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.

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 128	92.29 7	95.97 2	74.28 30	97.24 13	88.58 30	96.91 1	94.87 18
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
DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 17	96.68 2	94.95 12

PC_three_1452									68.21 284	92.02 12	94.00 56	82.09 5	95.98 57	84.58 64	96.68 2	94.95 12

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 22	96.63 4	94.88 16

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 296	92.39 6				88.94 25	96.63 4	94.85 21

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 22	96.58 6	94.26 52

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 17	96.57 7	94.67 29

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 54	82.45 3	96.87 20	83.77 75	96.48 8	94.88 16

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 42	94.27 41	75.89 19	96.81 23	87.45 41	96.44 9	93.05 120

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 20	96.41 12	93.33 103
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				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 20	96.41 12	94.21 53

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 35	95.09 19	71.06 67	96.67 29	87.67 38	96.37 14	94.09 58

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 42	96.34 15	93.95 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 117	86.34 61	95.29 17	70.86 69	96.00 55	88.78 28	96.04 16	94.58 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 100	89.16 23	95.10 18	75.65 21	96.19 47	87.07 43	96.01 17	94.79 23

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 37	94.06 52	76.43 16	96.84 21	88.48 33	95.99 18	94.34 48

PHI-MVS		86.43 46	86.17 53	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 182	85.22 71	91.90 109	69.47 85	96.42 40	83.28 79	95.94 19	94.35 47

test_prior2							88.85 125		75.41 107	84.91 75	93.54 69	74.28 30		83.31 78	95.86 20

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 36	94.80 23	73.76 34	97.11 15	87.51 40	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 70	94.32 39	71.76 55	96.93 19	85.53 54	95.79 22	94.32 49

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 150	88.57 29	94.67 25	75.57 22	95.79 59	86.77 45	95.76 23

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 49	78.98 12	96.58 35	85.66 51	95.72 24	94.58 34

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 276	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

test9_res														84.90 57	95.70 26	92.87 127

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 19	95.65 27	94.47 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 132	86.57 1	87.39 51	94.97 21	71.70 57	97.68 1	92.19 1	95.63 28	95.57 1

agg_prior2														82.91 84	95.45 29	92.70 132

CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 283	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 117	94.23 44	72.13 51	97.09 16	84.83 60	95.37 31	93.65 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 212	92.02 98	79.45 22	85.88 63	94.80 23	68.07 104	96.21 46	86.69 46	95.34 32	93.23 106

DeepC-MVS_fast	79.65 3	86.91 38	86.62 44	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 93	93.36 77	71.44 61	96.76 25	80.82 105	95.33 33	94.16 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 185	82.14 3	86.65 59	94.28 40	68.28 103	97.46 6	90.81 6	95.31 34	95.15 8

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 102	94.40 36	72.24 49	96.28 43	85.65 52	95.30 35	93.62 90
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 174	84.86 78	92.89 88	76.22 17	96.33 41	84.89 59	95.13 36	94.40 44

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 156	90.51 65	92.90 57	77.26 59	87.44 50	91.63 119	71.27 64	96.06 50	85.62 53	95.01 37	94.78 24

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 79	93.99 58	70.67 72	96.82 22	84.18 72	95.01 37	93.90 69

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 176	88.58 28	94.52 27	73.36 35	96.49 38	84.26 68	95.01 37	92.70 132
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 65	93.47 73	73.02 42	97.00 18	84.90 57	94.94 40	94.10 57

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 82	94.52 27	68.81 96	96.65 30	84.53 65	94.90 41	94.00 63

SPE-MVS-test		86.29 50	86.48 45	85.71 76	91.02 91	67.21 173	92.36 30	93.78 19	78.97 33	83.51 109	91.20 134	70.65 73	95.15 87	81.96 94	94.89 42	94.77 25

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 75	94.44 34	70.78 70	96.61 32	84.53 65	94.89 42	93.66 83

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 215	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	94.84 44

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 87	94.52 27	69.09 90	96.70 27	84.37 67	94.83 45	94.03 61

原ACMM1				84.35 124	93.01 62	68.79 113		92.44 78	63.96 341	81.09 141	91.57 122	66.06 129	95.45 71	67.19 253	94.82 46	88.81 288

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 103	93.95 61	69.77 82	96.01 54	85.15 55	94.66 47	94.32 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NormalMVS		86.29 50	85.88 59	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 89	92.18 102	64.64 143	95.53 67	80.70 108	94.65 48	94.56 37

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 48	96.27 44	86.87 44	94.65 48	93.70 82

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 190	93.04 42	69.80 247	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 184

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 132	88.90 26	93.85 64	75.75 20	96.00 55	87.80 37	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		86.68 42	86.27 49	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 104	83.86 101	94.42 35	67.87 108	96.64 31	82.70 90	94.57 52	93.66 83

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	94.17 46	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

X-MVStestdata		80.37 178	77.83 215	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 455	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 119		71.67 58	96.16 48		94.50 53	93.54 95

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 155	89.63 91	92.65 71	72.89 179	84.64 83	91.71 115	71.85 53	96.03 51	84.77 62	94.45 56	94.49 40

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 105	94.46 31	67.93 106	95.95 58	84.20 71	94.39 57	93.23 106

CSCG		86.41 48	86.19 52	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 148	83.16 112	91.07 139	75.94 18	95.19 85	79.94 116	94.38 58	93.55 94

MSLP-MVS++		85.43 69	85.76 63	84.45 120	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 198	80.36 111	94.35 59	90.16 232

mPP-MVS		86.67 43	86.32 47	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 118	94.25 43	66.44 122	96.24 45	82.88 85	94.28 60	93.38 99

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 56	74.83 23	93.78 151	87.63 39	94.27 61	93.65 87
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
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 47	78.35 13	96.77 24	89.59 15	94.22 62	94.67 29
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
DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 177	67.93 147	85.52 247	93.44 28	78.70 34	83.63 108	89.03 194	74.57 24	95.71 62	80.26 113	94.04 63	93.66 83
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
EPNet		83.72 95	82.92 108	86.14 68	84.22 309	69.48 97	91.05 59	85.27 295	81.30 6	76.83 223	91.65 117	66.09 128	95.56 64	76.00 158	93.85 64	93.38 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EC-MVSNet		86.01 53	86.38 46	84.91 105	89.31 143	66.27 186	92.32 31	93.63 22	79.37 23	84.17 95	91.88 110	69.04 94	95.43 73	83.93 74	93.77 65	93.01 123

3Dnovator+	77.84 4	85.48 67	84.47 86	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 228	93.37 76	60.40 212	96.75 26	77.20 142	93.73 66	95.29 6

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 158	92.83 18	93.30 33	79.67 19	84.57 86	92.27 100	71.47 60	95.02 96	84.24 70	93.46 69	95.13 9

CANet		86.45 45	86.10 55	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 136	91.43 127	70.34 74	97.23 14	84.26 68	93.36 70	94.37 46

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 129	88.80 27	95.61 11	70.29 76	96.44 39	86.20 50	93.08 71	93.16 113

新几何1				83.42 171	93.13 56	70.71 76		85.48 294	57.43 402	81.80 130	91.98 107	63.28 153	92.27 226	64.60 274	92.99 72	87.27 327

HPM-MVS_fast		85.35 73	84.95 79	86.57 59	93.69 42	70.58 80	92.15 36	91.62 118	73.89 151	82.67 120	94.09 50	62.60 165	95.54 66	80.93 103	92.93 73	93.57 92

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 133	86.84 58	94.65 26	67.31 113	95.77 60	84.80 61	92.85 74	92.84 130

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 165	87.32 231	65.13 214	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 216	89.52 16	92.78 75	93.20 111

旧先验1					91.96 76	65.79 198		86.37 281			93.08 85	69.31 88			92.74 76	88.74 293

3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 203	72.94 28	90.64 63	92.14 97	77.21 62	75.47 254	92.83 90	58.56 223	94.72 110	73.24 189	92.71 77	92.13 163

MVS_111021_HR		85.14 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 229	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 132	81.02 102	92.58 78	92.08 164

APD-MVS_3200maxsize		85.97 56	85.88 59	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 161	85.94 62	94.51 30	65.80 133	95.61 63	83.04 82	92.51 79	93.53 96

test2506		77.30 253	76.49 250	79.74 278	90.08 112	52.02 398	87.86 169	63.10 441	74.88 124	80.16 157	92.79 93	38.29 405	92.35 223	68.74 239	92.50 80	94.86 19

ECVR-MVS		79.61 189	79.26 182	80.67 258	90.08 112	54.69 381	87.89 167	77.44 394	74.88 124	80.27 154	92.79 93	48.96 331	92.45 217	68.55 240	92.50 80	94.86 19

test1111		79.43 196	79.18 185	80.15 270	89.99 117	53.31 394	87.33 185	77.05 398	75.04 118	80.23 156	92.77 95	48.97 330	92.33 225	68.87 237	92.40 82	94.81 22

patch_mono-2		83.65 96	84.54 83	80.99 250	90.06 116	65.83 195	84.21 280	88.74 227	71.60 198	85.01 72	92.44 98	74.51 26	83.50 376	82.15 93	92.15 83	93.64 89

dcpmvs_2		85.63 64	86.15 54	84.06 146	91.71 80	64.94 221	86.47 215	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 242	83.36 77	92.15 83	95.35 3

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 148	88.59 139	89.05 211	80.19 12	90.70 17	95.40 15	74.56 25	93.92 144	91.54 2	92.07 85	95.31 5

MAR-MVS		81.84 132	80.70 142	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 249	78.50 183	86.21 280	62.36 171	94.52 117	65.36 267	92.05 86	89.77 256
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
TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 261	76.41 85	85.80 64	90.22 162	74.15 32	95.37 81	81.82 95	91.88 87	92.65 136

SR-MVS-dyc-post		85.77 61	85.61 66	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	65.00 141	95.56 64	82.75 86	91.87 88	92.50 142

RE-MVS-def			85.48 69		93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	63.87 149		82.75 86	91.87 88	92.50 142

IS-MVSNet		83.15 111	82.81 109	84.18 136	89.94 119	63.30 262	91.59 46	88.46 234	79.04 30	79.49 164	92.16 104	65.10 138	94.28 124	67.71 246	91.86 90	94.95 12

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 208	67.85 149	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 241	95.43 73	84.03 73	91.75 91	95.24 7

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 186	87.08 241	65.21 211	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 248	91.30 3	91.60 92	92.34 149

Vis-MVSNet (Re-imp)		78.36 224	78.45 197	78.07 313	88.64 173	51.78 404	86.70 208	79.63 376	74.14 145	75.11 273	90.83 147	61.29 193	89.75 299	58.10 335	91.60 92	92.69 134

MG-MVS		83.41 104	83.45 97	83.28 176	92.74 67	62.28 282	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 192	91.58 94	92.45 146

CPTT-MVS		83.73 94	83.33 101	84.92 104	93.28 49	70.86 74	92.09 37	90.38 154	68.75 275	79.57 163	92.83 90	60.60 208	93.04 196	80.92 104	91.56 95	90.86 202

test222					91.50 82	68.26 133	84.16 281	83.20 329	54.63 413	79.74 160	91.63 119	58.97 220			91.42 96	86.77 341

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 112	87.76 215	65.62 202	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 129	90.83 5	91.39 97	94.38 45

ETV-MVS		84.90 82	84.67 82	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 96	85.71 289	69.32 87	95.38 78	80.82 105	91.37 98	92.72 131

testdata				79.97 273	90.90 94	64.21 237		84.71 302	59.27 384	85.40 68	92.91 87	62.02 178	89.08 313	68.95 236	91.37 98	86.63 345

API-MVS		81.99 129	81.23 133	84.26 133	90.94 93	70.18 87	91.10 58	89.32 195	71.51 200	78.66 179	88.28 218	65.26 136	95.10 93	64.74 273	91.23 100	87.51 320

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 219	67.22 172	88.69 135	93.04 42	79.64 21	85.33 69	92.54 97	73.30 36	94.50 118	83.49 76	91.14 101	95.37 2
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_7		83.34 107	84.03 90	81.28 241	85.73 271	65.13 214	85.40 248	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 331	86.56 47	91.05 102	90.80 203

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 134	86.26 257	67.40 164	89.18 108	89.31 196	72.50 181	88.31 31	93.86 63	69.66 83	91.96 236	89.81 11	91.05 102	93.38 99

Vis-MVSNet		83.46 103	82.80 110	85.43 85	90.25 108	68.74 117	90.30 75	90.13 166	76.33 91	80.87 145	92.89 88	61.00 199	94.20 129	72.45 201	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 187	78.33 202	84.09 142	85.17 286	69.91 89	90.57 64	90.97 137	66.70 299	72.17 317	91.91 108	54.70 258	93.96 137	61.81 300	90.95 105	88.41 302

SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 241	79.31 24	84.39 89	92.18 102	64.64 143	95.53 67	80.70 108	90.91 106	93.21 109

UA-Net		85.08 78	84.96 78	85.45 84	92.07 75	68.07 141	89.78 85	90.86 142	82.48 2	84.60 85	93.20 80	69.35 86	95.22 84	71.39 209	90.88 107	93.07 117

test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 293	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 177	90.37 7	90.75 108	93.96 64

ACMMP		85.89 60	85.39 70	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 148	93.82 65	64.33 145	96.29 42	82.67 91	90.69 109	93.23 106
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
test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 342	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 178	90.31 8	90.67 110	93.89 70

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 234	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 205	89.04 24	90.56 111	94.16 54

casdiffmvs		85.11 77	85.14 76	85.01 99	87.20 234	65.77 199	87.75 171	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 143	81.27 101	90.48 112	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 263	69.93 88	88.65 137	90.78 143	69.97 243	88.27 32	93.98 59	71.39 62	91.54 256	88.49 32	90.45 113	93.91 67

UGNet		80.83 156	79.59 173	84.54 116	88.04 198	68.09 140	89.42 99	88.16 236	76.95 70	76.22 240	89.46 184	49.30 325	93.94 140	68.48 241	90.31 114	91.60 175
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
baseline		84.93 80	84.98 77	84.80 110	87.30 232	65.39 208	87.30 186	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 137	81.31 99	90.30 115	95.03 11

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 225	70.21 82	90.50 67	90.38 154	68.55 278	81.32 136	89.47 182	61.68 182	93.46 168	78.98 122	90.26 116	92.05 165

lupinMVS		81.39 146	80.27 154	84.76 111	87.35 225	70.21 82	85.55 243	86.41 279	62.85 351	81.32 136	88.61 208	61.68 182	92.24 228	78.41 129	90.26 116	91.83 168

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 226	79.17 170	91.03 142	64.12 147	96.03 51	68.39 243	90.14 118	91.50 180

EIA-MVS		83.31 109	82.80 110	84.82 108	89.59 126	65.59 203	88.21 153	92.68 67	74.66 131	78.96 172	86.42 276	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

MVS_111021_LR		82.61 120	82.11 120	84.11 137	88.82 162	71.58 57	85.15 253	86.16 285	74.69 129	80.47 153	91.04 140	62.29 172	90.55 287	80.33 112	90.08 120	90.20 231

jason		81.39 146	80.29 153	84.70 113	86.63 253	69.90 90	85.95 230	86.77 274	63.24 344	81.07 142	89.47 182	61.08 198	92.15 230	78.33 130	90.07 121	92.05 165
jason: jason.
test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 119	84.12 311	69.37 104	88.15 157	87.96 244	70.01 241	83.95 100	93.23 79	68.80 97	91.51 259	88.61 29	89.96 122	92.57 137

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 384	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 178	90.26 9	89.95 123	93.78 79

LFMVS		81.82 133	81.23 133	83.57 168	91.89 78	63.43 260	89.84 81	81.85 348	77.04 69	83.21 110	93.10 81	52.26 281	93.43 170	71.98 204	89.95 123	93.85 71

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 241	67.56 159	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 236	94.07 135	77.77 136	89.89 125	94.56 37

MVS		78.19 229	76.99 238	81.78 227	85.66 272	66.99 175	84.66 265	90.47 151	55.08 412	72.02 319	85.27 302	63.83 150	94.11 134	66.10 261	89.80 126	84.24 382

GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 192	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 248	95.35 82	80.03 114	89.74 127	94.69 28

CANet_DTU		80.61 167	79.87 165	82.83 200	85.60 275	63.17 267	87.36 183	88.65 230	76.37 89	75.88 247	88.44 214	53.51 270	93.07 192	73.30 187	89.74 127	92.25 154

Elysia		81.53 141	80.16 156	85.62 79	85.51 277	68.25 135	88.84 126	92.19 92	71.31 203	80.50 151	89.83 168	46.89 342	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 141	80.16 156	85.62 79	85.51 277	68.25 135	88.84 126	92.19 92	71.31 203	80.50 151	89.83 168	46.89 342	94.82 104	76.85 147	89.57 129	93.80 77

PVSNet_Blended		80.98 152	80.34 151	82.90 197	88.85 159	65.40 206	84.43 275	92.00 100	67.62 289	78.11 194	85.05 310	66.02 130	94.27 125	71.52 206	89.50 131	89.01 278

PAPM_NR		83.02 115	82.41 115	84.82 108	92.47 72	66.37 184	87.93 165	91.80 111	73.82 152	77.32 211	90.66 149	67.90 107	94.90 100	70.37 219	89.48 132	93.19 112

114514_t		80.68 165	79.51 174	84.20 135	94.09 38	67.27 169	89.64 90	91.11 135	58.75 391	74.08 291	90.72 148	58.10 226	95.04 95	69.70 228	89.42 133	90.30 228

LCM-MVSNet-Re		77.05 256	76.94 239	77.36 326	87.20 234	51.60 405	80.06 346	80.46 364	75.20 114	67.69 363	86.72 261	62.48 168	88.98 315	63.44 281	89.25 134	91.51 179

fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 146	85.38 281	68.40 129	88.34 149	86.85 273	67.48 292	87.48 49	93.40 75	70.89 68	91.61 249	88.38 34	89.22 135	92.16 162

mvsmamba		80.60 169	79.38 177	84.27 131	89.74 124	67.24 171	87.47 178	86.95 269	70.02 240	75.38 260	88.93 198	51.24 299	92.56 211	75.47 166	89.22 135	93.00 124

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 131	85.42 280	68.81 112	88.49 142	87.26 263	68.08 285	88.03 38	93.49 70	72.04 52	91.77 244	88.90 26	89.14 137	92.24 156

alignmvs		85.48 67	85.32 73	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 46	91.46 126	70.32 75	93.78 151	81.51 96	88.95 138	94.63 33

VNet		82.21 124	82.41 115	81.62 230	90.82 96	60.93 298	84.47 271	89.78 175	76.36 90	84.07 97	91.88 110	64.71 142	90.26 289	70.68 216	88.89 139	93.66 83

PS-MVSNAJ		81.69 136	81.02 137	83.70 163	89.51 130	68.21 138	84.28 279	90.09 167	70.79 218	81.26 140	85.62 294	63.15 159	94.29 123	75.62 162	88.87 140	88.59 297

sasdasda		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 145	81.50 97	88.80 141	94.77 25

canonicalmvs		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 145	81.50 97	88.80 141	94.77 25

QAPM		80.88 154	79.50 175	85.03 98	88.01 201	68.97 110	91.59 46	92.00 100	66.63 305	75.15 272	92.16 104	57.70 230	95.45 71	63.52 279	88.76 143	90.66 211

MGCFI-Net		85.06 79	85.51 68	83.70 163	89.42 135	63.01 268	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 171	81.28 100	88.74 144	94.66 32

VDD-MVS		83.01 116	82.36 117	84.96 101	91.02 91	66.40 183	88.91 121	88.11 237	77.57 49	84.39 89	93.29 78	52.19 282	93.91 145	77.05 145	88.70 145	94.57 36

PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 101	90.80 97	69.76 93	88.74 133	91.70 115	69.39 255	78.96 172	88.46 213	65.47 135	94.87 103	74.42 175	88.57 146	90.24 230

xiu_mvs_v2_base		81.69 136	81.05 136	83.60 165	89.15 151	68.03 143	84.46 273	90.02 168	70.67 221	81.30 139	86.53 274	63.17 158	94.19 131	75.60 163	88.54 147	88.57 298

PAPR		81.66 138	80.89 140	83.99 154	90.27 107	64.00 240	86.76 207	91.77 114	68.84 274	77.13 221	89.50 180	67.63 109	94.88 102	67.55 248	88.52 148	93.09 116

MVS_Test		83.15 111	83.06 104	83.41 173	86.86 244	63.21 264	86.11 227	92.00 100	74.31 139	82.87 115	89.44 187	70.03 78	93.21 180	77.39 141	88.50 149	93.81 75

fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 127	86.70 250	65.83 195	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 193	91.30 3	88.44 150	94.02 62

AdaColmap		80.58 172	79.42 176	84.06 146	93.09 59	68.91 111	89.36 103	88.97 217	69.27 259	75.70 250	89.69 173	57.20 238	95.77 60	63.06 284	88.41 151	87.50 321

VDDNet		81.52 143	80.67 143	84.05 149	90.44 104	64.13 239	89.73 87	85.91 288	71.11 209	83.18 111	93.48 71	50.54 308	93.49 165	73.40 186	88.25 152	94.54 39

PCF-MVS	73.52 7	80.38 176	78.84 191	85.01 99	87.71 216	68.99 109	83.65 290	91.46 126	63.00 348	77.77 203	90.28 158	66.10 127	95.09 94	61.40 303	88.22 153	90.94 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RRT-MVS		82.60 122	82.10 121	84.10 138	87.98 202	62.94 273	87.45 180	91.27 128	77.42 56	79.85 159	90.28 158	56.62 244	94.70 112	79.87 117	88.15 154	94.67 29

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 161	86.17 261	65.00 219	86.96 196	87.28 261	74.35 137	88.25 33	94.23 44	61.82 180	92.60 208	89.85 10	88.09 155	93.84 73

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 183	67.45 161	88.89 122	89.15 207	75.50 105	82.27 121	88.28 218	69.61 84	94.45 121	77.81 135	87.84 156	93.84 73

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 159	85.62 274	64.94 221	87.03 193	86.62 277	74.32 138	87.97 41	94.33 38	60.67 204	92.60 208	89.72 12	87.79 157	93.96 64

gg-mvs-nofinetune		69.95 350	67.96 353	75.94 337	83.07 337	54.51 384	77.23 384	70.29 422	63.11 346	70.32 334	62.33 436	43.62 373	88.69 321	53.88 365	87.76 158	84.62 379

xiu_mvs_v1_base_debu		80.80 160	79.72 169	84.03 151	87.35 225	70.19 84	85.56 240	88.77 223	69.06 268	81.83 127	88.16 222	50.91 302	92.85 201	78.29 131	87.56 159	89.06 273

xiu_mvs_v1_base		80.80 160	79.72 169	84.03 151	87.35 225	70.19 84	85.56 240	88.77 223	69.06 268	81.83 127	88.16 222	50.91 302	92.85 201	78.29 131	87.56 159	89.06 273

xiu_mvs_v1_base_debi		80.80 160	79.72 169	84.03 151	87.35 225	70.19 84	85.56 240	88.77 223	69.06 268	81.83 127	88.16 222	50.91 302	92.85 201	78.29 131	87.56 159	89.06 273

CLD-MVS		82.31 123	81.65 129	84.29 128	88.47 178	67.73 153	85.81 237	92.35 83	75.78 99	78.33 189	86.58 271	64.01 148	94.35 122	76.05 157	87.48 162	90.79 204
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
myMVS_eth3d28		73.62 306	73.53 296	73.90 365	88.20 188	47.41 424	78.06 376	79.37 378	74.29 141	73.98 292	84.29 324	44.67 364	83.54 375	51.47 377	87.39 163	90.74 208

CDS-MVSNet		79.07 207	77.70 222	83.17 183	87.60 220	68.23 137	84.40 277	86.20 284	67.49 291	76.36 237	86.54 273	61.54 185	90.79 282	61.86 299	87.33 164	90.49 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
diffmvs		82.10 125	81.88 127	82.76 209	83.00 340	63.78 247	83.68 289	89.76 177	72.94 177	82.02 126	89.85 167	65.96 132	90.79 282	82.38 92	87.30 165	93.71 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPP-MVSNet		83.40 105	83.02 105	84.57 115	90.13 110	64.47 232	92.32 31	90.73 144	74.45 136	79.35 168	91.10 137	69.05 93	95.12 88	72.78 193	87.22 166	94.13 56

mamba_0404		81.91 130	80.84 141	85.13 95	89.24 147	68.26 133	87.84 170	89.25 201	71.06 212	80.62 149	90.39 155	59.57 215	94.65 114	72.45 201	87.19 167	92.47 145

TAMVS		78.89 212	77.51 228	83.03 191	87.80 210	67.79 152	84.72 263	85.05 300	67.63 288	76.75 226	87.70 234	62.25 173	90.82 281	58.53 330	87.13 168	90.49 219

TAPA-MVS	73.13 9	79.15 204	77.94 210	82.79 206	89.59 126	62.99 272	88.16 156	91.51 122	65.77 314	77.14 220	91.09 138	60.91 200	93.21 180	50.26 387	87.05 169	92.17 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 245	76.40 254	81.51 233	87.29 233	61.85 287	83.78 286	89.59 184	64.74 327	71.23 327	88.70 204	62.59 166	93.66 158	52.66 371	87.03 170	89.01 278

test_yl		81.17 148	80.47 149	83.24 179	89.13 152	63.62 248	86.21 224	89.95 171	72.43 185	81.78 131	89.61 177	57.50 233	93.58 159	70.75 214	86.90 171	92.52 140

DCV-MVSNet		81.17 148	80.47 149	83.24 179	89.13 152	63.62 248	86.21 224	89.95 171	72.43 185	81.78 131	89.61 177	57.50 233	93.58 159	70.75 214	86.90 171	92.52 140

LuminaMVS		80.68 165	79.62 172	83.83 159	85.07 292	68.01 144	86.99 195	88.83 220	70.36 231	81.38 135	87.99 229	50.11 313	92.51 215	79.02 120	86.89 173	90.97 198

BH-untuned		79.47 194	78.60 194	82.05 222	89.19 150	65.91 193	86.07 228	88.52 233	72.18 187	75.42 258	87.69 235	61.15 196	93.54 163	60.38 311	86.83 174	86.70 343

BH-RMVSNet		79.61 189	78.44 198	83.14 184	89.38 139	65.93 192	84.95 259	87.15 266	73.56 160	78.19 192	89.79 171	56.67 243	93.36 172	59.53 319	86.74 175	90.13 234

LS3D		76.95 259	74.82 277	83.37 174	90.45 103	67.36 166	89.15 113	86.94 270	61.87 364	69.52 347	90.61 150	51.71 295	94.53 116	46.38 409	86.71 176	88.21 306

Fast-Effi-MVS+		80.81 157	79.92 162	83.47 169	88.85 159	64.51 229	85.53 245	89.39 190	70.79 218	78.49 184	85.06 309	67.54 110	93.58 159	67.03 256	86.58 177	92.32 151

EPNet_dtu		75.46 284	74.86 276	77.23 329	82.57 352	54.60 382	86.89 200	83.09 330	71.64 194	66.25 385	85.86 287	55.99 246	88.04 330	54.92 359	86.55 178	89.05 276
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 167	70.95 71	89.13 114	91.52 121	77.55 52	80.96 144	91.75 114	60.71 202	94.50 118	79.67 119	86.51 179	89.97 248
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
OMC-MVS		82.69 118	81.97 126	84.85 107	88.75 169	67.42 162	87.98 161	90.87 141	74.92 123	79.72 161	91.65 117	62.19 175	93.96 137	75.26 168	86.42 180	93.16 113

HQP_MVS		83.64 97	83.14 102	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 174	91.00 144	60.42 210	95.38 78	78.71 125	86.32 181	91.33 185

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 181	91.33 185

FA-MVS(test-final)		80.96 153	79.91 163	84.10 138	88.30 186	65.01 218	84.55 270	90.01 169	73.25 171	79.61 162	87.57 238	58.35 225	94.72 110	71.29 210	86.25 183	92.56 138

thisisatest0515		77.33 252	75.38 269	83.18 182	85.27 285	63.80 246	82.11 316	83.27 325	65.06 323	75.91 246	83.84 334	49.54 320	94.27 125	67.24 252	86.19 184	91.48 182

plane_prior						68.71 119	90.38 73		77.62 47						86.16 185

UWE-MVS		72.13 328	71.49 318	74.03 363	86.66 252	47.70 421	81.40 326	76.89 400	63.60 343	75.59 251	84.22 328	39.94 395	85.62 357	48.98 394	86.13 186	88.77 290

mvs_anonymous		79.42 197	79.11 186	80.34 265	84.45 306	57.97 333	82.59 311	87.62 254	67.40 293	76.17 244	88.56 211	68.47 100	89.59 302	70.65 217	86.05 187	93.47 97

GeoE		81.71 135	81.01 138	83.80 162	89.51 130	64.45 233	88.97 119	88.73 228	71.27 206	78.63 180	89.76 172	66.32 124	93.20 183	69.89 226	86.02 188	93.74 80

HQP3-MVS								92.19 92							85.99 189

HQP-MVS		82.61 120	82.02 124	84.37 122	89.33 140	66.98 176	89.17 109	92.19 92	76.41 85	77.23 214	90.23 161	60.17 213	95.11 90	77.47 139	85.99 189	91.03 195

mamba_test_0407_2		77.67 246	77.52 227	78.12 311	88.81 163	67.96 145	65.03 439	88.66 229	70.96 216	79.48 165	89.80 170	58.69 221	74.23 431	70.35 220	85.93 191	92.18 159

mamba_test_0407		81.58 140	80.48 148	84.87 106	88.81 163	67.96 145	87.37 182	89.25 201	71.06 212	79.48 165	90.39 155	59.57 215	94.48 120	72.45 201	85.93 191	92.18 159

BH-w/o		78.21 227	77.33 232	80.84 254	88.81 163	65.13 214	84.87 260	87.85 249	69.75 250	74.52 286	84.74 316	61.34 191	93.11 190	58.24 334	85.84 193	84.27 381

FE-MVS		77.78 240	75.68 261	84.08 143	88.09 196	66.00 190	83.13 304	87.79 250	68.42 282	78.01 197	85.23 304	45.50 361	95.12 88	59.11 323	85.83 194	91.11 191

testing222		74.04 301	72.66 307	78.19 309	87.89 205	55.36 374	81.06 329	79.20 381	71.30 205	74.65 284	83.57 344	39.11 400	88.67 322	51.43 379	85.75 195	90.53 217

CHOSEN 1792x2688		77.63 247	75.69 260	83.44 170	89.98 118	68.58 125	78.70 366	87.50 257	56.38 407	75.80 249	86.84 257	58.67 222	91.40 264	61.58 302	85.75 195	90.34 225

icg_test_0407		80.61 167	79.90 164	82.75 210	87.13 237	63.59 252	85.33 249	89.33 192	70.51 227	77.82 200	89.03 194	61.84 179	92.91 199	72.56 198	85.56 197	91.74 171

ICG_test_0404		77.16 255	76.42 253	79.37 286	87.13 237	63.59 252	77.12 385	89.33 192	70.51 227	66.22 386	89.03 194	50.36 310	82.78 381	72.56 198	85.56 197	91.74 171

icg_test_0403		80.80 160	80.12 159	82.87 199	87.13 237	63.59 252	85.19 250	89.33 192	70.51 227	78.49 184	89.03 194	63.26 155	93.27 175	72.56 198	85.56 197	91.74 171

guyue		81.13 150	80.64 144	82.60 213	86.52 254	63.92 244	86.69 209	87.73 252	73.97 147	80.83 147	89.69 173	56.70 242	91.33 267	78.26 134	85.40 200	92.54 139

Anonymous202405211		78.25 225	77.01 236	81.99 224	91.03 90	60.67 303	84.77 262	83.90 315	70.65 225	80.00 158	91.20 134	41.08 390	91.43 263	65.21 268	85.26 201	93.85 71

cascas		76.72 263	74.64 279	82.99 193	85.78 270	65.88 194	82.33 313	89.21 204	60.85 370	72.74 307	81.02 376	47.28 338	93.75 155	67.48 249	85.02 202	89.34 268

FIs		82.07 127	82.42 114	81.04 249	88.80 166	58.34 327	88.26 152	93.49 27	76.93 71	78.47 186	91.04 140	69.92 80	92.34 224	69.87 227	84.97 203	92.44 147

viewmamba		80.41 174	79.84 166	82.12 219	82.95 344	62.50 278	83.39 297	88.06 241	67.11 294	80.98 143	90.31 157	66.20 126	91.01 278	74.62 172	84.90 204	92.86 128

test-LLR		72.94 320	72.43 309	74.48 357	81.35 372	58.04 331	78.38 370	77.46 392	66.66 300	69.95 342	79.00 399	48.06 334	79.24 398	66.13 259	84.83 205	86.15 351

test-mter		71.41 332	70.39 334	74.48 357	81.35 372	58.04 331	78.38 370	77.46 392	60.32 374	69.95 342	79.00 399	36.08 414	79.24 398	66.13 259	84.83 205	86.15 351

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 189	67.85 149	87.66 173	89.73 179	80.05 15	82.95 113	89.59 179	70.74 71	94.82 104	80.66 110	84.72 207	93.28 105

thisisatest0530		79.40 198	77.76 220	84.31 126	87.69 218	65.10 217	87.36 183	84.26 311	70.04 239	77.42 208	88.26 220	49.94 316	94.79 108	70.20 221	84.70 208	93.03 121

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 144	86.69 251	67.31 167	89.46 96	83.07 331	71.09 210	86.96 57	93.70 68	69.02 95	91.47 261	88.79 27	84.62 209	93.44 98

testing91		76.54 264	75.66 263	79.18 291	88.43 181	55.89 367	81.08 328	83.00 333	73.76 154	75.34 262	84.29 324	46.20 352	90.07 293	64.33 275	84.50 210	91.58 177

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 138	84.86 295	67.28 168	89.40 101	83.01 332	70.67 221	87.08 54	93.96 60	68.38 101	91.45 262	88.56 31	84.50 210	93.56 93

GG-mvs-BLEND				75.38 347	81.59 366	55.80 369	79.32 355	69.63 424		67.19 370	73.67 425	43.24 375	88.90 319	50.41 382	84.50 210	81.45 410

FC-MVSNet-test		81.52 143	82.02 124	80.03 272	88.42 182	55.97 366	87.95 163	93.42 30	77.10 67	77.38 209	90.98 146	69.96 79	91.79 243	68.46 242	84.50 210	92.33 150

PVSNet	64.34 18	72.08 329	70.87 328	75.69 340	86.21 259	56.44 358	74.37 404	80.73 359	62.06 362	70.17 337	82.23 367	42.86 378	83.31 378	54.77 360	84.45 214	87.32 325

ETVMVS		72.25 326	71.05 325	75.84 338	87.77 214	51.91 401	79.39 354	74.98 407	69.26 260	73.71 295	82.95 354	40.82 392	86.14 350	46.17 410	84.43 215	89.47 263

UBG		73.08 317	72.27 312	75.51 344	88.02 199	51.29 409	78.35 373	77.38 395	65.52 318	73.87 294	82.36 363	45.55 359	86.48 347	55.02 358	84.39 216	88.75 291

MS-PatchMatch		73.83 304	72.67 306	77.30 328	83.87 318	66.02 189	81.82 317	84.66 303	61.37 368	68.61 356	82.82 358	47.29 337	88.21 327	59.27 320	84.32 217	77.68 423

ET-MVSNet_ETH3D		78.63 217	76.63 249	84.64 114	86.73 249	69.47 98	85.01 257	84.61 304	69.54 253	66.51 383	86.59 269	50.16 312	91.75 245	76.26 154	84.24 218	92.69 134

testing99		76.09 276	75.12 275	79.00 292	88.16 190	55.50 373	80.79 332	81.40 353	73.30 169	75.17 270	84.27 327	44.48 367	90.02 294	64.28 276	84.22 219	91.48 182

TESTMET0.1,1		69.89 351	69.00 343	72.55 377	79.27 400	56.85 350	78.38 370	74.71 411	57.64 399	68.09 360	77.19 412	37.75 407	76.70 411	63.92 278	84.09 220	84.10 385

AstraMVS		80.81 157	80.14 158	82.80 203	86.05 266	63.96 241	86.46 216	85.90 289	73.71 155	80.85 146	90.56 151	54.06 265	91.57 253	79.72 118	83.97 221	92.86 128

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 97	87.87 206	67.53 160	87.44 181	89.66 180	79.74 18	82.23 122	89.41 188	70.24 77	94.74 109	79.95 115	83.92 222	92.99 125

LPG-MVS_test		82.08 126	81.27 132	84.50 117	89.23 148	68.76 115	90.22 76	91.94 104	75.37 109	76.64 229	91.51 123	54.29 261	94.91 98	78.44 127	83.78 223	89.83 253

LGP-MVS_train				84.50 117	89.23 148	68.76 115		91.94 104	75.37 109	76.64 229	91.51 123	54.29 261	94.91 98	78.44 127	83.78 223	89.83 253

testing11		75.14 290	74.01 288	78.53 303	88.16 190	56.38 360	80.74 335	80.42 366	70.67 221	72.69 310	83.72 339	43.61 374	89.86 296	62.29 293	83.76 225	89.36 267

thres100view900		76.50 266	75.55 265	79.33 287	89.52 129	56.99 349	85.83 236	83.23 326	73.94 149	76.32 238	87.12 253	51.89 291	91.95 237	48.33 397	83.75 226	89.07 271

tfpn200view9		76.42 270	75.37 270	79.55 285	89.13 152	57.65 340	85.17 251	83.60 318	73.41 166	76.45 234	86.39 277	52.12 283	91.95 237	48.33 397	83.75 226	89.07 271

thres400		76.50 266	75.37 270	79.86 275	89.13 152	57.65 340	85.17 251	83.60 318	73.41 166	76.45 234	86.39 277	52.12 283	91.95 237	48.33 397	83.75 226	90.00 244

thres600view7		76.50 266	75.44 266	79.68 280	89.40 137	57.16 346	85.53 245	83.23 326	73.79 153	76.26 239	87.09 254	51.89 291	91.89 240	48.05 402	83.72 229	90.00 244

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 129	86.14 262	68.12 139	89.43 97	82.87 336	70.27 236	87.27 53	93.80 66	69.09 90	91.58 251	88.21 35	83.65 230	93.14 115

thres200		75.55 282	74.47 283	78.82 295	87.78 213	57.85 336	83.07 307	83.51 321	72.44 184	75.84 248	84.42 319	52.08 286	91.75 245	47.41 404	83.64 231	86.86 339

SDMVSNet		80.38 176	80.18 155	80.99 250	89.03 157	64.94 221	80.45 341	89.40 189	75.19 115	76.61 231	89.98 164	60.61 207	87.69 335	76.83 150	83.55 232	90.33 226

sd_testset		77.70 244	77.40 229	78.60 299	89.03 157	60.02 312	79.00 361	85.83 290	75.19 115	76.61 231	89.98 164	54.81 253	85.46 360	62.63 290	83.55 232	90.33 226

testing3-2		75.12 291	75.19 273	74.91 352	90.40 105	45.09 434	80.29 344	78.42 386	78.37 40	76.54 233	87.75 232	44.36 368	87.28 340	57.04 345	83.49 234	92.37 148

XVG-OURS		80.41 174	79.23 183	83.97 155	85.64 273	69.02 108	83.03 309	90.39 153	71.09 210	77.63 205	91.49 125	54.62 260	91.35 265	75.71 160	83.47 235	91.54 178

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 129	83.79 319	68.07 141	89.34 104	82.85 337	69.80 247	87.36 52	94.06 52	68.34 102	91.56 254	87.95 36	83.46 236	93.21 109

SD_0403		74.65 294	74.77 278	74.29 360	86.20 260	47.42 423	83.71 288	85.12 297	69.30 258	68.50 358	87.95 230	59.40 217	86.05 351	49.38 391	83.35 237	89.40 265

CNLPA		78.08 231	76.79 243	81.97 225	90.40 105	71.07 67	87.59 175	84.55 305	66.03 312	72.38 314	89.64 176	57.56 232	86.04 352	59.61 318	83.35 237	88.79 289

MVP-Stereo		76.12 274	74.46 284	81.13 247	85.37 282	69.79 91	84.42 276	87.95 245	65.03 324	67.46 366	85.33 301	53.28 273	91.73 247	58.01 336	83.27 239	81.85 408
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 265	75.30 272	80.21 269	83.93 316	62.32 281	84.66 265	88.81 221	60.23 375	70.16 338	84.07 331	55.30 251	90.73 285	67.37 250	83.21 240	87.59 319

tttt0517		79.40 198	77.91 211	83.90 158	88.10 195	63.84 245	88.37 148	84.05 313	71.45 201	76.78 225	89.12 191	49.93 318	94.89 101	70.18 222	83.18 241	92.96 126

HyFIR lowres test		77.53 248	75.40 268	83.94 157	89.59 126	66.62 180	80.36 342	88.64 231	56.29 408	76.45 234	85.17 306	57.64 231	93.28 174	61.34 305	83.10 242	91.91 167

ACMP	74.13 6	81.51 145	80.57 145	84.36 123	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 276	90.41 154	53.82 267	94.54 115	77.56 138	82.91 243	89.86 252
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 164	79.84 166	83.58 167	89.31 143	68.37 130	89.99 79	91.60 119	70.28 235	77.25 212	89.66 175	53.37 272	93.53 164	74.24 178	82.85 244	88.85 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 355	68.67 344	71.35 387	75.67 414	62.03 284	75.17 396	73.46 414	50.00 425	68.68 354	79.05 397	52.07 287	78.13 403	61.16 306	82.77 245	73.90 429

PLC	70.83 11	78.05 233	76.37 255	83.08 188	91.88 79	67.80 151	88.19 154	89.46 188	64.33 333	69.87 344	88.38 215	53.66 268	93.58 159	58.86 326	82.73 246	87.86 312
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 249	76.18 256	81.20 244	88.24 187	63.24 263	84.61 268	86.40 280	67.55 290	77.81 201	86.48 275	54.10 263	93.15 187	57.75 338	82.72 247	87.20 328

Anonymous20240529		80.19 182	78.89 190	84.10 138	90.60 100	64.75 226	88.95 120	90.90 139	65.97 313	80.59 150	91.17 136	49.97 315	93.73 157	69.16 234	82.70 248	93.81 75

ab-mvs		79.51 192	78.97 189	81.14 246	88.46 179	60.91 299	83.84 285	89.24 203	70.36 231	79.03 171	88.87 201	63.23 157	90.21 291	65.12 269	82.57 249	92.28 153

HY-MVS	69.67 12	77.95 236	77.15 234	80.36 264	87.57 224	60.21 311	83.37 299	87.78 251	66.11 309	75.37 261	87.06 256	63.27 154	90.48 288	61.38 304	82.43 250	90.40 223

PS-MVSNAJss		82.07 127	81.31 131	84.34 125	86.51 255	67.27 169	89.27 105	91.51 122	71.75 193	79.37 167	90.22 162	63.15 159	94.27 125	77.69 137	82.36 251	91.49 181

UniMVSNet_ETH3D		79.10 206	78.24 204	81.70 229	86.85 245	60.24 310	87.28 187	88.79 222	74.25 142	76.84 222	90.53 153	49.48 321	91.56 254	67.98 244	82.15 252	93.29 104

WB-MVSnew		71.96 330	71.65 317	72.89 374	84.67 303	51.88 402	82.29 314	77.57 391	62.31 358	73.67 297	83.00 353	53.49 271	81.10 392	45.75 413	82.13 253	85.70 361

PVSNet_BlendedMVS		80.60 169	80.02 160	82.36 218	88.85 159	65.40 206	86.16 226	92.00 100	69.34 257	78.11 194	86.09 284	66.02 130	94.27 125	71.52 206	82.06 254	87.39 322

WTY-MVS		75.65 281	75.68 261	75.57 342	86.40 256	56.82 351	77.92 379	82.40 341	65.10 322	76.18 242	87.72 233	63.13 162	80.90 393	60.31 312	81.96 255	89.00 280

ACMMP++_ref															81.95 256

DP-MVS		76.78 262	74.57 280	83.42 171	93.29 48	69.46 100	88.55 141	83.70 317	63.98 340	70.20 335	88.89 200	54.01 266	94.80 107	46.66 406	81.88 257	86.01 355

CMPMVS	51.72 21	70.19 347	68.16 349	76.28 335	73.15 430	57.55 342	79.47 353	83.92 314	48.02 428	56.48 428	84.81 314	43.13 376	86.42 348	62.67 289	81.81 258	84.89 375
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 157	79.76 168	83.96 156	85.60 275	68.78 114	83.54 296	90.50 150	70.66 224	76.71 227	91.66 116	60.69 203	91.26 268	76.94 146	81.58 259	91.83 168

MIMVSNet		70.69 340	69.30 339	74.88 353	84.52 304	56.35 362	75.87 392	79.42 377	64.59 328	67.76 361	82.41 362	41.10 389	81.54 389	46.64 408	81.34 260	86.75 342

ACMMP++															81.25 261

D2MVS		74.82 292	73.21 300	79.64 282	79.81 391	62.56 277	80.34 343	87.35 260	64.37 332	68.86 353	82.66 360	46.37 348	90.10 292	67.91 245	81.24 262	86.25 348

test_vis1_n_1920		75.52 283	75.78 259	74.75 356	79.84 390	57.44 344	83.26 301	85.52 293	62.83 352	79.34 169	86.17 282	45.10 363	79.71 397	78.75 124	81.21 263	87.10 335

GA-MVS		76.87 260	75.17 274	81.97 225	82.75 347	62.58 276	81.44 325	86.35 282	72.16 189	74.74 281	82.89 356	46.20 352	92.02 234	68.85 238	81.09 264	91.30 187

sss		73.60 307	73.64 295	73.51 368	82.80 346	55.01 379	76.12 388	81.69 349	62.47 357	74.68 283	85.85 288	57.32 235	78.11 404	60.86 308	80.93 265	87.39 322

UWE-MVS-28		65.32 382	64.93 376	66.49 410	78.70 402	38.55 447	77.86 380	64.39 439	62.00 363	64.13 399	83.60 342	41.44 387	76.00 419	31.39 439	80.89 266	84.92 374

Effi-MVS+-dtu		80.03 184	78.57 195	84.42 121	85.13 290	68.74 117	88.77 129	88.10 238	74.99 119	74.97 278	83.49 345	57.27 236	93.36 172	73.53 183	80.88 267	91.18 189

EG-PatchMatch MVS		74.04 301	71.82 315	80.71 257	84.92 294	67.42 162	85.86 234	88.08 239	66.04 311	64.22 398	83.85 333	35.10 416	92.56 211	57.44 340	80.83 268	82.16 407

jajsoiax		79.29 201	77.96 209	83.27 177	84.68 300	66.57 182	89.25 106	90.16 165	69.20 264	75.46 256	89.49 181	45.75 358	93.13 189	76.84 149	80.80 269	90.11 236

1112_ss		77.40 251	76.43 252	80.32 266	89.11 156	60.41 308	83.65 290	87.72 253	62.13 361	73.05 304	86.72 261	62.58 167	89.97 295	62.11 297	80.80 269	90.59 215

mvs_tets		79.13 205	77.77 219	83.22 181	84.70 299	66.37 184	89.17 109	90.19 164	69.38 256	75.40 259	89.46 184	44.17 370	93.15 187	76.78 151	80.70 271	90.14 233

PatchMatch-RL		72.38 323	70.90 327	76.80 333	88.60 174	67.38 165	79.53 352	76.17 404	62.75 354	69.36 349	82.00 371	45.51 360	84.89 366	53.62 366	80.58 272	78.12 422

EI-MVSNet		80.52 173	79.98 161	82.12 219	84.28 307	63.19 266	86.41 217	88.95 218	74.18 144	78.69 177	87.54 241	66.62 118	92.43 218	72.57 196	80.57 273	90.74 208

MVSTER		79.01 208	77.88 214	82.38 217	83.07 337	64.80 225	84.08 284	88.95 218	69.01 271	78.69 177	87.17 252	54.70 258	92.43 218	74.69 171	80.57 273	89.89 251

XVG-ACMP-BASELINE		76.11 275	74.27 287	81.62 230	83.20 333	64.67 227	83.60 293	89.75 178	69.75 250	71.85 320	87.09 254	32.78 420	92.11 231	69.99 225	80.43 275	88.09 308

Fast-Effi-MVS+-dtu		78.02 234	76.49 250	82.62 212	83.16 336	66.96 178	86.94 198	87.45 259	72.45 182	71.49 325	84.17 329	54.79 257	91.58 251	67.61 247	80.31 276	89.30 269

LTVRE_ROB	69.57 13	76.25 273	74.54 282	81.41 236	88.60 174	64.38 235	79.24 356	89.12 210	70.76 220	69.79 346	87.86 231	49.09 328	93.20 183	56.21 354	80.16 277	86.65 344
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
Test_1112_low_res		76.40 271	75.44 266	79.27 288	89.28 145	58.09 329	81.69 320	87.07 267	59.53 382	72.48 312	86.67 266	61.30 192	89.33 306	60.81 309	80.15 278	90.41 222

test_djsdf		80.30 179	79.32 180	83.27 177	83.98 315	65.37 209	90.50 67	90.38 154	68.55 278	76.19 241	88.70 204	56.44 245	93.46 168	78.98 122	80.14 279	90.97 198

test_fmvs1		70.93 337	70.52 330	72.16 380	73.71 423	55.05 378	80.82 330	78.77 384	51.21 424	78.58 181	84.41 320	31.20 425	76.94 410	75.88 159	80.12 280	84.47 380

test_fmvs1_n		70.86 338	70.24 335	72.73 376	72.51 434	55.28 376	81.27 327	79.71 375	51.49 423	78.73 176	84.87 312	27.54 430	77.02 409	76.06 156	79.97 281	85.88 359

CHOSEN 280x420		66.51 376	64.71 378	71.90 381	81.45 369	63.52 256	57.98 445	68.95 428	53.57 415	62.59 408	76.70 413	46.22 351	75.29 427	55.25 356	79.68 282	76.88 425

baseline2		75.70 280	73.83 293	81.30 240	83.26 331	61.79 289	82.57 312	80.65 360	66.81 296	66.88 374	83.42 346	57.86 229	92.19 229	63.47 280	79.57 283	89.91 249

GBi-Net		78.40 222	77.40 229	81.40 237	87.60 220	63.01 268	88.39 145	89.28 197	71.63 195	75.34 262	87.28 245	54.80 254	91.11 271	62.72 286	79.57 283	90.09 238

test1		78.40 222	77.40 229	81.40 237	87.60 220	63.01 268	88.39 145	89.28 197	71.63 195	75.34 262	87.28 245	54.80 254	91.11 271	62.72 286	79.57 283	90.09 238

FMVSNet3		77.88 238	76.85 241	80.97 252	86.84 246	62.36 279	86.52 214	88.77 223	71.13 208	75.34 262	86.66 267	54.07 264	91.10 274	62.72 286	79.57 283	89.45 264

FMVSNet2		78.20 228	77.21 233	81.20 244	87.60 220	62.89 274	87.47 178	89.02 213	71.63 195	75.29 268	87.28 245	54.80 254	91.10 274	62.38 291	79.38 287	89.61 260

anonymousdsp		78.60 218	77.15 234	82.98 194	80.51 382	67.08 174	87.24 188	89.53 186	65.66 316	75.16 271	87.19 251	52.52 276	92.25 227	77.17 143	79.34 288	89.61 260

nrg030		83.88 90	83.53 96	84.96 101	86.77 248	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 191	80.79 107	79.28 289	92.50 142

VPA-MVSNet		80.60 169	80.55 146	80.76 256	88.07 197	60.80 301	86.86 201	91.58 120	75.67 103	80.24 155	89.45 186	63.34 152	90.25 290	70.51 218	79.22 290	91.23 188

tt0805		78.73 214	77.83 215	81.43 235	85.17 286	60.30 309	89.41 100	90.90 139	71.21 207	77.17 219	88.73 203	46.38 347	93.21 180	72.57 196	78.96 291	90.79 204

test_cas_vis1_n_1920		73.76 305	73.74 294	73.81 366	75.90 412	59.77 314	80.51 339	82.40 341	58.30 393	81.62 133	85.69 290	44.35 369	76.41 415	76.29 153	78.61 292	85.23 368

F-COLMAP		76.38 272	74.33 286	82.50 215	89.28 145	66.95 179	88.41 144	89.03 212	64.05 338	66.83 375	88.61 208	46.78 344	92.89 200	57.48 339	78.55 293	87.67 315

FMVSNet1		77.44 249	76.12 257	81.40 237	86.81 247	63.01 268	88.39 145	89.28 197	70.49 230	74.39 288	87.28 245	49.06 329	91.11 271	60.91 307	78.52 294	90.09 238

MDTV_nov1_ep13			69.97 337		83.18 334	53.48 391	77.10 386	80.18 372	60.45 372	69.33 350	80.44 382	48.89 332	86.90 342	51.60 376	78.51 295

CVMVSNet		72.99 319	72.58 308	74.25 361	84.28 307	50.85 412	86.41 217	83.45 323	44.56 432	73.23 302	87.54 241	49.38 323	85.70 355	65.90 263	78.44 296	86.19 350

tpm2		73.26 314	71.46 319	78.63 297	83.34 329	56.71 354	80.65 337	80.40 367	56.63 406	73.55 298	82.02 370	51.80 293	91.24 269	56.35 353	78.42 297	87.95 309

test_vis1_n		69.85 352	69.21 341	71.77 382	72.66 433	55.27 377	81.48 323	76.21 403	52.03 420	75.30 267	83.20 350	28.97 428	76.22 417	74.60 173	78.41 298	83.81 388

CostFormer		75.24 289	73.90 291	79.27 288	82.65 351	58.27 328	80.80 331	82.73 339	61.57 365	75.33 266	83.13 351	55.52 249	91.07 277	64.98 271	78.34 299	88.45 300

ACMH	67.68 16	75.89 278	73.93 290	81.77 228	88.71 171	66.61 181	88.62 138	89.01 214	69.81 246	66.78 376	86.70 265	41.95 386	91.51 259	55.64 355	78.14 300	87.17 329
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mamv4		76.81 261	78.23 206	72.54 378	86.12 263	65.75 200	78.76 365	82.07 345	64.12 335	72.97 305	91.02 143	67.97 105	68.08 443	83.04 82	78.02 301	83.80 389

WBMVS		73.43 309	72.81 305	75.28 348	87.91 204	50.99 411	78.59 369	81.31 355	65.51 320	74.47 287	84.83 313	46.39 346	86.68 344	58.41 331	77.86 302	88.17 307

dmvs_re		71.14 334	70.58 329	72.80 375	81.96 360	59.68 315	75.60 394	79.34 379	68.55 278	69.27 351	80.72 381	49.42 322	76.54 412	52.56 372	77.79 303	82.19 406

CR-MVSNet		73.37 310	71.27 323	79.67 281	81.32 374	65.19 212	75.92 390	80.30 368	59.92 378	72.73 308	81.19 373	52.50 277	86.69 343	59.84 315	77.71 304	87.11 333

RPMNet		73.51 308	70.49 331	82.58 214	81.32 374	65.19 212	75.92 390	92.27 85	57.60 400	72.73 308	76.45 415	52.30 280	95.43 73	48.14 401	77.71 304	87.11 333

SSC-MVS3.2		73.35 313	73.39 297	73.23 369	85.30 284	49.01 419	74.58 403	81.57 350	75.21 113	73.68 296	85.58 295	52.53 275	82.05 386	54.33 363	77.69 306	88.63 296

SCA		74.22 298	72.33 311	79.91 274	84.05 314	62.17 283	79.96 349	79.29 380	66.30 308	72.38 314	80.13 388	51.95 289	88.60 323	59.25 321	77.67 307	88.96 282

Anonymous20231211		78.97 210	77.69 223	82.81 202	90.54 102	64.29 236	90.11 78	91.51 122	65.01 325	76.16 245	88.13 227	50.56 307	93.03 197	69.68 229	77.56 308	91.11 191

v1144		80.03 184	79.03 187	83.01 192	83.78 320	64.51 229	87.11 191	90.57 149	71.96 192	78.08 196	86.20 281	61.41 189	93.94 140	74.93 170	77.23 309	90.60 214

WR-MVS		79.49 193	79.22 184	80.27 267	88.79 167	58.35 326	85.06 256	88.61 232	78.56 35	77.65 204	88.34 216	63.81 151	90.66 286	64.98 271	77.22 310	91.80 170

v1192		79.59 191	78.43 199	83.07 189	83.55 325	64.52 228	86.93 199	90.58 147	70.83 217	77.78 202	85.90 285	59.15 219	93.94 140	73.96 180	77.19 311	90.76 206

VPNet		78.69 216	78.66 193	78.76 296	88.31 185	55.72 370	84.45 274	86.63 276	76.79 75	78.26 190	90.55 152	59.30 218	89.70 301	66.63 257	77.05 312	90.88 201

v1240		78.99 209	77.78 218	82.64 211	83.21 332	63.54 255	86.62 211	90.30 160	69.74 252	77.33 210	85.68 291	57.04 239	93.76 154	73.13 190	76.92 313	90.62 212

MSDG		73.36 312	70.99 326	80.49 262	84.51 305	65.80 197	80.71 336	86.13 286	65.70 315	65.46 389	83.74 337	44.60 365	90.91 280	51.13 380	76.89 314	84.74 377

IterMVS-LS		80.06 183	79.38 177	82.11 221	85.89 267	63.20 265	86.79 204	89.34 191	74.19 143	75.45 257	86.72 261	66.62 118	92.39 220	72.58 195	76.86 315	90.75 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 202	78.03 208	82.80 203	83.30 330	63.94 243	86.80 203	90.33 158	69.91 245	77.48 207	85.53 296	58.44 224	93.75 155	73.60 182	76.85 316	90.71 210

XXY-MVS		75.41 286	75.56 264	74.96 351	83.59 324	57.82 337	80.59 338	83.87 316	66.54 306	74.93 279	88.31 217	63.24 156	80.09 396	62.16 295	76.85 316	86.97 337

v2v482		80.23 180	79.29 181	83.05 190	83.62 323	64.14 238	87.04 192	89.97 170	73.61 158	78.18 193	87.22 249	61.10 197	93.82 149	76.11 155	76.78 318	91.18 189

VortexMVS		78.57 220	77.89 213	80.59 259	85.89 267	62.76 275	85.61 238	89.62 183	72.06 190	74.99 277	85.38 300	55.94 247	90.77 284	74.99 169	76.58 319	88.23 304

v144192		79.47 194	78.37 200	82.78 207	83.35 328	63.96 241	86.96 196	90.36 157	69.99 242	77.50 206	85.67 292	60.66 205	93.77 153	74.27 177	76.58 319	90.62 212

UniMVSNet (Re)		81.60 139	81.11 135	83.09 186	88.38 183	64.41 234	87.60 174	93.02 46	78.42 37	78.56 182	88.16 222	69.78 81	93.26 176	69.58 230	76.49 321	91.60 175

UniMVSNet_NR-MVSNet		81.88 131	81.54 130	82.92 196	88.46 179	63.46 258	87.13 189	92.37 82	80.19 12	78.38 187	89.14 190	71.66 59	93.05 194	70.05 223	76.46 322	92.25 154

DU-MVS		81.12 151	80.52 147	82.90 197	87.80 210	63.46 258	87.02 194	91.87 108	79.01 31	78.38 187	89.07 192	65.02 139	93.05 194	70.05 223	76.46 322	92.20 157

cl22		78.07 232	77.01 236	81.23 243	82.37 357	61.83 288	83.55 294	87.98 243	68.96 272	75.06 275	83.87 332	61.40 190	91.88 241	73.53 183	76.39 324	89.98 247

miper_ehance_all_eth		78.59 219	77.76 220	81.08 248	82.66 350	61.56 291	83.65 290	89.15 207	68.87 273	75.55 253	83.79 336	66.49 121	92.03 233	73.25 188	76.39 324	89.64 259

miper_enhance_ethall		77.87 239	76.86 240	80.92 253	81.65 364	61.38 293	82.68 310	88.98 215	65.52 318	75.47 254	82.30 365	65.76 134	92.00 235	72.95 191	76.39 324	89.39 266

Syy-MVS		68.05 366	67.85 355	68.67 402	84.68 300	40.97 445	78.62 367	73.08 416	66.65 303	66.74 377	79.46 394	52.11 285	82.30 384	32.89 437	76.38 327	82.75 401

myMVS_eth3d		67.02 372	66.29 373	69.21 397	84.68 300	42.58 440	78.62 367	73.08 416	66.65 303	66.74 377	79.46 394	31.53 424	82.30 384	39.43 429	76.38 327	82.75 401

PatchmatchNet		73.12 316	71.33 322	78.49 305	83.18 334	60.85 300	79.63 351	78.57 385	64.13 334	71.73 321	79.81 393	51.20 300	85.97 353	57.40 341	76.36 329	88.66 294
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 345	68.37 346	76.21 336	80.60 380	56.23 363	79.19 358	86.49 278	60.89 369	61.29 411	85.47 298	31.78 423	89.47 305	53.37 368	76.21 330	82.94 400

OpenMVS_ROB	64.09 19	70.56 342	68.19 348	77.65 321	80.26 383	59.41 320	85.01 257	82.96 335	58.76 390	65.43 390	82.33 364	37.63 408	91.23 270	45.34 416	76.03 331	82.32 404

ACMH+	68.96 14	76.01 277	74.01 288	82.03 223	88.60 174	65.31 210	88.86 123	87.55 255	70.25 237	67.75 362	87.47 243	41.27 388	93.19 185	58.37 332	75.94 332	87.60 317

tpm		72.37 324	71.71 316	74.35 359	82.19 358	52.00 399	79.22 357	77.29 396	64.56 329	72.95 306	83.68 341	51.35 297	83.26 379	58.33 333	75.80 333	87.81 313

Anonymous20231206		68.60 360	67.80 358	71.02 390	80.23 385	50.75 413	78.30 374	80.47 363	56.79 405	66.11 387	82.63 361	46.35 349	78.95 400	43.62 419	75.70 334	83.36 393

v7n		78.97 210	77.58 226	83.14 184	83.45 327	65.51 204	88.32 150	91.21 130	73.69 156	72.41 313	86.32 279	57.93 227	93.81 150	69.18 233	75.65 335	90.11 236

NR-MVSNet		80.23 180	79.38 177	82.78 207	87.80 210	63.34 261	86.31 221	91.09 136	79.01 31	72.17 317	89.07 192	67.20 114	92.81 204	66.08 262	75.65 335	92.20 157

v10		79.74 188	78.67 192	82.97 195	84.06 313	64.95 220	87.88 168	90.62 146	73.11 173	75.11 273	86.56 272	61.46 188	94.05 136	73.68 181	75.55 337	89.90 250

IB-MVS	68.01 15	75.85 279	73.36 299	83.31 175	84.76 298	66.03 188	83.38 298	85.06 299	70.21 238	69.40 348	81.05 375	45.76 357	94.66 113	65.10 270	75.49 338	89.25 270
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
h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 206	76.02 96	84.67 80	91.39 128	61.54 185	95.50 69	82.71 88	75.48 339	91.72 174

c3_l		78.75 213	77.91 211	81.26 242	82.89 345	61.56 291	84.09 283	89.13 209	69.97 243	75.56 252	84.29 324	66.36 123	92.09 232	73.47 185	75.48 339	90.12 235

V42		79.38 200	78.24 204	82.83 200	81.10 376	65.50 205	85.55 243	89.82 174	71.57 199	78.21 191	86.12 283	60.66 205	93.18 186	75.64 161	75.46 341	89.81 255

testing3		68.56 362	67.67 361	71.22 389	87.33 230	42.87 439	83.06 308	71.54 419	70.36 231	69.08 352	84.38 321	30.33 427	85.69 356	37.50 432	75.45 342	85.09 373

cl____		77.72 242	76.76 244	80.58 260	82.49 354	60.48 306	83.09 305	87.87 247	69.22 262	74.38 289	85.22 305	62.10 176	91.53 257	71.09 211	75.41 343	89.73 258

DIV-MVS_self_test		77.72 242	76.76 244	80.58 260	82.48 355	60.48 306	83.09 305	87.86 248	69.22 262	74.38 289	85.24 303	62.10 176	91.53 257	71.09 211	75.40 344	89.74 257

v8		79.97 186	79.02 188	82.80 203	84.09 312	64.50 231	87.96 162	90.29 161	74.13 146	75.24 269	86.81 258	62.88 164	93.89 148	74.39 176	75.40 344	90.00 244

Baseline_NR-MVSNet		78.15 230	78.33 202	77.61 322	85.79 269	56.21 364	86.78 205	85.76 291	73.60 159	77.93 199	87.57 238	65.02 139	88.99 314	67.14 254	75.33 346	87.63 316

pmmvs5		71.55 331	70.20 336	75.61 341	77.83 405	56.39 359	81.74 319	80.89 356	57.76 398	67.46 366	84.49 317	49.26 326	85.32 362	57.08 344	75.29 347	85.11 372

EPMVS		69.02 357	68.16 349	71.59 383	79.61 395	49.80 418	77.40 382	66.93 432	62.82 353	70.01 339	79.05 397	45.79 356	77.86 406	56.58 351	75.26 348	87.13 332

TranMVSNet+NR-MVSNet		80.84 155	80.31 152	82.42 216	87.85 207	62.33 280	87.74 172	91.33 127	80.55 9	77.99 198	89.86 166	65.23 137	92.62 206	67.05 255	75.24 349	92.30 152

test_fmvs2		68.35 365	67.48 364	70.98 391	69.50 437	51.95 400	80.05 347	76.38 402	49.33 426	74.65 284	84.38 321	23.30 439	75.40 426	74.51 174	75.17 350	85.60 362

tfpnnormal		74.39 295	73.16 301	78.08 312	86.10 265	58.05 330	84.65 267	87.53 256	70.32 234	71.22 328	85.63 293	54.97 252	89.86 296	43.03 420	75.02 351	86.32 347

COLMAP_ROB	66.92 17	73.01 318	70.41 333	80.81 255	87.13 237	65.63 201	88.30 151	84.19 312	62.96 349	63.80 403	87.69 235	38.04 406	92.56 211	46.66 406	74.91 352	84.24 382
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 364	67.85 355	70.29 393	80.70 379	43.93 437	72.47 409	74.88 408	60.15 376	70.55 330	76.57 414	49.94 316	81.59 388	50.58 381	74.83 353	85.34 366

pmmvs4		74.03 303	71.91 314	80.39 263	81.96 360	68.32 131	81.45 324	82.14 343	59.32 383	69.87 344	85.13 307	52.40 279	88.13 329	60.21 313	74.74 354	84.73 378

ITE_SJBPF				78.22 308	81.77 363	60.57 304		83.30 324	69.25 261	67.54 364	87.20 250	36.33 413	87.28 340	54.34 362	74.62 355	86.80 340

test0.0.03 1		68.00 367	67.69 360	68.90 399	77.55 406	47.43 422	75.70 393	72.95 418	66.66 300	66.56 379	82.29 366	48.06 334	75.87 421	44.97 417	74.51 356	83.41 392

test_0402		72.79 321	70.44 332	79.84 276	88.13 193	65.99 191	85.93 231	84.29 309	65.57 317	67.40 369	85.49 297	46.92 341	92.61 207	35.88 434	74.38 357	80.94 413

CP-MVSNet		78.22 226	78.34 201	77.84 317	87.83 209	54.54 383	87.94 164	91.17 132	77.65 46	73.48 299	88.49 212	62.24 174	88.43 325	62.19 294	74.07 358	90.55 216

FMVSNet5		69.50 353	67.96 353	74.15 362	82.97 343	55.35 375	80.01 348	82.12 344	62.56 356	63.02 404	81.53 372	36.92 409	81.92 387	48.42 396	74.06 359	85.17 371

MVS-HIRNet		59.14 396	57.67 398	63.57 414	81.65 364	43.50 438	71.73 411	65.06 437	39.59 439	51.43 434	57.73 442	38.34 404	82.58 383	39.53 427	73.95 360	64.62 438

tpmrst		72.39 322	72.13 313	73.18 373	80.54 381	49.91 416	79.91 350	79.08 382	63.11 346	71.69 322	79.95 390	55.32 250	82.77 382	65.66 266	73.89 361	86.87 338

PS-CasMVS		78.01 235	78.09 207	77.77 319	87.71 216	54.39 385	88.02 160	91.22 129	77.50 54	73.26 301	88.64 207	60.73 201	88.41 326	61.88 298	73.88 362	90.53 217

v148		78.72 215	77.80 217	81.47 234	82.73 348	61.96 286	86.30 222	88.08 239	73.26 170	76.18 242	85.47 298	62.46 169	92.36 222	71.92 205	73.82 363	90.09 238

Patchmatch-test		64.82 385	63.24 386	69.57 395	79.42 398	49.82 417	63.49 442	69.05 427	51.98 421	59.95 417	80.13 388	50.91 302	70.98 436	40.66 426	73.57 364	87.90 311

WR-MVS_H		78.51 221	78.49 196	78.56 301	88.02 199	56.38 360	88.43 143	92.67 68	77.14 64	73.89 293	87.55 240	66.25 125	89.24 309	58.92 325	73.55 365	90.06 242

AUN-MVS		79.21 203	77.60 225	84.05 149	88.71 171	67.61 156	85.84 235	87.26 263	69.08 267	77.23 214	88.14 226	53.20 274	93.47 167	75.50 165	73.45 366	91.06 193

hse-mvs2		81.72 134	80.94 139	84.07 144	88.72 170	67.68 154	85.87 233	87.26 263	76.02 96	84.67 80	88.22 221	61.54 185	93.48 166	82.71 88	73.44 367	91.06 193

testgi		66.67 375	66.53 372	67.08 409	75.62 415	41.69 444	75.93 389	76.50 401	66.11 309	65.20 394	86.59 269	35.72 415	74.71 428	43.71 418	73.38 368	84.84 376

Anonymous20240521		68.80 359	67.22 368	73.55 367	74.33 419	54.11 386	83.18 302	85.61 292	58.15 394	61.68 410	80.94 378	30.71 426	81.27 391	57.00 346	73.34 369	85.28 367

pm-mvs1		77.25 254	76.68 248	78.93 294	84.22 309	58.62 324	86.41 217	88.36 235	71.37 202	73.31 300	88.01 228	61.22 195	89.15 312	64.24 277	73.01 370	89.03 277

eth_miper_zixun_eth		77.92 237	76.69 247	81.61 232	83.00 340	61.98 285	83.15 303	89.20 205	69.52 254	74.86 280	84.35 323	61.76 181	92.56 211	71.50 208	72.89 371	90.28 229

miper_lstm_enhance		74.11 300	73.11 302	77.13 330	80.11 386	59.62 316	72.23 410	86.92 272	66.76 298	70.40 333	82.92 355	56.93 240	82.92 380	69.06 235	72.63 372	88.87 285

tpmvs		71.09 335	69.29 340	76.49 334	82.04 359	56.04 365	78.92 363	81.37 354	64.05 338	67.18 371	78.28 405	49.74 319	89.77 298	49.67 390	72.37 373	83.67 390

PEN-MVS		77.73 241	77.69 223	77.84 317	87.07 243	53.91 388	87.91 166	91.18 131	77.56 51	73.14 303	88.82 202	61.23 194	89.17 311	59.95 314	72.37 373	90.43 221

DSMNet-mixed		57.77 398	56.90 400	60.38 418	67.70 439	35.61 449	69.18 423	53.97 450	32.30 448	57.49 425	79.88 391	40.39 394	68.57 442	38.78 430	72.37 373	76.97 424

MonoMVSNet		76.49 269	75.80 258	78.58 300	81.55 367	58.45 325	86.36 220	86.22 283	74.87 126	74.73 282	83.73 338	51.79 294	88.73 320	70.78 213	72.15 376	88.55 299

IterMVS-SCA-FT		75.43 285	73.87 292	80.11 271	82.69 349	64.85 224	81.57 322	83.47 322	69.16 265	70.49 332	84.15 330	51.95 289	88.15 328	69.23 232	72.14 377	87.34 324

tpm cat1		70.57 341	68.31 347	77.35 327	82.41 356	57.95 334	78.08 375	80.22 370	52.04 419	68.54 357	77.66 410	52.00 288	87.84 333	51.77 374	72.07 378	86.25 348

RPSCF		73.23 315	71.46 319	78.54 302	82.50 353	59.85 313	82.18 315	82.84 338	58.96 387	71.15 329	89.41 188	45.48 362	84.77 367	58.82 327	71.83 379	91.02 197

IterMVS		74.29 296	72.94 304	78.35 307	81.53 368	63.49 257	81.58 321	82.49 340	68.06 286	69.99 341	83.69 340	51.66 296	85.54 358	65.85 264	71.64 380	86.01 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 336	68.09 351	79.58 283	85.15 288	63.62 248	84.58 269	79.83 373	62.31 358	60.32 415	86.73 259	32.02 421	88.96 317	50.28 385	71.57 381	86.15 351

TestCases				79.58 283	85.15 288	63.62 248		79.83 373	62.31 358	60.32 415	86.73 259	32.02 421	88.96 317	50.28 385	71.57 381	86.15 351

baseline1		76.98 258	76.75 246	77.66 320	88.13 193	55.66 371	85.12 254	81.89 346	73.04 175	76.79 224	88.90 199	62.43 170	87.78 334	63.30 283	71.18 383	89.55 262

Patchmtry		70.74 339	69.16 342	75.49 345	80.72 378	54.07 387	74.94 401	80.30 368	58.34 392	70.01 339	81.19 373	52.50 277	86.54 345	53.37 368	71.09 384	85.87 360

DTE-MVSNet		76.99 257	76.80 242	77.54 325	86.24 258	53.06 397	87.52 176	90.66 145	77.08 68	72.50 311	88.67 206	60.48 209	89.52 303	57.33 342	70.74 385	90.05 243

reproduce_monomvs		75.40 287	74.38 285	78.46 306	83.92 317	57.80 338	83.78 286	86.94 270	73.47 164	72.25 316	84.47 318	38.74 401	89.27 308	75.32 167	70.53 386	88.31 303

MIMVSNet1		68.58 361	66.78 371	73.98 364	80.07 387	51.82 403	80.77 333	84.37 306	64.40 331	59.75 418	82.16 368	36.47 412	83.63 374	42.73 421	70.33 387	86.48 346

pmmvs6		74.69 293	73.39 297	78.61 298	81.38 371	57.48 343	86.64 210	87.95 245	64.99 326	70.18 336	86.61 268	50.43 309	89.52 303	62.12 296	70.18 388	88.83 287

test_vis1_rt		60.28 394	58.42 397	65.84 411	67.25 440	55.60 372	70.44 419	60.94 444	44.33 433	59.00 419	66.64 434	24.91 434	68.67 441	62.80 285	69.48 389	73.25 430

TinyColmap		67.30 371	64.81 377	74.76 355	81.92 362	56.68 355	80.29 344	81.49 352	60.33 373	56.27 429	83.22 348	24.77 435	87.66 336	45.52 414	69.47 390	79.95 418

OurMVSNet-221017-0		74.26 297	72.42 310	79.80 277	83.76 321	59.59 317	85.92 232	86.64 275	66.39 307	66.96 373	87.58 237	39.46 396	91.60 250	65.76 265	69.27 391	88.22 305

JIA-IIPM		66.32 378	62.82 390	76.82 332	77.09 409	61.72 290	65.34 437	75.38 405	58.04 397	64.51 396	62.32 437	42.05 385	86.51 346	51.45 378	69.22 392	82.21 405

ADS-MVSNet2		66.20 381	63.33 385	74.82 354	79.92 388	58.75 323	67.55 429	75.19 406	53.37 416	65.25 392	75.86 418	42.32 381	80.53 395	41.57 424	68.91 393	85.18 369

ADS-MVSNet		64.36 386	62.88 389	68.78 401	79.92 388	47.17 425	67.55 429	71.18 420	53.37 416	65.25 392	75.86 418	42.32 381	73.99 432	41.57 424	68.91 393	85.18 369

test20.03		67.45 369	66.95 370	68.94 398	75.48 416	44.84 435	77.50 381	77.67 390	66.66 300	63.01 405	83.80 335	47.02 340	78.40 402	42.53 423	68.86 395	83.58 391

EU-MVSNet		68.53 363	67.61 362	71.31 388	78.51 404	47.01 426	84.47 271	84.27 310	42.27 435	66.44 384	84.79 315	40.44 393	83.76 372	58.76 328	68.54 396	83.17 394

dmvs_testset		62.63 390	64.11 381	58.19 420	78.55 403	24.76 458	75.28 395	65.94 435	67.91 287	60.34 414	76.01 417	53.56 269	73.94 433	31.79 438	67.65 397	75.88 427

our_test_3		69.14 356	67.00 369	75.57 342	79.80 392	58.80 322	77.96 377	77.81 389	59.55 381	62.90 407	78.25 406	47.43 336	83.97 371	51.71 375	67.58 398	83.93 387

ppachtmachnet_test		70.04 349	67.34 367	78.14 310	79.80 392	61.13 294	79.19 358	80.59 361	59.16 385	65.27 391	79.29 396	46.75 345	87.29 339	49.33 392	66.72 399	86.00 357

LF4IMVS		64.02 387	62.19 391	69.50 396	70.90 435	53.29 395	76.13 387	77.18 397	52.65 418	58.59 420	80.98 377	23.55 438	76.52 413	53.06 370	66.66 400	78.68 421

Patchmatch-RL test		70.24 346	67.78 359	77.61 322	77.43 407	59.57 318	71.16 414	70.33 421	62.94 350	68.65 355	72.77 427	50.62 306	85.49 359	69.58 230	66.58 401	87.77 314

dp		66.80 373	65.43 375	70.90 392	79.74 394	48.82 420	75.12 399	74.77 409	59.61 380	64.08 400	77.23 411	42.89 377	80.72 394	48.86 395	66.58 401	83.16 395

test_fmvs3		63.36 389	61.82 392	67.98 406	62.51 446	46.96 427	77.37 383	74.03 413	45.24 431	67.50 365	78.79 402	12.16 451	72.98 435	72.77 194	66.02 403	83.99 386

CL-MVSNet_self_test		72.37 324	71.46 319	75.09 350	79.49 397	53.53 390	80.76 334	85.01 301	69.12 266	70.51 331	82.05 369	57.92 228	84.13 370	52.27 373	66.00 404	87.60 317

FPMVS		53.68 404	51.64 406	59.81 419	65.08 443	51.03 410	69.48 422	69.58 425	41.46 436	40.67 443	72.32 428	16.46 447	70.00 440	24.24 447	65.42 405	58.40 443

pmmvs-eth3d		70.50 343	67.83 357	78.52 304	77.37 408	66.18 187	81.82 317	81.51 351	58.90 388	63.90 402	80.42 383	42.69 379	86.28 349	58.56 329	65.30 406	83.11 396

N_pmnet		52.79 406	53.26 404	51.40 430	78.99 401	7.68 464	69.52 421	3.89 463	51.63 422	57.01 426	74.98 422	40.83 391	65.96 445	37.78 431	64.67 407	80.56 417

PM-MVS		66.41 377	64.14 380	73.20 372	73.92 422	56.45 357	78.97 362	64.96 438	63.88 342	64.72 395	80.24 387	19.84 443	83.44 377	66.24 258	64.52 408	79.71 419

KD-MVS_self_test		68.81 358	67.59 363	72.46 379	74.29 420	45.45 429	77.93 378	87.00 268	63.12 345	63.99 401	78.99 401	42.32 381	84.77 367	56.55 352	64.09 409	87.16 331

SixPastTwentyTwo		73.37 310	71.26 324	79.70 279	85.08 291	57.89 335	85.57 239	83.56 320	71.03 214	65.66 388	85.88 286	42.10 384	92.57 210	59.11 323	63.34 410	88.65 295

sc_t1		72.19 327	69.51 338	80.23 268	84.81 296	61.09 296	84.68 264	80.22 370	60.70 371	71.27 326	83.58 343	36.59 411	89.24 309	60.41 310	63.31 411	90.37 224

tt0320		70.49 344	68.03 352	77.89 315	84.78 297	59.12 321	83.55 294	80.44 365	58.13 395	67.43 368	80.41 384	39.26 398	87.54 337	55.12 357	63.18 412	86.99 336

EGC-MVSNET		52.07 408	47.05 412	67.14 408	83.51 326	60.71 302	80.50 340	67.75 430	0.07 458	0.43 459	75.85 420	24.26 436	81.54 389	28.82 441	62.25 413	59.16 441

TransMVSNet (Re)		75.39 288	74.56 281	77.86 316	85.50 279	57.10 348	86.78 205	86.09 287	72.17 188	71.53 324	87.34 244	63.01 163	89.31 307	56.84 348	61.83 414	87.17 329

MDA-MVSNet_test_wron		65.03 383	62.92 387	71.37 385	75.93 411	56.73 352	69.09 426	74.73 410	57.28 403	54.03 432	77.89 407	45.88 354	74.39 430	49.89 389	61.55 415	82.99 399

YYNet1		65.03 383	62.91 388	71.38 384	75.85 413	56.60 356	69.12 425	74.66 412	57.28 403	54.12 431	77.87 408	45.85 355	74.48 429	49.95 388	61.52 416	83.05 397

mvsany_test1		62.30 391	61.26 395	65.41 412	69.52 436	54.86 380	66.86 431	49.78 452	46.65 429	68.50 358	83.21 349	49.15 327	66.28 444	56.93 347	60.77 417	75.11 428

ambc				75.24 349	73.16 429	50.51 414	63.05 443	87.47 258		64.28 397	77.81 409	17.80 445	89.73 300	57.88 337	60.64 418	85.49 363

TDRefinement		67.49 368	64.34 379	76.92 331	73.47 427	61.07 297	84.86 261	82.98 334	59.77 379	58.30 422	85.13 307	26.06 431	87.89 332	47.92 403	60.59 419	81.81 409

Gipuma		45.18 415	41.86 418	55.16 427	77.03 410	51.52 406	32.50 451	80.52 362	32.46 447	27.12 450	35.02 451	9.52 454	75.50 423	22.31 448	60.21 420	38.45 450
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tt0320-xc		70.11 348	67.45 365	78.07 313	85.33 283	59.51 319	83.28 300	78.96 383	58.77 389	67.10 372	80.28 386	36.73 410	87.42 338	56.83 349	59.77 421	87.29 326

new-patchmatchnet		61.73 392	61.73 393	61.70 416	72.74 432	24.50 459	69.16 424	78.03 388	61.40 366	56.72 427	75.53 421	38.42 403	76.48 414	45.95 412	57.67 422	84.13 384

MDA-MVSNet-bldmvs		66.68 374	63.66 384	75.75 339	79.28 399	60.56 305	73.92 406	78.35 387	64.43 330	50.13 437	79.87 392	44.02 371	83.67 373	46.10 411	56.86 423	83.03 398

new_pmnet		50.91 409	50.29 409	52.78 429	68.58 438	34.94 451	63.71 440	56.63 449	39.73 438	44.95 440	65.47 435	21.93 440	58.48 449	34.98 435	56.62 424	64.92 437

test_f		52.09 407	50.82 408	55.90 424	53.82 454	42.31 443	59.42 444	58.31 448	36.45 443	56.12 430	70.96 431	12.18 450	57.79 450	53.51 367	56.57 425	67.60 435

test_vis3_rt		49.26 411	47.02 413	56.00 423	54.30 452	45.27 433	66.76 433	48.08 453	36.83 442	44.38 441	53.20 446	7.17 458	64.07 446	56.77 350	55.66 426	58.65 442

PMVS	37.38 22	44.16 416	40.28 420	55.82 425	40.82 460	42.54 442	65.12 438	63.99 440	34.43 445	24.48 451	57.12 444	3.92 461	76.17 418	17.10 452	55.52 427	48.75 446
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		53.31 405	49.93 410	63.42 415	65.68 442	50.13 415	71.59 413	66.90 433	34.43 445	40.58 444	71.56 430	8.65 456	76.27 416	34.64 436	55.36 428	63.86 439

mvs5depth		69.45 354	67.45 365	75.46 346	73.93 421	55.83 368	79.19 358	83.23 326	66.89 295	71.63 323	83.32 347	33.69 419	85.09 363	59.81 316	55.34 429	85.46 364

pmmvs3		57.79 397	54.26 402	68.37 403	64.02 445	56.72 353	75.12 399	65.17 436	40.20 437	52.93 433	69.86 433	20.36 442	75.48 424	45.45 415	55.25 430	72.90 431

UnsupCasMVSNet_eth		67.33 370	65.99 374	71.37 385	73.48 426	51.47 407	75.16 397	85.19 296	65.20 321	60.78 413	80.93 380	42.35 380	77.20 408	57.12 343	53.69 431	85.44 365

K. test v3		71.19 333	68.51 345	79.21 290	83.04 339	57.78 339	84.35 278	76.91 399	72.90 178	62.99 406	82.86 357	39.27 397	91.09 276	61.65 301	52.66 432	88.75 291

mmtdpeth		74.16 299	73.01 303	77.60 324	83.72 322	61.13 294	85.10 255	85.10 298	72.06 190	77.21 218	80.33 385	43.84 372	85.75 354	77.14 144	52.61 433	85.91 358

UnsupCasMVSNet_bld		63.70 388	61.53 394	70.21 394	73.69 424	51.39 408	72.82 408	81.89 346	55.63 410	57.81 424	71.80 429	38.67 402	78.61 401	49.26 393	52.21 434	80.63 415

LCM-MVSNet		54.25 401	49.68 411	67.97 407	53.73 455	45.28 432	66.85 432	80.78 358	35.96 444	39.45 445	62.23 438	8.70 455	78.06 405	48.24 400	51.20 435	80.57 416

KD-MVS_2432*1600		66.22 379	63.89 382	73.21 370	75.47 417	53.42 392	70.76 417	84.35 307	64.10 336	66.52 381	78.52 403	34.55 417	84.98 364	50.40 383	50.33 436	81.23 411

miper_refine_blended		66.22 379	63.89 382	73.21 370	75.47 417	53.42 392	70.76 417	84.35 307	64.10 336	66.52 381	78.52 403	34.55 417	84.98 364	50.40 383	50.33 436	81.23 411

mvsany_test3		53.99 402	51.45 407	61.61 417	55.51 451	44.74 436	63.52 441	45.41 456	43.69 434	58.11 423	76.45 415	17.99 444	63.76 447	54.77 360	47.59 438	76.34 426

lessismore_v0				78.97 293	81.01 377	57.15 347		65.99 434		61.16 412	82.82 358	39.12 399	91.34 266	59.67 317	46.92 439	88.43 301

testf1		45.72 412	41.96 416	57.00 421	56.90 449	45.32 430	66.14 434	59.26 446	26.19 449	30.89 448	60.96 440	4.14 459	70.64 438	26.39 445	46.73 440	55.04 444

APD_test2		45.72 412	41.96 416	57.00 421	56.90 449	45.32 430	66.14 434	59.26 446	26.19 449	30.89 448	60.96 440	4.14 459	70.64 438	26.39 445	46.73 440	55.04 444

ttmdpeth		59.91 395	57.10 399	68.34 404	67.13 441	46.65 428	74.64 402	67.41 431	48.30 427	62.52 409	85.04 311	20.40 441	75.93 420	42.55 422	45.90 442	82.44 403

MVStest1		56.63 399	52.76 405	68.25 405	61.67 447	53.25 396	71.67 412	68.90 429	38.59 440	50.59 436	83.05 352	25.08 433	70.66 437	36.76 433	38.56 443	80.83 414

PVSNet_0	57.27 20	61.67 393	59.27 396	68.85 400	79.61 395	57.44 344	68.01 427	73.44 415	55.93 409	58.54 421	70.41 432	44.58 366	77.55 407	47.01 405	35.91 444	71.55 432

WB-MVS		54.94 400	54.72 401	55.60 426	73.50 425	20.90 460	74.27 405	61.19 443	59.16 385	50.61 435	74.15 423	47.19 339	75.78 422	17.31 451	35.07 445	70.12 433

test_method		31.52 420	29.28 424	38.23 434	27.03 462	6.50 465	20.94 453	62.21 442	4.05 456	22.35 454	52.50 447	13.33 448	47.58 454	27.04 444	34.04 446	60.62 440

SSC-MVS		53.88 403	53.59 403	54.75 428	72.87 431	19.59 461	73.84 407	60.53 445	57.58 401	49.18 439	73.45 426	46.34 350	75.47 425	16.20 454	32.28 447	69.20 434

PMMVS2		40.82 417	38.86 421	46.69 431	53.84 453	16.45 462	48.61 448	49.92 451	37.49 441	31.67 446	60.97 439	8.14 457	56.42 451	28.42 442	30.72 448	67.19 436

dongtai		45.42 414	45.38 415	45.55 432	73.36 428	26.85 456	67.72 428	34.19 458	54.15 414	49.65 438	56.41 445	25.43 432	62.94 448	19.45 449	28.09 449	46.86 448

kuosan		39.70 418	40.40 419	37.58 435	64.52 444	26.98 454	65.62 436	33.02 459	46.12 430	42.79 442	48.99 448	24.10 437	46.56 456	12.16 457	26.30 450	39.20 449

DeepMVS_CX				27.40 438	40.17 461	26.90 455		24.59 462	17.44 454	23.95 452	48.61 449	9.77 453	26.48 457	18.06 450	24.47 451	28.83 451

MVE	26.22 23	30.37 422	25.89 426	43.81 433	44.55 459	35.46 450	28.87 452	39.07 457	18.20 453	18.58 455	40.18 450	2.68 462	47.37 455	17.07 453	23.78 452	48.60 447
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 419	30.64 422	35.15 436	52.87 456	27.67 453	57.09 446	47.86 454	24.64 451	16.40 456	33.05 452	11.23 452	54.90 452	14.46 455	18.15 453	22.87 452

EMVS		30.81 421	29.65 423	34.27 437	50.96 457	25.95 457	56.58 447	46.80 455	24.01 452	15.53 457	30.68 453	12.47 449	54.43 453	12.81 456	17.05 454	22.43 453

ANet_high		50.57 410	46.10 414	63.99 413	48.67 458	39.13 446	70.99 416	80.85 357	61.39 367	31.18 447	57.70 443	17.02 446	73.65 434	31.22 440	15.89 455	79.18 420

tmp_tt		18.61 424	21.40 427	10.23 440	4.82 463	10.11 463	34.70 450	30.74 461	1.48 457	23.91 453	26.07 454	28.42 429	13.41 459	27.12 443	15.35 456	7.17 454

wuyk23d		16.82 425	15.94 428	19.46 439	58.74 448	31.45 452	39.22 449	3.74 464	6.84 455	6.04 458	2.70 458	1.27 463	24.29 458	10.54 458	14.40 457	2.63 455

testmvs		6.04 428	8.02 431	0.10 442	0.08 464	0.03 467	69.74 420	0.04 465	0.05 459	0.31 460	1.68 459	0.02 465	0.04 460	0.24 459	0.02 458	0.25 457

test123		6.12 427	8.11 430	0.14 441	0.06 465	0.09 466	71.05 415	0.03 466	0.04 460	0.25 461	1.30 460	0.05 464	0.03 461	0.21 460	0.01 459	0.29 456

mmdepth		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

monomultidepth		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

test_blank		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

uanet_test		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

DCPMVS		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

cdsmvs_eth3d_5k		19.96 423	26.61 425	0.00 443	0.00 466	0.00 468	0.00 454	89.26 200	0.00 461	0.00 462	88.61 208	61.62 184	0.00 462	0.00 461	0.00 460	0.00 458

pcd_1.5k_mvsjas		5.26 429	7.02 432	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	63.15 159	0.00 462	0.00 461	0.00 460	0.00 458

sosnet-low-res		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

sosnet		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

uncertanet		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

Regformer		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

ab-mvs-re		7.23 426	9.64 429	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	86.72 261	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

uanet		0.00 430	0.00 433	0.00 443	0.00 466	0.00 468	0.00 454	0.00 467	0.00 461	0.00 462	0.00 461	0.00 466	0.00 462	0.00 461	0.00 460	0.00 458

WAC-MVS						42.58 440								39.46 428

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 134	91.30 15

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

eth-test2					0.00 466

eth-test					0.00 466

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

save fliter					93.80 40	72.35 44	90.47 69	91.17 132	74.31 139

test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

GSMVS																88.96 282

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 298				88.96 282

sam_mvs												50.01 314

MTGPA								92.02 98

test_post1							78.90 364				5.43 457	48.81 333	85.44 361	59.25 321

test_post											5.46 456	50.36 310	84.24 369

patchmatchnet-post											74.00 424	51.12 301	88.60 323

MTMP							92.18 35	32.83 460

gm-plane-assit					81.40 370	53.83 389			62.72 355		80.94 378		92.39 220	63.40 282

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 281	85.00 73	93.10 81	74.36 29	95.41 76

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 276	84.87 77	93.10 81	74.43 27	95.16 86

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

test_prior4						72.60 34	89.01 118

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 67

旧先验2							86.56 213		58.10 396	87.04 55			88.98 315	74.07 179

新几何2							86.29 223

无先验							87.48 177	88.98 215	60.00 377				94.12 133	67.28 251		88.97 281

原ACMM2							86.86 201

testdata2													91.01 278	62.37 292

segment_acmp												73.08 40

testdata1							84.14 282		75.71 100

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 210

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 174

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

n2								0.00 467

nn								0.00 467

door-mid								69.98 423

test11								92.23 88

door								69.44 426

HQP5-MVS						66.98 176

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 214

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 214

BP-MVS														77.47 139

HQP4-MVS										77.24 213			95.11 90			91.03 195

HQP2-MVS												60.17 213

NP-MVS					89.62 125	68.32 131					90.24 160

MDTV_nov1_ep13_2view						37.79 448	75.16 397		55.10 411	66.53 380		49.34 324		53.98 364		87.94 310

Test By Simon												64.33 145

High-resolution multi-view benchmark