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 13	85.07 50	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	84.21 148	96.51 7	57.84 294	88.93 85	92.84 87	91.92 3	96.16 3	96.23 18	86.95 48	95.99 9	79.05 109	98.57 14	98.80 6

PS-CasMVS		90.06 39	91.92 11	84.47 140	96.56 6	58.83 289	89.04 83	92.74 90	91.40 5	96.12 4	96.06 22	87.23 45	95.57 36	79.42 107	98.74 5	99.00 2

LCM-MVSNet-Re		83.48 151	85.06 119	78.75 244	85.94 248	55.75 310	80.05 237	94.27 19	76.47 125	96.09 5	94.54 62	83.31 82	89.75 228	59.95 288	94.89 163	90.75 206

PEN-MVS		90.03 41	91.88 14	84.48 139	96.57 5	58.88 286	88.95 84	93.19 69	91.62 4	96.01 6	96.16 20	87.02 47	95.60 34	78.69 112	98.72 8	98.97 3

CP-MVSNet		89.27 58	90.91 40	84.37 141	96.34 8	58.61 291	88.66 91	92.06 104	90.78 6	95.67 7	95.17 43	81.80 105	95.54 39	79.00 110	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 67	93.16 130	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	85.71 119	96.32 9	62.39 243	89.54 74	93.31 64	90.21 10	95.57 9	95.66 29	81.42 109	95.90 14	80.94 87	98.80 2	98.84 5

OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 76	80.37 68	91.91 33	93.11 73	81.10 73	95.32 10	97.24 5	72.94 194	94.85 65	85.07 50	97.78 53	97.26 16

anonymousdsp		89.73 49	88.88 66	92.27 7	89.82 167	86.67 14	90.51 50	90.20 161	69.87 213	95.06 11	96.14 21	84.28 72	93.07 134	87.68 12	96.34 105	97.09 21

wuyk23d		75.13 254	79.30 206	62.63 342	75.56 344	75.18 118	80.89 229	73.10 329	75.06 147	94.76 12	95.32 35	87.73 40	52.85 371	34.16 371	97.11 80	59.85 368

ACMH	76.49 14	89.34 55	91.14 31	83.96 153	92.50 92	70.36 164	89.55 72	93.84 46	81.89 65	94.70 13	95.44 34	90.69 8	88.31 252	83.33 65	98.30 24	93.20 132
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 85	87.45 83	86.45 101	92.52 91	69.19 177	87.84 102	88.05 199	81.66 67	94.64 14	96.53 14	65.94 233	94.75 67	83.02 69	96.83 88	95.41 51

mvs_tets		89.78 48	89.27 59	91.30 25	93.51 65	84.79 40	89.89 63	90.63 145	70.00 212	94.55 15	96.67 11	87.94 37	93.59 113	84.27 59	95.97 121	95.52 49

jajsoiax		89.41 53	88.81 68	91.19 28	93.38 69	84.72 41	89.70 66	90.29 158	69.27 216	94.39 16	96.38 15	86.02 60	93.52 117	83.96 61	95.92 126	95.34 53

test_0402		88.65 65	89.58 56	85.88 116	92.55 90	72.22 146	84.01 157	89.44 178	88.63 16	94.38 17	95.77 26	86.38 56	93.59 113	79.84 99	95.21 150	91.82 183

UniMVSNet_ETH3D		89.12 61	90.72 43	84.31 146	97.00 2	64.33 221	89.67 69	88.38 191	88.84 13	94.29 18	97.57 3	90.48 13	91.26 181	72.57 189	97.65 60	97.34 15

v7n		90.13 36	90.96 38	87.65 87	91.95 110	71.06 158	89.99 59	93.05 77	86.53 26	94.29 18	96.27 17	82.69 86	94.08 93	86.25 37	97.63 61	97.82 8

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 95	82.98 53	91.98 31	90.08 164	71.54 193	94.28 20	96.54 13	81.57 107	94.27 82	86.26 35	96.49 99	97.09 21

PS-MVSNAJss		88.31 69	87.90 76	89.56 57	93.31 71	77.96 89	87.94 100	91.97 107	70.73 202	94.19 21	96.67 11	76.94 151	94.57 74	83.07 67	96.28 107	96.15 33

SED-MVS		90.46 33	91.64 17	86.93 92	94.18 46	72.65 132	90.47 51	93.69 50	83.77 45	94.11 22	94.27 74	90.28 14	95.84 22	86.03 41	97.92 46	92.29 167

test_241102_ONE					94.18 46	72.65 132		93.69 50	83.62 47	94.11 22	93.78 103	90.28 14	95.50 44

DPE-MVS		90.53 32	91.08 33	88.88 66	93.38 69	78.65 83	89.15 82	94.05 36	84.68 39	93.90 24	94.11 86	88.13 34	96.30 3	84.51 58	97.81 52	91.70 187
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 80	78.04 86	92.84 15	94.14 31	83.33 51	93.90 24	95.73 27	88.77 25	96.41 1	87.60 15	97.98 42	92.98 140

ACMH+	77.89 11	90.73 27	91.50 21	88.44 75	93.00 79	76.26 111	89.65 70	95.55 7	87.72 21	93.89 26	94.94 48	91.62 3	93.44 121	78.35 115	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 60	79.20 96	93.83 27	93.60 108	90.81 7	92.96 136	85.02 52	98.45 18	92.41 160
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 27	88.75 14	93.79 28	94.43 67	88.83 24	95.51 42	87.16 26	97.60 64	92.73 146

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 27	88.75 14	93.79 28	94.43 67	90.64 10		87.16 26	97.60 64	92.73 146

DVP-MVS		90.06 39	91.32 28	86.29 104	94.16 49	72.56 138	90.54 48	91.01 135	83.61 48	93.75 30	94.65 57	89.76 18	95.78 27	86.42 31	97.97 43	90.55 215
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 32	93.75 30	94.65 57	87.44 43	95.78 27	87.41 19	98.21 29	92.98 140

test0726					94.16 49	72.56 138	90.63 45	93.90 42	83.61 48	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 19	82.35 60	93.67 33	94.82 52	91.18 4	95.52 40	85.36 47	98.73 6	95.23 59

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 19	82.35 60	93.67 33	94.82 52	91.18 4	95.52 40	85.36 47	98.73 6	95.23 59

RRT_MVS		88.30 70	87.83 77	89.70 52	93.62 64	75.70 115	92.36 26	89.06 183	77.34 118	93.63 35	95.83 25	65.40 236	95.90 14	85.01 53	98.23 27	97.49 13

DVP-MVS++		90.07 38	91.09 32	87.00 91	91.55 127	72.64 134	96.19 2	94.10 34	85.33 32	93.49 36	94.64 60	81.12 112	95.88 16	87.41 19	95.94 124	92.48 157

test_241102_TWO								93.71 49	83.77 45	93.49 36	94.27 74	89.27 21	95.84 22	86.03 41	97.82 51	92.04 176

test_one_0601					93.85 58	73.27 128		94.11 33	86.57 25	93.47 38	94.64 60	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 45	88.20 19	93.24 39	94.02 89	90.15 16	95.67 32	86.82 29	97.34 74	92.19 173

APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 78	85.17 35	92.47 25	95.05 13	87.65 22	93.21 40	94.39 72	90.09 17	95.08 59	86.67 30	97.60 64	94.18 92

testf1		89.30 56	89.12 60	89.84 48	88.67 186	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 233	74.12 165	96.10 116	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 186	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 233	74.12 165	96.10 116	94.45 82

Anonymous20231211		88.40 67	89.62 55	84.73 135	90.46 155	65.27 211	88.86 86	93.02 81	87.15 23	93.05 43	97.10 6	82.28 95	92.02 162	76.70 139	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 97	94.51 17	75.79 137	92.94 44	94.96 47	88.36 28	95.01 61	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	86.22 107	91.63 121	77.07 101	89.82 64	93.77 47	78.90 100	92.88 45	92.29 141	86.11 58	90.22 212	86.24 38	97.24 77	91.36 195
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 36	79.03 99	92.87 46	93.74 104	90.60 11	95.21 56	82.87 70	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 90	86.07 40	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 68	75.37 144	92.84 48	95.28 38	85.58 62	96.09 6	87.92 9	97.76 55	93.88 105
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 91				92.84 48

v10		86.54 93	87.10 88	84.84 131	88.16 200	63.28 231	86.64 122	92.20 101	75.42 143	92.81 50	94.50 63	74.05 179	94.06 94	83.88 62	96.28 107	97.17 20

dcpmvs_2		84.23 134	85.14 118	81.50 204	88.61 189	61.98 250	82.90 190	93.11 73	68.66 225	92.77 51	92.39 136	78.50 132	87.63 258	76.99 138	92.30 218	94.90 65

v8		86.22 98	86.83 95	84.36 143	87.82 204	62.35 245	86.42 125	91.33 126	76.78 124	92.73 52	94.48 65	73.41 188	93.72 105	83.10 66	95.41 142	97.01 23

nrg030		87.85 80	88.49 70	85.91 114	90.07 163	69.73 168	87.86 101	94.20 25	74.04 155	92.70 53	94.66 56	85.88 61	91.50 173	79.72 102	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 57	82.82 57	92.60 54	93.97 91	88.19 31	96.29 4	87.61 14	98.20 31	94.39 86
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 47	89.97 48	89.27 61	94.76 39	79.86 72	86.76 119	92.78 89	78.78 102	92.51 55	93.64 107	88.13 34	93.84 102	84.83 55	97.55 67	94.10 97
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 22	85.21 34	92.51 55	95.13 44	90.65 9	95.34 50	88.06 8	98.15 34	95.95 41

K. test v3		85.14 112	84.73 124	86.37 102	91.13 141	69.63 170	85.45 135	76.68 303	84.06 43	92.44 57	96.99 8	62.03 254	94.65 70	80.58 93	93.24 201	94.83 72

ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 27	82.52 59	92.39 58	94.14 84	89.15 23	95.62 33	87.35 21	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 73	92.86 84	77.09 100	91.19 40	95.74 5	81.38 70	92.28 59	93.80 101	86.89 49	94.64 71	85.52 46	97.51 71	94.30 89

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 99	76.51 108	91.09 42	91.87 111	72.61 180	92.16 60	95.23 41	66.01 232	95.59 35	86.02 43	97.78 53	97.24 17

COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 62	86.15 20	93.37 10	95.10 12	90.28 9	92.11 61	95.03 46	89.75 20	94.93 63	79.95 98	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 38	79.68 88	92.09 62	93.89 99	83.80 76	93.10 133	82.67 72	98.04 36	93.64 118

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 127	94.02 54	64.13 222	84.38 151	91.29 127	84.88 38	92.06 63	93.84 100	86.45 54	93.73 104	73.22 180	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	91.81 115	84.07 42	92.00 64	94.40 71	86.63 51	95.28 53	88.59 5	98.31 23	92.30 166

ACMMP_NAP		90.65 28	91.07 35	89.42 59	95.93 15	79.54 76	89.95 61	93.68 52	77.65 115	91.97 65	94.89 49	88.38 27	95.45 46	89.27 3	97.87 50	93.27 129

FC-MVSNet-test		85.93 103	87.05 90	82.58 187	92.25 100	56.44 305	85.75 131	93.09 75	77.33 119	91.94 66	94.65 57	74.78 171	93.41 123	75.11 157	98.58 13	97.88 7

lessismore_v0				85.95 113	91.10 142	70.99 159		70.91 342		91.79 67	94.42 69	61.76 255	92.93 138	79.52 106	93.03 206	93.93 103

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 14	82.88 56	91.77 68	93.94 97	90.55 12	95.73 30	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 42	80.32 81	91.74 69	94.41 70	88.17 32	95.98 10	86.37 33	97.99 40	93.96 102

mPP-MVS		91.69 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 17	92.60 93	83.09 53	91.54 70	94.25 78	87.67 41	95.51 42	87.21 25	98.11 35	93.12 136

HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 21	93.29 66	81.99 62	91.47 71	93.96 94	88.35 29	95.56 37	87.74 10	97.74 57	92.85 143

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 67	81.99 62	91.40 72	94.17 83	87.51 42	95.87 18	87.74 10	97.76 55	93.99 99

GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 48	80.98 75	91.38 73	93.80 101	87.20 46	95.80 24	87.10 28	97.69 59	93.93 103

ANet_high		83.17 157	85.68 112	75.65 287	81.24 296	45.26 363	79.94 239	92.91 84	83.83 44	91.33 74	96.88 10	80.25 121	85.92 282	68.89 220	95.89 127	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 24	80.14 84	91.29 75	93.97 91	87.93 38	95.87 18	88.65 4	97.96 45	94.12 96

bld_raw_dy_0_64		84.85 118	84.44 133	86.07 112	93.73 60	74.93 119	88.57 92	81.90 273	70.44 204	91.28 76	95.18 42	56.62 289	89.28 238	85.15 49	97.09 81	93.99 99

casdiffmvs_mvg		86.72 91	87.51 82	84.36 143	87.09 223	65.22 212	84.16 153	94.23 22	77.89 112	91.28 76	93.66 106	84.35 71	92.71 142	80.07 95	94.87 166	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 89	81.34 64	90.19 56	93.08 76	80.87 77	91.13 78	93.19 112	86.22 57	95.97 11	82.23 77	97.18 79	90.45 217
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 58		91.84 117		88.80 88	95.32 11	75.14 146	91.07 79	92.89 122	87.27 44	93.78 103	83.69 64	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 59	83.16 52	91.06 80	94.00 90	88.26 30	95.71 31	87.28 24	98.39 20	92.55 155

FIs		85.35 109	86.27 101	82.60 186	91.86 114	57.31 298	85.10 139	93.05 77	75.83 136	91.02 81	93.97 91	73.57 184	92.91 140	73.97 168	98.02 39	97.58 12

UniMVSNet_NR-MVSNet		86.84 89	87.06 89	86.17 110	92.86 84	67.02 194	82.55 199	91.56 118	83.08 54	90.92 82	91.82 153	78.25 135	93.99 95	74.16 163	98.35 21	97.49 13

DU-MVS		86.80 90	86.99 91	86.21 108	93.24 74	67.02 194	83.16 183	92.21 100	81.73 66	90.92 82	91.97 147	77.20 145	93.99 95	74.16 163	98.35 21	97.61 10

tt0805		88.09 74	89.79 51	82.98 176	93.26 73	63.94 225	91.10 41	89.64 173	85.07 35	90.91 84	91.09 170	89.16 22	91.87 167	82.03 78	95.87 128	93.13 134

V42		83.47 152	83.37 150	83.75 158	83.16 280	63.33 230	81.31 222	90.23 160	69.51 215	90.91 84	90.81 181	74.16 177	92.29 156	80.06 96	90.22 259	95.62 47

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 65	81.91 64	90.88 86	94.21 79	87.75 39	95.87 18	87.60 15	97.71 58	93.83 107

APD_test1		88.40 67	87.91 75	89.88 47	89.50 170	86.65 16	89.98 60	91.91 110	84.26 40	90.87 87	93.92 98	82.18 96	89.29 237	73.75 172	94.81 167	93.70 114

WR-MVS		83.56 149	84.40 136	81.06 212	93.43 68	54.88 316	78.67 261	85.02 244	81.24 71	90.74 88	91.56 159	72.85 195	91.08 187	68.00 230	98.04 36	97.23 18

v1240		84.30 130	84.51 132	83.65 160	87.65 209	61.26 256	82.85 191	91.54 119	67.94 234	90.68 89	90.65 187	71.71 208	93.64 107	82.84 71	94.78 168	96.07 36

ZD-MVS					92.22 102	80.48 67		91.85 112	71.22 198	90.38 90	92.98 117	86.06 59	96.11 5	81.99 80	96.75 91

MIMVSNet1		83.63 148	84.59 129	80.74 217	94.06 53	62.77 237	82.72 193	84.53 252	77.57 117	90.34 91	95.92 24	76.88 157	85.83 284	61.88 275	97.42 72	93.62 119

LS3D		90.60 30	90.34 47	91.38 24	89.03 180	84.23 45	93.58 6	94.68 16	90.65 7	90.33 92	93.95 96	84.50 69	95.37 49	80.87 88	95.50 141	94.53 79

KD-MVS_self_test		81.93 175	83.14 154	78.30 253	84.75 259	52.75 328	80.37 234	89.42 179	70.24 210	90.26 93	93.39 110	74.55 176	86.77 270	68.61 225	96.64 93	95.38 52

PMVS	80.48 6	90.08 37	90.66 44	88.34 78	96.71 3	92.97 1	90.31 54	89.57 176	88.51 17	90.11 94	95.12 45	90.98 6	88.92 242	77.55 129	97.07 82	83.13 313
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PC_three_1452									58.96 295	90.06 95	91.33 164	80.66 117	93.03 135	75.78 149	95.94 124	92.48 157

v1921920		84.23 134	84.37 137	83.79 156	87.64 210	61.71 251	82.91 189	91.20 130	67.94 234	90.06 95	90.34 192	72.04 205	93.59 113	82.32 76	94.91 161	96.07 36

ITE_SJBPF				90.11 45	90.72 150	84.97 37		90.30 156	81.56 68	90.02 97	91.20 168	82.40 91	90.81 197	73.58 175	94.66 172	94.56 76

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 35	89.99 98	94.03 88	86.57 52	95.80 24	87.35 21	97.62 62	94.20 90

X-MVStestdata		85.04 114	82.70 160	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 35	89.99 98	16.05 375	86.57 52	95.80 24	87.35 21	97.62 62	94.20 90

v1192		84.57 123	84.69 128	84.21 148	87.75 206	62.88 235	83.02 186	91.43 122	69.08 219	89.98 100	90.89 178	72.70 198	93.62 111	82.41 75	94.97 160	96.13 34

Anonymous20240529		86.20 99	87.13 87	83.42 166	90.19 159	64.55 219	84.55 146	90.71 142	85.85 31	89.94 101	95.24 40	82.13 97	90.40 208	69.19 216	96.40 104	95.31 55

pmmvs6		86.52 94	88.06 74	81.90 196	92.22 102	62.28 246	84.66 144	89.15 181	83.54 50	89.85 102	97.32 4	88.08 36	86.80 269	70.43 205	97.30 76	96.62 28

v144192		84.24 133	84.41 135	83.71 159	87.59 211	61.57 252	82.95 188	91.03 134	67.82 237	89.80 103	90.49 190	73.28 191	93.51 118	81.88 82	94.89 163	96.04 38

v1144		84.54 125	84.72 126	84.00 151	87.67 208	62.55 241	82.97 187	90.93 138	70.32 208	89.80 103	90.99 173	73.50 185	93.48 119	81.69 83	94.65 173	95.97 39

DeepC-MVS	82.31 4	89.15 60	89.08 62	89.37 60	93.64 63	79.07 79	88.54 93	94.20 25	73.53 161	89.71 105	94.82 52	85.09 63	95.77 29	84.17 60	98.03 38	93.26 130
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 93	91.22 27	90.08 161	89.30 4	89.68 68	91.11 132	79.26 95	89.68 106	94.81 55	82.44 89	87.74 256	76.54 142	88.74 274	96.61 29

IU-MVS					94.18 46	72.64 134		90.82 140	56.98 309	89.67 107				85.78 45	97.92 46	93.28 128

FMVSNet1		84.55 124	85.45 115	81.85 198	90.27 158	61.05 259	86.83 116	88.27 196	78.57 106	89.66 108	95.64 30	75.43 162	90.68 201	69.09 217	95.33 145	93.82 108

IterMVS-LS		84.73 120	84.98 121	83.96 153	87.35 214	63.66 226	83.25 179	89.88 168	76.06 129	89.62 109	92.37 140	73.40 190	92.52 147	78.16 120	94.77 170	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 89	91.30 134	77.50 93	87.55 104	92.97 83	87.95 20	89.62 109	92.87 123	84.56 68	93.89 99	77.65 127	96.62 94	90.70 209

UniMVSNet (Re)		86.87 87	86.98 92	86.55 99	93.11 77	68.48 182	83.80 166	92.87 85	80.37 79	89.61 111	91.81 154	77.72 139	94.18 88	75.00 158	98.53 15	96.99 24

IS-MVSNet		86.66 92	86.82 96	86.17 110	92.05 108	66.87 197	91.21 39	88.64 188	86.30 28	89.60 112	92.59 131	69.22 216	94.91 64	73.89 169	97.89 49	96.72 26

v2v482		84.09 137	84.24 139	83.62 161	87.13 219	61.40 253	82.71 194	89.71 171	72.19 189	89.55 113	91.41 162	70.70 212	93.20 128	81.02 86	93.76 190	96.25 32

Baseline_NR-MVSNet		84.00 141	85.90 107	78.29 254	91.47 132	53.44 324	82.29 207	87.00 219	79.06 98	89.55 113	95.72 28	77.20 145	86.14 280	72.30 191	98.51 16	95.28 56

CSCG		86.26 96	86.47 98	85.60 121	90.87 147	74.26 123	87.98 99	91.85 112	80.35 80	89.54 115	88.01 231	79.09 128	92.13 158	75.51 151	95.06 157	90.41 218

ambc				82.98 176	90.55 154	64.86 215	88.20 95	89.15 181		89.40 116	93.96 94	71.67 209	91.38 180	78.83 111	96.55 96	92.71 149

DeepPCF-MVS	81.24 5	87.28 84	86.21 103	90.49 38	91.48 131	84.90 38	83.41 175	92.38 98	70.25 209	89.35 117	90.68 185	82.85 85	94.57 74	79.55 104	95.95 123	92.00 178

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 42	78.43 107	89.16 118	92.25 143	72.03 206	96.36 2	88.21 7	90.93 248	92.98 140
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	85.91 128	93.60 55	80.16 83	89.13 119	93.44 109	83.82 75	90.98 189	83.86 63	95.30 149	93.60 120

MDA-MVSNet-bldmvs		77.47 230	76.90 234	79.16 240	79.03 321	64.59 216	66.58 345	75.67 309	73.15 172	88.86 120	88.99 219	66.94 226	81.23 312	64.71 254	88.22 282	91.64 189

EG-PatchMatch MVS		84.08 138	84.11 140	83.98 152	92.22 102	72.61 137	82.20 213	87.02 216	72.63 179	88.86 120	91.02 172	78.52 131	91.11 186	73.41 177	91.09 242	88.21 251

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 136	81.66 62	91.25 38	94.13 32	88.89 11	88.83 122	94.26 77	77.55 142	95.86 21	84.88 54	95.87 128	95.24 58

EI-MVSNet-UG-set		85.04 114	84.44 133	86.85 94	83.87 274	72.52 140	83.82 164	85.15 240	80.27 82	88.75 123	85.45 274	79.95 124	91.90 165	81.92 81	90.80 252	96.13 34

EI-MVSNet-Vis-set		85.12 113	84.53 131	86.88 93	84.01 271	72.76 131	83.91 162	85.18 239	80.44 78	88.75 123	85.49 272	80.08 122	91.92 164	82.02 79	90.85 251	95.97 39

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 112	81.68 61	87.49 106	93.17 70	76.02 131	88.64 125	91.22 166	84.24 73	93.37 124	77.97 125	97.03 83	95.52 49

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 88	88.22 18	88.53 126	97.64 2	83.45 80	94.55 76	86.02 43	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 80	82.59 58	88.52 127	94.37 73	86.74 50	95.41 48	86.32 34	98.21 29	93.19 133
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		85.50 106	86.14 104	83.58 162	87.97 201	67.13 192	87.55 104	94.32 18	73.44 163	88.47 128	87.54 241	86.45 54	91.06 188	75.76 150	93.76 190	92.54 156

NR-MVSNet		86.00 101	86.22 102	85.34 125	93.24 74	64.56 218	82.21 211	90.46 148	80.99 74	88.42 129	91.97 147	77.56 141	93.85 100	72.46 190	98.65 11	97.61 10

alignmvs		83.94 143	83.98 143	83.80 155	87.80 205	67.88 189	84.54 148	91.42 124	73.27 170	88.41 130	87.96 232	72.33 201	90.83 196	76.02 148	94.11 184	92.69 150

TransMVSNet (Re)		84.02 140	85.74 111	78.85 242	91.00 144	55.20 315	82.29 207	87.26 207	79.65 89	88.38 131	95.52 33	83.00 83	86.88 267	67.97 231	96.60 95	94.45 82

PM-MVS		80.20 202	79.00 208	83.78 157	88.17 199	86.66 15	81.31 222	66.81 356	69.64 214	88.33 132	90.19 197	64.58 238	83.63 302	71.99 193	90.03 260	81.06 339

tttt0517		81.07 183	79.58 203	85.52 122	88.99 182	66.45 201	87.03 112	75.51 311	73.76 159	88.32 133	90.20 196	37.96 364	94.16 92	79.36 108	95.13 153	95.93 42

casdiffmvs		85.21 110	85.85 108	83.31 169	86.17 244	62.77 237	83.03 185	93.93 40	74.69 150	88.21 134	92.68 130	82.29 94	91.89 166	77.87 126	93.75 192	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		71.42 288	70.67 290	73.64 297	69.66 368	70.46 162	66.97 344	89.73 169	42.68 364	88.20 135	83.04 303	43.77 349	60.07 366	65.35 250	86.66 296	90.39 219

v148		82.31 166	82.48 166	81.81 201	85.59 250	59.66 276	81.47 220	86.02 228	72.85 175	88.05 136	90.65 187	70.73 211	90.91 193	75.15 156	91.79 231	94.87 67

AllTest		87.97 77	87.40 85	89.68 53	91.59 122	83.40 48	89.50 75	95.44 9	79.47 90	88.00 137	93.03 115	82.66 87	91.47 174	70.81 197	96.14 113	94.16 93

TestCases				89.68 53	91.59 122	83.40 48		95.44 9	79.47 90	88.00 137	93.03 115	82.66 87	91.47 174	70.81 197	96.14 113	94.16 93

pm-mvs1		83.69 146	84.95 122	79.91 229	90.04 165	59.66 276	82.43 203	87.44 204	75.52 141	87.85 139	95.26 39	81.25 111	85.65 286	68.74 223	96.04 118	94.42 85

PCF-MVS	74.62 15	82.15 170	80.92 186	85.84 117	89.43 172	72.30 144	80.53 232	91.82 114	57.36 307	87.81 140	89.92 202	77.67 140	93.63 108	58.69 293	95.08 156	91.58 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 180	81.61 176	80.41 223	86.38 233	58.75 290	83.93 161	86.58 221	72.43 181	87.65 141	92.98 117	63.78 244	90.22 212	66.86 234	93.92 188	92.27 169

GeoE		85.45 108	85.81 109	84.37 141	90.08 161	67.07 193	85.86 130	91.39 125	72.33 186	87.59 142	90.25 195	84.85 66	92.37 152	78.00 123	91.94 230	93.66 115

VPA-MVSNet		83.47 152	84.73 124	79.69 233	90.29 157	57.52 297	81.30 224	88.69 187	76.29 126	87.58 143	94.44 66	80.60 118	87.20 262	66.60 239	96.82 89	94.34 88

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 29	92.30 99	79.74 87	87.50 144	92.38 137	81.42 109	93.28 126	83.07 67	97.24 77	91.67 188

VDDNet		84.35 128	85.39 116	81.25 207	95.13 31	59.32 279	85.42 136	81.11 278	86.41 27	87.41 145	96.21 19	73.61 183	90.61 204	66.33 240	96.85 86	93.81 111

c3_l		81.64 177	81.59 177	81.79 202	80.86 302	59.15 283	78.61 262	90.18 162	68.36 226	87.20 146	87.11 251	69.39 214	91.62 171	78.16 120	94.43 178	94.60 75

VDD-MVS		84.23 134	84.58 130	83.20 172	91.17 140	65.16 214	83.25 179	84.97 247	79.79 86	87.18 147	94.27 74	74.77 172	90.89 194	69.24 213	96.54 97	93.55 124

MSLP-MVS++		85.00 116	86.03 105	81.90 196	91.84 117	71.56 156	86.75 120	93.02 81	75.95 134	87.12 148	89.39 210	77.98 136	89.40 236	77.46 130	94.78 168	84.75 290

baseline		85.20 111	85.93 106	83.02 175	86.30 238	62.37 244	84.55 146	93.96 39	74.48 152	87.12 148	92.03 146	82.30 93	91.94 163	78.39 113	94.21 182	94.74 73

YYNet1		70.06 300	70.44 293	68.90 323	73.76 355	53.42 325	58.99 361	67.20 352	58.42 298	87.10 150	85.39 276	59.82 267	67.32 353	59.79 289	83.50 325	85.96 276

MDA-MVSNet_test_wron		70.05 301	70.44 293	68.88 324	73.84 354	53.47 323	58.93 362	67.28 351	58.43 297	87.09 151	85.40 275	59.80 268	67.25 354	59.66 290	83.54 324	85.92 278

test_fmvs3		75.72 250	75.20 250	77.27 269	75.01 351	69.47 171	78.93 255	84.88 248	46.67 349	87.08 152	87.84 236	50.44 315	71.62 338	77.42 133	88.53 275	90.72 207

CNVR-MVS		87.81 81	87.68 79	88.21 80	92.87 82	77.30 99	85.25 137	91.23 129	77.31 120	87.07 153	91.47 161	82.94 84	94.71 68	84.67 56	96.27 109	92.62 153

EPP-MVSNet		85.47 107	85.04 120	86.77 96	91.52 130	69.37 172	91.63 36	87.98 201	81.51 69	87.05 154	91.83 152	66.18 231	95.29 51	70.75 200	96.89 85	95.64 46

TinyColmap		81.25 181	82.34 168	77.99 259	85.33 253	60.68 267	82.32 206	88.33 194	71.26 197	86.97 155	92.22 145	77.10 148	86.98 266	62.37 269	95.17 152	86.31 274

eth_miper_zixun_eth		80.84 186	80.22 196	82.71 184	81.41 294	60.98 262	77.81 271	90.14 163	67.31 240	86.95 156	87.24 248	64.26 240	92.31 154	75.23 155	91.61 234	94.85 71

Anonymous20240521		80.18 203	81.25 180	76.95 272	83.15 281	60.84 264	82.46 202	85.99 229	68.76 223	86.78 157	93.73 105	59.13 272	77.44 323	73.71 173	97.55 67	92.56 154

Patchmatch-RL test		74.48 263	73.68 262	76.89 275	84.83 257	66.54 199	72.29 324	69.16 349	57.70 303	86.76 158	86.33 260	45.79 335	82.59 305	69.63 210	90.65 257	81.54 330

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 115	93.91 41	80.07 85	86.75 159	93.26 111	93.64 2	90.93 191	84.60 57	90.75 253	93.97 101

h-mvs33		84.25 132	82.76 159	88.72 70	91.82 119	82.60 56	84.00 158	84.98 246	71.27 195	86.70 160	90.55 189	63.04 250	93.92 98	78.26 118	94.20 183	89.63 229

hse-mvs2		83.47 152	81.81 173	88.47 74	91.03 143	82.27 57	82.61 195	83.69 257	71.27 195	86.70 160	86.05 266	63.04 250	92.41 150	78.26 118	93.62 196	90.71 208

HPM-MVS++		88.93 64	88.45 71	90.38 40	94.92 35	85.85 27	89.70 66	91.27 128	78.20 109	86.69 162	92.28 142	80.36 120	95.06 60	86.17 39	96.49 99	90.22 221

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 65	95.72 21	76.74 104	92.49 24	91.19 131	67.85 236	86.63 163	94.84 51	79.58 126	95.96 12	87.62 13	94.50 175	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		82.61 162	82.42 167	83.20 172	83.25 278	63.66 226	83.50 173	85.07 241	76.06 129	86.55 164	85.10 280	73.41 188	90.25 209	78.15 122	90.67 255	95.68 45

HQP_MVS		87.75 82	87.43 84	88.70 72	93.45 66	76.42 109	89.45 77	93.61 53	79.44 92	86.55 164	92.95 120	74.84 169	95.22 54	80.78 90	95.83 130	94.46 80

plane_prior3						76.85 103			77.79 114	86.55 164

BH-untuned		80.96 185	80.99 184	80.84 216	88.55 191	68.23 183	80.33 235	88.46 189	72.79 177	86.55 164	86.76 255	74.72 173	91.77 170	61.79 276	88.99 269	82.52 320

MVSTER		77.09 234	75.70 245	81.25 207	75.27 348	61.08 258	77.49 278	85.07 241	60.78 287	86.55 164	88.68 223	43.14 354	90.25 209	73.69 174	90.67 255	92.42 159

旧先验2							81.73 216		56.88 310	86.54 169			84.90 292	72.81 187

IterMVS-SCA-FT		80.64 190	79.41 204	84.34 145	83.93 272	69.66 169	76.28 294	81.09 279	72.43 181	86.47 170	90.19 197	60.46 260	93.15 131	77.45 131	86.39 300	90.22 221

DIV-MVS_self_test		80.43 193	80.23 194	81.02 213	79.99 310	59.25 280	77.07 282	87.02 216	67.38 238	86.19 171	89.22 213	63.09 248	90.16 214	76.32 143	95.80 133	93.66 115

CDPH-MVS		86.17 100	85.54 114	88.05 83	92.25 100	75.45 116	83.85 163	92.01 105	65.91 248	86.19 171	91.75 156	83.77 77	94.98 62	77.43 132	96.71 92	93.73 113

cl____		80.42 194	80.23 194	81.02 213	79.99 310	59.25 280	77.07 282	87.02 216	67.37 239	86.18 173	89.21 214	63.08 249	90.16 214	76.31 144	95.80 133	93.65 117

MVS_111021_LR		84.28 131	83.76 146	85.83 118	89.23 177	83.07 51	80.99 228	83.56 259	72.71 178	86.07 174	89.07 218	81.75 106	86.19 279	77.11 136	93.36 197	88.24 250

GBi-Net		82.02 172	82.07 169	81.85 198	86.38 233	61.05 259	86.83 116	88.27 196	72.43 181	86.00 175	95.64 30	63.78 244	90.68 201	65.95 242	93.34 198	93.82 108

test1		82.02 172	82.07 169	81.85 198	86.38 233	61.05 259	86.83 116	88.27 196	72.43 181	86.00 175	95.64 30	63.78 244	90.68 201	65.95 242	93.34 198	93.82 108

FMVSNet3		78.80 216	78.55 216	79.57 235	82.89 285	56.89 303	81.76 215	85.77 231	69.04 220	86.00 175	90.44 191	51.75 309	90.09 220	65.95 242	93.34 198	91.72 185

miper_ehance_all_eth		80.34 198	80.04 201	81.24 209	79.82 312	58.95 285	77.66 273	89.66 172	65.75 252	85.99 178	85.11 279	68.29 221	91.42 178	76.03 147	92.03 226	93.33 126

tfpnnormal		81.79 176	82.95 157	78.31 252	88.93 183	55.40 311	80.83 231	82.85 265	76.81 123	85.90 179	94.14 84	74.58 175	86.51 274	66.82 237	95.68 139	93.01 139

TAPA-MVS	77.73 12	85.71 105	84.83 123	88.37 77	88.78 185	79.72 73	87.15 110	93.50 56	69.17 217	85.80 180	89.56 207	80.76 115	92.13 158	73.21 185	95.51 140	93.25 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 142	82.69 161	87.72 85	89.27 176	81.45 63	83.72 168	81.58 277	74.73 149	85.66 181	86.06 265	72.56 200	92.69 144	75.44 153	95.21 150	89.01 245

EU-MVSNet		75.12 255	74.43 257	77.18 270	83.11 282	59.48 278	85.71 133	82.43 268	39.76 368	85.64 182	88.76 221	44.71 347	87.88 255	73.86 170	85.88 304	84.16 296

LF4IMVS		82.75 161	81.93 172	85.19 126	82.08 287	80.15 70	85.53 134	88.76 186	68.01 231	85.58 183	87.75 237	71.80 207	86.85 268	74.02 167	93.87 189	88.58 248

Patchmtry		76.56 242	77.46 225	73.83 296	79.37 318	46.60 360	82.41 204	76.90 300	73.81 158	85.56 184	92.38 137	48.07 322	83.98 299	63.36 264	95.31 148	90.92 202

MVS_111021_HR		84.63 121	84.34 138	85.49 124	90.18 160	75.86 114	79.23 253	87.13 211	73.35 164	85.56 184	89.34 211	83.60 79	90.50 206	76.64 140	94.05 186	90.09 226

testdata				79.54 236	92.87 82	72.34 143		80.14 286	59.91 293	85.47 186	91.75 156	67.96 223	85.24 288	68.57 227	92.18 225	81.06 339

test1111		78.53 220	78.85 211	77.56 265	92.22 102	47.49 356	82.61 195	69.24 348	72.43 181	85.28 187	94.20 80	51.91 307	90.07 221	65.36 249	96.45 102	95.11 62

thisisatest0530		79.07 210	77.33 229	84.26 147	87.13 219	64.58 217	83.66 170	75.95 306	68.86 222	85.22 188	87.36 245	38.10 362	93.57 116	75.47 152	94.28 181	94.62 74

DROMVSNet		88.01 75	88.32 72	87.09 90	89.28 175	72.03 148	90.31 54	96.31 3	80.88 76	85.12 189	89.67 206	84.47 70	95.46 45	82.56 73	96.26 110	93.77 112

CLD-MVS		83.18 156	82.64 162	84.79 132	89.05 179	67.82 190	77.93 269	92.52 94	68.33 227	85.07 190	81.54 321	82.06 98	92.96 136	69.35 212	97.91 48	93.57 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FA-MVS(test-final)		83.13 158	83.02 156	83.43 165	86.16 246	66.08 205	88.00 98	88.36 192	75.55 140	85.02 191	92.75 128	65.12 237	92.50 148	74.94 159	91.30 240	91.72 185

DeepC-MVS_fast	80.27 8	86.23 97	85.65 113	87.96 84	91.30 134	76.92 102	87.19 108	91.99 106	70.56 203	84.96 192	90.69 184	80.01 123	95.14 57	78.37 114	95.78 135	91.82 183
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	84.80 119	84.71 127	85.06 129	86.36 236	74.71 120	88.77 89	90.00 166	75.65 139	84.96 192	93.17 113	74.06 178	91.19 183	78.28 117	91.09 242	89.29 237

QAPM		82.59 163	82.59 164	82.58 187	86.44 231	66.69 198	89.94 62	90.36 152	67.97 233	84.94 194	92.58 133	72.71 197	92.18 157	70.63 203	87.73 287	88.85 246

VPNet		80.25 200	81.68 174	75.94 285	92.46 93	47.98 354	76.70 287	81.67 275	73.45 162	84.87 195	92.82 124	74.66 174	86.51 274	61.66 278	96.85 86	93.33 126

NCCC		87.36 83	86.87 94	88.83 67	92.32 98	78.84 82	86.58 123	91.09 133	78.77 103	84.85 196	90.89 178	80.85 114	95.29 51	81.14 85	95.32 146	92.34 164

PHI-MVS		86.38 95	85.81 109	88.08 81	88.44 194	77.34 97	89.35 80	93.05 77	73.15 172	84.76 197	87.70 238	78.87 130	94.18 88	80.67 92	96.29 106	92.73 146

pmmvs-eth3d		78.42 222	77.04 232	82.57 189	87.44 213	74.41 122	80.86 230	79.67 288	55.68 313	84.69 198	90.31 194	60.91 258	85.42 287	62.20 271	91.59 235	87.88 258

test_prior2							83.37 176		75.43 142	84.58 199	91.57 158	81.92 103		79.54 105	96.97 84

TEST9					92.34 96	79.70 74	83.94 159	90.32 153	65.41 258	84.49 200	90.97 174	82.03 99	93.63 108

train_agg		85.98 102	85.28 117	88.07 82	92.34 96	79.70 74	83.94 159	90.32 153	65.79 249	84.49 200	90.97 174	81.93 101	93.63 108	81.21 84	96.54 97	90.88 203

Gipuma		84.44 126	86.33 100	78.78 243	84.20 269	73.57 126	89.55 72	90.44 149	84.24 41	84.38 202	94.89 49	76.35 160	80.40 316	76.14 146	96.80 90	82.36 322
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		64.31 325	65.85 319	59.67 350	66.54 373	62.24 248	57.76 363	70.96 341	40.13 366	84.36 203	82.09 315	46.93 324	51.67 372	61.99 274	81.89 335	65.12 364

test_8					92.09 106	78.87 81	83.82 164	90.31 155	65.79 249	84.36 203	90.96 176	81.93 101	93.44 121

cl22		78.97 211	78.21 221	81.24 209	77.74 326	59.01 284	77.46 279	87.13 211	65.79 249	84.32 205	85.10 280	58.96 274	90.88 195	75.36 154	92.03 226	93.84 106

CS-MVS		88.14 72	87.67 80	89.54 58	89.56 169	79.18 78	90.47 51	94.77 15	79.37 94	84.32 205	89.33 212	83.87 74	94.53 77	82.45 74	94.89 163	94.90 65

agg_prior					91.58 125	77.69 92		90.30 156		84.32 205			93.18 129

Anonymous202405211		80.51 192	81.19 183	78.49 249	88.48 192	57.26 299	76.63 289	82.49 267	81.21 72	84.30 208	92.24 144	67.99 222	86.24 278	62.22 270	95.13 153	91.98 180

LFMVS		80.15 204	80.56 188	78.89 241	89.19 178	55.93 307	85.22 138	73.78 323	82.96 55	84.28 209	92.72 129	57.38 284	90.07 221	63.80 260	95.75 136	90.68 210

Vis-MVSNet		86.86 88	86.58 97	87.72 85	92.09 106	77.43 96	87.35 107	92.09 103	78.87 101	84.27 210	94.05 87	78.35 134	93.65 106	80.54 94	91.58 236	92.08 175
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ECVR-MVS		78.44 221	78.63 215	77.88 261	91.85 115	48.95 350	83.68 169	69.91 346	72.30 187	84.26 211	94.20 80	51.89 308	89.82 225	63.58 261	96.02 119	94.87 67

iter_conf05		78.81 215	77.35 228	83.21 171	82.98 284	60.75 266	84.09 155	88.34 193	63.12 267	84.25 212	89.48 208	31.41 372	94.51 79	76.64 140	95.83 130	94.38 87

FE-MVS		79.98 207	78.86 210	83.36 167	86.47 230	66.45 201	89.73 65	84.74 251	72.80 176	84.22 213	91.38 163	44.95 345	93.60 112	63.93 259	91.50 237	90.04 227

ETV-MVS		84.31 129	83.91 145	85.52 122	88.58 190	70.40 163	84.50 150	93.37 58	78.76 104	84.07 214	78.72 342	80.39 119	95.13 58	73.82 171	92.98 208	91.04 199

MCST-MVS		84.36 127	83.93 144	85.63 120	91.59 122	71.58 155	83.52 172	92.13 102	61.82 276	83.96 215	89.75 205	79.93 125	93.46 120	78.33 116	94.34 179	91.87 182

新几何1				82.95 178	93.96 55	78.56 84		80.24 285	55.45 314	83.93 216	91.08 171	71.19 210	88.33 251	65.84 245	93.07 205	81.95 326

BH-RMVSNet		80.53 191	80.22 196	81.49 205	87.19 218	66.21 204	77.79 272	86.23 225	74.21 154	83.69 217	88.50 225	73.25 192	90.75 198	63.18 266	87.90 284	87.52 261

USDC		76.63 240	76.73 236	76.34 281	83.46 276	57.20 300	80.02 238	88.04 200	52.14 331	83.65 218	91.25 165	63.24 247	86.65 273	54.66 319	94.11 184	85.17 285

miper_enhance_ethall		77.83 226	76.93 233	80.51 221	76.15 340	58.01 293	75.47 304	88.82 184	58.05 301	83.59 219	80.69 325	64.41 239	91.20 182	73.16 186	92.03 226	92.33 165

Effi-MVS+-dtu		85.82 104	83.38 149	93.14 3	87.13 219	91.15 2	87.70 103	88.42 190	74.57 151	83.56 220	85.65 270	78.49 133	94.21 86	72.04 192	92.88 210	94.05 98

CNLPA		83.55 150	83.10 155	84.90 130	89.34 174	83.87 46	84.54 148	88.77 185	79.09 97	83.54 221	88.66 224	74.87 168	81.73 310	66.84 236	92.29 220	89.11 239

OpenMVS_ROB	70.19 17	77.77 229	77.46 225	78.71 245	84.39 265	61.15 257	81.18 226	82.52 266	62.45 273	83.34 222	87.37 244	66.20 230	88.66 248	64.69 255	85.02 311	86.32 273

thres100view900		75.45 251	75.05 251	76.66 278	87.27 215	51.88 336	81.07 227	73.26 327	75.68 138	83.25 223	86.37 259	45.54 336	88.80 243	51.98 332	90.99 244	89.31 235

miper_lstm_enhance		76.45 244	76.10 241	77.51 266	76.72 335	60.97 263	64.69 349	85.04 243	63.98 264	83.20 224	88.22 228	56.67 288	78.79 321	73.22 180	93.12 204	92.78 145

IterMVS		76.91 236	76.34 239	78.64 246	80.91 300	64.03 223	76.30 293	79.03 291	64.88 261	83.11 225	89.16 215	59.90 266	84.46 295	68.61 225	85.15 310	87.42 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 247	75.35 249	77.85 263	87.01 225	51.84 337	80.45 233	73.26 327	75.20 145	83.10 226	86.31 262	45.54 336	89.05 239	55.03 317	92.24 222	92.66 151

mvs_anonymous		78.13 224	78.76 213	76.23 284	79.24 319	50.31 347	78.69 260	84.82 249	61.60 280	83.09 227	92.82 124	73.89 181	87.01 263	68.33 229	86.41 299	91.37 194

test_fmvs2		73.57 270	72.80 272	75.90 286	72.74 362	68.84 181	77.07 282	84.32 254	45.14 354	82.89 228	84.22 292	48.37 320	70.36 341	73.40 178	87.03 293	88.52 249

MVS_Test		82.47 165	83.22 151	80.22 226	82.62 286	57.75 296	82.54 200	91.96 108	71.16 199	82.89 228	92.52 135	77.41 143	90.50 206	80.04 97	87.84 286	92.40 161

test12				86.57 98	90.74 149	72.63 136		90.69 143		82.76 230		79.20 127	94.80 66		95.32 146	92.27 169

原ACMM1				84.60 138	92.81 87	74.01 124		91.50 120	62.59 270	82.73 231	90.67 186	76.53 158	94.25 84	69.24 213	95.69 138	85.55 281

test_yl		78.71 218	78.51 217	79.32 238	84.32 266	58.84 287	78.38 263	85.33 236	75.99 132	82.49 232	86.57 256	58.01 278	90.02 223	62.74 267	92.73 213	89.10 240

DCV-MVSNet		78.71 218	78.51 217	79.32 238	84.32 266	58.84 287	78.38 263	85.33 236	75.99 132	82.49 232	86.57 256	58.01 278	90.02 223	62.74 267	92.73 213	89.10 240

diffmvs		80.40 195	80.48 191	80.17 227	79.02 322	60.04 271	77.54 276	90.28 159	66.65 245	82.40 234	87.33 246	73.50 185	87.35 261	77.98 124	89.62 263	93.13 134
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 71	76.54 105	79.38 248	77.79 296	52.59 326	82.36 235	90.84 180	66.83 228			91.69 233	81.25 334

D2MVS		76.84 237	75.67 246	80.34 224	80.48 308	62.16 249	73.50 318	84.80 250	57.61 305	82.24 236	87.54 241	51.31 310	87.65 257	70.40 206	93.19 203	91.23 196

VNet		79.31 209	80.27 193	76.44 279	87.92 203	53.95 320	75.58 302	84.35 253	74.39 153	82.23 237	90.72 183	72.84 196	84.39 296	60.38 287	93.98 187	90.97 200

Vis-MVSNet (Re-imp)		77.82 227	77.79 224	77.92 260	88.82 184	51.29 341	83.28 177	71.97 335	74.04 155	82.23 237	89.78 204	57.38 284	89.41 235	57.22 301	95.41 142	93.05 138

API-MVS		82.28 167	82.61 163	81.30 206	86.29 239	69.79 166	88.71 90	87.67 203	78.42 108	82.15 239	84.15 294	77.98 136	91.59 172	65.39 248	92.75 212	82.51 321

iter_conf_final		80.36 197	78.88 209	84.79 132	86.29 239	66.36 203	86.95 113	86.25 224	68.16 230	82.09 240	89.48 208	36.59 367	94.51 79	79.83 100	94.30 180	93.50 125

DP-MVS Recon		84.05 139	83.22 151	86.52 100	91.73 120	75.27 117	83.23 181	92.40 96	72.04 190	82.04 241	88.33 227	77.91 138	93.95 97	66.17 241	95.12 155	90.34 220

MSDG		80.06 206	79.99 202	80.25 225	83.91 273	68.04 188	77.51 277	89.19 180	77.65 115	81.94 242	83.45 300	76.37 159	86.31 277	63.31 265	86.59 297	86.41 272

test2506		74.12 266	73.39 266	76.28 282	91.85 115	44.20 366	84.06 156	48.20 376	72.30 187	81.90 243	94.20 80	27.22 379	89.77 226	64.81 253	96.02 119	94.87 67

Fast-Effi-MVS+		81.04 184	80.57 187	82.46 191	87.50 212	63.22 232	78.37 265	89.63 174	68.01 231	81.87 244	82.08 316	82.31 92	92.65 145	67.10 233	88.30 281	91.51 193

testgi		72.36 280	74.61 253	65.59 335	80.56 307	42.82 370	68.29 337	73.35 326	66.87 243	81.84 245	89.93 201	72.08 204	66.92 356	46.05 356	92.54 215	87.01 268

tfpn200view9		74.86 259	74.23 258	76.74 277	86.24 241	52.12 333	79.24 251	73.87 321	73.34 165	81.82 246	84.60 289	46.02 330	88.80 243	51.98 332	90.99 244	89.31 235

thres400		75.14 253	74.23 258	77.86 262	86.24 241	52.12 333	79.24 251	73.87 321	73.34 165	81.82 246	84.60 289	46.02 330	88.80 243	51.98 332	90.99 244	92.66 151

CL-MVSNet_self_test		76.81 238	77.38 227	75.12 290	86.90 227	51.34 339	73.20 321	80.63 284	68.30 228	81.80 248	88.40 226	66.92 227	80.90 313	55.35 314	94.90 162	93.12 136

OpenMVS	76.72 13	81.98 174	82.00 171	81.93 195	84.42 264	68.22 184	88.50 94	89.48 177	66.92 242	81.80 248	91.86 149	72.59 199	90.16 214	71.19 196	91.25 241	87.40 263

AdaColmap		83.66 147	83.69 147	83.57 163	90.05 164	72.26 145	86.29 127	90.00 166	78.19 110	81.65 250	87.16 249	83.40 81	94.24 85	61.69 277	94.76 171	84.21 295

CS-MVS-test		87.00 86	86.43 99	88.71 71	89.46 171	77.46 94	89.42 79	95.73 6	77.87 113	81.64 251	87.25 247	82.43 90	94.53 77	77.65 127	96.46 101	94.14 95

DELS-MVS		81.44 179	81.25 180	82.03 194	84.27 268	62.87 236	76.47 292	92.49 95	70.97 200	81.64 251	83.83 295	75.03 166	92.70 143	74.29 161	92.22 224	90.51 216
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		83.10 159	82.54 165	84.77 134	92.90 81	69.10 179	86.65 121	90.62 146	54.66 317	81.46 253	90.81 181	76.98 150	94.38 81	72.62 188	96.18 111	90.82 205

TR-MVS		76.77 239	75.79 243	79.72 232	86.10 247	65.79 208	77.14 280	83.02 263	65.20 259	81.40 254	82.10 314	66.30 229	90.73 200	55.57 311	85.27 308	82.65 315

TAMVS		78.08 225	76.36 238	83.23 170	90.62 152	72.87 130	79.08 254	80.01 287	61.72 278	81.35 255	86.92 254	63.96 243	88.78 246	50.61 337	93.01 207	88.04 254

Effi-MVS+		83.90 144	84.01 142	83.57 163	87.22 217	65.61 210	86.55 124	92.40 96	78.64 105	81.34 256	84.18 293	83.65 78	92.93 138	74.22 162	87.87 285	92.17 174

new-patchmatchnet		70.10 299	73.37 267	60.29 349	81.23 297	16.95 380	59.54 358	74.62 314	62.93 268	80.97 257	87.93 234	62.83 253	71.90 337	55.24 315	95.01 159	92.00 178

PVSNet_Blended_VisFu		81.55 178	80.49 190	84.70 137	91.58 125	73.24 129	84.21 152	91.67 117	62.86 269	80.94 258	87.16 249	67.27 225	92.87 141	69.82 209	88.94 271	87.99 255

BH-w/o		76.57 241	76.07 242	78.10 257	86.88 228	65.92 207	77.63 274	86.33 222	65.69 253	80.89 259	79.95 334	68.97 219	90.74 199	53.01 328	85.25 309	77.62 349

PAPM_NR		83.23 155	83.19 153	83.33 168	90.90 146	65.98 206	88.19 96	90.78 141	78.13 111	80.87 260	87.92 235	73.49 187	92.42 149	70.07 207	88.40 276	91.60 190

ab-mvs		79.67 208	80.56 188	76.99 271	88.48 192	56.93 301	84.70 143	86.06 227	68.95 221	80.78 261	93.08 114	75.30 164	84.62 294	56.78 302	90.90 249	89.43 233

XXY-MVS		74.44 265	76.19 240	69.21 322	84.61 260	52.43 332	71.70 326	77.18 299	60.73 288	80.60 262	90.96 176	75.44 161	69.35 344	56.13 307	88.33 277	85.86 279

HQP4-MVS										80.56 263			94.61 72			93.56 122

HQP-NCC					91.19 137		84.77 140		73.30 167	80.55 264

ACMP_Plane					91.19 137		84.77 140		73.30 167	80.55 264

HQP-MVS		84.61 122	84.06 141	86.27 105	91.19 137	70.66 160	84.77 140	92.68 91	73.30 167	80.55 264	90.17 199	72.10 202	94.61 72	77.30 134	94.47 176	93.56 122

AUN-MVS		81.18 182	78.78 212	88.39 76	90.93 145	82.14 58	82.51 201	83.67 258	64.69 262	80.29 267	85.91 269	51.07 311	92.38 151	76.29 145	93.63 195	90.65 212

HyFIR lowres test		75.12 255	72.66 275	82.50 190	91.44 133	65.19 213	72.47 323	87.31 206	46.79 348	80.29 267	84.30 291	52.70 306	92.10 161	51.88 336	86.73 295	90.22 221

test20.03		73.75 269	74.59 255	71.22 311	81.11 298	51.12 343	70.15 332	72.10 334	70.42 205	80.28 269	91.50 160	64.21 241	74.72 333	46.96 353	94.58 174	87.82 260

mvsany_test3		65.48 322	62.97 328	73.03 302	69.99 367	76.17 113	64.83 347	43.71 378	43.68 359	80.25 270	87.05 253	52.83 305	63.09 365	51.92 335	72.44 361	79.84 345

F-COLMAP		84.97 117	83.42 148	89.63 55	92.39 94	83.40 48	88.83 87	91.92 109	73.19 171	80.18 271	89.15 216	77.04 149	93.28 126	65.82 246	92.28 221	92.21 172

GA-MVS		75.83 248	74.61 253	79.48 237	81.87 289	59.25 280	73.42 319	82.88 264	68.68 224	79.75 272	81.80 318	50.62 313	89.46 231	66.85 235	85.64 305	89.72 228

xiu_mvs_v1_base_debu		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

xiu_mvs_v1_base		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

xiu_mvs_v1_base_debi		80.84 186	80.14 198	82.93 179	88.31 195	71.73 151	79.53 244	87.17 208	65.43 255	79.59 273	82.73 310	76.94 151	90.14 217	73.22 180	88.33 277	86.90 269

test_fmvs1_n		70.94 292	70.41 295	72.53 306	73.92 353	66.93 196	75.99 297	84.21 256	43.31 361	79.40 276	79.39 338	43.47 350	68.55 349	69.05 218	84.91 314	82.10 324

patch_mono-2		78.89 212	79.39 205	77.41 268	84.78 258	68.11 186	75.60 300	83.11 262	60.96 285	79.36 277	89.89 203	75.18 165	72.97 334	73.32 179	92.30 218	91.15 197

UnsupCasMVSNet_eth		71.63 287	72.30 280	69.62 320	76.47 337	52.70 330	70.03 333	80.97 280	59.18 294	79.36 277	88.21 229	60.50 259	69.12 345	58.33 296	77.62 354	87.04 267

ppachtmachnet_test		74.73 262	74.00 260	76.90 274	80.71 305	56.89 303	71.53 327	78.42 293	58.24 299	79.32 279	82.92 307	57.91 281	84.26 297	65.60 247	91.36 239	89.56 230

MG-MVS		80.32 199	80.94 185	78.47 250	88.18 198	52.62 331	82.29 207	85.01 245	72.01 191	79.24 280	92.54 134	69.36 215	93.36 125	70.65 202	89.19 268	89.45 231

Fast-Effi-MVS+-dtu		82.54 164	81.41 179	85.90 115	85.60 249	76.53 107	83.07 184	89.62 175	73.02 174	79.11 281	83.51 298	80.74 116	90.24 211	68.76 222	89.29 265	90.94 201

CDS-MVSNet		77.32 232	75.40 247	83.06 174	89.00 181	72.48 141	77.90 270	82.17 270	60.81 286	78.94 282	83.49 299	59.30 270	88.76 247	54.64 320	92.37 217	87.93 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 272	73.54 264	72.43 307	84.92 256	47.79 355	79.89 240	74.00 319	65.93 247	78.81 283	86.28 263	56.36 291	81.63 311	56.63 303	79.04 349	87.87 259

EIA-MVS		82.19 169	81.23 182	85.10 128	87.95 202	69.17 178	83.22 182	93.33 61	70.42 205	78.58 284	79.77 337	77.29 144	94.20 87	71.51 194	88.96 270	91.93 181

thres200		72.34 281	71.55 287	74.70 293	83.48 275	51.60 338	75.02 307	73.71 324	70.14 211	78.56 285	80.57 328	46.20 328	88.20 253	46.99 352	89.29 265	84.32 294

our_test_3		71.85 284	71.59 284	72.62 304	80.71 305	53.78 321	69.72 334	71.71 339	58.80 296	78.03 286	80.51 330	56.61 290	78.84 320	62.20 271	86.04 303	85.23 284

KD-MVS_2432*1600		66.87 314	65.81 320	70.04 316	67.50 370	47.49 356	62.56 353	79.16 289	61.21 283	77.98 287	80.61 326	25.29 381	82.48 306	53.02 326	84.92 312	80.16 343

miper_refine_blended		66.87 314	65.81 320	70.04 316	67.50 370	47.49 356	62.56 353	79.16 289	61.21 283	77.98 287	80.61 326	25.29 381	82.48 306	53.02 326	84.92 312	80.16 343

jason		77.42 231	75.75 244	82.43 192	87.10 222	69.27 173	77.99 268	81.94 272	51.47 335	77.84 289	85.07 283	60.32 262	89.00 240	70.74 201	89.27 267	89.03 243
jason: jason.
MAR-MVS		80.24 201	78.74 214	84.73 135	86.87 229	78.18 85	85.75 131	87.81 202	65.67 254	77.84 289	78.50 343	73.79 182	90.53 205	61.59 279	90.87 250	85.49 283
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		72.29 282	72.00 281	73.14 300	88.63 188	85.00 36	74.65 310	67.39 350	71.94 192	77.80 291	87.66 239	50.48 314	75.83 329	49.95 339	79.51 343	58.58 370

test_fmvs1		69.57 305	69.05 304	71.14 313	69.15 369	65.77 209	73.98 314	83.32 260	42.83 363	77.77 292	78.27 345	43.39 353	68.50 350	68.39 228	84.38 321	79.15 346

pmmvs4		74.92 258	72.98 271	80.73 218	84.95 255	71.71 154	76.23 295	77.59 297	52.83 325	77.73 293	86.38 258	56.35 292	84.97 291	57.72 300	87.05 292	85.51 282

ET-MVSNet_ETH3D		75.28 252	72.77 273	82.81 183	83.03 283	68.11 186	77.09 281	76.51 304	60.67 289	77.60 294	80.52 329	38.04 363	91.15 185	70.78 199	90.68 254	89.17 238

UnsupCasMVSNet_bld		69.21 307	69.68 301	67.82 329	79.42 316	51.15 342	67.82 341	75.79 307	54.15 319	77.47 295	85.36 278	59.26 271	70.64 340	48.46 346	79.35 345	81.66 328

Anonymous20231206		71.38 289	71.88 282	69.88 318	86.31 237	54.37 317	70.39 331	74.62 314	52.57 327	76.73 296	88.76 221	59.94 265	72.06 336	44.35 359	93.23 202	83.23 311

CMPMVS	59.41 20	75.12 255	73.57 263	79.77 230	75.84 343	67.22 191	81.21 225	82.18 269	50.78 340	76.50 297	87.66 239	55.20 298	82.99 304	62.17 273	90.64 258	89.09 242
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 283	71.69 283	73.32 298	81.57 292	53.02 327	76.77 286	78.37 294	63.31 265	76.37 298	91.85 150	36.68 366	78.98 319	47.87 349	92.45 216	87.95 256

CVMVSNet		72.62 278	71.41 288	76.28 282	83.25 278	60.34 269	83.50 173	79.02 292	37.77 371	76.33 299	85.10 280	49.60 318	87.41 260	70.54 204	77.54 355	81.08 337

PLC	73.85 16	82.09 171	80.31 192	87.45 88	90.86 148	80.29 69	85.88 129	90.65 144	68.17 229	76.32 300	86.33 260	73.12 193	92.61 146	61.40 280	90.02 261	89.44 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 168	81.57 178	84.19 150	85.54 251	69.26 174	91.98 31	90.08 164	71.54 193	76.23 301	85.07 283	58.69 275	94.27 82	86.26 35	88.77 272	89.03 243

lupinMVS		76.37 245	74.46 256	82.09 193	85.54 251	69.26 174	76.79 285	80.77 283	50.68 342	76.23 301	82.82 308	58.69 275	88.94 241	69.85 208	88.77 272	88.07 252

PatchMatch-RL		74.48 263	73.22 268	78.27 255	87.70 207	85.26 34	75.92 298	70.09 344	64.34 263	76.09 303	81.25 323	65.87 234	78.07 322	53.86 322	83.82 323	71.48 357

thisisatest0515		73.00 276	70.52 292	80.46 222	81.45 293	59.90 274	73.16 322	74.31 318	57.86 302	76.08 304	77.78 346	37.60 365	92.12 160	65.00 251	91.45 238	89.35 234

MS-PatchMatch		70.93 293	70.22 296	73.06 301	81.85 290	62.50 242	73.82 317	77.90 295	52.44 328	75.92 305	81.27 322	55.67 295	81.75 309	55.37 313	77.70 353	74.94 353

CHOSEN 1792x2688		72.45 279	70.56 291	78.13 256	90.02 166	63.08 233	68.72 336	83.16 261	42.99 362	75.92 305	85.46 273	57.22 286	85.18 290	49.87 341	81.67 336	86.14 275

CR-MVSNet		74.00 267	73.04 270	76.85 276	79.58 313	62.64 239	82.58 197	76.90 300	50.50 343	75.72 307	92.38 137	48.07 322	84.07 298	68.72 224	82.91 329	83.85 300

RPMNet		78.88 213	78.28 220	80.68 220	79.58 313	62.64 239	82.58 197	94.16 27	74.80 148	75.72 307	92.59 131	48.69 319	95.56 37	73.48 176	82.91 329	83.85 300

DPM-MVS		80.10 205	79.18 207	82.88 182	90.71 151	69.74 167	78.87 258	90.84 139	60.29 291	75.64 309	85.92 268	67.28 224	93.11 132	71.24 195	91.79 231	85.77 280

test_vis1_n		70.29 296	69.99 299	71.20 312	75.97 342	66.50 200	76.69 288	80.81 281	44.22 357	75.43 310	77.23 351	50.00 316	68.59 348	66.71 238	82.85 331	78.52 348

PVSNet_BlendedMVS		78.80 216	77.84 223	81.65 203	84.43 262	63.41 228	79.49 247	90.44 149	61.70 279	75.43 310	87.07 252	69.11 217	91.44 176	60.68 285	92.24 222	90.11 225

PVSNet_Blended		76.49 243	75.40 247	79.76 231	84.43 262	63.41 228	75.14 306	90.44 149	57.36 307	75.43 310	78.30 344	69.11 217	91.44 176	60.68 285	87.70 288	84.42 293

PAPR		78.84 214	78.10 222	81.07 211	85.17 254	60.22 270	82.21 211	90.57 147	62.51 271	75.32 313	84.61 288	74.99 167	92.30 155	59.48 291	88.04 283	90.68 210

N_pmnet		70.20 297	68.80 307	74.38 294	80.91 300	84.81 39	59.12 360	76.45 305	55.06 315	75.31 314	82.36 313	55.74 294	54.82 370	47.02 351	87.24 291	83.52 304

cascas		76.29 246	74.81 252	80.72 219	84.47 261	62.94 234	73.89 316	87.34 205	55.94 312	75.16 315	76.53 356	63.97 242	91.16 184	65.00 251	90.97 247	88.06 253

SCA		73.32 271	72.57 277	75.58 288	81.62 291	55.86 308	78.89 257	71.37 340	61.73 277	74.93 316	83.42 301	60.46 260	87.01 263	58.11 298	82.63 334	83.88 297

test_vis1_n_1920		71.30 290	71.58 286	70.47 314	77.58 329	59.99 273	74.25 311	84.22 255	51.06 337	74.85 317	79.10 339	55.10 299	68.83 347	68.86 221	79.20 348	82.58 317

xiu_mvs_v2_base		77.19 233	76.75 235	78.52 248	87.01 225	61.30 255	75.55 303	87.12 214	61.24 282	74.45 318	78.79 341	77.20 145	90.93 191	64.62 257	84.80 318	83.32 309

CANet		83.79 145	82.85 158	86.63 97	86.17 244	72.21 147	83.76 167	91.43 122	77.24 121	74.39 319	87.45 243	75.36 163	95.42 47	77.03 137	92.83 211	92.25 171

PS-MVSNAJ		77.04 235	76.53 237	78.56 247	87.09 223	61.40 253	75.26 305	87.13 211	61.25 281	74.38 320	77.22 352	76.94 151	90.94 190	64.63 256	84.83 317	83.35 308

MVP-Stereo		75.81 249	73.51 265	82.71 184	89.35 173	73.62 125	80.06 236	85.20 238	60.30 290	73.96 321	87.94 233	57.89 282	89.45 232	52.02 331	74.87 359	85.06 287
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 160	81.64 175	86.21 108	86.20 243	76.24 112	86.86 114	85.68 232	77.07 122	73.76 322	92.82 124	69.64 213	91.82 169	69.04 219	93.69 193	90.56 214
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		74.82 260	73.74 261	78.04 258	89.57 168	60.04 271	76.49 291	87.09 215	54.31 318	73.66 323	79.80 335	60.25 263	86.76 272	58.37 294	84.15 322	87.32 264

Test_1112_low_res		73.90 268	73.08 269	76.35 280	90.35 156	55.95 306	73.40 320	86.17 226	50.70 341	73.14 324	85.94 267	58.31 277	85.90 283	56.51 304	83.22 326	87.20 265

1314		73.22 273	72.56 278	75.20 289	80.41 309	57.84 294	81.64 218	85.36 235	51.68 334	73.10 325	76.65 355	61.45 256	85.19 289	63.54 262	79.21 347	82.59 316

test_vis1_rt		65.64 321	64.09 325	70.31 315	66.09 374	70.20 165	61.16 356	81.60 276	38.65 369	72.87 326	69.66 364	52.84 304	60.04 367	56.16 306	77.77 352	80.68 341

Patchmatch-test		65.91 319	67.38 312	61.48 347	75.51 345	43.21 369	68.84 335	63.79 360	62.48 272	72.80 327	83.42 301	44.89 346	59.52 368	48.27 348	86.45 298	81.70 327

PatchmatchNet		69.71 304	68.83 306	72.33 308	77.66 328	53.60 322	79.29 249	69.99 345	57.66 304	72.53 328	82.93 306	46.45 327	80.08 318	60.91 283	72.09 362	83.31 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 310	68.08 311	67.55 330	78.74 324	43.53 368	75.60 300	67.10 355	54.92 316	72.23 329	88.10 230	42.87 355	75.97 328	52.21 330	80.95 342	83.15 312

pmmvs5		70.73 294	70.07 297	72.72 303	77.03 333	52.73 329	74.14 312	75.65 310	50.36 344	72.17 330	85.37 277	55.42 297	80.67 315	52.86 329	87.59 289	84.77 289

PatchT		70.52 295	72.76 274	63.79 341	79.38 317	33.53 376	77.63 274	65.37 358	73.61 160	71.77 331	92.79 127	44.38 348	75.65 330	64.53 258	85.37 307	82.18 323

MVS		73.21 274	72.59 276	75.06 291	80.97 299	60.81 265	81.64 218	85.92 230	46.03 352	71.68 332	77.54 347	68.47 220	89.77 226	55.70 310	85.39 306	74.60 354

MIMVSNet		71.09 291	71.59 284	69.57 321	87.23 216	50.07 348	78.91 256	71.83 336	60.20 292	71.26 333	91.76 155	55.08 300	76.09 327	41.06 364	87.02 294	82.54 319

WTY-MVS		67.91 311	68.35 309	66.58 333	80.82 303	48.12 353	65.96 346	72.60 330	53.67 321	71.20 334	81.68 320	58.97 273	69.06 346	48.57 345	81.67 336	82.55 318

test0.0.03 1		64.66 324	64.36 324	65.57 336	75.03 350	46.89 359	64.69 349	61.58 365	62.43 274	71.18 335	77.54 347	43.41 351	68.47 351	40.75 365	82.65 332	81.35 331

CostFormer		69.98 302	68.68 308	73.87 295	77.14 331	50.72 345	79.26 250	74.51 316	51.94 333	70.97 336	84.75 286	45.16 344	87.49 259	55.16 316	79.23 346	83.40 307

tpmvs		70.16 298	69.56 302	71.96 309	74.71 352	48.13 352	79.63 242	75.45 312	65.02 260	70.26 337	81.88 317	45.34 341	85.68 285	58.34 295	75.39 358	82.08 325

sss		66.92 313	67.26 313	65.90 334	77.23 330	51.10 344	64.79 348	71.72 338	52.12 332	70.13 338	80.18 332	57.96 280	65.36 361	50.21 338	81.01 341	81.25 334

tpm2		68.45 309	66.83 315	73.30 299	78.93 323	48.50 351	79.76 241	71.76 337	47.50 347	69.92 339	83.60 297	42.07 356	88.40 250	48.44 347	79.51 343	83.01 314

HY-MVS	64.64 18	73.03 275	72.47 279	74.71 292	83.36 277	54.19 318	82.14 214	81.96 271	56.76 311	69.57 340	86.21 264	60.03 264	84.83 293	49.58 342	82.65 332	85.11 286

tpm cat1		66.76 316	65.21 323	71.42 310	77.09 332	50.62 346	78.01 267	73.68 325	44.89 355	68.64 341	79.00 340	45.51 338	82.42 308	49.91 340	70.15 365	81.23 336

IB-MVS	62.13 19	71.64 286	68.97 305	79.66 234	80.80 304	62.26 247	73.94 315	76.90 300	63.27 266	68.63 342	76.79 354	33.83 370	91.84 168	59.28 292	87.26 290	84.88 288
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		80.37 196	78.41 219	86.23 106	76.75 334	73.28 127	87.18 109	77.45 298	76.24 128	68.14 343	88.93 220	65.41 235	93.85 100	69.47 211	96.12 115	91.55 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 317	65.63 322	68.75 325	81.96 288	49.88 349	62.19 355	72.51 332	51.03 338	68.04 344	75.34 359	50.84 312	74.77 331	45.82 357	82.96 327	81.60 329

tpmrst		66.28 318	66.69 317	65.05 338	72.82 361	39.33 371	78.20 266	70.69 343	53.16 324	67.88 345	80.36 331	48.18 321	74.75 332	58.13 297	70.79 364	81.08 337

CANet_DTU		77.81 228	77.05 231	80.09 228	81.37 295	59.90 274	83.26 178	88.29 195	69.16 218	67.83 346	83.72 296	60.93 257	89.47 230	69.22 215	89.70 262	90.88 203

EPMVS		62.47 326	62.63 330	62.01 343	70.63 366	38.74 372	74.76 308	52.86 373	53.91 320	67.71 347	80.01 333	39.40 360	66.60 357	55.54 312	68.81 369	80.68 341

MVS_0304		78.17 223	77.23 230	80.99 215	84.13 270	69.07 180	81.39 221	80.81 281	76.28 127	67.53 348	89.11 217	62.87 252	86.77 270	60.90 284	92.01 229	87.13 266

MDTV_nov1_ep13			68.29 310		78.03 325	43.87 367	74.12 313	72.22 333	52.17 329	67.02 349	85.54 271	45.36 340	80.85 314	55.73 308	84.42 320

pmmvs3		62.47 326	60.02 338	69.80 319	71.58 365	64.00 224	70.52 330	58.44 369	39.77 367	66.05 350	75.84 357	27.10 380	72.28 335	46.15 355	84.77 319	73.11 355

ADS-MVSNet2		65.87 320	63.64 327	72.55 305	73.16 358	56.92 302	67.10 342	74.81 313	49.74 345	66.04 351	82.97 304	46.71 325	77.26 324	42.29 361	69.96 366	83.46 305

ADS-MVSNet		61.90 328	62.19 331	61.03 348	73.16 358	36.42 374	67.10 342	61.75 363	49.74 345	66.04 351	82.97 304	46.71 325	63.21 363	42.29 361	69.96 366	83.46 305

mvsany_test1		58.48 337	56.47 342	64.50 339	65.90 376	68.21 185	56.95 364	42.11 379	38.30 370	65.69 353	77.19 353	56.96 287	59.35 369	46.16 354	58.96 372	65.93 363

DSMNet-mixed		60.98 334	61.61 333	59.09 352	72.88 360	45.05 364	74.70 309	46.61 377	26.20 373	65.34 354	90.32 193	55.46 296	63.12 364	41.72 363	81.30 340	69.09 361

JIA-IIPM		69.41 306	66.64 318	77.70 264	73.19 357	71.24 157	75.67 299	65.56 357	70.42 205	65.18 355	92.97 119	33.64 371	83.06 303	53.52 324	69.61 368	78.79 347

test-LLR		67.21 312	66.74 316	68.63 326	76.45 338	55.21 313	67.89 338	67.14 353	62.43 274	65.08 356	72.39 361	43.41 351	69.37 342	61.00 281	84.89 315	81.31 332

test-mter		65.00 323	63.79 326	68.63 326	76.45 338	55.21 313	67.89 338	67.14 353	50.98 339	65.08 356	72.39 361	28.27 377	69.37 342	61.00 281	84.89 315	81.31 332

PMMVS2		55.64 340	59.27 339	44.74 356	64.30 378	12.32 381	40.60 369	49.79 375	53.19 323	65.06 358	84.81 285	53.60 303	49.76 373	32.68 373	89.41 264	72.15 356

baseline2		69.77 303	66.89 314	78.41 251	79.51 315	58.09 292	76.23 295	69.57 347	57.50 306	64.82 359	77.45 349	46.02 330	88.44 249	53.08 325	77.83 351	88.70 247

gg-mvs-nofinetune		68.96 308	69.11 303	68.52 328	76.12 341	45.32 362	83.59 171	55.88 371	86.68 24	64.62 360	97.01 7	30.36 374	83.97 300	44.78 358	82.94 328	76.26 351

PAPM		71.77 285	70.06 298	76.92 273	86.39 232	53.97 319	76.62 290	86.62 220	53.44 322	63.97 361	84.73 287	57.79 283	92.34 153	39.65 366	81.33 339	84.45 292

new_pmnet		55.69 339	57.66 340	49.76 355	75.47 346	30.59 377	59.56 357	51.45 374	43.62 360	62.49 362	75.48 358	40.96 358	49.15 374	37.39 369	72.52 360	69.55 360

MDTV_nov1_ep13_2view						27.60 379	70.76 329		46.47 351	61.27 363		45.20 342		49.18 343		83.75 302

dp		60.70 335	60.29 337	61.92 345	72.04 364	38.67 373	70.83 328	64.08 359	51.28 336	60.75 364	77.28 350	36.59 367	71.58 339	47.41 350	62.34 371	75.52 352

TESTMET0.1,1		61.29 331	60.32 336	64.19 340	72.06 363	51.30 340	67.89 338	62.09 361	45.27 353	60.65 365	69.01 365	27.93 378	64.74 362	56.31 305	81.65 338	76.53 350

PMMVS		61.65 329	60.38 335	65.47 337	65.40 377	69.26 174	63.97 351	61.73 364	36.80 372	60.11 366	68.43 366	59.42 269	66.35 358	48.97 344	78.57 350	60.81 367

PVSNet_0	51.08 22	56.10 338	54.97 343	59.48 351	75.12 349	53.28 326	55.16 365	61.89 362	44.30 356	59.16 367	62.48 370	54.22 301	65.91 360	35.40 370	47.01 373	59.25 369

MVS-HIRNet		61.16 332	62.92 329	55.87 353	79.09 320	35.34 375	71.83 325	57.98 370	46.56 350	59.05 368	91.14 169	49.95 317	76.43 326	38.74 367	71.92 363	55.84 371

E-PMN		61.59 330	61.62 332	61.49 346	66.81 372	55.40 311	53.77 366	60.34 366	66.80 244	58.90 369	65.50 368	40.48 359	66.12 359	55.72 309	86.25 301	62.95 366

GG-mvs-BLEND				67.16 331	73.36 356	46.54 361	84.15 154	55.04 372		58.64 370	61.95 371	29.93 375	83.87 301	38.71 368	76.92 356	71.07 358

EPNet_dtu		72.87 277	71.33 289	77.49 267	77.72 327	60.55 268	82.35 205	75.79 307	66.49 246	58.39 371	81.06 324	53.68 302	85.98 281	53.55 323	92.97 209	85.95 277
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 333	60.81 334	61.99 344	65.96 375	55.86 308	53.10 367	58.97 368	67.06 241	56.89 372	63.33 369	40.98 357	67.03 355	54.79 318	86.18 302	63.08 365

CHOSEN 280x420		59.08 336	56.52 341	66.76 332	76.51 336	64.39 220	49.62 368	59.00 367	43.86 358	55.66 373	68.41 367	35.55 369	68.21 352	43.25 360	76.78 357	67.69 362

MVE	40.22 23	51.82 341	50.47 344	55.87 353	62.66 379	51.91 335	31.61 371	39.28 380	40.65 365	50.76 374	74.98 360	56.24 293	44.67 375	33.94 372	64.11 370	71.04 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 358	32.95 380	29.49 378		21.63 383	12.07 374	37.95 375	45.07 373	30.84 373	19.21 377	17.94 376	33.06 376	23.69 373

tmp_tt		20.25 344	24.50 347	7.49 359	4.47 382	8.70 382	34.17 370	25.16 382	1.00 377	32.43 376	18.49 374	39.37 361	9.21 378	21.64 375	43.75 374	4.57 374

test_method		30.46 342	29.60 345	33.06 357	17.99 381	3.84 383	13.62 372	73.92 320	2.79 375	18.29 377	53.41 372	28.53 376	43.25 376	22.56 374	35.27 375	52.11 372

EGC-MVSNET		74.79 261	69.99 299	89.19 63	94.89 37	87.00 11	91.89 34	86.28 223	1.09 376	2.23 378	95.98 23	81.87 104	89.48 229	79.76 101	95.96 122	91.10 198

testmvs		5.91 348	7.65 351	0.72 361	1.20 383	0.37 385	59.14 359	0.67 385	0.49 379	1.11 379	2.76 378	0.94 384	0.24 380	1.02 378	1.47 377	1.55 376

test123		6.27 347	8.08 350	0.84 360	1.11 384	0.57 384	62.90 352	0.82 384	0.54 378	1.07 380	2.75 379	1.26 383	0.30 379	1.04 377	1.26 378	1.66 375

test_blank		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

cdsmvs_eth3d_5k		20.81 343	27.75 346	0.00 362	0.00 385	0.00 386	0.00 373	85.44 234	0.00 380	0.00 381	82.82 308	81.46 108	0.00 381	0.00 379	0.00 379	0.00 377

pcd_1.5k_mvsjas		6.41 346	8.55 349	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	76.94 151	0.00 381	0.00 379	0.00 379	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

ab-mvs-re		6.65 345	8.87 348	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	79.80 335	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

MSC_two_6792asdad				88.81 68	91.55 127	77.99 87		91.01 135					96.05 7	87.45 17	98.17 32	92.40 161

No_MVS				88.81 68	91.55 127	77.99 87		91.01 135					96.05 7	87.45 17	98.17 32	92.40 161

eth-test2					0.00 385

eth-test					0.00 385

OPU-MVS				88.27 79	91.89 113	77.83 90	90.47 51				91.22 166	81.12 112	94.68 69	74.48 160	95.35 144	92.29 167

save fliter					93.75 59	77.44 95	86.31 126	89.72 170	70.80 201

test_0728_SECOND				86.79 95	94.25 45	72.45 142	90.54 48	94.10 34					95.88 16	86.42 31	97.97 43	92.02 177

GSMVS																83.88 297

sam_mvs1												46.11 329				83.88 297

sam_mvs												45.92 334

MTGPA								91.81 115

test_post1							78.85 259				3.13 376	45.19 343	80.13 317	58.11 298

test_post											3.10 377	45.43 339	77.22 325

patchmatchnet-post											81.71 319	45.93 333	87.01 263

MTMP							90.66 44	33.14 381

gm-plane-assit					75.42 347	44.97 365			52.17 329		72.36 363		87.90 254	54.10 321

test9_res														80.83 89	96.45 102	90.57 213

agg_prior2														79.68 103	96.16 112	90.22 221

test_prior4						78.97 80	84.59 145

test_prior				86.32 103	90.59 153	71.99 149		92.85 86					94.17 90			92.80 144

新几何2							81.72 217

旧先验1					91.97 109	71.77 150		81.78 274			91.84 151	73.92 180			93.65 194	83.61 303

无先验							82.81 192	85.62 233	58.09 300				91.41 179	67.95 232		84.48 291

原ACMM2							82.26 210

testdata2													86.43 276	63.52 263

segment_acmp												81.94 100

testdata1							79.62 243		73.95 157

plane_prior7					93.45 66	77.31 98

plane_prior6					92.61 88	76.54 105						74.84 169

plane_prior5								93.61 53					95.22 54	80.78 90	95.83 130	94.46 80

plane_prior4											92.95 120

plane_prior2							89.45 77		79.44 92

plane_prior1					92.83 86

plane_prior						76.42 109	87.15 110		75.94 135						95.03 158

n2								0.00 386

nn								0.00 386

door-mid								74.45 317

test11								91.46 121

door								72.57 331

HQP5-MVS						70.66 160

BP-MVS														77.30 134

HQP3-MVS								92.68 91							94.47 176

HQP2-MVS												72.10 202

NP-MVS					91.95 110	74.55 121					90.17 199

ACMMP++_ref															95.74 137

ACMMP++															97.35 73

Test By Simon												79.09 128

High-resolution multi-view benchmark