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 50	97.23 2	95.32 2	99.01 2	97.26 9	80.16 148	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 53	96.29 22	88.16 36	94.17 105	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 68	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 79	93.16 149	91.10 2	97.53 80	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 53	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 43	95.72 38	89.60 5	98.27 28	92.08 233

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 28	89.13 7	98.26 30	91.76 244

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 28	89.13 7	98.26 30	91.76 244

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 61	95.13 52	90.65 10	95.34 58	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 81	90.26 4	98.44 20	93.63 145

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 59	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 49	87.16 38	97.60 74	92.73 190

SR-MVS		92.23 11	92.34 12	91.91 17	94.89 38	87.85 10	92.51 25	93.87 52	88.20 24	93.24 43	94.02 100	90.15 17	95.67 40	86.82 43	97.34 84	92.19 228

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 71	91.77 75	93.94 108	90.55 13	95.73 37	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 87	85.17 39	92.47 27	95.05 15	87.65 28	93.21 46	94.39 81	90.09 18	95.08 69	86.67 45	97.60 74	94.18 113

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 69	86.15 24	93.37 10	95.10 14	90.28 10	92.11 67	95.03 54	89.75 21	94.93 73	79.95 132	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 74	92.39 64	94.14 93	89.15 26	95.62 41	87.35 33	98.24 32	94.56 90
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 114	83.09 68	91.54 77	94.25 87	87.67 46	95.51 49	87.21 37	98.11 40	93.12 173

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 73	83.16 67	91.06 87	94.00 101	88.26 33	95.71 39	87.28 36	98.39 23	92.55 203

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 76	85.07 45	89.99 109	94.03 99	86.57 58	95.80 30	87.35 33	97.62 72	94.20 110

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 37	91.81 141	84.07 55	92.00 70	94.40 80	86.63 57	95.28 61	88.59 11	98.31 26	92.30 220

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 106	88.22 23	88.53 146	97.64 6	83.45 93	94.55 89	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 82	81.99 77	91.40 79	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 121

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 66	94.27 25	82.35 75	93.67 38	94.82 60	91.18 5	95.52 47	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 80	81.91 79	90.88 94	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 130

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 81	81.99 77	91.47 78	93.96 105	88.35 32	95.56 44	87.74 22	97.74 62	92.85 187

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 100	91.29 83	93.97 102	87.93 42	95.87 20	88.65 10	97.96 51	94.12 117

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 89	78.04 96	92.84 16	94.14 37	83.33 65	93.90 29	95.73 34	88.77 28	96.41 3	87.60 27	97.98 48	92.98 183
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 44	91.93 15	95.67 23	85.85 31	90.00 66	93.90 49	80.32 97	91.74 76	94.41 79	88.17 35	95.98 13	86.37 49	97.99 46	93.96 123

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 64	80.97 70	91.49 44	93.48 71	82.82 72	92.60 60	93.97 102	88.19 34	96.29 6	87.61 26	98.20 36	94.39 104
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 45	91.83 20	96.18 11	86.88 17	92.20 31	93.03 96	82.59 73	88.52 147	94.37 82	86.74 56	95.41 56	86.32 50	98.21 34	93.19 168
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 41	90.82 37	95.45 28	82.73 59	91.75 42	93.74 58	80.98 90	91.38 80	93.80 112	87.20 51	95.80 30	87.10 40	97.69 65	93.93 124

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 109	94.51 19	75.79 158	92.94 50	94.96 55	88.36 31	95.01 71	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 82	93.00 88	76.26 122	89.65 79	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 140	78.35 154	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 40	89.42 62	95.93 16	79.54 82	89.95 70	93.68 63	77.65 135	91.97 71	94.89 57	88.38 30	95.45 54	89.27 6	97.87 56	93.27 163

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 80	93.35 75	79.20 113	93.83 32	93.60 122	90.81 8	92.96 156	85.02 74	98.45 19	92.41 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 54	91.38 28	89.03 202	84.23 49	93.58 6	94.68 18	90.65 8	90.33 103	93.95 107	84.50 81	95.37 57	80.87 122	95.50 156	94.53 94

ACMP	79.16 10	90.54 36	90.60 52	90.35 45	94.36 50	80.98 69	89.16 91	94.05 42	79.03 116	92.87 52	93.74 117	90.60 12	95.21 64	82.87 100	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 38	88.88 71	93.38 78	78.65 90	89.15 92	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 83	97.81 58	91.70 248
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 38	91.64 22	86.93 108	94.18 54	72.65 155	90.47 59	93.69 61	83.77 58	94.11 27	94.27 83	90.28 15	95.84 26	86.03 57	97.92 52	92.29 222

SMA-MVS		90.31 39	90.48 53	89.83 55	95.31 30	79.52 83	90.98 51	93.24 83	75.37 167	92.84 54	95.28 48	85.58 71	96.09 8	87.92 18	97.76 60	93.88 127
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 40	90.80 47	88.68 78	92.86 93	77.09 111	91.19 48	95.74 6	81.38 85	92.28 66	93.80 112	86.89 55	94.64 84	85.52 66	97.51 81	94.30 109

v7n		90.13 41	90.96 43	87.65 98	91.95 121	71.06 188	89.99 68	93.05 93	86.53 35	94.29 23	96.27 23	82.69 101	94.08 108	86.25 53	97.63 70	97.82 8

ME-MVS		90.09 42	90.66 50	88.38 84	92.82 96	76.12 126	89.40 89	93.70 60	83.72 60	92.39 64	93.18 131	88.02 40	95.47 52	84.99 75	97.69 65	93.54 155

PMVS	80.48 6	90.08 43	90.66 50	88.34 86	96.71 3	92.97 2	90.31 63	89.57 218	88.51 21	90.11 105	95.12 53	90.98 7	88.92 278	77.55 168	97.07 91	83.13 405
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 44	91.09 37	87.00 106	91.55 138	72.64 157	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 136	95.88 18	87.41 31	95.94 136	92.48 206

DVP-MVS		90.06 45	91.32 33	86.29 120	94.16 57	72.56 161	90.54 56	91.01 169	83.61 62	93.75 35	94.65 65	89.76 19	95.78 34	86.42 47	97.97 49	90.55 285
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 45	91.92 16	84.47 172	96.56 6	58.83 358	89.04 93	92.74 108	91.40 6	96.12 5	96.06 29	87.23 50	95.57 43	79.42 142	98.74 6	99.00 2

PEN-MVS		90.03 47	91.88 19	84.48 171	96.57 5	58.88 355	88.95 94	93.19 85	91.62 5	96.01 7	96.16 27	87.02 53	95.60 42	78.69 150	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 48	89.74 59	90.83 36	93.16 85	80.37 74	91.91 40	93.11 89	81.10 88	95.32 14	97.24 10	72.94 251	94.85 75	85.07 71	97.78 59	97.26 16

DTE-MVSNet		89.98 49	91.91 18	84.21 181	96.51 7	57.84 366	88.93 95	92.84 104	91.92 4	96.16 4	96.23 24	86.95 54	95.99 12	79.05 146	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 49	89.84 57	90.41 43	94.91 37	84.50 48	89.49 85	93.98 44	79.68 105	92.09 68	93.89 110	83.80 88	93.10 152	82.67 104	98.04 41	93.64 144

TestfortrainingZip a		89.97 51	90.77 48	87.58 99	94.38 48	73.21 149	92.12 33	93.85 53	77.53 139	93.24 43	93.18 131	87.06 52	95.85 24	87.89 19	97.69 65	93.68 139

3Dnovator+	83.92 2	89.97 51	89.66 60	90.92 35	91.27 147	81.66 66	91.25 46	94.13 38	88.89 15	88.83 138	94.26 86	77.55 177	95.86 23	84.88 77	95.87 142	95.24 65

WR-MVS_H		89.91 53	91.31 34	85.71 137	96.32 9	62.39 299	89.54 83	93.31 79	90.21 12	95.57 11	95.66 37	81.42 133	95.90 17	80.94 121	98.80 3	98.84 5

OPM-MVS		89.80 54	89.97 55	89.27 64	94.76 40	79.86 78	86.76 136	92.78 107	78.78 119	92.51 61	93.64 121	88.13 37	93.84 119	84.83 79	97.55 77	94.10 118
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 55	89.27 66	91.30 29	93.51 72	84.79 44	89.89 72	90.63 179	70.00 254	94.55 19	96.67 17	87.94 41	93.59 131	84.27 85	95.97 132	95.52 56

anonymousdsp		89.73 56	88.88 76	92.27 8	89.82 183	86.67 18	90.51 58	90.20 200	69.87 255	95.06 15	96.14 28	84.28 84	93.07 153	87.68 24	96.34 114	97.09 20

test_djsdf		89.62 57	89.01 70	91.45 26	92.36 106	82.98 57	91.98 38	90.08 203	71.54 233	94.28 25	96.54 19	81.57 131	94.27 95	86.26 51	96.49 108	97.09 20

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 46	94.47 43	85.95 27	86.84 132	93.91 48	80.07 101	86.75 196	93.26 128	93.64 2	90.93 216	84.60 82	90.75 313	93.97 122

APD-MVS		89.54 59	89.63 61	89.26 65	92.57 99	81.34 68	90.19 65	93.08 92	80.87 92	91.13 85	93.19 130	86.22 65	95.97 14	82.23 110	97.18 89	90.45 287
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 60	88.81 79	91.19 32	93.38 78	84.72 45	89.70 75	90.29 197	69.27 262	94.39 21	96.38 21	86.02 68	93.52 136	83.96 87	95.92 138	95.34 60

CPTT-MVS		89.39 61	88.98 72	90.63 40	95.09 33	86.95 16	92.09 36	92.30 123	79.74 104	87.50 180	92.38 166	81.42 133	93.28 145	83.07 96	97.24 87	91.67 249

ACMH	76.49 14	89.34 62	91.14 36	83.96 188	92.50 102	70.36 197	89.55 81	93.84 55	81.89 80	94.70 17	95.44 44	90.69 9	88.31 295	83.33 92	98.30 27	93.20 167
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 63	89.12 67	89.84 53	88.67 213	85.64 35	90.61 54	93.17 86	86.02 38	93.12 47	95.30 46	84.94 76	89.44 270	74.12 218	96.10 127	94.45 98

APD_test2		89.30 63	89.12 67	89.84 53	88.67 213	85.64 35	90.61 54	93.17 86	86.02 38	93.12 47	95.30 46	84.94 76	89.44 270	74.12 218	96.10 127	94.45 98

CP-MVSNet		89.27 65	90.91 45	84.37 173	96.34 8	58.61 361	88.66 102	92.06 130	90.78 7	95.67 8	95.17 51	81.80 128	95.54 46	79.00 147	98.69 10	98.95 4

XVG-OURS		89.18 66	88.83 78	90.23 47	94.28 51	86.11 26	85.91 151	93.60 66	80.16 99	89.13 134	93.44 124	83.82 87	90.98 213	83.86 89	95.30 164	93.60 148

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 63	93.64 70	79.07 86	88.54 105	94.20 31	73.53 192	89.71 117	94.82 60	85.09 75	95.77 36	84.17 86	98.03 43	93.26 165
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 68	90.72 49	84.31 179	97.00 2	64.33 270	89.67 78	88.38 238	88.84 17	94.29 23	97.57 7	90.48 14	91.26 204	72.57 249	97.65 69	97.34 15

MSP-MVS		89.08 69	88.16 86	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 124	89.16 132	92.25 175	72.03 265	96.36 4	88.21 13	90.93 305	92.98 183
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 70	89.88 56	86.22 124	91.63 132	77.07 112	89.82 73	93.77 57	78.90 117	92.88 51	92.29 173	86.11 66	90.22 244	86.24 54	97.24 87	91.36 257
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 71	88.45 82	90.38 44	94.92 36	85.85 31	89.70 75	91.27 161	78.20 127	86.69 200	92.28 174	80.36 146	95.06 70	86.17 55	96.49 108	90.22 291

Elysia		88.71 72	88.89 74	88.19 89	91.26 148	72.96 151	88.10 110	93.59 67	84.31 51	90.42 99	94.10 96	74.07 228	94.82 76	88.19 14	95.92 138	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 89	91.26 148	72.96 151	88.10 110	93.59 67	84.31 51	90.42 99	94.10 96	74.07 228	94.82 76	88.19 14	95.92 138	96.80 27

test_0402		88.65 74	89.58 63	85.88 133	92.55 100	72.22 169	84.01 199	89.44 221	88.63 20	94.38 22	95.77 32	86.38 64	93.59 131	79.84 133	95.21 165	91.82 242

DP-MVS		88.60 75	89.01 70	87.36 101	91.30 145	77.50 104	87.55 118	92.97 100	87.95 26	89.62 121	92.87 149	84.56 80	93.89 116	77.65 166	96.62 103	90.70 277

APD_test1		88.40 76	87.91 88	89.88 52	89.50 189	86.65 20	89.98 69	91.91 136	84.26 53	90.87 95	93.92 109	82.18 116	89.29 274	73.75 226	94.81 184	93.70 138

Anonymous20231211		88.40 76	89.62 62	84.73 163	90.46 168	65.27 259	88.86 96	93.02 97	87.15 30	93.05 49	97.10 11	82.28 114	92.02 182	76.70 178	97.99 46	96.88 26

PS-MVSNAJss		88.31 78	87.90 89	89.56 60	93.31 80	77.96 99	87.94 114	91.97 133	70.73 245	94.19 26	96.67 17	76.94 190	94.57 87	83.07 96	96.28 116	96.15 38

OMC-MVS		88.19 79	87.52 93	90.19 48	91.94 123	81.68 65	87.49 121	93.17 86	76.02 152	88.64 143	91.22 214	84.24 85	93.37 143	77.97 164	97.03 92	95.52 56

CS-MVS		88.14 80	87.67 92	89.54 61	89.56 187	79.18 85	90.47 59	94.77 17	79.37 111	84.32 266	89.33 280	83.87 86	94.53 90	82.45 106	94.89 180	94.90 76

TSAR-MVS + MP.		88.14 80	87.82 90	89.09 69	95.72 22	76.74 115	92.49 26	91.19 164	67.85 289	86.63 201	94.84 59	79.58 154	95.96 15	87.62 25	94.50 193	94.56 90
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 82	89.79 58	82.98 219	93.26 82	63.94 274	91.10 49	89.64 215	85.07 45	90.91 91	91.09 219	89.16 25	91.87 187	82.03 111	95.87 142	93.13 170

EC-MVSNet		88.01 83	88.32 85	87.09 103	89.28 194	72.03 172	90.31 63	96.31 4	80.88 91	85.12 241	89.67 273	84.47 82	95.46 53	82.56 105	96.26 119	93.77 136

RPSCF		88.00 84	86.93 107	91.22 31	90.08 176	89.30 5	89.68 77	91.11 165	79.26 112	89.68 118	94.81 63	82.44 105	87.74 306	76.54 183	88.74 349	96.61 32

AllTest		87.97 85	87.40 97	89.68 56	91.59 133	83.40 52	89.50 84	95.44 11	79.47 107	88.00 162	93.03 140	82.66 102	91.47 195	70.81 263	96.14 124	94.16 114

TranMVSNet+NR-MVSNet		87.86 86	88.76 80	85.18 149	94.02 62	64.13 271	84.38 191	91.29 157	84.88 48	92.06 69	93.84 111	86.45 61	93.73 121	73.22 240	98.66 11	97.69 9

nrg030		87.85 87	88.49 81	85.91 131	90.07 178	69.73 205	87.86 115	94.20 31	74.04 184	92.70 59	94.66 64	85.88 69	91.50 194	79.72 135	97.32 85	96.50 34

CNVR-MVS		87.81 88	87.68 91	88.21 88	92.87 91	77.30 110	85.25 168	91.23 162	77.31 142	87.07 190	91.47 204	82.94 98	94.71 80	84.67 81	96.27 118	92.62 198

HQP_MVS		87.75 89	87.43 96	88.70 77	93.45 74	76.42 119	89.45 86	93.61 64	79.44 109	86.55 202	92.95 146	74.84 215	95.22 62	80.78 124	95.83 144	94.46 96

sc_t1		87.70 90	88.94 73	83.99 186	93.47 73	67.15 236	85.05 173	88.21 245	86.81 32	91.87 73	97.65 5	85.51 73	87.91 301	74.22 213	97.63 70	96.92 25

MM		87.64 91	87.15 99	89.09 69	89.51 188	76.39 121	88.68 101	86.76 276	84.54 50	83.58 285	93.78 114	73.36 246	96.48 2	87.98 17	96.21 120	94.41 103

MVSMamba_PlusPlus		87.53 92	88.86 77	83.54 205	92.03 119	62.26 303	91.49 44	92.62 112	88.07 25	88.07 159	96.17 26	72.24 260	95.79 33	84.85 78	94.16 206	92.58 201

NCCC		87.36 93	86.87 108	88.83 72	92.32 109	78.84 89	86.58 140	91.09 167	78.77 120	84.85 253	90.89 230	80.85 139	95.29 59	81.14 119	95.32 161	92.34 218

DeepPCF-MVS	81.24 5	87.28 94	86.21 118	90.49 42	91.48 142	84.90 42	83.41 222	92.38 119	70.25 251	89.35 129	90.68 240	82.85 100	94.57 87	79.55 139	95.95 135	92.00 237

SixPastTwentyTwo		87.20 95	87.45 95	86.45 117	92.52 101	69.19 215	87.84 116	88.05 246	81.66 82	94.64 18	96.53 20	65.94 302	94.75 79	83.02 98	96.83 97	95.41 58

fmvsm_s_conf0.5_n_9		87.04 96	87.02 104	87.08 104	89.67 185	75.87 128	84.60 184	89.74 210	74.40 181	89.92 113	93.41 125	80.45 144	90.63 231	86.66 46	94.37 199	94.73 87

SPE-MVS-test		87.00 97	86.43 114	88.71 76	89.46 190	77.46 105	89.42 88	95.73 7	77.87 133	81.64 326	87.25 325	82.43 106	94.53 90	77.65 166	96.46 110	94.14 116

UniMVSNet (Re)		86.87 98	86.98 106	86.55 115	93.11 86	68.48 225	83.80 209	92.87 102	80.37 95	89.61 123	91.81 190	77.72 173	94.18 103	75.00 206	98.53 16	96.99 24

Vis-MVSNet		86.86 99	86.58 111	87.72 96	92.09 116	77.43 107	87.35 122	92.09 129	78.87 118	84.27 271	94.05 98	78.35 165	93.65 124	80.54 128	91.58 290	92.08 233
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 100	87.06 102	86.17 127	92.86 93	67.02 240	82.55 249	91.56 147	83.08 69	90.92 89	91.82 189	78.25 166	93.99 110	74.16 216	98.35 24	97.49 13

DU-MVS		86.80 101	86.99 105	86.21 125	93.24 83	67.02 240	83.16 232	92.21 124	81.73 81	90.92 89	91.97 181	77.20 184	93.99 110	74.16 216	98.35 24	97.61 10

casdiffmvs_mvg		86.72 102	87.51 94	84.36 175	87.09 264	65.22 260	84.16 195	94.23 28	77.89 131	91.28 84	93.66 120	84.35 83	92.71 162	80.07 129	94.87 183	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		86.68 103	86.52 112	87.18 102	85.94 302	78.30 92	86.93 129	92.20 125	65.94 309	89.16 132	93.16 135	83.10 96	89.89 259	87.81 21	94.43 197	93.35 158

tt0320-xc		86.67 104	88.41 83	81.44 262	93.45 74	60.44 332	83.96 201	88.50 234	87.26 29	90.90 93	97.90 3	85.61 70	86.40 332	70.14 275	98.01 45	97.47 14

IS-MVSNet		86.66 105	86.82 110	86.17 127	92.05 118	66.87 244	91.21 47	88.64 231	86.30 37	89.60 124	92.59 158	69.22 283	94.91 74	73.89 223	97.89 55	96.72 29

tt0320		86.63 106	88.36 84	81.41 263	93.57 71	60.73 329	84.37 192	88.61 233	87.00 31	90.75 96	97.98 2	85.54 72	86.45 330	69.75 280	97.70 64	97.06 22

v10		86.54 107	87.10 101	84.84 157	88.16 228	63.28 281	86.64 139	92.20 125	75.42 166	92.81 56	94.50 72	74.05 231	94.06 109	83.88 88	96.28 116	97.17 19

pmmvs6		86.52 108	88.06 87	81.90 248	92.22 112	62.28 302	84.66 183	89.15 225	83.54 64	89.85 114	97.32 8	88.08 39	86.80 323	70.43 272	97.30 86	96.62 31

NormalMVS		86.47 109	85.32 141	89.94 51	94.43 44	80.42 72	88.63 103	93.59 67	74.56 176	85.12 241	90.34 253	66.19 299	94.20 100	76.57 181	98.44 20	95.19 68

PHI-MVS		86.38 110	85.81 128	88.08 91	88.44 222	77.34 108	89.35 90	93.05 93	73.15 205	84.76 255	87.70 314	78.87 159	94.18 103	80.67 126	96.29 115	92.73 190

CSCG		86.26 111	86.47 113	85.60 139	90.87 160	74.26 138	87.98 113	91.85 137	80.35 96	89.54 127	88.01 301	79.09 157	92.13 178	75.51 199	95.06 172	90.41 288

DeepC-MVS_fast	80.27 8	86.23 112	85.65 134	87.96 94	91.30 145	76.92 113	87.19 124	91.99 132	70.56 246	84.96 248	90.69 239	80.01 150	95.14 67	78.37 153	95.78 148	91.82 242
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 113	86.83 109	84.36 175	87.82 236	62.35 301	86.42 143	91.33 156	76.78 146	92.73 58	94.48 74	73.41 243	93.72 122	83.10 95	95.41 157	97.01 23

Anonymous20240529		86.20 114	87.13 100	83.42 207	90.19 173	64.55 267	84.55 186	90.71 176	85.85 40	89.94 112	95.24 50	82.13 117	90.40 239	69.19 287	96.40 113	95.31 62

fmvsm_s_conf0.5_n_3		86.19 115	87.27 98	82.95 221	86.91 272	70.38 196	85.31 167	92.61 113	75.59 162	88.32 154	92.87 149	82.22 115	88.63 287	88.80 9	92.82 253	89.83 301

test_fmvsmconf0.1_n		86.18 116	85.88 126	87.08 104	85.26 318	78.25 93	85.82 155	91.82 139	65.33 324	88.55 145	92.35 172	82.62 104	89.80 261	86.87 42	94.32 201	93.18 169

CDPH-MVS		86.17 117	85.54 135	88.05 93	92.25 110	75.45 131	83.85 206	92.01 131	65.91 311	86.19 213	91.75 194	83.77 89	94.98 72	77.43 171	96.71 101	93.73 137

NR-MVSNet		86.00 118	86.22 117	85.34 146	93.24 83	64.56 266	82.21 263	90.46 185	80.99 89	88.42 150	91.97 181	77.56 176	93.85 117	72.46 250	98.65 12	97.61 10

train_agg		85.98 119	85.28 142	88.07 92	92.34 107	79.70 80	83.94 202	90.32 192	65.79 313	84.49 260	90.97 224	81.93 123	93.63 126	81.21 118	96.54 106	90.88 271

KinetiMVS		85.95 120	86.10 121	85.50 143	87.56 246	69.78 203	83.70 212	89.83 209	80.42 94	87.76 173	93.24 129	73.76 237	91.54 193	85.03 73	93.62 227	95.19 68

FC-MVSNet-test		85.93 121	87.05 103	82.58 232	92.25 110	56.44 377	85.75 156	93.09 91	77.33 141	91.94 72	94.65 65	74.78 217	93.41 142	75.11 205	98.58 14	97.88 7

test_fmvsmconf_n		85.88 122	85.51 136	86.99 107	84.77 327	78.21 94	85.40 165	91.39 154	65.32 325	87.72 175	91.81 190	82.33 109	89.78 262	86.68 44	94.20 204	92.99 181

Effi-MVS+-dtu		85.82 123	83.38 192	93.14 4	87.13 259	91.15 3	87.70 117	88.42 237	74.57 175	83.56 286	85.65 349	78.49 164	94.21 99	72.04 252	92.88 249	94.05 120

TAPA-MVS	77.73 12	85.71 124	84.83 152	88.37 85	88.78 212	79.72 79	87.15 126	93.50 70	69.17 263	85.80 224	89.56 274	80.76 140	92.13 178	73.21 245	95.51 155	93.25 166
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 125	86.14 119	83.58 201	87.97 230	67.13 237	87.55 118	94.32 22	73.44 195	88.47 148	87.54 317	86.45 61	91.06 211	75.76 196	93.76 218	92.54 204

canonicalmvs		85.50 125	86.14 119	83.58 201	87.97 230	67.13 237	87.55 118	94.32 22	73.44 195	88.47 148	87.54 317	86.45 61	91.06 211	75.76 196	93.76 218	92.54 204

fmvsm_s_conf0.5_n_8		85.48 127	85.75 131	84.68 166	87.10 262	69.98 201	84.28 193	92.68 109	74.77 172	87.90 166	92.36 171	73.94 232	90.41 238	85.95 62	92.74 255	93.66 140

EPP-MVSNet		85.47 128	85.04 147	86.77 112	91.52 141	69.37 210	91.63 43	87.98 249	81.51 84	87.05 191	91.83 188	66.18 301	95.29 59	70.75 266	96.89 94	95.64 53

GeoE		85.45 129	85.81 128	84.37 173	90.08 176	67.07 239	85.86 154	91.39 154	72.33 223	87.59 177	90.25 258	84.85 78	92.37 172	78.00 162	91.94 280	93.66 140

MGCNet		85.37 130	84.58 162	87.75 95	85.28 317	73.36 143	86.54 142	85.71 293	77.56 138	81.78 324	92.47 164	70.29 277	96.02 11	85.59 65	95.96 133	93.87 128

FIs		85.35 131	86.27 116	82.60 231	91.86 125	57.31 370	85.10 172	93.05 93	75.83 157	91.02 88	93.97 102	73.57 239	92.91 160	73.97 222	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 132	85.36 140	84.81 159	85.80 305	76.13 125	85.15 171	92.32 122	61.40 360	91.33 81	90.85 233	83.76 90	86.16 338	84.31 84	93.28 236	92.15 231

casdiffmvs		85.21 133	85.85 127	83.31 210	86.17 294	62.77 288	83.03 234	93.93 47	74.69 174	88.21 156	92.68 157	82.29 113	91.89 186	77.87 165	93.75 221	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		85.20 134	85.25 143	85.02 154	86.01 300	71.31 184	84.96 174	91.76 143	69.10 265	88.90 135	92.56 161	73.84 235	90.63 231	86.88 41	93.26 237	93.13 170

baseline		85.20 134	85.93 124	83.02 217	86.30 289	62.37 300	84.55 186	93.96 45	74.48 178	87.12 185	92.03 180	82.30 111	91.94 183	78.39 152	94.21 203	94.74 86

SSM_0404		85.16 136	85.09 145	85.36 145	90.14 175	69.52 208	86.17 148	91.58 145	74.41 179	86.55 202	91.49 201	78.54 160	93.97 112	73.71 227	93.21 241	92.59 200

K. test v3		85.14 137	84.73 154	86.37 118	91.13 154	69.63 207	85.45 163	76.68 378	84.06 56	92.44 63	96.99 13	62.03 329	94.65 83	80.58 127	93.24 238	94.83 83

mmtdpeth		85.13 138	85.78 130	83.17 215	84.65 329	74.71 134	85.87 153	90.35 191	77.94 130	83.82 278	96.96 15	77.75 171	80.03 397	78.44 151	96.21 120	94.79 85

EI-MVSNet-Vis-set		85.12 139	84.53 165	86.88 109	84.01 342	72.76 154	83.91 205	85.18 303	80.44 93	88.75 140	85.49 353	80.08 149	91.92 184	82.02 112	90.85 310	95.97 44

fmvsm_l_conf0.5_n_3		85.11 140	84.96 149	85.56 140	87.49 249	75.69 130	84.71 181	90.61 181	67.64 293	84.88 251	92.05 179	82.30 111	88.36 293	83.84 90	91.10 298	92.62 198

MGCFI-Net		85.04 141	85.95 123	82.31 241	87.52 247	63.59 277	86.23 147	93.96 45	73.46 193	88.07 159	87.83 312	86.46 60	90.87 221	76.17 190	93.89 214	92.47 208

EI-MVSNet-UG-set		85.04 141	84.44 168	86.85 110	83.87 346	72.52 163	83.82 207	85.15 304	80.27 98	88.75 140	85.45 355	79.95 151	91.90 185	81.92 115	90.80 312	96.13 39

X-MVStestdata		85.04 141	82.70 211	92.08 9	95.64 24	86.25 22	92.64 20	93.33 76	85.07 45	89.99 109	16.05 474	86.57 58	95.80 30	87.35 33	97.62 72	94.20 110

MSLP-MVS++		85.00 144	86.03 122	81.90 248	91.84 128	71.56 182	86.75 137	93.02 97	75.95 155	87.12 185	89.39 278	77.98 168	89.40 273	77.46 169	94.78 185	84.75 377

F-COLMAP		84.97 145	83.42 191	89.63 58	92.39 105	83.40 52	88.83 97	91.92 135	73.19 204	80.18 348	89.15 284	77.04 188	93.28 145	65.82 320	92.28 269	92.21 227

SSM_0407		84.89 146	84.85 151	85.01 155	89.13 198	68.97 218	85.60 160	91.58 145	74.41 179	85.68 225	91.49 201	78.54 160	93.69 123	73.71 227	93.47 229	92.38 215

balanced_conf03		84.80 147	85.40 138	83.00 218	88.95 205	61.44 311	90.42 62	92.37 121	71.48 235	88.72 142	93.13 136	70.16 279	95.15 66	79.26 144	94.11 207	92.41 210

3Dnovator	80.37 7	84.80 147	84.71 157	85.06 152	86.36 287	74.71 134	88.77 99	90.00 205	75.65 160	84.96 248	93.17 134	74.06 230	91.19 206	78.28 156	91.09 299	89.29 311

SymmetryMVS		84.79 149	83.54 186	88.55 79	92.44 104	80.42 72	88.63 103	82.37 338	74.56 176	85.12 241	90.34 253	66.19 299	94.20 100	76.57 181	95.68 152	91.03 265

IterMVS-LS		84.73 150	84.98 148	83.96 188	87.35 252	63.66 275	83.25 227	89.88 208	76.06 150	89.62 121	92.37 169	73.40 245	92.52 167	78.16 159	94.77 187	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 151	84.34 173	85.49 144	90.18 174	75.86 129	79.23 317	87.13 266	73.35 197	85.56 232	89.34 279	83.60 92	90.50 235	76.64 180	94.05 211	90.09 297

HQP-MVS		84.61 152	84.06 178	86.27 121	91.19 150	70.66 191	84.77 176	92.68 109	73.30 200	80.55 340	90.17 263	72.10 261	94.61 85	77.30 173	94.47 195	93.56 152

v1192		84.57 153	84.69 159	84.21 181	87.75 238	62.88 285	83.02 235	91.43 151	69.08 266	89.98 111	90.89 230	72.70 255	93.62 129	82.41 107	94.97 177	96.13 39

fmvsm_s_conf0.5_n_5		84.56 154	84.71 157	84.11 184	87.92 233	72.09 171	84.80 175	88.64 231	64.43 334	88.77 139	91.78 192	78.07 167	87.95 300	85.85 63	92.18 273	92.30 220

FMVSNet1		84.55 155	85.45 137	81.85 250	90.27 172	61.05 320	86.83 133	88.27 242	78.57 123	89.66 120	95.64 38	75.43 207	90.68 228	69.09 288	95.33 160	93.82 131

v1144		84.54 156	84.72 156	84.00 185	87.67 242	62.55 292	82.97 237	90.93 172	70.32 250	89.80 115	90.99 223	73.50 240	93.48 138	81.69 117	94.65 191	95.97 44

Gipuma		84.44 157	86.33 115	78.78 308	84.20 339	73.57 142	89.55 81	90.44 186	84.24 54	84.38 263	94.89 57	76.35 203	80.40 394	76.14 191	96.80 99	82.36 415
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 158	84.27 174	84.74 162	87.25 255	70.84 190	83.55 217	88.45 236	68.64 275	86.29 212	91.31 210	74.97 213	88.42 291	87.87 20	90.07 328	94.95 75

MCST-MVS		84.36 159	83.93 182	85.63 138	91.59 133	71.58 180	83.52 218	92.13 127	61.82 353	83.96 276	89.75 271	79.93 152	93.46 139	78.33 155	94.34 200	91.87 241

VDDNet		84.35 160	85.39 139	81.25 265	95.13 32	59.32 346	85.42 164	81.11 349	86.41 36	87.41 181	96.21 25	73.61 238	90.61 233	66.33 313	96.85 95	93.81 134

ETV-MVS		84.31 161	83.91 183	85.52 141	88.58 218	70.40 195	84.50 190	93.37 72	78.76 121	84.07 274	78.72 428	80.39 145	95.13 68	73.82 225	92.98 247	91.04 264

v1240		84.30 162	84.51 166	83.65 198	87.65 243	61.26 316	82.85 241	91.54 148	67.94 286	90.68 98	90.65 244	71.71 269	93.64 125	82.84 101	94.78 185	96.07 41

MVS_111021_LR		84.28 163	83.76 184	85.83 135	89.23 196	83.07 55	80.99 285	83.56 326	72.71 215	86.07 216	89.07 286	81.75 130	86.19 337	77.11 175	93.36 232	88.24 330

h-mvs33		84.25 164	82.76 210	88.72 75	91.82 130	82.60 60	84.00 200	84.98 310	71.27 236	86.70 198	90.55 249	63.04 326	93.92 115	78.26 157	94.20 204	89.63 303

v144192		84.24 165	84.41 169	83.71 197	87.59 245	61.57 310	82.95 238	91.03 168	67.82 290	89.80 115	90.49 250	73.28 247	93.51 137	81.88 116	94.89 180	96.04 43

dcpmvs_2		84.23 166	85.14 144	81.50 260	88.61 217	61.98 307	82.90 240	93.11 89	68.66 274	92.77 57	92.39 165	78.50 163	87.63 309	76.99 177	92.30 266	94.90 76

v1921920		84.23 166	84.37 171	83.79 193	87.64 244	61.71 309	82.91 239	91.20 163	67.94 286	90.06 106	90.34 253	72.04 264	93.59 131	82.32 108	94.91 178	96.07 41

VDD-MVS		84.23 166	84.58 162	83.20 213	91.17 153	65.16 262	83.25 227	84.97 311	79.79 103	87.18 184	94.27 83	74.77 218	90.89 219	69.24 284	96.54 106	93.55 154

v2v482		84.09 169	84.24 175	83.62 199	87.13 259	61.40 312	82.71 244	89.71 213	72.19 226	89.55 125	91.41 205	70.70 275	93.20 147	81.02 120	93.76 218	96.25 37

EG-PatchMatch MVS		84.08 170	84.11 177	83.98 187	92.22 112	72.61 160	82.20 265	87.02 272	72.63 216	88.86 136	91.02 222	78.52 162	91.11 209	73.41 235	91.09 299	88.21 331

E3		84.06 171	84.61 160	82.40 240	87.49 249	61.30 314	81.03 284	93.36 73	71.83 231	86.01 218	91.87 183	82.91 99	91.36 202	75.66 198	91.33 294	94.53 94

fmvsm_s_conf0.5_n_6		84.05 172	84.14 176	83.81 191	87.75 238	71.17 186	83.42 221	91.10 166	67.90 288	84.53 258	90.70 238	73.01 250	88.73 284	85.09 70	93.72 223	91.53 254

DP-MVS Recon		84.05 172	83.22 195	86.52 116	91.73 131	75.27 132	83.23 229	92.40 117	72.04 228	82.04 315	88.33 297	77.91 170	93.95 114	66.17 314	95.12 170	90.34 290

viewmacassd2359aftdt		84.04 174	84.78 153	81.81 253	86.43 281	60.32 334	81.95 267	92.82 105	71.56 232	86.06 217	92.98 142	81.79 129	90.28 240	76.18 189	93.24 238	94.82 84

TransMVSNet (Re)		84.02 175	85.74 132	78.85 307	91.00 157	55.20 389	82.29 259	87.26 261	79.65 106	88.38 152	95.52 41	83.00 97	86.88 321	67.97 302	96.60 104	94.45 98

Baseline_NR-MVSNet		84.00 176	85.90 125	78.29 319	91.47 143	53.44 401	82.29 259	87.00 275	79.06 115	89.55 125	95.72 36	77.20 184	86.14 339	72.30 251	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 177	84.46 167	82.53 235	86.11 297	70.65 193	82.45 254	89.17 224	67.72 292	86.74 197	91.49 201	79.20 155	85.86 348	84.71 80	92.60 259	91.07 263

TSAR-MVS + GP.		83.95 178	82.69 212	87.72 96	89.27 195	81.45 67	83.72 211	81.58 347	74.73 173	85.66 228	86.06 344	72.56 257	92.69 164	75.44 201	95.21 165	89.01 324

LuminaMVS		83.94 179	83.51 187	85.23 147	89.78 184	71.74 175	84.76 179	87.27 260	72.60 217	89.31 130	90.60 248	64.04 315	90.95 214	79.08 145	94.11 207	92.99 181

alignmvs		83.94 179	83.98 180	83.80 192	87.80 237	67.88 232	84.54 188	91.42 153	73.27 203	88.41 151	87.96 302	72.33 258	90.83 222	76.02 193	94.11 207	92.69 194

Effi-MVS+		83.90 181	84.01 179	83.57 203	87.22 257	65.61 258	86.55 141	92.40 117	78.64 122	81.34 331	84.18 374	83.65 91	92.93 158	74.22 213	87.87 363	92.17 230

fmvsm_s_conf0.1_n_2		83.82 182	83.49 188	84.84 157	85.99 301	70.19 199	80.93 286	87.58 256	67.26 299	87.94 165	92.37 169	71.40 271	88.01 297	86.03 57	91.87 281	96.31 36

mvs5depth		83.82 182	84.54 164	81.68 256	82.23 371	68.65 223	86.89 130	89.90 207	80.02 102	87.74 174	97.86 4	64.19 314	82.02 382	76.37 185	95.63 154	94.35 105

CANet		83.79 184	82.85 209	86.63 113	86.17 294	72.21 170	83.76 210	91.43 151	77.24 143	74.39 403	87.45 321	75.36 208	95.42 55	77.03 176	92.83 252	92.25 226

pm-mvs1		83.69 185	84.95 150	79.91 293	90.04 180	59.66 343	82.43 255	87.44 257	75.52 164	87.85 169	95.26 49	81.25 135	85.65 352	68.74 294	96.04 129	94.42 102

AdaColmap		83.66 186	83.69 185	83.57 203	90.05 179	72.26 168	86.29 145	90.00 205	78.19 128	81.65 325	87.16 327	83.40 94	94.24 98	61.69 356	94.76 188	84.21 387

viewdifsd2359ckpt09		83.64 187	83.18 198	85.03 153	87.26 254	66.99 242	85.32 166	93.83 56	65.57 319	84.99 247	89.40 277	77.30 180	93.57 134	71.16 262	93.80 217	94.54 93

MIMVSNet1		83.63 188	84.59 161	80.74 276	94.06 61	62.77 288	82.72 243	84.53 318	77.57 137	90.34 102	95.92 31	76.88 196	85.83 349	61.88 354	97.42 82	93.62 146

fmvsm_s_conf0.5_n_2		83.62 189	83.29 194	84.62 167	85.43 315	70.18 200	80.61 292	87.24 262	67.14 300	87.79 171	91.87 183	71.79 268	87.98 299	86.00 61	91.77 284	95.71 50

test_fmvsm_n_1920		83.60 190	82.89 206	85.74 136	85.22 319	77.74 102	84.12 197	90.48 183	59.87 380	86.45 211	91.12 218	75.65 205	85.89 346	82.28 109	90.87 308	93.58 150

WR-MVS		83.56 191	84.40 170	81.06 270	93.43 77	54.88 390	78.67 326	85.02 308	81.24 86	90.74 97	91.56 199	72.85 252	91.08 210	68.00 301	98.04 41	97.23 17

CNLPA		83.55 192	83.10 201	84.90 156	89.34 193	83.87 50	84.54 188	88.77 228	79.09 114	83.54 287	88.66 294	74.87 214	81.73 384	66.84 308	92.29 268	89.11 317

viewcassd2359sk11		83.53 193	83.96 181	82.25 242	86.97 271	61.13 318	80.80 290	93.22 84	70.97 242	85.36 236	91.08 220	81.84 127	91.29 203	74.79 208	90.58 324	94.33 107

LCM-MVSNet-Re		83.48 194	85.06 146	78.75 309	85.94 302	55.75 383	80.05 298	94.27 25	76.47 147	96.09 6	94.54 71	83.31 95	89.75 265	59.95 367	94.89 180	90.75 274

hse-mvs2		83.47 195	81.81 227	88.47 81	91.03 156	82.27 61	82.61 245	83.69 324	71.27 236	86.70 198	86.05 345	63.04 326	92.41 170	78.26 157	93.62 227	90.71 276

V42		83.47 195	83.37 193	83.75 195	83.16 365	63.33 280	81.31 278	90.23 199	69.51 259	90.91 91	90.81 235	74.16 227	92.29 176	80.06 130	90.22 326	95.62 54

VPA-MVSNet		83.47 195	84.73 154	79.69 298	90.29 171	57.52 369	81.30 280	88.69 230	76.29 148	87.58 179	94.44 75	80.60 143	87.20 315	66.60 311	96.82 98	94.34 106

mamba_0408		83.44 198	82.88 207	85.11 150	89.13 198	68.97 218	72.73 401	91.28 158	72.90 209	85.68 225	90.61 246	76.78 197	93.97 112	73.37 237	93.47 229	92.38 215

viewdifsd2359ckpt07		83.41 199	84.35 172	80.56 282	85.84 304	58.93 354	79.47 309	91.28 158	73.01 208	87.59 177	92.07 178	85.24 74	88.68 285	73.59 232	91.11 297	94.09 119

PAPM_NR		83.23 200	83.19 197	83.33 209	90.90 159	65.98 254	88.19 108	90.78 175	78.13 129	80.87 336	87.92 306	73.49 242	92.42 169	70.07 276	88.40 352	91.60 251

CLD-MVS		83.18 201	82.64 213	84.79 160	89.05 201	67.82 233	77.93 336	92.52 115	68.33 278	85.07 244	81.54 403	82.06 120	92.96 156	69.35 283	97.91 54	93.57 151
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 202	85.68 133	75.65 357	81.24 383	45.26 445	79.94 300	92.91 101	83.83 57	91.33 81	96.88 16	80.25 147	85.92 342	68.89 291	95.89 141	95.76 48

FA-MVS(test-final)		83.13 203	83.02 202	83.43 206	86.16 296	66.08 253	88.00 112	88.36 239	75.55 163	85.02 245	92.75 155	65.12 308	92.50 168	74.94 207	91.30 295	91.72 246

114514_t		83.10 204	82.54 216	84.77 161	92.90 90	69.10 217	86.65 138	90.62 180	54.66 412	81.46 328	90.81 235	76.98 189	94.38 93	72.62 248	96.18 122	90.82 273

RRT-MVS		82.97 205	83.44 189	81.57 258	85.06 322	58.04 364	87.20 123	90.37 189	77.88 132	88.59 144	93.70 119	63.17 323	93.05 154	76.49 184	88.47 351	93.62 146

viewmanbaseed2359cas		82.95 206	83.43 190	81.52 259	85.18 320	60.03 339	81.36 277	92.38 119	69.55 258	84.84 254	91.38 206	79.85 153	90.09 253	74.22 213	92.09 275	94.43 101

BP-MVS1		82.81 207	81.67 229	86.23 122	87.88 235	68.53 224	86.06 150	84.36 319	75.65 160	85.14 240	90.19 260	45.84 415	94.42 92	85.18 69	94.72 189	95.75 49

UGNet		82.78 208	81.64 230	86.21 125	86.20 293	76.24 123	86.86 131	85.68 294	77.07 144	73.76 407	92.82 151	69.64 280	91.82 189	69.04 290	93.69 224	90.56 284
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		82.75 209	81.93 225	85.19 148	82.08 372	80.15 76	85.53 161	88.76 229	68.01 283	85.58 231	87.75 313	71.80 267	86.85 322	74.02 221	93.87 215	88.58 327

EI-MVSNet		82.61 210	82.42 218	83.20 213	83.25 362	63.66 275	83.50 219	85.07 305	76.06 150	86.55 202	85.10 361	73.41 243	90.25 241	78.15 161	90.67 319	95.68 52

QAPM		82.59 211	82.59 215	82.58 232	86.44 280	66.69 245	89.94 71	90.36 190	67.97 285	84.94 250	92.58 160	72.71 254	92.18 177	70.63 269	87.73 366	88.85 325

fmvsm_s_conf0.1_n_a		82.58 212	81.93 225	84.50 170	87.68 241	73.35 144	86.14 149	77.70 367	61.64 358	85.02 245	91.62 196	77.75 171	86.24 334	82.79 102	87.07 374	93.91 126

Fast-Effi-MVS+-dtu		82.54 213	81.41 239	85.90 132	85.60 310	76.53 118	83.07 233	89.62 217	73.02 207	79.11 358	83.51 379	80.74 141	90.24 243	68.76 293	89.29 339	90.94 268

MVS_Test		82.47 214	83.22 195	80.22 289	82.62 370	57.75 368	82.54 250	91.96 134	71.16 240	82.89 298	92.52 163	77.41 178	90.50 235	80.04 131	87.84 365	92.40 212

viewdifsd2359ckpt11		82.46 215	82.98 204	80.88 273	83.53 349	61.00 323	79.46 310	85.97 289	69.48 260	87.89 167	91.31 210	82.10 118	88.61 288	74.28 211	92.86 250	93.02 177

viewmsd2359difaftdt		82.46 215	82.99 203	80.88 273	83.52 350	61.00 323	79.46 310	85.97 289	69.48 260	87.89 167	91.31 210	82.10 118	88.61 288	74.28 211	92.86 250	93.02 177

v148		82.31 217	82.48 217	81.81 253	85.59 311	59.66 343	81.47 275	86.02 287	72.85 211	88.05 161	90.65 244	70.73 274	90.91 218	75.15 204	91.79 282	94.87 78

API-MVS		82.28 218	82.61 214	81.30 264	86.29 290	69.79 202	88.71 100	87.67 255	78.42 125	82.15 311	84.15 375	77.98 168	91.59 192	65.39 323	92.75 254	82.51 414

MVSFormer		82.23 219	81.57 235	84.19 183	85.54 312	69.26 212	91.98 38	90.08 203	71.54 233	76.23 383	85.07 364	58.69 351	94.27 95	86.26 51	88.77 347	89.03 322

viewdifsd2359ckpt13		82.22 220	81.98 224	82.95 221	85.48 314	64.44 268	83.17 231	92.11 128	65.97 308	83.72 281	89.73 272	77.60 175	90.80 224	70.61 270	89.42 337	93.59 149

fmvsm_s_conf0.5_n_a		82.21 221	81.51 238	84.32 178	86.56 277	73.35 144	85.46 162	77.30 371	61.81 354	84.51 259	90.88 232	77.36 179	86.21 336	82.72 103	86.97 379	93.38 157

EIA-MVS		82.19 222	81.23 246	85.10 151	87.95 232	69.17 216	83.22 230	93.33 76	70.42 247	78.58 363	79.77 419	77.29 181	94.20 100	71.51 258	88.96 345	91.93 240

GDP-MVS		82.17 223	80.85 254	86.15 129	88.65 215	68.95 221	85.65 159	93.02 97	68.42 276	83.73 280	89.54 275	45.07 426	94.31 94	79.66 137	93.87 215	95.19 68

fmvsm_s_conf0.1_n		82.17 223	81.59 233	83.94 190	86.87 275	71.57 181	85.19 170	77.42 370	62.27 352	84.47 262	91.33 208	76.43 200	85.91 344	83.14 93	87.14 372	94.33 107

PCF-MVS	74.62 15	82.15 225	80.92 252	85.84 134	89.43 191	72.30 167	80.53 293	91.82 139	57.36 396	87.81 170	89.92 268	77.67 174	93.63 126	58.69 372	95.08 171	91.58 252
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 226	80.31 261	87.45 100	90.86 161	80.29 75	85.88 152	90.65 178	68.17 281	76.32 382	86.33 339	73.12 249	92.61 166	61.40 359	90.02 330	89.44 306
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 227	81.54 237	83.60 200	83.94 343	73.90 140	83.35 224	86.10 283	58.97 382	83.80 279	90.36 252	74.23 225	86.94 320	82.90 99	90.22 326	89.94 299

fmvsm_s_conf0.5_n_7		82.04 228	82.05 222	82.01 246	86.98 270	71.07 187	78.70 324	89.45 220	68.07 282	78.14 365	91.61 197	74.19 226	85.92 342	79.61 138	91.73 285	89.05 321

GBi-Net		82.02 229	82.07 220	81.85 250	86.38 284	61.05 320	86.83 133	88.27 242	72.43 218	86.00 219	95.64 38	63.78 319	90.68 228	65.95 316	93.34 233	93.82 131

test1		82.02 229	82.07 220	81.85 250	86.38 284	61.05 320	86.83 133	88.27 242	72.43 218	86.00 219	95.64 38	63.78 319	90.68 228	65.95 316	93.34 233	93.82 131

OpenMVS	76.72 13	81.98 231	82.00 223	81.93 247	84.42 334	68.22 227	88.50 106	89.48 219	66.92 303	81.80 322	91.86 185	72.59 256	90.16 247	71.19 261	91.25 296	87.40 347

KD-MVS_self_test		81.93 232	83.14 200	78.30 318	84.75 328	52.75 405	80.37 295	89.42 222	70.24 252	90.26 104	93.39 126	74.55 224	86.77 324	68.61 296	96.64 102	95.38 59

fmvsm_s_conf0.5_n		81.91 233	81.30 243	83.75 195	86.02 299	71.56 182	84.73 180	77.11 374	62.44 349	84.00 275	90.68 240	76.42 201	85.89 346	83.14 93	87.11 373	93.81 134

SDMVSNet		81.90 234	83.17 199	78.10 322	88.81 210	62.45 298	76.08 370	86.05 286	73.67 189	83.41 288	93.04 138	82.35 108	80.65 391	70.06 277	95.03 173	91.21 259

tfpnnormal		81.79 235	82.95 205	78.31 317	88.93 206	55.40 385	80.83 289	82.85 333	76.81 145	85.90 223	94.14 93	74.58 222	86.51 328	66.82 309	95.68 152	93.01 180

AstraMVS		81.67 236	81.40 240	82.48 237	87.06 267	66.47 248	81.41 276	81.68 344	68.78 271	88.00 162	90.95 228	65.70 304	87.86 305	76.66 179	92.38 263	93.12 173

c3_l		81.64 237	81.59 233	81.79 255	80.86 389	59.15 351	78.61 327	90.18 201	68.36 277	87.20 183	87.11 329	69.39 281	91.62 191	78.16 159	94.43 197	94.60 89

guyue		81.57 238	81.37 242	82.15 243	86.39 282	66.13 252	81.54 274	83.21 328	69.79 256	87.77 172	89.95 266	65.36 307	87.64 308	75.88 194	92.49 261	92.67 195

PVSNet_Blended_VisFu		81.55 239	80.49 259	84.70 165	91.58 136	73.24 148	84.21 194	91.67 144	62.86 343	80.94 334	87.16 327	67.27 293	92.87 161	69.82 279	88.94 346	87.99 337

fmvsm_l_conf0.5_n_a		81.46 240	80.87 253	83.25 211	83.73 348	73.21 149	83.00 236	85.59 296	58.22 388	82.96 297	90.09 265	72.30 259	86.65 326	81.97 114	89.95 331	89.88 300

SSM_04072		81.44 241	82.88 207	77.10 337	89.13 198	68.97 218	72.73 401	91.28 158	72.90 209	85.68 225	90.61 246	76.78 197	69.94 434	73.37 237	93.47 229	92.38 215

DELS-MVS		81.44 241	81.25 244	82.03 245	84.27 338	62.87 286	76.47 364	92.49 116	70.97 242	81.64 326	83.83 376	75.03 211	92.70 163	74.29 210	92.22 272	90.51 286
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		81.31 243	81.61 232	80.41 285	86.38 284	58.75 359	83.93 204	86.58 278	72.43 218	87.65 176	92.98 142	63.78 319	90.22 244	66.86 306	93.92 213	92.27 224

TinyColmap		81.25 244	82.34 219	77.99 325	85.33 316	60.68 330	82.32 258	88.33 240	71.26 238	86.97 192	92.22 177	77.10 187	86.98 319	62.37 348	95.17 167	86.31 360

diffmvs_AUTHOR		81.24 245	81.55 236	80.30 287	80.61 394	60.22 335	77.98 335	90.48 183	67.77 291	83.34 290	89.50 276	74.69 220	87.42 311	78.78 149	90.81 311	93.27 163

AUN-MVS		81.18 246	78.78 284	88.39 83	90.93 158	82.14 62	82.51 251	83.67 325	64.69 333	80.29 344	85.91 348	51.07 390	92.38 171	76.29 188	93.63 226	90.65 281

IMVS_0407		81.08 247	81.23 246	80.62 281	85.76 306	62.46 294	82.46 252	87.91 250	65.23 326	82.12 312	87.92 306	77.27 182	90.18 246	71.67 254	90.74 314	89.20 312

tttt0517		81.07 248	79.58 274	85.52 141	88.99 204	66.45 249	87.03 128	75.51 386	73.76 188	88.32 154	90.20 259	37.96 447	94.16 107	79.36 143	95.13 168	95.93 47

Fast-Effi-MVS+		81.04 249	80.57 256	82.46 238	87.50 248	63.22 282	78.37 330	89.63 216	68.01 283	81.87 318	82.08 397	82.31 110	92.65 165	67.10 305	88.30 358	91.51 255

BH-untuned		80.96 250	80.99 250	80.84 275	88.55 219	68.23 226	80.33 296	88.46 235	72.79 214	86.55 202	86.76 333	74.72 219	91.77 190	61.79 355	88.99 344	82.52 413

IMVS_0403		80.93 251	81.00 249	80.72 278	85.76 306	62.46 294	81.82 268	87.91 250	65.23 326	82.07 314	87.92 306	75.91 204	90.50 235	71.67 254	90.74 314	89.20 312

eth_miper_zixun_eth		80.84 252	80.22 265	82.71 229	81.41 381	60.98 325	77.81 338	90.14 202	67.31 298	86.95 193	87.24 326	64.26 312	92.31 174	75.23 203	91.61 288	94.85 82

xiu_mvs_v1_base_debu		80.84 252	80.14 267	82.93 224	88.31 223	71.73 176	79.53 305	87.17 263	65.43 320	79.59 350	82.73 391	76.94 190	90.14 250	73.22 240	88.33 354	86.90 354

xiu_mvs_v1_base		80.84 252	80.14 267	82.93 224	88.31 223	71.73 176	79.53 305	87.17 263	65.43 320	79.59 350	82.73 391	76.94 190	90.14 250	73.22 240	88.33 354	86.90 354

xiu_mvs_v1_base_debi		80.84 252	80.14 267	82.93 224	88.31 223	71.73 176	79.53 305	87.17 263	65.43 320	79.59 350	82.73 391	76.94 190	90.14 250	73.22 240	88.33 354	86.90 354

IterMVS-SCA-FT		80.64 256	79.41 275	84.34 177	83.93 344	69.66 206	76.28 366	81.09 350	72.43 218	86.47 209	90.19 260	60.46 336	93.15 150	77.45 170	86.39 385	90.22 291

BH-RMVSNet		80.53 257	80.22 265	81.49 261	87.19 258	66.21 251	77.79 339	86.23 281	74.21 183	83.69 282	88.50 295	73.25 248	90.75 225	63.18 344	87.90 362	87.52 345

VortexMVS		80.51 258	80.63 255	80.15 291	83.36 358	61.82 308	80.63 291	88.00 248	67.11 301	87.23 182	89.10 285	63.98 316	88.00 298	73.63 231	92.63 258	90.64 282

Anonymous202405211		80.51 258	81.19 248	78.49 314	88.48 220	57.26 371	76.63 359	82.49 336	81.21 87	84.30 269	92.24 176	67.99 289	86.24 334	62.22 349	95.13 168	91.98 239

DIV-MVS_self_test		80.43 260	80.23 263	81.02 271	79.99 399	59.25 348	77.07 352	87.02 272	67.38 295	86.19 213	89.22 281	63.09 324	90.16 247	76.32 186	95.80 146	93.66 140

cl____		80.42 261	80.23 263	81.02 271	79.99 399	59.25 348	77.07 352	87.02 272	67.37 296	86.18 215	89.21 282	63.08 325	90.16 247	76.31 187	95.80 146	93.65 143

diffmvs		80.40 262	80.48 260	80.17 290	79.02 412	60.04 337	77.54 343	90.28 198	66.65 306	82.40 305	87.33 324	73.50 240	87.35 313	77.98 163	89.62 335	93.13 170
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		80.37 263	78.41 292	86.23 122	76.75 426	73.28 146	87.18 125	77.45 369	76.24 149	68.14 437	88.93 288	65.41 306	93.85 117	69.47 282	96.12 126	91.55 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 264	80.04 270	81.24 267	79.82 402	58.95 353	77.66 340	89.66 214	65.75 316	85.99 222	85.11 360	68.29 288	91.42 199	76.03 192	92.03 276	93.33 159

MG-MVS		80.32 265	80.94 251	78.47 315	88.18 226	52.62 408	82.29 259	85.01 309	72.01 229	79.24 357	92.54 162	69.36 282	93.36 144	70.65 268	89.19 342	89.45 305

mvsmamba		80.30 266	78.87 281	84.58 169	88.12 229	67.55 234	92.35 30	84.88 312	63.15 341	85.33 237	90.91 229	50.71 392	95.20 65	66.36 312	87.98 361	90.99 266

VPNet		80.25 267	81.68 228	75.94 353	92.46 103	47.98 432	76.70 357	81.67 345	73.45 194	84.87 252	92.82 151	74.66 221	86.51 328	61.66 357	96.85 95	93.33 159

MAR-MVS		80.24 268	78.74 286	84.73 163	86.87 275	78.18 95	85.75 156	87.81 254	65.67 318	77.84 369	78.50 429	73.79 236	90.53 234	61.59 358	90.87 308	85.49 370
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		80.20 269	79.00 280	83.78 194	88.17 227	86.66 19	81.31 278	66.81 442	69.64 257	88.33 153	90.19 260	64.58 309	83.63 373	71.99 253	90.03 329	81.06 433

Anonymous20240521		80.18 270	81.25 244	76.95 339	83.15 366	60.84 327	82.46 252	85.99 288	68.76 272	86.78 194	93.73 118	59.13 348	77.44 408	73.71 227	97.55 77	92.56 202

LFMVS		80.15 271	80.56 257	78.89 306	89.19 197	55.93 379	85.22 169	73.78 398	82.96 70	84.28 270	92.72 156	57.38 360	90.07 255	63.80 338	95.75 149	90.68 278

DPM-MVS		80.10 272	79.18 279	82.88 227	90.71 164	69.74 204	78.87 322	90.84 173	60.29 376	75.64 392	85.92 347	67.28 292	93.11 151	71.24 260	91.79 282	85.77 366

MSDG		80.06 273	79.99 272	80.25 288	83.91 345	68.04 231	77.51 344	89.19 223	77.65 135	81.94 316	83.45 381	76.37 202	86.31 333	63.31 343	86.59 382	86.41 358

FE-MVS		79.98 274	78.86 282	83.36 208	86.47 279	66.45 249	89.73 74	84.74 316	72.80 213	84.22 273	91.38 206	44.95 427	93.60 130	63.93 336	91.50 291	90.04 298

sd_testset		79.95 275	81.39 241	75.64 358	88.81 210	58.07 363	76.16 369	82.81 334	73.67 189	83.41 288	93.04 138	80.96 138	77.65 407	58.62 373	95.03 173	91.21 259

ab-mvs		79.67 276	80.56 257	76.99 338	88.48 220	56.93 373	84.70 182	86.06 285	68.95 269	80.78 337	93.08 137	75.30 209	84.62 360	56.78 382	90.90 306	89.43 307

VNet		79.31 277	80.27 262	76.44 347	87.92 233	53.95 397	75.58 376	84.35 320	74.39 182	82.23 309	90.72 237	72.84 253	84.39 365	60.38 365	93.98 212	90.97 267

thisisatest0530		79.07 278	77.33 302	84.26 180	87.13 259	64.58 265	83.66 214	75.95 381	68.86 270	85.22 239	87.36 323	38.10 444	93.57 134	75.47 200	94.28 202	94.62 88

cl22		78.97 279	78.21 294	81.24 267	77.74 416	59.01 352	77.46 347	87.13 266	65.79 313	84.32 266	85.10 361	58.96 350	90.88 220	75.36 202	92.03 276	93.84 129

patch_mono-2		78.89 280	79.39 276	77.41 334	84.78 326	68.11 229	75.60 374	83.11 330	60.96 368	79.36 354	89.89 269	75.18 210	72.97 423	73.32 239	92.30 266	91.15 261

RPMNet		78.88 281	78.28 293	80.68 280	79.58 403	62.64 290	82.58 247	94.16 33	74.80 171	75.72 390	92.59 158	48.69 399	95.56 44	73.48 234	82.91 421	83.85 392

PAPR		78.84 282	78.10 295	81.07 269	85.17 321	60.22 335	82.21 263	90.57 182	62.51 345	75.32 396	84.61 369	74.99 212	92.30 175	59.48 370	88.04 360	90.68 278

viewmambaseed2359dif		78.80 283	78.47 291	79.78 294	80.26 398	59.28 347	77.31 349	87.13 266	60.42 374	82.37 306	88.67 293	74.58 222	87.87 304	67.78 304	87.73 366	92.19 228

PVSNet_BlendedMVS		78.80 283	77.84 296	81.65 257	84.43 332	63.41 278	79.49 308	90.44 186	61.70 357	75.43 393	87.07 330	69.11 284	91.44 197	60.68 363	92.24 270	90.11 296

FMVSNet3		78.80 283	78.55 288	79.57 300	82.89 369	56.89 375	81.76 269	85.77 292	69.04 267	86.00 219	90.44 251	51.75 388	90.09 253	65.95 316	93.34 233	91.72 246

test_yl		78.71 286	78.51 289	79.32 303	84.32 336	58.84 356	78.38 328	85.33 300	75.99 153	82.49 303	86.57 335	58.01 354	90.02 257	62.74 345	92.73 256	89.10 318

DCV-MVSNet		78.71 286	78.51 289	79.32 303	84.32 336	58.84 356	78.38 328	85.33 300	75.99 153	82.49 303	86.57 335	58.01 354	90.02 257	62.74 345	92.73 256	89.10 318

test1111		78.53 288	78.85 283	77.56 331	92.22 112	47.49 434	82.61 245	69.24 430	72.43 218	85.28 238	94.20 89	51.91 386	90.07 255	65.36 324	96.45 111	95.11 72

FE-MVSNET		78.46 289	79.36 277	75.75 355	86.53 278	54.53 392	78.03 332	85.35 299	69.01 268	85.41 235	90.68 240	64.27 311	85.73 350	62.59 347	92.35 265	87.00 353

icg_test_0407_2		78.46 289	79.68 273	74.78 365	85.76 306	62.46 294	68.51 430	87.91 250	65.23 326	82.12 312	87.92 306	77.27 182	72.67 424	71.67 254	90.74 314	89.20 312

ECVR-MVS		78.44 291	78.63 287	77.88 327	91.85 126	48.95 428	83.68 213	69.91 426	72.30 224	84.26 272	94.20 89	51.89 387	89.82 260	63.58 339	96.02 130	94.87 78

pmmvs-eth3d		78.42 292	77.04 305	82.57 234	87.44 251	74.41 137	80.86 288	79.67 358	55.68 405	84.69 256	90.31 257	60.91 334	85.42 353	62.20 350	91.59 289	87.88 341

mvs_anonymous		78.13 293	78.76 285	76.23 352	79.24 409	50.31 424	78.69 325	84.82 314	61.60 359	83.09 296	92.82 151	73.89 234	87.01 316	68.33 300	86.41 384	91.37 256

TAMVS		78.08 294	76.36 312	83.23 212	90.62 165	72.87 153	79.08 318	80.01 357	61.72 356	81.35 330	86.92 332	63.96 318	88.78 282	50.61 421	93.01 246	88.04 336

miper_enhance_ethall		77.83 295	76.93 306	80.51 283	76.15 433	58.01 365	75.47 378	88.82 227	58.05 390	83.59 284	80.69 407	64.41 310	91.20 205	73.16 246	92.03 276	92.33 219

Vis-MVSNet (Re-imp)		77.82 296	77.79 297	77.92 326	88.82 209	51.29 418	83.28 225	71.97 414	74.04 184	82.23 309	89.78 270	57.38 360	89.41 272	57.22 381	95.41 157	93.05 176

CANet_DTU		77.81 297	77.05 304	80.09 292	81.37 382	59.90 341	83.26 226	88.29 241	69.16 264	67.83 440	83.72 377	60.93 333	89.47 267	69.22 286	89.70 334	90.88 271

OpenMVS_ROB	70.19 17	77.77 298	77.46 299	78.71 310	84.39 335	61.15 317	81.18 282	82.52 335	62.45 348	83.34 290	87.37 322	66.20 298	88.66 286	64.69 331	85.02 401	86.32 359

SSC-MVS		77.55 299	81.64 230	65.29 431	90.46 168	20.33 478	73.56 394	68.28 432	85.44 41	88.18 158	94.64 68	70.93 273	81.33 386	71.25 259	92.03 276	94.20 110

MDA-MVSNet-bldmvs		77.47 300	76.90 307	79.16 305	79.03 411	64.59 264	66.58 442	75.67 384	73.15 205	88.86 136	88.99 287	66.94 294	81.23 387	64.71 330	88.22 359	91.64 250

jason		77.42 301	75.75 318	82.43 239	87.10 262	69.27 211	77.99 334	81.94 342	51.47 432	77.84 369	85.07 364	60.32 338	89.00 276	70.74 267	89.27 341	89.03 322
jason: jason.
CDS-MVSNet		77.32 302	75.40 322	83.06 216	89.00 203	72.48 164	77.90 337	82.17 340	60.81 369	78.94 360	83.49 380	59.30 346	88.76 283	54.64 401	92.37 264	87.93 340
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 303	77.75 298	75.73 356	85.76 306	62.46 294	70.84 416	87.91 250	65.23 326	72.21 415	87.92 306	67.48 291	75.53 416	71.67 254	90.74 314	89.20 312

xiu_mvs_v2_base		77.19 304	76.75 309	78.52 313	87.01 268	61.30 314	75.55 377	87.12 270	61.24 365	74.45 402	78.79 427	77.20 184	90.93 216	64.62 333	84.80 408	83.32 401

MVSTER		77.09 305	75.70 319	81.25 265	75.27 441	61.08 319	77.49 346	85.07 305	60.78 370	86.55 202	88.68 291	43.14 436	90.25 241	73.69 230	90.67 319	92.42 209

PS-MVSNAJ		77.04 306	76.53 311	78.56 312	87.09 264	61.40 312	75.26 379	87.13 266	61.25 364	74.38 404	77.22 441	76.94 190	90.94 215	64.63 332	84.83 407	83.35 400

IterMVS		76.91 307	76.34 313	78.64 311	80.91 387	64.03 272	76.30 365	79.03 361	64.88 332	83.11 294	89.16 283	59.90 342	84.46 363	68.61 296	85.15 399	87.42 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 308	75.67 320	80.34 286	80.48 396	62.16 306	73.50 395	84.80 315	57.61 394	82.24 308	87.54 317	51.31 389	87.65 307	70.40 273	93.19 242	91.23 258

CL-MVSNet_self_test		76.81 309	77.38 301	75.12 361	86.90 273	51.34 416	73.20 398	80.63 354	68.30 279	81.80 322	88.40 296	66.92 295	80.90 388	55.35 395	94.90 179	93.12 173

TR-MVS		76.77 310	75.79 317	79.72 297	86.10 298	65.79 256	77.14 350	83.02 331	65.20 330	81.40 329	82.10 395	66.30 297	90.73 227	55.57 392	85.27 395	82.65 408

MonoMVSNet		76.66 311	77.26 303	74.86 363	79.86 401	54.34 394	86.26 146	86.08 284	71.08 241	85.59 230	88.68 291	53.95 378	85.93 341	63.86 337	80.02 437	84.32 383

USDC		76.63 312	76.73 310	76.34 349	83.46 353	57.20 372	80.02 299	88.04 247	52.14 428	83.65 283	91.25 213	63.24 322	86.65 326	54.66 400	94.11 207	85.17 372

BH-w/o		76.57 313	76.07 316	78.10 322	86.88 274	65.92 255	77.63 341	86.33 279	65.69 317	80.89 335	79.95 416	68.97 286	90.74 226	53.01 411	85.25 396	77.62 444

Patchmtry		76.56 314	77.46 299	73.83 371	79.37 408	46.60 438	82.41 256	76.90 375	73.81 187	85.56 232	92.38 166	48.07 402	83.98 370	63.36 342	95.31 163	90.92 269

PVSNet_Blended		76.49 315	75.40 322	79.76 296	84.43 332	63.41 278	75.14 380	90.44 186	57.36 396	75.43 393	78.30 430	69.11 284	91.44 197	60.68 363	87.70 368	84.42 382

miper_lstm_enhance		76.45 316	76.10 315	77.51 332	76.72 427	60.97 326	64.69 446	85.04 307	63.98 337	83.20 293	88.22 298	56.67 364	78.79 404	73.22 240	93.12 243	92.78 189

lupinMVS		76.37 317	74.46 331	82.09 244	85.54 312	69.26 212	76.79 355	80.77 353	50.68 439	76.23 383	82.82 389	58.69 351	88.94 277	69.85 278	88.77 347	88.07 333

cascas		76.29 318	74.81 327	80.72 278	84.47 331	62.94 284	73.89 392	87.34 258	55.94 403	75.16 398	76.53 446	63.97 317	91.16 207	65.00 327	90.97 304	88.06 335

SD_0403		76.08 319	76.77 308	73.98 369	87.08 266	49.45 427	83.62 215	84.68 317	63.31 338	75.13 399	87.47 320	71.85 266	84.56 361	49.97 423	87.86 364	87.94 339

WB-MVS		76.06 320	80.01 271	64.19 434	89.96 182	20.58 477	72.18 405	68.19 433	83.21 66	86.46 210	93.49 123	70.19 278	78.97 402	65.96 315	90.46 325	93.02 177

thres600view7		75.97 321	75.35 324	77.85 329	87.01 268	51.84 414	80.45 294	73.26 403	75.20 168	83.10 295	86.31 341	45.54 417	89.05 275	55.03 398	92.24 270	92.66 196

GA-MVS		75.83 322	74.61 328	79.48 302	81.87 374	59.25 348	73.42 396	82.88 332	68.68 273	79.75 349	81.80 400	50.62 393	89.46 268	66.85 307	85.64 392	89.72 302

MVP-Stereo		75.81 323	73.51 340	82.71 229	89.35 192	73.62 141	80.06 297	85.20 302	60.30 375	73.96 405	87.94 303	57.89 358	89.45 269	52.02 415	74.87 455	85.06 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 324	75.20 325	77.27 335	75.01 444	69.47 209	78.93 319	84.88 312	46.67 446	87.08 189	87.84 311	50.44 395	71.62 429	77.42 172	88.53 350	90.72 275

thres100view900		75.45 325	75.05 326	76.66 345	87.27 253	51.88 413	81.07 283	73.26 403	75.68 159	83.25 292	86.37 338	45.54 417	88.80 279	51.98 416	90.99 301	89.31 309

ET-MVSNet_ETH3D		75.28 326	72.77 349	82.81 228	83.03 368	68.11 229	77.09 351	76.51 379	60.67 372	77.60 374	80.52 411	38.04 445	91.15 208	70.78 265	90.68 318	89.17 316

thres400		75.14 327	74.23 333	77.86 328	86.24 291	52.12 410	79.24 315	73.87 396	73.34 198	81.82 320	84.60 370	46.02 410	88.80 279	51.98 416	90.99 301	92.66 196

wuyk23d		75.13 328	79.30 278	62.63 437	75.56 437	75.18 133	80.89 287	73.10 405	75.06 170	94.76 16	95.32 45	87.73 45	52.85 469	34.16 467	97.11 90	59.85 465

EU-MVSNet		75.12 329	74.43 332	77.18 336	83.11 367	59.48 345	85.71 158	82.43 337	39.76 466	85.64 229	88.76 289	44.71 429	87.88 303	73.86 224	85.88 391	84.16 388

HyFIR lowres test		75.12 329	72.66 351	82.50 236	91.44 144	65.19 261	72.47 403	87.31 259	46.79 445	80.29 344	84.30 372	52.70 383	92.10 181	51.88 420	86.73 380	90.22 291

CMPMVS	59.41 20	75.12 329	73.57 338	79.77 295	75.84 436	67.22 235	81.21 281	82.18 339	50.78 437	76.50 379	87.66 315	55.20 374	82.99 376	62.17 352	90.64 323	89.09 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 332	72.98 347	80.73 277	84.95 323	71.71 179	76.23 367	77.59 368	52.83 422	77.73 373	86.38 337	56.35 367	84.97 357	57.72 380	87.05 375	85.51 369

tfpn200view9		74.86 333	74.23 333	76.74 344	86.24 291	52.12 410	79.24 315	73.87 396	73.34 198	81.82 320	84.60 370	46.02 410	88.80 279	51.98 416	90.99 301	89.31 309

1112_ss		74.82 334	73.74 336	78.04 324	89.57 186	60.04 337	76.49 363	87.09 271	54.31 413	73.66 408	79.80 417	60.25 339	86.76 325	58.37 374	84.15 412	87.32 348

EGC-MVSNET		74.79 335	69.99 379	89.19 67	94.89 38	87.00 15	91.89 41	86.28 280	1.09 475	2.23 477	95.98 30	81.87 126	89.48 266	79.76 134	95.96 133	91.10 262

ppachtmachnet_test		74.73 336	74.00 335	76.90 341	80.71 392	56.89 375	71.53 411	78.42 363	58.24 387	79.32 356	82.92 388	57.91 357	84.26 367	65.60 322	91.36 293	89.56 304

Patchmatch-RL test		74.48 337	73.68 337	76.89 342	84.83 325	66.54 246	72.29 404	69.16 431	57.70 392	86.76 195	86.33 339	45.79 416	82.59 377	69.63 281	90.65 322	81.54 424

PatchMatch-RL		74.48 337	73.22 344	78.27 320	87.70 240	85.26 38	75.92 372	70.09 424	64.34 335	76.09 386	81.25 405	65.87 303	78.07 406	53.86 403	83.82 414	71.48 453

XXY-MVS		74.44 339	76.19 314	69.21 406	84.61 330	52.43 409	71.70 408	77.18 373	60.73 371	80.60 338	90.96 226	75.44 206	69.35 437	56.13 387	88.33 354	85.86 365

test2506		74.12 340	73.39 341	76.28 350	91.85 126	44.20 448	84.06 198	48.20 473	72.30 224	81.90 317	94.20 89	27.22 472	89.77 263	64.81 329	96.02 130	94.87 78

reproduce_monomvs		74.09 341	73.23 343	76.65 346	76.52 428	54.54 391	77.50 345	81.40 348	65.85 312	82.86 300	86.67 334	27.38 470	84.53 362	70.24 274	90.66 321	90.89 270

CR-MVSNet		74.00 342	73.04 346	76.85 343	79.58 403	62.64 290	82.58 247	76.90 375	50.50 440	75.72 390	92.38 166	48.07 402	84.07 369	68.72 295	82.91 421	83.85 392

SSC-MVS3.2		73.90 343	75.67 320	68.61 414	84.11 341	41.28 456	64.17 448	72.83 406	72.09 227	79.08 359	87.94 303	70.31 276	73.89 422	55.99 388	94.49 194	90.67 280

Test_1112_low_res		73.90 343	73.08 345	76.35 348	90.35 170	55.95 378	73.40 397	86.17 282	50.70 438	73.14 409	85.94 346	58.31 353	85.90 345	56.51 384	83.22 418	87.20 350

test20.03		73.75 345	74.59 330	71.22 392	81.11 385	51.12 420	70.15 422	72.10 413	70.42 247	80.28 346	91.50 200	64.21 313	74.72 420	46.96 441	94.58 192	87.82 343

test_fmvs2		73.57 346	72.80 348	75.90 354	72.74 458	68.84 222	77.07 352	84.32 321	45.14 452	82.89 298	84.22 373	48.37 400	70.36 433	73.40 236	87.03 376	88.52 328

SCA		73.32 347	72.57 353	75.58 359	81.62 378	55.86 381	78.89 321	71.37 419	61.73 355	74.93 400	83.42 382	60.46 336	87.01 316	58.11 378	82.63 426	83.88 389

baseline1		73.26 348	73.54 339	72.43 385	84.92 324	47.79 433	79.89 301	74.00 394	65.93 310	78.81 361	86.28 342	56.36 366	81.63 385	56.63 383	79.04 444	87.87 342

1314		73.22 349	72.56 354	75.20 360	80.41 397	57.84 366	81.64 272	85.36 298	51.68 431	73.10 410	76.65 445	61.45 331	85.19 355	63.54 340	79.21 442	82.59 409

MVS		73.21 350	72.59 352	75.06 362	80.97 386	60.81 328	81.64 272	85.92 291	46.03 450	71.68 418	77.54 436	68.47 287	89.77 263	55.70 391	85.39 393	74.60 450

HY-MVS	64.64 18	73.03 351	72.47 355	74.71 366	83.36 358	54.19 395	82.14 266	81.96 341	56.76 402	69.57 432	86.21 343	60.03 340	84.83 359	49.58 428	82.65 424	85.11 373

thisisatest0515		73.00 352	70.52 371	80.46 284	81.45 380	59.90 341	73.16 399	74.31 393	57.86 391	76.08 387	77.78 433	37.60 448	92.12 180	65.00 327	91.45 292	89.35 308

EPNet_dtu		72.87 353	71.33 365	77.49 333	77.72 417	60.55 331	82.35 257	75.79 382	66.49 307	58.39 468	81.06 406	53.68 379	85.98 340	53.55 406	92.97 248	85.95 363
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 354	71.41 364	76.28 350	83.25 362	60.34 333	83.50 219	79.02 362	37.77 470	76.33 381	85.10 361	49.60 398	87.41 312	70.54 271	77.54 450	81.08 431

CHOSEN 1792x2688		72.45 355	70.56 370	78.13 321	90.02 181	63.08 283	68.72 429	83.16 329	42.99 460	75.92 388	85.46 354	57.22 362	85.18 356	49.87 426	81.67 428	86.14 361

testgi		72.36 356	74.61 328	65.59 428	80.56 395	42.82 453	68.29 431	73.35 402	66.87 304	81.84 319	89.93 267	72.08 263	66.92 451	46.05 445	92.54 260	87.01 352

thres200		72.34 357	71.55 363	74.70 367	83.48 352	51.60 415	75.02 381	73.71 399	70.14 253	78.56 364	80.57 410	46.20 408	88.20 296	46.99 440	89.29 339	84.32 383

FPMVS		72.29 358	72.00 357	73.14 376	88.63 216	85.00 40	74.65 385	67.39 436	71.94 230	77.80 371	87.66 315	50.48 394	75.83 414	49.95 424	79.51 438	58.58 467

FMVSNet5		72.10 359	71.69 359	73.32 374	81.57 379	53.02 404	76.77 356	78.37 364	63.31 338	76.37 380	91.85 186	36.68 449	78.98 401	47.87 437	92.45 262	87.95 338

our_test_3		71.85 360	71.59 360	72.62 382	80.71 392	53.78 398	69.72 425	71.71 418	58.80 384	78.03 366	80.51 412	56.61 365	78.84 403	62.20 350	86.04 390	85.23 371

PAPM		71.77 361	70.06 377	76.92 340	86.39 282	53.97 396	76.62 360	86.62 277	53.44 417	63.97 457	84.73 368	57.79 359	92.34 173	39.65 457	81.33 432	84.45 381

ttmdpeth		71.72 362	70.67 368	74.86 363	73.08 455	55.88 380	77.41 348	69.27 429	55.86 404	78.66 362	93.77 116	38.01 446	75.39 417	60.12 366	89.87 332	93.31 161

IB-MVS	62.13 19	71.64 363	68.97 389	79.66 299	80.80 391	62.26 303	73.94 391	76.90 375	63.27 340	68.63 436	76.79 443	33.83 453	91.84 188	59.28 371	87.26 370	84.88 375
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		71.63 364	72.30 356	69.62 403	76.47 430	52.70 407	70.03 423	80.97 351	59.18 381	79.36 354	88.21 299	60.50 335	69.12 438	58.33 376	77.62 449	87.04 351

testing3		71.53 365	70.79 367	73.77 372	88.89 208	41.86 455	76.60 362	59.12 462	72.83 212	80.97 332	82.08 397	19.80 478	87.33 314	65.12 326	91.68 287	92.13 232

test_vis3_rt		71.42 366	70.67 368	73.64 373	69.66 465	70.46 194	66.97 441	89.73 211	42.68 462	88.20 157	83.04 384	43.77 431	60.07 463	65.35 325	86.66 381	90.39 289

Anonymous20231206		71.38 367	71.88 358	69.88 400	86.31 288	54.37 393	70.39 420	74.62 389	52.57 424	76.73 378	88.76 289	59.94 341	72.06 426	44.35 449	93.23 240	83.23 403

test_vis1_n_1920		71.30 368	71.58 362	70.47 395	77.58 419	59.99 340	74.25 386	84.22 322	51.06 434	74.85 401	79.10 423	55.10 375	68.83 440	68.86 292	79.20 443	82.58 410

MIMVSNet		71.09 369	71.59 360	69.57 404	87.23 256	50.07 425	78.91 320	71.83 415	60.20 378	71.26 419	91.76 193	55.08 376	76.09 412	41.06 454	87.02 377	82.54 412

test_fmvs1_n		70.94 370	70.41 374	72.53 384	73.92 446	66.93 243	75.99 371	84.21 323	43.31 459	79.40 353	79.39 421	43.47 432	68.55 442	69.05 289	84.91 404	82.10 418

MS-PatchMatch		70.93 371	70.22 375	73.06 377	81.85 375	62.50 293	73.82 393	77.90 365	52.44 425	75.92 388	81.27 404	55.67 371	81.75 383	55.37 394	77.70 448	74.94 449

pmmvs5		70.73 372	70.07 376	72.72 380	77.03 424	52.73 406	74.14 387	75.65 385	50.36 441	72.17 416	85.37 358	55.42 373	80.67 390	52.86 412	87.59 369	84.77 376

testing3-2		70.72 373	70.97 366	69.95 399	88.93 206	34.80 469	69.85 424	66.59 443	78.42 125	77.58 375	85.55 350	31.83 459	82.08 381	46.28 442	93.73 222	92.98 183

PatchT		70.52 374	72.76 350	63.79 436	79.38 407	33.53 470	77.63 341	65.37 447	73.61 191	71.77 417	92.79 154	44.38 430	75.65 415	64.53 334	85.37 394	82.18 417

test_vis1_n		70.29 375	69.99 379	71.20 393	75.97 435	66.50 247	76.69 358	80.81 352	44.22 455	75.43 393	77.23 440	50.00 396	68.59 441	66.71 310	82.85 423	78.52 443

N_pmnet		70.20 376	68.80 391	74.38 368	80.91 387	84.81 43	59.12 459	76.45 380	55.06 408	75.31 397	82.36 394	55.74 370	54.82 468	47.02 439	87.24 371	83.52 396

tpmvs		70.16 377	69.56 382	71.96 388	74.71 445	48.13 430	79.63 303	75.45 387	65.02 331	70.26 427	81.88 399	45.34 422	85.68 351	58.34 375	75.39 454	82.08 419

new-patchmatchnet		70.10 378	73.37 342	60.29 445	81.23 384	16.95 480	59.54 457	74.62 389	62.93 342	80.97 332	87.93 305	62.83 328	71.90 427	55.24 396	95.01 176	92.00 237

YYNet1		70.06 379	70.44 372	68.90 408	73.76 448	53.42 402	58.99 460	67.20 438	58.42 386	87.10 187	85.39 357	59.82 343	67.32 448	59.79 368	83.50 417	85.96 362

MVStest1		70.05 380	69.26 383	72.41 386	58.62 477	55.59 384	76.61 361	65.58 445	53.44 417	89.28 131	93.32 127	22.91 476	71.44 431	74.08 220	89.52 336	90.21 295

MDA-MVSNet_test_wron		70.05 380	70.44 372	68.88 409	73.84 447	53.47 400	58.93 461	67.28 437	58.43 385	87.09 188	85.40 356	59.80 344	67.25 449	59.66 369	83.54 416	85.92 364

CostFormer		69.98 382	68.68 392	73.87 370	77.14 422	50.72 422	79.26 314	74.51 391	51.94 430	70.97 422	84.75 367	45.16 425	87.49 310	55.16 397	79.23 441	83.40 399

testing91		69.94 383	68.99 388	72.80 379	83.81 347	45.89 441	71.57 410	73.64 401	68.24 280	70.77 425	77.82 432	34.37 452	84.44 364	53.64 405	87.00 378	88.07 333

baseline2		69.77 384	66.89 401	78.41 316	79.51 405	58.09 362	76.23 367	69.57 427	57.50 395	64.82 455	77.45 438	46.02 410	88.44 290	53.08 408	77.83 446	88.70 326

PatchmatchNet		69.71 385	68.83 390	72.33 387	77.66 418	53.60 399	79.29 313	69.99 425	57.66 393	72.53 413	82.93 387	46.45 407	80.08 396	60.91 362	72.09 458	83.31 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 386	69.05 386	71.14 394	69.15 466	65.77 257	73.98 390	83.32 327	42.83 461	77.77 372	78.27 431	43.39 435	68.50 443	68.39 299	84.38 411	79.15 441

JIA-IIPM		69.41 387	66.64 405	77.70 330	73.19 452	71.24 185	75.67 373	65.56 446	70.42 247	65.18 451	92.97 145	33.64 455	83.06 374	53.52 407	69.61 464	78.79 442

Syy-MVS		69.40 388	70.03 378	67.49 419	81.72 376	38.94 461	71.00 413	61.99 453	61.38 361	70.81 423	72.36 457	61.37 332	79.30 399	64.50 335	85.18 397	84.22 385

testing99		69.27 389	68.15 396	72.63 381	83.29 360	45.45 443	71.15 412	71.08 420	67.34 297	70.43 426	77.77 434	32.24 458	84.35 366	53.72 404	86.33 386	88.10 332

UnsupCasMVSNet_bld		69.21 390	69.68 381	67.82 417	79.42 406	51.15 419	67.82 435	75.79 382	54.15 414	77.47 376	85.36 359	59.26 347	70.64 432	48.46 434	79.35 440	81.66 422

test_cas_vis1_n_1920		69.20 391	69.12 384	69.43 405	73.68 449	62.82 287	70.38 421	77.21 372	46.18 449	80.46 343	78.95 425	52.03 385	65.53 456	65.77 321	77.45 451	79.95 439

gg-mvs-nofinetune		68.96 392	69.11 385	68.52 415	76.12 434	45.32 444	83.59 216	55.88 467	86.68 33	64.62 456	97.01 12	30.36 463	83.97 371	44.78 448	82.94 420	76.26 446

WBMVS		68.76 393	68.43 393	69.75 402	83.29 360	40.30 459	67.36 437	72.21 412	57.09 399	77.05 377	85.53 352	33.68 454	80.51 392	48.79 432	90.90 306	88.45 329

WB-MVSnew		68.72 394	69.01 387	67.85 416	83.22 364	43.98 449	74.93 382	65.98 444	55.09 407	73.83 406	79.11 422	65.63 305	71.89 428	38.21 462	85.04 400	87.69 344

tpm2		68.45 395	66.83 402	73.30 375	78.93 413	48.50 429	79.76 302	71.76 416	47.50 444	69.92 429	83.60 378	42.07 438	88.40 292	48.44 435	79.51 438	83.01 406

tpm		67.95 396	68.08 397	67.55 418	78.74 414	43.53 451	75.60 374	67.10 441	54.92 409	72.23 414	88.10 300	42.87 437	75.97 413	52.21 414	80.95 436	83.15 404

WTY-MVS		67.91 397	68.35 394	66.58 424	80.82 390	48.12 431	65.96 443	72.60 407	53.67 416	71.20 420	81.68 402	58.97 349	69.06 439	48.57 433	81.67 428	82.55 411

testing11		67.38 398	65.93 406	71.73 390	83.37 357	46.60 438	70.95 415	69.40 428	62.47 347	66.14 444	76.66 444	31.22 460	84.10 368	49.10 430	84.10 413	84.49 379

test-LLR		67.21 399	66.74 403	68.63 412	76.45 431	55.21 387	67.89 432	67.14 439	62.43 350	65.08 452	72.39 455	43.41 433	69.37 435	61.00 360	84.89 405	81.31 426

testing222		66.93 400	65.30 413	71.81 389	83.38 356	45.83 442	72.06 406	67.50 435	64.12 336	69.68 431	76.37 447	27.34 471	83.00 375	38.88 458	88.38 353	86.62 357

sss		66.92 401	67.26 399	65.90 426	77.23 421	51.10 421	64.79 445	71.72 417	52.12 429	70.13 428	80.18 414	57.96 356	65.36 457	50.21 422	81.01 434	81.25 428

KD-MVS_2432*1600		66.87 402	65.81 409	70.04 397	67.50 467	47.49 434	62.56 451	79.16 359	61.21 366	77.98 367	80.61 408	25.29 474	82.48 378	53.02 409	84.92 402	80.16 437

miper_refine_blended		66.87 402	65.81 409	70.04 397	67.50 467	47.49 434	62.56 451	79.16 359	61.21 366	77.98 367	80.61 408	25.29 474	82.48 378	53.02 409	84.92 402	80.16 437

dmvs_re		66.81 404	66.98 400	66.28 425	76.87 425	58.68 360	71.66 409	72.24 410	60.29 376	69.52 433	73.53 454	52.38 384	64.40 459	44.90 447	81.44 431	75.76 447

tpm cat1		66.76 405	65.21 414	71.42 391	77.09 423	50.62 423	78.01 333	73.68 400	44.89 453	68.64 435	79.00 424	45.51 419	82.42 380	49.91 425	70.15 461	81.23 430

UWE-MVS		66.43 406	65.56 412	69.05 407	84.15 340	40.98 457	73.06 400	64.71 449	54.84 410	76.18 385	79.62 420	29.21 465	80.50 393	38.54 461	89.75 333	85.66 367

PVSNet	58.17 21	66.41 407	65.63 411	68.75 410	81.96 373	49.88 426	62.19 453	72.51 409	51.03 435	68.04 438	75.34 451	50.84 391	74.77 418	45.82 446	82.96 419	81.60 423

tpmrst		66.28 408	66.69 404	65.05 432	72.82 457	39.33 460	78.20 331	70.69 423	53.16 420	67.88 439	80.36 413	48.18 401	74.75 419	58.13 377	70.79 460	81.08 431

Patchmatch-test		65.91 409	67.38 398	61.48 442	75.51 438	43.21 452	68.84 428	63.79 451	62.48 346	72.80 412	83.42 382	44.89 428	59.52 465	48.27 436	86.45 383	81.70 421

ADS-MVSNet2		65.87 410	63.64 419	72.55 383	73.16 453	56.92 374	67.10 439	74.81 388	49.74 442	66.04 446	82.97 385	46.71 405	77.26 409	42.29 451	69.96 462	83.46 397

myMVS_eth3d28		65.83 411	65.85 407	65.78 427	83.42 355	35.71 467	67.29 438	68.01 434	67.58 294	69.80 430	77.72 435	32.29 457	74.30 421	37.49 463	89.06 343	87.32 348

test_vis1_rt		65.64 412	64.09 416	70.31 396	66.09 471	70.20 198	61.16 454	81.60 346	38.65 467	72.87 411	69.66 460	52.84 381	60.04 464	56.16 386	77.77 447	80.68 435

mvsany_test3		65.48 413	62.97 422	73.03 378	69.99 464	76.17 124	64.83 444	43.71 475	43.68 457	80.25 347	87.05 331	52.83 382	63.09 462	51.92 419	72.44 457	79.84 440

test-mter		65.00 414	63.79 418	68.63 412	76.45 431	55.21 387	67.89 432	67.14 439	50.98 436	65.08 452	72.39 455	28.27 468	69.37 435	61.00 360	84.89 405	81.31 426

ETVMVS		64.67 415	63.34 421	68.64 411	83.44 354	41.89 454	69.56 427	61.70 458	61.33 363	68.74 434	75.76 449	28.76 466	79.35 398	34.65 466	86.16 389	84.67 378

myMVS_eth3d		64.66 416	63.89 417	66.97 422	81.72 376	37.39 464	71.00 413	61.99 453	61.38 361	70.81 423	72.36 457	20.96 477	79.30 399	49.59 427	85.18 397	84.22 385

test0.0.03 1		64.66 416	64.36 415	65.57 429	75.03 443	46.89 437	64.69 446	61.58 459	62.43 350	71.18 421	77.54 436	43.41 433	68.47 444	40.75 456	82.65 424	81.35 425

UBG		64.34 418	63.35 420	67.30 420	83.50 351	40.53 458	67.46 436	65.02 448	54.77 411	67.54 442	74.47 453	32.99 456	78.50 405	40.82 455	83.58 415	82.88 407

test_f		64.31 419	65.85 407	59.67 446	66.54 470	62.24 305	57.76 463	70.96 421	40.13 464	84.36 264	82.09 396	46.93 404	51.67 470	61.99 353	81.89 427	65.12 461

pmmvs3		62.47 420	60.02 433	69.80 401	71.58 461	64.00 273	70.52 419	58.44 465	39.77 465	66.05 445	75.84 448	27.10 473	72.28 425	46.15 444	84.77 409	73.11 451

EPMVS		62.47 420	62.63 424	62.01 438	70.63 463	38.74 462	74.76 383	52.86 469	53.91 415	67.71 441	80.01 415	39.40 442	66.60 452	55.54 393	68.81 466	80.68 435

ADS-MVSNet		61.90 422	62.19 426	61.03 443	73.16 453	36.42 466	67.10 439	61.75 456	49.74 442	66.04 446	82.97 385	46.71 405	63.21 460	42.29 451	69.96 462	83.46 397

PMMVS		61.65 423	60.38 430	65.47 430	65.40 474	69.26 212	63.97 449	61.73 457	36.80 471	60.11 463	68.43 462	59.42 345	66.35 453	48.97 431	78.57 445	60.81 464

E-PMN		61.59 424	61.62 427	61.49 441	66.81 469	55.40 385	53.77 466	60.34 461	66.80 305	58.90 466	65.50 465	40.48 441	66.12 454	55.72 390	86.25 387	62.95 463

TESTMET0.1,1		61.29 425	60.32 431	64.19 434	72.06 459	51.30 417	67.89 432	62.09 452	45.27 451	60.65 462	69.01 461	27.93 469	64.74 458	56.31 385	81.65 430	76.53 445

MVS-HIRNet		61.16 426	62.92 423	55.87 449	79.09 410	35.34 468	71.83 407	57.98 466	46.56 447	59.05 465	91.14 217	49.95 397	76.43 411	38.74 459	71.92 459	55.84 468

EMVS		61.10 427	60.81 429	61.99 439	65.96 472	55.86 381	53.10 467	58.97 464	67.06 302	56.89 470	63.33 466	40.98 439	67.03 450	54.79 399	86.18 388	63.08 462

DSMNet-mixed		60.98 428	61.61 428	59.09 448	72.88 456	45.05 446	74.70 384	46.61 474	26.20 472	65.34 450	90.32 256	55.46 372	63.12 461	41.72 453	81.30 433	69.09 457

dp		60.70 429	60.29 432	61.92 440	72.04 460	38.67 463	70.83 417	64.08 450	51.28 433	60.75 461	77.28 439	36.59 450	71.58 430	47.41 438	62.34 468	75.52 448

dmvs_testset		60.59 430	62.54 425	54.72 451	77.26 420	27.74 474	74.05 389	61.00 460	60.48 373	65.62 449	67.03 464	55.93 369	68.23 446	32.07 470	69.46 465	68.17 458

CHOSEN 280x420		59.08 431	56.52 437	66.76 423	76.51 429	64.39 269	49.62 468	59.00 463	43.86 456	55.66 471	68.41 463	35.55 451	68.21 447	43.25 450	76.78 453	67.69 459

mvsany_test1		58.48 432	56.47 438	64.50 433	65.90 473	68.21 228	56.95 464	42.11 476	38.30 468	65.69 448	77.19 442	56.96 363	59.35 466	46.16 443	58.96 469	65.93 460

UWE-MVS-28		58.44 433	57.71 435	60.65 444	73.58 450	31.23 471	69.68 426	48.80 472	53.12 421	61.79 459	78.83 426	30.98 461	68.40 445	21.58 473	80.99 435	82.33 416

PVSNet_0	51.08 22	56.10 434	54.97 439	59.48 447	75.12 442	53.28 403	55.16 465	61.89 455	44.30 454	59.16 464	62.48 467	54.22 377	65.91 455	35.40 465	47.01 470	59.25 466

new_pmnet		55.69 435	57.66 436	49.76 452	75.47 439	30.59 472	59.56 456	51.45 470	43.62 458	62.49 458	75.48 450	40.96 440	49.15 472	37.39 464	72.52 456	69.55 456

PMMVS2		55.64 436	59.27 434	44.74 453	64.30 475	12.32 481	40.60 469	49.79 471	53.19 419	65.06 454	84.81 366	53.60 380	49.76 471	32.68 469	89.41 338	72.15 452

MVE	40.22 23	51.82 437	50.47 440	55.87 449	62.66 476	51.91 412	31.61 471	39.28 477	40.65 463	50.76 472	74.98 452	56.24 368	44.67 473	33.94 468	64.11 467	71.04 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 438	42.65 441	39.67 454	70.86 462	21.11 476	61.01 455	21.42 481	57.36 396	57.97 469	50.06 470	16.40 479	58.73 467	21.03 474	27.69 474	39.17 470

kuosan		30.83 439	32.17 442	26.83 456	53.36 478	19.02 479	57.90 462	20.44 482	38.29 469	38.01 473	37.82 472	15.18 480	33.45 475	7.74 476	20.76 475	28.03 471

test_method		30.46 440	29.60 443	33.06 455	17.99 480	3.84 483	13.62 472	73.92 395	2.79 474	18.29 476	53.41 469	28.53 467	43.25 474	22.56 471	35.27 472	52.11 469

cdsmvs_eth3d_5k		20.81 441	27.75 444	0.00 461	0.00 484	0.00 486	0.00 473	85.44 297	0.00 479	0.00 480	82.82 389	81.46 132	0.00 480	0.00 479	0.00 478	0.00 476

tmp_tt		20.25 442	24.50 445	7.49 458	4.47 481	8.70 482	34.17 470	25.16 479	1.00 476	32.43 475	18.49 473	39.37 443	9.21 477	21.64 472	43.75 471	4.57 473

ab-mvs-re		6.65 443	8.87 446	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	79.80 417	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

pcd_1.5k_mvsjas		6.41 444	8.55 447	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	76.94 190	0.00 480	0.00 479	0.00 478	0.00 476

test123		6.27 445	8.08 448	0.84 459	1.11 483	0.57 484	62.90 450	0.82 483	0.54 477	1.07 479	2.75 478	1.26 481	0.30 478	1.04 477	1.26 477	1.66 474

testmvs		5.91 446	7.65 449	0.72 460	1.20 482	0.37 485	59.14 458	0.67 484	0.49 478	1.11 478	2.76 477	0.94 482	0.24 479	1.02 478	1.47 476	1.55 475

mmdepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

monomultidepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

test_blank		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet_test		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

DCPMVS		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet-low-res		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uncertanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

Regformer		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 139	93.24 43	93.18 131		95.85 24	84.99 75	97.69 65	93.54 155

TestfortrainingZip							92.12 33

WAC-MVS						37.39 464								52.61 413

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	91.55 138	77.99 97		91.01 169					96.05 9	87.45 29	98.17 37	92.40 212

PC_three_1452									58.96 383	90.06 106	91.33 208	80.66 142	93.03 155	75.78 195	95.94 136	92.48 206

No_MVS				88.81 73	91.55 138	77.99 97		91.01 169					96.05 9	87.45 29	98.17 37	92.40 212

test_one_0601					93.85 66	73.27 147		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 484

eth-test					0.00 484

ZD-MVS					92.22 112	80.48 71		91.85 137	71.22 239	90.38 101	92.98 142	86.06 67	96.11 7	81.99 113	96.75 100

RE-MVS-def			92.61 9		94.13 59	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 74	92.73 190

IU-MVS					94.18 54	72.64 157		90.82 174	56.98 400	89.67 119				85.78 64	97.92 52	93.28 162

OPU-MVS				88.27 87	91.89 124	77.83 100	90.47 59				91.22 214	81.12 136	94.68 81	74.48 209	95.35 159	92.29 222

test_241102_TWO								93.71 59	83.77 58	93.49 40	94.27 83	89.27 24	95.84 26	86.03 57	97.82 57	92.04 235

test_241102_ONE					94.18 54	72.65 155		93.69 61	83.62 61	94.11 27	93.78 114	90.28 15	95.50 51

9.14			89.29 65		91.84 128		88.80 98	95.32 13	75.14 169	91.07 86	92.89 148	87.27 49	93.78 120	83.69 91	97.55 77

save fliter					93.75 67	77.44 106	86.31 144	89.72 212	70.80 244

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 34	87.41 31	98.21 34	92.98 183

test_0728_SECOND				86.79 111	94.25 52	72.45 165	90.54 56	94.10 40					95.88 18	86.42 47	97.97 49	92.02 236

test0726					94.16 57	72.56 161	90.63 53	93.90 49	83.61 62	93.75 35	94.49 73	89.76 19

GSMVS																83.88 389

test_part2					93.86 65	77.77 101				92.84 54

sam_mvs1												46.11 409				83.88 389

sam_mvs												45.92 414

ambc				82.98 219	90.55 167	64.86 263	88.20 107	89.15 225		89.40 128	93.96 105	71.67 270	91.38 201	78.83 148	96.55 105	92.71 193

MTGPA								91.81 141

test_post1							78.85 323				3.13 475	45.19 424	80.13 395	58.11 378

test_post											3.10 476	45.43 420	77.22 410

patchmatchnet-post											81.71 401	45.93 413	87.01 316

GG-mvs-BLEND				67.16 421	73.36 451	46.54 440	84.15 196	55.04 468		58.64 467	61.95 468	29.93 464	83.87 372	38.71 460	76.92 452	71.07 454

MTMP							90.66 52	33.14 478

gm-plane-assit					75.42 440	44.97 447			52.17 426		72.36 457		87.90 302	54.10 402

test9_res														80.83 123	96.45 111	90.57 283

TEST9					92.34 107	79.70 80	83.94 202	90.32 192	65.41 323	84.49 260	90.97 224	82.03 121	93.63 126

test_8					92.09 116	78.87 88	83.82 207	90.31 194	65.79 313	84.36 264	90.96 226	81.93 123	93.44 140

agg_prior2														79.68 136	96.16 123	90.22 291

agg_prior					91.58 136	77.69 103		90.30 195		84.32 266			93.18 148

TestCases				89.68 56	91.59 133	83.40 52		95.44 11	79.47 107	88.00 162	93.03 140	82.66 102	91.47 195	70.81 263	96.14 124	94.16 114

test_prior4						78.97 87	84.59 185

test_prior2							83.37 223		75.43 165	84.58 257	91.57 198	81.92 125		79.54 140	96.97 93

test_prior				86.32 119	90.59 166	71.99 173		92.85 103					94.17 105			92.80 188

旧先验2							81.73 270		56.88 401	86.54 208			84.90 358	72.81 247

新几何2							81.72 271

新几何1				82.95 221	93.96 63	78.56 91		80.24 355	55.45 406	83.93 277	91.08 220	71.19 272	88.33 294	65.84 319	93.07 244	81.95 420

旧先验1					91.97 120	71.77 174		81.78 343			91.84 187	73.92 233			93.65 225	83.61 395

无先验							82.81 242	85.62 295	58.09 389				91.41 200	67.95 303		84.48 380

原ACMM2							82.26 262

原ACMM1				84.60 168	92.81 97	74.01 139		91.50 149	62.59 344	82.73 302	90.67 243	76.53 199	94.25 97	69.24 284	95.69 151	85.55 368

test222					93.31 80	76.54 116	79.38 312	77.79 366	52.59 423	82.36 307	90.84 234	66.83 296			91.69 286	81.25 428

testdata2													86.43 331	63.52 341

segment_acmp												81.94 122

testdata				79.54 301	92.87 91	72.34 166		80.14 356	59.91 379	85.47 234	91.75 194	67.96 290	85.24 354	68.57 298	92.18 273	81.06 433

testdata1							79.62 304		73.95 186

test12				86.57 114	90.74 162	72.63 159		90.69 177		82.76 301		79.20 155	94.80 78		95.32 161	92.27 224

plane_prior7					93.45 74	77.31 109

plane_prior6					92.61 98	76.54 116						74.84 215

plane_prior5								93.61 64					95.22 62	80.78 124	95.83 144	94.46 96

plane_prior4											92.95 146

plane_prior3						76.85 114			77.79 134	86.55 202

plane_prior2							89.45 86		79.44 109

plane_prior1					92.83 95

plane_prior						76.42 119	87.15 126		75.94 156						95.03 173

n2								0.00 485

nn								0.00 485

door-mid								74.45 392

lessismore_v0				85.95 130	91.10 155	70.99 189		70.91 422		91.79 74	94.42 78	61.76 330	92.93 158	79.52 141	93.03 245	93.93 124

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 75	93.67 38	94.82 60	91.18 5	95.52 47	85.36 67	98.73 7	95.23 66

test11								91.46 150

door								72.57 408

HQP5-MVS						70.66 191

HQP-NCC					91.19 150		84.77 176		73.30 200	80.55 340

ACMP_Plane					91.19 150		84.77 176		73.30 200	80.55 340

BP-MVS														77.30 173

HQP4-MVS										80.56 339			94.61 85			93.56 152

HQP3-MVS								92.68 109							94.47 195

HQP2-MVS												72.10 261

NP-MVS					91.95 121	74.55 136					90.17 263

MDTV_nov1_ep13_2view						27.60 475	70.76 418		46.47 448	61.27 460		45.20 423		49.18 429		83.75 394

MDTV_nov1_ep13			68.29 395		78.03 415	43.87 450	74.12 388	72.22 411	52.17 426	67.02 443	85.54 351	45.36 421	80.85 389	55.73 389	84.42 410

ACMMP++_ref															95.74 150

ACMMP++															97.35 83

Test By Simon												79.09 157

ITE_SJBPF				90.11 49	90.72 163	84.97 41		90.30 195	81.56 83	90.02 108	91.20 216	82.40 107	90.81 223	73.58 233	94.66 190	94.56 90

DeepMVS_CX				24.13 457	32.95 479	29.49 473		21.63 480	12.07 473	37.95 474	45.07 471	30.84 462	19.21 476	17.94 475	33.06 473	23.69 472

High-resolution multi-view benchmark