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.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 69	88.18 1	87.15 3	65.04 17	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 32

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 79	87.82 7	86.78 10	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 40

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 74	87.85 5	85.03 43	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 170
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
MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 33	62.73 9	86.09 22	86.83 8	65.51 13	83.81 10	90.51 31	63.71 13	89.23 26	81.51 2	88.44 31	88.09 47
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
DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 16	63.28 52	83.27 15	91.83 10	64.96 7	90.47 11	76.41 41	89.67 20	86.84 100
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		80.42 6	80.87 6	79.07 25	85.30 51	59.25 64	86.84 11	85.86 24	63.31 49	83.65 12	91.48 12	64.70 10	89.91 16	77.02 35	89.69 18	88.06 50

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 21	60.02 48	88.12 3	86.42 16	62.94 61	82.40 16	92.12 2	59.64 24	89.76 20	78.70 15	88.32 35	86.79 102
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 24	61.95 19	86.33 17	85.75 28	62.49 72	82.20 20	92.28 1	56.53 45	89.70 21	79.85 6	91.48 1	88.19 42
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
aaEdge-Enhanced		80.04 10	80.36 9	79.08 24	86.63 23	59.25 64	85.62 32	86.73 12	63.10 58	82.27 19	90.57 28	61.90 17	89.88 19	77.02 35	89.43 24	88.10 45

MM		80.20 8	80.28 10	79.99 2	82.19 91	60.01 49	86.19 21	83.93 62	73.19 1	77.08 47	91.21 21	57.23 40	90.73 10	83.35 1	88.12 38	89.22 9

HPM-MVS++		79.88 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 16	83.70 81	65.37 14	78.78 31	90.64 25	58.63 32	87.24 61	79.00 14	90.37 14	85.26 182

CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 17	84.37 45	85.03 43	66.96 5	77.58 41	90.06 46	59.47 26	89.13 28	78.67 17	89.73 16	87.03 92

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 51	59.08 73	86.84 11	86.01 21	63.31 49	82.37 17	91.48 12	60.88 19	89.61 22	76.25 44	86.13 66	88.06 50

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 82	62.18 16	87.60 9	85.83 26	66.69 10	78.03 38	90.98 22	54.26 78	90.06 14	78.42 23	89.02 27	87.69 62
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		78.82 16	79.22 15	77.60 52	82.88 84	57.83 92	84.99 37	88.13 2	61.86 90	79.16 28	90.75 24	57.96 33	87.09 70	77.08 34	90.18 15	87.87 54

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 61	60.32 46	83.03 68	85.33 35	62.86 64	80.17 23	90.03 48	61.76 18	88.95 30	74.21 63	88.67 30	88.12 44

ACMMP_NAP		78.77 18	78.78 17	78.74 34	85.44 47	61.04 31	83.84 60	85.16 38	62.88 63	78.10 36	91.26 20	52.51 110	88.39 36	79.34 9	90.52 13	86.78 103

9.14			78.75 18		83.10 79		84.15 54	88.26 1	59.90 140	78.57 33	90.36 36	57.51 39	86.86 75	77.39 29	89.52 23

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 30	62.05 18	86.62 15	86.01 21	63.32 48	75.08 64	90.47 34	53.96 85	88.68 33	76.48 40	89.63 22	87.16 89

MP-MVS-pluss		78.35 23	78.46 20	78.03 45	84.96 57	59.52 58	82.93 70	85.39 34	62.15 82	76.41 51	91.51 11	52.47 112	86.78 77	80.66 4	89.64 21	87.80 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 53	66.73 8	74.67 78	89.38 59	55.30 67	89.18 27	74.19 64	87.34 50	86.38 120

MGCNet		78.45 21	78.28 22	78.98 29	80.73 116	57.91 91	84.68 41	81.64 134	68.35 2	75.77 53	90.38 35	53.98 83	90.26 13	81.30 3	87.68 46	88.77 19

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 31	84.96 57	61.41 26	84.03 56	83.82 76	59.34 156	79.37 27	89.76 55	59.84 21	87.62 58	76.69 38	86.74 59	87.68 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS		78.35 23	78.26 24	78.64 35	86.54 27	63.47 4	86.02 24	83.55 87	63.89 40	73.60 99	90.60 26	54.85 73	86.72 78	77.20 32	88.06 40	85.74 156
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 72	61.62 23	84.17 53	86.85 6	63.23 54	73.84 96	90.25 41	57.68 36	89.96 15	74.62 61	89.03 26	87.89 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		78.02 26	78.04 26	77.98 46	86.44 29	60.81 38	85.52 33	84.36 54	60.61 116	79.05 29	90.30 39	55.54 66	88.32 38	73.48 71	87.03 52	84.83 197
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		78.14 25	77.85 27	78.99 28	86.05 40	61.82 22	85.84 26	85.21 37	63.56 44	74.29 84	90.03 48	52.56 109	88.53 35	74.79 60	88.34 33	86.63 112

lecture		77.75 28	77.84 28	77.50 54	82.75 86	57.62 95	85.92 25	86.20 19	60.53 118	78.99 30	91.45 14	51.51 132	87.78 53	75.65 50	87.55 47	87.10 91

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 55	62.82 65	73.96 89	90.50 32	53.20 100	88.35 37	74.02 66	87.05 51	86.13 136

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 65	61.88 21	84.55 43	83.87 69	60.37 125	79.89 24	89.38 59	54.97 71	85.58 116	76.12 46	84.94 72	86.33 127
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
MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 86	83.22 66	86.93 5	56.91 211	74.91 69	88.19 78	59.15 29	87.68 57	73.67 69	87.45 49	86.57 113

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 56	62.82 65	73.55 101	90.56 30	49.80 159	88.24 39	74.02 66	87.03 52	86.32 129

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 58	62.81 67	73.30 106	90.58 27	49.90 156	88.21 40	73.78 68	87.03 52	86.29 133

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 56	60.81 38	82.91 71	85.08 40	62.57 70	73.09 117	89.97 51	50.90 144	87.48 59	75.30 54	86.85 57	87.33 83
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CSCG		76.92 37	76.75 35	77.41 56	83.96 70	59.60 56	82.95 69	86.50 14	60.78 112	75.27 59	84.83 185	60.76 20	86.56 84	67.86 121	87.87 45	86.06 138

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 40	63.18 5	87.55 10	83.45 90	62.44 74	72.68 127	90.50 32	48.18 182	87.34 60	73.59 70	85.71 68	84.76 201

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 36	60.86 36	84.71 40	84.85 47	61.98 89	73.06 118	88.88 68	53.72 91	89.06 29	68.27 109	88.04 41	87.42 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 68	60.46 43	84.75 38	83.34 95	60.22 132	77.85 39	91.42 16	50.67 145	87.69 55	72.46 77	84.53 76	85.46 168

our_new_method		76.90 38	76.58 38	77.87 48	83.99 68	60.46 43	84.75 38	83.34 95	60.22 132	77.85 39	91.42 16	50.67 145	87.69 55	72.46 77	84.53 76	85.46 168

XVS		77.17 35	76.56 40	79.00 26	86.32 31	62.62 11	85.83 27	83.92 63	64.55 26	72.17 135	90.01 50	47.95 184	88.01 46	71.55 91	86.74 59	86.37 122

BridgeMVS		76.58 43	76.55 41	76.68 68	81.73 97	52.90 189	80.94 99	85.70 30	61.12 105	74.90 70	87.17 113	56.46 46	88.14 42	72.87 74	88.03 42	89.00 12

Casviewmamba		76.62 42	76.52 42	76.90 62	77.91 200	53.66 166	80.76 103	84.47 50	66.73 8	75.75 55	88.63 75	59.17 28	86.66 80	72.28 80	83.01 93	90.39 1

MTAPA		76.90 38	76.42 43	78.35 39	86.08 39	63.57 2	74.92 258	80.97 161	65.13 16	75.77 53	90.88 23	48.63 177	86.66 80	77.23 31	88.17 37	84.81 198

casdiffmvs_mvg		76.14 51	76.30 44	75.66 89	76.46 261	51.83 221	79.67 122	85.08 40	65.02 20	75.84 52	88.58 76	59.42 27	85.08 128	72.75 75	83.93 84	90.08 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
train_agg		76.27 48	76.15 45	76.64 71	85.58 45	61.59 24	81.62 91	81.26 149	55.86 238	74.93 67	88.81 69	53.70 92	84.68 140	75.24 56	88.33 34	83.65 245

reproduce_model		76.43 46	76.08 46	77.49 55	83.47 76	60.09 47	84.60 42	82.90 115	59.65 146	77.31 42	91.43 15	49.62 162	87.24 61	71.99 85	83.75 88	85.14 184

PGM-MVS		76.77 41	76.06 47	78.88 32	86.14 37	62.73 9	82.55 78	83.74 78	61.71 91	72.45 133	90.34 38	48.48 180	88.13 43	72.32 79	86.85 57	85.78 150

CS-MVS		76.25 50	75.98 48	77.06 61	80.15 130	55.63 132	84.51 44	83.90 65	63.24 53	73.30 106	87.27 105	55.06 69	86.30 96	71.78 88	84.58 74	89.25 8

CANet		76.46 45	75.93 49	78.06 43	81.29 106	57.53 97	82.35 80	83.31 98	67.78 3	70.09 166	86.34 144	54.92 72	88.90 31	72.68 76	84.55 75	87.76 60

mPP-MVS		76.54 44	75.93 49	78.34 40	86.47 28	63.50 3	85.74 30	82.28 124	62.90 62	71.77 140	90.26 40	46.61 208	86.55 87	71.71 89	85.66 69	84.97 193

EC-MVSNet		75.84 55	75.87 51	75.74 87	78.86 161	52.65 198	83.73 61	86.08 20	63.47 46	72.77 126	87.25 110	53.13 101	87.93 48	71.97 86	85.57 70	86.66 110

NormalMVS		76.26 49	75.74 52	77.83 50	82.75 86	59.89 52	84.36 46	83.21 103	64.69 23	74.21 85	87.40 98	49.48 163	86.17 99	68.04 118	87.55 47	87.42 74

SR-MVS		76.13 52	75.70 53	77.40 58	85.87 42	61.20 29	85.52 33	82.19 125	59.99 138	75.10 63	90.35 37	47.66 189	86.52 88	71.64 90	82.99 95	84.47 210

CDPH-MVS		76.31 47	75.67 54	78.22 41	85.35 50	59.14 71	81.31 96	84.02 59	56.32 229	74.05 87	88.98 64	53.34 97	87.92 49	69.23 104	88.42 32	87.59 68

MVSMamba_PlusPlus		75.75 57	75.44 55	76.67 69	80.84 114	53.06 186	78.62 140	85.13 39	59.65 146	71.53 146	87.47 96	56.92 42	88.17 41	72.18 83	86.63 62	88.80 16

PHI-MVS		75.87 54	75.36 56	77.41 56	80.62 121	55.91 125	84.28 50	85.78 27	56.08 236	73.41 102	86.58 135	50.94 143	88.54 34	70.79 96	89.71 17	87.79 59

ACMMP		76.02 53	75.33 57	78.07 42	85.20 54	61.91 20	85.49 35	84.44 52	63.04 59	69.80 176	89.74 56	45.43 222	87.16 67	72.01 84	82.87 101	85.14 184
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
SPE-MVS-test		75.62 58	75.31 58	76.56 73	80.63 120	55.13 143	83.88 59	85.22 36	62.05 86	71.49 147	86.03 155	53.83 87	86.36 94	67.74 123	86.91 56	88.19 42

dcpmvs_2		74.55 72	75.23 59	72.48 200	82.34 89	53.34 178	77.87 167	81.46 138	57.80 193	75.49 56	86.81 121	62.22 15	77.75 325	71.09 94	82.02 110	86.34 124

fmvsm_s_conf0.5_n_9		75.16 61	75.22 60	75.01 102	78.34 183	55.37 140	77.30 189	73.95 321	61.40 97	79.46 25	90.14 42	57.07 41	81.15 234	80.00 5	79.31 157	88.51 31

hybridcas		74.86 64	75.07 61	74.24 129	76.30 262	50.58 244	79.30 128	83.88 68	63.15 57	74.69 76	88.13 80	58.91 30	82.98 178	68.30 108	82.93 98	89.15 11

DPM-MVS		75.47 59	75.00 62	76.88 63	81.38 105	59.16 67	79.94 115	85.71 29	56.59 222	72.46 131	86.76 122	56.89 43	87.86 51	66.36 144	88.91 29	83.64 246

sasdasda		74.67 68	74.98 63	73.71 158	78.94 159	50.56 247	80.23 108	83.87 69	60.30 129	77.15 44	86.56 136	59.65 22	82.00 213	66.01 148	82.12 107	88.58 29

canonicalmvs		74.67 68	74.98 63	73.71 158	78.94 159	50.56 247	80.23 108	83.87 69	60.30 129	77.15 44	86.56 136	59.65 22	82.00 213	66.01 148	82.12 107	88.58 29

casdiffmvs		74.80 65	74.89 65	74.53 119	75.59 276	50.37 253	78.17 157	85.06 42	62.80 68	74.40 81	87.86 89	57.88 34	83.61 161	69.46 103	82.79 103	89.59 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
baseline		74.61 70	74.70 66	74.34 124	75.70 271	49.99 264	77.54 179	84.63 49	62.73 69	73.98 88	87.79 92	57.67 37	83.82 157	69.49 101	82.74 104	89.20 10

SymmetryMVS		75.28 60	74.60 67	77.30 59	83.85 71	59.89 52	84.36 46	75.51 288	64.69 23	74.21 85	87.40 98	49.48 163	86.17 99	68.04 118	83.88 85	85.85 147

3Dnovator+	66.72 4	75.84 55	74.57 68	79.66 9	82.40 88	59.92 51	85.83 27	86.32 18	66.92 7	67.80 224	89.24 61	42.03 261	89.38 25	64.07 165	86.50 63	89.69 4

DELS-MVS		74.76 66	74.46 69	75.65 90	77.84 203	52.25 210	75.59 240	84.17 57	63.76 41	73.15 112	82.79 237	59.58 25	86.80 76	67.24 132	86.04 67	87.89 52
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
fmvsm_s_conf0.5_n_8		74.30 75	74.39 70	74.01 143	75.33 283	52.89 191	78.24 149	77.32 246	61.65 92	78.13 35	88.90 67	52.82 106	81.54 224	78.46 22	78.67 179	87.60 67

fmvsm_s_conf0.5_n_3		73.55 91	74.39 70	71.03 254	74.09 321	51.86 220	77.77 173	75.60 284	61.18 103	78.67 32	88.98 64	55.88 64	77.73 326	78.69 16	78.68 178	83.50 250

APD-MVS_3200maxsize		74.96 62	74.39 70	76.67 69	82.20 90	58.24 87	83.67 62	83.29 99	58.41 176	73.71 97	90.14 42	45.62 215	85.99 106	69.64 100	82.85 102	85.78 150

OPM-MVS		74.73 67	74.25 73	76.19 78	80.81 115	59.01 77	82.60 77	83.64 84	63.74 42	72.52 130	87.49 95	47.18 199	85.88 109	69.47 102	80.78 123	83.66 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TSAR-MVS + GP.		74.90 63	74.15 74	77.17 60	82.00 93	58.77 82	81.80 88	78.57 211	58.58 172	74.32 83	84.51 201	55.94 63	87.22 64	67.11 134	84.48 79	85.52 164

E5new		74.10 78	74.09 75	74.15 135	77.14 232	50.74 237	78.24 149	83.86 72	62.34 76	73.95 90	87.27 105	55.97 61	82.95 181	68.16 114	79.86 141	88.77 19

E6new		74.10 78	74.09 75	74.15 135	77.14 232	50.74 237	78.24 149	83.85 74	62.34 76	73.95 90	87.27 105	55.98 59	82.95 181	68.17 112	79.85 143	88.77 19

E6		74.10 78	74.09 75	74.15 135	77.14 232	50.74 237	78.24 149	83.85 74	62.34 76	73.95 90	87.27 105	55.98 59	82.95 181	68.17 112	79.85 143	88.77 19

E5		74.10 78	74.09 75	74.15 135	77.14 232	50.74 237	78.24 149	83.86 72	62.34 76	73.95 90	87.27 105	55.97 61	82.95 181	68.16 114	79.86 141	88.77 19

alignmvs		73.86 85	73.99 79	73.45 172	78.20 187	50.50 249	78.57 142	82.43 122	59.40 154	76.57 49	86.71 128	56.42 48	81.23 233	65.84 151	81.79 113	88.62 26

fmvsm_s_conf0.5_n_10		74.11 77	73.98 80	74.48 121	74.61 303	52.86 193	78.10 161	77.06 251	57.14 203	78.24 34	88.79 72	52.83 105	82.26 208	77.79 28	81.30 119	88.32 35

SR-MVS-dyc-post		74.57 71	73.90 81	76.58 72	83.49 74	59.87 54	84.29 48	81.36 142	58.07 182	73.14 113	90.07 44	44.74 232	85.84 110	68.20 110	81.76 114	84.03 222

MG-MVS		73.96 83	73.89 82	74.16 133	85.65 44	49.69 274	81.59 93	81.29 148	61.45 96	71.05 152	88.11 81	51.77 127	87.73 54	61.05 205	83.09 92	85.05 189

ETV-MVS		74.46 73	73.84 83	76.33 76	79.27 148	55.24 142	79.22 129	85.00 45	64.97 22	72.65 128	79.46 321	53.65 95	87.87 50	67.45 131	82.91 99	85.89 144

E4		73.91 84	73.83 84	74.15 135	77.13 236	50.47 250	77.15 196	83.79 77	62.21 81	73.61 98	87.19 112	56.08 57	83.03 173	67.91 120	79.35 155	88.94 14

HQP_MVS		74.31 74	73.73 85	76.06 79	81.41 103	56.31 114	84.22 51	84.01 60	64.52 28	69.27 185	86.10 152	45.26 226	87.21 65	68.16 114	80.58 129	84.65 202

RE-MVS-def			73.71 86		83.49 74	59.87 54	84.29 48	81.36 142	58.07 182	73.14 113	90.07 44	43.06 251		68.20 110	81.76 114	84.03 222

fmvsm_l_conf0.5_n_9		73.27 98	73.66 87	72.09 209	73.82 322	52.72 197	77.45 183	74.28 314	56.61 221	77.10 46	88.16 79	56.17 52	77.09 341	78.27 24	81.13 121	86.48 117

E2		73.72 88	73.60 88	74.06 140	77.16 230	50.40 251	76.97 201	83.74 78	61.64 93	73.36 103	86.75 125	56.14 53	82.99 175	67.50 129	79.18 165	88.80 16

E3		73.72 88	73.60 88	74.06 140	77.16 230	50.40 251	76.97 201	83.74 78	61.64 93	73.36 103	86.76 122	56.13 54	82.99 175	67.50 129	79.18 165	88.80 16

MSLP-MVS++		73.77 86	73.47 90	74.66 111	83.02 81	59.29 63	82.30 85	81.88 129	59.34 156	71.59 144	86.83 120	45.94 213	83.65 160	65.09 157	85.22 71	81.06 313

HPM-MVS_fast		74.30 75	73.46 91	76.80 65	84.45 66	59.04 76	83.65 63	81.05 157	60.15 134	70.43 161	89.84 53	41.09 284	85.59 115	67.61 127	82.90 100	85.77 153

MVS_111021_HR		74.02 82	73.46 91	75.69 88	83.01 82	60.63 40	77.29 190	78.40 222	61.18 103	70.58 159	85.97 158	54.18 80	84.00 154	67.52 128	82.98 97	82.45 280

viewcassd2359sk11		73.56 90	73.41 93	74.00 144	77.13 236	50.35 254	76.86 209	83.69 82	61.23 102	73.14 113	86.38 143	56.09 56	82.96 179	67.15 133	79.01 170	88.70 25

fmvsm_s_conf0.5_n_11		73.16 100	73.35 94	72.58 194	75.48 278	52.41 209	78.84 134	76.85 256	58.64 170	73.58 100	87.25 110	54.09 82	79.47 275	76.19 45	79.27 158	85.86 146

MGCFI-Net		72.45 118	73.34 95	69.81 281	77.77 205	43.21 368	75.84 237	81.18 153	59.59 151	75.45 57	86.64 129	57.74 35	77.94 317	63.92 169	81.90 112	88.30 36

casdiffseed414692147		73.73 87	73.22 96	75.28 99	76.76 252	52.16 212	80.05 112	83.01 112	63.38 47	73.35 105	87.11 114	53.22 98	84.14 148	61.71 199	80.38 134	89.55 6

E3new		73.41 94	73.22 96	73.95 147	77.06 241	50.31 255	76.78 212	83.66 83	60.90 108	72.93 121	86.02 156	55.99 58	82.95 181	66.89 141	78.77 175	88.61 27

viewmacassd2359aftdt		73.15 101	73.16 98	73.11 181	75.15 289	49.31 281	77.53 181	83.21 103	60.42 121	73.20 110	87.34 102	53.82 88	81.05 239	67.02 138	80.79 122	88.96 13

fmvsm_l_conf0.5_n_3		73.23 99	73.13 99	73.55 168	74.40 310	55.13 143	78.97 132	74.96 303	56.64 215	74.76 75	88.75 73	55.02 70	78.77 305	76.33 42	78.31 189	86.74 105

nrg030		72.96 106	73.01 100	72.84 188	75.41 281	50.24 256	80.02 113	82.89 117	58.36 178	74.44 80	86.73 126	58.90 31	80.83 247	65.84 151	74.46 250	87.44 73

UA-Net		73.13 102	72.93 101	73.76 153	83.58 73	51.66 223	78.75 135	77.66 235	67.75 4	72.61 129	89.42 57	49.82 158	83.29 168	53.61 273	83.14 91	86.32 129

viewmanbaseed2359cas		72.92 107	72.89 102	73.00 183	75.16 287	49.25 284	77.25 193	83.11 111	59.52 153	72.93 121	86.63 131	54.11 81	80.98 240	66.63 142	80.67 126	88.76 24

fmvsm_s_conf0.5_n_6		72.59 115	72.87 103	71.73 220	75.14 290	51.96 218	76.28 222	77.12 249	57.63 197	73.85 95	86.91 118	51.54 131	77.87 322	77.18 33	80.18 139	85.37 176

fmvsm_s_conf0.5_n_5		72.69 112	72.80 104	72.37 205	74.11 320	53.21 182	78.12 158	73.31 328	53.98 291	76.81 48	88.05 84	53.38 96	77.37 336	76.64 39	80.78 123	86.53 115

HQP-MVS		73.45 92	72.80 104	75.40 94	80.66 117	54.94 145	82.31 82	83.90 65	62.10 83	67.85 218	85.54 175	45.46 220	86.93 73	67.04 136	80.35 135	84.32 212

CLD-MVS		73.33 96	72.68 106	75.29 98	78.82 163	53.33 179	78.23 154	84.79 48	61.30 100	70.41 163	81.04 287	52.41 113	87.12 68	64.61 163	82.49 106	85.41 174
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
fmvsm_l_mol_unc0.5_1		72.30 122	72.61 107	71.37 239	72.96 339	48.16 305	72.91 306	64.68 409	58.47 175	81.24 22	91.38 18	56.26 50	79.00 298	72.19 81	83.35 90	86.95 95

test_fmvsmconf_n		73.01 104	72.59 108	74.27 127	71.28 377	55.88 126	78.21 156	75.56 286	54.31 286	74.86 71	87.80 91	54.72 74	80.23 263	78.07 26	78.48 184	86.70 106

balanced_ft_v1		72.98 105	72.55 109	74.27 127	79.52 142	50.64 242	77.78 172	83.29 99	56.76 212	67.88 217	85.95 159	49.42 166	85.29 126	68.64 106	83.76 87	86.87 98

Effi-MVS+		73.31 97	72.54 110	75.62 91	77.87 201	53.64 167	79.62 124	79.61 183	61.63 95	72.02 138	82.61 242	56.44 47	85.97 107	63.99 168	79.07 168	87.25 85

MVS_Test		72.45 118	72.46 111	72.42 204	74.88 292	48.50 299	76.28 222	83.14 109	59.40 154	72.46 131	84.68 190	55.66 65	81.12 235	65.98 150	79.66 148	87.63 65

viewdifsd2359ckpt09		73.42 93	72.45 112	76.30 77	77.25 228	53.27 180	80.36 107	82.48 121	57.96 187	72.24 134	85.73 168	53.22 98	86.27 97	63.79 175	79.06 169	89.36 7

test_fmvsmconf0.1_n		72.81 108	72.33 113	74.24 129	69.89 403	55.81 127	78.22 155	75.40 291	54.17 288	75.00 66	88.03 87	53.82 88	80.23 263	78.08 25	78.34 188	86.69 107

BP-MVS1		73.41 94	72.25 114	76.88 63	76.68 254	53.70 164	79.15 130	81.07 156	60.66 115	71.81 139	87.39 100	40.93 285	87.24 61	71.23 93	81.29 120	89.71 3

EPNet		73.09 103	72.16 115	75.90 81	75.95 268	56.28 116	83.05 67	72.39 340	66.53 11	65.27 277	87.00 116	50.40 149	85.47 121	62.48 191	86.32 65	85.94 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VDD-MVS		72.50 116	72.09 116	73.75 155	81.58 99	49.69 274	77.76 174	77.63 236	63.21 55	73.21 109	89.02 63	42.14 260	83.32 167	61.72 198	82.50 105	88.25 38

CPTT-MVS		72.78 109	72.08 117	74.87 105	84.88 62	61.41 26	84.15 54	77.86 230	55.27 256	67.51 230	88.08 83	41.93 264	81.85 216	69.04 105	80.01 140	81.35 303

viewdifsd2359ckpt07		71.90 132	71.97 118	71.69 223	74.81 296	48.08 309	75.30 245	80.49 169	60.00 137	71.63 143	86.33 145	56.34 49	79.25 280	65.40 155	77.41 203	87.76 60

fmvsm_s_conf0.5_n_4		72.04 130	71.85 119	72.58 194	73.74 325	52.49 205	76.69 213	72.42 339	56.42 226	75.32 58	87.04 115	52.13 120	78.01 316	79.29 12	73.65 264	87.26 84

PAPM_NR		72.63 114	71.80 120	75.13 100	81.72 98	53.42 177	79.91 117	83.28 101	59.14 158	66.31 255	85.90 161	51.86 124	86.06 103	57.45 238	80.62 127	85.91 143

viewdifsd2359ckpt13		72.40 121	71.79 121	74.22 131	75.63 273	51.77 222	78.67 138	83.13 110	57.08 204	71.59 144	85.36 179	53.10 102	82.64 199	63.07 185	78.51 183	88.24 39

LPG-MVS_test		72.74 110	71.74 122	75.76 85	80.22 125	57.51 98	82.55 78	83.40 92	61.32 98	66.67 248	87.33 103	39.15 305	86.59 82	67.70 125	77.30 208	83.19 258

EI-MVSNet-Vis-set		72.42 120	71.59 123	74.91 103	78.47 176	54.02 158	77.05 199	79.33 189	65.03 19	71.68 142	79.35 324	52.75 107	84.89 135	66.46 143	74.23 254	85.83 149

LFMVS		71.78 134	71.59 123	72.32 206	83.40 77	46.38 328	79.75 120	71.08 349	64.18 35	72.80 125	88.64 74	42.58 256	83.72 158	57.41 239	84.49 78	86.86 99

test_fmvsmconf0.01_n		72.17 126	71.50 125	74.16 133	67.96 435	55.58 135	78.06 162	74.67 307	54.19 287	74.54 79	88.23 77	50.35 151	80.24 262	78.07 26	77.46 202	86.65 111

h-mvs33		72.71 111	71.49 126	76.40 74	81.99 94	59.58 57	76.92 205	76.74 262	60.40 122	74.81 72	85.95 159	45.54 218	85.76 112	70.41 98	70.61 316	83.86 233

FIs		70.82 157	71.43 127	68.98 296	78.33 184	38.14 425	76.96 203	83.59 86	61.02 106	67.33 232	86.73 126	55.07 68	81.64 220	54.61 265	79.22 161	87.14 90

API-MVS		72.17 126	71.41 128	74.45 122	81.95 95	57.22 101	84.03 56	80.38 172	59.89 144	68.40 199	82.33 255	49.64 161	87.83 52	51.87 287	84.16 83	78.30 364

3Dnovator	64.47 5	72.49 117	71.39 129	75.79 84	77.70 208	58.99 78	80.66 105	83.15 108	62.24 80	65.46 273	86.59 134	42.38 259	85.52 117	59.59 218	84.72 73	82.85 268

Vis-MVSNet		72.18 125	71.37 130	74.61 114	81.29 106	55.41 138	80.90 100	78.28 225	60.73 113	69.23 188	88.09 82	44.36 238	82.65 198	57.68 236	81.75 116	85.77 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VDDNet		71.81 133	71.33 131	73.26 179	82.80 85	47.60 319	78.74 136	75.27 293	59.59 151	72.94 120	89.40 58	41.51 277	83.91 155	58.75 230	82.99 95	88.26 37

EPP-MVSNet		72.16 128	71.31 132	74.71 108	78.68 167	49.70 272	82.10 86	81.65 133	60.40 122	65.94 262	85.84 163	51.74 128	86.37 93	55.93 249	79.55 151	88.07 49

GDP-MVS		72.64 113	71.28 133	76.70 66	77.72 207	54.22 156	79.57 125	84.45 51	55.30 255	71.38 148	86.97 117	39.94 291	87.00 72	67.02 138	79.20 162	88.89 15

PS-MVSNAJss		72.24 124	71.21 134	75.31 96	78.50 174	55.93 124	81.63 90	82.12 126	56.24 233	70.02 170	85.68 170	47.05 201	84.34 146	65.27 156	74.41 253	85.67 159

ACMP	63.53 6	72.30 122	71.20 135	75.59 93	80.28 123	57.54 96	82.74 74	82.84 118	60.58 117	65.24 281	86.18 149	39.25 303	86.03 105	66.95 140	76.79 217	83.22 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvsm_n_1920		71.73 136	71.14 136	73.50 169	72.52 348	56.53 113	75.60 239	76.16 271	48.11 389	77.22 43	85.56 171	53.10 102	77.43 333	74.86 58	77.14 210	86.55 114

patch_mono-2		69.85 180	71.09 137	66.16 344	79.11 156	54.80 149	71.97 326	74.31 312	53.50 302	70.90 155	84.17 207	57.63 38	63.31 442	66.17 145	82.02 110	80.38 328

EI-MVSNet-UG-set		71.92 131	71.06 138	74.52 120	77.98 198	53.56 170	76.62 214	79.16 190	64.40 30	71.18 150	78.95 329	52.19 118	84.66 142	65.47 154	73.57 267	85.32 178

PRO-TEST		71.42 143	71.02 139	72.62 193	78.68 167	52.64 200	78.04 163	81.04 158	56.33 228	68.21 202	82.15 264	50.03 153	81.69 219	64.20 164	80.51 132	83.52 249

UniMVSNet_NR-MVSNet		71.11 147	71.00 140	71.44 233	79.20 151	44.13 354	76.02 232	82.60 120	66.48 12	68.20 203	84.60 198	56.82 44	82.82 194	54.62 263	70.43 318	87.36 81

IS-MVSNet		71.57 138	71.00 140	73.27 178	78.86 161	45.63 339	80.22 110	78.69 204	64.14 38	66.46 251	87.36 101	49.30 168	85.60 114	50.26 300	83.71 89	88.59 28

diffmvs_AUTHOR		71.02 149	70.87 142	71.45 232	69.89 403	48.97 290	73.16 301	78.33 224	57.79 194	72.11 137	85.26 180	51.84 125	77.89 321	71.00 95	78.47 186	87.49 71

fmvsm_l_conf0.5_n		70.99 152	70.82 143	71.48 229	71.45 370	54.40 152	77.18 195	70.46 358	48.67 377	75.17 61	86.86 119	53.77 90	76.86 349	76.33 42	77.51 201	83.17 262

PAPR		71.72 137	70.82 143	74.41 123	81.20 110	51.17 226	79.55 126	83.33 97	55.81 241	66.93 242	84.61 195	50.95 142	86.06 103	55.79 252	79.20 162	86.00 139

DP-MVS Recon		72.15 129	70.73 145	76.40 74	86.57 26	57.99 90	81.15 98	82.96 113	57.03 207	66.78 243	85.56 171	44.50 236	88.11 44	51.77 289	80.23 138	83.10 263

RRT-MVS		71.46 141	70.70 146	73.74 156	77.76 206	49.30 282	76.60 215	80.45 170	61.25 101	68.17 205	84.78 187	44.64 234	84.90 134	64.79 159	77.88 195	87.03 92

viewmamba		71.13 146	70.66 147	72.56 196	70.23 394	50.07 261	74.25 273	77.85 231	59.92 139	70.94 153	85.55 173	52.30 116	80.25 261	68.42 107	76.47 222	87.35 82

EIA-MVS		71.78 134	70.60 148	75.30 97	79.85 134	53.54 171	77.27 192	83.26 102	57.92 189	66.49 250	79.39 322	52.07 121	86.69 79	60.05 212	79.14 167	85.66 160

OMC-MVS		71.40 144	70.60 148	73.78 151	76.60 257	53.15 183	79.74 121	79.78 179	58.37 177	68.75 193	86.45 141	45.43 222	80.60 251	62.58 189	77.73 196	87.58 69

FC-MVSNet-test		69.80 183	70.58 150	67.46 319	77.61 217	34.73 459	76.05 230	83.19 107	60.84 110	65.88 266	86.46 140	54.52 77	80.76 250	52.52 280	78.12 191	86.91 96

diffmvs		70.69 159	70.43 151	71.46 230	69.45 410	48.95 291	72.93 304	78.46 217	57.27 201	71.69 141	83.97 214	51.48 133	77.92 320	70.70 97	77.95 194	87.53 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		71.45 142	70.39 152	74.65 112	82.01 92	58.82 81	79.93 116	80.35 173	55.09 262	65.82 269	82.16 263	49.17 171	82.64 199	60.34 210	78.62 181	82.50 279

test_fmvsmvis_n_1920		70.84 154	70.38 153	72.22 208	71.16 378	55.39 139	75.86 235	72.21 342	49.03 372	73.28 108	86.17 150	51.83 126	77.29 338	75.80 47	78.05 192	83.98 225

MVSFormer		71.50 140	70.38 153	74.88 104	78.76 164	57.15 106	82.79 72	78.48 215	51.26 342	69.49 179	83.22 232	43.99 242	83.24 169	66.06 146	79.37 152	84.23 216

onestephybrid01		71.00 151	70.34 155	72.99 184	70.38 391	50.88 234	74.14 276	77.41 241	58.80 164	71.36 149	84.93 182	50.96 141	80.87 246	67.73 124	77.35 204	87.23 86

fmvsm_l_conf0.5_n_a		70.50 163	70.27 156	71.18 246	71.30 376	54.09 157	76.89 206	69.87 362	47.90 393	74.37 82	86.49 139	53.07 104	76.69 355	75.41 53	77.11 211	82.76 269

UniMVSNet (Re)		70.63 160	70.20 157	71.89 213	78.55 173	45.29 342	75.94 233	82.92 114	63.68 43	68.16 206	83.59 223	53.89 86	83.49 165	53.97 269	71.12 309	86.89 97

VNet		69.68 187	70.19 158	68.16 309	79.73 136	41.63 390	70.53 351	77.38 243	60.37 125	70.69 156	86.63 131	51.08 139	77.09 341	53.61 273	81.69 118	85.75 155

KinetiMVS		71.26 145	70.16 159	74.57 117	74.59 304	52.77 196	75.91 234	81.20 152	60.72 114	69.10 191	85.71 169	41.67 272	83.53 163	63.91 171	78.62 181	87.42 74

GeoE		71.01 150	70.15 160	73.60 166	79.57 140	52.17 211	78.93 133	78.12 227	58.02 184	67.76 227	83.87 215	52.36 114	82.72 196	56.90 241	75.79 234	85.92 142

MAR-MVS		71.51 139	70.15 160	75.60 92	81.84 96	59.39 60	81.38 95	82.90 115	54.90 274	68.08 213	78.70 330	47.73 187	85.51 118	51.68 291	84.17 82	81.88 291
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
TranMVSNet+NR-MVSNet		70.36 167	70.10 162	71.17 248	78.64 172	42.97 375	76.53 217	81.16 155	66.95 6	68.53 197	85.42 177	51.61 130	83.07 172	52.32 281	69.70 340	87.46 72

hse-mvs2		71.04 148	69.86 163	74.60 115	79.58 139	57.12 108	73.96 279	75.25 294	60.40 122	74.81 72	81.95 269	45.54 218	82.90 187	70.41 98	66.83 373	83.77 238

xiu_mvs_v2_base		70.52 161	69.75 164	72.84 188	81.21 109	55.63 132	75.11 251	78.92 197	54.92 273	69.96 173	79.68 316	47.00 205	82.09 211	61.60 201	79.37 152	80.81 318

ACMM	61.98 7	70.80 158	69.73 165	74.02 142	80.59 122	58.59 84	82.68 75	82.02 128	55.46 251	67.18 237	84.39 204	38.51 314	83.17 171	60.65 208	76.10 230	80.30 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-MVSNAJ		70.51 162	69.70 166	72.93 186	81.52 100	55.79 128	74.92 258	79.00 195	55.04 268	69.88 174	78.66 332	47.05 201	82.19 209	61.61 200	79.58 149	80.83 317

fmvsm_s_conf0.5_n_7		69.54 193	69.67 167	69.15 295	73.47 330	51.41 225	70.35 355	73.34 327	57.05 206	68.41 198	85.83 164	49.86 157	72.84 379	71.86 87	76.83 216	83.19 258

114514_t		70.83 156	69.56 168	74.64 113	86.21 33	54.63 150	82.34 81	81.81 131	48.22 386	63.01 317	85.83 164	40.92 286	87.10 69	57.91 235	79.79 145	82.18 285

DU-MVS		70.01 175	69.53 169	71.44 233	78.05 195	44.13 354	75.01 254	81.51 137	64.37 31	68.20 203	84.52 199	49.12 174	82.82 194	54.62 263	70.43 318	87.37 79

PCF-MVS	61.88 8	70.95 153	69.49 170	75.35 95	77.63 212	55.71 129	76.04 231	81.81 131	50.30 355	69.66 177	85.40 178	52.51 110	84.89 135	51.82 288	80.24 137	85.45 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPA-MVSNet		69.02 209	69.47 171	67.69 315	77.42 222	41.00 397	74.04 277	79.68 181	60.06 135	69.26 187	84.81 186	51.06 140	77.58 331	54.44 266	74.43 252	84.48 209

v2v482		70.50 163	69.45 172	73.66 161	72.62 345	50.03 263	77.58 176	80.51 168	59.90 140	69.52 178	82.14 265	47.53 192	84.88 137	65.07 158	70.17 327	86.09 137

hybridnocas07		69.86 179	69.44 173	71.14 250	68.10 433	48.28 302	72.52 315	77.08 250	56.94 209	70.50 160	84.91 184	50.48 148	78.37 309	67.84 122	76.55 221	86.76 104

SSM_0404		70.84 154	69.41 174	75.12 101	79.20 151	53.86 160	77.89 166	80.00 177	53.88 293	69.40 182	84.61 195	43.21 248	86.56 84	58.80 228	77.68 198	84.95 194

v1144		70.42 165	69.31 175	73.76 153	73.22 332	50.64 242	77.83 170	81.43 139	58.58 172	69.40 182	81.16 284	47.53 192	85.29 126	64.01 167	70.64 314	85.34 177

v8		70.33 168	69.28 176	73.49 170	73.15 334	50.22 257	78.62 140	80.78 164	60.79 111	66.45 252	82.11 267	49.35 167	84.98 131	63.58 178	68.71 356	85.28 180

fmvsm_s_conf0.5_n_2		69.82 181	69.27 177	71.46 230	72.00 360	51.08 227	73.30 294	67.79 381	55.06 267	75.24 60	87.51 94	44.02 241	77.00 345	75.67 49	72.86 282	86.31 132

test_yl		69.69 185	69.13 178	71.36 240	78.37 181	45.74 335	74.71 262	80.20 174	57.91 190	70.01 171	83.83 216	42.44 257	82.87 190	54.97 259	79.72 146	85.48 166

DCV-MVSNet		69.69 185	69.13 178	71.36 240	78.37 181	45.74 335	74.71 262	80.20 174	57.91 190	70.01 171	83.83 216	42.44 257	82.87 190	54.97 259	79.72 146	85.48 166

Fast-Effi-MVS+		70.28 169	69.12 180	73.73 157	78.50 174	51.50 224	75.01 254	79.46 187	56.16 235	68.59 194	79.55 319	53.97 84	84.05 150	53.34 275	77.53 200	85.65 161

Anonymous20240529		69.91 178	69.02 181	72.56 196	80.19 128	47.65 316	77.56 178	80.99 160	55.45 252	69.88 174	86.76 122	39.24 304	82.18 210	54.04 268	77.10 212	87.85 55

v10		70.21 170	69.02 181	73.81 150	73.51 328	50.92 232	78.74 136	81.39 140	60.05 136	66.39 253	81.83 272	47.58 191	85.41 124	62.80 188	68.86 355	85.09 188

fmvsm_s_conf0.1_n_2		69.64 189	69.01 183	71.52 228	71.66 365	51.04 228	73.39 293	67.14 387	55.02 271	75.11 62	87.64 93	42.94 253	77.01 344	75.55 51	72.63 288	86.52 116

SSM_0407		70.41 166	68.96 184	74.75 107	78.65 169	53.46 173	77.28 191	80.00 177	53.88 293	68.14 207	84.61 195	43.21 248	86.26 98	58.80 228	76.11 227	84.54 204

hybrid		69.38 200	68.93 185	70.75 260	67.86 437	48.20 304	72.49 317	76.90 254	55.23 258	70.42 162	84.34 205	49.76 160	77.62 330	67.11 134	76.20 225	86.42 119

NR-MVSNet		69.54 193	68.85 186	71.59 227	78.05 195	43.81 359	74.20 274	80.86 163	65.18 15	62.76 321	84.52 199	52.35 115	83.59 162	50.96 296	70.78 313	87.37 79

fmvsm_s_conf0.5_n		69.58 191	68.84 187	71.79 218	72.31 356	52.90 189	77.90 165	62.43 435	49.97 360	72.85 124	85.90 161	52.21 117	76.49 358	75.75 48	70.26 325	85.97 140

QAPM		70.05 174	68.81 188	73.78 151	76.54 259	53.43 176	83.23 65	83.48 88	52.89 310	65.90 264	86.29 146	41.55 276	86.49 90	51.01 294	78.40 187	81.42 297

MVS_111021_LR		69.50 196	68.78 189	71.65 225	78.38 179	59.33 61	74.82 260	70.11 360	58.08 181	67.83 223	84.68 190	41.96 262	76.34 362	65.62 153	77.54 199	79.30 352

fmvsm_s_conf0.5_n_a		69.54 193	68.74 190	71.93 212	72.47 350	53.82 162	78.25 148	62.26 437	49.78 362	73.12 116	86.21 148	52.66 108	76.79 351	75.02 57	68.88 353	85.18 183

v1192		69.97 177	68.68 191	73.85 148	73.19 333	50.94 230	77.68 175	81.36 142	57.51 199	68.95 192	80.85 294	45.28 225	85.33 125	62.97 187	70.37 320	85.27 181

AdaColmap		69.99 176	68.66 192	73.97 146	84.94 59	57.83 92	82.63 76	78.71 203	56.28 232	64.34 296	84.14 208	41.57 274	87.06 71	46.45 339	78.88 171	77.02 385

fmvsm_s_conf0.1_n		69.41 199	68.60 193	71.83 215	71.07 379	52.88 192	77.85 169	62.44 434	49.58 365	72.97 119	86.22 147	51.68 129	76.48 359	75.53 52	70.10 329	86.14 135

v144192		69.71 184	68.51 194	73.33 177	73.10 335	50.13 259	77.54 179	80.64 165	56.65 214	68.57 196	80.55 297	46.87 206	84.96 133	62.98 186	69.66 341	84.89 196

FA-MVS(test-final)		69.82 181	68.48 195	73.84 149	78.44 177	50.04 262	75.58 242	78.99 196	58.16 180	67.59 228	82.14 265	42.66 254	85.63 113	56.60 242	76.19 226	85.84 148

IterMVS-LS		69.22 205	68.48 195	71.43 235	74.44 309	49.40 278	76.23 224	77.55 237	59.60 148	65.85 267	81.59 279	51.28 136	81.58 223	59.87 216	69.90 334	83.30 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20231211		69.28 202	68.47 197	71.73 220	80.28 123	47.18 323	79.98 114	82.37 123	54.61 279	67.24 235	84.01 212	39.43 298	82.41 206	55.45 257	72.83 283	85.62 162

WR-MVS		68.47 225	68.47 197	68.44 304	80.20 127	39.84 407	73.75 287	76.07 274	64.68 25	68.11 211	83.63 222	50.39 150	79.14 287	49.78 301	69.66 341	86.34 124

fmvsm_s_conf0.1_n_a		69.32 201	68.44 199	71.96 210	70.91 381	53.78 163	78.12 158	62.30 436	49.35 368	73.20 110	86.55 138	51.99 122	76.79 351	74.83 59	68.68 358	85.32 178

EI-MVSNet		69.27 203	68.44 199	71.73 220	74.47 307	49.39 279	75.20 249	78.45 218	59.60 148	69.16 189	76.51 379	51.29 135	82.50 203	59.86 217	71.45 306	83.30 253

jason		69.65 188	68.39 201	73.43 174	78.27 186	56.88 110	77.12 197	73.71 324	46.53 413	69.34 184	83.22 232	43.37 246	79.18 282	64.77 160	79.20 162	84.23 216
jason: jason.
viewdifsd2359ckpt11		69.13 206	68.38 202	71.38 237	71.57 367	48.61 296	73.22 299	73.18 331	57.65 195	70.67 157	84.73 188	50.03 153	79.80 267	63.25 181	71.10 310	85.74 156

viewmsd2359difaftdt		69.13 206	68.38 202	71.38 237	71.57 367	48.61 296	73.22 299	73.18 331	57.65 195	70.67 157	84.73 188	50.03 153	79.80 267	63.25 181	71.10 310	85.74 156

Elysia		70.19 172	68.29 204	75.88 82	74.15 317	54.33 154	78.26 146	83.21 103	55.04 268	67.28 233	83.59 223	30.16 412	86.11 101	63.67 176	79.26 159	87.20 87

StellarMVS		70.19 172	68.29 204	75.88 82	74.15 317	54.33 154	78.26 146	83.21 103	55.04 268	67.28 233	83.59 223	30.16 412	86.11 101	63.67 176	79.26 159	87.20 87

lupinMVS		69.57 192	68.28 206	73.44 173	78.76 164	57.15 106	76.57 216	73.29 330	46.19 416	69.49 179	82.18 260	43.99 242	79.23 281	64.66 161	79.37 152	83.93 227

viewmambaseed2359dif		68.91 211	68.18 207	71.11 251	70.21 395	48.05 312	72.28 321	75.90 277	51.96 326	70.93 154	84.47 202	51.37 134	78.59 307	61.55 203	74.97 245	86.68 108

v1921920		69.47 197	68.17 208	73.36 176	73.06 336	50.10 260	77.39 184	80.56 166	56.58 223	68.59 194	80.37 299	44.72 233	84.98 131	62.47 192	69.82 335	85.00 190

IMVS_0403		69.09 208	68.14 209	71.95 211	77.06 241	49.73 268	74.51 266	78.60 207	52.70 312	66.69 246	82.58 243	46.43 209	83.38 166	59.20 223	75.46 240	82.74 270

VPNet		67.52 249	68.11 210	65.74 354	79.18 153	36.80 440	72.17 323	72.83 336	62.04 87	67.79 225	85.83 164	48.88 176	76.60 357	51.30 292	72.97 281	83.81 234

SDMVSNet		68.03 236	68.10 211	67.84 311	77.13 236	48.72 295	65.32 408	79.10 191	58.02 184	65.08 284	82.55 248	47.83 186	73.40 376	63.92 169	73.92 258	81.41 298

IMVS_0407		68.90 212	67.93 212	71.82 216	77.06 241	49.73 268	74.40 271	78.60 207	52.70 312	66.19 256	82.58 243	45.17 228	83.00 174	59.20 223	75.46 240	82.74 270

v1240		69.24 204	67.91 213	73.25 180	73.02 338	49.82 266	77.21 194	80.54 167	56.43 225	68.34 201	80.51 298	43.33 247	84.99 129	62.03 196	69.77 338	84.95 194

dtuplus		68.48 224	67.76 214	70.63 264	70.33 393	48.09 308	72.62 311	75.88 279	52.33 320	71.09 151	84.66 192	50.09 152	77.93 319	58.02 234	74.82 248	85.87 145

test_djsdf		69.45 198	67.74 215	74.58 116	74.57 306	54.92 147	82.79 72	78.48 215	51.26 342	65.41 274	83.49 228	38.37 316	83.24 169	66.06 146	69.25 348	85.56 163

PVSNet_BlendedMVS		68.56 223	67.72 216	71.07 253	77.03 247	50.57 245	74.50 267	81.52 135	53.66 301	64.22 302	79.72 315	49.13 172	82.87 190	55.82 250	73.92 258	79.77 347

PVSNet_Blended		68.59 219	67.72 216	71.19 245	77.03 247	50.57 245	72.51 316	81.52 135	51.91 327	64.22 302	77.77 356	49.13 172	82.87 190	55.82 250	79.58 149	80.14 337

CANet_DTU		68.18 233	67.71 218	69.59 284	74.83 295	46.24 330	78.66 139	76.85 256	59.60 148	63.45 308	82.09 268	35.25 352	77.41 334	59.88 215	78.76 176	85.14 184

c3_l		68.33 228	67.56 219	70.62 265	70.87 382	46.21 331	74.47 268	78.80 201	56.22 234	66.19 256	78.53 337	51.88 123	81.40 227	62.08 193	69.04 351	84.25 215

Baseline_NR-MVSNet		67.05 260	67.56 219	65.50 358	75.65 272	37.70 431	75.42 243	74.65 308	59.90 140	68.14 207	83.15 235	49.12 174	77.20 339	52.23 282	69.78 336	81.60 293

OpenMVS	61.03 9	68.85 213	67.56 219	72.70 192	74.26 315	53.99 159	81.21 97	81.34 146	52.70 312	62.75 322	85.55 173	38.86 309	84.14 148	48.41 316	83.01 93	79.97 339

Effi-MVS+-dtu		69.64 189	67.53 222	75.95 80	76.10 266	62.29 15	80.20 111	76.06 275	59.83 145	65.26 280	77.09 366	41.56 275	84.02 153	60.60 209	71.09 312	81.53 296

ECVR-MVS		67.72 246	67.51 223	68.35 305	79.46 143	36.29 448	74.79 261	66.93 389	58.72 166	67.19 236	88.05 84	36.10 344	81.38 228	52.07 284	84.25 80	87.39 77

mvs_anonymous		68.03 236	67.51 223	69.59 284	72.08 358	44.57 351	71.99 325	75.23 295	51.67 329	67.06 239	82.57 247	54.68 75	77.94 317	56.56 245	75.71 236	86.26 134

XVG-OURS-SEG-HR		68.81 214	67.47 225	72.82 190	74.40 310	56.87 111	70.59 350	79.04 194	54.77 276	66.99 240	86.01 157	39.57 297	78.21 313	62.54 190	73.33 274	83.37 252

BH-RMVSNet		68.81 214	67.42 226	72.97 185	80.11 131	52.53 203	74.26 272	76.29 270	58.48 174	68.38 200	84.20 206	42.59 255	83.83 156	46.53 338	75.91 232	82.56 274

UGNet		68.81 214	67.39 227	73.06 182	78.33 184	54.47 151	79.77 119	75.40 291	60.45 120	63.22 310	84.40 203	32.71 388	80.91 245	51.71 290	80.56 131	83.81 234
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
XVG-OURS		68.76 217	67.37 228	72.90 187	74.32 313	57.22 101	70.09 359	78.81 200	55.24 257	67.79 225	85.81 167	36.54 340	78.28 312	62.04 195	75.74 235	83.19 258

v7n		69.01 210	67.36 229	73.98 145	72.51 349	52.65 198	78.54 144	81.30 147	60.26 131	62.67 323	81.62 276	43.61 244	84.49 143	57.01 240	68.70 357	84.79 199

V42		68.65 218	67.35 230	72.56 196	68.93 420	50.18 258	72.90 307	79.47 186	56.92 210	69.45 181	80.26 303	46.29 211	82.99 175	64.07 165	67.82 364	84.53 207

BH-untuned		68.27 229	67.29 231	71.21 244	79.74 135	53.22 181	76.06 229	77.46 240	57.19 202	66.10 259	81.61 277	45.37 224	83.50 164	45.42 358	76.68 219	76.91 389

xiu_mvs_v1_base_debu		68.58 220	67.28 232	72.48 200	78.19 188	57.19 103	75.28 246	75.09 299	51.61 331	70.04 167	81.41 281	32.79 384	79.02 295	63.81 172	77.31 205	81.22 306

xiu_mvs_v1_base		68.58 220	67.28 232	72.48 200	78.19 188	57.19 103	75.28 246	75.09 299	51.61 331	70.04 167	81.41 281	32.79 384	79.02 295	63.81 172	77.31 205	81.22 306

xiu_mvs_v1_base_debi		68.58 220	67.28 232	72.48 200	78.19 188	57.19 103	75.28 246	75.09 299	51.61 331	70.04 167	81.41 281	32.79 384	79.02 295	63.81 172	77.31 205	81.22 306

X-MVStestdata		70.21 170	67.28 232	79.00 26	86.32 31	62.62 11	85.83 27	83.92 63	64.55 26	72.17 135	6.49 530	47.95 184	88.01 46	71.55 91	86.74 59	86.37 122

tt0805		67.77 245	67.24 236	69.34 289	74.87 293	40.08 404	77.36 185	81.37 141	55.31 254	66.33 254	84.65 193	37.35 328	82.55 202	55.65 255	72.28 294	85.39 175

miper_ehance_all_eth		68.03 236	67.24 236	70.40 269	70.54 386	46.21 331	73.98 278	78.68 205	55.07 265	66.05 260	77.80 353	52.16 119	81.31 230	61.53 204	69.32 345	83.67 242

guyue		68.10 235	67.23 238	70.71 263	73.67 327	49.27 283	73.65 289	76.04 276	55.62 248	67.84 222	82.26 258	41.24 282	78.91 303	61.01 206	73.72 262	83.94 226

v148		68.24 231	67.19 239	71.40 236	70.43 389	47.77 315	75.76 238	77.03 252	58.91 162	67.36 231	80.10 307	48.60 179	81.89 215	60.01 213	66.52 376	84.53 207

test1111		67.21 253	67.14 240	67.42 320	79.24 149	34.76 458	73.89 284	65.65 399	58.71 168	66.96 241	87.95 88	36.09 345	80.53 253	52.03 285	83.79 86	86.97 94

UniMVSNet_ETH3D		67.60 248	67.07 241	69.18 293	77.39 223	42.29 381	74.18 275	75.59 285	60.37 125	66.77 244	86.06 154	37.64 324	78.93 301	52.16 283	73.49 269	86.32 129

WR-MVS_H		67.02 261	66.92 242	67.33 323	77.95 199	37.75 429	77.57 177	82.11 127	62.03 88	62.65 324	82.48 252	50.57 147	79.46 276	42.91 385	64.01 394	84.79 199

AstraMVS		67.86 242	66.83 243	70.93 256	73.50 329	49.34 280	73.28 297	74.01 319	55.45 252	68.10 212	83.28 230	38.93 308	79.14 287	63.22 183	71.74 301	84.30 214

LuminaMVS		68.24 231	66.82 244	72.51 199	73.46 331	53.60 169	76.23 224	78.88 198	52.78 311	68.08 213	80.13 305	32.70 389	81.41 226	63.16 184	75.97 231	82.53 276

icg_test_0407_2		66.41 276	66.75 245	65.37 362	77.06 241	49.73 268	63.79 424	78.60 207	52.70 312	66.19 256	82.58 243	45.17 228	63.65 441	59.20 223	75.46 240	82.74 270

PAPM		67.92 240	66.69 246	71.63 226	78.09 193	49.02 287	77.09 198	81.24 151	51.04 347	60.91 350	83.98 213	47.71 188	84.99 129	40.81 399	79.32 156	80.90 316

mvsmamba		68.47 225	66.56 247	74.21 132	79.60 138	52.95 187	74.94 257	75.48 289	52.09 325	60.10 356	83.27 231	36.54 340	84.70 139	59.32 222	77.69 197	84.99 192

GBi-Net		67.21 253	66.55 248	69.19 290	77.63 212	43.33 365	77.31 186	77.83 232	56.62 218	65.04 286	82.70 238	41.85 267	80.33 258	47.18 330	72.76 284	83.92 228

test1		67.21 253	66.55 248	69.19 290	77.63 212	43.33 365	77.31 186	77.83 232	56.62 218	65.04 286	82.70 238	41.85 267	80.33 258	47.18 330	72.76 284	83.92 228

cl22		67.47 250	66.45 250	70.54 267	69.85 405	46.49 327	73.85 285	77.35 244	55.07 265	65.51 272	77.92 346	47.64 190	81.10 236	61.58 202	69.32 345	84.01 224

jajsoiax		68.25 230	66.45 250	73.66 161	75.62 274	55.49 137	80.82 101	78.51 214	52.33 320	64.33 297	84.11 209	28.28 433	81.81 218	63.48 179	70.62 315	83.67 242

PEN-MVS		66.60 271	66.45 250	67.04 325	77.11 240	36.56 442	77.03 200	80.42 171	62.95 60	62.51 329	84.03 211	46.69 207	79.07 290	44.22 367	63.08 407	85.51 165

ab-mvs		66.65 270	66.42 253	67.37 321	76.17 265	41.73 387	70.41 354	76.14 273	53.99 290	65.98 261	83.51 227	49.48 163	76.24 363	48.60 314	73.46 271	84.14 220

AUN-MVS		68.45 227	66.41 254	74.57 117	79.53 141	57.08 109	73.93 282	75.23 295	54.44 284	66.69 246	81.85 271	37.10 334	82.89 188	62.07 194	66.84 372	83.75 239

CP-MVSNet		66.49 274	66.41 254	66.72 328	77.67 210	36.33 445	76.83 211	79.52 185	62.45 73	62.54 327	83.47 229	46.32 210	78.37 309	45.47 357	63.43 403	85.45 170

mvs_tets		68.18 233	66.36 256	73.63 164	75.61 275	55.35 141	80.77 102	78.56 212	52.48 319	64.27 299	84.10 210	27.45 442	81.84 217	63.45 180	70.56 317	83.69 241

MVS		67.37 251	66.33 257	70.51 268	75.46 279	50.94 230	73.95 280	81.85 130	41.57 454	62.54 327	78.57 336	47.98 183	85.47 121	52.97 278	82.05 109	75.14 406

PS-CasMVS		66.42 275	66.32 258	66.70 330	77.60 218	36.30 447	76.94 204	79.61 183	62.36 75	62.43 332	83.66 221	45.69 214	78.37 309	45.35 359	63.26 405	85.42 173

FMVSNet2		66.93 263	66.31 259	68.79 299	77.63 212	42.98 374	76.11 227	77.47 238	56.62 218	65.22 283	82.17 262	41.85 267	80.18 265	47.05 336	72.72 287	83.20 257

eth_miper_zixun_eth		67.63 247	66.28 260	71.67 224	71.60 366	48.33 301	73.68 288	77.88 229	55.80 242	65.91 263	78.62 335	47.35 198	82.88 189	59.45 219	66.25 377	83.81 234

cl____		67.18 256	66.26 261	69.94 276	70.20 396	45.74 335	73.30 294	76.83 258	55.10 260	65.27 277	79.57 318	47.39 196	80.53 253	59.41 221	69.22 349	83.53 248

DIV-MVS_self_test		67.18 256	66.26 261	69.94 276	70.20 396	45.74 335	73.29 296	76.83 258	55.10 260	65.27 277	79.58 317	47.38 197	80.53 253	59.43 220	69.22 349	83.54 247

miper_enhance_ethall		67.11 259	66.09 263	70.17 273	69.21 414	45.98 333	72.85 308	78.41 221	51.38 339	65.65 270	75.98 389	51.17 138	81.25 231	60.82 207	69.32 345	83.29 255

Anonymous202405211		66.84 265	65.99 264	69.40 288	80.19 128	42.21 383	71.11 341	71.31 348	58.80 164	67.90 215	86.39 142	29.83 417	79.65 270	49.60 307	78.78 174	86.33 127

FMVSNet1		66.70 269	65.87 265	69.19 290	77.49 220	43.33 365	77.31 186	77.83 232	56.45 224	64.60 295	82.70 238	38.08 322	80.33 258	46.08 344	72.31 293	83.92 228

BH-w/o		66.85 264	65.83 266	69.90 279	79.29 145	52.46 206	74.66 264	76.65 263	54.51 283	64.85 291	78.12 340	45.59 217	82.95 181	43.26 381	75.54 238	74.27 421

thisisatest0530		67.92 240	65.78 267	74.33 125	76.29 263	51.03 229	76.89 206	74.25 315	53.67 300	65.59 271	81.76 274	35.15 353	85.50 119	55.94 248	72.47 289	86.47 118

ET-MVSNet_ETH3D		67.96 239	65.72 268	74.68 110	76.67 255	55.62 134	75.11 251	74.74 304	52.91 309	60.03 358	80.12 306	33.68 373	82.64 199	61.86 197	76.34 223	85.78 150

tttt0517		67.83 243	65.66 269	74.33 125	76.69 253	50.82 235	77.86 168	73.99 320	54.54 282	64.64 294	82.53 251	35.06 354	85.50 119	55.71 253	69.91 333	86.67 109

FMVSNet3		66.32 278	65.61 270	68.46 303	76.48 260	42.34 380	74.98 256	77.15 248	55.83 240	65.04 286	81.16 284	39.91 292	80.14 266	47.18 330	72.76 284	82.90 267

MVSTER		67.16 258	65.58 271	71.88 214	70.37 392	49.70 272	70.25 357	78.45 218	51.52 334	69.16 189	80.37 299	38.45 315	82.50 203	60.19 211	71.46 305	83.44 251

VortexMVS		66.41 276	65.50 272	69.16 294	73.75 323	48.14 306	73.41 292	78.28 225	53.73 298	64.98 290	78.33 338	40.62 287	79.07 290	58.88 227	67.50 367	80.26 334

mamba_0408		67.78 244	65.42 273	74.85 106	78.65 169	53.46 173	50.83 483	79.09 192	53.75 296	68.14 207	83.83 216	41.79 270	86.56 84	56.58 243	76.11 227	84.54 204

SSM_04072		64.98 295	65.42 273	63.68 377	78.65 169	53.46 173	50.83 483	79.09 192	53.75 296	68.14 207	83.83 216	41.79 270	53.03 486	56.58 243	76.11 227	84.54 204

FBQ-MVS		66.84 265	65.39 275	71.18 246	79.22 150	47.61 318	76.89 206	74.70 306	56.31 231	65.84 268	77.22 362	36.21 343	82.07 212	45.20 361	76.94 214	83.87 231

CDS-MVSNet		66.80 267	65.37 276	71.10 252	78.98 158	53.13 185	73.27 298	71.07 350	52.15 323	64.72 292	80.23 304	43.56 245	77.10 340	45.48 356	78.88 171	83.05 264
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DTE-MVSNet		65.58 285	65.34 277	66.31 340	76.06 267	34.79 456	76.43 219	79.38 188	62.55 71	61.66 342	83.83 216	45.60 216	79.15 286	41.64 397	60.88 429	85.00 190

Fast-Effi-MVS+-dtu		67.37 251	65.33 278	73.48 171	72.94 340	57.78 94	77.47 182	76.88 255	57.60 198	61.97 335	76.85 370	39.31 301	80.49 256	54.72 262	70.28 324	82.17 287

TAMVS		66.78 268	65.27 279	71.33 243	79.16 155	53.67 165	73.84 286	69.59 366	52.32 322	65.28 276	81.72 275	44.49 237	77.40 335	42.32 389	78.66 180	82.92 265

TAPA-MVS	59.36 10	66.60 271	65.20 280	70.81 258	76.63 256	48.75 293	76.52 218	80.04 176	50.64 352	65.24 281	84.93 182	39.15 305	78.54 308	36.77 427	76.88 215	85.14 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 273	65.07 281	71.17 248	79.18 153	49.63 276	73.48 290	75.20 297	52.95 308	67.90 215	80.33 302	39.81 295	83.68 159	43.20 382	73.56 268	80.20 335

pm-mvs1		65.24 291	64.97 282	66.04 348	72.38 353	39.40 414	72.62 311	75.63 283	55.53 249	62.35 334	83.18 234	47.45 194	76.47 360	49.06 311	66.54 375	82.24 284

anonymousdsp		67.00 262	64.82 283	73.57 167	70.09 399	56.13 119	76.35 220	77.35 244	48.43 383	64.99 289	80.84 295	33.01 381	80.34 257	64.66 161	67.64 366	84.23 216

test2506		65.33 290	64.61 284	67.50 316	79.46 143	34.19 464	74.43 270	51.92 475	58.72 166	66.75 245	88.05 84	25.99 455	80.92 244	51.94 286	84.25 80	87.39 77

sd_testset		64.46 302	64.45 285	64.51 370	77.13 236	42.25 382	62.67 431	72.11 343	58.02 184	65.08 284	82.55 248	41.22 283	69.88 402	47.32 328	73.92 258	81.41 298

TransMVSNet (Re)		64.72 296	64.33 286	65.87 353	75.22 284	38.56 420	74.66 264	75.08 302	58.90 163	61.79 338	82.63 241	51.18 137	78.07 315	43.63 378	55.87 454	80.99 315

IMVS_0404		64.63 299	64.22 287	65.88 352	77.06 241	49.73 268	64.40 417	78.60 207	52.70 312	53.16 445	82.58 243	34.82 357	65.16 435	59.20 223	75.46 240	82.74 270

ACMH+	57.40 11	66.12 279	64.06 288	72.30 207	77.79 204	52.83 194	80.39 106	78.03 228	57.30 200	57.47 394	82.55 248	27.68 440	84.17 147	45.54 352	69.78 336	79.90 341

CNLPA		65.43 287	64.02 289	69.68 282	78.73 166	58.07 89	77.82 171	70.71 356	51.49 336	61.57 344	83.58 226	38.23 320	70.82 394	43.90 373	70.10 329	80.16 336

HY-MVS	56.14 13	64.55 301	63.89 290	66.55 336	74.73 299	41.02 394	69.96 360	74.43 309	49.29 369	61.66 342	80.92 291	47.43 195	76.68 356	44.91 364	71.69 302	81.94 289

Vis-MVSNet (Re-imp)		63.69 312	63.88 291	63.14 383	74.75 298	31.04 482	71.16 339	63.64 421	56.32 229	59.80 363	84.99 181	44.51 235	75.46 367	39.12 412	80.62 127	82.92 265

baseline1		63.81 311	63.87 292	63.62 378	76.29 263	36.36 443	71.78 330	67.29 385	56.05 237	64.23 301	82.95 236	47.11 200	74.41 372	47.30 329	61.85 423	80.10 338

testing91		64.46 302	63.80 293	66.47 337	78.43 178	40.06 405	67.63 385	69.59 366	59.06 159	63.18 312	78.05 342	34.05 366	76.99 346	48.30 317	75.87 233	82.37 282

MVP-Stereo		65.41 288	63.80 293	70.22 270	77.62 216	55.53 136	76.30 221	78.53 213	50.59 353	56.47 406	78.65 333	39.84 294	82.68 197	44.10 371	72.12 298	72.44 437
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GA-MVS		65.53 286	63.70 295	71.02 255	70.87 382	48.10 307	70.48 352	74.40 310	56.69 213	64.70 293	76.77 371	33.66 374	81.10 236	55.42 258	70.32 323	83.87 231

DP-MVS		65.68 283	63.66 296	71.75 219	84.93 60	56.87 111	80.74 104	73.16 333	53.06 307	59.09 373	82.35 254	36.79 339	85.94 108	32.82 451	69.96 332	72.45 436

usedtu_dtu_shiyan1		64.34 305	63.57 297	66.66 332	72.44 351	40.74 400	69.60 366	76.80 260	53.21 305	61.73 340	77.92 346	41.92 265	77.68 328	46.23 341	72.25 295	81.57 294

FE-MVSNET3		64.34 305	63.57 297	66.66 332	72.44 351	40.74 400	69.60 366	76.80 260	53.21 305	61.73 340	77.92 346	41.92 265	77.68 328	46.23 341	72.25 295	81.57 294

ACMH	55.70 15	65.20 292	63.57 297	70.07 274	78.07 194	52.01 217	79.48 127	79.69 180	55.75 243	56.59 403	80.98 289	27.12 445	80.94 242	42.90 386	71.58 304	77.25 383
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0515		65.83 282	63.50 300	72.82 190	73.75 323	49.50 277	71.32 335	73.12 335	49.39 367	63.82 304	76.50 381	34.95 356	84.84 138	53.20 277	75.49 239	84.13 221

SD_0403		63.07 321	63.49 301	61.82 392	75.16 287	31.14 481	71.89 329	73.47 325	53.34 304	58.22 385	81.81 273	45.17 228	73.86 375	37.43 421	74.87 247	80.45 325

cascas		65.98 280	63.42 302	73.64 163	77.26 227	52.58 202	72.26 322	77.21 247	48.56 379	61.21 347	74.60 404	32.57 395	85.82 111	50.38 299	76.75 218	82.52 278

1112_ss		64.00 310	63.36 303	65.93 350	79.28 147	42.58 379	71.35 334	72.36 341	46.41 414	60.55 353	77.89 350	46.27 212	73.28 377	46.18 343	69.97 331	81.92 290

FE-MVS		65.91 281	63.33 304	73.63 164	77.36 224	51.95 219	72.62 311	75.81 280	53.70 299	65.31 275	78.96 328	28.81 427	86.39 92	43.93 372	73.48 270	82.55 275

MonoMVSNet		64.15 307	63.31 305	66.69 331	70.51 387	44.12 356	74.47 268	74.21 316	57.81 192	63.03 315	76.62 375	38.33 317	77.31 337	54.22 267	60.59 435	78.64 361

testing99		64.05 308	63.29 306	66.34 339	78.17 191	39.76 409	67.33 390	68.00 380	58.60 171	63.03 315	78.10 341	32.57 395	76.94 348	48.22 318	75.58 237	82.34 283

1314		64.61 300	63.21 307	68.80 298	71.87 363	47.46 320	73.95 280	78.39 223	42.88 447	59.97 359	76.60 378	38.11 321	79.39 278	54.84 261	72.32 292	79.55 348

PLC	56.13 14	65.09 293	63.21 307	70.72 262	81.04 112	54.87 148	78.57 142	77.47 238	48.51 381	55.71 411	81.89 270	33.71 372	79.71 269	41.66 395	70.37 320	77.58 376
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HyFIR lowres test		65.67 284	63.01 309	73.67 160	79.97 133	55.65 131	69.07 374	75.52 287	42.68 448	63.53 307	77.95 344	40.43 289	81.64 220	46.01 345	71.91 299	83.73 240

EG-PatchMatch MVS		64.71 297	62.87 310	70.22 270	77.68 209	53.48 172	77.99 164	78.82 199	53.37 303	56.03 410	77.41 361	24.75 463	84.04 151	46.37 340	73.42 273	73.14 427

CHOSEN 1792x2688		65.08 294	62.84 311	71.82 216	81.49 102	56.26 117	66.32 396	74.20 317	40.53 460	63.16 313	78.65 333	41.30 278	77.80 324	45.80 347	74.09 255	81.40 300

pmmvs6		63.69 312	62.82 312	66.27 342	70.63 384	39.27 415	73.13 302	75.47 290	52.69 317	59.75 365	82.30 256	39.71 296	77.03 343	47.40 325	64.35 393	82.53 276

IB-MVS	56.42 12	65.40 289	62.73 313	73.40 175	74.89 291	52.78 195	73.09 303	75.13 298	55.69 244	58.48 382	73.73 412	32.86 383	86.32 95	50.63 297	70.11 328	81.10 311
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
CostFormer		64.04 309	62.51 314	68.61 301	71.88 362	45.77 334	71.30 336	70.60 357	47.55 400	64.31 298	76.61 377	41.63 273	79.62 272	49.74 303	69.00 352	80.42 326

LS3D		64.71 297	62.50 315	71.34 242	79.72 137	55.71 129	79.82 118	74.72 305	48.50 382	56.62 402	84.62 194	33.59 375	82.34 207	29.65 473	75.23 244	75.97 396

thres100view900		63.28 317	62.41 316	65.89 351	77.31 226	38.66 419	72.65 309	69.11 373	57.07 205	62.45 330	81.03 288	37.01 336	79.17 283	31.84 457	73.25 276	79.83 344

testing3-2		62.06 338	62.36 317	61.17 400	79.29 145	30.31 484	64.09 423	63.49 422	63.50 45	62.84 318	82.22 259	32.35 400	69.02 406	40.01 406	73.43 272	84.17 219

thres600view7		63.30 316	62.27 318	66.41 338	77.18 229	38.87 417	72.35 319	69.11 373	56.98 208	62.37 333	80.96 290	37.01 336	79.00 298	31.43 464	73.05 280	81.36 301

XVG-ACMP-BASELINE		64.36 304	62.23 319	70.74 261	72.35 354	52.45 207	70.80 348	78.45 218	53.84 295	59.87 361	81.10 286	16.24 483	79.32 279	55.64 256	71.76 300	80.47 324

tfpn200view9		63.18 319	62.18 320	66.21 343	76.85 250	39.62 411	71.96 327	69.44 369	56.63 216	62.61 325	79.83 310	37.18 330	79.17 283	31.84 457	73.25 276	79.83 344

thres400		63.31 315	62.18 320	66.72 328	76.85 250	39.62 411	71.96 327	69.44 369	56.63 216	62.61 325	79.83 310	37.18 330	79.17 283	31.84 457	73.25 276	81.36 301

EPNet_dtu		61.90 343	61.97 322	61.68 393	72.89 341	39.78 408	75.85 236	65.62 400	55.09 262	54.56 429	79.36 323	37.59 325	67.02 421	39.80 408	76.95 213	78.25 365
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		62.81 323	61.90 323	65.54 356	78.38 179	40.76 399	67.59 387	66.78 391	55.48 250	60.13 355	77.11 365	31.67 403	76.79 351	45.53 353	74.45 251	79.06 355

Test_1112_low_res		62.32 333	61.77 324	64.00 375	79.08 157	39.53 413	68.17 381	70.17 359	43.25 442	59.03 374	79.90 309	44.08 239	71.24 392	43.79 375	68.42 359	81.25 305

XXY-MVS		60.68 353	61.67 325	57.70 429	70.43 389	38.45 422	64.19 420	66.47 392	48.05 391	63.22 310	80.86 293	49.28 169	60.47 451	45.25 360	67.28 370	74.19 422

tfpnnormal		62.47 328	61.63 326	64.99 367	74.81 296	39.01 416	71.22 337	73.72 323	55.22 259	60.21 354	80.09 308	41.26 281	76.98 347	30.02 471	68.09 362	78.97 358

IterMVS-SCA-FT		62.49 327	61.52 327	65.40 361	71.99 361	50.80 236	71.15 340	69.63 365	45.71 422	60.61 352	77.93 345	37.45 326	65.99 430	55.67 254	63.50 402	79.42 350

MS-PatchMatch		62.42 332	61.46 328	65.31 364	75.21 285	52.10 213	72.05 324	74.05 318	46.41 414	57.42 396	74.36 405	34.35 363	77.57 332	45.62 350	73.67 263	66.26 475

SSC-MVS3.2		60.57 355	61.39 329	58.12 425	74.29 314	32.63 474	59.52 449	65.53 401	59.90 140	62.45 330	79.75 314	41.96 262	63.90 440	39.47 410	69.65 343	77.84 373

LCM-MVSNet-Re		61.88 344	61.35 330	63.46 379	74.58 305	31.48 480	61.42 439	58.14 453	58.71 168	53.02 447	79.55 319	43.07 250	76.80 350	45.69 348	77.96 193	82.11 288

testing222		62.29 335	61.31 331	65.25 365	77.87 201	38.53 421	68.34 379	66.31 395	56.37 227	63.15 314	77.58 359	28.47 429	76.18 365	37.04 425	76.65 220	81.05 314

IterMVS		62.79 324	61.27 332	67.35 322	69.37 411	52.04 216	71.17 338	68.24 379	52.63 318	59.82 362	76.91 369	37.32 329	72.36 382	52.80 279	63.19 406	77.66 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
baseline2		63.42 314	61.26 333	69.89 280	72.55 347	47.62 317	71.54 332	68.38 377	50.11 357	54.82 424	75.55 394	43.06 251	80.96 241	48.13 319	67.16 371	81.11 310

LTVRE_ROB	55.42 16	63.15 320	61.23 334	68.92 297	76.57 258	47.80 313	59.92 448	76.39 267	54.35 285	58.67 378	82.46 253	29.44 421	81.49 225	42.12 390	71.14 308	77.46 377
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
reproduce_monomvs		62.56 326	61.20 335	66.62 335	70.62 385	44.30 353	70.13 358	73.13 334	54.78 275	61.13 348	76.37 382	25.63 458	75.63 366	58.75 230	60.29 436	79.93 340

thres200		62.20 336	61.16 336	65.34 363	75.38 282	39.99 406	69.60 366	69.29 371	55.64 247	61.87 337	76.99 367	37.07 335	78.96 300	31.28 465	73.28 275	77.06 384

myMVS_eth3d28		60.66 354	61.04 337	59.51 408	77.32 225	31.58 479	63.11 428	63.87 418	59.00 160	60.90 351	78.26 339	32.69 390	66.15 429	36.10 436	78.13 190	80.81 318

test_0402		63.25 318	61.01 338	69.96 275	80.00 132	54.37 153	76.86 209	72.02 344	54.58 281	58.71 376	80.79 296	35.00 355	84.36 145	26.41 486	64.71 388	71.15 455

CL-MVSNet_self_test		61.53 347	60.94 339	63.30 381	68.95 418	36.93 439	67.60 386	72.80 337	55.67 245	59.95 360	76.63 374	45.01 231	72.22 386	39.74 409	62.09 422	80.74 320

FE-MVSNET2		62.01 340	60.88 340	65.42 360	68.74 422	38.43 423	72.92 305	77.39 242	54.74 278	55.40 416	76.71 372	35.46 350	76.72 354	44.25 366	62.31 419	81.10 311

miper_lstm_enhance		62.03 339	60.88 340	65.49 359	66.71 446	46.25 329	56.29 466	75.70 282	50.68 350	61.27 346	75.48 396	40.21 290	68.03 412	56.31 247	65.25 384	82.18 285

F-COLMAP		63.05 322	60.87 342	69.58 286	76.99 249	53.63 168	78.12 158	76.16 271	47.97 392	52.41 449	81.61 277	27.87 437	78.11 314	40.07 403	66.66 374	77.00 386

usedtu_blend_shiyan5		62.63 325	60.77 343	68.20 307	68.53 426	44.64 348	73.47 291	77.00 253	51.91 327	57.10 397	69.95 445	38.83 310	79.61 273	47.44 322	62.67 410	80.37 329

blended_shiyan8		62.46 329	60.71 344	67.71 313	69.15 416	43.43 363	70.83 345	76.52 264	51.49 336	57.67 390	71.36 433	39.38 299	79.07 290	47.37 326	62.67 410	80.62 322

blended_shiyan6		62.46 329	60.71 344	67.71 313	69.14 417	43.42 364	70.82 346	76.52 264	51.50 335	57.64 391	71.37 432	39.38 299	79.08 289	47.36 327	62.67 410	80.65 321

WBMVS		60.54 356	60.61 346	60.34 405	78.00 197	35.95 451	64.55 416	64.89 405	49.63 363	63.39 309	78.70 330	33.85 371	67.65 415	42.10 391	70.35 322	77.43 378

gbinet_0.2-2-1-0.02		62.43 331	60.41 347	68.49 302	68.91 421	43.71 360	71.73 331	75.89 278	52.10 324	58.33 383	69.67 452	36.86 338	80.59 252	47.18 330	63.05 408	81.16 309

WTY-MVS		59.75 365	60.39 348	57.85 427	72.32 355	37.83 428	61.05 444	64.18 414	45.95 421	61.91 336	79.11 327	47.01 204	60.88 450	42.50 388	69.49 344	74.83 412

D2MVS		62.30 334	60.29 349	68.34 306	66.46 449	48.42 300	65.70 400	73.42 326	47.71 397	58.16 386	75.02 400	30.51 407	77.71 327	53.96 270	71.68 303	78.90 359

wanda-best-256-512		62.00 341	60.17 350	67.49 317	68.53 426	43.07 372	69.65 363	76.38 268	51.26 342	57.10 397	69.95 445	38.83 310	79.04 293	47.14 334	62.67 410	80.37 329

FE-blended-shiyan7		62.00 341	60.17 350	67.49 317	68.53 426	43.07 372	69.65 363	76.38 268	51.26 342	57.10 397	69.95 445	38.83 310	79.04 293	47.14 334	62.67 410	80.37 329

tpm2		62.07 337	60.10 352	67.99 310	72.79 342	43.86 358	71.05 343	66.85 390	43.14 444	62.77 320	75.39 398	38.32 318	80.80 248	41.69 394	68.88 353	79.32 351

UWE-MVS		60.18 360	59.78 353	61.39 398	77.67 210	33.92 467	69.04 375	63.82 419	48.56 379	64.27 299	77.64 358	27.20 444	70.40 399	33.56 448	76.24 224	79.83 344

WB-MVSnew		59.66 366	59.69 354	59.56 407	75.19 286	35.78 453	69.34 371	64.28 413	46.88 410	61.76 339	75.79 390	40.61 288	65.20 434	32.16 453	71.21 307	77.70 374

UBG		59.62 368	59.53 355	59.89 406	78.12 192	35.92 452	64.11 422	60.81 445	49.45 366	61.34 345	75.55 394	33.05 379	67.39 419	38.68 414	74.62 249	76.35 394

pmmvs4		61.48 349	59.39 356	67.76 312	71.57 367	53.86 160	71.42 333	65.34 402	44.20 433	59.46 368	77.92 346	35.90 346	74.71 370	43.87 374	64.87 387	74.71 416

MSDG		61.81 345	59.23 357	69.55 287	72.64 344	52.63 201	70.45 353	75.81 280	51.38 339	53.70 436	76.11 384	29.52 419	81.08 238	37.70 419	65.79 381	74.93 411

CVMVSNet		59.63 367	59.14 358	61.08 402	74.47 307	38.84 418	75.20 249	68.74 375	31.15 482	58.24 384	76.51 379	32.39 398	68.58 408	49.77 302	65.84 380	75.81 398

mmtdpeth		60.40 359	59.12 359	64.27 373	69.59 407	48.99 288	70.67 349	70.06 361	54.96 272	62.78 319	73.26 417	27.00 447	67.66 414	58.44 233	45.29 484	76.16 395

blend_shiyan4		61.38 350	59.10 360	68.20 307	68.94 419	44.64 348	70.81 347	76.52 264	51.63 330	57.56 393	69.94 448	28.30 432	79.61 273	47.44 322	60.78 431	80.36 332

test_vis1_n_1920		58.86 372	59.06 361	58.25 421	63.76 462	43.14 370	67.49 388	66.36 394	40.22 462	65.89 265	71.95 427	31.04 404	59.75 456	59.94 214	64.90 386	71.85 445

ETVMVS		59.51 369	58.81 362	61.58 395	77.46 221	34.87 455	64.94 414	59.35 448	54.06 289	61.08 349	76.67 373	29.54 418	71.87 388	32.16 453	74.07 256	78.01 372

COLMAP_ROB	52.97 17	61.27 352	58.81 362	68.64 300	74.63 302	52.51 204	78.42 145	73.30 329	49.92 361	50.96 454	81.51 280	23.06 466	79.40 277	31.63 461	65.85 379	74.01 424
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		61.65 346	58.80 364	70.20 272	75.80 269	47.22 322	75.59 240	69.68 364	54.61 279	54.11 433	79.26 325	27.07 446	82.96 179	43.27 380	49.79 477	80.41 327

tpmrst		58.24 381	58.70 365	56.84 431	66.97 443	34.32 462	69.57 369	61.14 443	47.17 407	58.58 381	71.60 429	41.28 280	60.41 452	49.20 309	62.84 409	75.78 399

OurMVSNet-221017-0		61.37 351	58.63 366	69.61 283	72.05 359	48.06 310	73.93 282	72.51 338	47.23 406	54.74 425	80.92 291	21.49 473	81.24 232	48.57 315	56.22 453	79.53 349

RPMNet		61.53 347	58.42 367	70.86 257	69.96 401	52.07 214	65.31 409	81.36 142	43.20 443	59.36 369	70.15 443	35.37 351	85.47 121	36.42 434	64.65 389	75.06 407

SCA		60.49 357	58.38 368	66.80 327	74.14 319	48.06 310	63.35 427	63.23 426	49.13 371	59.33 372	72.10 424	37.45 326	74.27 373	44.17 368	62.57 416	78.05 368

PatchmatchNet		59.84 363	58.24 369	64.65 369	73.05 337	46.70 326	69.42 370	62.18 438	47.55 400	58.88 375	71.96 426	34.49 361	69.16 404	42.99 384	63.60 400	78.07 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		57.34 388	58.16 370	54.86 441	71.80 364	34.77 457	67.47 389	56.04 467	48.20 388	60.10 356	76.92 368	37.17 332	53.41 485	40.76 400	65.01 385	76.40 393

OpenMVS_ROB	52.78 18	60.03 361	58.14 371	65.69 355	70.47 388	44.82 344	75.33 244	70.86 355	45.04 425	56.06 409	76.00 386	26.89 449	79.65 270	35.36 440	67.29 369	72.60 432

test-LLR		58.15 383	58.13 372	58.22 422	68.57 424	44.80 345	65.46 405	57.92 454	50.08 358	55.44 414	69.82 449	32.62 392	57.44 468	49.66 305	73.62 265	72.41 438

CR-MVSNet		59.91 362	57.90 373	65.96 349	69.96 401	52.07 214	65.31 409	63.15 427	42.48 449	59.36 369	74.84 401	35.83 347	70.75 395	45.50 354	64.65 389	75.06 407

sc_t1		59.76 364	57.84 374	65.54 356	74.87 293	42.95 376	69.61 365	64.16 416	48.90 374	58.68 377	77.12 364	28.19 435	72.35 383	43.75 377	55.28 456	81.31 304

PVSNet	50.76 19	58.40 377	57.39 375	61.42 396	75.53 277	44.04 357	61.43 438	63.45 424	47.04 409	56.91 400	73.61 413	27.00 447	64.76 436	39.12 412	72.40 290	75.47 403

nomal-1		58.46 376	57.31 376	61.90 391	68.64 423	49.90 265	55.10 469	63.49 422	48.22 386	59.51 367	72.40 420	32.56 397	65.29 433	45.60 351	70.25 326	70.51 460

K. test v3		60.47 358	57.11 377	70.56 266	73.74 325	48.22 303	75.10 253	62.55 432	58.27 179	53.62 439	76.31 383	27.81 438	81.59 222	47.42 324	39.18 492	81.88 291

MIMVSNet		57.35 387	57.07 378	58.22 422	74.21 316	37.18 434	62.46 432	60.88 444	48.88 375	55.29 418	75.99 388	31.68 402	62.04 447	31.87 456	72.35 291	75.43 404

MDTV_nov1_ep13			57.00 379		72.73 343	38.26 424	65.02 413	64.73 408	44.74 427	55.46 413	72.48 419	32.61 394	70.47 396	37.47 420	67.75 365

tpmvs		58.47 375	56.95 380	63.03 385	70.20 396	41.21 393	67.90 384	67.23 386	49.62 364	54.73 426	70.84 436	34.14 365	76.24 363	36.64 431	61.29 427	71.64 447

tpm cat1		59.25 371	56.95 380	66.15 345	72.19 357	46.96 324	68.09 382	65.76 398	40.03 464	57.81 389	70.56 438	38.32 318	74.51 371	38.26 417	61.50 426	77.00 386

dmvs_re		56.77 392	56.83 382	56.61 432	69.23 413	41.02 394	58.37 454	64.18 414	50.59 353	57.45 395	71.42 430	35.54 349	58.94 461	37.23 423	67.45 368	69.87 466

tt0320		58.59 374	56.81 383	63.92 376	75.46 279	41.32 392	68.63 377	64.06 417	47.05 408	56.19 408	74.19 407	30.34 409	71.36 390	39.92 407	55.45 455	79.09 354

test_cas_vis1_n_1920		56.91 391	56.71 384	57.51 430	59.13 485	45.40 341	63.58 425	61.29 442	36.24 473	67.14 238	71.85 428	29.89 416	56.69 472	57.65 237	63.58 401	70.46 461

0.4-1-1-0.1		59.29 370	56.70 385	67.07 324	69.35 412	43.16 369	66.59 392	70.87 354	48.59 378	55.11 420	62.25 481	28.22 434	78.92 302	45.49 355	63.79 397	79.14 353

sss		56.17 399	56.57 386	54.96 440	66.93 444	36.32 446	57.94 457	61.69 440	41.67 452	58.64 379	75.32 399	38.72 313	56.25 475	42.04 392	66.19 378	72.31 441

Patchmtry		57.16 389	56.47 387	59.23 412	69.17 415	34.58 460	62.98 429	63.15 427	44.53 429	56.83 401	74.84 401	35.83 347	68.71 407	40.03 404	60.91 428	74.39 420

gg-mvs-nofinetune		57.86 385	56.43 388	62.18 389	72.62 345	35.35 454	66.57 393	56.33 463	50.65 351	57.64 391	57.10 489	30.65 406	76.36 361	37.38 422	78.88 171	74.82 413

tt0320-xc		58.33 379	56.41 389	64.08 374	75.79 270	41.34 391	68.30 380	62.72 431	47.90 393	56.29 407	74.16 409	28.53 428	71.04 393	41.50 398	52.50 468	79.88 342

pmmvs-eth3d		58.81 373	56.31 390	66.30 341	67.61 438	52.42 208	72.30 320	64.76 407	43.55 439	54.94 423	74.19 407	28.95 424	72.60 380	43.31 379	57.21 448	73.88 425

Syy-MVS		56.00 400	56.23 391	55.32 438	74.69 300	26.44 498	65.52 403	57.49 457	50.97 348	56.52 404	72.18 422	39.89 293	68.09 410	24.20 490	64.59 391	71.44 451

CMPMVS	42.80 21	57.81 386	55.97 392	63.32 380	60.98 479	47.38 321	64.66 415	69.50 368	32.06 480	46.83 472	77.80 353	29.50 420	71.36 390	48.68 313	73.75 261	71.21 454
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		56.54 393	55.92 393	58.41 420	77.52 219	27.93 492	69.72 362	56.36 462	54.75 277	58.63 380	77.80 353	20.88 474	71.75 389	25.31 489	62.25 420	75.53 402

test-mter		56.42 396	55.82 394	58.22 422	68.57 424	44.80 345	65.46 405	57.92 454	39.94 466	55.44 414	69.82 449	21.92 469	57.44 468	49.66 305	73.62 265	72.41 438

pmmvs5		56.47 395	55.68 395	58.86 417	61.41 475	36.71 441	66.37 395	62.75 430	40.38 461	53.70 436	76.62 375	34.56 359	67.05 420	40.02 405	65.27 383	72.83 430

0.3-1-1-0.015		58.40 377	55.56 396	66.91 326	68.08 434	43.09 371	65.25 411	70.96 353	47.89 395	53.10 446	59.82 484	26.48 450	78.79 304	45.07 363	63.43 403	78.84 360

0.4-1-1-0.2		58.31 380	55.53 397	66.64 334	67.46 440	42.78 378	64.38 418	70.97 352	47.65 398	53.38 444	59.02 485	28.39 431	78.72 306	44.86 365	63.63 399	78.42 363

Patchmatch-RL test		58.16 382	55.49 398	66.15 345	67.92 436	48.89 292	60.66 446	51.07 479	47.86 396	59.36 369	62.71 480	34.02 368	72.27 385	56.41 246	59.40 439	77.30 380

ppachtmachnet_test		58.06 384	55.38 399	66.10 347	69.51 408	48.99 288	68.01 383	66.13 397	44.50 430	54.05 434	70.74 437	32.09 401	72.34 384	36.68 430	56.71 452	76.99 388

Anonymous20231206		55.10 411	55.30 400	54.48 443	69.81 406	33.94 466	62.91 430	62.13 439	41.08 456	55.18 419	75.65 392	32.75 387	56.59 474	30.32 470	67.86 363	72.91 428

dtuonly		54.95 412	55.26 401	54.01 446	59.03 486	35.99 449	61.92 436	56.33 463	38.48 469	54.61 428	77.85 352	34.27 364	51.60 492	45.10 362	69.74 339	74.43 418

FMVSNet5		55.86 402	54.93 402	58.66 419	71.05 380	36.35 444	64.18 421	62.48 433	46.76 412	50.66 459	74.73 403	25.80 456	64.04 438	33.11 449	65.57 382	75.59 401

TESTMET0.1,1		55.28 407	54.90 403	56.42 433	66.56 447	43.67 361	65.46 405	56.27 465	39.18 468	53.83 435	67.44 464	24.21 464	55.46 479	48.04 320	73.11 279	70.13 464

dtuonlycased		55.96 401	54.88 404	59.22 413	68.38 431	40.38 402	69.17 373	63.12 429	40.00 465	53.62 439	68.84 457	36.27 342	66.23 428	40.57 401	53.92 463	71.06 457

AllTest		57.08 390	54.65 405	64.39 371	71.44 371	49.03 285	69.92 361	67.30 383	45.97 419	47.16 470	79.77 312	17.47 477	67.56 417	33.65 445	59.16 440	76.57 391

myMVS_eth3d		54.86 413	54.61 406	55.61 437	74.69 300	27.31 495	65.52 403	57.49 457	50.97 348	56.52 404	72.18 422	21.87 472	68.09 410	27.70 480	64.59 391	71.44 451

PatchMatch-RL		56.25 398	54.55 407	61.32 399	77.06 241	56.07 121	65.57 402	54.10 472	44.13 435	53.49 443	71.27 435	25.20 460	66.78 422	36.52 433	63.66 398	61.12 480

our_test_3		56.49 394	54.42 408	62.68 387	69.51 408	45.48 340	66.08 397	61.49 441	44.11 436	50.73 458	69.60 453	33.05 379	68.15 409	38.38 416	56.86 449	74.40 419

Anonymous20240521		55.30 406	54.41 409	57.96 426	60.92 481	41.73 387	71.09 342	71.06 351	41.18 455	48.65 466	73.31 415	16.93 480	59.25 458	42.54 387	64.01 394	72.90 429

EU-MVSNet		55.61 405	54.41 409	59.19 415	65.41 455	33.42 469	72.44 318	71.91 345	28.81 484	51.27 452	73.87 411	24.76 462	69.08 405	43.04 383	58.20 444	75.06 407

MIMVSNet1		55.17 409	54.31 411	57.77 428	70.03 400	32.01 477	65.68 401	64.81 406	49.19 370	46.75 473	76.00 386	25.53 459	64.04 438	28.65 476	62.13 421	77.26 382

USDC		56.35 397	54.24 412	62.69 386	64.74 458	40.31 403	65.05 412	73.83 322	43.93 437	47.58 468	77.71 357	15.36 486	75.05 369	38.19 418	61.81 424	72.70 431

RPSCF		55.80 403	54.22 413	60.53 404	65.13 457	42.91 377	64.30 419	57.62 456	36.84 472	58.05 388	82.28 257	28.01 436	56.24 476	37.14 424	58.61 443	82.44 281

test20.03		53.87 417	54.02 414	53.41 452	61.47 474	28.11 491	61.30 440	59.21 449	51.34 341	52.09 450	77.43 360	33.29 378	58.55 463	29.76 472	60.27 437	73.58 426

KD-MVS_self_test		55.22 408	53.89 415	59.21 414	57.80 489	27.47 494	57.75 460	74.32 311	47.38 402	50.90 455	70.00 444	28.45 430	70.30 400	40.44 402	57.92 445	79.87 343

mvs5depth		55.64 404	53.81 416	61.11 401	59.39 484	40.98 398	65.89 398	68.28 378	50.21 356	58.11 387	75.42 397	17.03 479	67.63 416	43.79 375	46.21 481	74.73 415

FE-MVSNET		55.16 410	53.75 417	59.41 409	65.29 456	33.20 471	67.21 391	66.21 396	48.39 385	49.56 464	73.53 414	29.03 423	72.51 381	30.38 469	54.10 462	72.52 434

EPMVS		53.96 415	53.69 418	54.79 442	66.12 452	31.96 478	62.34 434	49.05 483	44.42 432	55.54 412	71.33 434	30.22 411	56.70 471	41.65 396	62.54 417	75.71 400

test0.0.03 1		53.32 423	53.59 419	52.50 458	62.81 468	29.45 486	59.51 450	54.11 471	50.08 358	54.40 431	74.31 406	32.62 392	55.92 477	30.50 468	63.95 396	72.15 443

PatchT		53.17 424	53.44 420	52.33 459	68.29 432	25.34 502	58.21 455	54.41 470	44.46 431	54.56 429	69.05 456	33.32 377	60.94 449	36.93 426	61.76 425	70.73 459

PMMVS		53.96 415	53.26 421	56.04 434	62.60 469	50.92 232	61.17 442	56.09 466	32.81 479	53.51 442	66.84 469	34.04 367	59.93 455	44.14 370	68.18 361	57.27 488

UnsupCasMVSNet_eth		53.16 425	52.47 422	55.23 439	59.45 483	33.39 470	59.43 451	69.13 372	45.98 418	50.35 461	72.32 421	29.30 422	58.26 465	42.02 393	44.30 485	74.05 423

testgi		51.90 428	52.37 423	50.51 465	60.39 482	23.55 505	58.42 453	58.15 452	49.03 372	51.83 451	79.21 326	22.39 467	55.59 478	29.24 475	62.64 415	72.40 440

UWE-MVS-28		52.25 427	52.35 424	51.93 462	66.99 442	22.79 506	63.48 426	48.31 487	46.78 411	52.73 448	76.11 384	27.78 439	57.82 467	20.58 498	68.41 360	75.17 405

dmvs_testset		50.16 436	51.90 425	44.94 473	66.49 448	11.78 517	61.01 445	51.50 476	51.17 346	50.30 462	67.44 464	39.28 302	60.29 453	22.38 493	57.49 447	62.76 479

TinyColmap		54.14 414	51.72 426	61.40 397	66.84 445	41.97 384	66.52 394	68.51 376	44.81 426	42.69 485	75.77 391	11.66 493	72.94 378	31.96 455	56.77 451	69.27 470

dp		51.89 429	51.60 427	52.77 456	68.44 430	32.45 476	62.36 433	54.57 469	44.16 434	49.31 465	67.91 459	28.87 426	56.61 473	33.89 444	54.89 458	69.24 471

KD-MVS_2432*1600		53.45 419	51.50 428	59.30 410	62.82 466	37.14 435	55.33 467	71.79 346	47.34 404	55.09 421	70.52 439	21.91 470	70.45 397	35.72 438	42.97 487	70.31 462

miper_refine_blended		53.45 419	51.50 428	59.30 410	62.82 466	37.14 435	55.33 467	71.79 346	47.34 404	55.09 421	70.52 439	21.91 470	70.45 397	35.72 438	42.97 487	70.31 462

MDA-MVSNet-bldmvs		53.87 417	50.81 430	63.05 384	66.25 450	48.58 298	56.93 464	63.82 419	48.09 390	41.22 486	70.48 441	30.34 409	68.00 413	34.24 443	45.92 483	72.57 433

usedtu_dtu_shiyan2		53.34 422	50.78 431	61.00 403	61.86 473	39.63 410	68.47 378	64.58 410	42.94 445	45.22 477	67.61 463	19.25 476	66.71 423	28.08 478	59.05 442	76.66 390

TDRefinement		53.44 421	50.72 432	61.60 394	64.31 461	46.96 324	70.89 344	65.27 404	41.78 450	44.61 480	77.98 343	11.52 495	66.36 426	28.57 477	51.59 471	71.49 450

test_fmvs1		51.32 433	50.48 433	53.81 448	53.57 491	37.51 432	60.63 447	51.16 477	28.02 488	63.62 306	69.23 455	16.41 482	53.93 484	51.01 294	60.70 432	69.99 465

test_fmvs1_n		51.37 431	50.35 434	54.42 445	52.85 493	37.71 430	61.16 443	51.93 474	28.15 486	63.81 305	69.73 451	13.72 487	53.95 483	51.16 293	60.65 433	71.59 448

PM-MVS		52.33 426	50.19 435	58.75 418	62.10 471	45.14 343	65.75 399	40.38 502	43.60 438	53.52 441	72.65 418	9.16 501	65.87 431	50.41 298	54.18 461	65.24 478

YYNet1		50.73 434	48.96 436	56.03 435	61.10 477	41.78 386	51.94 478	56.44 461	40.94 458	44.84 478	67.80 461	30.08 414	55.08 481	36.77 427	50.71 473	71.22 453

MDA-MVSNet_test_wron		50.71 435	48.95 437	56.00 436	61.17 476	41.84 385	51.90 479	56.45 460	40.96 457	44.79 479	67.84 460	30.04 415	55.07 482	36.71 429	50.69 474	71.11 456

UnsupCasMVSNet_bld		50.07 437	48.87 438	53.66 449	60.97 480	33.67 468	57.62 461	64.56 411	39.47 467	47.38 469	64.02 478	27.47 441	59.32 457	34.69 442	43.68 486	67.98 474

ADS-MVSNet2		51.33 432	48.76 439	59.07 416	66.02 453	44.60 350	50.90 481	59.76 447	36.90 470	50.74 456	66.18 472	26.38 451	63.11 443	27.17 482	54.76 459	69.50 468

test_vis1_n		49.89 438	48.69 440	53.50 451	53.97 490	37.38 433	61.53 437	47.33 491	28.54 485	59.62 366	67.10 468	13.52 488	52.27 489	49.07 310	57.52 446	70.84 458

Patchmatch-test		49.08 439	48.28 441	51.50 463	64.40 460	30.85 483	45.68 494	48.46 486	35.60 474	46.10 476	72.10 424	34.47 362	46.37 498	27.08 484	60.65 433	77.27 381

ADS-MVSNet		48.48 441	47.77 442	50.63 464	66.02 453	29.92 485	50.90 481	50.87 481	36.90 470	50.74 456	66.18 472	26.38 451	52.47 488	27.17 482	54.76 459	69.50 468

new-patchmatchnet		47.56 443	47.73 443	47.06 468	58.81 487	9.37 520	48.78 487	59.21 449	43.28 441	44.22 481	68.66 458	25.67 457	57.20 470	31.57 463	49.35 478	74.62 417

JIA-IIPM		51.56 430	47.68 444	63.21 382	64.61 459	50.73 241	47.71 490	58.77 451	42.90 446	48.46 467	51.72 493	24.97 461	70.24 401	36.06 437	53.89 464	68.64 472

test_fmvs2		48.69 440	47.49 445	52.29 460	48.63 500	33.06 473	57.76 459	48.05 489	25.71 492	59.76 364	69.60 453	11.57 494	52.23 490	49.45 308	56.86 449	71.58 449

CHOSEN 280x420		47.83 442	46.36 446	52.24 461	67.37 441	49.78 267	38.91 502	43.11 500	35.00 475	43.27 484	63.30 479	28.95 424	49.19 494	36.53 432	60.80 430	57.76 487

PVSNet_0	43.31 20	47.46 444	45.64 447	52.92 455	67.60 439	44.65 347	54.06 473	54.64 468	41.59 453	46.15 475	58.75 486	30.99 405	58.66 462	32.18 452	24.81 503	55.46 490

MVS-HIRNet		45.52 446	44.48 448	48.65 467	68.49 429	34.05 465	59.41 452	44.50 497	27.03 489	37.96 496	50.47 499	26.16 454	64.10 437	26.74 485	59.52 438	47.82 497

WB-MVS		43.26 449	43.41 449	42.83 477	63.32 465	10.32 519	58.17 456	45.20 494	45.42 423	40.44 489	67.26 467	34.01 369	58.98 460	11.96 509	24.88 502	59.20 482

ttmdpeth		45.56 445	42.95 450	53.39 453	52.33 496	29.15 487	57.77 458	48.20 488	31.81 481	49.86 463	77.21 363	8.69 502	59.16 459	27.31 481	33.40 499	71.84 446

test_fmvs3		44.30 448	42.55 451	49.55 466	42.83 505	27.15 497	53.03 475	44.93 495	22.03 500	53.69 438	64.94 475	4.21 509	49.63 493	47.47 321	49.82 476	71.88 444

LF4IMVS		42.95 450	42.26 452	45.04 471	48.30 501	32.50 475	54.80 470	48.49 485	28.03 487	40.51 488	70.16 442	9.24 500	43.89 501	31.63 461	49.18 479	58.72 484

SSC-MVS		41.96 454	41.99 453	41.90 478	62.46 470	9.28 521	57.41 462	44.32 498	43.38 440	38.30 495	66.45 470	32.67 391	58.42 464	10.98 511	21.91 505	57.99 486

pmmvs3		44.92 447	41.95 454	53.86 447	52.58 495	43.55 362	62.11 435	46.90 493	26.05 491	40.63 487	60.19 483	11.08 498	57.91 466	31.83 460	46.15 482	60.11 481

FPMVS		42.18 453	41.11 455	45.39 470	58.03 488	41.01 396	49.50 485	53.81 473	30.07 483	33.71 498	64.03 476	11.69 492	52.08 491	14.01 504	55.11 457	43.09 499

N_pmnet		39.35 459	40.28 456	36.54 484	63.76 462	1.62 539	49.37 486	0.76 537	34.62 476	43.61 483	66.38 471	26.25 453	42.57 502	26.02 487	51.77 470	65.44 476

test_vis1_rt		41.35 456	39.45 457	47.03 469	46.65 504	37.86 427	47.76 489	38.65 503	23.10 496	44.21 482	51.22 497	11.20 497	44.08 500	39.27 411	53.02 466	59.14 483

MVStest1		42.65 451	39.29 458	52.71 457	47.26 503	34.58 460	54.41 472	50.84 482	23.35 494	39.31 494	74.08 410	12.57 490	55.09 480	23.32 491	28.47 501	68.47 473

DSMNet-mixed		39.30 460	38.72 459	41.03 479	51.22 497	19.66 509	45.53 495	31.35 509	15.83 507	39.80 491	67.42 466	22.19 468	45.13 499	22.43 492	52.69 467	58.31 485

EGC-MVSNET		42.47 452	38.48 460	54.46 444	74.33 312	48.73 294	70.33 356	51.10 478	0.03 557	0.18 556	67.78 462	13.28 489	66.49 425	18.91 500	50.36 475	48.15 495

mvsany_test1		39.38 458	38.16 461	43.02 476	49.05 498	34.28 463	44.16 498	25.94 513	22.74 498	46.57 474	62.21 482	23.85 465	41.16 506	33.01 450	35.91 495	53.63 491

ANet_high		41.38 455	37.47 462	53.11 454	39.73 511	24.45 503	56.94 463	69.69 363	47.65 398	26.04 503	52.32 492	12.44 491	62.38 446	21.80 494	10.61 514	72.49 435

LCM-MVSNet		40.30 457	35.88 463	53.57 450	42.24 506	29.15 487	45.21 496	60.53 446	22.23 499	28.02 501	50.98 498	3.72 511	61.78 448	31.22 466	38.76 493	69.78 467

dongtai		34.52 464	34.94 464	33.26 487	61.06 478	16.00 513	52.79 477	23.78 515	40.71 459	39.33 493	48.65 503	16.91 481	48.34 495	12.18 508	19.05 507	35.44 507

APD_test1		37.39 461	34.94 464	44.72 474	48.88 499	33.19 472	52.95 476	44.00 499	19.49 501	27.28 502	58.59 487	3.18 513	52.84 487	18.92 499	41.17 490	48.14 496

new_pmnet		34.13 465	34.29 466	33.64 486	52.63 494	18.23 511	44.43 497	33.90 508	22.81 497	30.89 500	53.18 491	10.48 499	35.72 510	20.77 497	39.51 491	46.98 498

PMVS	28.69 22	36.22 462	33.29 467	45.02 472	36.82 513	35.98 450	54.68 471	48.74 484	26.31 490	21.02 508	51.61 495	2.88 514	60.10 454	9.99 515	47.58 480	38.99 505
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 463	31.91 468	43.33 475	62.05 472	37.87 426	20.39 509	67.03 388	23.23 495	18.41 510	25.84 516	4.24 508	62.73 444	14.71 503	51.32 472	29.38 508
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		31.86 468	31.05 469	34.28 485	32.33 517	21.86 507	32.34 505	30.46 510	16.02 506	39.78 492	55.45 490	4.80 507	32.36 512	30.61 467	37.66 494	48.64 493

kuosan		29.62 471	30.82 470	26.02 492	52.99 492	16.22 512	51.09 480	22.71 516	33.91 478	33.99 497	40.85 505	15.89 484	33.11 511	7.59 522	18.37 508	28.72 509

mvsany_test3		32.62 466	30.57 471	38.77 482	36.16 514	24.20 504	38.10 503	20.63 517	19.14 502	40.36 490	57.43 488	5.06 506	36.63 509	29.59 474	28.66 500	55.49 489

test_vis3_rt		32.09 467	30.20 472	37.76 483	35.36 515	27.48 493	40.60 501	28.29 512	16.69 505	32.52 499	40.53 507	1.96 517	37.40 508	33.64 447	42.21 489	48.39 494

testf1		31.46 469	28.89 473	39.16 480	41.99 508	28.78 489	46.45 492	37.56 504	14.28 508	21.10 506	48.96 500	1.48 519	47.11 496	13.63 505	34.56 496	41.60 501

APD_test2		31.46 469	28.89 473	39.16 480	41.99 508	28.78 489	46.45 492	37.56 504	14.28 508	21.10 506	48.96 500	1.48 519	47.11 496	13.63 505	34.56 496	41.60 501

PMMVS2		27.40 472	25.91 475	31.87 489	39.46 512	6.57 524	31.17 506	28.52 511	23.96 493	20.45 509	48.94 502	4.20 510	37.94 507	16.51 501	19.97 506	51.09 492

cdsmvs_eth3d_5k		17.50 479	23.34 476	0.00 542	0.00 566	0.00 568	0.00 554	78.63 206	0.00 561	0.00 562	82.18 260	49.25 170	0.00 560	0.00 561	0.00 559	0.00 558

E-PMN		23.77 473	22.73 477	26.90 490	42.02 507	20.67 508	42.66 499	35.70 506	17.43 503	10.28 520	25.05 517	6.42 504	42.39 504	10.28 514	14.71 510	17.63 514

EMVS		22.97 474	21.84 478	26.36 491	40.20 510	19.53 510	41.95 500	34.64 507	17.09 504	9.73 521	22.83 519	7.29 503	42.22 505	9.18 517	13.66 512	17.32 515

ArgMatch-Sym		21.00 476	19.89 479	24.35 495	23.32 518	15.10 514	32.50 504	4.90 523	11.83 510	24.09 504	51.35 496	0.56 522	19.55 516	21.24 495	9.18 517	38.40 506

ArgMatch-SfM		20.82 477	19.10 480	25.97 493	21.54 519	13.77 515	29.84 508	6.08 522	9.69 512	22.36 505	51.71 494	0.53 523	21.69 515	20.98 496	9.18 517	42.43 500

test_method		19.68 478	18.10 481	24.41 494	13.68 523	3.11 533	12.06 516	42.37 501	2.00 522	11.97 516	36.38 508	5.77 505	29.35 514	15.06 502	23.65 504	40.76 503

MVE	17.77 23	21.41 475	17.77 482	32.34 488	34.34 516	25.44 501	16.11 511	24.11 514	11.19 511	13.22 514	31.92 511	1.58 518	30.95 513	10.47 513	17.03 509	40.62 504
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DenseAffine		14.16 480	13.16 483	17.15 496	17.01 521	8.89 522	19.68 510	2.17 526	7.89 513	15.00 512	40.64 506	0.19 526	15.28 518	11.16 510	4.69 522	27.27 510

wuyk23d		13.32 481	12.52 484	15.71 497	47.54 502	26.27 499	31.06 507	1.98 527	4.93 517	5.18 528	1.94 543	0.45 524	18.54 517	6.81 523	12.83 513	2.33 530

RoMa-SfM		11.96 482	11.39 485	13.68 498	10.24 525	6.80 523	15.83 512	1.33 530	6.34 515	13.06 515	41.41 504	0.16 527	12.72 519	10.58 512	3.56 525	21.52 511

tmp_tt		9.43 485	11.14 486	4.30 510	2.38 540	4.40 526	13.62 514	16.08 519	0.39 530	15.89 511	13.06 527	15.80 485	5.54 528	12.63 507	10.46 515	2.95 528

DKM		10.33 483	10.10 487	11.02 500	10.54 524	5.43 525	14.18 513	1.03 533	4.97 516	11.74 517	36.09 509	0.11 531	9.09 523	9.38 516	2.85 526	18.53 513

VLMVS_CLIP		8.61 487	9.36 488	6.34 507	7.07 529	4.23 528	8.66 521	10.16 521	1.75 523	13.91 513	20.41 521	2.33 515	10.32 522	6.21 524	13.74 511	4.49 525

LoFTR		9.45 484	9.00 489	10.79 501	10.22 526	4.31 527	11.11 517	4.11 524	2.40 521	10.53 519	30.89 512	0.13 528	10.75 521	3.12 527	8.52 519	17.31 516

PDCNetPlus		9.23 486	8.89 490	10.23 502	13.70 522	3.70 529	12.27 515	1.51 529	3.98 518	6.73 526	29.50 514	0.24 525	8.07 525	7.83 520	4.30 523	18.93 512

ab-mvs-re		6.49 491	8.65 491	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	77.89 350	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

RoMa-HiRes		8.28 488	8.27 492	8.28 503	6.12 530	3.67 530	10.07 519	0.74 538	3.93 519	9.17 522	34.46 510	0.12 530	7.12 526	7.80 521	2.05 532	14.04 518

DKM-HiRes		7.91 489	7.93 493	7.83 504	7.35 528	3.58 531	10.03 520	0.66 540	3.58 520	9.05 523	30.62 513	0.08 538	5.66 527	8.09 518	1.91 533	14.26 517

MatchFormer		7.03 490	6.96 494	7.26 505	7.64 527	3.36 532	10.21 518	3.04 525	1.31 524	9.02 524	22.94 518	0.08 538	8.15 524	1.46 531	6.91 520	10.26 521

test123		4.73 492	6.30 495	0.02 540	0.01 564	0.01 567	56.36 465	0.00 566	0.01 558	0.04 560	0.21 559	0.01 558	0.00 560	0.03 553	0.00 559	0.04 556

testmvs		4.52 493	6.03 496	0.01 541	0.01 564	0.00 568	53.86 474	0.00 566	0.01 558	0.04 560	0.27 558	0.00 564	0.00 560	0.04 545	0.00 559	0.03 557

pcd_1.5k_mvsjas		3.92 498	5.23 497	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	47.05 201	0.00 560	0.00 561	0.00 559	0.00 558

MVS_clip		4.22 496	4.98 498	1.95 513	5.46 532	1.99 534	3.96 523	0.34 544	0.36 531	7.04 525	17.25 522	0.66 521	0.80 538	4.04 525	5.70 521	3.07 527

PMatch-SfM		4.42 494	4.43 499	4.39 509	2.90 536	1.50 540	4.85 522	0.36 543	1.17 525	4.73 530	20.99 520	0.01 558	3.26 531	3.74 526	1.10 540	8.40 523

MASt3R-SfM		3.33 499	3.70 500	2.21 512	2.02 544	1.04 542	3.52 527	1.05 532	0.67 528	4.93 529	16.68 523	0.10 533	1.50 535	2.06 529	2.29 531	4.09 526

GLUNet-SfM		4.33 495	3.64 501	6.41 506	3.38 535	1.65 537	3.23 529	1.54 528	0.66 529	6.36 527	15.13 526	0.08 538	5.54 528	0.94 533	1.44 536	12.05 520

ELoFTR		4.04 497	3.55 502	5.50 508	2.33 541	1.25 541	3.58 525	1.18 531	0.90 526	4.23 532	16.28 524	0.03 546	5.46 530	1.95 530	1.42 537	9.81 522

PMatch-Up-SfM		3.14 500	3.26 503	2.81 511	1.97 545	1.00 544	3.35 528	0.23 550	0.79 527	3.44 533	16.19 525	0.01 558	2.11 532	2.62 528	0.70 553	5.32 524

ALIKED-LG		2.35 501	2.54 504	1.78 514	5.54 531	1.79 536	3.81 524	0.96 534	0.33 532	1.86 535	7.18 529	0.13 528	1.60 533	0.20 542	2.81 527	1.94 531

VLMVS		2.25 502	2.47 505	1.62 516	2.41 539	1.01 543	1.61 535	0.72 539	0.07 556	4.27 531	6.17 531	2.11 516	1.03 537	1.17 532	3.66 524	2.83 529

ALIKED-MNN		2.09 503	2.23 506	1.67 515	5.15 533	1.82 535	3.53 526	0.77 535	0.25 533	1.45 537	6.03 532	0.09 536	1.52 534	0.17 543	2.64 529	1.66 532

ALIKED-NN		1.96 504	2.12 507	1.48 517	4.72 534	1.65 537	3.19 530	0.77 535	0.23 534	1.43 538	5.87 533	0.10 533	1.37 536	0.16 544	2.61 530	1.42 538

MVS_baseline		1.38 505	1.71 508	0.39 528	1.08 559	0.02 566	0.39 552	0.06 564	0.01 558	2.77 534	7.83 528	0.07 543	0.00 560	0.47 535	2.72 528	1.14 540

XFeat-MNN		1.07 506	1.17 509	0.77 519	0.52 562	0.31 559	1.15 537	0.41 541	0.15 538	1.62 536	4.35 534	0.07 543	0.77 539	0.38 536	1.88 534	1.22 539

SP-DiffGlue		0.98 507	1.05 510	0.75 522	0.81 561	0.40 551	1.24 536	0.37 542	0.19 535	1.26 540	3.80 535	0.11 531	0.34 545	0.51 534	1.18 538	1.52 536

SP-LightGlue		0.94 508	0.99 511	0.78 518	2.60 537	0.38 552	1.71 531	0.34 544	0.17 536	0.50 542	2.14 539	0.09 536	0.38 542	0.26 538	1.13 539	1.59 533

SP-SuperGlue		0.93 509	0.98 512	0.77 519	2.54 538	0.38 552	1.70 532	0.34 544	0.17 536	0.52 541	2.13 540	0.10 533	0.36 544	0.26 538	1.10 540	1.57 535

XFeat-NN		0.87 511	0.97 513	0.59 524	0.48 563	0.24 562	0.94 538	0.29 549	0.12 541	1.41 539	3.45 538	0.06 545	0.56 540	0.29 537	1.65 535	0.95 541

SP-MNN		0.89 510	0.93 514	0.77 519	2.32 542	0.34 556	1.68 533	0.33 547	0.13 540	0.49 543	2.07 541	0.08 538	0.39 541	0.25 540	1.07 542	1.58 534

SP-NN		0.85 512	0.90 515	0.73 523	2.22 543	0.33 558	1.63 534	0.31 548	0.14 539	0.47 544	1.97 542	0.08 538	0.38 542	0.25 540	1.01 543	1.47 537

SIFT-NN		0.60 513	0.65 516	0.45 525	1.90 546	0.55 545	0.90 539	0.16 551	0.10 542	0.34 545	1.43 544	0.02 547	0.28 546	0.04 545	0.95 544	0.50 542

SIFT-MNN		0.56 514	0.61 517	0.43 526	1.75 547	0.50 546	0.82 540	0.16 551	0.10 542	0.30 546	1.38 545	0.02 547	0.28 546	0.04 545	0.92 546	0.50 542

SIFT-NN-NCMNet		0.53 515	0.58 518	0.40 527	1.60 549	0.49 547	0.80 541	0.15 553	0.09 545	0.28 548	1.29 546	0.02 547	0.27 548	0.04 545	0.94 545	0.44 546

SIFT-NCM-Cal		0.51 516	0.55 519	0.38 529	1.66 548	0.45 548	0.75 542	0.12 554	0.09 545	0.21 553	1.18 551	0.02 547	0.27 548	0.03 553	0.89 547	0.43 548

SIFT-NN-CMatch		0.49 517	0.53 520	0.38 529	1.35 553	0.41 550	0.70 544	0.12 554	0.09 545	0.30 546	1.28 548	0.02 547	0.26 550	0.04 545	0.83 549	0.47 544

SIFT-NN-UMatch		0.48 518	0.52 521	0.36 531	1.27 555	0.36 554	0.75 542	0.12 554	0.10 542	0.25 550	1.29 546	0.02 547	0.26 550	0.04 545	0.85 548	0.44 546

SIFT-ConvMatch		0.48 518	0.52 521	0.35 532	1.51 550	0.42 549	0.64 546	0.11 557	0.09 545	0.26 549	1.24 549	0.02 547	0.25 552	0.04 545	0.76 551	0.38 549

SIFT-UMatch		0.45 520	0.50 523	0.32 534	1.46 551	0.34 556	0.66 545	0.10 559	0.09 545	0.22 552	1.19 550	0.02 547	0.25 552	0.04 545	0.73 552	0.36 551

SIFT-NN-PointCN		0.44 521	0.47 524	0.33 533	1.17 556	0.29 560	0.64 546	0.11 557	0.09 545	0.25 550	1.14 552	0.02 547	0.25 552	0.03 553	0.78 550	0.46 545

SIFT-UM-Cal		0.41 523	0.46 525	0.28 536	1.35 553	0.29 560	0.57 548	0.08 561	0.09 545	0.20 554	1.10 553	0.02 547	0.23 555	0.03 553	0.68 554	0.30 554

SIFT-CM-Cal		0.42 522	0.46 525	0.31 535	1.40 552	0.35 555	0.56 549	0.09 560	0.09 545	0.20 554	1.09 554	0.02 547	0.23 555	0.03 553	0.66 555	0.34 552

SIFT-PCN-Cal		0.36 524	0.39 527	0.26 537	1.16 557	0.21 563	0.46 551	0.07 563	0.08 553	0.17 557	0.92 555	0.01 558	0.20 558	0.03 553	0.59 557	0.37 550

SIFT-PointCN		0.36 524	0.39 527	0.25 538	1.14 558	0.21 563	0.50 550	0.08 561	0.08 553	0.17 557	0.89 556	0.01 558	0.21 557	0.03 553	0.60 556	0.34 552

SIFT-NCMNet		0.30 526	0.33 529	0.19 539	1.04 560	0.18 565	0.39 552	0.05 565	0.08 553	0.14 559	0.77 557	0.01 558	0.16 559	0.02 560	0.49 558	0.22 555

mmdepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

monomultidepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

test_blank		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

uanet_test		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

DCPMVS		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

sosnet-low-res		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

sosnet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

uncertanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

Regformer		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

uanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 568	0.00 554	0.00 566	0.00 561	0.00 562	0.00 560	0.00 564	0.00 560	0.00 561	0.00 559	0.00 558

Meshroom													0.00 560
: In preparation.
AliceVision / Meshro													0.00 560
: In preparation.
AliceVision_Meshroom													0.00 560
: In preparation.
PatchmatchNet2					0.00 566	13.27 516	48.02 488	44.92 496	34.52 477
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														25.92 488	51.90 469	65.44 476
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													42.51 503
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					86.59 25	59.16 67		86.47 15		82.32 18		62.54 14	89.91 16	77.25 30	89.69 18

aaatest				79.09 23	85.30 51	59.25 64	86.84 11	85.86 24	60.95 107	83.65 12	90.57 28		89.91 16	77.02 35	89.43 24	88.10 45

TestfortrainingZip				78.05 44	84.66 63	58.22 88	86.84 11	85.98 23	63.31 49	79.39 26	88.94 66	62.01 16	89.61 22		86.45 64	86.34 124

WAC-MVS						27.31 495								27.77 479

FOURS1					86.12 38	60.82 37	88.18 1	83.61 85	60.87 109	81.50 21

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

PC_three_1452									55.09 262	84.46 4	89.84 53	66.68 5	89.41 24	74.24 62	91.38 2	88.42 32

No_MVS				79.95 4	87.24 14	61.04 31		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 21	83.80 11	91.86 6	64.03 12

eth-test2					0.00 566

eth-test					0.00 566

ZD-MVS					86.64 21	60.38 45		82.70 119	57.95 188	78.10 36	90.06 46	56.12 55	88.84 32	74.05 65	87.00 55

IU-MVS					87.77 4	59.15 69		85.53 33	53.93 292	84.64 3				79.07 13	90.87 5	88.37 34

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 52	67.01 1	90.33 12	73.16 72	91.15 4	88.23 40

test_241102_TWO								86.73 12	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 64

test_241102_ONE					87.77 4	58.90 79		86.78 10	64.20 34	85.97 1	91.34 19	66.87 3	90.78 7

save fliter					86.17 35	61.30 28	83.98 58	79.66 182	59.00 160

test_0728_THIRD									65.04 17	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 47

test_0728_SECOND				79.19 16	87.82 3	59.11 72	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 66

test0726					87.75 7	59.07 74	87.86 4	86.83 8	64.26 32	84.19 7	91.92 5	64.82 8

GSMVS																78.05 368

test_part2					87.58 9	60.47 42				83.42 14

sam_mvs1												34.74 358				78.05 368

sam_mvs												33.43 376

ambc				65.13 366	63.72 464	37.07 437	47.66 491	78.78 202		54.37 432	71.42 430	11.24 496	80.94 242	45.64 349	53.85 465	77.38 379

MTGPA								80.97 161

test_post1							68.67 376				3.64 536	32.39 398	69.49 403	44.17 368

test_post											3.55 537	33.90 370	66.52 424

patchmatchnet-post											64.03 476	34.50 360	74.27 373

GG-mvs-BLEND				62.34 388	71.36 375	37.04 438	69.20 372	57.33 459		54.73 426	65.48 474	30.37 408	77.82 323	34.82 441	74.93 246	72.17 442

MTMP							86.03 23	17.08 518

gm-plane-assit					71.40 374	41.72 389			48.85 376		73.31 415		82.48 205	48.90 312

test9_res														75.28 55	88.31 36	83.81 234

TEST9					85.58 45	61.59 24	81.62 91	81.26 149	55.65 246	74.93 67	88.81 69	53.70 92	84.68 140

test_8					85.40 48	60.96 34	81.54 94	81.18 153	55.86 238	74.81 72	88.80 71	53.70 92	84.45 144

agg_prior2														73.09 73	87.93 44	84.33 211

agg_prior					85.04 55	59.96 50		81.04 158		74.68 77			84.04 151

TestCases				64.39 371	71.44 371	49.03 285		67.30 383	45.97 419	47.16 470	79.77 312	17.47 477	67.56 417	33.65 445	59.16 440	76.57 391

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 125	75.01 65	89.06 62	56.22 51		72.19 81	88.96 28

test_prior				76.69 67	84.20 67	57.27 100		84.88 46					86.43 91			86.38 120

旧先验2							76.08 228		45.32 424	76.55 50			65.56 432	58.75 230

新几何2							76.12 226

新几何1				70.76 259	85.66 43	61.13 30		66.43 393	44.68 428	70.29 164	86.64 129	41.29 279	75.23 368	49.72 304	81.75 116	75.93 397

旧先验1					83.04 80	53.15 183		67.52 382			87.85 90	44.08 239			80.76 125	78.03 371

无先验							79.66 123	74.30 313	48.40 384				80.78 249	53.62 272		79.03 357

原ACMM2							79.02 131

原ACMM1				74.69 109	85.39 49	59.40 59		83.42 91	51.47 338	70.27 165	86.61 133	48.61 178	86.51 89	53.85 271	87.96 43	78.16 366

test222					83.14 78	58.68 83	72.57 314	63.45 424	41.78 450	67.56 229	86.12 151	37.13 333			78.73 177	74.98 410

testdata2													72.18 387	46.95 337

segment_acmp												54.23 79

testdata				64.66 368	81.52 100	52.93 188		65.29 403	46.09 417	73.88 94	87.46 97	38.08 322	66.26 427	53.31 276	78.48 184	74.78 414

testdata1							72.65 309		60.50 119

test12				77.76 51	84.52 64	58.41 85		83.36 94		72.93 121		54.61 76	88.05 45		88.12 38	86.81 101

plane_prior7					81.41 103	55.96 123

plane_prior6					81.20 110	56.24 118						45.26 226

plane_prior5								84.01 60					87.21 65	68.16 114	80.58 129	84.65 202

plane_prior4											86.10 152

plane_prior3						56.09 120			63.92 39	69.27 185

plane_prior2							84.22 51		64.52 28

plane_prior1					81.27 108

plane_prior						56.31 114	83.58 64		63.19 56						80.48 133

n2								0.00 566

nn								0.00 566

door-mid								47.19 492

lessismore_v0				69.91 278	71.42 373	47.80 313		50.90 480		50.39 460	75.56 393	27.43 443	81.33 229	45.91 346	34.10 498	80.59 323

LGP-MVS_train				75.76 85	80.22 125	57.51 98		83.40 92	61.32 98	66.67 248	87.33 103	39.15 305	86.59 82	67.70 125	77.30 208	83.19 258

test11								83.47 89

door								47.60 490

HQP5-MVS						54.94 145

HQP-NCC					80.66 117		82.31 82		62.10 83	67.85 218

ACMP_Plane					80.66 117		82.31 82		62.10 83	67.85 218

BP-MVS														67.04 136

HQP4-MVS										67.85 218			86.93 73			84.32 212

HQP3-MVS								83.90 65							80.35 135

HQP2-MVS												45.46 220

NP-MVS					80.98 113	56.05 122					85.54 175

MDTV_nov1_ep13_2view						25.89 500	61.22 441		40.10 463	51.10 453		32.97 382		38.49 415		78.61 362

ACMMP++_ref															74.07 256

ACMMP++															72.16 297

Test By Simon												48.33 181

ITE_SJBPF				62.09 390	66.16 451	44.55 352		64.32 412	47.36 403	55.31 417	80.34 301	19.27 475	62.68 445	36.29 435	62.39 418	79.04 356

DeepMVS_CX				12.03 499	17.97 520	10.91 518		10.60 520	7.46 514	11.07 518	28.36 515	3.28 512	11.29 520	8.01 519	9.74 516	13.89 519

High-resolution multi-view benchmark