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	80.16 151	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	92.08 237

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	91.76 248

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	91.76 248

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	93.63 147

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	92.73 193

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	92.19 232

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 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	92.60 117	83.09 69	91.54 78	94.25 88	87.67 46	95.51 50	87.21 37	98.11 40	93.12 176

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	94.00 102	88.26 33	95.71 40	87.28 36	98.39 23	92.55 207

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	94.03 100	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	91.81 144	84.07 55	92.00 71	94.40 81	86.63 58	95.28 62	88.59 11	98.31 26	92.30 224

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 108	88.22 23	88.53 148	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	93.99 123

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	93.83 132

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	93.96 106	88.35 32	95.56 45	87.74 22	97.74 62	92.85 190

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	94.12 119

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	92.98 186
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	93.96 125

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	93.97 103	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 98	82.59 74	88.52 149	94.37 83	86.74 57	95.41 57	86.32 50	98.21 34	93.19 171
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	93.93 126

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	93.27 166

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	93.60 123	90.81 8	92.96 157	85.02 74	98.45 19	92.41 214
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	89.03 204	84.23 49	93.58 6	94.68 18	90.65 8	90.33 105	93.95 108	84.50 82	95.37 58	80.87 124	95.50 158	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	93.74 118	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	91.70 252
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	93.18 132	87.06 52	95.85 24	84.99 75	97.69 65	93.54 157

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	92.29 226

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	93.88 129
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	94.30 111

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 190	89.99 69	93.05 95	86.53 35	94.29 23	96.27 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	93.18 132	88.02 40	95.47 53	84.99 75	97.69 65	93.54 157

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 222	88.51 21	90.11 107	95.12 54	90.98 7	88.92 281	77.55 170	97.07 92	83.13 409
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	92.48 210

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 162	90.54 57	91.01 172	83.61 63	93.75 35	94.65 66	89.76 19	95.78 35	86.42 47	97.97 49	90.55 289
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	58.83 362	89.04 94	92.74 110	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	58.88 359	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 91	81.10 89	95.32 14	97.24 10	72.94 255	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	57.84 370	88.93 96	92.84 106	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	93.89 111	83.80 89	93.10 153	82.67 106	98.04 41	93.64 146

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	93.18 132	87.06 52	95.85 24	87.89 19	97.69 65	93.68 141

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	77.55 180	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	92.78 109	78.78 121	92.51 62	93.64 122	88.13 37	93.84 120	84.83 80	97.55 78	94.10 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 56	89.27 67	91.30 29	93.51 73	84.79 44	89.89 73	90.63 182	70.00 258	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	90.20 204	69.87 259	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	90.08 207	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	86.75 198	93.26 129	93.64 2	90.93 218	84.60 83	90.75 317	93.97 124

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 94	80.87 93	91.13 86	93.19 131	86.22 66	95.97 14	82.23 112	97.18 90	90.45 291
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	90.29 201	69.27 266	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	92.30 126	79.74 105	87.50 182	92.38 168	81.42 136	93.28 146	83.07 98	97.24 88	91.67 253

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	88.31 299	83.33 94	98.30 27	93.20 170
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	88.67 215	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 47	84.94 77	89.44 273	74.12 221	96.10 129	94.45 100

APD_test2		89.30 64	89.12 68	89.84 53	88.67 215	85.64 35	90.61 55	93.17 88	86.02 38	93.12 48	95.30 47	84.94 77	89.44 273	74.12 221	96.10 129	94.45 100

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 365	88.66 103	92.06 133	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	93.44 125	83.82 88	90.98 215	83.86 90	95.30 166	93.60 150

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	93.26 168
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	88.38 242	88.84 17	94.29 23	97.57 7	90.48 14	91.26 206	72.57 252	97.65 70	97.34 15

MSP-MVS		89.08 70	88.16 87	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 126	89.16 134	92.25 177	72.03 269	96.36 4	88.21 13	90.93 309	92.98 186
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	92.29 175	86.11 67	90.22 246	86.24 54	97.24 88	91.36 261
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		88.93 72	88.45 83	90.38 44	94.92 36	85.85 31	89.70 76	91.27 164	78.20 129	86.69 202	92.28 176	80.36 149	95.06 71	86.17 55	96.49 109	90.22 295

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	74.07 232	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	74.07 232	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	89.44 225	88.63 20	94.38 22	95.77 33	86.38 65	93.59 132	79.84 135	95.21 167	91.82 246

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 147	77.50 104	87.55 119	92.97 102	87.95 26	89.62 123	92.87 151	84.56 81	93.89 117	77.65 168	96.62 104	90.70 281

APD_test1		88.40 77	87.91 89	89.88 52	89.50 191	86.65 20	89.98 70	91.91 139	84.26 53	90.87 96	93.92 110	82.18 118	89.29 277	73.75 229	94.81 186	93.70 140

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 170	65.27 261	88.86 97	93.02 99	87.15 30	93.05 50	97.10 11	82.28 116	92.02 183	76.70 180	97.99 46	96.88 26

PS-MVSNAJss		88.31 79	87.90 90	89.56 60	93.31 81	77.96 99	87.94 115	91.97 136	70.73 248	94.19 26	96.67 18	76.94 194	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	91.22 217	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	84.32 269	89.33 284	83.87 87	94.53 91	82.45 108	94.89 182	94.90 77

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 167	67.85 293	86.63 203	94.84 60	79.58 157	95.96 15	87.62 25	94.50 195	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	89.64 219	85.07 45	90.91 92	91.09 223	89.16 25	91.87 188	82.03 113	95.87 144	93.13 173

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	85.12 244	89.67 277	84.47 83	95.46 54	82.56 107	96.26 121	93.77 138

RPSCF		88.00 85	86.93 108	91.22 31	90.08 178	89.30 5	89.68 78	91.11 168	79.26 113	89.68 120	94.81 64	82.44 107	87.74 310	76.54 185	88.74 353	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	88.00 164	93.03 142	82.66 104	91.47 196	70.81 266	96.14 126	94.16 116

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 273	84.38 194	91.29 160	84.88 48	92.06 70	93.84 112	86.45 62	93.73 122	73.22 243	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 180	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	91.23 165	77.31 144	87.07 192	91.47 207	82.94 99	94.71 81	84.67 82	96.27 120	92.62 201

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	86.55 204	92.95 148	74.84 219	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	88.21 249	86.81 32	91.87 74	97.65 5	85.51 74	87.91 305	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	86.76 280	84.54 50	83.58 288	93.78 115	73.36 250	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	92.62 114	88.07 25	88.07 161	96.17 27	72.24 264	95.79 34	84.85 79	94.16 208	92.58 205

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 170	78.77 122	84.85 256	90.89 234	80.85 142	95.29 60	81.14 121	95.32 163	92.34 222

DeepPCF-MVS	81.24 5	87.28 95	86.21 120	90.49 42	91.48 144	84.90 42	83.41 225	92.38 122	70.25 255	89.35 131	90.68 244	82.85 102	94.57 88	79.55 141	95.95 137	92.00 241

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 217	87.84 117	88.05 250	81.66 83	94.64 18	96.53 21	65.94 306	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	89.74 214	74.40 183	89.92 115	93.41 126	80.45 147	90.63 233	86.66 46	94.37 201	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	81.64 330	87.25 329	82.43 108	94.53 91	77.65 168	96.46 111	94.14 118

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 227	83.80 212	92.87 104	80.37 96	89.61 125	91.81 193	77.72 176	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	92.09 132	78.87 120	84.27 274	94.05 99	78.35 168	93.65 125	80.54 130	91.58 293	92.08 237
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	91.56 150	83.08 70	90.92 90	91.82 192	78.25 169	93.99 111	74.16 219	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 242	83.16 235	92.21 127	81.73 82	90.92 90	91.97 183	77.20 188	93.99 111	74.16 219	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 267	65.22 262	84.16 198	94.23 28	77.89 133	91.28 85	93.66 121	84.35 84	92.71 163	80.07 131	94.87 185	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	85.94 306	78.30 92	86.93 130	92.20 128	65.94 313	89.16 134	93.16 137	83.10 97	89.89 262	87.81 21	94.43 199	93.35 161

tt0320-xc		86.67 105	88.41 84	81.44 266	93.45 75	60.44 336	83.96 204	88.50 238	87.26 29	90.90 94	97.90 3	85.61 71	86.40 336	70.14 278	98.01 45	97.47 14

IS-MVSNet		86.66 106	86.82 111	86.17 128	92.05 119	66.87 246	91.21 48	88.64 235	86.30 37	89.60 126	92.59 160	69.22 287	94.91 75	73.89 226	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 267	93.57 72	60.73 333	84.37 195	88.61 237	87.00 31	90.75 97	97.98 2	85.54 73	86.45 334	69.75 283	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 230	63.28 283	86.64 140	92.20 128	75.42 168	92.81 57	94.50 73	74.05 235	94.06 110	83.88 89	96.28 118	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 252	92.22 113	62.28 304	84.66 186	89.15 229	83.54 65	89.85 116	97.32 8	88.08 39	86.80 327	70.43 275	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	85.12 244	90.34 257	66.19 303	94.20 101	76.57 183	98.44 20	95.19 69

PHI-MVS		86.38 111	85.81 130	88.08 92	88.44 224	77.34 108	89.35 91	93.05 95	73.15 207	84.76 258	87.70 318	78.87 162	94.18 104	80.67 128	96.29 117	92.73 193

CSCG		86.26 112	86.47 115	85.60 140	90.87 162	74.26 139	87.98 114	91.85 140	80.35 97	89.54 129	88.01 305	79.09 160	92.13 179	75.51 202	95.06 174	90.41 292

DeepC-MVS_fast	80.27 8	86.23 113	85.65 136	87.96 95	91.30 147	76.92 113	87.19 125	91.99 135	70.56 249	84.96 251	90.69 243	80.01 153	95.14 68	78.37 155	95.78 150	91.82 246
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	87.82 238	62.35 303	86.42 144	91.33 159	76.78 148	92.73 59	94.48 75	73.41 247	93.72 123	83.10 97	95.41 159	97.01 23

Anonymous20240529		86.20 115	87.13 101	83.42 209	90.19 175	64.55 269	84.55 189	90.71 179	85.85 40	89.94 114	95.24 51	82.13 120	90.40 241	69.19 290	96.40 114	95.31 63

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 275	70.38 198	85.31 170	92.61 116	75.59 164	88.32 156	92.87 151	82.22 117	88.63 290	88.80 9	92.82 255	89.83 305

test_fmvsmconf0.1_n		86.18 117	85.88 128	87.08 105	85.26 322	78.25 93	85.82 157	91.82 142	65.33 328	88.55 147	92.35 174	82.62 106	89.80 264	86.87 42	94.32 203	93.18 172

CDPH-MVS		86.17 118	85.54 137	88.05 94	92.25 111	75.45 132	83.85 209	92.01 134	65.91 315	86.19 215	91.75 197	83.77 90	94.98 73	77.43 173	96.71 102	93.73 139

NR-MVSNet		86.00 119	86.22 119	85.34 147	93.24 84	64.56 268	82.21 266	90.46 188	80.99 90	88.42 152	91.97 183	77.56 179	93.85 118	72.46 253	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	90.32 196	65.79 317	84.49 263	90.97 228	81.93 126	93.63 127	81.21 120	96.54 107	90.88 275

KinetiMVS		85.95 121	86.10 123	85.50 144	87.56 248	69.78 205	83.70 215	89.83 213	80.42 95	87.76 175	93.24 130	73.76 241	91.54 194	85.03 73	93.62 229	95.19 69

FC-MVSNet-test		85.93 122	87.05 104	82.58 234	92.25 111	56.44 381	85.75 158	93.09 93	77.33 143	91.94 73	94.65 66	74.78 221	93.41 143	75.11 208	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 138	86.99 108	84.77 331	78.21 94	85.40 168	91.39 157	65.32 329	87.72 177	91.81 193	82.33 111	89.78 265	86.68 44	94.20 206	92.99 184

Effi-MVS+-dtu		85.82 124	83.38 196	93.14 4	87.13 262	91.15 3	87.70 118	88.42 241	74.57 177	83.56 289	85.65 353	78.49 167	94.21 100	72.04 255	92.88 251	94.05 122

TAPA-MVS	77.73 12	85.71 125	84.83 154	88.37 86	88.78 214	79.72 79	87.15 127	93.50 71	69.17 267	85.80 227	89.56 278	80.76 143	92.13 179	73.21 248	95.51 157	93.25 169
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	90.45 189	79.10 115	90.40 102	96.73 17	82.17 119	89.96 261	67.47 308	96.33 116	95.34 60

sasdasda		85.50 127	86.14 121	83.58 203	87.97 232	67.13 239	87.55 119	94.32 22	73.44 197	88.47 150	87.54 321	86.45 62	91.06 213	75.76 198	93.76 220	92.54 208

canonicalmvs		85.50 127	86.14 121	83.58 203	87.97 232	67.13 239	87.55 119	94.32 22	73.44 197	88.47 150	87.54 321	86.45 62	91.06 213	75.76 198	93.76 220	92.54 208

fmvsm_s_conf0.5_n_8		85.48 129	85.75 133	84.68 167	87.10 265	69.98 203	84.28 196	92.68 111	74.77 174	87.90 168	92.36 173	73.94 236	90.41 240	85.95 62	92.74 257	93.66 142

EPP-MVSNet		85.47 130	85.04 149	86.77 113	91.52 143	69.37 212	91.63 44	87.98 253	81.51 85	87.05 193	91.83 191	66.18 305	95.29 60	70.75 269	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	91.39 157	72.33 225	87.59 179	90.25 262	84.85 79	92.37 173	78.00 164	91.94 282	93.66 142

MGCNet		85.37 132	84.58 166	87.75 96	85.28 321	73.36 144	86.54 143	85.71 297	77.56 140	81.78 328	92.47 166	70.29 281	96.02 11	85.59 65	95.96 135	93.87 130

FIs		85.35 133	86.27 118	82.60 233	91.86 126	57.31 374	85.10 175	93.05 95	75.83 159	91.02 89	93.97 103	73.57 243	92.91 161	73.97 225	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 134	85.36 142	84.81 160	85.80 309	76.13 125	85.15 174	92.32 125	61.40 364	91.33 82	90.85 237	83.76 91	86.16 342	84.31 85	93.28 238	92.15 235

casdiffmvs		85.21 135	85.85 129	83.31 212	86.17 298	62.77 290	83.03 237	93.93 47	74.69 176	88.21 158	92.68 159	82.29 115	91.89 187	77.87 167	93.75 223	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	86.01 304	71.31 185	84.96 177	91.76 146	69.10 269	88.90 137	92.56 163	73.84 239	90.63 233	86.88 41	93.26 239	93.13 173

baseline		85.20 136	85.93 126	83.02 219	86.30 293	62.37 302	84.55 189	93.96 45	74.48 180	87.12 187	92.03 182	82.30 113	91.94 184	78.39 154	94.21 205	94.74 87

SSM_0404		85.16 138	85.09 147	85.36 146	90.14 177	69.52 210	86.17 149	91.58 148	74.41 181	86.55 204	91.49 204	78.54 163	93.97 113	73.71 230	93.21 243	92.59 204

K. test v3		85.14 139	84.73 156	86.37 119	91.13 156	69.63 209	85.45 166	76.68 382	84.06 56	92.44 64	96.99 13	62.03 333	94.65 84	80.58 129	93.24 240	94.83 84

mmtdpeth		85.13 140	85.78 132	83.17 217	84.65 333	74.71 135	85.87 155	90.35 195	77.94 132	83.82 281	96.96 15	77.75 174	80.03 401	78.44 153	96.21 122	94.79 86

EI-MVSNet-Vis-set		85.12 141	84.53 169	86.88 110	84.01 346	72.76 155	83.91 208	85.18 307	80.44 94	88.75 142	85.49 357	80.08 152	91.92 185	82.02 114	90.85 314	95.97 44

fmvsm_l_conf0.5_n_3		85.11 142	84.96 151	85.56 141	87.49 251	75.69 131	84.71 184	90.61 184	67.64 297	84.88 254	92.05 181	82.30 113	88.36 297	83.84 91	91.10 302	92.62 201

MGCFI-Net		85.04 143	85.95 125	82.31 245	87.52 249	63.59 279	86.23 148	93.96 45	73.46 195	88.07 161	87.83 316	86.46 61	90.87 223	76.17 192	93.89 216	92.47 212

EI-MVSNet-UG-set		85.04 143	84.44 172	86.85 111	83.87 350	72.52 164	83.82 210	85.15 308	80.27 99	88.75 142	85.45 359	79.95 154	91.90 186	81.92 117	90.80 316	96.13 39

X-MVStestdata		85.04 143	82.70 215	92.08 9	95.64 24	86.25 22	92.64 20	93.33 78	85.07 45	89.99 111	16.05 478	86.57 59	95.80 31	87.35 33	97.62 73	94.20 112

MSLP-MVS++		85.00 146	86.03 124	81.90 252	91.84 129	71.56 183	86.75 138	93.02 99	75.95 157	87.12 187	89.39 282	77.98 171	89.40 276	77.46 171	94.78 187	84.75 381

F-COLMAP		84.97 147	83.42 195	89.63 58	92.39 106	83.40 52	88.83 98	91.92 138	73.19 206	80.18 352	89.15 288	77.04 192	93.28 146	65.82 324	92.28 271	92.21 231

SSM_0407		84.89 148	84.85 153	85.01 156	89.13 200	68.97 220	85.60 162	91.58 148	74.41 181	85.68 228	91.49 204	78.54 163	93.69 124	73.71 230	93.47 231	92.38 219

balanced_conf03		84.80 149	85.40 140	83.00 220	88.95 207	61.44 314	90.42 63	92.37 124	71.48 238	88.72 144	93.13 138	70.16 283	95.15 67	79.26 146	94.11 209	92.41 214

3Dnovator	80.37 7	84.80 149	84.71 159	85.06 153	86.36 291	74.71 135	88.77 100	90.00 209	75.65 162	84.96 251	93.17 136	74.06 234	91.19 208	78.28 158	91.09 303	89.29 315

SymmetryMVS		84.79 151	83.54 190	88.55 79	92.44 105	80.42 72	88.63 104	82.37 342	74.56 178	85.12 244	90.34 257	66.19 303	94.20 101	76.57 183	95.68 154	91.03 269

IterMVS-LS		84.73 152	84.98 150	83.96 190	87.35 255	63.66 277	83.25 230	89.88 212	76.06 152	89.62 123	92.37 171	73.40 249	92.52 168	78.16 161	94.77 189	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	79.23 321	87.13 270	73.35 199	85.56 235	89.34 283	83.60 93	90.50 237	76.64 182	94.05 213	90.09 301

HQP-MVS		84.61 154	84.06 182	86.27 122	91.19 152	70.66 193	84.77 179	92.68 111	73.30 202	80.55 344	90.17 267	72.10 265	94.61 86	77.30 175	94.47 197	93.56 154

v1192		84.57 155	84.69 161	84.21 182	87.75 240	62.88 287	83.02 238	91.43 154	69.08 270	89.98 113	90.89 234	72.70 259	93.62 130	82.41 109	94.97 179	96.13 39

fmvsm_s_conf0.5_n_11		84.56 156	84.69 161	84.15 185	86.53 281	71.29 186	85.53 163	92.62 114	70.54 250	82.75 305	91.20 219	77.33 183	88.55 293	83.80 92	91.93 283	92.61 203

fmvsm_s_conf0.5_n_5		84.56 156	84.71 159	84.11 186	87.92 235	72.09 172	84.80 178	88.64 235	64.43 338	88.77 141	91.78 195	78.07 170	87.95 304	85.85 63	92.18 275	92.30 224

FMVSNet1		84.55 158	85.45 139	81.85 254	90.27 174	61.05 324	86.83 134	88.27 246	78.57 125	89.66 122	95.64 39	75.43 211	90.68 230	69.09 291	95.33 162	93.82 133

v1144		84.54 159	84.72 158	84.00 187	87.67 244	62.55 294	82.97 240	90.93 175	70.32 254	89.80 117	90.99 227	73.50 244	93.48 139	81.69 119	94.65 193	95.97 44

Gipuma		84.44 160	86.33 117	78.78 312	84.20 343	73.57 143	89.55 82	90.44 190	84.24 54	84.38 266	94.89 58	76.35 207	80.40 398	76.14 193	96.80 100	82.36 419
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	87.25 258	70.84 192	83.55 220	88.45 240	68.64 279	86.29 214	91.31 213	74.97 217	88.42 295	87.87 20	90.07 332	94.95 76

MCST-MVS		84.36 162	83.93 186	85.63 139	91.59 135	71.58 181	83.52 221	92.13 130	61.82 357	83.96 279	89.75 275	79.93 155	93.46 140	78.33 157	94.34 202	91.87 245

VDDNet		84.35 163	85.39 141	81.25 269	95.13 32	59.32 350	85.42 167	81.11 353	86.41 36	87.41 183	96.21 26	73.61 242	90.61 235	66.33 317	96.85 96	93.81 136

ETV-MVS		84.31 164	83.91 187	85.52 142	88.58 220	70.40 197	84.50 193	93.37 73	78.76 123	84.07 277	78.72 432	80.39 148	95.13 69	73.82 228	92.98 249	91.04 268

v1240		84.30 165	84.51 170	83.65 200	87.65 245	61.26 320	82.85 244	91.54 151	67.94 290	90.68 99	90.65 248	71.71 273	93.64 126	82.84 103	94.78 187	96.07 41

MVS_111021_LR		84.28 166	83.76 188	85.83 136	89.23 198	83.07 55	80.99 289	83.56 330	72.71 217	86.07 218	89.07 290	81.75 133	86.19 341	77.11 177	93.36 234	88.24 334

h-mvs33		84.25 167	82.76 214	88.72 75	91.82 131	82.60 60	84.00 203	84.98 314	71.27 239	86.70 200	90.55 253	63.04 330	93.92 116	78.26 159	94.20 206	89.63 307

v144192		84.24 168	84.41 173	83.71 199	87.59 247	61.57 313	82.95 241	91.03 171	67.82 294	89.80 117	90.49 254	73.28 251	93.51 138	81.88 118	94.89 182	96.04 43

dcpmvs_2		84.23 169	85.14 146	81.50 264	88.61 219	61.98 310	82.90 243	93.11 91	68.66 278	92.77 58	92.39 167	78.50 166	87.63 313	76.99 179	92.30 268	94.90 77

v1921920		84.23 169	84.37 175	83.79 195	87.64 246	61.71 312	82.91 242	91.20 166	67.94 290	90.06 108	90.34 257	72.04 268	93.59 132	82.32 110	94.91 180	96.07 41

VDD-MVS		84.23 169	84.58 166	83.20 215	91.17 155	65.16 264	83.25 230	84.97 315	79.79 104	87.18 186	94.27 84	74.77 222	90.89 221	69.24 287	96.54 107	93.55 156

v2v482		84.09 172	84.24 179	83.62 201	87.13 262	61.40 315	82.71 247	89.71 217	72.19 228	89.55 127	91.41 208	70.70 279	93.20 148	81.02 122	93.76 220	96.25 37

EG-PatchMatch MVS		84.08 173	84.11 181	83.98 189	92.22 113	72.61 161	82.20 268	87.02 276	72.63 218	88.86 138	91.02 226	78.52 165	91.11 211	73.41 238	91.09 303	88.21 335

E2		84.06 174	84.61 163	82.40 243	87.49 251	61.31 317	81.03 287	93.36 74	71.83 233	86.02 220	91.87 185	82.91 100	91.37 203	75.66 200	91.33 297	94.53 95

E3		84.06 174	84.61 163	82.40 243	87.49 251	61.30 318	81.03 287	93.36 74	71.83 233	86.01 221	91.87 185	82.91 100	91.36 204	75.66 200	91.33 297	94.53 95

fmvsm_s_conf0.5_n_6		84.05 176	84.14 180	83.81 193	87.75 240	71.17 188	83.42 224	91.10 169	67.90 292	84.53 261	90.70 242	73.01 254	88.73 287	85.09 70	93.72 225	91.53 258

DP-MVS Recon		84.05 176	83.22 199	86.52 117	91.73 133	75.27 133	83.23 232	92.40 120	72.04 230	82.04 319	88.33 301	77.91 173	93.95 115	66.17 318	95.12 172	90.34 294

viewmacassd2359aftdt		84.04 178	84.78 155	81.81 257	86.43 285	60.32 338	81.95 270	92.82 107	71.56 235	86.06 219	92.98 144	81.79 132	90.28 242	76.18 191	93.24 240	94.82 85

TransMVSNet (Re)		84.02 179	85.74 134	78.85 311	91.00 159	55.20 393	82.29 262	87.26 265	79.65 107	88.38 154	95.52 42	83.00 98	86.88 325	67.97 305	96.60 105	94.45 100

Baseline_NR-MVSNet		84.00 180	85.90 127	78.29 323	91.47 145	53.44 405	82.29 262	87.00 279	79.06 117	89.55 127	95.72 37	77.20 188	86.14 343	72.30 254	98.51 17	95.28 64

fmvsm_l_conf0.5_n_9		83.98 181	84.46 171	82.53 237	86.11 301	70.65 195	82.45 257	89.17 228	67.72 296	86.74 199	91.49 204	79.20 158	85.86 352	84.71 81	92.60 261	91.07 267

TSAR-MVS + GP.		83.95 182	82.69 216	87.72 97	89.27 197	81.45 67	83.72 214	81.58 351	74.73 175	85.66 231	86.06 348	72.56 261	92.69 165	75.44 204	95.21 167	89.01 328

LuminaMVS		83.94 183	83.51 191	85.23 148	89.78 186	71.74 176	84.76 182	87.27 264	72.60 219	89.31 132	90.60 252	64.04 319	90.95 216	79.08 147	94.11 209	92.99 184

alignmvs		83.94 183	83.98 184	83.80 194	87.80 239	67.88 234	84.54 191	91.42 156	73.27 205	88.41 153	87.96 306	72.33 262	90.83 224	76.02 195	94.11 209	92.69 197

Effi-MVS+		83.90 185	84.01 183	83.57 205	87.22 260	65.61 260	86.55 142	92.40 120	78.64 124	81.34 335	84.18 378	83.65 92	92.93 159	74.22 216	87.87 367	92.17 234

fmvsm_s_conf0.1_n_2		83.82 186	83.49 192	84.84 158	85.99 305	70.19 201	80.93 290	87.58 260	67.26 303	87.94 167	92.37 171	71.40 275	88.01 301	86.03 57	91.87 284	96.31 36

mvs5depth		83.82 186	84.54 168	81.68 260	82.23 375	68.65 225	86.89 131	89.90 211	80.02 103	87.74 176	97.86 4	64.19 318	82.02 386	76.37 187	95.63 156	94.35 107

CANet		83.79 188	82.85 213	86.63 114	86.17 298	72.21 171	83.76 213	91.43 154	77.24 145	74.39 407	87.45 325	75.36 212	95.42 56	77.03 178	92.83 254	92.25 230

pm-mvs1		83.69 189	84.95 152	79.91 297	90.04 182	59.66 347	82.43 258	87.44 261	75.52 166	87.85 171	95.26 50	81.25 138	85.65 356	68.74 297	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	90.00 209	78.19 130	81.65 329	87.16 331	83.40 95	94.24 99	61.69 360	94.76 190	84.21 391

viewdifsd2359ckpt09		83.64 191	83.18 202	85.03 154	87.26 257	66.99 244	85.32 169	93.83 57	65.57 323	84.99 250	89.40 281	77.30 184	93.57 135	71.16 265	93.80 219	94.54 94

MIMVSNet1		83.63 192	84.59 165	80.74 280	94.06 62	62.77 290	82.72 246	84.53 322	77.57 139	90.34 104	95.92 32	76.88 200	85.83 353	61.88 358	97.42 83	93.62 148

fmvsm_s_conf0.5_n_2		83.62 193	83.29 198	84.62 168	85.43 319	70.18 202	80.61 296	87.24 266	67.14 304	87.79 173	91.87 185	71.79 272	87.98 303	86.00 61	91.77 287	95.71 50

test_fmvsm_n_1920		83.60 194	82.89 210	85.74 137	85.22 323	77.74 102	84.12 200	90.48 186	59.87 384	86.45 213	91.12 222	75.65 209	85.89 350	82.28 111	90.87 312	93.58 152

WR-MVS		83.56 195	84.40 174	81.06 274	93.43 78	54.88 394	78.67 330	85.02 312	81.24 87	90.74 98	91.56 202	72.85 256	91.08 212	68.00 304	98.04 41	97.23 17

CNLPA		83.55 196	83.10 205	84.90 157	89.34 195	83.87 50	84.54 191	88.77 232	79.09 116	83.54 290	88.66 298	74.87 218	81.73 388	66.84 312	92.29 270	89.11 321

viewcassd2359sk11		83.53 197	83.96 185	82.25 246	86.97 274	61.13 322	80.80 294	93.22 86	70.97 245	85.36 239	91.08 224	81.84 130	91.29 205	74.79 211	90.58 328	94.33 109

LCM-MVSNet-Re		83.48 198	85.06 148	78.75 313	85.94 306	55.75 387	80.05 302	94.27 25	76.47 149	96.09 6	94.54 72	83.31 96	89.75 268	59.95 371	94.89 182	90.75 278

hse-mvs2		83.47 199	81.81 231	88.47 82	91.03 158	82.27 61	82.61 248	83.69 328	71.27 239	86.70 200	86.05 349	63.04 330	92.41 171	78.26 159	93.62 229	90.71 280

V42		83.47 199	83.37 197	83.75 197	83.16 369	63.33 282	81.31 281	90.23 203	69.51 263	90.91 92	90.81 239	74.16 231	92.29 177	80.06 132	90.22 330	95.62 54

VPA-MVSNet		83.47 199	84.73 156	79.69 302	90.29 173	57.52 373	81.30 283	88.69 234	76.29 150	87.58 181	94.44 76	80.60 146	87.20 319	66.60 315	96.82 99	94.34 108

mamba_0408		83.44 202	82.88 211	85.11 151	89.13 200	68.97 220	72.73 405	91.28 161	72.90 211	85.68 228	90.61 250	76.78 201	93.97 113	73.37 240	93.47 231	92.38 219

viewdifsd2359ckpt07		83.41 203	84.35 176	80.56 286	85.84 308	58.93 358	79.47 313	91.28 161	73.01 210	87.59 179	92.07 180	85.24 75	88.68 288	73.59 235	91.11 301	94.09 121

PAPM_NR		83.23 204	83.19 201	83.33 211	90.90 161	65.98 256	88.19 109	90.78 178	78.13 131	80.87 340	87.92 310	73.49 246	92.42 170	70.07 279	88.40 356	91.60 255

CLD-MVS		83.18 205	82.64 217	84.79 161	89.05 203	67.82 235	77.93 340	92.52 118	68.33 282	85.07 247	81.54 407	82.06 123	92.96 157	69.35 286	97.91 54	93.57 153
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	75.65 361	81.24 387	45.26 449	79.94 304	92.91 103	83.83 57	91.33 82	96.88 16	80.25 150	85.92 346	68.89 294	95.89 143	95.76 48

FA-MVS(test-final)		83.13 207	83.02 206	83.43 208	86.16 300	66.08 255	88.00 113	88.36 243	75.55 165	85.02 248	92.75 157	65.12 312	92.50 169	74.94 210	91.30 299	91.72 250

114514_t		83.10 208	82.54 220	84.77 162	92.90 91	69.10 219	86.65 139	90.62 183	54.66 416	81.46 332	90.81 239	76.98 193	94.38 94	72.62 251	96.18 124	90.82 277

RRT-MVS		82.97 209	83.44 193	81.57 262	85.06 326	58.04 368	87.20 124	90.37 193	77.88 134	88.59 146	93.70 120	63.17 327	93.05 155	76.49 186	88.47 355	93.62 148

viewmanbaseed2359cas		82.95 210	83.43 194	81.52 263	85.18 324	60.03 343	81.36 280	92.38 122	69.55 262	84.84 257	91.38 209	79.85 156	90.09 255	74.22 216	92.09 277	94.43 103

BP-MVS1		82.81 211	81.67 233	86.23 123	87.88 237	68.53 226	86.06 152	84.36 323	75.65 162	85.14 243	90.19 264	45.84 419	94.42 93	85.18 69	94.72 191	95.75 49

UGNet		82.78 212	81.64 234	86.21 126	86.20 297	76.24 123	86.86 132	85.68 298	77.07 146	73.76 411	92.82 153	69.64 284	91.82 190	69.04 293	93.69 226	90.56 288
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		82.75 213	81.93 229	85.19 149	82.08 376	80.15 76	85.53 163	88.76 233	68.01 287	85.58 234	87.75 317	71.80 271	86.85 326	74.02 224	93.87 217	88.58 331

EI-MVSNet		82.61 214	82.42 222	83.20 215	83.25 366	63.66 277	83.50 222	85.07 309	76.06 152	86.55 204	85.10 365	73.41 247	90.25 243	78.15 163	90.67 323	95.68 52

QAPM		82.59 215	82.59 219	82.58 234	86.44 284	66.69 247	89.94 72	90.36 194	67.97 289	84.94 253	92.58 162	72.71 258	92.18 178	70.63 272	87.73 370	88.85 329

fmvsm_s_conf0.1_n_a		82.58 216	81.93 229	84.50 171	87.68 243	73.35 145	86.14 150	77.70 371	61.64 362	85.02 248	91.62 199	77.75 174	86.24 338	82.79 104	87.07 378	93.91 128

Fast-Effi-MVS+-dtu		82.54 217	81.41 243	85.90 133	85.60 314	76.53 118	83.07 236	89.62 221	73.02 209	79.11 362	83.51 383	80.74 144	90.24 245	68.76 296	89.29 343	90.94 272

MVS_Test		82.47 218	83.22 199	80.22 293	82.62 374	57.75 372	82.54 253	91.96 137	71.16 243	82.89 301	92.52 165	77.41 181	90.50 237	80.04 133	87.84 369	92.40 216

viewdifsd2359ckpt11		82.46 219	82.98 208	80.88 277	83.53 353	61.00 327	79.46 314	85.97 293	69.48 264	87.89 169	91.31 213	82.10 121	88.61 291	74.28 214	92.86 252	93.02 180

viewmsd2359difaftdt		82.46 219	82.99 207	80.88 277	83.52 354	61.00 327	79.46 314	85.97 293	69.48 264	87.89 169	91.31 213	82.10 121	88.61 291	74.28 214	92.86 252	93.02 180

v148		82.31 221	82.48 221	81.81 257	85.59 315	59.66 347	81.47 278	86.02 291	72.85 213	88.05 163	90.65 248	70.73 278	90.91 220	75.15 207	91.79 285	94.87 79

API-MVS		82.28 222	82.61 218	81.30 268	86.29 294	69.79 204	88.71 101	87.67 259	78.42 127	82.15 315	84.15 379	77.98 171	91.59 193	65.39 327	92.75 256	82.51 418

MVSFormer		82.23 223	81.57 239	84.19 184	85.54 316	69.26 214	91.98 39	90.08 207	71.54 236	76.23 387	85.07 368	58.69 355	94.27 96	86.26 51	88.77 351	89.03 326

viewdifsd2359ckpt13		82.22 224	81.98 228	82.95 223	85.48 318	64.44 270	83.17 234	92.11 131	65.97 312	83.72 284	89.73 276	77.60 178	90.80 226	70.61 273	89.42 341	93.59 151

fmvsm_s_conf0.5_n_a		82.21 225	81.51 242	84.32 179	86.56 280	73.35 145	85.46 165	77.30 375	61.81 358	84.51 262	90.88 236	77.36 182	86.21 340	82.72 105	86.97 383	93.38 160

EIA-MVS		82.19 226	81.23 250	85.10 152	87.95 234	69.17 218	83.22 233	93.33 78	70.42 251	78.58 367	79.77 423	77.29 185	94.20 101	71.51 261	88.96 349	91.93 244

GDP-MVS		82.17 227	80.85 258	86.15 130	88.65 217	68.95 223	85.65 161	93.02 99	68.42 280	83.73 283	89.54 279	45.07 430	94.31 95	79.66 139	93.87 217	95.19 69

fmvsm_s_conf0.1_n		82.17 227	81.59 237	83.94 192	86.87 278	71.57 182	85.19 173	77.42 374	62.27 356	84.47 265	91.33 211	76.43 204	85.91 348	83.14 95	87.14 376	94.33 109

PCF-MVS	74.62 15	82.15 229	80.92 256	85.84 135	89.43 193	72.30 168	80.53 297	91.82 142	57.36 400	87.81 172	89.92 272	77.67 177	93.63 127	58.69 376	95.08 173	91.58 256
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 230	80.31 265	87.45 101	90.86 163	80.29 75	85.88 154	90.65 181	68.17 285	76.32 386	86.33 343	73.12 253	92.61 167	61.40 363	90.02 334	89.44 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 231	81.54 241	83.60 202	83.94 347	73.90 141	83.35 227	86.10 287	58.97 386	83.80 282	90.36 256	74.23 229	86.94 324	82.90 101	90.22 330	89.94 303

fmvsm_s_conf0.5_n_7		82.04 232	82.05 226	82.01 250	86.98 273	71.07 189	78.70 328	89.45 224	68.07 286	78.14 369	91.61 200	74.19 230	85.92 346	79.61 140	91.73 288	89.05 325

GBi-Net		82.02 233	82.07 224	81.85 254	86.38 288	61.05 324	86.83 134	88.27 246	72.43 220	86.00 222	95.64 39	63.78 323	90.68 230	65.95 320	93.34 235	93.82 133

test1		82.02 233	82.07 224	81.85 254	86.38 288	61.05 324	86.83 134	88.27 246	72.43 220	86.00 222	95.64 39	63.78 323	90.68 230	65.95 320	93.34 235	93.82 133

OpenMVS	76.72 13	81.98 235	82.00 227	81.93 251	84.42 338	68.22 229	88.50 107	89.48 223	66.92 307	81.80 326	91.86 188	72.59 260	90.16 249	71.19 264	91.25 300	87.40 351

KD-MVS_self_test		81.93 236	83.14 204	78.30 322	84.75 332	52.75 409	80.37 299	89.42 226	70.24 256	90.26 106	93.39 127	74.55 228	86.77 328	68.61 299	96.64 103	95.38 59

fmvsm_s_conf0.5_n		81.91 237	81.30 247	83.75 197	86.02 303	71.56 183	84.73 183	77.11 378	62.44 353	84.00 278	90.68 244	76.42 205	85.89 350	83.14 95	87.11 377	93.81 136

SDMVSNet		81.90 238	83.17 203	78.10 326	88.81 212	62.45 300	76.08 374	86.05 290	73.67 191	83.41 291	93.04 140	82.35 110	80.65 395	70.06 280	95.03 175	91.21 263

tfpnnormal		81.79 239	82.95 209	78.31 321	88.93 208	55.40 389	80.83 293	82.85 337	76.81 147	85.90 226	94.14 94	74.58 226	86.51 332	66.82 313	95.68 154	93.01 183

AstraMVS		81.67 240	81.40 244	82.48 240	87.06 270	66.47 250	81.41 279	81.68 348	68.78 275	88.00 164	90.95 232	65.70 308	87.86 309	76.66 181	92.38 265	93.12 176

c3_l		81.64 241	81.59 237	81.79 259	80.86 393	59.15 355	78.61 331	90.18 205	68.36 281	87.20 185	87.11 333	69.39 285	91.62 192	78.16 161	94.43 199	94.60 90

guyue		81.57 242	81.37 246	82.15 247	86.39 286	66.13 254	81.54 277	83.21 332	69.79 260	87.77 174	89.95 270	65.36 311	87.64 312	75.88 196	92.49 263	92.67 198

PVSNet_Blended_VisFu		81.55 243	80.49 263	84.70 166	91.58 138	73.24 149	84.21 197	91.67 147	62.86 347	80.94 338	87.16 331	67.27 297	92.87 162	69.82 282	88.94 350	87.99 341

fmvsm_l_conf0.5_n_a		81.46 244	80.87 257	83.25 213	83.73 352	73.21 150	83.00 239	85.59 300	58.22 392	82.96 300	90.09 269	72.30 263	86.65 330	81.97 116	89.95 335	89.88 304

SSM_04072		81.44 245	82.88 211	77.10 341	89.13 200	68.97 220	72.73 405	91.28 161	72.90 211	85.68 228	90.61 250	76.78 201	69.94 438	73.37 240	93.47 231	92.38 219

DELS-MVS		81.44 245	81.25 248	82.03 249	84.27 342	62.87 288	76.47 368	92.49 119	70.97 245	81.64 330	83.83 380	75.03 215	92.70 164	74.29 213	92.22 274	90.51 290
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		81.31 247	81.61 236	80.41 289	86.38 288	58.75 363	83.93 207	86.58 282	72.43 220	87.65 178	92.98 144	63.78 323	90.22 246	66.86 310	93.92 215	92.27 228

TinyColmap		81.25 248	82.34 223	77.99 329	85.33 320	60.68 334	82.32 261	88.33 244	71.26 241	86.97 194	92.22 179	77.10 191	86.98 323	62.37 352	95.17 169	86.31 364

diffmvs_AUTHOR		81.24 249	81.55 240	80.30 291	80.61 398	60.22 339	77.98 339	90.48 186	67.77 295	83.34 293	89.50 280	74.69 224	87.42 315	78.78 151	90.81 315	93.27 166

AUN-MVS		81.18 250	78.78 288	88.39 84	90.93 160	82.14 62	82.51 254	83.67 329	64.69 337	80.29 348	85.91 352	51.07 394	92.38 172	76.29 190	93.63 228	90.65 285

IMVS_0407		81.08 251	81.23 250	80.62 285	85.76 310	62.46 296	82.46 255	87.91 254	65.23 330	82.12 316	87.92 310	77.27 186	90.18 248	71.67 257	90.74 318	89.20 316

tttt0517		81.07 252	79.58 278	85.52 142	88.99 206	66.45 251	87.03 129	75.51 390	73.76 190	88.32 156	90.20 263	37.96 451	94.16 108	79.36 145	95.13 170	95.93 47

Fast-Effi-MVS+		81.04 253	80.57 260	82.46 241	87.50 250	63.22 284	78.37 334	89.63 220	68.01 287	81.87 322	82.08 401	82.31 112	92.65 166	67.10 309	88.30 362	91.51 259

BH-untuned		80.96 254	80.99 254	80.84 279	88.55 221	68.23 228	80.33 300	88.46 239	72.79 216	86.55 204	86.76 337	74.72 223	91.77 191	61.79 359	88.99 348	82.52 417

IMVS_0403		80.93 255	81.00 253	80.72 282	85.76 310	62.46 296	81.82 271	87.91 254	65.23 330	82.07 318	87.92 310	75.91 208	90.50 237	71.67 257	90.74 318	89.20 316

eth_miper_zixun_eth		80.84 256	80.22 269	82.71 231	81.41 385	60.98 329	77.81 342	90.14 206	67.31 302	86.95 195	87.24 330	64.26 316	92.31 175	75.23 206	91.61 291	94.85 83

xiu_mvs_v1_base_debu		80.84 256	80.14 271	82.93 226	88.31 225	71.73 177	79.53 309	87.17 267	65.43 324	79.59 354	82.73 395	76.94 194	90.14 252	73.22 243	88.33 358	86.90 358

xiu_mvs_v1_base		80.84 256	80.14 271	82.93 226	88.31 225	71.73 177	79.53 309	87.17 267	65.43 324	79.59 354	82.73 395	76.94 194	90.14 252	73.22 243	88.33 358	86.90 358

xiu_mvs_v1_base_debi		80.84 256	80.14 271	82.93 226	88.31 225	71.73 177	79.53 309	87.17 267	65.43 324	79.59 354	82.73 395	76.94 194	90.14 252	73.22 243	88.33 358	86.90 358

IterMVS-SCA-FT		80.64 260	79.41 279	84.34 178	83.93 348	69.66 208	76.28 370	81.09 354	72.43 220	86.47 211	90.19 264	60.46 340	93.15 151	77.45 172	86.39 389	90.22 295

BH-RMVSNet		80.53 261	80.22 269	81.49 265	87.19 261	66.21 253	77.79 343	86.23 285	74.21 185	83.69 285	88.50 299	73.25 252	90.75 227	63.18 348	87.90 366	87.52 349

VortexMVS		80.51 262	80.63 259	80.15 295	83.36 362	61.82 311	80.63 295	88.00 252	67.11 305	87.23 184	89.10 289	63.98 320	88.00 302	73.63 234	92.63 260	90.64 286

Anonymous202405211		80.51 262	81.19 252	78.49 318	88.48 222	57.26 375	76.63 363	82.49 340	81.21 88	84.30 272	92.24 178	67.99 293	86.24 338	62.22 353	95.13 170	91.98 243

DIV-MVS_self_test		80.43 264	80.23 267	81.02 275	79.99 403	59.25 352	77.07 356	87.02 276	67.38 299	86.19 215	89.22 285	63.09 328	90.16 249	76.32 188	95.80 148	93.66 142

cl____		80.42 265	80.23 267	81.02 275	79.99 403	59.25 352	77.07 356	87.02 276	67.37 300	86.18 217	89.21 286	63.08 329	90.16 249	76.31 189	95.80 148	93.65 145

diffmvs		80.40 266	80.48 264	80.17 294	79.02 416	60.04 341	77.54 347	90.28 202	66.65 310	82.40 309	87.33 328	73.50 244	87.35 317	77.98 165	89.62 339	93.13 173
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		80.37 267	78.41 296	86.23 123	76.75 430	73.28 147	87.18 126	77.45 373	76.24 151	68.14 441	88.93 292	65.41 310	93.85 118	69.47 285	96.12 128	91.55 257
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 268	80.04 274	81.24 271	79.82 406	58.95 357	77.66 344	89.66 218	65.75 320	85.99 225	85.11 364	68.29 292	91.42 200	76.03 194	92.03 278	93.33 162

MG-MVS		80.32 269	80.94 255	78.47 319	88.18 228	52.62 412	82.29 262	85.01 313	72.01 231	79.24 361	92.54 164	69.36 286	93.36 145	70.65 271	89.19 346	89.45 309

mvsmamba		80.30 270	78.87 285	84.58 170	88.12 231	67.55 236	92.35 30	84.88 316	63.15 345	85.33 240	90.91 233	50.71 396	95.20 66	66.36 316	87.98 365	90.99 270

VPNet		80.25 271	81.68 232	75.94 357	92.46 104	47.98 436	76.70 361	81.67 349	73.45 196	84.87 255	92.82 153	74.66 225	86.51 332	61.66 361	96.85 96	93.33 162

MAR-MVS		80.24 272	78.74 290	84.73 164	86.87 278	78.18 95	85.75 158	87.81 258	65.67 322	77.84 373	78.50 433	73.79 240	90.53 236	61.59 362	90.87 312	85.49 374
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		80.20 273	79.00 284	83.78 196	88.17 229	86.66 19	81.31 281	66.81 446	69.64 261	88.33 155	90.19 264	64.58 313	83.63 377	71.99 256	90.03 333	81.06 437

Anonymous20240521		80.18 274	81.25 248	76.95 343	83.15 370	60.84 331	82.46 255	85.99 292	68.76 276	86.78 196	93.73 119	59.13 352	77.44 412	73.71 230	97.55 78	92.56 206

LFMVS		80.15 275	80.56 261	78.89 310	89.19 199	55.93 383	85.22 172	73.78 402	82.96 71	84.28 273	92.72 158	57.38 364	90.07 257	63.80 342	95.75 151	90.68 282

DPM-MVS		80.10 276	79.18 283	82.88 229	90.71 166	69.74 206	78.87 326	90.84 176	60.29 380	75.64 396	85.92 351	67.28 296	93.11 152	71.24 263	91.79 285	85.77 370

MSDG		80.06 277	79.99 276	80.25 292	83.91 349	68.04 233	77.51 348	89.19 227	77.65 137	81.94 320	83.45 385	76.37 206	86.31 337	63.31 347	86.59 386	86.41 362

FE-MVS		79.98 278	78.86 286	83.36 210	86.47 283	66.45 251	89.73 75	84.74 320	72.80 215	84.22 276	91.38 209	44.95 431	93.60 131	63.93 340	91.50 294	90.04 302

sd_testset		79.95 279	81.39 245	75.64 362	88.81 212	58.07 367	76.16 373	82.81 338	73.67 191	83.41 291	93.04 140	80.96 141	77.65 411	58.62 377	95.03 175	91.21 263

ab-mvs		79.67 280	80.56 261	76.99 342	88.48 222	56.93 377	84.70 185	86.06 289	68.95 273	80.78 341	93.08 139	75.30 213	84.62 364	56.78 386	90.90 310	89.43 311

VNet		79.31 281	80.27 266	76.44 351	87.92 235	53.95 401	75.58 380	84.35 324	74.39 184	82.23 313	90.72 241	72.84 257	84.39 369	60.38 369	93.98 214	90.97 271

thisisatest0530		79.07 282	77.33 306	84.26 181	87.13 262	64.58 267	83.66 217	75.95 385	68.86 274	85.22 242	87.36 327	38.10 448	93.57 135	75.47 203	94.28 204	94.62 89

cl22		78.97 283	78.21 298	81.24 271	77.74 420	59.01 356	77.46 351	87.13 270	65.79 317	84.32 269	85.10 365	58.96 354	90.88 222	75.36 205	92.03 278	93.84 131

patch_mono-2		78.89 284	79.39 280	77.41 338	84.78 330	68.11 231	75.60 378	83.11 334	60.96 372	79.36 358	89.89 273	75.18 214	72.97 427	73.32 242	92.30 268	91.15 265

RPMNet		78.88 285	78.28 297	80.68 284	79.58 407	62.64 292	82.58 250	94.16 33	74.80 173	75.72 394	92.59 160	48.69 403	95.56 45	73.48 237	82.91 425	83.85 396

PAPR		78.84 286	78.10 299	81.07 273	85.17 325	60.22 339	82.21 266	90.57 185	62.51 349	75.32 400	84.61 373	74.99 216	92.30 176	59.48 374	88.04 364	90.68 282

viewmambaseed2359dif		78.80 287	78.47 295	79.78 298	80.26 402	59.28 351	77.31 353	87.13 270	60.42 378	82.37 310	88.67 297	74.58 226	87.87 308	67.78 307	87.73 370	92.19 232

PVSNet_BlendedMVS		78.80 287	77.84 300	81.65 261	84.43 336	63.41 280	79.49 312	90.44 190	61.70 361	75.43 397	87.07 334	69.11 288	91.44 198	60.68 367	92.24 272	90.11 300

FMVSNet3		78.80 287	78.55 292	79.57 304	82.89 373	56.89 379	81.76 272	85.77 296	69.04 271	86.00 222	90.44 255	51.75 392	90.09 255	65.95 320	93.34 235	91.72 250

test_yl		78.71 290	78.51 293	79.32 307	84.32 340	58.84 360	78.38 332	85.33 304	75.99 155	82.49 307	86.57 339	58.01 358	90.02 259	62.74 349	92.73 258	89.10 322

DCV-MVSNet		78.71 290	78.51 293	79.32 307	84.32 340	58.84 360	78.38 332	85.33 304	75.99 155	82.49 307	86.57 339	58.01 358	90.02 259	62.74 349	92.73 258	89.10 322

test1111		78.53 292	78.85 287	77.56 335	92.22 113	47.49 438	82.61 248	69.24 434	72.43 220	85.28 241	94.20 90	51.91 390	90.07 257	65.36 328	96.45 112	95.11 73

FE-MVSNET		78.46 293	79.36 281	75.75 359	86.53 281	54.53 396	78.03 336	85.35 303	69.01 272	85.41 238	90.68 244	64.27 315	85.73 354	62.59 351	92.35 267	87.00 357

icg_test_0407_2		78.46 293	79.68 277	74.78 369	85.76 310	62.46 296	68.51 434	87.91 254	65.23 330	82.12 316	87.92 310	77.27 186	72.67 428	71.67 257	90.74 318	89.20 316

ECVR-MVS		78.44 295	78.63 291	77.88 331	91.85 127	48.95 432	83.68 216	69.91 430	72.30 226	84.26 275	94.20 90	51.89 391	89.82 263	63.58 343	96.02 132	94.87 79

pmmvs-eth3d		78.42 296	77.04 309	82.57 236	87.44 254	74.41 138	80.86 292	79.67 362	55.68 409	84.69 259	90.31 261	60.91 338	85.42 357	62.20 354	91.59 292	87.88 345

mvs_anonymous		78.13 297	78.76 289	76.23 356	79.24 413	50.31 428	78.69 329	84.82 318	61.60 363	83.09 299	92.82 153	73.89 238	87.01 320	68.33 303	86.41 388	91.37 260

TAMVS		78.08 298	76.36 316	83.23 214	90.62 167	72.87 154	79.08 322	80.01 361	61.72 360	81.35 334	86.92 336	63.96 322	88.78 285	50.61 425	93.01 248	88.04 340

miper_enhance_ethall		77.83 299	76.93 310	80.51 287	76.15 437	58.01 369	75.47 382	88.82 231	58.05 394	83.59 287	80.69 411	64.41 314	91.20 207	73.16 249	92.03 278	92.33 223

Vis-MVSNet (Re-imp)		77.82 300	77.79 301	77.92 330	88.82 211	51.29 422	83.28 228	71.97 418	74.04 186	82.23 313	89.78 274	57.38 364	89.41 275	57.22 385	95.41 159	93.05 179

CANet_DTU		77.81 301	77.05 308	80.09 296	81.37 386	59.90 345	83.26 229	88.29 245	69.16 268	67.83 444	83.72 381	60.93 337	89.47 270	69.22 289	89.70 338	90.88 275

OpenMVS_ROB	70.19 17	77.77 302	77.46 303	78.71 314	84.39 339	61.15 321	81.18 285	82.52 339	62.45 352	83.34 293	87.37 326	66.20 302	88.66 289	64.69 335	85.02 405	86.32 363

SSC-MVS		77.55 303	81.64 234	65.29 435	90.46 170	20.33 482	73.56 398	68.28 436	85.44 41	88.18 160	94.64 69	70.93 277	81.33 390	71.25 262	92.03 278	94.20 112

MDA-MVSNet-bldmvs		77.47 304	76.90 311	79.16 309	79.03 415	64.59 266	66.58 446	75.67 388	73.15 207	88.86 138	88.99 291	66.94 298	81.23 391	64.71 334	88.22 363	91.64 254

jason		77.42 305	75.75 322	82.43 242	87.10 265	69.27 213	77.99 338	81.94 346	51.47 436	77.84 373	85.07 368	60.32 342	89.00 279	70.74 270	89.27 345	89.03 326
jason: jason.
CDS-MVSNet		77.32 306	75.40 326	83.06 218	89.00 205	72.48 165	77.90 341	82.17 344	60.81 373	78.94 364	83.49 384	59.30 350	88.76 286	54.64 405	92.37 266	87.93 344
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 307	77.75 302	75.73 360	85.76 310	62.46 296	70.84 420	87.91 254	65.23 330	72.21 419	87.92 310	67.48 295	75.53 420	71.67 257	90.74 318	89.20 316

xiu_mvs_v2_base		77.19 308	76.75 313	78.52 317	87.01 271	61.30 318	75.55 381	87.12 274	61.24 369	74.45 406	78.79 431	77.20 188	90.93 218	64.62 337	84.80 412	83.32 405

MVSTER		77.09 309	75.70 323	81.25 269	75.27 445	61.08 323	77.49 350	85.07 309	60.78 374	86.55 204	88.68 295	43.14 440	90.25 243	73.69 233	90.67 323	92.42 213

PS-MVSNAJ		77.04 310	76.53 315	78.56 316	87.09 267	61.40 315	75.26 383	87.13 270	61.25 368	74.38 408	77.22 445	76.94 194	90.94 217	64.63 336	84.83 411	83.35 404

IterMVS		76.91 311	76.34 317	78.64 315	80.91 391	64.03 274	76.30 369	79.03 365	64.88 336	83.11 297	89.16 287	59.90 346	84.46 367	68.61 299	85.15 403	87.42 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 312	75.67 324	80.34 290	80.48 400	62.16 309	73.50 399	84.80 319	57.61 398	82.24 312	87.54 321	51.31 393	87.65 311	70.40 276	93.19 244	91.23 262

CL-MVSNet_self_test		76.81 313	77.38 305	75.12 365	86.90 276	51.34 420	73.20 402	80.63 358	68.30 283	81.80 326	88.40 300	66.92 299	80.90 392	55.35 399	94.90 181	93.12 176

TR-MVS		76.77 314	75.79 321	79.72 301	86.10 302	65.79 258	77.14 354	83.02 335	65.20 334	81.40 333	82.10 399	66.30 301	90.73 229	55.57 396	85.27 399	82.65 412

MonoMVSNet		76.66 315	77.26 307	74.86 367	79.86 405	54.34 398	86.26 147	86.08 288	71.08 244	85.59 233	88.68 295	53.95 382	85.93 345	63.86 341	80.02 441	84.32 387

USDC		76.63 316	76.73 314	76.34 353	83.46 357	57.20 376	80.02 303	88.04 251	52.14 432	83.65 286	91.25 216	63.24 326	86.65 330	54.66 404	94.11 209	85.17 376

BH-w/o		76.57 317	76.07 320	78.10 326	86.88 277	65.92 257	77.63 345	86.33 283	65.69 321	80.89 339	79.95 420	68.97 290	90.74 228	53.01 415	85.25 400	77.62 448

Patchmtry		76.56 318	77.46 303	73.83 375	79.37 412	46.60 442	82.41 259	76.90 379	73.81 189	85.56 235	92.38 168	48.07 406	83.98 374	63.36 346	95.31 165	90.92 273

PVSNet_Blended		76.49 319	75.40 326	79.76 300	84.43 336	63.41 280	75.14 384	90.44 190	57.36 400	75.43 397	78.30 434	69.11 288	91.44 198	60.68 367	87.70 372	84.42 386

miper_lstm_enhance		76.45 320	76.10 319	77.51 336	76.72 431	60.97 330	64.69 450	85.04 311	63.98 341	83.20 296	88.22 302	56.67 368	78.79 408	73.22 243	93.12 245	92.78 192

lupinMVS		76.37 321	74.46 335	82.09 248	85.54 316	69.26 214	76.79 359	80.77 357	50.68 443	76.23 387	82.82 393	58.69 355	88.94 280	69.85 281	88.77 351	88.07 337

cascas		76.29 322	74.81 331	80.72 282	84.47 335	62.94 286	73.89 396	87.34 262	55.94 407	75.16 402	76.53 450	63.97 321	91.16 209	65.00 331	90.97 308	88.06 339

SD_0403		76.08 323	76.77 312	73.98 373	87.08 269	49.45 431	83.62 218	84.68 321	63.31 342	75.13 403	87.47 324	71.85 270	84.56 365	49.97 427	87.86 368	87.94 343

WB-MVS		76.06 324	80.01 275	64.19 438	89.96 184	20.58 481	72.18 409	68.19 437	83.21 67	86.46 212	93.49 124	70.19 282	78.97 406	65.96 319	90.46 329	93.02 180

thres600view7		75.97 325	75.35 328	77.85 333	87.01 271	51.84 418	80.45 298	73.26 407	75.20 170	83.10 298	86.31 345	45.54 421	89.05 278	55.03 402	92.24 272	92.66 199

GA-MVS		75.83 326	74.61 332	79.48 306	81.87 378	59.25 352	73.42 400	82.88 336	68.68 277	79.75 353	81.80 404	50.62 397	89.46 271	66.85 311	85.64 396	89.72 306

MVP-Stereo		75.81 327	73.51 344	82.71 231	89.35 194	73.62 142	80.06 301	85.20 306	60.30 379	73.96 409	87.94 307	57.89 362	89.45 272	52.02 419	74.87 459	85.06 378
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 328	75.20 329	77.27 339	75.01 448	69.47 211	78.93 323	84.88 316	46.67 450	87.08 191	87.84 315	50.44 399	71.62 433	77.42 174	88.53 354	90.72 279

thres100view900		75.45 329	75.05 330	76.66 349	87.27 256	51.88 417	81.07 286	73.26 407	75.68 161	83.25 295	86.37 342	45.54 421	88.80 282	51.98 420	90.99 305	89.31 313

ET-MVSNet_ETH3D		75.28 330	72.77 353	82.81 230	83.03 372	68.11 231	77.09 355	76.51 383	60.67 376	77.60 378	80.52 415	38.04 449	91.15 210	70.78 268	90.68 322	89.17 320

thres400		75.14 331	74.23 337	77.86 332	86.24 295	52.12 414	79.24 319	73.87 400	73.34 200	81.82 324	84.60 374	46.02 414	88.80 282	51.98 420	90.99 305	92.66 199

wuyk23d		75.13 332	79.30 282	62.63 441	75.56 441	75.18 134	80.89 291	73.10 409	75.06 172	94.76 16	95.32 46	87.73 45	52.85 473	34.16 471	97.11 91	59.85 469

EU-MVSNet		75.12 333	74.43 336	77.18 340	83.11 371	59.48 349	85.71 160	82.43 341	39.76 470	85.64 232	88.76 293	44.71 433	87.88 307	73.86 227	85.88 395	84.16 392

HyFIR lowres test		75.12 333	72.66 355	82.50 239	91.44 146	65.19 263	72.47 407	87.31 263	46.79 449	80.29 348	84.30 376	52.70 387	92.10 182	51.88 424	86.73 384	90.22 295

CMPMVS	59.41 20	75.12 333	73.57 342	79.77 299	75.84 440	67.22 237	81.21 284	82.18 343	50.78 441	76.50 383	87.66 319	55.20 378	82.99 380	62.17 356	90.64 327	89.09 324
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 336	72.98 351	80.73 281	84.95 327	71.71 180	76.23 371	77.59 372	52.83 426	77.73 377	86.38 341	56.35 371	84.97 361	57.72 384	87.05 379	85.51 373

tfpn200view9		74.86 337	74.23 337	76.74 348	86.24 295	52.12 414	79.24 319	73.87 400	73.34 200	81.82 324	84.60 374	46.02 414	88.80 282	51.98 420	90.99 305	89.31 313

1112_ss		74.82 338	73.74 340	78.04 328	89.57 188	60.04 341	76.49 367	87.09 275	54.31 417	73.66 412	79.80 421	60.25 343	86.76 329	58.37 378	84.15 416	87.32 352

EGC-MVSNET		74.79 339	69.99 383	89.19 67	94.89 38	87.00 15	91.89 42	86.28 284	1.09 479	2.23 481	95.98 31	81.87 129	89.48 269	79.76 136	95.96 135	91.10 266

ppachtmachnet_test		74.73 340	74.00 339	76.90 345	80.71 396	56.89 379	71.53 415	78.42 367	58.24 391	79.32 360	82.92 392	57.91 361	84.26 371	65.60 326	91.36 296	89.56 308

Patchmatch-RL test		74.48 341	73.68 341	76.89 346	84.83 329	66.54 248	72.29 408	69.16 435	57.70 396	86.76 197	86.33 343	45.79 420	82.59 381	69.63 284	90.65 326	81.54 428

PatchMatch-RL		74.48 341	73.22 348	78.27 324	87.70 242	85.26 38	75.92 376	70.09 428	64.34 339	76.09 390	81.25 409	65.87 307	78.07 410	53.86 407	83.82 418	71.48 457

XXY-MVS		74.44 343	76.19 318	69.21 410	84.61 334	52.43 413	71.70 412	77.18 377	60.73 375	80.60 342	90.96 230	75.44 210	69.35 441	56.13 391	88.33 358	85.86 369

test2506		74.12 344	73.39 345	76.28 354	91.85 127	44.20 452	84.06 201	48.20 477	72.30 226	81.90 321	94.20 90	27.22 476	89.77 266	64.81 333	96.02 132	94.87 79

reproduce_monomvs		74.09 345	73.23 347	76.65 350	76.52 432	54.54 395	77.50 349	81.40 352	65.85 316	82.86 303	86.67 338	27.38 474	84.53 366	70.24 277	90.66 325	90.89 274

CR-MVSNet		74.00 346	73.04 350	76.85 347	79.58 407	62.64 292	82.58 250	76.90 379	50.50 444	75.72 394	92.38 168	48.07 406	84.07 373	68.72 298	82.91 425	83.85 396

SSC-MVS3.2		73.90 347	75.67 324	68.61 418	84.11 345	41.28 460	64.17 452	72.83 410	72.09 229	79.08 363	87.94 307	70.31 280	73.89 426	55.99 392	94.49 196	90.67 284

Test_1112_low_res		73.90 347	73.08 349	76.35 352	90.35 172	55.95 382	73.40 401	86.17 286	50.70 442	73.14 413	85.94 350	58.31 357	85.90 349	56.51 388	83.22 422	87.20 354

test20.03		73.75 349	74.59 334	71.22 396	81.11 389	51.12 424	70.15 426	72.10 417	70.42 251	80.28 350	91.50 203	64.21 317	74.72 424	46.96 445	94.58 194	87.82 347

test_fmvs2		73.57 350	72.80 352	75.90 358	72.74 462	68.84 224	77.07 356	84.32 325	45.14 456	82.89 301	84.22 377	48.37 404	70.36 437	73.40 239	87.03 380	88.52 332

SCA		73.32 351	72.57 357	75.58 363	81.62 382	55.86 385	78.89 325	71.37 423	61.73 359	74.93 404	83.42 386	60.46 340	87.01 320	58.11 382	82.63 430	83.88 393

baseline1		73.26 352	73.54 343	72.43 389	84.92 328	47.79 437	79.89 305	74.00 398	65.93 314	78.81 365	86.28 346	56.36 370	81.63 389	56.63 387	79.04 448	87.87 346

1314		73.22 353	72.56 358	75.20 364	80.41 401	57.84 370	81.64 275	85.36 302	51.68 435	73.10 414	76.65 449	61.45 335	85.19 359	63.54 344	79.21 446	82.59 413

MVS		73.21 354	72.59 356	75.06 366	80.97 390	60.81 332	81.64 275	85.92 295	46.03 454	71.68 422	77.54 440	68.47 291	89.77 266	55.70 395	85.39 397	74.60 454

HY-MVS	64.64 18	73.03 355	72.47 359	74.71 370	83.36 362	54.19 399	82.14 269	81.96 345	56.76 406	69.57 436	86.21 347	60.03 344	84.83 363	49.58 432	82.65 428	85.11 377

thisisatest0515		73.00 356	70.52 375	80.46 288	81.45 384	59.90 345	73.16 403	74.31 397	57.86 395	76.08 391	77.78 437	37.60 452	92.12 181	65.00 331	91.45 295	89.35 312

EPNet_dtu		72.87 357	71.33 369	77.49 337	77.72 421	60.55 335	82.35 260	75.79 386	66.49 311	58.39 472	81.06 410	53.68 383	85.98 344	53.55 410	92.97 250	85.95 367
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 358	71.41 368	76.28 354	83.25 366	60.34 337	83.50 222	79.02 366	37.77 474	76.33 385	85.10 365	49.60 402	87.41 316	70.54 274	77.54 454	81.08 435

CHOSEN 1792x2688		72.45 359	70.56 374	78.13 325	90.02 183	63.08 285	68.72 433	83.16 333	42.99 464	75.92 392	85.46 358	57.22 366	85.18 360	49.87 430	81.67 432	86.14 365

testgi		72.36 360	74.61 332	65.59 432	80.56 399	42.82 457	68.29 435	73.35 406	66.87 308	81.84 323	89.93 271	72.08 267	66.92 455	46.05 449	92.54 262	87.01 356

thres200		72.34 361	71.55 367	74.70 371	83.48 356	51.60 419	75.02 385	73.71 403	70.14 257	78.56 368	80.57 414	46.20 412	88.20 300	46.99 444	89.29 343	84.32 387

FPMVS		72.29 362	72.00 361	73.14 380	88.63 218	85.00 40	74.65 389	67.39 440	71.94 232	77.80 375	87.66 319	50.48 398	75.83 418	49.95 428	79.51 442	58.58 471

FMVSNet5		72.10 363	71.69 363	73.32 378	81.57 383	53.02 408	76.77 360	78.37 368	63.31 342	76.37 384	91.85 189	36.68 453	78.98 405	47.87 441	92.45 264	87.95 342

our_test_3		71.85 364	71.59 364	72.62 386	80.71 396	53.78 402	69.72 429	71.71 422	58.80 388	78.03 370	80.51 416	56.61 369	78.84 407	62.20 354	86.04 394	85.23 375

PAPM		71.77 365	70.06 381	76.92 344	86.39 286	53.97 400	76.62 364	86.62 281	53.44 421	63.97 461	84.73 372	57.79 363	92.34 174	39.65 461	81.33 436	84.45 385

ttmdpeth		71.72 366	70.67 372	74.86 367	73.08 459	55.88 384	77.41 352	69.27 433	55.86 408	78.66 366	93.77 117	38.01 450	75.39 421	60.12 370	89.87 336	93.31 164

IB-MVS	62.13 19	71.64 367	68.97 393	79.66 303	80.80 395	62.26 305	73.94 395	76.90 379	63.27 344	68.63 440	76.79 447	33.83 457	91.84 189	59.28 375	87.26 374	84.88 379
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		71.63 368	72.30 360	69.62 407	76.47 434	52.70 411	70.03 427	80.97 355	59.18 385	79.36 358	88.21 303	60.50 339	69.12 442	58.33 380	77.62 453	87.04 355

testing3		71.53 369	70.79 371	73.77 376	88.89 210	41.86 459	76.60 366	59.12 466	72.83 214	80.97 336	82.08 401	19.80 482	87.33 318	65.12 330	91.68 290	92.13 236

test_vis3_rt		71.42 370	70.67 372	73.64 377	69.66 469	70.46 196	66.97 445	89.73 215	42.68 466	88.20 159	83.04 388	43.77 435	60.07 467	65.35 329	86.66 385	90.39 293

Anonymous20231206		71.38 371	71.88 362	69.88 404	86.31 292	54.37 397	70.39 424	74.62 393	52.57 428	76.73 382	88.76 293	59.94 345	72.06 430	44.35 453	93.23 242	83.23 407

test_vis1_n_1920		71.30 372	71.58 366	70.47 399	77.58 423	59.99 344	74.25 390	84.22 326	51.06 438	74.85 405	79.10 427	55.10 379	68.83 444	68.86 295	79.20 447	82.58 414

MIMVSNet		71.09 373	71.59 364	69.57 408	87.23 259	50.07 429	78.91 324	71.83 419	60.20 382	71.26 423	91.76 196	55.08 380	76.09 416	41.06 458	87.02 381	82.54 416

test_fmvs1_n		70.94 374	70.41 378	72.53 388	73.92 450	66.93 245	75.99 375	84.21 327	43.31 463	79.40 357	79.39 425	43.47 436	68.55 446	69.05 292	84.91 408	82.10 422

MS-PatchMatch		70.93 375	70.22 379	73.06 381	81.85 379	62.50 295	73.82 397	77.90 369	52.44 429	75.92 392	81.27 408	55.67 375	81.75 387	55.37 398	77.70 452	74.94 453

pmmvs5		70.73 376	70.07 380	72.72 384	77.03 428	52.73 410	74.14 391	75.65 389	50.36 445	72.17 420	85.37 362	55.42 377	80.67 394	52.86 416	87.59 373	84.77 380

testing3-2		70.72 377	70.97 370	69.95 403	88.93 208	34.80 473	69.85 428	66.59 447	78.42 127	77.58 379	85.55 354	31.83 463	82.08 385	46.28 446	93.73 224	92.98 186

PatchT		70.52 378	72.76 354	63.79 440	79.38 411	33.53 474	77.63 345	65.37 451	73.61 193	71.77 421	92.79 156	44.38 434	75.65 419	64.53 338	85.37 398	82.18 421

test_vis1_n		70.29 379	69.99 383	71.20 397	75.97 439	66.50 249	76.69 362	80.81 356	44.22 459	75.43 397	77.23 444	50.00 400	68.59 445	66.71 314	82.85 427	78.52 447

N_pmnet		70.20 380	68.80 395	74.38 372	80.91 391	84.81 43	59.12 463	76.45 384	55.06 412	75.31 401	82.36 398	55.74 374	54.82 472	47.02 443	87.24 375	83.52 400

tpmvs		70.16 381	69.56 386	71.96 392	74.71 449	48.13 434	79.63 307	75.45 391	65.02 335	70.26 431	81.88 403	45.34 426	85.68 355	58.34 379	75.39 458	82.08 423

new-patchmatchnet		70.10 382	73.37 346	60.29 449	81.23 388	16.95 484	59.54 461	74.62 393	62.93 346	80.97 336	87.93 309	62.83 332	71.90 431	55.24 400	95.01 178	92.00 241

YYNet1		70.06 383	70.44 376	68.90 412	73.76 452	53.42 406	58.99 464	67.20 442	58.42 390	87.10 189	85.39 361	59.82 347	67.32 452	59.79 372	83.50 421	85.96 366

MVStest1		70.05 384	69.26 387	72.41 390	58.62 481	55.59 388	76.61 365	65.58 449	53.44 421	89.28 133	93.32 128	22.91 480	71.44 435	74.08 223	89.52 340	90.21 299

MDA-MVSNet_test_wron		70.05 384	70.44 376	68.88 413	73.84 451	53.47 404	58.93 465	67.28 441	58.43 389	87.09 190	85.40 360	59.80 348	67.25 453	59.66 373	83.54 420	85.92 368

CostFormer		69.98 386	68.68 396	73.87 374	77.14 426	50.72 426	79.26 318	74.51 395	51.94 434	70.97 426	84.75 371	45.16 429	87.49 314	55.16 401	79.23 445	83.40 403

testing91		69.94 387	68.99 392	72.80 383	83.81 351	45.89 445	71.57 414	73.64 405	68.24 284	70.77 429	77.82 436	34.37 456	84.44 368	53.64 409	87.00 382	88.07 337

baseline2		69.77 388	66.89 405	78.41 320	79.51 409	58.09 366	76.23 371	69.57 431	57.50 399	64.82 459	77.45 442	46.02 414	88.44 294	53.08 412	77.83 450	88.70 330

PatchmatchNet		69.71 389	68.83 394	72.33 391	77.66 422	53.60 403	79.29 317	69.99 429	57.66 397	72.53 417	82.93 391	46.45 411	80.08 400	60.91 366	72.09 462	83.31 406
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 390	69.05 390	71.14 398	69.15 470	65.77 259	73.98 394	83.32 331	42.83 465	77.77 376	78.27 435	43.39 439	68.50 447	68.39 302	84.38 415	79.15 445

JIA-IIPM		69.41 391	66.64 409	77.70 334	73.19 456	71.24 187	75.67 377	65.56 450	70.42 251	65.18 455	92.97 147	33.64 459	83.06 378	53.52 411	69.61 468	78.79 446

Syy-MVS		69.40 392	70.03 382	67.49 423	81.72 380	38.94 465	71.00 417	61.99 457	61.38 365	70.81 427	72.36 461	61.37 336	79.30 403	64.50 339	85.18 401	84.22 389

testing99		69.27 393	68.15 400	72.63 385	83.29 364	45.45 447	71.15 416	71.08 424	67.34 301	70.43 430	77.77 438	32.24 462	84.35 370	53.72 408	86.33 390	88.10 336

UnsupCasMVSNet_bld		69.21 394	69.68 385	67.82 421	79.42 410	51.15 423	67.82 439	75.79 386	54.15 418	77.47 380	85.36 363	59.26 351	70.64 436	48.46 438	79.35 444	81.66 426

test_cas_vis1_n_1920		69.20 395	69.12 388	69.43 409	73.68 453	62.82 289	70.38 425	77.21 376	46.18 453	80.46 347	78.95 429	52.03 389	65.53 460	65.77 325	77.45 455	79.95 443

gg-mvs-nofinetune		68.96 396	69.11 389	68.52 419	76.12 438	45.32 448	83.59 219	55.88 471	86.68 33	64.62 460	97.01 12	30.36 467	83.97 375	44.78 452	82.94 424	76.26 450

WBMVS		68.76 397	68.43 397	69.75 406	83.29 364	40.30 463	67.36 441	72.21 416	57.09 403	77.05 381	85.53 356	33.68 458	80.51 396	48.79 436	90.90 310	88.45 333

WB-MVSnew		68.72 398	69.01 391	67.85 420	83.22 368	43.98 453	74.93 386	65.98 448	55.09 411	73.83 410	79.11 426	65.63 309	71.89 432	38.21 466	85.04 404	87.69 348

tpm2		68.45 399	66.83 406	73.30 379	78.93 417	48.50 433	79.76 306	71.76 420	47.50 448	69.92 433	83.60 382	42.07 442	88.40 296	48.44 439	79.51 442	83.01 410

tpm		67.95 400	68.08 401	67.55 422	78.74 418	43.53 455	75.60 378	67.10 445	54.92 413	72.23 418	88.10 304	42.87 441	75.97 417	52.21 418	80.95 440	83.15 408

WTY-MVS		67.91 401	68.35 398	66.58 428	80.82 394	48.12 435	65.96 447	72.60 411	53.67 420	71.20 424	81.68 406	58.97 353	69.06 443	48.57 437	81.67 432	82.55 415

testing11		67.38 402	65.93 410	71.73 394	83.37 361	46.60 442	70.95 419	69.40 432	62.47 351	66.14 448	76.66 448	31.22 464	84.10 372	49.10 434	84.10 417	84.49 383

test-LLR		67.21 403	66.74 407	68.63 416	76.45 435	55.21 391	67.89 436	67.14 443	62.43 354	65.08 456	72.39 459	43.41 437	69.37 439	61.00 364	84.89 409	81.31 430

testing222		66.93 404	65.30 417	71.81 393	83.38 360	45.83 446	72.06 410	67.50 439	64.12 340	69.68 435	76.37 451	27.34 475	83.00 379	38.88 462	88.38 357	86.62 361

sss		66.92 405	67.26 403	65.90 430	77.23 425	51.10 425	64.79 449	71.72 421	52.12 433	70.13 432	80.18 418	57.96 360	65.36 461	50.21 426	81.01 438	81.25 432

KD-MVS_2432*1600		66.87 406	65.81 413	70.04 401	67.50 471	47.49 438	62.56 455	79.16 363	61.21 370	77.98 371	80.61 412	25.29 478	82.48 382	53.02 413	84.92 406	80.16 441

miper_refine_blended		66.87 406	65.81 413	70.04 401	67.50 471	47.49 438	62.56 455	79.16 363	61.21 370	77.98 371	80.61 412	25.29 478	82.48 382	53.02 413	84.92 406	80.16 441

dmvs_re		66.81 408	66.98 404	66.28 429	76.87 429	58.68 364	71.66 413	72.24 414	60.29 380	69.52 437	73.53 458	52.38 388	64.40 463	44.90 451	81.44 435	75.76 451

tpm cat1		66.76 409	65.21 418	71.42 395	77.09 427	50.62 427	78.01 337	73.68 404	44.89 457	68.64 439	79.00 428	45.51 423	82.42 384	49.91 429	70.15 465	81.23 434

UWE-MVS		66.43 410	65.56 416	69.05 411	84.15 344	40.98 461	73.06 404	64.71 453	54.84 414	76.18 389	79.62 424	29.21 469	80.50 397	38.54 465	89.75 337	85.66 371

PVSNet	58.17 21	66.41 411	65.63 415	68.75 414	81.96 377	49.88 430	62.19 457	72.51 413	51.03 439	68.04 442	75.34 455	50.84 395	74.77 422	45.82 450	82.96 423	81.60 427

tpmrst		66.28 412	66.69 408	65.05 436	72.82 461	39.33 464	78.20 335	70.69 427	53.16 424	67.88 443	80.36 417	48.18 405	74.75 423	58.13 381	70.79 464	81.08 435

Patchmatch-test		65.91 413	67.38 402	61.48 446	75.51 442	43.21 456	68.84 432	63.79 455	62.48 350	72.80 416	83.42 386	44.89 432	59.52 469	48.27 440	86.45 387	81.70 425

ADS-MVSNet2		65.87 414	63.64 423	72.55 387	73.16 457	56.92 378	67.10 443	74.81 392	49.74 446	66.04 450	82.97 389	46.71 409	77.26 413	42.29 455	69.96 466	83.46 401

myMVS_eth3d28		65.83 415	65.85 411	65.78 431	83.42 359	35.71 471	67.29 442	68.01 438	67.58 298	69.80 434	77.72 439	32.29 461	74.30 425	37.49 467	89.06 347	87.32 352

test_vis1_rt		65.64 416	64.09 420	70.31 400	66.09 475	70.20 200	61.16 458	81.60 350	38.65 471	72.87 415	69.66 464	52.84 385	60.04 468	56.16 390	77.77 451	80.68 439

mvsany_test3		65.48 417	62.97 426	73.03 382	69.99 468	76.17 124	64.83 448	43.71 479	43.68 461	80.25 351	87.05 335	52.83 386	63.09 466	51.92 423	72.44 461	79.84 444

test-mter		65.00 418	63.79 422	68.63 416	76.45 435	55.21 391	67.89 436	67.14 443	50.98 440	65.08 456	72.39 459	28.27 472	69.37 439	61.00 364	84.89 409	81.31 430

ETVMVS		64.67 419	63.34 425	68.64 415	83.44 358	41.89 458	69.56 431	61.70 462	61.33 367	68.74 438	75.76 453	28.76 470	79.35 402	34.65 470	86.16 393	84.67 382

myMVS_eth3d		64.66 420	63.89 421	66.97 426	81.72 380	37.39 468	71.00 417	61.99 457	61.38 365	70.81 427	72.36 461	20.96 481	79.30 403	49.59 431	85.18 401	84.22 389

test0.0.03 1		64.66 420	64.36 419	65.57 433	75.03 447	46.89 441	64.69 450	61.58 463	62.43 354	71.18 425	77.54 440	43.41 437	68.47 448	40.75 460	82.65 428	81.35 429

UBG		64.34 422	63.35 424	67.30 424	83.50 355	40.53 462	67.46 440	65.02 452	54.77 415	67.54 446	74.47 457	32.99 460	78.50 409	40.82 459	83.58 419	82.88 411

test_f		64.31 423	65.85 411	59.67 450	66.54 474	62.24 307	57.76 467	70.96 425	40.13 468	84.36 267	82.09 400	46.93 408	51.67 474	61.99 357	81.89 431	65.12 465

pmmvs3		62.47 424	60.02 437	69.80 405	71.58 465	64.00 275	70.52 423	58.44 469	39.77 469	66.05 449	75.84 452	27.10 477	72.28 429	46.15 448	84.77 413	73.11 455

EPMVS		62.47 424	62.63 428	62.01 442	70.63 467	38.74 466	74.76 387	52.86 473	53.91 419	67.71 445	80.01 419	39.40 446	66.60 456	55.54 397	68.81 470	80.68 439

ADS-MVSNet		61.90 426	62.19 430	61.03 447	73.16 457	36.42 470	67.10 443	61.75 460	49.74 446	66.04 450	82.97 389	46.71 409	63.21 464	42.29 455	69.96 466	83.46 401

PMMVS		61.65 427	60.38 434	65.47 434	65.40 478	69.26 214	63.97 453	61.73 461	36.80 475	60.11 467	68.43 466	59.42 349	66.35 457	48.97 435	78.57 449	60.81 468

E-PMN		61.59 428	61.62 431	61.49 445	66.81 473	55.40 389	53.77 470	60.34 465	66.80 309	58.90 470	65.50 469	40.48 445	66.12 458	55.72 394	86.25 391	62.95 467

TESTMET0.1,1		61.29 429	60.32 435	64.19 438	72.06 463	51.30 421	67.89 436	62.09 456	45.27 455	60.65 466	69.01 465	27.93 473	64.74 462	56.31 389	81.65 434	76.53 449

MVS-HIRNet		61.16 430	62.92 427	55.87 453	79.09 414	35.34 472	71.83 411	57.98 470	46.56 451	59.05 469	91.14 221	49.95 401	76.43 415	38.74 463	71.92 463	55.84 472

EMVS		61.10 431	60.81 433	61.99 443	65.96 476	55.86 385	53.10 471	58.97 468	67.06 306	56.89 474	63.33 470	40.98 443	67.03 454	54.79 403	86.18 392	63.08 466

DSMNet-mixed		60.98 432	61.61 432	59.09 452	72.88 460	45.05 450	74.70 388	46.61 478	26.20 476	65.34 454	90.32 260	55.46 376	63.12 465	41.72 457	81.30 437	69.09 461

dp		60.70 433	60.29 436	61.92 444	72.04 464	38.67 467	70.83 421	64.08 454	51.28 437	60.75 465	77.28 443	36.59 454	71.58 434	47.41 442	62.34 472	75.52 452

dmvs_testset		60.59 434	62.54 429	54.72 455	77.26 424	27.74 478	74.05 393	61.00 464	60.48 377	65.62 453	67.03 468	55.93 373	68.23 450	32.07 474	69.46 469	68.17 462

CHOSEN 280x420		59.08 435	56.52 441	66.76 427	76.51 433	64.39 271	49.62 472	59.00 467	43.86 460	55.66 475	68.41 467	35.55 455	68.21 451	43.25 454	76.78 457	67.69 463

mvsany_test1		58.48 436	56.47 442	64.50 437	65.90 477	68.21 230	56.95 468	42.11 480	38.30 472	65.69 452	77.19 446	56.96 367	59.35 470	46.16 447	58.96 473	65.93 464

UWE-MVS-28		58.44 437	57.71 439	60.65 448	73.58 454	31.23 475	69.68 430	48.80 476	53.12 425	61.79 463	78.83 430	30.98 465	68.40 449	21.58 477	80.99 439	82.33 420

PVSNet_0	51.08 22	56.10 438	54.97 443	59.48 451	75.12 446	53.28 407	55.16 469	61.89 459	44.30 458	59.16 468	62.48 471	54.22 381	65.91 459	35.40 469	47.01 474	59.25 470

new_pmnet		55.69 439	57.66 440	49.76 456	75.47 443	30.59 476	59.56 460	51.45 474	43.62 462	62.49 462	75.48 454	40.96 444	49.15 476	37.39 468	72.52 460	69.55 460

PMMVS2		55.64 440	59.27 438	44.74 457	64.30 479	12.32 485	40.60 473	49.79 475	53.19 423	65.06 458	84.81 370	53.60 384	49.76 475	32.68 473	89.41 342	72.15 456

MVE	40.22 23	51.82 441	50.47 444	55.87 453	62.66 480	51.91 416	31.61 475	39.28 481	40.65 467	50.76 476	74.98 456	56.24 372	44.67 477	33.94 472	64.11 471	71.04 459
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 442	42.65 445	39.67 458	70.86 466	21.11 480	61.01 459	21.42 485	57.36 400	57.97 473	50.06 474	16.40 483	58.73 471	21.03 478	27.69 478	39.17 474

kuosan		30.83 443	32.17 446	26.83 460	53.36 482	19.02 483	57.90 466	20.44 486	38.29 473	38.01 477	37.82 476	15.18 484	33.45 479	7.74 480	20.76 479	28.03 475

test_method		30.46 444	29.60 447	33.06 459	17.99 484	3.84 487	13.62 476	73.92 399	2.79 478	18.29 480	53.41 473	28.53 471	43.25 478	22.56 475	35.27 476	52.11 473

cdsmvs_eth3d_5k		20.81 445	27.75 448	0.00 465	0.00 488	0.00 490	0.00 477	85.44 301	0.00 483	0.00 484	82.82 393	81.46 135	0.00 484	0.00 483	0.00 482	0.00 480

tmp_tt		20.25 446	24.50 449	7.49 462	4.47 485	8.70 486	34.17 474	25.16 483	1.00 480	32.43 479	18.49 477	39.37 447	9.21 481	21.64 476	43.75 475	4.57 477

ab-mvs-re		6.65 447	8.87 450	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	79.80 421	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

pcd_1.5k_mvsjas		6.41 448	8.55 451	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	76.94 194	0.00 484	0.00 483	0.00 482	0.00 480

test123		6.27 449	8.08 452	0.84 463	1.11 487	0.57 488	62.90 454	0.82 487	0.54 481	1.07 483	2.75 482	1.26 485	0.30 482	1.04 481	1.26 481	1.66 478

testmvs		5.91 450	7.65 453	0.72 464	1.20 486	0.37 489	59.14 462	0.67 488	0.49 482	1.11 482	2.76 481	0.94 486	0.24 483	1.02 482	1.47 480	1.55 479

mmdepth		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

monomultidepth		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

test_blank		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

uanet_test		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

DCPMVS		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

sosnet-low-res		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

sosnet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

uncertanet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

Regformer		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

uanet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 490	0.00 477	0.00 489	0.00 483	0.00 484	0.00 483	0.00 487	0.00 484	0.00 483	0.00 482	0.00 480

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 141	93.24 43	93.18 132		95.85 24	84.99 75	97.69 65	93.54 157

TestfortrainingZip							92.12 33

WAC-MVS						37.39 468								52.61 417

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		91.01 172					96.05 9	87.45 29	98.17 37	92.40 216

PC_three_1452									58.96 387	90.06 108	91.33 211	80.66 145	93.03 156	75.78 197	95.94 138	92.48 210

No_MVS				88.81 73	91.55 140	77.99 97		91.01 172					96.05 9	87.45 29	98.17 37	92.40 216

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					0.00 488

eth-test					0.00 488

ZD-MVS					92.22 113	80.48 71		91.85 140	71.22 242	90.38 103	92.98 144	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	92.73 193

IU-MVS					94.18 55	72.64 158		90.82 177	56.98 404	89.67 121				85.78 64	97.92 52	93.28 165

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 217	81.12 139	94.68 82	74.48 212	95.35 161	92.29 226

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	92.04 239

test_241102_ONE					94.18 55	72.65 156		93.69 62	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	75.14 171	91.07 87	92.89 150	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 216	70.80 247

test_0728_THIRD									85.33 42	93.75 35	94.65 66	87.44 48	95.78 35	87.41 31	98.21 34	92.98 186

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	92.02 240

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																83.88 393

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 413				83.88 393

sam_mvs												45.92 418

ambc				82.98 221	90.55 169	64.86 265	88.20 108	89.15 229		89.40 130	93.96 106	71.67 274	91.38 202	78.83 150	96.55 106	92.71 196

MTGPA								91.81 144

test_post1							78.85 327				3.13 479	45.19 428	80.13 399	58.11 382

test_post											3.10 480	45.43 424	77.22 414

patchmatchnet-post											81.71 405	45.93 417	87.01 320

GG-mvs-BLEND				67.16 425	73.36 455	46.54 444	84.15 199	55.04 472		58.64 471	61.95 472	29.93 468	83.87 376	38.71 464	76.92 456	71.07 458

MTMP							90.66 53	33.14 482

gm-plane-assit					75.42 444	44.97 451			52.17 430		72.36 461		87.90 306	54.10 406

test9_res														80.83 125	96.45 112	90.57 287

TEST9					92.34 108	79.70 80	83.94 205	90.32 196	65.41 327	84.49 263	90.97 228	82.03 124	93.63 127

test_8					92.09 117	78.87 88	83.82 210	90.31 198	65.79 317	84.36 267	90.96 230	81.93 126	93.44 141

agg_prior2														79.68 138	96.16 125	90.22 295

agg_prior					91.58 138	77.69 103		90.30 199		84.32 269			93.18 149

TestCases				89.68 56	91.59 135	83.40 52		95.44 11	79.47 108	88.00 164	93.03 142	82.66 104	91.47 196	70.81 266	96.14 126	94.16 116

test_prior4						78.97 87	84.59 188

test_prior2							83.37 226		75.43 167	84.58 260	91.57 201	81.92 128		79.54 142	96.97 94

test_prior				86.32 120	90.59 168	71.99 174		92.85 105					94.17 106			92.80 191

旧先验2							81.73 273		56.88 405	86.54 210			84.90 362	72.81 250

新几何2							81.72 274

新几何1				82.95 223	93.96 64	78.56 91		80.24 359	55.45 410	83.93 280	91.08 224	71.19 276	88.33 298	65.84 323	93.07 246	81.95 424

旧先验1					91.97 121	71.77 175		81.78 347			91.84 190	73.92 237			93.65 227	83.61 399

无先验							82.81 245	85.62 299	58.09 393				91.41 201	67.95 306		84.48 384

原ACMM2							82.26 265

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 152	62.59 348	82.73 306	90.67 247	76.53 203	94.25 98	69.24 287	95.69 153	85.55 372

test222					93.31 81	76.54 116	79.38 316	77.79 370	52.59 427	82.36 311	90.84 238	66.83 300			91.69 289	81.25 432

testdata2													86.43 335	63.52 345

segment_acmp												81.94 125

testdata				79.54 305	92.87 92	72.34 167		80.14 360	59.91 383	85.47 237	91.75 197	67.96 294	85.24 358	68.57 301	92.18 275	81.06 437

testdata1							79.62 308		73.95 188

test12				86.57 115	90.74 164	72.63 160		90.69 180		82.76 304		79.20 158	94.80 79		95.32 163	92.27 228

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 219

plane_prior5								93.61 65					95.22 63	80.78 126	95.83 146	94.46 98

plane_prior4											92.95 148

plane_prior3						76.85 114			77.79 136	86.55 204

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 158						95.03 175

n2								0.00 489

nn								0.00 489

door-mid								74.45 396

lessismore_v0				85.95 131	91.10 157	70.99 191		70.91 426		91.79 75	94.42 79	61.76 334	92.93 159	79.52 143	93.03 247	93.93 126

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								91.46 153

door								72.57 412

HQP5-MVS						70.66 193

HQP-NCC					91.19 152		84.77 179		73.30 202	80.55 344

ACMP_Plane					91.19 152		84.77 179		73.30 202	80.55 344

BP-MVS														77.30 175

HQP4-MVS										80.56 343			94.61 86			93.56 154

HQP3-MVS								92.68 111							94.47 197

HQP2-MVS												72.10 265

NP-MVS					91.95 122	74.55 137					90.17 267

MDTV_nov1_ep13_2view						27.60 479	70.76 422		46.47 452	61.27 464		45.20 427		49.18 433		83.75 398

MDTV_nov1_ep13			68.29 399		78.03 419	43.87 454	74.12 392	72.22 415	52.17 430	67.02 447	85.54 355	45.36 425	80.85 393	55.73 393	84.42 414

ACMMP++_ref															95.74 152

ACMMP++															97.35 84

Test By Simon												79.09 160

ITE_SJBPF				90.11 49	90.72 165	84.97 41		90.30 199	81.56 84	90.02 110	91.20 219	82.40 109	90.81 225	73.58 236	94.66 192	94.56 91

DeepMVS_CX				24.13 461	32.95 483	29.49 477		21.63 484	12.07 477	37.95 478	45.07 475	30.84 466	19.21 480	17.94 479	33.06 477	23.69 476

High-resolution multi-view benchmark