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	80.16 152	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 23	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 34	87.75 43	95.72 39	89.60 5	98.27 28	92.08 239

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 43	89.64 22	95.82 29	89.13 7	98.26 30	91.76 250

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 43	89.64 22	95.82 29	89.13 7	98.26 30	91.76 250

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 53	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 35	91.34 4	94.68 82	90.26 4	98.44 20	93.63 149

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 77	88.83 27	95.51 50	87.16 38	97.60 75	92.73 195

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	94.02 102	90.15 17	95.67 41	86.82 43	97.34 85	92.19 234

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	93.94 110	90.55 13	95.73 38	88.50 12	98.23 33	95.33 62
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 82	90.09 18	95.08 70	86.67 45	97.60 75	94.18 115

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 55	89.75 21	94.93 74	79.95 134	98.27 28	95.04 75
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 94	89.15 26	95.62 42	87.35 33	98.24 32	94.56 91
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	92.60 118	83.09 69	91.54 78	94.25 88	87.67 46	95.51 50	87.21 37	98.11 40	93.12 178

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	94.00 103	88.26 33	95.71 40	87.28 36	98.39 23	92.55 209

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	89.99 111	94.03 101	86.57 59	95.80 31	87.35 33	97.62 73	94.20 112

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 38	91.81 145	84.07 55	92.00 71	94.40 81	86.63 58	95.28 62	88.59 11	98.31 26	92.30 226

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 109	88.22 23	88.53 148	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 93	87.51 47	95.87 20	87.74 22	97.76 60	93.99 124

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 61	91.18 5	95.52 48	85.36 67	98.73 7	95.23 67

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 89	87.75 43	95.87 20	87.60 27	97.71 63	93.83 134

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	93.96 107	88.35 32	95.56 45	87.74 22	97.74 62	92.85 192

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	93.97 104	87.93 42	95.87 20	88.65 10	97.96 51	94.12 120

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 35	88.77 28	96.41 3	87.60 27	97.98 48	92.98 188
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 80	88.17 35	95.98 13	86.37 49	97.99 46	93.96 127

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	93.97 104	88.19 34	96.29 6	87.61 26	98.20 36	94.39 106
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 99	82.59 74	88.52 149	94.37 83	86.74 57	95.41 57	86.32 50	98.21 34	93.19 173
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	93.80 114	87.20 51	95.80 31	87.10 40	97.69 65	93.93 128

MP-MVS-pluss		90.81 31	91.08 39	89.99 50	95.97 14	79.88 77	88.13 110	94.51 19	75.79 160	92.94 51	94.96 56	88.36 31	95.01 72	90.70 3	98.40 22	95.09 74
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 57	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	77.65 137	91.97 72	94.89 58	88.38 30	95.45 55	89.27 6	97.87 56	93.27 168

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	93.60 124	90.81 8	92.96 157	85.02 74	98.45 19	92.41 216
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	89.03 205	84.23 49	93.58 6	94.68 18	90.65 8	90.33 105	93.95 109	84.50 82	95.37 58	80.87 124	95.50 159	94.53 95

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	79.03 118	92.87 53	93.74 119	90.60 12	95.21 65	82.87 102	98.76 4	94.87 79
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 96	88.13 37	96.30 5	84.51 84	97.81 58	91.70 254
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	93.18 133	87.06 52	95.85 24	84.99 75	97.69 65	93.54 159

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 84	90.28 15	95.84 27	86.03 57	97.92 52	92.29 228

SMA-MVS		90.31 40	90.48 54	89.83 55	95.31 30	79.52 83	90.98 52	93.24 85	75.37 169	92.84 55	95.28 49	85.58 72	96.09 8	87.92 18	97.76 60	93.88 131
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	93.80 114	86.89 56	94.64 85	85.52 66	97.51 82	94.30 111

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 190	89.99 69	93.05 96	86.53 35	94.29 23	96.27 24	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	93.18 133	88.02 40	95.47 53	84.99 75	97.69 65	93.54 159

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 223	88.51 21	90.11 107	95.12 54	90.98 7	88.92 283	77.55 170	97.07 92	83.13 411
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 45	91.09 38	87.00 107	91.55 140	72.64 158	96.19 2	94.10 40	85.33 42	93.49 40	94.64 69	81.12 139	95.88 18	87.41 31	95.94 138	92.48 212

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 162	90.54 57	91.01 173	83.61 63	93.75 35	94.65 66	89.76 19	95.78 35	86.42 47	97.97 49	90.55 291
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	58.83 363	89.04 94	92.74 111	91.40 6	96.12 5	96.06 30	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	58.88 360	88.95 95	93.19 87	91.62 5	96.01 7	96.16 28	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 92	81.10 89	95.32 14	97.24 10	72.94 257	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	57.84 372	88.93 96	92.84 107	91.92 4	96.16 4	96.23 25	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	93.89 112	83.80 89	93.10 153	82.67 106	98.04 41	93.64 148

TestfortrainingZip a		89.97 52	90.77 49	87.58 100	94.38 48	73.21 150	92.12 33	93.85 53	77.53 141	93.24 43	93.18 133	87.06 52	95.85 24	87.89 19	97.69 65	93.68 143

3Dnovator+	83.92 2	89.97 52	89.66 61	90.92 35	91.27 149	81.66 66	91.25 47	94.13 38	88.89 15	88.83 140	94.26 87	77.55 181	95.86 23	84.88 78	95.87 144	95.24 66

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 38	81.42 136	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	92.78 110	78.78 121	92.51 62	93.64 123	88.13 37	93.84 120	84.83 80	97.55 78	94.10 121
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	90.63 183	70.00 260	94.55 19	96.67 18	87.94 41	93.59 132	84.27 86	95.97 134	95.52 56

anonymousdsp		89.73 57	88.88 77	92.27 8	89.82 185	86.67 18	90.51 59	90.20 205	69.87 261	95.06 15	96.14 29	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	90.08 208	71.54 236	94.28 25	96.54 20	81.57 134	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	86.75 199	93.26 130	93.64 2	90.93 219	84.60 83	90.75 318	93.97 126

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 95	80.87 93	91.13 86	93.19 132	86.22 66	95.97 14	82.23 112	97.18 90	90.45 293
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	90.29 202	69.27 268	94.39 21	96.38 22	86.02 69	93.52 137	83.96 88	95.92 140	95.34 60

CPTT-MVS		89.39 62	88.98 73	90.63 40	95.09 33	86.95 16	92.09 37	92.30 127	79.74 105	87.50 183	92.38 169	81.42 136	93.28 146	83.07 98	97.24 88	91.67 255

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 45	90.69 9	88.31 301	83.33 94	98.30 27	93.20 172
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	88.67 216	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 47	84.94 77	89.44 275	74.12 222	96.10 129	94.45 100

APD_test2		89.30 64	89.12 68	89.84 53	88.67 216	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 47	84.94 77	89.44 275	74.12 222	96.10 129	94.45 100

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 366	88.66 103	92.06 134	90.78 7	95.67 8	95.17 52	81.80 131	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	85.91 153	93.60 67	80.16 100	89.13 136	93.44 126	83.82 88	90.98 216	83.86 90	95.30 167	93.60 152

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	73.53 194	89.71 119	94.82 61	85.09 76	95.77 37	84.17 87	98.03 43	93.26 170
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	88.38 244	88.84 17	94.29 23	97.57 7	90.48 14	91.26 206	72.57 253	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	78.43 126	89.16 134	92.25 178	72.03 271	96.36 4	88.21 13	90.93 310	92.98 188
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	91.63 134	77.07 112	89.82 74	93.77 58	78.90 119	92.88 52	92.29 176	86.11 67	90.22 247	86.24 54	97.24 88	91.36 263
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	91.27 165	78.20 129	86.69 203	92.28 177	80.36 150	95.06 71	86.17 55	96.49 109	90.22 297

Elysia		88.71 73	88.89 75	88.19 90	91.26 150	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 97	74.07 234	94.82 77	88.19 14	95.92 140	96.80 27

StellarMVS		88.71 73	88.89 75	88.19 90	91.26 150	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 97	74.07 234	94.82 77	88.19 14	95.92 140	96.80 27

test_0402		88.65 75	89.58 64	85.88 134	92.55 101	72.22 170	84.01 202	89.44 226	88.63 20	94.38 22	95.77 33	86.38 65	93.59 132	79.84 135	95.21 168	91.82 248

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 147	77.50 104	87.55 119	92.97 103	87.95 26	89.62 123	92.87 152	84.56 81	93.89 117	77.65 168	96.62 104	90.70 283

APD_test1		88.40 77	87.91 89	89.88 52	89.50 191	86.65 20	89.98 70	91.91 140	84.26 53	90.87 96	93.92 111	82.18 118	89.29 279	73.75 230	94.81 187	93.70 142

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 170	65.27 261	88.86 97	93.02 100	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	91.97 137	70.73 249	94.19 26	96.67 18	76.94 195	94.57 88	83.07 98	96.28 118	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	76.02 154	88.64 145	91.22 218	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	89.56 189	79.18 85	90.47 60	94.77 17	79.37 112	84.32 271	89.33 286	83.87 87	94.53 91	82.45 108	94.89 183	94.90 77

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 168	67.85 295	86.63 204	94.84 60	79.58 158	95.96 15	87.62 25	94.50 196	94.56 91
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	89.64 220	85.07 45	90.91 92	91.09 224	89.16 25	91.87 188	82.03 113	95.87 144	93.13 175

EC-MVSNet		88.01 84	88.32 86	87.09 104	89.28 196	72.03 173	90.31 64	96.31 4	80.88 92	85.12 245	89.67 279	84.47 83	95.46 54	82.56 107	96.26 121	93.77 140

RPSCF		88.00 85	86.93 108	91.22 31	90.08 178	89.30 5	89.68 78	91.11 169	79.26 113	89.68 120	94.81 64	82.44 107	87.74 312	76.54 185	88.74 355	96.61 32

AllTest		87.97 86	87.40 98	89.68 56	91.59 135	83.40 52	89.50 85	95.44 11	79.47 108	88.00 165	93.03 143	82.66 104	91.47 196	70.81 267	96.14 126	94.16 117

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 273	84.38 194	91.29 161	84.88 48	92.06 70	93.84 113	86.45 62	93.73 122	73.22 244	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 180	69.73 207	87.86 116	94.20 31	74.04 186	92.70 60	94.66 65	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	85.25 171	91.23 166	77.31 144	87.07 193	91.47 208	82.94 99	94.71 81	84.67 82	96.27 120	92.62 203

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	86.55 205	92.95 149	74.84 221	95.22 63	80.78 126	95.83 146	94.46 98

sc_t1		87.70 91	88.94 74	83.99 188	93.47 74	67.15 238	85.05 176	88.21 251	86.81 32	91.87 74	97.65 5	85.51 74	87.91 307	74.22 216	97.63 71	96.92 25

MM		87.64 92	87.15 100	89.09 69	89.51 190	76.39 121	88.68 102	86.76 282	84.54 50	83.58 290	93.78 116	73.36 252	96.48 2	87.98 17	96.21 122	94.41 105

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 207	92.03 120	62.26 305	91.49 45	92.62 115	88.07 25	88.07 162	96.17 27	72.24 266	95.79 34	84.85 79	94.16 209	92.58 207

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 171	78.77 122	84.85 257	90.89 235	80.85 142	95.29 60	81.14 121	95.32 164	92.34 224

DeepPCF-MVS	81.24 5	87.28 95	86.21 120	90.49 42	91.48 144	84.90 42	83.41 225	92.38 123	70.25 257	89.35 131	90.68 245	82.85 102	94.57 88	79.55 141	95.95 137	92.00 243

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 217	87.84 117	88.05 252	81.66 83	94.64 18	96.53 21	65.94 308	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	89.67 187	75.87 129	84.60 187	89.74 215	74.40 183	89.92 115	93.41 127	80.45 148	90.63 234	86.66 46	94.37 202	94.73 88

SPE-MVS-test		87.00 98	86.43 116	88.71 76	89.46 192	77.46 105	89.42 89	95.73 7	77.87 135	81.64 332	87.25 331	82.43 108	94.53 91	77.65 168	96.46 111	94.14 119

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 227	83.80 212	92.87 105	80.37 96	89.61 125	91.81 194	77.72 177	94.18 104	75.00 209	98.53 16	96.99 24

Vis-MVSNet		86.86 100	86.58 113	87.72 97	92.09 117	77.43 107	87.35 123	92.09 133	78.87 120	84.27 276	94.05 100	78.35 169	93.65 125	80.54 130	91.58 294	92.08 239
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	82.55 252	91.56 151	83.08 70	90.92 90	91.82 193	78.25 170	93.99 111	74.16 220	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 242	83.16 235	92.21 128	81.73 82	90.92 90	91.97 184	77.20 189	93.99 111	74.16 220	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 268	65.22 262	84.16 198	94.23 28	77.89 133	91.28 85	93.66 122	84.35 84	92.71 163	80.07 131	94.87 186	95.16 72
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	86.52 114	87.18 103	85.94 308	78.30 92	86.93 130	92.20 129	65.94 315	89.16 134	93.16 138	83.10 97	89.89 263	87.81 21	94.43 200	93.35 163

tt0320-xc		86.67 105	88.41 84	81.44 267	93.45 75	60.44 337	83.96 204	88.50 240	87.26 29	90.90 94	97.90 3	85.61 71	86.40 338	70.14 279	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	88.64 237	86.30 37	89.60 126	92.59 161	69.22 289	94.91 75	73.89 227	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 268	93.57 72	60.73 334	84.37 195	88.61 239	87.00 31	90.75 97	97.98 2	85.54 73	86.45 336	69.75 284	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 231	63.28 283	86.64 140	92.20 129	75.42 168	92.81 57	94.50 73	74.05 237	94.06 110	83.88 89	96.28 118	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 253	92.22 113	62.28 304	84.66 186	89.15 231	83.54 65	89.85 116	97.32 8	88.08 39	86.80 329	70.43 276	97.30 87	96.62 31

NormalMVS		86.47 110	85.32 143	89.94 51	94.43 44	80.42 72	88.63 104	93.59 68	74.56 178	85.12 245	90.34 258	66.19 305	94.20 101	76.57 183	98.44 20	95.19 69

PHI-MVS		86.38 111	85.81 130	88.08 92	88.44 225	77.34 108	89.35 91	93.05 96	73.15 207	84.76 260	87.70 320	78.87 163	94.18 104	80.67 128	96.29 117	92.73 195

CSCG		86.26 112	86.47 115	85.60 140	90.87 162	74.26 139	87.98 114	91.85 141	80.35 97	89.54 129	88.01 307	79.09 161	92.13 179	75.51 202	95.06 175	90.41 294

DeepC-MVS_fast	80.27 8	86.23 113	85.65 136	87.96 95	91.30 147	76.92 113	87.19 125	91.99 136	70.56 250	84.96 252	90.69 244	80.01 154	95.14 68	78.37 155	95.78 150	91.82 248
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	87.82 239	62.35 303	86.42 144	91.33 160	76.78 148	92.73 59	94.48 75	73.41 249	93.72 123	83.10 97	95.41 160	97.01 23

Anonymous20240529		86.20 115	87.13 101	83.42 209	90.19 175	64.55 269	84.55 189	90.71 180	85.85 40	89.94 114	95.24 51	82.13 120	90.40 242	69.19 291	96.40 114	95.31 63

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 276	70.38 198	85.31 170	92.61 117	75.59 164	88.32 156	92.87 152	82.22 117	88.63 292	88.80 9	92.82 256	89.83 307

test_fmvsmconf0.1_n		86.18 117	85.88 128	87.08 105	85.26 324	78.25 93	85.82 157	91.82 143	65.33 330	88.55 147	92.35 175	82.62 106	89.80 265	86.87 42	94.32 204	93.18 174

CDPH-MVS		86.17 118	85.54 137	88.05 94	92.25 111	75.45 132	83.85 209	92.01 135	65.91 317	86.19 216	91.75 198	83.77 90	94.98 73	77.43 173	96.71 102	93.73 141

NR-MVSNet		86.00 119	86.22 119	85.34 147	93.24 84	64.56 268	82.21 267	90.46 189	80.99 90	88.42 152	91.97 184	77.56 180	93.85 118	72.46 254	98.65 12	97.61 10

train_agg		85.98 120	85.28 144	88.07 93	92.34 108	79.70 80	83.94 205	90.32 197	65.79 319	84.49 265	90.97 229	81.93 126	93.63 127	81.21 120	96.54 107	90.88 277

KinetiMVS		85.95 121	86.10 123	85.50 144	87.56 249	69.78 205	83.70 215	89.83 214	80.42 95	87.76 176	93.24 131	73.76 243	91.54 194	85.03 73	93.62 230	95.19 69

FC-MVSNet-test		85.93 122	87.05 104	82.58 234	92.25 111	56.44 383	85.75 158	93.09 94	77.33 143	91.94 73	94.65 66	74.78 223	93.41 143	75.11 208	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 138	86.99 108	84.77 333	78.21 94	85.40 168	91.39 158	65.32 331	87.72 178	91.81 194	82.33 111	89.78 266	86.68 44	94.20 207	92.99 186

Effi-MVS+-dtu		85.82 124	83.38 198	93.14 4	87.13 263	91.15 3	87.70 118	88.42 243	74.57 177	83.56 291	85.65 355	78.49 168	94.21 100	72.04 256	92.88 252	94.05 123

TAPA-MVS	77.73 12	85.71 125	84.83 154	88.37 86	88.78 215	79.72 79	87.15 127	93.50 71	69.17 269	85.80 228	89.56 280	80.76 144	92.13 179	73.21 249	95.51 158	93.25 171
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FE-MVSNET1		85.67 126	86.73 112	82.51 238	91.76 132	62.23 308	86.13 151	90.45 190	79.10 115	90.40 102	96.73 17	82.17 119	89.96 262	67.47 309	96.33 116	95.34 60

sasdasda		85.50 127	86.14 121	83.58 203	87.97 233	67.13 239	87.55 119	94.32 22	73.44 197	88.47 150	87.54 323	86.45 62	91.06 214	75.76 198	93.76 221	92.54 210

canonicalmvs		85.50 127	86.14 121	83.58 203	87.97 233	67.13 239	87.55 119	94.32 22	73.44 197	88.47 150	87.54 323	86.45 62	91.06 214	75.76 198	93.76 221	92.54 210

fmvsm_s_conf0.5_n_8		85.48 129	85.75 133	84.68 167	87.10 266	69.98 203	84.28 196	92.68 112	74.77 174	87.90 169	92.36 174	73.94 238	90.41 241	85.95 62	92.74 258	93.66 144

EPP-MVSNet		85.47 130	85.04 149	86.77 113	91.52 143	69.37 212	91.63 44	87.98 255	81.51 85	87.05 194	91.83 192	66.18 307	95.29 60	70.75 270	96.89 95	95.64 53

GeoE		85.45 131	85.81 130	84.37 174	90.08 178	67.07 241	85.86 156	91.39 158	72.33 225	87.59 180	90.25 264	84.85 79	92.37 173	78.00 164	91.94 283	93.66 144

MGCNet		85.37 132	84.58 166	87.75 96	85.28 323	73.36 144	86.54 143	85.71 299	77.56 140	81.78 330	92.47 167	70.29 283	96.02 11	85.59 65	95.96 135	93.87 132

FIs		85.35 133	86.27 118	82.60 233	91.86 126	57.31 376	85.10 175	93.05 96	75.83 159	91.02 89	93.97 104	73.57 245	92.91 161	73.97 226	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 134	85.36 142	84.81 160	85.80 311	76.13 125	85.15 174	92.32 126	61.40 366	91.33 82	90.85 238	83.76 91	86.16 344	84.31 85	93.28 239	92.15 237

casdiffmvs		85.21 135	85.85 129	83.31 212	86.17 300	62.77 290	83.03 237	93.93 47	74.69 176	88.21 159	92.68 160	82.29 115	91.89 187	77.87 167	93.75 224	95.27 65
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		85.20 136	85.25 145	85.02 155	86.01 306	71.31 185	84.96 177	91.76 147	69.10 271	88.90 137	92.56 164	73.84 241	90.63 234	86.88 41	93.26 240	93.13 175

baseline		85.20 136	85.93 126	83.02 219	86.30 295	62.37 302	84.55 189	93.96 45	74.48 180	87.12 188	92.03 183	82.30 113	91.94 184	78.39 154	94.21 206	94.74 87

SSM_0404		85.16 138	85.09 147	85.36 146	90.14 177	69.52 210	86.17 149	91.58 149	74.41 181	86.55 205	91.49 205	78.54 164	93.97 113	73.71 231	93.21 244	92.59 206

K. test v3		85.14 139	84.73 156	86.37 119	91.13 156	69.63 209	85.45 166	76.68 384	84.06 56	92.44 64	96.99 13	62.03 335	94.65 84	80.58 129	93.24 241	94.83 84

mmtdpeth		85.13 140	85.78 132	83.17 217	84.65 335	74.71 135	85.87 155	90.35 196	77.94 132	83.82 283	96.96 15	77.75 175	80.03 403	78.44 153	96.21 122	94.79 86

EI-MVSNet-Vis-set		85.12 141	84.53 169	86.88 110	84.01 348	72.76 155	83.91 208	85.18 309	80.44 94	88.75 142	85.49 359	80.08 153	91.92 185	82.02 114	90.85 315	95.97 44

fmvsm_l_conf0.5_n_3		85.11 142	84.96 151	85.56 141	87.49 252	75.69 131	84.71 184	90.61 185	67.64 299	84.88 255	92.05 182	82.30 113	88.36 299	83.84 91	91.10 303	92.62 203

MGCFI-Net		85.04 143	85.95 125	82.31 245	87.52 250	63.59 279	86.23 148	93.96 45	73.46 195	88.07 162	87.83 318	86.46 61	90.87 224	76.17 192	93.89 217	92.47 214

EI-MVSNet-UG-set		85.04 143	84.44 172	86.85 111	83.87 352	72.52 164	83.82 210	85.15 310	80.27 99	88.75 142	85.45 361	79.95 155	91.90 186	81.92 117	90.80 317	96.13 39

X-MVStestdata		85.04 143	82.70 217	92.08 9	95.64 24	86.25 22	92.64 20	93.33 78	85.07 45	89.99 111	16.05 480	86.57 59	95.80 31	87.35 33	97.62 73	94.20 112

MSLP-MVS++		85.00 146	86.03 124	81.90 253	91.84 129	71.56 183	86.75 138	93.02 100	75.95 157	87.12 188	89.39 284	77.98 172	89.40 278	77.46 171	94.78 188	84.75 383

F-COLMAP		84.97 147	83.42 196	89.63 58	92.39 106	83.40 52	88.83 98	91.92 139	73.19 206	80.18 354	89.15 290	77.04 193	93.28 146	65.82 326	92.28 272	92.21 233

SSM_0407		84.89 148	84.85 153	85.01 156	89.13 200	68.97 220	85.60 162	91.58 149	74.41 181	85.68 229	91.49 205	78.54 164	93.69 124	73.71 231	93.47 232	92.38 221

balanced_conf03		84.80 149	85.40 140	83.00 220	88.95 208	61.44 314	90.42 63	92.37 125	71.48 238	88.72 144	93.13 139	70.16 285	95.15 67	79.26 146	94.11 210	92.41 216

3Dnovator	80.37 7	84.80 149	84.71 159	85.06 153	86.36 293	74.71 135	88.77 100	90.00 210	75.65 162	84.96 252	93.17 137	74.06 236	91.19 209	78.28 158	91.09 304	89.29 317

SymmetryMVS		84.79 151	83.54 190	88.55 79	92.44 105	80.42 72	88.63 104	82.37 344	74.56 178	85.12 245	90.34 258	66.19 305	94.20 101	76.57 183	95.68 154	91.03 271

IterMVS-LS		84.73 152	84.98 150	83.96 190	87.35 256	63.66 277	83.25 230	89.88 213	76.06 152	89.62 123	92.37 172	73.40 251	92.52 168	78.16 161	94.77 190	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 153	84.34 177	85.49 145	90.18 176	75.86 130	79.23 323	87.13 272	73.35 199	85.56 236	89.34 285	83.60 93	90.50 238	76.64 182	94.05 214	90.09 303

HQP-MVS		84.61 154	84.06 182	86.27 122	91.19 152	70.66 193	84.77 179	92.68 112	73.30 202	80.55 346	90.17 269	72.10 267	94.61 86	77.30 175	94.47 198	93.56 156

v1192		84.57 155	84.69 161	84.21 182	87.75 241	62.88 287	83.02 238	91.43 155	69.08 272	89.98 113	90.89 235	72.70 261	93.62 130	82.41 109	94.97 180	96.13 39

fmvsm_s_conf0.5_n_11		84.56 156	84.69 161	84.15 185	86.53 282	71.29 186	85.53 163	92.62 115	70.54 251	82.75 307	91.20 220	77.33 184	88.55 295	83.80 92	91.93 284	92.61 205

fmvsm_s_conf0.5_n_5		84.56 156	84.71 159	84.11 186	87.92 236	72.09 172	84.80 178	88.64 237	64.43 340	88.77 141	91.78 196	78.07 171	87.95 306	85.85 63	92.18 276	92.30 226

FMVSNet1		84.55 158	85.45 139	81.85 255	90.27 174	61.05 324	86.83 134	88.27 248	78.57 125	89.66 122	95.64 39	75.43 213	90.68 231	69.09 292	95.33 163	93.82 135

v1144		84.54 159	84.72 158	84.00 187	87.67 245	62.55 294	82.97 240	90.93 176	70.32 255	89.80 117	90.99 228	73.50 246	93.48 139	81.69 119	94.65 194	95.97 44

Gipuma		84.44 160	86.33 117	78.78 314	84.20 345	73.57 143	89.55 82	90.44 191	84.24 54	84.38 268	94.89 58	76.35 208	80.40 400	76.14 193	96.80 100	82.36 421
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 161	84.27 178	84.74 163	87.25 259	70.84 192	83.55 220	88.45 242	68.64 281	86.29 215	91.31 214	74.97 219	88.42 297	87.87 20	90.07 333	94.95 76

MCST-MVS		84.36 162	83.93 186	85.63 139	91.59 135	71.58 181	83.52 221	92.13 131	61.82 359	83.96 281	89.75 277	79.93 156	93.46 140	78.33 157	94.34 203	91.87 247

VDDNet		84.35 163	85.39 141	81.25 270	95.13 32	59.32 351	85.42 167	81.11 355	86.41 36	87.41 184	96.21 26	73.61 244	90.61 236	66.33 318	96.85 96	93.81 138

ETV-MVS		84.31 164	83.91 187	85.52 142	88.58 221	70.40 197	84.50 193	93.37 73	78.76 123	84.07 279	78.72 434	80.39 149	95.13 69	73.82 229	92.98 250	91.04 270

v1240		84.30 165	84.51 170	83.65 200	87.65 246	61.26 320	82.85 244	91.54 152	67.94 292	90.68 99	90.65 249	71.71 275	93.64 126	82.84 103	94.78 188	96.07 41

MVS_111021_LR		84.28 166	83.76 188	85.83 136	89.23 198	83.07 55	80.99 290	83.56 332	72.71 217	86.07 219	89.07 292	81.75 133	86.19 343	77.11 177	93.36 235	88.24 336

h-mvs33		84.25 167	82.76 216	88.72 75	91.82 131	82.60 60	84.00 203	84.98 316	71.27 239	86.70 201	90.55 254	63.04 332	93.92 116	78.26 159	94.20 207	89.63 309

v144192		84.24 168	84.41 173	83.71 199	87.59 248	61.57 313	82.95 241	91.03 172	67.82 296	89.80 117	90.49 255	73.28 253	93.51 138	81.88 118	94.89 183	96.04 43

dcpmvs_2		84.23 169	85.14 146	81.50 265	88.61 220	61.98 310	82.90 243	93.11 92	68.66 280	92.77 58	92.39 168	78.50 167	87.63 315	76.99 179	92.30 269	94.90 77

v1921920		84.23 169	84.37 175	83.79 195	87.64 247	61.71 312	82.91 242	91.20 167	67.94 292	90.06 108	90.34 258	72.04 270	93.59 132	82.32 110	94.91 181	96.07 41

VDD-MVS		84.23 169	84.58 166	83.20 215	91.17 155	65.16 264	83.25 230	84.97 317	79.79 104	87.18 187	94.27 84	74.77 224	90.89 222	69.24 288	96.54 107	93.55 158

v2v482		84.09 172	84.24 179	83.62 201	87.13 263	61.40 315	82.71 247	89.71 218	72.19 228	89.55 127	91.41 209	70.70 281	93.20 148	81.02 122	93.76 221	96.25 37

EG-PatchMatch MVS		84.08 173	84.11 181	83.98 189	92.22 113	72.61 161	82.20 269	87.02 278	72.63 218	88.86 138	91.02 227	78.52 166	91.11 212	73.41 239	91.09 304	88.21 337

E2		84.06 174	84.61 163	82.40 243	87.49 252	61.31 317	81.03 288	93.36 74	71.83 233	86.02 221	91.87 186	82.91 100	91.37 203	75.66 200	91.33 298	94.53 95

E3		84.06 174	84.61 163	82.40 243	87.49 252	61.30 318	81.03 288	93.36 74	71.83 233	86.01 222	91.87 186	82.91 100	91.36 204	75.66 200	91.33 298	94.53 95

fmvsm_s_conf0.5_n_6		84.05 176	84.14 180	83.81 193	87.75 241	71.17 188	83.42 224	91.10 170	67.90 294	84.53 263	90.70 243	73.01 256	88.73 289	85.09 70	93.72 226	91.53 260

DP-MVS Recon		84.05 176	83.22 201	86.52 117	91.73 133	75.27 133	83.23 232	92.40 121	72.04 230	82.04 321	88.33 303	77.91 174	93.95 115	66.17 319	95.12 173	90.34 296

viewmacassd2359aftdt		84.04 178	84.78 155	81.81 258	86.43 287	60.32 339	81.95 271	92.82 108	71.56 235	86.06 220	92.98 145	81.79 132	90.28 243	76.18 191	93.24 241	94.82 85

TransMVSNet (Re)		84.02 179	85.74 134	78.85 313	91.00 159	55.20 395	82.29 263	87.26 267	79.65 107	88.38 154	95.52 42	83.00 98	86.88 327	67.97 306	96.60 105	94.45 100

Baseline_NR-MVSNet		84.00 180	85.90 127	78.29 325	91.47 145	53.44 407	82.29 263	87.00 281	79.06 117	89.55 127	95.72 37	77.20 189	86.14 345	72.30 255	98.51 17	95.28 64

fmvsm_l_conf0.5_n_9		83.98 181	84.46 171	82.53 237	86.11 303	70.65 195	82.45 257	89.17 230	67.72 298	86.74 200	91.49 205	79.20 159	85.86 354	84.71 81	92.60 262	91.07 269

TSAR-MVS + GP.		83.95 182	82.69 218	87.72 97	89.27 197	81.45 67	83.72 214	81.58 353	74.73 175	85.66 232	86.06 350	72.56 263	92.69 165	75.44 204	95.21 168	89.01 330

LuminaMVS		83.94 183	83.51 191	85.23 148	89.78 186	71.74 176	84.76 182	87.27 266	72.60 219	89.31 132	90.60 253	64.04 321	90.95 217	79.08 147	94.11 210	92.99 186

alignmvs		83.94 183	83.98 184	83.80 194	87.80 240	67.88 234	84.54 191	91.42 157	73.27 205	88.41 153	87.96 308	72.33 264	90.83 225	76.02 195	94.11 210	92.69 199

Effi-MVS+		83.90 185	84.01 183	83.57 205	87.22 261	65.61 260	86.55 142	92.40 121	78.64 124	81.34 337	84.18 380	83.65 92	92.93 159	74.22 216	87.87 369	92.17 236

fmvsm_s_conf0.1_n_2		83.82 186	83.49 193	84.84 158	85.99 307	70.19 201	80.93 291	87.58 262	67.26 305	87.94 168	92.37 172	71.40 277	88.01 303	86.03 57	91.87 285	96.31 36

mvs5depth		83.82 186	84.54 168	81.68 261	82.23 377	68.65 225	86.89 131	89.90 212	80.02 103	87.74 177	97.86 4	64.19 320	82.02 388	76.37 187	95.63 157	94.35 107

CANet		83.79 188	82.85 215	86.63 114	86.17 300	72.21 171	83.76 213	91.43 155	77.24 145	74.39 409	87.45 327	75.36 214	95.42 56	77.03 178	92.83 255	92.25 232

pm-mvs1		83.69 189	84.95 152	79.91 299	90.04 182	59.66 348	82.43 258	87.44 263	75.52 166	87.85 172	95.26 50	81.25 138	85.65 358	68.74 298	96.04 131	94.42 104

AdaColmap		83.66 190	83.69 189	83.57 205	90.05 181	72.26 169	86.29 146	90.00 210	78.19 130	81.65 331	87.16 333	83.40 95	94.24 99	61.69 362	94.76 191	84.21 393

viewdifsd2359ckpt09		83.64 191	83.18 204	85.03 154	87.26 258	66.99 244	85.32 169	93.83 57	65.57 325	84.99 251	89.40 283	77.30 185	93.57 135	71.16 266	93.80 220	94.54 94

MIMVSNet1		83.63 192	84.59 165	80.74 281	94.06 62	62.77 290	82.72 246	84.53 324	77.57 139	90.34 104	95.92 32	76.88 201	85.83 355	61.88 360	97.42 83	93.62 150

fmvsm_s_conf0.5_n_2		83.62 193	83.29 200	84.62 168	85.43 321	70.18 202	80.61 298	87.24 268	67.14 306	87.79 174	91.87 186	71.79 274	87.98 305	86.00 61	91.77 288	95.71 50

test_fmvsm_n_1920		83.60 194	82.89 212	85.74 137	85.22 325	77.74 102	84.12 200	90.48 187	59.87 386	86.45 214	91.12 223	75.65 211	85.89 352	82.28 111	90.87 313	93.58 154

WR-MVS		83.56 195	84.40 174	81.06 275	93.43 78	54.88 396	78.67 332	85.02 314	81.24 87	90.74 98	91.56 203	72.85 258	91.08 213	68.00 305	98.04 41	97.23 17

CNLPA		83.55 196	83.10 207	84.90 157	89.34 195	83.87 50	84.54 191	88.77 234	79.09 116	83.54 292	88.66 300	74.87 220	81.73 390	66.84 313	92.29 271	89.11 323

viewcassd2359sk11		83.53 197	83.96 185	82.25 246	86.97 275	61.13 322	80.80 295	93.22 86	70.97 246	85.36 240	91.08 225	81.84 130	91.29 205	74.79 211	90.58 329	94.33 109

LCM-MVSNet-Re		83.48 198	85.06 148	78.75 315	85.94 308	55.75 389	80.05 304	94.27 25	76.47 149	96.09 6	94.54 72	83.31 96	89.75 269	59.95 373	94.89 183	90.75 280

hse-mvs2		83.47 199	81.81 233	88.47 82	91.03 158	82.27 61	82.61 248	83.69 330	71.27 239	86.70 201	86.05 351	63.04 332	92.41 171	78.26 159	93.62 230	90.71 282

V42		83.47 199	83.37 199	83.75 197	83.16 371	63.33 282	81.31 282	90.23 204	69.51 265	90.91 92	90.81 240	74.16 233	92.29 177	80.06 132	90.22 331	95.62 54

VPA-MVSNet		83.47 199	84.73 156	79.69 304	90.29 173	57.52 375	81.30 284	88.69 236	76.29 150	87.58 182	94.44 76	80.60 147	87.20 321	66.60 316	96.82 99	94.34 108

mamba_0408		83.44 202	82.88 213	85.11 151	89.13 200	68.97 220	72.73 407	91.28 162	72.90 211	85.68 229	90.61 251	76.78 202	93.97 113	73.37 241	93.47 232	92.38 221

viewdifsd2359ckpt07		83.41 203	84.35 176	80.56 288	85.84 310	58.93 359	79.47 315	91.28 162	73.01 210	87.59 180	92.07 181	85.24 75	88.68 290	73.59 236	91.11 302	94.09 122

PAPM_NR		83.23 204	83.19 203	83.33 211	90.90 161	65.98 256	88.19 109	90.78 179	78.13 131	80.87 342	87.92 312	73.49 248	92.42 170	70.07 280	88.40 358	91.60 257

CLD-MVS		83.18 205	82.64 219	84.79 161	89.05 204	67.82 235	77.93 342	92.52 119	68.33 284	85.07 248	81.54 409	82.06 123	92.96 157	69.35 287	97.91 54	93.57 155
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 206	85.68 135	75.65 363	81.24 389	45.26 451	79.94 306	92.91 104	83.83 57	91.33 82	96.88 16	80.25 151	85.92 348	68.89 295	95.89 143	95.76 48

FA-MVS(test-final)		83.13 207	83.02 208	83.43 208	86.16 302	66.08 255	88.00 113	88.36 245	75.55 165	85.02 249	92.75 158	65.12 314	92.50 169	74.94 210	91.30 300	91.72 252

114514_t		83.10 208	82.54 222	84.77 162	92.90 91	69.10 219	86.65 139	90.62 184	54.66 418	81.46 334	90.81 240	76.98 194	94.38 94	72.62 252	96.18 124	90.82 279

E3new		83.08 209	83.39 197	82.14 248	86.49 284	61.00 327	80.64 296	93.12 91	70.30 256	84.78 259	90.34 258	80.85 142	91.24 207	74.20 219	89.83 338	94.17 116

RRT-MVS		82.97 210	83.44 194	81.57 263	85.06 328	58.04 370	87.20 124	90.37 194	77.88 134	88.59 146	93.70 121	63.17 329	93.05 155	76.49 186	88.47 357	93.62 150

viewmanbaseed2359cas		82.95 211	83.43 195	81.52 264	85.18 326	60.03 344	81.36 281	92.38 123	69.55 264	84.84 258	91.38 210	79.85 157	90.09 256	74.22 216	92.09 278	94.43 103

BP-MVS1		82.81 212	81.67 235	86.23 123	87.88 238	68.53 226	86.06 152	84.36 325	75.65 162	85.14 244	90.19 266	45.84 421	94.42 93	85.18 69	94.72 192	95.75 49

FE-MVSNET2		82.80 213	83.51 191	80.67 286	89.08 203	58.46 367	82.40 260	89.26 228	71.25 242	88.24 158	94.07 99	75.75 210	89.56 270	65.91 324	95.67 156	93.98 125

UGNet		82.78 214	81.64 236	86.21 126	86.20 299	76.24 123	86.86 132	85.68 300	77.07 146	73.76 413	92.82 154	69.64 286	91.82 190	69.04 294	93.69 227	90.56 290
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		82.75 215	81.93 231	85.19 149	82.08 378	80.15 76	85.53 163	88.76 235	68.01 289	85.58 235	87.75 319	71.80 273	86.85 328	74.02 225	93.87 218	88.58 333

EI-MVSNet		82.61 216	82.42 224	83.20 215	83.25 368	63.66 277	83.50 222	85.07 311	76.06 152	86.55 205	85.10 367	73.41 249	90.25 244	78.15 163	90.67 324	95.68 52

QAPM		82.59 217	82.59 221	82.58 234	86.44 286	66.69 247	89.94 72	90.36 195	67.97 291	84.94 254	92.58 163	72.71 260	92.18 178	70.63 273	87.73 372	88.85 331

fmvsm_s_conf0.1_n_a		82.58 218	81.93 231	84.50 171	87.68 244	73.35 145	86.14 150	77.70 373	61.64 364	85.02 249	91.62 200	77.75 175	86.24 340	82.79 104	87.07 380	93.91 130

Fast-Effi-MVS+-dtu		82.54 219	81.41 245	85.90 133	85.60 316	76.53 118	83.07 236	89.62 222	73.02 209	79.11 364	83.51 385	80.74 145	90.24 246	68.76 297	89.29 345	90.94 274

MVS_Test		82.47 220	83.22 201	80.22 295	82.62 376	57.75 374	82.54 253	91.96 138	71.16 244	82.89 303	92.52 166	77.41 182	90.50 238	80.04 133	87.84 371	92.40 218

viewdifsd2359ckpt11		82.46 221	82.98 210	80.88 278	83.53 355	61.00 327	79.46 316	85.97 295	69.48 266	87.89 170	91.31 214	82.10 121	88.61 293	74.28 214	92.86 253	93.02 182

viewmsd2359difaftdt		82.46 221	82.99 209	80.88 278	83.52 356	61.00 327	79.46 316	85.97 295	69.48 266	87.89 170	91.31 214	82.10 121	88.61 293	74.28 214	92.86 253	93.02 182

v148		82.31 223	82.48 223	81.81 258	85.59 317	59.66 348	81.47 279	86.02 293	72.85 213	88.05 164	90.65 249	70.73 280	90.91 221	75.15 207	91.79 286	94.87 79

API-MVS		82.28 224	82.61 220	81.30 269	86.29 296	69.79 204	88.71 101	87.67 261	78.42 127	82.15 317	84.15 381	77.98 172	91.59 193	65.39 329	92.75 257	82.51 420

MVSFormer		82.23 225	81.57 241	84.19 184	85.54 318	69.26 214	91.98 39	90.08 208	71.54 236	76.23 389	85.07 370	58.69 357	94.27 96	86.26 51	88.77 353	89.03 328

viewdifsd2359ckpt13		82.22 226	81.98 230	82.95 223	85.48 320	64.44 270	83.17 234	92.11 132	65.97 314	83.72 286	89.73 278	77.60 179	90.80 227	70.61 274	89.42 343	93.59 153

fmvsm_s_conf0.5_n_a		82.21 227	81.51 244	84.32 179	86.56 281	73.35 145	85.46 165	77.30 377	61.81 360	84.51 264	90.88 237	77.36 183	86.21 342	82.72 105	86.97 385	93.38 162

EIA-MVS		82.19 228	81.23 252	85.10 152	87.95 235	69.17 218	83.22 233	93.33 78	70.42 252	78.58 369	79.77 425	77.29 186	94.20 101	71.51 262	88.96 351	91.93 246

GDP-MVS		82.17 229	80.85 260	86.15 130	88.65 218	68.95 223	85.65 161	93.02 100	68.42 282	83.73 285	89.54 281	45.07 432	94.31 95	79.66 139	93.87 218	95.19 69

fmvsm_s_conf0.1_n		82.17 229	81.59 239	83.94 192	86.87 279	71.57 182	85.19 173	77.42 376	62.27 358	84.47 267	91.33 212	76.43 205	85.91 350	83.14 95	87.14 378	94.33 109

PCF-MVS	74.62 15	82.15 231	80.92 258	85.84 135	89.43 193	72.30 168	80.53 299	91.82 143	57.36 402	87.81 173	89.92 274	77.67 178	93.63 127	58.69 378	95.08 174	91.58 258
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 232	80.31 267	87.45 101	90.86 163	80.29 75	85.88 154	90.65 182	68.17 287	76.32 388	86.33 345	73.12 255	92.61 167	61.40 365	90.02 335	89.44 312
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 233	81.54 243	83.60 202	83.94 349	73.90 141	83.35 227	86.10 289	58.97 388	83.80 284	90.36 257	74.23 231	86.94 326	82.90 101	90.22 331	89.94 305

fmvsm_s_conf0.5_n_7		82.04 234	82.05 228	82.01 251	86.98 274	71.07 189	78.70 330	89.45 225	68.07 288	78.14 371	91.61 201	74.19 232	85.92 348	79.61 140	91.73 289	89.05 327

GBi-Net		82.02 235	82.07 226	81.85 255	86.38 290	61.05 324	86.83 134	88.27 248	72.43 220	86.00 223	95.64 39	63.78 325	90.68 231	65.95 321	93.34 236	93.82 135

test1		82.02 235	82.07 226	81.85 255	86.38 290	61.05 324	86.83 134	88.27 248	72.43 220	86.00 223	95.64 39	63.78 325	90.68 231	65.95 321	93.34 236	93.82 135

OpenMVS	76.72 13	81.98 237	82.00 229	81.93 252	84.42 340	68.22 229	88.50 107	89.48 224	66.92 309	81.80 328	91.86 189	72.59 262	90.16 250	71.19 265	91.25 301	87.40 353

KD-MVS_self_test		81.93 238	83.14 206	78.30 324	84.75 334	52.75 411	80.37 301	89.42 227	70.24 258	90.26 106	93.39 128	74.55 230	86.77 330	68.61 300	96.64 103	95.38 59

fmvsm_s_conf0.5_n		81.91 239	81.30 249	83.75 197	86.02 305	71.56 183	84.73 183	77.11 380	62.44 355	84.00 280	90.68 245	76.42 206	85.89 352	83.14 95	87.11 379	93.81 138

SDMVSNet		81.90 240	83.17 205	78.10 328	88.81 213	62.45 300	76.08 376	86.05 292	73.67 191	83.41 293	93.04 141	82.35 110	80.65 397	70.06 281	95.03 176	91.21 265

tfpnnormal		81.79 241	82.95 211	78.31 323	88.93 209	55.40 391	80.83 294	82.85 339	76.81 147	85.90 227	94.14 94	74.58 228	86.51 334	66.82 314	95.68 154	93.01 185

AstraMVS		81.67 242	81.40 246	82.48 240	87.06 271	66.47 250	81.41 280	81.68 350	68.78 277	88.00 165	90.95 233	65.70 310	87.86 311	76.66 181	92.38 266	93.12 178

c3_l		81.64 243	81.59 239	81.79 260	80.86 395	59.15 356	78.61 333	90.18 206	68.36 283	87.20 186	87.11 335	69.39 287	91.62 192	78.16 161	94.43 200	94.60 90

guyue		81.57 244	81.37 248	82.15 247	86.39 288	66.13 254	81.54 278	83.21 334	69.79 262	87.77 175	89.95 272	65.36 313	87.64 314	75.88 196	92.49 264	92.67 200

PVSNet_Blended_VisFu		81.55 245	80.49 265	84.70 166	91.58 138	73.24 149	84.21 197	91.67 148	62.86 349	80.94 340	87.16 333	67.27 299	92.87 162	69.82 283	88.94 352	87.99 343

fmvsm_l_conf0.5_n_a		81.46 246	80.87 259	83.25 213	83.73 354	73.21 150	83.00 239	85.59 302	58.22 394	82.96 302	90.09 271	72.30 265	86.65 332	81.97 116	89.95 336	89.88 306

SSM_04072		81.44 247	82.88 213	77.10 343	89.13 200	68.97 220	72.73 407	91.28 162	72.90 211	85.68 229	90.61 251	76.78 202	69.94 440	73.37 241	93.47 232	92.38 221

DELS-MVS		81.44 247	81.25 250	82.03 250	84.27 344	62.87 288	76.47 370	92.49 120	70.97 246	81.64 332	83.83 382	75.03 217	92.70 164	74.29 213	92.22 275	90.51 292
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		81.31 249	81.61 238	80.41 291	86.38 290	58.75 364	83.93 207	86.58 284	72.43 220	87.65 179	92.98 145	63.78 325	90.22 247	66.86 311	93.92 216	92.27 230

TinyColmap		81.25 250	82.34 225	77.99 331	85.33 322	60.68 335	82.32 262	88.33 246	71.26 241	86.97 195	92.22 180	77.10 192	86.98 325	62.37 354	95.17 170	86.31 366

diffmvs_AUTHOR		81.24 251	81.55 242	80.30 293	80.61 400	60.22 340	77.98 341	90.48 187	67.77 297	83.34 295	89.50 282	74.69 226	87.42 317	78.78 151	90.81 316	93.27 168

AUN-MVS		81.18 252	78.78 290	88.39 84	90.93 160	82.14 62	82.51 254	83.67 331	64.69 339	80.29 350	85.91 354	51.07 396	92.38 172	76.29 190	93.63 229	90.65 287

IMVS_0407		81.08 253	81.23 252	80.62 287	85.76 312	62.46 296	82.46 255	87.91 256	65.23 332	82.12 318	87.92 312	77.27 187	90.18 249	71.67 258	90.74 319	89.20 318

tttt0517		81.07 254	79.58 280	85.52 142	88.99 207	66.45 251	87.03 129	75.51 392	73.76 190	88.32 156	90.20 265	37.96 453	94.16 108	79.36 145	95.13 171	95.93 47

Fast-Effi-MVS+		81.04 255	80.57 262	82.46 241	87.50 251	63.22 284	78.37 336	89.63 221	68.01 289	81.87 324	82.08 403	82.31 112	92.65 166	67.10 310	88.30 364	91.51 261

BH-untuned		80.96 256	80.99 256	80.84 280	88.55 222	68.23 228	80.33 302	88.46 241	72.79 216	86.55 205	86.76 339	74.72 225	91.77 191	61.79 361	88.99 350	82.52 419

IMVS_0403		80.93 257	81.00 255	80.72 283	85.76 312	62.46 296	81.82 272	87.91 256	65.23 332	82.07 320	87.92 312	75.91 209	90.50 238	71.67 258	90.74 319	89.20 318

eth_miper_zixun_eth		80.84 258	80.22 271	82.71 231	81.41 387	60.98 330	77.81 344	90.14 207	67.31 304	86.95 196	87.24 332	64.26 318	92.31 175	75.23 206	91.61 292	94.85 83

xiu_mvs_v1_base_debu		80.84 258	80.14 273	82.93 226	88.31 226	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 397	76.94 195	90.14 253	73.22 244	88.33 360	86.90 360

xiu_mvs_v1_base		80.84 258	80.14 273	82.93 226	88.31 226	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 397	76.94 195	90.14 253	73.22 244	88.33 360	86.90 360

xiu_mvs_v1_base_debi		80.84 258	80.14 273	82.93 226	88.31 226	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 397	76.94 195	90.14 253	73.22 244	88.33 360	86.90 360

IterMVS-SCA-FT		80.64 262	79.41 281	84.34 178	83.93 350	69.66 208	76.28 372	81.09 356	72.43 220	86.47 212	90.19 266	60.46 342	93.15 151	77.45 172	86.39 391	90.22 297

BH-RMVSNet		80.53 263	80.22 271	81.49 266	87.19 262	66.21 253	77.79 345	86.23 287	74.21 185	83.69 287	88.50 301	73.25 254	90.75 228	63.18 350	87.90 368	87.52 351

VortexMVS		80.51 264	80.63 261	80.15 297	83.36 364	61.82 311	80.63 297	88.00 254	67.11 307	87.23 185	89.10 291	63.98 322	88.00 304	73.63 235	92.63 261	90.64 288

Anonymous202405211		80.51 264	81.19 254	78.49 320	88.48 223	57.26 377	76.63 365	82.49 342	81.21 88	84.30 274	92.24 179	67.99 295	86.24 340	62.22 355	95.13 171	91.98 245

DIV-MVS_self_test		80.43 266	80.23 269	81.02 276	79.99 405	59.25 353	77.07 358	87.02 278	67.38 301	86.19 216	89.22 287	63.09 330	90.16 250	76.32 188	95.80 148	93.66 144

cl____		80.42 267	80.23 269	81.02 276	79.99 405	59.25 353	77.07 358	87.02 278	67.37 302	86.18 218	89.21 288	63.08 331	90.16 250	76.31 189	95.80 148	93.65 147

diffmvs		80.40 268	80.48 266	80.17 296	79.02 418	60.04 342	77.54 349	90.28 203	66.65 312	82.40 311	87.33 330	73.50 246	87.35 319	77.98 165	89.62 341	93.13 175
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		80.37 269	78.41 298	86.23 123	76.75 432	73.28 147	87.18 126	77.45 375	76.24 151	68.14 443	88.93 294	65.41 312	93.85 118	69.47 286	96.12 128	91.55 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 270	80.04 276	81.24 272	79.82 408	58.95 358	77.66 346	89.66 219	65.75 322	85.99 226	85.11 366	68.29 294	91.42 200	76.03 194	92.03 279	93.33 164

MG-MVS		80.32 271	80.94 257	78.47 321	88.18 229	52.62 414	82.29 263	85.01 315	72.01 231	79.24 363	92.54 165	69.36 288	93.36 145	70.65 272	89.19 348	89.45 311

mvsmamba		80.30 272	78.87 287	84.58 170	88.12 232	67.55 236	92.35 30	84.88 318	63.15 347	85.33 241	90.91 234	50.71 398	95.20 66	66.36 317	87.98 367	90.99 272

VPNet		80.25 273	81.68 234	75.94 359	92.46 104	47.98 438	76.70 363	81.67 351	73.45 196	84.87 256	92.82 154	74.66 227	86.51 334	61.66 363	96.85 96	93.33 164

MAR-MVS		80.24 274	78.74 292	84.73 164	86.87 279	78.18 95	85.75 158	87.81 260	65.67 324	77.84 375	78.50 435	73.79 242	90.53 237	61.59 364	90.87 313	85.49 376
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		80.20 275	79.00 286	83.78 196	88.17 230	86.66 19	81.31 282	66.81 448	69.64 263	88.33 155	90.19 266	64.58 315	83.63 379	71.99 257	90.03 334	81.06 439

Anonymous20240521		80.18 276	81.25 250	76.95 345	83.15 372	60.84 332	82.46 255	85.99 294	68.76 278	86.78 197	93.73 120	59.13 354	77.44 414	73.71 231	97.55 78	92.56 208

LFMVS		80.15 277	80.56 263	78.89 312	89.19 199	55.93 385	85.22 172	73.78 404	82.96 71	84.28 275	92.72 159	57.38 366	90.07 258	63.80 344	95.75 151	90.68 284

DPM-MVS		80.10 278	79.18 285	82.88 229	90.71 166	69.74 206	78.87 328	90.84 177	60.29 382	75.64 398	85.92 353	67.28 298	93.11 152	71.24 264	91.79 286	85.77 372

MSDG		80.06 279	79.99 278	80.25 294	83.91 351	68.04 233	77.51 350	89.19 229	77.65 137	81.94 322	83.45 387	76.37 207	86.31 339	63.31 349	86.59 388	86.41 364

FE-MVS		79.98 280	78.86 288	83.36 210	86.47 285	66.45 251	89.73 75	84.74 322	72.80 215	84.22 278	91.38 210	44.95 433	93.60 131	63.93 342	91.50 295	90.04 304

sd_testset		79.95 281	81.39 247	75.64 364	88.81 213	58.07 369	76.16 375	82.81 340	73.67 191	83.41 293	93.04 141	80.96 141	77.65 413	58.62 379	95.03 176	91.21 265

ab-mvs		79.67 282	80.56 263	76.99 344	88.48 223	56.93 379	84.70 185	86.06 291	68.95 275	80.78 343	93.08 140	75.30 215	84.62 366	56.78 388	90.90 311	89.43 313

VNet		79.31 283	80.27 268	76.44 353	87.92 236	53.95 403	75.58 382	84.35 326	74.39 184	82.23 315	90.72 242	72.84 259	84.39 371	60.38 371	93.98 215	90.97 273

thisisatest0530		79.07 284	77.33 308	84.26 181	87.13 263	64.58 267	83.66 217	75.95 387	68.86 276	85.22 243	87.36 329	38.10 450	93.57 135	75.47 203	94.28 205	94.62 89

cl22		78.97 285	78.21 300	81.24 272	77.74 422	59.01 357	77.46 353	87.13 272	65.79 319	84.32 271	85.10 367	58.96 356	90.88 223	75.36 205	92.03 279	93.84 133

patch_mono-2		78.89 286	79.39 282	77.41 340	84.78 332	68.11 231	75.60 380	83.11 336	60.96 374	79.36 360	89.89 275	75.18 216	72.97 429	73.32 243	92.30 269	91.15 267

RPMNet		78.88 287	78.28 299	80.68 285	79.58 409	62.64 292	82.58 250	94.16 33	74.80 173	75.72 396	92.59 161	48.69 405	95.56 45	73.48 238	82.91 427	83.85 398

PAPR		78.84 288	78.10 301	81.07 274	85.17 327	60.22 340	82.21 267	90.57 186	62.51 351	75.32 402	84.61 375	74.99 218	92.30 176	59.48 376	88.04 366	90.68 284

viewmambaseed2359dif		78.80 289	78.47 297	79.78 300	80.26 404	59.28 352	77.31 355	87.13 272	60.42 380	82.37 312	88.67 299	74.58 228	87.87 310	67.78 308	87.73 372	92.19 234

PVSNet_BlendedMVS		78.80 289	77.84 302	81.65 262	84.43 338	63.41 280	79.49 314	90.44 191	61.70 363	75.43 399	87.07 336	69.11 290	91.44 198	60.68 369	92.24 273	90.11 302

FMVSNet3		78.80 289	78.55 294	79.57 306	82.89 375	56.89 381	81.76 273	85.77 298	69.04 273	86.00 223	90.44 256	51.75 394	90.09 256	65.95 321	93.34 236	91.72 252

test_yl		78.71 292	78.51 295	79.32 309	84.32 342	58.84 361	78.38 334	85.33 306	75.99 155	82.49 309	86.57 341	58.01 360	90.02 260	62.74 351	92.73 259	89.10 324

DCV-MVSNet		78.71 292	78.51 295	79.32 309	84.32 342	58.84 361	78.38 334	85.33 306	75.99 155	82.49 309	86.57 341	58.01 360	90.02 260	62.74 351	92.73 259	89.10 324

test1111		78.53 294	78.85 289	77.56 337	92.22 113	47.49 440	82.61 248	69.24 436	72.43 220	85.28 242	94.20 90	51.91 392	90.07 258	65.36 330	96.45 112	95.11 73

FE-MVSNET		78.46 295	79.36 283	75.75 361	86.53 282	54.53 398	78.03 338	85.35 305	69.01 274	85.41 239	90.68 245	64.27 317	85.73 356	62.59 353	92.35 268	87.00 359

icg_test_0407_2		78.46 295	79.68 279	74.78 371	85.76 312	62.46 296	68.51 436	87.91 256	65.23 332	82.12 318	87.92 312	77.27 187	72.67 430	71.67 258	90.74 319	89.20 318

ECVR-MVS		78.44 297	78.63 293	77.88 333	91.85 127	48.95 434	83.68 216	69.91 432	72.30 226	84.26 277	94.20 90	51.89 393	89.82 264	63.58 345	96.02 132	94.87 79

pmmvs-eth3d		78.42 298	77.04 311	82.57 236	87.44 255	74.41 138	80.86 293	79.67 364	55.68 411	84.69 261	90.31 263	60.91 340	85.42 359	62.20 356	91.59 293	87.88 347

mvs_anonymous		78.13 299	78.76 291	76.23 358	79.24 415	50.31 430	78.69 331	84.82 320	61.60 365	83.09 301	92.82 154	73.89 240	87.01 322	68.33 304	86.41 390	91.37 262

TAMVS		78.08 300	76.36 318	83.23 214	90.62 167	72.87 154	79.08 324	80.01 363	61.72 362	81.35 336	86.92 338	63.96 324	88.78 287	50.61 427	93.01 249	88.04 342

miper_enhance_ethall		77.83 301	76.93 312	80.51 289	76.15 439	58.01 371	75.47 384	88.82 233	58.05 396	83.59 289	80.69 413	64.41 316	91.20 208	73.16 250	92.03 279	92.33 225

Vis-MVSNet (Re-imp)		77.82 302	77.79 303	77.92 332	88.82 212	51.29 424	83.28 228	71.97 420	74.04 186	82.23 315	89.78 276	57.38 366	89.41 277	57.22 387	95.41 160	93.05 181

CANet_DTU		77.81 303	77.05 310	80.09 298	81.37 388	59.90 346	83.26 229	88.29 247	69.16 270	67.83 446	83.72 383	60.93 339	89.47 272	69.22 290	89.70 340	90.88 277

OpenMVS_ROB	70.19 17	77.77 304	77.46 305	78.71 316	84.39 341	61.15 321	81.18 286	82.52 341	62.45 354	83.34 295	87.37 328	66.20 304	88.66 291	64.69 337	85.02 407	86.32 365

SSC-MVS		77.55 305	81.64 236	65.29 437	90.46 170	20.33 484	73.56 400	68.28 438	85.44 41	88.18 161	94.64 69	70.93 279	81.33 392	71.25 263	92.03 279	94.20 112

MDA-MVSNet-bldmvs		77.47 306	76.90 313	79.16 311	79.03 417	64.59 266	66.58 448	75.67 390	73.15 207	88.86 138	88.99 293	66.94 300	81.23 393	64.71 336	88.22 365	91.64 256

jason		77.42 307	75.75 324	82.43 242	87.10 266	69.27 213	77.99 340	81.94 348	51.47 438	77.84 375	85.07 370	60.32 344	89.00 281	70.74 271	89.27 347	89.03 328
jason: jason.
CDS-MVSNet		77.32 308	75.40 328	83.06 218	89.00 206	72.48 165	77.90 343	82.17 346	60.81 375	78.94 366	83.49 386	59.30 352	88.76 288	54.64 407	92.37 267	87.93 346
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 309	77.75 304	75.73 362	85.76 312	62.46 296	70.84 422	87.91 256	65.23 332	72.21 421	87.92 312	67.48 297	75.53 422	71.67 258	90.74 319	89.20 318

xiu_mvs_v2_base		77.19 310	76.75 315	78.52 319	87.01 272	61.30 318	75.55 383	87.12 276	61.24 371	74.45 408	78.79 433	77.20 189	90.93 219	64.62 339	84.80 414	83.32 407

MVSTER		77.09 311	75.70 325	81.25 270	75.27 447	61.08 323	77.49 352	85.07 311	60.78 376	86.55 205	88.68 297	43.14 442	90.25 244	73.69 234	90.67 324	92.42 215

PS-MVSNAJ		77.04 312	76.53 317	78.56 318	87.09 268	61.40 315	75.26 385	87.13 272	61.25 370	74.38 410	77.22 447	76.94 195	90.94 218	64.63 338	84.83 413	83.35 406

IterMVS		76.91 313	76.34 319	78.64 317	80.91 393	64.03 274	76.30 371	79.03 367	64.88 338	83.11 299	89.16 289	59.90 348	84.46 369	68.61 300	85.15 405	87.42 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 314	75.67 326	80.34 292	80.48 402	62.16 309	73.50 401	84.80 321	57.61 400	82.24 314	87.54 323	51.31 395	87.65 313	70.40 277	93.19 245	91.23 264

CL-MVSNet_self_test		76.81 315	77.38 307	75.12 367	86.90 277	51.34 422	73.20 404	80.63 360	68.30 285	81.80 328	88.40 302	66.92 301	80.90 394	55.35 401	94.90 182	93.12 178

TR-MVS		76.77 316	75.79 323	79.72 303	86.10 304	65.79 258	77.14 356	83.02 337	65.20 336	81.40 335	82.10 401	66.30 303	90.73 230	55.57 398	85.27 401	82.65 414

MonoMVSNet		76.66 317	77.26 309	74.86 369	79.86 407	54.34 400	86.26 147	86.08 290	71.08 245	85.59 234	88.68 297	53.95 384	85.93 347	63.86 343	80.02 443	84.32 389

USDC		76.63 318	76.73 316	76.34 355	83.46 359	57.20 378	80.02 305	88.04 253	52.14 434	83.65 288	91.25 217	63.24 328	86.65 332	54.66 406	94.11 210	85.17 378

BH-w/o		76.57 319	76.07 322	78.10 328	86.88 278	65.92 257	77.63 347	86.33 285	65.69 323	80.89 341	79.95 422	68.97 292	90.74 229	53.01 417	85.25 402	77.62 450

Patchmtry		76.56 320	77.46 305	73.83 377	79.37 414	46.60 444	82.41 259	76.90 381	73.81 189	85.56 236	92.38 169	48.07 408	83.98 376	63.36 348	95.31 166	90.92 275

PVSNet_Blended		76.49 321	75.40 328	79.76 302	84.43 338	63.41 280	75.14 386	90.44 191	57.36 402	75.43 399	78.30 436	69.11 290	91.44 198	60.68 369	87.70 374	84.42 388

miper_lstm_enhance		76.45 322	76.10 321	77.51 338	76.72 433	60.97 331	64.69 452	85.04 313	63.98 343	83.20 298	88.22 304	56.67 370	78.79 410	73.22 244	93.12 246	92.78 194

lupinMVS		76.37 323	74.46 337	82.09 249	85.54 318	69.26 214	76.79 361	80.77 359	50.68 445	76.23 389	82.82 395	58.69 357	88.94 282	69.85 282	88.77 353	88.07 339

cascas		76.29 324	74.81 333	80.72 283	84.47 337	62.94 286	73.89 398	87.34 264	55.94 409	75.16 404	76.53 452	63.97 323	91.16 210	65.00 333	90.97 309	88.06 341

SD_0403		76.08 325	76.77 314	73.98 375	87.08 270	49.45 433	83.62 218	84.68 323	63.31 344	75.13 405	87.47 326	71.85 272	84.56 367	49.97 429	87.86 370	87.94 345

WB-MVS		76.06 326	80.01 277	64.19 440	89.96 184	20.58 483	72.18 411	68.19 439	83.21 67	86.46 213	93.49 125	70.19 284	78.97 408	65.96 320	90.46 330	93.02 182

thres600view7		75.97 327	75.35 330	77.85 335	87.01 272	51.84 420	80.45 300	73.26 409	75.20 170	83.10 300	86.31 347	45.54 423	89.05 280	55.03 404	92.24 273	92.66 201

GA-MVS		75.83 328	74.61 334	79.48 308	81.87 380	59.25 353	73.42 402	82.88 338	68.68 279	79.75 355	81.80 406	50.62 399	89.46 273	66.85 312	85.64 398	89.72 308

MVP-Stereo		75.81 329	73.51 346	82.71 231	89.35 194	73.62 142	80.06 303	85.20 308	60.30 381	73.96 411	87.94 309	57.89 364	89.45 274	52.02 421	74.87 461	85.06 380
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 330	75.20 331	77.27 341	75.01 450	69.47 211	78.93 325	84.88 318	46.67 452	87.08 192	87.84 317	50.44 401	71.62 435	77.42 174	88.53 356	90.72 281

thres100view900		75.45 331	75.05 332	76.66 351	87.27 257	51.88 419	81.07 287	73.26 409	75.68 161	83.25 297	86.37 344	45.54 423	88.80 284	51.98 422	90.99 306	89.31 315

ET-MVSNet_ETH3D		75.28 332	72.77 355	82.81 230	83.03 374	68.11 231	77.09 357	76.51 385	60.67 378	77.60 380	80.52 417	38.04 451	91.15 211	70.78 269	90.68 323	89.17 322

thres400		75.14 333	74.23 339	77.86 334	86.24 297	52.12 416	79.24 321	73.87 402	73.34 200	81.82 326	84.60 376	46.02 416	88.80 284	51.98 422	90.99 306	92.66 201

wuyk23d		75.13 334	79.30 284	62.63 443	75.56 443	75.18 134	80.89 292	73.10 411	75.06 172	94.76 16	95.32 46	87.73 45	52.85 475	34.16 473	97.11 91	59.85 471

EU-MVSNet		75.12 335	74.43 338	77.18 342	83.11 373	59.48 350	85.71 160	82.43 343	39.76 472	85.64 233	88.76 295	44.71 435	87.88 309	73.86 228	85.88 397	84.16 394

HyFIR lowres test		75.12 335	72.66 357	82.50 239	91.44 146	65.19 263	72.47 409	87.31 265	46.79 451	80.29 350	84.30 378	52.70 389	92.10 182	51.88 426	86.73 386	90.22 297

CMPMVS	59.41 20	75.12 335	73.57 344	79.77 301	75.84 442	67.22 237	81.21 285	82.18 345	50.78 443	76.50 385	87.66 321	55.20 380	82.99 382	62.17 358	90.64 328	89.09 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 338	72.98 353	80.73 282	84.95 329	71.71 180	76.23 373	77.59 374	52.83 428	77.73 379	86.38 343	56.35 373	84.97 363	57.72 386	87.05 381	85.51 375

tfpn200view9		74.86 339	74.23 339	76.74 350	86.24 297	52.12 416	79.24 321	73.87 402	73.34 200	81.82 326	84.60 376	46.02 416	88.80 284	51.98 422	90.99 306	89.31 315

1112_ss		74.82 340	73.74 342	78.04 330	89.57 188	60.04 342	76.49 369	87.09 277	54.31 419	73.66 414	79.80 423	60.25 345	86.76 331	58.37 380	84.15 418	87.32 354

EGC-MVSNET		74.79 341	69.99 385	89.19 67	94.89 38	87.00 15	91.89 42	86.28 286	1.09 481	2.23 483	95.98 31	81.87 129	89.48 271	79.76 136	95.96 135	91.10 268

ppachtmachnet_test		74.73 342	74.00 341	76.90 347	80.71 398	56.89 381	71.53 417	78.42 369	58.24 393	79.32 362	82.92 394	57.91 363	84.26 373	65.60 328	91.36 297	89.56 310

Patchmatch-RL test		74.48 343	73.68 343	76.89 348	84.83 331	66.54 248	72.29 410	69.16 437	57.70 398	86.76 198	86.33 345	45.79 422	82.59 383	69.63 285	90.65 327	81.54 430

PatchMatch-RL		74.48 343	73.22 350	78.27 326	87.70 243	85.26 38	75.92 378	70.09 430	64.34 341	76.09 392	81.25 411	65.87 309	78.07 412	53.86 409	83.82 420	71.48 459

XXY-MVS		74.44 345	76.19 320	69.21 412	84.61 336	52.43 415	71.70 414	77.18 379	60.73 377	80.60 344	90.96 231	75.44 212	69.35 443	56.13 393	88.33 360	85.86 371

test2506		74.12 346	73.39 347	76.28 356	91.85 127	44.20 454	84.06 201	48.20 479	72.30 226	81.90 323	94.20 90	27.22 478	89.77 267	64.81 335	96.02 132	94.87 79

reproduce_monomvs		74.09 347	73.23 349	76.65 352	76.52 434	54.54 397	77.50 351	81.40 354	65.85 318	82.86 305	86.67 340	27.38 476	84.53 368	70.24 278	90.66 326	90.89 276

CR-MVSNet		74.00 348	73.04 352	76.85 349	79.58 409	62.64 292	82.58 250	76.90 381	50.50 446	75.72 396	92.38 169	48.07 408	84.07 375	68.72 299	82.91 427	83.85 398

SSC-MVS3.2		73.90 349	75.67 326	68.61 420	84.11 347	41.28 462	64.17 454	72.83 412	72.09 229	79.08 365	87.94 309	70.31 282	73.89 428	55.99 394	94.49 197	90.67 286

Test_1112_low_res		73.90 349	73.08 351	76.35 354	90.35 172	55.95 384	73.40 403	86.17 288	50.70 444	73.14 415	85.94 352	58.31 359	85.90 351	56.51 390	83.22 424	87.20 356

test20.03		73.75 351	74.59 336	71.22 398	81.11 391	51.12 426	70.15 428	72.10 419	70.42 252	80.28 352	91.50 204	64.21 319	74.72 426	46.96 447	94.58 195	87.82 349

test_fmvs2		73.57 352	72.80 354	75.90 360	72.74 464	68.84 224	77.07 358	84.32 327	45.14 458	82.89 303	84.22 379	48.37 406	70.36 439	73.40 240	87.03 382	88.52 334

SCA		73.32 353	72.57 359	75.58 365	81.62 384	55.86 387	78.89 327	71.37 425	61.73 361	74.93 406	83.42 388	60.46 342	87.01 322	58.11 384	82.63 432	83.88 395

baseline1		73.26 354	73.54 345	72.43 391	84.92 330	47.79 439	79.89 307	74.00 400	65.93 316	78.81 367	86.28 348	56.36 372	81.63 391	56.63 389	79.04 450	87.87 348

1314		73.22 355	72.56 360	75.20 366	80.41 403	57.84 372	81.64 276	85.36 304	51.68 437	73.10 416	76.65 451	61.45 337	85.19 361	63.54 346	79.21 448	82.59 415

MVS		73.21 356	72.59 358	75.06 368	80.97 392	60.81 333	81.64 276	85.92 297	46.03 456	71.68 424	77.54 442	68.47 293	89.77 267	55.70 397	85.39 399	74.60 456

HY-MVS	64.64 18	73.03 357	72.47 361	74.71 372	83.36 364	54.19 401	82.14 270	81.96 347	56.76 408	69.57 438	86.21 349	60.03 346	84.83 365	49.58 434	82.65 430	85.11 379

thisisatest0515		73.00 358	70.52 377	80.46 290	81.45 386	59.90 346	73.16 405	74.31 399	57.86 397	76.08 393	77.78 439	37.60 454	92.12 181	65.00 333	91.45 296	89.35 314

EPNet_dtu		72.87 359	71.33 371	77.49 339	77.72 423	60.55 336	82.35 261	75.79 388	66.49 313	58.39 474	81.06 412	53.68 385	85.98 346	53.55 412	92.97 251	85.95 369
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 360	71.41 370	76.28 356	83.25 368	60.34 338	83.50 222	79.02 368	37.77 476	76.33 387	85.10 367	49.60 404	87.41 318	70.54 275	77.54 456	81.08 437

CHOSEN 1792x2688		72.45 361	70.56 376	78.13 327	90.02 183	63.08 285	68.72 435	83.16 335	42.99 466	75.92 394	85.46 360	57.22 368	85.18 362	49.87 432	81.67 434	86.14 367

testgi		72.36 362	74.61 334	65.59 434	80.56 401	42.82 459	68.29 437	73.35 408	66.87 310	81.84 325	89.93 273	72.08 269	66.92 457	46.05 451	92.54 263	87.01 358

thres200		72.34 363	71.55 369	74.70 373	83.48 358	51.60 421	75.02 387	73.71 405	70.14 259	78.56 370	80.57 416	46.20 414	88.20 302	46.99 446	89.29 345	84.32 389

FPMVS		72.29 364	72.00 363	73.14 382	88.63 219	85.00 40	74.65 391	67.39 442	71.94 232	77.80 377	87.66 321	50.48 400	75.83 420	49.95 430	79.51 444	58.58 473

FMVSNet5		72.10 365	71.69 365	73.32 380	81.57 385	53.02 410	76.77 362	78.37 370	63.31 344	76.37 386	91.85 190	36.68 455	78.98 407	47.87 443	92.45 265	87.95 344

our_test_3		71.85 366	71.59 366	72.62 388	80.71 398	53.78 404	69.72 431	71.71 424	58.80 390	78.03 372	80.51 418	56.61 371	78.84 409	62.20 356	86.04 396	85.23 377

PAPM		71.77 367	70.06 383	76.92 346	86.39 288	53.97 402	76.62 366	86.62 283	53.44 423	63.97 463	84.73 374	57.79 365	92.34 174	39.65 463	81.33 438	84.45 387

ttmdpeth		71.72 368	70.67 374	74.86 369	73.08 461	55.88 386	77.41 354	69.27 435	55.86 410	78.66 368	93.77 118	38.01 452	75.39 423	60.12 372	89.87 337	93.31 166

IB-MVS	62.13 19	71.64 369	68.97 395	79.66 305	80.80 397	62.26 305	73.94 397	76.90 381	63.27 346	68.63 442	76.79 449	33.83 459	91.84 189	59.28 377	87.26 376	84.88 381
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		71.63 370	72.30 362	69.62 409	76.47 436	52.70 413	70.03 429	80.97 357	59.18 387	79.36 360	88.21 305	60.50 341	69.12 444	58.33 382	77.62 455	87.04 357

testing3		71.53 371	70.79 373	73.77 378	88.89 211	41.86 461	76.60 368	59.12 468	72.83 214	80.97 338	82.08 403	19.80 484	87.33 320	65.12 332	91.68 291	92.13 238

test_vis3_rt		71.42 372	70.67 374	73.64 379	69.66 471	70.46 196	66.97 447	89.73 216	42.68 468	88.20 160	83.04 390	43.77 437	60.07 469	65.35 331	86.66 387	90.39 295

Anonymous20231206		71.38 373	71.88 364	69.88 406	86.31 294	54.37 399	70.39 426	74.62 395	52.57 430	76.73 384	88.76 295	59.94 347	72.06 432	44.35 455	93.23 243	83.23 409

test_vis1_n_1920		71.30 374	71.58 368	70.47 401	77.58 425	59.99 345	74.25 392	84.22 328	51.06 440	74.85 407	79.10 429	55.10 381	68.83 446	68.86 296	79.20 449	82.58 416

MIMVSNet		71.09 375	71.59 366	69.57 410	87.23 260	50.07 431	78.91 326	71.83 421	60.20 384	71.26 425	91.76 197	55.08 382	76.09 418	41.06 460	87.02 383	82.54 418

test_fmvs1_n		70.94 376	70.41 380	72.53 390	73.92 452	66.93 245	75.99 377	84.21 329	43.31 465	79.40 359	79.39 427	43.47 438	68.55 448	69.05 293	84.91 410	82.10 424

MS-PatchMatch		70.93 377	70.22 381	73.06 383	81.85 381	62.50 295	73.82 399	77.90 371	52.44 431	75.92 394	81.27 410	55.67 377	81.75 389	55.37 400	77.70 454	74.94 455

pmmvs5		70.73 378	70.07 382	72.72 386	77.03 430	52.73 412	74.14 393	75.65 391	50.36 447	72.17 422	85.37 364	55.42 379	80.67 396	52.86 418	87.59 375	84.77 382

testing3-2		70.72 379	70.97 372	69.95 405	88.93 209	34.80 475	69.85 430	66.59 449	78.42 127	77.58 381	85.55 356	31.83 465	82.08 387	46.28 448	93.73 225	92.98 188

PatchT		70.52 380	72.76 356	63.79 442	79.38 413	33.53 476	77.63 347	65.37 453	73.61 193	71.77 423	92.79 157	44.38 436	75.65 421	64.53 340	85.37 400	82.18 423

test_vis1_n		70.29 381	69.99 385	71.20 399	75.97 441	66.50 249	76.69 364	80.81 358	44.22 461	75.43 399	77.23 446	50.00 402	68.59 447	66.71 315	82.85 429	78.52 449

N_pmnet		70.20 382	68.80 397	74.38 374	80.91 393	84.81 43	59.12 465	76.45 386	55.06 414	75.31 403	82.36 400	55.74 376	54.82 474	47.02 445	87.24 377	83.52 402

tpmvs		70.16 383	69.56 388	71.96 394	74.71 451	48.13 436	79.63 309	75.45 393	65.02 337	70.26 433	81.88 405	45.34 428	85.68 357	58.34 381	75.39 460	82.08 425

new-patchmatchnet		70.10 384	73.37 348	60.29 451	81.23 390	16.95 486	59.54 463	74.62 395	62.93 348	80.97 338	87.93 311	62.83 334	71.90 433	55.24 402	95.01 179	92.00 243

YYNet1		70.06 385	70.44 378	68.90 414	73.76 454	53.42 408	58.99 466	67.20 444	58.42 392	87.10 190	85.39 363	59.82 349	67.32 454	59.79 374	83.50 423	85.96 368

MVStest1		70.05 386	69.26 389	72.41 392	58.62 483	55.59 390	76.61 367	65.58 451	53.44 423	89.28 133	93.32 129	22.91 482	71.44 437	74.08 224	89.52 342	90.21 301

MDA-MVSNet_test_wron		70.05 386	70.44 378	68.88 415	73.84 453	53.47 406	58.93 467	67.28 443	58.43 391	87.09 191	85.40 362	59.80 350	67.25 455	59.66 375	83.54 422	85.92 370

CostFormer		69.98 388	68.68 398	73.87 376	77.14 428	50.72 428	79.26 320	74.51 397	51.94 436	70.97 428	84.75 373	45.16 431	87.49 316	55.16 403	79.23 447	83.40 405

testing91		69.94 389	68.99 394	72.80 385	83.81 353	45.89 447	71.57 416	73.64 407	68.24 286	70.77 431	77.82 438	34.37 458	84.44 370	53.64 411	87.00 384	88.07 339

baseline2		69.77 390	66.89 407	78.41 322	79.51 411	58.09 368	76.23 373	69.57 433	57.50 401	64.82 461	77.45 444	46.02 416	88.44 296	53.08 414	77.83 452	88.70 332

PatchmatchNet		69.71 391	68.83 396	72.33 393	77.66 424	53.60 405	79.29 319	69.99 431	57.66 399	72.53 419	82.93 393	46.45 413	80.08 402	60.91 368	72.09 464	83.31 408
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 392	69.05 392	71.14 400	69.15 472	65.77 259	73.98 396	83.32 333	42.83 467	77.77 378	78.27 437	43.39 441	68.50 449	68.39 303	84.38 417	79.15 447

JIA-IIPM		69.41 393	66.64 411	77.70 336	73.19 458	71.24 187	75.67 379	65.56 452	70.42 252	65.18 457	92.97 148	33.64 461	83.06 380	53.52 413	69.61 470	78.79 448

Syy-MVS		69.40 394	70.03 384	67.49 425	81.72 382	38.94 467	71.00 419	61.99 459	61.38 367	70.81 429	72.36 463	61.37 338	79.30 405	64.50 341	85.18 403	84.22 391

testing99		69.27 395	68.15 402	72.63 387	83.29 366	45.45 449	71.15 418	71.08 426	67.34 303	70.43 432	77.77 440	32.24 464	84.35 372	53.72 410	86.33 392	88.10 338

UnsupCasMVSNet_bld		69.21 396	69.68 387	67.82 423	79.42 412	51.15 425	67.82 441	75.79 388	54.15 420	77.47 382	85.36 365	59.26 353	70.64 438	48.46 440	79.35 446	81.66 428

test_cas_vis1_n_1920		69.20 397	69.12 390	69.43 411	73.68 455	62.82 289	70.38 427	77.21 378	46.18 455	80.46 349	78.95 431	52.03 391	65.53 462	65.77 327	77.45 457	79.95 445

gg-mvs-nofinetune		68.96 398	69.11 391	68.52 421	76.12 440	45.32 450	83.59 219	55.88 473	86.68 33	64.62 462	97.01 12	30.36 469	83.97 377	44.78 454	82.94 426	76.26 452

WBMVS		68.76 399	68.43 399	69.75 408	83.29 366	40.30 465	67.36 443	72.21 418	57.09 405	77.05 383	85.53 358	33.68 460	80.51 398	48.79 438	90.90 311	88.45 335

WB-MVSnew		68.72 400	69.01 393	67.85 422	83.22 370	43.98 455	74.93 388	65.98 450	55.09 413	73.83 412	79.11 428	65.63 311	71.89 434	38.21 468	85.04 406	87.69 350

tpm2		68.45 401	66.83 408	73.30 381	78.93 419	48.50 435	79.76 308	71.76 422	47.50 450	69.92 435	83.60 384	42.07 444	88.40 298	48.44 441	79.51 444	83.01 412

tpm		67.95 402	68.08 403	67.55 424	78.74 420	43.53 457	75.60 380	67.10 447	54.92 415	72.23 420	88.10 306	42.87 443	75.97 419	52.21 420	80.95 442	83.15 410

WTY-MVS		67.91 403	68.35 400	66.58 430	80.82 396	48.12 437	65.96 449	72.60 413	53.67 422	71.20 426	81.68 408	58.97 355	69.06 445	48.57 439	81.67 434	82.55 417

testing11		67.38 404	65.93 412	71.73 396	83.37 363	46.60 444	70.95 421	69.40 434	62.47 353	66.14 450	76.66 450	31.22 466	84.10 374	49.10 436	84.10 419	84.49 385

test-LLR		67.21 405	66.74 409	68.63 418	76.45 437	55.21 393	67.89 438	67.14 445	62.43 356	65.08 458	72.39 461	43.41 439	69.37 441	61.00 366	84.89 411	81.31 432

testing222		66.93 406	65.30 419	71.81 395	83.38 362	45.83 448	72.06 412	67.50 441	64.12 342	69.68 437	76.37 453	27.34 477	83.00 381	38.88 464	88.38 359	86.62 363

sss		66.92 407	67.26 405	65.90 432	77.23 427	51.10 427	64.79 451	71.72 423	52.12 435	70.13 434	80.18 420	57.96 362	65.36 463	50.21 428	81.01 440	81.25 434

KD-MVS_2432*1600		66.87 408	65.81 415	70.04 403	67.50 473	47.49 440	62.56 457	79.16 365	61.21 372	77.98 373	80.61 414	25.29 480	82.48 384	53.02 415	84.92 408	80.16 443

miper_refine_blended		66.87 408	65.81 415	70.04 403	67.50 473	47.49 440	62.56 457	79.16 365	61.21 372	77.98 373	80.61 414	25.29 480	82.48 384	53.02 415	84.92 408	80.16 443

dmvs_re		66.81 410	66.98 406	66.28 431	76.87 431	58.68 365	71.66 415	72.24 416	60.29 382	69.52 439	73.53 460	52.38 390	64.40 465	44.90 453	81.44 437	75.76 453

tpm cat1		66.76 411	65.21 420	71.42 397	77.09 429	50.62 429	78.01 339	73.68 406	44.89 459	68.64 441	79.00 430	45.51 425	82.42 386	49.91 431	70.15 467	81.23 436

UWE-MVS		66.43 412	65.56 418	69.05 413	84.15 346	40.98 463	73.06 406	64.71 455	54.84 416	76.18 391	79.62 426	29.21 471	80.50 399	38.54 467	89.75 339	85.66 373

PVSNet	58.17 21	66.41 413	65.63 417	68.75 416	81.96 379	49.88 432	62.19 459	72.51 415	51.03 441	68.04 444	75.34 457	50.84 397	74.77 424	45.82 452	82.96 425	81.60 429

tpmrst		66.28 414	66.69 410	65.05 438	72.82 463	39.33 466	78.20 337	70.69 429	53.16 426	67.88 445	80.36 419	48.18 407	74.75 425	58.13 383	70.79 466	81.08 437

Patchmatch-test		65.91 415	67.38 404	61.48 448	75.51 444	43.21 458	68.84 434	63.79 457	62.48 352	72.80 418	83.42 388	44.89 434	59.52 471	48.27 442	86.45 389	81.70 427

ADS-MVSNet2		65.87 416	63.64 425	72.55 389	73.16 459	56.92 380	67.10 445	74.81 394	49.74 448	66.04 452	82.97 391	46.71 411	77.26 415	42.29 457	69.96 468	83.46 403

myMVS_eth3d28		65.83 417	65.85 413	65.78 433	83.42 361	35.71 473	67.29 444	68.01 440	67.58 300	69.80 436	77.72 441	32.29 463	74.30 427	37.49 469	89.06 349	87.32 354

test_vis1_rt		65.64 418	64.09 422	70.31 402	66.09 477	70.20 200	61.16 460	81.60 352	38.65 473	72.87 417	69.66 466	52.84 387	60.04 470	56.16 392	77.77 453	80.68 441

mvsany_test3		65.48 419	62.97 428	73.03 384	69.99 470	76.17 124	64.83 450	43.71 481	43.68 463	80.25 353	87.05 337	52.83 388	63.09 468	51.92 425	72.44 463	79.84 446

test-mter		65.00 420	63.79 424	68.63 418	76.45 437	55.21 393	67.89 438	67.14 445	50.98 442	65.08 458	72.39 461	28.27 474	69.37 441	61.00 366	84.89 411	81.31 432

ETVMVS		64.67 421	63.34 427	68.64 417	83.44 360	41.89 460	69.56 433	61.70 464	61.33 369	68.74 440	75.76 455	28.76 472	79.35 404	34.65 472	86.16 395	84.67 384

myMVS_eth3d		64.66 422	63.89 423	66.97 428	81.72 382	37.39 470	71.00 419	61.99 459	61.38 367	70.81 429	72.36 463	20.96 483	79.30 405	49.59 433	85.18 403	84.22 391

test0.0.03 1		64.66 422	64.36 421	65.57 435	75.03 449	46.89 443	64.69 452	61.58 465	62.43 356	71.18 427	77.54 442	43.41 439	68.47 450	40.75 462	82.65 430	81.35 431

UBG		64.34 424	63.35 426	67.30 426	83.50 357	40.53 464	67.46 442	65.02 454	54.77 417	67.54 448	74.47 459	32.99 462	78.50 411	40.82 461	83.58 421	82.88 413

test_f		64.31 425	65.85 413	59.67 452	66.54 476	62.24 307	57.76 469	70.96 427	40.13 470	84.36 269	82.09 402	46.93 410	51.67 476	61.99 359	81.89 433	65.12 467

pmmvs3		62.47 426	60.02 439	69.80 407	71.58 467	64.00 275	70.52 425	58.44 471	39.77 471	66.05 451	75.84 454	27.10 479	72.28 431	46.15 450	84.77 415	73.11 457

EPMVS		62.47 426	62.63 430	62.01 444	70.63 469	38.74 468	74.76 389	52.86 475	53.91 421	67.71 447	80.01 421	39.40 448	66.60 458	55.54 399	68.81 472	80.68 441

ADS-MVSNet		61.90 428	62.19 432	61.03 449	73.16 459	36.42 472	67.10 445	61.75 462	49.74 448	66.04 452	82.97 391	46.71 411	63.21 466	42.29 457	69.96 468	83.46 403

PMMVS		61.65 429	60.38 436	65.47 436	65.40 480	69.26 214	63.97 455	61.73 463	36.80 477	60.11 469	68.43 468	59.42 351	66.35 459	48.97 437	78.57 451	60.81 470

E-PMN		61.59 430	61.62 433	61.49 447	66.81 475	55.40 391	53.77 472	60.34 467	66.80 311	58.90 472	65.50 471	40.48 447	66.12 460	55.72 396	86.25 393	62.95 469

TESTMET0.1,1		61.29 431	60.32 437	64.19 440	72.06 465	51.30 423	67.89 438	62.09 458	45.27 457	60.65 468	69.01 467	27.93 475	64.74 464	56.31 391	81.65 436	76.53 451

MVS-HIRNet		61.16 432	62.92 429	55.87 455	79.09 416	35.34 474	71.83 413	57.98 472	46.56 453	59.05 471	91.14 222	49.95 403	76.43 417	38.74 465	71.92 465	55.84 474

EMVS		61.10 433	60.81 435	61.99 445	65.96 478	55.86 387	53.10 473	58.97 470	67.06 308	56.89 476	63.33 472	40.98 445	67.03 456	54.79 405	86.18 394	63.08 468

DSMNet-mixed		60.98 434	61.61 434	59.09 454	72.88 462	45.05 452	74.70 390	46.61 480	26.20 478	65.34 456	90.32 262	55.46 378	63.12 467	41.72 459	81.30 439	69.09 463

dp		60.70 435	60.29 438	61.92 446	72.04 466	38.67 469	70.83 423	64.08 456	51.28 439	60.75 467	77.28 445	36.59 456	71.58 436	47.41 444	62.34 474	75.52 454

dmvs_testset		60.59 436	62.54 431	54.72 457	77.26 426	27.74 480	74.05 395	61.00 466	60.48 379	65.62 455	67.03 470	55.93 375	68.23 452	32.07 476	69.46 471	68.17 464

CHOSEN 280x420		59.08 437	56.52 443	66.76 429	76.51 435	64.39 271	49.62 474	59.00 469	43.86 462	55.66 477	68.41 469	35.55 457	68.21 453	43.25 456	76.78 459	67.69 465

mvsany_test1		58.48 438	56.47 444	64.50 439	65.90 479	68.21 230	56.95 470	42.11 482	38.30 474	65.69 454	77.19 448	56.96 369	59.35 472	46.16 449	58.96 475	65.93 466

UWE-MVS-28		58.44 439	57.71 441	60.65 450	73.58 456	31.23 477	69.68 432	48.80 478	53.12 427	61.79 465	78.83 432	30.98 467	68.40 451	21.58 479	80.99 441	82.33 422

PVSNet_0	51.08 22	56.10 440	54.97 445	59.48 453	75.12 448	53.28 409	55.16 471	61.89 461	44.30 460	59.16 470	62.48 473	54.22 383	65.91 461	35.40 471	47.01 476	59.25 472

new_pmnet		55.69 441	57.66 442	49.76 458	75.47 445	30.59 478	59.56 462	51.45 476	43.62 464	62.49 464	75.48 456	40.96 446	49.15 478	37.39 470	72.52 462	69.55 462

PMMVS2		55.64 442	59.27 440	44.74 459	64.30 481	12.32 487	40.60 475	49.79 477	53.19 425	65.06 460	84.81 372	53.60 386	49.76 477	32.68 475	89.41 344	72.15 458

MVE	40.22 23	51.82 443	50.47 446	55.87 455	62.66 482	51.91 418	31.61 477	39.28 483	40.65 469	50.76 478	74.98 458	56.24 374	44.67 479	33.94 474	64.11 473	71.04 461
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 444	42.65 447	39.67 460	70.86 468	21.11 482	61.01 461	21.42 487	57.36 402	57.97 475	50.06 476	16.40 485	58.73 473	21.03 480	27.69 480	39.17 476

kuosan		30.83 445	32.17 448	26.83 462	53.36 484	19.02 485	57.90 468	20.44 488	38.29 475	38.01 479	37.82 478	15.18 486	33.45 481	7.74 482	20.76 481	28.03 477

test_method		30.46 446	29.60 449	33.06 461	17.99 486	3.84 489	13.62 478	73.92 401	2.79 480	18.29 482	53.41 475	28.53 473	43.25 480	22.56 477	35.27 478	52.11 475

cdsmvs_eth3d_5k		20.81 447	27.75 450	0.00 467	0.00 490	0.00 492	0.00 479	85.44 303	0.00 485	0.00 486	82.82 395	81.46 135	0.00 486	0.00 485	0.00 484	0.00 482

tmp_tt		20.25 448	24.50 451	7.49 464	4.47 487	8.70 488	34.17 476	25.16 485	1.00 482	32.43 481	18.49 479	39.37 449	9.21 483	21.64 478	43.75 477	4.57 479

ab-mvs-re		6.65 449	8.87 452	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	79.80 423	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		6.41 450	8.55 453	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	76.94 195	0.00 486	0.00 485	0.00 484	0.00 482

test123		6.27 451	8.08 454	0.84 465	1.11 489	0.57 490	62.90 456	0.82 489	0.54 483	1.07 485	2.75 484	1.26 487	0.30 484	1.04 483	1.26 483	1.66 480

testmvs		5.91 452	7.65 455	0.72 466	1.20 488	0.37 491	59.14 464	0.67 490	0.49 484	1.11 484	2.76 483	0.94 488	0.24 485	1.02 484	1.47 482	1.55 481

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 141	93.24 43	93.18 133		95.85 24	84.99 75	97.69 65	93.54 159

TestfortrainingZip							92.12 33

WAC-MVS						37.39 470								52.61 419

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	91.55 140	77.99 97		91.01 173					96.05 9	87.45 29	98.17 37	92.40 218

PC_three_1452									58.96 389	90.06 108	91.33 212	80.66 146	93.03 156	75.78 197	95.94 138	92.48 212

No_MVS				88.81 73	91.55 140	77.99 97		91.01 173					96.05 9	87.45 29	98.17 37	92.40 218

test_one_0601					93.85 67	73.27 148		94.11 39	86.57 34	93.47 42	94.64 69	88.42 29

eth-test2					0.00 490

eth-test					0.00 490

ZD-MVS					92.22 113	80.48 71		91.85 141	71.22 243	90.38 103	92.98 145	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 77	90.64 11		87.16 38	97.60 75	92.73 195

IU-MVS					94.18 55	72.64 158		90.82 178	56.98 406	89.67 121				85.78 64	97.92 52	93.28 167

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 218	81.12 139	94.68 82	74.48 212	95.35 162	92.29 228

test_241102_TWO								93.71 60	83.77 58	93.49 40	94.27 84	89.27 24	95.84 27	86.03 57	97.82 57	92.04 241

test_241102_ONE					94.18 55	72.65 156		93.69 62	83.62 62	94.11 27	93.78 116	90.28 15	95.50 52

9.14			89.29 66		91.84 129		88.80 99	95.32 13	75.14 171	91.07 87	92.89 151	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 217	70.80 248

test_0728_THIRD									85.33 42	93.75 35	94.65 66	87.44 48	95.78 35	87.41 31	98.21 34	92.98 188

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	92.02 242

test0726					94.16 58	72.56 162	90.63 54	93.90 49	83.61 63	93.75 35	94.49 74	89.76 19

GSMVS																83.88 395

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 415				83.88 395

sam_mvs												45.92 420

ambc				82.98 221	90.55 169	64.86 265	88.20 108	89.15 231		89.40 130	93.96 107	71.67 276	91.38 202	78.83 150	96.55 106	92.71 198

MTGPA								91.81 145

test_post1							78.85 329				3.13 481	45.19 430	80.13 401	58.11 384

test_post											3.10 482	45.43 426	77.22 416

patchmatchnet-post											81.71 407	45.93 419	87.01 322

GG-mvs-BLEND				67.16 427	73.36 457	46.54 446	84.15 199	55.04 474		58.64 473	61.95 474	29.93 470	83.87 378	38.71 466	76.92 458	71.07 460

MTMP							90.66 53	33.14 484

gm-plane-assit					75.42 446	44.97 453			52.17 432		72.36 463		87.90 308	54.10 408

test9_res														80.83 125	96.45 112	90.57 289

TEST9					92.34 108	79.70 80	83.94 205	90.32 197	65.41 329	84.49 265	90.97 229	82.03 124	93.63 127

test_8					92.09 117	78.87 88	83.82 210	90.31 199	65.79 319	84.36 269	90.96 231	81.93 126	93.44 141

agg_prior2														79.68 138	96.16 125	90.22 297

agg_prior					91.58 138	77.69 103		90.30 200		84.32 271			93.18 149

TestCases				89.68 56	91.59 135	83.40 52		95.44 11	79.47 108	88.00 165	93.03 143	82.66 104	91.47 196	70.81 267	96.14 126	94.16 117

test_prior4						78.97 87	84.59 188

test_prior2							83.37 226		75.43 167	84.58 262	91.57 202	81.92 128		79.54 142	96.97 94

test_prior				86.32 120	90.59 168	71.99 174		92.85 106					94.17 106			92.80 193

旧先验2							81.73 274		56.88 407	86.54 211			84.90 364	72.81 251

新几何2							81.72 275

新几何1				82.95 223	93.96 64	78.56 91		80.24 361	55.45 412	83.93 282	91.08 225	71.19 278	88.33 300	65.84 325	93.07 247	81.95 426

旧先验1					91.97 121	71.77 175		81.78 349			91.84 191	73.92 239			93.65 228	83.61 401

无先验							82.81 245	85.62 301	58.09 395				91.41 201	67.95 307		84.48 386

原ACMM2							82.26 266

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 153	62.59 350	82.73 308	90.67 248	76.53 204	94.25 98	69.24 288	95.69 153	85.55 374

test222					93.31 81	76.54 116	79.38 318	77.79 372	52.59 429	82.36 313	90.84 239	66.83 302			91.69 290	81.25 434

testdata2													86.43 337	63.52 347

segment_acmp												81.94 125

testdata				79.54 307	92.87 92	72.34 167		80.14 362	59.91 385	85.47 238	91.75 198	67.96 296	85.24 360	68.57 302	92.18 276	81.06 439

testdata1							79.62 310		73.95 188

test12				86.57 115	90.74 164	72.63 160		90.69 181		82.76 306		79.20 159	94.80 79		95.32 164	92.27 230

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 221

plane_prior5								93.61 65					95.22 63	80.78 126	95.83 146	94.46 98

plane_prior4											92.95 149

plane_prior3						76.85 114			77.79 136	86.55 205

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 158						95.03 176

n2								0.00 491

nn								0.00 491

door-mid								74.45 398

lessismore_v0				85.95 131	91.10 157	70.99 191		70.91 428		91.79 75	94.42 79	61.76 336	92.93 159	79.52 143	93.03 248	93.93 128

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 76	93.67 38	94.82 61	91.18 5	95.52 48	85.36 67	98.73 7	95.23 67

test11								91.46 154

door								72.57 414

HQP5-MVS						70.66 193

HQP-NCC					91.19 152		84.77 179		73.30 202	80.55 346

ACMP_Plane					91.19 152		84.77 179		73.30 202	80.55 346

BP-MVS														77.30 175

HQP4-MVS										80.56 345			94.61 86			93.56 156

HQP3-MVS								92.68 112							94.47 198

HQP2-MVS												72.10 267

NP-MVS					91.95 122	74.55 137					90.17 269

MDTV_nov1_ep13_2view						27.60 481	70.76 424		46.47 454	61.27 466		45.20 429		49.18 435		83.75 400

MDTV_nov1_ep13			68.29 401		78.03 421	43.87 456	74.12 394	72.22 417	52.17 432	67.02 449	85.54 357	45.36 427	80.85 395	55.73 395	84.42 416

ACMMP++_ref															95.74 152

ACMMP++															97.35 84

Test By Simon												79.09 161

ITE_SJBPF				90.11 49	90.72 165	84.97 41		90.30 200	81.56 84	90.02 110	91.20 220	82.40 109	90.81 226	73.58 237	94.66 193	94.56 91

DeepMVS_CX				24.13 463	32.95 485	29.49 479		21.63 486	12.07 479	37.95 480	45.07 477	30.84 468	19.21 482	17.94 481	33.06 479	23.69 478

High-resolution multi-view benchmark