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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

UniMVSNet_ETH3D		97.13 5	97.72 3	95.35 84	99.51 2	87.38 134	97.70 8	97.54 112	98.16 2	98.94 2	99.33 2	97.84 4	99.08 94	90.73 141	99.73 13	99.59 13

FOURS1					99.21 3	94.68 12	98.45 4	98.81 8	97.73 6	98.27 20

PEN-MVS		96.69 20	97.39 8	94.61 118	99.16 4	84.50 195	96.54 34	98.05 64	98.06 4	98.64 13	98.25 37	95.01 51	99.65 3	92.95 89	99.83 5	99.68 4

MIMVSNet1		95.52 69	95.45 77	95.72 73	99.14 5	89.02 102	96.23 57	96.87 167	93.73 60	97.87 28	98.49 29	90.73 155	99.05 99	86.43 243	99.60 26	99.10 47

PS-CasMVS		96.69 20	97.43 5	94.49 128	99.13 6	84.09 205	96.61 32	97.97 77	97.91 5	98.64 13	98.13 41	95.24 38	99.65 3	93.39 72	99.84 3	99.72 2

DTE-MVSNet		96.74 17	97.43 5	94.67 113	99.13 6	84.68 194	96.51 35	97.94 83	98.14 3	98.67 12	98.32 34	95.04 48	99.69 2	93.27 77	99.82 7	99.62 10

pmmvs6		96.80 12	97.36 9	95.15 97	99.12 8	87.82 129	96.68 30	97.86 85	96.10 27	98.14 24	99.28 3	97.94 3	98.21 209	91.38 129	99.69 14	99.42 19

HPM-MVS_fast		97.01 6	96.89 14	97.39 21	99.12 8	93.92 28	97.16 14	98.17 45	93.11 74	96.48 90	97.36 94	96.92 6	99.34 63	94.31 40	99.38 59	98.92 72

MP-MVS-pluss		96.08 48	95.92 58	96.57 44	99.06 10	91.21 65	93.25 165	98.32 24	87.89 198	96.86 75	97.38 90	95.55 26	99.39 49	95.47 25	99.47 43	99.11 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OurMVSNet-221017-0		96.80 12	96.75 17	96.96 35	99.03 11	91.85 57	97.98 7	98.01 72	94.15 51	98.93 3	99.07 5	88.07 188	99.57 14	95.86 15	99.69 14	99.46 18

WR-MVS_H		96.60 25	97.05 13	95.24 92	99.02 12	86.44 160	96.78 27	98.08 57	97.42 9	98.48 16	97.86 62	91.76 128	99.63 6	94.23 42	99.84 3	99.66 6

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 8	98.61 3	98.81 8	97.41 10	97.28 56	98.46 30	94.62 62	98.84 129	94.64 34	99.53 38	98.99 56

testf1		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 22	94.96 38	97.30 54	97.93 55	96.05 16	97.90 235	89.32 180	99.23 86	98.19 144

APD_test2		96.77 14	96.49 26	97.60 8	99.01 14	96.70 3	96.31 50	98.33 22	94.96 38	97.30 54	97.93 55	96.05 16	97.90 235	89.32 180	99.23 86	98.19 144

CP-MVSNet		96.19 45	96.80 16	94.38 133	98.99 16	83.82 208	96.31 50	97.53 114	97.60 7	98.34 19	97.52 81	91.98 122	99.63 6	93.08 85	99.81 8	99.70 3

PMVS	87.21 14	94.97 94	95.33 85	93.91 149	98.97 17	97.16 2	95.54 85	95.85 220	96.47 22	93.40 217	97.46 87	95.31 35	95.47 338	86.18 247	98.78 144	89.11 379
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MTAPA		96.65 22	96.38 33	97.47 15	98.95 18	94.05 23	95.88 70	97.62 105	94.46 47	96.29 99	96.94 129	93.56 79	99.37 57	94.29 41	99.42 52	98.99 56

ACMMP_NAP		96.21 44	96.12 46	96.49 48	98.90 19	91.42 63	94.57 119	98.03 69	90.42 148	96.37 93	97.35 97	95.68 21	99.25 75	94.44 37	99.34 64	98.80 86

HPM-MVS		96.81 11	96.62 22	97.36 23	98.89 20	93.53 38	97.51 10	98.44 16	92.35 88	95.95 116	96.41 162	96.71 8	99.42 33	93.99 47	99.36 60	99.13 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDDNet		94.03 131	94.27 127	93.31 173	98.87 21	82.36 229	95.51 86	91.78 318	97.19 12	96.32 96	98.60 22	84.24 238	98.75 147	87.09 230	98.83 138	98.81 84

TSAR-MVS + MP.		94.96 95	94.75 107	95.57 78	98.86 22	88.69 108	96.37 44	96.81 171	85.23 244	94.75 179	97.12 116	91.85 124	99.40 46	93.45 67	98.33 189	98.62 116
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT_MVS		95.41 77	95.20 92	96.05 55	98.86 22	88.92 104	97.49 11	94.48 266	93.12 73	97.94 27	98.54 25	81.19 272	99.63 6	95.48 24	99.69 14	99.60 12

EGC-MVSNET		80.97 351	75.73 365	96.67 42	98.85 24	94.55 15	96.83 23	96.60 184	2.44 401	5.32 402	98.25 37	92.24 115	98.02 226	91.85 115	99.21 90	97.45 212

mvs_tets		96.83 8	96.71 18	97.17 27	98.83 25	92.51 48	96.58 33	97.61 107	87.57 207	98.80 7	98.90 9	96.50 9	99.59 13	96.15 13	99.47 43	99.40 21

APD_test1		95.91 53	95.42 80	97.36 23	98.82 26	96.62 6	95.64 80	97.64 103	93.38 69	95.89 121	97.23 106	93.35 87	97.66 263	88.20 206	98.66 159	97.79 188

PS-MVSNAJss		96.01 50	96.04 52	95.89 67	98.82 26	88.51 116	95.57 84	97.88 84	88.72 180	98.81 6	98.86 10	90.77 151	99.60 9	95.43 27	99.53 38	99.57 14

MP-MVS		96.14 46	95.68 69	97.51 13	98.81 28	94.06 21	96.10 60	97.78 96	92.73 78	93.48 214	96.72 147	94.23 71	99.42 33	91.99 110	99.29 74	99.05 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 28	93.86 31	99.07 2	98.98 6	97.01 13	98.92 4	98.78 14	95.22 40	98.61 170	96.85 3	99.77 9	99.31 28
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
ZNCC-MVS		96.42 35	96.20 41	97.07 30	98.80 30	92.79 46	96.08 61	98.16 48	91.74 115	95.34 151	96.36 170	95.68 21	99.44 29	94.41 38	99.28 79	98.97 62

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 31	92.49 49	96.44 41	97.42 121	86.96 216	98.71 10	98.72 17	95.36 32	99.56 17	95.92 14	99.45 47	99.32 27

tt0805		95.42 76	95.93 57	93.86 152	98.75 32	88.47 117	97.68 9	94.29 270	96.48 21	95.38 147	93.63 283	94.89 55	97.94 234	95.38 28	96.92 269	95.17 304

MSP-MVS		95.34 80	94.63 115	97.48 14	98.67 33	94.05 23	96.41 43	98.18 41	91.26 126	95.12 163	95.15 228	86.60 217	99.50 21	93.43 71	96.81 273	98.89 75
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
GST-MVS		96.24 43	95.99 54	97.00 33	98.65 34	92.71 47	95.69 78	98.01 72	92.08 96	95.74 129	96.28 176	95.22 40	99.42 33	93.17 81	99.06 103	98.88 77

SteuartSystems-ACMMP		96.40 37	96.30 36	96.71 40	98.63 35	91.96 55	95.70 76	98.01 72	93.34 70	96.64 85	96.57 155	94.99 52	99.36 58	93.48 64	99.34 64	98.82 82
Skip Steuart: Steuart Systems R&D Blog.
region2R		96.41 36	96.09 47	97.38 22	98.62 36	93.81 35	96.32 49	97.96 78	92.26 91	95.28 155	96.57 155	95.02 50	99.41 39	93.63 56	99.11 101	98.94 66

mPP-MVS		96.46 31	96.05 51	97.69 4	98.62 36	94.65 13	96.45 39	97.74 98	92.59 82	95.47 142	96.68 149	94.50 66	99.42 33	93.10 83	99.26 82	98.99 56

ACMMP		96.61 24	96.34 34	97.43 18	98.61 38	93.88 29	96.95 21	98.18 41	92.26 91	96.33 95	96.84 137	95.10 46	99.40 46	93.47 65	99.33 66	99.02 53
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
VPNet		93.08 159	93.76 140	91.03 255	98.60 39	75.83 331	91.51 234	95.62 225	91.84 107	95.74 129	97.10 119	89.31 176	98.32 200	85.07 262	99.06 103	98.93 68

ACMMPR		96.46 31	96.14 45	97.41 20	98.60 39	93.82 33	96.30 54	97.96 78	92.35 88	95.57 137	96.61 153	94.93 54	99.41 39	93.78 52	99.15 98	99.00 54

PGM-MVS		96.32 40	95.94 55	97.43 18	98.59 41	93.84 32	95.33 90	98.30 27	91.40 124	95.76 126	96.87 134	95.26 37	99.45 27	92.77 91	99.21 90	99.00 54

XVS		96.49 29	96.18 42	97.44 16	98.56 42	93.99 26	96.50 36	97.95 80	94.58 43	94.38 189	96.49 157	94.56 64	99.39 49	93.57 58	99.05 106	98.93 68

X-MVStestdata		90.70 214	88.45 261	97.44 16	98.56 42	93.99 26	96.50 36	97.95 80	94.58 43	94.38 189	26.89 399	94.56 64	99.39 49	93.57 58	99.05 106	98.93 68

ACMH	88.36 12	96.59 27	97.43 5	94.07 141	98.56 42	85.33 188	96.33 47	98.30 27	94.66 42	98.72 8	98.30 35	97.51 5	98.00 228	94.87 31	99.59 28	98.86 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
bld_raw_dy_0_64		94.27 120	94.15 130	94.65 116	98.55 45	86.28 166	95.80 73	95.55 233	88.41 188	97.09 61	98.08 44	78.69 286	98.87 125	95.63 17	99.53 38	98.81 84

test_0728_SECOND				94.88 104	98.55 45	86.72 151	95.20 96	98.22 36					99.38 55	93.44 68	99.31 69	98.53 121

test_djsdf		96.62 23	96.49 26	97.01 32	98.55 45	91.77 59	97.15 15	97.37 123	88.98 174	98.26 22	98.86 10	93.35 87	99.60 9	96.41 9	99.45 47	99.66 6

v7n		96.82 9	97.31 10	95.33 86	98.54 48	86.81 148	96.83 23	98.07 60	96.59 20	98.46 17	98.43 32	92.91 102	99.52 19	96.25 12	99.76 10	99.65 8

ACMH+	88.43 11	96.48 30	96.82 15	95.47 81	98.54 48	89.06 101	95.65 79	98.61 12	96.10 27	98.16 23	97.52 81	96.90 7	98.62 169	90.30 155	99.60 26	98.72 97

SixPastTwentyTwo		94.91 96	95.21 90	93.98 143	98.52 50	83.19 217	95.93 67	94.84 256	94.86 41	98.49 15	98.74 16	81.45 266	99.60 9	94.69 33	99.39 58	99.15 39

SED-MVS		96.00 51	96.41 32	94.76 109	98.51 51	86.97 144	95.21 94	98.10 54	91.95 98	97.63 35	97.25 104	96.48 10	99.35 60	93.29 75	99.29 74	97.95 169

IU-MVS					98.51 51	86.66 154		96.83 170	72.74 357	95.83 123				93.00 87	99.29 74	98.64 112

test_241102_ONE					98.51 51	86.97 144		98.10 54	91.85 104	97.63 35	97.03 123	96.48 10	98.95 114

DVP-MVS		95.82 58	96.18 42	94.72 111	98.51 51	86.69 152	95.20 96	97.00 155	91.85 104	97.40 52	97.35 97	95.58 24	99.34 63	93.44 68	99.31 69	98.13 150
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
test0726					98.51 51	86.69 152	95.34 89	98.18 41	91.85 104	97.63 35	97.37 91	95.58 24

HFP-MVS		96.39 38	96.17 44	97.04 31	98.51 51	93.37 39	96.30 54	97.98 75	92.35 88	95.63 134	96.47 158	95.37 30	99.27 74	93.78 52	99.14 99	98.48 125

Baseline_NR-MVSNet		94.47 113	95.09 97	92.60 201	98.50 57	80.82 250	92.08 212	96.68 180	93.82 59	96.29 99	98.56 24	90.10 168	97.75 256	90.10 166	99.66 21	99.24 32

OPM-MVS		95.61 65	95.45 77	96.08 54	98.49 58	91.00 68	92.65 186	97.33 131	90.05 153	96.77 80	96.85 135	95.04 48	98.56 177	92.77 91	99.06 103	98.70 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FC-MVSNet-test		95.32 81	95.88 59	93.62 159	98.49 58	81.77 234	95.90 69	98.32 24	93.93 56	97.53 42	97.56 76	88.48 181	99.40 46	92.91 90	99.83 5	99.68 4

mvsmamba		95.61 65	95.40 81	96.22 51	98.44 60	89.86 84	97.14 17	97.45 120	91.25 128	97.49 44	98.14 39	83.49 242	99.45 27	95.52 22	99.66 21	99.36 24

XVG-ACMP-BASELINE		95.68 63	95.34 84	96.69 41	98.40 61	93.04 41	94.54 123	98.05 64	90.45 147	96.31 97	96.76 141	92.91 102	98.72 152	91.19 130	99.42 52	98.32 133

ACMM	88.83 9	96.30 42	96.07 50	96.97 34	98.39 62	92.95 44	94.74 111	98.03 69	90.82 137	97.15 59	96.85 135	96.25 14	99.00 106	93.10 83	99.33 66	98.95 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		95.43 73	95.94 55	93.93 148	98.38 63	85.08 191	95.46 87	97.12 148	91.84 107	97.28 56	98.46 30	95.30 36	97.71 260	90.17 162	99.42 52	98.99 56

COLMAP_ROB	91.06 5	96.75 16	96.62 22	97.13 28	98.38 63	94.31 17	96.79 26	98.32 24	96.69 17	96.86 75	97.56 76	95.48 27	98.77 146	90.11 164	99.44 50	98.31 135
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TransMVSNet (Re)		95.27 87	96.04 52	92.97 181	98.37 65	81.92 233	95.07 101	96.76 176	93.97 55	97.77 31	98.57 23	95.72 20	97.90 235	88.89 197	99.23 86	99.08 48

LPG-MVS_test		96.38 39	96.23 39	96.84 38	98.36 66	92.13 52	95.33 90	98.25 31	91.78 111	97.07 63	97.22 108	96.38 12	99.28 72	92.07 108	99.59 28	99.11 44

LGP-MVS_train				96.84 38	98.36 66	92.13 52		98.25 31	91.78 111	97.07 63	97.22 108	96.38 12	99.28 72	92.07 108	99.59 28	99.11 44

CP-MVS		96.44 34	96.08 49	97.54 11	98.29 68	94.62 14	96.80 25	98.08 57	92.67 81	95.08 167	96.39 167	94.77 58	99.42 33	93.17 81	99.44 50	98.58 119

FIs		94.90 97	95.35 83	93.55 162	98.28 69	81.76 235	95.33 90	98.14 49	93.05 76	97.07 63	97.18 111	87.65 195	99.29 70	91.72 119	99.69 14	99.61 11

SMA-MVS		95.77 59	95.54 74	96.47 49	98.27 70	91.19 66	95.09 99	97.79 95	86.48 219	97.42 50	97.51 84	94.47 69	99.29 70	93.55 60	99.29 74	98.93 68
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_one_0601					98.26 71	87.14 140		98.18 41	94.25 48	96.99 70	97.36 94	95.13 43

TranMVSNet+NR-MVSNet		96.07 49	96.26 38	95.50 80	98.26 71	87.69 131	93.75 150	97.86 85	95.96 32	97.48 46	97.14 114	95.33 34	99.44 29	90.79 139	99.76 10	99.38 22

IS-MVSNet		94.49 112	94.35 123	94.92 102	98.25 73	86.46 159	97.13 18	94.31 269	96.24 25	96.28 101	96.36 170	82.88 250	99.35 60	88.19 207	99.52 41	98.96 64

UA-Net		97.35 4	97.24 11	97.69 4	98.22 74	93.87 30	98.42 6	98.19 39	96.95 14	95.46 144	99.23 4	93.45 82	99.57 14	95.34 30	99.89 2	99.63 9

test_part2					98.21 75	89.41 93				96.72 81

test_0402		95.73 61	96.22 40	94.26 135	98.19 76	85.77 179	93.24 166	97.24 139	96.88 16	97.69 33	97.77 65	94.12 73	99.13 88	91.54 126	99.29 74	97.88 177

ACMP	88.15 13	95.71 62	95.43 79	96.54 45	98.17 77	91.73 60	94.24 132	98.08 57	89.46 163	96.61 87	96.47 158	95.85 18	99.12 91	90.45 147	99.56 36	98.77 91
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CPTT-MVS		94.74 102	94.12 131	96.60 43	98.15 78	93.01 42	95.84 71	97.66 102	89.21 171	93.28 221	95.46 215	88.89 179	98.98 107	89.80 171	98.82 139	97.80 187

SF-MVS		95.88 56	95.88 59	95.87 68	98.12 79	89.65 87	95.58 83	98.56 14	91.84 107	96.36 94	96.68 149	94.37 70	99.32 69	92.41 101	99.05 106	98.64 112

Vis-MVSNet		95.50 70	95.48 76	95.56 79	98.11 80	89.40 94	95.35 88	98.22 36	92.36 87	94.11 192	98.07 45	92.02 120	99.44 29	93.38 73	97.67 239	97.85 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVG-OURS-SEG-HR		95.38 78	95.00 100	96.51 46	98.10 81	94.07 20	92.46 194	98.13 50	90.69 140	93.75 206	96.25 179	98.03 2	97.02 295	92.08 107	95.55 300	98.45 127

EPP-MVSNet		93.91 137	93.68 144	94.59 122	98.08 82	85.55 185	97.44 12	94.03 275	94.22 50	94.94 171	96.19 181	82.07 261	99.57 14	87.28 227	98.89 126	98.65 107

SR-MVS-dyc-post		96.84 7	96.60 24	97.56 10	98.07 83	95.27 9	96.37 44	98.12 51	95.66 33	97.00 68	97.03 123	94.85 56	99.42 33	93.49 62	98.84 133	98.00 161

RE-MVS-def			96.66 19		98.07 83	95.27 9	96.37 44	98.12 51	95.66 33	97.00 68	97.03 123	95.40 29		93.49 62	98.84 133	98.00 161

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 85	94.69 11	96.13 59	98.07 60	95.17 37	96.82 77	96.73 146	95.09 47	99.43 32	92.99 88	98.71 151	98.50 122

K. test v3		93.37 149	93.27 159	93.66 158	98.05 85	82.62 225	94.35 126	86.62 355	96.05 29	97.51 43	98.85 12	76.59 312	99.65 3	93.21 79	98.20 204	98.73 96

lessismore_v0				93.87 151	98.05 85	83.77 209		80.32 390		97.13 60	97.91 59	77.49 297	99.11 93	92.62 97	98.08 213	98.74 95

test1111		90.39 225	90.61 221	89.74 292	98.04 88	71.50 361	95.59 81	79.72 392	89.41 164	95.94 117	98.14 39	70.79 334	98.81 136	88.52 204	99.32 68	98.90 74

AllTest		94.88 98	94.51 117	96.00 56	98.02 89	92.17 50	95.26 93	98.43 17	90.48 145	95.04 168	96.74 144	92.54 111	97.86 243	85.11 260	98.98 114	97.98 165

TestCases				96.00 56	98.02 89	92.17 50		98.43 17	90.48 145	95.04 168	96.74 144	92.54 111	97.86 243	85.11 260	98.98 114	97.98 165

anonymousdsp		96.74 17	96.42 29	97.68 6	98.00 91	94.03 25	96.97 20	97.61 107	87.68 205	98.45 18	98.77 15	94.20 72	99.50 21	96.70 5	99.40 57	99.53 15

XVG-OURS		94.72 103	94.12 131	96.50 47	98.00 91	94.23 18	91.48 235	98.17 45	90.72 139	95.30 153	96.47 158	87.94 192	96.98 296	91.41 128	97.61 242	98.30 136

114514_t		90.51 219	89.80 239	92.63 198	98.00 91	82.24 230	93.40 162	97.29 135	65.84 386	89.40 314	94.80 244	86.99 207	98.75 147	83.88 273	98.61 161	96.89 241

Gipuma		95.31 84	95.80 65	93.81 155	97.99 94	90.91 70	96.42 42	97.95 80	96.69 17	91.78 272	98.85 12	91.77 126	95.49 337	91.72 119	99.08 102	95.02 310
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD-MVS_3200maxsize		96.82 9	96.65 20	97.32 25	97.95 95	93.82 33	96.31 50	98.25 31	95.51 35	96.99 70	97.05 122	95.63 23	99.39 49	93.31 74	98.88 128	98.75 92

SDMVSNet		94.43 114	95.02 98	92.69 194	97.93 96	82.88 223	91.92 221	95.99 216	93.65 65	95.51 139	98.63 20	94.60 63	96.48 312	87.57 221	99.35 61	98.70 101

sd_testset		93.94 135	94.39 119	92.61 200	97.93 96	83.24 214	93.17 169	95.04 250	93.65 65	95.51 139	98.63 20	94.49 67	95.89 330	81.72 294	99.35 61	98.70 101

DPE-MVS		95.89 55	95.88 59	95.92 64	97.93 96	89.83 85	93.46 159	98.30 27	92.37 86	97.75 32	96.95 128	95.14 42	99.51 20	91.74 118	99.28 79	98.41 129
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SSC-MVS		90.16 234	92.96 162	81.78 370	97.88 99	48.48 402	90.75 251	87.69 347	96.02 31	96.70 82	97.63 72	85.60 229	97.80 248	85.73 251	98.60 163	99.06 50

HPM-MVS++		95.02 92	94.39 119	96.91 37	97.88 99	93.58 37	94.09 140	96.99 157	91.05 132	92.40 257	95.22 227	91.03 147	99.25 75	92.11 105	98.69 154	97.90 174

EG-PatchMatch MVS		94.54 111	94.67 114	94.14 138	97.87 101	86.50 156	92.00 216	96.74 177	88.16 194	96.93 72	97.61 73	93.04 99	97.90 235	91.60 123	98.12 209	98.03 159

nrg030		96.32 40	96.55 25	95.62 76	97.83 102	88.55 115	95.77 74	98.29 30	92.68 79	98.03 26	97.91 59	95.13 43	98.95 114	93.85 50	99.49 42	99.36 24

test2506		85.42 317	84.57 320	87.96 325	97.81 103	66.53 380	96.14 58	56.35 404	89.04 172	93.55 213	98.10 42	42.88 403	98.68 163	88.09 211	99.18 94	98.67 105

ECVR-MVS		90.12 236	90.16 230	90.00 288	97.81 103	72.68 355	95.76 75	78.54 395	89.04 172	95.36 150	98.10 42	70.51 335	98.64 168	87.10 229	99.18 94	98.67 105

UniMVSNet (Re)		95.32 81	95.15 93	95.80 70	97.79 105	88.91 105	92.91 176	98.07 60	93.46 67	96.31 97	95.97 192	90.14 165	99.34 63	92.11 105	99.64 24	99.16 38

VPA-MVSNet		95.14 89	95.67 70	93.58 161	97.76 106	83.15 218	94.58 118	97.58 109	93.39 68	97.05 66	98.04 48	93.25 90	98.51 182	89.75 174	99.59 28	99.08 48

DU-MVS		95.28 85	95.12 95	95.75 72	97.75 107	88.59 113	92.58 188	97.81 91	93.99 53	96.80 78	95.90 193	90.10 168	99.41 39	91.60 123	99.58 33	99.26 30

NR-MVSNet		95.28 85	95.28 88	95.26 90	97.75 107	87.21 138	95.08 100	97.37 123	93.92 58	97.65 34	95.90 193	90.10 168	99.33 68	90.11 164	99.66 21	99.26 30

XXY-MVS		92.58 176	93.16 161	90.84 264	97.75 107	79.84 265	91.87 225	96.22 206	85.94 229	95.53 138	97.68 67	92.69 108	94.48 352	83.21 277	97.51 244	98.21 142

WB-MVS		89.44 252	92.15 184	81.32 371	97.73 110	48.22 403	89.73 286	87.98 345	95.24 36	96.05 113	96.99 127	85.18 231	96.95 297	82.45 286	97.97 223	98.78 88

PVSNet_Blended_VisFu		91.63 198	91.20 207	92.94 185	97.73 110	83.95 207	92.14 211	97.46 118	78.85 320	92.35 260	94.98 236	84.16 239	99.08 94	86.36 244	96.77 275	95.79 287

tfpnnormal		94.27 120	94.87 103	92.48 205	97.71 112	80.88 249	94.55 122	95.41 240	93.70 61	96.67 84	97.72 66	91.40 134	98.18 213	87.45 223	99.18 94	98.36 131

HQP_MVS		94.26 122	93.93 134	95.23 93	97.71 112	88.12 122	94.56 120	97.81 91	91.74 115	93.31 218	95.59 209	86.93 209	98.95 114	89.26 186	98.51 173	98.60 117

plane_prior7					97.71 112	88.68 109

UniMVSNet_NR-MVSNet		95.35 79	95.21 90	95.76 71	97.69 115	88.59 113	92.26 208	97.84 88	94.91 40	96.80 78	95.78 202	90.42 160	99.41 39	91.60 123	99.58 33	99.29 29

APDe-MVS		96.46 31	96.64 21	95.93 62	97.68 116	89.38 95	96.90 22	98.41 19	92.52 83	97.43 48	97.92 58	95.11 45	99.50 21	94.45 36	99.30 71	98.92 72
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS	91.39 4	95.43 73	95.33 85	95.71 74	97.67 117	90.17 80	93.86 147	98.02 71	87.35 209	96.22 105	97.99 53	94.48 68	99.05 99	92.73 94	99.68 18	97.93 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
KD-MVS_self_test		94.10 129	94.73 110	92.19 212	97.66 118	79.49 275	94.86 108	97.12 148	89.59 162	96.87 74	97.65 70	90.40 162	98.34 199	89.08 192	99.35 61	98.75 92

Vis-MVSNet (Re-imp)		90.42 222	90.16 230	91.20 251	97.66 118	77.32 309	94.33 128	87.66 348	91.20 129	92.99 234	95.13 230	75.40 317	98.28 202	77.86 329	99.19 92	97.99 164

dcpmvs_2		93.96 134	95.01 99	90.82 265	97.60 120	74.04 345	93.68 154	98.85 7	89.80 158	97.82 29	97.01 126	91.14 145	99.21 78	90.56 145	98.59 164	99.19 36

FMVSNet1		94.84 99	95.13 94	93.97 144	97.60 120	84.29 198	95.99 63	96.56 188	92.38 85	97.03 67	98.53 26	90.12 166	98.98 107	88.78 199	99.16 97	98.65 107

RPSCF		95.58 68	94.89 102	97.62 7	97.58 122	96.30 7	95.97 66	97.53 114	92.42 84	93.41 215	97.78 63	91.21 140	97.77 253	91.06 132	97.06 261	98.80 86

WR-MVS		93.49 146	93.72 141	92.80 191	97.57 123	80.03 260	90.14 273	95.68 224	93.70 61	96.62 86	95.39 222	87.21 203	99.04 102	87.50 222	99.64 24	99.33 26

CSCG		94.69 105	94.75 107	94.52 125	97.55 124	87.87 127	95.01 104	97.57 110	92.68 79	96.20 107	93.44 289	91.92 123	98.78 143	89.11 191	99.24 85	96.92 239

MCST-MVS		92.91 164	92.51 176	94.10 140	97.52 125	85.72 181	91.36 239	97.13 147	80.33 302	92.91 238	94.24 262	91.23 139	98.72 152	89.99 168	97.93 226	97.86 179

F-COLMAP		92.28 186	91.06 211	95.95 59	97.52 125	91.90 56	93.53 156	97.18 142	83.98 263	88.70 327	94.04 269	88.41 183	98.55 179	80.17 310	95.99 291	97.39 219

9.14			94.81 104		97.49 127		94.11 139	98.37 20	87.56 208	95.38 147	96.03 189	94.66 60	99.08 94	90.70 142	98.97 119

VDD-MVS		94.37 115	94.37 121	94.40 132	97.49 127	86.07 172	93.97 144	93.28 289	94.49 45	96.24 103	97.78 63	87.99 191	98.79 140	88.92 195	99.14 99	98.34 132

testgi		90.38 226	91.34 205	87.50 331	97.49 127	71.54 360	89.43 295	95.16 247	88.38 189	94.54 185	94.68 249	92.88 104	93.09 367	71.60 369	97.85 230	97.88 177

save fliter					97.46 130	88.05 124	92.04 214	97.08 150	87.63 206

Anonymous20231211		96.60 25	97.13 12	95.00 100	97.46 130	86.35 164	97.11 19	98.24 34	97.58 8	98.72 8	98.97 7	93.15 94	99.15 84	93.18 80	99.74 12	99.50 17

plane_prior1					97.38 132

APD-MVS		95.00 93	94.69 111	95.93 62	97.38 132	90.88 71	94.59 116	97.81 91	89.22 170	95.46 144	96.17 184	93.42 85	99.34 63	89.30 182	98.87 131	97.56 206
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.1_n_a		94.26 122	94.37 121	93.95 147	97.36 134	85.72 181	94.15 136	95.44 237	83.25 271	95.51 139	98.05 46	92.54 111	97.19 288	95.55 21	97.46 248	98.94 66

ITE_SJBPF				95.95 59	97.34 135	93.36 40		96.55 191	91.93 100	94.82 176	95.39 222	91.99 121	97.08 293	85.53 253	97.96 224	97.41 215

Anonymous20240529		95.50 70	95.83 63	94.50 126	97.33 136	85.93 174	95.19 98	96.77 175	96.64 19	97.61 38	98.05 46	93.23 91	98.79 140	88.60 203	99.04 111	98.78 88

test_fmvsmconf0.01_n		95.90 54	96.09 47	95.31 89	97.30 137	89.21 97	94.24 132	98.76 10	86.25 223	97.56 39	98.66 18	95.73 19	98.44 190	97.35 2	98.99 113	98.27 138

OMC-MVS		94.22 125	93.69 143	95.81 69	97.25 138	91.27 64	92.27 207	97.40 122	87.10 215	94.56 184	95.42 218	93.74 77	98.11 218	86.62 237	98.85 132	98.06 153

GeoE		94.55 110	94.68 113	94.15 137	97.23 139	85.11 190	94.14 138	97.34 130	88.71 181	95.26 156	95.50 214	94.65 61	99.12 91	90.94 136	98.40 179	98.23 140

ZD-MVS					97.23 139	90.32 78		97.54 112	84.40 260	94.78 178	95.79 199	92.76 107	99.39 49	88.72 201	98.40 179

fmvsm_s_conf0.1_n		94.19 128	94.41 118	93.52 167	97.22 141	84.37 196	93.73 151	95.26 245	84.45 259	95.76 126	98.00 51	91.85 124	97.21 285	95.62 18	97.82 231	98.98 60

plane_prior6					97.21 142	88.23 121						86.93 209

DP-MVS Recon		92.31 185	91.88 191	93.60 160	97.18 143	86.87 147	91.10 244	97.37 123	84.92 253	92.08 268	94.08 268	88.59 180	98.20 210	83.50 274	98.14 208	95.73 289

新几何1				93.17 177	97.16 144	87.29 135		94.43 267	67.95 380	91.29 278	94.94 238	86.97 208	98.23 208	81.06 302	97.75 233	93.98 338

DP-MVS		95.62 64	95.84 62	94.97 101	97.16 144	88.62 111	94.54 123	97.64 103	96.94 15	96.58 88	97.32 101	93.07 98	98.72 152	90.45 147	98.84 133	97.57 204

CHOSEN 1792x2688		87.19 303	85.92 312	91.00 258	97.13 146	79.41 276	84.51 370	95.60 226	64.14 389	90.07 302	94.81 242	78.26 292	97.14 291	73.34 358	95.38 307	96.46 259

HyFIR lowres test		87.19 303	85.51 314	92.24 210	97.12 147	80.51 251	85.03 364	96.06 211	66.11 385	91.66 274	92.98 300	70.12 336	99.14 86	75.29 348	95.23 311	97.07 231

ab-mvs		92.40 182	92.62 174	91.74 227	97.02 148	81.65 236	95.84 71	95.50 236	86.95 217	92.95 237	97.56 76	90.70 156	97.50 270	79.63 317	97.43 249	96.06 274

tttt0517		89.81 246	88.90 255	92.55 203	97.00 149	79.73 270	95.03 103	83.65 377	89.88 156	95.30 153	94.79 245	53.64 391	99.39 49	91.99 110	98.79 143	98.54 120

h-mvs33		92.89 165	91.99 188	95.58 77	96.97 150	90.55 76	93.94 145	94.01 278	89.23 168	93.95 201	96.19 181	76.88 308	99.14 86	91.02 133	95.71 297	97.04 235

test222					96.95 151	85.27 189	88.83 311	93.61 281	65.09 388	90.74 289	94.85 241	84.62 237			97.36 252	93.91 339

CDPH-MVS		92.67 174	91.83 193	95.18 96	96.94 152	88.46 118	90.70 254	97.07 151	77.38 326	92.34 262	95.08 233	92.67 109	98.88 121	85.74 250	98.57 166	98.20 143

CNVR-MVS		94.58 109	94.29 124	95.46 82	96.94 152	89.35 96	91.81 229	96.80 172	89.66 160	93.90 204	95.44 217	92.80 106	98.72 152	92.74 93	98.52 171	98.32 133

EC-MVSNet		95.44 72	95.62 71	94.89 103	96.93 154	87.69 131	96.48 38	99.14 4	93.93 56	92.77 243	94.52 255	93.95 76	99.49 24	93.62 57	99.22 89	97.51 209

原ACMM1				92.87 188	96.91 155	84.22 201		97.01 154	76.84 332	89.64 312	94.46 256	88.00 190	98.70 159	81.53 296	98.01 220	95.70 292

ambc				92.98 180	96.88 156	83.01 221	95.92 68	96.38 198		96.41 92	97.48 86	88.26 184	97.80 248	89.96 169	98.93 125	98.12 151

testdata				91.03 255	96.87 157	82.01 231		94.28 271	71.55 361	92.46 253	95.42 218	85.65 227	97.38 281	82.64 282	97.27 254	93.70 345

CS-MVS-test		95.32 81	95.10 96	95.96 58	96.86 158	90.75 74	96.33 47	99.20 2	93.99 53	91.03 285	93.73 281	93.52 81	99.55 18	91.81 116	99.45 47	97.58 203

test_fmvsmconf0.1_n		95.61 65	95.72 68	95.26 90	96.85 159	89.20 98	93.51 157	98.60 13	85.68 235	97.42 50	98.30 35	95.34 33	98.39 191	96.85 3	98.98 114	98.19 144

OPU-MVS				95.15 97	96.84 160	89.43 92	95.21 94				95.66 207	93.12 95	98.06 221	86.28 246	98.61 161	97.95 169

CS-MVS		95.77 59	95.58 73	96.37 50	96.84 160	91.72 61	96.73 29	99.06 5	94.23 49	92.48 252	94.79 245	93.56 79	99.49 24	93.47 65	99.05 106	97.89 176

NP-MVS					96.82 162	87.10 141					93.40 290

3Dnovator+	92.74 2	95.86 57	95.77 66	96.13 53	96.81 163	90.79 73	96.30 54	97.82 90	96.13 26	94.74 180	97.23 106	91.33 135	99.16 83	93.25 78	98.30 192	98.46 126

Test_1112_low_res		87.50 295	86.58 302	90.25 280	96.80 164	77.75 303	87.53 330	96.25 202	69.73 375	86.47 351	93.61 285	75.67 315	97.88 239	79.95 312	93.20 351	95.11 308

PAPM_NR		91.03 209	90.81 216	91.68 231	96.73 165	81.10 246	93.72 152	96.35 199	88.19 192	88.77 325	92.12 320	85.09 233	97.25 283	82.40 287	93.90 341	96.68 250

fmvsm_s_conf0.5_n_a		94.02 132	94.08 133	93.84 153	96.72 166	85.73 180	93.65 155	95.23 246	83.30 269	95.13 162	97.56 76	92.22 116	97.17 289	95.51 23	97.41 250	98.64 112

fmvsm_s_conf0.5_n		94.00 133	94.20 129	93.42 171	96.69 167	84.37 196	93.38 163	95.13 248	84.50 258	95.40 146	97.55 80	91.77 126	97.20 286	95.59 19	97.79 232	98.69 104

1112_ss		88.42 276	87.41 285	91.45 239	96.69 167	80.99 247	89.72 287	96.72 178	73.37 351	87.00 349	90.69 341	77.38 300	98.20 210	81.38 297	93.72 344	95.15 306

test_fmvsmvis_n_1920		95.08 91	95.40 81	94.13 139	96.66 169	87.75 130	93.44 161	98.49 15	85.57 240	98.27 20	97.11 117	94.11 74	97.75 256	96.26 11	98.72 149	96.89 241

patch_mono-2		92.46 180	92.72 172	91.71 229	96.65 170	78.91 287	88.85 310	97.17 143	83.89 265	92.45 254	96.76 141	89.86 172	97.09 292	90.24 159	98.59 164	99.12 43

v8		94.65 107	95.29 87	92.74 192	96.65 170	79.77 269	94.59 116	97.17 143	91.86 103	97.47 47	97.93 55	88.16 186	99.08 94	94.32 39	99.47 43	99.38 22

MVS_111021_HR		93.63 143	93.42 155	94.26 135	96.65 170	86.96 146	89.30 300	96.23 204	88.36 190	93.57 212	94.60 252	93.45 82	97.77 253	90.23 160	98.38 183	98.03 159

ANet_high		94.83 100	96.28 37	90.47 273	96.65 170	73.16 350	94.33 128	98.74 11	96.39 24	98.09 25	98.93 8	93.37 86	98.70 159	90.38 150	99.68 18	99.53 15

SD-MVS		95.19 88	95.73 67	93.55 162	96.62 174	88.88 107	94.67 113	98.05 64	91.26 126	97.25 58	96.40 163	95.42 28	94.36 356	92.72 95	99.19 92	97.40 218
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
PM-MVS		93.33 150	92.67 173	95.33 86	96.58 175	94.06 21	92.26 208	92.18 309	85.92 230	96.22 105	96.61 153	85.64 228	95.99 328	90.35 152	98.23 199	95.93 280

Anonymous20240521		92.86 168	93.57 150	90.74 267	96.57 176	75.50 333	94.15 136	95.60 226	89.38 165	95.90 120	97.90 61	80.39 276	97.96 232	92.60 98	99.68 18	98.75 92

v10		94.68 106	95.27 89	92.90 187	96.57 176	80.15 254	94.65 115	97.57 110	90.68 141	97.43 48	98.00 51	88.18 185	99.15 84	94.84 32	99.55 37	99.41 20

Anonymous202405211		92.58 176	92.50 177	92.83 190	96.55 178	83.22 216	92.43 197	91.64 320	94.10 52	95.59 136	96.64 151	81.88 265	97.50 270	85.12 259	98.52 171	97.77 190

DVP-MVS++		95.93 52	96.34 34	94.70 112	96.54 179	86.66 154	98.45 4	98.22 36	93.26 71	97.54 40	97.36 94	93.12 95	99.38 55	93.88 48	98.68 155	98.04 156

MSC_two_6792asdad				95.90 65	96.54 179	89.57 88		96.87 167					99.41 39	94.06 45	99.30 71	98.72 97

No_MVS				95.90 65	96.54 179	89.57 88		96.87 167					99.41 39	94.06 45	99.30 71	98.72 97

PLC	85.34 15	90.40 223	88.92 253	94.85 105	96.53 182	90.02 81	91.58 233	96.48 194	80.16 303	86.14 353	92.18 317	85.73 225	98.25 207	76.87 339	94.61 327	96.30 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	88.58 10	92.49 179	91.75 195	94.73 110	96.50 183	89.69 86	92.91 176	97.68 101	78.02 324	92.79 242	94.10 267	90.85 149	97.96 232	84.76 266	98.16 206	96.54 252
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
NCCC		94.08 130	93.54 152	95.70 75	96.49 184	89.90 83	92.39 200	96.91 164	90.64 142	92.33 263	94.60 252	90.58 159	98.96 112	90.21 161	97.70 237	98.23 140

TAMVS		90.16 234	89.05 249	93.49 169	96.49 184	86.37 162	90.34 267	92.55 305	80.84 300	92.99 234	94.57 254	81.94 264	98.20 210	73.51 357	98.21 202	95.90 283

test_fmvsmconf_n		95.43 73	95.50 75	95.22 94	96.48 186	89.19 99	93.23 167	98.36 21	85.61 238	96.92 73	98.02 50	95.23 39	98.38 194	96.69 6	98.95 123	98.09 152

TEST9					96.45 187	89.46 90	90.60 257	96.92 162	79.09 316	90.49 292	94.39 258	91.31 136	98.88 121

train_agg		92.71 173	91.83 193	95.35 84	96.45 187	89.46 90	90.60 257	96.92 162	79.37 311	90.49 292	94.39 258	91.20 141	98.88 121	88.66 202	98.43 178	97.72 195

test_8					96.37 189	89.14 100	90.51 260	96.89 165	79.37 311	90.42 294	94.36 260	91.20 141	98.82 131

CLD-MVS		91.82 193	91.41 203	93.04 178	96.37 189	83.65 210	86.82 344	97.29 135	84.65 257	92.27 264	89.67 352	92.20 118	97.85 245	83.95 272	99.47 43	97.62 201
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					96.36 191		91.37 236		87.16 212	88.81 321

ACMP_Plane					96.36 191		91.37 236		87.16 212	88.81 321

HQP-MVS		92.09 190	91.49 201	93.88 150	96.36 191	84.89 192	91.37 236	97.31 132	87.16 212	88.81 321	93.40 290	84.76 235	98.60 172	86.55 240	97.73 234	98.14 149

v2v482		93.29 151	93.63 146	92.29 208	96.35 194	78.82 289	91.77 231	96.28 200	88.45 186	95.70 133	96.26 178	86.02 223	98.90 118	93.02 86	98.81 141	99.14 40

MSLP-MVS++		93.25 155	93.88 135	91.37 241	96.34 195	82.81 224	93.11 170	97.74 98	89.37 166	94.08 194	95.29 226	90.40 162	96.35 319	90.35 152	98.25 197	94.96 311

thisisatest0530		88.69 273	87.52 284	92.20 211	96.33 196	79.36 277	92.81 178	84.01 376	86.44 220	93.67 209	92.68 308	53.62 392	99.25 75	89.65 176	98.45 177	98.00 161

FPMVS		84.50 325	83.28 330	88.16 323	96.32 197	94.49 16	85.76 358	85.47 366	83.09 275	85.20 358	94.26 261	63.79 367	86.58 392	63.72 389	91.88 369	83.40 390

Anonymous20231206		88.77 270	88.29 266	90.20 283	96.31 198	78.81 290	89.56 291	93.49 286	74.26 347	92.38 258	95.58 212	82.21 258	95.43 340	72.07 365	98.75 148	96.34 263

MVP-Stereo		90.07 240	88.92 253	93.54 164	96.31 198	86.49 157	90.93 247	95.59 230	79.80 304	91.48 275	95.59 209	80.79 273	97.39 279	78.57 327	91.19 371	96.76 248
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvsm_n_1920		94.72 103	94.74 109	94.67 113	96.30 200	88.62 111	93.19 168	98.07 60	85.63 237	97.08 62	97.35 97	90.86 148	97.66 263	95.70 16	98.48 176	97.74 194

v1144		93.50 145	93.81 136	92.57 202	96.28 201	79.61 272	91.86 227	96.96 158	86.95 217	95.91 119	96.32 172	87.65 195	98.96 112	93.51 61	98.88 128	99.13 41

LFMVS		91.33 205	91.16 210	91.82 224	96.27 202	79.36 277	95.01 104	85.61 365	96.04 30	94.82 176	97.06 121	72.03 330	98.46 188	84.96 263	98.70 153	97.65 200

VNet		92.67 174	92.96 162	91.79 225	96.27 202	80.15 254	91.95 217	94.98 252	92.19 94	94.52 186	96.07 187	87.43 199	97.39 279	84.83 264	98.38 183	97.83 183

IterMVS-LS		93.78 140	94.28 125	92.27 209	96.27 202	79.21 282	91.87 225	96.78 173	91.77 113	96.57 89	97.07 120	87.15 204	98.74 150	91.99 110	99.03 112	98.86 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		92.87 167	93.29 156	91.62 233	96.25 205	77.72 304	91.28 240	95.05 249	89.69 159	95.93 118	96.04 188	87.34 200	98.38 194	90.05 167	97.99 221	98.78 88

casdiffmvs_mvg		95.10 90	95.62 71	93.53 165	96.25 205	83.23 215	92.66 185	98.19 39	93.06 75	97.49 44	97.15 113	94.78 57	98.71 158	92.27 103	98.72 149	98.65 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_LR		93.66 142	93.28 158	94.80 107	96.25 205	90.95 69	90.21 270	95.43 239	87.91 196	93.74 208	94.40 257	92.88 104	96.38 317	90.39 149	98.28 193	97.07 231

agg_prior					96.20 208	88.89 106		96.88 166		90.21 299			98.78 143

旧先验1					96.20 208	84.17 203		94.82 257			95.57 213	89.57 174			97.89 228	96.32 264

CNLPA		91.72 196	91.20 207	93.26 175	96.17 210	91.02 67	91.14 242	95.55 233	90.16 152	90.87 286	93.56 287	86.31 219	94.40 355	79.92 316	97.12 259	94.37 329

fmvsm_l_conf0.5_n		93.79 139	93.81 136	93.73 156	96.16 211	86.26 167	92.46 194	96.72 178	81.69 292	95.77 125	97.11 117	90.83 150	97.82 246	95.58 20	97.99 221	97.11 230

hse-mvs2		92.24 188	91.20 207	95.38 83	96.16 211	90.65 75	92.52 190	92.01 316	89.23 168	93.95 201	92.99 299	76.88 308	98.69 161	91.02 133	96.03 289	96.81 245

v1192		93.49 146	93.78 139	92.62 199	96.16 211	79.62 271	91.83 228	97.22 141	86.07 227	96.10 112	96.38 168	87.22 202	99.02 104	94.14 44	98.88 128	99.22 33

thres100view900		87.35 298	86.89 297	88.72 310	96.14 214	73.09 351	93.00 173	85.31 368	92.13 95	93.26 223	90.96 336	63.42 368	98.28 202	71.27 371	96.54 281	94.79 319

DeepC-MVS_fast	89.96 7	93.73 141	93.44 154	94.60 121	96.14 214	87.90 126	93.36 164	97.14 145	85.53 241	93.90 204	95.45 216	91.30 137	98.59 174	89.51 177	98.62 160	97.31 224
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPM-MVS		89.35 253	88.40 262	92.18 215	96.13 216	84.20 202	86.96 339	96.15 210	75.40 340	87.36 346	91.55 329	83.30 245	98.01 227	82.17 290	96.62 279	94.32 331

fmvsm_l_conf0.5_n_a		93.59 144	93.63 146	93.49 169	96.10 217	85.66 183	92.32 203	96.57 187	81.32 294	95.63 134	97.14 114	90.19 164	97.73 259	95.37 29	98.03 217	97.07 231

AUN-MVS		90.05 241	88.30 265	95.32 88	96.09 218	90.52 77	92.42 198	92.05 315	82.08 289	88.45 331	92.86 301	65.76 356	98.69 161	88.91 196	96.07 288	96.75 249

baseline		94.26 122	94.80 105	92.64 196	96.08 219	80.99 247	93.69 153	98.04 68	90.80 138	94.89 174	96.32 172	93.19 92	98.48 187	91.68 121	98.51 173	98.43 128

PCF-MVS	84.52 17	89.12 257	87.71 281	93.34 172	96.06 220	85.84 177	86.58 352	97.31 132	68.46 379	93.61 211	93.89 277	87.51 198	98.52 181	67.85 382	98.11 210	95.66 294
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v144192		93.20 158	93.54 152	92.16 216	96.05 221	78.26 296	91.95 217	97.14 145	84.98 252	95.96 115	96.11 185	87.08 206	99.04 102	93.79 51	98.84 133	99.17 37

thres600view7		87.66 289	87.10 295	89.36 299	96.05 221	73.17 349	92.72 181	85.31 368	91.89 102	93.29 220	90.97 335	63.42 368	98.39 191	73.23 359	96.99 268	96.51 254

casdiffmvs		94.32 119	94.80 105	92.85 189	96.05 221	81.44 241	92.35 201	98.05 64	91.53 122	95.75 128	96.80 138	93.35 87	98.49 183	91.01 135	98.32 191	98.64 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MIMVSNet		87.13 305	86.54 304	88.89 307	96.05 221	76.11 326	94.39 125	88.51 337	81.37 293	88.27 334	96.75 143	72.38 327	95.52 335	65.71 387	95.47 303	95.03 309

v1921920		93.26 153	93.61 148	92.19 212	96.04 225	78.31 295	91.88 224	97.24 139	85.17 246	96.19 109	96.19 181	86.76 213	99.05 99	94.18 43	98.84 133	99.22 33

v1240		93.29 151	93.71 142	92.06 219	96.01 226	77.89 301	91.81 229	97.37 123	85.12 248	96.69 83	96.40 163	86.67 215	99.07 98	94.51 35	98.76 146	99.22 33

BH-untuned		90.68 215	90.90 212	90.05 287	95.98 227	79.57 273	90.04 276	94.94 254	87.91 196	94.07 195	93.00 298	87.76 194	97.78 252	79.19 323	95.17 312	92.80 360

DeepPCF-MVS	90.46 6	94.20 126	93.56 151	96.14 52	95.96 228	92.96 43	89.48 293	97.46 118	85.14 247	96.23 104	95.42 218	93.19 92	98.08 220	90.37 151	98.76 146	97.38 221

test_prior				94.61 118	95.95 229	87.23 137		97.36 128					98.68 163			97.93 171

test12				94.43 131	95.95 229	86.75 150		96.24 203		89.76 310		89.79 173	98.79 140		97.95 225	97.75 193

MVS_0304		93.92 136	93.68 144	94.64 117	95.94 231	85.83 178	94.34 127	88.14 343	92.98 77	91.09 284	97.68 67	86.73 214	99.36 58	96.64 7	99.59 28	98.72 97

LCM-MVSNet-Re		94.20 126	94.58 116	93.04 178	95.91 232	83.13 219	93.79 149	99.19 3	92.00 97	98.84 5	98.04 48	93.64 78	99.02 104	81.28 298	98.54 169	96.96 238

PatchMatch-RL		89.18 255	88.02 278	92.64 196	95.90 233	92.87 45	88.67 317	91.06 323	80.34 301	90.03 303	91.67 326	83.34 244	94.42 354	76.35 343	94.84 321	90.64 376

ETV-MVS		92.99 162	92.74 169	93.72 157	95.86 234	86.30 165	92.33 202	97.84 88	91.70 118	92.81 240	86.17 379	92.22 116	99.19 81	88.03 214	97.73 234	95.66 294

testing3		83.66 331	82.52 336	87.08 334	95.84 235	65.84 383	89.80 285	77.17 398	88.17 193	90.84 287	88.63 362	30.95 406	98.11 218	84.05 271	97.19 257	97.28 226

TSAR-MVS + GP.		93.07 161	92.41 179	95.06 99	95.82 236	90.87 72	90.97 246	92.61 304	88.04 195	94.61 183	93.79 280	88.08 187	97.81 247	89.41 179	98.39 182	96.50 257

QAPM		92.88 166	92.77 167	93.22 176	95.82 236	83.31 212	96.45 39	97.35 129	83.91 264	93.75 206	96.77 139	89.25 177	98.88 121	84.56 268	97.02 263	97.49 210

EIA-MVS		92.35 184	92.03 186	93.30 174	95.81 238	83.97 206	92.80 179	98.17 45	87.71 203	89.79 309	87.56 369	91.17 144	99.18 82	87.97 215	97.27 254	96.77 247

tfpn200view9		87.05 306	86.52 305	88.67 311	95.77 239	72.94 352	91.89 222	86.00 360	90.84 135	92.61 247	89.80 347	63.93 365	98.28 202	71.27 371	96.54 281	94.79 319

thres400		87.20 302	86.52 305	89.24 303	95.77 239	72.94 352	91.89 222	86.00 360	90.84 135	92.61 247	89.80 347	63.93 365	98.28 202	71.27 371	96.54 281	96.51 254

pmmvs-eth3d		91.54 200	90.73 219	93.99 142	95.76 241	87.86 128	90.83 249	93.98 279	78.23 323	94.02 199	96.22 180	82.62 257	96.83 303	86.57 238	98.33 189	97.29 225

jason		89.17 256	88.32 264	91.70 230	95.73 242	80.07 257	88.10 321	93.22 290	71.98 360	90.09 300	92.79 304	78.53 290	98.56 177	87.43 224	97.06 261	96.46 259
jason: jason.
alignmvs		93.26 153	92.85 166	94.50 126	95.70 243	87.45 133	93.45 160	95.76 221	91.58 120	95.25 158	92.42 315	81.96 263	98.72 152	91.61 122	97.87 229	97.33 223

xiu_mvs_v1_base_debu		91.47 202	91.52 198	91.33 243	95.69 244	81.56 237	89.92 280	96.05 213	83.22 272	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 292	93.62 348

xiu_mvs_v1_base		91.47 202	91.52 198	91.33 243	95.69 244	81.56 237	89.92 280	96.05 213	83.22 272	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 292	93.62 348

xiu_mvs_v1_base_debi		91.47 202	91.52 198	91.33 243	95.69 244	81.56 237	89.92 280	96.05 213	83.22 272	91.26 279	90.74 338	91.55 131	98.82 131	89.29 183	95.91 292	93.62 348

PHI-MVS		94.34 118	93.80 138	95.95 59	95.65 247	91.67 62	94.82 109	97.86 85	87.86 199	93.04 233	94.16 266	91.58 130	98.78 143	90.27 157	98.96 121	97.41 215

LF4IMVS		92.72 172	92.02 187	94.84 106	95.65 247	91.99 54	92.92 175	96.60 184	85.08 250	92.44 255	93.62 284	86.80 212	96.35 319	86.81 232	98.25 197	96.18 270

test20.03		90.80 211	90.85 215	90.63 270	95.63 249	79.24 280	89.81 284	92.87 295	89.90 155	94.39 188	96.40 163	85.77 224	95.27 345	73.86 356	99.05 106	97.39 219

TinyColmap		92.00 192	92.76 168	89.71 293	95.62 250	77.02 312	90.72 253	96.17 209	87.70 204	95.26 156	96.29 174	92.54 111	96.45 314	81.77 292	98.77 145	95.66 294

canonicalmvs		94.59 108	94.69 111	94.30 134	95.60 251	87.03 143	95.59 81	98.24 34	91.56 121	95.21 161	92.04 321	94.95 53	98.66 165	91.45 127	97.57 243	97.20 228

AdaColmap		91.63 198	91.36 204	92.47 206	95.56 252	86.36 163	92.24 210	96.27 201	88.88 178	89.90 306	92.69 307	91.65 129	98.32 200	77.38 336	97.64 240	92.72 361

UnsupCasMVSNet_bld		88.50 275	88.03 277	89.90 289	95.52 253	78.88 288	87.39 332	94.02 277	79.32 314	93.06 231	94.02 271	80.72 274	94.27 357	75.16 349	93.08 355	96.54 252

3Dnovator	92.54 3	94.80 101	94.90 101	94.47 129	95.47 254	87.06 142	96.63 31	97.28 137	91.82 110	94.34 191	97.41 88	90.60 158	98.65 167	92.47 100	98.11 210	97.70 196

Fast-Effi-MVS+		91.28 207	90.86 214	92.53 204	95.45 255	82.53 226	89.25 303	96.52 192	85.00 251	89.91 305	88.55 364	92.94 100	98.84 129	84.72 267	95.44 304	96.22 268

GBi-Net		93.21 156	92.96 162	93.97 144	95.40 256	84.29 198	95.99 63	96.56 188	88.63 182	95.10 164	98.53 26	81.31 268	98.98 107	86.74 233	98.38 183	98.65 107

test1		93.21 156	92.96 162	93.97 144	95.40 256	84.29 198	95.99 63	96.56 188	88.63 182	95.10 164	98.53 26	81.31 268	98.98 107	86.74 233	98.38 183	98.65 107

FMVSNet2		92.78 170	92.73 171	92.95 183	95.40 256	81.98 232	94.18 135	95.53 235	88.63 182	96.05 113	97.37 91	81.31 268	98.81 136	87.38 226	98.67 157	98.06 153

CDS-MVSNet		89.55 248	88.22 272	93.53 165	95.37 259	86.49 157	89.26 301	93.59 282	79.76 306	91.15 282	92.31 316	77.12 303	98.38 194	77.51 334	97.92 227	95.71 290
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
V42		93.43 148	93.58 149	92.97 181	95.34 260	81.22 244	92.67 184	96.49 193	87.25 211	96.20 107	96.37 169	87.32 201	98.85 128	92.39 102	98.21 202	98.85 81

Patchmatch-RL test		88.81 269	88.52 259	89.69 294	95.33 261	79.94 263	86.22 355	92.71 300	78.46 321	95.80 124	94.18 265	66.25 354	95.33 343	89.22 188	98.53 170	93.78 342

CL-MVSNet_self_test		90.04 242	89.90 237	90.47 273	95.24 262	77.81 302	86.60 351	92.62 303	85.64 236	93.25 225	93.92 275	83.84 240	96.06 326	79.93 314	98.03 217	97.53 208

BH-RMVSNet		90.47 221	90.44 225	90.56 272	95.21 263	78.65 293	89.15 304	93.94 280	88.21 191	92.74 244	94.22 263	86.38 218	97.88 239	78.67 326	95.39 306	95.14 307

Effi-MVS+		92.79 169	92.74 169	92.94 185	95.10 264	83.30 213	94.00 142	97.53 114	91.36 125	89.35 315	90.65 343	94.01 75	98.66 165	87.40 225	95.30 309	96.88 243

USDC		89.02 260	89.08 248	88.84 308	95.07 265	74.50 340	88.97 306	96.39 197	73.21 353	93.27 222	96.28 176	82.16 260	96.39 316	77.55 333	98.80 142	95.62 297

WTY-MVS		86.93 308	86.50 307	88.24 321	94.96 266	74.64 336	87.19 335	92.07 314	78.29 322	88.32 333	91.59 328	78.06 293	94.27 357	74.88 350	93.15 353	95.80 286

FA-MVS(test-final)		91.81 194	91.85 192	91.68 231	94.95 267	79.99 262	96.00 62	93.44 287	87.80 200	94.02 199	97.29 102	77.60 296	98.45 189	88.04 213	97.49 245	96.61 251

PS-MVSNAJ		88.86 268	88.99 252	88.48 317	94.88 268	74.71 335	86.69 347	95.60 226	80.88 298	87.83 340	87.37 372	90.77 151	98.82 131	82.52 284	94.37 331	91.93 367

MG-MVS		89.54 249	89.80 239	88.76 309	94.88 268	72.47 357	89.60 289	92.44 307	85.82 231	89.48 313	95.98 191	82.85 252	97.74 258	81.87 291	95.27 310	96.08 273

xiu_mvs_v2_base		89.00 263	89.19 246	88.46 318	94.86 270	74.63 337	86.97 338	95.60 226	80.88 298	87.83 340	88.62 363	91.04 146	98.81 136	82.51 285	94.38 330	91.93 367

MAR-MVS		90.32 230	88.87 256	94.66 115	94.82 271	91.85 57	94.22 134	94.75 260	80.91 297	87.52 345	88.07 367	86.63 216	97.87 242	76.67 340	96.21 287	94.25 332
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
PVSNet_BlendedMVS		90.35 228	89.96 235	91.54 236	94.81 272	78.80 291	90.14 273	96.93 160	79.43 310	88.68 328	95.06 234	86.27 220	98.15 216	80.27 306	98.04 216	97.68 198

PVSNet_Blended		88.74 271	88.16 275	90.46 275	94.81 272	78.80 291	86.64 348	96.93 160	74.67 343	88.68 328	89.18 359	86.27 220	98.15 216	80.27 306	96.00 290	94.44 328

FE-MVS		89.06 259	88.29 266	91.36 242	94.78 274	79.57 273	96.77 28	90.99 324	84.87 254	92.96 236	96.29 174	60.69 379	98.80 139	80.18 309	97.11 260	95.71 290

BH-w/o		87.21 301	87.02 296	87.79 329	94.77 275	77.27 310	87.90 323	93.21 292	81.74 291	89.99 304	88.39 366	83.47 243	96.93 300	71.29 370	92.43 363	89.15 378

LS3D		96.11 47	95.83 63	96.95 36	94.75 276	94.20 19	97.34 13	97.98 75	97.31 11	95.32 152	96.77 139	93.08 97	99.20 80	91.79 117	98.16 206	97.44 214

Effi-MVS+-dtu		93.90 138	92.60 175	97.77 3	94.74 277	96.67 5	94.00 142	95.41 240	89.94 154	91.93 271	92.13 319	90.12 166	98.97 111	87.68 220	97.48 246	97.67 199

MVSFormer		92.18 189	92.23 181	92.04 220	94.74 277	80.06 258	97.15 15	97.37 123	88.98 174	88.83 319	92.79 304	77.02 305	99.60 9	96.41 9	96.75 276	96.46 259

lupinMVS		88.34 278	87.31 286	91.45 239	94.74 277	80.06 258	87.23 333	92.27 308	71.10 365	88.83 319	91.15 332	77.02 305	98.53 180	86.67 236	96.75 276	95.76 288

baseline1		87.62 291	87.31 286	88.54 314	94.71 280	74.27 343	93.10 171	88.20 341	86.20 224	92.18 266	93.04 297	73.21 324	95.52 335	79.32 321	85.82 386	95.83 285

MDA-MVSNet-bldmvs		91.04 208	90.88 213	91.55 235	94.68 281	80.16 253	85.49 360	92.14 312	90.41 149	94.93 172	95.79 199	85.10 232	96.93 300	85.15 257	94.19 337	97.57 204

Fast-Effi-MVS+-dtu		92.77 171	92.16 182	94.58 124	94.66 282	88.25 120	92.05 213	96.65 182	89.62 161	90.08 301	91.23 331	92.56 110	98.60 172	86.30 245	96.27 286	96.90 240

iter_conf_final		90.23 232	89.32 245	92.95 183	94.65 283	81.46 240	94.32 130	95.40 242	85.61 238	92.84 239	95.37 224	54.58 388	99.13 88	92.16 104	98.94 124	98.25 139

UnsupCasMVSNet_eth		90.33 229	90.34 228	90.28 278	94.64 284	80.24 252	89.69 288	95.88 218	85.77 232	93.94 203	95.69 206	81.99 262	92.98 368	84.21 270	91.30 370	97.62 201

OpenMVS_ROB	85.12 16	89.52 250	89.05 249	90.92 260	94.58 285	81.21 245	91.10 244	93.41 288	77.03 330	93.41 215	93.99 273	83.23 246	97.80 248	79.93 314	94.80 322	93.74 344

OpenMVS	89.45 8	92.27 187	92.13 185	92.68 195	94.53 286	84.10 204	95.70 76	97.03 153	82.44 286	91.14 283	96.42 161	88.47 182	98.38 194	85.95 248	97.47 247	95.55 298

thres200		85.85 314	85.18 315	87.88 328	94.44 287	72.52 356	89.08 305	86.21 357	88.57 185	91.44 276	88.40 365	64.22 363	98.00 228	68.35 380	95.88 295	93.12 354

DELS-MVS		92.05 191	92.16 182	91.72 228	94.44 287	80.13 256	87.62 325	97.25 138	87.34 210	92.22 265	93.18 296	89.54 175	98.73 151	89.67 175	98.20 204	96.30 265
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
N_pmnet		88.90 267	87.25 289	93.83 154	94.40 289	93.81 35	84.73 366	87.09 352	79.36 313	93.26 223	92.43 314	79.29 282	91.68 373	77.50 335	97.22 256	96.00 276

pmmvs4		88.95 265	87.70 282	92.70 193	94.30 290	85.60 184	87.22 334	92.16 311	74.62 344	89.75 311	94.19 264	77.97 294	96.41 315	82.71 281	96.36 285	96.09 272

new-patchmatchnet		88.97 264	90.79 217	83.50 365	94.28 291	55.83 400	85.34 362	93.56 284	86.18 225	95.47 142	95.73 205	83.10 247	96.51 311	85.40 254	98.06 214	98.16 147

API-MVS		91.52 201	91.61 196	91.26 247	94.16 292	86.26 167	94.66 114	94.82 257	91.17 130	92.13 267	91.08 334	90.03 171	97.06 294	79.09 324	97.35 253	90.45 377

MSDG		90.82 210	90.67 220	91.26 247	94.16 292	83.08 220	86.63 349	96.19 207	90.60 144	91.94 270	91.89 322	89.16 178	95.75 332	80.96 303	94.51 328	94.95 312

TR-MVS		87.70 287	87.17 291	89.27 301	94.11 294	79.26 279	88.69 315	91.86 317	81.94 290	90.69 290	89.79 349	82.82 253	97.42 276	72.65 363	91.98 367	91.14 373

test_yl		90.11 237	89.73 242	91.26 247	94.09 295	79.82 266	90.44 261	92.65 301	90.90 133	93.19 228	93.30 292	73.90 321	98.03 223	82.23 288	96.87 270	95.93 280

DCV-MVSNet		90.11 237	89.73 242	91.26 247	94.09 295	79.82 266	90.44 261	92.65 301	90.90 133	93.19 228	93.30 292	73.90 321	98.03 223	82.23 288	96.87 270	95.93 280

D2MVS		89.93 243	89.60 244	90.92 260	94.03 297	78.40 294	88.69 315	94.85 255	78.96 318	93.08 230	95.09 232	74.57 319	96.94 298	88.19 207	98.96 121	97.41 215

sss		87.23 300	86.82 298	88.46 318	93.96 298	77.94 298	86.84 342	92.78 299	77.59 325	87.61 344	91.83 323	78.75 285	91.92 372	77.84 330	94.20 335	95.52 299

PVSNet	76.22 20	82.89 337	82.37 337	84.48 358	93.96 298	64.38 390	78.60 388	88.61 336	71.50 362	84.43 367	86.36 378	74.27 320	94.60 351	69.87 378	93.69 345	94.46 327

IterMVS-SCA-FT		91.65 197	91.55 197	91.94 221	93.89 300	79.22 281	87.56 328	93.51 285	91.53 122	95.37 149	96.62 152	78.65 287	98.90 118	91.89 114	94.95 317	97.70 196

UGNet		93.08 159	92.50 177	94.79 108	93.87 301	87.99 125	95.07 101	94.26 272	90.64 142	87.33 347	97.67 69	86.89 211	98.49 183	88.10 210	98.71 151	97.91 173
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
PAPM		81.91 345	80.11 355	87.31 333	93.87 301	72.32 358	84.02 374	93.22 290	69.47 376	76.13 395	89.84 346	72.15 328	97.23 284	53.27 397	89.02 379	92.37 364

CANet		92.38 183	91.99 188	93.52 167	93.82 303	83.46 211	91.14 242	97.00 155	89.81 157	86.47 351	94.04 269	87.90 193	99.21 78	89.50 178	98.27 194	97.90 174

test_fmvs3		92.42 181	92.40 180	92.46 207	93.80 304	87.28 136	93.86 147	97.05 152	76.86 331	96.25 102	98.66 18	82.87 251	91.26 375	95.44 26	96.83 272	98.82 82

HY-MVS	82.50 18	86.81 309	85.93 311	89.47 295	93.63 305	77.93 299	94.02 141	91.58 321	75.68 336	83.64 371	93.64 282	77.40 299	97.42 276	71.70 368	92.07 366	93.05 357

test_vis1_n_1920		89.45 251	89.85 238	88.28 320	93.59 306	76.71 320	90.67 255	97.78 96	79.67 308	90.30 298	96.11 185	76.62 311	92.17 371	90.31 154	93.57 346	95.96 278

MVS_Test		92.57 178	93.29 156	90.40 276	93.53 307	75.85 329	92.52 190	96.96 158	88.73 179	92.35 260	96.70 148	90.77 151	98.37 198	92.53 99	95.49 302	96.99 237

EU-MVSNet		87.39 297	86.71 301	89.44 296	93.40 308	76.11 326	94.93 107	90.00 332	57.17 395	95.71 132	97.37 91	64.77 362	97.68 262	92.67 96	94.37 331	94.52 326

MS-PatchMatch		88.05 282	87.75 280	88.95 305	93.28 309	77.93 299	87.88 324	92.49 306	75.42 339	92.57 250	93.59 286	80.44 275	94.24 359	81.28 298	92.75 358	94.69 324

GA-MVS		87.70 287	86.82 298	90.31 277	93.27 310	77.22 311	84.72 368	92.79 298	85.11 249	89.82 307	90.07 344	66.80 349	97.76 255	84.56 268	94.27 334	95.96 278

pmmvs5		87.87 284	87.14 292	90.07 285	93.26 311	76.97 316	88.89 308	92.18 309	73.71 350	88.36 332	93.89 277	76.86 310	96.73 306	80.32 305	96.81 273	96.51 254

IterMVS		90.18 233	90.16 230	90.21 282	93.15 312	75.98 328	87.56 328	92.97 294	86.43 221	94.09 193	96.40 163	78.32 291	97.43 275	87.87 217	94.69 325	97.23 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		78.83 361	80.60 351	73.51 380	93.07 313	47.37 404	87.10 337	78.00 396	68.94 377	77.53 393	97.26 103	71.45 332	94.62 350	63.28 390	88.74 380	78.55 395

diffmvs		91.74 195	91.93 190	91.15 253	93.06 314	78.17 297	88.77 313	97.51 117	86.28 222	92.42 256	93.96 274	88.04 189	97.46 273	90.69 143	96.67 278	97.82 185
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ET-MVSNet_ETH3D		86.15 312	84.27 323	91.79 225	93.04 315	81.28 242	87.17 336	86.14 358	79.57 309	83.65 370	88.66 361	57.10 383	98.18 213	87.74 219	95.40 305	95.90 283

FMVSNet3		90.78 212	90.32 229	92.16 216	93.03 316	79.92 264	92.54 189	94.95 253	86.17 226	95.10 164	96.01 190	69.97 337	98.75 147	86.74 233	98.38 183	97.82 185

thisisatest0515		84.72 323	82.99 333	89.90 289	92.96 317	75.33 334	84.36 371	83.42 378	77.37 327	88.27 334	86.65 374	53.94 390	98.72 152	82.56 283	97.40 251	95.67 293

PAPR		87.65 290	86.77 300	90.27 279	92.85 318	77.38 308	88.56 318	96.23 204	76.82 333	84.98 362	89.75 351	86.08 222	97.16 290	72.33 364	93.35 349	96.26 267

iter_conf05		88.94 266	88.09 276	91.50 238	92.74 319	76.97 316	92.80 179	95.92 217	82.82 280	93.65 210	95.37 224	49.41 395	99.13 88	90.82 138	99.28 79	98.40 130

test_vis3_rt		90.40 223	90.03 234	91.52 237	92.58 320	88.95 103	90.38 265	97.72 100	73.30 352	97.79 30	97.51 84	77.05 304	87.10 390	89.03 193	94.89 318	98.50 122

test_vis1_n		89.01 262	89.01 251	89.03 304	92.57 321	82.46 228	92.62 187	96.06 211	73.02 355	90.40 295	95.77 203	74.86 318	89.68 383	90.78 140	94.98 316	94.95 312

EI-MVSNet-Vis-set		94.36 116	94.28 125	94.61 118	92.55 322	85.98 173	92.44 196	94.69 262	93.70 61	96.12 111	95.81 198	91.24 138	98.86 126	93.76 55	98.22 201	98.98 60

testing222		80.54 355	78.53 361	86.58 340	92.54 323	68.60 374	86.24 354	82.72 380	83.78 267	82.68 378	84.24 386	39.25 404	95.94 329	60.25 392	95.09 314	95.20 303

EI-MVSNet-UG-set		94.35 117	94.27 127	94.59 122	92.46 324	85.87 176	92.42 198	94.69 262	93.67 64	96.13 110	95.84 197	91.20 141	98.86 126	93.78 52	98.23 199	99.03 52

FMVSNet5		87.82 286	86.56 303	91.62 233	92.31 325	79.81 268	93.49 158	94.81 259	83.26 270	91.36 277	96.93 130	52.77 393	97.49 272	76.07 344	98.03 217	97.55 207

c3_l		91.32 206	91.42 202	91.00 258	92.29 326	76.79 319	87.52 331	96.42 196	85.76 233	94.72 182	93.89 277	82.73 254	98.16 215	90.93 137	98.55 167	98.04 156

dmvs_re		84.69 324	83.94 326	86.95 337	92.24 327	82.93 222	89.51 292	87.37 350	84.38 261	85.37 356	85.08 383	72.44 326	86.59 391	68.05 381	91.03 374	91.33 371

MDA-MVSNet_test_wron		88.16 281	88.23 271	87.93 326	92.22 328	73.71 346	80.71 386	88.84 334	82.52 284	94.88 175	95.14 229	82.70 255	93.61 362	83.28 276	93.80 343	96.46 259

YYNet1		88.17 280	88.24 270	87.93 326	92.21 329	73.62 347	80.75 385	88.77 335	82.51 285	94.99 170	95.11 231	82.70 255	93.70 361	83.33 275	93.83 342	96.48 258

CANet_DTU		89.85 245	89.17 247	91.87 222	92.20 330	80.02 261	90.79 250	95.87 219	86.02 228	82.53 379	91.77 324	80.01 277	98.57 176	85.66 252	97.70 237	97.01 236

test_cas_vis1_n_1920		88.25 279	88.27 268	88.20 322	92.19 331	78.92 286	89.45 294	95.44 237	75.29 342	93.23 226	95.65 208	71.58 331	90.23 381	88.05 212	93.55 347	95.44 300

mvs_anonymous		90.37 227	91.30 206	87.58 330	92.17 332	68.00 375	89.84 283	94.73 261	83.82 266	93.22 227	97.40 89	87.54 197	97.40 278	87.94 216	95.05 315	97.34 222

EI-MVSNet		92.99 162	93.26 160	92.19 212	92.12 333	79.21 282	92.32 203	94.67 264	91.77 113	95.24 159	95.85 195	87.14 205	98.49 183	91.99 110	98.26 195	98.86 78

CVMVSNet		85.16 319	84.72 317	86.48 341	92.12 333	70.19 366	92.32 203	88.17 342	56.15 396	90.64 291	95.85 195	67.97 344	96.69 307	88.78 199	90.52 375	92.56 362

test_fmvs1_n		88.73 272	88.38 263	89.76 291	92.06 335	82.53 226	92.30 206	96.59 186	71.14 364	92.58 249	95.41 221	68.55 340	89.57 385	91.12 131	95.66 298	97.18 229

eth_miper_zixun_eth		90.72 213	90.61 221	91.05 254	92.04 336	76.84 318	86.91 340	96.67 181	85.21 245	94.41 187	93.92 275	79.53 280	98.26 206	89.76 173	97.02 263	98.06 153

SCA		87.43 296	87.21 290	88.10 324	92.01 337	71.98 359	89.43 295	88.11 344	82.26 288	88.71 326	92.83 302	78.65 287	97.59 266	79.61 318	93.30 350	94.75 321

dmvs_testset		78.23 362	78.99 358	75.94 378	91.99 338	55.34 401	88.86 309	78.70 394	82.69 281	81.64 386	79.46 393	75.93 314	85.74 393	48.78 399	82.85 392	86.76 386

test_fmvs2		90.62 218	90.40 227	91.29 246	91.93 339	85.46 186	92.70 183	96.48 194	74.44 345	94.91 173	97.59 74	75.52 316	90.57 377	93.44 68	96.56 280	97.84 182

cl____		90.65 216	90.56 223	90.91 262	91.85 340	76.98 315	86.75 345	95.36 243	85.53 241	94.06 196	94.89 239	77.36 302	97.98 231	90.27 157	98.98 114	97.76 191

DIV-MVS_self_test		90.65 216	90.56 223	90.91 262	91.85 340	76.99 314	86.75 345	95.36 243	85.52 243	94.06 196	94.89 239	77.37 301	97.99 230	90.28 156	98.97 119	97.76 191

our_test_3		87.55 293	87.59 283	87.44 332	91.76 342	70.48 365	83.83 375	90.55 330	79.79 305	92.06 269	92.17 318	78.63 289	95.63 333	84.77 265	94.73 323	96.22 268

ppachtmachnet_test		88.61 274	88.64 258	88.50 316	91.76 342	70.99 364	84.59 369	92.98 293	79.30 315	92.38 258	93.53 288	79.57 279	97.45 274	86.50 242	97.17 258	97.07 231

Syy-MVS		84.81 322	84.93 316	84.42 359	91.71 344	63.36 393	85.89 356	81.49 384	81.03 295	85.13 359	81.64 391	77.44 298	95.00 347	85.94 249	94.12 338	94.91 315

myMVS_eth3d		79.62 358	78.26 362	83.72 363	91.71 344	61.25 395	85.89 356	81.49 384	81.03 295	85.13 359	81.64 391	32.12 405	95.00 347	71.17 374	94.12 338	94.91 315

1314		86.46 311	86.33 308	86.87 338	91.65 346	74.54 338	91.94 219	94.10 274	74.28 346	84.78 364	87.33 373	83.03 249	95.00 347	78.72 325	91.16 372	91.06 374

WB-MVSnew		84.20 328	83.89 327	85.16 353	91.62 347	66.15 382	88.44 320	81.00 387	76.23 335	87.98 338	87.77 368	84.98 234	93.35 365	62.85 391	94.10 340	95.98 277

miper_ehance_all_eth		90.48 220	90.42 226	90.69 268	91.62 347	76.57 322	86.83 343	96.18 208	83.38 268	94.06 196	92.66 309	82.20 259	98.04 222	89.79 172	97.02 263	97.45 212

cascas		87.02 307	86.28 309	89.25 302	91.56 349	76.45 323	84.33 372	96.78 173	71.01 366	86.89 350	85.91 380	81.35 267	96.94 298	83.09 278	95.60 299	94.35 330

baseline2		83.38 333	81.54 342	88.90 306	91.38 350	72.84 354	88.78 312	81.22 386	78.97 317	79.82 390	87.56 369	61.73 375	97.80 248	74.30 354	90.05 377	96.05 275

miper_lstm_enhance		89.90 244	89.80 239	90.19 284	91.37 351	77.50 306	83.82 376	95.00 251	84.84 255	93.05 232	94.96 237	76.53 313	95.20 346	89.96 169	98.67 157	97.86 179

mvsany_test3		89.11 258	88.21 273	91.83 223	91.30 352	90.25 79	88.09 322	78.76 393	76.37 334	96.43 91	98.39 33	83.79 241	90.43 380	86.57 238	94.20 335	94.80 318

IB-MVS	77.21 19	83.11 334	81.05 345	89.29 300	91.15 353	75.85 329	85.66 359	86.00 360	79.70 307	82.02 383	86.61 375	48.26 396	98.39 191	77.84 330	92.22 364	93.63 347
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
MVS		84.98 321	84.30 322	87.01 335	91.03 354	77.69 305	91.94 219	94.16 273	59.36 394	84.23 368	87.50 371	85.66 226	96.80 304	71.79 366	93.05 356	86.54 387

CR-MVSNet		87.89 283	87.12 294	90.22 281	91.01 355	78.93 284	92.52 190	92.81 296	73.08 354	89.10 316	96.93 130	67.11 346	97.64 265	88.80 198	92.70 359	94.08 333

RPMNet		90.31 231	90.14 233	90.81 266	91.01 355	78.93 284	92.52 190	98.12 51	91.91 101	89.10 316	96.89 133	68.84 339	99.41 39	90.17 162	92.70 359	94.08 333

new_pmnet		81.22 348	81.01 347	81.86 369	90.92 357	70.15 367	84.03 373	80.25 391	70.83 367	85.97 354	89.78 350	67.93 345	84.65 395	67.44 383	91.90 368	90.78 375

PatchT		87.51 294	88.17 274	85.55 348	90.64 358	66.91 377	92.02 215	86.09 359	92.20 93	89.05 318	97.16 112	64.15 364	96.37 318	89.21 189	92.98 357	93.37 352

Patchmatch-test		86.10 313	86.01 310	86.38 345	90.63 359	74.22 344	89.57 290	86.69 354	85.73 234	89.81 308	92.83 302	65.24 360	91.04 376	77.82 332	95.78 296	93.88 341

PVSNet_0	70.34 21	74.58 363	72.96 366	79.47 375	90.63 359	66.24 381	73.26 389	83.40 379	63.67 391	78.02 392	78.35 395	72.53 325	89.59 384	56.68 395	60.05 399	82.57 393

PMMVS2		81.31 347	83.44 329	74.92 379	90.52 361	46.49 405	69.19 393	85.23 371	84.30 262	87.95 339	94.71 248	76.95 307	84.36 396	64.07 388	98.09 212	93.89 340

tpm		84.38 326	84.08 324	85.30 351	90.47 362	63.43 392	89.34 298	85.63 364	77.24 329	87.62 343	95.03 235	61.00 378	97.30 282	79.26 322	91.09 373	95.16 305

wuyk23d		87.83 285	90.79 217	78.96 376	90.46 363	88.63 110	92.72 181	90.67 329	91.65 119	98.68 11	97.64 71	96.06 15	77.53 398	59.84 393	99.41 56	70.73 396

Patchmtry		90.11 237	89.92 236	90.66 269	90.35 364	77.00 313	92.96 174	92.81 296	90.25 151	94.74 180	96.93 130	67.11 346	97.52 269	85.17 255	98.98 114	97.46 211

test_f		86.65 310	87.13 293	85.19 352	90.28 365	86.11 171	86.52 353	91.66 319	69.76 374	95.73 131	97.21 110	69.51 338	81.28 397	89.15 190	94.40 329	88.17 383

CHOSEN 280x420		80.04 357	77.97 364	86.23 346	90.13 366	74.53 339	72.87 391	89.59 333	66.38 384	76.29 394	85.32 382	56.96 384	95.36 341	69.49 379	94.72 324	88.79 381

MVSTER		89.32 254	88.75 257	91.03 255	90.10 367	76.62 321	90.85 248	94.67 264	82.27 287	95.24 159	95.79 199	61.09 377	98.49 183	90.49 146	98.26 195	97.97 168

tpm2		81.46 346	80.35 353	84.80 355	89.90 368	65.14 386	90.44 261	85.36 367	65.82 387	82.05 382	92.44 313	57.94 382	96.69 307	70.71 375	88.49 381	92.56 362

cl22		89.02 260	88.50 260	90.59 271	89.76 369	76.45 323	86.62 350	94.03 275	82.98 278	92.65 246	92.49 310	72.05 329	97.53 268	88.93 194	97.02 263	97.78 189

test0.0.03 1		82.48 339	81.47 343	85.48 349	89.70 370	73.57 348	84.73 366	81.64 383	83.07 276	88.13 336	86.61 375	62.86 371	89.10 388	66.24 386	90.29 376	93.77 343

test-LLR		83.58 332	83.17 331	84.79 356	89.68 371	66.86 378	83.08 377	84.52 373	83.07 276	82.85 376	84.78 384	62.86 371	93.49 363	82.85 279	94.86 319	94.03 336

test-mter		81.21 349	80.01 356	84.79 356	89.68 371	66.86 378	83.08 377	84.52 373	73.85 349	82.85 376	84.78 384	43.66 400	93.49 363	82.85 279	94.86 319	94.03 336

DSMNet-mixed		82.21 341	81.56 340	84.16 361	89.57 373	70.00 370	90.65 256	77.66 397	54.99 397	83.30 374	97.57 75	77.89 295	90.50 379	66.86 385	95.54 301	91.97 366

PatchmatchNet		85.22 318	84.64 318	86.98 336	89.51 374	69.83 371	90.52 259	87.34 351	78.87 319	87.22 348	92.74 306	66.91 348	96.53 309	81.77 292	86.88 384	94.58 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.88 328		89.42 375	61.52 394	88.74 314	87.41 349	73.99 348	84.96 363	94.01 272	65.25 359	95.53 334	78.02 328	93.16 352

CostFormer		83.09 335	82.21 338	85.73 347	89.27 376	67.01 376	90.35 266	86.47 356	70.42 371	83.52 373	93.23 295	61.18 376	96.85 302	77.21 337	88.26 382	93.34 353

ADS-MVSNet2		84.01 329	82.20 339	89.41 297	89.04 377	76.37 325	87.57 326	90.98 325	72.71 358	84.46 365	92.45 311	68.08 342	96.48 312	70.58 376	83.97 388	95.38 301

ADS-MVSNet		82.25 340	81.55 341	84.34 360	89.04 377	65.30 384	87.57 326	85.13 372	72.71 358	84.46 365	92.45 311	68.08 342	92.33 370	70.58 376	83.97 388	95.38 301

tpm cat1		80.61 354	79.46 357	84.07 362	88.78 379	65.06 388	89.26 301	88.23 340	62.27 392	81.90 384	89.66 353	62.70 373	95.29 344	71.72 367	80.60 395	91.86 369

CMPMVS	68.83 22	87.28 299	85.67 313	92.09 218	88.77 380	85.42 187	90.31 268	94.38 268	70.02 373	88.00 337	93.30 292	73.78 323	94.03 360	75.96 346	96.54 281	96.83 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
miper_enhance_ethall		88.42 276	87.87 279	90.07 285	88.67 381	75.52 332	85.10 363	95.59 230	75.68 336	92.49 251	89.45 355	78.96 283	97.88 239	87.86 218	97.02 263	96.81 245

test_fmvs1		87.59 292	87.27 288	88.54 314	88.32 382	81.26 243	90.43 264	95.72 223	70.55 370	91.70 273	94.63 250	68.13 341	89.42 386	90.59 144	95.34 308	94.94 314

test_vis1_rt		85.58 316	84.58 319	88.60 313	87.97 383	86.76 149	85.45 361	93.59 282	66.43 383	87.64 342	89.20 358	79.33 281	85.38 394	81.59 295	89.98 378	93.66 346

tpmrst		82.85 338	82.93 334	82.64 367	87.65 384	58.99 398	90.14 273	87.90 346	75.54 338	83.93 369	91.63 327	66.79 351	95.36 341	81.21 300	81.54 394	93.57 351

JIA-IIPM		85.08 320	83.04 332	91.19 252	87.56 385	86.14 170	89.40 297	84.44 375	88.98 174	82.20 380	97.95 54	56.82 385	96.15 322	76.55 342	83.45 390	91.30 372

TESTMET0.1,1		79.09 360	78.04 363	82.25 368	87.52 386	64.03 391	83.08 377	80.62 389	70.28 372	80.16 389	83.22 388	44.13 399	90.56 378	79.95 312	93.36 348	92.15 365

gg-mvs-nofinetune		82.10 344	81.02 346	85.34 350	87.46 387	71.04 362	94.74 111	67.56 401	96.44 23	79.43 391	98.99 6	45.24 397	96.15 322	67.18 384	92.17 365	88.85 380

pmmvs3		80.83 352	78.96 359	86.45 342	87.23 388	77.48 307	84.87 365	82.31 381	63.83 390	85.03 361	89.50 354	49.66 394	93.10 366	73.12 361	95.10 313	88.78 382

tpmvs		84.22 327	83.97 325	84.94 354	87.09 389	65.18 385	91.21 241	88.35 338	82.87 279	85.21 357	90.96 336	65.24 360	96.75 305	79.60 320	85.25 387	92.90 359

gm-plane-assit					87.08 390	59.33 397			71.22 363		83.58 387		97.20 286	73.95 355

MVE	59.87 23	73.86 364	72.65 367	77.47 377	87.00 391	74.35 341	61.37 395	60.93 403	67.27 381	69.69 398	86.49 377	81.24 271	72.33 399	56.45 396	83.45 390	85.74 388
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPNet_dtu		85.63 315	84.37 321	89.40 298	86.30 392	74.33 342	91.64 232	88.26 339	84.84 255	72.96 397	89.85 345	71.27 333	97.69 261	76.60 341	97.62 241	96.18 270
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		83.91 330	82.93 334	86.84 339	86.18 393	85.93 174	81.11 384	75.03 399	70.80 369	88.57 330	94.63 250	83.08 248	87.38 389	80.39 304	86.57 385	87.21 385

dp		79.28 359	78.62 360	81.24 372	85.97 394	56.45 399	86.91 340	85.26 370	72.97 356	81.45 387	89.17 360	56.01 387	95.45 339	73.19 360	76.68 396	91.82 370

EPMVS		81.17 350	80.37 352	83.58 364	85.58 395	65.08 387	90.31 268	71.34 400	77.31 328	85.80 355	91.30 330	59.38 380	92.70 369	79.99 311	82.34 393	92.96 358

E-PMN		80.72 353	80.86 348	80.29 374	85.11 396	68.77 373	72.96 390	81.97 382	87.76 202	83.25 375	83.01 389	62.22 374	89.17 387	77.15 338	94.31 333	82.93 391

GG-mvs-BLEND				83.24 366	85.06 397	71.03 363	94.99 106	65.55 402		74.09 396	75.51 396	44.57 398	94.46 353	59.57 394	87.54 383	84.24 389

EMVS		80.35 356	80.28 354	80.54 373	84.73 398	69.07 372	72.54 392	80.73 388	87.80 200	81.66 385	81.73 390	62.89 370	89.84 382	75.79 347	94.65 326	82.71 392

EPNet		89.80 247	88.25 269	94.45 130	83.91 399	86.18 169	93.87 146	87.07 353	91.16 131	80.64 388	94.72 247	78.83 284	98.89 120	85.17 255	98.89 126	98.28 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS		83.00 336	81.11 344	88.66 312	83.81 400	86.44 160	82.24 381	85.65 363	61.75 393	82.07 381	85.64 381	79.75 278	91.59 374	75.99 345	93.09 354	87.94 384

KD-MVS_2432*1600		82.17 342	80.75 349	86.42 343	82.04 401	70.09 368	81.75 382	90.80 327	82.56 282	90.37 296	89.30 356	42.90 401	96.11 324	74.47 352	92.55 361	93.06 355

miper_refine_blended		82.17 342	80.75 349	86.42 343	82.04 401	70.09 368	81.75 382	90.80 327	82.56 282	90.37 296	89.30 356	42.90 401	96.11 324	74.47 352	92.55 361	93.06 355

DeepMVS_CX				53.83 382	70.38 403	64.56 389		48.52 406	33.01 398	65.50 399	74.21 397	56.19 386	46.64 401	38.45 401	70.07 397	50.30 397

test_method		50.44 365	48.94 368	54.93 381	39.68 404	12.38 408	28.59 396	90.09 331	6.82 399	41.10 401	78.41 394	54.41 389	70.69 400	50.12 398	51.26 400	81.72 394

tmp_tt		37.97 366	44.33 369	18.88 383	11.80 405	21.54 407	63.51 394	45.66 407	4.23 400	51.34 400	50.48 398	59.08 381	22.11 402	44.50 400	68.35 398	13.00 398

test123		9.49 368	12.01 371	1.91 384	2.87 406	1.30 409	82.38 380	1.34 409	1.36 402	2.84 403	6.56 401	2.45 407	0.97 403	2.73 402	5.56 401	3.47 399

testmvs		9.02 369	11.42 372	1.81 385	2.77 407	1.13 410	79.44 387	1.90 408	1.18 403	2.65 404	6.80 400	1.95 408	0.87 404	2.62 403	3.45 402	3.44 400

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

eth-test2					0.00 408

eth-test					0.00 408

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		23.35 367	31.13 370	0.00 386	0.00 408	0.00 411	0.00 397	95.58 232	0.00 404	0.00 405	91.15 332	93.43 84	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		7.56 370	10.09 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	90.77 151	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		7.56 370	10.08 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	90.69 341	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				95.22 94		87.21 138	94.31 131	90.92 326	94.48 46	92.80 241	97.52 81	85.27 230	99.49 24	96.58 8	99.57 35	98.97 62

WAC-MVS						61.25 395								74.55 351

PC_three_1452									75.31 341	95.87 122	95.75 204	92.93 101	96.34 321	87.18 228	98.68 155	98.04 156

test_241102_TWO								98.10 54	91.95 98	97.54 40	97.25 104	95.37 30	99.35 60	93.29 75	99.25 83	98.49 124

test_0728_THIRD									93.26 71	97.40 52	97.35 97	94.69 59	99.34 63	93.88 48	99.42 52	98.89 75

GSMVS																94.75 321

sam_mvs1												66.64 352				94.75 321

sam_mvs												66.41 353

MTGPA								97.62 105

test_post1							90.21 270				5.85 403	65.36 358	96.00 327	79.61 318

test_post											6.07 402	65.74 357	95.84 331

patchmatchnet-post											91.71 325	66.22 355	97.59 266

MTMP							94.82 109	54.62 405

test9_res														88.16 209	98.40 179	97.83 183

agg_prior2														87.06 231	98.36 188	97.98 165

test_prior4						89.91 82	90.74 252

test_prior2							90.21 270		89.33 167	90.77 288	94.81 242	90.41 161		88.21 205	98.55 167

旧先验2							90.00 278		68.65 378	92.71 245			96.52 310	85.15 257

新几何2							90.02 277

无先验							89.94 279	95.75 222	70.81 368				98.59 174	81.17 301		94.81 317

原ACMM2							89.34 298

testdata2													98.03 223	80.24 308

segment_acmp												92.14 119

testdata1							88.96 307		88.44 187

plane_prior5								97.81 91					98.95 114	89.26 186	98.51 173	98.60 117

plane_prior4											95.59 209

plane_prior3						88.43 119			90.35 150	93.31 218

plane_prior2							94.56 120		91.74 115

plane_prior						88.12 122	93.01 172		88.98 174						98.06 214

n2								0.00 410

nn								0.00 410

door-mid								92.13 313

test11								96.65 182

door								91.26 322

HQP5-MVS						84.89 192

BP-MVS														86.55 240

HQP4-MVS										88.81 321			98.61 170			98.15 148

HQP3-MVS								97.31 132							97.73 234

HQP2-MVS												84.76 235

MDTV_nov1_ep13_2view						42.48 406	88.45 319		67.22 382	83.56 372		66.80 349		72.86 362		94.06 335

ACMMP++_ref															98.82 139

ACMMP++															99.25 83

Test By Simon												90.61 157

High-resolution multi-view benchmark