141	95.95 136	92.00 240

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 217	87.84 117	88.05 249	81.66 83	94.64 18	96.53 20	65.94 305	94.75 80	83.02 100	96.83 98	95.41 58

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 105	89.67 186	75.87 129	84.60 186	89.74 213	74.40 182	89.92 114	93.41 125	80.45 146	90.63 233	86.66 46	94.37 200	94.73 87

SPE-MVS-test		87.00 98	86.43 115	88.71 76	89.46 191	77.46 105	89.42 89	95.73 7	77.87 134	81.64 329	87.25 328	82.43 108	94.53 91	77.65 168	96.46 111	94.14 117

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 227	83.80 211	92.87 104	80.37 96	89.61 124	91.81 192	77.72 175	94.18 104	75.00 209	98.53 16	96.99 24

Vis-MVSNet		86.86 100	86.58 112	87.72 97	92.09 117	77.43 107	87.35 123	92.09 132	78.87 119	84.27 273	94.05 98	78.35 167	93.65 125	80.54 130	91.58 292	92.08 236
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 128	92.86 94	67.02 242	82.55 251	91.56 150	83.08 70	90.92 90	91.82 191	78.25 168	93.99 111	74.16 219	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 242	83.16 234	92.21 127	81.73 82	90.92 90	91.97 182	77.20 187	93.99 111	74.16 219	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 266	65.22 262	84.16 197	94.23 28	77.89 132	91.28 85	93.66 120	84.35 84	92.71 163	80.07 131	94.87 184	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 104	86.52 113	87.18 103	85.94 305	78.30 92	86.93 130	92.20 128	65.94 312	89.16 133	93.16 136	83.10 97	89.89 261	87.81 21	94.43 198	93.35 160

tt0320-xc		86.67 105	88.41 84	81.44 265	93.45 75	60.44 335	83.96 203	88.50 237	87.26 29	90.90 94	97.90 3	85.61 71	86.40 335	70.14 278	98.01 45	97.47 14

IS-MVSNet		86.66 106	86.82 111	86.17 128	92.05 119	66.87 246	91.21 48	88.64 234	86.30 37	89.60 125	92.59 159	69.22 286	94.91 75	73.89 226	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 266	93.57 72	60.73 332	84.37 194	88.61 236	87.00 31	90.75 97	97.98 2	85.54 73	86.45 333	69.75 283	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 229	63.28 283	86.64 140	92.20 128	75.42 167	92.81 57	94.50 72	74.05 234	94.06 110	83.88 89	96.28 117	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 251	92.22 113	62.28 304	84.66 185	89.15 228	83.54 65	89.85 115	97.32 8	88.08 39	86.80 326	70.43 275	97.30 87	96.62 31

NormalMVS		86.47 110	85.32 142	89.94 51	94.43 44	80.42 72	88.63 104	93.59 68	74.56 177	85.12 243	90.34 256	66.19 302	94.20 101	76.57 183	98.44 20	95.19 68

PHI-MVS		86.38 111	85.81 129	88.08 92	88.44 223	77.34 108	89.35 91	93.05 95	73.15 206	84.76 257	87.70 317	78.87 161	94.18 104	80.67 128	96.29 116	92.73 192

CSCG		86.26 112	86.47 114	85.60 140	90.87 161	74.26 139	87.98 114	91.85 140	80.35 97	89.54 128	88.01 304	79.09 159	92.13 179	75.51 202	95.06 173	90.41 291

DeepC-MVS_fast	80.27 8	86.23 113	85.65 135	87.96 95	91.30 146	76.92 113	87.19 125	91.99 135	70.56 248	84.96 250	90.69 242	80.01 152	95.14 68	78.37 155	95.78 149	91.82 245
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 114	86.83 110	84.36 176	87.82 237	62.35 303	86.42 144	91.33 159	76.78 147	92.73 59	94.48 74	73.41 246	93.72 123	83.10 97	95.41 158	97.01 23

Anonymous20240529		86.20 115	87.13 101	83.42 209	90.19 174	64.55 269	84.55 188	90.71 179	85.85 40	89.94 113	95.24 50	82.13 119	90.40 241	69.19 290	96.40 114	95.31 62

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 274	70.38 198	85.31 169	92.61 116	75.59 163	88.32 155	92.87 150	82.22 117	88.63 289	88.80 9	92.82 254	89.83 304

test_fmvsmconf0.1_n		86.18 117	85.88 127	87.08 105	85.26 321	78.25 93	85.82 156	91.82 142	65.33 327	88.55 146	92.35 173	82.62 106	89.80 263	86.87 42	94.32 202	93.18 171

CDPH-MVS		86.17 118	85.54 136	88.05 94	92.25 111	75.45 132	83.85 208	92.01 134	65.91 314	86.19 214	91.75 196	83.77 90	94.98 73	77.43 173	96.71 102	93.73 138

NR-MVSNet		86.00 119	86.22 118	85.34 147	93.24 84	64.56 268	82.21 265	90.46 188	80.99 90	88.42 151	91.97 182	77.56 178	93.85 118	72.46 253	98.65 12	97.61 10

train_agg		85.98 120	85.28 143	88.07 93	92.34 108	79.70 80	83.94 204	90.32 195	65.79 316	84.49 262	90.97 227	81.93 125	93.63 127	81.21 120	96.54 107	90.88 274

KinetiMVS		85.95 121	86.10 122	85.50 144	87.56 247	69.78 205	83.70 214	89.83 212	80.42 95	87.76 174	93.24 129	73.76 240	91.54 194	85.03 73	93.62 228	95.19 68

FC-MVSNet-test		85.93 122	87.05 104	82.58 234	92.25 111	56.44 380	85.75 157	93.09 93	77.33 142	91.94 73	94.65 65	74.78 220	93.41 143	75.11 208	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 137	86.99 108	84.77 330	78.21 94	85.40 167	91.39 157	65.32 328	87.72 176	91.81 192	82.33 111	89.78 264	86.68 44	94.20 205	92.99 183

Effi-MVS+-dtu		85.82 124	83.38 195	93.14 4	87.13 261	91.15 3	87.70 118	88.42 240	74.57 176	83.56 288	85.65 352	78.49 166	94.21 100	72.04 255	92.88 250	94.05 121

TAPA-MVS	77.73 12	85.71 125	84.83 153	88.37 86	88.78 213	79.72 79	87.15 127	93.50 71	69.17 266	85.80 226	89.56 277	80.76 142	92.13 179	73.21 248	95.51 156	93.25 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 126	86.14 120	83.58 203	87.97 231	67.13 239	87.55 119	94.32 22	73.44 196	88.47 149	87.54 320	86.45 62	91.06 213	75.76 198	93.76 219	92.54 207

canonicalmvs		85.50 126	86.14 120	83.58 203	87.97 231	67.13 239	87.55 119	94.32 22	73.44 196	88.47 149	87.54 320	86.45 62	91.06 213	75.76 198	93.76 219	92.54 207

fmvsm_s_conf0.5_n_8		85.48 128	85.75 132	84.68 167	87.10 264	69.98 203	84.28 195	92.68 111	74.77 173	87.90 167	92.36 172	73.94 235	90.41 240	85.95 62	92.74 256	93.66 141

EPP-MVSNet		85.47 129	85.04 148	86.77 113	91.52 142	69.37 212	91.63 44	87.98 252	81.51 85	87.05 192	91.83 190	66.18 304	95.29 60	70.75 269	96.89 95	95.64 53

GeoE		85.45 130	85.81 129	84.37 174	90.08 177	67.07 241	85.86 155	91.39 157	72.33 224	87.59 178	90.25 261	84.85 79	92.37 173	78.00 164	91.94 281	93.66 141

MGCNet		85.37 131	84.58 165	87.75 96	85.28 320	73.36 144	86.54 143	85.71 296	77.56 139	81.78 327	92.47 165	70.29 280	96.02 11	85.59 65	95.96 134	93.87 129

FIs		85.35 132	86.27 117	82.60 233	91.86 126	57.31 373	85.10 174	93.05 95	75.83 158	91.02 89	93.97 102	73.57 242	92.91 161	73.97 225	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 133	85.36 141	84.81 160	85.80 308	76.13 125	85.15 173	92.32 125	61.40 363	91.33 82	90.85 236	83.76 91	86.16 341	84.31 85	93.28 237	92.15 234

casdiffmvs		85.21 134	85.85 128	83.31 212	86.17 297	62.77 290	83.03 236	93.93 47	74.69 175	88.21 157	92.68 158	82.29 115	91.89 187	77.87 167	93.75 222	95.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_10		85.20 135	85.25 144	85.02 155	86.01 303	71.31 185	84.96 176	91.76 146	69.10 268	88.90 136	92.56 162	73.84 238	90.63 233	86.88 41	93.26 238	93.13 172

baseline		85.20 135	85.93 125	83.02 219	86.30 292	62.37 302	84.55 188	93.96 45	74.48 179	87.12 186	92.03 181	82.30 113	91.94 184	78.39 154	94.21 204	94.74 86

SSM_0404		85.16 137	85.09 146	85.36 146	90.14 176	69.52 210	86.17 149	91.58 148	74.41 180	86.55 203	91.49 203	78.54 162	93.97 113	73.71 230	93.21 242	92.59 203

K. test v3		85.14 138	84.73 155	86.37 119	91.13 155	69.63 209	85.45 165	76.68 381	84.06 56	92.44 64	96.99 13	62.03 332	94.65 84	80.58 129	93.24 239	94.83 83

mmtdpeth		85.13 139	85.78 131	83.17 217	84.65 332	74.71 135	85.87 154	90.35 194	77.94 131	83.82 280	96.96 15	77.75 173	80.03 400	78.44 153	96.21 121	94.79 85

EI-MVSNet-Vis-set		85.12 140	84.53 168	86.88 110	84.01 345	72.76 155	83.91 207	85.18 306	80.44 94	88.75 141	85.49 356	80.08 151	91.92 185	82.02 114	90.85 313	95.97 44

fmvsm_l_conf0.5_n_3		85.11 141	84.96 150	85.56 141	87.49 250	75.69 131	84.71 183	90.61 184	67.64 296	84.88 253	92.05 180	82.30 113	88.36 296	83.84 91	91.10 301	92.62 200

MGCFI-Net		85.04 142	85.95 124	82.31 244	87.52 248	63.59 279	86.23 148	93.96 45	73.46 194	88.07 160	87.83 315	86.46 61	90.87 223	76.17 192	93.89 215	92.47 211

EI-MVSNet-UG-set		85.04 142	84.44 171	86.85 111	83.87 349	72.52 164	83.82 209	85.15 307	80.27 99	88.75 141	85.45 358	79.95 153	91.90 186	81.92 117	90.80 315	96.13 39

X-MVStestdata		85.04 142	82.70 214	92.08 9	95.64 24	86.25 22	92.64 20	93.33 78	85.07 45	89.99 110	16.05 477	86.57 59	95.80 31	87.35 33	97.62 73	94.20 111

MSLP-MVS++		85.00 145	86.03 123	81.90 251	91.84 129	71.56 183	86.75 138	93.02 99	75.95 156	87.12 186	89.39 281	77.98 170	89.40 275	77.46 171	94.78 186	84.75 380

F-COLMAP		84.97 146	83.42 194	89.63 58	92.39 106	83.40 52	88.83 98	91.92 138	73.19 205	80.18 351	89.15 287	77.04 191	93.28 146	65.82 323	92.28 270	92.21 230

SSM_0407		84.89 147	84.85 152	85.01 156	89.13 199	68.97 220	85.60 161	91.58 148	74.41 180	85.68 227	91.49 203	78.54 162	93.69 124	73.71 230	93.47 230	92.38 218

balanced_conf03		84.80 148	85.40 139	83.00 220	88.95 206	61.44 313	90.42 63	92.37 124	71.48 237	88.72 143	93.13 137	70.16 282	95.15 67	79.26 146	94.11 208	92.41 213

3Dnovator	80.37 7	84.80 148	84.71 158	85.06 153	86.36 290	74.71 135	88.77 100	90.00 208	75.65 161	84.96 250	93.17 135	74.06 233	91.19 208	78.28 158	91.09 302	89.29 314

SymmetryMVS		84.79 150	83.54 189	88.55 79	92.44 105	80.42 72	88.63 104	82.37 341	74.56 177	85.12 243	90.34 256	66.19 302	94.20 101	76.57 183	95.68 153	91.03 268

IterMVS-LS		84.73 151	84.98 149	83.96 190	87.35 254	63.66 277	83.25 229	89.88 211	76.06 151	89.62 122	92.37 170	73.40 248	92.52 168	78.16 161	94.77 188	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 152	84.34 176	85.49 145	90.18 175	75.86 130	79.23 320	87.13 269	73.35 198	85.56 234	89.34 282	83.60 93	90.50 237	76.64 182	94.05 212	90.09 300

HQP-MVS		84.61 153	84.06 181	86.27 122	91.19 151	70.66 193	84.77 178	92.68 111	73.30 201	80.55 343	90.17 266	72.10 264	94.61 86	77.30 175	94.47 196	93.56 153

v1192		84.57 154	84.69 160	84.21 182	87.75 239	62.88 287	83.02 237	91.43 154	69.08 269	89.98 112	90.89 233	72.70 258	93.62 130	82.41 109	94.97 178	96.13 39

fmvsm_s_conf0.5_n_11		84.56 155	84.69 160	84.15 185	86.53 280	71.29 186	85.53 162	92.62 114	70.54 249	82.75 304	91.20 218	77.33 182	88.55 292	83.80 92	91.93 282	92.61 202

fmvsm_s_conf0.5_n_5		84.56 155	84.71 158	84.11 186	87.92 234	72.09 172	84.80 177	88.64 234	64.43 337	88.77 140	91.78 194	78.07 169	87.95 303	85.85 63	92.18 274	92.30 223

FMVSNet1		84.55 157	85.45 138	81.85 253	90.27 173	61.05 323	86.83 134	88.27 245	78.57 124	89.66 121	95.64 38	75.43 210	90.68 230	69.09 291	95.33 161	93.82 132

v1144		84.54 158	84.72 157	84.00 187	87.67 243	62.55 294	82.97 239	90.93 175	70.32 253	89.80 116	90.99 226	73.50 243	93.48 139	81.69 119	94.65 192	95.97 44

Gipuma		84.44 159	86.33 116	78.78 311	84.20 342	73.57 143	89.55 82	90.44 189	84.24 54	84.38 265	94.89 57	76.35 206	80.40 397	76.14 193	96.80 100	82.36 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 160	84.27 177	84.74 163	87.25 257	70.84 192	83.55 219	88.45 239	68.64 278	86.29 213	91.31 212	74.97 216	88.42 294	87.87 20	90.07 331	94.95 75

MCST-MVS		84.36 161	83.93 185	85.63 139	91.59 134	71.58 181	83.52 220	92.13 130	61.82 356	83.96 278	89.75 274	79.93 154	93.46 140	78.33 157	94.34 201	91.87 244

VDDNet		84.35 162	85.39 140	81.25 268	95.13 32	59.32 349	85.42 166	81.11 352	86.41 36	87.41 182	96.21 25	73.61 241	90.61 235	66.33 316	96.85 96	93.81 135

ETV-MVS		84.31 163	83.91 186	85.52 142	88.58 219	70.40 197	84.50 192	93.37 73	78.76 122	84.07 276	78.72 431	80.39 147	95.13 69	73.82 228	92.98 248	91.04 267

v1240		84.30 164	84.51 169	83.65 200	87.65 244	61.26 319	82.85 243	91.54 151	67.94 289	90.68 99	90.65 247	71.71 272	93.64 126	82.84 103	94.78 186	96.07 41

MVS_111021_LR		84.28 165	83.76 187	85.83 136	89.23 197	83.07 55	80.99 288	83.56 329	72.71 216	86.07 217	89.07 289	81.75 132	86.19 340	77.11 177	93.36 233	88.24 333

h-mvs33		84.25 166	82.76 213	88.72 75	91.82 131	82.60 60	84.00 202	84.98 313	71.27 238	86.70 199	90.55 252	63.04 329	93.92 116	78.26 159	94.20 205	89.63 306

v144192		84.24 167	84.41 172	83.71 199	87.59 246	61.57 312	82.95 240	91.03 171	67.82 293	89.80 116	90.49 253	73.28 250	93.51 138	81.88 118	94.89 181	96.04 43

dcpmvs_2		84.23 168	85.14 145	81.50 263	88.61 218	61.98 309	82.90 242	93.11 91	68.66 277	92.77 58	92.39 166	78.50 165	87.63 312	76.99 179	92.30 267	94.90 76

v1921920		84.23 168	84.37 174	83.79 195	87.64 245	61.71 311	82.91 241	91.20 166	67.94 289	90.06 107	90.34 256	72.04 267	93.59 132	82.32 110	94.91 179	96.07 41

VDD-MVS		84.23 168	84.58 165	83.20 215	91.17 154	65.16 264	83.25 229	84.97 314	79.79 104	87.18 185	94.27 83	74.77 221	90.89 221	69.24 287	96.54 107	93.55 155

v2v482		84.09 171	84.24 178	83.62 201	87.13 261	61.40 314	82.71 246	89.71 216	72.19 227	89.55 126	91.41 207	70.70 278	93.20 148	81.02 122	93.76 219	96.25 37

EG-PatchMatch MVS		84.08 172	84.11 180	83.98 189	92.22 113	72.61 161	82.20 267	87.02 275	72.63 217	88.86 137	91.02 225	78.52 164	91.11 211	73.41 238	91.09 302	88.21 334

E2		84.06 173	84.61 162	82.40 242	87.49 250	61.31 316	81.03 286	93.36 74	71.83 232	86.02 219	91.87 184	82.91 100	91.37 203	75.66 200	91.33 296	94.53 94

E3		84.06 173	84.61 162	82.40 242	87.49 250	61.30 317	81.03 286	93.36 74	71.83 232	86.01 220	91.87 184	82.91 100	91.36 204	75.66 200	91.33 296	94.53 94

fmvsm_s_conf0.5_n_6		84.05 175	84.14 179	83.81 193	87.75 239	71.17 188	83.42 223	91.10 169	67.90 291	84.53 260	90.70 241	73.01 253	88.73 286	85.09 70	93.72 224	91.53 257

DP-MVS Recon		84.05 175	83.22 198	86.52 117	91.73 132	75.27 133	83.23 231	92.40 120	72.04 229	82.04 318	88.33 300	77.91 172	93.95 115	66.17 317	95.12 171	90.34 293

viewmacassd2359aftdt		84.04 177	84.78 154	81.81 256	86.43 284	60.32 337	81.95 269	92.82 107	71.56 234	86.06 218	92.98 143	81.79 131	90.28 242	76.18 191	93.24 239	94.82 84

TransMVSNet (Re)		84.02 178	85.74 133	78.85 310	91.00 158	55.20 392	82.29 261	87.26 264	79.65 107	88.38 153	95.52 41	83.00 98	86.88 324	67.97 305	96.60 105	94.45 99

Baseline_NR-MVSNet		84.00 179	85.90 126	78.29 322	91.47 144	53.44 404	82.29 261	87.00 278	79.06 116	89.55 126	95.72 36	77.20 187	86.14 342	72.30 254	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 180	84.46 170	82.53 237	86.11 300	70.65 195	82.45 256	89.17 227	67.72 295	86.74 198	91.49 203	79.20 157	85.86 351	84.71 81	92.60 260	91.07 266

TSAR-MVS + GP.		83.95 181	82.69 215	87.72 97	89.27 196	81.45 67	83.72 213	81.58 350	74.73 174	85.66 230	86.06 347	72.56 260	92.69 165	75.44 204	95.21 166	89.01 327

LuminaMVS		83.94 182	83.51 190	85.23 148	89.78 185	71.74 176	84.76 181	87.27 263	72.60 218	89.31 131	90.60 251	64.04 318	90.95 216	79.08 147	94.11 208	92.99 183

alignmvs		83.94 182	83.98 183	83.80 194	87.80 238	67.88 234	84.54 190	91.42 156	73.27 204	88.41 152	87.96 305	72.33 261	90.83 224	76.02 195	94.11 208	92.69 196

Effi-MVS+		83.90 184	84.01 182	83.57 205	87.22 259	65.61 260	86.55 142	92.40 120	78.64 123	81.34 334	84.18 377	83.65 92	92.93 159	74.22 216	87.87 366	92.17 233

fmvsm_s_conf0.1_n_2		83.82 185	83.49 191	84.84 158	85.99 304	70.19 201	80.93 289	87.58 259	67.26 302	87.94 166	92.37 170	71.40 274	88.01 300	86.03 57	91.87 283	96.31 36

mvs5depth		83.82 185	84.54 167	81.68 259	82.23 374	68.65 225	86.89 131	89.90 210	80.02 103	87.74 175	97.86 4	64.19 317	82.02 385	76.37 187	95.63 155	94.35 106

CANet		83.79 187	82.85 212	86.63 114	86.17 297	72.21 171	83.76 212	91.43 154	77.24 144	74.39 406	87.45 324	75.36 211	95.42 56	77.03 178	92.83 253	92.25 229

pm-mvs1		83.69 188	84.95 151	79.91 296	90.04 181	59.66 346	82.43 257	87.44 260	75.52 165	87.85 170	95.26 49	81.25 137	85.65 355	68.74 297	96.04 130	94.42 103

AdaColmap		83.66 189	83.69 188	83.57 205	90.05 180	72.26 169	86.29 146	90.00 208	78.19 129	81.65 328	87.16 330	83.40 95	94.24 99	61.69 359	94.76 189	84.21 390

viewdifsd2359ckpt09		83.64 190	83.18 201	85.03 154	87.26 256	66.99 244	85.32 168	93.83 57	65.57 322	84.99 249	89.40 280	77.30 183	93.57 135	71.16 265	93.80 218	94.54 93

MIMVSNet1		83.63 191	84.59 164	80.74 279	94.06 62	62.77 290	82.72 245	84.53 321	77.57 138	90.34 103	95.92 31	76.88 199	85.83 352	61.88 357	97.42 83	93.62 147

fmvsm_s_conf0.5_n_2		83.62 192	83.29 197	84.62 168	85.43 318	70.18 202	80.61 295	87.24 265	67.14 303	87.79 172	91.87 184	71.79 271	87.98 302	86.00 61	91.77 286	95.71 50

test_fmvsm_n_1920		83.60 193	82.89 209	85.74 137	85.22 322	77.74 102	84.12 199	90.48 186	59.87 383	86.45 212	91.12 221	75.65 208	85.89 349	82.28 111	90.87 311	93.58 151

WR-MVS		83.56 194	84.40 173	81.06 273	93.43 78	54.88 393	78.67 329	85.02 311	81.24 87	90.74 98	91.56 201	72.85 255	91.08 212	68.00 304	98.04 41	97.23 17

CNLPA		83.55 195	83.10 204	84.90 157	89.34 194	83.87 50	84.54 190	88.77 231	79.09 115	83.54 289	88.66 297	74.87 217	81.73 387	66.84 311	92.29 269	89.11 320

viewcassd2359sk11		83.53 196	83.96 184	82.25 245	86.97 273	61.13 321	80.80 293	93.22 86	70.97 244	85.36 238	91.08 223	81.84 129	91.29 205	74.79 211	90.58 327	94.33 108

LCM-MVSNet-Re		83.48 197	85.06 147	78.75 312	85.94 305	55.75 386	80.05 301	94.27 25	76.47 148	96.09 6	94.54 71	83.31 96	89.75 267	59.95 370	94.89 181	90.75 277

hse-mvs2		83.47 198	81.81 230	88.47 82	91.03 157	82.27 61	82.61 247	83.69 327	71.27 238	86.70 199	86.05 348	63.04 329	92.41 171	78.26 159	93.62 228	90.71 279

V42		83.47 198	83.37 196	83.75 197	83.16 368	63.33 282	81.31 280	90.23 202	69.51 262	90.91 92	90.81 238	74.16 230	92.29 177	80.06 132	90.22 329	95.62 54

VPA-MVSNet		83.47 198	84.73 155	79.69 301	90.29 172	57.52 372	81.30 282	88.69 233	76.29 149	87.58 180	94.44 75	80.60 145	87.20 318	66.60 314	96.82 99	94.34 107

mamba_0408		83.44 201	82.88 210	85.11 151	89.13 199	68.97 220	72.73 404	91.28 161	72.90 210	85.68 227	90.61 249	76.78 200	93.97 113	73.37 240	93.47 230	92.38 218

viewdifsd2359ckpt07		83.41 202	84.35 175	80.56 285	85.84 307	58.93 357	79.47 312	91.28 161	73.01 209	87.59 178	92.07 179	85.24 75	88.68 287	73.59 235	91.11 300	94.09 120

PAPM_NR		83.23 203	83.19 200	83.33 211	90.90 160	65.98 256	88.19 109	90.78 178	78.13 130	80.87 339	87.92 309	73.49 245	92.42 170	70.07 279	88.40 355	91.60 254

CLD-MVS		83.18 204	82.64 216	84.79 161	89.05 202	67.82 235	77.93 339	92.52 118	68.33 281	85.07 246	81.54 406	82.06 122	92.96 157	69.35 286	97.91 54	93.57 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 205	85.68 134	75.65 360	81.24 386	45.26 448	79.94 303	92.91 103	83.83 57	91.33 82	96.88 16	80.25 149	85.92 345	68.89 294	95.89 142	95.76 48

FA-MVS(test-final)		83.13 206	83.02 205	83.43 208	86.16 299	66.08 255	88.00 113	88.36 242	75.55 164	85.02 247	92.75 156	65.12 311	92.50 169	74.94 210	91.30 298	91.72 249

114514_t		83.10 207	82.54 219	84.77 162	92.90 91	69.10 219	86.65 139	90.62 183	54.66 415	81.46 331	90.81 238	76.98 192	94.38 94	72.62 251	96.18 123	90.82 276

RRT-MVS		82.97 208	83.44 192	81.57 261	85.06 325	58.04 367	87.20 124	90.37 192	77.88 133	88.59 145	93.70 119	63.17 326	93.05 155	76.49 186	88.47 354	93.62 147

viewmanbaseed2359cas		82.95 209	83.43 193	81.52 262	85.18 323	60.03 342	81.36 279	92.38 122	69.55 261	84.84 256	91.38 208	79.85 155	90.09 255	74.22 216	92.09 276	94.43 102

BP-MVS1		82.81 210	81.67 232	86.23 123	87.88 236	68.53 226	86.06 151	84.36 322	75.65 161	85.14 242	90.19 263	45.84 418	94.42 93	85.18 69	94.72 190	95.75 49

UGNet		82.78 211	81.64 233	86.21 126	86.20 296	76.24 123	86.86 132	85.68 297	77.07 145	73.76 410	92.82 152	69.64 283	91.82 190	69.04 293	93.69 225	90.56 287
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		82.75 212	81.93 228	85.19 149	82.08 375	80.15 76	85.53 162	88.76 232	68.01 286	85.58 233	87.75 316	71.80 270	86.85 325	74.02 224	93.87 216	88.58 330

EI-MVSNet		82.61 213	82.42 221	83.20 215	83.25 365	63.66 277	83.50 221	85.07 308	76.06 151	86.55 203	85.10 364	73.41 246	90.25 243	78.15 163	90.67 322	95.68 52

QAPM		82.59 214	82.59 218	82.58 234	86.44 283	66.69 247	89.94 72	90.36 193	67.97 288	84.94 252	92.58 161	72.71 257	92.18 178	70.63 272	87.73 369	88.85 328

fmvsm_s_conf0.1_n_a		82.58 215	81.93 228	84.50 171	87.68 242	73.35 145	86.14 150	77.70 370	61.64 361	85.02 247	91.62 198	77.75 173	86.24 337	82.79 104	87.07 377	93.91 127

Fast-Effi-MVS+-dtu		82.54 216	81.41 242	85.90 133	85.60 313	76.53 118	83.07 235	89.62 220	73.02 208	79.11 361	83.51 382	80.74 143	90.24 245	68.76 296	89.29 342	90.94 271

MVS_Test		82.47 217	83.22 198	80.22 292	82.62 373	57.75 371	82.54 252	91.96 137	71.16 242	82.89 300	92.52 164	77.41 180	90.50 237	80.04 133	87.84 368	92.40 215

viewdifsd2359ckpt11		82.46 218	82.98 207	80.88 276	83.53 352	61.00 326	79.46 313	85.97 292	69.48 263	87.89 168	91.31 212	82.10 120	88.61 290	74.28 214	92.86 251	93.02 179

viewmsd2359difaftdt		82.46 218	82.99 206	80.88 276	83.52 353	61.00 326	79.46 313	85.97 292	69.48 263	87.89 168	91.31 212	82.10 120	88.61 290	74.28 214	92.86 251	93.02 179

v148		82.31 220	82.48 220	81.81 256	85.59 314	59.66 346	81.47 277	86.02 290	72.85 212	88.05 162	90.65 247	70.73 277	90.91 220	75.15 207	91.79 284	94.87 78

API-MVS		82.28 221	82.61 217	81.30 267	86.29 293	69.79 204	88.71 101	87.67 258	78.42 126	82.15 314	84.15 378	77.98 170	91.59 193	65.39 326	92.75 255	82.51 417

MVSFormer		82.23 222	81.57 238	84.19 184	85.54 315	69.26 214	91.98 39	90.08 206	71.54 235	76.23 386	85.07 367	58.69 354	94.27 96	86.26 51	88.77 350	89.03 325

viewdifsd2359ckpt13		82.22 223	81.98 227	82.95 223	85.48 317	64.44 270	83.17 233	92.11 131	65.97 311	83.72 283	89.73 275	77.60 177	90.80 226	70.61 273	89.42 340	93.59 150

fmvsm_s_conf0.5_n_a		82.21 224	81.51 241	84.32 179	86.56 279	73.35 145	85.46 164	77.30 374	61.81 357	84.51 261	90.88 235	77.36 181	86.21 339	82.72 105	86.97 382	93.38 159

EIA-MVS		82.19 225	81.23 249	85.10 152	87.95 233	69.17 218	83.22 232	93.33 78	70.42 250	78.58 366	79.77 422	77.29 184	94.20 101	71.51 261	88.96 348	91.93 243

GDP-MVS		82.17 226	80.85 257	86.15 130	88.65 216	68.95 223	85.65 160	93.02 99	68.42 279	83.73 282	89.54 278	45.07 429	94.31 95	79.66 139	93.87 216	95.19 68

fmvsm_s_conf0.1_n		82.17 226	81.59 236	83.94 192	86.87 277	71.57 182	85.19 172	77.42 373	62.27 355	84.47 264	91.33 210	76.43 203	85.91 347	83.14 95	87.14 375	94.33 108

PCF-MVS	74.62 15	82.15 228	80.92 255	85.84 135	89.43 192	72.30 168	80.53 296	91.82 142	57.36 399	87.81 171	89.92 271	77.67 176	93.63 127	58.69 375	95.08 172	91.58 255
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 229	80.31 264	87.45 101	90.86 162	80.29 75	85.88 153	90.65 181	68.17 284	76.32 385	86.33 342	73.12 252	92.61 167	61.40 362	90.02 333	89.44 309
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 230	81.54 240	83.60 202	83.94 346	73.90 141	83.35 226	86.10 286	58.97 385	83.80 281	90.36 255	74.23 228	86.94 323	82.90 101	90.22 329	89.94 302

fmvsm_s_conf0.5_n_7		82.04 231	82.05 225	82.01 249	86.98 272	71.07 189	78.70 327	89.45 223	68.07 285	78.14 368	91.61 199	74.19 229	85.92 345	79.61 140	91.73 287	89.05 324

GBi-Net		82.02 232	82.07 223	81.85 253	86.38 287	61.05 323	86.83 134	88.27 245	72.43 219	86.00 221	95.64 38	63.78 322	90.68 230	65.95 319	93.34 234	93.82 132

test1		82.02 232	82.07 223	81.85 253	86.38 287	61.05 323	86.83 134	88.27 245	72.43 219	86.00 221	95.64 38	63.78 322	90.68 230	65.95 319	93.34 234	93.82 132

OpenMVS	76.72 13	81.98 234	82.00 226	81.93 250	84.42 337	68.22 229	88.50 107	89.48 222	66.92 306	81.80 325	91.86 187	72.59 259	90.16 249	71.19 264	91.25 299	87.40 350

KD-MVS_self_test		81.93 235	83.14 203	78.30 321	84.75 331	52.75 408	80.37 298	89.42 225	70.24 255	90.26 105	93.39 126	74.55 227	86.77 327	68.61 299	96.64 103	95.38 59

fmvsm_s_conf0.5_n		81.91 236	81.30 246	83.75 197	86.02 302	71.56 183	84.73 182	77.11 377	62.44 352	84.00 277	90.68 243	76.42 204	85.89 349	83.14 95	87.11 376	93.81 135

SDMVSNet		81.90 237	83.17 202	78.10 325	88.81 211	62.45 300	76.08 373	86.05 289	73.67 190	83.41 290	93.04 139	82.35 110	80.65 394	70.06 280	95.03 174	91.21 262

tfpnnormal		81.79 238	82.95 208	78.31 320	88.93 207	55.40 388	80.83 292	82.85 336	76.81 146	85.90 225	94.14 93	74.58 225	86.51 331	66.82 312	95.68 153	93.01 182

AstraMVS		81.67 239	81.40 243	82.48 239	87.06 269	66.47 250	81.41 278	81.68 347	68.78 274	88.00 163	90.95 231	65.70 307	87.86 308	76.66 181	92.38 264	93.12 175

c3_l		81.64 240	81.59 236	81.79 258	80.86 392	59.15 354	78.61 330	90.18 204	68.36 280	87.20 184	87.11 332	69.39 284	91.62 192	78.16 161	94.43 198	94.60 89

guyue		81.57 241	81.37 245	82.15 246	86.39 285	66.13 254	81.54 276	83.21 331	69.79 259	87.77 173	89.95 269	65.36 310	87.64 311	75.88 196	92.49 262	92.67 197

PVSNet_Blended_VisFu		81.55 242	80.49 262	84.70 166	91.58 137	73.24 149	84.21 196	91.67 147	62.86 346	80.94 337	87.16 330	67.27 296	92.87 162	69.82 282	88.94 349	87.99 340

fmvsm_l_conf0.5_n_a		81.46 243	80.87 256	83.25 213	83.73 351	73.21 150	83.00 238	85.59 299	58.22 391	82.96 299	90.09 268	72.30 262	86.65 329	81.97 116	89.95 334	89.88 303

SSM_04072		81.44 244	82.88 210	77.10 340	89.13 199	68.97 220	72.73 404	91.28 161	72.90 210	85.68 227	90.61 249	76.78 200	69.94 437	73.37 240	93.47 230	92.38 218

DELS-MVS		81.44 244	81.25 247	82.03 248	84.27 341	62.87 288	76.47 367	92.49 119	70.97 244	81.64 329	83.83 379	75.03 214	92.70 164	74.29 213	92.22 273	90.51 289
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		81.31 246	81.61 235	80.41 288	86.38 287	58.75 362	83.93 206	86.58 281	72.43 219	87.65 177	92.98 143	63.78 322	90.22 246	66.86 309	93.92 214	92.27 227

TinyColmap		81.25 247	82.34 222	77.99 328	85.33 319	60.68 333	82.32 260	88.33 243	71.26 240	86.97 193	92.22 178	77.10 190	86.98 322	62.37 351	95.17 168	86.31 363

diffmvs_AUTHOR		81.24 248	81.55 239	80.30 290	80.61 397	60.22 338	77.98 338	90.48 186	67.77 294	83.34 292	89.50 279	74.69 223	87.42 314	78.78 151	90.81 314	93.27 165

AUN-MVS		81.18 249	78.78 287	88.39 84	90.93 159	82.14 62	82.51 253	83.67 328	64.69 336	80.29 347	85.91 351	51.07 393	92.38 172	76.29 190	93.63 227	90.65 284

IMVS_0407		81.08 250	81.23 249	80.62 284	85.76 309	62.46 296	82.46 254	87.91 253	65.23 329	82.12 315	87.92 309	77.27 185	90.18 248	71.67 257	90.74 317	89.20 315

tttt0517		81.07 251	79.58 277	85.52 142	88.99 205	66.45 251	87.03 129	75.51 389	73.76 189	88.32 155	90.20 262	37.96 450	94.16 108	79.36 145	95.13 169	95.93 47

Fast-Effi-MVS+		81.04 252	80.57 259	82.46 240	87.50 249	63.22 284	78.37 333	89.63 219	68.01 286	81.87 321	82.08 400	82.31 112	92.65 166	67.10 308	88.30 361	91.51 258

BH-untuned		80.96 253	80.99 253	80.84 278	88.55 220	68.23 228	80.33 299	88.46 238	72.79 215	86.55 203	86.76 336	74.72 222	91.77 191	61.79 358	88.99 347	82.52 416

IMVS_0403		80.93 254	81.00 252	80.72 281	85.76 309	62.46 296	81.82 270	87.91 253	65.23 329	82.07 317	87.92 309	75.91 207	90.50 237	71.67 257	90.74 317	89.20 315

eth_miper_zixun_eth		80.84 255	80.22 268	82.71 231	81.41 384	60.98 328	77.81 341	90.14 205	67.31 301	86.95 194	87.24 329	64.26 315	92.31 175	75.23 206	91.61 290	94.85 82

xiu_mvs_v1_base_debu		80.84 255	80.14 270	82.93 226	88.31 224	71.73 177	79.53 308	87.17 266	65.43 323	79.59 353	82.73 394	76.94 193	90.14 252	73.22 243	88.33 357	86.90 357

xiu_mvs_v1_base		80.84 255	80.14 270	82.93 226	88.31 224	71.73 177	79.53 308	87.17 266	65.43 323	79.59 353	82.73 394	76.94 193	90.14 252	73.22 243	88.33 357	86.90 357

xiu_mvs_v1_base_debi		80.84 255	80.14 270	82.93 226	88.31 224	71.73 177	79.53 308	87.17 266	65.43 323	79.59 353	82.73 394	76.94 193	90.14 252	73.22 243	88.33 357	86.90 357

IterMVS-SCA-FT		80.64 259	79.41 278	84.34 178	83.93 347	69.66 208	76.28 369	81.09 353	72.43 219	86.47 210	90.19 263	60.46 339	93.15 151	77.45 172	86.39 388	90.22 294

BH-RMVSNet		80.53 260	80.22 268	81.49 264	87.19 260	66.21 253	77.79 342	86.23 284	74.21 184	83.69 284	88.50 298	73.25 251	90.75 227	63.18 347	87.90 365	87.52 348

VortexMVS		80.51 261	80.63 258	80.15 294	83.36 361	61.82 310	80.63 294	88.00 251	67.11 304	87.23 183	89.10 288	63.98 319	88.00 301	73.63 234	92.63 259	90.64 285

Anonymous202405211		80.51 261	81.19 251	78.49 317	88.48 221	57.26 374	76.63 362	82.49 339	81.21 88	84.30 271	92.24 177	67.99 292	86.24 337	62.22 352	95.13 169	91.98 242

DIV-MVS_self_test		80.43 263	80.23 266	81.02 274	79.99 402	59.25 351	77.07 355	87.02 275	67.38 298	86.19 214	89.22 284	63.09 327	90.16 249	76.32 188	95.80 147	93.66 141

cl____		80.42 264	80.23 266	81.02 274	79.99 402	59.25 351	77.07 355	87.02 275	67.37 299	86.18 216	89.21 285	63.08 328	90.16 249	76.31 189	95.80 147	93.65 144

diffmvs		80.40 265	80.48 263	80.17 293	79.02 415	60.04 340	77.54 346	90.28 201	66.65 309	82.40 308	87.33 327	73.50 243	87.35 316	77.98 165	89.62 338	93.13 172
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet		80.37 266	78.41 295	86.23 123	76.75 429	73.28 147	87.18 126	77.45 372	76.24 150	68.14 440	88.93 291	65.41 309	93.85 118	69.47 285	96.12 127	91.55 256
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 267	80.04 273	81.24 270	79.82 405	58.95 356	77.66 343	89.66 217	65.75 319	85.99 224	85.11 363	68.29 291	91.42 200	76.03 194	92.03 277	93.33 161

MG-MVS		80.32 268	80.94 254	78.47 318	88.18 227	52.62 411	82.29 261	85.01 312	72.01 230	79.24 360	92.54 163	69.36 285	93.36 145	70.65 271	89.19 345	89.45 308

mvsmamba		80.30 269	78.87 284	84.58 170	88.12 230	67.55 236	92.35 30	84.88 315	63.15 344	85.33 239	90.91 232	50.71 395	95.20 66	66.36 315	87.98 364	90.99 269

VPNet		80.25 270	81.68 231	75.94 356	92.46 104	47.98 435	76.70 360	81.67 348	73.45 195	84.87 254	92.82 152	74.66 224	86.51 331	61.66 360	96.85 96	93.33 161

MAR-MVS		80.24 271	78.74 289	84.73 164	86.87 277	78.18 95	85.75 157	87.81 257	65.67 321	77.84 372	78.50 432	73.79 239	90.53 236	61.59 361	90.87 311	85.49 373
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		80.20 272	79.00 283	83.78 196	88.17 228	86.66 19	81.31 280	66.81 445	69.64 260	88.33 154	90.19 263	64.58 312	83.63 376	71.99 256	90.03 332	81.06 436

Anonymous20240521		80.18 273	81.25 247	76.95 342	83.15 369	60.84 330	82.46 254	85.99 291	68.76 275	86.78 195	93.73 118	59.13 351	77.44 411	73.71 230	97.55 78	92.56 205

LFMVS		80.15 274	80.56 260	78.89 309	89.19 198	55.93 382	85.22 171	73.78 401	82.96 71	84.28 272	92.72 157	57.38 363	90.07 257	63.80 341	95.75 150	90.68 281

DPM-MVS		80.10 275	79.18 282	82.88 229	90.71 165	69.74 206	78.87 325	90.84 176	60.29 379	75.64 395	85.92 350	67.28 295	93.11 152	71.24 263	91.79 284	85.77 369

MSDG		80.06 276	79.99 275	80.25 291	83.91 348	68.04 233	77.51 347	89.19 226	77.65 136	81.94 319	83.45 384	76.37 205	86.31 336	63.31 346	86.59 385	86.41 361

FE-MVS		79.98 277	78.86 285	83.36 210	86.47 282	66.45 251	89.73 75	84.74 319	72.80 214	84.22 275	91.38 208	44.95 430	93.60 131	63.93 339	91.50 293	90.04 301

sd_testset		79.95 278	81.39 244	75.64 361	88.81 211	58.07 366	76.16 372	82.81 337	73.67 190	83.41 290	93.04 139	80.96 140	77.65 410	58.62 376	95.03 174	91.21 262

ab-mvs		79.67 279	80.56 260	76.99 341	88.48 221	56.93 376	84.70 184	86.06 288	68.95 272	80.78 340	93.08 138	75.30 212	84.62 363	56.78 385	90.90 309	89.43 310

VNet		79.31 280	80.27 265	76.44 350	87.92 234	53.95 400	75.58 379	84.35 323	74.39 183	82.23 312	90.72 240	72.84 256	84.39 368	60.38 368	93.98 213	90.97 270

thisisatest0530		79.07 281	77.33 305	84.26 181	87.13 261	64.58 267	83.66 216	75.95 384	68.86 273	85.22 241	87.36 326	38.10 447	93.57 135	75.47 203	94.28 203	94.62 88

cl22		78.97 282	78.21 297	81.24 270	77.74 419	59.01 355	77.46 350	87.13 269	65.79 316	84.32 268	85.10 364	58.96 353	90.88 222	75.36 205	92.03 277	93.84 130

patch_mono-2		78.89 283	79.39 279	77.41 337	84.78 329	68.11 231	75.60 377	83.11 333	60.96 371	79.36 357	89.89 272	75.18 213	72.97 426	73.32 242	92.30 267	91.15 264

RPMNet		78.88 284	78.28 296	80.68 283	79.58 406	62.64 292	82.58 249	94.16 33	74.80 172	75.72 393	92.59 159	48.69 402	95.56 45	73.48 237	82.91 424	83.85 395

PAPR		78.84 285	78.10 298	81.07 272	85.17 324	60.22 338	82.21 265	90.57 185	62.51 348	75.32 399	84.61 372	74.99 215	92.30 176	59.48 373	88.04 363	90.68 281

viewmambaseed2359dif		78.80 286	78.47 294	79.78 297	80.26 401	59.28 350	77.31 352	87.13 269	60.42 377	82.37 309	88.67 296	74.58 225	87.87 307	67.78 307	87.73 369	92.19 231

PVSNet_BlendedMVS		78.80 286	77.84 299	81.65 260	84.43 335	63.41 280	79.49 311	90.44 189	61.70 360	75.43 396	87.07 333	69.11 287	91.44 198	60.68 366	92.24 271	90.11 299

FMVSNet3		78.80 286	78.55 291	79.57 303	82.89 372	56.89 378	81.76 271	85.77 295	69.04 270	86.00 221	90.44 254	51.75 391	90.09 255	65.95 319	93.34 234	91.72 249

test_yl		78.71 289	78.51 292	79.32 306	84.32 339	58.84 359	78.38 331	85.33 303	75.99 154	82.49 306	86.57 338	58.01 357	90.02 259	62.74 348	92.73 257	89.10 321

DCV-MVSNet		78.71 289	78.51 292	79.32 306	84.32 339	58.84 359	78.38 331	85.33 303	75.99 154	82.49 306	86.57 338	58.01 357	90.02 259	62.74 348	92.73 257	89.10 321

test1111		78.53 291	78.85 286	77.56 334	92.22 113	47.49 437	82.61 247	69.24 433	72.43 219	85.28 240	94.20 89	51.91 389	90.07 257	65.36 327	96.45 112	95.11 72

FE-MVSNET		78.46 292	79.36 280	75.75 358	86.53 280	54.53 395	78.03 335	85.35 302	69.01 271	85.41 237	90.68 243	64.27 314	85.73 353	62.59 350	92.35 266	87.00 356

icg_test_0407_2		78.46 292	79.68 276	74.78 368	85.76 309	62.46 296	68.51 433	87.91 253	65.23 329	82.12 315	87.92 309	77.27 185	72.67 427	71.67 257	90.74 317	89.20 315

ECVR-MVS		78.44 294	78.63 290	77.88 330	91.85 127	48.95 431	83.68 215	69.91 429	72.30 225	84.26 274	94.20 89	51.89 390	89.82 262	63.58 342	96.02 131	94.87 78

pmmvs-eth3d		78.42 295	77.04 308	82.57 236	87.44 253	74.41 138	80.86 291	79.67 361	55.68 408	84.69 258	90.31 260	60.91 337	85.42 356	62.20 353	91.59 291	87.88 344

mvs_anonymous		78.13 296	78.76 288	76.23 355	79.24 412	50.31 427	78.69 328	84.82 317	61.60 362	83.09 298	92.82 152	73.89 237	87.01 319	68.33 303	86.41 387	91.37 259

TAMVS		78.08 297	76.36 315	83.23 214	90.62 166	72.87 154	79.08 321	80.01 360	61.72 359	81.35 333	86.92 335	63.96 321	88.78 284	50.61 424	93.01 247	88.04 339

miper_enhance_ethall		77.83 298	76.93 309	80.51 286	76.15 436	58.01 368	75.47 381	88.82 230	58.05 393	83.59 286	80.69 410	64.41 313	91.20 207	73.16 249	92.03 277	92.33 222

Vis-MVSNet (Re-imp)		77.82 299	77.79 300	77.92 329	88.82 210	51.29 421	83.28 227	71.97 417	74.04 185	82.23 312	89.78 273	57.38 363	89.41 274	57.22 384	95.41 158	93.05 178

CANet_DTU		77.81 300	77.05 307	80.09 295	81.37 385	59.90 344	83.26 228	88.29 244	69.16 267	67.83 443	83.72 380	60.93 336	89.47 269	69.22 289	89.70 337	90.88 274

OpenMVS_ROB	70.19 17	77.77 301	77.46 302	78.71 313	84.39 338	61.15 320	81.18 284	82.52 338	62.45 351	83.34 292	87.37 325	66.20 301	88.66 288	64.69 334	85.02 404	86.32 362

SSC-MVS		77.55 302	81.64 233	65.29 434	90.46 169	20.33 481	73.56 397	68.28 435	85.44 41	88.18 159	94.64 68	70.93 276	81.33 389	71.25 262	92.03 277	94.20 111

MDA-MVSNet-bldmvs		77.47 303	76.90 310	79.16 308	79.03 414	64.59 266	66.58 445	75.67 387	73.15 206	88.86 137	88.99 290	66.94 297	81.23 390	64.71 333	88.22 362	91.64 253

jason		77.42 304	75.75 321	82.43 241	87.10 264	69.27 213	77.99 337	81.94 345	51.47 435	77.84 372	85.07 367	60.32 341	89.00 278	70.74 270	89.27 344	89.03 325
jason: jason.
CDS-MVSNet		77.32 305	75.40 325	83.06 218	89.00 204	72.48 165	77.90 340	82.17 343	60.81 372	78.94 363	83.49 383	59.30 349	88.76 285	54.64 404	92.37 265	87.93 343
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 306	77.75 301	75.73 359	85.76 309	62.46 296	70.84 419	87.91 253	65.23 329	72.21 418	87.92 309	67.48 294	75.53 419	71.67 257	90.74 317	89.20 315

xiu_mvs_v2_base		77.19 307	76.75 312	78.52 316	87.01 270	61.30 317	75.55 380	87.12 273	61.24 368	74.45 405	78.79 430	77.20 187	90.93 218	64.62 336	84.80 411	83.32 404

MVSTER		77.09 308	75.70 322	81.25 268	75.27 444	61.08 322	77.49 349	85.07 308	60.78 373	86.55 203	88.68 294	43.14 439	90.25 243	73.69 233	90.67 322	92.42 212

PS-MVSNAJ		77.04 309	76.53 314	78.56 315	87.09 266	61.40 314	75.26 382	87.13 269	61.25 367	74.38 407	77.22 444	76.94 193	90.94 217	64.63 335	84.83 410	83.35 403

IterMVS		76.91 310	76.34 316	78.64 314	80.91 390	64.03 274	76.30 368	79.03 364	64.88 335	83.11 296	89.16 286	59.90 345	84.46 366	68.61 299	85.15 402	87.42 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 311	75.67 323	80.34 289	80.48 399	62.16 308	73.50 398	84.80 318	57.61 397	82.24 311	87.54 320	51.31 392	87.65 310	70.40 276	93.19 243	91.23 261

CL-MVSNet_self_test		76.81 312	77.38 304	75.12 364	86.90 275	51.34 419	73.20 401	80.63 357	68.30 282	81.80 325	88.40 299	66.92 298	80.90 391	55.35 398	94.90 180	93.12 175

TR-MVS		76.77 313	75.79 320	79.72 300	86.10 301	65.79 258	77.14 353	83.02 334	65.20 333	81.40 332	82.10 398	66.30 300	90.73 229	55.57 395	85.27 398	82.65 411

MonoMVSNet		76.66 314	77.26 306	74.86 366	79.86 404	54.34 397	86.26 147	86.08 287	71.08 243	85.59 232	88.68 294	53.95 381	85.93 344	63.86 340	80.02 440	84.32 386

USDC		76.63 315	76.73 313	76.34 352	83.46 356	57.20 375	80.02 302	88.04 250	52.14 431	83.65 285	91.25 215	63.24 325	86.65 329	54.66 403	94.11 208	85.17 375

BH-w/o		76.57 316	76.07 319	78.10 325	86.88 276	65.92 257	77.63 344	86.33 282	65.69 320	80.89 338	79.95 419	68.97 289	90.74 228	53.01 414	85.25 399	77.62 447

Patchmtry		76.56 317	77.46 302	73.83 374	79.37 411	46.60 441	82.41 258	76.90 378	73.81 188	85.56 234	92.38 167	48.07 405	83.98 373	63.36 345	95.31 164	90.92 272

PVSNet_Blended		76.49 318	75.40 325	79.76 299	84.43 335	63.41 280	75.14 383	90.44 189	57.36 399	75.43 396	78.30 433	69.11 287	91.44 198	60.68 366	87.70 371	84.42 385

miper_lstm_enhance		76.45 319	76.10 318	77.51 335	76.72 430	60.97 329	64.69 449	85.04 310	63.98 340	83.20 295	88.22 301	56.67 367	78.79 407	73.22 243	93.12 244	92.78 191

lupinMVS		76.37 320	74.46 334	82.09 247	85.54 315	69.26 214	76.79 358	80.77 356	50.68 442	76.23 386	82.82 392	58.69 354	88.94 279	69.85 281	88.77 350	88.07 336

cascas		76.29 321	74.81 330	80.72 281	84.47 334	62.94 286	73.89 395	87.34 261	55.94 406	75.16 401	76.53 449	63.97 320	91.16 209	65.00 330	90.97 307	88.06 338

SD_0403		76.08 322	76.77 311	73.98 372	87.08 268	49.45 430	83.62 217	84.68 320	63.31 341	75.13 402	87.47 323	71.85 269	84.56 364	49.97 426	87.86 367	87.94 342

WB-MVS		76.06 323	80.01 274	64.19 437	89.96 183	20.58 480	72.18 408	68.19 436	83.21 67	86.46 211	93.49 123	70.19 281	78.97 405	65.96 318	90.46 328	93.02 179

thres600view7		75.97 324	75.35 327	77.85 332	87.01 270	51.84 417	80.45 297	73.26 406	75.20 169	83.10 297	86.31 344	45.54 420	89.05 277	55.03 401	92.24 271	92.66 198

GA-MVS		75.83 325	74.61 331	79.48 305	81.87 377	59.25 351	73.42 399	82.88 335	68.68 276	79.75 352	81.80 403	50.62 396	89.46 270	66.85 310	85.64 395	89.72 305

MVP-Stereo		75.81 326	73.51 343	82.71 231	89.35 193	73.62 142	80.06 300	85.20 305	60.30 378	73.96 408	87.94 306	57.89 361	89.45 271	52.02 418	74.87 458	85.06 377
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 327	75.20 328	77.27 338	75.01 447	69.47 211	78.93 322	84.88 315	46.67 449	87.08 190	87.84 314	50.44 398	71.62 432	77.42 174	88.53 353	90.72 278

thres100view900		75.45 328	75.05 329	76.66 348	87.27 255	51.88 416	81.07 285	73.26 406	75.68 160	83.25 294	86.37 341	45.54 420	88.80 281	51.98 419	90.99 304	89.31 312

ET-MVSNet_ETH3D		75.28 329	72.77 352	82.81 230	83.03 371	68.11 231	77.09 354	76.51 382	60.67 375	77.60 377	80.52 414	38.04 448	91.15 210	70.78 268	90.68 321	89.17 319

thres400		75.14 330	74.23 336	77.86 331	86.24 294	52.12 413	79.24 318	73.87 399	73.34 199	81.82 323	84.60 373	46.02 413	88.80 281	51.98 419	90.99 304	92.66 198

wuyk23d		75.13 331	79.30 281	62.63 440	75.56 440	75.18 134	80.89 290	73.10 408	75.06 171	94.76 16	95.32 45	87.73 45	52.85 472	34.16 470	97.11 91	59.85 468

EU-MVSNet		75.12 332	74.43 335	77.18 339	83.11 370	59.48 348	85.71 159	82.43 340	39.76 469	85.64 231	88.76 292	44.71 432	87.88 306	73.86 227	85.88 394	84.16 391

HyFIR lowres test		75.12 332	72.66 354	82.50 238	91.44 145	65.19 263	72.47 406	87.31 262	46.79 448	80.29 347	84.30 375	52.70 386	92.10 182	51.88 423	86.73 383	90.22 294

CMPMVS	59.41 20	75.12 332	73.57 341	79.77 298	75.84 439	67.22 237	81.21 283	82.18 342	50.78 440	76.50 382	87.66 318	55.20 377	82.99 379	62.17 355	90.64 326	89.09 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 335	72.98 350	80.73 280	84.95 326	71.71 180	76.23 370	77.59 371	52.83 425	77.73 376	86.38 340	56.35 370	84.97 360	57.72 383	87.05 378	85.51 372

tfpn200view9		74.86 336	74.23 336	76.74 347	86.24 294	52.12 413	79.24 318	73.87 399	73.34 199	81.82 323	84.60 373	46.02 413	88.80 281	51.98 419	90.99 304	89.31 312

1112_ss		74.82 337	73.74 339	78.04 327	89.57 187	60.04 340	76.49 366	87.09 274	54.31 416	73.66 411	79.80 420	60.25 342	86.76 328	58.37 377	84.15 415	87.32 351

EGC-MVSNET		74.79 338	69.99 382	89.19 67	94.89 38	87.00 15	91.89 42	86.28 283	1.09 478	2.23 480	95.98 30	81.87 128	89.48 268	79.76 136	95.96 134	91.10 265

ppachtmachnet_test		74.73 339	74.00 338	76.90 344	80.71 395	56.89 378	71.53 414	78.42 366	58.24 390	79.32 359	82.92 391	57.91 360	84.26 370	65.60 325	91.36 295	89.56 307

Patchmatch-RL test		74.48 340	73.68 340	76.89 345	84.83 328	66.54 248	72.29 407	69.16 434	57.70 395	86.76 196	86.33 342	45.79 419	82.59 380	69.63 284	90.65 325	81.54 427

PatchMatch-RL		74.48 340	73.22 347	78.27 323	87.70 241	85.26 38	75.92 375	70.09 427	64.34 338	76.09 389	81.25 408	65.87 306	78.07 409	53.86 406	83.82 417	71.48 456

XXY-MVS		74.44 342	76.19 317	69.21 409	84.61 333	52.43 412	71.70 411	77.18 376	60.73 374	80.60 341	90.96 229	75.44 209	69.35 440	56.13 390	88.33 357	85.86 368

test2506		74.12 343	73.39 344	76.28 353	91.85 127	44.20 451	84.06 200	48.20 476	72.30 225	81.90 320	94.20 89	27.22 475	89.77 265	64.81 332	96.02 131	94.87 78

reproduce_monomvs		74.09 344	73.23 346	76.65 349	76.52 431	54.54 394	77.50 348	81.40 351	65.85 315	82.86 302	86.67 337	27.38 473	84.53 365	70.24 277	90.66 324	90.89 273

CR-MVSNet		74.00 345	73.04 349	76.85 346	79.58 406	62.64 292	82.58 249	76.90 378	50.50 443	75.72 393	92.38 167	48.07 405	84.07 372	68.72 298	82.91 424	83.85 395

SSC-MVS3.2		73.90 346	75.67 323	68.61 417	84.11 344	41.28 459	64.17 451	72.83 409	72.09 228	79.08 362	87.94 306	70.31 279	73.89 425	55.99 391	94.49 195	90.67 283

Test_1112_low_res		73.90 346	73.08 348	76.35 351	90.35 171	55.95 381	73.40 400	86.17 285	50.70 441	73.14 412	85.94 349	58.31 356	85.90 348	56.51 387	83.22 421	87.20 353

test20.03		73.75 348	74.59 333	71.22 395	81.11 388	51.12 423	70.15 425	72.10 416	70.42 250	80.28 349	91.50 202	64.21 316	74.72 423	46.96 444	94.58 193	87.82 346

test_fmvs2		73.57 349	72.80 351	75.90 357	72.74 461	68.84 224	77.07 355	84.32 324	45.14 455	82.89 300	84.22 376	48.37 403	70.36 436	73.40 239	87.03 379	88.52 331

SCA		73.32 350	72.57 356	75.58 362	81.62 381	55.86 384	78.89 324	71.37 422	61.73 358	74.93 403	83.42 385	60.46 339	87.01 319	58.11 381	82.63 429	83.88 392

baseline1		73.26 351	73.54 342	72.43 388	84.92 327	47.79 436	79.89 304	74.00 397	65.93 313	78.81 364	86.28 345	56.36 369	81.63 388	56.63 386	79.04 447	87.87 345

1314		73.22 352	72.56 357	75.20 363	80.41 400	57.84 369	81.64 274	85.36 301	51.68 434	73.10 413	76.65 448	61.45 334	85.19 358	63.54 343	79.21 445	82.59 412

MVS		73.21 353	72.59 355	75.06 365	80.97 389	60.81 331	81.64 274	85.92 294	46.03 453	71.68 421	77.54 439	68.47 290	89.77 265	55.70 394	85.39 396	74.60 453

HY-MVS	64.64 18	73.03 354	72.47 358	74.71 369	83.36 361	54.19 398	82.14 268	81.96 344	56.76 405	69.57 435	86.21 346	60.03 343	84.83 362	49.58 431	82.65 427	85.11 376

thisisatest0515		73.00 355	70.52 374	80.46 287	81.45 383	59.90 344	73.16 402	74.31 396	57.86 394	76.08 390	77.78 436	37.60 451	92.12 181	65.00 330	91.45 294	89.35 311

EPNet_dtu		72.87 356	71.33 368	77.49 336	77.72 420	60.55 334	82.35 259	75.79 385	66.49 310	58.39 471	81.06 409	53.68 382	85.98 343	53.55 409	92.97 249	85.95 366
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 357	71.41 367	76.28 353	83.25 365	60.34 336	83.50 221	79.02 365	37.77 473	76.33 384	85.10 364	49.60 401	87.41 315	70.54 274	77.54 453	81.08 434

CHOSEN 1792x2688		72.45 358	70.56 373	78.13 324	90.02 182	63.08 285	68.72 432	83.16 332	42.99 463	75.92 391	85.46 357	57.22 365	85.18 359	49.87 429	81.67 431	86.14 364

testgi		72.36 359	74.61 331	65.59 431	80.56 398	42.82 456	68.29 434	73.35 405	66.87 307	81.84 322	89.93 270	72.08 266	66.92 454	46.05 448	92.54 261	87.01 355

thres200		72.34 360	71.55 366	74.70 370	83.48 355	51.60 418	75.02 384	73.71 402	70.14 256	78.56 367	80.57 413	46.20 411	88.20 299	46.99 443	89.29 342	84.32 386

FPMVS		72.29 361	72.00 360	73.14 379	88.63 217	85.00 40	74.65 388	67.39 439	71.94 231	77.80 374	87.66 318	50.48 397	75.83 417	49.95 427	79.51 441	58.58 470

FMVSNet5		72.10 362	71.69 362	73.32 377	81.57 382	53.02 407	76.77 359	78.37 367	63.31 341	76.37 383	91.85 188	36.68 452	78.98 404	47.87 440	92.45 263	87.95 341

our_test_3		71.85 363	71.59 363	72.62 385	80.71 395	53.78 401	69.72 428	71.71 421	58.80 387	78.03 369	80.51 415	56.61 368	78.84 406	62.20 353	86.04 393	85.23 374

PAPM		71.77 364	70.06 380	76.92 343	86.39 285	53.97 399	76.62 363	86.62 280	53.44 420	63.97 460	84.73 371	57.79 362	92.34 174	39.65 460	81.33 435	84.45 384

ttmdpeth		71.72 365	70.67 371	74.86 366	73.08 458	55.88 383	77.41 351	69.27 432	55.86 407	78.66 365	93.77 116	38.01 449	75.39 420	60.12 369	89.87 335	93.31 163

IB-MVS	62.13 19	71.64 366	68.97 392	79.66 302	80.80 394	62.26 305	73.94 394	76.90 378	63.27 343	68.63 439	76.79 446	33.83 456	91.84 189	59.28 374	87.26 373	84.88 378
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		71.63 367	72.30 359	69.62 406	76.47 433	52.70 410	70.03 426	80.97 354	59.18 384	79.36 357	88.21 302	60.50 338	69.12 441	58.33 379	77.62 452	87.04 354

testing3		71.53 368	70.79 370	73.77 375	88.89 209	41.86 458	76.60 365	59.12 465	72.83 213	80.97 335	82.08 400	19.80 481	87.33 317	65.12 329	91.68 289	92.13 235

test_vis3_rt		71.42 369	70.67 371	73.64 376	69.66 468	70.46 196	66.97 444	89.73 214	42.68 465	88.20 158	83.04 387	43.77 434	60.07 466	65.35 328	86.66 384	90.39 292

Anonymous20231206		71.38 370	71.88 361	69.88 403	86.31 291	54.37 396	70.39 423	74.62 392	52.57 427	76.73 381	88.76 292	59.94 344	72.06 429	44.35 452	93.23 241	83.23 406

test_vis1_n_1920		71.30 371	71.58 365	70.47 398	77.58 422	59.99 343	74.25 389	84.22 325	51.06 437	74.85 404	79.10 426	55.10 378	68.83 443	68.86 295	79.20 446	82.58 413

MIMVSNet		71.09 372	71.59 363	69.57 407	87.23 258	50.07 428	78.91 323	71.83 418	60.20 381	71.26 422	91.76 195	55.08 379	76.09 415	41.06 457	87.02 380	82.54 415

test_fmvs1_n		70.94 373	70.41 377	72.53 387	73.92 449	66.93 245	75.99 374	84.21 326	43.31 462	79.40 356	79.39 424	43.47 435	68.55 445	69.05 292	84.91 407	82.10 421

MS-PatchMatch		70.93 374	70.22 378	73.06 380	81.85 378	62.50 295	73.82 396	77.90 368	52.44 428	75.92 391	81.27 407	55.67 374	81.75 386	55.37 397	77.70 451	74.94 452

pmmvs5		70.73 375	70.07 379	72.72 383	77.03 427	52.73 409	74.14 390	75.65 388	50.36 444	72.17 419	85.37 361	55.42 376	80.67 393	52.86 415	87.59 372	84.77 379

testing3-2		70.72 376	70.97 369	69.95 402	88.93 207	34.80 472	69.85 427	66.59 446	78.42 126	77.58 378	85.55 353	31.83 462	82.08 384	46.28 445	93.73 223	92.98 185

PatchT		70.52 377	72.76 353	63.79 439	79.38 410	33.53 473	77.63 344	65.37 450	73.61 192	71.77 420	92.79 155	44.38 433	75.65 418	64.53 337	85.37 397	82.18 420

test_vis1_n		70.29 378	69.99 382	71.20 396	75.97 438	66.50 249	76.69 361	80.81 355	44.22 458	75.43 396	77.23 443	50.00 399	68.59 444	66.71 313	82.85 426	78.52 446

N_pmnet		70.20 379	68.80 394	74.38 371	80.91 390	84.81 43	59.12 462	76.45 383	55.06 411	75.31 400	82.36 397	55.74 373	54.82 471	47.02 442	87.24 374	83.52 399

tpmvs		70.16 380	69.56 385	71.96 391	74.71 448	48.13 433	79.63 306	75.45 390	65.02 334	70.26 430	81.88 402	45.34 425	85.68 354	58.34 378	75.39 457	82.08 422

new-patchmatchnet		70.10 381	73.37 345	60.29 448	81.23 387	16.95 483	59.54 460	74.62 392	62.93 345	80.97 335	87.93 308	62.83 331	71.90 430	55.24 399	95.01 177	92.00 240

YYNet1		70.06 382	70.44 375	68.90 411	73.76 451	53.42 405	58.99 463	67.20 441	58.42 389	87.10 188	85.39 360	59.82 346	67.32 451	59.79 371	83.50 420	85.96 365

MVStest1		70.05 383	69.26 386	72.41 389	58.62 480	55.59 387	76.61 364	65.58 448	53.44 420	89.28 132	93.32 127	22.91 479	71.44 434	74.08 223	89.52 339	90.21 298

MDA-MVSNet_test_wron		70.05 383	70.44 375	68.88 412	73.84 450	53.47 403	58.93 464	67.28 440	58.43 388	87.09 189	85.40 359	59.80 347	67.25 452	59.66 372	83.54 419	85.92 367

CostFormer		69.98 385	68.68 395	73.87 373	77.14 425	50.72 425	79.26 317	74.51 394	51.94 433	70.97 425	84.75 370	45.16 428	87.49 313	55.16 400	79.23 444	83.40 402

testing91		69.94 386	68.99 391	72.80 382	83.81 350	45.89 444	71.57 413	73.64 404	68.24 283	70.77 428	77.82 435	34.37 455	84.44 367	53.64 408	87.00 381	88.07 336

baseline2		69.77 387	66.89 404	78.41 319	79.51 408	58.09 365	76.23 370	69.57 430	57.50 398	64.82 458	77.45 441	46.02 413	88.44 293	53.08 411	77.83 449	88.70 329

PatchmatchNet		69.71 388	68.83 393	72.33 390	77.66 421	53.60 402	79.29 316	69.99 428	57.66 396	72.53 416	82.93 390	46.45 410	80.08 399	60.91 365	72.09 461	83.31 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 389	69.05 389	71.14 397	69.15 469	65.77 259	73.98 393	83.32 330	42.83 464	77.77 375	78.27 434	43.39 438	68.50 446	68.39 302	84.38 414	79.15 444

JIA-IIPM		69.41 390	66.64 408	77.70 333	73.19 455	71.24 187	75.67 376	65.56 449	70.42 250	65.18 454	92.97 146	33.64 458	83.06 377	53.52 410	69.61 467	78.79 445

Syy-MVS		69.40 391	70.03 381	67.49 422	81.72 379	38.94 464	71.00 416	61.99 456	61.38 364	70.81 426	72.36 460	61.37 335	79.30 402	64.50 338	85.18 400	84.22 388

testing99		69.27 392	68.15 399	72.63 384	83.29 363	45.45 446	71.15 415	71.08 423	67.34 300	70.43 429	77.77 437	32.24 461	84.35 369	53.72 407	86.33 389	88.10 335

UnsupCasMVSNet_bld		69.21 393	69.68 384	67.82 420	79.42 409	51.15 422	67.82 438	75.79 385	54.15 417	77.47 379	85.36 362	59.26 350	70.64 435	48.46 437	79.35 443	81.66 425

test_cas_vis1_n_1920		69.20 394	69.12 387	69.43 408	73.68 452	62.82 289	70.38 424	77.21 375	46.18 452	80.46 346	78.95 428	52.03 388	65.53 459	65.77 324	77.45 454	79.95 442

gg-mvs-nofinetune		68.96 395	69.11 388	68.52 418	76.12 437	45.32 447	83.59 218	55.88 470	86.68 33	64.62 459	97.01 12	30.36 466	83.97 374	44.78 451	82.94 423	76.26 449

WBMVS		68.76 396	68.43 396	69.75 405	83.29 363	40.30 462	67.36 440	72.21 415	57.09 402	77.05 380	85.53 355	33.68 457	80.51 395	48.79 435	90.90 309	88.45 332

WB-MVSnew		68.72 397	69.01 390	67.85 419	83.22 367	43.98 452	74.93 385	65.98 447	55.09 410	73.83 409	79.11 425	65.63 308	71.89 431	38.21 465	85.04 403	87.69 347

tpm2		68.45 398	66.83 405	73.30 378	78.93 416	48.50 432	79.76 305	71.76 419	47.50 447	69.92 432	83.60 381	42.07 441	88.40 295	48.44 438	79.51 441	83.01 409

tpm		67.95 399	68.08 400	67.55 421	78.74 417	43.53 454	75.60 377	67.10 444	54.92 412	72.23 417	88.10 303	42.87 440	75.97 416	52.21 417	80.95 439	83.15 407

WTY-MVS		67.91 400	68.35 397	66.58 427	80.82 393	48.12 434	65.96 446	72.60 410	53.67 419	71.20 423	81.68 405	58.97 352	69.06 442	48.57 436	81.67 431	82.55 414

testing11		67.38 401	65.93 409	71.73 393	83.37 360	46.60 441	70.95 418	69.40 431	62.47 350	66.14 447	76.66 447	31.22 463	84.10 371	49.10 433	84.10 416	84.49 382

test-LLR		67.21 402	66.74 406	68.63 415	76.45 434	55.21 390	67.89 435	67.14 442	62.43 353	65.08 455	72.39 458	43.41 436	69.37 438	61.00 363	84.89 408	81.31 429

testing222		66.93 403	65.30 416	71.81 392	83.38 359	45.83 445	72.06 409	67.50 438	64.12 339	69.68 434	76.37 450	27.34 474	83.00 378	38.88 461	88.38 356	86.62 360

sss		66.92 404	67.26 402	65.90 429	77.23 424	51.10 424	64.79 448	71.72 420	52.12 432	70.13 431	80.18 417	57.96 359	65.36 460	50.21 425	81.01 437	81.25 431

KD-MVS_2432*1600		66.87 405	65.81 412	70.04 400	67.50 470	47.49 437	62.56 454	79.16 362	61.21 369	77.98 370	80.61 411	25.29 477	82.48 381	53.02 412	84.92 405	80.16 440

miper_refine_blended		66.87 405	65.81 412	70.04 400	67.50 470	47.49 437	62.56 454	79.16 362	61.21 369	77.98 370	80.61 411	25.29 477	82.48 381	53.02 412	84.92 405	80.16 440

dmvs_re		66.81 407	66.98 403	66.28 428	76.87 428	58.68 363	71.66 412	72.24 413	60.29 379	69.52 436	73.53 457	52.38 387	64.40 462	44.90 450	81.44 434	75.76 450

tpm cat1		66.76 408	65.21 417	71.42 394	77.09 426	50.62 426	78.01 336	73.68 403	44.89 456	68.64 438	79.00 427	45.51 422	82.42 383	49.91 428	70.15 464	81.23 433

UWE-MVS		66.43 409	65.56 415	69.05 410	84.15 343	40.98 460	73.06 403	64.71 452	54.84 413	76.18 388	79.62 423	29.21 468	80.50 396	38.54 464	89.75 336	85.66 370

PVSNet	58.17 21	66.41 410	65.63 414	68.75 413	81.96 376	49.88 429	62.19 456	72.51 412	51.03 438	68.04 441	75.34 454	50.84 394	74.77 421	45.82 449	82.96 422	81.60 426

tpmrst		66.28 411	66.69 407	65.05 435	72.82 460	39.33 463	78.20 334	70.69 426	53.16 423	67.88 442	80.36 416	48.18 404	74.75 422	58.13 380	70.79 463	81.08 434

Patchmatch-test		65.91 412	67.38 401	61.48 445	75.51 441	43.21 455	68.84 431	63.79 454	62.48 349	72.80 415	83.42 385	44.89 431	59.52 468	48.27 439	86.45 386	81.70 424

ADS-MVSNet2		65.87 413	63.64 422	72.55 386	73.16 456	56.92 377	67.10 442	74.81 391	49.74 445	66.04 449	82.97 388	46.71 408	77.26 412	42.29 454	69.96 465	83.46 400

myMVS_eth3d28		65.83 414	65.85 410	65.78 430	83.42 358	35.71 470	67.29 441	68.01 437	67.58 297	69.80 433	77.72 438	32.29 460	74.30 424	37.49 466	89.06 346	87.32 351

test_vis1_rt		65.64 415	64.09 419	70.31 399	66.09 474	70.20 200	61.16 457	81.60 349	38.65 470	72.87 414	69.66 463	52.84 384	60.04 467	56.16 389	77.77 450	80.68 438

mvsany_test3		65.48 416	62.97 425	73.03 381	69.99 467	76.17 124	64.83 447	43.71 478	43.68 460	80.25 350	87.05 334	52.83 385	63.09 465	51.92 422	72.44 460	79.84 443

test-mter		65.00 417	63.79 421	68.63 415	76.45 434	55.21 390	67.89 435	67.14 442	50.98 439	65.08 455	72.39 458	28.27 471	69.37 438	61.00 363	84.89 408	81.31 429

ETVMVS		64.67 418	63.34 424	68.64 414	83.44 357	41.89 457	69.56 430	61.70 461	61.33 366	68.74 437	75.76 452	28.76 469	79.35 401	34.65 469	86.16 392	84.67 381

myMVS_eth3d		64.66 419	63.89 420	66.97 425	81.72 379	37.39 467	71.00 416	61.99 456	61.38 364	70.81 426	72.36 460	20.96 480	79.30 402	49.59 430	85.18 400	84.22 388

test0.0.03 1		64.66 419	64.36 418	65.57 432	75.03 446	46.89 440	64.69 449	61.58 462	62.43 353	71.18 424	77.54 439	43.41 436	68.47 447	40.75 459	82.65 427	81.35 428

UBG		64.34 421	63.35 423	67.30 423	83.50 354	40.53 461	67.46 439	65.02 451	54.77 414	67.54 445	74.47 456	32.99 459	78.50 408	40.82 458	83.58 418	82.88 410

test_f		64.31 422	65.85 410	59.67 449	66.54 473	62.24 307	57.76 466	70.96 424	40.13 467	84.36 266	82.09 399	46.93 407	51.67 473	61.99 356	81.89 430	65.12 464

pmmvs3		62.47 423	60.02 436	69.80 404	71.58 464	64.00 275	70.52 422	58.44 468	39.77 468	66.05 448	75.84 451	27.10 476	72.28 428	46.15 447	84.77 412	73.11 454

EPMVS		62.47 423	62.63 427	62.01 441	70.63 466	38.74 465	74.76 386	52.86 472	53.91 418	67.71 444	80.01 418	39.40 445	66.60 455	55.54 396	68.81 469	80.68 438

ADS-MVSNet		61.90 425	62.19 429	61.03 446	73.16 456	36.42 469	67.10 442	61.75 459	49.74 445	66.04 449	82.97 388	46.71 408	63.21 463	42.29 454	69.96 465	83.46 400

PMMVS		61.65 426	60.38 433	65.47 433	65.40 477	69.26 214	63.97 452	61.73 460	36.80 474	60.11 466	68.43 465	59.42 348	66.35 456	48.97 434	78.57 448	60.81 467

E-PMN		61.59 427	61.62 430	61.49 444	66.81 472	55.40 388	53.77 469	60.34 464	66.80 308	58.90 469	65.50 468	40.48 444	66.12 457	55.72 393	86.25 390	62.95 466

TESTMET0.1,1		61.29 428	60.32 434	64.19 437	72.06 462	51.30 420	67.89 435	62.09 455	45.27 454	60.65 465	69.01 464	27.93 472	64.74 461	56.31 388	81.65 433	76.53 448

MVS-HIRNet		61.16 429	62.92 426	55.87 452	79.09 413	35.34 471	71.83 410	57.98 469	46.56 450	59.05 468	91.14 220	49.95 400	76.43 414	38.74 462	71.92 462	55.84 471

EMVS		61.10 430	60.81 432	61.99 442	65.96 475	55.86 384	53.10 470	58.97 467	67.06 305	56.89 473	63.33 469	40.98 442	67.03 453	54.79 402	86.18 391	63.08 465

DSMNet-mixed		60.98 431	61.61 431	59.09 451	72.88 459	45.05 449	74.70 387	46.61 477	26.20 475	65.34 453	90.32 259	55.46 375	63.12 464	41.72 456	81.30 436	69.09 460

dp		60.70 432	60.29 435	61.92 443	72.04 463	38.67 466	70.83 420	64.08 453	51.28 436	60.75 464	77.28 442	36.59 453	71.58 433	47.41 441	62.34 471	75.52 451

dmvs_testset		60.59 433	62.54 428	54.72 454	77.26 423	27.74 477	74.05 392	61.00 463	60.48 376	65.62 452	67.03 467	55.93 372	68.23 449	32.07 473	69.46 468	68.17 461

CHOSEN 280x420		59.08 434	56.52 440	66.76 426	76.51 432	64.39 271	49.62 471	59.00 466	43.86 459	55.66 474	68.41 466	35.55 454	68.21 450	43.25 453	76.78 456	67.69 462

mvsany_test1		58.48 435	56.47 441	64.50 436	65.90 476	68.21 230	56.95 467	42.11 479	38.30 471	65.69 451	77.19 445	56.96 366	59.35 469	46.16 446	58.96 472	65.93 463

UWE-MVS-28		58.44 436	57.71 438	60.65 447	73.58 453	31.23 474	69.68 429	48.80 475	53.12 424	61.79 462	78.83 429	30.98 464	68.40 448	21.58 476	80.99 438	82.33 419

PVSNet_0	51.08 22	56.10 437	54.97 442	59.48 450	75.12 445	53.28 406	55.16 468	61.89 458	44.30 457	59.16 467	62.48 470	54.22 380	65.91 458	35.40 468	47.01 473	59.25 469

new_pmnet		55.69 438	57.66 439	49.76 455	75.47 442	30.59 475	59.56 459	51.45 473	43.62 461	62.49 461	75.48 453	40.96 443	49.15 475	37.39 467	72.52 459	69.55 459

PMMVS2		55.64 439	59.27 437	44.74 456	64.30 478	12.32 484	40.60 472	49.79 474	53.19 422	65.06 457	84.81 369	53.60 383	49.76 474	32.68 472	89.41 341	72.15 455

MVE	40.22 23	51.82 440	50.47 443	55.87 452	62.66 479	51.91 415	31.61 474	39.28 480	40.65 466	50.76 475	74.98 455	56.24 371	44.67 476	33.94 471	64.11 470	71.04 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 441	42.65 444	39.67 457	70.86 465	21.11 479	61.01 458	21.42 484	57.36 399	57.97 472	50.06 473	16.40 482	58.73 470	21.03 477	27.69 477	39.17 473

kuosan		30.83 442	32.17 445	26.83 459	53.36 481	19.02 482	57.90 465	20.44 485	38.29 472	38.01 476	37.82 475	15.18 483	33.45 478	7.74 479	20.76 478	28.03 474

test_method		30.46 443	29.60 446	33.06 458	17.99 483	3.84 486	13.62 475	73.92 398	2.79 477	18.29 479	53.41 472	28.53 470	43.25 477	22.56 474	35.27 475	52.11 472

cdsmvs_eth3d_5k		20.81 444	27.75 447	0.00 464	0.00 487	0.00 489	0.00 476	85.44 300	0.00 482	0.00 483	82.82 392	81.46 134	0.00 483	0.00 482	0.00 481	0.00 479

tmp_tt		20.25 445	24.50 448	7.49 461	4.47 484	8.70 485	34.17 473	25.16 482	1.00 479	32.43 478	18.49 476	39.37 446	9.21 480	21.64 475	43.75 474	4.57 476

ab-mvs-re		6.65 446	8.87 449	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	79.80 420	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		6.41 447	8.55 450	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	76.94 193	0.00 483	0.00 482	0.00 481	0.00 479

test123		6.27 448	8.08 451	0.84 462	1.11 486	0.57 487	62.90 453	0.82 486	0.54 480	1.07 482	2.75 481	1.26 484	0.30 481	1.04 480	1.26 480	1.66 477

testmvs		5.91 449	7.65 452	0.72 463	1.20 485	0.37 488	59.14 461	0.67 487	0.49 481	1.11 481	2.76 480	0.94 485	0.24 482	1.02 481	1.47 479	1.55 478

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 140	93.24 43	93.18 131		95.85 24	84.99 75	97.69 65	93.54 156

TestfortrainingZip							92.12 33

WAC-MVS						37.39 467								52.61 416

FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

MSC_two_6792asdad				88.81 73	91.55 139	77.99 97		91.01 172					96.05 9	87.45 29	98.17 37	92.40 215

PC_three_1452									58.96 386	90.06 107	91.33 210	80.66 144	93.03 156	75.78 197	95.94 137	92.48 209

No_MVS				88.81 73	91.55 139	77.99 97		91.01 172					96.05 9	87.45 29	98.17 37	92.40 215

test_one_0601					93.85 67	73.27 148		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 487

eth-test					0.00 487

ZD-MVS					92.22 113	80.48 71		91.85 140	71.22 241	90.38 102	92.98 143	86.06 68	96.11 7	81.99 115	96.75 101

RE-MVS-def			92.61 9		94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 38	97.60 75	92.73 192

IU-MVS					94.18 55	72.64 158		90.82 177	56.98 403	89.67 120				85.78 64	97.92 52	93.28 164

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 216	81.12 138	94.68 82	74.48 212	95.35 160	92.29 225

test_241102_TWO								93.71 60	83.77 58	93.49 40	94.27 83	89.27 24	95.84 27	86.03 57	97.82 57	92.04 238

test_241102_ONE					94.18 55	72.65 156		93.69 62	83.62 62	94.11 27	93.78 114	90.28 15	95.50 52

9.14			89.29 66		91.84 129		88.80 99	95.32 13	75.14 170	91.07 87	92.89 149	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 215	70.80 246

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 35	87.41 31	98.21 34	92.98 185

test_0728_SECOND				86.79 112	94.25 53	72.45 166	90.54 57	94.10 40					95.88 18	86.42 47	97.97 49	92.02 239

test0726					94.16 58	72.56 162	90.63 54	93.90 49	83.61 63	93.75 35	94.49 73	89.76 19

GSMVS																83.88 392

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 412				83.88 392

sam_mvs												45.92 417

ambc				82.98 221	90.55 168	64.86 265	88.20 108	89.15 228		89.40 129	93.96 105	71.67 273	91.38 202	78.83 150	96.55 106	92.71 195

MTGPA								91.81 144

test_post1							78.85 326				3.13 478	45.19 427	80.13 398	58.11 381

test_post											3.10 479	45.43 423	77.22 413

patchmatchnet-post											81.71 404	45.93 416	87.01 319

GG-mvs-BLEND				67.16 424	73.36 454	46.54 443	84.15 198	55.04 471		58.64 470	61.95 471	29.93 467	83.87 375	38.71 463	76.92 455	71.07 457

MTMP							90.66 53	33.14 481

gm-plane-assit					75.42 443	44.97 450			52.17 429		72.36 460		87.90 305	54.10 405

test9_res														80.83 125	96.45 112	90.57 286

TEST9					92.34 108	79.70 80	83.94 204	90.32 195	65.41 326	84.49 262	90.97 227	82.03 123	93.63 127

test_8					92.09 117	78.87 88	83.82 209	90.31 197	65.79 316	84.36 266	90.96 229	81.93 125	93.44 141

agg_prior2														79.68 138	96.16 124	90.22 294

agg_prior					91.58 137	77.69 103		90.30 198		84.32 268			93.18 149

TestCases				89.68 56	91.59 134	83.40 52		95.44 11	79.47 108	88.00 163	93.03 141	82.66 104	91.47 196	70.81 266	96.14 125	94.16 115

test_prior4						78.97 87	84.59 187

test_prior2							83.37 225		75.43 166	84.58 259	91.57 200	81.92 127		79.54 142	96.97 94

test_prior				86.32 120	90.59 167	71.99 174		92.85 105					94.17 106			92.80 190

旧先验2							81.73 272		56.88 404	86.54 209			84.90 361	72.81 250

新几何2							81.72 273

新几何1				82.95 223	93.96 64	78.56 91		80.24 358	55.45 409	83.93 279	91.08 223	71.19 275	88.33 297	65.84 322	93.07 245	81.95 423

旧先验1					91.97 121	71.77 175		81.78 346			91.84 189	73.92 236			93.65 226	83.61 398

无先验							82.81 244	85.62 298	58.09 392				91.41 201	67.95 306		84.48 383

原ACMM2							82.26 264

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 152	62.59 347	82.73 305	90.67 246	76.53 202	94.25 98	69.24 287	95.69 152	85.55 371

test222					93.31 81	76.54 116	79.38 315	77.79 369	52.59 426	82.36 310	90.84 237	66.83 299			91.69 288	81.25 431

testdata2													86.43 334	63.52 344

segment_acmp												81.94 124

testdata				79.54 304	92.87 92	72.34 167		80.14 359	59.91 382	85.47 236	91.75 196	67.96 293	85.24 357	68.57 301	92.18 274	81.06 436

testdata1							79.62 307		73.95 187

test12				86.57 115	90.74 163	72.63 160		90.69 180		82.76 303		79.20 157	94.80 79		95.32 162	92.27 227

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 218

plane_prior5								93.61 65					95.22 63	80.78 126	95.83 145	94.46 97

plane_prior4											92.95 147

plane_prior3						76.85 114			77.79 135	86.55 203

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 157						95.03 174

n2								0.00 488

nn								0.00 488

door-mid								74.45 395

lessismore_v0				85.95 131	91.10 156	70.99 191		70.91 425		91.79 75	94.42 78	61.76 333	92.93 159	79.52 143	93.03 246	93.93 125

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	98.73 7	95.23 66

test11								91.46 153

door								72.57 411

HQP5-MVS						70.66 193

HQP-NCC					91.19 151		84.77 178		73.30 201	80.55 343

ACMP_Plane					91.19 151		84.77 178		73.30 201	80.55 343

BP-MVS														77.30 175

HQP4-MVS										80.56 342			94.61 86			93.56 153

HQP3-MVS								92.68 111							94.47 196

HQP2-MVS												72.10 264

NP-MVS					91.95 122	74.55 137					90.17 266

MDTV_nov1_ep13_2view						27.60 478	70.76 421		46.47 451	61.27 463		45.20 426		49.18 432		83.75 397

MDTV_nov1_ep13			68.29 398		78.03 418	43.87 453	74.12 391	72.22 414	52.17 429	67.02 446	85.54 354	45.36 424	80.85 392	55.73 392	84.42 413

ACMMP++_ref															95.74 151

ACMMP++															97.35 84

Test By Simon												79.09 159

ITE_SJBPF				90.11 49	90.72 164	84.97 41		90.30 198	81.56 84	90.02 109	91.20 218	82.40 109	90.81 225	73.58 236	94.66 191	94.56 90

DeepMVS_CX				24.13 460	32.95 482	29.49 476		21.63 483	12.07 476	37.95 477	45.07 474	30.84 465	19.21 479	17.94 478	33.06 476	23.69 475

High-resolution multi-view benchmark