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 29
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	89.68 152	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 96	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	72.48 183	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 39

SMA-MVS		88.14 17	88.29 21	87.67 33	93.21 68	68.72 70	93.85 78	94.03 62	74.18 146	91.74 12	96.67 21	65.61 70	98.42 33	89.24 45	96.08 7	95.88 46
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 54	94.91 71	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 53	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	87.04 308	88.32 4	92.60 5	96.57 23	32.61 347	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 38

PHI-MVS		86.83 39	86.85 40	86.78 61	93.47 62	65.55 151	95.39 31	95.10 22	71.77 209	85.69 55	96.52 24	62.07 117	98.77 22	86.06 73	95.60 12	96.03 41

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 131	93.00 74	58.16 303	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	71.65 213	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 28

test_241102_TWO								94.41 48	71.65 213	92.07 9	97.21 4	74.58 17	99.11 6	92.34 21	95.36 14	96.59 19

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 15

test_0728_THIRD									72.48 183	90.55 20	96.93 11	76.24 11	99.08 11	91.53 31	94.99 18	96.43 29

test9_res														89.41 41	94.96 19	95.29 67

ACMMP_NAP		86.05 51	85.80 56	86.80 60	91.58 114	67.53 102	91.79 170	93.49 83	74.93 137	84.61 65	95.30 55	59.42 145	97.92 41	86.13 71	94.92 20	94.94 84

DPE-MVS		88.77 16	89.21 16	87.45 43	96.26 20	67.56 100	94.17 58	94.15 59	68.77 263	90.74 18	97.27 2	76.09 12	98.49 29	90.58 39	94.91 21	96.30 32
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	71.92 199	90.55 20	96.93 11	73.77 21	99.08 11	91.91 29	94.90 22	96.29 33
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 24

train_agg		87.21 32	87.42 31	86.60 66	94.18 41	67.28 107	94.16 59	93.51 80	71.87 204	85.52 56	95.33 53	68.19 48	97.27 82	89.09 46	94.90 22	95.25 73

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 75	96.19 35	64.53 84	98.44 31	83.42 95	94.88 25	96.61 18
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 24

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 24

TSAR-MVS + MP.		88.11 19	88.64 17	86.54 70	91.73 110	68.04 88	90.36 226	93.55 79	82.89 21	91.29 16	92.89 122	72.27 31	96.03 141	87.99 53	94.77 26	95.54 55
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		75.40 131	85.25 62	95.61 47	67.94 51		87.47 59	94.77 26

agg_prior2														86.41 69	94.75 30	95.33 63

MVS_0304		90.01 8	90.50 9	88.53 23	90.14 143	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 45

MVS		84.66 75	82.86 102	90.06 2	90.93 128	74.56 6	87.91 277	95.54 13	68.55 265	72.35 199	94.71 76	59.78 141	98.90 19	81.29 112	94.69 32	96.74 16

SF-MVS		87.03 34	87.09 34	86.84 57	92.70 83	67.45 105	93.64 91	93.76 69	70.78 237	86.25 47	96.44 28	66.98 57	97.79 47	88.68 50	94.56 33	95.28 69

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 45	96.20 34	66.56 62	98.76 23	89.03 48	94.56 33	95.92 44

3Dnovator	73.91 6	82.69 116	80.82 130	88.31 26	89.57 154	71.26 23	92.60 134	94.39 51	78.84 84	67.89 256	92.48 132	48.42 258	98.52 28	68.80 212	94.40 35	95.15 75

CDPH-MVS		85.71 59	85.46 60	86.46 72	94.75 34	67.19 109	93.89 76	92.83 108	70.90 233	83.09 78	95.28 56	63.62 96	97.36 73	80.63 115	94.18 36	94.84 88

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 87	83.87 74	92.94 120	64.34 85	96.94 105	75.19 153	94.09 37	95.66 50

9.14			87.63 27		93.86 47		94.41 53	94.18 57	72.76 178	86.21 48	96.51 25	66.64 60	97.88 44	90.08 40	94.04 38

原ACMM1				84.42 144	93.21 68	64.27 185		93.40 88	65.39 288	79.51 114	92.50 129	58.11 158	96.69 115	65.27 250	93.96 39	92.32 175

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 82	81.50 89	96.50 26	58.98 151	96.78 113	83.49 94	93.93 40	96.29 33

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 21

MP-MVS-pluss		85.24 66	85.13 65	85.56 100	91.42 119	65.59 149	91.54 180	92.51 121	74.56 140	80.62 100	95.64 46	59.15 149	97.00 96	86.94 66	93.80 42	94.07 123
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVP-Stereo		77.12 211	76.23 203	79.79 264	81.72 301	66.34 132	89.29 253	90.88 190	70.56 241	62.01 310	82.88 269	49.34 249	94.13 217	65.55 247	93.80 42	78.88 364
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	75.02 364	94.75 33		78.67 129	90.85 164	77.91 7	94.56 200	72.25 177	93.74 44	95.36 62

ZNCC-MVS		85.33 65	85.08 66	86.06 82	93.09 73	65.65 147	93.89 76	93.41 87	73.75 157	79.94 109	94.68 77	60.61 132	98.03 38	82.63 99	93.72 45	94.52 106

CSCG		86.87 36	86.26 45	88.72 17	95.05 31	70.79 31	93.83 83	95.33 16	68.48 267	77.63 137	94.35 89	73.04 24	98.45 30	84.92 83	93.71 46	96.92 14

test12				87.09 51	94.60 36	68.86 66		92.91 105		82.67 83		65.44 71	97.55 63		93.69 47	94.84 88

PAPM		85.89 56	85.46 60	87.18 48	88.20 195	72.42 14	92.41 142	92.77 109	82.11 31	80.34 105	93.07 117	68.27 47	95.02 179	78.39 135	93.59 48	94.09 121

SteuartSystems-ACMMP		86.82 40	86.90 38	86.58 68	90.42 137	66.38 130	96.09 18	93.87 64	77.73 99	84.01 73	95.66 45	63.39 101	97.94 40	87.40 60	93.55 49	95.42 56
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	69.35 254	88.39 34	96.34 30	67.74 53	97.66 54	90.62 38	93.44 50	96.01 42
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	83.52 168	92.63 85	59.36 291	95.49 28	91.92 141	80.09 60	85.46 58	95.53 49	61.82 121	95.77 149	86.77 68	93.37 51	95.41 57

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	53.55 212	97.89 43	91.10 33	93.31 52	94.54 104

MAR-MVS		84.18 86	83.43 88	86.44 73	96.25 21	65.93 142	94.28 56	94.27 56	74.41 141	79.16 120	95.61 47	53.99 207	98.88 21	69.62 201	93.26 53	94.50 108
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		77.18 209	74.31 230	85.80 92	91.42 119	68.36 77	71.78 367	94.72 34	49.61 368	77.12 144	45.92 391	77.41 8	93.98 229	67.62 222	93.16 54	95.05 79

ZD-MVS					96.63 9	65.50 153		93.50 82	70.74 238	85.26 61	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	66.54 280	86.17 49	95.88 41	63.83 91	97.00 96	86.39 70	92.94 56	95.06 78
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
新几何1				84.73 130	92.32 90	64.28 184		91.46 167	59.56 335	79.77 111	92.90 121	56.95 172	96.57 119	63.40 260	92.91 57	93.34 145

DeepC-MVS	77.85 3	85.52 63	85.24 63	86.37 76	88.80 177	66.64 124	92.15 149	93.68 74	81.07 46	76.91 147	93.64 107	62.59 112	98.44 31	85.50 75	92.84 58	94.03 125
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	53.45 216	97.68 50	91.07 34	92.62 59	94.54 104

MP-MVS		85.02 69	84.97 68	85.17 115	92.60 86	64.27 185	93.24 107	92.27 126	73.13 168	79.63 113	94.43 83	61.90 118	97.17 85	85.00 81	92.56 60	94.06 124
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		83.91 91	83.38 92	85.50 101	91.89 106	65.16 160	81.75 325	92.23 127	75.32 132	80.53 102	95.21 63	56.06 184	97.16 87	84.86 84	92.55 61	94.18 115

GST-MVS		84.63 76	84.29 76	85.66 98	92.82 79	65.27 156	93.04 115	93.13 97	73.20 166	78.89 122	94.18 96	59.41 146	97.85 45	81.45 108	92.48 62	93.86 133

HFP-MVS		84.73 74	84.40 75	85.72 96	93.75 51	65.01 164	93.50 99	93.19 94	72.19 193	79.22 119	94.93 69	59.04 150	97.67 51	81.55 106	92.21 63	94.49 109

ACMMPR		84.37 78	84.06 77	85.28 110	93.56 58	64.37 180	93.50 99	93.15 96	72.19 193	78.85 127	94.86 72	56.69 176	97.45 66	81.55 106	92.20 64	94.02 126

MS-PatchMatch		77.90 201	76.50 199	82.12 206	85.99 241	69.95 42	91.75 175	92.70 111	73.97 151	62.58 307	84.44 255	41.11 302	95.78 147	63.76 259	92.17 65	80.62 350

region2R		84.36 79	84.03 78	85.36 107	93.54 59	64.31 183	93.43 104	92.95 104	72.16 196	78.86 126	94.84 73	56.97 171	97.53 64	81.38 110	92.11 66	94.24 113

CS-MVS		85.80 57	86.65 41	83.27 176	92.00 101	58.92 296	95.31 32	91.86 146	79.97 61	84.82 64	95.40 51	62.26 115	95.51 167	86.11 72	92.08 67	95.37 60

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 35

dcpmvs_2		87.37 30	87.55 29	86.85 56	95.04 32	68.20 85	90.36 226	90.66 196	79.37 72	81.20 91	93.67 106	74.73 15	96.55 121	90.88 36	92.00 68	95.82 47

旧先验1					91.94 102	60.74 268		91.50 165			94.36 85	65.23 73			91.84 70	94.55 102

MVSFormer		83.75 96	82.88 101	86.37 76	89.24 167	71.18 25	89.07 259	90.69 193	65.80 285	87.13 41	94.34 90	64.99 75	92.67 267	72.83 169	91.80 71	95.27 70

lupinMVS		87.74 24	87.77 26	87.63 38	89.24 167	71.18 25	96.57 12	92.90 106	82.70 25	87.13 41	95.27 58	64.99 75	95.80 146	89.34 43	91.80 71	95.93 43

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 80	66.79 59	97.34 75	83.89 92	91.68 73	95.29 67
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	73.60 7	82.10 125	80.60 137	86.60 66	90.89 130	66.80 121	95.20 35	93.44 85	74.05 148	67.42 262	92.49 131	49.46 248	97.65 55	70.80 190	91.68 73	95.33 63

XVS		83.87 92	83.47 86	85.05 116	93.22 66	63.78 194	92.92 119	92.66 114	73.99 149	78.18 131	94.31 92	55.25 190	97.41 70	79.16 126	91.58 75	93.95 128

X-MVStestdata		76.86 214	74.13 234	85.05 116	93.22 66	63.78 194	92.92 119	92.66 114	73.99 149	78.18 131	10.19 406	55.25 190	97.41 70	79.16 126	91.58 75	93.95 128

SD-MVS		87.49 27	87.49 30	87.50 42	93.60 56	68.82 68	93.90 75	92.63 117	76.86 111	87.90 36	95.76 43	66.17 63	97.63 56	89.06 47	91.48 77	96.05 40
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 77	85.04 67	83.01 180	89.34 159	61.37 255	94.42 52	91.09 182	77.91 96	83.24 76	94.20 95	58.37 154	95.40 168	85.35 76	91.41 78	92.27 180

PGM-MVS		83.25 104	82.70 105	84.92 120	92.81 81	64.07 189	90.44 222	92.20 131	71.28 225	77.23 143	94.43 83	55.17 194	97.31 77	79.33 125	91.38 79	93.37 144

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 96	93.08 116	63.19 105	97.29 78	87.08 64	91.38 79	94.13 119

HPM-MVS		83.25 104	82.95 99	84.17 153	92.25 92	62.88 226	90.91 207	91.86 146	70.30 243	77.12 144	93.96 101	56.75 174	96.28 129	82.04 103	91.34 81	93.34 145
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EIA-MVS		84.84 72	84.88 69	84.69 133	91.30 122	62.36 234	93.85 78	92.04 136	79.45 69	79.33 118	94.28 93	62.42 113	96.35 127	80.05 119	91.25 82	95.38 59

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 80	78.88 125	93.99 100	62.25 116	98.15 36	85.93 74	91.15 83	94.15 118

test222					89.77 150	61.60 251	89.55 247	89.42 244	56.83 348	77.28 142	92.43 133	52.76 220			91.14 84	93.09 153

jason		86.40 44	86.17 48	87.11 50	86.16 239	70.54 34	95.71 25	92.19 132	82.00 32	84.58 66	94.34 90	61.86 119	95.53 166	87.76 55	90.89 85	95.27 70
jason: jason.
mPP-MVS		82.96 110	82.44 110	84.52 141	92.83 77	62.92 224	92.76 123	91.85 148	71.52 221	75.61 159	94.24 94	53.48 215	96.99 99	78.97 129	90.73 86	93.64 139

CP-MVS		83.71 97	83.40 91	84.65 135	93.14 71	63.84 192	94.59 50	92.28 125	71.03 231	77.41 140	94.92 70	55.21 193	96.19 131	81.32 111	90.70 87	93.91 130

OpenMVS	70.45 11	78.54 189	75.92 208	86.41 75	85.93 245	71.68 19	92.74 124	92.51 121	66.49 281	64.56 286	91.96 143	43.88 292	98.10 37	54.61 303	90.65 88	89.44 227

PAPM_NR		82.97 109	81.84 117	86.37 76	94.10 44	66.76 122	87.66 281	92.84 107	69.96 247	74.07 176	93.57 109	63.10 108	97.50 65	70.66 193	90.58 89	94.85 85

testdata				81.34 223	89.02 171	57.72 307		89.84 228	58.65 339	85.32 60	94.09 97	57.03 167	93.28 245	69.34 204	90.56 90	93.03 156

Vis-MVSNet		80.92 143	79.98 146	83.74 161	88.48 182	61.80 245	93.44 103	88.26 293	73.96 152	77.73 135	91.76 148	49.94 244	94.76 187	65.84 242	90.37 91	94.65 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		84.98 71	83.45 87	89.57 10	89.94 147	75.14 5	92.07 155	92.32 124	81.87 33	75.68 156	88.27 202	60.18 135	98.60 27	80.46 117	90.27 92	94.96 82

test_fmvsm_n_1920		87.69 25	88.50 18	85.27 111	87.05 223	63.55 207	93.69 88	91.08 184	84.18 15	90.17 24	97.04 8	67.58 54	97.99 39	95.72 5	90.03 93	94.26 112

ETV-MVS		86.01 52	86.11 49	85.70 97	90.21 142	67.02 116	93.43 104	91.92 141	81.21 45	84.13 72	94.07 99	60.93 129	95.63 157	89.28 44	89.81 94	94.46 110

QAPM		79.95 162	77.39 188	87.64 34	89.63 153	71.41 21	93.30 106	93.70 73	65.34 290	67.39 264	91.75 149	47.83 265	98.96 16	57.71 293	89.81 94	92.54 169

CANet_DTU		84.09 88	83.52 82	85.81 91	90.30 140	66.82 119	91.87 166	89.01 264	85.27 11	86.09 50	93.74 104	47.71 267	96.98 100	77.90 138	89.78 96	93.65 138

API-MVS		82.28 120	80.53 138	87.54 41	96.13 22	70.59 33	93.63 92	91.04 188	65.72 287	75.45 161	92.83 125	56.11 183	98.89 20	64.10 256	89.75 97	93.15 151

test2506		83.29 102	82.92 100	84.37 147	88.39 187	63.18 217	92.01 158	91.35 170	77.66 101	78.49 130	91.42 155	64.58 83	95.09 178	73.19 165	89.23 98	94.85 85

ECVR-MVS		81.29 135	80.38 141	84.01 157	88.39 187	61.96 243	92.56 139	86.79 312	77.66 101	76.63 148	91.42 155	46.34 276	95.24 175	74.36 162	89.23 98	94.85 85

MVS_Test		84.16 87	83.20 94	87.05 53	91.56 115	69.82 46	89.99 240	92.05 135	77.77 98	82.84 79	86.57 230	63.93 90	96.09 135	74.91 158	89.18 100	95.25 73

PAPR		85.15 68	84.47 73	87.18 48	96.02 25	68.29 79	91.85 168	93.00 103	76.59 118	79.03 121	95.00 66	61.59 122	97.61 58	78.16 136	89.00 101	95.63 51

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 92	85.93 52	94.80 74	75.80 13	98.21 34	89.38 42	88.78 102	96.59 19

SR-MVS		82.81 112	82.58 107	83.50 171	93.35 63	61.16 258	92.23 147	91.28 174	64.48 294	81.27 90	95.28 56	53.71 211	95.86 145	82.87 97	88.77 103	93.49 142

test1111		80.84 144	80.02 143	83.33 174	87.87 203	60.76 266	92.62 132	86.86 311	77.86 97	75.73 155	91.39 157	46.35 275	94.70 193	72.79 171	88.68 104	94.52 106

fmvsm_l_conf0.5_n_a		87.44 29	88.15 23	85.30 109	87.10 221	64.19 187	94.41 53	88.14 294	80.24 59	92.54 6	96.97 10	69.52 43	97.17 85	95.89 2	88.51 105	94.56 101

HPM-MVS_fast		80.25 155	79.55 154	82.33 196	91.55 116	59.95 281	91.32 194	89.16 255	65.23 291	74.71 168	93.07 117	47.81 266	95.74 150	74.87 160	88.23 106	91.31 199

PVSNet_Blended_VisFu		83.97 90	83.50 84	85.39 105	90.02 145	66.59 127	93.77 85	91.73 152	77.43 107	77.08 146	89.81 184	63.77 93	96.97 102	79.67 122	88.21 107	92.60 167

Vis-MVSNet (Re-imp)		79.24 172	79.57 151	78.24 287	88.46 183	52.29 342	90.41 224	89.12 258	74.24 145	69.13 233	91.91 146	65.77 68	90.09 319	59.00 289	88.09 108	92.33 174

fmvsm_l_conf0.5_n		87.49 27	88.19 22	85.39 105	86.95 224	64.37 180	94.30 55	88.45 285	80.51 51	92.70 4	96.86 15	69.98 41	97.15 88	95.83 3	88.08 109	94.65 98

APD-MVS_3200maxsize		81.64 131	81.32 122	82.59 190	92.36 89	58.74 298	91.39 187	91.01 189	63.35 303	79.72 112	94.62 79	51.82 226	96.14 133	79.71 121	87.93 110	92.89 162

Effi-MVS+		83.82 93	82.76 103	86.99 55	89.56 155	69.40 53	91.35 192	86.12 319	72.59 180	83.22 77	92.81 126	59.60 143	96.01 143	81.76 105	87.80 111	95.56 54

casdiffmvs_mvg		85.66 61	85.18 64	87.09 51	88.22 194	69.35 57	93.74 87	91.89 144	81.47 37	80.10 107	91.45 154	64.80 80	96.35 127	87.23 63	87.69 112	95.58 53
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		80.70 146	78.95 163	85.94 86	87.77 208	67.56 100	87.91 277	92.55 120	72.17 195	67.44 261	93.09 115	50.27 241	97.04 94	71.68 185	87.64 113	93.23 149

test_fmvsmconf_n		86.58 43	87.17 33	84.82 124	85.28 254	62.55 231	94.26 57	89.78 229	83.81 18	87.78 37	96.33 31	65.33 72	96.98 100	94.40 11	87.55 114	94.95 83

PMMVS		81.98 127	82.04 114	81.78 213	89.76 151	56.17 323	91.13 203	90.69 193	77.96 94	80.09 108	93.57 109	46.33 277	94.99 181	81.41 109	87.46 115	94.17 116

casdiffmvs		85.37 64	84.87 70	86.84 57	88.25 192	69.07 61	93.04 115	91.76 151	81.27 44	80.84 98	92.07 142	64.23 86	96.06 139	84.98 82	87.43 116	95.39 58
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	84.62 138	80.83 307	62.33 235	93.84 81	88.81 272	83.50 20	87.00 44	96.01 39	63.36 102	96.93 107	94.04 12	87.29 117	94.61 100

UGNet		79.87 163	78.68 165	83.45 173	89.96 146	61.51 252	92.13 150	90.79 191	76.83 113	78.85 127	86.33 234	38.16 317	96.17 132	67.93 219	87.17 118	92.67 165
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		82.02 126	81.52 120	83.51 170	88.42 185	62.88 226	89.77 244	88.93 268	76.78 114	75.55 160	93.10 114	50.31 240	95.38 170	83.82 93	87.02 119	92.26 181

test_fmvsmvis_n_1920		83.80 94	83.48 85	84.77 128	82.51 293	63.72 198	91.37 190	83.99 339	81.42 41	77.68 136	95.74 44	58.37 154	97.58 59	93.38 14	86.87 120	93.00 158

xiu_mvs_v1_base_debu		82.16 122	81.12 124	85.26 112	86.42 232	68.72 70	92.59 136	90.44 203	73.12 169	84.20 69	94.36 85	38.04 319	95.73 151	84.12 89	86.81 121	91.33 195

xiu_mvs_v1_base		82.16 122	81.12 124	85.26 112	86.42 232	68.72 70	92.59 136	90.44 203	73.12 169	84.20 69	94.36 85	38.04 319	95.73 151	84.12 89	86.81 121	91.33 195

xiu_mvs_v1_base_debi		82.16 122	81.12 124	85.26 112	86.42 232	68.72 70	92.59 136	90.44 203	73.12 169	84.20 69	94.36 85	38.04 319	95.73 151	84.12 89	86.81 121	91.33 195

SR-MVS-dyc-post		81.06 140	80.70 133	82.15 204	92.02 98	58.56 300	90.90 208	90.45 200	62.76 310	78.89 122	94.46 81	51.26 234	95.61 159	78.77 132	86.77 124	92.28 177

RE-MVS-def			80.48 139		92.02 98	58.56 300	90.90 208	90.45 200	62.76 310	78.89 122	94.46 81	49.30 250		78.77 132	86.77 124	92.28 177

baseline		85.01 70	84.44 74	86.71 62	88.33 189	68.73 69	90.24 231	91.82 150	81.05 47	81.18 92	92.50 129	63.69 94	96.08 138	84.45 87	86.71 126	95.32 65

TESTMET0.1,1		82.41 118	81.98 116	83.72 164	88.08 196	63.74 196	92.70 127	93.77 68	79.30 73	77.61 138	87.57 217	58.19 157	94.08 220	73.91 164	86.68 127	93.33 147

IS-MVSNet		80.14 157	79.41 156	82.33 196	87.91 201	60.08 280	91.97 162	88.27 291	72.90 176	71.44 210	91.73 150	61.44 123	93.66 239	62.47 270	86.53 128	93.24 148

CPTT-MVS		79.59 166	79.16 161	80.89 238	91.54 117	59.80 283	92.10 152	88.54 284	60.42 328	72.96 185	93.28 113	48.27 259	92.80 261	78.89 131	86.50 129	90.06 214

BH-w/o		80.49 150	79.30 159	84.05 156	90.83 132	64.36 182	93.60 93	89.42 244	74.35 143	69.09 234	90.15 179	55.23 192	95.61 159	64.61 253	86.43 130	92.17 183

PVSNet	73.49 8	80.05 159	78.63 166	84.31 149	90.92 129	64.97 165	92.47 140	91.05 187	79.18 76	72.43 197	90.51 169	37.05 331	94.06 222	68.06 216	86.00 131	93.90 132

test_fmvsmconf0.01_n		83.70 98	83.52 82	84.25 152	75.26 358	61.72 249	92.17 148	87.24 307	82.36 28	84.91 63	95.41 50	55.60 188	96.83 112	92.85 17	85.87 132	94.21 114

mvs_anonymous		81.36 134	79.99 145	85.46 102	90.39 139	68.40 76	86.88 292	90.61 198	74.41 141	70.31 222	84.67 251	63.79 92	92.32 282	73.13 166	85.70 133	95.67 49

DP-MVS Recon		82.73 113	81.65 119	85.98 84	97.31 4	67.06 113	95.15 37	91.99 138	69.08 260	76.50 151	93.89 102	54.48 202	98.20 35	70.76 191	85.66 134	92.69 164

BH-RMVSNet		79.46 170	77.65 180	84.89 121	91.68 112	65.66 146	93.55 95	88.09 296	72.93 173	73.37 182	91.12 161	46.20 279	96.12 134	56.28 298	85.61 135	92.91 160

diffmvs		84.28 81	83.83 79	85.61 99	87.40 214	68.02 89	90.88 210	89.24 250	80.54 50	81.64 88	92.52 128	59.83 140	94.52 203	87.32 61	85.11 136	94.29 111
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+		81.14 137	80.01 144	84.51 142	90.24 141	65.86 143	94.12 63	89.15 256	73.81 156	75.37 162	88.26 203	57.26 164	94.53 202	66.97 230	84.92 137	93.15 151

LFMVS		84.34 80	82.73 104	89.18 12	94.76 33	73.25 9	94.99 43	91.89 144	71.90 201	82.16 85	93.49 111	47.98 263	97.05 91	82.55 100	84.82 138	97.25 9

BH-untuned		78.68 185	77.08 191	83.48 172	89.84 148	63.74 196	92.70 127	88.59 282	71.57 219	66.83 271	88.65 195	51.75 228	95.39 169	59.03 288	84.77 139	91.32 198

test-LLR		80.10 158	79.56 152	81.72 215	86.93 227	61.17 256	92.70 127	91.54 162	71.51 222	75.62 157	86.94 226	53.83 208	92.38 278	72.21 178	84.76 140	91.60 189

test-mter		79.96 161	79.38 158	81.72 215	86.93 227	61.17 256	92.70 127	91.54 162	73.85 154	75.62 157	86.94 226	49.84 246	92.38 278	72.21 178	84.76 140	91.60 189

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	91.30 122	71.14 27	95.61 26	93.56 78	79.30 73	87.07 43	95.25 60	68.43 46	96.93 107	87.87 54	84.33 144	96.65 17

VNet		86.20 48	85.65 58	87.84 30	93.92 46	69.99 39	95.73 24	95.94 7	78.43 89	86.00 51	93.07 117	58.22 156	97.00 96	85.22 77	84.33 144	96.52 23

UA-Net		80.02 160	79.65 150	81.11 229	89.33 161	57.72 307	86.33 295	89.00 267	77.44 106	81.01 95	89.15 191	59.33 147	95.90 144	61.01 277	84.28 146	89.73 221

LCM-MVSNet-Re		72.93 265	71.84 264	76.18 309	88.49 181	48.02 362	80.07 343	70.17 379	73.96 152	52.25 352	80.09 313	49.98 243	88.24 331	67.35 223	84.23 147	92.28 177

ACMMP		81.49 132	80.67 134	83.93 158	91.71 111	62.90 225	92.13 150	92.22 130	71.79 208	71.68 207	93.49 111	50.32 239	96.96 103	78.47 134	84.22 148	91.93 187
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
114514_t		79.17 173	77.67 179	83.68 165	95.32 29	65.53 152	92.85 121	91.60 161	63.49 301	67.92 253	90.63 167	46.65 272	95.72 155	67.01 229	83.54 149	89.79 219

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 99	92.21 140	72.30 30	96.46 126	85.18 79	83.43 150	94.82 91

test_vis1_n_1920		81.66 130	82.01 115	80.64 240	82.24 296	55.09 331	94.76 47	86.87 310	81.67 36	84.40 68	94.63 78	38.17 316	94.67 194	91.98 28	83.34 151	92.16 184

testing222		85.18 67	84.69 72	86.63 65	92.91 76	69.91 43	92.61 133	95.80 9	80.31 55	80.38 104	92.27 137	68.73 44	95.19 176	75.94 148	83.27 152	94.81 92

EPMVS		78.49 190	75.98 207	86.02 83	91.21 124	69.68 51	80.23 340	91.20 175	75.25 133	72.48 195	78.11 327	54.65 198	93.69 238	57.66 294	83.04 153	94.69 94

AdaColmap		78.94 178	77.00 194	84.76 129	96.34 17	65.86 143	92.66 131	87.97 300	62.18 315	70.56 216	92.37 135	43.53 293	97.35 74	64.50 254	82.86 154	91.05 204

CDS-MVSNet		81.43 133	80.74 132	83.52 168	86.26 236	64.45 174	92.09 153	90.65 197	75.83 125	73.95 178	89.81 184	63.97 89	92.91 257	71.27 186	82.82 155	93.20 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 280x420		77.35 207	76.95 195	78.55 282	87.07 222	62.68 230	69.71 373	82.95 346	68.80 262	71.48 209	87.27 223	66.03 65	84.00 360	76.47 145	82.81 156	88.95 228

UWE-MVS		80.81 145	81.01 129	80.20 250	89.33 161	57.05 317	91.91 164	94.71 35	75.67 126	75.01 165	89.37 188	63.13 107	91.44 304	67.19 227	82.80 157	92.12 185

ETVMVS		84.22 85	83.71 80	85.76 94	92.58 87	68.25 83	92.45 141	95.53 14	79.54 68	79.46 115	91.64 152	70.29 39	94.18 216	69.16 207	82.76 158	94.84 88

PCF-MVS	73.15 9	79.29 171	77.63 181	84.29 150	86.06 240	65.96 141	87.03 288	91.10 181	69.86 249	69.79 230	90.64 165	57.54 163	96.59 117	64.37 255	82.29 159	90.32 211
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	84.82 124	87.36 216	63.54 208	94.74 48	90.02 223	82.52 26	90.14 25	96.92 13	62.93 110	97.84 46	95.28 8	82.26 160	93.07 155

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 86	94.73 75	67.93 52	97.63 56	79.55 123	82.25 161	96.54 22

testing99		86.01 52	85.47 59	87.63 38	93.62 55	71.25 24	93.47 102	95.23 18	80.42 54	80.60 101	91.95 144	71.73 35	96.50 124	80.02 120	82.22 162	95.13 76

HY-MVS	76.49 5	84.28 81	83.36 93	87.02 54	92.22 93	67.74 95	84.65 302	94.50 43	79.15 77	82.23 84	87.93 211	66.88 58	96.94 105	80.53 116	82.20 163	96.39 31

testing91		85.93 54	85.31 62	87.78 32	93.59 57	71.47 20	93.50 99	95.08 25	80.26 56	80.53 102	91.93 145	70.43 38	96.51 123	80.32 118	82.13 164	95.37 60

VDD-MVS		83.06 107	81.81 118	86.81 59	90.86 131	67.70 96	95.40 30	91.50 165	75.46 129	81.78 87	92.34 136	40.09 305	97.13 89	86.85 67	82.04 165	95.60 52

fmvsm_s_conf0.1_n		85.61 62	85.93 53	84.68 134	82.95 291	63.48 210	94.03 69	89.46 241	81.69 35	89.86 26	96.74 20	61.85 120	97.75 49	94.74 9	82.01 166	92.81 163

TAMVS		80.37 152	79.45 155	83.13 179	85.14 257	63.37 211	91.23 198	90.76 192	74.81 139	72.65 190	88.49 196	60.63 131	92.95 252	69.41 203	81.95 167	93.08 154

test_yl		84.28 81	83.16 95	87.64 34	94.52 37	69.24 58	95.78 19	95.09 23	69.19 257	81.09 93	92.88 123	57.00 169	97.44 68	81.11 113	81.76 168	96.23 36

DCV-MVSNet		84.28 81	83.16 95	87.64 34	94.52 37	69.24 58	95.78 19	95.09 23	69.19 257	81.09 93	92.88 123	57.00 169	97.44 68	81.11 113	81.76 168	96.23 36

FA-MVS(test-final)		79.12 174	77.23 190	84.81 127	90.54 135	63.98 191	81.35 331	91.71 154	71.09 230	74.85 167	82.94 268	52.85 219	97.05 91	67.97 217	81.73 170	93.41 143

thisisatest0515		83.41 100	82.49 109	86.16 81	89.46 158	68.26 81	93.54 96	94.70 36	74.31 144	75.75 154	90.92 162	72.62 28	96.52 122	69.64 199	81.50 171	93.71 136

baseline2		83.68 99	83.42 90	84.48 143	87.37 215	66.00 139	90.06 235	95.93 8	79.71 66	69.08 235	90.39 172	77.92 6	96.28 129	78.91 130	81.38 172	91.16 202

PatchmatchNet		77.46 205	74.63 223	85.96 85	89.55 156	70.35 36	79.97 345	89.55 239	72.23 192	70.94 212	76.91 338	57.03 167	92.79 262	54.27 305	81.17 173	94.74 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VDDNet		80.50 149	78.26 171	87.21 47	86.19 237	69.79 47	94.48 51	91.31 171	60.42 328	79.34 117	90.91 163	38.48 314	96.56 120	82.16 101	81.05 174	95.27 70

EPNet_dtu		78.80 182	79.26 160	77.43 295	88.06 197	49.71 355	91.96 163	91.95 140	77.67 100	76.56 150	91.28 159	58.51 153	90.20 317	56.37 297	80.95 175	92.39 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		82.71 115	82.38 111	83.73 163	89.25 164	59.58 286	92.24 146	94.89 28	77.96 94	79.86 110	92.38 134	56.70 175	97.05 91	77.26 141	80.86 176	94.55 102

FE-MVS		75.97 231	73.02 247	84.82 124	89.78 149	65.56 150	77.44 356	91.07 185	64.55 293	72.66 189	79.85 315	46.05 281	96.69 115	54.97 302	80.82 177	92.21 182

GeoE		78.90 179	77.43 184	83.29 175	88.95 173	62.02 241	92.31 143	86.23 317	70.24 244	71.34 211	89.27 189	54.43 203	94.04 225	63.31 262	80.81 178	93.81 135

fmvsm_s_conf0.5_n_a		85.75 58	86.09 50	84.72 131	85.73 248	63.58 205	93.79 84	89.32 247	81.42 41	90.21 23	96.91 14	62.41 114	97.67 51	94.48 10	80.56 179	92.90 161

TR-MVS		78.77 184	77.37 189	82.95 181	90.49 136	60.88 262	93.67 89	90.07 219	70.08 246	74.51 170	91.37 158	45.69 282	95.70 156	60.12 283	80.32 180	92.29 176

fmvsm_s_conf0.1_n_a		84.76 73	84.84 71	84.53 140	80.23 317	63.50 209	92.79 122	88.73 276	80.46 52	89.84 27	96.65 22	60.96 128	97.57 61	93.80 13	80.14 181	92.53 170

TAPA-MVS	70.22 12	74.94 246	73.53 242	79.17 275	90.40 138	52.07 343	89.19 257	89.61 238	62.69 312	70.07 224	92.67 127	48.89 257	94.32 207	38.26 368	79.97 182	91.12 203
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_cas_vis1_n_1920		80.45 151	80.61 136	79.97 259	78.25 343	57.01 319	94.04 68	88.33 288	79.06 81	82.81 80	93.70 105	38.65 311	91.63 296	90.82 37	79.81 183	91.27 201

cascas		78.18 194	75.77 210	85.41 104	87.14 220	69.11 60	92.96 118	91.15 179	66.71 279	70.47 217	86.07 237	37.49 325	96.48 125	70.15 196	79.80 184	90.65 207

HyFIR lowres test		81.03 141	79.56 152	85.43 103	87.81 206	68.11 87	90.18 232	90.01 224	70.65 239	72.95 186	86.06 238	63.61 97	94.50 204	75.01 156	79.75 185	93.67 137

WB-MVSnew		77.14 210	76.18 205	80.01 256	86.18 238	63.24 214	91.26 196	94.11 60	71.72 211	73.52 181	87.29 222	45.14 287	93.00 250	56.98 295	79.42 186	83.80 311

LS3D		69.17 295	66.40 299	77.50 293	91.92 104	56.12 324	85.12 299	80.37 355	46.96 374	56.50 338	87.51 218	37.25 326	93.71 237	32.52 384	79.40 187	82.68 331

EI-MVSNet-Vis-set		83.77 95	83.67 81	84.06 155	92.79 82	63.56 206	91.76 173	94.81 31	79.65 67	77.87 134	94.09 97	63.35 103	97.90 42	79.35 124	79.36 188	90.74 206

CVMVSNet		74.04 254	74.27 231	73.33 328	85.33 252	43.94 378	89.53 249	88.39 286	54.33 356	70.37 220	90.13 180	49.17 253	84.05 358	61.83 274	79.36 188	91.99 186

EPP-MVSNet		81.79 129	81.52 120	82.61 189	88.77 178	60.21 278	93.02 117	93.66 75	68.52 266	72.90 187	90.39 172	72.19 32	94.96 182	74.93 157	79.29 190	92.67 165

CLD-MVS		82.73 113	82.35 112	83.86 159	87.90 202	67.65 98	95.45 29	92.18 133	85.06 12	72.58 192	92.27 137	52.46 223	95.78 147	84.18 88	79.06 191	88.16 243
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							78.90 192

HQP-MVS		81.14 137	80.64 135	82.64 188	87.54 210	63.66 203	94.06 64	91.70 157	79.80 63	74.18 172	90.30 174	51.63 230	95.61 159	77.63 139	78.90 192	88.63 233

plane_prior						62.42 232	93.85 78		79.38 71						78.80 194

thres200		79.66 165	78.33 169	83.66 167	92.54 88	65.82 145	93.06 113	96.31 3	74.90 138	73.30 183	88.66 194	59.67 142	95.61 159	47.84 331	78.67 195	89.56 224

ET-MVSNet_ETH3D		84.01 89	83.15 97	86.58 68	90.78 133	70.89 30	94.74 48	94.62 40	81.44 40	58.19 328	93.64 107	73.64 23	92.35 281	82.66 98	78.66 196	96.50 27

HQP_MVS		80.34 153	79.75 149	82.12 206	86.94 225	62.42 232	93.13 111	91.31 171	78.81 85	72.53 193	89.14 192	50.66 237	95.55 164	76.74 142	78.53 197	88.39 240

plane_prior5								91.31 171					95.55 164	76.74 142	78.53 197	88.39 240

EI-MVSNet-UG-set		83.14 106	82.96 98	83.67 166	92.28 91	63.19 216	91.38 189	94.68 37	79.22 75	76.60 149	93.75 103	62.64 111	97.76 48	78.07 137	78.01 199	90.05 215

OMC-MVS		78.67 187	77.91 178	80.95 236	85.76 247	57.40 314	88.49 268	88.67 279	73.85 154	72.43 197	92.10 141	49.29 251	94.55 201	72.73 172	77.89 200	90.91 205

1112_ss		80.56 148	79.83 148	82.77 184	88.65 179	60.78 264	92.29 144	88.36 287	72.58 181	72.46 196	94.95 67	65.09 74	93.42 244	66.38 236	77.71 201	94.10 120

OPM-MVS		79.00 176	78.09 173	81.73 214	83.52 283	63.83 193	91.64 179	90.30 210	76.36 121	71.97 202	89.93 183	46.30 278	95.17 177	75.10 154	77.70 202	86.19 276
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		72.06 275	69.98 278	78.28 285	89.51 157	55.70 327	83.49 309	83.39 344	61.24 323	63.72 295	82.76 270	34.77 339	93.03 249	53.37 310	77.59 203	86.12 280

thres100view900		78.37 191	77.01 193	82.46 191	91.89 106	63.21 215	91.19 202	96.33 1	72.28 191	70.45 219	87.89 212	60.31 133	95.32 171	45.16 342	77.58 204	88.83 229

tfpn200view9		78.79 183	77.43 184	82.88 182	92.21 94	64.49 171	92.05 156	96.28 4	73.48 163	71.75 205	88.26 203	60.07 138	95.32 171	45.16 342	77.58 204	88.83 229

thres400		78.68 185	77.43 184	82.43 192	92.21 94	64.49 171	92.05 156	96.28 4	73.48 163	71.75 205	88.26 203	60.07 138	95.32 171	45.16 342	77.58 204	87.48 249

CostFormer		82.33 119	81.15 123	85.86 89	89.01 172	68.46 75	82.39 322	93.01 101	75.59 127	80.25 106	81.57 287	72.03 33	94.96 182	79.06 128	77.48 207	94.16 117

tpm2		79.80 164	77.95 177	85.34 108	88.28 190	68.26 81	81.56 328	91.42 168	70.11 245	77.59 139	80.50 305	67.40 55	94.26 213	67.34 224	77.35 208	93.51 141

Test_1112_low_res		79.56 167	78.60 167	82.43 192	88.24 193	60.39 275	92.09 153	87.99 298	72.10 197	71.84 203	87.42 219	64.62 82	93.04 248	65.80 243	77.30 209	93.85 134

tpmrst		80.57 147	79.14 162	84.84 123	90.10 144	68.28 80	81.70 326	89.72 236	77.63 103	75.96 153	79.54 319	64.94 77	92.71 264	75.43 151	77.28 210	93.55 140

bld_raw_dy_0_64		82.84 111	80.75 131	89.09 14	93.74 52	72.16 15	93.16 110	77.36 359	89.69 1	74.55 169	96.48 27	32.35 349	97.56 62	92.21 24	77.24 211	97.53 6

Anonymous202405211		77.96 198	75.33 217	85.87 88	93.73 54	64.52 170	94.85 45	85.36 325	62.52 313	76.11 152	90.18 177	29.43 361	97.29 78	68.51 214	77.24 211	95.81 48

GA-MVS		78.33 193	76.23 203	84.65 135	83.65 281	66.30 133	91.44 181	90.14 217	76.01 123	70.32 221	84.02 258	42.50 297	94.72 190	70.98 188	77.00 213	92.94 159

thisisatest0530		81.15 136	80.07 142	84.39 146	88.26 191	65.63 148	91.40 185	94.62 40	71.27 226	70.93 213	89.18 190	72.47 29	96.04 140	65.62 245	76.89 214	91.49 191

thres600view7		78.00 196	76.66 198	82.03 211	91.93 103	63.69 201	91.30 195	96.33 1	72.43 186	70.46 218	87.89 212	60.31 133	94.92 185	42.64 354	76.64 215	87.48 249

PLC	68.80 14	75.23 242	73.68 241	79.86 262	92.93 75	58.68 299	90.64 219	88.30 289	60.90 325	64.43 290	90.53 168	42.38 298	94.57 198	56.52 296	76.54 216	86.33 271
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MIMVSNet		71.64 277	68.44 290	81.23 225	81.97 300	64.44 175	73.05 366	88.80 273	69.67 251	64.59 284	74.79 350	32.79 345	87.82 335	53.99 306	76.35 217	91.42 193

test_fmvs1		74.07 253	73.69 240	75.22 313	78.91 335	47.34 367	89.06 261	74.69 369	63.68 300	79.41 116	91.59 153	24.36 370	87.77 337	85.22 77	76.26 218	90.55 210

MVS-HIRNet		60.25 341	55.55 348	74.35 321	84.37 271	56.57 322	71.64 368	74.11 370	34.44 389	45.54 375	42.24 396	31.11 357	89.81 320	40.36 362	76.10 219	76.67 373

CNLPA		74.31 251	72.30 259	80.32 244	91.49 118	61.66 250	90.85 211	80.72 353	56.67 349	63.85 294	90.64 165	46.75 271	90.84 307	53.79 307	75.99 220	88.47 239

ab-mvs		80.18 156	78.31 170	85.80 92	88.44 184	65.49 154	83.00 319	92.67 113	71.82 207	77.36 141	85.01 246	54.50 199	96.59 117	76.35 146	75.63 221	95.32 65

test_fmvs1_n		72.69 272	71.92 263	74.99 316	71.15 371	47.08 369	87.34 286	75.67 364	63.48 302	78.08 133	91.17 160	20.16 381	87.87 334	84.65 85	75.57 222	90.01 216

iter_conf05		83.27 103	82.70 105	84.98 119	93.32 64	71.84 18	94.16 59	81.76 349	82.74 23	73.83 179	88.40 199	72.77 27	94.61 195	82.10 102	75.21 223	88.48 237

FIs		79.47 169	79.41 156	79.67 266	85.95 242	59.40 288	91.68 177	93.94 63	78.06 93	68.96 239	88.28 201	66.61 61	91.77 293	66.20 239	74.99 224	87.82 245

SDMVSNet		80.26 154	78.88 164	84.40 145	89.25 164	67.63 99	85.35 298	93.02 100	76.77 115	70.84 214	87.12 224	47.95 264	96.09 135	85.04 80	74.55 225	89.48 225

sd_testset		77.08 212	75.37 215	82.20 202	89.25 164	62.11 240	82.06 323	89.09 260	76.77 115	70.84 214	87.12 224	41.43 301	95.01 180	67.23 226	74.55 225	89.48 225

CMPMVS	48.56 21	66.77 315	64.41 316	73.84 325	70.65 374	50.31 352	77.79 355	85.73 323	45.54 378	44.76 377	82.14 278	35.40 337	90.14 318	63.18 264	74.54 227	81.07 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dmvs_re		76.93 213	75.36 216	81.61 217	87.78 207	60.71 269	80.00 344	87.99 298	79.42 70	69.02 237	89.47 187	46.77 270	94.32 207	63.38 261	74.45 228	89.81 218

test_vis1_n		71.63 278	70.73 274	74.31 323	69.63 377	47.29 368	86.91 290	72.11 374	63.21 306	75.18 163	90.17 178	20.40 379	85.76 349	84.59 86	74.42 229	89.87 217

XVG-OURS		74.25 252	72.46 258	79.63 267	78.45 341	57.59 311	80.33 338	87.39 303	63.86 298	68.76 243	89.62 186	40.50 304	91.72 294	69.00 209	74.25 230	89.58 222

tpm cat1		75.30 241	72.21 260	84.58 139	88.52 180	67.77 94	78.16 354	88.02 297	61.88 320	68.45 248	76.37 342	60.65 130	94.03 227	53.77 308	74.11 231	91.93 187

XVG-OURS-SEG-HR		74.70 248	73.08 246	79.57 269	78.25 343	57.33 315	80.49 336	87.32 304	63.22 305	68.76 243	90.12 182	44.89 289	91.59 297	70.55 194	74.09 232	89.79 219

FC-MVSNet-test		77.99 197	78.08 174	77.70 290	84.89 262	55.51 328	90.27 229	93.75 72	76.87 110	66.80 272	87.59 216	65.71 69	90.23 316	62.89 267	73.94 233	87.37 252

PVSNet_BlendedMVS		83.38 101	83.43 88	83.22 177	93.76 49	67.53 102	94.06 64	93.61 76	79.13 78	81.00 96	85.14 245	63.19 105	97.29 78	87.08 64	73.91 234	84.83 303

tttt0517		79.50 168	78.53 168	82.41 195	87.22 218	61.43 254	89.75 245	94.76 32	69.29 255	67.91 254	88.06 210	72.92 25	95.63 157	62.91 266	73.90 235	90.16 213

MDTV_nov1_ep13			72.61 255		89.06 170	68.48 74	80.33 338	90.11 218	71.84 206	71.81 204	75.92 346	53.01 218	93.92 232	48.04 328	73.38 236

SCA		75.82 234	72.76 251	85.01 118	86.63 229	70.08 38	81.06 333	89.19 253	71.60 218	70.01 225	77.09 336	45.53 283	90.25 312	60.43 280	73.27 237	94.68 95

CR-MVSNet		73.79 258	70.82 273	82.70 186	83.15 286	67.96 90	70.25 370	84.00 337	73.67 161	69.97 227	72.41 356	57.82 160	89.48 323	52.99 311	73.13 238	90.64 208

RPMNet		70.42 285	65.68 304	84.63 137	83.15 286	67.96 90	70.25 370	90.45 200	46.83 376	69.97 227	65.10 375	56.48 180	95.30 174	35.79 373	73.13 238	90.64 208

Fast-Effi-MVS+-dtu		75.04 244	73.37 244	80.07 253	80.86 306	59.52 287	91.20 201	85.38 324	71.90 201	65.20 279	84.84 249	41.46 300	92.97 251	66.50 235	72.96 240	87.73 246

mvsmamba		76.85 216	75.71 212	80.25 248	83.07 288	59.16 293	91.44 181	80.64 354	76.84 112	67.95 252	86.33 234	46.17 280	94.24 214	76.06 147	72.92 241	87.36 253

LPG-MVS_test		75.82 234	74.58 225	79.56 270	84.31 272	59.37 289	90.44 222	89.73 234	69.49 252	64.86 281	88.42 197	38.65 311	94.30 209	72.56 174	72.76 242	85.01 301

LGP-MVS_train				79.56 270	84.31 272	59.37 289		89.73 234	69.49 252	64.86 281	88.42 197	38.65 311	94.30 209	72.56 174	72.76 242	85.01 301

EG-PatchMatch MVS		68.55 301	65.41 307	77.96 289	78.69 338	62.93 222	89.86 242	89.17 254	60.55 327	50.27 360	77.73 330	22.60 375	94.06 222	47.18 334	72.65 244	76.88 372

EI-MVSNet		78.97 177	78.22 172	81.25 224	85.33 252	62.73 229	89.53 249	93.21 91	72.39 188	72.14 200	90.13 180	60.99 126	94.72 190	67.73 221	72.49 245	86.29 272

MVSTER		82.47 117	82.05 113	83.74 161	92.68 84	69.01 63	91.90 165	93.21 91	79.83 62	72.14 200	85.71 242	74.72 16	94.72 190	75.72 149	72.49 245	87.50 248

Anonymous20240529		76.84 217	74.15 233	84.88 122	91.02 126	64.95 166	93.84 81	91.09 182	53.57 357	73.00 184	87.42 219	35.91 335	97.32 76	69.14 208	72.41 247	92.36 173

D2MVS		73.80 257	72.02 262	79.15 277	79.15 330	62.97 220	88.58 267	90.07 219	72.94 172	59.22 322	78.30 324	42.31 299	92.70 266	65.59 246	72.00 248	81.79 339

PS-MVSNAJss		77.26 208	76.31 202	80.13 252	80.64 311	59.16 293	90.63 221	91.06 186	72.80 177	68.58 246	84.57 253	53.55 212	93.96 230	72.97 167	71.96 249	87.27 257

Effi-MVS+-dtu		76.14 224	75.28 218	78.72 281	83.22 285	55.17 330	89.87 241	87.78 301	75.42 130	67.98 251	81.43 289	45.08 288	92.52 274	75.08 155	71.63 250	88.48 237

ACMMP++_ref															71.63 250

ACMM	69.62 13	74.34 250	72.73 253	79.17 275	84.25 274	57.87 305	90.36 226	89.93 225	63.17 307	65.64 276	86.04 239	37.79 323	94.10 218	65.89 241	71.52 252	85.55 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	71.68 10	75.58 239	74.23 232	79.62 268	84.97 261	59.64 284	90.80 213	89.07 262	70.39 242	62.95 303	87.30 221	38.28 315	93.87 234	72.89 168	71.45 253	85.36 297
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dp		75.01 245	72.09 261	83.76 160	89.28 163	66.22 136	79.96 346	89.75 231	71.16 227	67.80 258	77.19 335	51.81 227	92.54 273	50.39 316	71.44 254	92.51 171

tpm		78.58 188	77.03 192	83.22 177	85.94 244	64.56 169	83.21 316	91.14 180	78.31 90	73.67 180	79.68 317	64.01 88	92.09 287	66.07 240	71.26 255	93.03 156

DP-MVS		69.90 290	66.48 297	80.14 251	95.36 28	62.93 222	89.56 246	76.11 362	50.27 367	57.69 334	85.23 244	39.68 306	95.73 151	33.35 378	71.05 256	81.78 340

UniMVSNet_ETH3D		72.74 269	70.53 276	79.36 272	78.62 340	56.64 321	85.01 300	89.20 252	63.77 299	64.84 283	84.44 255	34.05 342	91.86 291	63.94 257	70.89 257	89.57 223

jajsoiax		73.05 263	71.51 268	77.67 291	77.46 349	54.83 332	88.81 263	90.04 222	69.13 259	62.85 305	83.51 263	31.16 356	92.75 263	70.83 189	69.80 258	85.43 296

ACMMP++															69.72 259

mvs_tets		72.71 270	71.11 269	77.52 292	77.41 350	54.52 334	88.45 269	89.76 230	68.76 264	62.70 306	83.26 266	29.49 360	92.71 264	70.51 195	69.62 260	85.34 298

tpmvs		72.88 267	69.76 283	82.22 201	90.98 127	67.05 114	78.22 353	88.30 289	63.10 308	64.35 291	74.98 349	55.09 195	94.27 211	43.25 348	69.57 261	85.34 298

GBi-Net		75.65 236	73.83 238	81.10 230	88.85 174	65.11 161	90.01 237	90.32 206	70.84 234	67.04 267	80.25 310	48.03 260	91.54 299	59.80 285	69.34 262	86.64 265

test1		75.65 236	73.83 238	81.10 230	88.85 174	65.11 161	90.01 237	90.32 206	70.84 234	67.04 267	80.25 310	48.03 260	91.54 299	59.80 285	69.34 262	86.64 265

FMVSNet3		77.73 202	76.04 206	82.80 183	91.20 125	68.99 64	91.87 166	91.99 138	73.35 165	67.04 267	83.19 267	56.62 177	92.14 284	59.80 285	69.34 262	87.28 256

Syy-MVS		69.65 292	69.52 284	70.03 347	87.87 203	43.21 380	88.07 273	89.01 264	72.91 174	63.11 300	88.10 207	45.28 286	85.54 350	22.07 393	69.23 265	81.32 342

myMVS_eth3d		72.58 274	72.74 252	72.10 340	87.87 203	49.45 357	88.07 273	89.01 264	72.91 174	63.11 300	88.10 207	63.63 95	85.54 350	32.73 382	69.23 265	81.32 342

MSDG		69.54 293	65.73 303	80.96 235	85.11 259	63.71 199	84.19 304	83.28 345	56.95 346	54.50 343	84.03 257	31.50 353	96.03 141	42.87 352	69.13 267	83.14 323

JIA-IIPM		66.06 318	62.45 327	76.88 304	81.42 304	54.45 335	57.49 392	88.67 279	49.36 369	63.86 293	46.86 390	56.06 184	90.25 312	49.53 321	68.83 268	85.95 284

OpenMVS_ROB	61.12 18	66.39 316	62.92 324	76.80 305	76.51 353	57.77 306	89.22 255	83.41 343	55.48 353	53.86 347	77.84 329	26.28 369	93.95 231	34.90 375	68.76 269	78.68 366

FMVSNet2		76.07 225	74.01 236	82.26 200	88.85 174	67.66 97	91.33 193	91.61 160	70.84 234	65.98 274	82.25 276	48.03 260	92.00 289	58.46 290	68.73 270	87.10 259

test_djsdf		73.76 259	72.56 256	77.39 296	77.00 352	53.93 336	89.07 259	90.69 193	65.80 285	63.92 292	82.03 279	43.14 296	92.67 267	72.83 169	68.53 271	85.57 292

F-COLMAP		70.66 282	68.44 290	77.32 297	86.37 235	55.91 325	88.00 275	86.32 314	56.94 347	57.28 336	88.07 209	33.58 343	92.49 275	51.02 314	68.37 272	83.55 313

XVG-ACMP-BASELINE		68.04 306	65.53 306	75.56 311	74.06 363	52.37 341	78.43 350	85.88 321	62.03 317	58.91 326	81.21 297	20.38 380	91.15 306	60.69 279	68.18 273	83.16 322

LTVRE_ROB	59.60 19	66.27 317	63.54 320	74.45 320	84.00 277	51.55 345	67.08 380	83.53 341	58.78 338	54.94 342	80.31 308	34.54 340	93.23 246	40.64 361	68.03 274	78.58 367
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		77.94 199	76.44 200	82.43 192	82.60 292	64.44 175	92.01 158	91.83 149	73.59 162	70.00 226	85.82 240	54.43 203	94.76 187	69.63 200	68.02 275	88.10 244

ADS-MVSNet2		66.90 314	63.44 321	77.26 299	88.06 197	60.70 270	68.01 377	75.56 366	57.57 341	64.48 287	69.87 366	38.68 309	84.10 357	40.87 359	67.89 276	86.97 260

ADS-MVSNet		68.54 302	64.38 317	81.03 234	88.06 197	66.90 118	68.01 377	84.02 336	57.57 341	64.48 287	69.87 366	38.68 309	89.21 325	40.87 359	67.89 276	86.97 260

test0.0.03 1		72.76 268	72.71 254	72.88 332	80.25 316	47.99 363	91.22 199	89.45 242	71.51 222	62.51 308	87.66 215	53.83 208	85.06 354	50.16 318	67.84 278	85.58 291

anonymousdsp		71.14 281	69.37 285	76.45 306	72.95 366	54.71 333	84.19 304	88.88 269	61.92 319	62.15 309	79.77 316	38.14 318	91.44 304	68.90 211	67.45 279	83.21 321

tt0805		73.07 262	70.73 274	80.07 253	78.37 342	57.05 317	87.78 279	92.18 133	61.23 324	67.04 267	86.49 231	31.35 355	94.58 196	65.06 251	67.12 280	88.57 235

VPA-MVSNet		79.03 175	78.00 175	82.11 209	85.95 242	64.48 173	93.22 109	94.66 38	75.05 136	74.04 177	84.95 247	52.17 225	93.52 241	74.90 159	67.04 281	88.32 242

nrg030		80.93 142	79.86 147	84.13 154	83.69 280	68.83 67	93.23 108	91.20 175	75.55 128	75.06 164	88.22 206	63.04 109	94.74 189	81.88 104	66.88 282	88.82 231

FMVSNet1		72.71 270	69.91 281	81.10 230	83.60 282	65.11 161	90.01 237	90.32 206	63.92 297	63.56 296	80.25 310	36.35 334	91.54 299	54.46 304	66.75 283	86.64 265

PatchT		69.11 296	65.37 308	80.32 244	82.07 299	63.68 202	67.96 379	87.62 302	50.86 365	69.37 231	65.18 374	57.09 166	88.53 329	41.59 357	66.60 284	88.74 232

IB-MVS	77.80 4	82.18 121	80.46 140	87.35 45	89.14 169	70.28 37	95.59 27	95.17 21	78.85 83	70.19 223	85.82 240	70.66 37	97.67 51	72.19 180	66.52 285	94.09 121
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		74.44 249	72.97 249	78.84 280	82.36 295	57.66 309	89.83 243	88.79 275	70.61 240	64.58 285	84.89 248	39.24 307	92.65 270	70.11 197	66.34 286	86.21 275

test_fmvs2		65.78 321	64.84 309	68.60 353	66.54 382	41.71 382	83.27 313	69.81 380	54.38 355	67.91 254	84.54 254	15.35 386	81.22 377	75.65 150	66.16 287	82.88 324

pmmvs5		73.35 260	71.52 267	78.86 279	78.64 339	60.61 273	91.08 204	86.90 309	67.69 270	63.32 298	83.64 261	44.33 291	90.53 309	62.04 272	66.02 288	85.46 295

dmvs_testset		65.55 322	66.45 298	62.86 363	79.87 320	22.35 406	76.55 358	71.74 376	77.42 108	55.85 339	87.77 214	51.39 232	80.69 378	31.51 388	65.92 289	85.55 293

pmmvs4		73.92 256	71.81 265	80.25 248	79.17 329	65.24 157	87.43 284	87.26 306	67.64 273	63.46 297	83.91 260	48.96 256	91.53 302	62.94 265	65.49 290	83.96 308

cl22		77.94 199	76.78 196	81.42 221	87.57 209	64.93 167	90.67 217	88.86 271	72.45 185	67.63 260	82.68 272	64.07 87	92.91 257	71.79 181	65.30 291	86.44 270

miper_ehance_all_eth		77.60 203	76.44 200	81.09 233	85.70 249	64.41 178	90.65 218	88.64 281	72.31 189	67.37 265	82.52 273	64.77 81	92.64 271	70.67 192	65.30 291	86.24 274

miper_enhance_ethall		78.86 180	77.97 176	81.54 219	88.00 200	65.17 159	91.41 183	89.15 256	75.19 134	68.79 242	83.98 259	67.17 56	92.82 259	72.73 172	65.30 291	86.62 269

v1144		76.73 220	74.88 220	82.27 198	80.23 317	66.60 126	91.68 177	90.21 216	73.69 159	69.06 236	81.89 280	52.73 221	94.40 206	69.21 206	65.23 294	85.80 287

DSMNet-mixed		56.78 347	54.44 350	63.79 362	63.21 386	29.44 401	64.43 383	64.10 388	42.12 386	51.32 356	71.60 361	31.76 352	75.04 383	36.23 370	65.20 295	86.87 263

v1192		75.98 230	73.92 237	82.15 204	79.73 321	66.24 135	91.22 199	89.75 231	72.67 179	68.49 247	81.42 290	49.86 245	94.27 211	67.08 228	65.02 296	85.95 284

v2v482		77.42 206	75.65 213	82.73 185	80.38 313	67.13 112	91.85 168	90.23 214	75.09 135	69.37 231	83.39 265	53.79 210	94.44 205	71.77 182	65.00 297	86.63 268

V42		76.46 222	74.55 226	82.19 203	79.14 331	67.82 93	90.26 230	89.42 244	73.75 157	68.63 245	81.89 280	51.31 233	94.09 219	71.69 184	64.84 298	84.66 304

ACMH	63.93 17	68.62 300	64.81 310	80.03 255	85.22 255	63.25 213	87.72 280	84.66 331	60.83 326	51.57 355	79.43 320	27.29 366	94.96 182	41.76 355	64.84 298	81.88 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		81.84 128	81.03 128	84.28 151	91.60 113	66.62 125	91.08 204	91.66 159	81.87 33	74.86 166	91.67 151	69.98 41	94.92 185	71.76 183	64.75 300	91.29 200

v1240		75.21 243	72.98 248	81.88 212	79.20 328	66.00 139	90.75 215	89.11 259	71.63 217	67.41 263	81.22 295	47.36 268	93.87 234	65.46 248	64.72 301	85.77 288

IterMVS-LS		76.49 221	75.18 219	80.43 243	84.49 268	62.74 228	90.64 219	88.80 273	72.40 187	65.16 280	81.72 283	60.98 127	92.27 283	67.74 220	64.65 302	86.29 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		75.63 238	73.49 243	82.06 210	79.38 326	66.35 131	91.07 206	89.48 240	71.98 198	67.99 250	81.22 295	49.16 254	93.90 233	66.56 232	64.56 303	85.92 286

v144192		76.05 228	74.03 235	82.12 206	79.50 325	66.55 128	91.39 187	89.71 237	72.30 190	68.17 249	81.33 292	51.75 228	94.03 227	67.94 218	64.19 304	85.77 288

Anonymous20231211		73.08 261	70.39 277	81.13 228	90.62 134	63.33 212	91.40 185	90.06 221	51.84 362	64.46 289	80.67 303	36.49 333	94.07 221	63.83 258	64.17 305	85.98 283

testing3		70.38 286	70.83 271	69.03 351	85.82 246	43.93 379	90.72 216	90.56 199	68.06 268	60.24 316	86.82 228	64.83 79	84.12 356	26.33 389	64.10 306	79.04 363

Patchmatch-test		65.86 319	60.94 333	80.62 241	83.75 279	58.83 297	58.91 391	75.26 368	44.50 381	50.95 359	77.09 336	58.81 152	87.90 333	35.13 374	64.03 307	95.12 77

USDC		67.43 313	64.51 314	76.19 308	77.94 347	55.29 329	78.38 351	85.00 328	73.17 167	48.36 367	80.37 307	21.23 377	92.48 276	52.15 312	64.02 308	80.81 348

VPNet		78.82 181	77.53 183	82.70 186	84.52 267	66.44 129	93.93 73	92.23 127	80.46 52	72.60 191	88.38 200	49.18 252	93.13 247	72.47 176	63.97 309	88.55 236

Anonymous20231206		67.53 311	65.78 302	72.79 333	74.95 359	47.59 365	88.23 271	87.32 304	61.75 322	58.07 330	77.29 333	37.79 323	87.29 343	42.91 350	63.71 310	83.48 316

WR-MVS		76.76 219	75.74 211	79.82 263	84.60 265	62.27 238	92.60 134	92.51 121	76.06 122	67.87 257	85.34 243	56.76 173	90.24 315	62.20 271	63.69 311	86.94 262

h-mvs33		83.01 108	82.56 108	84.35 148	89.34 159	62.02 241	92.72 125	93.76 69	81.45 38	82.73 81	92.25 139	60.11 136	97.13 89	87.69 56	62.96 312	93.91 130

c3_l		76.83 218	75.47 214	80.93 237	85.02 260	64.18 188	90.39 225	88.11 295	71.66 212	66.65 273	81.64 285	63.58 100	92.56 272	69.31 205	62.86 313	86.04 281

test_vis1_rt		59.09 345	57.31 344	64.43 361	68.44 380	46.02 373	83.05 318	48.63 401	51.96 361	49.57 363	63.86 377	16.30 384	80.20 379	71.21 187	62.79 314	67.07 386

mvsany_test1		68.77 299	68.56 288	69.39 349	73.57 364	45.88 374	80.93 334	60.88 392	59.65 334	71.56 208	90.26 176	43.22 295	75.05 382	74.26 163	62.70 315	87.25 258

UniMVSNet_NR-MVSNet		78.15 195	77.55 182	79.98 257	84.46 269	60.26 276	92.25 145	93.20 93	77.50 105	68.88 240	86.61 229	66.10 64	92.13 285	66.38 236	62.55 316	87.54 247

DU-MVS		76.86 214	75.84 209	79.91 260	82.96 289	60.26 276	91.26 196	91.54 162	76.46 120	68.88 240	86.35 232	56.16 181	92.13 285	66.38 236	62.55 316	87.35 254

UniMVSNet (Re)		77.58 204	76.78 196	79.98 257	84.11 275	60.80 263	91.76 173	93.17 95	76.56 119	69.93 229	84.78 250	63.32 104	92.36 280	64.89 252	62.51 318	86.78 264

v8		75.35 240	73.26 245	81.61 217	80.67 310	66.82 119	89.54 248	89.27 249	71.65 213	63.30 299	80.30 309	54.99 196	94.06 222	67.33 225	62.33 319	83.94 309

cl____		76.07 225	74.67 221	80.28 246	85.15 256	61.76 247	90.12 233	88.73 276	71.16 227	65.43 277	81.57 287	61.15 124	92.95 252	66.54 233	62.17 320	86.13 279

v10		74.77 247	72.54 257	81.46 220	80.33 315	66.71 123	89.15 258	89.08 261	70.94 232	63.08 302	79.86 314	52.52 222	94.04 225	65.70 244	62.17 320	83.64 312

DIV-MVS_self_test		76.07 225	74.67 221	80.28 246	85.14 257	61.75 248	90.12 233	88.73 276	71.16 227	65.42 278	81.60 286	61.15 124	92.94 256	66.54 233	62.16 322	86.14 277

IterMVS-SCA-FT		71.55 279	69.97 279	76.32 307	81.48 302	60.67 271	87.64 282	85.99 320	66.17 283	59.50 320	78.88 321	45.53 283	83.65 362	62.58 269	61.93 323	84.63 306

IterMVS		72.65 273	70.83 271	78.09 288	82.17 297	62.96 221	87.64 282	86.28 315	71.56 220	60.44 315	78.85 322	45.42 285	86.66 345	63.30 263	61.83 324	84.65 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 306	65.66 305	75.18 315	84.43 270	57.89 304	83.54 308	86.26 316	61.83 321	53.64 348	73.30 353	37.15 329	85.08 353	48.99 323	61.77 325	82.56 333

v7n		71.31 280	68.65 287	79.28 273	76.40 354	60.77 265	86.71 293	89.45 242	64.17 296	58.77 327	78.24 325	44.59 290	93.54 240	57.76 292	61.75 326	83.52 315

v148		76.19 223	74.47 228	81.36 222	80.05 319	64.44 175	91.75 175	90.23 214	73.68 160	67.13 266	80.84 300	55.92 186	93.86 236	68.95 210	61.73 327	85.76 290

tfpnnormal		70.10 287	67.36 295	78.32 284	83.45 284	60.97 261	88.85 262	92.77 109	64.85 292	60.83 314	78.53 323	43.52 294	93.48 242	31.73 385	61.70 328	80.52 351

ACMH+	65.35 16	67.65 309	64.55 313	76.96 303	84.59 266	57.10 316	88.08 272	80.79 352	58.59 340	53.00 349	81.09 299	26.63 368	92.95 252	46.51 336	61.69 329	80.82 347

ITE_SJBPF				70.43 346	74.44 361	47.06 370		77.32 360	60.16 331	54.04 346	83.53 262	23.30 374	84.01 359	43.07 349	61.58 330	80.21 356

NR-MVSNet		76.05 228	74.59 224	80.44 242	82.96 289	62.18 239	90.83 212	91.73 152	77.12 109	60.96 313	86.35 232	59.28 148	91.80 292	60.74 278	61.34 331	87.35 254

test_0402		64.54 326	61.09 332	74.92 317	84.10 276	60.75 267	87.95 276	79.71 357	52.03 360	52.41 351	77.20 334	32.21 351	91.64 295	23.14 391	61.03 332	72.36 380

Baseline_NR-MVSNet		73.99 255	72.83 250	77.48 294	80.78 308	59.29 292	91.79 170	84.55 332	68.85 261	68.99 238	80.70 301	56.16 181	92.04 288	62.67 268	60.98 333	81.11 344

TranMVSNet+NR-MVSNet		75.86 233	74.52 227	79.89 261	82.44 294	60.64 272	91.37 190	91.37 169	76.63 117	67.65 259	86.21 236	52.37 224	91.55 298	61.84 273	60.81 334	87.48 249

testgi		64.48 327	62.87 325	69.31 350	71.24 369	40.62 385	85.49 297	79.92 356	65.36 289	54.18 345	83.49 264	23.74 373	84.55 355	41.60 356	60.79 335	82.77 326

eth_miper_zixun_eth		75.96 232	74.40 229	80.66 239	84.66 264	63.02 219	89.28 254	88.27 291	71.88 203	65.73 275	81.65 284	59.45 144	92.81 260	68.13 215	60.53 336	86.14 277

COLMAP_ROB	57.96 20	62.98 334	59.65 336	72.98 331	81.44 303	53.00 340	83.75 307	75.53 367	48.34 372	48.81 366	81.40 291	24.14 371	90.30 311	32.95 380	60.52 337	75.65 375
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		78.37 191	77.43 184	81.17 226	86.60 230	57.45 313	89.46 251	91.16 177	74.11 147	74.40 171	90.49 170	55.52 189	94.57 198	74.73 161	60.43 338	91.48 192

hse-mvs2		81.12 139	81.11 127	81.16 227	86.52 231	57.48 312	89.40 252	91.16 177	81.45 38	82.73 81	90.49 170	60.11 136	94.58 196	87.69 56	60.41 339	91.41 194

RPSCF		64.24 328	61.98 330	71.01 345	76.10 356	45.00 375	75.83 362	75.94 363	46.94 375	58.96 325	84.59 252	31.40 354	82.00 374	47.76 332	60.33 340	86.04 281

miper_lstm_enhance		73.05 263	71.73 266	77.03 300	83.80 278	58.32 302	81.76 324	88.88 269	69.80 250	61.01 312	78.23 326	57.19 165	87.51 341	65.34 249	59.53 341	85.27 300

CP-MVSNet		70.50 284	69.91 281	72.26 337	80.71 309	51.00 349	87.23 287	90.30 210	67.84 269	59.64 319	82.69 271	50.23 242	82.30 372	51.28 313	59.28 342	83.46 317

PS-CasMVS		69.86 291	69.13 286	72.07 341	80.35 314	50.57 351	87.02 289	89.75 231	67.27 275	59.19 323	82.28 275	46.58 273	82.24 373	50.69 315	59.02 343	83.39 319

pm-mvs1		72.89 266	71.09 270	78.26 286	79.10 332	57.62 310	90.80 213	89.30 248	67.66 271	62.91 304	81.78 282	49.11 255	92.95 252	60.29 282	58.89 344	84.22 307

Anonymous20240521		62.09 335	59.08 338	71.10 344	67.19 381	48.72 361	83.91 306	85.23 326	50.38 366	47.84 368	71.22 365	20.74 378	85.51 352	46.47 337	58.75 345	79.06 362

WR-MVS_H		70.59 283	69.94 280	72.53 334	81.03 305	51.43 346	87.35 285	92.03 137	67.38 274	60.23 317	80.70 301	55.84 187	83.45 364	46.33 338	58.58 346	82.72 328

PEN-MVS		69.46 294	68.56 288	72.17 339	79.27 327	49.71 355	86.90 291	89.24 250	67.24 278	59.08 324	82.51 274	47.23 269	83.54 363	48.42 326	57.12 347	83.25 320

EU-MVSNet		64.01 329	63.01 323	67.02 359	74.40 362	38.86 390	83.27 313	86.19 318	45.11 379	54.27 344	81.15 298	36.91 332	80.01 380	48.79 325	57.02 348	82.19 337

AllTest		61.66 336	58.06 340	72.46 335	79.57 322	51.42 347	80.17 341	68.61 382	51.25 363	45.88 371	81.23 293	19.86 382	86.58 346	38.98 365	57.01 349	79.39 359

TestCases				72.46 335	79.57 322	51.42 347		68.61 382	51.25 363	45.88 371	81.23 293	19.86 382	86.58 346	38.98 365	57.01 349	79.39 359

Patchmtry		67.53 311	63.93 318	78.34 283	82.12 298	64.38 179	68.72 374	84.00 337	48.23 373	59.24 321	72.41 356	57.82 160	89.27 324	46.10 339	56.68 351	81.36 341

our_test_3		68.29 304	64.69 312	79.11 278	78.92 333	64.85 168	88.40 270	85.06 327	60.32 330	52.68 350	76.12 344	40.81 303	89.80 322	44.25 347	55.65 352	82.67 332

FPMVS		45.64 356	43.10 360	53.23 373	51.42 398	36.46 391	64.97 382	71.91 375	29.13 393	27.53 393	61.55 382	9.83 395	65.01 397	16.00 399	55.58 353	58.22 389

DTE-MVSNet		68.46 303	67.33 296	71.87 343	77.94 347	49.00 360	86.16 296	88.58 283	66.36 282	58.19 328	82.21 277	46.36 274	83.87 361	44.97 345	55.17 354	82.73 327

MIMVSNet1		60.16 342	57.33 343	68.67 352	69.71 376	44.13 377	78.92 348	84.21 333	55.05 354	44.63 378	71.85 360	23.91 372	81.54 376	32.63 383	55.03 355	80.35 352

pmmvs6		67.57 310	64.76 311	76.00 310	72.82 368	53.37 338	88.71 264	86.78 313	53.19 358	57.58 335	78.03 328	35.33 338	92.41 277	55.56 300	54.88 356	82.21 336

TinyColmap		60.32 340	56.42 347	72.00 342	78.78 336	53.18 339	78.36 352	75.64 365	52.30 359	41.59 384	75.82 347	14.76 389	88.35 330	35.84 371	54.71 357	74.46 376

test20.03		63.83 330	62.65 326	67.38 358	70.58 375	39.94 386	86.57 294	84.17 334	63.29 304	51.86 353	77.30 332	37.09 330	82.47 370	38.87 367	54.13 358	79.73 357

OurMVSNet-221017-0		64.68 325	62.17 329	72.21 338	76.08 357	47.35 366	80.67 335	81.02 351	56.19 350	51.60 354	79.66 318	27.05 367	88.56 328	53.60 309	53.63 359	80.71 349

test_fmvs3		56.82 346	54.86 349	62.69 364	53.59 395	35.47 392	75.87 361	65.64 387	43.91 382	55.10 341	71.43 364	6.91 400	74.40 385	68.64 213	52.63 360	78.20 369

Patchmatch-RL test		68.17 305	64.49 315	79.19 274	71.22 370	53.93 336	70.07 372	71.54 378	69.22 256	56.79 337	62.89 378	56.58 178	88.61 326	69.53 202	52.61 361	95.03 81

ppachtmachnet_test		67.72 308	63.70 319	79.77 265	78.92 333	66.04 138	88.68 265	82.90 347	60.11 332	55.45 340	75.96 345	39.19 308	90.55 308	39.53 363	52.55 362	82.71 329

LF4IMVS		54.01 350	52.12 351	59.69 365	62.41 388	39.91 388	68.59 375	68.28 384	42.96 385	44.55 379	75.18 348	14.09 391	68.39 391	41.36 358	51.68 363	70.78 381

N_pmnet		50.55 351	49.11 354	54.88 371	77.17 351	4.02 414	84.36 303	2.00 412	48.59 370	45.86 373	68.82 368	32.22 350	82.80 369	31.58 386	51.38 364	77.81 370

pmmvs-eth3d		65.53 323	62.32 328	75.19 314	69.39 378	59.59 285	82.80 320	83.43 342	62.52 313	51.30 357	72.49 354	32.86 344	87.16 344	55.32 301	50.73 365	78.83 365

CL-MVSNet_self_test		69.92 289	68.09 293	75.41 312	73.25 365	55.90 326	90.05 236	89.90 226	69.96 247	61.96 311	76.54 339	51.05 235	87.64 338	49.51 322	50.59 366	82.70 330

PM-MVS		59.40 343	56.59 345	67.84 354	63.63 385	41.86 381	76.76 357	63.22 389	59.01 337	51.07 358	72.27 359	11.72 392	83.25 366	61.34 275	50.28 367	78.39 368

MDA-MVSNet_test_wron		63.78 331	60.16 334	74.64 318	78.15 345	60.41 274	83.49 309	84.03 335	56.17 352	39.17 386	71.59 362	37.22 327	83.24 367	42.87 352	48.73 368	80.26 354

YYNet1		63.76 332	60.14 335	74.62 319	78.06 346	60.19 279	83.46 311	83.99 339	56.18 351	39.25 385	71.56 363	37.18 328	83.34 365	42.90 351	48.70 369	80.32 353

KD-MVS_self_test		60.87 339	58.60 339	67.68 356	66.13 383	39.93 387	75.63 363	84.70 330	57.32 344	49.57 363	68.45 369	29.55 359	82.87 368	48.09 327	47.94 370	80.25 355

SixPastTwentyTwo		64.92 324	61.78 331	74.34 322	78.74 337	49.76 354	83.42 312	79.51 358	62.86 309	50.27 360	77.35 331	30.92 358	90.49 310	45.89 340	47.06 371	82.78 325

new_pmnet		49.31 352	46.44 355	57.93 366	62.84 387	40.74 384	68.47 376	62.96 390	36.48 388	35.09 388	57.81 385	14.97 388	72.18 387	32.86 381	46.44 372	60.88 388

EGC-MVSNET		42.35 358	38.09 361	55.11 370	74.57 360	46.62 371	71.63 369	55.77 393	0.04 407	0.24 408	62.70 379	14.24 390	74.91 384	17.59 396	46.06 373	43.80 393

TransMVSNet (Re)		70.07 288	67.66 294	77.31 298	80.62 312	59.13 295	91.78 172	84.94 329	65.97 284	60.08 318	80.44 306	50.78 236	91.87 290	48.84 324	45.46 374	80.94 346

ambc				69.61 348	61.38 390	41.35 383	49.07 397	85.86 322		50.18 362	66.40 372	10.16 394	88.14 332	45.73 341	44.20 375	79.32 361

TDRefinement		55.28 349	51.58 352	66.39 360	59.53 392	46.15 372	76.23 360	72.80 372	44.60 380	42.49 382	76.28 343	15.29 387	82.39 371	33.20 379	43.75 376	70.62 382

Gipuma		34.91 365	31.44 368	45.30 380	70.99 372	39.64 389	19.85 402	72.56 373	20.10 398	16.16 402	21.47 403	5.08 403	71.16 388	13.07 400	43.70 377	25.08 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		46.58 354	43.45 358	55.96 368	45.18 402	32.05 396	61.18 386	49.49 400	33.39 390	42.05 383	62.48 380	7.00 399	65.56 395	47.08 335	43.21 378	70.27 383

MDA-MVSNet-bldmvs		61.54 338	57.70 342	73.05 330	79.53 324	57.00 320	83.08 317	81.23 350	57.57 341	34.91 389	72.45 355	32.79 345	86.26 348	35.81 372	41.95 379	75.89 374

new-patchmatchnet		59.30 344	56.48 346	67.79 355	65.86 384	44.19 376	82.47 321	81.77 348	59.94 333	43.65 381	66.20 373	27.67 365	81.68 375	39.34 364	41.40 380	77.50 371

UnsupCasMVSNet_eth		65.79 320	63.10 322	73.88 324	70.71 373	50.29 353	81.09 332	89.88 227	72.58 181	49.25 365	74.77 351	32.57 348	87.43 342	55.96 299	41.04 381	83.90 310

test_vis3_rt		40.46 361	37.79 362	48.47 378	44.49 403	33.35 395	66.56 381	32.84 409	32.39 391	29.65 391	39.13 399	3.91 407	68.65 390	50.17 317	40.99 382	43.40 394

pmmvs3		55.51 348	51.50 353	67.53 357	57.90 393	50.93 350	80.37 337	73.66 371	40.63 387	44.15 380	64.75 376	16.30 384	78.97 381	44.77 346	40.98 383	72.69 378

APD_test1		40.50 360	37.31 363	50.09 376	51.88 396	35.27 393	59.45 390	52.59 397	21.64 396	26.12 394	57.80 386	4.56 404	66.56 393	22.64 392	39.09 384	48.43 392

UnsupCasMVSNet_bld		61.60 337	57.71 341	73.29 329	68.73 379	51.64 344	78.61 349	89.05 263	57.20 345	46.11 370	61.96 381	28.70 363	88.60 327	50.08 319	38.90 385	79.63 358

PMVS	26.43 22	31.84 368	28.16 371	42.89 381	25.87 411	27.58 402	50.92 396	49.78 399	21.37 397	14.17 403	40.81 398	2.01 410	66.62 392	9.61 403	38.88 386	34.49 399
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
K. test v3		63.09 333	59.61 337	73.53 327	76.26 355	49.38 359	83.27 313	77.15 361	64.35 295	47.77 369	72.32 358	28.73 362	87.79 336	49.93 320	36.69 387	83.41 318

KD-MVS_2432*1600		69.03 297	66.37 300	77.01 301	85.56 250	61.06 259	81.44 329	90.25 212	67.27 275	58.00 331	76.53 340	54.49 200	87.63 339	48.04 328	35.77 388	82.34 334

miper_refine_blended		69.03 297	66.37 300	77.01 301	85.56 250	61.06 259	81.44 329	90.25 212	67.27 275	58.00 331	76.53 340	54.49 200	87.63 339	48.04 328	35.77 388	82.34 334

mvsany_test3		48.86 353	46.35 356	56.41 367	46.00 401	31.67 397	62.26 385	47.25 402	43.71 383	45.54 375	68.15 370	10.84 393	64.44 399	57.95 291	35.44 390	73.13 377

LCM-MVSNet		40.54 359	35.79 364	54.76 372	36.92 408	30.81 398	51.41 395	69.02 381	22.07 395	24.63 395	45.37 392	4.56 404	65.81 394	33.67 377	34.50 391	67.67 384

test_method		38.59 363	35.16 366	48.89 377	54.33 394	21.35 407	45.32 398	53.71 396	7.41 404	28.74 392	51.62 388	8.70 397	52.87 402	33.73 376	32.89 392	72.47 379

lessismore_v0				73.72 326	72.93 367	47.83 364		61.72 391		45.86 373	73.76 352	28.63 364	89.81 320	47.75 333	31.37 393	83.53 314

testf1		32.77 366	29.47 369	42.67 382	41.89 405	30.81 398	52.07 393	43.45 403	15.45 399	18.52 399	44.82 393	2.12 408	58.38 400	16.05 397	30.87 394	38.83 395

APD_test2		32.77 366	29.47 369	42.67 382	41.89 405	30.81 398	52.07 393	43.45 403	15.45 399	18.52 399	44.82 393	2.12 408	58.38 400	16.05 397	30.87 394	38.83 395

PVSNet_0	68.08 15	71.81 276	68.32 292	82.27 198	84.68 263	62.31 237	88.68 265	90.31 209	75.84 124	57.93 333	80.65 304	37.85 322	94.19 215	69.94 198	29.05 396	90.31 212

WB-MVS		46.23 355	44.94 357	50.11 375	62.13 389	21.23 408	76.48 359	55.49 394	45.89 377	35.78 387	61.44 383	35.54 336	72.83 386	9.96 402	21.75 397	56.27 390

SSC-MVS		44.51 357	43.35 359	47.99 379	61.01 391	18.90 410	74.12 365	54.36 395	43.42 384	34.10 390	60.02 384	34.42 341	70.39 389	9.14 404	19.57 398	54.68 391

DeepMVS_CX				34.71 385	51.45 397	24.73 405		28.48 411	31.46 392	17.49 401	52.75 387	5.80 402	42.60 406	18.18 395	19.42 399	36.81 398

PMMVS2		37.93 364	33.61 367	50.92 374	46.31 400	24.76 404	60.55 389	50.05 398	28.94 394	20.93 396	47.59 389	4.41 406	65.13 396	25.14 390	18.55 400	62.87 387

MVE	24.84 23	24.35 370	19.77 376	38.09 384	34.56 410	26.92 403	26.57 400	38.87 407	11.73 403	11.37 404	27.44 400	1.37 411	50.42 403	11.41 401	14.60 401	36.93 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 369	24.00 373	26.45 386	43.74 404	18.44 411	60.86 387	39.66 405	15.11 401	9.53 405	22.10 402	6.52 401	46.94 404	8.31 405	10.14 402	13.98 402

EMVS		23.76 371	23.20 375	25.46 387	41.52 407	16.90 412	60.56 388	38.79 408	14.62 402	8.99 406	20.24 405	7.35 398	45.82 405	7.25 406	9.46 403	13.64 403

tmp_tt		22.26 372	23.75 374	17.80 388	5.23 412	12.06 413	35.26 399	39.48 406	2.82 406	18.94 397	44.20 395	22.23 376	24.64 407	36.30 369	9.31 404	16.69 401

ANet_high		40.27 362	35.20 365	55.47 369	34.74 409	34.47 394	63.84 384	71.56 377	48.42 371	18.80 398	41.08 397	9.52 396	64.45 398	20.18 394	8.66 405	67.49 385

wuyk23d		11.30 374	10.95 377	12.33 389	48.05 399	19.89 409	25.89 401	1.92 413	3.58 405	3.12 407	1.37 407	0.64 412	15.77 408	6.23 407	7.77 406	1.35 404

testmvs		7.23 376	9.62 379	0.06 391	0.04 413	0.02 416	84.98 301	0.02 414	0.03 408	0.18 409	1.21 408	0.01 414	0.02 409	0.14 408	0.01 407	0.13 406

test_blank		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

uanet_test		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

cdsmvs_eth3d_5k		19.86 373	26.47 372	0.00 392	0.00 415	0.00 417	0.00 403	93.45 84	0.00 410	0.00 411	95.27 58	49.56 247	0.00 411	0.00 410	0.00 408	0.00 407

pcd_1.5k_mvsjas		4.46 378	5.95 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	53.55 212	0.00 411	0.00 410	0.00 408	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

test123		6.92 377	9.21 380	0.08 390	0.03 414	0.05 415	81.65 327	0.01 415	0.02 409	0.14 410	0.85 409	0.03 413	0.02 409	0.12 409	0.00 408	0.16 405

ab-mvs-re		7.91 375	10.55 378	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	94.95 67	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 408	0.00 407

WAC-MVS						49.45 357								31.56 387

FOURS1					93.95 45	61.77 246	93.96 71	91.92 141	62.14 316	86.57 46

test_one_0601					96.32 18	69.74 49		94.18 57	71.42 224	90.67 19	96.85 16	74.45 18

eth-test2					0.00 415

eth-test					0.00 415

test_241102_ONE					96.45 12	69.38 54		94.44 46	71.65 213	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 91

test0726					96.40 15	69.99 39	96.76 8	94.33 54	71.92 199	91.89 11	97.11 6	73.77 21

GSMVS																94.68 95

test_part2					96.29 19	68.16 86				90.78 17

sam_mvs1												57.85 159				94.68 95

sam_mvs												54.91 197

MTGPA								92.23 127

test_post1							78.95 347				20.70 404	53.05 217	91.50 303	60.43 280

test_post											23.01 401	56.49 179	92.67 267

patchmatchnet-post											67.62 371	57.62 162	90.25 312

MTMP							93.77 85	32.52 410

gm-plane-assit					88.42 185	67.04 115			78.62 88		91.83 147		97.37 72	76.57 144

TEST9					94.18 41	67.28 107	94.16 59	93.51 80	71.75 210	85.52 56	95.33 53	68.01 50	97.27 82

test_8					94.19 40	67.19 109	94.15 62	93.42 86	71.87 204	85.38 59	95.35 52	68.19 48	96.95 104

agg_prior					94.16 43	66.97 117		93.31 89		84.49 67			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 79

旧先验2							92.00 161		59.37 336	87.54 40			93.47 243	75.39 152

新几何2							91.41 183

无先验							92.71 126	92.61 118	62.03 317				97.01 95	66.63 231		93.97 127

原ACMM2							92.01 158

testdata2													96.09 135	61.26 276

segment_acmp												65.94 66

testdata1							89.21 256		77.55 104

plane_prior7					86.94 225	61.51 252

plane_prior6					87.23 217	62.32 236						50.66 237

plane_prior4											89.14 192

plane_prior3						61.95 244			79.09 79	72.53 193

plane_prior2							93.13 111		78.81 85

plane_prior1					87.15 219

n2								0.00 416

nn								0.00 416

door-mid								66.01 386

test11								93.01 101

door								66.57 385

HQP5-MVS						63.66 203

HQP-NCC					87.54 210		94.06 64		79.80 63	74.18 172

ACMP_Plane					87.54 210		94.06 64		79.80 63	74.18 172

BP-MVS														77.63 139

HQP4-MVS										74.18 172			95.61 159			88.63 233

HQP2-MVS												51.63 230

NP-MVS					87.41 213	63.04 218					90.30 174

MDTV_nov1_ep13_2view						59.90 282	80.13 342		67.65 272	72.79 188		54.33 205		59.83 284		92.58 168

Test By Simon												54.21 206

High-resolution multi-view benchmark