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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 54	99.27 1	99.54 1

FOURS1					96.08 11	87.41 10	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 43	91.91 13	84.21 155	96.51 7	57.84 312	88.93 85	92.84 90	91.92 3	96.16 3	96.23 18	86.95 48	95.99 10	79.05 120	98.57 14	98.80 6

PS-CasMVS		90.06 39	91.92 11	84.47 146	96.56 6	58.83 305	89.04 83	92.74 93	91.40 5	96.12 4	96.06 22	87.23 45	95.57 38	79.42 118	98.74 5	99.00 2

LCM-MVSNet-Re		83.48 159	85.06 127	78.75 258	85.94 259	55.75 328	80.05 250	94.27 21	76.47 129	96.09 5	94.54 62	83.31 85	89.75 236	59.95 308	94.89 167	90.75 222

PEN-MVS		90.03 41	91.88 14	84.48 145	96.57 5	58.88 302	88.95 84	93.19 72	91.62 4	96.01 6	96.16 20	87.02 47	95.60 36	78.69 123	98.72 8	98.97 3

CP-MVSNet		89.27 58	90.91 40	84.37 147	96.34 8	58.61 308	88.66 92	92.06 109	90.78 6	95.67 7	95.17 42	81.80 112	95.54 41	79.00 121	98.69 9	98.95 4

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 20	93.73 60	85.72 30	96.79 1	95.51 8	88.86 12	95.63 8	96.99 8	84.81 69	93.16 133	91.10 1	97.53 70	96.58 30
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
WR-MVS_H		89.91 46	91.31 29	85.71 124	96.32 9	62.39 257	89.54 74	93.31 67	90.21 10	95.57 9	95.66 29	81.42 116	95.90 15	80.94 99	98.80 2	98.84 5

OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 75	80.37 68	91.91 33	93.11 76	81.10 77	95.32 10	97.24 5	72.94 209	94.85 67	85.07 54	97.78 53	97.26 16

anonymousdsp		89.73 49	88.88 66	92.27 7	89.82 168	86.67 14	90.51 50	90.20 169	69.87 220	95.06 11	96.14 21	84.28 74	93.07 137	87.68 15	96.34 105	97.09 21

wuyk23d		75.13 272	79.30 225	62.63 372	75.56 374	75.18 124	80.89 242	73.10 347	75.06 150	94.76 12	95.32 35	87.73 40	52.85 402	34.16 402	97.11 80	59.85 399

ACMH	76.49 14	89.34 55	91.14 31	83.96 160	92.50 91	70.36 176	89.55 72	93.84 49	81.89 68	94.70 13	95.44 34	90.69 8	88.31 259	83.33 70	98.30 24	93.20 139
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 86	87.45 83	86.45 106	92.52 90	69.19 189	87.84 103	88.05 207	81.66 70	94.64 14	96.53 14	65.94 252	94.75 69	83.02 76	96.83 87	95.41 51

mvs_tets		89.78 48	89.27 59	91.30 25	93.51 64	84.79 40	89.89 63	90.63 152	70.00 219	94.55 15	96.67 11	87.94 37	93.59 116	84.27 64	95.97 123	95.52 49

jajsoiax		89.41 53	88.81 68	91.19 28	93.38 68	84.72 41	89.70 66	90.29 166	69.27 223	94.39 16	96.38 15	86.02 62	93.52 120	83.96 66	95.92 128	95.34 53

test_0402		88.65 65	89.58 56	85.88 120	92.55 89	72.22 156	84.01 169	89.44 186	88.63 16	94.38 17	95.77 26	86.38 58	93.59 116	79.84 111	95.21 152	91.82 197

UniMVSNet_ETH3D		89.12 61	90.72 43	84.31 153	97.00 2	64.33 232	89.67 69	88.38 199	88.84 13	94.29 18	97.57 3	90.48 13	91.26 184	72.57 202	97.65 60	97.34 15

v7n		90.13 36	90.96 38	87.65 89	91.95 109	71.06 170	89.99 59	93.05 80	86.53 26	94.29 18	96.27 17	82.69 90	94.08 95	86.25 42	97.63 61	97.82 8

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 94	82.98 53	91.98 31	90.08 172	71.54 201	94.28 20	96.54 13	81.57 114	94.27 84	86.26 40	96.49 99	97.09 21

PS-MVSNAJss		88.31 69	87.90 76	89.56 57	93.31 70	77.96 92	87.94 101	91.97 112	70.73 210	94.19 21	96.67 11	76.94 161	94.57 76	83.07 74	96.28 108	96.15 33

SED-MVS		90.46 33	91.64 17	86.93 97	94.18 46	72.65 142	90.47 51	93.69 53	83.77 47	94.11 22	94.27 74	90.28 14	95.84 23	86.03 46	97.92 46	92.29 179

test_241102_ONE					94.18 46	72.65 142		93.69 53	83.62 49	94.11 22	93.78 104	90.28 14	95.50 46

DPE-MVS		90.53 32	91.08 33	88.88 67	93.38 68	78.65 83	89.15 82	94.05 38	84.68 40	93.90 24	94.11 87	88.13 34	96.30 4	84.51 62	97.81 52	91.70 201
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		91.22 21	91.92 11	89.14 64	92.97 79	78.04 89	92.84 15	94.14 33	83.33 53	93.90 24	95.73 27	88.77 25	96.41 2	87.60 18	97.98 42	92.98 150
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMH+	77.89 11	90.73 27	91.50 21	88.44 76	93.00 78	76.26 116	89.65 70	95.55 7	87.72 21	93.89 26	94.94 47	91.62 3	93.44 124	78.35 126	98.76 3	95.61 48

ACMM	79.39 9	90.65 28	90.99 37	89.63 55	95.03 33	83.53 47	89.62 71	93.35 63	79.20 100	93.83 27	93.60 110	90.81 7	92.96 139	85.02 56	98.45 18	92.41 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 13	94.16 29	88.75 14	93.79 28	94.43 67	88.83 24	95.51 44	87.16 29	97.60 64	92.73 156

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 29	88.75 14	93.79 28	94.43 67	90.64 10		87.16 29	97.60 64	92.73 156

DVP-MVS		90.06 39	91.32 28	86.29 109	94.16 49	72.56 148	90.54 48	91.01 142	83.61 50	93.75 30	94.65 56	89.76 18	95.78 28	86.42 36	97.97 43	90.55 231
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									85.33 33	93.75 30	94.65 56	87.44 43	95.78 28	87.41 22	98.21 29	92.98 150

test0726					94.16 49	72.56 148	90.63 45	93.90 45	83.61 50	93.75 30	94.49 64	89.76 18

LPG-MVS_test		91.47 17	91.68 16	90.82 33	94.75 40	81.69 59	90.00 57	94.27 21	82.35 63	93.67 33	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 21	82.35 63	93.67 33	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

RRT_MVS		88.30 70	87.83 77	89.70 52	93.62 63	75.70 121	92.36 26	89.06 191	77.34 122	93.63 35	95.83 25	65.40 257	95.90 15	85.01 57	98.23 27	97.49 13

DVP-MVS++		90.07 38	91.09 32	87.00 95	91.55 126	72.64 144	96.19 2	94.10 36	85.33 33	93.49 36	94.64 59	81.12 119	95.88 17	87.41 22	95.94 126	92.48 168

test_241102_TWO								93.71 52	83.77 47	93.49 36	94.27 74	89.27 21	95.84 23	86.03 46	97.82 51	92.04 190

test_one_0601					93.85 58	73.27 136		94.11 35	86.57 25	93.47 38	94.64 59	88.42 26

SR-MVS		92.23 6	92.34 7	91.91 15	94.89 37	87.85 8	92.51 23	93.87 48	88.20 19	93.24 39	94.02 90	90.15 16	95.67 34	86.82 33	97.34 74	92.19 185

APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 77	85.17 35	92.47 25	95.05 14	87.65 22	93.21 40	94.39 72	90.09 17	95.08 61	86.67 35	97.60 64	94.18 95

testf1		89.30 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 73	86.02 29	93.12 41	95.30 36	84.94 66	89.44 241	74.12 178	96.10 118	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 73	86.02 29	93.12 41	95.30 36	84.94 66	89.44 241	74.12 178	96.10 118	94.45 82

Anonymous20231211		88.40 67	89.62 55	84.73 140	90.46 154	65.27 222	88.86 86	93.02 84	87.15 23	93.05 43	97.10 6	82.28 102	92.02 165	76.70 150	97.99 40	96.88 25

MP-MVS-pluss		90.81 26	91.08 33	89.99 46	95.97 13	79.88 71	88.13 98	94.51 18	75.79 140	92.94 44	94.96 46	88.36 28	95.01 63	90.70 2	98.40 19	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 63	89.88 49	86.22 112	91.63 120	77.07 105	89.82 64	93.77 50	78.90 104	92.88 45	92.29 148	86.11 60	90.22 217	86.24 43	97.24 77	91.36 209
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
ACMP	79.16 10	90.54 31	90.60 45	90.35 41	94.36 43	80.98 65	89.16 81	94.05 38	79.03 103	92.87 46	93.74 106	90.60 11	95.21 58	82.87 78	98.76 3	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 33	94.17 92	86.07 45	98.48 17	97.22 19

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 71	75.37 147	92.84 48	95.28 38	85.58 64	96.09 7	87.92 10	97.76 55	93.88 109
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
test_part2					93.86 57	77.77 94				92.84 48

v10		86.54 95	87.10 89	84.84 136	88.16 206	63.28 243	86.64 124	92.20 105	75.42 146	92.81 50	94.50 63	74.05 193	94.06 96	83.88 67	96.28 108	97.17 20

dcpmvs_2		84.23 141	85.14 126	81.50 218	88.61 195	61.98 264	82.90 204	93.11 76	68.66 232	92.77 51	92.39 142	78.50 140	87.63 265	76.99 149	92.30 228	94.90 65

v8		86.22 101	86.83 96	84.36 149	87.82 211	62.35 259	86.42 127	91.33 133	76.78 128	92.73 52	94.48 65	73.41 202	93.72 108	83.10 73	95.41 144	97.01 23

nrg030		87.85 80	88.49 70	85.91 118	90.07 163	69.73 180	87.86 102	94.20 27	74.04 158	92.70 53	94.66 55	85.88 63	91.50 176	79.72 113	97.32 75	96.50 31

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 37	93.48 60	82.82 60	92.60 54	93.97 92	88.19 31	96.29 5	87.61 17	98.20 31	94.39 87
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 47	89.97 48	89.27 61	94.76 39	79.86 72	86.76 121	92.78 92	78.78 106	92.51 55	93.64 109	88.13 34	93.84 105	84.83 59	97.55 67	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HPM-MVS_fast		92.50 4	92.54 5	92.37 5	95.93 15	85.81 29	92.99 12	94.23 24	85.21 35	92.51 55	95.13 43	90.65 9	95.34 52	88.06 8	98.15 34	95.95 41

K. test v3		85.14 119	84.73 132	86.37 107	91.13 140	69.63 182	85.45 142	76.68 320	84.06 45	92.44 57	96.99 8	62.03 275	94.65 72	80.58 105	93.24 211	94.83 72

ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 29	82.52 62	92.39 58	94.14 84	89.15 23	95.62 35	87.35 24	98.24 26	94.56 76
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
SF-MVS		90.27 35	90.80 42	88.68 74	92.86 83	77.09 104	91.19 40	95.74 5	81.38 73	92.28 59	93.80 102	86.89 49	94.64 73	85.52 51	97.51 71	94.30 91

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 98	76.51 112	91.09 42	91.87 116	72.61 188	92.16 60	95.23 41	66.01 251	95.59 37	86.02 48	97.78 53	97.24 17

COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 61	86.15 20	93.37 10	95.10 13	90.28 9	92.11 61	95.03 45	89.75 20	94.93 65	79.95 110	98.27 25	95.04 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 43	89.84 50	90.41 39	94.91 36	84.50 44	89.49 76	93.98 40	79.68 92	92.09 62	93.89 100	83.80 78	93.10 136	82.67 82	98.04 36	93.64 123

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 132	94.02 54	64.13 233	84.38 162	91.29 134	84.88 39	92.06 63	93.84 101	86.45 55	93.73 107	73.22 193	98.66 10	97.69 9

MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 30	91.81 121	84.07 44	92.00 64	94.40 71	86.63 51	95.28 55	88.59 5	98.31 23	92.30 178

ACMMP_NAP		90.65 28	91.07 35	89.42 59	95.93 15	79.54 76	89.95 61	93.68 55	77.65 119	91.97 65	94.89 48	88.38 27	95.45 48	89.27 3	97.87 50	93.27 136

FC-MVSNet-test		85.93 107	87.05 91	82.58 199	92.25 99	56.44 323	85.75 137	93.09 78	77.33 123	91.94 66	94.65 56	74.78 184	93.41 126	75.11 170	98.58 13	97.88 7

lessismore_v0				85.95 117	91.10 141	70.99 171		70.91 362		91.79 67	94.42 69	61.76 276	92.93 141	79.52 117	93.03 216	93.93 106

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 15	82.88 59	91.77 68	93.94 98	90.55 12	95.73 31	88.50 6	98.23 27	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 22	90.95 39	91.93 13	95.67 22	85.85 27	90.00 57	93.90 45	80.32 85	91.74 69	94.41 70	88.17 32	95.98 11	86.37 38	97.99 40	93.96 105

mPP-MVS		91.69 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 17	92.60 96	83.09 56	91.54 70	94.25 78	87.67 41	95.51 44	87.21 28	98.11 35	93.12 144

HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 21	93.29 69	81.99 65	91.47 71	93.96 95	88.35 29	95.56 39	87.74 13	97.74 57	92.85 153

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 70	81.99 65	91.40 72	94.17 83	87.51 42	95.87 19	87.74 13	97.76 55	93.99 103

GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 51	80.98 79	91.38 73	93.80 102	87.20 46	95.80 25	87.10 31	97.69 59	93.93 106

test_fmvsmvis_n_1920		85.22 116	85.36 124	84.81 137	85.80 261	76.13 119	85.15 148	92.32 102	61.40 298	91.33 74	90.85 192	83.76 80	86.16 291	84.31 63	93.28 210	92.15 187

ANet_high		83.17 165	85.68 118	75.65 302	81.24 324	45.26 385	79.94 252	92.91 87	83.83 46	91.33 74	96.88 10	80.25 129	85.92 294	68.89 237	95.89 129	95.76 43

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 28	94.22 26	80.14 88	91.29 76	93.97 92	87.93 38	95.87 19	88.65 4	97.96 45	94.12 99

casdiffmvs_mvg		86.72 92	87.51 82	84.36 149	87.09 232	65.22 223	84.16 164	94.23 24	77.89 116	91.28 77	93.66 108	84.35 73	92.71 145	80.07 107	94.87 170	95.16 61
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS		89.54 52	89.63 54	89.26 62	92.57 88	81.34 64	90.19 56	93.08 79	80.87 81	91.13 78	93.19 115	86.22 59	95.97 12	82.23 88	97.18 79	90.45 233
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 58		91.84 116		88.80 88	95.32 12	75.14 149	91.07 79	92.89 128	87.27 44	93.78 106	83.69 69	97.55 67

CP-MVS		91.67 12	91.58 19	91.96 12	95.29 30	87.62 9	93.38 9	93.36 62	83.16 55	91.06 80	94.00 91	88.26 30	95.71 32	87.28 27	98.39 20	92.55 165

FIs		85.35 115	86.27 103	82.60 198	91.86 113	57.31 316	85.10 149	93.05 80	75.83 139	91.02 81	93.97 92	73.57 198	92.91 143	73.97 181	98.02 39	97.58 12

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 115	92.86 83	67.02 206	82.55 213	91.56 124	83.08 57	90.92 82	91.82 160	78.25 143	93.99 97	74.16 176	98.35 21	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 113	93.24 73	67.02 206	83.16 196	92.21 104	81.73 69	90.92 82	91.97 154	77.20 155	93.99 97	74.16 176	98.35 21	97.61 10

tt0805		88.09 74	89.79 51	82.98 188	93.26 72	63.94 236	91.10 41	89.64 181	85.07 36	90.91 84	91.09 181	89.16 22	91.87 170	82.03 89	95.87 130	93.13 142

V42		83.47 160	83.37 157	83.75 166	83.16 306	63.33 242	81.31 235	90.23 168	69.51 222	90.91 84	90.81 194	74.16 191	92.29 159	80.06 108	90.22 272	95.62 47

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 68	81.91 67	90.88 86	94.21 79	87.75 39	95.87 19	87.60 18	97.71 58	93.83 111

APD_test1		88.40 67	87.91 75	89.88 47	89.50 172	86.65 16	89.98 60	91.91 115	84.26 42	90.87 87	93.92 99	82.18 103	89.29 245	73.75 185	94.81 171	93.70 119

bld_raw_dy_0_64		81.25 196	81.17 200	81.49 219	85.55 264	60.85 279	86.36 128	95.45 9	57.08 336	90.81 88	82.69 329	65.85 254	93.91 101	70.37 221	96.34 105	89.72 246

WR-MVS		83.56 157	84.40 143	81.06 227	93.43 67	54.88 334	78.67 274	85.02 256	81.24 75	90.74 89	91.56 168	72.85 210	91.08 190	68.00 247	98.04 36	97.23 18

v1240		84.30 137	84.51 140	83.65 169	87.65 217	61.26 270	82.85 205	91.54 125	67.94 241	90.68 90	90.65 201	71.71 224	93.64 110	82.84 79	94.78 172	96.07 36

ZD-MVS					92.22 101	80.48 67		91.85 117	71.22 206	90.38 91	92.98 123	86.06 61	96.11 6	81.99 91	96.75 90

MIMVSNet1		83.63 155	84.59 137	80.74 231	94.06 53	62.77 250	82.72 207	84.53 264	77.57 121	90.34 92	95.92 24	76.88 167	85.83 299	61.88 296	97.42 72	93.62 124

LS3D		90.60 30	90.34 47	91.38 24	89.03 183	84.23 45	93.58 6	94.68 17	90.65 7	90.33 93	93.95 97	84.50 71	95.37 51	80.87 100	95.50 143	94.53 79

KD-MVS_self_test		81.93 187	83.14 162	78.30 267	84.75 277	52.75 346	80.37 247	89.42 187	70.24 217	90.26 94	93.39 113	74.55 189	86.77 279	68.61 242	96.64 92	95.38 52

PMVS	80.48 6	90.08 37	90.66 44	88.34 79	96.71 3	92.97 1	90.31 54	89.57 184	88.51 17	90.11 95	95.12 44	90.98 6	88.92 249	77.55 140	97.07 81	83.13 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PC_three_1452									58.96 321	90.06 96	91.33 173	80.66 125	93.03 138	75.78 161	95.94 126	92.48 168

v1921920		84.23 141	84.37 144	83.79 164	87.64 218	61.71 265	82.91 203	91.20 137	67.94 241	90.06 96	90.34 207	72.04 221	93.59 116	82.32 86	94.91 165	96.07 36

ITE_SJBPF				90.11 45	90.72 149	84.97 37		90.30 164	81.56 71	90.02 98	91.20 178	82.40 96	90.81 202	73.58 188	94.66 176	94.56 76

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 64	85.07 36	89.99 99	94.03 89	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

X-MVStestdata		85.04 121	82.70 169	92.08 8	95.64 23	86.25 18	92.64 18	93.33 64	85.07 36	89.99 99	16.05 406	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

v1192		84.57 130	84.69 136	84.21 155	87.75 213	62.88 247	83.02 199	91.43 128	69.08 226	89.98 101	90.89 189	72.70 213	93.62 114	82.41 85	94.97 164	96.13 34

Anonymous20240529		86.20 102	87.13 88	83.42 177	90.19 159	64.55 230	84.55 157	90.71 149	85.85 31	89.94 102	95.24 40	82.13 104	90.40 213	69.19 233	96.40 104	95.31 55

pmmvs6		86.52 96	88.06 74	81.90 209	92.22 101	62.28 260	84.66 155	89.15 189	83.54 52	89.85 103	97.32 4	88.08 36	86.80 278	70.43 219	97.30 76	96.62 28

v144192		84.24 140	84.41 142	83.71 168	87.59 219	61.57 266	82.95 202	91.03 141	67.82 244	89.80 104	90.49 204	73.28 206	93.51 121	81.88 94	94.89 167	96.04 38

v1144		84.54 132	84.72 134	84.00 158	87.67 216	62.55 254	82.97 201	90.93 145	70.32 215	89.80 104	90.99 184	73.50 199	93.48 122	81.69 95	94.65 177	95.97 39

DeepC-MVS	82.31 4	89.15 60	89.08 62	89.37 60	93.64 62	79.07 79	88.54 93	94.20 27	73.53 166	89.71 106	94.82 51	85.09 65	95.77 30	84.17 65	98.03 38	93.26 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 76	86.93 94	91.22 27	90.08 161	89.30 4	89.68 68	91.11 139	79.26 99	89.68 107	94.81 54	82.44 94	87.74 263	76.54 153	88.74 290	96.61 29

IU-MVS					94.18 46	72.64 144		90.82 147	56.98 337	89.67 108				85.78 50	97.92 46	93.28 135

FMVSNet1		84.55 131	85.45 122	81.85 211	90.27 158	61.05 273	86.83 118	88.27 204	78.57 110	89.66 109	95.64 30	75.43 175	90.68 206	69.09 234	95.33 147	93.82 112

IterMVS-LS		84.73 127	84.98 129	83.96 160	87.35 223	63.66 237	83.25 192	89.88 176	76.06 132	89.62 110	92.37 146	73.40 204	92.52 150	78.16 131	94.77 174	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 66	89.01 63	87.36 91	91.30 133	77.50 97	87.55 105	92.97 86	87.95 20	89.62 110	92.87 129	84.56 70	93.89 102	77.65 138	96.62 93	90.70 225

UniMVSNet (Re)		86.87 88	86.98 93	86.55 104	93.11 76	68.48 193	83.80 178	92.87 88	80.37 83	89.61 112	91.81 161	77.72 148	94.18 90	75.00 171	98.53 15	96.99 24

IS-MVSNet		86.66 94	86.82 97	86.17 115	92.05 107	66.87 209	91.21 39	88.64 196	86.30 28	89.60 113	92.59 137	69.22 235	94.91 66	73.89 182	97.89 49	96.72 26

v2v482		84.09 144	84.24 146	83.62 170	87.13 228	61.40 267	82.71 208	89.71 179	72.19 197	89.55 114	91.41 171	70.70 230	93.20 131	81.02 98	93.76 198	96.25 32

Baseline_NR-MVSNet		84.00 148	85.90 112	78.29 268	91.47 131	53.44 342	82.29 221	87.00 227	79.06 102	89.55 114	95.72 28	77.20 155	86.14 292	72.30 204	98.51 16	95.28 56

CSCG		86.26 99	86.47 100	85.60 126	90.87 146	74.26 128	87.98 100	91.85 117	80.35 84	89.54 116	88.01 245	79.09 136	92.13 161	75.51 164	95.06 159	90.41 234

ambc				82.98 188	90.55 153	64.86 226	88.20 96	89.15 189		89.40 117	93.96 95	71.67 225	91.38 183	78.83 122	96.55 95	92.71 159

DeepPCF-MVS	81.24 5	87.28 85	86.21 105	90.49 38	91.48 130	84.90 38	83.41 187	92.38 101	70.25 216	89.35 118	90.68 198	82.85 89	94.57 76	79.55 115	95.95 125	92.00 192

test_fmvsmconf0.01_n		86.68 93	86.52 99	87.18 92	85.94 259	78.30 85	86.93 115	92.20 105	65.94 255	89.16 119	93.16 117	83.10 86	89.89 230	87.81 11	94.43 182	93.35 132

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 45	78.43 111	89.16 119	92.25 150	72.03 222	96.36 3	88.21 7	90.93 259	92.98 150
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
XVG-OURS		89.18 59	88.83 67	90.23 43	94.28 44	86.11 22	85.91 133	93.60 58	80.16 87	89.13 121	93.44 112	83.82 77	90.98 193	83.86 68	95.30 151	93.60 125

MDA-MVSNet-bldmvs		77.47 247	76.90 252	79.16 254	79.03 349	64.59 227	66.58 376	75.67 326	73.15 179	88.86 122	88.99 233	66.94 245	81.23 332	64.71 274	88.22 299	91.64 203

EG-PatchMatch MVS		84.08 145	84.11 147	83.98 159	92.22 101	72.61 147	82.20 227	87.02 224	72.63 187	88.86 122	91.02 183	78.52 139	91.11 189	73.41 190	91.09 253	88.21 271

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 135	81.66 62	91.25 38	94.13 34	88.89 11	88.83 124	94.26 77	77.55 151	95.86 22	84.88 58	95.87 130	95.24 58

EI-MVSNet-UG-set		85.04 121	84.44 141	86.85 99	83.87 293	72.52 150	83.82 176	85.15 252	80.27 86	88.75 125	85.45 290	79.95 132	91.90 168	81.92 93	90.80 264	96.13 34

EI-MVSNet-Vis-set		85.12 120	84.53 139	86.88 98	84.01 289	72.76 141	83.91 174	85.18 251	80.44 82	88.75 125	85.49 288	80.08 130	91.92 167	82.02 90	90.85 263	95.97 39

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 111	81.68 61	87.49 108	93.17 73	76.02 134	88.64 127	91.22 176	84.24 75	93.37 127	77.97 136	97.03 82	95.52 49

test_fmvsmconf0.1_n		86.18 103	85.88 113	87.08 94	85.26 269	78.25 86	85.82 136	91.82 119	65.33 268	88.55 128	92.35 147	82.62 93	89.80 232	86.87 32	94.32 185	93.18 141

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 91	88.22 18	88.53 129	97.64 2	83.45 83	94.55 78	86.02 48	98.60 12	96.67 27

MP-MVS		91.14 24	90.91 40	91.83 18	96.18 10	86.88 13	92.20 27	93.03 83	82.59 61	88.52 130	94.37 73	86.74 50	95.41 50	86.32 39	98.21 29	93.19 140
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
sasdasda		85.50 111	86.14 106	83.58 172	87.97 207	67.13 203	87.55 105	94.32 19	73.44 169	88.47 131	87.54 256	86.45 55	91.06 191	75.76 162	93.76 198	92.54 166

canonicalmvs		85.50 111	86.14 106	83.58 172	87.97 207	67.13 203	87.55 105	94.32 19	73.44 169	88.47 131	87.54 256	86.45 55	91.06 191	75.76 162	93.76 198	92.54 166

NR-MVSNet		86.00 105	86.22 104	85.34 130	93.24 73	64.56 229	82.21 225	90.46 156	80.99 78	88.42 133	91.97 154	77.56 150	93.85 103	72.46 203	98.65 11	97.61 10

alignmvs		83.94 150	83.98 150	83.80 163	87.80 212	67.88 200	84.54 159	91.42 130	73.27 177	88.41 134	87.96 246	72.33 216	90.83 201	76.02 160	94.11 190	92.69 160

TransMVSNet (Re)		84.02 147	85.74 117	78.85 256	91.00 143	55.20 333	82.29 221	87.26 215	79.65 93	88.38 135	95.52 33	83.00 87	86.88 276	67.97 248	96.60 94	94.45 82

PM-MVS		80.20 217	79.00 227	83.78 165	88.17 205	86.66 15	81.31 235	66.81 380	69.64 221	88.33 136	90.19 212	64.58 259	83.63 320	71.99 206	90.03 274	81.06 367

tttt0517		81.07 199	79.58 222	85.52 127	88.99 185	66.45 213	87.03 114	75.51 328	73.76 162	88.32 137	90.20 211	37.96 388	94.16 94	79.36 119	95.13 155	95.93 42

casdiffmvs		85.21 117	85.85 114	83.31 180	86.17 254	62.77 250	83.03 198	93.93 43	74.69 153	88.21 138	92.68 136	82.29 101	91.89 169	77.87 137	93.75 201	95.27 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_vis3_rt		71.42 307	70.67 309	73.64 314	69.66 399	70.46 174	66.97 375	89.73 177	42.68 395	88.20 139	83.04 319	43.77 373	60.07 397	65.35 269	86.66 318	90.39 235

SSC-MVS		77.55 246	81.64 185	65.29 366	90.46 154	20.33 411	73.56 337	68.28 371	85.44 32	88.18 140	94.64 59	70.93 228	81.33 331	71.25 208	92.03 236	94.20 92

MGCFI-Net		85.04 121	85.95 109	82.31 205	87.52 220	63.59 239	86.23 131	93.96 41	73.46 167	88.07 141	87.83 251	86.46 54	90.87 200	76.17 157	93.89 196	92.47 170

v148		82.31 175	82.48 175	81.81 214	85.59 263	59.66 292	81.47 234	86.02 239	72.85 182	88.05 142	90.65 201	70.73 229	90.91 197	75.15 169	91.79 241	94.87 67

AllTest		87.97 77	87.40 85	89.68 53	91.59 121	83.40 48	89.50 75	95.44 10	79.47 94	88.00 143	93.03 121	82.66 91	91.47 177	70.81 211	96.14 115	94.16 96

TestCases				89.68 53	91.59 121	83.40 48		95.44 10	79.47 94	88.00 143	93.03 121	82.66 91	91.47 177	70.81 211	96.14 115	94.16 96

pm-mvs1		83.69 153	84.95 130	79.91 243	90.04 165	59.66 292	82.43 217	87.44 212	75.52 144	87.85 145	95.26 39	81.25 118	85.65 301	68.74 240	96.04 120	94.42 85

PCF-MVS	74.62 15	82.15 181	80.92 203	85.84 121	89.43 174	72.30 154	80.53 245	91.82 119	57.36 334	87.81 146	89.92 218	77.67 149	93.63 111	58.69 313	95.08 158	91.58 205
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_fmvsmconf_n		85.88 108	85.51 121	86.99 96	84.77 276	78.21 87	85.40 144	91.39 131	65.32 269	87.72 147	91.81 161	82.33 98	89.78 233	86.68 34	94.20 188	92.99 149

FMVSNet2		81.31 195	81.61 187	80.41 237	86.38 244	58.75 306	83.93 173	86.58 231	72.43 189	87.65 148	92.98 123	63.78 266	90.22 217	66.86 251	93.92 194	92.27 181

GeoE		85.45 114	85.81 115	84.37 147	90.08 161	67.07 205	85.86 135	91.39 131	72.33 194	87.59 149	90.25 210	84.85 68	92.37 155	78.00 134	91.94 240	93.66 120

VPA-MVSNet		83.47 160	84.73 132	79.69 247	90.29 157	57.52 315	81.30 237	88.69 195	76.29 130	87.58 150	94.44 66	80.60 126	87.20 270	66.60 256	96.82 88	94.34 89

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 29	92.30 103	79.74 91	87.50 151	92.38 143	81.42 116	93.28 129	83.07 74	97.24 77	91.67 202

VDDNet		84.35 135	85.39 123	81.25 222	95.13 31	59.32 295	85.42 143	81.11 291	86.41 27	87.41 152	96.21 19	73.61 197	90.61 209	66.33 257	96.85 85	93.81 115

c3_l		81.64 191	81.59 188	81.79 215	80.86 330	59.15 299	78.61 275	90.18 170	68.36 233	87.20 153	87.11 267	69.39 233	91.62 174	78.16 131	94.43 182	94.60 75

VDD-MVS		84.23 141	84.58 138	83.20 184	91.17 139	65.16 225	83.25 192	84.97 259	79.79 90	87.18 154	94.27 74	74.77 185	90.89 198	69.24 230	96.54 96	93.55 130

MSLP-MVS++		85.00 124	86.03 108	81.90 209	91.84 116	71.56 167	86.75 122	93.02 84	75.95 137	87.12 155	89.39 225	77.98 144	89.40 244	77.46 141	94.78 172	84.75 314

baseline		85.20 118	85.93 110	83.02 187	86.30 249	62.37 258	84.55 157	93.96 41	74.48 155	87.12 155	92.03 153	82.30 100	91.94 166	78.39 124	94.21 187	94.74 73

YYNet1		70.06 319	70.44 312	68.90 346	73.76 385	53.42 343	58.99 392	67.20 376	58.42 324	87.10 157	85.39 292	59.82 289	67.32 382	59.79 309	83.50 353	85.96 299

MDA-MVSNet_test_wron		70.05 320	70.44 312	68.88 347	73.84 384	53.47 341	58.93 393	67.28 375	58.43 323	87.09 158	85.40 291	59.80 290	67.25 383	59.66 310	83.54 352	85.92 301

test_fmvs3		75.72 268	75.20 268	77.27 284	75.01 381	69.47 183	78.93 268	84.88 260	46.67 379	87.08 159	87.84 250	50.44 339	71.62 366	77.42 144	88.53 291	90.72 223

CNVR-MVS		87.81 81	87.68 79	88.21 81	92.87 81	77.30 103	85.25 145	91.23 136	77.31 124	87.07 160	91.47 170	82.94 88	94.71 70	84.67 60	96.27 110	92.62 163

EPP-MVSNet		85.47 113	85.04 128	86.77 101	91.52 129	69.37 184	91.63 36	87.98 209	81.51 72	87.05 161	91.83 159	66.18 250	95.29 53	70.75 214	96.89 84	95.64 46

TinyColmap		81.25 196	82.34 177	77.99 274	85.33 268	60.68 283	82.32 220	88.33 202	71.26 205	86.97 162	92.22 152	77.10 158	86.98 274	62.37 290	95.17 154	86.31 297

eth_miper_zixun_eth		80.84 202	80.22 214	82.71 196	81.41 322	60.98 276	77.81 284	90.14 171	67.31 248	86.95 163	87.24 264	64.26 261	92.31 157	75.23 168	91.61 245	94.85 71

Anonymous20240521		80.18 218	81.25 196	76.95 287	83.15 307	60.84 280	82.46 216	85.99 240	68.76 230	86.78 164	93.73 107	59.13 294	77.44 350	73.71 186	97.55 67	92.56 164

Patchmatch-RL test		74.48 281	73.68 280	76.89 290	84.83 274	66.54 211	72.29 345	69.16 370	57.70 330	86.76 165	86.33 276	45.79 359	82.59 324	69.63 227	90.65 269	81.54 358

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 117	93.91 44	80.07 89	86.75 166	93.26 114	93.64 2	90.93 195	84.60 61	90.75 265	93.97 104

h-mvs33		84.25 139	82.76 168	88.72 71	91.82 118	82.60 56	84.00 170	84.98 258	71.27 203	86.70 167	90.55 203	63.04 272	93.92 100	78.26 129	94.20 188	89.63 249

hse-mvs2		83.47 160	81.81 183	88.47 75	91.03 142	82.27 57	82.61 209	83.69 269	71.27 203	86.70 167	86.05 282	63.04 272	92.41 153	78.26 129	93.62 205	90.71 224

HPM-MVS++		88.93 64	88.45 71	90.38 40	94.92 35	85.85 27	89.70 66	91.27 135	78.20 113	86.69 169	92.28 149	80.36 128	95.06 62	86.17 44	96.49 99	90.22 237

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 65	95.72 21	76.74 108	92.49 24	91.19 138	67.85 243	86.63 170	94.84 50	79.58 134	95.96 13	87.62 16	94.50 179	94.56 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet		82.61 170	82.42 176	83.20 184	83.25 303	63.66 237	83.50 185	85.07 253	76.06 132	86.55 171	85.10 296	73.41 202	90.25 214	78.15 133	90.67 267	95.68 45

HQP_MVS		87.75 82	87.43 84	88.70 73	93.45 65	76.42 113	89.45 77	93.61 56	79.44 96	86.55 171	92.95 126	74.84 182	95.22 56	80.78 102	95.83 132	94.46 80

plane_prior3						76.85 107			77.79 118	86.55 171

BH-untuned		80.96 201	80.99 201	80.84 230	88.55 197	68.23 194	80.33 248	88.46 197	72.79 185	86.55 171	86.76 271	74.72 186	91.77 173	61.79 297	88.99 285	82.52 348

MVSTER		77.09 251	75.70 263	81.25 222	75.27 378	61.08 272	77.49 291	85.07 253	60.78 308	86.55 171	88.68 237	43.14 378	90.25 214	73.69 187	90.67 267	92.42 171

旧先验2							81.73 230		56.88 338	86.54 176			84.90 307	72.81 200

IterMVS-SCA-FT		80.64 206	79.41 223	84.34 151	83.93 291	69.66 181	76.28 309	81.09 292	72.43 189	86.47 177	90.19 212	60.46 282	93.15 134	77.45 142	86.39 322	90.22 237

WB-MVS		76.06 264	80.01 220	64.19 369	89.96 167	20.58 410	72.18 346	68.19 372	83.21 54	86.46 178	93.49 111	70.19 231	78.97 345	65.96 259	90.46 271	93.02 147

test_fmvsm_n_1920		83.60 156	82.89 166	85.74 123	85.22 270	77.74 95	84.12 166	90.48 155	59.87 318	86.45 179	91.12 180	75.65 173	85.89 297	82.28 87	90.87 261	93.58 126

DIV-MVS_self_test		80.43 209	80.23 212	81.02 228	79.99 338	59.25 296	77.07 296	87.02 224	67.38 245	86.19 180	89.22 228	63.09 270	90.16 219	76.32 154	95.80 135	93.66 120

CDPH-MVS		86.17 104	85.54 120	88.05 84	92.25 99	75.45 122	83.85 175	92.01 110	65.91 257	86.19 180	91.75 164	83.77 79	94.98 64	77.43 143	96.71 91	93.73 118

cl____		80.42 210	80.23 212	81.02 228	79.99 338	59.25 296	77.07 296	87.02 224	67.37 246	86.18 182	89.21 229	63.08 271	90.16 219	76.31 155	95.80 135	93.65 122

MVS_111021_LR		84.28 138	83.76 153	85.83 122	89.23 179	83.07 51	80.99 241	83.56 272	72.71 186	86.07 183	89.07 232	81.75 113	86.19 290	77.11 147	93.36 206	88.24 270

GBi-Net		82.02 184	82.07 178	81.85 211	86.38 244	61.05 273	86.83 118	88.27 204	72.43 189	86.00 184	95.64 30	63.78 266	90.68 206	65.95 260	93.34 207	93.82 112

test1		82.02 184	82.07 178	81.85 211	86.38 244	61.05 273	86.83 118	88.27 204	72.43 189	86.00 184	95.64 30	63.78 266	90.68 206	65.95 260	93.34 207	93.82 112

FMVSNet3		78.80 232	78.55 234	79.57 249	82.89 311	56.89 321	81.76 229	85.77 242	69.04 227	86.00 184	90.44 205	51.75 333	90.09 225	65.95 260	93.34 207	91.72 199

miper_ehance_all_eth		80.34 213	80.04 219	81.24 224	79.82 340	58.95 301	77.66 286	89.66 180	65.75 261	85.99 187	85.11 295	68.29 240	91.42 181	76.03 159	92.03 236	93.33 133

tfpnnormal		81.79 190	82.95 165	78.31 266	88.93 186	55.40 329	80.83 244	82.85 278	76.81 127	85.90 188	94.14 84	74.58 188	86.51 283	66.82 254	95.68 141	93.01 148

TAPA-MVS	77.73 12	85.71 110	84.83 131	88.37 78	88.78 191	79.72 73	87.15 112	93.50 59	69.17 224	85.80 189	89.56 223	80.76 123	92.13 161	73.21 198	95.51 142	93.25 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 149	82.69 170	87.72 86	89.27 178	81.45 63	83.72 180	81.58 290	74.73 152	85.66 190	86.06 281	72.56 215	92.69 147	75.44 166	95.21 152	89.01 265

EU-MVSNet		75.12 273	74.43 275	77.18 285	83.11 308	59.48 294	85.71 139	82.43 282	39.76 399	85.64 191	88.76 235	44.71 371	87.88 262	73.86 183	85.88 328	84.16 324

LF4IMVS		82.75 169	81.93 181	85.19 131	82.08 313	80.15 70	85.53 140	88.76 194	68.01 238	85.58 192	87.75 252	71.80 223	86.85 277	74.02 180	93.87 197	88.58 268

Patchmtry		76.56 259	77.46 243	73.83 312	79.37 346	46.60 378	82.41 218	76.90 317	73.81 161	85.56 193	92.38 143	48.07 346	83.98 317	63.36 285	95.31 150	90.92 218

MVS_111021_HR		84.63 128	84.34 145	85.49 129	90.18 160	75.86 120	79.23 266	87.13 219	73.35 171	85.56 193	89.34 226	83.60 82	90.50 211	76.64 151	94.05 192	90.09 242

testdata				79.54 250	92.87 81	72.34 153		80.14 298	59.91 317	85.47 195	91.75 164	67.96 242	85.24 303	68.57 244	92.18 235	81.06 367

test1111		78.53 236	78.85 229	77.56 280	92.22 101	47.49 374	82.61 209	69.24 369	72.43 189	85.28 196	94.20 80	51.91 331	90.07 226	65.36 268	96.45 102	95.11 62

thisisatest0530		79.07 226	77.33 247	84.26 154	87.13 228	64.58 228	83.66 182	75.95 323	68.86 229	85.22 197	87.36 261	38.10 386	93.57 119	75.47 165	94.28 186	94.62 74

EC-MVSNet		88.01 75	88.32 72	87.09 93	89.28 177	72.03 158	90.31 54	96.31 3	80.88 80	85.12 198	89.67 222	84.47 72	95.46 47	82.56 83	96.26 111	93.77 117

CLD-MVS		83.18 164	82.64 171	84.79 138	89.05 182	67.82 201	77.93 282	92.52 97	68.33 234	85.07 199	81.54 340	82.06 105	92.96 139	69.35 229	97.91 48	93.57 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
fmvsm_s_conf0.1_n_a		82.58 172	81.93 181	84.50 144	87.68 215	73.35 133	86.14 132	77.70 309	61.64 296	85.02 200	91.62 166	77.75 147	86.24 287	82.79 80	87.07 311	93.91 108

FA-MVS(test-final)		83.13 166	83.02 164	83.43 176	86.16 256	66.08 216	88.00 99	88.36 200	75.55 143	85.02 200	92.75 134	65.12 258	92.50 151	74.94 172	91.30 251	91.72 199

DeepC-MVS_fast	80.27 8	86.23 100	85.65 119	87.96 85	91.30 133	76.92 106	87.19 110	91.99 111	70.56 211	84.96 202	90.69 197	80.01 131	95.14 59	78.37 125	95.78 137	91.82 197
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 126	84.71 135	85.06 134	86.36 247	74.71 125	88.77 89	90.00 174	75.65 142	84.96 202	93.17 116	74.06 192	91.19 186	78.28 128	91.09 253	89.29 257

QAPM		82.59 171	82.59 173	82.58 199	86.44 242	66.69 210	89.94 62	90.36 160	67.97 240	84.94 204	92.58 139	72.71 212	92.18 160	70.63 217	87.73 304	88.85 266

VPNet		80.25 215	81.68 184	75.94 300	92.46 92	47.98 372	76.70 301	81.67 288	73.45 168	84.87 205	92.82 130	74.66 187	86.51 283	61.66 299	96.85 85	93.33 133

NCCC		87.36 84	86.87 95	88.83 68	92.32 97	78.84 82	86.58 125	91.09 140	78.77 107	84.85 206	90.89 189	80.85 122	95.29 53	81.14 97	95.32 148	92.34 176

PHI-MVS		86.38 97	85.81 115	88.08 82	88.44 200	77.34 101	89.35 80	93.05 80	73.15 179	84.76 207	87.70 253	78.87 138	94.18 90	80.67 104	96.29 107	92.73 156

pmmvs-eth3d		78.42 238	77.04 250	82.57 201	87.44 222	74.41 127	80.86 243	79.67 300	55.68 341	84.69 208	90.31 209	60.91 280	85.42 302	62.20 292	91.59 246	87.88 280

test_prior2							83.37 188		75.43 145	84.58 209	91.57 167	81.92 110		79.54 116	96.97 83

fmvsm_s_conf0.5_n_a		82.21 178	81.51 192	84.32 152	86.56 240	73.35 133	85.46 141	77.30 313	61.81 292	84.51 210	90.88 191	77.36 153	86.21 289	82.72 81	86.97 316	93.38 131

TEST9					92.34 95	79.70 74	83.94 171	90.32 161	65.41 267	84.49 211	90.97 185	82.03 106	93.63 111

train_agg		85.98 106	85.28 125	88.07 83	92.34 95	79.70 74	83.94 171	90.32 161	65.79 258	84.49 211	90.97 185	81.93 108	93.63 111	81.21 96	96.54 96	90.88 219

fmvsm_s_conf0.1_n		82.17 180	81.59 188	83.94 162	86.87 238	71.57 166	85.19 147	77.42 312	62.27 290	84.47 213	91.33 173	76.43 169	85.91 295	83.14 71	87.14 309	94.33 90

Gipuma		84.44 133	86.33 102	78.78 257	84.20 287	73.57 132	89.55 72	90.44 157	84.24 43	84.38 214	94.89 48	76.35 172	80.40 338	76.14 158	96.80 89	82.36 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		64.31 355	65.85 345	59.67 380	66.54 404	62.24 262	57.76 394	70.96 361	40.13 397	84.36 215	82.09 332	46.93 348	51.67 403	61.99 295	81.89 363	65.12 395

test_8					92.09 105	78.87 81	83.82 176	90.31 163	65.79 258	84.36 215	90.96 187	81.93 108	93.44 124

cl22		78.97 227	78.21 239	81.24 224	77.74 354	59.01 300	77.46 292	87.13 219	65.79 258	84.32 217	85.10 296	58.96 296	90.88 199	75.36 167	92.03 236	93.84 110

CS-MVS		88.14 72	87.67 80	89.54 58	89.56 170	79.18 78	90.47 51	94.77 16	79.37 98	84.32 217	89.33 227	83.87 76	94.53 79	82.45 84	94.89 167	94.90 65

agg_prior					91.58 124	77.69 96		90.30 164		84.32 217			93.18 132

Anonymous202405211		80.51 208	81.19 199	78.49 263	88.48 198	57.26 317	76.63 303	82.49 281	81.21 76	84.30 220	92.24 151	67.99 241	86.24 287	62.22 291	95.13 155	91.98 194

LFMVS		80.15 219	80.56 206	78.89 255	89.19 181	55.93 325	85.22 146	73.78 340	82.96 58	84.28 221	92.72 135	57.38 306	90.07 226	63.80 281	95.75 138	90.68 226

Vis-MVSNet		86.86 89	86.58 98	87.72 86	92.09 105	77.43 100	87.35 109	92.09 108	78.87 105	84.27 222	94.05 88	78.35 142	93.65 109	80.54 106	91.58 247	92.08 189
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ECVR-MVS		78.44 237	78.63 233	77.88 276	91.85 114	48.95 368	83.68 181	69.91 366	72.30 195	84.26 223	94.20 80	51.89 332	89.82 231	63.58 282	96.02 121	94.87 67

iter_conf05		78.81 231	77.35 246	83.21 183	82.98 310	60.75 282	84.09 167	88.34 201	63.12 279	84.25 224	89.48 224	31.41 397	94.51 81	76.64 151	95.83 132	94.38 88

FE-MVS		79.98 222	78.86 228	83.36 178	86.47 241	66.45 213	89.73 65	84.74 263	72.80 184	84.22 225	91.38 172	44.95 369	93.60 115	63.93 280	91.50 248	90.04 243

ETV-MVS		84.31 136	83.91 152	85.52 127	88.58 196	70.40 175	84.50 161	93.37 61	78.76 108	84.07 226	78.72 364	80.39 127	95.13 60	73.82 184	92.98 218	91.04 215

fmvsm_s_conf0.5_n		81.91 188	81.30 195	83.75 166	86.02 258	71.56 167	84.73 153	77.11 316	62.44 287	84.00 227	90.68 198	76.42 170	85.89 297	83.14 71	87.11 310	93.81 115

MCST-MVS		84.36 134	83.93 151	85.63 125	91.59 121	71.58 165	83.52 184	92.13 107	61.82 291	83.96 228	89.75 221	79.93 133	93.46 123	78.33 127	94.34 184	91.87 196

新几何1				82.95 190	93.96 55	78.56 84		80.24 297	55.45 342	83.93 229	91.08 182	71.19 227	88.33 258	65.84 263	93.07 215	81.95 354

fmvsm_l_conf0.5_n		82.06 183	81.54 191	83.60 171	83.94 290	73.90 130	83.35 189	86.10 236	58.97 320	83.80 230	90.36 206	74.23 190	86.94 275	82.90 77	90.22 272	89.94 244

BH-RMVSNet		80.53 207	80.22 214	81.49 219	87.19 227	66.21 215	77.79 285	86.23 234	74.21 157	83.69 231	88.50 239	73.25 207	90.75 203	63.18 287	87.90 301	87.52 284

USDC		76.63 257	76.73 254	76.34 296	83.46 297	57.20 318	80.02 251	88.04 208	52.14 361	83.65 232	91.25 175	63.24 269	86.65 281	54.66 340	94.11 190	85.17 309

miper_enhance_ethall		77.83 242	76.93 251	80.51 235	76.15 370	58.01 311	75.47 321	88.82 192	58.05 328	83.59 233	80.69 344	64.41 260	91.20 185	73.16 199	92.03 236	92.33 177

MM		87.64 83	87.15 87	89.09 65	89.51 171	76.39 115	88.68 91	86.76 229	84.54 41	83.58 234	93.78 104	73.36 205	96.48 1	87.98 9	96.21 112	94.41 86

Effi-MVS+-dtu		85.82 109	83.38 156	93.14 3	87.13 228	91.15 2	87.70 104	88.42 198	74.57 154	83.56 235	85.65 286	78.49 141	94.21 88	72.04 205	92.88 220	94.05 102

CNLPA		83.55 158	83.10 163	84.90 135	89.34 176	83.87 46	84.54 159	88.77 193	79.09 101	83.54 236	88.66 238	74.87 181	81.73 329	66.84 253	92.29 230	89.11 259

SDMVSNet		81.90 189	83.17 161	78.10 271	88.81 189	62.45 256	76.08 313	86.05 238	73.67 163	83.41 237	93.04 119	82.35 97	80.65 336	70.06 224	95.03 160	91.21 211

sd_testset		79.95 223	81.39 194	75.64 303	88.81 189	58.07 310	76.16 312	82.81 279	73.67 163	83.41 237	93.04 119	80.96 121	77.65 349	58.62 314	95.03 160	91.21 211

OpenMVS_ROB	70.19 17	77.77 245	77.46 243	78.71 259	84.39 283	61.15 271	81.18 239	82.52 280	62.45 286	83.34 239	87.37 260	66.20 249	88.66 255	64.69 275	85.02 338	86.32 296

thres100view900		75.45 269	75.05 269	76.66 293	87.27 224	51.88 354	81.07 240	73.26 345	75.68 141	83.25 240	86.37 275	45.54 360	88.80 250	51.98 356	90.99 255	89.31 255

miper_lstm_enhance		76.45 261	76.10 259	77.51 281	76.72 365	60.97 277	64.69 380	85.04 255	63.98 276	83.20 241	88.22 242	56.67 310	78.79 347	73.22 193	93.12 214	92.78 155

IterMVS		76.91 253	76.34 257	78.64 260	80.91 328	64.03 234	76.30 308	79.03 303	64.88 272	83.11 242	89.16 230	59.90 288	84.46 310	68.61 242	85.15 336	87.42 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 265	75.35 267	77.85 278	87.01 234	51.84 355	80.45 246	73.26 345	75.20 148	83.10 243	86.31 278	45.54 360	89.05 246	55.03 338	92.24 232	92.66 161

mvs_anonymous		78.13 240	78.76 231	76.23 299	79.24 347	50.31 365	78.69 273	84.82 261	61.60 297	83.09 244	92.82 130	73.89 195	87.01 271	68.33 246	86.41 321	91.37 208

fmvsm_l_conf0.5_n_a		81.46 193	80.87 204	83.25 181	83.73 295	73.21 138	83.00 200	85.59 245	58.22 326	82.96 245	90.09 216	72.30 217	86.65 281	81.97 92	89.95 276	89.88 245

test_fmvs2		73.57 288	72.80 290	75.90 301	72.74 393	68.84 192	77.07 296	84.32 266	45.14 385	82.89 246	84.22 308	48.37 344	70.36 369	73.40 191	87.03 313	88.52 269

MVS_Test		82.47 174	83.22 158	80.22 240	82.62 312	57.75 314	82.54 214	91.96 113	71.16 207	82.89 246	92.52 141	77.41 152	90.50 211	80.04 109	87.84 303	92.40 173

test12				86.57 103	90.74 148	72.63 146		90.69 150		82.76 248		79.20 135	94.80 68		95.32 148	92.27 181

原ACMM1				84.60 143	92.81 86	74.01 129		91.50 126	62.59 282	82.73 249	90.67 200	76.53 168	94.25 86	69.24 230	95.69 140	85.55 305

test_yl		78.71 234	78.51 235	79.32 252	84.32 284	58.84 303	78.38 276	85.33 248	75.99 135	82.49 250	86.57 272	58.01 300	90.02 228	62.74 288	92.73 223	89.10 260

DCV-MVSNet		78.71 234	78.51 235	79.32 252	84.32 284	58.84 303	78.38 276	85.33 248	75.99 135	82.49 250	86.57 272	58.01 300	90.02 228	62.74 288	92.73 223	89.10 260

diffmvs		80.40 211	80.48 209	80.17 241	79.02 350	60.04 287	77.54 289	90.28 167	66.65 253	82.40 252	87.33 262	73.50 199	87.35 268	77.98 135	89.62 279	93.13 142
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test222					93.31 70	76.54 109	79.38 261	77.79 308	52.59 356	82.36 253	90.84 193	66.83 247			91.69 243	81.25 362

D2MVS		76.84 254	75.67 264	80.34 238	80.48 336	62.16 263	73.50 338	84.80 262	57.61 332	82.24 254	87.54 256	51.31 334	87.65 264	70.40 220	93.19 213	91.23 210

VNet		79.31 225	80.27 211	76.44 294	87.92 210	53.95 338	75.58 319	84.35 265	74.39 156	82.23 255	90.72 196	72.84 211	84.39 312	60.38 307	93.98 193	90.97 216

Vis-MVSNet (Re-imp)		77.82 243	77.79 242	77.92 275	88.82 188	51.29 359	83.28 190	71.97 354	74.04 158	82.23 255	89.78 220	57.38 306	89.41 243	57.22 322	95.41 144	93.05 146

API-MVS		82.28 176	82.61 172	81.30 221	86.29 250	69.79 178	88.71 90	87.67 211	78.42 112	82.15 257	84.15 310	77.98 144	91.59 175	65.39 267	92.75 222	82.51 349

DP-MVS Recon		84.05 146	83.22 158	86.52 105	91.73 119	75.27 123	83.23 194	92.40 99	72.04 198	82.04 258	88.33 241	77.91 146	93.95 99	66.17 258	95.12 157	90.34 236

MSDG		80.06 221	79.99 221	80.25 239	83.91 292	68.04 199	77.51 290	89.19 188	77.65 119	81.94 259	83.45 316	76.37 171	86.31 286	63.31 286	86.59 319	86.41 295

test2506		74.12 284	73.39 284	76.28 297	91.85 114	44.20 388	84.06 168	48.20 407	72.30 195	81.90 260	94.20 80	27.22 408	89.77 234	64.81 273	96.02 121	94.87 67

Fast-Effi-MVS+		81.04 200	80.57 205	82.46 203	87.50 221	63.22 244	78.37 278	89.63 182	68.01 238	81.87 261	82.08 333	82.31 99	92.65 148	67.10 250	88.30 298	91.51 207

testgi		72.36 298	74.61 271	65.59 363	80.56 335	42.82 393	68.29 368	73.35 344	66.87 251	81.84 262	89.93 217	72.08 220	66.92 385	46.05 382	92.54 225	87.01 290

tfpn200view9		74.86 277	74.23 276	76.74 292	86.24 251	52.12 351	79.24 264	73.87 338	73.34 172	81.82 263	84.60 305	46.02 354	88.80 250	51.98 356	90.99 255	89.31 255

thres400		75.14 271	74.23 276	77.86 277	86.24 251	52.12 351	79.24 264	73.87 338	73.34 172	81.82 263	84.60 305	46.02 354	88.80 250	51.98 356	90.99 255	92.66 161

CL-MVSNet_self_test		76.81 255	77.38 245	75.12 306	86.90 236	51.34 357	73.20 341	80.63 296	68.30 235	81.80 265	88.40 240	66.92 246	80.90 333	55.35 335	94.90 166	93.12 144

OpenMVS	76.72 13	81.98 186	82.00 180	81.93 208	84.42 282	68.22 195	88.50 94	89.48 185	66.92 250	81.80 265	91.86 156	72.59 214	90.16 219	71.19 210	91.25 252	87.40 286

AdaColmap		83.66 154	83.69 154	83.57 174	90.05 164	72.26 155	86.29 130	90.00 174	78.19 114	81.65 267	87.16 265	83.40 84	94.24 87	61.69 298	94.76 175	84.21 323

CS-MVS-test		87.00 87	86.43 101	88.71 72	89.46 173	77.46 98	89.42 79	95.73 6	77.87 117	81.64 268	87.25 263	82.43 95	94.53 79	77.65 138	96.46 101	94.14 98

DELS-MVS		81.44 194	81.25 196	82.03 207	84.27 286	62.87 248	76.47 307	92.49 98	70.97 208	81.64 268	83.83 311	75.03 179	92.70 146	74.29 174	92.22 234	90.51 232
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
114514_t		83.10 167	82.54 174	84.77 139	92.90 80	69.10 191	86.65 123	90.62 153	54.66 347	81.46 270	90.81 194	76.98 160	94.38 83	72.62 201	96.18 113	90.82 221

TR-MVS		76.77 256	75.79 261	79.72 246	86.10 257	65.79 219	77.14 294	83.02 276	65.20 270	81.40 271	82.10 331	66.30 248	90.73 205	55.57 332	85.27 332	82.65 343

TAMVS		78.08 241	76.36 256	83.23 182	90.62 151	72.87 140	79.08 267	80.01 299	61.72 294	81.35 272	86.92 270	63.96 265	88.78 253	50.61 361	93.01 217	88.04 276

Effi-MVS+		83.90 151	84.01 149	83.57 174	87.22 226	65.61 221	86.55 126	92.40 99	78.64 109	81.34 273	84.18 309	83.65 81	92.93 141	74.22 175	87.87 302	92.17 186

testing3		71.53 306	70.79 308	73.77 313	88.89 187	41.86 395	76.60 305	59.12 397	72.83 183	80.97 274	82.08 333	19.80 413	87.33 269	65.12 270	91.68 244	92.13 188

new-patchmatchnet		70.10 318	73.37 285	60.29 379	81.23 325	16.95 412	59.54 389	74.62 331	62.93 280	80.97 274	87.93 248	62.83 274	71.90 364	55.24 336	95.01 163	92.00 192

PVSNet_Blended_VisFu		81.55 192	80.49 208	84.70 142	91.58 124	73.24 137	84.21 163	91.67 123	62.86 281	80.94 276	87.16 265	67.27 244	92.87 144	69.82 226	88.94 287	87.99 277

BH-w/o		76.57 258	76.07 260	78.10 271	86.88 237	65.92 218	77.63 287	86.33 232	65.69 262	80.89 277	79.95 353	68.97 238	90.74 204	53.01 351	85.25 333	77.62 378

MVS_0304		86.35 98	85.92 111	87.66 88	89.21 180	73.16 139	88.40 95	83.63 271	81.27 74	80.87 278	94.12 86	71.49 226	95.71 32	87.79 12	96.50 98	94.11 100

PAPM_NR		83.23 163	83.19 160	83.33 179	90.90 145	65.98 217	88.19 97	90.78 148	78.13 115	80.87 278	87.92 249	73.49 201	92.42 152	70.07 223	88.40 292	91.60 204

ab-mvs		79.67 224	80.56 206	76.99 286	88.48 198	56.93 319	84.70 154	86.06 237	68.95 228	80.78 280	93.08 118	75.30 177	84.62 309	56.78 323	90.90 260	89.43 253

XXY-MVS		74.44 283	76.19 258	69.21 344	84.61 278	52.43 350	71.70 349	77.18 315	60.73 309	80.60 281	90.96 187	75.44 174	69.35 372	56.13 328	88.33 294	85.86 302

HQP4-MVS										80.56 282			94.61 74			93.56 128

HQP-NCC					91.19 136		84.77 150		73.30 174	80.55 283

ACMP_Plane					91.19 136		84.77 150		73.30 174	80.55 283

HQP-MVS		84.61 129	84.06 148	86.27 110	91.19 136	70.66 172	84.77 150	92.68 94	73.30 174	80.55 283	90.17 214	72.10 218	94.61 74	77.30 145	94.47 180	93.56 128

test_cas_vis1_n_1920		69.20 330	69.12 323	69.43 343	73.68 386	62.82 249	70.38 361	77.21 314	46.18 382	80.46 286	78.95 362	52.03 330	65.53 390	65.77 265	77.45 385	79.95 373

AUN-MVS		81.18 198	78.78 230	88.39 77	90.93 144	82.14 58	82.51 215	83.67 270	64.69 273	80.29 287	85.91 285	51.07 335	92.38 154	76.29 156	93.63 204	90.65 228

HyFIR lowres test		75.12 273	72.66 293	82.50 202	91.44 132	65.19 224	72.47 344	87.31 214	46.79 378	80.29 287	84.30 307	52.70 328	92.10 164	51.88 360	86.73 317	90.22 237

test20.03		73.75 287	74.59 273	71.22 332	81.11 326	51.12 361	70.15 362	72.10 353	70.42 212	80.28 289	91.50 169	64.21 262	74.72 360	46.96 379	94.58 178	87.82 282

mvsany_test3		65.48 350	62.97 358	73.03 319	69.99 398	76.17 118	64.83 378	43.71 409	43.68 390	80.25 290	87.05 269	52.83 327	63.09 396	51.92 359	72.44 391	79.84 374

F-COLMAP		84.97 125	83.42 155	89.63 55	92.39 93	83.40 48	88.83 87	91.92 114	73.19 178	80.18 291	89.15 231	77.04 159	93.28 129	65.82 264	92.28 231	92.21 184

GA-MVS		75.83 266	74.61 271	79.48 251	81.87 315	59.25 296	73.42 339	82.88 277	68.68 231	79.75 292	81.80 336	50.62 337	89.46 239	66.85 252	85.64 329	89.72 246

xiu_mvs_v1_base_debu		80.84 202	80.14 216	82.93 191	88.31 201	71.73 161	79.53 257	87.17 216	65.43 264	79.59 293	82.73 326	76.94 161	90.14 222	73.22 193	88.33 294	86.90 291

xiu_mvs_v1_base		80.84 202	80.14 216	82.93 191	88.31 201	71.73 161	79.53 257	87.17 216	65.43 264	79.59 293	82.73 326	76.94 161	90.14 222	73.22 193	88.33 294	86.90 291

xiu_mvs_v1_base_debi		80.84 202	80.14 216	82.93 191	88.31 201	71.73 161	79.53 257	87.17 216	65.43 264	79.59 293	82.73 326	76.94 161	90.14 222	73.22 193	88.33 294	86.90 291

test_fmvs1_n		70.94 311	70.41 314	72.53 325	73.92 383	66.93 208	75.99 314	84.21 268	43.31 392	79.40 296	79.39 358	43.47 374	68.55 377	69.05 235	84.91 341	82.10 352

patch_mono-2		78.89 228	79.39 224	77.41 283	84.78 275	68.11 197	75.60 317	83.11 275	60.96 306	79.36 297	89.89 219	75.18 178	72.97 361	73.32 192	92.30 228	91.15 213

UnsupCasMVSNet_eth		71.63 305	72.30 298	69.62 341	76.47 367	52.70 348	70.03 363	80.97 293	59.18 319	79.36 297	88.21 243	60.50 281	69.12 373	58.33 317	77.62 383	87.04 289

ppachtmachnet_test		74.73 280	74.00 278	76.90 289	80.71 333	56.89 321	71.53 352	78.42 305	58.24 325	79.32 299	82.92 323	57.91 303	84.26 314	65.60 266	91.36 250	89.56 250

MG-MVS		80.32 214	80.94 202	78.47 264	88.18 204	52.62 349	82.29 221	85.01 257	72.01 199	79.24 300	92.54 140	69.36 234	93.36 128	70.65 216	89.19 284	89.45 251

Fast-Effi-MVS+-dtu		82.54 173	81.41 193	85.90 119	85.60 262	76.53 111	83.07 197	89.62 183	73.02 181	79.11 301	83.51 314	80.74 124	90.24 216	68.76 239	89.29 281	90.94 217

CDS-MVSNet		77.32 249	75.40 265	83.06 186	89.00 184	72.48 151	77.90 283	82.17 284	60.81 307	78.94 302	83.49 315	59.30 292	88.76 254	54.64 341	92.37 227	87.93 279
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 290	73.54 282	72.43 326	84.92 273	47.79 373	79.89 253	74.00 336	65.93 256	78.81 303	86.28 279	56.36 312	81.63 330	56.63 324	79.04 378	87.87 281

EIA-MVS		82.19 179	81.23 198	85.10 133	87.95 209	69.17 190	83.22 195	93.33 64	70.42 212	78.58 304	79.77 356	77.29 154	94.20 89	71.51 207	88.96 286	91.93 195

thres200		72.34 299	71.55 305	74.70 309	83.48 296	51.60 356	75.02 324	73.71 341	70.14 218	78.56 305	80.57 347	46.20 352	88.20 260	46.99 378	89.29 281	84.32 320

our_test_3		71.85 302	71.59 302	72.62 323	80.71 333	53.78 339	69.72 364	71.71 358	58.80 322	78.03 306	80.51 349	56.61 311	78.84 346	62.20 292	86.04 327	85.23 308

KD-MVS_2432*1600		66.87 340	65.81 346	70.04 337	67.50 401	47.49 374	62.56 384	79.16 301	61.21 304	77.98 307	80.61 345	25.29 410	82.48 325	53.02 349	84.92 339	80.16 371

miper_refine_blended		66.87 340	65.81 346	70.04 337	67.50 401	47.49 374	62.56 384	79.16 301	61.21 304	77.98 307	80.61 345	25.29 410	82.48 325	53.02 349	84.92 339	80.16 371

jason		77.42 248	75.75 262	82.43 204	87.10 231	69.27 185	77.99 281	81.94 286	51.47 365	77.84 309	85.07 299	60.32 284	89.00 247	70.74 215	89.27 283	89.03 263
jason: jason.
MAR-MVS		80.24 216	78.74 232	84.73 140	86.87 238	78.18 88	85.75 137	87.81 210	65.67 263	77.84 309	78.50 365	73.79 196	90.53 210	61.59 300	90.87 261	85.49 307
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
FPMVS		72.29 300	72.00 299	73.14 317	88.63 194	85.00 36	74.65 328	67.39 374	71.94 200	77.80 311	87.66 254	50.48 338	75.83 356	49.95 363	79.51 372	58.58 401

test_fmvs1		69.57 325	69.05 325	71.14 334	69.15 400	65.77 220	73.98 333	83.32 273	42.83 394	77.77 312	78.27 367	43.39 377	68.50 378	68.39 245	84.38 348	79.15 375

pmmvs4		74.92 276	72.98 289	80.73 232	84.95 272	71.71 164	76.23 310	77.59 310	52.83 355	77.73 313	86.38 274	56.35 313	84.97 306	57.72 321	87.05 312	85.51 306

ET-MVSNet_ETH3D		75.28 270	72.77 291	82.81 195	83.03 309	68.11 197	77.09 295	76.51 321	60.67 310	77.60 314	80.52 348	38.04 387	91.15 188	70.78 213	90.68 266	89.17 258

UnsupCasMVSNet_bld		69.21 329	69.68 321	67.82 354	79.42 344	51.15 360	67.82 372	75.79 324	54.15 349	77.47 315	85.36 294	59.26 293	70.64 368	48.46 372	79.35 374	81.66 356

Anonymous20231206		71.38 308	71.88 300	69.88 339	86.31 248	54.37 335	70.39 360	74.62 331	52.57 357	76.73 316	88.76 235	59.94 287	72.06 363	44.35 386	93.23 212	83.23 339

CMPMVS	59.41 20	75.12 273	73.57 281	79.77 244	75.84 373	67.22 202	81.21 238	82.18 283	50.78 370	76.50 317	87.66 254	55.20 320	82.99 323	62.17 294	90.64 270	89.09 262
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 301	71.69 301	73.32 315	81.57 320	53.02 345	76.77 300	78.37 306	63.31 277	76.37 318	91.85 157	36.68 390	78.98 344	47.87 375	92.45 226	87.95 278

CVMVSNet		72.62 296	71.41 306	76.28 297	83.25 303	60.34 285	83.50 185	79.02 304	37.77 402	76.33 319	85.10 296	49.60 342	87.41 267	70.54 218	77.54 384	81.08 365

PLC	73.85 16	82.09 182	80.31 210	87.45 90	90.86 147	80.29 69	85.88 134	90.65 151	68.17 237	76.32 320	86.33 276	73.12 208	92.61 149	61.40 301	90.02 275	89.44 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 177	81.57 190	84.19 157	85.54 266	69.26 186	91.98 31	90.08 172	71.54 201	76.23 321	85.07 299	58.69 297	94.27 84	86.26 40	88.77 288	89.03 263

lupinMVS		76.37 262	74.46 274	82.09 206	85.54 266	69.26 186	76.79 299	80.77 295	50.68 372	76.23 321	82.82 324	58.69 297	88.94 248	69.85 225	88.77 288	88.07 273

UWE-MVS		66.43 344	65.56 349	69.05 345	84.15 288	40.98 396	73.06 343	64.71 384	54.84 346	76.18 323	79.62 357	29.21 402	80.50 337	38.54 397	89.75 277	85.66 304

PatchMatch-RL		74.48 281	73.22 286	78.27 269	87.70 214	85.26 34	75.92 315	70.09 364	64.34 274	76.09 324	81.25 342	65.87 253	78.07 348	53.86 343	83.82 351	71.48 387

thisisatest0515		73.00 294	70.52 311	80.46 236	81.45 321	59.90 290	73.16 342	74.31 335	57.86 329	76.08 325	77.78 369	37.60 389	92.12 163	65.00 271	91.45 249	89.35 254

MS-PatchMatch		70.93 312	70.22 315	73.06 318	81.85 316	62.50 255	73.82 336	77.90 307	52.44 358	75.92 326	81.27 341	55.67 317	81.75 328	55.37 334	77.70 382	74.94 383

CHOSEN 1792x2688		72.45 297	70.56 310	78.13 270	90.02 166	63.08 245	68.72 367	83.16 274	42.99 393	75.92 326	85.46 289	57.22 308	85.18 305	49.87 365	81.67 364	86.14 298

iter_conf05_11		78.40 239	77.29 248	81.71 216	85.55 264	60.95 278	77.22 293	86.90 228	60.10 316	75.79 328	81.73 337	64.08 263	94.47 82	70.37 221	93.92 194	89.72 246

CR-MVSNet		74.00 285	73.04 288	76.85 291	79.58 341	62.64 252	82.58 211	76.90 317	50.50 373	75.72 329	92.38 143	48.07 346	84.07 316	68.72 241	82.91 357	83.85 328

RPMNet		78.88 229	78.28 238	80.68 234	79.58 341	62.64 252	82.58 211	94.16 29	74.80 151	75.72 329	92.59 137	48.69 343	95.56 39	73.48 189	82.91 357	83.85 328

DPM-MVS		80.10 220	79.18 226	82.88 194	90.71 150	69.74 179	78.87 271	90.84 146	60.29 313	75.64 331	85.92 284	67.28 243	93.11 135	71.24 209	91.79 241	85.77 303

test_vis1_n		70.29 315	69.99 319	71.20 333	75.97 372	66.50 212	76.69 302	80.81 294	44.22 388	75.43 332	77.23 375	50.00 340	68.59 376	66.71 255	82.85 359	78.52 377

PVSNet_BlendedMVS		78.80 232	77.84 241	81.65 217	84.43 280	63.41 240	79.49 260	90.44 157	61.70 295	75.43 332	87.07 268	69.11 236	91.44 179	60.68 305	92.24 232	90.11 241

PVSNet_Blended		76.49 260	75.40 265	79.76 245	84.43 280	63.41 240	75.14 323	90.44 157	57.36 334	75.43 332	78.30 366	69.11 236	91.44 179	60.68 305	87.70 305	84.42 319

PAPR		78.84 230	78.10 240	81.07 226	85.17 271	60.22 286	82.21 225	90.57 154	62.51 283	75.32 335	84.61 304	74.99 180	92.30 158	59.48 311	88.04 300	90.68 226

N_pmnet		70.20 316	68.80 330	74.38 310	80.91 328	84.81 39	59.12 391	76.45 322	55.06 344	75.31 336	82.36 330	55.74 316	54.82 401	47.02 377	87.24 308	83.52 332

cascas		76.29 263	74.81 270	80.72 233	84.47 279	62.94 246	73.89 335	87.34 213	55.94 340	75.16 337	76.53 381	63.97 264	91.16 187	65.00 271	90.97 258	88.06 275

SCA		73.32 289	72.57 295	75.58 304	81.62 319	55.86 326	78.89 270	71.37 359	61.73 293	74.93 338	83.42 317	60.46 282	87.01 271	58.11 319	82.63 362	83.88 325

test_vis1_n_1920		71.30 309	71.58 304	70.47 335	77.58 357	59.99 289	74.25 329	84.22 267	51.06 367	74.85 339	79.10 360	55.10 321	68.83 375	68.86 238	79.20 377	82.58 345

xiu_mvs_v2_base		77.19 250	76.75 253	78.52 262	87.01 234	61.30 269	75.55 320	87.12 222	61.24 303	74.45 340	78.79 363	77.20 155	90.93 195	64.62 277	84.80 345	83.32 337

CANet		83.79 152	82.85 167	86.63 102	86.17 254	72.21 157	83.76 179	91.43 128	77.24 125	74.39 341	87.45 259	75.36 176	95.42 49	77.03 148	92.83 221	92.25 183

PS-MVSNAJ		77.04 252	76.53 255	78.56 261	87.09 232	61.40 267	75.26 322	87.13 219	61.25 302	74.38 342	77.22 376	76.94 161	90.94 194	64.63 276	84.83 344	83.35 336

MVP-Stereo		75.81 267	73.51 283	82.71 196	89.35 175	73.62 131	80.06 249	85.20 250	60.30 312	73.96 343	87.94 247	57.89 304	89.45 240	52.02 355	74.87 389	85.06 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WB-MVSnew		68.72 332	69.01 326	67.85 353	83.22 305	43.98 389	74.93 325	65.98 381	55.09 343	73.83 344	79.11 359	65.63 255	71.89 365	38.21 398	85.04 337	87.69 283

UGNet		82.78 168	81.64 185	86.21 113	86.20 253	76.24 117	86.86 116	85.68 243	77.07 126	73.76 345	92.82 130	69.64 232	91.82 172	69.04 236	93.69 202	90.56 230
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
1112_ss		74.82 278	73.74 279	78.04 273	89.57 169	60.04 287	76.49 306	87.09 223	54.31 348	73.66 346	79.80 354	60.25 285	86.76 280	58.37 315	84.15 349	87.32 287

Test_1112_low_res		73.90 286	73.08 287	76.35 295	90.35 156	55.95 324	73.40 340	86.17 235	50.70 371	73.14 347	85.94 283	58.31 299	85.90 296	56.51 325	83.22 354	87.20 288

1314		73.22 291	72.56 296	75.20 305	80.41 337	57.84 312	81.64 232	85.36 247	51.68 364	73.10 348	76.65 380	61.45 277	85.19 304	63.54 283	79.21 376	82.59 344

test_vis1_rt		65.64 349	64.09 353	70.31 336	66.09 405	70.20 177	61.16 387	81.60 289	38.65 400	72.87 349	69.66 394	52.84 326	60.04 398	56.16 327	77.77 381	80.68 369

Patchmatch-test		65.91 347	67.38 336	61.48 377	75.51 375	43.21 392	68.84 366	63.79 386	62.48 284	72.80 350	83.42 317	44.89 370	59.52 399	48.27 374	86.45 320	81.70 355

PatchmatchNet		69.71 324	68.83 329	72.33 327	77.66 356	53.60 340	79.29 262	69.99 365	57.66 331	72.53 351	82.93 322	46.45 351	80.08 340	60.91 304	72.09 392	83.31 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 334	68.08 335	67.55 355	78.74 352	43.53 391	75.60 317	67.10 379	54.92 345	72.23 352	88.10 244	42.87 379	75.97 355	52.21 354	80.95 371	83.15 340

pmmvs5		70.73 313	70.07 316	72.72 321	77.03 362	52.73 347	74.14 330	75.65 327	50.36 374	72.17 353	85.37 293	55.42 319	80.67 335	52.86 352	87.59 306	84.77 313

PatchT		70.52 314	72.76 292	63.79 371	79.38 345	33.53 405	77.63 287	65.37 383	73.61 165	71.77 354	92.79 133	44.38 372	75.65 357	64.53 278	85.37 331	82.18 351

MVS		73.21 292	72.59 294	75.06 307	80.97 327	60.81 281	81.64 232	85.92 241	46.03 383	71.68 355	77.54 371	68.47 239	89.77 234	55.70 331	85.39 330	74.60 384

MIMVSNet		71.09 310	71.59 302	69.57 342	87.23 225	50.07 366	78.91 269	71.83 355	60.20 315	71.26 356	91.76 163	55.08 322	76.09 354	41.06 391	87.02 314	82.54 347

WTY-MVS		67.91 335	68.35 332	66.58 360	80.82 331	48.12 371	65.96 377	72.60 348	53.67 351	71.20 357	81.68 339	58.97 295	69.06 374	48.57 371	81.67 364	82.55 346

test0.0.03 1		64.66 353	64.36 352	65.57 364	75.03 380	46.89 377	64.69 380	61.58 394	62.43 288	71.18 358	77.54 371	43.41 375	68.47 379	40.75 392	82.65 360	81.35 359

CostFormer		69.98 321	68.68 331	73.87 311	77.14 360	50.72 363	79.26 263	74.51 333	51.94 363	70.97 359	84.75 302	45.16 368	87.49 266	55.16 337	79.23 375	83.40 335

Syy-MVS		69.40 327	70.03 318	67.49 356	81.72 317	38.94 398	71.00 354	61.99 388	61.38 299	70.81 360	72.36 391	61.37 278	79.30 342	64.50 279	85.18 334	84.22 321

myMVS_eth3d		64.66 353	63.89 354	66.97 358	81.72 317	37.39 401	71.00 354	61.99 388	61.38 299	70.81 360	72.36 391	20.96 412	79.30 342	49.59 366	85.18 334	84.22 321

testing91		69.94 322	68.99 327	72.80 320	83.81 294	45.89 381	71.57 351	73.64 343	68.24 236	70.77 362	77.82 368	34.37 393	84.44 311	53.64 345	87.00 315	88.07 273

testing99		69.27 328	68.15 334	72.63 322	83.29 302	45.45 383	71.15 353	71.08 360	67.34 247	70.43 363	77.77 370	32.24 396	84.35 313	53.72 344	86.33 323	88.10 272

tpmvs		70.16 317	69.56 322	71.96 328	74.71 382	48.13 370	79.63 255	75.45 329	65.02 271	70.26 364	81.88 335	45.34 365	85.68 300	58.34 316	75.39 388	82.08 353

sss		66.92 339	67.26 337	65.90 362	77.23 359	51.10 362	64.79 379	71.72 357	52.12 362	70.13 365	80.18 351	57.96 302	65.36 391	50.21 362	81.01 370	81.25 362

tpm2		68.45 333	66.83 340	73.30 316	78.93 351	48.50 369	79.76 254	71.76 356	47.50 377	69.92 366	83.60 313	42.07 380	88.40 257	48.44 373	79.51 372	83.01 342

testing222		66.93 338	65.30 350	71.81 329	83.38 299	45.83 382	72.06 347	67.50 373	64.12 275	69.68 367	76.37 382	27.34 407	83.00 322	38.88 394	88.38 293	86.62 294

HY-MVS	64.64 18	73.03 293	72.47 297	74.71 308	83.36 301	54.19 336	82.14 228	81.96 285	56.76 339	69.57 368	86.21 280	60.03 286	84.83 308	49.58 367	82.65 360	85.11 310

dmvs_re		66.81 342	66.98 338	66.28 361	76.87 363	58.68 307	71.66 350	72.24 351	60.29 313	69.52 369	73.53 388	52.38 329	64.40 393	44.90 384	81.44 367	75.76 381

ETVMVS		64.67 352	63.34 357	68.64 349	83.44 298	41.89 394	69.56 365	61.70 393	61.33 301	68.74 370	75.76 384	28.76 403	79.35 341	34.65 401	86.16 326	84.67 315

tpm cat1		66.76 343	65.21 351	71.42 331	77.09 361	50.62 364	78.01 280	73.68 342	44.89 386	68.64 371	79.00 361	45.51 362	82.42 327	49.91 364	70.15 395	81.23 364

IB-MVS	62.13 19	71.64 304	68.97 328	79.66 248	80.80 332	62.26 261	73.94 334	76.90 317	63.27 278	68.63 372	76.79 378	33.83 394	91.84 171	59.28 312	87.26 307	84.88 312
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
EPNet		80.37 212	78.41 237	86.23 111	76.75 364	73.28 135	87.18 111	77.45 311	76.24 131	68.14 373	88.93 234	65.41 256	93.85 103	69.47 228	96.12 117	91.55 206
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 345	65.63 348	68.75 348	81.96 314	49.88 367	62.19 386	72.51 350	51.03 368	68.04 374	75.34 386	50.84 336	74.77 358	45.82 383	82.96 355	81.60 357

tpmrst		66.28 346	66.69 342	65.05 367	72.82 392	39.33 397	78.20 279	70.69 363	53.16 354	67.88 375	80.36 350	48.18 345	74.75 359	58.13 318	70.79 394	81.08 365

CANet_DTU		77.81 244	77.05 249	80.09 242	81.37 323	59.90 290	83.26 191	88.29 203	69.16 225	67.83 376	83.72 312	60.93 279	89.47 238	69.22 232	89.70 278	90.88 219

EPMVS		62.47 356	62.63 360	62.01 373	70.63 397	38.74 399	74.76 326	52.86 404	53.91 350	67.71 377	80.01 352	39.40 384	66.60 386	55.54 333	68.81 400	80.68 369

MDTV_nov1_ep13			68.29 333		78.03 353	43.87 390	74.12 331	72.22 352	52.17 359	67.02 378	85.54 287	45.36 364	80.85 334	55.73 329	84.42 347

testing11		67.38 336	65.93 344	71.73 330	83.37 300	46.60 378	70.95 356	69.40 368	62.47 285	66.14 379	76.66 379	31.22 398	84.10 315	49.10 369	84.10 350	84.49 316

pmmvs3		62.47 356	60.02 369	69.80 340	71.58 396	64.00 235	70.52 359	58.44 400	39.77 398	66.05 380	75.84 383	27.10 409	72.28 362	46.15 381	84.77 346	73.11 385

ADS-MVSNet2		65.87 348	63.64 356	72.55 324	73.16 389	56.92 320	67.10 373	74.81 330	49.74 375	66.04 381	82.97 320	46.71 349	77.26 351	42.29 388	69.96 396	83.46 333

ADS-MVSNet		61.90 358	62.19 362	61.03 378	73.16 389	36.42 403	67.10 373	61.75 391	49.74 375	66.04 381	82.97 320	46.71 349	63.21 394	42.29 388	69.96 396	83.46 333

mvsany_test1		58.48 368	56.47 373	64.50 368	65.90 407	68.21 196	56.95 395	42.11 410	38.30 401	65.69 383	77.19 377	56.96 309	59.35 400	46.16 380	58.96 403	65.93 394

dmvs_testset		60.59 366	62.54 361	54.72 385	77.26 358	27.74 408	74.05 332	61.00 395	60.48 311	65.62 384	67.03 398	55.93 315	68.23 380	32.07 405	69.46 399	68.17 392

DSMNet-mixed		60.98 364	61.61 364	59.09 382	72.88 391	45.05 386	74.70 327	46.61 408	26.20 404	65.34 385	90.32 208	55.46 318	63.12 395	41.72 390	81.30 369	69.09 391

JIA-IIPM		69.41 326	66.64 343	77.70 279	73.19 388	71.24 169	75.67 316	65.56 382	70.42 212	65.18 386	92.97 125	33.64 395	83.06 321	53.52 347	69.61 398	78.79 376

test-LLR		67.21 337	66.74 341	68.63 350	76.45 368	55.21 331	67.89 369	67.14 377	62.43 288	65.08 387	72.39 389	43.41 375	69.37 370	61.00 302	84.89 342	81.31 360

test-mter		65.00 351	63.79 355	68.63 350	76.45 368	55.21 331	67.89 369	67.14 377	50.98 369	65.08 387	72.39 389	28.27 405	69.37 370	61.00 302	84.89 342	81.31 360

PMMVS2		55.64 371	59.27 370	44.74 387	64.30 409	12.32 413	40.60 400	49.79 406	53.19 353	65.06 389	84.81 301	53.60 325	49.76 404	32.68 404	89.41 280	72.15 386

baseline2		69.77 323	66.89 339	78.41 265	79.51 343	58.09 309	76.23 310	69.57 367	57.50 333	64.82 390	77.45 373	46.02 354	88.44 256	53.08 348	77.83 380	88.70 267

gg-mvs-nofinetune		68.96 331	69.11 324	68.52 352	76.12 371	45.32 384	83.59 183	55.88 402	86.68 24	64.62 391	97.01 7	30.36 400	83.97 318	44.78 385	82.94 356	76.26 380

PAPM		71.77 303	70.06 317	76.92 288	86.39 243	53.97 337	76.62 304	86.62 230	53.44 352	63.97 392	84.73 303	57.79 305	92.34 156	39.65 393	81.33 368	84.45 318

new_pmnet		55.69 370	57.66 371	49.76 386	75.47 376	30.59 406	59.56 388	51.45 405	43.62 391	62.49 393	75.48 385	40.96 382	49.15 405	37.39 399	72.52 390	69.55 390

MDTV_nov1_ep13_2view						27.60 409	70.76 358		46.47 381	61.27 394		45.20 366		49.18 368		83.75 330

dp		60.70 365	60.29 368	61.92 375	72.04 395	38.67 400	70.83 357	64.08 385	51.28 366	60.75 395	77.28 374	36.59 391	71.58 367	47.41 376	62.34 402	75.52 382

TESTMET0.1,1		61.29 361	60.32 367	64.19 369	72.06 394	51.30 358	67.89 369	62.09 387	45.27 384	60.65 396	69.01 395	27.93 406	64.74 392	56.31 326	81.65 366	76.53 379

PMMVS		61.65 359	60.38 366	65.47 365	65.40 408	69.26 186	63.97 382	61.73 392	36.80 403	60.11 397	68.43 396	59.42 291	66.35 387	48.97 370	78.57 379	60.81 398

PVSNet_0	51.08 22	56.10 369	54.97 374	59.48 381	75.12 379	53.28 344	55.16 396	61.89 390	44.30 387	59.16 398	62.48 401	54.22 323	65.91 389	35.40 400	47.01 404	59.25 400

MVS-HIRNet		61.16 362	62.92 359	55.87 383	79.09 348	35.34 404	71.83 348	57.98 401	46.56 380	59.05 399	91.14 179	49.95 341	76.43 353	38.74 395	71.92 393	55.84 402

E-PMN		61.59 360	61.62 363	61.49 376	66.81 403	55.40 329	53.77 397	60.34 396	66.80 252	58.90 400	65.50 399	40.48 383	66.12 388	55.72 330	86.25 324	62.95 397

GG-mvs-BLEND				67.16 357	73.36 387	46.54 380	84.15 165	55.04 403		58.64 401	61.95 402	29.93 401	83.87 319	38.71 396	76.92 386	71.07 388

EPNet_dtu		72.87 295	71.33 307	77.49 282	77.72 355	60.55 284	82.35 219	75.79 324	66.49 254	58.39 402	81.06 343	53.68 324	85.98 293	53.55 346	92.97 219	85.95 300
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 363	60.81 365	61.99 374	65.96 406	55.86 326	53.10 398	58.97 399	67.06 249	56.89 403	63.33 400	40.98 381	67.03 384	54.79 339	86.18 325	63.08 396

CHOSEN 280x420		59.08 367	56.52 372	66.76 359	76.51 366	64.39 231	49.62 399	59.00 398	43.86 389	55.66 404	68.41 397	35.55 392	68.21 381	43.25 387	76.78 387	67.69 393

MVE	40.22 23	51.82 372	50.47 375	55.87 383	62.66 410	51.91 353	31.61 402	39.28 411	40.65 396	50.76 405	74.98 387	56.24 314	44.67 406	33.94 403	64.11 401	71.04 389
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 389	32.95 411	29.49 407		21.63 414	12.07 405	37.95 406	45.07 404	30.84 399	19.21 408	17.94 408	33.06 407	23.69 404

tmp_tt		20.25 375	24.50 378	7.49 390	4.47 413	8.70 414	34.17 401	25.16 413	1.00 408	32.43 407	18.49 405	39.37 385	9.21 409	21.64 407	43.75 405	4.57 405

test_method		30.46 373	29.60 376	33.06 388	17.99 412	3.84 415	13.62 403	73.92 337	2.79 406	18.29 408	53.41 403	28.53 404	43.25 407	22.56 406	35.27 406	52.11 403

EGC-MVSNET		74.79 279	69.99 319	89.19 63	94.89 37	87.00 11	91.89 34	86.28 233	1.09 407	2.23 409	95.98 23	81.87 111	89.48 237	79.76 112	95.96 124	91.10 214

testmvs		5.91 379	7.65 382	0.72 392	1.20 414	0.37 417	59.14 390	0.67 416	0.49 410	1.11 410	2.76 409	0.94 415	0.24 411	1.02 410	1.47 408	1.55 407

test123		6.27 378	8.08 381	0.84 391	1.11 415	0.57 416	62.90 383	0.82 415	0.54 409	1.07 411	2.75 410	1.26 414	0.30 410	1.04 409	1.26 409	1.66 406

test_blank		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet_test		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

DCPMVS		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

cdsmvs_eth3d_5k		20.81 374	27.75 377	0.00 393	0.00 416	0.00 418	0.00 404	85.44 246	0.00 411	0.00 412	82.82 324	81.46 115	0.00 412	0.00 411	0.00 410	0.00 408

pcd_1.5k_mvsjas		6.41 377	8.55 380	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	76.94 161	0.00 412	0.00 411	0.00 410	0.00 408

sosnet-low-res		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

sosnet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uncertanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

Regformer		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

ab-mvs-re		6.65 376	8.87 379	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	79.80 354	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

WAC-MVS						37.39 401								52.61 353

MSC_two_6792asdad				88.81 69	91.55 126	77.99 90		91.01 142					96.05 8	87.45 20	98.17 32	92.40 173

No_MVS				88.81 69	91.55 126	77.99 90		91.01 142					96.05 8	87.45 20	98.17 32	92.40 173

eth-test2					0.00 416

eth-test					0.00 416

OPU-MVS				88.27 80	91.89 112	77.83 93	90.47 51				91.22 176	81.12 119	94.68 71	74.48 173	95.35 146	92.29 179

save fliter					93.75 59	77.44 99	86.31 129	89.72 178	70.80 209

test_0728_SECOND				86.79 100	94.25 45	72.45 152	90.54 48	94.10 36					95.88 17	86.42 36	97.97 43	92.02 191

GSMVS																83.88 325

sam_mvs1												46.11 353				83.88 325

sam_mvs												45.92 358

MTGPA								91.81 121

test_post1							78.85 272				3.13 407	45.19 367	80.13 339	58.11 319

test_post											3.10 408	45.43 363	77.22 352

patchmatchnet-post											81.71 338	45.93 357	87.01 271

MTMP							90.66 44	33.14 412

gm-plane-assit					75.42 377	44.97 387			52.17 359		72.36 391		87.90 261	54.10 342

test9_res														80.83 101	96.45 102	90.57 229

agg_prior2														79.68 114	96.16 114	90.22 237

test_prior4						78.97 80	84.59 156

test_prior				86.32 108	90.59 152	71.99 159		92.85 89					94.17 92			92.80 154

新几何2							81.72 231

旧先验1					91.97 108	71.77 160		81.78 287			91.84 158	73.92 194			93.65 203	83.61 331

无先验							82.81 206	85.62 244	58.09 327				91.41 182	67.95 249		84.48 317

原ACMM2							82.26 224

testdata2													86.43 285	63.52 284

segment_acmp												81.94 107

testdata1							79.62 256		73.95 160

plane_prior7					93.45 65	77.31 102

plane_prior6					92.61 87	76.54 109						74.84 182

plane_prior5								93.61 56					95.22 56	80.78 102	95.83 132	94.46 80

plane_prior4											92.95 126

plane_prior2							89.45 77		79.44 96

plane_prior1					92.83 85

plane_prior						76.42 113	87.15 112		75.94 138						95.03 160

n2								0.00 417

nn								0.00 417

door-mid								74.45 334

test11								91.46 127

door								72.57 349

HQP5-MVS						70.66 172

BP-MVS														77.30 145

HQP3-MVS								92.68 94							94.47 180

HQP2-MVS												72.10 218

NP-MVS					91.95 109	74.55 126					90.17 214

ACMMP++_ref															95.74 139

ACMMP++															97.35 73

Test By Simon												79.09 136

High-resolution multi-view benchmark