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.

DP-MVS Recon		91.72 77	90.85 87	94.34 35	99.50 1	85.00 64	98.51 26	95.96 141	80.57 226	88.08 133	97.63 80	76.84 120	99.89 7	85.67 145	94.88 121	98.13 79

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 26	95.17 2	92.11 73	98.46 26	87.33 24	99.97 2	97.21 17	99.31 4	99.63 7

MG-MVS		94.25 26	93.72 36	95.85 11	99.38 3	89.35 11	97.98 49	98.09 8	89.99 37	92.34 70	96.97 109	81.30 64	98.99 106	88.54 123	98.88 21	99.20 23

AdaColmap		88.81 137	87.61 146	92.39 113	99.33 4	79.95 169	96.70 153	95.58 161	77.51 280	83.05 182	96.69 122	61.90 259	99.72 36	84.29 156	93.47 137	97.50 131

CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 24	94.66 3	96.79 12	98.78 11	86.42 29	99.95 3	97.59 13	99.18 7	99.00 28

testtj		94.09 31	94.08 31	94.09 45	99.28 6	83.32 96	97.59 77	96.61 80	83.60 176	94.77 41	98.46 26	82.72 57	99.64 47	95.29 38	98.42 46	99.32 18

NCCC		95.63 6	95.94 8	94.69 27	99.21 7	85.15 60	99.16 3	96.96 35	94.11 6	95.59 25	98.64 21	85.07 34	99.91 4	95.61 33	99.10 9	99.00 28

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 22	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

ZD-MVS					99.09 9	83.22 98		96.60 83	82.88 191	93.61 56	98.06 51	82.93 53	99.14 96	95.51 35	98.49 42

DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 10	85.34 50	98.13 39	96.77 55	88.38 63	97.70 6	98.77 12	92.06 3	99.84 12	97.47 14	99.37 1	99.70 3

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

No_MVS				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

DVP-MVS		95.58 8	95.91 9	94.57 29	99.05 10	85.18 55	99.06 9	96.46 100	88.75 54	96.69 13	98.76 14	87.69 22	99.76 25	97.90 9	98.85 22	98.77 35
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					99.05 10	85.18 55	99.11 8	96.78 49	88.75 54	97.65 9	98.91 3	87.69 22

test_0728_SECOND				95.14 17	99.04 15	86.14 34	99.06 9	96.77 55					99.84 12	97.90 9	98.85 22	99.45 10

SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 62	99.12 6	96.78 49	88.72 56	97.79 4	98.91 3	88.48 17	99.82 18	98.15 4	98.97 17	99.74 1

IU-MVS					99.03 16	85.34 50		96.86 44	92.05 16	98.74 1				98.15 4	98.97 17	99.42 13

test_241102_ONE					99.03 16	85.03 62		96.78 49	88.72 56	97.79 4	98.90 6	88.48 17	99.82 18

ETH3 D test6400		95.56 9	95.41 13	96.00 9	99.02 19	89.42 9	98.75 17	96.80 48	87.28 86	95.88 23	98.95 2	85.92 31	99.41 67	97.15 18	98.95 20	99.18 25

test_one_0601					98.91 20	84.56 72		96.70 65	88.06 69	96.57 16	98.77 12	88.04 20

test_part2					98.90 21	85.14 61				96.07 21

PAPR		92.74 54	92.17 68	94.45 31	98.89 22	84.87 67	97.20 107	96.20 127	87.73 78	88.40 128	98.12 44	78.71 92	99.76 25	87.99 130	96.28 104	98.74 36

DeepC-MVS_fast	89.06 2	94.48 20	94.30 28	95.02 19	98.86 23	85.68 45	98.06 45	96.64 77	93.64 8	91.74 79	98.54 22	80.17 75	99.90 5	92.28 78	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		94.56 19	94.75 16	93.96 48	98.84 24	83.40 93	98.04 47	96.41 106	85.79 113	95.00 35	98.28 32	84.32 41	99.18 92	97.35 16	98.77 29	99.28 20

DPE-MVS		95.32 10	95.55 10	94.64 28	98.79 25	84.87 67	97.77 61	96.74 60	86.11 105	96.54 17	98.89 7	88.39 19	99.74 33	97.67 12	99.05 12	99.31 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APD-MVS		93.61 39	93.59 39	93.69 57	98.76 26	83.26 97	97.21 105	96.09 134	82.41 200	94.65 42	98.21 34	81.96 62	98.81 119	94.65 45	98.36 54	99.01 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		92.89 51	92.86 53	92.98 89	98.71 27	81.12 141	97.58 78	96.70 65	85.20 130	91.75 77	97.97 59	78.47 94	99.71 37	90.95 89	98.41 48	98.12 80

#test#		92.99 48	92.99 49	92.98 89	98.71 27	81.12 141	97.77 61	96.70 65	85.75 114	91.75 77	97.97 59	78.47 94	99.71 37	91.36 85	98.41 48	98.12 80

region2R		92.72 58	92.70 56	92.79 98	98.68 29	80.53 158	97.53 82	96.51 94	85.22 128	91.94 75	97.98 57	77.26 113	99.67 45	90.83 93	98.37 53	98.18 73

test_prior3		94.03 33	94.34 26	93.09 84	98.68 29	81.91 121	98.37 29	96.40 109	86.08 107	94.57 43	98.02 52	83.14 49	99.06 102	95.05 40	98.79 27	98.29 65

test_prior				93.09 84	98.68 29	81.91 121		96.40 109					99.06 102			98.29 65

ACMMPR		92.69 60	92.67 57	92.75 99	98.66 32	80.57 155	97.58 78	96.69 68	85.20 130	91.57 81	97.92 61	77.01 118	99.67 45	90.95 89	98.41 48	98.00 92

API-MVS		90.18 111	88.97 122	93.80 52	98.66 32	82.95 104	97.50 86	95.63 160	75.16 298	86.31 146	97.69 72	72.49 186	99.90 5	81.26 188	96.07 107	98.56 48

CDPH-MVS		93.12 45	92.91 51	93.74 54	98.65 34	83.88 81	97.67 72	96.26 122	83.00 188	93.22 60	98.24 33	81.31 63	99.21 85	89.12 118	98.74 31	98.14 78

TEST9					98.64 35	83.71 86	97.82 57	96.65 74	84.29 155	95.16 29	98.09 46	84.39 37	99.36 75

train_agg		94.28 24	94.45 22	93.74 54	98.64 35	83.71 86	97.82 57	96.65 74	84.50 147	95.16 29	98.09 46	84.33 38	99.36 75	95.91 29	98.96 19	98.16 75

test_8					98.63 37	83.64 89	97.81 59	96.63 79	84.50 147	95.10 31	98.11 45	84.33 38	99.23 81

HPM-MVS++		95.32 10	95.48 12	94.85 23	98.62 38	86.04 35	97.81 59	96.93 38	92.45 12	95.69 24	98.50 24	85.38 32	99.85 10	94.75 43	99.18 7	98.65 44

agg_prior1		94.10 30	94.31 27	93.48 70	98.59 39	83.13 99	97.77 61	96.56 88	84.38 151	94.19 48	98.13 41	84.66 36	99.16 94	95.74 31	98.74 31	98.15 77

agg_prior					98.59 39	83.13 99		96.56 88		94.19 48			99.16 94

CSCG		92.02 71	91.65 77	93.12 82	98.53 41	80.59 154	97.47 87	97.18 22	77.06 288	84.64 162	97.98 57	83.98 43	99.52 58	90.72 95	97.33 83	99.23 22

XVS		92.69 60	92.71 54	92.63 105	98.52 42	80.29 161	97.37 98	96.44 102	87.04 93	91.38 83	97.83 68	77.24 115	99.59 52	90.46 99	98.07 62	98.02 87

X-MVStestdata		86.26 182	84.14 199	92.63 105	98.52 42	80.29 161	97.37 98	96.44 102	87.04 93	91.38 83	20.73 378	77.24 115	99.59 52	90.46 99	98.07 62	98.02 87

FOURS1					98.51 44	78.01 225	98.13 39	96.21 126	83.04 186	94.39 46

CP-MVS		92.54 65	92.60 59	92.34 114	98.50 45	79.90 171	98.40 28	96.40 109	84.75 138	90.48 101	98.09 46	77.40 112	99.21 85	91.15 88	98.23 59	97.92 100

PAPM_NR		91.46 84	90.82 88	93.37 74	98.50 45	81.81 128	95.03 237	96.13 131	84.65 143	86.10 149	97.65 78	79.24 84	99.75 31	83.20 176	96.88 96	98.56 48

MAR-MVS		90.63 102	90.22 98	91.86 132	98.47 47	78.20 221	97.18 109	96.61 80	83.87 167	88.18 132	98.18 35	68.71 214	99.75 31	83.66 169	97.15 88	97.63 122
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
patch_mono-2		95.14 12	96.08 7	92.33 115	98.44 48	77.84 233	98.43 27	97.21 20	92.58 11	97.68 8	97.65 78	86.88 26	99.83 16	98.25 3	97.60 74	99.33 17

Regformer-1		94.00 34	94.04 33	93.87 50	98.41 49	84.29 76	97.43 93	97.04 28	89.50 43	92.75 67	98.13 41	82.60 59	99.26 80	93.55 58	96.99 91	98.06 84

Regformer-2		93.92 35	94.01 34	93.67 59	98.41 49	83.75 85	97.43 93	97.00 30	89.43 45	92.69 68	98.13 41	82.48 60	99.22 83	93.51 59	96.99 91	98.04 85

mPP-MVS		91.88 73	91.82 73	92.07 124	98.38 51	78.63 205	97.29 101	96.09 134	85.12 132	88.45 127	97.66 74	75.53 145	99.68 43	89.83 109	98.02 65	97.88 101

SR-MVS		92.16 69	92.27 64	91.83 135	98.37 52	78.41 211	96.67 154	95.76 152	82.19 204	91.97 74	98.07 50	76.44 127	98.64 123	93.71 55	97.27 85	98.45 55

test12				94.25 38	98.34 53	85.55 47		96.35 116		92.36 69		80.84 65	99.22 83		98.31 56	97.98 94

CPTT-MVS		89.72 118	89.87 109	89.29 204	98.33 54	73.30 295	97.70 69	95.35 177	75.68 294	87.40 137	97.44 90	70.43 206	98.25 140	89.56 114	96.90 94	96.33 175

MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 55	80.10 168	97.42 95	96.78 49	92.20 14	97.11 11	98.29 31	93.46 1	99.10 100	96.01 26	99.30 5	99.38 14
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
MSLP-MVS++		94.28 24	94.39 25	93.97 47	98.30 56	84.06 80	98.64 21	96.93 38	90.71 27	93.08 62	98.70 18	79.98 76	99.21 85	94.12 51	99.07 11	98.63 45

PGM-MVS		91.93 72	91.80 74	92.32 117	98.27 57	79.74 176	95.28 222	97.27 18	83.83 168	90.89 96	97.78 70	76.12 134	99.56 56	88.82 121	97.93 68	97.66 119

xxxxxxxxxxxxxcwj		94.38 22	94.62 19	93.68 58	98.24 58	83.34 94	98.61 23	92.69 297	91.32 19	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 60

ZNCC-MVS		92.75 53	92.60 59	93.23 78	98.24 58	81.82 127	97.63 73	96.50 96	85.00 135	91.05 93	97.74 71	78.38 96	99.80 24	90.48 98	98.34 55	98.07 83

save fliter					98.24 58	83.34 94	98.61 23	96.57 86	91.32 19

114514_t		88.79 139	87.57 147	92.45 110	98.21 61	81.74 130	96.99 128	95.45 169	75.16 298	82.48 185	95.69 138	68.59 215	98.50 131	80.33 194	95.18 119	97.10 149

test1172		91.64 79	92.00 71	90.54 172	98.20 62	74.48 285	96.45 166	95.65 157	81.97 208	91.63 80	98.02 52	75.76 140	98.61 124	93.16 66	97.17 87	98.52 51

GST-MVS		92.43 67	92.22 67	93.04 87	98.17 63	81.64 134	97.40 97	96.38 113	84.71 141	90.90 95	97.40 92	77.55 110	99.76 25	89.75 111	97.74 71	97.72 114

DP-MVS		81.47 258	78.28 272	91.04 156	98.14 64	78.48 207	95.09 235	86.97 349	61.14 356	71.12 303	92.78 200	59.59 269	99.38 69	53.11 350	86.61 190	95.27 198

MP-MVS		92.61 63	92.67 57	92.42 112	98.13 65	79.73 177	97.33 100	96.20 127	85.63 117	90.53 99	97.66 74	78.14 100	99.70 40	92.12 80	98.30 57	97.85 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
9.14			94.26 29		98.10 66		98.14 36	96.52 93	84.74 139	94.83 39	98.80 9	82.80 56	99.37 73	95.95 28	98.42 46

Regformer-3		93.19 43	93.19 47	93.19 80	98.10 66	83.01 103	97.08 124	96.98 32	88.98 51	91.35 87	97.89 62	80.80 66	99.23 81	92.30 77	95.20 117	97.32 139

Regformer-4		93.06 47	93.12 48	92.89 94	98.10 66	82.20 117	97.08 124	96.92 40	88.87 53	91.23 90	97.89 62	80.57 69	99.19 90	92.21 79	95.20 117	97.29 143

PHI-MVS		93.59 40	93.63 38	93.48 70	98.05 69	81.76 129	98.64 21	97.13 23	82.60 198	94.09 52	98.49 25	80.35 70	99.85 10	94.74 44	98.62 35	98.83 33

SMA-MVS		94.70 17	94.68 17	94.76 25	98.02 70	85.94 38	97.47 87	96.77 55	85.32 124	97.92 3	98.70 18	83.09 52	99.84 12	95.79 30	99.08 10	98.49 52
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
PLC	83.97 7	88.00 159	87.38 153	89.83 197	98.02 70	76.46 257	97.16 113	94.43 226	79.26 258	81.98 196	96.28 126	69.36 211	99.27 78	77.71 220	92.25 150	93.77 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		92.74 54	92.71 54	92.86 95	97.90 72	80.85 148	96.47 163	96.33 117	87.92 72	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 98

MTAPA		92.45 66	92.31 63	92.86 95	97.90 72	80.85 148	92.88 287	96.33 117	87.92 72	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 98

APD-MVS_3200maxsize		91.23 91	91.35 81	90.89 162	97.89 74	76.35 260	96.30 178	95.52 165	79.82 245	91.03 94	97.88 65	74.70 162	98.54 129	92.11 81	96.89 95	97.77 111

HPM-MVS		91.62 81	91.53 79	91.89 131	97.88 75	79.22 189	96.99 128	95.73 154	82.07 205	89.50 116	97.19 101	75.59 144	98.93 114	90.91 91	97.94 66	97.54 126
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SD-MVS		94.84 15	95.02 15	94.29 37	97.87 76	84.61 71	97.76 65	96.19 129	89.59 42	96.66 15	98.17 39	84.33 38	99.60 51	96.09 24	98.50 41	98.66 43
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
ETH3D-3000-0.1		94.43 21	94.42 24	94.45 31	97.78 77	85.78 41	97.98 49	96.53 92	85.29 127	95.45 26	98.81 8	83.36 47	99.38 69	96.07 25	98.53 37	98.19 72

dcpmvs_2		93.10 46	93.46 43	92.02 127	97.77 78	79.73 177	94.82 241	93.86 253	86.91 95	91.33 88	96.76 118	85.20 33	98.06 145	96.90 20	97.60 74	98.27 68

原ACMM1				91.22 152	97.77 78	78.10 223		96.61 80	81.05 217	91.28 89	97.42 91	77.92 104	98.98 107	79.85 202	98.51 38	96.59 166

SR-MVS-dyc-post		91.29 89	91.45 80	90.80 164	97.76 80	76.03 265	96.20 185	95.44 170	80.56 227	90.72 97	97.84 66	75.76 140	98.61 124	91.99 82	96.79 99	97.75 112

RE-MVS-def			91.18 85		97.76 80	76.03 265	96.20 185	95.44 170	80.56 227	90.72 97	97.84 66	73.36 180		91.99 82	96.79 99	97.75 112

TSAR-MVS + MP.		94.79 16	95.17 14	93.64 60	97.66 82	84.10 79	95.85 203	96.42 105	91.26 21	97.49 10	96.80 117	86.50 28	98.49 132	95.54 34	99.03 13	98.33 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS_fast		90.38 109	90.17 101	91.03 157	97.61 83	77.35 244	97.15 114	95.48 167	79.51 251	88.79 123	96.90 110	71.64 196	98.81 119	87.01 139	97.44 79	96.94 152

EI-MVSNet-Vis-set		91.84 74	91.77 75	92.04 126	97.60 84	81.17 140	96.61 155	96.87 42	88.20 67	89.19 118	97.55 85	78.69 93	99.14 96	90.29 105	90.94 159	95.80 185

CNLPA		86.96 169	85.37 177	91.72 137	97.59 85	79.34 187	97.21 105	91.05 319	74.22 305	78.90 224	96.75 120	67.21 223	98.95 111	74.68 250	90.77 160	96.88 157

ACMMP		90.39 107	89.97 104	91.64 139	97.58 86	78.21 220	96.78 145	96.72 63	84.73 140	84.72 160	97.23 99	71.22 199	99.63 49	88.37 128	92.41 148	97.08 150
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SF-MVS		94.17 27	94.05 32	94.55 30	97.56 87	85.95 36	97.73 67	96.43 104	84.02 160	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 60

CANet		94.89 14	94.64 18	95.63 12	97.55 88	88.12 15	99.06 9	96.39 112	94.07 7	95.34 28	97.80 69	76.83 121	99.87 8	97.08 19	97.64 73	98.89 31

PVSNet_BlendedMVS		90.05 113	89.96 105	90.33 180	97.47 89	83.86 82	98.02 48	96.73 61	87.98 71	89.53 114	89.61 244	76.42 128	99.57 54	94.29 48	79.59 241	87.57 311

PVSNet_Blended		93.13 44	92.98 50	93.57 64	97.47 89	83.86 82	99.32 1	96.73 61	91.02 25	89.53 114	96.21 127	76.42 128	99.57 54	94.29 48	95.81 113	97.29 143

新几何1				93.12 82	97.44 91	81.60 135		96.71 64	74.54 303	91.22 91	97.57 81	79.13 86	99.51 61	77.40 225	98.46 43	98.26 69

LS3D		82.22 249	79.94 262	89.06 206	97.43 92	74.06 290	93.20 281	92.05 303	61.90 351	73.33 287	95.21 150	59.35 272	99.21 85	54.54 346	92.48 147	93.90 219

test_yl		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 218	89.85 106	96.14 128	75.61 142	98.81 119	90.42 103	88.56 176	98.74 36

DCV-MVSNet		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 218	89.85 106	96.14 128	75.61 142	98.81 119	90.42 103	88.56 176	98.74 36

EI-MVSNet-UG-set		91.35 88	91.22 82	91.73 136	97.39 95	80.68 152	96.47 163	96.83 45	87.92 72	88.30 131	97.36 93	77.84 105	99.13 98	89.43 116	89.45 166	95.37 195

旧先验1					97.39 95	79.58 181		96.54 90			98.08 49	84.00 42			97.42 81	97.62 123

1121		90.66 101	89.82 110	93.16 81	97.39 95	81.71 132	93.33 274	96.66 73	74.45 304	91.38 83	97.55 85	79.27 82	99.52 58	79.95 199	98.43 45	98.26 69

TSAR-MVS + GP.		94.35 23	94.50 20	93.89 49	97.38 98	83.04 102	98.10 41	95.29 180	91.57 17	93.81 53	97.45 87	86.64 27	99.43 66	96.28 23	94.01 130	99.20 23

MVS_111021_HR		93.41 42	93.39 44	93.47 73	97.34 99	82.83 105	97.56 80	98.27 6	89.16 49	89.71 109	97.14 102	79.77 78	99.56 56	93.65 56	97.94 66	98.02 87

MP-MVS-pluss		92.58 64	92.35 62	93.29 75	97.30 100	82.53 109	96.44 168	96.04 138	84.68 142	89.12 119	98.37 29	77.48 111	99.74 33	93.31 64	98.38 52	97.59 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPNet		94.06 32	94.15 30	93.76 53	97.27 101	84.35 74	98.29 31	97.64 13	94.57 4	95.36 27	96.88 112	79.96 77	99.12 99	91.30 86	96.11 106	97.82 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_NAP		93.46 41	93.23 46	94.17 42	97.16 102	84.28 77	96.82 142	96.65 74	86.24 103	94.27 47	97.99 55	77.94 102	99.83 16	93.39 60	98.57 36	98.39 58

LFMVS		89.27 127	87.64 143	94.16 44	97.16 102	85.52 48	97.18 109	94.66 211	79.17 259	89.63 112	96.57 123	55.35 306	98.22 141	89.52 115	89.54 165	98.74 36

DeepPCF-MVS	89.82 1	94.61 18	96.17 5	89.91 194	97.09 104	70.21 324	98.99 14	96.69 68	95.57 1	95.08 32	99.23 1	86.40 30	99.87 8	97.84 11	98.66 34	99.65 6

VNet		92.11 70	91.22 82	94.79 24	96.91 105	86.98 26	97.91 52	97.96 9	86.38 102	93.65 55	95.74 135	70.16 209	98.95 111	93.39 60	88.87 172	98.43 56

TAPA-MVS	81.61 12	85.02 200	83.67 203	89.06 206	96.79 106	73.27 297	95.92 197	94.79 204	74.81 301	80.47 211	96.83 114	71.07 201	98.19 143	49.82 358	92.57 144	95.71 188
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		84.41 211	81.93 232	91.85 134	96.78 107	78.41 211	97.44 89	91.34 314	70.29 330	84.06 166	94.26 175	41.09 351	98.96 108	79.46 204	82.65 226	98.17 74

ETH3D cwj APD-0.16		93.91 37	93.76 35	94.36 34	96.70 108	85.74 42	97.22 102	96.41 106	83.94 163	94.13 51	98.69 20	83.13 51	99.37 73	95.25 39	98.39 51	97.97 97

abl_6		89.80 116	89.71 113	90.07 186	96.53 109	75.52 274	94.48 246	95.04 189	81.12 216	89.22 117	97.00 108	68.83 213	98.96 108	89.86 108	95.27 116	95.73 187

CS-MVS-test		92.98 49	93.67 37	90.90 161	96.52 110	76.87 251	98.68 18	94.73 206	90.36 34	94.84 38	97.89 62	77.94 102	97.15 193	94.28 50	97.80 70	98.70 42

DELS-MVS		94.98 13	94.49 21	96.44 6	96.42 111	90.59 7	99.21 2	97.02 29	94.40 5	91.46 82	97.08 106	83.32 48	99.69 41	92.83 71	98.70 33	99.04 26
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
thres200		88.92 133	87.65 142	92.73 101	96.30 112	85.62 46	97.85 55	98.86 1	84.38 151	84.82 158	93.99 183	75.12 158	98.01 146	70.86 280	86.67 189	94.56 210

CS-MVS		92.73 56	93.48 42	90.48 174	96.27 113	75.93 270	98.55 25	94.93 193	89.32 46	94.54 45	97.67 73	78.91 88	97.02 197	93.80 53	97.32 84	98.49 52

DPM-MVS		96.21 2	95.53 11	98.26 1	96.26 114	95.09 1	99.15 4	96.98 32	93.39 9	96.45 18	98.79 10	90.17 10	99.99 1	89.33 117	99.25 6	99.70 3

tfpn200view9		88.48 146	87.15 158	92.47 109	96.21 115	85.30 53	97.44 89	98.85 2	83.37 178	83.99 168	93.82 186	75.36 152	97.93 148	69.04 286	86.24 195	94.17 212

thres400		88.42 149	87.15 158	92.23 119	96.21 115	85.30 53	97.44 89	98.85 2	83.37 178	83.99 168	93.82 186	75.36 152	97.93 148	69.04 286	86.24 195	93.45 225

test222					96.15 117	78.41 211	95.87 201	96.46 100	71.97 323	89.66 111	97.45 87	76.33 131			98.24 58	98.30 64

HY-MVS	84.06 6	91.63 80	90.37 96	95.39 16	96.12 118	88.25 14	90.22 311	97.58 14	88.33 65	90.50 100	91.96 206	79.26 83	99.06 102	90.29 105	89.07 169	98.88 32

thres100view900		88.30 152	86.95 164	92.33 115	96.10 119	84.90 66	97.14 115	98.85 2	82.69 196	83.41 176	93.66 189	75.43 149	97.93 148	69.04 286	86.24 195	94.17 212

thres600view7		88.06 157	86.70 167	92.15 122	96.10 119	85.17 59	97.14 115	98.85 2	82.70 195	83.41 176	93.66 189	75.43 149	97.82 155	67.13 295	85.88 199	93.45 225

WTY-MVS		92.65 62	91.68 76	95.56 13	96.00 121	88.90 12	98.23 33	97.65 12	88.57 59	89.82 108	97.22 100	79.29 81	99.06 102	89.57 113	88.73 174	98.73 40

MVSTER		89.25 128	88.92 125	90.24 182	95.98 122	84.66 70	96.79 144	95.36 175	87.19 91	80.33 214	90.61 231	90.02 12	95.97 238	85.38 148	78.64 252	90.09 252

testdata				90.13 185	95.92 123	74.17 288		96.49 99	73.49 312	94.82 40	97.99 55	78.80 91	97.93 148	83.53 173	97.52 76	98.29 65

PatchMatch-RL		85.00 201	83.66 204	89.02 208	95.86 124	74.55 284	92.49 291	93.60 269	79.30 256	79.29 223	91.47 213	58.53 280	98.45 134	70.22 283	92.17 152	94.07 216

iter_conf05		90.14 112	89.79 111	91.17 153	95.85 125	86.93 27	97.68 71	88.67 344	89.93 38	81.73 201	92.80 197	90.37 8	96.03 233	90.44 101	80.65 234	90.56 239

canonicalmvs		92.27 68	91.22 82	95.41 15	95.80 126	88.31 13	97.09 122	94.64 214	88.49 61	92.99 64	97.31 94	72.68 185	98.57 128	93.38 62	88.58 175	99.36 16

Anonymous20240529		83.15 232	80.60 251	90.80 164	95.74 127	78.27 215	96.81 143	94.92 194	60.10 360	81.89 198	92.54 201	45.82 336	98.82 118	79.25 208	78.32 258	95.31 197

MVS_111021_LR		91.60 82	91.64 78	91.47 145	95.74 127	78.79 202	96.15 187	96.77 55	88.49 61	88.64 125	97.07 107	72.33 188	99.19 90	93.13 69	96.48 103	96.43 170

test_part1		84.72 204	82.85 218	90.34 179	95.73 129	84.79 69	96.75 148	94.10 242	79.05 265	75.97 263	89.51 245	67.69 216	95.94 242	79.34 205	67.50 322	90.30 246

PS-MVSNAJ		94.17 27	93.52 41	96.10 8	95.65 130	92.35 2	98.21 34	95.79 151	92.42 13	96.24 19	98.18 35	71.04 202	99.17 93	96.77 21	97.39 82	96.79 159

iter_conf_final		89.51 121	89.21 118	90.39 176	95.60 131	84.44 73	97.22 102	89.09 337	89.11 50	82.07 195	92.80 197	87.03 25	96.03 233	89.10 119	80.89 231	90.70 237

Anonymous20231211		79.72 274	77.19 280	87.33 245	95.59 132	77.16 249	95.18 229	94.18 237	59.31 362	72.57 295	86.20 295	47.89 330	95.66 260	74.53 254	69.24 305	89.18 271

alignmvs		92.97 50	92.26 65	95.12 18	95.54 133	87.77 19	98.67 19	96.38 113	88.04 70	93.01 63	97.45 87	79.20 85	98.60 126	93.25 65	88.76 173	98.99 30

PVSNet	82.34 9	89.02 130	87.79 140	92.71 102	95.49 134	81.50 136	97.70 69	97.29 17	87.76 77	85.47 152	95.12 158	56.90 294	98.90 115	80.33 194	94.02 129	97.71 116

tpmvs		83.04 235	80.77 246	89.84 196	95.43 135	77.96 227	85.59 343	95.32 179	75.31 297	76.27 258	83.70 324	73.89 172	97.41 176	59.53 326	81.93 229	94.14 214

SteuartSystems-ACMMP		94.13 29	94.44 23	93.20 79	95.41 136	81.35 138	99.02 13	96.59 84	89.50 43	94.18 50	98.36 30	83.68 46	99.45 65	94.77 42	98.45 44	98.81 34
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		87.47 166	85.90 172	92.18 121	95.41 136	82.26 116	87.00 334	96.28 121	85.88 112	84.23 165	85.57 302	75.07 159	96.26 227	71.14 278	92.50 146	98.03 86

BH-RMVSNet		86.84 172	85.28 178	91.49 144	95.35 138	80.26 164	96.95 135	92.21 301	82.86 192	81.77 200	95.46 145	59.34 273	97.64 160	69.79 284	93.81 134	96.57 167

OMC-MVS		88.80 138	88.16 134	90.72 167	95.30 139	77.92 230	94.81 242	94.51 220	86.80 98	84.97 156	96.85 113	67.53 219	98.60 126	85.08 149	87.62 183	95.63 189

MVS_Test		90.29 110	89.18 119	93.62 62	95.23 140	84.93 65	94.41 249	94.66 211	84.31 153	90.37 103	91.02 223	75.13 157	97.82 155	83.11 178	94.42 126	98.12 80

F-COLMAP		84.50 210	83.44 210	87.67 235	95.22 141	72.22 302	95.95 195	93.78 260	75.74 293	76.30 257	95.18 153	59.50 271	98.45 134	72.67 266	86.59 191	92.35 229

baseline1		88.85 136	87.49 149	92.93 93	95.21 142	86.85 28	95.47 216	94.61 216	87.29 85	83.11 181	94.99 163	80.70 67	96.89 204	82.28 182	73.72 274	95.05 199

CHOSEN 1792x2688		91.07 93	90.21 99	93.64 60	95.18 143	83.53 90	96.26 180	96.13 131	88.92 52	84.90 157	93.10 195	72.86 183	99.62 50	88.86 120	95.67 114	97.79 110

UGNet		87.73 163	86.55 168	91.27 150	95.16 144	79.11 193	96.35 175	96.23 124	88.14 68	87.83 135	90.48 232	50.65 319	99.09 101	80.13 198	94.03 128	95.60 190
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
VDD-MVS		88.28 153	87.02 163	92.06 125	95.09 145	80.18 167	97.55 81	94.45 225	83.09 184	89.10 120	95.92 134	47.97 329	98.49 132	93.08 70	86.91 188	97.52 130

PVSNet_Blended_VisFu		91.24 90	90.77 89	92.66 103	95.09 145	82.40 113	97.77 61	95.87 148	88.26 66	86.39 145	93.94 184	76.77 122	99.27 78	88.80 122	94.00 131	96.31 176

h-mvs33		89.30 126	88.95 124	90.36 178	95.07 147	76.04 264	96.96 134	97.11 25	90.39 32	92.22 71	95.10 159	74.70 162	98.86 116	93.14 67	65.89 332	96.16 178

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 10	95.07 147	92.02 6	98.19 35	95.68 156	92.06 15	96.01 22	98.14 40	70.83 205	98.96 108	96.74 22	96.57 102	96.76 162

cl22		85.11 199	84.17 198	87.92 231	95.06 149	78.82 199	95.51 214	94.22 233	79.74 247	76.77 247	87.92 266	75.96 136	95.68 259	79.93 201	72.42 281	89.27 269

BH-w/o		88.24 154	87.47 151	90.54 172	95.03 150	78.54 206	97.41 96	93.82 255	84.08 158	78.23 232	94.51 171	69.34 212	97.21 187	80.21 197	94.58 125	95.87 184

CHOSEN 280x420		91.71 78	91.85 72	91.29 149	94.94 151	82.69 106	87.89 328	96.17 130	85.94 110	87.27 140	94.31 173	90.27 9	95.65 262	94.04 52	95.86 111	95.53 192

GG-mvs-BLEND				93.49 69	94.94 151	86.26 32	81.62 350	97.00 30		88.32 130	94.30 174	91.23 5	96.21 230	88.49 125	97.43 80	98.00 92

HyFIR lowres test		89.36 124	88.60 128	91.63 141	94.91 153	80.76 151	95.60 212	95.53 163	82.56 199	84.03 167	91.24 220	78.03 101	96.81 210	87.07 138	88.41 178	97.32 139

miper_enhance_ethall		85.95 186	85.20 179	88.19 228	94.85 154	79.76 173	96.00 192	94.06 245	82.98 189	77.74 235	88.76 253	79.42 79	95.46 272	80.58 192	72.42 281	89.36 268

mvs_anonymous		88.68 140	87.62 145	91.86 132	94.80 155	81.69 133	93.53 270	94.92 194	82.03 206	78.87 226	90.43 234	75.77 139	95.34 276	85.04 150	93.16 141	98.55 50

CANet_DTU		90.98 94	90.04 103	93.83 51	94.76 156	86.23 33	96.32 177	93.12 290	93.11 10	93.71 54	96.82 116	63.08 248	99.48 63	84.29 156	95.12 120	95.77 186

PMMVS		89.46 123	89.92 107	88.06 229	94.64 157	69.57 331	96.22 182	94.95 192	87.27 87	91.37 86	96.54 124	65.88 230	97.39 177	88.54 123	93.89 132	97.23 145

TR-MVS		86.30 181	84.93 187	90.42 175	94.63 158	77.58 239	96.57 157	93.82 255	80.30 235	82.42 187	95.16 154	58.74 278	97.55 166	74.88 248	87.82 182	96.13 180

EPNet_dtu		87.65 164	87.89 137	86.93 255	94.57 159	71.37 317	96.72 149	96.50 96	88.56 60	87.12 141	95.02 161	75.91 138	94.01 314	66.62 297	90.00 163	95.42 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		84.71 205	82.71 221	90.70 168	94.55 160	87.71 20	95.92 197	94.67 210	81.73 210	75.82 267	88.08 264	66.99 224	94.47 306	71.23 275	75.38 268	89.91 257

ETV-MVS		92.72 58	92.87 52	92.28 118	94.54 161	81.89 123	97.98 49	95.21 183	89.77 41	93.11 61	96.83 114	77.23 117	97.50 171	95.74 31	95.38 115	97.44 133

EIA-MVS		91.73 75	92.05 70	90.78 166	94.52 162	76.40 259	98.06 45	95.34 178	89.19 48	88.90 122	97.28 98	77.56 109	97.73 158	90.77 94	96.86 98	98.20 71

BH-untuned		86.95 170	85.94 171	89.99 189	94.52 162	77.46 241	96.78 145	93.37 280	81.80 209	76.62 250	93.81 188	66.64 227	97.02 197	76.06 238	93.88 133	95.48 193

DeepC-MVS	86.58 3	91.53 83	91.06 86	92.94 92	94.52 162	81.89 123	95.95 195	95.98 140	90.76 26	83.76 174	96.76 118	73.24 181	99.71 37	91.67 84	96.96 93	97.22 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
gg-mvs-nofinetune		85.48 194	82.90 217	93.24 77	94.51 165	85.82 40	79.22 354	96.97 34	61.19 355	87.33 139	53.01 368	90.58 6	96.07 232	86.07 143	97.23 86	97.81 109

3Dnovator+	82.88 8	89.63 120	87.85 138	94.99 20	94.49 166	86.76 30	97.84 56	95.74 153	86.10 106	75.47 272	96.02 131	65.00 238	99.51 61	82.91 180	97.07 90	98.72 41

ET-MVSNet_ETH3D		90.01 114	89.03 120	92.95 91	94.38 167	86.77 29	98.14 36	96.31 120	89.30 47	63.33 339	96.72 121	90.09 11	93.63 321	90.70 96	82.29 228	98.46 54

tpmrst		88.36 150	87.38 153	91.31 147	94.36 168	79.92 170	87.32 332	95.26 182	85.32 124	88.34 129	86.13 296	80.60 68	96.70 214	83.78 163	85.34 206	97.30 142

MVS		90.60 103	88.64 127	96.50 5	94.25 169	90.53 8	93.33 274	97.21 20	77.59 279	78.88 225	97.31 94	71.52 197	99.69 41	89.60 112	98.03 64	99.27 21

dp		84.30 213	82.31 227	90.28 181	94.24 170	77.97 226	86.57 337	95.53 163	79.94 244	80.75 208	85.16 310	71.49 198	96.39 223	63.73 312	83.36 216	96.48 169

sss		90.87 98	89.96 105	93.60 63	94.15 171	83.84 84	97.14 115	98.13 7	85.93 111	89.68 110	96.09 130	71.67 194	99.30 77	87.69 131	89.16 168	97.66 119

PatchmatchNet		86.83 173	85.12 183	91.95 129	94.12 172	82.27 115	86.55 338	95.64 159	84.59 145	82.98 183	84.99 314	77.26 113	95.96 241	68.61 290	91.34 157	97.64 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.69 202		94.09 173	81.01 143	86.78 336	96.09 134	83.81 169	84.75 159	84.32 319	74.44 167	96.54 218	63.88 311	85.07 207

UA-Net		88.92 133	88.48 130	90.24 182	94.06 174	77.18 248	93.04 283	94.66 211	87.39 84	91.09 92	93.89 185	74.92 160	98.18 144	75.83 241	91.43 156	95.35 196

Fast-Effi-MVS+		87.93 161	86.94 165	90.92 160	94.04 175	79.16 191	98.26 32	93.72 264	81.29 214	83.94 171	92.90 196	69.83 210	96.68 215	76.70 231	91.74 155	96.93 153

QAPM		86.88 171	84.51 191	93.98 46	94.04 175	85.89 39	97.19 108	96.05 137	73.62 309	75.12 275	95.62 141	62.02 255	99.74 33	70.88 279	96.06 108	96.30 177

thisisatest0515		90.95 96	90.26 97	93.01 88	94.03 177	84.27 78	97.91 52	96.67 70	83.18 181	86.87 143	95.51 144	88.66 16	97.85 154	80.46 193	89.01 170	96.92 155

Vis-MVSNet (Re-imp)		88.88 135	88.87 126	88.91 210	93.89 178	74.43 286	96.93 137	94.19 236	84.39 150	83.22 179	95.67 139	78.24 98	94.70 301	78.88 212	94.40 127	97.61 124

ADS-MVSNet2		79.57 275	77.53 277	85.71 276	93.78 179	72.13 304	79.48 352	86.11 354	73.09 315	80.14 216	79.99 344	62.15 253	90.14 354	59.49 327	83.52 213	94.85 202

ADS-MVSNet		81.26 261	78.36 271	89.96 192	93.78 179	79.78 172	79.48 352	93.60 269	73.09 315	80.14 216	79.99 344	62.15 253	95.24 283	59.49 327	83.52 213	94.85 202

EPP-MVSNet		89.76 117	89.72 112	89.87 195	93.78 179	76.02 267	97.22 102	96.51 94	79.35 253	85.11 154	95.01 162	84.82 35	97.10 195	87.46 134	88.21 180	96.50 168

3Dnovator	82.32 10	89.33 125	87.64 143	94.42 33	93.73 182	85.70 44	97.73 67	96.75 59	86.73 101	76.21 259	95.93 132	62.17 252	99.68 43	81.67 186	97.81 69	97.88 101

Effi-MVS+		90.70 100	89.90 108	93.09 84	93.61 183	83.48 91	95.20 227	92.79 295	83.22 180	91.82 76	95.70 137	71.82 193	97.48 173	91.25 87	93.67 135	98.32 60

IS-MVSNet		88.67 141	88.16 134	90.20 184	93.61 183	76.86 252	96.77 147	93.07 291	84.02 160	83.62 175	95.60 142	74.69 165	96.24 229	78.43 215	93.66 136	97.49 132

AUN-MVS		86.25 183	85.57 173	88.26 224	93.57 185	73.38 293	95.45 217	95.88 146	83.94 163	85.47 152	94.21 177	73.70 177	96.67 216	83.54 172	64.41 336	94.73 208

test2506		90.96 95	90.39 94	92.65 104	93.54 186	82.46 112	96.37 173	97.35 16	86.78 99	87.55 136	95.25 147	77.83 106	97.50 171	84.07 158	94.80 122	97.98 94

ECVR-MVS		88.35 151	87.25 155	91.65 138	93.54 186	79.40 184	96.56 159	90.78 324	86.78 99	85.57 151	95.25 147	57.25 292	97.56 164	84.73 154	94.80 122	97.98 94

hse-mvs2		88.22 155	88.21 132	88.25 225	93.54 186	73.41 292	95.41 219	95.89 145	90.39 32	92.22 71	94.22 176	74.70 162	96.66 217	93.14 67	64.37 337	94.69 209

LCM-MVSNet-Re		83.75 220	83.54 208	84.39 299	93.54 186	64.14 347	92.51 290	84.03 362	83.90 166	66.14 328	86.59 285	67.36 221	92.68 328	84.89 152	92.87 142	96.35 172

DROMVSNet		91.73 75	92.11 69	90.58 170	93.54 186	77.77 235	98.07 44	94.40 227	87.44 82	92.99 64	97.11 105	74.59 166	96.87 206	93.75 54	97.08 89	97.11 148

tpm cat1		83.63 223	81.38 240	90.39 176	93.53 191	78.19 222	85.56 344	95.09 186	70.78 328	78.51 229	83.28 327	74.80 161	97.03 196	66.77 296	84.05 211	95.95 181

thisisatest0530		89.65 119	89.02 121	91.53 143	93.46 192	80.78 150	96.52 160	96.67 70	81.69 211	83.79 173	94.90 164	88.85 15	97.68 159	77.80 216	87.49 186	96.14 179

MSDG		80.62 268	77.77 276	89.14 205	93.43 193	77.24 245	91.89 298	90.18 328	69.86 333	68.02 317	91.94 208	52.21 317	98.84 117	59.32 329	83.12 217	91.35 231

ab-mvs		87.08 168	84.94 186	93.48 70	93.34 194	83.67 88	88.82 319	95.70 155	81.18 215	84.55 163	90.14 240	62.72 249	98.94 113	85.49 147	82.54 227	97.85 105

1314		88.94 132	87.20 156	94.17 42	93.21 195	85.73 43	93.33 274	96.64 77	82.89 190	75.98 262	96.36 125	66.83 226	99.39 68	83.52 174	96.02 109	97.39 137

1112_ss		88.60 144	87.47 151	92.00 128	93.21 195	80.97 145	96.47 163	92.46 299	83.64 174	80.86 207	97.30 96	80.24 73	97.62 161	77.60 221	85.49 203	97.40 136

GeoE		86.36 180	85.20 179	89.83 197	93.17 197	76.13 262	97.53 82	92.11 302	79.58 250	80.99 205	94.01 182	66.60 228	96.17 231	73.48 262	89.30 167	97.20 147

test1111		88.11 156	87.04 162	91.35 146	93.15 198	78.79 202	96.57 157	90.78 324	86.88 97	85.04 155	95.20 151	57.23 293	97.39 177	83.88 161	94.59 124	97.87 103

Test_1112_low_res		88.03 158	86.73 166	91.94 130	93.15 198	80.88 147	96.44 168	92.41 300	83.59 177	80.74 209	91.16 221	80.18 74	97.59 162	77.48 224	85.40 204	97.36 138

CostFormer		89.08 129	88.39 131	91.15 154	93.13 200	79.15 192	88.61 322	96.11 133	83.14 182	89.58 113	86.93 280	83.83 45	96.87 206	88.22 129	85.92 198	97.42 134

IB-MVS	85.34 4	88.67 141	87.14 160	93.26 76	93.12 201	84.32 75	98.76 16	97.27 18	87.19 91	79.36 222	90.45 233	83.92 44	98.53 130	84.41 155	69.79 299	96.93 153
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
diffmvs		91.17 92	90.74 90	92.44 111	93.11 202	82.50 111	96.25 181	93.62 268	87.79 76	90.40 102	95.93 132	73.44 179	97.42 175	93.62 57	92.55 145	97.41 135

tttt0517		88.57 145	88.19 133	89.71 201	93.00 203	75.99 268	95.67 208	96.67 70	80.78 221	81.82 199	94.40 172	88.97 14	97.58 163	76.05 239	86.31 192	95.57 191

MVSFormer		91.36 87	90.57 91	93.73 56	93.00 203	88.08 16	94.80 243	94.48 221	80.74 222	94.90 36	97.13 103	78.84 89	95.10 291	83.77 164	97.46 77	98.02 87

lupinMVS		93.87 38	93.58 40	94.75 26	93.00 203	88.08 16	99.15 4	95.50 166	91.03 24	94.90 36	97.66 74	78.84 89	97.56 164	94.64 46	97.46 77	98.62 46

tpm2		87.35 167	86.26 169	90.62 169	92.93 206	78.67 204	88.06 327	95.99 139	79.33 254	87.40 137	86.43 291	80.28 72	96.40 222	80.23 196	85.73 202	96.79 159

baseline		90.76 99	90.10 102	92.74 100	92.90 207	82.56 108	94.60 245	94.56 219	87.69 79	89.06 121	95.67 139	73.76 174	97.51 170	90.43 102	92.23 151	98.16 75

casdiffmvs		90.95 96	90.39 94	92.63 105	92.82 208	82.53 109	96.83 141	94.47 223	87.69 79	88.47 126	95.56 143	74.04 171	97.54 168	90.90 92	92.74 143	97.83 107

Vis-MVSNet		88.67 141	87.82 139	91.24 151	92.68 209	78.82 199	96.95 135	93.85 254	87.55 81	87.07 142	95.13 157	63.43 246	97.21 187	77.58 222	96.15 105	97.70 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		82.42 245	80.43 254	88.39 220	92.66 210	81.95 118	94.30 255	93.38 277	79.06 262	75.82 267	85.66 298	56.38 300	93.84 316	71.23 275	75.38 268	89.38 265

test1		82.42 245	80.43 254	88.39 220	92.66 210	81.95 118	94.30 255	93.38 277	79.06 262	75.82 267	85.66 298	56.38 300	93.84 316	71.23 275	75.38 268	89.38 265

FMVSNet2		82.79 239	80.44 253	89.83 197	92.66 210	85.43 49	95.42 218	94.35 228	79.06 262	74.46 279	87.28 272	56.38 300	94.31 309	69.72 285	74.68 271	89.76 260

miper_ehance_all_eth		84.57 208	83.60 207	87.50 242	92.64 213	78.25 216	95.40 220	93.47 273	79.28 257	76.41 254	87.64 269	76.53 126	95.24 283	78.58 213	72.42 281	89.01 280

cascas		86.50 178	84.48 193	92.55 108	92.64 213	85.95 36	97.04 127	95.07 188	75.32 296	80.50 210	91.02 223	54.33 313	97.98 147	86.79 141	87.62 183	93.71 221

TESTMET0.1,1		89.83 115	89.34 117	91.31 147	92.54 215	80.19 166	97.11 118	96.57 86	86.15 104	86.85 144	91.83 210	79.32 80	96.95 200	81.30 187	92.35 149	96.77 161

COLMAP_ROB	73.24 19	75.74 304	73.00 310	83.94 301	92.38 216	69.08 333	91.85 299	86.93 350	61.48 354	65.32 331	90.27 236	42.27 347	96.93 203	50.91 355	75.63 267	85.80 336
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		90.54 104	89.54 114	93.55 65	92.31 217	87.58 22	96.99 128	94.87 197	87.23 88	93.27 57	97.56 82	57.43 288	98.32 137	92.72 72	93.46 138	94.74 205

xiu_mvs_v1_base		90.54 104	89.54 114	93.55 65	92.31 217	87.58 22	96.99 128	94.87 197	87.23 88	93.27 57	97.56 82	57.43 288	98.32 137	92.72 72	93.46 138	94.74 205

xiu_mvs_v1_base_debi		90.54 104	89.54 114	93.55 65	92.31 217	87.58 22	96.99 128	94.87 197	87.23 88	93.27 57	97.56 82	57.43 288	98.32 137	92.72 72	93.46 138	94.74 205

SCA		85.63 191	83.64 205	91.60 142	92.30 220	81.86 125	92.88 287	95.56 162	84.85 136	82.52 184	85.12 312	58.04 283	95.39 273	73.89 258	87.58 185	97.54 126

gm-plane-assit					92.27 221	79.64 180			84.47 149		95.15 155		97.93 148	85.81 144

test-LLR		88.48 146	87.98 136	89.98 190	92.26 222	77.23 246	97.11 118	95.96 141	83.76 171	86.30 147	91.38 216	72.30 189	96.78 212	80.82 190	91.92 153	95.94 182

test-mter		88.95 131	88.60 128	89.98 190	92.26 222	77.23 246	97.11 118	95.96 141	85.32 124	86.30 147	91.38 216	76.37 130	96.78 212	80.82 190	91.92 153	95.94 182

PAPM		92.87 52	92.40 61	94.30 36	92.25 224	87.85 18	96.40 172	96.38 113	91.07 23	88.72 124	96.90 110	82.11 61	97.37 179	90.05 107	97.70 72	97.67 118

cl____		83.27 229	82.12 228	86.74 256	92.20 225	75.95 269	95.11 232	93.27 284	78.44 272	74.82 277	87.02 279	74.19 169	95.19 285	74.67 251	69.32 303	89.09 275

DIV-MVS_self_test		83.27 229	82.12 228	86.74 256	92.19 226	75.92 271	95.11 232	93.26 285	78.44 272	74.81 278	87.08 278	74.19 169	95.19 285	74.66 252	69.30 304	89.11 274

AllTest		75.92 302	73.06 309	84.47 295	92.18 227	67.29 338	91.07 307	84.43 360	67.63 337	63.48 336	90.18 237	38.20 355	97.16 190	57.04 337	73.37 276	88.97 283

TestCases				84.47 295	92.18 227	67.29 338		84.43 360	67.63 337	63.48 336	90.18 237	38.20 355	97.16 190	57.04 337	73.37 276	88.97 283

CLD-MVS		87.97 160	87.48 150	89.44 202	92.16 229	80.54 157	98.14 36	94.92 194	91.41 18	79.43 221	95.40 146	62.34 251	97.27 185	90.60 97	82.90 222	90.50 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
c3_l		83.80 219	82.65 222	87.25 249	92.10 230	77.74 237	95.25 225	93.04 292	78.58 269	76.01 261	87.21 276	75.25 156	95.11 290	77.54 223	68.89 307	88.91 286

HQP-NCC					92.08 231		97.63 73		90.52 29	82.30 188

ACMP_Plane					92.08 231		97.63 73		90.52 29	82.30 188

HQP-MVS		87.91 162	87.55 148	88.98 209	92.08 231	78.48 207	97.63 73	94.80 202	90.52 29	82.30 188	94.56 169	65.40 234	97.32 180	87.67 132	83.01 219	91.13 232

PCF-MVS	84.09 5	86.77 176	85.00 185	92.08 123	92.06 234	83.07 101	92.14 295	94.47 223	79.63 249	76.90 245	94.78 165	71.15 200	99.20 89	72.87 264	91.05 158	93.98 217
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					92.04 235	78.22 217					94.56 169

plane_prior6					91.98 236	77.92 230						64.77 240

Effi-MVS+-dtu		84.61 207	84.90 188	83.72 306	91.96 237	63.14 351	94.95 238	93.34 281	85.57 118	79.79 219	87.12 277	61.99 256	95.61 266	83.55 170	85.83 200	92.41 228

mvs-test1		86.83 173	87.17 157	85.81 273	91.96 237	65.24 344	97.90 54	93.34 281	85.57 118	84.51 164	95.14 156	61.99 256	97.19 189	83.55 170	90.55 161	95.00 200

plane_prior1					91.95 239

CDS-MVSNet		89.50 122	88.96 123	91.14 155	91.94 240	80.93 146	97.09 122	95.81 150	84.26 156	84.72 160	94.20 178	80.31 71	95.64 263	83.37 175	88.96 171	96.85 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 165	87.09 161	88.74 214	91.86 241	77.96 227	97.18 109	94.69 207	89.89 39	81.33 202	94.15 179	64.77 240	97.30 182	87.08 136	82.82 223	90.96 234

plane_prior7					91.86 241	77.55 240

eth_miper_zixun_eth		83.12 233	82.01 230	86.47 262	91.85 243	74.80 281	94.33 253	93.18 287	79.11 260	75.74 270	87.25 275	72.71 184	95.32 278	76.78 230	67.13 326	89.27 269

VDDNet		86.44 179	84.51 191	92.22 120	91.56 244	81.83 126	97.10 121	94.64 214	69.50 334	87.84 134	95.19 152	48.01 328	97.92 153	89.82 110	86.92 187	96.89 156

EI-MVSNet		85.80 188	85.20 179	87.59 238	91.55 245	77.41 242	95.13 230	95.36 175	80.43 232	80.33 214	94.71 166	73.72 175	95.97 238	76.96 229	78.64 252	89.39 263

CVMVSNet		84.83 203	85.57 173	82.63 317	91.55 245	60.38 358	95.13 230	95.03 190	80.60 225	82.10 194	94.71 166	66.40 229	90.19 353	74.30 255	90.32 162	97.31 141

ACMP	81.66 11	84.00 215	83.22 213	86.33 263	91.53 247	72.95 300	95.91 199	93.79 259	83.70 173	73.79 282	92.22 203	54.31 314	96.89 204	83.98 159	79.74 240	89.16 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 216	82.80 220	87.31 247	91.46 248	77.39 243	95.66 209	93.43 275	80.44 230	75.51 271	87.26 274	73.72 175	95.16 287	76.99 227	70.72 290	89.39 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		78.25 285	74.72 298	88.83 212	91.20 249	74.10 289	73.91 366	88.70 343	59.89 361	66.82 324	85.12 312	78.38 96	94.54 305	48.84 360	79.58 242	97.86 104

miper_lstm_enhance		81.66 257	80.66 250	84.67 291	91.19 250	71.97 309	91.94 297	93.19 286	77.86 276	72.27 297	85.26 306	73.46 178	93.42 323	73.71 261	67.05 327	88.61 288

ACMM	80.70 13	83.72 222	82.85 218	86.31 266	91.19 250	72.12 305	95.88 200	94.29 231	80.44 230	77.02 243	91.96 206	55.24 307	97.14 194	79.30 207	80.38 235	89.67 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		88.48 146	87.79 140	90.56 171	91.09 252	79.18 190	96.45 166	95.88 146	83.64 174	83.12 180	93.33 191	75.94 137	95.74 258	82.40 181	88.27 179	96.75 163

ACMH+	76.62 16	77.47 293	74.94 295	85.05 285	91.07 253	71.58 315	93.26 279	90.01 329	71.80 324	64.76 333	88.55 256	41.62 349	96.48 220	62.35 318	71.00 287	87.09 319

OpenMVS	79.58 14	86.09 184	83.62 206	93.50 68	90.95 254	86.71 31	97.44 89	95.83 149	75.35 295	72.64 294	95.72 136	57.42 291	99.64 47	71.41 273	95.85 112	94.13 215

LPG-MVS_test		84.20 214	83.49 209	86.33 263	90.88 255	73.06 298	95.28 222	94.13 239	82.20 202	76.31 255	93.20 192	54.83 311	96.95 200	83.72 166	80.83 232	88.98 281

LGP-MVS_train				86.33 263	90.88 255	73.06 298		94.13 239	82.20 202	76.31 255	93.20 192	54.83 311	96.95 200	83.72 166	80.83 232	88.98 281

KD-MVS_2432*1600		77.63 291	74.92 296	85.77 274	90.86 257	79.44 182	88.08 325	93.92 249	76.26 290	67.05 322	82.78 329	72.15 191	91.92 337	61.53 319	41.62 369	85.94 333

miper_refine_blended		77.63 291	74.92 296	85.77 274	90.86 257	79.44 182	88.08 325	93.92 249	76.26 290	67.05 322	82.78 329	72.15 191	91.92 337	61.53 319	41.62 369	85.94 333

baseline2		90.39 107	90.21 99	90.93 159	90.86 257	80.99 144	95.20 227	97.41 15	86.03 109	80.07 218	94.61 168	90.58 6	97.47 174	87.29 135	89.86 164	94.35 211

PVSNet_0	77.72 15	81.70 255	78.95 269	89.94 193	90.77 260	76.72 255	95.96 194	96.95 36	85.01 134	70.24 310	88.53 258	52.32 316	98.20 142	86.68 142	44.08 368	94.89 201

ACMH	75.40 17	77.99 287	74.96 294	87.10 252	90.67 261	76.41 258	93.19 282	91.64 310	72.47 321	63.44 338	87.61 270	43.34 342	97.16 190	58.34 331	73.94 273	87.72 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS-HIRNet		71.36 323	67.00 327	84.46 297	90.58 262	69.74 329	79.15 355	87.74 348	46.09 367	61.96 346	50.50 369	45.14 337	95.64 263	53.74 348	88.11 181	88.00 302

jason		92.73 56	92.23 66	94.21 41	90.50 263	87.30 25	98.65 20	95.09 186	90.61 28	92.76 66	97.13 103	75.28 155	97.30 182	93.32 63	96.75 101	98.02 87
jason: jason.
LTVRE_ROB	73.68 18	77.99 287	75.74 291	84.74 288	90.45 264	72.02 307	86.41 339	91.12 316	72.57 320	66.63 325	87.27 273	54.95 310	96.98 199	56.29 341	75.98 264	85.21 339
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
XVG-OURS		85.18 197	84.38 195	87.59 238	90.42 265	71.73 313	91.06 308	94.07 244	82.00 207	83.29 178	95.08 160	56.42 299	97.55 166	83.70 168	83.42 215	93.49 224

VPA-MVSNet		85.32 195	83.83 201	89.77 200	90.25 266	82.63 107	96.36 174	97.07 27	83.03 187	81.21 204	89.02 250	61.58 260	96.31 226	85.02 151	70.95 288	90.36 242

XVG-OURS-SEG-HR		85.74 190	85.16 182	87.49 243	90.22 267	71.45 316	91.29 305	94.09 243	81.37 213	83.90 172	95.22 149	60.30 266	97.53 169	85.58 146	84.42 210	93.50 223

tpm		85.55 192	84.47 194	88.80 213	90.19 268	75.39 276	88.79 320	94.69 207	84.83 137	83.96 170	85.21 308	78.22 99	94.68 302	76.32 237	78.02 260	96.34 173

CR-MVSNet		83.53 224	81.36 241	90.06 187	90.16 269	79.75 174	79.02 356	91.12 316	84.24 157	82.27 192	80.35 341	75.45 147	93.67 320	63.37 315	86.25 193	96.75 163

RPMNet		79.85 272	75.92 290	91.64 139	90.16 269	79.75 174	79.02 356	95.44 170	58.43 364	82.27 192	72.55 360	73.03 182	98.41 136	46.10 364	86.25 193	96.75 163

FIs		86.73 177	86.10 170	88.61 216	90.05 271	80.21 165	96.14 188	96.95 36	85.56 121	78.37 231	92.30 202	76.73 123	95.28 280	79.51 203	79.27 246	90.35 243

FMVSNet5		76.46 300	74.16 304	83.35 312	90.05 271	76.17 261	89.58 314	89.85 330	71.39 327	65.29 332	80.42 340	50.61 320	87.70 360	61.05 324	69.24 305	86.18 329

IterMVS		80.67 267	79.16 267	85.20 284	89.79 273	76.08 263	92.97 285	91.86 305	80.28 236	71.20 302	85.14 311	57.93 286	91.34 343	72.52 267	70.74 289	88.18 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		85.31 196	84.23 197	88.55 217	89.75 274	80.55 156	96.72 149	96.89 41	85.42 122	78.40 230	88.93 251	75.38 151	95.52 270	78.58 213	68.02 316	89.57 262

Patchmtry		77.36 294	74.59 299	85.67 278	89.75 274	75.75 273	77.85 359	91.12 316	60.28 358	71.23 301	80.35 341	75.45 147	93.56 322	57.94 332	67.34 325	87.68 307

JIA-IIPM		79.00 281	77.20 279	84.40 298	89.74 276	64.06 348	75.30 363	95.44 170	62.15 350	81.90 197	59.08 366	78.92 87	95.59 267	66.51 300	85.78 201	93.54 222

MS-PatchMatch		83.05 234	81.82 234	86.72 260	89.64 277	79.10 194	94.88 240	94.59 218	79.70 248	70.67 306	89.65 243	50.43 321	96.82 209	70.82 282	95.99 110	84.25 345

IterMVS-SCA-FT		80.51 269	79.10 268	84.73 289	89.63 278	74.66 282	92.98 284	91.81 307	80.05 241	71.06 304	85.18 309	58.04 283	91.40 342	72.48 268	70.70 291	88.12 300

Fast-Effi-MVS+-dtu		83.33 228	82.60 223	85.50 280	89.55 279	69.38 332	96.09 191	91.38 311	82.30 201	75.96 264	91.41 214	56.71 295	95.58 268	75.13 247	84.90 208	91.54 230

PatchT		79.75 273	76.85 283	88.42 218	89.55 279	75.49 275	77.37 360	94.61 216	63.07 347	82.46 186	73.32 359	75.52 146	93.41 324	51.36 353	84.43 209	96.36 171

GA-MVS		85.79 189	84.04 200	91.02 158	89.47 281	80.27 163	96.90 138	94.84 200	85.57 118	80.88 206	89.08 248	56.56 298	96.47 221	77.72 219	85.35 205	96.34 173

UniMVSNet_NR-MVSNet		85.49 193	84.59 189	88.21 227	89.44 282	79.36 185	96.71 151	96.41 106	85.22 128	78.11 233	90.98 225	76.97 119	95.14 288	79.14 209	68.30 313	90.12 249

FC-MVSNet-test		85.96 185	85.39 176	87.66 236	89.38 283	78.02 224	95.65 211	96.87 42	85.12 132	77.34 238	91.94 208	76.28 132	94.74 300	77.09 226	78.82 250	90.21 247

WR-MVS		84.32 212	82.96 215	88.41 219	89.38 283	80.32 160	96.59 156	96.25 123	83.97 162	76.63 249	90.36 235	67.53 219	94.86 298	75.82 242	70.09 297	90.06 254

VPNet		84.69 206	82.92 216	90.01 188	89.01 285	83.45 92	96.71 151	95.46 168	85.71 116	79.65 220	92.18 204	56.66 297	96.01 237	83.05 179	67.84 319	90.56 239

nrg030		86.79 175	85.43 175	90.87 163	88.76 286	85.34 50	97.06 126	94.33 229	84.31 153	80.45 212	91.98 205	72.36 187	96.36 224	88.48 126	71.13 286	90.93 236

DU-MVS		84.57 208	83.33 211	88.28 223	88.76 286	79.36 185	96.43 170	95.41 174	85.42 122	78.11 233	90.82 227	67.61 217	95.14 288	79.14 209	68.30 313	90.33 244

NR-MVSNet		83.35 227	81.52 239	88.84 211	88.76 286	81.31 139	94.45 248	95.16 184	84.65 143	67.81 318	90.82 227	70.36 207	94.87 297	74.75 249	66.89 329	90.33 244

test_0402		72.68 317	69.54 323	82.09 321	88.67 289	71.81 312	92.72 289	86.77 351	61.52 353	62.21 344	83.91 322	43.22 343	93.76 319	34.60 369	72.23 284	80.72 361

RPSCF		77.73 290	76.63 285	81.06 325	88.66 290	55.76 367	87.77 329	87.88 346	64.82 346	74.14 281	92.79 199	49.22 325	96.81 210	67.47 294	76.88 262	90.62 238

FMVSNet1		79.50 276	76.54 286	88.39 220	88.47 291	81.95 118	94.30 255	93.38 277	73.14 314	72.04 299	85.66 298	43.86 339	93.84 316	65.48 304	72.53 280	89.38 265

OPM-MVS		85.84 187	85.10 184	88.06 229	88.34 292	77.83 234	95.72 206	94.20 235	87.89 75	80.45 212	94.05 181	58.57 279	97.26 186	83.88 161	82.76 225	89.09 275
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		78.14 286	75.42 292	86.31 266	88.33 293	79.24 188	94.41 249	96.22 125	73.51 310	69.81 312	85.52 304	55.43 305	95.75 255	47.65 362	67.86 318	83.95 348

MVS_0304		78.43 283	76.70 284	83.60 308	88.22 294	69.81 327	92.91 286	95.10 185	72.32 322	78.71 227	80.29 343	33.78 362	93.37 325	68.77 289	80.23 236	87.63 308

TinyColmap		72.41 318	68.99 325	82.68 316	88.11 295	69.59 330	88.41 323	85.20 357	65.55 343	57.91 356	84.82 316	30.80 368	95.94 242	51.38 352	68.70 308	82.49 356

WR-MVS_H		81.02 263	80.09 257	83.79 303	88.08 296	71.26 318	94.46 247	96.54 90	80.08 240	72.81 293	86.82 281	70.36 207	92.65 329	64.18 309	67.50 322	87.46 315

mvsmamba		85.17 198	84.54 190	87.05 253	87.94 297	75.11 279	96.22 182	87.79 347	86.91 95	78.55 228	91.77 211	64.93 239	95.91 245	86.94 140	79.80 237	90.12 249

CP-MVSNet		81.01 264	80.08 258	83.79 303	87.91 298	70.51 321	94.29 258	95.65 157	80.83 220	72.54 296	88.84 252	63.71 244	92.32 332	68.58 291	68.36 312	88.55 289

D2MVS		82.67 241	81.55 237	86.04 271	87.77 299	76.47 256	95.21 226	96.58 85	82.66 197	70.26 309	85.46 305	60.39 265	95.80 252	76.40 235	79.18 247	85.83 335

TranMVSNet+NR-MVSNet		83.24 231	81.71 235	87.83 232	87.71 300	78.81 201	96.13 190	94.82 201	84.52 146	76.18 260	90.78 229	64.07 243	94.60 303	74.60 253	66.59 331	90.09 252

USDC		78.65 282	76.25 287	85.85 272	87.58 301	74.60 283	89.58 314	90.58 327	84.05 159	63.13 340	88.23 261	40.69 353	96.86 208	66.57 299	75.81 266	86.09 331

PS-CasMVS		80.27 270	79.18 266	83.52 310	87.56 302	69.88 326	94.08 261	95.29 180	80.27 237	72.08 298	88.51 259	59.22 275	92.23 334	67.49 293	68.15 315	88.45 293

RRT_MVS		83.88 217	83.27 212	85.71 276	87.53 303	72.12 305	95.35 221	94.33 229	83.81 169	75.86 266	91.28 219	60.55 264	95.09 293	83.93 160	76.76 263	89.90 258

bld_raw_conf005		83.53 224	82.51 224	86.62 261	87.38 304	73.99 291	95.66 209	85.25 356	85.74 115	76.81 246	91.39 215	55.95 303	95.86 248	84.87 153	79.51 244	90.00 256

MIMVSNet		79.18 280	75.99 289	88.72 215	87.37 305	80.66 153	79.96 351	91.82 306	77.38 282	74.33 280	81.87 333	41.78 348	90.74 349	66.36 302	83.10 218	94.76 204

XXY-MVS		83.84 218	82.00 231	89.35 203	87.13 306	81.38 137	95.72 206	94.26 232	80.15 239	75.92 265	90.63 230	61.96 258	96.52 219	78.98 211	73.28 279	90.14 248

ITE_SJBPF				82.38 318	87.00 307	65.59 343		89.55 332	79.99 243	69.37 314	91.30 218	41.60 350	95.33 277	62.86 317	74.63 272	86.24 328

test_low_dy_conf_001		83.73 221	83.18 214	85.40 282	86.81 308	71.09 319	95.06 236	94.22 233	82.79 193	77.42 236	91.69 212	59.07 276	95.26 282	82.20 184	79.32 245	89.86 259

test0.0.03 1		82.79 239	82.48 225	83.74 305	86.81 308	72.22 302	96.52 160	95.03 190	83.76 171	73.00 290	93.20 192	72.30 189	88.88 356	64.15 310	77.52 261	90.12 249

v8		81.88 253	80.06 260	87.32 246	86.63 310	79.04 197	94.41 249	93.65 267	78.77 267	73.19 289	85.57 302	66.87 225	95.81 251	73.84 260	67.61 321	87.11 318

v10		81.43 259	79.53 265	87.11 251	86.38 311	78.87 198	94.31 254	93.43 275	77.88 275	73.24 288	85.26 306	65.44 233	95.75 255	72.14 269	67.71 320	86.72 322

PEN-MVS		79.47 277	78.26 273	83.08 313	86.36 312	68.58 334	93.85 264	94.77 205	79.76 246	71.37 300	88.55 256	59.79 267	92.46 330	64.50 308	65.40 333	88.19 298

UniMVSNet_ETH3D		80.86 266	78.75 270	87.22 250	86.31 313	72.02 307	91.95 296	93.76 263	73.51 310	75.06 276	90.16 239	43.04 345	95.66 260	76.37 236	78.55 256	93.98 217

v1144		82.90 238	81.27 242	87.78 234	86.29 314	79.07 196	96.14 188	93.93 248	80.05 241	77.38 237	86.80 282	65.50 232	95.93 244	75.21 246	70.13 294	88.33 296

V42		83.04 235	81.53 238	87.57 240	86.27 315	79.09 195	95.87 201	94.11 241	80.35 234	77.22 241	86.79 283	65.32 236	96.02 236	77.74 218	70.14 293	87.61 310

v2v482		83.46 226	81.86 233	88.25 225	86.19 316	79.65 179	96.34 176	94.02 246	81.56 212	77.32 239	88.23 261	65.62 231	96.03 233	77.77 217	69.72 301	89.09 275

v148		82.41 247	80.89 244	86.99 254	86.18 317	76.81 253	96.27 179	93.82 255	80.49 229	75.28 274	86.11 297	67.32 222	95.75 255	75.48 244	67.03 328	88.42 294

pmmvs4		82.54 243	80.79 245	87.79 233	86.11 318	80.49 159	93.55 269	93.18 287	77.29 283	73.35 286	89.40 247	65.26 237	95.05 295	75.32 245	73.61 275	87.83 304

MVP-Stereo		82.65 242	81.67 236	85.59 279	86.10 319	78.29 214	93.33 274	92.82 294	77.75 277	69.17 316	87.98 265	59.28 274	95.76 254	71.77 270	96.88 96	82.73 353
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		82.31 248	80.55 252	87.60 237	85.94 320	78.47 210	95.85 203	93.80 258	79.33 254	76.97 244	86.51 286	63.33 247	95.87 247	73.11 263	70.13 294	88.46 292

TransMVSNet (Re)		76.94 297	74.38 301	84.62 293	85.92 321	75.25 277	95.28 222	89.18 336	73.88 308	67.22 319	86.46 288	59.64 268	94.10 312	59.24 330	52.57 359	84.50 343

PS-MVSNAJss		84.91 202	84.30 196	86.74 256	85.89 322	74.40 287	94.95 238	94.16 238	83.93 165	76.45 253	90.11 241	71.04 202	95.77 253	83.16 177	79.02 249	90.06 254

v144192		82.43 244	80.73 248	87.54 241	85.81 323	78.22 217	95.98 193	93.78 260	79.09 261	77.11 242	86.49 287	64.66 242	95.91 245	74.20 256	69.42 302	88.49 290

bld_raw_dy_0_64		82.13 250	80.76 247	86.24 268	85.78 324	75.03 280	94.40 252	82.62 367	83.12 183	76.46 252	90.96 226	53.83 315	94.55 304	81.04 189	78.60 255	89.14 273

v1921920		82.02 252	80.23 256	87.41 244	85.62 325	77.92 230	95.79 205	93.69 265	78.86 266	76.67 248	86.44 289	62.50 250	95.83 250	72.69 265	69.77 300	88.47 291

v1240		81.70 255	79.83 263	87.30 248	85.50 326	77.70 238	95.48 215	93.44 274	78.46 271	76.53 251	86.44 289	60.85 263	95.84 249	71.59 272	70.17 292	88.35 295

pm-mvs1		80.05 271	78.02 274	86.15 269	85.42 327	75.81 272	95.11 232	92.69 297	77.13 285	70.36 308	87.43 271	58.44 281	95.27 281	71.36 274	64.25 338	87.36 316

our_test_3		77.90 289	75.37 293	85.48 281	85.39 328	76.74 254	93.63 266	91.67 308	73.39 313	65.72 330	84.65 317	58.20 282	93.13 327	57.82 333	67.87 317	86.57 324

ppachtmachnet_test		77.19 295	74.22 303	86.13 270	85.39 328	78.22 217	93.98 262	91.36 313	71.74 325	67.11 321	84.87 315	56.67 296	93.37 325	52.21 351	64.59 335	86.80 321

MDA-MVSNet-bldmvs		71.45 322	67.94 326	81.98 322	85.33 330	68.50 335	92.35 294	88.76 341	70.40 329	42.99 367	81.96 332	46.57 334	91.31 344	48.75 361	54.39 353	86.11 330

Baseline_NR-MVSNet		81.22 262	80.07 259	84.68 290	85.32 331	75.12 278	96.48 162	88.80 340	76.24 292	77.28 240	86.40 292	67.61 217	94.39 308	75.73 243	66.73 330	84.54 342

DTE-MVSNet		78.37 284	77.06 281	82.32 320	85.22 332	67.17 340	93.40 271	93.66 266	78.71 268	70.53 307	88.29 260	59.06 277	92.23 334	61.38 322	63.28 342	87.56 312

pmmvs5		81.34 260	79.54 264	86.73 259	85.02 333	76.91 250	96.22 182	91.65 309	77.65 278	73.55 283	88.61 255	55.70 304	94.43 307	74.12 257	73.35 278	88.86 287

XVG-ACMP-BASELINE		79.38 278	77.90 275	83.81 302	84.98 334	67.14 341	89.03 318	93.18 287	80.26 238	72.87 292	88.15 263	38.55 354	96.26 227	76.05 239	78.05 259	88.02 301

MDA-MVSNet_test_wron		73.54 312	70.43 319	82.86 314	84.55 335	71.85 310	91.74 301	91.32 315	67.63 337	46.73 366	81.09 338	55.11 308	90.42 352	55.91 343	59.76 347	86.31 327

SixPastTwentyTwo		76.04 301	74.32 302	81.22 324	84.54 336	61.43 357	91.16 306	89.30 335	77.89 274	64.04 335	86.31 293	48.23 326	94.29 310	63.54 314	63.84 340	87.93 303

YYNet1		73.53 313	70.43 319	82.85 315	84.52 337	71.73 313	91.69 302	91.37 312	67.63 337	46.79 365	81.21 337	55.04 309	90.43 351	55.93 342	59.70 348	86.38 326

N_pmnet		61.30 332	60.20 335	64.60 350	84.32 338	17.00 385	91.67 303	10.98 384	61.77 352	58.45 355	78.55 348	49.89 323	91.83 339	42.27 367	63.94 339	84.97 340

mvs_tets		81.74 254	80.71 249	84.84 287	84.22 339	70.29 323	93.91 263	93.78 260	82.77 194	73.37 285	89.46 246	47.36 333	95.31 279	81.99 185	79.55 243	88.92 285

jajsoiax		82.12 251	81.15 243	85.03 286	84.19 340	70.70 320	94.22 259	93.95 247	83.07 185	73.48 284	89.75 242	49.66 324	95.37 275	82.24 183	79.76 238	89.02 279

EU-MVSNet		76.92 298	76.95 282	76.83 339	84.10 341	54.73 369	91.77 300	92.71 296	72.74 318	69.57 313	88.69 254	58.03 285	87.43 361	64.91 307	70.00 298	88.33 296

test_djsdf		83.00 237	82.45 226	84.64 292	84.07 342	69.78 328	94.80 243	94.48 221	80.74 222	75.41 273	87.70 268	61.32 262	95.10 291	83.77 164	79.76 238	89.04 278

v7n		79.32 279	77.34 278	85.28 283	84.05 343	72.89 301	93.38 272	93.87 252	75.02 300	70.68 305	84.37 318	59.58 270	95.62 265	67.60 292	67.50 322	87.32 317

OurMVSNet-221017-0		77.18 296	76.06 288	80.55 328	83.78 344	60.00 360	90.35 310	91.05 319	77.01 289	66.62 326	87.92 266	47.73 331	94.03 313	71.63 271	68.44 311	87.62 309

EG-PatchMatch MVS		74.92 307	72.02 312	83.62 307	83.76 345	73.28 296	93.62 267	92.04 304	68.57 336	58.88 353	83.80 323	31.87 366	95.57 269	56.97 339	78.67 251	82.00 359

K. test v3		73.62 310	71.59 314	79.69 331	82.98 346	59.85 361	90.85 309	88.83 339	77.13 285	58.90 352	82.11 331	43.62 340	91.72 340	65.83 303	54.10 354	87.50 314

EGC-MVSNET		52.46 336	47.56 339	67.15 347	81.98 347	60.11 359	82.54 349	72.44 375	0.11 381	0.70 382	74.59 354	25.11 370	83.26 367	29.04 371	61.51 345	58.09 369

anonymousdsp		80.98 265	79.97 261	84.01 300	81.73 348	70.44 322	92.49 291	93.58 271	77.10 287	72.98 291	86.31 293	57.58 287	94.90 296	79.32 206	78.63 254	86.69 323

Anonymous20231206		75.29 306	73.64 307	80.22 329	80.75 349	63.38 350	93.36 273	90.71 326	73.09 315	67.12 320	83.70 324	50.33 322	90.85 348	53.63 349	70.10 296	86.44 325

Gipuma		45.11 339	42.05 341	54.30 355	80.69 350	51.30 371	35.80 373	83.81 363	28.13 371	27.94 373	34.53 373	11.41 379	76.70 371	21.45 373	54.65 352	34.90 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
lessismore_v0				79.98 330	80.59 351	58.34 363		80.87 369		58.49 354	83.46 326	43.10 344	93.89 315	63.11 316	48.68 361	87.72 305

OpenMVS_ROB	68.52 20	73.02 316	69.57 322	83.37 311	80.54 352	71.82 311	93.60 268	88.22 345	62.37 349	61.98 345	83.15 328	35.31 361	95.47 271	45.08 365	75.88 265	82.82 351

testgi		74.88 308	73.40 308	79.32 333	80.13 353	61.75 354	93.21 280	86.64 352	79.49 252	66.56 327	91.06 222	35.51 360	88.67 357	56.79 340	71.25 285	87.56 312

CMPMVS	54.94 21	75.71 305	74.56 300	79.17 334	79.69 354	55.98 365	89.59 313	93.30 283	60.28 358	53.85 362	89.07 249	47.68 332	96.33 225	76.55 232	81.02 230	85.22 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		72.36 319	70.82 316	76.95 338	79.18 355	56.33 364	86.12 340	86.11 354	69.30 335	63.06 341	86.66 284	33.03 364	92.25 333	65.33 305	68.64 309	82.28 357

pmmvs6		74.65 309	71.67 313	83.60 308	79.13 356	69.94 325	93.31 278	90.88 323	61.05 357	65.83 329	84.15 321	43.43 341	94.83 299	66.62 297	60.63 346	86.02 332

DeepMVS_CX				64.06 351	78.53 357	43.26 375		68.11 378	69.94 332	38.55 368	76.14 353	18.53 373	79.34 368	43.72 366	41.62 369	69.57 367

CL-MVSNet_self_test		75.81 303	74.14 305	80.83 327	78.33 358	67.79 337	94.22 259	93.52 272	77.28 284	69.82 311	81.54 335	61.47 261	89.22 355	57.59 335	53.51 355	85.48 337

test20.03		72.36 319	71.15 315	75.98 343	77.79 359	59.16 362	92.40 293	89.35 334	74.09 306	61.50 347	84.32 319	48.09 327	85.54 366	50.63 356	62.15 344	83.24 349

UnsupCasMVSNet_eth		73.25 314	70.57 318	81.30 323	77.53 360	66.33 342	87.24 333	93.89 251	80.38 233	57.90 357	81.59 334	42.91 346	90.56 350	65.18 306	48.51 362	87.01 320

DSMNet-mixed		73.13 315	72.45 311	75.19 345	77.51 361	46.82 372	85.09 345	82.01 368	67.61 341	69.27 315	81.33 336	50.89 318	86.28 363	54.54 346	83.80 212	92.46 227

Patchmatch-RL test		76.65 299	74.01 306	84.55 294	77.37 362	64.23 346	78.49 358	82.84 366	78.48 270	64.63 334	73.40 358	76.05 135	91.70 341	76.99 227	57.84 349	97.72 114

Anonymous20240521		72.06 321	69.91 321	78.50 335	77.11 363	61.67 356	91.62 304	90.97 321	65.52 344	62.37 343	79.05 347	36.32 357	90.96 347	57.75 334	68.52 310	82.87 350

test_method		56.77 333	54.53 336	63.49 352	76.49 364	40.70 377	75.68 362	74.24 374	19.47 375	48.73 364	71.89 362	19.31 372	65.80 375	57.46 336	47.51 365	83.97 347

MIMVSNet1		69.44 325	66.65 329	77.84 336	76.48 365	62.84 352	87.42 331	88.97 338	66.96 342	57.75 358	79.72 346	32.77 365	85.83 365	46.32 363	63.42 341	84.85 341

pmmvs-eth3d		73.59 311	70.66 317	82.38 318	76.40 366	73.38 293	89.39 317	89.43 333	72.69 319	60.34 351	77.79 350	46.43 335	91.26 345	66.42 301	57.06 350	82.51 354

new_pmnet		66.18 330	63.18 333	75.18 346	76.27 367	61.74 355	83.79 347	84.66 359	56.64 365	51.57 363	71.85 363	31.29 367	87.93 359	49.98 357	62.55 343	75.86 364

KD-MVS_self_test		70.97 324	69.31 324	75.95 344	76.24 368	55.39 368	87.45 330	90.94 322	70.20 331	62.96 342	77.48 351	44.01 338	88.09 358	61.25 323	53.26 356	84.37 344

UnsupCasMVSNet_bld		68.60 329	64.50 332	80.92 326	74.63 369	67.80 336	83.97 346	92.94 293	65.12 345	54.63 361	68.23 364	35.97 358	92.17 336	60.13 325	44.83 366	82.78 352

PM-MVS		69.32 326	66.93 328	76.49 340	73.60 370	55.84 366	85.91 341	79.32 372	74.72 302	61.09 348	78.18 349	21.76 371	91.10 346	70.86 280	56.90 351	82.51 354

new-patchmatchnet		68.85 328	65.93 330	77.61 337	73.57 371	63.94 349	90.11 312	88.73 342	71.62 326	55.08 360	73.60 357	40.84 352	87.22 362	51.35 354	48.49 363	81.67 360

ambc				76.02 342	68.11 372	51.43 370	64.97 369	89.59 331		60.49 350	74.49 355	17.17 374	92.46 330	61.50 321	52.85 358	84.17 346

pmmvs3		65.75 331	62.18 334	76.45 341	67.12 373	64.54 345	88.68 321	85.05 358	54.77 366	57.54 359	73.79 356	29.40 369	86.21 364	55.49 345	47.77 364	78.62 362

TDRefinement		69.20 327	65.78 331	79.48 332	66.04 374	62.21 353	88.21 324	86.12 353	62.92 348	61.03 349	85.61 301	33.23 363	94.16 311	55.82 344	53.02 357	82.08 358

FPMVS		55.09 334	52.93 337	61.57 353	55.98 375	40.51 378	83.11 348	83.41 365	37.61 369	34.95 370	71.95 361	14.40 375	76.95 369	29.81 370	65.16 334	67.25 368

PMMVS2		50.90 337	46.31 340	64.67 349	55.53 376	46.67 373	77.30 361	71.02 376	40.89 368	34.16 371	59.32 365	9.83 380	76.14 372	40.09 368	28.63 372	71.21 365

wuyk23d		14.10 346	13.89 349	14.72 361	55.23 377	22.91 384	33.83 374	3.56 385	4.94 378	4.11 379	2.28 381	2.06 384	19.66 380	10.23 378	8.74 378	1.59 378

E-PMN		32.70 343	32.39 345	33.65 359	53.35 378	25.70 382	74.07 365	53.33 382	21.08 373	17.17 377	33.63 375	11.85 378	54.84 377	12.98 376	14.04 374	20.42 374

EMVS		31.70 344	31.45 346	32.48 360	50.72 379	23.95 383	74.78 364	52.30 383	20.36 374	16.08 378	31.48 376	12.80 376	53.60 378	11.39 377	13.10 377	19.88 375

LCM-MVSNet		52.52 335	48.24 338	65.35 348	47.63 380	41.45 376	72.55 367	83.62 364	31.75 370	37.66 369	57.92 367	9.19 381	76.76 370	49.26 359	44.60 367	77.84 363

MVE	35.65 22	33.85 342	29.49 347	46.92 357	41.86 381	36.28 379	50.45 372	56.52 381	18.75 376	18.28 375	37.84 372	2.41 383	58.41 376	18.71 374	20.62 373	46.06 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		46.22 338	41.28 343	61.04 354	39.91 382	46.25 374	70.59 368	76.18 373	58.87 363	23.09 374	48.00 371	12.58 377	66.54 374	28.65 372	13.62 375	70.35 366

PMVS	34.80 23	39.19 341	35.53 344	50.18 356	29.72 383	30.30 380	59.60 371	66.20 379	26.06 372	17.91 376	49.53 370	3.12 382	74.09 373	18.19 375	49.40 360	46.14 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 340	41.93 342	40.38 358	20.10 384	26.84 381	61.93 370	59.09 380	14.81 377	28.51 372	80.58 339	35.53 359	48.33 379	63.70 313	13.11 376	45.96 372

testmvs		9.92 347	12.94 350	0.84 363	0.65 385	0.29 387	93.78 265	0.39 386	0.42 379	2.85 380	15.84 379	0.17 386	0.30 382	2.18 379	0.21 379	1.91 377

test123		9.07 348	11.73 351	1.11 362	0.50 386	0.77 386	89.44 316	0.20 387	0.34 380	2.15 381	10.72 380	0.34 385	0.32 381	1.79 380	0.08 380	2.23 376

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

eth-test2					0.00 387

eth-test					0.00 387

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		21.43 345	28.57 348	0.00 364	0.00 387	0.00 388	0.00 375	95.93 144	0.00 382	0.00 383	97.66 74	63.57 245	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		5.92 350	7.89 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	71.04 202	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.11 349	10.81 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	97.30 96	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

PC_three_1452									91.12 22	98.33 2	98.42 28	92.51 2	99.81 21	98.96 2	99.37 1	99.70 3

test_241102_TWO								96.78 49	88.72 56	97.70 6	98.91 3	87.86 21	99.82 18	98.15 4	99.00 15	99.47 9

test_0728_THIRD									88.38 63	96.69 13	98.76 14	89.64 13	99.76 25	97.47 14	98.84 24	99.38 14

GSMVS																97.54 126

sam_mvs1												77.59 108				97.54 126

sam_mvs												75.35 154

MTGPA								96.33 117

test_post1							85.88 342				30.24 377	73.77 173	95.07 294	73.89 258

test_post											33.80 374	76.17 133	95.97 238

patchmatchnet-post											77.09 352	77.78 107	95.39 273

MTMP							97.53 82	68.16 377

test9_res														96.00 27	99.03 13	98.31 63

agg_prior2														94.30 47	99.00 15	98.57 47

test_prior4						82.34 114	97.75 66

test_prior2							98.37 29		86.08 107	94.57 43	98.02 52	83.14 49		95.05 40	98.79 27

旧先验2							96.97 133		74.06 307	96.10 20			97.76 157	88.38 127

新几何2							96.42 171

无先验							96.87 139	96.78 49	77.39 281				99.52 58	79.95 199		98.43 56

原ACMM2							96.84 140

testdata2													99.48 63	76.45 234

segment_acmp												82.69 58

testdata1							95.57 213		87.44 82

plane_prior5								94.69 207					97.30 182	87.08 136	82.82 223	90.96 234

plane_prior4											94.15 179

plane_prior3						77.75 236			90.17 36	81.33 202

plane_prior2							97.18 109		89.89 39

plane_prior						77.96 227	97.52 85		90.36 34						82.96 221

n2								0.00 388

nn								0.00 388

door-mid								79.75 371

test11								96.50 96

door								80.13 370

HQP5-MVS						78.48 207

BP-MVS														87.67 132

HQP4-MVS										82.30 188			97.32 180			91.13 232

HQP3-MVS								94.80 202							83.01 219

HQP2-MVS												65.40 234

MDTV_nov1_ep13_2view						81.74 130	86.80 335		80.65 224	85.65 150		74.26 168		76.52 233		96.98 151

ACMMP++_ref															78.45 257

ACMMP++															79.05 248

Test By Simon												71.65 195

High-resolution multi-view benchmark