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 69	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 47	97.23 2	95.32 2	99.01 2	97.26 9	80.16 140	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 51	96.29 22	88.16 36	94.17 102	86.07 54	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 66	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 76	93.16 143	91.10 2	97.53 77	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 51	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 42	95.72 36	89.60 5	98.27 28	92.08 215

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 26	89.13 7	98.26 30	91.76 226

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 26	89.13 7	98.26 30	91.76 226

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 59	95.13 52	90.65 10	95.34 55	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 78	90.26 4	98.44 20	93.63 138

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 47	87.16 37	97.60 71	92.73 176

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 38	86.82 41	97.34 81	92.19 211

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 70	91.77 72	93.94 108	90.55 13	95.73 35	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 85	85.17 39	92.47 27	95.05 15	87.65 28	93.21 44	94.39 81	90.09 18	95.08 66	86.67 43	97.60 71	94.18 108

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 67	86.15 24	93.37 10	95.10 14	90.28 10	92.11 64	95.03 54	89.75 21	94.93 70	79.95 127	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 73	92.39 62	94.14 93	89.15 26	95.62 39	87.35 32	98.24 32	94.56 89
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	92.60 107	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 161

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 69	83.16 66	91.06 84	94.00 101	88.26 33	95.71 37	87.28 35	98.39 23	92.55 189

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	94.03 99	86.57 56	95.80 28	87.35 32	97.62 69	94.20 105

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 132	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 203

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 99	88.22 23	88.53 142	97.64 6	83.45 90	94.55 86	86.02 58	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 77	81.99 76	91.40 76	94.17 92	87.51 46	95.87 20	87.74 21	97.76 60	93.99 115

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 63	94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	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 75	81.91 78	90.88 91	94.21 88	87.75 42	95.87 20	87.60 26	97.71 63	93.83 124

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 76	81.99 76	91.47 75	93.96 105	88.35 32	95.56 42	87.74 21	97.74 62	92.85 173

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 99	91.29 80	93.97 102	87.93 41	95.87 20	88.65 10	97.96 51	94.12 112

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 87	78.04 96	92.84 16	94.14 37	83.33 64	93.90 29	95.73 34	88.77 28	96.41 3	87.60 26	97.98 48	92.98 169
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 63	93.90 49	80.32 96	91.74 73	94.41 79	88.17 35	95.98 13	86.37 47	97.99 46	93.96 117

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 62	80.97 70	91.49 41	93.48 67	82.82 71	92.60 58	93.97 102	88.19 34	96.29 6	87.61 25	98.20 36	94.39 100
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 90	82.59 72	88.52 143	94.37 82	86.74 54	95.41 53	86.32 48	98.21 34	93.19 157
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 39	93.74 55	80.98 89	91.38 77	93.80 112	87.20 50	95.80 28	87.10 39	97.69 65	93.93 118

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 105	94.51 19	75.79 155	92.94 48	94.96 55	88.36 31	95.01 68	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 86	76.26 122	89.65 76	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 134	78.35 148	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 67	93.68 59	77.65 134	91.97 68	94.89 57	88.38 30	95.45 51	89.27 6	97.87 56	93.27 153

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 77	93.35 70	79.20 112	93.83 32	93.60 122	90.81 8	92.96 150	85.02 72	98.45 19	92.41 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 52	91.38 28	89.03 196	84.23 49	93.58 6	94.68 18	90.65 8	90.33 100	93.95 107	84.50 78	95.37 54	80.87 117	95.50 153	94.53 92

ACMP	79.16 10	90.54 36	90.60 50	90.35 45	94.36 48	80.98 69	89.16 87	94.05 42	79.03 115	92.87 50	93.74 117	90.60 12	95.21 61	82.87 95	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 76	78.65 90	89.15 88	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 78	97.81 58	91.70 230
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	86.93 105	94.18 52	72.65 152	90.47 56	93.69 57	83.77 58	94.11 27	94.27 83	90.28 15	95.84 24	86.03 55	97.92 52	92.29 205

SMA-MVS		90.31 39	90.48 51	89.83 55	95.31 30	79.52 83	90.98 48	93.24 78	75.37 164	92.84 52	95.28 48	85.58 69	96.09 8	87.92 18	97.76 60	93.88 121
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 91	77.09 111	91.19 45	95.74 6	81.38 84	92.28 63	93.80 112	86.89 53	94.64 81	85.52 64	97.51 78	94.30 104

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 184	89.99 65	93.05 87	86.53 35	94.29 23	96.27 23	82.69 97	94.08 105	86.25 51	97.63 67	97.82 8

PMVS	80.48 6	90.08 42	90.66 49	88.34 84	96.71 3	92.97 2	90.31 60	89.57 203	88.51 21	90.11 102	95.12 53	90.98 7	88.92 265	77.55 162	97.07 88	83.13 383
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 43	91.09 37	87.00 103	91.55 135	72.64 154	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 128	95.88 18	87.41 30	95.94 133	92.48 192

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 155	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 266
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 44	91.92 16	84.47 165	96.56 6	58.83 337	89.04 89	92.74 101	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 137	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 164	96.57 5	58.88 334	88.95 90	93.19 79	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 144	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 47	89.74 57	90.83 36	93.16 83	80.37 74	91.91 37	93.11 83	81.10 87	95.32 14	97.24 10	72.94 231	94.85 72	85.07 69	97.78 59	97.26 16

DTE-MVSNet		89.98 48	91.91 18	84.21 174	96.51 7	57.84 345	88.93 91	92.84 98	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 141	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 48	89.84 55	90.41 43	94.91 37	84.50 48	89.49 82	93.98 44	79.68 104	92.09 65	93.89 110	83.80 85	93.10 146	82.67 99	98.04 41	93.64 137

3Dnovator+	83.92 2	89.97 50	89.66 58	90.92 35	91.27 144	81.66 66	91.25 43	94.13 38	88.89 15	88.83 134	94.26 86	77.55 165	95.86 23	84.88 73	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 287	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 125	95.90 17	80.94 116	98.80 3	98.84 5

OPM-MVS		89.80 52	89.97 53	89.27 64	94.76 40	79.86 78	86.76 132	92.78 100	78.78 118	92.51 59	93.64 121	88.13 37	93.84 115	84.83 75	97.55 74	94.10 113
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 53	89.27 64	91.30 29	93.51 70	84.79 44	89.89 69	90.63 165	70.00 244	94.55 19	96.67 17	87.94 40	93.59 126	84.27 80	95.97 129	95.52 56

anonymousdsp		89.73 54	88.88 74	92.27 8	89.82 180	86.67 18	90.51 55	90.20 185	69.87 245	95.06 15	96.14 28	84.28 81	93.07 147	87.68 23	96.34 111	97.09 20

test_djsdf		89.62 55	89.01 68	91.45 26	92.36 103	82.98 57	91.98 35	90.08 188	71.54 224	94.28 25	96.54 19	81.57 123	94.27 92	86.26 49	96.49 105	97.09 20

XVG-OURS-SEG-HR		89.59 56	89.37 62	90.28 46	94.47 43	85.95 27	86.84 128	93.91 48	80.07 100	86.75 189	93.26 128	93.64 2	90.93 208	84.60 77	90.75 296	93.97 116

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 86	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 105	97.18 86	90.45 268
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 58	88.81 77	91.19 32	93.38 76	84.72 45	89.70 72	90.29 182	69.27 249	94.39 21	96.38 21	86.02 66	93.52 130	83.96 82	95.92 135	95.34 60

CPTT-MVS		89.39 59	88.98 70	90.63 40	95.09 33	86.95 16	92.09 33	92.30 115	79.74 103	87.50 173	92.38 161	81.42 125	93.28 139	83.07 91	97.24 84	91.67 231

ACMH	76.49 14	89.34 60	91.14 36	83.96 181	92.50 99	70.36 192	89.55 78	93.84 53	81.89 79	94.70 17	95.44 44	90.69 9	88.31 279	83.33 87	98.30 27	93.20 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 61	89.12 65	89.84 53	88.67 207	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 257	74.12 206	96.10 124	94.45 95

APD_test2		89.30 61	89.12 65	89.84 53	88.67 207	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 257	74.12 206	96.10 124	94.45 95

CP-MVSNet		89.27 63	90.91 45	84.37 166	96.34 8	58.61 340	88.66 98	92.06 121	90.78 7	95.67 8	95.17 51	81.80 121	95.54 44	79.00 142	98.69 10	98.95 4

XVG-OURS		89.18 64	88.83 76	90.23 47	94.28 49	86.11 26	85.91 147	93.60 62	80.16 98	89.13 131	93.44 124	83.82 84	90.98 205	83.86 84	95.30 161	93.60 141

DeepC-MVS	82.31 4	89.15 65	89.08 67	89.37 63	93.64 68	79.07 86	88.54 101	94.20 31	73.53 188	89.71 114	94.82 60	85.09 72	95.77 34	84.17 81	98.03 43	93.26 154
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 66	90.72 48	84.31 172	97.00 2	64.33 260	89.67 75	88.38 222	88.84 17	94.29 23	97.57 7	90.48 14	91.26 196	72.57 233	97.65 66	97.34 15

MSP-MVS		89.08 67	88.16 84	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 123	89.16 129	92.25 170	72.03 245	96.36 4	88.21 13	90.93 289	92.98 169
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 68	89.88 54	86.22 121	91.63 129	77.07 112	89.82 70	93.77 54	78.90 116	92.88 49	92.29 168	86.11 64	90.22 233	86.24 52	97.24 84	91.36 239
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 69	88.45 80	90.38 44	94.92 36	85.85 31	89.70 72	91.27 147	78.20 126	86.69 192	92.28 169	80.36 138	95.06 67	86.17 53	96.49 105	90.22 272

Elysia		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 209	94.82 73	88.19 14	95.92 135	96.80 27

StellarMVS		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 209	94.82 73	88.19 14	95.92 135	96.80 27

test_0402		88.65 72	89.58 61	85.88 130	92.55 97	72.22 166	84.01 192	89.44 206	88.63 20	94.38 22	95.77 32	86.38 62	93.59 126	79.84 128	95.21 162	91.82 224

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 94	87.95 26	89.62 118	92.87 145	84.56 77	93.89 112	77.65 160	96.62 100	90.70 258

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 127	84.26 53	90.87 92	93.92 109	82.18 112	89.29 261	73.75 214	94.81 181	93.70 132

Anonymous20231211		88.40 74	89.62 60	84.73 156	90.46 165	65.27 250	88.86 92	93.02 91	87.15 30	93.05 47	97.10 11	82.28 110	92.02 176	76.70 172	97.99 46	96.88 26

PS-MVSNAJss		88.31 76	87.90 87	89.56 60	93.31 78	77.96 99	87.94 110	91.97 124	70.73 235	94.19 26	96.67 17	76.94 175	94.57 84	83.07 91	96.28 113	96.15 38

OMC-MVS		88.19 77	87.52 91	90.19 48	91.94 120	81.68 65	87.49 117	93.17 80	76.02 149	88.64 139	91.22 202	84.24 82	93.37 137	77.97 158	97.03 89	95.52 56

CS-MVS		88.14 78	87.67 90	89.54 61	89.56 184	79.18 85	90.47 56	94.77 17	79.37 110	84.32 249	89.33 261	83.87 83	94.53 87	82.45 101	94.89 177	94.90 76

TSAR-MVS + MP.		88.14 78	87.82 88	89.09 69	95.72 22	76.74 115	92.49 26	91.19 150	67.85 274	86.63 193	94.84 59	79.58 145	95.96 15	87.62 24	94.50 190	94.56 89
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 80	89.79 56	82.98 212	93.26 80	63.94 264	91.10 46	89.64 200	85.07 45	90.91 88	91.09 207	89.16 25	91.87 181	82.03 106	95.87 139	93.13 159

EC-MVSNet		88.01 81	88.32 83	87.09 100	89.28 191	72.03 169	90.31 60	96.31 4	80.88 90	85.12 226	89.67 256	84.47 79	95.46 50	82.56 100	96.26 116	93.77 130

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 151	79.26 111	89.68 115	94.81 63	82.44 101	87.74 289	76.54 177	88.74 328	96.61 32

AllTest		87.97 83	87.40 95	89.68 56	91.59 130	83.40 52	89.50 81	95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 189	70.81 244	96.14 121	94.16 109

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 261	84.38 184	91.29 146	84.88 48	92.06 66	93.84 111	86.45 59	93.73 117	73.22 224	98.66 11	97.69 9

nrg030		87.85 85	88.49 79	85.91 128	90.07 175	69.73 200	87.86 111	94.20 31	74.04 180	92.70 57	94.66 64	85.88 67	91.50 188	79.72 130	97.32 82	96.50 34

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 162	91.23 148	77.31 139	87.07 183	91.47 195	82.94 95	94.71 77	84.67 76	96.27 115	92.62 184

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 60	79.44 108	86.55 194	92.95 142	74.84 198	95.22 59	80.78 119	95.83 141	94.46 93

sc_t1		87.70 88	88.94 71	83.99 179	93.47 71	67.15 228	85.05 167	88.21 229	86.81 32	91.87 70	97.65 5	85.51 71	87.91 285	74.22 202	97.63 67	96.92 25

MM		87.64 89	87.15 97	89.09 69	89.51 185	76.39 121	88.68 97	86.76 257	84.54 50	83.58 267	93.78 114	73.36 226	96.48 2	87.98 17	96.21 117	94.41 99

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 198	92.03 116	62.26 291	91.49 41	92.62 105	88.07 25	88.07 155	96.17 26	72.24 240	95.79 31	84.85 74	94.16 203	92.58 187

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 153	78.77 119	84.85 237	90.89 217	80.85 131	95.29 56	81.14 114	95.32 158	92.34 201

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 215	92.38 112	70.25 241	89.35 126	90.68 227	82.85 96	94.57 84	79.55 134	95.95 132	92.00 219

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 210	87.84 112	88.05 230	81.66 81	94.64 18	96.53 20	65.94 282	94.75 76	83.02 93	96.83 94	95.41 58

fmvsm_s_conf0.5_n_9		87.04 94	87.02 102	87.08 101	89.67 182	75.87 126	84.60 177	89.74 195	74.40 177	89.92 110	93.41 125	80.45 136	90.63 222	86.66 44	94.37 196	94.73 86

SPE-MVS-test		87.00 95	86.43 112	88.71 76	89.46 187	77.46 105	89.42 85	95.73 7	77.87 132	81.64 304	87.25 303	82.43 102	94.53 87	77.65 160	96.46 107	94.14 111

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 217	83.80 202	92.87 96	80.37 94	89.61 120	91.81 183	77.72 162	94.18 100	75.00 198	98.53 16	96.99 24

Vis-MVSNet		86.86 97	86.58 109	87.72 94	92.09 113	77.43 107	87.35 118	92.09 120	78.87 117	84.27 254	94.05 98	78.35 154	93.65 119	80.54 123	91.58 276	92.08 215
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 98	87.06 100	86.17 124	92.86 91	67.02 232	82.55 241	91.56 136	83.08 68	90.92 86	91.82 182	78.25 155	93.99 107	74.16 204	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 232	83.16 224	92.21 116	81.73 80	90.92 86	91.97 175	77.20 169	93.99 107	74.16 204	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 168	87.09 256	65.22 251	84.16 188	94.23 28	77.89 130	91.28 81	93.66 120	84.35 80	92.71 156	80.07 124	94.87 180	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		86.68 101	86.52 110	87.18 99	85.94 289	78.30 92	86.93 125	92.20 117	65.94 291	89.16 129	93.16 132	83.10 93	89.89 246	87.81 20	94.43 194	93.35 148

tt0320-xc		86.67 102	88.41 81	81.44 249	93.45 72	60.44 316	83.96 194	88.50 218	87.26 29	90.90 90	97.90 3	85.61 68	86.40 314	70.14 255	98.01 45	97.47 14

IS-MVSNet		86.66 103	86.82 108	86.17 124	92.05 115	66.87 235	91.21 44	88.64 215	86.30 37	89.60 121	92.59 154	69.22 263	94.91 71	73.89 211	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 250	93.57 69	60.73 313	84.37 185	88.61 217	87.00 31	90.75 93	97.98 2	85.54 70	86.45 312	69.75 260	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 150	88.16 222	63.28 271	86.64 135	92.20 117	75.42 163	92.81 54	94.50 72	74.05 212	94.06 106	83.88 83	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 237	92.22 109	62.28 290	84.66 176	89.15 209	83.54 63	89.85 111	97.32 8	88.08 39	86.80 305	70.43 252	97.30 83	96.62 31

NormalMVS		86.47 107	85.32 139	89.94 51	94.43 44	80.42 72	88.63 99	93.59 63	74.56 173	85.12 226	90.34 237	66.19 279	94.20 97	76.57 175	98.44 20	95.19 68

PHI-MVS		86.38 108	85.81 126	88.08 89	88.44 216	77.34 108	89.35 86	93.05 87	73.15 201	84.76 238	87.70 292	78.87 149	94.18 100	80.67 121	96.29 112	92.73 176

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 128	80.35 95	89.54 124	88.01 281	79.09 147	92.13 172	75.51 191	95.06 169	90.41 269

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 123	70.56 236	84.96 232	90.69 226	80.01 142	95.14 64	78.37 147	95.78 145	91.82 224
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 111	86.83 107	84.36 168	87.82 230	62.35 289	86.42 139	91.33 145	76.78 143	92.73 56	94.48 74	73.41 223	93.72 118	83.10 90	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 200	90.19 170	64.55 258	84.55 179	90.71 162	85.85 40	89.94 109	95.24 50	82.13 113	90.40 229	69.19 267	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 214	86.91 263	70.38 191	85.31 161	92.61 106	75.59 159	88.32 150	92.87 145	82.22 111	88.63 273	88.80 9	92.82 242	89.83 282

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 301	78.25 93	85.82 151	91.82 130	65.33 305	88.55 141	92.35 167	82.62 100	89.80 248	86.87 40	94.32 198	93.18 158

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 199	92.01 122	65.91 293	86.19 205	91.75 187	83.77 86	94.98 69	77.43 165	96.71 98	93.73 131

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 257	82.21 253	90.46 170	80.99 88	88.42 146	91.97 175	77.56 164	93.85 113	72.46 234	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 195	90.32 177	65.79 295	84.49 243	90.97 211	81.93 117	93.63 121	81.21 113	96.54 103	90.88 252

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 240	69.78 198	83.70 205	89.83 194	80.42 93	87.76 167	93.24 129	73.76 217	91.54 187	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 224	92.25 107	56.44 356	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 200	93.41 136	75.11 197	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 309	78.21 94	85.40 160	91.39 143	65.32 306	87.72 169	91.81 183	82.33 105	89.78 249	86.68 42	94.20 201	92.99 167

Effi-MVS+-dtu		85.82 121	83.38 182	93.14 4	87.13 251	91.15 3	87.70 113	88.42 221	74.57 172	83.56 268	85.65 327	78.49 153	94.21 96	72.04 236	92.88 240	94.05 114

TAPA-MVS	77.73 12	85.71 122	84.83 148	88.37 83	88.78 206	79.72 79	87.15 122	93.50 66	69.17 250	85.80 214	89.56 257	80.76 132	92.13 172	73.21 229	95.51 152	93.25 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 123	86.14 117	83.58 194	87.97 224	67.13 229	87.55 114	94.32 22	73.44 191	88.47 144	87.54 295	86.45 59	91.06 203	75.76 189	93.76 214	92.54 190

canonicalmvs		85.50 123	86.14 117	83.58 194	87.97 224	67.13 229	87.55 114	94.32 22	73.44 191	88.47 144	87.54 295	86.45 59	91.06 203	75.76 189	93.76 214	92.54 190

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 159	87.10 254	69.98 196	84.28 186	92.68 102	74.77 169	87.90 162	92.36 166	73.94 213	90.41 228	85.95 60	92.74 244	93.66 133

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 205	91.63 40	87.98 233	81.51 83	87.05 184	91.83 181	66.18 281	95.29 56	70.75 247	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 166	90.08 173	67.07 231	85.86 150	91.39 143	72.33 216	87.59 171	90.25 242	84.85 75	92.37 166	78.00 156	91.94 266	93.66 133

MVS_0304		85.37 128	84.58 156	87.75 93	85.28 300	73.36 141	86.54 138	85.71 272	77.56 137	81.78 302	92.47 159	70.29 257	96.02 11	85.59 63	95.96 130	93.87 122

FIs		85.35 129	86.27 114	82.60 223	91.86 122	57.31 349	85.10 166	93.05 87	75.83 154	91.02 85	93.97 102	73.57 219	92.91 154	73.97 210	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 152	85.80 291	76.13 125	85.15 165	92.32 114	61.40 339	91.33 78	90.85 220	83.76 87	86.16 320	84.31 79	93.28 229	92.15 213

casdiffmvs		85.21 131	85.85 125	83.31 203	86.17 283	62.77 278	83.03 226	93.93 47	74.69 171	88.21 152	92.68 153	82.29 109	91.89 180	77.87 159	93.75 217	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
baseline		85.20 132	85.93 122	83.02 210	86.30 278	62.37 288	84.55 179	93.96 45	74.48 175	87.12 178	92.03 174	82.30 107	91.94 177	78.39 146	94.21 200	94.74 85

mamba_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 203	86.17 144	91.58 135	74.41 176	86.55 194	91.49 194	78.54 150	93.97 109	73.71 215	93.21 232	92.59 186

K. test v3		85.14 134	84.73 149	86.37 115	91.13 151	69.63 202	85.45 158	76.68 356	84.06 56	92.44 61	96.99 13	62.03 308	94.65 80	80.58 122	93.24 230	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 208	84.65 311	74.71 132	85.87 149	90.35 176	77.94 129	83.82 261	96.96 15	77.75 160	80.03 377	78.44 145	96.21 117	94.79 84

EI-MVSNet-Vis-set		85.12 136	84.53 159	86.88 106	84.01 324	72.76 151	83.91 198	85.18 281	80.44 92	88.75 136	85.49 331	80.08 141	91.92 178	82.02 107	90.85 294	95.97 44

fmvsm_l_conf0.5_n_3		85.11 137	84.96 146	85.56 137	87.49 243	75.69 128	84.71 174	90.61 167	67.64 276	84.88 235	92.05 173	82.30 107	88.36 277	83.84 85	91.10 282	92.62 184

MGCFI-Net		85.04 138	85.95 121	82.31 231	87.52 241	63.59 267	86.23 143	93.96 45	73.46 189	88.07 155	87.83 290	86.46 58	90.87 213	76.17 183	93.89 211	92.47 194

EI-MVSNet-UG-set		85.04 138	84.44 161	86.85 107	83.87 328	72.52 160	83.82 200	85.15 282	80.27 97	88.75 136	85.45 333	79.95 143	91.90 179	81.92 110	90.80 295	96.13 39

X-MVStestdata		85.04 138	82.70 196	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 452	86.57 56	95.80 28	87.35 32	97.62 69	94.20 105

MSLP-MVS++		85.00 141	86.03 120	81.90 237	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 178	89.39 259	77.98 157	89.40 260	77.46 163	94.78 182	84.75 355

F-COLMAP		84.97 142	83.42 181	89.63 58	92.39 102	83.40 52	88.83 93	91.92 126	73.19 200	80.18 326	89.15 265	77.04 173	93.28 139	65.82 299	92.28 256	92.21 210

balanced_conf03		84.80 143	85.40 136	83.00 211	88.95 199	61.44 299	90.42 59	92.37 113	71.48 226	88.72 138	93.13 133	70.16 259	95.15 63	79.26 139	94.11 204	92.41 196

3Dnovator	80.37 7	84.80 143	84.71 152	85.06 148	86.36 276	74.71 132	88.77 95	90.00 190	75.65 157	84.96 232	93.17 131	74.06 211	91.19 198	78.28 150	91.09 283	89.29 292

SymmetryMVS		84.79 145	83.54 177	88.55 79	92.44 101	80.42 72	88.63 99	82.37 316	74.56 173	85.12 226	90.34 237	66.19 279	94.20 97	76.57 175	95.68 149	91.03 246

IterMVS-LS		84.73 146	84.98 145	83.96 181	87.35 245	63.66 265	83.25 220	89.88 193	76.06 147	89.62 118	92.37 164	73.40 225	92.52 161	78.16 153	94.77 184	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 147	84.34 165	85.49 141	90.18 171	75.86 127	79.23 300	87.13 248	73.35 193	85.56 219	89.34 260	83.60 89	90.50 225	76.64 174	94.05 208	90.09 278

HQP-MVS		84.61 148	84.06 170	86.27 118	91.19 147	70.66 187	84.77 169	92.68 102	73.30 196	80.55 318	90.17 247	72.10 241	94.61 82	77.30 167	94.47 192	93.56 144

v1192		84.57 149	84.69 154	84.21 174	87.75 232	62.88 275	83.02 227	91.43 140	69.08 252	89.98 108	90.89 217	72.70 235	93.62 124	82.41 102	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 150	84.71 152	84.11 177	87.92 227	72.09 168	84.80 168	88.64 215	64.43 313	88.77 135	91.78 185	78.07 156	87.95 284	85.85 61	92.18 260	92.30 203

FMVSNet1		84.55 151	85.45 135	81.85 239	90.27 169	61.05 306	86.83 129	88.27 226	78.57 122	89.66 117	95.64 38	75.43 190	90.68 219	69.09 268	95.33 157	93.82 125

v1144		84.54 152	84.72 151	84.00 178	87.67 236	62.55 282	82.97 229	90.93 158	70.32 240	89.80 112	90.99 210	73.50 220	93.48 132	81.69 112	94.65 188	95.97 44

Gipuma		84.44 153	86.33 113	78.78 289	84.20 321	73.57 140	89.55 78	90.44 171	84.24 54	84.38 246	94.89 57	76.35 186	80.40 374	76.14 184	96.80 96	82.36 393
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 154	84.27 166	84.74 155	87.25 247	70.84 186	83.55 210	88.45 220	68.64 260	86.29 204	91.31 200	74.97 196	88.42 275	87.87 19	90.07 308	94.95 75

MCST-MVS		84.36 155	83.93 173	85.63 135	91.59 130	71.58 177	83.52 211	92.13 119	61.82 332	83.96 259	89.75 255	79.93 144	93.46 133	78.33 149	94.34 197	91.87 223

VDDNet		84.35 156	85.39 137	81.25 252	95.13 32	59.32 327	85.42 159	81.11 327	86.41 36	87.41 174	96.21 25	73.61 218	90.61 223	66.33 292	96.85 92	93.81 128

ETV-MVS		84.31 157	83.91 174	85.52 138	88.58 212	70.40 190	84.50 183	93.37 68	78.76 120	84.07 257	78.72 406	80.39 137	95.13 65	73.82 213	92.98 238	91.04 245

v1240		84.30 158	84.51 160	83.65 191	87.65 237	61.26 303	82.85 233	91.54 137	67.94 271	90.68 95	90.65 230	71.71 249	93.64 120	82.84 96	94.78 182	96.07 41

MVS_111021_LR		84.28 159	83.76 175	85.83 132	89.23 193	83.07 55	80.99 272	83.56 304	72.71 208	86.07 208	89.07 267	81.75 122	86.19 319	77.11 169	93.36 225	88.24 309

h-mvs33		84.25 160	82.76 195	88.72 75	91.82 127	82.60 60	84.00 193	84.98 288	71.27 227	86.70 190	90.55 233	63.04 305	93.92 111	78.26 151	94.20 201	89.63 284

v144192		84.24 161	84.41 162	83.71 190	87.59 239	61.57 298	82.95 230	91.03 154	67.82 275	89.80 112	90.49 234	73.28 227	93.51 131	81.88 111	94.89 177	96.04 43

dcpmvs_2		84.23 162	85.14 141	81.50 247	88.61 211	61.98 295	82.90 232	93.11 83	68.66 259	92.77 55	92.39 160	78.50 152	87.63 292	76.99 171	92.30 253	94.90 76

v1921920		84.23 162	84.37 164	83.79 186	87.64 238	61.71 297	82.91 231	91.20 149	67.94 271	90.06 103	90.34 237	72.04 244	93.59 126	82.32 103	94.91 175	96.07 41

VDD-MVS		84.23 162	84.58 156	83.20 206	91.17 150	65.16 253	83.25 220	84.97 289	79.79 102	87.18 177	94.27 83	74.77 201	90.89 211	69.24 264	96.54 103	93.55 146

v2v482		84.09 165	84.24 167	83.62 192	87.13 251	61.40 300	82.71 236	89.71 198	72.19 219	89.55 122	91.41 196	70.70 255	93.20 141	81.02 115	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 166	84.11 169	83.98 180	92.22 109	72.61 157	82.20 255	87.02 253	72.63 209	88.86 132	91.02 209	78.52 151	91.11 201	73.41 221	91.09 283	88.21 310

fmvsm_s_conf0.5_n_6		84.05 167	84.14 168	83.81 184	87.75 232	71.17 182	83.42 214	91.10 152	67.90 273	84.53 241	90.70 225	73.01 230	88.73 271	85.09 68	93.72 219	91.53 236

DP-MVS Recon		84.05 167	83.22 185	86.52 113	91.73 128	75.27 130	83.23 222	92.40 110	72.04 221	82.04 293	88.33 277	77.91 159	93.95 110	66.17 293	95.12 167	90.34 271

TransMVSNet (Re)		84.02 169	85.74 130	78.85 288	91.00 154	55.20 368	82.29 249	87.26 243	79.65 105	88.38 148	95.52 41	83.00 94	86.88 303	67.97 282	96.60 101	94.45 95

Baseline_NR-MVSNet		84.00 170	85.90 123	78.29 300	91.47 140	53.44 379	82.29 249	87.00 256	79.06 114	89.55 122	95.72 36	77.20 169	86.14 321	72.30 235	98.51 17	95.28 63

TSAR-MVS + GP.		83.95 171	82.69 197	87.72 94	89.27 192	81.45 67	83.72 204	81.58 325	74.73 170	85.66 215	86.06 322	72.56 237	92.69 158	75.44 193	95.21 162	89.01 303

LuminaMVS		83.94 172	83.51 178	85.23 144	89.78 181	71.74 172	84.76 172	87.27 242	72.60 210	89.31 127	90.60 232	64.04 294	90.95 206	79.08 140	94.11 204	92.99 167

alignmvs		83.94 172	83.98 172	83.80 185	87.80 231	67.88 224	84.54 181	91.42 142	73.27 199	88.41 147	87.96 282	72.33 238	90.83 214	76.02 186	94.11 204	92.69 180

Effi-MVS+		83.90 174	84.01 171	83.57 196	87.22 249	65.61 249	86.55 137	92.40 110	78.64 121	81.34 309	84.18 352	83.65 88	92.93 152	74.22 202	87.87 342	92.17 212

fmvsm_s_conf0.1_n_2		83.82 175	83.49 179	84.84 150	85.99 288	70.19 194	80.93 273	87.58 238	67.26 282	87.94 161	92.37 164	71.40 251	88.01 281	86.03 55	91.87 267	96.31 36

mvs5depth		83.82 175	84.54 158	81.68 244	82.23 351	68.65 215	86.89 126	89.90 192	80.02 101	87.74 168	97.86 4	64.19 293	82.02 362	76.37 179	95.63 151	94.35 101

CANet		83.79 177	82.85 194	86.63 110	86.17 283	72.21 167	83.76 203	91.43 140	77.24 140	74.39 381	87.45 299	75.36 191	95.42 52	77.03 170	92.83 241	92.25 209

pm-mvs1		83.69 178	84.95 147	79.91 275	90.04 177	59.66 324	82.43 245	87.44 239	75.52 161	87.85 163	95.26 49	81.25 127	85.65 332	68.74 274	96.04 126	94.42 98

AdaColmap		83.66 179	83.69 176	83.57 196	90.05 176	72.26 165	86.29 141	90.00 190	78.19 127	81.65 303	87.16 305	83.40 91	94.24 95	61.69 334	94.76 185	84.21 365

MIMVSNet1		83.63 180	84.59 155	80.74 261	94.06 59	62.77 278	82.72 235	84.53 296	77.57 136	90.34 99	95.92 31	76.88 181	85.83 330	61.88 332	97.42 79	93.62 139

fmvsm_s_conf0.5_n_2		83.62 181	83.29 184	84.62 160	85.43 298	70.18 195	80.61 278	87.24 244	67.14 283	87.79 165	91.87 177	71.79 248	87.98 283	86.00 59	91.77 270	95.71 50

test_fmvsm_n_1920		83.60 182	82.89 193	85.74 133	85.22 302	77.74 102	84.12 190	90.48 169	59.87 358	86.45 203	91.12 206	75.65 188	85.89 328	82.28 104	90.87 292	93.58 142

WR-MVS		83.56 183	84.40 163	81.06 257	93.43 75	54.88 369	78.67 309	85.02 286	81.24 85	90.74 94	91.56 192	72.85 232	91.08 202	68.00 281	98.04 41	97.23 17

CNLPA		83.55 184	83.10 190	84.90 149	89.34 190	83.87 50	84.54 181	88.77 212	79.09 113	83.54 269	88.66 274	74.87 197	81.73 364	66.84 287	92.29 255	89.11 296

LCM-MVSNet-Re		83.48 185	85.06 143	78.75 290	85.94 289	55.75 362	80.05 284	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 252	59.95 345	94.89 177	90.75 255

hse-mvs2		83.47 186	81.81 211	88.47 80	91.03 153	82.27 61	82.61 237	83.69 302	71.27 227	86.70 190	86.05 323	63.04 305	92.41 164	78.26 151	93.62 223	90.71 257

V42		83.47 186	83.37 183	83.75 188	83.16 345	63.33 270	81.31 266	90.23 184	69.51 248	90.91 88	90.81 222	74.16 208	92.29 170	80.06 125	90.22 306	95.62 54

VPA-MVSNet		83.47 186	84.73 149	79.69 279	90.29 168	57.52 348	81.30 268	88.69 214	76.29 145	87.58 172	94.44 75	80.60 135	87.20 297	66.60 290	96.82 95	94.34 102

PAPM_NR		83.23 189	83.19 187	83.33 202	90.90 156	65.98 245	88.19 104	90.78 161	78.13 128	80.87 314	87.92 286	73.49 222	92.42 163	70.07 256	88.40 331	91.60 233

CLD-MVS		83.18 190	82.64 198	84.79 153	89.05 195	67.82 225	77.93 317	92.52 108	68.33 263	85.07 229	81.54 381	82.06 114	92.96 150	69.35 263	97.91 54	93.57 143
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 191	85.68 131	75.65 336	81.24 363	45.26 423	79.94 286	92.91 95	83.83 57	91.33 78	96.88 16	80.25 139	85.92 324	68.89 271	95.89 138	95.76 48

FA-MVS(test-final)		83.13 192	83.02 191	83.43 199	86.16 285	66.08 244	88.00 108	88.36 223	75.55 160	85.02 230	92.75 151	65.12 288	92.50 162	74.94 199	91.30 280	91.72 228

114514_t		83.10 193	82.54 201	84.77 154	92.90 88	69.10 212	86.65 134	90.62 166	54.66 390	81.46 306	90.81 222	76.98 174	94.38 90	72.62 232	96.18 119	90.82 254

RRT-MVS		82.97 194	83.44 180	81.57 246	85.06 304	58.04 343	87.20 119	90.37 174	77.88 131	88.59 140	93.70 119	63.17 302	93.05 148	76.49 178	88.47 330	93.62 139

BP-MVS1		82.81 195	81.67 213	86.23 119	87.88 229	68.53 216	86.06 146	84.36 297	75.65 157	85.14 225	90.19 244	45.84 394	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 196	81.64 214	86.21 122	86.20 282	76.24 123	86.86 127	85.68 273	77.07 141	73.76 385	92.82 147	69.64 260	91.82 183	69.04 270	93.69 220	90.56 265
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		82.75 197	81.93 209	85.19 145	82.08 352	80.15 76	85.53 156	88.76 213	68.01 268	85.58 218	87.75 291	71.80 247	86.85 304	74.02 209	93.87 212	88.58 306

EI-MVSNet		82.61 198	82.42 203	83.20 206	83.25 342	63.66 265	83.50 212	85.07 283	76.06 147	86.55 194	85.10 339	73.41 223	90.25 230	78.15 155	90.67 300	95.68 52

QAPM		82.59 199	82.59 200	82.58 224	86.44 270	66.69 236	89.94 68	90.36 175	67.97 270	84.94 234	92.58 156	72.71 234	92.18 171	70.63 250	87.73 345	88.85 304

fmvsm_s_conf0.1_n_a		82.58 200	81.93 209	84.50 163	87.68 235	73.35 142	86.14 145	77.70 345	61.64 337	85.02 230	91.62 189	77.75 160	86.24 316	82.79 97	87.07 352	93.91 120

Fast-Effi-MVS+-dtu		82.54 201	81.41 222	85.90 129	85.60 294	76.53 118	83.07 225	89.62 202	73.02 203	79.11 336	83.51 357	80.74 133	90.24 232	68.76 273	89.29 318	90.94 249

MVS_Test		82.47 202	83.22 185	80.22 271	82.62 350	57.75 347	82.54 242	91.96 125	71.16 231	82.89 279	92.52 158	77.41 166	90.50 225	80.04 126	87.84 344	92.40 198

v148		82.31 203	82.48 202	81.81 242	85.59 295	59.66 324	81.47 264	86.02 268	72.85 204	88.05 157	90.65 230	70.73 254	90.91 210	75.15 196	91.79 268	94.87 78

API-MVS		82.28 204	82.61 199	81.30 251	86.29 279	69.79 197	88.71 96	87.67 237	78.42 124	82.15 291	84.15 353	77.98 157	91.59 186	65.39 302	92.75 243	82.51 392

MVSFormer		82.23 205	81.57 219	84.19 176	85.54 296	69.26 207	91.98 35	90.08 188	71.54 224	76.23 361	85.07 342	58.69 330	94.27 92	86.26 49	88.77 326	89.03 301

fmvsm_s_conf0.5_n_a		82.21 206	81.51 221	84.32 171	86.56 268	73.35 142	85.46 157	77.30 349	61.81 333	84.51 242	90.88 219	77.36 167	86.21 318	82.72 98	86.97 357	93.38 147

EIA-MVS		82.19 207	81.23 229	85.10 147	87.95 226	69.17 211	83.22 223	93.33 71	70.42 237	78.58 341	79.77 397	77.29 168	94.20 97	71.51 240	88.96 324	91.93 222

GDP-MVS		82.17 208	80.85 236	86.15 126	88.65 209	68.95 213	85.65 155	93.02 91	68.42 261	83.73 263	89.54 258	45.07 405	94.31 91	79.66 132	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 208	81.59 217	83.94 183	86.87 266	71.57 178	85.19 164	77.42 348	62.27 331	84.47 245	91.33 198	76.43 183	85.91 326	83.14 88	87.14 350	94.33 103

PCF-MVS	74.62 15	82.15 210	80.92 234	85.84 131	89.43 188	72.30 164	80.53 279	91.82 130	57.36 374	87.81 164	89.92 252	77.67 163	93.63 121	58.69 350	95.08 168	91.58 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 211	80.31 243	87.45 97	90.86 158	80.29 75	85.88 148	90.65 164	68.17 266	76.32 360	86.33 317	73.12 229	92.61 160	61.40 337	90.02 310	89.44 287
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 212	81.54 220	83.60 193	83.94 325	73.90 138	83.35 217	86.10 264	58.97 360	83.80 262	90.36 236	74.23 206	86.94 302	82.90 94	90.22 306	89.94 280

fmvsm_s_conf0.5_n_7		82.04 213	82.05 207	82.01 235	86.98 262	71.07 183	78.70 307	89.45 205	68.07 267	78.14 343	91.61 190	74.19 207	85.92 324	79.61 133	91.73 271	89.05 300

GBi-Net		82.02 214	82.07 205	81.85 239	86.38 273	61.05 306	86.83 129	88.27 226	72.43 211	86.00 209	95.64 38	63.78 298	90.68 219	65.95 295	93.34 226	93.82 125

test1		82.02 214	82.07 205	81.85 239	86.38 273	61.05 306	86.83 129	88.27 226	72.43 211	86.00 209	95.64 38	63.78 298	90.68 219	65.95 295	93.34 226	93.82 125

OpenMVS	76.72 13	81.98 216	82.00 208	81.93 236	84.42 316	68.22 219	88.50 102	89.48 204	66.92 286	81.80 300	91.86 178	72.59 236	90.16 235	71.19 243	91.25 281	87.40 326

KD-MVS_self_test		81.93 217	83.14 189	78.30 299	84.75 310	52.75 383	80.37 281	89.42 207	70.24 242	90.26 101	93.39 126	74.55 205	86.77 306	68.61 276	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 218	81.30 226	83.75 188	86.02 287	71.56 179	84.73 173	77.11 352	62.44 328	84.00 258	90.68 227	76.42 184	85.89 328	83.14 88	87.11 351	93.81 128

SDMVSNet		81.90 219	83.17 188	78.10 303	88.81 204	62.45 286	76.08 350	86.05 267	73.67 185	83.41 270	93.04 135	82.35 104	80.65 371	70.06 257	95.03 170	91.21 241

tfpnnormal		81.79 220	82.95 192	78.31 298	88.93 200	55.40 364	80.83 276	82.85 311	76.81 142	85.90 213	94.14 93	74.58 204	86.51 310	66.82 288	95.68 149	93.01 166

AstraMVS		81.67 221	81.40 223	82.48 228	87.06 259	66.47 239	81.41 265	81.68 322	68.78 256	88.00 158	90.95 215	65.70 284	87.86 288	76.66 173	92.38 251	93.12 161

c3_l		81.64 222	81.59 217	81.79 243	80.86 369	59.15 331	78.61 310	90.18 186	68.36 262	87.20 176	87.11 307	69.39 261	91.62 185	78.16 153	94.43 194	94.60 88

guyue		81.57 223	81.37 225	82.15 232	86.39 271	66.13 243	81.54 263	83.21 306	69.79 246	87.77 166	89.95 250	65.36 287	87.64 291	75.88 187	92.49 249	92.67 181

PVSNet_Blended_VisFu		81.55 224	80.49 241	84.70 158	91.58 133	73.24 146	84.21 187	91.67 134	62.86 322	80.94 312	87.16 305	67.27 273	92.87 155	69.82 259	88.94 325	87.99 316

fmvsm_l_conf0.5_n_a		81.46 225	80.87 235	83.25 204	83.73 330	73.21 147	83.00 228	85.59 275	58.22 366	82.96 278	90.09 249	72.30 239	86.65 308	81.97 109	89.95 311	89.88 281

DELS-MVS		81.44 226	81.25 227	82.03 234	84.27 320	62.87 276	76.47 344	92.49 109	70.97 233	81.64 304	83.83 354	75.03 194	92.70 157	74.29 201	92.22 259	90.51 267
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		81.31 227	81.61 216	80.41 268	86.38 273	58.75 338	83.93 197	86.58 259	72.43 211	87.65 170	92.98 139	63.78 298	90.22 233	66.86 285	93.92 210	92.27 207

TinyColmap		81.25 228	82.34 204	77.99 306	85.33 299	60.68 314	82.32 248	88.33 224	71.26 229	86.97 185	92.22 172	77.10 172	86.98 301	62.37 326	95.17 164	86.31 338

AUN-MVS		81.18 229	78.78 264	88.39 82	90.93 155	82.14 62	82.51 243	83.67 303	64.69 312	80.29 322	85.91 326	51.07 369	92.38 165	76.29 182	93.63 222	90.65 262

tttt0517		81.07 230	79.58 255	85.52 138	88.99 198	66.45 240	87.03 124	75.51 364	73.76 184	88.32 150	90.20 243	37.96 426	94.16 104	79.36 138	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 231	80.57 238	82.46 229	87.50 242	63.22 272	78.37 313	89.63 201	68.01 268	81.87 296	82.08 375	82.31 106	92.65 159	67.10 284	88.30 337	91.51 237

BH-untuned		80.96 232	80.99 232	80.84 260	88.55 213	68.23 218	80.33 282	88.46 219	72.79 207	86.55 194	86.76 311	74.72 202	91.77 184	61.79 333	88.99 323	82.52 391

icg_test_0403		80.93 233	81.00 231	80.72 263	85.76 292	62.46 284	81.82 257	87.91 234	65.23 307	82.07 292	87.92 286	75.91 187	90.50 225	71.67 238	90.74 297	89.20 293

eth_miper_zixun_eth		80.84 234	80.22 247	82.71 221	81.41 361	60.98 309	77.81 319	90.14 187	67.31 281	86.95 186	87.24 304	64.26 291	92.31 168	75.23 195	91.61 274	94.85 82

xiu_mvs_v1_base_debu		80.84 234	80.14 249	82.93 216	88.31 217	71.73 173	79.53 291	87.17 245	65.43 301	79.59 328	82.73 369	76.94 175	90.14 238	73.22 224	88.33 333	86.90 332

xiu_mvs_v1_base		80.84 234	80.14 249	82.93 216	88.31 217	71.73 173	79.53 291	87.17 245	65.43 301	79.59 328	82.73 369	76.94 175	90.14 238	73.22 224	88.33 333	86.90 332

xiu_mvs_v1_base_debi		80.84 234	80.14 249	82.93 216	88.31 217	71.73 173	79.53 291	87.17 245	65.43 301	79.59 328	82.73 369	76.94 175	90.14 238	73.22 224	88.33 333	86.90 332

IterMVS-SCA-FT		80.64 238	79.41 256	84.34 170	83.93 326	69.66 201	76.28 346	81.09 328	72.43 211	86.47 201	90.19 244	60.46 315	93.15 144	77.45 164	86.39 363	90.22 272

BH-RMVSNet		80.53 239	80.22 247	81.49 248	87.19 250	66.21 242	77.79 320	86.23 262	74.21 179	83.69 264	88.50 275	73.25 228	90.75 216	63.18 323	87.90 341	87.52 324

VortexMVS		80.51 240	80.63 237	80.15 273	83.36 338	61.82 296	80.63 277	88.00 232	67.11 284	87.23 175	89.10 266	63.98 295	88.00 282	73.63 218	92.63 247	90.64 263

Anonymous202405211		80.51 240	81.19 230	78.49 295	88.48 214	57.26 350	76.63 339	82.49 314	81.21 86	84.30 252	92.24 171	67.99 269	86.24 316	62.22 327	95.13 165	91.98 221

DIV-MVS_self_test		80.43 242	80.23 245	81.02 258	79.99 377	59.25 328	77.07 332	87.02 253	67.38 278	86.19 205	89.22 262	63.09 303	90.16 235	76.32 180	95.80 143	93.66 133

cl____		80.42 243	80.23 245	81.02 258	79.99 377	59.25 328	77.07 332	87.02 253	67.37 279	86.18 207	89.21 263	63.08 304	90.16 235	76.31 181	95.80 143	93.65 136

diffmvs		80.40 244	80.48 242	80.17 272	79.02 390	60.04 319	77.54 324	90.28 183	66.65 289	82.40 286	87.33 302	73.50 220	87.35 295	77.98 157	89.62 315	93.13 159
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		80.37 245	78.41 271	86.23 119	76.75 404	73.28 144	87.18 121	77.45 347	76.24 146	68.14 415	88.93 269	65.41 286	93.85 113	69.47 262	96.12 123	91.55 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 246	80.04 252	81.24 254	79.82 380	58.95 333	77.66 321	89.66 199	65.75 298	85.99 212	85.11 338	68.29 268	91.42 193	76.03 185	92.03 262	93.33 149

MG-MVS		80.32 247	80.94 233	78.47 296	88.18 220	52.62 386	82.29 249	85.01 287	72.01 222	79.24 335	92.54 157	69.36 262	93.36 138	70.65 249	89.19 321	89.45 286

mvsmamba		80.30 248	78.87 261	84.58 162	88.12 223	67.55 226	92.35 30	84.88 290	63.15 320	85.33 222	90.91 216	50.71 371	95.20 62	66.36 291	87.98 340	90.99 247

VPNet		80.25 249	81.68 212	75.94 333	92.46 100	47.98 410	76.70 337	81.67 323	73.45 190	84.87 236	92.82 147	74.66 203	86.51 310	61.66 335	96.85 92	93.33 149

MAR-MVS		80.24 250	78.74 266	84.73 156	86.87 266	78.18 95	85.75 152	87.81 236	65.67 300	77.84 347	78.50 407	73.79 216	90.53 224	61.59 336	90.87 292	85.49 348
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		80.20 251	79.00 260	83.78 187	88.17 221	86.66 19	81.31 266	66.81 420	69.64 247	88.33 149	90.19 244	64.58 289	83.63 353	71.99 237	90.03 309	81.06 411

Anonymous20240521		80.18 252	81.25 227	76.95 319	83.15 346	60.84 311	82.46 244	85.99 269	68.76 257	86.78 187	93.73 118	59.13 327	77.44 388	73.71 215	97.55 74	92.56 188

LFMVS		80.15 253	80.56 239	78.89 287	89.19 194	55.93 358	85.22 163	73.78 376	82.96 69	84.28 253	92.72 152	57.38 339	90.07 242	63.80 317	95.75 146	90.68 259

DPM-MVS		80.10 254	79.18 259	82.88 219	90.71 161	69.74 199	78.87 305	90.84 159	60.29 354	75.64 370	85.92 325	67.28 272	93.11 145	71.24 242	91.79 268	85.77 344

MSDG		80.06 255	79.99 254	80.25 270	83.91 327	68.04 223	77.51 325	89.19 208	77.65 134	81.94 294	83.45 359	76.37 185	86.31 315	63.31 322	86.59 360	86.41 336

FE-MVS		79.98 256	78.86 262	83.36 201	86.47 269	66.45 240	89.73 71	84.74 294	72.80 206	84.22 256	91.38 197	44.95 406	93.60 125	63.93 315	91.50 277	90.04 279

sd_testset		79.95 257	81.39 224	75.64 337	88.81 204	58.07 342	76.16 349	82.81 312	73.67 185	83.41 270	93.04 135	80.96 130	77.65 387	58.62 351	95.03 170	91.21 241

ab-mvs		79.67 258	80.56 239	76.99 318	88.48 214	56.93 352	84.70 175	86.06 266	68.95 254	80.78 315	93.08 134	75.30 192	84.62 340	56.78 360	90.90 290	89.43 288

VNet		79.31 259	80.27 244	76.44 327	87.92 227	53.95 375	75.58 356	84.35 298	74.39 178	82.23 289	90.72 224	72.84 233	84.39 345	60.38 343	93.98 209	90.97 248

thisisatest0530		79.07 260	77.33 281	84.26 173	87.13 251	64.58 256	83.66 207	75.95 359	68.86 255	85.22 224	87.36 301	38.10 423	93.57 129	75.47 192	94.28 199	94.62 87

cl22		78.97 261	78.21 273	81.24 254	77.74 394	59.01 332	77.46 328	87.13 248	65.79 295	84.32 249	85.10 339	58.96 329	90.88 212	75.36 194	92.03 262	93.84 123

patch_mono-2		78.89 262	79.39 257	77.41 315	84.78 308	68.11 221	75.60 354	83.11 308	60.96 347	79.36 332	89.89 253	75.18 193	72.97 403	73.32 223	92.30 253	91.15 243

RPMNet		78.88 263	78.28 272	80.68 265	79.58 381	62.64 280	82.58 239	94.16 33	74.80 168	75.72 368	92.59 154	48.69 378	95.56 42	73.48 220	82.91 399	83.85 370

PAPR		78.84 264	78.10 274	81.07 256	85.17 303	60.22 318	82.21 253	90.57 168	62.51 324	75.32 374	84.61 347	74.99 195	92.30 169	59.48 348	88.04 339	90.68 259

PVSNet_BlendedMVS		78.80 265	77.84 275	81.65 245	84.43 314	63.41 268	79.49 294	90.44 171	61.70 336	75.43 371	87.07 308	69.11 264	91.44 191	60.68 341	92.24 257	90.11 277

FMVSNet3		78.80 265	78.55 268	79.57 281	82.89 349	56.89 354	81.76 258	85.77 271	69.04 253	86.00 209	90.44 235	51.75 367	90.09 241	65.95 295	93.34 226	91.72 228

test_yl		78.71 267	78.51 269	79.32 284	84.32 318	58.84 335	78.38 311	85.33 278	75.99 150	82.49 284	86.57 313	58.01 333	90.02 244	62.74 324	92.73 245	89.10 297

DCV-MVSNet		78.71 267	78.51 269	79.32 284	84.32 318	58.84 335	78.38 311	85.33 278	75.99 150	82.49 284	86.57 313	58.01 333	90.02 244	62.74 324	92.73 245	89.10 297

test1111		78.53 269	78.85 263	77.56 312	92.22 109	47.49 412	82.61 237	69.24 408	72.43 211	85.28 223	94.20 89	51.91 365	90.07 242	65.36 303	96.45 108	95.11 72

ECVR-MVS		78.44 270	78.63 267	77.88 308	91.85 123	48.95 406	83.68 206	69.91 404	72.30 217	84.26 255	94.20 89	51.89 366	89.82 247	63.58 318	96.02 127	94.87 78

pmmvs-eth3d		78.42 271	77.04 284	82.57 226	87.44 244	74.41 135	80.86 275	79.67 336	55.68 383	84.69 239	90.31 241	60.91 313	85.42 333	62.20 328	91.59 275	87.88 320

mvs_anonymous		78.13 272	78.76 265	76.23 332	79.24 387	50.31 402	78.69 308	84.82 292	61.60 338	83.09 277	92.82 147	73.89 215	87.01 298	68.33 280	86.41 362	91.37 238

TAMVS		78.08 273	76.36 291	83.23 205	90.62 162	72.87 150	79.08 301	80.01 335	61.72 335	81.35 308	86.92 310	63.96 297	88.78 269	50.61 399	93.01 237	88.04 315

miper_enhance_ethall		77.83 274	76.93 285	80.51 266	76.15 411	58.01 344	75.47 358	88.82 211	58.05 368	83.59 266	80.69 385	64.41 290	91.20 197	73.16 230	92.03 262	92.33 202

Vis-MVSNet (Re-imp)		77.82 275	77.79 276	77.92 307	88.82 203	51.29 396	83.28 218	71.97 392	74.04 180	82.23 289	89.78 254	57.38 339	89.41 259	57.22 359	95.41 154	93.05 164

CANet_DTU		77.81 276	77.05 283	80.09 274	81.37 362	59.90 322	83.26 219	88.29 225	69.16 251	67.83 418	83.72 355	60.93 312	89.47 254	69.22 266	89.70 314	90.88 252

OpenMVS_ROB	70.19 17	77.77 277	77.46 278	78.71 291	84.39 317	61.15 304	81.18 270	82.52 313	62.45 327	83.34 272	87.37 300	66.20 278	88.66 272	64.69 310	85.02 379	86.32 337

SSC-MVS		77.55 278	81.64 214	65.29 409	90.46 165	20.33 456	73.56 374	68.28 410	85.44 41	88.18 154	94.64 68	70.93 253	81.33 366	71.25 241	92.03 262	94.20 105

MDA-MVSNet-bldmvs		77.47 279	76.90 286	79.16 286	79.03 389	64.59 255	66.58 419	75.67 362	73.15 201	88.86 132	88.99 268	66.94 274	81.23 367	64.71 309	88.22 338	91.64 232

jason		77.42 280	75.75 297	82.43 230	87.10 254	69.27 206	77.99 316	81.94 320	51.47 410	77.84 347	85.07 342	60.32 317	89.00 263	70.74 248	89.27 320	89.03 301
jason: jason.
CDS-MVSNet		77.32 281	75.40 301	83.06 209	89.00 197	72.48 161	77.90 318	82.17 318	60.81 348	78.94 338	83.49 358	59.30 325	88.76 270	54.64 379	92.37 252	87.93 319
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ICG_test_0404		77.24 282	77.75 277	75.73 335	85.76 292	62.46 284	70.84 394	87.91 234	65.23 307	72.21 393	87.92 286	67.48 271	75.53 396	71.67 238	90.74 297	89.20 293

xiu_mvs_v2_base		77.19 283	76.75 288	78.52 294	87.01 260	61.30 302	75.55 357	87.12 251	61.24 344	74.45 380	78.79 405	77.20 169	90.93 208	64.62 312	84.80 386	83.32 379

MVSTER		77.09 284	75.70 298	81.25 252	75.27 419	61.08 305	77.49 327	85.07 283	60.78 349	86.55 194	88.68 272	43.14 415	90.25 230	73.69 217	90.67 300	92.42 195

PS-MVSNAJ		77.04 285	76.53 290	78.56 293	87.09 256	61.40 300	75.26 359	87.13 248	61.25 343	74.38 382	77.22 419	76.94 175	90.94 207	64.63 311	84.83 385	83.35 378

IterMVS		76.91 286	76.34 292	78.64 292	80.91 367	64.03 262	76.30 345	79.03 339	64.88 311	83.11 275	89.16 264	59.90 321	84.46 343	68.61 276	85.15 377	87.42 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 287	75.67 299	80.34 269	80.48 375	62.16 294	73.50 375	84.80 293	57.61 372	82.24 288	87.54 295	51.31 368	87.65 290	70.40 253	93.19 233	91.23 240

CL-MVSNet_self_test		76.81 288	77.38 280	75.12 340	86.90 264	51.34 394	73.20 378	80.63 332	68.30 264	81.80 300	88.40 276	66.92 275	80.90 368	55.35 373	94.90 176	93.12 161

TR-MVS		76.77 289	75.79 296	79.72 278	86.10 286	65.79 247	77.14 330	83.02 309	65.20 309	81.40 307	82.10 373	66.30 277	90.73 218	55.57 370	85.27 373	82.65 386

MonoMVSNet		76.66 290	77.26 282	74.86 342	79.86 379	54.34 372	86.26 142	86.08 265	71.08 232	85.59 217	88.68 272	53.95 357	85.93 323	63.86 316	80.02 415	84.32 361

USDC		76.63 291	76.73 289	76.34 329	83.46 333	57.20 351	80.02 285	88.04 231	52.14 406	83.65 265	91.25 201	63.24 301	86.65 308	54.66 378	94.11 204	85.17 350

BH-w/o		76.57 292	76.07 295	78.10 303	86.88 265	65.92 246	77.63 322	86.33 260	65.69 299	80.89 313	79.95 394	68.97 266	90.74 217	53.01 389	85.25 374	77.62 422

Patchmtry		76.56 293	77.46 278	73.83 349	79.37 386	46.60 416	82.41 246	76.90 353	73.81 183	85.56 219	92.38 161	48.07 381	83.98 350	63.36 321	95.31 160	90.92 250

PVSNet_Blended		76.49 294	75.40 301	79.76 277	84.43 314	63.41 268	75.14 360	90.44 171	57.36 374	75.43 371	78.30 408	69.11 264	91.44 191	60.68 341	87.70 346	84.42 360

miper_lstm_enhance		76.45 295	76.10 294	77.51 313	76.72 405	60.97 310	64.69 423	85.04 285	63.98 316	83.20 274	88.22 278	56.67 343	78.79 384	73.22 224	93.12 234	92.78 175

lupinMVS		76.37 296	74.46 310	82.09 233	85.54 296	69.26 207	76.79 335	80.77 331	50.68 417	76.23 361	82.82 367	58.69 330	88.94 264	69.85 258	88.77 326	88.07 312

cascas		76.29 297	74.81 306	80.72 263	84.47 313	62.94 274	73.89 372	87.34 240	55.94 381	75.16 376	76.53 424	63.97 296	91.16 199	65.00 306	90.97 288	88.06 314

SD_0403		76.08 298	76.77 287	73.98 347	87.08 258	49.45 405	83.62 208	84.68 295	63.31 317	75.13 377	87.47 298	71.85 246	84.56 341	49.97 401	87.86 343	87.94 318

WB-MVS		76.06 299	80.01 253	64.19 412	89.96 179	20.58 455	72.18 383	68.19 411	83.21 65	86.46 202	93.49 123	70.19 258	78.97 382	65.96 294	90.46 305	93.02 165

thres600view7		75.97 300	75.35 303	77.85 310	87.01 260	51.84 392	80.45 280	73.26 381	75.20 165	83.10 276	86.31 319	45.54 396	89.05 262	55.03 376	92.24 257	92.66 182

GA-MVS		75.83 301	74.61 307	79.48 283	81.87 354	59.25 328	73.42 376	82.88 310	68.68 258	79.75 327	81.80 378	50.62 372	89.46 255	66.85 286	85.64 370	89.72 283

MVP-Stereo		75.81 302	73.51 319	82.71 221	89.35 189	73.62 139	80.06 283	85.20 280	60.30 353	73.96 383	87.94 283	57.89 337	89.45 256	52.02 393	74.87 433	85.06 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 303	75.20 304	77.27 316	75.01 422	69.47 204	78.93 302	84.88 290	46.67 424	87.08 182	87.84 289	50.44 374	71.62 408	77.42 166	88.53 329	90.72 256

thres100view900		75.45 304	75.05 305	76.66 325	87.27 246	51.88 391	81.07 271	73.26 381	75.68 156	83.25 273	86.37 316	45.54 396	88.80 266	51.98 394	90.99 285	89.31 290

ET-MVSNet_ETH3D		75.28 305	72.77 328	82.81 220	83.03 348	68.11 221	77.09 331	76.51 357	60.67 351	77.60 352	80.52 389	38.04 424	91.15 200	70.78 246	90.68 299	89.17 295

thres400		75.14 306	74.23 312	77.86 309	86.24 280	52.12 388	79.24 298	73.87 374	73.34 194	81.82 298	84.60 348	46.02 389	88.80 266	51.98 394	90.99 285	92.66 182

wuyk23d		75.13 307	79.30 258	62.63 415	75.56 415	75.18 131	80.89 274	73.10 383	75.06 167	94.76 16	95.32 45	87.73 44	52.85 447	34.16 445	97.11 87	59.85 443

EU-MVSNet		75.12 308	74.43 311	77.18 317	83.11 347	59.48 326	85.71 154	82.43 315	39.76 444	85.64 216	88.76 270	44.71 408	87.88 287	73.86 212	85.88 369	84.16 366

HyFIR lowres test		75.12 308	72.66 330	82.50 227	91.44 141	65.19 252	72.47 381	87.31 241	46.79 423	80.29 322	84.30 350	52.70 362	92.10 175	51.88 398	86.73 358	90.22 272

CMPMVS	59.41 20	75.12 308	73.57 317	79.77 276	75.84 414	67.22 227	81.21 269	82.18 317	50.78 415	76.50 357	87.66 293	55.20 353	82.99 356	62.17 330	90.64 304	89.09 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 311	72.98 326	80.73 262	84.95 305	71.71 176	76.23 347	77.59 346	52.83 400	77.73 351	86.38 315	56.35 346	84.97 337	57.72 358	87.05 353	85.51 347

tfpn200view9		74.86 312	74.23 312	76.74 324	86.24 280	52.12 388	79.24 298	73.87 374	73.34 194	81.82 298	84.60 348	46.02 389	88.80 266	51.98 394	90.99 285	89.31 290

1112_ss		74.82 313	73.74 315	78.04 305	89.57 183	60.04 319	76.49 343	87.09 252	54.31 391	73.66 386	79.80 395	60.25 318	86.76 307	58.37 352	84.15 390	87.32 327

EGC-MVSNET		74.79 314	69.99 358	89.19 67	94.89 38	87.00 15	91.89 38	86.28 261	1.09 453	2.23 455	95.98 30	81.87 120	89.48 253	79.76 129	95.96 130	91.10 244

ppachtmachnet_test		74.73 315	74.00 314	76.90 321	80.71 372	56.89 354	71.53 389	78.42 341	58.24 365	79.32 334	82.92 366	57.91 336	84.26 347	65.60 301	91.36 279	89.56 285

Patchmatch-RL test		74.48 316	73.68 316	76.89 322	84.83 307	66.54 237	72.29 382	69.16 409	57.70 370	86.76 188	86.33 317	45.79 395	82.59 357	69.63 261	90.65 303	81.54 402

PatchMatch-RL		74.48 316	73.22 323	78.27 301	87.70 234	85.26 38	75.92 352	70.09 402	64.34 314	76.09 364	81.25 383	65.87 283	78.07 386	53.86 381	83.82 392	71.48 431

XXY-MVS		74.44 318	76.19 293	69.21 384	84.61 312	52.43 387	71.70 386	77.18 351	60.73 350	80.60 316	90.96 213	75.44 189	69.35 415	56.13 365	88.33 333	85.86 343

test2506		74.12 319	73.39 320	76.28 330	91.85 123	44.20 426	84.06 191	48.20 451	72.30 217	81.90 295	94.20 89	27.22 451	89.77 250	64.81 308	96.02 127	94.87 78

reproduce_monomvs		74.09 320	73.23 322	76.65 326	76.52 406	54.54 370	77.50 326	81.40 326	65.85 294	82.86 281	86.67 312	27.38 449	84.53 342	70.24 254	90.66 302	90.89 251

CR-MVSNet		74.00 321	73.04 325	76.85 323	79.58 381	62.64 280	82.58 239	76.90 353	50.50 418	75.72 368	92.38 161	48.07 381	84.07 349	68.72 275	82.91 399	83.85 370

SSC-MVS3.2		73.90 322	75.67 299	68.61 392	84.11 323	41.28 434	64.17 425	72.83 384	72.09 220	79.08 337	87.94 283	70.31 256	73.89 402	55.99 366	94.49 191	90.67 261

Test_1112_low_res		73.90 322	73.08 324	76.35 328	90.35 167	55.95 357	73.40 377	86.17 263	50.70 416	73.14 387	85.94 324	58.31 332	85.90 327	56.51 362	83.22 396	87.20 329

test20.03		73.75 324	74.59 309	71.22 370	81.11 365	51.12 398	70.15 400	72.10 391	70.42 237	80.28 324	91.50 193	64.21 292	74.72 400	46.96 419	94.58 189	87.82 322

test_fmvs2		73.57 325	72.80 327	75.90 334	72.74 436	68.84 214	77.07 332	84.32 299	45.14 430	82.89 279	84.22 351	48.37 379	70.36 412	73.40 222	87.03 354	88.52 307

SCA		73.32 326	72.57 332	75.58 338	81.62 358	55.86 360	78.89 304	71.37 397	61.73 334	74.93 378	83.42 360	60.46 315	87.01 298	58.11 356	82.63 404	83.88 367

baseline1		73.26 327	73.54 318	72.43 363	84.92 306	47.79 411	79.89 287	74.00 372	65.93 292	78.81 339	86.28 320	56.36 345	81.63 365	56.63 361	79.04 422	87.87 321

1314		73.22 328	72.56 333	75.20 339	80.41 376	57.84 345	81.64 261	85.36 277	51.68 409	73.10 388	76.65 423	61.45 310	85.19 335	63.54 319	79.21 420	82.59 387

MVS		73.21 329	72.59 331	75.06 341	80.97 366	60.81 312	81.64 261	85.92 270	46.03 428	71.68 396	77.54 414	68.47 267	89.77 250	55.70 369	85.39 371	74.60 428

HY-MVS	64.64 18	73.03 330	72.47 334	74.71 344	83.36 338	54.19 373	82.14 256	81.96 319	56.76 380	69.57 410	86.21 321	60.03 319	84.83 339	49.58 406	82.65 402	85.11 351

thisisatest0515		73.00 331	70.52 350	80.46 267	81.45 360	59.90 322	73.16 379	74.31 371	57.86 369	76.08 365	77.78 411	37.60 427	92.12 174	65.00 306	91.45 278	89.35 289

EPNet_dtu		72.87 332	71.33 344	77.49 314	77.72 395	60.55 315	82.35 247	75.79 360	66.49 290	58.39 446	81.06 384	53.68 358	85.98 322	53.55 384	92.97 239	85.95 341
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 333	71.41 343	76.28 330	83.25 342	60.34 317	83.50 212	79.02 340	37.77 448	76.33 359	85.10 339	49.60 377	87.41 294	70.54 251	77.54 428	81.08 409

CHOSEN 1792x2688		72.45 334	70.56 349	78.13 302	90.02 178	63.08 273	68.72 407	83.16 307	42.99 438	75.92 366	85.46 332	57.22 341	85.18 336	49.87 404	81.67 406	86.14 339

testgi		72.36 335	74.61 307	65.59 406	80.56 374	42.82 431	68.29 408	73.35 380	66.87 287	81.84 297	89.93 251	72.08 243	66.92 429	46.05 423	92.54 248	87.01 331

thres200		72.34 336	71.55 342	74.70 345	83.48 332	51.60 393	75.02 361	73.71 377	70.14 243	78.56 342	80.57 388	46.20 387	88.20 280	46.99 418	89.29 318	84.32 361

FPMVS		72.29 337	72.00 336	73.14 354	88.63 210	85.00 40	74.65 365	67.39 414	71.94 223	77.80 349	87.66 293	50.48 373	75.83 394	49.95 402	79.51 416	58.58 445

FMVSNet5		72.10 338	71.69 338	73.32 352	81.57 359	53.02 382	76.77 336	78.37 342	63.31 317	76.37 358	91.85 179	36.68 428	78.98 381	47.87 415	92.45 250	87.95 317

our_test_3		71.85 339	71.59 339	72.62 360	80.71 372	53.78 376	69.72 403	71.71 396	58.80 362	78.03 344	80.51 390	56.61 344	78.84 383	62.20 328	86.04 368	85.23 349

PAPM		71.77 340	70.06 356	76.92 320	86.39 271	53.97 374	76.62 340	86.62 258	53.44 395	63.97 435	84.73 346	57.79 338	92.34 167	39.65 435	81.33 410	84.45 359

ttmdpeth		71.72 341	70.67 347	74.86 342	73.08 433	55.88 359	77.41 329	69.27 407	55.86 382	78.66 340	93.77 116	38.01 425	75.39 397	60.12 344	89.87 312	93.31 151

IB-MVS	62.13 19	71.64 342	68.97 368	79.66 280	80.80 371	62.26 291	73.94 371	76.90 353	63.27 319	68.63 414	76.79 421	33.83 432	91.84 182	59.28 349	87.26 348	84.88 353
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		71.63 343	72.30 335	69.62 381	76.47 408	52.70 385	70.03 401	80.97 329	59.18 359	79.36 332	88.21 279	60.50 314	69.12 416	58.33 354	77.62 427	87.04 330

testing3		71.53 344	70.79 346	73.77 350	88.89 202	41.86 433	76.60 342	59.12 440	72.83 205	80.97 310	82.08 375	19.80 457	87.33 296	65.12 305	91.68 273	92.13 214

test_vis3_rt		71.42 345	70.67 347	73.64 351	69.66 443	70.46 189	66.97 418	89.73 196	42.68 440	88.20 153	83.04 362	43.77 410	60.07 441	65.35 304	86.66 359	90.39 270

Anonymous20231206		71.38 346	71.88 337	69.88 378	86.31 277	54.37 371	70.39 398	74.62 367	52.57 402	76.73 356	88.76 270	59.94 320	72.06 405	44.35 427	93.23 231	83.23 381

test_vis1_n_1920		71.30 347	71.58 341	70.47 373	77.58 397	59.99 321	74.25 366	84.22 300	51.06 412	74.85 379	79.10 401	55.10 354	68.83 418	68.86 272	79.20 421	82.58 388

MIMVSNet		71.09 348	71.59 339	69.57 382	87.23 248	50.07 403	78.91 303	71.83 393	60.20 356	71.26 397	91.76 186	55.08 355	76.09 392	41.06 432	87.02 355	82.54 390

test_fmvs1_n		70.94 349	70.41 353	72.53 362	73.92 424	66.93 234	75.99 351	84.21 301	43.31 437	79.40 331	79.39 399	43.47 411	68.55 420	69.05 269	84.91 382	82.10 396

MS-PatchMatch		70.93 350	70.22 354	73.06 355	81.85 355	62.50 283	73.82 373	77.90 343	52.44 403	75.92 366	81.27 382	55.67 350	81.75 363	55.37 372	77.70 426	74.94 427

pmmvs5		70.73 351	70.07 355	72.72 358	77.03 402	52.73 384	74.14 367	75.65 363	50.36 419	72.17 394	85.37 336	55.42 352	80.67 370	52.86 390	87.59 347	84.77 354

testing3-2		70.72 352	70.97 345	69.95 377	88.93 200	34.80 447	69.85 402	66.59 421	78.42 124	77.58 353	85.55 328	31.83 438	82.08 361	46.28 420	93.73 218	92.98 169

PatchT		70.52 353	72.76 329	63.79 414	79.38 385	33.53 448	77.63 322	65.37 425	73.61 187	71.77 395	92.79 150	44.38 409	75.65 395	64.53 313	85.37 372	82.18 395

test_vis1_n		70.29 354	69.99 358	71.20 371	75.97 413	66.50 238	76.69 338	80.81 330	44.22 433	75.43 371	77.23 418	50.00 375	68.59 419	66.71 289	82.85 401	78.52 421

N_pmnet		70.20 355	68.80 370	74.38 346	80.91 367	84.81 43	59.12 436	76.45 358	55.06 386	75.31 375	82.36 372	55.74 349	54.82 446	47.02 417	87.24 349	83.52 374

tpmvs		70.16 356	69.56 361	71.96 366	74.71 423	48.13 408	79.63 289	75.45 365	65.02 310	70.26 405	81.88 377	45.34 401	85.68 331	58.34 353	75.39 432	82.08 397

new-patchmatchnet		70.10 357	73.37 321	60.29 423	81.23 364	16.95 458	59.54 434	74.62 367	62.93 321	80.97 310	87.93 285	62.83 307	71.90 406	55.24 374	95.01 173	92.00 219

YYNet1		70.06 358	70.44 351	68.90 386	73.76 426	53.42 380	58.99 437	67.20 416	58.42 364	87.10 180	85.39 335	59.82 322	67.32 426	59.79 346	83.50 395	85.96 340

MVStest1		70.05 359	69.26 362	72.41 364	58.62 455	55.59 363	76.61 341	65.58 423	53.44 395	89.28 128	93.32 127	22.91 455	71.44 410	74.08 208	89.52 316	90.21 276

MDA-MVSNet_test_wron		70.05 359	70.44 351	68.88 387	73.84 425	53.47 378	58.93 438	67.28 415	58.43 363	87.09 181	85.40 334	59.80 323	67.25 427	59.66 347	83.54 394	85.92 342

CostFormer		69.98 361	68.68 371	73.87 348	77.14 400	50.72 400	79.26 297	74.51 369	51.94 408	70.97 400	84.75 345	45.16 404	87.49 293	55.16 375	79.23 419	83.40 377

testing91		69.94 362	68.99 367	72.80 357	83.81 329	45.89 419	71.57 388	73.64 379	68.24 265	70.77 403	77.82 410	34.37 431	84.44 344	53.64 383	87.00 356	88.07 312

baseline2		69.77 363	66.89 380	78.41 297	79.51 383	58.09 341	76.23 347	69.57 405	57.50 373	64.82 433	77.45 416	46.02 389	88.44 274	53.08 386	77.83 424	88.70 305

PatchmatchNet		69.71 364	68.83 369	72.33 365	77.66 396	53.60 377	79.29 296	69.99 403	57.66 371	72.53 391	82.93 365	46.45 386	80.08 376	60.91 340	72.09 436	83.31 380
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 365	69.05 365	71.14 372	69.15 444	65.77 248	73.98 370	83.32 305	42.83 439	77.77 350	78.27 409	43.39 414	68.50 421	68.39 279	84.38 389	79.15 419

JIA-IIPM		69.41 366	66.64 384	77.70 311	73.19 430	71.24 181	75.67 353	65.56 424	70.42 237	65.18 429	92.97 141	33.64 434	83.06 354	53.52 385	69.61 442	78.79 420

Syy-MVS		69.40 367	70.03 357	67.49 397	81.72 356	38.94 439	71.00 391	61.99 431	61.38 340	70.81 401	72.36 435	61.37 311	79.30 379	64.50 314	85.18 375	84.22 363

testing99		69.27 368	68.15 375	72.63 359	83.29 340	45.45 421	71.15 390	71.08 398	67.34 280	70.43 404	77.77 412	32.24 437	84.35 346	53.72 382	86.33 364	88.10 311

UnsupCasMVSNet_bld		69.21 369	69.68 360	67.82 395	79.42 384	51.15 397	67.82 412	75.79 360	54.15 392	77.47 354	85.36 337	59.26 326	70.64 411	48.46 412	79.35 418	81.66 400

test_cas_vis1_n_1920		69.20 370	69.12 363	69.43 383	73.68 427	62.82 277	70.38 399	77.21 350	46.18 427	80.46 321	78.95 403	52.03 364	65.53 434	65.77 300	77.45 429	79.95 417

gg-mvs-nofinetune		68.96 371	69.11 364	68.52 393	76.12 412	45.32 422	83.59 209	55.88 445	86.68 33	64.62 434	97.01 12	30.36 442	83.97 351	44.78 426	82.94 398	76.26 424

WBMVS		68.76 372	68.43 372	69.75 380	83.29 340	40.30 437	67.36 414	72.21 390	57.09 377	77.05 355	85.53 330	33.68 433	80.51 372	48.79 410	90.90 290	88.45 308

WB-MVSnew		68.72 373	69.01 366	67.85 394	83.22 344	43.98 427	74.93 362	65.98 422	55.09 385	73.83 384	79.11 400	65.63 285	71.89 407	38.21 440	85.04 378	87.69 323

tpm2		68.45 374	66.83 381	73.30 353	78.93 391	48.50 407	79.76 288	71.76 394	47.50 422	69.92 407	83.60 356	42.07 417	88.40 276	48.44 413	79.51 416	83.01 384

tpm		67.95 375	68.08 376	67.55 396	78.74 392	43.53 429	75.60 354	67.10 419	54.92 387	72.23 392	88.10 280	42.87 416	75.97 393	52.21 392	80.95 414	83.15 382

WTY-MVS		67.91 376	68.35 373	66.58 402	80.82 370	48.12 409	65.96 420	72.60 385	53.67 394	71.20 398	81.68 380	58.97 328	69.06 417	48.57 411	81.67 406	82.55 389

testing11		67.38 377	65.93 385	71.73 368	83.37 337	46.60 416	70.95 393	69.40 406	62.47 326	66.14 422	76.66 422	31.22 439	84.10 348	49.10 408	84.10 391	84.49 357

test-LLR		67.21 378	66.74 382	68.63 390	76.45 409	55.21 366	67.89 409	67.14 417	62.43 329	65.08 430	72.39 433	43.41 412	69.37 413	61.00 338	84.89 383	81.31 404

testing222		66.93 379	65.30 392	71.81 367	83.38 336	45.83 420	72.06 384	67.50 413	64.12 315	69.68 409	76.37 425	27.34 450	83.00 355	38.88 436	88.38 332	86.62 335

sss		66.92 380	67.26 378	65.90 404	77.23 399	51.10 399	64.79 422	71.72 395	52.12 407	70.13 406	80.18 392	57.96 335	65.36 435	50.21 400	81.01 412	81.25 406

KD-MVS_2432*1600		66.87 381	65.81 388	70.04 375	67.50 445	47.49 412	62.56 428	79.16 337	61.21 345	77.98 345	80.61 386	25.29 453	82.48 358	53.02 387	84.92 380	80.16 415

miper_refine_blended		66.87 381	65.81 388	70.04 375	67.50 445	47.49 412	62.56 428	79.16 337	61.21 345	77.98 345	80.61 386	25.29 453	82.48 358	53.02 387	84.92 380	80.16 415

dmvs_re		66.81 383	66.98 379	66.28 403	76.87 403	58.68 339	71.66 387	72.24 388	60.29 354	69.52 411	73.53 432	52.38 363	64.40 437	44.90 425	81.44 409	75.76 425

tpm cat1		66.76 384	65.21 393	71.42 369	77.09 401	50.62 401	78.01 315	73.68 378	44.89 431	68.64 413	79.00 402	45.51 398	82.42 360	49.91 403	70.15 439	81.23 408

UWE-MVS		66.43 385	65.56 391	69.05 385	84.15 322	40.98 435	73.06 380	64.71 427	54.84 388	76.18 363	79.62 398	29.21 444	80.50 373	38.54 439	89.75 313	85.66 345

PVSNet	58.17 21	66.41 386	65.63 390	68.75 388	81.96 353	49.88 404	62.19 430	72.51 387	51.03 413	68.04 416	75.34 429	50.84 370	74.77 398	45.82 424	82.96 397	81.60 401

tpmrst		66.28 387	66.69 383	65.05 410	72.82 435	39.33 438	78.20 314	70.69 401	53.16 398	67.88 417	80.36 391	48.18 380	74.75 399	58.13 355	70.79 438	81.08 409

Patchmatch-test		65.91 388	67.38 377	61.48 420	75.51 416	43.21 430	68.84 406	63.79 429	62.48 325	72.80 390	83.42 360	44.89 407	59.52 443	48.27 414	86.45 361	81.70 399

ADS-MVSNet2		65.87 389	63.64 398	72.55 361	73.16 431	56.92 353	67.10 416	74.81 366	49.74 420	66.04 424	82.97 363	46.71 384	77.26 389	42.29 429	69.96 440	83.46 375

myMVS_eth3d28		65.83 390	65.85 386	65.78 405	83.42 335	35.71 445	67.29 415	68.01 412	67.58 277	69.80 408	77.72 413	32.29 436	74.30 401	37.49 441	89.06 322	87.32 327

test_vis1_rt		65.64 391	64.09 395	70.31 374	66.09 449	70.20 193	61.16 431	81.60 324	38.65 445	72.87 389	69.66 438	52.84 360	60.04 442	56.16 364	77.77 425	80.68 413

mvsany_test3		65.48 392	62.97 401	73.03 356	69.99 442	76.17 124	64.83 421	43.71 453	43.68 435	80.25 325	87.05 309	52.83 361	63.09 440	51.92 397	72.44 435	79.84 418

test-mter		65.00 393	63.79 397	68.63 390	76.45 409	55.21 366	67.89 409	67.14 417	50.98 414	65.08 430	72.39 433	28.27 447	69.37 413	61.00 338	84.89 383	81.31 404

ETVMVS		64.67 394	63.34 400	68.64 389	83.44 334	41.89 432	69.56 405	61.70 436	61.33 342	68.74 412	75.76 427	28.76 445	79.35 378	34.65 444	86.16 367	84.67 356

myMVS_eth3d		64.66 395	63.89 396	66.97 400	81.72 356	37.39 442	71.00 391	61.99 431	61.38 340	70.81 401	72.36 435	20.96 456	79.30 379	49.59 405	85.18 375	84.22 363

test0.0.03 1		64.66 395	64.36 394	65.57 407	75.03 421	46.89 415	64.69 423	61.58 437	62.43 329	71.18 399	77.54 414	43.41 412	68.47 422	40.75 434	82.65 402	81.35 403

UBG		64.34 397	63.35 399	67.30 398	83.50 331	40.53 436	67.46 413	65.02 426	54.77 389	67.54 420	74.47 431	32.99 435	78.50 385	40.82 433	83.58 393	82.88 385

test_f		64.31 398	65.85 386	59.67 424	66.54 448	62.24 293	57.76 440	70.96 399	40.13 442	84.36 247	82.09 374	46.93 383	51.67 448	61.99 331	81.89 405	65.12 439

pmmvs3		62.47 399	60.02 412	69.80 379	71.58 439	64.00 263	70.52 397	58.44 443	39.77 443	66.05 423	75.84 426	27.10 452	72.28 404	46.15 422	84.77 387	73.11 429

EPMVS		62.47 399	62.63 403	62.01 416	70.63 441	38.74 440	74.76 363	52.86 447	53.91 393	67.71 419	80.01 393	39.40 421	66.60 430	55.54 371	68.81 444	80.68 413

ADS-MVSNet		61.90 401	62.19 405	61.03 421	73.16 431	36.42 444	67.10 416	61.75 434	49.74 420	66.04 424	82.97 363	46.71 384	63.21 438	42.29 429	69.96 440	83.46 375

PMMVS		61.65 402	60.38 409	65.47 408	65.40 452	69.26 207	63.97 426	61.73 435	36.80 449	60.11 441	68.43 440	59.42 324	66.35 431	48.97 409	78.57 423	60.81 442

E-PMN		61.59 403	61.62 406	61.49 419	66.81 447	55.40 364	53.77 443	60.34 439	66.80 288	58.90 444	65.50 443	40.48 420	66.12 432	55.72 368	86.25 365	62.95 441

TESTMET0.1,1		61.29 404	60.32 410	64.19 412	72.06 437	51.30 395	67.89 409	62.09 430	45.27 429	60.65 440	69.01 439	27.93 448	64.74 436	56.31 363	81.65 408	76.53 423

MVS-HIRNet		61.16 405	62.92 402	55.87 427	79.09 388	35.34 446	71.83 385	57.98 444	46.56 425	59.05 443	91.14 205	49.95 376	76.43 391	38.74 437	71.92 437	55.84 446

EMVS		61.10 406	60.81 408	61.99 417	65.96 450	55.86 360	53.10 444	58.97 442	67.06 285	56.89 448	63.33 444	40.98 418	67.03 428	54.79 377	86.18 366	63.08 440

DSMNet-mixed		60.98 407	61.61 407	59.09 426	72.88 434	45.05 424	74.70 364	46.61 452	26.20 450	65.34 428	90.32 240	55.46 351	63.12 439	41.72 431	81.30 411	69.09 435

dp		60.70 408	60.29 411	61.92 418	72.04 438	38.67 441	70.83 395	64.08 428	51.28 411	60.75 439	77.28 417	36.59 429	71.58 409	47.41 416	62.34 446	75.52 426

dmvs_testset		60.59 409	62.54 404	54.72 429	77.26 398	27.74 452	74.05 369	61.00 438	60.48 352	65.62 427	67.03 442	55.93 348	68.23 424	32.07 448	69.46 443	68.17 436

CHOSEN 280x420		59.08 410	56.52 416	66.76 401	76.51 407	64.39 259	49.62 445	59.00 441	43.86 434	55.66 449	68.41 441	35.55 430	68.21 425	43.25 428	76.78 431	67.69 437

mvsany_test1		58.48 411	56.47 417	64.50 411	65.90 451	68.21 220	56.95 441	42.11 454	38.30 446	65.69 426	77.19 420	56.96 342	59.35 444	46.16 421	58.96 447	65.93 438

UWE-MVS-28		58.44 412	57.71 414	60.65 422	73.58 428	31.23 449	69.68 404	48.80 450	53.12 399	61.79 437	78.83 404	30.98 440	68.40 423	21.58 451	80.99 413	82.33 394

PVSNet_0	51.08 22	56.10 413	54.97 418	59.48 425	75.12 420	53.28 381	55.16 442	61.89 433	44.30 432	59.16 442	62.48 445	54.22 356	65.91 433	35.40 443	47.01 448	59.25 444

new_pmnet		55.69 414	57.66 415	49.76 430	75.47 417	30.59 450	59.56 433	51.45 448	43.62 436	62.49 436	75.48 428	40.96 419	49.15 450	37.39 442	72.52 434	69.55 434

PMMVS2		55.64 415	59.27 413	44.74 431	64.30 453	12.32 459	40.60 446	49.79 449	53.19 397	65.06 432	84.81 344	53.60 359	49.76 449	32.68 447	89.41 317	72.15 430

MVE	40.22 23	51.82 416	50.47 419	55.87 427	62.66 454	51.91 390	31.61 448	39.28 455	40.65 441	50.76 450	74.98 430	56.24 347	44.67 451	33.94 446	64.11 445	71.04 433
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 417	42.65 420	39.67 432	70.86 440	21.11 454	61.01 432	21.42 459	57.36 374	57.97 447	50.06 448	16.40 458	58.73 445	21.03 452	27.69 452	39.17 448

kuosan		30.83 418	32.17 421	26.83 434	53.36 456	19.02 457	57.90 439	20.44 460	38.29 447	38.01 451	37.82 450	15.18 459	33.45 453	7.74 454	20.76 453	28.03 449

test_method		30.46 419	29.60 422	33.06 433	17.99 458	3.84 461	13.62 449	73.92 373	2.79 452	18.29 454	53.41 447	28.53 446	43.25 452	22.56 449	35.27 450	52.11 447

cdsmvs_eth3d_5k		20.81 420	27.75 423	0.00 439	0.00 462	0.00 464	0.00 450	85.44 276	0.00 457	0.00 458	82.82 367	81.46 124	0.00 458	0.00 457	0.00 456	0.00 454

tmp_tt		20.25 421	24.50 424	7.49 436	4.47 459	8.70 460	34.17 447	25.16 457	1.00 454	32.43 453	18.49 451	39.37 422	9.21 455	21.64 450	43.75 449	4.57 451

ab-mvs-re		6.65 422	8.87 425	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	79.80 395	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

pcd_1.5k_mvsjas		6.41 423	8.55 426	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	76.94 175	0.00 458	0.00 457	0.00 456	0.00 454

test123		6.27 424	8.08 427	0.84 437	1.11 461	0.57 462	62.90 427	0.82 461	0.54 455	1.07 457	2.75 456	1.26 460	0.30 456	1.04 455	1.26 455	1.66 452

testmvs		5.91 425	7.65 428	0.72 438	1.20 460	0.37 463	59.14 435	0.67 462	0.49 456	1.11 456	2.76 455	0.94 461	0.24 457	1.02 456	1.47 454	1.55 453

mmdepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

monomultidepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

test_blank		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet_test		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

DCPMVS		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet-low-res		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uncertanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

Regformer		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

WAC-MVS						37.39 442								52.61 391

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	91.55 135	77.99 97		91.01 155					96.05 9	87.45 28	98.17 37	92.40 198

PC_three_1452									58.96 361	90.06 103	91.33 198	80.66 134	93.03 149	75.78 188	95.94 133	92.48 192

No_MVS				88.81 73	91.55 135	77.99 97		91.01 155					96.05 9	87.45 28	98.17 37	92.40 198

test_one_0601					93.85 64	73.27 145		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 462

eth-test					0.00 462

ZD-MVS					92.22 109	80.48 71		91.85 128	71.22 230	90.38 98	92.98 139	86.06 65	96.11 7	81.99 108	96.75 97

RE-MVS-def			92.61 9		94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 37	97.60 71	92.73 176

IU-MVS					94.18 52	72.64 154		90.82 160	56.98 378	89.67 116				85.78 62	97.92 52	93.28 152

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 202	81.12 128	94.68 78	74.48 200	95.35 156	92.29 205

test_241102_TWO								93.71 56	83.77 58	93.49 40	94.27 83	89.27 24	95.84 24	86.03 55	97.82 57	92.04 217

test_241102_ONE					94.18 52	72.65 152		93.69 57	83.62 60	94.11 27	93.78 114	90.28 15	95.50 49

9.14			89.29 63		91.84 125		88.80 94	95.32 13	75.14 166	91.07 83	92.89 144	87.27 48	93.78 116	83.69 86	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 197	70.80 234

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 169

test_0728_SECOND				86.79 108	94.25 50	72.45 162	90.54 53	94.10 40					95.88 18	86.42 45	97.97 49	92.02 218

test0726					94.16 55	72.56 158	90.63 50	93.90 49	83.61 61	93.75 35	94.49 73	89.76 19

GSMVS																83.88 367

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 388				83.88 367

sam_mvs												45.92 393

ambc				82.98 212	90.55 164	64.86 254	88.20 103	89.15 209		89.40 125	93.96 105	71.67 250	91.38 195	78.83 143	96.55 102	92.71 179

MTGPA								91.81 132

test_post1							78.85 306				3.13 453	45.19 403	80.13 375	58.11 356

test_post											3.10 454	45.43 399	77.22 390

patchmatchnet-post											81.71 379	45.93 392	87.01 298

GG-mvs-BLEND				67.16 399	73.36 429	46.54 418	84.15 189	55.04 446		58.64 445	61.95 446	29.93 443	83.87 352	38.71 438	76.92 430	71.07 432

MTMP							90.66 49	33.14 456

gm-plane-assit					75.42 418	44.97 425			52.17 404		72.36 435		87.90 286	54.10 380

test9_res														80.83 118	96.45 108	90.57 264

TEST9					92.34 104	79.70 80	83.94 195	90.32 177	65.41 304	84.49 243	90.97 211	82.03 115	93.63 121

test_8					92.09 113	78.87 88	83.82 200	90.31 179	65.79 295	84.36 247	90.96 213	81.93 117	93.44 134

agg_prior2														79.68 131	96.16 120	90.22 272

agg_prior					91.58 133	77.69 103		90.30 180		84.32 249			93.18 142

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 189	70.81 244	96.14 121	94.16 109

test_prior4						78.97 87	84.59 178

test_prior2							83.37 216		75.43 162	84.58 240	91.57 191	81.92 119		79.54 135	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 174

旧先验2							81.73 259		56.88 379	86.54 200			84.90 338	72.81 231

新几何2							81.72 260

新几何1				82.95 214	93.96 61	78.56 91		80.24 333	55.45 384	83.93 260	91.08 208	71.19 252	88.33 278	65.84 298	93.07 235	81.95 398

旧先验1					91.97 117	71.77 171		81.78 321			91.84 180	73.92 214			93.65 221	83.61 373

无先验							82.81 234	85.62 274	58.09 367				91.41 194	67.95 283		84.48 358

原ACMM2							82.26 252

原ACMM1				84.60 161	92.81 94	74.01 137		91.50 138	62.59 323	82.73 283	90.67 229	76.53 182	94.25 94	69.24 264	95.69 148	85.55 346

test222					93.31 78	76.54 116	79.38 295	77.79 344	52.59 401	82.36 287	90.84 221	66.83 276			91.69 272	81.25 406

testdata2													86.43 313	63.52 320

segment_acmp												81.94 116

testdata				79.54 282	92.87 89	72.34 163		80.14 334	59.91 357	85.47 221	91.75 187	67.96 270	85.24 334	68.57 278	92.18 260	81.06 411

testdata1							79.62 290		73.95 182

test12				86.57 111	90.74 159	72.63 156		90.69 163		82.76 282		79.20 146	94.80 75		95.32 158	92.27 207

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 198

plane_prior5								93.61 60					95.22 59	80.78 119	95.83 141	94.46 93

plane_prior4											92.95 142

plane_prior3						76.85 114			77.79 133	86.55 194

plane_prior2							89.45 83		79.44 108

plane_prior1					92.83 93

plane_prior						76.42 119	87.15 122		75.94 153						95.03 170

n2								0.00 463

nn								0.00 463

door-mid								74.45 370

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 400		91.79 71	94.42 78	61.76 309	92.93 152	79.52 136	93.03 236	93.93 118

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	98.73 7	95.23 66

test11								91.46 139

door								72.57 386

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 169		73.30 196	80.55 318

ACMP_Plane					91.19 147		84.77 169		73.30 196	80.55 318

BP-MVS														77.30 167

HQP4-MVS										80.56 317			94.61 82			93.56 144

HQP3-MVS								92.68 102							94.47 192

HQP2-MVS												72.10 241

NP-MVS					91.95 118	74.55 134					90.17 247

MDTV_nov1_ep13_2view						27.60 453	70.76 396		46.47 426	61.27 438		45.20 402		49.18 407		83.75 372

MDTV_nov1_ep13			68.29 374		78.03 393	43.87 428	74.12 368	72.22 389	52.17 404	67.02 421	85.54 329	45.36 400	80.85 369	55.73 367	84.42 388

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 147

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 180	81.56 82	90.02 105	91.20 204	82.40 103	90.81 215	73.58 219	94.66 187	94.56 89

DeepMVS_CX				24.13 435	32.95 457	29.49 451		21.63 458	12.07 451	37.95 452	45.07 449	30.84 441	19.21 454	17.94 453	33.06 451	23.69 450

High-resolution multi-view benchmark