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 49	97.23 2	95.32 2	99.01 2	97.26 9	80.16 147	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 52	96.29 22	88.16 36	94.17 104	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 67	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 79	93.16 148	91.10 2	97.53 79	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 52	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 43	95.72 37	89.60 5	98.27 28	92.08 231

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 27	89.13 7	98.26 30	91.76 242

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 27	89.13 7	98.26 30	91.76 242

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 60	95.13 52	90.65 10	95.34 57	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 80	90.26 4	98.44 20	93.63 144

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 58	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 48	87.16 38	97.60 73	92.73 188

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 39	86.82 43	97.34 83	92.19 226

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 74	93.94 108	90.55 13	95.73 36	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 86	85.17 39	92.47 27	95.05 15	87.65 28	93.21 45	94.39 81	90.09 18	95.08 68	86.67 45	97.60 73	94.18 112

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 68	86.15 24	93.37 10	95.10 14	90.28 10	92.11 66	95.03 54	89.75 21	94.93 72	79.95 131	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 63	94.14 93	89.15 26	95.62 40	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	92.60 112	83.09 68	91.54 76	94.25 87	87.67 46	95.51 48	87.21 37	98.11 40	93.12 171

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 72	83.16 67	91.06 86	94.00 101	88.26 33	95.71 38	87.28 36	98.39 23	92.55 201

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 74	85.07 45	89.99 108	94.03 99	86.57 58	95.80 29	87.35 33	97.62 71	94.20 109

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 36	91.81 139	84.07 55	92.00 69	94.40 80	86.63 57	95.28 60	88.59 11	98.31 26	92.30 218

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 104	88.22 23	88.53 145	97.64 6	83.45 93	94.55 88	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 80	81.99 77	91.40 78	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 120

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 65	94.27 25	82.35 75	93.67 38	94.82 60	91.18 5	95.52 46	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 78	81.91 79	90.88 93	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 129

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 79	81.99 77	91.47 77	93.96 105	88.35 32	95.56 43	87.74 22	97.74 62	92.85 185

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 82	93.97 102	87.93 42	95.87 20	88.65 10	97.96 51	94.12 116

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 88	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	92.98 181
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 65	93.90 49	80.32 97	91.74 75	94.41 79	88.17 35	95.98 13	86.37 49	97.99 46	93.96 122

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 63	80.97 70	91.49 43	93.48 70	82.82 72	92.60 59	93.97 102	88.19 34	96.29 6	87.61 26	98.20 36	94.39 103
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 94	82.59 73	88.52 146	94.37 82	86.74 56	95.41 55	86.32 50	98.21 34	93.19 166
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 41	93.74 57	80.98 90	91.38 79	93.80 112	87.20 51	95.80 29	87.10 40	97.69 65	93.93 123

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 108	94.51 19	75.79 157	92.94 49	94.96 55	88.36 31	95.01 70	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 81	93.00 87	76.26 122	89.65 78	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 139	78.35 153	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 69	93.68 62	77.65 135	91.97 70	94.89 57	88.38 30	95.45 53	89.27 6	97.87 56	93.27 161

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 79	93.35 73	79.20 113	93.83 32	93.60 122	90.81 8	92.96 155	85.02 74	98.45 19	92.41 208
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	89.03 201	84.23 49	93.58 6	94.68 18	90.65 8	90.33 102	93.95 107	84.50 81	95.37 56	80.87 121	95.50 155	94.53 94

ACMP	79.16 10	90.54 36	90.60 52	90.35 45	94.36 49	80.98 69	89.16 90	94.05 42	79.03 116	92.87 51	93.74 117	90.60 12	95.21 63	82.87 99	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 77	78.65 90	89.15 91	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 82	97.81 58	91.70 246
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	86.93 107	94.18 53	72.65 154	90.47 58	93.69 60	83.77 58	94.11 27	94.27 83	90.28 15	95.84 25	86.03 57	97.92 52	92.29 220

SMA-MVS		90.31 39	90.48 53	89.83 55	95.31 30	79.52 83	90.98 50	93.24 81	75.37 166	92.84 53	95.28 48	85.58 71	96.09 8	87.92 18	97.76 60	93.88 126
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 92	77.09 111	91.19 47	95.74 6	81.38 85	92.28 65	93.80 112	86.89 55	94.64 83	85.52 66	97.51 80	94.30 108

v7n		90.13 41	90.96 43	87.65 97	91.95 120	71.06 187	89.99 67	93.05 91	86.53 35	94.29 23	96.27 23	82.69 100	94.08 107	86.25 53	97.63 69	97.82 8

ME-MVS		90.09 42	90.66 50	88.38 83	92.82 95	76.12 126	89.40 88	93.70 59	83.72 60	92.39 63	93.18 131	88.02 40	95.47 51	84.99 75	97.69 65	93.54 154

PMVS	80.48 6	90.08 43	90.66 50	88.34 85	96.71 3	92.97 2	90.31 62	89.57 216	88.51 21	90.11 104	95.12 53	90.98 7	88.92 276	77.55 167	97.07 90	83.13 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 44	91.09 37	87.00 105	91.55 137	72.64 156	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 135	95.88 18	87.41 31	95.94 135	92.48 204

DVP-MVS		90.06 45	91.32 33	86.29 119	94.16 56	72.56 160	90.54 55	91.01 167	83.61 62	93.75 35	94.65 65	89.76 19	95.78 33	86.42 47	97.97 49	90.55 283
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	84.47 171	96.56 6	58.83 356	89.04 92	92.74 106	91.40 6	96.12 5	96.06 29	87.23 50	95.57 42	79.42 141	98.74 6	99.00 2

PEN-MVS		90.03 47	91.88 19	84.48 170	96.57 5	58.88 353	88.95 93	93.19 83	91.62 5	96.01 7	96.16 27	87.02 53	95.60 41	78.69 149	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 48	89.74 59	90.83 36	93.16 84	80.37 74	91.91 39	93.11 87	81.10 88	95.32 14	97.24 10	72.94 250	94.85 74	85.07 71	97.78 59	97.26 16

DTE-MVSNet		89.98 49	91.91 18	84.21 180	96.51 7	57.84 364	88.93 94	92.84 102	91.92 4	96.16 4	96.23 24	86.95 54	95.99 12	79.05 145	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 84	93.98 44	79.68 105	92.09 67	93.89 110	83.80 88	93.10 151	82.67 103	98.04 41	93.64 143

TestfortrainingZip a		89.97 51	90.77 48	87.58 98	94.38 48	73.21 148	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	93.68 138

3Dnovator+	83.92 2	89.97 51	89.66 60	90.92 35	91.27 146	81.66 66	91.25 45	94.13 38	88.89 15	88.83 137	94.26 86	77.55 176	95.86 23	84.88 76	95.87 141	95.24 65

WR-MVS_H		89.91 53	91.31 34	85.71 136	96.32 9	62.39 298	89.54 82	93.31 77	90.21 12	95.57 11	95.66 37	81.42 132	95.90 17	80.94 120	98.80 3	98.84 5

OPM-MVS		89.80 54	89.97 55	89.27 64	94.76 40	79.86 78	86.76 135	92.78 105	78.78 119	92.51 60	93.64 121	88.13 37	93.84 118	84.83 78	97.55 76	94.10 117
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 71	84.79 44	89.89 71	90.63 177	70.00 252	94.55 19	96.67 17	87.94 41	93.59 130	84.27 84	95.97 131	95.52 56

anonymousdsp		89.73 56	88.88 76	92.27 8	89.82 182	86.67 18	90.51 57	90.20 198	69.87 253	95.06 15	96.14 28	84.28 84	93.07 152	87.68 24	96.34 113	97.09 20

test_djsdf		89.62 57	89.01 70	91.45 26	92.36 105	82.98 57	91.98 37	90.08 201	71.54 231	94.28 25	96.54 19	81.57 130	94.27 94	86.26 51	96.49 107	97.09 20

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 46	94.47 43	85.95 27	86.84 131	93.91 48	80.07 101	86.75 195	93.26 128	93.64 2	90.93 214	84.60 81	90.75 311	93.97 121

APD-MVS		89.54 59	89.63 61	89.26 65	92.57 98	81.34 68	90.19 64	93.08 90	80.87 92	91.13 84	93.19 130	86.22 65	95.97 14	82.23 109	97.18 88	90.45 285
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 77	84.72 45	89.70 74	90.29 195	69.27 260	94.39 21	96.38 21	86.02 68	93.52 135	83.96 86	95.92 137	95.34 60

CPTT-MVS		89.39 61	88.98 72	90.63 40	95.09 33	86.95 16	92.09 35	92.30 121	79.74 104	87.50 179	92.38 165	81.42 132	93.28 144	83.07 95	97.24 86	91.67 247

ACMH	76.49 14	89.34 62	91.14 36	83.96 187	92.50 101	70.36 196	89.55 80	93.84 54	81.89 80	94.70 17	95.44 44	90.69 9	88.31 293	83.33 91	98.30 27	93.20 165
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	88.67 212	85.64 35	90.61 53	93.17 84	86.02 38	93.12 46	95.30 46	84.94 76	89.44 268	74.12 216	96.10 126	94.45 97

APD_test2		89.30 63	89.12 67	89.84 53	88.67 212	85.64 35	90.61 53	93.17 84	86.02 38	93.12 46	95.30 46	84.94 76	89.44 268	74.12 216	96.10 126	94.45 97

CP-MVSNet		89.27 65	90.91 45	84.37 172	96.34 8	58.61 359	88.66 101	92.06 128	90.78 7	95.67 8	95.17 51	81.80 127	95.54 45	79.00 146	98.69 10	98.95 4

XVG-OURS		89.18 66	88.83 78	90.23 47	94.28 50	86.11 26	85.91 150	93.60 65	80.16 99	89.13 133	93.44 124	83.82 87	90.98 211	83.86 88	95.30 163	93.60 147

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 63	93.64 69	79.07 86	88.54 104	94.20 31	73.53 191	89.71 116	94.82 60	85.09 75	95.77 35	84.17 85	98.03 43	93.26 163
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	84.31 178	97.00 2	64.33 269	89.67 77	88.38 236	88.84 17	94.29 23	97.57 7	90.48 14	91.26 202	72.57 247	97.65 68	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	89.16 131	92.25 174	72.03 264	96.36 4	88.21 13	90.93 303	92.98 181
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	86.22 123	91.63 131	77.07 112	89.82 72	93.77 56	78.90 117	92.88 50	92.29 172	86.11 66	90.22 242	86.24 54	97.24 86	91.36 255
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 74	91.27 159	78.20 127	86.69 199	92.28 173	80.36 145	95.06 69	86.17 55	96.49 107	90.22 289

Elysia		88.71 72	88.89 74	88.19 88	91.26 147	72.96 150	88.10 109	93.59 66	84.31 51	90.42 98	94.10 96	74.07 227	94.82 75	88.19 14	95.92 137	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 88	91.26 147	72.96 150	88.10 109	93.59 66	84.31 51	90.42 98	94.10 96	74.07 227	94.82 75	88.19 14	95.92 137	96.80 27

test_0402		88.65 74	89.58 63	85.88 132	92.55 99	72.22 168	84.01 198	89.44 219	88.63 20	94.38 22	95.77 32	86.38 64	93.59 130	79.84 132	95.21 164	91.82 240

DP-MVS		88.60 75	89.01 70	87.36 100	91.30 144	77.50 104	87.55 117	92.97 98	87.95 26	89.62 120	92.87 148	84.56 80	93.89 115	77.65 165	96.62 102	90.70 275

APD_test1		88.40 76	87.91 88	89.88 52	89.50 188	86.65 20	89.98 68	91.91 134	84.26 53	90.87 94	93.92 109	82.18 115	89.29 272	73.75 224	94.81 183	93.70 137

Anonymous20231211		88.40 76	89.62 62	84.73 162	90.46 167	65.27 258	88.86 95	93.02 95	87.15 30	93.05 48	97.10 11	82.28 113	92.02 181	76.70 177	97.99 46	96.88 26

PS-MVSNAJss		88.31 78	87.90 89	89.56 60	93.31 79	77.96 99	87.94 113	91.97 131	70.73 243	94.19 26	96.67 17	76.94 189	94.57 86	83.07 95	96.28 115	96.15 38

OMC-MVS		88.19 79	87.52 93	90.19 48	91.94 122	81.68 65	87.49 120	93.17 84	76.02 151	88.64 142	91.22 212	84.24 85	93.37 142	77.97 163	97.03 91	95.52 56

CS-MVS		88.14 80	87.67 92	89.54 61	89.56 186	79.18 85	90.47 58	94.77 17	79.37 111	84.32 264	89.33 278	83.87 86	94.53 89	82.45 105	94.89 179	94.90 76

TSAR-MVS + MP.		88.14 80	87.82 90	89.09 69	95.72 22	76.74 115	92.49 26	91.19 162	67.85 287	86.63 200	94.84 59	79.58 153	95.96 15	87.62 25	94.50 192	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	82.98 218	93.26 81	63.94 273	91.10 48	89.64 213	85.07 45	90.91 90	91.09 217	89.16 25	91.87 186	82.03 110	95.87 141	93.13 168

EC-MVSNet		88.01 83	88.32 85	87.09 102	89.28 193	72.03 171	90.31 62	96.31 4	80.88 91	85.12 239	89.67 271	84.47 82	95.46 52	82.56 104	96.26 118	93.77 135

RPSCF		88.00 84	86.93 107	91.22 31	90.08 175	89.30 5	89.68 76	91.11 163	79.26 112	89.68 117	94.81 63	82.44 104	87.74 304	76.54 182	88.74 347	96.61 32

AllTest		87.97 85	87.40 97	89.68 56	91.59 132	83.40 52	89.50 83	95.44 11	79.47 107	88.00 161	93.03 139	82.66 101	91.47 194	70.81 261	96.14 123	94.16 113

TranMVSNet+NR-MVSNet		87.86 86	88.76 80	85.18 148	94.02 61	64.13 270	84.38 190	91.29 155	84.88 48	92.06 68	93.84 111	86.45 61	93.73 120	73.22 238	98.66 11	97.69 9

nrg030		87.85 87	88.49 81	85.91 130	90.07 177	69.73 204	87.86 114	94.20 31	74.04 183	92.70 58	94.66 64	85.88 69	91.50 193	79.72 134	97.32 84	96.50 34

CNVR-MVS		87.81 88	87.68 91	88.21 87	92.87 90	77.30 110	85.25 167	91.23 160	77.31 141	87.07 189	91.47 202	82.94 98	94.71 79	84.67 80	96.27 117	92.62 196

HQP_MVS		87.75 89	87.43 96	88.70 77	93.45 73	76.42 119	89.45 85	93.61 63	79.44 109	86.55 201	92.95 145	74.84 214	95.22 61	80.78 123	95.83 143	94.46 95

sc_t1		87.70 90	88.94 73	83.99 185	93.47 72	67.15 235	85.05 172	88.21 243	86.81 32	91.87 72	97.65 5	85.51 73	87.91 299	74.22 211	97.63 69	96.92 25

MM		87.64 91	87.15 99	89.09 69	89.51 187	76.39 121	88.68 100	86.76 274	84.54 50	83.58 283	93.78 114	73.36 245	96.48 2	87.98 17	96.21 119	94.41 102

MVSMamba_PlusPlus		87.53 92	88.86 77	83.54 204	92.03 118	62.26 302	91.49 43	92.62 110	88.07 25	88.07 158	96.17 26	72.24 259	95.79 32	84.85 77	94.16 205	92.58 199

NCCC		87.36 93	86.87 108	88.83 72	92.32 108	78.84 89	86.58 139	91.09 165	78.77 120	84.85 251	90.89 228	80.85 138	95.29 58	81.14 118	95.32 160	92.34 216

DeepPCF-MVS	81.24 5	87.28 94	86.21 118	90.49 42	91.48 141	84.90 42	83.41 221	92.38 117	70.25 249	89.35 128	90.68 238	82.85 99	94.57 86	79.55 138	95.95 134	92.00 235

SixPastTwentyTwo		87.20 95	87.45 95	86.45 116	92.52 100	69.19 214	87.84 115	88.05 244	81.66 82	94.64 18	96.53 20	65.94 301	94.75 78	83.02 97	96.83 96	95.41 58

fmvsm_s_conf0.5_n_9		87.04 96	87.02 104	87.08 103	89.67 184	75.87 127	84.60 183	89.74 208	74.40 180	89.92 112	93.41 125	80.45 143	90.63 229	86.66 46	94.37 198	94.73 87

SPE-MVS-test		87.00 97	86.43 114	88.71 76	89.46 189	77.46 105	89.42 87	95.73 7	77.87 133	81.64 324	87.25 323	82.43 105	94.53 89	77.65 165	96.46 109	94.14 115

UniMVSNet (Re)		86.87 98	86.98 106	86.55 114	93.11 85	68.48 224	83.80 208	92.87 100	80.37 95	89.61 122	91.81 188	77.72 172	94.18 102	75.00 204	98.53 16	96.99 24

Vis-MVSNet		86.86 99	86.58 111	87.72 95	92.09 115	77.43 107	87.35 121	92.09 127	78.87 118	84.27 269	94.05 98	78.35 164	93.65 123	80.54 127	91.58 289	92.08 231
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	86.17 126	92.86 92	67.02 239	82.55 248	91.56 145	83.08 69	90.92 88	91.82 187	78.25 165	93.99 109	74.16 214	98.35 24	97.49 13

DU-MVS		86.80 101	86.99 105	86.21 124	93.24 82	67.02 239	83.16 231	92.21 122	81.73 81	90.92 88	91.97 180	77.20 183	93.99 109	74.16 214	98.35 24	97.61 10

casdiffmvs_mvg		86.72 102	87.51 94	84.36 174	87.09 262	65.22 259	84.16 194	94.23 28	77.89 131	91.28 83	93.66 120	84.35 83	92.71 161	80.07 128	94.87 182	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	87.18 101	85.94 300	78.30 92	86.93 128	92.20 123	65.94 307	89.16 131	93.16 134	83.10 96	89.89 257	87.81 21	94.43 196	93.35 156

tt0320-xc		86.67 104	88.41 83	81.44 260	93.45 73	60.44 330	83.96 200	88.50 232	87.26 29	90.90 92	97.90 3	85.61 70	86.40 330	70.14 273	98.01 45	97.47 14

IS-MVSNet		86.66 105	86.82 110	86.17 126	92.05 117	66.87 243	91.21 46	88.64 229	86.30 37	89.60 123	92.59 157	69.22 282	94.91 73	73.89 221	97.89 55	96.72 29

tt0320		86.63 106	88.36 84	81.41 261	93.57 70	60.73 327	84.37 191	88.61 231	87.00 31	90.75 95	97.98 2	85.54 72	86.45 328	69.75 278	97.70 64	97.06 22

v10		86.54 107	87.10 101	84.84 156	88.16 227	63.28 280	86.64 138	92.20 123	75.42 165	92.81 55	94.50 72	74.05 230	94.06 108	83.88 87	96.28 115	97.17 19

pmmvs6		86.52 108	88.06 87	81.90 246	92.22 111	62.28 301	84.66 182	89.15 223	83.54 64	89.85 113	97.32 8	88.08 39	86.80 321	70.43 270	97.30 85	96.62 31

NormalMVS		86.47 109	85.32 141	89.94 51	94.43 44	80.42 72	88.63 102	93.59 66	74.56 175	85.12 239	90.34 251	66.19 298	94.20 99	76.57 180	98.44 20	95.19 68

PHI-MVS		86.38 110	85.81 128	88.08 90	88.44 221	77.34 108	89.35 89	93.05 91	73.15 204	84.76 253	87.70 312	78.87 158	94.18 102	80.67 125	96.29 114	92.73 188

CSCG		86.26 111	86.47 113	85.60 138	90.87 159	74.26 137	87.98 112	91.85 135	80.35 96	89.54 126	88.01 299	79.09 156	92.13 177	75.51 197	95.06 171	90.41 286

DeepC-MVS_fast	80.27 8	86.23 112	85.65 134	87.96 93	91.30 144	76.92 113	87.19 123	91.99 130	70.56 244	84.96 246	90.69 237	80.01 149	95.14 66	78.37 152	95.78 147	91.82 240
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	84.36 174	87.82 235	62.35 300	86.42 142	91.33 154	76.78 145	92.73 57	94.48 74	73.41 242	93.72 121	83.10 94	95.41 156	97.01 23

Anonymous20240529		86.20 114	87.13 100	83.42 206	90.19 172	64.55 266	84.55 185	90.71 174	85.85 40	89.94 111	95.24 50	82.13 116	90.40 237	69.19 285	96.40 112	95.31 62

fmvsm_s_conf0.5_n_3		86.19 115	87.27 98	82.95 220	86.91 270	70.38 195	85.31 166	92.61 111	75.59 161	88.32 153	92.87 148	82.22 114	88.63 285	88.80 9	92.82 252	89.83 299

test_fmvsmconf0.1_n		86.18 116	85.88 126	87.08 103	85.26 316	78.25 93	85.82 154	91.82 137	65.33 322	88.55 144	92.35 171	82.62 103	89.80 259	86.87 42	94.32 200	93.18 167

CDPH-MVS		86.17 117	85.54 135	88.05 92	92.25 109	75.45 130	83.85 205	92.01 129	65.91 309	86.19 212	91.75 192	83.77 89	94.98 71	77.43 170	96.71 100	93.73 136

NR-MVSNet		86.00 118	86.22 117	85.34 145	93.24 82	64.56 265	82.21 262	90.46 183	80.99 89	88.42 149	91.97 180	77.56 175	93.85 116	72.46 248	98.65 12	97.61 10

train_agg		85.98 119	85.28 142	88.07 91	92.34 106	79.70 80	83.94 201	90.32 190	65.79 311	84.49 258	90.97 222	81.93 122	93.63 125	81.21 117	96.54 105	90.88 269

KinetiMVS		85.95 120	86.10 121	85.50 142	87.56 245	69.78 202	83.70 211	89.83 207	80.42 94	87.76 172	93.24 129	73.76 236	91.54 192	85.03 73	93.62 226	95.19 68

FC-MVSNet-test		85.93 121	87.05 103	82.58 231	92.25 109	56.44 375	85.75 155	93.09 89	77.33 140	91.94 71	94.65 65	74.78 216	93.41 141	75.11 203	98.58 14	97.88 7

test_fmvsmconf_n		85.88 122	85.51 136	86.99 106	84.77 325	78.21 94	85.40 164	91.39 152	65.32 323	87.72 174	91.81 188	82.33 108	89.78 260	86.68 44	94.20 203	92.99 179

Effi-MVS+-dtu		85.82 123	83.38 191	93.14 4	87.13 257	91.15 3	87.70 116	88.42 235	74.57 174	83.56 284	85.65 347	78.49 163	94.21 98	72.04 250	92.88 248	94.05 119

TAPA-MVS	77.73 12	85.71 124	84.83 152	88.37 84	88.78 211	79.72 79	87.15 125	93.50 69	69.17 261	85.80 222	89.56 272	80.76 139	92.13 177	73.21 243	95.51 154	93.25 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 125	86.14 119	83.58 200	87.97 229	67.13 236	87.55 117	94.32 22	73.44 194	88.47 147	87.54 315	86.45 61	91.06 209	75.76 195	93.76 217	92.54 202

canonicalmvs		85.50 125	86.14 119	83.58 200	87.97 229	67.13 236	87.55 117	94.32 22	73.44 194	88.47 147	87.54 315	86.45 61	91.06 209	75.76 195	93.76 217	92.54 202

fmvsm_s_conf0.5_n_8		85.48 127	85.75 131	84.68 165	87.10 260	69.98 200	84.28 192	92.68 107	74.77 171	87.90 165	92.36 170	73.94 231	90.41 236	85.95 62	92.74 254	93.66 139

EPP-MVSNet		85.47 128	85.04 147	86.77 111	91.52 140	69.37 209	91.63 42	87.98 247	81.51 84	87.05 190	91.83 186	66.18 300	95.29 58	70.75 264	96.89 93	95.64 53

GeoE		85.45 129	85.81 128	84.37 172	90.08 175	67.07 238	85.86 153	91.39 152	72.33 222	87.59 176	90.25 256	84.85 78	92.37 171	78.00 161	91.94 279	93.66 139

MGCNet		85.37 130	84.58 161	87.75 94	85.28 315	73.36 142	86.54 141	85.71 291	77.56 138	81.78 322	92.47 163	70.29 276	96.02 11	85.59 65	95.96 132	93.87 127

FIs		85.35 131	86.27 116	82.60 230	91.86 124	57.31 368	85.10 171	93.05 91	75.83 156	91.02 87	93.97 102	73.57 238	92.91 159	73.97 220	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 132	85.36 140	84.81 158	85.80 303	76.13 125	85.15 170	92.32 120	61.40 358	91.33 80	90.85 231	83.76 90	86.16 336	84.31 83	93.28 235	92.15 229

casdiffmvs		85.21 133	85.85 127	83.31 209	86.17 292	62.77 287	83.03 233	93.93 47	74.69 173	88.21 155	92.68 156	82.29 112	91.89 185	77.87 164	93.75 220	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	85.02 153	86.01 298	71.31 183	84.96 173	91.76 141	69.10 263	88.90 134	92.56 160	73.84 234	90.63 229	86.88 41	93.26 236	93.13 168

baseline		85.20 134	85.93 124	83.02 216	86.30 287	62.37 299	84.55 185	93.96 45	74.48 177	87.12 184	92.03 179	82.30 110	91.94 182	78.39 151	94.21 202	94.74 86

SSM_0404		85.16 136	85.09 145	85.36 144	90.14 174	69.52 207	86.17 147	91.58 143	74.41 178	86.55 201	91.49 199	78.54 159	93.97 111	73.71 225	93.21 240	92.59 198

K. test v3		85.14 137	84.73 154	86.37 117	91.13 153	69.63 206	85.45 162	76.68 376	84.06 56	92.44 62	96.99 13	62.03 328	94.65 82	80.58 126	93.24 237	94.83 83

mmtdpeth		85.13 138	85.78 130	83.17 214	84.65 327	74.71 133	85.87 152	90.35 189	77.94 130	83.82 276	96.96 15	77.75 170	80.03 395	78.44 150	96.21 119	94.79 85

EI-MVSNet-Vis-set		85.12 139	84.53 164	86.88 108	84.01 340	72.76 153	83.91 204	85.18 301	80.44 93	88.75 139	85.49 351	80.08 148	91.92 183	82.02 111	90.85 308	95.97 44

fmvsm_l_conf0.5_n_3		85.11 140	84.96 149	85.56 139	87.49 248	75.69 129	84.71 180	90.61 179	67.64 291	84.88 249	92.05 178	82.30 110	88.36 291	83.84 89	91.10 296	92.62 196

MGCFI-Net		85.04 141	85.95 123	82.31 239	87.52 246	63.59 276	86.23 146	93.96 45	73.46 192	88.07 158	87.83 310	86.46 60	90.87 219	76.17 189	93.89 213	92.47 206

EI-MVSNet-UG-set		85.04 141	84.44 167	86.85 109	83.87 344	72.52 162	83.82 206	85.15 302	80.27 98	88.75 139	85.45 353	79.95 150	91.90 184	81.92 114	90.80 310	96.13 39

X-MVStestdata		85.04 141	82.70 210	92.08 9	95.64 24	86.25 22	92.64 20	93.33 74	85.07 45	89.99 108	16.05 472	86.57 58	95.80 29	87.35 33	97.62 71	94.20 109

MSLP-MVS++		85.00 144	86.03 122	81.90 246	91.84 127	71.56 181	86.75 136	93.02 95	75.95 154	87.12 184	89.39 276	77.98 167	89.40 271	77.46 168	94.78 184	84.75 375

F-COLMAP		84.97 145	83.42 190	89.63 58	92.39 104	83.40 52	88.83 96	91.92 133	73.19 203	80.18 346	89.15 282	77.04 187	93.28 144	65.82 318	92.28 268	92.21 225

SSM_0407		84.89 146	84.85 151	85.01 154	89.13 197	68.97 217	85.60 159	91.58 143	74.41 178	85.68 223	91.49 199	78.54 159	93.69 122	73.71 225	93.47 228	92.38 213

balanced_conf03		84.80 147	85.40 138	83.00 217	88.95 204	61.44 310	90.42 61	92.37 119	71.48 233	88.72 141	93.13 135	70.16 278	95.15 65	79.26 143	94.11 206	92.41 208

3Dnovator	80.37 7	84.80 147	84.71 157	85.06 151	86.36 285	74.71 133	88.77 98	90.00 203	75.65 159	84.96 246	93.17 133	74.06 229	91.19 204	78.28 155	91.09 297	89.29 309

SymmetryMVS		84.79 149	83.54 185	88.55 79	92.44 103	80.42 72	88.63 102	82.37 336	74.56 175	85.12 239	90.34 251	66.19 298	94.20 99	76.57 180	95.68 151	91.03 263

IterMVS-LS		84.73 150	84.98 148	83.96 187	87.35 250	63.66 274	83.25 226	89.88 206	76.06 149	89.62 120	92.37 168	73.40 244	92.52 166	78.16 158	94.77 186	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	84.34 172	85.49 143	90.18 173	75.86 128	79.23 315	87.13 264	73.35 196	85.56 230	89.34 277	83.60 92	90.50 233	76.64 179	94.05 210	90.09 295

HQP-MVS		84.61 152	84.06 177	86.27 120	91.19 149	70.66 190	84.77 175	92.68 107	73.30 199	80.55 338	90.17 261	72.10 260	94.61 84	77.30 172	94.47 194	93.56 151

v1192		84.57 153	84.69 159	84.21 180	87.75 237	62.88 284	83.02 234	91.43 149	69.08 264	89.98 110	90.89 228	72.70 254	93.62 128	82.41 106	94.97 176	96.13 39

fmvsm_s_conf0.5_n_5		84.56 154	84.71 157	84.11 183	87.92 232	72.09 170	84.80 174	88.64 229	64.43 332	88.77 138	91.78 190	78.07 166	87.95 298	85.85 63	92.18 272	92.30 218

FMVSNet1		84.55 155	85.45 137	81.85 248	90.27 171	61.05 318	86.83 132	88.27 240	78.57 123	89.66 119	95.64 38	75.43 206	90.68 226	69.09 286	95.33 159	93.82 130

v1144		84.54 156	84.72 156	84.00 184	87.67 241	62.55 291	82.97 236	90.93 170	70.32 248	89.80 114	90.99 221	73.50 239	93.48 137	81.69 116	94.65 190	95.97 44

Gipuma		84.44 157	86.33 115	78.78 306	84.20 337	73.57 141	89.55 80	90.44 184	84.24 54	84.38 261	94.89 57	76.35 202	80.40 392	76.14 190	96.80 98	82.36 413
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	84.27 173	84.74 161	87.25 253	70.84 189	83.55 216	88.45 234	68.64 273	86.29 211	91.31 208	74.97 212	88.42 289	87.87 20	90.07 326	94.95 75

MCST-MVS		84.36 159	83.93 181	85.63 137	91.59 132	71.58 179	83.52 217	92.13 125	61.82 351	83.96 274	89.75 269	79.93 151	93.46 138	78.33 154	94.34 199	91.87 239

VDDNet		84.35 160	85.39 139	81.25 263	95.13 32	59.32 344	85.42 163	81.11 347	86.41 36	87.41 180	96.21 25	73.61 237	90.61 231	66.33 311	96.85 94	93.81 133

ETV-MVS		84.31 161	83.91 182	85.52 140	88.58 217	70.40 194	84.50 189	93.37 71	78.76 121	84.07 272	78.72 426	80.39 144	95.13 67	73.82 223	92.98 246	91.04 262

v1240		84.30 162	84.51 165	83.65 197	87.65 242	61.26 314	82.85 240	91.54 146	67.94 284	90.68 97	90.65 242	71.71 268	93.64 124	82.84 100	94.78 184	96.07 41

MVS_111021_LR		84.28 163	83.76 183	85.83 134	89.23 195	83.07 55	80.99 283	83.56 324	72.71 214	86.07 215	89.07 284	81.75 129	86.19 335	77.11 174	93.36 231	88.24 328

h-mvs33		84.25 164	82.76 209	88.72 75	91.82 129	82.60 60	84.00 199	84.98 308	71.27 234	86.70 197	90.55 247	63.04 325	93.92 114	78.26 156	94.20 203	89.63 301

v144192		84.24 165	84.41 168	83.71 196	87.59 244	61.57 309	82.95 237	91.03 166	67.82 288	89.80 114	90.49 248	73.28 246	93.51 136	81.88 115	94.89 179	96.04 43

dcpmvs_2		84.23 166	85.14 144	81.50 258	88.61 216	61.98 306	82.90 239	93.11 87	68.66 272	92.77 56	92.39 164	78.50 162	87.63 307	76.99 176	92.30 265	94.90 76

v1921920		84.23 166	84.37 170	83.79 192	87.64 243	61.71 308	82.91 238	91.20 161	67.94 284	90.06 105	90.34 251	72.04 263	93.59 130	82.32 107	94.91 177	96.07 41

VDD-MVS		84.23 166	84.58 161	83.20 212	91.17 152	65.16 261	83.25 226	84.97 309	79.79 103	87.18 183	94.27 83	74.77 217	90.89 217	69.24 282	96.54 105	93.55 153

v2v482		84.09 169	84.24 174	83.62 198	87.13 257	61.40 311	82.71 243	89.71 211	72.19 225	89.55 124	91.41 203	70.70 274	93.20 146	81.02 119	93.76 217	96.25 37

EG-PatchMatch MVS		84.08 170	84.11 176	83.98 186	92.22 111	72.61 159	82.20 264	87.02 270	72.63 215	88.86 135	91.02 220	78.52 161	91.11 207	73.41 233	91.09 297	88.21 329

fmvsm_s_conf0.5_n_6		84.05 171	84.14 175	83.81 190	87.75 237	71.17 185	83.42 220	91.10 164	67.90 286	84.53 256	90.70 236	73.01 249	88.73 282	85.09 70	93.72 222	91.53 252

DP-MVS Recon		84.05 171	83.22 194	86.52 115	91.73 130	75.27 131	83.23 228	92.40 115	72.04 227	82.04 313	88.33 295	77.91 169	93.95 113	66.17 312	95.12 169	90.34 288

viewmacassd2359aftdt		84.04 173	84.78 153	81.81 251	86.43 279	60.32 332	81.95 266	92.82 103	71.56 230	86.06 216	92.98 141	81.79 128	90.28 238	76.18 188	93.24 237	94.82 84

TransMVSNet (Re)		84.02 174	85.74 132	78.85 305	91.00 156	55.20 387	82.29 258	87.26 259	79.65 106	88.38 151	95.52 41	83.00 97	86.88 319	67.97 300	96.60 103	94.45 97

Baseline_NR-MVSNet		84.00 175	85.90 125	78.29 317	91.47 142	53.44 399	82.29 258	87.00 273	79.06 115	89.55 124	95.72 36	77.20 183	86.14 337	72.30 249	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 176	84.46 166	82.53 234	86.11 295	70.65 192	82.45 253	89.17 222	67.72 290	86.74 196	91.49 199	79.20 154	85.86 346	84.71 79	92.60 258	91.07 261

TSAR-MVS + GP.		83.95 177	82.69 211	87.72 95	89.27 194	81.45 67	83.72 210	81.58 345	74.73 172	85.66 226	86.06 342	72.56 256	92.69 163	75.44 199	95.21 164	89.01 322

LuminaMVS		83.94 178	83.51 186	85.23 146	89.78 183	71.74 174	84.76 178	87.27 258	72.60 216	89.31 129	90.60 246	64.04 314	90.95 212	79.08 144	94.11 206	92.99 179

alignmvs		83.94 178	83.98 179	83.80 191	87.80 236	67.88 231	84.54 187	91.42 151	73.27 202	88.41 150	87.96 300	72.33 257	90.83 220	76.02 192	94.11 206	92.69 192

Effi-MVS+		83.90 180	84.01 178	83.57 202	87.22 255	65.61 257	86.55 140	92.40 115	78.64 122	81.34 329	84.18 372	83.65 91	92.93 157	74.22 211	87.87 361	92.17 228

fmvsm_s_conf0.1_n_2		83.82 181	83.49 187	84.84 156	85.99 299	70.19 198	80.93 284	87.58 254	67.26 297	87.94 164	92.37 168	71.40 270	88.01 295	86.03 57	91.87 280	96.31 36

mvs5depth		83.82 181	84.54 163	81.68 254	82.23 369	68.65 222	86.89 129	89.90 205	80.02 102	87.74 173	97.86 4	64.19 313	82.02 380	76.37 184	95.63 153	94.35 104

CANet		83.79 183	82.85 208	86.63 112	86.17 292	72.21 169	83.76 209	91.43 149	77.24 142	74.39 401	87.45 319	75.36 207	95.42 54	77.03 175	92.83 251	92.25 224

pm-mvs1		83.69 184	84.95 150	79.91 291	90.04 179	59.66 341	82.43 254	87.44 255	75.52 163	87.85 168	95.26 49	81.25 134	85.65 350	68.74 292	96.04 128	94.42 101

AdaColmap		83.66 185	83.69 184	83.57 202	90.05 178	72.26 167	86.29 144	90.00 203	78.19 128	81.65 323	87.16 325	83.40 94	94.24 97	61.69 354	94.76 187	84.21 385

viewdifsd2359ckpt09		83.64 186	83.18 197	85.03 152	87.26 252	66.99 241	85.32 165	93.83 55	65.57 317	84.99 245	89.40 275	77.30 179	93.57 133	71.16 260	93.80 216	94.54 93

MIMVSNet1		83.63 187	84.59 160	80.74 274	94.06 60	62.77 287	82.72 242	84.53 316	77.57 137	90.34 101	95.92 31	76.88 195	85.83 347	61.88 352	97.42 81	93.62 145

fmvsm_s_conf0.5_n_2		83.62 188	83.29 193	84.62 166	85.43 313	70.18 199	80.61 290	87.24 260	67.14 298	87.79 170	91.87 182	71.79 267	87.98 297	86.00 61	91.77 283	95.71 50

test_fmvsm_n_1920		83.60 189	82.89 205	85.74 135	85.22 317	77.74 102	84.12 196	90.48 181	59.87 378	86.45 210	91.12 216	75.65 204	85.89 344	82.28 108	90.87 306	93.58 149

WR-MVS		83.56 190	84.40 169	81.06 268	93.43 76	54.88 388	78.67 324	85.02 306	81.24 86	90.74 96	91.56 197	72.85 251	91.08 208	68.00 299	98.04 41	97.23 17

CNLPA		83.55 191	83.10 200	84.90 155	89.34 192	83.87 50	84.54 187	88.77 226	79.09 114	83.54 285	88.66 292	74.87 213	81.73 382	66.84 306	92.29 267	89.11 315

viewcassd2359sk11		83.53 192	83.96 180	82.25 240	86.97 269	61.13 316	80.80 288	93.22 82	70.97 240	85.36 234	91.08 218	81.84 126	91.29 201	74.79 206	90.58 322	94.33 106

LCM-MVSNet-Re		83.48 193	85.06 146	78.75 307	85.94 300	55.75 381	80.05 296	94.27 25	76.47 146	96.09 6	94.54 71	83.31 95	89.75 263	59.95 365	94.89 179	90.75 272

hse-mvs2		83.47 194	81.81 226	88.47 80	91.03 155	82.27 61	82.61 244	83.69 322	71.27 234	86.70 197	86.05 343	63.04 325	92.41 169	78.26 156	93.62 226	90.71 274

V42		83.47 194	83.37 192	83.75 194	83.16 363	63.33 279	81.31 277	90.23 197	69.51 257	90.91 90	90.81 233	74.16 226	92.29 175	80.06 129	90.22 324	95.62 54

VPA-MVSNet		83.47 194	84.73 154	79.69 296	90.29 170	57.52 367	81.30 279	88.69 228	76.29 147	87.58 178	94.44 75	80.60 142	87.20 313	66.60 309	96.82 97	94.34 105

mamba_0408		83.44 197	82.88 206	85.11 149	89.13 197	68.97 217	72.73 399	91.28 156	72.90 208	85.68 223	90.61 244	76.78 196	93.97 111	73.37 235	93.47 228	92.38 213

viewdifsd2359ckpt07		83.41 198	84.35 171	80.56 280	85.84 302	58.93 352	79.47 307	91.28 156	73.01 207	87.59 176	92.07 177	85.24 74	88.68 283	73.59 230	91.11 295	94.09 118

PAPM_NR		83.23 199	83.19 196	83.33 208	90.90 158	65.98 253	88.19 107	90.78 173	78.13 129	80.87 334	87.92 304	73.49 241	92.42 168	70.07 274	88.40 350	91.60 249

CLD-MVS		83.18 200	82.64 212	84.79 159	89.05 200	67.82 232	77.93 334	92.52 113	68.33 276	85.07 242	81.54 401	82.06 119	92.96 155	69.35 281	97.91 54	93.57 150
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 201	85.68 133	75.65 355	81.24 381	45.26 443	79.94 298	92.91 99	83.83 57	91.33 80	96.88 16	80.25 146	85.92 340	68.89 289	95.89 140	95.76 48

FA-MVS(test-final)		83.13 202	83.02 201	83.43 205	86.16 294	66.08 252	88.00 111	88.36 237	75.55 162	85.02 243	92.75 154	65.12 307	92.50 167	74.94 205	91.30 293	91.72 244

114514_t		83.10 203	82.54 215	84.77 160	92.90 89	69.10 216	86.65 137	90.62 178	54.66 410	81.46 326	90.81 233	76.98 188	94.38 92	72.62 246	96.18 121	90.82 271

RRT-MVS		82.97 204	83.44 188	81.57 256	85.06 320	58.04 362	87.20 122	90.37 187	77.88 132	88.59 143	93.70 119	63.17 322	93.05 153	76.49 183	88.47 349	93.62 145

viewmanbaseed2359cas		82.95 205	83.43 189	81.52 257	85.18 318	60.03 337	81.36 276	92.38 117	69.55 256	84.84 252	91.38 204	79.85 152	90.09 251	74.22 211	92.09 274	94.43 100

BP-MVS1		82.81 206	81.67 228	86.23 121	87.88 234	68.53 223	86.06 149	84.36 317	75.65 159	85.14 238	90.19 258	45.84 414	94.42 91	85.18 69	94.72 188	95.75 49

UGNet		82.78 207	81.64 229	86.21 124	86.20 291	76.24 123	86.86 130	85.68 292	77.07 143	73.76 405	92.82 150	69.64 279	91.82 188	69.04 288	93.69 223	90.56 282
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		82.75 208	81.93 224	85.19 147	82.08 370	80.15 76	85.53 160	88.76 227	68.01 281	85.58 229	87.75 311	71.80 266	86.85 320	74.02 219	93.87 214	88.58 325

EI-MVSNet		82.61 209	82.42 217	83.20 212	83.25 360	63.66 274	83.50 218	85.07 303	76.06 149	86.55 201	85.10 359	73.41 242	90.25 239	78.15 160	90.67 317	95.68 52

QAPM		82.59 210	82.59 214	82.58 231	86.44 278	66.69 244	89.94 70	90.36 188	67.97 283	84.94 248	92.58 159	72.71 253	92.18 176	70.63 267	87.73 364	88.85 323

fmvsm_s_conf0.1_n_a		82.58 211	81.93 224	84.50 169	87.68 240	73.35 143	86.14 148	77.70 365	61.64 356	85.02 243	91.62 194	77.75 170	86.24 332	82.79 101	87.07 372	93.91 125

Fast-Effi-MVS+-dtu		82.54 212	81.41 238	85.90 131	85.60 308	76.53 118	83.07 232	89.62 215	73.02 206	79.11 356	83.51 377	80.74 140	90.24 241	68.76 291	89.29 337	90.94 266

MVS_Test		82.47 213	83.22 194	80.22 287	82.62 368	57.75 366	82.54 249	91.96 132	71.16 238	82.89 296	92.52 162	77.41 177	90.50 233	80.04 130	87.84 363	92.40 210

viewdifsd2359ckpt11		82.46 214	82.98 203	80.88 271	83.53 347	61.00 321	79.46 308	85.97 287	69.48 258	87.89 166	91.31 208	82.10 117	88.61 286	74.28 209	92.86 249	93.02 175

viewmsd2359difaftdt		82.46 214	82.99 202	80.88 271	83.52 348	61.00 321	79.46 308	85.97 287	69.48 258	87.89 166	91.31 208	82.10 117	88.61 286	74.28 209	92.86 249	93.02 175

v148		82.31 216	82.48 216	81.81 251	85.59 309	59.66 341	81.47 274	86.02 285	72.85 210	88.05 160	90.65 242	70.73 273	90.91 216	75.15 202	91.79 281	94.87 78

API-MVS		82.28 217	82.61 213	81.30 262	86.29 288	69.79 201	88.71 99	87.67 253	78.42 125	82.15 309	84.15 373	77.98 167	91.59 191	65.39 321	92.75 253	82.51 412

MVSFormer		82.23 218	81.57 234	84.19 182	85.54 310	69.26 211	91.98 37	90.08 201	71.54 231	76.23 381	85.07 362	58.69 350	94.27 94	86.26 51	88.77 345	89.03 320

viewdifsd2359ckpt13		82.22 219	81.98 223	82.95 220	85.48 312	64.44 267	83.17 230	92.11 126	65.97 306	83.72 279	89.73 270	77.60 174	90.80 222	70.61 268	89.42 335	93.59 148

fmvsm_s_conf0.5_n_a		82.21 220	81.51 237	84.32 177	86.56 275	73.35 143	85.46 161	77.30 369	61.81 352	84.51 257	90.88 230	77.36 178	86.21 334	82.72 102	86.97 377	93.38 155

EIA-MVS		82.19 221	81.23 245	85.10 150	87.95 231	69.17 215	83.22 229	93.33 74	70.42 245	78.58 361	79.77 417	77.29 180	94.20 99	71.51 256	88.96 343	91.93 238

GDP-MVS		82.17 222	80.85 253	86.15 128	88.65 214	68.95 220	85.65 158	93.02 95	68.42 274	83.73 278	89.54 273	45.07 425	94.31 93	79.66 136	93.87 214	95.19 68

fmvsm_s_conf0.1_n		82.17 222	81.59 232	83.94 189	86.87 273	71.57 180	85.19 169	77.42 368	62.27 350	84.47 260	91.33 206	76.43 199	85.91 342	83.14 92	87.14 370	94.33 106

PCF-MVS	74.62 15	82.15 224	80.92 251	85.84 133	89.43 190	72.30 166	80.53 291	91.82 137	57.36 394	87.81 169	89.92 266	77.67 173	93.63 125	58.69 370	95.08 170	91.58 250
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 225	80.31 260	87.45 99	90.86 160	80.29 75	85.88 151	90.65 176	68.17 279	76.32 380	86.33 337	73.12 248	92.61 165	61.40 357	90.02 328	89.44 304
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 226	81.54 236	83.60 199	83.94 341	73.90 139	83.35 223	86.10 281	58.97 380	83.80 277	90.36 250	74.23 224	86.94 318	82.90 98	90.22 324	89.94 297

fmvsm_s_conf0.5_n_7		82.04 227	82.05 221	82.01 244	86.98 268	71.07 186	78.70 322	89.45 218	68.07 280	78.14 363	91.61 195	74.19 225	85.92 340	79.61 137	91.73 284	89.05 319

GBi-Net		82.02 228	82.07 219	81.85 248	86.38 282	61.05 318	86.83 132	88.27 240	72.43 217	86.00 217	95.64 38	63.78 318	90.68 226	65.95 314	93.34 232	93.82 130

test1		82.02 228	82.07 219	81.85 248	86.38 282	61.05 318	86.83 132	88.27 240	72.43 217	86.00 217	95.64 38	63.78 318	90.68 226	65.95 314	93.34 232	93.82 130

OpenMVS	76.72 13	81.98 230	82.00 222	81.93 245	84.42 332	68.22 226	88.50 105	89.48 217	66.92 301	81.80 320	91.86 183	72.59 255	90.16 245	71.19 259	91.25 294	87.40 345

KD-MVS_self_test		81.93 231	83.14 199	78.30 316	84.75 326	52.75 403	80.37 293	89.42 220	70.24 250	90.26 103	93.39 126	74.55 223	86.77 322	68.61 294	96.64 101	95.38 59

fmvsm_s_conf0.5_n		81.91 232	81.30 242	83.75 194	86.02 297	71.56 181	84.73 179	77.11 372	62.44 347	84.00 273	90.68 238	76.42 200	85.89 344	83.14 92	87.11 371	93.81 133

SDMVSNet		81.90 233	83.17 198	78.10 320	88.81 209	62.45 297	76.08 368	86.05 284	73.67 188	83.41 286	93.04 137	82.35 107	80.65 389	70.06 275	95.03 172	91.21 257

tfpnnormal		81.79 234	82.95 204	78.31 315	88.93 205	55.40 383	80.83 287	82.85 331	76.81 144	85.90 221	94.14 93	74.58 221	86.51 326	66.82 307	95.68 151	93.01 178

AstraMVS		81.67 235	81.40 239	82.48 236	87.06 265	66.47 247	81.41 275	81.68 342	68.78 269	88.00 161	90.95 226	65.70 303	87.86 303	76.66 178	92.38 262	93.12 171

c3_l		81.64 236	81.59 232	81.79 253	80.86 387	59.15 349	78.61 325	90.18 199	68.36 275	87.20 182	87.11 327	69.39 280	91.62 190	78.16 158	94.43 196	94.60 89

guyue		81.57 237	81.37 241	82.15 241	86.39 280	66.13 251	81.54 273	83.21 326	69.79 254	87.77 171	89.95 264	65.36 306	87.64 306	75.88 193	92.49 260	92.67 193

PVSNet_Blended_VisFu		81.55 238	80.49 258	84.70 164	91.58 135	73.24 147	84.21 193	91.67 142	62.86 341	80.94 332	87.16 325	67.27 292	92.87 160	69.82 277	88.94 344	87.99 335

fmvsm_l_conf0.5_n_a		81.46 239	80.87 252	83.25 210	83.73 346	73.21 148	83.00 235	85.59 294	58.22 386	82.96 295	90.09 263	72.30 258	86.65 324	81.97 113	89.95 329	89.88 298

SSM_04072		81.44 240	82.88 206	77.10 335	89.13 197	68.97 217	72.73 399	91.28 156	72.90 208	85.68 223	90.61 244	76.78 196	69.94 432	73.37 235	93.47 228	92.38 213

DELS-MVS		81.44 240	81.25 243	82.03 243	84.27 336	62.87 285	76.47 362	92.49 114	70.97 240	81.64 324	83.83 374	75.03 210	92.70 162	74.29 208	92.22 271	90.51 284
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		81.31 242	81.61 231	80.41 283	86.38 282	58.75 357	83.93 203	86.58 276	72.43 217	87.65 175	92.98 141	63.78 318	90.22 242	66.86 304	93.92 212	92.27 222

TinyColmap		81.25 243	82.34 218	77.99 323	85.33 314	60.68 328	82.32 257	88.33 238	71.26 236	86.97 191	92.22 176	77.10 186	86.98 317	62.37 346	95.17 166	86.31 358

diffmvs_AUTHOR		81.24 244	81.55 235	80.30 285	80.61 392	60.22 333	77.98 333	90.48 181	67.77 289	83.34 288	89.50 274	74.69 219	87.42 309	78.78 148	90.81 309	93.27 161

AUN-MVS		81.18 245	78.78 283	88.39 82	90.93 157	82.14 62	82.51 250	83.67 323	64.69 331	80.29 342	85.91 346	51.07 389	92.38 170	76.29 187	93.63 225	90.65 279

IMVS_0407		81.08 246	81.23 245	80.62 279	85.76 304	62.46 293	82.46 251	87.91 248	65.23 324	82.12 310	87.92 304	77.27 181	90.18 244	71.67 252	90.74 312	89.20 310

tttt0517		81.07 247	79.58 273	85.52 140	88.99 203	66.45 248	87.03 127	75.51 384	73.76 187	88.32 153	90.20 257	37.96 446	94.16 106	79.36 142	95.13 167	95.93 47

Fast-Effi-MVS+		81.04 248	80.57 255	82.46 237	87.50 247	63.22 281	78.37 328	89.63 214	68.01 281	81.87 316	82.08 395	82.31 109	92.65 164	67.10 303	88.30 356	91.51 253

BH-untuned		80.96 249	80.99 249	80.84 273	88.55 218	68.23 225	80.33 294	88.46 233	72.79 213	86.55 201	86.76 331	74.72 218	91.77 189	61.79 353	88.99 342	82.52 411

IMVS_0403		80.93 250	81.00 248	80.72 276	85.76 304	62.46 293	81.82 267	87.91 248	65.23 324	82.07 312	87.92 304	75.91 203	90.50 233	71.67 252	90.74 312	89.20 310

eth_miper_zixun_eth		80.84 251	80.22 264	82.71 228	81.41 379	60.98 323	77.81 336	90.14 200	67.31 296	86.95 192	87.24 324	64.26 311	92.31 173	75.23 201	91.61 287	94.85 82

xiu_mvs_v1_base_debu		80.84 251	80.14 266	82.93 223	88.31 222	71.73 175	79.53 303	87.17 261	65.43 318	79.59 348	82.73 389	76.94 189	90.14 248	73.22 238	88.33 352	86.90 352

xiu_mvs_v1_base		80.84 251	80.14 266	82.93 223	88.31 222	71.73 175	79.53 303	87.17 261	65.43 318	79.59 348	82.73 389	76.94 189	90.14 248	73.22 238	88.33 352	86.90 352

xiu_mvs_v1_base_debi		80.84 251	80.14 266	82.93 223	88.31 222	71.73 175	79.53 303	87.17 261	65.43 318	79.59 348	82.73 389	76.94 189	90.14 248	73.22 238	88.33 352	86.90 352

IterMVS-SCA-FT		80.64 255	79.41 274	84.34 176	83.93 342	69.66 205	76.28 364	81.09 348	72.43 217	86.47 208	90.19 258	60.46 335	93.15 149	77.45 169	86.39 383	90.22 289

BH-RMVSNet		80.53 256	80.22 264	81.49 259	87.19 256	66.21 250	77.79 337	86.23 279	74.21 182	83.69 280	88.50 293	73.25 247	90.75 223	63.18 342	87.90 360	87.52 343

VortexMVS		80.51 257	80.63 254	80.15 289	83.36 356	61.82 307	80.63 289	88.00 246	67.11 299	87.23 181	89.10 283	63.98 315	88.00 296	73.63 229	92.63 257	90.64 280

Anonymous202405211		80.51 257	81.19 247	78.49 312	88.48 219	57.26 369	76.63 357	82.49 334	81.21 87	84.30 267	92.24 175	67.99 288	86.24 332	62.22 347	95.13 167	91.98 237

DIV-MVS_self_test		80.43 259	80.23 262	81.02 269	79.99 397	59.25 346	77.07 350	87.02 270	67.38 293	86.19 212	89.22 279	63.09 323	90.16 245	76.32 185	95.80 145	93.66 139

cl____		80.42 260	80.23 262	81.02 269	79.99 397	59.25 346	77.07 350	87.02 270	67.37 294	86.18 214	89.21 280	63.08 324	90.16 245	76.31 186	95.80 145	93.65 142

diffmvs		80.40 261	80.48 259	80.17 288	79.02 410	60.04 335	77.54 341	90.28 196	66.65 304	82.40 303	87.33 322	73.50 239	87.35 311	77.98 162	89.62 333	93.13 168
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		80.37 262	78.41 291	86.23 121	76.75 424	73.28 145	87.18 124	77.45 367	76.24 148	68.14 435	88.93 286	65.41 305	93.85 116	69.47 280	96.12 125	91.55 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 263	80.04 269	81.24 265	79.82 400	58.95 351	77.66 338	89.66 212	65.75 314	85.99 220	85.11 358	68.29 287	91.42 198	76.03 191	92.03 275	93.33 157

MG-MVS		80.32 264	80.94 250	78.47 313	88.18 225	52.62 406	82.29 258	85.01 307	72.01 228	79.24 355	92.54 161	69.36 281	93.36 143	70.65 266	89.19 340	89.45 303

mvsmamba		80.30 265	78.87 280	84.58 168	88.12 228	67.55 233	92.35 30	84.88 310	63.15 339	85.33 235	90.91 227	50.71 391	95.20 64	66.36 310	87.98 359	90.99 264

VPNet		80.25 266	81.68 227	75.94 351	92.46 102	47.98 430	76.70 355	81.67 343	73.45 193	84.87 250	92.82 150	74.66 220	86.51 326	61.66 355	96.85 94	93.33 157

MAR-MVS		80.24 267	78.74 285	84.73 162	86.87 273	78.18 95	85.75 155	87.81 252	65.67 316	77.84 367	78.50 427	73.79 235	90.53 232	61.59 356	90.87 306	85.49 368
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		80.20 268	79.00 279	83.78 193	88.17 226	86.66 19	81.31 277	66.81 440	69.64 255	88.33 152	90.19 258	64.58 308	83.63 371	71.99 251	90.03 327	81.06 431

Anonymous20240521		80.18 269	81.25 243	76.95 337	83.15 364	60.84 325	82.46 251	85.99 286	68.76 270	86.78 193	93.73 118	59.13 347	77.44 406	73.71 225	97.55 76	92.56 200

LFMVS		80.15 270	80.56 256	78.89 304	89.19 196	55.93 377	85.22 168	73.78 396	82.96 70	84.28 268	92.72 155	57.38 359	90.07 253	63.80 336	95.75 148	90.68 276

DPM-MVS		80.10 271	79.18 278	82.88 226	90.71 163	69.74 203	78.87 320	90.84 171	60.29 374	75.64 390	85.92 345	67.28 291	93.11 150	71.24 258	91.79 281	85.77 364

MSDG		80.06 272	79.99 271	80.25 286	83.91 343	68.04 230	77.51 342	89.19 221	77.65 135	81.94 314	83.45 379	76.37 201	86.31 331	63.31 341	86.59 380	86.41 356

FE-MVS		79.98 273	78.86 281	83.36 207	86.47 277	66.45 248	89.73 73	84.74 314	72.80 212	84.22 271	91.38 204	44.95 426	93.60 129	63.93 334	91.50 290	90.04 296

sd_testset		79.95 274	81.39 240	75.64 356	88.81 209	58.07 361	76.16 367	82.81 332	73.67 188	83.41 286	93.04 137	80.96 137	77.65 405	58.62 371	95.03 172	91.21 257

ab-mvs		79.67 275	80.56 256	76.99 336	88.48 219	56.93 371	84.70 181	86.06 283	68.95 267	80.78 335	93.08 136	75.30 208	84.62 358	56.78 380	90.90 304	89.43 305

VNet		79.31 276	80.27 261	76.44 345	87.92 232	53.95 395	75.58 374	84.35 318	74.39 181	82.23 307	90.72 235	72.84 252	84.39 363	60.38 363	93.98 211	90.97 265

thisisatest0530		79.07 277	77.33 301	84.26 179	87.13 257	64.58 264	83.66 213	75.95 379	68.86 268	85.22 237	87.36 321	38.10 443	93.57 133	75.47 198	94.28 201	94.62 88

cl22		78.97 278	78.21 293	81.24 265	77.74 414	59.01 350	77.46 345	87.13 264	65.79 311	84.32 264	85.10 359	58.96 349	90.88 218	75.36 200	92.03 275	93.84 128

patch_mono-2		78.89 279	79.39 275	77.41 332	84.78 324	68.11 228	75.60 372	83.11 328	60.96 366	79.36 352	89.89 267	75.18 209	72.97 421	73.32 237	92.30 265	91.15 259

RPMNet		78.88 280	78.28 292	80.68 278	79.58 401	62.64 289	82.58 246	94.16 33	74.80 170	75.72 388	92.59 157	48.69 398	95.56 43	73.48 232	82.91 419	83.85 390

PAPR		78.84 281	78.10 294	81.07 267	85.17 319	60.22 333	82.21 262	90.57 180	62.51 343	75.32 394	84.61 367	74.99 211	92.30 174	59.48 368	88.04 358	90.68 276

viewmambaseed2359dif		78.80 282	78.47 290	79.78 292	80.26 396	59.28 345	77.31 347	87.13 264	60.42 372	82.37 304	88.67 291	74.58 221	87.87 302	67.78 302	87.73 364	92.19 226

PVSNet_BlendedMVS		78.80 282	77.84 295	81.65 255	84.43 330	63.41 277	79.49 306	90.44 184	61.70 355	75.43 391	87.07 328	69.11 283	91.44 196	60.68 361	92.24 269	90.11 294

FMVSNet3		78.80 282	78.55 287	79.57 298	82.89 367	56.89 373	81.76 268	85.77 290	69.04 265	86.00 217	90.44 249	51.75 387	90.09 251	65.95 314	93.34 232	91.72 244

test_yl		78.71 285	78.51 288	79.32 301	84.32 334	58.84 354	78.38 326	85.33 298	75.99 152	82.49 301	86.57 333	58.01 353	90.02 255	62.74 343	92.73 255	89.10 316

DCV-MVSNet		78.71 285	78.51 288	79.32 301	84.32 334	58.84 354	78.38 326	85.33 298	75.99 152	82.49 301	86.57 333	58.01 353	90.02 255	62.74 343	92.73 255	89.10 316

test1111		78.53 287	78.85 282	77.56 329	92.22 111	47.49 432	82.61 244	69.24 428	72.43 217	85.28 236	94.20 89	51.91 385	90.07 253	65.36 322	96.45 110	95.11 72

FE-MVSNET		78.46 288	79.36 276	75.75 353	86.53 276	54.53 390	78.03 330	85.35 297	69.01 266	85.41 233	90.68 238	64.27 310	85.73 348	62.59 345	92.35 264	87.00 351

icg_test_0407_2		78.46 288	79.68 272	74.78 363	85.76 304	62.46 293	68.51 428	87.91 248	65.23 324	82.12 310	87.92 304	77.27 181	72.67 422	71.67 252	90.74 312	89.20 310

ECVR-MVS		78.44 290	78.63 286	77.88 325	91.85 125	48.95 426	83.68 212	69.91 424	72.30 223	84.26 270	94.20 89	51.89 386	89.82 258	63.58 337	96.02 129	94.87 78

pmmvs-eth3d		78.42 291	77.04 304	82.57 233	87.44 249	74.41 136	80.86 286	79.67 356	55.68 403	84.69 254	90.31 255	60.91 333	85.42 351	62.20 348	91.59 288	87.88 339

mvs_anonymous		78.13 292	78.76 284	76.23 350	79.24 407	50.31 422	78.69 323	84.82 312	61.60 357	83.09 294	92.82 150	73.89 233	87.01 314	68.33 298	86.41 382	91.37 254

TAMVS		78.08 293	76.36 311	83.23 211	90.62 164	72.87 152	79.08 316	80.01 355	61.72 354	81.35 328	86.92 330	63.96 317	88.78 280	50.61 419	93.01 245	88.04 334

miper_enhance_ethall		77.83 294	76.93 305	80.51 281	76.15 431	58.01 363	75.47 376	88.82 225	58.05 388	83.59 282	80.69 405	64.41 309	91.20 203	73.16 244	92.03 275	92.33 217

Vis-MVSNet (Re-imp)		77.82 295	77.79 296	77.92 324	88.82 208	51.29 416	83.28 224	71.97 412	74.04 183	82.23 307	89.78 268	57.38 359	89.41 270	57.22 379	95.41 156	93.05 174

CANet_DTU		77.81 296	77.05 303	80.09 290	81.37 380	59.90 339	83.26 225	88.29 239	69.16 262	67.83 438	83.72 375	60.93 332	89.47 265	69.22 284	89.70 332	90.88 269

OpenMVS_ROB	70.19 17	77.77 297	77.46 298	78.71 308	84.39 333	61.15 315	81.18 281	82.52 333	62.45 346	83.34 288	87.37 320	66.20 297	88.66 284	64.69 329	85.02 399	86.32 357

SSC-MVS		77.55 298	81.64 229	65.29 429	90.46 167	20.33 476	73.56 392	68.28 430	85.44 41	88.18 157	94.64 68	70.93 272	81.33 384	71.25 257	92.03 275	94.20 109

MDA-MVSNet-bldmvs		77.47 299	76.90 306	79.16 303	79.03 409	64.59 263	66.58 440	75.67 382	73.15 204	88.86 135	88.99 285	66.94 293	81.23 385	64.71 328	88.22 357	91.64 248

jason		77.42 300	75.75 317	82.43 238	87.10 260	69.27 210	77.99 332	81.94 340	51.47 430	77.84 367	85.07 362	60.32 337	89.00 274	70.74 265	89.27 339	89.03 320
jason: jason.
CDS-MVSNet		77.32 301	75.40 321	83.06 215	89.00 202	72.48 163	77.90 335	82.17 338	60.81 367	78.94 358	83.49 378	59.30 345	88.76 281	54.64 399	92.37 263	87.93 338
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 302	77.75 297	75.73 354	85.76 304	62.46 293	70.84 414	87.91 248	65.23 324	72.21 413	87.92 304	67.48 290	75.53 414	71.67 252	90.74 312	89.20 310

xiu_mvs_v2_base		77.19 303	76.75 308	78.52 311	87.01 266	61.30 313	75.55 375	87.12 268	61.24 363	74.45 400	78.79 425	77.20 183	90.93 214	64.62 331	84.80 406	83.32 399

MVSTER		77.09 304	75.70 318	81.25 263	75.27 439	61.08 317	77.49 344	85.07 303	60.78 368	86.55 201	88.68 289	43.14 435	90.25 239	73.69 228	90.67 317	92.42 207

PS-MVSNAJ		77.04 305	76.53 310	78.56 310	87.09 262	61.40 311	75.26 377	87.13 264	61.25 362	74.38 402	77.22 439	76.94 189	90.94 213	64.63 330	84.83 405	83.35 398

IterMVS		76.91 306	76.34 312	78.64 309	80.91 385	64.03 271	76.30 363	79.03 359	64.88 330	83.11 292	89.16 281	59.90 341	84.46 361	68.61 294	85.15 397	87.42 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 307	75.67 319	80.34 284	80.48 394	62.16 305	73.50 393	84.80 313	57.61 392	82.24 306	87.54 315	51.31 388	87.65 305	70.40 271	93.19 241	91.23 256

CL-MVSNet_self_test		76.81 308	77.38 300	75.12 359	86.90 271	51.34 414	73.20 396	80.63 352	68.30 277	81.80 320	88.40 294	66.92 294	80.90 386	55.35 393	94.90 178	93.12 171

TR-MVS		76.77 309	75.79 316	79.72 295	86.10 296	65.79 255	77.14 348	83.02 329	65.20 328	81.40 327	82.10 393	66.30 296	90.73 225	55.57 390	85.27 393	82.65 406

MonoMVSNet		76.66 310	77.26 302	74.86 361	79.86 399	54.34 392	86.26 145	86.08 282	71.08 239	85.59 228	88.68 289	53.95 377	85.93 339	63.86 335	80.02 435	84.32 381

USDC		76.63 311	76.73 309	76.34 347	83.46 351	57.20 370	80.02 297	88.04 245	52.14 426	83.65 281	91.25 211	63.24 321	86.65 324	54.66 398	94.11 206	85.17 370

BH-w/o		76.57 312	76.07 315	78.10 320	86.88 272	65.92 254	77.63 339	86.33 277	65.69 315	80.89 333	79.95 414	68.97 285	90.74 224	53.01 409	85.25 394	77.62 442

Patchmtry		76.56 313	77.46 298	73.83 369	79.37 406	46.60 436	82.41 255	76.90 373	73.81 186	85.56 230	92.38 165	48.07 401	83.98 368	63.36 340	95.31 162	90.92 267

PVSNet_Blended		76.49 314	75.40 321	79.76 294	84.43 330	63.41 277	75.14 378	90.44 184	57.36 394	75.43 391	78.30 428	69.11 283	91.44 196	60.68 361	87.70 366	84.42 380

miper_lstm_enhance		76.45 315	76.10 314	77.51 330	76.72 425	60.97 324	64.69 444	85.04 305	63.98 335	83.20 291	88.22 296	56.67 363	78.79 402	73.22 238	93.12 242	92.78 187

lupinMVS		76.37 316	74.46 330	82.09 242	85.54 310	69.26 211	76.79 353	80.77 351	50.68 437	76.23 381	82.82 387	58.69 350	88.94 275	69.85 276	88.77 345	88.07 331

cascas		76.29 317	74.81 326	80.72 276	84.47 329	62.94 283	73.89 390	87.34 256	55.94 401	75.16 396	76.53 444	63.97 316	91.16 205	65.00 325	90.97 302	88.06 333

SD_0403		76.08 318	76.77 307	73.98 367	87.08 264	49.45 425	83.62 214	84.68 315	63.31 336	75.13 397	87.47 318	71.85 265	84.56 359	49.97 421	87.86 362	87.94 337

WB-MVS		76.06 319	80.01 270	64.19 432	89.96 181	20.58 475	72.18 403	68.19 431	83.21 66	86.46 209	93.49 123	70.19 277	78.97 400	65.96 313	90.46 323	93.02 175

thres600view7		75.97 320	75.35 323	77.85 327	87.01 266	51.84 412	80.45 292	73.26 401	75.20 167	83.10 293	86.31 339	45.54 416	89.05 273	55.03 396	92.24 269	92.66 194

GA-MVS		75.83 321	74.61 327	79.48 300	81.87 372	59.25 346	73.42 394	82.88 330	68.68 271	79.75 347	81.80 398	50.62 392	89.46 266	66.85 305	85.64 390	89.72 300

MVP-Stereo		75.81 322	73.51 339	82.71 228	89.35 191	73.62 140	80.06 295	85.20 300	60.30 373	73.96 403	87.94 301	57.89 357	89.45 267	52.02 413	74.87 453	85.06 372
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 323	75.20 324	77.27 333	75.01 442	69.47 208	78.93 317	84.88 310	46.67 444	87.08 188	87.84 309	50.44 394	71.62 427	77.42 171	88.53 348	90.72 273

thres100view900		75.45 324	75.05 325	76.66 343	87.27 251	51.88 411	81.07 282	73.26 401	75.68 158	83.25 290	86.37 336	45.54 416	88.80 277	51.98 414	90.99 299	89.31 307

ET-MVSNet_ETH3D		75.28 325	72.77 348	82.81 227	83.03 366	68.11 228	77.09 349	76.51 377	60.67 370	77.60 372	80.52 409	38.04 444	91.15 206	70.78 263	90.68 316	89.17 314

thres400		75.14 326	74.23 332	77.86 326	86.24 289	52.12 408	79.24 313	73.87 394	73.34 197	81.82 318	84.60 368	46.02 409	88.80 277	51.98 414	90.99 299	92.66 194

wuyk23d		75.13 327	79.30 277	62.63 435	75.56 435	75.18 132	80.89 285	73.10 403	75.06 169	94.76 16	95.32 45	87.73 45	52.85 467	34.16 465	97.11 89	59.85 463

EU-MVSNet		75.12 328	74.43 331	77.18 334	83.11 365	59.48 343	85.71 157	82.43 335	39.76 464	85.64 227	88.76 287	44.71 428	87.88 301	73.86 222	85.88 389	84.16 386

HyFIR lowres test		75.12 328	72.66 350	82.50 235	91.44 143	65.19 260	72.47 401	87.31 257	46.79 443	80.29 342	84.30 370	52.70 382	92.10 180	51.88 418	86.73 378	90.22 289

CMPMVS	59.41 20	75.12 328	73.57 337	79.77 293	75.84 434	67.22 234	81.21 280	82.18 337	50.78 435	76.50 377	87.66 313	55.20 373	82.99 374	62.17 350	90.64 321	89.09 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 331	72.98 346	80.73 275	84.95 321	71.71 178	76.23 365	77.59 366	52.83 420	77.73 371	86.38 335	56.35 366	84.97 355	57.72 378	87.05 373	85.51 367

tfpn200view9		74.86 332	74.23 332	76.74 342	86.24 289	52.12 408	79.24 313	73.87 394	73.34 197	81.82 318	84.60 368	46.02 409	88.80 277	51.98 414	90.99 299	89.31 307

1112_ss		74.82 333	73.74 335	78.04 322	89.57 185	60.04 335	76.49 361	87.09 269	54.31 411	73.66 406	79.80 415	60.25 338	86.76 323	58.37 372	84.15 410	87.32 346

EGC-MVSNET		74.79 334	69.99 378	89.19 67	94.89 38	87.00 15	91.89 40	86.28 278	1.09 473	2.23 475	95.98 30	81.87 125	89.48 264	79.76 133	95.96 132	91.10 260

ppachtmachnet_test		74.73 335	74.00 334	76.90 339	80.71 390	56.89 373	71.53 409	78.42 361	58.24 385	79.32 354	82.92 386	57.91 356	84.26 365	65.60 320	91.36 292	89.56 302

Patchmatch-RL test		74.48 336	73.68 336	76.89 340	84.83 323	66.54 245	72.29 402	69.16 429	57.70 390	86.76 194	86.33 337	45.79 415	82.59 375	69.63 279	90.65 320	81.54 422

PatchMatch-RL		74.48 336	73.22 343	78.27 318	87.70 239	85.26 38	75.92 370	70.09 422	64.34 333	76.09 384	81.25 403	65.87 302	78.07 404	53.86 401	83.82 412	71.48 451

XXY-MVS		74.44 338	76.19 313	69.21 404	84.61 328	52.43 407	71.70 406	77.18 371	60.73 369	80.60 336	90.96 224	75.44 205	69.35 435	56.13 385	88.33 352	85.86 363

test2506		74.12 339	73.39 340	76.28 348	91.85 125	44.20 446	84.06 197	48.20 471	72.30 223	81.90 315	94.20 89	27.22 471	89.77 261	64.81 327	96.02 129	94.87 78

reproduce_monomvs		74.09 340	73.23 342	76.65 344	76.52 426	54.54 389	77.50 343	81.40 346	65.85 310	82.86 298	86.67 332	27.38 469	84.53 360	70.24 272	90.66 319	90.89 268

CR-MVSNet		74.00 341	73.04 345	76.85 341	79.58 401	62.64 289	82.58 246	76.90 373	50.50 438	75.72 388	92.38 165	48.07 401	84.07 367	68.72 293	82.91 419	83.85 390

SSC-MVS3.2		73.90 342	75.67 319	68.61 412	84.11 339	41.28 454	64.17 446	72.83 404	72.09 226	79.08 357	87.94 301	70.31 275	73.89 420	55.99 386	94.49 193	90.67 278

Test_1112_low_res		73.90 342	73.08 344	76.35 346	90.35 169	55.95 376	73.40 395	86.17 280	50.70 436	73.14 407	85.94 344	58.31 352	85.90 343	56.51 382	83.22 416	87.20 348

test20.03		73.75 344	74.59 329	71.22 390	81.11 383	51.12 418	70.15 420	72.10 411	70.42 245	80.28 344	91.50 198	64.21 312	74.72 418	46.96 439	94.58 191	87.82 341

test_fmvs2		73.57 345	72.80 347	75.90 352	72.74 456	68.84 221	77.07 350	84.32 319	45.14 450	82.89 296	84.22 371	48.37 399	70.36 431	73.40 234	87.03 374	88.52 326

SCA		73.32 346	72.57 352	75.58 357	81.62 376	55.86 379	78.89 319	71.37 417	61.73 353	74.93 398	83.42 380	60.46 335	87.01 314	58.11 376	82.63 424	83.88 387

baseline1		73.26 347	73.54 338	72.43 383	84.92 322	47.79 431	79.89 299	74.00 392	65.93 308	78.81 359	86.28 340	56.36 365	81.63 383	56.63 381	79.04 442	87.87 340

1314		73.22 348	72.56 353	75.20 358	80.41 395	57.84 364	81.64 271	85.36 296	51.68 429	73.10 408	76.65 443	61.45 330	85.19 353	63.54 338	79.21 440	82.59 407

MVS		73.21 349	72.59 351	75.06 360	80.97 384	60.81 326	81.64 271	85.92 289	46.03 448	71.68 416	77.54 434	68.47 286	89.77 261	55.70 389	85.39 391	74.60 448

HY-MVS	64.64 18	73.03 350	72.47 354	74.71 364	83.36 356	54.19 393	82.14 265	81.96 339	56.76 400	69.57 430	86.21 341	60.03 339	84.83 357	49.58 426	82.65 422	85.11 371

thisisatest0515		73.00 351	70.52 370	80.46 282	81.45 378	59.90 339	73.16 397	74.31 391	57.86 389	76.08 385	77.78 431	37.60 447	92.12 179	65.00 325	91.45 291	89.35 306

EPNet_dtu		72.87 352	71.33 364	77.49 331	77.72 415	60.55 329	82.35 256	75.79 380	66.49 305	58.39 466	81.06 404	53.68 378	85.98 338	53.55 404	92.97 247	85.95 361
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 353	71.41 363	76.28 348	83.25 360	60.34 331	83.50 218	79.02 360	37.77 468	76.33 379	85.10 359	49.60 397	87.41 310	70.54 269	77.54 448	81.08 429

CHOSEN 1792x2688		72.45 354	70.56 369	78.13 319	90.02 180	63.08 282	68.72 427	83.16 327	42.99 458	75.92 386	85.46 352	57.22 361	85.18 354	49.87 424	81.67 426	86.14 359

testgi		72.36 355	74.61 327	65.59 426	80.56 393	42.82 451	68.29 429	73.35 400	66.87 302	81.84 317	89.93 265	72.08 262	66.92 449	46.05 443	92.54 259	87.01 350

thres200		72.34 356	71.55 362	74.70 365	83.48 350	51.60 413	75.02 379	73.71 397	70.14 251	78.56 362	80.57 408	46.20 407	88.20 294	46.99 438	89.29 337	84.32 381

FPMVS		72.29 357	72.00 356	73.14 374	88.63 215	85.00 40	74.65 383	67.39 434	71.94 229	77.80 369	87.66 313	50.48 393	75.83 412	49.95 422	79.51 436	58.58 465

FMVSNet5		72.10 358	71.69 358	73.32 372	81.57 377	53.02 402	76.77 354	78.37 362	63.31 336	76.37 378	91.85 184	36.68 448	78.98 399	47.87 435	92.45 261	87.95 336

our_test_3		71.85 359	71.59 359	72.62 380	80.71 390	53.78 396	69.72 423	71.71 416	58.80 382	78.03 364	80.51 410	56.61 364	78.84 401	62.20 348	86.04 388	85.23 369

PAPM		71.77 360	70.06 376	76.92 338	86.39 280	53.97 394	76.62 358	86.62 275	53.44 415	63.97 455	84.73 366	57.79 358	92.34 172	39.65 455	81.33 430	84.45 379

ttmdpeth		71.72 361	70.67 367	74.86 361	73.08 453	55.88 378	77.41 346	69.27 427	55.86 402	78.66 360	93.77 116	38.01 445	75.39 415	60.12 364	89.87 330	93.31 159

IB-MVS	62.13 19	71.64 362	68.97 388	79.66 297	80.80 389	62.26 302	73.94 389	76.90 373	63.27 338	68.63 434	76.79 441	33.83 452	91.84 187	59.28 369	87.26 368	84.88 373
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		71.63 363	72.30 355	69.62 401	76.47 428	52.70 405	70.03 421	80.97 349	59.18 379	79.36 352	88.21 297	60.50 334	69.12 436	58.33 374	77.62 447	87.04 349

testing3		71.53 364	70.79 366	73.77 370	88.89 207	41.86 453	76.60 360	59.12 460	72.83 211	80.97 330	82.08 395	19.80 477	87.33 312	65.12 324	91.68 286	92.13 230

test_vis3_rt		71.42 365	70.67 367	73.64 371	69.66 463	70.46 193	66.97 439	89.73 209	42.68 460	88.20 156	83.04 382	43.77 430	60.07 461	65.35 323	86.66 379	90.39 287

Anonymous20231206		71.38 366	71.88 357	69.88 398	86.31 286	54.37 391	70.39 418	74.62 387	52.57 422	76.73 376	88.76 287	59.94 340	72.06 424	44.35 447	93.23 239	83.23 401

test_vis1_n_1920		71.30 367	71.58 361	70.47 393	77.58 417	59.99 338	74.25 384	84.22 320	51.06 432	74.85 399	79.10 421	55.10 374	68.83 438	68.86 290	79.20 441	82.58 408

MIMVSNet		71.09 368	71.59 359	69.57 402	87.23 254	50.07 423	78.91 318	71.83 413	60.20 376	71.26 417	91.76 191	55.08 375	76.09 410	41.06 452	87.02 375	82.54 410

test_fmvs1_n		70.94 369	70.41 373	72.53 382	73.92 444	66.93 242	75.99 369	84.21 321	43.31 457	79.40 351	79.39 419	43.47 431	68.55 440	69.05 287	84.91 402	82.10 416

MS-PatchMatch		70.93 370	70.22 374	73.06 375	81.85 373	62.50 292	73.82 391	77.90 363	52.44 423	75.92 386	81.27 402	55.67 370	81.75 381	55.37 392	77.70 446	74.94 447

pmmvs5		70.73 371	70.07 375	72.72 378	77.03 422	52.73 404	74.14 385	75.65 383	50.36 439	72.17 414	85.37 356	55.42 372	80.67 388	52.86 410	87.59 367	84.77 374

testing3-2		70.72 372	70.97 365	69.95 397	88.93 205	34.80 467	69.85 422	66.59 441	78.42 125	77.58 373	85.55 348	31.83 458	82.08 379	46.28 440	93.73 221	92.98 181

PatchT		70.52 373	72.76 349	63.79 434	79.38 405	33.53 468	77.63 339	65.37 445	73.61 190	71.77 415	92.79 153	44.38 429	75.65 413	64.53 332	85.37 392	82.18 415

test_vis1_n		70.29 374	69.99 378	71.20 391	75.97 433	66.50 246	76.69 356	80.81 350	44.22 453	75.43 391	77.23 438	50.00 395	68.59 439	66.71 308	82.85 421	78.52 441

N_pmnet		70.20 375	68.80 390	74.38 366	80.91 385	84.81 43	59.12 457	76.45 378	55.06 406	75.31 395	82.36 392	55.74 369	54.82 466	47.02 437	87.24 369	83.52 394

tpmvs		70.16 376	69.56 381	71.96 386	74.71 443	48.13 428	79.63 301	75.45 385	65.02 329	70.26 425	81.88 397	45.34 421	85.68 349	58.34 373	75.39 452	82.08 417

new-patchmatchnet		70.10 377	73.37 341	60.29 443	81.23 382	16.95 478	59.54 455	74.62 387	62.93 340	80.97 330	87.93 303	62.83 327	71.90 425	55.24 394	95.01 175	92.00 235

YYNet1		70.06 378	70.44 371	68.90 406	73.76 446	53.42 400	58.99 458	67.20 436	58.42 384	87.10 186	85.39 355	59.82 342	67.32 446	59.79 366	83.50 415	85.96 360

MVStest1		70.05 379	69.26 382	72.41 384	58.62 475	55.59 382	76.61 359	65.58 443	53.44 415	89.28 130	93.32 127	22.91 475	71.44 429	74.08 218	89.52 334	90.21 293

MDA-MVSNet_test_wron		70.05 379	70.44 371	68.88 407	73.84 445	53.47 398	58.93 459	67.28 435	58.43 383	87.09 187	85.40 354	59.80 343	67.25 447	59.66 367	83.54 414	85.92 362

CostFormer		69.98 381	68.68 391	73.87 368	77.14 420	50.72 420	79.26 312	74.51 389	51.94 428	70.97 420	84.75 365	45.16 424	87.49 308	55.16 395	79.23 439	83.40 397

testing91		69.94 382	68.99 387	72.80 377	83.81 345	45.89 439	71.57 408	73.64 399	68.24 278	70.77 423	77.82 430	34.37 451	84.44 362	53.64 403	87.00 376	88.07 331

baseline2		69.77 383	66.89 400	78.41 314	79.51 403	58.09 360	76.23 365	69.57 425	57.50 393	64.82 453	77.45 436	46.02 409	88.44 288	53.08 406	77.83 444	88.70 324

PatchmatchNet		69.71 384	68.83 389	72.33 385	77.66 416	53.60 397	79.29 311	69.99 423	57.66 391	72.53 411	82.93 385	46.45 406	80.08 394	60.91 360	72.09 456	83.31 400
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 385	69.05 385	71.14 392	69.15 464	65.77 256	73.98 388	83.32 325	42.83 459	77.77 370	78.27 429	43.39 434	68.50 441	68.39 297	84.38 409	79.15 439

JIA-IIPM		69.41 386	66.64 404	77.70 328	73.19 450	71.24 184	75.67 371	65.56 444	70.42 245	65.18 449	92.97 144	33.64 454	83.06 372	53.52 405	69.61 462	78.79 440

Syy-MVS		69.40 387	70.03 377	67.49 417	81.72 374	38.94 459	71.00 411	61.99 451	61.38 359	70.81 421	72.36 455	61.37 331	79.30 397	64.50 333	85.18 395	84.22 383

testing99		69.27 388	68.15 395	72.63 379	83.29 358	45.45 441	71.15 410	71.08 418	67.34 295	70.43 424	77.77 432	32.24 457	84.35 364	53.72 402	86.33 384	88.10 330

UnsupCasMVSNet_bld		69.21 389	69.68 380	67.82 415	79.42 404	51.15 417	67.82 433	75.79 380	54.15 412	77.47 374	85.36 357	59.26 346	70.64 430	48.46 432	79.35 438	81.66 420

test_cas_vis1_n_1920		69.20 390	69.12 383	69.43 403	73.68 447	62.82 286	70.38 419	77.21 370	46.18 447	80.46 341	78.95 423	52.03 384	65.53 454	65.77 319	77.45 449	79.95 437

gg-mvs-nofinetune		68.96 391	69.11 384	68.52 413	76.12 432	45.32 442	83.59 215	55.88 465	86.68 33	64.62 454	97.01 12	30.36 462	83.97 369	44.78 446	82.94 418	76.26 444

WBMVS		68.76 392	68.43 392	69.75 400	83.29 358	40.30 457	67.36 435	72.21 410	57.09 397	77.05 375	85.53 350	33.68 453	80.51 390	48.79 430	90.90 304	88.45 327

WB-MVSnew		68.72 393	69.01 386	67.85 414	83.22 362	43.98 447	74.93 380	65.98 442	55.09 405	73.83 404	79.11 420	65.63 304	71.89 426	38.21 460	85.04 398	87.69 342

tpm2		68.45 394	66.83 401	73.30 373	78.93 411	48.50 427	79.76 300	71.76 414	47.50 442	69.92 427	83.60 376	42.07 437	88.40 290	48.44 433	79.51 436	83.01 404

tpm		67.95 395	68.08 396	67.55 416	78.74 412	43.53 449	75.60 372	67.10 439	54.92 407	72.23 412	88.10 298	42.87 436	75.97 411	52.21 412	80.95 434	83.15 402

WTY-MVS		67.91 396	68.35 393	66.58 422	80.82 388	48.12 429	65.96 441	72.60 405	53.67 414	71.20 418	81.68 400	58.97 348	69.06 437	48.57 431	81.67 426	82.55 409

testing11		67.38 397	65.93 405	71.73 388	83.37 355	46.60 436	70.95 413	69.40 426	62.47 345	66.14 442	76.66 442	31.22 459	84.10 366	49.10 428	84.10 411	84.49 377

test-LLR		67.21 398	66.74 402	68.63 410	76.45 429	55.21 385	67.89 430	67.14 437	62.43 348	65.08 450	72.39 453	43.41 432	69.37 433	61.00 358	84.89 403	81.31 424

testing222		66.93 399	65.30 412	71.81 387	83.38 354	45.83 440	72.06 404	67.50 433	64.12 334	69.68 429	76.37 445	27.34 470	83.00 373	38.88 456	88.38 351	86.62 355

sss		66.92 400	67.26 398	65.90 424	77.23 419	51.10 419	64.79 443	71.72 415	52.12 427	70.13 426	80.18 412	57.96 355	65.36 455	50.21 420	81.01 432	81.25 426

KD-MVS_2432*1600		66.87 401	65.81 408	70.04 395	67.50 465	47.49 432	62.56 449	79.16 357	61.21 364	77.98 365	80.61 406	25.29 473	82.48 376	53.02 407	84.92 400	80.16 435

miper_refine_blended		66.87 401	65.81 408	70.04 395	67.50 465	47.49 432	62.56 449	79.16 357	61.21 364	77.98 365	80.61 406	25.29 473	82.48 376	53.02 407	84.92 400	80.16 435

dmvs_re		66.81 403	66.98 399	66.28 423	76.87 423	58.68 358	71.66 407	72.24 408	60.29 374	69.52 431	73.53 452	52.38 383	64.40 457	44.90 445	81.44 429	75.76 445

tpm cat1		66.76 404	65.21 413	71.42 389	77.09 421	50.62 421	78.01 331	73.68 398	44.89 451	68.64 433	79.00 422	45.51 418	82.42 378	49.91 423	70.15 459	81.23 428

UWE-MVS		66.43 405	65.56 411	69.05 405	84.15 338	40.98 455	73.06 398	64.71 447	54.84 408	76.18 383	79.62 418	29.21 464	80.50 391	38.54 459	89.75 331	85.66 365

PVSNet	58.17 21	66.41 406	65.63 410	68.75 408	81.96 371	49.88 424	62.19 451	72.51 407	51.03 433	68.04 436	75.34 449	50.84 390	74.77 416	45.82 444	82.96 417	81.60 421

tpmrst		66.28 407	66.69 403	65.05 430	72.82 455	39.33 458	78.20 329	70.69 421	53.16 418	67.88 437	80.36 411	48.18 400	74.75 417	58.13 375	70.79 458	81.08 429

Patchmatch-test		65.91 408	67.38 397	61.48 440	75.51 436	43.21 450	68.84 426	63.79 449	62.48 344	72.80 410	83.42 380	44.89 427	59.52 463	48.27 434	86.45 381	81.70 419

ADS-MVSNet2		65.87 409	63.64 418	72.55 381	73.16 451	56.92 372	67.10 437	74.81 386	49.74 440	66.04 444	82.97 383	46.71 404	77.26 407	42.29 449	69.96 460	83.46 395

myMVS_eth3d28		65.83 410	65.85 406	65.78 425	83.42 353	35.71 465	67.29 436	68.01 432	67.58 292	69.80 428	77.72 433	32.29 456	74.30 419	37.49 461	89.06 341	87.32 346

test_vis1_rt		65.64 411	64.09 415	70.31 394	66.09 469	70.20 197	61.16 452	81.60 344	38.65 465	72.87 409	69.66 458	52.84 380	60.04 462	56.16 384	77.77 445	80.68 433

mvsany_test3		65.48 412	62.97 421	73.03 376	69.99 462	76.17 124	64.83 442	43.71 473	43.68 455	80.25 345	87.05 329	52.83 381	63.09 460	51.92 417	72.44 455	79.84 438

test-mter		65.00 413	63.79 417	68.63 410	76.45 429	55.21 385	67.89 430	67.14 437	50.98 434	65.08 450	72.39 453	28.27 467	69.37 433	61.00 358	84.89 403	81.31 424

ETVMVS		64.67 414	63.34 420	68.64 409	83.44 352	41.89 452	69.56 425	61.70 456	61.33 361	68.74 432	75.76 447	28.76 465	79.35 396	34.65 464	86.16 387	84.67 376

myMVS_eth3d		64.66 415	63.89 416	66.97 420	81.72 374	37.39 462	71.00 411	61.99 451	61.38 359	70.81 421	72.36 455	20.96 476	79.30 397	49.59 425	85.18 395	84.22 383

test0.0.03 1		64.66 415	64.36 414	65.57 427	75.03 441	46.89 435	64.69 444	61.58 457	62.43 348	71.18 419	77.54 434	43.41 432	68.47 442	40.75 454	82.65 422	81.35 423

UBG		64.34 417	63.35 419	67.30 418	83.50 349	40.53 456	67.46 434	65.02 446	54.77 409	67.54 440	74.47 451	32.99 455	78.50 403	40.82 453	83.58 413	82.88 405

test_f		64.31 418	65.85 406	59.67 444	66.54 468	62.24 304	57.76 461	70.96 419	40.13 462	84.36 262	82.09 394	46.93 403	51.67 468	61.99 351	81.89 425	65.12 459

pmmvs3		62.47 419	60.02 432	69.80 399	71.58 459	64.00 272	70.52 417	58.44 463	39.77 463	66.05 443	75.84 446	27.10 472	72.28 423	46.15 442	84.77 407	73.11 449

EPMVS		62.47 419	62.63 423	62.01 436	70.63 461	38.74 460	74.76 381	52.86 467	53.91 413	67.71 439	80.01 413	39.40 441	66.60 450	55.54 391	68.81 464	80.68 433

ADS-MVSNet		61.90 421	62.19 425	61.03 441	73.16 451	36.42 464	67.10 437	61.75 454	49.74 440	66.04 444	82.97 383	46.71 404	63.21 458	42.29 449	69.96 460	83.46 395

PMMVS		61.65 422	60.38 429	65.47 428	65.40 472	69.26 211	63.97 447	61.73 455	36.80 469	60.11 461	68.43 460	59.42 344	66.35 451	48.97 429	78.57 443	60.81 462

E-PMN		61.59 423	61.62 426	61.49 439	66.81 467	55.40 383	53.77 464	60.34 459	66.80 303	58.90 464	65.50 463	40.48 440	66.12 452	55.72 388	86.25 385	62.95 461

TESTMET0.1,1		61.29 424	60.32 430	64.19 432	72.06 457	51.30 415	67.89 430	62.09 450	45.27 449	60.65 460	69.01 459	27.93 468	64.74 456	56.31 383	81.65 428	76.53 443

MVS-HIRNet		61.16 425	62.92 422	55.87 447	79.09 408	35.34 466	71.83 405	57.98 464	46.56 445	59.05 463	91.14 215	49.95 396	76.43 409	38.74 457	71.92 457	55.84 466

EMVS		61.10 426	60.81 428	61.99 437	65.96 470	55.86 379	53.10 465	58.97 462	67.06 300	56.89 468	63.33 464	40.98 438	67.03 448	54.79 397	86.18 386	63.08 460

DSMNet-mixed		60.98 427	61.61 427	59.09 446	72.88 454	45.05 444	74.70 382	46.61 472	26.20 470	65.34 448	90.32 254	55.46 371	63.12 459	41.72 451	81.30 431	69.09 455

dp		60.70 428	60.29 431	61.92 438	72.04 458	38.67 461	70.83 415	64.08 448	51.28 431	60.75 459	77.28 437	36.59 449	71.58 428	47.41 436	62.34 466	75.52 446

dmvs_testset		60.59 429	62.54 424	54.72 449	77.26 418	27.74 472	74.05 387	61.00 458	60.48 371	65.62 447	67.03 462	55.93 368	68.23 444	32.07 468	69.46 463	68.17 456

CHOSEN 280x420		59.08 430	56.52 436	66.76 421	76.51 427	64.39 268	49.62 466	59.00 461	43.86 454	55.66 469	68.41 461	35.55 450	68.21 445	43.25 448	76.78 451	67.69 457

mvsany_test1		58.48 431	56.47 437	64.50 431	65.90 471	68.21 227	56.95 462	42.11 474	38.30 466	65.69 446	77.19 440	56.96 362	59.35 464	46.16 441	58.96 467	65.93 458

UWE-MVS-28		58.44 432	57.71 434	60.65 442	73.58 448	31.23 469	69.68 424	48.80 470	53.12 419	61.79 457	78.83 424	30.98 460	68.40 443	21.58 471	80.99 433	82.33 414

PVSNet_0	51.08 22	56.10 433	54.97 438	59.48 445	75.12 440	53.28 401	55.16 463	61.89 453	44.30 452	59.16 462	62.48 465	54.22 376	65.91 453	35.40 463	47.01 468	59.25 464

new_pmnet		55.69 434	57.66 435	49.76 450	75.47 437	30.59 470	59.56 454	51.45 468	43.62 456	62.49 456	75.48 448	40.96 439	49.15 470	37.39 462	72.52 454	69.55 454

PMMVS2		55.64 435	59.27 433	44.74 451	64.30 473	12.32 479	40.60 467	49.79 469	53.19 417	65.06 452	84.81 364	53.60 379	49.76 469	32.68 467	89.41 336	72.15 450

MVE	40.22 23	51.82 436	50.47 439	55.87 447	62.66 474	51.91 410	31.61 469	39.28 475	40.65 461	50.76 470	74.98 450	56.24 367	44.67 471	33.94 466	64.11 465	71.04 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 437	42.65 440	39.67 452	70.86 460	21.11 474	61.01 453	21.42 479	57.36 394	57.97 467	50.06 468	16.40 478	58.73 465	21.03 472	27.69 472	39.17 468

kuosan		30.83 438	32.17 441	26.83 454	53.36 476	19.02 477	57.90 460	20.44 480	38.29 467	38.01 471	37.82 470	15.18 479	33.45 473	7.74 474	20.76 473	28.03 469

test_method		30.46 439	29.60 442	33.06 453	17.99 478	3.84 481	13.62 470	73.92 393	2.79 472	18.29 474	53.41 467	28.53 466	43.25 472	22.56 469	35.27 470	52.11 467

cdsmvs_eth3d_5k		20.81 440	27.75 443	0.00 459	0.00 482	0.00 484	0.00 471	85.44 295	0.00 477	0.00 478	82.82 387	81.46 131	0.00 478	0.00 477	0.00 476	0.00 474

tmp_tt		20.25 441	24.50 444	7.49 456	4.47 479	8.70 480	34.17 468	25.16 477	1.00 474	32.43 473	18.49 471	39.37 442	9.21 475	21.64 470	43.75 469	4.57 471

ab-mvs-re		6.65 442	8.87 445	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	79.80 415	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

pcd_1.5k_mvsjas		6.41 443	8.55 446	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	76.94 189	0.00 478	0.00 477	0.00 476	0.00 474

test123		6.27 444	8.08 447	0.84 457	1.11 481	0.57 482	62.90 448	0.82 481	0.54 475	1.07 477	2.75 476	1.26 480	0.30 476	1.04 475	1.26 475	1.66 472

testmvs		5.91 445	7.65 448	0.72 458	1.20 480	0.37 483	59.14 456	0.67 482	0.49 476	1.11 476	2.76 475	0.94 481	0.24 477	1.02 476	1.47 474	1.55 473

mmdepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

monomultidepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

test_blank		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet_test		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

DCPMVS		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet-low-res		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uncertanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

Regformer		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

TestfortrainingZip							92.12 33

WAC-MVS						37.39 462								52.61 411

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	91.55 137	77.99 97		91.01 167					96.05 9	87.45 29	98.17 37	92.40 210

PC_three_1452									58.96 381	90.06 105	91.33 206	80.66 141	93.03 154	75.78 194	95.94 135	92.48 204

No_MVS				88.81 73	91.55 137	77.99 97		91.01 167					96.05 9	87.45 29	98.17 37	92.40 210

test_one_0601					93.85 65	73.27 146		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 482

eth-test					0.00 482

ZD-MVS					92.22 111	80.48 71		91.85 135	71.22 237	90.38 100	92.98 141	86.06 67	96.11 7	81.99 112	96.75 99

RE-MVS-def			92.61 9		94.13 58	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 73	92.73 188

IU-MVS					94.18 53	72.64 156		90.82 172	56.98 398	89.67 118				85.78 64	97.92 52	93.28 160

OPU-MVS				88.27 86	91.89 123	77.83 100	90.47 58				91.22 212	81.12 135	94.68 80	74.48 207	95.35 158	92.29 220

test_241102_TWO								93.71 58	83.77 58	93.49 40	94.27 83	89.27 24	95.84 25	86.03 57	97.82 57	92.04 233

test_241102_ONE					94.18 53	72.65 154		93.69 60	83.62 61	94.11 27	93.78 114	90.28 15	95.50 50

9.14			89.29 65		91.84 127		88.80 97	95.32 13	75.14 168	91.07 85	92.89 147	87.27 49	93.78 119	83.69 90	97.55 76

save fliter					93.75 66	77.44 106	86.31 143	89.72 210	70.80 242

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 33	87.41 31	98.21 34	92.98 181

test_0728_SECOND				86.79 110	94.25 51	72.45 164	90.54 55	94.10 40					95.88 18	86.42 47	97.97 49	92.02 234

test0726					94.16 56	72.56 160	90.63 52	93.90 49	83.61 62	93.75 35	94.49 73	89.76 19

GSMVS																83.88 387

test_part2					93.86 64	77.77 101				92.84 53

sam_mvs1												46.11 408				83.88 387

sam_mvs												45.92 413

ambc				82.98 218	90.55 166	64.86 262	88.20 106	89.15 223		89.40 127	93.96 105	71.67 269	91.38 200	78.83 147	96.55 104	92.71 191

MTGPA								91.81 139

test_post1							78.85 321				3.13 473	45.19 423	80.13 393	58.11 376

test_post											3.10 474	45.43 419	77.22 408

patchmatchnet-post											81.71 399	45.93 412	87.01 314

GG-mvs-BLEND				67.16 419	73.36 449	46.54 438	84.15 195	55.04 466		58.64 465	61.95 466	29.93 463	83.87 370	38.71 458	76.92 450	71.07 452

MTMP							90.66 51	33.14 476

gm-plane-assit					75.42 438	44.97 445			52.17 424		72.36 455		87.90 300	54.10 400

test9_res														80.83 122	96.45 110	90.57 281

TEST9					92.34 106	79.70 80	83.94 201	90.32 190	65.41 321	84.49 258	90.97 222	82.03 120	93.63 125

test_8					92.09 115	78.87 88	83.82 206	90.31 192	65.79 311	84.36 262	90.96 224	81.93 122	93.44 139

agg_prior2														79.68 135	96.16 122	90.22 289

agg_prior					91.58 135	77.69 103		90.30 193		84.32 264			93.18 147

TestCases				89.68 56	91.59 132	83.40 52		95.44 11	79.47 107	88.00 161	93.03 139	82.66 101	91.47 194	70.81 261	96.14 123	94.16 113

test_prior4						78.97 87	84.59 184

test_prior2							83.37 222		75.43 164	84.58 255	91.57 196	81.92 124		79.54 139	96.97 92

test_prior				86.32 118	90.59 165	71.99 172		92.85 101					94.17 104			92.80 186

旧先验2							81.73 269		56.88 399	86.54 207			84.90 356	72.81 245

新几何2							81.72 270

新几何1				82.95 220	93.96 62	78.56 91		80.24 353	55.45 404	83.93 275	91.08 218	71.19 271	88.33 292	65.84 317	93.07 243	81.95 418

旧先验1					91.97 119	71.77 173		81.78 341			91.84 185	73.92 232			93.65 224	83.61 393

无先验							82.81 241	85.62 293	58.09 387				91.41 199	67.95 301		84.48 378

原ACMM2							82.26 261

原ACMM1				84.60 167	92.81 96	74.01 138		91.50 147	62.59 342	82.73 300	90.67 241	76.53 198	94.25 96	69.24 282	95.69 150	85.55 366

test222					93.31 79	76.54 116	79.38 310	77.79 364	52.59 421	82.36 305	90.84 232	66.83 295			91.69 285	81.25 426

testdata2													86.43 329	63.52 339

segment_acmp												81.94 121

testdata				79.54 299	92.87 90	72.34 165		80.14 354	59.91 377	85.47 232	91.75 192	67.96 289	85.24 352	68.57 296	92.18 272	81.06 431

testdata1							79.62 302		73.95 185

test12				86.57 113	90.74 161	72.63 158		90.69 175		82.76 299		79.20 154	94.80 77		95.32 160	92.27 222

plane_prior7					93.45 73	77.31 109

plane_prior6					92.61 97	76.54 116						74.84 214

plane_prior5								93.61 63					95.22 61	80.78 123	95.83 143	94.46 95

plane_prior4											92.95 145

plane_prior3						76.85 114			77.79 134	86.55 201

plane_prior2							89.45 85		79.44 109

plane_prior1					92.83 94

plane_prior						76.42 119	87.15 125		75.94 155						95.03 172

n2								0.00 483

nn								0.00 483

door-mid								74.45 390

lessismore_v0				85.95 129	91.10 154	70.99 188		70.91 420		91.79 73	94.42 78	61.76 329	92.93 157	79.52 140	93.03 244	93.93 123

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 46	85.36 67	98.73 7	95.23 66

test11								91.46 148

door								72.57 406

HQP5-MVS						70.66 190

HQP-NCC					91.19 149		84.77 175		73.30 199	80.55 338

ACMP_Plane					91.19 149		84.77 175		73.30 199	80.55 338

BP-MVS														77.30 172

HQP4-MVS										80.56 337			94.61 84			93.56 151

HQP3-MVS								92.68 107							94.47 194

HQP2-MVS												72.10 260

NP-MVS					91.95 120	74.55 135					90.17 261

MDTV_nov1_ep13_2view						27.60 473	70.76 416		46.47 446	61.27 458		45.20 422		49.18 427		83.75 392

MDTV_nov1_ep13			68.29 394		78.03 413	43.87 448	74.12 386	72.22 409	52.17 424	67.02 441	85.54 349	45.36 420	80.85 387	55.73 387	84.42 408

ACMMP++_ref															95.74 149

ACMMP++															97.35 82

Test By Simon												79.09 156

ITE_SJBPF				90.11 49	90.72 162	84.97 41		90.30 193	81.56 83	90.02 107	91.20 214	82.40 106	90.81 221	73.58 231	94.66 189	94.56 90

DeepMVS_CX				24.13 455	32.95 477	29.49 471		21.63 478	12.07 471	37.95 472	45.07 469	30.84 461	19.21 474	17.94 473	33.06 471	23.69 470

High-resolution multi-view benchmark