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 57	91.63 1	86.34 1	97.97 1	94.77 3	66.57 122	95.38 1	87.74 1	97.72 1	93.00 7

MP-MVS-pluss		82.54 30	83.46 29	79.76 45	88.88 31	68.44 80	81.57 64	86.33 19	63.17 112	85.38 55	91.26 37	76.33 33	84.67 71	83.30 2	94.96 26	86.17 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		82.33 31	83.28 32	79.46 51	89.28 19	69.09 78	83.62 46	84.98 45	64.77 94	83.97 72	91.02 41	75.53 42	85.93 38	82.00 3	94.36 48	83.35 154

LTVRE_ROB	75.46 1	84.22 10	84.98 11	81.94 24	84.82 76	75.40 29	91.60 3	87.80 8	73.52 28	88.90 15	93.06 7	71.39 73	81.53 119	81.53 4	92.15 84	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
MTAPA		83.19 22	83.87 22	81.13 34	91.16 3	78.16 12	84.87 33	80.63 132	72.08 41	84.93 59	90.79 48	74.65 49	84.42 75	80.98 5	94.75 32	80.82 216

HPM-MVS		84.12 12	84.63 13	82.60 14	88.21 36	74.40 35	85.24 31	87.21 14	70.69 50	85.14 57	90.42 61	78.99 17	86.62 15	80.83 6	94.93 27	86.79 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ZNCC-MVS		83.12 24	83.68 25	81.45 28	89.14 25	73.28 46	86.32 26	85.97 25	67.39 65	84.02 71	90.39 65	74.73 48	86.46 17	80.73 7	94.43 43	84.60 113

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 38	79.26 7	89.12 12	92.10 19	77.52 25	85.92 39	80.47 8	95.20 18	82.10 191

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 176

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 176

MSP-MVS		80.49 49	79.67 62	82.96 6	89.70 12	77.46 23	87.16 12	85.10 43	64.94 93	81.05 106	88.38 117	57.10 221	87.10 9	79.75 11	83.87 236	84.31 125
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
SteuartSystems-ACMMP		83.07 25	83.64 26	81.35 30	85.14 72	71.00 58	85.53 29	84.78 49	70.91 48	85.64 48	90.41 62	75.55 41	87.69 5	79.75 11	95.08 23	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.12 32	82.68 42	80.43 40	88.90 30	69.52 69	85.12 32	84.76 50	63.53 106	84.23 69	91.47 34	72.02 67	87.16 8	79.74 13	94.36 48	84.61 111
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
HFP-MVS		83.39 21	84.03 20	81.48 27	89.25 21	75.69 28	87.01 17	84.27 64	70.23 51	84.47 67	90.43 60	76.79 29	85.94 36	79.58 14	94.23 54	82.82 172

ACMMPR		83.62 16	83.93 21	82.69 12	89.78 11	77.51 22	87.01 17	84.19 68	70.23 51	84.49 66	90.67 53	75.15 44	86.37 20	79.58 14	94.26 52	84.18 128

HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 16	73.69 27	86.17 40	91.70 29	78.23 21	85.20 61	79.45 16	94.91 28	88.15 48

TSAR-MVS + MP.		79.05 61	78.81 66	79.74 46	88.94 28	67.52 87	86.61 22	81.38 113	51.71 234	77.15 152	91.42 36	65.49 132	87.20 7	79.44 17	87.17 189	84.51 119
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
region2R		83.54 18	83.86 23	82.58 15	89.82 10	77.53 18	87.06 16	84.23 67	70.19 53	83.86 73	90.72 52	75.20 43	86.27 23	79.41 18	94.25 53	83.95 133

ACMMP		84.22 10	84.84 12	82.35 18	89.23 22	76.66 26	87.65 7	85.89 26	71.03 47	85.85 45	90.58 54	78.77 18	85.78 44	79.37 19	95.17 20	84.62 110
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		84.01 14	84.39 15	82.88 7	90.65 4	81.38 4	87.08 13	82.79 87	72.41 39	85.11 58	90.85 47	76.65 31	84.89 66	79.30 20	94.63 36	82.35 185

CP-MVS		84.12 12	84.55 14	82.80 11	89.42 18	79.74 6	88.19 5	84.43 61	71.96 43	84.70 64	90.56 55	77.12 28	86.18 28	79.24 21	95.36 13	82.49 183

GST-MVS		82.79 28	83.27 33	81.34 31	88.99 27	73.29 45	85.94 28	85.13 41	68.58 62	84.14 70	90.21 75	73.37 59	86.41 18	79.09 22	93.98 59	84.30 127

XVS		83.51 19	83.73 24	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	90.39 65	73.86 55	86.31 21	78.84 23	94.03 56	84.64 108

X-MVStestdata		76.81 81	74.79 103	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	9.95 430	73.86 55	86.31 21	78.84 23	94.03 56	84.64 108

MP-MVS		83.19 22	83.54 27	82.14 20	90.54 5	79.00 9	86.42 25	83.59 77	71.31 44	81.26 103	90.96 42	74.57 50	84.69 70	78.41 25	94.78 31	82.74 175
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		83.07 25	83.25 34	82.54 16	89.57 14	77.21 24	82.04 61	85.40 36	67.96 64	84.91 62	90.88 45	75.59 39	86.57 16	78.16 26	94.71 34	83.82 135

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	79.20 16	85.58 51	78.11 27	94.46 39	84.89 98

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	81.38 7		78.11 27	94.46 39	84.89 98

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 78	66.72 94	86.54 23	85.11 42	72.00 42	86.65 35	91.75 28	78.20 22	87.04 11	77.93 29	94.32 51	83.47 148
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 40	77.42 17	86.15 41	90.24 73	81.69 5	85.94 36	77.77 30	93.58 64	83.09 161

APD-MVS_3200maxsize		83.57 17	84.33 16	81.31 32	82.83 109	73.53 44	85.50 30	87.45 13	74.11 23	86.45 38	90.52 58	80.02 10	84.48 73	77.73 31	94.34 50	85.93 76

SD-MVS		80.28 53	81.55 51	76.47 90	83.57 93	67.83 84	83.39 51	85.35 39	64.42 96	86.14 42	87.07 136	74.02 54	80.97 133	77.70 32	92.32 82	80.62 224
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		79.89 55	79.80 61	80.18 43	89.02 26	78.44 11	83.49 49	80.18 142	64.71 95	78.11 140	88.39 116	65.46 133	83.14 93	77.64 33	91.20 98	78.94 249

DVP-MVS++		81.24 39	82.74 41	76.76 84	83.14 99	60.90 148	91.64 1	85.49 32	74.03 25	84.93 59	90.38 67	66.82 115	85.90 40	77.43 34	90.78 115	83.49 145

test_0728_THIRD									74.03 25	85.83 46	90.41 62	75.58 40	85.69 47	77.43 34	94.74 33	84.31 125

MM		78.15 73	77.68 78	79.55 50	80.10 139	65.47 104	80.94 68	78.74 168	71.22 45	72.40 239	88.70 107	60.51 182	87.70 4	77.40 36	89.13 152	85.48 87

MSC_two_6792asdad				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 137

No_MVS				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 137

LPG-MVS_test		83.47 20	84.33 16	80.90 36	87.00 40	70.41 64	82.04 61	86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 72

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 72

SF-MVS		80.72 47	81.80 46	77.48 77	82.03 119	64.40 115	83.41 50	88.46 6	65.28 85	84.29 68	89.18 94	73.73 58	83.22 92	76.01 41	93.77 61	84.81 105

DVP-MVS		81.15 41	83.12 36	75.24 107	86.16 52	60.78 150	83.77 44	80.58 134	72.48 37	85.83 46	90.41 62	78.57 19	85.69 47	75.86 42	94.39 44	79.24 245
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
test_0728_SECOND				76.57 87	86.20 49	60.57 153	83.77 44	85.49 32					85.90 40	75.86 42	94.39 44	83.25 156

fmvsm_s_conf0.5_n_3		72.97 136	74.13 114	69.47 205	71.40 274	58.36 175	73.07 168	80.64 131	56.86 166	75.49 189	84.67 188	67.86 106	72.33 258	75.68 44	81.54 267	77.73 267

IU-MVS					86.12 54	60.90 148		80.38 138	45.49 303	81.31 102				75.64 45	94.39 44	84.65 107

SED-MVS		81.78 35	83.48 28	76.67 85	86.12 54	61.06 144	83.62 46	84.72 52	72.61 35	87.38 28	89.70 83	77.48 26	85.89 42	75.29 46	94.39 44	83.08 162

test_241102_TWO								84.80 48	72.61 35	84.93 59	89.70 83	77.73 24	85.89 42	75.29 46	94.22 55	83.25 156

XVG-OURS		79.51 57	79.82 60	78.58 65	86.11 57	74.96 32	76.33 127	84.95 47	66.89 67	82.75 86	88.99 102	66.82 115	78.37 181	74.80 48	90.76 118	82.40 184

CPTT-MVS		81.51 38	81.76 47	80.76 38	89.20 23	78.75 10	86.48 24	82.03 101	68.80 58	80.92 108	88.52 113	72.00 68	82.39 105	74.80 48	93.04 70	81.14 206

XVG-OURS-SEG-HR		79.62 56	79.99 59	78.49 66	86.46 47	74.79 33	77.15 115	85.39 37	66.73 70	80.39 115	88.85 105	74.43 53	78.33 183	74.73 50	85.79 206	82.35 185

DPE-MVS		82.00 34	83.02 37	78.95 60	85.36 69	67.25 89	82.91 54	84.98 45	73.52 28	85.43 54	90.03 77	76.37 32	86.97 13	74.56 51	94.02 58	82.62 180
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.5_n_4		70.18 176	69.83 184	71.24 177	71.65 270	58.59 174	69.29 226	71.66 241	48.69 274	71.62 248	82.11 234	59.94 188	70.03 283	74.52 52	78.96 300	85.10 93

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 14	79.37 15	84.79 69	74.51 53	96.15 3	92.88 8

test_fmvsmconf0.01_n		73.91 112	73.64 124	74.71 108	69.79 302	66.25 97	75.90 133	79.90 146	46.03 297	76.48 175	85.02 185	67.96 105	73.97 239	74.47 54	87.22 186	83.90 134

ACMP	69.50 8	82.64 29	83.38 30	80.40 41	86.50 46	69.44 71	82.30 58	86.08 24	66.80 69	86.70 34	89.99 78	81.64 6	85.95 35	74.35 55	96.11 4	85.81 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	69.25 9	82.11 33	83.31 31	78.49 66	88.17 37	73.96 38	83.11 53	84.52 60	66.40 73	87.45 26	89.16 96	81.02 8	80.52 142	74.27 56	95.73 8	80.98 212
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_0304		75.45 93	74.66 105	77.83 74	75.58 210	61.53 137	78.29 99	77.18 194	63.15 114	69.97 274	87.20 131	57.54 217	87.05 10	74.05 57	88.96 155	84.89 98

OPM-MVS		80.99 45	81.63 50	79.07 56	86.86 44	69.39 72	79.41 88	84.00 73	65.64 77	85.54 52	89.28 89	76.32 34	83.47 88	74.03 58	93.57 65	84.35 124
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CNVR-MVS		78.49 68	78.59 70	78.16 70	85.86 63	67.40 88	78.12 104	81.50 109	63.92 100	77.51 148	86.56 156	68.43 98	84.82 68	73.83 59	91.61 90	82.26 189

9.14			80.22 57		80.68 134		80.35 77	87.69 11	59.90 136	83.00 80	88.20 120	74.57 50	81.75 117	73.75 60	93.78 60

DeepC-MVS	72.44 4	81.00 44	80.83 54	81.50 26	86.70 45	70.03 68	82.06 60	87.00 15	59.89 137	80.91 109	90.53 56	72.19 64	88.56 2	73.67 61	94.52 38	85.92 77
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
anonymousdsp		78.60 65	77.80 77	81.00 35	78.01 170	74.34 37	80.09 81	76.12 202	50.51 252	89.19 11	90.88 45	71.45 72	77.78 195	73.38 62	90.60 120	90.90 17

test_fmvsmconf0.1_n		73.26 125	72.82 143	74.56 110	69.10 309	66.18 99	74.65 152	79.34 156	45.58 300	75.54 187	83.91 204	67.19 110	73.88 242	73.26 63	86.86 192	83.63 142

3Dnovator+	73.19 2	81.08 43	80.48 55	82.87 8	81.41 127	72.03 49	84.38 38	86.23 23	77.28 18	80.65 112	90.18 76	59.80 192	87.58 6	73.06 64	91.34 95	89.01 34

test_fmvsmconf_n		72.91 138	72.40 150	74.46 111	68.62 313	66.12 100	74.21 159	78.80 166	45.64 299	74.62 203	83.25 220	66.80 118	73.86 243	72.97 65	86.66 198	83.39 151

v7n		79.37 60	80.41 56	76.28 92	78.67 163	55.81 191	79.22 90	82.51 95	70.72 49	87.54 25	92.44 15	68.00 103	81.34 121	72.84 66	91.72 86	91.69 11

ZD-MVS					83.91 90	69.36 73		81.09 121	58.91 147	82.73 87	89.11 97	75.77 38	86.63 14	72.73 67	92.93 72

UA-Net		81.56 37	82.28 44	79.40 52	88.91 29	69.16 76	84.67 36	80.01 145	75.34 19	79.80 119	94.91 2	69.79 88	80.25 146	72.63 68	94.46 39	88.78 42

APD-MVS		81.13 42	81.73 48	79.36 53	84.47 83	70.53 63	83.85 42	83.70 75	69.43 57	83.67 75	88.96 103	75.89 37	86.41 18	72.62 69	92.95 71	81.14 206
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 39	72.91 47	80.23 80	85.56 31	66.56 72	85.64 48	89.57 85	69.12 92	80.55 141	72.51 70	93.37 66	83.48 147

train_agg		76.38 84	76.55 88	75.86 98	85.47 67	69.32 74	76.42 123	78.69 169	54.00 211	76.97 154	86.74 146	66.60 120	81.10 127	72.50 71	91.56 91	77.15 274

BP-MVS1		71.60 157	70.06 180	76.20 94	74.07 236	55.22 196	74.29 157	73.44 224	57.29 162	73.87 220	84.65 189	32.57 361	83.49 87	72.43 72	87.94 170	89.89 23

MVSFormer		69.93 180	69.03 193	72.63 157	74.93 216	59.19 162	83.98 40	75.72 207	52.27 227	63.53 340	76.74 311	43.19 303	80.56 139	72.28 73	78.67 304	78.14 260

test_djsdf		78.88 63	78.27 73	80.70 39	81.42 126	71.24 56	83.98 40	75.72 207	52.27 227	87.37 30	92.25 17	68.04 102	80.56 139	72.28 73	91.15 100	90.32 21

test9_res														72.12 75	91.37 94	77.40 269

fmvsm_s_conf0.5_n_5		71.46 160	71.62 162	70.99 180	73.89 240	59.95 158	73.02 170	73.08 226	45.15 308	77.30 151	84.06 202	64.73 142	70.08 282	71.20 76	82.10 254	82.92 167

HQP_MVS		78.77 64	78.78 68	78.72 62	85.18 70	65.18 108	82.74 55	85.49 32	65.45 80	78.23 137	89.11 97	60.83 180	86.15 29	71.09 77	90.94 107	84.82 103

plane_prior5								85.49 32					86.15 29	71.09 77	90.94 107	84.82 103

v10		75.69 89	76.20 91	74.16 118	74.44 229	48.69 246	75.84 135	82.93 86	59.02 145	85.92 44	89.17 95	58.56 202	82.74 101	70.73 79	89.14 151	91.05 14

agg_prior2														70.70 80	90.93 109	78.55 254

fmvsm_s_conf0.1_n_2		69.14 193	68.42 203	71.28 175	68.30 318	57.60 180	65.06 289	69.91 268	48.24 277	74.56 205	82.84 224	55.55 233	69.73 285	70.66 81	80.69 276	86.52 68

fmvsm_s_conf0.5_n_2		68.93 196	68.23 208	71.02 179	67.78 326	57.58 181	64.74 292	69.56 272	48.16 279	74.38 209	82.32 232	56.00 232	69.68 288	70.65 82	80.52 280	85.80 82

MVSMamba_PlusPlus		76.88 80	78.21 74	72.88 148	80.83 132	48.71 245	83.28 52	82.79 87	72.78 31	79.17 126	91.94 22	56.47 228	83.95 78	70.51 83	86.15 201	85.99 75

NCCC		78.25 71	78.04 76	78.89 61	85.61 65	69.45 70	79.80 85	80.99 124	65.77 76	75.55 186	86.25 165	67.42 108	85.42 52	70.10 84	90.88 113	81.81 197

OurMVSNet-221017-0		78.57 66	78.53 71	78.67 63	80.48 136	64.16 116	80.24 79	82.06 100	61.89 121	88.77 16	93.32 5	57.15 219	82.60 103	70.08 85	92.80 73	89.25 28

test_prior2							75.57 136		58.92 146	76.53 173	86.78 144	67.83 107		69.81 86	92.76 75

EC-MVSNet		77.08 79	77.39 81	76.14 95	76.86 191	56.87 184	80.32 78	87.52 12	63.45 108	74.66 202	84.52 194	69.87 87	84.94 64	69.76 87	89.59 139	86.60 67

test_fmvsmvis_n_1920		72.36 147	72.49 147	71.96 167	71.29 276	64.06 118	72.79 172	81.82 104	40.23 350	81.25 104	81.04 249	70.62 80	68.69 295	69.74 88	83.60 242	83.14 160

v8		75.07 100	75.64 97	73.35 131	73.42 245	47.46 266	75.20 138	81.45 111	60.05 135	85.64 48	89.26 90	58.08 210	81.80 116	69.71 89	87.97 169	90.79 18

CS-MVS		76.51 83	76.00 93	78.06 73	77.02 183	64.77 112	80.78 70	82.66 92	60.39 133	74.15 212	83.30 218	69.65 89	82.07 112	69.27 90	86.75 196	87.36 56

fmvsm_l_conf0.5_n_3		71.98 153	71.68 159	72.88 148	72.84 261	64.15 117	73.48 164	77.11 195	48.97 272	71.31 258	84.18 199	67.98 104	71.60 270	68.86 91	80.43 281	82.89 168

v1240		73.06 130	73.14 134	72.84 150	74.74 222	47.27 270	71.88 189	81.11 119	51.80 233	82.28 90	84.21 198	56.22 230	82.34 107	68.82 92	87.17 189	88.91 38

v1192		73.40 121	73.42 126	73.32 133	74.65 226	48.67 247	72.21 177	81.73 106	52.76 223	81.85 93	84.56 192	57.12 220	82.24 110	68.58 93	87.33 181	89.06 33

mvs_tets		78.93 62	78.67 69	79.72 47	84.81 77	73.93 39	80.65 71	76.50 200	51.98 232	87.40 27	91.86 26	76.09 36	78.53 173	68.58 93	90.20 124	86.69 66

v1921920		72.96 137	72.98 140	72.89 147	74.67 223	47.58 264	71.92 187	80.69 128	51.70 235	81.69 99	83.89 205	56.58 226	82.25 109	68.34 95	87.36 178	88.82 40

jajsoiax		78.51 67	78.16 75	79.59 49	84.65 80	73.83 41	80.42 74	76.12 202	51.33 243	87.19 31	91.51 33	73.79 57	78.44 177	68.27 96	90.13 128	86.49 69

v1144		73.29 124	73.39 127	73.01 139	74.12 235	48.11 253	72.01 182	81.08 122	53.83 215	81.77 95	84.68 187	58.07 211	81.91 114	68.10 97	86.86 192	88.99 36

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 124	84.80 35	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 98	97.05 2	96.93 1

PHI-MVS		74.92 103	74.36 110	76.61 86	76.40 196	62.32 130	80.38 75	83.15 82	54.16 208	73.23 229	80.75 253	62.19 162	83.86 80	68.02 99	90.92 110	83.65 141

CDPH-MVS		77.33 77	77.06 85	78.14 71	84.21 87	63.98 119	76.07 131	83.45 78	54.20 206	77.68 147	87.18 132	69.98 85	85.37 53	68.01 100	92.72 76	85.08 95

v144192		72.99 134	73.06 138	72.77 151	74.58 227	47.48 265	71.90 188	80.44 137	51.57 236	81.46 101	84.11 201	58.04 212	82.12 111	67.98 101	87.47 176	88.70 43

OMC-MVS		79.41 59	78.79 67	81.28 33	80.62 135	70.71 62	80.91 69	84.76 50	62.54 117	81.77 95	86.65 152	71.46 71	83.53 86	67.95 102	92.44 78	89.60 24

PS-MVSNAJss		77.54 75	77.35 82	78.13 72	84.88 75	66.37 96	78.55 96	79.59 152	53.48 218	86.29 39	92.43 16	62.39 159	80.25 146	67.90 103	90.61 119	87.77 50

EI-MVSNet-Vis-set		72.78 140	71.87 155	75.54 103	74.77 221	59.02 168	72.24 176	71.56 244	63.92 100	78.59 132	71.59 352	66.22 125	78.60 172	67.58 104	80.32 282	89.00 35

ACMH	63.62 14	77.50 76	80.11 58	69.68 202	79.61 143	56.28 186	78.81 93	83.62 76	63.41 110	87.14 33	90.23 74	76.11 35	73.32 244	67.58 104	94.44 42	79.44 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_l_conf0.5_n		67.48 219	66.88 231	69.28 210	67.41 331	62.04 131	70.69 208	69.85 269	39.46 353	69.59 279	81.09 248	58.15 206	68.73 294	67.51 106	78.16 313	77.07 278

SixPastTwentyTwo		75.77 87	76.34 89	74.06 120	81.69 124	54.84 198	76.47 120	75.49 209	64.10 99	87.73 21	92.24 18	50.45 262	81.30 123	67.41 107	91.46 93	86.04 74

casdiffmvs_mvg		75.26 96	76.18 92	72.52 158	72.87 260	49.47 240	72.94 171	84.71 54	59.49 139	80.90 110	88.81 106	70.07 84	79.71 154	67.40 108	88.39 161	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
fmvsm_s_conf0.5_n		66.34 236	65.27 244	69.57 204	68.20 319	59.14 167	71.66 191	56.48 348	40.92 343	67.78 302	79.46 276	61.23 173	66.90 314	67.39 109	74.32 346	82.66 179

DeepPCF-MVS	71.07 5	78.48 69	77.14 84	82.52 17	84.39 86	77.04 25	76.35 125	84.05 71	56.66 171	80.27 116	85.31 182	68.56 95	87.03 12	67.39 109	91.26 96	83.50 144

BP-MVS														67.38 111

HQP-MVS		75.24 97	75.01 102	75.94 96	82.37 113	58.80 170	77.32 111	84.12 69	59.08 141	71.58 250	85.96 175	58.09 208	85.30 55	67.38 111	89.16 148	83.73 140

fmvsm_s_conf0.1_n		66.60 230	65.54 241	69.77 201	68.99 310	59.15 165	72.12 178	56.74 347	40.72 347	68.25 300	80.14 265	61.18 176	66.92 313	67.34 113	74.40 343	83.23 158

EI-MVSNet-UG-set		72.63 143	71.68 159	75.47 104	74.67 223	58.64 173	72.02 181	71.50 245	63.53 106	78.58 134	71.39 356	65.98 126	78.53 173	67.30 114	80.18 285	89.23 29

v2v482		72.55 146	72.58 146	72.43 160	72.92 259	46.72 273	71.41 195	79.13 159	55.27 184	81.17 105	85.25 183	55.41 234	81.13 126	67.25 115	85.46 209	89.43 26

fmvsm_s_conf0.1_n_a		67.37 223	66.36 233	70.37 188	70.86 278	61.17 142	74.00 161	57.18 342	40.77 345	68.83 294	80.88 251	63.11 151	67.61 306	66.94 116	74.72 338	82.33 188

COLMAP_ROB	72.78 3	83.75 15	84.11 19	82.68 13	82.97 106	74.39 36	87.18 11	88.18 7	78.98 8	86.11 43	91.47 34	79.70 14	85.76 45	66.91 117	95.46 12	87.89 49
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		67.00 228	65.95 240	70.17 193	69.72 303	61.16 143	73.34 166	56.83 345	40.96 342	68.36 297	80.08 266	62.84 152	67.57 307	66.90 118	74.50 342	81.78 198

LS3D		80.99 45	80.85 53	81.41 29	78.37 164	71.37 54	87.45 8	85.87 27	77.48 16	81.98 92	89.95 80	69.14 91	85.26 57	66.15 119	91.24 97	87.61 53

fmvsm_l_conf0.5_n_a		66.66 229	65.97 239	68.72 226	67.09 334	61.38 139	70.03 215	69.15 276	38.59 361	68.41 296	80.36 259	56.56 227	68.32 299	66.10 120	77.45 318	76.46 280

MVS_Test		69.84 181	70.71 175	67.24 243	67.49 330	43.25 304	69.87 218	81.22 118	52.69 224	71.57 253	86.68 149	62.09 163	74.51 232	66.05 121	78.74 302	83.96 132

WR-MVS_H		80.22 54	82.17 45	74.39 115	89.46 15	42.69 309	78.24 101	82.24 97	78.21 13	89.57 10	92.10 19	68.05 101	85.59 50	66.04 122	95.62 10	94.88 5

V42		71.06 163	70.83 173	71.72 169	67.25 332	47.14 271	65.94 275	80.35 140	51.35 242	83.40 78	83.23 221	59.25 196	78.80 168	65.91 123	80.81 274	89.23 29

test_fmvsm_n_1920		69.63 183	68.45 202	73.16 135	70.56 285	65.86 102	70.26 213	78.35 175	37.69 367	74.29 210	78.89 289	61.10 177	68.10 301	65.87 124	79.07 298	85.53 86

K. test v3		73.67 115	73.61 125	73.87 123	79.78 141	55.62 195	74.69 150	62.04 323	66.16 75	84.76 63	93.23 6	49.47 267	80.97 133	65.66 125	86.67 197	85.02 97

DeepC-MVS_fast	69.89 7	77.17 78	76.33 90	79.70 48	83.90 91	67.94 82	80.06 83	83.75 74	56.73 170	74.88 197	85.32 181	65.54 131	87.79 3	65.61 126	91.14 101	83.35 154
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_0402		78.17 72	79.48 63	74.24 117	83.50 94	59.15 165	72.52 173	74.60 217	75.34 19	88.69 17	91.81 27	75.06 45	82.37 106	65.10 127	88.68 158	81.20 204

diffmvs		67.42 222	67.50 220	67.20 244	62.26 370	45.21 287	64.87 291	77.04 196	48.21 278	71.74 246	79.70 272	58.40 203	71.17 273	64.99 128	80.27 283	85.22 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMVS	70.70 6	81.70 36	83.15 35	77.36 79	90.35 6	82.82 3	82.15 59	79.22 158	74.08 24	87.16 32	91.97 21	84.80 2	76.97 202	64.98 129	93.61 63	72.28 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GDP-MVS		70.84 167	69.24 189	75.62 101	76.44 195	55.65 193	74.62 153	82.78 89	49.63 262	72.10 244	83.79 207	31.86 369	82.84 99	64.93 130	87.01 191	88.39 47

ACMH+	66.64 10	81.20 40	82.48 43	77.35 80	81.16 131	62.39 129	80.51 72	87.80 8	73.02 30	87.57 24	91.08 40	80.28 9	82.44 104	64.82 131	96.10 5	87.21 58

MCST-MVS		73.42 120	73.34 131	73.63 127	81.28 129	59.17 164	74.80 146	83.13 83	45.50 301	72.84 232	83.78 208	65.15 137	80.99 131	64.54 132	89.09 154	80.73 220

ambc				70.10 196	77.74 174	50.21 230	74.28 158	77.93 185		79.26 124	88.29 119	54.11 242	79.77 153	64.43 133	91.10 104	80.30 230

lessismore_v0				72.75 152	79.60 144	56.83 185		57.37 338		83.80 74	89.01 101	47.45 282	78.74 170	64.39 134	86.49 200	82.69 178

tt0805		76.12 86	78.43 72	69.20 211	81.32 128	41.37 317	76.72 119	77.64 187	63.78 103	82.06 91	87.88 126	79.78 11	79.05 163	64.33 135	92.40 79	87.17 61

baseline		73.10 127	73.96 118	70.51 186	71.46 273	46.39 278	72.08 179	84.40 62	55.95 178	76.62 167	86.46 159	67.20 109	78.03 190	64.22 136	87.27 185	87.11 62

EGC-MVSNET		64.77 249	61.17 283	75.60 102	86.90 43	74.47 34	84.04 39	68.62 280	0.60 432	1.13 434	91.61 32	65.32 135	74.15 238	64.01 137	88.28 162	78.17 259

CANet		73.00 133	71.84 156	76.48 89	75.82 207	61.28 140	74.81 144	80.37 139	63.17 112	62.43 346	80.50 257	61.10 177	85.16 63	64.00 138	84.34 232	83.01 165

balanced_conf03		73.59 117	74.06 115	72.17 166	77.48 179	47.72 262	81.43 65	82.20 98	54.38 199	79.19 125	87.68 128	54.41 239	83.57 84	63.98 139	85.78 207	85.22 89

tttt0517		69.46 187	67.79 217	74.46 111	75.34 211	52.72 213	75.05 140	63.27 316	54.69 193	78.87 130	84.37 196	26.63 396	81.15 125	63.95 140	87.93 171	89.51 25

casdiffmvs		73.06 130	73.84 119	70.72 182	71.32 275	46.71 274	70.93 204	84.26 65	55.62 181	77.46 149	87.10 133	67.09 111	77.81 193	63.95 140	86.83 194	87.64 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		74.85 108	74.56 106	75.72 99	81.63 125	64.64 113	76.35 125	79.06 160	62.85 115	73.33 227	88.41 115	62.54 157	79.59 157	63.94 142	82.92 247	82.94 166
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-CasMVS		80.41 51	82.86 40	73.07 138	89.93 7	39.21 336	77.15 115	81.28 115	79.74 6	90.87 5	92.73 12	75.03 46	84.93 65	63.83 143	95.19 19	95.07 3

DTE-MVSNet		80.35 52	82.89 39	72.74 153	89.84 8	37.34 356	77.16 114	81.81 105	80.45 4	90.92 4	92.95 8	74.57 50	86.12 31	63.65 144	94.68 35	94.76 6

h-mvs33		73.08 128	71.61 163	77.48 77	83.89 92	72.89 48	70.47 210	71.12 258	54.28 202	77.89 141	83.41 211	49.04 271	80.98 132	63.62 145	90.77 117	78.58 253

hse-mvs2		72.32 148	70.66 176	77.31 81	83.10 103	71.77 51	69.19 229	71.45 247	54.28 202	77.89 141	78.26 295	49.04 271	79.23 160	63.62 145	89.13 152	80.92 213

c3_l		69.82 182	69.89 182	69.61 203	66.24 343	43.48 300	68.12 247	79.61 151	51.43 238	77.72 145	80.18 264	54.61 238	78.15 189	63.62 145	87.50 175	87.20 59

CP-MVSNet		79.48 58	81.65 49	72.98 141	89.66 13	39.06 338	76.76 118	80.46 136	78.91 9	90.32 8	91.70 29	68.49 96	84.89 66	63.40 148	95.12 22	95.01 4

GeoE		73.14 126	73.77 122	71.26 176	78.09 168	52.64 214	74.32 155	79.56 153	56.32 174	76.35 179	83.36 216	70.76 79	77.96 191	63.32 149	81.84 259	83.18 159

PC_three_1452									46.98 291	81.83 94	86.28 162	66.55 123	84.47 74	63.31 150	90.78 115	83.49 145

PEN-MVS		80.46 50	82.91 38	73.11 137	89.83 9	39.02 339	77.06 117	82.61 93	80.04 5	90.60 7	92.85 10	74.93 47	85.21 60	63.15 151	95.15 21	95.09 2

MSLP-MVS++		74.48 109	75.78 95	70.59 184	84.66 79	62.40 128	78.65 94	84.24 66	60.55 132	77.71 146	81.98 237	63.12 150	77.64 197	62.95 152	88.14 164	71.73 329

EI-MVSNet		69.61 185	69.01 194	71.41 174	73.94 238	49.90 235	71.31 198	71.32 250	58.22 150	75.40 191	70.44 359	58.16 205	75.85 211	62.51 153	79.81 291	88.48 44

IterMVS-LS		73.01 132	73.12 136	72.66 155	73.79 241	49.90 235	71.63 192	78.44 174	58.22 150	80.51 113	86.63 153	58.15 206	79.62 155	62.51 153	88.20 163	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		78.44 70	79.29 64	75.90 97	81.86 122	65.33 106	79.05 91	84.63 58	74.83 22	80.41 114	86.27 163	71.68 69	83.45 89	62.45 155	92.40 79	78.92 250

AUN-MVS		70.22 174	67.88 215	77.22 82	82.96 107	71.61 52	69.08 230	71.39 248	49.17 268	71.70 247	78.07 300	37.62 340	79.21 161	61.81 156	89.15 150	80.82 216

MVS_111021_LR		72.10 151	71.82 157	72.95 142	79.53 145	73.90 40	70.45 211	66.64 288	56.87 165	76.81 162	81.76 241	68.78 93	71.76 266	61.81 156	83.74 238	73.18 310

SPE-MVS-test		74.89 106	74.23 112	76.86 83	77.01 184	62.94 127	78.98 92	84.61 59	58.62 148	70.17 271	80.80 252	66.74 119	81.96 113	61.74 158	89.40 146	85.69 84

OPU-MVS				78.65 64	83.44 97	66.85 93	83.62 46				86.12 170	66.82 115	86.01 34	61.72 159	89.79 136	83.08 162

dcpmvs_2		71.02 165	72.65 145	66.16 255	76.06 204	50.49 226	71.97 183	79.36 155	50.34 253	82.81 85	83.63 209	64.38 144	67.27 310	61.54 160	83.71 240	80.71 222

MVS_111021_HR		72.98 135	72.97 141	72.99 140	80.82 133	65.47 104	68.81 234	72.77 232	57.67 157	75.76 183	82.38 231	71.01 77	77.17 200	61.38 161	86.15 201	76.32 282

nrg030		74.87 107	75.99 94	71.52 172	74.90 218	49.88 239	74.10 160	82.58 94	54.55 198	83.50 77	89.21 92	71.51 70	75.74 215	61.24 162	92.34 81	88.94 37

IterMVS-SCA-FT		67.68 217	66.07 237	72.49 159	73.34 247	58.20 177	63.80 303	65.55 297	48.10 280	76.91 157	82.64 228	45.20 290	78.84 167	61.20 163	77.89 316	80.44 228

miper_ehance_all_eth		68.36 206	68.16 211	68.98 218	65.14 355	43.34 302	67.07 262	78.92 163	49.11 269	76.21 180	77.72 302	53.48 244	77.92 192	61.16 164	84.59 227	85.68 85

ITE_SJBPF				80.35 42	76.94 186	73.60 42		80.48 135	66.87 68	83.64 76	86.18 166	70.25 83	79.90 152	61.12 165	88.95 156	87.56 54

DIV-MVS_self_test		68.27 210	68.26 206	68.29 231	64.98 356	43.67 298	65.89 276	74.67 215	50.04 259	76.86 160	82.43 229	48.74 275	75.38 217	60.94 166	89.81 134	85.81 78

cl____		68.26 211	68.26 206	68.29 231	64.98 356	43.67 298	65.89 276	74.67 215	50.04 259	76.86 160	82.42 230	48.74 275	75.38 217	60.92 167	89.81 134	85.80 82

3Dnovator	65.95 11	71.50 159	71.22 169	72.34 162	73.16 250	63.09 125	78.37 98	78.32 176	57.67 157	72.22 242	84.61 191	54.77 235	78.47 175	60.82 168	81.07 270	75.45 288

cl22		67.14 224	66.51 232	69.03 217	63.20 365	43.46 301	66.88 267	76.25 201	49.22 267	74.48 206	77.88 301	45.49 289	77.40 199	60.64 169	84.59 227	86.24 70

testf1		75.66 90	76.57 86	72.95 142	67.07 336	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 268	60.46 170	91.13 102	79.56 239

APD_test2		75.66 90	76.57 86	72.95 142	67.07 336	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 268	60.46 170	91.13 102	79.56 239

Effi-MVS+		72.10 151	72.28 152	71.58 170	74.21 233	50.33 228	74.72 149	82.73 90	62.62 116	70.77 263	76.83 310	69.96 86	80.97 133	60.20 172	78.43 307	83.45 150

eth_miper_zixun_eth		69.42 188	68.73 200	71.50 173	67.99 322	46.42 276	67.58 252	78.81 164	50.72 250	78.13 139	80.34 260	50.15 264	80.34 144	60.18 173	84.65 225	87.74 51

RRT-MVS		70.33 173	70.73 174	69.14 214	71.93 268	45.24 286	75.10 139	75.08 214	60.85 130	78.62 131	87.36 130	49.54 266	78.64 171	60.16 174	77.90 315	83.55 143

TSAR-MVS + GP.		73.08 128	71.60 164	77.54 76	78.99 159	70.73 61	74.96 141	69.38 273	60.73 131	74.39 208	78.44 293	57.72 215	82.78 100	60.16 174	89.60 138	79.11 247

DPM-MVS		69.98 179	69.22 191	72.26 164	82.69 111	58.82 169	70.53 209	81.23 117	47.79 285	64.16 328	80.21 261	51.32 258	83.12 94	60.14 176	84.95 222	74.83 294

114514_t		73.40 121	73.33 132	73.64 126	84.15 89	57.11 182	78.20 102	80.02 144	43.76 319	72.55 236	86.07 173	64.00 146	83.35 91	60.14 176	91.03 106	80.45 227

TAPA-MVS	65.27 12	75.16 98	74.29 111	77.77 75	74.86 219	68.08 81	77.89 105	84.04 72	55.15 186	76.19 181	83.39 212	66.91 113	80.11 150	60.04 178	90.14 127	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MG-MVS		70.47 172	71.34 168	67.85 236	79.26 149	40.42 330	74.67 151	75.15 213	58.41 149	68.74 295	88.14 124	56.08 231	83.69 82	59.90 179	81.71 264	79.43 244

CSCG		74.12 111	74.39 108	73.33 132	79.35 147	61.66 136	77.45 110	81.98 102	62.47 119	79.06 128	80.19 263	61.83 164	78.79 169	59.83 180	87.35 179	79.54 242

APD_test1		75.04 101	75.38 101	74.02 121	69.89 298	70.15 66	76.46 121	79.71 148	65.50 79	82.99 81	88.60 112	66.94 112	72.35 257	59.77 181	88.54 159	79.56 239

FA-MVS(test-final)		71.27 161	71.06 170	71.92 168	73.96 237	52.32 216	76.45 122	76.12 202	59.07 144	74.04 217	86.18 166	52.18 251	79.43 159	59.75 182	81.76 260	84.03 131

Gipuma		69.55 186	72.83 142	59.70 314	63.63 364	53.97 205	80.08 82	75.93 205	64.24 98	73.49 224	88.93 104	57.89 214	62.46 340	59.75 182	91.55 92	62.67 392
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
thisisatest0530		67.05 227	65.16 247	72.73 154	73.10 254	50.55 225	71.26 200	63.91 311	50.22 256	74.46 207	80.75 253	26.81 395	80.25 146	59.43 184	86.50 199	87.37 55

v148		69.38 190	69.39 186	69.36 207	69.14 308	44.56 291	68.83 233	72.70 233	54.79 191	78.59 132	84.12 200	54.69 236	76.74 208	59.40 185	82.20 252	86.79 64

旧先验2							71.17 201		45.11 309	78.54 135			61.28 346	59.19 186

LF4IMVS		67.50 218	67.31 223	68.08 234	58.86 393	61.93 132	71.43 194	75.90 206	44.67 313	72.42 238	80.20 262	57.16 218	70.44 279	58.99 187	86.12 203	71.88 327

mmtdpeth		68.76 200	70.55 177	63.40 279	67.06 338	56.26 187	68.73 239	71.22 256	55.47 183	70.09 272	88.64 111	65.29 136	56.89 364	58.94 188	89.50 141	77.04 279

ETV-MVS		72.72 141	72.16 154	74.38 116	76.90 189	55.95 188	73.34 166	84.67 55	62.04 120	72.19 243	70.81 357	65.90 128	85.24 59	58.64 189	84.96 221	81.95 194

DELS-MVS		68.83 198	68.31 204	70.38 187	70.55 287	48.31 249	63.78 304	82.13 99	54.00 211	68.96 286	75.17 323	58.95 199	80.06 151	58.55 190	82.74 249	82.76 173
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
PAPM_NR		73.91 112	74.16 113	73.16 135	81.90 121	53.50 209	81.28 66	81.40 112	66.17 74	73.30 228	83.31 217	59.96 187	83.10 95	58.45 191	81.66 265	82.87 170

Anonymous20231211		75.54 92	77.19 83	70.59 184	77.67 176	45.70 284	74.73 148	80.19 141	68.80 58	82.95 82	92.91 9	66.26 124	76.76 207	58.41 192	92.77 74	89.30 27

miper_enhance_ethall		65.86 238	65.05 254	68.28 233	61.62 374	42.62 310	64.74 292	77.97 183	42.52 329	73.42 226	72.79 345	49.66 265	77.68 196	58.12 193	84.59 227	84.54 115

IS-MVSNet		75.10 99	75.42 100	74.15 119	79.23 150	48.05 255	79.43 86	78.04 182	70.09 54	79.17 126	88.02 125	53.04 247	83.60 83	58.05 194	93.76 62	90.79 18

FC-MVSNet-test		73.32 123	74.78 104	68.93 221	79.21 151	36.57 358	71.82 190	79.54 154	57.63 160	82.57 88	90.38 67	59.38 195	78.99 165	57.91 195	94.56 37	91.23 13

MGCFI-Net		71.70 156	73.10 137	67.49 240	73.23 249	43.08 305	72.06 180	82.43 96	54.58 196	75.97 182	82.00 235	72.42 63	75.22 221	57.84 196	87.34 180	84.18 128

RPSCF		75.76 88	74.37 109	79.93 44	74.81 220	77.53 18	77.53 109	79.30 157	59.44 140	78.88 129	89.80 82	71.26 74	73.09 246	57.45 197	80.89 271	89.17 31

alignmvs		70.54 171	71.00 171	69.15 213	73.50 243	48.04 256	69.85 219	79.62 149	53.94 214	76.54 172	82.00 235	59.00 198	74.68 230	57.32 198	87.21 187	84.72 106

sasdasda		72.29 149	73.38 128	69.04 215	74.23 230	47.37 267	73.93 162	83.18 80	54.36 200	76.61 168	81.64 243	72.03 65	75.34 219	57.12 199	87.28 183	84.40 121

canonicalmvs		72.29 149	73.38 128	69.04 215	74.23 230	47.37 267	73.93 162	83.18 80	54.36 200	76.61 168	81.64 243	72.03 65	75.34 219	57.12 199	87.28 183	84.40 121

UniMVSNet (Re)		75.00 102	75.48 99	73.56 129	83.14 99	47.92 257	70.41 212	81.04 123	63.67 104	79.54 121	86.37 161	62.83 153	81.82 115	57.10 201	95.25 15	90.94 16

原ACMM1				73.90 122	85.90 60	65.15 110		81.67 107	50.97 247	74.25 211	86.16 168	61.60 167	83.54 85	56.75 202	91.08 105	73.00 312

FIs		72.56 144	73.80 120	68.84 224	78.74 162	37.74 352	71.02 202	79.83 147	56.12 175	80.88 111	89.45 87	58.18 204	78.28 184	56.63 203	93.36 67	90.51 20

xiu_mvs_v1_base_debu		67.87 213	67.07 226	70.26 190	79.13 154	61.90 133	67.34 256	71.25 253	47.98 281	67.70 303	74.19 335	61.31 170	72.62 251	56.51 204	78.26 310	76.27 283

xiu_mvs_v1_base		67.87 213	67.07 226	70.26 190	79.13 154	61.90 133	67.34 256	71.25 253	47.98 281	67.70 303	74.19 335	61.31 170	72.62 251	56.51 204	78.26 310	76.27 283

xiu_mvs_v1_base_debi		67.87 213	67.07 226	70.26 190	79.13 154	61.90 133	67.34 256	71.25 253	47.98 281	67.70 303	74.19 335	61.31 170	72.62 251	56.51 204	78.26 310	76.27 283

Effi-MVS+-dtu		75.43 94	72.28 152	84.91 3	77.05 181	83.58 2	78.47 97	77.70 186	57.68 156	74.89 196	78.13 299	64.80 140	84.26 77	56.46 207	85.32 214	86.88 63

MVSTER		63.29 266	61.60 280	68.36 229	59.77 388	46.21 279	60.62 326	71.32 250	41.83 333	75.40 191	79.12 285	30.25 384	75.85 211	56.30 208	79.81 291	83.03 164

UniMVSNet_NR-MVSNet		74.90 105	75.65 96	72.64 156	83.04 104	45.79 281	69.26 227	78.81 164	66.66 71	81.74 97	86.88 141	63.26 149	81.07 129	56.21 209	94.98 24	91.05 14

DU-MVS		74.91 104	75.57 98	72.93 145	83.50 94	45.79 281	69.47 222	80.14 143	65.22 86	81.74 97	87.08 134	61.82 165	81.07 129	56.21 209	94.98 24	91.93 9

RPMNet		65.77 239	65.08 253	67.84 237	66.37 340	48.24 251	70.93 204	86.27 20	54.66 194	61.35 350	86.77 145	33.29 355	85.67 49	55.93 211	70.17 376	69.62 351

CLD-MVS		72.88 139	72.36 151	74.43 114	77.03 182	54.30 202	68.77 237	83.43 79	52.12 229	76.79 163	74.44 330	69.54 90	83.91 79	55.88 212	93.25 69	85.09 94
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
mvs5depth		66.35 235	67.98 212	61.47 299	62.43 368	51.05 221	69.38 224	69.24 275	56.74 169	73.62 221	89.06 100	46.96 284	58.63 357	55.87 213	88.49 160	74.73 295

miper_lstm_enhance		61.97 279	61.63 279	62.98 283	60.04 382	45.74 283	47.53 398	70.95 259	44.04 315	73.06 230	78.84 290	39.72 325	60.33 348	55.82 214	84.64 226	82.88 169

AllTest		77.66 74	77.43 80	78.35 68	79.19 152	70.81 59	78.60 95	88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 178	55.60 215	90.90 111	85.81 78

TestCases				78.35 68	79.19 152	70.81 59		88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 178	55.60 215	90.90 111	85.81 78

EU-MVSNet		60.82 289	60.80 288	60.86 307	68.37 315	41.16 318	72.27 175	68.27 282	26.96 412	69.08 283	75.71 316	32.09 365	67.44 308	55.59 217	78.90 301	73.97 303

TranMVSNet+NR-MVSNet		76.13 85	77.66 79	71.56 171	84.61 81	42.57 311	70.98 203	78.29 178	68.67 61	83.04 79	89.26 90	72.99 61	80.75 138	55.58 218	95.47 11	91.35 12

OpenMVS	62.51 15	68.76 200	68.75 198	68.78 225	70.56 285	53.91 206	78.29 99	77.35 190	48.85 273	70.22 269	83.52 210	52.65 249	76.93 203	55.31 219	81.99 255	75.49 287

QAPM		69.18 192	69.26 188	68.94 220	71.61 271	52.58 215	80.37 76	78.79 167	49.63 262	73.51 223	85.14 184	53.66 243	79.12 162	55.11 220	75.54 331	75.11 293

MVStest1		55.38 327	54.97 334	56.58 334	43.72 431	40.07 332	59.13 335	47.09 397	34.83 383	76.53 173	84.65 189	13.55 435	53.30 374	55.04 221	80.23 284	76.38 281

NR-MVSNet		73.62 116	74.05 116	72.33 163	83.50 94	43.71 297	65.65 281	77.32 191	64.32 97	75.59 185	87.08 134	62.45 158	81.34 121	54.90 222	95.63 9	91.93 9

EG-PatchMatch MVS		70.70 169	70.88 172	70.16 194	82.64 112	58.80 170	71.48 193	73.64 222	54.98 187	76.55 171	81.77 240	61.10 177	78.94 166	54.87 223	80.84 273	72.74 318

SSC-MVS		61.79 282	66.08 236	48.89 378	76.91 187	10.00 436	53.56 375	47.37 396	68.20 63	76.56 170	89.21 92	54.13 241	57.59 362	54.75 224	74.07 347	79.08 248

jason		64.47 254	62.84 272	69.34 209	76.91 187	59.20 161	67.15 261	65.67 294	35.29 381	65.16 321	76.74 311	44.67 294	70.68 275	54.74 225	79.28 297	78.14 260
jason: jason.
Baseline_NR-MVSNet		70.62 170	73.19 133	62.92 286	76.97 185	34.44 374	68.84 232	70.88 261	60.25 134	79.50 122	90.53 56	61.82 165	69.11 292	54.67 226	95.27 14	85.22 89

UniMVSNet_ETH3D		76.74 82	79.02 65	69.92 200	89.27 20	43.81 296	74.47 154	71.70 240	72.33 40	85.50 53	93.65 4	77.98 23	76.88 205	54.60 227	91.64 88	89.08 32

无先验							74.82 143	70.94 260	47.75 286				76.85 206	54.47 228		72.09 326

testdata				64.13 268	85.87 62	63.34 123		61.80 324	47.83 284	76.42 178	86.60 155	48.83 274	62.31 342	54.46 229	81.26 269	66.74 372

SDMVSNet		66.36 234	67.85 216	61.88 294	73.04 257	46.14 280	58.54 342	71.36 249	51.42 239	68.93 289	82.72 226	65.62 130	62.22 343	54.41 230	84.67 223	77.28 270

PVSNet_Blended_VisFu		70.04 177	68.88 195	73.53 130	82.71 110	63.62 121	74.81 144	81.95 103	48.53 276	67.16 310	79.18 284	51.42 257	78.38 180	54.39 231	79.72 294	78.60 252

EPNet		69.10 194	67.32 222	74.46 111	68.33 317	61.27 141	77.56 107	63.57 313	60.95 128	56.62 380	82.75 225	51.53 256	81.24 124	54.36 232	90.20 124	80.88 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EIA-MVS		68.59 204	67.16 225	72.90 146	75.18 214	55.64 194	69.39 223	81.29 114	52.44 226	64.53 324	70.69 358	60.33 184	82.30 108	54.27 233	76.31 325	80.75 219

patch_mono-2		62.73 275	64.08 258	58.68 322	70.36 291	55.87 190	60.84 324	64.11 310	41.23 338	64.04 329	78.22 296	60.00 186	48.80 385	54.17 234	83.71 240	71.37 332

ET-MVSNet_ETH3D		63.32 265	60.69 289	71.20 178	70.15 296	55.66 192	65.02 290	64.32 308	43.28 328	68.99 285	72.05 350	25.46 402	78.19 188	54.16 235	82.80 248	79.74 238

EPP-MVSNet		73.86 114	73.38 128	75.31 105	78.19 166	53.35 211	80.45 73	77.32 191	65.11 89	76.47 176	86.80 142	49.47 267	83.77 81	53.89 236	92.72 76	88.81 41

lupinMVS		63.36 264	61.49 281	68.97 219	74.93 216	59.19 162	65.80 279	64.52 307	34.68 387	63.53 340	74.25 333	43.19 303	70.62 276	53.88 237	78.67 304	77.10 275

CNLPA		73.44 119	73.03 139	74.66 109	78.27 165	75.29 30	75.99 132	78.49 173	65.39 82	75.67 184	83.22 223	61.23 173	66.77 319	53.70 238	85.33 213	81.92 195

CVMVSNet		59.21 302	58.44 306	61.51 297	73.94 238	47.76 261	71.31 198	64.56 306	26.91 414	60.34 358	70.44 359	36.24 346	67.65 304	53.57 239	68.66 385	69.12 356

CANet_DTU		64.04 260	63.83 260	64.66 264	68.39 314	42.97 307	73.45 165	74.50 218	52.05 231	54.78 390	75.44 321	43.99 298	70.42 280	53.49 240	78.41 308	80.59 225

D2MVS		62.58 276	61.05 285	67.20 244	63.85 361	47.92 257	56.29 356	69.58 271	39.32 354	70.07 273	78.19 297	34.93 350	72.68 249	53.44 241	83.74 238	81.00 211

test_fmvs3		56.78 317	55.99 326	59.12 319	53.96 418	48.09 254	58.76 341	66.22 290	27.54 410	76.66 165	68.69 383	25.32 404	51.31 377	53.42 242	73.38 352	77.97 265

Anonymous20240521		63.55 262	66.07 237	55.99 337	66.18 345	44.04 295	68.77 237	68.80 277	46.99 290	72.57 235	85.84 177	39.87 324	50.22 381	53.40 243	92.23 83	73.71 307

PM-MVS		64.49 253	63.61 263	67.14 246	76.68 192	75.15 31	68.49 243	42.85 411	51.17 246	77.85 143	80.51 256	45.76 286	66.31 322	52.83 244	76.35 324	59.96 401

API-MVS		70.97 166	71.51 166	69.37 206	75.20 213	55.94 189	80.99 67	76.84 197	62.48 118	71.24 259	77.51 305	61.51 169	80.96 136	52.04 245	85.76 208	71.22 335

Fast-Effi-MVS+-dtu		70.00 178	68.74 199	73.77 124	73.47 244	64.53 114	71.36 196	78.14 181	55.81 180	68.84 293	74.71 327	65.36 134	75.75 214	52.00 246	79.00 299	81.03 209

mvs_anonymous		65.08 245	65.49 242	63.83 272	63.79 362	37.60 354	66.52 271	69.82 270	43.44 324	73.46 225	86.08 172	58.79 201	71.75 267	51.90 247	75.63 330	82.15 190

Patchmatch-RL test		59.95 297	59.12 299	62.44 289	72.46 263	54.61 201	59.63 333	47.51 395	41.05 341	74.58 204	74.30 332	31.06 378	65.31 328	51.61 248	79.85 290	67.39 365

F-COLMAP		75.29 95	73.99 117	79.18 54	81.73 123	71.90 50	81.86 63	82.98 84	59.86 138	72.27 240	84.00 203	64.56 143	83.07 96	51.48 249	87.19 188	82.56 182

reproduce_monomvs		58.94 304	58.14 309	61.35 301	59.70 389	40.98 321	60.24 330	63.51 314	45.85 298	68.95 287	75.31 322	18.27 426	65.82 324	51.47 250	79.97 287	77.26 273

pmmvs6		71.82 154	73.66 123	66.31 254	75.94 205	42.01 313	66.99 263	72.53 235	63.45 108	76.43 177	92.78 11	72.95 62	69.69 287	51.41 251	90.46 121	87.22 57

IterMVS		63.12 268	62.48 275	65.02 263	66.34 342	52.86 212	63.81 302	62.25 318	46.57 293	71.51 255	80.40 258	44.60 295	66.82 318	51.38 252	75.47 332	75.38 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		70.81 168	71.44 167	68.91 222	79.07 157	46.51 275	67.82 250	70.83 262	61.23 124	74.07 215	88.69 108	59.86 190	75.62 216	51.11 253	90.28 123	84.61 111

KD-MVS_self_test		66.38 233	67.51 219	62.97 284	61.76 372	34.39 375	58.11 347	75.30 210	50.84 249	77.12 153	85.42 180	56.84 224	69.44 289	51.07 254	91.16 99	85.08 95

新几何1				69.99 198	88.37 35	71.34 55		62.08 321	43.85 316	74.99 195	86.11 171	52.85 248	70.57 277	50.99 255	83.23 246	68.05 363

Anonymous20240529		72.56 144	73.79 121	68.86 223	76.89 190	45.21 287	68.80 236	77.25 193	67.16 66	76.89 158	90.44 59	65.95 127	74.19 237	50.75 256	90.00 129	87.18 60

UGNet		70.20 175	69.05 192	73.65 125	76.24 198	63.64 120	75.87 134	72.53 235	61.48 123	60.93 356	86.14 169	52.37 250	77.12 201	50.67 257	85.21 215	80.17 233
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
GA-MVS		62.91 270	61.66 277	66.66 252	67.09 334	44.49 292	61.18 322	69.36 274	51.33 243	69.33 282	74.47 329	36.83 343	74.94 226	50.60 258	74.72 338	80.57 226

Fast-Effi-MVS+		68.81 199	68.30 205	70.35 189	74.66 225	48.61 248	66.06 274	78.32 176	50.62 251	71.48 256	75.54 318	68.75 94	79.59 157	50.55 259	78.73 303	82.86 171

WR-MVS		71.20 162	72.48 148	67.36 242	84.98 74	35.70 366	64.43 298	68.66 279	65.05 90	81.49 100	86.43 160	57.57 216	76.48 209	50.36 260	93.32 68	89.90 22

FMVSNet1		71.06 163	72.48 148	66.81 248	77.65 177	40.68 326	71.96 184	73.03 227	61.14 125	79.45 123	90.36 70	60.44 183	75.20 223	50.20 261	88.05 166	84.54 115

ANet_high		67.08 225	69.94 181	58.51 324	57.55 399	27.09 407	58.43 344	76.80 198	63.56 105	82.40 89	91.93 23	59.82 191	64.98 331	50.10 262	88.86 157	83.46 149

TransMVSNet (Re)		69.62 184	71.63 161	63.57 275	76.51 194	35.93 364	65.75 280	71.29 252	61.05 126	75.02 194	89.90 81	65.88 129	70.41 281	49.79 263	89.48 142	84.38 123

DP-MVS Recon		73.57 118	72.69 144	76.23 93	82.85 108	63.39 122	74.32 155	82.96 85	57.75 155	70.35 267	81.98 237	64.34 145	84.41 76	49.69 264	89.95 131	80.89 214

pm-mvs1		68.40 205	69.85 183	64.04 271	73.10 254	39.94 333	64.61 296	70.50 264	55.52 182	73.97 218	89.33 88	63.91 147	68.38 298	49.68 265	88.02 167	83.81 136

test_fmvs2		54.80 331	54.11 341	56.88 333	51.76 422	49.95 234	56.70 354	65.80 293	26.22 415	69.42 280	65.25 397	31.82 370	49.98 382	49.63 266	70.36 374	70.71 341

mvsmamba		68.87 197	67.30 224	73.57 128	76.58 193	53.70 208	84.43 37	74.25 219	45.38 305	76.63 166	84.55 193	35.85 347	85.27 56	49.54 267	78.49 306	81.75 199

1314		59.83 298	58.86 302	62.74 287	65.71 348	44.78 290	68.59 240	72.63 234	33.54 394	61.05 354	67.29 392	43.62 301	71.26 272	49.49 268	67.84 390	72.19 325

WB-MVS		60.04 296	64.19 257	47.59 381	76.09 201	10.22 435	52.44 381	46.74 398	65.17 88	74.07 215	87.48 129	53.48 244	55.28 368	49.36 269	72.84 355	77.28 270

CMPMVS	48.73 20	61.54 285	60.89 286	63.52 276	61.08 376	51.55 218	68.07 248	68.00 283	33.88 389	65.87 315	81.25 246	37.91 337	67.71 303	49.32 270	82.60 250	71.31 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJ		64.27 258	63.73 262	65.90 258	77.82 173	51.42 219	63.33 308	72.33 237	45.09 310	61.60 348	68.04 386	62.39 159	73.95 240	49.07 271	73.87 349	72.34 322

xiu_mvs_v2_base		64.43 255	63.96 259	65.85 259	77.72 175	51.32 220	63.63 305	72.31 238	45.06 311	61.70 347	69.66 371	62.56 155	73.93 241	49.06 272	73.91 348	72.31 323

thisisatest0515		60.48 293	57.86 311	68.34 230	67.25 332	46.42 276	60.58 327	62.14 319	40.82 344	63.58 339	69.12 375	26.28 398	78.34 182	48.83 273	82.13 253	80.26 231

OpenMVS_ROB	54.93 17	63.23 267	63.28 267	63.07 282	69.81 299	45.34 285	68.52 242	67.14 285	43.74 320	70.61 265	79.22 282	47.90 281	72.66 250	48.75 274	73.84 350	71.21 336

PCF-MVS	63.80 13	72.70 142	71.69 158	75.72 99	78.10 167	60.01 157	73.04 169	81.50 109	45.34 306	79.66 120	84.35 197	65.15 137	82.65 102	48.70 275	89.38 147	84.50 120
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		68.69 203	68.20 210	70.14 195	76.40 196	53.90 207	64.62 295	73.48 223	58.01 152	73.91 219	81.78 239	59.09 197	78.22 185	48.59 276	77.96 314	78.31 256

VDDNet		71.60 157	73.13 135	67.02 247	86.29 48	41.11 319	69.97 216	66.50 289	68.72 60	74.74 198	91.70 29	59.90 189	75.81 213	48.58 277	91.72 86	84.15 130

CR-MVSNet		58.96 303	58.49 305	60.36 311	66.37 340	48.24 251	70.93 204	56.40 350	32.87 395	61.35 350	86.66 150	33.19 356	63.22 339	48.50 278	70.17 376	69.62 351

FE-MVS		68.29 209	66.96 229	72.26 164	74.16 234	54.24 203	77.55 108	73.42 225	57.65 159	72.66 234	84.91 186	32.02 368	81.49 120	48.43 279	81.85 258	81.04 208

testdata2													67.30 309	48.34 280

tfpnnormal		66.48 232	67.93 213	62.16 292	73.40 246	36.65 357	63.45 306	64.99 301	55.97 177	72.82 233	87.80 127	57.06 222	69.10 293	48.31 281	87.54 173	80.72 221

test_vis1_n_1920		52.96 344	53.50 343	51.32 361	59.15 391	44.90 289	56.13 359	64.29 309	30.56 406	59.87 363	60.68 410	40.16 322	47.47 391	48.25 282	62.46 403	61.58 398

PAPR		69.20 191	68.66 201	70.82 181	75.15 215	47.77 260	75.31 137	81.11 119	49.62 264	66.33 313	79.27 281	61.53 168	82.96 97	48.12 283	81.50 268	81.74 200

testing3		58.28 309	58.38 307	58.00 327	77.45 180	26.12 414	60.78 325	43.00 410	56.02 176	70.18 270	75.76 315	13.27 436	67.24 311	48.02 284	80.89 271	80.65 223

FMVSNet2		67.48 219	68.21 209	65.29 260	73.14 251	38.94 340	68.81 234	71.21 257	54.81 188	76.73 164	86.48 158	48.63 277	74.60 231	47.98 285	86.11 204	82.35 185

AdaColmap		74.22 110	74.56 106	73.20 134	81.95 120	60.97 146	79.43 86	80.90 125	65.57 78	72.54 237	81.76 241	70.98 78	85.26 57	47.88 286	90.00 129	73.37 308

cascas		64.59 251	62.77 273	70.05 197	75.27 212	50.02 232	61.79 317	71.61 242	42.46 330	63.68 337	68.89 380	49.33 269	80.35 143	47.82 287	84.05 235	79.78 237

VPA-MVSNet		68.71 202	70.37 178	63.72 273	76.13 200	38.06 350	64.10 300	71.48 246	56.60 173	74.10 214	88.31 118	64.78 141	69.72 286	47.69 288	90.15 126	83.37 153

MSDG		67.47 221	67.48 221	67.46 241	70.70 281	54.69 200	66.90 266	78.17 179	60.88 129	70.41 266	74.76 325	61.22 175	73.18 245	47.38 289	76.87 321	74.49 299

GBi-Net		68.30 207	68.79 196	66.81 248	73.14 251	40.68 326	71.96 184	73.03 227	54.81 188	74.72 199	90.36 70	48.63 277	75.20 223	47.12 290	85.37 210	84.54 115

test1		68.30 207	68.79 196	66.81 248	73.14 251	40.68 326	71.96 184	73.03 227	54.81 188	74.72 199	90.36 70	48.63 277	75.20 223	47.12 290	85.37 210	84.54 115

FMVSNet3		65.00 246	65.16 247	64.52 266	69.47 304	37.56 355	66.63 269	70.38 265	51.55 237	74.72 199	83.27 219	37.89 338	74.44 233	47.12 290	85.37 210	81.57 202

PLC	62.01 16	71.79 155	70.28 179	76.33 91	80.31 138	68.63 79	78.18 103	81.24 116	54.57 197	67.09 311	80.63 255	59.44 193	81.74 118	46.91 293	84.17 233	78.63 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ppachtmachnet_test		60.26 295	59.61 296	62.20 291	67.70 328	44.33 293	58.18 346	60.96 326	40.75 346	65.80 316	72.57 346	41.23 313	63.92 335	46.87 294	82.42 251	78.33 255

test1111		64.62 250	65.19 246	62.93 285	79.01 158	29.91 397	65.45 284	54.41 359	54.09 209	71.47 257	88.48 114	37.02 342	74.29 236	46.83 295	89.94 132	84.58 114

MAR-MVS		67.72 216	66.16 235	72.40 161	74.45 228	64.99 111	74.87 142	77.50 189	48.67 275	65.78 317	68.58 384	57.01 223	77.79 194	46.68 296	81.92 256	74.42 301
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
MonoMVSNet		62.75 273	63.42 265	60.73 308	65.60 349	40.77 324	72.49 174	70.56 263	52.49 225	75.07 193	79.42 278	39.52 328	69.97 284	46.59 297	69.06 382	71.44 331

LFMVS		67.06 226	67.89 214	64.56 265	78.02 169	38.25 347	70.81 207	59.60 330	65.18 87	71.06 261	86.56 156	43.85 299	75.22 221	46.35 298	89.63 137	80.21 232

test2506		61.23 286	60.85 287	62.38 290	78.80 160	27.88 405	67.33 259	37.42 424	54.23 204	67.55 306	88.68 109	17.87 428	74.39 234	46.33 299	89.41 144	84.86 101

Syy-MVS		54.13 334	55.45 330	50.18 366	68.77 311	23.59 419	55.02 365	44.55 404	43.80 317	58.05 371	64.07 399	46.22 285	58.83 355	46.16 300	72.36 359	68.12 361

BH-untuned		69.39 189	69.46 185	69.18 212	77.96 171	56.88 183	68.47 244	77.53 188	56.77 168	77.79 144	79.63 274	60.30 185	80.20 149	46.04 301	80.65 277	70.47 342

MDA-MVSNet-bldmvs		62.34 278	61.73 276	64.16 267	61.64 373	49.90 235	48.11 396	57.24 341	53.31 219	80.95 107	79.39 279	49.00 273	61.55 345	45.92 302	80.05 286	81.03 209

test_fmvs1_n		52.70 347	52.01 354	54.76 342	53.83 419	50.36 227	55.80 361	65.90 292	24.96 419	65.39 318	60.64 411	27.69 393	48.46 387	45.88 303	67.99 388	65.46 377

TinyColmap		67.98 212	69.28 187	64.08 269	67.98 323	46.82 272	70.04 214	75.26 211	53.05 220	77.36 150	86.79 143	59.39 194	72.59 254	45.64 304	88.01 168	72.83 316

test_cas_vis1_n_1920		50.90 360	50.92 364	50.83 364	54.12 417	47.80 259	51.44 385	54.61 357	26.95 413	63.95 331	60.85 409	37.86 339	44.97 402	45.53 305	62.97 402	59.72 402

test_yl		65.11 243	65.09 251	65.18 261	70.59 283	40.86 322	63.22 311	72.79 230	57.91 153	68.88 291	79.07 287	42.85 306	74.89 227	45.50 306	84.97 218	79.81 235

DCV-MVSNet		65.11 243	65.09 251	65.18 261	70.59 283	40.86 322	63.22 311	72.79 230	57.91 153	68.88 291	79.07 287	42.85 306	74.89 227	45.50 306	84.97 218	79.81 235

test_fmvs1		51.51 357	50.86 365	53.48 349	49.72 425	49.35 243	54.11 372	64.96 302	24.64 421	63.66 338	59.61 414	28.33 392	48.45 388	45.38 308	67.30 392	62.66 393

ECVR-MVS		64.82 247	65.22 245	63.60 274	78.80 160	31.14 391	66.97 264	56.47 349	54.23 204	69.94 275	88.68 109	37.23 341	74.81 229	45.28 309	89.41 144	84.86 101

PVSNet_BlendedMVS		65.38 241	64.30 255	68.61 227	69.81 299	49.36 241	65.60 283	78.96 161	45.50 301	59.98 359	78.61 291	51.82 253	78.20 186	44.30 310	84.11 234	78.27 257

PVSNet_Blended		62.90 271	61.64 278	66.69 251	69.81 299	49.36 241	61.23 321	78.96 161	42.04 331	59.98 359	68.86 381	51.82 253	78.20 186	44.30 310	77.77 317	72.52 319

Anonymous202405211		66.02 237	66.89 230	63.43 278	74.22 232	38.14 348	59.00 337	66.13 291	63.33 111	69.76 278	85.95 176	51.88 252	70.50 278	44.23 312	87.52 174	81.64 201

VPNet		65.58 240	67.56 218	59.65 315	79.72 142	30.17 396	60.27 329	62.14 319	54.19 207	71.24 259	86.63 153	58.80 200	67.62 305	44.17 313	90.87 114	81.18 205

Patchmtry		60.91 288	63.01 271	54.62 344	66.10 346	26.27 413	67.47 254	56.40 350	54.05 210	72.04 245	86.66 150	33.19 356	60.17 349	43.69 314	87.45 177	77.42 268

PatchT		53.35 342	56.47 322	43.99 397	64.19 360	17.46 428	59.15 334	43.10 409	52.11 230	54.74 391	86.95 139	29.97 387	49.98 382	43.62 315	74.40 343	64.53 387

IB-MVS	49.67 18	59.69 299	56.96 318	67.90 235	68.19 320	50.30 229	61.42 319	65.18 300	47.57 287	55.83 384	67.15 393	23.77 408	79.60 156	43.56 316	79.97 287	73.79 306
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
our_test_3		56.46 318	56.51 321	56.30 335	67.70 328	39.66 335	55.36 364	52.34 373	40.57 349	63.85 332	69.91 370	40.04 323	58.22 359	43.49 317	75.29 336	71.03 340

test_vis1_n		51.27 359	50.41 369	53.83 346	56.99 401	50.01 233	56.75 353	60.53 327	25.68 417	59.74 364	57.86 415	29.40 389	47.41 392	43.10 318	63.66 400	64.08 388

PatchmatchNet		54.60 332	54.27 339	55.59 340	65.17 354	39.08 337	66.92 265	51.80 375	39.89 351	58.39 368	73.12 343	31.69 372	58.33 358	43.01 319	58.38 415	69.38 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs-eth3d		64.41 256	63.27 268	67.82 238	75.81 208	60.18 156	69.49 221	62.05 322	38.81 360	74.13 213	82.23 233	43.76 300	68.65 296	42.53 320	80.63 279	74.63 296

ttmdpeth		56.40 319	55.45 330	59.25 317	55.63 409	40.69 325	58.94 339	49.72 384	36.22 376	65.39 318	86.97 138	23.16 411	56.69 365	42.30 321	80.74 275	80.36 229

LCM-MVSNet-Re		69.10 194	71.57 165	61.70 295	70.37 290	34.30 376	61.45 318	79.62 149	56.81 167	89.59 9	88.16 123	68.44 97	72.94 247	42.30 321	87.33 181	77.85 266

VNet		64.01 261	65.15 249	60.57 309	73.28 248	35.61 367	57.60 349	67.08 286	54.61 195	66.76 312	83.37 214	56.28 229	66.87 315	42.19 323	85.20 216	79.23 246

test-LLR		50.43 362	50.69 367	49.64 370	60.76 377	41.87 314	53.18 376	45.48 402	43.41 325	49.41 410	60.47 412	29.22 390	44.73 404	42.09 324	72.14 362	62.33 396

test-mter		48.56 371	48.20 376	49.64 370	60.76 377	41.87 314	53.18 376	45.48 402	31.91 401	49.41 410	60.47 412	18.34 425	44.73 404	42.09 324	72.14 362	62.33 396

MVS		60.62 292	59.97 293	62.58 288	68.13 321	47.28 269	68.59 240	73.96 221	32.19 396	59.94 361	68.86 381	50.48 261	77.64 197	41.85 326	75.74 328	62.83 390

MIMVSNet1		66.57 231	69.23 190	58.59 323	81.26 130	37.73 353	64.06 301	57.62 335	57.02 164	78.40 136	90.75 49	62.65 154	58.10 361	41.77 327	89.58 140	79.95 234

test_vis3_rt		51.94 355	51.04 362	54.65 343	46.32 429	50.13 231	44.34 409	78.17 179	23.62 423	68.95 287	62.81 403	21.41 417	38.52 422	41.49 328	72.22 361	75.30 292

Vis-MVSNet (Re-imp)		62.74 274	63.21 269	61.34 302	72.19 265	31.56 388	67.31 260	53.87 361	53.60 217	69.88 276	83.37 214	40.52 320	70.98 274	41.40 329	86.78 195	81.48 203

YYNet1		52.58 348	53.50 343	49.85 368	54.15 415	36.45 360	40.53 414	46.55 400	38.09 364	75.52 188	73.31 342	41.08 317	43.88 408	41.10 330	71.14 370	69.21 355

sd_testset		63.55 262	65.38 243	58.07 326	73.04 257	38.83 342	57.41 350	65.44 298	51.42 239	68.93 289	82.72 226	63.76 148	58.11 360	41.05 331	84.67 223	77.28 270

MDA-MVSNet_test_wron		52.57 349	53.49 345	49.81 369	54.24 414	36.47 359	40.48 415	46.58 399	38.13 363	75.47 190	73.32 341	41.05 318	43.85 409	40.98 332	71.20 369	69.10 357

1112_ss		59.48 300	58.99 301	60.96 306	77.84 172	42.39 312	61.42 319	68.45 281	37.96 365	59.93 362	67.46 389	45.11 292	65.07 330	40.89 333	71.81 364	75.41 289

tpmvs		55.84 321	55.45 330	57.01 331	60.33 380	33.20 381	65.89 276	59.29 332	47.52 288	56.04 382	73.60 338	31.05 379	68.06 302	40.64 334	64.64 397	69.77 349

TR-MVS		64.59 251	63.54 264	67.73 239	75.75 209	50.83 224	63.39 307	70.29 266	49.33 266	71.55 254	74.55 328	50.94 259	78.46 176	40.43 335	75.69 329	73.89 305

test_post1							66.63 269				2.08 432	30.66 382	59.33 353	40.34 336

SCA		58.57 308	58.04 310	60.17 312	70.17 294	41.07 320	65.19 287	53.38 367	43.34 327	61.00 355	73.48 339	45.20 290	69.38 290	40.34 336	70.31 375	70.05 345

baseline1		57.82 312	58.36 308	56.19 336	69.17 307	30.76 394	62.94 313	55.21 354	46.04 296	63.83 334	78.47 292	41.20 314	63.68 336	39.44 338	68.99 383	74.13 302

ab-mvs		64.11 259	65.13 250	61.05 304	71.99 267	38.03 351	67.59 251	68.79 278	49.08 270	65.32 320	86.26 164	58.02 213	66.85 317	39.33 339	79.79 293	78.27 257

tpmrst		50.15 365	51.38 359	46.45 387	56.05 405	24.77 417	64.40 299	49.98 382	36.14 377	53.32 397	69.59 372	35.16 349	48.69 386	39.24 340	58.51 414	65.89 374

test_f		43.79 387	45.63 381	38.24 408	42.29 434	38.58 343	34.76 423	47.68 394	22.22 426	67.34 308	63.15 402	31.82 370	30.60 427	39.19 341	62.28 404	45.53 420

CostFormer		57.35 314	56.14 324	60.97 305	63.76 363	38.43 344	67.50 253	60.22 328	37.14 372	59.12 367	76.34 313	32.78 359	71.99 263	39.12 342	69.27 381	72.47 320

pmmvs4		60.78 290	59.04 300	66.00 257	73.06 256	57.67 179	64.53 297	60.22 328	36.91 373	65.96 314	77.27 306	39.66 326	68.54 297	38.87 343	74.89 337	71.80 328

gm-plane-assit					62.51 367	33.91 378			37.25 371		62.71 404		72.74 248	38.70 344

Test_1112_low_res		58.78 306	58.69 303	59.04 321	79.41 146	38.13 349	57.62 348	66.98 287	34.74 385	59.62 365	77.56 304	42.92 305	63.65 337	38.66 345	70.73 372	75.35 291

thres600view7		61.82 281	61.38 282	63.12 281	71.81 269	34.93 371	64.64 294	56.99 343	54.78 192	70.33 268	79.74 270	32.07 366	72.42 256	38.61 346	83.46 243	82.02 192

UnsupCasMVSNet_eth		52.26 351	53.29 346	49.16 375	55.08 411	33.67 379	50.03 390	58.79 333	37.67 368	63.43 342	74.75 326	41.82 311	45.83 395	38.59 347	59.42 411	67.98 364

CL-MVSNet_self_test		62.44 277	63.40 266	59.55 316	72.34 264	32.38 383	56.39 355	64.84 303	51.21 245	67.46 307	81.01 250	50.75 260	63.51 338	38.47 348	88.12 165	82.75 174

MDTV_nov1_ep13			54.05 342		65.54 350	29.30 400	59.00 337	55.22 353	35.96 379	52.44 398	75.98 314	30.77 381	59.62 351	38.21 349	73.33 353

BH-w/o		64.81 248	64.29 256	66.36 253	76.08 203	54.71 199	65.61 282	75.23 212	50.10 258	71.05 262	71.86 351	54.33 240	79.02 164	38.20 350	76.14 326	65.36 378

TESTMET0.1,1		45.17 380	44.93 386	45.89 389	56.02 406	38.31 345	53.18 376	41.94 417	27.85 409	44.86 421	56.47 417	17.93 427	41.50 417	38.08 351	68.06 387	57.85 405

USDC		62.80 272	63.10 270	61.89 293	65.19 352	43.30 303	67.42 255	74.20 220	35.80 380	72.25 241	84.48 195	45.67 287	71.95 264	37.95 352	84.97 218	70.42 344

SSC-MVS3.2		57.01 315	59.50 297	49.57 372	67.73 327	25.95 415	46.68 401	51.75 376	51.41 241	63.84 333	79.66 273	53.28 246	50.34 380	37.85 353	83.28 245	72.41 321

E-PMN		45.17 380	45.36 383	44.60 394	50.07 423	42.75 308	38.66 418	42.29 415	46.39 294	39.55 426	51.15 422	26.00 399	45.37 400	37.68 354	76.41 323	45.69 419

CDS-MVSNet		64.33 257	62.66 274	69.35 208	80.44 137	58.28 176	65.26 286	65.66 295	44.36 314	67.30 309	75.54 318	43.27 302	71.77 265	37.68 354	84.44 230	78.01 263
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		55.59 325	54.89 335	57.68 328	69.18 306	49.05 244	61.00 323	62.93 317	35.98 378	58.36 369	68.93 379	36.71 344	66.59 320	37.62 356	63.30 401	57.39 407

FPMVS		59.43 301	60.07 292	57.51 329	77.62 178	71.52 53	62.33 315	50.92 378	57.40 161	69.40 281	80.00 267	39.14 330	61.92 344	37.47 357	66.36 393	39.09 424

EPMVS		45.74 377	46.53 380	43.39 399	54.14 416	22.33 424	55.02 365	35.00 427	34.69 386	51.09 404	70.20 364	25.92 400	42.04 414	37.19 358	55.50 419	65.78 375

baseline2		55.57 326	52.74 347	64.05 270	65.26 351	44.11 294	62.38 314	54.43 358	39.03 358	51.21 403	67.35 391	33.66 354	72.45 255	37.14 359	64.22 399	75.60 286

EMVS		44.61 384	44.45 389	45.10 393	48.91 426	43.00 306	37.92 419	41.10 421	46.75 292	38.00 428	48.43 425	26.42 397	46.27 394	37.11 360	75.38 334	46.03 418

testing99		55.16 329	54.56 338	56.98 332	70.13 297	30.58 395	54.55 371	54.11 360	49.53 265	56.76 378	70.14 366	22.76 413	65.79 325	36.99 361	76.04 327	74.57 297

testing91		55.74 323	55.29 333	57.08 330	70.63 282	30.85 393	54.94 368	56.31 352	50.34 253	57.08 374	70.10 367	24.50 406	65.86 323	36.98 362	76.75 322	74.53 298

XXY-MVS		55.19 328	57.40 316	48.56 380	64.45 359	34.84 373	51.54 384	53.59 363	38.99 359	63.79 335	79.43 277	56.59 225	45.57 397	36.92 363	71.29 368	65.25 379

HyFIR lowres test		63.01 269	60.47 290	70.61 183	83.04 104	54.10 204	59.93 332	72.24 239	33.67 392	69.00 284	75.63 317	38.69 332	76.93 203	36.60 364	75.45 333	80.81 218

EPNet_dtu		58.93 305	58.52 304	60.16 313	67.91 324	47.70 263	69.97 216	58.02 334	49.73 261	47.28 415	73.02 344	38.14 334	62.34 341	36.57 365	85.99 205	70.43 343
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		52.05 353	51.58 357	53.44 350	52.11 420	31.20 389	44.88 407	64.83 304	41.53 335	64.37 325	70.03 368	15.61 432	64.20 332	36.25 366	74.61 340	64.93 383

miper_refine_blended		52.05 353	51.58 357	53.44 350	52.11 420	31.20 389	44.88 407	64.83 304	41.53 335	64.37 325	70.03 368	15.61 432	64.20 332	36.25 366	74.61 340	64.93 383

new-patchmatchnet		52.89 346	55.76 328	44.26 396	59.94 386	6.31 437	37.36 421	50.76 380	41.10 339	64.28 327	79.82 269	44.77 293	48.43 389	36.24 368	87.61 172	78.03 262

JIA-IIPM		54.03 336	51.62 356	61.25 303	59.14 392	55.21 197	59.10 336	47.72 393	50.85 248	50.31 409	85.81 178	20.10 420	63.97 334	36.16 369	55.41 420	64.55 386

WAC-MVS						22.69 421								36.10 370

PatchMatch-RL		58.68 307	57.72 312	61.57 296	76.21 199	73.59 43	61.83 316	49.00 390	47.30 289	61.08 352	68.97 377	50.16 263	59.01 354	36.06 371	68.84 384	52.10 411

thres100view900		61.17 287	61.09 284	61.39 300	72.14 266	35.01 370	65.42 285	56.99 343	55.23 185	70.71 264	79.90 268	32.07 366	72.09 260	35.61 372	81.73 261	77.08 276

tfpn200view9		60.35 294	59.97 293	61.51 297	70.78 279	35.35 368	63.27 309	57.47 336	53.00 221	68.31 298	77.09 308	32.45 363	72.09 260	35.61 372	81.73 261	77.08 276

thres400		60.77 291	59.97 293	63.15 280	70.78 279	35.35 368	63.27 309	57.47 336	53.00 221	68.31 298	77.09 308	32.45 363	72.09 260	35.61 372	81.73 261	82.02 192

test_vis1_rt		46.70 376	45.24 384	51.06 363	44.58 430	51.04 222	39.91 416	67.56 284	21.84 427	51.94 401	50.79 423	33.83 353	39.77 419	35.25 375	61.50 406	62.38 395

MVP-Stereo		61.56 284	59.22 298	68.58 228	79.28 148	60.44 154	69.20 228	71.57 243	43.58 322	56.42 381	78.37 294	39.57 327	76.46 210	34.86 376	60.16 409	68.86 358
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TAMVS		65.31 242	63.75 261	69.97 199	82.23 117	59.76 160	66.78 268	63.37 315	45.20 307	69.79 277	79.37 280	47.42 283	72.17 259	34.48 377	85.15 217	77.99 264

tpm cat1		54.02 337	52.63 349	58.19 325	64.85 358	39.86 334	66.26 273	57.28 339	32.16 397	56.90 376	70.39 361	32.75 360	65.30 329	34.29 378	58.79 412	69.41 353

pmmvs5		52.49 350	52.58 350	52.21 356	54.99 412	32.38 383	55.45 363	53.84 362	32.15 398	55.49 386	74.81 324	38.08 335	57.37 363	34.02 379	74.40 343	66.88 369

CHOSEN 1792x2688		58.09 310	56.30 323	63.45 277	79.95 140	50.93 223	54.07 373	65.59 296	28.56 408	61.53 349	74.33 331	41.09 316	66.52 321	33.91 380	67.69 391	72.92 313

myMVS_eth3d		50.36 363	50.52 368	49.88 367	68.77 311	22.69 421	55.02 365	44.55 404	43.80 317	58.05 371	64.07 399	14.16 434	58.83 355	33.90 381	72.36 359	68.12 361

HY-MVS	49.31 19	57.96 311	57.59 314	59.10 320	66.85 339	36.17 361	65.13 288	65.39 299	39.24 357	54.69 392	78.14 298	44.28 297	67.18 312	33.75 382	70.79 371	73.95 304

tpm2		56.12 320	54.64 337	60.55 310	66.24 343	36.01 362	68.14 246	56.77 346	33.60 393	58.25 370	75.52 320	30.25 384	74.33 235	33.27 383	69.76 380	71.32 333

MDTV_nov1_ep13_2view						18.41 427	53.74 374		31.57 402	44.89 420		29.90 388		32.93 384		71.48 330

tpm		50.60 361	52.42 352	45.14 392	65.18 353	26.29 412	60.30 328	43.50 407	37.41 370	57.01 375	79.09 286	30.20 386	42.32 412	32.77 385	66.36 393	66.81 371

testing11		53.13 343	52.26 353	55.75 339	70.44 289	31.73 387	54.75 369	52.40 372	44.81 312	52.36 400	68.40 385	21.83 416	65.74 326	32.64 386	72.73 356	69.78 348

WBMVS		53.38 340	54.14 340	51.11 362	70.16 295	26.66 409	50.52 389	51.64 377	39.32 354	63.08 343	77.16 307	23.53 409	55.56 366	31.99 387	79.88 289	71.11 338

sss		47.59 374	48.32 374	45.40 391	56.73 404	33.96 377	45.17 405	48.51 391	32.11 400	52.37 399	65.79 395	40.39 321	41.91 415	31.85 388	61.97 405	60.35 400

PMMVS		44.69 382	43.95 391	46.92 384	50.05 424	53.47 210	48.08 397	42.40 413	22.36 425	44.01 424	53.05 420	42.60 308	45.49 398	31.69 389	61.36 407	41.79 422

thres200		57.55 313	57.02 317	59.17 318	67.89 325	34.93 371	58.91 340	57.25 340	50.24 255	64.01 330	71.46 354	32.49 362	71.39 271	31.31 390	79.57 295	71.19 337

WTY-MVS		49.39 368	50.31 370	46.62 386	61.22 375	32.00 386	46.61 402	49.77 383	33.87 390	54.12 394	69.55 373	41.96 310	45.40 399	31.28 391	64.42 398	62.47 394

testing3-2		56.85 316	57.62 313	54.53 345	75.84 206	22.23 425	51.26 386	49.10 388	61.04 127	63.74 336	79.73 271	22.29 415	59.44 352	31.16 392	84.43 231	81.92 195

UnsupCasMVSNet_bld		50.01 366	51.03 363	46.95 383	58.61 394	32.64 382	48.31 394	53.27 368	34.27 388	60.47 357	71.53 353	41.40 312	47.07 393	30.68 393	60.78 408	61.13 399

PVSNet	43.83 21	51.56 356	51.17 360	52.73 353	68.34 316	38.27 346	48.22 395	53.56 365	36.41 375	54.29 393	64.94 398	34.60 351	54.20 372	30.34 394	69.87 378	65.71 376

test20.03		55.74 323	57.51 315	50.42 365	59.89 387	32.09 385	50.63 387	49.01 389	50.11 257	65.07 322	83.23 221	45.61 288	48.11 390	30.22 395	83.82 237	71.07 339

FMVSNet5		55.08 330	55.54 329	53.71 347	65.80 347	33.50 380	56.22 357	52.50 371	43.72 321	61.06 353	83.38 213	25.46 402	54.87 369	30.11 396	81.64 266	72.75 317

gg-mvs-nofinetune		55.75 322	56.75 320	52.72 354	62.87 366	28.04 404	68.92 231	41.36 419	71.09 46	50.80 405	92.63 13	20.74 418	66.86 316	29.97 397	72.41 358	63.25 389

dp		44.09 386	44.88 387	41.72 403	58.53 396	23.18 420	54.70 370	42.38 414	34.80 384	44.25 423	65.61 396	24.48 407	44.80 403	29.77 398	49.42 423	57.18 408

PAPM		61.79 282	60.37 291	66.05 256	76.09 201	41.87 314	69.30 225	76.79 199	40.64 348	53.80 395	79.62 275	44.38 296	82.92 98	29.64 399	73.11 354	73.36 309

testgi		54.00 338	56.86 319	45.45 390	58.20 397	25.81 416	49.05 392	49.50 386	45.43 304	67.84 301	81.17 247	51.81 255	43.20 411	29.30 400	79.41 296	67.34 367

Patchmatch-test		47.93 372	49.96 371	41.84 401	57.42 400	24.26 418	48.75 393	41.49 418	39.30 356	56.79 377	73.48 339	30.48 383	33.87 425	29.29 401	72.61 357	67.39 365

pmmvs3		46.71 375	45.09 385	51.55 359	56.76 403	48.25 250	55.78 362	39.53 423	24.13 422	50.35 408	63.40 401	15.90 431	51.08 378	29.29 401	70.69 373	55.33 410

mvsany_test3		43.76 388	41.01 392	52.01 357	48.09 427	57.74 178	42.47 411	23.85 434	23.30 424	64.80 323	62.17 406	27.12 394	40.59 418	29.17 403	48.11 424	57.69 406

dmvs_re		49.91 367	50.77 366	47.34 382	59.98 383	38.86 341	53.18 376	53.58 364	39.75 352	55.06 387	61.58 408	36.42 345	44.40 406	29.15 404	68.23 386	58.75 404

N_pmnet		52.06 352	51.11 361	54.92 341	59.64 390	71.03 57	37.42 420	61.62 325	33.68 391	57.12 373	72.10 347	37.94 336	31.03 426	29.13 405	71.35 367	62.70 391

Anonymous20231206		54.13 334	55.82 327	49.04 377	70.89 277	35.96 363	51.73 383	50.87 379	34.86 382	62.49 345	79.22 282	42.52 309	44.29 407	27.95 406	81.88 257	66.88 369

CHOSEN 280x420		41.62 390	39.89 395	46.80 385	61.81 371	51.59 217	33.56 424	35.74 426	27.48 411	37.64 429	53.53 418	23.24 410	42.09 413	27.39 407	58.64 413	46.72 417

UBG		49.18 369	49.35 373	48.66 379	70.36 291	26.56 411	50.53 388	45.61 401	37.43 369	53.37 396	65.97 394	23.03 412	54.20 372	26.29 408	71.54 366	65.20 380

mvsany_test1		37.88 392	35.74 397	44.28 395	47.28 428	49.90 235	36.54 422	24.37 433	19.56 428	45.76 417	53.46 419	32.99 358	37.97 423	26.17 409	35.52 426	44.99 421

MIMVSNet		54.39 333	56.12 325	49.20 374	72.57 262	30.91 392	59.98 331	48.43 392	41.66 334	55.94 383	83.86 206	41.19 315	50.42 379	26.05 410	75.38 334	66.27 373

ADS-MVSNet2		48.76 370	47.25 379	53.29 352	55.90 407	40.54 329	47.34 399	54.99 356	31.41 403	50.48 406	72.06 348	31.23 375	54.26 371	25.93 411	55.93 417	65.07 381

ADS-MVSNet		44.62 383	45.58 382	41.73 402	55.90 407	20.83 426	47.34 399	39.94 422	31.41 403	50.48 406	72.06 348	31.23 375	39.31 420	25.93 411	55.93 417	65.07 381

testing222		53.37 341	52.50 351	55.98 338	70.51 288	29.68 398	56.20 358	51.85 374	46.19 295	56.76 378	68.94 378	19.18 424	65.39 327	25.87 413	76.98 320	72.87 315

test0.0.03 1		47.72 373	48.31 375	45.93 388	55.53 410	29.39 399	46.40 403	41.21 420	43.41 325	55.81 385	67.65 388	29.22 390	43.77 410	25.73 414	69.87 378	64.62 385

GG-mvs-BLEND				52.24 355	60.64 379	29.21 401	69.73 220	42.41 412		45.47 418	52.33 421	20.43 419	68.16 300	25.52 415	65.42 395	59.36 403

DSMNet-mixed		43.18 389	44.66 388	38.75 406	54.75 413	28.88 402	57.06 352	27.42 431	13.47 429	47.27 416	77.67 303	38.83 331	39.29 421	25.32 416	60.12 410	48.08 415

WB-MVSnew		53.94 339	54.76 336	51.49 360	71.53 272	28.05 403	58.22 345	50.36 381	37.94 366	59.16 366	70.17 365	49.21 270	51.94 376	24.49 417	71.80 365	74.47 300

MVS-HIRNet		45.53 378	47.29 378	40.24 404	62.29 369	26.82 408	56.02 360	37.41 425	29.74 407	43.69 425	81.27 245	33.96 352	55.48 367	24.46 418	56.79 416	38.43 425

myMVS_eth3d28		51.35 358	51.99 355	49.44 373	69.21 305	22.51 423	49.82 391	49.11 387	49.00 271	55.03 388	70.31 362	22.73 414	52.88 375	24.33 419	78.39 309	72.92 313

UWE-MVS		52.94 345	52.70 348	53.65 348	73.56 242	27.49 406	57.30 351	49.57 385	38.56 362	62.79 344	71.42 355	19.49 423	60.41 347	24.33 419	77.33 319	73.06 311

PVSNet_0	36.71 22	41.12 391	40.78 394	42.14 400	59.97 384	40.13 331	40.97 413	42.24 416	30.81 405	44.86 421	49.41 424	40.70 319	45.12 401	23.15 421	34.96 427	41.16 423

ETVMVS		50.32 364	49.87 372	51.68 358	70.30 293	26.66 409	52.33 382	43.93 406	43.54 323	54.91 389	67.95 387	20.01 421	60.17 349	22.47 422	73.40 351	68.22 360

new_pmnet		37.55 394	39.80 396	30.79 409	56.83 402	16.46 430	39.35 417	30.65 429	25.59 418	45.26 419	61.60 407	24.54 405	28.02 429	21.60 423	52.80 422	47.90 416

dmvs_testset		45.26 379	47.51 377	38.49 407	59.96 385	14.71 431	58.50 343	43.39 408	41.30 337	51.79 402	56.48 416	39.44 329	49.91 384	21.42 424	55.35 421	50.85 412

MVE	27.91 23	36.69 395	35.64 398	39.84 405	43.37 432	35.85 365	19.49 426	24.61 432	24.68 420	39.05 427	62.63 405	38.67 333	27.10 430	21.04 425	47.25 425	56.56 409
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		61.97 279	66.25 234	49.12 376	58.19 398	60.77 152	66.32 272	52.97 369	55.93 179	90.62 6	86.91 140	73.07 60	35.98 424	20.63 426	91.63 89	50.62 413

PMMVS2		37.74 393	40.87 393	28.36 410	42.41 433	5.35 438	24.61 425	27.75 430	32.15 398	47.85 414	70.27 363	35.85 347	29.51 428	19.08 427	67.85 389	50.22 414

dongtai		31.66 396	32.98 399	27.71 411	58.58 395	12.61 433	45.02 406	14.24 437	41.90 332	47.93 413	43.91 426	10.65 437	41.81 416	14.06 428	20.53 430	28.72 427

UWE-MVS-28		44.18 385	44.37 390	43.61 398	60.10 381	16.96 429	52.62 380	33.27 428	36.79 374	48.86 412	69.47 374	19.96 422	45.65 396	13.40 429	64.83 396	68.23 359

test_method		19.26 398	19.12 402	19.71 412	9.09 437	1.91 440	7.79 428	53.44 366	1.42 431	10.27 433	35.80 427	17.42 429	25.11 431	12.44 430	24.38 429	32.10 426

tmp_tt		11.98 400	14.73 403	3.72 415	2.28 438	4.62 439	19.44 427	14.50 436	0.47 433	21.55 431	9.58 431	25.78 401	4.57 434	11.61 431	27.37 428	1.96 430

DeepMVS_CX				11.83 414	15.51 436	13.86 432		11.25 439	5.76 430	20.85 432	26.46 429	17.06 430	9.22 433	9.69 432	13.82 432	12.42 429

kuosan		22.02 397	23.52 401	17.54 413	41.56 435	11.24 434	41.99 412	13.39 438	26.13 416	28.87 430	30.75 428	9.72 438	21.94 432	4.77 433	14.49 431	19.43 428

testmvs		4.06 404	5.28 407	0.41 416	0.64 440	0.16 442	42.54 410	0.31 441	0.26 435	0.50 436	1.40 435	0.77 439	0.17 435	0.56 434	0.55 434	0.90 431

test123		4.43 403	5.78 406	0.39 417	0.97 439	0.28 441	46.33 404	0.45 440	0.31 434	0.62 435	1.50 434	0.61 440	0.11 436	0.56 434	0.63 433	0.77 432

mmdepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

monomultidepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

test_blank		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet_test		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

DCPMVS		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

cdsmvs_eth3d_5k		17.71 399	23.62 400	0.00 418	0.00 441	0.00 443	0.00 429	70.17 267	0.00 436	0.00 437	74.25 333	68.16 100	0.00 437	0.00 436	0.00 435	0.00 433

pcd_1.5k_mvsjas		5.20 402	6.93 405	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	62.39 159	0.00 437	0.00 436	0.00 435	0.00 433

sosnet-low-res		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uncertanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

Regformer		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

ab-mvs-re		5.62 401	7.50 404	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	67.46 389	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

FOURS1					89.19 24	77.84 14	91.64 1	89.11 3	84.05 3	91.57 3

test_one_0601					85.84 64	61.45 138		85.63 30	75.27 21	85.62 51	90.38 67	76.72 30

eth-test2					0.00 441

eth-test					0.00 441

test_241102_ONE					86.12 54	61.06 144		84.72 52	72.64 34	87.38 28	89.47 86	77.48 26	85.74 46

save fliter					87.00 40	67.23 90	79.24 89	77.94 184	56.65 172

test0726					86.16 52	60.78 150	83.81 43	85.10 43	72.48 37	85.27 56	89.96 79	78.57 19

GSMVS																70.05 345

test_part2					85.90 60	66.44 95				84.61 65

sam_mvs1												31.41 373				70.05 345

sam_mvs												31.21 377

MTGPA								80.63 132

test_post											1.99 433	30.91 380	54.76 370

patchmatchnet-post											68.99 376	31.32 374	69.38 290

MTMP							84.83 34	19.26 435

TEST9					85.47 67	69.32 74	76.42 123	78.69 169	53.73 216	76.97 154	86.74 146	66.84 114	81.10 127

test_8					85.09 73	67.89 83	76.26 128	78.66 171	54.00 211	76.89 158	86.72 148	66.60 120	80.89 137

agg_prior					84.44 85	66.02 101		78.62 172		76.95 156			80.34 144

test_prior4						70.14 67	77.57 106

test_prior				75.27 106	82.15 118	59.85 159		84.33 63					83.39 90			82.58 181

新几何2							71.33 197

旧先验1					84.55 82	60.36 155		63.69 312			87.05 137	54.65 237			83.34 244	69.66 350

原ACMM2							74.78 147

test222					87.30 38	69.15 77	67.85 249	59.59 331	41.06 340	73.05 231	85.72 179	48.03 280			80.65 277	66.92 368

segment_acmp												68.30 99

testdata1							68.34 245		57.24 163

test12				76.51 88	82.28 116	60.94 147		81.64 108		73.60 222		64.88 139	85.19 62		90.42 122	83.38 152

plane_prior7					85.18 70	66.21 98

plane_prior6					84.18 88	65.31 107						60.83 180

plane_prior4											89.11 97

plane_prior3						65.67 103			63.82 102	78.23 137

plane_prior2							82.74 55		65.45 80

plane_prior1					84.46 84

plane_prior						65.18 108	80.06 83		61.88 122						89.91 133

n2								0.00 442

nn								0.00 442

door-mid								55.02 355

test11								82.71 91

door								52.91 370

HQP5-MVS						58.80 170

HQP-NCC					82.37 113		77.32 111		59.08 141	71.58 250

ACMP_Plane					82.37 113		77.32 111		59.08 141	71.58 250

HQP4-MVS										71.59 249			85.31 54			83.74 139

HQP3-MVS								84.12 69							89.16 148

HQP2-MVS												58.09 208

NP-MVS					83.34 98	63.07 126					85.97 174

ACMMP++_ref															89.47 143

ACMMP++															91.96 85

Test By Simon												62.56 155

High-resolution multi-view benchmark