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.

MSP-MVS		90.38 5	91.87 1	85.88 87	92.83 77	64.03 190	93.06 113	94.33 54	82.19 30	93.65 3	96.15 37	85.89 1	97.19 84	91.02 35	97.75 1	96.43 30
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
DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 153	76.68 2	97.29 2	95.35 15	82.87 22	91.58 13	97.22 3	79.93 5	99.10 9	83.12 97	97.64 2	97.94 1

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 6				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 71	94.37 52	72.48 184	92.07 9	96.85 16	83.82 2	99.15 2	91.53 31	97.42 4	97.55 4

PC_three_1452									80.91 48	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 84	95.24 34	94.49 44	82.43 27	88.90 33	96.35 29	71.89 34	98.63 26	88.76 49	96.40 6	96.06 40

SMA-MVS		88.14 17	88.29 21	87.67 33	93.21 68	68.72 70	93.85 78	94.03 62	74.18 147	91.74 12	96.67 21	65.61 70	98.42 33	89.24 45	96.08 7	95.88 47
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
DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 71	73.88 7	97.01 5	94.40 50	88.32 4	85.71 55	94.91 72	74.11 19	98.91 17	87.26 62	95.94 8	97.03 13
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
MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 8	97.13 3	95.58 11	89.33 2	85.77 54	96.26 32	72.84 26	99.38 1	92.64 19	95.93 9	97.08 12

iter_conf05_11		86.99 35	86.27 43	89.15 13	93.74 52	72.45 13	97.56 1	87.04 309	88.32 4	92.60 5	96.57 23	32.61 348	97.45 66	92.21 24	95.80 10	97.53 6

CNVR-MVS		90.32 6	90.89 7	88.61 22	96.76 8	70.65 32	96.47 14	94.83 30	84.83 13	89.07 32	96.80 19	70.86 36	99.06 15	92.64 19	95.71 11	96.12 39

PHI-MVS		86.83 39	86.85 40	86.78 61	93.47 62	65.55 151	95.39 31	95.10 22	71.77 210	85.69 56	96.52 24	62.07 118	98.77 22	86.06 74	95.60 12	96.03 42

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 132	93.00 74	58.16 304	96.72 9	94.41 48	86.50 10	90.25 22	97.83 1	75.46 14	98.67 25	92.78 18	95.49 13	97.32 8

SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 54	96.89 6	94.44 46	71.65 214	92.11 7	97.21 4	76.79 9	99.11 6	92.34 21	95.36 14	97.62 2

IU-MVS					96.46 11	69.91 43		95.18 20	80.75 49	95.28 1				92.34 21	95.36 14	96.47 29

test_241102_TWO								94.41 48	71.65 214	92.07 9	97.21 4	74.58 17	99.11 6	92.34 21	95.36 14	96.59 20

MM		90.87 2	91.52 2	88.92 15	92.12 96	71.10 28	97.02 4	96.04 6	88.70 3	91.57 14	96.19 35	70.12 40	98.91 17	96.83 1	95.06 17	96.76 16

test_0728_THIRD									72.48 184	90.55 20	96.93 11	76.24 11	99.08 11	91.53 31	94.99 18	96.43 30

test9_res														89.41 41	94.96 19	95.29 68

ACMMP_NAP		86.05 51	85.80 56	86.80 60	91.58 114	67.53 102	91.79 171	93.49 83	74.93 138	84.61 66	95.30 55	59.42 146	97.92 41	86.13 72	94.92 20	94.94 85

DPE-MVS		88.77 16	89.21 16	87.45 43	96.26 20	67.56 100	94.17 58	94.15 59	68.77 264	90.74 18	97.27 2	76.09 12	98.49 29	90.58 39	94.91 21	96.30 33
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 42	71.92 200	90.55 20	96.93 11	73.77 21	99.08 11	91.91 29	94.90 22	96.29 34
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_SECOND				88.70 18	96.45 12	70.43 35	96.64 10	94.37 52					99.15 2	91.91 29	94.90 22	96.51 25

train_agg		87.21 32	87.42 31	86.60 66	94.18 41	67.28 107	94.16 59	93.51 80	71.87 205	85.52 57	95.33 53	68.19 48	97.27 82	89.09 46	94.90 22	95.25 74

DeepC-MVS_fast	79.48 2	87.95 21	88.00 24	87.79 31	95.86 27	68.32 78	95.74 22	94.11 60	83.82 17	83.49 76	96.19 35	64.53 84	98.44 31	83.42 96	94.88 25	96.61 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 26					99.07 13	92.01 26	94.77 26	96.51 25

No_MVS				89.60 8	97.31 4	73.22 10		95.05 26					99.07 13	92.01 26	94.77 26	96.51 25

TSAR-MVS + MP.		88.11 19	88.64 17	86.54 70	91.73 110	68.04 88	90.36 227	93.55 79	82.89 21	91.29 16	92.89 123	72.27 31	96.03 142	87.99 53	94.77 26	95.54 56
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_prior2							95.10 39		75.40 132	85.25 63	95.61 47	67.94 51		87.47 59	94.77 26

agg_prior2														86.41 70	94.75 30	95.33 64

MVS_0304		90.01 8	90.50 9	88.53 23	90.14 144	70.94 29	96.47 14	95.72 10	87.33 6	89.60 29	96.26 32	68.44 45	98.74 24	95.82 4	94.72 31	95.90 46

MVS		84.66 76	82.86 103	90.06 2	90.93 129	74.56 6	87.91 278	95.54 13	68.55 266	72.35 200	94.71 77	59.78 142	98.90 19	81.29 113	94.69 32	96.74 17

SF-MVS		87.03 34	87.09 34	86.84 57	92.70 83	67.45 105	93.64 91	93.76 69	70.78 238	86.25 48	96.44 28	66.98 57	97.79 47	88.68 50	94.56 33	95.28 70

NCCC		89.07 15	89.46 15	87.91 28	96.60 10	69.05 62	96.38 16	94.64 39	84.42 14	86.74 46	96.20 34	66.56 62	98.76 23	89.03 48	94.56 33	95.92 45

3Dnovator	73.91 6	82.69 117	80.82 131	88.31 26	89.57 155	71.26 23	92.60 135	94.39 51	78.84 85	67.89 257	92.48 133	48.42 259	98.52 28	68.80 213	94.40 35	95.15 76

CDPH-MVS		85.71 59	85.46 61	86.46 72	94.75 34	67.19 109	93.89 76	92.83 108	70.90 234	83.09 79	95.28 56	63.62 96	97.36 73	80.63 116	94.18 36	94.84 89

MG-MVS		87.11 33	86.27 43	89.62 7	97.79 1	76.27 4	94.96 44	94.49 44	78.74 88	83.87 75	92.94 121	64.34 85	96.94 105	75.19 154	94.09 37	95.66 51

9.14			87.63 27		93.86 47		94.41 53	94.18 57	72.76 179	86.21 49	96.51 25	66.64 60	97.88 44	90.08 40	94.04 38

原ACMM1				84.42 145	93.21 68	64.27 185		93.40 88	65.39 289	79.51 115	92.50 130	58.11 159	96.69 115	65.27 251	93.96 39	92.32 176

MSLP-MVS++		86.27 47	85.91 54	87.35 45	92.01 100	68.97 65	95.04 41	92.70 111	79.04 83	81.50 90	96.50 26	58.98 152	96.78 113	83.49 95	93.93 40	96.29 34

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 50	96.53 13	93.78 66	86.89 8	89.68 28	95.78 42	65.94 66	99.10 9	92.99 16	93.91 41	96.58 22

MP-MVS-pluss		85.24 67	85.13 66	85.56 100	91.42 119	65.59 149	91.54 181	92.51 121	74.56 141	80.62 101	95.64 46	59.15 150	97.00 96	86.94 67	93.80 42	94.07 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVP-Stereo		77.12 212	76.23 204	79.79 265	81.72 302	66.34 132	89.29 254	90.88 191	70.56 242	62.01 311	82.88 270	49.34 250	94.13 218	65.55 248	93.80 42	78.88 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GG-mvs-BLEND				86.53 71	91.91 105	69.67 52	75.02 365	94.75 33		78.67 130	90.85 165	77.91 7	94.56 201	72.25 178	93.74 44	95.36 63

ZNCC-MVS		85.33 66	85.08 67	86.06 82	93.09 73	65.65 147	93.89 76	93.41 87	73.75 158	79.94 110	94.68 78	60.61 133	98.03 38	82.63 100	93.72 45	94.52 107

CSCG		86.87 36	86.26 45	88.72 17	95.05 31	70.79 31	93.83 83	95.33 16	68.48 268	77.63 138	94.35 90	73.04 24	98.45 30	84.92 84	93.71 46	96.92 15

test12				87.09 51	94.60 36	68.86 66		92.91 105		82.67 84		65.44 71	97.55 63		93.69 47	94.84 89

PAPM		85.89 56	85.46 61	87.18 48	88.20 196	72.42 14	92.41 143	92.77 109	82.11 31	80.34 106	93.07 118	68.27 47	95.02 180	78.39 136	93.59 48	94.09 122

SteuartSystems-ACMMP		86.82 40	86.90 38	86.58 68	90.42 138	66.38 130	96.09 18	93.87 64	77.73 100	84.01 74	95.66 45	63.39 101	97.94 40	87.40 60	93.55 49	95.42 57
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		87.54 26	87.84 25	86.65 64	96.07 23	66.30 133	94.84 46	93.78 66	69.35 255	88.39 34	96.34 30	67.74 53	97.66 54	90.62 38	93.44 50	96.01 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CS-MVS-test		86.14 50	87.01 35	83.52 169	92.63 85	59.36 292	95.49 28	91.92 141	80.09 60	85.46 59	95.53 49	61.82 122	95.77 150	86.77 69	93.37 51	95.41 58

PS-MVSNAJ		88.14 17	87.61 28	89.71 6	92.06 97	76.72 1	95.75 21	93.26 90	83.86 16	89.55 30	96.06 38	53.55 213	97.89 43	91.10 33	93.31 52	94.54 105

MAR-MVS		84.18 87	83.43 89	86.44 73	96.25 21	65.93 142	94.28 56	94.27 56	74.41 142	79.16 121	95.61 47	53.99 208	98.88 21	69.62 202	93.26 53	94.50 109
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
gg-mvs-nofinetune		77.18 210	74.31 231	85.80 92	91.42 119	68.36 77	71.78 368	94.72 34	49.61 369	77.12 145	45.92 392	77.41 8	93.98 230	67.62 223	93.16 54	95.05 80

ZD-MVS					96.63 9	65.50 153		93.50 82	70.74 239	85.26 62	95.19 64	64.92 78	97.29 78	87.51 58	93.01 55

APD-MVS		85.93 54	85.99 52	85.76 94	95.98 26	65.21 158	93.59 94	92.58 119	66.54 281	86.17 50	95.88 41	63.83 91	97.00 96	86.39 71	92.94 56	95.06 79
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
新几何1				84.73 131	92.32 90	64.28 184		91.46 167	59.56 336	79.77 112	92.90 122	56.95 173	96.57 119	63.40 261	92.91 57	93.34 146

DeepC-MVS	77.85 3	85.52 64	85.24 64	86.37 76	88.80 178	66.64 124	92.15 150	93.68 74	81.07 46	76.91 148	93.64 108	62.59 113	98.44 31	85.50 76	92.84 58	94.03 126
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 22	87.38 32	89.55 11	91.41 121	76.43 3	95.74 22	93.12 98	83.53 19	89.55 30	95.95 40	53.45 217	97.68 50	91.07 34	92.62 59	94.54 105

MP-MVS		85.02 70	84.97 69	85.17 115	92.60 86	64.27 185	93.24 107	92.27 126	73.13 169	79.63 114	94.43 84	61.90 119	97.17 85	85.00 82	92.56 60	94.06 125
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		83.91 92	83.38 93	85.50 101	91.89 106	65.16 160	81.75 326	92.23 127	75.32 133	80.53 103	95.21 63	56.06 185	97.16 87	84.86 85	92.55 61	94.18 116

GST-MVS		84.63 77	84.29 77	85.66 98	92.82 79	65.27 156	93.04 115	93.13 97	73.20 167	78.89 123	94.18 97	59.41 147	97.85 45	81.45 109	92.48 62	93.86 134

HFP-MVS		84.73 75	84.40 76	85.72 96	93.75 51	65.01 164	93.50 99	93.19 94	72.19 194	79.22 120	94.93 70	59.04 151	97.67 51	81.55 107	92.21 63	94.49 110

ACMMPR		84.37 79	84.06 78	85.28 110	93.56 58	64.37 180	93.50 99	93.15 96	72.19 194	78.85 128	94.86 73	56.69 177	97.45 66	81.55 107	92.20 64	94.02 127

MS-PatchMatch		77.90 202	76.50 200	82.12 207	85.99 242	69.95 42	91.75 176	92.70 111	73.97 152	62.58 308	84.44 256	41.11 303	95.78 148	63.76 260	92.17 65	80.62 351

region2R		84.36 80	84.03 79	85.36 107	93.54 59	64.31 183	93.43 104	92.95 104	72.16 197	78.86 127	94.84 74	56.97 172	97.53 64	81.38 111	92.11 66	94.24 114

CS-MVS		85.80 57	86.65 41	83.27 177	92.00 101	58.92 297	95.31 32	91.86 146	79.97 61	84.82 65	95.40 51	62.26 116	95.51 168	86.11 73	92.08 67	95.37 61

patch_mono-2		89.71 11	90.99 6	85.85 90	96.04 24	63.70 200	95.04 41	95.19 19	86.74 9	91.53 15	95.15 65	73.86 20	97.58 59	93.38 14	92.00 68	96.28 36

dcpmvs_2		87.37 30	87.55 29	86.85 56	95.04 32	68.20 85	90.36 227	90.66 197	79.37 73	81.20 92	93.67 107	74.73 15	96.55 121	90.88 36	92.00 68	95.82 48

旧先验1					91.94 102	60.74 269		91.50 165			94.36 86	65.23 73			91.84 70	94.55 103

MVSFormer		83.75 97	82.88 102	86.37 76	89.24 168	71.18 25	89.07 260	90.69 194	65.80 286	87.13 41	94.34 91	64.99 75	92.67 268	72.83 170	91.80 71	95.27 71

lupinMVS		87.74 24	87.77 26	87.63 38	89.24 168	71.18 25	96.57 12	92.90 106	82.70 25	87.13 41	95.27 58	64.99 75	95.80 147	89.34 43	91.80 71	95.93 44

EPNet		87.84 23	88.38 19	86.23 80	93.30 65	66.05 137	95.26 33	94.84 29	87.09 7	88.06 35	94.53 81	66.79 59	97.34 75	83.89 93	91.68 73	95.29 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	73.60 7	82.10 126	80.60 138	86.60 66	90.89 131	66.80 121	95.20 35	93.44 85	74.05 149	67.42 263	92.49 132	49.46 249	97.65 55	70.80 191	91.68 73	95.33 64

XVS		83.87 93	83.47 87	85.05 117	93.22 66	63.78 194	92.92 119	92.66 114	73.99 150	78.18 132	94.31 93	55.25 191	97.41 70	79.16 127	91.58 75	93.95 129

X-MVStestdata		76.86 215	74.13 235	85.05 117	93.22 66	63.78 194	92.92 119	92.66 114	73.99 150	78.18 132	10.19 407	55.25 191	97.41 70	79.16 127	91.58 75	93.95 129

SD-MVS		87.49 27	87.49 30	87.50 42	93.60 56	68.82 68	93.90 75	92.63 117	76.86 112	87.90 36	95.76 43	66.17 63	97.63 56	89.06 47	91.48 77	96.05 41
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
EC-MVSNet		84.53 78	85.04 68	83.01 181	89.34 160	61.37 256	94.42 52	91.09 183	77.91 97	83.24 77	94.20 96	58.37 155	95.40 169	85.35 77	91.41 78	92.27 181

PGM-MVS		83.25 105	82.70 106	84.92 121	92.81 81	64.07 189	90.44 223	92.20 131	71.28 226	77.23 144	94.43 84	55.17 195	97.31 77	79.33 126	91.38 79	93.37 145

PVSNet_Blended		86.73 41	86.86 39	86.31 79	93.76 49	67.53 102	96.33 17	93.61 76	82.34 29	81.00 97	93.08 117	63.19 105	97.29 78	87.08 65	91.38 79	94.13 120

HPM-MVS		83.25 105	82.95 100	84.17 154	92.25 92	62.88 226	90.91 208	91.86 146	70.30 244	77.12 145	93.96 102	56.75 175	96.28 129	82.04 104	91.34 81	93.34 146
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EIA-MVS		84.84 73	84.88 70	84.69 134	91.30 123	62.36 235	93.85 78	92.04 136	79.45 70	79.33 119	94.28 94	62.42 114	96.35 127	80.05 120	91.25 82	95.38 60

MVS_111021_HR		86.19 49	85.80 56	87.37 44	93.17 70	69.79 47	93.99 70	93.76 69	79.08 81	78.88 126	93.99 101	62.25 117	98.15 36	85.93 75	91.15 83	94.15 119

test222					89.77 151	61.60 252	89.55 248	89.42 245	56.83 349	77.28 143	92.43 134	52.76 221			91.14 84	93.09 154

jason		86.40 44	86.17 48	87.11 50	86.16 240	70.54 34	95.71 25	92.19 132	82.00 32	84.58 67	94.34 91	61.86 120	95.53 167	87.76 55	90.89 85	95.27 71
jason: jason.
mPP-MVS		82.96 111	82.44 111	84.52 142	92.83 77	62.92 224	92.76 124	91.85 148	71.52 222	75.61 160	94.24 95	53.48 216	96.99 99	78.97 130	90.73 86	93.64 140

CP-MVS		83.71 98	83.40 92	84.65 136	93.14 71	63.84 192	94.59 50	92.28 125	71.03 232	77.41 141	94.92 71	55.21 194	96.19 131	81.32 112	90.70 87	93.91 131

OpenMVS	70.45 11	78.54 190	75.92 209	86.41 75	85.93 246	71.68 19	92.74 125	92.51 121	66.49 282	64.56 287	91.96 144	43.88 293	98.10 37	54.61 304	90.65 88	89.44 228

PAPM_NR		82.97 110	81.84 118	86.37 76	94.10 44	66.76 122	87.66 282	92.84 107	69.96 248	74.07 177	93.57 110	63.10 108	97.50 65	70.66 194	90.58 89	94.85 86

testdata				81.34 224	89.02 172	57.72 308		89.84 229	58.65 340	85.32 61	94.09 98	57.03 168	93.28 246	69.34 205	90.56 90	93.03 157

Vis-MVSNet		80.92 144	79.98 147	83.74 162	88.48 183	61.80 246	93.44 103	88.26 294	73.96 153	77.73 136	91.76 149	49.94 245	94.76 188	65.84 243	90.37 91	94.65 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		84.98 72	83.45 88	89.57 10	89.94 148	75.14 5	92.07 156	92.32 124	81.87 33	75.68 157	88.27 203	60.18 136	98.60 27	80.46 118	90.27 92	94.96 83

test_fmvsm_n_1920		87.69 25	88.50 18	85.27 111	87.05 224	63.55 207	93.69 88	91.08 185	84.18 15	90.17 24	97.04 8	67.58 54	97.99 39	95.72 5	90.03 93	94.26 113

ETV-MVS		86.01 52	86.11 49	85.70 97	90.21 143	67.02 116	93.43 104	91.92 141	81.21 45	84.13 73	94.07 100	60.93 130	95.63 158	89.28 44	89.81 94	94.46 111

QAPM		79.95 163	77.39 189	87.64 34	89.63 154	71.41 21	93.30 106	93.70 73	65.34 291	67.39 265	91.75 150	47.83 266	98.96 16	57.71 294	89.81 94	92.54 170

CANet_DTU		84.09 89	83.52 83	85.81 91	90.30 141	66.82 119	91.87 167	89.01 265	85.27 11	86.09 51	93.74 105	47.71 268	96.98 100	77.90 139	89.78 96	93.65 139

API-MVS		82.28 121	80.53 139	87.54 41	96.13 22	70.59 33	93.63 92	91.04 189	65.72 288	75.45 162	92.83 126	56.11 184	98.89 20	64.10 257	89.75 97	93.15 152

test2506		83.29 103	82.92 101	84.37 148	88.39 188	63.18 217	92.01 159	91.35 170	77.66 102	78.49 131	91.42 156	64.58 83	95.09 179	73.19 166	89.23 98	94.85 86

ECVR-MVS		81.29 136	80.38 142	84.01 158	88.39 188	61.96 244	92.56 140	86.79 313	77.66 102	76.63 149	91.42 156	46.34 277	95.24 176	74.36 163	89.23 98	94.85 86

MVS_Test		84.16 88	83.20 95	87.05 53	91.56 115	69.82 46	89.99 241	92.05 135	77.77 99	82.84 80	86.57 231	63.93 90	96.09 136	74.91 159	89.18 100	95.25 74

PAPR		85.15 69	84.47 74	87.18 48	96.02 25	68.29 79	91.85 169	93.00 103	76.59 119	79.03 122	95.00 67	61.59 123	97.61 58	78.16 137	89.00 101	95.63 52

TSAR-MVS + GP.		87.96 20	88.37 20	86.70 63	93.51 61	65.32 155	95.15 37	93.84 65	78.17 93	85.93 53	94.80 75	75.80 13	98.21 34	89.38 42	88.78 102	96.59 20

SR-MVS		82.81 113	82.58 108	83.50 172	93.35 63	61.16 259	92.23 148	91.28 175	64.48 295	81.27 91	95.28 56	53.71 212	95.86 146	82.87 98	88.77 103	93.49 143

test1111		80.84 145	80.02 144	83.33 175	87.87 204	60.76 267	92.62 133	86.86 312	77.86 98	75.73 156	91.39 158	46.35 276	94.70 194	72.79 172	88.68 104	94.52 107

fmvsm_l_conf0.5_n_a		87.44 29	88.15 23	85.30 109	87.10 222	64.19 187	94.41 53	88.14 295	80.24 59	92.54 6	96.97 10	69.52 43	97.17 85	95.89 2	88.51 105	94.56 102

HPM-MVS_fast		80.25 156	79.55 155	82.33 197	91.55 116	59.95 282	91.32 195	89.16 256	65.23 292	74.71 169	93.07 118	47.81 267	95.74 151	74.87 161	88.23 106	91.31 200

PVSNet_Blended_VisFu		83.97 91	83.50 85	85.39 105	90.02 146	66.59 127	93.77 85	91.73 152	77.43 108	77.08 147	89.81 185	63.77 93	96.97 102	79.67 123	88.21 107	92.60 168

Vis-MVSNet (Re-imp)		79.24 173	79.57 152	78.24 288	88.46 184	52.29 343	90.41 225	89.12 259	74.24 146	69.13 234	91.91 147	65.77 68	90.09 320	59.00 290	88.09 108	92.33 175

fmvsm_l_conf0.5_n		87.49 27	88.19 22	85.39 105	86.95 225	64.37 180	94.30 55	88.45 286	80.51 51	92.70 4	96.86 15	69.98 41	97.15 88	95.83 3	88.08 109	94.65 99

APD-MVS_3200maxsize		81.64 132	81.32 123	82.59 191	92.36 89	58.74 299	91.39 188	91.01 190	63.35 304	79.72 113	94.62 80	51.82 227	96.14 133	79.71 122	87.93 110	92.89 163

Effi-MVS+		83.82 94	82.76 104	86.99 55	89.56 156	69.40 53	91.35 193	86.12 320	72.59 181	83.22 78	92.81 127	59.60 144	96.01 144	81.76 106	87.80 111	95.56 55

casdiffmvs_mvg		85.66 61	85.18 65	87.09 51	88.22 195	69.35 57	93.74 87	91.89 144	81.47 37	80.10 108	91.45 155	64.80 80	96.35 127	87.23 63	87.69 112	95.58 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
1314		80.70 147	78.95 164	85.94 86	87.77 209	67.56 100	87.91 278	92.55 120	72.17 196	67.44 262	93.09 116	50.27 242	97.04 94	71.68 186	87.64 113	93.23 150

test_fmvsmconf_n		86.58 43	87.17 33	84.82 125	85.28 255	62.55 231	94.26 57	89.78 230	83.81 18	87.78 37	96.33 31	65.33 72	96.98 100	94.40 11	87.55 114	94.95 84

PMMVS		81.98 128	82.04 115	81.78 214	89.76 152	56.17 324	91.13 204	90.69 194	77.96 95	80.09 109	93.57 110	46.33 278	94.99 182	81.41 110	87.46 115	94.17 117

casdiffmvs		85.37 65	84.87 71	86.84 57	88.25 193	69.07 61	93.04 115	91.76 151	81.27 44	80.84 99	92.07 143	64.23 86	96.06 140	84.98 83	87.43 116	95.39 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.1_n		85.71 59	86.08 51	84.62 139	80.83 308	62.33 236	93.84 81	88.81 273	83.50 20	87.00 44	96.01 39	63.36 102	96.93 107	94.04 12	87.29 117	94.61 101

UGNet		79.87 164	78.68 166	83.45 174	89.96 147	61.51 253	92.13 151	90.79 192	76.83 114	78.85 128	86.33 235	38.16 318	96.17 132	67.93 220	87.17 118	92.67 166
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
MVS_111021_LR		82.02 127	81.52 121	83.51 171	88.42 186	62.88 226	89.77 245	88.93 269	76.78 115	75.55 161	93.10 115	50.31 241	95.38 171	83.82 94	87.02 119	92.26 182

test_fmvsmvis_n_1920		83.80 95	83.48 86	84.77 129	82.51 294	63.72 198	91.37 191	83.99 340	81.42 41	77.68 137	95.74 44	58.37 155	97.58 59	93.38 14	86.87 120	93.00 159

xiu_mvs_v1_base_debu		82.16 123	81.12 125	85.26 112	86.42 233	68.72 70	92.59 137	90.44 204	73.12 170	84.20 70	94.36 86	38.04 320	95.73 152	84.12 90	86.81 121	91.33 196

xiu_mvs_v1_base		82.16 123	81.12 125	85.26 112	86.42 233	68.72 70	92.59 137	90.44 204	73.12 170	84.20 70	94.36 86	38.04 320	95.73 152	84.12 90	86.81 121	91.33 196

xiu_mvs_v1_base_debi		82.16 123	81.12 125	85.26 112	86.42 233	68.72 70	92.59 137	90.44 204	73.12 170	84.20 70	94.36 86	38.04 320	95.73 152	84.12 90	86.81 121	91.33 196

SR-MVS-dyc-post		81.06 141	80.70 134	82.15 205	92.02 98	58.56 301	90.90 209	90.45 201	62.76 311	78.89 123	94.46 82	51.26 235	95.61 160	78.77 133	86.77 124	92.28 178

RE-MVS-def			80.48 140		92.02 98	58.56 301	90.90 209	90.45 201	62.76 311	78.89 123	94.46 82	49.30 251		78.77 133	86.77 124	92.28 178

baseline		85.01 71	84.44 75	86.71 62	88.33 190	68.73 69	90.24 232	91.82 150	81.05 47	81.18 93	92.50 130	63.69 94	96.08 139	84.45 88	86.71 126	95.32 66

TESTMET0.1,1		82.41 119	81.98 117	83.72 165	88.08 197	63.74 196	92.70 128	93.77 68	79.30 74	77.61 139	87.57 218	58.19 158	94.08 221	73.91 165	86.68 127	93.33 148

IS-MVSNet		80.14 158	79.41 157	82.33 197	87.91 202	60.08 281	91.97 163	88.27 292	72.90 177	71.44 211	91.73 151	61.44 124	93.66 240	62.47 271	86.53 128	93.24 149

CPTT-MVS		79.59 167	79.16 162	80.89 239	91.54 117	59.80 284	92.10 153	88.54 285	60.42 329	72.96 186	93.28 114	48.27 260	92.80 262	78.89 132	86.50 129	90.06 215

BH-w/o		80.49 151	79.30 160	84.05 157	90.83 133	64.36 182	93.60 93	89.42 245	74.35 144	69.09 235	90.15 180	55.23 193	95.61 160	64.61 254	86.43 130	92.17 184

PVSNet	73.49 8	80.05 160	78.63 167	84.31 150	90.92 130	64.97 165	92.47 141	91.05 188	79.18 77	72.43 198	90.51 170	37.05 332	94.06 223	68.06 217	86.00 131	93.90 133

test_fmvsmconf0.01_n		83.70 99	83.52 83	84.25 153	75.26 359	61.72 250	92.17 149	87.24 308	82.36 28	84.91 64	95.41 50	55.60 189	96.83 112	92.85 17	85.87 132	94.21 115

mvs_anonymous		81.36 135	79.99 146	85.46 102	90.39 140	68.40 76	86.88 293	90.61 199	74.41 142	70.31 223	84.67 252	63.79 92	92.32 283	73.13 167	85.70 133	95.67 50

DP-MVS Recon		82.73 114	81.65 120	85.98 84	97.31 4	67.06 113	95.15 37	91.99 138	69.08 261	76.50 152	93.89 103	54.48 203	98.20 35	70.76 192	85.66 134	92.69 165

BH-RMVSNet		79.46 171	77.65 181	84.89 122	91.68 112	65.66 146	93.55 95	88.09 297	72.93 174	73.37 183	91.12 162	46.20 280	96.12 134	56.28 299	85.61 135	92.91 161

diffmvs		84.28 82	83.83 80	85.61 99	87.40 215	68.02 89	90.88 211	89.24 251	80.54 50	81.64 89	92.52 129	59.83 141	94.52 204	87.32 61	85.11 136	94.29 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Fast-Effi-MVS+		81.14 138	80.01 145	84.51 143	90.24 142	65.86 143	94.12 63	89.15 257	73.81 157	75.37 163	88.26 204	57.26 165	94.53 203	66.97 231	84.92 137	93.15 152

LFMVS		84.34 81	82.73 105	89.18 12	94.76 33	73.25 9	94.99 43	91.89 144	71.90 202	82.16 86	93.49 112	47.98 264	97.05 91	82.55 101	84.82 138	97.25 9

BH-untuned		78.68 186	77.08 192	83.48 173	89.84 149	63.74 196	92.70 128	88.59 283	71.57 220	66.83 272	88.65 196	51.75 229	95.39 170	59.03 289	84.77 139	91.32 199

test-LLR		80.10 159	79.56 153	81.72 216	86.93 228	61.17 257	92.70 128	91.54 162	71.51 223	75.62 158	86.94 227	53.83 209	92.38 279	72.21 179	84.76 140	91.60 190

test-mter		79.96 162	79.38 159	81.72 216	86.93 228	61.17 257	92.70 128	91.54 162	73.85 155	75.62 158	86.94 227	49.84 247	92.38 279	72.21 179	84.76 140	91.60 190

sasdasda		86.85 37	86.25 46	88.66 20	91.80 108	71.92 16	93.54 96	91.71 154	80.26 56	87.55 38	95.25 60	63.59 98	96.93 107	88.18 51	84.34 142	97.11 10

canonicalmvs		86.85 37	86.25 46	88.66 20	91.80 108	71.92 16	93.54 96	91.71 154	80.26 56	87.55 38	95.25 60	63.59 98	96.93 107	88.18 51	84.34 142	97.11 10

alignmvs		87.28 31	86.97 36	88.24 27	91.30 123	71.14 27	95.61 26	93.56 78	79.30 74	87.07 43	95.25 60	68.43 46	96.93 107	87.87 54	84.33 144	96.65 18

VNet		86.20 48	85.65 59	87.84 30	93.92 46	69.99 39	95.73 24	95.94 7	78.43 90	86.00 52	93.07 118	58.22 157	97.00 96	85.22 78	84.33 144	96.52 24

UA-Net		80.02 161	79.65 151	81.11 230	89.33 162	57.72 308	86.33 296	89.00 268	77.44 107	81.01 96	89.15 192	59.33 148	95.90 145	61.01 278	84.28 146	89.73 222

LCM-MVSNet-Re		72.93 266	71.84 265	76.18 310	88.49 182	48.02 363	80.07 344	70.17 380	73.96 153	52.25 353	80.09 314	49.98 244	88.24 332	67.35 224	84.23 147	92.28 178

ACMMP		81.49 133	80.67 135	83.93 159	91.71 111	62.90 225	92.13 151	92.22 130	71.79 209	71.68 208	93.49 112	50.32 240	96.96 103	78.47 135	84.22 148	91.93 188
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
MGCFI-Net		85.59 63	85.73 58	85.17 115	91.41 121	62.44 232	92.87 121	91.31 171	79.65 67	86.99 45	95.14 66	62.90 111	96.12 134	87.13 64	84.13 149	96.96 14

114514_t		79.17 174	77.67 180	83.68 166	95.32 29	65.53 152	92.85 122	91.60 161	63.49 302	67.92 254	90.63 168	46.65 273	95.72 156	67.01 230	83.54 150	89.79 220

testing11		86.71 42	86.44 42	87.55 40	93.54 59	71.35 22	93.65 90	95.58 11	81.36 43	80.69 100	92.21 141	72.30 30	96.46 126	85.18 80	83.43 151	94.82 92

test_vis1_n_1920		81.66 131	82.01 116	80.64 241	82.24 297	55.09 332	94.76 47	86.87 311	81.67 36	84.40 69	94.63 79	38.17 317	94.67 195	91.98 28	83.34 152	92.16 185

testing222		85.18 68	84.69 73	86.63 65	92.91 76	69.91 43	92.61 134	95.80 9	80.31 55	80.38 105	92.27 138	68.73 44	95.19 177	75.94 149	83.27 153	94.81 93

EPMVS		78.49 191	75.98 208	86.02 83	91.21 125	69.68 51	80.23 341	91.20 176	75.25 134	72.48 196	78.11 328	54.65 199	93.69 239	57.66 295	83.04 154	94.69 95

AdaColmap		78.94 179	77.00 195	84.76 130	96.34 17	65.86 143	92.66 132	87.97 301	62.18 316	70.56 217	92.37 136	43.53 294	97.35 74	64.50 255	82.86 155	91.05 205

CDS-MVSNet		81.43 134	80.74 133	83.52 169	86.26 237	64.45 174	92.09 154	90.65 198	75.83 126	73.95 179	89.81 185	63.97 89	92.91 258	71.27 187	82.82 156	93.20 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 280x420		77.35 208	76.95 196	78.55 283	87.07 223	62.68 230	69.71 374	82.95 347	68.80 263	71.48 210	87.27 224	66.03 65	84.00 361	76.47 146	82.81 157	88.95 229

UWE-MVS		80.81 146	81.01 130	80.20 251	89.33 162	57.05 318	91.91 165	94.71 35	75.67 127	75.01 166	89.37 189	63.13 107	91.44 305	67.19 228	82.80 158	92.12 186

ETVMVS		84.22 86	83.71 81	85.76 94	92.58 87	68.25 83	92.45 142	95.53 14	79.54 69	79.46 116	91.64 153	70.29 39	94.18 217	69.16 208	82.76 159	94.84 89

PCF-MVS	73.15 9	79.29 172	77.63 182	84.29 151	86.06 241	65.96 141	87.03 289	91.10 182	69.86 250	69.79 231	90.64 166	57.54 164	96.59 117	64.37 256	82.29 160	90.32 212
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n		86.39 45	86.91 37	84.82 125	87.36 217	63.54 208	94.74 48	90.02 224	82.52 26	90.14 25	96.92 13	62.93 110	97.84 46	95.28 8	82.26 161	93.07 156

WTY-MVS		86.32 46	85.81 55	87.85 29	92.82 79	69.37 56	95.20 35	95.25 17	82.71 24	81.91 87	94.73 76	67.93 52	97.63 56	79.55 124	82.25 162	96.54 23

testing99		86.01 52	85.47 60	87.63 38	93.62 55	71.25 24	93.47 102	95.23 18	80.42 54	80.60 102	91.95 145	71.73 35	96.50 124	80.02 121	82.22 163	95.13 77

HY-MVS	76.49 5	84.28 82	83.36 94	87.02 54	92.22 93	67.74 95	84.65 303	94.50 43	79.15 78	82.23 85	87.93 212	66.88 58	96.94 105	80.53 117	82.20 164	96.39 32

testing91		85.93 54	85.31 63	87.78 32	93.59 57	71.47 20	93.50 99	95.08 25	80.26 56	80.53 103	91.93 146	70.43 38	96.51 123	80.32 119	82.13 165	95.37 61

VDD-MVS		83.06 108	81.81 119	86.81 59	90.86 132	67.70 96	95.40 30	91.50 165	75.46 130	81.78 88	92.34 137	40.09 306	97.13 89	86.85 68	82.04 166	95.60 53

fmvsm_s_conf0.1_n		85.61 62	85.93 53	84.68 135	82.95 292	63.48 210	94.03 69	89.46 242	81.69 35	89.86 26	96.74 20	61.85 121	97.75 49	94.74 9	82.01 167	92.81 164

TAMVS		80.37 153	79.45 156	83.13 180	85.14 258	63.37 211	91.23 199	90.76 193	74.81 140	72.65 191	88.49 197	60.63 132	92.95 253	69.41 204	81.95 168	93.08 155

test_yl		84.28 82	83.16 96	87.64 34	94.52 37	69.24 58	95.78 19	95.09 23	69.19 258	81.09 94	92.88 124	57.00 170	97.44 68	81.11 114	81.76 169	96.23 37

DCV-MVSNet		84.28 82	83.16 96	87.64 34	94.52 37	69.24 58	95.78 19	95.09 23	69.19 258	81.09 94	92.88 124	57.00 170	97.44 68	81.11 114	81.76 169	96.23 37

FA-MVS(test-final)		79.12 175	77.23 191	84.81 128	90.54 136	63.98 191	81.35 332	91.71 154	71.09 231	74.85 168	82.94 269	52.85 220	97.05 91	67.97 218	81.73 171	93.41 144

thisisatest0515		83.41 101	82.49 110	86.16 81	89.46 159	68.26 81	93.54 96	94.70 36	74.31 145	75.75 155	90.92 163	72.62 28	96.52 122	69.64 200	81.50 172	93.71 137

baseline2		83.68 100	83.42 91	84.48 144	87.37 216	66.00 139	90.06 236	95.93 8	79.71 66	69.08 236	90.39 173	77.92 6	96.28 129	78.91 131	81.38 173	91.16 203

PatchmatchNet		77.46 206	74.63 224	85.96 85	89.55 157	70.35 36	79.97 346	89.55 240	72.23 193	70.94 213	76.91 339	57.03 168	92.79 263	54.27 306	81.17 174	94.74 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VDDNet		80.50 150	78.26 172	87.21 47	86.19 238	69.79 47	94.48 51	91.31 171	60.42 329	79.34 118	90.91 164	38.48 315	96.56 120	82.16 102	81.05 175	95.27 71

EPNet_dtu		78.80 183	79.26 161	77.43 296	88.06 198	49.71 356	91.96 164	91.95 140	77.67 101	76.56 151	91.28 160	58.51 154	90.20 318	56.37 298	80.95 176	92.39 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		82.71 116	82.38 112	83.73 164	89.25 165	59.58 287	92.24 147	94.89 28	77.96 95	79.86 111	92.38 135	56.70 176	97.05 91	77.26 142	80.86 177	94.55 103

FE-MVS		75.97 232	73.02 248	84.82 125	89.78 150	65.56 150	77.44 357	91.07 186	64.55 294	72.66 190	79.85 316	46.05 282	96.69 115	54.97 303	80.82 178	92.21 183

GeoE		78.90 180	77.43 185	83.29 176	88.95 174	62.02 242	92.31 144	86.23 318	70.24 245	71.34 212	89.27 190	54.43 204	94.04 226	63.31 263	80.81 179	93.81 136

fmvsm_s_conf0.5_n_a		85.75 58	86.09 50	84.72 132	85.73 249	63.58 205	93.79 84	89.32 248	81.42 41	90.21 23	96.91 14	62.41 115	97.67 51	94.48 10	80.56 180	92.90 162

TR-MVS		78.77 185	77.37 190	82.95 182	90.49 137	60.88 263	93.67 89	90.07 220	70.08 247	74.51 171	91.37 159	45.69 283	95.70 157	60.12 284	80.32 181	92.29 177

fmvsm_s_conf0.1_n_a		84.76 74	84.84 72	84.53 141	80.23 318	63.50 209	92.79 123	88.73 277	80.46 52	89.84 27	96.65 22	60.96 129	97.57 61	93.80 13	80.14 182	92.53 171

TAPA-MVS	70.22 12	74.94 247	73.53 243	79.17 276	90.40 139	52.07 344	89.19 258	89.61 239	62.69 313	70.07 225	92.67 128	48.89 258	94.32 208	38.26 369	79.97 183	91.12 204
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_cas_vis1_n_1920		80.45 152	80.61 137	79.97 260	78.25 344	57.01 320	94.04 68	88.33 289	79.06 82	82.81 81	93.70 106	38.65 312	91.63 297	90.82 37	79.81 184	91.27 202

cascas		78.18 195	75.77 211	85.41 104	87.14 221	69.11 60	92.96 118	91.15 180	66.71 280	70.47 218	86.07 238	37.49 326	96.48 125	70.15 197	79.80 185	90.65 208

HyFIR lowres test		81.03 142	79.56 153	85.43 103	87.81 207	68.11 87	90.18 233	90.01 225	70.65 240	72.95 187	86.06 239	63.61 97	94.50 205	75.01 157	79.75 186	93.67 138

WB-MVSnew		77.14 211	76.18 206	80.01 257	86.18 239	63.24 214	91.26 197	94.11 60	71.72 212	73.52 182	87.29 223	45.14 288	93.00 251	56.98 296	79.42 187	83.80 312

LS3D		69.17 296	66.40 300	77.50 294	91.92 104	56.12 325	85.12 300	80.37 356	46.96 375	56.50 339	87.51 219	37.25 327	93.71 238	32.52 385	79.40 188	82.68 332

EI-MVSNet-Vis-set		83.77 96	83.67 82	84.06 156	92.79 82	63.56 206	91.76 174	94.81 31	79.65 67	77.87 135	94.09 98	63.35 103	97.90 42	79.35 125	79.36 189	90.74 207

CVMVSNet		74.04 255	74.27 232	73.33 329	85.33 253	43.94 379	89.53 250	88.39 287	54.33 357	70.37 221	90.13 181	49.17 254	84.05 359	61.83 275	79.36 189	91.99 187

EPP-MVSNet		81.79 130	81.52 121	82.61 190	88.77 179	60.21 279	93.02 117	93.66 75	68.52 267	72.90 188	90.39 173	72.19 32	94.96 183	74.93 158	79.29 191	92.67 166

CLD-MVS		82.73 114	82.35 113	83.86 160	87.90 203	67.65 98	95.45 29	92.18 133	85.06 12	72.58 193	92.27 138	52.46 224	95.78 148	84.18 89	79.06 192	88.16 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP3-MVS								91.70 157							78.90 193

HQP-MVS		81.14 138	80.64 136	82.64 189	87.54 211	63.66 203	94.06 64	91.70 157	79.80 63	74.18 173	90.30 175	51.63 231	95.61 160	77.63 140	78.90 193	88.63 234

plane_prior						62.42 233	93.85 78		79.38 72						78.80 195

thres200		79.66 166	78.33 170	83.66 168	92.54 88	65.82 145	93.06 113	96.31 3	74.90 139	73.30 184	88.66 195	59.67 143	95.61 160	47.84 332	78.67 196	89.56 225

ET-MVSNet_ETH3D		84.01 90	83.15 98	86.58 68	90.78 134	70.89 30	94.74 48	94.62 40	81.44 40	58.19 329	93.64 108	73.64 23	92.35 282	82.66 99	78.66 197	96.50 28

HQP_MVS		80.34 154	79.75 150	82.12 207	86.94 226	62.42 233	93.13 111	91.31 171	78.81 86	72.53 194	89.14 193	50.66 238	95.55 165	76.74 143	78.53 198	88.39 241

plane_prior5								91.31 171					95.55 165	76.74 143	78.53 198	88.39 241

EI-MVSNet-UG-set		83.14 107	82.96 99	83.67 167	92.28 91	63.19 216	91.38 190	94.68 37	79.22 76	76.60 150	93.75 104	62.64 112	97.76 48	78.07 138	78.01 200	90.05 216

OMC-MVS		78.67 188	77.91 179	80.95 237	85.76 248	57.40 315	88.49 269	88.67 280	73.85 155	72.43 198	92.10 142	49.29 252	94.55 202	72.73 173	77.89 201	90.91 206

1112_ss		80.56 149	79.83 149	82.77 185	88.65 180	60.78 265	92.29 145	88.36 288	72.58 182	72.46 197	94.95 68	65.09 74	93.42 245	66.38 237	77.71 202	94.10 121

OPM-MVS		79.00 177	78.09 174	81.73 215	83.52 284	63.83 193	91.64 180	90.30 211	76.36 122	71.97 203	89.93 184	46.30 279	95.17 178	75.10 155	77.70 203	86.19 277
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		72.06 276	69.98 279	78.28 286	89.51 158	55.70 328	83.49 310	83.39 345	61.24 324	63.72 296	82.76 271	34.77 340	93.03 250	53.37 311	77.59 204	86.12 281

thres100view900		78.37 192	77.01 194	82.46 192	91.89 106	63.21 215	91.19 203	96.33 1	72.28 192	70.45 220	87.89 213	60.31 134	95.32 172	45.16 343	77.58 205	88.83 230

tfpn200view9		78.79 184	77.43 185	82.88 183	92.21 94	64.49 171	92.05 157	96.28 4	73.48 164	71.75 206	88.26 204	60.07 139	95.32 172	45.16 343	77.58 205	88.83 230

thres400		78.68 186	77.43 185	82.43 193	92.21 94	64.49 171	92.05 157	96.28 4	73.48 164	71.75 206	88.26 204	60.07 139	95.32 172	45.16 343	77.58 205	87.48 250

CostFormer		82.33 120	81.15 124	85.86 89	89.01 173	68.46 75	82.39 323	93.01 101	75.59 128	80.25 107	81.57 288	72.03 33	94.96 183	79.06 129	77.48 208	94.16 118

tpm2		79.80 165	77.95 178	85.34 108	88.28 191	68.26 81	81.56 329	91.42 168	70.11 246	77.59 140	80.50 306	67.40 55	94.26 214	67.34 225	77.35 209	93.51 142

Test_1112_low_res		79.56 168	78.60 168	82.43 193	88.24 194	60.39 276	92.09 154	87.99 299	72.10 198	71.84 204	87.42 220	64.62 82	93.04 249	65.80 244	77.30 210	93.85 135

tpmrst		80.57 148	79.14 163	84.84 124	90.10 145	68.28 80	81.70 327	89.72 237	77.63 104	75.96 154	79.54 320	64.94 77	92.71 265	75.43 152	77.28 211	93.55 141

bld_raw_dy_0_64		82.84 112	80.75 132	89.09 14	93.74 52	72.16 15	93.16 110	77.36 360	89.69 1	74.55 170	96.48 27	32.35 350	97.56 62	92.21 24	77.24 212	97.53 6

Anonymous202405211		77.96 199	75.33 218	85.87 88	93.73 54	64.52 170	94.85 45	85.36 326	62.52 314	76.11 153	90.18 178	29.43 362	97.29 78	68.51 215	77.24 212	95.81 49

GA-MVS		78.33 194	76.23 204	84.65 136	83.65 282	66.30 133	91.44 182	90.14 218	76.01 124	70.32 222	84.02 259	42.50 298	94.72 191	70.98 189	77.00 214	92.94 160

thisisatest0530		81.15 137	80.07 143	84.39 147	88.26 192	65.63 148	91.40 186	94.62 40	71.27 227	70.93 214	89.18 191	72.47 29	96.04 141	65.62 246	76.89 215	91.49 192

thres600view7		78.00 197	76.66 199	82.03 212	91.93 103	63.69 201	91.30 196	96.33 1	72.43 187	70.46 219	87.89 213	60.31 134	94.92 186	42.64 355	76.64 216	87.48 250

PLC	68.80 14	75.23 243	73.68 242	79.86 263	92.93 75	58.68 300	90.64 220	88.30 290	60.90 326	64.43 291	90.53 169	42.38 299	94.57 199	56.52 297	76.54 217	86.33 272
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MIMVSNet		71.64 278	68.44 291	81.23 226	81.97 301	64.44 175	73.05 367	88.80 274	69.67 252	64.59 285	74.79 351	32.79 346	87.82 336	53.99 307	76.35 218	91.42 194

test_fmvs1		74.07 254	73.69 241	75.22 314	78.91 336	47.34 368	89.06 262	74.69 370	63.68 301	79.41 117	91.59 154	24.36 371	87.77 338	85.22 78	76.26 219	90.55 211

MVS-HIRNet		60.25 342	55.55 349	74.35 322	84.37 272	56.57 323	71.64 369	74.11 371	34.44 390	45.54 376	42.24 397	31.11 358	89.81 321	40.36 363	76.10 220	76.67 374

CNLPA		74.31 252	72.30 260	80.32 245	91.49 118	61.66 251	90.85 212	80.72 354	56.67 350	63.85 295	90.64 166	46.75 272	90.84 308	53.79 308	75.99 221	88.47 240

ab-mvs		80.18 157	78.31 171	85.80 92	88.44 185	65.49 154	83.00 320	92.67 113	71.82 208	77.36 142	85.01 247	54.50 200	96.59 117	76.35 147	75.63 222	95.32 66

test_fmvs1_n		72.69 273	71.92 264	74.99 317	71.15 372	47.08 370	87.34 287	75.67 365	63.48 303	78.08 134	91.17 161	20.16 382	87.87 335	84.65 86	75.57 223	90.01 217

iter_conf05		83.27 104	82.70 106	84.98 120	93.32 64	71.84 18	94.16 59	81.76 350	82.74 23	73.83 180	88.40 200	72.77 27	94.61 196	82.10 103	75.21 224	88.48 238

FIs		79.47 170	79.41 157	79.67 267	85.95 243	59.40 289	91.68 178	93.94 63	78.06 94	68.96 240	88.28 202	66.61 61	91.77 294	66.20 240	74.99 225	87.82 246

SDMVSNet		80.26 155	78.88 165	84.40 146	89.25 165	67.63 99	85.35 299	93.02 100	76.77 116	70.84 215	87.12 225	47.95 265	96.09 136	85.04 81	74.55 226	89.48 226

sd_testset		77.08 213	75.37 216	82.20 203	89.25 165	62.11 241	82.06 324	89.09 261	76.77 116	70.84 215	87.12 225	41.43 302	95.01 181	67.23 227	74.55 226	89.48 226

CMPMVS	48.56 21	66.77 316	64.41 317	73.84 326	70.65 375	50.31 353	77.79 356	85.73 324	45.54 379	44.76 378	82.14 279	35.40 338	90.14 319	63.18 265	74.54 228	81.07 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dmvs_re		76.93 214	75.36 217	81.61 218	87.78 208	60.71 270	80.00 345	87.99 299	79.42 71	69.02 238	89.47 188	46.77 271	94.32 208	63.38 262	74.45 229	89.81 219

test_vis1_n		71.63 279	70.73 275	74.31 324	69.63 378	47.29 369	86.91 291	72.11 375	63.21 307	75.18 164	90.17 179	20.40 380	85.76 350	84.59 87	74.42 230	89.87 218

XVG-OURS		74.25 253	72.46 259	79.63 268	78.45 342	57.59 312	80.33 339	87.39 304	63.86 299	68.76 244	89.62 187	40.50 305	91.72 295	69.00 210	74.25 231	89.58 223

tpm cat1		75.30 242	72.21 261	84.58 140	88.52 181	67.77 94	78.16 355	88.02 298	61.88 321	68.45 249	76.37 343	60.65 131	94.03 228	53.77 309	74.11 232	91.93 188

XVG-OURS-SEG-HR		74.70 249	73.08 247	79.57 270	78.25 344	57.33 316	80.49 337	87.32 305	63.22 306	68.76 244	90.12 183	44.89 290	91.59 298	70.55 195	74.09 233	89.79 220

FC-MVSNet-test		77.99 198	78.08 175	77.70 291	84.89 263	55.51 329	90.27 230	93.75 72	76.87 111	66.80 273	87.59 217	65.71 69	90.23 317	62.89 268	73.94 234	87.37 253

PVSNet_BlendedMVS		83.38 102	83.43 89	83.22 178	93.76 49	67.53 102	94.06 64	93.61 76	79.13 79	81.00 97	85.14 246	63.19 105	97.29 78	87.08 65	73.91 235	84.83 304

tttt0517		79.50 169	78.53 169	82.41 196	87.22 219	61.43 255	89.75 246	94.76 32	69.29 256	67.91 255	88.06 211	72.92 25	95.63 158	62.91 267	73.90 236	90.16 214

MDTV_nov1_ep13			72.61 256		89.06 171	68.48 74	80.33 339	90.11 219	71.84 207	71.81 205	75.92 347	53.01 219	93.92 233	48.04 329	73.38 237

SCA		75.82 235	72.76 252	85.01 119	86.63 230	70.08 38	81.06 334	89.19 254	71.60 219	70.01 226	77.09 337	45.53 284	90.25 313	60.43 281	73.27 238	94.68 96

CR-MVSNet		73.79 259	70.82 274	82.70 187	83.15 287	67.96 90	70.25 371	84.00 338	73.67 162	69.97 228	72.41 357	57.82 161	89.48 324	52.99 312	73.13 239	90.64 209

RPMNet		70.42 286	65.68 305	84.63 138	83.15 287	67.96 90	70.25 371	90.45 201	46.83 377	69.97 228	65.10 376	56.48 181	95.30 175	35.79 374	73.13 239	90.64 209

Fast-Effi-MVS+-dtu		75.04 245	73.37 245	80.07 254	80.86 307	59.52 288	91.20 202	85.38 325	71.90 202	65.20 280	84.84 250	41.46 301	92.97 252	66.50 236	72.96 241	87.73 247

mvsmamba		76.85 217	75.71 213	80.25 249	83.07 289	59.16 294	91.44 182	80.64 355	76.84 113	67.95 253	86.33 235	46.17 281	94.24 215	76.06 148	72.92 242	87.36 254

LPG-MVS_test		75.82 235	74.58 226	79.56 271	84.31 273	59.37 290	90.44 223	89.73 235	69.49 253	64.86 282	88.42 198	38.65 312	94.30 210	72.56 175	72.76 243	85.01 302

LGP-MVS_train				79.56 271	84.31 273	59.37 290		89.73 235	69.49 253	64.86 282	88.42 198	38.65 312	94.30 210	72.56 175	72.76 243	85.01 302

EG-PatchMatch MVS		68.55 302	65.41 308	77.96 290	78.69 339	62.93 222	89.86 243	89.17 255	60.55 328	50.27 361	77.73 331	22.60 376	94.06 223	47.18 335	72.65 245	76.88 373

EI-MVSNet		78.97 178	78.22 173	81.25 225	85.33 253	62.73 229	89.53 250	93.21 91	72.39 189	72.14 201	90.13 181	60.99 127	94.72 191	67.73 222	72.49 246	86.29 273

MVSTER		82.47 118	82.05 114	83.74 162	92.68 84	69.01 63	91.90 166	93.21 91	79.83 62	72.14 201	85.71 243	74.72 16	94.72 191	75.72 150	72.49 246	87.50 249

Anonymous20240529		76.84 218	74.15 234	84.88 123	91.02 127	64.95 166	93.84 81	91.09 183	53.57 358	73.00 185	87.42 220	35.91 336	97.32 76	69.14 209	72.41 248	92.36 174

D2MVS		73.80 258	72.02 263	79.15 278	79.15 331	62.97 220	88.58 268	90.07 220	72.94 173	59.22 323	78.30 325	42.31 300	92.70 267	65.59 247	72.00 249	81.79 340

PS-MVSNAJss		77.26 209	76.31 203	80.13 253	80.64 312	59.16 294	90.63 222	91.06 187	72.80 178	68.58 247	84.57 254	53.55 213	93.96 231	72.97 168	71.96 250	87.27 258

Effi-MVS+-dtu		76.14 225	75.28 219	78.72 282	83.22 286	55.17 331	89.87 242	87.78 302	75.42 131	67.98 252	81.43 290	45.08 289	92.52 275	75.08 156	71.63 251	88.48 238

ACMMP++_ref															71.63 251

ACMM	69.62 13	74.34 251	72.73 254	79.17 276	84.25 275	57.87 306	90.36 227	89.93 226	63.17 308	65.64 277	86.04 240	37.79 324	94.10 219	65.89 242	71.52 253	85.55 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	71.68 10	75.58 240	74.23 233	79.62 269	84.97 262	59.64 285	90.80 214	89.07 263	70.39 243	62.95 304	87.30 222	38.28 316	93.87 235	72.89 169	71.45 254	85.36 298
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dp		75.01 246	72.09 262	83.76 161	89.28 164	66.22 136	79.96 347	89.75 232	71.16 228	67.80 259	77.19 336	51.81 228	92.54 274	50.39 317	71.44 255	92.51 172

tpm		78.58 189	77.03 193	83.22 178	85.94 245	64.56 169	83.21 317	91.14 181	78.31 91	73.67 181	79.68 318	64.01 88	92.09 288	66.07 241	71.26 256	93.03 157

DP-MVS		69.90 291	66.48 298	80.14 252	95.36 28	62.93 222	89.56 247	76.11 363	50.27 368	57.69 335	85.23 245	39.68 307	95.73 152	33.35 379	71.05 257	81.78 341

UniMVSNet_ETH3D		72.74 270	70.53 277	79.36 273	78.62 341	56.64 322	85.01 301	89.20 253	63.77 300	64.84 284	84.44 256	34.05 343	91.86 292	63.94 258	70.89 258	89.57 224

jajsoiax		73.05 264	71.51 269	77.67 292	77.46 350	54.83 333	88.81 264	90.04 223	69.13 260	62.85 306	83.51 264	31.16 357	92.75 264	70.83 190	69.80 259	85.43 297

ACMMP++															69.72 260

mvs_tets		72.71 271	71.11 270	77.52 293	77.41 351	54.52 335	88.45 270	89.76 231	68.76 265	62.70 307	83.26 267	29.49 361	92.71 265	70.51 196	69.62 261	85.34 299

tpmvs		72.88 268	69.76 284	82.22 202	90.98 128	67.05 114	78.22 354	88.30 290	63.10 309	64.35 292	74.98 350	55.09 196	94.27 212	43.25 349	69.57 262	85.34 299

GBi-Net		75.65 237	73.83 239	81.10 231	88.85 175	65.11 161	90.01 238	90.32 207	70.84 235	67.04 268	80.25 311	48.03 261	91.54 300	59.80 286	69.34 263	86.64 266

test1		75.65 237	73.83 239	81.10 231	88.85 175	65.11 161	90.01 238	90.32 207	70.84 235	67.04 268	80.25 311	48.03 261	91.54 300	59.80 286	69.34 263	86.64 266

FMVSNet3		77.73 203	76.04 207	82.80 184	91.20 126	68.99 64	91.87 167	91.99 138	73.35 166	67.04 268	83.19 268	56.62 178	92.14 285	59.80 286	69.34 263	87.28 257

Syy-MVS		69.65 293	69.52 285	70.03 348	87.87 204	43.21 381	88.07 274	89.01 265	72.91 175	63.11 301	88.10 208	45.28 287	85.54 351	22.07 394	69.23 266	81.32 343

myMVS_eth3d		72.58 275	72.74 253	72.10 341	87.87 204	49.45 358	88.07 274	89.01 265	72.91 175	63.11 301	88.10 208	63.63 95	85.54 351	32.73 383	69.23 266	81.32 343

MSDG		69.54 294	65.73 304	80.96 236	85.11 260	63.71 199	84.19 305	83.28 346	56.95 347	54.50 344	84.03 258	31.50 354	96.03 142	42.87 353	69.13 268	83.14 324

JIA-IIPM		66.06 319	62.45 328	76.88 305	81.42 305	54.45 336	57.49 393	88.67 280	49.36 370	63.86 294	46.86 391	56.06 185	90.25 313	49.53 322	68.83 269	85.95 285

OpenMVS_ROB	61.12 18	66.39 317	62.92 325	76.80 306	76.51 354	57.77 307	89.22 256	83.41 344	55.48 354	53.86 348	77.84 330	26.28 370	93.95 232	34.90 376	68.76 270	78.68 367

FMVSNet2		76.07 226	74.01 237	82.26 201	88.85 175	67.66 97	91.33 194	91.61 160	70.84 235	65.98 275	82.25 277	48.03 261	92.00 290	58.46 291	68.73 271	87.10 260

test_djsdf		73.76 260	72.56 257	77.39 297	77.00 353	53.93 337	89.07 260	90.69 194	65.80 286	63.92 293	82.03 280	43.14 297	92.67 268	72.83 170	68.53 272	85.57 293

F-COLMAP		70.66 283	68.44 291	77.32 298	86.37 236	55.91 326	88.00 276	86.32 315	56.94 348	57.28 337	88.07 210	33.58 344	92.49 276	51.02 315	68.37 273	83.55 314

XVG-ACMP-BASELINE		68.04 307	65.53 307	75.56 312	74.06 364	52.37 342	78.43 351	85.88 322	62.03 318	58.91 327	81.21 298	20.38 381	91.15 307	60.69 280	68.18 274	83.16 323

LTVRE_ROB	59.60 19	66.27 318	63.54 321	74.45 321	84.00 278	51.55 346	67.08 381	83.53 342	58.78 339	54.94 343	80.31 309	34.54 341	93.23 247	40.64 362	68.03 275	78.58 368
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
XXY-MVS		77.94 200	76.44 201	82.43 193	82.60 293	64.44 175	92.01 159	91.83 149	73.59 163	70.00 227	85.82 241	54.43 204	94.76 188	69.63 201	68.02 276	88.10 245

ADS-MVSNet2		66.90 315	63.44 322	77.26 300	88.06 198	60.70 271	68.01 378	75.56 367	57.57 342	64.48 288	69.87 367	38.68 310	84.10 358	40.87 360	67.89 277	86.97 261

ADS-MVSNet		68.54 303	64.38 318	81.03 235	88.06 198	66.90 118	68.01 378	84.02 337	57.57 342	64.48 288	69.87 367	38.68 310	89.21 326	40.87 360	67.89 277	86.97 261

test0.0.03 1		72.76 269	72.71 255	72.88 333	80.25 317	47.99 364	91.22 200	89.45 243	71.51 223	62.51 309	87.66 216	53.83 209	85.06 355	50.16 319	67.84 279	85.58 292

anonymousdsp		71.14 282	69.37 286	76.45 307	72.95 367	54.71 334	84.19 305	88.88 270	61.92 320	62.15 310	79.77 317	38.14 319	91.44 305	68.90 212	67.45 280	83.21 322

tt0805		73.07 263	70.73 275	80.07 254	78.37 343	57.05 318	87.78 280	92.18 133	61.23 325	67.04 268	86.49 232	31.35 356	94.58 197	65.06 252	67.12 281	88.57 236

VPA-MVSNet		79.03 176	78.00 176	82.11 210	85.95 243	64.48 173	93.22 109	94.66 38	75.05 137	74.04 178	84.95 248	52.17 226	93.52 242	74.90 160	67.04 282	88.32 243

nrg030		80.93 143	79.86 148	84.13 155	83.69 281	68.83 67	93.23 108	91.20 176	75.55 129	75.06 165	88.22 207	63.04 109	94.74 190	81.88 105	66.88 283	88.82 232

FMVSNet1		72.71 271	69.91 282	81.10 231	83.60 283	65.11 161	90.01 238	90.32 207	63.92 298	63.56 297	80.25 311	36.35 335	91.54 300	54.46 305	66.75 284	86.64 266

PatchT		69.11 297	65.37 309	80.32 245	82.07 300	63.68 202	67.96 380	87.62 303	50.86 366	69.37 232	65.18 375	57.09 167	88.53 330	41.59 358	66.60 285	88.74 233

IB-MVS	77.80 4	82.18 122	80.46 141	87.35 45	89.14 170	70.28 37	95.59 27	95.17 21	78.85 84	70.19 224	85.82 241	70.66 37	97.67 51	72.19 181	66.52 286	94.09 122
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
RRT_MVS		74.44 250	72.97 250	78.84 281	82.36 296	57.66 310	89.83 244	88.79 276	70.61 241	64.58 286	84.89 249	39.24 308	92.65 271	70.11 198	66.34 287	86.21 276

test_fmvs2		65.78 322	64.84 310	68.60 354	66.54 383	41.71 383	83.27 314	69.81 381	54.38 356	67.91 255	84.54 255	15.35 387	81.22 378	75.65 151	66.16 288	82.88 325

pmmvs5		73.35 261	71.52 268	78.86 280	78.64 340	60.61 274	91.08 205	86.90 310	67.69 271	63.32 299	83.64 262	44.33 292	90.53 310	62.04 273	66.02 289	85.46 296

dmvs_testset		65.55 323	66.45 299	62.86 364	79.87 321	22.35 407	76.55 359	71.74 377	77.42 109	55.85 340	87.77 215	51.39 233	80.69 379	31.51 389	65.92 290	85.55 294

pmmvs4		73.92 257	71.81 266	80.25 249	79.17 330	65.24 157	87.43 285	87.26 307	67.64 274	63.46 298	83.91 261	48.96 257	91.53 303	62.94 266	65.49 291	83.96 309

cl22		77.94 200	76.78 197	81.42 222	87.57 210	64.93 167	90.67 218	88.86 272	72.45 186	67.63 261	82.68 273	64.07 87	92.91 258	71.79 182	65.30 292	86.44 271

miper_ehance_all_eth		77.60 204	76.44 201	81.09 234	85.70 250	64.41 178	90.65 219	88.64 282	72.31 190	67.37 266	82.52 274	64.77 81	92.64 272	70.67 193	65.30 292	86.24 275

miper_enhance_ethall		78.86 181	77.97 177	81.54 220	88.00 201	65.17 159	91.41 184	89.15 257	75.19 135	68.79 243	83.98 260	67.17 56	92.82 260	72.73 173	65.30 292	86.62 270

v1144		76.73 221	74.88 221	82.27 199	80.23 318	66.60 126	91.68 178	90.21 217	73.69 160	69.06 237	81.89 281	52.73 222	94.40 207	69.21 207	65.23 295	85.80 288

DSMNet-mixed		56.78 348	54.44 351	63.79 363	63.21 387	29.44 402	64.43 384	64.10 389	42.12 387	51.32 357	71.60 362	31.76 353	75.04 384	36.23 371	65.20 296	86.87 264

v1192		75.98 231	73.92 238	82.15 205	79.73 322	66.24 135	91.22 200	89.75 232	72.67 180	68.49 248	81.42 291	49.86 246	94.27 212	67.08 229	65.02 297	85.95 285

v2v482		77.42 207	75.65 214	82.73 186	80.38 314	67.13 112	91.85 169	90.23 215	75.09 136	69.37 232	83.39 266	53.79 211	94.44 206	71.77 183	65.00 298	86.63 269

V42		76.46 223	74.55 227	82.19 204	79.14 332	67.82 93	90.26 231	89.42 245	73.75 158	68.63 246	81.89 281	51.31 234	94.09 220	71.69 185	64.84 299	84.66 305

ACMH	63.93 17	68.62 301	64.81 311	80.03 256	85.22 256	63.25 213	87.72 281	84.66 332	60.83 327	51.57 356	79.43 321	27.29 367	94.96 183	41.76 356	64.84 299	81.88 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		81.84 129	81.03 129	84.28 152	91.60 113	66.62 125	91.08 205	91.66 159	81.87 33	74.86 167	91.67 152	69.98 41	94.92 186	71.76 184	64.75 301	91.29 201

v1240		75.21 244	72.98 249	81.88 213	79.20 329	66.00 139	90.75 216	89.11 260	71.63 218	67.41 264	81.22 296	47.36 269	93.87 235	65.46 249	64.72 302	85.77 289

IterMVS-LS		76.49 222	75.18 220	80.43 244	84.49 269	62.74 228	90.64 220	88.80 274	72.40 188	65.16 281	81.72 284	60.98 128	92.27 284	67.74 221	64.65 303	86.29 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		75.63 239	73.49 244	82.06 211	79.38 327	66.35 131	91.07 207	89.48 241	71.98 199	67.99 251	81.22 296	49.16 255	93.90 234	66.56 233	64.56 304	85.92 287

v144192		76.05 229	74.03 236	82.12 207	79.50 326	66.55 128	91.39 188	89.71 238	72.30 191	68.17 250	81.33 293	51.75 229	94.03 228	67.94 219	64.19 305	85.77 289

Anonymous20231211		73.08 262	70.39 278	81.13 229	90.62 135	63.33 212	91.40 186	90.06 222	51.84 363	64.46 290	80.67 304	36.49 334	94.07 222	63.83 259	64.17 306	85.98 284

testing3		70.38 287	70.83 272	69.03 352	85.82 247	43.93 380	90.72 217	90.56 200	68.06 269	60.24 317	86.82 229	64.83 79	84.12 357	26.33 390	64.10 307	79.04 364

Patchmatch-test		65.86 320	60.94 334	80.62 242	83.75 280	58.83 298	58.91 392	75.26 369	44.50 382	50.95 360	77.09 337	58.81 153	87.90 334	35.13 375	64.03 308	95.12 78

USDC		67.43 314	64.51 315	76.19 309	77.94 348	55.29 330	78.38 352	85.00 329	73.17 168	48.36 368	80.37 308	21.23 378	92.48 277	52.15 313	64.02 309	80.81 349

VPNet		78.82 182	77.53 184	82.70 187	84.52 268	66.44 129	93.93 73	92.23 127	80.46 52	72.60 192	88.38 201	49.18 253	93.13 248	72.47 177	63.97 310	88.55 237

Anonymous20231206		67.53 312	65.78 303	72.79 334	74.95 360	47.59 366	88.23 272	87.32 305	61.75 323	58.07 331	77.29 334	37.79 324	87.29 344	42.91 351	63.71 311	83.48 317

WR-MVS		76.76 220	75.74 212	79.82 264	84.60 266	62.27 239	92.60 135	92.51 121	76.06 123	67.87 258	85.34 244	56.76 174	90.24 316	62.20 272	63.69 312	86.94 263

h-mvs33		83.01 109	82.56 109	84.35 149	89.34 160	62.02 242	92.72 126	93.76 69	81.45 38	82.73 82	92.25 140	60.11 137	97.13 89	87.69 56	62.96 313	93.91 131

c3_l		76.83 219	75.47 215	80.93 238	85.02 261	64.18 188	90.39 226	88.11 296	71.66 213	66.65 274	81.64 286	63.58 100	92.56 273	69.31 206	62.86 314	86.04 282

test_vis1_rt		59.09 346	57.31 345	64.43 362	68.44 381	46.02 374	83.05 319	48.63 402	51.96 362	49.57 364	63.86 378	16.30 385	80.20 380	71.21 188	62.79 315	67.07 387

mvsany_test1		68.77 300	68.56 289	69.39 350	73.57 365	45.88 375	80.93 335	60.88 393	59.65 335	71.56 209	90.26 177	43.22 296	75.05 383	74.26 164	62.70 316	87.25 259

UniMVSNet_NR-MVSNet		78.15 196	77.55 183	79.98 258	84.46 270	60.26 277	92.25 146	93.20 93	77.50 106	68.88 241	86.61 230	66.10 64	92.13 286	66.38 237	62.55 317	87.54 248

DU-MVS		76.86 215	75.84 210	79.91 261	82.96 290	60.26 277	91.26 197	91.54 162	76.46 121	68.88 241	86.35 233	56.16 182	92.13 286	66.38 237	62.55 317	87.35 255

UniMVSNet (Re)		77.58 205	76.78 197	79.98 258	84.11 276	60.80 264	91.76 174	93.17 95	76.56 120	69.93 230	84.78 251	63.32 104	92.36 281	64.89 253	62.51 319	86.78 265

v8		75.35 241	73.26 246	81.61 218	80.67 311	66.82 119	89.54 249	89.27 250	71.65 214	63.30 300	80.30 310	54.99 197	94.06 223	67.33 226	62.33 320	83.94 310

cl____		76.07 226	74.67 222	80.28 247	85.15 257	61.76 248	90.12 234	88.73 277	71.16 228	65.43 278	81.57 288	61.15 125	92.95 253	66.54 234	62.17 321	86.13 280

v10		74.77 248	72.54 258	81.46 221	80.33 316	66.71 123	89.15 259	89.08 262	70.94 233	63.08 303	79.86 315	52.52 223	94.04 226	65.70 245	62.17 321	83.64 313

DIV-MVS_self_test		76.07 226	74.67 222	80.28 247	85.14 258	61.75 249	90.12 234	88.73 277	71.16 228	65.42 279	81.60 287	61.15 125	92.94 257	66.54 234	62.16 323	86.14 278

IterMVS-SCA-FT		71.55 280	69.97 280	76.32 308	81.48 303	60.67 272	87.64 283	85.99 321	66.17 284	59.50 321	78.88 322	45.53 284	83.65 363	62.58 270	61.93 324	84.63 307

IterMVS		72.65 274	70.83 272	78.09 289	82.17 298	62.96 221	87.64 283	86.28 316	71.56 221	60.44 316	78.85 323	45.42 286	86.66 346	63.30 264	61.83 325	84.65 306
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 307	65.66 306	75.18 316	84.43 271	57.89 305	83.54 309	86.26 317	61.83 322	53.64 349	73.30 354	37.15 330	85.08 354	48.99 324	61.77 326	82.56 334

v7n		71.31 281	68.65 288	79.28 274	76.40 355	60.77 266	86.71 294	89.45 243	64.17 297	58.77 328	78.24 326	44.59 291	93.54 241	57.76 293	61.75 327	83.52 316

v148		76.19 224	74.47 229	81.36 223	80.05 320	64.44 175	91.75 176	90.23 215	73.68 161	67.13 267	80.84 301	55.92 187	93.86 237	68.95 211	61.73 328	85.76 291

tfpnnormal		70.10 288	67.36 296	78.32 285	83.45 285	60.97 262	88.85 263	92.77 109	64.85 293	60.83 315	78.53 324	43.52 295	93.48 243	31.73 386	61.70 329	80.52 352

ACMH+	65.35 16	67.65 310	64.55 314	76.96 304	84.59 267	57.10 317	88.08 273	80.79 353	58.59 341	53.00 350	81.09 300	26.63 369	92.95 253	46.51 337	61.69 330	80.82 348

ITE_SJBPF				70.43 347	74.44 362	47.06 371		77.32 361	60.16 332	54.04 347	83.53 263	23.30 375	84.01 360	43.07 350	61.58 331	80.21 357

NR-MVSNet		76.05 229	74.59 225	80.44 243	82.96 290	62.18 240	90.83 213	91.73 152	77.12 110	60.96 314	86.35 233	59.28 149	91.80 293	60.74 279	61.34 332	87.35 255

test_0402		64.54 327	61.09 333	74.92 318	84.10 277	60.75 268	87.95 277	79.71 358	52.03 361	52.41 352	77.20 335	32.21 352	91.64 296	23.14 392	61.03 333	72.36 381

Baseline_NR-MVSNet		73.99 256	72.83 251	77.48 295	80.78 309	59.29 293	91.79 171	84.55 333	68.85 262	68.99 239	80.70 302	56.16 182	92.04 289	62.67 269	60.98 334	81.11 345

TranMVSNet+NR-MVSNet		75.86 234	74.52 228	79.89 262	82.44 295	60.64 273	91.37 191	91.37 169	76.63 118	67.65 260	86.21 237	52.37 225	91.55 299	61.84 274	60.81 335	87.48 250

testgi		64.48 328	62.87 326	69.31 351	71.24 370	40.62 386	85.49 298	79.92 357	65.36 290	54.18 346	83.49 265	23.74 374	84.55 356	41.60 357	60.79 336	82.77 327

eth_miper_zixun_eth		75.96 233	74.40 230	80.66 240	84.66 265	63.02 219	89.28 255	88.27 292	71.88 204	65.73 276	81.65 285	59.45 145	92.81 261	68.13 216	60.53 337	86.14 278

COLMAP_ROB	57.96 20	62.98 335	59.65 337	72.98 332	81.44 304	53.00 341	83.75 308	75.53 368	48.34 373	48.81 367	81.40 292	24.14 372	90.30 312	32.95 381	60.52 338	75.65 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		78.37 192	77.43 185	81.17 227	86.60 231	57.45 314	89.46 252	91.16 178	74.11 148	74.40 172	90.49 171	55.52 190	94.57 199	74.73 162	60.43 339	91.48 193

hse-mvs2		81.12 140	81.11 128	81.16 228	86.52 232	57.48 313	89.40 253	91.16 178	81.45 38	82.73 82	90.49 171	60.11 137	94.58 197	87.69 56	60.41 340	91.41 195

RPSCF		64.24 329	61.98 331	71.01 346	76.10 357	45.00 376	75.83 363	75.94 364	46.94 376	58.96 326	84.59 253	31.40 355	82.00 375	47.76 333	60.33 341	86.04 282

miper_lstm_enhance		73.05 264	71.73 267	77.03 301	83.80 279	58.32 303	81.76 325	88.88 270	69.80 251	61.01 313	78.23 327	57.19 166	87.51 342	65.34 250	59.53 342	85.27 301

CP-MVSNet		70.50 285	69.91 282	72.26 338	80.71 310	51.00 350	87.23 288	90.30 211	67.84 270	59.64 320	82.69 272	50.23 243	82.30 373	51.28 314	59.28 343	83.46 318

PS-CasMVS		69.86 292	69.13 287	72.07 342	80.35 315	50.57 352	87.02 290	89.75 232	67.27 276	59.19 324	82.28 276	46.58 274	82.24 374	50.69 316	59.02 344	83.39 320

pm-mvs1		72.89 267	71.09 271	78.26 287	79.10 333	57.62 311	90.80 214	89.30 249	67.66 272	62.91 305	81.78 283	49.11 256	92.95 253	60.29 283	58.89 345	84.22 308

Anonymous20240521		62.09 336	59.08 339	71.10 345	67.19 382	48.72 362	83.91 307	85.23 327	50.38 367	47.84 369	71.22 366	20.74 379	85.51 353	46.47 338	58.75 346	79.06 363

WR-MVS_H		70.59 284	69.94 281	72.53 335	81.03 306	51.43 347	87.35 286	92.03 137	67.38 275	60.23 318	80.70 302	55.84 188	83.45 365	46.33 339	58.58 347	82.72 329

PEN-MVS		69.46 295	68.56 289	72.17 340	79.27 328	49.71 356	86.90 292	89.24 251	67.24 279	59.08 325	82.51 275	47.23 270	83.54 364	48.42 327	57.12 348	83.25 321

EU-MVSNet		64.01 330	63.01 324	67.02 360	74.40 363	38.86 391	83.27 314	86.19 319	45.11 380	54.27 345	81.15 299	36.91 333	80.01 381	48.79 326	57.02 349	82.19 338

AllTest		61.66 337	58.06 341	72.46 336	79.57 323	51.42 348	80.17 342	68.61 383	51.25 364	45.88 372	81.23 294	19.86 383	86.58 347	38.98 366	57.01 350	79.39 360

TestCases				72.46 336	79.57 323	51.42 348		68.61 383	51.25 364	45.88 372	81.23 294	19.86 383	86.58 347	38.98 366	57.01 350	79.39 360

Patchmtry		67.53 312	63.93 319	78.34 284	82.12 299	64.38 179	68.72 375	84.00 338	48.23 374	59.24 322	72.41 357	57.82 161	89.27 325	46.10 340	56.68 352	81.36 342

our_test_3		68.29 305	64.69 313	79.11 279	78.92 334	64.85 168	88.40 271	85.06 328	60.32 331	52.68 351	76.12 345	40.81 304	89.80 323	44.25 348	55.65 353	82.67 333

FPMVS		45.64 357	43.10 361	53.23 374	51.42 399	36.46 392	64.97 383	71.91 376	29.13 394	27.53 394	61.55 383	9.83 396	65.01 398	16.00 400	55.58 354	58.22 390

DTE-MVSNet		68.46 304	67.33 297	71.87 344	77.94 348	49.00 361	86.16 297	88.58 284	66.36 283	58.19 329	82.21 278	46.36 275	83.87 362	44.97 346	55.17 355	82.73 328

MIMVSNet1		60.16 343	57.33 344	68.67 353	69.71 377	44.13 378	78.92 349	84.21 334	55.05 355	44.63 379	71.85 361	23.91 373	81.54 377	32.63 384	55.03 356	80.35 353

pmmvs6		67.57 311	64.76 312	76.00 311	72.82 369	53.37 339	88.71 265	86.78 314	53.19 359	57.58 336	78.03 329	35.33 339	92.41 278	55.56 301	54.88 357	82.21 337

TinyColmap		60.32 341	56.42 348	72.00 343	78.78 337	53.18 340	78.36 353	75.64 366	52.30 360	41.59 385	75.82 348	14.76 390	88.35 331	35.84 372	54.71 358	74.46 377

test20.03		63.83 331	62.65 327	67.38 359	70.58 376	39.94 387	86.57 295	84.17 335	63.29 305	51.86 354	77.30 333	37.09 331	82.47 371	38.87 368	54.13 359	79.73 358

OurMVSNet-221017-0		64.68 326	62.17 330	72.21 339	76.08 358	47.35 367	80.67 336	81.02 352	56.19 351	51.60 355	79.66 319	27.05 368	88.56 329	53.60 310	53.63 360	80.71 350

test_fmvs3		56.82 347	54.86 350	62.69 365	53.59 396	35.47 393	75.87 362	65.64 388	43.91 383	55.10 342	71.43 365	6.91 401	74.40 386	68.64 214	52.63 361	78.20 370

Patchmatch-RL test		68.17 306	64.49 316	79.19 275	71.22 371	53.93 337	70.07 373	71.54 379	69.22 257	56.79 338	62.89 379	56.58 179	88.61 327	69.53 203	52.61 362	95.03 82

ppachtmachnet_test		67.72 309	63.70 320	79.77 266	78.92 334	66.04 138	88.68 266	82.90 348	60.11 333	55.45 341	75.96 346	39.19 309	90.55 309	39.53 364	52.55 363	82.71 330

LF4IMVS		54.01 351	52.12 352	59.69 366	62.41 389	39.91 389	68.59 376	68.28 385	42.96 386	44.55 380	75.18 349	14.09 392	68.39 392	41.36 359	51.68 364	70.78 382

N_pmnet		50.55 352	49.11 355	54.88 372	77.17 352	4.02 415	84.36 304	2.00 413	48.59 371	45.86 374	68.82 369	32.22 351	82.80 370	31.58 387	51.38 365	77.81 371

pmmvs-eth3d		65.53 324	62.32 329	75.19 315	69.39 379	59.59 286	82.80 321	83.43 343	62.52 314	51.30 358	72.49 355	32.86 345	87.16 345	55.32 302	50.73 366	78.83 366

CL-MVSNet_self_test		69.92 290	68.09 294	75.41 313	73.25 366	55.90 327	90.05 237	89.90 227	69.96 248	61.96 312	76.54 340	51.05 236	87.64 339	49.51 323	50.59 367	82.70 331

PM-MVS		59.40 344	56.59 346	67.84 355	63.63 386	41.86 382	76.76 358	63.22 390	59.01 338	51.07 359	72.27 360	11.72 393	83.25 367	61.34 276	50.28 368	78.39 369

MDA-MVSNet_test_wron		63.78 332	60.16 335	74.64 319	78.15 346	60.41 275	83.49 310	84.03 336	56.17 353	39.17 387	71.59 363	37.22 328	83.24 368	42.87 353	48.73 369	80.26 355

YYNet1		63.76 333	60.14 336	74.62 320	78.06 347	60.19 280	83.46 312	83.99 340	56.18 352	39.25 386	71.56 364	37.18 329	83.34 366	42.90 352	48.70 370	80.32 354

KD-MVS_self_test		60.87 340	58.60 340	67.68 357	66.13 384	39.93 388	75.63 364	84.70 331	57.32 345	49.57 364	68.45 370	29.55 360	82.87 369	48.09 328	47.94 371	80.25 356

SixPastTwentyTwo		64.92 325	61.78 332	74.34 323	78.74 338	49.76 355	83.42 313	79.51 359	62.86 310	50.27 361	77.35 332	30.92 359	90.49 311	45.89 341	47.06 372	82.78 326

new_pmnet		49.31 353	46.44 356	57.93 367	62.84 388	40.74 385	68.47 377	62.96 391	36.48 389	35.09 389	57.81 386	14.97 389	72.18 388	32.86 382	46.44 373	60.88 389

EGC-MVSNET		42.35 359	38.09 362	55.11 371	74.57 361	46.62 372	71.63 370	55.77 394	0.04 408	0.24 409	62.70 380	14.24 391	74.91 385	17.59 397	46.06 374	43.80 394

TransMVSNet (Re)		70.07 289	67.66 295	77.31 299	80.62 313	59.13 296	91.78 173	84.94 330	65.97 285	60.08 319	80.44 307	50.78 237	91.87 291	48.84 325	45.46 375	80.94 347

ambc				69.61 349	61.38 391	41.35 384	49.07 398	85.86 323		50.18 363	66.40 373	10.16 395	88.14 333	45.73 342	44.20 376	79.32 362

TDRefinement		55.28 350	51.58 353	66.39 361	59.53 393	46.15 373	76.23 361	72.80 373	44.60 381	42.49 383	76.28 344	15.29 388	82.39 372	33.20 380	43.75 377	70.62 383

Gipuma		34.91 366	31.44 369	45.30 381	70.99 373	39.64 390	19.85 403	72.56 374	20.10 399	16.16 403	21.47 404	5.08 404	71.16 389	13.07 401	43.70 378	25.08 401
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		46.58 355	43.45 359	55.96 369	45.18 403	32.05 397	61.18 387	49.49 401	33.39 391	42.05 384	62.48 381	7.00 400	65.56 396	47.08 336	43.21 379	70.27 384

MDA-MVSNet-bldmvs		61.54 339	57.70 343	73.05 331	79.53 325	57.00 321	83.08 318	81.23 351	57.57 342	34.91 390	72.45 356	32.79 346	86.26 349	35.81 373	41.95 380	75.89 375

new-patchmatchnet		59.30 345	56.48 347	67.79 356	65.86 385	44.19 377	82.47 322	81.77 349	59.94 334	43.65 382	66.20 374	27.67 366	81.68 376	39.34 365	41.40 381	77.50 372

UnsupCasMVSNet_eth		65.79 321	63.10 323	73.88 325	70.71 374	50.29 354	81.09 333	89.88 228	72.58 182	49.25 366	74.77 352	32.57 349	87.43 343	55.96 300	41.04 382	83.90 311

test_vis3_rt		40.46 362	37.79 363	48.47 379	44.49 404	33.35 396	66.56 382	32.84 410	32.39 392	29.65 392	39.13 400	3.91 408	68.65 391	50.17 318	40.99 383	43.40 395

pmmvs3		55.51 349	51.50 354	67.53 358	57.90 394	50.93 351	80.37 338	73.66 372	40.63 388	44.15 381	64.75 377	16.30 385	78.97 382	44.77 347	40.98 384	72.69 379

APD_test1		40.50 361	37.31 364	50.09 377	51.88 397	35.27 394	59.45 391	52.59 398	21.64 397	26.12 395	57.80 387	4.56 405	66.56 394	22.64 393	39.09 385	48.43 393

UnsupCasMVSNet_bld		61.60 338	57.71 342	73.29 330	68.73 380	51.64 345	78.61 350	89.05 264	57.20 346	46.11 371	61.96 382	28.70 364	88.60 328	50.08 320	38.90 386	79.63 359

PMVS	26.43 22	31.84 369	28.16 372	42.89 382	25.87 412	27.58 403	50.92 397	49.78 400	21.37 398	14.17 404	40.81 399	2.01 411	66.62 393	9.61 404	38.88 387	34.49 400
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
K. test v3		63.09 334	59.61 338	73.53 328	76.26 356	49.38 360	83.27 314	77.15 362	64.35 296	47.77 370	72.32 359	28.73 363	87.79 337	49.93 321	36.69 388	83.41 319

KD-MVS_2432*1600		69.03 298	66.37 301	77.01 302	85.56 251	61.06 260	81.44 330	90.25 213	67.27 276	58.00 332	76.53 341	54.49 201	87.63 340	48.04 329	35.77 389	82.34 335

miper_refine_blended		69.03 298	66.37 301	77.01 302	85.56 251	61.06 260	81.44 330	90.25 213	67.27 276	58.00 332	76.53 341	54.49 201	87.63 340	48.04 329	35.77 389	82.34 335

mvsany_test3		48.86 354	46.35 357	56.41 368	46.00 402	31.67 398	62.26 386	47.25 403	43.71 384	45.54 376	68.15 371	10.84 394	64.44 400	57.95 292	35.44 391	73.13 378

LCM-MVSNet		40.54 360	35.79 365	54.76 373	36.92 409	30.81 399	51.41 396	69.02 382	22.07 396	24.63 396	45.37 393	4.56 405	65.81 395	33.67 378	34.50 392	67.67 385

test_method		38.59 364	35.16 367	48.89 378	54.33 395	21.35 408	45.32 399	53.71 397	7.41 405	28.74 393	51.62 389	8.70 398	52.87 403	33.73 377	32.89 393	72.47 380

lessismore_v0				73.72 327	72.93 368	47.83 365		61.72 392		45.86 374	73.76 353	28.63 365	89.81 321	47.75 334	31.37 394	83.53 315

testf1		32.77 367	29.47 370	42.67 383	41.89 406	30.81 399	52.07 394	43.45 404	15.45 400	18.52 400	44.82 394	2.12 409	58.38 401	16.05 398	30.87 395	38.83 396

APD_test2		32.77 367	29.47 370	42.67 383	41.89 406	30.81 399	52.07 394	43.45 404	15.45 400	18.52 400	44.82 394	2.12 409	58.38 401	16.05 398	30.87 395	38.83 396

PVSNet_0	68.08 15	71.81 277	68.32 293	82.27 199	84.68 264	62.31 238	88.68 266	90.31 210	75.84 125	57.93 334	80.65 305	37.85 323	94.19 216	69.94 199	29.05 397	90.31 213

WB-MVS		46.23 356	44.94 358	50.11 376	62.13 390	21.23 409	76.48 360	55.49 395	45.89 378	35.78 388	61.44 384	35.54 337	72.83 387	9.96 403	21.75 398	56.27 391

SSC-MVS		44.51 358	43.35 360	47.99 380	61.01 392	18.90 411	74.12 366	54.36 396	43.42 385	34.10 391	60.02 385	34.42 342	70.39 390	9.14 405	19.57 399	54.68 392

DeepMVS_CX				34.71 386	51.45 398	24.73 406		28.48 412	31.46 393	17.49 402	52.75 388	5.80 403	42.60 407	18.18 396	19.42 400	36.81 399

PMMVS2		37.93 365	33.61 368	50.92 375	46.31 401	24.76 405	60.55 390	50.05 399	28.94 395	20.93 397	47.59 390	4.41 407	65.13 397	25.14 391	18.55 401	62.87 388

MVE	24.84 23	24.35 371	19.77 377	38.09 385	34.56 411	26.92 404	26.57 401	38.87 408	11.73 404	11.37 405	27.44 401	1.37 412	50.42 404	11.41 402	14.60 402	36.93 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 370	24.00 374	26.45 387	43.74 405	18.44 412	60.86 388	39.66 406	15.11 402	9.53 406	22.10 403	6.52 402	46.94 405	8.31 406	10.14 403	13.98 403

EMVS		23.76 372	23.20 376	25.46 388	41.52 408	16.90 413	60.56 389	38.79 409	14.62 403	8.99 407	20.24 406	7.35 399	45.82 406	7.25 407	9.46 404	13.64 404

tmp_tt		22.26 373	23.75 375	17.80 389	5.23 413	12.06 414	35.26 400	39.48 407	2.82 407	18.94 398	44.20 396	22.23 377	24.64 408	36.30 370	9.31 405	16.69 402

ANet_high		40.27 363	35.20 366	55.47 370	34.74 410	34.47 395	63.84 385	71.56 378	48.42 372	18.80 399	41.08 398	9.52 397	64.45 399	20.18 395	8.66 406	67.49 386

wuyk23d		11.30 375	10.95 378	12.33 390	48.05 400	19.89 410	25.89 402	1.92 414	3.58 406	3.12 408	1.37 408	0.64 413	15.77 409	6.23 408	7.77 407	1.35 405

testmvs		7.23 377	9.62 380	0.06 392	0.04 414	0.02 417	84.98 302	0.02 415	0.03 409	0.18 410	1.21 409	0.01 415	0.02 410	0.14 409	0.01 408	0.13 407

test_blank		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

uanet_test		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

DCPMVS		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

cdsmvs_eth3d_5k		19.86 374	26.47 373	0.00 393	0.00 416	0.00 418	0.00 404	93.45 84	0.00 411	0.00 412	95.27 58	49.56 248	0.00 412	0.00 411	0.00 409	0.00 408

pcd_1.5k_mvsjas		4.46 379	5.95 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	53.55 213	0.00 412	0.00 411	0.00 409	0.00 408

sosnet-low-res		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

sosnet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

uncertanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

Regformer		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

test123		6.92 378	9.21 381	0.08 391	0.03 415	0.05 416	81.65 328	0.01 416	0.02 410	0.14 411	0.85 410	0.03 414	0.02 410	0.12 410	0.00 409	0.16 406

ab-mvs-re		7.91 376	10.55 379	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	94.95 68	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

uanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 409	0.00 408

WAC-MVS						49.45 358								31.56 388

FOURS1					93.95 45	61.77 247	93.96 71	91.92 141	62.14 317	86.57 47

test_one_0601					96.32 18	69.74 49		94.18 57	71.42 225	90.67 19	96.85 16	74.45 18

eth-test2					0.00 416

eth-test					0.00 416

test_241102_ONE					96.45 12	69.38 54		94.44 46	71.65 214	92.11 7	97.05 7	76.79 9	99.11 6

save fliter					93.84 48	67.89 92	95.05 40	92.66 114	78.19 92

test0726					96.40 15	69.99 39	96.76 8	94.33 54	71.92 200	91.89 11	97.11 6	73.77 21

GSMVS																94.68 96

test_part2					96.29 19	68.16 86				90.78 17

sam_mvs1												57.85 160				94.68 96

sam_mvs												54.91 198

MTGPA								92.23 127

test_post1							78.95 348				20.70 405	53.05 218	91.50 304	60.43 281

test_post											23.01 402	56.49 180	92.67 268

patchmatchnet-post											67.62 372	57.62 163	90.25 313

MTMP							93.77 85	32.52 411

gm-plane-assit					88.42 186	67.04 115			78.62 89		91.83 148		97.37 72	76.57 145

TEST9					94.18 41	67.28 107	94.16 59	93.51 80	71.75 211	85.52 57	95.33 53	68.01 50	97.27 82

test_8					94.19 40	67.19 109	94.15 62	93.42 86	71.87 205	85.38 60	95.35 52	68.19 48	96.95 104

agg_prior					94.16 43	66.97 117		93.31 89		84.49 68			96.75 114

test_prior4						67.18 111	93.92 74

test_prior				86.42 74	94.71 35	67.35 106		93.10 99					96.84 111			95.05 80

旧先验2							92.00 162		59.37 337	87.54 40			93.47 244	75.39 153

新几何2							91.41 184

无先验							92.71 127	92.61 118	62.03 318				97.01 95	66.63 232		93.97 128

原ACMM2							92.01 159

testdata2													96.09 136	61.26 277

segment_acmp												65.94 66

testdata1							89.21 257		77.55 105

plane_prior7					86.94 226	61.51 253

plane_prior6					87.23 218	62.32 237						50.66 238

plane_prior4											89.14 193

plane_prior3						61.95 245			79.09 80	72.53 194

plane_prior2							93.13 111		78.81 86

plane_prior1					87.15 220

n2								0.00 417

nn								0.00 417

door-mid								66.01 387

test11								93.01 101

door								66.57 386

HQP5-MVS						63.66 203

HQP-NCC					87.54 211		94.06 64		79.80 63	74.18 173

ACMP_Plane					87.54 211		94.06 64		79.80 63	74.18 173

BP-MVS														77.63 140

HQP4-MVS										74.18 173			95.61 160			88.63 234

HQP2-MVS												51.63 231

NP-MVS					87.41 214	63.04 218					90.30 175

MDTV_nov1_ep13_2view						59.90 283	80.13 343		67.65 273	72.79 189		54.33 206		59.83 285		92.58 169

Test By Simon												54.21 207

High-resolution multi-view benchmark