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.

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 32	68.44 83	81.57 63	86.33 22	63.17 110	85.38 51	91.26 34	76.33 32	84.67 68	83.30 1	94.96 23	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 81	83.62 45	84.98 48	64.77 91	83.97 72	91.02 38	75.53 41	85.93 36	82.00 2	94.36 47	83.35 147

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 78	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 121	81.53 3	92.15 89	88.91 37
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
zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 56	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 72	80.98 4	94.75 29	80.82 200

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 32	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 72	80.98 4	94.75 29	80.82 200

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 39	74.40 36	85.24 29	87.21 16	70.69 48	85.14 54	90.42 58	78.99 16	86.62 14	80.83 6	94.93 24	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 22	83.68 23	81.45 29	89.14 26	73.28 47	86.32 24	85.97 30	67.39 64	84.02 71	90.39 62	74.73 48	86.46 16	80.73 7	94.43 42	84.60 109

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 46	64.94 90	81.05 105	88.38 110	57.10 207	87.10 8	79.75 8	83.87 228	84.31 121
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 23	83.64 24	81.35 31	85.14 74	71.00 60	85.53 27	84.78 52	70.91 46	85.64 44	90.41 59	75.55 40	87.69 3	79.75 8	95.08 20	85.36 84
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.12 32	82.68 42	80.43 42	88.90 31	69.52 72	85.12 30	84.76 53	63.53 104	84.23 69	91.47 30	72.02 67	87.16 7	79.74 10	94.36 47	84.61 107
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 19	84.03 18	81.48 27	89.25 21	75.69 28	87.01 15	84.27 68	70.23 49	84.47 65	90.43 56	76.79 27	85.94 33	79.58 11	94.23 53	82.82 162

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 73	70.23 49	84.49 64	90.67 49	75.15 43	86.37 19	79.58 11	94.26 51	84.18 124

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 36	91.70 24	78.23 20	85.20 58	79.45 13	94.91 25	88.15 45

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 29	67.52 88	86.61 20	81.38 112	51.71 226	77.15 152	91.42 33	65.49 128	87.20 6	79.44 14	87.17 185	84.51 115
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 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 72	70.19 51	83.86 73	90.72 48	75.20 42	86.27 22	79.41 15	94.25 52	83.95 128

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 31	71.03 45	85.85 41	90.58 50	78.77 17	85.78 41	79.37 16	95.17 17	84.62 106
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 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 91	72.41 36	85.11 55	90.85 42	76.65 30	84.89 63	79.30 17	94.63 34	82.35 175

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 64	71.96 42	84.70 62	90.56 51	77.12 26	86.18 27	79.24 18	95.36 13	82.49 172

GST-MVS		82.79 27	83.27 32	81.34 32	88.99 28	73.29 46	85.94 26	85.13 44	68.58 60	84.14 70	90.21 73	73.37 61	86.41 17	79.09 19	93.98 60	84.30 123

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	90.39 62	73.86 56	86.31 20	78.84 20	94.03 57	84.64 104

X-MVStestdata		76.81 84	74.79 105	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	9.95 373	73.86 56	86.31 20	78.84 20	94.03 57	84.64 104

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 82	71.31 43	81.26 103	90.96 39	74.57 51	84.69 67	78.41 22	94.78 28	82.74 166
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 60	85.40 39	67.96 62	84.91 60	90.88 40	75.59 38	86.57 15	78.16 23	94.71 32	83.82 129

abl_6		84.92 3	85.70 3	82.57 17	86.72 49	79.27 8	87.56 7	86.08 28	77.48 13	88.12 14	91.53 28	81.18 8	84.31 75	78.12 24	94.47 37	84.15 125

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	79.20 14	85.58 48	78.11 25	94.46 38	84.89 94

RE-MVS-def			85.50 4		86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	81.38 7		78.11 25	94.46 38	84.89 94

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 43	81.06 3	87.20 28	90.28 70	79.20 14	85.58 48	78.04 27	94.08 56	83.55 137

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 80	66.72 96	86.54 21	85.11 45	72.00 41	86.65 33	91.75 23	78.20 21	87.04 9	77.93 28	94.32 50	83.47 142

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 42	77.42 15	86.15 37	90.24 71	81.69 5	85.94 33	77.77 29	93.58 67	83.09 153

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 113	73.53 45	85.50 28	87.45 14	74.11 21	86.45 34	90.52 54	80.02 11	84.48 70	77.73 30	94.34 49	85.93 75

SD-MVS		80.28 55	81.55 52	76.47 96	83.57 97	67.83 87	83.39 51	85.35 41	64.42 95	86.14 38	87.07 127	74.02 55	80.97 134	77.70 31	92.32 87	80.62 208
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 57	79.80 63	80.18 45	89.02 27	78.44 13	83.49 49	80.18 139	64.71 93	78.11 140	88.39 109	65.46 129	83.14 94	77.64 32	91.20 105	78.94 228

DVP-MVS++		81.24 39	82.74 40	76.76 90	83.14 103	60.90 144	91.64 1	85.49 35	74.03 23	84.93 56	90.38 64	66.82 114	85.90 37	77.43 33	90.78 120	83.49 139

test_0728_THIRD									74.03 23	85.83 42	90.41 59	75.58 39	85.69 44	77.43 33	94.74 31	84.31 121

MSC_two_6792asdad				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 132

No_MVS				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 132

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 43	70.41 66	82.04 60	86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 71

LGP-MVS_train				80.90 38	87.00 43	70.41 66		86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 71

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 70	87.00 43	67.23 91	79.24 87	88.61 5	56.65 167	84.29 67	89.18 91	73.73 59	83.22 92	76.01 39	93.77 62	84.81 100

SF-MVS		80.72 48	81.80 46	77.48 82	82.03 124	64.40 116	83.41 50	88.46 6	65.28 85	84.29 67	89.18 91	73.73 59	83.22 92	76.01 39	93.77 62	84.81 100

#test#		82.40 30	82.71 41	81.48 27	89.25 21	75.69 28	84.47 36	84.27 68	64.45 94	84.47 65	90.43 56	76.79 27	85.94 33	76.01 39	94.23 53	82.82 162

DVP-MVS		81.15 42	83.12 35	75.24 113	86.16 57	60.78 146	83.77 43	80.58 131	72.48 34	85.83 42	90.41 59	78.57 18	85.69 44	75.86 42	94.39 43	79.24 225
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 93	86.20 54	60.57 149	83.77 43	85.49 35					85.90 37	75.86 42	94.39 43	83.25 149

IU-MVS					86.12 59	60.90 144		80.38 135	45.49 281	81.31 102				75.64 44	94.39 43	84.65 103

SED-MVS		81.78 35	83.48 27	76.67 91	86.12 59	61.06 140	83.62 45	84.72 55	72.61 32	87.38 25	89.70 81	77.48 24	85.89 39	75.29 45	94.39 43	83.08 154

test_241102_TWO								84.80 51	72.61 32	84.93 56	89.70 81	77.73 23	85.89 39	75.29 45	94.22 55	83.25 149

XVG-OURS		79.51 59	79.82 62	78.58 67	86.11 62	74.96 33	76.33 125	84.95 50	66.89 66	82.75 86	88.99 99	66.82 114	78.37 183	74.80 47	90.76 123	82.40 174

CPTT-MVS		81.51 38	81.76 47	80.76 40	89.20 24	78.75 12	86.48 22	82.03 101	68.80 56	80.92 108	88.52 106	72.00 68	82.39 106	74.80 47	93.04 73	81.14 191

XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 68	86.46 52	74.79 34	77.15 115	85.39 40	66.73 70	80.39 115	88.85 102	74.43 54	78.33 185	74.73 49	85.79 200	82.35 175

DPE-MVS		82.00 34	83.02 36	78.95 62	85.36 71	67.25 90	82.91 53	84.98 48	73.52 26	85.43 50	90.03 75	76.37 31	86.97 11	74.56 50	94.02 59	82.62 168
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 66	74.51 51	96.15 2	92.88 7

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 51	69.44 74	82.30 57	86.08 28	66.80 69	86.70 32	89.99 76	81.64 6	85.95 32	74.35 52	96.11 3	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 30	78.49 68	88.17 40	73.96 39	83.11 52	84.52 63	66.40 74	87.45 23	89.16 94	81.02 9	80.52 144	74.27 53	95.73 7	80.98 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 48	69.39 75	79.41 86	84.00 78	65.64 78	85.54 48	89.28 87	76.32 33	83.47 87	74.03 54	93.57 68	84.35 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CNVR-MVS		78.49 71	78.59 74	78.16 73	85.86 66	67.40 89	78.12 105	81.50 108	63.92 99	77.51 149	86.56 148	68.43 100	84.82 65	73.83 55	91.61 96	82.26 178

9.14			80.22 59		80.68 138		80.35 74	87.69 11	59.90 133	83.00 80	88.20 114	74.57 51	81.75 119	73.75 56	93.78 61

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 50	70.03 70	82.06 59	87.00 17	59.89 134	80.91 109	90.53 52	72.19 65	88.56 1	73.67 57	94.52 36	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 68	77.80 80	81.00 37	78.01 175	74.34 38	80.09 79	76.12 192	50.51 241	89.19 10	90.88 40	71.45 74	77.78 197	73.38 58	90.60 125	90.90 16

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 89	80.67 139	64.57 113	80.26 76	87.60 12	60.74 127	82.47 89	88.03 119	71.73 70	81.81 117	73.12 59	93.61 65	85.09 89

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 133	72.03 50	84.38 37	86.23 26	77.28 16	80.65 111	90.18 74	59.80 180	87.58 4	73.06 60	91.34 102	89.01 33

v7n		79.37 62	80.41 58	76.28 98	78.67 168	55.81 181	79.22 89	82.51 97	70.72 47	87.54 22	92.44 14	68.00 105	81.34 122	72.84 61	91.72 91	91.69 10

ZD-MVS					83.91 94	69.36 76		81.09 120	58.91 144	82.73 87	89.11 95	75.77 37	86.63 13	72.73 62	92.93 75

UA-Net		81.56 37	82.28 44	79.40 54	88.91 30	69.16 79	84.67 35	80.01 142	75.34 17	79.80 120	94.91 2	69.79 87	80.25 149	72.63 63	94.46 38	88.78 41

APD-MVS		81.13 43	81.73 48	79.36 55	84.47 85	70.53 65	83.85 41	83.70 80	69.43 55	83.67 75	88.96 100	75.89 36	86.41 17	72.62 64	92.95 74	81.14 191
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 61	87.01 42	72.91 48	80.23 78	85.56 34	66.56 73	85.64 44	89.57 83	69.12 92	80.55 143	72.51 65	93.37 69	83.48 141

train_agg		76.38 88	76.55 89	75.86 104	85.47 69	69.32 77	76.42 121	78.69 161	54.00 203	76.97 155	86.74 138	66.60 117	81.10 128	72.50 66	91.56 97	77.15 248

agg_prior1		75.89 90	76.41 90	74.31 122	84.44 87	66.02 102	76.12 127	78.62 164	54.40 192	76.95 157	86.85 132	66.44 120	80.34 146	72.45 67	91.42 100	76.57 252

MVSFormer		69.93 171	69.03 183	72.63 157	74.93 215	59.19 159	83.98 39	75.72 196	52.27 219	63.53 302	76.74 277	43.19 279	80.56 141	72.28 68	78.67 285	78.14 240

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 132	71.24 58	83.98 39	75.72 196	52.27 219	87.37 27	92.25 16	68.04 104	80.56 141	72.28 68	91.15 107	90.32 20

test9_res														72.12 70	91.37 101	77.40 247

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 84	80.09 144	66.16 101	79.08 90	86.13 27	57.55 157	80.93 107	87.76 122	71.98 69	82.73 102	72.11 71	92.83 77	83.25 149

testtj		81.19 41	81.70 49	79.67 51	83.95 93	69.77 71	83.58 48	84.63 60	72.13 38	82.85 84	88.36 111	75.00 46	86.79 12	71.99 72	92.84 76	82.44 173

RRT_MVS		73.80 118	71.19 164	81.60 24	71.04 265	70.33 68	78.78 93	74.91 206	56.96 161	77.83 144	85.56 172	32.82 328	87.39 5	71.16 73	91.68 93	87.07 62

HQP_MVS		78.77 67	78.78 71	78.72 64	85.18 72	65.18 108	82.74 54	85.49 35	65.45 80	78.23 137	89.11 95	60.83 170	86.15 28	71.09 74	90.94 112	84.82 98

plane_prior5								85.49 35					86.15 28	71.09 74	90.94 112	84.82 98

v10		75.69 95	76.20 93	74.16 124	74.44 230	48.69 222	75.84 131	82.93 90	59.02 141	85.92 40	89.17 93	58.56 190	82.74 101	70.73 76	89.14 155	91.05 13

agg_prior2														70.70 77	90.93 114	78.55 233

Regformer-4		74.64 110	73.67 122	77.55 80	74.74 221	64.49 115	72.91 158	75.42 200	67.45 63	80.24 117	72.07 316	68.98 93	80.19 153	70.29 78	80.91 260	87.98 46

NCCC		78.25 75	78.04 79	78.89 63	85.61 68	69.45 73	79.80 83	80.99 123	65.77 77	75.55 182	86.25 157	67.42 108	85.42 50	70.10 79	90.88 118	81.81 184

Regformer-2		75.32 98	74.47 109	77.88 76	74.22 232	66.65 97	72.77 160	77.54 179	68.47 61	80.44 113	72.08 314	70.60 80	80.97 134	70.08 80	84.02 226	86.01 74

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 65	80.48 141	64.16 117	80.24 77	82.06 100	61.89 118	88.77 12	93.32 4	57.15 205	82.60 104	70.08 80	92.80 78	89.25 27

test_prior3		76.71 86	77.19 85	75.27 111	82.15 122	59.85 155	75.57 132	84.33 66	58.92 142	76.53 171	86.78 135	67.83 106	83.39 89	69.81 82	92.76 80	82.58 169

test_prior2							75.57 132		58.92 142	76.53 171	86.78 135	67.83 106		69.81 82	92.76 80

DROMVSNet		77.08 82	77.39 83	76.14 101	76.86 195	56.87 175	80.32 75	87.52 13	63.45 106	74.66 196	84.52 182	69.87 86	84.94 61	69.76 84	89.59 145	86.60 67

v8		75.07 103	75.64 99	73.35 135	73.42 242	47.46 240	75.20 136	81.45 110	60.05 132	85.64 44	89.26 88	58.08 197	81.80 118	69.71 85	87.97 168	90.79 17

CS-MVS-test		75.88 91	75.24 103	77.78 77	77.30 185	63.59 122	77.02 118	86.83 19	60.82 126	74.17 202	79.23 249	65.42 130	82.29 110	69.48 86	86.87 187	87.47 53

CS-MVS		76.42 87	76.03 94	77.57 79	77.07 186	63.35 124	80.80 67	82.77 92	60.39 130	74.11 204	83.29 202	69.71 88	81.97 114	69.28 87	86.87 187	87.38 55

v1240		73.06 132	73.14 133	72.84 150	74.74 221	47.27 243	71.88 175	81.11 118	51.80 225	82.28 91	84.21 186	56.22 214	82.34 108	68.82 88	87.17 185	88.91 37

Regformer-1		74.28 112	73.63 124	76.21 100	74.22 232	64.12 118	72.77 160	75.46 199	66.86 68	79.27 125	72.08 314	69.29 90	78.74 171	68.73 89	84.02 226	85.77 82

v1192		73.40 124	73.42 126	73.32 137	74.65 227	48.67 223	72.21 165	81.73 105	52.76 216	81.85 93	84.56 181	57.12 206	82.24 112	68.58 90	87.33 179	89.06 32

mvs_tets		78.93 65	78.67 73	79.72 49	84.81 79	73.93 40	80.65 68	76.50 190	51.98 224	87.40 24	91.86 21	76.09 35	78.53 175	68.58 90	90.20 129	86.69 66

v1921920		72.96 138	72.98 138	72.89 149	74.67 224	47.58 238	71.92 173	80.69 127	51.70 227	81.69 99	83.89 190	56.58 212	82.25 111	68.34 92	87.36 177	88.82 39

jajsoiax		78.51 70	78.16 78	79.59 52	84.65 82	73.83 42	80.42 71	76.12 192	51.33 232	87.19 29	91.51 29	73.79 58	78.44 179	68.27 93	90.13 134	86.49 68

v1144		73.29 127	73.39 127	73.01 143	74.12 236	48.11 230	72.01 168	81.08 121	53.83 207	81.77 95	84.68 179	58.07 198	81.91 115	68.10 94	86.86 189	88.99 35

LCM-MVSNet		86.90 1	88.67 1	81.57 25	91.50 1	63.30 126	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 95	97.05 1	96.93 1

PHI-MVS		74.92 105	74.36 112	76.61 92	76.40 198	62.32 131	80.38 72	83.15 86	54.16 200	73.23 216	80.75 227	62.19 155	83.86 80	68.02 96	90.92 115	83.65 136

CDPH-MVS		77.33 80	77.06 88	78.14 74	84.21 90	63.98 119	76.07 128	83.45 83	54.20 198	77.68 148	87.18 123	69.98 84	85.37 51	68.01 97	92.72 82	85.08 91

v144192		72.99 136	73.06 136	72.77 151	74.58 228	47.48 239	71.90 174	80.44 134	51.57 228	81.46 101	84.11 188	58.04 199	82.12 113	67.98 98	87.47 175	88.70 42

Regformer-3		72.86 140	72.28 147	74.62 116	74.74 221	60.18 152	72.91 158	71.76 228	64.74 92	78.42 135	72.07 316	67.00 111	76.28 215	67.97 99	80.91 260	87.39 54

OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 140	70.71 64	80.91 66	84.76 53	62.54 114	81.77 95	86.65 144	71.46 73	83.53 86	67.95 100	92.44 84	89.60 23

PS-MVSNAJss		77.54 78	77.35 84	78.13 75	84.88 77	66.37 99	78.55 96	79.59 147	53.48 210	86.29 35	92.43 15	62.39 152	80.25 149	67.90 101	90.61 124	87.77 48

EI-MVSNet-Vis-set		72.78 141	71.87 151	75.54 108	74.77 220	59.02 163	72.24 164	71.56 232	63.92 99	78.59 131	71.59 322	66.22 122	78.60 173	67.58 102	80.32 268	89.00 34

ACMH	63.62 14	77.50 79	80.11 60	69.68 192	79.61 148	56.28 177	78.81 92	83.62 81	63.41 108	87.14 31	90.23 72	76.11 34	73.32 244	67.58 102	94.44 41	79.44 223
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		75.77 92	76.34 91	74.06 126	81.69 130	54.84 186	76.47 120	75.49 198	64.10 98	87.73 18	92.24 17	50.45 241	81.30 124	67.41 104	91.46 99	86.04 73

DeepPCF-MVS	71.07 5	78.48 72	77.14 87	82.52 19	84.39 89	77.04 25	76.35 123	84.05 76	56.66 166	80.27 116	85.31 175	68.56 97	87.03 10	67.39 105	91.26 103	83.50 138

BP-MVS														67.38 106

HQP-MVS		75.24 100	75.01 104	75.94 102	82.37 117	58.80 165	77.32 111	84.12 74	59.08 138	71.58 235	85.96 167	58.09 195	85.30 53	67.38 106	89.16 152	83.73 135

EI-MVSNet-UG-set		72.63 144	71.68 155	75.47 109	74.67 224	58.64 168	72.02 167	71.50 233	63.53 104	78.58 133	71.39 325	65.98 123	78.53 175	67.30 108	80.18 270	89.23 28

v2v482		72.55 147	72.58 143	72.43 159	72.92 254	46.72 246	71.41 180	79.13 153	55.27 178	81.17 104	85.25 176	55.41 217	81.13 127	67.25 109	85.46 202	89.43 25

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 110	74.39 37	87.18 11	88.18 7	78.98 7	86.11 39	91.47 30	79.70 12	85.76 42	66.91 110	95.46 11	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LS3D		80.99 46	80.85 55	81.41 30	78.37 169	71.37 56	87.45 8	85.87 32	77.48 13	81.98 92	89.95 78	69.14 91	85.26 54	66.15 111	91.24 104	87.61 51

MVS_Test		69.84 172	70.71 168	67.24 226	67.49 298	43.25 275	69.87 200	81.22 117	52.69 217	71.57 238	86.68 141	62.09 156	74.51 235	66.05 112	78.74 283	83.96 127

WR-MVS_H		80.22 56	82.17 45	74.39 120	89.46 15	42.69 279	78.24 102	82.24 98	78.21 10	89.57 9	92.10 18	68.05 103	85.59 47	66.04 113	95.62 9	94.88 5

V42		71.06 157	70.83 167	71.72 165	67.25 299	47.14 244	65.94 256	80.35 137	51.35 231	83.40 78	83.23 204	59.25 184	78.80 169	65.91 114	80.81 264	89.23 28

test_part1		76.97 83	78.21 77	73.25 138	77.87 177	45.76 255	78.27 101	87.26 15	66.69 71	85.31 52	91.43 32	55.95 216	84.24 77	65.71 115	95.43 12	89.75 22

K. test v3		73.67 119	73.61 125	73.87 128	79.78 146	55.62 184	74.69 147	62.04 298	66.16 76	84.76 61	93.23 5	49.47 245	80.97 134	65.66 116	86.67 193	85.02 93

DeepC-MVS_fast	69.89 7	77.17 81	76.33 92	79.70 50	83.90 95	67.94 85	80.06 81	83.75 79	56.73 165	74.88 190	85.32 174	65.54 127	87.79 2	65.61 117	91.14 108	83.35 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3 D test6400		75.73 94	76.00 95	74.92 114	81.75 128	56.93 173	78.31 99	84.60 62	52.83 215	77.15 152	85.14 177	68.59 96	84.03 78	65.44 118	90.20 129	83.82 129

test_0402		78.17 76	79.48 66	74.24 123	83.50 98	59.15 162	72.52 162	74.60 209	75.34 17	88.69 13	91.81 22	75.06 44	82.37 107	65.10 119	88.68 160	81.20 189

diffmvs		67.42 207	67.50 206	67.20 227	62.26 332	45.21 259	64.87 271	77.04 186	48.21 262	71.74 232	79.70 242	58.40 191	71.17 268	64.99 120	80.27 269	85.22 85

PMVS	70.70 6	81.70 36	83.15 34	77.36 85	90.35 6	82.82 2	82.15 58	79.22 152	74.08 22	87.16 30	91.97 19	84.80 2	76.97 205	64.98 121	93.61 65	72.28 287
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMH+	66.64 10	81.20 40	82.48 43	77.35 86	81.16 136	62.39 130	80.51 69	87.80 8	73.02 28	87.57 21	91.08 37	80.28 10	82.44 105	64.82 122	96.10 4	87.21 58

MCST-MVS		73.42 123	73.34 130	73.63 132	81.28 134	59.17 161	74.80 143	83.13 87	45.50 279	72.84 219	83.78 192	65.15 133	80.99 132	64.54 123	89.09 157	80.73 205

ambc				70.10 187	77.74 180	50.21 213	74.28 152	77.93 176		79.26 126	88.29 113	54.11 223	79.77 157	64.43 124	91.10 109	80.30 213

lessismore_v0				72.75 152	79.60 149	56.83 176		57.37 313		83.80 74	89.01 98	47.45 259	78.74 171	64.39 125	86.49 195	82.69 167

baseline		73.10 129	73.96 117	70.51 178	71.46 263	46.39 251	72.08 166	84.40 65	55.95 173	76.62 167	86.46 151	67.20 109	78.03 192	64.22 126	87.27 182	87.11 61

EGC-MVSNET		64.77 229	61.17 260	75.60 107	86.90 47	74.47 35	84.04 38	68.62 261	0.60 375	1.13 377	91.61 27	65.32 132	74.15 241	64.01 127	88.28 161	78.17 239

CANet		73.00 135	71.84 152	76.48 95	75.82 207	61.28 138	74.81 141	80.37 136	63.17 110	62.43 306	80.50 231	61.10 168	85.16 60	64.00 128	84.34 222	83.01 157

tttt0517		69.46 177	67.79 203	74.46 117	75.34 210	52.72 200	75.05 137	63.27 289	54.69 187	78.87 130	84.37 184	26.63 357	81.15 126	63.95 129	87.93 169	89.51 24

casdiffmvs		73.06 132	73.84 118	70.72 174	71.32 264	46.71 247	70.93 189	84.26 70	55.62 176	77.46 150	87.10 124	67.09 110	77.81 195	63.95 129	86.83 190	87.64 50

Vis-MVSNet		74.85 109	74.56 107	75.72 105	81.63 131	64.64 112	76.35 123	79.06 154	62.85 112	73.33 214	88.41 108	62.54 150	79.59 160	63.94 131	82.92 238	82.94 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-CasMVS		80.41 52	82.86 39	73.07 142	89.93 7	39.21 302	77.15 115	81.28 114	79.74 6	90.87 4	92.73 11	75.03 45	84.93 62	63.83 132	95.19 16	95.07 3

DTE-MVSNet		80.35 53	82.89 38	72.74 153	89.84 8	37.34 320	77.16 114	81.81 104	80.45 4	90.92 3	92.95 7	74.57 51	86.12 30	63.65 133	94.68 33	94.76 6

h-mvs33		73.08 130	71.61 157	77.48 82	83.89 96	72.89 49	70.47 194	71.12 244	54.28 194	77.89 141	83.41 195	49.04 248	80.98 133	63.62 134	90.77 122	78.58 232

hse-mvs2		72.32 148	70.66 170	77.31 87	83.10 107	71.77 52	69.19 209	71.45 235	54.28 194	77.89 141	78.26 262	49.04 248	79.23 162	63.62 134	89.13 156	80.92 197

c3_l		69.82 173	69.89 174	69.61 193	66.24 307	43.48 271	68.12 227	79.61 146	51.43 230	77.72 146	80.18 237	54.61 221	78.15 191	63.62 134	87.50 174	87.20 59

bset_n11_16_dypcd		66.91 212	65.84 220	70.12 186	72.95 253	53.54 195	63.64 285	68.65 260	48.54 260	72.54 224	74.28 298	40.58 296	78.54 174	63.52 137	87.82 170	78.29 236

CP-MVSNet		79.48 60	81.65 50	72.98 145	89.66 13	39.06 304	76.76 119	80.46 133	78.91 8	90.32 7	91.70 24	68.49 98	84.89 63	63.40 138	95.12 19	95.01 4

GeoE		73.14 128	73.77 121	71.26 171	78.09 173	52.64 201	74.32 150	79.56 148	56.32 170	76.35 176	83.36 200	70.76 79	77.96 193	63.32 139	81.84 249	83.18 152

PC_three_1452									46.98 274	81.83 94	86.28 154	66.55 119	84.47 71	63.31 140	90.78 120	83.49 139

PEN-MVS		80.46 51	82.91 37	73.11 141	89.83 9	39.02 305	77.06 117	82.61 95	80.04 5	90.60 6	92.85 9	74.93 47	85.21 57	63.15 141	95.15 18	95.09 2

MSLP-MVS++		74.48 111	75.78 97	70.59 176	84.66 81	62.40 129	78.65 94	84.24 71	60.55 129	77.71 147	81.98 214	63.12 145	77.64 199	62.95 142	88.14 163	71.73 292

EI-MVSNet		69.61 175	69.01 184	71.41 170	73.94 237	49.90 215	71.31 183	71.32 237	58.22 146	75.40 185	70.44 328	58.16 193	75.85 216	62.51 143	79.81 274	88.48 43

IterMVS-LS		73.01 134	73.12 135	72.66 155	73.79 239	49.90 215	71.63 177	78.44 167	58.22 146	80.51 112	86.63 145	58.15 194	79.62 158	62.51 143	88.20 162	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		78.44 73	79.29 67	75.90 103	81.86 127	65.33 106	79.05 91	84.63 60	74.83 20	80.41 114	86.27 155	71.68 71	83.45 88	62.45 145	92.40 85	78.92 229

AUN-MVS		70.22 166	67.88 202	77.22 88	82.96 111	71.61 53	69.08 210	71.39 236	49.17 255	71.70 233	78.07 267	37.62 313	79.21 163	61.81 146	89.15 154	80.82 200

MVS_111021_LR		72.10 150	71.82 153	72.95 146	79.53 150	73.90 41	70.45 195	66.64 268	56.87 162	76.81 164	81.76 218	68.78 94	71.76 264	61.81 146	83.74 230	73.18 277

OPU-MVS				78.65 66	83.44 101	66.85 95	83.62 45				86.12 162	66.82 114	86.01 31	61.72 148	89.79 142	83.08 154

RRT_test8_iter05		65.80 218	65.13 227	67.80 222	67.02 302	40.85 292	67.13 242	75.33 201	49.73 249	72.69 221	81.32 221	24.45 367	77.37 202	61.69 149	86.82 191	85.18 87

dcpmvs_2		71.02 159	72.65 142	66.16 238	76.06 205	50.49 210	71.97 169	79.36 150	50.34 242	82.81 85	83.63 193	64.38 140	67.27 295	61.54 150	83.71 232	80.71 207

MVS_111021_HR		72.98 137	72.97 139	72.99 144	80.82 137	65.47 105	68.81 214	72.77 220	57.67 154	75.76 179	82.38 211	71.01 77	77.17 203	61.38 151	86.15 196	76.32 253

nrg030		74.87 108	75.99 96	71.52 168	74.90 217	49.88 218	74.10 153	82.58 96	54.55 191	83.50 77	89.21 90	71.51 72	75.74 220	61.24 152	92.34 86	88.94 36

IterMVS-SCA-FT		67.68 203	66.07 218	72.49 158	73.34 244	58.20 170	63.80 283	65.55 274	48.10 263	76.91 159	82.64 208	45.20 266	78.84 168	61.20 153	77.89 293	80.44 212

miper_ehance_all_eth		68.36 193	68.16 199	68.98 202	65.14 318	43.34 273	67.07 243	78.92 157	49.11 256	76.21 177	77.72 269	53.48 225	77.92 194	61.16 154	84.59 218	85.68 83

ITE_SJBPF				80.35 44	76.94 191	73.60 43		80.48 132	66.87 67	83.64 76	86.18 158	70.25 83	79.90 156	61.12 155	88.95 158	87.56 52

DIV-MVS_self_test		68.27 196	68.26 194	68.29 214	64.98 319	43.67 269	65.89 257	74.67 207	50.04 247	76.86 162	82.43 209	48.74 252	75.38 222	60.94 156	89.81 140	85.81 77

cl____		68.26 197	68.26 194	68.29 214	64.98 319	43.67 269	65.89 257	74.67 207	50.04 247	76.86 162	82.42 210	48.74 252	75.38 222	60.92 157	89.81 140	85.80 81

3Dnovator	65.95 11	71.50 155	71.22 163	72.34 161	73.16 246	63.09 127	78.37 98	78.32 168	57.67 154	72.22 229	84.61 180	54.77 218	78.47 177	60.82 158	81.07 259	75.45 259

cl22		67.14 208	66.51 215	69.03 201	63.20 328	43.46 272	66.88 248	76.25 191	49.22 254	74.48 198	77.88 268	45.49 265	77.40 201	60.64 159	84.59 218	86.24 69

Effi-MVS+		72.10 150	72.28 147	71.58 166	74.21 235	50.33 211	74.72 146	82.73 93	62.62 113	70.77 248	76.83 276	69.96 85	80.97 134	60.20 160	78.43 287	83.45 144

eth_miper_zixun_eth		69.42 178	68.73 190	71.50 169	67.99 292	46.42 249	67.58 232	78.81 158	50.72 239	78.13 139	80.34 233	50.15 243	80.34 146	60.18 161	84.65 216	87.74 49

TSAR-MVS + GP.		73.08 130	71.60 158	77.54 81	78.99 164	70.73 63	74.96 138	69.38 255	60.73 128	74.39 200	78.44 260	57.72 202	82.78 100	60.16 162	89.60 144	79.11 227

DPM-MVS		69.98 170	69.22 181	72.26 163	82.69 115	58.82 164	70.53 193	81.23 116	47.79 268	64.16 294	80.21 234	51.32 237	83.12 95	60.14 163	84.95 215	74.83 265

114514_t		73.40 124	73.33 131	73.64 131	84.15 92	57.11 172	78.20 103	80.02 141	43.76 293	72.55 223	86.07 165	64.00 142	83.35 91	60.14 163	91.03 111	80.45 211

TAPA-MVS	65.27 12	75.16 101	74.29 113	77.77 78	74.86 218	68.08 84	77.89 106	84.04 77	55.15 180	76.19 178	83.39 196	66.91 112	80.11 154	60.04 165	90.14 133	85.13 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MG-MVS		70.47 165	71.34 162	67.85 219	79.26 154	40.42 297	74.67 148	75.15 205	58.41 145	68.74 269	88.14 118	56.08 215	83.69 82	59.90 166	81.71 253	79.43 224

CSCG		74.12 114	74.39 110	73.33 136	79.35 152	61.66 136	77.45 110	81.98 102	62.47 116	79.06 128	80.19 236	61.83 157	78.79 170	59.83 167	87.35 178	79.54 222

Gipuma		69.55 176	72.83 140	59.70 293	63.63 327	53.97 192	80.08 80	75.93 194	64.24 97	73.49 211	88.93 101	57.89 201	62.46 318	59.75 168	91.55 98	62.67 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
thisisatest0530		67.05 211	65.16 224	72.73 154	73.10 250	50.55 209	71.26 185	63.91 285	50.22 244	74.46 199	80.75 227	26.81 356	80.25 149	59.43 169	86.50 194	87.37 56

v148		69.38 180	69.39 177	69.36 195	69.14 285	44.56 262	68.83 213	72.70 221	54.79 185	78.59 131	84.12 187	54.69 219	76.74 212	59.40 170	82.20 244	86.79 64

旧先验2							71.17 186		45.11 285	78.54 134			61.28 323	59.19 171

LF4IMVS		67.50 204	67.31 209	68.08 217	58.86 351	61.93 132	71.43 179	75.90 195	44.67 288	72.42 226	80.20 235	57.16 204	70.44 274	58.99 172	86.12 197	71.88 290

ETV-MVS		72.72 142	72.16 150	74.38 121	76.90 193	55.95 178	73.34 156	84.67 57	62.04 117	72.19 230	70.81 326	65.90 125	85.24 56	58.64 173	84.96 214	81.95 182

DELS-MVS		68.83 186	68.31 192	70.38 179	70.55 273	48.31 226	63.78 284	82.13 99	54.00 203	68.96 265	75.17 287	58.95 187	80.06 155	58.55 174	82.74 240	82.76 164
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		73.91 115	74.16 114	73.16 140	81.90 126	53.50 196	81.28 64	81.40 111	66.17 75	73.30 215	83.31 201	59.96 176	83.10 96	58.45 175	81.66 254	82.87 160

Anonymous20231211		75.54 96	77.19 85	70.59 176	77.67 182	45.70 257	74.73 145	80.19 138	68.80 56	82.95 81	92.91 8	66.26 121	76.76 211	58.41 176	92.77 79	89.30 26

miper_enhance_ethall		65.86 217	65.05 232	68.28 216	61.62 336	42.62 280	64.74 272	77.97 174	42.52 302	73.42 213	72.79 311	49.66 244	77.68 198	58.12 177	84.59 218	84.54 111

IS-MVSNet		75.10 102	75.42 102	74.15 125	79.23 155	48.05 231	79.43 84	78.04 173	70.09 52	79.17 127	88.02 120	53.04 226	83.60 84	58.05 178	93.76 64	90.79 17

FC-MVSNet-test		73.32 126	74.78 106	68.93 205	79.21 156	36.57 322	71.82 176	79.54 149	57.63 156	82.57 88	90.38 64	59.38 183	78.99 166	57.91 179	94.56 35	91.23 12

RPSCF		75.76 93	74.37 111	79.93 46	74.81 219	77.53 20	77.53 109	79.30 151	59.44 137	78.88 129	89.80 80	71.26 76	73.09 246	57.45 180	80.89 262	89.17 30

alignmvs		70.54 164	71.00 165	69.15 199	73.50 240	48.04 232	69.85 201	79.62 144	53.94 206	76.54 170	82.00 213	59.00 186	74.68 233	57.32 181	87.21 183	84.72 102

canonicalmvs		72.29 149	73.38 128	69.04 200	74.23 231	47.37 241	73.93 154	83.18 85	54.36 193	76.61 168	81.64 220	72.03 66	75.34 224	57.12 182	87.28 181	84.40 118

UniMVSNet (Re)		75.00 104	75.48 101	73.56 133	83.14 103	47.92 233	70.41 196	81.04 122	63.67 102	79.54 122	86.37 153	62.83 146	81.82 116	57.10 183	95.25 15	90.94 15

原ACMM1				73.90 127	85.90 63	65.15 110		81.67 106	50.97 236	74.25 201	86.16 160	61.60 160	83.54 85	56.75 184	91.08 110	73.00 278

FIs		72.56 145	73.80 119	68.84 208	78.74 167	37.74 316	71.02 187	79.83 143	56.12 171	80.88 110	89.45 85	58.18 192	78.28 186	56.63 185	93.36 70	90.51 19

xiu_mvs_v1_base_debu		67.87 199	67.07 211	70.26 181	79.13 159	61.90 133	67.34 236	71.25 240	47.98 264	67.70 273	74.19 301	61.31 163	72.62 251	56.51 186	78.26 289	76.27 254

xiu_mvs_v1_base		67.87 199	67.07 211	70.26 181	79.13 159	61.90 133	67.34 236	71.25 240	47.98 264	67.70 273	74.19 301	61.31 163	72.62 251	56.51 186	78.26 289	76.27 254

xiu_mvs_v1_base_debi		67.87 199	67.07 211	70.26 181	79.13 159	61.90 133	67.34 236	71.25 240	47.98 264	67.70 273	74.19 301	61.31 163	72.62 251	56.51 186	78.26 289	76.27 254

Effi-MVS+-dtu		75.43 97	72.28 147	84.91 2	77.05 187	83.58 1	78.47 97	77.70 177	57.68 152	74.89 189	78.13 266	64.80 136	84.26 76	56.46 189	85.32 207	86.88 63

mvs-test1		73.81 117	70.69 169	83.18 5	77.05 187	81.39 3	75.39 134	77.70 177	57.68 152	71.19 245	74.72 291	64.80 136	83.66 83	56.46 189	81.19 258	84.50 116

MVSTER		63.29 245	61.60 257	68.36 212	59.77 348	46.21 252	60.62 307	71.32 237	41.83 305	75.40 185	79.12 253	30.25 349	75.85 216	56.30 191	79.81 274	83.03 156

UniMVSNet_NR-MVSNet		74.90 107	75.65 98	72.64 156	83.04 108	45.79 253	69.26 207	78.81 158	66.66 72	81.74 97	86.88 131	63.26 144	81.07 130	56.21 192	94.98 21	91.05 13

DU-MVS		74.91 106	75.57 100	72.93 147	83.50 98	45.79 253	69.47 204	80.14 140	65.22 86	81.74 97	87.08 125	61.82 158	81.07 130	56.21 192	94.98 21	91.93 8

RPMNet		65.77 219	65.08 231	67.84 220	66.37 304	48.24 228	70.93 189	86.27 23	54.66 188	61.35 310	86.77 137	33.29 325	85.67 46	55.93 194	70.17 333	69.62 311

CLD-MVS		72.88 139	72.36 146	74.43 119	77.03 189	54.30 190	68.77 217	83.43 84	52.12 221	76.79 165	74.44 295	69.54 89	83.91 79	55.88 195	93.25 72	85.09 89
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_lstm_enhance		61.97 258	61.63 256	62.98 266	60.04 344	45.74 256	47.53 349	70.95 245	44.04 290	73.06 217	78.84 257	39.72 301	60.33 324	55.82 196	84.64 217	82.88 159

AllTest		77.66 77	77.43 82	78.35 71	79.19 157	70.81 61	78.60 95	88.64 3	65.37 83	80.09 118	88.17 115	70.33 81	78.43 180	55.60 197	90.90 116	85.81 77

TestCases				78.35 71	79.19 157	70.81 61		88.64 3	65.37 83	80.09 118	88.17 115	70.33 81	78.43 180	55.60 197	90.90 116	85.81 77

EU-MVSNet		60.82 267	60.80 265	60.86 287	68.37 287	41.16 287	72.27 163	68.27 263	26.96 366	69.08 262	75.71 281	32.09 334	67.44 292	55.59 199	78.90 282	73.97 270

TranMVSNet+NR-MVSNet		76.13 89	77.66 81	71.56 167	84.61 83	42.57 281	70.98 188	78.29 170	68.67 59	83.04 79	89.26 88	72.99 63	80.75 140	55.58 200	95.47 10	91.35 11

OpenMVS	62.51 15	68.76 188	68.75 188	68.78 209	70.56 272	53.91 193	78.29 100	77.35 182	48.85 258	70.22 254	83.52 194	52.65 229	76.93 206	55.31 201	81.99 246	75.49 258

QAPM		69.18 183	69.26 179	68.94 204	71.61 262	52.58 202	80.37 73	78.79 160	49.63 251	73.51 210	85.14 177	53.66 224	79.12 164	55.11 202	75.54 303	75.11 263

NR-MVSNet		73.62 120	74.05 115	72.33 162	83.50 98	43.71 268	65.65 262	77.32 183	64.32 96	75.59 181	87.08 125	62.45 151	81.34 122	54.90 203	95.63 8	91.93 8

EG-PatchMatch MVS		70.70 162	70.88 166	70.16 184	82.64 116	58.80 165	71.48 178	73.64 213	54.98 181	76.55 169	81.77 217	61.10 168	78.94 167	54.87 204	80.84 263	72.74 282

jason		64.47 234	62.84 248	69.34 197	76.91 192	59.20 158	67.15 241	65.67 271	35.29 338	65.16 288	76.74 277	44.67 270	70.68 270	54.74 205	79.28 280	78.14 240
jason: jason.
Baseline_NR-MVSNet		70.62 163	73.19 132	62.92 269	76.97 190	34.44 338	68.84 212	70.88 247	60.25 131	79.50 123	90.53 52	61.82 158	69.11 282	54.67 206	95.27 14	85.22 85

UniMVSNet_ETH3D		76.74 85	79.02 68	69.92 191	89.27 20	43.81 267	74.47 149	71.70 229	72.33 37	85.50 49	93.65 3	77.98 22	76.88 208	54.60 207	91.64 94	89.08 31

无先验							74.82 140	70.94 246	47.75 269				76.85 209	54.47 208		72.09 289

1121		69.23 181	68.26 194	72.12 164	88.36 37	71.40 55	68.59 219	62.06 296	43.80 292	74.75 191	86.18 158	52.92 227	76.85 209	54.47 208	83.27 236	68.12 319

testdata				64.13 251	85.87 65	63.34 125		61.80 299	47.83 267	76.42 175	86.60 147	48.83 251	62.31 320	54.46 210	81.26 257	66.74 329

PVSNet_Blended_VisFu		70.04 168	68.88 185	73.53 134	82.71 114	63.62 121	74.81 141	81.95 103	48.53 261	67.16 279	79.18 252	51.42 236	78.38 182	54.39 211	79.72 277	78.60 231

EPNet		69.10 184	67.32 208	74.46 117	68.33 289	61.27 139	77.56 108	63.57 287	60.95 124	56.62 332	82.75 207	51.53 235	81.24 125	54.36 212	90.20 129	80.88 199
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EIA-MVS		68.59 191	67.16 210	72.90 148	75.18 213	55.64 183	69.39 205	81.29 113	52.44 218	64.53 290	70.69 327	60.33 173	82.30 109	54.27 213	76.31 298	80.75 204

patch_mono-2		62.73 253	64.08 235	58.68 300	70.36 275	55.87 180	60.84 306	64.11 284	41.23 309	64.04 295	78.22 263	60.00 175	48.80 342	54.17 214	83.71 232	71.37 294

ET-MVSNet_ETH3D		63.32 244	60.69 266	71.20 172	70.15 277	55.66 182	65.02 270	64.32 283	43.28 301	68.99 264	72.05 320	25.46 363	78.19 190	54.16 215	82.80 239	79.74 221

EPP-MVSNet		73.86 116	73.38 128	75.31 110	78.19 171	53.35 198	80.45 70	77.32 183	65.11 88	76.47 173	86.80 133	49.47 245	83.77 81	53.89 216	92.72 82	88.81 40

lupinMVS		63.36 243	61.49 258	68.97 203	74.93 215	59.19 159	65.80 260	64.52 282	34.68 343	63.53 302	74.25 299	43.19 279	70.62 271	53.88 217	78.67 285	77.10 249

CNLPA		73.44 122	73.03 137	74.66 115	78.27 170	75.29 31	75.99 129	78.49 166	65.39 82	75.67 180	83.22 206	61.23 166	66.77 301	53.70 218	85.33 206	81.92 183

CVMVSNet		59.21 279	58.44 282	61.51 279	73.94 237	47.76 236	71.31 183	64.56 281	26.91 367	60.34 318	70.44 328	36.24 318	67.65 290	53.57 219	68.66 341	69.12 316

CANet_DTU		64.04 240	63.83 237	64.66 247	68.39 286	42.97 277	73.45 155	74.50 210	52.05 223	54.78 339	75.44 286	43.99 274	70.42 275	53.49 220	78.41 288	80.59 209

D2MVS		62.58 254	61.05 262	67.20 227	63.85 324	47.92 233	56.29 326	69.58 254	39.32 318	70.07 255	78.19 264	34.93 321	72.68 249	53.44 221	83.74 230	81.00 195

Anonymous20240521		63.55 242	66.07 218	55.99 309	66.18 309	44.04 266	68.77 217	68.80 257	46.99 273	72.57 222	85.84 169	39.87 300	50.22 340	53.40 222	92.23 88	73.71 274

PM-MVS		64.49 233	63.61 240	67.14 229	76.68 196	75.15 32	68.49 223	42.85 359	51.17 235	77.85 143	80.51 230	45.76 261	66.31 304	52.83 223	76.35 297	59.96 352

API-MVS		70.97 160	71.51 160	69.37 194	75.20 212	55.94 179	80.99 65	76.84 187	62.48 115	71.24 243	77.51 272	61.51 162	80.96 138	52.04 224	85.76 201	71.22 297

Fast-Effi-MVS+-dtu		70.00 169	68.74 189	73.77 129	73.47 241	64.53 114	71.36 181	78.14 172	55.81 175	68.84 268	74.71 292	65.36 131	75.75 219	52.00 225	79.00 281	81.03 193

mvs_anonymous		65.08 225	65.49 221	63.83 255	63.79 325	37.60 318	66.52 252	69.82 253	43.44 297	73.46 212	86.08 164	58.79 189	71.75 265	51.90 226	75.63 302	82.15 179

Patchmatch-RL test		59.95 274	59.12 275	62.44 272	72.46 256	54.61 189	59.63 313	47.51 352	41.05 312	74.58 197	74.30 297	31.06 343	65.31 306	51.61 227	79.85 273	67.39 322

F-COLMAP		75.29 99	73.99 116	79.18 56	81.73 129	71.90 51	81.86 62	82.98 88	59.86 135	72.27 227	84.00 189	64.56 139	83.07 97	51.48 228	87.19 184	82.56 171

pmmvs6		71.82 152	73.66 123	66.31 237	75.94 206	42.01 283	66.99 244	72.53 223	63.45 106	76.43 174	92.78 10	72.95 64	69.69 278	51.41 229	90.46 126	87.22 57

IterMVS		63.12 247	62.48 251	65.02 246	66.34 306	52.86 199	63.81 282	62.25 292	46.57 276	71.51 240	80.40 232	44.60 271	66.82 300	51.38 230	75.47 304	75.38 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		70.81 161	71.44 161	68.91 206	79.07 162	46.51 248	67.82 230	70.83 248	61.23 121	74.07 206	88.69 103	59.86 178	75.62 221	51.11 231	90.28 128	84.61 107

KD-MVS_self_test		66.38 215	67.51 205	62.97 267	61.76 334	34.39 339	58.11 321	75.30 202	50.84 238	77.12 154	85.42 173	56.84 210	69.44 279	51.07 232	91.16 106	85.08 91

新几何1				69.99 189	88.37 36	71.34 57		62.08 295	43.85 291	74.99 188	86.11 163	52.85 228	70.57 272	50.99 233	83.23 237	68.05 320

Anonymous20240529		72.56 145	73.79 120	68.86 207	76.89 194	45.21 259	68.80 216	77.25 185	67.16 65	76.89 160	90.44 55	65.95 124	74.19 240	50.75 234	90.00 135	87.18 60

UGNet		70.20 167	69.05 182	73.65 130	76.24 200	63.64 120	75.87 130	72.53 223	61.48 120	60.93 316	86.14 161	52.37 230	77.12 204	50.67 235	85.21 208	80.17 216
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		62.91 249	61.66 254	66.66 235	67.09 301	44.49 263	61.18 304	69.36 256	51.33 232	69.33 261	74.47 294	36.83 316	74.94 229	50.60 236	74.72 310	80.57 210

Fast-Effi-MVS+		68.81 187	68.30 193	70.35 180	74.66 226	48.61 224	66.06 255	78.32 168	50.62 240	71.48 241	75.54 283	68.75 95	79.59 160	50.55 237	78.73 284	82.86 161

WR-MVS		71.20 156	72.48 144	67.36 225	84.98 76	35.70 330	64.43 277	68.66 259	65.05 89	81.49 100	86.43 152	57.57 203	76.48 213	50.36 238	93.32 71	89.90 21

FMVSNet1		71.06 157	72.48 144	66.81 231	77.65 183	40.68 293	71.96 170	73.03 215	61.14 122	79.45 124	90.36 67	60.44 172	75.20 226	50.20 239	88.05 165	84.54 111

ANet_high		67.08 209	69.94 173	58.51 302	57.55 356	27.09 365	58.43 319	76.80 188	63.56 103	82.40 90	91.93 20	59.82 179	64.98 309	50.10 240	88.86 159	83.46 143

TransMVSNet (Re)		69.62 174	71.63 156	63.57 258	76.51 197	35.93 328	65.75 261	71.29 239	61.05 123	75.02 187	89.90 79	65.88 126	70.41 276	49.79 241	89.48 147	84.38 119

DP-MVS Recon		73.57 121	72.69 141	76.23 99	82.85 112	63.39 123	74.32 150	82.96 89	57.75 151	70.35 252	81.98 214	64.34 141	84.41 74	49.69 242	89.95 137	80.89 198

pm-mvs1		68.40 192	69.85 175	64.04 254	73.10 250	39.94 299	64.61 275	70.50 249	55.52 177	73.97 207	89.33 86	63.91 143	68.38 286	49.68 243	88.02 166	83.81 131

1314		59.83 275	58.86 278	62.74 270	65.71 312	44.78 261	68.59 219	72.63 222	33.54 350	61.05 314	67.29 352	43.62 277	71.26 267	49.49 244	67.84 344	72.19 288

CMPMVS	48.73 20	61.54 263	60.89 263	63.52 259	61.08 339	51.55 204	68.07 228	68.00 264	33.88 345	65.87 284	81.25 223	37.91 311	67.71 289	49.32 245	82.60 241	71.31 296
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJ		64.27 238	63.73 239	65.90 241	77.82 179	51.42 205	63.33 289	72.33 225	45.09 286	61.60 308	68.04 347	62.39 152	73.95 242	49.07 246	73.87 316	72.34 285

xiu_mvs_v2_base		64.43 235	63.96 236	65.85 242	77.72 181	51.32 206	63.63 286	72.31 226	45.06 287	61.70 307	69.66 336	62.56 148	73.93 243	49.06 247	73.91 315	72.31 286

thisisatest0515		60.48 271	57.86 285	68.34 213	67.25 299	46.42 249	60.58 308	62.14 293	40.82 313	63.58 301	69.12 339	26.28 359	78.34 184	48.83 248	82.13 245	80.26 214

OpenMVS_ROB	54.93 17	63.23 246	63.28 243	63.07 265	69.81 278	45.34 258	68.52 222	67.14 265	43.74 294	70.61 250	79.22 250	47.90 258	72.66 250	48.75 249	73.84 317	71.21 298

PCF-MVS	63.80 13	72.70 143	71.69 154	75.72 105	78.10 172	60.01 154	73.04 157	81.50 108	45.34 283	79.66 121	84.35 185	65.15 133	82.65 103	48.70 250	89.38 151	84.50 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		68.69 190	68.20 198	70.14 185	76.40 198	53.90 194	64.62 274	73.48 214	58.01 148	73.91 208	81.78 216	59.09 185	78.22 187	48.59 251	77.96 292	78.31 235

VDDNet		71.60 154	73.13 134	67.02 230	86.29 53	41.11 288	69.97 198	66.50 269	68.72 58	74.74 192	91.70 24	59.90 177	75.81 218	48.58 252	91.72 91	84.15 125

CR-MVSNet		58.96 280	58.49 281	60.36 290	66.37 304	48.24 228	70.93 189	56.40 321	32.87 351	61.35 310	86.66 142	33.19 326	63.22 317	48.50 253	70.17 333	69.62 311

testdata2													67.30 294	48.34 254

tfpnnormal		66.48 214	67.93 200	62.16 275	73.40 243	36.65 321	63.45 287	64.99 277	55.97 172	72.82 220	87.80 121	57.06 208	69.10 283	48.31 255	87.54 172	80.72 206

PAPR		69.20 182	68.66 191	70.82 173	75.15 214	47.77 235	75.31 135	81.11 118	49.62 252	66.33 282	79.27 248	61.53 161	82.96 98	48.12 256	81.50 256	81.74 185

FMVSNet2		67.48 205	68.21 197	65.29 243	73.14 247	38.94 306	68.81 214	71.21 243	54.81 182	76.73 166	86.48 150	48.63 254	74.60 234	47.98 257	86.11 198	82.35 175

AdaColmap		74.22 113	74.56 107	73.20 139	81.95 125	60.97 142	79.43 84	80.90 124	65.57 79	72.54 224	81.76 218	70.98 78	85.26 54	47.88 258	90.00 135	73.37 275

cascas		64.59 231	62.77 249	70.05 188	75.27 211	50.02 214	61.79 299	71.61 230	42.46 303	63.68 300	68.89 343	49.33 247	80.35 145	47.82 259	84.05 225	79.78 220

VPA-MVSNet		68.71 189	70.37 171	63.72 256	76.13 202	38.06 314	64.10 279	71.48 234	56.60 169	74.10 205	88.31 112	64.78 138	69.72 277	47.69 260	90.15 132	83.37 146

MSDG		67.47 206	67.48 207	67.46 224	70.70 269	54.69 188	66.90 247	78.17 171	60.88 125	70.41 251	74.76 289	61.22 167	73.18 245	47.38 261	76.87 295	74.49 267

GBi-Net		68.30 194	68.79 186	66.81 231	73.14 247	40.68 293	71.96 170	73.03 215	54.81 182	74.72 193	90.36 67	48.63 254	75.20 226	47.12 262	85.37 203	84.54 111

test1		68.30 194	68.79 186	66.81 231	73.14 247	40.68 293	71.96 170	73.03 215	54.81 182	74.72 193	90.36 67	48.63 254	75.20 226	47.12 262	85.37 203	84.54 111

FMVSNet3		65.00 226	65.16 224	64.52 249	69.47 281	37.56 319	66.63 250	70.38 250	51.55 229	74.72 193	83.27 203	37.89 312	74.44 236	47.12 262	85.37 203	81.57 187

PLC	62.01 16	71.79 153	70.28 172	76.33 97	80.31 143	68.63 82	78.18 104	81.24 115	54.57 190	67.09 280	80.63 229	59.44 181	81.74 120	46.91 265	84.17 223	78.63 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ppachtmachnet_test		60.26 273	59.61 273	62.20 274	67.70 296	44.33 264	58.18 320	60.96 301	40.75 314	65.80 285	72.57 312	41.23 289	63.92 313	46.87 266	82.42 243	78.33 234

test1111		64.62 230	65.19 223	62.93 268	79.01 163	29.91 358	65.45 265	54.41 328	54.09 201	71.47 242	88.48 107	37.02 315	74.29 239	46.83 267	89.94 138	84.58 110

MAR-MVS		67.72 202	66.16 217	72.40 160	74.45 229	64.99 111	74.87 139	77.50 181	48.67 259	65.78 286	68.58 346	57.01 209	77.79 196	46.68 268	81.92 247	74.42 268
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
LFMVS		67.06 210	67.89 201	64.56 248	78.02 174	38.25 311	70.81 192	59.60 304	65.18 87	71.06 246	86.56 148	43.85 275	75.22 225	46.35 269	89.63 143	80.21 215

test2506		61.23 264	60.85 264	62.38 273	78.80 165	27.88 364	67.33 239	37.42 372	54.23 196	67.55 276	88.68 104	17.87 376	74.39 237	46.33 270	89.41 149	84.86 96

BH-untuned		69.39 179	69.46 176	69.18 198	77.96 176	56.88 174	68.47 224	77.53 180	56.77 164	77.79 145	79.63 243	60.30 174	80.20 152	46.04 271	80.65 265	70.47 302

MDA-MVSNet-bldmvs		62.34 257	61.73 253	64.16 250	61.64 335	49.90 215	48.11 347	57.24 316	53.31 211	80.95 106	79.39 246	49.00 250	61.55 322	45.92 272	80.05 271	81.03 193

TinyColmap		67.98 198	69.28 178	64.08 252	67.98 293	46.82 245	70.04 197	75.26 203	53.05 212	77.36 151	86.79 134	59.39 182	72.59 254	45.64 273	88.01 167	72.83 280

test_yl		65.11 223	65.09 229	65.18 244	70.59 270	40.86 290	63.22 292	72.79 218	57.91 149	68.88 266	79.07 255	42.85 282	74.89 230	45.50 274	84.97 211	79.81 218

DCV-MVSNet		65.11 223	65.09 229	65.18 244	70.59 270	40.86 290	63.22 292	72.79 218	57.91 149	68.88 266	79.07 255	42.85 282	74.89 230	45.50 274	84.97 211	79.81 218

ECVR-MVS		64.82 227	65.22 222	63.60 257	78.80 165	31.14 354	66.97 245	56.47 320	54.23 196	69.94 256	88.68 104	37.23 314	74.81 232	45.28 276	89.41 149	84.86 96

DWT-MVSNet_test		53.04 306	51.12 319	58.77 299	61.23 337	38.67 307	62.16 297	57.74 309	38.24 324	51.76 349	59.07 363	21.36 370	67.40 293	44.80 277	63.76 352	70.25 305

PVSNet_BlendedMVS		65.38 221	64.30 233	68.61 210	69.81 278	49.36 219	65.60 264	78.96 155	45.50 279	59.98 319	78.61 258	51.82 232	78.20 188	44.30 278	84.11 224	78.27 237

PVSNet_Blended		62.90 250	61.64 255	66.69 234	69.81 278	49.36 219	61.23 303	78.96 155	42.04 304	59.98 319	68.86 344	51.82 232	78.20 188	44.30 278	77.77 294	72.52 283

Anonymous202405211		66.02 216	66.89 214	63.43 261	74.22 232	38.14 312	59.00 316	66.13 270	63.33 109	69.76 259	85.95 168	51.88 231	70.50 273	44.23 280	87.52 173	81.64 186

VPNet		65.58 220	67.56 204	59.65 294	79.72 147	30.17 357	60.27 310	62.14 293	54.19 199	71.24 243	86.63 145	58.80 188	67.62 291	44.17 281	90.87 119	81.18 190

Patchmtry		60.91 266	63.01 247	54.62 312	66.10 310	26.27 368	67.47 234	56.40 321	54.05 202	72.04 231	86.66 142	33.19 326	60.17 325	43.69 282	87.45 176	77.42 246

PatchT		53.35 305	56.47 295	43.99 345	64.19 323	17.46 376	59.15 314	43.10 358	52.11 222	54.74 340	86.95 129	29.97 352	49.98 341	43.62 283	74.40 313	64.53 342

IB-MVS	49.67 18	59.69 276	56.96 291	67.90 218	68.19 290	50.30 212	61.42 301	65.18 276	47.57 270	55.83 336	67.15 353	23.77 368	79.60 159	43.56 284	79.97 272	73.79 273
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		56.46 290	56.51 294	56.30 307	67.70 296	39.66 301	55.36 331	52.34 339	40.57 316	63.85 297	69.91 335	40.04 299	58.22 330	43.49 285	75.29 308	71.03 301

PatchmatchNet		54.60 299	54.27 305	55.59 310	65.17 317	39.08 303	66.92 246	51.80 340	39.89 317	58.39 325	73.12 309	31.69 337	58.33 329	43.01 286	58.38 363	69.38 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs-eth3d		64.41 236	63.27 244	67.82 221	75.81 208	60.18 152	69.49 203	62.05 297	38.81 323	74.13 203	82.23 212	43.76 276	68.65 284	42.53 287	80.63 267	74.63 266

LCM-MVSNet-Re		69.10 184	71.57 159	61.70 277	70.37 274	34.30 340	61.45 300	79.62 144	56.81 163	89.59 8	88.16 117	68.44 99	72.94 247	42.30 288	87.33 179	77.85 245

VNet		64.01 241	65.15 226	60.57 288	73.28 245	35.61 331	57.60 323	67.08 266	54.61 189	66.76 281	83.37 198	56.28 213	66.87 297	42.19 289	85.20 209	79.23 226

test-LLR		50.43 317	50.69 322	49.64 325	60.76 340	41.87 284	53.18 336	45.48 355	43.41 298	49.41 357	60.47 361	29.22 354	44.73 355	42.09 290	72.14 322	62.33 348

test-mter		48.56 322	48.20 327	49.64 325	60.76 340	41.87 284	53.18 336	45.48 355	31.91 357	49.41 357	60.47 361	18.34 374	44.73 355	42.09 290	72.14 322	62.33 348

MVS		60.62 270	59.97 270	62.58 271	68.13 291	47.28 242	68.59 219	73.96 212	32.19 352	59.94 321	68.86 344	50.48 240	77.64 199	41.85 292	75.74 300	62.83 344

MIMVSNet1		66.57 213	69.23 180	58.59 301	81.26 135	37.73 317	64.06 280	57.62 310	57.02 160	78.40 136	90.75 45	62.65 147	58.10 331	41.77 293	89.58 146	79.95 217

Vis-MVSNet (Re-imp)		62.74 252	63.21 245	61.34 282	72.19 258	31.56 351	67.31 240	53.87 329	53.60 209	69.88 257	83.37 198	40.52 297	70.98 269	41.40 294	86.78 192	81.48 188

MVS_0304		62.51 255	62.27 252	63.25 262	69.39 282	48.47 225	64.05 281	62.48 291	59.69 136	54.10 344	81.04 225	45.71 262	66.31 304	41.38 295	82.58 242	74.96 264

YYNet1		52.58 308	53.50 307	49.85 323	54.15 370	36.45 324	40.53 360	46.55 354	38.09 326	75.52 183	73.31 308	41.08 293	43.88 358	41.10 296	71.14 328	69.21 315

MDA-MVSNet_test_wron		52.57 309	53.49 308	49.81 324	54.24 369	36.47 323	40.48 361	46.58 353	38.13 325	75.47 184	73.32 307	41.05 294	43.85 359	40.98 297	71.20 327	69.10 317

1112_ss		59.48 277	58.99 277	60.96 286	77.84 178	42.39 282	61.42 301	68.45 262	37.96 327	59.93 322	67.46 349	45.11 268	65.07 308	40.89 298	71.81 324	75.41 260

tpmvs		55.84 292	55.45 302	57.01 306	60.33 343	33.20 345	65.89 257	59.29 306	47.52 271	56.04 334	73.60 304	31.05 344	68.06 288	40.64 299	64.64 349	69.77 309

TR-MVS		64.59 231	63.54 241	67.73 223	75.75 209	50.83 208	63.39 288	70.29 251	49.33 253	71.55 239	74.55 293	50.94 238	78.46 178	40.43 300	75.69 301	73.89 272

test_post1							66.63 250				2.08 375	30.66 347	59.33 327	40.34 301

SCA		58.57 284	58.04 284	60.17 291	70.17 276	41.07 289	65.19 268	53.38 334	43.34 300	61.00 315	73.48 305	45.20 266	69.38 280	40.34 301	70.31 332	70.05 306

baseline1		57.82 287	58.36 283	56.19 308	69.17 284	30.76 356	62.94 294	55.21 324	46.04 278	63.83 298	78.47 259	41.20 290	63.68 314	39.44 303	68.99 339	74.13 269

ab-mvs		64.11 239	65.13 227	61.05 284	71.99 260	38.03 315	67.59 231	68.79 258	49.08 257	65.32 287	86.26 156	58.02 200	66.85 299	39.33 304	79.79 276	78.27 237

tpmrst		50.15 318	51.38 317	46.45 336	56.05 361	24.77 370	64.40 278	49.98 344	36.14 334	53.32 346	69.59 337	35.16 320	48.69 343	39.24 305	58.51 362	65.89 331

CostFormer		57.35 289	56.14 297	60.97 285	63.76 326	38.43 308	67.50 233	60.22 302	37.14 331	59.12 324	76.34 279	32.78 329	71.99 261	39.12 306	69.27 338	72.47 284

pmmvs4		60.78 268	59.04 276	66.00 240	73.06 252	57.67 171	64.53 276	60.22 302	36.91 332	65.96 283	77.27 273	39.66 302	68.54 285	38.87 307	74.89 309	71.80 291

gm-plane-assit					62.51 330	33.91 342			37.25 330		62.71 358		72.74 248	38.70 308

Test_1112_low_res		58.78 282	58.69 279	59.04 298	79.41 151	38.13 313	57.62 322	66.98 267	34.74 341	59.62 323	77.56 271	42.92 281	63.65 315	38.66 309	70.73 330	75.35 262

thres600view7		61.82 260	61.38 259	63.12 264	71.81 261	34.93 335	64.64 273	56.99 317	54.78 186	70.33 253	79.74 241	32.07 335	72.42 256	38.61 310	83.46 234	82.02 180

UnsupCasMVSNet_eth		52.26 311	53.29 309	49.16 328	55.08 366	33.67 343	50.03 342	58.79 307	37.67 328	63.43 304	74.75 290	41.82 287	45.83 348	38.59 311	59.42 359	67.98 321

CL-MVSNet_self_test		62.44 256	63.40 242	59.55 295	72.34 257	32.38 347	56.39 325	64.84 278	51.21 234	67.46 277	81.01 226	50.75 239	63.51 316	38.47 312	88.12 164	82.75 165

MDTV_nov1_ep13			54.05 306		65.54 313	29.30 360	59.00 316	55.22 323	35.96 336	52.44 347	75.98 280	30.77 346	59.62 326	38.21 313	73.33 318

BH-w/o		64.81 228	64.29 234	66.36 236	76.08 204	54.71 187	65.61 263	75.23 204	50.10 246	71.05 247	71.86 321	54.33 222	79.02 165	38.20 314	76.14 299	65.36 334

TESTMET0.1,1		45.17 329	44.93 334	45.89 338	56.02 362	38.31 309	53.18 336	41.94 365	27.85 364	44.86 365	56.47 364	17.93 375	41.50 366	38.08 315	68.06 342	57.85 354

USDC		62.80 251	63.10 246	61.89 276	65.19 315	43.30 274	67.42 235	74.20 211	35.80 337	72.25 228	84.48 183	45.67 263	71.95 262	37.95 316	84.97 211	70.42 304

E-PMN		45.17 329	45.36 332	44.60 343	50.07 374	42.75 278	38.66 363	42.29 363	46.39 277	39.55 370	51.15 368	26.00 360	45.37 352	37.68 317	76.41 296	45.69 366

CDS-MVSNet		64.33 237	62.66 250	69.35 196	80.44 142	58.28 169	65.26 267	65.66 272	44.36 289	67.30 278	75.54 283	43.27 278	71.77 263	37.68 317	84.44 221	78.01 243
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		55.59 295	54.89 303	57.68 304	69.18 283	49.05 221	61.00 305	62.93 290	35.98 335	58.36 326	68.93 342	36.71 317	66.59 302	37.62 319	63.30 353	57.39 355

FPMVS		59.43 278	60.07 269	57.51 305	77.62 184	71.52 54	62.33 296	50.92 341	57.40 158	69.40 260	80.00 238	39.14 304	61.92 321	37.47 320	66.36 346	39.09 369

EPMVS		45.74 327	46.53 330	43.39 346	54.14 371	22.33 373	55.02 332	35.00 375	34.69 342	51.09 351	70.20 332	25.92 361	42.04 364	37.19 321	55.50 367	65.78 332

baseline2		55.57 296	52.74 310	64.05 253	65.26 314	44.11 265	62.38 295	54.43 327	39.03 321	51.21 350	67.35 351	33.66 324	72.45 255	37.14 322	64.22 351	75.60 257

EMVS		44.61 333	44.45 337	45.10 342	48.91 376	43.00 276	37.92 364	41.10 369	46.75 275	38.00 372	48.43 370	26.42 358	46.27 347	37.11 323	75.38 306	46.03 365

XXY-MVS		55.19 297	57.40 289	48.56 331	64.45 322	34.84 337	51.54 340	53.59 331	38.99 322	63.79 299	79.43 245	56.59 211	45.57 349	36.92 324	71.29 326	65.25 335

HyFIR lowres test		63.01 248	60.47 267	70.61 175	83.04 108	54.10 191	59.93 312	72.24 227	33.67 348	69.00 263	75.63 282	38.69 306	76.93 206	36.60 325	75.45 305	80.81 203

EPNet_dtu		58.93 281	58.52 280	60.16 292	67.91 294	47.70 237	69.97 198	58.02 308	49.73 249	47.28 360	73.02 310	38.14 308	62.34 319	36.57 326	85.99 199	70.43 303
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		52.05 313	51.58 315	53.44 314	52.11 372	31.20 352	44.88 356	64.83 279	41.53 307	64.37 291	70.03 333	15.61 380	64.20 310	36.25 327	74.61 311	64.93 338

miper_refine_blended		52.05 313	51.58 315	53.44 314	52.11 372	31.20 352	44.88 356	64.83 279	41.53 307	64.37 291	70.03 333	15.61 380	64.20 310	36.25 327	74.61 311	64.93 338

new-patchmatchnet		52.89 307	55.76 300	44.26 344	59.94 346	6.31 379	37.36 366	50.76 343	41.10 310	64.28 293	79.82 240	44.77 269	48.43 344	36.24 329	87.61 171	78.03 242

JIA-IIPM		54.03 302	51.62 314	61.25 283	59.14 350	55.21 185	59.10 315	47.72 351	50.85 237	50.31 356	85.81 170	20.10 373	63.97 312	36.16 330	55.41 368	64.55 341

PatchMatch-RL		58.68 283	57.72 286	61.57 278	76.21 201	73.59 44	61.83 298	49.00 348	47.30 272	61.08 312	68.97 341	50.16 242	59.01 328	36.06 331	68.84 340	52.10 359

thres100view900		61.17 265	61.09 261	61.39 281	72.14 259	35.01 334	65.42 266	56.99 317	55.23 179	70.71 249	79.90 239	32.07 335	72.09 258	35.61 332	81.73 250	77.08 250

tfpn200view9		60.35 272	59.97 270	61.51 279	70.78 267	35.35 332	63.27 290	57.47 311	53.00 213	68.31 270	77.09 274	32.45 332	72.09 258	35.61 332	81.73 250	77.08 250

thres400		60.77 269	59.97 270	63.15 263	70.78 267	35.35 332	63.27 290	57.47 311	53.00 213	68.31 270	77.09 274	32.45 332	72.09 258	35.61 332	81.73 250	82.02 180

MVP-Stereo		61.56 262	59.22 274	68.58 211	79.28 153	60.44 150	69.20 208	71.57 231	43.58 296	56.42 333	78.37 261	39.57 303	76.46 214	34.86 335	60.16 357	68.86 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TAMVS		65.31 222	63.75 238	69.97 190	82.23 121	59.76 157	66.78 249	63.37 288	45.20 284	69.79 258	79.37 247	47.42 260	72.17 257	34.48 336	85.15 210	77.99 244

tpm cat1		54.02 303	52.63 311	58.19 303	64.85 321	39.86 300	66.26 254	57.28 314	32.16 353	56.90 330	70.39 330	32.75 330	65.30 307	34.29 337	58.79 360	69.41 313

pmmvs5		52.49 310	52.58 312	52.21 320	54.99 367	32.38 347	55.45 330	53.84 330	32.15 354	55.49 338	74.81 288	38.08 309	57.37 332	34.02 338	74.40 313	66.88 326

CHOSEN 1792x2688		58.09 285	56.30 296	63.45 260	79.95 145	50.93 207	54.07 334	65.59 273	28.56 363	61.53 309	74.33 296	41.09 292	66.52 303	33.91 339	67.69 345	72.92 279

HY-MVS	49.31 19	57.96 286	57.59 287	59.10 297	66.85 303	36.17 325	65.13 269	65.39 275	39.24 320	54.69 341	78.14 265	44.28 273	67.18 296	33.75 340	70.79 329	73.95 271

tpm2		56.12 291	54.64 304	60.55 289	66.24 307	36.01 326	68.14 226	56.77 319	33.60 349	58.25 327	75.52 285	30.25 349	74.33 238	33.27 341	69.76 337	71.32 295

MDTV_nov1_ep13_2view						18.41 375	53.74 335		31.57 358	44.89 364		29.90 353		32.93 342		71.48 293

tpm		50.60 316	52.42 313	45.14 341	65.18 316	26.29 367	60.30 309	43.50 357	37.41 329	57.01 329	79.09 254	30.20 351	42.32 362	32.77 343	66.36 346	66.81 328

sss		47.59 325	48.32 325	45.40 340	56.73 360	33.96 341	45.17 355	48.51 349	32.11 356	52.37 348	65.79 354	40.39 298	41.91 365	31.85 344	61.97 354	60.35 351

PMMVS		44.69 331	43.95 338	46.92 333	50.05 375	53.47 197	48.08 348	42.40 361	22.36 371	44.01 368	53.05 366	42.60 284	45.49 350	31.69 345	61.36 355	41.79 367

thres200		57.55 288	57.02 290	59.17 296	67.89 295	34.93 335	58.91 318	57.25 315	50.24 243	64.01 296	71.46 324	32.49 331	71.39 266	31.31 346	79.57 278	71.19 299

WTY-MVS		49.39 320	50.31 323	46.62 335	61.22 338	32.00 350	46.61 352	49.77 345	33.87 346	54.12 343	69.55 338	41.96 286	45.40 351	31.28 347	64.42 350	62.47 347

UnsupCasMVSNet_bld		50.01 319	51.03 321	46.95 332	58.61 352	32.64 346	48.31 345	53.27 335	34.27 344	60.47 317	71.53 323	41.40 288	47.07 346	30.68 348	60.78 356	61.13 350

PVSNet	43.83 21	51.56 315	51.17 318	52.73 317	68.34 288	38.27 310	48.22 346	53.56 332	36.41 333	54.29 342	64.94 356	34.60 322	54.20 337	30.34 349	69.87 335	65.71 333

test20.03		55.74 294	57.51 288	50.42 322	59.89 347	32.09 349	50.63 341	49.01 347	50.11 245	65.07 289	83.23 204	45.61 264	48.11 345	30.22 350	83.82 229	71.07 300

FMVSNet5		55.08 298	55.54 301	53.71 313	65.80 311	33.50 344	56.22 327	52.50 338	43.72 295	61.06 313	83.38 197	25.46 363	54.87 334	30.11 351	81.64 255	72.75 281

gg-mvs-nofinetune		55.75 293	56.75 293	52.72 318	62.87 329	28.04 363	68.92 211	41.36 367	71.09 44	50.80 352	92.63 12	20.74 371	66.86 298	29.97 352	72.41 321	63.25 343

dp		44.09 334	44.88 335	41.72 350	58.53 353	23.18 372	54.70 333	42.38 362	34.80 340	44.25 367	65.61 355	24.48 366	44.80 354	29.77 353	49.42 370	57.18 356

PAPM		61.79 261	60.37 268	66.05 239	76.09 203	41.87 284	69.30 206	76.79 189	40.64 315	53.80 345	79.62 244	44.38 272	82.92 99	29.64 354	73.11 319	73.36 276

testgi		54.00 304	56.86 292	45.45 339	58.20 354	25.81 369	49.05 343	49.50 346	45.43 282	67.84 272	81.17 224	51.81 234	43.20 361	29.30 355	79.41 279	67.34 324

Patchmatch-test		47.93 323	49.96 324	41.84 348	57.42 357	24.26 371	48.75 344	41.49 366	39.30 319	56.79 331	73.48 305	30.48 348	33.87 370	29.29 356	72.61 320	67.39 322

pmmvs3		46.71 326	45.09 333	51.55 321	56.76 359	48.25 227	55.78 329	39.53 371	24.13 370	50.35 355	63.40 357	15.90 379	51.08 338	29.29 356	70.69 331	55.33 358

N_pmnet		52.06 312	51.11 320	54.92 311	59.64 349	71.03 59	37.42 365	61.62 300	33.68 347	57.12 328	72.10 313	37.94 310	31.03 371	29.13 358	71.35 325	62.70 345

Anonymous20231206		54.13 301	55.82 299	49.04 330	70.89 266	35.96 327	51.73 339	50.87 342	34.86 339	62.49 305	79.22 250	42.52 285	44.29 357	27.95 359	81.88 248	66.88 326

CHOSEN 280x420		41.62 336	39.89 341	46.80 334	61.81 333	51.59 203	33.56 367	35.74 374	27.48 365	37.64 373	53.53 365	23.24 369	42.09 363	27.39 360	58.64 361	46.72 364

MIMVSNet		54.39 300	56.12 298	49.20 327	72.57 255	30.91 355	59.98 311	48.43 350	41.66 306	55.94 335	83.86 191	41.19 291	50.42 339	26.05 361	75.38 306	66.27 330

ADS-MVSNet2		48.76 321	47.25 329	53.29 316	55.90 363	40.54 296	47.34 350	54.99 326	31.41 359	50.48 353	72.06 318	31.23 340	54.26 336	25.93 362	55.93 365	65.07 336

ADS-MVSNet		44.62 332	45.58 331	41.73 349	55.90 363	20.83 374	47.34 350	39.94 370	31.41 359	50.48 353	72.06 318	31.23 340	39.31 367	25.93 362	55.93 365	65.07 336

test0.0.03 1		47.72 324	48.31 326	45.93 337	55.53 365	29.39 359	46.40 353	41.21 368	43.41 298	55.81 337	67.65 348	29.22 354	43.77 360	25.73 364	69.87 335	64.62 340

GG-mvs-BLEND				52.24 319	60.64 342	29.21 361	69.73 202	42.41 360		45.47 362	52.33 367	20.43 372	68.16 287	25.52 365	65.42 348	59.36 353

DSMNet-mixed		43.18 335	44.66 336	38.75 353	54.75 368	28.88 362	57.06 324	27.42 378	13.47 372	47.27 361	77.67 270	38.83 305	39.29 368	25.32 366	60.12 358	48.08 362

MVS-HIRNet		45.53 328	47.29 328	40.24 351	62.29 331	26.82 366	56.02 328	37.41 373	29.74 362	43.69 369	81.27 222	33.96 323	55.48 333	24.46 367	56.79 364	38.43 370

PVSNet_0	36.71 22	41.12 337	40.78 340	42.14 347	59.97 345	40.13 298	40.97 359	42.24 364	30.81 361	44.86 365	49.41 369	40.70 295	45.12 353	23.15 368	34.96 372	41.16 368

new_pmnet		37.55 339	39.80 342	30.79 354	56.83 358	16.46 377	39.35 362	30.65 376	25.59 368	45.26 363	61.60 360	24.54 365	28.02 373	21.60 369	52.80 369	47.90 363

MVE	27.91 23	36.69 340	35.64 343	39.84 352	43.37 377	35.85 329	19.49 369	24.61 379	24.68 369	39.05 371	62.63 359	38.67 307	27.10 374	21.04 370	47.25 371	56.56 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		61.97 258	66.25 216	49.12 329	58.19 355	60.77 148	66.32 253	52.97 336	55.93 174	90.62 5	86.91 130	73.07 62	35.98 369	20.63 371	91.63 95	50.62 360

PMMVS2		37.74 338	40.87 339	28.36 355	42.41 378	5.35 380	24.61 368	27.75 377	32.15 354	47.85 359	70.27 331	35.85 319	29.51 372	19.08 372	67.85 343	50.22 361

test_method		19.26 341	19.12 345	19.71 356	9.09 380	1.91 382	7.79 371	53.44 333	1.42 374	10.27 376	35.80 371	17.42 377	25.11 375	12.44 373	24.38 374	32.10 371

tmp_tt		11.98 343	14.73 346	3.72 358	2.28 381	4.62 381	19.44 370	14.50 381	0.47 376	21.55 374	9.58 374	25.78 362	4.57 377	11.61 374	27.37 373	1.96 373

DeepMVS_CX				11.83 357	15.51 379	13.86 378		11.25 382	5.76 373	20.85 375	26.46 372	17.06 378	9.22 376	9.69 375	13.82 375	12.42 372

testmvs		4.06 347	5.28 350	0.41 359	0.64 383	0.16 384	42.54 358	0.31 384	0.26 378	0.50 379	1.40 378	0.77 382	0.17 378	0.56 376	0.55 377	0.90 374

test123		4.43 346	5.78 349	0.39 360	0.97 382	0.28 383	46.33 354	0.45 383	0.31 377	0.62 378	1.50 377	0.61 383	0.11 379	0.56 376	0.63 376	0.77 375

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		17.71 342	23.62 344	0.00 361	0.00 384	0.00 385	0.00 372	70.17 252	0.00 379	0.00 380	74.25 299	68.16 102	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		5.20 345	6.93 348	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	62.39 152	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		5.62 344	7.50 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	67.46 349	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

test_one_0601					85.84 67	61.45 137		85.63 33	75.27 19	85.62 47	90.38 64	76.72 29

eth-test2					0.00 384

eth-test					0.00 384

test_241102_ONE					86.12 59	61.06 140		84.72 55	72.64 31	87.38 25	89.47 84	77.48 24	85.74 43

save fliter					87.00 43	67.23 91	79.24 87	77.94 175	56.65 167

test0726					86.16 57	60.78 146	83.81 42	85.10 46	72.48 34	85.27 53	89.96 77	78.57 18

GSMVS																70.05 306

test_part2					85.90 63	66.44 98				84.61 63

sam_mvs1												31.41 338				70.05 306

sam_mvs												31.21 342

MTGPA								80.63 128

test_post											1.99 376	30.91 345	54.76 335

patchmatchnet-post											68.99 340	31.32 339	69.38 280

MTMP							84.83 33	19.26 380

TEST9					85.47 69	69.32 77	76.42 121	78.69 161	53.73 208	76.97 155	86.74 138	66.84 113	81.10 128

test_8					85.09 75	67.89 86	76.26 126	78.66 163	54.00 203	76.89 160	86.72 140	66.60 117	80.89 139

agg_prior					84.44 87	66.02 102		78.62 164		76.95 157			80.34 146

test_prior4						70.14 69	77.57 107

test_prior				75.27 111	82.15 122	59.85 155		84.33 66					83.39 89			82.58 169

新几何2							71.33 182

旧先验1					84.55 84	60.36 151		63.69 286			87.05 128	54.65 220			83.34 235	69.66 310

原ACMM2							74.78 144

test222					87.30 41	69.15 80	67.85 229	59.59 305	41.06 311	73.05 218	85.72 171	48.03 257			80.65 265	66.92 325

segment_acmp												68.30 101

testdata1							68.34 225		57.24 159

test12				76.51 94	82.28 120	60.94 143		81.64 107		73.60 209		64.88 135	85.19 59		90.42 127	83.38 145

plane_prior7					85.18 72	66.21 100

plane_prior6					84.18 91	65.31 107						60.83 170

plane_prior4											89.11 95

plane_prior3						65.67 104			63.82 101	78.23 137

plane_prior2							82.74 54		65.45 80

plane_prior1					84.46 86

plane_prior						65.18 108	80.06 81		61.88 119						89.91 139

n2								0.00 385

nn								0.00 385

door-mid								55.02 325

test11								82.71 94

door								52.91 337

HQP5-MVS						58.80 165

HQP-NCC					82.37 117		77.32 111		59.08 138	71.58 235

ACMP_Plane					82.37 117		77.32 111		59.08 138	71.58 235

HQP4-MVS										71.59 234			85.31 52			83.74 134

HQP3-MVS								84.12 74							89.16 152

HQP2-MVS												58.09 195

NP-MVS					83.34 102	63.07 128					85.97 166

ACMMP++_ref															89.47 148

ACMMP++															91.96 90

Test By Simon												62.56 148

High-resolution multi-view benchmark