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 92	92.83 80	64.03 199	93.06 122	94.33 58	82.19 39	93.65 3	96.15 42	85.89 1	97.19 89	91.02 43	97.75 1	96.43 31
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 9	91.24 1	89.68 162	76.68 2	97.29 1	95.35 17	82.87 31	91.58 16	97.22 5	79.93 5	99.10 9	83.12 111	97.64 2	97.94 1

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 11	83.82 2	99.15 2	95.72 6	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 78	94.37 56	72.48 206	92.07 10	96.85 19	83.82 2	99.15 2	91.53 39	97.42 4	97.55 4

PC_three_1452									80.91 58	94.07 2	96.83 21	83.57 4	99.12 5	95.70 8	97.42 4	97.55 4

HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 87	95.24 33	94.49 48	82.43 36	88.90 37	96.35 33	71.89 40	98.63 26	88.76 57	96.40 6	96.06 41

SMA-MVS		88.14 18	88.29 24	87.67 33	93.21 68	68.72 73	93.85 85	94.03 66	74.18 169	91.74 13	96.67 25	65.61 79	98.42 33	89.24 53	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 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 54	88.32 3	85.71 64	94.91 82	74.11 21	98.91 18	87.26 71	95.94 8	97.03 12
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 10	97.13 2	95.58 11	89.33 1	85.77 63	96.26 38	72.84 30	99.38 1	92.64 29	95.93 9	97.08 11

balanced_conf03		89.08 15	88.84 18	89.81 6	93.66 54	75.15 5	90.61 241	93.43 92	84.06 18	86.20 58	90.17 195	72.42 35	96.98 106	93.09 25	95.92 10	97.29 7

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 33	84.83 13	89.07 36	96.80 22	70.86 43	99.06 15	92.64 29	95.71 11	96.12 40

PHI-MVS		86.83 43	86.85 47	86.78 63	93.47 63	65.55 157	95.39 30	95.10 25	71.77 232	85.69 65	96.52 27	62.07 133	98.77 23	86.06 83	95.60 12	96.03 43

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 139	93.00 76	58.16 332	96.72 9	94.41 52	86.50 8	90.25 27	97.83 1	75.46 14	98.67 25	92.78 28	95.49 13	97.32 6

SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 56	96.89 6	94.44 50	71.65 236	92.11 8	97.21 6	76.79 9	99.11 6	92.34 31	95.36 14	97.62 2

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 59	95.28 1				92.34 31	95.36 14	96.47 28

test_241102_TWO								94.41 52	71.65 236	92.07 10	97.21 6	74.58 18	99.11 6	92.34 31	95.36 14	96.59 19

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 17	96.19 40	70.12 47	98.91 18	96.83 1	95.06 17	96.76 15

test_0728_THIRD									72.48 206	90.55 23	96.93 13	76.24 11	99.08 11	91.53 39	94.99 18	96.43 31

test9_res														89.41 49	94.96 19	95.29 71

ACMMP_NAP		86.05 60	85.80 66	86.80 62	91.58 121	67.53 106	91.79 186	93.49 89	74.93 159	84.61 75	95.30 65	59.42 162	97.92 43	86.13 81	94.92 20	94.94 91

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 104	94.17 64	94.15 63	68.77 285	90.74 21	97.27 3	76.09 12	98.49 29	90.58 47	94.91 21	96.30 34
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 46	71.92 222	90.55 23	96.93 13	73.77 23	99.08 11	91.91 37	94.90 22	96.29 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 56					99.15 2	91.91 37	94.90 22	96.51 24

train_agg		87.21 36	87.42 37	86.60 69	94.18 41	67.28 111	94.16 65	93.51 86	71.87 227	85.52 66	95.33 63	68.19 54	97.27 85	89.09 54	94.90 22	95.25 77

DeepC-MVS_fast	79.48 2	87.95 23	88.00 28	87.79 31	95.86 27	68.32 81	95.74 21	94.11 64	83.82 20	83.49 86	96.19 40	64.53 95	98.44 31	83.42 110	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 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 34	94.77 26	96.51 24

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 34	94.77 26	96.51 24

TSAR-MVS + MP.		88.11 20	88.64 20	86.54 73	91.73 117	68.04 91	90.36 247	93.55 84	82.89 29	91.29 19	92.89 135	72.27 37	96.03 157	87.99 61	94.77 26	95.54 58
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 38		75.40 152	85.25 72	95.61 54	67.94 57		87.47 68	94.77 26

agg_prior2														86.41 79	94.75 30	95.33 67

MVSMamba_PlusPlus		84.97 85	83.65 96	88.93 14	90.17 153	74.04 8	87.84 303	92.69 124	62.18 342	81.47 106	87.64 236	71.47 42	96.28 142	84.69 95	94.74 31	96.47 28

MVS		84.66 89	82.86 121	90.06 2	90.93 138	74.56 7	87.91 301	95.54 14	68.55 287	72.35 222	94.71 87	59.78 158	98.90 20	81.29 130	94.69 32	96.74 16

MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 77	87.30 4	92.15 7	96.15 42	66.38 69	98.94 17	96.71 2	94.67 33	96.47 28

SF-MVS		87.03 38	87.09 40	86.84 59	92.70 86	67.45 109	93.64 98	93.76 73	70.78 260	86.25 56	96.44 30	66.98 63	97.79 49	88.68 58	94.56 34	95.28 73

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 64	96.38 15	94.64 42	84.42 15	86.74 53	96.20 39	66.56 68	98.76 24	89.03 56	94.56 34	95.92 46

3Dnovator	73.91 6	82.69 133	80.82 150	88.31 26	89.57 164	71.26 22	92.60 148	94.39 55	78.84 98	67.89 281	92.48 145	48.42 288	98.52 28	68.80 239	94.40 36	95.15 79

CDPH-MVS		85.71 68	85.46 72	86.46 75	94.75 34	67.19 113	93.89 83	92.83 117	70.90 256	83.09 91	95.28 66	63.62 109	97.36 76	80.63 135	94.18 37	94.84 96

MG-MVS		87.11 37	86.27 53	89.62 8	97.79 1	76.27 4	94.96 45	94.49 48	78.74 101	83.87 84	92.94 133	64.34 96	96.94 112	75.19 177	94.09 38	95.66 53

9.14			87.63 32		93.86 48		94.41 56	94.18 61	72.76 201	86.21 57	96.51 28	66.64 66	97.88 46	90.08 48	94.04 39

原ACMM1				84.42 153	93.21 68	64.27 193		93.40 95	65.39 312	79.51 131	92.50 142	58.11 180	96.69 123	65.27 278	93.96 40	92.32 196

MSLP-MVS++		86.27 56	85.91 64	87.35 45	92.01 106	68.97 67	95.04 41	92.70 121	79.04 96	81.50 104	96.50 29	58.98 171	96.78 120	83.49 109	93.93 41	96.29 35

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 51	96.53 13	93.78 70	86.89 6	89.68 33	95.78 49	65.94 74	99.10 9	92.99 26	93.91 42	96.58 21

MP-MVS-pluss		85.24 77	85.13 78	85.56 105	91.42 126	65.59 155	91.54 196	92.51 133	74.56 162	80.62 117	95.64 53	59.15 166	97.00 102	86.94 76	93.80 43	94.07 138
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVP-Stereo		77.12 237	76.23 230	79.79 291	81.72 328	66.34 138	89.29 275	90.88 212	70.56 263	62.01 339	82.88 295	49.34 279	94.13 238	65.55 275	93.80 43	78.88 394
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GG-mvs-BLEND				86.53 74	91.91 112	69.67 53	75.02 398	94.75 36		78.67 147	90.85 179	77.91 7	94.56 221	72.25 204	93.74 45	95.36 66

ZNCC-MVS		85.33 76	85.08 79	86.06 87	93.09 73	65.65 153	93.89 83	93.41 94	73.75 180	79.94 126	94.68 88	60.61 148	98.03 40	82.63 117	93.72 46	94.52 115

CSCG		86.87 40	86.26 54	88.72 17	95.05 31	70.79 29	93.83 90	95.33 18	68.48 289	77.63 155	94.35 100	73.04 28	98.45 30	84.92 93	93.71 47	96.92 14

test12				87.09 52	94.60 36	68.86 68		92.91 114		82.67 98		65.44 80	97.55 66		93.69 48	94.84 96

PAPM		85.89 65	85.46 72	87.18 49	88.20 207	72.42 15	92.41 157	92.77 119	82.11 40	80.34 122	93.07 130	68.27 53	95.02 198	78.39 158	93.59 49	94.09 136

SteuartSystems-ACMMP		86.82 45	86.90 45	86.58 71	90.42 147	66.38 136	96.09 17	93.87 68	77.73 119	84.01 83	95.66 52	63.39 114	97.94 42	87.40 69	93.55 50	95.42 60
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		87.54 28	87.84 30	86.65 67	96.07 23	66.30 139	94.84 47	93.78 70	69.35 276	88.39 39	96.34 34	67.74 59	97.66 58	90.62 46	93.44 51	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SPE-MVS-test		86.14 59	87.01 41	83.52 185	92.63 88	59.36 320	95.49 27	91.92 159	80.09 70	85.46 68	95.53 58	61.82 137	95.77 165	86.77 78	93.37 52	95.41 61

PS-MVSNAJ		88.14 18	87.61 34	89.71 7	92.06 102	76.72 1	95.75 20	93.26 98	83.86 19	89.55 34	96.06 44	53.55 236	97.89 45	91.10 41	93.31 53	94.54 113

MAR-MVS		84.18 101	83.43 103	86.44 76	96.25 21	65.93 148	94.28 62	94.27 60	74.41 164	79.16 137	95.61 54	53.99 231	98.88 22	69.62 228	93.26 54	94.50 117
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		77.18 235	74.31 257	85.80 97	91.42 126	68.36 80	71.78 403	94.72 37	49.61 400	77.12 163	45.92 429	77.41 8	93.98 250	67.62 250	93.16 55	95.05 85

ZD-MVS					96.63 9	65.50 159		93.50 88	70.74 261	85.26 71	95.19 74	64.92 88	97.29 81	87.51 66	93.01 56

APD-MVS		85.93 63	85.99 62	85.76 99	95.98 26	65.21 165	93.59 101	92.58 131	66.54 304	86.17 59	95.88 48	63.83 104	97.00 102	86.39 80	92.94 57	95.06 84
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
新几何1				84.73 138	92.32 93	64.28 192		91.46 186	59.56 364	79.77 128	92.90 134	56.95 194	96.57 127	63.40 288	92.91 58	93.34 161

DeepC-MVS	77.85 3	85.52 74	85.24 76	86.37 79	88.80 187	66.64 130	92.15 165	93.68 79	81.07 56	76.91 166	93.64 120	62.59 127	98.44 31	85.50 85	92.84 59	94.03 140
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 24	87.38 38	89.55 12	91.41 129	76.43 3	95.74 21	93.12 106	83.53 23	89.55 34	95.95 47	53.45 240	97.68 53	91.07 42	92.62 60	94.54 113

MP-MVS		85.02 82	84.97 81	85.17 122	92.60 89	64.27 193	93.24 116	92.27 139	73.13 191	79.63 130	94.43 94	61.90 134	97.17 90	85.00 91	92.56 61	94.06 139
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		83.91 106	83.38 107	85.50 106	91.89 113	65.16 167	81.75 357	92.23 140	75.32 154	80.53 119	95.21 73	56.06 207	97.16 93	84.86 94	92.55 62	94.18 130

GST-MVS		84.63 90	84.29 90	85.66 103	92.82 82	65.27 163	93.04 124	93.13 105	73.20 189	78.89 139	94.18 108	59.41 163	97.85 47	81.45 126	92.48 63	93.86 149

HFP-MVS		84.73 88	84.40 89	85.72 101	93.75 52	65.01 171	93.50 106	93.19 102	72.19 216	79.22 136	94.93 80	59.04 169	97.67 55	81.55 124	92.21 64	94.49 118

ACMMPR		84.37 93	84.06 91	85.28 117	93.56 58	64.37 188	93.50 106	93.15 104	72.19 216	78.85 144	94.86 83	56.69 198	97.45 70	81.55 124	92.20 65	94.02 141

MS-PatchMatch		77.90 226	76.50 225	82.12 231	85.99 263	69.95 42	91.75 191	92.70 121	73.97 174	62.58 336	84.44 279	41.11 335	95.78 163	63.76 287	92.17 66	80.62 379

region2R		84.36 94	84.03 92	85.36 113	93.54 60	64.31 191	93.43 111	92.95 113	72.16 219	78.86 143	94.84 84	56.97 193	97.53 67	81.38 128	92.11 67	94.24 127

CS-MVS		85.80 66	86.65 51	83.27 196	92.00 107	58.92 324	95.31 31	91.86 164	79.97 71	84.82 74	95.40 61	62.26 131	95.51 184	86.11 82	92.08 68	95.37 64

patch_mono-2		89.71 11	90.99 6	85.85 95	96.04 24	63.70 211	95.04 41	95.19 22	86.74 7	91.53 18	95.15 75	73.86 22	97.58 63	93.38 23	92.00 69	96.28 37

dcpmvs_2		87.37 34	87.55 35	86.85 58	95.04 32	68.20 88	90.36 247	90.66 219	79.37 85	81.20 108	93.67 119	74.73 16	96.55 129	90.88 44	92.00 69	95.82 48

fmvsm_s_conf0.5_n_6		87.50 30	88.72 19	83.84 173	86.89 246	60.04 308	95.05 39	92.17 149	84.80 14	92.27 6	96.37 31	64.62 92	96.54 130	94.43 15	91.86 71	94.94 91

旧先验1					91.94 108	60.74 288		91.50 184			94.36 96	65.23 83			91.84 72	94.55 111

MVSFormer		83.75 111	82.88 120	86.37 79	89.24 177	71.18 24	89.07 281	90.69 216	65.80 309	87.13 48	94.34 101	64.99 85	92.67 291	72.83 195	91.80 73	95.27 74

lupinMVS		87.74 26	87.77 31	87.63 38	89.24 177	71.18 24	96.57 12	92.90 115	82.70 33	87.13 48	95.27 68	64.99 85	95.80 162	89.34 51	91.80 73	95.93 45

EPNet		87.84 25	88.38 22	86.23 83	93.30 65	66.05 143	95.26 32	94.84 32	87.09 5	88.06 40	94.53 91	66.79 65	97.34 78	83.89 104	91.68 75	95.29 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	73.60 7	82.10 143	80.60 157	86.60 69	90.89 140	66.80 127	95.20 34	93.44 91	74.05 171	67.42 288	92.49 144	49.46 278	97.65 59	70.80 218	91.68 75	95.33 67

XVS		83.87 107	83.47 101	85.05 125	93.22 66	63.78 204	92.92 130	92.66 126	73.99 172	78.18 149	94.31 103	55.25 213	97.41 73	79.16 148	91.58 77	93.95 143

X-MVStestdata		76.86 241	74.13 262	85.05 125	93.22 66	63.78 204	92.92 130	92.66 126	73.99 172	78.18 149	10.19 444	55.25 213	97.41 73	79.16 148	91.58 77	93.95 143

SD-MVS		87.49 31	87.49 36	87.50 42	93.60 56	68.82 70	93.90 82	92.63 129	76.86 132	87.90 42	95.76 50	66.17 71	97.63 60	89.06 55	91.48 79	96.05 42
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 91	85.04 80	83.01 202	89.34 169	61.37 275	94.42 55	91.09 204	77.91 114	83.24 87	94.20 107	58.37 176	95.40 186	85.35 86	91.41 80	92.27 201

PGM-MVS		83.25 120	82.70 124	84.92 128	92.81 84	64.07 198	90.44 242	92.20 144	71.28 248	77.23 162	94.43 94	55.17 217	97.31 80	79.33 147	91.38 81	93.37 160

PVSNet_Blended		86.73 47	86.86 46	86.31 82	93.76 50	67.53 106	96.33 16	93.61 81	82.34 38	81.00 113	93.08 129	63.19 118	97.29 81	87.08 74	91.38 81	94.13 134

HPM-MVS		83.25 120	82.95 118	84.17 163	92.25 95	62.88 239	90.91 224	91.86 164	70.30 265	77.12 163	93.96 114	56.75 196	96.28 142	82.04 121	91.34 83	93.34 161
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EIA-MVS		84.84 86	84.88 82	84.69 142	91.30 131	62.36 249	93.85 85	92.04 152	79.45 81	79.33 135	94.28 105	62.42 129	96.35 140	80.05 140	91.25 84	95.38 63

MVS_111021_HR		86.19 58	85.80 66	87.37 44	93.17 70	69.79 48	93.99 77	93.76 73	79.08 93	78.88 142	93.99 113	62.25 132	98.15 38	85.93 84	91.15 85	94.15 133

test222					89.77 160	61.60 269	89.55 268	89.42 272	56.83 379	77.28 161	92.43 146	52.76 244			91.14 86	93.09 171

jason		86.40 51	86.17 57	87.11 51	86.16 260	70.54 32	95.71 24	92.19 146	82.00 41	84.58 76	94.34 101	61.86 135	95.53 183	87.76 63	90.89 87	95.27 74
jason: jason.
mPP-MVS		82.96 128	82.44 128	84.52 150	92.83 80	62.92 237	92.76 137	91.85 166	71.52 244	75.61 178	94.24 106	53.48 239	96.99 105	78.97 151	90.73 88	93.64 155

CP-MVS		83.71 112	83.40 106	84.65 144	93.14 71	63.84 202	94.59 53	92.28 138	71.03 254	77.41 158	94.92 81	55.21 216	96.19 146	81.32 129	90.70 89	93.91 146

OpenMVS	70.45 11	78.54 213	75.92 235	86.41 78	85.93 267	71.68 18	92.74 138	92.51 133	66.49 305	64.56 314	91.96 158	43.88 323	98.10 39	54.61 332	90.65 90	89.44 252

PAPM_NR		82.97 127	81.84 135	86.37 79	94.10 44	66.76 128	87.66 307	92.84 116	69.96 269	74.07 198	93.57 122	63.10 121	97.50 69	70.66 221	90.58 91	94.85 93

testdata				81.34 248	89.02 181	57.72 336		89.84 256	58.65 368	85.32 70	94.09 110	57.03 189	93.28 268	69.34 231	90.56 92	93.03 174

mvsmamba		81.55 151	80.72 152	84.03 169	91.42 126	66.93 123	83.08 346	89.13 286	78.55 105	67.50 286	87.02 248	51.79 253	90.07 347	87.48 67	90.49 93	95.10 82

fmvsm_s_conf0.5_n_3		86.88 39	87.99 29	83.58 184	87.26 231	60.74 288	93.21 119	87.94 329	84.22 16	91.70 14	97.27 3	65.91 76	95.02 198	93.95 20	90.42 94	94.99 88

fmvsm_s_conf0.5_n_7		85.24 77	86.69 49	80.91 264	84.52 293	60.10 306	93.35 114	90.35 231	83.41 25	86.54 55	96.27 37	60.50 149	90.02 348	94.84 12	90.38 95	92.61 185

Vis-MVSNet		80.92 165	79.98 168	83.74 175	88.48 193	61.80 261	93.44 110	88.26 321	73.96 175	77.73 153	91.76 163	49.94 272	94.76 208	65.84 270	90.37 96	94.65 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		84.98 84	83.45 102	89.57 11	89.94 157	75.14 6	92.07 171	92.32 137	81.87 42	75.68 175	88.27 222	60.18 152	98.60 27	80.46 137	90.27 97	94.96 89

fmvsm_s_conf0.5_n_8		87.96 21	88.93 17	85.07 124	88.43 196	61.78 262	94.73 51	91.74 170	85.87 9	91.66 15	97.50 2	64.03 100	98.33 34	96.28 3	90.08 98	95.10 82

fmvsm_l_conf0.5_n_3		87.54 28	88.29 24	85.30 115	86.92 244	62.63 244	95.02 43	90.28 238	84.95 12	90.27 26	96.86 17	65.36 81	97.52 68	94.93 11	90.03 99	95.76 50

test_fmvsm_n_1920		87.69 27	88.50 21	85.27 118	87.05 238	63.55 218	93.69 95	91.08 206	84.18 17	90.17 29	97.04 10	67.58 60	97.99 41	95.72 6	90.03 99	94.26 125

fmvsm_s_conf0.5_n_5		86.38 53	86.94 43	84.71 141	84.67 288	63.29 224	94.04 74	89.99 252	82.88 30	87.85 43	96.03 45	62.89 125	96.36 139	94.15 17	89.95 101	94.48 119

ETV-MVS		86.01 61	86.11 59	85.70 102	90.21 152	67.02 121	93.43 111	91.92 159	81.21 55	84.13 82	94.07 112	60.93 145	95.63 173	89.28 52	89.81 102	94.46 120

QAPM		79.95 185	77.39 213	87.64 34	89.63 163	71.41 20	93.30 115	93.70 78	65.34 314	67.39 290	91.75 164	47.83 295	98.96 16	57.71 321	89.81 102	92.54 189

CANet_DTU		84.09 103	83.52 97	85.81 96	90.30 150	66.82 125	91.87 182	89.01 293	85.27 10	86.09 60	93.74 117	47.71 297	96.98 106	77.90 161	89.78 104	93.65 154

API-MVS		82.28 138	80.53 159	87.54 41	96.13 22	70.59 31	93.63 99	91.04 210	65.72 311	75.45 181	92.83 138	56.11 206	98.89 21	64.10 284	89.75 105	93.15 168

test2506		83.29 119	82.92 119	84.37 156	88.39 199	63.18 230	92.01 174	91.35 189	77.66 121	78.49 148	91.42 170	64.58 94	95.09 197	73.19 191	89.23 106	94.85 93

ECVR-MVS		81.29 156	80.38 162	84.01 170	88.39 199	61.96 258	92.56 153	86.79 341	77.66 121	76.63 167	91.42 170	46.34 307	95.24 194	74.36 186	89.23 106	94.85 93

MVS_Test		84.16 102	83.20 111	87.05 54	91.56 122	69.82 46	89.99 261	92.05 151	77.77 118	82.84 93	86.57 253	63.93 103	96.09 151	74.91 182	89.18 108	95.25 77

reproduce-ours		83.51 115	83.33 109	84.06 165	92.18 99	60.49 296	90.74 233	92.04 152	64.35 319	83.24 87	95.59 56	59.05 167	97.27 85	83.61 106	89.17 109	94.41 122

our_new_method		83.51 115	83.33 109	84.06 165	92.18 99	60.49 296	90.74 233	92.04 152	64.35 319	83.24 87	95.59 56	59.05 167	97.27 85	83.61 106	89.17 109	94.41 122

PAPR		85.15 80	84.47 87	87.18 49	96.02 25	68.29 82	91.85 184	93.00 112	76.59 139	79.03 138	95.00 77	61.59 138	97.61 62	78.16 159	89.00 111	95.63 54

BP-MVS1		86.54 50	86.68 50	86.13 86	87.80 219	67.18 115	92.97 127	95.62 10	79.92 72	82.84 93	94.14 109	74.95 15	96.46 135	82.91 114	88.96 112	94.74 101

TSAR-MVS + GP.		87.96 21	88.37 23	86.70 66	93.51 62	65.32 162	95.15 36	93.84 69	78.17 110	85.93 62	94.80 85	75.80 13	98.21 36	89.38 50	88.78 113	96.59 19

SR-MVS		82.81 129	82.58 125	83.50 188	93.35 64	61.16 278	92.23 162	91.28 195	64.48 318	81.27 107	95.28 66	53.71 235	95.86 161	82.87 115	88.77 114	93.49 158

test1111		80.84 166	80.02 165	83.33 192	87.87 215	60.76 286	92.62 146	86.86 340	77.86 115	75.73 174	91.39 172	46.35 306	94.70 214	72.79 197	88.68 115	94.52 115

fmvsm_l_conf0.5_n_a		87.44 33	88.15 27	85.30 115	87.10 236	64.19 195	94.41 56	88.14 322	80.24 69	92.54 5	96.97 12	69.52 50	97.17 90	95.89 4	88.51 116	94.56 110

reproduce_model		83.15 122	82.96 116	83.73 177	92.02 103	59.74 312	90.37 246	92.08 150	63.70 326	82.86 92	95.48 59	58.62 173	97.17 90	83.06 112	88.42 117	94.26 125

HPM-MVS_fast		80.25 178	79.55 177	82.33 221	91.55 123	59.95 309	91.32 209	89.16 283	65.23 315	74.71 191	93.07 130	47.81 296	95.74 166	74.87 184	88.23 118	91.31 223

PVSNet_Blended_VisFu		83.97 105	83.50 99	85.39 110	90.02 155	66.59 133	93.77 92	91.73 171	77.43 127	77.08 165	89.81 203	63.77 106	96.97 109	79.67 143	88.21 119	92.60 186

Vis-MVSNet (Re-imp)		79.24 196	79.57 174	78.24 314	88.46 194	52.29 372	90.41 244	89.12 287	74.24 168	69.13 258	91.91 161	65.77 77	90.09 346	59.00 317	88.09 120	92.33 195

fmvsm_l_conf0.5_n		87.49 31	88.19 26	85.39 110	86.95 239	64.37 188	94.30 61	88.45 313	80.51 61	92.70 4	96.86 17	69.98 48	97.15 94	95.83 5	88.08 121	94.65 107

APD-MVS_3200maxsize		81.64 150	81.32 140	82.59 215	92.36 92	58.74 326	91.39 202	91.01 211	63.35 330	79.72 129	94.62 90	51.82 251	96.14 148	79.71 142	87.93 122	92.89 180

RRT-MVS		82.61 134	81.16 141	86.96 57	91.10 135	68.75 71	87.70 306	92.20 144	76.97 130	72.68 210	87.10 247	51.30 260	96.41 137	83.56 108	87.84 123	95.74 51

Effi-MVS+		83.82 108	82.76 122	86.99 56	89.56 165	69.40 54	91.35 207	86.12 349	72.59 203	83.22 90	92.81 139	59.60 160	96.01 159	81.76 123	87.80 124	95.56 57

casdiffmvs_mvg		85.66 70	85.18 77	87.09 52	88.22 206	69.35 59	93.74 94	91.89 162	81.47 47	80.10 124	91.45 169	64.80 90	96.35 140	87.23 72	87.69 125	95.58 56
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		80.70 168	78.95 187	85.94 91	87.77 221	67.56 104	87.91 301	92.55 132	72.17 218	67.44 287	93.09 128	50.27 268	97.04 100	71.68 212	87.64 126	93.23 165

test_fmvsmconf_n		86.58 49	87.17 39	84.82 132	85.28 276	62.55 245	94.26 63	89.78 257	83.81 21	87.78 44	96.33 35	65.33 82	96.98 106	94.40 16	87.55 127	94.95 90

PMMVS		81.98 145	82.04 132	81.78 238	89.76 161	56.17 352	91.13 220	90.69 216	77.96 112	80.09 125	93.57 122	46.33 308	94.99 201	81.41 127	87.46 128	94.17 131

casdiffmvs		85.37 75	84.87 83	86.84 59	88.25 204	69.07 63	93.04 124	91.76 169	81.27 54	80.84 115	92.07 156	64.23 98	96.06 155	84.98 92	87.43 129	95.39 62
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 68	86.08 61	84.62 147	80.83 335	62.33 250	93.84 88	88.81 301	83.50 24	87.00 51	96.01 46	63.36 115	96.93 114	94.04 19	87.29 130	94.61 109

UGNet		79.87 186	78.68 189	83.45 190	89.96 156	61.51 270	92.13 166	90.79 214	76.83 134	78.85 144	86.33 257	38.16 349	96.17 147	67.93 247	87.17 131	92.67 183
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		82.02 144	81.52 138	83.51 187	88.42 197	62.88 239	89.77 264	88.93 297	76.78 135	75.55 179	93.10 127	50.31 267	95.38 188	83.82 105	87.02 132	92.26 202

fmvsm_s_conf0.5_n_4		86.79 46	87.63 32	84.27 161	86.15 261	61.48 272	94.69 52	91.16 198	83.79 22	90.51 25	96.28 36	64.24 97	98.22 35	95.00 10	86.88 133	93.11 170

test_fmvsmvis_n_1920		83.80 109	83.48 100	84.77 136	82.51 321	63.72 209	91.37 205	83.99 372	81.42 51	77.68 154	95.74 51	58.37 176	97.58 63	93.38 23	86.87 134	93.00 176

xiu_mvs_v1_base_debu		82.16 140	81.12 143	85.26 119	86.42 253	68.72 73	92.59 150	90.44 228	73.12 192	84.20 79	94.36 96	38.04 351	95.73 167	84.12 101	86.81 135	91.33 219

xiu_mvs_v1_base		82.16 140	81.12 143	85.26 119	86.42 253	68.72 73	92.59 150	90.44 228	73.12 192	84.20 79	94.36 96	38.04 351	95.73 167	84.12 101	86.81 135	91.33 219

xiu_mvs_v1_base_debi		82.16 140	81.12 143	85.26 119	86.42 253	68.72 73	92.59 150	90.44 228	73.12 192	84.20 79	94.36 96	38.04 351	95.73 167	84.12 101	86.81 135	91.33 219

SR-MVS-dyc-post		81.06 162	80.70 153	82.15 229	92.02 103	58.56 329	90.90 225	90.45 224	62.76 337	78.89 139	94.46 92	51.26 261	95.61 175	78.77 155	86.77 138	92.28 198

RE-MVS-def			80.48 160		92.02 103	58.56 329	90.90 225	90.45 224	62.76 337	78.89 139	94.46 92	49.30 280		78.77 155	86.77 138	92.28 198

baseline		85.01 83	84.44 88	86.71 65	88.33 201	68.73 72	90.24 252	91.82 168	81.05 57	81.18 109	92.50 142	63.69 107	96.08 154	84.45 98	86.71 140	95.32 69

TESTMET0.1,1		82.41 136	81.98 134	83.72 179	88.08 208	63.74 206	92.70 141	93.77 72	79.30 86	77.61 156	87.57 238	58.19 179	94.08 241	73.91 189	86.68 141	93.33 163

IS-MVSNet		80.14 180	79.41 179	82.33 221	87.91 213	60.08 307	91.97 178	88.27 319	72.90 199	71.44 235	91.73 165	61.44 139	93.66 262	62.47 298	86.53 142	93.24 164

CPTT-MVS		79.59 189	79.16 184	80.89 265	91.54 124	59.80 311	92.10 168	88.54 312	60.42 357	72.96 206	93.28 126	48.27 289	92.80 285	78.89 154	86.50 143	90.06 239

KinetiMVS		81.43 153	80.11 163	85.38 112	86.60 249	65.47 161	92.90 133	93.54 85	75.33 153	77.31 160	90.39 187	46.81 300	96.75 121	71.65 213	86.46 144	93.93 145

BH-w/o		80.49 173	79.30 182	84.05 168	90.83 142	64.36 190	93.60 100	89.42 272	74.35 166	69.09 259	90.15 197	55.23 215	95.61 175	64.61 281	86.43 145	92.17 204

PVSNet	73.49 8	80.05 182	78.63 190	84.31 158	90.92 139	64.97 172	92.47 155	91.05 209	79.18 89	72.43 220	90.51 184	37.05 363	94.06 243	68.06 244	86.00 146	93.90 148

GDP-MVS		85.54 73	85.32 74	86.18 84	87.64 222	67.95 95	92.91 132	92.36 136	77.81 116	83.69 85	94.31 103	72.84 30	96.41 137	80.39 138	85.95 147	94.19 129

test_fmvsmconf0.01_n		83.70 113	83.52 97	84.25 162	75.26 393	61.72 266	92.17 164	87.24 337	82.36 37	84.91 73	95.41 60	55.60 211	96.83 119	92.85 27	85.87 148	94.21 128

myMVS_eth3d28		86.31 55	86.15 58	86.78 63	93.56 58	70.49 33	92.94 129	95.28 19	82.47 35	78.70 146	92.07 156	72.45 34	95.41 185	82.11 120	85.78 149	94.44 121

mvs_anonymous		81.36 155	79.99 167	85.46 107	90.39 149	68.40 79	86.88 318	90.61 221	74.41 164	70.31 247	84.67 275	63.79 105	92.32 306	73.13 192	85.70 150	95.67 52

DP-MVS Recon		82.73 130	81.65 137	85.98 89	97.31 4	67.06 118	95.15 36	91.99 156	69.08 282	76.50 170	93.89 115	54.48 225	98.20 37	70.76 219	85.66 151	92.69 182

BH-RMVSNet		79.46 194	77.65 204	84.89 129	91.68 119	65.66 152	93.55 102	88.09 324	72.93 196	73.37 203	91.12 176	46.20 310	96.12 149	56.28 327	85.61 152	92.91 178

UBG		86.83 43	86.70 48	87.20 48	93.07 74	69.81 47	93.43 111	95.56 13	81.52 46	81.50 104	92.12 154	73.58 26	96.28 142	84.37 99	85.20 153	95.51 59

diffmvs		84.28 96	83.83 93	85.61 104	87.40 228	68.02 92	90.88 227	89.24 278	80.54 60	81.64 103	92.52 141	59.83 157	94.52 224	87.32 70	85.11 154	94.29 124
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+		81.14 159	80.01 166	84.51 151	90.24 151	65.86 149	94.12 69	89.15 284	73.81 179	75.37 182	88.26 223	57.26 186	94.53 223	66.97 258	84.92 155	93.15 168

LFMVS		84.34 95	82.73 123	89.18 13	94.76 33	73.25 11	94.99 44	91.89 162	71.90 224	82.16 100	93.49 124	47.98 293	97.05 97	82.55 118	84.82 156	97.25 8

BH-untuned		78.68 209	77.08 216	83.48 189	89.84 158	63.74 206	92.70 141	88.59 310	71.57 242	66.83 297	88.65 216	51.75 254	95.39 187	59.03 316	84.77 157	91.32 222

test-LLR		80.10 181	79.56 175	81.72 240	86.93 242	61.17 276	92.70 141	91.54 181	71.51 245	75.62 176	86.94 249	53.83 232	92.38 301	72.21 205	84.76 158	91.60 213

test-mter		79.96 184	79.38 181	81.72 240	86.93 242	61.17 276	92.70 141	91.54 181	73.85 177	75.62 176	86.94 249	49.84 274	92.38 301	72.21 205	84.76 158	91.60 213

fmvsm_s_conf0.5_n_2		85.06 81	85.60 70	83.44 191	86.92 244	60.53 295	94.41 56	87.31 335	83.30 26	88.72 38	96.72 24	54.28 229	97.75 51	94.07 18	84.68 160	92.04 207

sasdasda		86.85 41	86.25 55	88.66 20	91.80 115	71.92 16	93.54 103	91.71 173	80.26 66	87.55 45	95.25 70	63.59 111	96.93 114	88.18 59	84.34 161	97.11 9

canonicalmvs		86.85 41	86.25 55	88.66 20	91.80 115	71.92 16	93.54 103	91.71 173	80.26 66	87.55 45	95.25 70	63.59 111	96.93 114	88.18 59	84.34 161	97.11 9

alignmvs		87.28 35	86.97 42	88.24 27	91.30 131	71.14 26	95.61 25	93.56 83	79.30 86	87.07 50	95.25 70	68.43 52	96.93 114	87.87 62	84.33 163	96.65 17

VNet		86.20 57	85.65 69	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 106	86.00 61	93.07 130	58.22 178	97.00 102	85.22 87	84.33 163	96.52 23

UA-Net		80.02 183	79.65 173	81.11 255	89.33 171	57.72 336	86.33 322	89.00 296	77.44 126	81.01 112	89.15 211	59.33 164	95.90 160	61.01 305	84.28 165	89.73 246

LCM-MVSNet-Re		72.93 293	71.84 292	76.18 337	88.49 192	48.02 396	80.07 375	70.17 416	73.96 175	52.25 386	80.09 341	49.98 271	88.24 363	67.35 251	84.23 166	92.28 198

ACMMP		81.49 152	80.67 154	83.93 171	91.71 118	62.90 238	92.13 166	92.22 143	71.79 231	71.68 231	93.49 124	50.32 266	96.96 110	78.47 157	84.22 167	91.93 209
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 72	85.73 68	85.17 122	91.41 129	62.44 246	92.87 134	91.31 190	79.65 78	86.99 52	95.14 76	62.90 124	96.12 149	87.13 73	84.13 168	96.96 13

fmvsm_s_conf0.1_n_2		84.40 92	84.78 85	83.27 196	85.25 277	60.41 298	94.13 68	85.69 355	83.05 28	87.99 41	96.37 31	52.75 245	97.68 53	93.75 22	84.05 169	91.71 212

StellarMVS		76.45 250	74.17 260	83.30 193	80.43 342	64.12 197	89.58 266	90.83 213	61.78 350	72.53 215	85.92 262	34.30 374	94.81 207	68.10 243	84.01 170	90.97 229

114514_t		79.17 197	77.67 203	83.68 181	95.32 29	65.53 158	92.85 135	91.60 180	63.49 328	67.92 278	90.63 182	46.65 303	95.72 171	67.01 257	83.54 171	89.79 244

testing11		86.71 48	86.44 52	87.55 40	93.54 60	71.35 21	93.65 97	95.58 11	81.36 53	80.69 116	92.21 153	72.30 36	96.46 135	85.18 89	83.43 172	94.82 99

test_vis1_n_1920		81.66 149	82.01 133	80.64 267	82.24 323	55.09 361	94.76 48	86.87 339	81.67 45	84.40 78	94.63 89	38.17 348	94.67 215	91.98 36	83.34 173	92.16 205

testing222		85.18 79	84.69 86	86.63 68	92.91 78	69.91 43	92.61 147	95.80 9	80.31 65	80.38 121	92.27 150	68.73 51	95.19 195	75.94 171	83.27 174	94.81 100

EPMVS		78.49 214	75.98 234	86.02 88	91.21 133	69.68 52	80.23 372	91.20 196	75.25 155	72.48 218	78.11 356	54.65 221	93.69 261	57.66 322	83.04 175	94.69 103

AdaColmap		78.94 202	77.00 219	84.76 137	96.34 17	65.86 149	92.66 145	87.97 328	62.18 342	70.56 241	92.37 148	43.53 324	97.35 77	64.50 282	82.86 176	91.05 228

CDS-MVSNet		81.43 153	80.74 151	83.52 185	86.26 257	64.45 182	92.09 169	90.65 220	75.83 146	73.95 200	89.81 203	63.97 102	92.91 281	71.27 214	82.82 177	93.20 167
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 280x420		77.35 233	76.95 220	78.55 309	87.07 237	62.68 243	69.71 409	82.95 379	68.80 284	71.48 234	87.27 244	66.03 73	84.00 393	76.47 168	82.81 178	88.95 254

UWE-MVS		80.81 167	81.01 148	80.20 277	89.33 171	57.05 346	91.91 180	94.71 38	75.67 147	75.01 185	89.37 208	63.13 120	91.44 330	67.19 255	82.80 179	92.12 206

ETVMVS		84.22 100	83.71 94	85.76 99	92.58 90	68.25 86	92.45 156	95.53 15	79.54 80	79.46 132	91.64 167	70.29 46	94.18 236	69.16 234	82.76 180	94.84 96

PCF-MVS	73.15 9	79.29 195	77.63 205	84.29 159	86.06 262	65.96 147	87.03 314	91.10 203	69.86 271	69.79 255	90.64 180	57.54 185	96.59 125	64.37 283	82.29 181	90.32 236
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n		86.39 52	86.91 44	84.82 132	87.36 230	63.54 219	94.74 49	90.02 250	82.52 34	90.14 30	96.92 15	62.93 123	97.84 48	95.28 9	82.26 182	93.07 173

WTY-MVS		86.32 54	85.81 65	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 32	81.91 101	94.73 86	67.93 58	97.63 60	79.55 144	82.25 183	96.54 22

testing99		86.01 61	85.47 71	87.63 38	93.62 55	71.25 23	93.47 109	95.23 21	80.42 64	80.60 118	91.95 159	71.73 41	96.50 133	80.02 141	82.22 184	95.13 80

HY-MVS	76.49 5	84.28 96	83.36 108	87.02 55	92.22 96	67.74 99	84.65 329	94.50 47	79.15 90	82.23 99	87.93 231	66.88 64	96.94 112	80.53 136	82.20 185	96.39 33

testing91		85.93 63	85.31 75	87.78 32	93.59 57	71.47 19	93.50 106	95.08 28	80.26 66	80.53 119	91.93 160	70.43 45	96.51 132	80.32 139	82.13 186	95.37 64

VDD-MVS		83.06 125	81.81 136	86.81 61	90.86 141	67.70 100	95.40 29	91.50 184	75.46 150	81.78 102	92.34 149	40.09 338	97.13 95	86.85 77	82.04 187	95.60 55

fmvsm_s_conf0.1_n		85.61 71	85.93 63	84.68 143	82.95 318	63.48 221	94.03 76	89.46 269	81.69 44	89.86 31	96.74 23	61.85 136	97.75 51	94.74 13	82.01 188	92.81 181

TAMVS		80.37 175	79.45 178	83.13 201	85.14 280	63.37 222	91.23 214	90.76 215	74.81 161	72.65 212	88.49 217	60.63 147	92.95 276	69.41 230	81.95 189	93.08 172

test_yl		84.28 96	83.16 112	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 279	81.09 110	92.88 136	57.00 191	97.44 71	81.11 132	81.76 190	96.23 38

DCV-MVSNet		84.28 96	83.16 112	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 279	81.09 110	92.88 136	57.00 191	97.44 71	81.11 132	81.76 190	96.23 38

FA-MVS(test-final)		79.12 198	77.23 215	84.81 135	90.54 145	63.98 201	81.35 363	91.71 173	71.09 253	74.85 188	82.94 294	52.85 243	97.05 97	67.97 245	81.73 192	93.41 159

thisisatest0515		83.41 117	82.49 127	86.16 85	89.46 168	68.26 84	93.54 103	94.70 39	74.31 167	75.75 173	90.92 177	72.62 32	96.52 131	69.64 226	81.50 193	93.71 152

baseline2		83.68 114	83.42 105	84.48 152	87.37 229	66.00 145	90.06 256	95.93 8	79.71 77	69.08 260	90.39 187	77.92 6	96.28 142	78.91 153	81.38 194	91.16 226

PatchmatchNet		77.46 231	74.63 250	85.96 90	89.55 166	70.35 35	79.97 377	89.55 267	72.23 215	70.94 237	76.91 368	57.03 189	92.79 286	54.27 334	81.17 195	94.74 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VDDNet		80.50 172	78.26 195	87.21 47	86.19 258	69.79 48	94.48 54	91.31 190	60.42 357	79.34 134	90.91 178	38.48 346	96.56 128	82.16 119	81.05 196	95.27 74

EPNet_dtu		78.80 206	79.26 183	77.43 322	88.06 209	49.71 388	91.96 179	91.95 158	77.67 120	76.56 169	91.28 174	58.51 174	90.20 344	56.37 326	80.95 197	92.39 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		82.71 132	82.38 129	83.73 177	89.25 174	59.58 315	92.24 161	94.89 31	77.96 112	79.86 127	92.38 147	56.70 197	97.05 97	77.26 164	80.86 198	94.55 111

FE-MVS		75.97 259	73.02 276	84.82 132	89.78 159	65.56 156	77.44 388	91.07 207	64.55 317	72.66 211	79.85 343	46.05 311	96.69 123	54.97 331	80.82 199	92.21 203

GeoE		78.90 203	77.43 209	83.29 194	88.95 183	62.02 256	92.31 158	86.23 347	70.24 266	71.34 236	89.27 209	54.43 226	94.04 246	63.31 290	80.81 200	93.81 151

UWE-MVS-28		76.83 244	77.60 206	74.51 350	84.58 292	50.34 384	88.22 295	94.60 45	74.46 163	66.66 299	88.98 214	62.53 128	85.50 385	57.55 323	80.80 201	87.69 273

LuminaMVS		78.14 220	76.66 223	82.60 214	80.82 336	64.64 176	89.33 274	90.45 224	68.25 290	74.73 190	85.51 267	41.15 334	94.14 237	78.96 152	80.69 202	89.04 253

fmvsm_s_conf0.5_n_a		85.75 67	86.09 60	84.72 139	85.73 270	63.58 216	93.79 91	89.32 275	81.42 51	90.21 28	96.91 16	62.41 130	97.67 55	94.48 14	80.56 203	92.90 179

TR-MVS		78.77 208	77.37 214	82.95 204	90.49 146	60.88 282	93.67 96	90.07 246	70.08 268	74.51 192	91.37 173	45.69 313	95.70 172	60.12 311	80.32 204	92.29 197

fmvsm_s_conf0.1_n_a		84.76 87	84.84 84	84.53 149	80.23 347	63.50 220	92.79 136	88.73 304	80.46 62	89.84 32	96.65 26	60.96 144	97.57 65	93.80 21	80.14 205	92.53 190

TAPA-MVS	70.22 12	74.94 274	73.53 270	79.17 303	90.40 148	52.07 373	89.19 279	89.61 266	62.69 339	70.07 249	92.67 140	48.89 287	94.32 228	38.26 402	79.97 206	91.12 227
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_cas_vis1_n_1920		80.45 174	80.61 156	79.97 286	78.25 374	57.01 348	94.04 74	88.33 316	79.06 95	82.81 95	93.70 118	38.65 343	91.63 322	90.82 45	79.81 207	91.27 225

cascas		78.18 218	75.77 237	85.41 109	87.14 235	69.11 62	92.96 128	91.15 201	66.71 303	70.47 242	86.07 259	37.49 357	96.48 134	70.15 224	79.80 208	90.65 232

HyFIR lowres test		81.03 163	79.56 175	85.43 108	87.81 218	68.11 90	90.18 253	90.01 251	70.65 262	72.95 207	86.06 260	63.61 110	94.50 225	75.01 180	79.75 209	93.67 153

WB-MVSnew		77.14 236	76.18 232	80.01 283	86.18 259	63.24 226	91.26 211	94.11 64	71.72 234	73.52 202	87.29 243	45.14 318	93.00 274	56.98 324	79.42 210	83.80 340

LS3D		69.17 323	66.40 328	77.50 320	91.92 110	56.12 353	85.12 326	80.37 387	46.96 407	56.50 370	87.51 239	37.25 358	93.71 260	32.52 419	79.40 211	82.68 360

EI-MVSNet-Vis-set		83.77 110	83.67 95	84.06 165	92.79 85	63.56 217	91.76 189	94.81 34	79.65 78	77.87 152	94.09 110	63.35 116	97.90 44	79.35 146	79.36 212	90.74 231

CVMVSNet		74.04 282	74.27 258	73.33 360	85.33 274	43.94 414	89.53 270	88.39 314	54.33 387	70.37 245	90.13 198	49.17 283	84.05 391	61.83 302	79.36 212	91.99 208

guyue		81.23 157	80.57 158	83.21 200	86.64 247	61.85 260	92.52 154	92.78 118	78.69 102	74.92 186	89.42 207	50.07 270	95.35 189	80.79 134	79.31 214	92.42 192

EPP-MVSNet		81.79 147	81.52 138	82.61 213	88.77 188	60.21 304	93.02 126	93.66 80	68.52 288	72.90 208	90.39 187	72.19 38	94.96 202	74.93 181	79.29 215	92.67 183

CLD-MVS		82.73 130	82.35 130	83.86 172	87.90 214	67.65 102	95.45 28	92.18 147	85.06 11	72.58 214	92.27 150	52.46 248	95.78 163	84.18 100	79.06 216	88.16 268
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP3-MVS								91.70 176							78.90 217

HQP-MVS		81.14 159	80.64 155	82.64 212	87.54 224	63.66 214	94.06 70	91.70 176	79.80 74	74.18 194	90.30 190	51.63 256	95.61 175	77.63 162	78.90 217	88.63 259

plane_prior						62.42 247	93.85 85		79.38 84						78.80 219

thres200		79.66 188	78.33 193	83.66 183	92.54 91	65.82 151	93.06 122	96.31 3	74.90 160	73.30 204	88.66 215	59.67 159	95.61 175	47.84 363	78.67 220	89.56 249

ET-MVSNet_ETH3D		84.01 104	83.15 114	86.58 71	90.78 143	70.89 28	94.74 49	94.62 43	81.44 50	58.19 359	93.64 120	73.64 25	92.35 304	82.66 116	78.66 221	96.50 27

HQP_MVS		80.34 176	79.75 172	82.12 231	86.94 240	62.42 247	93.13 120	91.31 190	78.81 99	72.53 215	89.14 212	50.66 264	95.55 181	76.74 165	78.53 222	88.39 265

plane_prior5								91.31 190					95.55 181	76.74 165	78.53 222	88.39 265

EI-MVSNet-UG-set		83.14 123	82.96 116	83.67 182	92.28 94	63.19 229	91.38 204	94.68 40	79.22 88	76.60 168	93.75 116	62.64 126	97.76 50	78.07 160	78.01 224	90.05 240

OMC-MVS		78.67 211	77.91 202	80.95 262	85.76 269	57.40 343	88.49 291	88.67 307	73.85 177	72.43 220	92.10 155	49.29 281	94.55 222	72.73 199	77.89 225	90.91 230

1112_ss		80.56 171	79.83 170	82.77 207	88.65 189	60.78 284	92.29 159	88.36 315	72.58 204	72.46 219	94.95 78	65.09 84	93.42 267	66.38 264	77.71 226	94.10 135

OPM-MVS		79.00 200	78.09 197	81.73 239	83.52 310	63.83 203	91.64 195	90.30 236	76.36 142	71.97 226	89.93 202	46.30 309	95.17 196	75.10 178	77.70 227	86.19 302
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		72.06 303	69.98 306	78.28 312	89.51 167	55.70 357	83.49 338	83.39 377	61.24 352	63.72 324	82.76 296	34.77 371	93.03 273	53.37 339	77.59 228	86.12 306

thres100view900		78.37 215	77.01 218	82.46 216	91.89 113	63.21 228	91.19 218	96.33 1	72.28 214	70.45 244	87.89 232	60.31 150	95.32 190	45.16 376	77.58 229	88.83 255

tfpn200view9		78.79 207	77.43 209	82.88 205	92.21 97	64.49 179	92.05 172	96.28 4	73.48 186	71.75 229	88.26 223	60.07 155	95.32 190	45.16 376	77.58 229	88.83 255

thres400		78.68 209	77.43 209	82.43 217	92.21 97	64.49 179	92.05 172	96.28 4	73.48 186	71.75 229	88.26 223	60.07 155	95.32 190	45.16 376	77.58 229	87.48 276

CostFormer		82.33 137	81.15 142	85.86 94	89.01 182	68.46 78	82.39 354	93.01 110	75.59 148	80.25 123	81.57 315	72.03 39	94.96 202	79.06 150	77.48 232	94.16 132

tpm2		79.80 187	77.95 201	85.34 114	88.28 202	68.26 84	81.56 360	91.42 187	70.11 267	77.59 157	80.50 333	67.40 61	94.26 234	67.34 252	77.35 233	93.51 157

Test_1112_low_res		79.56 190	78.60 191	82.43 217	88.24 205	60.39 300	92.09 169	87.99 326	72.10 220	71.84 227	87.42 240	64.62 92	93.04 272	65.80 271	77.30 234	93.85 150

tpmrst		80.57 170	79.14 185	84.84 131	90.10 154	68.28 83	81.70 358	89.72 264	77.63 123	75.96 172	79.54 347	64.94 87	92.71 288	75.43 175	77.28 235	93.55 156

Anonymous202405211		77.96 223	75.33 243	85.87 93	93.73 53	64.52 178	94.85 46	85.36 357	62.52 340	76.11 171	90.18 193	29.43 393	97.29 81	68.51 241	77.24 236	95.81 49

GA-MVS		78.33 217	76.23 230	84.65 144	83.65 308	66.30 139	91.44 197	90.14 244	76.01 144	70.32 246	84.02 283	42.50 328	94.72 211	70.98 216	77.00 237	92.94 177

AstraMVS		80.66 169	79.79 171	83.28 195	85.07 283	61.64 268	92.19 163	90.58 222	79.40 83	74.77 189	90.18 193	45.93 312	95.61 175	83.04 113	76.96 238	92.60 186

thisisatest0530		81.15 158	80.07 164	84.39 155	88.26 203	65.63 154	91.40 200	94.62 43	71.27 249	70.93 238	89.18 210	72.47 33	96.04 156	65.62 273	76.89 239	91.49 215

thres600view7		78.00 221	76.66 223	82.03 236	91.93 109	63.69 212	91.30 210	96.33 1	72.43 209	70.46 243	87.89 232	60.31 150	94.92 205	42.64 388	76.64 240	87.48 276

PLC	68.80 14	75.23 270	73.68 269	79.86 289	92.93 77	58.68 327	90.64 238	88.30 317	60.90 354	64.43 318	90.53 183	42.38 329	94.57 218	56.52 325	76.54 241	86.33 298
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MIMVSNet		71.64 305	68.44 318	81.23 250	81.97 327	64.44 183	73.05 400	88.80 302	69.67 273	64.59 313	74.79 381	32.79 378	87.82 367	53.99 335	76.35 242	91.42 217

test_fmvs1		74.07 281	73.69 268	75.22 342	78.91 365	47.34 401	89.06 283	74.69 401	63.68 327	79.41 133	91.59 168	24.36 404	87.77 369	85.22 87	76.26 243	90.55 235

MVS-HIRNet		60.25 376	55.55 383	74.35 352	84.37 298	56.57 351	71.64 404	74.11 402	34.44 426	45.54 411	42.24 434	31.11 388	89.81 349	40.36 396	76.10 244	76.67 406

CNLPA		74.31 279	72.30 287	80.32 272	91.49 125	61.66 267	90.85 228	80.72 385	56.67 380	63.85 323	90.64 180	46.75 302	90.84 333	53.79 336	75.99 245	88.47 264

ab-mvs		80.18 179	78.31 194	85.80 97	88.44 195	65.49 160	83.00 349	92.67 125	71.82 230	77.36 159	85.01 271	54.50 222	96.59 125	76.35 170	75.63 246	95.32 69

test_fmvs1_n		72.69 300	71.92 291	74.99 346	71.15 406	47.08 403	87.34 312	75.67 396	63.48 329	78.08 151	91.17 175	20.16 418	87.87 366	84.65 96	75.57 247	90.01 241

testing3-2		83.11 124	83.15 114	82.98 203	91.92 110	64.01 200	94.39 59	95.37 16	78.32 107	75.53 180	90.06 201	73.18 27	93.18 270	74.34 187	75.27 248	91.77 211

FIs		79.47 193	79.41 179	79.67 294	85.95 264	59.40 317	91.68 193	93.94 67	78.06 111	68.96 265	88.28 221	66.61 67	91.77 318	66.20 267	74.99 249	87.82 271

SDMVSNet		80.26 177	78.88 188	84.40 154	89.25 174	67.63 103	85.35 325	93.02 109	76.77 136	70.84 239	87.12 245	47.95 294	96.09 151	85.04 90	74.55 250	89.48 250

sd_testset		77.08 238	75.37 241	82.20 227	89.25 174	62.11 255	82.06 355	89.09 289	76.77 136	70.84 239	87.12 245	41.43 333	95.01 200	67.23 254	74.55 250	89.48 250

CMPMVS	48.56 21	66.77 344	64.41 346	73.84 357	70.65 409	50.31 385	77.79 387	85.73 354	45.54 412	44.76 413	82.14 305	35.40 369	90.14 345	63.18 292	74.54 252	81.07 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dmvs_re		76.93 240	75.36 242	81.61 242	87.78 220	60.71 290	80.00 376	87.99 326	79.42 82	69.02 262	89.47 206	46.77 301	94.32 228	63.38 289	74.45 253	89.81 243

test_vis1_n		71.63 306	70.73 302	74.31 354	69.63 412	47.29 402	86.91 316	72.11 409	63.21 333	75.18 183	90.17 195	20.40 416	85.76 381	84.59 97	74.42 254	89.87 242

XVG-OURS		74.25 280	72.46 286	79.63 295	78.45 372	57.59 340	80.33 370	87.39 332	63.86 324	68.76 269	89.62 205	40.50 337	91.72 319	69.00 236	74.25 255	89.58 247

tpm cat1		75.30 269	72.21 288	84.58 148	88.52 191	67.77 98	78.16 386	88.02 325	61.88 348	68.45 274	76.37 372	60.65 146	94.03 248	53.77 337	74.11 256	91.93 209

XVG-OURS-SEG-HR		74.70 277	73.08 275	79.57 297	78.25 374	57.33 344	80.49 368	87.32 333	63.22 332	68.76 269	90.12 200	44.89 320	91.59 323	70.55 222	74.09 257	89.79 244

FC-MVSNet-test		77.99 222	78.08 198	77.70 317	84.89 286	55.51 358	90.27 250	93.75 76	76.87 131	66.80 298	87.59 237	65.71 78	90.23 343	62.89 295	73.94 258	87.37 279

PVSNet_BlendedMVS		83.38 118	83.43 103	83.22 198	93.76 50	67.53 106	94.06 70	93.61 81	79.13 91	81.00 113	85.14 270	63.19 118	97.29 81	87.08 74	73.91 259	84.83 331

tttt0517		79.50 191	78.53 192	82.41 220	87.22 233	61.43 274	89.75 265	94.76 35	69.29 277	67.91 279	88.06 230	72.92 29	95.63 173	62.91 294	73.90 260	90.16 238

MDTV_nov1_ep13			72.61 283		89.06 180	68.48 77	80.33 370	90.11 245	71.84 229	71.81 228	75.92 376	53.01 242	93.92 253	48.04 360	73.38 261

SCA		75.82 262	72.76 279	85.01 127	86.63 248	70.08 38	81.06 365	89.19 281	71.60 241	70.01 250	77.09 366	45.53 314	90.25 339	60.43 308	73.27 262	94.68 104

CR-MVSNet		73.79 286	70.82 301	82.70 210	83.15 314	67.96 93	70.25 406	84.00 370	73.67 184	69.97 252	72.41 389	57.82 182	89.48 352	52.99 340	73.13 263	90.64 233

RPMNet		70.42 313	65.68 334	84.63 146	83.15 314	67.96 93	70.25 406	90.45 224	46.83 409	69.97 252	65.10 412	56.48 203	95.30 193	35.79 407	73.13 263	90.64 233

Fast-Effi-MVS+-dtu		75.04 272	73.37 272	80.07 280	80.86 334	59.52 316	91.20 217	85.38 356	71.90 224	65.20 308	84.84 273	41.46 332	92.97 275	66.50 263	72.96 265	87.73 272

LPG-MVS_test		75.82 262	74.58 252	79.56 298	84.31 299	59.37 318	90.44 242	89.73 262	69.49 274	64.86 310	88.42 218	38.65 343	94.30 230	72.56 201	72.76 266	85.01 329

LGP-MVS_train				79.56 298	84.31 299	59.37 318		89.73 262	69.49 274	64.86 310	88.42 218	38.65 343	94.30 230	72.56 201	72.76 266	85.01 329

EG-PatchMatch MVS		68.55 329	65.41 337	77.96 316	78.69 368	62.93 235	89.86 263	89.17 282	60.55 356	50.27 395	77.73 360	22.60 412	94.06 243	47.18 367	72.65 268	76.88 405

EI-MVSNet		78.97 201	78.22 196	81.25 249	85.33 274	62.73 242	89.53 270	93.21 99	72.39 211	72.14 223	90.13 198	60.99 142	94.72 211	67.73 249	72.49 269	86.29 299

MVSTER		82.47 135	82.05 131	83.74 175	92.68 87	69.01 65	91.90 181	93.21 99	79.83 73	72.14 223	85.71 265	74.72 17	94.72 211	75.72 173	72.49 269	87.50 275

Anonymous20240529		76.84 243	74.15 261	84.88 130	91.02 136	64.95 173	93.84 88	91.09 204	53.57 388	73.00 205	87.42 240	35.91 367	97.32 79	69.14 235	72.41 271	92.36 194

D2MVS		73.80 285	72.02 290	79.15 305	79.15 360	62.97 233	88.58 290	90.07 246	72.94 195	59.22 352	78.30 353	42.31 330	92.70 290	65.59 274	72.00 272	81.79 368

PS-MVSNAJss		77.26 234	76.31 229	80.13 279	80.64 340	59.16 322	90.63 240	91.06 208	72.80 200	68.58 272	84.57 277	53.55 236	93.96 251	72.97 193	71.96 273	87.27 283

Effi-MVS+-dtu		76.14 252	75.28 244	78.72 308	83.22 313	55.17 360	89.87 262	87.78 330	75.42 151	67.98 277	81.43 317	45.08 319	92.52 297	75.08 179	71.63 274	88.48 263

ACMMP++_ref															71.63 274

ACMM	69.62 13	74.34 278	72.73 281	79.17 303	84.25 301	57.87 334	90.36 247	89.93 253	63.17 334	65.64 305	86.04 261	37.79 355	94.10 239	65.89 269	71.52 276	85.55 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	71.68 10	75.58 267	74.23 259	79.62 296	84.97 285	59.64 313	90.80 230	89.07 291	70.39 264	62.95 332	87.30 242	38.28 347	93.87 256	72.89 194	71.45 277	85.36 325
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dp		75.01 273	72.09 289	83.76 174	89.28 173	66.22 142	79.96 378	89.75 259	71.16 250	67.80 283	77.19 365	51.81 252	92.54 296	50.39 347	71.44 278	92.51 191

tpm		78.58 212	77.03 217	83.22 198	85.94 266	64.56 177	83.21 345	91.14 202	78.31 108	73.67 201	79.68 345	64.01 101	92.09 312	66.07 268	71.26 279	93.03 174

DP-MVS		69.90 318	66.48 326	80.14 278	95.36 28	62.93 235	89.56 267	76.11 394	50.27 399	57.69 366	85.23 269	39.68 339	95.73 167	33.35 412	71.05 280	81.78 369

UniMVSNet_ETH3D		72.74 297	70.53 304	79.36 300	78.62 370	56.64 350	85.01 327	89.20 280	63.77 325	64.84 312	84.44 279	34.05 375	91.86 316	63.94 285	70.89 281	89.57 248

jajsoiax		73.05 291	71.51 296	77.67 318	77.46 383	54.83 362	88.81 286	90.04 249	69.13 281	62.85 334	83.51 288	31.16 387	92.75 287	70.83 217	69.80 282	85.43 324

ACMMP++															69.72 283

mvs_tets		72.71 298	71.11 297	77.52 319	77.41 384	54.52 364	88.45 292	89.76 258	68.76 286	62.70 335	83.26 292	29.49 392	92.71 288	70.51 223	69.62 284	85.34 326

tpmvs		72.88 295	69.76 311	82.22 226	90.98 137	67.05 119	78.22 385	88.30 317	63.10 335	64.35 319	74.98 379	55.09 218	94.27 232	43.25 382	69.57 285	85.34 326

GBi-Net		75.65 264	73.83 266	81.10 256	88.85 184	65.11 168	90.01 258	90.32 232	70.84 257	67.04 293	80.25 338	48.03 290	91.54 325	59.80 313	69.34 286	86.64 292

test1		75.65 264	73.83 266	81.10 256	88.85 184	65.11 168	90.01 258	90.32 232	70.84 257	67.04 293	80.25 338	48.03 290	91.54 325	59.80 313	69.34 286	86.64 292

FMVSNet3		77.73 227	76.04 233	82.80 206	91.20 134	68.99 66	91.87 182	91.99 156	73.35 188	67.04 293	83.19 293	56.62 199	92.14 309	59.80 313	69.34 286	87.28 282

Syy-MVS		69.65 320	69.52 312	70.03 380	87.87 215	43.21 416	88.07 297	89.01 293	72.91 197	63.11 329	88.10 227	45.28 317	85.54 382	22.07 430	69.23 289	81.32 371

myMVS_eth3d		72.58 302	72.74 280	72.10 372	87.87 215	49.45 390	88.07 297	89.01 293	72.91 197	63.11 329	88.10 227	63.63 108	85.54 382	32.73 417	69.23 289	81.32 371

MSDG		69.54 321	65.73 333	80.96 261	85.11 282	63.71 210	84.19 333	83.28 378	56.95 377	54.50 375	84.03 282	31.50 384	96.03 157	42.87 386	69.13 291	83.14 352

JIA-IIPM		66.06 347	62.45 357	76.88 332	81.42 332	54.45 365	57.49 430	88.67 307	49.36 401	63.86 322	46.86 428	56.06 207	90.25 339	49.53 352	68.83 292	85.95 310

OpenMVS_ROB	61.12 18	66.39 345	62.92 354	76.80 333	76.51 387	57.77 335	89.22 277	83.41 376	55.48 384	53.86 379	77.84 358	26.28 402	93.95 252	34.90 409	68.76 293	78.68 397

FMVSNet2		76.07 253	74.01 264	82.26 225	88.85 184	67.66 101	91.33 208	91.61 179	70.84 257	65.98 302	82.25 303	48.03 290	92.00 314	58.46 318	68.73 294	87.10 285

test_djsdf		73.76 287	72.56 284	77.39 323	77.00 386	53.93 366	89.07 281	90.69 216	65.80 309	63.92 321	82.03 306	43.14 327	92.67 291	72.83 195	68.53 295	85.57 320

F-COLMAP		70.66 310	68.44 318	77.32 324	86.37 256	55.91 355	88.00 299	86.32 344	56.94 378	57.28 368	88.07 229	33.58 376	92.49 298	51.02 344	68.37 296	83.55 342

XVG-ACMP-BASELINE		68.04 335	65.53 336	75.56 339	74.06 398	52.37 371	78.43 382	85.88 351	62.03 345	58.91 356	81.21 325	20.38 417	91.15 332	60.69 307	68.18 297	83.16 351

WBMVS		81.67 148	80.98 149	83.72 179	93.07 74	69.40 54	94.33 60	93.05 108	76.84 133	72.05 225	84.14 281	74.49 19	93.88 255	72.76 198	68.09 298	87.88 270

LTVRE_ROB	59.60 19	66.27 346	63.54 350	74.45 351	84.00 304	51.55 376	67.08 418	83.53 374	58.78 367	54.94 374	80.31 336	34.54 372	93.23 269	40.64 395	68.03 299	78.58 398
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		77.94 224	76.44 226	82.43 217	82.60 320	64.44 183	92.01 174	91.83 167	73.59 185	70.00 251	85.82 263	54.43 226	94.76 208	69.63 227	68.02 300	88.10 269

ADS-MVSNet2		66.90 343	63.44 351	77.26 326	88.06 209	60.70 291	68.01 414	75.56 398	57.57 371	64.48 315	69.87 399	38.68 341	84.10 390	40.87 393	67.89 301	86.97 286

ADS-MVSNet		68.54 330	64.38 347	81.03 260	88.06 209	66.90 124	68.01 414	84.02 369	57.57 371	64.48 315	69.87 399	38.68 341	89.21 354	40.87 393	67.89 301	86.97 286

test0.0.03 1		72.76 296	72.71 282	72.88 364	80.25 346	47.99 397	91.22 215	89.45 270	71.51 245	62.51 337	87.66 235	53.83 232	85.06 387	50.16 349	67.84 303	85.58 319

anonymousdsp		71.14 309	69.37 313	76.45 334	72.95 401	54.71 363	84.19 333	88.88 298	61.92 347	62.15 338	79.77 344	38.14 350	91.44 330	68.90 238	67.45 304	83.21 350

tt0805		73.07 290	70.73 302	80.07 280	78.37 373	57.05 346	87.78 304	92.18 147	61.23 353	67.04 293	86.49 254	31.35 386	94.58 216	65.06 279	67.12 305	88.57 261

VPA-MVSNet		79.03 199	78.00 199	82.11 234	85.95 264	64.48 181	93.22 118	94.66 41	75.05 158	74.04 199	84.95 272	52.17 250	93.52 264	74.90 183	67.04 306	88.32 267

nrg030		80.93 164	79.86 169	84.13 164	83.69 307	68.83 69	93.23 117	91.20 196	75.55 149	75.06 184	88.22 226	63.04 122	94.74 210	81.88 122	66.88 307	88.82 257

FMVSNet1		72.71 298	69.91 309	81.10 256	83.60 309	65.11 168	90.01 258	90.32 232	63.92 323	63.56 325	80.25 338	36.35 366	91.54 325	54.46 333	66.75 308	86.64 292

PatchT		69.11 324	65.37 338	80.32 272	82.07 326	63.68 213	67.96 416	87.62 331	50.86 397	69.37 256	65.18 411	57.09 188	88.53 359	41.59 391	66.60 309	88.74 258

IB-MVS	77.80 4	82.18 139	80.46 161	87.35 45	89.14 179	70.28 36	95.59 26	95.17 24	78.85 97	70.19 248	85.82 263	70.66 44	97.67 55	72.19 207	66.52 310	94.09 136
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
test_fmvs2		65.78 350	64.84 339	68.60 386	66.54 418	41.71 418	83.27 342	69.81 417	54.38 386	67.91 279	84.54 278	15.35 423	81.22 410	75.65 174	66.16 311	82.88 353

pmmvs5		73.35 288	71.52 295	78.86 307	78.64 369	60.61 294	91.08 221	86.90 338	67.69 293	63.32 327	83.64 286	44.33 322	90.53 336	62.04 300	66.02 312	85.46 323

SSC-MVS3.2		74.92 275	73.32 273	79.74 293	86.53 251	60.31 301	89.03 284	92.70 121	78.61 104	68.98 264	83.34 291	41.93 331	92.23 308	52.77 341	65.97 313	86.69 291

dmvs_testset		65.55 351	66.45 327	62.86 398	79.87 350	22.35 444	76.55 390	71.74 411	77.42 128	55.85 371	87.77 234	51.39 258	80.69 411	31.51 423	65.92 314	85.55 321

MonoMVSNet		76.99 239	75.08 246	82.73 208	83.32 312	63.24 226	86.47 321	86.37 343	79.08 93	66.31 301	79.30 349	49.80 275	91.72 319	79.37 145	65.70 315	93.23 165

pmmvs4		73.92 284	71.81 293	80.25 276	79.17 359	65.24 164	87.43 310	87.26 336	67.64 296	63.46 326	83.91 285	48.96 286	91.53 328	62.94 293	65.49 316	83.96 337

cl22		77.94 224	76.78 221	81.42 246	87.57 223	64.93 174	90.67 236	88.86 300	72.45 208	67.63 285	82.68 298	64.07 99	92.91 281	71.79 208	65.30 317	86.44 297

miper_ehance_all_eth		77.60 229	76.44 226	81.09 259	85.70 271	64.41 186	90.65 237	88.64 309	72.31 212	67.37 291	82.52 299	64.77 91	92.64 294	70.67 220	65.30 317	86.24 301

miper_enhance_ethall		78.86 204	77.97 200	81.54 244	88.00 212	65.17 166	91.41 198	89.15 284	75.19 156	68.79 268	83.98 284	67.17 62	92.82 283	72.73 199	65.30 317	86.62 296

VortexMVS		77.62 228	76.44 226	81.13 253	88.58 190	63.73 208	91.24 213	91.30 194	77.81 116	65.76 303	81.97 307	49.69 276	93.72 259	76.40 169	65.26 320	85.94 312

v1144		76.73 247	74.88 247	82.27 223	80.23 347	66.60 132	91.68 193	90.21 243	73.69 182	69.06 261	81.89 308	52.73 246	94.40 227	69.21 233	65.23 321	85.80 315

DSMNet-mixed		56.78 382	54.44 386	63.79 396	63.21 423	29.44 439	64.43 421	64.10 426	42.12 423	51.32 391	71.60 394	31.76 383	75.04 418	36.23 404	65.20 322	86.87 289

v1192		75.98 258	73.92 265	82.15 229	79.73 351	66.24 141	91.22 215	89.75 259	72.67 202	68.49 273	81.42 318	49.86 273	94.27 232	67.08 256	65.02 323	85.95 310

v2v482		77.42 232	75.65 239	82.73 208	80.38 343	67.13 117	91.85 184	90.23 241	75.09 157	69.37 256	83.39 290	53.79 234	94.44 226	71.77 209	65.00 324	86.63 295

V42		76.46 249	74.55 253	82.19 228	79.14 361	67.82 97	90.26 251	89.42 272	73.75 180	68.63 271	81.89 308	51.31 259	94.09 240	71.69 211	64.84 325	84.66 332

ACMH	63.93 17	68.62 328	64.81 340	80.03 282	85.22 278	63.25 225	87.72 305	84.66 363	60.83 355	51.57 390	79.43 348	27.29 399	94.96 202	41.76 389	64.84 325	81.88 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		81.84 146	81.03 147	84.28 160	91.60 120	66.62 131	91.08 221	91.66 178	81.87 42	74.86 187	91.67 166	69.98 48	94.92 205	71.76 210	64.75 327	91.29 224

v1240		75.21 271	72.98 277	81.88 237	79.20 358	66.00 145	90.75 232	89.11 288	71.63 240	67.41 289	81.22 323	47.36 298	93.87 256	65.46 276	64.72 328	85.77 316

IterMVS-LS		76.49 248	75.18 245	80.43 271	84.49 295	62.74 241	90.64 238	88.80 302	72.40 210	65.16 309	81.72 311	60.98 143	92.27 307	67.74 248	64.65 329	86.29 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		75.63 266	73.49 271	82.06 235	79.38 356	66.35 137	91.07 223	89.48 268	71.98 221	67.99 276	81.22 323	49.16 284	93.90 254	66.56 260	64.56 330	85.92 313

mamv4		65.18 353	67.43 323	58.44 402	77.88 380	49.36 393	69.40 410	70.99 415	48.31 405	57.78 365	85.53 266	59.01 170	51.88 440	73.67 190	64.32 331	74.07 410

v144192		76.05 256	74.03 263	82.12 231	79.50 355	66.55 134	91.39 202	89.71 265	72.30 213	68.17 275	81.33 320	51.75 254	94.03 248	67.94 246	64.19 332	85.77 316

Anonymous20231211		73.08 289	70.39 305	81.13 253	90.62 144	63.33 223	91.40 200	90.06 248	51.84 393	64.46 317	80.67 331	36.49 365	94.07 242	63.83 286	64.17 333	85.98 309

testing3		70.38 314	70.83 299	69.03 384	85.82 268	43.93 415	90.72 235	90.56 223	68.06 291	60.24 346	86.82 251	64.83 89	84.12 389	26.33 425	64.10 334	79.04 392

Patchmatch-test		65.86 348	60.94 363	80.62 269	83.75 306	58.83 325	58.91 429	75.26 400	44.50 415	50.95 394	77.09 366	58.81 172	87.90 365	35.13 408	64.03 335	95.12 81

USDC		67.43 342	64.51 344	76.19 336	77.94 378	55.29 359	78.38 383	85.00 360	73.17 190	48.36 403	80.37 335	21.23 414	92.48 299	52.15 342	64.02 336	80.81 377

VPNet		78.82 205	77.53 208	82.70 210	84.52 293	66.44 135	93.93 80	92.23 140	80.46 62	72.60 213	88.38 220	49.18 282	93.13 271	72.47 203	63.97 337	88.55 262

Anonymous20231206		67.53 340	65.78 332	72.79 365	74.95 394	47.59 399	88.23 294	87.32 333	61.75 351	58.07 361	77.29 363	37.79 355	87.29 375	42.91 384	63.71 338	83.48 345

WR-MVS		76.76 246	75.74 238	79.82 290	84.60 290	62.27 253	92.60 148	92.51 133	76.06 143	67.87 282	85.34 268	56.76 195	90.24 342	62.20 299	63.69 339	86.94 288

h-mvs33		83.01 126	82.56 126	84.35 157	89.34 169	62.02 256	92.72 139	93.76 73	81.45 48	82.73 96	92.25 152	60.11 153	97.13 95	87.69 64	62.96 340	93.91 146

c3_l		76.83 244	75.47 240	80.93 263	85.02 284	64.18 196	90.39 245	88.11 323	71.66 235	66.65 300	81.64 313	63.58 113	92.56 295	69.31 232	62.86 341	86.04 307

test_vis1_rt		59.09 380	57.31 378	64.43 395	68.44 415	46.02 409	83.05 348	48.63 439	51.96 392	49.57 398	63.86 415	16.30 421	80.20 412	71.21 215	62.79 342	67.07 422

mvsany_test1		68.77 327	68.56 316	69.39 382	73.57 399	45.88 410	80.93 366	60.88 430	59.65 363	71.56 232	90.26 192	43.22 326	75.05 417	74.26 188	62.70 343	87.25 284

UniMVSNet_NR-MVSNet		78.15 219	77.55 207	79.98 284	84.46 296	60.26 302	92.25 160	93.20 101	77.50 125	68.88 266	86.61 252	66.10 72	92.13 310	66.38 264	62.55 344	87.54 274

DU-MVS		76.86 241	75.84 236	79.91 287	82.96 316	60.26 302	91.26 211	91.54 181	76.46 141	68.88 266	86.35 255	56.16 204	92.13 310	66.38 264	62.55 344	87.35 280

UniMVSNet (Re)		77.58 230	76.78 221	79.98 284	84.11 302	60.80 283	91.76 189	93.17 103	76.56 140	69.93 254	84.78 274	63.32 117	92.36 303	64.89 280	62.51 346	86.78 290

v8		75.35 268	73.26 274	81.61 242	80.67 339	66.82 125	89.54 269	89.27 277	71.65 236	63.30 328	80.30 337	54.99 219	94.06 243	67.33 253	62.33 347	83.94 338

cl____		76.07 253	74.67 248	80.28 274	85.15 279	61.76 264	90.12 254	88.73 304	71.16 250	65.43 306	81.57 315	61.15 140	92.95 276	66.54 261	62.17 348	86.13 305

v10		74.77 276	72.54 285	81.46 245	80.33 345	66.71 129	89.15 280	89.08 290	70.94 255	63.08 331	79.86 342	52.52 247	94.04 246	65.70 272	62.17 348	83.64 341

DIV-MVS_self_test		76.07 253	74.67 248	80.28 274	85.14 280	61.75 265	90.12 254	88.73 304	71.16 250	65.42 307	81.60 314	61.15 140	92.94 280	66.54 261	62.16 350	86.14 303

IterMVS-SCA-FT		71.55 307	69.97 307	76.32 335	81.48 330	60.67 292	87.64 308	85.99 350	66.17 307	59.50 350	78.88 350	45.53 314	83.65 395	62.58 297	61.93 351	84.63 335

IterMVS		72.65 301	70.83 299	78.09 315	82.17 324	62.96 234	87.64 308	86.28 345	71.56 243	60.44 345	78.85 351	45.42 316	86.66 377	63.30 291	61.83 352	84.65 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 335	65.66 335	75.18 344	84.43 297	57.89 333	83.54 337	86.26 346	61.83 349	53.64 381	73.30 384	37.15 361	85.08 386	48.99 355	61.77 353	82.56 362

v7n		71.31 308	68.65 315	79.28 301	76.40 388	60.77 285	86.71 319	89.45 270	64.17 322	58.77 357	78.24 354	44.59 321	93.54 263	57.76 320	61.75 354	83.52 344

v148		76.19 251	74.47 255	81.36 247	80.05 349	64.44 183	91.75 191	90.23 241	73.68 183	67.13 292	80.84 328	55.92 209	93.86 258	68.95 237	61.73 355	85.76 318

tfpnnormal		70.10 315	67.36 324	78.32 311	83.45 311	60.97 281	88.85 285	92.77 119	64.85 316	60.83 343	78.53 352	43.52 325	93.48 265	31.73 420	61.70 356	80.52 380

ACMH+	65.35 16	67.65 338	64.55 343	76.96 331	84.59 291	57.10 345	88.08 296	80.79 384	58.59 369	53.00 383	81.09 327	26.63 401	92.95 276	46.51 369	61.69 357	80.82 376

ITE_SJBPF				70.43 379	74.44 396	47.06 404		77.32 392	60.16 360	54.04 378	83.53 287	23.30 409	84.01 392	43.07 383	61.58 358	80.21 385

NR-MVSNet		76.05 256	74.59 251	80.44 270	82.96 316	62.18 254	90.83 229	91.73 171	77.12 129	60.96 342	86.35 255	59.28 165	91.80 317	60.74 306	61.34 359	87.35 280

test_0402		64.54 356	61.09 362	74.92 347	84.10 303	60.75 287	87.95 300	79.71 389	52.03 391	52.41 385	77.20 364	32.21 382	91.64 321	23.14 428	61.03 360	72.36 416

Baseline_NR-MVSNet		73.99 283	72.83 278	77.48 321	80.78 337	59.29 321	91.79 186	84.55 365	68.85 283	68.99 263	80.70 329	56.16 204	92.04 313	62.67 296	60.98 361	81.11 373

TranMVSNet+NR-MVSNet		75.86 261	74.52 254	79.89 288	82.44 322	60.64 293	91.37 205	91.37 188	76.63 138	67.65 284	86.21 258	52.37 249	91.55 324	61.84 301	60.81 362	87.48 276

testgi		64.48 357	62.87 355	69.31 383	71.24 404	40.62 421	85.49 324	79.92 388	65.36 313	54.18 377	83.49 289	23.74 407	84.55 388	41.60 390	60.79 363	82.77 355

eth_miper_zixun_eth		75.96 260	74.40 256	80.66 266	84.66 289	63.02 232	89.28 276	88.27 319	71.88 226	65.73 304	81.65 312	59.45 161	92.81 284	68.13 242	60.53 364	86.14 303

COLMAP_ROB	57.96 20	62.98 365	59.65 367	72.98 363	81.44 331	53.00 370	83.75 336	75.53 399	48.34 404	48.81 402	81.40 319	24.14 405	90.30 338	32.95 414	60.52 365	75.65 408
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		78.37 215	77.43 209	81.17 251	86.60 249	57.45 342	89.46 272	91.16 198	74.11 170	74.40 193	90.49 185	55.52 212	94.57 218	74.73 185	60.43 366	91.48 216

hse-mvs2		81.12 161	81.11 146	81.16 252	86.52 252	57.48 341	89.40 273	91.16 198	81.45 48	82.73 96	90.49 185	60.11 153	94.58 216	87.69 64	60.41 367	91.41 218

RPSCF		64.24 358	61.98 360	71.01 378	76.10 390	45.00 411	75.83 395	75.94 395	46.94 408	58.96 355	84.59 276	31.40 385	82.00 407	47.76 365	60.33 368	86.04 307

miper_lstm_enhance		73.05 291	71.73 294	77.03 328	83.80 305	58.32 331	81.76 356	88.88 298	69.80 272	61.01 341	78.23 355	57.19 187	87.51 373	65.34 277	59.53 369	85.27 328

CP-MVSNet		70.50 312	69.91 309	72.26 369	80.71 338	51.00 381	87.23 313	90.30 236	67.84 292	59.64 349	82.69 297	50.23 269	82.30 405	51.28 343	59.28 370	83.46 346

PS-CasMVS		69.86 319	69.13 314	72.07 373	80.35 344	50.57 383	87.02 315	89.75 259	67.27 298	59.19 353	82.28 302	46.58 304	82.24 406	50.69 346	59.02 371	83.39 348

pm-mvs1		72.89 294	71.09 298	78.26 313	79.10 362	57.62 338	90.80 230	89.30 276	67.66 294	62.91 333	81.78 310	49.11 285	92.95 276	60.29 310	58.89 372	84.22 336

Anonymous20240521		62.09 366	59.08 370	71.10 377	67.19 416	48.72 395	83.91 335	85.23 358	50.38 398	47.84 404	71.22 398	20.74 415	85.51 384	46.47 370	58.75 373	79.06 391

WR-MVS_H		70.59 311	69.94 308	72.53 366	81.03 333	51.43 377	87.35 311	92.03 155	67.38 297	60.23 347	80.70 329	55.84 210	83.45 397	46.33 371	58.58 374	82.72 357

reproduce_monomvs		79.49 192	79.11 186	80.64 267	92.91 78	61.47 273	91.17 219	93.28 97	83.09 27	64.04 320	82.38 301	66.19 70	94.57 218	81.19 131	57.71 375	85.88 314

PEN-MVS		69.46 322	68.56 316	72.17 371	79.27 357	49.71 388	86.90 317	89.24 278	67.24 301	59.08 354	82.51 300	47.23 299	83.54 396	48.42 358	57.12 376	83.25 349

EU-MVSNet		64.01 359	63.01 353	67.02 392	74.40 397	38.86 427	83.27 342	86.19 348	45.11 413	54.27 376	81.15 326	36.91 364	80.01 413	48.79 357	57.02 377	82.19 366

AllTest		61.66 368	58.06 372	72.46 367	79.57 352	51.42 378	80.17 373	68.61 419	51.25 395	45.88 407	81.23 321	19.86 419	86.58 378	38.98 399	57.01 378	79.39 388

TestCases				72.46 367	79.57 352	51.42 378		68.61 419	51.25 395	45.88 407	81.23 321	19.86 419	86.58 378	38.98 399	57.01 378	79.39 388

Patchmtry		67.53 340	63.93 348	78.34 310	82.12 325	64.38 187	68.72 411	84.00 370	48.23 406	59.24 351	72.41 389	57.82 182	89.27 353	46.10 372	56.68 380	81.36 370

our_test_3		68.29 333	64.69 342	79.11 306	78.92 363	64.85 175	88.40 293	85.06 359	60.32 359	52.68 384	76.12 374	40.81 336	89.80 351	44.25 381	55.65 381	82.67 361

FPMVS		45.64 394	43.10 398	53.23 411	51.42 436	36.46 429	64.97 420	71.91 410	29.13 431	27.53 431	61.55 420	9.83 433	65.01 434	16.00 437	55.58 382	58.22 427

DTE-MVSNet		68.46 331	67.33 325	71.87 375	77.94 378	49.00 394	86.16 323	88.58 311	66.36 306	58.19 359	82.21 304	46.36 305	83.87 394	44.97 379	55.17 383	82.73 356

MIMVSNet1		60.16 377	57.33 377	68.67 385	69.71 411	44.13 413	78.92 380	84.21 366	55.05 385	44.63 414	71.85 393	23.91 406	81.54 409	32.63 418	55.03 384	80.35 381

pmmvs6		67.57 339	64.76 341	76.00 338	72.82 403	53.37 368	88.71 287	86.78 342	53.19 389	57.58 367	78.03 357	35.33 370	92.41 300	55.56 329	54.88 385	82.21 365

TinyColmap		60.32 375	56.42 382	72.00 374	78.78 366	53.18 369	78.36 384	75.64 397	52.30 390	41.59 421	75.82 377	14.76 426	88.35 362	35.84 405	54.71 386	74.46 409

test20.03		63.83 360	62.65 356	67.38 391	70.58 410	39.94 423	86.57 320	84.17 367	63.29 331	51.86 388	77.30 362	37.09 362	82.47 403	38.87 401	54.13 387	79.73 386

OurMVSNet-221017-0		64.68 355	62.17 359	72.21 370	76.08 391	47.35 400	80.67 367	81.02 383	56.19 381	51.60 389	79.66 346	27.05 400	88.56 358	53.60 338	53.63 388	80.71 378

test_fmvs3		56.82 381	54.86 385	62.69 400	53.59 433	35.47 430	75.87 394	65.64 424	43.91 417	55.10 373	71.43 397	6.91 438	74.40 420	68.64 240	52.63 389	78.20 401

Patchmatch-RL test		68.17 334	64.49 345	79.19 302	71.22 405	53.93 366	70.07 408	71.54 413	69.22 278	56.79 369	62.89 416	56.58 200	88.61 356	69.53 229	52.61 390	95.03 87

ppachtmachnet_test		67.72 337	63.70 349	79.77 292	78.92 363	66.04 144	88.68 288	82.90 380	60.11 361	55.45 372	75.96 375	39.19 340	90.55 335	39.53 397	52.55 391	82.71 358

LF4IMVS		54.01 386	52.12 387	59.69 401	62.41 425	39.91 425	68.59 412	68.28 421	42.96 421	44.55 415	75.18 378	14.09 428	68.39 427	41.36 392	51.68 392	70.78 417

N_pmnet		50.55 389	49.11 391	54.88 408	77.17 385	4.02 452	84.36 330	2.00 450	48.59 402	45.86 409	68.82 402	32.22 381	82.80 402	31.58 421	51.38 393	77.81 403

pmmvs-eth3d		65.53 352	62.32 358	75.19 343	69.39 413	59.59 314	82.80 350	83.43 375	62.52 340	51.30 392	72.49 387	32.86 377	87.16 376	55.32 330	50.73 394	78.83 395

CL-MVSNet_self_test		69.92 317	68.09 321	75.41 340	73.25 400	55.90 356	90.05 257	89.90 254	69.96 269	61.96 340	76.54 369	51.05 262	87.64 370	49.51 353	50.59 395	82.70 359

PM-MVS		59.40 378	56.59 380	67.84 387	63.63 422	41.86 417	76.76 389	63.22 427	59.01 366	51.07 393	72.27 392	11.72 430	83.25 399	61.34 303	50.28 396	78.39 400

MDA-MVSNet_test_wron		63.78 362	60.16 365	74.64 348	78.15 376	60.41 298	83.49 338	84.03 368	56.17 383	39.17 423	71.59 395	37.22 359	83.24 400	42.87 386	48.73 397	80.26 383

YYNet1		63.76 363	60.14 366	74.62 349	78.06 377	60.19 305	83.46 340	83.99 372	56.18 382	39.25 422	71.56 396	37.18 360	83.34 398	42.90 385	48.70 398	80.32 382

KD-MVS_self_test		60.87 373	58.60 371	67.68 389	66.13 419	39.93 424	75.63 397	84.70 362	57.32 375	49.57 398	68.45 404	29.55 391	82.87 401	48.09 359	47.94 399	80.25 384

SixPastTwentyTwo		64.92 354	61.78 361	74.34 353	78.74 367	49.76 387	83.42 341	79.51 390	62.86 336	50.27 395	77.35 361	30.92 389	90.49 337	45.89 373	47.06 400	82.78 354

sc_t1		63.81 361	59.39 369	77.10 327	77.62 381	56.03 354	84.32 332	73.56 405	46.66 410	58.22 358	73.06 385	23.28 410	90.62 334	50.93 345	46.84 401	84.64 334

tt0320		61.85 367	57.45 376	75.03 345	77.49 382	57.60 339	82.74 351	73.65 404	43.65 419	53.65 380	68.18 405	25.47 403	88.66 355	45.56 375	46.68 402	78.81 396

new_pmnet		49.31 390	46.44 393	57.93 403	62.84 424	40.74 420	68.47 413	62.96 428	36.48 425	35.09 426	57.81 423	14.97 425	72.18 422	32.86 416	46.44 403	60.88 425

EGC-MVSNET		42.35 396	38.09 399	55.11 407	74.57 395	46.62 406	71.63 405	55.77 431	0.04 445	0.24 446	62.70 417	14.24 427	74.91 419	17.59 434	46.06 404	43.80 431

TransMVSNet (Re)		70.07 316	67.66 322	77.31 325	80.62 341	59.13 323	91.78 188	84.94 361	65.97 308	60.08 348	80.44 334	50.78 263	91.87 315	48.84 356	45.46 405	80.94 375

ambc				69.61 381	61.38 428	41.35 419	49.07 435	85.86 353		50.18 397	66.40 409	10.16 432	88.14 364	45.73 374	44.20 406	79.32 390

TDRefinement		55.28 384	51.58 388	66.39 393	59.53 430	46.15 408	76.23 392	72.80 406	44.60 414	42.49 419	76.28 373	15.29 424	82.39 404	33.20 413	43.75 407	70.62 418

Gipuma		34.91 403	31.44 406	45.30 418	70.99 407	39.64 426	19.85 440	72.56 408	20.10 436	16.16 440	21.47 441	5.08 441	71.16 423	13.07 438	43.70 408	25.08 438
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		46.58 392	43.45 396	55.96 405	45.18 440	32.05 434	61.18 424	49.49 438	33.39 427	42.05 420	62.48 418	7.00 437	65.56 432	47.08 368	43.21 409	70.27 419

tt0320-xc		61.51 371	56.89 379	75.37 341	78.50 371	58.61 328	82.61 352	71.27 414	44.31 416	53.17 382	68.03 407	23.38 408	88.46 360	47.77 364	43.00 410	79.03 393

MDA-MVSNet-bldmvs		61.54 370	57.70 374	73.05 362	79.53 354	57.00 349	83.08 346	81.23 382	57.57 371	34.91 427	72.45 388	32.79 378	86.26 380	35.81 406	41.95 411	75.89 407

new-patchmatchnet		59.30 379	56.48 381	67.79 388	65.86 420	44.19 412	82.47 353	81.77 381	59.94 362	43.65 417	66.20 410	27.67 398	81.68 408	39.34 398	41.40 412	77.50 404

UnsupCasMVSNet_eth		65.79 349	63.10 352	73.88 356	70.71 408	50.29 386	81.09 364	89.88 255	72.58 204	49.25 400	74.77 382	32.57 380	87.43 374	55.96 328	41.04 413	83.90 339

test_vis3_rt		40.46 399	37.79 400	48.47 416	44.49 441	33.35 433	66.56 419	32.84 447	32.39 428	29.65 429	39.13 437	3.91 445	68.65 426	50.17 348	40.99 414	43.40 432

pmmvs3		55.51 383	51.50 389	67.53 390	57.90 431	50.93 382	80.37 369	73.66 403	40.63 424	44.15 416	64.75 413	16.30 421	78.97 414	44.77 380	40.98 415	72.69 414

APD_test1		40.50 398	37.31 401	50.09 414	51.88 434	35.27 431	59.45 428	52.59 435	21.64 434	26.12 432	57.80 424	4.56 442	66.56 430	22.64 429	39.09 416	48.43 430

mvs5depth		61.03 372	57.65 375	71.18 376	67.16 417	47.04 405	72.74 401	77.49 391	57.47 374	60.52 344	72.53 386	22.84 411	88.38 361	49.15 354	38.94 417	78.11 402

UnsupCasMVSNet_bld		61.60 369	57.71 373	73.29 361	68.73 414	51.64 375	78.61 381	89.05 292	57.20 376	46.11 406	61.96 419	28.70 395	88.60 357	50.08 350	38.90 418	79.63 387

PMVS	26.43 22	31.84 406	28.16 409	42.89 419	25.87 449	27.58 440	50.92 434	49.78 437	21.37 435	14.17 441	40.81 436	2.01 448	66.62 429	9.61 441	38.88 419	34.49 437
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
K. test v3		63.09 364	59.61 368	73.53 359	76.26 389	49.38 392	83.27 342	77.15 393	64.35 319	47.77 405	72.32 391	28.73 394	87.79 368	49.93 351	36.69 420	83.41 347

mmtdpeth		68.33 332	66.37 329	74.21 355	82.81 319	51.73 374	84.34 331	80.42 386	67.01 302	71.56 232	68.58 403	30.52 390	92.35 304	75.89 172	36.21 421	78.56 399

kuosan		60.86 374	60.24 364	62.71 399	81.57 329	46.43 407	75.70 396	85.88 351	57.98 370	48.95 401	69.53 401	58.42 175	76.53 415	28.25 424	35.87 422	65.15 423

KD-MVS_2432*1600		69.03 325	66.37 329	77.01 329	85.56 272	61.06 279	81.44 361	90.25 239	67.27 298	58.00 362	76.53 370	54.49 223	87.63 371	48.04 360	35.77 423	82.34 363

miper_refine_blended		69.03 325	66.37 329	77.01 329	85.56 272	61.06 279	81.44 361	90.25 239	67.27 298	58.00 362	76.53 370	54.49 223	87.63 371	48.04 360	35.77 423	82.34 363

mvsany_test3		48.86 391	46.35 394	56.41 404	46.00 439	31.67 435	62.26 423	47.25 440	43.71 418	45.54 411	68.15 406	10.84 431	64.44 436	57.95 319	35.44 425	73.13 413

LCM-MVSNet		40.54 397	35.79 402	54.76 409	36.92 446	30.81 436	51.41 433	69.02 418	22.07 433	24.63 433	45.37 430	4.56 442	65.81 431	33.67 411	34.50 426	67.67 420

test_method		38.59 401	35.16 404	48.89 415	54.33 432	21.35 445	45.32 436	53.71 434	7.41 442	28.74 430	51.62 426	8.70 435	52.87 439	33.73 410	32.89 427	72.47 415

lessismore_v0				73.72 358	72.93 402	47.83 398		61.72 429		45.86 409	73.76 383	28.63 396	89.81 349	47.75 366	31.37 428	83.53 343

testf1		32.77 404	29.47 407	42.67 420	41.89 443	30.81 436	52.07 431	43.45 441	15.45 437	18.52 437	44.82 431	2.12 446	58.38 437	16.05 435	30.87 429	38.83 433

APD_test2		32.77 404	29.47 407	42.67 420	41.89 443	30.81 436	52.07 431	43.45 441	15.45 437	18.52 437	44.82 431	2.12 446	58.38 437	16.05 435	30.87 429	38.83 433

ttmdpeth		53.34 387	49.96 390	63.45 397	62.07 427	40.04 422	72.06 402	65.64 424	42.54 422	51.88 387	77.79 359	13.94 429	76.48 416	32.93 415	30.82 431	73.84 411

PVSNet_0	68.08 15	71.81 304	68.32 320	82.27 223	84.68 287	62.31 252	88.68 288	90.31 235	75.84 145	57.93 364	80.65 332	37.85 354	94.19 235	69.94 225	29.05 432	90.31 237

dongtai		55.18 385	55.46 384	54.34 410	76.03 392	36.88 428	76.07 393	84.61 364	51.28 394	43.41 418	64.61 414	56.56 201	67.81 428	18.09 433	28.50 433	58.32 426

MVStest1		51.35 388	46.89 392	64.74 394	65.06 421	51.10 380	67.33 417	72.58 407	30.20 430	35.30 425	74.82 380	27.70 397	69.89 425	24.44 427	24.57 434	73.22 412

WB-MVS		46.23 393	44.94 395	50.11 413	62.13 426	21.23 446	76.48 391	55.49 432	45.89 411	35.78 424	61.44 421	35.54 368	72.83 421	9.96 440	21.75 435	56.27 428

SSC-MVS		44.51 395	43.35 397	47.99 417	61.01 429	18.90 448	74.12 399	54.36 433	43.42 420	34.10 428	60.02 422	34.42 373	70.39 424	9.14 442	19.57 436	54.68 429

DeepMVS_CX				34.71 423	51.45 435	24.73 443		28.48 449	31.46 429	17.49 439	52.75 425	5.80 440	42.60 444	18.18 432	19.42 437	36.81 436

PMMVS2		37.93 402	33.61 405	50.92 412	46.31 438	24.76 442	60.55 427	50.05 436	28.94 432	20.93 434	47.59 427	4.41 444	65.13 433	25.14 426	18.55 438	62.87 424

MVE	24.84 23	24.35 408	19.77 414	38.09 422	34.56 448	26.92 441	26.57 438	38.87 445	11.73 441	11.37 442	27.44 438	1.37 449	50.42 441	11.41 439	14.60 439	36.93 435
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 407	24.00 411	26.45 424	43.74 442	18.44 449	60.86 425	39.66 443	15.11 439	9.53 443	22.10 440	6.52 439	46.94 442	8.31 443	10.14 440	13.98 440

EMVS		23.76 409	23.20 413	25.46 425	41.52 445	16.90 450	60.56 426	38.79 446	14.62 440	8.99 444	20.24 443	7.35 436	45.82 443	7.25 444	9.46 441	13.64 441

tmp_tt		22.26 410	23.75 412	17.80 426	5.23 450	12.06 451	35.26 437	39.48 444	2.82 444	18.94 435	44.20 433	22.23 413	24.64 445	36.30 403	9.31 442	16.69 439

ANet_high		40.27 400	35.20 403	55.47 406	34.74 447	34.47 432	63.84 422	71.56 412	48.42 403	18.80 436	41.08 435	9.52 434	64.45 435	20.18 431	8.66 443	67.49 421

wuyk23d		11.30 412	10.95 415	12.33 427	48.05 437	19.89 447	25.89 439	1.92 451	3.58 443	3.12 445	1.37 445	0.64 450	15.77 446	6.23 445	7.77 444	1.35 442

testmvs		7.23 414	9.62 417	0.06 429	0.04 451	0.02 454	84.98 328	0.02 452	0.03 446	0.18 447	1.21 446	0.01 452	0.02 447	0.14 446	0.01 445	0.13 444

mmdepth		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

monomultidepth		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

test_blank		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

uanet_test		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

DCPMVS		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

cdsmvs_eth3d_5k		19.86 411	26.47 410	0.00 430	0.00 453	0.00 455	0.00 441	93.45 90	0.00 448	0.00 449	95.27 68	49.56 277	0.00 449	0.00 448	0.00 446	0.00 445

pcd_1.5k_mvsjas		4.46 416	5.95 419	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	53.55 236	0.00 449	0.00 448	0.00 446	0.00 445

sosnet-low-res		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

sosnet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

uncertanet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

Regformer		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

test123		6.92 415	9.21 418	0.08 428	0.03 452	0.05 453	81.65 359	0.01 453	0.02 447	0.14 448	0.85 447	0.03 451	0.02 447	0.12 447	0.00 446	0.16 443

ab-mvs-re		7.91 413	10.55 416	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	94.95 78	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

uanet		0.00 417	0.00 420	0.00 430	0.00 453	0.00 455	0.00 441	0.00 454	0.00 448	0.00 449	0.00 448	0.00 453	0.00 449	0.00 448	0.00 446	0.00 445

WAC-MVS						49.45 390								31.56 422

FOURS1					93.95 46	61.77 263	93.96 78	91.92 159	62.14 344	86.57 54

test_one_0601					96.32 18	69.74 50		94.18 61	71.42 247	90.67 22	96.85 19	74.45 20

eth-test2					0.00 453

eth-test					0.00 453

test_241102_ONE					96.45 12	69.38 56		94.44 50	71.65 236	92.11 8	97.05 9	76.79 9	99.11 6

save fliter					93.84 49	67.89 96	95.05 39	92.66 126	78.19 109

test0726					96.40 15	69.99 39	96.76 8	94.33 58	71.92 222	91.89 12	97.11 8	73.77 23

GSMVS																94.68 104

test_part2					96.29 19	68.16 89				90.78 20

sam_mvs1												57.85 181				94.68 104

sam_mvs												54.91 220

MTGPA								92.23 140

test_post1							78.95 379				20.70 442	53.05 241	91.50 329	60.43 308

test_post											23.01 439	56.49 202	92.67 291

patchmatchnet-post											67.62 408	57.62 184	90.25 339

MTMP							93.77 92	32.52 448

gm-plane-assit					88.42 197	67.04 120			78.62 103		91.83 162		97.37 75	76.57 167

TEST9					94.18 41	67.28 111	94.16 65	93.51 86	71.75 233	85.52 66	95.33 63	68.01 56	97.27 85

test_8					94.19 40	67.19 113	94.15 67	93.42 93	71.87 227	85.38 69	95.35 62	68.19 54	96.95 111

agg_prior					94.16 43	66.97 122		93.31 96		84.49 77			96.75 121

test_prior4						67.18 115	93.92 81

test_prior				86.42 77	94.71 35	67.35 110		93.10 107					96.84 118			95.05 85

旧先验2							92.00 177		59.37 365	87.54 47			93.47 266	75.39 176

新几何2							91.41 198

无先验							92.71 140	92.61 130	62.03 345				97.01 101	66.63 259		93.97 142

原ACMM2							92.01 174

testdata2													96.09 151	61.26 304

segment_acmp												65.94 74

testdata1							89.21 278		77.55 124

plane_prior7					86.94 240	61.51 270

plane_prior6					87.23 232	62.32 251						50.66 264

plane_prior4											89.14 212

plane_prior3						61.95 259			79.09 92	72.53 215

plane_prior2							93.13 120		78.81 99

plane_prior1					87.15 234

n2								0.00 454

nn								0.00 454

door-mid								66.01 423

test11								93.01 110

door								66.57 422

HQP5-MVS						63.66 214

HQP-NCC					87.54 224		94.06 70		79.80 74	74.18 194

ACMP_Plane					87.54 224		94.06 70		79.80 74	74.18 194

BP-MVS														77.63 162

HQP4-MVS										74.18 194			95.61 175			88.63 259

HQP2-MVS												51.63 256

NP-MVS					87.41 227	63.04 231					90.30 190

MDTV_nov1_ep13_2view						59.90 310	80.13 374		67.65 295	72.79 209		54.33 228		59.83 312		92.58 188

Test By Simon												54.21 230

High-resolution multi-view benchmark