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.

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 43	94.27 42	75.89 19	96.81 23	87.45 42	96.44 9	93.05 124

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 55	82.45 3	96.87 20	83.77 76	96.48 8	94.88 16

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 215	92.02 98	79.45 22	85.88 64	94.80 23	68.07 107	96.21 46	86.69 47	95.34 32	93.23 109

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 103	94.40 36	72.24 50	96.28 43	85.65 53	95.30 35	93.62 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 106	94.46 31	67.93 109	95.95 58	84.20 72	94.39 57	93.23 109

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 38	94.06 53	76.43 16	96.84 21	88.48 34	95.99 18	94.34 50

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 131	92.29 7	95.97 2	74.28 30	97.24 13	88.58 31	96.91 1	94.87 18
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
MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 134	86.57 1	87.39 52	94.97 21	71.70 58	97.68 1	92.19 1	95.63 28	95.57 1

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 66	93.47 74	73.02 42	97.00 18	84.90 58	94.94 40	94.10 59

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 83	94.52 27	68.81 97	96.65 30	84.53 66	94.90 41	94.00 65

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 88	94.52 27	69.09 91	96.70 27	84.37 68	94.83 45	94.03 63

mPP-MVS		86.67 43	86.32 48	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 121	94.25 44	66.44 125	96.24 45	82.88 86	94.28 60	93.38 102

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 76	94.44 34	70.78 71	96.61 32	84.53 66	94.89 42	93.66 85

3Dnovator+	77.84 4	85.48 68	84.47 87	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 236	93.37 77	60.40 219	96.75 26	77.20 146	93.73 66	95.29 6

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 48	78.35 13	96.77 24	89.59 16	94.22 62	94.67 30
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		86.68 42	86.27 50	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 106	83.86 102	94.42 35	67.87 111	96.64 31	82.70 92	94.57 52	93.66 85

ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 71	94.32 39	71.76 56	96.93 19	85.53 55	95.79 22	94.32 51

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	94.17 47	67.45 114	96.60 33	83.06 81	94.50 53	94.07 61

X-MVStestdata		80.37 182	77.83 222	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	12.47 463	67.45 114	96.60 33	83.06 81	94.50 53	94.07 61

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 36	95.09 19	71.06 68	96.67 29	87.67 39	96.37 14	94.09 60

DPM-MVS		84.93 81	84.29 88	86.84 52	90.20 109	73.04 23	87.12 191	93.04 42	69.80 255	82.85 119	91.22 136	73.06 41	96.02 53	76.72 158	94.63 50	91.46 192

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 80	93.99 59	70.67 73	96.82 22	84.18 73	95.01 37	93.90 71

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 289	85.00 74	93.10 82	74.36 29	95.41 76

train_agg		86.43 46	86.20 51	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 284	85.00 74	93.10 82	74.43 27	95.41 76	84.97 57	95.71 25	93.02 126

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 37	94.80 23	73.76 34	97.11 15	87.51 41	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 109	82.31 121	86.59 57	87.94 204	72.94 28	90.64 63	92.14 97	77.21 62	75.47 262	92.83 91	58.56 231	94.72 110	73.24 195	92.71 77	92.13 170

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 57	74.83 23	93.78 152	87.63 40	94.27 61	93.65 89
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
MVS_111021_HR		85.14 77	84.75 82	86.32 61	91.65 81	72.70 30	85.98 232	90.33 160	76.11 94	82.08 129	91.61 124	71.36 64	94.17 133	81.02 104	92.58 78	92.08 171

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 43	96.34 15	93.95 68
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					95.06 8	72.65 32				91.80 13

ACMMP		85.89 60	85.39 71	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 154	93.82 66	64.33 149	96.29 42	82.67 93	90.69 110	93.23 109
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
test_prior4						72.60 34	89.01 118

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 284	84.87 78	93.10 82	74.43 27	95.16 86

TSAR-MVS + GP.		85.71 64	85.33 73	86.84 52	91.34 84	72.50 36	89.07 117	87.28 267	76.41 85	85.80 65	90.22 168	74.15 32	95.37 81	81.82 97	91.88 88	92.65 142

CSCG		86.41 48	86.19 53	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 151	83.16 114	91.07 142	75.94 18	95.19 85	79.94 118	94.38 58	93.55 97

NormalMVS		86.29 50	85.88 60	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 90	92.18 103	64.64 147	95.53 67	80.70 110	94.65 48	94.56 39

SymmetryMVS		85.38 73	84.81 81	87.07 46	91.47 83	72.47 38	91.65 43	88.06 247	79.31 24	84.39 90	92.18 103	64.64 147	95.53 67	80.70 110	90.91 107	93.21 112

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 180	84.86 79	92.89 89	76.22 17	96.33 41	84.89 60	95.13 36	94.40 46

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 137	91.30 15

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 182	88.58 29	94.52 27	73.36 35	96.49 38	84.26 69	95.01 37	92.70 138
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 38	86.62 45	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 94	93.36 78	71.44 62	96.76 25	80.82 107	95.33 33	94.16 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter					93.80 40	72.35 44	90.47 69	91.17 134	74.31 142

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 120	94.23 45	72.13 52	97.09 16	84.83 61	95.37 31	93.65 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 221	87.75 45	94.07 52	74.01 33	96.70 27	84.66 64	94.84 44

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 104	93.95 62	69.77 83	96.01 54	85.15 56	94.66 47	94.32 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 135	88.90 27	93.85 65	75.75 20	96.00 55	87.80 38	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 136	86.84 59	94.65 26	67.31 116	95.77 60	84.80 62	92.85 74	92.84 136

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 119	86.34 62	95.29 17	70.86 70	96.00 55	88.78 29	96.04 16	94.58 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 101	89.16 24	95.10 18	75.65 21	96.19 47	87.07 44	96.01 17	94.79 23

agg_prior					92.85 64	71.94 52		91.78 114		84.41 89			94.93 97

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 188	82.14 3	86.65 60	94.28 41	68.28 105	97.46 6	90.81 6	95.31 34	95.15 8

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 103	91.06 16	96.03 1	76.84 14	97.03 17	89.09 20	95.65 27	94.47 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 122

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 122

MVS_111021_LR		82.61 123	82.11 124	84.11 138	88.82 162	71.58 57	85.15 256	86.16 293	74.69 132	80.47 159	91.04 143	62.29 178	90.55 294	80.33 114	90.08 121	90.20 239

MAR-MVS		81.84 136	80.70 146	85.27 89	91.32 85	71.53 58	89.82 82	90.92 140	69.77 257	78.50 190	86.21 288	62.36 177	94.52 118	65.36 275	92.05 87	89.77 264
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
test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 21	96.41 12	94.21 55

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 18	96.68 2	94.95 12

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 304	92.39 6				88.94 26	96.63 4	94.85 21

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 21	96.41 12	93.33 106
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 132	88.80 28	95.61 11	70.29 77	96.44 39	86.20 51	93.08 71	93.16 116

CDPH-MVS		85.76 63	85.29 76	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 291	84.61 85	93.48 72	72.32 48	96.15 49	79.00 125	95.43 30	94.28 53

CNLPA		78.08 239	76.79 251	81.97 232	90.40 105	71.07 67	87.59 176	84.55 313	66.03 320	72.38 322	89.64 183	57.56 240	86.04 359	59.61 326	83.35 243	88.79 297

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 23	96.63 4	94.88 16

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

PHI-MVS		86.43 46	86.17 54	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 188	85.22 72	91.90 111	69.47 86	96.42 40	83.28 80	95.94 19	94.35 49

OPM-MVS		83.50 105	82.95 110	85.14 92	88.79 168	70.95 71	89.13 114	91.52 123	77.55 52	80.96 148	91.75 116	60.71 209	94.50 119	79.67 121	86.51 183	89.97 256
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 45	86.10 56	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 140	91.43 130	70.34 75	97.23 14	84.26 69	93.36 70	94.37 48

DP-MVS Recon		83.11 117	82.09 126	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 233	79.17 177	91.03 145	64.12 151	96.03 51	68.39 251	90.14 119	91.50 188

CPTT-MVS		83.73 96	83.33 104	84.92 105	93.28 49	70.86 74	92.09 37	90.38 156	68.75 283	79.57 169	92.83 91	60.60 215	93.04 197	80.92 106	91.56 96	90.86 210

h-mvs33		83.15 114	82.19 123	86.02 72	90.56 101	70.85 75	88.15 158	89.16 211	76.02 96	84.67 81	91.39 131	61.54 192	95.50 69	82.71 90	75.48 347	91.72 182

新几何1				83.42 175	93.13 56	70.71 76		85.48 302	57.43 410	81.80 134	91.98 109	63.28 157	92.27 229	64.60 282	92.99 72	87.27 335

test12				86.80 54	92.63 69	70.70 77		91.79 113		82.71 122		71.67 59	96.16 48		94.50 53	93.54 98

SR-MVS-dyc-post		85.77 62	85.61 67	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	65.00 145	95.56 64	82.75 88	91.87 89	92.50 148

RE-MVS-def			85.48 70		93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	63.87 153		82.75 88	91.87 89	92.50 148

HPM-MVS_fast		85.35 74	84.95 80	86.57 59	93.69 42	70.58 80	92.15 36	91.62 120	73.89 154	82.67 123	94.09 51	62.60 171	95.54 66	80.93 105	92.93 73	93.57 95

MSLP-MVS++		85.43 70	85.76 64	84.45 121	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 94	92.81 93	67.16 118	92.94 199	80.36 113	94.35 59	90.16 240

MVSFormer		82.85 120	82.05 127	85.24 90	87.35 226	70.21 82	90.50 67	90.38 156	68.55 286	81.32 140	89.47 189	61.68 189	93.46 169	78.98 126	90.26 117	92.05 172

lupinMVS		81.39 150	80.27 158	84.76 112	87.35 226	70.21 82	85.55 246	86.41 287	62.85 359	81.32 140	88.61 216	61.68 189	92.24 231	78.41 133	90.26 117	91.83 175

xiu_mvs_v1_base_debu		80.80 164	79.72 175	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 276	81.83 131	88.16 230	50.91 310	92.85 203	78.29 135	87.56 163	89.06 281

xiu_mvs_v1_base		80.80 164	79.72 175	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 276	81.83 131	88.16 230	50.91 310	92.85 203	78.29 135	87.56 163	89.06 281

xiu_mvs_v1_base_debi		80.80 164	79.72 175	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 276	81.83 131	88.16 230	50.91 310	92.85 203	78.29 135	87.56 163	89.06 281

API-MVS		81.99 133	81.23 137	84.26 134	90.94 93	70.18 87	91.10 58	89.32 200	71.51 206	78.66 186	88.28 226	65.26 140	95.10 93	64.74 281	91.23 101	87.51 328

test_fmvsm_n_1920		85.29 75	85.34 72	85.13 95	86.12 268	69.93 88	88.65 137	90.78 145	69.97 251	88.27 33	93.98 60	71.39 63	91.54 261	88.49 33	90.45 114	93.91 69

OpenMVS	72.83 10	79.77 193	78.33 209	84.09 143	85.17 291	69.91 89	90.57 64	90.97 139	66.70 307	72.17 325	91.91 110	54.70 266	93.96 138	61.81 308	90.95 106	88.41 310

jason		81.39 150	80.29 157	84.70 114	86.63 257	69.90 90	85.95 233	86.77 280	63.24 352	81.07 146	89.47 189	61.08 205	92.15 233	78.33 134	90.07 122	92.05 172
jason: jason.
MVP-Stereo		76.12 282	74.46 292	81.13 254	85.37 287	69.79 91	84.42 279	87.95 251	65.03 332	67.46 374	85.33 309	53.28 281	91.73 250	58.01 344	83.27 245	81.85 416
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 49	96.27 44	86.87 45	94.65 48	93.70 84

PVSNet_Blended_VisFu		82.62 122	81.83 132	84.96 101	90.80 97	69.76 93	88.74 133	91.70 117	69.39 263	78.96 179	88.46 221	65.47 139	94.87 103	74.42 181	88.57 149	90.24 238

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 69

APD-MVS_3200maxsize		85.97 56	85.88 60	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 164	85.94 63	94.51 30	65.80 137	95.61 63	83.04 83	92.51 79	93.53 99

test_fmvsmconf_n		85.92 57	86.04 58	85.57 82	85.03 298	69.51 96	89.62 92	90.58 149	73.42 168	87.75 45	94.02 55	72.85 45	93.24 178	90.37 7	90.75 109	93.96 66

EPNet		83.72 97	82.92 111	86.14 68	84.22 314	69.48 97	91.05 59	85.27 303	81.30 6	76.83 231	91.65 120	66.09 132	95.56 64	76.00 164	93.85 64	93.38 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 225	76.63 257	84.64 115	86.73 253	69.47 98	85.01 260	84.61 312	69.54 261	66.51 391	86.59 277	50.16 320	91.75 248	76.26 160	84.24 224	92.69 140

alignmvs		85.48 68	85.32 74	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 47	91.46 129	70.32 76	93.78 152	81.51 98	88.95 141	94.63 34

DP-MVS		76.78 270	74.57 288	83.42 175	93.29 48	69.46 100	88.55 142	83.70 325	63.98 348	70.20 343	88.89 208	54.01 274	94.80 107	46.66 414	81.88 263	86.01 363

sasdasda		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

canonicalmvs		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

test_fmvsmconf0.1_n		85.61 66	85.65 66	85.50 83	82.99 350	69.39 103	89.65 89	90.29 163	73.31 172	87.77 44	94.15 49	71.72 57	93.23 179	90.31 8	90.67 111	93.89 72

test_fmvsmvis_n_1920		84.02 90	83.87 92	84.49 120	84.12 316	69.37 104	88.15 158	87.96 250	70.01 249	83.95 101	93.23 80	68.80 98	91.51 264	88.61 30	89.96 123	92.57 143

nrg030		83.88 91	83.53 99	84.96 101	86.77 252	69.28 105	90.46 70	92.67 68	74.79 130	82.95 116	91.33 133	72.70 47	93.09 192	80.79 109	79.28 295	92.50 148

test_fmvsmconf0.01_n		84.73 84	84.52 86	85.34 87	80.25 392	69.03 106	89.47 95	89.65 184	73.24 176	86.98 57	94.27 42	66.62 121	93.23 179	90.26 9	89.95 124	93.78 81

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 50	78.98 12	96.58 35	85.66 52	95.72 24	94.58 36

XVG-OURS		80.41 178	79.23 189	83.97 158	85.64 278	69.02 108	83.03 315	90.39 155	71.09 216	77.63 213	91.49 128	54.62 268	91.35 270	75.71 166	83.47 241	91.54 186

PCF-MVS	73.52 7	80.38 180	78.84 198	85.01 99	87.71 217	68.99 109	83.65 296	91.46 128	63.00 356	77.77 211	90.28 164	66.10 131	95.09 94	61.40 311	88.22 156	90.94 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 158	79.50 181	85.03 98	88.01 202	68.97 110	91.59 46	92.00 100	66.63 313	75.15 280	92.16 105	57.70 238	95.45 71	63.52 287	88.76 146	90.66 219

AdaColmap		80.58 176	79.42 182	84.06 148	93.09 59	68.91 111	89.36 103	88.97 222	69.27 267	75.70 258	89.69 180	57.20 246	95.77 60	63.06 292	88.41 154	87.50 329

fmvsm_l_conf0.5_n		84.47 85	84.54 84	84.27 132	85.42 285	68.81 112	88.49 143	87.26 269	68.08 293	88.03 39	93.49 71	72.04 53	91.77 247	88.90 27	89.14 140	92.24 162

原ACMM1				84.35 125	93.01 62	68.79 113		92.44 78	63.96 349	81.09 145	91.57 125	66.06 133	95.45 71	67.19 261	94.82 46	88.81 296

XVG-OURS-SEG-HR		80.81 161	79.76 172	83.96 159	85.60 280	68.78 114	83.54 302	90.50 152	70.66 231	76.71 235	91.66 119	60.69 210	91.26 273	76.94 150	81.58 265	91.83 175

LPG-MVS_test		82.08 130	81.27 136	84.50 118	89.23 148	68.76 115	90.22 76	91.94 104	75.37 111	76.64 237	91.51 126	54.29 269	94.91 98	78.44 131	83.78 229	89.83 261

LGP-MVS_train				84.50 118	89.23 148	68.76 115		91.94 104	75.37 111	76.64 237	91.51 126	54.29 269	94.91 98	78.44 131	83.78 229	89.83 261

Effi-MVS+-dtu		80.03 190	78.57 202	84.42 122	85.13 295	68.74 117	88.77 129	88.10 244	74.99 121	74.97 286	83.49 353	57.27 244	93.36 173	73.53 189	80.88 273	91.18 197

Vis-MVSNet		83.46 106	82.80 113	85.43 85	90.25 108	68.74 117	90.30 75	90.13 168	76.33 91	80.87 151	92.89 89	61.00 206	94.20 130	72.45 208	90.97 105	93.35 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 100	83.14 105	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 181	91.00 147	60.42 217	95.38 78	78.71 129	86.32 185	91.33 193

plane_prior						68.71 119	90.38 73		77.62 47						86.16 189

plane_prior6					89.84 121	68.70 121						60.42 217

ACMP	74.13 6	81.51 149	80.57 149	84.36 124	89.42 135	68.69 122	89.97 80	91.50 127	74.46 138	75.04 284	90.41 160	53.82 275	94.54 116	77.56 142	82.91 249	89.86 260
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 83	84.67 83	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 97	85.71 297	69.32 88	95.38 78	80.82 107	91.37 99	92.72 137

plane_prior3						68.60 124			78.44 36	78.92 181

CHOSEN 1792x2688		77.63 255	75.69 268	83.44 174	89.98 118	68.58 125	78.70 372	87.50 263	56.38 415	75.80 257	86.84 265	58.67 230	91.40 269	61.58 310	85.75 200	90.34 233

fmvsm_l_conf0.5_n_3		86.02 52	86.32 48	85.14 92	87.20 235	68.54 126	89.57 93	90.44 154	75.31 113	87.49 49	94.39 37	72.86 44	92.72 208	89.04 25	90.56 112	94.16 56

plane_prior7					90.08 112	68.51 127

GDP-MVS		83.52 104	82.64 115	86.16 65	88.14 193	68.45 128	89.13 114	92.69 66	72.82 186	83.71 105	91.86 114	55.69 256	95.35 82	80.03 116	89.74 128	94.69 29

fmvsm_l_conf0.5_n_a		84.13 88	84.16 89	84.06 148	85.38 286	68.40 129	88.34 150	86.85 279	67.48 300	87.48 50	93.40 76	70.89 69	91.61 252	88.38 35	89.22 137	92.16 169

ACMM	73.20 8	80.78 168	79.84 170	83.58 170	89.31 143	68.37 130	89.99 79	91.60 121	70.28 243	77.25 220	89.66 182	53.37 280	93.53 165	74.24 184	82.85 250	88.85 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 311	71.91 322	80.39 270	81.96 368	68.32 131	81.45 330	82.14 351	59.32 391	69.87 352	85.13 315	52.40 287	88.13 336	60.21 321	74.74 362	84.73 386

NP-MVS					89.62 125	68.32 131					90.24 166

SSM_0404		81.91 134	80.84 145	85.13 95	89.24 147	68.26 133	87.84 171	89.25 206	71.06 218	80.62 155	90.39 161	59.57 222	94.65 114	72.45 208	87.19 171	92.47 151

test222					91.50 82	68.26 133	84.16 286	83.20 337	54.63 421	79.74 166	91.63 122	58.97 227			91.42 97	86.77 349

Elysia		81.53 145	80.16 160	85.62 79	85.51 282	68.25 135	88.84 126	92.19 92	71.31 209	80.50 157	89.83 174	46.89 350	94.82 104	76.85 151	89.57 130	93.80 79

StellarMVS		81.53 145	80.16 160	85.62 79	85.51 282	68.25 135	88.84 126	92.19 92	71.31 209	80.50 157	89.83 174	46.89 350	94.82 104	76.85 151	89.57 130	93.80 79

CDS-MVSNet		79.07 214	77.70 229	83.17 187	87.60 221	68.23 137	84.40 280	86.20 292	67.49 299	76.36 245	86.54 281	61.54 192	90.79 288	61.86 307	87.33 168	90.49 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 140	81.02 141	83.70 166	89.51 130	68.21 138	84.28 282	90.09 169	70.79 225	81.26 144	85.62 302	63.15 163	94.29 124	75.62 168	88.87 143	88.59 305

fmvsm_s_conf0.5_n_a		83.63 101	83.41 101	84.28 130	86.14 267	68.12 139	89.43 97	82.87 344	70.27 244	87.27 54	93.80 67	69.09 91	91.58 254	88.21 36	83.65 236	93.14 119

UGNet		80.83 160	79.59 179	84.54 117	88.04 199	68.09 140	89.42 99	88.16 242	76.95 70	76.22 248	89.46 191	49.30 333	93.94 141	68.48 249	90.31 115	91.60 183
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
fmvsm_s_conf0.1_n_a		83.32 111	82.99 109	84.28 130	83.79 324	68.07 141	89.34 104	82.85 345	69.80 255	87.36 53	94.06 53	68.34 104	91.56 257	87.95 37	83.46 242	93.21 112

UA-Net		85.08 79	84.96 79	85.45 84	92.07 75	68.07 141	89.78 85	90.86 144	82.48 2	84.60 86	93.20 81	69.35 87	95.22 84	71.39 216	90.88 108	93.07 121

xiu_mvs_v2_base		81.69 140	81.05 140	83.60 168	89.15 151	68.03 143	84.46 276	90.02 170	70.67 228	81.30 143	86.53 282	63.17 162	94.19 132	75.60 169	88.54 150	88.57 306

LuminaMVS		80.68 169	79.62 178	83.83 162	85.07 297	68.01 144	86.99 196	88.83 225	70.36 239	81.38 139	87.99 237	50.11 321	92.51 218	79.02 123	86.89 177	90.97 206

mamba_0408		79.37 207	77.52 234	84.93 104	88.81 163	67.96 145	65.03 446	88.66 234	70.96 222	79.48 171	89.80 176	58.69 228	94.65 114	70.35 227	85.93 195	92.18 165

SSM_04072		77.67 254	77.52 234	78.12 319	88.81 163	67.96 145	65.03 446	88.66 234	70.96 222	79.48 171	89.80 176	58.69 228	74.23 439	70.35 227	85.93 195	92.18 165

SSM_0407		81.58 144	80.48 152	84.87 107	88.81 163	67.96 145	87.37 183	89.25 206	71.06 218	79.48 171	90.39 161	59.57 222	94.48 121	72.45 208	85.93 195	92.18 165

DELS-MVS		85.41 71	85.30 75	85.77 75	88.49 178	67.93 148	85.52 250	93.44 28	78.70 34	83.63 109	89.03 201	74.57 24	95.71 62	80.26 115	94.04 63	93.66 85
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
fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 149	88.59 139	89.05 216	80.19 12	90.70 17	95.40 15	74.56 25	93.92 145	91.54 2	92.07 86	95.31 5

BP-MVS1		84.32 86	83.71 95	86.17 64	87.84 209	67.85 150	89.38 102	89.64 185	77.73 45	83.98 100	92.12 108	56.89 249	95.43 73	84.03 74	91.75 92	95.24 7

EI-MVSNet-Vis-set		84.19 87	83.81 93	85.31 88	88.18 190	67.85 150	87.66 174	89.73 182	80.05 15	82.95 116	89.59 186	70.74 72	94.82 104	80.66 112	84.72 213	93.28 108

PLC	70.83 11	78.05 241	76.37 263	83.08 192	91.88 79	67.80 152	88.19 155	89.46 191	64.33 341	69.87 352	88.38 223	53.66 276	93.58 160	58.86 334	82.73 252	87.86 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 220	77.51 236	83.03 195	87.80 211	67.79 153	84.72 266	85.05 308	67.63 296	76.75 234	87.70 242	62.25 179	90.82 287	58.53 338	87.13 172	90.49 227

CLD-MVS		82.31 127	81.65 133	84.29 129	88.47 179	67.73 154	85.81 240	92.35 83	75.78 99	78.33 196	86.58 279	64.01 152	94.35 123	76.05 163	87.48 166	90.79 212
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		81.72 138	80.94 143	84.07 145	88.72 171	67.68 155	85.87 236	87.26 269	76.02 96	84.67 81	88.22 229	61.54 192	93.48 167	82.71 90	73.44 375	91.06 201

MVSMamba_PlusPlus		85.99 54	85.96 59	86.05 69	91.09 88	67.64 156	89.63 91	92.65 71	72.89 185	84.64 84	91.71 117	71.85 54	96.03 51	84.77 63	94.45 56	94.49 42

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 157	90.51 65	92.90 57	77.26 59	87.44 51	91.63 122	71.27 65	96.06 50	85.62 54	95.01 37	94.78 24

AUN-MVS		79.21 210	77.60 232	84.05 151	88.71 172	67.61 157	85.84 238	87.26 269	69.08 275	77.23 222	88.14 234	53.20 282	93.47 168	75.50 171	73.45 374	91.06 201

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 159	92.83 18	93.30 33	79.67 19	84.57 87	92.27 101	71.47 61	95.02 96	84.24 71	93.46 69	95.13 9

KinetiMVS		83.31 112	82.61 116	85.39 86	87.08 244	67.56 160	88.06 160	91.65 118	77.80 44	82.21 127	91.79 115	57.27 244	94.07 136	77.77 140	89.89 126	94.56 39

EI-MVSNet-UG-set		83.81 92	83.38 102	85.09 97	87.87 207	67.53 161	87.44 182	89.66 183	79.74 18	82.23 126	89.41 195	70.24 78	94.74 109	79.95 117	83.92 228	92.99 129

Effi-MVS+		83.62 102	83.08 106	85.24 90	88.38 184	67.45 162	88.89 122	89.15 212	75.50 107	82.27 125	88.28 226	69.61 85	94.45 122	77.81 139	87.84 160	93.84 75

EG-PatchMatch MVS		74.04 309	71.82 323	80.71 264	84.92 299	67.42 163	85.86 237	88.08 245	66.04 319	64.22 406	83.85 341	35.10 424	92.56 214	57.44 348	80.83 274	82.16 415

OMC-MVS		82.69 121	81.97 130	84.85 108	88.75 170	67.42 163	87.98 162	90.87 143	74.92 125	79.72 167	91.65 120	62.19 181	93.96 138	75.26 174	86.42 184	93.16 116

fmvsm_s_conf0.5_n_5		85.22 76	85.55 68	84.25 135	86.26 262	67.40 165	89.18 108	89.31 201	72.50 187	88.31 32	93.86 64	69.66 84	91.96 239	89.81 12	91.05 103	93.38 102

PatchMatch-RL		72.38 331	70.90 335	76.80 341	88.60 175	67.38 166	79.53 358	76.17 412	62.75 362	69.36 357	82.00 379	45.51 368	84.89 373	53.62 374	80.58 278	78.12 430

LS3D		76.95 267	74.82 285	83.37 178	90.45 103	67.36 167	89.15 113	86.94 276	61.87 372	69.52 355	90.61 156	51.71 303	94.53 117	46.38 417	86.71 180	88.21 314

fmvsm_s_conf0.5_n		83.80 93	83.71 95	84.07 145	86.69 255	67.31 168	89.46 96	83.07 339	71.09 216	86.96 58	93.70 69	69.02 96	91.47 266	88.79 28	84.62 215	93.44 101

fmvsm_s_conf0.1_n		83.56 103	83.38 102	84.10 139	84.86 300	67.28 169	89.40 101	83.01 340	70.67 228	87.08 55	93.96 61	68.38 103	91.45 267	88.56 32	84.50 216	93.56 96

PS-MVSNAJss		82.07 131	81.31 135	84.34 126	86.51 260	67.27 170	89.27 105	91.51 124	71.75 199	79.37 174	90.22 168	63.15 163	94.27 126	77.69 141	82.36 257	91.49 189

114514_t		80.68 169	79.51 180	84.20 136	94.09 38	67.27 170	89.64 90	91.11 137	58.75 399	74.08 299	90.72 152	58.10 234	95.04 95	69.70 236	89.42 134	90.30 236

mvsmamba		80.60 173	79.38 183	84.27 132	89.74 124	67.24 172	87.47 179	86.95 275	70.02 248	75.38 268	88.93 206	51.24 307	92.56 214	75.47 172	89.22 137	93.00 128

casdiffmvs_mvg		85.99 54	86.09 57	85.70 77	87.65 220	67.22 173	88.69 135	93.04 42	79.64 21	85.33 70	92.54 98	73.30 36	94.50 119	83.49 77	91.14 102	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		86.29 50	86.48 46	85.71 76	91.02 91	67.21 174	92.36 30	93.78 19	78.97 33	83.51 110	91.20 137	70.65 74	95.15 87	81.96 96	94.89 42	94.77 25

anonymousdsp		78.60 226	77.15 242	82.98 198	80.51 390	67.08 175	87.24 189	89.53 189	65.66 324	75.16 279	87.19 259	52.52 284	92.25 230	77.17 147	79.34 294	89.61 268

MVS		78.19 237	76.99 246	81.78 234	85.66 277	66.99 176	84.66 268	90.47 153	55.08 420	72.02 327	85.27 310	63.83 154	94.11 135	66.10 269	89.80 127	84.24 390

HQP5-MVS						66.98 177

HQP-MVS		82.61 123	82.02 128	84.37 123	89.33 140	66.98 177	89.17 109	92.19 92	76.41 85	77.23 222	90.23 167	60.17 220	95.11 90	77.47 143	85.99 193	91.03 203

Fast-Effi-MVS+-dtu		78.02 242	76.49 258	82.62 217	83.16 344	66.96 179	86.94 199	87.45 265	72.45 188	71.49 333	84.17 337	54.79 265	91.58 254	67.61 255	80.31 282	89.30 277

F-COLMAP		76.38 280	74.33 294	82.50 220	89.28 145	66.95 180	88.41 145	89.03 217	64.05 346	66.83 383	88.61 216	46.78 352	92.89 201	57.48 347	78.55 299	87.67 323

HyFIR lowres test		77.53 256	75.40 276	83.94 160	89.59 126	66.62 181	80.36 348	88.64 237	56.29 416	76.45 242	85.17 314	57.64 239	93.28 175	61.34 313	83.10 248	91.91 174

ACMH	67.68 16	75.89 286	73.93 298	81.77 235	88.71 172	66.61 182	88.62 138	89.01 219	69.81 254	66.78 384	86.70 273	41.95 394	91.51 264	55.64 363	78.14 308	87.17 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 208	77.96 216	83.27 181	84.68 305	66.57 183	89.25 106	90.16 167	69.20 272	75.46 264	89.49 188	45.75 366	93.13 190	76.84 153	80.80 275	90.11 244

VDD-MVS		83.01 119	82.36 120	84.96 101	91.02 91	66.40 184	88.91 121	88.11 243	77.57 49	84.39 90	93.29 79	52.19 290	93.91 146	77.05 149	88.70 148	94.57 38

mvs_tets		79.13 212	77.77 226	83.22 185	84.70 304	66.37 185	89.17 109	90.19 166	69.38 264	75.40 267	89.46 191	44.17 378	93.15 188	76.78 157	80.70 277	90.14 241

PAPM_NR		83.02 118	82.41 118	84.82 109	92.47 72	66.37 185	87.93 166	91.80 112	73.82 155	77.32 219	90.66 153	67.90 110	94.90 100	70.37 226	89.48 133	93.19 115

EC-MVSNet		86.01 53	86.38 47	84.91 106	89.31 143	66.27 187	92.32 31	93.63 22	79.37 23	84.17 96	91.88 112	69.04 95	95.43 73	83.93 75	93.77 65	93.01 127

pmmvs-eth3d		70.50 351	67.83 365	78.52 312	77.37 416	66.18 188	81.82 323	81.51 359	58.90 396	63.90 410	80.42 391	42.69 387	86.28 356	58.56 337	65.30 414	83.11 404

IB-MVS	68.01 15	75.85 287	73.36 307	83.31 179	84.76 303	66.03 189	83.38 304	85.06 307	70.21 246	69.40 356	81.05 383	45.76 365	94.66 113	65.10 278	75.49 346	89.25 278
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
MS-PatchMatch		73.83 312	72.67 314	77.30 336	83.87 323	66.02 190	81.82 323	84.66 311	61.37 376	68.61 364	82.82 366	47.29 345	88.21 334	59.27 328	84.32 223	77.68 431

fmvsm_l_conf0.5_n_9		85.84 61	86.63 44	83.46 173	87.12 243	66.01 191	88.56 141	89.43 192	75.59 105	89.32 23	94.32 39	72.89 43	91.21 276	90.11 10	92.33 83	93.16 116

FE-MVS		77.78 248	75.68 269	84.08 144	88.09 197	66.00 192	83.13 310	87.79 256	68.42 290	78.01 204	85.23 312	45.50 369	95.12 88	59.11 331	85.83 199	91.11 199

test_0402		72.79 329	70.44 340	79.84 283	88.13 194	65.99 193	85.93 234	84.29 317	65.57 325	67.40 377	85.49 305	46.92 349	92.61 210	35.88 442	74.38 365	80.94 421

BH-RMVSNet		79.61 195	78.44 205	83.14 188	89.38 139	65.93 194	84.95 262	87.15 272	73.56 163	78.19 199	89.79 178	56.67 251	93.36 173	59.53 327	86.74 179	90.13 242

BH-untuned		79.47 200	78.60 201	82.05 229	89.19 150	65.91 195	86.07 231	88.52 239	72.18 193	75.42 266	87.69 243	61.15 203	93.54 164	60.38 319	86.83 178	86.70 351

cascas		76.72 271	74.64 287	82.99 197	85.78 275	65.88 196	82.33 319	89.21 209	60.85 378	72.74 315	81.02 384	47.28 346	93.75 156	67.48 257	85.02 208	89.34 276

fmvsm_s_conf0.5_n_4		85.39 72	85.75 65	84.30 128	86.70 254	65.83 197	88.77 129	89.78 177	75.46 108	88.35 31	93.73 68	69.19 90	93.06 194	91.30 3	88.44 153	94.02 64

patch_mono-2		83.65 99	84.54 84	80.99 257	90.06 116	65.83 197	84.21 283	88.74 232	71.60 204	85.01 73	92.44 99	74.51 26	83.50 383	82.15 95	92.15 84	93.64 91

MSDG		73.36 320	70.99 334	80.49 269	84.51 310	65.80 199	80.71 342	86.13 294	65.70 323	65.46 397	83.74 345	44.60 373	90.91 286	51.13 388	76.89 322	84.74 385

旧先验1					91.96 76	65.79 200		86.37 289			93.08 86	69.31 89			92.74 76	88.74 301

casdiffmvs		85.11 78	85.14 77	85.01 99	87.20 235	65.77 201	87.75 172	92.83 61	77.84 43	84.36 93	92.38 100	72.15 51	93.93 144	81.27 103	90.48 113	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mamv4		76.81 269	78.23 213	72.54 386	86.12 268	65.75 202	78.76 371	82.07 353	64.12 343	72.97 313	91.02 146	67.97 108	68.08 451	83.04 83	78.02 309	83.80 397

COLMAP_ROB	66.92 17	73.01 326	70.41 341	80.81 262	87.13 238	65.63 203	88.30 152	84.19 320	62.96 357	63.80 411	87.69 243	38.04 414	92.56 214	46.66 414	74.91 360	84.24 390
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 113	87.76 216	65.62 204	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 100	94.20 130	90.83 5	91.39 98	94.38 47

EIA-MVS		83.31 112	82.80 113	84.82 109	89.59 126	65.59 205	88.21 154	92.68 67	74.66 134	78.96 179	86.42 284	69.06 93	95.26 83	75.54 170	90.09 120	93.62 92

v7n		78.97 217	77.58 233	83.14 188	83.45 334	65.51 206	88.32 151	91.21 132	73.69 159	72.41 321	86.32 287	57.93 235	93.81 151	69.18 241	75.65 343	90.11 244

V42		79.38 206	78.24 211	82.83 204	81.10 384	65.50 207	85.55 246	89.82 176	71.57 205	78.21 198	86.12 291	60.66 212	93.18 187	75.64 167	75.46 349	89.81 263

PVSNet_BlendedMVS		80.60 173	80.02 164	82.36 223	88.85 159	65.40 208	86.16 229	92.00 100	69.34 265	78.11 201	86.09 292	66.02 134	94.27 126	71.52 213	82.06 260	87.39 330

PVSNet_Blended		80.98 156	80.34 155	82.90 201	88.85 159	65.40 208	84.43 278	92.00 100	67.62 297	78.11 201	85.05 318	66.02 134	94.27 126	71.52 213	89.50 132	89.01 286

baseline		84.93 81	84.98 78	84.80 111	87.30 233	65.39 210	87.30 187	92.88 58	77.62 47	84.04 99	92.26 102	71.81 55	93.96 138	81.31 101	90.30 116	95.03 11

test_djsdf		80.30 185	79.32 186	83.27 181	83.98 320	65.37 211	90.50 67	90.38 156	68.55 286	76.19 249	88.70 212	56.44 253	93.46 169	78.98 126	80.14 285	90.97 206

ACMH+	68.96 14	76.01 285	74.01 296	82.03 230	88.60 175	65.31 212	88.86 123	87.55 261	70.25 245	67.75 370	87.47 251	41.27 396	93.19 186	58.37 340	75.94 340	87.60 325

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 190	87.08 244	65.21 213	89.09 116	90.21 165	79.67 19	89.98 19	95.02 20	73.17 39	91.71 251	91.30 3	91.60 93	92.34 155

CR-MVSNet		73.37 318	71.27 331	79.67 288	81.32 382	65.19 214	75.92 397	80.30 376	59.92 386	72.73 316	81.19 381	52.50 285	86.69 350	59.84 323	77.71 312	87.11 341

RPMNet		73.51 316	70.49 339	82.58 219	81.32 382	65.19 214	75.92 397	92.27 85	57.60 408	72.73 316	76.45 423	52.30 288	95.43 73	48.14 409	77.71 312	87.11 341

fmvsm_s_conf0.5_n_7		83.34 110	84.03 91	81.28 248	85.73 276	65.13 216	85.40 251	89.90 175	74.96 124	82.13 128	93.89 63	66.65 120	87.92 338	86.56 48	91.05 103	90.80 211

fmvsm_s_conf0.5_n_6		85.55 67	86.20 51	83.60 168	87.32 232	65.13 216	88.86 123	91.63 119	75.41 109	88.23 35	93.45 75	68.56 101	92.47 219	89.52 17	92.78 75	93.20 114

BH-w/o		78.21 235	77.33 240	80.84 261	88.81 163	65.13 216	84.87 263	87.85 255	69.75 258	74.52 294	84.74 324	61.34 198	93.11 191	58.24 342	85.84 198	84.27 389

thisisatest0530		79.40 204	77.76 227	84.31 127	87.69 219	65.10 219	87.36 184	84.26 319	70.04 247	77.42 216	88.26 228	49.94 324	94.79 108	70.20 229	84.70 214	93.03 125

FA-MVS(test-final)		80.96 157	79.91 167	84.10 139	88.30 187	65.01 220	84.55 273	90.01 171	73.25 175	79.61 168	87.57 246	58.35 233	94.72 110	71.29 217	86.25 187	92.56 144

fmvsm_s_conf0.5_n_2		84.04 89	84.11 90	83.81 164	86.17 266	65.00 221	86.96 197	87.28 267	74.35 140	88.25 34	94.23 45	61.82 187	92.60 211	89.85 11	88.09 158	93.84 75

v10		79.74 194	78.67 199	82.97 199	84.06 318	64.95 222	87.88 169	90.62 148	73.11 179	75.11 281	86.56 280	61.46 195	94.05 137	73.68 187	75.55 345	89.90 258

fmvsm_s_conf0.1_n_2		83.80 93	83.79 94	83.83 162	85.62 279	64.94 223	87.03 194	86.62 285	74.32 141	87.97 42	94.33 38	60.67 211	92.60 211	89.72 13	87.79 161	93.96 66

SDMVSNet		80.38 180	80.18 159	80.99 257	89.03 157	64.94 223	80.45 347	89.40 193	75.19 117	76.61 239	89.98 170	60.61 214	87.69 342	76.83 154	83.55 238	90.33 234

dcpmvs_2		85.63 65	86.15 55	84.06 148	91.71 80	64.94 223	86.47 218	91.87 108	73.63 160	86.60 61	93.02 87	76.57 15	91.87 245	83.36 78	92.15 84	95.35 3

IterMVS-SCA-FT		75.43 293	73.87 300	80.11 278	82.69 357	64.85 226	81.57 328	83.47 330	69.16 273	70.49 340	84.15 338	51.95 297	88.15 335	69.23 240	72.14 385	87.34 332

MVSTER		79.01 215	77.88 221	82.38 222	83.07 345	64.80 227	84.08 289	88.95 223	69.01 279	78.69 184	87.17 260	54.70 266	92.43 221	74.69 177	80.57 279	89.89 259

Anonymous20240529		80.19 188	78.89 197	84.10 139	90.60 100	64.75 228	88.95 120	90.90 141	65.97 321	80.59 156	91.17 139	49.97 323	93.73 158	69.16 242	82.70 254	93.81 77

XVG-ACMP-BASELINE		76.11 283	74.27 295	81.62 237	83.20 341	64.67 229	83.60 299	89.75 181	69.75 258	71.85 328	87.09 262	32.78 428	92.11 234	69.99 233	80.43 281	88.09 316

viewmacassd2359aftdt		83.76 95	83.66 97	84.07 145	86.59 258	64.56 230	86.88 202	91.82 111	75.72 100	83.34 111	92.15 107	68.24 106	92.88 202	79.05 122	89.15 139	94.77 25

viewmanbaseed2359cas		83.66 98	83.55 98	84.00 156	86.81 250	64.53 231	86.65 212	91.75 116	74.89 126	83.15 115	91.68 118	68.74 99	92.83 206	79.02 123	89.24 136	94.63 34

v1192		79.59 197	78.43 206	83.07 193	83.55 332	64.52 232	86.93 200	90.58 149	70.83 224	77.78 210	85.90 293	59.15 226	93.94 141	73.96 186	77.19 319	90.76 214

Fast-Effi-MVS+		80.81 161	79.92 166	83.47 172	88.85 159	64.51 233	85.53 248	89.39 194	70.79 225	78.49 191	85.06 317	67.54 113	93.58 160	67.03 264	86.58 181	92.32 157

v1144		80.03 190	79.03 193	83.01 196	83.78 325	64.51 233	87.11 192	90.57 151	71.96 198	78.08 203	86.20 289	61.41 196	93.94 141	74.93 176	77.23 317	90.60 222

v8		79.97 192	79.02 194	82.80 207	84.09 317	64.50 235	87.96 163	90.29 163	74.13 149	75.24 277	86.81 266	62.88 170	93.89 149	74.39 182	75.40 352	90.00 252

EPP-MVSNet		83.40 108	83.02 108	84.57 116	90.13 110	64.47 236	92.32 31	90.73 146	74.45 139	79.35 175	91.10 140	69.05 94	95.12 88	72.78 199	87.22 170	94.13 58

GeoE		81.71 139	81.01 142	83.80 165	89.51 130	64.45 237	88.97 119	88.73 233	71.27 212	78.63 187	89.76 179	66.32 127	93.20 184	69.89 234	86.02 192	93.74 82

UniMVSNet (Re)		81.60 143	81.11 139	83.09 190	88.38 184	64.41 238	87.60 175	93.02 46	78.42 37	78.56 189	88.16 230	69.78 82	93.26 177	69.58 238	76.49 329	91.60 183

LTVRE_ROB	69.57 13	76.25 281	74.54 290	81.41 243	88.60 175	64.38 239	79.24 362	89.12 215	70.76 227	69.79 354	87.86 239	49.09 336	93.20 184	56.21 362	80.16 283	86.65 352
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
Anonymous20231211		78.97 217	77.69 230	82.81 206	90.54 102	64.29 240	90.11 78	91.51 124	65.01 333	76.16 253	88.13 235	50.56 315	93.03 198	69.68 237	77.56 316	91.11 199

testdata				79.97 280	90.90 94	64.21 241		84.71 310	59.27 392	85.40 69	92.91 88	62.02 184	89.08 320	68.95 244	91.37 99	86.63 353

v2v482		80.23 186	79.29 187	83.05 194	83.62 330	64.14 242	87.04 193	89.97 172	73.61 161	78.18 200	87.22 257	61.10 204	93.82 150	76.11 161	76.78 326	91.18 197

VDDNet		81.52 147	80.67 147	84.05 151	90.44 104	64.13 243	89.73 87	85.91 296	71.11 215	83.18 113	93.48 72	50.54 316	93.49 166	73.40 192	88.25 155	94.54 41

PAPR		81.66 142	80.89 144	83.99 157	90.27 107	64.00 244	86.76 209	91.77 115	68.84 282	77.13 229	89.50 187	67.63 112	94.88 102	67.55 256	88.52 151	93.09 120

AstraMVS		80.81 161	80.14 162	82.80 207	86.05 271	63.96 245	86.46 219	85.90 297	73.71 158	80.85 152	90.56 157	54.06 273	91.57 256	79.72 120	83.97 227	92.86 134

v144192		79.47 200	78.37 207	82.78 211	83.35 335	63.96 245	86.96 197	90.36 159	69.99 250	77.50 214	85.67 300	60.66 212	93.77 154	74.27 183	76.58 327	90.62 220

v1921920		79.22 209	78.03 215	82.80 207	83.30 337	63.94 247	86.80 205	90.33 160	69.91 253	77.48 215	85.53 304	58.44 232	93.75 156	73.60 188	76.85 324	90.71 218

guyue		81.13 154	80.64 148	82.60 218	86.52 259	63.92 248	86.69 211	87.73 258	73.97 150	80.83 153	89.69 180	56.70 250	91.33 272	78.26 138	85.40 206	92.54 145

tttt0517		79.40 204	77.91 218	83.90 161	88.10 196	63.84 249	88.37 149	84.05 321	71.45 207	76.78 233	89.12 198	49.93 326	94.89 101	70.18 230	83.18 247	92.96 130

diffmvs_AUTHOR		82.38 126	82.27 122	82.73 215	83.26 338	63.80 250	83.89 290	89.76 179	73.35 171	82.37 124	90.84 150	66.25 128	90.79 288	82.77 87	87.93 159	93.59 94

thisisatest0515		77.33 260	75.38 277	83.18 186	85.27 290	63.80 250	82.11 322	83.27 333	65.06 331	75.91 254	83.84 342	49.54 328	94.27 126	67.24 260	86.19 188	91.48 190

diffmvs		82.10 129	81.88 131	82.76 213	83.00 348	63.78 252	83.68 295	89.76 179	72.94 183	82.02 130	89.85 173	65.96 136	90.79 288	82.38 94	87.30 169	93.71 83
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_yl		81.17 152	80.47 153	83.24 183	89.13 152	63.62 253	86.21 227	89.95 173	72.43 191	81.78 135	89.61 184	57.50 241	93.58 160	70.75 221	86.90 175	92.52 146

DCV-MVSNet		81.17 152	80.47 153	83.24 183	89.13 152	63.62 253	86.21 227	89.95 173	72.43 191	81.78 135	89.61 184	57.50 241	93.58 160	70.75 221	86.90 175	92.52 146

AllTest		70.96 344	68.09 359	79.58 290	85.15 293	63.62 253	84.58 272	79.83 381	62.31 366	60.32 423	86.73 267	32.02 429	88.96 324	50.28 393	71.57 389	86.15 359

TestCases				79.58 290	85.15 293	63.62 253		79.83 381	62.31 366	60.32 423	86.73 267	32.02 429	88.96 324	50.28 393	71.57 389	86.15 359

icg_test_0407_2		78.92 219	78.93 196	78.90 302	87.13 238	63.59 257	76.58 393	89.33 196	70.51 234	77.82 207	89.03 201	61.84 185	81.38 398	72.56 204	85.56 202	91.74 178

IMVS_0407		80.61 171	79.90 168	82.75 214	87.13 238	63.59 257	85.33 252	89.33 196	70.51 234	77.82 207	89.03 201	61.84 185	92.91 200	72.56 204	85.56 202	91.74 178

IMVS_0404		77.16 263	76.42 261	79.37 293	87.13 238	63.59 257	77.12 391	89.33 196	70.51 234	66.22 394	89.03 201	50.36 318	82.78 388	72.56 204	85.56 202	91.74 178

IMVS_0403		80.80 164	80.12 163	82.87 203	87.13 238	63.59 257	85.19 253	89.33 196	70.51 234	78.49 191	89.03 201	63.26 159	93.27 176	72.56 204	85.56 202	91.74 178

v1240		78.99 216	77.78 225	82.64 216	83.21 340	63.54 261	86.62 214	90.30 162	69.74 260	77.33 218	85.68 299	57.04 247	93.76 155	73.13 196	76.92 321	90.62 220

CHOSEN 280x420		66.51 384	64.71 386	71.90 389	81.45 377	63.52 262	57.98 453	68.95 436	53.57 423	62.59 416	76.70 421	46.22 359	75.29 435	55.25 364	79.68 288	76.88 433

IterMVS		74.29 304	72.94 312	78.35 315	81.53 376	63.49 263	81.58 327	82.49 348	68.06 294	69.99 349	83.69 348	51.66 304	85.54 365	65.85 272	71.64 388	86.01 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 135	81.54 134	82.92 200	88.46 180	63.46 264	87.13 190	92.37 82	80.19 12	78.38 194	89.14 197	71.66 60	93.05 195	70.05 231	76.46 330	92.25 160

DU-MVS		81.12 155	80.52 151	82.90 201	87.80 211	63.46 264	87.02 195	91.87 108	79.01 31	78.38 194	89.07 199	65.02 143	93.05 195	70.05 231	76.46 330	92.20 163

LFMVS		81.82 137	81.23 137	83.57 171	91.89 78	63.43 266	89.84 81	81.85 356	77.04 69	83.21 112	93.10 82	52.26 289	93.43 171	71.98 211	89.95 124	93.85 73

NR-MVSNet		80.23 186	79.38 183	82.78 211	87.80 211	63.34 267	86.31 224	91.09 138	79.01 31	72.17 325	89.07 199	67.20 117	92.81 207	66.08 270	75.65 343	92.20 163

IS-MVSNet		83.15 114	82.81 112	84.18 137	89.94 119	63.30 268	91.59 46	88.46 240	79.04 30	79.49 170	92.16 105	65.10 142	94.28 125	67.71 254	91.86 91	94.95 12

TR-MVS		77.44 257	76.18 264	81.20 251	88.24 188	63.24 269	84.61 271	86.40 288	67.55 298	77.81 209	86.48 283	54.10 271	93.15 188	57.75 346	82.72 253	87.20 336

MVS_Test		83.15 114	83.06 107	83.41 177	86.86 247	63.21 270	86.11 230	92.00 100	74.31 142	82.87 118	89.44 194	70.03 79	93.21 181	77.39 145	88.50 152	93.81 77

IterMVS-LS		80.06 189	79.38 183	82.11 228	85.89 272	63.20 271	86.79 206	89.34 195	74.19 146	75.45 265	86.72 269	66.62 121	92.39 223	72.58 201	76.86 323	90.75 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 177	79.98 165	82.12 226	84.28 312	63.19 272	86.41 220	88.95 223	74.18 147	78.69 184	87.54 249	66.62 121	92.43 221	72.57 202	80.57 279	90.74 216

CANet_DTU		80.61 171	79.87 169	82.83 204	85.60 280	63.17 273	87.36 184	88.65 236	76.37 89	75.88 255	88.44 222	53.51 278	93.07 193	73.30 193	89.74 128	92.25 160

MGCFI-Net		85.06 80	85.51 69	83.70 166	89.42 135	63.01 274	89.43 97	92.62 74	76.43 84	87.53 48	91.34 132	72.82 46	93.42 172	81.28 102	88.74 147	94.66 33

GBi-Net		78.40 230	77.40 237	81.40 244	87.60 221	63.01 274	88.39 146	89.28 202	71.63 201	75.34 270	87.28 253	54.80 262	91.11 277	62.72 294	79.57 289	90.09 246

test1		78.40 230	77.40 237	81.40 244	87.60 221	63.01 274	88.39 146	89.28 202	71.63 201	75.34 270	87.28 253	54.80 262	91.11 277	62.72 294	79.57 289	90.09 246

FMVSNet1		77.44 257	76.12 265	81.40 244	86.81 250	63.01 274	88.39 146	89.28 202	70.49 238	74.39 296	87.28 253	49.06 337	91.11 277	60.91 315	78.52 300	90.09 246

TAPA-MVS	73.13 9	79.15 211	77.94 217	82.79 210	89.59 126	62.99 278	88.16 157	91.51 124	65.77 322	77.14 228	91.09 141	60.91 207	93.21 181	50.26 395	87.05 173	92.17 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS		82.60 125	82.10 125	84.10 139	87.98 203	62.94 279	87.45 181	91.27 130	77.42 56	79.85 165	90.28 164	56.62 252	94.70 112	79.87 119	88.15 157	94.67 30

FMVSNet2		78.20 236	77.21 241	81.20 251	87.60 221	62.89 280	87.47 179	89.02 218	71.63 201	75.29 276	87.28 253	54.80 262	91.10 280	62.38 299	79.38 293	89.61 268

VortexMVS		78.57 228	77.89 220	80.59 266	85.89 272	62.76 281	85.61 241	89.62 186	72.06 196	74.99 285	85.38 308	55.94 255	90.77 291	74.99 175	76.58 327	88.23 312

GA-MVS		76.87 268	75.17 282	81.97 232	82.75 355	62.58 282	81.44 331	86.35 290	72.16 195	74.74 289	82.89 364	46.20 360	92.02 237	68.85 246	81.09 270	91.30 195

D2MVS		74.82 300	73.21 308	79.64 289	79.81 399	62.56 283	80.34 349	87.35 266	64.37 340	68.86 361	82.66 368	46.37 356	90.10 299	67.91 253	81.24 268	86.25 356

viewmambaseed2359dif		80.41 178	79.84 170	82.12 226	82.95 352	62.50 284	83.39 303	88.06 247	67.11 302	80.98 147	90.31 163	66.20 130	91.01 284	74.62 178	84.90 210	92.86 134

viewdifsd2359ckpt11		80.37 182	79.73 173	82.30 224	83.70 328	62.39 285	84.20 284	86.67 281	73.22 177	80.90 149	90.62 154	63.00 168	91.56 257	76.81 155	78.44 302	92.95 131

viewmsd2359difaftdt		80.37 182	79.73 173	82.30 224	83.70 328	62.39 285	84.20 284	86.67 281	73.22 177	80.90 149	90.62 154	63.00 168	91.56 257	76.81 155	78.44 302	92.95 131

FMVSNet3		77.88 246	76.85 249	80.97 259	86.84 249	62.36 287	86.52 217	88.77 228	71.13 214	75.34 270	86.66 275	54.07 272	91.10 280	62.72 294	79.57 289	89.45 272

TranMVSNet+NR-MVSNet		80.84 159	80.31 156	82.42 221	87.85 208	62.33 288	87.74 173	91.33 129	80.55 9	77.99 205	89.86 172	65.23 141	92.62 209	67.05 263	75.24 357	92.30 158

1314		76.53 273	75.30 280	80.21 276	83.93 321	62.32 289	84.66 268	88.81 226	60.23 383	70.16 346	84.07 339	55.30 259	90.73 292	67.37 258	83.21 246	87.59 327

MG-MVS		83.41 107	83.45 100	83.28 180	92.74 67	62.28 290	88.17 156	89.50 190	75.22 114	81.49 138	92.74 97	66.75 119	95.11 90	72.85 198	91.58 95	92.45 152

SCA		74.22 306	72.33 319	79.91 281	84.05 319	62.17 291	79.96 355	79.29 388	66.30 316	72.38 322	80.13 396	51.95 297	88.60 330	59.25 329	77.67 315	88.96 290

PMMVS		69.34 363	68.67 352	71.35 395	75.67 422	62.03 292	75.17 403	73.46 422	50.00 433	68.68 362	79.05 405	52.07 295	78.13 411	61.16 314	82.77 251	73.90 437

eth_miper_zixun_eth		77.92 245	76.69 255	81.61 239	83.00 348	61.98 293	83.15 309	89.20 210	69.52 262	74.86 288	84.35 331	61.76 188	92.56 214	71.50 215	72.89 379	90.28 237

v148		78.72 223	77.80 224	81.47 241	82.73 356	61.96 294	86.30 225	88.08 245	73.26 174	76.18 250	85.47 306	62.46 175	92.36 225	71.92 212	73.82 371	90.09 246

PAPM		77.68 253	76.40 262	81.51 240	87.29 234	61.85 295	83.78 292	89.59 187	64.74 335	71.23 335	88.70 212	62.59 172	93.66 159	52.66 379	87.03 174	89.01 286

cl22		78.07 240	77.01 244	81.23 250	82.37 365	61.83 296	83.55 300	87.98 249	68.96 280	75.06 283	83.87 340	61.40 197	91.88 244	73.53 189	76.39 332	89.98 255

baseline2		75.70 288	73.83 301	81.30 247	83.26 338	61.79 297	82.57 318	80.65 368	66.81 304	66.88 382	83.42 354	57.86 237	92.19 232	63.47 288	79.57 289	89.91 257

JIA-IIPM		66.32 386	62.82 398	76.82 340	77.09 417	61.72 298	65.34 444	75.38 413	58.04 405	64.51 404	62.32 445	42.05 393	86.51 353	51.45 386	69.22 400	82.21 413

miper_ehance_all_eth		78.59 227	77.76 227	81.08 255	82.66 358	61.56 299	83.65 296	89.15 212	68.87 281	75.55 261	83.79 344	66.49 124	92.03 236	73.25 194	76.39 332	89.64 267

c3_l		78.75 221	77.91 218	81.26 249	82.89 353	61.56 299	84.09 288	89.13 214	69.97 251	75.56 260	84.29 332	66.36 126	92.09 235	73.47 191	75.48 347	90.12 243

miper_enhance_ethall		77.87 247	76.86 248	80.92 260	81.65 372	61.38 301	82.68 316	88.98 220	65.52 326	75.47 262	82.30 373	65.76 138	92.00 238	72.95 197	76.39 332	89.39 274

mmtdpeth		74.16 307	73.01 311	77.60 332	83.72 327	61.13 302	85.10 258	85.10 306	72.06 196	77.21 226	80.33 393	43.84 380	85.75 361	77.14 148	52.61 441	85.91 366

ppachtmachnet_test		70.04 357	67.34 375	78.14 318	79.80 400	61.13 302	79.19 364	80.59 369	59.16 393	65.27 399	79.29 404	46.75 353	87.29 346	49.33 400	66.72 407	86.00 365

sc_t1		72.19 335	69.51 346	80.23 275	84.81 301	61.09 304	84.68 267	80.22 378	60.70 379	71.27 334	83.58 351	36.59 419	89.24 316	60.41 318	63.31 419	90.37 232

TDRefinement		67.49 376	64.34 387	76.92 339	73.47 435	61.07 305	84.86 264	82.98 342	59.77 387	58.30 430	85.13 315	26.06 439	87.89 339	47.92 411	60.59 427	81.81 417

VNet		82.21 128	82.41 118	81.62 237	90.82 96	60.93 306	84.47 274	89.78 177	76.36 90	84.07 98	91.88 112	64.71 146	90.26 296	70.68 223	88.89 142	93.66 85

ab-mvs		79.51 198	78.97 195	81.14 253	88.46 180	60.91 307	83.84 291	89.24 208	70.36 239	79.03 178	88.87 209	63.23 161	90.21 298	65.12 277	82.57 255	92.28 159

PatchmatchNet		73.12 324	71.33 330	78.49 313	83.18 342	60.85 308	79.63 357	78.57 393	64.13 342	71.73 329	79.81 401	51.20 308	85.97 360	57.40 349	76.36 337	88.66 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 173	80.55 150	80.76 263	88.07 198	60.80 309	86.86 203	91.58 122	75.67 104	80.24 161	89.45 193	63.34 156	90.25 297	70.51 225	79.22 296	91.23 196

EGC-MVSNET		52.07 416	47.05 420	67.14 416	83.51 333	60.71 310	80.50 346	67.75 438	0.07 466	0.43 467	75.85 428	24.26 444	81.54 396	28.82 449	62.25 421	59.16 449

Anonymous202405211		78.25 233	77.01 244	81.99 231	91.03 90	60.67 311	84.77 265	83.90 323	70.65 232	80.00 164	91.20 137	41.08 398	91.43 268	65.21 276	85.26 207	93.85 73

ITE_SJBPF				78.22 316	81.77 371	60.57 312		83.30 332	69.25 269	67.54 372	87.20 258	36.33 421	87.28 347	54.34 370	74.62 363	86.80 348

MDA-MVSNet-bldmvs		66.68 382	63.66 392	75.75 347	79.28 407	60.56 313	73.92 413	78.35 395	64.43 338	50.13 445	79.87 400	44.02 379	83.67 380	46.10 419	56.86 431	83.03 406

cl____		77.72 250	76.76 252	80.58 267	82.49 362	60.48 314	83.09 311	87.87 253	69.22 270	74.38 297	85.22 313	62.10 182	91.53 262	71.09 218	75.41 351	89.73 266

DIV-MVS_self_test		77.72 250	76.76 252	80.58 267	82.48 363	60.48 314	83.09 311	87.86 254	69.22 270	74.38 297	85.24 311	62.10 182	91.53 262	71.09 218	75.40 352	89.74 265

1112_ss		77.40 259	76.43 260	80.32 273	89.11 156	60.41 316	83.65 296	87.72 259	62.13 369	73.05 312	86.72 269	62.58 173	89.97 302	62.11 305	80.80 275	90.59 223

tt0805		78.73 222	77.83 222	81.43 242	85.17 291	60.30 317	89.41 100	90.90 141	71.21 213	77.17 227	88.73 211	46.38 355	93.21 181	72.57 202	78.96 297	90.79 212

UniMVSNet_ETH3D		79.10 213	78.24 211	81.70 236	86.85 248	60.24 318	87.28 188	88.79 227	74.25 145	76.84 230	90.53 159	49.48 329	91.56 257	67.98 252	82.15 258	93.29 107

HY-MVS	69.67 12	77.95 244	77.15 242	80.36 271	87.57 225	60.21 319	83.37 305	87.78 257	66.11 317	75.37 269	87.06 264	63.27 158	90.48 295	61.38 312	82.43 256	90.40 231

sd_testset		77.70 252	77.40 237	78.60 307	89.03 157	60.02 320	79.00 367	85.83 298	75.19 117	76.61 239	89.98 170	54.81 261	85.46 367	62.63 298	83.55 238	90.33 234

RPSCF		73.23 323	71.46 327	78.54 310	82.50 361	59.85 321	82.18 321	82.84 346	58.96 395	71.15 337	89.41 195	45.48 370	84.77 374	58.82 335	71.83 387	91.02 205

test_cas_vis1_n_1920		73.76 313	73.74 302	73.81 374	75.90 420	59.77 322	80.51 345	82.40 349	58.30 401	81.62 137	85.69 298	44.35 377	76.41 423	76.29 159	78.61 298	85.23 376

dmvs_re		71.14 342	70.58 337	72.80 383	81.96 368	59.68 323	75.60 401	79.34 387	68.55 286	69.27 359	80.72 389	49.42 330	76.54 420	52.56 380	77.79 311	82.19 414

miper_lstm_enhance		74.11 308	73.11 310	77.13 338	80.11 394	59.62 324	72.23 417	86.92 278	66.76 306	70.40 341	82.92 363	56.93 248	82.92 387	69.06 243	72.63 380	88.87 293

OurMVSNet-221017-0		74.26 305	72.42 318	79.80 284	83.76 326	59.59 325	85.92 235	86.64 283	66.39 315	66.96 381	87.58 245	39.46 404	91.60 253	65.76 273	69.27 399	88.22 313

Patchmatch-RL test		70.24 354	67.78 367	77.61 330	77.43 415	59.57 326	71.16 421	70.33 429	62.94 358	68.65 363	72.77 435	50.62 314	85.49 366	69.58 238	66.58 409	87.77 322

tt0320-xc		70.11 356	67.45 373	78.07 321	85.33 288	59.51 327	83.28 306	78.96 391	58.77 397	67.10 380	80.28 394	36.73 418	87.42 345	56.83 357	59.77 429	87.29 334

OpenMVS_ROB	64.09 19	70.56 350	68.19 356	77.65 329	80.26 391	59.41 328	85.01 260	82.96 343	58.76 398	65.43 398	82.33 372	37.63 416	91.23 275	45.34 424	76.03 339	82.32 412

tt0320		70.49 352	68.03 360	77.89 323	84.78 302	59.12 329	83.55 300	80.44 373	58.13 403	67.43 376	80.41 392	39.26 406	87.54 344	55.12 365	63.18 420	86.99 344

our_test_3		69.14 364	67.00 377	75.57 350	79.80 400	58.80 330	77.96 383	77.81 397	59.55 389	62.90 415	78.25 414	47.43 344	83.97 378	51.71 383	67.58 406	83.93 395

ADS-MVSNet2		66.20 389	63.33 393	74.82 362	79.92 396	58.75 331	67.55 436	75.19 414	53.37 424	65.25 400	75.86 426	42.32 389	80.53 403	41.57 432	68.91 401	85.18 377

pm-mvs1		77.25 262	76.68 256	78.93 301	84.22 314	58.62 332	86.41 220	88.36 241	71.37 208	73.31 308	88.01 236	61.22 202	89.15 319	64.24 285	73.01 378	89.03 285

MonoMVSNet		76.49 277	75.80 266	78.58 308	81.55 375	58.45 333	86.36 223	86.22 291	74.87 129	74.73 290	83.73 346	51.79 302	88.73 327	70.78 220	72.15 384	88.55 307

WR-MVS		79.49 199	79.22 190	80.27 274	88.79 168	58.35 334	85.06 259	88.61 238	78.56 35	77.65 212	88.34 224	63.81 155	90.66 293	64.98 279	77.22 318	91.80 177

FIs		82.07 131	82.42 117	81.04 256	88.80 167	58.34 335	88.26 153	93.49 27	76.93 71	78.47 193	91.04 143	69.92 81	92.34 227	69.87 235	84.97 209	92.44 153

CostFormer		75.24 297	73.90 299	79.27 295	82.65 359	58.27 336	80.80 337	82.73 347	61.57 373	75.33 274	83.13 359	55.52 257	91.07 283	64.98 279	78.34 307	88.45 308

Test_1112_low_res		76.40 279	75.44 274	79.27 295	89.28 145	58.09 337	81.69 326	87.07 273	59.53 390	72.48 320	86.67 274	61.30 199	89.33 313	60.81 317	80.15 284	90.41 230

tfpnnormal		74.39 303	73.16 309	78.08 320	86.10 270	58.05 338	84.65 270	87.53 262	70.32 242	71.22 336	85.63 301	54.97 260	89.86 303	43.03 428	75.02 359	86.32 355

test-LLR		72.94 328	72.43 317	74.48 365	81.35 380	58.04 339	78.38 376	77.46 400	66.66 308	69.95 350	79.00 407	48.06 342	79.24 406	66.13 267	84.83 211	86.15 359

test-mter		71.41 340	70.39 342	74.48 365	81.35 380	58.04 339	78.38 376	77.46 400	60.32 382	69.95 350	79.00 407	36.08 422	79.24 406	66.13 267	84.83 211	86.15 359

mvs_anonymous		79.42 203	79.11 192	80.34 272	84.45 311	57.97 341	82.59 317	87.62 260	67.40 301	76.17 252	88.56 219	68.47 102	89.59 309	70.65 224	86.05 191	93.47 100

tpm cat1		70.57 349	68.31 355	77.35 335	82.41 364	57.95 342	78.08 381	80.22 378	52.04 427	68.54 365	77.66 418	52.00 296	87.84 340	51.77 382	72.07 386	86.25 356

SixPastTwentyTwo		73.37 318	71.26 332	79.70 286	85.08 296	57.89 343	85.57 242	83.56 328	71.03 220	65.66 396	85.88 294	42.10 392	92.57 213	59.11 331	63.34 418	88.65 303

thres200		75.55 290	74.47 291	78.82 303	87.78 214	57.85 344	83.07 313	83.51 329	72.44 190	75.84 256	84.42 327	52.08 294	91.75 248	47.41 412	83.64 237	86.86 347

XXY-MVS		75.41 294	75.56 272	74.96 359	83.59 331	57.82 345	80.59 344	83.87 324	66.54 314	74.93 287	88.31 225	63.24 160	80.09 404	62.16 303	76.85 324	86.97 345

reproduce_monomvs		75.40 295	74.38 293	78.46 314	83.92 322	57.80 346	83.78 292	86.94 276	73.47 167	72.25 324	84.47 326	38.74 409	89.27 315	75.32 173	70.53 394	88.31 311

K. test v3		71.19 341	68.51 353	79.21 297	83.04 347	57.78 347	84.35 281	76.91 407	72.90 184	62.99 414	82.86 365	39.27 405	91.09 282	61.65 309	52.66 440	88.75 299

tfpn200view9		76.42 278	75.37 278	79.55 292	89.13 152	57.65 348	85.17 254	83.60 326	73.41 169	76.45 242	86.39 285	52.12 291	91.95 240	48.33 405	83.75 232	89.07 279

thres400		76.50 274	75.37 278	79.86 282	89.13 152	57.65 348	85.17 254	83.60 326	73.41 169	76.45 242	86.39 285	52.12 291	91.95 240	48.33 405	83.75 232	90.00 252

CMPMVS	51.72 21	70.19 355	68.16 357	76.28 343	73.15 438	57.55 350	79.47 359	83.92 322	48.02 436	56.48 436	84.81 322	43.13 384	86.42 355	62.67 297	81.81 264	84.89 383
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 301	73.39 305	78.61 306	81.38 379	57.48 351	86.64 213	87.95 251	64.99 334	70.18 344	86.61 276	50.43 317	89.52 310	62.12 304	70.18 396	88.83 295

test_vis1_n_1920		75.52 291	75.78 267	74.75 364	79.84 398	57.44 352	83.26 307	85.52 301	62.83 360	79.34 176	86.17 290	45.10 371	79.71 405	78.75 128	81.21 269	87.10 343

PVSNet_0	57.27 20	61.67 401	59.27 404	68.85 408	79.61 403	57.44 352	68.01 434	73.44 423	55.93 417	58.54 429	70.41 440	44.58 374	77.55 415	47.01 413	35.91 452	71.55 440

thres600view7		76.50 274	75.44 274	79.68 287	89.40 137	57.16 354	85.53 248	83.23 334	73.79 156	76.26 247	87.09 262	51.89 299	91.89 243	48.05 410	83.72 235	90.00 252

lessismore_v0				78.97 300	81.01 385	57.15 355		65.99 442		61.16 420	82.82 366	39.12 407	91.34 271	59.67 325	46.92 447	88.43 309

TransMVSNet (Re)		75.39 296	74.56 289	77.86 324	85.50 284	57.10 356	86.78 207	86.09 295	72.17 194	71.53 332	87.34 252	63.01 167	89.31 314	56.84 356	61.83 422	87.17 337

thres100view900		76.50 274	75.55 273	79.33 294	89.52 129	56.99 357	85.83 239	83.23 334	73.94 152	76.32 246	87.12 261	51.89 299	91.95 240	48.33 405	83.75 232	89.07 279

TESTMET0.1,1		69.89 359	69.00 351	72.55 385	79.27 408	56.85 358	78.38 376	74.71 419	57.64 407	68.09 368	77.19 420	37.75 415	76.70 419	63.92 286	84.09 226	84.10 393

WTY-MVS		75.65 289	75.68 269	75.57 350	86.40 261	56.82 359	77.92 385	82.40 349	65.10 330	76.18 250	87.72 241	63.13 166	80.90 401	60.31 320	81.96 261	89.00 288

MDA-MVSNet_test_wron		65.03 391	62.92 395	71.37 393	75.93 419	56.73 360	69.09 433	74.73 418	57.28 411	54.03 440	77.89 415	45.88 362	74.39 438	49.89 397	61.55 423	82.99 407

pmmvs3		57.79 405	54.26 410	68.37 411	64.02 453	56.72 361	75.12 406	65.17 444	40.20 445	52.93 441	69.86 441	20.36 450	75.48 432	45.45 423	55.25 438	72.90 439

tpm2		73.26 322	71.46 327	78.63 305	83.34 336	56.71 362	80.65 343	80.40 375	56.63 414	73.55 306	82.02 378	51.80 301	91.24 274	56.35 361	78.42 305	87.95 317

TinyColmap		67.30 379	64.81 385	74.76 363	81.92 370	56.68 363	80.29 350	81.49 360	60.33 381	56.27 437	83.22 356	24.77 443	87.66 343	45.52 422	69.47 398	79.95 426

YYNet1		65.03 391	62.91 396	71.38 392	75.85 421	56.60 364	69.12 432	74.66 420	57.28 411	54.12 439	77.87 416	45.85 363	74.48 437	49.95 396	61.52 424	83.05 405

PM-MVS		66.41 385	64.14 388	73.20 380	73.92 430	56.45 365	78.97 368	64.96 446	63.88 350	64.72 403	80.24 395	19.84 451	83.44 384	66.24 266	64.52 416	79.71 427

PVSNet	64.34 18	72.08 337	70.87 336	75.69 348	86.21 264	56.44 366	74.37 411	80.73 367	62.06 370	70.17 345	82.23 375	42.86 386	83.31 385	54.77 368	84.45 220	87.32 333

pmmvs5		71.55 339	70.20 344	75.61 349	77.83 413	56.39 367	81.74 325	80.89 364	57.76 406	67.46 374	84.49 325	49.26 334	85.32 369	57.08 352	75.29 355	85.11 380

testing11		75.14 298	74.01 296	78.53 311	88.16 191	56.38 368	80.74 341	80.42 374	70.67 228	72.69 318	83.72 347	43.61 382	89.86 303	62.29 301	83.76 231	89.36 275

WR-MVS_H		78.51 229	78.49 203	78.56 309	88.02 200	56.38 368	88.43 144	92.67 68	77.14 64	73.89 301	87.55 248	66.25 128	89.24 316	58.92 333	73.55 373	90.06 250

MIMVSNet		70.69 348	69.30 347	74.88 361	84.52 309	56.35 370	75.87 399	79.42 385	64.59 336	67.76 369	82.41 370	41.10 397	81.54 396	46.64 416	81.34 266	86.75 350

USDC		70.33 353	68.37 354	76.21 344	80.60 388	56.23 371	79.19 364	86.49 286	60.89 377	61.29 419	85.47 306	31.78 431	89.47 312	53.37 376	76.21 338	82.94 408

Baseline_NR-MVSNet		78.15 238	78.33 209	77.61 330	85.79 274	56.21 372	86.78 207	85.76 299	73.60 162	77.93 206	87.57 246	65.02 143	88.99 321	67.14 262	75.33 354	87.63 324

tpmvs		71.09 343	69.29 348	76.49 342	82.04 367	56.04 373	78.92 369	81.37 362	64.05 346	67.18 379	78.28 413	49.74 327	89.77 305	49.67 398	72.37 381	83.67 398

FC-MVSNet-test		81.52 147	82.02 128	80.03 279	88.42 183	55.97 374	87.95 164	93.42 30	77.10 67	77.38 217	90.98 149	69.96 80	91.79 246	68.46 250	84.50 216	92.33 156

testing91		76.54 272	75.66 271	79.18 298	88.43 182	55.89 375	81.08 334	83.00 341	73.76 157	75.34 270	84.29 332	46.20 360	90.07 300	64.33 283	84.50 216	91.58 185

mvs5depth		69.45 362	67.45 373	75.46 354	73.93 429	55.83 376	79.19 364	83.23 334	66.89 303	71.63 331	83.32 355	33.69 427	85.09 370	59.81 324	55.34 437	85.46 372

GG-mvs-BLEND				75.38 355	81.59 374	55.80 377	79.32 361	69.63 432		67.19 378	73.67 433	43.24 383	88.90 326	50.41 390	84.50 216	81.45 418

VPNet		78.69 224	78.66 200	78.76 304	88.31 186	55.72 378	84.45 277	86.63 284	76.79 75	78.26 197	90.55 158	59.30 225	89.70 308	66.63 265	77.05 320	90.88 209

baseline1		76.98 266	76.75 254	77.66 328	88.13 194	55.66 379	85.12 257	81.89 354	73.04 181	76.79 232	88.90 207	62.43 176	87.78 341	63.30 291	71.18 391	89.55 270

test_vis1_rt		60.28 402	58.42 405	65.84 419	67.25 448	55.60 380	70.44 426	60.94 452	44.33 441	59.00 427	66.64 442	24.91 442	68.67 449	62.80 293	69.48 397	73.25 438

testing99		76.09 284	75.12 283	79.00 299	88.16 191	55.50 381	80.79 338	81.40 361	73.30 173	75.17 278	84.27 335	44.48 375	90.02 301	64.28 284	84.22 225	91.48 190

testing222		74.04 309	72.66 315	78.19 317	87.89 206	55.36 382	81.06 335	79.20 389	71.30 211	74.65 292	83.57 352	39.11 408	88.67 329	51.43 387	85.75 200	90.53 225

FMVSNet5		69.50 361	67.96 361	74.15 370	82.97 351	55.35 383	80.01 354	82.12 352	62.56 364	63.02 412	81.53 380	36.92 417	81.92 394	48.42 404	74.06 367	85.17 379

test_fmvs1_n		70.86 346	70.24 343	72.73 384	72.51 442	55.28 384	81.27 333	79.71 383	51.49 431	78.73 183	84.87 320	27.54 438	77.02 417	76.06 162	79.97 287	85.88 367

test_vis1_n		69.85 360	69.21 349	71.77 390	72.66 441	55.27 385	81.48 329	76.21 411	52.03 428	75.30 275	83.20 358	28.97 436	76.22 425	74.60 179	78.41 306	83.81 396

test_fmvs1		70.93 345	70.52 338	72.16 388	73.71 431	55.05 386	80.82 336	78.77 392	51.21 432	78.58 188	84.41 328	31.20 433	76.94 418	75.88 165	80.12 286	84.47 388

sss		73.60 315	73.64 303	73.51 376	82.80 354	55.01 387	76.12 395	81.69 357	62.47 365	74.68 291	85.85 296	57.32 243	78.11 412	60.86 316	80.93 271	87.39 330

mvsany_test1		62.30 399	61.26 403	65.41 420	69.52 444	54.86 388	66.86 438	49.78 460	46.65 437	68.50 366	83.21 357	49.15 335	66.28 452	56.93 355	60.77 425	75.11 436

ECVR-MVS		79.61 195	79.26 188	80.67 265	90.08 112	54.69 389	87.89 168	77.44 402	74.88 127	80.27 160	92.79 94	48.96 339	92.45 220	68.55 248	92.50 80	94.86 19

EPNet_dtu		75.46 292	74.86 284	77.23 337	82.57 360	54.60 390	86.89 201	83.09 338	71.64 200	66.25 393	85.86 295	55.99 254	88.04 337	54.92 367	86.55 182	89.05 284
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 234	78.34 208	77.84 325	87.83 210	54.54 391	87.94 165	91.17 134	77.65 46	73.48 307	88.49 220	62.24 180	88.43 332	62.19 302	74.07 366	90.55 224

gg-mvs-nofinetune		69.95 358	67.96 361	75.94 345	83.07 345	54.51 392	77.23 390	70.29 430	63.11 354	70.32 342	62.33 444	43.62 381	88.69 328	53.88 373	87.76 162	84.62 387

PS-CasMVS		78.01 243	78.09 214	77.77 327	87.71 217	54.39 393	88.02 161	91.22 131	77.50 54	73.26 309	88.64 215	60.73 208	88.41 333	61.88 306	73.88 370	90.53 225

Anonymous20240521		68.80 367	67.22 376	73.55 375	74.33 427	54.11 394	83.18 308	85.61 300	58.15 402	61.68 418	80.94 386	30.71 434	81.27 399	57.00 354	73.34 377	85.28 375

Patchmtry		70.74 347	69.16 350	75.49 353	80.72 386	54.07 395	74.94 408	80.30 376	58.34 400	70.01 347	81.19 381	52.50 285	86.54 352	53.37 376	71.09 392	85.87 368

PEN-MVS		77.73 249	77.69 230	77.84 325	87.07 246	53.91 396	87.91 167	91.18 133	77.56 51	73.14 311	88.82 210	61.23 201	89.17 318	59.95 322	72.37 381	90.43 229

gm-plane-assit					81.40 378	53.83 397			62.72 363		80.94 386		92.39 223	63.40 290

CL-MVSNet_self_test		72.37 332	71.46 327	75.09 358	79.49 405	53.53 398	80.76 340	85.01 309	69.12 274	70.51 339	82.05 377	57.92 236	84.13 377	52.27 381	66.00 412	87.60 325

MDTV_nov1_ep13			69.97 345		83.18 342	53.48 399	77.10 392	80.18 380	60.45 380	69.33 358	80.44 390	48.89 340	86.90 349	51.60 384	78.51 301

KD-MVS_2432*1600		66.22 387	63.89 390	73.21 378	75.47 425	53.42 400	70.76 424	84.35 315	64.10 344	66.52 389	78.52 411	34.55 425	84.98 371	50.40 391	50.33 444	81.23 419

miper_refine_blended		66.22 387	63.89 390	73.21 378	75.47 425	53.42 400	70.76 424	84.35 315	64.10 344	66.52 389	78.52 411	34.55 425	84.98 371	50.40 391	50.33 444	81.23 419

test1111		79.43 202	79.18 191	80.15 277	89.99 117	53.31 402	87.33 186	77.05 406	75.04 120	80.23 162	92.77 96	48.97 338	92.33 228	68.87 245	92.40 82	94.81 22

LF4IMVS		64.02 395	62.19 399	69.50 404	70.90 443	53.29 403	76.13 394	77.18 405	52.65 426	58.59 428	80.98 385	23.55 446	76.52 421	53.06 378	66.66 408	78.68 429

MVStest1		56.63 407	52.76 413	68.25 413	61.67 455	53.25 404	71.67 419	68.90 437	38.59 448	50.59 444	83.05 360	25.08 441	70.66 445	36.76 441	38.56 451	80.83 422

DTE-MVSNet		76.99 265	76.80 250	77.54 333	86.24 263	53.06 405	87.52 177	90.66 147	77.08 68	72.50 319	88.67 214	60.48 216	89.52 310	57.33 350	70.74 393	90.05 251

test2506		77.30 261	76.49 258	79.74 285	90.08 112	52.02 406	87.86 170	63.10 449	74.88 127	80.16 163	92.79 94	38.29 413	92.35 226	68.74 247	92.50 80	94.86 19

tpm		72.37 332	71.71 324	74.35 367	82.19 366	52.00 407	79.22 363	77.29 404	64.56 337	72.95 314	83.68 349	51.35 305	83.26 386	58.33 341	75.80 341	87.81 321

test_fmvs2		68.35 373	67.48 372	70.98 399	69.50 445	51.95 408	80.05 353	76.38 410	49.33 434	74.65 292	84.38 329	23.30 447	75.40 434	74.51 180	75.17 358	85.60 370

ETVMVS		72.25 334	71.05 333	75.84 346	87.77 215	51.91 409	79.39 360	74.98 415	69.26 268	73.71 303	82.95 362	40.82 400	86.14 357	46.17 418	84.43 221	89.47 271

WB-MVSnew		71.96 338	71.65 325	72.89 382	84.67 308	51.88 410	82.29 320	77.57 399	62.31 366	73.67 305	83.00 361	53.49 279	81.10 400	45.75 421	82.13 259	85.70 369

MIMVSNet1		68.58 369	66.78 379	73.98 372	80.07 395	51.82 411	80.77 339	84.37 314	64.40 339	59.75 426	82.16 376	36.47 420	83.63 381	42.73 429	70.33 395	86.48 354

Vis-MVSNet (Re-imp)		78.36 232	78.45 204	78.07 321	88.64 174	51.78 412	86.70 210	79.63 384	74.14 148	75.11 281	90.83 151	61.29 200	89.75 306	58.10 343	91.60 93	92.69 140

LCM-MVSNet-Re		77.05 264	76.94 247	77.36 334	87.20 235	51.60 413	80.06 352	80.46 372	75.20 116	67.69 371	86.72 269	62.48 174	88.98 322	63.44 289	89.25 135	91.51 187

Gipuma		45.18 423	41.86 426	55.16 435	77.03 418	51.52 414	32.50 459	80.52 370	32.46 455	27.12 458	35.02 459	9.52 462	75.50 431	22.31 456	60.21 428	38.45 458
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 378	65.99 382	71.37 393	73.48 434	51.47 415	75.16 404	85.19 304	65.20 329	60.78 421	80.93 388	42.35 388	77.20 416	57.12 351	53.69 439	85.44 373

UnsupCasMVSNet_bld		63.70 396	61.53 402	70.21 402	73.69 432	51.39 416	72.82 415	81.89 354	55.63 418	57.81 432	71.80 437	38.67 410	78.61 409	49.26 401	52.21 442	80.63 423

UBG		73.08 325	72.27 320	75.51 352	88.02 200	51.29 417	78.35 379	77.38 403	65.52 326	73.87 302	82.36 371	45.55 367	86.48 354	55.02 366	84.39 222	88.75 299

FPMVS		53.68 412	51.64 414	59.81 427	65.08 451	51.03 418	69.48 429	69.58 433	41.46 444	40.67 451	72.32 436	16.46 455	70.00 448	24.24 455	65.42 413	58.40 451

WBMVS		73.43 317	72.81 313	75.28 356	87.91 205	50.99 419	78.59 375	81.31 363	65.51 328	74.47 295	84.83 321	46.39 354	86.68 351	58.41 339	77.86 310	88.17 315

CVMVSNet		72.99 327	72.58 316	74.25 369	84.28 312	50.85 420	86.41 220	83.45 331	44.56 440	73.23 310	87.54 249	49.38 331	85.70 362	65.90 271	78.44 302	86.19 358

Anonymous20231206		68.60 368	67.80 366	71.02 398	80.23 393	50.75 421	78.30 380	80.47 371	56.79 413	66.11 395	82.63 369	46.35 357	78.95 408	43.62 427	75.70 342	83.36 401

ambc				75.24 357	73.16 437	50.51 422	63.05 451	87.47 264		64.28 405	77.81 417	17.80 453	89.73 307	57.88 345	60.64 426	85.49 371

APD_test1		53.31 413	49.93 418	63.42 423	65.68 450	50.13 423	71.59 420	66.90 441	34.43 453	40.58 452	71.56 438	8.65 464	76.27 424	34.64 444	55.36 436	63.86 447

tpmrst		72.39 330	72.13 321	73.18 381	80.54 389	49.91 424	79.91 356	79.08 390	63.11 354	71.69 330	79.95 398	55.32 258	82.77 389	65.66 274	73.89 369	86.87 346

Patchmatch-test		64.82 393	63.24 394	69.57 403	79.42 406	49.82 425	63.49 450	69.05 435	51.98 429	59.95 425	80.13 396	50.91 310	70.98 444	40.66 434	73.57 372	87.90 319

EPMVS		69.02 365	68.16 357	71.59 391	79.61 403	49.80 426	77.40 388	66.93 440	62.82 361	70.01 347	79.05 405	45.79 364	77.86 414	56.58 359	75.26 356	87.13 340

SSC-MVS3.2		73.35 321	73.39 305	73.23 377	85.30 289	49.01 427	74.58 410	81.57 358	75.21 115	73.68 304	85.58 303	52.53 283	82.05 393	54.33 371	77.69 314	88.63 304

dp		66.80 381	65.43 383	70.90 400	79.74 402	48.82 428	75.12 406	74.77 417	59.61 388	64.08 408	77.23 419	42.89 385	80.72 402	48.86 403	66.58 409	83.16 403

UWE-MVS		72.13 336	71.49 326	74.03 371	86.66 256	47.70 429	81.40 332	76.89 408	63.60 351	75.59 259	84.22 336	39.94 403	85.62 364	48.98 402	86.13 190	88.77 298

test0.0.03 1		68.00 375	67.69 368	68.90 407	77.55 414	47.43 430	75.70 400	72.95 426	66.66 308	66.56 387	82.29 374	48.06 342	75.87 429	44.97 425	74.51 364	83.41 400

SD_0403		74.65 302	74.77 286	74.29 368	86.20 265	47.42 431	83.71 294	85.12 305	69.30 266	68.50 366	87.95 238	59.40 224	86.05 358	49.38 399	83.35 243	89.40 273

myMVS_eth3d28		73.62 314	73.53 304	73.90 373	88.20 189	47.41 432	78.06 382	79.37 386	74.29 144	73.98 300	84.29 332	44.67 372	83.54 382	51.47 385	87.39 167	90.74 216

ADS-MVSNet		64.36 394	62.88 397	68.78 409	79.92 396	47.17 433	67.55 436	71.18 428	53.37 424	65.25 400	75.86 426	42.32 389	73.99 440	41.57 432	68.91 401	85.18 377

EU-MVSNet		68.53 371	67.61 370	71.31 396	78.51 412	47.01 434	84.47 274	84.27 318	42.27 443	66.44 392	84.79 323	40.44 401	83.76 379	58.76 336	68.54 404	83.17 402

test_fmvs3		63.36 397	61.82 400	67.98 414	62.51 454	46.96 435	77.37 389	74.03 421	45.24 439	67.50 373	78.79 410	12.16 459	72.98 443	72.77 200	66.02 411	83.99 394

ttmdpeth		59.91 403	57.10 407	68.34 412	67.13 449	46.65 436	74.64 409	67.41 439	48.30 435	62.52 417	85.04 319	20.40 449	75.93 428	42.55 430	45.90 450	82.44 411

KD-MVS_self_test		68.81 366	67.59 371	72.46 387	74.29 428	45.45 437	77.93 384	87.00 274	63.12 353	63.99 409	78.99 409	42.32 389	84.77 374	56.55 360	64.09 417	87.16 339

testf1		45.72 420	41.96 424	57.00 429	56.90 457	45.32 438	66.14 441	59.26 454	26.19 457	30.89 456	60.96 448	4.14 467	70.64 446	26.39 453	46.73 448	55.04 452

APD_test2		45.72 420	41.96 424	57.00 429	56.90 457	45.32 438	66.14 441	59.26 454	26.19 457	30.89 456	60.96 448	4.14 467	70.64 446	26.39 453	46.73 448	55.04 452

LCM-MVSNet		54.25 409	49.68 419	67.97 415	53.73 463	45.28 440	66.85 439	80.78 366	35.96 452	39.45 453	62.23 446	8.70 463	78.06 413	48.24 408	51.20 443	80.57 424

test_vis3_rt		49.26 419	47.02 421	56.00 431	54.30 460	45.27 441	66.76 440	48.08 461	36.83 450	44.38 449	53.20 454	7.17 466	64.07 454	56.77 358	55.66 434	58.65 450

testing3-2		75.12 299	75.19 281	74.91 360	90.40 105	45.09 442	80.29 350	78.42 394	78.37 40	76.54 241	87.75 240	44.36 376	87.28 347	57.04 353	83.49 240	92.37 154

test20.03		67.45 377	66.95 378	68.94 406	75.48 424	44.84 443	77.50 387	77.67 398	66.66 308	63.01 413	83.80 343	47.02 348	78.40 410	42.53 431	68.86 403	83.58 399

mvsany_test3		53.99 410	51.45 415	61.61 425	55.51 459	44.74 444	63.52 449	45.41 464	43.69 442	58.11 431	76.45 423	17.99 452	63.76 455	54.77 368	47.59 446	76.34 434

PatchT		68.46 372	67.85 363	70.29 401	80.70 387	43.93 445	72.47 416	74.88 416	60.15 384	70.55 338	76.57 422	49.94 324	81.59 395	50.58 389	74.83 361	85.34 374

MVS-HIRNet		59.14 404	57.67 406	63.57 422	81.65 372	43.50 446	71.73 418	65.06 445	39.59 447	51.43 442	57.73 450	38.34 412	82.58 390	39.53 435	73.95 368	64.62 446

testing3		68.56 370	67.67 369	71.22 397	87.33 231	42.87 447	83.06 314	71.54 427	70.36 239	69.08 360	84.38 329	30.33 435	85.69 363	37.50 440	75.45 350	85.09 381

WAC-MVS						42.58 448								39.46 436

myMVS_eth3d		67.02 380	66.29 381	69.21 405	84.68 305	42.58 448	78.62 373	73.08 424	66.65 311	66.74 385	79.46 402	31.53 432	82.30 391	39.43 437	76.38 335	82.75 409

PMVS	37.38 22	44.16 424	40.28 428	55.82 433	40.82 468	42.54 450	65.12 445	63.99 448	34.43 453	24.48 459	57.12 452	3.92 469	76.17 426	17.10 460	55.52 435	48.75 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 415	50.82 416	55.90 432	53.82 462	42.31 451	59.42 452	58.31 456	36.45 451	56.12 438	70.96 439	12.18 458	57.79 458	53.51 375	56.57 433	67.60 443

testgi		66.67 383	66.53 380	67.08 417	75.62 423	41.69 452	75.93 396	76.50 409	66.11 317	65.20 402	86.59 277	35.72 423	74.71 436	43.71 426	73.38 376	84.84 384

Syy-MVS		68.05 374	67.85 363	68.67 410	84.68 305	40.97 453	78.62 373	73.08 424	66.65 311	66.74 385	79.46 402	52.11 293	82.30 391	32.89 445	76.38 335	82.75 409

ANet_high		50.57 418	46.10 422	63.99 421	48.67 466	39.13 454	70.99 423	80.85 365	61.39 375	31.18 455	57.70 451	17.02 454	73.65 442	31.22 448	15.89 463	79.18 428

UWE-MVS-28		65.32 390	64.93 384	66.49 418	78.70 410	38.55 455	77.86 386	64.39 447	62.00 371	64.13 407	83.60 350	41.44 395	76.00 427	31.39 447	80.89 272	84.92 382

MDTV_nov1_ep13_2view						37.79 456	75.16 404		55.10 419	66.53 388		49.34 332		53.98 372		87.94 318

DSMNet-mixed		57.77 406	56.90 408	60.38 426	67.70 447	35.61 457	69.18 430	53.97 458	32.30 456	57.49 433	79.88 399	40.39 402	68.57 450	38.78 438	72.37 381	76.97 432

MVE	26.22 23	30.37 430	25.89 434	43.81 441	44.55 467	35.46 458	28.87 460	39.07 465	18.20 461	18.58 463	40.18 458	2.68 470	47.37 463	17.07 461	23.78 460	48.60 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 417	50.29 417	52.78 437	68.58 446	34.94 459	63.71 448	56.63 457	39.73 446	44.95 448	65.47 443	21.93 448	58.48 457	34.98 443	56.62 432	64.92 445

wuyk23d		16.82 433	15.94 436	19.46 447	58.74 456	31.45 460	39.22 457	3.74 472	6.84 463	6.04 466	2.70 466	1.27 471	24.29 466	10.54 466	14.40 465	2.63 463

E-PMN		31.77 427	30.64 430	35.15 444	52.87 464	27.67 461	57.09 454	47.86 462	24.64 459	16.40 464	33.05 460	11.23 460	54.90 460	14.46 463	18.15 461	22.87 460

kuosan		39.70 426	40.40 427	37.58 443	64.52 452	26.98 462	65.62 443	33.02 467	46.12 438	42.79 450	48.99 456	24.10 445	46.56 464	12.16 465	26.30 458	39.20 457

DeepMVS_CX				27.40 446	40.17 469	26.90 463		24.59 470	17.44 462	23.95 460	48.61 457	9.77 461	26.48 465	18.06 458	24.47 459	28.83 459

dongtai		45.42 422	45.38 423	45.55 440	73.36 436	26.85 464	67.72 435	34.19 466	54.15 422	49.65 446	56.41 453	25.43 440	62.94 456	19.45 457	28.09 457	46.86 456

EMVS		30.81 429	29.65 431	34.27 445	50.96 465	25.95 465	56.58 455	46.80 463	24.01 460	15.53 465	30.68 461	12.47 457	54.43 461	12.81 464	17.05 462	22.43 461

dmvs_testset		62.63 398	64.11 389	58.19 428	78.55 411	24.76 466	75.28 402	65.94 443	67.91 295	60.34 422	76.01 425	53.56 277	73.94 441	31.79 446	67.65 405	75.88 435

new-patchmatchnet		61.73 400	61.73 401	61.70 424	72.74 440	24.50 467	69.16 431	78.03 396	61.40 374	56.72 435	75.53 429	38.42 411	76.48 422	45.95 420	57.67 430	84.13 392

WB-MVS		54.94 408	54.72 409	55.60 434	73.50 433	20.90 468	74.27 412	61.19 451	59.16 393	50.61 443	74.15 431	47.19 347	75.78 430	17.31 459	35.07 453	70.12 441

SSC-MVS		53.88 411	53.59 411	54.75 436	72.87 439	19.59 469	73.84 414	60.53 453	57.58 409	49.18 447	73.45 434	46.34 358	75.47 433	16.20 462	32.28 455	69.20 442

PMMVS2		40.82 425	38.86 429	46.69 439	53.84 461	16.45 470	48.61 456	49.92 459	37.49 449	31.67 454	60.97 447	8.14 465	56.42 459	28.42 450	30.72 456	67.19 444

tmp_tt		18.61 432	21.40 435	10.23 448	4.82 471	10.11 471	34.70 458	30.74 469	1.48 465	23.91 461	26.07 462	28.42 437	13.41 467	27.12 451	15.35 464	7.17 462

N_pmnet		52.79 414	53.26 412	51.40 438	78.99 409	7.68 472	69.52 428	3.89 471	51.63 430	57.01 434	74.98 430	40.83 399	65.96 453	37.78 439	64.67 415	80.56 425

test_method		31.52 428	29.28 432	38.23 442	27.03 470	6.50 473	20.94 461	62.21 450	4.05 464	22.35 462	52.50 455	13.33 456	47.58 462	27.04 452	34.04 454	60.62 448

test123		6.12 435	8.11 438	0.14 449	0.06 473	0.09 474	71.05 422	0.03 474	0.04 468	0.25 469	1.30 468	0.05 472	0.03 469	0.21 468	0.01 467	0.29 464

testmvs		6.04 436	8.02 439	0.10 450	0.08 472	0.03 475	69.74 427	0.04 473	0.05 467	0.31 468	1.68 467	0.02 473	0.04 468	0.24 467	0.02 466	0.25 465

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

cdsmvs_eth3d_5k		19.96 431	26.61 433	0.00 451	0.00 474	0.00 476	0.00 462	89.26 205	0.00 469	0.00 470	88.61 216	61.62 191	0.00 470	0.00 469	0.00 468	0.00 466

pcd_1.5k_mvsjas		5.26 437	7.02 440	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	63.15 163	0.00 470	0.00 469	0.00 468	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

ab-mvs-re		7.23 434	9.64 437	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	86.72 269	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

PC_three_1452									68.21 292	92.02 12	94.00 57	82.09 5	95.98 57	84.58 65	96.68 2	94.95 12

eth-test2					0.00 474

eth-test					0.00 474

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 23	96.58 6	94.26 54

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 153	88.57 30	94.67 25	75.57 22	95.79 59	86.77 46	95.76 23

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 18	96.57 7	94.67 30

GSMVS																88.96 290

sam_mvs1												51.32 306				88.96 290

sam_mvs												50.01 322

MTGPA								92.02 98

test_post1							78.90 370				5.43 465	48.81 341	85.44 368	59.25 329

test_post											5.46 464	50.36 318	84.24 376

patchmatchnet-post											74.00 432	51.12 309	88.60 330

MTMP							92.18 35	32.83 468

test9_res														84.90 58	95.70 26	92.87 133

agg_prior2														82.91 85	95.45 29	92.70 138

test_prior2							88.85 125		75.41 109	84.91 76	93.54 70	74.28 30		83.31 79	95.86 20

旧先验2							86.56 216		58.10 404	87.04 56			88.98 322	74.07 185

新几何2							86.29 226

无先验							87.48 178	88.98 220	60.00 385				94.12 134	67.28 259		88.97 289

原ACMM2							86.86 203

testdata2													91.01 284	62.37 300

segment_acmp												73.08 40

testdata1							84.14 287		75.71 101

plane_prior5								92.44 78					95.38 78	78.71 129	86.32 185	91.33 193

plane_prior4											91.00 147

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

n2								0.00 475

nn								0.00 475

door-mid								69.98 431

test11								92.23 88

door								69.44 434

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 222

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 222

BP-MVS														77.47 143

HQP4-MVS										77.24 221			95.11 90			91.03 203

HQP3-MVS								92.19 92							85.99 193

HQP2-MVS												60.17 220

ACMMP++_ref															81.95 262

ACMMP++															81.25 267

Test By Simon												64.33 149

High-resolution multi-view benchmark