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.

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 35	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 8

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 42

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 52	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 12

test_241102_TWO								94.06 12	77.24 52	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 41

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 50	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 86
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 24

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 43	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 56	93.10 1	95.39 11	82.99 1	97.27 10

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 23	93.63 23	74.77 112	92.29 7	95.97 2	74.28 36	97.24 11	88.58 13	96.91 1	94.87 14
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
CANet		86.45 45	86.10 54	87.51 40	90.09 114	70.94 74	89.70 88	92.59 72	81.78 4	81.32 113	91.43 104	70.34 69	97.23 12	84.26 47	93.36 71	94.37 35

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 22	80.19 14	88.10 27	94.80 16	73.76 41	97.11 13	87.51 18	95.82 24	94.90 11
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	79.81 2	87.08 37	86.88 39	87.69 36	91.16 92	72.32 47	90.31 72	93.94 17	77.12 58	82.82 97	94.23 39	72.13 54	97.09 14	84.83 36	95.37 35	93.65 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 31

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 32	93.49 29	74.75 113	88.33 25	94.43 32	73.27 44	97.02 16	84.18 51	94.84 48	93.82 62

NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 52	92.83 61	81.50 6	85.79 46	93.47 59	73.02 47	97.00 17	84.90 33	94.94 44	94.10 45

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 66	85.24 52	94.32 35	71.76 56	96.93 18	85.53 29	95.79 25	94.32 38

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 55	96.48 8	94.88 12

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 64	93.00 47	80.90 9	88.06 28	94.06 46	76.43 17	96.84 20	88.48 14	95.99 19	94.34 37

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 25	93.43 32	76.89 64	84.68 64	93.99 50	70.67 67	96.82 21	84.18 51	95.01 42	93.90 57

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 60	80.26 13	87.78 30	94.27 36	75.89 20	96.81 22	87.45 19	96.44 9	93.05 97

MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 64	94.67 24
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
DeepC-MVS_fast	79.65 3	86.91 38	86.62 42	87.76 29	93.52 52	72.37 44	91.26 49	93.04 43	76.62 73	84.22 77	93.36 61	71.44 60	96.76 24	80.82 86	95.33 38	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 62	84.47 76	88.51 6	91.08 93	73.49 17	93.18 11	93.78 21	80.79 10	76.66 189	93.37 60	60.40 190	96.75 25	77.20 116	93.73 68	95.29 3

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 181	87.75 31	94.07 45	74.01 40	96.70 26	84.66 39	94.84 48

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 71	84.45 72	94.52 23	69.09 82	96.70 26	84.37 44	94.83 50	94.03 49

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 57	93.59 25	76.27 82	88.14 26	95.09 15	71.06 62	96.67 28	87.67 16	96.37 14	94.09 46

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 68	84.66 67	94.52 23	68.81 86	96.65 29	84.53 41	94.90 45	94.00 52

PGM-MVS		86.68 41	86.27 48	87.90 21	94.22 35	73.38 19	90.22 75	93.04 43	75.53 94	83.86 83	94.42 33	67.87 92	96.64 30	82.70 72	94.57 55	93.66 68

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 62	94.05 15	70.80 183	87.59 33	93.51 56	77.57 14	96.63 31	83.31 58	95.77 26	94.72 23

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 68	84.91 58	94.44 30	70.78 64	96.61 32	84.53 41	94.89 46	93.66 68

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 28	93.24 37	75.23 100	84.91 58	94.44 30	70.78 64	96.61 32	83.75 56	94.89 46	93.66 68

XVS		87.18 34	86.91 38	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 24	83.67 87	94.17 40	67.45 95	96.60 34	83.06 63	94.50 56	94.07 47

X-MVStestdata		80.37 145	77.83 181	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 24	83.67 87	12.47 373	67.45 95	96.60 34	83.06 63	94.50 56	94.07 47

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 110	89.57 90	93.39 34	77.53 47	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 27

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 40	92.83 61	73.01 151	88.58 23	94.52 23	73.36 42	96.49 37	84.26 47	95.01 42	92.70 108
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		86.43 46	86.17 52	87.24 46	90.88 98	70.96 72	92.27 31	94.07 11	72.45 154	85.22 53	91.90 90	69.47 78	96.42 38	83.28 61	95.94 20	94.35 36

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 82	93.82 19	73.07 149	84.86 63	92.89 72	76.22 18	96.33 39	84.89 35	95.13 41	94.40 34

ACMMP		85.89 56	85.39 62	87.38 44	93.59 51	72.63 34	92.74 20	93.18 41	76.78 68	80.73 122	93.82 53	64.33 125	96.29 40	82.67 73	90.69 102	93.23 89
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
MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 22	92.65 70	77.57 43	83.84 84	94.40 34	72.24 52	96.28 41	85.65 28	95.30 40	93.62 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		86.67 42	86.32 47	87.72 32	94.41 24	73.55 13	92.74 20	92.22 87	76.87 65	82.81 98	94.25 38	66.44 104	96.24 42	82.88 67	94.28 62	93.38 83

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 74	92.02 94	79.45 21	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 185	92.02 94	79.45 21	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 67	89.69 169	74.31 122	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 19

SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 23	94.74 2	75.71 90	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 19

test12				86.80 55	92.63 73	70.70 81		91.79 109		82.71 99		71.67 57	96.16 47		94.50 56	93.54 80

CDPH-MVS		85.76 58	85.29 66	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 235	84.61 68	93.48 57	72.32 51	96.15 48	79.00 96	95.43 34	94.28 40

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 88	72.45 41	90.02 78	94.37 4	71.76 165	87.28 34	94.27 36	75.18 26	96.08 49	85.16 30	95.77 26	93.80 65

DP-MVS Recon		83.11 92	82.09 99	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 190	79.17 137	91.03 116	64.12 127	96.03 50	68.39 197	90.14 110	91.50 145

DPM-MVS		84.93 72	84.29 77	86.84 53	90.20 112	73.04 24	87.12 167	93.04 43	69.80 203	82.85 96	91.22 108	73.06 46	96.02 51	76.72 123	94.63 53	91.46 148

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 33	93.33 35	76.07 85	83.81 85	93.95 51	69.77 76	96.01 52	85.15 31	94.66 52	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 84	93.50 28	75.17 103	86.34 41	95.29 12	70.86 63	96.00 53	88.78 12	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 48	92.35 80	74.62 116	88.90 21	93.85 52	75.75 21	96.00 53	87.80 15	94.63 53	95.04 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PC_three_1452									68.21 236	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 8

CP-MVS		87.11 35	86.92 37	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 73	83.68 86	94.46 27	67.93 90	95.95 56	84.20 50	94.39 59	93.23 89

abl_6		85.23 67	84.95 71	86.07 71	92.23 79	70.48 86	90.80 59	92.08 92	73.51 144	85.26 51	94.16 41	62.75 146	95.92 57	82.46 75	91.30 97	91.81 138

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 47	94.70 3	74.47 119	88.86 22	94.61 21	75.23 25	95.84 58	86.62 26	95.92 21	94.78 21

9.14			88.26 15		92.84 68		91.52 46	94.75 1	73.93 132	88.57 24	94.67 19	75.57 24	95.79 59	86.77 23	95.76 28

SR-MVS		86.73 39	86.67 41	86.91 52	94.11 40	72.11 51	92.37 27	92.56 73	74.50 117	86.84 37	94.65 20	67.31 97	95.77 60	84.80 37	92.85 75	92.84 106

AdaColmap		80.58 141	79.42 143	84.06 126	93.09 61	68.91 115	89.36 92	88.97 195	69.27 213	75.70 211	89.69 142	57.20 211	95.77 60	63.06 238	88.41 131	87.50 270

DELS-MVS		85.41 65	85.30 65	85.77 75	88.49 174	67.93 139	85.52 218	93.44 31	78.70 31	83.63 89	89.03 163	74.57 29	95.71 62	80.26 92	94.04 65	93.66 68
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
Regformer-2		86.63 43	86.53 43	86.95 51	89.33 138	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 132	74.45 31	95.61 63	84.38 43	90.63 103	94.01 51

APD-MVS_3200maxsize		85.97 54	85.88 56	86.22 67	92.69 72	69.53 103	91.93 37	92.99 49	73.54 141	85.94 42	94.51 26	65.80 114	95.61 63	83.04 65	92.51 80	93.53 81

test1172		86.20 52	86.22 49	86.12 70	93.95 42	69.89 96	91.79 42	92.28 82	75.07 104	86.40 40	94.58 22	65.00 122	95.56 65	84.34 45	92.60 78	92.90 104

SR-MVS-dyc-post		85.77 57	85.61 59	86.23 66	93.06 62	70.63 82	91.88 38	92.27 83	73.53 142	85.69 47	94.45 28	65.00 122	95.56 65	82.75 68	91.87 88	92.50 115

EPNet		83.72 79	82.92 88	86.14 69	84.22 257	69.48 104	91.05 55	85.27 259	81.30 7	76.83 184	91.65 95	66.09 109	95.56 65	76.00 128	93.85 66	93.38 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HPM-MVS_fast		85.35 66	84.95 71	86.57 61	93.69 48	70.58 85	92.15 35	91.62 114	73.89 133	82.67 100	94.09 44	62.60 147	95.54 68	80.93 84	92.93 73	93.57 78

h-mvs33		83.15 89	82.19 97	86.02 73	90.56 105	70.85 78	88.15 140	89.16 185	76.02 86	84.67 65	91.39 105	61.54 165	95.50 69	82.71 70	75.48 281	91.72 140

test_prior3		86.73 39	86.86 40	86.33 63	92.61 74	69.59 101	88.85 109	92.97 54	75.41 96	84.91 58	93.54 54	74.28 36	95.48 70	83.31 58	95.86 22	93.91 55

test_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 70			93.91 55

原ACMM1				84.35 115	93.01 64	68.79 116		92.44 75	63.96 285	81.09 118	91.57 99	66.06 110	95.45 72	67.19 208	94.82 51	88.81 242

QAPM		80.88 126	79.50 142	85.03 90	88.01 190	68.97 114	91.59 43	92.00 97	66.63 251	75.15 228	92.16 85	57.70 203	95.45 72	63.52 232	88.76 125	90.66 171

RPMNet		73.51 260	70.49 276	82.58 184	81.32 316	65.19 191	75.92 326	92.27 83	57.60 335	72.73 253	76.45 345	52.30 243	95.43 74	48.14 335	77.71 248	87.11 282

DROMVSNet		86.01 53	86.38 45	84.91 97	89.31 143	66.27 169	92.32 29	93.63 23	79.37 23	84.17 79	91.88 91	69.04 85	95.43 74	83.93 54	93.77 67	93.01 100

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 234	85.00 56	93.10 65	74.36 35	95.41 76

train_agg		86.43 46	86.20 50	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 230	85.00 56	93.10 65	74.43 32	95.41 76	84.97 32	95.71 30	93.02 99

test_part1		82.78 96	82.08 100	84.89 98	90.66 103	66.97 158	90.96 56	92.93 57	77.19 55	80.53 124	90.04 134	63.44 132	95.39 78	76.04 127	76.90 258	92.31 122

ETV-MVS		84.90 74	84.67 74	85.59 78	89.39 136	68.66 126	88.74 115	92.64 71	79.97 17	84.10 80	85.71 250	69.32 80	95.38 79	80.82 86	91.37 95	92.72 107

HQP_MVS		83.64 81	83.14 83	85.14 87	90.08 115	68.71 122	91.25 50	92.44 75	79.12 26	78.92 141	91.00 117	60.42 188	95.38 79	78.71 99	86.32 157	91.33 150

plane_prior5								92.44 75					95.38 79	78.71 99	86.32 157	91.33 150

TSAR-MVS + GP.		85.71 59	85.33 63	86.84 53	91.34 90	72.50 37	89.07 102	87.28 234	76.41 75	85.80 45	90.22 130	74.15 39	95.37 82	81.82 78	91.88 87	92.65 112

Regformer-4		85.68 60	85.45 61	86.35 62	88.95 156	69.67 100	88.29 133	91.29 125	81.73 5	85.36 50	90.01 135	72.62 49	95.35 83	83.28 61	87.57 136	94.03 49

EIA-MVS		83.31 88	82.80 90	84.82 100	89.59 127	65.59 183	88.21 136	92.68 66	74.66 115	78.96 139	86.42 238	69.06 83	95.26 84	75.54 133	90.09 111	93.62 76

UA-Net		85.08 71	84.96 70	85.45 80	92.07 81	68.07 137	89.78 85	90.86 138	82.48 2	84.60 69	93.20 63	69.35 79	95.22 85	71.39 166	90.88 101	93.07 96

CSCG		86.41 48	86.19 51	87.07 50	92.91 65	72.48 38	90.81 58	93.56 26	73.95 130	83.16 92	91.07 113	75.94 19	95.19 86	79.94 94	94.38 60	93.55 79

test_8					93.13 58	72.57 36	88.68 118	91.84 107	68.69 230	84.87 62	93.10 65	74.43 32	95.16 87

EPP-MVSNet		83.40 86	83.02 86	84.57 106	90.13 113	64.47 205	92.32 29	90.73 139	74.45 121	79.35 136	91.10 111	69.05 84	95.12 88	72.78 157	87.22 144	94.13 44

HQP4-MVS										77.24 176			95.11 89			91.03 159

HQP-MVS		82.61 99	82.02 102	84.37 113	89.33 138	66.98 156	89.17 96	92.19 89	76.41 75	77.23 177	90.23 129	60.17 191	95.11 89	77.47 113	85.99 163	91.03 159

MG-MVS		83.41 85	83.45 80	83.28 151	92.74 71	62.28 246	88.17 138	89.50 173	75.22 101	81.49 112	92.74 80	66.75 100	95.11 89	72.85 156	91.58 92	92.45 118

API-MVS		81.99 107	81.23 111	84.26 119	90.94 96	70.18 93	91.10 54	89.32 177	71.51 173	78.66 146	88.28 182	65.26 117	95.10 92	64.74 228	91.23 98	87.51 269

PCF-MVS	73.52 7	80.38 144	78.84 158	85.01 91	87.71 199	68.99 113	83.65 255	91.46 122	63.00 291	77.77 166	90.28 127	66.10 108	95.09 93	61.40 254	88.22 133	90.94 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS-test		86.26 50	86.48 44	85.60 77	90.84 99	66.60 163	91.16 53	93.56 26	79.82 18	84.57 70	89.89 137	70.67 67	95.04 94	84.30 46	93.48 69	95.16 4

114514_t		80.68 137	79.51 141	84.20 120	94.09 41	67.27 152	89.64 89	91.11 132	58.75 328	74.08 242	90.72 121	58.10 199	95.04 94	69.70 182	89.42 119	90.30 185

Regformer-1		86.41 48	86.33 46	86.64 58	89.33 138	70.93 75	88.43 123	91.39 123	82.14 3	86.65 38	90.09 132	74.39 34	95.01 96	83.97 53	90.63 103	93.97 53

CS-MVS		86.61 44	86.96 36	85.56 79	90.78 102	66.54 165	92.84 17	93.30 36	79.67 20	84.55 71	92.25 84	71.46 59	95.00 97	84.25 49	93.48 69	95.15 5

agg_prior1		86.22 51	86.09 55	86.62 59	92.85 66	71.94 54	88.59 120	91.78 110	68.96 225	84.41 73	93.18 64	74.94 27	94.93 98	84.75 38	95.33 38	93.01 100

agg_prior					92.85 66	71.94 54		91.78 110		84.41 73			94.93 98

LPG-MVS_test		82.08 104	81.27 110	84.50 108	89.23 147	68.76 118	90.22 75	91.94 101	75.37 98	76.64 190	91.51 100	54.29 228	94.91 100	78.44 103	83.78 181	89.83 210

LGP-MVS_train				84.50 108	89.23 147	68.76 118		91.94 101	75.37 98	76.64 190	91.51 100	54.29 228	94.91 100	78.44 103	83.78 181	89.83 210

PAPM_NR		83.02 93	82.41 93	84.82 100	92.47 77	66.37 167	87.93 146	91.80 108	73.82 134	77.32 174	90.66 122	67.90 91	94.90 102	70.37 174	89.48 118	93.19 93

tttt0517		79.40 166	77.91 178	83.90 138	88.10 186	63.84 216	88.37 130	84.05 275	71.45 174	76.78 186	89.12 160	49.93 276	94.89 103	70.18 176	83.18 192	92.96 103

PAPR		81.66 115	80.89 118	83.99 134	90.27 110	64.00 213	86.76 181	91.77 112	68.84 228	77.13 182	89.50 148	67.63 93	94.88 104	67.55 202	88.52 129	93.09 95

PVSNet_Blended_VisFu		82.62 98	81.83 106	84.96 93	90.80 101	69.76 98	88.74 115	91.70 113	69.39 210	78.96 139	88.46 177	65.47 116	94.87 105	74.42 138	88.57 127	90.24 187

EI-MVSNet-Vis-set		84.19 75	83.81 78	85.31 82	88.18 183	67.85 140	87.66 153	89.73 168	80.05 16	82.95 93	89.59 147	70.74 66	94.82 106	80.66 89	84.72 172	93.28 88

DP-MVS		76.78 225	74.57 237	83.42 146	93.29 54	69.46 107	88.55 122	83.70 279	63.98 284	70.20 277	88.89 165	54.01 232	94.80 107	46.66 340	81.88 208	86.01 302

thisisatest0530		79.40 166	77.76 185	84.31 117	87.69 201	65.10 194	87.36 160	84.26 273	70.04 197	77.42 171	88.26 184	49.94 274	94.79 108	70.20 175	84.70 173	93.03 98

EI-MVSNet-UG-set		83.81 77	83.38 81	85.09 89	87.87 192	67.53 146	87.44 159	89.66 170	79.74 19	82.23 102	89.41 156	70.24 71	94.74 109	79.95 93	83.92 180	92.99 102

3Dnovator	76.31 5	83.38 87	82.31 96	86.59 60	87.94 191	72.94 29	90.64 61	92.14 91	77.21 54	75.47 214	92.83 74	58.56 197	94.72 110	73.24 153	92.71 77	92.13 130

IB-MVS	68.01 15	75.85 239	73.36 251	83.31 150	84.76 249	66.03 171	83.38 261	85.06 261	70.21 196	69.40 290	81.05 310	45.76 307	94.66 111	65.10 225	75.49 280	89.25 224
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
ACMP	74.13 6	81.51 119	80.57 121	84.36 114	89.42 134	68.69 125	89.97 80	91.50 121	74.46 120	75.04 232	90.41 126	53.82 233	94.54 112	77.56 112	82.91 195	89.86 209
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D		76.95 223	74.82 235	83.37 149	90.45 107	67.36 151	89.15 100	86.94 240	61.87 304	69.52 289	90.61 123	51.71 256	94.53 113	46.38 343	86.71 152	88.21 255

MAR-MVS		81.84 109	80.70 119	85.27 84	91.32 91	71.53 60	89.82 82	90.92 135	69.77 204	78.50 149	86.21 242	62.36 153	94.52 114	65.36 222	92.05 86	89.77 213
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
OPM-MVS		83.50 83	82.95 87	85.14 87	88.79 164	70.95 73	89.13 101	91.52 117	77.55 46	80.96 120	91.75 93	60.71 182	94.50 115	79.67 95	86.51 155	89.97 205
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Effi-MVS+		83.62 82	83.08 84	85.24 85	88.38 179	67.45 147	88.89 107	89.15 186	75.50 95	82.27 101	88.28 182	69.61 77	94.45 116	77.81 110	87.84 134	93.84 61

CLD-MVS		82.31 101	81.65 107	84.29 118	88.47 175	67.73 143	85.81 209	92.35 80	75.78 89	78.33 154	86.58 233	64.01 128	94.35 117	76.05 126	87.48 141	90.79 166
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PS-MVSNAJ		81.69 113	81.02 115	83.70 140	89.51 131	68.21 135	84.28 245	90.09 158	70.79 184	81.26 117	85.62 254	63.15 140	94.29 118	75.62 131	88.87 123	88.59 248

IS-MVSNet		83.15 89	82.81 89	84.18 121	89.94 122	63.30 230	91.59 43	88.46 211	79.04 28	79.49 134	92.16 85	65.10 119	94.28 119	67.71 200	91.86 90	94.95 8

thisisatest0515		77.33 216	75.38 229	83.18 157	85.27 241	63.80 217	82.11 275	83.27 288	65.06 268	75.91 206	83.84 280	49.54 278	94.27 120	67.24 207	86.19 159	91.48 147

PS-MVSNAJss		82.07 105	81.31 109	84.34 116	86.51 225	67.27 152	89.27 94	91.51 118	71.75 166	79.37 135	90.22 130	63.15 140	94.27 120	77.69 111	82.36 203	91.49 146

PVSNet_BlendedMVS		80.60 139	80.02 130	82.36 188	88.85 158	65.40 186	86.16 198	92.00 97	69.34 212	78.11 159	86.09 245	66.02 111	94.27 120	71.52 163	82.06 205	87.39 271

PVSNet_Blended		80.98 125	80.34 126	82.90 171	88.85 158	65.40 186	84.43 241	92.00 97	67.62 239	78.11 159	85.05 267	66.02 111	94.27 120	71.52 163	89.50 117	89.01 232

mvs-test1		80.88 126	79.40 144	85.29 83	85.13 245	69.75 99	89.28 93	88.10 215	74.99 106	76.44 195	86.72 222	57.27 209	94.26 124	73.53 146	83.18 192	91.87 135

Vis-MVSNet		83.46 84	82.80 90	85.43 81	90.25 111	68.74 120	90.30 73	90.13 157	76.33 81	80.87 121	92.89 72	61.00 179	94.20 125	72.45 160	90.97 99	93.35 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
xiu_mvs_v2_base		81.69 113	81.05 114	83.60 141	89.15 150	68.03 138	84.46 239	90.02 159	70.67 187	81.30 116	86.53 236	63.17 139	94.19 126	75.60 132	88.54 128	88.57 249

Regformer-3		85.23 67	85.07 68	85.70 76	88.95 156	69.01 112	88.29 133	89.91 163	80.95 8	85.01 54	90.01 135	72.45 50	94.19 126	82.50 74	87.57 136	93.90 57

MVS_111021_HR		85.14 69	84.75 73	86.32 65	91.65 87	72.70 31	85.98 201	90.33 151	76.11 84	82.08 103	91.61 98	71.36 61	94.17 128	81.02 83	92.58 79	92.08 131

无先验							87.48 157	88.98 193	60.00 316				94.12 129	67.28 205		88.97 235

1121		80.84 128	79.77 135	84.05 128	93.11 60	70.78 79	84.66 231	85.42 258	57.37 337	81.76 111	92.02 87	63.41 133	94.12 129	67.28 205	92.93 73	87.26 276

MVS		78.19 196	76.99 202	81.78 197	85.66 234	66.99 155	84.66 231	90.47 145	55.08 347	72.02 262	85.27 260	63.83 130	94.11 131	66.10 216	89.80 115	84.24 321

v10		79.74 156	78.67 159	82.97 169	84.06 260	64.95 195	87.88 149	90.62 141	73.11 148	75.11 229	86.56 234	61.46 168	94.05 132	73.68 144	75.55 279	89.90 207

baseline		84.93 72	84.98 69	84.80 102	87.30 212	65.39 188	87.30 163	92.88 58	77.62 41	84.04 82	92.26 83	71.81 55	93.96 133	81.31 81	90.30 107	95.03 7

OMC-MVS		82.69 97	81.97 104	84.85 99	88.75 166	67.42 148	87.98 142	90.87 137	74.92 108	79.72 132	91.65 95	62.19 157	93.96 133	75.26 135	86.42 156	93.16 94

OpenMVS	72.83 10	79.77 155	78.33 170	84.09 124	85.17 242	69.91 94	90.57 63	90.97 134	66.70 247	72.17 260	91.91 89	54.70 225	93.96 133	61.81 251	90.95 100	88.41 253

v1192		79.59 159	78.43 166	83.07 163	83.55 269	64.52 201	86.93 173	90.58 142	70.83 182	77.78 165	85.90 246	59.15 194	93.94 136	73.96 143	77.19 255	90.76 167

v1144		80.03 151	79.03 154	83.01 166	83.78 265	64.51 202	87.11 168	90.57 143	71.96 164	78.08 161	86.20 243	61.41 169	93.94 136	74.93 136	77.23 253	90.60 174

UGNet		80.83 130	79.59 140	84.54 107	88.04 188	68.09 136	89.42 91	88.16 213	76.95 62	76.22 199	89.46 152	49.30 282	93.94 136	68.48 195	90.31 106	91.60 141
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
casdiffmvs		85.11 70	85.14 67	85.01 91	87.20 214	65.77 181	87.75 151	92.83 61	77.84 39	84.36 76	92.38 82	72.15 53	93.93 139	81.27 82	90.48 105	95.33 2

canonicalmvs		85.91 55	85.87 57	86.04 72	89.84 124	69.44 108	90.45 70	93.00 47	76.70 72	88.01 29	91.23 107	73.28 43	93.91 140	81.50 80	88.80 124	94.77 22

VDD-MVS		83.01 94	82.36 95	84.96 93	91.02 95	66.40 166	88.91 106	88.11 214	77.57 43	84.39 75	93.29 62	52.19 245	93.91 140	77.05 118	88.70 126	94.57 29

v8		79.97 153	79.02 155	82.80 176	84.09 259	64.50 204	87.96 143	90.29 154	74.13 129	75.24 226	86.81 219	62.88 145	93.89 142	74.39 139	75.40 285	90.00 201

v2v482		80.23 147	79.29 148	83.05 164	83.62 267	64.14 211	87.04 169	89.97 160	73.61 138	78.18 158	87.22 210	61.10 177	93.82 143	76.11 125	76.78 263	91.18 154

v7n		78.97 178	77.58 191	83.14 159	83.45 271	65.51 184	88.32 131	91.21 128	73.69 136	72.41 257	86.32 241	57.93 200	93.81 144	69.18 187	75.65 277	90.11 193

alignmvs		85.48 62	85.32 64	85.96 74	89.51 131	69.47 105	89.74 86	92.47 74	76.17 83	87.73 32	91.46 103	70.32 70	93.78 145	81.51 79	88.95 121	94.63 26

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 41	93.19 40	77.87 38	90.32 17	94.00 48	74.83 28	93.78 145	87.63 17	94.27 63	93.65 73
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
v144192		79.47 162	78.37 168	82.78 179	83.35 272	63.96 214	86.96 171	90.36 150	69.99 198	77.50 169	85.67 252	60.66 184	93.77 147	74.27 140	76.58 264	90.62 172

v1240		78.99 177	77.78 183	82.64 182	83.21 276	63.54 223	86.62 184	90.30 153	69.74 207	77.33 173	85.68 251	57.04 212	93.76 148	73.13 154	76.92 257	90.62 172

v1921920		79.22 170	78.03 175	82.80 176	83.30 274	63.94 215	86.80 177	90.33 151	69.91 201	77.48 170	85.53 255	58.44 198	93.75 149	73.60 145	76.85 261	90.71 170

cascas		76.72 226	74.64 236	82.99 167	85.78 233	65.88 177	82.33 273	89.21 183	60.85 310	72.74 252	81.02 311	47.28 295	93.75 149	67.48 203	85.02 168	89.34 222

Anonymous20240529		80.19 149	78.89 157	84.10 123	90.60 104	64.75 199	88.95 105	90.90 136	65.97 259	80.59 123	91.17 110	49.97 273	93.73 151	69.16 188	82.70 200	93.81 63

RRT_test8_iter05		78.38 190	77.40 193	81.34 209	86.00 230	58.86 280	86.55 187	91.26 126	72.13 163	75.91 206	87.42 204	44.97 311	93.73 151	77.02 119	75.30 288	91.45 149

PAPM		77.68 210	76.40 217	81.51 203	87.29 213	61.85 251	83.78 253	89.59 171	64.74 272	71.23 268	88.70 168	62.59 148	93.66 153	52.66 310	87.03 147	89.01 232

test_yl		81.17 122	80.47 124	83.24 154	89.13 151	63.62 219	86.21 196	89.95 161	72.43 157	81.78 109	89.61 145	57.50 206	93.58 154	70.75 169	86.90 148	92.52 113

DCV-MVSNet		81.17 122	80.47 124	83.24 154	89.13 151	63.62 219	86.21 196	89.95 161	72.43 157	81.78 109	89.61 145	57.50 206	93.58 154	70.75 169	86.90 148	92.52 113

Fast-Effi-MVS+		80.81 131	79.92 132	83.47 144	88.85 158	64.51 202	85.53 216	89.39 175	70.79 184	78.49 150	85.06 266	67.54 94	93.58 154	67.03 211	86.58 153	92.32 121

PLC	70.83 11	78.05 200	76.37 218	83.08 162	91.88 85	67.80 141	88.19 137	89.46 174	64.33 278	69.87 286	88.38 179	53.66 234	93.58 154	58.86 275	82.73 198	87.86 261
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		79.47 162	78.60 161	82.05 192	89.19 149	65.91 176	86.07 200	88.52 210	72.18 160	75.42 218	87.69 196	61.15 176	93.54 158	60.38 261	86.83 150	86.70 290

ACMM	73.20 8	80.78 136	79.84 134	83.58 142	89.31 143	68.37 130	89.99 79	91.60 115	70.28 194	77.25 175	89.66 143	53.37 236	93.53 159	74.24 141	82.85 196	88.85 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		81.52 117	80.67 120	84.05 128	90.44 108	64.13 212	89.73 87	85.91 254	71.11 178	83.18 91	93.48 57	50.54 268	93.49 160	73.40 150	88.25 132	94.54 30

hse-mvs2		81.72 111	80.94 117	84.07 125	88.72 167	67.68 144	85.87 205	87.26 235	76.02 86	84.67 65	88.22 185	61.54 165	93.48 161	82.71 70	73.44 308	91.06 157

AUN-MVS		79.21 171	77.60 190	84.05 128	88.71 168	67.61 145	85.84 207	87.26 235	69.08 220	77.23 177	88.14 190	53.20 238	93.47 162	75.50 134	73.45 307	91.06 157

MVSFormer		82.85 95	82.05 101	85.24 85	87.35 207	70.21 88	90.50 65	90.38 147	68.55 232	81.32 113	89.47 150	61.68 162	93.46 163	78.98 97	90.26 108	92.05 132

test_djsdf		80.30 146	79.32 147	83.27 152	83.98 262	65.37 189	90.50 65	90.38 147	68.55 232	76.19 200	88.70 168	56.44 215	93.46 163	78.98 97	80.14 228	90.97 162

LFMVS		81.82 110	81.23 111	83.57 143	91.89 84	63.43 228	89.84 81	81.85 305	77.04 61	83.21 90	93.10 65	52.26 244	93.43 165	71.98 161	89.95 114	93.85 59

Effi-MVS+-dtu		80.03 151	78.57 162	84.42 111	85.13 245	68.74 120	88.77 112	88.10 215	74.99 106	74.97 233	83.49 286	57.27 209	93.36 166	73.53 146	80.88 216	91.18 154

BH-RMVSNet		79.61 157	78.44 165	83.14 159	89.38 137	65.93 175	84.95 226	87.15 237	73.56 140	78.19 157	89.79 140	56.67 214	93.36 166	59.53 268	86.74 151	90.13 191

HyFIR lowres test		77.53 212	75.40 228	83.94 137	89.59 127	66.62 161	80.36 292	88.64 208	56.29 343	76.45 192	85.17 263	57.64 204	93.28 168	61.34 256	83.10 194	91.91 134

UniMVSNet (Re)		81.60 116	81.11 113	83.09 161	88.38 179	64.41 207	87.60 154	93.02 46	78.42 34	78.56 148	88.16 186	69.78 75	93.26 169	69.58 184	76.49 265	91.60 141

MVS_Test		83.15 89	83.06 85	83.41 148	86.86 218	63.21 232	86.11 199	92.00 97	74.31 122	82.87 95	89.44 155	70.03 72	93.21 170	77.39 115	88.50 130	93.81 63

TAPA-MVS	73.13 9	79.15 172	77.94 177	82.79 178	89.59 127	62.99 239	88.16 139	91.51 118	65.77 260	77.14 181	91.09 112	60.91 180	93.21 170	50.26 323	87.05 146	92.17 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		81.71 112	81.01 116	83.80 139	89.51 131	64.45 206	88.97 104	88.73 206	71.27 176	78.63 147	89.76 141	66.32 106	93.20 172	69.89 180	86.02 162	93.74 66

LTVRE_ROB	69.57 13	76.25 234	74.54 239	81.41 205	88.60 171	64.38 208	79.24 303	89.12 189	70.76 186	69.79 288	87.86 193	49.09 284	93.20 172	56.21 298	80.16 226	86.65 291
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
ACMH+	68.96 14	76.01 237	74.01 244	82.03 193	88.60 171	65.31 190	88.86 108	87.55 228	70.25 195	67.75 300	87.47 203	41.27 331	93.19 174	58.37 280	75.94 274	87.60 266

V42		79.38 168	78.24 172	82.83 173	81.10 319	65.50 185	85.55 214	89.82 164	71.57 172	78.21 156	86.12 244	60.66 184	93.18 175	75.64 130	75.46 283	89.81 212

mvs_tets		79.13 173	77.77 184	83.22 156	84.70 250	66.37 167	89.17 96	90.19 155	69.38 211	75.40 219	89.46 152	44.17 316	93.15 176	76.78 122	80.70 220	90.14 190

TR-MVS		77.44 213	76.18 219	81.20 213	88.24 182	63.24 231	84.61 235	86.40 247	67.55 240	77.81 164	86.48 237	54.10 230	93.15 176	57.75 286	82.72 199	87.20 277

jajsoiax		79.29 169	77.96 176	83.27 152	84.68 251	66.57 164	89.25 95	90.16 156	69.20 217	75.46 216	89.49 149	45.75 308	93.13 178	76.84 121	80.80 218	90.11 193

BH-w/o		78.21 194	77.33 196	80.84 222	88.81 162	65.13 193	84.87 227	87.85 224	69.75 205	74.52 238	84.74 270	61.34 171	93.11 179	58.24 282	85.84 165	84.27 320

nrg030		83.88 76	83.53 79	84.96 93	86.77 222	69.28 109	90.46 69	92.67 67	74.79 111	82.95 93	91.33 106	72.70 48	93.09 180	80.79 88	79.28 238	92.50 115

CANet_DTU		80.61 138	79.87 133	82.83 173	85.60 236	63.17 235	87.36 160	88.65 207	76.37 79	75.88 208	88.44 178	53.51 235	93.07 181	73.30 151	89.74 116	92.25 125

UniMVSNet_NR-MVSNet		81.88 108	81.54 108	82.92 170	88.46 176	63.46 226	87.13 166	92.37 79	80.19 14	78.38 152	89.14 158	71.66 58	93.05 182	70.05 177	76.46 266	92.25 125

DU-MVS		81.12 124	80.52 123	82.90 171	87.80 195	63.46 226	87.02 170	91.87 105	79.01 29	78.38 152	89.07 161	65.02 120	93.05 182	70.05 177	76.46 266	92.20 127

CPTT-MVS		83.73 78	83.33 82	84.92 96	93.28 55	70.86 77	92.09 36	90.38 147	68.75 229	79.57 133	92.83 74	60.60 186	93.04 184	80.92 85	91.56 93	90.86 165

Anonymous20231211		78.97 178	77.69 188	82.81 175	90.54 106	64.29 209	90.11 77	91.51 118	65.01 270	76.16 204	88.13 191	50.56 267	93.03 185	69.68 183	77.56 251	91.11 156

MSLP-MVS++		85.43 64	85.76 58	84.45 110	91.93 83	70.24 87	90.71 60	92.86 59	77.46 49	84.22 77	92.81 76	67.16 99	92.94 186	80.36 90	94.35 61	90.16 189

F-COLMAP		76.38 233	74.33 242	82.50 185	89.28 145	66.95 160	88.41 126	89.03 190	64.05 282	66.83 311	88.61 172	46.78 298	92.89 187	57.48 287	78.55 240	87.67 264

xiu_mvs_v1_base_debu		80.80 133	79.72 137	84.03 131	87.35 207	70.19 90	85.56 211	88.77 200	69.06 221	81.83 105	88.16 186	50.91 262	92.85 188	78.29 107	87.56 138	89.06 227

xiu_mvs_v1_base		80.80 133	79.72 137	84.03 131	87.35 207	70.19 90	85.56 211	88.77 200	69.06 221	81.83 105	88.16 186	50.91 262	92.85 188	78.29 107	87.56 138	89.06 227

xiu_mvs_v1_base_debi		80.80 133	79.72 137	84.03 131	87.35 207	70.19 90	85.56 211	88.77 200	69.06 221	81.83 105	88.16 186	50.91 262	92.85 188	78.29 107	87.56 138	89.06 227

NR-MVSNet		80.23 147	79.38 145	82.78 179	87.80 195	63.34 229	86.31 193	91.09 133	79.01 29	72.17 260	89.07 161	67.20 98	92.81 191	66.08 217	75.65 277	92.20 127

TranMVSNet+NR-MVSNet		80.84 128	80.31 127	82.42 186	87.85 193	62.33 244	87.74 152	91.33 124	80.55 11	77.99 162	89.86 138	65.23 118	92.62 192	67.05 210	75.24 291	92.30 123

test_0402		72.79 270	70.44 277	79.84 242	88.13 184	65.99 173	85.93 203	84.29 271	65.57 263	67.40 305	85.49 256	46.92 297	92.61 193	35.88 361	74.38 298	80.94 345

SixPastTwentyTwo		73.37 261	71.26 271	79.70 245	85.08 247	57.89 293	85.57 210	83.56 282	71.03 180	65.66 321	85.88 247	42.10 328	92.57 194	59.11 272	63.34 346	88.65 247

eth_miper_zixun_eth		77.92 204	76.69 211	81.61 202	83.00 284	61.98 249	83.15 264	89.20 184	69.52 209	74.86 235	84.35 273	61.76 161	92.56 195	71.50 165	72.89 312	90.28 186

EG-PatchMatch MVS		74.04 255	71.82 264	80.71 225	84.92 248	67.42 148	85.86 206	88.08 217	66.04 257	64.22 331	83.85 279	35.10 352	92.56 195	57.44 288	80.83 217	82.16 339

COLMAP_ROB	66.92 17	73.01 267	70.41 278	80.81 223	87.13 216	65.63 182	88.30 132	84.19 274	62.96 292	63.80 335	87.69 196	38.04 344	92.56 195	46.66 340	74.91 293	84.24 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ECVR-MVS		79.61 157	79.26 149	80.67 226	90.08 115	54.69 327	87.89 148	77.44 337	74.88 109	80.27 126	92.79 77	48.96 287	92.45 198	68.55 194	92.50 81	94.86 15

EI-MVSNet		80.52 142	79.98 131	82.12 189	84.28 255	63.19 234	86.41 190	88.95 196	74.18 127	78.69 144	87.54 201	66.62 101	92.43 199	72.57 159	80.57 222	90.74 169

MVSTER		79.01 176	77.88 180	82.38 187	83.07 281	64.80 198	84.08 251	88.95 196	69.01 224	78.69 144	87.17 213	54.70 225	92.43 199	74.69 137	80.57 222	89.89 208

gm-plane-assit					81.40 312	53.83 335			62.72 297		80.94 313		92.39 201	63.40 235

IterMVS-LS		80.06 150	79.38 145	82.11 190	85.89 231	63.20 233	86.79 178	89.34 176	74.19 126	75.45 217	86.72 222	66.62 101	92.39 201	72.58 158	76.86 260	90.75 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		78.72 182	77.80 182	81.47 204	82.73 291	61.96 250	86.30 194	88.08 217	73.26 147	76.18 201	85.47 257	62.46 151	92.36 203	71.92 162	73.82 304	90.09 195

test2506		77.30 217	76.49 214	79.74 244	90.08 115	52.02 343	87.86 150	63.10 370	74.88 109	80.16 129	92.79 77	38.29 343	92.35 204	68.74 193	92.50 81	94.86 15

FIs		82.07 105	82.42 92	81.04 218	88.80 163	58.34 285	88.26 135	93.49 29	76.93 63	78.47 151	91.04 114	69.92 74	92.34 205	69.87 181	84.97 169	92.44 119

test1111		79.43 164	79.18 152	80.15 236	89.99 120	53.31 340	87.33 162	77.05 340	75.04 105	80.23 128	92.77 79	48.97 286	92.33 206	68.87 191	92.40 83	94.81 18

新几何1				83.42 146	93.13 58	70.71 80		85.48 257	57.43 336	81.80 108	91.98 88	63.28 135	92.27 207	64.60 229	92.99 72	87.27 275

bset_n11_16_dypcd		77.12 219	75.47 225	82.06 191	81.12 318	65.99 173	81.37 284	83.20 291	69.94 200	76.09 205	83.38 288	47.75 292	92.26 208	78.51 101	77.91 247	87.95 257

anonymousdsp		78.60 185	77.15 198	82.98 168	80.51 325	67.08 154	87.24 165	89.53 172	65.66 262	75.16 227	87.19 212	52.52 239	92.25 209	77.17 117	79.34 236	89.61 217

lupinMVS		81.39 120	80.27 129	84.76 103	87.35 207	70.21 88	85.55 214	86.41 246	62.85 294	81.32 113	88.61 172	61.68 162	92.24 210	78.41 105	90.26 108	91.83 136

baseline2		75.70 240	73.83 248	81.30 210	83.26 275	61.79 253	82.57 271	80.65 314	66.81 244	66.88 309	83.42 287	57.86 202	92.19 211	63.47 233	79.57 231	89.91 206

RRT_MVS		79.88 154	78.38 167	84.38 112	85.42 239	70.60 84	88.71 117	88.75 204	72.30 159	78.83 143	89.14 158	44.44 314	92.18 212	78.50 102	79.33 237	90.35 183

jason		81.39 120	80.29 128	84.70 104	86.63 224	69.90 95	85.95 202	86.77 242	63.24 287	81.07 119	89.47 150	61.08 178	92.15 213	78.33 106	90.07 113	92.05 132
jason: jason.
XVG-ACMP-BASELINE		76.11 236	74.27 243	81.62 200	83.20 277	64.67 200	83.60 258	89.75 167	69.75 205	71.85 263	87.09 215	32.78 356	92.11 214	69.99 179	80.43 224	88.09 256

c3_l		78.75 181	77.91 178	81.26 211	82.89 288	61.56 255	84.09 250	89.13 188	69.97 199	75.56 212	84.29 274	66.36 105	92.09 215	73.47 149	75.48 281	90.12 192

miper_ehance_all_eth		78.59 186	77.76 185	81.08 217	82.66 293	61.56 255	83.65 255	89.15 186	68.87 227	75.55 213	83.79 282	66.49 103	92.03 216	73.25 152	76.39 268	89.64 216

GA-MVS		76.87 224	75.17 233	81.97 195	82.75 290	62.58 241	81.44 283	86.35 249	72.16 162	74.74 236	82.89 292	46.20 303	92.02 217	68.85 192	81.09 214	91.30 152

miper_enhance_ethall		77.87 206	76.86 204	80.92 221	81.65 307	61.38 257	82.68 269	88.98 193	65.52 264	75.47 214	82.30 300	65.76 115	92.00 218	72.95 155	76.39 268	89.39 221

thres100view900		76.50 228	75.55 224	79.33 252	89.52 130	56.99 305	85.83 208	83.23 289	73.94 131	76.32 197	87.12 214	51.89 253	91.95 219	48.33 331	83.75 183	89.07 225

tfpn200view9		76.42 231	75.37 230	79.55 251	89.13 151	57.65 297	85.17 219	83.60 280	73.41 145	76.45 192	86.39 239	52.12 246	91.95 219	48.33 331	83.75 183	89.07 225

thres400		76.50 228	75.37 230	79.86 241	89.13 151	57.65 297	85.17 219	83.60 280	73.41 145	76.45 192	86.39 239	52.12 246	91.95 219	48.33 331	83.75 183	90.00 201

thres600view7		76.50 228	75.44 226	79.68 246	89.40 135	57.16 302	85.53 216	83.23 289	73.79 135	76.26 198	87.09 215	51.89 253	91.89 222	48.05 336	83.72 186	90.00 201

cl22		78.07 199	77.01 200	81.23 212	82.37 300	61.83 252	83.55 259	87.98 219	68.96 225	75.06 231	83.87 278	61.40 170	91.88 223	73.53 146	76.39 268	89.98 204

dcpmvs_2		85.63 61	86.15 53	84.06 126	91.71 86	64.94 196	86.47 189	91.87 105	73.63 137	86.60 39	93.02 70	76.57 16	91.87 224	83.36 57	92.15 84	95.35 1

FC-MVSNet-test		81.52 117	82.02 102	80.03 238	88.42 178	55.97 321	87.95 144	93.42 33	77.10 59	77.38 172	90.98 119	69.96 73	91.79 225	68.46 196	84.50 174	92.33 120

ET-MVSNet_ETH3D		78.63 184	76.63 213	84.64 105	86.73 223	69.47 105	85.01 224	84.61 266	69.54 208	66.51 317	86.59 231	50.16 271	91.75 226	76.26 124	84.24 178	92.69 110

thres200		75.55 242	74.47 240	78.82 259	87.78 198	57.85 294	83.07 267	83.51 283	72.44 156	75.84 209	84.42 272	52.08 248	91.75 226	47.41 338	83.64 187	86.86 286

MVP-Stereo		76.12 235	74.46 241	81.13 216	85.37 240	69.79 97	84.42 242	87.95 220	65.03 269	67.46 303	85.33 259	53.28 237	91.73 228	58.01 284	83.27 190	81.85 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		74.26 252	72.42 259	79.80 243	83.76 266	59.59 276	85.92 204	86.64 243	66.39 253	66.96 308	87.58 198	39.46 337	91.60 229	65.76 220	69.27 329	88.22 254

Fast-Effi-MVS+-dtu		78.02 201	76.49 214	82.62 183	83.16 280	66.96 159	86.94 172	87.45 232	72.45 154	71.49 267	84.17 275	54.79 224	91.58 230	67.61 201	80.31 225	89.30 223

UniMVSNet_ETH3D		79.10 174	78.24 172	81.70 199	86.85 219	60.24 271	87.28 164	88.79 199	74.25 125	76.84 183	90.53 125	49.48 279	91.56 231	67.98 198	82.15 204	93.29 87

cl____		77.72 208	76.76 208	80.58 227	82.49 297	60.48 268	83.09 265	87.87 222	69.22 215	74.38 240	85.22 262	62.10 158	91.53 232	71.09 167	75.41 284	89.73 215

DIV-MVS_self_test		77.72 208	76.76 208	80.58 227	82.48 298	60.48 268	83.09 265	87.86 223	69.22 215	74.38 240	85.24 261	62.10 158	91.53 232	71.09 167	75.40 285	89.74 214

ACMH	67.68 16	75.89 238	73.93 245	81.77 198	88.71 168	66.61 162	88.62 119	89.01 192	69.81 202	66.78 312	86.70 227	41.95 330	91.51 234	55.64 299	78.14 246	87.17 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211		78.25 192	77.01 200	81.99 194	91.03 94	60.67 265	84.77 229	83.90 277	70.65 189	80.00 130	91.20 109	41.08 333	91.43 235	65.21 223	85.26 167	93.85 59

CHOSEN 1792x2688		77.63 211	75.69 221	83.44 145	89.98 121	68.58 128	78.70 310	87.50 230	56.38 342	75.80 210	86.84 218	58.67 196	91.40 236	61.58 253	85.75 166	90.34 184

XVG-OURS		80.41 143	79.23 150	83.97 135	85.64 235	69.02 111	83.03 268	90.39 146	71.09 179	77.63 168	91.49 102	54.62 227	91.35 237	75.71 129	83.47 188	91.54 143

lessismore_v0				78.97 257	81.01 320	57.15 303		65.99 365		61.16 343	82.82 294	39.12 339	91.34 238	59.67 266	46.92 365	88.43 252

XVG-OURS-SEG-HR		80.81 131	79.76 136	83.96 136	85.60 236	68.78 117	83.54 260	90.50 144	70.66 188	76.71 188	91.66 94	60.69 183	91.26 239	76.94 120	81.58 210	91.83 136

tpm2		73.26 264	71.46 266	78.63 261	83.34 273	56.71 310	80.65 289	80.40 319	56.63 341	73.55 244	82.02 305	51.80 255	91.24 240	56.35 297	78.42 244	87.95 257

OpenMVS_ROB	64.09 19	70.56 285	68.19 290	77.65 277	80.26 326	59.41 278	85.01 224	82.96 295	58.76 327	65.43 323	82.33 299	37.63 346	91.23 241	45.34 348	76.03 273	82.32 337

GBi-Net		78.40 188	77.40 193	81.40 206	87.60 202	63.01 236	88.39 127	89.28 178	71.63 168	75.34 221	87.28 206	54.80 221	91.11 242	62.72 239	79.57 231	90.09 195

test1		78.40 188	77.40 193	81.40 206	87.60 202	63.01 236	88.39 127	89.28 178	71.63 168	75.34 221	87.28 206	54.80 221	91.11 242	62.72 239	79.57 231	90.09 195

FMVSNet1		77.44 213	76.12 220	81.40 206	86.81 221	63.01 236	88.39 127	89.28 178	70.49 191	74.39 239	87.28 206	49.06 285	91.11 242	60.91 258	78.52 241	90.09 195

FMVSNet3		77.88 205	76.85 205	80.97 220	86.84 220	62.36 243	86.52 188	88.77 200	71.13 177	75.34 221	86.66 229	54.07 231	91.10 245	62.72 239	79.57 231	89.45 220

FMVSNet2		78.20 195	77.21 197	81.20 213	87.60 202	62.89 240	87.47 158	89.02 191	71.63 168	75.29 225	87.28 206	54.80 221	91.10 245	62.38 243	79.38 235	89.61 217

K. test v3		71.19 279	68.51 287	79.21 255	83.04 283	57.78 296	84.35 244	76.91 341	72.90 153	62.99 338	82.86 293	39.27 338	91.09 247	61.65 252	52.66 360	88.75 244

CostFormer		75.24 247	73.90 246	79.27 253	82.65 294	58.27 286	80.80 285	82.73 297	61.57 305	75.33 224	83.13 290	55.52 217	91.07 248	64.98 226	78.34 245	88.45 251

testdata2													91.01 249	62.37 244

MSDG		73.36 263	70.99 272	80.49 229	84.51 253	65.80 179	80.71 288	86.13 252	65.70 261	65.46 322	83.74 283	44.60 312	90.91 250	51.13 316	76.89 259	84.74 316

TAMVS		78.89 180	77.51 192	83.03 165	87.80 195	67.79 142	84.72 230	85.05 262	67.63 238	76.75 187	87.70 195	62.25 155	90.82 251	58.53 279	87.13 145	90.49 178

diffmvs		82.10 103	81.88 105	82.76 181	83.00 284	63.78 218	83.68 254	89.76 166	72.94 152	82.02 104	89.85 139	65.96 113	90.79 252	82.38 76	87.30 143	93.71 67

CDS-MVSNet		79.07 175	77.70 187	83.17 158	87.60 202	68.23 134	84.40 243	86.20 250	67.49 241	76.36 196	86.54 235	61.54 165	90.79 252	61.86 250	87.33 142	90.49 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		76.53 227	75.30 232	80.21 235	83.93 263	62.32 245	84.66 231	88.81 198	60.23 314	70.16 280	84.07 277	55.30 219	90.73 254	67.37 204	83.21 191	87.59 268

WR-MVS		79.49 161	79.22 151	80.27 234	88.79 164	58.35 284	85.06 223	88.61 209	78.56 32	77.65 167	88.34 180	63.81 131	90.66 255	64.98 226	77.22 254	91.80 139

MVS_111021_LR		82.61 99	82.11 98	84.11 122	88.82 161	71.58 59	85.15 221	86.16 251	74.69 114	80.47 125	91.04 114	62.29 154	90.55 256	80.33 91	90.08 112	90.20 188

HY-MVS	69.67 12	77.95 203	77.15 198	80.36 231	87.57 206	60.21 272	83.37 262	87.78 225	66.11 255	75.37 220	87.06 217	63.27 136	90.48 257	61.38 255	82.43 202	90.40 182

VNet		82.21 102	82.41 93	81.62 200	90.82 100	60.93 260	84.47 237	89.78 165	76.36 80	84.07 81	91.88 91	64.71 124	90.26 258	70.68 171	88.89 122	93.66 68

VPA-MVSNet		80.60 139	80.55 122	80.76 224	88.07 187	60.80 263	86.86 175	91.58 116	75.67 93	80.24 127	89.45 154	63.34 134	90.25 259	70.51 173	79.22 239	91.23 153

ab-mvs		79.51 160	78.97 156	81.14 215	88.46 176	60.91 261	83.84 252	89.24 182	70.36 192	79.03 138	88.87 166	63.23 138	90.21 260	65.12 224	82.57 201	92.28 124

DWT-MVSNet_test		73.70 258	71.86 263	79.21 255	82.91 287	58.94 279	82.34 272	82.17 300	65.21 265	71.05 271	78.31 334	44.21 315	90.17 261	63.29 237	77.28 252	88.53 250

D2MVS		74.82 248	73.21 252	79.64 248	79.81 332	62.56 242	80.34 293	87.35 233	64.37 277	68.86 293	82.66 296	46.37 300	90.10 262	67.91 199	81.24 213	86.25 295

1112_ss		77.40 215	76.43 216	80.32 233	89.11 155	60.41 270	83.65 255	87.72 226	62.13 302	73.05 250	86.72 222	62.58 149	89.97 263	62.11 248	80.80 218	90.59 175

tfpnnormal		74.39 250	73.16 253	78.08 270	86.10 229	58.05 288	84.65 234	87.53 229	70.32 193	71.22 269	85.63 253	54.97 220	89.86 264	43.03 352	75.02 292	86.32 294

tpmvs		71.09 280	69.29 283	76.49 290	82.04 303	56.04 320	78.92 308	81.37 309	64.05 282	67.18 307	78.28 335	49.74 277	89.77 265	49.67 326	72.37 314	83.67 326

Vis-MVSNet (Re-imp)		78.36 191	78.45 164	78.07 271	88.64 170	51.78 346	86.70 182	79.63 326	74.14 128	75.11 229	90.83 120	61.29 173	89.75 266	58.10 283	91.60 91	92.69 110

ambc				75.24 301	73.16 363	50.51 354	63.05 362	87.47 231		64.28 330	77.81 339	17.80 369	89.73 267	57.88 285	60.64 351	85.49 306

VPNet		78.69 183	78.66 160	78.76 260	88.31 181	55.72 323	84.45 240	86.63 244	76.79 67	78.26 155	90.55 124	59.30 193	89.70 268	66.63 212	77.05 256	90.88 164

mvs_anonymous		79.42 165	79.11 153	80.34 232	84.45 254	57.97 291	82.59 270	87.62 227	67.40 242	76.17 203	88.56 175	68.47 87	89.59 269	70.65 172	86.05 161	93.47 82

pmmvs6		74.69 249	73.39 250	78.61 262	81.38 313	57.48 300	86.64 183	87.95 220	64.99 271	70.18 278	86.61 230	50.43 269	89.52 270	62.12 247	70.18 327	88.83 241

DTE-MVSNet		76.99 221	76.80 206	77.54 280	86.24 227	53.06 342	87.52 156	90.66 140	77.08 60	72.50 255	88.67 170	60.48 187	89.52 270	57.33 290	70.74 325	90.05 200

USDC		70.33 287	68.37 288	76.21 292	80.60 323	56.23 318	79.19 305	86.49 245	60.89 309	61.29 342	85.47 257	31.78 359	89.47 272	53.37 307	76.21 272	82.94 336

Test_1112_low_res		76.40 232	75.44 226	79.27 253	89.28 145	58.09 287	81.69 279	87.07 238	59.53 321	72.48 256	86.67 228	61.30 172	89.33 273	60.81 260	80.15 227	90.41 181

TransMVSNet (Re)		75.39 246	74.56 238	77.86 272	85.50 238	57.10 304	86.78 179	86.09 253	72.17 161	71.53 266	87.34 205	63.01 144	89.31 274	56.84 294	61.83 348	87.17 278

WR-MVS_H		78.51 187	78.49 163	78.56 263	88.02 189	56.38 316	88.43 123	92.67 67	77.14 57	73.89 243	87.55 200	66.25 107	89.24 275	58.92 274	73.55 306	90.06 199

PEN-MVS		77.73 207	77.69 188	77.84 273	87.07 217	53.91 334	87.91 147	91.18 129	77.56 45	73.14 249	88.82 167	61.23 174	89.17 276	59.95 264	72.37 314	90.43 180

pm-mvs1		77.25 218	76.68 212	78.93 258	84.22 257	58.62 283	86.41 190	88.36 212	71.37 175	73.31 246	88.01 192	61.22 175	89.15 277	64.24 230	73.01 311	89.03 231

testdata				79.97 239	90.90 97	64.21 210		84.71 264	59.27 323	85.40 49	92.91 71	62.02 160	89.08 278	68.95 190	91.37 95	86.63 292

Baseline_NR-MVSNet		78.15 197	78.33 170	77.61 278	85.79 232	56.21 319	86.78 179	85.76 255	73.60 139	77.93 163	87.57 199	65.02 120	88.99 279	67.14 209	75.33 287	87.63 265

旧先验2							86.56 186		58.10 331	87.04 35			88.98 280	74.07 142

LCM-MVSNet-Re		77.05 220	76.94 203	77.36 281	87.20 214	51.60 347	80.06 295	80.46 318	75.20 102	67.69 301	86.72 222	62.48 150	88.98 280	63.44 234	89.25 120	91.51 144

AllTest		70.96 281	68.09 293	79.58 249	85.15 243	63.62 219	84.58 236	79.83 324	62.31 300	60.32 345	86.73 220	32.02 357	88.96 282	50.28 321	71.57 321	86.15 298

TestCases				79.58 249	85.15 243	63.62 219		79.83 324	62.31 300	60.32 345	86.73 220	32.02 357	88.96 282	50.28 321	71.57 321	86.15 298

GG-mvs-BLEND				75.38 300	81.59 309	55.80 322	79.32 302	69.63 358		67.19 306	73.67 352	43.24 319	88.90 284	50.41 318	84.50 174	81.45 342

gg-mvs-nofinetune		69.95 291	67.96 294	75.94 293	83.07 281	54.51 330	77.23 321	70.29 356	63.11 289	70.32 276	62.33 359	43.62 318	88.69 285	53.88 305	87.76 135	84.62 319

patchmatchnet-post											74.00 351	51.12 261	88.60 286

SCA		74.22 253	72.33 260	79.91 240	84.05 261	62.17 247	79.96 297	79.29 328	66.30 254	72.38 258	80.13 320	51.95 251	88.60 286	59.25 270	77.67 250	88.96 236

CP-MVSNet		78.22 193	78.34 169	77.84 273	87.83 194	54.54 329	87.94 145	91.17 130	77.65 40	73.48 245	88.49 176	62.24 156	88.43 288	62.19 245	74.07 299	90.55 176

PS-CasMVS		78.01 202	78.09 174	77.77 275	87.71 199	54.39 331	88.02 141	91.22 127	77.50 48	73.26 247	88.64 171	60.73 181	88.41 289	61.88 249	73.88 303	90.53 177

MS-PatchMatch		73.83 257	72.67 256	77.30 283	83.87 264	66.02 172	81.82 276	84.66 265	61.37 308	68.61 296	82.82 294	47.29 294	88.21 290	59.27 269	84.32 177	77.68 354

IterMVS-SCA-FT		75.43 244	73.87 247	80.11 237	82.69 292	64.85 197	81.57 281	83.47 285	69.16 218	70.49 274	84.15 276	51.95 251	88.15 291	69.23 186	72.14 317	87.34 273

pmmvs4		74.03 256	71.91 262	80.39 230	81.96 304	68.32 131	81.45 282	82.14 301	59.32 322	69.87 286	85.13 264	52.40 242	88.13 292	60.21 263	74.74 295	84.73 317

EPNet_dtu		75.46 243	74.86 234	77.23 285	82.57 295	54.60 328	86.89 174	83.09 293	71.64 167	66.25 319	85.86 248	55.99 216	88.04 293	54.92 301	86.55 154	89.05 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		67.49 304	64.34 313	76.92 287	73.47 362	61.07 259	84.86 228	82.98 294	59.77 318	58.30 351	85.13 264	26.06 362	87.89 294	47.92 337	60.59 352	81.81 341

tpm cat1		70.57 284	68.31 289	77.35 282	82.41 299	57.95 292	78.08 315	80.22 322	52.04 353	68.54 297	77.66 340	52.00 250	87.84 295	51.77 312	72.07 318	86.25 295

baseline1		76.98 222	76.75 210	77.66 276	88.13 184	55.66 324	85.12 222	81.89 303	73.04 150	76.79 185	88.90 164	62.43 152	87.78 296	63.30 236	71.18 323	89.55 219

TinyColmap		67.30 307	64.81 311	74.76 306	81.92 305	56.68 311	80.29 294	81.49 308	60.33 312	56.27 357	83.22 289	24.77 363	87.66 297	45.52 346	69.47 328	79.95 349

MVS_0304		72.48 271	70.89 274	77.24 284	82.20 301	59.68 274	84.11 249	83.49 284	67.10 243	66.87 310	80.59 316	35.00 353	87.40 298	59.07 273	79.58 230	84.63 318

ppachtmachnet_test		70.04 290	67.34 303	78.14 269	79.80 333	61.13 258	79.19 305	80.59 315	59.16 324	65.27 324	79.29 326	46.75 299	87.29 299	49.33 327	66.72 336	86.00 304

ITE_SJBPF				78.22 268	81.77 306	60.57 266		83.30 287	69.25 214	67.54 302	87.20 211	36.33 349	87.28 300	54.34 303	74.62 296	86.80 287

MDTV_nov1_ep13			69.97 281		83.18 278	53.48 337	77.10 322	80.18 323	60.45 311	69.33 292	80.44 317	48.89 288	86.90 301	51.60 314	78.51 242

CR-MVSNet		73.37 261	71.27 270	79.67 247	81.32 316	65.19 191	75.92 326	80.30 320	59.92 317	72.73 253	81.19 308	52.50 240	86.69 302	59.84 265	77.71 248	87.11 282

Patchmtry		70.74 282	69.16 284	75.49 299	80.72 321	54.07 333	74.94 335	80.30 320	58.34 329	70.01 281	81.19 308	52.50 240	86.54 303	53.37 307	71.09 324	85.87 305

JIA-IIPM		66.32 313	62.82 323	76.82 288	77.09 348	61.72 254	65.34 358	75.38 344	58.04 332	64.51 329	62.32 360	42.05 329	86.51 304	51.45 315	69.22 330	82.21 338

CMPMVS	51.72 21	70.19 289	68.16 291	76.28 291	73.15 364	57.55 299	79.47 301	83.92 276	48.02 357	56.48 356	84.81 268	43.13 320	86.42 305	62.67 242	81.81 209	84.89 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		70.50 286	67.83 297	78.52 265	77.37 347	66.18 170	81.82 276	81.51 307	58.90 326	63.90 334	80.42 318	42.69 323	86.28 306	58.56 278	65.30 342	83.11 332

CNLPA		78.08 198	76.79 207	81.97 195	90.40 109	71.07 69	87.59 155	84.55 267	66.03 258	72.38 258	89.64 144	57.56 205	86.04 307	59.61 267	83.35 189	88.79 243

PatchmatchNet		73.12 266	71.33 269	78.49 266	83.18 278	60.85 262	79.63 299	78.57 330	64.13 279	71.73 264	79.81 325	51.20 260	85.97 308	57.40 289	76.36 271	88.66 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		72.99 268	72.58 257	74.25 310	84.28 255	50.85 352	86.41 190	83.45 286	44.56 358	73.23 248	87.54 201	49.38 280	85.70 309	65.90 218	78.44 243	86.19 297

IterMVS		74.29 251	72.94 255	78.35 267	81.53 310	63.49 225	81.58 280	82.49 298	68.06 237	69.99 283	83.69 284	51.66 257	85.54 310	65.85 219	71.64 320	86.01 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-RL test		70.24 288	67.78 299	77.61 278	77.43 346	59.57 277	71.16 342	70.33 355	62.94 293	68.65 295	72.77 353	50.62 266	85.49 311	69.58 184	66.58 338	87.77 263

test_post1							78.90 309				5.43 375	48.81 289	85.44 312	59.25 270

pmmvs5		71.55 277	70.20 280	75.61 296	77.83 344	56.39 315	81.74 278	80.89 310	57.76 333	67.46 303	84.49 271	49.26 283	85.32 313	57.08 292	75.29 289	85.11 313

KD-MVS_2432*1600		66.22 314	63.89 315	73.21 315	75.47 355	53.42 338	70.76 345	84.35 269	64.10 280	66.52 315	78.52 332	34.55 354	84.98 314	50.40 319	50.33 363	81.23 343

miper_refine_blended		66.22 314	63.89 315	73.21 315	75.47 355	53.42 338	70.76 345	84.35 269	64.10 280	66.52 315	78.52 332	34.55 354	84.98 314	50.40 319	50.33 363	81.23 343

PatchMatch-RL		72.38 273	70.90 273	76.80 289	88.60 171	67.38 150	79.53 300	76.17 343	62.75 296	69.36 291	82.00 306	45.51 309	84.89 316	53.62 306	80.58 221	78.12 353

KD-MVS_self_test		68.81 297	67.59 302	72.46 320	74.29 358	45.45 362	77.93 317	87.00 239	63.12 288	63.99 333	78.99 331	42.32 325	84.77 317	56.55 296	64.09 345	87.16 280

RPSCF		73.23 265	71.46 266	78.54 264	82.50 296	59.85 273	82.18 274	82.84 296	58.96 325	71.15 270	89.41 156	45.48 310	84.77 317	58.82 276	71.83 319	91.02 161

test_post											5.46 374	50.36 270	84.24 319

CL-MVSNet_self_test		72.37 274	71.46 266	75.09 302	79.49 338	53.53 336	80.76 287	85.01 263	69.12 219	70.51 273	82.05 304	57.92 201	84.13 320	52.27 311	66.00 340	87.60 266

our_test_3		69.14 295	67.00 305	75.57 297	79.80 333	58.80 281	77.96 316	77.81 333	59.55 320	62.90 339	78.25 336	47.43 293	83.97 321	51.71 313	67.58 335	83.93 325

EU-MVSNet		68.53 301	67.61 301	71.31 327	78.51 343	47.01 361	84.47 237	84.27 272	42.27 359	66.44 318	84.79 269	40.44 335	83.76 322	58.76 277	68.54 334	83.17 330

MDA-MVSNet-bldmvs		66.68 309	63.66 317	75.75 294	79.28 340	60.56 267	73.92 337	78.35 331	64.43 275	50.13 362	79.87 324	44.02 317	83.67 323	46.10 344	56.86 355	83.03 334

MIMVSNet1		68.58 300	66.78 307	73.98 312	80.07 329	51.82 345	80.77 286	84.37 268	64.40 276	59.75 348	82.16 303	36.47 348	83.63 324	42.73 353	70.33 326	86.48 293

patch_mono-2		83.65 80	84.54 75	80.99 219	90.06 119	65.83 178	84.21 246	88.74 205	71.60 171	85.01 54	92.44 81	74.51 30	83.50 325	82.15 77	92.15 84	93.64 75

PM-MVS		66.41 312	64.14 314	73.20 317	73.92 359	56.45 313	78.97 307	64.96 368	63.88 286	64.72 328	80.24 319	19.84 368	83.44 326	66.24 213	64.52 344	79.71 350

PVSNet	64.34 18	72.08 276	70.87 275	75.69 295	86.21 228	56.44 314	74.37 336	80.73 313	62.06 303	70.17 279	82.23 302	42.86 322	83.31 327	54.77 302	84.45 176	87.32 274

tpm		72.37 274	71.71 265	74.35 309	82.19 302	52.00 344	79.22 304	77.29 338	64.56 274	72.95 251	83.68 285	51.35 258	83.26 328	58.33 281	75.80 275	87.81 262

miper_lstm_enhance		74.11 254	73.11 254	77.13 286	80.11 328	59.62 275	72.23 340	86.92 241	66.76 246	70.40 275	82.92 291	56.93 213	82.92 329	69.06 189	72.63 313	88.87 239

tpmrst		72.39 272	72.13 261	73.18 318	80.54 324	49.91 355	79.91 298	79.08 329	63.11 289	71.69 265	79.95 322	55.32 218	82.77 330	65.66 221	73.89 302	86.87 285

MVS-HIRNet		59.14 325	57.67 328	63.57 343	81.65 307	43.50 366	71.73 341	65.06 367	39.59 363	51.43 361	57.73 363	38.34 342	82.58 331	39.53 358	73.95 301	64.62 362

FMVSNet5		69.50 293	67.96 294	74.15 311	82.97 286	55.35 325	80.01 296	82.12 302	62.56 298	63.02 336	81.53 307	36.92 347	81.92 332	48.42 330	74.06 300	85.17 312

PatchT		68.46 302	67.85 296	70.29 330	80.70 322	43.93 365	72.47 339	74.88 346	60.15 315	70.55 272	76.57 344	49.94 274	81.59 333	50.58 317	74.83 294	85.34 308

EGC-MVSNET		52.07 331	47.05 335	67.14 340	83.51 270	60.71 264	80.50 291	67.75 363	0.07 376	0.43 377	75.85 348	24.26 364	81.54 334	28.82 364	62.25 347	59.16 364

MIMVSNet		70.69 283	69.30 282	74.88 304	84.52 252	56.35 317	75.87 328	79.42 327	64.59 273	67.76 299	82.41 298	41.10 332	81.54 334	46.64 342	81.34 211	86.75 289

Anonymous20240521		68.80 298	67.22 304	73.55 313	74.33 357	54.11 332	83.18 263	85.61 256	58.15 330	61.68 341	80.94 313	30.71 360	81.27 336	57.00 293	73.34 310	85.28 309

WTY-MVS		75.65 241	75.68 222	75.57 297	86.40 226	56.82 307	77.92 318	82.40 299	65.10 267	76.18 201	87.72 194	63.13 143	80.90 337	60.31 262	81.96 206	89.00 234

dp		66.80 308	65.43 310	70.90 329	79.74 335	48.82 358	75.12 333	74.77 347	59.61 319	64.08 332	77.23 341	42.89 321	80.72 338	48.86 329	66.58 338	83.16 331

ADS-MVSNet2		66.20 316	63.33 318	74.82 305	79.92 330	58.75 282	67.55 355	75.19 345	53.37 350	65.25 325	75.86 346	42.32 325	80.53 339	41.57 355	68.91 331	85.18 310

XXY-MVS		75.41 245	75.56 223	74.96 303	83.59 268	57.82 295	80.59 290	83.87 278	66.54 252	74.93 234	88.31 181	63.24 137	80.09 340	62.16 246	76.85 261	86.97 284

test-LLR		72.94 269	72.43 258	74.48 307	81.35 314	58.04 289	78.38 311	77.46 335	66.66 248	69.95 284	79.00 329	48.06 290	79.24 341	66.13 214	84.83 170	86.15 298

test-mter		71.41 278	70.39 279	74.48 307	81.35 314	58.04 289	78.38 311	77.46 335	60.32 313	69.95 284	79.00 329	36.08 350	79.24 341	66.13 214	84.83 170	86.15 298

Anonymous20231206		68.60 299	67.80 298	71.02 328	80.23 327	50.75 353	78.30 314	80.47 317	56.79 340	66.11 320	82.63 297	46.35 301	78.95 343	43.62 351	75.70 276	83.36 329

UnsupCasMVSNet_bld		63.70 322	61.53 326	70.21 331	73.69 360	51.39 350	72.82 338	81.89 303	55.63 345	57.81 352	71.80 355	38.67 340	78.61 344	49.26 328	52.21 361	80.63 346

test20.03		67.45 305	66.95 306	68.94 334	75.48 354	44.84 364	77.50 319	77.67 334	66.66 248	63.01 337	83.80 281	47.02 296	78.40 345	42.53 354	68.86 333	83.58 327

PMMVS		69.34 294	68.67 286	71.35 326	75.67 352	62.03 248	75.17 330	73.46 351	50.00 356	68.68 294	79.05 327	52.07 249	78.13 346	61.16 257	82.77 197	73.90 357

sss		73.60 259	73.64 249	73.51 314	82.80 289	55.01 326	76.12 324	81.69 306	62.47 299	74.68 237	85.85 249	57.32 208	78.11 347	60.86 259	80.93 215	87.39 271

LCM-MVSNet		54.25 328	49.68 334	67.97 339	53.73 373	45.28 363	66.85 357	80.78 312	35.96 365	39.45 365	62.23 361	8.70 377	78.06 348	48.24 334	51.20 362	80.57 347

EPMVS		69.02 296	68.16 291	71.59 322	79.61 336	49.80 357	77.40 320	66.93 364	62.82 295	70.01 281	79.05 327	45.79 306	77.86 349	56.58 295	75.26 290	87.13 281

PVSNet_0	57.27 20	61.67 324	59.27 327	68.85 336	79.61 336	57.44 301	68.01 354	73.44 352	55.93 344	58.54 350	70.41 356	44.58 313	77.55 350	47.01 339	35.91 366	71.55 359

UnsupCasMVSNet_eth		67.33 306	65.99 309	71.37 324	73.48 361	51.47 349	75.16 331	85.19 260	65.20 266	60.78 344	80.93 315	42.35 324	77.20 351	57.12 291	53.69 359	85.44 307

TESTMET0.1,1		69.89 292	69.00 285	72.55 319	79.27 341	56.85 306	78.38 311	74.71 349	57.64 334	68.09 298	77.19 342	37.75 345	76.70 352	63.92 231	84.09 179	84.10 324

LF4IMVS		64.02 321	62.19 324	69.50 333	70.90 366	53.29 341	76.13 323	77.18 339	52.65 352	58.59 349	80.98 312	23.55 365	76.52 353	53.06 309	66.66 337	78.68 352

new-patchmatchnet		61.73 323	61.73 325	61.70 344	72.74 365	24.50 378	69.16 351	78.03 332	61.40 306	56.72 355	75.53 349	38.42 341	76.48 354	45.95 345	57.67 354	84.13 323

PMVS	37.38 22	44.16 335	40.28 338	55.82 347	40.82 378	42.54 367	65.12 359	63.99 369	34.43 366	24.48 369	57.12 365	3.92 379	76.17 355	17.10 371	55.52 357	48.75 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test0.0.03 1		68.00 303	67.69 300	68.90 335	77.55 345	47.43 359	75.70 329	72.95 353	66.66 248	66.56 313	82.29 301	48.06 290	75.87 356	44.97 349	74.51 297	83.41 328

Gipuma		45.18 334	41.86 337	55.16 348	77.03 349	51.52 348	32.50 369	80.52 316	32.46 367	27.12 368	35.02 369	9.52 376	75.50 357	22.31 369	60.21 353	38.45 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		57.79 326	54.26 330	68.37 338	64.02 370	56.72 309	75.12 333	65.17 366	40.20 361	52.93 360	69.86 357	20.36 367	75.48 358	45.45 347	55.25 358	72.90 358

CHOSEN 280x420		66.51 311	64.71 312	71.90 321	81.45 311	63.52 224	57.98 363	68.95 362	53.57 349	62.59 340	76.70 343	46.22 302	75.29 359	55.25 300	79.68 229	76.88 356

testgi		66.67 310	66.53 308	67.08 341	75.62 353	41.69 368	75.93 325	76.50 342	66.11 255	65.20 327	86.59 231	35.72 351	74.71 360	43.71 350	73.38 309	84.84 315

YYNet1		65.03 317	62.91 321	71.38 323	75.85 351	56.60 312	69.12 352	74.66 350	57.28 338	54.12 358	77.87 338	45.85 305	74.48 361	49.95 324	61.52 350	83.05 333

MDA-MVSNet_test_wron		65.03 317	62.92 320	71.37 324	75.93 350	56.73 308	69.09 353	74.73 348	57.28 338	54.03 359	77.89 337	45.88 304	74.39 362	49.89 325	61.55 349	82.99 335

ADS-MVSNet		64.36 320	62.88 322	68.78 337	79.92 330	47.17 360	67.55 355	71.18 354	53.37 350	65.25 325	75.86 346	42.32 325	73.99 363	41.57 355	68.91 331	85.18 310

ANet_high		50.57 333	46.10 336	63.99 342	48.67 376	39.13 369	70.99 344	80.85 311	61.39 307	31.18 367	57.70 364	17.02 370	73.65 364	31.22 363	15.89 373	79.18 351

Patchmatch-test		64.82 319	63.24 319	69.57 332	79.42 339	49.82 356	63.49 361	69.05 361	51.98 354	59.95 347	80.13 320	50.91 262	70.98 365	40.66 357	73.57 305	87.90 260

FPMVS		53.68 329	51.64 332	59.81 346	65.08 369	51.03 351	69.48 349	69.58 359	41.46 360	40.67 364	72.32 354	16.46 371	70.00 366	24.24 368	65.42 341	58.40 365

DSMNet-mixed		57.77 327	56.90 329	60.38 345	67.70 368	35.61 371	69.18 350	53.97 373	32.30 368	57.49 353	79.88 323	40.39 336	68.57 367	38.78 359	72.37 314	76.97 355

N_pmnet		52.79 330	53.26 331	51.40 350	78.99 342	7.68 381	69.52 348	3.89 381	51.63 355	57.01 354	74.98 350	40.83 334	65.96 368	37.78 360	64.67 343	80.56 348

new_pmnet		50.91 332	50.29 333	52.78 349	68.58 367	34.94 373	63.71 360	56.63 372	39.73 362	44.95 363	65.47 358	21.93 366	58.48 369	34.98 362	56.62 356	64.92 361

PMMVS2		40.82 336	38.86 339	46.69 351	53.84 372	16.45 379	48.61 366	49.92 374	37.49 364	31.67 366	60.97 362	8.14 378	56.42 370	28.42 365	30.72 368	67.19 360

E-PMN		31.77 337	30.64 340	35.15 354	52.87 374	27.67 375	57.09 364	47.86 375	24.64 369	16.40 374	33.05 370	11.23 374	54.90 371	14.46 373	18.15 371	22.87 370

EMVS		30.81 339	29.65 341	34.27 355	50.96 375	25.95 377	56.58 365	46.80 376	24.01 370	15.53 375	30.68 371	12.47 373	54.43 372	12.81 374	17.05 372	22.43 371

test_method		31.52 338	29.28 342	38.23 353	27.03 380	6.50 382	20.94 371	62.21 371	4.05 374	22.35 372	52.50 366	13.33 372	47.58 373	27.04 367	34.04 367	60.62 363

MVE	26.22 23	30.37 340	25.89 344	43.81 352	44.55 377	35.46 372	28.87 370	39.07 377	18.20 371	18.58 373	40.18 368	2.68 380	47.37 374	17.07 372	23.78 370	48.60 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 356	40.17 379	26.90 376		24.59 380	17.44 372	23.95 370	48.61 367	9.77 375	26.48 375	18.06 370	24.47 369	28.83 369

wuyk23d		16.82 343	15.94 346	19.46 357	58.74 371	31.45 374	39.22 367	3.74 382	6.84 373	6.04 376	2.70 376	1.27 381	24.29 376	10.54 375	14.40 375	2.63 373

tmp_tt		18.61 342	21.40 345	10.23 358	4.82 381	10.11 380	34.70 368	30.74 379	1.48 375	23.91 371	26.07 372	28.42 361	13.41 377	27.12 366	15.35 374	7.17 372

testmvs		6.04 346	8.02 349	0.10 360	0.08 382	0.03 384	69.74 347	0.04 383	0.05 377	0.31 378	1.68 377	0.02 383	0.04 378	0.24 376	0.02 376	0.25 375

test123		6.12 345	8.11 348	0.14 359	0.06 383	0.09 383	71.05 343	0.03 384	0.04 378	0.25 379	1.30 378	0.05 382	0.03 379	0.21 377	0.01 377	0.29 374

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		19.96 341	26.61 343	0.00 361	0.00 384	0.00 385	0.00 372	89.26 181	0.00 379	0.00 380	88.61 172	61.62 164	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		5.26 347	7.02 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	63.15 140	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		7.23 344	9.64 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	86.72 222	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 118	91.30 15

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 37	92.05 11	95.74 6	80.83 11

eth-test2					0.00 384

eth-test					0.00 384

RE-MVS-def			85.48 60		93.06 62	70.63 82	91.88 38	92.27 83	73.53 142	85.69 47	94.45 28	63.87 129		82.75 68	91.87 88	92.50 115

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 244	92.39 6				88.94 11	96.63 4	94.85 17

save fliter					93.80 44	72.35 45	90.47 67	91.17 130	74.31 122

test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 50	92.81 3	95.79 3	80.98 9

GSMVS																88.96 236

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 259				88.96 236

sam_mvs												50.01 272

MTGPA								92.02 94

MTMP							92.18 34	32.83 378

test9_res														84.90 33	95.70 31	92.87 105

agg_prior2														82.91 66	95.45 33	92.70 108

test_prior4						72.60 35	89.01 103

test_prior2							88.85 109		75.41 96	84.91 58	93.54 54	74.28 36		83.31 58	95.86 22

新几何2							86.29 195

旧先验1					91.96 82	65.79 180		86.37 248			93.08 69	69.31 81			92.74 76	88.74 245

原ACMM2							86.86 175

test222					91.50 89	68.26 133	84.16 247	83.20 291	54.63 348	79.74 131	91.63 97	58.97 195			91.42 94	86.77 288

segment_acmp												73.08 45

testdata1							84.14 248		75.71 90

plane_prior7					90.08 115	68.51 129

plane_prior6					89.84 124	68.70 124						60.42 188

plane_prior4											91.00 117

plane_prior3						68.60 127			78.44 33	78.92 141

plane_prior2							91.25 50		79.12 26

plane_prior1					89.90 123

plane_prior						68.71 122	90.38 71		77.62 41						86.16 160

n2								0.00 385

nn								0.00 385

door-mid								69.98 357

test11								92.23 86

door								69.44 360

HQP5-MVS						66.98 156

HQP-NCC					89.33 138		89.17 96		76.41 75	77.23 177

ACMP_Plane					89.33 138		89.17 96		76.41 75	77.23 177

BP-MVS														77.47 113

HQP3-MVS								92.19 89							85.99 163

HQP2-MVS												60.17 191

NP-MVS					89.62 126	68.32 131					90.24 128

MDTV_nov1_ep13_2view						37.79 370	75.16 331		55.10 346	66.53 314		49.34 281		53.98 304		87.94 259

ACMMP++_ref															81.95 207

ACMMP++															81.25 212

Test By Simon												64.33 125

High-resolution multi-view benchmark