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	66.57 120	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 70
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	83.35 151

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	80.63 131	72.08 41	84.93 59	90.79 48	74.65 49	84.42 75	80.98 5	94.75 32	80.82 210

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	84.60 110

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	82.10 186

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	82.72 171

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	82.72 171

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	57.10 217	87.10 9	79.75 11	83.87 235	84.31 122
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 86
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	84.61 108
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	82.82 167

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	84.18 125

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	51.71 232	77.15 151	91.42 36	65.49 130	87.20 7	79.44 17	87.17 189	84.51 116
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	83.95 130

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	84.62 107
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	82.35 180

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	82.49 178

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	84.30 124

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	84.64 105

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	9.95 420	73.86 55	86.31 21	78.84 23	94.03 56	84.64 105

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	82.74 170
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	83.82 132

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 95

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 95

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	83.47 145
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	83.09 158

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 75

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	80.62 218
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	80.18 141	64.71 95	78.11 140	88.39 116	65.46 131	83.14 93	77.64 33	91.20 98	78.94 243

DVP-MVS++		81.24 39	82.74 41	76.76 84	83.14 99	60.90 147	91.64 1	85.49 32	74.03 25	84.93 59	90.38 67	66.82 113	85.90 40	77.43 34	90.78 115	83.49 142

test_0728_THIRD									74.03 25	85.83 46	90.41 62	75.58 40	85.69 47	77.43 34	94.74 33	84.31 122

MM		78.15 73	77.68 78	79.55 50	80.10 139	65.47 104	80.94 68	78.74 167	71.22 45	72.40 235	88.70 107	60.51 179	87.70 4	77.40 36	89.13 152	85.48 85

MSC_two_6792asdad				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 134

No_MVS				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 134

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 71

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 71

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	84.81 102

DVP-MVS		81.15 41	83.12 36	75.24 107	86.16 52	60.78 149	83.77 44	80.58 133	72.48 37	85.83 46	90.41 62	78.57 19	85.69 47	75.86 42	94.39 44	79.24 239
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	60.57 152	83.77 44	85.49 32					85.90 40	75.86 42	94.39 44	83.25 153

IU-MVS					86.12 54	60.90 147		80.38 137	45.49 295	81.31 102				75.64 44	94.39 44	84.65 104

SED-MVS		81.78 35	83.48 28	76.67 85	86.12 54	61.06 143	83.62 46	84.72 52	72.61 35	87.38 28	89.70 83	77.48 26	85.89 42	75.29 45	94.39 44	83.08 159

test_241102_TWO								84.80 48	72.61 35	84.93 59	89.70 83	77.73 24	85.89 42	75.29 45	94.22 55	83.25 153

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	66.82 113	78.37 181	74.80 47	90.76 118	82.40 179

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 47	93.04 70	81.14 200

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 49	85.79 206	82.35 180

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 50	94.02 58	82.62 175
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
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 51	96.15 3	92.88 8

test_fmvsmconf0.01_n		73.91 112	73.64 123	74.71 108	69.79 297	66.25 97	75.90 133	79.90 145	46.03 289	76.48 174	85.02 185	67.96 104	73.97 239	74.47 52	87.22 186	83.90 131

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 53	96.11 4	85.81 77
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 54	95.73 8	80.98 206
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	75.58 209	61.53 136	78.29 99	77.18 193	63.15 114	69.97 268	87.20 131	57.54 213	87.05 10	74.05 55	88.96 155	84.89 95

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 56	93.57 65	84.35 121
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 57	91.61 90	82.26 184

9.14			80.22 57		80.68 134		80.35 77	87.69 11	59.90 135	83.00 80	88.20 120	74.57 50	81.75 117	73.75 58	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	59.89 136	80.91 109	90.53 56	72.19 64	88.56 2	73.67 59	94.52 38	85.92 76
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	76.12 200	50.51 249	89.19 11	90.88 45	71.45 72	77.78 195	73.38 60	90.60 120	90.90 17

test_fmvsmconf0.1_n		73.26 125	72.82 142	74.56 110	69.10 303	66.18 99	74.65 152	79.34 155	45.58 292	75.54 186	83.91 201	67.19 108	73.88 242	73.26 61	86.86 192	83.63 139

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	59.80 188	87.58 6	73.06 62	91.34 95	89.01 34

test_fmvsmconf_n		72.91 137	72.40 149	74.46 111	68.62 307	66.12 100	74.21 159	78.80 165	45.64 291	74.62 201	83.25 217	66.80 116	73.86 243	72.97 63	86.66 198	83.39 148

v7n		79.37 60	80.41 56	76.28 92	78.67 163	55.81 185	79.22 90	82.51 95	70.72 49	87.54 25	92.44 15	68.00 103	81.34 121	72.84 64	91.72 86	91.69 11

ZD-MVS					83.91 90	69.36 73		81.09 121	58.91 146	82.73 87	89.11 97	75.77 38	86.63 14	72.73 65	92.93 72

UA-Net		81.56 37	82.28 44	79.40 52	88.91 29	69.16 76	84.67 36	80.01 144	75.34 19	79.80 119	94.91 2	69.79 88	80.25 146	72.63 66	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 67	92.95 71	81.14 200
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 68	93.37 66	83.48 144

train_agg		76.38 84	76.55 88	75.86 98	85.47 67	69.32 74	76.42 123	78.69 168	54.00 209	76.97 153	86.74 146	66.60 118	81.10 127	72.50 69	91.56 91	77.15 267

BP-MVS1		71.60 155	70.06 177	76.20 94	74.07 235	55.22 190	74.29 157	73.44 222	57.29 161	73.87 216	84.65 188	32.57 354	83.49 87	72.43 70	87.94 170	89.89 23

MVSFormer		69.93 176	69.03 189	72.63 156	74.93 215	59.19 160	83.98 40	75.72 205	52.27 225	63.53 332	76.74 303	43.19 296	80.56 139	72.28 71	78.67 296	78.14 254

test_djsdf		78.88 63	78.27 73	80.70 39	81.42 126	71.24 56	83.98 40	75.72 205	52.27 225	87.37 30	92.25 17	68.04 102	80.56 139	72.28 71	91.15 100	90.32 21

test9_res														72.12 73	91.37 94	77.40 262

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	60.83 177	86.15 29	71.09 74	90.94 107	84.82 100

plane_prior5								85.49 32					86.15 29	71.09 74	90.94 107	84.82 100

v10		75.69 89	76.20 91	74.16 118	74.44 228	48.69 240	75.84 135	82.93 86	59.02 144	85.92 44	89.17 95	58.56 198	82.74 101	70.73 76	89.14 151	91.05 14

agg_prior2														70.70 77	90.93 109	78.55 248

MVSMamba_PlusPlus		76.88 80	78.21 74	72.88 148	80.83 132	48.71 239	83.28 52	82.79 87	72.78 31	79.17 126	91.94 22	56.47 224	83.95 78	70.51 78	86.15 201	85.99 74

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	75.55 185	86.25 165	67.42 106	85.42 52	70.10 79	90.88 113	81.81 191

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	57.15 215	82.60 103	70.08 80	92.80 73	89.25 28

test_prior2							75.57 136		58.92 145	76.53 172	86.78 144	67.83 105		69.81 81	92.76 75

EC-MVSNet		77.08 79	77.39 81	76.14 95	76.86 191	56.87 178	80.32 78	87.52 12	63.45 108	74.66 200	84.52 193	69.87 87	84.94 64	69.76 82	89.59 139	86.60 67

test_fmvsmvis_n_1920		72.36 146	72.49 146	71.96 166	71.29 271	64.06 117	72.79 169	81.82 104	40.23 341	81.25 104	81.04 243	70.62 80	68.69 289	69.74 83	83.60 241	83.14 157

v8		75.07 100	75.64 97	73.35 131	73.42 243	47.46 260	75.20 138	81.45 111	60.05 134	85.64 48	89.26 90	58.08 206	81.80 116	69.71 84	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	60.39 132	74.15 208	83.30 215	69.65 89	82.07 112	69.27 85	86.75 196	87.36 56

v1240		73.06 130	73.14 133	72.84 149	74.74 221	47.27 264	71.88 186	81.11 119	51.80 231	82.28 90	84.21 197	56.22 226	82.34 107	68.82 86	87.17 189	88.91 38

v1192		73.40 121	73.42 125	73.32 133	74.65 225	48.67 241	72.21 174	81.73 106	52.76 221	81.85 93	84.56 191	57.12 216	82.24 110	68.58 87	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	76.50 198	51.98 230	87.40 27	91.86 26	76.09 36	78.53 173	68.58 87	90.20 124	86.69 66

v1921920		72.96 136	72.98 139	72.89 147	74.67 222	47.58 258	71.92 184	80.69 128	51.70 233	81.69 99	83.89 202	56.58 222	82.25 109	68.34 89	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	76.12 200	51.33 240	87.19 31	91.51 33	73.79 57	78.44 177	68.27 90	90.13 128	86.49 68

v1144		73.29 124	73.39 126	73.01 139	74.12 234	48.11 247	72.01 179	81.08 122	53.83 213	81.77 95	84.68 187	58.07 207	81.91 114	68.10 91	86.86 192	88.99 36

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 123	84.80 35	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 92	97.05 2	96.93 1

PHI-MVS		74.92 103	74.36 110	76.61 86	76.40 196	62.32 129	80.38 75	83.15 82	54.16 206	73.23 225	80.75 247	62.19 159	83.86 80	68.02 93	90.92 110	83.65 138

CDPH-MVS		77.33 77	77.06 85	78.14 71	84.21 87	63.98 118	76.07 131	83.45 78	54.20 204	77.68 147	87.18 132	69.98 85	85.37 53	68.01 94	92.72 76	85.08 92

v144192		72.99 134	73.06 137	72.77 150	74.58 226	47.48 259	71.90 185	80.44 136	51.57 234	81.46 101	84.11 199	58.04 208	82.12 111	67.98 95	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 96	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	79.59 151	53.48 216	86.29 39	92.43 16	62.39 156	80.25 146	67.90 97	90.61 119	87.77 50

EI-MVSNet-Vis-set		72.78 139	71.87 154	75.54 103	74.77 220	59.02 166	72.24 173	71.56 240	63.92 100	78.59 132	71.59 344	66.22 123	78.60 172	67.58 98	80.32 275	89.00 35

ACMH	63.62 14	77.50 76	80.11 58	69.68 197	79.61 143	56.28 180	78.81 93	83.62 76	63.41 110	87.14 33	90.23 74	76.11 35	73.32 244	67.58 98	94.44 42	79.44 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_l_conf0.5_n		67.48 213	66.88 225	69.28 204	67.41 322	62.04 130	70.69 205	69.85 264	39.46 344	69.59 273	81.09 242	58.15 202	68.73 288	67.51 100	78.16 304	77.07 271

SixPastTwentyTwo		75.77 87	76.34 89	74.06 120	81.69 124	54.84 192	76.47 120	75.49 207	64.10 99	87.73 21	92.24 18	50.45 255	81.30 123	67.41 101	91.46 93	86.04 73

casdiffmvs_mvg		75.26 96	76.18 92	72.52 157	72.87 258	49.47 234	72.94 168	84.71 54	59.49 138	80.90 110	88.81 106	70.07 84	79.71 154	67.40 102	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		66.34 230	65.27 238	69.57 199	68.20 312	59.14 165	71.66 188	56.48 342	40.92 334	67.78 296	79.46 268	61.23 170	66.90 308	67.39 103	74.32 337	82.66 174

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	56.66 169	80.27 116	85.31 182	68.56 95	87.03 12	67.39 103	91.26 96	83.50 141

BP-MVS														67.38 105

HQP-MVS		75.24 97	75.01 102	75.94 96	82.37 113	58.80 168	77.32 111	84.12 69	59.08 140	71.58 245	85.96 175	58.09 204	85.30 55	67.38 105	89.16 148	83.73 137

fmvsm_s_conf0.1_n		66.60 224	65.54 235	69.77 196	68.99 304	59.15 163	72.12 175	56.74 341	40.72 338	68.25 294	80.14 259	61.18 173	66.92 307	67.34 107	74.40 334	83.23 155

EI-MVSNet-UG-set		72.63 142	71.68 158	75.47 104	74.67 222	58.64 171	72.02 178	71.50 241	63.53 106	78.58 134	71.39 348	65.98 124	78.53 173	67.30 108	80.18 278	89.23 29

v2v482		72.55 145	72.58 145	72.43 159	72.92 257	46.72 267	71.41 192	79.13 158	55.27 182	81.17 105	85.25 183	55.41 228	81.13 126	67.25 109	85.46 209	89.43 26

fmvsm_s_conf0.1_n_a		67.37 217	66.36 227	70.37 183	70.86 273	61.17 141	74.00 161	57.18 336	40.77 336	68.83 288	80.88 245	63.11 148	67.61 300	66.94 110	74.72 329	82.33 183

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	66.91 111	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		67.00 222	65.95 234	70.17 188	69.72 298	61.16 142	73.34 165	56.83 339	40.96 333	68.36 291	80.08 260	62.84 149	67.57 301	66.90 112	74.50 333	81.78 192

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	66.15 113	91.24 97	87.61 53

fmvsm_l_conf0.5_n_a		66.66 223	65.97 233	68.72 220	67.09 325	61.38 138	70.03 212	69.15 270	38.59 352	68.41 290	80.36 253	56.56 223	68.32 293	66.10 114	77.45 309	76.46 273

MVS_Test		69.84 177	70.71 172	67.24 237	67.49 321	43.25 298	69.87 215	81.22 118	52.69 222	71.57 248	86.68 149	62.09 160	74.51 232	66.05 115	78.74 294	83.96 129

WR-MVS_H		80.22 54	82.17 45	74.39 115	89.46 15	42.69 303	78.24 101	82.24 97	78.21 13	89.57 10	92.10 19	68.05 101	85.59 50	66.04 116	95.62 10	94.88 5

V42		71.06 160	70.83 170	71.72 168	67.25 323	47.14 265	65.94 271	80.35 139	51.35 239	83.40 78	83.23 218	59.25 192	78.80 168	65.91 117	80.81 270	89.23 29

test_fmvsm_n_1920		69.63 179	68.45 198	73.16 135	70.56 280	65.86 102	70.26 210	78.35 174	37.69 358	74.29 206	78.89 281	61.10 174	68.10 295	65.87 118	79.07 291	85.53 84

K. test v3		73.67 115	73.61 124	73.87 123	79.78 141	55.62 189	74.69 150	62.04 317	66.16 75	84.76 63	93.23 6	49.47 260	80.97 133	65.66 119	86.67 197	85.02 94

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	56.73 168	74.88 195	85.32 181	65.54 129	87.79 3	65.61 120	91.14 101	83.35 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_0402		78.17 72	79.48 63	74.24 117	83.50 94	59.15 163	72.52 170	74.60 215	75.34 19	88.69 17	91.81 27	75.06 45	82.37 106	65.10 121	88.68 158	81.20 198

diffmvs		67.42 216	67.50 214	67.20 238	62.26 361	45.21 281	64.87 286	77.04 194	48.21 271	71.74 242	79.70 265	58.40 199	71.17 271	64.99 122	80.27 276	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
PMVS	70.70 6	81.70 36	83.15 35	77.36 79	90.35 6	82.82 3	82.15 59	79.22 157	74.08 24	87.16 32	91.97 21	84.80 2	76.97 202	64.98 123	93.61 63	72.28 315
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GDP-MVS		70.84 164	69.24 185	75.62 101	76.44 195	55.65 187	74.62 153	82.78 89	49.63 259	72.10 240	83.79 204	31.86 362	82.84 99	64.93 124	87.01 191	88.39 47

ACMH+	66.64 10	81.20 40	82.48 43	77.35 80	81.16 131	62.39 128	80.51 72	87.80 8	73.02 30	87.57 24	91.08 40	80.28 9	82.44 104	64.82 125	96.10 5	87.21 58

MCST-MVS		73.42 120	73.34 130	73.63 127	81.28 129	59.17 162	74.80 146	83.13 83	45.50 293	72.84 228	83.78 205	65.15 135	80.99 131	64.54 126	89.09 154	80.73 214

ambc				70.10 191	77.74 174	50.21 224	74.28 158	77.93 184		79.26 124	88.29 119	54.11 236	79.77 153	64.43 127	91.10 104	80.30 224

lessismore_v0				72.75 151	79.60 144	56.83 179		57.37 332		83.80 74	89.01 101	47.45 275	78.74 170	64.39 128	86.49 200	82.69 173

tt0805		76.12 86	78.43 72	69.20 205	81.32 128	41.37 311	76.72 119	77.64 186	63.78 103	82.06 91	87.88 126	79.78 11	79.05 163	64.33 129	92.40 79	87.17 61

baseline		73.10 127	73.96 117	70.51 181	71.46 269	46.39 272	72.08 176	84.40 62	55.95 176	76.62 166	86.46 159	67.20 107	78.03 190	64.22 130	87.27 185	87.11 62

EGC-MVSNET		64.77 243	61.17 277	75.60 102	86.90 43	74.47 34	84.04 39	68.62 274	0.60 422	1.13 424	91.61 32	65.32 133	74.15 238	64.01 131	88.28 162	78.17 253

CANet		73.00 133	71.84 155	76.48 89	75.82 206	61.28 139	74.81 144	80.37 138	63.17 112	62.43 338	80.50 251	61.10 174	85.16 63	64.00 132	84.34 231	83.01 162

balanced_conf03		73.59 117	74.06 114	72.17 165	77.48 179	47.72 256	81.43 65	82.20 98	54.38 197	79.19 125	87.68 128	54.41 233	83.57 84	63.98 133	85.78 207	85.22 87

tttt0517		69.46 183	67.79 211	74.46 111	75.34 210	52.72 207	75.05 140	63.27 310	54.69 191	78.87 130	84.37 195	26.63 389	81.15 125	63.95 134	87.93 171	89.51 25

casdiffmvs		73.06 130	73.84 118	70.72 177	71.32 270	46.71 268	70.93 201	84.26 65	55.62 179	77.46 149	87.10 133	67.09 109	77.81 193	63.95 134	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	79.06 159	62.85 115	73.33 223	88.41 115	62.54 154	79.59 157	63.94 136	82.92 245	82.94 163
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	39.21 330	77.15 115	81.28 115	79.74 6	90.87 5	92.73 12	75.03 46	84.93 65	63.83 137	95.19 19	95.07 3

DTE-MVSNet		80.35 52	82.89 39	72.74 152	89.84 8	37.34 350	77.16 114	81.81 105	80.45 4	90.92 4	92.95 8	74.57 50	86.12 31	63.65 138	94.68 35	94.76 6

h-mvs33		73.08 128	71.61 160	77.48 77	83.89 92	72.89 48	70.47 207	71.12 254	54.28 200	77.89 141	83.41 208	49.04 264	80.98 132	63.62 139	90.77 117	78.58 247

hse-mvs2		72.32 147	70.66 173	77.31 81	83.10 103	71.77 51	69.19 225	71.45 243	54.28 200	77.89 141	78.26 287	49.04 264	79.23 160	63.62 139	89.13 152	80.92 207

c3_l		69.82 178	69.89 179	69.61 198	66.24 334	43.48 294	68.12 243	79.61 150	51.43 236	77.72 145	80.18 258	54.61 232	78.15 189	63.62 139	87.50 175	87.20 59

CP-MVSNet		79.48 58	81.65 49	72.98 141	89.66 13	39.06 332	76.76 118	80.46 135	78.91 9	90.32 8	91.70 29	68.49 96	84.89 66	63.40 142	95.12 22	95.01 4

GeoE		73.14 126	73.77 121	71.26 174	78.09 168	52.64 208	74.32 155	79.56 152	56.32 172	76.35 178	83.36 213	70.76 79	77.96 191	63.32 143	81.84 256	83.18 156

PC_three_1452									46.98 283	81.83 94	86.28 162	66.55 121	84.47 74	63.31 144	90.78 115	83.49 142

PEN-MVS		80.46 50	82.91 38	73.11 137	89.83 9	39.02 333	77.06 117	82.61 93	80.04 5	90.60 7	92.85 10	74.93 47	85.21 60	63.15 145	95.15 21	95.09 2

MSLP-MVS++		74.48 109	75.78 95	70.59 179	84.66 79	62.40 127	78.65 94	84.24 66	60.55 131	77.71 146	81.98 231	63.12 147	77.64 197	62.95 146	88.14 164	71.73 320

EI-MVSNet		69.61 181	69.01 190	71.41 173	73.94 237	49.90 229	71.31 195	71.32 246	58.22 149	75.40 189	70.44 351	58.16 201	75.85 211	62.51 147	79.81 284	88.48 44

IterMVS-LS		73.01 132	73.12 135	72.66 154	73.79 239	49.90 229	71.63 189	78.44 173	58.22 149	80.51 113	86.63 153	58.15 202	79.62 155	62.51 147	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	62.45 149	92.40 79	78.92 244

AUN-MVS		70.22 171	67.88 209	77.22 82	82.96 107	71.61 52	69.08 226	71.39 244	49.17 265	71.70 243	78.07 292	37.62 333	79.21 161	61.81 150	89.15 150	80.82 210

MVS_111021_LR		72.10 150	71.82 156	72.95 142	79.53 145	73.90 40	70.45 208	66.64 282	56.87 164	76.81 161	81.76 235	68.78 93	71.76 265	61.81 150	83.74 237	73.18 303

SPE-MVS-test		74.89 106	74.23 112	76.86 83	77.01 184	62.94 126	78.98 92	84.61 59	58.62 147	70.17 265	80.80 246	66.74 117	81.96 113	61.74 152	89.40 146	85.69 82

OPU-MVS				78.65 64	83.44 97	66.85 93	83.62 46				86.12 170	66.82 113	86.01 34	61.72 153	89.79 136	83.08 159

dcpmvs_2		71.02 162	72.65 144	66.16 249	76.06 204	50.49 220	71.97 180	79.36 154	50.34 250	82.81 85	83.63 206	64.38 141	67.27 304	61.54 154	83.71 239	80.71 216

MVS_111021_HR		72.98 135	72.97 140	72.99 140	80.82 133	65.47 104	68.81 230	72.77 229	57.67 156	75.76 182	82.38 227	71.01 77	77.17 200	61.38 155	86.15 201	76.32 275

nrg030		74.87 107	75.99 94	71.52 171	74.90 217	49.88 233	74.10 160	82.58 94	54.55 196	83.50 77	89.21 92	71.51 70	75.74 215	61.24 156	92.34 81	88.94 37

IterMVS-SCA-FT		67.68 211	66.07 231	72.49 158	73.34 245	58.20 173	63.80 297	65.55 291	48.10 272	76.91 156	82.64 224	45.20 283	78.84 167	61.20 157	77.89 307	80.44 222

miper_ehance_all_eth		68.36 200	68.16 205	68.98 212	65.14 346	43.34 296	67.07 258	78.92 162	49.11 266	76.21 179	77.72 294	53.48 238	77.92 192	61.16 158	84.59 227	85.68 83

ITE_SJBPF				80.35 42	76.94 186	73.60 42		80.48 134	66.87 68	83.64 76	86.18 166	70.25 83	79.90 152	61.12 159	88.95 156	87.56 54

DIV-MVS_self_test		68.27 204	68.26 201	68.29 225	64.98 347	43.67 292	65.89 272	74.67 213	50.04 256	76.86 159	82.43 225	48.74 268	75.38 217	60.94 160	89.81 134	85.81 77

cl____		68.26 205	68.26 201	68.29 225	64.98 347	43.67 292	65.89 272	74.67 213	50.04 256	76.86 159	82.42 226	48.74 268	75.38 217	60.92 161	89.81 134	85.80 81

3Dnovator	65.95 11	71.50 157	71.22 166	72.34 161	73.16 248	63.09 124	78.37 98	78.32 175	57.67 156	72.22 238	84.61 190	54.77 229	78.47 175	60.82 162	81.07 266	75.45 281

cl22		67.14 218	66.51 226	69.03 211	63.20 356	43.46 295	66.88 263	76.25 199	49.22 264	74.48 203	77.88 293	45.49 282	77.40 199	60.64 163	84.59 227	86.24 69

testf1		75.66 90	76.57 86	72.95 142	67.07 327	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 267	60.46 164	91.13 102	79.56 233

APD_test2		75.66 90	76.57 86	72.95 142	67.07 327	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 267	60.46 164	91.13 102	79.56 233

Effi-MVS+		72.10 150	72.28 151	71.58 169	74.21 232	50.33 222	74.72 149	82.73 90	62.62 116	70.77 257	76.83 302	69.96 86	80.97 133	60.20 166	78.43 299	83.45 147

eth_miper_zixun_eth		69.42 184	68.73 196	71.50 172	67.99 315	46.42 270	67.58 248	78.81 163	50.72 247	78.13 139	80.34 254	50.15 257	80.34 144	60.18 167	84.65 225	87.74 51

RRT-MVS		70.33 170	70.73 171	69.14 208	71.93 265	45.24 280	75.10 139	75.08 212	60.85 129	78.62 131	87.36 130	49.54 259	78.64 171	60.16 168	77.90 306	83.55 140

TSAR-MVS + GP.		73.08 128	71.60 161	77.54 76	78.99 159	70.73 61	74.96 141	69.38 267	60.73 130	74.39 205	78.44 285	57.72 211	82.78 100	60.16 168	89.60 138	79.11 241

DPM-MVS		69.98 175	69.22 187	72.26 163	82.69 111	58.82 167	70.53 206	81.23 117	47.79 277	64.16 322	80.21 255	51.32 251	83.12 94	60.14 170	84.95 222	74.83 287

114514_t		73.40 121	73.33 131	73.64 126	84.15 89	57.11 176	78.20 102	80.02 143	43.76 310	72.55 232	86.07 173	64.00 143	83.35 91	60.14 170	91.03 106	80.45 221

TAPA-MVS	65.27 12	75.16 98	74.29 111	77.77 75	74.86 218	68.08 81	77.89 105	84.04 72	55.15 184	76.19 180	83.39 209	66.91 111	80.11 150	60.04 172	90.14 127	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MG-MVS		70.47 169	71.34 165	67.85 230	79.26 149	40.42 324	74.67 151	75.15 211	58.41 148	68.74 289	88.14 124	56.08 227	83.69 82	59.90 173	81.71 261	79.43 238

CSCG		74.12 111	74.39 108	73.33 132	79.35 147	61.66 135	77.45 110	81.98 102	62.47 119	79.06 128	80.19 257	61.83 161	78.79 169	59.83 174	87.35 179	79.54 236

APD_test1		75.04 101	75.38 101	74.02 121	69.89 293	70.15 66	76.46 121	79.71 147	65.50 79	82.99 81	88.60 112	66.94 110	72.35 257	59.77 175	88.54 159	79.56 233

FA-MVS(test-final)		71.27 158	71.06 167	71.92 167	73.96 236	52.32 210	76.45 122	76.12 200	59.07 143	74.04 213	86.18 166	52.18 244	79.43 159	59.75 176	81.76 257	84.03 128

Gipuma		69.55 182	72.83 141	59.70 308	63.63 355	53.97 199	80.08 82	75.93 203	64.24 98	73.49 220	88.93 104	57.89 210	62.46 334	59.75 176	91.55 92	62.67 382
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
thisisatest0530		67.05 221	65.16 241	72.73 153	73.10 252	50.55 219	71.26 197	63.91 305	50.22 253	74.46 204	80.75 247	26.81 388	80.25 146	59.43 178	86.50 199	87.37 55

v148		69.38 186	69.39 182	69.36 201	69.14 302	44.56 285	68.83 229	72.70 230	54.79 189	78.59 132	84.12 198	54.69 230	76.74 208	59.40 179	82.20 250	86.79 64

旧先验2							71.17 198		45.11 300	78.54 135			61.28 340	59.19 180

LF4IMVS		67.50 212	67.31 217	68.08 228	58.86 383	61.93 131	71.43 191	75.90 204	44.67 304	72.42 234	80.20 256	57.16 214	70.44 277	58.99 181	86.12 203	71.88 318

mmtdpeth		68.76 194	70.55 174	63.40 273	67.06 329	56.26 181	68.73 235	71.22 252	55.47 181	70.09 266	88.64 111	65.29 134	56.89 357	58.94 182	89.50 141	77.04 272

ETV-MVS		72.72 140	72.16 153	74.38 116	76.90 189	55.95 182	73.34 165	84.67 55	62.04 120	72.19 239	70.81 349	65.90 126	85.24 59	58.64 183	84.96 221	81.95 189

DELS-MVS		68.83 192	68.31 199	70.38 182	70.55 282	48.31 243	63.78 298	82.13 99	54.00 209	68.96 280	75.17 315	58.95 195	80.06 151	58.55 184	82.74 247	82.76 168
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
PAPM_NR		73.91 112	74.16 113	73.16 135	81.90 121	53.50 203	81.28 66	81.40 112	66.17 74	73.30 224	83.31 214	59.96 184	83.10 95	58.45 185	81.66 262	82.87 165

Anonymous20231211		75.54 92	77.19 83	70.59 179	77.67 176	45.70 278	74.73 148	80.19 140	68.80 58	82.95 82	92.91 9	66.26 122	76.76 207	58.41 186	92.77 74	89.30 27

miper_enhance_ethall		65.86 232	65.05 248	68.28 227	61.62 365	42.62 304	64.74 287	77.97 182	42.52 320	73.42 222	72.79 337	49.66 258	77.68 196	58.12 187	84.59 227	84.54 112

IS-MVSNet		75.10 99	75.42 100	74.15 119	79.23 150	48.05 249	79.43 86	78.04 181	70.09 54	79.17 126	88.02 125	53.04 240	83.60 83	58.05 188	93.76 62	90.79 18

FC-MVSNet-test		73.32 123	74.78 104	68.93 215	79.21 151	36.57 352	71.82 187	79.54 153	57.63 159	82.57 88	90.38 67	59.38 191	78.99 165	57.91 189	94.56 37	91.23 13

MGCFI-Net		71.70 154	73.10 136	67.49 234	73.23 247	43.08 299	72.06 177	82.43 96	54.58 194	75.97 181	82.00 229	72.42 63	75.22 221	57.84 190	87.34 180	84.18 125

RPSCF		75.76 88	74.37 109	79.93 44	74.81 219	77.53 18	77.53 109	79.30 156	59.44 139	78.88 129	89.80 82	71.26 74	73.09 246	57.45 191	80.89 267	89.17 31

alignmvs		70.54 168	71.00 168	69.15 207	73.50 241	48.04 250	69.85 216	79.62 148	53.94 212	76.54 171	82.00 229	59.00 194	74.68 230	57.32 192	87.21 187	84.72 103

sasdasda		72.29 148	73.38 127	69.04 209	74.23 229	47.37 261	73.93 162	83.18 80	54.36 198	76.61 167	81.64 237	72.03 65	75.34 219	57.12 193	87.28 183	84.40 118

canonicalmvs		72.29 148	73.38 127	69.04 209	74.23 229	47.37 261	73.93 162	83.18 80	54.36 198	76.61 167	81.64 237	72.03 65	75.34 219	57.12 193	87.28 183	84.40 118

UniMVSNet (Re)		75.00 102	75.48 99	73.56 129	83.14 99	47.92 251	70.41 209	81.04 123	63.67 104	79.54 121	86.37 161	62.83 150	81.82 115	57.10 195	95.25 15	90.94 16

原ACMM1				73.90 122	85.90 60	65.15 110		81.67 107	50.97 244	74.25 207	86.16 168	61.60 164	83.54 85	56.75 196	91.08 105	73.00 305

FIs		72.56 143	73.80 119	68.84 218	78.74 162	37.74 346	71.02 199	79.83 146	56.12 173	80.88 111	89.45 87	58.18 200	78.28 184	56.63 197	93.36 67	90.51 20

xiu_mvs_v1_base_debu		67.87 207	67.07 220	70.26 185	79.13 154	61.90 132	67.34 252	71.25 249	47.98 273	67.70 297	74.19 327	61.31 167	72.62 251	56.51 198	78.26 301	76.27 276

xiu_mvs_v1_base		67.87 207	67.07 220	70.26 185	79.13 154	61.90 132	67.34 252	71.25 249	47.98 273	67.70 297	74.19 327	61.31 167	72.62 251	56.51 198	78.26 301	76.27 276

xiu_mvs_v1_base_debi		67.87 207	67.07 220	70.26 185	79.13 154	61.90 132	67.34 252	71.25 249	47.98 273	67.70 297	74.19 327	61.31 167	72.62 251	56.51 198	78.26 301	76.27 276

Effi-MVS+-dtu		75.43 94	72.28 151	84.91 3	77.05 181	83.58 2	78.47 97	77.70 185	57.68 155	74.89 194	78.13 291	64.80 138	84.26 77	56.46 201	85.32 214	86.88 63

MVSTER		63.29 260	61.60 274	68.36 223	59.77 378	46.21 273	60.62 320	71.32 246	41.83 324	75.40 189	79.12 277	30.25 377	75.85 211	56.30 202	79.81 284	83.03 161

UniMVSNet_NR-MVSNet		74.90 105	75.65 96	72.64 155	83.04 104	45.79 275	69.26 223	78.81 163	66.66 71	81.74 97	86.88 141	63.26 146	81.07 129	56.21 203	94.98 24	91.05 14

DU-MVS		74.91 104	75.57 98	72.93 145	83.50 94	45.79 275	69.47 219	80.14 142	65.22 86	81.74 97	87.08 134	61.82 162	81.07 129	56.21 203	94.98 24	91.93 9

RPMNet		65.77 233	65.08 247	67.84 231	66.37 331	48.24 245	70.93 201	86.27 20	54.66 192	61.35 342	86.77 145	33.29 348	85.67 49	55.93 205	70.17 367	69.62 342

CLD-MVS		72.88 138	72.36 150	74.43 114	77.03 182	54.30 196	68.77 233	83.43 79	52.12 227	76.79 162	74.44 322	69.54 90	83.91 79	55.88 206	93.25 69	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
mvs5depth		66.35 229	67.98 206	61.47 293	62.43 359	51.05 215	69.38 221	69.24 269	56.74 167	73.62 217	89.06 100	46.96 277	58.63 350	55.87 207	88.49 160	74.73 288

miper_lstm_enhance		61.97 273	61.63 273	62.98 277	60.04 372	45.74 277	47.53 389	70.95 255	44.04 306	73.06 226	78.84 282	39.72 318	60.33 342	55.82 208	84.64 226	82.88 164

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	55.60 209	90.90 111	85.81 77

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	55.60 209	90.90 111	85.81 77

EU-MVSNet		60.82 283	60.80 282	60.86 301	68.37 309	41.16 312	72.27 172	68.27 276	26.96 402	69.08 277	75.71 308	32.09 358	67.44 302	55.59 211	78.90 293	73.97 296

TranMVSNet+NR-MVSNet		76.13 85	77.66 79	71.56 170	84.61 81	42.57 305	70.98 200	78.29 177	68.67 61	83.04 79	89.26 90	72.99 61	80.75 138	55.58 212	95.47 11	91.35 12

OpenMVS	62.51 15	68.76 194	68.75 194	68.78 219	70.56 280	53.91 200	78.29 99	77.35 189	48.85 268	70.22 263	83.52 207	52.65 242	76.93 203	55.31 213	81.99 252	75.49 280

QAPM		69.18 188	69.26 184	68.94 214	71.61 267	52.58 209	80.37 76	78.79 166	49.63 259	73.51 219	85.14 184	53.66 237	79.12 162	55.11 214	75.54 322	75.11 286

MVStest1		55.38 319	54.97 326	56.58 328	43.72 421	40.07 326	59.13 329	47.09 388	34.83 373	76.53 172	84.65 188	13.55 425	53.30 367	55.04 215	80.23 277	76.38 274

NR-MVSNet		73.62 116	74.05 115	72.33 162	83.50 94	43.71 291	65.65 277	77.32 190	64.32 97	75.59 184	87.08 134	62.45 155	81.34 121	54.90 216	95.63 9	91.93 9

EG-PatchMatch MVS		70.70 166	70.88 169	70.16 189	82.64 112	58.80 168	71.48 190	73.64 220	54.98 185	76.55 170	81.77 234	61.10 174	78.94 166	54.87 217	80.84 269	72.74 310

SSC-MVS		61.79 276	66.08 230	48.89 369	76.91 187	10.00 426	53.56 369	47.37 387	68.20 63	76.56 169	89.21 92	54.13 235	57.59 355	54.75 218	74.07 338	79.08 242

jason		64.47 248	62.84 266	69.34 203	76.91 187	59.20 159	67.15 257	65.67 288	35.29 371	65.16 315	76.74 303	44.67 287	70.68 273	54.74 219	79.28 290	78.14 254
jason: jason.
Baseline_NR-MVSNet		70.62 167	73.19 132	62.92 280	76.97 185	34.44 368	68.84 228	70.88 257	60.25 133	79.50 122	90.53 56	61.82 162	69.11 286	54.67 220	95.27 14	85.22 87

UniMVSNet_ETH3D		76.74 82	79.02 65	69.92 195	89.27 20	43.81 290	74.47 154	71.70 237	72.33 40	85.50 53	93.65 4	77.98 23	76.88 205	54.60 221	91.64 88	89.08 32

无先验							74.82 143	70.94 256	47.75 278				76.85 206	54.47 222		72.09 317

testdata				64.13 262	85.87 62	63.34 122		61.80 318	47.83 276	76.42 177	86.60 155	48.83 267	62.31 336	54.46 223	81.26 265	66.74 362

SDMVSNet		66.36 228	67.85 210	61.88 288	73.04 255	46.14 274	58.54 336	71.36 245	51.42 237	68.93 283	82.72 222	65.62 128	62.22 337	54.41 224	84.67 223	77.28 263

PVSNet_Blended_VisFu		70.04 173	68.88 191	73.53 130	82.71 110	63.62 120	74.81 144	81.95 103	48.53 270	67.16 304	79.18 276	51.42 250	78.38 180	54.39 225	79.72 287	78.60 246

EPNet		69.10 189	67.32 216	74.46 111	68.33 311	61.27 140	77.56 107	63.57 307	60.95 127	56.62 372	82.75 221	51.53 249	81.24 124	54.36 226	90.20 124	80.88 209
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EIA-MVS		68.59 198	67.16 219	72.90 146	75.18 213	55.64 188	69.39 220	81.29 114	52.44 224	64.53 318	70.69 350	60.33 181	82.30 108	54.27 227	76.31 316	80.75 213

patch_mono-2		62.73 269	64.08 252	58.68 316	70.36 286	55.87 184	60.84 318	64.11 304	41.23 329	64.04 323	78.22 288	60.00 183	48.80 376	54.17 228	83.71 239	71.37 323

ET-MVSNet_ETH3D		63.32 259	60.69 283	71.20 175	70.15 291	55.66 186	65.02 285	64.32 302	43.28 319	68.99 279	72.05 342	25.46 395	78.19 188	54.16 229	82.80 246	79.74 232

EPP-MVSNet		73.86 114	73.38 127	75.31 105	78.19 166	53.35 205	80.45 73	77.32 190	65.11 89	76.47 175	86.80 142	49.47 260	83.77 81	53.89 230	92.72 76	88.81 41

lupinMVS		63.36 258	61.49 275	68.97 213	74.93 215	59.19 160	65.80 275	64.52 301	34.68 377	63.53 332	74.25 325	43.19 296	70.62 274	53.88 231	78.67 296	77.10 268

CNLPA		73.44 119	73.03 138	74.66 109	78.27 165	75.29 30	75.99 132	78.49 172	65.39 82	75.67 183	83.22 220	61.23 170	66.77 313	53.70 232	85.33 213	81.92 190

CVMVSNet		59.21 296	58.44 299	61.51 291	73.94 237	47.76 255	71.31 195	64.56 300	26.91 404	60.34 350	70.44 351	36.24 339	67.65 298	53.57 233	68.66 376	69.12 347

CANet_DTU		64.04 254	63.83 254	64.66 258	68.39 308	42.97 301	73.45 164	74.50 216	52.05 229	54.78 381	75.44 313	43.99 291	70.42 278	53.49 234	78.41 300	80.59 219

D2MVS		62.58 270	61.05 279	67.20 238	63.85 352	47.92 251	56.29 350	69.58 266	39.32 345	70.07 267	78.19 289	34.93 343	72.68 249	53.44 235	83.74 237	81.00 205

test_fmvs3		56.78 309	55.99 318	59.12 313	53.96 408	48.09 248	58.76 335	66.22 284	27.54 400	76.66 164	68.69 373	25.32 397	51.31 369	53.42 236	73.38 343	77.97 259

Anonymous20240521		63.55 256	66.07 231	55.99 331	66.18 336	44.04 289	68.77 233	68.80 271	46.99 282	72.57 231	85.84 177	39.87 317	50.22 372	53.40 237	92.23 83	73.71 300

PM-MVS		64.49 247	63.61 257	67.14 240	76.68 192	75.15 31	68.49 239	42.85 402	51.17 243	77.85 143	80.51 250	45.76 279	66.31 316	52.83 238	76.35 315	59.96 391

API-MVS		70.97 163	71.51 163	69.37 200	75.20 212	55.94 183	80.99 67	76.84 195	62.48 118	71.24 253	77.51 297	61.51 166	80.96 136	52.04 239	85.76 208	71.22 326

Fast-Effi-MVS+-dtu		70.00 174	68.74 195	73.77 124	73.47 242	64.53 114	71.36 193	78.14 180	55.81 178	68.84 287	74.71 319	65.36 132	75.75 214	52.00 240	79.00 292	81.03 203

mvs_anonymous		65.08 239	65.49 236	63.83 266	63.79 353	37.60 348	66.52 267	69.82 265	43.44 315	73.46 221	86.08 172	58.79 197	71.75 266	51.90 241	75.63 321	82.15 185

Patchmatch-RL test		59.95 291	59.12 292	62.44 283	72.46 260	54.61 195	59.63 327	47.51 386	41.05 332	74.58 202	74.30 324	31.06 371	65.31 322	51.61 242	79.85 283	67.39 355

F-COLMAP		75.29 95	73.99 116	79.18 54	81.73 123	71.90 50	81.86 63	82.98 84	59.86 137	72.27 236	84.00 200	64.56 140	83.07 96	51.48 243	87.19 188	82.56 177

reproduce_monomvs		58.94 298	58.14 302	61.35 295	59.70 379	40.98 315	60.24 324	63.51 308	45.85 290	68.95 281	75.31 314	18.27 416	65.82 318	51.47 244	79.97 280	77.26 266

pmmvs6		71.82 152	73.66 122	66.31 248	75.94 205	42.01 307	66.99 259	72.53 232	63.45 108	76.43 176	92.78 11	72.95 62	69.69 282	51.41 245	90.46 121	87.22 57

IterMVS		63.12 262	62.48 269	65.02 257	66.34 333	52.86 206	63.81 296	62.25 312	46.57 285	71.51 250	80.40 252	44.60 288	66.82 312	51.38 246	75.47 323	75.38 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		70.81 165	71.44 164	68.91 216	79.07 157	46.51 269	67.82 246	70.83 258	61.23 124	74.07 211	88.69 108	59.86 186	75.62 216	51.11 247	90.28 123	84.61 108

KD-MVS_self_test		66.38 227	67.51 213	62.97 278	61.76 363	34.39 369	58.11 341	75.30 208	50.84 246	77.12 152	85.42 180	56.84 220	69.44 283	51.07 248	91.16 99	85.08 92

新几何1				69.99 193	88.37 35	71.34 55		62.08 315	43.85 307	74.99 193	86.11 171	52.85 241	70.57 275	50.99 249	83.23 244	68.05 353

Anonymous20240529		72.56 143	73.79 120	68.86 217	76.89 190	45.21 281	68.80 232	77.25 192	67.16 66	76.89 157	90.44 59	65.95 125	74.19 237	50.75 250	90.00 129	87.18 60

UGNet		70.20 172	69.05 188	73.65 125	76.24 198	63.64 119	75.87 134	72.53 232	61.48 123	60.93 348	86.14 169	52.37 243	77.12 201	50.67 251	85.21 215	80.17 227
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		62.91 264	61.66 271	66.66 246	67.09 325	44.49 286	61.18 316	69.36 268	51.33 240	69.33 276	74.47 321	36.83 336	74.94 226	50.60 252	74.72 329	80.57 220

Fast-Effi-MVS+		68.81 193	68.30 200	70.35 184	74.66 224	48.61 242	66.06 270	78.32 175	50.62 248	71.48 251	75.54 310	68.75 94	79.59 157	50.55 253	78.73 295	82.86 166

WR-MVS		71.20 159	72.48 147	67.36 236	84.98 74	35.70 360	64.43 292	68.66 273	65.05 90	81.49 100	86.43 160	57.57 212	76.48 209	50.36 254	93.32 68	89.90 22

FMVSNet1		71.06 160	72.48 147	66.81 242	77.65 177	40.68 320	71.96 181	73.03 224	61.14 125	79.45 123	90.36 70	60.44 180	75.20 223	50.20 255	88.05 166	84.54 112

ANet_high		67.08 219	69.94 178	58.51 318	57.55 389	27.09 401	58.43 338	76.80 196	63.56 105	82.40 89	91.93 23	59.82 187	64.98 325	50.10 256	88.86 157	83.46 146

TransMVSNet (Re)		69.62 180	71.63 159	63.57 269	76.51 194	35.93 358	65.75 276	71.29 248	61.05 126	75.02 192	89.90 81	65.88 127	70.41 279	49.79 257	89.48 142	84.38 120

DP-MVS Recon		73.57 118	72.69 143	76.23 93	82.85 108	63.39 121	74.32 155	82.96 85	57.75 154	70.35 261	81.98 231	64.34 142	84.41 76	49.69 258	89.95 131	80.89 208

pm-mvs1		68.40 199	69.85 180	64.04 265	73.10 252	39.94 327	64.61 290	70.50 260	55.52 180	73.97 214	89.33 88	63.91 144	68.38 292	49.68 259	88.02 167	83.81 133

test_fmvs2		54.80 323	54.11 333	56.88 327	51.76 412	49.95 228	56.70 348	65.80 287	26.22 405	69.42 274	65.25 387	31.82 363	49.98 373	49.63 260	70.36 365	70.71 332

mvsmamba		68.87 191	67.30 218	73.57 128	76.58 193	53.70 202	84.43 37	74.25 217	45.38 297	76.63 165	84.55 192	35.85 340	85.27 56	49.54 261	78.49 298	81.75 193

1314		59.83 292	58.86 295	62.74 281	65.71 339	44.78 284	68.59 236	72.63 231	33.54 384	61.05 346	67.29 382	43.62 294	71.26 270	49.49 262	67.84 381	72.19 316

WB-MVS		60.04 290	64.19 251	47.59 372	76.09 201	10.22 425	52.44 374	46.74 389	65.17 88	74.07 211	87.48 129	53.48 238	55.28 361	49.36 263	72.84 346	77.28 263

CMPMVS	48.73 20	61.54 279	60.89 280	63.52 270	61.08 367	51.55 212	68.07 244	68.00 277	33.88 379	65.87 309	81.25 240	37.91 330	67.71 297	49.32 264	82.60 248	71.31 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJ		64.27 252	63.73 256	65.90 252	77.82 173	51.42 213	63.33 302	72.33 234	45.09 301	61.60 340	68.04 376	62.39 156	73.95 240	49.07 265	73.87 340	72.34 313

xiu_mvs_v2_base		64.43 249	63.96 253	65.85 253	77.72 175	51.32 214	63.63 299	72.31 235	45.06 302	61.70 339	69.66 362	62.56 152	73.93 241	49.06 266	73.91 339	72.31 314

thisisatest0515		60.48 287	57.86 304	68.34 224	67.25 323	46.42 270	60.58 321	62.14 313	40.82 335	63.58 331	69.12 365	26.28 391	78.34 182	48.83 267	82.13 251	80.26 225

OpenMVS_ROB	54.93 17	63.23 261	63.28 261	63.07 276	69.81 294	45.34 279	68.52 238	67.14 279	43.74 311	70.61 259	79.22 274	47.90 274	72.66 250	48.75 268	73.84 341	71.21 327

PCF-MVS	63.80 13	72.70 141	71.69 157	75.72 99	78.10 167	60.01 156	73.04 167	81.50 109	45.34 298	79.66 120	84.35 196	65.15 135	82.65 102	48.70 269	89.38 147	84.50 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		68.69 197	68.20 204	70.14 190	76.40 196	53.90 201	64.62 289	73.48 221	58.01 151	73.91 215	81.78 233	59.09 193	78.22 185	48.59 270	77.96 305	78.31 250

VDDNet		71.60 155	73.13 134	67.02 241	86.29 48	41.11 313	69.97 213	66.50 283	68.72 60	74.74 196	91.70 29	59.90 185	75.81 213	48.58 271	91.72 86	84.15 127

CR-MVSNet		58.96 297	58.49 298	60.36 305	66.37 331	48.24 245	70.93 201	56.40 344	32.87 385	61.35 342	86.66 150	33.19 349	63.22 333	48.50 272	70.17 367	69.62 342

FE-MVS		68.29 203	66.96 223	72.26 163	74.16 233	54.24 197	77.55 108	73.42 223	57.65 158	72.66 230	84.91 186	32.02 361	81.49 120	48.43 273	81.85 255	81.04 202

testdata2													67.30 303	48.34 274

tfpnnormal		66.48 226	67.93 207	62.16 286	73.40 244	36.65 351	63.45 300	64.99 295	55.97 175	72.82 229	87.80 127	57.06 218	69.10 287	48.31 275	87.54 173	80.72 215

test_vis1_n_1920		52.96 336	53.50 335	51.32 354	59.15 381	44.90 283	56.13 353	64.29 303	30.56 396	59.87 355	60.68 400	40.16 315	47.47 382	48.25 276	62.46 393	61.58 388

PAPR		69.20 187	68.66 197	70.82 176	75.15 214	47.77 254	75.31 137	81.11 119	49.62 261	66.33 307	79.27 273	61.53 165	82.96 97	48.12 277	81.50 264	81.74 194

testing3		58.28 303	58.38 300	58.00 321	77.45 180	26.12 408	60.78 319	43.00 401	56.02 174	70.18 264	75.76 307	13.27 426	67.24 305	48.02 278	80.89 267	80.65 217

FMVSNet2		67.48 213	68.21 203	65.29 254	73.14 249	38.94 334	68.81 230	71.21 253	54.81 186	76.73 163	86.48 158	48.63 270	74.60 231	47.98 279	86.11 204	82.35 180

AdaColmap		74.22 110	74.56 106	73.20 134	81.95 120	60.97 145	79.43 86	80.90 125	65.57 78	72.54 233	81.76 235	70.98 78	85.26 57	47.88 280	90.00 129	73.37 301

cascas		64.59 245	62.77 267	70.05 192	75.27 211	50.02 226	61.79 311	71.61 238	42.46 321	63.68 329	68.89 370	49.33 262	80.35 143	47.82 281	84.05 234	79.78 231

VPA-MVSNet		68.71 196	70.37 175	63.72 267	76.13 200	38.06 344	64.10 294	71.48 242	56.60 171	74.10 210	88.31 118	64.78 139	69.72 281	47.69 282	90.15 126	83.37 150

MSDG		67.47 215	67.48 215	67.46 235	70.70 276	54.69 194	66.90 262	78.17 178	60.88 128	70.41 260	74.76 317	61.22 172	73.18 245	47.38 283	76.87 312	74.49 292

GBi-Net		68.30 201	68.79 192	66.81 242	73.14 249	40.68 320	71.96 181	73.03 224	54.81 186	74.72 197	90.36 70	48.63 270	75.20 223	47.12 284	85.37 210	84.54 112

test1		68.30 201	68.79 192	66.81 242	73.14 249	40.68 320	71.96 181	73.03 224	54.81 186	74.72 197	90.36 70	48.63 270	75.20 223	47.12 284	85.37 210	84.54 112

FMVSNet3		65.00 240	65.16 241	64.52 260	69.47 299	37.56 349	66.63 265	70.38 261	51.55 235	74.72 197	83.27 216	37.89 331	74.44 233	47.12 284	85.37 210	81.57 196

PLC	62.01 16	71.79 153	70.28 176	76.33 91	80.31 138	68.63 79	78.18 103	81.24 116	54.57 195	67.09 305	80.63 249	59.44 189	81.74 118	46.91 287	84.17 232	78.63 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ppachtmachnet_test		60.26 289	59.61 290	62.20 285	67.70 319	44.33 287	58.18 340	60.96 320	40.75 337	65.80 310	72.57 338	41.23 306	63.92 329	46.87 288	82.42 249	78.33 249

test1111		64.62 244	65.19 240	62.93 279	79.01 158	29.91 391	65.45 280	54.41 353	54.09 207	71.47 252	88.48 114	37.02 335	74.29 236	46.83 289	89.94 132	84.58 111

MAR-MVS		67.72 210	66.16 229	72.40 160	74.45 227	64.99 111	74.87 142	77.50 188	48.67 269	65.78 311	68.58 374	57.01 219	77.79 194	46.68 290	81.92 253	74.42 294
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		62.75 267	63.42 259	60.73 302	65.60 340	40.77 318	72.49 171	70.56 259	52.49 223	75.07 191	79.42 270	39.52 321	69.97 280	46.59 291	69.06 373	71.44 322

LFMVS		67.06 220	67.89 208	64.56 259	78.02 169	38.25 341	70.81 204	59.60 324	65.18 87	71.06 255	86.56 156	43.85 292	75.22 221	46.35 292	89.63 137	80.21 226

test2506		61.23 280	60.85 281	62.38 284	78.80 160	27.88 399	67.33 255	37.42 415	54.23 202	67.55 300	88.68 109	17.87 418	74.39 234	46.33 293	89.41 144	84.86 98

Syy-MVS		54.13 326	55.45 322	50.18 359	68.77 305	23.59 412	55.02 359	44.55 395	43.80 308	58.05 363	64.07 389	46.22 278	58.83 348	46.16 294	72.36 350	68.12 351

BH-untuned		69.39 185	69.46 181	69.18 206	77.96 171	56.88 177	68.47 240	77.53 187	56.77 166	77.79 144	79.63 266	60.30 182	80.20 149	46.04 295	80.65 272	70.47 333

MDA-MVSNet-bldmvs		62.34 272	61.73 270	64.16 261	61.64 364	49.90 229	48.11 387	57.24 335	53.31 217	80.95 107	79.39 271	49.00 266	61.55 339	45.92 296	80.05 279	81.03 203

test_fmvs1_n		52.70 339	52.01 346	54.76 336	53.83 409	50.36 221	55.80 355	65.90 286	24.96 409	65.39 312	60.64 401	27.69 386	48.46 378	45.88 297	67.99 379	65.46 367

TinyColmap		67.98 206	69.28 183	64.08 263	67.98 316	46.82 266	70.04 211	75.26 209	53.05 218	77.36 150	86.79 143	59.39 190	72.59 254	45.64 298	88.01 168	72.83 308

test_cas_vis1_n_1920		50.90 351	50.92 355	50.83 357	54.12 407	47.80 253	51.44 378	54.61 351	26.95 403	63.95 325	60.85 399	37.86 332	44.97 392	45.53 299	62.97 392	59.72 392

test_yl		65.11 237	65.09 245	65.18 255	70.59 278	40.86 316	63.22 305	72.79 227	57.91 152	68.88 285	79.07 279	42.85 299	74.89 227	45.50 300	84.97 218	79.81 229

DCV-MVSNet		65.11 237	65.09 245	65.18 255	70.59 278	40.86 316	63.22 305	72.79 227	57.91 152	68.88 285	79.07 279	42.85 299	74.89 227	45.50 300	84.97 218	79.81 229

test_fmvs1		51.51 349	50.86 356	53.48 342	49.72 415	49.35 237	54.11 366	64.96 296	24.64 411	63.66 330	59.61 404	28.33 385	48.45 379	45.38 302	67.30 383	62.66 383

ECVR-MVS		64.82 241	65.22 239	63.60 268	78.80 160	31.14 385	66.97 260	56.47 343	54.23 202	69.94 269	88.68 109	37.23 334	74.81 229	45.28 303	89.41 144	84.86 98

PVSNet_BlendedMVS		65.38 235	64.30 249	68.61 221	69.81 294	49.36 235	65.60 279	78.96 160	45.50 293	59.98 351	78.61 283	51.82 246	78.20 186	44.30 304	84.11 233	78.27 251

PVSNet_Blended		62.90 265	61.64 272	66.69 245	69.81 294	49.36 235	61.23 315	78.96 160	42.04 322	59.98 351	68.86 371	51.82 246	78.20 186	44.30 304	77.77 308	72.52 311

Anonymous202405211		66.02 231	66.89 224	63.43 272	74.22 231	38.14 342	59.00 331	66.13 285	63.33 111	69.76 272	85.95 176	51.88 245	70.50 276	44.23 306	87.52 174	81.64 195

VPNet		65.58 234	67.56 212	59.65 309	79.72 142	30.17 390	60.27 323	62.14 313	54.19 205	71.24 253	86.63 153	58.80 196	67.62 299	44.17 307	90.87 114	81.18 199

Patchmtry		60.91 282	63.01 265	54.62 338	66.10 337	26.27 407	67.47 250	56.40 344	54.05 208	72.04 241	86.66 150	33.19 349	60.17 343	43.69 308	87.45 177	77.42 261

PatchT		53.35 334	56.47 314	43.99 388	64.19 351	17.46 419	59.15 328	43.10 400	52.11 228	54.74 382	86.95 139	29.97 380	49.98 373	43.62 309	74.40 334	64.53 377

IB-MVS	49.67 18	59.69 293	56.96 310	67.90 229	68.19 313	50.30 223	61.42 313	65.18 294	47.57 279	55.83 376	67.15 383	23.77 401	79.60 156	43.56 310	79.97 280	73.79 299
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		56.46 310	56.51 313	56.30 329	67.70 319	39.66 329	55.36 358	52.34 367	40.57 340	63.85 326	69.91 361	40.04 316	58.22 352	43.49 311	75.29 327	71.03 331

test_vis1_n		51.27 350	50.41 360	53.83 339	56.99 391	50.01 227	56.75 347	60.53 321	25.68 407	59.74 356	57.86 405	29.40 382	47.41 383	43.10 312	63.66 390	64.08 378

PatchmatchNet		54.60 324	54.27 331	55.59 334	65.17 345	39.08 331	66.92 261	51.80 369	39.89 342	58.39 360	73.12 335	31.69 365	58.33 351	43.01 313	58.38 405	69.38 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs-eth3d		64.41 250	63.27 262	67.82 232	75.81 207	60.18 155	69.49 218	62.05 316	38.81 351	74.13 209	82.23 228	43.76 293	68.65 290	42.53 314	80.63 274	74.63 289

ttmdpeth		56.40 311	55.45 322	59.25 311	55.63 399	40.69 319	58.94 333	49.72 377	36.22 366	65.39 312	86.97 138	23.16 404	56.69 358	42.30 315	80.74 271	80.36 223

LCM-MVSNet-Re		69.10 189	71.57 162	61.70 289	70.37 285	34.30 370	61.45 312	79.62 148	56.81 165	89.59 9	88.16 123	68.44 97	72.94 247	42.30 315	87.33 181	77.85 260

VNet		64.01 255	65.15 243	60.57 303	73.28 246	35.61 361	57.60 343	67.08 280	54.61 193	66.76 306	83.37 211	56.28 225	66.87 309	42.19 317	85.20 216	79.23 240

test-LLR		50.43 353	50.69 358	49.64 363	60.76 368	41.87 308	53.18 370	45.48 393	43.41 316	49.41 401	60.47 402	29.22 383	44.73 394	42.09 318	72.14 353	62.33 386

test-mter		48.56 362	48.20 367	49.64 363	60.76 368	41.87 308	53.18 370	45.48 393	31.91 391	49.41 401	60.47 402	18.34 415	44.73 394	42.09 318	72.14 353	62.33 386

MVS		60.62 286	59.97 287	62.58 282	68.13 314	47.28 263	68.59 236	73.96 219	32.19 386	59.94 353	68.86 371	50.48 254	77.64 197	41.85 320	75.74 319	62.83 380

MIMVSNet1		66.57 225	69.23 186	58.59 317	81.26 130	37.73 347	64.06 295	57.62 329	57.02 163	78.40 136	90.75 49	62.65 151	58.10 354	41.77 321	89.58 140	79.95 228

test_vis3_rt		51.94 347	51.04 353	54.65 337	46.32 419	50.13 225	44.34 399	78.17 178	23.62 413	68.95 281	62.81 393	21.41 408	38.52 412	41.49 322	72.22 352	75.30 285

Vis-MVSNet (Re-imp)		62.74 268	63.21 263	61.34 296	72.19 262	31.56 382	67.31 256	53.87 355	53.60 215	69.88 270	83.37 211	40.52 313	70.98 272	41.40 323	86.78 195	81.48 197

YYNet1		52.58 340	53.50 335	49.85 361	54.15 405	36.45 354	40.53 404	46.55 391	38.09 355	75.52 187	73.31 334	41.08 310	43.88 398	41.10 324	71.14 361	69.21 346

sd_testset		63.55 256	65.38 237	58.07 320	73.04 255	38.83 336	57.41 344	65.44 292	51.42 237	68.93 283	82.72 222	63.76 145	58.11 353	41.05 325	84.67 223	77.28 263

MDA-MVSNet_test_wron		52.57 341	53.49 337	49.81 362	54.24 404	36.47 353	40.48 405	46.58 390	38.13 354	75.47 188	73.32 333	41.05 311	43.85 399	40.98 326	71.20 360	69.10 348

1112_ss		59.48 294	58.99 294	60.96 300	77.84 172	42.39 306	61.42 313	68.45 275	37.96 356	59.93 354	67.46 379	45.11 285	65.07 324	40.89 327	71.81 355	75.41 282

tpmvs		55.84 313	55.45 322	57.01 325	60.33 371	33.20 375	65.89 272	59.29 326	47.52 280	56.04 374	73.60 330	31.05 372	68.06 296	40.64 328	64.64 387	69.77 340

TR-MVS		64.59 245	63.54 258	67.73 233	75.75 208	50.83 218	63.39 301	70.29 262	49.33 263	71.55 249	74.55 320	50.94 252	78.46 176	40.43 329	75.69 320	73.89 298

test_post1							66.63 265				2.08 422	30.66 375	59.33 346	40.34 330

SCA		58.57 302	58.04 303	60.17 306	70.17 289	41.07 314	65.19 283	53.38 361	43.34 318	61.00 347	73.48 331	45.20 283	69.38 284	40.34 330	70.31 366	70.05 336

baseline1		57.82 306	58.36 301	56.19 330	69.17 301	30.76 388	62.94 307	55.21 348	46.04 288	63.83 327	78.47 284	41.20 307	63.68 330	39.44 332	68.99 374	74.13 295

ab-mvs		64.11 253	65.13 244	61.05 298	71.99 264	38.03 345	67.59 247	68.79 272	49.08 267	65.32 314	86.26 164	58.02 209	66.85 311	39.33 333	79.79 286	78.27 251

tpmrst		50.15 356	51.38 350	46.45 378	56.05 395	24.77 410	64.40 293	49.98 375	36.14 367	53.32 388	69.59 363	35.16 342	48.69 377	39.24 334	58.51 404	65.89 364

test_f		43.79 377	45.63 372	38.24 398	42.29 424	38.58 337	34.76 413	47.68 385	22.22 416	67.34 302	63.15 392	31.82 363	30.60 417	39.19 335	62.28 394	45.53 410

CostFormer		57.35 308	56.14 316	60.97 299	63.76 354	38.43 338	67.50 249	60.22 322	37.14 363	59.12 359	76.34 305	32.78 352	71.99 262	39.12 336	69.27 372	72.47 312

pmmvs4		60.78 284	59.04 293	66.00 251	73.06 254	57.67 175	64.53 291	60.22 322	36.91 364	65.96 308	77.27 298	39.66 319	68.54 291	38.87 337	74.89 328	71.80 319

gm-plane-assit					62.51 358	33.91 372			37.25 362		62.71 394		72.74 248	38.70 338

Test_1112_low_res		58.78 300	58.69 296	59.04 315	79.41 146	38.13 343	57.62 342	66.98 281	34.74 375	59.62 357	77.56 296	42.92 298	63.65 331	38.66 339	70.73 363	75.35 284

thres600view7		61.82 275	61.38 276	63.12 275	71.81 266	34.93 365	64.64 288	56.99 337	54.78 190	70.33 262	79.74 264	32.07 359	72.42 256	38.61 340	83.46 242	82.02 187

UnsupCasMVSNet_eth		52.26 343	53.29 338	49.16 366	55.08 401	33.67 373	50.03 382	58.79 327	37.67 359	63.43 334	74.75 318	41.82 304	45.83 386	38.59 341	59.42 401	67.98 354

CL-MVSNet_self_test		62.44 271	63.40 260	59.55 310	72.34 261	32.38 377	56.39 349	64.84 297	51.21 242	67.46 301	81.01 244	50.75 253	63.51 332	38.47 342	88.12 165	82.75 169

MDTV_nov1_ep13			54.05 334		65.54 341	29.30 394	59.00 331	55.22 347	35.96 369	52.44 389	75.98 306	30.77 374	59.62 345	38.21 343	73.33 344

BH-w/o		64.81 242	64.29 250	66.36 247	76.08 203	54.71 193	65.61 278	75.23 210	50.10 255	71.05 256	71.86 343	54.33 234	79.02 164	38.20 344	76.14 317	65.36 368

TESTMET0.1,1		45.17 371	44.93 377	45.89 380	56.02 396	38.31 339	53.18 370	41.94 408	27.85 399	44.86 411	56.47 407	17.93 417	41.50 407	38.08 345	68.06 378	57.85 395

USDC		62.80 266	63.10 264	61.89 287	65.19 343	43.30 297	67.42 251	74.20 218	35.80 370	72.25 237	84.48 194	45.67 280	71.95 263	37.95 346	84.97 218	70.42 335

E-PMN		45.17 371	45.36 374	44.60 385	50.07 413	42.75 302	38.66 408	42.29 406	46.39 286	39.55 416	51.15 412	26.00 392	45.37 390	37.68 347	76.41 314	45.69 409

CDS-MVSNet		64.33 251	62.66 268	69.35 202	80.44 137	58.28 172	65.26 282	65.66 289	44.36 305	67.30 303	75.54 310	43.27 295	71.77 264	37.68 347	84.44 230	78.01 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		55.59 317	54.89 327	57.68 322	69.18 300	49.05 238	61.00 317	62.93 311	35.98 368	58.36 361	68.93 369	36.71 337	66.59 314	37.62 349	63.30 391	57.39 397

FPMVS		59.43 295	60.07 286	57.51 323	77.62 178	71.52 53	62.33 309	50.92 371	57.40 160	69.40 275	80.00 261	39.14 323	61.92 338	37.47 350	66.36 384	39.09 414

EPMVS		45.74 368	46.53 371	43.39 389	54.14 406	22.33 416	55.02 359	35.00 418	34.69 376	51.09 395	70.20 355	25.92 393	42.04 404	37.19 351	55.50 409	65.78 365

baseline2		55.57 318	52.74 339	64.05 264	65.26 342	44.11 288	62.38 308	54.43 352	39.03 349	51.21 394	67.35 381	33.66 347	72.45 255	37.14 352	64.22 389	75.60 279

EMVS		44.61 375	44.45 380	45.10 384	48.91 416	43.00 300	37.92 409	41.10 412	46.75 284	38.00 418	48.43 415	26.42 390	46.27 385	37.11 353	75.38 325	46.03 408

testing99		55.16 321	54.56 330	56.98 326	70.13 292	30.58 389	54.55 365	54.11 354	49.53 262	56.76 370	70.14 357	22.76 406	65.79 319	36.99 354	76.04 318	74.57 290

testing91		55.74 315	55.29 325	57.08 324	70.63 277	30.85 387	54.94 362	56.31 346	50.34 250	57.08 366	70.10 358	24.50 399	65.86 317	36.98 355	76.75 313	74.53 291

XXY-MVS		55.19 320	57.40 308	48.56 371	64.45 350	34.84 367	51.54 377	53.59 357	38.99 350	63.79 328	79.43 269	56.59 221	45.57 387	36.92 356	71.29 359	65.25 369

HyFIR lowres test		63.01 263	60.47 284	70.61 178	83.04 104	54.10 198	59.93 326	72.24 236	33.67 382	69.00 278	75.63 309	38.69 325	76.93 203	36.60 357	75.45 324	80.81 212

EPNet_dtu		58.93 299	58.52 297	60.16 307	67.91 317	47.70 257	69.97 213	58.02 328	49.73 258	47.28 405	73.02 336	38.14 327	62.34 335	36.57 358	85.99 205	70.43 334
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		52.05 345	51.58 348	53.44 343	52.11 410	31.20 383	44.88 397	64.83 298	41.53 326	64.37 319	70.03 359	15.61 422	64.20 326	36.25 359	74.61 331	64.93 373

miper_refine_blended		52.05 345	51.58 348	53.44 343	52.11 410	31.20 383	44.88 397	64.83 298	41.53 326	64.37 319	70.03 359	15.61 422	64.20 326	36.25 359	74.61 331	64.93 373

new-patchmatchnet		52.89 338	55.76 320	44.26 387	59.94 376	6.31 427	37.36 411	50.76 373	41.10 330	64.28 321	79.82 263	44.77 286	48.43 380	36.24 361	87.61 172	78.03 256

JIA-IIPM		54.03 328	51.62 347	61.25 297	59.14 382	55.21 191	59.10 330	47.72 384	50.85 245	50.31 400	85.81 178	20.10 411	63.97 328	36.16 362	55.41 410	64.55 376

WAC-MVS						22.69 414								36.10 363

PatchMatch-RL		58.68 301	57.72 305	61.57 290	76.21 199	73.59 43	61.83 310	49.00 381	47.30 281	61.08 344	68.97 367	50.16 256	59.01 347	36.06 364	68.84 375	52.10 401

thres100view900		61.17 281	61.09 278	61.39 294	72.14 263	35.01 364	65.42 281	56.99 337	55.23 183	70.71 258	79.90 262	32.07 359	72.09 259	35.61 365	81.73 258	77.08 269

tfpn200view9		60.35 288	59.97 287	61.51 291	70.78 274	35.35 362	63.27 303	57.47 330	53.00 219	68.31 292	77.09 300	32.45 356	72.09 259	35.61 365	81.73 258	77.08 269

thres400		60.77 285	59.97 287	63.15 274	70.78 274	35.35 362	63.27 303	57.47 330	53.00 219	68.31 292	77.09 300	32.45 356	72.09 259	35.61 365	81.73 258	82.02 187

test_vis1_rt		46.70 367	45.24 375	51.06 356	44.58 420	51.04 216	39.91 406	67.56 278	21.84 417	51.94 392	50.79 413	33.83 346	39.77 409	35.25 368	61.50 396	62.38 385

MVP-Stereo		61.56 278	59.22 291	68.58 222	79.28 148	60.44 153	69.20 224	71.57 239	43.58 313	56.42 373	78.37 286	39.57 320	76.46 210	34.86 369	60.16 399	68.86 349
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TAMVS		65.31 236	63.75 255	69.97 194	82.23 117	59.76 158	66.78 264	63.37 309	45.20 299	69.79 271	79.37 272	47.42 276	72.17 258	34.48 370	85.15 217	77.99 258

tpm cat1		54.02 329	52.63 341	58.19 319	64.85 349	39.86 328	66.26 269	57.28 333	32.16 387	56.90 368	70.39 353	32.75 353	65.30 323	34.29 371	58.79 402	69.41 344

pmmvs5		52.49 342	52.58 342	52.21 349	54.99 402	32.38 377	55.45 357	53.84 356	32.15 388	55.49 378	74.81 316	38.08 328	57.37 356	34.02 372	74.40 334	66.88 359

CHOSEN 1792x2688		58.09 304	56.30 315	63.45 271	79.95 140	50.93 217	54.07 367	65.59 290	28.56 398	61.53 341	74.33 323	41.09 309	66.52 315	33.91 373	67.69 382	72.92 306

myMVS_eth3d		50.36 354	50.52 359	49.88 360	68.77 305	22.69 414	55.02 359	44.55 395	43.80 308	58.05 363	64.07 389	14.16 424	58.83 348	33.90 374	72.36 350	68.12 351

HY-MVS	49.31 19	57.96 305	57.59 306	59.10 314	66.85 330	36.17 355	65.13 284	65.39 293	39.24 348	54.69 383	78.14 290	44.28 290	67.18 306	33.75 375	70.79 362	73.95 297

tpm2		56.12 312	54.64 329	60.55 304	66.24 334	36.01 356	68.14 242	56.77 340	33.60 383	58.25 362	75.52 312	30.25 377	74.33 235	33.27 376	69.76 371	71.32 324

MDTV_nov1_ep13_2view						18.41 418	53.74 368		31.57 392	44.89 410		29.90 381		32.93 377		71.48 321

tpm		50.60 352	52.42 344	45.14 383	65.18 344	26.29 406	60.30 322	43.50 398	37.41 361	57.01 367	79.09 278	30.20 379	42.32 402	32.77 378	66.36 384	66.81 361

testing11		53.13 335	52.26 345	55.75 333	70.44 284	31.73 381	54.75 363	52.40 366	44.81 303	52.36 391	68.40 375	21.83 407	65.74 320	32.64 379	72.73 347	69.78 339

WBMVS		53.38 332	54.14 332	51.11 355	70.16 290	26.66 403	50.52 381	51.64 370	39.32 345	63.08 335	77.16 299	23.53 402	55.56 359	31.99 380	79.88 282	71.11 329

sss		47.59 365	48.32 365	45.40 382	56.73 394	33.96 371	45.17 395	48.51 382	32.11 390	52.37 390	65.79 385	40.39 314	41.91 405	31.85 381	61.97 395	60.35 390

PMMVS		44.69 373	43.95 381	46.92 375	50.05 414	53.47 204	48.08 388	42.40 404	22.36 415	44.01 414	53.05 410	42.60 301	45.49 388	31.69 382	61.36 397	41.79 412

thres200		57.55 307	57.02 309	59.17 312	67.89 318	34.93 365	58.91 334	57.25 334	50.24 252	64.01 324	71.46 346	32.49 355	71.39 269	31.31 383	79.57 288	71.19 328

WTY-MVS		49.39 359	50.31 361	46.62 377	61.22 366	32.00 380	46.61 392	49.77 376	33.87 380	54.12 385	69.55 364	41.96 303	45.40 389	31.28 384	64.42 388	62.47 384

UnsupCasMVSNet_bld		50.01 357	51.03 354	46.95 374	58.61 384	32.64 376	48.31 385	53.27 362	34.27 378	60.47 349	71.53 345	41.40 305	47.07 384	30.68 385	60.78 398	61.13 389

PVSNet	43.83 21	51.56 348	51.17 351	52.73 346	68.34 310	38.27 340	48.22 386	53.56 359	36.41 365	54.29 384	64.94 388	34.60 344	54.20 365	30.34 386	69.87 369	65.71 366

test20.03		55.74 315	57.51 307	50.42 358	59.89 377	32.09 379	50.63 379	49.01 380	50.11 254	65.07 316	83.23 218	45.61 281	48.11 381	30.22 387	83.82 236	71.07 330

FMVSNet5		55.08 322	55.54 321	53.71 340	65.80 338	33.50 374	56.22 351	52.50 365	43.72 312	61.06 345	83.38 210	25.46 395	54.87 362	30.11 388	81.64 263	72.75 309

gg-mvs-nofinetune		55.75 314	56.75 312	52.72 347	62.87 357	28.04 398	68.92 227	41.36 410	71.09 46	50.80 396	92.63 13	20.74 409	66.86 310	29.97 389	72.41 349	63.25 379

dp		44.09 376	44.88 378	41.72 393	58.53 386	23.18 413	54.70 364	42.38 405	34.80 374	44.25 413	65.61 386	24.48 400	44.80 393	29.77 390	49.42 413	57.18 398

PAPM		61.79 276	60.37 285	66.05 250	76.09 201	41.87 308	69.30 222	76.79 197	40.64 339	53.80 386	79.62 267	44.38 289	82.92 98	29.64 391	73.11 345	73.36 302

testgi		54.00 330	56.86 311	45.45 381	58.20 387	25.81 409	49.05 383	49.50 379	45.43 296	67.84 295	81.17 241	51.81 248	43.20 401	29.30 392	79.41 289	67.34 357

Patchmatch-test		47.93 363	49.96 362	41.84 391	57.42 390	24.26 411	48.75 384	41.49 409	39.30 347	56.79 369	73.48 331	30.48 376	33.87 415	29.29 393	72.61 348	67.39 355

pmmvs3		46.71 366	45.09 376	51.55 352	56.76 393	48.25 244	55.78 356	39.53 414	24.13 412	50.35 399	63.40 391	15.90 421	51.08 370	29.29 393	70.69 364	55.33 400

mvsany_test3		43.76 378	41.01 382	52.01 350	48.09 417	57.74 174	42.47 401	23.85 424	23.30 414	64.80 317	62.17 396	27.12 387	40.59 408	29.17 395	48.11 414	57.69 396

dmvs_re		49.91 358	50.77 357	47.34 373	59.98 373	38.86 335	53.18 370	53.58 358	39.75 343	55.06 379	61.58 398	36.42 338	44.40 396	29.15 396	68.23 377	58.75 394

N_pmnet		52.06 344	51.11 352	54.92 335	59.64 380	71.03 57	37.42 410	61.62 319	33.68 381	57.12 365	72.10 339	37.94 329	31.03 416	29.13 397	71.35 358	62.70 381

Anonymous20231206		54.13 326	55.82 319	49.04 368	70.89 272	35.96 357	51.73 376	50.87 372	34.86 372	62.49 337	79.22 274	42.52 302	44.29 397	27.95 398	81.88 254	66.88 359

CHOSEN 280x420		41.62 380	39.89 385	46.80 376	61.81 362	51.59 211	33.56 414	35.74 417	27.48 401	37.64 419	53.53 408	23.24 403	42.09 403	27.39 399	58.64 403	46.72 407

UBG		49.18 360	49.35 364	48.66 370	70.36 286	26.56 405	50.53 380	45.61 392	37.43 360	53.37 387	65.97 384	23.03 405	54.20 365	26.29 400	71.54 357	65.20 370

mvsany_test1		37.88 382	35.74 387	44.28 386	47.28 418	49.90 229	36.54 412	24.37 423	19.56 418	45.76 407	53.46 409	32.99 351	37.97 413	26.17 401	35.52 416	44.99 411

MIMVSNet		54.39 325	56.12 317	49.20 365	72.57 259	30.91 386	59.98 325	48.43 383	41.66 325	55.94 375	83.86 203	41.19 308	50.42 371	26.05 402	75.38 325	66.27 363

ADS-MVSNet2		48.76 361	47.25 370	53.29 345	55.90 397	40.54 323	47.34 390	54.99 350	31.41 393	50.48 397	72.06 340	31.23 368	54.26 364	25.93 403	55.93 407	65.07 371

ADS-MVSNet		44.62 374	45.58 373	41.73 392	55.90 397	20.83 417	47.34 390	39.94 413	31.41 393	50.48 397	72.06 340	31.23 368	39.31 410	25.93 403	55.93 407	65.07 371

testing222		53.37 333	52.50 343	55.98 332	70.51 283	29.68 392	56.20 352	51.85 368	46.19 287	56.76 370	68.94 368	19.18 414	65.39 321	25.87 405	76.98 311	72.87 307

test0.0.03 1		47.72 364	48.31 366	45.93 379	55.53 400	29.39 393	46.40 393	41.21 411	43.41 316	55.81 377	67.65 378	29.22 383	43.77 400	25.73 406	69.87 369	64.62 375

GG-mvs-BLEND				52.24 348	60.64 370	29.21 395	69.73 217	42.41 403		45.47 408	52.33 411	20.43 410	68.16 294	25.52 407	65.42 386	59.36 393

DSMNet-mixed		43.18 379	44.66 379	38.75 396	54.75 403	28.88 396	57.06 346	27.42 421	13.47 419	47.27 406	77.67 295	38.83 324	39.29 411	25.32 408	60.12 400	48.08 405

WB-MVSnew		53.94 331	54.76 328	51.49 353	71.53 268	28.05 397	58.22 339	50.36 374	37.94 357	59.16 358	70.17 356	49.21 263	51.94 368	24.49 409	71.80 356	74.47 293

MVS-HIRNet		45.53 369	47.29 369	40.24 394	62.29 360	26.82 402	56.02 354	37.41 416	29.74 397	43.69 415	81.27 239	33.96 345	55.48 360	24.46 410	56.79 406	38.43 415

UWE-MVS		52.94 337	52.70 340	53.65 341	73.56 240	27.49 400	57.30 345	49.57 378	38.56 353	62.79 336	71.42 347	19.49 413	60.41 341	24.33 411	77.33 310	73.06 304

PVSNet_0	36.71 22	41.12 381	40.78 384	42.14 390	59.97 374	40.13 325	40.97 403	42.24 407	30.81 395	44.86 411	49.41 414	40.70 312	45.12 391	23.15 412	34.96 417	41.16 413

ETVMVS		50.32 355	49.87 363	51.68 351	70.30 288	26.66 403	52.33 375	43.93 397	43.54 314	54.91 380	67.95 377	20.01 412	60.17 343	22.47 413	73.40 342	68.22 350

new_pmnet		37.55 384	39.80 386	30.79 399	56.83 392	16.46 420	39.35 407	30.65 419	25.59 408	45.26 409	61.60 397	24.54 398	28.02 419	21.60 414	52.80 412	47.90 406

dmvs_testset		45.26 370	47.51 368	38.49 397	59.96 375	14.71 421	58.50 337	43.39 399	41.30 328	51.79 393	56.48 406	39.44 322	49.91 375	21.42 415	55.35 411	50.85 402

MVE	27.91 23	36.69 385	35.64 388	39.84 395	43.37 422	35.85 359	19.49 416	24.61 422	24.68 410	39.05 417	62.63 395	38.67 326	27.10 420	21.04 416	47.25 415	56.56 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		61.97 273	66.25 228	49.12 367	58.19 388	60.77 151	66.32 268	52.97 363	55.93 177	90.62 6	86.91 140	73.07 60	35.98 414	20.63 417	91.63 89	50.62 403

PMMVS2		37.74 383	40.87 383	28.36 400	42.41 423	5.35 428	24.61 415	27.75 420	32.15 388	47.85 404	70.27 354	35.85 340	29.51 418	19.08 418	67.85 380	50.22 404

dongtai		31.66 386	32.98 389	27.71 401	58.58 385	12.61 423	45.02 396	14.24 427	41.90 323	47.93 403	43.91 416	10.65 427	41.81 406	14.06 419	20.53 420	28.72 417

test_method		19.26 388	19.12 392	19.71 402	9.09 427	1.91 430	7.79 418	53.44 360	1.42 421	10.27 423	35.80 417	17.42 419	25.11 421	12.44 420	24.38 419	32.10 416

tmp_tt		11.98 390	14.73 393	3.72 405	2.28 428	4.62 429	19.44 417	14.50 426	0.47 423	21.55 421	9.58 421	25.78 394	4.57 424	11.61 421	27.37 418	1.96 420

DeepMVS_CX				11.83 404	15.51 426	13.86 422		11.25 429	5.76 420	20.85 422	26.46 419	17.06 420	9.22 423	9.69 422	13.82 422	12.42 419

kuosan		22.02 387	23.52 391	17.54 403	41.56 425	11.24 424	41.99 402	13.39 428	26.13 406	28.87 420	30.75 418	9.72 428	21.94 422	4.77 423	14.49 421	19.43 418

testmvs		4.06 394	5.28 397	0.41 406	0.64 430	0.16 432	42.54 400	0.31 431	0.26 425	0.50 426	1.40 425	0.77 429	0.17 425	0.56 424	0.55 424	0.90 421

test123		4.43 393	5.78 396	0.39 407	0.97 429	0.28 431	46.33 394	0.45 430	0.31 424	0.62 425	1.50 424	0.61 430	0.11 426	0.56 424	0.63 423	0.77 422

mmdepth		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

monomultidepth		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

test_blank		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

uanet_test		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

DCPMVS		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

cdsmvs_eth3d_5k		17.71 389	23.62 390	0.00 408	0.00 431	0.00 433	0.00 419	70.17 263	0.00 426	0.00 427	74.25 325	68.16 100	0.00 427	0.00 426	0.00 425	0.00 423

pcd_1.5k_mvsjas		5.20 392	6.93 395	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	62.39 156	0.00 427	0.00 426	0.00 425	0.00 423

sosnet-low-res		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

sosnet		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

uncertanet		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

Regformer		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

ab-mvs-re		5.62 391	7.50 394	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	67.46 379	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

uanet		0.00 395	0.00 398	0.00 408	0.00 431	0.00 433	0.00 419	0.00 432	0.00 426	0.00 427	0.00 426	0.00 431	0.00 427	0.00 426	0.00 425	0.00 423

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	61.45 137		85.63 30	75.27 21	85.62 51	90.38 67	76.72 30

eth-test2					0.00 431

eth-test					0.00 431

test_241102_ONE					86.12 54	61.06 143		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	77.94 183	56.65 170

test0726					86.16 52	60.78 149	83.81 43	85.10 43	72.48 37	85.27 56	89.96 79	78.57 19

GSMVS																70.05 336

test_part2					85.90 60	66.44 95				84.61 65

sam_mvs1												31.41 366				70.05 336

sam_mvs												31.21 370

MTGPA								80.63 131

test_post											1.99 423	30.91 373	54.76 363

patchmatchnet-post											68.99 366	31.32 367	69.38 284

MTMP							84.83 34	19.26 425

TEST9					85.47 67	69.32 74	76.42 123	78.69 168	53.73 214	76.97 153	86.74 146	66.84 112	81.10 127

test_8					85.09 73	67.89 83	76.26 128	78.66 170	54.00 209	76.89 157	86.72 148	66.60 118	80.89 137

agg_prior					84.44 85	66.02 101		78.62 171		76.95 155			80.34 144

test_prior4						70.14 67	77.57 106

test_prior				75.27 106	82.15 118	59.85 157		84.33 63					83.39 90			82.58 176

新几何2							71.33 194

旧先验1					84.55 82	60.36 154		63.69 306			87.05 137	54.65 231			83.34 243	69.66 341

原ACMM2							74.78 147

test222					87.30 38	69.15 77	67.85 245	59.59 325	41.06 331	73.05 227	85.72 179	48.03 273			80.65 272	66.92 358

segment_acmp												68.30 99

testdata1							68.34 241		57.24 162

test12				76.51 88	82.28 116	60.94 146		81.64 108		73.60 218		64.88 137	85.19 62		90.42 122	83.38 149

plane_prior7					85.18 70	66.21 98

plane_prior6					84.18 88	65.31 107						60.83 177

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								0.00 432

nn								0.00 432

door-mid								55.02 349

test11								82.71 91

door								52.91 364

HQP5-MVS						58.80 168

HQP-NCC					82.37 113		77.32 111		59.08 140	71.58 245

ACMP_Plane					82.37 113		77.32 111		59.08 140	71.58 245

HQP4-MVS										71.59 244			85.31 54			83.74 136

HQP3-MVS								84.12 69							89.16 148

HQP2-MVS												58.09 204

NP-MVS					83.34 98	63.07 125					85.97 174

ACMMP++_ref															89.47 143

ACMMP++															91.96 85

Test By Simon												62.56 152

High-resolution multi-view benchmark