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.

APDe-MVS		97.82 4	97.73 3	98.08 15	99.15 33	94.82 25	98.81 2	98.30 23	94.76 34	98.30 13	98.90 3	93.77 14	99.68 47	97.93 1	99.69 3	99.75 3

SED-MVS		98.05 1	97.99 1	98.24 7	99.42 6	95.30 15	98.25 29	98.27 28	95.13 17	99.19 1	98.89 4	95.54 3	99.85 14	97.52 2	99.66 8	99.56 22

test_241102_TWO								98.27 28	95.13 17	98.93 6	98.89 4	94.99 8	99.85 14	97.52 2	99.65 10	99.74 5

DVP-MVS		97.91 2	97.81 2	98.22 9	99.45 2	95.36 10	98.21 36	97.85 111	94.92 24	98.73 8	98.87 6	95.08 5	99.84 19	97.52 2	99.67 6	99.48 41
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				98.51 2	99.45 2	95.93 3	98.21 36	98.28 26					99.86 8	97.52 2	99.67 6	99.75 3

test_0728_THIRD									94.78 33	98.73 8	98.87 6	95.87 2	99.84 19	97.45 6	99.72 2	99.77 1

IU-MVS					99.42 6	95.39 9		97.94 102	90.40 172	98.94 5				97.41 7	99.66 8	99.74 5

TSAR-MVS + MP.		97.42 8	97.33 9	97.69 42	99.25 27	94.24 39	98.07 44	97.85 111	93.72 60	98.57 11	98.35 38	93.69 15	99.40 109	97.06 8	99.46 38	99.44 47
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS		96.12 62	96.17 58	95.97 121	96.69 169	91.17 139	98.49 14	97.72 121	93.80 57	96.17 76	97.13 125	89.42 85	98.60 179	97.05 9	99.03 83	98.15 150

CNVR-MVS		97.68 5	97.44 8	98.37 5	98.90 51	95.86 4	97.27 123	98.08 64	95.81 3	97.87 23	98.31 47	94.26 10	99.68 47	97.02 10	99.49 34	99.57 19

SD-MVS		97.41 9	97.53 6	97.06 71	98.57 72	94.46 30	97.92 57	98.14 53	94.82 30	99.01 3	98.55 19	94.18 11	97.41 300	96.94 11	99.64 11	99.32 60
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
Regformer-4		96.97 29	96.87 21	97.25 61	98.34 83	92.66 85	96.96 153	98.01 91	95.12 19	97.14 41	98.42 31	91.82 46	99.61 62	96.90 12	99.13 75	99.50 37

CANet		96.39 55	96.02 60	97.50 50	97.62 129	93.38 67	97.02 145	97.96 100	95.42 7	94.86 112	97.81 82	87.38 114	99.82 25	96.88 13	99.20 70	99.29 62

TSAR-MVS + GP.		96.69 45	96.49 46	97.27 60	98.31 88	93.39 66	96.79 169	96.72 226	94.17 47	97.44 29	97.66 93	92.76 23	99.33 114	96.86 14	97.76 118	99.08 80

Regformer-3		96.85 38	96.80 29	97.01 72	98.34 83	92.02 108	96.96 153	97.76 115	95.01 23	97.08 46	98.42 31	91.71 49	99.54 86	96.80 15	99.13 75	99.48 41

Regformer-2		97.16 18	96.99 16	97.67 43	98.32 86	93.84 53	96.83 165	98.10 61	95.24 11	97.49 26	98.25 54	92.57 30	99.61 62	96.80 15	99.29 57	99.56 22

Regformer-1		97.10 20	96.96 18	97.54 49	98.32 86	93.48 64	96.83 165	97.99 97	95.20 13	97.46 27	98.25 54	92.48 34	99.58 71	96.79 17	99.29 57	99.55 26

DeepPCF-MVS	93.97 1	96.61 48	97.09 12	95.15 160	98.09 105	86.63 264	96.00 234	98.15 51	95.43 6	97.95 19	98.56 17	93.40 16	99.36 113	96.77 18	99.48 35	99.45 45

SMA-MVS		97.35 12	97.03 14	98.30 6	99.06 40	95.42 8	97.94 55	98.18 46	90.57 168	98.85 7	98.94 1	93.33 17	99.83 22	96.72 19	99.68 4	99.63 11
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
DPE-MVS		97.86 3	97.65 4	98.47 3	99.17 32	95.78 5	97.21 132	98.35 19	95.16 16	98.71 10	98.80 9	95.05 7	99.89 3	96.70 20	99.73 1	99.73 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		97.59 7	97.54 5	97.73 38	99.40 11	93.77 58	98.53 9	98.29 24	95.55 5	98.56 12	97.81 82	93.90 12	99.65 53	96.62 21	99.21 69	99.77 1
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
MSLP-MVS++		96.94 32	97.06 13	96.59 83	98.72 59	91.86 112	97.67 82	98.49 12	94.66 37	97.24 36	98.41 34	92.31 37	98.94 150	96.61 22	99.46 38	98.96 92

MP-MVS-pluss		96.70 44	96.27 53	97.98 21	99.23 30	94.71 26	96.96 153	98.06 73	90.67 159	95.55 102	98.78 10	91.07 65	99.86 8	96.58 23	99.55 21	99.38 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		97.62 6	97.53 6	97.87 24	98.39 80	94.25 38	98.43 18	98.27 28	95.34 10	98.11 16	98.56 17	94.53 9	99.71 38	96.57 24	99.62 13	99.65 9
Skip Steuart: Steuart Systems R&D Blog.
MCST-MVS		97.18 16	96.84 25	98.20 10	99.30 24	95.35 12	97.12 140	98.07 70	93.54 68	96.08 80	97.69 90	93.86 13	99.71 38	96.50 25	99.39 47	99.55 26

xxxxxxxxxxxxxcwj		97.36 11	97.20 10	97.83 26	98.91 49	94.28 35	97.02 145	97.22 183	95.35 8	98.27 14	98.65 13	93.33 17	99.72 35	96.49 26	99.52 25	99.51 34

SF-MVS		97.39 10	97.13 11	98.17 11	99.02 43	95.28 17	98.23 33	98.27 28	92.37 108	98.27 14	98.65 13	93.33 17	99.72 35	96.49 26	99.52 25	99.51 34

EI-MVSNet-Vis-set		96.51 51	96.47 47	96.63 80	98.24 93	91.20 134	96.89 160	97.73 118	94.74 35	96.49 64	98.49 24	90.88 70	99.58 71	96.44 28	98.32 102	99.13 74

VDD-MVS		93.82 125	93.08 131	96.02 118	97.88 116	89.96 174	97.72 77	95.85 267	92.43 106	95.86 89	98.44 28	68.42 326	99.39 110	96.31 29	94.85 175	98.71 115

test1172		96.93 33	96.86 22	97.15 67	99.10 34	92.34 94	97.96 54	98.04 81	93.79 58	97.35 33	98.53 21	91.40 57	99.56 81	96.30 30	99.30 56	99.55 26

ACMMP_NAP		97.20 15	96.86 22	98.23 8	99.09 36	95.16 20	97.60 92	98.19 44	92.82 96	97.93 20	98.74 11	91.60 53	99.86 8	96.26 31	99.52 25	99.67 8

diffmvs		95.25 83	95.13 81	95.63 138	96.43 185	89.34 195	95.99 235	97.35 174	92.83 95	96.31 71	97.37 113	86.44 125	98.67 173	96.26 31	97.19 135	98.87 103

EI-MVSNet-UG-set		96.34 56	96.30 52	96.47 91	98.20 98	90.93 146	96.86 161	97.72 121	94.67 36	96.16 77	98.46 26	90.43 76	99.58 71	96.23 33	97.96 112	98.90 99

SR-MVS		97.01 28	96.86 22	97.47 51	99.09 36	93.27 71	97.98 49	98.07 70	93.75 59	97.45 28	98.48 25	91.43 56	99.59 68	96.22 34	99.27 61	99.54 29

xiu_mvs_v1_base_debu		95.01 89	94.76 87	95.75 130	96.58 173	91.71 113	96.25 219	97.35 174	92.99 86	96.70 52	96.63 153	82.67 184	99.44 104	96.22 34	97.46 122	96.11 218

xiu_mvs_v1_base		95.01 89	94.76 87	95.75 130	96.58 173	91.71 113	96.25 219	97.35 174	92.99 86	96.70 52	96.63 153	82.67 184	99.44 104	96.22 34	97.46 122	96.11 218

xiu_mvs_v1_base_debi		95.01 89	94.76 87	95.75 130	96.58 173	91.71 113	96.25 219	97.35 174	92.99 86	96.70 52	96.63 153	82.67 184	99.44 104	96.22 34	97.46 122	96.11 218

alignmvs		95.87 70	95.23 78	97.78 33	97.56 134	95.19 18	97.86 60	97.17 186	94.39 43	96.47 66	96.40 167	85.89 133	99.20 123	96.21 38	95.11 173	98.95 94

canonicalmvs		96.02 65	95.45 71	97.75 37	97.59 132	95.15 21	98.28 26	97.60 135	94.52 40	96.27 73	96.12 179	87.65 107	99.18 126	96.20 39	94.82 177	98.91 98

zzz-MVS		97.07 22	96.77 32	97.97 22	99.37 16	94.42 32	97.15 138	98.08 64	95.07 21	96.11 78	98.59 15	90.88 70	99.90 1	96.18 40	99.50 32	99.58 17

MTAPA		97.08 21	96.78 31	97.97 22	99.37 16	94.42 32	97.24 125	98.08 64	95.07 21	96.11 78	98.59 15	90.88 70	99.90 1	96.18 40	99.50 32	99.58 17

APD-MVS_3200maxsize		96.81 40	96.71 36	97.12 69	99.01 46	92.31 97	97.98 49	98.06 73	93.11 83	97.44 29	98.55 19	90.93 68	99.55 84	96.06 42	99.25 65	99.51 34

SR-MVS-dyc-post		96.88 36	96.80 29	97.11 70	99.02 43	92.34 94	97.98 49	98.03 84	93.52 69	97.43 31	98.51 22	91.40 57	99.56 81	96.05 43	99.26 63	99.43 49

RE-MVS-def			96.72 35		99.02 43	92.34 94	97.98 49	98.03 84	93.52 69	97.43 31	98.51 22	90.71 73		96.05 43	99.26 63	99.43 49

MVS_111021_HR		96.68 47	96.58 42	96.99 73	98.46 74	92.31 97	96.20 224	98.90 2	94.30 46	95.86 89	97.74 87	92.33 35	99.38 112	96.04 45	99.42 43	99.28 65

PHI-MVS		96.77 42	96.46 48	97.71 41	98.40 78	94.07 48	98.21 36	98.45 15	89.86 180	97.11 44	98.01 68	92.52 32	99.69 44	96.03 46	99.53 24	99.36 58

HPM-MVS++		97.34 13	96.97 17	98.47 3	99.08 38	96.16 2	97.55 96	97.97 99	95.59 4	96.61 58	97.89 72	92.57 30	99.84 19	95.95 47	99.51 29	99.40 53

DELS-MVS		96.61 48	96.38 51	97.30 57	97.79 121	93.19 72	95.96 236	98.18 46	95.23 12	95.87 88	97.65 94	91.45 55	99.70 43	95.87 48	99.44 42	99.00 90
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
MVS_111021_LR		96.24 59	96.19 57	96.39 98	98.23 97	91.35 127	96.24 222	98.79 4	93.99 51	95.80 91	97.65 94	89.92 83	99.24 121	95.87 48	99.20 70	98.58 119

hse-mvs3		94.15 110	93.52 118	96.04 117	97.81 119	90.22 165	97.62 91	97.58 138	95.19 14	96.74 50	97.45 109	83.67 162	99.61 62	95.85 50	79.73 330	98.29 146

hse-mvs2		93.45 137	92.99 133	94.81 177	97.02 154	88.59 216	96.69 179	96.47 244	95.19 14	96.74 50	96.16 178	83.67 162	98.48 191	95.85 50	79.13 334	97.35 186

NCCC		97.30 14	97.03 14	98.11 14	98.77 57	95.06 22	97.34 115	98.04 81	95.96 2	97.09 45	97.88 74	93.18 20	99.71 38	95.84 52	99.17 72	99.56 22

VNet		95.89 69	95.45 71	97.21 65	98.07 107	92.94 79	97.50 99	98.15 51	93.87 53	97.52 25	97.61 100	85.29 140	99.53 89	95.81 53	95.27 169	99.16 70

9.14			96.75 33		98.93 47		97.73 74	98.23 38	91.28 144	97.88 22	98.44 28	93.00 21	99.65 53	95.76 54	99.47 36

XVS		97.18 16	96.96 18	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 26	96.59 60	98.29 50	91.70 50	99.80 27	95.66 55	99.40 45	99.62 13

X-MVStestdata		91.71 197	89.67 256	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 26	96.59 60	32.69 362	91.70 50	99.80 27	95.66 55	99.40 45	99.62 13

baseline		95.58 75	95.42 73	96.08 113	96.78 164	90.41 162	97.16 136	97.45 158	93.69 63	95.65 100	97.85 78	87.29 115	98.68 172	95.66 55	97.25 133	99.13 74

ETV-MVS		96.02 65	95.89 63	96.40 96	97.16 142	92.44 92	97.47 104	97.77 114	94.55 39	96.48 65	94.51 253	91.23 62	98.92 151	95.65 58	98.19 105	97.82 168

casdiffmvs		95.64 73	95.49 69	96.08 113	96.76 167	90.45 160	97.29 122	97.44 162	94.00 50	95.46 106	97.98 70	87.52 111	98.73 167	95.64 59	97.33 130	99.08 80

HFP-MVS		97.14 19	96.92 20	97.83 26	99.42 6	94.12 45	98.52 10	98.32 20	93.21 77	97.18 38	98.29 50	92.08 39	99.83 22	95.63 60	99.59 15	99.54 29

ACMMPR		97.07 22	96.84 25	97.79 32	99.44 5	93.88 52	98.52 10	98.31 22	93.21 77	97.15 40	98.33 44	91.35 59	99.86 8	95.63 60	99.59 15	99.62 13

HPM-MVS		96.69 45	96.45 49	97.40 53	99.36 19	93.11 74	98.87 1	98.06 73	91.17 148	96.40 69	97.99 69	90.99 67	99.58 71	95.61 62	99.61 14	99.49 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		97.02 26	96.81 28	97.64 46	99.33 22	93.54 62	98.80 3	98.28 26	92.99 86	96.45 68	98.30 49	91.90 45	99.85 14	95.61 62	99.68 4	99.54 29

DeepC-MVS	93.07 3	96.06 63	95.66 66	97.29 58	97.96 109	93.17 73	97.30 121	98.06 73	93.92 52	93.38 141	98.66 12	86.83 120	99.73 32	95.60 64	99.22 68	98.96 92
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		96.96 30	96.67 38	97.85 25	99.37 16	94.12 45	98.49 14	98.18 46	92.64 103	96.39 70	98.18 58	91.61 52	99.88 4	95.59 65	99.55 21	99.57 19

ETH3D-3000-0.1		97.07 22	96.71 36	98.14 13	98.90 51	95.33 14	97.68 81	98.24 34	91.57 130	97.90 21	98.37 36	92.61 29	99.66 52	95.59 65	99.51 29	99.43 49

region2R		97.07 22	96.84 25	97.77 35	99.46 1	93.79 55	98.52 10	98.24 34	93.19 80	97.14 41	98.34 41	91.59 54	99.87 7	95.46 67	99.59 15	99.64 10

OPU-MVS				98.55 1	98.82 56	96.86 1	98.25 29				98.26 53	96.04 1	99.24 121	95.36 68	99.59 15	99.56 22

lupinMVS		94.99 93	94.56 93	96.29 106	96.34 189	91.21 132	95.83 242	96.27 252	88.93 208	96.22 74	96.88 137	86.20 130	98.85 157	95.27 69	99.05 81	98.82 107

mPP-MVS		96.86 37	96.60 40	97.64 46	99.40 11	93.44 65	98.50 13	98.09 63	93.27 76	95.95 87	98.33 44	91.04 66	99.88 4	95.20 70	99.57 20	99.60 16

DeepC-MVS_fast	93.89 2	96.93 33	96.64 39	97.78 33	98.64 67	94.30 34	97.41 107	98.04 81	94.81 31	96.59 60	98.37 36	91.24 61	99.64 61	95.16 71	99.52 25	99.42 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
jason		94.84 98	94.39 101	96.18 111	95.52 222	90.93 146	96.09 228	96.52 242	89.28 196	96.01 85	97.32 114	84.70 147	98.77 164	95.15 72	98.91 89	98.85 104
jason: jason.
#test#		97.02 26	96.75 33	97.83 26	99.42 6	94.12 45	98.15 39	98.32 20	92.57 104	97.18 38	98.29 50	92.08 39	99.83 22	95.12 73	99.59 15	99.54 29

abl_6		96.40 54	96.21 55	96.98 74	98.89 54	92.20 102	97.89 58	98.03 84	93.34 75	97.22 37	98.42 31	87.93 103	99.72 35	95.10 74	99.07 80	99.02 83

train_agg		96.30 57	95.83 64	97.72 39	98.70 60	94.19 40	96.41 201	98.02 88	88.58 220	96.03 81	97.56 105	92.73 25	99.59 68	95.04 75	99.37 52	99.39 54

agg_prior1		96.22 60	95.77 65	97.56 48	98.67 62	93.79 55	96.28 217	98.00 93	88.76 217	95.68 96	97.55 107	92.70 27	99.57 79	95.01 76	99.32 53	99.32 60

test_prior3		96.46 53	96.20 56	97.23 62	98.67 62	92.99 76	96.35 209	98.00 93	92.80 97	96.03 81	97.59 101	92.01 41	99.41 107	95.01 76	99.38 48	99.29 62

test_prior2							96.35 209		92.80 97	96.03 81	97.59 101	92.01 41		95.01 76	99.38 48

nrg030		94.05 117	93.31 127	96.27 107	95.22 244	94.59 28	98.34 21	97.46 152	92.93 93	91.21 191	96.64 149	87.23 117	98.22 206	94.99 79	85.80 282	95.98 222

VDDNet		93.05 150	92.07 162	96.02 118	96.84 160	90.39 163	98.08 43	95.85 267	86.22 276	95.79 92	98.46 26	67.59 329	99.19 124	94.92 80	94.85 175	98.47 131

APD-MVS		96.95 31	96.60 40	98.01 19	99.03 42	94.93 24	97.72 77	98.10 61	91.50 132	98.01 18	98.32 46	92.33 35	99.58 71	94.85 81	99.51 29	99.53 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ETH3D cwj APD-0.16		96.56 50	96.06 59	98.05 17	98.26 92	95.19 18	96.99 150	98.05 80	89.85 182	97.26 35	98.22 56	91.80 47	99.69 44	94.84 82	99.28 59	99.27 66

GST-MVS		96.85 38	96.52 45	97.82 29	99.36 19	94.14 44	98.29 25	98.13 54	92.72 100	96.70 52	98.06 64	91.35 59	99.86 8	94.83 83	99.28 59	99.47 44

MP-MVS		96.77 42	96.45 49	97.72 39	99.39 13	93.80 54	98.41 19	98.06 73	93.37 72	95.54 104	98.34 41	90.59 75	99.88 4	94.83 83	99.54 23	99.49 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test9_res														94.81 85	99.38 48	99.45 45

PS-MVSNAJ		95.37 79	95.33 76	95.49 149	97.35 136	90.66 155	95.31 263	97.48 147	93.85 54	96.51 63	95.70 205	88.65 94	99.65 53	94.80 86	98.27 103	96.17 213

HPM-MVS_fast		96.51 51	96.27 53	97.22 64	99.32 23	92.74 82	98.74 4	98.06 73	90.57 168	96.77 49	98.35 38	90.21 79	99.53 89	94.80 86	99.63 12	99.38 56

xiu_mvs_v2_base		95.32 81	95.29 77	95.40 154	97.22 138	90.50 158	95.44 257	97.44 162	93.70 62	96.46 67	96.18 175	88.59 97	99.53 89	94.79 88	97.81 115	96.17 213

CSCG		96.05 64	95.91 62	96.46 93	99.24 28	90.47 159	98.30 24	98.57 11	89.01 203	93.97 128	97.57 103	92.62 28	99.76 30	94.66 89	99.27 61	99.15 72

EIA-MVS		95.53 77	95.47 70	95.71 135	97.06 150	89.63 179	97.82 65	97.87 107	93.57 64	93.92 129	95.04 230	90.61 74	98.95 149	94.62 90	98.68 94	98.54 121

ZD-MVS					99.05 41	94.59 28		98.08 64	89.22 198	97.03 47	98.10 60	92.52 32	99.65 53	94.58 91	99.31 55

ACMMP		96.27 58	95.93 61	97.28 59	99.24 28	92.62 87	98.25 29	98.81 3	92.99 86	94.56 116	98.39 35	88.96 89	99.85 14	94.57 92	97.63 119	99.36 58
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
PGM-MVS		96.81 40	96.53 44	97.65 44	99.35 21	93.53 63	97.65 85	98.98 1	92.22 111	97.14 41	98.44 28	91.17 64	99.85 14	94.35 93	99.46 38	99.57 19

ET-MVSNet_ETH3D		91.49 209	90.11 238	95.63 138	96.40 186	91.57 121	95.34 260	93.48 333	90.60 167	75.58 347	95.49 216	80.08 230	96.79 320	94.25 94	89.76 247	98.52 123

testtj		96.93 33	96.56 43	98.05 17	99.10 34	94.66 27	97.78 69	98.22 39	92.74 99	97.59 24	98.20 57	91.96 44	99.86 8	94.21 95	99.25 65	99.63 11

bset_n11_16_dypcd		91.55 205	90.59 217	94.44 193	91.51 334	90.25 164	92.70 325	93.42 334	92.27 110	90.22 205	94.74 244	78.42 260	97.80 264	94.19 96	87.86 263	95.29 265

LFMVS		93.60 132	92.63 145	96.52 85	98.13 104	91.27 129	97.94 55	93.39 335	90.57 168	96.29 72	98.31 47	69.00 322	99.16 128	94.18 97	95.87 158	99.12 77

MVSFormer		95.37 79	95.16 80	95.99 120	96.34 189	91.21 132	98.22 34	97.57 139	91.42 136	96.22 74	97.32 114	86.20 130	97.92 253	94.07 98	99.05 81	98.85 104

test_djsdf		93.07 149	92.76 139	94.00 210	93.49 309	88.70 214	98.22 34	97.57 139	91.42 136	90.08 216	95.55 213	82.85 181	97.92 253	94.07 98	91.58 221	95.40 252

mvs_anonymous		93.82 125	93.74 109	94.06 207	96.44 184	85.41 282	95.81 243	97.05 199	89.85 182	90.09 215	96.36 169	87.44 113	97.75 270	93.97 100	96.69 145	99.02 83

VPA-MVSNet		93.24 143	92.48 154	95.51 146	95.70 216	92.39 93	97.86 60	98.66 9	92.30 109	92.09 171	95.37 219	80.49 222	98.40 194	93.95 101	85.86 281	95.75 235

agg_prior2														93.94 102	99.38 48	99.50 37

mvs_tets		92.31 177	91.76 172	93.94 217	93.41 311	88.29 223	97.63 90	97.53 143	92.04 120	88.76 254	96.45 163	74.62 292	98.09 225	93.91 103	91.48 223	95.45 248

Effi-MVS+		94.93 94	94.45 99	96.36 101	96.61 170	91.47 123	96.41 201	97.41 167	91.02 153	94.50 117	95.92 188	87.53 110	98.78 162	93.89 104	96.81 140	98.84 106

jajsoiax		92.42 172	91.89 170	94.03 209	93.33 314	88.50 220	97.73 74	97.53 143	92.00 122	88.85 250	96.50 161	75.62 288	98.11 220	93.88 105	91.56 222	95.48 243

XVG-OURS-SEG-HR		93.86 124	93.55 115	94.81 177	97.06 150	88.53 219	95.28 264	97.45 158	91.68 128	94.08 125	97.68 91	82.41 192	98.90 154	93.84 106	92.47 206	96.98 191

PS-MVSNAJss		93.74 128	93.51 119	94.44 193	93.91 296	89.28 200	97.75 71	97.56 142	92.50 105	89.94 218	96.54 159	88.65 94	98.18 212	93.83 107	90.90 234	95.86 224

EPNet		95.20 86	94.56 93	97.14 68	92.80 322	92.68 84	97.85 63	94.87 312	96.64 1	92.46 157	97.80 84	86.23 127	99.65 53	93.72 108	98.62 96	99.10 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_test8_iter05		91.19 228	90.78 209	92.41 273	95.76 215	83.14 309	97.32 118	97.46 152	91.37 140	89.07 246	95.57 210	70.33 315	98.21 207	93.56 109	86.62 276	95.89 223

PVSNet_Blended_VisFu		95.27 82	94.91 85	96.38 99	98.20 98	90.86 148	97.27 123	98.25 33	90.21 173	94.18 123	97.27 116	87.48 112	99.73 32	93.53 110	97.77 117	98.55 120

CPTT-MVS		95.57 76	95.19 79	96.70 77	99.27 26	91.48 122	98.33 22	98.11 59	87.79 245	95.17 109	98.03 66	87.09 118	99.61 62	93.51 111	99.42 43	99.02 83

MVSTER		93.20 145	92.81 138	94.37 197	96.56 176	89.59 182	97.06 141	97.12 190	91.24 145	91.30 185	95.96 186	82.02 199	98.05 232	93.48 112	90.55 238	95.47 245

PVSNet_BlendedMVS		94.06 116	93.92 105	94.47 192	98.27 89	89.46 190	96.73 173	98.36 16	90.17 174	94.36 119	95.24 224	88.02 100	99.58 71	93.44 113	90.72 236	94.36 306

PVSNet_Blended		94.87 97	94.56 93	95.81 127	98.27 89	89.46 190	95.47 256	98.36 16	88.84 211	94.36 119	96.09 183	88.02 100	99.58 71	93.44 113	98.18 106	98.40 139

3Dnovator	91.36 5	95.19 87	94.44 100	97.44 52	96.56 176	93.36 69	98.65 6	98.36 16	94.12 48	89.25 243	98.06 64	82.20 196	99.77 29	93.41 115	99.32 53	99.18 69

EPP-MVSNet		95.22 85	95.04 83	95.76 128	97.49 135	89.56 183	98.67 5	97.00 205	90.69 158	94.24 122	97.62 99	89.79 84	98.81 160	93.39 116	96.49 150	98.92 97

RRT_MVS		93.21 144	92.32 158	95.91 123	94.92 259	94.15 43	96.92 157	96.86 220	91.42 136	91.28 188	96.43 164	79.66 239	98.10 221	93.29 117	90.06 243	95.46 246

CHOSEN 280x420		93.12 147	92.72 143	94.34 199	96.71 168	87.27 246	90.29 341	97.72 121	86.61 271	91.34 182	95.29 221	84.29 155	98.41 193	93.25 118	98.94 87	97.35 186

3Dnovator+	91.43 4	95.40 78	94.48 98	98.16 12	96.90 158	95.34 13	98.48 16	97.87 107	94.65 38	88.53 259	98.02 67	83.69 161	99.71 38	93.18 119	98.96 86	99.44 47

test_yl		94.78 100	94.23 102	96.43 94	97.74 123	91.22 130	96.85 162	97.10 192	91.23 146	95.71 94	96.93 132	84.30 153	99.31 116	93.10 120	95.12 171	98.75 109

DCV-MVSNet		94.78 100	94.23 102	96.43 94	97.74 123	91.22 130	96.85 162	97.10 192	91.23 146	95.71 94	96.93 132	84.30 153	99.31 116	93.10 120	95.12 171	98.75 109

HQP_MVS		93.78 127	93.43 123	94.82 175	96.21 193	89.99 170	97.74 72	97.51 145	94.85 26	91.34 182	96.64 149	81.32 210	98.60 179	93.02 122	92.23 209	95.86 224

plane_prior5								97.51 145					98.60 179	93.02 122	92.23 209	95.86 224

MVS_Test		94.89 96	94.62 91	95.68 136	96.83 162	89.55 184	96.70 177	97.17 186	91.17 148	95.60 101	96.11 182	87.87 104	98.76 165	93.01 124	97.17 136	98.72 113

CLD-MVS		92.98 153	92.53 151	94.32 200	96.12 202	89.20 202	95.28 264	97.47 150	92.66 101	89.90 219	95.62 208	80.58 220	98.40 194	92.73 125	92.40 207	95.38 254
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XVG-OURS		93.72 129	93.35 126	94.80 180	97.07 147	88.61 215	94.79 274	97.46 152	91.97 123	93.99 126	97.86 77	81.74 205	98.88 156	92.64 126	92.67 204	96.92 195

ETH3 D test6400		96.16 61	95.52 68	98.07 16	98.90 51	95.06 22	97.03 142	98.21 40	88.16 234	96.64 57	97.70 89	91.18 63	99.67 49	92.44 127	99.47 36	99.48 41

旧先验2							95.94 237		81.66 325	97.34 34			98.82 159	92.26 128

CDPH-MVS		95.97 67	95.38 74	97.77 35	98.93 47	94.44 31	96.35 209	97.88 105	86.98 264	96.65 56	97.89 72	91.99 43	99.47 100	92.26 128	99.46 38	99.39 54

FIs		94.09 115	93.70 110	95.27 156	95.70 216	92.03 107	98.10 41	98.68 7	93.36 74	90.39 201	96.70 144	87.63 108	97.94 250	92.25 130	90.50 240	95.84 227

LPG-MVS_test		92.94 156	92.56 148	94.10 205	96.16 198	88.26 225	97.65 85	97.46 152	91.29 141	90.12 212	97.16 122	79.05 247	98.73 167	92.25 130	91.89 217	95.31 258

LGP-MVS_train				94.10 205	96.16 198	88.26 225		97.46 152	91.29 141	90.12 212	97.16 122	79.05 247	98.73 167	92.25 130	91.89 217	95.31 258

cascas		91.20 225	90.08 239	94.58 190	94.97 255	89.16 205	93.65 309	97.59 137	79.90 336	89.40 235	92.92 306	75.36 289	98.36 198	92.14 133	94.75 179	96.23 210

OPM-MVS		93.28 142	92.76 139	94.82 175	94.63 275	90.77 152	96.65 183	97.18 184	93.72 60	91.68 176	97.26 117	79.33 244	98.63 176	92.13 134	92.28 208	95.07 270

BP-MVS														92.13 134

HQP-MVS		93.19 146	92.74 142	94.54 191	95.86 208	89.33 196	96.65 183	97.39 168	93.55 65	90.14 206	95.87 190	80.95 213	98.50 187	92.13 134	92.10 214	95.78 231

DP-MVS Recon		95.68 72	95.12 82	97.37 54	99.19 31	94.19 40	97.03 142	98.08 64	88.35 227	95.09 110	97.65 94	89.97 82	99.48 99	92.08 137	98.59 97	98.44 136

VPNet		92.23 183	91.31 189	94.99 166	95.56 220	90.96 144	97.22 131	97.86 110	92.96 92	90.96 193	96.62 156	75.06 290	98.20 209	91.90 138	83.65 315	95.80 230

sss		94.51 104	93.80 108	96.64 78	97.07 147	91.97 110	96.32 213	98.06 73	88.94 207	94.50 117	96.78 139	84.60 148	99.27 119	91.90 138	96.02 154	98.68 117

anonymousdsp		92.16 186	91.55 180	93.97 213	92.58 326	89.55 184	97.51 98	97.42 166	89.42 193	88.40 260	94.84 238	80.66 219	97.88 258	91.87 140	91.28 227	94.48 302

ACMP	89.59 10	92.62 167	92.14 161	94.05 208	96.40 186	88.20 228	97.36 114	97.25 182	91.52 131	88.30 263	96.64 149	78.46 259	98.72 170	91.86 141	91.48 223	95.23 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		93.66 130	92.92 136	95.87 125	98.24 93	89.88 175	94.58 278	98.49 12	85.06 293	93.78 131	95.78 199	82.86 180	98.67 173	91.77 142	95.71 163	99.07 82

UGNet		94.04 118	93.28 128	96.31 103	96.85 159	91.19 135	97.88 59	97.68 127	94.40 42	93.00 149	96.18 175	73.39 302	99.61 62	91.72 143	98.46 99	98.13 151
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
UniMVSNet_NR-MVSNet		93.37 139	92.67 144	95.47 152	95.34 233	92.83 80	97.17 135	98.58 10	92.98 91	90.13 210	95.80 195	88.37 99	97.85 259	91.71 144	83.93 310	95.73 237

DU-MVS		92.90 158	92.04 163	95.49 149	94.95 257	92.83 80	97.16 136	98.24 34	93.02 85	90.13 210	95.71 203	83.47 165	97.85 259	91.71 144	83.93 310	95.78 231

Effi-MVS+-dtu		93.08 148	93.21 130	92.68 268	96.02 205	83.25 308	97.14 139	96.72 226	93.85 54	91.20 192	93.44 300	83.08 173	98.30 202	91.69 146	95.73 162	96.50 206

mvs-test1		93.63 131	93.69 111	93.46 240	96.02 205	84.61 294	97.24 125	96.72 226	93.85 54	92.30 164	95.76 200	83.08 173	98.89 155	91.69 146	96.54 148	96.87 197

UniMVSNet (Re)		93.31 141	92.55 149	95.61 140	95.39 227	93.34 70	97.39 111	98.71 5	93.14 82	90.10 214	94.83 239	87.71 105	98.03 236	91.67 148	83.99 309	95.46 246

LCM-MVSNet-Re		92.50 168	92.52 152	92.44 271	96.82 163	81.89 317	96.92 157	93.71 331	92.41 107	84.30 315	94.60 251	85.08 143	97.03 311	91.51 149	97.36 128	98.40 139

FC-MVSNet-test		93.94 121	93.57 114	95.04 164	95.48 224	91.45 125	98.12 40	98.71 5	93.37 72	90.23 204	96.70 144	87.66 106	97.85 259	91.49 150	90.39 241	95.83 228

PMMVS		92.86 160	92.34 156	94.42 196	94.92 259	86.73 260	94.53 280	96.38 248	84.78 298	94.27 121	95.12 229	83.13 172	98.40 194	91.47 151	96.49 150	98.12 152

Vis-MVSNet		95.23 84	94.81 86	96.51 88	97.18 141	91.58 120	98.26 28	98.12 56	94.38 44	94.90 111	98.15 59	82.28 194	98.92 151	91.45 152	98.58 98	99.01 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		94.15 110	93.51 119	96.06 115	98.27 89	89.38 193	95.18 270	98.48 14	85.60 284	93.76 132	97.11 126	83.15 171	99.61 62	91.33 153	98.72 93	99.19 68

OMC-MVS		95.09 88	94.70 90	96.25 109	98.46 74	91.28 128	96.43 199	97.57 139	92.04 120	94.77 114	97.96 71	87.01 119	99.09 137	91.31 154	96.77 141	98.36 143

MG-MVS		95.61 74	95.38 74	96.31 103	98.42 77	90.53 157	96.04 230	97.48 147	93.47 71	95.67 99	98.10 60	89.17 87	99.25 120	91.27 155	98.77 91	99.13 74

ACMM	89.79 8	92.96 154	92.50 153	94.35 198	96.30 191	88.71 213	97.58 93	97.36 173	91.40 139	90.53 197	96.65 148	79.77 236	98.75 166	91.24 156	91.64 219	95.59 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WTY-MVS		94.71 102	94.02 104	96.79 76	97.71 125	92.05 106	96.59 192	97.35 174	90.61 165	94.64 115	96.93 132	86.41 126	99.39 110	91.20 157	94.71 181	98.94 95

Anonymous20240529		91.98 191	90.73 212	95.73 133	98.14 103	89.40 192	97.99 48	97.72 121	79.63 337	93.54 136	97.41 112	69.94 320	99.56 81	91.04 158	91.11 229	98.22 147

test_part1		92.21 185	91.10 199	95.51 146	97.80 120	92.66 85	98.02 47	97.68 127	89.79 185	88.80 253	96.02 184	76.85 277	98.18 212	90.86 159	84.11 308	95.69 238

AUN-MVS		91.76 196	90.75 211	94.81 177	97.00 155	88.57 217	96.65 183	96.49 243	89.63 187	92.15 167	96.12 179	78.66 256	98.50 187	90.83 160	79.18 333	97.36 185

CANet_DTU		94.37 105	93.65 113	96.55 84	96.46 183	92.13 104	96.21 223	96.67 234	94.38 44	93.53 137	97.03 130	79.34 243	99.71 38	90.76 161	98.45 100	97.82 168

ab-mvs		93.57 134	92.55 149	96.64 78	97.28 137	91.96 111	95.40 258	97.45 158	89.81 184	93.22 147	96.28 172	79.62 240	99.46 101	90.74 162	93.11 198	98.50 126

CostFormer		91.18 229	90.70 213	92.62 269	94.84 265	81.76 318	94.09 297	94.43 319	84.15 304	92.72 156	93.77 289	79.43 242	98.20 209	90.70 163	92.18 212	97.90 161

Anonymous202405211		92.07 189	90.83 208	95.76 128	98.19 100	88.75 212	97.58 93	95.00 303	86.00 279	93.64 133	97.45 109	66.24 338	99.53 89	90.68 164	92.71 202	99.01 87

tpmrst		91.44 211	91.32 188	91.79 288	95.15 247	79.20 340	93.42 313	95.37 285	88.55 223	93.49 138	93.67 294	82.49 190	98.27 203	90.41 165	89.34 250	97.90 161

thisisatest0530		93.03 151	92.21 160	95.49 149	97.07 147	89.11 206	97.49 103	92.19 343	90.16 175	94.09 124	96.41 166	76.43 282	99.05 143	90.38 166	95.68 164	98.31 145

UA-Net		95.95 68	95.53 67	97.20 66	97.67 126	92.98 78	97.65 85	98.13 54	94.81 31	96.61 58	98.35 38	88.87 90	99.51 94	90.36 167	97.35 129	99.11 78

UniMVSNet_ETH3D		91.34 219	90.22 235	94.68 185	94.86 264	87.86 238	97.23 130	97.46 152	87.99 237	89.90 219	96.92 135	66.35 336	98.23 205	90.30 168	90.99 232	97.96 157

tttt0517		92.96 154	92.33 157	94.87 174	97.11 145	87.16 252	97.97 53	92.09 344	90.63 163	93.88 130	97.01 131	76.50 279	99.06 142	90.29 169	95.45 166	98.38 141

IS-MVSNet		94.90 95	94.52 96	96.05 116	97.67 126	90.56 156	98.44 17	96.22 255	93.21 77	93.99 126	97.74 87	85.55 138	98.45 192	89.98 170	97.86 113	99.14 73

miper_enhance_ethall		91.54 207	91.01 200	93.15 252	95.35 232	87.07 254	93.97 299	96.90 214	86.79 268	89.17 244	93.43 302	86.55 123	97.64 278	89.97 171	86.93 271	94.74 296

EI-MVSNet		93.03 151	92.88 137	93.48 238	95.77 213	86.98 255	96.44 197	97.12 190	90.66 161	91.30 185	97.64 97	86.56 122	98.05 232	89.91 172	90.55 238	95.41 249

IterMVS-LS		92.29 179	91.94 168	93.34 245	96.25 192	86.97 256	96.57 195	97.05 199	90.67 159	89.50 234	94.80 241	86.59 121	97.64 278	89.91 172	86.11 280	95.40 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl-mvsnet2		91.21 224	90.56 220	93.14 253	96.09 204	86.80 258	94.41 285	96.58 241	87.80 244	88.58 258	93.99 282	80.85 218	97.62 281	89.87 174	86.93 271	94.99 273

CDS-MVSNet		94.14 113	93.54 116	95.93 122	96.18 196	91.46 124	96.33 212	97.04 201	88.97 206	93.56 134	96.51 160	87.55 109	97.89 257	89.80 175	95.95 156	98.44 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WR-MVS		92.34 175	91.53 181	94.77 182	95.13 249	90.83 149	96.40 204	97.98 98	91.88 124	89.29 240	95.54 214	82.50 189	97.80 264	89.79 176	85.27 290	95.69 238

NR-MVSNet		92.34 175	91.27 192	95.53 145	94.95 257	93.05 75	97.39 111	98.07 70	92.65 102	84.46 313	95.71 203	85.00 144	97.77 269	89.71 177	83.52 316	95.78 231

Anonymous20231211		90.63 249	89.42 260	94.27 201	98.24 93	89.19 204	98.05 45	97.89 103	79.95 335	88.25 266	94.96 231	72.56 303	98.13 216	89.70 178	85.14 292	95.49 242

testdata				95.46 153	98.18 102	88.90 210		97.66 129	82.73 319	97.03 47	98.07 63	90.06 80	98.85 157	89.67 179	98.98 85	98.64 118

Baseline_NR-MVSNet		91.20 225	90.62 215	92.95 259	93.83 299	88.03 233	97.01 149	95.12 299	88.42 225	89.70 225	95.13 228	83.47 165	97.44 297	89.66 180	83.24 318	93.37 324

DPM-MVS		95.69 71	94.92 84	98.01 19	98.08 106	95.71 7	95.27 266	97.62 134	90.43 171	95.55 102	97.07 128	91.72 48	99.50 97	89.62 181	98.94 87	98.82 107

XXY-MVS		92.16 186	91.23 194	94.95 171	94.75 269	90.94 145	97.47 104	97.43 165	89.14 200	88.90 247	96.43 164	79.71 237	98.24 204	89.56 182	87.68 264	95.67 240

miper_ehance_all_eth		91.59 201	91.13 198	92.97 258	95.55 221	86.57 265	94.47 281	96.88 217	87.77 246	88.88 249	94.01 280	86.22 128	97.54 287	89.49 183	86.93 271	94.79 292

XVG-ACMP-BASELINE		90.93 238	90.21 236	93.09 254	94.31 287	85.89 275	95.33 261	97.26 180	91.06 152	89.38 236	95.44 218	68.61 324	98.60 179	89.46 184	91.05 230	94.79 292

thisisatest0515		92.29 179	91.30 190	95.25 157	96.60 171	88.90 210	94.36 287	92.32 342	87.92 239	93.43 140	94.57 252	77.28 275	99.00 146	89.42 185	95.86 159	97.86 164

cl_fuxian		91.38 214	90.89 202	92.88 261	95.58 219	86.30 268	94.68 276	96.84 222	88.17 232	88.83 252	94.23 272	85.65 137	97.47 294	89.36 186	84.63 300	94.89 282

AdaColmap		94.34 106	93.68 112	96.31 103	98.59 69	91.68 116	96.59 192	97.81 113	89.87 179	92.15 167	97.06 129	83.62 164	99.54 86	89.34 187	98.07 109	97.70 172

TranMVSNet+NR-MVSNet		92.50 168	91.63 177	95.14 161	94.76 268	92.07 105	97.53 97	98.11 59	92.90 94	89.56 231	96.12 179	83.16 170	97.60 283	89.30 188	83.20 319	95.75 235

D2MVS		91.30 221	90.95 201	92.35 274	94.71 271	85.52 280	96.18 225	98.21 40	88.89 209	86.60 296	93.82 287	79.92 234	97.95 249	89.29 189	90.95 233	93.56 320

1314		92.81 164	92.03 164	95.14 161	95.33 236	89.52 187	96.04 230	97.44 162	87.72 249	86.25 299	95.33 220	83.84 159	98.79 161	89.26 190	97.05 138	97.11 189

v2v482		91.59 201	90.85 206	93.80 223	93.87 298	88.17 230	96.94 156	96.88 217	89.54 188	89.53 232	94.90 235	81.70 206	98.02 237	89.25 191	85.04 296	95.20 267

114514_t		93.95 120	93.06 132	96.63 80	99.07 39	91.61 117	97.46 106	97.96 100	77.99 343	93.00 149	97.57 103	86.14 132	99.33 114	89.22 192	99.15 73	98.94 95

PAPM_NR		95.01 89	94.59 92	96.26 108	98.89 54	90.68 154	97.24 125	97.73 118	91.80 125	92.93 154	96.62 156	89.13 88	99.14 131	89.21 193	97.78 116	98.97 91

baseline1		92.82 163	91.90 169	95.55 144	97.20 140	90.77 152	97.19 133	94.58 317	92.20 113	92.36 161	96.34 170	84.16 156	98.21 207	89.20 194	83.90 313	97.68 173

IB-MVS	87.33 17	89.91 264	88.28 276	94.79 181	95.26 243	87.70 241	95.12 272	93.95 330	89.35 195	87.03 290	92.49 312	70.74 313	99.19 124	89.18 195	81.37 326	97.49 183
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
HY-MVS	89.66 9	93.87 123	92.95 135	96.63 80	97.10 146	92.49 91	95.64 250	96.64 235	89.05 202	93.00 149	95.79 198	85.77 136	99.45 103	89.16 196	94.35 183	97.96 157

V42		91.58 203	90.87 203	93.73 225	94.05 293	88.50 220	97.32 118	96.97 206	88.80 216	89.71 224	94.33 264	82.54 188	98.05 232	89.01 197	85.07 294	94.64 300

OurMVSNet-221017-0		90.51 252	90.19 237	91.44 297	93.41 311	81.25 321	96.98 152	96.28 251	91.68 128	86.55 297	96.30 171	74.20 295	97.98 240	88.96 198	87.40 269	95.09 269

API-MVS		94.84 98	94.49 97	95.90 124	97.90 115	92.00 109	97.80 67	97.48 147	89.19 199	94.81 113	96.71 142	88.84 91	99.17 127	88.91 199	98.76 92	96.53 204

test-LLR		91.42 212	91.19 196	92.12 278	94.59 276	80.66 324	94.29 291	92.98 337	91.11 150	90.76 195	92.37 314	79.02 249	98.07 229	88.81 200	96.74 142	97.63 174

test-mter		90.19 260	89.54 259	92.12 278	94.59 276	80.66 324	94.29 291	92.98 337	87.68 250	90.76 195	92.37 314	67.67 328	98.07 229	88.81 200	96.74 142	97.63 174

eth_miper_zixun_eth		91.02 233	90.59 217	92.34 275	95.33 236	84.35 295	94.10 296	96.90 214	88.56 222	88.84 251	94.33 264	84.08 157	97.60 283	88.77 202	84.37 305	95.06 271

TAMVS		94.01 119	93.46 121	95.64 137	96.16 198	90.45 160	96.71 176	96.89 216	89.27 197	93.46 139	96.92 135	87.29 115	97.94 250	88.70 203	95.74 161	98.53 122

Patchmatch-RL test		87.38 292	86.24 293	90.81 307	88.74 350	78.40 344	88.12 350	93.17 336	87.11 263	82.17 330	89.29 339	81.95 201	95.60 336	88.64 204	77.02 337	98.41 138

baseline2		91.63 200	90.86 204	93.94 217	94.33 285	86.32 267	95.92 238	91.64 348	89.37 194	86.94 292	94.69 246	81.62 207	98.69 171	88.64 204	94.57 182	96.81 199

TESTMET0.1,1		90.06 262	89.42 260	91.97 281	94.41 283	80.62 326	94.29 291	91.97 346	87.28 260	90.44 200	92.47 313	68.79 323	97.67 275	88.50 206	96.60 147	97.61 178

Vis-MVSNet (Re-imp)		94.15 110	93.88 106	94.95 171	97.61 130	87.92 235	98.10 41	95.80 269	92.22 111	93.02 148	97.45 109	84.53 150	97.91 256	88.24 207	97.97 111	99.02 83

DWT-MVSNet_test		90.76 242	89.89 246	93.38 243	95.04 253	83.70 304	95.85 241	94.30 325	88.19 230	90.46 199	92.80 307	73.61 300	98.50 187	88.16 208	90.58 237	97.95 159

1112_ss		93.37 139	92.42 155	96.21 110	97.05 152	90.99 142	96.31 214	96.72 226	86.87 267	89.83 222	96.69 146	86.51 124	99.14 131	88.12 209	93.67 192	98.50 126

CVMVSNet		91.23 223	91.75 173	89.67 321	95.77 213	74.69 349	96.44 197	94.88 309	85.81 281	92.18 166	97.64 97	79.07 246	95.58 337	88.06 210	95.86 159	98.74 111

MAR-MVS		94.22 108	93.46 121	96.51 88	98.00 108	92.19 103	97.67 82	97.47 150	88.13 236	93.00 149	95.84 192	84.86 146	99.51 94	87.99 211	98.17 107	97.83 167
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
原ACMM1				96.38 99	98.59 69	91.09 141		97.89 103	87.41 256	95.22 108	97.68 91	90.25 77	99.54 86	87.95 212	99.12 78	98.49 128

CP-MVSNet		91.89 193	91.24 193	93.82 222	95.05 252	88.57 217	97.82 65	98.19 44	91.70 127	88.21 267	95.76 200	81.96 200	97.52 291	87.86 213	84.65 299	95.37 255

v148		90.99 234	90.38 224	92.81 264	93.83 299	85.80 276	96.78 171	96.68 232	89.45 192	88.75 255	93.93 284	82.96 179	97.82 263	87.83 214	83.25 317	94.80 290

v1144		91.37 216	90.60 216	93.68 230	93.89 297	88.23 227	96.84 164	97.03 203	88.37 226	89.69 226	94.39 260	82.04 198	97.98 240	87.80 215	85.37 287	94.84 284

cl-mvsnet1		90.97 236	90.33 225	92.88 261	95.36 231	86.19 272	94.46 283	96.63 238	87.82 242	88.18 268	94.23 272	82.99 176	97.53 289	87.72 216	85.57 284	94.93 278

gm-plane-assit					93.22 315	78.89 343			84.82 297		93.52 297		98.64 175	87.72 216

GeoE		93.89 122	93.28 128	95.72 134	96.96 157	89.75 178	98.24 32	96.92 213	89.47 191	92.12 169	97.21 120	84.42 151	98.39 197	87.71 218	96.50 149	99.01 87

cl-mvsnet____		90.96 237	90.32 226	92.89 260	95.37 230	86.21 271	94.46 283	96.64 235	87.82 242	88.15 269	94.18 275	82.98 177	97.54 287	87.70 219	85.59 283	94.92 280

pmmvs4		90.93 238	89.85 248	94.17 203	93.34 313	90.79 151	94.60 277	96.02 261	84.62 299	87.45 280	95.15 226	81.88 203	97.45 296	87.70 219	87.87 262	94.27 311

Test_1112_low_res		92.84 162	91.84 171	95.85 126	97.04 153	89.97 173	95.53 254	96.64 235	85.38 287	89.65 228	95.18 225	85.86 134	99.10 134	87.70 219	93.58 197	98.49 128

无先验							95.79 244	97.87 107	83.87 309				99.65 53	87.68 222		98.89 101

1121		94.71 102	93.83 107	97.34 55	98.57 72	93.64 60	96.04 230	97.73 118	81.56 327	95.68 96	97.85 78	90.23 78	99.65 53	87.68 222	99.12 78	98.73 112

Fast-Effi-MVS+		93.46 136	92.75 141	95.59 141	96.77 165	90.03 167	96.81 168	97.13 189	88.19 230	91.30 185	94.27 269	86.21 129	98.63 176	87.66 224	96.46 152	98.12 152

CNLPA		94.28 107	93.53 117	96.52 85	98.38 81	92.55 89	96.59 192	96.88 217	90.13 176	91.91 173	97.24 118	85.21 141	99.09 137	87.64 225	97.83 114	97.92 160

v8		91.29 222	90.53 221	93.57 235	94.15 289	88.12 232	97.34 115	97.06 198	88.99 204	88.32 262	94.26 271	83.08 173	98.01 238	87.62 226	83.92 312	94.57 301

pmmvs5		89.86 267	88.87 269	92.82 263	92.86 320	86.23 270	96.26 218	95.39 283	84.24 303	87.12 287	94.51 253	74.27 294	97.36 303	87.61 227	87.57 265	94.86 283

Fast-Effi-MVS+-dtu		92.29 179	91.99 166	93.21 251	95.27 240	85.52 280	97.03 142	96.63 238	92.09 118	89.11 245	95.14 227	80.33 226	98.08 226	87.54 228	94.74 180	96.03 221

OpenMVS	89.19 12	92.86 160	91.68 176	96.40 96	95.34 233	92.73 83	98.27 27	98.12 56	84.86 296	85.78 302	97.75 86	78.89 254	99.74 31	87.50 229	98.65 95	96.73 201

miper_lstm_enhance		90.50 253	90.06 242	91.83 285	95.33 236	83.74 301	93.86 302	96.70 231	87.56 253	87.79 275	93.81 288	83.45 167	96.92 317	87.39 230	84.62 301	94.82 287

IterMVS-SCA-FT		90.31 255	89.81 250	91.82 286	95.52 222	84.20 298	94.30 290	96.15 258	90.61 165	87.39 283	94.27 269	75.80 285	96.44 323	87.34 231	86.88 275	94.82 287

PLC	91.00 6	94.11 114	93.43 123	96.13 112	98.58 71	91.15 140	96.69 179	97.39 168	87.29 259	91.37 181	96.71 142	88.39 98	99.52 93	87.33 232	97.13 137	97.73 170
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		90.25 257	89.74 255	91.76 291	93.92 295	79.73 336	93.98 298	93.54 332	88.28 228	91.99 172	93.25 303	77.51 274	97.44 297	87.30 233	87.94 261	98.12 152

GA-MVS		91.38 214	90.31 227	94.59 186	94.65 273	87.62 242	94.34 288	96.19 257	90.73 157	90.35 202	93.83 285	71.84 305	97.96 247	87.22 234	93.61 195	98.21 148

BH-untuned		92.94 156	92.62 146	93.92 219	97.22 138	86.16 273	96.40 204	96.25 254	90.06 177	89.79 223	96.17 177	83.19 169	98.35 199	87.19 235	97.27 132	97.24 188

v144192		91.06 231	90.28 229	93.39 242	93.66 304	87.23 249	96.83 165	97.07 196	87.43 255	89.69 226	94.28 268	81.48 208	98.00 239	87.18 236	84.92 298	94.93 278

RPSCF		90.75 244	90.86 204	90.42 314	96.84 160	76.29 347	95.61 251	96.34 249	83.89 307	91.38 180	97.87 75	76.45 280	98.78 162	87.16 237	92.23 209	96.20 211

PS-CasMVS		91.55 205	90.84 207	93.69 229	94.96 256	88.28 224	97.84 64	98.24 34	91.46 134	88.04 271	95.80 195	79.67 238	97.48 293	87.02 238	84.54 303	95.31 258

pm-mvs1		90.72 246	89.65 258	93.96 214	94.29 288	89.63 179	97.79 68	96.82 223	89.07 201	86.12 301	95.48 217	78.61 257	97.78 267	86.97 239	81.67 324	94.46 303

IterMVS		90.15 261	89.67 256	91.61 293	95.48 224	83.72 302	94.33 289	96.12 259	89.99 178	87.31 286	94.15 277	75.78 287	96.27 326	86.97 239	86.89 274	94.83 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		93.58 133	92.98 134	95.37 155	98.40 78	88.98 208	97.18 134	97.29 179	87.75 248	90.49 198	97.10 127	85.21 141	99.50 97	86.70 241	96.72 144	97.63 174

PVSNet	86.66 18	92.24 182	91.74 175	93.73 225	97.77 122	83.69 305	92.88 322	96.72 226	87.91 240	93.00 149	94.86 237	78.51 258	99.05 143	86.53 242	97.45 126	98.47 131

v1192		91.07 230	90.23 233	93.58 234	93.70 302	87.82 239	96.73 173	97.07 196	87.77 246	89.58 229	94.32 266	80.90 217	97.97 243	86.52 243	85.48 285	94.95 274

新几何1				97.32 56	98.60 68	93.59 61		97.75 116	81.58 326	95.75 93	97.85 78	90.04 81	99.67 49	86.50 244	99.13 75	98.69 116

v10		91.04 232	90.23 233	93.49 237	94.12 290	88.16 231	97.32 118	97.08 195	88.26 229	88.29 264	94.22 274	82.17 197	97.97 243	86.45 245	84.12 307	94.33 307

v1921920		90.85 240	90.03 243	93.29 247	93.55 305	86.96 257	96.74 172	97.04 201	87.36 257	89.52 233	94.34 263	80.23 228	97.97 243	86.27 246	85.21 291	94.94 276

MDTV_nov1_ep13_2view						70.35 354	93.10 320		83.88 308	93.55 135		82.47 191		86.25 247		98.38 141

test_post1							92.81 324				16.58 366	80.53 221	97.68 274	86.20 248

SCA		91.84 194	91.18 197	93.83 221	95.59 218	84.95 290	94.72 275	95.58 279	90.82 154	92.25 165	93.69 291	75.80 285	98.10 221	86.20 248	95.98 155	98.45 133

PAPR		94.18 109	93.42 125	96.48 90	97.64 128	91.42 126	95.55 252	97.71 126	88.99 204	92.34 163	95.82 194	89.19 86	99.11 133	86.14 250	97.38 127	98.90 99

GBi-Net		91.35 217	90.27 230	94.59 186	96.51 179	91.18 136	97.50 99	96.93 209	88.82 213	89.35 237	94.51 253	73.87 296	97.29 306	86.12 251	88.82 253	95.31 258

test1		91.35 217	90.27 230	94.59 186	96.51 179	91.18 136	97.50 99	96.93 209	88.82 213	89.35 237	94.51 253	73.87 296	97.29 306	86.12 251	88.82 253	95.31 258

FMVSNet3		91.78 195	90.69 214	95.03 165	96.53 178	92.27 99	97.02 145	96.93 209	89.79 185	89.35 237	94.65 249	77.01 276	97.47 294	86.12 251	88.82 253	95.35 256

EPMVS		90.70 247	89.81 250	93.37 244	94.73 270	84.21 297	93.67 308	88.02 355	89.50 190	92.38 160	93.49 298	77.82 272	97.78 267	86.03 254	92.68 203	98.11 155

MVS		91.71 197	90.44 222	95.51 146	95.20 246	91.59 119	96.04 230	97.45 158	73.44 350	87.36 284	95.60 209	85.42 139	99.10 134	85.97 255	97.46 122	95.83 228

testdata2													99.67 49	85.96 256

K. test v3		87.64 291	86.75 292	90.32 315	93.02 319	79.48 338	96.61 189	92.08 345	90.66 161	80.25 339	94.09 278	67.21 332	96.65 322	85.96 256	80.83 328	94.83 285

WR-MVS_H		92.00 190	91.35 186	93.95 215	95.09 251	89.47 188	98.04 46	98.68 7	91.46 134	88.34 261	94.68 247	85.86 134	97.56 285	85.77 258	84.24 306	94.82 287

gg-mvs-nofinetune		87.82 289	85.61 298	94.44 193	94.46 280	89.27 201	91.21 336	84.61 360	80.88 330	89.89 221	74.98 354	71.50 307	97.53 289	85.75 259	97.21 134	96.51 205

tpm2		89.96 263	89.21 264	92.23 277	94.91 262	81.25 321	93.78 304	94.42 320	80.62 333	91.56 177	93.44 300	76.44 281	97.94 250	85.60 260	92.08 216	97.49 183

v1240		90.70 247	89.85 248	93.23 249	93.51 308	86.80 258	96.61 189	97.02 204	87.16 262	89.58 229	94.31 267	79.55 241	97.98 240	85.52 261	85.44 286	94.90 281

PEN-MVS		91.20 225	90.44 222	93.48 238	94.49 279	87.91 237	97.76 70	98.18 46	91.29 141	87.78 276	95.74 202	80.35 225	97.33 304	85.46 262	82.96 320	95.19 268

QAPM		93.45 137	92.27 159	96.98 74	96.77 165	92.62 87	98.39 20	98.12 56	84.50 301	88.27 265	97.77 85	82.39 193	99.81 26	85.40 263	98.81 90	98.51 125

EU-MVSNet		88.72 281	88.90 268	88.20 326	93.15 317	74.21 350	96.63 188	94.22 326	85.18 290	87.32 285	95.97 185	76.16 283	94.98 341	85.27 264	86.17 278	95.41 249

BH-w/o		92.14 188	91.75 173	93.31 246	96.99 156	85.73 277	95.67 247	95.69 273	88.73 218	89.26 242	94.82 240	82.97 178	98.07 229	85.26 265	96.32 153	96.13 217

FMVSNet2		91.31 220	90.08 239	94.99 166	96.51 179	92.21 100	97.41 107	96.95 207	88.82 213	88.62 256	94.75 243	73.87 296	97.42 299	85.20 266	88.55 258	95.35 256

PM-MVS		83.48 315	81.86 319	88.31 325	87.83 353	77.59 345	93.43 312	91.75 347	86.91 265	80.63 335	89.91 336	44.42 358	95.84 332	85.17 267	76.73 339	91.50 344

LF4IMVS		87.94 288	87.25 285	89.98 318	92.38 330	80.05 334	94.38 286	95.25 293	87.59 252	84.34 314	94.74 244	64.31 343	97.66 277	84.83 268	87.45 266	92.23 337

PatchmatchNet		91.91 192	91.35 186	93.59 233	95.38 228	84.11 299	93.15 318	95.39 283	89.54 188	92.10 170	93.68 293	82.82 182	98.13 216	84.81 269	95.32 168	98.52 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs6		87.81 290	86.19 294	92.69 267	91.32 335	86.30 268	97.34 115	96.41 247	80.59 334	84.05 321	94.37 262	67.37 331	97.67 275	84.75 270	79.51 332	94.09 315

v7n		90.76 242	89.86 247	93.45 241	93.54 306	87.60 243	97.70 80	97.37 171	88.85 210	87.65 278	94.08 279	81.08 212	98.10 221	84.68 271	83.79 314	94.66 299

SixPastTwentyTwo		89.15 273	88.54 273	90.98 304	93.49 309	80.28 331	96.70 177	94.70 313	90.78 155	84.15 318	95.57 210	71.78 306	97.71 273	84.63 272	85.07 294	94.94 276

TDRefinement		86.53 297	84.76 307	91.85 284	82.23 357	84.25 296	96.38 207	95.35 286	84.97 295	84.09 319	94.94 232	65.76 341	98.34 201	84.60 273	74.52 342	92.97 326

MVS_0304		88.79 279	87.57 281	92.46 270	94.65 273	86.15 274	96.40 204	97.17 186	86.44 272	88.02 272	91.71 326	56.68 353	97.03 311	84.47 274	92.58 205	94.19 312

ACMH	87.59 16	90.53 251	89.42 260	93.87 220	96.21 193	87.92 235	97.24 125	96.94 208	88.45 224	83.91 322	96.27 173	71.92 304	98.62 178	84.43 275	89.43 249	95.05 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	87.92 14	90.20 259	89.18 265	93.25 248	96.48 182	86.45 266	96.99 150	96.68 232	88.83 212	84.79 312	96.22 174	70.16 318	98.53 185	84.42 276	88.04 260	94.77 295

MS-PatchMatch		90.27 256	89.77 252	91.78 289	94.33 285	84.72 293	95.55 252	96.73 225	86.17 277	86.36 298	95.28 223	71.28 309	97.80 264	84.09 277	98.14 108	92.81 329

PatchMatch-RL		92.90 158	92.02 165	95.56 142	98.19 100	90.80 150	95.27 266	97.18 184	87.96 238	91.86 175	95.68 206	80.44 223	98.99 147	84.01 278	97.54 121	96.89 196

lessismore_v0				90.45 313	91.96 333	79.09 342		87.19 358		80.32 338	94.39 260	66.31 337	97.55 286	84.00 279	76.84 338	94.70 297

CMPMVS	62.92 21	85.62 308	84.92 305	87.74 328	89.14 348	73.12 352	94.17 294	96.80 224	73.98 348	73.65 349	94.93 233	66.36 335	97.61 282	83.95 280	91.28 227	92.48 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVP-Stereo		90.74 245	90.08 239	92.71 266	93.19 316	88.20 228	95.86 240	96.27 252	86.07 278	84.86 311	94.76 242	77.84 271	97.75 270	83.88 281	98.01 110	92.17 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LS3D		93.57 134	92.61 147	96.47 91	97.59 132	91.61 117	97.67 82	97.72 121	85.17 291	90.29 203	98.34 41	84.60 148	99.73 32	83.85 282	98.27 103	98.06 156

DTE-MVSNet		90.56 250	89.75 254	93.01 256	93.95 294	87.25 247	97.64 89	97.65 131	90.74 156	87.12 287	95.68 206	79.97 233	97.00 315	83.33 283	81.66 325	94.78 294

BH-RMVSNet		92.72 166	91.97 167	94.97 169	97.16 142	87.99 234	96.15 226	95.60 277	90.62 164	91.87 174	97.15 124	78.41 261	98.57 183	83.16 284	97.60 120	98.36 143

pmmvs-eth3d		86.22 302	84.45 308	91.53 294	88.34 351	87.25 247	94.47 281	95.01 302	83.47 314	79.51 342	89.61 338	69.75 321	95.71 334	83.13 285	76.73 339	91.64 341

FMVSNet1		89.88 266	88.31 275	94.59 186	95.41 226	91.18 136	97.50 99	96.93 209	86.62 270	87.41 282	94.51 253	65.94 340	97.29 306	83.04 286	87.43 267	95.31 258

MDTV_nov1_ep13			90.76 210		95.22 244	80.33 329	93.03 321	95.28 290	88.14 235	92.84 155	93.83 285	81.34 209	98.08 226	82.86 287	94.34 184

TR-MVS		91.48 210	90.59 217	94.16 204	96.40 186	87.33 244	95.67 247	95.34 289	87.68 250	91.46 179	95.52 215	76.77 278	98.35 199	82.85 288	93.61 195	96.79 200

JIA-IIPM		88.26 286	87.04 290	91.91 282	93.52 307	81.42 320	89.38 347	94.38 321	80.84 331	90.93 194	80.74 352	79.22 245	97.92 253	82.76 289	91.62 220	96.38 209

PVSNet_0	82.17 19	85.46 309	83.64 312	90.92 305	95.27 240	79.49 337	90.55 340	95.60 277	83.76 310	83.00 328	89.95 335	71.09 310	97.97 243	82.75 290	60.79 356	95.31 258

ambc				86.56 332	83.60 355	70.00 355	85.69 352	94.97 305		80.60 336	88.45 340	37.42 360	96.84 319	82.69 291	75.44 341	92.86 328

USDC		88.94 275	87.83 280	92.27 276	94.66 272	84.96 289	93.86 302	95.90 265	87.34 258	83.40 324	95.56 212	67.43 330	98.19 211	82.64 292	89.67 248	93.66 319

ITE_SJBPF				92.43 272	95.34 233	85.37 283		95.92 263	91.47 133	87.75 277	96.39 168	71.00 311	97.96 247	82.36 293	89.86 246	93.97 316

UnsupCasMVSNet_eth		85.99 304	84.45 308	90.62 311	89.97 343	82.40 315	93.62 310	97.37 171	89.86 180	78.59 344	92.37 314	65.25 342	95.35 340	82.27 294	70.75 348	94.10 313

GG-mvs-BLEND				93.62 231	93.69 303	89.20 202	92.39 330	83.33 361		87.98 274	89.84 337	71.00 311	96.87 318	82.08 295	95.40 167	94.80 290

thres600view7		92.49 170	91.60 178	95.18 159	97.91 114	89.47 188	97.65 85	94.66 314	92.18 117	93.33 142	94.91 234	78.06 268	99.10 134	81.61 296	94.06 189	96.98 191

LTVRE_ROB	88.41 13	90.99 234	89.92 245	94.19 202	96.18 196	89.55 184	96.31 214	97.09 194	87.88 241	85.67 303	95.91 189	78.79 255	98.57 183	81.50 297	89.98 244	94.44 304
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
tpmvs		89.83 268	89.15 266	91.89 283	94.92 259	80.30 330	93.11 319	95.46 282	86.28 274	88.08 270	92.65 309	80.44 223	98.52 186	81.47 298	89.92 245	96.84 198

thres100view900		92.43 171	91.58 179	94.98 168	97.92 113	89.37 194	97.71 79	94.66 314	92.20 113	93.31 143	94.90 235	78.06 268	99.08 139	81.40 299	94.08 186	96.48 207

tfpn200view9		92.38 174	91.52 182	94.95 171	97.85 117	89.29 198	97.41 107	94.88 309	92.19 115	93.27 145	94.46 258	78.17 264	99.08 139	81.40 299	94.08 186	96.48 207

thres400		92.42 172	91.52 182	95.12 163	97.85 117	89.29 198	97.41 107	94.88 309	92.19 115	93.27 145	94.46 258	78.17 264	99.08 139	81.40 299	94.08 186	96.98 191

DP-MVS		92.76 165	91.51 184	96.52 85	98.77 57	90.99 142	97.38 113	96.08 260	82.38 320	89.29 240	97.87 75	83.77 160	99.69 44	81.37 302	96.69 145	98.89 101

thres200		92.23 183	91.39 185	94.75 184	97.61 130	89.03 207	96.60 191	95.09 300	92.08 119	93.28 144	94.00 281	78.39 262	99.04 145	81.26 303	94.18 185	96.19 212

CR-MVSNet		90.82 241	89.77 252	93.95 215	94.45 281	87.19 250	90.23 342	95.68 275	86.89 266	92.40 158	92.36 317	80.91 215	97.05 310	81.09 304	93.95 190	97.60 179

MSDG		91.42 212	90.24 232	94.96 170	97.15 144	88.91 209	93.69 307	96.32 250	85.72 283	86.93 293	96.47 162	80.24 227	98.98 148	80.57 305	95.05 174	96.98 191

dp		88.90 277	88.26 277	90.81 307	94.58 278	76.62 346	92.85 323	94.93 307	85.12 292	90.07 217	93.07 304	75.81 284	98.12 219	80.53 306	87.42 268	97.71 171

tpm cat1		88.36 284	87.21 287	91.81 287	95.13 249	80.55 327	92.58 327	95.70 272	74.97 347	87.45 280	91.96 322	78.01 270	98.17 214	80.39 307	88.74 256	96.72 202

DIV-MVS_2432*1600		85.95 305	84.95 304	88.96 323	89.55 347	79.11 341	95.13 271	96.42 246	85.91 280	84.07 320	90.48 331	70.03 319	94.82 342	80.04 308	72.94 346	92.94 327

AllTest		90.23 258	88.98 267	93.98 211	97.94 111	86.64 261	96.51 196	95.54 280	85.38 287	85.49 305	96.77 140	70.28 316	99.15 129	80.02 309	92.87 199	96.15 215

TestCases				93.98 211	97.94 111	86.64 261		95.54 280	85.38 287	85.49 305	96.77 140	70.28 316	99.15 129	80.02 309	92.87 199	96.15 215

ADS-MVSNet2		89.45 270	88.59 272	92.03 280	95.86 208	82.26 316	90.93 337	94.32 324	83.23 316	91.28 188	91.81 324	79.01 251	95.99 328	79.52 311	91.39 225	97.84 165

ADS-MVSNet		89.89 265	88.68 271	93.53 236	95.86 208	84.89 291	90.93 337	95.07 301	83.23 316	91.28 188	91.81 324	79.01 251	97.85 259	79.52 311	91.39 225	97.84 165

our_test_3		88.78 280	87.98 279	91.20 302	92.45 328	82.53 312	93.61 311	95.69 273	85.77 282	84.88 310	93.71 290	79.99 232	96.78 321	79.47 313	86.24 277	94.28 310

EPNet_dtu		91.71 197	91.28 191	92.99 257	93.76 301	83.71 303	96.69 179	95.28 290	93.15 81	87.02 291	95.95 187	83.37 168	97.38 302	79.46 314	96.84 139	97.88 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		88.94 275	87.56 282	93.08 255	94.35 284	88.45 222	97.73 74	95.23 294	87.47 254	84.26 316	95.29 221	79.86 235	97.33 304	79.44 315	74.44 343	93.45 323

EG-PatchMatch MVS		87.02 295	85.44 299	91.76 291	92.67 324	85.00 288	96.08 229	96.45 245	83.41 315	79.52 341	93.49 298	57.10 352	97.72 272	79.34 316	90.87 235	92.56 333

Patchmtry		88.64 282	87.25 285	92.78 265	94.09 291	86.64 261	89.82 345	95.68 275	80.81 332	87.63 279	92.36 317	80.91 215	97.03 311	78.86 317	85.12 293	94.67 298

FMVSNet5		87.29 293	85.79 297	91.78 289	94.80 267	87.28 245	95.49 255	95.28 290	84.09 305	83.85 323	91.82 323	62.95 346	94.17 346	78.48 318	85.34 289	93.91 317

COLMAP_ROB	87.81 15	90.40 254	89.28 263	93.79 224	97.95 110	87.13 253	96.92 157	95.89 266	82.83 318	86.88 295	97.18 121	73.77 299	99.29 118	78.44 319	93.62 194	94.95 274
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		86.42 299	85.44 299	89.34 322	90.33 340	79.79 335	96.73 173	95.92 263	83.71 311	83.25 325	91.36 329	63.92 344	96.01 327	78.39 320	85.36 288	92.22 338

test0.0.03 1		89.37 272	88.70 270	91.41 298	92.47 327	85.63 278	95.22 269	92.70 340	91.11 150	86.91 294	93.65 295	79.02 249	93.19 351	78.00 321	89.18 251	95.41 249

MIMVSNet		88.50 283	86.76 291	93.72 227	94.84 265	87.77 240	91.39 332	94.05 327	86.41 273	87.99 273	92.59 311	63.27 345	95.82 333	77.44 322	92.84 201	97.57 181

MDA-MVSNet_test_wron		85.87 306	84.23 310	90.80 309	92.38 330	82.57 311	93.17 316	95.15 297	82.15 321	67.65 351	92.33 320	78.20 263	95.51 338	77.33 323	79.74 329	94.31 309

YYNet1		85.87 306	84.23 310	90.78 310	92.38 330	82.46 314	93.17 316	95.14 298	82.12 322	67.69 350	92.36 317	78.16 266	95.50 339	77.31 324	79.73 330	94.39 305

UnsupCasMVSNet_bld		82.13 319	79.46 322	90.14 317	88.00 352	82.47 313	90.89 339	96.62 240	78.94 340	75.61 346	84.40 350	56.63 354	96.31 325	77.30 325	66.77 352	91.63 342

KD-MVS_2432*1600		84.81 312	82.64 315	91.31 299	91.07 337	85.34 284	91.22 334	95.75 270	85.56 285	83.09 326	90.21 333	67.21 332	95.89 329	77.18 326	62.48 354	92.69 330

miper_refine_blended		84.81 312	82.64 315	91.31 299	91.07 337	85.34 284	91.22 334	95.75 270	85.56 285	83.09 326	90.21 333	67.21 332	95.89 329	77.18 326	62.48 354	92.69 330

PCF-MVS	89.48 11	91.56 204	89.95 244	96.36 101	96.60 171	92.52 90	92.51 328	97.26 180	79.41 338	88.90 247	96.56 158	84.04 158	99.55 84	77.01 328	97.30 131	97.01 190
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testgi		87.97 287	87.21 287	90.24 316	92.86 320	80.76 323	96.67 182	94.97 305	91.74 126	85.52 304	95.83 193	62.66 347	94.47 345	76.25 329	88.36 259	95.48 243

TinyColmap		86.82 296	85.35 302	91.21 301	94.91 262	82.99 310	93.94 300	94.02 329	83.58 312	81.56 331	94.68 247	62.34 348	98.13 216	75.78 330	87.35 270	92.52 334

ppachtmachnet_test		88.35 285	87.29 284	91.53 294	92.45 328	83.57 306	93.75 305	95.97 262	84.28 302	85.32 308	94.18 275	79.00 253	96.93 316	75.71 331	84.99 297	94.10 313

PAPM		91.52 208	90.30 228	95.20 158	95.30 239	89.83 176	93.38 314	96.85 221	86.26 275	88.59 257	95.80 195	84.88 145	98.15 215	75.67 332	95.93 157	97.63 174

CL-MVSNet_2432*1600		86.31 301	85.15 303	89.80 320	88.83 349	81.74 319	93.93 301	96.22 255	86.67 269	85.03 309	90.80 330	78.09 267	94.50 343	74.92 333	71.86 347	93.15 325

tfpnnormal		89.70 269	88.40 274	93.60 232	95.15 247	90.10 166	97.56 95	98.16 50	87.28 260	86.16 300	94.63 250	77.57 273	98.05 232	74.48 334	84.59 302	92.65 332

DSMNet-mixed		86.34 300	86.12 296	87.00 331	89.88 344	70.43 353	94.93 273	90.08 353	77.97 344	85.42 307	92.78 308	74.44 293	93.96 347	74.43 335	95.14 170	96.62 203

Patchmatch-test		89.42 271	87.99 278	93.70 228	95.27 240	85.11 286	88.98 348	94.37 322	81.11 328	87.10 289	93.69 291	82.28 194	97.50 292	74.37 336	94.76 178	98.48 130

LCM-MVSNet		72.55 322	69.39 326	82.03 334	70.81 364	65.42 359	90.12 344	94.36 323	55.02 356	65.88 353	81.72 351	24.16 367	89.96 353	74.32 337	68.10 351	90.71 348

new-patchmatchnet		83.18 316	81.87 318	87.11 330	86.88 354	75.99 348	93.70 306	95.18 296	85.02 294	77.30 345	88.40 341	65.99 339	93.88 348	74.19 338	70.18 349	91.47 345

MDA-MVSNet-bldmvs		85.00 310	82.95 314	91.17 303	93.13 318	83.33 307	94.56 279	95.00 303	84.57 300	65.13 355	92.65 309	70.45 314	95.85 331	73.57 339	77.49 336	94.33 307

pmmvs3		79.97 320	77.50 324	87.39 329	82.80 356	79.38 339	92.70 325	90.75 352	70.69 351	78.66 343	87.47 348	51.34 356	93.40 349	73.39 340	69.65 350	89.38 350

test_method		66.11 326	64.89 329	69.79 341	72.62 362	35.23 369	65.19 360	92.83 339	20.35 362	65.20 354	88.08 345	43.14 359	82.70 358	73.12 341	63.46 353	91.45 346

PatchT		88.87 278	87.42 283	93.22 250	94.08 292	85.10 287	89.51 346	94.64 316	81.92 323	92.36 161	88.15 344	80.05 231	97.01 314	72.43 342	93.65 193	97.54 182

Anonymous20231206		87.09 294	86.14 295	89.93 319	91.22 336	80.35 328	96.11 227	95.35 286	83.57 313	84.16 317	93.02 305	73.54 301	95.61 335	72.16 343	86.14 279	93.84 318

MVS-HIRNet		82.47 318	81.21 320	86.26 333	95.38 228	69.21 356	88.96 349	89.49 354	66.28 352	80.79 334	74.08 356	68.48 325	97.39 301	71.93 344	95.47 165	92.18 339

new_pmnet		82.89 317	81.12 321	88.18 327	89.63 345	80.18 332	91.77 331	92.57 341	76.79 346	75.56 348	88.23 343	61.22 349	94.48 344	71.43 345	82.92 321	89.87 349

TAPA-MVS	90.10 7	92.30 178	91.22 195	95.56 142	98.33 85	89.60 181	96.79 169	97.65 131	81.83 324	91.52 178	97.23 119	87.94 102	98.91 153	71.31 346	98.37 101	98.17 149
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		86.14 303	85.40 301	88.35 324	90.12 341	80.06 333	95.90 239	95.20 295	88.59 219	81.29 332	93.62 296	71.43 308	92.65 352	71.26 347	81.17 327	92.34 336

tmp_tt		51.94 332	53.82 332	46.29 346	33.73 368	45.30 367	78.32 357	67.24 367	18.02 363	50.93 359	87.05 349	52.99 355	53.11 364	70.76 348	25.29 362	40.46 360

MIMVSNet1		84.93 311	83.05 313	90.56 312	89.56 346	84.84 292	95.40 258	95.35 286	83.91 306	80.38 337	92.21 321	57.23 351	93.34 350	70.69 349	82.75 323	93.50 321

RPMNet		88.98 274	87.05 289	94.77 182	94.45 281	87.19 250	90.23 342	98.03 84	77.87 345	92.40 158	87.55 347	80.17 229	99.51 94	68.84 350	93.95 190	97.60 179

N_pmnet		78.73 321	78.71 323	78.79 336	92.80 322	46.50 365	94.14 295	43.71 368	78.61 341	80.83 333	91.66 327	74.94 291	96.36 324	67.24 351	84.45 304	93.50 321

OpenMVS_ROB	81.14 20	84.42 314	82.28 317	90.83 306	90.06 342	84.05 300	95.73 246	94.04 328	73.89 349	80.17 340	91.53 328	59.15 350	97.64 278	66.92 352	89.05 252	90.80 347

PMMVS2		70.19 324	66.92 327	80.01 335	76.35 358	65.67 358	86.22 351	87.58 357	64.83 354	62.38 356	80.29 353	26.78 365	88.49 355	63.79 353	54.07 357	85.88 351

test_0402		86.46 298	84.79 306	91.45 296	95.02 254	85.55 279	96.29 216	94.89 308	80.90 329	82.21 329	93.97 283	68.21 327	97.29 306	62.98 354	88.68 257	91.51 343

DeepMVS_CX				74.68 340	90.84 339	64.34 360		81.61 363	65.34 353	67.47 352	88.01 346	48.60 357	80.13 360	62.33 355	73.68 345	79.58 354

FPMVS		71.27 323	69.85 325	75.50 338	74.64 359	59.03 361	91.30 333	91.50 349	58.80 355	57.92 357	88.28 342	29.98 363	85.53 357	53.43 356	82.84 322	81.95 353

ANet_high		63.94 327	59.58 330	77.02 337	61.24 366	66.06 357	85.66 353	87.93 356	78.53 342	42.94 360	71.04 357	25.42 366	80.71 359	52.60 357	30.83 360	84.28 352

Gipuma		67.86 325	65.41 328	75.18 339	92.66 325	73.45 351	66.50 359	94.52 318	53.33 357	57.80 358	66.07 358	30.81 361	89.20 354	48.15 358	78.88 335	62.90 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	53.92 22	58.58 328	55.40 331	68.12 342	51.00 367	48.64 363	78.86 356	87.10 359	46.77 358	35.84 364	74.28 355	8.76 368	86.34 356	42.07 359	73.91 344	69.38 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 330	48.81 335	66.58 343	65.34 365	57.50 362	72.49 358	70.94 366	40.15 361	39.28 363	63.51 359	6.89 370	73.48 363	38.29 360	42.38 358	68.76 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		53.28 329	52.56 333	55.43 344	74.43 360	47.13 364	83.63 355	76.30 364	42.23 359	42.59 361	62.22 360	28.57 364	74.40 361	31.53 361	31.51 359	44.78 358

EMVS		52.08 331	51.31 334	54.39 345	72.62 362	45.39 366	83.84 354	75.51 365	41.13 360	40.77 362	59.65 361	30.08 362	73.60 362	28.31 362	29.90 361	44.18 359

wuyk23d		25.11 333	24.57 337	26.74 347	73.98 361	39.89 368	57.88 361	9.80 369	12.27 364	10.39 365	6.97 367	7.03 369	36.44 365	25.43 363	17.39 363	3.89 363

testmvs		13.36 335	16.33 338	4.48 349	5.04 369	2.26 371	93.18 315	3.28 370	2.70 365	8.24 366	21.66 363	2.29 372	2.19 366	7.58 364	2.96 364	9.00 362

test123		13.04 336	15.66 339	5.18 348	4.51 370	3.45 370	92.50 329	1.81 371	2.50 366	7.58 367	20.15 364	3.67 371	2.18 367	7.13 365	1.07 365	9.90 361

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		23.24 334	30.99 336	0.00 350	0.00 371	0.00 372	0.00 362	97.63 133	0.00 367	0.00 368	96.88 137	84.38 152	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		7.39 338	9.85 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	88.65 94	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.06 337	10.74 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	96.69 146	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

test_241102_ONE					99.42 6	95.30 15		98.27 28	95.09 20	99.19 1	98.81 8	95.54 3	99.65 53

save fliter					98.91 49	94.28 35	97.02 145	98.02 88	95.35 8

test0726					99.45 2	95.36 10	98.31 23	98.29 24	94.92 24	98.99 4	98.92 2	95.08 5

GSMVS																98.45 133

test_part2					99.28 25	95.74 6				98.10 17

sam_mvs1												82.76 183				98.45 133

sam_mvs												81.94 202

MTGPA								98.08 64

test_post											17.58 365	81.76 204	98.08 226

patchmatchnet-post											90.45 332	82.65 187	98.10 221

MTMP							97.86 60	82.03 362

TEST9					98.70 60	94.19 40	96.41 201	98.02 88	88.17 232	96.03 81	97.56 105	92.74 24	99.59 68

test_8					98.67 62	94.06 49	96.37 208	98.01 91	88.58 220	95.98 86	97.55 107	92.73 25	99.58 71

agg_prior					98.67 62	93.79 55		98.00 93		95.68 96			99.57 79

test_prior4						93.66 59	96.42 200

test_prior				97.23 62	98.67 62	92.99 76		98.00 93					99.41 107			99.29 62

新几何2							95.79 244

旧先验1					98.38 81	93.38 67		97.75 116			98.09 62	92.30 38			99.01 84	99.16 70

原ACMM2							95.67 247

test222					98.24 93	92.21 100	95.33 261	97.60 135	79.22 339	95.25 107	97.84 81	88.80 92			99.15 73	98.72 113

segment_acmp												92.89 22

testdata1							95.26 268		93.10 84

test12				97.65 44	98.46 74	94.26 37		97.66 129		95.52 105		90.89 69	99.46 101		99.25 65	99.22 67

plane_prior7					96.21 193	89.98 172

plane_prior6					96.10 203	90.00 168						81.32 210

plane_prior4											96.64 149

plane_prior3						90.00 168			94.46 41	91.34 182

plane_prior2							97.74 72		94.85 26

plane_prior1					96.14 201

plane_prior						89.99 170	97.24 125		94.06 49						92.16 213

n2								0.00 372

nn								0.00 372

door-mid								91.06 351

test11								97.88 105

door								91.13 350

HQP5-MVS						89.33 196

HQP-NCC					95.86 208		96.65 183		93.55 65	90.14 206

ACMP_Plane					95.86 208		96.65 183		93.55 65	90.14 206

HQP4-MVS										90.14 206			98.50 187			95.78 231

HQP3-MVS								97.39 168							92.10 214

HQP2-MVS												80.95 213

NP-MVS					95.99 207	89.81 177					95.87 190

ACMMP++_ref															90.30 242

ACMMP++															91.02 231

Test By Simon												88.73 93

High-resolution multi-view benchmark