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		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 20	95.65 27	94.47 42
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 130	92.29 7	95.97 2	74.28 30	97.24 13	88.58 31	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 21	96.41 12	93.33 105
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_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 18	96.57 7	94.67 29

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 18	96.68 2	94.95 12

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

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 23	96.63 4	94.88 16

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 23	96.58 6	94.26 53

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 43	96.34 15	93.95 67
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 131	88.80 28	95.61 11	70.29 77	96.44 39	86.20 51	93.08 71	93.16 115

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 121	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 121

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 121	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 121

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 49	96.27 44	86.87 45	94.65 48	93.70 83

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 149	88.59 139	89.05 215	80.19 12	90.70 17	95.40 15	74.56 25	93.92 145	91.54 2	92.07 86	95.31 5

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

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 118	86.34 62	95.29 17	70.86 70	96.00 55	88.78 29	96.04 16	94.58 35
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 100	89.16 24	95.10 18	75.65 21	96.19 47	87.07 44	96.01 17	94.79 23

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 36	95.09 19	71.06 68	96.67 29	87.67 39	96.37 14	94.09 59

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 189	87.08 244	65.21 213	89.09 116	90.21 164	79.67 19	89.98 19	95.02 20	73.17 39	91.71 250	91.30 3	91.60 93	92.34 153

MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 133	86.57 1	87.39 52	94.97 21	71.70 58	97.68 1	92.19 1	95.63 28	95.57 1

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 113	87.76 216	65.62 204	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 100	94.20 130	90.83 5	91.39 98	94.38 46

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 214	92.02 98	79.45 22	85.88 64	94.80 23	68.07 106	96.21 46	86.69 47	95.34 32	93.23 108

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 37	94.80 23	73.76 34	97.11 15	87.51 41	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 152	88.57 30	94.67 25	75.57 22	95.79 59	86.77 46	95.76 23

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 135	86.84 59	94.65 26	67.31 115	95.77 60	84.80 62	92.85 74	92.84 134

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 88	94.52 27	69.09 91	96.70 27	84.37 68	94.83 45	94.03 62

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 83	94.52 27	68.81 97	96.65 30	84.53 66	94.90 41	94.00 64

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 180	88.58 29	94.52 27	73.36 35	96.49 38	84.26 69	95.01 37	92.70 136
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 56	85.88 60	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 163	85.94 63	94.51 30	65.80 136	95.61 63	83.04 83	92.51 79	93.53 98

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 106	94.46 31	67.93 108	95.95 58	84.20 72	94.39 57	93.23 108

SR-MVS-dyc-post		85.77 62	85.61 67	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 164	85.69 67	94.45 32	65.00 144	95.56 64	82.75 88	91.87 89	92.50 146

RE-MVS-def			85.48 70		93.06 60	70.63 78	91.88 39	92.27 85	73.53 164	85.69 67	94.45 32	63.87 152		82.75 88	91.87 89	92.50 146

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 76	94.44 34	70.78 71	96.61 32	84.53 66	94.89 42	93.66 84

PGM-MVS		86.68 42	86.27 50	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 105	83.86 102	94.42 35	67.87 110	96.64 31	82.70 92	94.57 52	93.66 84

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 103	94.40 36	72.24 50	96.28 43	85.65 53	95.30 35	93.62 91
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		86.02 52	86.32 48	85.14 92	87.20 235	68.54 126	89.57 93	90.44 153	75.31 112	87.49 49	94.39 37	72.86 44	92.72 207	89.04 25	90.56 112	94.16 55

fmvsm_s_conf0.1_n_2		83.80 93	83.79 94	83.83 161	85.62 278	64.94 223	87.03 194	86.62 283	74.32 140	87.97 42	94.33 38	60.67 209	92.60 210	89.72 13	87.79 160	93.96 65

fmvsm_l_conf0.5_n_9		85.84 61	86.63 44	83.46 172	87.12 243	66.01 191	88.56 141	89.43 191	75.59 104	89.32 23	94.32 39	72.89 43	91.21 274	90.11 10	92.33 83	93.16 115

ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 71	94.32 39	71.76 56	96.93 19	85.53 55	95.79 22	94.32 50

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 187	82.14 3	86.65 60	94.28 41	68.28 105	97.46 6	90.81 6	95.31 34	95.15 8

test_fmvsmconf0.01_n		84.73 84	84.52 86	85.34 87	80.25 390	69.03 106	89.47 95	89.65 183	73.24 175	86.98 57	94.27 42	66.62 120	93.23 179	90.26 9	89.95 124	93.78 80

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 43	94.27 42	75.89 19	96.81 23	87.45 42	96.44 9	93.05 123

mPP-MVS		86.67 43	86.32 48	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 120	94.25 44	66.44 124	96.24 45	82.88 86	94.28 60	93.38 101

fmvsm_s_conf0.5_n_2		84.04 89	84.11 90	83.81 163	86.17 265	65.00 221	86.96 197	87.28 266	74.35 139	88.25 34	94.23 45	61.82 185	92.60 210	89.85 11	88.09 157	93.84 74

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 119	94.23 45	72.13 52	97.09 16	84.83 61	95.37 31	93.65 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	94.17 47	67.45 113	96.60 33	83.06 81	94.50 53	94.07 60

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 48	78.35 13	96.77 24	89.59 16	94.22 62	94.67 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
test_fmvsmconf0.1_n		85.61 66	85.65 66	85.50 83	82.99 348	69.39 103	89.65 89	90.29 162	73.31 171	87.77 44	94.15 49	71.72 57	93.23 179	90.31 8	90.67 111	93.89 71

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 50	78.98 12	96.58 35	85.66 52	95.72 24	94.58 35

HPM-MVS_fast		85.35 74	84.95 80	86.57 59	93.69 42	70.58 80	92.15 36	91.62 119	73.89 153	82.67 122	94.09 51	62.60 169	95.54 66	80.93 105	92.93 73	93.57 94

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 219	87.75 45	94.07 52	74.01 33	96.70 27	84.66 64	94.84 44

fmvsm_s_conf0.1_n_a		83.32 110	82.99 108	84.28 130	83.79 323	68.07 141	89.34 104	82.85 343	69.80 253	87.36 53	94.06 53	68.34 104	91.56 256	87.95 37	83.46 241	93.21 111

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 38	94.06 53	76.43 16	96.84 21	88.48 34	95.99 18	94.34 49

test_fmvsmconf_n		85.92 57	86.04 58	85.57 82	85.03 297	69.51 96	89.62 92	90.58 148	73.42 167	87.75 45	94.02 55	72.85 45	93.24 178	90.37 7	90.75 109	93.96 65

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 55	82.45 3	96.87 20	83.77 76	96.48 8	94.88 16

PC_three_1452									68.21 290	92.02 12	94.00 57	82.09 5	95.98 57	84.58 65	96.68 2	94.95 12

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 57	74.83 23	93.78 152	87.63 40	94.27 61	93.65 88
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
GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 80	93.99 59	70.67 73	96.82 22	84.18 73	95.01 37	93.90 70

test_fmvsm_n_1920		85.29 75	85.34 72	85.13 95	86.12 267	69.93 88	88.65 137	90.78 144	69.97 249	88.27 33	93.98 60	71.39 63	91.54 259	88.49 33	90.45 114	93.91 68

fmvsm_s_conf0.1_n		83.56 102	83.38 101	84.10 139	84.86 299	67.28 169	89.40 101	83.01 338	70.67 226	87.08 55	93.96 61	68.38 103	91.45 265	88.56 32	84.50 215	93.56 95

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 104	93.95 62	69.77 83	96.01 54	85.15 56	94.66 47	94.32 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_7		83.34 109	84.03 91	81.28 246	85.73 275	65.13 216	85.40 250	89.90 174	74.96 123	82.13 127	93.89 63	66.65 119	87.92 336	86.56 48	91.05 103	90.80 209

fmvsm_s_conf0.5_n_5		85.22 76	85.55 68	84.25 135	86.26 261	67.40 165	89.18 108	89.31 200	72.50 185	88.31 32	93.86 64	69.66 84	91.96 238	89.81 12	91.05 103	93.38 101

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 134	88.90 27	93.85 65	75.75 20	96.00 55	87.80 38	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 60	85.39 71	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 152	93.82 66	64.33 148	96.29 42	82.67 93	90.69 110	93.23 108
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
fmvsm_s_conf0.5_n_a		83.63 100	83.41 100	84.28 130	86.14 266	68.12 139	89.43 97	82.87 342	70.27 242	87.27 54	93.80 67	69.09 91	91.58 253	88.21 36	83.65 235	93.14 118

fmvsm_s_conf0.5_n_4		85.39 72	85.75 65	84.30 128	86.70 254	65.83 197	88.77 129	89.78 176	75.46 107	88.35 31	93.73 68	69.19 90	93.06 194	91.30 3	88.44 152	94.02 63

fmvsm_s_conf0.5_n		83.80 93	83.71 95	84.07 145	86.69 255	67.31 168	89.46 96	83.07 337	71.09 214	86.96 58	93.70 69	69.02 96	91.47 264	88.79 28	84.62 214	93.44 100

test_prior2							88.85 125		75.41 108	84.91 76	93.54 70	74.28 30		83.31 79	95.86 20

fmvsm_l_conf0.5_n		84.47 85	84.54 84	84.27 132	85.42 284	68.81 112	88.49 143	87.26 268	68.08 291	88.03 39	93.49 71	72.04 53	91.77 246	88.90 27	89.14 139	92.24 160

VDDNet		81.52 146	80.67 146	84.05 150	90.44 104	64.13 242	89.73 87	85.91 294	71.11 213	83.18 112	93.48 72	50.54 314	93.49 166	73.40 190	88.25 154	94.54 40

CDPH-MVS		85.76 63	85.29 76	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 289	84.61 85	93.48 72	72.32 48	96.15 49	79.00 124	95.43 30	94.28 52

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 66	93.47 74	73.02 42	97.00 18	84.90 58	94.94 40	94.10 58

fmvsm_s_conf0.5_n_6		85.55 67	86.20 51	83.60 167	87.32 232	65.13 216	88.86 123	91.63 118	75.41 108	88.23 35	93.45 75	68.56 101	92.47 218	89.52 17	92.78 75	93.20 113

fmvsm_l_conf0.5_n_a		84.13 88	84.16 89	84.06 147	85.38 285	68.40 129	88.34 150	86.85 278	67.48 298	87.48 50	93.40 76	70.89 69	91.61 251	88.38 35	89.22 137	92.16 167

3Dnovator+	77.84 4	85.48 68	84.47 87	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 234	93.37 77	60.40 217	96.75 26	77.20 145	93.73 66	95.29 6

DeepC-MVS_fast	79.65 3	86.91 38	86.62 45	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 94	93.36 78	71.44 62	96.76 25	80.82 107	95.33 33	94.16 55
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 118	82.36 119	84.96 101	91.02 91	66.40 184	88.91 121	88.11 242	77.57 49	84.39 90	93.29 79	52.19 288	93.91 146	77.05 148	88.70 147	94.57 37

test_fmvsmvis_n_1920		84.02 90	83.87 92	84.49 120	84.12 315	69.37 104	88.15 158	87.96 249	70.01 247	83.95 101	93.23 80	68.80 98	91.51 262	88.61 30	89.96 123	92.57 141

UA-Net		85.08 79	84.96 79	85.45 84	92.07 75	68.07 141	89.78 85	90.86 143	82.48 2	84.60 86	93.20 81	69.35 87	95.22 84	71.39 214	90.88 108	93.07 120

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 287	85.00 74	93.10 82	74.36 29	95.41 76

train_agg		86.43 46	86.20 51	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 282	85.00 74	93.10 82	74.43 27	95.41 76	84.97 57	95.71 25	93.02 125

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 282	84.87 78	93.10 82	74.43 27	95.16 86

LFMVS		81.82 136	81.23 136	83.57 170	91.89 78	63.43 265	89.84 81	81.85 354	77.04 69	83.21 111	93.10 82	52.26 287	93.43 171	71.98 209	89.95 124	93.85 72

旧先验1					91.96 76	65.79 200		86.37 287			93.08 86	69.31 89			92.74 76	88.74 299

dcpmvs_2		85.63 65	86.15 55	84.06 147	91.71 80	64.94 223	86.47 217	91.87 108	73.63 159	86.60 61	93.02 87	76.57 15	91.87 244	83.36 78	92.15 84	95.35 3

testdata				79.97 278	90.90 94	64.21 240		84.71 308	59.27 390	85.40 69	92.91 88	62.02 182	89.08 318	68.95 242	91.37 99	86.63 351

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 178	84.86 79	92.89 89	76.22 17	96.33 41	84.89 60	95.13 36	94.40 45

Vis-MVSNet		83.46 105	82.80 112	85.43 85	90.25 108	68.74 117	90.30 75	90.13 167	76.33 91	80.87 149	92.89 89	61.00 204	94.20 130	72.45 206	90.97 105	93.35 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 95	83.33 103	84.92 105	93.28 49	70.86 74	92.09 37	90.38 155	68.75 281	79.57 167	92.83 91	60.60 213	93.04 197	80.92 106	91.56 96	90.86 208

3Dnovator	76.31 5	83.38 108	82.31 120	86.59 57	87.94 204	72.94 28	90.64 63	92.14 97	77.21 62	75.47 260	92.83 91	58.56 229	94.72 110	73.24 193	92.71 77	92.13 168

MSLP-MVS++		85.43 70	85.76 64	84.45 121	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 94	92.81 93	67.16 117	92.94 199	80.36 113	94.35 59	90.16 238

test2506		77.30 259	76.49 256	79.74 283	90.08 112	52.02 404	87.86 170	63.10 447	74.88 126	80.16 161	92.79 94	38.29 411	92.35 225	68.74 245	92.50 80	94.86 19

ECVR-MVS		79.61 193	79.26 186	80.67 263	90.08 112	54.69 387	87.89 168	77.44 400	74.88 126	80.27 158	92.79 94	48.96 337	92.45 219	68.55 246	92.50 80	94.86 19

test1111		79.43 200	79.18 189	80.15 275	89.99 117	53.31 400	87.33 186	77.05 404	75.04 119	80.23 160	92.77 96	48.97 336	92.33 227	68.87 243	92.40 82	94.81 22

MG-MVS		83.41 106	83.45 99	83.28 179	92.74 67	62.28 288	88.17 156	89.50 189	75.22 113	81.49 137	92.74 97	66.75 118	95.11 90	72.85 196	91.58 95	92.45 150

casdiffmvs_mvg		85.99 54	86.09 57	85.70 77	87.65 220	67.22 173	88.69 135	93.04 42	79.64 21	85.33 70	92.54 98	73.30 36	94.50 119	83.49 77	91.14 102	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
patch_mono-2		83.65 98	84.54 84	80.99 255	90.06 116	65.83 197	84.21 282	88.74 231	71.60 202	85.01 73	92.44 99	74.51 26	83.50 381	82.15 95	92.15 84	93.64 90

casdiffmvs		85.11 78	85.14 77	85.01 99	87.20 235	65.77 201	87.75 172	92.83 61	77.84 43	84.36 93	92.38 100	72.15 51	93.93 144	81.27 103	90.48 113	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
CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 159	92.83 18	93.30 33	79.67 19	84.57 87	92.27 101	71.47 61	95.02 96	84.24 71	93.46 69	95.13 9

baseline		84.93 81	84.98 78	84.80 111	87.30 233	65.39 210	87.30 187	92.88 58	77.62 47	84.04 99	92.26 102	71.81 55	93.96 138	81.31 101	90.30 116	95.03 11

NormalMVS		86.29 50	85.88 60	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 90	92.18 103	64.64 146	95.53 67	80.70 110	94.65 48	94.56 38

SymmetryMVS		85.38 73	84.81 81	87.07 46	91.47 83	72.47 38	91.65 43	88.06 246	79.31 24	84.39 90	92.18 103	64.64 146	95.53 67	80.70 110	90.91 107	93.21 111

QAPM		80.88 157	79.50 179	85.03 98	88.01 202	68.97 110	91.59 46	92.00 100	66.63 311	75.15 278	92.16 105	57.70 236	95.45 71	63.52 285	88.76 145	90.66 217

IS-MVSNet		83.15 113	82.81 111	84.18 137	89.94 119	63.30 267	91.59 46	88.46 239	79.04 30	79.49 168	92.16 105	65.10 141	94.28 125	67.71 252	91.86 91	94.95 12

BP-MVS1		84.32 86	83.71 95	86.17 64	87.84 209	67.85 150	89.38 102	89.64 184	77.73 45	83.98 100	92.12 107	56.89 247	95.43 73	84.03 74	91.75 92	95.24 7

新几何1				83.42 174	93.13 56	70.71 76		85.48 300	57.43 408	81.80 133	91.98 108	63.28 156	92.27 228	64.60 280	92.99 72	87.27 333

OpenMVS	72.83 10	79.77 191	78.33 207	84.09 143	85.17 290	69.91 89	90.57 64	90.97 138	66.70 305	72.17 323	91.91 109	54.70 264	93.96 138	61.81 306	90.95 106	88.41 308

PHI-MVS		86.43 46	86.17 54	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 186	85.22 72	91.90 110	69.47 86	96.42 40	83.28 80	95.94 19	94.35 48

VNet		82.21 127	82.41 117	81.62 235	90.82 96	60.93 304	84.47 273	89.78 176	76.36 90	84.07 98	91.88 111	64.71 145	90.26 294	70.68 221	88.89 141	93.66 84

EC-MVSNet		86.01 53	86.38 47	84.91 106	89.31 143	66.27 187	92.32 31	93.63 22	79.37 23	84.17 96	91.88 111	69.04 95	95.43 73	83.93 75	93.77 65	93.01 126

GDP-MVS		83.52 103	82.64 114	86.16 65	88.14 193	68.45 128	89.13 114	92.69 66	72.82 184	83.71 105	91.86 113	55.69 254	95.35 82	80.03 116	89.74 128	94.69 28

KinetiMVS		83.31 111	82.61 115	85.39 86	87.08 244	67.56 160	88.06 160	91.65 117	77.80 44	82.21 126	91.79 114	57.27 242	94.07 136	77.77 139	89.89 126	94.56 38

OPM-MVS		83.50 104	82.95 109	85.14 92	88.79 168	70.95 71	89.13 114	91.52 122	77.55 52	80.96 147	91.75 115	60.71 207	94.50 119	79.67 121	86.51 182	89.97 254
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 54	85.96 59	86.05 69	91.09 88	67.64 156	89.63 91	92.65 71	72.89 183	84.64 84	91.71 116	71.85 54	96.03 51	84.77 63	94.45 56	94.49 41

viewmanbaseed2359cas		83.66 97	83.55 97	84.00 155	86.81 250	64.53 230	86.65 211	91.75 115	74.89 125	83.15 114	91.68 117	68.74 99	92.83 205	79.02 122	89.24 136	94.63 33

XVG-OURS-SEG-HR		80.81 160	79.76 171	83.96 158	85.60 279	68.78 114	83.54 300	90.50 151	70.66 229	76.71 233	91.66 118	60.69 208	91.26 271	76.94 149	81.58 264	91.83 173

EPNet		83.72 96	82.92 110	86.14 68	84.22 313	69.48 97	91.05 59	85.27 301	81.30 6	76.83 229	91.65 119	66.09 131	95.56 64	76.00 162	93.85 64	93.38 101
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 120	81.97 129	84.85 108	88.75 170	67.42 163	87.98 162	90.87 142	74.92 124	79.72 165	91.65 119	62.19 179	93.96 138	75.26 172	86.42 183	93.16 115

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 157	90.51 65	92.90 57	77.26 59	87.44 51	91.63 121	71.27 65	96.06 50	85.62 54	95.01 37	94.78 24

test222					91.50 82	68.26 133	84.16 284	83.20 335	54.63 419	79.74 164	91.63 121	58.97 225			91.42 97	86.77 347

MVS_111021_HR		85.14 77	84.75 82	86.32 61	91.65 81	72.70 30	85.98 231	90.33 159	76.11 94	82.08 128	91.61 123	71.36 64	94.17 133	81.02 104	92.58 78	92.08 169

原ACMM1				84.35 125	93.01 62	68.79 113		92.44 78	63.96 347	81.09 144	91.57 124	66.06 132	95.45 71	67.19 259	94.82 46	88.81 294

LPG-MVS_test		82.08 129	81.27 135	84.50 118	89.23 148	68.76 115	90.22 76	91.94 104	75.37 110	76.64 235	91.51 125	54.29 267	94.91 98	78.44 130	83.78 228	89.83 259

LGP-MVS_train				84.50 118	89.23 148	68.76 115		91.94 104	75.37 110	76.64 235	91.51 125	54.29 267	94.91 98	78.44 130	83.78 228	89.83 259

XVG-OURS		80.41 177	79.23 187	83.97 157	85.64 277	69.02 108	83.03 313	90.39 154	71.09 214	77.63 211	91.49 127	54.62 266	91.35 268	75.71 164	83.47 240	91.54 184

alignmvs		85.48 68	85.32 74	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 47	91.46 128	70.32 76	93.78 152	81.51 98	88.95 140	94.63 33

CANet		86.45 45	86.10 56	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 139	91.43 129	70.34 75	97.23 14	84.26 69	93.36 70	94.37 47

h-mvs33		83.15 113	82.19 122	86.02 72	90.56 101	70.85 75	88.15 158	89.16 210	76.02 96	84.67 81	91.39 130	61.54 190	95.50 69	82.71 90	75.48 345	91.72 180

MGCFI-Net		85.06 80	85.51 69	83.70 165	89.42 135	63.01 273	89.43 97	92.62 74	76.43 84	87.53 48	91.34 131	72.82 46	93.42 172	81.28 102	88.74 146	94.66 32

nrg030		83.88 91	83.53 98	84.96 101	86.77 252	69.28 105	90.46 70	92.67 68	74.79 129	82.95 115	91.33 132	72.70 47	93.09 192	80.79 109	79.28 294	92.50 146

sasdasda		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 133	73.28 37	93.91 146	81.50 99	88.80 143	94.77 25

canonicalmvs		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 133	73.28 37	93.91 146	81.50 99	88.80 143	94.77 25

DPM-MVS		84.93 81	84.29 88	86.84 52	90.20 109	73.04 23	87.12 191	93.04 42	69.80 253	82.85 118	91.22 135	73.06 41	96.02 53	76.72 156	94.63 50	91.46 190

Anonymous202405211		78.25 231	77.01 242	81.99 229	91.03 90	60.67 309	84.77 264	83.90 321	70.65 230	80.00 162	91.20 136	41.08 396	91.43 266	65.21 274	85.26 206	93.85 72

SPE-MVS-test		86.29 50	86.48 46	85.71 76	91.02 91	67.21 174	92.36 30	93.78 19	78.97 33	83.51 110	91.20 136	70.65 74	95.15 87	81.96 96	94.89 42	94.77 25

Anonymous20240529		80.19 186	78.89 195	84.10 139	90.60 100	64.75 228	88.95 120	90.90 140	65.97 319	80.59 154	91.17 138	49.97 321	93.73 158	69.16 240	82.70 253	93.81 76

EPP-MVSNet		83.40 107	83.02 107	84.57 116	90.13 110	64.47 235	92.32 31	90.73 145	74.45 138	79.35 173	91.10 139	69.05 94	95.12 88	72.78 197	87.22 169	94.13 57

TAPA-MVS	73.13 9	79.15 209	77.94 215	82.79 209	89.59 126	62.99 277	88.16 157	91.51 123	65.77 320	77.14 226	91.09 140	60.91 205	93.21 181	50.26 393	87.05 172	92.17 166
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 48	86.19 53	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 150	83.16 113	91.07 141	75.94 18	95.19 85	79.94 118	94.38 58	93.55 96

FIs		82.07 130	82.42 116	81.04 254	88.80 167	58.34 333	88.26 153	93.49 27	76.93 71	78.47 191	91.04 142	69.92 81	92.34 226	69.87 233	84.97 208	92.44 151

MVS_111021_LR		82.61 122	82.11 123	84.11 138	88.82 162	71.58 57	85.15 255	86.16 291	74.69 131	80.47 157	91.04 142	62.29 176	90.55 292	80.33 114	90.08 121	90.20 237

DP-MVS Recon		83.11 116	82.09 125	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 231	79.17 175	91.03 144	64.12 150	96.03 51	68.39 249	90.14 119	91.50 186

mamv4		76.81 267	78.23 211	72.54 384	86.12 267	65.75 202	78.76 369	82.07 351	64.12 341	72.97 311	91.02 145	67.97 107	68.08 449	83.04 83	78.02 307	83.80 395

HQP_MVS		83.64 99	83.14 104	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 179	91.00 146	60.42 215	95.38 78	78.71 128	86.32 184	91.33 191

plane_prior4											91.00 146

FC-MVSNet-test		81.52 146	82.02 127	80.03 277	88.42 183	55.97 372	87.95 164	93.42 30	77.10 67	77.38 215	90.98 148	69.96 80	91.79 245	68.46 248	84.50 215	92.33 154

diffmvs_AUTHOR		82.38 125	82.27 121	82.73 214	83.26 336	63.80 249	83.89 288	89.76 178	73.35 170	82.37 123	90.84 149	66.25 127	90.79 286	82.77 87	87.93 158	93.59 93

Vis-MVSNet (Re-imp)		78.36 230	78.45 202	78.07 319	88.64 174	51.78 410	86.70 209	79.63 382	74.14 147	75.11 279	90.83 150	61.29 198	89.75 304	58.10 341	91.60 93	92.69 138

114514_t		80.68 168	79.51 178	84.20 136	94.09 38	67.27 170	89.64 90	91.11 136	58.75 397	74.08 297	90.72 151	58.10 232	95.04 95	69.70 234	89.42 134	90.30 234

PAPM_NR		83.02 117	82.41 117	84.82 109	92.47 72	66.37 185	87.93 166	91.80 111	73.82 154	77.32 217	90.66 152	67.90 109	94.90 100	70.37 224	89.48 133	93.19 114

viewmsd2359difaftdt		80.37 181	79.73 172	82.30 223	83.70 327	62.39 284	84.20 283	86.67 280	73.22 176	80.90 148	90.62 153	63.00 167	91.56 256	76.81 154	78.44 301	92.95 130

LS3D		76.95 265	74.82 283	83.37 177	90.45 103	67.36 167	89.15 113	86.94 275	61.87 370	69.52 353	90.61 154	51.71 301	94.53 117	46.38 415	86.71 179	88.21 312

AstraMVS		80.81 160	80.14 161	82.80 206	86.05 270	63.96 244	86.46 218	85.90 295	73.71 157	80.85 150	90.56 155	54.06 271	91.57 255	79.72 120	83.97 226	92.86 132

VPNet		78.69 222	78.66 198	78.76 302	88.31 186	55.72 376	84.45 276	86.63 282	76.79 75	78.26 195	90.55 156	59.30 223	89.70 306	66.63 263	77.05 318	90.88 207

UniMVSNet_ETH3D		79.10 211	78.24 209	81.70 234	86.85 248	60.24 316	87.28 188	88.79 226	74.25 144	76.84 228	90.53 157	49.48 327	91.56 256	67.98 250	82.15 257	93.29 106

ACMP	74.13 6	81.51 148	80.57 148	84.36 124	89.42 135	68.69 122	89.97 80	91.50 126	74.46 137	75.04 282	90.41 158	53.82 273	94.54 116	77.56 141	82.91 248	89.86 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_0407		81.58 143	80.48 151	84.87 107	88.81 163	67.96 145	87.37 183	89.25 205	71.06 216	79.48 169	90.39 159	59.57 220	94.48 121	72.45 206	85.93 194	92.18 163

SSM_0404		81.91 133	80.84 144	85.13 95	89.24 147	68.26 133	87.84 171	89.25 205	71.06 216	80.62 153	90.39 159	59.57 220	94.65 114	72.45 206	87.19 170	92.47 149

viewmambaseed2359dif		80.41 177	79.84 169	82.12 224	82.95 350	62.50 283	83.39 301	88.06 246	67.11 300	80.98 146	90.31 161	66.20 129	91.01 282	74.62 176	84.90 209	92.86 132

RRT-MVS		82.60 124	82.10 124	84.10 139	87.98 203	62.94 278	87.45 181	91.27 129	77.42 56	79.85 163	90.28 162	56.62 250	94.70 112	79.87 119	88.15 156	94.67 29

PCF-MVS	73.52 7	80.38 179	78.84 196	85.01 99	87.71 217	68.99 109	83.65 294	91.46 127	63.00 354	77.77 209	90.28 162	66.10 130	95.09 94	61.40 309	88.22 155	90.94 206
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 125	68.32 131					90.24 164

HQP-MVS		82.61 122	82.02 127	84.37 123	89.33 140	66.98 177	89.17 109	92.19 92	76.41 85	77.23 220	90.23 165	60.17 218	95.11 90	77.47 142	85.99 192	91.03 201

PS-MVSNAJss		82.07 130	81.31 134	84.34 126	86.51 259	67.27 170	89.27 105	91.51 123	71.75 197	79.37 172	90.22 166	63.15 162	94.27 126	77.69 140	82.36 256	91.49 187

TSAR-MVS + GP.		85.71 64	85.33 73	86.84 52	91.34 84	72.50 36	89.07 117	87.28 266	76.41 85	85.80 65	90.22 166	74.15 32	95.37 81	81.82 97	91.88 88	92.65 140

SDMVSNet		80.38 179	80.18 158	80.99 255	89.03 157	64.94 223	80.45 345	89.40 192	75.19 116	76.61 237	89.98 168	60.61 212	87.69 340	76.83 153	83.55 237	90.33 232

sd_testset		77.70 250	77.40 235	78.60 305	89.03 157	60.02 318	79.00 365	85.83 296	75.19 116	76.61 237	89.98 168	54.81 259	85.46 365	62.63 296	83.55 237	90.33 232

TranMVSNet+NR-MVSNet		80.84 158	80.31 155	82.42 220	87.85 208	62.33 286	87.74 173	91.33 128	80.55 9	77.99 203	89.86 170	65.23 140	92.62 208	67.05 261	75.24 355	92.30 156

diffmvs		82.10 128	81.88 130	82.76 212	83.00 346	63.78 251	83.68 293	89.76 178	72.94 181	82.02 129	89.85 171	65.96 135	90.79 286	82.38 94	87.30 168	93.71 82
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Elysia		81.53 144	80.16 159	85.62 79	85.51 281	68.25 135	88.84 126	92.19 92	71.31 207	80.50 155	89.83 172	46.89 348	94.82 104	76.85 150	89.57 130	93.80 78

StellarMVS		81.53 144	80.16 159	85.62 79	85.51 281	68.25 135	88.84 126	92.19 92	71.31 207	80.50 155	89.83 172	46.89 348	94.82 104	76.85 150	89.57 130	93.80 78

mamba_0408		79.37 205	77.52 232	84.93 104	88.81 163	67.96 145	65.03 444	88.66 233	70.96 220	79.48 169	89.80 174	58.69 226	94.65 114	70.35 225	85.93 194	92.18 163

SSM_04072		77.67 252	77.52 232	78.12 317	88.81 163	67.96 145	65.03 444	88.66 233	70.96 220	79.48 169	89.80 174	58.69 226	74.23 437	70.35 225	85.93 194	92.18 163

BH-RMVSNet		79.61 193	78.44 203	83.14 187	89.38 139	65.93 194	84.95 261	87.15 271	73.56 162	78.19 197	89.79 176	56.67 249	93.36 173	59.53 325	86.74 178	90.13 240

GeoE		81.71 138	81.01 141	83.80 164	89.51 130	64.45 236	88.97 119	88.73 232	71.27 210	78.63 185	89.76 177	66.32 126	93.20 184	69.89 232	86.02 191	93.74 81

guyue		81.13 153	80.64 147	82.60 217	86.52 258	63.92 247	86.69 210	87.73 257	73.97 149	80.83 151	89.69 178	56.70 248	91.33 270	78.26 137	85.40 205	92.54 143

AdaColmap		80.58 175	79.42 180	84.06 147	93.09 59	68.91 111	89.36 103	88.97 221	69.27 265	75.70 256	89.69 178	57.20 244	95.77 60	63.06 290	88.41 153	87.50 327

ACMM	73.20 8	80.78 167	79.84 169	83.58 169	89.31 143	68.37 130	89.99 79	91.60 120	70.28 241	77.25 218	89.66 180	53.37 278	93.53 165	74.24 182	82.85 249	88.85 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 237	76.79 249	81.97 230	90.40 105	71.07 67	87.59 176	84.55 311	66.03 318	72.38 320	89.64 181	57.56 238	86.04 357	59.61 324	83.35 242	88.79 295

test_yl		81.17 151	80.47 152	83.24 182	89.13 152	63.62 252	86.21 226	89.95 172	72.43 189	81.78 134	89.61 182	57.50 239	93.58 160	70.75 219	86.90 174	92.52 144

DCV-MVSNet		81.17 151	80.47 152	83.24 182	89.13 152	63.62 252	86.21 226	89.95 172	72.43 189	81.78 134	89.61 182	57.50 239	93.58 160	70.75 219	86.90 174	92.52 144

EI-MVSNet-Vis-set		84.19 87	83.81 93	85.31 88	88.18 190	67.85 150	87.66 174	89.73 181	80.05 15	82.95 115	89.59 184	70.74 72	94.82 104	80.66 112	84.72 212	93.28 107

PAPR		81.66 141	80.89 143	83.99 156	90.27 107	64.00 243	86.76 208	91.77 114	68.84 280	77.13 227	89.50 185	67.63 111	94.88 102	67.55 254	88.52 150	93.09 119

jajsoiax		79.29 206	77.96 214	83.27 180	84.68 304	66.57 183	89.25 106	90.16 166	69.20 270	75.46 262	89.49 186	45.75 364	93.13 190	76.84 152	80.80 274	90.11 242

MVSFormer		82.85 119	82.05 126	85.24 90	87.35 226	70.21 82	90.50 67	90.38 155	68.55 284	81.32 139	89.47 187	61.68 187	93.46 169	78.98 125	90.26 117	92.05 170

jason		81.39 149	80.29 156	84.70 114	86.63 257	69.90 90	85.95 232	86.77 279	63.24 350	81.07 145	89.47 187	61.08 203	92.15 232	78.33 133	90.07 122	92.05 170
jason: jason.
mvs_tets		79.13 210	77.77 224	83.22 184	84.70 303	66.37 185	89.17 109	90.19 165	69.38 262	75.40 265	89.46 189	44.17 376	93.15 188	76.78 155	80.70 276	90.14 239

UGNet		80.83 159	79.59 177	84.54 117	88.04 199	68.09 140	89.42 99	88.16 241	76.95 70	76.22 246	89.46 189	49.30 331	93.94 141	68.48 247	90.31 115	91.60 181
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
VPA-MVSNet		80.60 172	80.55 149	80.76 261	88.07 198	60.80 307	86.86 202	91.58 121	75.67 103	80.24 159	89.45 191	63.34 155	90.25 295	70.51 223	79.22 295	91.23 194

MVS_Test		83.15 113	83.06 106	83.41 176	86.86 247	63.21 269	86.11 229	92.00 100	74.31 141	82.87 117	89.44 192	70.03 79	93.21 181	77.39 144	88.50 151	93.81 76

EI-MVSNet-UG-set		83.81 92	83.38 101	85.09 97	87.87 207	67.53 161	87.44 182	89.66 182	79.74 18	82.23 125	89.41 193	70.24 78	94.74 109	79.95 117	83.92 227	92.99 128

RPSCF		73.23 321	71.46 325	78.54 308	82.50 359	59.85 319	82.18 319	82.84 344	58.96 393	71.15 335	89.41 193	45.48 368	84.77 372	58.82 333	71.83 385	91.02 203

UniMVSNet_NR-MVSNet		81.88 134	81.54 133	82.92 199	88.46 180	63.46 263	87.13 190	92.37 82	80.19 12	78.38 192	89.14 195	71.66 60	93.05 195	70.05 229	76.46 328	92.25 158

tttt0517		79.40 202	77.91 216	83.90 160	88.10 196	63.84 248	88.37 149	84.05 319	71.45 205	76.78 231	89.12 196	49.93 324	94.89 101	70.18 228	83.18 246	92.96 129

DU-MVS		81.12 154	80.52 150	82.90 200	87.80 211	63.46 263	87.02 195	91.87 108	79.01 31	78.38 192	89.07 197	65.02 142	93.05 195	70.05 229	76.46 328	92.20 161

NR-MVSNet		80.23 184	79.38 181	82.78 210	87.80 211	63.34 266	86.31 223	91.09 137	79.01 31	72.17 323	89.07 197	67.20 116	92.81 206	66.08 268	75.65 341	92.20 161

icg_test_0407_2		78.92 217	78.93 194	78.90 300	87.13 238	63.59 256	76.58 391	89.33 195	70.51 232	77.82 205	89.03 199	61.84 183	81.38 396	72.56 202	85.56 201	91.74 176

IMVS_0407		80.61 170	79.90 167	82.75 213	87.13 238	63.59 256	85.33 251	89.33 195	70.51 232	77.82 205	89.03 199	61.84 183	92.91 200	72.56 202	85.56 201	91.74 176

IMVS_0404		77.16 261	76.42 259	79.37 291	87.13 238	63.59 256	77.12 389	89.33 195	70.51 232	66.22 392	89.03 199	50.36 316	82.78 386	72.56 202	85.56 201	91.74 176

IMVS_0403		80.80 163	80.12 162	82.87 202	87.13 238	63.59 256	85.19 252	89.33 195	70.51 232	78.49 189	89.03 199	63.26 158	93.27 176	72.56 202	85.56 201	91.74 176

DELS-MVS		85.41 71	85.30 75	85.77 75	88.49 178	67.93 148	85.52 249	93.44 28	78.70 34	83.63 109	89.03 199	74.57 24	95.71 62	80.26 115	94.04 63	93.66 84
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
mvsmamba		80.60 172	79.38 181	84.27 132	89.74 124	67.24 172	87.47 179	86.95 274	70.02 246	75.38 266	88.93 204	51.24 305	92.56 213	75.47 170	89.22 137	93.00 127

baseline1		76.98 264	76.75 252	77.66 326	88.13 194	55.66 377	85.12 256	81.89 352	73.04 179	76.79 230	88.90 205	62.43 174	87.78 339	63.30 289	71.18 389	89.55 268

DP-MVS		76.78 268	74.57 286	83.42 174	93.29 48	69.46 100	88.55 142	83.70 323	63.98 346	70.20 341	88.89 206	54.01 272	94.80 107	46.66 412	81.88 262	86.01 361

ab-mvs		79.51 196	78.97 193	81.14 251	88.46 180	60.91 305	83.84 289	89.24 207	70.36 237	79.03 176	88.87 207	63.23 160	90.21 296	65.12 275	82.57 254	92.28 157

PEN-MVS		77.73 247	77.69 228	77.84 323	87.07 246	53.91 394	87.91 167	91.18 132	77.56 51	73.14 309	88.82 208	61.23 199	89.17 316	59.95 320	72.37 379	90.43 227

tt0805		78.73 220	77.83 220	81.43 240	85.17 290	60.30 315	89.41 100	90.90 140	71.21 211	77.17 225	88.73 209	46.38 353	93.21 181	72.57 200	78.96 296	90.79 210

test_djsdf		80.30 183	79.32 184	83.27 180	83.98 319	65.37 211	90.50 67	90.38 155	68.55 284	76.19 247	88.70 210	56.44 251	93.46 169	78.98 125	80.14 284	90.97 204

PAPM		77.68 251	76.40 260	81.51 238	87.29 234	61.85 293	83.78 290	89.59 186	64.74 333	71.23 333	88.70 210	62.59 170	93.66 159	52.66 377	87.03 173	89.01 284

DTE-MVSNet		76.99 263	76.80 248	77.54 331	86.24 262	53.06 403	87.52 177	90.66 146	77.08 68	72.50 317	88.67 212	60.48 214	89.52 308	57.33 348	70.74 391	90.05 249

PS-CasMVS		78.01 241	78.09 212	77.77 325	87.71 217	54.39 391	88.02 161	91.22 130	77.50 54	73.26 307	88.64 213	60.73 206	88.41 331	61.88 304	73.88 368	90.53 223

cdsmvs_eth3d_5k		19.96 429	26.61 431	0.00 449	0.00 472	0.00 474	0.00 460	89.26 204	0.00 467	0.00 468	88.61 214	61.62 189	0.00 468	0.00 467	0.00 466	0.00 464

lupinMVS		81.39 149	80.27 157	84.76 112	87.35 226	70.21 82	85.55 245	86.41 285	62.85 357	81.32 139	88.61 214	61.68 187	92.24 230	78.41 132	90.26 117	91.83 173

F-COLMAP		76.38 278	74.33 292	82.50 219	89.28 145	66.95 180	88.41 145	89.03 216	64.05 344	66.83 381	88.61 214	46.78 350	92.89 201	57.48 345	78.55 298	87.67 321

mvs_anonymous		79.42 201	79.11 190	80.34 270	84.45 310	57.97 339	82.59 315	87.62 259	67.40 299	76.17 250	88.56 217	68.47 102	89.59 307	70.65 222	86.05 190	93.47 99

CP-MVSNet		78.22 232	78.34 206	77.84 323	87.83 210	54.54 389	87.94 165	91.17 133	77.65 46	73.48 305	88.49 218	62.24 178	88.43 330	62.19 300	74.07 364	90.55 222

PVSNet_Blended_VisFu		82.62 121	81.83 131	84.96 101	90.80 97	69.76 93	88.74 133	91.70 116	69.39 261	78.96 177	88.46 219	65.47 138	94.87 103	74.42 179	88.57 148	90.24 236

CANet_DTU		80.61 170	79.87 168	82.83 203	85.60 279	63.17 272	87.36 184	88.65 235	76.37 89	75.88 253	88.44 220	53.51 276	93.07 193	73.30 191	89.74 128	92.25 158

PLC	70.83 11	78.05 239	76.37 261	83.08 191	91.88 79	67.80 152	88.19 155	89.46 190	64.33 339	69.87 350	88.38 221	53.66 274	93.58 160	58.86 332	82.73 251	87.86 318
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 197	79.22 188	80.27 272	88.79 168	58.35 332	85.06 258	88.61 237	78.56 35	77.65 210	88.34 222	63.81 154	90.66 291	64.98 277	77.22 316	91.80 175

XXY-MVS		75.41 292	75.56 270	74.96 357	83.59 329	57.82 343	80.59 342	83.87 322	66.54 312	74.93 285	88.31 223	63.24 159	80.09 402	62.16 301	76.85 322	86.97 343

Effi-MVS+		83.62 101	83.08 105	85.24 90	88.38 184	67.45 162	88.89 122	89.15 211	75.50 106	82.27 124	88.28 224	69.61 85	94.45 122	77.81 138	87.84 159	93.84 74

API-MVS		81.99 132	81.23 136	84.26 134	90.94 93	70.18 87	91.10 58	89.32 199	71.51 204	78.66 184	88.28 224	65.26 139	95.10 93	64.74 279	91.23 101	87.51 326

thisisatest0530		79.40 202	77.76 225	84.31 127	87.69 219	65.10 219	87.36 184	84.26 317	70.04 245	77.42 214	88.26 226	49.94 322	94.79 108	70.20 227	84.70 213	93.03 124

hse-mvs2		81.72 137	80.94 142	84.07 145	88.72 171	67.68 155	85.87 235	87.26 268	76.02 96	84.67 81	88.22 227	61.54 190	93.48 167	82.71 90	73.44 373	91.06 199

xiu_mvs_v1_base_debu		80.80 163	79.72 173	84.03 152	87.35 226	70.19 84	85.56 242	88.77 227	69.06 274	81.83 130	88.16 228	50.91 308	92.85 202	78.29 134	87.56 162	89.06 279

xiu_mvs_v1_base		80.80 163	79.72 173	84.03 152	87.35 226	70.19 84	85.56 242	88.77 227	69.06 274	81.83 130	88.16 228	50.91 308	92.85 202	78.29 134	87.56 162	89.06 279

xiu_mvs_v1_base_debi		80.80 163	79.72 173	84.03 152	87.35 226	70.19 84	85.56 242	88.77 227	69.06 274	81.83 130	88.16 228	50.91 308	92.85 202	78.29 134	87.56 162	89.06 279

UniMVSNet (Re)		81.60 142	81.11 138	83.09 189	88.38 184	64.41 237	87.60 175	93.02 46	78.42 37	78.56 187	88.16 228	69.78 82	93.26 177	69.58 236	76.49 327	91.60 181

AUN-MVS		79.21 208	77.60 230	84.05 150	88.71 172	67.61 157	85.84 237	87.26 268	69.08 273	77.23 220	88.14 232	53.20 280	93.47 168	75.50 169	73.45 372	91.06 199

Anonymous20231211		78.97 215	77.69 228	82.81 205	90.54 102	64.29 239	90.11 78	91.51 123	65.01 331	76.16 251	88.13 233	50.56 313	93.03 198	69.68 235	77.56 314	91.11 197

pm-mvs1		77.25 260	76.68 254	78.93 299	84.22 313	58.62 330	86.41 219	88.36 240	71.37 206	73.31 306	88.01 234	61.22 200	89.15 317	64.24 283	73.01 376	89.03 283

LuminaMVS		80.68 168	79.62 176	83.83 161	85.07 296	68.01 144	86.99 196	88.83 224	70.36 237	81.38 138	87.99 235	50.11 319	92.51 217	79.02 122	86.89 176	90.97 204

SD_0403		74.65 300	74.77 284	74.29 366	86.20 264	47.42 429	83.71 292	85.12 303	69.30 264	68.50 364	87.95 236	59.40 222	86.05 356	49.38 397	83.35 242	89.40 271

LTVRE_ROB	69.57 13	76.25 279	74.54 288	81.41 241	88.60 175	64.38 238	79.24 360	89.12 214	70.76 225	69.79 352	87.86 237	49.09 334	93.20 184	56.21 360	80.16 282	86.65 350
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
testing3-2		75.12 297	75.19 279	74.91 358	90.40 105	45.09 440	80.29 348	78.42 392	78.37 40	76.54 239	87.75 238	44.36 374	87.28 345	57.04 351	83.49 239	92.37 152

WTY-MVS		75.65 287	75.68 267	75.57 348	86.40 260	56.82 357	77.92 383	82.40 347	65.10 328	76.18 248	87.72 239	63.13 165	80.90 399	60.31 318	81.96 260	89.00 286

TAMVS		78.89 218	77.51 234	83.03 194	87.80 211	67.79 153	84.72 265	85.05 306	67.63 294	76.75 232	87.70 240	62.25 177	90.82 285	58.53 336	87.13 171	90.49 225

BH-untuned		79.47 198	78.60 199	82.05 227	89.19 150	65.91 195	86.07 230	88.52 238	72.18 191	75.42 264	87.69 241	61.15 201	93.54 164	60.38 317	86.83 177	86.70 349

COLMAP_ROB	66.92 17	73.01 324	70.41 339	80.81 260	87.13 238	65.63 203	88.30 152	84.19 318	62.96 355	63.80 409	87.69 241	38.04 412	92.56 213	46.66 412	74.91 358	84.24 388
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 303	72.42 316	79.80 282	83.76 325	59.59 323	85.92 234	86.64 281	66.39 313	66.96 379	87.58 243	39.46 402	91.60 252	65.76 271	69.27 397	88.22 311

FA-MVS(test-final)		80.96 156	79.91 166	84.10 139	88.30 187	65.01 220	84.55 272	90.01 170	73.25 174	79.61 166	87.57 244	58.35 231	94.72 110	71.29 215	86.25 186	92.56 142

Baseline_NR-MVSNet		78.15 236	78.33 207	77.61 328	85.79 273	56.21 370	86.78 206	85.76 297	73.60 161	77.93 204	87.57 244	65.02 142	88.99 319	67.14 260	75.33 352	87.63 322

WR-MVS_H		78.51 227	78.49 201	78.56 307	88.02 200	56.38 366	88.43 144	92.67 68	77.14 64	73.89 299	87.55 246	66.25 127	89.24 314	58.92 331	73.55 371	90.06 248

EI-MVSNet		80.52 176	79.98 164	82.12 224	84.28 311	63.19 271	86.41 219	88.95 222	74.18 146	78.69 182	87.54 247	66.62 120	92.43 220	72.57 200	80.57 278	90.74 214

CVMVSNet		72.99 325	72.58 314	74.25 367	84.28 311	50.85 418	86.41 219	83.45 329	44.56 438	73.23 308	87.54 247	49.38 329	85.70 360	65.90 269	78.44 301	86.19 356

ACMH+	68.96 14	76.01 283	74.01 294	82.03 228	88.60 175	65.31 212	88.86 123	87.55 260	70.25 243	67.75 368	87.47 249	41.27 394	93.19 186	58.37 338	75.94 338	87.60 323

TransMVSNet (Re)		75.39 294	74.56 287	77.86 322	85.50 283	57.10 354	86.78 206	86.09 293	72.17 192	71.53 330	87.34 250	63.01 166	89.31 312	56.84 354	61.83 420	87.17 335

GBi-Net		78.40 228	77.40 235	81.40 242	87.60 221	63.01 273	88.39 146	89.28 201	71.63 199	75.34 268	87.28 251	54.80 260	91.11 275	62.72 292	79.57 288	90.09 244

test1		78.40 228	77.40 235	81.40 242	87.60 221	63.01 273	88.39 146	89.28 201	71.63 199	75.34 268	87.28 251	54.80 260	91.11 275	62.72 292	79.57 288	90.09 244

FMVSNet2		78.20 234	77.21 239	81.20 249	87.60 221	62.89 279	87.47 179	89.02 217	71.63 199	75.29 274	87.28 251	54.80 260	91.10 278	62.38 297	79.38 292	89.61 266

FMVSNet1		77.44 255	76.12 263	81.40 242	86.81 250	63.01 273	88.39 146	89.28 201	70.49 236	74.39 294	87.28 251	49.06 335	91.11 275	60.91 313	78.52 299	90.09 244

v2v482		80.23 184	79.29 185	83.05 193	83.62 328	64.14 241	87.04 193	89.97 171	73.61 160	78.18 198	87.22 255	61.10 202	93.82 150	76.11 159	76.78 324	91.18 195

ITE_SJBPF				78.22 314	81.77 369	60.57 310		83.30 330	69.25 267	67.54 370	87.20 256	36.33 419	87.28 345	54.34 368	74.62 361	86.80 346

anonymousdsp		78.60 224	77.15 240	82.98 197	80.51 388	67.08 175	87.24 189	89.53 188	65.66 322	75.16 277	87.19 257	52.52 282	92.25 229	77.17 146	79.34 293	89.61 266

MVSTER		79.01 213	77.88 219	82.38 221	83.07 343	64.80 227	84.08 287	88.95 222	69.01 277	78.69 182	87.17 258	54.70 264	92.43 220	74.69 175	80.57 278	89.89 257

thres100view900		76.50 272	75.55 271	79.33 292	89.52 129	56.99 355	85.83 238	83.23 332	73.94 151	76.32 244	87.12 259	51.89 297	91.95 239	48.33 403	83.75 231	89.07 277

thres600view7		76.50 272	75.44 272	79.68 285	89.40 137	57.16 352	85.53 247	83.23 332	73.79 155	76.26 245	87.09 260	51.89 297	91.89 242	48.05 408	83.72 234	90.00 250

XVG-ACMP-BASELINE		76.11 281	74.27 293	81.62 235	83.20 339	64.67 229	83.60 297	89.75 180	69.75 256	71.85 326	87.09 260	32.78 426	92.11 233	69.99 231	80.43 280	88.09 314

HY-MVS	69.67 12	77.95 242	77.15 240	80.36 269	87.57 225	60.21 317	83.37 303	87.78 256	66.11 315	75.37 267	87.06 262	63.27 157	90.48 293	61.38 310	82.43 255	90.40 229

CHOSEN 1792x2688		77.63 253	75.69 266	83.44 173	89.98 118	68.58 125	78.70 370	87.50 262	56.38 413	75.80 255	86.84 263	58.67 228	91.40 267	61.58 308	85.75 199	90.34 231

v8		79.97 190	79.02 192	82.80 206	84.09 316	64.50 234	87.96 163	90.29 162	74.13 148	75.24 275	86.81 264	62.88 168	93.89 149	74.39 180	75.40 350	90.00 250

AllTest		70.96 342	68.09 357	79.58 288	85.15 292	63.62 252	84.58 271	79.83 379	62.31 364	60.32 421	86.73 265	32.02 427	88.96 322	50.28 391	71.57 387	86.15 357

TestCases				79.58 288	85.15 292	63.62 252		79.83 379	62.31 364	60.32 421	86.73 265	32.02 427	88.96 322	50.28 391	71.57 387	86.15 357

LCM-MVSNet-Re		77.05 262	76.94 245	77.36 332	87.20 235	51.60 411	80.06 350	80.46 370	75.20 115	67.69 369	86.72 267	62.48 172	88.98 320	63.44 287	89.25 135	91.51 185

1112_ss		77.40 257	76.43 258	80.32 271	89.11 156	60.41 314	83.65 294	87.72 258	62.13 367	73.05 310	86.72 267	62.58 171	89.97 300	62.11 303	80.80 274	90.59 221

ab-mvs-re		7.23 432	9.64 435	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	86.72 267	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

IterMVS-LS		80.06 187	79.38 181	82.11 226	85.89 271	63.20 270	86.79 205	89.34 194	74.19 145	75.45 263	86.72 267	66.62 120	92.39 222	72.58 199	76.86 321	90.75 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 284	73.93 296	81.77 233	88.71 172	66.61 182	88.62 138	89.01 218	69.81 252	66.78 382	86.70 271	41.95 392	91.51 262	55.64 361	78.14 306	87.17 335
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 277	75.44 272	79.27 293	89.28 145	58.09 335	81.69 324	87.07 272	59.53 388	72.48 318	86.67 272	61.30 197	89.33 311	60.81 315	80.15 283	90.41 228

FMVSNet3		77.88 244	76.85 247	80.97 257	86.84 249	62.36 285	86.52 216	88.77 227	71.13 212	75.34 268	86.66 273	54.07 270	91.10 278	62.72 292	79.57 288	89.45 270

pmmvs6		74.69 299	73.39 303	78.61 304	81.38 377	57.48 349	86.64 212	87.95 250	64.99 332	70.18 342	86.61 274	50.43 315	89.52 308	62.12 302	70.18 394	88.83 293

ET-MVSNet_ETH3D		78.63 223	76.63 255	84.64 115	86.73 253	69.47 98	85.01 259	84.61 310	69.54 259	66.51 389	86.59 275	50.16 318	91.75 247	76.26 158	84.24 223	92.69 138

testgi		66.67 381	66.53 378	67.08 415	75.62 421	41.69 450	75.93 394	76.50 407	66.11 315	65.20 400	86.59 275	35.72 421	74.71 434	43.71 424	73.38 374	84.84 382

CLD-MVS		82.31 126	81.65 132	84.29 129	88.47 179	67.73 154	85.81 239	92.35 83	75.78 99	78.33 194	86.58 277	64.01 151	94.35 123	76.05 161	87.48 165	90.79 210
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 192	78.67 197	82.97 198	84.06 317	64.95 222	87.88 169	90.62 147	73.11 177	75.11 279	86.56 278	61.46 193	94.05 137	73.68 185	75.55 343	89.90 256

CDS-MVSNet		79.07 212	77.70 227	83.17 186	87.60 221	68.23 137	84.40 279	86.20 290	67.49 297	76.36 243	86.54 279	61.54 190	90.79 286	61.86 305	87.33 167	90.49 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 139	81.05 139	83.60 167	89.15 151	68.03 143	84.46 275	90.02 169	70.67 226	81.30 142	86.53 280	63.17 161	94.19 132	75.60 167	88.54 149	88.57 304

TR-MVS		77.44 255	76.18 262	81.20 249	88.24 188	63.24 268	84.61 270	86.40 286	67.55 296	77.81 207	86.48 281	54.10 269	93.15 188	57.75 344	82.72 252	87.20 334

EIA-MVS		83.31 111	82.80 112	84.82 109	89.59 126	65.59 205	88.21 154	92.68 67	74.66 133	78.96 177	86.42 282	69.06 93	95.26 83	75.54 168	90.09 120	93.62 91

tfpn200view9		76.42 276	75.37 276	79.55 290	89.13 152	57.65 346	85.17 253	83.60 324	73.41 168	76.45 240	86.39 283	52.12 289	91.95 239	48.33 403	83.75 231	89.07 277

thres400		76.50 272	75.37 276	79.86 280	89.13 152	57.65 346	85.17 253	83.60 324	73.41 168	76.45 240	86.39 283	52.12 289	91.95 239	48.33 403	83.75 231	90.00 250

v7n		78.97 215	77.58 231	83.14 187	83.45 332	65.51 206	88.32 151	91.21 131	73.69 158	72.41 319	86.32 285	57.93 233	93.81 151	69.18 239	75.65 341	90.11 242

MAR-MVS		81.84 135	80.70 145	85.27 89	91.32 85	71.53 58	89.82 82	90.92 139	69.77 255	78.50 188	86.21 286	62.36 175	94.52 118	65.36 273	92.05 87	89.77 262
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
v1144		80.03 188	79.03 191	83.01 195	83.78 324	64.51 232	87.11 192	90.57 150	71.96 196	78.08 201	86.20 287	61.41 194	93.94 141	74.93 174	77.23 315	90.60 220

test_vis1_n_1920		75.52 289	75.78 265	74.75 362	79.84 396	57.44 350	83.26 305	85.52 299	62.83 358	79.34 174	86.17 288	45.10 369	79.71 403	78.75 127	81.21 268	87.10 341

V42		79.38 204	78.24 209	82.83 203	81.10 382	65.50 207	85.55 245	89.82 175	71.57 203	78.21 196	86.12 289	60.66 210	93.18 187	75.64 165	75.46 347	89.81 261

PVSNet_BlendedMVS		80.60 172	80.02 163	82.36 222	88.85 159	65.40 208	86.16 228	92.00 100	69.34 263	78.11 199	86.09 290	66.02 133	94.27 126	71.52 211	82.06 259	87.39 328

v1192		79.59 195	78.43 204	83.07 192	83.55 330	64.52 231	86.93 200	90.58 148	70.83 222	77.78 208	85.90 291	59.15 224	93.94 141	73.96 184	77.19 317	90.76 212

SixPastTwentyTwo		73.37 316	71.26 330	79.70 284	85.08 295	57.89 341	85.57 241	83.56 326	71.03 218	65.66 394	85.88 292	42.10 390	92.57 212	59.11 329	63.34 416	88.65 301

EPNet_dtu		75.46 290	74.86 282	77.23 335	82.57 358	54.60 388	86.89 201	83.09 336	71.64 198	66.25 391	85.86 293	55.99 252	88.04 335	54.92 365	86.55 181	89.05 282
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 313	73.64 301	73.51 374	82.80 352	55.01 385	76.12 393	81.69 355	62.47 363	74.68 289	85.85 294	57.32 241	78.11 410	60.86 314	80.93 270	87.39 328

ETV-MVS		84.90 83	84.67 83	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 97	85.71 295	69.32 88	95.38 78	80.82 107	91.37 99	92.72 135

test_cas_vis1_n_1920		73.76 311	73.74 300	73.81 372	75.90 418	59.77 320	80.51 343	82.40 347	58.30 399	81.62 136	85.69 296	44.35 375	76.41 421	76.29 157	78.61 297	85.23 374

v1240		78.99 214	77.78 223	82.64 215	83.21 338	63.54 260	86.62 213	90.30 161	69.74 258	77.33 216	85.68 297	57.04 245	93.76 155	73.13 194	76.92 319	90.62 218

v144192		79.47 198	78.37 205	82.78 210	83.35 333	63.96 244	86.96 197	90.36 158	69.99 248	77.50 212	85.67 298	60.66 210	93.77 154	74.27 181	76.58 325	90.62 218

tfpnnormal		74.39 301	73.16 307	78.08 318	86.10 269	58.05 336	84.65 269	87.53 261	70.32 240	71.22 334	85.63 299	54.97 258	89.86 301	43.03 426	75.02 357	86.32 353

PS-MVSNAJ		81.69 139	81.02 140	83.70 165	89.51 130	68.21 138	84.28 281	90.09 168	70.79 223	81.26 143	85.62 300	63.15 162	94.29 124	75.62 166	88.87 142	88.59 303

SSC-MVS3.2		73.35 319	73.39 303	73.23 375	85.30 288	49.01 425	74.58 408	81.57 356	75.21 114	73.68 302	85.58 301	52.53 281	82.05 391	54.33 369	77.69 312	88.63 302

v1921920		79.22 207	78.03 213	82.80 206	83.30 335	63.94 246	86.80 204	90.33 159	69.91 251	77.48 213	85.53 302	58.44 230	93.75 156	73.60 186	76.85 322	90.71 216

test_0402		72.79 327	70.44 338	79.84 281	88.13 194	65.99 193	85.93 233	84.29 315	65.57 323	67.40 375	85.49 303	46.92 347	92.61 209	35.88 440	74.38 363	80.94 419

v148		78.72 221	77.80 222	81.47 239	82.73 354	61.96 292	86.30 224	88.08 244	73.26 173	76.18 248	85.47 304	62.46 173	92.36 224	71.92 210	73.82 369	90.09 244

USDC		70.33 351	68.37 352	76.21 342	80.60 386	56.23 369	79.19 362	86.49 284	60.89 375	61.29 417	85.47 304	31.78 429	89.47 310	53.37 374	76.21 336	82.94 406

VortexMVS		78.57 226	77.89 218	80.59 264	85.89 271	62.76 280	85.61 240	89.62 185	72.06 194	74.99 283	85.38 306	55.94 253	90.77 289	74.99 173	76.58 325	88.23 310

MVP-Stereo		76.12 280	74.46 290	81.13 252	85.37 286	69.79 91	84.42 278	87.95 250	65.03 330	67.46 372	85.33 307	53.28 279	91.73 249	58.01 342	83.27 244	81.85 414
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 235	76.99 244	81.78 232	85.66 276	66.99 176	84.66 267	90.47 152	55.08 418	72.02 325	85.27 308	63.83 153	94.11 135	66.10 267	89.80 127	84.24 388

DIV-MVS_self_test		77.72 248	76.76 250	80.58 265	82.48 361	60.48 312	83.09 309	87.86 253	69.22 268	74.38 295	85.24 309	62.10 180	91.53 260	71.09 216	75.40 350	89.74 263

FE-MVS		77.78 246	75.68 267	84.08 144	88.09 197	66.00 192	83.13 308	87.79 255	68.42 288	78.01 202	85.23 310	45.50 367	95.12 88	59.11 329	85.83 198	91.11 197

cl____		77.72 248	76.76 250	80.58 265	82.49 360	60.48 312	83.09 309	87.87 252	69.22 268	74.38 295	85.22 311	62.10 180	91.53 260	71.09 216	75.41 349	89.73 264

HyFIR lowres test		77.53 254	75.40 274	83.94 159	89.59 126	66.62 181	80.36 346	88.64 236	56.29 414	76.45 240	85.17 312	57.64 237	93.28 175	61.34 311	83.10 247	91.91 172

pmmvs4		74.03 309	71.91 320	80.39 268	81.96 366	68.32 131	81.45 328	82.14 349	59.32 389	69.87 350	85.13 313	52.40 285	88.13 334	60.21 319	74.74 360	84.73 384

TDRefinement		67.49 374	64.34 385	76.92 337	73.47 433	61.07 303	84.86 263	82.98 340	59.77 385	58.30 428	85.13 313	26.06 437	87.89 337	47.92 409	60.59 425	81.81 415

Fast-Effi-MVS+		80.81 160	79.92 165	83.47 171	88.85 159	64.51 232	85.53 247	89.39 193	70.79 223	78.49 189	85.06 315	67.54 112	93.58 160	67.03 262	86.58 180	92.32 155

PVSNet_Blended		80.98 155	80.34 154	82.90 200	88.85 159	65.40 208	84.43 277	92.00 100	67.62 295	78.11 199	85.05 316	66.02 133	94.27 126	71.52 211	89.50 132	89.01 284

ttmdpeth		59.91 401	57.10 405	68.34 410	67.13 447	46.65 434	74.64 407	67.41 437	48.30 433	62.52 415	85.04 317	20.40 447	75.93 426	42.55 428	45.90 448	82.44 409

test_fmvs1_n		70.86 344	70.24 341	72.73 382	72.51 440	55.28 382	81.27 331	79.71 381	51.49 429	78.73 181	84.87 318	27.54 436	77.02 415	76.06 160	79.97 286	85.88 365

WBMVS		73.43 315	72.81 311	75.28 354	87.91 205	50.99 417	78.59 373	81.31 361	65.51 326	74.47 293	84.83 319	46.39 352	86.68 349	58.41 337	77.86 308	88.17 313

CMPMVS	51.72 21	70.19 353	68.16 355	76.28 341	73.15 436	57.55 348	79.47 357	83.92 320	48.02 434	56.48 434	84.81 320	43.13 382	86.42 353	62.67 295	81.81 263	84.89 381
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 369	67.61 368	71.31 394	78.51 410	47.01 432	84.47 273	84.27 316	42.27 441	66.44 390	84.79 321	40.44 399	83.76 377	58.76 334	68.54 402	83.17 400

BH-w/o		78.21 233	77.33 238	80.84 259	88.81 163	65.13 216	84.87 262	87.85 254	69.75 256	74.52 292	84.74 322	61.34 196	93.11 191	58.24 340	85.84 197	84.27 387

pmmvs5		71.55 337	70.20 342	75.61 347	77.83 411	56.39 365	81.74 323	80.89 362	57.76 404	67.46 372	84.49 323	49.26 332	85.32 367	57.08 350	75.29 353	85.11 378

reproduce_monomvs		75.40 293	74.38 291	78.46 312	83.92 321	57.80 344	83.78 290	86.94 275	73.47 166	72.25 322	84.47 324	38.74 407	89.27 313	75.32 171	70.53 392	88.31 309

thres200		75.55 288	74.47 289	78.82 301	87.78 214	57.85 342	83.07 311	83.51 327	72.44 188	75.84 254	84.42 325	52.08 292	91.75 247	47.41 410	83.64 236	86.86 345

test_fmvs1		70.93 343	70.52 336	72.16 386	73.71 429	55.05 384	80.82 334	78.77 390	51.21 430	78.58 186	84.41 326	31.20 431	76.94 416	75.88 163	80.12 285	84.47 386

testing3		68.56 368	67.67 367	71.22 395	87.33 231	42.87 445	83.06 312	71.54 425	70.36 237	69.08 358	84.38 327	30.33 433	85.69 361	37.50 438	75.45 348	85.09 379

test_fmvs2		68.35 371	67.48 370	70.98 397	69.50 443	51.95 406	80.05 351	76.38 408	49.33 432	74.65 290	84.38 327	23.30 445	75.40 432	74.51 178	75.17 356	85.60 368

eth_miper_zixun_eth		77.92 243	76.69 253	81.61 237	83.00 346	61.98 291	83.15 307	89.20 209	69.52 260	74.86 286	84.35 329	61.76 186	92.56 213	71.50 213	72.89 377	90.28 235

myMVS_eth3d28		73.62 312	73.53 302	73.90 371	88.20 189	47.41 430	78.06 380	79.37 384	74.29 143	73.98 298	84.29 330	44.67 370	83.54 380	51.47 383	87.39 166	90.74 214

testing91		76.54 270	75.66 269	79.18 296	88.43 182	55.89 373	81.08 332	83.00 339	73.76 156	75.34 268	84.29 330	46.20 358	90.07 298	64.33 281	84.50 215	91.58 183

c3_l		78.75 219	77.91 216	81.26 247	82.89 351	61.56 297	84.09 286	89.13 213	69.97 249	75.56 258	84.29 330	66.36 125	92.09 234	73.47 189	75.48 345	90.12 241

testing99		76.09 282	75.12 281	79.00 297	88.16 191	55.50 379	80.79 336	81.40 359	73.30 172	75.17 276	84.27 333	44.48 373	90.02 299	64.28 282	84.22 224	91.48 188

UWE-MVS		72.13 334	71.49 324	74.03 369	86.66 256	47.70 427	81.40 330	76.89 406	63.60 349	75.59 257	84.22 334	39.94 401	85.62 362	48.98 400	86.13 189	88.77 296

Fast-Effi-MVS+-dtu		78.02 240	76.49 256	82.62 216	83.16 342	66.96 179	86.94 199	87.45 264	72.45 186	71.49 331	84.17 335	54.79 263	91.58 253	67.61 253	80.31 281	89.30 275

IterMVS-SCA-FT		75.43 291	73.87 298	80.11 276	82.69 355	64.85 226	81.57 326	83.47 328	69.16 271	70.49 338	84.15 336	51.95 295	88.15 333	69.23 238	72.14 383	87.34 330

1314		76.53 271	75.30 278	80.21 274	83.93 320	62.32 287	84.66 267	88.81 225	60.23 381	70.16 344	84.07 337	55.30 257	90.73 290	67.37 256	83.21 245	87.59 325

cl22		78.07 238	77.01 242	81.23 248	82.37 363	61.83 294	83.55 298	87.98 248	68.96 278	75.06 281	83.87 338	61.40 195	91.88 243	73.53 187	76.39 330	89.98 253

EG-PatchMatch MVS		74.04 307	71.82 321	80.71 262	84.92 298	67.42 163	85.86 236	88.08 244	66.04 317	64.22 404	83.85 339	35.10 422	92.56 213	57.44 346	80.83 273	82.16 413

thisisatest0515		77.33 258	75.38 275	83.18 185	85.27 289	63.80 249	82.11 320	83.27 331	65.06 329	75.91 252	83.84 340	49.54 326	94.27 126	67.24 258	86.19 187	91.48 188

test20.03		67.45 375	66.95 376	68.94 404	75.48 422	44.84 441	77.50 385	77.67 396	66.66 306	63.01 411	83.80 341	47.02 346	78.40 408	42.53 429	68.86 401	83.58 397

miper_ehance_all_eth		78.59 225	77.76 225	81.08 253	82.66 356	61.56 297	83.65 294	89.15 211	68.87 279	75.55 259	83.79 342	66.49 123	92.03 235	73.25 192	76.39 330	89.64 265

MSDG		73.36 318	70.99 332	80.49 267	84.51 309	65.80 199	80.71 340	86.13 292	65.70 321	65.46 395	83.74 343	44.60 371	90.91 284	51.13 386	76.89 320	84.74 383

MonoMVSNet		76.49 275	75.80 264	78.58 306	81.55 373	58.45 331	86.36 222	86.22 289	74.87 128	74.73 288	83.73 344	51.79 300	88.73 325	70.78 218	72.15 382	88.55 305

testing11		75.14 296	74.01 294	78.53 309	88.16 191	56.38 366	80.74 339	80.42 372	70.67 226	72.69 316	83.72 345	43.61 380	89.86 301	62.29 299	83.76 230	89.36 273

IterMVS		74.29 302	72.94 310	78.35 313	81.53 374	63.49 262	81.58 325	82.49 346	68.06 292	69.99 347	83.69 346	51.66 302	85.54 363	65.85 270	71.64 386	86.01 361
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 330	71.71 322	74.35 365	82.19 364	52.00 405	79.22 361	77.29 402	64.56 335	72.95 312	83.68 347	51.35 303	83.26 384	58.33 339	75.80 339	87.81 319

UWE-MVS-28		65.32 388	64.93 382	66.49 416	78.70 408	38.55 453	77.86 384	64.39 445	62.00 369	64.13 405	83.60 348	41.44 393	76.00 425	31.39 445	80.89 271	84.92 380

sc_t1		72.19 333	69.51 344	80.23 273	84.81 300	61.09 302	84.68 266	80.22 376	60.70 377	71.27 332	83.58 349	36.59 417	89.24 314	60.41 316	63.31 417	90.37 230

testing222		74.04 307	72.66 313	78.19 315	87.89 206	55.36 380	81.06 333	79.20 387	71.30 209	74.65 290	83.57 350	39.11 406	88.67 327	51.43 385	85.75 199	90.53 223

Effi-MVS+-dtu		80.03 188	78.57 200	84.42 122	85.13 294	68.74 117	88.77 129	88.10 243	74.99 120	74.97 284	83.49 351	57.27 242	93.36 173	73.53 187	80.88 272	91.18 195

baseline2		75.70 286	73.83 299	81.30 245	83.26 336	61.79 295	82.57 316	80.65 366	66.81 302	66.88 380	83.42 352	57.86 235	92.19 231	63.47 286	79.57 288	89.91 255

mvs5depth		69.45 360	67.45 371	75.46 352	73.93 427	55.83 374	79.19 362	83.23 332	66.89 301	71.63 329	83.32 353	33.69 425	85.09 368	59.81 322	55.34 435	85.46 370

TinyColmap		67.30 377	64.81 383	74.76 361	81.92 368	56.68 361	80.29 348	81.49 358	60.33 379	56.27 435	83.22 354	24.77 441	87.66 341	45.52 420	69.47 396	79.95 424

mvsany_test1		62.30 397	61.26 401	65.41 418	69.52 442	54.86 386	66.86 436	49.78 458	46.65 435	68.50 364	83.21 355	49.15 333	66.28 450	56.93 353	60.77 423	75.11 434

test_vis1_n		69.85 358	69.21 347	71.77 388	72.66 439	55.27 383	81.48 327	76.21 409	52.03 426	75.30 273	83.20 356	28.97 434	76.22 423	74.60 177	78.41 304	83.81 394

CostFormer		75.24 295	73.90 297	79.27 293	82.65 357	58.27 334	80.80 335	82.73 345	61.57 371	75.33 272	83.13 357	55.52 255	91.07 281	64.98 277	78.34 305	88.45 306

MVStest1		56.63 405	52.76 411	68.25 411	61.67 453	53.25 402	71.67 417	68.90 435	38.59 446	50.59 442	83.05 358	25.08 439	70.66 443	36.76 439	38.56 449	80.83 420

WB-MVSnew		71.96 336	71.65 323	72.89 380	84.67 307	51.88 408	82.29 318	77.57 397	62.31 364	73.67 303	83.00 359	53.49 277	81.10 398	45.75 419	82.13 258	85.70 367

ETVMVS		72.25 332	71.05 331	75.84 344	87.77 215	51.91 407	79.39 358	74.98 413	69.26 266	73.71 301	82.95 360	40.82 398	86.14 355	46.17 416	84.43 220	89.47 269

miper_lstm_enhance		74.11 306	73.11 308	77.13 336	80.11 392	59.62 322	72.23 415	86.92 277	66.76 304	70.40 339	82.92 361	56.93 246	82.92 385	69.06 241	72.63 378	88.87 291

GA-MVS		76.87 266	75.17 280	81.97 230	82.75 353	62.58 281	81.44 329	86.35 288	72.16 193	74.74 287	82.89 362	46.20 358	92.02 236	68.85 244	81.09 269	91.30 193

K. test v3		71.19 339	68.51 351	79.21 295	83.04 345	57.78 345	84.35 280	76.91 405	72.90 182	62.99 412	82.86 363	39.27 403	91.09 280	61.65 307	52.66 438	88.75 297

MS-PatchMatch		73.83 310	72.67 312	77.30 334	83.87 322	66.02 190	81.82 321	84.66 309	61.37 374	68.61 362	82.82 364	47.29 343	88.21 332	59.27 326	84.32 222	77.68 429

lessismore_v0				78.97 298	81.01 383	57.15 353		65.99 440		61.16 418	82.82 364	39.12 405	91.34 269	59.67 323	46.92 445	88.43 307

D2MVS		74.82 298	73.21 306	79.64 287	79.81 397	62.56 282	80.34 347	87.35 265	64.37 338	68.86 359	82.66 366	46.37 354	90.10 297	67.91 251	81.24 267	86.25 354

Anonymous20231206		68.60 366	67.80 364	71.02 396	80.23 391	50.75 419	78.30 378	80.47 369	56.79 411	66.11 393	82.63 367	46.35 355	78.95 406	43.62 425	75.70 340	83.36 399

MIMVSNet		70.69 346	69.30 345	74.88 359	84.52 308	56.35 368	75.87 397	79.42 383	64.59 334	67.76 367	82.41 368	41.10 395	81.54 394	46.64 414	81.34 265	86.75 348

UBG		73.08 323	72.27 318	75.51 350	88.02 200	51.29 415	78.35 377	77.38 401	65.52 324	73.87 300	82.36 369	45.55 365	86.48 352	55.02 364	84.39 221	88.75 297

OpenMVS_ROB	64.09 19	70.56 348	68.19 354	77.65 327	80.26 389	59.41 326	85.01 259	82.96 341	58.76 396	65.43 396	82.33 370	37.63 414	91.23 273	45.34 422	76.03 337	82.32 410

miper_enhance_ethall		77.87 245	76.86 246	80.92 258	81.65 370	61.38 299	82.68 314	88.98 219	65.52 324	75.47 260	82.30 371	65.76 137	92.00 237	72.95 195	76.39 330	89.39 272

test0.0.03 1		68.00 373	67.69 366	68.90 405	77.55 412	47.43 428	75.70 398	72.95 424	66.66 306	66.56 385	82.29 372	48.06 340	75.87 427	44.97 423	74.51 362	83.41 398

PVSNet	64.34 18	72.08 335	70.87 334	75.69 346	86.21 263	56.44 364	74.37 409	80.73 365	62.06 368	70.17 343	82.23 373	42.86 384	83.31 383	54.77 366	84.45 219	87.32 331

MIMVSNet1		68.58 367	66.78 377	73.98 370	80.07 393	51.82 409	80.77 337	84.37 312	64.40 337	59.75 424	82.16 374	36.47 418	83.63 379	42.73 427	70.33 393	86.48 352

CL-MVSNet_self_test		72.37 330	71.46 325	75.09 356	79.49 403	53.53 396	80.76 338	85.01 307	69.12 272	70.51 337	82.05 375	57.92 234	84.13 375	52.27 379	66.00 410	87.60 323

tpm2		73.26 320	71.46 325	78.63 303	83.34 334	56.71 360	80.65 341	80.40 373	56.63 412	73.55 304	82.02 376	51.80 299	91.24 272	56.35 359	78.42 303	87.95 315

PatchMatch-RL		72.38 329	70.90 333	76.80 339	88.60 175	67.38 166	79.53 356	76.17 410	62.75 360	69.36 355	82.00 377	45.51 366	84.89 371	53.62 372	80.58 277	78.12 428

FMVSNet5		69.50 359	67.96 359	74.15 368	82.97 349	55.35 381	80.01 352	82.12 350	62.56 362	63.02 410	81.53 378	36.92 415	81.92 392	48.42 402	74.06 365	85.17 377

CR-MVSNet		73.37 316	71.27 329	79.67 286	81.32 380	65.19 214	75.92 395	80.30 374	59.92 384	72.73 314	81.19 379	52.50 283	86.69 348	59.84 321	77.71 310	87.11 339

Patchmtry		70.74 345	69.16 348	75.49 351	80.72 384	54.07 393	74.94 406	80.30 374	58.34 398	70.01 345	81.19 379	52.50 283	86.54 350	53.37 374	71.09 390	85.87 366

IB-MVS	68.01 15	75.85 285	73.36 305	83.31 178	84.76 302	66.03 189	83.38 302	85.06 305	70.21 244	69.40 354	81.05 381	45.76 363	94.66 113	65.10 276	75.49 344	89.25 276
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
cascas		76.72 269	74.64 285	82.99 196	85.78 274	65.88 196	82.33 317	89.21 208	60.85 376	72.74 313	81.02 382	47.28 344	93.75 156	67.48 255	85.02 207	89.34 274

LF4IMVS		64.02 393	62.19 397	69.50 402	70.90 441	53.29 401	76.13 392	77.18 403	52.65 424	58.59 426	80.98 383	23.55 444	76.52 419	53.06 376	66.66 406	78.68 427

Anonymous20240521		68.80 365	67.22 374	73.55 373	74.33 425	54.11 392	83.18 306	85.61 298	58.15 400	61.68 416	80.94 384	30.71 432	81.27 397	57.00 352	73.34 375	85.28 373

gm-plane-assit					81.40 376	53.83 395			62.72 361		80.94 384		92.39 222	63.40 288

UnsupCasMVSNet_eth		67.33 376	65.99 380	71.37 391	73.48 432	51.47 413	75.16 402	85.19 302	65.20 327	60.78 419	80.93 386	42.35 386	77.20 414	57.12 349	53.69 437	85.44 371

dmvs_re		71.14 340	70.58 335	72.80 381	81.96 366	59.68 321	75.60 399	79.34 385	68.55 284	69.27 357	80.72 387	49.42 328	76.54 418	52.56 378	77.79 309	82.19 412

MDTV_nov1_ep13			69.97 343		83.18 340	53.48 397	77.10 390	80.18 378	60.45 378	69.33 356	80.44 388	48.89 338	86.90 347	51.60 382	78.51 300

pmmvs-eth3d		70.50 349	67.83 363	78.52 310	77.37 414	66.18 188	81.82 321	81.51 357	58.90 394	63.90 408	80.42 389	42.69 385	86.28 354	58.56 335	65.30 412	83.11 402

tt0320		70.49 350	68.03 358	77.89 321	84.78 301	59.12 327	83.55 298	80.44 371	58.13 401	67.43 374	80.41 390	39.26 404	87.54 342	55.12 363	63.18 418	86.99 342

mmtdpeth		74.16 305	73.01 309	77.60 330	83.72 326	61.13 300	85.10 257	85.10 304	72.06 194	77.21 224	80.33 391	43.84 378	85.75 359	77.14 147	52.61 439	85.91 364

tt0320-xc		70.11 354	67.45 371	78.07 319	85.33 287	59.51 325	83.28 304	78.96 389	58.77 395	67.10 378	80.28 392	36.73 416	87.42 343	56.83 355	59.77 427	87.29 332

PM-MVS		66.41 383	64.14 386	73.20 378	73.92 428	56.45 363	78.97 366	64.96 444	63.88 348	64.72 401	80.24 393	19.84 449	83.44 382	66.24 264	64.52 414	79.71 425

SCA		74.22 304	72.33 317	79.91 279	84.05 318	62.17 289	79.96 353	79.29 386	66.30 314	72.38 320	80.13 394	51.95 295	88.60 328	59.25 327	77.67 313	88.96 288

Patchmatch-test		64.82 391	63.24 392	69.57 401	79.42 404	49.82 423	63.49 448	69.05 433	51.98 427	59.95 423	80.13 394	50.91 308	70.98 442	40.66 432	73.57 370	87.90 317

tpmrst		72.39 328	72.13 319	73.18 379	80.54 387	49.91 422	79.91 354	79.08 388	63.11 352	71.69 328	79.95 396	55.32 256	82.77 387	65.66 272	73.89 367	86.87 344

DSMNet-mixed		57.77 404	56.90 406	60.38 424	67.70 445	35.61 455	69.18 428	53.97 456	32.30 454	57.49 431	79.88 397	40.39 400	68.57 448	38.78 436	72.37 379	76.97 430

MDA-MVSNet-bldmvs		66.68 380	63.66 390	75.75 345	79.28 405	60.56 311	73.92 411	78.35 393	64.43 336	50.13 443	79.87 398	44.02 377	83.67 378	46.10 417	56.86 429	83.03 404

PatchmatchNet		73.12 322	71.33 328	78.49 311	83.18 340	60.85 306	79.63 355	78.57 391	64.13 340	71.73 327	79.81 399	51.20 306	85.97 358	57.40 347	76.36 335	88.66 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Syy-MVS		68.05 372	67.85 361	68.67 408	84.68 304	40.97 451	78.62 371	73.08 422	66.65 309	66.74 383	79.46 400	52.11 291	82.30 389	32.89 443	76.38 333	82.75 407

myMVS_eth3d		67.02 378	66.29 379	69.21 403	84.68 304	42.58 446	78.62 371	73.08 422	66.65 309	66.74 383	79.46 400	31.53 430	82.30 389	39.43 435	76.38 333	82.75 407

ppachtmachnet_test		70.04 355	67.34 373	78.14 316	79.80 398	61.13 300	79.19 362	80.59 367	59.16 391	65.27 397	79.29 402	46.75 351	87.29 344	49.33 398	66.72 405	86.00 363

EPMVS		69.02 363	68.16 355	71.59 389	79.61 401	49.80 424	77.40 386	66.93 438	62.82 359	70.01 345	79.05 403	45.79 362	77.86 412	56.58 357	75.26 354	87.13 338

PMMVS		69.34 361	68.67 350	71.35 393	75.67 420	62.03 290	75.17 401	73.46 420	50.00 431	68.68 360	79.05 403	52.07 293	78.13 409	61.16 312	82.77 250	73.90 435

test-LLR		72.94 326	72.43 315	74.48 363	81.35 378	58.04 337	78.38 374	77.46 398	66.66 306	69.95 348	79.00 405	48.06 340	79.24 404	66.13 265	84.83 210	86.15 357

test-mter		71.41 338	70.39 340	74.48 363	81.35 378	58.04 337	78.38 374	77.46 398	60.32 380	69.95 348	79.00 405	36.08 420	79.24 404	66.13 265	84.83 210	86.15 357

KD-MVS_self_test		68.81 364	67.59 369	72.46 385	74.29 426	45.45 435	77.93 382	87.00 273	63.12 351	63.99 407	78.99 407	42.32 387	84.77 372	56.55 358	64.09 415	87.16 337

test_fmvs3		63.36 395	61.82 398	67.98 412	62.51 452	46.96 433	77.37 387	74.03 419	45.24 437	67.50 371	78.79 408	12.16 457	72.98 441	72.77 198	66.02 409	83.99 392

KD-MVS_2432*1600		66.22 385	63.89 388	73.21 376	75.47 423	53.42 398	70.76 422	84.35 313	64.10 342	66.52 387	78.52 409	34.55 423	84.98 369	50.40 389	50.33 442	81.23 417

miper_refine_blended		66.22 385	63.89 388	73.21 376	75.47 423	53.42 398	70.76 422	84.35 313	64.10 342	66.52 387	78.52 409	34.55 423	84.98 369	50.40 389	50.33 442	81.23 417

tpmvs		71.09 341	69.29 346	76.49 340	82.04 365	56.04 371	78.92 367	81.37 360	64.05 344	67.18 377	78.28 411	49.74 325	89.77 303	49.67 396	72.37 379	83.67 396

our_test_3		69.14 362	67.00 375	75.57 348	79.80 398	58.80 328	77.96 381	77.81 395	59.55 387	62.90 413	78.25 412	47.43 342	83.97 376	51.71 381	67.58 404	83.93 393

MDA-MVSNet_test_wron		65.03 389	62.92 393	71.37 391	75.93 417	56.73 358	69.09 431	74.73 416	57.28 409	54.03 438	77.89 413	45.88 360	74.39 436	49.89 395	61.55 421	82.99 405

YYNet1		65.03 389	62.91 394	71.38 390	75.85 419	56.60 362	69.12 430	74.66 418	57.28 409	54.12 437	77.87 414	45.85 361	74.48 435	49.95 394	61.52 422	83.05 403

ambc				75.24 355	73.16 435	50.51 420	63.05 449	87.47 263		64.28 403	77.81 415	17.80 451	89.73 305	57.88 343	60.64 424	85.49 369

tpm cat1		70.57 347	68.31 353	77.35 333	82.41 362	57.95 340	78.08 379	80.22 376	52.04 425	68.54 363	77.66 416	52.00 294	87.84 338	51.77 380	72.07 384	86.25 354

dp		66.80 379	65.43 381	70.90 398	79.74 400	48.82 426	75.12 404	74.77 415	59.61 386	64.08 406	77.23 417	42.89 383	80.72 400	48.86 401	66.58 407	83.16 401

TESTMET0.1,1		69.89 357	69.00 349	72.55 383	79.27 406	56.85 356	78.38 374	74.71 417	57.64 405	68.09 366	77.19 418	37.75 413	76.70 417	63.92 284	84.09 225	84.10 391

CHOSEN 280x420		66.51 382	64.71 384	71.90 387	81.45 375	63.52 261	57.98 451	68.95 434	53.57 421	62.59 414	76.70 419	46.22 357	75.29 433	55.25 362	79.68 287	76.88 431

PatchT		68.46 370	67.85 361	70.29 399	80.70 385	43.93 443	72.47 414	74.88 414	60.15 382	70.55 336	76.57 420	49.94 322	81.59 393	50.58 387	74.83 359	85.34 372

mvsany_test3		53.99 408	51.45 413	61.61 423	55.51 457	44.74 442	63.52 447	45.41 462	43.69 440	58.11 429	76.45 421	17.99 450	63.76 453	54.77 366	47.59 444	76.34 432

RPMNet		73.51 314	70.49 337	82.58 218	81.32 380	65.19 214	75.92 395	92.27 85	57.60 406	72.73 314	76.45 421	52.30 286	95.43 73	48.14 407	77.71 310	87.11 339

dmvs_testset		62.63 396	64.11 387	58.19 426	78.55 409	24.76 464	75.28 400	65.94 441	67.91 293	60.34 420	76.01 423	53.56 275	73.94 439	31.79 444	67.65 403	75.88 433

ADS-MVSNet2		66.20 387	63.33 391	74.82 360	79.92 394	58.75 329	67.55 434	75.19 412	53.37 422	65.25 398	75.86 424	42.32 387	80.53 401	41.57 430	68.91 399	85.18 375

ADS-MVSNet		64.36 392	62.88 395	68.78 407	79.92 394	47.17 431	67.55 434	71.18 426	53.37 422	65.25 398	75.86 424	42.32 387	73.99 438	41.57 430	68.91 399	85.18 375

EGC-MVSNET		52.07 414	47.05 418	67.14 414	83.51 331	60.71 308	80.50 344	67.75 436	0.07 464	0.43 465	75.85 426	24.26 442	81.54 394	28.82 447	62.25 419	59.16 447

new-patchmatchnet		61.73 398	61.73 399	61.70 422	72.74 438	24.50 465	69.16 429	78.03 394	61.40 372	56.72 433	75.53 427	38.42 409	76.48 420	45.95 418	57.67 428	84.13 390

N_pmnet		52.79 412	53.26 410	51.40 436	78.99 407	7.68 470	69.52 426	3.89 469	51.63 428	57.01 432	74.98 428	40.83 397	65.96 451	37.78 437	64.67 413	80.56 423

WB-MVS		54.94 406	54.72 407	55.60 432	73.50 431	20.90 466	74.27 410	61.19 449	59.16 391	50.61 441	74.15 429	47.19 345	75.78 428	17.31 457	35.07 451	70.12 439

patchmatchnet-post											74.00 430	51.12 307	88.60 328

GG-mvs-BLEND				75.38 353	81.59 372	55.80 375	79.32 359	69.63 430		67.19 376	73.67 431	43.24 381	88.90 324	50.41 388	84.50 215	81.45 416

SSC-MVS		53.88 409	53.59 409	54.75 434	72.87 437	19.59 467	73.84 412	60.53 451	57.58 407	49.18 445	73.45 432	46.34 356	75.47 431	16.20 460	32.28 453	69.20 440

Patchmatch-RL test		70.24 352	67.78 365	77.61 328	77.43 413	59.57 324	71.16 419	70.33 427	62.94 356	68.65 361	72.77 433	50.62 312	85.49 364	69.58 236	66.58 407	87.77 320

FPMVS		53.68 410	51.64 412	59.81 425	65.08 449	51.03 416	69.48 427	69.58 431	41.46 442	40.67 449	72.32 434	16.46 453	70.00 446	24.24 453	65.42 411	58.40 449

UnsupCasMVSNet_bld		63.70 394	61.53 400	70.21 400	73.69 430	51.39 414	72.82 413	81.89 352	55.63 416	57.81 430	71.80 435	38.67 408	78.61 407	49.26 399	52.21 440	80.63 421

APD_test1		53.31 411	49.93 416	63.42 421	65.68 448	50.13 421	71.59 418	66.90 439	34.43 451	40.58 450	71.56 436	8.65 462	76.27 422	34.64 442	55.36 434	63.86 445

test_f		52.09 413	50.82 414	55.90 430	53.82 460	42.31 449	59.42 450	58.31 454	36.45 449	56.12 436	70.96 437	12.18 456	57.79 456	53.51 373	56.57 431	67.60 441

PVSNet_0	57.27 20	61.67 399	59.27 402	68.85 406	79.61 401	57.44 350	68.01 432	73.44 421	55.93 415	58.54 427	70.41 438	44.58 372	77.55 413	47.01 411	35.91 450	71.55 438

pmmvs3		57.79 403	54.26 408	68.37 409	64.02 451	56.72 359	75.12 404	65.17 442	40.20 443	52.93 439	69.86 439	20.36 448	75.48 430	45.45 421	55.25 436	72.90 437

test_vis1_rt		60.28 400	58.42 403	65.84 417	67.25 446	55.60 378	70.44 424	60.94 450	44.33 439	59.00 425	66.64 440	24.91 440	68.67 447	62.80 291	69.48 395	73.25 436

new_pmnet		50.91 415	50.29 415	52.78 435	68.58 444	34.94 457	63.71 446	56.63 455	39.73 444	44.95 446	65.47 441	21.93 446	58.48 455	34.98 441	56.62 430	64.92 443

gg-mvs-nofinetune		69.95 356	67.96 359	75.94 343	83.07 343	54.51 390	77.23 388	70.29 428	63.11 352	70.32 340	62.33 442	43.62 379	88.69 326	53.88 371	87.76 161	84.62 385

JIA-IIPM		66.32 384	62.82 396	76.82 338	77.09 415	61.72 296	65.34 442	75.38 411	58.04 403	64.51 402	62.32 443	42.05 391	86.51 351	51.45 384	69.22 398	82.21 411

LCM-MVSNet		54.25 407	49.68 417	67.97 413	53.73 461	45.28 438	66.85 437	80.78 364	35.96 450	39.45 451	62.23 444	8.70 461	78.06 411	48.24 406	51.20 441	80.57 422

PMMVS2		40.82 423	38.86 427	46.69 437	53.84 459	16.45 468	48.61 454	49.92 457	37.49 447	31.67 452	60.97 445	8.14 463	56.42 457	28.42 448	30.72 454	67.19 442

testf1		45.72 418	41.96 422	57.00 427	56.90 455	45.32 436	66.14 439	59.26 452	26.19 455	30.89 454	60.96 446	4.14 465	70.64 444	26.39 451	46.73 446	55.04 450

APD_test2		45.72 418	41.96 422	57.00 427	56.90 455	45.32 436	66.14 439	59.26 452	26.19 455	30.89 454	60.96 446	4.14 465	70.64 444	26.39 451	46.73 446	55.04 450

MVS-HIRNet		59.14 402	57.67 404	63.57 420	81.65 370	43.50 444	71.73 416	65.06 443	39.59 445	51.43 440	57.73 448	38.34 410	82.58 388	39.53 433	73.95 366	64.62 444

ANet_high		50.57 416	46.10 420	63.99 419	48.67 464	39.13 452	70.99 421	80.85 363	61.39 373	31.18 453	57.70 449	17.02 452	73.65 440	31.22 446	15.89 461	79.18 426

PMVS	37.38 22	44.16 422	40.28 426	55.82 431	40.82 466	42.54 448	65.12 443	63.99 446	34.43 451	24.48 457	57.12 450	3.92 467	76.17 424	17.10 458	55.52 433	48.75 452
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 420	45.38 421	45.55 438	73.36 434	26.85 462	67.72 433	34.19 464	54.15 420	49.65 444	56.41 451	25.43 438	62.94 454	19.45 455	28.09 455	46.86 454

test_vis3_rt		49.26 417	47.02 419	56.00 429	54.30 458	45.27 439	66.76 438	48.08 459	36.83 448	44.38 447	53.20 452	7.17 464	64.07 452	56.77 356	55.66 432	58.65 448

test_method		31.52 426	29.28 430	38.23 440	27.03 468	6.50 471	20.94 459	62.21 448	4.05 462	22.35 460	52.50 453	13.33 454	47.58 460	27.04 450	34.04 452	60.62 446

kuosan		39.70 424	40.40 425	37.58 441	64.52 450	26.98 460	65.62 441	33.02 465	46.12 436	42.79 448	48.99 454	24.10 443	46.56 462	12.16 463	26.30 456	39.20 455

DeepMVS_CX				27.40 444	40.17 467	26.90 461		24.59 468	17.44 460	23.95 458	48.61 455	9.77 459	26.48 463	18.06 456	24.47 457	28.83 457

MVE	26.22 23	30.37 428	25.89 432	43.81 439	44.55 465	35.46 456	28.87 458	39.07 463	18.20 459	18.58 461	40.18 456	2.68 468	47.37 461	17.07 459	23.78 458	48.60 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 421	41.86 424	55.16 433	77.03 416	51.52 412	32.50 457	80.52 368	32.46 453	27.12 456	35.02 457	9.52 460	75.50 429	22.31 454	60.21 426	38.45 456
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 425	30.64 428	35.15 442	52.87 462	27.67 459	57.09 452	47.86 460	24.64 457	16.40 462	33.05 458	11.23 458	54.90 458	14.46 461	18.15 459	22.87 458

EMVS		30.81 427	29.65 429	34.27 443	50.96 463	25.95 463	56.58 453	46.80 461	24.01 458	15.53 463	30.68 459	12.47 455	54.43 459	12.81 462	17.05 460	22.43 459

tmp_tt		18.61 430	21.40 433	10.23 446	4.82 469	10.11 469	34.70 456	30.74 467	1.48 463	23.91 459	26.07 460	28.42 435	13.41 465	27.12 449	15.35 462	7.17 460

X-MVStestdata		80.37 181	77.83 220	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	12.47 461	67.45 113	96.60 33	83.06 81	94.50 53	94.07 60

test_post											5.46 462	50.36 316	84.24 374

test_post1							78.90 368				5.43 463	48.81 339	85.44 366	59.25 327

wuyk23d		16.82 431	15.94 434	19.46 445	58.74 454	31.45 458	39.22 455	3.74 470	6.84 461	6.04 464	2.70 464	1.27 469	24.29 464	10.54 464	14.40 463	2.63 461

testmvs		6.04 434	8.02 437	0.10 448	0.08 470	0.03 473	69.74 425	0.04 471	0.05 465	0.31 466	1.68 465	0.02 471	0.04 466	0.24 465	0.02 464	0.25 463

test123		6.12 433	8.11 436	0.14 447	0.06 471	0.09 472	71.05 420	0.03 472	0.04 466	0.25 467	1.30 466	0.05 470	0.03 467	0.21 466	0.01 465	0.29 462

mmdepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

monomultidepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

test_blank		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet_test		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

DCPMVS		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

pcd_1.5k_mvsjas		5.26 435	7.02 438	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	63.15 162	0.00 468	0.00 467	0.00 466	0.00 464

sosnet-low-res		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

sosnet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uncertanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

Regformer		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

WAC-MVS						42.58 446								39.46 434

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 136	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 43

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 43

eth-test2					0.00 472

eth-test					0.00 472

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 302	92.39 6				88.94 26	96.63 4	94.85 21

save fliter					93.80 40	72.35 44	90.47 69	91.17 133	74.31 141

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 21	96.41 12	94.21 54

GSMVS																88.96 288

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 304				88.96 288

sam_mvs												50.01 320

MTGPA								92.02 98

MTMP							92.18 35	32.83 466

test9_res														84.90 58	95.70 26	92.87 131

agg_prior2														82.91 85	95.45 29	92.70 136

agg_prior					92.85 64	71.94 52		91.78 113		84.41 89			94.93 97

test_prior4						72.60 34	89.01 118

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 68

旧先验2							86.56 215		58.10 402	87.04 56			88.98 320	74.07 183

新几何2							86.29 225

无先验							87.48 178	88.98 219	60.00 383				94.12 134	67.28 257		88.97 287

原ACMM2							86.86 202

testdata2													91.01 282	62.37 298

segment_acmp												73.08 40

testdata1							84.14 285		75.71 100

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 121		71.67 59	96.16 48		94.50 53	93.54 97

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 215

plane_prior5								92.44 78					95.38 78	78.71 128	86.32 184	91.33 191

plane_prior3						68.60 124			78.44 36	78.92 179

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

plane_prior						68.71 119	90.38 73		77.62 47						86.16 188

n2								0.00 473

nn								0.00 473

door-mid								69.98 429

test11								92.23 88

door								69.44 432

HQP5-MVS						66.98 177

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 220

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 220

BP-MVS														77.47 142

HQP4-MVS										77.24 219			95.11 90			91.03 201

HQP3-MVS								92.19 92							85.99 192

HQP2-MVS												60.17 218

MDTV_nov1_ep13_2view						37.79 454	75.16 402		55.10 417	66.53 386		49.34 330		53.98 370		87.94 316

ACMMP++_ref															81.95 261

ACMMP++															81.25 266

Test By Simon												64.33 148

High-resolution multi-view benchmark