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 24	93.63 21	74.77 124	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
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 59	95.06 1	94.23 3	78.38 35	92.78 4	95.74 6	82.45 3	97.49 4	89.42 16	96.68 2	94.95 11

PC_three_1452									68.21 272	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 57	93.10 1	95.72 8	82.99 1	97.44 7	89.07 21	96.63 4	94.88 15

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 283	92.39 6				88.94 24	96.63 4	94.85 20

test_241102_TWO								94.06 10	77.24 57	92.78 4	95.72 8	81.26 8	97.44 7	89.07 21	96.58 6	94.26 50

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 9	89.42 16	96.57 7	94.67 28

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 52	82.45 3	96.87 20	83.77 73	96.48 8	94.88 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 115

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 54	92.12 9	95.78 4	80.98 9	97.40 9	89.08 19	96.41 12	93.33 99
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 58	93.49 9	94.23 3					97.49 4	89.08 19	96.41 12	94.21 51

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 88	88.14 33	95.09 17	71.06 65	96.67 29	87.67 37	96.37 14	94.09 56

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 94	92.29 7	95.66 10	81.67 6	97.38 11	87.44 41	96.34 15	93.95 64
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 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 113	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 96	89.16 21	95.10 16	75.65 21	96.19 46	87.07 42	96.01 17	94.79 22

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 35	94.06 50	76.43 16	96.84 21	88.48 32	95.99 18	94.34 46

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 178	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 45

test_prior2							88.85 122		75.41 103	84.91 73	93.54 67	74.28 29		83.31 76	95.86 20

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 70	85.24 68	94.32 37	71.76 53	96.93 19	85.53 52	95.79 22	94.32 47

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 146	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 50	89.79 19	94.12 47	78.98 12	96.58 35	85.66 49	95.72 24	94.58 33

train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 127	91.89 103	68.69 264	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 117

test9_res														84.90 55	95.70 26	92.87 122

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 98	91.06 16	96.03 1	76.84 14	97.03 17	89.09 18	95.65 27	94.47 39
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 95	73.65 10	92.66 23	91.17 129	86.57 1	87.39 49	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

agg_prior2														82.91 82	95.45 29	92.70 126

CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 135	92.42 80	68.32 271	84.61 82	93.48 69	72.32 46	96.15 48	79.00 117	95.43 30	94.28 49

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 62	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 83
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 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 205	92.02 95	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 102

DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 78	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 52
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 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 182	82.14 3	86.65 57	94.28 38	68.28 101	97.46 6	90.81 5	95.31 34	95.15 7

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 46	83.84 98	94.40 34	72.24 47	96.28 43	85.65 50	95.30 35	93.62 86
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 170	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 42

balanced_conf03		86.78 37	86.99 35	86.15 63	91.24 83	67.61 148	90.51 62	92.90 56	77.26 56	87.44 48	91.63 115	71.27 62	96.06 49	85.62 51	95.01 37	94.78 23

GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 68	84.68 77	93.99 56	70.67 70	96.82 22	84.18 70	95.01 37	93.90 67

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 172	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 126
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 63	93.47 71	73.02 41	97.00 18	84.90 55	94.94 40	94.10 55

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 72	84.66 80	94.52 25	68.81 94	96.65 30	84.53 63	94.90 41	94.00 61

SPE-MVS-test		86.29 48	86.48 43	85.71 73	91.02 88	67.21 165	92.36 29	93.78 18	78.97 30	83.51 105	91.20 130	70.65 71	95.15 84	81.96 92	94.89 42	94.77 24

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 72	84.91 73	94.44 32	70.78 68	96.61 32	84.53 63	94.89 42	93.66 79

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 208	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 75	84.45 85	94.52 25	69.09 88	96.70 27	84.37 65	94.83 45	94.03 59

原ACMM1				84.35 117	93.01 60	68.79 110		92.44 77	63.96 328	81.09 137	91.57 118	66.06 126	95.45 68	67.19 241	94.82 46	88.81 275

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 91	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 183	93.04 41	69.80 236	82.85 112	91.22 129	73.06 40	96.02 52	76.72 147	94.63 48	91.46 172

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 128	88.90 24	93.85 62	75.75 20	96.00 54	87.80 36	94.63 48	95.04 9
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 40	86.27 47	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 100	83.86 97	94.42 33	67.87 106	96.64 31	82.70 88	94.57 50	93.66 79

XVS		87.18 32	86.91 39	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	94.17 44	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

X-MVStestdata		80.37 168	77.83 205	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 442	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

test12				86.80 52	92.63 67	70.70 75		91.79 109		82.71 115		71.67 56	96.16 47		94.50 51	93.54 91

MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 147	89.63 88	92.65 70	72.89 175	84.64 81	91.71 111	71.85 51	96.03 50	84.77 60	94.45 54	94.49 38

CP-MVS		87.11 33	86.92 38	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 78	83.68 101	94.46 29	67.93 104	95.95 57	84.20 69	94.39 55	93.23 102

CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 144	83.16 108	91.07 135	75.94 18	95.19 82	79.94 112	94.38 56	93.55 90

MSLP-MVS++		85.43 66	85.76 60	84.45 113	91.93 75	70.24 79	90.71 59	92.86 58	77.46 52	84.22 89	92.81 90	67.16 113	92.94 190	80.36 107	94.35 57	90.16 220

mPP-MVS		86.67 41	86.32 45	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 69	82.81 114	94.25 41	66.44 120	96.24 44	82.88 83	94.28 58	93.38 95

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 144	87.63 38	94.27 59	93.65 83
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 16	94.11 6	80.27 10	91.35 14	94.16 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
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 67	85.30 71	85.77 72	88.49 170	67.93 140	85.52 240	93.44 27	78.70 31	83.63 104	89.03 185	74.57 24	95.71 61	80.26 109	94.04 61	93.66 79
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 91	82.92 104	86.14 65	84.22 297	69.48 94	91.05 56	85.27 283	81.30 6	76.83 212	91.65 113	66.09 125	95.56 63	76.00 153	93.85 62	93.38 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EC-MVSNet		86.01 50	86.38 44	84.91 99	89.31 139	66.27 178	92.32 30	93.63 21	79.37 22	84.17 91	91.88 106	69.04 92	95.43 70	83.93 72	93.77 63	93.01 118

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 217	93.37 74	60.40 205	96.75 26	77.20 138	93.73 64	95.29 5

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 113

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 113

CS-MVS		86.69 39	86.95 37	85.90 71	90.76 96	67.57 150	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

CANet		86.45 43	86.10 53	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 132	91.43 123	70.34 72	97.23 14	84.26 66	93.36 68	94.37 44

reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 125	88.80 25	95.61 11	70.29 74	96.44 39	86.20 48	93.08 69	93.16 108

新几何1				83.42 164	93.13 54	70.71 74		85.48 282	57.43 389	81.80 126	91.98 103	63.28 148	92.27 217	64.60 262	92.99 70	87.27 314

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 115	73.89 147	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 88

SR-MVS		86.73 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 129	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 124

fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 158	87.32 224	65.13 206	88.86 120	91.63 114	75.41 103	88.23 32	93.45 72	68.56 97	92.47 207	89.52 15	92.78 73	93.20 106

旧先验1					91.96 74	65.79 190		86.37 269			93.08 83	69.31 86			92.74 74	88.74 280

3Dnovator	76.31 5	83.38 102	82.31 114	86.59 55	87.94 196	72.94 28	90.64 60	92.14 94	77.21 59	75.47 243	92.83 88	58.56 212	94.72 106	73.24 183	92.71 75	92.13 154

MVS_111021_HR		85.14 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 222	90.33 155	76.11 90	82.08 121	91.61 117	71.36 61	94.17 126	81.02 100	92.58 76	92.08 155

APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 157	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 92

test2506		77.30 242	76.49 239	79.74 268	90.08 109	52.02 386	87.86 164	63.10 428	74.88 120	80.16 150	92.79 91	38.29 392	92.35 214	68.74 227	92.50 78	94.86 18

ECVR-MVS		79.61 179	79.26 172	80.67 248	90.08 109	54.69 369	87.89 162	77.44 381	74.88 120	80.27 147	92.79 91	48.96 319	92.45 208	68.55 228	92.50 78	94.86 18

test1111		79.43 186	79.18 175	80.15 260	89.99 114	53.31 382	87.33 178	77.05 385	75.04 114	80.23 149	92.77 93	48.97 318	92.33 216	68.87 225	92.40 80	94.81 21

patch_mono-2		83.65 92	84.54 79	80.99 240	90.06 113	65.83 187	84.21 271	88.74 218	71.60 194	85.01 70	92.44 96	74.51 25	83.50 365	82.15 91	92.15 81	93.64 85

dcpmvs_2		85.63 61	86.15 52	84.06 139	91.71 78	64.94 213	86.47 208	91.87 105	73.63 153	86.60 58	93.02 84	76.57 15	91.87 233	83.36 75	92.15 81	95.35 3

MAR-MVS		81.84 127	80.70 137	85.27 84	91.32 82	71.53 56	89.82 79	90.92 135	69.77 238	78.50 174	86.21 267	62.36 165	94.52 112	65.36 255	92.05 83	89.77 244
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 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 249	76.41 81	85.80 62	90.22 155	74.15 31	95.37 78	81.82 93	91.88 84	92.65 130

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 136

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 136

IS-MVSNet		83.15 107	82.81 105	84.18 129	89.94 116	63.30 251	91.59 43	88.46 224	79.04 27	79.49 157	92.16 100	65.10 135	94.28 118	67.71 234	91.86 87	94.95 11

BP-MVS1		84.32 81	83.71 90	86.17 61	87.84 201	67.85 141	89.38 99	89.64 179	77.73 42	83.98 95	92.12 102	56.89 230	95.43 70	84.03 71	91.75 88	95.24 6

fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 179	87.08 231	65.21 203	89.09 113	90.21 160	79.67 18	89.98 18	95.02 18	73.17 38	91.71 239	91.30 2	91.60 89	92.34 142

Vis-MVSNet (Re-imp)		78.36 214	78.45 187	78.07 301	88.64 166	51.78 392	86.70 201	79.63 363	74.14 141	75.11 262	90.83 143	61.29 186	89.75 289	58.10 323	91.60 89	92.69 128

MG-MVS		83.41 100	83.45 93	83.28 169	92.74 65	62.28 270	88.17 150	89.50 184	75.22 108	81.49 130	92.74 94	66.75 114	95.11 87	72.85 186	91.58 91	92.45 139

CPTT-MVS		83.73 90	83.33 97	84.92 98	93.28 49	70.86 72	92.09 36	90.38 151	68.75 263	79.57 156	92.83 88	60.60 201	93.04 188	80.92 102	91.56 92	90.86 190

test222					91.50 80	68.26 130	84.16 272	83.20 316	54.63 400	79.74 153	91.63 115	58.97 210			91.42 93	86.77 328

fmvsm_s_conf0.5_n_8		86.56 42	87.17 33	84.73 105	87.76 208	65.62 194	89.20 104	92.21 89	79.94 16	89.74 20	94.86 20	68.63 96	94.20 123	90.83 4	91.39 94	94.38 43

ETV-MVS		84.90 78	84.67 78	85.59 76	89.39 134	68.66 120	88.74 129	92.64 72	79.97 15	84.10 92	85.71 276	69.32 85	95.38 75	80.82 103	91.37 95	92.72 125

testdata				79.97 263	90.90 91	64.21 229		84.71 289	59.27 371	85.40 66	92.91 85	62.02 172	89.08 303	68.95 224	91.37 95	86.63 332

API-MVS		81.99 125	81.23 129	84.26 126	90.94 90	70.18 85	91.10 55	89.32 189	71.51 196	78.66 170	88.28 206	65.26 133	95.10 90	64.74 261	91.23 97	87.51 307

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 164	88.69 131	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 113	83.49 74	91.14 98	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		83.34 103	84.03 86	81.28 231	85.73 260	65.13 206	85.40 241	89.90 170	74.96 118	82.13 120	93.89 60	66.65 115	87.92 321	86.56 45	91.05 99	90.80 191

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 127	86.26 247	67.40 156	89.18 105	89.31 190	72.50 177	88.31 29	93.86 61	69.66 81	91.96 227	89.81 10	91.05 99	93.38 95

Vis-MVSNet		83.46 99	82.80 106	85.43 80	90.25 105	68.74 114	90.30 72	90.13 163	76.33 87	80.87 140	92.89 86	61.00 192	94.20 123	72.45 192	90.97 101	93.35 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 177	78.33 192	84.09 135	85.17 274	69.91 87	90.57 61	90.97 134	66.70 286	72.17 306	91.91 104	54.70 247	93.96 131	61.81 288	90.95 102	88.41 289

UA-Net		85.08 74	84.96 75	85.45 79	92.07 73	68.07 136	89.78 82	90.86 139	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 198	90.88 103	93.07 112

test_fmvsmconf_n		85.92 54	86.04 55	85.57 77	85.03 281	69.51 93	89.62 89	90.58 144	73.42 161	87.75 42	94.02 52	72.85 43	93.24 169	90.37 6	90.75 104	93.96 62

ACMMP		85.89 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 72	80.73 143	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 102
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 62	85.65 62	85.50 78	82.99 330	69.39 100	89.65 86	90.29 158	73.31 164	87.77 41	94.15 46	71.72 54	93.23 170	90.31 7	90.67 106	93.89 68

fmvsm_l_conf0.5_n_3		86.02 49	86.32 45	85.14 87	87.20 227	68.54 123	89.57 90	90.44 149	75.31 107	87.49 46	94.39 35	72.86 42	92.72 196	89.04 23	90.56 107	94.16 52

casdiffmvs		85.11 73	85.14 73	85.01 93	87.20 227	65.77 191	87.75 165	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 137	81.27 99	90.48 108	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 70	85.34 68	85.13 90	86.12 252	69.93 86	88.65 133	90.78 140	69.97 232	88.27 30	93.98 57	71.39 60	91.54 247	88.49 31	90.45 109	93.91 65

UGNet		80.83 149	79.59 163	84.54 109	88.04 191	68.09 135	89.42 96	88.16 226	76.95 66	76.22 229	89.46 175	49.30 313	93.94 134	68.48 229	90.31 110	91.60 163
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 76	84.98 74	84.80 103	87.30 225	65.39 200	87.30 179	92.88 57	77.62 44	84.04 94	92.26 99	71.81 52	93.96 131	81.31 97	90.30 111	95.03 10

MVSFormer		82.85 113	82.05 119	85.24 85	87.35 218	70.21 80	90.50 64	90.38 151	68.55 266	81.32 132	89.47 173	61.68 175	93.46 161	78.98 118	90.26 112	92.05 156

lupinMVS		81.39 139	80.27 148	84.76 104	87.35 218	70.21 80	85.55 236	86.41 267	62.85 338	81.32 132	88.61 196	61.68 175	92.24 219	78.41 125	90.26 112	91.83 159

DP-MVS Recon		83.11 110	82.09 118	86.15 63	94.44 19	70.92 71	88.79 124	92.20 90	70.53 218	79.17 161	91.03 138	64.12 142	96.03 50	68.39 231	90.14 114	91.50 168

EIA-MVS		83.31 105	82.80 106	84.82 101	89.59 123	65.59 195	88.21 148	92.68 66	74.66 127	78.96 163	86.42 263	69.06 90	95.26 80	75.54 159	90.09 115	93.62 86

MVS_111021_LR		82.61 116	82.11 116	84.11 130	88.82 157	71.58 55	85.15 244	86.16 273	74.69 125	80.47 146	91.04 136	62.29 166	90.55 277	80.33 108	90.08 116	90.20 219

jason		81.39 139	80.29 147	84.70 106	86.63 243	69.90 88	85.95 223	86.77 262	63.24 331	81.07 138	89.47 173	61.08 191	92.15 221	78.33 126	90.07 117	92.05 156
jason: jason.
test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 112	84.12 299	69.37 101	88.15 152	87.96 232	70.01 230	83.95 96	93.23 77	68.80 95	91.51 250	88.61 28	89.96 118	92.57 131

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 82	80.25 371	69.03 103	89.47 92	89.65 178	73.24 168	86.98 54	94.27 39	66.62 116	93.23 170	90.26 8	89.95 119	93.78 76

LFMVS		81.82 128	81.23 129	83.57 161	91.89 76	63.43 249	89.84 78	81.85 335	77.04 65	83.21 106	93.10 79	52.26 270	93.43 163	71.98 193	89.95 119	93.85 69

KinetiMVS		83.31 105	82.61 109	85.39 81	87.08 231	67.56 151	88.06 154	91.65 113	77.80 41	82.21 119	91.79 109	57.27 225	94.07 129	77.77 132	89.89 121	94.56 36

MVS		78.19 219	76.99 227	81.78 217	85.66 261	66.99 167	84.66 256	90.47 148	55.08 399	72.02 308	85.27 289	63.83 145	94.11 128	66.10 249	89.80 122	84.24 369

GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 176	83.71 100	91.86 108	55.69 237	95.35 79	80.03 110	89.74 123	94.69 27

CANet_DTU		80.61 159	79.87 156	82.83 192	85.60 264	63.17 256	87.36 176	88.65 220	76.37 85	75.88 236	88.44 202	53.51 259	93.07 184	73.30 181	89.74 123	92.25 147

StellarMVS		81.53 135	80.16 150	85.62 75	85.51 266	68.25 131	88.84 123	92.19 91	71.31 199	80.50 145	89.83 161	46.89 330	94.82 101	76.85 143	89.57 125	93.80 75

PVSNet_Blended		80.98 145	80.34 145	82.90 190	88.85 154	65.40 198	84.43 266	92.00 97	67.62 277	78.11 184	85.05 297	66.02 127	94.27 119	71.52 195	89.50 126	89.01 265

PAPM_NR		83.02 111	82.41 111	84.82 101	92.47 70	66.37 176	87.93 160	91.80 108	73.82 148	77.32 200	90.66 145	67.90 105	94.90 97	70.37 208	89.48 127	93.19 107

114514_t		80.68 157	79.51 164	84.20 128	94.09 38	67.27 161	89.64 87	91.11 132	58.75 378	74.08 280	90.72 144	58.10 215	95.04 92	69.70 216	89.42 128	90.30 216

LCM-MVSNet-Re		77.05 244	76.94 228	77.36 314	87.20 227	51.60 393	80.06 335	80.46 351	75.20 110	67.69 351	86.72 248	62.48 162	88.98 305	63.44 269	89.25 129	91.51 167

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 139	85.38 269	68.40 126	88.34 144	86.85 261	67.48 280	87.48 47	93.40 73	70.89 66	91.61 240	88.38 33	89.22 130	92.16 153

mvsmamba		80.60 160	79.38 167	84.27 124	89.74 121	67.24 163	87.47 172	86.95 257	70.02 229	75.38 249	88.93 186	51.24 288	92.56 202	75.47 161	89.22 130	93.00 119

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 124	85.42 268	68.81 109	88.49 137	87.26 251	68.08 273	88.03 36	93.49 68	72.04 50	91.77 235	88.90 25	89.14 132	92.24 149

alignmvs		85.48 64	85.32 70	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 89	87.73 44	91.46 122	70.32 73	93.78 144	81.51 94	88.95 133	94.63 32

VNet		82.21 120	82.41 111	81.62 220	90.82 93	60.93 286	84.47 262	89.78 172	76.36 86	84.07 93	91.88 106	64.71 139	90.26 279	70.68 205	88.89 134	93.66 79

PS-MVSNAJ		81.69 131	81.02 133	83.70 156	89.51 127	68.21 133	84.28 270	90.09 164	70.79 210	81.26 136	85.62 281	63.15 153	94.29 117	75.62 157	88.87 135	88.59 284

sasdasda		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 138	81.50 95	88.80 136	94.77 24

canonicalmvs		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 138	81.50 95	88.80 136	94.77 24

QAPM		80.88 147	79.50 165	85.03 92	88.01 194	68.97 107	91.59 43	92.00 97	66.63 292	75.15 261	92.16 100	57.70 219	95.45 68	63.52 267	88.76 138	90.66 199

MGCFI-Net		85.06 75	85.51 65	83.70 156	89.42 131	63.01 257	89.43 94	92.62 73	76.43 80	87.53 45	91.34 125	72.82 44	93.42 164	81.28 98	88.74 139	94.66 31

VDD-MVS		83.01 112	82.36 113	84.96 95	91.02 88	66.40 175	88.91 118	88.11 227	77.57 46	84.39 87	93.29 76	52.19 271	93.91 138	77.05 141	88.70 140	94.57 35

PVSNet_Blended_VisFu		82.62 115	81.83 124	84.96 95	90.80 94	69.76 90	88.74 129	91.70 112	69.39 244	78.96 163	88.46 201	65.47 132	94.87 100	74.42 169	88.57 141	90.24 218

xiu_mvs_v2_base		81.69 131	81.05 132	83.60 158	89.15 146	68.03 138	84.46 264	90.02 165	70.67 213	81.30 135	86.53 261	63.17 152	94.19 125	75.60 158	88.54 142	88.57 285

PAPR		81.66 133	80.89 136	83.99 147	90.27 104	64.00 232	86.76 200	91.77 111	68.84 262	77.13 210	89.50 171	67.63 107	94.88 99	67.55 236	88.52 143	93.09 111

MVS_Test		83.15 107	83.06 100	83.41 166	86.86 234	63.21 253	86.11 220	92.00 97	74.31 135	82.87 111	89.44 178	70.03 76	93.21 172	77.39 137	88.50 144	93.81 73

fmvsm_s_conf0.5_n_4		85.39 68	85.75 61	84.30 120	86.70 240	65.83 187	88.77 125	89.78 172	75.46 102	88.35 28	93.73 65	69.19 87	93.06 185	91.30 2	88.44 145	94.02 60

AdaColmap		80.58 163	79.42 166	84.06 139	93.09 57	68.91 108	89.36 100	88.97 208	69.27 247	75.70 239	89.69 164	57.20 227	95.77 59	63.06 272	88.41 146	87.50 308

VDDNet		81.52 136	80.67 138	84.05 142	90.44 101	64.13 231	89.73 84	85.91 276	71.11 204	83.18 107	93.48 69	50.54 297	93.49 158	73.40 180	88.25 147	94.54 37

PCF-MVS	73.52 7	80.38 166	78.84 181	85.01 93	87.71 209	68.99 106	83.65 280	91.46 123	63.00 335	77.77 192	90.28 151	66.10 124	95.09 91	61.40 291	88.22 148	90.94 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
RRT-MVS		82.60 118	82.10 117	84.10 131	87.98 195	62.94 262	87.45 174	91.27 125	77.42 53	79.85 152	90.28 151	56.62 233	94.70 108	79.87 113	88.15 149	94.67 28

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 154	86.17 250	65.00 211	86.96 189	87.28 249	74.35 133	88.25 31	94.23 42	61.82 173	92.60 199	89.85 9	88.09 150	93.84 71

Effi-MVS+		83.62 95	83.08 99	85.24 85	88.38 176	67.45 153	88.89 119	89.15 199	75.50 101	82.27 117	88.28 206	69.61 82	94.45 115	77.81 131	87.84 151	93.84 71

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 152	85.62 263	64.94 213	87.03 186	86.62 265	74.32 134	87.97 39	94.33 36	60.67 197	92.60 199	89.72 11	87.79 152	93.96 62

gg-mvs-nofinetune		69.95 337	67.96 340	75.94 325	83.07 325	54.51 372	77.23 373	70.29 409	63.11 333	70.32 323	62.33 423	43.62 360	88.69 311	53.88 353	87.76 153	84.62 366

xiu_mvs_v1_base_debu		80.80 153	79.72 159	84.03 144	87.35 218	70.19 82	85.56 233	88.77 214	69.06 256	81.83 123	88.16 210	50.91 291	92.85 192	78.29 127	87.56 154	89.06 260

xiu_mvs_v1_base		80.80 153	79.72 159	84.03 144	87.35 218	70.19 82	85.56 233	88.77 214	69.06 256	81.83 123	88.16 210	50.91 291	92.85 192	78.29 127	87.56 154	89.06 260

xiu_mvs_v1_base_debi		80.80 153	79.72 159	84.03 144	87.35 218	70.19 82	85.56 233	88.77 214	69.06 256	81.83 123	88.16 210	50.91 291	92.85 192	78.29 127	87.56 154	89.06 260

CLD-MVS		82.31 119	81.65 125	84.29 121	88.47 171	67.73 145	85.81 230	92.35 82	75.78 95	78.33 179	86.58 258	64.01 143	94.35 116	76.05 152	87.48 157	90.79 192
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
myMVS_eth3d28		73.62 293	73.53 283	73.90 352	88.20 181	47.41 411	78.06 365	79.37 365	74.29 137	73.98 281	84.29 311	44.67 351	83.54 364	51.47 365	87.39 158	90.74 196

CDS-MVSNet		79.07 197	77.70 212	83.17 176	87.60 213	68.23 132	84.40 268	86.20 272	67.49 279	76.36 226	86.54 260	61.54 178	90.79 272	61.86 287	87.33 159	90.49 207
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
diffmvs		82.10 121	81.88 123	82.76 201	83.00 328	63.78 239	83.68 279	89.76 174	72.94 173	82.02 122	89.85 160	65.96 129	90.79 272	82.38 90	87.30 160	93.71 78
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		83.40 101	83.02 101	84.57 108	90.13 107	64.47 224	92.32 30	90.73 141	74.45 132	79.35 159	91.10 133	69.05 91	95.12 85	72.78 187	87.22 161	94.13 54

TAMVS		78.89 202	77.51 217	83.03 184	87.80 203	67.79 144	84.72 254	85.05 287	67.63 276	76.75 215	87.70 221	62.25 167	90.82 271	58.53 318	87.13 162	90.49 207

TAPA-MVS	73.13 9	79.15 194	77.94 200	82.79 198	89.59 123	62.99 261	88.16 151	91.51 119	65.77 301	77.14 209	91.09 134	60.91 193	93.21 172	50.26 375	87.05 163	92.17 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 235	76.40 242	81.51 223	87.29 226	61.85 275	83.78 277	89.59 181	64.74 314	71.23 316	88.70 192	62.59 160	93.66 151	52.66 359	87.03 164	89.01 265

test_yl		81.17 141	80.47 143	83.24 172	89.13 147	63.62 240	86.21 217	89.95 168	72.43 181	81.78 127	89.61 168	57.50 222	93.58 152	70.75 203	86.90 165	92.52 134

DCV-MVSNet		81.17 141	80.47 143	83.24 172	89.13 147	63.62 240	86.21 217	89.95 168	72.43 181	81.78 127	89.61 168	57.50 222	93.58 152	70.75 203	86.90 165	92.52 134

LuminaMVS		80.68 157	79.62 162	83.83 152	85.07 280	68.01 139	86.99 188	88.83 211	70.36 220	81.38 131	87.99 217	50.11 301	92.51 206	79.02 116	86.89 167	90.97 186

BH-untuned		79.47 184	78.60 184	82.05 212	89.19 145	65.91 185	86.07 221	88.52 223	72.18 183	75.42 247	87.69 222	61.15 189	93.54 156	60.38 299	86.83 168	86.70 330

BH-RMVSNet		79.61 179	78.44 188	83.14 177	89.38 135	65.93 184	84.95 250	87.15 254	73.56 156	78.19 182	89.79 162	56.67 232	93.36 165	59.53 307	86.74 169	90.13 222

LS3D		76.95 247	74.82 265	83.37 167	90.45 100	67.36 158	89.15 110	86.94 258	61.87 351	69.52 336	90.61 146	51.71 284	94.53 111	46.38 396	86.71 170	88.21 293

Fast-Effi-MVS+		80.81 150	79.92 154	83.47 162	88.85 154	64.51 221	85.53 238	89.39 187	70.79 210	78.49 175	85.06 296	67.54 108	93.58 152	67.03 244	86.58 171	92.32 144

EPNet_dtu		75.46 272	74.86 264	77.23 317	82.57 339	54.60 370	86.89 193	83.09 317	71.64 190	66.25 373	85.86 274	55.99 235	88.04 320	54.92 347	86.55 172	89.05 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 98	82.95 103	85.14 87	88.79 160	70.95 69	89.13 111	91.52 118	77.55 49	80.96 139	91.75 110	60.71 195	94.50 113	79.67 115	86.51 173	89.97 236
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
OMC-MVS		82.69 114	81.97 122	84.85 100	88.75 162	67.42 154	87.98 156	90.87 138	74.92 119	79.72 154	91.65 113	62.19 169	93.96 131	75.26 163	86.42 174	93.16 108

HQP_MVS		83.64 93	83.14 98	85.14 87	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 165	91.00 140	60.42 203	95.38 75	78.71 121	86.32 175	91.33 173

plane_prior5								92.44 77					95.38 75	78.71 121	86.32 175	91.33 173

FA-MVS(test-final)		80.96 146	79.91 155	84.10 131	88.30 179	65.01 210	84.55 261	90.01 166	73.25 167	79.61 155	87.57 225	58.35 214	94.72 106	71.29 199	86.25 177	92.56 132

thisisatest0515		77.33 241	75.38 257	83.18 175	85.27 273	63.80 238	82.11 305	83.27 312	65.06 310	75.91 235	83.84 321	49.54 308	94.27 119	67.24 240	86.19 178	91.48 170

plane_prior						68.71 116	90.38 70		77.62 44						86.16 179

UWE-MVS		72.13 315	71.49 305	74.03 350	86.66 242	47.70 409	81.40 315	76.89 387	63.60 330	75.59 240	84.22 315	39.94 382	85.62 346	48.98 381	86.13 180	88.77 277

mvs_anonymous		79.42 187	79.11 176	80.34 255	84.45 294	57.97 321	82.59 300	87.62 242	67.40 281	76.17 233	88.56 199	68.47 98	89.59 292	70.65 206	86.05 181	93.47 93

GeoE		81.71 130	81.01 134	83.80 155	89.51 127	64.45 225	88.97 116	88.73 219	71.27 201	78.63 171	89.76 163	66.32 122	93.20 175	69.89 214	86.02 182	93.74 77

HQP3-MVS								92.19 91							85.99 183

HQP-MVS		82.61 116	82.02 120	84.37 115	89.33 136	66.98 168	89.17 106	92.19 91	76.41 81	77.23 203	90.23 154	60.17 206	95.11 87	77.47 135	85.99 183	91.03 183

BH-w/o		78.21 217	77.33 221	80.84 244	88.81 158	65.13 206	84.87 251	87.85 237	69.75 239	74.52 275	84.74 303	61.34 184	93.11 182	58.24 322	85.84 185	84.27 368

FE-MVS		77.78 230	75.68 249	84.08 136	88.09 189	66.00 182	83.13 293	87.79 238	68.42 270	78.01 187	85.23 291	45.50 348	95.12 85	59.11 311	85.83 186	91.11 179

testing222		74.04 288	72.66 294	78.19 298	87.89 198	55.36 362	81.06 318	79.20 368	71.30 200	74.65 273	83.57 331	39.11 387	88.67 312	51.43 367	85.75 187	90.53 205

CHOSEN 1792x2688		77.63 236	75.69 248	83.44 163	89.98 115	68.58 122	78.70 355	87.50 245	56.38 394	75.80 238	86.84 244	58.67 211	91.40 255	61.58 290	85.75 187	90.34 213

guyue		81.13 143	80.64 139	82.60 204	86.52 244	63.92 236	86.69 202	87.73 240	73.97 143	80.83 142	89.69 164	56.70 231	91.33 258	78.26 130	85.40 189	92.54 133

Anonymous202405211		78.25 215	77.01 225	81.99 214	91.03 87	60.67 291	84.77 253	83.90 302	70.65 217	80.00 151	91.20 130	41.08 377	91.43 254	65.21 256	85.26 190	93.85 69

cascas		76.72 251	74.64 266	82.99 186	85.78 259	65.88 186	82.33 302	89.21 196	60.85 357	72.74 296	81.02 363	47.28 326	93.75 148	67.48 237	85.02 191	89.34 255

FIs		82.07 123	82.42 110	81.04 239	88.80 159	58.34 315	88.26 147	93.49 26	76.93 67	78.47 176	91.04 136	69.92 78	92.34 215	69.87 215	84.97 192	92.44 140

test-LLR		72.94 307	72.43 296	74.48 345	81.35 359	58.04 319	78.38 359	77.46 379	66.66 287	69.95 331	79.00 386	48.06 322	79.24 386	66.13 247	84.83 193	86.15 338

test-mter		71.41 319	70.39 321	74.48 345	81.35 359	58.04 319	78.38 359	77.46 379	60.32 361	69.95 331	79.00 386	36.08 401	79.24 386	66.13 247	84.83 193	86.15 338

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 83	88.18 182	67.85 141	87.66 167	89.73 176	80.05 14	82.95 109	89.59 170	70.74 69	94.82 101	80.66 106	84.72 195	93.28 101

thisisatest0530		79.40 188	77.76 210	84.31 119	87.69 211	65.10 209	87.36 176	84.26 298	70.04 228	77.42 197	88.26 208	49.94 304	94.79 104	70.20 209	84.70 196	93.03 116

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 137	86.69 241	67.31 159	89.46 93	83.07 318	71.09 205	86.96 55	93.70 66	69.02 93	91.47 252	88.79 26	84.62 197	93.44 94

testing91		76.54 252	75.66 251	79.18 280	88.43 174	55.89 355	81.08 317	83.00 320	73.76 150	75.34 251	84.29 311	46.20 339	90.07 283	64.33 263	84.50 198	91.58 165

fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 131	84.86 283	67.28 160	89.40 98	83.01 319	70.67 213	87.08 52	93.96 58	68.38 99	91.45 253	88.56 30	84.50 198	93.56 89

GG-mvs-BLEND				75.38 335	81.59 353	55.80 357	79.32 344	69.63 411		67.19 358	73.67 412	43.24 362	88.90 309	50.41 370	84.50 198	81.45 397

FC-MVSNet-test		81.52 136	82.02 120	80.03 262	88.42 175	55.97 354	87.95 158	93.42 29	77.10 63	77.38 198	90.98 142	69.96 77	91.79 234	68.46 230	84.50 198	92.33 143

PVSNet	64.34 18	72.08 316	70.87 315	75.69 328	86.21 249	56.44 346	74.37 392	80.73 346	62.06 349	70.17 326	82.23 354	42.86 365	83.31 367	54.77 348	84.45 202	87.32 312

ETVMVS		72.25 313	71.05 312	75.84 326	87.77 207	51.91 389	79.39 343	74.98 394	69.26 248	73.71 284	82.95 341	40.82 379	86.14 340	46.17 397	84.43 203	89.47 251

UBG		73.08 304	72.27 299	75.51 332	88.02 192	51.29 397	78.35 362	77.38 382	65.52 305	73.87 283	82.36 350	45.55 346	86.48 337	55.02 346	84.39 204	88.75 278

MS-PatchMatch		73.83 291	72.67 293	77.30 316	83.87 306	66.02 181	81.82 306	84.66 290	61.37 355	68.61 345	82.82 345	47.29 325	88.21 317	59.27 308	84.32 205	77.68 410

ET-MVSNet_ETH3D		78.63 207	76.63 238	84.64 107	86.73 239	69.47 95	85.01 248	84.61 291	69.54 242	66.51 371	86.59 256	50.16 300	91.75 236	76.26 149	84.24 206	92.69 128

testing99		76.09 264	75.12 263	79.00 281	88.16 183	55.50 361	80.79 321	81.40 340	73.30 165	75.17 259	84.27 314	44.48 354	90.02 284	64.28 264	84.22 207	91.48 170

TESTMET0.1,1		69.89 338	69.00 330	72.55 364	79.27 387	56.85 338	78.38 359	74.71 398	57.64 386	68.09 348	77.19 399	37.75 394	76.70 399	63.92 266	84.09 208	84.10 372

AstraMVS		80.81 150	80.14 151	82.80 195	86.05 255	63.96 233	86.46 209	85.90 277	73.71 151	80.85 141	90.56 147	54.06 254	91.57 244	79.72 114	83.97 209	92.86 123

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 91	87.87 199	67.53 152	87.44 175	89.66 177	79.74 17	82.23 118	89.41 179	70.24 75	94.74 105	79.95 111	83.92 210	92.99 120

LPG-MVS_test		82.08 122	81.27 128	84.50 110	89.23 143	68.76 112	90.22 73	91.94 101	75.37 105	76.64 218	91.51 119	54.29 250	94.91 95	78.44 123	83.78 211	89.83 241

LGP-MVS_train				84.50 110	89.23 143	68.76 112		91.94 101	75.37 105	76.64 218	91.51 119	54.29 250	94.91 95	78.44 123	83.78 211	89.83 241

testing11		75.14 278	74.01 275	78.53 292	88.16 183	56.38 348	80.74 324	80.42 353	70.67 213	72.69 299	83.72 326	43.61 361	89.86 286	62.29 281	83.76 213	89.36 254

thres100view900		76.50 254	75.55 253	79.33 276	89.52 126	56.99 337	85.83 229	83.23 313	73.94 145	76.32 227	87.12 240	51.89 280	91.95 228	48.33 384	83.75 214	89.07 258

tfpn200view9		76.42 258	75.37 258	79.55 275	89.13 147	57.65 328	85.17 242	83.60 305	73.41 162	76.45 223	86.39 264	52.12 272	91.95 228	48.33 384	83.75 214	89.07 258

thres400		76.50 254	75.37 258	79.86 265	89.13 147	57.65 328	85.17 242	83.60 305	73.41 162	76.45 223	86.39 264	52.12 272	91.95 228	48.33 384	83.75 214	90.00 232

thres600view7		76.50 254	75.44 254	79.68 270	89.40 133	57.16 334	85.53 238	83.23 313	73.79 149	76.26 228	87.09 241	51.89 280	91.89 231	48.05 389	83.72 217	90.00 232

fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 122	86.14 251	68.12 134	89.43 94	82.87 323	70.27 225	87.27 51	93.80 64	69.09 88	91.58 242	88.21 34	83.65 218	93.14 110

thres200		75.55 270	74.47 270	78.82 284	87.78 206	57.85 324	83.07 296	83.51 308	72.44 180	75.84 237	84.42 306	52.08 275	91.75 236	47.41 391	83.64 219	86.86 326

SDMVSNet		80.38 166	80.18 149	80.99 240	89.03 152	64.94 213	80.45 330	89.40 186	75.19 111	76.61 220	89.98 157	60.61 200	87.69 325	76.83 145	83.55 220	90.33 214

sd_testset		77.70 234	77.40 218	78.60 288	89.03 152	60.02 300	79.00 350	85.83 278	75.19 111	76.61 220	89.98 157	54.81 242	85.46 349	62.63 278	83.55 220	90.33 214

testing3-2		75.12 279	75.19 261	74.91 340	90.40 102	45.09 421	80.29 333	78.42 373	78.37 37	76.54 222	87.75 219	44.36 355	87.28 330	57.04 333	83.49 222	92.37 141

XVG-OURS		80.41 165	79.23 173	83.97 148	85.64 262	69.02 105	83.03 298	90.39 150	71.09 205	77.63 194	91.49 121	54.62 249	91.35 256	75.71 155	83.47 223	91.54 166

fmvsm_s_conf0.1_n_a		83.32 104	82.99 102	84.28 122	83.79 307	68.07 136	89.34 101	82.85 324	69.80 236	87.36 50	94.06 50	68.34 100	91.56 245	87.95 35	83.46 224	93.21 105

CNLPA		78.08 221	76.79 232	81.97 215	90.40 102	71.07 65	87.59 169	84.55 292	66.03 299	72.38 303	89.64 167	57.56 221	86.04 341	59.61 306	83.35 225	88.79 276

MVP-Stereo		76.12 262	74.46 271	81.13 237	85.37 270	69.79 89	84.42 267	87.95 233	65.03 311	67.46 354	85.33 288	53.28 262	91.73 238	58.01 324	83.27 226	81.85 395
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 253	75.30 260	80.21 259	83.93 304	62.32 269	84.66 256	88.81 212	60.23 362	70.16 327	84.07 318	55.30 240	90.73 275	67.37 238	83.21 227	87.59 306

tttt0517		79.40 188	77.91 201	83.90 151	88.10 188	63.84 237	88.37 143	84.05 300	71.45 197	76.78 214	89.12 182	49.93 306	94.89 98	70.18 210	83.18 228	92.96 121

HyFIR lowres test		77.53 237	75.40 256	83.94 150	89.59 123	66.62 172	80.36 331	88.64 221	56.29 395	76.45 223	85.17 293	57.64 220	93.28 167	61.34 293	83.10 229	91.91 158

ACMP	74.13 6	81.51 138	80.57 140	84.36 116	89.42 131	68.69 119	89.97 77	91.50 122	74.46 131	75.04 265	90.41 150	53.82 256	94.54 110	77.56 134	82.91 230	89.86 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 156	79.84 157	83.58 160	89.31 139	68.37 127	89.99 76	91.60 116	70.28 224	77.25 201	89.66 166	53.37 261	93.53 157	74.24 172	82.85 231	88.85 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 342	68.67 331	71.35 374	75.67 401	62.03 272	75.17 384	73.46 401	50.00 412	68.68 343	79.05 384	52.07 276	78.13 391	61.16 294	82.77 232	73.90 416

PLC	70.83 11	78.05 223	76.37 243	83.08 181	91.88 77	67.80 143	88.19 149	89.46 185	64.33 320	69.87 333	88.38 203	53.66 257	93.58 152	58.86 314	82.73 233	87.86 299
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 238	76.18 244	81.20 234	88.24 180	63.24 252	84.61 259	86.40 268	67.55 278	77.81 190	86.48 262	54.10 252	93.15 179	57.75 326	82.72 234	87.20 315

Anonymous20240529		80.19 172	78.89 180	84.10 131	90.60 97	64.75 218	88.95 117	90.90 136	65.97 300	80.59 144	91.17 132	49.97 303	93.73 150	69.16 222	82.70 235	93.81 73

ab-mvs		79.51 182	78.97 179	81.14 236	88.46 172	60.91 287	83.84 276	89.24 195	70.36 220	79.03 162	88.87 189	63.23 151	90.21 281	65.12 257	82.57 236	92.28 146

HY-MVS	69.67 12	77.95 226	77.15 223	80.36 254	87.57 217	60.21 299	83.37 288	87.78 239	66.11 296	75.37 250	87.06 243	63.27 149	90.48 278	61.38 292	82.43 237	90.40 211

PS-MVSNAJss		82.07 123	81.31 127	84.34 118	86.51 245	67.27 161	89.27 102	91.51 119	71.75 189	79.37 158	90.22 155	63.15 153	94.27 119	77.69 133	82.36 238	91.49 169

UniMVSNet_ETH3D		79.10 196	78.24 194	81.70 219	86.85 235	60.24 298	87.28 180	88.79 213	74.25 138	76.84 211	90.53 149	49.48 309	91.56 245	67.98 232	82.15 239	93.29 100

WB-MVSnew		71.96 317	71.65 304	72.89 361	84.67 291	51.88 390	82.29 303	77.57 378	62.31 345	73.67 286	83.00 340	53.49 260	81.10 380	45.75 400	82.13 240	85.70 348

PVSNet_BlendedMVS		80.60 160	80.02 152	82.36 209	88.85 154	65.40 198	86.16 219	92.00 97	69.34 246	78.11 184	86.09 271	66.02 127	94.27 119	71.52 195	82.06 241	87.39 309

WTY-MVS		75.65 269	75.68 249	75.57 330	86.40 246	56.82 339	77.92 368	82.40 328	65.10 309	76.18 231	87.72 220	63.13 156	80.90 381	60.31 300	81.96 242	89.00 267

ACMMP++_ref															81.95 243

DP-MVS		76.78 250	74.57 267	83.42 164	93.29 48	69.46 97	88.55 136	83.70 304	63.98 327	70.20 324	88.89 188	54.01 255	94.80 103	46.66 393	81.88 244	86.01 342

CMPMVS	51.72 21	70.19 334	68.16 336	76.28 323	73.15 417	57.55 330	79.47 342	83.92 301	48.02 415	56.48 415	84.81 301	43.13 363	86.42 338	62.67 277	81.81 245	84.89 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 150	79.76 158	83.96 149	85.60 264	68.78 111	83.54 286	90.50 147	70.66 216	76.71 216	91.66 112	60.69 196	91.26 259	76.94 142	81.58 246	91.83 159

MIMVSNet		70.69 327	69.30 326	74.88 341	84.52 292	56.35 350	75.87 380	79.42 364	64.59 315	67.76 349	82.41 349	41.10 376	81.54 377	46.64 395	81.34 247	86.75 329

ACMMP++															81.25 248

D2MVS		74.82 280	73.21 287	79.64 272	79.81 378	62.56 266	80.34 332	87.35 248	64.37 319	68.86 342	82.66 347	46.37 335	90.10 282	67.91 233	81.24 249	86.25 335

test_vis1_n_1920		75.52 271	75.78 247	74.75 344	79.84 377	57.44 332	83.26 290	85.52 281	62.83 339	79.34 160	86.17 269	45.10 350	79.71 385	78.75 120	81.21 250	87.10 322

GA-MVS		76.87 248	75.17 262	81.97 215	82.75 334	62.58 265	81.44 314	86.35 270	72.16 185	74.74 270	82.89 343	46.20 339	92.02 225	68.85 226	81.09 251	91.30 175

sss		73.60 294	73.64 282	73.51 355	82.80 333	55.01 367	76.12 376	81.69 336	62.47 344	74.68 272	85.85 275	57.32 224	78.11 392	60.86 296	80.93 252	87.39 309

UWE-MVS-28		65.32 369	64.93 363	66.49 397	78.70 389	38.55 434	77.86 369	64.39 426	62.00 350	64.13 386	83.60 329	41.44 374	76.00 407	31.39 426	80.89 253	84.92 361

Effi-MVS+-dtu		80.03 174	78.57 185	84.42 114	85.13 278	68.74 114	88.77 125	88.10 228	74.99 115	74.97 267	83.49 332	57.27 225	93.36 165	73.53 177	80.88 254	91.18 177

EG-PatchMatch MVS		74.04 288	71.82 302	80.71 247	84.92 282	67.42 154	85.86 227	88.08 229	66.04 298	64.22 385	83.85 320	35.10 403	92.56 202	57.44 328	80.83 255	82.16 394

jajsoiax		79.29 191	77.96 199	83.27 170	84.68 288	66.57 174	89.25 103	90.16 162	69.20 252	75.46 245	89.49 172	45.75 345	93.13 181	76.84 144	80.80 256	90.11 224

1112_ss		77.40 240	76.43 241	80.32 256	89.11 151	60.41 296	83.65 280	87.72 241	62.13 348	73.05 293	86.72 248	62.58 161	89.97 285	62.11 285	80.80 256	90.59 203

mvs_tets		79.13 195	77.77 209	83.22 174	84.70 287	66.37 176	89.17 106	90.19 161	69.38 245	75.40 248	89.46 175	44.17 357	93.15 179	76.78 146	80.70 258	90.14 221

PatchMatch-RL		72.38 310	70.90 314	76.80 321	88.60 167	67.38 157	79.53 341	76.17 391	62.75 341	69.36 338	82.00 358	45.51 347	84.89 355	53.62 354	80.58 259	78.12 409

EI-MVSNet		80.52 164	79.98 153	82.12 210	84.28 295	63.19 255	86.41 210	88.95 209	74.18 140	78.69 168	87.54 228	66.62 116	92.43 209	72.57 190	80.57 260	90.74 196

MVSTER		79.01 198	77.88 204	82.38 208	83.07 325	64.80 217	84.08 275	88.95 209	69.01 259	78.69 168	87.17 239	54.70 247	92.43 209	74.69 166	80.57 260	89.89 239

XVG-ACMP-BASELINE		76.11 263	74.27 274	81.62 220	83.20 321	64.67 219	83.60 283	89.75 175	69.75 239	71.85 309	87.09 241	32.78 407	92.11 222	69.99 213	80.43 262	88.09 295

Fast-Effi-MVS+-dtu		78.02 224	76.49 239	82.62 203	83.16 324	66.96 170	86.94 191	87.45 247	72.45 178	71.49 314	84.17 316	54.79 246	91.58 242	67.61 235	80.31 263	89.30 256

LTVRE_ROB	69.57 13	76.25 261	74.54 269	81.41 226	88.60 167	64.38 227	79.24 345	89.12 202	70.76 212	69.79 335	87.86 218	49.09 316	93.20 175	56.21 342	80.16 264	86.65 331
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		76.40 259	75.44 254	79.27 277	89.28 141	58.09 317	81.69 309	87.07 255	59.53 369	72.48 301	86.67 253	61.30 185	89.33 296	60.81 297	80.15 265	90.41 210

test_djsdf		80.30 169	79.32 170	83.27 170	83.98 303	65.37 201	90.50 64	90.38 151	68.55 266	76.19 230	88.70 192	56.44 234	93.46 161	78.98 118	80.14 266	90.97 186

test_fmvs1		70.93 324	70.52 317	72.16 367	73.71 410	55.05 366	80.82 319	78.77 371	51.21 411	78.58 172	84.41 307	31.20 412	76.94 398	75.88 154	80.12 267	84.47 367

test_fmvs1_n		70.86 325	70.24 322	72.73 363	72.51 421	55.28 364	81.27 316	79.71 362	51.49 410	78.73 167	84.87 299	27.54 417	77.02 397	76.06 151	79.97 268	85.88 346

CHOSEN 280x420		66.51 363	64.71 365	71.90 368	81.45 356	63.52 245	57.98 432	68.95 415	53.57 402	62.59 395	76.70 400	46.22 338	75.29 415	55.25 344	79.68 269	76.88 412

baseline2		75.70 268	73.83 280	81.30 230	83.26 319	61.79 277	82.57 301	80.65 347	66.81 283	66.88 362	83.42 333	57.86 218	92.19 220	63.47 268	79.57 270	89.91 237

GBi-Net		78.40 212	77.40 218	81.40 227	87.60 213	63.01 257	88.39 140	89.28 191	71.63 191	75.34 251	87.28 232	54.80 243	91.11 262	62.72 274	79.57 270	90.09 226

test1		78.40 212	77.40 218	81.40 227	87.60 213	63.01 257	88.39 140	89.28 191	71.63 191	75.34 251	87.28 232	54.80 243	91.11 262	62.72 274	79.57 270	90.09 226

FMVSNet3		77.88 228	76.85 230	80.97 242	86.84 236	62.36 267	86.52 207	88.77 214	71.13 203	75.34 251	86.66 254	54.07 253	91.10 265	62.72 274	79.57 270	89.45 252

FMVSNet2		78.20 218	77.21 222	81.20 234	87.60 213	62.89 263	87.47 172	89.02 204	71.63 191	75.29 257	87.28 232	54.80 243	91.10 265	62.38 279	79.38 274	89.61 248

anonymousdsp		78.60 208	77.15 223	82.98 187	80.51 369	67.08 166	87.24 181	89.53 183	65.66 303	75.16 260	87.19 238	52.52 265	92.25 218	77.17 139	79.34 275	89.61 248

nrg030		83.88 86	83.53 92	84.96 95	86.77 238	69.28 102	90.46 67	92.67 67	74.79 123	82.95 109	91.33 126	72.70 45	93.09 183	80.79 105	79.28 276	92.50 136

VPA-MVSNet		80.60 160	80.55 141	80.76 246	88.07 190	60.80 289	86.86 194	91.58 117	75.67 99	80.24 148	89.45 177	63.34 147	90.25 280	70.51 207	79.22 277	91.23 176

tt0805		78.73 204	77.83 205	81.43 225	85.17 274	60.30 297	89.41 97	90.90 136	71.21 202	77.17 208	88.73 191	46.38 334	93.21 172	72.57 190	78.96 278	90.79 192

test_cas_vis1_n_1920		73.76 292	73.74 281	73.81 353	75.90 399	59.77 302	80.51 328	82.40 328	58.30 380	81.62 129	85.69 277	44.35 356	76.41 403	76.29 148	78.61 279	85.23 355

F-COLMAP		76.38 260	74.33 273	82.50 206	89.28 141	66.95 171	88.41 139	89.03 203	64.05 325	66.83 363	88.61 196	46.78 331	92.89 191	57.48 327	78.55 280	87.67 302

FMVSNet1		77.44 238	76.12 245	81.40 227	86.81 237	63.01 257	88.39 140	89.28 191	70.49 219	74.39 277	87.28 232	49.06 317	91.11 262	60.91 295	78.52 281	90.09 226

MDTV_nov1_ep13			69.97 324		83.18 322	53.48 379	77.10 374	80.18 359	60.45 359	69.33 339	80.44 369	48.89 320	86.90 332	51.60 364	78.51 282

CVMVSNet		72.99 306	72.58 295	74.25 348	84.28 295	50.85 400	86.41 210	83.45 310	44.56 419	73.23 291	87.54 228	49.38 311	85.70 344	65.90 251	78.44 283	86.19 337

tpm2		73.26 301	71.46 306	78.63 286	83.34 317	56.71 342	80.65 326	80.40 354	56.63 393	73.55 287	82.02 357	51.80 282	91.24 260	56.35 341	78.42 284	87.95 296

test_vis1_n		69.85 339	69.21 328	71.77 369	72.66 420	55.27 365	81.48 312	76.21 390	52.03 407	75.30 256	83.20 337	28.97 415	76.22 405	74.60 167	78.41 285	83.81 375

CostFormer		75.24 277	73.90 278	79.27 277	82.65 338	58.27 316	80.80 320	82.73 326	61.57 352	75.33 255	83.13 338	55.52 238	91.07 268	64.98 259	78.34 286	88.45 287

ACMH	67.68 16	75.89 266	73.93 277	81.77 218	88.71 164	66.61 173	88.62 134	89.01 205	69.81 235	66.78 364	86.70 252	41.95 373	91.51 250	55.64 343	78.14 287	87.17 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mamv4		76.81 249	78.23 196	72.54 365	86.12 252	65.75 192	78.76 354	82.07 332	64.12 322	72.97 294	91.02 139	67.97 103	68.08 430	83.04 80	78.02 288	83.80 376

WBMVS		73.43 296	72.81 292	75.28 336	87.91 197	50.99 399	78.59 358	81.31 342	65.51 307	74.47 276	84.83 300	46.39 333	86.68 334	58.41 319	77.86 289	88.17 294

dmvs_re		71.14 321	70.58 316	72.80 362	81.96 347	59.68 303	75.60 382	79.34 366	68.55 266	69.27 340	80.72 368	49.42 310	76.54 400	52.56 360	77.79 290	82.19 393

CR-MVSNet		73.37 297	71.27 310	79.67 271	81.32 361	65.19 204	75.92 378	80.30 355	59.92 365	72.73 297	81.19 360	52.50 266	86.69 333	59.84 303	77.71 291	87.11 320

RPMNet		73.51 295	70.49 318	82.58 205	81.32 361	65.19 204	75.92 378	92.27 84	57.60 387	72.73 297	76.45 402	52.30 269	95.43 70	48.14 388	77.71 291	87.11 320

SSC-MVS3.2		73.35 300	73.39 284	73.23 356	85.30 272	49.01 407	74.58 391	81.57 337	75.21 109	73.68 285	85.58 282	52.53 264	82.05 374	54.33 351	77.69 293	88.63 283

SCA		74.22 285	72.33 298	79.91 264	84.05 302	62.17 271	79.96 338	79.29 367	66.30 295	72.38 303	80.13 375	51.95 278	88.60 313	59.25 309	77.67 294	88.96 269

Anonymous20231211		78.97 200	77.69 213	82.81 194	90.54 99	64.29 228	90.11 75	91.51 119	65.01 312	76.16 234	88.13 215	50.56 296	93.03 189	69.68 217	77.56 295	91.11 179

v1144		80.03 174	79.03 177	83.01 185	83.78 308	64.51 221	87.11 184	90.57 146	71.96 188	78.08 186	86.20 268	61.41 182	93.94 134	74.93 165	77.23 296	90.60 202

WR-MVS		79.49 183	79.22 174	80.27 257	88.79 160	58.35 314	85.06 247	88.61 222	78.56 32	77.65 193	88.34 204	63.81 146	90.66 276	64.98 259	77.22 297	91.80 161

v1192		79.59 181	78.43 189	83.07 182	83.55 313	64.52 220	86.93 192	90.58 144	70.83 209	77.78 191	85.90 272	59.15 209	93.94 134	73.96 174	77.19 298	90.76 194

VPNet		78.69 206	78.66 183	78.76 285	88.31 178	55.72 358	84.45 265	86.63 264	76.79 71	78.26 180	90.55 148	59.30 208	89.70 291	66.63 245	77.05 299	90.88 189

v1240		78.99 199	77.78 208	82.64 202	83.21 320	63.54 244	86.62 204	90.30 157	69.74 241	77.33 199	85.68 278	57.04 228	93.76 147	73.13 184	76.92 300	90.62 200

MSDG		73.36 299	70.99 313	80.49 252	84.51 293	65.80 189	80.71 325	86.13 274	65.70 302	65.46 376	83.74 324	44.60 352	90.91 270	51.13 368	76.89 301	84.74 364

IterMVS-LS		80.06 173	79.38 167	82.11 211	85.89 256	63.20 254	86.79 197	89.34 188	74.19 139	75.45 246	86.72 248	66.62 116	92.39 211	72.58 189	76.86 302	90.75 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 192	78.03 198	82.80 195	83.30 318	63.94 235	86.80 196	90.33 155	69.91 234	77.48 196	85.53 283	58.44 213	93.75 148	73.60 176	76.85 303	90.71 198

XXY-MVS		75.41 274	75.56 252	74.96 339	83.59 312	57.82 325	80.59 327	83.87 303	66.54 293	74.93 268	88.31 205	63.24 150	80.09 384	62.16 283	76.85 303	86.97 324

v2v482		80.23 170	79.29 171	83.05 183	83.62 311	64.14 230	87.04 185	89.97 167	73.61 154	78.18 183	87.22 236	61.10 190	93.82 142	76.11 150	76.78 305	91.18 177

VortexMVS		78.57 210	77.89 203	80.59 249	85.89 256	62.76 264	85.61 231	89.62 180	72.06 186	74.99 266	85.38 287	55.94 236	90.77 274	74.99 164	76.58 306	88.23 291

v144192		79.47 184	78.37 190	82.78 199	83.35 316	63.96 233	86.96 189	90.36 154	69.99 231	77.50 195	85.67 279	60.66 198	93.77 146	74.27 171	76.58 306	90.62 200

UniMVSNet (Re)		81.60 134	81.11 131	83.09 179	88.38 176	64.41 226	87.60 168	93.02 45	78.42 34	78.56 173	88.16 210	69.78 79	93.26 168	69.58 218	76.49 308	91.60 163

UniMVSNet_NR-MVSNet		81.88 126	81.54 126	82.92 189	88.46 172	63.46 247	87.13 182	92.37 81	80.19 12	78.38 177	89.14 181	71.66 57	93.05 186	70.05 211	76.46 309	92.25 147

DU-MVS		81.12 144	80.52 142	82.90 190	87.80 203	63.46 247	87.02 187	91.87 105	79.01 28	78.38 177	89.07 183	65.02 136	93.05 186	70.05 211	76.46 309	92.20 150

cl22		78.07 222	77.01 225	81.23 233	82.37 344	61.83 276	83.55 284	87.98 231	68.96 260	75.06 264	83.87 319	61.40 183	91.88 232	73.53 177	76.39 311	89.98 235

miper_ehance_all_eth		78.59 209	77.76 210	81.08 238	82.66 337	61.56 279	83.65 280	89.15 199	68.87 261	75.55 242	83.79 323	66.49 119	92.03 224	73.25 182	76.39 311	89.64 247

miper_enhance_ethall		77.87 229	76.86 229	80.92 243	81.65 351	61.38 281	82.68 299	88.98 206	65.52 305	75.47 243	82.30 352	65.76 131	92.00 226	72.95 185	76.39 311	89.39 253

Syy-MVS		68.05 353	67.85 342	68.67 389	84.68 288	40.97 432	78.62 356	73.08 403	66.65 290	66.74 365	79.46 381	52.11 274	82.30 372	32.89 424	76.38 314	82.75 388

myMVS_eth3d		67.02 359	66.29 360	69.21 384	84.68 288	42.58 427	78.62 356	73.08 403	66.65 290	66.74 365	79.46 381	31.53 411	82.30 372	39.43 416	76.38 314	82.75 388

PatchmatchNet		73.12 303	71.33 309	78.49 294	83.18 322	60.85 288	79.63 340	78.57 372	64.13 321	71.73 310	79.81 380	51.20 289	85.97 342	57.40 329	76.36 316	88.66 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 332	68.37 333	76.21 324	80.60 367	56.23 351	79.19 347	86.49 266	60.89 356	61.29 398	85.47 285	31.78 410	89.47 295	53.37 356	76.21 317	82.94 387

OpenMVS_ROB	64.09 19	70.56 329	68.19 335	77.65 309	80.26 370	59.41 308	85.01 248	82.96 322	58.76 377	65.43 377	82.33 351	37.63 395	91.23 261	45.34 403	76.03 318	82.32 391

ACMH+	68.96 14	76.01 265	74.01 275	82.03 213	88.60 167	65.31 202	88.86 120	87.55 243	70.25 226	67.75 350	87.47 230	41.27 375	93.19 177	58.37 320	75.94 319	87.60 304

tpm		72.37 311	71.71 303	74.35 347	82.19 345	52.00 387	79.22 346	77.29 383	64.56 316	72.95 295	83.68 328	51.35 286	83.26 368	58.33 321	75.80 320	87.81 300

Anonymous20231206		68.60 347	67.80 345	71.02 377	80.23 372	50.75 401	78.30 363	80.47 350	56.79 392	66.11 374	82.63 348	46.35 336	78.95 388	43.62 406	75.70 321	83.36 380

v7n		78.97 200	77.58 216	83.14 177	83.45 315	65.51 196	88.32 145	91.21 127	73.69 152	72.41 302	86.32 266	57.93 216	93.81 143	69.18 221	75.65 322	90.11 224

NR-MVSNet		80.23 170	79.38 167	82.78 199	87.80 203	63.34 250	86.31 214	91.09 133	79.01 28	72.17 306	89.07 183	67.20 112	92.81 195	66.08 250	75.65 322	92.20 150

v10		79.74 178	78.67 182	82.97 188	84.06 301	64.95 212	87.88 163	90.62 143	73.11 169	75.11 262	86.56 259	61.46 181	94.05 130	73.68 175	75.55 324	89.90 238

IB-MVS	68.01 15	75.85 267	73.36 286	83.31 168	84.76 286	66.03 180	83.38 287	85.06 286	70.21 227	69.40 337	81.05 362	45.76 344	94.66 109	65.10 258	75.49 325	89.25 257
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		83.15 107	82.19 115	86.02 69	90.56 98	70.85 73	88.15 152	89.16 198	76.02 92	84.67 78	91.39 124	61.54 178	95.50 66	82.71 86	75.48 326	91.72 162

c3_l		78.75 203	77.91 201	81.26 232	82.89 332	61.56 279	84.09 274	89.13 201	69.97 232	75.56 241	84.29 311	66.36 121	92.09 223	73.47 179	75.48 326	90.12 223

V42		79.38 190	78.24 194	82.83 192	81.10 363	65.50 197	85.55 236	89.82 171	71.57 195	78.21 181	86.12 270	60.66 198	93.18 178	75.64 156	75.46 328	89.81 243

testing3		68.56 349	67.67 348	71.22 376	87.33 223	42.87 426	83.06 297	71.54 406	70.36 220	69.08 341	84.38 308	30.33 414	85.69 345	37.50 419	75.45 329	85.09 360

cl____		77.72 232	76.76 233	80.58 250	82.49 341	60.48 294	83.09 294	87.87 235	69.22 250	74.38 278	85.22 292	62.10 170	91.53 248	71.09 200	75.41 330	89.73 246

DIV-MVS_self_test		77.72 232	76.76 233	80.58 250	82.48 342	60.48 294	83.09 294	87.86 236	69.22 250	74.38 278	85.24 290	62.10 170	91.53 248	71.09 200	75.40 331	89.74 245

v8		79.97 176	79.02 178	82.80 195	84.09 300	64.50 223	87.96 157	90.29 158	74.13 142	75.24 258	86.81 245	62.88 158	93.89 141	74.39 170	75.40 331	90.00 232

Baseline_NR-MVSNet		78.15 220	78.33 192	77.61 310	85.79 258	56.21 352	86.78 198	85.76 279	73.60 155	77.93 189	87.57 225	65.02 136	88.99 304	67.14 242	75.33 333	87.63 303

pmmvs5		71.55 318	70.20 323	75.61 329	77.83 392	56.39 347	81.74 308	80.89 343	57.76 385	67.46 354	84.49 304	49.26 314	85.32 351	57.08 332	75.29 334	85.11 359

EPMVS		69.02 344	68.16 336	71.59 370	79.61 382	49.80 406	77.40 371	66.93 419	62.82 340	70.01 328	79.05 384	45.79 343	77.86 394	56.58 339	75.26 335	87.13 319

TranMVSNet+NR-MVSNet		80.84 148	80.31 146	82.42 207	87.85 200	62.33 268	87.74 166	91.33 124	80.55 9	77.99 188	89.86 159	65.23 134	92.62 197	67.05 243	75.24 336	92.30 145

test_fmvs2		68.35 352	67.48 351	70.98 378	69.50 424	51.95 388	80.05 336	76.38 389	49.33 413	74.65 273	84.38 308	23.30 426	75.40 414	74.51 168	75.17 337	85.60 349

tfpnnormal		74.39 282	73.16 288	78.08 300	86.10 254	58.05 318	84.65 258	87.53 244	70.32 223	71.22 317	85.63 280	54.97 241	89.86 286	43.03 407	75.02 338	86.32 334

COLMAP_ROB	66.92 17	73.01 305	70.41 320	80.81 245	87.13 230	65.63 193	88.30 146	84.19 299	62.96 336	63.80 390	87.69 222	38.04 393	92.56 202	46.66 393	74.91 339	84.24 369
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 351	67.85 342	70.29 380	80.70 366	43.93 424	72.47 397	74.88 395	60.15 363	70.55 319	76.57 401	49.94 304	81.59 376	50.58 369	74.83 340	85.34 353

pmmvs4		74.03 290	71.91 301	80.39 253	81.96 347	68.32 128	81.45 313	82.14 330	59.32 370	69.87 333	85.13 294	52.40 268	88.13 319	60.21 301	74.74 341	84.73 365

ITE_SJBPF				78.22 297	81.77 350	60.57 292		83.30 311	69.25 249	67.54 352	87.20 237	36.33 400	87.28 330	54.34 350	74.62 342	86.80 327

test0.0.03 1		68.00 354	67.69 347	68.90 386	77.55 393	47.43 410	75.70 381	72.95 405	66.66 287	66.56 367	82.29 353	48.06 322	75.87 409	44.97 404	74.51 343	83.41 379

test_0402		72.79 308	70.44 319	79.84 266	88.13 186	65.99 183	85.93 224	84.29 296	65.57 304	67.40 357	85.49 284	46.92 329	92.61 198	35.88 421	74.38 344	80.94 400

CP-MVSNet		78.22 216	78.34 191	77.84 305	87.83 202	54.54 371	87.94 159	91.17 129	77.65 43	73.48 288	88.49 200	62.24 168	88.43 315	62.19 282	74.07 345	90.55 204

FMVSNet5		69.50 340	67.96 340	74.15 349	82.97 331	55.35 363	80.01 337	82.12 331	62.56 343	63.02 391	81.53 359	36.92 396	81.92 375	48.42 383	74.06 346	85.17 358

MVS-HIRNet		59.14 383	57.67 385	63.57 401	81.65 351	43.50 425	71.73 399	65.06 424	39.59 426	51.43 421	57.73 429	38.34 391	82.58 371	39.53 414	73.95 347	64.62 425

tpmrst		72.39 309	72.13 300	73.18 360	80.54 368	49.91 404	79.91 339	79.08 369	63.11 333	71.69 311	79.95 377	55.32 239	82.77 370	65.66 254	73.89 348	86.87 325

PS-CasMVS		78.01 225	78.09 197	77.77 307	87.71 209	54.39 373	88.02 155	91.22 126	77.50 51	73.26 290	88.64 195	60.73 194	88.41 316	61.88 286	73.88 349	90.53 205

v148		78.72 205	77.80 207	81.47 224	82.73 335	61.96 274	86.30 215	88.08 229	73.26 166	76.18 231	85.47 285	62.46 163	92.36 213	71.92 194	73.82 350	90.09 226

Patchmatch-test		64.82 372	63.24 373	69.57 382	79.42 385	49.82 405	63.49 429	69.05 414	51.98 408	59.95 404	80.13 375	50.91 291	70.98 423	40.66 413	73.57 351	87.90 298

WR-MVS_H		78.51 211	78.49 186	78.56 290	88.02 192	56.38 348	88.43 138	92.67 67	77.14 61	73.89 282	87.55 227	66.25 123	89.24 299	58.92 313	73.55 352	90.06 230

AUN-MVS		79.21 193	77.60 215	84.05 142	88.71 164	67.61 148	85.84 228	87.26 251	69.08 255	77.23 203	88.14 214	53.20 263	93.47 160	75.50 160	73.45 353	91.06 181

hse-mvs2		81.72 129	80.94 135	84.07 137	88.72 163	67.68 146	85.87 226	87.26 251	76.02 92	84.67 78	88.22 209	61.54 178	93.48 159	82.71 86	73.44 354	91.06 181

testgi		66.67 362	66.53 359	67.08 396	75.62 402	41.69 431	75.93 377	76.50 388	66.11 296	65.20 381	86.59 256	35.72 402	74.71 416	43.71 405	73.38 355	84.84 363

Anonymous20240521		68.80 346	67.22 355	73.55 354	74.33 406	54.11 374	83.18 291	85.61 280	58.15 381	61.68 397	80.94 365	30.71 413	81.27 379	57.00 334	73.34 356	85.28 354

pm-mvs1		77.25 243	76.68 237	78.93 283	84.22 297	58.62 312	86.41 210	88.36 225	71.37 198	73.31 289	88.01 216	61.22 188	89.15 302	64.24 265	73.01 357	89.03 264

eth_miper_zixun_eth		77.92 227	76.69 236	81.61 222	83.00 328	61.98 273	83.15 292	89.20 197	69.52 243	74.86 269	84.35 310	61.76 174	92.56 202	71.50 197	72.89 358	90.28 217

miper_lstm_enhance		74.11 287	73.11 289	77.13 318	80.11 373	59.62 304	72.23 398	86.92 260	66.76 285	70.40 322	82.92 342	56.93 229	82.92 369	69.06 223	72.63 359	88.87 272

tpmvs		71.09 322	69.29 327	76.49 322	82.04 346	56.04 353	78.92 352	81.37 341	64.05 325	67.18 359	78.28 392	49.74 307	89.77 288	49.67 378	72.37 360	83.67 377

PEN-MVS		77.73 231	77.69 213	77.84 305	87.07 233	53.91 376	87.91 161	91.18 128	77.56 48	73.14 292	88.82 190	61.23 187	89.17 301	59.95 302	72.37 360	90.43 209

DSMNet-mixed		57.77 385	56.90 387	60.38 405	67.70 426	35.61 436	69.18 411	53.97 437	32.30 435	57.49 412	79.88 378	40.39 381	68.57 429	38.78 417	72.37 360	76.97 411

MonoMVSNet		76.49 257	75.80 246	78.58 289	81.55 354	58.45 313	86.36 213	86.22 271	74.87 122	74.73 271	83.73 325	51.79 283	88.73 310	70.78 202	72.15 363	88.55 286

IterMVS-SCA-FT		75.43 273	73.87 279	80.11 261	82.69 336	64.85 216	81.57 311	83.47 309	69.16 253	70.49 321	84.15 317	51.95 278	88.15 318	69.23 220	72.14 364	87.34 311

tpm cat1		70.57 328	68.31 334	77.35 315	82.41 343	57.95 322	78.08 364	80.22 357	52.04 406	68.54 346	77.66 397	52.00 277	87.84 323	51.77 362	72.07 365	86.25 335

RPSCF		73.23 302	71.46 306	78.54 291	82.50 340	59.85 301	82.18 304	82.84 325	58.96 374	71.15 318	89.41 179	45.48 349	84.77 356	58.82 315	71.83 366	91.02 185

IterMVS		74.29 283	72.94 291	78.35 296	81.53 355	63.49 246	81.58 310	82.49 327	68.06 274	69.99 330	83.69 327	51.66 285	85.54 347	65.85 252	71.64 367	86.01 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 323	68.09 338	79.58 273	85.15 276	63.62 240	84.58 260	79.83 360	62.31 345	60.32 402	86.73 246	32.02 408	88.96 307	50.28 373	71.57 368	86.15 338

TestCases				79.58 273	85.15 276	63.62 240		79.83 360	62.31 345	60.32 402	86.73 246	32.02 408	88.96 307	50.28 373	71.57 368	86.15 338

baseline1		76.98 246	76.75 235	77.66 308	88.13 186	55.66 359	85.12 245	81.89 333	73.04 171	76.79 213	88.90 187	62.43 164	87.78 324	63.30 271	71.18 370	89.55 250

Patchmtry		70.74 326	69.16 329	75.49 333	80.72 365	54.07 375	74.94 389	80.30 355	58.34 379	70.01 328	81.19 360	52.50 266	86.54 335	53.37 356	71.09 371	85.87 347

DTE-MVSNet		76.99 245	76.80 231	77.54 313	86.24 248	53.06 385	87.52 170	90.66 142	77.08 64	72.50 300	88.67 194	60.48 202	89.52 293	57.33 330	70.74 372	90.05 231

reproduce_monomvs		75.40 275	74.38 272	78.46 295	83.92 305	57.80 326	83.78 277	86.94 258	73.47 160	72.25 305	84.47 305	38.74 388	89.27 298	75.32 162	70.53 373	88.31 290

MIMVSNet1		68.58 348	66.78 358	73.98 351	80.07 374	51.82 391	80.77 322	84.37 293	64.40 318	59.75 405	82.16 355	36.47 399	83.63 363	42.73 408	70.33 374	86.48 333

pmmvs6		74.69 281	73.39 284	78.61 287	81.38 358	57.48 331	86.64 203	87.95 233	64.99 313	70.18 325	86.61 255	50.43 298	89.52 293	62.12 284	70.18 375	88.83 274

test_vis1_rt		60.28 381	58.42 384	65.84 398	67.25 427	55.60 360	70.44 407	60.94 431	44.33 420	59.00 406	66.64 421	24.91 421	68.67 428	62.80 273	69.48 376	73.25 417

TinyColmap		67.30 358	64.81 364	74.76 343	81.92 349	56.68 343	80.29 333	81.49 339	60.33 360	56.27 416	83.22 335	24.77 422	87.66 326	45.52 401	69.47 377	79.95 405

OurMVSNet-221017-0		74.26 284	72.42 297	79.80 267	83.76 309	59.59 305	85.92 225	86.64 263	66.39 294	66.96 361	87.58 224	39.46 383	91.60 241	65.76 253	69.27 378	88.22 292

JIA-IIPM		66.32 365	62.82 377	76.82 320	77.09 396	61.72 278	65.34 425	75.38 392	58.04 384	64.51 383	62.32 424	42.05 372	86.51 336	51.45 366	69.22 379	82.21 392

ADS-MVSNet2		66.20 368	63.33 372	74.82 342	79.92 375	58.75 311	67.55 417	75.19 393	53.37 403	65.25 379	75.86 405	42.32 368	80.53 383	41.57 411	68.91 380	85.18 356

ADS-MVSNet		64.36 373	62.88 376	68.78 388	79.92 375	47.17 412	67.55 417	71.18 407	53.37 403	65.25 379	75.86 405	42.32 368	73.99 419	41.57 411	68.91 380	85.18 356

test20.03		67.45 356	66.95 357	68.94 385	75.48 403	44.84 422	77.50 370	77.67 377	66.66 287	63.01 392	83.80 322	47.02 328	78.40 390	42.53 410	68.86 382	83.58 378

EU-MVSNet		68.53 350	67.61 349	71.31 375	78.51 391	47.01 413	84.47 262	84.27 297	42.27 422	66.44 372	84.79 302	40.44 380	83.76 361	58.76 316	68.54 383	83.17 381

dmvs_testset		62.63 377	64.11 368	58.19 407	78.55 390	24.76 445	75.28 383	65.94 422	67.91 275	60.34 401	76.01 404	53.56 258	73.94 420	31.79 425	67.65 384	75.88 414

our_test_3		69.14 343	67.00 356	75.57 330	79.80 379	58.80 310	77.96 366	77.81 376	59.55 368	62.90 394	78.25 393	47.43 324	83.97 360	51.71 363	67.58 385	83.93 374

ppachtmachnet_test		70.04 336	67.34 354	78.14 299	79.80 379	61.13 282	79.19 347	80.59 348	59.16 372	65.27 378	79.29 383	46.75 332	87.29 329	49.33 379	66.72 386	86.00 344

LF4IMVS		64.02 374	62.19 378	69.50 383	70.90 422	53.29 383	76.13 375	77.18 384	52.65 405	58.59 407	80.98 364	23.55 425	76.52 401	53.06 358	66.66 387	78.68 408

Patchmatch-RL test		70.24 333	67.78 346	77.61 310	77.43 394	59.57 306	71.16 402	70.33 408	62.94 337	68.65 344	72.77 414	50.62 295	85.49 348	69.58 218	66.58 388	87.77 301

dp		66.80 360	65.43 362	70.90 379	79.74 381	48.82 408	75.12 387	74.77 396	59.61 367	64.08 387	77.23 398	42.89 364	80.72 382	48.86 382	66.58 388	83.16 382

test_fmvs3		63.36 376	61.82 379	67.98 393	62.51 433	46.96 414	77.37 372	74.03 400	45.24 418	67.50 353	78.79 389	12.16 438	72.98 422	72.77 188	66.02 390	83.99 373

CL-MVSNet_self_test		72.37 311	71.46 306	75.09 338	79.49 384	53.53 378	80.76 323	85.01 288	69.12 254	70.51 320	82.05 356	57.92 217	84.13 359	52.27 361	66.00 391	87.60 304

FPMVS		53.68 391	51.64 393	59.81 406	65.08 430	51.03 398	69.48 410	69.58 412	41.46 423	40.67 430	72.32 415	16.46 434	70.00 427	24.24 434	65.42 392	58.40 430

pmmvs-eth3d		70.50 330	67.83 344	78.52 293	77.37 395	66.18 179	81.82 306	81.51 338	58.90 375	63.90 389	80.42 370	42.69 366	86.28 339	58.56 317	65.30 393	83.11 383

N_pmnet		52.79 393	53.26 391	51.40 417	78.99 388	7.68 451	69.52 409	3.89 450	51.63 409	57.01 413	74.98 409	40.83 378	65.96 432	37.78 418	64.67 394	80.56 404

PM-MVS		66.41 364	64.14 367	73.20 359	73.92 409	56.45 345	78.97 351	64.96 425	63.88 329	64.72 382	80.24 374	19.84 430	83.44 366	66.24 246	64.52 395	79.71 406

KD-MVS_self_test		68.81 345	67.59 350	72.46 366	74.29 407	45.45 416	77.93 367	87.00 256	63.12 332	63.99 388	78.99 388	42.32 368	84.77 356	56.55 340	64.09 396	87.16 318

SixPastTwentyTwo		73.37 297	71.26 311	79.70 269	85.08 279	57.89 323	85.57 232	83.56 307	71.03 207	65.66 375	85.88 273	42.10 371	92.57 201	59.11 311	63.34 397	88.65 282

sc_t1		72.19 314	69.51 325	80.23 258	84.81 284	61.09 284	84.68 255	80.22 357	60.70 358	71.27 315	83.58 330	36.59 398	89.24 299	60.41 298	63.31 398	90.37 212

tt0320		70.49 331	68.03 339	77.89 303	84.78 285	59.12 309	83.55 284	80.44 352	58.13 382	67.43 356	80.41 371	39.26 385	87.54 327	55.12 345	63.18 399	86.99 323

EGC-MVSNET		52.07 395	47.05 399	67.14 395	83.51 314	60.71 290	80.50 329	67.75 417	0.07 445	0.43 446	75.85 407	24.26 423	81.54 377	28.82 428	62.25 400	59.16 428

TransMVSNet (Re)		75.39 276	74.56 268	77.86 304	85.50 267	57.10 336	86.78 198	86.09 275	72.17 184	71.53 313	87.34 231	63.01 157	89.31 297	56.84 336	61.83 401	87.17 316

MDA-MVSNet_test_wron		65.03 370	62.92 374	71.37 372	75.93 398	56.73 340	69.09 414	74.73 397	57.28 390	54.03 419	77.89 394	45.88 341	74.39 418	49.89 377	61.55 402	82.99 386

YYNet1		65.03 370	62.91 375	71.38 371	75.85 400	56.60 344	69.12 413	74.66 399	57.28 390	54.12 418	77.87 395	45.85 342	74.48 417	49.95 376	61.52 403	83.05 384

mvsany_test1		62.30 378	61.26 382	65.41 399	69.52 423	54.86 368	66.86 419	49.78 439	46.65 416	68.50 347	83.21 336	49.15 315	66.28 431	56.93 335	60.77 404	75.11 415

ambc				75.24 337	73.16 416	50.51 402	63.05 430	87.47 246		64.28 384	77.81 396	17.80 432	89.73 290	57.88 325	60.64 405	85.49 350

TDRefinement		67.49 355	64.34 366	76.92 319	73.47 414	61.07 285	84.86 252	82.98 321	59.77 366	58.30 409	85.13 294	26.06 418	87.89 322	47.92 390	60.59 406	81.81 396

Gipuma		45.18 402	41.86 405	55.16 414	77.03 397	51.52 394	32.50 438	80.52 349	32.46 434	27.12 437	35.02 438	9.52 441	75.50 411	22.31 435	60.21 407	38.45 437
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tt0320-xc		70.11 335	67.45 352	78.07 301	85.33 271	59.51 307	83.28 289	78.96 370	58.77 376	67.10 360	80.28 373	36.73 397	87.42 328	56.83 337	59.77 408	87.29 313

new-patchmatchnet		61.73 379	61.73 380	61.70 403	72.74 419	24.50 446	69.16 412	78.03 375	61.40 353	56.72 414	75.53 408	38.42 390	76.48 402	45.95 399	57.67 409	84.13 371

MDA-MVSNet-bldmvs		66.68 361	63.66 371	75.75 327	79.28 386	60.56 293	73.92 394	78.35 374	64.43 317	50.13 424	79.87 379	44.02 358	83.67 362	46.10 398	56.86 410	83.03 385

new_pmnet		50.91 396	50.29 396	52.78 416	68.58 425	34.94 438	63.71 427	56.63 436	39.73 425	44.95 427	65.47 422	21.93 427	58.48 436	34.98 422	56.62 411	64.92 424

test_f		52.09 394	50.82 395	55.90 411	53.82 441	42.31 430	59.42 431	58.31 435	36.45 430	56.12 417	70.96 418	12.18 437	57.79 437	53.51 355	56.57 412	67.60 422

test_vis3_rt		49.26 398	47.02 400	56.00 410	54.30 439	45.27 420	66.76 421	48.08 440	36.83 429	44.38 428	53.20 433	7.17 445	64.07 433	56.77 338	55.66 413	58.65 429

PMVS	37.38 22	44.16 403	40.28 407	55.82 412	40.82 447	42.54 429	65.12 426	63.99 427	34.43 432	24.48 438	57.12 431	3.92 448	76.17 406	17.10 439	55.52 414	48.75 433
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		53.31 392	49.93 397	63.42 402	65.68 429	50.13 403	71.59 401	66.90 420	34.43 432	40.58 431	71.56 417	8.65 443	76.27 404	34.64 423	55.36 415	63.86 426

mvs5depth		69.45 341	67.45 352	75.46 334	73.93 408	55.83 356	79.19 347	83.23 313	66.89 282	71.63 312	83.32 334	33.69 406	85.09 352	59.81 304	55.34 416	85.46 351

pmmvs3		57.79 384	54.26 389	68.37 390	64.02 432	56.72 341	75.12 387	65.17 423	40.20 424	52.93 420	69.86 420	20.36 429	75.48 412	45.45 402	55.25 417	72.90 418

UnsupCasMVSNet_eth		67.33 357	65.99 361	71.37 372	73.48 413	51.47 395	75.16 385	85.19 284	65.20 308	60.78 400	80.93 367	42.35 367	77.20 396	57.12 331	53.69 418	85.44 352

K. test v3		71.19 320	68.51 332	79.21 279	83.04 327	57.78 327	84.35 269	76.91 386	72.90 174	62.99 393	82.86 344	39.27 384	91.09 267	61.65 289	52.66 419	88.75 278

mmtdpeth		74.16 286	73.01 290	77.60 312	83.72 310	61.13 282	85.10 246	85.10 285	72.06 186	77.21 207	80.33 372	43.84 359	85.75 343	77.14 140	52.61 420	85.91 345

UnsupCasMVSNet_bld		63.70 375	61.53 381	70.21 381	73.69 411	51.39 396	72.82 396	81.89 333	55.63 397	57.81 411	71.80 416	38.67 389	78.61 389	49.26 380	52.21 421	80.63 402

LCM-MVSNet		54.25 388	49.68 398	67.97 394	53.73 442	45.28 419	66.85 420	80.78 345	35.96 431	39.45 432	62.23 425	8.70 442	78.06 393	48.24 387	51.20 422	80.57 403

KD-MVS_2432*1600		66.22 366	63.89 369	73.21 357	75.47 404	53.42 380	70.76 405	84.35 294	64.10 323	66.52 369	78.52 390	34.55 404	84.98 353	50.40 371	50.33 423	81.23 398

miper_refine_blended		66.22 366	63.89 369	73.21 357	75.47 404	53.42 380	70.76 405	84.35 294	64.10 323	66.52 369	78.52 390	34.55 404	84.98 353	50.40 371	50.33 423	81.23 398

mvsany_test3		53.99 389	51.45 394	61.61 404	55.51 438	44.74 423	63.52 428	45.41 443	43.69 421	58.11 410	76.45 402	17.99 431	63.76 434	54.77 348	47.59 425	76.34 413

lessismore_v0				78.97 282	81.01 364	57.15 335		65.99 421		61.16 399	82.82 345	39.12 386	91.34 257	59.67 305	46.92 426	88.43 288

testf1		45.72 399	41.96 403	57.00 408	56.90 436	45.32 417	66.14 422	59.26 433	26.19 436	30.89 435	60.96 427	4.14 446	70.64 425	26.39 432	46.73 427	55.04 431

APD_test2		45.72 399	41.96 403	57.00 408	56.90 436	45.32 417	66.14 422	59.26 433	26.19 436	30.89 435	60.96 427	4.14 446	70.64 425	26.39 432	46.73 427	55.04 431

ttmdpeth		59.91 382	57.10 386	68.34 391	67.13 428	46.65 415	74.64 390	67.41 418	48.30 414	62.52 396	85.04 298	20.40 428	75.93 408	42.55 409	45.90 429	82.44 390

MVStest1		56.63 386	52.76 392	68.25 392	61.67 434	53.25 384	71.67 400	68.90 416	38.59 427	50.59 423	83.05 339	25.08 420	70.66 424	36.76 420	38.56 430	80.83 401

PVSNet_0	57.27 20	61.67 380	59.27 383	68.85 387	79.61 382	57.44 332	68.01 415	73.44 402	55.93 396	58.54 408	70.41 419	44.58 353	77.55 395	47.01 392	35.91 431	71.55 419

WB-MVS		54.94 387	54.72 388	55.60 413	73.50 412	20.90 447	74.27 393	61.19 430	59.16 372	50.61 422	74.15 410	47.19 327	75.78 410	17.31 438	35.07 432	70.12 420

test_method		31.52 407	29.28 411	38.23 421	27.03 449	6.50 452	20.94 440	62.21 429	4.05 443	22.35 441	52.50 434	13.33 435	47.58 441	27.04 431	34.04 433	60.62 427

SSC-MVS		53.88 390	53.59 390	54.75 415	72.87 418	19.59 448	73.84 395	60.53 432	57.58 388	49.18 426	73.45 413	46.34 337	75.47 413	16.20 441	32.28 434	69.20 421

PMMVS2		40.82 404	38.86 408	46.69 418	53.84 440	16.45 449	48.61 435	49.92 438	37.49 428	31.67 433	60.97 426	8.14 444	56.42 438	28.42 429	30.72 435	67.19 423

dongtai		45.42 401	45.38 402	45.55 419	73.36 415	26.85 443	67.72 416	34.19 445	54.15 401	49.65 425	56.41 432	25.43 419	62.94 435	19.45 436	28.09 436	46.86 435

kuosan		39.70 405	40.40 406	37.58 422	64.52 431	26.98 441	65.62 424	33.02 446	46.12 417	42.79 429	48.99 435	24.10 424	46.56 443	12.16 444	26.30 437	39.20 436

DeepMVS_CX				27.40 425	40.17 448	26.90 442		24.59 449	17.44 441	23.95 439	48.61 436	9.77 440	26.48 444	18.06 437	24.47 438	28.83 438

MVE	26.22 23	30.37 409	25.89 413	43.81 420	44.55 446	35.46 437	28.87 439	39.07 444	18.20 440	18.58 442	40.18 437	2.68 449	47.37 442	17.07 440	23.78 439	48.60 434
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 406	30.64 409	35.15 423	52.87 443	27.67 440	57.09 433	47.86 441	24.64 438	16.40 443	33.05 439	11.23 439	54.90 439	14.46 442	18.15 440	22.87 439

EMVS		30.81 408	29.65 410	34.27 424	50.96 444	25.95 444	56.58 434	46.80 442	24.01 439	15.53 444	30.68 440	12.47 436	54.43 440	12.81 443	17.05 441	22.43 440

ANet_high		50.57 397	46.10 401	63.99 400	48.67 445	39.13 433	70.99 404	80.85 344	61.39 354	31.18 434	57.70 430	17.02 433	73.65 421	31.22 427	15.89 442	79.18 407

tmp_tt		18.61 411	21.40 414	10.23 427	4.82 450	10.11 450	34.70 437	30.74 448	1.48 444	23.91 440	26.07 441	28.42 416	13.41 446	27.12 430	15.35 443	7.17 441

wuyk23d		16.82 412	15.94 415	19.46 426	58.74 435	31.45 439	39.22 436	3.74 451	6.84 442	6.04 445	2.70 445	1.27 450	24.29 445	10.54 445	14.40 444	2.63 442

testmvs		6.04 415	8.02 418	0.10 429	0.08 451	0.03 454	69.74 408	0.04 452	0.05 446	0.31 447	1.68 446	0.02 452	0.04 447	0.24 446	0.02 445	0.25 444

test123		6.12 414	8.11 417	0.14 428	0.06 452	0.09 453	71.05 403	0.03 453	0.04 447	0.25 448	1.30 447	0.05 451	0.03 448	0.21 447	0.01 446	0.29 443

mmdepth		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

monomultidepth		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

test_blank		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

uanet_test		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

DCPMVS		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

cdsmvs_eth3d_5k		19.96 410	26.61 412	0.00 430	0.00 453	0.00 455	0.00 441	89.26 194	0.00 448	0.00 449	88.61 196	61.62 177	0.00 449	0.00 448	0.00 447	0.00 445

pcd_1.5k_mvsjas		5.26 416	7.02 419	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	63.15 153	0.00 449	0.00 448	0.00 447	0.00 445

sosnet-low-res		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

sosnet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

uncertanet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

Regformer		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

ab-mvs-re		7.23 413	9.64 416	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	86.72 248	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

uanet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 447	0.00 445

WAC-MVS						42.58 427								39.46 415

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 130	91.30 15

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 38	92.05 11	95.74 6	80.83 11

eth-test2					0.00 453

eth-test					0.00 453

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 60	93.10 1	95.39 14	82.99 1	97.27 12

save fliter					93.80 40	72.35 42	90.47 66	91.17 129	74.31 135

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 54	92.81 3	95.79 3	80.98 9

GSMVS																88.96 269

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 287				88.96 269

sam_mvs												50.01 302

MTGPA								92.02 95

test_post1							78.90 353				5.43 444	48.81 321	85.44 350	59.25 309

test_post											5.46 443	50.36 299	84.24 358

patchmatchnet-post											74.00 411	51.12 290	88.60 313

MTMP							92.18 34	32.83 447

gm-plane-assit					81.40 357	53.83 377			62.72 342		80.94 365		92.39 211	63.40 270

TEST9					93.26 52	72.96 25	88.75 127	91.89 103	68.44 269	85.00 71	93.10 79	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 132	91.84 107	68.69 264	84.87 75	93.10 79	74.43 26	95.16 83

agg_prior					92.85 62	71.94 50		91.78 110		84.41 86			94.93 94

test_prior4						72.60 34	89.01 115

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 65

旧先验2							86.56 206		58.10 383	87.04 53			88.98 305	74.07 173

新几何2							86.29 216

无先验							87.48 171	88.98 206	60.00 364				94.12 127	67.28 239		88.97 268

原ACMM2							86.86 194

testdata2													91.01 269	62.37 280

segment_acmp												73.08 39

testdata1							84.14 273		75.71 96

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 203

plane_prior4											91.00 140

plane_prior3						68.60 121			78.44 33	78.92 165

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 117

n2								0.00 454

nn								0.00 454

door-mid								69.98 410

test11								92.23 87

door								69.44 413

HQP5-MVS						66.98 168

HQP-NCC					89.33 136		89.17 106		76.41 81	77.23 203

ACMP_Plane					89.33 136		89.17 106		76.41 81	77.23 203

BP-MVS														77.47 135

HQP4-MVS										77.24 202			95.11 87			91.03 183

HQP2-MVS												60.17 206

NP-MVS					89.62 122	68.32 128					90.24 153

MDTV_nov1_ep13_2view						37.79 435	75.16 385		55.10 398	66.53 368		49.34 312		53.98 352		87.94 297

Test By Simon												64.33 140

High-resolution multi-view benchmark