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 42	94.27 41	75.89 19	96.81 23	87.45 41	96.44 9	93.05 120

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 54	82.45 3	96.87 20	83.77 75	96.48 8	94.88 16

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 211	92.02 98	79.45 22	85.88 63	94.80 23	68.07 104	96.21 46	86.69 46	95.34 32	93.23 106

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	83.84 102	94.40 36	72.24 49	96.28 43	85.65 52	95.30 35	93.62 90
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	83.68 105	94.46 31	67.93 106	95.95 58	84.20 71	94.39 57	93.23 106

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 37	94.06 52	76.43 16	96.84 21	88.48 33	95.99 18	94.34 48

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 128	92.29 7	95.97 2	74.28 30	97.24 13	88.58 30	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	91.17 132	86.57 1	87.39 51	94.97 21	71.70 57	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 65	93.47 73	73.02 42	97.00 18	84.90 57	94.94 40	94.10 57

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 82	94.52 27	68.81 96	96.65 30	84.53 65	94.90 41	94.00 63

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 87	94.52 27	69.09 90	96.70 27	84.37 67	94.83 45	94.03 61

mPP-MVS		86.67 43	86.32 47	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 118	94.25 43	66.44 122	96.24 45	82.88 85	94.28 60	93.38 99

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 75	94.44 34	70.78 70	96.61 32	84.53 65	94.89 42	93.66 83

3Dnovator+	77.84 4	85.48 67	84.47 86	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 224	93.37 76	60.40 210	96.75 26	77.20 142	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 47	78.35 13	96.77 24	89.59 15	94.22 62	94.67 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		86.68 42	86.27 49	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 104	83.86 101	94.42 35	67.87 108	96.64 31	82.70 90	94.57 52	93.66 83

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 70	94.32 39	71.76 55	96.93 19	85.53 54	95.79 22	94.32 49

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	83.67 106	94.17 46	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

X-MVStestdata		80.37 175	77.83 212	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 450	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

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 35	95.09 19	71.06 67	96.67 29	87.67 38	96.37 14	94.09 58

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 189	93.04 42	69.80 243	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 179

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 79	93.99 58	70.67 72	96.82 22	84.18 72	95.01 37	93.90 69

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 277	85.00 73	93.10 81	74.36 29	95.41 76

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 272	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

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 36	94.80 23	73.76 34	97.11 15	87.51 40	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 201	72.94 28	90.64 63	92.14 97	77.21 62	75.47 250	92.83 90	58.56 219	94.72 110	73.24 188	92.71 77	92.13 160

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 56	74.83 23	93.78 150	87.63 39	94.27 61	93.65 87
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 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 228	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 131	81.02 102	92.58 78	92.08 161

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 42	96.34 15	93.95 66
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 70	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 147	93.82 65	64.33 144	96.29 42	82.67 91	90.69 109	93.23 106
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	68.69 272	84.87 77	93.10 81	74.43 27	95.16 86

TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 256	76.41 85	85.80 64	90.22 160	74.15 32	95.37 81	81.82 95	91.88 87	92.65 135

CSCG		86.41 48	86.19 52	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 148	83.16 112	91.07 139	75.94 18	95.19 85	79.94 116	94.38 58	93.55 94

NormalMVS		86.29 50	85.88 59	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	94.65 48	94.56 37

SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 237	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	90.91 106	93.21 109

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 174	84.86 78	92.89 88	76.22 17	96.33 41	84.89 59	95.13 36	94.40 44

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 134	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	73.01 176	88.58 28	94.52 27	73.36 35	96.49 38	84.26 68	95.01 37	92.70 131
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 44	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 93	93.36 77	71.44 61	96.76 25	80.82 105	95.33 33	94.16 54
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	91.17 132	74.31 139

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	82.82 117	94.23 44	72.13 51	97.09 16	84.83 60	95.37 31	93.65 87
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	70.98 214	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	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	83.81 103	93.95 61	69.77 82	96.01 54	85.15 55	94.66 47	94.32 49
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	74.62 132	88.90 26	93.85 64	75.75 20	96.00 55	87.80 37	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	74.50 133	86.84 58	94.65 26	67.31 113	95.77 60	84.80 61	92.85 74	92.84 129

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 117	86.34 61	95.29 17	70.86 69	96.00 55	88.78 28	96.04 16	94.58 34
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	75.71 100	89.16 23	95.10 18	75.65 21	96.19 47	87.07 43	96.01 17	94.79 23

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 185	82.14 3	86.65 59	94.28 40	68.28 103	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	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 19	95.65 27	94.47 41
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	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

MVS_111021_LR		82.61 120	82.11 120	84.11 136	88.82 162	71.58 57	85.15 251	86.16 280	74.69 129	80.47 152	91.04 140	62.29 171	90.55 284	80.33 112	90.08 120	90.20 226

MAR-MVS		81.84 132	80.70 142	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 245	78.50 180	86.21 275	62.36 170	94.52 117	65.36 262	92.05 86	89.77 251
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 20	96.41 12	94.21 53

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 17	96.68 2	94.95 12

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 291	92.39 6				88.94 25	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 20	96.41 12	93.33 103
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	74.69 129	88.80 27	95.61 11	70.29 76	96.44 39	86.20 50	93.08 71	93.16 113

CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 279	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

CNLPA		78.08 228	76.79 239	81.97 222	90.40 105	71.07 67	87.59 175	84.55 300	66.03 307	72.38 310	89.64 173	57.56 228	86.04 349	59.61 313	83.35 232	88.79 284

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 22	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 53	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 182	85.22 71	91.90 109	69.47 85	96.42 40	83.28 79	95.94 19	94.35 47

OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 165	70.95 71	89.13 114	91.52 121	77.55 52	80.96 143	91.75 114	60.71 200	94.50 118	79.67 119	86.51 179	89.97 243
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 45	86.10 55	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 136	91.43 127	70.34 74	97.23 14	84.26 68	93.36 70	94.37 46

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 224	79.17 167	91.03 142	64.12 146	96.03 51	68.39 238	90.14 118	91.50 175

CPTT-MVS		83.73 94	83.33 101	84.92 104	93.28 49	70.86 74	92.09 37	90.38 154	68.75 271	79.57 162	92.83 90	60.60 206	93.04 195	80.92 104	91.56 95	90.86 197

h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 203	76.02 96	84.67 80	91.39 128	61.54 183	95.50 69	82.71 88	75.48 334	91.72 169

新几何1				83.42 170	93.13 56	70.71 76		85.48 289	57.43 397	81.80 130	91.98 107	63.28 152	92.27 224	64.60 269	92.99 72	87.27 322

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 119		71.67 58	96.16 48		94.50 53	93.54 95

SR-MVS-dyc-post		85.77 61	85.61 66	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	65.00 140	95.56 64	82.75 86	91.87 88	92.50 141

RE-MVS-def			85.48 69		93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	63.87 148		82.75 86	91.87 88	92.50 141

HPM-MVS_fast		85.35 73	84.95 79	86.57 59	93.69 42	70.58 80	92.15 36	91.62 118	73.89 151	82.67 120	94.09 50	62.60 164	95.54 66	80.93 103	92.93 73	93.57 92

MSLP-MVS++		85.43 69	85.76 63	84.45 119	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 197	80.36 111	94.35 59	90.16 227

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 223	70.21 82	90.50 67	90.38 154	68.55 274	81.32 136	89.47 179	61.68 180	93.46 167	78.98 122	90.26 116	92.05 162

lupinMVS		81.39 145	80.27 153	84.76 110	87.35 223	70.21 82	85.55 242	86.41 274	62.85 346	81.32 136	88.61 203	61.68 180	92.24 226	78.41 129	90.26 116	91.83 165

xiu_mvs_v1_base_debu		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 220	69.06 264	81.83 127	88.16 217	50.91 298	92.85 199	78.29 131	87.56 159	89.06 268

xiu_mvs_v1_base		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 220	69.06 264	81.83 127	88.16 217	50.91 298	92.85 199	78.29 131	87.56 159	89.06 268

xiu_mvs_v1_base_debi		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 220	69.06 264	81.83 127	88.16 217	50.91 298	92.85 199	78.29 131	87.56 159	89.06 268

API-MVS		81.99 129	81.23 133	84.26 132	90.94 93	70.18 87	91.10 58	89.32 193	71.51 200	78.66 176	88.28 213	65.26 135	95.10 93	64.74 268	91.23 100	87.51 315

test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 259	69.93 88	88.65 137	90.78 143	69.97 239	88.27 32	93.98 59	71.39 62	91.54 254	88.49 32	90.45 113	93.91 67

OpenMVS	72.83 10	79.77 184	78.33 199	84.09 141	85.17 282	69.91 89	90.57 64	90.97 137	66.70 294	72.17 313	91.91 108	54.70 254	93.96 136	61.81 295	90.95 105	88.41 297

jason		81.39 145	80.29 152	84.70 112	86.63 249	69.90 90	85.95 229	86.77 269	63.24 339	81.07 142	89.47 179	61.08 196	92.15 228	78.33 130	90.07 121	92.05 162
jason: jason.
MVP-Stereo		76.12 269	74.46 279	81.13 244	85.37 278	69.79 91	84.42 274	87.95 240	65.03 319	67.46 362	85.33 296	53.28 269	91.73 245	58.01 331	83.27 234	81.85 403
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 48	96.27 44	86.87 44	94.65 48	93.70 82

PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 101	90.80 97	69.76 93	88.74 133	91.70 115	69.39 251	78.96 169	88.46 208	65.47 134	94.87 103	74.42 174	88.57 146	90.24 225

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 67

APD-MVS_3200maxsize		85.97 56	85.88 59	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 161	85.94 62	94.51 30	65.80 132	95.61 63	83.04 82	92.51 79	93.53 96

test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 289	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 176	90.37 7	90.75 108	93.96 64

EPNet		83.72 95	82.92 108	86.14 68	84.22 305	69.48 97	91.05 59	85.27 290	81.30 6	76.83 219	91.65 117	66.09 127	95.56 64	76.00 158	93.85 64	93.38 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 214	76.63 245	84.64 113	86.73 245	69.47 98	85.01 255	84.61 299	69.54 249	66.51 379	86.59 264	50.16 307	91.75 243	76.26 154	84.24 213	92.69 133

alignmvs		85.48 67	85.32 73	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 46	91.46 126	70.32 75	93.78 150	81.51 96	88.95 138	94.63 33

DP-MVS		76.78 257	74.57 275	83.42 170	93.29 48	69.46 100	88.55 141	83.70 312	63.98 335	70.20 331	88.89 195	54.01 262	94.80 107	46.66 401	81.88 252	86.01 350

sasdasda		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 144	81.50 97	88.80 141	94.77 25

canonicalmvs		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 144	81.50 97	88.80 141	94.77 25

test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 338	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 177	90.31 8	90.67 110	93.89 70

test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 118	84.12 307	69.37 104	88.15 157	87.96 239	70.01 237	83.95 100	93.23 79	68.80 97	91.51 257	88.61 29	89.96 122	92.57 136

nrg030		83.88 90	83.53 96	84.96 101	86.77 244	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 190	80.79 107	79.28 284	92.50 141

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 379	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 177	90.26 9	89.95 123	93.78 79

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 49	78.98 12	96.58 35	85.66 51	95.72 24	94.58 34

XVG-OURS		80.41 172	79.23 180	83.97 154	85.64 269	69.02 108	83.03 306	90.39 153	71.09 210	77.63 201	91.49 125	54.62 256	91.35 263	75.71 160	83.47 230	91.54 173

PCF-MVS	73.52 7	80.38 173	78.84 188	85.01 99	87.71 214	68.99 109	83.65 288	91.46 126	63.00 343	77.77 199	90.28 156	66.10 126	95.09 94	61.40 298	88.22 153	90.94 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 153	79.50 172	85.03 98	88.01 199	68.97 110	91.59 46	92.00 100	66.63 300	75.15 268	92.16 104	57.70 226	95.45 71	63.52 274	88.76 143	90.66 206

AdaColmap		80.58 170	79.42 173	84.06 145	93.09 59	68.91 111	89.36 103	88.97 214	69.27 255	75.70 246	89.69 170	57.20 234	95.77 60	63.06 279	88.41 151	87.50 316

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 130	85.42 276	68.81 112	88.49 142	87.26 258	68.08 281	88.03 38	93.49 70	72.04 52	91.77 242	88.90 26	89.14 137	92.24 155

原ACMM1				84.35 123	93.01 62	68.79 113		92.44 78	63.96 336	81.09 141	91.57 122	66.06 128	95.45 71	67.19 248	94.82 46	88.81 283

XVG-OURS-SEG-HR		80.81 156	79.76 165	83.96 155	85.60 271	68.78 114	83.54 294	90.50 150	70.66 222	76.71 223	91.66 116	60.69 201	91.26 266	76.94 146	81.58 254	91.83 165

LPG-MVS_test		82.08 126	81.27 132	84.50 116	89.23 148	68.76 115	90.22 76	91.94 104	75.37 109	76.64 225	91.51 123	54.29 257	94.91 98	78.44 127	83.78 218	89.83 248

LGP-MVS_train				84.50 116	89.23 148	68.76 115		91.94 104	75.37 109	76.64 225	91.51 123	54.29 257	94.91 98	78.44 127	83.78 218	89.83 248

Effi-MVS+-dtu		80.03 181	78.57 192	84.42 120	85.13 286	68.74 117	88.77 129	88.10 234	74.99 119	74.97 274	83.49 340	57.27 232	93.36 171	73.53 182	80.88 262	91.18 184

Vis-MVSNet		83.46 103	82.80 110	85.43 85	90.25 108	68.74 117	90.30 75	90.13 166	76.33 91	80.87 144	92.89 88	61.00 197	94.20 128	72.45 198	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 97	83.14 102	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 171	91.00 144	60.42 208	95.38 78	78.71 125	86.32 181	91.33 180

plane_prior						68.71 119	90.38 73		77.62 47						86.16 185

plane_prior6					89.84 121	68.70 121						60.42 208

ACMP	74.13 6	81.51 144	80.57 145	84.36 122	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 272	90.41 154	53.82 263	94.54 115	77.56 138	82.91 238	89.86 247
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 82	84.67 82	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 96	85.71 284	69.32 87	95.38 78	80.82 105	91.37 98	92.72 130

plane_prior3						68.60 124			78.44 36	78.92 171

CHOSEN 1792x2688		77.63 243	75.69 255	83.44 169	89.98 118	68.58 125	78.70 363	87.50 252	56.38 402	75.80 245	86.84 252	58.67 218	91.40 262	61.58 297	85.75 193	90.34 220

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 232	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 203	89.04 24	90.56 111	94.16 54

plane_prior7					90.08 112	68.51 127

GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 190	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 244	95.35 82	80.03 114	89.74 127	94.69 28

fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 145	85.38 277	68.40 129	88.34 149	86.85 268	67.48 288	87.48 49	93.40 75	70.89 68	91.61 247	88.38 34	89.22 135	92.16 159

ACMM	73.20 8	80.78 163	79.84 164	83.58 166	89.31 143	68.37 130	89.99 79	91.60 119	70.28 231	77.25 208	89.66 172	53.37 268	93.53 163	74.24 177	82.85 239	88.85 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 298	71.91 309	80.39 260	81.96 355	68.32 131	81.45 321	82.14 338	59.32 378	69.87 340	85.13 302	52.40 275	88.13 326	60.21 308	74.74 349	84.73 373

NP-MVS					89.62 125	68.32 131					90.24 158

mamba_0404		81.91 130	80.84 141	85.13 95	89.24 147	68.26 133	87.84 170	89.25 199	71.06 212	80.62 148	90.39 155	59.57 213	94.65 114	72.45 198	87.19 167	92.47 144

test222					91.50 82	68.26 133	84.16 279	83.20 324	54.63 408	79.74 159	91.63 119	58.97 217			91.42 96	86.77 336

Elysia		81.53 140	80.16 155	85.62 79	85.51 273	68.25 135	88.84 126	92.19 92	71.31 203	80.50 150	89.83 166	46.89 337	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 140	80.16 155	85.62 79	85.51 273	68.25 135	88.84 126	92.19 92	71.31 203	80.50 150	89.83 166	46.89 337	94.82 104	76.85 147	89.57 129	93.80 77

CDS-MVSNet		79.07 204	77.70 219	83.17 182	87.60 218	68.23 137	84.40 275	86.20 279	67.49 287	76.36 233	86.54 268	61.54 183	90.79 279	61.86 294	87.33 164	90.49 214
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 136	81.02 137	83.70 162	89.51 130	68.21 138	84.28 277	90.09 167	70.79 216	81.26 140	85.62 289	63.15 158	94.29 122	75.62 162	88.87 140	88.59 292

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 128	86.14 258	68.12 139	89.43 97	82.87 331	70.27 232	87.27 53	93.80 66	69.09 90	91.58 249	88.21 35	83.65 225	93.14 115

UGNet		80.83 155	79.59 170	84.54 115	88.04 196	68.09 140	89.42 99	88.16 232	76.95 70	76.22 236	89.46 181	49.30 320	93.94 139	68.48 236	90.31 114	91.60 170
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		83.32 108	82.99 106	84.28 128	83.79 315	68.07 141	89.34 104	82.85 332	69.80 243	87.36 52	94.06 52	68.34 102	91.56 252	87.95 36	83.46 231	93.21 109

UA-Net		85.08 78	84.96 78	85.45 84	92.07 75	68.07 141	89.78 85	90.86 142	82.48 2	84.60 85	93.20 80	69.35 86	95.22 84	71.39 205	90.88 107	93.07 117

xiu_mvs_v2_base		81.69 136	81.05 136	83.60 164	89.15 151	68.03 143	84.46 271	90.02 168	70.67 219	81.30 139	86.53 269	63.17 157	94.19 130	75.60 163	88.54 147	88.57 293

LuminaMVS		80.68 164	79.62 169	83.83 158	85.07 288	68.01 144	86.99 194	88.83 217	70.36 227	81.38 135	87.99 224	50.11 308	92.51 213	79.02 120	86.89 173	90.97 193

DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 175	67.93 145	85.52 246	93.44 28	78.70 34	83.63 108	89.03 191	74.57 24	95.71 62	80.26 113	94.04 63	93.66 83
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	67.88 146	88.59 139	89.05 208	80.19 12	90.70 17	95.40 15	74.56 25	93.92 143	91.54 2	92.07 85	95.31 5

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 206	67.85 147	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 237	95.43 73	84.03 73	91.75 91	95.24 7

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 187	67.85 147	87.66 173	89.73 179	80.05 15	82.95 113	89.59 176	70.74 71	94.82 104	80.66 110	84.72 202	93.28 105

PLC	70.83 11	78.05 230	76.37 250	83.08 187	91.88 79	67.80 149	88.19 154	89.46 188	64.33 328	69.87 340	88.38 210	53.66 264	93.58 158	58.86 321	82.73 241	87.86 307
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 209	77.51 224	83.03 190	87.80 208	67.79 150	84.72 261	85.05 295	67.63 284	76.75 222	87.70 229	62.25 172	90.82 278	58.53 325	87.13 168	90.49 214

CLD-MVS		82.31 123	81.65 129	84.29 127	88.47 176	67.73 151	85.81 236	92.35 83	75.78 99	78.33 186	86.58 266	64.01 147	94.35 121	76.05 157	87.48 162	90.79 199
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		81.72 134	80.94 139	84.07 143	88.72 168	67.68 152	85.87 232	87.26 258	76.02 96	84.67 80	88.22 216	61.54 183	93.48 165	82.71 88	73.44 362	91.06 188

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 153	89.63 91	92.65 71	72.89 179	84.64 83	91.71 115	71.85 53	96.03 51	84.77 62	94.45 56	94.49 40

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 154	90.51 65	92.90 57	77.26 59	87.44 50	91.63 119	71.27 64	96.06 50	85.62 53	95.01 37	94.78 24

AUN-MVS		79.21 200	77.60 222	84.05 148	88.71 169	67.61 154	85.84 234	87.26 258	69.08 263	77.23 210	88.14 221	53.20 270	93.47 166	75.50 165	73.45 361	91.06 188

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 156	92.83 18	93.30 33	79.67 19	84.57 86	92.27 100	71.47 60	95.02 96	84.24 70	93.46 69	95.13 9

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 237	67.56 157	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 232	94.07 134	77.77 136	89.89 125	94.56 37

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 97	87.87 204	67.53 158	87.44 181	89.66 180	79.74 18	82.23 122	89.41 185	70.24 77	94.74 109	79.95 115	83.92 217	92.99 125

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 181	67.45 159	88.89 122	89.15 204	75.50 105	82.27 121	88.28 213	69.61 84	94.45 120	77.81 135	87.84 156	93.84 73

EG-PatchMatch MVS		74.04 296	71.82 310	80.71 254	84.92 290	67.42 160	85.86 233	88.08 235	66.04 306	64.22 393	83.85 328	35.10 411	92.56 209	57.44 335	80.83 263	82.16 402

OMC-MVS		82.69 118	81.97 126	84.85 106	88.75 167	67.42 160	87.98 161	90.87 141	74.92 123	79.72 160	91.65 117	62.19 174	93.96 136	75.26 168	86.42 180	93.16 113

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 133	86.26 253	67.40 162	89.18 108	89.31 194	72.50 181	88.31 31	93.86 63	69.66 83	91.96 234	89.81 11	91.05 102	93.38 99

PatchMatch-RL		72.38 318	70.90 322	76.80 328	88.60 172	67.38 163	79.53 349	76.17 399	62.75 349	69.36 345	82.00 366	45.51 355	84.89 363	53.62 361	80.58 267	78.12 417

LS3D		76.95 254	74.82 272	83.37 173	90.45 103	67.36 164	89.15 113	86.94 265	61.87 359	69.52 343	90.61 150	51.71 291	94.53 116	46.38 404	86.71 176	88.21 301

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 143	86.69 247	67.31 165	89.46 96	83.07 326	71.09 210	86.96 57	93.70 68	69.02 95	91.47 259	88.79 27	84.62 204	93.44 98

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 137	84.86 291	67.28 166	89.40 101	83.01 327	70.67 219	87.08 54	93.96 60	68.38 101	91.45 260	88.56 31	84.50 205	93.56 93

PS-MVSNAJss		82.07 127	81.31 131	84.34 124	86.51 251	67.27 167	89.27 105	91.51 122	71.75 193	79.37 164	90.22 160	63.15 158	94.27 124	77.69 137	82.36 246	91.49 176

114514_t		80.68 164	79.51 171	84.20 134	94.09 38	67.27 167	89.64 90	91.11 135	58.75 386	74.08 287	90.72 148	58.10 222	95.04 95	69.70 223	89.42 133	90.30 223

mvsmamba		80.60 167	79.38 174	84.27 130	89.74 124	67.24 169	87.47 178	86.95 264	70.02 236	75.38 256	88.93 193	51.24 295	92.56 209	75.47 166	89.22 135	93.00 124

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 217	67.22 170	88.69 135	93.04 42	79.64 21	85.33 69	92.54 97	73.30 36	94.50 118	83.49 76	91.14 101	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 45	85.71 76	91.02 91	67.21 171	92.36 30	93.78 19	78.97 33	83.51 109	91.20 134	70.65 73	95.15 87	81.96 94	94.89 42	94.77 25

anonymousdsp		78.60 215	77.15 230	82.98 193	80.51 377	67.08 172	87.24 187	89.53 186	65.66 311	75.16 267	87.19 246	52.52 272	92.25 225	77.17 143	79.34 283	89.61 255

MVS		78.19 226	76.99 234	81.78 224	85.66 268	66.99 173	84.66 263	90.47 151	55.08 407	72.02 315	85.27 297	63.83 149	94.11 133	66.10 256	89.80 126	84.24 377

HQP5-MVS						66.98 174

HQP-MVS		82.61 120	82.02 124	84.37 121	89.33 140	66.98 174	89.17 109	92.19 92	76.41 85	77.23 210	90.23 159	60.17 211	95.11 90	77.47 139	85.99 189	91.03 190

Fast-Effi-MVS+-dtu		78.02 231	76.49 246	82.62 210	83.16 332	66.96 176	86.94 197	87.45 254	72.45 182	71.49 321	84.17 324	54.79 253	91.58 249	67.61 242	80.31 271	89.30 264

F-COLMAP		76.38 267	74.33 281	82.50 213	89.28 145	66.95 177	88.41 144	89.03 209	64.05 333	66.83 371	88.61 203	46.78 339	92.89 198	57.48 334	78.55 288	87.67 310

HyFIR lowres test		77.53 244	75.40 263	83.94 156	89.59 126	66.62 178	80.36 339	88.64 227	56.29 403	76.45 230	85.17 301	57.64 227	93.28 173	61.34 300	83.10 237	91.91 164

ACMH	67.68 16	75.89 273	73.93 285	81.77 225	88.71 169	66.61 179	88.62 138	89.01 211	69.81 242	66.78 372	86.70 260	41.95 381	91.51 257	55.64 350	78.14 295	87.17 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 198	77.96 206	83.27 176	84.68 296	66.57 180	89.25 106	90.16 165	69.20 260	75.46 252	89.49 178	45.75 353	93.13 188	76.84 149	80.80 264	90.11 231

VDD-MVS		83.01 116	82.36 117	84.96 101	91.02 91	66.40 181	88.91 121	88.11 233	77.57 49	84.39 89	93.29 78	52.19 278	93.91 144	77.05 145	88.70 145	94.57 36

mvs_tets		79.13 202	77.77 216	83.22 180	84.70 295	66.37 182	89.17 109	90.19 164	69.38 252	75.40 255	89.46 181	44.17 365	93.15 186	76.78 151	80.70 266	90.14 228

PAPM_NR		83.02 115	82.41 115	84.82 107	92.47 72	66.37 182	87.93 165	91.80 111	73.82 152	77.32 207	90.66 149	67.90 107	94.90 100	70.37 215	89.48 132	93.19 112

EC-MVSNet		86.01 53	86.38 46	84.91 105	89.31 143	66.27 184	92.32 31	93.63 22	79.37 23	84.17 95	91.88 110	69.04 94	95.43 73	83.93 74	93.77 65	93.01 123

pmmvs-eth3d		70.50 338	67.83 352	78.52 300	77.37 403	66.18 185	81.82 314	81.51 346	58.90 383	63.90 397	80.42 378	42.69 374	86.28 346	58.56 324	65.30 401	83.11 391

IB-MVS	68.01 15	75.85 274	73.36 294	83.31 174	84.76 294	66.03 186	83.38 295	85.06 294	70.21 234	69.40 344	81.05 370	45.76 352	94.66 113	65.10 265	75.49 333	89.25 265
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		73.83 299	72.67 301	77.30 323	83.87 314	66.02 187	81.82 314	84.66 298	61.37 363	68.61 352	82.82 353	47.29 332	88.21 324	59.27 315	84.32 212	77.68 418

FE-MVS		77.78 237	75.68 256	84.08 142	88.09 194	66.00 188	83.13 301	87.79 245	68.42 278	78.01 194	85.23 299	45.50 356	95.12 88	59.11 318	85.83 192	91.11 186

test_0402		72.79 316	70.44 327	79.84 273	88.13 191	65.99 189	85.93 230	84.29 304	65.57 312	67.40 365	85.49 292	46.92 336	92.61 205	35.88 429	74.38 352	80.94 408

BH-RMVSNet		79.61 186	78.44 195	83.14 183	89.38 139	65.93 190	84.95 257	87.15 261	73.56 160	78.19 189	89.79 168	56.67 239	93.36 171	59.53 314	86.74 175	90.13 229

BH-untuned		79.47 191	78.60 191	82.05 219	89.19 150	65.91 191	86.07 227	88.52 229	72.18 187	75.42 254	87.69 230	61.15 194	93.54 162	60.38 306	86.83 174	86.70 338

cascas		76.72 258	74.64 274	82.99 192	85.78 266	65.88 192	82.33 310	89.21 201	60.85 365	72.74 303	81.02 371	47.28 333	93.75 154	67.48 244	85.02 198	89.34 263

fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 126	86.70 246	65.83 193	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 192	91.30 3	88.44 150	94.02 62

patch_mono-2		83.65 96	84.54 83	80.99 247	90.06 116	65.83 193	84.21 278	88.74 224	71.60 198	85.01 72	92.44 98	74.51 26	83.50 373	82.15 93	92.15 83	93.64 89

MSDG		73.36 307	70.99 321	80.49 259	84.51 301	65.80 195	80.71 333	86.13 281	65.70 310	65.46 384	83.74 332	44.60 360	90.91 277	51.13 375	76.89 309	84.74 372

旧先验1					91.96 76	65.79 196		86.37 276			93.08 85	69.31 88			92.74 76	88.74 288

casdiffmvs		85.11 77	85.14 76	85.01 99	87.20 232	65.77 197	87.75 171	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 142	81.27 101	90.48 112	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		76.81 256	78.23 203	72.54 373	86.12 259	65.75 198	78.76 362	82.07 340	64.12 330	72.97 301	91.02 143	67.97 105	68.08 438	83.04 82	78.02 296	83.80 384

COLMAP_ROB	66.92 17	73.01 313	70.41 328	80.81 252	87.13 235	65.63 199	88.30 151	84.19 307	62.96 344	63.80 398	87.69 230	38.04 401	92.56 209	46.66 401	74.91 347	84.24 377
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	84.73 111	87.76 213	65.62 200	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 128	90.83 5	91.39 97	94.38 45

EIA-MVS		83.31 109	82.80 110	84.82 107	89.59 126	65.59 201	88.21 153	92.68 67	74.66 131	78.96 169	86.42 271	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

v7n		78.97 207	77.58 223	83.14 183	83.45 323	65.51 202	88.32 150	91.21 130	73.69 156	72.41 309	86.32 274	57.93 223	93.81 149	69.18 228	75.65 330	90.11 231

V42		79.38 197	78.24 201	82.83 199	81.10 371	65.50 203	85.55 242	89.82 174	71.57 199	78.21 188	86.12 278	60.66 203	93.18 185	75.64 161	75.46 336	89.81 250

PVSNet_BlendedMVS		80.60 167	80.02 159	82.36 216	88.85 159	65.40 204	86.16 225	92.00 100	69.34 253	78.11 191	86.09 279	66.02 129	94.27 124	71.52 202	82.06 249	87.39 317

PVSNet_Blended		80.98 151	80.34 150	82.90 196	88.85 159	65.40 204	84.43 273	92.00 100	67.62 285	78.11 191	85.05 305	66.02 129	94.27 124	71.52 202	89.50 131	89.01 273

baseline		84.93 80	84.98 77	84.80 109	87.30 230	65.39 206	87.30 185	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 136	81.31 99	90.30 115	95.03 11

test_djsdf		80.30 176	79.32 177	83.27 176	83.98 311	65.37 207	90.50 67	90.38 154	68.55 274	76.19 237	88.70 199	56.44 241	93.46 167	78.98 122	80.14 274	90.97 193

ACMH+	68.96 14	76.01 272	74.01 283	82.03 220	88.60 172	65.31 208	88.86 123	87.55 250	70.25 233	67.75 358	87.47 238	41.27 383	93.19 184	58.37 327	75.94 327	87.60 312

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 185	87.08 237	65.21 209	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 246	91.30 3	91.60 92	92.34 148

CR-MVSNet		73.37 305	71.27 318	79.67 278	81.32 369	65.19 210	75.92 386	80.30 363	59.92 373	72.73 304	81.19 368	52.50 273	86.69 340	59.84 310	77.71 299	87.11 328

RPMNet		73.51 303	70.49 326	82.58 212	81.32 369	65.19 210	75.92 386	92.27 85	57.60 395	72.73 304	76.45 410	52.30 276	95.43 73	48.14 396	77.71 299	87.11 328

fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 238	85.73 267	65.13 212	85.40 247	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 328	86.56 47	91.05 102	90.80 198

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 164	87.32 229	65.13 212	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 214	89.52 16	92.78 75	93.20 111

BH-w/o		78.21 224	77.33 228	80.84 251	88.81 163	65.13 212	84.87 258	87.85 244	69.75 246	74.52 282	84.74 311	61.34 189	93.11 189	58.24 329	85.84 191	84.27 376

thisisatest0530		79.40 195	77.76 217	84.31 125	87.69 216	65.10 215	87.36 182	84.26 306	70.04 235	77.42 204	88.26 215	49.94 311	94.79 108	70.20 216	84.70 203	93.03 121

FA-MVS(test-final)		80.96 152	79.91 162	84.10 137	88.30 184	65.01 216	84.55 268	90.01 169	73.25 171	79.61 161	87.57 233	58.35 221	94.72 110	71.29 206	86.25 183	92.56 137

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 160	86.17 257	65.00 217	86.96 195	87.28 256	74.35 137	88.25 33	94.23 44	61.82 178	92.60 206	89.85 10	88.09 155	93.84 73

v10		79.74 185	78.67 189	82.97 194	84.06 309	64.95 218	87.88 168	90.62 146	73.11 173	75.11 269	86.56 267	61.46 186	94.05 135	73.68 180	75.55 332	89.90 245

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 158	85.62 270	64.94 219	87.03 192	86.62 272	74.32 138	87.97 41	94.33 38	60.67 202	92.60 206	89.72 12	87.79 157	93.96 64

SDMVSNet		80.38 173	80.18 154	80.99 247	89.03 157	64.94 219	80.45 338	89.40 189	75.19 115	76.61 227	89.98 162	60.61 205	87.69 332	76.83 150	83.55 227	90.33 221

dcpmvs_2		85.63 64	86.15 54	84.06 145	91.71 80	64.94 219	86.47 214	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 240	83.36 77	92.15 83	95.35 3

IterMVS-SCA-FT		75.43 280	73.87 287	80.11 268	82.69 344	64.85 222	81.57 319	83.47 317	69.16 261	70.49 328	84.15 325	51.95 285	88.15 325	69.23 227	72.14 372	87.34 319

MVSTER		79.01 205	77.88 211	82.38 215	83.07 333	64.80 223	84.08 282	88.95 215	69.01 267	78.69 174	87.17 247	54.70 254	92.43 216	74.69 171	80.57 268	89.89 246

Anonymous20240529		80.19 179	78.89 187	84.10 137	90.60 100	64.75 224	88.95 120	90.90 139	65.97 308	80.59 149	91.17 136	49.97 310	93.73 156	69.16 229	82.70 243	93.81 75

XVG-ACMP-BASELINE		76.11 270	74.27 282	81.62 227	83.20 329	64.67 225	83.60 291	89.75 178	69.75 246	71.85 316	87.09 249	32.78 415	92.11 229	69.99 220	80.43 270	88.09 303

v1192		79.59 188	78.43 196	83.07 188	83.55 321	64.52 226	86.93 198	90.58 147	70.83 215	77.78 198	85.90 280	59.15 216	93.94 139	73.96 179	77.19 306	90.76 201

Fast-Effi-MVS+		80.81 156	79.92 161	83.47 168	88.85 159	64.51 227	85.53 244	89.39 190	70.79 216	78.49 181	85.06 304	67.54 110	93.58 158	67.03 251	86.58 177	92.32 150

v1144		80.03 181	79.03 184	83.01 191	83.78 316	64.51 227	87.11 190	90.57 149	71.96 192	78.08 193	86.20 276	61.41 187	93.94 139	74.93 170	77.23 304	90.60 209

v8		79.97 183	79.02 185	82.80 202	84.09 308	64.50 229	87.96 162	90.29 161	74.13 146	75.24 265	86.81 253	62.88 163	93.89 147	74.39 175	75.40 339	90.00 239

EPP-MVSNet		83.40 105	83.02 105	84.57 114	90.13 110	64.47 230	92.32 31	90.73 144	74.45 136	79.35 165	91.10 137	69.05 93	95.12 88	72.78 192	87.22 166	94.13 56

GeoE		81.71 135	81.01 138	83.80 161	89.51 130	64.45 231	88.97 119	88.73 225	71.27 206	78.63 177	89.76 169	66.32 124	93.20 182	69.89 221	86.02 188	93.74 80

UniMVSNet (Re)		81.60 139	81.11 135	83.09 185	88.38 181	64.41 232	87.60 174	93.02 46	78.42 37	78.56 179	88.16 217	69.78 81	93.26 175	69.58 225	76.49 316	91.60 170

LTVRE_ROB	69.57 13	76.25 268	74.54 277	81.41 233	88.60 172	64.38 233	79.24 353	89.12 207	70.76 218	69.79 342	87.86 226	49.09 323	93.20 182	56.21 349	80.16 272	86.65 339
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Anonymous20231211		78.97 207	77.69 220	82.81 201	90.54 102	64.29 234	90.11 78	91.51 122	65.01 320	76.16 241	88.13 222	50.56 303	93.03 196	69.68 224	77.56 303	91.11 186

testdata				79.97 270	90.90 94	64.21 235		84.71 297	59.27 379	85.40 68	92.91 87	62.02 177	89.08 310	68.95 231	91.37 98	86.63 340

v2v482		80.23 177	79.29 178	83.05 189	83.62 319	64.14 236	87.04 191	89.97 170	73.61 158	78.18 190	87.22 244	61.10 195	93.82 148	76.11 155	76.78 313	91.18 184

VDDNet		81.52 142	80.67 143	84.05 148	90.44 104	64.13 237	89.73 87	85.91 283	71.11 209	83.18 111	93.48 71	50.54 304	93.49 164	73.40 185	88.25 152	94.54 39

PAPR		81.66 138	80.89 140	83.99 153	90.27 107	64.00 238	86.76 206	91.77 114	68.84 270	77.13 217	89.50 177	67.63 109	94.88 102	67.55 243	88.52 148	93.09 116

AstraMVS		80.81 156	80.14 157	82.80 202	86.05 262	63.96 239	86.46 215	85.90 284	73.71 155	80.85 145	90.56 151	54.06 261	91.57 251	79.72 118	83.97 216	92.86 128

v144192		79.47 191	78.37 197	82.78 206	83.35 324	63.96 239	86.96 195	90.36 157	69.99 238	77.50 202	85.67 287	60.66 203	93.77 152	74.27 176	76.58 314	90.62 207

v1921920		79.22 199	78.03 205	82.80 202	83.30 326	63.94 241	86.80 202	90.33 158	69.91 241	77.48 203	85.53 291	58.44 220	93.75 154	73.60 181	76.85 311	90.71 205

guyue		81.13 149	80.64 144	82.60 211	86.52 250	63.92 242	86.69 208	87.73 247	73.97 147	80.83 146	89.69 170	56.70 238	91.33 265	78.26 134	85.40 196	92.54 138

tttt0517		79.40 195	77.91 208	83.90 157	88.10 193	63.84 243	88.37 148	84.05 308	71.45 201	76.78 221	89.12 188	49.93 313	94.89 101	70.18 217	83.18 236	92.96 126

thisisatest0515		77.33 248	75.38 264	83.18 181	85.27 281	63.80 244	82.11 313	83.27 320	65.06 318	75.91 242	83.84 329	49.54 315	94.27 124	67.24 247	86.19 184	91.48 177

diffmvs		82.10 125	81.88 127	82.76 208	83.00 336	63.78 245	83.68 287	89.76 177	72.94 177	82.02 126	89.85 165	65.96 131	90.79 279	82.38 92	87.30 165	93.71 81
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		81.17 147	80.47 148	83.24 178	89.13 152	63.62 246	86.21 223	89.95 171	72.43 185	81.78 131	89.61 174	57.50 229	93.58 158	70.75 210	86.90 171	92.52 139

DCV-MVSNet		81.17 147	80.47 148	83.24 178	89.13 152	63.62 246	86.21 223	89.95 171	72.43 185	81.78 131	89.61 174	57.50 229	93.58 158	70.75 210	86.90 171	92.52 139

AllTest		70.96 331	68.09 346	79.58 280	85.15 284	63.62 246	84.58 267	79.83 368	62.31 353	60.32 410	86.73 254	32.02 416	88.96 314	50.28 380	71.57 376	86.15 346

TestCases				79.58 280	85.15 284	63.62 246		79.83 368	62.31 353	60.32 410	86.73 254	32.02 416	88.96 314	50.28 380	71.57 376	86.15 346

icg_test_0403		80.80 159	80.12 158	82.87 198	87.13 235	63.59 250	85.19 248	89.33 192	70.51 225	78.49 181	89.03 191	63.26 154	93.27 174	72.56 197	85.56 195	91.74 168

v1240		78.99 206	77.78 215	82.64 209	83.21 328	63.54 251	86.62 210	90.30 160	69.74 248	77.33 206	85.68 286	57.04 235	93.76 153	73.13 189	76.92 308	90.62 207

CHOSEN 280x420		66.51 371	64.71 373	71.90 376	81.45 364	63.52 252	57.98 440	68.95 423	53.57 410	62.59 403	76.70 408	46.22 346	75.29 423	55.25 351	79.68 277	76.88 420

IterMVS		74.29 291	72.94 299	78.35 303	81.53 363	63.49 253	81.58 318	82.49 335	68.06 282	69.99 337	83.69 335	51.66 292	85.54 355	65.85 259	71.64 375	86.01 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 131	81.54 130	82.92 195	88.46 177	63.46 254	87.13 188	92.37 82	80.19 12	78.38 184	89.14 187	71.66 59	93.05 193	70.05 218	76.46 317	92.25 153

DU-MVS		81.12 150	80.52 147	82.90 196	87.80 208	63.46 254	87.02 193	91.87 108	79.01 31	78.38 184	89.07 189	65.02 138	93.05 193	70.05 218	76.46 317	92.20 156

LFMVS		81.82 133	81.23 133	83.57 167	91.89 78	63.43 256	89.84 81	81.85 343	77.04 69	83.21 110	93.10 81	52.26 277	93.43 169	71.98 200	89.95 123	93.85 71

NR-MVSNet		80.23 177	79.38 174	82.78 206	87.80 208	63.34 257	86.31 220	91.09 136	79.01 31	72.17 313	89.07 189	67.20 114	92.81 202	66.08 257	75.65 330	92.20 156

IS-MVSNet		83.15 111	82.81 109	84.18 135	89.94 119	63.30 258	91.59 46	88.46 230	79.04 30	79.49 163	92.16 104	65.10 137	94.28 123	67.71 241	91.86 90	94.95 12

TR-MVS		77.44 245	76.18 251	81.20 241	88.24 185	63.24 259	84.61 266	86.40 275	67.55 286	77.81 197	86.48 270	54.10 259	93.15 186	57.75 333	82.72 242	87.20 323

MVS_Test		83.15 111	83.06 104	83.41 172	86.86 240	63.21 260	86.11 226	92.00 100	74.31 139	82.87 115	89.44 184	70.03 78	93.21 179	77.39 141	88.50 149	93.81 75

IterMVS-LS		80.06 180	79.38 174	82.11 218	85.89 263	63.20 261	86.79 203	89.34 191	74.19 143	75.45 253	86.72 256	66.62 118	92.39 218	72.58 194	76.86 310	90.75 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 171	79.98 160	82.12 217	84.28 303	63.19 262	86.41 216	88.95 215	74.18 144	78.69 174	87.54 236	66.62 118	92.43 216	72.57 195	80.57 268	90.74 203

CANet_DTU		80.61 166	79.87 163	82.83 199	85.60 271	63.17 263	87.36 182	88.65 226	76.37 89	75.88 243	88.44 209	53.51 266	93.07 191	73.30 186	89.74 127	92.25 153

MGCFI-Net		85.06 79	85.51 68	83.70 162	89.42 135	63.01 264	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 170	81.28 100	88.74 144	94.66 32

GBi-Net		78.40 219	77.40 225	81.40 234	87.60 218	63.01 264	88.39 145	89.28 195	71.63 195	75.34 258	87.28 240	54.80 250	91.11 269	62.72 281	79.57 278	90.09 233

test1		78.40 219	77.40 225	81.40 234	87.60 218	63.01 264	88.39 145	89.28 195	71.63 195	75.34 258	87.28 240	54.80 250	91.11 269	62.72 281	79.57 278	90.09 233

FMVSNet1		77.44 245	76.12 252	81.40 234	86.81 243	63.01 264	88.39 145	89.28 195	70.49 226	74.39 284	87.28 240	49.06 324	91.11 269	60.91 302	78.52 289	90.09 233

TAPA-MVS	73.13 9	79.15 201	77.94 207	82.79 205	89.59 126	62.99 268	88.16 156	91.51 122	65.77 309	77.14 216	91.09 138	60.91 198	93.21 179	50.26 382	87.05 169	92.17 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS		82.60 122	82.10 121	84.10 137	87.98 200	62.94 269	87.45 180	91.27 128	77.42 56	79.85 158	90.28 156	56.62 240	94.70 112	79.87 117	88.15 154	94.67 29

FMVSNet2		78.20 225	77.21 229	81.20 241	87.60 218	62.89 270	87.47 178	89.02 210	71.63 195	75.29 264	87.28 240	54.80 250	91.10 272	62.38 286	79.38 282	89.61 255

VortexMVS		78.57 217	77.89 210	80.59 256	85.89 263	62.76 271	85.61 237	89.62 183	72.06 190	74.99 273	85.38 295	55.94 243	90.77 281	74.99 169	76.58 314	88.23 299

GA-MVS		76.87 255	75.17 269	81.97 222	82.75 342	62.58 272	81.44 322	86.35 277	72.16 189	74.74 277	82.89 351	46.20 347	92.02 232	68.85 233	81.09 259	91.30 182

D2MVS		74.82 287	73.21 295	79.64 279	79.81 386	62.56 273	80.34 340	87.35 255	64.37 327	68.86 349	82.66 355	46.37 343	90.10 289	67.91 240	81.24 257	86.25 343

FMVSNet3		77.88 235	76.85 237	80.97 249	86.84 242	62.36 274	86.52 213	88.77 220	71.13 208	75.34 258	86.66 262	54.07 260	91.10 272	62.72 281	79.57 278	89.45 259

TranMVSNet+NR-MVSNet		80.84 154	80.31 151	82.42 214	87.85 205	62.33 275	87.74 172	91.33 127	80.55 9	77.99 195	89.86 164	65.23 136	92.62 204	67.05 250	75.24 344	92.30 151

1314		76.53 260	75.30 267	80.21 266	83.93 312	62.32 276	84.66 263	88.81 218	60.23 370	70.16 334	84.07 326	55.30 247	90.73 282	67.37 245	83.21 235	87.59 314

MG-MVS		83.41 104	83.45 97	83.28 175	92.74 67	62.28 277	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 191	91.58 94	92.45 145

SCA		74.22 293	72.33 306	79.91 271	84.05 310	62.17 278	79.96 346	79.29 375	66.30 303	72.38 310	80.13 383	51.95 285	88.60 320	59.25 316	77.67 302	88.96 277

PMMVS		69.34 350	68.67 339	71.35 382	75.67 409	62.03 279	75.17 392	73.46 409	50.00 420	68.68 350	79.05 392	52.07 283	78.13 399	61.16 301	82.77 240	73.90 424

eth_miper_zixun_eth		77.92 234	76.69 243	81.61 229	83.00 336	61.98 280	83.15 300	89.20 202	69.52 250	74.86 276	84.35 318	61.76 179	92.56 209	71.50 204	72.89 366	90.28 224

v148		78.72 212	77.80 214	81.47 231	82.73 343	61.96 281	86.30 221	88.08 235	73.26 170	76.18 238	85.47 293	62.46 168	92.36 220	71.92 201	73.82 358	90.09 233

PAPM		77.68 242	76.40 249	81.51 230	87.29 231	61.85 282	83.78 284	89.59 184	64.74 322	71.23 323	88.70 199	62.59 165	93.66 157	52.66 366	87.03 170	89.01 273

cl22		78.07 229	77.01 232	81.23 240	82.37 352	61.83 283	83.55 292	87.98 238	68.96 268	75.06 271	83.87 327	61.40 188	91.88 239	73.53 182	76.39 319	89.98 242

baseline2		75.70 275	73.83 288	81.30 237	83.26 327	61.79 284	82.57 309	80.65 355	66.81 291	66.88 370	83.42 341	57.86 225	92.19 227	63.47 275	79.57 278	89.91 244

JIA-IIPM		66.32 373	62.82 385	76.82 327	77.09 404	61.72 285	65.34 433	75.38 400	58.04 392	64.51 391	62.32 432	42.05 380	86.51 343	51.45 373	69.22 387	82.21 400

miper_ehance_all_eth		78.59 216	77.76 217	81.08 245	82.66 345	61.56 286	83.65 288	89.15 204	68.87 269	75.55 249	83.79 331	66.49 121	92.03 231	73.25 187	76.39 319	89.64 254

c3_l		78.75 210	77.91 208	81.26 239	82.89 340	61.56 286	84.09 281	89.13 206	69.97 239	75.56 248	84.29 319	66.36 123	92.09 230	73.47 184	75.48 334	90.12 230

miper_enhance_ethall		77.87 236	76.86 236	80.92 250	81.65 359	61.38 288	82.68 307	88.98 212	65.52 313	75.47 250	82.30 360	65.76 133	92.00 233	72.95 190	76.39 319	89.39 261

mmtdpeth		74.16 294	73.01 298	77.60 319	83.72 318	61.13 289	85.10 253	85.10 293	72.06 190	77.21 214	80.33 380	43.84 367	85.75 351	77.14 144	52.61 428	85.91 353

ppachtmachnet_test		70.04 344	67.34 362	78.14 306	79.80 387	61.13 289	79.19 355	80.59 356	59.16 380	65.27 386	79.29 391	46.75 340	87.29 336	49.33 387	66.72 394	86.00 352

sc_t1		72.19 322	69.51 333	80.23 265	84.81 292	61.09 291	84.68 262	80.22 365	60.70 366	71.27 322	83.58 338	36.59 406	89.24 306	60.41 305	63.31 406	90.37 219

TDRefinement		67.49 363	64.34 374	76.92 326	73.47 422	61.07 292	84.86 259	82.98 329	59.77 374	58.30 417	85.13 302	26.06 426	87.89 329	47.92 398	60.59 414	81.81 404

VNet		82.21 124	82.41 115	81.62 227	90.82 96	60.93 293	84.47 269	89.78 175	76.36 90	84.07 97	91.88 110	64.71 141	90.26 286	70.68 212	88.89 139	93.66 83

ab-mvs		79.51 189	78.97 186	81.14 243	88.46 177	60.91 294	83.84 283	89.24 200	70.36 227	79.03 168	88.87 196	63.23 156	90.21 288	65.12 264	82.57 244	92.28 152

PatchmatchNet		73.12 311	71.33 317	78.49 301	83.18 330	60.85 295	79.63 348	78.57 380	64.13 329	71.73 317	79.81 388	51.20 296	85.97 350	57.40 336	76.36 324	88.66 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 167	80.55 146	80.76 253	88.07 195	60.80 296	86.86 200	91.58 120	75.67 103	80.24 154	89.45 183	63.34 151	90.25 287	70.51 214	79.22 285	91.23 183

EGC-MVSNET		52.07 403	47.05 407	67.14 403	83.51 322	60.71 297	80.50 337	67.75 425	0.07 453	0.43 454	75.85 415	24.26 431	81.54 385	28.82 436	62.25 408	59.16 436

Anonymous202405211		78.25 222	77.01 232	81.99 221	91.03 90	60.67 298	84.77 260	83.90 310	70.65 223	80.00 157	91.20 134	41.08 385	91.43 261	65.21 263	85.26 197	93.85 71

ITE_SJBPF				78.22 304	81.77 358	60.57 299		83.30 319	69.25 257	67.54 360	87.20 245	36.33 408	87.28 337	54.34 357	74.62 350	86.80 335

MDA-MVSNet-bldmvs		66.68 369	63.66 379	75.75 334	79.28 394	60.56 300	73.92 402	78.35 382	64.43 325	50.13 432	79.87 387	44.02 366	83.67 370	46.10 406	56.86 418	83.03 393

cl____		77.72 239	76.76 240	80.58 257	82.49 349	60.48 301	83.09 302	87.87 242	69.22 258	74.38 285	85.22 300	62.10 175	91.53 255	71.09 207	75.41 338	89.73 253

DIV-MVS_self_test		77.72 239	76.76 240	80.58 257	82.48 350	60.48 301	83.09 302	87.86 243	69.22 258	74.38 285	85.24 298	62.10 175	91.53 255	71.09 207	75.40 339	89.74 252

1112_ss		77.40 247	76.43 248	80.32 263	89.11 156	60.41 303	83.65 288	87.72 248	62.13 356	73.05 300	86.72 256	62.58 166	89.97 292	62.11 292	80.80 264	90.59 210

tt0805		78.73 211	77.83 212	81.43 232	85.17 282	60.30 304	89.41 100	90.90 139	71.21 207	77.17 215	88.73 198	46.38 342	93.21 179	72.57 195	78.96 286	90.79 199

UniMVSNet_ETH3D		79.10 203	78.24 201	81.70 226	86.85 241	60.24 305	87.28 186	88.79 219	74.25 142	76.84 218	90.53 153	49.48 316	91.56 252	67.98 239	82.15 247	93.29 104

HY-MVS	69.67 12	77.95 233	77.15 230	80.36 261	87.57 222	60.21 306	83.37 296	87.78 246	66.11 304	75.37 257	87.06 251	63.27 153	90.48 285	61.38 299	82.43 245	90.40 218

sd_testset		77.70 241	77.40 225	78.60 295	89.03 157	60.02 307	79.00 358	85.83 285	75.19 115	76.61 227	89.98 162	54.81 249	85.46 357	62.63 285	83.55 227	90.33 221

RPSCF		73.23 310	71.46 314	78.54 298	82.50 348	59.85 308	82.18 312	82.84 333	58.96 382	71.15 325	89.41 185	45.48 357	84.77 364	58.82 322	71.83 374	91.02 192

test_cas_vis1_n_1920		73.76 300	73.74 289	73.81 361	75.90 407	59.77 309	80.51 336	82.40 336	58.30 388	81.62 133	85.69 285	44.35 364	76.41 411	76.29 153	78.61 287	85.23 363

dmvs_re		71.14 329	70.58 324	72.80 370	81.96 355	59.68 310	75.60 390	79.34 374	68.55 274	69.27 347	80.72 376	49.42 317	76.54 408	52.56 367	77.79 298	82.19 401

miper_lstm_enhance		74.11 295	73.11 297	77.13 325	80.11 381	59.62 311	72.23 406	86.92 267	66.76 293	70.40 329	82.92 350	56.93 236	82.92 377	69.06 230	72.63 367	88.87 280

OurMVSNet-221017-0		74.26 292	72.42 305	79.80 274	83.76 317	59.59 312	85.92 231	86.64 270	66.39 302	66.96 369	87.58 232	39.46 391	91.60 248	65.76 260	69.27 386	88.22 300

Patchmatch-RL test		70.24 341	67.78 354	77.61 317	77.43 402	59.57 313	71.16 410	70.33 416	62.94 345	68.65 351	72.77 422	50.62 302	85.49 356	69.58 225	66.58 396	87.77 309

tt0320-xc		70.11 343	67.45 360	78.07 308	85.33 279	59.51 314	83.28 297	78.96 378	58.77 384	67.10 368	80.28 381	36.73 405	87.42 335	56.83 344	59.77 416	87.29 321

OpenMVS_ROB	64.09 19	70.56 337	68.19 343	77.65 316	80.26 378	59.41 315	85.01 255	82.96 330	58.76 385	65.43 385	82.33 359	37.63 403	91.23 268	45.34 411	76.03 326	82.32 399

tt0320		70.49 339	68.03 347	77.89 310	84.78 293	59.12 316	83.55 292	80.44 360	58.13 390	67.43 364	80.41 379	39.26 393	87.54 334	55.12 352	63.18 407	86.99 331

our_test_3		69.14 351	67.00 364	75.57 337	79.80 387	58.80 317	77.96 374	77.81 384	59.55 376	62.90 402	78.25 401	47.43 331	83.97 368	51.71 370	67.58 393	83.93 382

ADS-MVSNet2		66.20 376	63.33 380	74.82 349	79.92 383	58.75 318	67.55 425	75.19 401	53.37 411	65.25 387	75.86 413	42.32 376	80.53 391	41.57 419	68.91 388	85.18 364

pm-mvs1		77.25 250	76.68 244	78.93 290	84.22 305	58.62 319	86.41 216	88.36 231	71.37 202	73.31 296	88.01 223	61.22 193	89.15 309	64.24 272	73.01 365	89.03 272

MonoMVSNet		76.49 264	75.80 253	78.58 296	81.55 362	58.45 320	86.36 219	86.22 278	74.87 126	74.73 278	83.73 333	51.79 290	88.73 317	70.78 209	72.15 371	88.55 294

WR-MVS		79.49 190	79.22 181	80.27 264	88.79 165	58.35 321	85.06 254	88.61 228	78.56 35	77.65 200	88.34 211	63.81 150	90.66 283	64.98 266	77.22 305	91.80 167

FIs		82.07 127	82.42 114	81.04 246	88.80 164	58.34 322	88.26 152	93.49 27	76.93 71	78.47 183	91.04 140	69.92 80	92.34 222	69.87 222	84.97 199	92.44 146

CostFormer		75.24 284	73.90 286	79.27 284	82.65 346	58.27 323	80.80 328	82.73 334	61.57 360	75.33 262	83.13 346	55.52 245	91.07 275	64.98 266	78.34 294	88.45 295

Test_1112_low_res		76.40 266	75.44 261	79.27 284	89.28 145	58.09 324	81.69 317	87.07 262	59.53 377	72.48 308	86.67 261	61.30 190	89.33 303	60.81 304	80.15 273	90.41 217

tfpnnormal		74.39 290	73.16 296	78.08 307	86.10 261	58.05 325	84.65 265	87.53 251	70.32 230	71.22 324	85.63 288	54.97 248	89.86 293	43.03 415	75.02 346	86.32 342

test-LLR		72.94 315	72.43 304	74.48 352	81.35 367	58.04 326	78.38 367	77.46 387	66.66 295	69.95 338	79.00 394	48.06 329	79.24 394	66.13 254	84.83 200	86.15 346

test-mter		71.41 327	70.39 329	74.48 352	81.35 367	58.04 326	78.38 367	77.46 387	60.32 369	69.95 338	79.00 394	36.08 409	79.24 394	66.13 254	84.83 200	86.15 346

mvs_anonymous		79.42 194	79.11 183	80.34 262	84.45 302	57.97 328	82.59 308	87.62 249	67.40 289	76.17 240	88.56 206	68.47 100	89.59 299	70.65 213	86.05 187	93.47 97

tpm cat1		70.57 336	68.31 342	77.35 322	82.41 351	57.95 329	78.08 372	80.22 365	52.04 414	68.54 353	77.66 405	52.00 284	87.84 330	51.77 369	72.07 373	86.25 343

SixPastTwentyTwo		73.37 305	71.26 319	79.70 276	85.08 287	57.89 330	85.57 238	83.56 315	71.03 213	65.66 383	85.88 281	42.10 379	92.57 208	59.11 318	63.34 405	88.65 290

thres200		75.55 277	74.47 278	78.82 291	87.78 211	57.85 331	83.07 304	83.51 316	72.44 184	75.84 244	84.42 314	52.08 282	91.75 243	47.41 399	83.64 226	86.86 334

XXY-MVS		75.41 281	75.56 259	74.96 346	83.59 320	57.82 332	80.59 335	83.87 311	66.54 301	74.93 275	88.31 212	63.24 155	80.09 392	62.16 290	76.85 311	86.97 332

reproduce_monomvs		75.40 282	74.38 280	78.46 302	83.92 313	57.80 333	83.78 284	86.94 265	73.47 164	72.25 312	84.47 313	38.74 396	89.27 305	75.32 167	70.53 381	88.31 298

K. test v3		71.19 328	68.51 340	79.21 286	83.04 335	57.78 334	84.35 276	76.91 394	72.90 178	62.99 401	82.86 352	39.27 392	91.09 274	61.65 296	52.66 427	88.75 286

tfpn200view9		76.42 265	75.37 265	79.55 282	89.13 152	57.65 335	85.17 249	83.60 313	73.41 166	76.45 230	86.39 272	52.12 279	91.95 235	48.33 392	83.75 221	89.07 266

thres400		76.50 261	75.37 265	79.86 272	89.13 152	57.65 335	85.17 249	83.60 313	73.41 166	76.45 230	86.39 272	52.12 279	91.95 235	48.33 392	83.75 221	90.00 239

CMPMVS	51.72 21	70.19 342	68.16 344	76.28 330	73.15 425	57.55 337	79.47 350	83.92 309	48.02 423	56.48 423	84.81 309	43.13 371	86.42 345	62.67 284	81.81 253	84.89 370
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 288	73.39 292	78.61 294	81.38 366	57.48 338	86.64 209	87.95 240	64.99 321	70.18 332	86.61 263	50.43 305	89.52 300	62.12 291	70.18 383	88.83 282

test_vis1_n_1920		75.52 278	75.78 254	74.75 351	79.84 385	57.44 339	83.26 298	85.52 288	62.83 347	79.34 166	86.17 277	45.10 358	79.71 393	78.75 124	81.21 258	87.10 330

PVSNet_0	57.27 20	61.67 388	59.27 391	68.85 395	79.61 390	57.44 339	68.01 423	73.44 410	55.93 404	58.54 416	70.41 427	44.58 361	77.55 403	47.01 400	35.91 439	71.55 427

thres600view7		76.50 261	75.44 261	79.68 277	89.40 137	57.16 341	85.53 244	83.23 321	73.79 153	76.26 235	87.09 249	51.89 287	91.89 238	48.05 397	83.72 224	90.00 239

lessismore_v0				78.97 289	81.01 372	57.15 342		65.99 429		61.16 407	82.82 353	39.12 394	91.34 264	59.67 312	46.92 434	88.43 296

TransMVSNet (Re)		75.39 283	74.56 276	77.86 311	85.50 275	57.10 343	86.78 204	86.09 282	72.17 188	71.53 320	87.34 239	63.01 162	89.31 304	56.84 343	61.83 409	87.17 324

thres100view900		76.50 261	75.55 260	79.33 283	89.52 129	56.99 344	85.83 235	83.23 321	73.94 149	76.32 234	87.12 248	51.89 287	91.95 235	48.33 392	83.75 221	89.07 266

TESTMET0.1,1		69.89 346	69.00 338	72.55 372	79.27 395	56.85 345	78.38 367	74.71 406	57.64 394	68.09 356	77.19 407	37.75 402	76.70 407	63.92 273	84.09 215	84.10 380

WTY-MVS		75.65 276	75.68 256	75.57 337	86.40 252	56.82 346	77.92 376	82.40 336	65.10 317	76.18 238	87.72 228	63.13 161	80.90 389	60.31 307	81.96 250	89.00 275

MDA-MVSNet_test_wron		65.03 378	62.92 382	71.37 380	75.93 406	56.73 347	69.09 422	74.73 405	57.28 398	54.03 427	77.89 402	45.88 349	74.39 426	49.89 384	61.55 410	82.99 394

pmmvs3		57.79 392	54.26 397	68.37 398	64.02 440	56.72 348	75.12 395	65.17 431	40.20 432	52.93 428	69.86 428	20.36 437	75.48 420	45.45 410	55.25 425	72.90 426

tpm2		73.26 309	71.46 314	78.63 293	83.34 325	56.71 349	80.65 334	80.40 362	56.63 401	73.55 294	82.02 365	51.80 289	91.24 267	56.35 348	78.42 292	87.95 304

TinyColmap		67.30 366	64.81 372	74.76 350	81.92 357	56.68 350	80.29 341	81.49 347	60.33 368	56.27 424	83.22 343	24.77 430	87.66 333	45.52 409	69.47 385	79.95 413

YYNet1		65.03 378	62.91 383	71.38 379	75.85 408	56.60 351	69.12 421	74.66 407	57.28 398	54.12 426	77.87 403	45.85 350	74.48 425	49.95 383	61.52 411	83.05 392

PM-MVS		66.41 372	64.14 375	73.20 367	73.92 417	56.45 352	78.97 359	64.96 433	63.88 337	64.72 390	80.24 382	19.84 438	83.44 374	66.24 253	64.52 403	79.71 414

PVSNet	64.34 18	72.08 324	70.87 323	75.69 335	86.21 255	56.44 353	74.37 400	80.73 354	62.06 357	70.17 333	82.23 362	42.86 373	83.31 375	54.77 355	84.45 209	87.32 320

pmmvs5		71.55 326	70.20 331	75.61 336	77.83 400	56.39 354	81.74 316	80.89 351	57.76 393	67.46 362	84.49 312	49.26 321	85.32 359	57.08 339	75.29 342	85.11 367

testing11		75.14 285	74.01 283	78.53 299	88.16 188	56.38 355	80.74 332	80.42 361	70.67 219	72.69 306	83.72 334	43.61 369	89.86 293	62.29 288	83.76 220	89.36 262

WR-MVS_H		78.51 218	78.49 193	78.56 297	88.02 197	56.38 355	88.43 143	92.67 68	77.14 64	73.89 289	87.55 235	66.25 125	89.24 306	58.92 320	73.55 360	90.06 237

MIMVSNet		70.69 335	69.30 334	74.88 348	84.52 300	56.35 357	75.87 388	79.42 372	64.59 323	67.76 357	82.41 357	41.10 384	81.54 385	46.64 403	81.34 255	86.75 337

USDC		70.33 340	68.37 341	76.21 331	80.60 375	56.23 358	79.19 355	86.49 273	60.89 364	61.29 406	85.47 293	31.78 418	89.47 302	53.37 363	76.21 325	82.94 395

Baseline_NR-MVSNet		78.15 227	78.33 199	77.61 317	85.79 265	56.21 359	86.78 204	85.76 286	73.60 159	77.93 196	87.57 233	65.02 138	88.99 311	67.14 249	75.33 341	87.63 311

tpmvs		71.09 330	69.29 335	76.49 329	82.04 354	56.04 360	78.92 360	81.37 349	64.05 333	67.18 367	78.28 400	49.74 314	89.77 295	49.67 385	72.37 368	83.67 385

FC-MVSNet-test		81.52 142	82.02 124	80.03 269	88.42 180	55.97 361	87.95 163	93.42 30	77.10 67	77.38 205	90.98 146	69.96 79	91.79 241	68.46 237	84.50 205	92.33 149

testing91		76.54 259	75.66 258	79.18 287	88.43 179	55.89 362	81.08 325	83.00 328	73.76 154	75.34 258	84.29 319	46.20 347	90.07 290	64.33 270	84.50 205	91.58 172

mvs5depth		69.45 349	67.45 360	75.46 341	73.93 416	55.83 363	79.19 355	83.23 321	66.89 290	71.63 319	83.32 342	33.69 414	85.09 360	59.81 311	55.34 424	85.46 359

GG-mvs-BLEND				75.38 342	81.59 361	55.80 364	79.32 352	69.63 419		67.19 366	73.67 420	43.24 370	88.90 316	50.41 377	84.50 205	81.45 405

VPNet		78.69 213	78.66 190	78.76 292	88.31 183	55.72 365	84.45 272	86.63 271	76.79 75	78.26 187	90.55 152	59.30 215	89.70 298	66.63 252	77.05 307	90.88 196

baseline1		76.98 253	76.75 242	77.66 315	88.13 191	55.66 366	85.12 252	81.89 341	73.04 175	76.79 220	88.90 194	62.43 169	87.78 331	63.30 278	71.18 378	89.55 257

test_vis1_rt		60.28 389	58.42 392	65.84 406	67.25 435	55.60 367	70.44 415	60.94 439	44.33 428	59.00 414	66.64 429	24.91 429	68.67 436	62.80 280	69.48 384	73.25 425

testing99		76.09 271	75.12 270	79.00 288	88.16 188	55.50 368	80.79 329	81.40 348	73.30 169	75.17 266	84.27 322	44.48 362	90.02 291	64.28 271	84.22 214	91.48 177

testing222		74.04 296	72.66 302	78.19 305	87.89 203	55.36 369	81.06 326	79.20 376	71.30 205	74.65 280	83.57 339	39.11 395	88.67 319	51.43 374	85.75 193	90.53 212

FMVSNet5		69.50 348	67.96 348	74.15 357	82.97 339	55.35 370	80.01 345	82.12 339	62.56 351	63.02 399	81.53 367	36.92 404	81.92 383	48.42 391	74.06 354	85.17 366

test_fmvs1_n		70.86 333	70.24 330	72.73 371	72.51 429	55.28 371	81.27 324	79.71 370	51.49 418	78.73 173	84.87 307	27.54 425	77.02 405	76.06 156	79.97 276	85.88 354

test_vis1_n		69.85 347	69.21 336	71.77 377	72.66 428	55.27 372	81.48 320	76.21 398	52.03 415	75.30 263	83.20 345	28.97 423	76.22 413	74.60 172	78.41 293	83.81 383

test_fmvs1		70.93 332	70.52 325	72.16 375	73.71 418	55.05 373	80.82 327	78.77 379	51.21 419	78.58 178	84.41 315	31.20 420	76.94 406	75.88 159	80.12 275	84.47 375

sss		73.60 302	73.64 290	73.51 363	82.80 341	55.01 374	76.12 384	81.69 344	62.47 352	74.68 279	85.85 283	57.32 231	78.11 400	60.86 303	80.93 260	87.39 317

mvsany_test1		62.30 386	61.26 390	65.41 407	69.52 431	54.86 375	66.86 427	49.78 447	46.65 424	68.50 354	83.21 344	49.15 322	66.28 439	56.93 342	60.77 412	75.11 423

ECVR-MVS		79.61 186	79.26 179	80.67 255	90.08 112	54.69 376	87.89 167	77.44 389	74.88 124	80.27 153	92.79 93	48.96 326	92.45 215	68.55 235	92.50 80	94.86 19

EPNet_dtu		75.46 279	74.86 271	77.23 324	82.57 347	54.60 377	86.89 199	83.09 325	71.64 194	66.25 381	85.86 282	55.99 242	88.04 327	54.92 354	86.55 178	89.05 271
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 223	78.34 198	77.84 312	87.83 207	54.54 378	87.94 164	91.17 132	77.65 46	73.48 295	88.49 207	62.24 173	88.43 322	62.19 289	74.07 353	90.55 211

gg-mvs-nofinetune		69.95 345	67.96 348	75.94 332	83.07 333	54.51 379	77.23 381	70.29 417	63.11 341	70.32 330	62.33 431	43.62 368	88.69 318	53.88 360	87.76 158	84.62 374

PS-CasMVS		78.01 232	78.09 204	77.77 314	87.71 214	54.39 380	88.02 160	91.22 129	77.50 54	73.26 297	88.64 202	60.73 199	88.41 323	61.88 293	73.88 357	90.53 212

Anonymous20240521		68.80 354	67.22 363	73.55 362	74.33 414	54.11 381	83.18 299	85.61 287	58.15 389	61.68 405	80.94 373	30.71 421	81.27 387	57.00 341	73.34 364	85.28 362

Patchmtry		70.74 334	69.16 337	75.49 340	80.72 373	54.07 382	74.94 397	80.30 363	58.34 387	70.01 335	81.19 368	52.50 273	86.54 342	53.37 363	71.09 379	85.87 355

PEN-MVS		77.73 238	77.69 220	77.84 312	87.07 239	53.91 383	87.91 166	91.18 131	77.56 51	73.14 299	88.82 197	61.23 192	89.17 308	59.95 309	72.37 368	90.43 216

gm-plane-assit					81.40 365	53.83 384			62.72 350		80.94 373		92.39 218	63.40 277

CL-MVSNet_self_test		72.37 319	71.46 314	75.09 345	79.49 392	53.53 385	80.76 331	85.01 296	69.12 262	70.51 327	82.05 364	57.92 224	84.13 367	52.27 368	66.00 399	87.60 312

MDTV_nov1_ep13			69.97 332		83.18 330	53.48 386	77.10 382	80.18 367	60.45 367	69.33 346	80.44 377	48.89 327	86.90 339	51.60 371	78.51 290

KD-MVS_2432*1600		66.22 374	63.89 377	73.21 365	75.47 412	53.42 387	70.76 413	84.35 302	64.10 331	66.52 377	78.52 398	34.55 412	84.98 361	50.40 378	50.33 431	81.23 406

miper_refine_blended		66.22 374	63.89 377	73.21 365	75.47 412	53.42 387	70.76 413	84.35 302	64.10 331	66.52 377	78.52 398	34.55 412	84.98 361	50.40 378	50.33 431	81.23 406

test1111		79.43 193	79.18 182	80.15 267	89.99 117	53.31 389	87.33 184	77.05 393	75.04 118	80.23 155	92.77 95	48.97 325	92.33 223	68.87 232	92.40 82	94.81 22

LF4IMVS		64.02 382	62.19 386	69.50 391	70.90 430	53.29 390	76.13 383	77.18 392	52.65 413	58.59 415	80.98 372	23.55 433	76.52 409	53.06 365	66.66 395	78.68 416

MVStest1		56.63 394	52.76 400	68.25 400	61.67 442	53.25 391	71.67 408	68.90 424	38.59 435	50.59 431	83.05 347	25.08 428	70.66 432	36.76 428	38.56 438	80.83 409

DTE-MVSNet		76.99 252	76.80 238	77.54 320	86.24 254	53.06 392	87.52 176	90.66 145	77.08 68	72.50 307	88.67 201	60.48 207	89.52 300	57.33 337	70.74 380	90.05 238

test2506		77.30 249	76.49 246	79.74 275	90.08 112	52.02 393	87.86 169	63.10 436	74.88 124	80.16 156	92.79 93	38.29 400	92.35 221	68.74 234	92.50 80	94.86 19

tpm		72.37 319	71.71 311	74.35 354	82.19 353	52.00 394	79.22 354	77.29 391	64.56 324	72.95 302	83.68 336	51.35 293	83.26 376	58.33 328	75.80 328	87.81 308

test_fmvs2		68.35 360	67.48 359	70.98 386	69.50 432	51.95 395	80.05 344	76.38 397	49.33 421	74.65 280	84.38 316	23.30 434	75.40 422	74.51 173	75.17 345	85.60 357

ETVMVS		72.25 321	71.05 320	75.84 333	87.77 212	51.91 396	79.39 351	74.98 402	69.26 256	73.71 291	82.95 349	40.82 387	86.14 347	46.17 405	84.43 210	89.47 258

WB-MVSnew		71.96 325	71.65 312	72.89 369	84.67 299	51.88 397	82.29 311	77.57 386	62.31 353	73.67 293	83.00 348	53.49 267	81.10 388	45.75 408	82.13 248	85.70 356

MIMVSNet1		68.58 356	66.78 366	73.98 359	80.07 382	51.82 398	80.77 330	84.37 301	64.40 326	59.75 413	82.16 363	36.47 407	83.63 371	42.73 416	70.33 382	86.48 341

Vis-MVSNet (Re-imp)		78.36 221	78.45 194	78.07 308	88.64 171	51.78 399	86.70 207	79.63 371	74.14 145	75.11 269	90.83 147	61.29 191	89.75 296	58.10 330	91.60 92	92.69 133

LCM-MVSNet-Re		77.05 251	76.94 235	77.36 321	87.20 232	51.60 400	80.06 343	80.46 359	75.20 114	67.69 359	86.72 256	62.48 167	88.98 312	63.44 276	89.25 134	91.51 174

Gipuma		45.18 410	41.86 413	55.16 422	77.03 405	51.52 401	32.50 446	80.52 357	32.46 442	27.12 445	35.02 446	9.52 449	75.50 419	22.31 443	60.21 415	38.45 445
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 365	65.99 369	71.37 380	73.48 421	51.47 402	75.16 393	85.19 291	65.20 316	60.78 408	80.93 375	42.35 375	77.20 404	57.12 338	53.69 426	85.44 360

UnsupCasMVSNet_bld		63.70 383	61.53 389	70.21 389	73.69 419	51.39 403	72.82 404	81.89 341	55.63 405	57.81 419	71.80 424	38.67 397	78.61 397	49.26 388	52.21 429	80.63 410

UBG		73.08 312	72.27 307	75.51 339	88.02 197	51.29 404	78.35 370	77.38 390	65.52 313	73.87 290	82.36 358	45.55 354	86.48 344	55.02 353	84.39 211	88.75 286

FPMVS		53.68 399	51.64 401	59.81 414	65.08 438	51.03 405	69.48 418	69.58 420	41.46 431	40.67 438	72.32 423	16.46 442	70.00 435	24.24 442	65.42 400	58.40 438

WBMVS		73.43 304	72.81 300	75.28 343	87.91 202	50.99 406	78.59 366	81.31 350	65.51 315	74.47 283	84.83 308	46.39 341	86.68 341	58.41 326	77.86 297	88.17 302

CVMVSNet		72.99 314	72.58 303	74.25 356	84.28 303	50.85 407	86.41 216	83.45 318	44.56 427	73.23 298	87.54 236	49.38 318	85.70 352	65.90 258	78.44 291	86.19 345

Anonymous20231206		68.60 355	67.80 353	71.02 385	80.23 380	50.75 408	78.30 371	80.47 358	56.79 400	66.11 382	82.63 356	46.35 344	78.95 396	43.62 414	75.70 329	83.36 388

ambc				75.24 344	73.16 424	50.51 409	63.05 438	87.47 253		64.28 392	77.81 404	17.80 440	89.73 297	57.88 332	60.64 413	85.49 358

APD_test1		53.31 400	49.93 405	63.42 410	65.68 437	50.13 410	71.59 409	66.90 428	34.43 440	40.58 439	71.56 425	8.65 451	76.27 412	34.64 431	55.36 423	63.86 434

tpmrst		72.39 317	72.13 308	73.18 368	80.54 376	49.91 411	79.91 347	79.08 377	63.11 341	71.69 318	79.95 385	55.32 246	82.77 378	65.66 261	73.89 356	86.87 333

Patchmatch-test		64.82 380	63.24 381	69.57 390	79.42 393	49.82 412	63.49 437	69.05 422	51.98 416	59.95 412	80.13 383	50.91 298	70.98 431	40.66 421	73.57 359	87.90 306

EPMVS		69.02 352	68.16 344	71.59 378	79.61 390	49.80 413	77.40 379	66.93 427	62.82 348	70.01 335	79.05 392	45.79 351	77.86 402	56.58 346	75.26 343	87.13 327

SSC-MVS3.2		73.35 308	73.39 292	73.23 364	85.30 280	49.01 414	74.58 399	81.57 345	75.21 113	73.68 292	85.58 290	52.53 271	82.05 382	54.33 358	77.69 301	88.63 291

dp		66.80 368	65.43 370	70.90 387	79.74 389	48.82 415	75.12 395	74.77 404	59.61 375	64.08 395	77.23 406	42.89 372	80.72 390	48.86 390	66.58 396	83.16 390

UWE-MVS		72.13 323	71.49 313	74.03 358	86.66 248	47.70 416	81.40 323	76.89 395	63.60 338	75.59 247	84.22 323	39.94 390	85.62 354	48.98 389	86.13 186	88.77 285

test0.0.03 1		68.00 362	67.69 355	68.90 394	77.55 401	47.43 417	75.70 389	72.95 413	66.66 295	66.56 375	82.29 361	48.06 329	75.87 417	44.97 412	74.51 351	83.41 387

SD_0403		74.65 289	74.77 273	74.29 355	86.20 256	47.42 418	83.71 286	85.12 292	69.30 254	68.50 354	87.95 225	59.40 214	86.05 348	49.38 386	83.35 232	89.40 260

myMVS_eth3d28		73.62 301	73.53 291	73.90 360	88.20 186	47.41 419	78.06 373	79.37 373	74.29 141	73.98 288	84.29 319	44.67 359	83.54 372	51.47 372	87.39 163	90.74 203

ADS-MVSNet		64.36 381	62.88 384	68.78 396	79.92 383	47.17 420	67.55 425	71.18 415	53.37 411	65.25 387	75.86 413	42.32 376	73.99 427	41.57 419	68.91 388	85.18 364

EU-MVSNet		68.53 358	67.61 357	71.31 383	78.51 399	47.01 421	84.47 269	84.27 305	42.27 430	66.44 380	84.79 310	40.44 388	83.76 369	58.76 323	68.54 391	83.17 389

test_fmvs3		63.36 384	61.82 387	67.98 401	62.51 441	46.96 422	77.37 380	74.03 408	45.24 426	67.50 361	78.79 397	12.16 446	72.98 430	72.77 193	66.02 398	83.99 381

ttmdpeth		59.91 390	57.10 394	68.34 399	67.13 436	46.65 423	74.64 398	67.41 426	48.30 422	62.52 404	85.04 306	20.40 436	75.93 416	42.55 417	45.90 437	82.44 398

KD-MVS_self_test		68.81 353	67.59 358	72.46 374	74.29 415	45.45 424	77.93 375	87.00 263	63.12 340	63.99 396	78.99 396	42.32 376	84.77 364	56.55 347	64.09 404	87.16 326

testf1		45.72 407	41.96 411	57.00 416	56.90 444	45.32 425	66.14 430	59.26 441	26.19 444	30.89 443	60.96 435	4.14 454	70.64 433	26.39 440	46.73 435	55.04 439

APD_test2		45.72 407	41.96 411	57.00 416	56.90 444	45.32 425	66.14 430	59.26 441	26.19 444	30.89 443	60.96 435	4.14 454	70.64 433	26.39 440	46.73 435	55.04 439

LCM-MVSNet		54.25 396	49.68 406	67.97 402	53.73 450	45.28 427	66.85 428	80.78 353	35.96 439	39.45 440	62.23 433	8.70 450	78.06 401	48.24 395	51.20 430	80.57 411

test_vis3_rt		49.26 406	47.02 408	56.00 418	54.30 447	45.27 428	66.76 429	48.08 448	36.83 437	44.38 436	53.20 441	7.17 453	64.07 441	56.77 345	55.66 421	58.65 437

testing3-2		75.12 286	75.19 268	74.91 347	90.40 105	45.09 429	80.29 341	78.42 381	78.37 40	76.54 229	87.75 227	44.36 363	87.28 337	57.04 340	83.49 229	92.37 147

test20.03		67.45 364	66.95 365	68.94 393	75.48 411	44.84 430	77.50 378	77.67 385	66.66 295	63.01 400	83.80 330	47.02 335	78.40 398	42.53 418	68.86 390	83.58 386

mvsany_test3		53.99 397	51.45 402	61.61 412	55.51 446	44.74 431	63.52 436	45.41 451	43.69 429	58.11 418	76.45 410	17.99 439	63.76 442	54.77 355	47.59 433	76.34 421

PatchT		68.46 359	67.85 350	70.29 388	80.70 374	43.93 432	72.47 405	74.88 403	60.15 371	70.55 326	76.57 409	49.94 311	81.59 384	50.58 376	74.83 348	85.34 361

MVS-HIRNet		59.14 391	57.67 393	63.57 409	81.65 359	43.50 433	71.73 407	65.06 432	39.59 434	51.43 429	57.73 437	38.34 399	82.58 379	39.53 422	73.95 355	64.62 433

testing3		68.56 357	67.67 356	71.22 384	87.33 228	42.87 434	83.06 305	71.54 414	70.36 227	69.08 348	84.38 316	30.33 422	85.69 353	37.50 427	75.45 337	85.09 368

WAC-MVS						42.58 435								39.46 423

myMVS_eth3d		67.02 367	66.29 368	69.21 392	84.68 296	42.58 435	78.62 364	73.08 411	66.65 298	66.74 373	79.46 389	31.53 419	82.30 380	39.43 424	76.38 322	82.75 396

PMVS	37.38 22	44.16 411	40.28 415	55.82 420	40.82 455	42.54 437	65.12 434	63.99 435	34.43 440	24.48 446	57.12 439	3.92 456	76.17 414	17.10 447	55.52 422	48.75 441
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 402	50.82 403	55.90 419	53.82 449	42.31 438	59.42 439	58.31 443	36.45 438	56.12 425	70.96 426	12.18 445	57.79 445	53.51 362	56.57 420	67.60 430

testgi		66.67 370	66.53 367	67.08 404	75.62 410	41.69 439	75.93 385	76.50 396	66.11 304	65.20 389	86.59 264	35.72 410	74.71 424	43.71 413	73.38 363	84.84 371

Syy-MVS		68.05 361	67.85 350	68.67 397	84.68 296	40.97 440	78.62 364	73.08 411	66.65 298	66.74 373	79.46 389	52.11 281	82.30 380	32.89 432	76.38 322	82.75 396

ANet_high		50.57 405	46.10 409	63.99 408	48.67 453	39.13 441	70.99 412	80.85 352	61.39 362	31.18 442	57.70 438	17.02 441	73.65 429	31.22 435	15.89 450	79.18 415

UWE-MVS-28		65.32 377	64.93 371	66.49 405	78.70 397	38.55 442	77.86 377	64.39 434	62.00 358	64.13 394	83.60 337	41.44 382	76.00 415	31.39 434	80.89 261	84.92 369

MDTV_nov1_ep13_2view						37.79 443	75.16 393		55.10 406	66.53 376		49.34 319		53.98 359		87.94 305

DSMNet-mixed		57.77 393	56.90 395	60.38 413	67.70 434	35.61 444	69.18 419	53.97 445	32.30 443	57.49 420	79.88 386	40.39 389	68.57 437	38.78 425	72.37 368	76.97 419

MVE	26.22 23	30.37 417	25.89 421	43.81 428	44.55 454	35.46 445	28.87 447	39.07 452	18.20 448	18.58 450	40.18 445	2.68 457	47.37 450	17.07 448	23.78 447	48.60 442
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 404	50.29 404	52.78 424	68.58 433	34.94 446	63.71 435	56.63 444	39.73 433	44.95 435	65.47 430	21.93 435	58.48 444	34.98 430	56.62 419	64.92 432

wuyk23d		16.82 420	15.94 423	19.46 434	58.74 443	31.45 447	39.22 444	3.74 459	6.84 450	6.04 453	2.70 453	1.27 458	24.29 453	10.54 453	14.40 452	2.63 450

E-PMN		31.77 414	30.64 417	35.15 431	52.87 451	27.67 448	57.09 441	47.86 449	24.64 446	16.40 451	33.05 447	11.23 447	54.90 447	14.46 450	18.15 448	22.87 447

kuosan		39.70 413	40.40 414	37.58 430	64.52 439	26.98 449	65.62 432	33.02 454	46.12 425	42.79 437	48.99 443	24.10 432	46.56 451	12.16 452	26.30 445	39.20 444

DeepMVS_CX				27.40 433	40.17 456	26.90 450		24.59 457	17.44 449	23.95 447	48.61 444	9.77 448	26.48 452	18.06 445	24.47 446	28.83 446

dongtai		45.42 409	45.38 410	45.55 427	73.36 423	26.85 451	67.72 424	34.19 453	54.15 409	49.65 433	56.41 440	25.43 427	62.94 443	19.45 444	28.09 444	46.86 443

EMVS		30.81 416	29.65 418	34.27 432	50.96 452	25.95 452	56.58 442	46.80 450	24.01 447	15.53 452	30.68 448	12.47 444	54.43 448	12.81 451	17.05 449	22.43 448

dmvs_testset		62.63 385	64.11 376	58.19 415	78.55 398	24.76 453	75.28 391	65.94 430	67.91 283	60.34 409	76.01 412	53.56 265	73.94 428	31.79 433	67.65 392	75.88 422

new-patchmatchnet		61.73 387	61.73 388	61.70 411	72.74 427	24.50 454	69.16 420	78.03 383	61.40 361	56.72 422	75.53 416	38.42 398	76.48 410	45.95 407	57.67 417	84.13 379

WB-MVS		54.94 395	54.72 396	55.60 421	73.50 420	20.90 455	74.27 401	61.19 438	59.16 380	50.61 430	74.15 418	47.19 334	75.78 418	17.31 446	35.07 440	70.12 428

SSC-MVS		53.88 398	53.59 398	54.75 423	72.87 426	19.59 456	73.84 403	60.53 440	57.58 396	49.18 434	73.45 421	46.34 345	75.47 421	16.20 449	32.28 442	69.20 429

PMMVS2		40.82 412	38.86 416	46.69 426	53.84 448	16.45 457	48.61 443	49.92 446	37.49 436	31.67 441	60.97 434	8.14 452	56.42 446	28.42 437	30.72 443	67.19 431

tmp_tt		18.61 419	21.40 422	10.23 435	4.82 458	10.11 458	34.70 445	30.74 456	1.48 452	23.91 448	26.07 449	28.42 424	13.41 454	27.12 438	15.35 451	7.17 449

N_pmnet		52.79 401	53.26 399	51.40 425	78.99 396	7.68 459	69.52 417	3.89 458	51.63 417	57.01 421	74.98 417	40.83 386	65.96 440	37.78 426	64.67 402	80.56 412

test_method		31.52 415	29.28 419	38.23 429	27.03 457	6.50 460	20.94 448	62.21 437	4.05 451	22.35 449	52.50 442	13.33 443	47.58 449	27.04 439	34.04 441	60.62 435

test123		6.12 422	8.11 425	0.14 436	0.06 460	0.09 461	71.05 411	0.03 461	0.04 455	0.25 456	1.30 455	0.05 459	0.03 456	0.21 455	0.01 454	0.29 451

testmvs		6.04 423	8.02 426	0.10 437	0.08 459	0.03 462	69.74 416	0.04 460	0.05 454	0.31 455	1.68 454	0.02 460	0.04 455	0.24 454	0.02 453	0.25 452

mmdepth		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

monomultidepth		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

test_blank		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet_test		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

DCPMVS		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

cdsmvs_eth3d_5k		19.96 418	26.61 420	0.00 438	0.00 461	0.00 463	0.00 449	89.26 198	0.00 456	0.00 457	88.61 203	61.62 182	0.00 457	0.00 456	0.00 455	0.00 453

pcd_1.5k_mvsjas		5.26 424	7.02 427	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	63.15 158	0.00 457	0.00 456	0.00 455	0.00 453

sosnet-low-res		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

sosnet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uncertanet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

Regformer		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

ab-mvs-re		7.23 421	9.64 424	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	86.72 256	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

PC_three_1452									68.21 280	92.02 12	94.00 56	82.09 5	95.98 57	84.58 64	96.68 2	94.95 12

eth-test2					0.00 461

eth-test					0.00 461

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 22	96.58 6	94.26 52

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 150	88.57 29	94.67 25	75.57 22	95.79 59	86.77 45	95.76 23

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 17	96.57 7	94.67 29

GSMVS																88.96 277

sam_mvs1												51.32 294				88.96 277

sam_mvs												50.01 309

MTGPA								92.02 98

test_post1							78.90 361				5.43 452	48.81 328	85.44 358	59.25 316

test_post											5.46 451	50.36 306	84.24 366

patchmatchnet-post											74.00 419	51.12 297	88.60 320

MTMP							92.18 35	32.83 455

test9_res														84.90 57	95.70 26	92.87 127

agg_prior2														82.91 84	95.45 29	92.70 131

test_prior2							88.85 125		75.41 107	84.91 75	93.54 69	74.28 30		83.31 78	95.86 20

旧先验2							86.56 212		58.10 391	87.04 55			88.98 312	74.07 178

新几何2							86.29 222

无先验							87.48 177	88.98 212	60.00 372				94.12 132	67.28 246		88.97 276

原ACMM2							86.86 200

testdata2													91.01 276	62.37 287

segment_acmp												73.08 40

testdata1							84.14 280		75.71 100

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 181	91.33 180

plane_prior4											91.00 144

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

n2								0.00 462

nn								0.00 462

door-mid								69.98 418

test11								92.23 88

door								69.44 421

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 210

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 210

BP-MVS														77.47 139

HQP4-MVS										77.24 209			95.11 90			91.03 190

HQP3-MVS								92.19 92							85.99 189

HQP2-MVS												60.17 211

ACMMP++_ref															81.95 251

ACMMP++															81.25 256

Test By Simon												64.33 144

High-resolution multi-view benchmark