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 70	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	80.16 145	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 55	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 77	93.16 146	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	92.08 229

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	91.76 240

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	91.76 240

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	93.63 143

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	92.73 186

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 42	97.34 81	92.19 224

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 44	97.60 71	94.18 112

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	79.95 129	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 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	92.60 110	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 169

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 70	83.16 66	91.06 84	94.00 101	88.26 33	95.71 37	87.28 35	98.39 23	92.55 199

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 72	85.07 45	89.99 106	94.03 99	86.57 56	95.80 28	87.35 32	97.62 69	94.20 109

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 137	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 216

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 102	88.22 23	88.53 143	97.64 6	83.45 91	94.55 86	86.02 59	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 78	81.99 76	91.40 76	94.17 92	87.51 46	95.87 20	87.74 21	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 63	94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 66	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 76	81.91 78	90.88 91	94.21 88	87.75 42	95.87 20	87.60 26	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 77	81.99 76	91.47 75	93.96 105	88.35 32	95.56 42	87.74 21	97.74 62	92.85 183

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	94.12 116

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	92.98 179
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 48	97.99 46	93.96 122

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 62	80.97 70	91.49 41	93.48 68	82.82 71	92.60 58	93.97 102	88.19 34	96.29 6	87.61 25	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 92	82.59 72	88.52 144	94.37 82	86.74 54	95.41 53	86.32 49	98.21 34	93.19 164
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 56	80.98 89	91.38 77	93.80 112	87.20 50	95.80 28	87.10 39	97.69 65	93.93 123

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 105	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	93.44 137	78.35 151	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 60	77.65 134	91.97 68	94.89 57	88.38 30	95.45 51	89.27 6	97.87 56	93.27 159

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 77	93.35 71	79.20 112	93.83 32	93.60 122	90.81 8	92.96 153	85.02 73	98.45 19	92.41 206
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	89.03 199	84.23 49	93.58 6	94.68 18	90.65 8	90.33 100	93.95 107	84.50 79	95.37 54	80.87 119	95.50 153	94.53 94

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 97	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 80	97.81 58	91.70 244
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 58	83.77 58	94.11 27	94.27 83	90.28 15	95.84 24	86.03 56	97.92 52	92.29 218

SMA-MVS		90.31 39	90.48 51	89.83 55	95.31 30	79.52 83	90.98 48	93.24 79	75.37 164	92.84 52	95.28 48	85.58 69	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 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 65	97.51 78	94.30 108

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 185	89.99 65	93.05 89	86.53 35	94.29 23	96.27 23	82.69 98	94.08 105	86.25 52	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	89.57 214	88.51 21	90.11 102	95.12 53	90.98 7	88.92 274	77.55 165	97.07 88	83.13 401
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	81.12 133	95.88 18	87.41 30	95.94 133	92.48 202

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 165	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 46	97.97 49	90.55 281
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	84.47 169	96.56 6	58.83 354	89.04 89	92.74 104	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 139	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 168	96.57 5	58.88 351	88.95 90	93.19 81	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 147	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 85	81.10 87	95.32 14	97.24 10	72.94 248	94.85 72	85.07 70	97.78 59	97.26 16

DTE-MVSNet		89.98 48	91.91 18	84.21 178	96.51 7	57.84 362	88.93 91	92.84 100	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 143	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 86	93.10 149	82.67 101	98.04 41	93.64 142

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	88.83 135	94.26 86	77.55 174	95.86 23	84.88 74	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 296	89.54 80	93.31 75	90.21 12	95.57 11	95.66 37	81.42 130	95.90 17	80.94 118	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	92.78 103	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 76	97.55 74	94.10 117
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	90.63 175	70.00 250	94.55 19	96.67 17	87.94 40	93.59 128	84.27 82	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	90.20 196	69.87 251	95.06 15	96.14 28	84.28 82	93.07 150	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	90.08 199	71.54 229	94.28 25	96.54 19	81.57 128	94.27 92	86.26 50	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	86.75 193	93.26 128	93.64 2	90.93 212	84.60 79	90.75 309	93.97 121

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 88	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 107	97.18 86	90.45 283
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	90.29 193	69.27 258	94.39 21	96.38 21	86.02 66	93.52 133	83.96 84	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	92.30 119	79.74 103	87.50 177	92.38 163	81.42 130	93.28 142	83.07 93	97.24 84	91.67 245

ACMH	76.49 14	89.34 60	91.14 36	83.96 185	92.50 99	70.36 194	89.55 78	93.84 53	81.89 79	94.70 17	95.44 44	90.69 9	88.31 291	83.33 89	98.30 27	93.20 163
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	88.67 210	85.64 35	90.61 51	93.17 82	86.02 38	93.12 45	95.30 46	84.94 74	89.44 266	74.12 214	96.10 124	94.45 97

APD_test2		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 82	86.02 38	93.12 45	95.30 46	84.94 74	89.44 266	74.12 214	96.10 124	94.45 97

CP-MVSNet		89.27 63	90.91 45	84.37 170	96.34 8	58.61 357	88.66 98	92.06 126	90.78 7	95.67 8	95.17 51	81.80 125	95.54 44	79.00 144	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 63	80.16 98	89.13 131	93.44 124	83.82 85	90.98 209	83.86 86	95.30 161	93.60 146

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	73.53 189	89.71 114	94.82 60	85.09 73	95.77 34	84.17 83	98.03 43	93.26 161
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	84.31 176	97.00 2	64.33 267	89.67 75	88.38 234	88.84 17	94.29 23	97.57 7	90.48 14	91.26 200	72.57 245	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	92.25 172	72.03 262	96.36 4	88.21 13	90.93 301	92.98 179
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 55	78.90 116	92.88 49	92.29 170	86.11 64	90.22 240	86.24 53	97.24 84	91.36 253
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	91.27 157	78.20 126	86.69 197	92.28 171	80.36 143	95.06 67	86.17 54	96.49 105	90.22 287

Elysia		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 64	84.31 51	90.42 96	94.10 96	74.07 225	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 64	84.31 51	90.42 96	94.10 96	74.07 225	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	84.01 195	89.44 217	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 130	95.21 162	91.82 238

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 96	87.95 26	89.62 118	92.87 146	84.56 78	93.89 113	77.65 163	96.62 100	90.70 273

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 132	84.26 53	90.87 92	93.92 109	82.18 113	89.29 270	73.75 222	94.81 181	93.70 137

Anonymous20231211		88.40 74	89.62 60	84.73 160	90.46 165	65.27 256	88.86 92	93.02 93	87.15 30	93.05 47	97.10 11	82.28 111	92.02 179	76.70 175	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	91.97 129	70.73 241	94.19 26	96.67 17	76.94 187	94.57 84	83.07 93	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 82	76.02 149	88.64 140	91.22 210	84.24 83	93.37 140	77.97 161	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	84.32 262	89.33 276	83.87 84	94.53 87	82.45 103	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	91.19 160	67.85 285	86.63 198	94.84 59	79.58 151	95.96 15	87.62 24	94.50 190	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 80	89.79 56	82.98 216	93.26 80	63.94 271	91.10 46	89.64 211	85.07 45	90.91 88	91.09 215	89.16 25	91.87 184	82.03 108	95.87 139	93.13 166

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	85.12 237	89.67 269	84.47 80	95.46 50	82.56 102	96.26 116	93.77 135

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 161	79.26 111	89.68 115	94.81 63	82.44 102	87.74 302	76.54 180	88.74 345	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	88.00 159	93.03 137	82.66 99	91.47 192	70.81 259	96.14 121	94.16 113

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 268	84.38 187	91.29 153	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 236	98.66 11	97.69 9

nrg030		87.85 85	88.49 79	85.91 128	90.07 175	69.73 202	87.86 111	94.20 31	74.04 181	92.70 57	94.66 64	85.88 67	91.50 191	79.72 132	97.32 82	96.50 34

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 164	91.23 158	77.31 139	87.07 187	91.47 200	82.94 96	94.71 77	84.67 78	96.27 115	92.62 194

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 61	79.44 108	86.55 199	92.95 143	74.84 212	95.22 59	80.78 121	95.83 141	94.46 95

sc_t1		87.70 88	88.94 71	83.99 183	93.47 71	67.15 233	85.05 169	88.21 241	86.81 32	91.87 70	97.65 5	85.51 71	87.91 297	74.22 209	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	86.76 272	84.54 50	83.58 281	93.78 114	73.36 243	96.48 2	87.98 17	96.21 117	94.41 102

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 202	92.03 116	62.26 300	91.49 41	92.62 108	88.07 25	88.07 156	96.17 26	72.24 257	95.79 31	84.85 75	94.16 203	92.58 197

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 163	78.77 119	84.85 249	90.89 226	80.85 136	95.29 56	81.14 116	95.32 158	92.34 214

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 218	92.38 115	70.25 247	89.35 126	90.68 236	82.85 97	94.57 84	79.55 136	95.95 132	92.00 233

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 212	87.84 112	88.05 242	81.66 81	94.64 18	96.53 20	65.94 299	94.75 76	83.02 95	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	84.60 180	89.74 206	74.40 178	89.92 110	93.41 125	80.45 141	90.63 227	86.66 45	94.37 196	94.73 87

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	81.64 322	87.25 321	82.43 103	94.53 87	77.65 163	96.46 107	94.14 115

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 222	83.80 205	92.87 98	80.37 94	89.61 120	91.81 186	77.72 170	94.18 100	75.00 202	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	92.09 125	78.87 117	84.27 267	94.05 98	78.35 162	93.65 121	80.54 125	91.58 287	92.08 229
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	67.02 237	82.55 245	91.56 143	83.08 68	90.92 86	91.82 185	78.25 163	93.99 107	74.16 212	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 237	83.16 228	92.21 120	81.73 80	90.92 86	91.97 178	77.20 181	93.99 107	74.16 212	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 172	87.09 260	65.22 257	84.16 191	94.23 28	77.89 130	91.28 81	93.66 120	84.35 81	92.71 159	80.07 126	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	85.94 298	78.30 92	86.93 125	92.20 121	65.94 305	89.16 129	93.16 132	83.10 94	89.89 255	87.81 20	94.43 194	93.35 154

tt0320-xc		86.67 102	88.41 81	81.44 258	93.45 72	60.44 328	83.96 197	88.50 230	87.26 29	90.90 90	97.90 3	85.61 68	86.40 328	70.14 271	98.01 45	97.47 14

IS-MVSNet		86.66 103	86.82 108	86.17 124	92.05 115	66.87 241	91.21 44	88.64 227	86.30 37	89.60 121	92.59 155	69.22 280	94.91 71	73.89 219	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 259	93.57 69	60.73 325	84.37 188	88.61 229	87.00 31	90.75 93	97.98 2	85.54 70	86.45 326	69.75 276	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 154	88.16 225	63.28 278	86.64 135	92.20 121	75.42 163	92.81 54	94.50 72	74.05 228	94.06 106	83.88 85	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 244	92.22 109	62.28 299	84.66 179	89.15 221	83.54 63	89.85 111	97.32 8	88.08 39	86.80 319	70.43 268	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 64	74.56 173	85.12 237	90.34 249	66.19 296	94.20 97	76.57 178	98.44 20	95.19 68

PHI-MVS		86.38 108	85.81 126	88.08 89	88.44 219	77.34 108	89.35 86	93.05 89	73.15 202	84.76 251	87.70 310	78.87 156	94.18 100	80.67 123	96.29 112	92.73 186

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 133	80.35 95	89.54 124	88.01 297	79.09 154	92.13 175	75.51 195	95.06 169	90.41 284

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 128	70.56 242	84.96 244	90.69 235	80.01 147	95.14 64	78.37 150	95.78 145	91.82 238
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	84.36 172	87.82 233	62.35 298	86.42 139	91.33 152	76.78 143	92.73 56	94.48 74	73.41 240	93.72 119	83.10 92	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 204	90.19 170	64.55 264	84.55 182	90.71 172	85.85 40	89.94 109	95.24 50	82.13 114	90.40 235	69.19 283	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 218	86.91 268	70.38 193	85.31 163	92.61 109	75.59 159	88.32 151	92.87 146	82.22 112	88.63 283	88.80 9	92.82 250	89.83 297

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 314	78.25 93	85.82 151	91.82 135	65.33 320	88.55 142	92.35 169	82.62 101	89.80 257	86.87 41	94.32 198	93.18 165

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 202	92.01 127	65.91 307	86.19 210	91.75 190	83.77 87	94.98 69	77.43 168	96.71 98	93.73 136

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 263	82.21 259	90.46 181	80.99 88	88.42 147	91.97 178	77.56 173	93.85 114	72.46 246	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 198	90.32 188	65.79 309	84.49 256	90.97 220	81.93 120	93.63 123	81.21 115	96.54 103	90.88 267

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 200	83.70 208	89.83 205	80.42 93	87.76 170	93.24 129	73.76 234	91.54 190	85.03 72	93.62 224	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 229	92.25 107	56.44 373	85.75 152	93.09 87	77.33 138	91.94 69	94.65 65	74.78 214	93.41 139	75.11 201	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 323	78.21 94	85.40 161	91.39 150	65.32 321	87.72 172	91.81 186	82.33 106	89.78 258	86.68 43	94.20 201	92.99 177

Effi-MVS+-dtu		85.82 121	83.38 189	93.14 4	87.13 255	91.15 3	87.70 113	88.42 233	74.57 172	83.56 282	85.65 345	78.49 161	94.21 96	72.04 248	92.88 246	94.05 119

TAPA-MVS	77.73 12	85.71 122	84.83 150	88.37 83	88.78 209	79.72 79	87.15 122	93.50 67	69.17 259	85.80 220	89.56 270	80.76 137	92.13 175	73.21 241	95.51 152	93.25 162
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 123	86.14 117	83.58 198	87.97 227	67.13 234	87.55 114	94.32 22	73.44 192	88.47 145	87.54 313	86.45 59	91.06 207	75.76 193	93.76 215	92.54 200

canonicalmvs		85.50 123	86.14 117	83.58 198	87.97 227	67.13 234	87.55 114	94.32 22	73.44 192	88.47 145	87.54 313	86.45 59	91.06 207	75.76 193	93.76 215	92.54 200

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 163	87.10 258	69.98 198	84.28 189	92.68 105	74.77 169	87.90 163	92.36 168	73.94 229	90.41 234	85.95 61	92.74 252	93.66 138

EPP-MVSNet		85.47 126	85.04 145	86.77 109	91.52 138	69.37 207	91.63 40	87.98 245	81.51 83	87.05 188	91.83 184	66.18 298	95.29 56	70.75 262	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 170	90.08 173	67.07 236	85.86 150	91.39 150	72.33 220	87.59 174	90.25 254	84.85 76	92.37 169	78.00 159	91.94 277	93.66 138

MGCNet		85.37 128	84.58 159	87.75 93	85.28 313	73.36 141	86.54 138	85.71 289	77.56 137	81.78 320	92.47 161	70.29 274	96.02 11	85.59 64	95.96 130	93.87 127

FIs		85.35 129	86.27 114	82.60 228	91.86 122	57.31 366	85.10 168	93.05 89	75.83 154	91.02 85	93.97 102	73.57 236	92.91 157	73.97 218	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 156	85.80 301	76.13 125	85.15 167	92.32 118	61.40 356	91.33 78	90.85 229	83.76 88	86.16 334	84.31 81	93.28 233	92.15 227

casdiffmvs		85.21 131	85.85 125	83.31 207	86.17 290	62.77 285	83.03 230	93.93 47	74.69 171	88.21 153	92.68 154	82.29 110	91.89 183	77.87 162	93.75 218	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		85.20 132	85.25 141	85.02 151	86.01 296	71.31 181	84.96 170	91.76 139	69.10 261	88.90 132	92.56 158	73.84 232	90.63 227	86.88 40	93.26 234	93.13 166

baseline		85.20 132	85.93 122	83.02 214	86.30 285	62.37 297	84.55 182	93.96 45	74.48 175	87.12 182	92.03 177	82.30 108	91.94 180	78.39 149	94.21 200	94.74 86

SSM_0404		85.16 134	85.09 143	85.36 142	90.14 172	69.52 205	86.17 144	91.58 141	74.41 176	86.55 199	91.49 197	78.54 157	93.97 109	73.71 223	93.21 238	92.59 196

K. test v3		85.14 135	84.73 152	86.37 115	91.13 151	69.63 204	85.45 159	76.68 374	84.06 56	92.44 61	96.99 13	62.03 326	94.65 80	80.58 124	93.24 235	94.83 83

mmtdpeth		85.13 136	85.78 128	83.17 212	84.65 325	74.71 132	85.87 149	90.35 187	77.94 129	83.82 274	96.96 15	77.75 168	80.03 393	78.44 148	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 137	84.53 162	86.88 106	84.01 338	72.76 151	83.91 201	85.18 299	80.44 92	88.75 137	85.49 349	80.08 146	91.92 181	82.02 109	90.85 306	95.97 44

fmvsm_l_conf0.5_n_3		85.11 138	84.96 147	85.56 137	87.49 246	75.69 128	84.71 177	90.61 177	67.64 289	84.88 247	92.05 176	82.30 108	88.36 289	83.84 87	91.10 294	92.62 194

MGCFI-Net		85.04 139	85.95 121	82.31 237	87.52 244	63.59 274	86.23 143	93.96 45	73.46 190	88.07 156	87.83 308	86.46 58	90.87 217	76.17 187	93.89 211	92.47 204

EI-MVSNet-UG-set		85.04 139	84.44 165	86.85 107	83.87 342	72.52 160	83.82 203	85.15 300	80.27 97	88.75 137	85.45 351	79.95 148	91.90 182	81.92 112	90.80 308	96.13 39

X-MVStestdata		85.04 139	82.70 208	92.08 9	95.64 24	86.25 22	92.64 20	93.33 72	85.07 45	89.99 106	16.05 470	86.57 56	95.80 28	87.35 32	97.62 69	94.20 109

MSLP-MVS++		85.00 142	86.03 120	81.90 244	91.84 125	71.56 179	86.75 133	93.02 93	75.95 152	87.12 182	89.39 274	77.98 165	89.40 269	77.46 166	94.78 182	84.75 373

F-COLMAP		84.97 143	83.42 188	89.63 58	92.39 102	83.40 52	88.83 93	91.92 131	73.19 201	80.18 344	89.15 280	77.04 185	93.28 142	65.82 316	92.28 266	92.21 223

SSM_0407		84.89 144	84.85 149	85.01 152	89.13 195	68.97 215	85.60 156	91.58 141	74.41 176	85.68 221	91.49 197	78.54 157	93.69 120	73.71 223	93.47 226	92.38 211

balanced_conf03		84.80 145	85.40 136	83.00 215	88.95 202	61.44 308	90.42 59	92.37 117	71.48 231	88.72 139	93.13 133	70.16 276	95.15 63	79.26 141	94.11 204	92.41 206

3Dnovator	80.37 7	84.80 145	84.71 155	85.06 149	86.36 283	74.71 132	88.77 95	90.00 201	75.65 157	84.96 244	93.17 131	74.06 227	91.19 202	78.28 153	91.09 295	89.29 307

SymmetryMVS		84.79 147	83.54 183	88.55 79	92.44 101	80.42 72	88.63 99	82.37 334	74.56 173	85.12 237	90.34 249	66.19 296	94.20 97	76.57 178	95.68 149	91.03 261

IterMVS-LS		84.73 148	84.98 146	83.96 185	87.35 248	63.66 272	83.25 223	89.88 204	76.06 147	89.62 118	92.37 166	73.40 242	92.52 164	78.16 156	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		84.63 149	84.34 170	85.49 141	90.18 171	75.86 127	79.23 312	87.13 262	73.35 194	85.56 228	89.34 275	83.60 90	90.50 231	76.64 177	94.05 208	90.09 293

HQP-MVS		84.61 150	84.06 175	86.27 118	91.19 147	70.66 188	84.77 172	92.68 105	73.30 197	80.55 336	90.17 259	72.10 258	94.61 82	77.30 170	94.47 192	93.56 150

v1192		84.57 151	84.69 157	84.21 178	87.75 235	62.88 282	83.02 231	91.43 147	69.08 262	89.98 108	90.89 226	72.70 252	93.62 126	82.41 104	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 152	84.71 155	84.11 181	87.92 230	72.09 168	84.80 171	88.64 227	64.43 330	88.77 136	91.78 188	78.07 164	87.95 296	85.85 62	92.18 270	92.30 216

FMVSNet1		84.55 153	85.45 135	81.85 246	90.27 169	61.05 316	86.83 129	88.27 238	78.57 122	89.66 117	95.64 38	75.43 204	90.68 224	69.09 284	95.33 157	93.82 130

v1144		84.54 154	84.72 154	84.00 182	87.67 239	62.55 289	82.97 233	90.93 168	70.32 246	89.80 112	90.99 219	73.50 237	93.48 135	81.69 114	94.65 188	95.97 44

Gipuma		84.44 155	86.33 113	78.78 304	84.20 335	73.57 140	89.55 78	90.44 182	84.24 54	84.38 259	94.89 57	76.35 200	80.40 390	76.14 188	96.80 96	82.36 411
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 156	84.27 171	84.74 159	87.25 251	70.84 187	83.55 213	88.45 232	68.64 271	86.29 209	91.31 206	74.97 210	88.42 287	87.87 19	90.07 324	94.95 75

MCST-MVS		84.36 157	83.93 179	85.63 135	91.59 130	71.58 177	83.52 214	92.13 123	61.82 349	83.96 272	89.75 267	79.93 149	93.46 136	78.33 152	94.34 197	91.87 237

VDDNet		84.35 158	85.39 137	81.25 261	95.13 32	59.32 342	85.42 160	81.11 345	86.41 36	87.41 178	96.21 25	73.61 235	90.61 229	66.33 309	96.85 92	93.81 133

ETV-MVS		84.31 159	83.91 180	85.52 138	88.58 215	70.40 192	84.50 186	93.37 69	78.76 120	84.07 270	78.72 424	80.39 142	95.13 65	73.82 221	92.98 244	91.04 260

v1240		84.30 160	84.51 163	83.65 195	87.65 240	61.26 312	82.85 237	91.54 144	67.94 282	90.68 95	90.65 240	71.71 266	93.64 122	82.84 98	94.78 182	96.07 41

MVS_111021_LR		84.28 161	83.76 181	85.83 132	89.23 193	83.07 55	80.99 280	83.56 322	72.71 212	86.07 213	89.07 282	81.75 127	86.19 333	77.11 172	93.36 229	88.24 326

h-mvs33		84.25 162	82.76 207	88.72 75	91.82 127	82.60 60	84.00 196	84.98 306	71.27 232	86.70 195	90.55 245	63.04 323	93.92 112	78.26 154	94.20 201	89.63 299

v144192		84.24 163	84.41 166	83.71 194	87.59 242	61.57 307	82.95 234	91.03 164	67.82 286	89.80 112	90.49 246	73.28 244	93.51 134	81.88 113	94.89 177	96.04 43

dcpmvs_2		84.23 164	85.14 142	81.50 256	88.61 214	61.98 304	82.90 236	93.11 85	68.66 270	92.77 55	92.39 162	78.50 160	87.63 305	76.99 174	92.30 263	94.90 76

v1921920		84.23 164	84.37 168	83.79 190	87.64 241	61.71 306	82.91 235	91.20 159	67.94 282	90.06 103	90.34 249	72.04 261	93.59 128	82.32 105	94.91 175	96.07 41

VDD-MVS		84.23 164	84.58 159	83.20 210	91.17 150	65.16 259	83.25 223	84.97 307	79.79 102	87.18 181	94.27 83	74.77 215	90.89 215	69.24 280	96.54 103	93.55 152

v2v482		84.09 167	84.24 172	83.62 196	87.13 255	61.40 309	82.71 240	89.71 209	72.19 223	89.55 122	91.41 201	70.70 272	93.20 144	81.02 117	93.76 215	96.25 37

EG-PatchMatch MVS		84.08 168	84.11 174	83.98 184	92.22 109	72.61 157	82.20 261	87.02 268	72.63 213	88.86 133	91.02 218	78.52 159	91.11 205	73.41 231	91.09 295	88.21 327

fmvsm_s_conf0.5_n_6		84.05 169	84.14 173	83.81 188	87.75 235	71.17 183	83.42 217	91.10 162	67.90 284	84.53 254	90.70 234	73.01 247	88.73 280	85.09 69	93.72 220	91.53 250

DP-MVS Recon		84.05 169	83.22 192	86.52 113	91.73 128	75.27 130	83.23 225	92.40 113	72.04 225	82.04 311	88.33 293	77.91 167	93.95 111	66.17 310	95.12 167	90.34 286

viewmacassd2359aftdt		84.04 171	84.78 151	81.81 249	86.43 277	60.32 330	81.95 263	92.82 101	71.56 228	86.06 214	92.98 139	81.79 126	90.28 236	76.18 186	93.24 235	94.82 84

TransMVSNet (Re)		84.02 172	85.74 130	78.85 303	91.00 154	55.20 385	82.29 255	87.26 257	79.65 105	88.38 149	95.52 41	83.00 95	86.88 317	67.97 298	96.60 101	94.45 97

Baseline_NR-MVSNet		84.00 173	85.90 123	78.29 315	91.47 140	53.44 397	82.29 255	87.00 271	79.06 114	89.55 122	95.72 36	77.20 181	86.14 335	72.30 247	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 174	84.46 164	82.53 232	86.11 293	70.65 190	82.45 250	89.17 220	67.72 288	86.74 194	91.49 197	79.20 152	85.86 344	84.71 77	92.60 256	91.07 259

TSAR-MVS + GP.		83.95 175	82.69 209	87.72 94	89.27 192	81.45 67	83.72 207	81.58 343	74.73 170	85.66 224	86.06 340	72.56 254	92.69 161	75.44 197	95.21 162	89.01 320

LuminaMVS		83.94 176	83.51 184	85.23 144	89.78 181	71.74 172	84.76 175	87.27 256	72.60 214	89.31 127	90.60 244	64.04 312	90.95 210	79.08 142	94.11 204	92.99 177

alignmvs		83.94 176	83.98 177	83.80 189	87.80 234	67.88 229	84.54 184	91.42 149	73.27 200	88.41 148	87.96 298	72.33 255	90.83 218	76.02 190	94.11 204	92.69 190

Effi-MVS+		83.90 178	84.01 176	83.57 200	87.22 253	65.61 255	86.55 137	92.40 113	78.64 121	81.34 327	84.18 370	83.65 89	92.93 155	74.22 209	87.87 359	92.17 226

fmvsm_s_conf0.1_n_2		83.82 179	83.49 185	84.84 154	85.99 297	70.19 196	80.93 281	87.58 252	67.26 295	87.94 162	92.37 166	71.40 268	88.01 293	86.03 56	91.87 278	96.31 36

mvs5depth		83.82 179	84.54 161	81.68 252	82.23 367	68.65 220	86.89 126	89.90 203	80.02 101	87.74 171	97.86 4	64.19 311	82.02 378	76.37 182	95.63 151	94.35 104

CANet		83.79 181	82.85 206	86.63 110	86.17 290	72.21 167	83.76 206	91.43 147	77.24 140	74.39 399	87.45 317	75.36 205	95.42 52	77.03 173	92.83 249	92.25 222

pm-mvs1		83.69 182	84.95 148	79.91 289	90.04 177	59.66 339	82.43 251	87.44 253	75.52 161	87.85 166	95.26 49	81.25 132	85.65 348	68.74 290	96.04 126	94.42 101

AdaColmap		83.66 183	83.69 182	83.57 200	90.05 176	72.26 165	86.29 141	90.00 201	78.19 127	81.65 321	87.16 323	83.40 92	94.24 95	61.69 352	94.76 185	84.21 383

viewdifsd2359ckpt09		83.64 184	83.18 195	85.03 150	87.26 250	66.99 239	85.32 162	93.83 54	65.57 315	84.99 243	89.40 273	77.30 177	93.57 131	71.16 258	93.80 214	94.54 93

MIMVSNet1		83.63 185	84.59 158	80.74 272	94.06 59	62.77 285	82.72 239	84.53 314	77.57 136	90.34 99	95.92 31	76.88 193	85.83 345	61.88 350	97.42 79	93.62 144

fmvsm_s_conf0.5_n_2		83.62 186	83.29 191	84.62 164	85.43 311	70.18 197	80.61 287	87.24 258	67.14 296	87.79 168	91.87 180	71.79 265	87.98 295	86.00 60	91.77 281	95.71 50

test_fmvsm_n_1920		83.60 187	82.89 203	85.74 133	85.22 315	77.74 102	84.12 193	90.48 179	59.87 376	86.45 208	91.12 214	75.65 202	85.89 342	82.28 106	90.87 304	93.58 148

WR-MVS		83.56 188	84.40 167	81.06 266	93.43 75	54.88 386	78.67 321	85.02 304	81.24 85	90.74 94	91.56 195	72.85 249	91.08 206	68.00 297	98.04 41	97.23 17

CNLPA		83.55 189	83.10 198	84.90 153	89.34 190	83.87 50	84.54 184	88.77 224	79.09 113	83.54 283	88.66 290	74.87 211	81.73 380	66.84 304	92.29 265	89.11 313

viewcassd2359sk11		83.53 190	83.96 178	82.25 238	86.97 267	61.13 314	80.80 285	93.22 80	70.97 238	85.36 232	91.08 216	81.84 124	91.29 199	74.79 204	90.58 320	94.33 106

LCM-MVSNet-Re		83.48 191	85.06 144	78.75 305	85.94 298	55.75 379	80.05 293	94.27 25	76.47 144	96.09 6	94.54 71	83.31 93	89.75 261	59.95 363	94.89 177	90.75 270

hse-mvs2		83.47 192	81.81 224	88.47 80	91.03 153	82.27 61	82.61 241	83.69 320	71.27 232	86.70 195	86.05 341	63.04 323	92.41 167	78.26 154	93.62 224	90.71 272

V42		83.47 192	83.37 190	83.75 192	83.16 361	63.33 277	81.31 274	90.23 195	69.51 255	90.91 88	90.81 231	74.16 224	92.29 173	80.06 127	90.22 322	95.62 54

VPA-MVSNet		83.47 192	84.73 152	79.69 294	90.29 168	57.52 365	81.30 276	88.69 226	76.29 145	87.58 176	94.44 75	80.60 140	87.20 311	66.60 307	96.82 95	94.34 105

mamba_0408		83.44 195	82.88 204	85.11 147	89.13 195	68.97 215	72.73 396	91.28 154	72.90 206	85.68 221	90.61 242	76.78 194	93.97 109	73.37 233	93.47 226	92.38 211

viewdifsd2359ckpt07		83.41 196	84.35 169	80.56 278	85.84 300	58.93 350	79.47 304	91.28 154	73.01 205	87.59 174	92.07 175	85.24 72	88.68 281	73.59 228	91.11 293	94.09 118

PAPM_NR		83.23 197	83.19 194	83.33 206	90.90 156	65.98 251	88.19 104	90.78 171	78.13 128	80.87 332	87.92 302	73.49 239	92.42 166	70.07 272	88.40 348	91.60 247

CLD-MVS		83.18 198	82.64 210	84.79 157	89.05 198	67.82 230	77.93 331	92.52 111	68.33 274	85.07 240	81.54 399	82.06 117	92.96 153	69.35 279	97.91 54	93.57 149
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 199	85.68 131	75.65 353	81.24 379	45.26 441	79.94 295	92.91 97	83.83 57	91.33 78	96.88 16	80.25 144	85.92 338	68.89 287	95.89 138	95.76 48

FA-MVS(test-final)		83.13 200	83.02 199	83.43 203	86.16 292	66.08 250	88.00 108	88.36 235	75.55 160	85.02 241	92.75 152	65.12 305	92.50 165	74.94 203	91.30 291	91.72 242

114514_t		83.10 201	82.54 213	84.77 158	92.90 88	69.10 214	86.65 134	90.62 176	54.66 408	81.46 324	90.81 231	76.98 186	94.38 90	72.62 244	96.18 119	90.82 269

RRT-MVS		82.97 202	83.44 186	81.57 254	85.06 318	58.04 360	87.20 119	90.37 185	77.88 131	88.59 141	93.70 119	63.17 320	93.05 151	76.49 181	88.47 347	93.62 144

viewmanbaseed2359cas		82.95 203	83.43 187	81.52 255	85.18 316	60.03 335	81.36 273	92.38 115	69.55 254	84.84 250	91.38 202	79.85 150	90.09 249	74.22 209	92.09 272	94.43 100

BP-MVS1		82.81 204	81.67 226	86.23 119	87.88 232	68.53 221	86.06 146	84.36 315	75.65 157	85.14 236	90.19 256	45.84 412	94.42 89	85.18 68	94.72 186	95.75 49

UGNet		82.78 205	81.64 227	86.21 122	86.20 289	76.24 123	86.86 127	85.68 290	77.07 141	73.76 403	92.82 148	69.64 277	91.82 186	69.04 286	93.69 221	90.56 280
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		82.75 206	81.93 222	85.19 145	82.08 368	80.15 76	85.53 157	88.76 225	68.01 279	85.58 227	87.75 309	71.80 264	86.85 318	74.02 217	93.87 212	88.58 323

EI-MVSNet		82.61 207	82.42 215	83.20 210	83.25 358	63.66 272	83.50 215	85.07 301	76.06 147	86.55 199	85.10 357	73.41 240	90.25 237	78.15 158	90.67 315	95.68 52

QAPM		82.59 208	82.59 212	82.58 229	86.44 276	66.69 242	89.94 68	90.36 186	67.97 281	84.94 246	92.58 157	72.71 251	92.18 174	70.63 265	87.73 362	88.85 321

fmvsm_s_conf0.1_n_a		82.58 209	81.93 222	84.50 167	87.68 238	73.35 142	86.14 145	77.70 363	61.64 354	85.02 241	91.62 192	77.75 168	86.24 330	82.79 99	87.07 370	93.91 125

Fast-Effi-MVS+-dtu		82.54 210	81.41 236	85.90 129	85.60 306	76.53 118	83.07 229	89.62 213	73.02 204	79.11 354	83.51 375	80.74 138	90.24 239	68.76 289	89.29 335	90.94 264

MVS_Test		82.47 211	83.22 192	80.22 285	82.62 366	57.75 364	82.54 246	91.96 130	71.16 236	82.89 294	92.52 160	77.41 175	90.50 231	80.04 128	87.84 361	92.40 208

viewdifsd2359ckpt11		82.46 212	82.98 201	80.88 269	83.53 345	61.00 319	79.46 305	85.97 285	69.48 256	87.89 164	91.31 206	82.10 115	88.61 284	74.28 207	92.86 247	93.02 173

viewmsd2359difaftdt		82.46 212	82.99 200	80.88 269	83.52 346	61.00 319	79.46 305	85.97 285	69.48 256	87.89 164	91.31 206	82.10 115	88.61 284	74.28 207	92.86 247	93.02 173

v148		82.31 214	82.48 214	81.81 249	85.59 307	59.66 339	81.47 271	86.02 283	72.85 208	88.05 158	90.65 240	70.73 271	90.91 214	75.15 200	91.79 279	94.87 78

API-MVS		82.28 215	82.61 211	81.30 260	86.29 286	69.79 199	88.71 96	87.67 251	78.42 124	82.15 307	84.15 371	77.98 165	91.59 189	65.39 319	92.75 251	82.51 410

MVSFormer		82.23 216	81.57 232	84.19 180	85.54 308	69.26 209	91.98 35	90.08 199	71.54 229	76.23 379	85.07 360	58.69 348	94.27 92	86.26 50	88.77 343	89.03 318

viewdifsd2359ckpt13		82.22 217	81.98 221	82.95 218	85.48 310	64.44 265	83.17 227	92.11 124	65.97 304	83.72 277	89.73 268	77.60 172	90.80 220	70.61 266	89.42 333	93.59 147

fmvsm_s_conf0.5_n_a		82.21 218	81.51 235	84.32 175	86.56 273	73.35 142	85.46 158	77.30 367	61.81 350	84.51 255	90.88 228	77.36 176	86.21 332	82.72 100	86.97 375	93.38 153

EIA-MVS		82.19 219	81.23 243	85.10 148	87.95 229	69.17 213	83.22 226	93.33 72	70.42 243	78.58 359	79.77 415	77.29 178	94.20 97	71.51 254	88.96 341	91.93 236

GDP-MVS		82.17 220	80.85 251	86.15 126	88.65 212	68.95 218	85.65 155	93.02 93	68.42 272	83.73 276	89.54 271	45.07 423	94.31 91	79.66 134	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 220	81.59 230	83.94 187	86.87 271	71.57 178	85.19 166	77.42 366	62.27 348	84.47 258	91.33 204	76.43 197	85.91 340	83.14 90	87.14 368	94.33 106

PCF-MVS	74.62 15	82.15 222	80.92 249	85.84 131	89.43 188	72.30 164	80.53 288	91.82 135	57.36 392	87.81 167	89.92 264	77.67 171	93.63 123	58.69 368	95.08 168	91.58 248
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 223	80.31 258	87.45 97	90.86 158	80.29 75	85.88 148	90.65 174	68.17 277	76.32 378	86.33 335	73.12 246	92.61 163	61.40 355	90.02 326	89.44 302
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 224	81.54 234	83.60 197	83.94 339	73.90 138	83.35 220	86.10 279	58.97 378	83.80 275	90.36 248	74.23 222	86.94 316	82.90 96	90.22 322	89.94 295

fmvsm_s_conf0.5_n_7		82.04 225	82.05 219	82.01 242	86.98 266	71.07 184	78.70 319	89.45 216	68.07 278	78.14 361	91.61 193	74.19 223	85.92 338	79.61 135	91.73 282	89.05 317

GBi-Net		82.02 226	82.07 217	81.85 246	86.38 280	61.05 316	86.83 129	88.27 238	72.43 215	86.00 215	95.64 38	63.78 316	90.68 224	65.95 312	93.34 230	93.82 130

test1		82.02 226	82.07 217	81.85 246	86.38 280	61.05 316	86.83 129	88.27 238	72.43 215	86.00 215	95.64 38	63.78 316	90.68 224	65.95 312	93.34 230	93.82 130

OpenMVS	76.72 13	81.98 228	82.00 220	81.93 243	84.42 330	68.22 224	88.50 102	89.48 215	66.92 299	81.80 318	91.86 181	72.59 253	90.16 243	71.19 257	91.25 292	87.40 343

KD-MVS_self_test		81.93 229	83.14 197	78.30 314	84.75 324	52.75 401	80.37 290	89.42 218	70.24 248	90.26 101	93.39 126	74.55 221	86.77 320	68.61 292	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 230	81.30 240	83.75 192	86.02 295	71.56 179	84.73 176	77.11 370	62.44 345	84.00 271	90.68 236	76.42 198	85.89 342	83.14 90	87.11 369	93.81 133

SDMVSNet		81.90 231	83.17 196	78.10 318	88.81 207	62.45 295	76.08 365	86.05 282	73.67 186	83.41 284	93.04 135	82.35 105	80.65 387	70.06 273	95.03 170	91.21 255

tfpnnormal		81.79 232	82.95 202	78.31 313	88.93 203	55.40 381	80.83 284	82.85 329	76.81 142	85.90 219	94.14 93	74.58 219	86.51 324	66.82 305	95.68 149	93.01 176

AstraMVS		81.67 233	81.40 237	82.48 234	87.06 263	66.47 245	81.41 272	81.68 340	68.78 267	88.00 159	90.95 224	65.70 301	87.86 301	76.66 176	92.38 260	93.12 169

c3_l		81.64 234	81.59 230	81.79 251	80.86 385	59.15 347	78.61 322	90.18 197	68.36 273	87.20 180	87.11 325	69.39 278	91.62 188	78.16 156	94.43 194	94.60 89

guyue		81.57 235	81.37 239	82.15 239	86.39 278	66.13 249	81.54 270	83.21 324	69.79 252	87.77 169	89.95 262	65.36 304	87.64 304	75.88 191	92.49 258	92.67 191

PVSNet_Blended_VisFu		81.55 236	80.49 256	84.70 162	91.58 133	73.24 146	84.21 190	91.67 140	62.86 339	80.94 330	87.16 323	67.27 290	92.87 158	69.82 275	88.94 342	87.99 333

fmvsm_l_conf0.5_n_a		81.46 237	80.87 250	83.25 208	83.73 344	73.21 147	83.00 232	85.59 292	58.22 384	82.96 293	90.09 261	72.30 256	86.65 322	81.97 111	89.95 327	89.88 296

SSM_04072		81.44 238	82.88 204	77.10 333	89.13 195	68.97 215	72.73 396	91.28 154	72.90 206	85.68 221	90.61 242	76.78 194	69.94 430	73.37 233	93.47 226	92.38 211

DELS-MVS		81.44 238	81.25 241	82.03 241	84.27 334	62.87 283	76.47 359	92.49 112	70.97 238	81.64 322	83.83 372	75.03 208	92.70 160	74.29 206	92.22 269	90.51 282
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		81.31 240	81.61 229	80.41 281	86.38 280	58.75 355	83.93 200	86.58 274	72.43 215	87.65 173	92.98 139	63.78 316	90.22 240	66.86 302	93.92 210	92.27 220

TinyColmap		81.25 241	82.34 216	77.99 321	85.33 312	60.68 326	82.32 254	88.33 236	71.26 234	86.97 189	92.22 174	77.10 184	86.98 315	62.37 344	95.17 164	86.31 356

diffmvs_AUTHOR		81.24 242	81.55 233	80.30 283	80.61 390	60.22 331	77.98 330	90.48 179	67.77 287	83.34 286	89.50 272	74.69 217	87.42 307	78.78 146	90.81 307	93.27 159

AUN-MVS		81.18 243	78.78 281	88.39 82	90.93 155	82.14 62	82.51 247	83.67 321	64.69 329	80.29 340	85.91 344	51.07 387	92.38 168	76.29 185	93.63 223	90.65 277

IMVS_0407		81.08 244	81.23 243	80.62 277	85.76 302	62.46 291	82.46 248	87.91 246	65.23 322	82.12 308	87.92 302	77.27 179	90.18 242	71.67 250	90.74 310	89.20 308

tttt0517		81.07 245	79.58 271	85.52 138	88.99 201	66.45 246	87.03 124	75.51 382	73.76 185	88.32 151	90.20 255	37.96 444	94.16 104	79.36 140	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 246	80.57 253	82.46 235	87.50 245	63.22 279	78.37 325	89.63 212	68.01 279	81.87 314	82.08 393	82.31 107	92.65 162	67.10 301	88.30 354	91.51 251

BH-untuned		80.96 247	80.99 247	80.84 271	88.55 216	68.23 223	80.33 291	88.46 231	72.79 211	86.55 199	86.76 329	74.72 216	91.77 187	61.79 351	88.99 340	82.52 409

IMVS_0403		80.93 248	81.00 246	80.72 274	85.76 302	62.46 291	81.82 264	87.91 246	65.23 322	82.07 310	87.92 302	75.91 201	90.50 231	71.67 250	90.74 310	89.20 308

eth_miper_zixun_eth		80.84 249	80.22 262	82.71 226	81.41 377	60.98 321	77.81 333	90.14 198	67.31 294	86.95 190	87.24 322	64.26 309	92.31 171	75.23 199	91.61 285	94.85 82

xiu_mvs_v1_base_debu		80.84 249	80.14 264	82.93 221	88.31 220	71.73 173	79.53 300	87.17 259	65.43 316	79.59 346	82.73 387	76.94 187	90.14 246	73.22 236	88.33 350	86.90 350

xiu_mvs_v1_base		80.84 249	80.14 264	82.93 221	88.31 220	71.73 173	79.53 300	87.17 259	65.43 316	79.59 346	82.73 387	76.94 187	90.14 246	73.22 236	88.33 350	86.90 350

xiu_mvs_v1_base_debi		80.84 249	80.14 264	82.93 221	88.31 220	71.73 173	79.53 300	87.17 259	65.43 316	79.59 346	82.73 387	76.94 187	90.14 246	73.22 236	88.33 350	86.90 350

IterMVS-SCA-FT		80.64 253	79.41 272	84.34 174	83.93 340	69.66 203	76.28 361	81.09 346	72.43 215	86.47 206	90.19 256	60.46 333	93.15 147	77.45 167	86.39 381	90.22 287

BH-RMVSNet		80.53 254	80.22 262	81.49 257	87.19 254	66.21 248	77.79 334	86.23 277	74.21 180	83.69 278	88.50 291	73.25 245	90.75 221	63.18 340	87.90 358	87.52 341

VortexMVS		80.51 255	80.63 252	80.15 287	83.36 354	61.82 305	80.63 286	88.00 244	67.11 297	87.23 179	89.10 281	63.98 313	88.00 294	73.63 227	92.63 255	90.64 278

Anonymous202405211		80.51 255	81.19 245	78.49 310	88.48 217	57.26 367	76.63 354	82.49 332	81.21 86	84.30 265	92.24 173	67.99 286	86.24 330	62.22 345	95.13 165	91.98 235

DIV-MVS_self_test		80.43 257	80.23 260	81.02 267	79.99 395	59.25 344	77.07 347	87.02 268	67.38 291	86.19 210	89.22 277	63.09 321	90.16 243	76.32 183	95.80 143	93.66 138

cl____		80.42 258	80.23 260	81.02 267	79.99 395	59.25 344	77.07 347	87.02 268	67.37 292	86.18 212	89.21 278	63.08 322	90.16 243	76.31 184	95.80 143	93.65 141

diffmvs		80.40 259	80.48 257	80.17 286	79.02 408	60.04 333	77.54 338	90.28 194	66.65 302	82.40 301	87.33 320	73.50 237	87.35 309	77.98 160	89.62 331	93.13 166
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		80.37 260	78.41 289	86.23 119	76.75 422	73.28 144	87.18 121	77.45 365	76.24 146	68.14 433	88.93 284	65.41 303	93.85 114	69.47 278	96.12 123	91.55 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 261	80.04 267	81.24 263	79.82 398	58.95 349	77.66 335	89.66 210	65.75 312	85.99 218	85.11 356	68.29 285	91.42 196	76.03 189	92.03 273	93.33 155

MG-MVS		80.32 262	80.94 248	78.47 311	88.18 223	52.62 404	82.29 255	85.01 305	72.01 226	79.24 353	92.54 159	69.36 279	93.36 141	70.65 264	89.19 338	89.45 301

mvsmamba		80.30 263	78.87 278	84.58 166	88.12 226	67.55 231	92.35 30	84.88 308	63.15 337	85.33 233	90.91 225	50.71 389	95.20 62	66.36 308	87.98 357	90.99 262

VPNet		80.25 264	81.68 225	75.94 349	92.46 100	47.98 428	76.70 352	81.67 341	73.45 191	84.87 248	92.82 148	74.66 218	86.51 324	61.66 353	96.85 92	93.33 155

MAR-MVS		80.24 265	78.74 283	84.73 160	86.87 271	78.18 95	85.75 152	87.81 250	65.67 314	77.84 365	78.50 425	73.79 233	90.53 230	61.59 354	90.87 304	85.49 366
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		80.20 266	79.00 277	83.78 191	88.17 224	86.66 19	81.31 274	66.81 438	69.64 253	88.33 150	90.19 256	64.58 306	83.63 369	71.99 249	90.03 325	81.06 429

Anonymous20240521		80.18 267	81.25 241	76.95 335	83.15 362	60.84 323	82.46 248	85.99 284	68.76 268	86.78 191	93.73 118	59.13 345	77.44 404	73.71 223	97.55 74	92.56 198

LFMVS		80.15 268	80.56 254	78.89 302	89.19 194	55.93 375	85.22 165	73.78 394	82.96 69	84.28 266	92.72 153	57.38 357	90.07 251	63.80 334	95.75 146	90.68 274

DPM-MVS		80.10 269	79.18 276	82.88 224	90.71 161	69.74 201	78.87 317	90.84 169	60.29 372	75.64 388	85.92 343	67.28 289	93.11 148	71.24 256	91.79 279	85.77 362

MSDG		80.06 270	79.99 269	80.25 284	83.91 341	68.04 228	77.51 339	89.19 219	77.65 134	81.94 312	83.45 377	76.37 199	86.31 329	63.31 339	86.59 378	86.41 354

FE-MVS		79.98 271	78.86 279	83.36 205	86.47 275	66.45 246	89.73 71	84.74 312	72.80 210	84.22 269	91.38 202	44.95 424	93.60 127	63.93 332	91.50 288	90.04 294

sd_testset		79.95 272	81.39 238	75.64 354	88.81 207	58.07 359	76.16 364	82.81 330	73.67 186	83.41 284	93.04 135	80.96 135	77.65 403	58.62 369	95.03 170	91.21 255

ab-mvs		79.67 273	80.56 254	76.99 334	88.48 217	56.93 369	84.70 178	86.06 281	68.95 265	80.78 333	93.08 134	75.30 206	84.62 356	56.78 378	90.90 302	89.43 303

VNet		79.31 274	80.27 259	76.44 343	87.92 230	53.95 393	75.58 371	84.35 316	74.39 179	82.23 305	90.72 233	72.84 250	84.39 361	60.38 361	93.98 209	90.97 263

thisisatest0530		79.07 275	77.33 299	84.26 177	87.13 255	64.58 262	83.66 210	75.95 377	68.86 266	85.22 235	87.36 319	38.10 441	93.57 131	75.47 196	94.28 199	94.62 88

cl22		78.97 276	78.21 291	81.24 263	77.74 412	59.01 348	77.46 342	87.13 262	65.79 309	84.32 262	85.10 357	58.96 347	90.88 216	75.36 198	92.03 273	93.84 128

patch_mono-2		78.89 277	79.39 273	77.41 330	84.78 322	68.11 226	75.60 369	83.11 326	60.96 364	79.36 350	89.89 265	75.18 207	72.97 419	73.32 235	92.30 263	91.15 257

RPMNet		78.88 278	78.28 290	80.68 276	79.58 399	62.64 287	82.58 243	94.16 33	74.80 168	75.72 386	92.59 155	48.69 396	95.56 42	73.48 230	82.91 417	83.85 388

PAPR		78.84 279	78.10 292	81.07 265	85.17 317	60.22 331	82.21 259	90.57 178	62.51 341	75.32 392	84.61 365	74.99 209	92.30 172	59.48 366	88.04 356	90.68 274

viewmambaseed2359dif		78.80 280	78.47 288	79.78 290	80.26 394	59.28 343	77.31 344	87.13 262	60.42 370	82.37 302	88.67 289	74.58 219	87.87 300	67.78 300	87.73 362	92.19 224

PVSNet_BlendedMVS		78.80 280	77.84 293	81.65 253	84.43 328	63.41 275	79.49 303	90.44 182	61.70 353	75.43 389	87.07 326	69.11 281	91.44 194	60.68 359	92.24 267	90.11 292

FMVSNet3		78.80 280	78.55 285	79.57 296	82.89 365	56.89 371	81.76 265	85.77 288	69.04 263	86.00 215	90.44 247	51.75 385	90.09 249	65.95 312	93.34 230	91.72 242

test_yl		78.71 283	78.51 286	79.32 299	84.32 332	58.84 352	78.38 323	85.33 296	75.99 150	82.49 299	86.57 331	58.01 351	90.02 253	62.74 341	92.73 253	89.10 314

DCV-MVSNet		78.71 283	78.51 286	79.32 299	84.32 332	58.84 352	78.38 323	85.33 296	75.99 150	82.49 299	86.57 331	58.01 351	90.02 253	62.74 341	92.73 253	89.10 314

test1111		78.53 285	78.85 280	77.56 327	92.22 109	47.49 430	82.61 241	69.24 426	72.43 215	85.28 234	94.20 89	51.91 383	90.07 251	65.36 320	96.45 108	95.11 72

FE-MVSNET		78.46 286	79.36 274	75.75 351	86.53 274	54.53 388	78.03 327	85.35 295	69.01 264	85.41 231	90.68 236	64.27 308	85.73 346	62.59 343	92.35 262	87.00 349

icg_test_0407_2		78.46 286	79.68 270	74.78 361	85.76 302	62.46 291	68.51 425	87.91 246	65.23 322	82.12 308	87.92 302	77.27 179	72.67 420	71.67 250	90.74 310	89.20 308

ECVR-MVS		78.44 288	78.63 284	77.88 323	91.85 123	48.95 424	83.68 209	69.91 422	72.30 221	84.26 268	94.20 89	51.89 384	89.82 256	63.58 335	96.02 127	94.87 78

pmmvs-eth3d		78.42 289	77.04 302	82.57 231	87.44 247	74.41 135	80.86 283	79.67 354	55.68 401	84.69 252	90.31 253	60.91 331	85.42 349	62.20 346	91.59 286	87.88 337

mvs_anonymous		78.13 290	78.76 282	76.23 348	79.24 405	50.31 420	78.69 320	84.82 310	61.60 355	83.09 292	92.82 148	73.89 231	87.01 312	68.33 296	86.41 380	91.37 252

TAMVS		78.08 291	76.36 309	83.23 209	90.62 162	72.87 150	79.08 313	80.01 353	61.72 352	81.35 326	86.92 328	63.96 315	88.78 278	50.61 417	93.01 243	88.04 332

miper_enhance_ethall		77.83 292	76.93 303	80.51 279	76.15 429	58.01 361	75.47 373	88.82 223	58.05 386	83.59 280	80.69 403	64.41 307	91.20 201	73.16 242	92.03 273	92.33 215

Vis-MVSNet (Re-imp)		77.82 293	77.79 294	77.92 322	88.82 206	51.29 414	83.28 221	71.97 410	74.04 181	82.23 305	89.78 266	57.38 357	89.41 268	57.22 377	95.41 154	93.05 172

CANet_DTU		77.81 294	77.05 301	80.09 288	81.37 378	59.90 337	83.26 222	88.29 237	69.16 260	67.83 436	83.72 373	60.93 330	89.47 263	69.22 282	89.70 330	90.88 267

OpenMVS_ROB	70.19 17	77.77 295	77.46 296	78.71 306	84.39 331	61.15 313	81.18 278	82.52 331	62.45 344	83.34 286	87.37 318	66.20 295	88.66 282	64.69 327	85.02 397	86.32 355

SSC-MVS		77.55 296	81.64 227	65.29 427	90.46 165	20.33 474	73.56 389	68.28 428	85.44 41	88.18 155	94.64 68	70.93 270	81.33 382	71.25 255	92.03 273	94.20 109

MDA-MVSNet-bldmvs		77.47 297	76.90 304	79.16 301	79.03 407	64.59 261	66.58 437	75.67 380	73.15 202	88.86 133	88.99 283	66.94 291	81.23 383	64.71 326	88.22 355	91.64 246

jason		77.42 298	75.75 315	82.43 236	87.10 258	69.27 208	77.99 329	81.94 338	51.47 428	77.84 365	85.07 360	60.32 335	89.00 272	70.74 263	89.27 337	89.03 318
jason: jason.
CDS-MVSNet		77.32 299	75.40 319	83.06 213	89.00 200	72.48 161	77.90 332	82.17 336	60.81 365	78.94 356	83.49 376	59.30 343	88.76 279	54.64 397	92.37 261	87.93 336
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 300	77.75 295	75.73 352	85.76 302	62.46 291	70.84 411	87.91 246	65.23 322	72.21 411	87.92 302	67.48 288	75.53 412	71.67 250	90.74 310	89.20 308

xiu_mvs_v2_base		77.19 301	76.75 306	78.52 309	87.01 264	61.30 311	75.55 372	87.12 266	61.24 361	74.45 398	78.79 423	77.20 181	90.93 212	64.62 329	84.80 404	83.32 397

MVSTER		77.09 302	75.70 316	81.25 261	75.27 437	61.08 315	77.49 341	85.07 301	60.78 366	86.55 199	88.68 287	43.14 433	90.25 237	73.69 226	90.67 315	92.42 205

PS-MVSNAJ		77.04 303	76.53 308	78.56 308	87.09 260	61.40 309	75.26 374	87.13 262	61.25 360	74.38 400	77.22 437	76.94 187	90.94 211	64.63 328	84.83 403	83.35 396

IterMVS		76.91 304	76.34 310	78.64 307	80.91 383	64.03 269	76.30 360	79.03 357	64.88 328	83.11 290	89.16 279	59.90 339	84.46 359	68.61 292	85.15 395	87.42 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 305	75.67 317	80.34 282	80.48 392	62.16 303	73.50 390	84.80 311	57.61 390	82.24 304	87.54 313	51.31 386	87.65 303	70.40 269	93.19 239	91.23 254

CL-MVSNet_self_test		76.81 306	77.38 298	75.12 357	86.90 269	51.34 412	73.20 393	80.63 350	68.30 275	81.80 318	88.40 292	66.92 292	80.90 384	55.35 391	94.90 176	93.12 169

TR-MVS		76.77 307	75.79 314	79.72 293	86.10 294	65.79 253	77.14 345	83.02 327	65.20 326	81.40 325	82.10 391	66.30 294	90.73 223	55.57 388	85.27 391	82.65 404

MonoMVSNet		76.66 308	77.26 300	74.86 359	79.86 397	54.34 390	86.26 142	86.08 280	71.08 237	85.59 226	88.68 287	53.95 375	85.93 337	63.86 333	80.02 433	84.32 379

USDC		76.63 309	76.73 307	76.34 345	83.46 349	57.20 368	80.02 294	88.04 243	52.14 424	83.65 279	91.25 209	63.24 319	86.65 322	54.66 396	94.11 204	85.17 368

BH-w/o		76.57 310	76.07 313	78.10 318	86.88 270	65.92 252	77.63 336	86.33 275	65.69 313	80.89 331	79.95 412	68.97 283	90.74 222	53.01 407	85.25 392	77.62 440

Patchmtry		76.56 311	77.46 296	73.83 367	79.37 404	46.60 434	82.41 252	76.90 371	73.81 184	85.56 228	92.38 163	48.07 399	83.98 366	63.36 338	95.31 160	90.92 265

PVSNet_Blended		76.49 312	75.40 319	79.76 292	84.43 328	63.41 275	75.14 375	90.44 182	57.36 392	75.43 389	78.30 426	69.11 281	91.44 194	60.68 359	87.70 364	84.42 378

miper_lstm_enhance		76.45 313	76.10 312	77.51 328	76.72 423	60.97 322	64.69 441	85.04 303	63.98 333	83.20 289	88.22 294	56.67 361	78.79 400	73.22 236	93.12 240	92.78 185

lupinMVS		76.37 314	74.46 328	82.09 240	85.54 308	69.26 209	76.79 350	80.77 349	50.68 435	76.23 379	82.82 385	58.69 348	88.94 273	69.85 274	88.77 343	88.07 329

cascas		76.29 315	74.81 324	80.72 274	84.47 327	62.94 281	73.89 387	87.34 254	55.94 399	75.16 394	76.53 442	63.97 314	91.16 203	65.00 323	90.97 300	88.06 331

SD_0403		76.08 316	76.77 305	73.98 365	87.08 262	49.45 423	83.62 211	84.68 313	63.31 334	75.13 395	87.47 316	71.85 263	84.56 357	49.97 419	87.86 360	87.94 335

WB-MVS		76.06 317	80.01 268	64.19 430	89.96 179	20.58 473	72.18 400	68.19 429	83.21 65	86.46 207	93.49 123	70.19 275	78.97 398	65.96 311	90.46 321	93.02 173

thres600view7		75.97 318	75.35 321	77.85 325	87.01 264	51.84 410	80.45 289	73.26 399	75.20 165	83.10 291	86.31 337	45.54 414	89.05 271	55.03 394	92.24 267	92.66 192

GA-MVS		75.83 319	74.61 325	79.48 298	81.87 370	59.25 344	73.42 391	82.88 328	68.68 269	79.75 345	81.80 396	50.62 390	89.46 264	66.85 303	85.64 388	89.72 298

MVP-Stereo		75.81 320	73.51 337	82.71 226	89.35 189	73.62 139	80.06 292	85.20 298	60.30 371	73.96 401	87.94 299	57.89 355	89.45 265	52.02 411	74.87 451	85.06 370
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 321	75.20 322	77.27 331	75.01 440	69.47 206	78.93 314	84.88 308	46.67 442	87.08 186	87.84 307	50.44 392	71.62 425	77.42 169	88.53 346	90.72 271

thres100view900		75.45 322	75.05 323	76.66 341	87.27 249	51.88 409	81.07 279	73.26 399	75.68 156	83.25 288	86.37 334	45.54 414	88.80 275	51.98 412	90.99 297	89.31 305

ET-MVSNet_ETH3D		75.28 323	72.77 346	82.81 225	83.03 364	68.11 226	77.09 346	76.51 375	60.67 368	77.60 370	80.52 407	38.04 442	91.15 204	70.78 261	90.68 314	89.17 312

thres400		75.14 324	74.23 330	77.86 324	86.24 287	52.12 406	79.24 310	73.87 392	73.34 195	81.82 316	84.60 366	46.02 407	88.80 275	51.98 412	90.99 297	92.66 192

wuyk23d		75.13 325	79.30 275	62.63 433	75.56 433	75.18 131	80.89 282	73.10 401	75.06 167	94.76 16	95.32 45	87.73 44	52.85 465	34.16 463	97.11 87	59.85 461

EU-MVSNet		75.12 326	74.43 329	77.18 332	83.11 363	59.48 341	85.71 154	82.43 333	39.76 462	85.64 225	88.76 285	44.71 426	87.88 299	73.86 220	85.88 387	84.16 384

HyFIR lowres test		75.12 326	72.66 348	82.50 233	91.44 141	65.19 258	72.47 398	87.31 255	46.79 441	80.29 340	84.30 368	52.70 380	92.10 178	51.88 416	86.73 376	90.22 287

CMPMVS	59.41 20	75.12 326	73.57 335	79.77 291	75.84 432	67.22 232	81.21 277	82.18 335	50.78 433	76.50 375	87.66 311	55.20 371	82.99 372	62.17 348	90.64 319	89.09 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 329	72.98 344	80.73 273	84.95 319	71.71 176	76.23 362	77.59 364	52.83 418	77.73 369	86.38 333	56.35 364	84.97 353	57.72 376	87.05 371	85.51 365

tfpn200view9		74.86 330	74.23 330	76.74 340	86.24 287	52.12 406	79.24 310	73.87 392	73.34 195	81.82 316	84.60 366	46.02 407	88.80 275	51.98 412	90.99 297	89.31 305

1112_ss		74.82 331	73.74 333	78.04 320	89.57 183	60.04 333	76.49 358	87.09 267	54.31 409	73.66 404	79.80 413	60.25 336	86.76 321	58.37 370	84.15 408	87.32 344

EGC-MVSNET		74.79 332	69.99 376	89.19 67	94.89 38	87.00 15	91.89 38	86.28 276	1.09 471	2.23 473	95.98 30	81.87 123	89.48 262	79.76 131	95.96 130	91.10 258

ppachtmachnet_test		74.73 333	74.00 332	76.90 337	80.71 388	56.89 371	71.53 406	78.42 359	58.24 383	79.32 352	82.92 384	57.91 354	84.26 363	65.60 318	91.36 290	89.56 300

Patchmatch-RL test		74.48 334	73.68 334	76.89 338	84.83 321	66.54 243	72.29 399	69.16 427	57.70 388	86.76 192	86.33 335	45.79 413	82.59 373	69.63 277	90.65 318	81.54 420

PatchMatch-RL		74.48 334	73.22 341	78.27 316	87.70 237	85.26 38	75.92 367	70.09 420	64.34 331	76.09 382	81.25 401	65.87 300	78.07 402	53.86 399	83.82 410	71.48 449

XXY-MVS		74.44 336	76.19 311	69.21 402	84.61 326	52.43 405	71.70 403	77.18 369	60.73 367	80.60 334	90.96 222	75.44 203	69.35 433	56.13 383	88.33 350	85.86 361

test2506		74.12 337	73.39 338	76.28 346	91.85 123	44.20 444	84.06 194	48.20 469	72.30 221	81.90 313	94.20 89	27.22 469	89.77 259	64.81 325	96.02 127	94.87 78

reproduce_monomvs		74.09 338	73.23 340	76.65 342	76.52 424	54.54 387	77.50 340	81.40 344	65.85 308	82.86 296	86.67 330	27.38 467	84.53 358	70.24 270	90.66 317	90.89 266

CR-MVSNet		74.00 339	73.04 343	76.85 339	79.58 399	62.64 287	82.58 243	76.90 371	50.50 436	75.72 386	92.38 163	48.07 399	84.07 365	68.72 291	82.91 417	83.85 388

SSC-MVS3.2		73.90 340	75.67 317	68.61 410	84.11 337	41.28 452	64.17 443	72.83 402	72.09 224	79.08 355	87.94 299	70.31 273	73.89 418	55.99 384	94.49 191	90.67 276

Test_1112_low_res		73.90 340	73.08 342	76.35 344	90.35 167	55.95 374	73.40 392	86.17 278	50.70 434	73.14 405	85.94 342	58.31 350	85.90 341	56.51 380	83.22 414	87.20 346

test20.03		73.75 342	74.59 327	71.22 388	81.11 381	51.12 416	70.15 417	72.10 409	70.42 243	80.28 342	91.50 196	64.21 310	74.72 416	46.96 437	94.58 189	87.82 339

test_fmvs2		73.57 343	72.80 345	75.90 350	72.74 454	68.84 219	77.07 347	84.32 317	45.14 448	82.89 294	84.22 369	48.37 397	70.36 429	73.40 232	87.03 372	88.52 324

SCA		73.32 344	72.57 350	75.58 355	81.62 374	55.86 377	78.89 316	71.37 415	61.73 351	74.93 396	83.42 378	60.46 333	87.01 312	58.11 374	82.63 422	83.88 385

baseline1		73.26 345	73.54 336	72.43 381	84.92 320	47.79 429	79.89 296	74.00 390	65.93 306	78.81 357	86.28 338	56.36 363	81.63 381	56.63 379	79.04 440	87.87 338

1314		73.22 346	72.56 351	75.20 356	80.41 393	57.84 362	81.64 268	85.36 294	51.68 427	73.10 406	76.65 441	61.45 328	85.19 351	63.54 336	79.21 438	82.59 405

MVS		73.21 347	72.59 349	75.06 358	80.97 382	60.81 324	81.64 268	85.92 287	46.03 446	71.68 414	77.54 432	68.47 284	89.77 259	55.70 387	85.39 389	74.60 446

HY-MVS	64.64 18	73.03 348	72.47 352	74.71 362	83.36 354	54.19 391	82.14 262	81.96 337	56.76 398	69.57 428	86.21 339	60.03 337	84.83 355	49.58 424	82.65 420	85.11 369

thisisatest0515		73.00 349	70.52 368	80.46 280	81.45 376	59.90 337	73.16 394	74.31 389	57.86 387	76.08 383	77.78 429	37.60 445	92.12 177	65.00 323	91.45 289	89.35 304

EPNet_dtu		72.87 350	71.33 362	77.49 329	77.72 413	60.55 327	82.35 253	75.79 378	66.49 303	58.39 464	81.06 402	53.68 376	85.98 336	53.55 402	92.97 245	85.95 359
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 351	71.41 361	76.28 346	83.25 358	60.34 329	83.50 215	79.02 358	37.77 466	76.33 377	85.10 357	49.60 395	87.41 308	70.54 267	77.54 446	81.08 427

CHOSEN 1792x2688		72.45 352	70.56 367	78.13 317	90.02 178	63.08 280	68.72 424	83.16 325	42.99 456	75.92 384	85.46 350	57.22 359	85.18 352	49.87 422	81.67 424	86.14 357

testgi		72.36 353	74.61 325	65.59 424	80.56 391	42.82 449	68.29 426	73.35 398	66.87 300	81.84 315	89.93 263	72.08 260	66.92 447	46.05 441	92.54 257	87.01 348

thres200		72.34 354	71.55 360	74.70 363	83.48 348	51.60 411	75.02 376	73.71 395	70.14 249	78.56 360	80.57 406	46.20 405	88.20 292	46.99 436	89.29 335	84.32 379

FPMVS		72.29 355	72.00 354	73.14 372	88.63 213	85.00 40	74.65 380	67.39 432	71.94 227	77.80 367	87.66 311	50.48 391	75.83 410	49.95 420	79.51 434	58.58 463

FMVSNet5		72.10 356	71.69 356	73.32 370	81.57 375	53.02 400	76.77 351	78.37 360	63.31 334	76.37 376	91.85 182	36.68 446	78.98 397	47.87 433	92.45 259	87.95 334

our_test_3		71.85 357	71.59 357	72.62 378	80.71 388	53.78 394	69.72 420	71.71 414	58.80 380	78.03 362	80.51 408	56.61 362	78.84 399	62.20 346	86.04 386	85.23 367

PAPM		71.77 358	70.06 374	76.92 336	86.39 278	53.97 392	76.62 355	86.62 273	53.44 413	63.97 453	84.73 364	57.79 356	92.34 170	39.65 453	81.33 428	84.45 377

ttmdpeth		71.72 359	70.67 365	74.86 359	73.08 451	55.88 376	77.41 343	69.27 425	55.86 400	78.66 358	93.77 116	38.01 443	75.39 413	60.12 362	89.87 328	93.31 157

IB-MVS	62.13 19	71.64 360	68.97 386	79.66 295	80.80 387	62.26 300	73.94 386	76.90 371	63.27 336	68.63 432	76.79 439	33.83 450	91.84 185	59.28 367	87.26 366	84.88 371
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		71.63 361	72.30 353	69.62 399	76.47 426	52.70 403	70.03 418	80.97 347	59.18 377	79.36 350	88.21 295	60.50 332	69.12 434	58.33 372	77.62 445	87.04 347

testing3		71.53 362	70.79 364	73.77 368	88.89 205	41.86 451	76.60 357	59.12 458	72.83 209	80.97 328	82.08 393	19.80 475	87.33 310	65.12 322	91.68 284	92.13 228

test_vis3_rt		71.42 363	70.67 365	73.64 369	69.66 461	70.46 191	66.97 436	89.73 207	42.68 458	88.20 154	83.04 380	43.77 428	60.07 459	65.35 321	86.66 377	90.39 285

Anonymous20231206		71.38 364	71.88 355	69.88 396	86.31 284	54.37 389	70.39 415	74.62 385	52.57 420	76.73 374	88.76 285	59.94 338	72.06 422	44.35 445	93.23 237	83.23 399

test_vis1_n_1920		71.30 365	71.58 359	70.47 391	77.58 415	59.99 336	74.25 381	84.22 318	51.06 430	74.85 397	79.10 419	55.10 372	68.83 436	68.86 288	79.20 439	82.58 406

MIMVSNet		71.09 366	71.59 357	69.57 400	87.23 252	50.07 421	78.91 315	71.83 411	60.20 374	71.26 415	91.76 189	55.08 373	76.09 408	41.06 450	87.02 373	82.54 408

test_fmvs1_n		70.94 367	70.41 371	72.53 380	73.92 442	66.93 240	75.99 366	84.21 319	43.31 455	79.40 349	79.39 417	43.47 429	68.55 438	69.05 285	84.91 400	82.10 414

MS-PatchMatch		70.93 368	70.22 372	73.06 373	81.85 371	62.50 290	73.82 388	77.90 361	52.44 421	75.92 384	81.27 400	55.67 368	81.75 379	55.37 390	77.70 444	74.94 445

pmmvs5		70.73 369	70.07 373	72.72 376	77.03 420	52.73 402	74.14 382	75.65 381	50.36 437	72.17 412	85.37 354	55.42 370	80.67 386	52.86 408	87.59 365	84.77 372

testing3-2		70.72 370	70.97 363	69.95 395	88.93 203	34.80 465	69.85 419	66.59 439	78.42 124	77.58 371	85.55 346	31.83 456	82.08 377	46.28 438	93.73 219	92.98 179

PatchT		70.52 371	72.76 347	63.79 432	79.38 403	33.53 466	77.63 336	65.37 443	73.61 188	71.77 413	92.79 151	44.38 427	75.65 411	64.53 330	85.37 390	82.18 413

test_vis1_n		70.29 372	69.99 376	71.20 389	75.97 431	66.50 244	76.69 353	80.81 348	44.22 451	75.43 389	77.23 436	50.00 393	68.59 437	66.71 306	82.85 419	78.52 439

N_pmnet		70.20 373	68.80 388	74.38 364	80.91 383	84.81 43	59.12 454	76.45 376	55.06 404	75.31 393	82.36 390	55.74 367	54.82 464	47.02 435	87.24 367	83.52 392

tpmvs		70.16 374	69.56 379	71.96 384	74.71 441	48.13 426	79.63 298	75.45 383	65.02 327	70.26 423	81.88 395	45.34 419	85.68 347	58.34 371	75.39 450	82.08 415

new-patchmatchnet		70.10 375	73.37 339	60.29 441	81.23 380	16.95 476	59.54 452	74.62 385	62.93 338	80.97 328	87.93 301	62.83 325	71.90 423	55.24 392	95.01 173	92.00 233

YYNet1		70.06 376	70.44 369	68.90 404	73.76 444	53.42 398	58.99 455	67.20 434	58.42 382	87.10 184	85.39 353	59.82 340	67.32 444	59.79 364	83.50 413	85.96 358

MVStest1		70.05 377	69.26 380	72.41 382	58.62 473	55.59 380	76.61 356	65.58 441	53.44 413	89.28 128	93.32 127	22.91 473	71.44 427	74.08 216	89.52 332	90.21 291

MDA-MVSNet_test_wron		70.05 377	70.44 369	68.88 405	73.84 443	53.47 396	58.93 456	67.28 433	58.43 381	87.09 185	85.40 352	59.80 341	67.25 445	59.66 365	83.54 412	85.92 360

CostFormer		69.98 379	68.68 389	73.87 366	77.14 418	50.72 418	79.26 309	74.51 387	51.94 426	70.97 418	84.75 363	45.16 422	87.49 306	55.16 393	79.23 437	83.40 395

testing91		69.94 380	68.99 385	72.80 375	83.81 343	45.89 437	71.57 405	73.64 397	68.24 276	70.77 421	77.82 428	34.37 449	84.44 360	53.64 401	87.00 374	88.07 329

baseline2		69.77 381	66.89 398	78.41 312	79.51 401	58.09 358	76.23 362	69.57 423	57.50 391	64.82 451	77.45 434	46.02 407	88.44 286	53.08 404	77.83 442	88.70 322

PatchmatchNet		69.71 382	68.83 387	72.33 383	77.66 414	53.60 395	79.29 308	69.99 421	57.66 389	72.53 409	82.93 383	46.45 404	80.08 392	60.91 358	72.09 454	83.31 398
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 383	69.05 383	71.14 390	69.15 462	65.77 254	73.98 385	83.32 323	42.83 457	77.77 368	78.27 427	43.39 432	68.50 439	68.39 295	84.38 407	79.15 437

JIA-IIPM		69.41 384	66.64 402	77.70 326	73.19 448	71.24 182	75.67 368	65.56 442	70.42 243	65.18 447	92.97 142	33.64 452	83.06 370	53.52 403	69.61 460	78.79 438

Syy-MVS		69.40 385	70.03 375	67.49 415	81.72 372	38.94 457	71.00 408	61.99 449	61.38 357	70.81 419	72.36 453	61.37 329	79.30 395	64.50 331	85.18 393	84.22 381

testing99		69.27 386	68.15 393	72.63 377	83.29 356	45.45 439	71.15 407	71.08 416	67.34 293	70.43 422	77.77 430	32.24 455	84.35 362	53.72 400	86.33 382	88.10 328

UnsupCasMVSNet_bld		69.21 387	69.68 378	67.82 413	79.42 402	51.15 415	67.82 430	75.79 378	54.15 410	77.47 372	85.36 355	59.26 344	70.64 428	48.46 430	79.35 436	81.66 418

test_cas_vis1_n_1920		69.20 388	69.12 381	69.43 401	73.68 445	62.82 284	70.38 416	77.21 368	46.18 445	80.46 339	78.95 421	52.03 382	65.53 452	65.77 317	77.45 447	79.95 435

gg-mvs-nofinetune		68.96 389	69.11 382	68.52 411	76.12 430	45.32 440	83.59 212	55.88 463	86.68 33	64.62 452	97.01 12	30.36 460	83.97 367	44.78 444	82.94 416	76.26 442

WBMVS		68.76 390	68.43 390	69.75 398	83.29 356	40.30 455	67.36 432	72.21 408	57.09 395	77.05 373	85.53 348	33.68 451	80.51 388	48.79 428	90.90 302	88.45 325

WB-MVSnew		68.72 391	69.01 384	67.85 412	83.22 360	43.98 445	74.93 377	65.98 440	55.09 403	73.83 402	79.11 418	65.63 302	71.89 424	38.21 458	85.04 396	87.69 340

tpm2		68.45 392	66.83 399	73.30 371	78.93 409	48.50 425	79.76 297	71.76 412	47.50 440	69.92 425	83.60 374	42.07 435	88.40 288	48.44 431	79.51 434	83.01 402

tpm		67.95 393	68.08 394	67.55 414	78.74 410	43.53 447	75.60 369	67.10 437	54.92 405	72.23 410	88.10 296	42.87 434	75.97 409	52.21 410	80.95 432	83.15 400

WTY-MVS		67.91 394	68.35 391	66.58 420	80.82 386	48.12 427	65.96 438	72.60 403	53.67 412	71.20 416	81.68 398	58.97 346	69.06 435	48.57 429	81.67 424	82.55 407

testing11		67.38 395	65.93 403	71.73 386	83.37 353	46.60 434	70.95 410	69.40 424	62.47 343	66.14 440	76.66 440	31.22 457	84.10 364	49.10 426	84.10 409	84.49 375

test-LLR		67.21 396	66.74 400	68.63 408	76.45 427	55.21 383	67.89 427	67.14 435	62.43 346	65.08 448	72.39 451	43.41 430	69.37 431	61.00 356	84.89 401	81.31 422

testing222		66.93 397	65.30 410	71.81 385	83.38 352	45.83 438	72.06 401	67.50 431	64.12 332	69.68 427	76.37 443	27.34 468	83.00 371	38.88 454	88.38 349	86.62 353

sss		66.92 398	67.26 396	65.90 422	77.23 417	51.10 417	64.79 440	71.72 413	52.12 425	70.13 424	80.18 410	57.96 353	65.36 453	50.21 418	81.01 430	81.25 424

KD-MVS_2432*1600		66.87 399	65.81 406	70.04 393	67.50 463	47.49 430	62.56 446	79.16 355	61.21 362	77.98 363	80.61 404	25.29 471	82.48 374	53.02 405	84.92 398	80.16 433

miper_refine_blended		66.87 399	65.81 406	70.04 393	67.50 463	47.49 430	62.56 446	79.16 355	61.21 362	77.98 363	80.61 404	25.29 471	82.48 374	53.02 405	84.92 398	80.16 433

dmvs_re		66.81 401	66.98 397	66.28 421	76.87 421	58.68 356	71.66 404	72.24 406	60.29 372	69.52 429	73.53 450	52.38 381	64.40 455	44.90 443	81.44 427	75.76 443

tpm cat1		66.76 402	65.21 411	71.42 387	77.09 419	50.62 419	78.01 328	73.68 396	44.89 449	68.64 431	79.00 420	45.51 416	82.42 376	49.91 421	70.15 457	81.23 426

UWE-MVS		66.43 403	65.56 409	69.05 403	84.15 336	40.98 453	73.06 395	64.71 445	54.84 406	76.18 381	79.62 416	29.21 462	80.50 389	38.54 457	89.75 329	85.66 363

PVSNet	58.17 21	66.41 404	65.63 408	68.75 406	81.96 369	49.88 422	62.19 448	72.51 405	51.03 431	68.04 434	75.34 447	50.84 388	74.77 414	45.82 442	82.96 415	81.60 419

tpmrst		66.28 405	66.69 401	65.05 428	72.82 453	39.33 456	78.20 326	70.69 419	53.16 416	67.88 435	80.36 409	48.18 398	74.75 415	58.13 373	70.79 456	81.08 427

Patchmatch-test		65.91 406	67.38 395	61.48 438	75.51 434	43.21 448	68.84 423	63.79 447	62.48 342	72.80 408	83.42 378	44.89 425	59.52 461	48.27 432	86.45 379	81.70 417

ADS-MVSNet2		65.87 407	63.64 416	72.55 379	73.16 449	56.92 370	67.10 434	74.81 384	49.74 438	66.04 442	82.97 381	46.71 402	77.26 405	42.29 447	69.96 458	83.46 393

myMVS_eth3d28		65.83 408	65.85 404	65.78 423	83.42 351	35.71 463	67.29 433	68.01 430	67.58 290	69.80 426	77.72 431	32.29 454	74.30 417	37.49 459	89.06 339	87.32 344

test_vis1_rt		65.64 409	64.09 413	70.31 392	66.09 467	70.20 195	61.16 449	81.60 342	38.65 463	72.87 407	69.66 456	52.84 378	60.04 460	56.16 382	77.77 443	80.68 431

mvsany_test3		65.48 410	62.97 419	73.03 374	69.99 460	76.17 124	64.83 439	43.71 471	43.68 453	80.25 343	87.05 327	52.83 379	63.09 458	51.92 415	72.44 453	79.84 436

test-mter		65.00 411	63.79 415	68.63 408	76.45 427	55.21 383	67.89 427	67.14 435	50.98 432	65.08 448	72.39 451	28.27 465	69.37 431	61.00 356	84.89 401	81.31 422

ETVMVS		64.67 412	63.34 418	68.64 407	83.44 350	41.89 450	69.56 422	61.70 454	61.33 359	68.74 430	75.76 445	28.76 463	79.35 394	34.65 462	86.16 385	84.67 374

myMVS_eth3d		64.66 413	63.89 414	66.97 418	81.72 372	37.39 460	71.00 408	61.99 449	61.38 357	70.81 419	72.36 453	20.96 474	79.30 395	49.59 423	85.18 393	84.22 381

test0.0.03 1		64.66 413	64.36 412	65.57 425	75.03 439	46.89 433	64.69 441	61.58 455	62.43 346	71.18 417	77.54 432	43.41 430	68.47 440	40.75 452	82.65 420	81.35 421

UBG		64.34 415	63.35 417	67.30 416	83.50 347	40.53 454	67.46 431	65.02 444	54.77 407	67.54 438	74.47 449	32.99 453	78.50 401	40.82 451	83.58 411	82.88 403

test_f		64.31 416	65.85 404	59.67 442	66.54 466	62.24 302	57.76 458	70.96 417	40.13 460	84.36 260	82.09 392	46.93 401	51.67 466	61.99 349	81.89 423	65.12 457

pmmvs3		62.47 417	60.02 430	69.80 397	71.58 457	64.00 270	70.52 414	58.44 461	39.77 461	66.05 441	75.84 444	27.10 470	72.28 421	46.15 440	84.77 405	73.11 447

EPMVS		62.47 417	62.63 421	62.01 434	70.63 459	38.74 458	74.76 378	52.86 465	53.91 411	67.71 437	80.01 411	39.40 439	66.60 448	55.54 389	68.81 462	80.68 431

ADS-MVSNet		61.90 419	62.19 423	61.03 439	73.16 449	36.42 462	67.10 434	61.75 452	49.74 438	66.04 442	82.97 381	46.71 402	63.21 456	42.29 447	69.96 458	83.46 393

PMMVS		61.65 420	60.38 427	65.47 426	65.40 470	69.26 209	63.97 444	61.73 453	36.80 467	60.11 459	68.43 458	59.42 342	66.35 449	48.97 427	78.57 441	60.81 460

E-PMN		61.59 421	61.62 424	61.49 437	66.81 465	55.40 381	53.77 461	60.34 457	66.80 301	58.90 462	65.50 461	40.48 438	66.12 450	55.72 386	86.25 383	62.95 459

TESTMET0.1,1		61.29 422	60.32 428	64.19 430	72.06 455	51.30 413	67.89 427	62.09 448	45.27 447	60.65 458	69.01 457	27.93 466	64.74 454	56.31 381	81.65 426	76.53 441

MVS-HIRNet		61.16 423	62.92 420	55.87 445	79.09 406	35.34 464	71.83 402	57.98 462	46.56 443	59.05 461	91.14 213	49.95 394	76.43 407	38.74 455	71.92 455	55.84 464

EMVS		61.10 424	60.81 426	61.99 435	65.96 468	55.86 377	53.10 462	58.97 460	67.06 298	56.89 466	63.33 462	40.98 436	67.03 446	54.79 395	86.18 384	63.08 458

DSMNet-mixed		60.98 425	61.61 425	59.09 444	72.88 452	45.05 442	74.70 379	46.61 470	26.20 468	65.34 446	90.32 252	55.46 369	63.12 457	41.72 449	81.30 429	69.09 453

dp		60.70 426	60.29 429	61.92 436	72.04 456	38.67 459	70.83 412	64.08 446	51.28 429	60.75 457	77.28 435	36.59 447	71.58 426	47.41 434	62.34 464	75.52 444

dmvs_testset		60.59 427	62.54 422	54.72 447	77.26 416	27.74 470	74.05 384	61.00 456	60.48 369	65.62 445	67.03 460	55.93 366	68.23 442	32.07 466	69.46 461	68.17 454

CHOSEN 280x420		59.08 428	56.52 434	66.76 419	76.51 425	64.39 266	49.62 463	59.00 459	43.86 452	55.66 467	68.41 459	35.55 448	68.21 443	43.25 446	76.78 449	67.69 455

mvsany_test1		58.48 429	56.47 435	64.50 429	65.90 469	68.21 225	56.95 459	42.11 472	38.30 464	65.69 444	77.19 438	56.96 360	59.35 462	46.16 439	58.96 465	65.93 456

UWE-MVS-28		58.44 430	57.71 432	60.65 440	73.58 446	31.23 467	69.68 421	48.80 468	53.12 417	61.79 455	78.83 422	30.98 458	68.40 441	21.58 469	80.99 431	82.33 412

PVSNet_0	51.08 22	56.10 431	54.97 436	59.48 443	75.12 438	53.28 399	55.16 460	61.89 451	44.30 450	59.16 460	62.48 463	54.22 374	65.91 451	35.40 461	47.01 466	59.25 462

new_pmnet		55.69 432	57.66 433	49.76 448	75.47 435	30.59 468	59.56 451	51.45 466	43.62 454	62.49 454	75.48 446	40.96 437	49.15 468	37.39 460	72.52 452	69.55 452

PMMVS2		55.64 433	59.27 431	44.74 449	64.30 471	12.32 477	40.60 464	49.79 467	53.19 415	65.06 450	84.81 362	53.60 377	49.76 467	32.68 465	89.41 334	72.15 448

MVE	40.22 23	51.82 434	50.47 437	55.87 445	62.66 472	51.91 408	31.61 466	39.28 473	40.65 459	50.76 468	74.98 448	56.24 365	44.67 469	33.94 464	64.11 463	71.04 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 435	42.65 438	39.67 450	70.86 458	21.11 472	61.01 450	21.42 477	57.36 392	57.97 465	50.06 466	16.40 476	58.73 463	21.03 470	27.69 470	39.17 466

kuosan		30.83 436	32.17 439	26.83 452	53.36 474	19.02 475	57.90 457	20.44 478	38.29 465	38.01 469	37.82 468	15.18 477	33.45 471	7.74 472	20.76 471	28.03 467

test_method		30.46 437	29.60 440	33.06 451	17.99 476	3.84 479	13.62 467	73.92 391	2.79 470	18.29 472	53.41 465	28.53 464	43.25 470	22.56 467	35.27 468	52.11 465

cdsmvs_eth3d_5k		20.81 438	27.75 441	0.00 457	0.00 480	0.00 482	0.00 468	85.44 293	0.00 475	0.00 476	82.82 385	81.46 129	0.00 476	0.00 475	0.00 474	0.00 472

tmp_tt		20.25 439	24.50 442	7.49 454	4.47 477	8.70 478	34.17 465	25.16 475	1.00 472	32.43 471	18.49 469	39.37 440	9.21 473	21.64 468	43.75 467	4.57 469

ab-mvs-re		6.65 440	8.87 443	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	79.80 413	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

pcd_1.5k_mvsjas		6.41 441	8.55 444	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	76.94 187	0.00 476	0.00 475	0.00 474	0.00 472

test123		6.27 442	8.08 445	0.84 455	1.11 479	0.57 480	62.90 445	0.82 479	0.54 473	1.07 475	2.75 474	1.26 478	0.30 474	1.04 473	1.26 473	1.66 470

testmvs		5.91 443	7.65 446	0.72 456	1.20 478	0.37 481	59.14 453	0.67 480	0.49 474	1.11 474	2.76 473	0.94 479	0.24 475	1.02 474	1.47 472	1.55 471

mmdepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet_test		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

WAC-MVS						37.39 460								52.61 409

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		91.01 165					96.05 9	87.45 28	98.17 37	92.40 208

PC_three_1452									58.96 379	90.06 103	91.33 204	80.66 139	93.03 152	75.78 192	95.94 133	92.48 202

No_MVS				88.81 73	91.55 135	77.99 97		91.01 165					96.05 9	87.45 28	98.17 37	92.40 208

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					0.00 480

eth-test					0.00 480

ZD-MVS					92.22 109	80.48 71		91.85 133	71.22 235	90.38 98	92.98 139	86.06 65	96.11 7	81.99 110	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	92.73 186

IU-MVS					94.18 52	72.64 154		90.82 170	56.98 396	89.67 116				85.78 63	97.92 52	93.28 158

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 210	81.12 133	94.68 78	74.48 205	95.35 156	92.29 218

test_241102_TWO								93.71 57	83.77 58	93.49 40	94.27 83	89.27 24	95.84 24	86.03 56	97.82 57	92.04 231

test_241102_ONE					94.18 52	72.65 152		93.69 58	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	92.89 145	87.27 48	93.78 117	83.69 88	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 208	70.80 240

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 179

test_0728_SECOND				86.79 108	94.25 50	72.45 162	90.54 53	94.10 40					95.88 18	86.42 46	97.97 49	92.02 232

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																83.88 385

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 406				83.88 385

sam_mvs												45.92 411

ambc				82.98 216	90.55 164	64.86 260	88.20 103	89.15 221		89.40 125	93.96 105	71.67 267	91.38 198	78.83 145	96.55 102	92.71 189

MTGPA								91.81 137

test_post1							78.85 318				3.13 471	45.19 421	80.13 391	58.11 374

test_post											3.10 472	45.43 417	77.22 406

patchmatchnet-post											81.71 397	45.93 410	87.01 312

GG-mvs-BLEND				67.16 417	73.36 447	46.54 436	84.15 192	55.04 464		58.64 463	61.95 464	29.93 461	83.87 368	38.71 456	76.92 448	71.07 450

MTMP							90.66 49	33.14 474

gm-plane-assit					75.42 436	44.97 443			52.17 422		72.36 453		87.90 298	54.10 398

test9_res														80.83 120	96.45 108	90.57 279

TEST9					92.34 104	79.70 80	83.94 198	90.32 188	65.41 319	84.49 256	90.97 220	82.03 118	93.63 123

test_8					92.09 113	78.87 88	83.82 203	90.31 190	65.79 309	84.36 260	90.96 222	81.93 120	93.44 137

agg_prior2														79.68 133	96.16 120	90.22 287

agg_prior					91.58 133	77.69 103		90.30 191		84.32 262			93.18 145

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 159	93.03 137	82.66 99	91.47 192	70.81 259	96.14 121	94.16 113

test_prior4						78.97 87	84.59 181

test_prior2							83.37 219		75.43 162	84.58 253	91.57 194	81.92 122		79.54 137	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 99					94.17 102			92.80 184

旧先验2							81.73 266		56.88 397	86.54 205			84.90 354	72.81 243

新几何2							81.72 267

新几何1				82.95 218	93.96 61	78.56 91		80.24 351	55.45 402	83.93 273	91.08 216	71.19 269	88.33 290	65.84 315	93.07 241	81.95 416

旧先验1					91.97 117	71.77 171		81.78 339			91.84 183	73.92 230			93.65 222	83.61 391

无先验							82.81 238	85.62 291	58.09 385				91.41 197	67.95 299		84.48 376

原ACMM2							82.26 258

原ACMM1				84.60 165	92.81 94	74.01 137		91.50 145	62.59 340	82.73 298	90.67 239	76.53 196	94.25 94	69.24 280	95.69 148	85.55 364

test222					93.31 78	76.54 116	79.38 307	77.79 362	52.59 419	82.36 303	90.84 230	66.83 293			91.69 283	81.25 424

testdata2													86.43 327	63.52 337

segment_acmp												81.94 119

testdata				79.54 297	92.87 89	72.34 163		80.14 352	59.91 375	85.47 230	91.75 190	67.96 287	85.24 350	68.57 294	92.18 270	81.06 429

testdata1							79.62 299		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 173		82.76 297		79.20 152	94.80 75		95.32 158	92.27 220

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 212

plane_prior5								93.61 61					95.22 59	80.78 121	95.83 141	94.46 95

plane_prior4											92.95 143

plane_prior3						76.85 114			77.79 133	86.55 199

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								0.00 481

nn								0.00 481

door-mid								74.45 388

lessismore_v0				85.95 127	91.10 152	70.99 186		70.91 418		91.79 71	94.42 78	61.76 327	92.93 155	79.52 138	93.03 242	93.93 123

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 66	98.73 7	95.23 66

test11								91.46 146

door								72.57 404

HQP5-MVS						70.66 188

HQP-NCC					91.19 147		84.77 172		73.30 197	80.55 336

ACMP_Plane					91.19 147		84.77 172		73.30 197	80.55 336

BP-MVS														77.30 170

HQP4-MVS										80.56 335			94.61 82			93.56 150

HQP3-MVS								92.68 105							94.47 192

HQP2-MVS												72.10 258

NP-MVS					91.95 118	74.55 134					90.17 259

MDTV_nov1_ep13_2view						27.60 471	70.76 413		46.47 444	61.27 456		45.20 420		49.18 425		83.75 390

MDTV_nov1_ep13			68.29 392		78.03 411	43.87 446	74.12 383	72.22 407	52.17 422	67.02 439	85.54 347	45.36 418	80.85 385	55.73 385	84.42 406

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 154

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 191	81.56 82	90.02 105	91.20 212	82.40 104	90.81 219	73.58 229	94.66 187	94.56 90

DeepMVS_CX				24.13 453	32.95 475	29.49 469		21.63 476	12.07 469	37.95 470	45.07 467	30.84 459	19.21 472	17.94 471	33.06 469	23.69 468

High-resolution multi-view benchmark