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.

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 66	74.51 48	96.15 2	92.88 7

FOURS1					89.19 23	77.84 12	91.64 1	89.11 2	84.05 2	91.57 2

AllTest		77.66 71	77.43 76	78.35 66	79.19 150	70.81 55	78.60 91	88.64 3	65.37 79	80.09 116	88.17 117	70.33 76	78.43 173	55.60 201	90.90 109	85.81 76

TestCases				78.35 66	79.19 150	70.81 55		88.64 3	65.37 79	80.09 116	88.17 117	70.33 76	78.43 173	55.60 201	90.90 109	85.81 76

SF-MVS		80.72 43	81.80 42	77.48 74	82.03 116	64.40 111	83.41 46	88.46 5	65.28 81	84.29 65	89.18 91	73.73 55	83.22 88	76.01 38	93.77 58	84.81 100

COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 103	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 107	95.46 11	87.89 48
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 128	62.39 124	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 100	64.82 120	96.10 4	87.21 57

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 72	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 68	81.53 115	81.53 3	92.15 82	88.91 38
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
LCM-MVSNet		86.90 1	88.67 1	81.57 21	91.50 1	63.30 119	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 88	97.05 1	96.93 1

9.14			80.22 53		80.68 130		80.35 72	87.69 10	59.90 129	83.00 78	88.20 116	74.57 47	81.75 113	73.75 54	93.78 57

EC-MVSNet		77.08 77	77.39 77	76.14 92	76.86 188	56.87 176	80.32 73	87.52 11	63.45 104	74.66 191	84.52 182	69.87 82	84.94 61	69.76 78	89.59 138	86.60 67

APD-MVS_3200maxsize		83.57 13	84.33 12	81.31 28	82.83 106	73.53 40	85.50 27	87.45 12	74.11 19	86.45 35	90.52 55	80.02 10	84.48 70	77.73 27	94.34 47	85.93 74

HPM-MVS		84.12 8	84.63 9	82.60 13	88.21 35	74.40 31	85.24 28	87.21 13	70.69 45	85.14 54	90.42 58	78.99 15	86.62 13	80.83 5	94.93 23	86.79 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS	72.44 4	81.00 40	80.83 50	81.50 22	86.70 44	70.03 64	82.06 55	87.00 14	59.89 130	80.91 108	90.53 53	72.19 60	88.56 1	73.67 55	94.52 35	85.92 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		84.59 4	85.10 6	83.06 4	88.60 32	75.83 23	86.27 24	86.89 15	73.69 23	86.17 37	91.70 25	78.23 19	85.20 58	79.45 12	94.91 24	88.15 47

bld_raw_dy_0_64		69.94 167	69.64 172	70.84 171	73.28 238	46.85 258	75.82 131	86.52 16	40.43 328	81.41 100	74.77 301	48.70 262	83.01 93	56.25 194	89.59 138	82.66 167

LPG-MVS_test		83.47 16	84.33 12	80.90 32	87.00 39	70.41 60	82.04 56	86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

LGP-MVS_train				80.90 32	87.00 39	70.41 60		86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

MP-MVS-pluss		82.54 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 19	63.17 108	85.38 52	91.26 33	76.33 30	84.67 68	83.30 1	94.96 22	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SR-MVS-dyc-post		84.75 3	85.26 5	83.21 3	86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	79.20 14	85.58 49	78.11 23	94.46 36	84.89 94

RE-MVS-def			85.50 3		86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	81.38 7		78.11 23	94.46 36	84.89 94

RPMNet		65.77 224	65.08 238	67.84 225	66.37 321	48.24 237	70.93 192	86.27 20	54.66 184	61.35 328	86.77 137	33.29 339	85.67 47	55.93 198	70.17 353	69.62 328

3Dnovator+	73.19 2	81.08 39	80.48 51	82.87 7	81.41 124	72.03 45	84.38 34	86.23 23	77.28 14	80.65 111	90.18 73	59.80 181	87.58 5	73.06 59	91.34 93	89.01 34

ACMP	69.50 8	82.64 25	83.38 26	80.40 37	86.50 45	69.44 67	82.30 53	86.08 24	66.80 65	86.70 30	89.99 75	81.64 6	85.95 34	74.35 50	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ZNCC-MVS		83.12 20	83.68 21	81.45 24	89.14 24	73.28 42	86.32 23	85.97 25	67.39 60	84.02 68	90.39 62	74.73 45	86.46 15	80.73 6	94.43 40	84.60 108

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 26	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 105
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
LS3D		80.99 41	80.85 49	81.41 25	78.37 162	71.37 50	87.45 7	85.87 27	77.48 12	81.98 90	89.95 77	69.14 86	85.26 54	66.15 109	91.24 95	87.61 52

test_one_0601					85.84 61	61.45 133		85.63 28	75.27 17	85.62 48	90.38 64	76.72 27

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 57	87.01 38	72.91 43	80.23 75	85.56 29	66.56 68	85.64 45	89.57 82	69.12 87	80.55 137	72.51 65	93.37 63	83.48 139

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 95	60.90 144	91.64 1	85.49 30	74.03 21	84.93 56	90.38 64	66.82 108	85.90 38	77.43 30	90.78 113	83.49 137

test_0728_SECOND				76.57 85	86.20 48	60.57 150	83.77 40	85.49 30					85.90 38	75.86 39	94.39 41	83.25 148

HQP_MVS		78.77 60	78.78 64	78.72 60	85.18 66	65.18 104	82.74 51	85.49 30	65.45 76	78.23 133	89.11 94	60.83 170	86.15 27	71.09 71	90.94 105	84.82 98

plane_prior5								85.49 30					86.15 27	71.09 71	90.94 105	84.82 98

PGM-MVS		83.07 21	83.25 30	82.54 15	89.57 13	77.21 20	82.04 56	85.40 34	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 128

XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 64	86.46 46	74.79 29	77.15 110	85.39 35	66.73 66	80.39 114	88.85 102	74.43 50	78.33 178	74.73 46	85.79 200	82.35 175

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 89	67.83 80	83.39 47	85.35 36	64.42 92	86.14 39	87.07 129	74.02 51	80.97 129	77.70 28	92.32 80	80.62 211
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
SR-MVS		84.51 5	85.27 4	82.25 18	88.52 33	77.71 13	86.81 16	85.25 37	77.42 13	86.15 38	90.24 70	81.69 5	85.94 35	77.77 26	93.58 61	83.09 153

GST-MVS		82.79 24	83.27 29	81.34 27	88.99 26	73.29 41	85.94 25	85.13 38	68.58 57	84.14 67	90.21 72	73.37 56	86.41 16	79.09 18	93.98 56	84.30 121

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 74	66.72 90	86.54 20	85.11 39	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 140
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					86.16 51	60.78 147	83.81 39	85.10 40	72.48 32	85.27 53	89.96 76	78.57 17

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 40	64.94 89	81.05 105	88.38 113	57.10 210	87.10 8	79.75 7	83.87 228	84.31 119
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
DPE-MVS		82.00 30	83.02 33	78.95 58	85.36 65	67.25 85	82.91 50	84.98 42	73.52 24	85.43 51	90.03 74	76.37 29	86.97 11	74.56 47	94.02 55	82.62 170
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP_NAP		82.33 27	83.28 28	79.46 49	89.28 18	69.09 74	83.62 42	84.98 42	64.77 90	83.97 69	91.02 38	75.53 39	85.93 37	82.00 2	94.36 45	83.35 146

XVG-OURS		79.51 53	79.82 56	78.58 63	86.11 56	74.96 28	76.33 122	84.95 44	66.89 63	82.75 84	88.99 98	66.82 108	78.37 176	74.80 44	90.76 116	82.40 174

test_241102_TWO								84.80 45	72.61 30	84.93 56	89.70 80	77.73 22	85.89 40	75.29 42	94.22 52	83.25 148

SteuartSystems-ACMMP		83.07 21	83.64 22	81.35 26	85.14 68	71.00 54	85.53 26	84.78 46	70.91 43	85.64 45	90.41 59	75.55 38	87.69 4	79.75 7	95.08 19	85.36 85
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.12 28	82.68 38	80.43 36	88.90 29	69.52 65	85.12 29	84.76 47	63.53 102	84.23 66	91.47 30	72.02 62	87.16 7	79.74 9	94.36 45	84.61 106
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
OMC-MVS		79.41 55	78.79 63	81.28 29	80.62 131	70.71 58	80.91 63	84.76 47	62.54 112	81.77 93	86.65 144	71.46 66	83.53 82	67.95 92	92.44 76	89.60 24

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 140	83.62 42	84.72 49	72.61 30	87.38 24	89.70 80	77.48 23	85.89 40	75.29 42	94.39 41	83.08 154

test_241102_ONE					86.12 53	61.06 140		84.72 49	72.64 29	87.38 24	89.47 83	77.48 23	85.74 44

casdiffmvs_mvg		75.26 93	76.18 89	72.52 151	72.87 251	49.47 227	72.94 160	84.71 51	59.49 132	80.90 109	88.81 103	70.07 79	79.71 150	67.40 98	88.39 159	88.40 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		72.72 136	72.16 147	74.38 112	76.90 186	55.95 179	73.34 157	84.67 52	62.04 115	72.19 229	70.81 335	65.90 120	85.24 56	58.64 176	84.96 214	81.95 183

XVS		83.51 15	83.73 20	82.85 8	89.43 15	77.61 14	86.80 17	84.66 53	72.71 27	82.87 81	90.39 62	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

X-MVStestdata		76.81 78	74.79 101	82.85 8	89.43 15	77.61 14	86.80 17	84.66 53	72.71 27	82.87 81	9.95 403	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 119	65.33 102	79.05 87	84.63 55	74.83 18	80.41 113	86.27 155	71.68 64	83.45 84	62.45 143	92.40 77	78.92 236

CS-MVS-test		74.89 103	74.23 109	76.86 81	77.01 181	62.94 122	78.98 88	84.61 56	58.62 141	70.17 254	80.80 233	66.74 112	81.96 109	61.74 146	89.40 145	85.69 81

ACMM	69.25 9	82.11 29	83.31 27	78.49 64	88.17 36	73.96 34	83.11 49	84.52 57	66.40 69	87.45 22	89.16 93	81.02 8	80.52 138	74.27 51	95.73 7	80.98 199
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVS		84.12 8	84.55 10	82.80 10	89.42 17	79.74 5	88.19 5	84.43 58	71.96 38	84.70 61	90.56 52	77.12 25	86.18 26	79.24 17	95.36 12	82.49 173

baseline		73.10 123	73.96 113	70.51 177	71.46 261	46.39 265	72.08 168	84.40 59	55.95 169	76.62 161	86.46 151	67.20 102	78.03 185	64.22 125	87.27 180	87.11 61

test_prior				75.27 102	82.15 115	59.85 155		84.33 60					83.39 85			82.58 171

HFP-MVS		83.39 17	84.03 16	81.48 23	89.25 20	75.69 24	87.01 14	84.27 61	70.23 46	84.47 64	90.43 57	76.79 26	85.94 35	79.58 10	94.23 51	82.82 162

casdiffmvs		73.06 126	73.84 114	70.72 173	71.32 262	46.71 261	70.93 192	84.26 62	55.62 172	77.46 145	87.10 126	67.09 104	77.81 188	63.95 128	86.83 188	87.64 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MSLP-MVS++		74.48 106	75.78 93	70.59 175	84.66 75	62.40 123	78.65 90	84.24 63	60.55 125	77.71 142	81.98 219	63.12 140	77.64 192	62.95 140	88.14 162	71.73 308

region2R		83.54 14	83.86 19	82.58 14	89.82 9	77.53 16	87.06 13	84.23 64	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 126

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 65	70.23 46	84.49 63	90.67 50	75.15 41	86.37 18	79.58 10	94.26 49	84.18 122

HQP3-MVS								84.12 66							89.16 147

HQP-MVS		75.24 94	75.01 100	75.94 93	82.37 110	58.80 166	77.32 106	84.12 66	59.08 134	71.58 234	85.96 167	58.09 197	85.30 53	67.38 101	89.16 147	83.73 133

DeepPCF-MVS	71.07 5	78.48 65	77.14 80	82.52 16	84.39 82	77.04 21	76.35 120	84.05 68	56.66 162	80.27 115	85.31 174	68.56 90	87.03 10	67.39 99	91.26 94	83.50 136

TAPA-MVS	65.27 12	75.16 95	74.29 108	77.77 72	74.86 212	68.08 77	77.89 100	84.04 69	55.15 176	76.19 173	83.39 197	66.91 106	80.11 146	60.04 166	90.14 125	85.13 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 70	65.64 73	85.54 49	89.28 86	76.32 31	83.47 83	74.03 52	93.57 62	84.35 118
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS_fast	69.89 7	77.17 76	76.33 87	79.70 44	83.90 87	67.94 78	80.06 79	83.75 71	56.73 161	74.88 186	85.32 173	65.54 123	87.79 2	65.61 116	91.14 99	83.35 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		81.13 38	81.73 44	79.36 51	84.47 79	70.53 59	83.85 38	83.70 72	69.43 52	83.67 73	88.96 99	75.89 34	86.41 16	72.62 64	92.95 69	81.14 193
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH	63.62 14	77.50 73	80.11 54	69.68 193	79.61 140	56.28 178	78.81 89	83.62 73	63.41 106	87.14 29	90.23 71	76.11 32	73.32 237	67.58 94	94.44 39	79.44 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 74	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 165
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		77.33 74	77.06 81	78.14 69	84.21 83	63.98 114	76.07 126	83.45 75	54.20 193	77.68 143	87.18 125	69.98 80	85.37 51	68.01 90	92.72 74	85.08 91

CLD-MVS		72.88 134	72.36 144	74.43 110	77.03 179	54.30 191	68.77 223	83.43 76	52.12 216	76.79 158	74.44 307	69.54 85	83.91 75	55.88 199	93.25 66	85.09 90
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		72.29 144	73.38 123	69.04 204	74.23 223	47.37 252	73.93 155	83.18 77	54.36 188	76.61 162	81.64 225	72.03 61	75.34 214	57.12 185	87.28 179	84.40 116

PHI-MVS		74.92 100	74.36 107	76.61 84	76.40 191	62.32 125	80.38 70	83.15 78	54.16 195	73.23 214	80.75 234	62.19 152	83.86 76	68.02 89	90.92 108	83.65 134

MCST-MVS		73.42 116	73.34 125	73.63 123	81.28 126	59.17 160	74.80 141	83.13 79	45.50 280	72.84 218	83.78 193	65.15 128	80.99 127	64.54 121	89.09 153	80.73 207

F-COLMAP		75.29 92	73.99 112	79.18 52	81.73 120	71.90 46	81.86 58	82.98 80	59.86 131	72.27 226	84.00 189	64.56 133	83.07 92	51.48 234	87.19 183	82.56 172

DP-MVS Recon		73.57 114	72.69 137	76.23 91	82.85 105	63.39 117	74.32 149	82.96 81	57.75 148	70.35 250	81.98 219	64.34 135	84.41 73	49.69 248	89.95 129	80.89 201

v10		75.69 87	76.20 88	74.16 114	74.44 222	48.69 232	75.84 130	82.93 82	59.02 138	85.92 41	89.17 92	58.56 191	82.74 97	70.73 73	89.14 150	91.05 15

mPP-MVS		84.01 10	84.39 11	82.88 6	90.65 3	81.38 3	87.08 12	82.79 83	72.41 34	85.11 55	90.85 44	76.65 28	84.89 63	79.30 16	94.63 33	82.35 175

Effi-MVS+		72.10 145	72.28 145	71.58 163	74.21 225	50.33 215	74.72 144	82.73 84	62.62 111	70.77 246	76.83 287	69.96 81	80.97 129	60.20 161	78.43 287	83.45 142

test11								82.71 85

CS-MVS		76.51 80	76.00 90	78.06 71	77.02 180	64.77 108	80.78 64	82.66 86	60.39 126	74.15 199	83.30 203	69.65 84	82.07 108	69.27 81	86.75 190	87.36 55

PEN-MVS		80.46 46	82.91 34	73.11 132	89.83 8	39.02 320	77.06 112	82.61 87	80.04 4	90.60 6	92.85 9	74.93 44	85.21 57	63.15 139	95.15 17	95.09 2

nrg030		74.87 104	75.99 91	71.52 165	74.90 211	49.88 226	74.10 153	82.58 88	54.55 187	83.50 75	89.21 89	71.51 65	75.74 210	61.24 150	92.34 79	88.94 37

v7n		79.37 56	80.41 52	76.28 90	78.67 161	55.81 182	79.22 86	82.51 89	70.72 44	87.54 21	92.44 14	68.00 98	81.34 117	72.84 61	91.72 84	91.69 10

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 294	78.24 96	82.24 90	78.21 9	89.57 9	92.10 18	68.05 96	85.59 48	66.04 112	95.62 9	94.88 5

DELS-MVS		68.83 183	68.31 190	70.38 178	70.55 274	48.31 235	63.78 288	82.13 91	54.00 198	68.96 268	75.17 299	58.95 188	80.06 147	58.55 177	82.74 240	82.76 163
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
mvsmamba		77.20 75	76.37 85	79.69 45	80.34 134	61.52 132	80.58 66	82.12 92	53.54 205	83.93 70	91.03 37	49.49 251	85.97 33	73.26 57	93.08 67	91.59 12

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 132	64.16 112	80.24 74	82.06 93	61.89 116	88.77 12	93.32 4	57.15 208	82.60 99	70.08 76	92.80 71	89.25 28

CPTT-MVS		81.51 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 94	68.80 53	80.92 107	88.52 109	72.00 63	82.39 101	74.80 44	93.04 68	81.14 193

CSCG		74.12 108	74.39 105	73.33 127	79.35 144	61.66 131	77.45 105	81.98 95	62.47 114	79.06 125	80.19 244	61.83 154	78.79 165	59.83 168	87.35 176	79.54 228

PVSNet_Blended_VisFu		70.04 164	68.88 182	73.53 125	82.71 107	63.62 116	74.81 139	81.95 96	48.53 258	67.16 291	79.18 262	51.42 241	78.38 175	54.39 216	79.72 276	78.60 238

test_fmvsmvis_n_1920		72.36 142	72.49 140	71.96 160	71.29 263	64.06 113	72.79 161	81.82 97	40.23 329	81.25 103	81.04 230	70.62 75	68.69 281	69.74 79	83.60 234	83.14 152

DTE-MVSNet		80.35 48	82.89 35	72.74 146	89.84 7	37.34 337	77.16 109	81.81 98	80.45 3	90.92 3	92.95 7	74.57 47	86.12 29	63.65 132	94.68 31	94.76 6

v1192		73.40 117	73.42 121	73.32 128	74.65 219	48.67 233	72.21 165	81.73 99	52.76 211	81.85 91	84.56 181	57.12 209	82.24 106	68.58 83	87.33 177	89.06 33

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 100	50.97 233	74.25 198	86.16 160	61.60 157	83.54 81	56.75 187	91.08 103	73.00 293

test12				76.51 86	82.28 113	60.94 143		81.64 101		73.60 207		64.88 130	85.19 59		90.42 120	83.38 144

CNVR-MVS		78.49 64	78.59 66	78.16 68	85.86 60	67.40 84	78.12 99	81.50 102	63.92 96	77.51 144	86.56 148	68.43 93	84.82 65	73.83 53	91.61 88	82.26 179

PCF-MVS	63.80 13	72.70 137	71.69 151	75.72 96	78.10 165	60.01 154	73.04 159	81.50 102	45.34 284	79.66 119	84.35 185	65.15 128	82.65 98	48.70 258	89.38 146	84.50 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		75.07 97	75.64 95	73.35 126	73.42 235	47.46 251	75.20 134	81.45 104	60.05 128	85.64 45	89.26 87	58.08 199	81.80 112	69.71 80	87.97 167	90.79 19

PAPM_NR		73.91 109	74.16 110	73.16 130	81.90 118	53.50 197	81.28 60	81.40 105	66.17 70	73.30 213	83.31 202	59.96 177	83.10 91	58.45 178	81.66 255	82.87 160

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 106	51.71 221	77.15 147	91.42 32	65.49 124	87.20 6	79.44 13	87.17 184	84.51 114
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EIA-MVS		68.59 188	67.16 208	72.90 141	75.18 207	55.64 184	69.39 211	81.29 107	52.44 213	64.53 304	70.69 336	60.33 174	82.30 104	54.27 218	76.31 303	80.75 206

PS-CasMVS		80.41 47	82.86 36	73.07 133	89.93 6	39.21 317	77.15 110	81.28 108	79.74 5	90.87 4	92.73 11	75.03 43	84.93 62	63.83 131	95.19 15	95.07 3

PLC	62.01 16	71.79 148	70.28 168	76.33 89	80.31 135	68.63 75	78.18 98	81.24 109	54.57 186	67.09 292	80.63 236	59.44 182	81.74 114	46.91 276	84.17 225	78.63 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		69.98 166	69.22 178	72.26 158	82.69 108	58.82 165	70.53 197	81.23 110	47.79 265	64.16 308	80.21 242	51.32 242	83.12 90	60.14 164	84.95 215	74.83 276

MVS_Test		69.84 169	70.71 165	67.24 230	67.49 312	43.25 290	69.87 206	81.22 111	52.69 212	71.57 237	86.68 141	62.09 153	74.51 225	66.05 111	78.74 283	83.96 125

v1240		73.06 126	73.14 128	72.84 143	74.74 215	47.27 254	71.88 177	81.11 112	51.80 220	82.28 88	84.21 186	56.22 218	82.34 103	68.82 82	87.17 184	88.91 38

PAPR		69.20 179	68.66 188	70.82 172	75.15 208	47.77 246	75.31 133	81.11 112	49.62 249	66.33 294	79.27 259	61.53 158	82.96 94	48.12 266	81.50 257	81.74 187

ZD-MVS					83.91 86	69.36 69		81.09 114	58.91 140	82.73 85	89.11 94	75.77 35	86.63 12	72.73 62	92.93 70

v1144		73.29 120	73.39 122	73.01 134	74.12 227	48.11 239	72.01 170	81.08 115	53.83 202	81.77 93	84.68 179	58.07 200	81.91 110	68.10 87	86.86 186	88.99 36

UniMVSNet (Re)		75.00 99	75.48 97	73.56 124	83.14 95	47.92 243	70.41 200	81.04 116	63.67 100	79.54 120	86.37 153	62.83 143	81.82 111	57.10 186	95.25 14	90.94 17

NCCC		78.25 67	78.04 71	78.89 59	85.61 62	69.45 66	79.80 81	80.99 117	65.77 72	75.55 177	86.25 157	67.42 101	85.42 50	70.10 75	90.88 111	81.81 185

AdaColmap		74.22 107	74.56 103	73.20 129	81.95 117	60.97 142	79.43 82	80.90 118	65.57 74	72.54 223	81.76 223	70.98 73	85.26 54	47.88 269	90.00 127	73.37 289

MSC_two_6792asdad				79.02 55	83.14 95	67.03 87		80.75 119					86.24 22	77.27 33	94.85 25	83.78 130

No_MVS				79.02 55	83.14 95	67.03 87		80.75 119					86.24 22	77.27 33	94.85 25	83.78 130

v1921920		72.96 132	72.98 133	72.89 142	74.67 216	47.58 249	71.92 175	80.69 121	51.70 222	81.69 97	83.89 191	56.58 215	82.25 105	68.34 85	87.36 175	88.82 40

testf1		75.66 88	76.57 82	72.95 137	67.07 318	67.62 81	76.10 124	80.68 122	64.95 87	86.58 33	90.94 40	71.20 70	71.68 260	60.46 159	91.13 100	79.56 225

APD_test2		75.66 88	76.57 82	72.95 137	67.07 318	67.62 81	76.10 124	80.68 122	64.95 87	86.58 33	90.94 40	71.20 70	71.68 260	60.46 159	91.13 100	79.56 225

MTGPA								80.63 124

MTAPA		83.19 18	83.87 18	81.13 30	91.16 2	78.16 11	84.87 30	80.63 124	72.08 36	84.93 56	90.79 45	74.65 46	84.42 72	80.98 4	94.75 28	80.82 203

DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 147	83.77 40	80.58 126	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 231
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
ITE_SJBPF				80.35 38	76.94 183	73.60 38		80.48 127	66.87 64	83.64 74	86.18 158	70.25 78	79.90 148	61.12 154	88.95 155	87.56 53

CP-MVSNet		79.48 54	81.65 45	72.98 136	89.66 12	39.06 319	76.76 113	80.46 128	78.91 7	90.32 7	91.70 25	68.49 91	84.89 63	63.40 136	95.12 18	95.01 4

v144192		72.99 130	73.06 131	72.77 144	74.58 220	47.48 250	71.90 176	80.44 129	51.57 223	81.46 99	84.11 188	58.04 201	82.12 107	67.98 91	87.47 173	88.70 43

IU-MVS					86.12 53	60.90 144		80.38 130	45.49 282	81.31 101				75.64 41	94.39 41	84.65 102

CANet		73.00 129	71.84 149	76.48 87	75.82 201	61.28 136	74.81 139	80.37 131	63.17 108	62.43 324	80.50 238	61.10 167	85.16 60	64.00 127	84.34 224	83.01 157

V42		71.06 153	70.83 164	71.72 162	67.25 314	47.14 255	65.94 261	80.35 132	51.35 228	83.40 76	83.23 206	59.25 185	78.80 164	65.91 113	80.81 263	89.23 29

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 95	61.34 135	83.29 48	80.34 133	57.43 154	86.65 31	91.79 23	50.52 245	86.01 31	71.36 70	94.65 32	91.62 11

Anonymous20231211		75.54 90	77.19 79	70.59 175	77.67 174	45.70 271	74.73 143	80.19 134	68.80 53	82.95 80	92.91 8	66.26 116	76.76 202	58.41 179	92.77 72	89.30 27

HPM-MVS++		79.89 51	79.80 57	80.18 39	89.02 25	78.44 10	83.49 45	80.18 135	64.71 91	78.11 136	88.39 112	65.46 125	83.14 89	77.64 29	91.20 96	78.94 235

DU-MVS		74.91 101	75.57 96	72.93 140	83.50 90	45.79 268	69.47 210	80.14 136	65.22 82	81.74 95	87.08 127	61.82 155	81.07 125	56.21 196	94.98 20	91.93 8

114514_t		73.40 117	73.33 126	73.64 122	84.15 85	57.11 174	78.20 97	80.02 137	43.76 297	72.55 222	86.07 165	64.00 136	83.35 86	60.14 164	91.03 104	80.45 214

UA-Net		81.56 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 138	75.34 15	79.80 118	94.91 2	69.79 83	80.25 142	72.63 63	94.46 36	88.78 42

test_fmvsmconf0.01_n		73.91 109	73.64 119	74.71 104	69.79 288	66.25 93	75.90 128	79.90 139	46.03 277	76.48 167	85.02 177	67.96 99	73.97 232	74.47 49	87.22 181	83.90 127

FIs		72.56 139	73.80 115	68.84 212	78.74 160	37.74 333	71.02 190	79.83 140	56.12 166	80.88 110	89.45 84	58.18 193	78.28 179	56.63 188	93.36 64	90.51 21

APD_test1		75.04 98	75.38 99	74.02 117	69.89 283	70.15 62	76.46 116	79.71 141	65.50 75	82.99 79	88.60 108	66.94 105	72.35 250	59.77 169	88.54 158	79.56 225

alignmvs		70.54 160	71.00 162	69.15 203	73.50 233	48.04 242	69.85 207	79.62 142	53.94 201	76.54 165	82.00 218	59.00 187	74.68 223	57.32 184	87.21 182	84.72 101

LCM-MVSNet-Re		69.10 181	71.57 156	61.70 281	70.37 277	34.30 357	61.45 302	79.62 142	56.81 159	89.59 8	88.16 119	68.44 92	72.94 240	42.30 303	87.33 177	77.85 252

c3_l		69.82 170	69.89 170	69.61 194	66.24 324	43.48 286	68.12 232	79.61 144	51.43 225	77.72 141	80.18 245	54.61 224	78.15 184	63.62 133	87.50 172	87.20 58

PS-MVSNAJss		77.54 72	77.35 78	78.13 70	84.88 71	66.37 92	78.55 92	79.59 145	53.48 206	86.29 36	92.43 15	62.39 149	80.25 142	67.90 93	90.61 117	87.77 49

GeoE		73.14 122	73.77 117	71.26 168	78.09 166	52.64 202	74.32 149	79.56 146	56.32 165	76.35 171	83.36 201	70.76 74	77.96 186	63.32 137	81.84 249	83.18 151

FC-MVSNet-test		73.32 119	74.78 102	68.93 209	79.21 149	36.57 339	71.82 178	79.54 147	57.63 153	82.57 86	90.38 64	59.38 184	78.99 161	57.91 182	94.56 34	91.23 14

dcpmvs_2		71.02 155	72.65 138	66.16 242	76.06 199	50.49 213	71.97 171	79.36 148	50.34 239	82.81 83	83.63 194	64.38 134	67.27 296	61.54 148	83.71 232	80.71 209

test_fmvsmconf0.1_n		73.26 121	72.82 136	74.56 106	69.10 294	66.18 95	74.65 147	79.34 149	45.58 279	75.54 178	83.91 190	67.19 103	73.88 235	73.26 57	86.86 186	83.63 135

RPSCF		75.76 86	74.37 106	79.93 40	74.81 213	77.53 16	77.53 104	79.30 150	59.44 133	78.88 126	89.80 79	71.26 69	73.09 239	57.45 183	80.89 260	89.17 31

PMVS	70.70 6	81.70 32	83.15 31	77.36 77	90.35 5	82.82 2	82.15 54	79.22 151	74.08 20	87.16 28	91.97 19	84.80 2	76.97 197	64.98 119	93.61 60	72.28 303
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v2v482		72.55 141	72.58 139	72.43 154	72.92 250	46.72 260	71.41 183	79.13 152	55.27 174	81.17 104	85.25 175	55.41 220	81.13 122	67.25 105	85.46 202	89.43 26

Vis-MVSNet		74.85 105	74.56 103	75.72 96	81.63 122	64.64 109	76.35 120	79.06 153	62.85 110	73.33 212	88.41 111	62.54 147	79.59 153	63.94 130	82.92 238	82.94 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet_BlendedMVS		65.38 226	64.30 240	68.61 215	69.81 285	49.36 228	65.60 269	78.96 154	45.50 280	59.98 337	78.61 269	51.82 237	78.20 181	44.30 292	84.11 226	78.27 243

PVSNet_Blended		62.90 256	61.64 262	66.69 238	69.81 285	49.36 228	61.23 305	78.96 154	42.04 310	59.98 337	68.86 357	51.82 237	78.20 181	44.30 292	77.77 295	72.52 299

miper_ehance_all_eth		68.36 190	68.16 196	68.98 206	65.14 335	43.34 288	67.07 247	78.92 156	49.11 254	76.21 172	77.72 280	53.48 229	77.92 187	61.16 152	84.59 220	85.68 82

eth_miper_zixun_eth		69.42 176	68.73 187	71.50 166	67.99 306	46.42 263	67.58 237	78.81 157	50.72 236	78.13 135	80.34 241	50.15 249	80.34 140	60.18 162	84.65 218	87.74 50

UniMVSNet_NR-MVSNet		74.90 102	75.65 94	72.64 149	83.04 101	45.79 268	69.26 213	78.81 157	66.66 67	81.74 95	86.88 133	63.26 139	81.07 125	56.21 196	94.98 20	91.05 15

test_fmvsmconf_n		72.91 133	72.40 143	74.46 107	68.62 298	66.12 96	74.21 152	78.80 159	45.64 278	74.62 192	83.25 205	66.80 111	73.86 236	72.97 60	86.66 192	83.39 143

QAPM		69.18 180	69.26 176	68.94 208	71.61 259	52.58 203	80.37 71	78.79 160	49.63 248	73.51 208	85.14 176	53.66 228	79.12 158	55.11 206	75.54 309	75.11 275

MM		78.15 70	77.68 74	79.55 48	80.10 136	65.47 100	80.94 62	78.74 161	71.22 40	72.40 225	88.70 104	60.51 172	87.70 3	77.40 32	89.13 151	85.48 84

TEST9					85.47 63	69.32 70	76.42 118	78.69 162	53.73 203	76.97 149	86.74 138	66.84 107	81.10 123

train_agg		76.38 81	76.55 84	75.86 95	85.47 63	69.32 70	76.42 118	78.69 162	54.00 198	76.97 149	86.74 138	66.60 113	81.10 123	72.50 66	91.56 89	77.15 258

test_8					85.09 69	67.89 79	76.26 123	78.66 164	54.00 198	76.89 153	86.72 140	66.60 113	80.89 133

agg_prior					84.44 81	66.02 97		78.62 165		76.95 151			80.34 140

CNLPA		73.44 115	73.03 132	74.66 105	78.27 163	75.29 26	75.99 127	78.49 166	65.39 78	75.67 175	83.22 208	61.23 163	66.77 305	53.70 223	85.33 206	81.92 184

IterMVS-LS		73.01 128	73.12 130	72.66 148	73.79 231	49.90 222	71.63 180	78.44 167	58.22 143	80.51 112	86.63 145	58.15 195	79.62 151	62.51 141	88.20 161	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvsm_n_1920		69.63 171	68.45 189	73.16 130	70.56 272	65.86 98	70.26 201	78.35 168	37.69 345	74.29 197	78.89 267	61.10 167	68.10 287	65.87 114	79.07 280	85.53 83

Fast-Effi-MVS+		68.81 184	68.30 191	70.35 180	74.66 218	48.61 234	66.06 260	78.32 169	50.62 237	71.48 240	75.54 295	68.75 89	79.59 153	50.55 243	78.73 284	82.86 161

3Dnovator	65.95 11	71.50 150	71.22 160	72.34 156	73.16 241	63.09 120	78.37 94	78.32 169	57.67 150	72.22 228	84.61 180	54.77 221	78.47 170	60.82 157	81.07 259	75.45 270

TranMVSNet+NR-MVSNet		76.13 83	77.66 75	71.56 164	84.61 77	42.57 296	70.98 191	78.29 171	68.67 56	83.04 77	89.26 87	72.99 58	80.75 134	55.58 204	95.47 10	91.35 13

test_vis3_rt		51.94 333	51.04 339	54.65 324	46.32 404	50.13 218	44.34 383	78.17 172	23.62 396	68.95 269	62.81 378	21.41 394	38.52 396	41.49 309	72.22 339	75.30 274

MSDG		67.47 205	67.48 205	67.46 228	70.70 268	54.69 189	66.90 251	78.17 172	60.88 123	70.41 249	74.76 302	61.22 165	73.18 238	47.38 272	76.87 299	74.49 280

Fast-Effi-MVS+-dtu		70.00 165	68.74 186	73.77 120	73.47 234	64.53 110	71.36 184	78.14 174	55.81 171	68.84 274	74.71 304	65.36 126	75.75 209	52.00 231	79.00 281	81.03 196

IS-MVSNet		75.10 96	75.42 98	74.15 115	79.23 148	48.05 241	79.43 82	78.04 175	70.09 49	79.17 124	88.02 121	53.04 231	83.60 80	58.05 181	93.76 59	90.79 19

miper_enhance_ethall		65.86 223	65.05 239	68.28 221	61.62 353	42.62 295	64.74 277	77.97 176	42.52 308	73.42 211	72.79 323	49.66 250	77.68 191	58.12 180	84.59 220	84.54 110

save fliter					87.00 39	67.23 86	79.24 85	77.94 177	56.65 163

ambc				70.10 187	77.74 172	50.21 217	74.28 151	77.93 178		79.26 123	88.29 115	54.11 227	79.77 149	64.43 122	91.10 102	80.30 216

Effi-MVS+-dtu		75.43 91	72.28 145	84.91 2	77.05 178	83.58 1	78.47 93	77.70 179	57.68 149	74.89 185	78.13 277	64.80 131	84.26 74	56.46 192	85.32 207	86.88 62

tt0805		76.12 84	78.43 68	69.20 201	81.32 125	41.37 302	76.72 114	77.64 180	63.78 99	82.06 89	87.88 122	79.78 11	79.05 159	64.33 124	92.40 77	87.17 60

BH-untuned		69.39 177	69.46 173	69.18 202	77.96 169	56.88 175	68.47 229	77.53 181	56.77 160	77.79 140	79.63 253	60.30 175	80.20 145	46.04 283	80.65 264	70.47 319

MAR-MVS		67.72 200	66.16 218	72.40 155	74.45 221	64.99 107	74.87 137	77.50 182	48.67 257	65.78 298	68.58 360	57.01 212	77.79 189	46.68 279	81.92 246	74.42 282
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
OpenMVS	62.51 15	68.76 185	68.75 185	68.78 213	70.56 272	53.91 195	78.29 95	77.35 183	48.85 256	70.22 252	83.52 195	52.65 233	76.93 198	55.31 205	81.99 245	75.49 269

NR-MVSNet		73.62 113	74.05 111	72.33 157	83.50 90	43.71 283	65.65 267	77.32 184	64.32 93	75.59 176	87.08 127	62.45 148	81.34 117	54.90 207	95.63 8	91.93 8

EPP-MVSNet		73.86 111	73.38 123	75.31 101	78.19 164	53.35 199	80.45 68	77.32 184	65.11 85	76.47 168	86.80 134	49.47 252	83.77 77	53.89 221	92.72 74	88.81 41

Anonymous20240529		72.56 139	73.79 116	68.86 211	76.89 187	45.21 273	68.80 222	77.25 186	67.16 61	76.89 153	90.44 56	65.95 119	74.19 230	50.75 240	90.00 127	87.18 59

diffmvs		67.42 206	67.50 204	67.20 231	62.26 349	45.21 273	64.87 276	77.04 187	48.21 259	71.74 231	79.70 252	58.40 192	71.17 264	64.99 118	80.27 268	85.22 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
iter_conf05_11		66.64 215	65.20 230	70.94 170	73.28 238	46.89 257	66.09 259	77.03 188	43.44 302	63.43 320	74.09 315	47.19 270	83.26 87	56.25 194	86.01 198	82.66 167

iter_conf05		67.34 208	65.62 224	72.50 152	69.82 284	47.06 256	72.19 166	76.86 189	45.32 285	72.86 217	82.85 209	20.53 396	83.73 78	61.13 153	89.02 154	86.70 65

API-MVS		70.97 156	71.51 157	69.37 196	75.20 206	55.94 180	80.99 61	76.84 190	62.48 113	71.24 242	77.51 283	61.51 159	80.96 132	52.04 230	85.76 201	71.22 313

ANet_high		67.08 210	69.94 169	58.51 306	57.55 375	27.09 388	58.43 325	76.80 191	63.56 101	82.40 87	91.93 20	59.82 180	64.98 316	50.10 246	88.86 156	83.46 141

PAPM		61.79 266	60.37 275	66.05 243	76.09 196	41.87 299	69.30 212	76.79 192	40.64 326	53.80 372	79.62 254	44.38 282	82.92 95	29.64 377	73.11 332	73.36 290

mvs_tets		78.93 58	78.67 65	79.72 43	84.81 73	73.93 35	80.65 65	76.50 193	51.98 219	87.40 23	91.86 21	76.09 33	78.53 168	68.58 83	90.20 122	86.69 66

cl22		67.14 209	66.51 215	69.03 205	63.20 345	43.46 287	66.88 252	76.25 194	49.22 252	74.48 194	77.88 279	45.49 275	77.40 194	60.64 158	84.59 220	86.24 69

FA-MVS(test-final)		71.27 151	71.06 161	71.92 161	73.96 228	52.32 204	76.45 117	76.12 195	59.07 137	74.04 204	86.18 158	52.18 235	79.43 155	59.75 170	81.76 250	84.03 124

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 168	74.34 33	80.09 77	76.12 195	50.51 238	89.19 10	90.88 42	71.45 67	77.78 190	73.38 56	90.60 118	90.90 18

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 76	73.83 37	80.42 69	76.12 195	51.33 229	87.19 27	91.51 29	73.79 54	78.44 172	68.27 86	90.13 126	86.49 68

Gipuma		69.55 174	72.83 135	59.70 297	63.63 344	53.97 194	80.08 78	75.93 198	64.24 94	73.49 209	88.93 101	57.89 203	62.46 325	59.75 170	91.55 90	62.67 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		67.50 202	67.31 207	68.08 222	58.86 370	61.93 127	71.43 182	75.90 199	44.67 291	72.42 224	80.20 243	57.16 207	70.44 270	58.99 175	86.12 196	71.88 306

MVSFormer		69.93 168	69.03 180	72.63 150	74.93 209	59.19 158	83.98 36	75.72 200	52.27 214	63.53 318	76.74 288	43.19 289	80.56 135	72.28 67	78.67 285	78.14 246

test_djsdf		78.88 59	78.27 69	80.70 35	81.42 123	71.24 52	83.98 36	75.72 200	52.27 214	87.37 26	92.25 16	68.04 97	80.56 135	72.28 67	91.15 98	90.32 22

SixPastTwentyTwo		75.77 85	76.34 86	74.06 116	81.69 121	54.84 187	76.47 115	75.49 202	64.10 95	87.73 17	92.24 17	50.45 247	81.30 119	67.41 97	91.46 91	86.04 73

KD-MVS_self_test		66.38 219	67.51 203	62.97 270	61.76 351	34.39 356	58.11 328	75.30 203	50.84 235	77.12 148	85.42 172	56.84 213	69.44 275	51.07 238	91.16 97	85.08 91

TinyColmap		67.98 196	69.28 175	64.08 256	67.98 307	46.82 259	70.04 202	75.26 204	53.05 208	77.36 146	86.79 135	59.39 183	72.59 247	45.64 286	88.01 166	72.83 296

BH-w/o		64.81 233	64.29 241	66.36 240	76.08 198	54.71 188	65.61 268	75.23 205	50.10 244	71.05 245	71.86 329	54.33 225	79.02 160	38.20 331	76.14 304	65.36 354

MG-MVS		70.47 161	71.34 159	67.85 224	79.26 147	40.42 312	74.67 146	75.15 206	58.41 142	68.74 276	88.14 120	56.08 219	83.69 79	59.90 167	81.71 254	79.43 230

MVS_0304		76.32 82	75.96 92	77.42 76	79.33 145	60.86 146	80.18 76	74.88 207	66.93 62	69.11 264	88.95 100	57.84 204	86.12 29	76.63 37	89.77 135	85.28 86

cl____		68.26 195	68.26 192	68.29 219	64.98 336	43.67 284	65.89 262	74.67 208	50.04 245	76.86 155	82.42 215	48.74 260	75.38 212	60.92 156	89.81 132	85.80 80

DIV-MVS_self_test		68.27 194	68.26 192	68.29 219	64.98 336	43.67 284	65.89 262	74.67 208	50.04 245	76.86 155	82.43 214	48.74 260	75.38 212	60.94 155	89.81 132	85.81 76

test_0402		78.17 69	79.48 59	74.24 113	83.50 90	59.15 161	72.52 162	74.60 210	75.34 15	88.69 13	91.81 22	75.06 42	82.37 102	65.10 117	88.68 157	81.20 191

CANet_DTU		64.04 245	63.83 245	64.66 251	68.39 299	42.97 292	73.45 156	74.50 211	52.05 218	54.78 367	75.44 298	43.99 284	70.42 271	53.49 225	78.41 288	80.59 212

USDC		62.80 257	63.10 254	61.89 279	65.19 332	43.30 289	67.42 240	74.20 212	35.80 355	72.25 227	84.48 183	45.67 273	71.95 256	37.95 333	84.97 211	70.42 321

MVS		60.62 276	59.97 277	62.58 274	68.13 305	47.28 253	68.59 225	73.96 213	32.19 370	59.94 339	68.86 357	50.48 246	77.64 192	41.85 307	75.74 306	62.83 365

EG-PatchMatch MVS		70.70 158	70.88 163	70.16 185	82.64 109	58.80 166	71.48 181	73.64 214	54.98 177	76.55 164	81.77 222	61.10 167	78.94 162	54.87 208	80.84 262	72.74 298

BH-RMVSNet		68.69 187	68.20 195	70.14 186	76.40 191	53.90 196	64.62 279	73.48 215	58.01 145	73.91 206	81.78 221	59.09 186	78.22 180	48.59 259	77.96 293	78.31 242

FE-MVS		68.29 193	66.96 212	72.26 158	74.16 226	54.24 192	77.55 103	73.42 216	57.65 152	72.66 220	84.91 178	32.02 351	81.49 116	48.43 262	81.85 248	81.04 195

GBi-Net		68.30 191	68.79 183	66.81 235	73.14 242	40.68 308	71.96 172	73.03 217	54.81 178	74.72 188	90.36 67	48.63 263	75.20 216	47.12 273	85.37 203	84.54 110

test1		68.30 191	68.79 183	66.81 235	73.14 242	40.68 308	71.96 172	73.03 217	54.81 178	74.72 188	90.36 67	48.63 263	75.20 216	47.12 273	85.37 203	84.54 110

FMVSNet1		71.06 153	72.48 141	66.81 235	77.65 175	40.68 308	71.96 172	73.03 217	61.14 120	79.45 122	90.36 67	60.44 173	75.20 216	50.20 245	88.05 164	84.54 110

test_yl		65.11 228	65.09 236	65.18 248	70.59 270	40.86 306	63.22 295	72.79 220	57.91 146	68.88 272	79.07 265	42.85 292	74.89 220	45.50 288	84.97 211	79.81 221

DCV-MVSNet		65.11 228	65.09 236	65.18 248	70.59 270	40.86 306	63.22 295	72.79 220	57.91 146	68.88 272	79.07 265	42.85 292	74.89 220	45.50 288	84.97 211	79.81 221

MVS_111021_HR		72.98 131	72.97 134	72.99 135	80.82 129	65.47 100	68.81 220	72.77 222	57.67 150	75.76 174	82.38 216	71.01 72	77.17 195	61.38 149	86.15 195	76.32 264

v148		69.38 178	69.39 174	69.36 197	69.14 293	44.56 277	68.83 219	72.70 223	54.79 181	78.59 128	84.12 187	54.69 222	76.74 203	59.40 173	82.20 243	86.79 63

1314		59.83 282	58.86 285	62.74 273	65.71 329	44.78 276	68.59 225	72.63 224	33.54 368	61.05 332	67.29 368	43.62 287	71.26 263	49.49 251	67.84 366	72.19 304

pmmvs6		71.82 147	73.66 118	66.31 241	75.94 200	42.01 298	66.99 248	72.53 225	63.45 104	76.43 169	92.78 10	72.95 59	69.69 274	51.41 235	90.46 119	87.22 56

UGNet		70.20 163	69.05 179	73.65 121	76.24 193	63.64 115	75.87 129	72.53 225	61.48 118	60.93 334	86.14 161	52.37 234	77.12 196	50.67 241	85.21 208	80.17 219
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
PS-MVSNAJ		64.27 243	63.73 247	65.90 245	77.82 171	51.42 207	63.33 292	72.33 227	45.09 288	61.60 326	68.04 362	62.39 149	73.95 233	49.07 254	73.87 327	72.34 301

xiu_mvs_v2_base		64.43 240	63.96 244	65.85 246	77.72 173	51.32 208	63.63 289	72.31 228	45.06 289	61.70 325	69.66 348	62.56 145	73.93 234	49.06 255	73.91 326	72.31 302

HyFIR lowres test		63.01 254	60.47 274	70.61 174	83.04 101	54.10 193	59.93 315	72.24 229	33.67 366	69.00 266	75.63 294	38.69 317	76.93 198	36.60 344	75.45 311	80.81 205

UniMVSNet_ETH3D		76.74 79	79.02 61	69.92 191	89.27 19	43.81 282	74.47 148	71.70 230	72.33 35	85.50 50	93.65 3	77.98 21	76.88 200	54.60 212	91.64 86	89.08 32

cascas		64.59 236	62.77 257	70.05 188	75.27 205	50.02 219	61.79 301	71.61 231	42.46 309	63.68 315	68.89 356	49.33 254	80.35 139	47.82 270	84.05 227	79.78 223

MVP-Stereo		61.56 268	59.22 281	68.58 216	79.28 146	60.44 151	69.20 214	71.57 232	43.58 300	56.42 359	78.37 272	39.57 313	76.46 205	34.86 356	60.16 384	68.86 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EI-MVSNet-Vis-set		72.78 135	71.87 148	75.54 99	74.77 214	59.02 164	72.24 164	71.56 233	63.92 96	78.59 128	71.59 330	66.22 117	78.60 167	67.58 94	80.32 267	89.00 35

EI-MVSNet-UG-set		72.63 138	71.68 152	75.47 100	74.67 216	58.64 169	72.02 169	71.50 234	63.53 102	78.58 130	71.39 334	65.98 118	78.53 168	67.30 104	80.18 269	89.23 29

VPA-MVSNet		68.71 186	70.37 167	63.72 260	76.13 195	38.06 331	64.10 284	71.48 235	56.60 164	74.10 201	88.31 114	64.78 132	69.72 273	47.69 271	90.15 124	83.37 145

hse-mvs2		72.32 143	70.66 166	77.31 79	83.10 100	71.77 47	69.19 215	71.45 236	54.28 189	77.89 137	78.26 273	49.04 256	79.23 156	63.62 133	89.13 151	80.92 200

AUN-MVS		70.22 162	67.88 199	77.22 80	82.96 104	71.61 48	69.08 216	71.39 237	49.17 253	71.70 232	78.07 278	37.62 325	79.21 157	61.81 144	89.15 149	80.82 203

SDMVSNet		66.36 220	67.85 200	61.88 280	73.04 248	46.14 267	58.54 323	71.36 238	51.42 226	68.93 270	82.72 211	65.62 122	62.22 328	54.41 215	84.67 216	77.28 255

EI-MVSNet		69.61 173	69.01 181	71.41 167	73.94 229	49.90 222	71.31 186	71.32 239	58.22 143	75.40 181	70.44 337	58.16 194	75.85 206	62.51 141	79.81 273	88.48 44

MVSTER		63.29 251	61.60 264	68.36 217	59.77 366	46.21 266	60.62 310	71.32 239	41.83 311	75.40 181	79.12 263	30.25 366	75.85 206	56.30 193	79.81 273	83.03 156

TransMVSNet (Re)		69.62 172	71.63 153	63.57 262	76.51 190	35.93 345	65.75 266	71.29 241	61.05 121	75.02 183	89.90 78	65.88 121	70.41 272	49.79 247	89.48 141	84.38 117

xiu_mvs_v1_base_debu		67.87 197	67.07 209	70.26 181	79.13 152	61.90 128	67.34 241	71.25 242	47.98 261	67.70 284	74.19 312	61.31 160	72.62 244	56.51 189	78.26 289	76.27 265

xiu_mvs_v1_base		67.87 197	67.07 209	70.26 181	79.13 152	61.90 128	67.34 241	71.25 242	47.98 261	67.70 284	74.19 312	61.31 160	72.62 244	56.51 189	78.26 289	76.27 265

xiu_mvs_v1_base_debi		67.87 197	67.07 209	70.26 181	79.13 152	61.90 128	67.34 241	71.25 242	47.98 261	67.70 284	74.19 312	61.31 160	72.62 244	56.51 189	78.26 289	76.27 265

FMVSNet2		67.48 203	68.21 194	65.29 247	73.14 242	38.94 321	68.81 220	71.21 245	54.81 178	76.73 159	86.48 150	48.63 263	74.60 224	47.98 268	86.11 197	82.35 175

h-mvs33		73.08 124	71.61 154	77.48 74	83.89 88	72.89 44	70.47 198	71.12 246	54.28 189	77.89 137	83.41 196	49.04 256	80.98 128	63.62 133	90.77 115	78.58 239

miper_lstm_enhance		61.97 263	61.63 263	62.98 269	60.04 360	45.74 270	47.53 374	70.95 247	44.04 293	73.06 215	78.84 268	39.72 311	60.33 333	55.82 200	84.64 219	82.88 159

无先验							74.82 138	70.94 248	47.75 266				76.85 201	54.47 213		72.09 305

Baseline_NR-MVSNet		70.62 159	73.19 127	62.92 272	76.97 182	34.44 355	68.84 218	70.88 249	60.25 127	79.50 121	90.53 53	61.82 155	69.11 278	54.67 211	95.27 13	85.22 87

VDD-MVS		70.81 157	71.44 158	68.91 210	79.07 155	46.51 262	67.82 235	70.83 250	61.23 119	74.07 202	88.69 105	59.86 179	75.62 211	51.11 237	90.28 121	84.61 106

pm-mvs1		68.40 189	69.85 171	64.04 258	73.10 245	39.94 314	64.61 280	70.50 251	55.52 173	73.97 205	89.33 85	63.91 137	68.38 284	49.68 249	88.02 165	83.81 129

FMVSNet3		65.00 231	65.16 232	64.52 253	69.47 290	37.56 336	66.63 254	70.38 252	51.55 224	74.72 188	83.27 204	37.89 323	74.44 226	47.12 273	85.37 203	81.57 189

TR-MVS		64.59 236	63.54 249	67.73 227	75.75 203	50.83 211	63.39 291	70.29 253	49.33 251	71.55 238	74.55 305	50.94 243	78.46 171	40.43 316	75.69 307	73.89 286

cdsmvs_eth3d_5k		17.71 372	23.62 374	0.00 391	0.00 414	0.00 416	0.00 402	70.17 254	0.00 409	0.00 410	74.25 310	68.16 95	0.00 410	0.00 409	0.00 408	0.00 406

fmvsm_l_conf0.5_n		67.48 203	66.88 214	69.28 200	67.41 313	62.04 126	70.69 196	69.85 255	39.46 332	69.59 260	81.09 229	58.15 195	68.73 280	67.51 96	78.16 292	77.07 262

mvs_anonymous		65.08 230	65.49 226	63.83 259	63.79 342	37.60 335	66.52 256	69.82 256	43.44 302	73.46 210	86.08 164	58.79 190	71.75 259	51.90 232	75.63 308	82.15 180

D2MVS		62.58 260	61.05 269	67.20 231	63.85 341	47.92 243	56.29 337	69.58 257	39.32 333	70.07 255	78.19 275	34.93 334	72.68 242	53.44 226	83.74 230	81.00 198

TSAR-MVS + GP.		73.08 124	71.60 155	77.54 73	78.99 157	70.73 57	74.96 136	69.38 258	60.73 124	74.39 196	78.44 271	57.72 205	82.78 96	60.16 163	89.60 137	79.11 233

GA-MVS		62.91 255	61.66 261	66.66 239	67.09 316	44.49 278	61.18 306	69.36 259	51.33 229	69.33 263	74.47 306	36.83 328	74.94 219	50.60 242	74.72 316	80.57 213

fmvsm_l_conf0.5_n_a		66.66 214	65.97 222	68.72 214	67.09 316	61.38 134	70.03 203	69.15 260	38.59 339	68.41 277	80.36 240	56.56 216	68.32 285	66.10 110	77.45 296	76.46 263

Anonymous20240521		63.55 247	66.07 220	55.99 318	66.18 326	44.04 281	68.77 223	68.80 261	46.99 270	72.57 221	85.84 169	39.87 310	50.22 357	53.40 228	92.23 81	73.71 288

ab-mvs		64.11 244	65.13 235	61.05 288	71.99 257	38.03 332	67.59 236	68.79 262	49.08 255	65.32 300	86.26 156	58.02 202	66.85 303	39.33 320	79.79 275	78.27 243

WR-MVS		71.20 152	72.48 141	67.36 229	84.98 70	35.70 347	64.43 282	68.66 263	65.05 86	81.49 98	86.43 152	57.57 206	76.48 204	50.36 244	93.32 65	89.90 23

EGC-MVSNET		64.77 234	61.17 267	75.60 98	86.90 42	74.47 30	84.04 35	68.62 264	0.60 405	1.13 407	91.61 28	65.32 127	74.15 231	64.01 126	88.28 160	78.17 245

1112_ss		59.48 284	58.99 284	60.96 290	77.84 170	42.39 297	61.42 303	68.45 265	37.96 343	59.93 340	67.46 365	45.11 278	65.07 315	40.89 314	71.81 342	75.41 271

EU-MVSNet		60.82 273	60.80 272	60.86 291	68.37 300	41.16 303	72.27 163	68.27 266	26.96 386	69.08 265	75.71 293	32.09 348	67.44 294	55.59 203	78.90 282	73.97 284

CMPMVS	48.73 20	61.54 269	60.89 270	63.52 263	61.08 355	51.55 206	68.07 233	68.00 267	33.88 363	65.87 296	81.25 227	37.91 322	67.71 289	49.32 253	82.60 241	71.31 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_rt		46.70 352	45.24 360	51.06 342	44.58 405	51.04 209	39.91 389	67.56 268	21.84 400	51.94 377	50.79 398	33.83 337	39.77 393	35.25 355	61.50 381	62.38 370

OpenMVS_ROB	54.93 17	63.23 252	63.28 251	63.07 268	69.81 285	45.34 272	68.52 227	67.14 269	43.74 298	70.61 248	79.22 260	47.90 267	72.66 243	48.75 257	73.84 328	71.21 314

VNet		64.01 246	65.15 234	60.57 292	73.28 238	35.61 348	57.60 330	67.08 270	54.61 185	66.76 293	83.37 199	56.28 217	66.87 301	42.19 304	85.20 209	79.23 232

Test_1112_low_res		58.78 289	58.69 286	59.04 303	79.41 143	38.13 330	57.62 329	66.98 271	34.74 359	59.62 343	77.56 282	42.92 291	63.65 322	38.66 326	70.73 349	75.35 273

MVS_111021_LR		72.10 145	71.82 150	72.95 137	79.53 142	73.90 36	70.45 199	66.64 272	56.87 158	76.81 157	81.76 223	68.78 88	71.76 258	61.81 144	83.74 230	73.18 291

VDDNet		71.60 149	73.13 129	67.02 234	86.29 47	41.11 304	69.97 204	66.50 273	68.72 55	74.74 187	91.70 25	59.90 178	75.81 208	48.58 260	91.72 84	84.15 123

test_fmvs3		56.78 298	55.99 307	59.12 301	53.96 393	48.09 240	58.76 322	66.22 274	27.54 384	76.66 160	68.69 359	25.32 386	51.31 354	53.42 227	73.38 330	77.97 251

Anonymous202405211		66.02 222	66.89 213	63.43 265	74.22 224	38.14 329	59.00 319	66.13 275	63.33 107	69.76 259	85.95 168	51.88 236	70.50 269	44.23 294	87.52 171	81.64 188

test_fmvs1_n		52.70 325	52.01 332	54.76 323	53.83 394	50.36 214	55.80 342	65.90 276	24.96 392	65.39 299	60.64 386	27.69 375	48.46 363	45.88 285	67.99 364	65.46 353

test_fmvs2		54.80 310	54.11 319	56.88 315	51.76 397	49.95 221	56.70 335	65.80 277	26.22 389	69.42 261	65.25 372	31.82 352	49.98 358	49.63 250	70.36 351	70.71 318

jason		64.47 239	62.84 256	69.34 199	76.91 184	59.20 157	67.15 246	65.67 278	35.29 356	65.16 301	76.74 288	44.67 280	70.68 266	54.74 210	79.28 279	78.14 246
jason: jason.
CDS-MVSNet		64.33 242	62.66 258	69.35 198	80.44 133	58.28 170	65.26 272	65.66 279	44.36 292	67.30 290	75.54 295	43.27 288	71.77 257	37.68 334	84.44 223	78.01 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 1792x2688		58.09 293	56.30 304	63.45 264	79.95 137	50.93 210	54.07 354	65.59 280	28.56 382	61.53 327	74.33 308	41.09 302	66.52 307	33.91 360	67.69 367	72.92 294

IterMVS-SCA-FT		67.68 201	66.07 220	72.49 153	73.34 237	58.20 171	63.80 287	65.55 281	48.10 260	76.91 152	82.64 213	45.20 276	78.84 163	61.20 151	77.89 294	80.44 215

sd_testset		63.55 247	65.38 227	58.07 308	73.04 248	38.83 323	57.41 331	65.44 282	51.42 226	68.93 270	82.72 211	63.76 138	58.11 343	41.05 312	84.67 216	77.28 255

HY-MVS	49.31 19	57.96 294	57.59 295	59.10 302	66.85 320	36.17 342	65.13 274	65.39 283	39.24 335	54.69 369	78.14 276	44.28 283	67.18 298	33.75 362	70.79 348	73.95 285

IB-MVS	49.67 18	59.69 283	56.96 299	67.90 223	68.19 304	50.30 216	61.42 303	65.18 284	47.57 267	55.83 362	67.15 369	23.77 390	79.60 152	43.56 298	79.97 271	73.79 287
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
tfpnnormal		66.48 218	67.93 197	62.16 278	73.40 236	36.65 338	63.45 290	64.99 285	55.97 168	72.82 219	87.80 123	57.06 211	69.10 279	48.31 264	87.54 170	80.72 208

test_fmvs1		51.51 335	50.86 342	53.48 329	49.72 400	49.35 230	54.11 353	64.96 286	24.64 394	63.66 316	59.61 389	28.33 374	48.45 364	45.38 290	67.30 368	62.66 368

CL-MVSNet_self_test		62.44 261	63.40 250	59.55 299	72.34 254	32.38 364	56.39 336	64.84 287	51.21 231	67.46 288	81.01 231	50.75 244	63.51 323	38.47 329	88.12 163	82.75 164

KD-MVS_2432*1600		52.05 331	51.58 334	53.44 330	52.11 395	31.20 370	44.88 381	64.83 288	41.53 313	64.37 305	70.03 345	15.61 408	64.20 317	36.25 346	74.61 318	64.93 358

miper_refine_blended		52.05 331	51.58 334	53.44 330	52.11 395	31.20 370	44.88 381	64.83 288	41.53 313	64.37 305	70.03 345	15.61 408	64.20 317	36.25 346	74.61 318	64.93 358

CVMVSNet		59.21 286	58.44 289	61.51 283	73.94 229	47.76 247	71.31 186	64.56 290	26.91 388	60.34 336	70.44 337	36.24 331	67.65 290	53.57 224	68.66 361	69.12 333

lupinMVS		63.36 249	61.49 265	68.97 207	74.93 209	59.19 158	65.80 265	64.52 291	34.68 361	63.53 318	74.25 310	43.19 289	70.62 267	53.88 222	78.67 285	77.10 259

ET-MVSNet_ETH3D		63.32 250	60.69 273	71.20 169	70.15 281	55.66 183	65.02 275	64.32 292	43.28 307	68.99 267	72.05 328	25.46 384	78.19 183	54.16 220	82.80 239	79.74 224

test_vis1_n_1920		52.96 322	53.50 321	51.32 341	59.15 368	44.90 275	56.13 340	64.29 293	30.56 380	59.87 341	60.68 385	40.16 308	47.47 367	48.25 265	62.46 378	61.58 373

patch_mono-2		62.73 259	64.08 243	58.68 304	70.36 278	55.87 181	60.84 308	64.11 294	41.23 316	64.04 309	78.22 274	60.00 176	48.80 361	54.17 219	83.71 232	71.37 310

thisisatest0530		67.05 212	65.16 232	72.73 147	73.10 245	50.55 212	71.26 188	63.91 295	50.22 242	74.46 195	80.75 234	26.81 377	80.25 142	59.43 172	86.50 193	87.37 54

旧先验1					84.55 78	60.36 152		63.69 296			87.05 130	54.65 223			83.34 236	69.66 327

EPNet		69.10 181	67.32 206	74.46 107	68.33 302	61.27 137	77.56 102	63.57 297	60.95 122	56.62 358	82.75 210	51.53 240	81.24 120	54.36 217	90.20 122	80.88 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAMVS		65.31 227	63.75 246	69.97 190	82.23 114	59.76 156	66.78 253	63.37 298	45.20 286	69.79 258	79.37 258	47.42 269	72.17 251	34.48 357	85.15 210	77.99 250

tttt0517		69.46 175	67.79 201	74.46 107	75.34 204	52.72 201	75.05 135	63.27 299	54.69 183	78.87 127	84.37 184	26.63 378	81.15 121	63.95 128	87.93 168	89.51 25

MS-PatchMatch		55.59 305	54.89 314	57.68 310	69.18 291	49.05 231	61.00 307	62.93 300	35.98 353	58.36 347	68.93 355	36.71 329	66.59 306	37.62 336	63.30 376	57.39 382

IterMVS		63.12 253	62.48 259	65.02 250	66.34 323	52.86 200	63.81 286	62.25 301	46.57 273	71.51 239	80.40 239	44.60 281	66.82 304	51.38 236	75.47 310	75.38 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thisisatest0515		60.48 277	57.86 293	68.34 218	67.25 314	46.42 263	60.58 311	62.14 302	40.82 322	63.58 317	69.12 351	26.28 380	78.34 177	48.83 256	82.13 244	80.26 217

VPNet		65.58 225	67.56 202	59.65 298	79.72 139	30.17 377	60.27 313	62.14 302	54.19 194	71.24 242	86.63 145	58.80 189	67.62 291	44.17 295	90.87 112	81.18 192

新几何1				69.99 189	88.37 34	71.34 51		62.08 304	43.85 294	74.99 184	86.11 163	52.85 232	70.57 268	50.99 239	83.23 237	68.05 339

pmmvs-eth3d		64.41 241	63.27 252	67.82 226	75.81 202	60.18 153	69.49 209	62.05 305	38.81 338	74.13 200	82.23 217	43.76 286	68.65 282	42.53 302	80.63 266	74.63 277

K. test v3		73.67 112	73.61 120	73.87 119	79.78 138	55.62 185	74.69 145	62.04 306	66.16 71	84.76 60	93.23 5	49.47 252	80.97 129	65.66 115	86.67 191	85.02 93

testdata				64.13 255	85.87 59	63.34 118		61.80 307	47.83 264	76.42 170	86.60 147	48.83 259	62.31 327	54.46 214	81.26 258	66.74 348

N_pmnet		52.06 330	51.11 338	54.92 322	59.64 367	71.03 53	37.42 393	61.62 308	33.68 365	57.12 351	72.10 325	37.94 321	31.03 400	29.13 383	71.35 344	62.70 366

ppachtmachnet_test		60.26 279	59.61 280	62.20 277	67.70 310	44.33 279	58.18 327	60.96 309	40.75 324	65.80 297	72.57 324	41.23 299	63.92 320	46.87 277	82.42 242	78.33 241

test_vis1_n		51.27 336	50.41 346	53.83 326	56.99 377	50.01 220	56.75 334	60.53 310	25.68 390	59.74 342	57.86 390	29.40 371	47.41 368	43.10 300	63.66 375	64.08 363

pmmvs4		60.78 274	59.04 283	66.00 244	73.06 247	57.67 173	64.53 281	60.22 311	36.91 350	65.96 295	77.27 284	39.66 312	68.54 283	38.87 324	74.89 315	71.80 307

CostFormer		57.35 297	56.14 305	60.97 289	63.76 343	38.43 325	67.50 238	60.22 311	37.14 349	59.12 345	76.34 290	32.78 343	71.99 255	39.12 323	69.27 358	72.47 300

LFMVS		67.06 211	67.89 198	64.56 252	78.02 167	38.25 328	70.81 195	59.60 313	65.18 83	71.06 244	86.56 148	43.85 285	75.22 215	46.35 280	89.63 136	80.21 218

test222					87.30 37	69.15 73	67.85 234	59.59 314	41.06 318	73.05 216	85.72 171	48.03 266			80.65 264	66.92 344

tpmvs		55.84 301	55.45 311	57.01 313	60.33 359	33.20 362	65.89 262	59.29 315	47.52 268	56.04 360	73.60 316	31.05 361	68.06 288	40.64 315	64.64 372	69.77 326

UnsupCasMVSNet_eth		52.26 329	53.29 324	49.16 352	55.08 386	33.67 360	50.03 367	58.79 316	37.67 346	63.43 320	74.75 303	41.82 297	45.83 371	38.59 328	59.42 386	67.98 340

EPNet_dtu		58.93 288	58.52 287	60.16 296	67.91 308	47.70 248	69.97 204	58.02 317	49.73 247	47.28 389	73.02 322	38.14 319	62.34 326	36.57 345	85.99 199	70.43 320
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MIMVSNet1		66.57 217	69.23 177	58.59 305	81.26 127	37.73 334	64.06 285	57.62 318	57.02 157	78.40 132	90.75 46	62.65 144	58.10 344	41.77 308	89.58 140	79.95 220

tfpn200view9		60.35 278	59.97 277	61.51 283	70.78 266	35.35 349	63.27 293	57.47 319	53.00 209	68.31 279	77.09 285	32.45 346	72.09 252	35.61 352	81.73 251	77.08 260

thres400		60.77 275	59.97 277	63.15 266	70.78 266	35.35 349	63.27 293	57.47 319	53.00 209	68.31 279	77.09 285	32.45 346	72.09 252	35.61 352	81.73 251	82.02 181

lessismore_v0				72.75 145	79.60 141	56.83 177		57.37 321		83.80 72	89.01 97	47.45 268	78.74 166	64.39 123	86.49 194	82.69 166

tpm cat1		54.02 316	52.63 327	58.19 307	64.85 338	39.86 315	66.26 258	57.28 322	32.16 371	56.90 354	70.39 339	32.75 344	65.30 314	34.29 358	58.79 387	69.41 330

thres200		57.55 296	57.02 298	59.17 300	67.89 309	34.93 352	58.91 321	57.25 323	50.24 241	64.01 310	71.46 332	32.49 345	71.39 262	31.31 369	79.57 277	71.19 315

MDA-MVSNet-bldmvs		62.34 262	61.73 260	64.16 254	61.64 352	49.90 222	48.11 372	57.24 324	53.31 207	80.95 106	79.39 257	49.00 258	61.55 330	45.92 284	80.05 270	81.03 196

fmvsm_s_conf0.1_n_a		67.37 207	66.36 216	70.37 179	70.86 265	61.17 138	74.00 154	57.18 325	40.77 323	68.83 275	80.88 232	63.11 141	67.61 292	66.94 106	74.72 316	82.33 178

thres100view900		61.17 271	61.09 268	61.39 285	72.14 256	35.01 351	65.42 271	56.99 326	55.23 175	70.71 247	79.90 249	32.07 349	72.09 252	35.61 352	81.73 251	77.08 260

thres600view7		61.82 265	61.38 266	63.12 267	71.81 258	34.93 352	64.64 278	56.99 326	54.78 182	70.33 251	79.74 251	32.07 349	72.42 249	38.61 327	83.46 235	82.02 181

fmvsm_s_conf0.5_n_a		67.00 213	65.95 223	70.17 184	69.72 289	61.16 139	73.34 157	56.83 328	40.96 320	68.36 278	80.08 247	62.84 142	67.57 293	66.90 108	74.50 320	81.78 186

tpm2		56.12 300	54.64 316	60.55 293	66.24 324	36.01 343	68.14 231	56.77 329	33.60 367	58.25 348	75.52 297	30.25 366	74.33 228	33.27 363	69.76 357	71.32 311

fmvsm_s_conf0.1_n		66.60 216	65.54 225	69.77 192	68.99 295	59.15 161	72.12 167	56.74 330	40.72 325	68.25 281	80.14 246	61.18 166	66.92 299	67.34 103	74.40 321	83.23 150

fmvsm_s_conf0.5_n		66.34 221	65.27 228	69.57 195	68.20 303	59.14 163	71.66 179	56.48 331	40.92 321	67.78 283	79.46 255	61.23 163	66.90 300	67.39 99	74.32 324	82.66 167

ECVR-MVS		64.82 232	65.22 229	63.60 261	78.80 158	31.14 372	66.97 249	56.47 332	54.23 191	69.94 256	88.68 106	37.23 326	74.81 222	45.28 291	89.41 143	84.86 96

CR-MVSNet		58.96 287	58.49 288	60.36 294	66.37 321	48.24 237	70.93 192	56.40 333	32.87 369	61.35 328	86.66 142	33.19 340	63.22 324	48.50 261	70.17 353	69.62 328

Patchmtry		60.91 272	63.01 255	54.62 325	66.10 327	26.27 392	67.47 239	56.40 333	54.05 197	72.04 230	86.66 142	33.19 340	60.17 334	43.69 296	87.45 174	77.42 253

testing91		55.74 303	55.29 313	57.08 312	70.63 269	30.85 374	54.94 349	56.31 335	50.34 239	57.08 352	70.10 344	24.50 388	65.86 309	36.98 342	76.75 300	74.53 279

MDTV_nov1_ep13			54.05 320		65.54 330	29.30 381	59.00 319	55.22 336	35.96 354	52.44 374	75.98 291	30.77 363	59.62 336	38.21 330	73.33 331

baseline1		57.82 295	58.36 291	56.19 317	69.17 292	30.76 375	62.94 297	55.21 337	46.04 276	63.83 313	78.47 270	41.20 300	63.68 321	39.44 319	68.99 359	74.13 283

door-mid								55.02 338

ADS-MVSNet2		48.76 346	47.25 355	53.29 332	55.90 383	40.54 311	47.34 375	54.99 339	31.41 377	50.48 382	72.06 326	31.23 357	54.26 351	25.93 388	55.93 392	65.07 356

test_cas_vis1_n_1920		50.90 337	50.92 341	50.83 343	54.12 392	47.80 245	51.44 365	54.61 340	26.95 387	63.95 311	60.85 384	37.86 324	44.97 377	45.53 287	62.97 377	59.72 377

baseline2		55.57 306	52.74 325	64.05 257	65.26 331	44.11 280	62.38 298	54.43 341	39.03 336	51.21 379	67.35 367	33.66 338	72.45 248	37.14 339	64.22 374	75.60 268

test1111		64.62 235	65.19 231	62.93 271	79.01 156	29.91 378	65.45 270	54.41 342	54.09 196	71.47 241	88.48 110	37.02 327	74.29 229	46.83 278	89.94 130	84.58 109

testing99		55.16 308	54.56 317	56.98 314	70.13 282	30.58 376	54.55 352	54.11 343	49.53 250	56.76 356	70.14 343	22.76 392	65.79 310	36.99 341	76.04 305	74.57 278

Vis-MVSNet (Re-imp)		62.74 258	63.21 253	61.34 286	72.19 255	31.56 369	67.31 245	53.87 344	53.60 204	69.88 257	83.37 199	40.52 306	70.98 265	41.40 310	86.78 189	81.48 190

pmmvs5		52.49 328	52.58 328	52.21 336	54.99 387	32.38 364	55.45 344	53.84 345	32.15 372	55.49 364	74.81 300	38.08 320	57.37 346	34.02 359	74.40 321	66.88 345

XXY-MVS		55.19 307	57.40 297	48.56 356	64.45 339	34.84 354	51.54 364	53.59 346	38.99 337	63.79 314	79.43 256	56.59 214	45.57 372	36.92 343	71.29 345	65.25 355

dmvs_re		49.91 344	50.77 343	47.34 358	59.98 361	38.86 322	53.18 357	53.58 347	39.75 331	55.06 365	61.58 383	36.42 330	44.40 381	29.15 382	68.23 362	58.75 379

PVSNet	43.83 21	51.56 334	51.17 337	52.73 333	68.34 301	38.27 327	48.22 371	53.56 348	36.41 351	54.29 370	64.94 373	34.60 335	54.20 352	30.34 372	69.87 355	65.71 352

test_method		19.26 371	19.12 375	19.71 386	9.09 410	1.91 413	7.79 401	53.44 349	1.42 404	10.27 406	35.80 401	17.42 405	25.11 405	12.44 404	24.38 404	32.10 401

SCA		58.57 291	58.04 292	60.17 295	70.17 280	41.07 305	65.19 273	53.38 350	43.34 306	61.00 333	73.48 317	45.20 276	69.38 276	40.34 317	70.31 352	70.05 322

UnsupCasMVSNet_bld		50.01 343	51.03 340	46.95 359	58.61 371	32.64 363	48.31 370	53.27 351	34.27 362	60.47 335	71.53 331	41.40 298	47.07 369	30.68 371	60.78 383	61.13 374

wuyk23d		61.97 263	66.25 217	49.12 353	58.19 374	60.77 149	66.32 257	52.97 352	55.93 170	90.62 5	86.91 132	73.07 57	35.98 398	20.63 402	91.63 87	50.62 388

door								52.91 353

FMVSNet5		55.08 309	55.54 310	53.71 327	65.80 328	33.50 361	56.22 338	52.50 354	43.72 299	61.06 331	83.38 198	25.46 384	54.87 349	30.11 374	81.64 256	72.75 297

testing11		53.13 321	52.26 331	55.75 320	70.44 276	31.73 368	54.75 350	52.40 355	44.81 290	52.36 376	68.40 361	21.83 393	65.74 311	32.64 366	72.73 334	69.78 325

our_test_3		56.46 299	56.51 302	56.30 316	67.70 310	39.66 316	55.36 345	52.34 356	40.57 327	63.85 312	69.91 347	40.04 309	58.22 342	43.49 299	75.29 314	71.03 317

testing222		53.37 319	52.50 329	55.98 319	70.51 275	29.68 379	56.20 339	51.85 357	46.19 275	56.76 356	68.94 354	19.18 401	65.39 312	25.87 390	76.98 298	72.87 295

PatchmatchNet		54.60 311	54.27 318	55.59 321	65.17 334	39.08 318	66.92 250	51.80 358	39.89 330	58.39 346	73.12 321	31.69 354	58.33 341	43.01 301	58.38 390	69.38 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FPMVS		59.43 285	60.07 276	57.51 311	77.62 176	71.52 49	62.33 299	50.92 359	57.40 155	69.40 262	80.00 248	39.14 315	61.92 329	37.47 337	66.36 369	39.09 399

Anonymous20231206		54.13 313	55.82 308	49.04 354	70.89 264	35.96 344	51.73 363	50.87 360	34.86 357	62.49 323	79.22 260	42.52 295	44.29 382	27.95 384	81.88 247	66.88 345

new-patchmatchnet		52.89 324	55.76 309	44.26 372	59.94 364	6.31 410	37.36 394	50.76 361	41.10 317	64.28 307	79.82 250	44.77 279	48.43 365	36.24 348	87.61 169	78.03 248

WB-MVSnew		53.94 318	54.76 315	51.49 340	71.53 260	28.05 384	58.22 326	50.36 362	37.94 344	59.16 344	70.17 342	49.21 255	51.94 353	24.49 394	71.80 343	74.47 281

tpmrst		50.15 342	51.38 336	46.45 363	56.05 381	24.77 395	64.40 283	49.98 363	36.14 352	53.32 373	69.59 349	35.16 333	48.69 362	39.24 321	58.51 389	65.89 350

WTY-MVS		49.39 345	50.31 347	46.62 362	61.22 354	32.00 367	46.61 377	49.77 364	33.87 364	54.12 371	69.55 350	41.96 296	45.40 374	31.28 370	64.42 373	62.47 369

UWE-MVS		52.94 323	52.70 326	53.65 328	73.56 232	27.49 387	57.30 332	49.57 365	38.56 340	62.79 322	71.42 333	19.49 400	60.41 332	24.33 396	77.33 297	73.06 292

testgi		54.00 317	56.86 300	45.45 366	58.20 373	25.81 394	49.05 368	49.50 366	45.43 283	67.84 282	81.17 228	51.81 239	43.20 386	29.30 378	79.41 278	67.34 343

test20.03		55.74 303	57.51 296	50.42 344	59.89 365	32.09 366	50.63 366	49.01 367	50.11 243	65.07 302	83.23 206	45.61 274	48.11 366	30.22 373	83.82 229	71.07 316

PatchMatch-RL		58.68 290	57.72 294	61.57 282	76.21 194	73.59 39	61.83 300	49.00 368	47.30 269	61.08 330	68.97 353	50.16 248	59.01 338	36.06 351	68.84 360	52.10 386

sss		47.59 350	48.32 350	45.40 367	56.73 380	33.96 358	45.17 380	48.51 369	32.11 374	52.37 375	65.79 370	40.39 307	41.91 390	31.85 367	61.97 380	60.35 375

MIMVSNet		54.39 312	56.12 306	49.20 351	72.57 252	30.91 373	59.98 314	48.43 370	41.66 312	55.94 361	83.86 192	41.19 301	50.42 356	26.05 387	75.38 312	66.27 349

JIA-IIPM		54.03 315	51.62 333	61.25 287	59.14 369	55.21 186	59.10 318	47.72 371	50.85 234	50.31 385	85.81 170	20.10 398	63.97 319	36.16 349	55.41 395	64.55 361

test_f		43.79 362	45.63 357	38.24 383	42.29 408	38.58 324	34.76 396	47.68 372	22.22 399	67.34 289	63.15 377	31.82 352	30.60 401	39.19 322	62.28 379	45.53 395

Patchmatch-RL test		59.95 281	59.12 282	62.44 275	72.46 253	54.61 190	59.63 316	47.51 373	41.05 319	74.58 193	74.30 309	31.06 360	65.31 313	51.61 233	79.85 272	67.39 341

SSC-MVS		61.79 266	66.08 219	48.89 355	76.91 184	10.00 409	53.56 356	47.37 374	68.20 58	76.56 163	89.21 89	54.13 226	57.59 345	54.75 209	74.07 325	79.08 234

WB-MVS		60.04 280	64.19 242	47.59 357	76.09 196	10.22 408	52.44 361	46.74 375	65.17 84	74.07 202	87.48 124	53.48 229	55.28 348	49.36 252	72.84 333	77.28 255

MDA-MVSNet_test_wron		52.57 327	53.49 323	49.81 348	54.24 389	36.47 340	40.48 388	46.58 376	38.13 341	75.47 180	73.32 319	41.05 304	43.85 384	40.98 313	71.20 346	69.10 334

YYNet1		52.58 326	53.50 321	49.85 347	54.15 390	36.45 341	40.53 387	46.55 377	38.09 342	75.52 179	73.31 320	41.08 303	43.88 383	41.10 311	71.14 347	69.21 332

test-LLR		50.43 339	50.69 344	49.64 349	60.76 356	41.87 299	53.18 357	45.48 378	43.41 304	49.41 386	60.47 387	29.22 372	44.73 379	42.09 305	72.14 340	62.33 371

test-mter		48.56 347	48.20 352	49.64 349	60.76 356	41.87 299	53.18 357	45.48 378	31.91 375	49.41 386	60.47 387	18.34 402	44.73 379	42.09 305	72.14 340	62.33 371

Syy-MVS		54.13 313	55.45 311	50.18 345	68.77 296	23.59 397	55.02 346	44.55 380	43.80 295	58.05 349	64.07 374	46.22 271	58.83 339	46.16 282	72.36 337	68.12 337

myMVS_eth3d		50.36 340	50.52 345	49.88 346	68.77 296	22.69 399	55.02 346	44.55 380	43.80 295	58.05 349	64.07 374	14.16 410	58.83 339	33.90 361	72.36 337	68.12 337

ETVMVS		50.32 341	49.87 349	51.68 338	70.30 279	26.66 390	52.33 362	43.93 382	43.54 301	54.91 366	67.95 363	20.01 399	60.17 334	22.47 398	73.40 329	68.22 336

tpm		50.60 338	52.42 330	45.14 368	65.18 333	26.29 391	60.30 312	43.50 383	37.41 347	57.01 353	79.09 264	30.20 368	42.32 387	32.77 365	66.36 369	66.81 347

dmvs_testset		45.26 355	47.51 353	38.49 382	59.96 363	14.71 406	58.50 324	43.39 384	41.30 315	51.79 378	56.48 391	39.44 314	49.91 360	21.42 400	55.35 396	50.85 387

PatchT		53.35 320	56.47 303	43.99 373	64.19 340	17.46 404	59.15 317	43.10 385	52.11 217	54.74 368	86.95 131	29.97 369	49.98 358	43.62 297	74.40 321	64.53 362

testing3		58.28 292	58.38 290	58.00 309	77.45 177	26.12 393	60.78 309	43.00 386	56.02 167	70.18 253	75.76 292	13.27 411	67.24 297	48.02 267	80.89 260	80.65 210

PM-MVS		64.49 238	63.61 248	67.14 233	76.68 189	75.15 27	68.49 228	42.85 387	51.17 232	77.85 139	80.51 237	45.76 272	66.31 308	52.83 229	76.35 302	59.96 376

GG-mvs-BLEND				52.24 335	60.64 358	29.21 382	69.73 208	42.41 388		45.47 392	52.33 396	20.43 397	68.16 286	25.52 392	65.42 371	59.36 378

PMMVS		44.69 358	43.95 366	46.92 360	50.05 399	53.47 198	48.08 373	42.40 389	22.36 398	44.01 398	53.05 395	42.60 294	45.49 373	31.69 368	61.36 382	41.79 397

dp		44.09 361	44.88 363	41.72 378	58.53 372	23.18 398	54.70 351	42.38 390	34.80 358	44.25 397	65.61 371	24.48 389	44.80 378	29.77 376	49.42 398	57.18 383

E-PMN		45.17 356	45.36 359	44.60 370	50.07 398	42.75 293	38.66 391	42.29 391	46.39 274	39.55 400	51.15 397	26.00 381	45.37 375	37.68 334	76.41 301	45.69 394

PVSNet_0	36.71 22	41.12 366	40.78 369	42.14 375	59.97 362	40.13 313	40.97 386	42.24 392	30.81 379	44.86 395	49.41 399	40.70 305	45.12 376	23.15 397	34.96 402	41.16 398

TESTMET0.1,1		45.17 356	44.93 362	45.89 365	56.02 382	38.31 326	53.18 357	41.94 393	27.85 383	44.86 395	56.47 392	17.93 403	41.50 391	38.08 332	68.06 363	57.85 380

Patchmatch-test		47.93 348	49.96 348	41.84 376	57.42 376	24.26 396	48.75 369	41.49 394	39.30 334	56.79 355	73.48 317	30.48 365	33.87 399	29.29 379	72.61 335	67.39 341

gg-mvs-nofinetune		55.75 302	56.75 301	52.72 334	62.87 346	28.04 385	68.92 217	41.36 395	71.09 41	50.80 381	92.63 12	20.74 395	66.86 302	29.97 375	72.41 336	63.25 364

test0.0.03 1		47.72 349	48.31 351	45.93 364	55.53 385	29.39 380	46.40 378	41.21 396	43.41 304	55.81 363	67.65 364	29.22 372	43.77 385	25.73 391	69.87 355	64.62 360

EMVS		44.61 360	44.45 365	45.10 369	48.91 401	43.00 291	37.92 392	41.10 397	46.75 272	38.00 402	48.43 400	26.42 379	46.27 370	37.11 340	75.38 312	46.03 393

ADS-MVSNet		44.62 359	45.58 358	41.73 377	55.90 383	20.83 402	47.34 375	39.94 398	31.41 377	50.48 382	72.06 326	31.23 357	39.31 394	25.93 388	55.93 392	65.07 356

pmmvs3		46.71 351	45.09 361	51.55 339	56.76 379	48.25 236	55.78 343	39.53 399	24.13 395	50.35 384	63.40 376	15.90 407	51.08 355	29.29 379	70.69 350	55.33 385

test2506		61.23 270	60.85 271	62.38 276	78.80 158	27.88 386	67.33 244	37.42 400	54.23 191	67.55 287	88.68 106	17.87 404	74.39 227	46.33 281	89.41 143	84.86 96

MVS-HIRNet		45.53 354	47.29 354	40.24 379	62.29 348	26.82 389	56.02 341	37.41 401	29.74 381	43.69 399	81.27 226	33.96 336	55.48 347	24.46 395	56.79 391	38.43 400

CHOSEN 280x420		41.62 365	39.89 370	46.80 361	61.81 350	51.59 205	33.56 397	35.74 402	27.48 385	37.64 403	53.53 393	23.24 391	42.09 388	27.39 385	58.64 388	46.72 392

EPMVS		45.74 353	46.53 356	43.39 374	54.14 391	22.33 401	55.02 346	35.00 403	34.69 360	51.09 380	70.20 341	25.92 382	42.04 389	37.19 338	55.50 394	65.78 351

new_pmnet		37.55 369	39.80 371	30.79 384	56.83 378	16.46 405	39.35 390	30.65 404	25.59 391	45.26 393	61.60 382	24.54 387	28.02 403	21.60 399	52.80 397	47.90 391

PMMVS2		37.74 368	40.87 368	28.36 385	42.41 407	5.35 411	24.61 398	27.75 405	32.15 372	47.85 388	70.27 340	35.85 332	29.51 402	19.08 403	67.85 365	50.22 389

DSMNet-mixed		43.18 364	44.66 364	38.75 381	54.75 388	28.88 383	57.06 333	27.42 406	13.47 402	47.27 390	77.67 281	38.83 316	39.29 395	25.32 393	60.12 385	48.08 390

MVE	27.91 23	36.69 370	35.64 373	39.84 380	43.37 406	35.85 346	19.49 399	24.61 407	24.68 393	39.05 401	62.63 380	38.67 318	27.10 404	21.04 401	47.25 400	56.56 384
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test1		37.88 367	35.74 372	44.28 371	47.28 403	49.90 222	36.54 395	24.37 408	19.56 401	45.76 391	53.46 394	32.99 342	37.97 397	26.17 386	35.52 401	44.99 396

mvsany_test3		43.76 363	41.01 367	52.01 337	48.09 402	57.74 172	42.47 385	23.85 409	23.30 397	64.80 303	62.17 381	27.12 376	40.59 392	29.17 381	48.11 399	57.69 381

MTMP							84.83 31	19.26 410

tmp_tt		11.98 373	14.73 376	3.72 388	2.28 411	4.62 412	19.44 400	14.50 411	0.47 406	21.55 404	9.58 404	25.78 383	4.57 407	11.61 405	27.37 403	1.96 403

DeepMVS_CX				11.83 387	15.51 409	13.86 407		11.25 412	5.76 403	20.85 405	26.46 402	17.06 406	9.22 406	9.69 406	13.82 405	12.42 402

test123		4.43 376	5.78 379	0.39 390	0.97 412	0.28 414	46.33 379	0.45 413	0.31 407	0.62 408	1.50 407	0.61 413	0.11 409	0.56 407	0.63 406	0.77 405

testmvs		4.06 377	5.28 380	0.41 389	0.64 413	0.16 415	42.54 384	0.31 414	0.26 408	0.50 409	1.40 408	0.77 412	0.17 408	0.56 407	0.55 407	0.90 404

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		5.20 375	6.93 378	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	62.39 149	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

n2								0.00 415

nn								0.00 415

ab-mvs-re		5.62 374	7.50 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	67.46 365	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						22.69 399								36.10 350

PC_three_1452									46.98 271	81.83 92	86.28 154	66.55 115	84.47 71	63.31 138	90.78 113	83.49 137

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				78.65 62	83.44 93	66.85 89	83.62 42				86.12 162	66.82 108	86.01 31	61.72 147	89.79 134	83.08 154

test_0728_THIRD									74.03 21	85.83 43	90.41 59	75.58 37	85.69 45	77.43 30	94.74 29	84.31 119

GSMVS																70.05 322

test_part2					85.90 57	66.44 91				84.61 62

sam_mvs1												31.41 355				70.05 322

sam_mvs												31.21 359

test_post1							66.63 254				2.08 405	30.66 364	59.33 337	40.34 317

test_post											1.99 406	30.91 362	54.76 350

patchmatchnet-post											68.99 352	31.32 356	69.38 276

gm-plane-assit					62.51 347	33.91 359			37.25 348		62.71 379		72.74 241	38.70 325

test9_res														72.12 69	91.37 92	77.40 254

agg_prior2														70.70 74	90.93 107	78.55 240

test_prior4						70.14 63	77.57 101

test_prior2							75.57 132		58.92 139	76.53 166	86.78 136	67.83 100		69.81 77	92.76 73

旧先验2							71.17 189		45.11 287	78.54 131			61.28 331	59.19 174

新几何2							71.33 185

原ACMM2							74.78 142

testdata2													67.30 295	48.34 263

segment_acmp												68.30 94

testdata1							68.34 230		57.24 156

plane_prior7					85.18 66	66.21 94

plane_prior6					84.18 84	65.31 103						60.83 170

plane_prior4											89.11 94

plane_prior3						65.67 99			63.82 98	78.23 133

plane_prior2							82.74 51		65.45 76

plane_prior1					84.46 80

plane_prior						65.18 104	80.06 79		61.88 117						89.91 131

HQP5-MVS						58.80 166

HQP-NCC					82.37 110		77.32 106		59.08 134	71.58 234

ACMP_Plane					82.37 110		77.32 106		59.08 134	71.58 234

BP-MVS														67.38 101

HQP4-MVS										71.59 233			85.31 52			83.74 132

HQP2-MVS												58.09 197

NP-MVS					83.34 94	63.07 121					85.97 166

MDTV_nov1_ep13_2view						18.41 403	53.74 355		31.57 376	44.89 394		29.90 370		32.93 364		71.48 309

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 145

High-resolution multi-view benchmark