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.

MM		89.16 7	89.23 9	88.97 4	90.79 103	73.65 10	92.66 28	91.17 154	86.57 1	87.39 58	94.97 25	71.70 65	97.68 1	92.19 1	95.63 31	95.57 2

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 61

No_MVS				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 61

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 65	95.06 1	94.23 6	78.38 39	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 74

MGCNet		87.69 24	87.55 29	88.12 13	89.45 140	71.76 54	91.47 57	89.54 211	82.14 3	86.65 67	94.28 46	68.28 123	97.46 6	90.81 6	95.31 37	95.15 9

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 73	93.57 8	94.06 14	77.24 65	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

test_241102_TWO								94.06 14	77.24 65	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 65	93.49 10	92.73 71	77.33 60	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 127
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									78.38 39	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

MED-MVS test				87.86 27	94.57 17	71.43 61	93.28 12	94.36 3	75.24 131	92.25 9	95.03 22		97.39 11	88.15 39	95.96 19	94.75 35

MED-MVS		89.75 3	90.37 3	87.89 24	94.57 17	71.43 61	93.28 12	94.36 3	77.30 62	92.25 9	95.87 3	81.59 7	97.39 11	88.15 39	95.96 19	94.85 24

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 12	75.90 112	92.29 7	95.66 12	81.67 6	97.38 13	87.44 48	96.34 15	93.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_ONE					95.30 2	70.98 73		94.06 14	77.17 68	93.10 1	95.39 18	82.99 1	97.27 14

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 151	92.29 7	95.97 2	74.28 34	97.24 15	88.58 33	96.91 1	94.87 21
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
CANet		86.45 48	86.10 61	87.51 42	90.09 116	70.94 77	89.70 94	92.59 81	81.78 4	81.32 162	91.43 149	70.34 82	97.23 16	84.26 75	93.36 73	94.37 65

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 79	72.96 25	93.73 5	93.67 25	80.19 13	88.10 43	94.80 27	73.76 38	97.11 17	87.51 46	95.82 24	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 92	72.32 45	90.31 79	93.94 18	77.12 71	82.82 138	94.23 50	72.13 59	97.09 18	84.83 67	95.37 34	93.65 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ME-MVS		88.98 11	89.39 8	87.75 30	94.54 20	71.43 61	91.61 49	94.25 5	76.30 104	90.62 21	95.03 22	78.06 15	97.07 19	88.15 39	95.96 19	94.75 35

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 22	71.69 55	93.83 4	93.96 17	75.70 119	91.06 19	96.03 1	76.84 18	97.03 20	89.09 21	95.65 30	94.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 66	81.50 5	85.79 73	93.47 81	73.02 46	97.00 21	84.90 64	94.94 43	94.10 80

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 17	71.25 65	93.28 12	93.91 19	77.30 62	91.13 18	95.87 3	77.62 16	96.95 22	86.12 57	93.07 75	94.85 24

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 80	85.24 78	94.32 44	71.76 63	96.93 23	85.53 61	95.79 25	94.32 69

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 19

CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 52	80.90 7	88.06 44	94.06 59	76.43 20	96.84 25	88.48 36	95.99 18	94.34 67

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 78	84.68 88	93.99 65	70.67 80	96.82 26	84.18 79	95.01 40	93.90 92

HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 65	80.26 12	87.78 49	94.27 47	75.89 23	96.81 27	87.45 47	96.44 9	93.05 148

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 10	80.27 11	91.35 16	94.16 54	78.35 14	96.77 28	89.59 17	94.22 65	94.67 42
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
DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 47	76.62 88	84.22 103	93.36 85	71.44 69	96.76 29	80.82 114	95.33 36	94.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 74	84.47 94	88.51 7	91.08 94	73.49 16	93.18 16	93.78 23	80.79 8	76.66 261	93.37 84	60.40 242	96.75 30	77.20 165	93.73 69	95.29 7

ZD-MVS					94.38 29	72.22 46		92.67 74	70.98 244	87.75 51	94.07 58	74.01 37	96.70 31	84.66 70	94.84 47

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 46	76.73 85	84.45 96	94.52 32	69.09 108	96.70 31	84.37 74	94.83 48	94.03 84

TestfortrainingZip				87.28 46	92.85 68	72.05 50	93.28 12	93.32 37	76.52 90	88.91 32	93.52 77	77.30 17	96.67 33		91.98 94	93.13 142

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 105	88.14 42	95.09 21	71.06 75	96.67 33	87.67 44	96.37 14	94.09 81

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 40	76.78 82	84.66 91	94.52 32	68.81 114	96.65 35	84.53 72	94.90 44	94.00 86

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 47	75.53 122	83.86 111	94.42 40	67.87 128	96.64 36	82.70 99	94.57 55	93.66 106

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 39	76.78 82	84.91 83	94.44 39	70.78 78	96.61 37	84.53 72	94.89 45	93.66 106

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 27	83.67 116	94.17 53	67.45 131	96.60 38	83.06 87	94.50 56	94.07 82

X-MVStestdata		80.37 204	77.83 244	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 27	83.67 116	12.47 516	67.45 131	96.60 38	83.06 87	94.50 56	94.07 82

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 60	92.24 78	69.03 111	89.57 99	93.39 35	77.53 55	89.79 25	94.12 56	78.98 13	96.58 40	85.66 58	95.72 27	94.58 51

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 142	88.96 30	95.54 14	71.20 73	96.54 41	86.28 54	93.49 70	93.06 146

our_new_method		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 142	88.96 30	95.54 14	71.20 73	96.54 41	86.28 54	93.49 70	93.06 146

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 36	72.39 41	91.86 45	92.83 66	73.01 203	88.58 35	94.52 32	73.36 39	96.49 43	84.26 75	95.01 40	92.70 162
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
reproduce_model		87.28 35	87.39 33	86.95 55	93.10 62	71.24 70	91.60 50	93.19 41	74.69 152	88.80 34	95.61 13	70.29 84	96.44 44	86.20 56	93.08 74	93.16 138

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 100	70.96 75	92.27 37	94.07 13	72.45 209	85.22 79	91.90 125	69.47 98	96.42 45	83.28 86	95.94 22	94.35 66

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 201	84.86 86	92.89 96	76.22 21	96.33 46	84.89 66	95.13 39	94.40 63

ACMMP		85.89 66	85.39 77	87.38 44	93.59 49	72.63 33	92.74 25	93.18 45	76.78 82	80.73 177	93.82 72	64.33 171	96.29 47	82.67 100	90.69 119	93.23 130
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
MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 77	77.57 51	83.84 112	94.40 41	72.24 56	96.28 48	85.65 59	95.30 38	93.62 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lecture		88.09 17	88.59 16	86.58 63	93.26 56	69.77 97	93.70 6	94.16 8	77.13 70	89.76 26	95.52 16	72.26 55	96.27 49	86.87 50	94.65 51	93.70 105

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 103	76.87 79	82.81 139	94.25 49	66.44 145	96.24 50	82.88 92	94.28 63	93.38 123

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 232	92.02 114	79.45 23	85.88 71	94.80 27	68.07 125	96.21 51	86.69 52	95.34 35	93.23 130

SF-MVS		88.46 15	88.74 15	87.64 39	92.78 71	71.95 52	92.40 29	94.74 2	75.71 117	89.16 29	95.10 20	75.65 25	96.19 52	87.07 49	96.01 17	94.79 28

test12				86.80 59	92.63 74	70.70 82		91.79 129		82.71 141		71.67 66	96.16 53		94.50 56	93.54 119

CDPH-MVS		85.76 69	85.29 82	87.17 49	93.49 51	71.08 71	88.58 149	92.42 87	68.32 318	84.61 93	93.48 79	72.32 54	96.15 54	79.00 143	95.43 33	94.28 72

BridgeMVS		86.78 42	86.99 40	86.15 72	91.24 91	67.61 164	90.51 70	92.90 62	77.26 64	87.44 57	91.63 139	71.27 72	96.06 55	85.62 60	95.01 40	94.78 29

MVSMamba_PlusPlus		85.99 60	85.96 65	86.05 75	91.09 93	67.64 163	89.63 97	92.65 77	72.89 206	84.64 92	91.71 134	71.85 61	96.03 56	84.77 69	94.45 59	94.49 59

DP-MVS Recon		83.11 136	82.09 147	86.15 72	94.44 23	70.92 78	88.79 136	92.20 106	70.53 257	79.17 202	91.03 165	64.12 173	96.03 56	68.39 274	90.14 129	91.50 213

DPM-MVS		84.93 88	84.29 95	86.84 57	90.20 114	73.04 23	87.12 208	93.04 47	69.80 279	82.85 137	91.22 156	73.06 45	96.02 58	76.72 177	94.63 53	91.46 217

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 109	83.81 113	93.95 68	69.77 95	96.01 59	85.15 62	94.66 50	94.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 51	89.80 90	93.50 30	75.17 138	86.34 69	95.29 19	70.86 77	96.00 60	88.78 31	96.04 16	94.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 90	74.62 155	88.90 33	93.85 71	75.75 24	96.00 60	87.80 43	94.63 53	95.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PC_three_1452									68.21 319	92.02 14	94.00 63	82.09 5	95.98 62	84.58 71	96.68 2	94.95 15

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 69	76.62 88	83.68 115	94.46 36	67.93 126	95.95 63	84.20 78	94.39 60	93.23 130

9.14			88.26 19		92.84 70		91.52 56	94.75 1	73.93 174	88.57 36	94.67 30	75.57 26	95.79 64	86.77 51	95.76 26

SR-MVS		86.73 43	86.67 48	86.91 56	94.11 41	72.11 49	92.37 33	92.56 82	74.50 156	86.84 65	94.65 31	67.31 133	95.77 65	84.80 68	92.85 78	92.84 160

AdaColmap		80.58 198	79.42 204	84.06 169	93.09 63	68.91 116	89.36 111	88.97 245	69.27 292	75.70 283	89.69 204	57.20 269	95.77 65	63.06 319	88.41 164	87.50 359

DELS-MVS		85.41 77	85.30 81	85.77 81	88.49 184	67.93 154	85.52 272	93.44 32	78.70 35	83.63 118	89.03 225	74.57 28	95.71 67	80.26 123	94.04 66	93.66 106
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
APD-MVS_3200maxsize		85.97 62	85.88 66	86.22 69	92.69 73	69.53 100	91.93 42	92.99 55	73.54 185	85.94 70	94.51 35	65.80 157	95.61 68	83.04 89	92.51 83	93.53 120

SR-MVS-dyc-post		85.77 68	85.61 73	86.23 68	93.06 64	70.63 83	91.88 43	92.27 96	73.53 186	85.69 74	94.45 37	65.00 165	95.56 69	82.75 95	91.87 96	92.50 173

EPNet		83.72 114	82.92 129	86.14 74	84.22 336	69.48 102	91.05 64	85.27 338	81.30 6	76.83 256	91.65 137	66.09 152	95.56 69	76.00 184	93.85 67	93.38 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HPM-MVS_fast		85.35 80	84.95 87	86.57 64	93.69 46	70.58 85	92.15 40	91.62 139	73.89 175	82.67 142	94.09 57	62.60 194	95.54 71	80.93 112	92.93 77	93.57 116

NormalMVS		86.29 54	85.88 66	87.52 41	93.26 56	72.47 38	91.65 47	92.19 108	79.31 25	84.39 98	92.18 116	64.64 168	95.53 72	80.70 117	94.65 51	94.56 55

SymmetryMVS		85.38 79	84.81 88	87.07 51	91.47 88	72.47 38	91.65 47	88.06 274	79.31 25	84.39 98	92.18 116	64.64 168	95.53 72	80.70 117	90.91 116	93.21 133

h-mvs33		83.15 133	82.19 144	86.02 78	90.56 106	70.85 80	88.15 170	89.16 234	76.02 110	84.67 89	91.39 150	61.54 215	95.50 74	82.71 97	75.48 374	91.72 207

test_prior				86.33 65	92.61 75	69.59 99		92.97 60					95.48 75			93.91 90

原ACMM1				84.35 144	93.01 66	68.79 118		92.44 84	63.96 383	81.09 167	91.57 143	66.06 153	95.45 76	67.19 284	94.82 49	88.81 321

QAPM		80.88 180	79.50 203	85.03 107	88.01 209	68.97 115	91.59 51	92.00 116	66.63 341	75.15 305	92.16 118	57.70 261	95.45 76	63.52 310	88.76 156	90.66 244

BP-MVS1		84.32 93	83.71 110	86.17 70	87.84 216	67.85 156	89.38 110	89.64 208	77.73 47	83.98 109	92.12 121	56.89 272	95.43 78	84.03 80	91.75 99	95.24 8

RPMNet		73.51 340	70.49 370	82.58 242	81.32 408	65.19 230	75.92 437	92.27 96	57.60 447	72.73 342	76.45 454	52.30 313	95.43 78	48.14 447	77.71 339	87.11 377

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 149	66.27 197	92.32 35	93.63 26	79.37 24	84.17 105	91.88 126	69.04 112	95.43 78	83.93 81	93.77 68	93.01 151

TEST9					93.26 56	72.96 25	88.75 139	91.89 122	68.44 316	85.00 81	93.10 89	74.36 33	95.41 81

train_agg		86.43 49	86.20 56	87.13 50	93.26 56	72.96 25	88.75 139	91.89 122	68.69 311	85.00 81	93.10 89	74.43 31	95.41 81	84.97 63	95.71 28	93.02 150

ETV-MVS		84.90 90	84.67 90	85.59 88	89.39 144	68.66 128	88.74 141	92.64 79	79.97 17	84.10 106	85.71 322	69.32 101	95.38 83	80.82 114	91.37 106	92.72 161

HQP_MVS		83.64 117	83.14 122	85.14 101	90.08 117	68.71 124	91.25 60	92.44 84	79.12 29	78.92 206	91.00 166	60.42 240	95.38 83	78.71 147	86.32 207	91.33 218

plane_prior5								92.44 84					95.38 83	78.71 147	86.32 207	91.33 218

TSAR-MVS + GP.		85.71 70	85.33 79	86.84 57	91.34 89	72.50 36	89.07 125	87.28 296	76.41 96	85.80 72	90.22 192	74.15 36	95.37 86	81.82 104	91.88 95	92.65 166

GDP-MVS		83.52 122	82.64 134	86.16 71	88.14 200	68.45 133	89.13 122	92.69 72	72.82 207	83.71 114	91.86 128	55.69 281	95.35 87	80.03 124	89.74 138	94.69 37

EIA-MVS		83.31 131	82.80 131	84.82 120	89.59 132	65.59 217	88.21 166	92.68 73	74.66 154	78.96 204	86.42 308	69.06 110	95.26 88	75.54 191	90.09 130	93.62 113

UA-Net		85.08 86	84.96 86	85.45 91	92.07 80	68.07 146	89.78 91	90.86 165	82.48 2	84.60 94	93.20 88	69.35 100	95.22 89	71.39 238	90.88 117	93.07 145

CSCG		86.41 51	86.19 58	87.07 51	92.91 67	72.48 37	90.81 66	93.56 29	73.95 172	83.16 130	91.07 162	75.94 22	95.19 90	79.94 126	94.38 61	93.55 118

test_8					93.13 60	72.57 35	88.68 145	91.84 126	68.69 311	84.87 85	93.10 89	74.43 31	95.16 91

SPE-MVS-test		86.29 54	86.48 51	85.71 82	91.02 96	67.21 183	92.36 34	93.78 23	78.97 34	83.51 123	91.20 157	70.65 81	95.15 92	81.96 103	94.89 45	94.77 30

FE-MVS		77.78 271	75.68 291	84.08 165	88.09 204	66.00 203	83.13 337	87.79 284	68.42 317	78.01 229	85.23 337	45.50 403	95.12 93	59.11 368	85.83 223	91.11 224

EPP-MVSNet		83.40 126	83.02 125	84.57 128	90.13 115	64.47 257	92.32 35	90.73 169	74.45 159	79.35 200	91.10 160	69.05 111	95.12 93	72.78 221	87.22 191	94.13 78

HQP4-MVS										77.24 246			95.11 95			91.03 228

HQP-MVS		82.61 144	82.02 149	84.37 142	89.33 146	66.98 186	89.17 117	92.19 108	76.41 96	77.23 247	90.23 191	60.17 243	95.11 95	77.47 162	85.99 217	91.03 228

MG-MVS		83.41 125	83.45 117	83.28 202	92.74 72	62.28 314	88.17 168	89.50 213	75.22 132	81.49 160	92.74 105	66.75 139	95.11 95	72.85 220	91.58 102	92.45 177

API-MVS		81.99 154	81.23 158	84.26 155	90.94 98	70.18 92	91.10 63	89.32 223	71.51 229	78.66 211	88.28 250	65.26 160	95.10 98	64.74 304	91.23 109	87.51 358

PCF-MVS	73.52 7	80.38 202	78.84 221	85.01 109	87.71 227	68.99 114	83.65 321	91.46 148	63.00 392	77.77 236	90.28 188	66.10 151	95.09 99	61.40 347	88.22 170	90.94 233
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cashybrid2		86.09 56	86.04 63	86.24 67	88.17 197	68.05 148	89.44 104	92.79 70	80.30 10	84.71 87	92.78 103	72.83 50	95.05 100	82.81 93	90.57 121	95.62 1

114514_t		80.68 191	79.51 202	84.20 157	94.09 42	67.27 179	89.64 96	91.11 157	58.75 438	74.08 324	90.72 172	58.10 257	95.04 101	69.70 259	89.42 144	90.30 261

CS-MVS		86.69 44	86.95 42	85.90 80	90.76 104	67.57 166	92.83 22	93.30 38	79.67 20	84.57 95	92.27 110	71.47 68	95.02 102	84.24 77	93.46 72	95.13 11

agg_prior					92.85 68	71.94 53		91.78 130		84.41 97			94.93 103

LPG-MVS_test		82.08 151	81.27 157	84.50 134	89.23 154	68.76 120	90.22 81	91.94 120	75.37 128	76.64 262	91.51 145	54.29 294	94.91 104	78.44 149	83.78 254	89.83 286

LGP-MVS_train				84.50 134	89.23 154	68.76 120		91.94 120	75.37 128	76.64 262	91.51 145	54.29 294	94.91 104	78.44 149	83.78 254	89.83 286

balanced_ft_v1		83.98 105	83.64 113	85.03 107	89.76 129	65.86 208	88.31 163	91.71 134	74.41 160	80.41 184	90.82 171	62.90 192	94.90 106	83.04 89	91.37 106	94.32 69

PAPM_NR		83.02 137	82.41 138	84.82 120	92.47 77	66.37 195	87.93 178	91.80 128	73.82 176	77.32 244	90.66 175	67.90 127	94.90 106	70.37 249	89.48 143	93.19 136

tttt0517		79.40 227	77.91 240	83.90 183	88.10 203	63.84 270	88.37 160	84.05 356	71.45 230	76.78 258	89.12 222	49.93 357	94.89 108	70.18 253	83.18 272	92.96 154

PAPR		81.66 163	80.89 165	83.99 179	90.27 112	64.00 265	86.76 226	91.77 131	68.84 309	77.13 254	89.50 211	67.63 129	94.88 109	67.55 279	88.52 161	93.09 144

PVSNet_Blended_VisFu		82.62 143	81.83 153	84.96 111	90.80 102	69.76 98	88.74 141	91.70 135	69.39 288	78.96 204	88.46 245	65.47 159	94.87 110	74.42 202	88.57 159	90.24 263

Elysia		81.53 166	80.16 182	85.62 86	85.51 304	68.25 140	88.84 134	92.19 108	71.31 232	80.50 181	89.83 198	46.89 383	94.82 111	76.85 170	89.57 140	93.80 100

StellarMVS		81.53 166	80.16 182	85.62 86	85.51 304	68.25 140	88.84 134	92.19 108	71.31 232	80.50 181	89.83 198	46.89 383	94.82 111	76.85 170	89.57 140	93.80 100

EI-MVSNet-Vis-set		84.19 98	83.81 107	85.31 96	88.18 196	67.85 156	87.66 186	89.73 205	80.05 16	82.95 133	89.59 210	70.74 79	94.82 111	80.66 119	84.72 238	93.28 129

DP-MVS		76.78 292	74.57 312	83.42 197	93.29 52	69.46 105	88.55 151	83.70 360	63.98 382	70.20 369	88.89 232	54.01 299	94.80 114	46.66 452	81.88 288	86.01 401

thisisatest0530		79.40 227	77.76 249	84.31 147	87.69 231	65.10 235	87.36 201	84.26 354	70.04 271	77.42 241	88.26 252	49.94 355	94.79 115	70.20 252	84.70 239	93.03 149

viewdifsd2359ckpt09		83.34 128	82.55 136	85.70 83	87.64 233	67.72 161	88.43 154	91.68 136	71.91 221	81.65 158	90.68 174	67.10 136	94.75 116	76.17 180	87.70 183	94.62 50

EI-MVSNet-UG-set		83.81 108	83.38 119	85.09 106	87.87 214	67.53 168	87.44 199	89.66 206	79.74 19	82.23 146	89.41 219	70.24 85	94.74 117	79.95 125	83.92 253	92.99 153

FA-MVS(test-final)		80.96 179	79.91 189	84.10 160	88.30 193	65.01 236	84.55 296	90.01 194	73.25 196	79.61 193	87.57 270	58.35 256	94.72 118	71.29 239	86.25 210	92.56 168

3Dnovator	76.31 5	83.38 127	82.31 141	86.59 62	87.94 211	72.94 28	90.64 68	92.14 113	77.21 67	75.47 287	92.83 98	58.56 254	94.72 118	73.24 216	92.71 81	92.13 195

RRT-MVS		82.60 146	82.10 146	84.10 160	87.98 210	62.94 301	87.45 194	91.27 150	77.42 58	79.85 190	90.28 188	56.62 275	94.70 120	79.87 131	88.15 171	94.67 42

IB-MVS	68.01 15	75.85 311	73.36 331	83.31 201	84.76 325	66.03 200	83.38 331	85.06 342	70.21 270	69.40 382	81.05 411	45.76 399	94.66 121	65.10 301	75.49 373	89.25 303
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
mamba_0408		79.37 230	77.52 256	84.93 114	88.81 169	67.96 151	65.03 487	88.66 261	70.96 245	79.48 196	89.80 200	58.69 251	94.65 122	70.35 250	85.93 219	92.18 190

SSM_0404		81.91 155	80.84 166	85.13 104	89.24 153	68.26 138	87.84 183	89.25 229	71.06 241	80.62 178	90.39 185	59.57 245	94.65 122	72.45 230	87.19 192	92.47 176

ACMP	74.13 6	81.51 170	80.57 171	84.36 143	89.42 141	68.69 127	89.97 85	91.50 147	74.46 158	75.04 309	90.41 183	53.82 300	94.54 124	77.56 161	82.91 274	89.86 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D		76.95 290	74.82 309	83.37 200	90.45 108	67.36 175	89.15 121	86.94 310	61.87 410	69.52 381	90.61 178	51.71 330	94.53 125	46.38 455	86.71 202	88.21 341

MAR-MVS		81.84 157	80.70 167	85.27 97	91.32 90	71.53 59	89.82 88	90.92 161	69.77 281	78.50 215	86.21 313	62.36 200	94.52 126	65.36 298	92.05 93	89.77 289
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
OPM-MVS		83.50 123	82.95 128	85.14 101	88.79 174	70.95 76	89.13 122	91.52 143	77.55 54	80.96 171	91.75 132	60.71 232	94.50 127	79.67 134	86.51 205	89.97 281
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
casdiffmvs_mvg		85.99 60	86.09 62	85.70 83	87.65 232	67.22 182	88.69 144	93.04 47	79.64 22	85.33 77	92.54 106	73.30 40	94.50 127	83.49 83	91.14 110	95.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSM_0407		81.58 165	80.48 174	84.87 118	88.81 169	67.96 151	87.37 200	89.25 229	71.06 241	79.48 196	90.39 185	59.57 245	94.48 129	72.45 230	85.93 219	92.18 190

Effi-MVS+		83.62 119	83.08 123	85.24 98	88.38 190	67.45 170	88.89 130	89.15 235	75.50 123	82.27 145	88.28 250	69.61 97	94.45 130	77.81 157	87.84 179	93.84 96

CLD-MVS		82.31 148	81.65 154	84.29 150	88.47 185	67.73 160	85.81 262	92.35 90	75.78 115	78.33 221	86.58 303	64.01 174	94.35 131	76.05 183	87.48 187	90.79 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PS-MVSNAJ		81.69 161	81.02 162	83.70 188	89.51 136	68.21 143	84.28 307	90.09 192	70.79 248	81.26 166	85.62 327	63.15 185	94.29 132	75.62 189	88.87 153	88.59 330

IS-MVSNet		83.15 133	82.81 130	84.18 158	89.94 124	63.30 289	91.59 51	88.46 267	79.04 31	79.49 195	92.16 118	65.10 162	94.28 133	67.71 277	91.86 98	94.95 15

thisisatest0515		77.33 283	75.38 299	83.18 208	85.27 312	63.80 271	82.11 353	83.27 368	65.06 365	75.91 279	83.84 369	49.54 360	94.27 134	67.24 283	86.19 211	91.48 215

PS-MVSNAJss		82.07 152	81.31 156	84.34 145	86.51 282	67.27 179	89.27 113	91.51 144	71.75 222	79.37 199	90.22 192	63.15 185	94.27 134	77.69 160	82.36 282	91.49 214

PVSNet_BlendedMVS		80.60 195	80.02 186	82.36 246	88.85 165	65.40 220	86.16 251	92.00 116	69.34 290	78.11 226	86.09 317	66.02 154	94.27 134	71.52 235	82.06 285	87.39 361

PVSNet_Blended		80.98 178	80.34 177	82.90 224	88.85 165	65.40 220	84.43 302	92.00 116	67.62 324	78.11 226	85.05 343	66.02 154	94.27 134	71.52 235	89.50 142	89.01 311

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 98	87.33 250	67.30 177	89.50 101	90.98 159	76.25 106	90.56 22	94.75 29	68.38 120	94.24 138	90.80 7	92.32 89	94.19 75

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 224	65.62 216	89.20 115	92.21 105	79.94 18	89.74 27	94.86 26	68.63 117	94.20 139	90.83 5	91.39 105	94.38 64

Vis-MVSNet		83.46 124	82.80 131	85.43 92	90.25 113	68.74 122	90.30 80	90.13 191	76.33 103	80.87 174	92.89 96	61.00 229	94.20 139	72.45 230	90.97 113	93.35 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
xiu_mvs_v2_base		81.69 161	81.05 161	83.60 190	89.15 157	68.03 149	84.46 299	90.02 193	70.67 252	81.30 165	86.53 306	63.17 184	94.19 141	75.60 190	88.54 160	88.57 331

MVS_111021_HR		85.14 83	84.75 89	86.32 66	91.65 86	72.70 30	85.98 254	90.33 183	76.11 108	82.08 149	91.61 142	71.36 71	94.17 142	81.02 111	92.58 82	92.08 196

无先验							87.48 190	88.98 243	60.00 424				94.12 143	67.28 282		88.97 314

MVS		78.19 260	76.99 268	81.78 259	85.66 299	66.99 185	84.66 291	90.47 176	55.08 461	72.02 353	85.27 335	63.83 176	94.11 144	66.10 292	89.80 137	84.24 430

KinetiMVS		83.31 131	82.61 135	85.39 94	87.08 265	67.56 167	88.06 172	91.65 137	77.80 46	82.21 147	91.79 129	57.27 267	94.07 145	77.77 158	89.89 136	94.56 55

v10		79.74 217	78.67 222	82.97 222	84.06 340	64.95 239	87.88 181	90.62 171	73.11 200	75.11 306	86.56 304	61.46 218	94.05 146	73.68 208	75.55 372	89.90 283

baseline		84.93 88	84.98 85	84.80 122	87.30 254	65.39 222	87.30 204	92.88 63	77.62 49	84.04 108	92.26 111	71.81 62	93.96 147	81.31 108	90.30 126	95.03 13

OMC-MVS		82.69 142	81.97 151	84.85 119	88.75 176	67.42 171	87.98 174	90.87 164	74.92 145	79.72 192	91.65 137	62.19 204	93.96 147	75.26 195	86.42 206	93.16 138

OpenMVS	72.83 10	79.77 216	78.33 232	84.09 164	85.17 313	69.91 94	90.57 69	90.97 160	66.70 335	72.17 351	91.91 124	54.70 291	93.96 147	61.81 342	90.95 115	88.41 335

v1192		79.59 220	78.43 229	83.07 215	83.55 354	64.52 253	86.93 217	90.58 172	70.83 247	77.78 235	85.90 318	59.15 249	93.94 150	73.96 207	77.19 346	90.76 239

v1144		80.03 213	79.03 216	83.01 218	83.78 347	64.51 254	87.11 209	90.57 174	71.96 220	78.08 228	86.20 314	61.41 219	93.94 150	74.93 197	77.23 344	90.60 247

UGNet		80.83 182	79.59 201	84.54 129	88.04 206	68.09 145	89.42 107	88.16 269	76.95 76	76.22 273	89.46 215	49.30 366	93.94 150	68.48 272	90.31 125	91.60 208
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
casdiffmvs		85.11 84	85.14 84	85.01 109	87.20 256	65.77 213	87.75 184	92.83 66	77.84 45	84.36 101	92.38 109	72.15 58	93.93 153	81.27 110	90.48 123	95.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 83	89.48 139	67.88 155	88.59 148	89.05 239	80.19 13	90.70 20	95.40 17	74.56 29	93.92 154	91.54 2	92.07 92	95.31 6

sasdasda		85.91 64	85.87 68	86.04 76	89.84 126	69.44 106	90.45 76	93.00 52	76.70 86	88.01 46	91.23 153	73.28 41	93.91 155	81.50 106	88.80 154	94.77 30

canonicalmvs		85.91 64	85.87 68	86.04 76	89.84 126	69.44 106	90.45 76	93.00 52	76.70 86	88.01 46	91.23 153	73.28 41	93.91 155	81.50 106	88.80 154	94.77 30

VDD-MVS		83.01 138	82.36 140	84.96 111	91.02 96	66.40 194	88.91 129	88.11 270	77.57 51	84.39 98	93.29 86	52.19 315	93.91 155	77.05 168	88.70 158	94.57 53

v8		79.97 215	79.02 217	82.80 230	84.09 339	64.50 256	87.96 175	90.29 186	74.13 170	75.24 302	86.81 290	62.88 193	93.89 158	74.39 203	75.40 379	90.00 277

hybridcas		85.11 84	85.18 83	84.90 117	87.47 244	65.68 214	88.53 152	92.38 88	77.91 43	84.27 102	92.48 107	72.19 57	93.88 159	80.37 120	90.97 113	95.15 9

v2v482		80.23 208	79.29 210	83.05 216	83.62 352	64.14 263	87.04 210	89.97 195	73.61 182	78.18 225	87.22 281	61.10 227	93.82 160	76.11 181	76.78 353	91.18 222

v7n		78.97 240	77.58 255	83.14 210	83.45 356	65.51 218	88.32 162	91.21 152	73.69 180	72.41 347	86.32 311	57.93 258	93.81 161	69.18 264	75.65 370	90.11 269

alignmvs		85.48 74	85.32 80	85.96 79	89.51 136	69.47 103	89.74 92	92.47 83	76.17 107	87.73 53	91.46 148	70.32 83	93.78 162	81.51 105	88.95 151	94.63 48

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 76	72.71 29	91.81 46	93.19 41	77.87 44	90.32 23	94.00 63	74.83 27	93.78 162	87.63 45	94.27 64	93.65 110
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
v144192		79.47 223	78.37 230	82.78 234	83.35 357	63.96 266	86.96 214	90.36 182	69.99 274	77.50 239	85.67 325	60.66 235	93.77 164	74.27 204	76.58 354	90.62 245

v1240		78.99 239	77.78 247	82.64 239	83.21 362	63.54 282	86.62 231	90.30 185	69.74 284	77.33 243	85.68 324	57.04 270	93.76 165	73.13 217	76.92 348	90.62 245

v1921920		79.22 232	78.03 237	82.80 230	83.30 359	63.94 268	86.80 222	90.33 183	69.91 277	77.48 240	85.53 329	58.44 255	93.75 166	73.60 209	76.85 351	90.71 243

cascas		76.72 293	74.64 311	82.99 219	85.78 297	65.88 207	82.33 349	89.21 232	60.85 416	72.74 341	81.02 412	47.28 379	93.75 166	67.48 280	85.02 232	89.34 301

Anonymous20240529		80.19 210	78.89 220	84.10 160	90.60 105	64.75 249	88.95 128	90.90 162	65.97 350	80.59 179	91.17 159	49.97 354	93.73 168	69.16 265	82.70 279	93.81 98

casdiffseed414692147		83.62 119	83.02 125	85.40 93	87.31 253	67.50 169	88.70 143	91.72 133	76.97 75	82.77 140	91.72 133	66.85 138	93.71 169	73.06 218	88.12 172	94.98 14

PAPM		77.68 276	76.40 284	81.51 265	87.29 255	61.85 321	83.78 317	89.59 210	64.74 369	71.23 361	88.70 236	62.59 195	93.66 170	52.66 417	87.03 196	89.01 311

E4		84.10 100	83.99 103	84.45 137	87.58 242	64.99 238	86.54 234	92.25 99	76.38 100	83.37 124	92.09 122	69.88 93	93.58 171	79.78 132	88.03 176	94.77 30

test_yl		81.17 173	80.47 175	83.24 205	89.13 158	63.62 274	86.21 249	89.95 196	72.43 212	81.78 155	89.61 208	57.50 264	93.58 171	70.75 244	86.90 197	92.52 171

DCV-MVSNet		81.17 173	80.47 175	83.24 205	89.13 158	63.62 274	86.21 249	89.95 196	72.43 212	81.78 155	89.61 208	57.50 264	93.58 171	70.75 244	86.90 197	92.52 171

Fast-Effi-MVS+		80.81 183	79.92 188	83.47 194	88.85 165	64.51 254	85.53 270	89.39 217	70.79 248	78.49 216	85.06 342	67.54 130	93.58 171	67.03 287	86.58 203	92.32 182

PLC	70.83 11	78.05 264	76.37 285	83.08 214	91.88 84	67.80 158	88.19 167	89.46 214	64.33 376	69.87 378	88.38 247	53.66 301	93.58 171	58.86 371	82.73 277	87.86 348
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
E2		84.00 103	83.87 104	84.39 140	87.70 229	64.95 239	86.40 241	92.23 100	75.85 113	83.21 126	91.78 130	70.09 88	93.55 176	79.52 136	88.05 174	94.66 45

E3		84.00 103	83.87 104	84.39 140	87.70 229	64.95 239	86.40 241	92.23 100	75.85 113	83.21 126	91.78 130	70.09 88	93.55 176	79.52 136	88.05 174	94.66 45

BH-untuned		79.47 223	78.60 224	82.05 253	89.19 156	65.91 206	86.07 253	88.52 266	72.18 215	75.42 291	87.69 267	61.15 226	93.54 178	60.38 355	86.83 200	86.70 388

viewcassd2359sk11		83.89 106	83.74 109	84.34 145	87.76 224	64.91 245	86.30 245	92.22 103	75.47 124	83.04 132	91.52 144	70.15 86	93.53 179	79.26 138	87.96 177	94.57 53

ACMM	73.20 8	80.78 190	79.84 192	83.58 192	89.31 149	68.37 135	89.99 84	91.60 141	70.28 267	77.25 245	89.66 206	53.37 305	93.53 179	74.24 205	82.85 275	88.85 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E3new		83.78 111	83.60 114	84.31 147	87.76 224	64.89 246	86.24 248	92.20 106	75.15 139	82.87 135	91.23 153	70.11 87	93.52 181	79.05 139	87.79 180	94.51 58

E5new		84.22 94	84.12 97	84.51 132	87.60 234	65.36 224	87.45 194	92.31 92	76.51 91	83.53 119	92.26 111	69.25 105	93.50 182	79.88 127	88.26 165	94.69 37

E5		84.22 94	84.12 97	84.51 132	87.60 234	65.36 224	87.45 194	92.31 92	76.51 91	83.53 119	92.26 111	69.25 105	93.50 182	79.88 127	88.26 165	94.69 37

E6new		84.22 94	84.12 97	84.52 130	87.60 234	65.36 224	87.45 194	92.30 94	76.51 91	83.53 119	92.26 111	69.26 103	93.49 184	79.88 127	88.26 165	94.69 37

E6		84.22 94	84.12 97	84.52 130	87.60 234	65.36 224	87.45 194	92.30 94	76.51 91	83.53 119	92.26 111	69.26 103	93.49 184	79.88 127	88.26 165	94.69 37

VDDNet		81.52 168	80.67 168	84.05 172	90.44 109	64.13 264	89.73 93	85.91 331	71.11 238	83.18 129	93.48 79	50.54 347	93.49 184	73.40 213	88.25 169	94.54 57

hse-mvs2		81.72 159	80.94 164	84.07 166	88.72 177	67.68 162	85.87 258	87.26 301	76.02 110	84.67 89	88.22 253	61.54 215	93.48 187	82.71 97	73.44 402	91.06 226

AUN-MVS		79.21 233	77.60 254	84.05 172	88.71 178	67.61 164	85.84 260	87.26 301	69.08 300	77.23 247	88.14 258	53.20 307	93.47 188	75.50 192	73.45 401	91.06 226

MVSFormer		82.85 140	82.05 148	85.24 98	87.35 245	70.21 87	90.50 72	90.38 179	68.55 313	81.32 162	89.47 213	61.68 212	93.46 189	78.98 144	90.26 127	92.05 197

test_djsdf		80.30 207	79.32 209	83.27 203	83.98 342	65.37 223	90.50 72	90.38 179	68.55 313	76.19 274	88.70 236	56.44 276	93.46 189	78.98 144	80.14 311	90.97 231

LFMVS		81.82 158	81.23 158	83.57 193	91.89 83	63.43 287	89.84 87	81.85 393	77.04 74	83.21 126	93.10 89	52.26 314	93.43 191	71.98 233	89.95 134	93.85 94

MGCFI-Net		85.06 87	85.51 75	83.70 188	89.42 141	63.01 295	89.43 105	92.62 80	76.43 95	87.53 54	91.34 151	72.82 51	93.42 192	81.28 109	88.74 157	94.66 45

Effi-MVS+-dtu		80.03 213	78.57 225	84.42 139	85.13 317	68.74 122	88.77 137	88.10 271	74.99 141	74.97 311	83.49 380	57.27 267	93.36 193	73.53 210	80.88 299	91.18 222

BH-RMVSNet		79.61 218	78.44 228	83.14 210	89.38 145	65.93 205	84.95 285	87.15 304	73.56 184	78.19 224	89.79 202	56.67 274	93.36 193	59.53 363	86.74 201	90.13 267

viewdifsd2359ckpt13		82.91 139	82.29 142	84.77 123	86.96 268	66.90 190	87.47 191	91.62 139	72.19 214	81.68 157	90.71 173	66.92 137	93.28 195	75.90 185	87.15 193	94.12 79

HyFIR lowres test		77.53 279	75.40 298	83.94 182	89.59 132	66.62 191	80.36 384	88.64 264	56.29 456	76.45 267	85.17 339	57.64 262	93.28 195	61.34 349	83.10 273	91.91 199

IMVS_0403		80.80 186	80.12 185	82.87 226	87.13 259	63.59 278	85.19 275	89.33 219	70.51 258	78.49 216	89.03 225	63.26 181	93.27 197	72.56 226	85.56 226	91.74 203

UniMVSNet (Re)		81.60 164	81.11 160	83.09 212	88.38 190	64.41 259	87.60 187	93.02 51	78.42 38	78.56 214	88.16 254	69.78 94	93.26 198	69.58 261	76.49 356	91.60 208

test_fmvsmconf_n		85.92 63	86.04 63	85.57 89	85.03 320	69.51 101	89.62 98	90.58 172	73.42 189	87.75 51	94.02 61	72.85 49	93.24 199	90.37 8	90.75 118	93.96 87

test_fmvsmconf0.1_n		85.61 72	85.65 72	85.50 90	82.99 375	69.39 108	89.65 95	90.29 186	73.31 193	87.77 50	94.15 55	71.72 64	93.23 200	90.31 9	90.67 120	93.89 93

test_fmvsmconf0.01_n		84.73 91	84.52 93	85.34 95	80.25 418	69.03 111	89.47 102	89.65 207	73.24 197	86.98 63	94.27 47	66.62 141	93.23 200	90.26 10	89.95 134	93.78 102

tt0805		78.73 245	77.83 244	81.43 267	85.17 313	60.30 353	89.41 108	90.90 162	71.21 236	77.17 252	88.73 235	46.38 389	93.21 202	72.57 224	78.96 325	90.79 237

MVS_Test		83.15 133	83.06 124	83.41 199	86.86 269	63.21 291	86.11 252	92.00 116	74.31 163	82.87 135	89.44 218	70.03 90	93.21 202	77.39 164	88.50 162	93.81 98

TAPA-MVS	73.13 9	79.15 234	77.94 239	82.79 233	89.59 132	62.99 299	88.16 169	91.51 144	65.77 351	77.14 253	91.09 161	60.91 230	93.21 202	50.26 433	87.05 195	92.17 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		81.71 160	81.01 163	83.80 187	89.51 136	64.45 258	88.97 127	88.73 259	71.27 235	78.63 212	89.76 203	66.32 147	93.20 205	69.89 257	86.02 216	93.74 103

LTVRE_ROB	69.57 13	76.25 305	74.54 314	81.41 268	88.60 181	64.38 260	79.24 400	89.12 238	70.76 250	69.79 380	87.86 263	49.09 369	93.20 205	56.21 399	80.16 309	86.65 390
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
ACMH+	68.96 14	76.01 309	74.01 320	82.03 254	88.60 181	65.31 228	88.86 131	87.55 289	70.25 269	67.75 404	87.47 275	41.27 431	93.19 207	58.37 377	75.94 367	87.60 353

V42		79.38 229	78.24 234	82.83 227	81.10 410	65.50 219	85.55 268	89.82 199	71.57 228	78.21 223	86.12 316	60.66 235	93.18 208	75.64 188	75.46 376	89.81 288

mvs_tets		79.13 235	77.77 248	83.22 207	84.70 326	66.37 195	89.17 117	90.19 189	69.38 289	75.40 292	89.46 215	44.17 412	93.15 209	76.78 176	80.70 303	90.14 266

TR-MVS		77.44 280	76.18 286	81.20 276	88.24 194	63.24 290	84.61 294	86.40 323	67.55 325	77.81 234	86.48 307	54.10 296	93.15 209	57.75 383	82.72 278	87.20 371

jajsoiax		79.29 231	77.96 238	83.27 203	84.68 327	66.57 193	89.25 114	90.16 190	69.20 297	75.46 289	89.49 212	45.75 400	93.13 211	76.84 172	80.80 301	90.11 269

BH-w/o		78.21 258	77.33 262	80.84 286	88.81 169	65.13 232	84.87 286	87.85 283	69.75 282	74.52 319	84.74 349	61.34 221	93.11 212	58.24 379	85.84 222	84.27 429

nrg030		83.88 107	83.53 116	84.96 111	86.77 274	69.28 110	90.46 75	92.67 74	74.79 150	82.95 133	91.33 152	72.70 52	93.09 213	80.79 116	79.28 323	92.50 173

CANet_DTU		80.61 193	79.87 191	82.83 227	85.60 302	63.17 294	87.36 201	88.65 263	76.37 101	75.88 280	88.44 246	53.51 303	93.07 214	73.30 214	89.74 138	92.25 185

fmvsm_s_conf0.5_n_4		85.39 78	85.75 71	84.30 149	86.70 276	65.83 209	88.77 137	89.78 200	75.46 125	88.35 37	93.73 74	69.19 107	93.06 215	91.30 3	88.44 163	94.02 85

UniMVSNet_NR-MVSNet		81.88 156	81.54 155	82.92 223	88.46 186	63.46 285	87.13 207	92.37 89	80.19 13	78.38 219	89.14 221	71.66 67	93.05 216	70.05 254	76.46 357	92.25 185

DU-MVS		81.12 176	80.52 173	82.90 224	87.80 218	63.46 285	87.02 212	91.87 124	79.01 32	78.38 219	89.07 223	65.02 163	93.05 216	70.05 254	76.46 357	92.20 188

CPTT-MVS		83.73 113	83.33 121	84.92 115	93.28 53	70.86 79	92.09 41	90.38 179	68.75 310	79.57 194	92.83 98	60.60 238	93.04 218	80.92 113	91.56 103	90.86 235

Anonymous20231211		78.97 240	77.69 252	82.81 229	90.54 107	64.29 261	90.11 83	91.51 144	65.01 367	76.16 278	88.13 259	50.56 346	93.03 219	69.68 260	77.56 343	91.11 224

MSLP-MVS++		85.43 76	85.76 70	84.45 137	91.93 82	70.24 86	90.71 67	92.86 64	77.46 57	84.22 103	92.81 100	67.16 135	92.94 220	80.36 121	94.35 62	90.16 265

IMVS_0407		80.61 193	79.90 190	82.75 237	87.13 259	63.59 278	85.33 274	89.33 219	70.51 258	77.82 232	89.03 225	61.84 208	92.91 221	72.56 226	85.56 226	91.74 203

F-COLMAP		76.38 304	74.33 318	82.50 243	89.28 151	66.95 189	88.41 156	89.03 240	64.05 380	66.83 418	88.61 240	46.78 385	92.89 222	57.48 384	78.55 327	87.67 351

viewmacassd2359aftdt		83.76 112	83.66 112	84.07 166	86.59 280	64.56 251	86.88 219	91.82 127	75.72 116	83.34 125	92.15 120	68.24 124	92.88 223	79.05 139	89.15 149	94.77 30

xiu_mvs_v1_base_debu		80.80 186	79.72 197	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 301	81.83 151	88.16 254	50.91 340	92.85 224	78.29 153	87.56 184	89.06 306

xiu_mvs_v1_base		80.80 186	79.72 197	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 301	81.83 151	88.16 254	50.91 340	92.85 224	78.29 153	87.56 184	89.06 306

xiu_mvs_v1_base_debi		80.80 186	79.72 197	84.03 174	87.35 245	70.19 89	85.56 265	88.77 252	69.06 301	81.83 151	88.16 254	50.91 340	92.85 224	78.29 153	87.56 184	89.06 306

viewmanbaseed2359cas		83.66 115	83.55 115	84.00 177	86.81 272	64.53 252	86.65 229	91.75 132	74.89 146	83.15 131	91.68 135	68.74 116	92.83 227	79.02 141	89.24 146	94.63 48

NR-MVSNet		80.23 208	79.38 206	82.78 234	87.80 218	63.34 288	86.31 244	91.09 158	79.01 32	72.17 351	89.07 223	67.20 134	92.81 228	66.08 293	75.65 370	92.20 188

fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 177	87.78 221	66.09 199	89.96 86	90.80 167	77.37 59	86.72 66	94.20 52	72.51 53	92.78 229	89.08 22	92.33 87	93.13 142

fmvsm_l_conf0.5_n_3		86.02 58	86.32 53	85.14 101	87.20 256	68.54 131	89.57 99	90.44 177	75.31 130	87.49 55	94.39 42	72.86 48	92.72 230	89.04 27	90.56 122	94.16 76

TranMVSNet+NR-MVSNet		80.84 181	80.31 178	82.42 244	87.85 215	62.33 312	87.74 185	91.33 149	80.55 9	77.99 230	89.86 196	65.23 161	92.62 231	67.05 286	75.24 384	92.30 183

test_0402		72.79 360	70.44 371	79.84 314	88.13 201	65.99 204	85.93 256	84.29 352	65.57 354	67.40 412	85.49 330	46.92 382	92.61 232	35.88 482	74.38 392	80.94 461

fmvsm_s_conf0.5_n_2		84.04 101	84.11 101	83.81 186	86.17 289	65.00 237	86.96 214	87.28 296	74.35 161	88.25 40	94.23 50	61.82 210	92.60 233	89.85 12	88.09 173	93.84 96

fmvsm_s_conf0.1_n_2		83.80 109	83.79 108	83.83 184	85.62 301	64.94 242	87.03 211	86.62 320	74.32 162	87.97 48	94.33 43	60.67 234	92.60 233	89.72 14	87.79 180	93.96 87

SixPastTwentyTwo		73.37 344	71.26 357	79.70 322	85.08 318	57.89 379	85.57 264	83.56 363	71.03 243	65.66 432	85.88 319	42.10 426	92.57 235	59.11 368	63.34 456	88.65 328

eth_miper_zixun_eth		77.92 268	76.69 277	81.61 264	83.00 371	61.98 319	83.15 336	89.20 233	69.52 287	74.86 313	84.35 356	61.76 211	92.56 236	71.50 237	72.89 406	90.28 262

mvsmamba		80.60 195	79.38 206	84.27 153	89.74 130	67.24 181	87.47 191	86.95 309	70.02 272	75.38 293	88.93 230	51.24 337	92.56 236	75.47 193	89.22 147	93.00 152

EG-PatchMatch MVS		74.04 333	71.82 347	80.71 289	84.92 321	67.42 171	85.86 259	88.08 272	66.04 347	64.22 444	83.85 368	35.10 463	92.56 236	57.44 385	80.83 300	82.16 454

COLMAP_ROB	66.92 17	73.01 354	70.41 372	80.81 287	87.13 259	65.63 215	88.30 164	84.19 355	62.96 393	63.80 449	87.69 267	38.04 452	92.56 236	46.66 452	74.91 387	84.24 430
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LuminaMVS		80.68 191	79.62 200	83.83 184	85.07 319	68.01 150	86.99 213	88.83 249	70.36 263	81.38 161	87.99 261	50.11 352	92.51 240	79.02 141	86.89 199	90.97 231

fmvsm_s_conf0.5_n_6		85.55 73	86.20 56	83.60 190	87.32 252	65.13 232	88.86 131	91.63 138	75.41 126	88.23 41	93.45 82	68.56 118	92.47 241	89.52 18	92.78 79	93.20 135

ECVR-MVS		79.61 218	79.26 211	80.67 290	90.08 117	54.69 427	87.89 180	77.44 442	74.88 147	80.27 185	92.79 101	48.96 372	92.45 242	68.55 271	92.50 84	94.86 22

EI-MVSNet		80.52 199	79.98 187	82.12 250	84.28 334	63.19 293	86.41 238	88.95 246	74.18 168	78.69 209	87.54 273	66.62 141	92.43 243	72.57 224	80.57 305	90.74 241

MVSTER		79.01 238	77.88 243	82.38 245	83.07 368	64.80 248	84.08 314	88.95 246	69.01 304	78.69 209	87.17 284	54.70 291	92.43 243	74.69 198	80.57 305	89.89 284

gm-plane-assit					81.40 404	53.83 435			62.72 399		80.94 414		92.39 245	63.40 313

IterMVS-LS		80.06 211	79.38 206	82.11 252	85.89 294	63.20 292	86.79 223	89.34 218	74.19 167	75.45 290	86.72 293	66.62 141	92.39 245	72.58 223	76.86 350	90.75 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		78.72 246	77.80 246	81.47 266	82.73 381	61.96 320	86.30 245	88.08 272	73.26 195	76.18 275	85.47 331	62.46 198	92.36 247	71.92 234	73.82 398	90.09 271

test2506		77.30 284	76.49 280	79.74 320	90.08 117	52.02 446	87.86 182	63.10 490	74.88 147	80.16 188	92.79 101	38.29 451	92.35 248	68.74 270	92.50 84	94.86 22

FIs		82.07 152	82.42 137	81.04 281	88.80 173	58.34 371	88.26 165	93.49 31	76.93 77	78.47 218	91.04 163	69.92 92	92.34 249	69.87 258	84.97 233	92.44 178

test1111		79.43 225	79.18 214	80.15 305	89.99 122	53.31 440	87.33 203	77.05 446	75.04 140	80.23 187	92.77 104	48.97 371	92.33 250	68.87 268	92.40 86	94.81 27

新几何1				83.42 197	93.13 60	70.71 81		85.48 337	57.43 450	81.80 154	91.98 123	63.28 179	92.27 251	64.60 305	92.99 76	87.27 369

anonymousdsp		78.60 249	77.15 264	82.98 221	80.51 416	67.08 184	87.24 206	89.53 212	65.66 353	75.16 304	87.19 283	52.52 309	92.25 252	77.17 166	79.34 322	89.61 293

lupinMVS		81.39 171	80.27 180	84.76 124	87.35 245	70.21 87	85.55 268	86.41 322	62.85 395	81.32 162	88.61 240	61.68 212	92.24 253	78.41 151	90.26 127	91.83 200

baseline2		75.70 312	73.83 325	81.30 272	83.26 360	61.79 323	82.57 346	80.65 406	66.81 332	66.88 417	83.42 381	57.86 260	92.19 254	63.47 311	79.57 315	89.91 282

jason		81.39 171	80.29 179	84.70 126	86.63 279	69.90 95	85.95 255	86.77 314	63.24 388	81.07 168	89.47 213	61.08 228	92.15 255	78.33 152	90.07 132	92.05 197
jason: jason.
XVG-ACMP-BASELINE		76.11 307	74.27 319	81.62 262	83.20 363	64.67 250	83.60 325	89.75 204	69.75 282	71.85 354	87.09 286	32.78 467	92.11 256	69.99 256	80.43 307	88.09 343

c3_l		78.75 244	77.91 240	81.26 274	82.89 378	61.56 326	84.09 313	89.13 237	69.97 275	75.56 285	84.29 357	66.36 146	92.09 257	73.47 212	75.48 374	90.12 268

viewdifsd2359ckpt07		82.83 141	82.78 133	82.99 219	86.51 282	62.58 305	85.09 281	90.83 166	75.22 132	82.28 144	91.63 139	69.43 99	92.03 258	77.71 159	86.32 207	94.34 67

miper_ehance_all_eth		78.59 250	77.76 249	81.08 280	82.66 383	61.56 326	83.65 321	89.15 235	68.87 308	75.55 286	83.79 371	66.49 144	92.03 258	73.25 215	76.39 359	89.64 292

GA-MVS		76.87 291	75.17 306	81.97 256	82.75 380	62.58 305	81.44 365	86.35 325	72.16 217	74.74 314	82.89 391	46.20 394	92.02 260	68.85 269	81.09 296	91.30 220

miper_enhance_ethall		77.87 270	76.86 270	80.92 285	81.65 398	61.38 330	82.68 344	88.98 243	65.52 355	75.47 287	82.30 400	65.76 158	92.00 261	72.95 219	76.39 359	89.39 299

fmvsm_s_conf0.5_n_5		85.22 82	85.55 74	84.25 156	86.26 285	67.40 173	89.18 116	89.31 224	72.50 208	88.31 38	93.86 70	69.66 96	91.96 262	89.81 13	91.05 111	93.38 123

thres100view900		76.50 296	75.55 295	79.33 331	89.52 135	56.99 394	85.83 261	83.23 369	73.94 173	76.32 271	87.12 285	51.89 326	91.95 263	48.33 443	83.75 257	89.07 304

tfpn200view9		76.42 302	75.37 300	79.55 328	89.13 158	57.65 385	85.17 276	83.60 361	73.41 190	76.45 267	86.39 309	52.12 316	91.95 263	48.33 443	83.75 257	89.07 304

thres400		76.50 296	75.37 300	79.86 313	89.13 158	57.65 385	85.17 276	83.60 361	73.41 190	76.45 267	86.39 309	52.12 316	91.95 263	48.33 443	83.75 257	90.00 277

thres600view7		76.50 296	75.44 296	79.68 323	89.40 143	57.16 391	85.53 270	83.23 369	73.79 177	76.26 272	87.09 286	51.89 326	91.89 266	48.05 448	83.72 260	90.00 277

cl22		78.07 263	77.01 266	81.23 275	82.37 390	61.83 322	83.55 326	87.98 276	68.96 307	75.06 308	83.87 367	61.40 220	91.88 267	73.53 210	76.39 359	89.98 280

dcpmvs_2		85.63 71	86.15 60	84.06 169	91.71 85	64.94 242	86.47 236	91.87 124	73.63 181	86.60 68	93.02 94	76.57 19	91.87 268	83.36 84	92.15 90	95.35 4

FC-MVSNet-test		81.52 168	82.02 149	80.03 307	88.42 189	55.97 411	87.95 176	93.42 34	77.10 72	77.38 242	90.98 168	69.96 91	91.79 269	68.46 273	84.50 241	92.33 181

fmvsm_l_conf0.5_n		84.47 92	84.54 91	84.27 153	85.42 307	68.81 117	88.49 153	87.26 301	68.08 320	88.03 45	93.49 78	72.04 60	91.77 270	88.90 29	89.14 150	92.24 187

ET-MVSNet_ETH3D		78.63 248	76.63 279	84.64 127	86.73 275	69.47 103	85.01 283	84.61 347	69.54 286	66.51 426	86.59 301	50.16 351	91.75 271	76.26 179	84.24 249	92.69 164

thres200		75.55 314	74.47 315	78.82 340	87.78 221	57.85 380	83.07 341	83.51 364	72.44 211	75.84 281	84.42 352	52.08 319	91.75 271	47.41 450	83.64 262	86.86 383

MVP-Stereo		76.12 306	74.46 316	81.13 279	85.37 309	69.79 96	84.42 304	87.95 279	65.03 366	67.46 409	85.33 334	53.28 306	91.73 273	58.01 381	83.27 270	81.85 456
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 212	87.08 265	65.21 229	89.09 124	90.21 188	79.67 20	89.98 24	95.02 24	73.17 43	91.71 274	91.30 3	91.60 100	92.34 180

fmvsm_l_conf0.5_n_a		84.13 99	84.16 96	84.06 169	85.38 308	68.40 134	88.34 161	86.85 313	67.48 327	87.48 56	93.40 83	70.89 76	91.61 275	88.38 37	89.22 147	92.16 194

OurMVSNet-221017-0		74.26 329	72.42 342	79.80 315	83.76 348	59.59 361	85.92 257	86.64 318	66.39 343	66.96 416	87.58 269	39.46 442	91.60 276	65.76 296	69.27 426	88.22 340

fmvsm_s_conf0.5_n_a		83.63 118	83.41 118	84.28 151	86.14 290	68.12 144	89.43 105	82.87 379	70.27 268	87.27 60	93.80 73	69.09 108	91.58 277	88.21 38	83.65 261	93.14 141

Fast-Effi-MVS+-dtu		78.02 265	76.49 280	82.62 240	83.16 366	66.96 188	86.94 216	87.45 293	72.45 209	71.49 359	84.17 364	54.79 290	91.58 277	67.61 278	80.31 308	89.30 302

AstraMVS		80.81 183	80.14 184	82.80 230	86.05 293	63.96 266	86.46 237	85.90 332	73.71 179	80.85 175	90.56 179	54.06 298	91.57 279	79.72 133	83.97 252	92.86 158

viewdifsd2359ckpt11		80.37 204	79.73 195	82.30 247	83.70 350	62.39 309	84.20 309	86.67 316	73.22 198	80.90 172	90.62 176	63.00 190	91.56 280	76.81 174	78.44 330	92.95 155

viewmsd2359difaftdt		80.37 204	79.73 195	82.30 247	83.70 350	62.39 309	84.20 309	86.67 316	73.22 198	80.90 172	90.62 176	63.00 190	91.56 280	76.81 174	78.44 330	92.95 155

fmvsm_s_conf0.1_n_a		83.32 130	82.99 127	84.28 151	83.79 346	68.07 146	89.34 112	82.85 380	69.80 279	87.36 59	94.06 59	68.34 122	91.56 280	87.95 42	83.46 267	93.21 133

UniMVSNet_ETH3D		79.10 236	78.24 234	81.70 261	86.85 270	60.24 354	87.28 205	88.79 251	74.25 166	76.84 255	90.53 181	49.48 361	91.56 280	67.98 275	82.15 283	93.29 128

test_fmvsm_n_1920		85.29 81	85.34 78	85.13 104	86.12 291	69.93 93	88.65 146	90.78 168	69.97 275	88.27 39	93.98 66	71.39 70	91.54 284	88.49 35	90.45 124	93.91 90

cl____		77.72 273	76.76 274	80.58 292	82.49 387	60.48 350	83.09 339	87.87 281	69.22 295	74.38 322	85.22 338	62.10 205	91.53 285	71.09 241	75.41 378	89.73 291

DIV-MVS_self_test		77.72 273	76.76 274	80.58 292	82.48 388	60.48 350	83.09 339	87.86 282	69.22 295	74.38 322	85.24 336	62.10 205	91.53 285	71.09 241	75.40 379	89.74 290

gbinet_0.2-2-1-0.02		73.24 350	70.86 365	80.39 295	78.03 446	61.62 325	83.10 338	86.69 315	65.98 349	69.29 385	76.15 461	49.77 358	91.51 287	62.75 323	66.00 443	88.03 344

test_fmvsmvis_n_1920		84.02 102	83.87 104	84.49 136	84.12 338	69.37 109	88.15 170	87.96 278	70.01 273	83.95 110	93.23 87	68.80 115	91.51 287	88.61 32	89.96 133	92.57 167

ACMH	67.68 16	75.89 310	73.93 322	81.77 260	88.71 178	66.61 192	88.62 147	89.01 242	69.81 278	66.78 419	86.70 297	41.95 428	91.51 287	55.64 400	78.14 336	87.17 373
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n		83.80 109	83.71 110	84.07 166	86.69 277	67.31 176	89.46 103	83.07 374	71.09 239	86.96 64	93.70 75	69.02 113	91.47 290	88.79 30	84.62 240	93.44 122

fmvsm_s_conf0.1_n		83.56 121	83.38 119	84.10 160	84.86 322	67.28 178	89.40 109	83.01 375	70.67 252	87.08 61	93.96 67	68.38 120	91.45 291	88.56 34	84.50 241	93.56 117

Anonymous202405211		78.25 256	77.01 266	81.99 255	91.03 95	60.67 346	84.77 288	83.90 358	70.65 256	80.00 189	91.20 157	41.08 433	91.43 292	65.21 299	85.26 231	93.85 94

CHOSEN 1792x2688		77.63 278	75.69 290	83.44 196	89.98 123	68.58 130	78.70 410	87.50 291	56.38 455	75.80 282	86.84 289	58.67 253	91.40 293	61.58 345	85.75 224	90.34 258

XVG-OURS		80.41 200	79.23 212	83.97 180	85.64 300	69.02 113	83.03 343	90.39 178	71.09 239	77.63 238	91.49 147	54.62 293	91.35 294	75.71 187	83.47 266	91.54 211

lessismore_v0				78.97 337	81.01 411	57.15 392		65.99 483		61.16 459	82.82 393	39.12 445	91.34 295	59.67 361	46.92 488	88.43 334

guyue		81.13 175	80.64 170	82.60 241	86.52 281	63.92 269	86.69 228	87.73 286	73.97 171	80.83 176	89.69 204	56.70 273	91.33 296	78.26 156	85.40 230	92.54 169

XVG-OURS-SEG-HR		80.81 183	79.76 194	83.96 181	85.60 302	68.78 119	83.54 328	90.50 175	70.66 255	76.71 260	91.66 136	60.69 233	91.26 297	76.94 169	81.58 291	91.83 200

tpm2		73.26 349	71.46 351	78.63 342	83.34 358	56.71 399	80.65 379	80.40 414	56.63 454	73.55 331	82.02 405	51.80 328	91.24 298	56.35 398	78.42 333	87.95 345

usedtu_blend_shiyan5		73.29 348	70.96 362	80.25 301	77.80 448	62.16 316	84.44 301	87.38 294	64.41 373	68.09 398	76.28 458	51.32 333	91.23 299	63.21 317	65.76 445	87.35 363

blend_shiyan4		72.29 365	69.65 378	80.21 303	78.24 444	62.16 316	82.29 350	87.27 299	65.41 358	68.43 397	76.42 457	39.91 440	91.23 299	63.21 317	65.66 450	87.22 370

OpenMVS_ROB	64.09 19	70.56 383	68.19 389	77.65 367	80.26 417	59.41 364	85.01 283	82.96 378	58.76 437	65.43 435	82.33 399	37.63 454	91.23 299	45.34 462	76.03 366	82.32 451

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 195	87.12 264	66.01 202	88.56 150	89.43 215	75.59 121	89.32 28	94.32 44	72.89 47	91.21 302	90.11 11	92.33 87	93.16 138

GBi-Net		78.40 253	77.40 259	81.40 269	87.60 234	63.01 295	88.39 157	89.28 225	71.63 224	75.34 295	87.28 277	54.80 287	91.11 303	62.72 324	79.57 315	90.09 271

test1		78.40 253	77.40 259	81.40 269	87.60 234	63.01 295	88.39 157	89.28 225	71.63 224	75.34 295	87.28 277	54.80 287	91.11 303	62.72 324	79.57 315	90.09 271

FMVSNet1		77.44 280	76.12 287	81.40 269	86.81 272	63.01 295	88.39 157	89.28 225	70.49 262	74.39 321	87.28 277	49.06 370	91.11 303	60.91 351	78.52 328	90.09 271

FMVSNet3		77.88 269	76.85 271	80.97 284	86.84 271	62.36 311	86.52 235	88.77 252	71.13 237	75.34 295	86.66 299	54.07 297	91.10 306	62.72 324	79.57 315	89.45 297

FMVSNet2		78.20 259	77.21 263	81.20 276	87.60 234	62.89 302	87.47 191	89.02 241	71.63 224	75.29 301	87.28 277	54.80 287	91.10 306	62.38 332	79.38 321	89.61 293

K. test v3		71.19 373	68.51 386	79.21 334	83.04 370	57.78 383	84.35 306	76.91 447	72.90 205	62.99 452	82.86 392	39.27 443	91.09 308	61.65 344	52.66 481	88.75 324

CostFormer		75.24 321	73.90 323	79.27 332	82.65 384	58.27 372	80.80 373	82.73 382	61.57 411	75.33 299	83.13 386	55.52 282	91.07 309	64.98 302	78.34 335	88.45 333

0.4-1-1-0.1		70.93 377	67.94 396	79.91 311	79.35 434	61.27 331	78.95 407	82.19 388	63.36 387	67.50 407	69.40 480	39.83 441	91.04 310	62.44 329	68.40 432	87.40 360

blended_shiyan6		73.38 342	71.17 358	80.01 309	78.36 441	61.48 329	82.43 347	87.27 299	65.40 359	68.56 393	77.55 448	51.94 324	91.01 311	63.27 316	65.76 445	87.55 356

viewmambaseed2359dif		80.41 200	79.84 192	82.12 250	82.95 377	62.50 308	83.39 330	88.06 274	67.11 330	80.98 170	90.31 187	66.20 150	91.01 311	74.62 199	84.90 234	92.86 158

testdata2													91.01 311	62.37 333

blended_shiyan8		73.38 342	71.17 358	80.02 308	78.36 441	61.51 328	82.43 347	87.28 296	65.40 359	68.61 391	77.53 449	51.91 325	91.00 314	63.28 315	65.76 445	87.53 357

wanda-best-256-512		72.94 356	70.66 366	79.79 316	77.80 448	61.03 337	81.31 367	87.15 304	65.18 362	68.09 398	76.28 458	51.32 333	90.97 315	63.06 319	65.76 445	87.35 363

FE-blended-shiyan7		72.94 356	70.66 366	79.79 316	77.80 448	61.03 337	81.31 367	87.15 304	65.18 362	68.09 398	76.28 458	51.32 333	90.97 315	63.06 319	65.76 445	87.35 363

0.3-1-1-0.015		70.03 391	66.80 415	79.72 321	78.18 445	61.07 335	77.63 425	82.32 387	62.65 400	65.50 433	67.29 481	37.62 455	90.91 317	61.99 339	68.04 434	87.19 372

MSDG		73.36 346	70.99 361	80.49 294	84.51 332	65.80 211	80.71 378	86.13 329	65.70 352	65.46 434	83.74 372	44.60 407	90.91 317	51.13 426	76.89 349	84.74 425

0.4-1-1-0.2		70.01 392	66.86 414	79.44 329	77.61 451	60.64 347	76.77 432	82.34 386	62.40 403	65.91 431	66.65 482	40.05 438	90.83 319	61.77 343	68.24 433	86.86 383

TAMVS		78.89 243	77.51 258	83.03 217	87.80 218	67.79 159	84.72 289	85.05 343	67.63 323	76.75 259	87.70 266	62.25 202	90.82 320	58.53 375	87.13 194	90.49 252

diffmvs_AUTHOR		82.38 147	82.27 143	82.73 238	83.26 360	63.80 271	83.89 315	89.76 202	73.35 192	82.37 143	90.84 169	66.25 148	90.79 321	82.77 94	87.93 178	93.59 115

diffmvs		82.10 150	81.88 152	82.76 236	83.00 371	63.78 273	83.68 320	89.76 202	72.94 204	82.02 150	89.85 197	65.96 156	90.79 321	82.38 101	87.30 190	93.71 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDS-MVSNet		79.07 237	77.70 251	83.17 209	87.60 234	68.23 142	84.40 305	86.20 327	67.49 326	76.36 270	86.54 305	61.54 215	90.79 321	61.86 341	87.33 189	90.49 252
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dtuplus		80.04 212	79.40 205	81.97 256	83.08 367	62.61 304	83.63 324	87.98 276	67.47 328	81.02 169	90.50 182	64.86 166	90.77 324	71.28 240	84.76 237	92.53 170

VortexMVS		78.57 251	77.89 242	80.59 291	85.89 294	62.76 303	85.61 263	89.62 209	72.06 218	74.99 310	85.38 333	55.94 280	90.77 324	74.99 196	76.58 354	88.23 339

1314		76.53 295	75.30 304	80.21 303	83.93 343	62.32 313	84.66 291	88.81 250	60.23 421	70.16 372	84.07 366	55.30 284	90.73 326	67.37 281	83.21 271	87.59 355

WR-MVS		79.49 222	79.22 213	80.27 300	88.79 174	58.35 370	85.06 282	88.61 265	78.56 36	77.65 237	88.34 248	63.81 177	90.66 327	64.98 302	77.22 345	91.80 202

MVS_111021_LR		82.61 144	82.11 145	84.11 159	88.82 168	71.58 58	85.15 278	86.16 328	74.69 152	80.47 183	91.04 163	62.29 201	90.55 328	80.33 122	90.08 131	90.20 264

hybrid		81.05 177	80.66 169	82.22 249	81.97 393	62.99 299	83.42 329	88.68 260	70.76 250	80.56 180	90.40 184	64.49 170	90.48 329	79.57 135	86.06 214	93.19 136

HY-MVS	69.67 12	77.95 267	77.15 264	80.36 297	87.57 243	60.21 355	83.37 332	87.78 285	66.11 345	75.37 294	87.06 288	63.27 180	90.48 329	61.38 348	82.43 281	90.40 256

usedtu_dtu_shiyan1		76.43 300	75.32 302	79.76 318	83.00 371	60.72 343	81.74 357	88.76 256	68.99 305	72.98 338	84.19 362	56.41 277	90.27 331	62.39 330	79.40 319	88.31 336

FE-MVSNET3		76.43 300	75.32 302	79.76 318	83.00 371	60.72 343	81.74 357	88.76 256	68.99 305	72.98 338	84.19 362	56.41 277	90.27 331	62.39 330	79.40 319	88.31 336

VNet		82.21 149	82.41 138	81.62 262	90.82 101	60.93 339	84.47 297	89.78 200	76.36 102	84.07 107	91.88 126	64.71 167	90.26 333	70.68 246	88.89 152	93.66 106

VPA-MVSNet		80.60 195	80.55 172	80.76 288	88.07 205	60.80 342	86.86 220	91.58 142	75.67 120	80.24 186	89.45 217	63.34 178	90.25 334	70.51 248	79.22 324	91.23 221

ab-mvs		79.51 221	78.97 218	81.14 278	88.46 186	60.91 340	83.84 316	89.24 231	70.36 263	79.03 203	88.87 233	63.23 183	90.21 335	65.12 300	82.57 280	92.28 184

D2MVS		74.82 324	73.21 332	79.64 325	79.81 426	62.56 307	80.34 385	87.35 295	64.37 375	68.86 388	82.66 395	46.37 390	90.10 336	67.91 276	81.24 294	86.25 394

testing91		76.54 294	75.66 293	79.18 335	88.43 188	55.89 412	81.08 370	83.00 376	73.76 178	75.34 295	84.29 357	46.20 394	90.07 337	64.33 306	84.50 241	91.58 210

testing99		76.09 308	75.12 307	79.00 336	88.16 198	55.50 418	80.79 374	81.40 398	73.30 194	75.17 303	84.27 360	44.48 409	90.02 338	64.28 307	84.22 250	91.48 215

1112_ss		77.40 282	76.43 282	80.32 299	89.11 162	60.41 352	83.65 321	87.72 287	62.13 407	73.05 337	86.72 293	62.58 196	89.97 339	62.11 338	80.80 301	90.59 248

testing11		75.14 322	74.01 320	78.53 348	88.16 198	56.38 405	80.74 377	80.42 413	70.67 252	72.69 344	83.72 374	43.61 416	89.86 340	62.29 334	83.76 256	89.36 300

tfpnnormal		74.39 327	73.16 333	78.08 357	86.10 292	58.05 374	84.65 293	87.53 290	70.32 266	71.22 362	85.63 326	54.97 285	89.86 340	43.03 467	75.02 386	86.32 393

tpmvs		71.09 375	69.29 381	76.49 380	82.04 392	56.04 410	78.92 408	81.37 399	64.05 380	67.18 414	78.28 442	49.74 359	89.77 342	49.67 436	72.37 408	83.67 437

Vis-MVSNet (Re-imp)		78.36 255	78.45 227	78.07 358	88.64 180	51.78 452	86.70 227	79.63 424	74.14 169	75.11 306	90.83 170	61.29 223	89.75 343	58.10 380	91.60 100	92.69 164

ambc				75.24 395	73.16 476	50.51 462	63.05 492	87.47 292		64.28 443	77.81 446	17.80 493	89.73 344	57.88 382	60.64 467	85.49 411

VPNet		78.69 247	78.66 223	78.76 341	88.31 192	55.72 415	84.45 300	86.63 319	76.79 81	78.26 222	90.55 180	59.30 248	89.70 345	66.63 288	77.05 347	90.88 234

mvs_anonymous		79.42 226	79.11 215	80.34 298	84.45 333	57.97 377	82.59 345	87.62 288	67.40 329	76.17 277	88.56 243	68.47 119	89.59 346	70.65 247	86.05 215	93.47 121

pmmvs6		74.69 325	73.39 329	78.61 343	81.38 405	57.48 388	86.64 230	87.95 279	64.99 368	70.18 370	86.61 300	50.43 348	89.52 347	62.12 337	70.18 423	88.83 320

DTE-MVSNet		76.99 288	76.80 272	77.54 371	86.24 286	53.06 444	87.52 189	90.66 170	77.08 73	72.50 345	88.67 238	60.48 239	89.52 347	57.33 387	70.74 420	90.05 276

USDC		70.33 386	68.37 387	76.21 382	80.60 414	56.23 408	79.19 402	86.49 321	60.89 415	61.29 458	85.47 331	31.78 470	89.47 349	53.37 414	76.21 365	82.94 447

Test_1112_low_res		76.40 303	75.44 296	79.27 332	89.28 151	58.09 373	81.69 360	87.07 307	59.53 429	72.48 346	86.67 298	61.30 222	89.33 350	60.81 353	80.15 310	90.41 255

TransMVSNet (Re)		75.39 320	74.56 313	77.86 361	85.50 306	57.10 393	86.78 224	86.09 330	72.17 216	71.53 358	87.34 276	63.01 189	89.31 351	56.84 393	61.83 462	87.17 373

reproduce_monomvs		75.40 319	74.38 317	78.46 351	83.92 344	57.80 382	83.78 317	86.94 310	73.47 188	72.25 350	84.47 351	38.74 447	89.27 352	75.32 194	70.53 421	88.31 336

sc_t1		72.19 367	69.51 379	80.23 302	84.81 323	61.09 334	84.68 290	80.22 418	60.70 417	71.27 360	83.58 378	36.59 458	89.24 353	60.41 354	63.31 457	90.37 257

WR-MVS_H		78.51 252	78.49 226	78.56 346	88.02 207	56.38 405	88.43 154	92.67 74	77.14 69	73.89 326	87.55 272	66.25 148	89.24 353	58.92 370	73.55 400	90.06 275

PEN-MVS		77.73 272	77.69 252	77.84 362	87.07 267	53.91 434	87.91 179	91.18 153	77.56 53	73.14 336	88.82 234	61.23 224	89.17 355	59.95 358	72.37 408	90.43 254

pm-mvs1		77.25 285	76.68 278	78.93 338	84.22 336	58.62 368	86.41 238	88.36 268	71.37 231	73.31 333	88.01 260	61.22 225	89.15 356	64.24 308	73.01 405	89.03 310

testdata				79.97 310	90.90 99	64.21 262		84.71 345	59.27 431	85.40 76	92.91 95	62.02 207	89.08 357	68.95 267	91.37 106	86.63 391

Baseline_NR-MVSNet		78.15 261	78.33 232	77.61 368	85.79 296	56.21 409	86.78 224	85.76 334	73.60 183	77.93 231	87.57 270	65.02 163	88.99 358	67.14 285	75.33 381	87.63 352

旧先验2							86.56 233		58.10 443	87.04 62			88.98 359	74.07 206

LCM-MVSNet-Re		77.05 287	76.94 269	77.36 372	87.20 256	51.60 453	80.06 389	80.46 411	75.20 135	67.69 405	86.72 293	62.48 197	88.98 359	63.44 312	89.25 145	91.51 212

AllTest		70.96 376	68.09 392	79.58 326	85.15 315	63.62 274	84.58 295	79.83 421	62.31 404	60.32 463	86.73 291	32.02 468	88.96 361	50.28 431	71.57 416	86.15 397

TestCases				79.58 326	85.15 315	63.62 274		79.83 421	62.31 404	60.32 463	86.73 291	32.02 468	88.96 361	50.28 431	71.57 416	86.15 397

GG-mvs-BLEND				75.38 393	81.59 400	55.80 414	79.32 399	69.63 473		67.19 413	73.67 471	43.24 417	88.90 363	50.41 428	84.50 241	81.45 458

MonoMVSNet		76.49 299	75.80 288	78.58 345	81.55 401	58.45 369	86.36 243	86.22 326	74.87 149	74.73 315	83.73 373	51.79 329	88.73 364	70.78 243	72.15 411	88.55 332

gg-mvs-nofinetune		69.95 393	67.96 394	75.94 383	83.07 368	54.51 430	77.23 429	70.29 471	63.11 390	70.32 368	62.33 485	43.62 415	88.69 365	53.88 411	87.76 182	84.62 427

testing222		74.04 333	72.66 339	78.19 354	87.89 213	55.36 419	81.06 371	79.20 429	71.30 234	74.65 317	83.57 379	39.11 446	88.67 366	51.43 425	85.75 224	90.53 250

patchmatchnet-post											74.00 470	51.12 339	88.60 367

SCA		74.22 330	72.33 343	79.91 311	84.05 341	62.17 315	79.96 392	79.29 428	66.30 344	72.38 348	80.13 424	51.95 322	88.60 367	59.25 366	77.67 342	88.96 315

FE-MVSNET2		72.88 359	71.28 355	77.67 365	78.30 443	57.78 383	84.43 302	88.92 248	69.56 285	64.61 441	81.67 407	46.73 387	88.54 369	59.33 364	67.99 435	86.69 389

CP-MVSNet		78.22 257	78.34 231	77.84 362	87.83 217	54.54 429	87.94 177	91.17 154	77.65 48	73.48 332	88.49 244	62.24 203	88.43 370	62.19 335	74.07 393	90.55 249

PS-CasMVS		78.01 266	78.09 236	77.77 364	87.71 227	54.39 431	88.02 173	91.22 151	77.50 56	73.26 334	88.64 239	60.73 231	88.41 371	61.88 340	73.88 397	90.53 250

MS-PatchMatch		73.83 336	72.67 338	77.30 374	83.87 345	66.02 201	81.82 355	84.66 346	61.37 414	68.61 391	82.82 393	47.29 378	88.21 372	59.27 365	84.32 248	77.68 472

IterMVS-SCA-FT		75.43 317	73.87 324	80.11 306	82.69 382	64.85 247	81.57 362	83.47 365	69.16 298	70.49 366	84.15 365	51.95 322	88.15 373	69.23 263	72.14 412	87.34 366

pmmvs4		74.03 335	71.91 346	80.39 295	81.96 394	68.32 136	81.45 364	82.14 389	59.32 430	69.87 378	85.13 340	52.40 312	88.13 374	60.21 357	74.74 389	84.73 426

EPNet_dtu		75.46 316	74.86 308	77.23 375	82.57 385	54.60 428	86.89 218	83.09 373	71.64 223	66.25 428	85.86 320	55.99 279	88.04 375	54.92 405	86.55 204	89.05 309
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_7		83.34 128	84.03 102	81.28 273	85.73 298	65.13 232	85.40 273	89.90 198	74.96 144	82.13 148	93.89 69	66.65 140	87.92 376	86.56 53	91.05 111	90.80 236

TDRefinement		67.49 413	64.34 425	76.92 377	73.47 474	61.07 335	84.86 287	82.98 377	59.77 426	58.30 470	85.13 340	26.06 479	87.89 377	47.92 449	60.59 468	81.81 457

tpm cat1		70.57 382	68.31 388	77.35 373	82.41 389	57.95 378	78.08 419	80.22 418	52.04 468	68.54 394	77.66 447	52.00 321	87.84 378	51.77 420	72.07 413	86.25 394

baseline1		76.98 289	76.75 276	77.66 366	88.13 201	55.66 416	85.12 279	81.89 391	73.04 202	76.79 257	88.90 231	62.43 199	87.78 379	63.30 314	71.18 418	89.55 295

SDMVSNet		80.38 202	80.18 181	80.99 282	89.03 163	64.94 242	80.45 383	89.40 216	75.19 136	76.61 264	89.98 194	60.61 237	87.69 380	76.83 173	83.55 263	90.33 259

TinyColmap		67.30 416	64.81 423	74.76 401	81.92 396	56.68 400	80.29 386	81.49 397	60.33 419	56.27 478	83.22 383	24.77 483	87.66 381	45.52 460	69.47 425	79.95 466

tt0320		70.49 385	68.03 393	77.89 360	84.78 324	59.12 365	83.55 326	80.44 412	58.13 442	67.43 411	80.41 420	39.26 444	87.54 382	55.12 402	63.18 458	86.99 380

tt0320-xc		70.11 389	67.45 407	78.07 358	85.33 310	59.51 363	83.28 333	78.96 431	58.77 436	67.10 415	80.28 422	36.73 457	87.42 383	56.83 394	59.77 470	87.29 368

ppachtmachnet_test		70.04 390	67.34 409	78.14 355	79.80 427	61.13 332	79.19 402	80.59 407	59.16 432	65.27 436	79.29 433	46.75 386	87.29 384	49.33 438	66.72 438	86.00 403

testing3-2		75.12 323	75.19 305	74.91 398	90.40 110	45.09 483	80.29 386	78.42 434	78.37 41	76.54 266	87.75 264	44.36 410	87.28 385	57.04 390	83.49 265	92.37 179

ITE_SJBPF				78.22 353	81.77 397	60.57 348		83.30 367	69.25 294	67.54 406	87.20 282	36.33 460	87.28 385	54.34 408	74.62 390	86.80 385

MDTV_nov1_ep13			69.97 377		83.18 364	53.48 437	77.10 431	80.18 420	60.45 418	69.33 384	80.44 418	48.89 373	86.90 387	51.60 422	78.51 329

CR-MVSNet		73.37 344	71.27 356	79.67 324	81.32 408	65.19 230	75.92 437	80.30 416	59.92 425	72.73 342	81.19 409	52.50 310	86.69 388	59.84 359	77.71 339	87.11 377

WBMVS		73.43 341	72.81 337	75.28 394	87.91 212	50.99 459	78.59 413	81.31 400	65.51 357	74.47 320	84.83 346	46.39 388	86.68 389	58.41 376	77.86 337	88.17 342

Patchmtry		70.74 380	69.16 383	75.49 391	80.72 412	54.07 433	74.94 448	80.30 416	58.34 439	70.01 373	81.19 409	52.50 310	86.54 390	53.37 414	71.09 419	85.87 406

JIA-IIPM		66.32 424	62.82 436	76.82 378	77.09 455	61.72 324	65.34 485	75.38 454	58.04 444	64.51 442	62.32 486	42.05 427	86.51 391	51.45 424	69.22 427	82.21 452

UBG		73.08 353	72.27 344	75.51 390	88.02 207	51.29 457	78.35 417	77.38 443	65.52 355	73.87 327	82.36 398	45.55 401	86.48 392	55.02 404	84.39 247	88.75 324

CMPMVS	51.72 21	70.19 388	68.16 390	76.28 381	73.15 477	57.55 387	79.47 397	83.92 357	48.02 477	56.48 476	84.81 347	43.13 418	86.42 393	62.67 327	81.81 289	84.89 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		70.50 384	67.83 399	78.52 349	77.37 454	66.18 198	81.82 355	81.51 396	58.90 435	63.90 448	80.42 419	42.69 421	86.28 394	58.56 374	65.30 452	83.11 443

ETVMVS		72.25 366	71.05 360	75.84 384	87.77 223	51.91 449	79.39 398	74.98 456	69.26 293	73.71 328	82.95 389	40.82 435	86.14 395	46.17 456	84.43 246	89.47 296

SD_0403		74.65 326	74.77 310	74.29 406	86.20 288	47.42 472	83.71 319	85.12 340	69.30 291	68.50 395	87.95 262	59.40 247	86.05 396	49.38 437	83.35 268	89.40 298

CNLPA		78.08 262	76.79 273	81.97 256	90.40 110	71.07 72	87.59 188	84.55 348	66.03 348	72.38 348	89.64 207	57.56 263	86.04 397	59.61 362	83.35 268	88.79 322

PatchmatchNet		73.12 352	71.33 354	78.49 350	83.18 364	60.85 341	79.63 395	78.57 433	64.13 377	71.73 355	79.81 429	51.20 338	85.97 398	57.40 386	76.36 364	88.66 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mmtdpeth		74.16 331	73.01 335	77.60 370	83.72 349	61.13 332	85.10 280	85.10 341	72.06 218	77.21 251	80.33 421	43.84 414	85.75 399	77.14 167	52.61 482	85.91 404

CVMVSNet		72.99 355	72.58 340	74.25 407	84.28 334	50.85 460	86.41 238	83.45 366	44.56 481	73.23 335	87.54 273	49.38 363	85.70 400	65.90 294	78.44 330	86.19 396

testing3		68.56 406	67.67 403	71.22 437	87.33 250	42.87 488	83.06 342	71.54 468	70.36 263	69.08 387	84.38 354	30.33 474	85.69 401	37.50 480	75.45 377	85.09 421

UWE-MVS		72.13 368	71.49 350	74.03 410	86.66 278	47.70 470	81.40 366	76.89 448	63.60 386	75.59 284	84.22 361	39.94 439	85.62 402	48.98 440	86.13 213	88.77 323

IterMVS		74.29 328	72.94 336	78.35 352	81.53 402	63.49 284	81.58 361	82.49 383	68.06 321	69.99 375	83.69 375	51.66 331	85.54 403	65.85 295	71.64 415	86.01 401
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-RL test		70.24 387	67.78 401	77.61 368	77.43 453	59.57 362	71.16 462	70.33 470	62.94 394	68.65 390	72.77 473	50.62 345	85.49 404	69.58 261	66.58 440	87.77 350

sd_testset		77.70 275	77.40 259	78.60 344	89.03 163	60.02 356	79.00 405	85.83 333	75.19 136	76.61 264	89.98 194	54.81 286	85.46 405	62.63 328	83.55 263	90.33 259

test_post1							78.90 409				5.43 524	48.81 374	85.44 406	59.25 366

pmmvs5		71.55 371	70.20 375	75.61 387	77.83 447	56.39 404	81.74 357	80.89 402	57.76 445	67.46 409	84.49 350	49.26 367	85.32 407	57.08 389	75.29 382	85.11 420

mvs5depth		69.45 398	67.45 407	75.46 392	73.93 468	55.83 413	79.19 402	83.23 369	66.89 331	71.63 357	83.32 382	33.69 466	85.09 408	59.81 360	55.34 478	85.46 412

KD-MVS_2432*1600		66.22 425	63.89 428	73.21 417	75.47 464	53.42 438	70.76 465	84.35 350	64.10 378	66.52 424	78.52 440	34.55 464	84.98 409	50.40 429	50.33 485	81.23 459

miper_refine_blended		66.22 425	63.89 428	73.21 417	75.47 464	53.42 438	70.76 465	84.35 350	64.10 378	66.52 424	78.52 440	34.55 464	84.98 409	50.40 429	50.33 485	81.23 459

PatchMatch-RL		72.38 362	70.90 363	76.80 379	88.60 181	67.38 174	79.53 396	76.17 453	62.75 398	69.36 383	82.00 406	45.51 402	84.89 411	53.62 412	80.58 304	78.12 471

KD-MVS_self_test		68.81 402	67.59 405	72.46 426	74.29 467	45.45 478	77.93 422	87.00 308	63.12 389	63.99 447	78.99 438	42.32 423	84.77 412	56.55 397	64.09 455	87.16 375

RPSCF		73.23 351	71.46 351	78.54 347	82.50 386	59.85 357	82.18 352	82.84 381	58.96 434	71.15 363	89.41 219	45.48 404	84.77 412	58.82 372	71.83 414	91.02 230

FE-MVSNET		67.25 417	65.33 421	73.02 421	75.86 459	52.54 445	80.26 388	80.56 408	63.80 385	60.39 461	79.70 430	41.41 430	84.66 414	43.34 466	62.62 460	81.86 455

test_post											5.46 523	50.36 349	84.24 415

CL-MVSNet_self_test		72.37 363	71.46 351	75.09 396	79.49 432	53.53 436	80.76 376	85.01 344	69.12 299	70.51 365	82.05 404	57.92 259	84.13 416	52.27 419	66.00 443	87.60 353

our_test_3		69.14 400	67.00 412	75.57 388	79.80 427	58.80 366	77.96 421	77.81 437	59.55 428	62.90 453	78.25 443	47.43 377	83.97 417	51.71 421	67.58 437	83.93 435

EU-MVSNet		68.53 407	67.61 404	71.31 436	78.51 440	47.01 475	84.47 297	84.27 353	42.27 484	66.44 427	84.79 348	40.44 436	83.76 418	58.76 373	68.54 431	83.17 441

MDA-MVSNet-bldmvs		66.68 420	63.66 430	75.75 385	79.28 435	60.56 349	73.92 454	78.35 435	64.43 372	50.13 486	79.87 428	44.02 413	83.67 419	46.10 457	56.86 472	83.03 445

MIMVSNet1		68.58 405	66.78 416	73.98 411	80.07 422	51.82 451	80.77 375	84.37 349	64.40 374	59.75 466	82.16 403	36.47 459	83.63 420	42.73 468	70.33 422	86.48 392

usedtu_dtu_shiyan2		64.75 432	61.63 440	74.10 409	70.64 484	53.18 443	82.10 354	81.27 401	56.22 457	56.39 477	74.67 468	27.94 477	83.56 421	42.71 469	62.73 459	85.57 410

myMVS_eth3d28		73.62 338	73.53 328	73.90 412	88.20 195	47.41 473	78.06 420	79.37 426	74.29 165	73.98 325	84.29 357	44.67 406	83.54 422	51.47 423	87.39 188	90.74 241

patch_mono-2		83.65 116	84.54 91	80.99 282	90.06 121	65.83 209	84.21 308	88.74 258	71.60 227	85.01 80	92.44 108	74.51 30	83.50 423	82.15 102	92.15 90	93.64 112

PM-MVS		66.41 423	64.14 426	73.20 419	73.92 469	56.45 402	78.97 406	64.96 487	63.88 384	64.72 440	80.24 423	19.84 491	83.44 424	66.24 289	64.52 454	79.71 467

PVSNet	64.34 18	72.08 369	70.87 364	75.69 386	86.21 287	56.44 403	74.37 452	80.73 405	62.06 408	70.17 371	82.23 402	42.86 420	83.31 425	54.77 406	84.45 245	87.32 367

tpm		72.37 363	71.71 348	74.35 405	82.19 391	52.00 447	79.22 401	77.29 444	64.56 371	72.95 340	83.68 376	51.35 332	83.26 426	58.33 378	75.80 368	87.81 349

miper_lstm_enhance		74.11 332	73.11 334	77.13 376	80.11 421	59.62 360	72.23 458	86.92 312	66.76 334	70.40 367	82.92 390	56.93 271	82.92 427	69.06 266	72.63 407	88.87 318

IMVS_0404		77.16 286	76.42 283	79.37 330	87.13 259	63.59 278	77.12 430	89.33 219	70.51 258	66.22 429	89.03 225	50.36 349	82.78 428	72.56 226	85.56 226	91.74 203

tpmrst		72.39 361	72.13 345	73.18 420	80.54 415	49.91 464	79.91 393	79.08 430	63.11 390	71.69 356	79.95 426	55.32 283	82.77 429	65.66 297	73.89 396	86.87 382

MVS-HIRNet		59.14 443	57.67 445	63.57 463	81.65 398	43.50 487	71.73 459	65.06 486	39.59 488	51.43 483	57.73 492	38.34 450	82.58 430	39.53 475	73.95 395	64.62 487

dtuonlycased		68.45 409	67.29 410	71.92 428	80.18 420	54.90 425	79.76 394	80.38 415	60.11 423	62.57 455	76.44 456	49.34 364	82.31 431	55.05 403	61.77 463	78.53 470

Syy-MVS		68.05 411	67.85 397	68.67 451	84.68 327	40.97 494	78.62 411	73.08 465	66.65 339	66.74 420	79.46 431	52.11 318	82.30 432	32.89 485	76.38 362	82.75 448

myMVS_eth3d		67.02 418	66.29 418	69.21 446	84.68 327	42.58 489	78.62 411	73.08 465	66.65 339	66.74 420	79.46 431	31.53 471	82.30 432	39.43 477	76.38 362	82.75 448

SSC-MVS3.2		73.35 347	73.39 329	73.23 416	85.30 311	49.01 468	74.58 450	81.57 395	75.21 134	73.68 329	85.58 328	52.53 308	82.05 434	54.33 409	77.69 341	88.63 329

FMVSNet5		69.50 397	67.96 394	74.15 408	82.97 376	55.35 420	80.01 391	82.12 390	62.56 401	63.02 450	81.53 408	36.92 456	81.92 435	48.42 442	74.06 394	85.17 419

PatchT		68.46 408	67.85 397	70.29 441	80.70 413	43.93 486	72.47 457	74.88 457	60.15 422	70.55 364	76.57 453	49.94 355	81.59 436	50.58 427	74.83 388	85.34 414

EGC-MVSNET		52.07 455	47.05 459	67.14 457	83.51 355	60.71 345	80.50 382	67.75 479	0.07 538	0.43 539	75.85 465	24.26 484	81.54 437	28.82 489	62.25 461	59.16 490

MIMVSNet		70.69 381	69.30 380	74.88 399	84.52 331	56.35 407	75.87 439	79.42 425	64.59 370	67.76 403	82.41 397	41.10 432	81.54 437	46.64 454	81.34 292	86.75 387

icg_test_0407_2		78.92 242	78.93 219	78.90 339	87.13 259	63.59 278	76.58 433	89.33 219	70.51 258	77.82 232	89.03 225	61.84 208	81.38 439	72.56 226	85.56 226	91.74 203

Anonymous20240521		68.80 403	67.22 411	73.55 414	74.33 466	54.11 432	83.18 335	85.61 335	58.15 441	61.68 457	80.94 414	30.71 473	81.27 440	57.00 391	73.34 404	85.28 415

WB-MVSnew		71.96 370	71.65 349	72.89 422	84.67 330	51.88 450	82.29 350	77.57 439	62.31 404	73.67 330	83.00 388	53.49 304	81.10 441	45.75 459	82.13 284	85.70 408

WTY-MVS		75.65 313	75.68 291	75.57 388	86.40 284	56.82 396	77.92 423	82.40 384	65.10 364	76.18 275	87.72 265	63.13 188	80.90 442	60.31 356	81.96 286	89.00 313

dp		66.80 419	65.43 420	70.90 440	79.74 429	48.82 469	75.12 446	74.77 458	59.61 427	64.08 446	77.23 450	42.89 419	80.72 443	48.86 441	66.58 440	83.16 442

ADS-MVSNet2		66.20 427	63.33 431	74.82 400	79.92 423	58.75 367	67.55 477	75.19 455	53.37 465	65.25 437	75.86 463	42.32 423	80.53 444	41.57 472	68.91 428	85.18 417

XXY-MVS		75.41 318	75.56 294	74.96 397	83.59 353	57.82 381	80.59 380	83.87 359	66.54 342	74.93 312	88.31 249	63.24 182	80.09 445	62.16 336	76.85 351	86.97 381

test_vis1_n_1920		75.52 315	75.78 289	74.75 402	79.84 425	57.44 389	83.26 334	85.52 336	62.83 396	79.34 201	86.17 315	45.10 405	79.71 446	78.75 146	81.21 295	87.10 379

test-LLR		72.94 356	72.43 341	74.48 403	81.35 406	58.04 375	78.38 414	77.46 440	66.66 336	69.95 376	79.00 436	48.06 375	79.24 447	66.13 290	84.83 235	86.15 397

test-mter		71.41 372	70.39 373	74.48 403	81.35 406	58.04 375	78.38 414	77.46 440	60.32 420	69.95 376	79.00 436	36.08 461	79.24 447	66.13 290	84.83 235	86.15 397

Anonymous20231206		68.60 404	67.80 400	71.02 438	80.23 419	50.75 461	78.30 418	80.47 410	56.79 453	66.11 430	82.63 396	46.35 391	78.95 449	43.62 465	75.70 369	83.36 440

UnsupCasMVSNet_bld		63.70 435	61.53 441	70.21 442	73.69 471	51.39 456	72.82 456	81.89 391	55.63 459	57.81 472	71.80 475	38.67 448	78.61 450	49.26 439	52.21 483	80.63 463

test20.03		67.45 414	66.95 413	68.94 447	75.48 463	44.84 484	77.50 426	77.67 438	66.66 336	63.01 451	83.80 370	47.02 381	78.40 451	42.53 471	68.86 430	83.58 438

PMMVS		69.34 399	68.67 385	71.35 435	75.67 461	62.03 318	75.17 443	73.46 463	50.00 474	68.68 389	79.05 434	52.07 320	78.13 452	61.16 350	82.77 276	73.90 478

sss		73.60 339	73.64 327	73.51 415	82.80 379	55.01 424	76.12 435	81.69 394	62.47 402	74.68 316	85.85 321	57.32 266	78.11 453	60.86 352	80.93 297	87.39 361

LCM-MVSNet		54.25 448	49.68 458	67.97 456	53.73 504	45.28 481	66.85 480	80.78 404	35.96 493	39.45 494	62.23 487	8.70 503	78.06 454	48.24 446	51.20 484	80.57 464

EPMVS		69.02 401	68.16 390	71.59 431	79.61 430	49.80 466	77.40 427	66.93 481	62.82 397	70.01 373	79.05 434	45.79 398	77.86 455	56.58 396	75.26 383	87.13 376

PVSNet_0	57.27 20	61.67 440	59.27 443	68.85 449	79.61 430	57.44 389	68.01 475	73.44 464	55.93 458	58.54 469	70.41 478	44.58 408	77.55 456	47.01 451	35.91 493	71.55 481

UnsupCasMVSNet_eth		67.33 415	65.99 419	71.37 433	73.48 473	51.47 455	75.16 444	85.19 339	65.20 361	60.78 460	80.93 416	42.35 422	77.20 457	57.12 388	53.69 480	85.44 413

test_fmvs1_n		70.86 379	70.24 374	72.73 424	72.51 482	55.28 421	81.27 369	79.71 423	51.49 472	78.73 208	84.87 345	27.54 478	77.02 458	76.06 182	79.97 313	85.88 405

test_fmvs1		70.93 377	70.52 369	72.16 427	73.71 470	55.05 423	80.82 372	78.77 432	51.21 473	78.58 213	84.41 353	31.20 472	76.94 459	75.88 186	80.12 312	84.47 428

TESTMET0.1,1		69.89 395	69.00 384	72.55 425	79.27 436	56.85 395	78.38 414	74.71 460	57.64 446	68.09 398	77.19 451	37.75 453	76.70 460	63.92 309	84.09 251	84.10 433

dmvs_re		71.14 374	70.58 368	72.80 423	81.96 394	59.68 359	75.60 441	79.34 427	68.55 313	69.27 386	80.72 417	49.42 362	76.54 461	52.56 418	77.79 338	82.19 453

LF4IMVS		64.02 434	62.19 437	69.50 445	70.90 483	53.29 441	76.13 434	77.18 445	52.65 467	58.59 468	80.98 413	23.55 486	76.52 462	53.06 416	66.66 439	78.68 469

new-patchmatchnet		61.73 439	61.73 439	61.70 465	72.74 480	24.50 508	69.16 472	78.03 436	61.40 412	56.72 475	75.53 466	38.42 449	76.48 463	45.95 458	57.67 471	84.13 432

test_cas_vis1_n_1920		73.76 337	73.74 326	73.81 413	75.90 458	59.77 358	80.51 381	82.40 384	58.30 440	81.62 159	85.69 323	44.35 411	76.41 464	76.29 178	78.61 326	85.23 416

APD_test1		53.31 452	49.93 457	63.42 464	65.68 491	50.13 463	71.59 461	66.90 482	34.43 494	40.58 493	71.56 476	8.65 504	76.27 465	34.64 484	55.36 477	63.86 488

test_vis1_n		69.85 396	69.21 382	71.77 430	72.66 481	55.27 422	81.48 363	76.21 452	52.03 469	75.30 300	83.20 385	28.97 475	76.22 466	74.60 200	78.41 334	83.81 436

PMVS	37.38 22	44.16 463	40.28 467	55.82 474	40.82 509	42.54 491	65.12 486	63.99 489	34.43 494	24.48 501	57.12 494	3.92 509	76.17 467	17.10 503	55.52 476	48.75 497
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		65.32 428	64.93 422	66.49 459	78.70 438	38.55 496	77.86 424	64.39 488	62.00 409	64.13 445	83.60 377	41.44 429	76.00 468	31.39 487	80.89 298	84.92 422

ttmdpeth		59.91 442	57.10 446	68.34 453	67.13 490	46.65 477	74.64 449	67.41 480	48.30 476	62.52 456	85.04 344	20.40 489	75.93 469	42.55 470	45.90 491	82.44 450

test0.0.03 1		68.00 412	67.69 402	68.90 448	77.55 452	47.43 471	75.70 440	72.95 467	66.66 336	66.56 422	82.29 401	48.06 375	75.87 470	44.97 463	74.51 391	83.41 439

WB-MVS		54.94 447	54.72 448	55.60 475	73.50 472	20.90 510	74.27 453	61.19 492	59.16 432	50.61 484	74.15 469	47.19 380	75.78 471	17.31 502	35.07 494	70.12 482

Gipuma		45.18 462	41.86 465	55.16 476	77.03 456	51.52 454	32.50 503	80.52 409	32.46 496	27.12 499	35.02 506	9.52 502	75.50 472	22.31 499	60.21 469	38.45 503
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		57.79 444	54.26 449	68.37 452	64.02 494	56.72 398	75.12 446	65.17 485	40.20 486	52.93 482	69.86 479	20.36 490	75.48 473	45.45 461	55.25 479	72.90 480

SSC-MVS		53.88 450	53.59 450	54.75 477	72.87 479	19.59 511	73.84 455	60.53 494	57.58 448	49.18 488	73.45 472	46.34 392	75.47 474	16.20 505	32.28 496	69.20 483

test_fmvs2		68.35 410	67.48 406	70.98 439	69.50 486	51.95 448	80.05 390	76.38 451	49.33 475	74.65 317	84.38 354	23.30 487	75.40 475	74.51 201	75.17 385	85.60 409

CHOSEN 280x420		66.51 422	64.71 424	71.90 429	81.45 403	63.52 283	57.98 494	68.95 477	53.57 464	62.59 454	76.70 452	46.22 393	75.29 476	55.25 401	79.68 314	76.88 474

testgi		66.67 421	66.53 417	67.08 458	75.62 462	41.69 493	75.93 436	76.50 449	66.11 345	65.20 439	86.59 301	35.72 462	74.71 477	43.71 464	73.38 403	84.84 424

YYNet1		65.03 429	62.91 434	71.38 432	75.85 460	56.60 401	69.12 473	74.66 461	57.28 451	54.12 480	77.87 445	45.85 397	74.48 478	49.95 434	61.52 465	83.05 444

MDA-MVSNet_test_wron		65.03 429	62.92 433	71.37 433	75.93 457	56.73 397	69.09 474	74.73 459	57.28 451	54.03 481	77.89 444	45.88 396	74.39 479	49.89 435	61.55 464	82.99 446

dtuonly		69.95 393	69.98 376	69.85 443	73.09 478	49.46 467	74.55 451	76.40 450	57.56 449	67.82 402	86.31 312	50.89 344	74.23 480	61.46 346	81.71 290	85.86 407

SSM_04072		77.67 277	77.52 256	78.12 356	88.81 169	67.96 151	65.03 487	88.66 261	70.96 245	79.48 196	89.80 200	58.69 251	74.23 480	70.35 250	85.93 219	92.18 190

ADS-MVSNet		64.36 433	62.88 435	68.78 450	79.92 423	47.17 474	67.55 477	71.18 469	53.37 465	65.25 437	75.86 463	42.32 423	73.99 482	41.57 472	68.91 428	85.18 417

dmvs_testset		62.63 437	64.11 427	58.19 469	78.55 439	24.76 507	75.28 442	65.94 484	67.91 322	60.34 462	76.01 462	53.56 302	73.94 483	31.79 486	67.65 436	75.88 476

ANet_high		50.57 457	46.10 461	63.99 462	48.67 507	39.13 495	70.99 464	80.85 403	61.39 413	31.18 496	57.70 493	17.02 494	73.65 484	31.22 488	15.89 506	79.18 468

test_fmvs3		63.36 436	61.82 438	67.98 455	62.51 495	46.96 476	77.37 428	74.03 462	45.24 480	67.50 407	78.79 439	12.16 499	72.98 485	72.77 222	66.02 442	83.99 434

Patchmatch-test		64.82 431	63.24 432	69.57 444	79.42 433	49.82 465	63.49 491	69.05 476	51.98 470	59.95 465	80.13 424	50.91 340	70.98 486	40.66 474	73.57 399	87.90 347

MVStest1		56.63 446	52.76 452	68.25 454	61.67 496	53.25 442	71.67 460	68.90 478	38.59 489	50.59 485	83.05 387	25.08 481	70.66 487	36.76 481	38.56 492	80.83 462

testf1		45.72 459	41.96 463	57.00 470	56.90 498	45.32 479	66.14 482	59.26 495	26.19 498	30.89 497	60.96 489	4.14 507	70.64 488	26.39 495	46.73 489	55.04 493

APD_test2		45.72 459	41.96 463	57.00 470	56.90 498	45.32 479	66.14 482	59.26 495	26.19 498	30.89 497	60.96 489	4.14 507	70.64 488	26.39 495	46.73 489	55.04 493

FPMVS		53.68 451	51.64 453	59.81 468	65.08 492	51.03 458	69.48 470	69.58 474	41.46 485	40.67 492	72.32 474	16.46 495	70.00 490	24.24 497	65.42 451	58.40 492

test_vis1_rt		60.28 441	58.42 444	65.84 460	67.25 489	55.60 417	70.44 467	60.94 493	44.33 482	59.00 467	66.64 483	24.91 482	68.67 491	62.80 322	69.48 424	73.25 479

DSMNet-mixed		57.77 445	56.90 447	60.38 467	67.70 488	35.61 498	69.18 471	53.97 499	32.30 497	57.49 473	79.88 427	40.39 437	68.57 492	38.78 478	72.37 408	76.97 473

mvsany_test1		62.30 438	61.26 442	65.41 461	69.52 485	54.86 426	66.86 479	49.78 501	46.65 478	68.50 395	83.21 384	49.15 368	66.28 493	56.93 392	60.77 466	75.11 477

N_pmnet		52.79 453	53.26 451	51.40 479	78.99 437	7.68 522	69.52 469	3.89 521	51.63 471	57.01 474	74.98 467	40.83 434	65.96 494	37.78 479	64.67 453	80.56 465

test_vis3_rt		49.26 458	47.02 460	56.00 472	54.30 501	45.27 482	66.76 481	48.08 502	36.83 491	44.38 490	53.20 498	7.17 506	64.07 495	56.77 395	55.66 475	58.65 491

mvsany_test3		53.99 449	51.45 454	61.61 466	55.51 500	44.74 485	63.52 490	45.41 505	43.69 483	58.11 471	76.45 454	17.99 492	63.76 496	54.77 406	47.59 487	76.34 475

dongtai		45.42 461	45.38 462	45.55 481	73.36 475	26.85 505	67.72 476	34.19 507	54.15 463	49.65 487	56.41 496	25.43 480	62.94 497	19.45 500	28.09 498	46.86 500

new_pmnet		50.91 456	50.29 456	52.78 478	68.58 487	34.94 500	63.71 489	56.63 498	39.73 487	44.95 489	65.47 484	21.93 488	58.48 498	34.98 483	56.62 473	64.92 486

test_f		52.09 454	50.82 455	55.90 473	53.82 503	42.31 492	59.42 493	58.31 497	36.45 492	56.12 479	70.96 477	12.18 498	57.79 499	53.51 413	56.57 474	67.60 484

PMMVS2		40.82 464	38.86 468	46.69 480	53.84 502	16.45 515	48.61 497	49.92 500	37.49 490	31.67 495	60.97 488	8.14 505	56.42 500	28.42 490	30.72 497	67.19 485

E-PMN		31.77 466	30.64 469	35.15 487	52.87 505	27.67 502	57.09 495	47.86 503	24.64 500	16.40 510	33.05 507	11.23 500	54.90 501	14.46 506	18.15 504	22.87 508

EMVS		30.81 468	29.65 470	34.27 488	50.96 506	25.95 506	56.58 496	46.80 504	24.01 501	15.53 511	30.68 509	12.47 497	54.43 502	12.81 508	17.05 505	22.43 509

test_method		31.52 467	29.28 471	38.23 484	27.03 516	6.50 524	20.94 507	62.21 491	4.05 512	22.35 505	52.50 499	13.33 496	47.58 503	27.04 492	34.04 495	60.62 489

MVE	26.22 23	30.37 469	25.89 473	43.81 482	44.55 508	35.46 499	28.87 506	39.07 506	18.20 505	18.58 508	40.18 504	2.68 510	47.37 504	17.07 504	23.78 501	48.60 498
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
kuosan		39.70 465	40.40 466	37.58 485	64.52 493	26.98 503	65.62 484	33.02 508	46.12 479	42.79 491	48.99 501	24.10 485	46.56 505	12.16 509	26.30 499	39.20 502

RoMa-SfM		28.67 470	25.38 474	38.54 483	32.61 513	22.48 509	40.24 498	7.23 517	21.81 502	26.66 500	60.46 491	0.96 513	41.72 506	26.47 494	11.95 509	51.40 496

LoFTR		27.52 471	24.27 475	37.29 486	34.75 512	19.27 512	33.78 502	21.60 512	12.42 507	21.61 506	56.59 495	0.91 514	40.37 507	13.94 507	22.80 502	52.22 495

DKM		25.67 472	23.01 476	33.64 489	32.08 514	19.25 513	37.50 500	5.52 518	18.67 503	23.58 504	55.44 497	0.64 517	34.02 508	23.95 498	9.73 510	47.66 499

MatchFormer		22.13 474	19.86 479	28.93 491	28.66 515	15.74 516	31.91 505	17.10 513	7.75 508	18.87 507	47.50 503	0.62 519	33.92 509	7.49 512	18.87 503	37.14 504

PDCNetPlus		24.75 473	22.46 477	31.64 490	35.53 511	17.00 514	32.00 504	9.46 514	18.43 504	18.56 509	51.31 500	1.65 511	33.00 510	26.51 493	8.70 512	44.91 501

DeepMVS_CX				27.40 492	40.17 510	26.90 504		24.59 511	17.44 506	23.95 502	48.61 502	9.77 501	26.48 511	18.06 501	24.47 500	28.83 507

ELoFTR		14.23 478	11.56 481	22.24 493	11.02 521	6.56 523	13.59 510	7.57 516	5.55 510	11.96 513	39.09 505	0.21 528	24.93 512	9.43 511	5.66 517	35.22 505

wuyk23d		16.82 477	15.94 480	19.46 495	58.74 497	31.45 501	39.22 499	3.74 523	6.84 509	6.04 514	2.70 538	1.27 512	24.29 513	10.54 510	14.40 508	2.63 521

GLUNet-SfM		12.90 479	10.00 482	21.62 494	13.58 520	8.30 520	10.19 512	9.30 515	4.31 511	12.18 512	30.90 508	0.50 523	22.76 514	4.89 513	4.14 523	33.79 506

tmp_tt		18.61 476	21.40 478	10.23 496	4.82 540	10.11 517	34.70 501	30.74 510	1.48 516	23.91 503	26.07 510	28.42 476	13.41 515	27.12 491	15.35 507	7.17 516

ALIKED-LG		8.61 480	8.70 484	8.33 497	20.63 517	8.70 519	15.50 508	4.61 519	2.19 513	5.84 515	18.70 511	0.80 515	8.06 516	1.03 521	8.97 511	8.25 510

ALIKED-MNN		7.86 481	7.83 487	7.97 498	19.40 518	8.86 518	14.48 509	3.90 520	1.59 514	4.74 520	16.49 512	0.59 520	7.65 517	0.91 522	8.34 514	7.39 513

ALIKED-NN		7.51 482	7.61 488	7.21 499	18.26 519	8.10 521	13.45 511	3.88 522	1.50 515	4.87 518	16.47 513	0.64 517	7.00 518	0.88 523	8.50 513	6.52 518

XFeat-MNN		4.39 487	4.49 490	4.10 500	2.88 542	1.91 537	5.86 518	2.57 524	1.06 518	5.04 516	13.99 514	0.43 526	4.47 519	2.00 515	6.55 515	5.92 519

XFeat-NN		3.78 493	3.96 496	3.23 506	2.65 543	1.53 542	4.99 519	1.92 529	0.81 523	4.77 519	12.37 517	0.38 527	3.39 520	1.64 516	6.13 516	4.77 520

SP-MNN		4.14 491	4.24 494	3.82 502	10.32 524	1.83 538	8.11 515	1.99 528	0.82 522	2.23 523	8.27 520	0.47 525	2.14 521	1.20 519	4.77 521	7.49 511

SP-LightGlue		4.27 489	4.41 492	3.86 501	10.99 522	1.99 534	8.19 513	2.06 527	0.98 520	2.37 522	8.29 518	0.56 521	2.10 522	1.27 517	4.99 519	7.48 512

SP-NN		4.00 492	4.12 495	3.63 505	9.92 525	1.81 539	7.94 516	1.90 530	0.86 521	2.15 524	8.00 521	0.50 523	2.09 523	1.20 519	4.63 522	6.98 517

SP-SuperGlue		4.24 490	4.38 493	3.81 503	10.75 523	2.00 533	8.18 514	2.09 526	1.00 519	2.41 521	8.29 518	0.56 521	2.05 524	1.27 517	4.91 520	7.39 513

SP-DiffGlue		4.29 488	4.46 491	3.77 504	3.68 541	2.12 531	5.97 517	2.22 525	1.10 517	4.89 517	13.93 515	0.66 516	1.95 525	2.47 514	5.24 518	7.22 515

SIFT-NN		2.77 494	2.92 497	2.34 507	8.70 526	3.08 525	4.46 520	1.01 532	0.68 524	1.46 525	5.49 522	0.16 529	1.65 526	0.26 524	4.04 524	2.27 522

SIFT-MNN		2.63 495	2.75 498	2.25 508	8.10 527	2.84 526	4.08 521	1.02 531	0.68 524	1.28 526	5.34 525	0.15 530	1.64 527	0.26 524	3.88 526	2.27 522

SIFT-NCM-Cal		2.40 497	2.52 500	2.05 510	7.74 528	2.54 528	3.75 524	0.84 534	0.65 527	0.89 533	4.78 531	0.13 534	1.60 528	0.19 535	3.71 527	2.01 528

SIFT-NN-NCMNet		2.52 496	2.64 499	2.14 509	7.53 529	2.74 527	4.00 522	0.98 533	0.65 527	1.24 528	5.08 528	0.14 531	1.60 528	0.23 527	3.94 525	2.07 526

SIFT-NN-UMatch		2.26 499	2.39 502	1.89 513	6.21 535	2.08 532	3.76 523	0.83 535	0.66 526	1.04 530	5.09 526	0.14 531	1.52 530	0.23 527	3.51 528	2.07 526

SIFT-NN-CMatch		2.31 498	2.41 501	2.00 511	6.59 533	2.34 530	3.48 525	0.83 535	0.65 527	1.28 526	5.09 526	0.14 531	1.52 530	0.23 527	3.41 529	2.14 524

SIFT-ConvMatch		2.25 500	2.37 503	1.90 512	7.29 530	2.37 529	3.21 528	0.75 537	0.65 527	1.03 531	4.91 529	0.12 537	1.51 532	0.22 530	3.13 531	1.81 529

SIFT-UMatch		2.16 501	2.30 504	1.72 515	6.99 531	1.97 536	3.32 526	0.70 539	0.64 531	0.91 532	4.86 530	0.12 537	1.49 533	0.22 530	2.97 532	1.72 531

SIFT-NN-PointCN		2.07 502	2.18 505	1.74 514	5.75 536	1.65 541	3.27 527	0.73 538	0.60 534	1.07 529	4.62 532	0.13 534	1.43 534	0.21 532	3.22 530	2.12 525

SIFT-CM-Cal		2.02 503	2.13 506	1.67 516	6.79 532	1.99 534	2.79 530	0.64 540	0.63 532	0.87 534	4.48 534	0.13 534	1.41 535	0.19 535	2.70 533	1.61 533

SIFT-UM-Cal		1.97 504	2.12 507	1.52 517	6.57 534	1.67 540	2.93 529	0.57 542	0.62 533	0.83 535	4.55 533	0.11 539	1.37 536	0.20 534	2.69 534	1.53 534

SIFT-PointCN		1.72 505	1.83 508	1.36 519	5.55 538	1.22 543	2.59 531	0.59 541	0.55 536	0.71 537	3.77 536	0.08 541	1.24 537	0.17 537	2.48 535	1.63 532

SIFT-PCN-Cal		1.72 505	1.82 509	1.39 518	5.64 537	1.19 544	2.39 532	0.53 543	0.55 536	0.72 536	3.90 535	0.09 540	1.22 538	0.17 537	2.42 536	1.76 530

SIFT-NCMNet		1.44 507	1.56 510	1.08 520	5.14 539	1.07 545	1.97 533	0.32 544	0.56 535	0.64 538	3.23 537	0.07 542	1.01 539	0.14 539	1.95 537	1.15 535

testmvs		6.04 485	8.02 486	0.10 522	0.08 544	0.03 547	69.74 468	0.04 545	0.05 539	0.31 540	1.68 539	0.02 544	0.04 540	0.24 526	0.02 538	0.25 537

test123		6.12 484	8.11 485	0.14 521	0.06 545	0.09 546	71.05 463	0.03 546	0.04 540	0.25 541	1.30 540	0.05 543	0.03 541	0.21 532	0.01 539	0.29 536

mmdepth		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

monomultidepth		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

test_blank		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

uanet_test		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

DCPMVS		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

cdsmvs_eth3d_5k		19.96 475	26.61 472	0.00 523	0.00 546	0.00 548	0.00 534	89.26 228	0.00 541	0.00 542	88.61 240	61.62 214	0.00 542	0.00 540	0.00 540	0.00 538

pcd_1.5k_mvsjas		5.26 486	7.02 489	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	63.15 185	0.00 542	0.00 540	0.00 540	0.00 538

sosnet-low-res		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

sosnet		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

uncertanet		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

Regformer		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

ab-mvs-re		7.23 483	9.64 483	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	86.72 293	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

uanet		0.00 508	0.00 511	0.00 523	0.00 546	0.00 548	0.00 534	0.00 547	0.00 541	0.00 542	0.00 541	0.00 545	0.00 542	0.00 540	0.00 540	0.00 538

WAC-MVS						42.58 489								39.46 476

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 157	91.30 17

test_one_0601					95.07 7	71.46 60		94.14 9	78.27 42	92.05 13	95.74 8	80.83 12

eth-test2					0.00 546

eth-test					0.00 546

RE-MVS-def			85.48 76		93.06 64	70.63 83	91.88 43	92.27 96	73.53 186	85.69 74	94.45 37	63.87 175		82.75 95	91.87 96	92.50 173

IU-MVS					95.30 2	71.25 65		92.95 61	66.81 332	92.39 6				88.94 28	96.63 4	94.85 24

save fliter					93.80 44	72.35 44	90.47 74	91.17 154	74.31 163

test0726					95.27 5	71.25 65	93.60 7	94.11 10	77.33 60	92.81 3	95.79 5	80.98 10

GSMVS																88.96 315

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 333				88.96 315

sam_mvs												50.01 353

MTGPA								92.02 114

MTMP							92.18 39	32.83 509

test9_res														84.90 64	95.70 29	92.87 157

agg_prior2														82.91 91	95.45 32	92.70 162

test_prior4						72.60 34	89.01 126

test_prior2							88.85 133		75.41 126	84.91 83	93.54 76	74.28 34		83.31 85	95.86 23

新几何2							86.29 247

旧先验1					91.96 81	65.79 212		86.37 324			93.08 93	69.31 102			92.74 80	88.74 326

原ACMM2							86.86 220

test222					91.50 87	68.26 138	84.16 311	83.20 372	54.63 462	79.74 191	91.63 139	58.97 250			91.42 104	86.77 386

segment_acmp												73.08 44

testdata1							84.14 312		75.71 117

plane_prior7					90.08 117	68.51 132

plane_prior6					89.84 126	68.70 126						60.42 240

plane_prior4											91.00 166

plane_prior3						68.60 129			78.44 37	78.92 206

plane_prior2							91.25 60		79.12 29

plane_prior1					89.90 125

plane_prior						68.71 124	90.38 78		77.62 49						86.16 212

n2								0.00 547

nn								0.00 547

door-mid								69.98 472

test11								92.23 100

door								69.44 475

HQP5-MVS						66.98 186

HQP-NCC					89.33 146		89.17 117		76.41 96	77.23 247

ACMP_Plane					89.33 146		89.17 117		76.41 96	77.23 247

BP-MVS														77.47 162

HQP3-MVS								92.19 108							85.99 217

HQP2-MVS												60.17 243

NP-MVS					89.62 131	68.32 136					90.24 190

MDTV_nov1_ep13_2view						37.79 497	75.16 444		55.10 460	66.53 423		49.34 364		53.98 410		87.94 346

ACMMP++_ref															81.95 287

ACMMP++															81.25 293

Test By Simon												64.33 171

High-resolution multi-view benchmark