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.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 55	91.63 1	86.34 1	97.97 1	94.77 3	66.57 117	95.38 1	87.74 1	97.72 1	93.00 8

LCM-MVSNet		86.90 2	88.67 2	81.57 22	91.50 2	63.30 120	84.80 32	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 70	68.08 91	97.05 2	96.93 1

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 9	87.95 15	92.53 15	79.37 13	84.79 66	74.51 49	96.15 3	92.88 9

SR-MVS-dyc-post		84.75 4	85.26 6	83.21 4	86.19 50	79.18 7	87.23 8	86.27 21	77.51 11	87.65 19	90.73 50	79.20 14	85.58 49	78.11 24	94.46 36	84.89 97

HPM-MVS_fast		84.59 5	85.10 7	83.06 5	88.60 33	75.83 24	86.27 24	86.89 16	73.69 24	86.17 38	91.70 28	78.23 19	85.20 58	79.45 13	94.91 25	88.15 50

SR-MVS		84.51 6	85.27 5	82.25 19	88.52 34	77.71 14	86.81 16	85.25 40	77.42 14	86.15 39	90.24 73	81.69 5	85.94 35	77.77 27	93.58 61	83.09 158

ACMMP		84.22 7	84.84 9	82.35 18	89.23 22	76.66 23	87.65 6	85.89 28	71.03 46	85.85 43	90.58 54	78.77 16	85.78 42	79.37 16	95.17 17	84.62 108
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
LTVRE_ROB	75.46 1	84.22 7	84.98 8	81.94 21	84.82 73	75.40 26	91.60 3	87.80 8	73.52 25	88.90 12	93.06 7	71.39 70	81.53 116	81.53 4	92.15 82	88.91 40
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
HPM-MVS		84.12 9	84.63 10	82.60 14	88.21 36	74.40 32	85.24 28	87.21 14	70.69 49	85.14 56	90.42 61	78.99 15	86.62 14	80.83 6	94.93 24	86.79 66
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 9	84.55 11	82.80 11	89.42 18	79.74 6	88.19 5	84.43 61	71.96 41	84.70 63	90.56 55	77.12 25	86.18 27	79.24 18	95.36 13	82.49 176

mPP-MVS		84.01 11	84.39 12	82.88 7	90.65 4	81.38 4	87.08 12	82.79 87	72.41 37	85.11 57	90.85 47	76.65 28	84.89 63	79.30 17	94.63 33	82.35 178

COLMAP_ROB	72.78 3	83.75 12	84.11 16	82.68 13	82.97 103	74.39 33	87.18 10	88.18 7	78.98 7	86.11 41	91.47 33	79.70 12	85.76 43	66.91 111	95.46 12	87.89 51
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		83.62 13	83.93 18	82.69 12	89.78 11	77.51 19	87.01 14	84.19 68	70.23 50	84.49 65	90.67 53	75.15 41	86.37 19	79.58 11	94.26 49	84.18 126

APD-MVS_3200maxsize		83.57 14	84.33 13	81.31 29	82.83 106	73.53 41	85.50 27	87.45 13	74.11 20	86.45 36	90.52 58	80.02 10	84.48 71	77.73 28	94.34 47	85.93 77

region2R		83.54 15	83.86 20	82.58 15	89.82 10	77.53 17	87.06 13	84.23 67	70.19 52	83.86 73	90.72 52	75.20 40	86.27 22	79.41 15	94.25 50	83.95 131

XVS		83.51 16	83.73 21	82.85 9	89.43 16	77.61 15	86.80 17	84.66 56	72.71 30	82.87 83	90.39 65	73.86 52	86.31 20	78.84 20	94.03 53	84.64 106

LPG-MVS_test		83.47 17	84.33 13	80.90 33	87.00 40	70.41 61	82.04 60	86.35 18	69.77 54	87.75 16	91.13 37	81.83 3	86.20 25	77.13 36	95.96 6	86.08 73

HFP-MVS		83.39 18	84.03 17	81.48 24	89.25 21	75.69 25	87.01 14	84.27 64	70.23 50	84.47 66	90.43 60	76.79 26	85.94 35	79.58 11	94.23 51	82.82 167

MTAPA		83.19 19	83.87 19	81.13 31	91.16 3	78.16 12	84.87 30	80.63 130	72.08 39	84.93 58	90.79 48	74.65 46	84.42 73	80.98 5	94.75 29	80.82 206

MP-MVS		83.19 19	83.54 24	82.14 20	90.54 5	79.00 9	86.42 22	83.59 77	71.31 43	81.26 103	90.96 42	74.57 47	84.69 67	78.41 22	94.78 28	82.74 170
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		83.12 21	83.68 22	81.45 25	89.14 25	73.28 43	86.32 23	85.97 27	67.39 64	84.02 70	90.39 65	74.73 45	86.46 16	80.73 7	94.43 40	84.60 111

PGM-MVS		83.07 22	83.25 31	82.54 16	89.57 14	77.21 21	82.04 60	85.40 37	67.96 63	84.91 61	90.88 45	75.59 36	86.57 15	78.16 23	94.71 31	83.82 133

SteuartSystems-ACMMP		83.07 22	83.64 23	81.35 27	85.14 69	71.00 55	85.53 26	84.78 49	70.91 47	85.64 46	90.41 62	75.55 38	87.69 5	79.75 8	95.08 20	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		82.88 24	84.14 15	79.08 53	84.80 75	66.72 91	86.54 20	85.11 42	72.00 40	86.65 32	91.75 27	78.20 20	87.04 10	77.93 26	94.32 48	83.47 145
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
GST-MVS		82.79 25	83.27 30	81.34 28	88.99 27	73.29 42	85.94 25	85.13 41	68.58 61	84.14 69	90.21 75	73.37 56	86.41 17	79.09 19	93.98 56	84.30 125

ACMP	69.50 8	82.64 26	83.38 27	80.40 38	86.50 46	69.44 68	82.30 56	86.08 26	66.80 69	86.70 31	89.99 78	81.64 6	85.95 34	74.35 51	96.11 4	85.81 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MP-MVS-pluss		82.54 27	83.46 26	79.76 42	88.88 31	68.44 77	81.57 63	86.33 20	63.17 112	85.38 54	91.26 36	76.33 30	84.67 68	83.30 2	94.96 23	86.17 72
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		82.33 28	83.28 29	79.46 49	89.28 19	69.09 75	83.62 43	84.98 45	64.77 94	83.97 71	91.02 41	75.53 39	85.93 37	82.00 3	94.36 45	83.35 151

SMA-MVS		82.12 29	82.68 39	80.43 37	88.90 30	69.52 66	85.12 29	84.76 50	63.53 106	84.23 68	91.47 33	72.02 64	87.16 8	79.74 10	94.36 45	84.61 109
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
ACMM	69.25 9	82.11 30	83.31 28	78.49 64	88.17 37	73.96 35	83.11 51	84.52 60	66.40 73	87.45 23	89.16 96	81.02 8	80.52 139	74.27 52	95.73 8	80.98 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPE-MVS		82.00 31	83.02 34	78.95 58	85.36 66	67.25 86	82.91 52	84.98 45	73.52 25	85.43 53	90.03 77	76.37 29	86.97 12	74.56 48	94.02 55	82.62 173
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		81.78 32	83.48 25	76.67 83	86.12 54	61.06 140	83.62 43	84.72 52	72.61 33	87.38 25	89.70 83	77.48 23	85.89 40	75.29 43	94.39 41	83.08 159

PMVS	70.70 6	81.70 33	83.15 32	77.36 77	90.35 6	82.82 3	82.15 57	79.22 156	74.08 21	87.16 29	91.97 21	84.80 2	76.97 198	64.98 123	93.61 60	72.28 306
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UA-Net		81.56 34	82.28 41	79.40 50	88.91 29	69.16 73	84.67 33	80.01 143	75.34 16	79.80 119	94.91 2	69.79 85	80.25 143	72.63 64	94.46 36	88.78 44

CPTT-MVS		81.51 35	81.76 44	80.76 35	89.20 23	78.75 10	86.48 21	82.03 100	68.80 57	80.92 108	88.52 112	72.00 65	82.39 102	74.80 45	93.04 68	81.14 196

DVP-MVS++		81.24 36	82.74 38	76.76 82	83.14 96	60.90 144	91.64 1	85.49 33	74.03 22	84.93 58	90.38 67	66.82 110	85.90 38	77.43 31	90.78 113	83.49 142

ACMH+	66.64 10	81.20 37	82.48 40	77.35 78	81.16 129	62.39 125	80.51 71	87.80 8	73.02 27	87.57 21	91.08 39	80.28 9	82.44 101	64.82 124	96.10 5	87.21 60

DVP-MVS		81.15 38	83.12 33	75.24 105	86.16 52	60.78 147	83.77 41	80.58 132	72.48 35	85.83 44	90.41 62	78.57 17	85.69 45	75.86 40	94.39 41	79.24 234
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
APD-MVS		81.13 39	81.73 45	79.36 51	84.47 80	70.53 60	83.85 39	83.70 75	69.43 56	83.67 75	88.96 102	75.89 34	86.41 17	72.62 65	92.95 69	81.14 196
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+	73.19 2	81.08 40	80.48 52	82.87 8	81.41 124	72.03 46	84.38 35	86.23 25	77.28 15	80.65 112	90.18 76	59.80 184	87.58 6	73.06 60	91.34 93	89.01 36

DeepC-MVS	72.44 4	81.00 41	80.83 51	81.50 23	86.70 45	70.03 65	82.06 58	87.00 15	59.89 134	80.91 109	90.53 56	72.19 61	88.56 2	73.67 56	94.52 35	85.92 78
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		80.99 42	81.63 47	79.07 54	86.86 44	69.39 69	79.41 88	84.00 73	65.64 77	85.54 50	89.28 89	76.32 31	83.47 86	74.03 53	93.57 62	84.35 122
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		80.99 42	80.85 50	81.41 26	78.37 165	71.37 51	87.45 7	85.87 29	77.48 13	81.98 92	89.95 80	69.14 88	85.26 54	66.15 113	91.24 95	87.61 55

SF-MVS		80.72 44	81.80 43	77.48 74	82.03 116	64.40 112	83.41 47	88.46 6	65.28 85	84.29 67	89.18 94	73.73 55	83.22 90	76.01 39	93.77 58	84.81 103

XVG-ACMP-BASELINE		80.54 45	81.06 49	78.98 57	87.01 39	72.91 44	80.23 79	85.56 32	66.56 72	85.64 46	89.57 85	69.12 89	80.55 138	72.51 66	93.37 63	83.48 144

MSP-MVS		80.49 46	79.67 59	82.96 6	89.70 12	77.46 20	87.16 11	85.10 43	64.94 93	81.05 106	88.38 116	57.10 213	87.10 9	79.75 8	83.87 231	84.31 123
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
PEN-MVS		80.46 47	82.91 35	73.11 135	89.83 9	39.02 323	77.06 116	82.61 92	80.04 5	90.60 7	92.85 11	74.93 44	85.21 57	63.15 143	95.15 18	95.09 2

PS-CasMVS		80.41 48	82.86 37	73.07 136	89.93 7	39.21 320	77.15 114	81.28 114	79.74 6	90.87 5	92.73 13	75.03 43	84.93 62	63.83 135	95.19 16	95.07 3

DTE-MVSNet		80.35 49	82.89 36	72.74 150	89.84 8	37.34 340	77.16 113	81.81 104	80.45 4	90.92 4	92.95 9	74.57 47	86.12 30	63.65 136	94.68 32	94.76 6

SD-MVS		80.28 50	81.55 48	76.47 88	83.57 90	67.83 81	83.39 48	85.35 39	64.42 96	86.14 40	87.07 132	74.02 51	80.97 130	77.70 29	92.32 80	80.62 214
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
WR-MVS_H		80.22 51	82.17 42	74.39 113	89.46 15	42.69 297	78.24 100	82.24 96	78.21 10	89.57 10	92.10 20	68.05 98	85.59 48	66.04 116	95.62 10	94.88 5

HPM-MVS++		79.89 52	79.80 58	80.18 40	89.02 26	78.44 11	83.49 46	80.18 140	64.71 95	78.11 139	88.39 115	65.46 128	83.14 91	77.64 30	91.20 96	78.94 238

XVG-OURS-SEG-HR		79.62 53	79.99 56	78.49 64	86.46 47	74.79 30	77.15 114	85.39 38	66.73 70	80.39 115	88.85 105	74.43 50	78.33 179	74.73 47	85.79 203	82.35 178

XVG-OURS		79.51 54	79.82 57	78.58 63	86.11 57	74.96 29	76.33 126	84.95 47	66.89 67	82.75 86	88.99 101	66.82 110	78.37 177	74.80 45	90.76 116	82.40 177

CP-MVSNet		79.48 55	81.65 46	72.98 139	89.66 13	39.06 322	76.76 117	80.46 134	78.91 8	90.32 8	91.70 28	68.49 93	84.89 63	63.40 140	95.12 19	95.01 4

OMC-MVS		79.41 56	78.79 66	81.28 30	80.62 134	70.71 59	80.91 67	84.76 50	62.54 116	81.77 95	86.65 147	71.46 68	83.53 85	67.95 96	92.44 76	89.60 26

v7n		79.37 57	80.41 53	76.28 90	78.67 164	55.81 182	79.22 90	82.51 94	70.72 48	87.54 22	92.44 16	68.00 100	81.34 118	72.84 62	91.72 84	91.69 12

TSAR-MVS + MP.		79.05 58	78.81 65	79.74 43	88.94 28	67.52 84	86.61 19	81.38 112	51.71 227	77.15 150	91.42 35	65.49 127	87.20 7	79.44 14	87.17 187	84.51 117
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mvs_tets		78.93 59	78.67 68	79.72 44	84.81 74	73.93 36	80.65 69	76.50 196	51.98 225	87.40 24	91.86 25	76.09 33	78.53 169	68.58 86	90.20 122	86.69 68

test_djsdf		78.88 60	78.27 72	80.70 36	81.42 123	71.24 53	83.98 37	75.72 203	52.27 220	87.37 27	92.25 18	68.04 99	80.56 136	72.28 68	91.15 98	90.32 23

HQP_MVS		78.77 61	78.78 67	78.72 60	85.18 67	65.18 105	82.74 53	85.49 33	65.45 80	78.23 136	89.11 97	60.83 173	86.15 28	71.09 71	90.94 105	84.82 101

bld_raw_dy_0_64		78.68 62	79.65 60	75.78 96	81.22 128	48.82 233	84.61 34	85.68 30	71.63 42	86.52 35	93.06 7	55.45 225	84.65 69	69.70 83	88.98 153	93.87 7

anonymousdsp		78.60 63	77.80 76	81.00 32	78.01 171	74.34 34	80.09 81	76.12 198	50.51 244	89.19 11	90.88 45	71.45 69	77.78 191	73.38 57	90.60 118	90.90 19

OurMVSNet-221017-0		78.57 64	78.53 70	78.67 61	80.48 135	64.16 113	80.24 78	82.06 99	61.89 120	88.77 13	93.32 5	57.15 211	82.60 100	70.08 78	92.80 71	89.25 30

jajsoiax		78.51 65	78.16 74	79.59 47	84.65 77	73.83 38	80.42 73	76.12 198	51.33 235	87.19 28	91.51 32	73.79 54	78.44 173	68.27 89	90.13 126	86.49 70

CNVR-MVS		78.49 66	78.59 69	78.16 68	85.86 61	67.40 85	78.12 103	81.50 108	63.92 100	77.51 147	86.56 151	68.43 95	84.82 65	73.83 54	91.61 88	82.26 182

DeepPCF-MVS	71.07 5	78.48 67	77.14 83	82.52 17	84.39 83	77.04 22	76.35 124	84.05 71	56.66 165	80.27 116	85.31 178	68.56 92	87.03 11	67.39 103	91.26 94	83.50 141

DP-MVS		78.44 68	79.29 63	75.90 94	81.86 119	65.33 103	79.05 91	84.63 58	74.83 19	80.41 114	86.27 159	71.68 66	83.45 87	62.45 147	92.40 77	78.92 239

NCCC		78.25 69	78.04 75	78.89 59	85.61 63	69.45 67	79.80 85	80.99 123	65.77 76	75.55 182	86.25 161	67.42 103	85.42 50	70.10 77	90.88 111	81.81 188

test_0402		78.17 70	79.48 61	74.24 115	83.50 91	59.15 161	72.52 166	74.60 213	75.34 16	88.69 14	91.81 26	75.06 42	82.37 103	65.10 121	88.68 156	81.20 194

MM		78.15 71	77.68 77	79.55 48	80.10 139	65.47 101	80.94 66	78.74 166	71.22 44	72.40 229	88.70 107	60.51 175	87.70 4	77.40 33	89.13 150	85.48 87

iter_conf05		77.90 72	79.33 62	73.61 126	80.83 130	46.85 260	82.06 58	86.72 17	72.78 28	85.44 52	91.94 22	56.47 220	83.95 77	70.51 75	87.24 183	90.02 24

AllTest		77.66 73	77.43 79	78.35 66	79.19 153	70.81 56	78.60 95	88.64 4	65.37 83	80.09 117	88.17 120	70.33 78	78.43 174	55.60 204	90.90 109	85.81 79

PS-MVSNAJss		77.54 74	77.35 81	78.13 70	84.88 72	66.37 93	78.55 96	79.59 150	53.48 212	86.29 37	92.43 17	62.39 152	80.25 143	67.90 97	90.61 117	87.77 52

ACMH	63.62 14	77.50 75	80.11 55	69.68 194	79.61 143	56.28 178	78.81 93	83.62 76	63.41 110	87.14 30	90.23 74	76.11 32	73.32 240	67.58 98	94.44 39	79.44 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		77.33 76	77.06 84	78.14 69	84.21 84	63.98 115	76.07 130	83.45 78	54.20 199	77.68 146	87.18 128	69.98 82	85.37 51	68.01 94	92.72 74	85.08 94

mvsmamba		77.20 77	76.37 88	79.69 46	80.34 137	61.52 133	80.58 70	82.12 98	53.54 211	83.93 72	91.03 40	49.49 257	85.97 33	73.26 58	93.08 67	91.59 13

DeepC-MVS_fast	69.89 7	77.17 78	76.33 90	79.70 45	83.90 88	67.94 79	80.06 83	83.75 74	56.73 164	74.88 191	85.32 177	65.54 126	87.79 3	65.61 120	91.14 99	83.35 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		77.08 79	77.39 80	76.14 92	76.86 191	56.87 176	80.32 77	87.52 12	63.45 108	74.66 196	84.52 186	69.87 84	84.94 61	69.76 80	89.59 138	86.60 69

MVSMamba_PlusPlus		76.88 80	78.21 73	72.88 146	80.83 130	48.71 234	83.28 49	82.79 87	72.78 28	79.17 125	91.94 22	56.47 220	83.95 77	70.51 75	86.15 198	85.99 76

X-MVStestdata		76.81 81	74.79 105	82.85 9	89.43 16	77.61 15	86.80 17	84.66 56	72.71 30	82.87 83	9.95 408	73.86 52	86.31 20	78.84 20	94.03 53	84.64 106

UniMVSNet_ETH3D		76.74 82	79.02 64	69.92 192	89.27 20	43.81 284	74.47 151	71.70 233	72.33 38	85.50 51	93.65 4	77.98 21	76.88 201	54.60 215	91.64 86	89.08 34

CS-MVS		76.51 83	76.00 93	78.06 71	77.02 183	64.77 109	80.78 68	82.66 91	60.39 130	74.15 204	83.30 207	69.65 86	82.07 109	69.27 84	86.75 193	87.36 58

train_agg		76.38 84	76.55 87	75.86 95	85.47 64	69.32 71	76.42 122	78.69 167	54.00 204	76.97 152	86.74 141	66.60 115	81.10 124	72.50 67	91.56 89	77.15 261

MVS_0304		76.32 85	75.96 95	77.42 76	79.33 148	60.86 146	80.18 80	74.88 210	66.93 66	69.11 268	88.95 103	57.84 207	86.12 30	76.63 38	89.77 135	85.28 89

TranMVSNet+NR-MVSNet		76.13 86	77.66 78	71.56 167	84.61 78	42.57 299	70.98 195	78.29 176	68.67 60	83.04 79	89.26 90	72.99 58	80.75 135	55.58 207	95.47 11	91.35 14

tt0805		76.12 87	78.43 71	69.20 202	81.32 125	41.37 305	76.72 118	77.64 185	63.78 103	82.06 91	87.88 125	79.78 11	79.05 160	64.33 128	92.40 77	87.17 63

SixPastTwentyTwo		75.77 88	76.34 89	74.06 118	81.69 121	54.84 187	76.47 119	75.49 205	64.10 99	87.73 18	92.24 19	50.45 253	81.30 120	67.41 101	91.46 91	86.04 75

RPSCF		75.76 89	74.37 110	79.93 41	74.81 217	77.53 17	77.53 108	79.30 155	59.44 137	78.88 129	89.80 82	71.26 71	73.09 242	57.45 187	80.89 263	89.17 33

v10		75.69 90	76.20 91	74.16 116	74.44 226	48.69 235	75.84 134	82.93 86	59.02 142	85.92 42	89.17 95	58.56 194	82.74 98	70.73 73	89.14 149	91.05 16

testf1		75.66 91	76.57 85	72.95 140	67.07 321	67.62 82	76.10 128	80.68 128	64.95 91	86.58 33	90.94 43	71.20 72	71.68 263	60.46 162	91.13 100	79.56 228

APD_test2		75.66 91	76.57 85	72.95 140	67.07 321	67.62 82	76.10 128	80.68 128	64.95 91	86.58 33	90.94 43	71.20 72	71.68 263	60.46 162	91.13 100	79.56 228

Anonymous20231211		75.54 93	77.19 82	70.59 176	77.67 177	45.70 273	74.73 146	80.19 139	68.80 57	82.95 82	92.91 10	66.26 119	76.76 203	58.41 182	92.77 72	89.30 29

Effi-MVS+-dtu		75.43 94	72.28 151	84.91 3	77.05 181	83.58 2	78.47 97	77.70 184	57.68 153	74.89 190	78.13 282	64.80 134	84.26 75	56.46 197	85.32 210	86.88 65

iter_conf05_11		75.31 95	75.20 103	75.61 99	76.77 192	54.48 191	83.28 49	86.25 24	55.47 177	79.17 125	86.44 155	52.98 238	84.17 76	68.03 92	88.46 158	88.17 49

F-COLMAP		75.29 96	73.99 116	79.18 52	81.73 120	71.90 47	81.86 62	82.98 84	59.86 135	72.27 230	84.00 193	64.56 136	83.07 94	51.48 237	87.19 186	82.56 175

casdiffmvs_mvg		75.26 97	76.18 92	72.52 155	72.87 255	49.47 228	72.94 164	84.71 54	59.49 136	80.90 110	88.81 106	70.07 81	79.71 151	67.40 102	88.39 159	88.40 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HQP-MVS		75.24 98	75.01 104	75.94 93	82.37 110	58.80 166	77.32 110	84.12 69	59.08 138	71.58 238	85.96 171	58.09 200	85.30 53	67.38 105	89.16 146	83.73 138

TAPA-MVS	65.27 12	75.16 99	74.29 112	77.77 72	74.86 216	68.08 78	77.89 104	84.04 72	55.15 180	76.19 177	83.39 201	66.91 108	80.11 147	60.04 169	90.14 125	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS-MVSNet		75.10 100	75.42 101	74.15 117	79.23 151	48.05 244	79.43 86	78.04 180	70.09 53	79.17 125	88.02 124	53.04 237	83.60 83	58.05 184	93.76 59	90.79 20

v8		75.07 101	75.64 98	73.35 129	73.42 240	47.46 254	75.20 137	81.45 110	60.05 132	85.64 46	89.26 90	58.08 202	81.80 113	69.71 82	87.97 167	90.79 20

APD_test1		75.04 102	75.38 102	74.02 119	69.89 287	70.15 63	76.46 120	79.71 146	65.50 79	82.99 81	88.60 111	66.94 107	72.35 253	59.77 172	88.54 157	79.56 228

UniMVSNet (Re)		75.00 103	75.48 100	73.56 127	83.14 96	47.92 246	70.41 204	81.04 122	63.67 104	79.54 121	86.37 157	62.83 146	81.82 112	57.10 191	95.25 15	90.94 18

PHI-MVS		74.92 104	74.36 111	76.61 84	76.40 195	62.32 126	80.38 74	83.15 82	54.16 201	73.23 219	80.75 239	62.19 155	83.86 80	68.02 93	90.92 108	83.65 139

DU-MVS		74.91 105	75.57 99	72.93 143	83.50 91	45.79 270	69.47 214	80.14 141	65.22 86	81.74 97	87.08 130	61.82 158	81.07 126	56.21 199	94.98 21	91.93 10

UniMVSNet_NR-MVSNet		74.90 106	75.65 97	72.64 153	83.04 101	45.79 270	69.26 217	78.81 162	66.66 71	81.74 97	86.88 136	63.26 142	81.07 126	56.21 199	94.98 21	91.05 16

CS-MVS-test		74.89 107	74.23 113	76.86 81	77.01 184	62.94 123	78.98 92	84.61 59	58.62 145	70.17 258	80.80 238	66.74 114	81.96 110	61.74 150	89.40 144	85.69 84

nrg030		74.87 108	75.99 94	71.52 168	74.90 215	49.88 227	74.10 156	82.58 93	54.55 192	83.50 77	89.21 92	71.51 67	75.74 211	61.24 154	92.34 79	88.94 39

Vis-MVSNet		74.85 109	74.56 107	75.72 97	81.63 122	64.64 110	76.35 124	79.06 158	62.85 114	73.33 217	88.41 114	62.54 150	79.59 154	63.94 134	82.92 241	82.94 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MSLP-MVS++		74.48 110	75.78 96	70.59 176	84.66 76	62.40 124	78.65 94	84.24 66	60.55 129	77.71 145	81.98 223	63.12 143	77.64 193	62.95 144	88.14 162	71.73 311

AdaColmap		74.22 111	74.56 107	73.20 132	81.95 117	60.97 142	79.43 86	80.90 124	65.57 78	72.54 227	81.76 227	70.98 75	85.26 54	47.88 272	90.00 127	73.37 292

CSCG		74.12 112	74.39 109	73.33 130	79.35 147	61.66 132	77.45 109	81.98 101	62.47 118	79.06 128	80.19 249	61.83 157	78.79 166	59.83 171	87.35 176	79.54 231

test_fmvsmconf0.01_n		73.91 113	73.64 123	74.71 106	69.79 291	66.25 94	75.90 132	79.90 144	46.03 283	76.48 171	85.02 181	67.96 101	73.97 235	74.47 50	87.22 184	83.90 132

PAPM_NR		73.91 113	74.16 114	73.16 133	81.90 118	53.50 198	81.28 64	81.40 111	66.17 74	73.30 218	83.31 206	59.96 180	83.10 93	58.45 181	81.66 258	82.87 165

EPP-MVSNet		73.86 115	73.38 127	75.31 103	78.19 167	53.35 200	80.45 72	77.32 189	65.11 89	76.47 172	86.80 137	49.47 258	83.77 81	53.89 224	92.72 74	88.81 43

K. test v3		73.67 116	73.61 124	73.87 121	79.78 141	55.62 185	74.69 148	62.04 309	66.16 75	84.76 62	93.23 6	49.47 258	80.97 130	65.66 119	86.67 194	85.02 96

NR-MVSNet		73.62 117	74.05 115	72.33 160	83.50 91	43.71 285	65.65 270	77.32 189	64.32 97	75.59 181	87.08 130	62.45 151	81.34 118	54.90 210	95.63 9	91.93 10

DP-MVS Recon		73.57 118	72.69 143	76.23 91	82.85 105	63.39 118	74.32 152	82.96 85	57.75 152	70.35 254	81.98 223	64.34 138	84.41 74	49.69 251	89.95 129	80.89 204

CNLPA		73.44 119	73.03 138	74.66 107	78.27 166	75.29 27	75.99 131	78.49 171	65.39 82	75.67 180	83.22 212	61.23 166	66.77 308	53.70 226	85.33 209	81.92 187

MCST-MVS		73.42 120	73.34 130	73.63 125	81.28 126	59.17 160	74.80 144	83.13 83	45.50 286	72.84 222	83.78 197	65.15 131	80.99 128	64.54 125	89.09 152	80.73 210

v1192		73.40 121	73.42 125	73.32 131	74.65 223	48.67 236	72.21 169	81.73 105	52.76 217	81.85 93	84.56 185	57.12 212	82.24 107	68.58 86	87.33 178	89.06 35

114514_t		73.40 121	73.33 131	73.64 124	84.15 86	57.11 174	78.20 101	80.02 142	43.76 302	72.55 226	86.07 169	64.00 139	83.35 89	60.14 167	91.03 104	80.45 217

FC-MVSNet-test		73.32 123	74.78 106	68.93 211	79.21 152	36.57 342	71.82 182	79.54 152	57.63 157	82.57 88	90.38 67	59.38 187	78.99 162	57.91 185	94.56 34	91.23 15

v1144		73.29 124	73.39 126	73.01 137	74.12 232	48.11 242	72.01 174	81.08 121	53.83 208	81.77 95	84.68 183	58.07 203	81.91 111	68.10 90	86.86 189	88.99 38

test_fmvsmconf0.1_n		73.26 125	72.82 142	74.56 108	69.10 297	66.18 96	74.65 150	79.34 154	45.58 285	75.54 183	83.91 194	67.19 105	73.88 238	73.26 58	86.86 189	83.63 140

GeoE		73.14 126	73.77 121	71.26 171	78.09 169	52.64 203	74.32 152	79.56 151	56.32 168	76.35 175	83.36 205	70.76 76	77.96 187	63.32 141	81.84 252	83.18 156

baseline		73.10 127	73.96 117	70.51 178	71.46 265	46.39 267	72.08 171	84.40 62	55.95 172	76.62 164	86.46 154	67.20 104	78.03 186	64.22 129	87.27 182	87.11 64

h-mvs33		73.08 128	71.61 160	77.48 74	83.89 89	72.89 45	70.47 202	71.12 249	54.28 195	77.89 140	83.41 200	49.04 262	80.98 129	63.62 137	90.77 115	78.58 242

TSAR-MVS + GP.		73.08 128	71.60 161	77.54 73	78.99 160	70.73 58	74.96 139	69.38 261	60.73 128	74.39 201	78.44 276	57.72 208	82.78 97	60.16 166	89.60 137	79.11 236

v1240		73.06 130	73.14 133	72.84 147	74.74 219	47.27 258	71.88 181	81.11 118	51.80 226	82.28 90	84.21 190	56.22 223	82.34 104	68.82 85	87.17 187	88.91 40

casdiffmvs		73.06 130	73.84 118	70.72 174	71.32 266	46.71 263	70.93 196	84.26 65	55.62 175	77.46 148	87.10 129	67.09 106	77.81 189	63.95 132	86.83 191	87.64 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		73.01 132	73.12 135	72.66 152	73.79 236	49.90 223	71.63 184	78.44 172	58.22 147	80.51 113	86.63 148	58.15 198	79.62 152	62.51 145	88.20 161	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet		73.00 133	71.84 155	76.48 87	75.82 205	61.28 136	74.81 142	80.37 137	63.17 112	62.43 327	80.50 243	61.10 170	85.16 60	64.00 131	84.34 227	83.01 162

v144192		72.99 134	73.06 137	72.77 148	74.58 224	47.48 253	71.90 180	80.44 135	51.57 229	81.46 101	84.11 192	58.04 204	82.12 108	67.98 95	87.47 173	88.70 45

MVS_111021_HR		72.98 135	72.97 140	72.99 138	80.82 132	65.47 101	68.81 224	72.77 225	57.67 154	75.76 179	82.38 219	71.01 74	77.17 196	61.38 153	86.15 198	76.32 267

v1921920		72.96 136	72.98 139	72.89 145	74.67 220	47.58 252	71.92 179	80.69 127	51.70 228	81.69 99	83.89 195	56.58 218	82.25 106	68.34 88	87.36 175	88.82 42

test_fmvsmconf_n		72.91 137	72.40 149	74.46 109	68.62 301	66.12 97	74.21 155	78.80 164	45.64 284	74.62 197	83.25 209	66.80 113	73.86 239	72.97 61	86.66 195	83.39 148

CLD-MVS		72.88 138	72.36 150	74.43 112	77.03 182	54.30 192	68.77 227	83.43 79	52.12 222	76.79 161	74.44 311	69.54 87	83.91 79	55.88 202	93.25 66	85.09 93
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EI-MVSNet-Vis-set		72.78 139	71.87 154	75.54 101	74.77 218	59.02 164	72.24 168	71.56 236	63.92 100	78.59 131	71.59 333	66.22 120	78.60 168	67.58 98	80.32 270	89.00 37

ETV-MVS		72.72 140	72.16 153	74.38 114	76.90 189	55.95 179	73.34 161	84.67 55	62.04 119	72.19 233	70.81 338	65.90 123	85.24 56	58.64 179	84.96 217	81.95 186

PCF-MVS	63.80 13	72.70 141	71.69 157	75.72 97	78.10 168	60.01 154	73.04 163	81.50 108	45.34 290	79.66 120	84.35 189	65.15 131	82.65 99	48.70 261	89.38 145	84.50 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 142	71.68 158	75.47 102	74.67 220	58.64 169	72.02 173	71.50 237	63.53 106	78.58 133	71.39 337	65.98 121	78.53 169	67.30 108	80.18 272	89.23 31

Anonymous20240529		72.56 143	73.79 120	68.86 213	76.89 190	45.21 275	68.80 226	77.25 191	67.16 65	76.89 156	90.44 59	65.95 122	74.19 233	50.75 243	90.00 127	87.18 62

FIs		72.56 143	73.80 119	68.84 214	78.74 163	37.74 336	71.02 194	79.83 145	56.12 169	80.88 111	89.45 87	58.18 196	78.28 180	56.63 193	93.36 64	90.51 22

v2v482		72.55 145	72.58 145	72.43 157	72.92 254	46.72 262	71.41 187	79.13 157	55.27 178	81.17 105	85.25 179	55.41 226	81.13 123	67.25 109	85.46 205	89.43 28

test_fmvsmvis_n_1920		72.36 146	72.49 146	71.96 163	71.29 267	64.06 114	72.79 165	81.82 103	40.23 333	81.25 104	81.04 235	70.62 77	68.69 284	69.74 81	83.60 237	83.14 157

hse-mvs2		72.32 147	70.66 172	77.31 79	83.10 100	71.77 48	69.19 219	71.45 239	54.28 195	77.89 140	78.26 278	49.04 262	79.23 157	63.62 137	89.13 150	80.92 203

sasdasda		72.29 148	73.38 127	69.04 205	74.23 227	47.37 255	73.93 158	83.18 80	54.36 193	76.61 165	81.64 229	72.03 62	75.34 215	57.12 189	87.28 180	84.40 119

canonicalmvs		72.29 148	73.38 127	69.04 205	74.23 227	47.37 255	73.93 158	83.18 80	54.36 193	76.61 165	81.64 229	72.03 62	75.34 215	57.12 189	87.28 180	84.40 119

Effi-MVS+		72.10 150	72.28 151	71.58 166	74.21 230	50.33 216	74.72 147	82.73 89	62.62 115	70.77 250	76.83 292	69.96 83	80.97 130	60.20 164	78.43 290	83.45 147

MVS_111021_LR		72.10 150	71.82 156	72.95 140	79.53 145	73.90 37	70.45 203	66.64 275	56.87 161	76.81 160	81.76 227	68.78 90	71.76 261	61.81 148	83.74 233	73.18 294

pmmvs6		71.82 152	73.66 122	66.31 244	75.94 204	42.01 301	66.99 252	72.53 228	63.45 108	76.43 173	92.78 12	72.95 59	69.69 277	51.41 238	90.46 119	87.22 59

PLC	62.01 16	71.79 153	70.28 174	76.33 89	80.31 138	68.63 76	78.18 102	81.24 115	54.57 191	67.09 296	80.63 241	59.44 185	81.74 115	46.91 279	84.17 228	78.63 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MGCFI-Net		71.70 154	73.10 136	67.49 230	73.23 244	43.08 293	72.06 172	82.43 95	54.58 190	75.97 178	82.00 221	72.42 60	75.22 217	57.84 186	87.34 177	84.18 126

VDDNet		71.60 155	73.13 134	67.02 237	86.29 48	41.11 307	69.97 208	66.50 276	68.72 59	74.74 192	91.70 28	59.90 181	75.81 209	48.58 263	91.72 84	84.15 128

3Dnovator	65.95 11	71.50 156	71.22 166	72.34 159	73.16 245	63.09 121	78.37 98	78.32 174	57.67 154	72.22 232	84.61 184	54.77 227	78.47 171	60.82 160	81.07 262	75.45 273

FA-MVS(test-final)		71.27 157	71.06 167	71.92 164	73.96 233	52.32 205	76.45 121	76.12 198	59.07 141	74.04 209	86.18 162	52.18 242	79.43 156	59.75 173	81.76 253	84.03 129

WR-MVS		71.20 158	72.48 147	67.36 232	84.98 71	35.70 350	64.43 285	68.66 266	65.05 90	81.49 100	86.43 156	57.57 209	76.48 205	50.36 247	93.32 65	89.90 25

V42		71.06 159	70.83 170	71.72 165	67.25 317	47.14 259	65.94 264	80.35 138	51.35 234	83.40 78	83.23 210	59.25 188	78.80 165	65.91 117	80.81 266	89.23 31

FMVSNet1		71.06 159	72.48 147	66.81 238	77.65 178	40.68 311	71.96 176	73.03 220	61.14 124	79.45 123	90.36 70	60.44 176	75.20 219	50.20 248	88.05 164	84.54 113

dcpmvs_2		71.02 161	72.65 144	66.16 245	76.06 203	50.49 214	71.97 175	79.36 153	50.34 245	82.81 85	83.63 198	64.38 137	67.27 299	61.54 152	83.71 235	80.71 212

API-MVS		70.97 162	71.51 163	69.37 197	75.20 210	55.94 180	80.99 65	76.84 193	62.48 117	71.24 246	77.51 288	61.51 162	80.96 133	52.04 233	85.76 204	71.22 316

VDD-MVS		70.81 163	71.44 164	68.91 212	79.07 158	46.51 264	67.82 239	70.83 253	61.23 123	74.07 207	88.69 108	59.86 182	75.62 212	51.11 240	90.28 121	84.61 109

EG-PatchMatch MVS		70.70 164	70.88 169	70.16 186	82.64 109	58.80 166	71.48 185	73.64 217	54.98 181	76.55 168	81.77 226	61.10 170	78.94 163	54.87 211	80.84 265	72.74 301

Baseline_NR-MVSNet		70.62 165	73.19 132	62.92 275	76.97 185	34.44 358	68.84 222	70.88 252	60.25 131	79.50 122	90.53 56	61.82 158	69.11 281	54.67 214	95.27 14	85.22 90

alignmvs		70.54 166	71.00 168	69.15 204	73.50 238	48.04 245	69.85 211	79.62 147	53.94 207	76.54 169	82.00 221	59.00 190	74.68 226	57.32 188	87.21 185	84.72 104

MG-MVS		70.47 167	71.34 165	67.85 226	79.26 150	40.42 315	74.67 149	75.15 209	58.41 146	68.74 280	88.14 123	56.08 224	83.69 82	59.90 170	81.71 257	79.43 233

AUN-MVS		70.22 168	67.88 204	77.22 80	82.96 104	71.61 49	69.08 220	71.39 240	49.17 259	71.70 236	78.07 283	37.62 329	79.21 158	61.81 148	89.15 148	80.82 206

UGNet		70.20 169	69.05 184	73.65 123	76.24 197	63.64 116	75.87 133	72.53 228	61.48 122	60.93 337	86.14 165	52.37 241	77.12 197	50.67 244	85.21 211	80.17 222
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
PVSNet_Blended_VisFu		70.04 170	68.88 187	73.53 128	82.71 107	63.62 117	74.81 142	81.95 102	48.53 264	67.16 295	79.18 267	51.42 248	78.38 176	54.39 219	79.72 279	78.60 241

Fast-Effi-MVS+-dtu		70.00 171	68.74 191	73.77 122	73.47 239	64.53 111	71.36 188	78.14 179	55.81 174	68.84 278	74.71 308	65.36 129	75.75 210	52.00 234	79.00 284	81.03 199

DPM-MVS		69.98 172	69.22 183	72.26 161	82.69 108	58.82 165	70.53 201	81.23 116	47.79 271	64.16 312	80.21 247	51.32 249	83.12 92	60.14 167	84.95 218	74.83 279

MVSFormer		69.93 173	69.03 185	72.63 154	74.93 213	59.19 158	83.98 37	75.72 203	52.27 220	63.53 322	76.74 293	43.19 293	80.56 136	72.28 68	78.67 288	78.14 249

MVS_Test		69.84 174	70.71 171	67.24 233	67.49 315	43.25 292	69.87 210	81.22 117	52.69 218	71.57 241	86.68 144	62.09 156	74.51 228	66.05 115	78.74 286	83.96 130

c3_l		69.82 175	69.89 176	69.61 195	66.24 327	43.48 288	68.12 236	79.61 149	51.43 231	77.72 144	80.18 250	54.61 230	78.15 185	63.62 137	87.50 172	87.20 61

test_fmvsm_n_1920		69.63 176	68.45 194	73.16 133	70.56 276	65.86 99	70.26 205	78.35 173	37.69 349	74.29 202	78.89 272	61.10 170	68.10 290	65.87 118	79.07 283	85.53 86

TransMVSNet (Re)		69.62 177	71.63 159	63.57 265	76.51 194	35.93 348	65.75 269	71.29 244	61.05 125	75.02 188	89.90 81	65.88 124	70.41 275	49.79 250	89.48 140	84.38 121

EI-MVSNet		69.61 178	69.01 186	71.41 170	73.94 234	49.90 223	71.31 190	71.32 242	58.22 147	75.40 186	70.44 340	58.16 197	75.85 207	62.51 145	79.81 276	88.48 46

Gipuma		69.55 179	72.83 141	59.70 300	63.63 347	53.97 195	80.08 82	75.93 201	64.24 98	73.49 214	88.93 104	57.89 206	62.46 328	59.75 173	91.55 90	62.67 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tttt0517		69.46 180	67.79 206	74.46 109	75.34 208	52.72 202	75.05 138	63.27 302	54.69 187	78.87 130	84.37 188	26.63 382	81.15 122	63.95 132	87.93 168	89.51 27

eth_miper_zixun_eth		69.42 181	68.73 192	71.50 169	67.99 309	46.42 265	67.58 241	78.81 162	50.72 242	78.13 138	80.34 246	50.15 255	80.34 141	60.18 165	84.65 221	87.74 53

BH-untuned		69.39 182	69.46 178	69.18 203	77.96 172	56.88 175	68.47 233	77.53 186	56.77 163	77.79 143	79.63 258	60.30 178	80.20 146	46.04 286	80.65 267	70.47 322

v148		69.38 183	69.39 179	69.36 198	69.14 296	44.56 279	68.83 223	72.70 226	54.79 185	78.59 131	84.12 191	54.69 228	76.74 204	59.40 176	82.20 246	86.79 66

PAPR		69.20 184	68.66 193	70.82 173	75.15 212	47.77 249	75.31 136	81.11 118	49.62 255	66.33 298	79.27 264	61.53 161	82.96 95	48.12 269	81.50 260	81.74 190

QAPM		69.18 185	69.26 181	68.94 210	71.61 263	52.58 204	80.37 75	78.79 165	49.63 254	73.51 213	85.14 180	53.66 234	79.12 159	55.11 209	75.54 312	75.11 278

LCM-MVSNet-Re		69.10 186	71.57 162	61.70 284	70.37 281	34.30 360	61.45 305	79.62 147	56.81 162	89.59 9	88.16 122	68.44 94	72.94 243	42.30 306	87.33 178	77.85 255

EPNet		69.10 186	67.32 211	74.46 109	68.33 305	61.27 137	77.56 106	63.57 300	60.95 126	56.62 361	82.75 213	51.53 247	81.24 121	54.36 220	90.20 122	80.88 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		68.83 188	68.31 195	70.38 179	70.55 278	48.31 238	63.78 291	82.13 97	54.00 204	68.96 272	75.17 304	58.95 191	80.06 148	58.55 180	82.74 243	82.76 168
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
Fast-Effi-MVS+		68.81 189	68.30 196	70.35 181	74.66 222	48.61 237	66.06 263	78.32 174	50.62 243	71.48 244	75.54 300	68.75 91	79.59 154	50.55 246	78.73 287	82.86 166

OpenMVS	62.51 15	68.76 190	68.75 190	68.78 215	70.56 276	53.91 196	78.29 99	77.35 188	48.85 262	70.22 256	83.52 199	52.65 240	76.93 199	55.31 208	81.99 248	75.49 272

VPA-MVSNet		68.71 191	70.37 173	63.72 263	76.13 199	38.06 334	64.10 287	71.48 238	56.60 167	74.10 206	88.31 117	64.78 135	69.72 276	47.69 274	90.15 124	83.37 150

BH-RMVSNet		68.69 192	68.20 200	70.14 187	76.40 195	53.90 197	64.62 282	73.48 218	58.01 149	73.91 211	81.78 225	59.09 189	78.22 181	48.59 262	77.96 296	78.31 245

EIA-MVS		68.59 193	67.16 213	72.90 144	75.18 211	55.64 184	69.39 215	81.29 113	52.44 219	64.53 308	70.69 339	60.33 177	82.30 105	54.27 221	76.31 306	80.75 209

pm-mvs1		68.40 194	69.85 177	64.04 261	73.10 249	39.94 317	64.61 283	70.50 254	55.52 176	73.97 210	89.33 88	63.91 140	68.38 287	49.68 252	88.02 165	83.81 134

miper_ehance_all_eth		68.36 195	68.16 201	68.98 208	65.14 338	43.34 290	67.07 251	78.92 161	49.11 260	76.21 176	77.72 285	53.48 235	77.92 188	61.16 156	84.59 223	85.68 85

GBi-Net		68.30 196	68.79 188	66.81 238	73.14 246	40.68 311	71.96 176	73.03 220	54.81 182	74.72 193	90.36 70	48.63 268	75.20 219	47.12 276	85.37 206	84.54 113

test1		68.30 196	68.79 188	66.81 238	73.14 246	40.68 311	71.96 176	73.03 220	54.81 182	74.72 193	90.36 70	48.63 268	75.20 219	47.12 276	85.37 206	84.54 113

FE-MVS		68.29 198	66.96 217	72.26 161	74.16 231	54.24 193	77.55 107	73.42 219	57.65 156	72.66 224	84.91 182	32.02 355	81.49 117	48.43 265	81.85 251	81.04 198

DIV-MVS_self_test		68.27 199	68.26 197	68.29 221	64.98 339	43.67 286	65.89 265	74.67 211	50.04 251	76.86 158	82.43 217	48.74 266	75.38 213	60.94 158	89.81 132	85.81 79

cl____		68.26 200	68.26 197	68.29 221	64.98 339	43.67 286	65.89 265	74.67 211	50.04 251	76.86 158	82.42 218	48.74 266	75.38 213	60.92 159	89.81 132	85.80 83

TinyColmap		67.98 201	69.28 180	64.08 259	67.98 310	46.82 261	70.04 206	75.26 207	53.05 214	77.36 149	86.79 138	59.39 186	72.59 250	45.64 289	88.01 166	72.83 299

xiu_mvs_v1_base_debu		67.87 202	67.07 214	70.26 182	79.13 155	61.90 129	67.34 245	71.25 245	47.98 267	67.70 288	74.19 316	61.31 163	72.62 247	56.51 194	78.26 292	76.27 268

xiu_mvs_v1_base		67.87 202	67.07 214	70.26 182	79.13 155	61.90 129	67.34 245	71.25 245	47.98 267	67.70 288	74.19 316	61.31 163	72.62 247	56.51 194	78.26 292	76.27 268

xiu_mvs_v1_base_debi		67.87 202	67.07 214	70.26 182	79.13 155	61.90 129	67.34 245	71.25 245	47.98 267	67.70 288	74.19 316	61.31 163	72.62 247	56.51 194	78.26 292	76.27 268

MAR-MVS		67.72 205	66.16 223	72.40 158	74.45 225	64.99 108	74.87 140	77.50 187	48.67 263	65.78 302	68.58 363	57.01 215	77.79 190	46.68 282	81.92 249	74.42 285
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
IterMVS-SCA-FT		67.68 206	66.07 225	72.49 156	73.34 242	58.20 171	63.80 290	65.55 284	48.10 266	76.91 155	82.64 216	45.20 280	78.84 164	61.20 155	77.89 297	80.44 218

LF4IMVS		67.50 207	67.31 212	68.08 224	58.86 373	61.93 128	71.43 186	75.90 202	44.67 296	72.42 228	80.20 248	57.16 210	70.44 273	58.99 178	86.12 200	71.88 309

fmvsm_l_conf0.5_n		67.48 208	66.88 219	69.28 201	67.41 316	62.04 127	70.69 200	69.85 258	39.46 336	69.59 264	81.09 234	58.15 198	68.73 283	67.51 100	78.16 295	77.07 265

FMVSNet2		67.48 208	68.21 199	65.29 250	73.14 246	38.94 324	68.81 224	71.21 248	54.81 182	76.73 162	86.48 153	48.63 268	74.60 227	47.98 271	86.11 201	82.35 178

MSDG		67.47 210	67.48 210	67.46 231	70.70 272	54.69 189	66.90 255	78.17 177	60.88 127	70.41 253	74.76 306	61.22 168	73.18 241	47.38 275	76.87 302	74.49 283

diffmvs		67.42 211	67.50 209	67.20 234	62.26 352	45.21 275	64.87 279	77.04 192	48.21 265	71.74 235	79.70 257	58.40 195	71.17 267	64.99 122	80.27 271	85.22 90
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_conf0.1_n_a		67.37 212	66.36 221	70.37 180	70.86 269	61.17 138	74.00 157	57.18 328	40.77 328	68.83 279	80.88 237	63.11 144	67.61 295	66.94 110	74.72 319	82.33 181

cl22		67.14 213	66.51 220	69.03 207	63.20 348	43.46 289	66.88 256	76.25 197	49.22 258	74.48 199	77.88 284	45.49 279	77.40 195	60.64 161	84.59 223	86.24 71

ANet_high		67.08 214	69.94 175	58.51 309	57.55 379	27.09 391	58.43 328	76.80 194	63.56 105	82.40 89	91.93 24	59.82 183	64.98 319	50.10 249	88.86 155	83.46 146

LFMVS		67.06 215	67.89 203	64.56 255	78.02 170	38.25 331	70.81 199	59.60 316	65.18 87	71.06 248	86.56 151	43.85 289	75.22 217	46.35 283	89.63 136	80.21 221

thisisatest0530		67.05 216	65.16 235	72.73 151	73.10 249	50.55 213	71.26 192	63.91 298	50.22 248	74.46 200	80.75 239	26.81 381	80.25 143	59.43 175	86.50 196	87.37 57

fmvsm_s_conf0.5_n_a		67.00 217	65.95 228	70.17 185	69.72 292	61.16 139	73.34 161	56.83 331	40.96 325	68.36 282	80.08 252	62.84 145	67.57 296	66.90 112	74.50 323	81.78 189

fmvsm_l_conf0.5_n_a		66.66 218	65.97 227	68.72 216	67.09 319	61.38 135	70.03 207	69.15 263	38.59 343	68.41 281	80.36 245	56.56 219	68.32 288	66.10 114	77.45 299	76.46 266

fmvsm_s_conf0.1_n		66.60 219	65.54 229	69.77 193	68.99 298	59.15 161	72.12 170	56.74 333	40.72 330	68.25 285	80.14 251	61.18 169	66.92 302	67.34 107	74.40 324	83.23 155

MIMVSNet1		66.57 220	69.23 182	58.59 308	81.26 127	37.73 337	64.06 288	57.62 321	57.02 160	78.40 135	90.75 49	62.65 147	58.10 347	41.77 311	89.58 139	79.95 223

tfpnnormal		66.48 221	67.93 202	62.16 281	73.40 241	36.65 341	63.45 293	64.99 288	55.97 171	72.82 223	87.80 126	57.06 214	69.10 282	48.31 267	87.54 170	80.72 211

KD-MVS_self_test		66.38 222	67.51 208	62.97 273	61.76 354	34.39 359	58.11 331	75.30 206	50.84 241	77.12 151	85.42 176	56.84 216	69.44 278	51.07 241	91.16 97	85.08 94

SDMVSNet		66.36 223	67.85 205	61.88 283	73.04 252	46.14 269	58.54 326	71.36 241	51.42 232	68.93 274	82.72 214	65.62 125	62.22 331	54.41 218	84.67 219	77.28 258

fmvsm_s_conf0.5_n		66.34 224	65.27 232	69.57 196	68.20 306	59.14 163	71.66 183	56.48 334	40.92 326	67.78 287	79.46 260	61.23 166	66.90 303	67.39 103	74.32 327	82.66 172

Anonymous202405211		66.02 225	66.89 218	63.43 268	74.22 229	38.14 332	59.00 322	66.13 278	63.33 111	69.76 263	85.95 172	51.88 243	70.50 272	44.23 297	87.52 171	81.64 191

miper_enhance_ethall		65.86 226	65.05 242	68.28 223	61.62 356	42.62 298	64.74 280	77.97 181	42.52 312	73.42 216	72.79 326	49.66 256	77.68 192	58.12 183	84.59 223	84.54 113

RPMNet		65.77 227	65.08 241	67.84 227	66.37 324	48.24 240	70.93 196	86.27 21	54.66 188	61.35 331	86.77 140	33.29 343	85.67 47	55.93 201	70.17 356	69.62 331

VPNet		65.58 228	67.56 207	59.65 301	79.72 142	30.17 380	60.27 316	62.14 305	54.19 200	71.24 246	86.63 148	58.80 192	67.62 294	44.17 298	90.87 112	81.18 195

PVSNet_BlendedMVS		65.38 229	64.30 243	68.61 217	69.81 288	49.36 229	65.60 272	78.96 159	45.50 286	59.98 340	78.61 274	51.82 244	78.20 182	44.30 295	84.11 229	78.27 246

TAMVS		65.31 230	63.75 249	69.97 191	82.23 114	59.76 156	66.78 257	63.37 301	45.20 291	69.79 262	79.37 263	47.42 274	72.17 254	34.48 360	85.15 213	77.99 253

test_yl		65.11 231	65.09 239	65.18 251	70.59 274	40.86 309	63.22 298	72.79 223	57.91 150	68.88 276	79.07 270	42.85 296	74.89 223	45.50 291	84.97 214	79.81 224

DCV-MVSNet		65.11 231	65.09 239	65.18 251	70.59 274	40.86 309	63.22 298	72.79 223	57.91 150	68.88 276	79.07 270	42.85 296	74.89 223	45.50 291	84.97 214	79.81 224

mvs_anonymous		65.08 233	65.49 230	63.83 262	63.79 345	37.60 338	66.52 260	69.82 259	43.44 307	73.46 215	86.08 168	58.79 193	71.75 262	51.90 235	75.63 311	82.15 183

FMVSNet3		65.00 234	65.16 235	64.52 256	69.47 293	37.56 339	66.63 258	70.38 255	51.55 230	74.72 193	83.27 208	37.89 327	74.44 229	47.12 276	85.37 206	81.57 192

ECVR-MVS		64.82 235	65.22 233	63.60 264	78.80 161	31.14 375	66.97 253	56.47 335	54.23 197	69.94 260	88.68 109	37.23 330	74.81 225	45.28 294	89.41 142	84.86 99

BH-w/o		64.81 236	64.29 244	66.36 243	76.08 202	54.71 188	65.61 271	75.23 208	50.10 250	71.05 249	71.86 332	54.33 231	79.02 161	38.20 334	76.14 307	65.36 357

EGC-MVSNET		64.77 237	61.17 270	75.60 100	86.90 43	74.47 31	84.04 36	68.62 267	0.60 410	1.13 412	91.61 31	65.32 130	74.15 234	64.01 130	88.28 160	78.17 248

test1111		64.62 238	65.19 234	62.93 274	79.01 159	29.91 381	65.45 273	54.41 345	54.09 202	71.47 245	88.48 113	37.02 331	74.29 232	46.83 281	89.94 130	84.58 112

cascas		64.59 239	62.77 260	70.05 189	75.27 209	50.02 220	61.79 304	71.61 234	42.46 313	63.68 319	68.89 359	49.33 260	80.35 140	47.82 273	84.05 230	79.78 226

TR-MVS		64.59 239	63.54 252	67.73 229	75.75 207	50.83 212	63.39 294	70.29 256	49.33 257	71.55 242	74.55 309	50.94 250	78.46 172	40.43 319	75.69 310	73.89 289

PM-MVS		64.49 241	63.61 251	67.14 236	76.68 193	75.15 28	68.49 232	42.85 390	51.17 238	77.85 142	80.51 242	45.76 276	66.31 311	52.83 232	76.35 305	59.96 379

jason		64.47 242	62.84 259	69.34 200	76.91 187	59.20 157	67.15 250	65.67 281	35.29 360	65.16 305	76.74 293	44.67 284	70.68 269	54.74 213	79.28 282	78.14 249
jason: jason.
xiu_mvs_v2_base		64.43 243	63.96 247	65.85 249	77.72 176	51.32 209	63.63 292	72.31 231	45.06 294	61.70 328	69.66 351	62.56 148	73.93 237	49.06 258	73.91 329	72.31 305

pmmvs-eth3d		64.41 244	63.27 255	67.82 228	75.81 206	60.18 153	69.49 213	62.05 308	38.81 342	74.13 205	82.23 220	43.76 290	68.65 285	42.53 305	80.63 269	74.63 280

CDS-MVSNet		64.33 245	62.66 261	69.35 199	80.44 136	58.28 170	65.26 275	65.66 282	44.36 297	67.30 294	75.54 300	43.27 292	71.77 260	37.68 337	84.44 226	78.01 252
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		64.27 246	63.73 250	65.90 248	77.82 174	51.42 208	63.33 295	72.33 230	45.09 293	61.60 329	68.04 365	62.39 152	73.95 236	49.07 257	73.87 330	72.34 304

ab-mvs		64.11 247	65.13 238	61.05 291	71.99 261	38.03 335	67.59 240	68.79 265	49.08 261	65.32 304	86.26 160	58.02 205	66.85 306	39.33 323	79.79 278	78.27 246

CANet_DTU		64.04 248	63.83 248	64.66 254	68.39 302	42.97 295	73.45 160	74.50 214	52.05 224	54.78 370	75.44 303	43.99 288	70.42 274	53.49 228	78.41 291	80.59 215

VNet		64.01 249	65.15 237	60.57 295	73.28 243	35.61 351	57.60 333	67.08 273	54.61 189	66.76 297	83.37 203	56.28 222	66.87 304	42.19 307	85.20 212	79.23 235

sd_testset		63.55 250	65.38 231	58.07 311	73.04 252	38.83 326	57.41 334	65.44 285	51.42 232	68.93 274	82.72 214	63.76 141	58.11 346	41.05 315	84.67 219	77.28 258

Anonymous20240521		63.55 250	66.07 225	55.99 321	66.18 329	44.04 283	68.77 227	68.80 264	46.99 276	72.57 225	85.84 173	39.87 314	50.22 360	53.40 231	92.23 81	73.71 291

lupinMVS		63.36 252	61.49 268	68.97 209	74.93 213	59.19 158	65.80 268	64.52 294	34.68 365	63.53 322	74.25 314	43.19 293	70.62 270	53.88 225	78.67 288	77.10 262

ET-MVSNet_ETH3D		63.32 253	60.69 276	71.20 172	70.15 285	55.66 183	65.02 278	64.32 295	43.28 311	68.99 271	72.05 331	25.46 388	78.19 184	54.16 223	82.80 242	79.74 227

MVSTER		63.29 254	61.60 267	68.36 219	59.77 369	46.21 268	60.62 313	71.32 242	41.83 316	75.40 186	79.12 268	30.25 370	75.85 207	56.30 198	79.81 276	83.03 161

OpenMVS_ROB	54.93 17	63.23 255	63.28 254	63.07 271	69.81 288	45.34 274	68.52 231	67.14 272	43.74 303	70.61 252	79.22 265	47.90 272	72.66 246	48.75 260	73.84 331	71.21 317

IterMVS		63.12 256	62.48 262	65.02 253	66.34 326	52.86 201	63.81 289	62.25 304	46.57 279	71.51 243	80.40 244	44.60 285	66.82 307	51.38 239	75.47 313	75.38 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 257	60.47 277	70.61 175	83.04 101	54.10 194	59.93 318	72.24 232	33.67 370	69.00 270	75.63 299	38.69 321	76.93 199	36.60 347	75.45 314	80.81 208

GA-MVS		62.91 258	61.66 264	66.66 242	67.09 319	44.49 280	61.18 309	69.36 262	51.33 235	69.33 267	74.47 310	36.83 332	74.94 222	50.60 245	74.72 319	80.57 216

PVSNet_Blended		62.90 259	61.64 265	66.69 241	69.81 288	49.36 229	61.23 308	78.96 159	42.04 314	59.98 340	68.86 360	51.82 244	78.20 182	44.30 295	77.77 298	72.52 302

USDC		62.80 260	63.10 257	61.89 282	65.19 335	43.30 291	67.42 244	74.20 215	35.80 359	72.25 231	84.48 187	45.67 277	71.95 259	37.95 336	84.97 214	70.42 324

Vis-MVSNet (Re-imp)		62.74 261	63.21 256	61.34 289	72.19 259	31.56 372	67.31 249	53.87 347	53.60 210	69.88 261	83.37 203	40.52 310	70.98 268	41.40 313	86.78 192	81.48 193

patch_mono-2		62.73 262	64.08 246	58.68 307	70.36 282	55.87 181	60.84 311	64.11 297	41.23 321	64.04 313	78.22 279	60.00 179	48.80 364	54.17 222	83.71 235	71.37 313

D2MVS		62.58 263	61.05 272	67.20 234	63.85 344	47.92 246	56.29 340	69.58 260	39.32 337	70.07 259	78.19 280	34.93 338	72.68 245	53.44 229	83.74 233	81.00 201

CL-MVSNet_self_test		62.44 264	63.40 253	59.55 302	72.34 258	32.38 367	56.39 339	64.84 290	51.21 237	67.46 292	81.01 236	50.75 251	63.51 326	38.47 332	88.12 163	82.75 169

MDA-MVSNet-bldmvs		62.34 265	61.73 263	64.16 257	61.64 355	49.90 223	48.11 375	57.24 327	53.31 213	80.95 107	79.39 262	49.00 264	61.55 333	45.92 287	80.05 273	81.03 199

miper_lstm_enhance		61.97 266	61.63 266	62.98 272	60.04 363	45.74 272	47.53 377	70.95 250	44.04 298	73.06 220	78.84 273	39.72 315	60.33 336	55.82 203	84.64 222	82.88 164

wuyk23d		61.97 266	66.25 222	49.12 356	58.19 378	60.77 149	66.32 261	52.97 355	55.93 173	90.62 6	86.91 135	73.07 57	35.98 402	20.63 405	91.63 87	50.62 391

thres600view7		61.82 268	61.38 269	63.12 270	71.81 262	34.93 355	64.64 281	56.99 329	54.78 186	70.33 255	79.74 256	32.07 353	72.42 252	38.61 330	83.46 238	82.02 184

SSC-MVS		61.79 269	66.08 224	48.89 358	76.91 187	10.00 414	53.56 359	47.37 377	68.20 62	76.56 167	89.21 92	54.13 232	57.59 348	54.75 212	74.07 328	79.08 237

PAPM		61.79 269	60.37 278	66.05 246	76.09 200	41.87 302	69.30 216	76.79 195	40.64 331	53.80 375	79.62 259	44.38 286	82.92 96	29.64 380	73.11 335	73.36 293

MVP-Stereo		61.56 271	59.22 284	68.58 218	79.28 149	60.44 151	69.20 218	71.57 235	43.58 305	56.42 362	78.37 277	39.57 317	76.46 206	34.86 359	60.16 387	68.86 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	48.73 20	61.54 272	60.89 273	63.52 266	61.08 358	51.55 207	68.07 237	68.00 270	33.88 367	65.87 300	81.25 232	37.91 326	67.71 292	49.32 256	82.60 244	71.31 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		61.23 273	60.85 274	62.38 279	78.80 161	27.88 389	67.33 248	37.42 403	54.23 197	67.55 291	88.68 109	17.87 407	74.39 230	46.33 284	89.41 142	84.86 99

thres100view900		61.17 274	61.09 271	61.39 288	72.14 260	35.01 354	65.42 274	56.99 329	55.23 179	70.71 251	79.90 254	32.07 353	72.09 255	35.61 355	81.73 254	77.08 263

Patchmtry		60.91 275	63.01 258	54.62 328	66.10 330	26.27 395	67.47 243	56.40 336	54.05 203	72.04 234	86.66 145	33.19 344	60.17 337	43.69 299	87.45 174	77.42 256

EU-MVSNet		60.82 276	60.80 275	60.86 294	68.37 303	41.16 306	72.27 167	68.27 269	26.96 390	69.08 269	75.71 298	32.09 352	67.44 297	55.59 206	78.90 285	73.97 287

pmmvs4		60.78 277	59.04 286	66.00 247	73.06 251	57.67 173	64.53 284	60.22 314	36.91 354	65.96 299	77.27 289	39.66 316	68.54 286	38.87 327	74.89 318	71.80 310

thres400		60.77 278	59.97 280	63.15 269	70.78 270	35.35 352	63.27 296	57.47 322	53.00 215	68.31 283	77.09 290	32.45 350	72.09 255	35.61 355	81.73 254	82.02 184

MVS		60.62 279	59.97 280	62.58 277	68.13 308	47.28 257	68.59 229	73.96 216	32.19 374	59.94 342	68.86 360	50.48 252	77.64 193	41.85 310	75.74 309	62.83 368

thisisatest0515		60.48 280	57.86 296	68.34 220	67.25 317	46.42 265	60.58 314	62.14 305	40.82 327	63.58 321	69.12 354	26.28 384	78.34 178	48.83 259	82.13 247	80.26 220

tfpn200view9		60.35 281	59.97 280	61.51 286	70.78 270	35.35 352	63.27 296	57.47 322	53.00 215	68.31 283	77.09 290	32.45 350	72.09 255	35.61 355	81.73 254	77.08 263

ppachtmachnet_test		60.26 282	59.61 283	62.20 280	67.70 313	44.33 281	58.18 330	60.96 312	40.75 329	65.80 301	72.57 327	41.23 303	63.92 323	46.87 280	82.42 245	78.33 244

WB-MVS		60.04 283	64.19 245	47.59 360	76.09 200	10.22 413	52.44 364	46.74 378	65.17 88	74.07 207	87.48 127	53.48 235	55.28 351	49.36 255	72.84 336	77.28 258

Patchmatch-RL test		59.95 284	59.12 285	62.44 278	72.46 257	54.61 190	59.63 319	47.51 376	41.05 324	74.58 198	74.30 313	31.06 364	65.31 316	51.61 236	79.85 275	67.39 344

1314		59.83 285	58.86 288	62.74 276	65.71 332	44.78 278	68.59 229	72.63 227	33.54 372	61.05 335	67.29 371	43.62 291	71.26 266	49.49 254	67.84 369	72.19 307

IB-MVS	49.67 18	59.69 286	56.96 302	67.90 225	68.19 307	50.30 217	61.42 306	65.18 287	47.57 273	55.83 365	67.15 372	23.77 394	79.60 153	43.56 301	79.97 274	73.79 290
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
1112_ss		59.48 287	58.99 287	60.96 293	77.84 173	42.39 300	61.42 306	68.45 268	37.96 347	59.93 343	67.46 368	45.11 282	65.07 318	40.89 317	71.81 345	75.41 274

FPMVS		59.43 288	60.07 279	57.51 314	77.62 179	71.52 50	62.33 302	50.92 362	57.40 158	69.40 266	80.00 253	39.14 319	61.92 332	37.47 340	66.36 372	39.09 402

CVMVSNet		59.21 289	58.44 292	61.51 286	73.94 234	47.76 250	71.31 190	64.56 293	26.91 392	60.34 339	70.44 340	36.24 335	67.65 293	53.57 227	68.66 364	69.12 336

CR-MVSNet		58.96 290	58.49 291	60.36 297	66.37 324	48.24 240	70.93 196	56.40 336	32.87 373	61.35 331	86.66 145	33.19 344	63.22 327	48.50 264	70.17 356	69.62 331

EPNet_dtu		58.93 291	58.52 290	60.16 299	67.91 311	47.70 251	69.97 208	58.02 320	49.73 253	47.28 393	73.02 325	38.14 323	62.34 329	36.57 348	85.99 202	70.43 323
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		58.78 292	58.69 289	59.04 306	79.41 146	38.13 333	57.62 332	66.98 274	34.74 363	59.62 346	77.56 287	42.92 295	63.65 325	38.66 329	70.73 352	75.35 276

PatchMatch-RL		58.68 293	57.72 297	61.57 285	76.21 198	73.59 40	61.83 303	49.00 371	47.30 275	61.08 333	68.97 356	50.16 254	59.01 341	36.06 354	68.84 363	52.10 389

SCA		58.57 294	58.04 295	60.17 298	70.17 284	41.07 308	65.19 276	53.38 353	43.34 310	61.00 336	73.48 320	45.20 280	69.38 279	40.34 320	70.31 355	70.05 325

testing3		58.28 295	58.38 293	58.00 312	77.45 180	26.12 396	60.78 312	43.00 389	56.02 170	70.18 257	75.76 297	13.27 414	67.24 300	48.02 270	80.89 263	80.65 213

CHOSEN 1792x2688		58.09 296	56.30 307	63.45 267	79.95 140	50.93 211	54.07 357	65.59 283	28.56 386	61.53 330	74.33 312	41.09 306	66.52 310	33.91 363	67.69 370	72.92 297

HY-MVS	49.31 19	57.96 297	57.59 298	59.10 305	66.85 323	36.17 345	65.13 277	65.39 286	39.24 339	54.69 372	78.14 281	44.28 287	67.18 301	33.75 365	70.79 351	73.95 288

baseline1		57.82 298	58.36 294	56.19 320	69.17 295	30.76 378	62.94 300	55.21 340	46.04 282	63.83 317	78.47 275	41.20 304	63.68 324	39.44 322	68.99 362	74.13 286

thres200		57.55 299	57.02 301	59.17 303	67.89 312	34.93 355	58.91 324	57.25 326	50.24 247	64.01 314	71.46 335	32.49 349	71.39 265	31.31 372	79.57 280	71.19 318

CostFormer		57.35 300	56.14 308	60.97 292	63.76 346	38.43 328	67.50 242	60.22 314	37.14 353	59.12 348	76.34 295	32.78 347	71.99 258	39.12 326	69.27 361	72.47 303

test_fmvs3		56.78 301	55.99 310	59.12 304	53.96 397	48.09 243	58.76 325	66.22 277	27.54 388	76.66 163	68.69 362	25.32 390	51.31 357	53.42 230	73.38 333	77.97 254

our_test_3		56.46 302	56.51 305	56.30 319	67.70 313	39.66 319	55.36 348	52.34 359	40.57 332	63.85 316	69.91 350	40.04 313	58.22 345	43.49 302	75.29 317	71.03 320

tpm2		56.12 303	54.64 319	60.55 296	66.24 327	36.01 346	68.14 235	56.77 332	33.60 371	58.25 351	75.52 302	30.25 370	74.33 231	33.27 366	69.76 360	71.32 314

tpmvs		55.84 304	55.45 314	57.01 316	60.33 362	33.20 365	65.89 265	59.29 318	47.52 274	56.04 363	73.60 319	31.05 365	68.06 291	40.64 318	64.64 375	69.77 329

gg-mvs-nofinetune		55.75 305	56.75 304	52.72 337	62.87 349	28.04 388	68.92 221	41.36 398	71.09 45	50.80 384	92.63 14	20.74 399	66.86 305	29.97 378	72.41 339	63.25 367

testing91		55.74 306	55.29 316	57.08 315	70.63 273	30.85 377	54.94 352	56.31 338	50.34 245	57.08 355	70.10 347	24.50 392	65.86 312	36.98 345	76.75 303	74.53 282

test20.03		55.74 306	57.51 299	50.42 347	59.89 368	32.09 369	50.63 369	49.01 370	50.11 249	65.07 306	83.23 210	45.61 278	48.11 369	30.22 376	83.82 232	71.07 319

MS-PatchMatch		55.59 308	54.89 317	57.68 313	69.18 294	49.05 232	61.00 310	62.93 303	35.98 357	58.36 350	68.93 358	36.71 333	66.59 309	37.62 339	63.30 379	57.39 385

baseline2		55.57 309	52.74 328	64.05 260	65.26 334	44.11 282	62.38 301	54.43 344	39.03 340	51.21 382	67.35 370	33.66 342	72.45 251	37.14 342	64.22 377	75.60 271

XXY-MVS		55.19 310	57.40 300	48.56 359	64.45 342	34.84 357	51.54 367	53.59 349	38.99 341	63.79 318	79.43 261	56.59 217	45.57 375	36.92 346	71.29 348	65.25 358

testing99		55.16 311	54.56 320	56.98 317	70.13 286	30.58 379	54.55 355	54.11 346	49.53 256	56.76 359	70.14 346	22.76 396	65.79 313	36.99 344	76.04 308	74.57 281

FMVSNet5		55.08 312	55.54 313	53.71 330	65.80 331	33.50 364	56.22 341	52.50 357	43.72 304	61.06 334	83.38 202	25.46 388	54.87 352	30.11 377	81.64 259	72.75 300

test_fmvs2		54.80 313	54.11 322	56.88 318	51.76 401	49.95 222	56.70 338	65.80 280	26.22 393	69.42 265	65.25 375	31.82 356	49.98 361	49.63 253	70.36 354	70.71 321

PatchmatchNet		54.60 314	54.27 321	55.59 324	65.17 337	39.08 321	66.92 254	51.80 361	39.89 334	58.39 349	73.12 324	31.69 358	58.33 344	43.01 304	58.38 393	69.38 334
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		54.39 315	56.12 309	49.20 354	72.57 256	30.91 376	59.98 317	48.43 373	41.66 317	55.94 364	83.86 196	41.19 305	50.42 359	26.05 390	75.38 315	66.27 352

Syy-MVS		54.13 316	55.45 314	50.18 348	68.77 299	23.59 400	55.02 349	44.55 383	43.80 300	58.05 352	64.07 377	46.22 275	58.83 342	46.16 285	72.36 340	68.12 340

Anonymous20231206		54.13 316	55.82 311	49.04 357	70.89 268	35.96 347	51.73 366	50.87 363	34.86 361	62.49 326	79.22 265	42.52 299	44.29 385	27.95 387	81.88 250	66.88 348

JIA-IIPM		54.03 318	51.62 336	61.25 290	59.14 372	55.21 186	59.10 321	47.72 374	50.85 240	50.31 388	85.81 174	20.10 401	63.97 322	36.16 352	55.41 398	64.55 364

tpm cat1		54.02 319	52.63 330	58.19 310	64.85 341	39.86 318	66.26 262	57.28 325	32.16 375	56.90 357	70.39 342	32.75 348	65.30 317	34.29 361	58.79 390	69.41 333

testgi		54.00 320	56.86 303	45.45 369	58.20 377	25.81 397	49.05 371	49.50 369	45.43 289	67.84 286	81.17 233	51.81 246	43.20 389	29.30 381	79.41 281	67.34 346

WB-MVSnew		53.94 321	54.76 318	51.49 343	71.53 264	28.05 387	58.22 329	50.36 365	37.94 348	59.16 347	70.17 345	49.21 261	51.94 356	24.49 397	71.80 346	74.47 284

testing222		53.37 322	52.50 332	55.98 322	70.51 279	29.68 382	56.20 342	51.85 360	46.19 281	56.76 359	68.94 357	19.18 404	65.39 315	25.87 393	76.98 301	72.87 298

PatchT		53.35 323	56.47 306	43.99 376	64.19 343	17.46 407	59.15 320	43.10 388	52.11 223	54.74 371	86.95 134	29.97 373	49.98 361	43.62 300	74.40 324	64.53 365

testing11		53.13 324	52.26 334	55.75 323	70.44 280	31.73 371	54.75 353	52.40 358	44.81 295	52.36 379	68.40 364	21.83 397	65.74 314	32.64 369	72.73 337	69.78 328

test_vis1_n_1920		52.96 325	53.50 324	51.32 344	59.15 371	44.90 277	56.13 343	64.29 296	30.56 384	59.87 344	60.68 388	40.16 312	47.47 370	48.25 268	62.46 381	61.58 376

UWE-MVS		52.94 326	52.70 329	53.65 331	73.56 237	27.49 390	57.30 335	49.57 368	38.56 344	62.79 325	71.42 336	19.49 403	60.41 335	24.33 399	77.33 300	73.06 295

new-patchmatchnet		52.89 327	55.76 312	44.26 375	59.94 367	6.31 415	37.36 399	50.76 364	41.10 322	64.28 311	79.82 255	44.77 283	48.43 368	36.24 351	87.61 169	78.03 251

test_fmvs1_n		52.70 328	52.01 335	54.76 326	53.83 398	50.36 215	55.80 345	65.90 279	24.96 397	65.39 303	60.64 389	27.69 379	48.46 366	45.88 288	67.99 367	65.46 356

YYNet1		52.58 329	53.50 324	49.85 350	54.15 394	36.45 344	40.53 392	46.55 380	38.09 346	75.52 184	73.31 323	41.08 307	43.88 386	41.10 314	71.14 350	69.21 335

MDA-MVSNet_test_wron		52.57 330	53.49 326	49.81 351	54.24 393	36.47 343	40.48 393	46.58 379	38.13 345	75.47 185	73.32 322	41.05 308	43.85 387	40.98 316	71.20 349	69.10 337

pmmvs5		52.49 331	52.58 331	52.21 339	54.99 391	32.38 367	55.45 347	53.84 348	32.15 376	55.49 367	74.81 305	38.08 324	57.37 349	34.02 362	74.40 324	66.88 348

UnsupCasMVSNet_eth		52.26 332	53.29 327	49.16 355	55.08 390	33.67 363	50.03 370	58.79 319	37.67 350	63.43 324	74.75 307	41.82 301	45.83 374	38.59 331	59.42 389	67.98 343

N_pmnet		52.06 333	51.11 341	54.92 325	59.64 370	71.03 54	37.42 398	61.62 311	33.68 369	57.12 354	72.10 328	37.94 325	31.03 404	29.13 386	71.35 347	62.70 369

KD-MVS_2432*1600		52.05 334	51.58 337	53.44 333	52.11 399	31.20 373	44.88 385	64.83 291	41.53 318	64.37 309	70.03 348	15.61 411	64.20 320	36.25 349	74.61 321	64.93 361

miper_refine_blended		52.05 334	51.58 337	53.44 333	52.11 399	31.20 373	44.88 385	64.83 291	41.53 318	64.37 309	70.03 348	15.61 411	64.20 320	36.25 349	74.61 321	64.93 361

test_vis3_rt		51.94 336	51.04 342	54.65 327	46.32 408	50.13 219	44.34 387	78.17 177	23.62 401	68.95 273	62.81 381	21.41 398	38.52 400	41.49 312	72.22 342	75.30 277

PVSNet	43.83 21	51.56 337	51.17 340	52.73 336	68.34 304	38.27 330	48.22 374	53.56 351	36.41 355	54.29 373	64.94 376	34.60 339	54.20 355	30.34 375	69.87 358	65.71 355

test_fmvs1		51.51 338	50.86 345	53.48 332	49.72 404	49.35 231	54.11 356	64.96 289	24.64 399	63.66 320	59.61 392	28.33 378	48.45 367	45.38 293	67.30 371	62.66 371

test_vis1_n		51.27 339	50.41 349	53.83 329	56.99 381	50.01 221	56.75 337	60.53 313	25.68 395	59.74 345	57.86 393	29.40 375	47.41 371	43.10 303	63.66 378	64.08 366

test_cas_vis1_n_1920		50.90 340	50.92 344	50.83 346	54.12 396	47.80 248	51.44 368	54.61 343	26.95 391	63.95 315	60.85 387	37.86 328	44.97 380	45.53 290	62.97 380	59.72 380

tpm		50.60 341	52.42 333	45.14 371	65.18 336	26.29 394	60.30 315	43.50 386	37.41 351	57.01 356	79.09 269	30.20 372	42.32 390	32.77 368	66.36 372	66.81 350

test-LLR		50.43 342	50.69 347	49.64 352	60.76 359	41.87 302	53.18 360	45.48 381	43.41 308	49.41 389	60.47 390	29.22 376	44.73 382	42.09 308	72.14 343	62.33 374

myMVS_eth3d		50.36 343	50.52 348	49.88 349	68.77 299	22.69 402	55.02 349	44.55 383	43.80 300	58.05 352	64.07 377	14.16 413	58.83 342	33.90 364	72.36 340	68.12 340

ETVMVS		50.32 344	49.87 352	51.68 341	70.30 283	26.66 393	52.33 365	43.93 385	43.54 306	54.91 369	67.95 366	20.01 402	60.17 337	22.47 401	73.40 332	68.22 339

tpmrst		50.15 345	51.38 339	46.45 366	56.05 385	24.77 398	64.40 286	49.98 366	36.14 356	53.32 376	69.59 352	35.16 337	48.69 365	39.24 324	58.51 392	65.89 353

UnsupCasMVSNet_bld		50.01 346	51.03 343	46.95 362	58.61 374	32.64 366	48.31 373	53.27 354	34.27 366	60.47 338	71.53 334	41.40 302	47.07 372	30.68 374	60.78 386	61.13 377

dmvs_re		49.91 347	50.77 346	47.34 361	59.98 364	38.86 325	53.18 360	53.58 350	39.75 335	55.06 368	61.58 386	36.42 334	44.40 384	29.15 385	68.23 365	58.75 382

WTY-MVS		49.39 348	50.31 350	46.62 365	61.22 357	32.00 370	46.61 380	49.77 367	33.87 368	54.12 374	69.55 353	41.96 300	45.40 377	31.28 373	64.42 376	62.47 372

ADS-MVSNet2		48.76 349	47.25 358	53.29 335	55.90 387	40.54 314	47.34 378	54.99 342	31.41 381	50.48 385	72.06 329	31.23 361	54.26 354	25.93 391	55.93 395	65.07 359

test-mter		48.56 350	48.20 355	49.64 352	60.76 359	41.87 302	53.18 360	45.48 381	31.91 379	49.41 389	60.47 390	18.34 405	44.73 382	42.09 308	72.14 343	62.33 374

Patchmatch-test		47.93 351	49.96 351	41.84 379	57.42 380	24.26 399	48.75 372	41.49 397	39.30 338	56.79 358	73.48 320	30.48 369	33.87 403	29.29 382	72.61 338	67.39 344

test0.0.03 1		47.72 352	48.31 354	45.93 367	55.53 389	29.39 383	46.40 381	41.21 399	43.41 308	55.81 366	67.65 367	29.22 376	43.77 388	25.73 394	69.87 358	64.62 363

sss		47.59 353	48.32 353	45.40 370	56.73 384	33.96 361	45.17 383	48.51 372	32.11 378	52.37 378	65.79 373	40.39 311	41.91 393	31.85 370	61.97 383	60.35 378

pmmvs3		46.71 354	45.09 364	51.55 342	56.76 383	48.25 239	55.78 346	39.53 402	24.13 400	50.35 387	63.40 379	15.90 410	51.08 358	29.29 382	70.69 353	55.33 388

test_vis1_rt		46.70 355	45.24 363	51.06 345	44.58 409	51.04 210	39.91 394	67.56 271	21.84 405	51.94 380	50.79 401	33.83 341	39.77 397	35.25 358	61.50 384	62.38 373

EPMVS		45.74 356	46.53 359	43.39 377	54.14 395	22.33 404	55.02 349	35.00 406	34.69 364	51.09 383	70.20 344	25.92 386	42.04 392	37.19 341	55.50 397	65.78 354

MVS-HIRNet		45.53 357	47.29 357	40.24 382	62.29 351	26.82 392	56.02 344	37.41 404	29.74 385	43.69 403	81.27 231	33.96 340	55.48 350	24.46 398	56.79 394	38.43 403

dmvs_testset		45.26 358	47.51 356	38.49 385	59.96 366	14.71 409	58.50 327	43.39 387	41.30 320	51.79 381	56.48 394	39.44 318	49.91 363	21.42 403	55.35 399	50.85 390

TESTMET0.1,1		45.17 359	44.93 365	45.89 368	56.02 386	38.31 329	53.18 360	41.94 396	27.85 387	44.86 399	56.47 395	17.93 406	41.50 395	38.08 335	68.06 366	57.85 383

E-PMN		45.17 359	45.36 362	44.60 373	50.07 402	42.75 296	38.66 396	42.29 394	46.39 280	39.55 404	51.15 400	26.00 385	45.37 378	37.68 337	76.41 304	45.69 397

PMMVS		44.69 361	43.95 369	46.92 363	50.05 403	53.47 199	48.08 376	42.40 392	22.36 403	44.01 402	53.05 398	42.60 298	45.49 376	31.69 371	61.36 385	41.79 400

ADS-MVSNet		44.62 362	45.58 361	41.73 380	55.90 387	20.83 405	47.34 378	39.94 401	31.41 381	50.48 385	72.06 329	31.23 361	39.31 398	25.93 391	55.93 395	65.07 359

EMVS		44.61 363	44.45 368	45.10 372	48.91 405	43.00 294	37.92 397	41.10 400	46.75 278	38.00 406	48.43 403	26.42 383	46.27 373	37.11 343	75.38 315	46.03 396

dp		44.09 364	44.88 366	41.72 381	58.53 376	23.18 401	54.70 354	42.38 393	34.80 362	44.25 401	65.61 374	24.48 393	44.80 381	29.77 379	49.42 401	57.18 386

test_f		43.79 365	45.63 360	38.24 386	42.29 412	38.58 327	34.76 401	47.68 375	22.22 404	67.34 293	63.15 380	31.82 356	30.60 405	39.19 325	62.28 382	45.53 398

mvsany_test3		43.76 366	41.01 370	52.01 340	48.09 406	57.74 172	42.47 389	23.85 412	23.30 402	64.80 307	62.17 384	27.12 380	40.59 396	29.17 384	48.11 402	57.69 384

DSMNet-mixed		43.18 367	44.66 367	38.75 384	54.75 392	28.88 386	57.06 336	27.42 409	13.47 407	47.27 394	77.67 286	38.83 320	39.29 399	25.32 396	60.12 388	48.08 393

CHOSEN 280x420		41.62 368	39.89 373	46.80 364	61.81 353	51.59 206	33.56 402	35.74 405	27.48 389	37.64 407	53.53 396	23.24 395	42.09 391	27.39 388	58.64 391	46.72 395

PVSNet_0	36.71 22	41.12 369	40.78 372	42.14 378	59.97 365	40.13 316	40.97 391	42.24 395	30.81 383	44.86 399	49.41 402	40.70 309	45.12 379	23.15 400	34.96 405	41.16 401

mvsany_test1		37.88 370	35.74 375	44.28 374	47.28 407	49.90 223	36.54 400	24.37 411	19.56 406	45.76 395	53.46 397	32.99 346	37.97 401	26.17 389	35.52 404	44.99 399

PMMVS2		37.74 371	40.87 371	28.36 388	42.41 411	5.35 416	24.61 403	27.75 408	32.15 376	47.85 392	70.27 343	35.85 336	29.51 406	19.08 406	67.85 368	50.22 392

new_pmnet		37.55 372	39.80 374	30.79 387	56.83 382	16.46 408	39.35 395	30.65 407	25.59 396	45.26 397	61.60 385	24.54 391	28.02 407	21.60 402	52.80 400	47.90 394

MVE	27.91 23	36.69 373	35.64 376	39.84 383	43.37 410	35.85 349	19.49 404	24.61 410	24.68 398	39.05 405	62.63 383	38.67 322	27.10 408	21.04 404	47.25 403	56.56 387
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		31.66 374	32.98 377	27.71 389	58.58 375	12.61 411	45.02 384	14.24 415	41.90 315	47.93 391	43.91 404	10.65 415	41.81 394	14.06 407	20.53 408	28.72 405

kuosan		22.02 375	23.52 379	17.54 391	41.56 413	11.24 412	41.99 390	13.39 416	26.13 394	28.87 408	30.75 406	9.72 416	21.94 410	4.77 411	14.49 409	19.43 406

test_method		19.26 376	19.12 380	19.71 390	9.09 415	1.91 418	7.79 406	53.44 352	1.42 409	10.27 411	35.80 405	17.42 408	25.11 409	12.44 408	24.38 407	32.10 404

cdsmvs_eth3d_5k		17.71 377	23.62 378	0.00 396	0.00 419	0.00 421	0.00 407	70.17 257	0.00 414	0.00 415	74.25 314	68.16 97	0.00 415	0.00 414	0.00 413	0.00 411

tmp_tt		11.98 378	14.73 381	3.72 393	2.28 416	4.62 417	19.44 405	14.50 414	0.47 411	21.55 409	9.58 409	25.78 387	4.57 412	11.61 409	27.37 406	1.96 408

ab-mvs-re		5.62 379	7.50 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	67.46 368	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		5.20 380	6.93 383	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	62.39 152	0.00 415	0.00 414	0.00 413	0.00 411

test123		4.43 381	5.78 384	0.39 395	0.97 417	0.28 419	46.33 382	0.45 418	0.31 412	0.62 413	1.50 412	0.61 418	0.11 414	0.56 412	0.63 411	0.77 410

testmvs		4.06 382	5.28 385	0.41 394	0.64 418	0.16 420	42.54 388	0.31 419	0.26 413	0.50 414	1.40 413	0.77 417	0.17 413	0.56 412	0.55 412	0.90 409

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						22.69 402								36.10 353

FOURS1					89.19 24	77.84 13	91.64 1	89.11 3	84.05 3	91.57 3

MSC_two_6792asdad				79.02 55	83.14 96	67.03 88		80.75 125					86.24 23	77.27 34	94.85 26	83.78 135

PC_three_1452									46.98 277	81.83 94	86.28 158	66.55 118	84.47 72	63.31 142	90.78 113	83.49 142

No_MVS				79.02 55	83.14 96	67.03 88		80.75 125					86.24 23	77.27 34	94.85 26	83.78 135

test_one_0601					85.84 62	61.45 134		85.63 31	75.27 18	85.62 49	90.38 67	76.72 27

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					83.91 87	69.36 70		81.09 120	58.91 144	82.73 87	89.11 97	75.77 35	86.63 13	72.73 63	92.93 70

RE-MVS-def			85.50 4		86.19 50	79.18 7	87.23 8	86.27 21	77.51 11	87.65 19	90.73 50	81.38 7		78.11 24	94.46 36	84.89 97

IU-MVS					86.12 54	60.90 144		80.38 136	45.49 288	81.31 102				75.64 42	94.39 41	84.65 105

OPU-MVS				78.65 62	83.44 94	66.85 90	83.62 43				86.12 166	66.82 110	86.01 32	61.72 151	89.79 134	83.08 159

test_241102_TWO								84.80 48	72.61 33	84.93 58	89.70 83	77.73 22	85.89 40	75.29 43	94.22 52	83.25 153

test_241102_ONE					86.12 54	61.06 140		84.72 52	72.64 32	87.38 25	89.47 86	77.48 23	85.74 44

9.14			80.22 54		80.68 133		80.35 76	87.69 11	59.90 133	83.00 80	88.20 119	74.57 47	81.75 114	73.75 55	93.78 57

save fliter					87.00 40	67.23 87	79.24 89	77.94 182	56.65 166

test_0728_THIRD									74.03 22	85.83 44	90.41 62	75.58 37	85.69 45	77.43 31	94.74 30	84.31 123

test_0728_SECOND				76.57 85	86.20 49	60.57 150	83.77 41	85.49 33					85.90 38	75.86 40	94.39 41	83.25 153

test0726					86.16 52	60.78 147	83.81 40	85.10 43	72.48 35	85.27 55	89.96 79	78.57 17

GSMVS																70.05 325

test_part2					85.90 58	66.44 92				84.61 64

sam_mvs1												31.41 359				70.05 325

sam_mvs												31.21 363

ambc				70.10 188	77.74 175	50.21 218	74.28 154	77.93 183		79.26 124	88.29 118	54.11 233	79.77 150	64.43 126	91.10 102	80.30 219

MTGPA								80.63 130

test_post1							66.63 258				2.08 410	30.66 368	59.33 340	40.34 320

test_post											1.99 411	30.91 366	54.76 353

patchmatchnet-post											68.99 355	31.32 360	69.38 279

GG-mvs-BLEND				52.24 338	60.64 361	29.21 385	69.73 212	42.41 391		45.47 396	52.33 399	20.43 400	68.16 289	25.52 395	65.42 374	59.36 381

MTMP							84.83 31	19.26 413

gm-plane-assit					62.51 350	33.91 362			37.25 352		62.71 382		72.74 244	38.70 328

test9_res														72.12 70	91.37 92	77.40 257

TEST9					85.47 64	69.32 71	76.42 122	78.69 167	53.73 209	76.97 152	86.74 141	66.84 109	81.10 124

test_8					85.09 70	67.89 80	76.26 127	78.66 169	54.00 204	76.89 156	86.72 143	66.60 115	80.89 134

agg_prior2														70.70 74	90.93 107	78.55 243

agg_prior					84.44 82	66.02 98		78.62 170		76.95 154			80.34 141

TestCases				78.35 66	79.19 153	70.81 56		88.64 4	65.37 83	80.09 117	88.17 120	70.33 78	78.43 174	55.60 204	90.90 109	85.81 79

test_prior4						70.14 64	77.57 105

test_prior2							75.57 135		58.92 143	76.53 170	86.78 139	67.83 102		69.81 79	92.76 73

test_prior				75.27 104	82.15 115	59.85 155		84.33 63					83.39 88			82.58 174

旧先验2							71.17 193		45.11 292	78.54 134			61.28 334	59.19 177

新几何2							71.33 189

新几何1				69.99 190	88.37 35	71.34 52		62.08 307	43.85 299	74.99 189	86.11 167	52.85 239	70.57 271	50.99 242	83.23 240	68.05 342

旧先验1					84.55 79	60.36 152		63.69 299			87.05 133	54.65 229			83.34 239	69.66 330

无先验							74.82 141	70.94 251	47.75 272				76.85 202	54.47 216		72.09 308

原ACMM2							74.78 145

原ACMM1				73.90 120	85.90 58	65.15 107		81.67 106	50.97 239	74.25 203	86.16 164	61.60 160	83.54 84	56.75 192	91.08 103	73.00 296

test222					87.30 38	69.15 74	67.85 238	59.59 317	41.06 323	73.05 221	85.72 175	48.03 271			80.65 267	66.92 347

testdata2													67.30 298	48.34 266

segment_acmp												68.30 96

testdata				64.13 258	85.87 60	63.34 119		61.80 310	47.83 270	76.42 174	86.60 150	48.83 265	62.31 330	54.46 217	81.26 261	66.74 351

testdata1							68.34 234		57.24 159

test12				76.51 86	82.28 113	60.94 143		81.64 107		73.60 212		64.88 133	85.19 59		90.42 120	83.38 149

plane_prior7					85.18 67	66.21 95

plane_prior6					84.18 85	65.31 104						60.83 173

plane_prior5								85.49 33					86.15 28	71.09 71	90.94 105	84.82 101

plane_prior4											89.11 97

plane_prior3						65.67 100			63.82 102	78.23 136

plane_prior2							82.74 53		65.45 80

plane_prior1					84.46 81

plane_prior						65.18 105	80.06 83		61.88 121						89.91 131

n2								0.00 420

nn								0.00 420

door-mid								55.02 341

lessismore_v0				72.75 149	79.60 144	56.83 177		57.37 324		83.80 74	89.01 100	47.45 273	78.74 167	64.39 127	86.49 197	82.69 171

LGP-MVS_train				80.90 33	87.00 40	70.41 61		86.35 18	69.77 54	87.75 16	91.13 37	81.83 3	86.20 25	77.13 36	95.96 6	86.08 73

test11								82.71 90

door								52.91 356

HQP5-MVS						58.80 166

HQP-NCC					82.37 110		77.32 110		59.08 138	71.58 238

ACMP_Plane					82.37 110		77.32 110		59.08 138	71.58 238

BP-MVS														67.38 105

HQP4-MVS										71.59 237			85.31 52			83.74 137

HQP3-MVS								84.12 69							89.16 146

HQP2-MVS												58.09 200

NP-MVS					83.34 95	63.07 122					85.97 170

MDTV_nov1_ep13_2view						18.41 406	53.74 358		31.57 380	44.89 398		29.90 374		32.93 367		71.48 312

MDTV_nov1_ep13			54.05 323		65.54 333	29.30 384	59.00 322	55.22 339	35.96 358	52.44 377	75.98 296	30.77 367	59.62 339	38.21 333	73.33 334

ACMMP++_ref															89.47 141

ACMMP++															91.96 83

Test By Simon												62.56 148

ITE_SJBPF				80.35 39	76.94 186	73.60 39		80.48 133	66.87 68	83.64 76	86.18 162	70.25 80	79.90 149	61.12 157	88.95 154	87.56 56

DeepMVS_CX				11.83 392	15.51 414	13.86 410		11.25 417	5.76 408	20.85 410	26.46 407	17.06 409	9.22 411	9.69 410	13.82 410	12.42 407

High-resolution multi-view benchmark