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	80.16 156	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 84	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	92.08 243

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	91.76 254

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	91.76 254

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	93.63 153

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	92.73 199

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	94.02 101	90.15 17	95.67 41	86.82 43	97.34 85	92.19 238

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	93.94 109	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	94.18 119

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 95
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	92.60 123	83.09 69	91.54 78	94.25 87	87.67 46	95.51 50	87.21 37	98.11 40	93.12 182

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 79	83.16 68	91.06 88	94.00 102	88.26 33	95.71 40	87.28 36	98.39 23	92.55 213

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 83	85.07 45	89.99 110	94.03 100	86.57 59	95.80 31	87.35 33	97.62 73	94.20 116

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 38	91.81 150	84.07 55	92.00 71	94.40 80	86.63 58	95.28 62	88.59 11	98.31 26	92.30 230

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 114	88.22 23	88.53 147	97.64 6	83.45 99	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 89	81.99 78	91.40 80	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 128

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 87	81.91 80	90.88 95	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 138

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 88	81.99 78	91.47 79	93.96 106	88.35 32	95.56 45	87.74 22	97.74 62	92.85 196

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	93.97 103	87.93 42	95.87 20	88.65 10	97.96 51	94.12 124

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	92.98 192
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	93.96 131

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 65	80.97 70	91.49 45	93.48 77	82.82 73	92.60 61	93.97 103	88.19 34	96.29 6	87.61 26	98.20 36	94.39 110
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	93.03 104	82.59 74	88.52 148	94.37 82	86.74 57	95.41 57	86.32 50	98.21 34	93.19 177
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	93.80 113	87.20 51	95.80 31	87.10 40	97.69 65	93.93 132

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 68	77.65 136	91.97 72	94.89 57	88.38 30	95.45 55	89.27 6	97.87 56	93.27 172

ACMM	79.39 9	90.65 33	90.99 43	89.63 58	95.03 34	83.53 51	89.62 81	93.35 82	79.20 114	93.83 32	93.60 123	90.81 8	92.96 157	85.02 74	98.45 19	92.41 220
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	90.33 104	93.95 108	84.50 86	95.37 58	80.87 124	95.50 159	94.53 99

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	93.74 118	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	91.70 258
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	93.18 136	87.06 52	95.85 24	84.99 75	97.69 65	93.54 163

SED-MVS		90.46 39	91.64 22	86.93 109	94.18 55	72.65 157	90.47 60	93.69 64	83.77 58	94.11 27	94.27 83	90.28 15	95.84 27	86.03 57	97.92 52	92.29 232

SMA-MVS		90.31 40	90.48 54	89.83 55	95.31 30	79.52 83	90.98 52	93.24 90	75.37 168	92.84 55	95.28 48	85.58 76	96.09 8	87.92 18	97.76 60	93.88 135
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	93.80 113	86.89 56	94.64 85	85.52 66	97.51 82	94.30 115

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 191	89.99 69	93.05 101	86.53 35	94.29 23	96.27 23	82.69 108	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	93.18 136	88.02 40	95.47 53	84.99 75	97.69 65	93.54 163

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 227	88.51 21	90.11 106	95.12 53	90.98 7	88.92 287	77.55 174	97.07 92	83.13 424
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	72.64 159	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 143	95.88 18	87.41 31	95.94 138	92.48 216

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 163	90.54 57	91.01 178	83.61 63	93.75 35	94.65 65	89.76 19	95.78 35	86.42 47	97.97 49	90.55 295
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	58.83 372	89.04 94	92.74 116	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	58.88 369	88.95 95	93.19 92	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 97	81.10 89	95.32 14	97.24 10	72.94 261	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	57.84 383	88.93 96	92.84 112	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	93.89 111	83.80 94	93.10 153	82.67 106	98.04 41	93.64 152

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	93.18 136	87.06 52	95.85 24	87.89 19	97.69 65	93.68 147

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	88.83 139	94.26 86	77.55 185	95.86 23	84.88 78	95.87 144	95.24 65

WR-MVS_H		89.91 54	91.31 34	85.71 138	96.32 9	62.39 302	89.54 84	93.31 86	90.21 12	95.57 11	95.66 37	81.42 140	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	92.78 115	78.78 120	92.51 62	93.64 122	88.13 37	93.84 120	84.83 80	97.55 78	94.10 125
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	90.63 188	70.00 266	94.55 19	96.67 17	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	90.20 209	69.87 267	95.06 15	96.14 28	84.28 89	93.07 154	87.68 24	96.34 116	97.09 20

test_djsdf		89.62 58	89.01 71	91.45 26	92.36 107	82.98 57	91.98 39	90.08 212	71.54 242	94.28 25	96.54 19	81.57 138	94.27 96	86.26 51	96.49 110	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	86.75 203	93.26 133	93.64 2	90.93 224	84.60 83	90.75 330	93.97 130

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 100	80.87 93	91.13 86	93.19 135	86.22 66	95.97 14	82.23 112	97.18 90	90.45 297
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	90.29 206	69.27 274	94.39 21	96.38 21	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	92.30 132	79.74 105	87.50 186	92.38 173	81.42 140	93.28 146	83.07 98	97.24 88	91.67 259

ACMH	76.49 14	89.34 63	91.14 36	83.96 190	92.50 103	70.36 200	89.55 82	93.84 56	81.89 81	94.70 17	95.44 44	90.69 9	88.31 311	83.33 94	98.30 27	93.20 176
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 93	86.02 38	93.12 48	95.30 46	84.94 81	89.44 279	74.12 227	96.10 129	94.45 104

APD_test2		89.30 64	89.12 68	89.84 53	88.67 216	85.64 35	90.61 55	93.17 93	86.02 38	93.12 48	95.30 46	84.94 81	89.44 279	74.12 227	96.10 129	94.45 104

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 375	88.66 103	92.06 139	90.78 7	95.67 8	95.17 51	81.80 135	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 71	80.16 100	89.13 135	93.44 125	83.82 93	90.98 221	83.86 90	95.30 167	93.60 156

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	73.53 199	89.71 118	94.82 60	85.09 80	95.77 37	84.17 87	98.03 43	93.26 174
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	64.33 273	89.67 79	88.38 248	88.84 17	94.29 23	97.57 7	90.48 14	91.26 207	72.57 258	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	92.25 182	72.03 275	96.36 4	88.21 13	90.93 320	92.98 192
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	92.29 180	86.11 67	90.22 252	86.24 54	97.24 88	91.36 267
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	91.27 170	78.20 128	86.69 207	92.28 181	80.36 154	95.06 71	86.17 55	96.49 110	90.22 301

Elysia		88.71 73	88.89 75	88.19 90	91.26 150	72.96 153	88.10 111	93.59 72	84.31 51	90.42 100	94.10 96	74.07 238	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	72.96 153	88.10 111	93.59 72	84.31 51	90.42 100	94.10 96	74.07 238	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	72.22 171	84.01 201	89.44 230	88.63 20	94.38 22	95.77 32	86.38 65	93.59 132	79.84 135	95.21 168	91.82 252

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 147	77.50 104	87.55 119	92.97 108	87.95 26	89.62 122	92.87 155	84.56 85	93.89 117	77.65 172	96.62 105	90.70 287

APD_test1		88.40 77	87.91 89	89.88 52	89.50 191	86.65 20	89.98 70	91.91 145	84.26 53	90.87 96	93.92 110	82.18 123	89.29 283	73.75 235	94.81 187	93.70 146

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 170	65.27 262	88.86 97	93.02 105	87.15 30	93.05 50	97.10 11	82.28 121	92.02 183	76.70 185	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	91.97 142	70.73 255	94.19 26	96.67 17	76.94 199	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 93	76.02 153	88.64 144	91.22 223	84.24 90	93.37 144	77.97 170	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	84.32 275	89.33 291	83.87 92	94.53 91	82.45 108	94.89 183	94.90 76

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 173	67.85 301	86.63 208	94.84 59	79.58 162	95.96 15	87.62 25	94.50 196	94.56 95
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	63.94 277	91.10 50	89.64 224	85.07 45	90.91 92	91.09 229	89.16 25	91.87 188	82.03 113	95.87 144	93.13 179

EC-MVSNet		88.01 84	88.32 86	87.09 104	89.28 196	72.03 174	90.31 64	96.31 4	80.88 92	85.12 249	89.67 284	84.47 87	95.46 54	82.56 107	96.26 121	93.77 144

RPSCF		88.00 85	86.93 108	91.22 31	90.08 178	89.30 5	89.68 78	91.11 174	79.26 113	89.68 119	94.81 63	82.44 112	87.74 322	76.54 190	88.74 367	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	88.00 164	93.03 146	82.66 109	91.47 197	70.81 272	96.14 126	94.16 121

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 274	84.38 193	91.29 166	84.88 48	92.06 70	93.84 112	86.45 62	93.73 122	73.22 249	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 180	69.73 208	87.86 116	94.20 31	74.04 187	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	91.23 171	77.31 143	87.07 196	91.47 213	82.94 104	94.71 81	84.67 82	96.27 120	92.62 207

HQP_MVS		87.75 90	87.43 97	88.70 77	93.45 75	76.42 119	89.45 87	93.61 69	79.44 110	86.55 209	92.95 152	74.84 225	95.22 63	80.78 126	95.83 146	94.46 102

sc_t1		87.70 91	88.94 74	83.99 188	93.47 74	67.15 239	85.05 175	88.21 256	86.81 32	91.87 74	97.65 5	85.51 78	87.91 317	74.22 221	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	86.76 287	84.54 50	83.58 294	93.78 115	73.36 256	96.48 2	87.98 17	96.21 122	94.41 109

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 207	92.03 120	62.26 306	91.49 45	92.62 120	88.07 25	88.07 161	96.17 26	72.24 270	95.79 34	84.85 79	94.16 209	92.58 211

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 176	78.77 121	84.85 261	90.89 240	80.85 146	95.29 60	81.14 121	95.32 164	92.34 228

DeepPCF-MVS	81.24 5	87.28 95	86.21 123	90.49 42	91.48 143	84.90 42	83.41 228	92.38 128	70.25 263	89.35 130	90.68 250	82.85 107	94.57 88	79.55 141	95.95 137	92.00 247

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 218	87.84 117	88.05 257	81.66 83	94.64 18	96.53 20	65.94 313	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	84.60 186	89.74 219	74.40 183	89.92 114	93.41 126	80.45 152	90.63 239	86.66 46	94.37 202	94.73 92

SPE-MVS-test		87.00 98	86.43 115	88.71 76	89.46 192	77.46 105	89.42 89	95.73 7	77.87 134	81.64 336	87.25 336	82.43 113	94.53 91	77.65 172	96.46 112	94.14 123

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 228	83.80 211	92.87 110	80.37 96	89.61 124	91.81 199	77.72 181	94.18 104	75.00 214	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	92.09 138	78.87 119	84.27 280	94.05 99	78.35 173	93.65 125	80.54 130	91.58 304	92.08 243
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	67.02 243	82.55 256	91.56 156	83.08 70	90.92 90	91.82 198	78.25 174	93.99 111	74.16 225	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 243	83.16 239	92.21 133	81.73 82	90.92 90	91.97 189	77.20 193	93.99 111	74.16 225	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 273	65.22 263	84.16 197	94.23 28	77.89 132	91.28 85	93.66 121	84.35 88	92.71 163	80.07 131	94.87 186	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	85.94 313	78.30 92	86.93 130	92.20 134	65.94 321	89.16 133	93.16 141	83.10 102	89.89 267	87.81 21	94.43 200	93.35 167

tt0320-xc		86.67 105	88.41 84	81.44 271	93.45 75	60.44 342	83.96 203	88.50 244	87.26 29	90.90 94	97.90 3	85.61 75	86.40 350	70.14 284	98.01 45	97.47 14

IS-MVSNet		86.66 106	86.82 111	86.17 128	92.05 119	66.87 247	91.21 48	88.64 241	86.30 37	89.60 125	92.59 165	69.22 293	94.91 75	73.89 232	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 272	93.57 72	60.73 339	84.37 194	88.61 243	87.00 31	90.75 97	97.98 2	85.54 77	86.45 348	69.75 289	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 236	63.28 284	86.64 140	92.20 134	75.42 167	92.81 57	94.50 72	74.05 241	94.06 110	83.88 89	96.28 118	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 257	92.22 113	62.28 305	84.66 185	89.15 235	83.54 65	89.85 115	97.32 8	88.08 39	86.80 341	70.43 281	97.30 87	96.62 31

NormalMVS		86.47 110	85.32 147	89.94 51	94.43 44	80.42 72	88.63 104	93.59 72	74.56 178	85.12 249	90.34 263	66.19 310	94.20 101	76.57 188	98.44 20	95.19 68

PHI-MVS		86.38 111	85.81 133	88.08 92	88.44 225	77.34 108	89.35 91	93.05 101	73.15 212	84.76 264	87.70 325	78.87 167	94.18 104	80.67 128	96.29 117	92.73 199

CSCG		86.26 112	86.47 114	85.60 140	90.87 162	74.26 139	87.98 114	91.85 146	80.35 97	89.54 128	88.01 312	79.09 165	92.13 179	75.51 207	95.06 175	90.41 298

DeepC-MVS_fast	80.27 8	86.23 113	85.65 139	87.96 95	91.30 147	76.92 113	87.19 125	91.99 141	70.56 256	84.96 256	90.69 249	80.01 158	95.14 68	78.37 155	95.78 150	91.82 252
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	87.82 244	62.35 304	86.42 144	91.33 165	76.78 147	92.73 59	94.48 74	73.41 253	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	64.55 270	84.55 188	90.71 185	85.85 40	89.94 113	95.24 50	82.13 124	90.40 247	69.19 296	96.40 115	95.31 62

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 281	70.38 199	85.31 169	92.61 122	75.59 163	88.32 155	92.87 155	82.22 122	88.63 300	88.80 9	92.82 260	89.83 311

test_fmvsmconf0.1_n		86.18 117	85.88 131	87.08 105	85.26 329	78.25 93	85.82 156	91.82 148	65.33 336	88.55 146	92.35 179	82.62 111	89.80 269	86.87 42	94.32 204	93.18 178

CDPH-MVS		86.17 118	85.54 140	88.05 94	92.25 111	75.45 132	83.85 208	92.01 140	65.91 323	86.19 220	91.75 203	83.77 95	94.98 73	77.43 177	96.71 103	93.73 145

NR-MVSNet		86.00 119	86.22 122	85.34 147	93.24 84	64.56 269	82.21 271	90.46 194	80.99 90	88.42 151	91.97 189	77.56 184	93.85 118	72.46 259	98.65 12	97.61 10

train_agg		85.98 120	85.28 148	88.07 93	92.34 108	79.70 80	83.94 204	90.32 201	65.79 325	84.49 269	90.97 234	81.93 130	93.63 127	81.21 120	96.54 108	90.88 281

KinetiMVS		85.95 121	86.10 126	85.50 144	87.56 254	69.78 206	83.70 214	89.83 218	80.42 95	87.76 175	93.24 134	73.76 247	91.54 194	85.03 73	93.62 230	95.19 68

FC-MVSNet-test		85.93 122	87.05 104	82.58 239	92.25 111	56.44 394	85.75 157	93.09 99	77.33 142	91.94 73	94.65 65	74.78 227	93.41 143	75.11 213	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 141	86.99 108	84.77 338	78.21 94	85.40 167	91.39 163	65.32 337	87.72 177	91.81 199	82.33 116	89.78 270	86.68 44	94.20 207	92.99 190

Effi-MVS+-dtu		85.82 124	83.38 202	93.14 4	87.13 268	91.15 3	87.70 118	88.42 247	74.57 177	83.56 295	85.65 360	78.49 172	94.21 100	72.04 261	92.88 256	94.05 127

TAPA-MVS	77.73 12	85.71 125	84.83 158	88.37 86	88.78 215	79.72 79	87.15 127	93.50 76	69.17 275	85.80 232	89.56 285	80.76 148	92.13 179	73.21 254	95.51 158	93.25 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 126	86.14 124	83.58 203	87.97 238	67.13 240	87.55 119	94.32 22	73.44 202	88.47 149	87.54 328	86.45 62	91.06 219	75.76 203	93.76 221	92.54 214

canonicalmvs		85.50 126	86.14 124	83.58 203	87.97 238	67.13 240	87.55 119	94.32 22	73.44 202	88.47 149	87.54 328	86.45 62	91.06 219	75.76 203	93.76 221	92.54 214

fmvsm_s_conf0.5_n_8		85.48 128	85.75 136	84.68 167	87.10 271	69.98 204	84.28 195	92.68 117	74.77 174	87.90 168	92.36 178	73.94 242	90.41 246	85.95 62	92.74 262	93.66 148

EPP-MVSNet		85.47 129	85.04 153	86.77 113	91.52 142	69.37 213	91.63 44	87.98 260	81.51 85	87.05 197	91.83 197	66.18 312	95.29 60	70.75 275	96.89 95	95.64 53

GeoE		85.45 130	85.81 133	84.37 174	90.08 178	67.07 242	85.86 155	91.39 163	72.33 231	87.59 183	90.25 269	84.85 83	92.37 173	78.00 168	91.94 293	93.66 148

E5new		85.44 131	86.37 116	82.66 233	88.22 231	61.86 311	83.59 218	93.70 61	73.64 194	87.62 179	93.30 129	85.85 71	91.26 207	78.02 164	93.40 235	94.86 82

E6new		85.44 131	86.37 116	82.66 233	88.23 229	61.86 311	83.59 218	93.69 64	73.64 194	87.61 181	93.30 129	85.85 71	91.26 207	78.02 164	93.40 235	94.86 82

E6		85.44 131	86.37 116	82.66 233	88.23 229	61.86 311	83.59 218	93.69 64	73.64 194	87.61 181	93.30 129	85.85 71	91.26 207	78.02 164	93.40 235	94.86 82

E5		85.44 131	86.37 116	82.66 233	88.22 231	61.86 311	83.59 218	93.70 61	73.64 194	87.62 179	93.30 129	85.85 71	91.26 207	78.02 164	93.40 235	94.86 82

MGCNet		85.37 135	84.58 170	87.75 96	85.28 328	73.36 144	86.54 143	85.71 304	77.56 139	81.78 334	92.47 171	70.29 287	96.02 11	85.59 65	95.96 135	93.87 136

FIs		85.35 136	86.27 121	82.60 238	91.86 126	57.31 387	85.10 174	93.05 101	75.83 158	91.02 89	93.97 103	73.57 249	92.91 161	73.97 231	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 137	85.36 146	84.81 160	85.80 316	76.13 125	85.15 173	92.32 131	61.40 379	91.33 82	90.85 243	83.76 96	86.16 356	84.31 85	93.28 243	92.15 241

casdiffmvs		85.21 138	85.85 132	83.31 212	86.17 305	62.77 291	83.03 241	93.93 47	74.69 176	88.21 158	92.68 164	82.29 120	91.89 187	77.87 171	93.75 224	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		85.20 139	85.25 149	85.02 155	86.01 311	71.31 186	84.96 176	91.76 152	69.10 277	88.90 136	92.56 168	73.84 245	90.63 239	86.88 41	93.26 244	93.13 179

baseline		85.20 139	85.93 129	83.02 219	86.30 300	62.37 303	84.55 188	93.96 45	74.48 180	87.12 191	92.03 188	82.30 118	91.94 184	78.39 154	94.21 206	94.74 91

SSM_0404		85.16 141	85.09 151	85.36 146	90.14 177	69.52 211	86.17 149	91.58 154	74.41 181	86.55 209	91.49 210	78.54 168	93.97 113	73.71 236	93.21 248	92.59 210

K. test v3		85.14 142	84.73 160	86.37 119	91.13 156	69.63 210	85.45 165	76.68 397	84.06 56	92.44 64	96.99 13	62.03 340	94.65 84	80.58 129	93.24 245	94.83 87

mmtdpeth		85.13 143	85.78 135	83.17 217	84.65 340	74.71 135	85.87 154	90.35 200	77.94 131	83.82 287	96.96 15	77.75 179	80.03 416	78.44 153	96.21 122	94.79 90

EI-MVSNet-Vis-set		85.12 144	84.53 173	86.88 110	84.01 355	72.76 156	83.91 207	85.18 314	80.44 94	88.75 141	85.49 364	80.08 157	91.92 185	82.02 114	90.85 325	95.97 44

fmvsm_l_conf0.5_n_3		85.11 145	84.96 155	85.56 141	87.49 257	75.69 131	84.71 183	90.61 190	67.64 305	84.88 259	92.05 186	82.30 118	88.36 309	83.84 91	91.10 313	92.62 207

MGCFI-Net		85.04 146	85.95 128	82.31 249	87.52 255	63.59 280	86.23 148	93.96 45	73.46 200	88.07 161	87.83 323	86.46 61	90.87 229	76.17 197	93.89 217	92.47 218

EI-MVSNet-UG-set		85.04 146	84.44 176	86.85 111	83.87 359	72.52 165	83.82 209	85.15 315	80.27 99	88.75 141	85.45 366	79.95 159	91.90 186	81.92 117	90.80 329	96.13 39

X-MVStestdata		85.04 146	82.70 221	92.08 9	95.64 24	86.25 22	92.64 20	93.33 83	85.07 45	89.99 110	16.05 493	86.57 59	95.80 31	87.35 33	97.62 73	94.20 116

MSLP-MVS++		85.00 149	86.03 127	81.90 257	91.84 129	71.56 184	86.75 138	93.02 105	75.95 156	87.12 191	89.39 289	77.98 176	89.40 282	77.46 175	94.78 188	84.75 396

F-COLMAP		84.97 150	83.42 200	89.63 58	92.39 106	83.40 52	88.83 98	91.92 144	73.19 211	80.18 358	89.15 295	77.04 197	93.28 146	65.82 330	92.28 281	92.21 237

SSM_0407		84.89 151	84.85 157	85.01 156	89.13 200	68.97 221	85.60 161	91.58 154	74.41 181	85.68 233	91.49 210	78.54 168	93.69 124	73.71 236	93.47 232	92.38 225

balanced_conf03		84.80 152	85.40 144	83.00 220	88.95 208	61.44 319	90.42 63	92.37 130	71.48 244	88.72 143	93.13 142	70.16 289	95.15 67	79.26 146	94.11 210	92.41 220

3Dnovator	80.37 7	84.80 152	84.71 163	85.06 153	86.36 298	74.71 135	88.77 100	90.00 214	75.65 161	84.96 256	93.17 140	74.06 240	91.19 214	78.28 158	91.09 314	89.29 324

SymmetryMVS		84.79 154	83.54 194	88.55 79	92.44 105	80.42 72	88.63 104	82.37 356	74.56 178	85.12 249	90.34 263	66.19 310	94.20 101	76.57 188	95.68 154	91.03 275

E4		84.75 155	85.46 142	82.61 237	88.17 234	61.55 318	81.39 285	93.55 75	73.13 214	86.83 200	92.83 157	84.17 91	91.48 196	76.92 184	92.19 285	94.80 89

IterMVS-LS		84.73 156	84.98 154	83.96 190	87.35 261	63.66 278	83.25 233	89.88 217	76.06 151	89.62 122	92.37 176	73.40 255	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		84.63 157	84.34 181	85.49 145	90.18 176	75.86 130	79.23 329	87.13 277	73.35 204	85.56 240	89.34 290	83.60 98	90.50 243	76.64 187	94.05 214	90.09 307

HQP-MVS		84.61 158	84.06 186	86.27 122	91.19 152	70.66 194	84.77 178	92.68 117	73.30 207	80.55 350	90.17 274	72.10 271	94.61 86	77.30 179	94.47 198	93.56 160

v1192		84.57 159	84.69 165	84.21 182	87.75 246	62.88 288	83.02 242	91.43 160	69.08 278	89.98 112	90.89 240	72.70 265	93.62 130	82.41 109	94.97 180	96.13 39

fmvsm_s_conf0.5_n_11		84.56 160	84.69 165	84.15 185	86.53 287	71.29 187	85.53 162	92.62 120	70.54 257	82.75 311	91.20 225	77.33 188	88.55 305	83.80 92	91.93 294	92.61 209

fmvsm_s_conf0.5_n_5		84.56 160	84.71 163	84.11 186	87.92 241	72.09 173	84.80 177	88.64 241	64.43 349	88.77 140	91.78 201	78.07 175	87.95 316	85.85 63	92.18 286	92.30 230

FMVSNet1		84.55 162	85.45 143	81.85 259	90.27 174	61.05 329	86.83 134	88.27 253	78.57 124	89.66 121	95.64 38	75.43 217	90.68 236	69.09 297	95.33 163	93.82 139

v1144		84.54 163	84.72 162	84.00 187	87.67 250	62.55 295	82.97 244	90.93 181	70.32 261	89.80 116	90.99 233	73.50 250	93.48 139	81.69 119	94.65 194	95.97 44

Gipuma		84.44 164	86.33 120	78.78 323	84.20 350	73.57 143	89.55 82	90.44 195	84.24 54	84.38 272	94.89 57	76.35 212	80.40 413	76.14 198	96.80 101	82.36 434
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 165	84.27 182	84.74 163	87.25 264	70.84 193	83.55 223	88.45 246	68.64 287	86.29 219	91.31 219	74.97 223	88.42 307	87.87 20	90.07 345	94.95 75

MCST-MVS		84.36 166	83.93 190	85.63 139	91.59 134	71.58 182	83.52 224	92.13 136	61.82 372	83.96 285	89.75 282	79.93 160	93.46 140	78.33 157	94.34 203	91.87 251

VDDNet		84.35 167	85.39 145	81.25 274	95.13 32	59.32 358	85.42 166	81.11 367	86.41 36	87.41 187	96.21 25	73.61 248	90.61 241	66.33 322	96.85 96	93.81 142

ETV-MVS		84.31 168	83.91 191	85.52 142	88.58 221	70.40 198	84.50 192	93.37 78	78.76 122	84.07 283	78.72 441	80.39 153	95.13 69	73.82 234	92.98 254	91.04 274

v1240		84.30 169	84.51 174	83.65 200	87.65 251	61.26 325	82.85 248	91.54 157	67.94 298	90.68 99	90.65 254	71.71 279	93.64 126	82.84 103	94.78 188	96.07 41

MVS_111021_LR		84.28 170	83.76 192	85.83 136	89.23 198	83.07 55	80.99 295	83.56 342	72.71 223	86.07 223	89.07 297	81.75 137	86.19 355	77.11 181	93.36 239	88.24 348

h-mvs33		84.25 171	82.76 220	88.72 75	91.82 131	82.60 60	84.00 202	84.98 321	71.27 245	86.70 205	90.55 259	63.04 337	93.92 116	78.26 159	94.20 207	89.63 315

v144192		84.24 172	84.41 177	83.71 199	87.59 253	61.57 317	82.95 245	91.03 177	67.82 302	89.80 116	90.49 260	73.28 257	93.51 138	81.88 118	94.89 183	96.04 43

dcpmvs_2		84.23 173	85.14 150	81.50 269	88.61 220	61.98 310	82.90 247	93.11 97	68.66 286	92.77 58	92.39 172	78.50 171	87.63 325	76.99 183	92.30 278	94.90 76

v1921920		84.23 173	84.37 179	83.79 195	87.64 252	61.71 316	82.91 246	91.20 172	67.94 298	90.06 107	90.34 263	72.04 274	93.59 132	82.32 110	94.91 181	96.07 41

VDD-MVS		84.23 173	84.58 170	83.20 215	91.17 155	65.16 265	83.25 233	84.97 322	79.79 104	87.18 190	94.27 83	74.77 228	90.89 227	69.24 293	96.54 108	93.55 162

v2v482		84.09 176	84.24 183	83.62 201	87.13 268	61.40 320	82.71 251	89.71 222	72.19 234	89.55 126	91.41 214	70.70 285	93.20 148	81.02 122	93.76 221	96.25 37

EG-PatchMatch MVS		84.08 177	84.11 185	83.98 189	92.22 113	72.61 162	82.20 273	87.02 283	72.63 224	88.86 137	91.02 232	78.52 170	91.11 217	73.41 244	91.09 314	88.21 349

E2		84.06 178	84.61 167	82.40 247	87.49 257	61.31 322	81.03 293	93.36 79	71.83 239	86.02 225	91.87 191	82.91 105	91.37 204	75.66 205	91.33 308	94.53 99

E3		84.06 178	84.61 167	82.40 247	87.49 257	61.30 323	81.03 293	93.36 79	71.83 239	86.01 226	91.87 191	82.91 105	91.36 205	75.66 205	91.33 308	94.53 99

fmvsm_s_conf0.5_n_6		84.05 180	84.14 184	83.81 193	87.75 246	71.17 189	83.42 227	91.10 175	67.90 300	84.53 267	90.70 248	73.01 260	88.73 295	85.09 70	93.72 226	91.53 264

DP-MVS Recon		84.05 180	83.22 205	86.52 117	91.73 132	75.27 133	83.23 236	92.40 126	72.04 236	82.04 325	88.33 308	77.91 178	93.95 115	66.17 323	95.12 173	90.34 300

viewmacassd2359aftdt		84.04 182	84.78 159	81.81 262	86.43 292	60.32 344	81.95 275	92.82 113	71.56 241	86.06 224	92.98 148	81.79 136	90.28 248	76.18 196	93.24 245	94.82 88

TransMVSNet (Re)		84.02 183	85.74 137	78.85 321	91.00 159	55.20 408	82.29 267	87.26 272	79.65 107	88.38 153	95.52 41	83.00 103	86.88 339	67.97 311	96.60 106	94.45 104

Baseline_NR-MVSNet		84.00 184	85.90 130	78.29 336	91.47 144	53.44 420	82.29 267	87.00 286	79.06 116	89.55 126	95.72 36	77.20 193	86.14 357	72.30 260	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 185	84.46 175	82.53 242	86.11 308	70.65 196	82.45 261	89.17 234	67.72 304	86.74 204	91.49 210	79.20 163	85.86 366	84.71 81	92.60 270	91.07 273

TSAR-MVS + GP.		83.95 186	82.69 222	87.72 97	89.27 197	81.45 67	83.72 213	81.58 365	74.73 175	85.66 236	86.06 355	72.56 267	92.69 165	75.44 209	95.21 168	89.01 337

LuminaMVS		83.94 187	83.51 195	85.23 148	89.78 186	71.74 177	84.76 181	87.27 271	72.60 225	89.31 131	90.60 258	64.04 326	90.95 222	79.08 147	94.11 210	92.99 190

alignmvs		83.94 187	83.98 188	83.80 194	87.80 245	67.88 235	84.54 190	91.42 162	73.27 210	88.41 152	87.96 313	72.33 268	90.83 230	76.02 200	94.11 210	92.69 203

Effi-MVS+		83.90 189	84.01 187	83.57 205	87.22 266	65.61 261	86.55 142	92.40 126	78.64 123	81.34 341	84.18 387	83.65 97	92.93 159	74.22 221	87.87 381	92.17 240

fmvsm_s_conf0.1_n_2		83.82 190	83.49 197	84.84 158	85.99 312	70.19 202	80.93 296	87.58 267	67.26 311	87.94 167	92.37 176	71.40 281	88.01 313	86.03 57	91.87 295	96.31 36

mvs5depth		83.82 190	84.54 172	81.68 265	82.23 384	68.65 226	86.89 131	89.90 216	80.02 103	87.74 176	97.86 4	64.19 325	82.02 400	76.37 192	95.63 157	94.35 111

CANet		83.79 192	82.85 219	86.63 114	86.17 305	72.21 172	83.76 212	91.43 160	77.24 144	74.39 422	87.45 332	75.36 218	95.42 56	77.03 182	92.83 259	92.25 236

pm-mvs1		83.69 193	84.95 156	79.91 304	90.04 182	59.66 355	82.43 262	87.44 268	75.52 165	87.85 171	95.26 49	81.25 142	85.65 370	68.74 303	96.04 131	94.42 108

AdaColmap		83.66 194	83.69 193	83.57 205	90.05 181	72.26 170	86.29 146	90.00 214	78.19 129	81.65 335	87.16 338	83.40 100	94.24 99	61.69 368	94.76 191	84.21 406

viewdifsd2359ckpt09		83.64 195	83.18 208	85.03 154	87.26 263	66.99 245	85.32 168	93.83 57	65.57 331	84.99 255	89.40 288	77.30 189	93.57 135	71.16 271	93.80 220	94.54 98

MIMVSNet1		83.63 196	84.59 169	80.74 286	94.06 62	62.77 291	82.72 250	84.53 331	77.57 138	90.34 103	95.92 31	76.88 205	85.83 367	61.88 366	97.42 83	93.62 154

fmvsm_s_conf0.5_n_2		83.62 197	83.29 204	84.62 168	85.43 326	70.18 203	80.61 304	87.24 273	67.14 312	87.79 173	91.87 191	71.79 278	87.98 315	86.00 61	91.77 298	95.71 50

test_fmvsm_n_1920		83.60 198	82.89 216	85.74 137	85.22 330	77.74 102	84.12 199	90.48 192	59.87 399	86.45 218	91.12 228	75.65 215	85.89 364	82.28 111	90.87 323	93.58 158

WR-MVS		83.56 199	84.40 178	81.06 280	93.43 78	54.88 409	78.67 338	85.02 319	81.24 87	90.74 98	91.56 208	72.85 262	91.08 218	68.00 310	98.04 41	97.23 17

CNLPA		83.55 200	83.10 211	84.90 157	89.34 195	83.87 50	84.54 190	88.77 238	79.09 115	83.54 296	88.66 305	74.87 224	81.73 402	66.84 317	92.29 280	89.11 330

viewcassd2359sk11		83.53 201	83.96 189	82.25 250	86.97 280	61.13 327	80.80 300	93.22 91	70.97 252	85.36 244	91.08 230	81.84 134	91.29 206	74.79 216	90.58 341	94.33 113

LCM-MVSNet-Re		83.48 202	85.06 152	78.75 324	85.94 313	55.75 400	80.05 310	94.27 25	76.47 148	96.09 6	94.54 71	83.31 101	89.75 273	59.95 381	94.89 183	90.75 284

hse-mvs2		83.47 203	81.81 237	88.47 82	91.03 158	82.27 61	82.61 252	83.69 340	71.27 245	86.70 205	86.05 356	63.04 337	92.41 171	78.26 159	93.62 230	90.71 286

V42		83.47 203	83.37 203	83.75 197	83.16 378	63.33 283	81.31 287	90.23 208	69.51 271	90.91 92	90.81 245	74.16 237	92.29 177	80.06 132	90.22 343	95.62 54

VPA-MVSNet		83.47 203	84.73 160	79.69 309	90.29 173	57.52 386	81.30 289	88.69 240	76.29 149	87.58 185	94.44 75	80.60 151	87.20 333	66.60 320	96.82 99	94.34 112

mamba_0408		83.44 206	82.88 217	85.11 151	89.13 200	68.97 221	72.73 420	91.28 167	72.90 217	85.68 233	90.61 256	76.78 206	93.97 113	73.37 246	93.47 232	92.38 225

viewdifsd2359ckpt07		83.41 207	84.35 180	80.56 293	85.84 315	58.93 368	79.47 321	91.28 167	73.01 216	87.59 183	92.07 185	85.24 79	88.68 297	73.59 241	91.11 312	94.09 126

PAPM_NR		83.23 208	83.19 207	83.33 211	90.90 161	65.98 257	88.19 109	90.78 184	78.13 130	80.87 346	87.92 317	73.49 252	92.42 170	70.07 285	88.40 370	91.60 261

CLD-MVS		83.18 209	82.64 223	84.79 161	89.05 204	67.82 236	77.93 348	92.52 124	68.33 290	85.07 252	81.54 416	82.06 127	92.96 157	69.35 292	97.91 54	93.57 159
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 210	85.68 138	75.65 376	81.24 398	45.26 464	79.94 312	92.91 109	83.83 57	91.33 82	96.88 16	80.25 155	85.92 360	68.89 300	95.89 143	95.76 48

FA-MVS(test-final)		83.13 211	83.02 212	83.43 208	86.16 307	66.08 256	88.00 113	88.36 249	75.55 164	85.02 253	92.75 162	65.12 319	92.50 169	74.94 215	91.30 310	91.72 256

114514_t		83.10 212	82.54 226	84.77 162	92.90 91	69.10 220	86.65 139	90.62 189	54.66 431	81.46 338	90.81 245	76.98 198	94.38 94	72.62 257	96.18 124	90.82 283

E3new		83.08 213	83.39 201	82.14 252	86.49 289	61.00 332	80.64 302	93.12 96	70.30 262	84.78 263	90.34 263	80.85 146	91.24 212	74.20 224	89.83 350	94.17 120

RRT-MVS		82.97 214	83.44 198	81.57 267	85.06 333	58.04 381	87.20 124	90.37 198	77.88 133	88.59 145	93.70 120	63.17 334	93.05 155	76.49 191	88.47 369	93.62 154

viewmanbaseed2359cas		82.95 215	83.43 199	81.52 268	85.18 331	60.03 349	81.36 286	92.38 128	69.55 270	84.84 262	91.38 215	79.85 161	90.09 261	74.22 221	92.09 288	94.43 107

BP-MVS1		82.81 216	81.67 239	86.23 123	87.88 243	68.53 227	86.06 151	84.36 332	75.65 161	85.14 248	90.19 271	45.84 433	94.42 93	85.18 69	94.72 192	95.75 49

FE-MVSNET2		82.80 217	83.51 195	80.67 291	89.08 203	58.46 376	82.40 264	89.26 232	71.25 248	88.24 157	94.07 98	75.75 214	89.56 274	65.91 328	95.67 156	93.98 129

UGNet		82.78 218	81.64 240	86.21 126	86.20 304	76.24 123	86.86 132	85.68 305	77.07 145	73.76 426	92.82 158	69.64 290	91.82 190	69.04 299	93.69 227	90.56 294
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		82.75 219	81.93 235	85.19 149	82.08 385	80.15 76	85.53 162	88.76 239	68.01 295	85.58 239	87.75 324	71.80 277	86.85 340	74.02 230	93.87 218	88.58 342

EI-MVSNet		82.61 220	82.42 228	83.20 215	83.25 375	63.66 278	83.50 225	85.07 316	76.06 151	86.55 209	85.10 372	73.41 253	90.25 249	78.15 163	90.67 336	95.68 52

QAPM		82.59 221	82.59 225	82.58 239	86.44 291	66.69 248	89.94 72	90.36 199	67.97 297	84.94 258	92.58 167	72.71 264	92.18 178	70.63 278	87.73 384	88.85 338

fmvsm_s_conf0.1_n_a		82.58 222	81.93 235	84.50 171	87.68 249	73.35 145	86.14 150	77.70 386	61.64 377	85.02 253	91.62 205	77.75 179	86.24 352	82.79 104	87.07 392	93.91 134

Fast-Effi-MVS+-dtu		82.54 223	81.41 249	85.90 133	85.60 321	76.53 118	83.07 240	89.62 226	73.02 215	79.11 371	83.51 392	80.74 149	90.24 251	68.76 302	89.29 357	90.94 278

MVS_Test		82.47 224	83.22 205	80.22 300	82.62 383	57.75 385	82.54 257	91.96 143	71.16 250	82.89 307	92.52 170	77.41 186	90.50 243	80.04 133	87.84 383	92.40 222

viewdifsd2359ckpt11		82.46 225	82.98 214	80.88 283	83.53 362	61.00 332	79.46 322	85.97 300	69.48 272	87.89 169	91.31 219	82.10 125	88.61 301	74.28 219	92.86 257	93.02 186

viewmsd2359difaftdt		82.46 225	82.99 213	80.88 283	83.52 363	61.00 332	79.46 322	85.97 300	69.48 272	87.89 169	91.31 219	82.10 125	88.61 301	74.28 219	92.86 257	93.02 186

v148		82.31 227	82.48 227	81.81 262	85.59 322	59.66 355	81.47 283	86.02 298	72.85 219	88.05 163	90.65 254	70.73 284	90.91 226	75.15 212	91.79 296	94.87 78

API-MVS		82.28 228	82.61 224	81.30 273	86.29 301	69.79 205	88.71 101	87.67 266	78.42 126	82.15 321	84.15 388	77.98 176	91.59 193	65.39 333	92.75 261	82.51 433

MVSFormer		82.23 229	81.57 245	84.19 184	85.54 323	69.26 215	91.98 39	90.08 212	71.54 242	76.23 402	85.07 375	58.69 362	94.27 96	86.26 51	88.77 365	89.03 335

viewdifsd2359ckpt13		82.22 230	81.98 234	82.95 223	85.48 325	64.44 271	83.17 238	92.11 137	65.97 320	83.72 290	89.73 283	77.60 183	90.80 232	70.61 279	89.42 355	93.59 157

fmvsm_s_conf0.5_n_a		82.21 231	81.51 248	84.32 179	86.56 286	73.35 145	85.46 164	77.30 390	61.81 373	84.51 268	90.88 242	77.36 187	86.21 354	82.72 105	86.97 397	93.38 166

EIA-MVS		82.19 232	81.23 256	85.10 152	87.95 240	69.17 219	83.22 237	93.33 83	70.42 258	78.58 376	79.77 432	77.29 190	94.20 101	71.51 267	88.96 363	91.93 250

GDP-MVS		82.17 233	80.85 264	86.15 130	88.65 218	68.95 224	85.65 160	93.02 105	68.42 288	83.73 289	89.54 286	45.07 444	94.31 95	79.66 139	93.87 218	95.19 68

fmvsm_s_conf0.1_n		82.17 233	81.59 243	83.94 192	86.87 284	71.57 183	85.19 172	77.42 389	62.27 371	84.47 271	91.33 217	76.43 209	85.91 362	83.14 95	87.14 390	94.33 113

PCF-MVS	74.62 15	82.15 235	80.92 262	85.84 135	89.43 193	72.30 169	80.53 305	91.82 148	57.36 415	87.81 172	89.92 279	77.67 182	93.63 127	58.69 390	95.08 174	91.58 262
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 236	80.31 271	87.45 101	90.86 163	80.29 75	85.88 153	90.65 187	68.17 293	76.32 401	86.33 350	73.12 259	92.61 167	61.40 373	90.02 347	89.44 318
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 237	81.54 247	83.60 202	83.94 356	73.90 141	83.35 230	86.10 294	58.97 401	83.80 288	90.36 262	74.23 235	86.94 338	82.90 101	90.22 343	89.94 309

fmvsm_s_conf0.5_n_7		82.04 238	82.05 232	82.01 255	86.98 279	71.07 190	78.70 336	89.45 229	68.07 294	78.14 380	91.61 206	74.19 236	85.92 360	79.61 140	91.73 299	89.05 334

GBi-Net		82.02 239	82.07 230	81.85 259	86.38 295	61.05 329	86.83 134	88.27 253	72.43 226	86.00 227	95.64 38	63.78 330	90.68 236	65.95 325	93.34 240	93.82 139

test1		82.02 239	82.07 230	81.85 259	86.38 295	61.05 329	86.83 134	88.27 253	72.43 226	86.00 227	95.64 38	63.78 330	90.68 236	65.95 325	93.34 240	93.82 139

OpenMVS	76.72 13	81.98 241	82.00 233	81.93 256	84.42 345	68.22 230	88.50 107	89.48 228	66.92 315	81.80 332	91.86 194	72.59 266	90.16 255	71.19 270	91.25 311	87.40 366

KD-MVS_self_test		81.93 242	83.14 210	78.30 335	84.75 339	52.75 424	80.37 307	89.42 231	70.24 264	90.26 105	93.39 127	74.55 234	86.77 342	68.61 305	96.64 104	95.38 59

fmvsm_s_conf0.5_n		81.91 243	81.30 253	83.75 197	86.02 310	71.56 184	84.73 182	77.11 393	62.44 368	84.00 284	90.68 250	76.42 210	85.89 364	83.14 95	87.11 391	93.81 142

SDMVSNet		81.90 244	83.17 209	78.10 339	88.81 213	62.45 301	76.08 382	86.05 297	73.67 192	83.41 297	93.04 144	82.35 115	80.65 410	70.06 286	95.03 176	91.21 269

tfpnnormal		81.79 245	82.95 215	78.31 334	88.93 209	55.40 404	80.83 299	82.85 351	76.81 146	85.90 231	94.14 93	74.58 232	86.51 346	66.82 318	95.68 154	93.01 189

AstraMVS		81.67 246	81.40 250	82.48 244	87.06 276	66.47 251	81.41 284	81.68 362	68.78 283	88.00 164	90.95 238	65.70 315	87.86 321	76.66 186	92.38 275	93.12 182

c3_l		81.64 247	81.59 243	81.79 264	80.86 405	59.15 364	78.61 339	90.18 210	68.36 289	87.20 189	87.11 340	69.39 291	91.62 192	78.16 161	94.43 200	94.60 94

guyue		81.57 248	81.37 252	82.15 251	86.39 293	66.13 255	81.54 282	83.21 346	69.79 268	87.77 174	89.95 277	65.36 318	87.64 324	75.88 201	92.49 273	92.67 204

PVSNet_Blended_VisFu		81.55 249	80.49 269	84.70 166	91.58 137	73.24 149	84.21 196	91.67 153	62.86 360	80.94 344	87.16 338	67.27 303	92.87 162	69.82 288	88.94 364	87.99 355

fmvsm_l_conf0.5_n_a		81.46 250	80.87 263	83.25 213	83.73 361	73.21 150	83.00 243	85.59 307	58.22 407	82.96 306	90.09 276	72.30 269	86.65 344	81.97 116	89.95 348	89.88 310

SSM_04072		81.44 251	82.88 217	77.10 356	89.13 200	68.97 221	72.73 420	91.28 167	72.90 217	85.68 233	90.61 256	76.78 206	69.94 453	73.37 246	93.47 232	92.38 225

DELS-MVS		81.44 251	81.25 254	82.03 254	84.27 349	62.87 289	76.47 376	92.49 125	70.97 252	81.64 336	83.83 389	75.03 221	92.70 164	74.29 218	92.22 284	90.51 296
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		81.31 253	81.61 242	80.41 296	86.38 295	58.75 373	83.93 206	86.58 289	72.43 226	87.65 178	92.98 148	63.78 330	90.22 252	66.86 315	93.92 216	92.27 234

TinyColmap		81.25 254	82.34 229	77.99 342	85.33 327	60.68 340	82.32 266	88.33 250	71.26 247	86.97 198	92.22 184	77.10 196	86.98 337	62.37 358	95.17 170	86.31 379

diffmvs_AUTHOR		81.24 255	81.55 246	80.30 298	80.61 410	60.22 345	77.98 347	90.48 192	67.77 303	83.34 299	89.50 287	74.69 230	87.42 329	78.78 151	90.81 328	93.27 172

AUN-MVS		81.18 256	78.78 294	88.39 84	90.93 160	82.14 62	82.51 258	83.67 341	64.69 347	80.29 354	85.91 359	51.07 408	92.38 172	76.29 195	93.63 229	90.65 291

IMVS_0407		81.08 257	81.23 256	80.62 292	85.76 317	62.46 297	82.46 259	87.91 261	65.23 338	82.12 322	87.92 317	77.27 191	90.18 254	71.67 263	90.74 331	89.20 325

tttt0517		81.07 258	79.58 284	85.52 142	88.99 207	66.45 252	87.03 129	75.51 405	73.76 191	88.32 155	90.20 270	37.96 465	94.16 108	79.36 145	95.13 171	95.93 47

Fast-Effi-MVS+		81.04 259	80.57 266	82.46 245	87.50 256	63.22 285	78.37 342	89.63 225	68.01 295	81.87 328	82.08 410	82.31 117	92.65 166	67.10 314	88.30 376	91.51 265

BH-untuned		80.96 260	80.99 260	80.84 285	88.55 222	68.23 229	80.33 308	88.46 245	72.79 222	86.55 209	86.76 344	74.72 229	91.77 191	61.79 367	88.99 362	82.52 432

IMVS_0403		80.93 261	81.00 259	80.72 288	85.76 317	62.46 297	81.82 276	87.91 261	65.23 338	82.07 324	87.92 317	75.91 213	90.50 243	71.67 263	90.74 331	89.20 325

eth_miper_zixun_eth		80.84 262	80.22 275	82.71 231	81.41 396	60.98 335	77.81 350	90.14 211	67.31 310	86.95 199	87.24 337	64.26 323	92.31 175	75.23 211	91.61 302	94.85 86

xiu_mvs_v1_base_debu		80.84 262	80.14 277	82.93 226	88.31 226	71.73 178	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 199	90.14 258	73.22 249	88.33 372	86.90 373

xiu_mvs_v1_base		80.84 262	80.14 277	82.93 226	88.31 226	71.73 178	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 199	90.14 258	73.22 249	88.33 372	86.90 373

xiu_mvs_v1_base_debi		80.84 262	80.14 277	82.93 226	88.31 226	71.73 178	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 199	90.14 258	73.22 249	88.33 372	86.90 373

IterMVS-SCA-FT		80.64 266	79.41 285	84.34 178	83.93 357	69.66 209	76.28 378	81.09 368	72.43 226	86.47 216	90.19 271	60.46 347	93.15 151	77.45 176	86.39 403	90.22 301

BH-RMVSNet		80.53 267	80.22 275	81.49 270	87.19 267	66.21 254	77.79 351	86.23 292	74.21 185	83.69 291	88.50 306	73.25 258	90.75 233	63.18 354	87.90 380	87.52 364

VortexMVS		80.51 268	80.63 265	80.15 302	83.36 371	61.82 315	80.63 303	88.00 259	67.11 313	87.23 188	89.10 296	63.98 327	88.00 314	73.63 240	92.63 265	90.64 292

Anonymous202405211		80.51 268	81.19 258	78.49 329	88.48 223	57.26 388	76.63 371	82.49 354	81.21 88	84.30 278	92.24 183	67.99 299	86.24 352	62.22 359	95.13 171	91.98 249

DIV-MVS_self_test		80.43 270	80.23 273	81.02 281	79.99 418	59.25 360	77.07 364	87.02 283	67.38 307	86.19 220	89.22 292	63.09 335	90.16 255	76.32 193	95.80 148	93.66 148

cl____		80.42 271	80.23 273	81.02 281	79.99 418	59.25 360	77.07 364	87.02 283	67.37 308	86.18 222	89.21 293	63.08 336	90.16 255	76.31 194	95.80 148	93.65 151

diffmvs		80.40 272	80.48 270	80.17 301	79.02 431	60.04 347	77.54 355	90.28 207	66.65 318	82.40 315	87.33 335	73.50 250	87.35 331	77.98 169	89.62 353	93.13 179
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		80.37 273	78.41 302	86.23 123	76.75 445	73.28 147	87.18 126	77.45 388	76.24 150	68.14 456	88.93 299	65.41 317	93.85 118	69.47 291	96.12 128	91.55 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 274	80.04 280	81.24 277	79.82 421	58.95 367	77.66 352	89.66 223	65.75 328	85.99 230	85.11 371	68.29 298	91.42 201	76.03 199	92.03 289	93.33 168

MG-MVS		80.32 275	80.94 261	78.47 330	88.18 233	52.62 427	82.29 267	85.01 320	72.01 237	79.24 369	92.54 169	69.36 292	93.36 145	70.65 277	89.19 360	89.45 317

mvsmamba		80.30 276	78.87 291	84.58 170	88.12 237	67.55 237	92.35 30	84.88 325	63.15 358	85.33 245	90.91 239	50.71 410	95.20 66	66.36 321	87.98 379	90.99 276

VPNet		80.25 277	81.68 238	75.94 372	92.46 104	47.98 451	76.70 369	81.67 363	73.45 201	84.87 260	92.82 158	74.66 231	86.51 346	61.66 369	96.85 96	93.33 168

MAR-MVS		80.24 278	78.74 296	84.73 164	86.87 284	78.18 95	85.75 157	87.81 265	65.67 330	77.84 384	78.50 442	73.79 246	90.53 242	61.59 370	90.87 323	85.49 389
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		80.20 279	79.00 290	83.78 196	88.17 234	86.66 19	81.31 287	66.81 461	69.64 269	88.33 154	90.19 271	64.58 320	83.63 391	71.99 262	90.03 346	81.06 452

Anonymous20240521		80.18 280	81.25 254	76.95 358	83.15 379	60.84 337	82.46 259	85.99 299	68.76 284	86.78 201	93.73 119	59.13 359	77.44 427	73.71 236	97.55 78	92.56 212

LFMVS		80.15 281	80.56 267	78.89 318	89.19 199	55.93 396	85.22 171	73.78 417	82.96 71	84.28 279	92.72 163	57.38 375	90.07 263	63.80 348	95.75 151	90.68 288

DPM-MVS		80.10 282	79.18 289	82.88 229	90.71 166	69.74 207	78.87 334	90.84 182	60.29 395	75.64 411	85.92 358	67.28 302	93.11 152	71.24 269	91.79 296	85.77 385

MSDG		80.06 283	79.99 282	80.25 299	83.91 358	68.04 234	77.51 356	89.19 233	77.65 136	81.94 326	83.45 394	76.37 211	86.31 351	63.31 353	86.59 400	86.41 377

FE-MVS		79.98 284	78.86 292	83.36 210	86.47 290	66.45 252	89.73 75	84.74 329	72.80 221	84.22 282	91.38 215	44.95 445	93.60 131	63.93 346	91.50 305	90.04 308

sd_testset		79.95 285	81.39 251	75.64 377	88.81 213	58.07 380	76.16 381	82.81 352	73.67 192	83.41 297	93.04 144	80.96 145	77.65 426	58.62 391	95.03 176	91.21 269

ab-mvs		79.67 286	80.56 267	76.99 357	88.48 223	56.93 390	84.70 184	86.06 296	68.95 281	80.78 347	93.08 143	75.30 219	84.62 378	56.78 400	90.90 321	89.43 319

VNet		79.31 287	80.27 272	76.44 366	87.92 241	53.95 416	75.58 388	84.35 333	74.39 184	82.23 319	90.72 247	72.84 263	84.39 383	60.38 379	93.98 215	90.97 277

thisisatest0530		79.07 288	77.33 313	84.26 181	87.13 268	64.58 268	83.66 216	75.95 400	68.86 282	85.22 247	87.36 334	38.10 462	93.57 135	75.47 208	94.28 205	94.62 93

cl22		78.97 289	78.21 304	81.24 277	77.74 435	59.01 366	77.46 359	87.13 277	65.79 325	84.32 275	85.10 372	58.96 361	90.88 228	75.36 210	92.03 289	93.84 137

usedtu_dtu_shiyan2		78.92 290	78.15 305	81.25 274	91.33 146	73.10 152	80.75 301	79.00 381	74.19 186	79.17 370	92.04 187	67.17 304	81.33 404	42.86 469	96.81 100	89.31 321

patch_mono-2		78.89 291	79.39 286	77.41 353	84.78 337	68.11 232	75.60 386	83.11 348	60.96 387	79.36 366	89.89 280	75.18 220	72.97 442	73.32 248	92.30 278	91.15 271

RPMNet		78.88 292	78.28 303	80.68 290	79.58 422	62.64 293	82.58 254	94.16 33	74.80 173	75.72 409	92.59 165	48.69 417	95.56 45	73.48 243	82.91 439	83.85 411

PAPR		78.84 293	78.10 306	81.07 279	85.17 332	60.22 345	82.21 271	90.57 191	62.51 362	75.32 415	84.61 380	74.99 222	92.30 176	59.48 384	88.04 378	90.68 288

viewmambaseed2359dif		78.80 294	78.47 301	79.78 305	80.26 417	59.28 359	77.31 361	87.13 277	60.42 393	82.37 316	88.67 304	74.58 232	87.87 320	67.78 313	87.73 384	92.19 238

PVSNet_BlendedMVS		78.80 294	77.84 307	81.65 266	84.43 343	63.41 281	79.49 320	90.44 195	61.70 376	75.43 412	87.07 341	69.11 294	91.44 199	60.68 377	92.24 282	90.11 306

FMVSNet3		78.80 294	78.55 298	79.57 311	82.89 382	56.89 392	81.76 277	85.77 303	69.04 279	86.00 227	90.44 261	51.75 406	90.09 261	65.95 325	93.34 240	91.72 256

test_yl		78.71 297	78.51 299	79.32 314	84.32 347	58.84 370	78.38 340	85.33 311	75.99 154	82.49 313	86.57 346	58.01 369	90.02 265	62.74 355	92.73 263	89.10 331

DCV-MVSNet		78.71 297	78.51 299	79.32 314	84.32 347	58.84 370	78.38 340	85.33 311	75.99 154	82.49 313	86.57 346	58.01 369	90.02 265	62.74 355	92.73 263	89.10 331

test1111		78.53 299	78.85 293	77.56 348	92.22 113	47.49 453	82.61 252	69.24 449	72.43 226	85.28 246	94.20 89	51.91 404	90.07 263	65.36 334	96.45 113	95.11 72

FE-MVSNET		78.46 300	79.36 287	75.75 374	86.53 287	54.53 411	78.03 344	85.35 310	69.01 280	85.41 243	90.68 250	64.27 322	85.73 368	62.59 357	92.35 277	87.00 372

icg_test_0407_2		78.46 300	79.68 283	74.78 384	85.76 317	62.46 297	68.51 449	87.91 261	65.23 338	82.12 322	87.92 317	77.27 191	72.67 443	71.67 263	90.74 331	89.20 325

ECVR-MVS		78.44 302	78.63 297	77.88 344	91.85 127	48.95 447	83.68 215	69.91 445	72.30 232	84.26 281	94.20 89	51.89 405	89.82 268	63.58 349	96.02 132	94.87 78

pmmvs-eth3d		78.42 303	77.04 316	82.57 241	87.44 260	74.41 138	80.86 298	79.67 376	55.68 424	84.69 265	90.31 268	60.91 345	85.42 371	62.20 360	91.59 303	87.88 359

mvs_anonymous		78.13 304	78.76 295	76.23 371	79.24 428	50.31 443	78.69 337	84.82 327	61.60 378	83.09 305	92.82 158	73.89 244	87.01 334	68.33 309	86.41 402	91.37 266

TAMVS		78.08 305	76.36 324	83.23 214	90.62 167	72.87 155	79.08 330	80.01 375	61.72 375	81.35 340	86.92 343	63.96 329	88.78 293	50.61 439	93.01 253	88.04 354

miper_enhance_ethall		77.83 306	76.93 317	80.51 294	76.15 452	58.01 382	75.47 390	88.82 237	58.05 409	83.59 293	80.69 420	64.41 321	91.20 213	73.16 255	92.03 289	92.33 229

Vis-MVSNet (Re-imp)		77.82 307	77.79 308	77.92 343	88.82 212	51.29 437	83.28 231	71.97 433	74.04 187	82.23 319	89.78 281	57.38 375	89.41 281	57.22 399	95.41 160	93.05 185

CANet_DTU		77.81 308	77.05 315	80.09 303	81.37 397	59.90 351	83.26 232	88.29 252	69.16 276	67.83 459	83.72 390	60.93 344	89.47 276	69.22 295	89.70 352	90.88 281

OpenMVS_ROB	70.19 17	77.77 309	77.46 310	78.71 325	84.39 346	61.15 326	81.18 291	82.52 353	62.45 367	83.34 299	87.37 333	66.20 309	88.66 299	64.69 341	85.02 419	86.32 378

SSC-MVS		77.55 310	81.64 240	65.29 450	90.46 170	20.33 497	73.56 411	68.28 451	85.44 41	88.18 160	94.64 68	70.93 283	81.33 404	71.25 268	92.03 289	94.20 116

MDA-MVSNet-bldmvs		77.47 311	76.90 318	79.16 316	79.03 430	64.59 267	66.58 461	75.67 403	73.15 212	88.86 137	88.99 298	66.94 305	81.23 406	64.71 340	88.22 377	91.64 260

jason		77.42 312	75.75 330	82.43 246	87.10 271	69.27 214	77.99 346	81.94 360	51.47 451	77.84 384	85.07 375	60.32 349	89.00 285	70.74 276	89.27 359	89.03 335
jason: jason.
CDS-MVSNet		77.32 313	75.40 334	83.06 218	89.00 206	72.48 166	77.90 349	82.17 358	60.81 388	78.94 373	83.49 393	59.30 357	88.76 294	54.64 419	92.37 276	87.93 358
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 314	77.75 309	75.73 375	85.76 317	62.46 297	70.84 435	87.91 261	65.23 338	72.21 434	87.92 317	67.48 301	75.53 435	71.67 263	90.74 331	89.20 325

xiu_mvs_v2_base		77.19 315	76.75 320	78.52 328	87.01 277	61.30 323	75.55 389	87.12 281	61.24 384	74.45 421	78.79 440	77.20 193	90.93 224	64.62 343	84.80 426	83.32 420

MVSTER		77.09 316	75.70 331	81.25 274	75.27 460	61.08 328	77.49 358	85.07 316	60.78 389	86.55 209	88.68 302	43.14 454	90.25 249	73.69 239	90.67 336	92.42 219

usedtu_blend_shiyan5		77.07 317	76.43 323	78.99 317	80.36 414	59.77 353	83.25 233	88.32 251	74.91 172	77.62 389	75.71 465	56.22 384	88.89 288	58.91 388	92.61 266	88.32 345

PS-MVSNAJ		77.04 318	76.53 322	78.56 327	87.09 273	61.40 320	75.26 391	87.13 277	61.25 383	74.38 423	77.22 456	76.94 199	90.94 223	64.63 342	84.83 425	83.35 419

IterMVS		76.91 319	76.34 325	78.64 326	80.91 403	64.03 275	76.30 377	79.03 379	64.88 345	83.11 303	89.16 294	59.90 353	84.46 381	68.61 305	85.15 417	87.42 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 320	75.67 332	80.34 297	80.48 412	62.16 309	73.50 412	84.80 328	57.61 413	82.24 318	87.54 328	51.31 407	87.65 323	70.40 282	93.19 249	91.23 268

CL-MVSNet_self_test		76.81 321	77.38 312	75.12 380	86.90 282	51.34 435	73.20 415	80.63 372	68.30 291	81.80 332	88.40 307	66.92 306	80.90 407	55.35 413	94.90 182	93.12 182

TR-MVS		76.77 322	75.79 329	79.72 308	86.10 309	65.79 259	77.14 362	83.02 349	65.20 342	81.40 339	82.10 408	66.30 308	90.73 235	55.57 410	85.27 413	82.65 427

MonoMVSNet		76.66 323	77.26 314	74.86 382	79.86 420	54.34 413	86.26 147	86.08 295	71.08 251	85.59 238	88.68 302	53.95 396	85.93 359	63.86 347	80.02 455	84.32 402

USDC		76.63 324	76.73 321	76.34 368	83.46 366	57.20 389	80.02 311	88.04 258	52.14 447	83.65 292	91.25 222	63.24 333	86.65 344	54.66 418	94.11 210	85.17 391

BH-w/o		76.57 325	76.07 328	78.10 339	86.88 283	65.92 258	77.63 353	86.33 290	65.69 329	80.89 345	79.95 429	68.97 296	90.74 234	53.01 429	85.25 414	77.62 463

Patchmtry		76.56 326	77.46 310	73.83 390	79.37 427	46.60 457	82.41 263	76.90 394	73.81 190	85.56 240	92.38 173	48.07 420	83.98 388	63.36 352	95.31 166	90.92 279

PVSNet_Blended		76.49 327	75.40 334	79.76 307	84.43 343	63.41 281	75.14 392	90.44 195	57.36 415	75.43 412	78.30 445	69.11 294	91.44 199	60.68 377	87.70 386	84.42 401

miper_lstm_enhance		76.45 328	76.10 327	77.51 351	76.72 446	60.97 336	64.69 465	85.04 318	63.98 354	83.20 302	88.22 309	56.67 379	78.79 423	73.22 249	93.12 250	92.78 198

lupinMVS		76.37 329	74.46 347	82.09 253	85.54 323	69.26 215	76.79 367	80.77 371	50.68 458	76.23 402	82.82 402	58.69 362	88.94 286	69.85 287	88.77 365	88.07 351

cascas		76.29 330	74.81 343	80.72 288	84.47 342	62.94 287	73.89 407	87.34 269	55.94 422	75.16 417	76.53 461	63.97 328	91.16 215	65.00 337	90.97 319	88.06 353

SD_0403		76.08 331	76.77 319	73.98 388	87.08 275	49.45 446	83.62 217	84.68 330	63.31 355	75.13 418	87.47 331	71.85 276	84.56 379	49.97 441	87.86 382	87.94 357

WB-MVS		76.06 332	80.01 281	64.19 453	89.96 184	20.58 496	72.18 424	68.19 452	83.21 67	86.46 217	93.49 124	70.19 288	78.97 421	65.96 324	90.46 342	93.02 186

blended_shiyan8		76.05 333	75.11 338	78.86 320	81.76 389	59.18 363	75.09 393	83.81 337	64.70 346	79.37 364	78.35 444	58.30 365	88.68 297	62.03 363	92.56 271	88.73 340

blended_shiyan6		76.05 333	75.11 338	78.87 319	81.74 390	59.15 364	75.08 394	83.79 338	64.69 347	79.37 364	78.37 443	58.30 365	88.69 296	61.99 364	92.61 266	88.77 339

thres600view7		75.97 335	75.35 336	77.85 346	87.01 277	51.84 433	80.45 306	73.26 422	75.20 169	83.10 304	86.31 352	45.54 435	89.05 284	55.03 416	92.24 282	92.66 205

GA-MVS		75.83 336	74.61 344	79.48 313	81.87 387	59.25 360	73.42 413	82.88 350	68.68 285	79.75 359	81.80 413	50.62 411	89.46 277	66.85 316	85.64 410	89.72 312

MVP-Stereo		75.81 337	73.51 358	82.71 231	89.35 194	73.62 142	80.06 309	85.20 313	60.30 394	73.96 424	87.94 314	57.89 373	89.45 278	52.02 433	74.87 473	85.06 393
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 338	75.20 337	77.27 354	75.01 463	69.47 212	78.93 331	84.88 325	46.67 465	87.08 195	87.84 322	50.44 413	71.62 448	77.42 178	88.53 368	90.72 285

usedtu_dtu_shiyan1		75.70 339	75.08 340	77.56 348	84.10 353	55.50 402	73.58 409	84.89 323	62.48 363	78.16 378	84.24 384	58.14 367	87.47 327	59.35 385	90.82 326	89.72 312

FE-MVSNET3		75.70 339	75.08 340	77.56 348	84.10 353	55.50 402	73.58 409	84.89 323	62.48 363	78.16 378	84.24 384	58.14 367	87.47 327	59.34 386	90.82 326	89.72 312

thres100view900		75.45 341	75.05 342	76.66 364	87.27 262	51.88 432	81.07 292	73.26 422	75.68 160	83.25 301	86.37 349	45.54 435	88.80 290	51.98 434	90.99 316	89.31 321

ET-MVSNet_ETH3D		75.28 342	72.77 367	82.81 230	83.03 381	68.11 232	77.09 363	76.51 398	60.67 391	77.60 392	80.52 424	38.04 463	91.15 216	70.78 274	90.68 335	89.17 329

thres400		75.14 343	74.23 349	77.86 345	86.24 302	52.12 429	79.24 327	73.87 415	73.34 205	81.82 330	84.60 381	46.02 428	88.80 290	51.98 434	90.99 316	92.66 205

wuyk23d		75.13 344	79.30 288	62.63 456	75.56 456	75.18 134	80.89 297	73.10 424	75.06 171	94.76 16	95.32 45	87.73 45	52.85 488	34.16 486	97.11 91	59.85 484

EU-MVSNet		75.12 345	74.43 348	77.18 355	83.11 380	59.48 357	85.71 159	82.43 355	39.76 485	85.64 237	88.76 300	44.71 447	87.88 319	73.86 233	85.88 409	84.16 407

HyFIR lowres test		75.12 345	72.66 369	82.50 243	91.44 145	65.19 264	72.47 422	87.31 270	46.79 464	80.29 354	84.30 383	52.70 401	92.10 182	51.88 438	86.73 398	90.22 301

CMPMVS	59.41 20	75.12 345	73.57 356	79.77 306	75.84 455	67.22 238	81.21 290	82.18 357	50.78 456	76.50 398	87.66 326	55.20 392	82.99 394	62.17 362	90.64 340	89.09 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
wanda-best-256-512		74.97 348	73.85 352	78.35 332	80.36 414	58.13 377	73.10 417	83.53 343	64.04 352	77.62 389	75.71 465	56.22 384	88.60 303	61.42 371	92.61 266	88.32 345

FE-blended-shiyan7		74.97 348	73.85 352	78.35 332	80.36 414	58.13 377	73.10 417	83.53 343	64.03 353	77.62 389	75.71 465	56.22 384	88.60 303	61.42 371	92.61 266	88.32 345

pmmvs4		74.92 350	72.98 365	80.73 287	84.95 334	71.71 181	76.23 379	77.59 387	52.83 441	77.73 388	86.38 348	56.35 382	84.97 375	57.72 398	87.05 393	85.51 388

tfpn200view9		74.86 351	74.23 349	76.74 363	86.24 302	52.12 429	79.24 327	73.87 415	73.34 205	81.82 330	84.60 381	46.02 428	88.80 290	51.98 434	90.99 316	89.31 321

1112_ss		74.82 352	73.74 354	78.04 341	89.57 188	60.04 347	76.49 375	87.09 282	54.31 432	73.66 427	79.80 430	60.25 350	86.76 343	58.37 392	84.15 430	87.32 367

EGC-MVSNET		74.79 353	69.99 397	89.19 67	94.89 38	87.00 15	91.89 42	86.28 291	1.09 494	2.23 496	95.98 30	81.87 133	89.48 275	79.76 136	95.96 135	91.10 272

ppachtmachnet_test		74.73 354	74.00 351	76.90 360	80.71 408	56.89 392	71.53 430	78.42 382	58.24 406	79.32 368	82.92 401	57.91 372	84.26 385	65.60 332	91.36 307	89.56 316

Patchmatch-RL test		74.48 355	73.68 355	76.89 361	84.83 336	66.54 249	72.29 423	69.16 450	57.70 411	86.76 202	86.33 350	45.79 434	82.59 395	69.63 290	90.65 339	81.54 443

PatchMatch-RL		74.48 355	73.22 362	78.27 337	87.70 248	85.26 38	75.92 384	70.09 443	64.34 350	76.09 405	81.25 418	65.87 314	78.07 425	53.86 421	83.82 432	71.48 472

XXY-MVS		74.44 357	76.19 326	69.21 425	84.61 341	52.43 428	71.70 427	77.18 392	60.73 390	80.60 348	90.96 236	75.44 216	69.35 456	56.13 405	88.33 372	85.86 384

test2506		74.12 358	73.39 359	76.28 369	91.85 127	44.20 467	84.06 200	48.20 492	72.30 232	81.90 327	94.20 89	27.22 491	89.77 271	64.81 339	96.02 132	94.87 78

reproduce_monomvs		74.09 359	73.23 361	76.65 365	76.52 447	54.54 410	77.50 357	81.40 366	65.85 324	82.86 309	86.67 345	27.38 489	84.53 380	70.24 283	90.66 338	90.89 280

CR-MVSNet		74.00 360	73.04 364	76.85 362	79.58 422	62.64 293	82.58 254	76.90 394	50.50 459	75.72 409	92.38 173	48.07 420	84.07 387	68.72 304	82.91 439	83.85 411

SSC-MVS3.2		73.90 361	75.67 332	68.61 433	84.11 352	41.28 475	64.17 467	72.83 425	72.09 235	79.08 372	87.94 314	70.31 286	73.89 441	55.99 406	94.49 197	90.67 290

Test_1112_low_res		73.90 361	73.08 363	76.35 367	90.35 172	55.95 395	73.40 414	86.17 293	50.70 457	73.14 428	85.94 357	58.31 364	85.90 363	56.51 402	83.22 436	87.20 369

test20.03		73.75 363	74.59 346	71.22 411	81.11 400	51.12 439	70.15 441	72.10 432	70.42 258	80.28 356	91.50 209	64.21 324	74.72 439	46.96 459	94.58 195	87.82 362

test_fmvs2		73.57 364	72.80 366	75.90 373	72.74 477	68.84 225	77.07 364	84.32 334	45.14 471	82.89 307	84.22 386	48.37 418	70.36 452	73.40 245	87.03 394	88.52 343

SCA		73.32 365	72.57 371	75.58 378	81.62 393	55.86 398	78.89 333	71.37 438	61.73 374	74.93 419	83.42 395	60.46 347	87.01 334	58.11 396	82.63 444	83.88 408

baseline1		73.26 366	73.54 357	72.43 404	84.92 335	47.79 452	79.89 313	74.00 413	65.93 322	78.81 374	86.28 353	56.36 381	81.63 403	56.63 401	79.04 462	87.87 360

1314		73.22 367	72.56 372	75.20 379	80.41 413	57.84 383	81.64 280	85.36 309	51.68 450	73.10 429	76.65 460	61.45 342	85.19 373	63.54 350	79.21 460	82.59 428

MVS		73.21 368	72.59 370	75.06 381	80.97 402	60.81 338	81.64 280	85.92 302	46.03 469	71.68 437	77.54 451	68.47 297	89.77 271	55.70 409	85.39 411	74.60 469

HY-MVS	64.64 18	73.03 369	72.47 373	74.71 385	83.36 371	54.19 414	82.14 274	81.96 359	56.76 421	69.57 451	86.21 354	60.03 351	84.83 377	49.58 446	82.65 442	85.11 392

thisisatest0515		73.00 370	70.52 389	80.46 295	81.45 395	59.90 351	73.16 416	74.31 412	57.86 410	76.08 406	77.78 448	37.60 466	92.12 181	65.00 337	91.45 306	89.35 320

EPNet_dtu		72.87 371	71.33 383	77.49 352	77.72 436	60.55 341	82.35 265	75.79 401	66.49 319	58.39 487	81.06 419	53.68 397	85.98 358	53.55 424	92.97 255	85.95 382
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 372	71.41 382	76.28 369	83.25 375	60.34 343	83.50 225	79.02 380	37.77 489	76.33 400	85.10 372	49.60 416	87.41 330	70.54 280	77.54 468	81.08 450

CHOSEN 1792x2688		72.45 373	70.56 388	78.13 338	90.02 183	63.08 286	68.72 448	83.16 347	42.99 479	75.92 407	85.46 365	57.22 377	85.18 374	49.87 444	81.67 446	86.14 380

testgi		72.36 374	74.61 344	65.59 447	80.56 411	42.82 472	68.29 450	73.35 421	66.87 316	81.84 329	89.93 278	72.08 273	66.92 470	46.05 463	92.54 272	87.01 371

thres200		72.34 375	71.55 381	74.70 386	83.48 365	51.60 434	75.02 395	73.71 418	70.14 265	78.56 377	80.57 423	46.20 426	88.20 312	46.99 458	89.29 357	84.32 402

FPMVS		72.29 376	72.00 375	73.14 395	88.63 219	85.00 40	74.65 399	67.39 455	71.94 238	77.80 386	87.66 326	50.48 412	75.83 433	49.95 442	79.51 456	58.58 486

FMVSNet5		72.10 377	71.69 377	73.32 393	81.57 394	53.02 423	76.77 368	78.37 383	63.31 355	76.37 399	91.85 195	36.68 467	78.98 420	47.87 455	92.45 274	87.95 356

our_test_3		71.85 378	71.59 378	72.62 401	80.71 408	53.78 417	69.72 444	71.71 437	58.80 403	78.03 381	80.51 425	56.61 380	78.84 422	62.20 360	86.04 408	85.23 390

PAPM		71.77 379	70.06 395	76.92 359	86.39 293	53.97 415	76.62 372	86.62 288	53.44 436	63.97 476	84.73 379	57.79 374	92.34 174	39.65 476	81.33 450	84.45 400

ttmdpeth		71.72 380	70.67 386	74.86 382	73.08 474	55.88 397	77.41 360	69.27 448	55.86 423	78.66 375	93.77 117	38.01 464	75.39 436	60.12 380	89.87 349	93.31 170

IB-MVS	62.13 19	71.64 381	68.97 407	79.66 310	80.80 407	62.26 306	73.94 406	76.90 394	63.27 357	68.63 455	76.79 458	33.83 471	91.84 189	59.28 387	87.26 388	84.88 394
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		71.63 382	72.30 374	69.62 422	76.47 449	52.70 426	70.03 442	80.97 369	59.18 400	79.36 366	88.21 310	60.50 346	69.12 457	58.33 394	77.62 467	87.04 370

testing3		71.53 383	70.79 385	73.77 391	88.89 211	41.86 474	76.60 374	59.12 481	72.83 220	80.97 342	82.08 410	19.80 497	87.33 332	65.12 336	91.68 301	92.13 242

test_vis3_rt		71.42 384	70.67 386	73.64 392	69.66 484	70.46 197	66.97 460	89.73 220	42.68 481	88.20 159	83.04 397	43.77 449	60.07 482	65.35 335	86.66 399	90.39 299

Anonymous20231206		71.38 385	71.88 376	69.88 419	86.31 299	54.37 412	70.39 439	74.62 408	52.57 443	76.73 397	88.76 300	59.94 352	72.06 445	44.35 467	93.23 247	83.23 422

test_vis1_n_1920		71.30 386	71.58 380	70.47 414	77.58 438	59.99 350	74.25 401	84.22 335	51.06 453	74.85 420	79.10 436	55.10 393	68.83 459	68.86 301	79.20 461	82.58 429

MIMVSNet		71.09 387	71.59 378	69.57 423	87.23 265	50.07 444	78.91 332	71.83 434	60.20 397	71.26 438	91.76 202	55.08 394	76.09 431	41.06 473	87.02 395	82.54 431

test_fmvs1_n		70.94 388	70.41 392	72.53 403	73.92 465	66.93 246	75.99 383	84.21 336	43.31 478	79.40 363	79.39 434	43.47 450	68.55 461	69.05 298	84.91 422	82.10 437

MS-PatchMatch		70.93 389	70.22 393	73.06 396	81.85 388	62.50 296	73.82 408	77.90 384	52.44 444	75.92 407	81.27 417	55.67 389	81.75 401	55.37 412	77.70 466	74.94 468

blend_shiyan4		70.82 390	68.15 414	78.83 322	81.06 401	59.77 353	74.58 400	83.79 338	64.94 344	77.34 395	75.47 469	29.39 483	88.89 288	58.91 388	67.86 485	87.84 361

pmmvs5		70.73 391	70.07 394	72.72 399	77.03 443	52.73 425	74.14 402	75.65 404	50.36 460	72.17 435	85.37 369	55.42 391	80.67 409	52.86 430	87.59 387	84.77 395

testing3-2		70.72 392	70.97 384	69.95 418	88.93 209	34.80 488	69.85 443	66.59 462	78.42 126	77.58 393	85.55 361	31.83 477	82.08 399	46.28 460	93.73 225	92.98 192

PatchT		70.52 393	72.76 368	63.79 455	79.38 426	33.53 489	77.63 353	65.37 466	73.61 198	71.77 436	92.79 161	44.38 448	75.65 434	64.53 344	85.37 412	82.18 436

test_vis1_n		70.29 394	69.99 397	71.20 412	75.97 454	66.50 250	76.69 370	80.81 370	44.22 474	75.43 412	77.23 455	50.00 414	68.59 460	66.71 319	82.85 441	78.52 462

N_pmnet		70.20 395	68.80 409	74.38 387	80.91 403	84.81 43	59.12 478	76.45 399	55.06 427	75.31 416	82.36 407	55.74 388	54.82 487	47.02 457	87.24 389	83.52 415

tpmvs		70.16 396	69.56 400	71.96 407	74.71 464	48.13 449	79.63 315	75.45 406	65.02 343	70.26 446	81.88 412	45.34 440	85.68 369	58.34 393	75.39 472	82.08 438

new-patchmatchnet		70.10 397	73.37 360	60.29 464	81.23 399	16.95 499	59.54 476	74.62 408	62.93 359	80.97 342	87.93 316	62.83 339	71.90 446	55.24 414	95.01 179	92.00 247

YYNet1		70.06 398	70.44 390	68.90 427	73.76 467	53.42 421	58.99 479	67.20 457	58.42 405	87.10 193	85.39 368	59.82 354	67.32 467	59.79 382	83.50 435	85.96 381

MVStest1		70.05 399	69.26 401	72.41 405	58.62 496	55.59 401	76.61 373	65.58 464	53.44 436	89.28 132	93.32 128	22.91 495	71.44 450	74.08 229	89.52 354	90.21 305

MDA-MVSNet_test_wron		70.05 399	70.44 390	68.88 428	73.84 466	53.47 419	58.93 480	67.28 456	58.43 404	87.09 194	85.40 367	59.80 355	67.25 468	59.66 383	83.54 434	85.92 383

CostFormer		69.98 401	68.68 410	73.87 389	77.14 441	50.72 441	79.26 326	74.51 410	51.94 449	70.97 441	84.75 378	45.16 443	87.49 326	55.16 415	79.23 459	83.40 418

testing91		69.94 402	68.99 406	72.80 398	83.81 360	45.89 460	71.57 429	73.64 420	68.24 292	70.77 444	77.82 447	34.37 470	84.44 382	53.64 423	87.00 396	88.07 351

baseline2		69.77 403	66.89 420	78.41 331	79.51 424	58.09 379	76.23 379	69.57 446	57.50 414	64.82 474	77.45 453	46.02 428	88.44 306	53.08 426	77.83 464	88.70 341

PatchmatchNet		69.71 404	68.83 408	72.33 406	77.66 437	53.60 418	79.29 325	69.99 444	57.66 412	72.53 432	82.93 400	46.45 425	80.08 415	60.91 376	72.09 476	83.31 421
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 405	69.05 404	71.14 413	69.15 485	65.77 260	73.98 405	83.32 345	42.83 480	77.77 387	78.27 446	43.39 453	68.50 462	68.39 308	84.38 429	79.15 460

JIA-IIPM		69.41 406	66.64 424	77.70 347	73.19 471	71.24 188	75.67 385	65.56 465	70.42 258	65.18 470	92.97 151	33.64 473	83.06 392	53.52 425	69.61 482	78.79 461

Syy-MVS		69.40 407	70.03 396	67.49 438	81.72 391	38.94 480	71.00 432	61.99 472	61.38 380	70.81 442	72.36 476	61.37 343	79.30 418	64.50 345	85.18 415	84.22 404

testing99		69.27 408	68.15 414	72.63 400	83.29 373	45.45 462	71.15 431	71.08 439	67.34 309	70.43 445	77.77 449	32.24 476	84.35 384	53.72 422	86.33 404	88.10 350

UnsupCasMVSNet_bld		69.21 409	69.68 399	67.82 436	79.42 425	51.15 438	67.82 454	75.79 401	54.15 433	77.47 394	85.36 370	59.26 358	70.64 451	48.46 452	79.35 458	81.66 441

test_cas_vis1_n_1920		69.20 410	69.12 402	69.43 424	73.68 468	62.82 290	70.38 440	77.21 391	46.18 468	80.46 353	78.95 438	52.03 403	65.53 475	65.77 331	77.45 469	79.95 458

gg-mvs-nofinetune		68.96 411	69.11 403	68.52 434	76.12 453	45.32 463	83.59 218	55.88 486	86.68 33	64.62 475	97.01 12	30.36 481	83.97 389	44.78 466	82.94 438	76.26 465

WBMVS		68.76 412	68.43 411	69.75 421	83.29 373	40.30 478	67.36 456	72.21 431	57.09 418	77.05 396	85.53 363	33.68 472	80.51 411	48.79 450	90.90 321	88.45 344

WB-MVSnew		68.72 413	69.01 405	67.85 435	83.22 377	43.98 468	74.93 396	65.98 463	55.09 426	73.83 425	79.11 435	65.63 316	71.89 447	38.21 481	85.04 418	87.69 363

tpm2		68.45 414	66.83 421	73.30 394	78.93 432	48.50 448	79.76 314	71.76 435	47.50 463	69.92 448	83.60 391	42.07 456	88.40 308	48.44 453	79.51 456	83.01 425

tpm		67.95 415	68.08 416	67.55 437	78.74 433	43.53 470	75.60 386	67.10 460	54.92 428	72.23 433	88.10 311	42.87 455	75.97 432	52.21 432	80.95 454	83.15 423

WTY-MVS		67.91 416	68.35 412	66.58 443	80.82 406	48.12 450	65.96 462	72.60 426	53.67 435	71.20 439	81.68 415	58.97 360	69.06 458	48.57 451	81.67 446	82.55 430

testing11		67.38 417	65.93 425	71.73 409	83.37 370	46.60 457	70.95 434	69.40 447	62.47 366	66.14 463	76.66 459	31.22 478	84.10 386	49.10 448	84.10 431	84.49 398

test-LLR		67.21 418	66.74 422	68.63 431	76.45 450	55.21 406	67.89 451	67.14 458	62.43 369	65.08 471	72.39 474	43.41 451	69.37 454	61.00 374	84.89 423	81.31 445

testing222		66.93 419	65.30 432	71.81 408	83.38 369	45.83 461	72.06 425	67.50 454	64.12 351	69.68 450	76.37 462	27.34 490	83.00 393	38.88 477	88.38 371	86.62 376

sss		66.92 420	67.26 418	65.90 445	77.23 440	51.10 440	64.79 464	71.72 436	52.12 448	70.13 447	80.18 427	57.96 371	65.36 476	50.21 440	81.01 452	81.25 447

KD-MVS_2432*1600		66.87 421	65.81 428	70.04 416	67.50 486	47.49 453	62.56 470	79.16 377	61.21 385	77.98 382	80.61 421	25.29 493	82.48 396	53.02 427	84.92 420	80.16 456

miper_refine_blended		66.87 421	65.81 428	70.04 416	67.50 486	47.49 453	62.56 470	79.16 377	61.21 385	77.98 382	80.61 421	25.29 493	82.48 396	53.02 427	84.92 420	80.16 456

dmvs_re		66.81 423	66.98 419	66.28 444	76.87 444	58.68 374	71.66 428	72.24 429	60.29 395	69.52 452	73.53 473	52.38 402	64.40 478	44.90 465	81.44 449	75.76 466

tpm cat1		66.76 424	65.21 433	71.42 410	77.09 442	50.62 442	78.01 345	73.68 419	44.89 472	68.64 454	79.00 437	45.51 437	82.42 398	49.91 443	70.15 479	81.23 449

UWE-MVS		66.43 425	65.56 431	69.05 426	84.15 351	40.98 476	73.06 419	64.71 468	54.84 429	76.18 404	79.62 433	29.21 484	80.50 412	38.54 480	89.75 351	85.66 386

PVSNet	58.17 21	66.41 426	65.63 430	68.75 429	81.96 386	49.88 445	62.19 472	72.51 428	51.03 454	68.04 457	75.34 470	50.84 409	74.77 437	45.82 464	82.96 437	81.60 442

tpmrst		66.28 427	66.69 423	65.05 451	72.82 476	39.33 479	78.20 343	70.69 442	53.16 439	67.88 458	80.36 426	48.18 419	74.75 438	58.13 395	70.79 478	81.08 450

Patchmatch-test		65.91 428	67.38 417	61.48 461	75.51 457	43.21 471	68.84 447	63.79 470	62.48 363	72.80 431	83.42 395	44.89 446	59.52 484	48.27 454	86.45 401	81.70 440

ADS-MVSNet2		65.87 429	63.64 438	72.55 402	73.16 472	56.92 391	67.10 458	74.81 407	49.74 461	66.04 465	82.97 398	46.71 423	77.26 428	42.29 470	69.96 480	83.46 416

myMVS_eth3d28		65.83 430	65.85 426	65.78 446	83.42 368	35.71 486	67.29 457	68.01 453	67.58 306	69.80 449	77.72 450	32.29 475	74.30 440	37.49 482	89.06 361	87.32 367

test_vis1_rt		65.64 431	64.09 435	70.31 415	66.09 490	70.20 201	61.16 473	81.60 364	38.65 486	72.87 430	69.66 479	52.84 399	60.04 483	56.16 404	77.77 465	80.68 454

mvsany_test3		65.48 432	62.97 441	73.03 397	69.99 483	76.17 124	64.83 463	43.71 494	43.68 476	80.25 357	87.05 342	52.83 400	63.09 481	51.92 437	72.44 475	79.84 459

test-mter		65.00 433	63.79 437	68.63 431	76.45 450	55.21 406	67.89 451	67.14 458	50.98 455	65.08 471	72.39 474	28.27 487	69.37 454	61.00 374	84.89 423	81.31 445

ETVMVS		64.67 434	63.34 440	68.64 430	83.44 367	41.89 473	69.56 446	61.70 477	61.33 382	68.74 453	75.76 464	28.76 485	79.35 417	34.65 485	86.16 407	84.67 397

myMVS_eth3d		64.66 435	63.89 436	66.97 441	81.72 391	37.39 483	71.00 432	61.99 472	61.38 380	70.81 442	72.36 476	20.96 496	79.30 418	49.59 445	85.18 415	84.22 404

test0.0.03 1		64.66 435	64.36 434	65.57 448	75.03 462	46.89 456	64.69 465	61.58 478	62.43 369	71.18 440	77.54 451	43.41 451	68.47 463	40.75 475	82.65 442	81.35 444

UBG		64.34 437	63.35 439	67.30 439	83.50 364	40.53 477	67.46 455	65.02 467	54.77 430	67.54 461	74.47 472	32.99 474	78.50 424	40.82 474	83.58 433	82.88 426

test_f		64.31 438	65.85 426	59.67 465	66.54 489	62.24 308	57.76 482	70.96 440	40.13 483	84.36 273	82.09 409	46.93 422	51.67 489	61.99 364	81.89 445	65.12 480

pmmvs3		62.47 439	60.02 452	69.80 420	71.58 480	64.00 276	70.52 438	58.44 484	39.77 484	66.05 464	75.84 463	27.10 492	72.28 444	46.15 462	84.77 427	73.11 470

EPMVS		62.47 439	62.63 443	62.01 457	70.63 482	38.74 481	74.76 397	52.86 488	53.91 434	67.71 460	80.01 428	39.40 460	66.60 471	55.54 411	68.81 484	80.68 454

ADS-MVSNet		61.90 441	62.19 445	61.03 462	73.16 472	36.42 485	67.10 458	61.75 475	49.74 461	66.04 465	82.97 398	46.71 423	63.21 479	42.29 470	69.96 480	83.46 416

PMMVS		61.65 442	60.38 449	65.47 449	65.40 493	69.26 215	63.97 468	61.73 476	36.80 490	60.11 482	68.43 481	59.42 356	66.35 472	48.97 449	78.57 463	60.81 483

E-PMN		61.59 443	61.62 446	61.49 460	66.81 488	55.40 404	53.77 485	60.34 480	66.80 317	58.90 485	65.50 484	40.48 459	66.12 473	55.72 408	86.25 405	62.95 482

TESTMET0.1,1		61.29 444	60.32 450	64.19 453	72.06 478	51.30 436	67.89 451	62.09 471	45.27 470	60.65 481	69.01 480	27.93 488	64.74 477	56.31 403	81.65 448	76.53 464

MVS-HIRNet		61.16 445	62.92 442	55.87 468	79.09 429	35.34 487	71.83 426	57.98 485	46.56 466	59.05 484	91.14 227	49.95 415	76.43 430	38.74 478	71.92 477	55.84 487

EMVS		61.10 446	60.81 448	61.99 458	65.96 491	55.86 398	53.10 486	58.97 483	67.06 314	56.89 489	63.33 485	40.98 457	67.03 469	54.79 417	86.18 406	63.08 481

DSMNet-mixed		60.98 447	61.61 447	59.09 467	72.88 475	45.05 465	74.70 398	46.61 493	26.20 491	65.34 469	90.32 267	55.46 390	63.12 480	41.72 472	81.30 451	69.09 476

dp		60.70 448	60.29 451	61.92 459	72.04 479	38.67 482	70.83 436	64.08 469	51.28 452	60.75 480	77.28 454	36.59 468	71.58 449	47.41 456	62.34 487	75.52 467

dmvs_testset		60.59 449	62.54 444	54.72 470	77.26 439	27.74 493	74.05 404	61.00 479	60.48 392	65.62 468	67.03 483	55.93 387	68.23 465	32.07 489	69.46 483	68.17 477

CHOSEN 280x420		59.08 450	56.52 456	66.76 442	76.51 448	64.39 272	49.62 487	59.00 482	43.86 475	55.66 490	68.41 482	35.55 469	68.21 466	43.25 468	76.78 471	67.69 478

mvsany_test1		58.48 451	56.47 457	64.50 452	65.90 492	68.21 231	56.95 483	42.11 495	38.30 487	65.69 467	77.19 457	56.96 378	59.35 485	46.16 461	58.96 488	65.93 479

UWE-MVS-28		58.44 452	57.71 454	60.65 463	73.58 469	31.23 490	69.68 445	48.80 491	53.12 440	61.79 478	78.83 439	30.98 479	68.40 464	21.58 492	80.99 453	82.33 435

PVSNet_0	51.08 22	56.10 453	54.97 458	59.48 466	75.12 461	53.28 422	55.16 484	61.89 474	44.30 473	59.16 483	62.48 486	54.22 395	65.91 474	35.40 484	47.01 489	59.25 485

new_pmnet		55.69 454	57.66 455	49.76 471	75.47 458	30.59 491	59.56 475	51.45 489	43.62 477	62.49 477	75.48 468	40.96 458	49.15 491	37.39 483	72.52 474	69.55 475

PMMVS2		55.64 455	59.27 453	44.74 472	64.30 494	12.32 500	40.60 488	49.79 490	53.19 438	65.06 473	84.81 377	53.60 398	49.76 490	32.68 488	89.41 356	72.15 471

MVE	40.22 23	51.82 456	50.47 459	55.87 468	62.66 495	51.91 431	31.61 490	39.28 496	40.65 482	50.76 491	74.98 471	56.24 383	44.67 492	33.94 487	64.11 486	71.04 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 457	42.65 460	39.67 473	70.86 481	21.11 495	61.01 474	21.42 500	57.36 415	57.97 488	50.06 489	16.40 498	58.73 486	21.03 493	27.69 493	39.17 489

kuosan		30.83 458	32.17 461	26.83 475	53.36 497	19.02 498	57.90 481	20.44 501	38.29 488	38.01 492	37.82 491	15.18 499	33.45 494	7.74 495	20.76 494	28.03 490

test_method		30.46 459	29.60 462	33.06 474	17.99 499	3.84 502	13.62 491	73.92 414	2.79 493	18.29 495	53.41 488	28.53 486	43.25 493	22.56 490	35.27 491	52.11 488

cdsmvs_eth3d_5k		20.81 460	27.75 463	0.00 480	0.00 503	0.00 505	0.00 492	85.44 308	0.00 498	0.00 499	82.82 402	81.46 139	0.00 499	0.00 498	0.00 497	0.00 495

tmp_tt		20.25 461	24.50 464	7.49 477	4.47 500	8.70 501	34.17 489	25.16 498	1.00 495	32.43 494	18.49 492	39.37 461	9.21 496	21.64 491	43.75 490	4.57 492

ab-mvs-re		6.65 462	8.87 465	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	79.80 430	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

pcd_1.5k_mvsjas		6.41 463	8.55 466	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	76.94 199	0.00 499	0.00 498	0.00 497	0.00 495

test123		6.27 464	8.08 467	0.84 478	1.11 502	0.57 503	62.90 469	0.82 502	0.54 496	1.07 498	2.75 497	1.26 500	0.30 497	1.04 496	1.26 496	1.66 493

testmvs		5.91 465	7.65 468	0.72 479	1.20 501	0.37 504	59.14 477	0.67 503	0.49 497	1.11 497	2.76 496	0.94 501	0.24 498	1.02 497	1.47 495	1.55 494

mmdepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

monomultidepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

test_blank		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet_test		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

DCPMVS		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet-low-res		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uncertanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

Regformer		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 140	93.24 43	93.18 136		95.85 24	84.99 75	97.69 65	93.54 163

TestfortrainingZip							92.12 33

WAC-MVS						37.39 483								52.61 431

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		91.01 178					96.05 9	87.45 29	98.17 37	92.40 222

PC_three_1452									58.96 402	90.06 107	91.33 217	80.66 150	93.03 156	75.78 202	95.94 138	92.48 216

No_MVS				88.81 73	91.55 139	77.99 97		91.01 178					96.05 9	87.45 29	98.17 37	92.40 222

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					0.00 503

eth-test					0.00 503

ZD-MVS					92.22 113	80.48 71		91.85 146	71.22 249	90.38 102	92.98 148	86.06 68	96.11 7	81.99 115	96.75 102

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	92.73 199

IU-MVS					94.18 55	72.64 159		90.82 183	56.98 419	89.67 120				85.78 64	97.92 52	93.28 171

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 223	81.12 143	94.68 82	74.48 217	95.35 162	92.29 232

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	92.04 245

test_241102_ONE					94.18 55	72.65 157		93.69 64	83.62 62	94.11 27	93.78 115	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	92.89 154	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 221	70.80 254

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 35	87.41 31	98.21 34	92.98 192

test_0728_SECOND				86.79 112	94.25 53	72.45 167	90.54 57	94.10 40					95.88 18	86.42 47	97.97 49	92.02 246

test0726					94.16 58	72.56 163	90.63 54	93.90 49	83.61 63	93.75 35	94.49 73	89.76 19

GSMVS																83.88 408

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 427				83.88 408

sam_mvs												45.92 432

ambc				82.98 221	90.55 169	64.86 266	88.20 108	89.15 235		89.40 129	93.96 106	71.67 280	91.38 203	78.83 150	96.55 107	92.71 202

MTGPA								91.81 150

test_post1							78.85 335				3.13 494	45.19 442	80.13 414	58.11 396

test_post											3.10 495	45.43 438	77.22 429

patchmatchnet-post											81.71 414	45.93 431	87.01 334

GG-mvs-BLEND				67.16 440	73.36 470	46.54 459	84.15 198	55.04 487		58.64 486	61.95 487	29.93 482	83.87 390	38.71 479	76.92 470	71.07 473

MTMP							90.66 53	33.14 497

gm-plane-assit					75.42 459	44.97 466			52.17 445		72.36 476		87.90 318	54.10 420

test9_res														80.83 125	96.45 113	90.57 293

TEST9					92.34 108	79.70 80	83.94 204	90.32 201	65.41 335	84.49 269	90.97 234	82.03 128	93.63 127

test_8					92.09 117	78.87 88	83.82 209	90.31 203	65.79 325	84.36 273	90.96 236	81.93 130	93.44 141

agg_prior2														79.68 138	96.16 125	90.22 301

agg_prior					91.58 137	77.69 103		90.30 204		84.32 275			93.18 149

TestCases				89.68 56	91.59 134	83.40 52		95.44 11	79.47 108	88.00 164	93.03 146	82.66 109	91.47 197	70.81 272	96.14 126	94.16 121

test_prior4						78.97 87	84.59 187

test_prior2							83.37 229		75.43 166	84.58 266	91.57 207	81.92 132		79.54 142	96.97 94

test_prior				86.32 120	90.59 168	71.99 175		92.85 111					94.17 106			92.80 197

旧先验2							81.73 278		56.88 420	86.54 215			84.90 376	72.81 256

新几何2							81.72 279

新几何1				82.95 223	93.96 64	78.56 91		80.24 373	55.45 425	83.93 286	91.08 230	71.19 282	88.33 310	65.84 329	93.07 251	81.95 439

旧先验1					91.97 121	71.77 176		81.78 361			91.84 196	73.92 243			93.65 228	83.61 414

无先验							82.81 249	85.62 306	58.09 408				91.41 202	67.95 312		84.48 399

原ACMM2							82.26 270

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 158	62.59 361	82.73 312	90.67 253	76.53 208	94.25 98	69.24 293	95.69 153	85.55 387

test222					93.31 81	76.54 116	79.38 324	77.79 385	52.59 442	82.36 317	90.84 244	66.83 307			91.69 300	81.25 447

testdata2													86.43 349	63.52 351

segment_acmp												81.94 129

testdata				79.54 312	92.87 92	72.34 168		80.14 374	59.91 398	85.47 242	91.75 203	67.96 300	85.24 372	68.57 307	92.18 286	81.06 452

testdata1							79.62 316		73.95 189

test12				86.57 115	90.74 164	72.63 161		90.69 186		82.76 310		79.20 163	94.80 79		95.32 164	92.27 234

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 225

plane_prior5								93.61 69					95.22 63	80.78 126	95.83 146	94.46 102

plane_prior4											92.95 152

plane_prior3						76.85 114			77.79 135	86.55 209

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 157						95.03 176

n2								0.00 504

nn								0.00 504

door-mid								74.45 411

lessismore_v0				85.95 131	91.10 157	70.99 192		70.91 441		91.79 75	94.42 78	61.76 341	92.93 159	79.52 143	93.03 252	93.93 132

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								91.46 159

door								72.57 427

HQP5-MVS						70.66 194

HQP-NCC					91.19 152		84.77 178		73.30 207	80.55 350

ACMP_Plane					91.19 152		84.77 178		73.30 207	80.55 350

BP-MVS														77.30 179

HQP4-MVS										80.56 349			94.61 86			93.56 160

HQP3-MVS								92.68 117							94.47 198

HQP2-MVS												72.10 271

NP-MVS					91.95 122	74.55 137					90.17 274

MDTV_nov1_ep13_2view						27.60 494	70.76 437		46.47 467	61.27 479		45.20 441		49.18 447		83.75 413

MDTV_nov1_ep13			68.29 413		78.03 434	43.87 469	74.12 403	72.22 430	52.17 445	67.02 462	85.54 362	45.36 439	80.85 408	55.73 407	84.42 428

ACMMP++_ref															95.74 152

ACMMP++															97.35 84

Test By Simon												79.09 165

ITE_SJBPF				90.11 49	90.72 165	84.97 41		90.30 204	81.56 84	90.02 109	91.20 225	82.40 114	90.81 231	73.58 242	94.66 193	94.56 95

DeepMVS_CX				24.13 476	32.95 498	29.49 492		21.63 499	12.07 492	37.95 493	45.07 490	30.84 480	19.21 495	17.94 494	33.06 492	23.69 491

High-resolution multi-view benchmark