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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 11	97.13 2	95.58 11	89.33 1	85.77 71	96.26 46	72.84 32	99.38 1	92.64 33	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 106	71.10 28	97.02 3	96.04 6	88.70 2	91.57 19	96.19 48	70.12 49	98.91 21	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 170	76.68 2	97.29 1	95.35 17	82.87 37	91.58 18	97.22 8	79.93 5	99.10 9	83.12 129	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 44	93.96 91	94.37 64	72.48 243	92.07 11	96.85 27	83.82 2	99.15 2	91.53 47	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 114	92.83 85	64.03 240	93.06 136	94.33 66	82.19 45	93.65 3	96.15 50	85.89 1	97.19 98	91.02 51	97.75 1	96.43 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MGCNet		90.32 6	90.90 7	88.55 23	94.05 49	70.23 38	97.00 5	93.73 85	87.30 4	92.15 8	96.15 50	66.38 75	98.94 20	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 31	96.47 14	94.83 36	84.83 17	89.07 43	96.80 30	70.86 45	99.06 15	92.64 33	95.71 11	96.12 41

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 76	73.88 9	97.01 4	94.40 62	88.32 3	85.71 72	94.91 91	74.11 23	98.91 21	87.26 79	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 60	96.89 6	94.44 55	71.65 273	92.11 9	97.21 9	76.79 9	99.11 6	92.34 36	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 172	93.00 81	58.16 383	96.72 9	94.41 60	86.50 9	90.25 34	97.83 1	75.46 16	98.67 29	92.78 32	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 117	96.04 25	63.70 257	95.04 43	95.19 22	86.74 8	91.53 20	95.15 84	73.86 24	97.58 69	93.38 27	92.00 75	96.28 38

CANet		89.61 12	89.99 12	88.46 24	94.39 43	69.71 53	96.53 13	93.78 78	86.89 7	89.68 40	95.78 57	65.94 80	99.10 9	92.99 30	93.91 46	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 40	96.64 10	94.52 51	71.92 259	90.55 30	96.93 21	73.77 25	99.08 11	91.91 42	94.90 22	96.29 36
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
HPM-MVS++		89.37 14	89.95 13	87.64 36	95.10 31	68.23 104	95.24 34	94.49 53	82.43 42	88.90 45	96.35 41	71.89 42	98.63 30	88.76 65	96.40 6	96.06 42

balanced_conf03		89.08 15	88.84 23	89.81 6	93.66 58	75.15 5	90.61 279	93.43 100	84.06 24	86.20 66	90.17 226	72.42 37	96.98 115	93.09 29	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 29	96.60 10	69.05 76	96.38 15	94.64 46	84.42 21	86.74 61	96.20 47	66.56 74	98.76 27	89.03 64	94.56 34	95.92 51

MED-MVS		88.94 17	89.45 16	87.42 46	94.76 34	67.28 130	94.47 64	94.87 32	70.09 305	91.27 23	96.95 17	76.77 11	98.98 16	91.55 44	94.28 37	95.99 47

DPE-MVS		88.77 18	89.21 19	87.45 45	96.26 21	67.56 123	94.17 77	94.15 71	68.77 326	90.74 28	97.27 6	76.09 14	98.49 33	90.58 55	94.91 21	96.30 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		88.66 19	88.99 21	87.70 34	94.76 34	68.73 86	94.47 64	94.87 32	73.09 230	91.27 23	96.95 17	76.77 11	98.98 16	84.41 112	94.28 37	95.37 73

ME-MVS		88.25 20	88.55 27	87.33 51	96.33 18	67.28 130	93.93 93	94.81 37	70.09 305	88.91 44	96.95 17	70.12 49	98.73 28	91.55 44	94.28 37	95.99 47

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 161	88.15 231	61.94 308	95.65 25	89.70 304	85.54 12	92.07 11	97.33 5	67.51 66	97.27 93	96.23 5	92.07 74	95.35 77

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 156	89.29 181	61.41 325	92.97 141	88.36 359	86.96 6	91.49 21	97.49 3	69.48 54	97.46 76	97.00 1	89.88 112	95.89 53

SMA-MVS		88.14 22	88.29 31	87.67 35	93.21 73	68.72 88	93.85 99	94.03 74	74.18 203	91.74 15	96.67 33	65.61 85	98.42 37	89.24 61	96.08 7	95.88 54
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
PS-MVSNAJ		88.14 22	87.61 41	89.71 7	92.06 109	76.72 1	95.75 20	93.26 106	83.86 25	89.55 41	96.06 52	53.55 269	97.89 51	91.10 49	93.31 57	94.54 135

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 91	91.73 124	68.04 109	90.36 286	93.55 92	82.89 35	91.29 22	92.89 146	72.27 39	96.03 169	87.99 69	94.77 26	95.54 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 198	89.07 189	61.60 318	94.87 51	89.06 332	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 187

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 151	88.43 218	61.78 311	94.73 59	91.74 182	85.87 10	91.66 17	97.50 2	64.03 106	98.33 38	96.28 4	90.08 108	95.10 94

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 74	93.51 66	65.32 194	95.15 37	93.84 77	78.17 130	85.93 70	94.80 94	75.80 15	98.21 40	89.38 58	88.78 124	96.59 19

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 32	95.86 28	68.32 98	95.74 21	94.11 72	83.82 26	83.49 97	96.19 48	64.53 101	98.44 35	83.42 128	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 124	88.69 200	63.71 255	94.56 62	90.22 280	85.04 15	92.27 6	97.05 12	63.67 114	98.15 42	95.09 12	91.39 87	95.27 85

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 12	91.41 136	76.43 3	95.74 21	93.12 114	83.53 29	89.55 41	95.95 55	53.45 273	97.68 59	91.07 50	92.62 65	94.54 135

EPNet		87.84 32	88.38 29	86.23 104	93.30 70	66.05 173	95.26 33	94.84 35	87.09 5	88.06 48	94.53 100	66.79 71	97.34 86	83.89 119	91.68 81	95.29 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 33	87.77 38	87.63 40	89.24 186	71.18 25	96.57 12	92.90 125	82.70 39	87.13 56	95.27 77	64.99 91	95.80 185	89.34 59	91.80 79	95.93 50

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 144	87.05 266	63.55 264	93.69 109	91.08 227	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 109	94.26 155

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 141	86.92 277	62.63 291	95.02 45	90.28 275	84.95 16	90.27 33	96.86 25	65.36 87	97.52 74	94.93 15	90.03 109	95.76 57

APDe-MVS		87.54 35	87.84 37	86.65 77	96.07 24	66.30 168	94.84 53	93.78 78	69.35 315	88.39 47	96.34 42	67.74 64	97.66 64	90.62 54	93.44 55	96.01 45
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 210	86.89 279	60.04 359	95.05 41	92.17 161	84.80 18	92.27 6	96.37 39	64.62 98	96.54 141	94.43 19	91.86 77	94.94 103

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 135	86.95 272	64.37 226	94.30 74	88.45 357	80.51 70	92.70 4	96.86 25	69.98 51	97.15 103	95.83 7	88.08 132	94.65 128

SD-MVS		87.49 38	87.49 43	87.50 44	93.60 60	68.82 83	93.90 96	92.63 140	76.86 158	87.90 50	95.76 58	66.17 77	97.63 66	89.06 63	91.48 85	96.05 43
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
fmvsm_l_conf0.5_n_a		87.44 40	88.15 34	85.30 141	87.10 264	64.19 235	94.41 69	88.14 367	80.24 81	92.54 5	96.97 16	69.52 53	97.17 99	95.89 6	88.51 127	94.56 132

dcpmvs_2		87.37 41	87.55 42	86.85 63	95.04 33	68.20 106	90.36 286	90.66 255	79.37 105	81.20 121	93.67 130	74.73 18	96.55 140	90.88 52	92.00 75	95.82 55

alignmvs		87.28 42	86.97 49	88.24 28	91.30 138	71.14 27	95.61 26	93.56 91	79.30 106	87.07 58	95.25 79	68.43 56	96.93 123	87.87 70	84.33 183	96.65 17

train_agg		87.21 43	87.42 44	86.60 80	94.18 45	67.28 130	94.16 78	93.51 94	71.87 264	85.52 75	95.33 71	68.19 59	97.27 93	89.09 62	94.90 22	95.25 89

MG-MVS		87.11 44	86.27 62	89.62 8	97.79 1	76.27 4	94.96 48	94.49 53	78.74 121	83.87 93	92.94 144	64.34 102	96.94 121	75.19 210	94.09 42	95.66 61

SF-MVS		87.03 45	87.09 47	86.84 64	92.70 91	67.45 128	93.64 112	93.76 81	70.78 297	86.25 64	96.44 38	66.98 69	97.79 55	88.68 66	94.56 34	95.28 84

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 224	87.26 258	60.74 339	93.21 133	87.94 374	84.22 22	91.70 16	97.27 6	65.91 82	95.02 232	93.95 24	90.42 103	94.99 100

CSCG		86.87 47	86.26 63	88.72 17	95.05 32	70.79 30	93.83 104	95.33 18	68.48 330	77.63 181	94.35 109	73.04 30	98.45 34	84.92 104	93.71 51	96.92 14

sasdasda		86.85 48	86.25 64	88.66 20	91.80 122	71.92 17	93.54 117	91.71 185	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 67	84.34 181	97.11 9

canonicalmvs		86.85 48	86.25 64	88.66 20	91.80 122	71.92 17	93.54 117	91.71 185	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 67	84.34 181	97.11 9

UBG		86.83 50	86.70 55	87.20 53	93.07 79	69.81 48	93.43 125	95.56 13	81.52 52	81.50 116	92.12 167	73.58 28	96.28 153	84.37 113	85.20 170	95.51 67

PHI-MVS		86.83 50	86.85 54	86.78 69	93.47 67	65.55 189	95.39 31	95.10 25	71.77 269	85.69 73	96.52 35	62.07 146	98.77 26	86.06 92	95.60 12	96.03 44

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 83	90.42 155	66.38 165	96.09 17	93.87 76	77.73 139	84.01 92	95.66 60	63.39 121	97.94 47	87.40 77	93.55 54	95.42 69
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 196	86.15 295	61.48 322	94.69 60	91.16 213	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 144	93.11 205

PVSNet_Blended		86.73 54	86.86 53	86.31 103	93.76 54	67.53 125	96.33 16	93.61 89	82.34 44	81.00 127	93.08 140	63.19 126	97.29 89	87.08 83	91.38 88	94.13 164

testing11		86.71 55	86.44 60	87.55 42	93.54 64	71.35 22	93.65 111	95.58 11	81.36 59	80.69 132	92.21 165	72.30 38	96.46 146	85.18 100	83.43 198	94.82 113

test_fmvsmconf_n		86.58 56	87.17 46	84.82 163	85.28 316	62.55 292	94.26 76	89.78 295	83.81 27	87.78 52	96.33 43	65.33 88	96.98 115	94.40 20	87.55 138	94.95 102

BP-MVS1		86.54 57	86.68 57	86.13 107	87.80 246	67.18 137	92.97 141	95.62 10	79.92 86	82.84 104	94.14 118	74.95 17	96.46 146	82.91 133	88.96 123	94.74 118

jason		86.40 58	86.17 66	87.11 56	86.16 294	70.54 33	95.71 24	92.19 158	82.00 47	84.58 85	94.34 110	61.86 149	95.53 213	87.76 71	90.89 96	95.27 85
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 129	92.12 106	65.95 178	94.88 49	90.83 243	84.69 19	83.67 95	94.10 119	63.16 128	96.91 127	85.31 96	91.15 92	93.93 175

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 163	87.36 257	63.54 265	94.74 56	90.02 288	82.52 40	90.14 37	96.92 23	62.93 133	97.84 54	95.28 11	82.26 209	93.07 208

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 174	84.67 328	63.29 271	94.04 87	89.99 290	82.88 36	87.85 51	96.03 53	62.89 135	96.36 150	94.15 21	89.95 111	94.48 145

SymmetryMVS		86.32 62	86.39 61	86.12 108	90.52 153	65.95 178	94.88 49	94.58 50	84.69 19	83.67 95	94.10 119	63.16 128	96.91 127	85.31 96	86.59 153	95.51 67

WTY-MVS		86.32 62	85.81 74	87.85 30	92.82 87	69.37 62	95.20 35	95.25 20	82.71 38	81.91 112	94.73 95	67.93 63	97.63 66	79.55 172	82.25 211	96.54 22

myMVS_eth3d28		86.31 64	86.15 67	86.78 69	93.56 62	70.49 34	92.94 144	95.28 19	82.47 41	78.70 171	92.07 169	72.45 36	95.41 215	82.11 142	85.78 163	94.44 147

MSLP-MVS++		86.27 65	85.91 73	87.35 49	92.01 113	68.97 79	95.04 43	92.70 131	79.04 116	81.50 116	96.50 37	58.98 194	96.78 131	83.49 127	93.93 45	96.29 36

VNet		86.20 66	85.65 78	87.84 31	93.92 51	69.99 40	95.73 23	95.94 7	78.43 126	86.00 69	93.07 141	58.22 204	97.00 111	85.22 98	84.33 183	96.52 23

MVS_111021_HR		86.19 67	85.80 75	87.37 48	93.17 75	69.79 49	93.99 90	93.76 81	79.08 113	78.88 167	93.99 124	62.25 144	98.15 42	85.93 93	91.15 92	94.15 163

SPE-MVS-test		86.14 68	87.01 48	83.52 225	92.63 93	59.36 371	95.49 28	91.92 171	80.09 82	85.46 77	95.53 66	61.82 151	95.77 189	86.77 87	93.37 56	95.41 70

ACMMP_NAP		86.05 69	85.80 75	86.80 68	91.58 128	67.53 125	91.79 212	93.49 97	74.93 191	84.61 84	95.30 73	59.42 184	97.92 48	86.13 90	94.92 20	94.94 103

testing99		86.01 70	85.47 80	87.63 40	93.62 59	71.25 24	93.47 123	95.23 21	80.42 73	80.60 134	91.95 176	71.73 43	96.50 144	80.02 169	82.22 212	95.13 92

ETV-MVS		86.01 70	86.11 68	85.70 125	90.21 160	67.02 144	93.43 125	91.92 171	81.21 61	84.13 91	94.07 123	60.93 160	95.63 201	89.28 60	89.81 113	94.46 146

testing91		85.93 72	85.31 84	87.78 33	93.59 61	71.47 20	93.50 120	95.08 28	80.26 78	80.53 137	91.93 177	70.43 47	96.51 143	80.32 167	82.13 214	95.37 73

APD-MVS		85.93 72	85.99 71	85.76 121	95.98 27	65.21 197	93.59 115	92.58 142	66.54 349	86.17 67	95.88 56	63.83 110	97.00 111	86.39 89	92.94 61	95.06 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 74	85.46 81	87.18 54	88.20 230	72.42 16	92.41 178	92.77 129	82.11 46	80.34 140	93.07 141	68.27 57	95.02 232	78.39 188	93.59 53	94.09 166

CS-MVS		85.80 75	86.65 59	83.27 237	92.00 114	58.92 375	95.31 32	91.86 176	79.97 83	84.82 83	95.40 69	62.26 143	95.51 214	86.11 91	92.08 73	95.37 73

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 172	85.73 308	63.58 262	93.79 105	89.32 315	81.42 57	90.21 35	96.91 24	62.41 140	97.67 61	94.48 18	80.56 237	92.90 214

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 182	80.83 379	62.33 297	93.84 102	88.81 344	83.50 30	87.00 59	96.01 54	63.36 122	96.93 123	94.04 23	87.29 141	94.61 130

CDPH-MVS		85.71 77	85.46 81	86.46 94	94.75 38	67.19 135	93.89 97	92.83 127	70.90 293	83.09 102	95.28 75	63.62 116	97.36 84	80.63 163	94.18 41	94.84 109

casdiffmvs_mvg		85.66 79	85.18 86	87.09 57	88.22 229	69.35 63	93.74 108	91.89 174	81.47 53	80.10 143	91.45 189	64.80 96	96.35 151	87.23 80	87.69 136	95.58 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 176	82.95 360	63.48 267	94.03 89	89.46 309	81.69 50	89.86 38	96.74 31	61.85 150	97.75 57	94.74 17	82.01 216	92.81 218

MGCFI-Net		85.59 81	85.73 77	85.17 148	91.41 136	62.44 293	92.87 149	91.31 203	79.65 93	86.99 60	95.14 85	62.90 134	96.12 161	87.13 82	84.13 189	96.96 13

GDP-MVS		85.54 82	85.32 83	86.18 105	87.64 249	67.95 113	92.91 147	92.36 148	77.81 136	83.69 94	94.31 112	72.84 32	96.41 148	80.39 166	85.95 160	94.19 159

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 99	88.80 198	66.64 159	92.15 188	93.68 87	81.07 63	76.91 195	93.64 131	62.59 137	98.44 35	85.50 94	92.84 63	94.03 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 84	84.87 92	86.84 64	88.25 227	69.07 73	93.04 138	91.76 181	81.27 60	80.84 130	92.07 169	64.23 104	96.06 167	84.98 103	87.43 140	95.39 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 85	85.08 88	86.06 109	93.09 78	65.65 185	93.89 97	93.41 102	73.75 214	79.94 145	94.68 97	60.61 165	98.03 45	82.63 137	93.72 50	94.52 137

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 313	84.52 333	60.10 357	93.35 128	90.35 268	83.41 31	86.54 63	96.27 45	60.50 166	90.02 398	94.84 16	90.38 104	92.61 222

MP-MVS-pluss		85.24 86	85.13 87	85.56 130	91.42 133	65.59 187	91.54 231	92.51 144	74.56 194	80.62 133	95.64 61	59.15 190	97.00 111	86.94 85	93.80 47	94.07 168
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 88	84.69 96	86.63 79	92.91 83	69.91 44	92.61 164	95.80 9	80.31 77	80.38 139	92.27 161	68.73 55	95.19 229	75.94 204	83.27 200	94.81 115

PAPR		85.15 89	84.47 97	87.18 54	96.02 26	68.29 99	91.85 210	93.00 120	76.59 169	79.03 163	95.00 86	61.59 152	97.61 68	78.16 189	89.00 122	95.63 62

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 231	86.92 277	60.53 346	94.41 69	87.31 382	83.30 32	88.72 46	96.72 32	54.28 261	97.75 57	94.07 22	84.68 180	92.04 245

MP-MVS		85.02 91	84.97 90	85.17 148	92.60 94	64.27 231	93.24 130	92.27 151	73.13 226	79.63 155	94.43 103	61.90 147	97.17 99	85.00 102	92.56 66	94.06 169
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 92	84.44 98	86.71 73	88.33 224	68.73 86	90.24 291	91.82 180	81.05 64	81.18 122	92.50 153	63.69 113	96.08 166	84.45 111	86.71 151	95.32 80

CHOSEN 1792x2688		84.98 93	83.45 119	89.57 11	89.94 165	75.14 6	92.07 194	92.32 149	81.87 48	75.68 205	88.27 261	60.18 170	98.60 31	80.46 165	90.27 107	94.96 101

MVSMamba_PlusPlus		84.97 94	83.65 112	88.93 14	90.17 161	74.04 8	87.84 349	92.69 134	62.18 393	81.47 118	87.64 275	71.47 44	96.28 153	84.69 106	94.74 31	96.47 28

balanced_ft_v1		84.95 95	83.81 107	88.38 26	93.31 69	73.59 10	85.95 372	92.51 144	77.25 152	73.97 237	89.14 247	59.30 187	95.25 227	92.50 35	90.34 106	96.31 34

E3new		84.94 96	84.36 100	86.69 76	89.06 190	69.31 64	92.68 161	91.29 208	80.72 67	81.03 125	92.14 166	61.89 148	95.91 173	84.59 108	85.85 162	94.86 105

viewmanbaseed2359cas		84.89 97	84.26 102	86.78 69	88.50 209	69.77 51	92.69 160	91.13 219	81.11 62	81.54 115	91.98 173	60.35 167	95.73 191	84.47 110	86.56 154	94.84 109

EIA-MVS		84.84 98	84.88 91	84.69 175	91.30 138	62.36 296	93.85 99	92.04 164	79.45 101	79.33 160	94.28 114	62.42 139	96.35 151	80.05 168	91.25 91	95.38 72

lecture		84.77 99	84.81 94	84.65 178	92.12 106	62.27 300	94.74 56	92.64 139	68.35 331	85.53 74	95.30 73	59.77 177	97.91 49	83.73 123	91.15 92	93.77 184

fmvsm_s_conf0.1_n_a		84.76 100	84.84 93	84.53 184	80.23 392	63.50 266	92.79 151	88.73 347	80.46 71	89.84 39	96.65 34	60.96 159	97.57 71	93.80 25	80.14 239	92.53 227

viewcassd2359sk11		84.74 101	84.11 103	86.64 78	88.57 203	69.20 71	92.61 164	91.23 210	80.58 68	80.85 129	91.96 174	61.39 154	95.89 175	84.28 114	85.49 167	94.82 113

HFP-MVS		84.73 102	84.40 99	85.72 123	93.75 56	65.01 203	93.50 120	93.19 110	72.19 253	79.22 161	94.93 89	59.04 193	97.67 61	81.55 152	92.21 69	94.49 144

MVS		84.66 103	82.86 141	90.06 2	90.93 145	74.56 7	87.91 347	95.54 14	68.55 328	72.35 263	94.71 96	59.78 176	98.90 23	81.29 158	94.69 32	96.74 16

GST-MVS		84.63 104	84.29 101	85.66 126	92.82 87	65.27 195	93.04 138	93.13 113	73.20 224	78.89 164	94.18 117	59.41 185	97.85 53	81.45 154	92.48 68	93.86 181

EC-MVSNet		84.53 105	85.04 89	83.01 243	89.34 177	61.37 326	94.42 68	91.09 223	77.91 134	83.24 98	94.20 116	58.37 202	95.40 216	85.35 95	91.41 86	92.27 239

E2		84.45 106	83.74 108	86.56 85	87.90 239	69.06 74	92.53 172	91.13 219	80.35 75	80.58 135	91.69 184	60.70 161	95.84 178	83.80 121	84.99 172	94.79 116

E3		84.45 106	83.74 108	86.56 85	87.90 239	69.06 74	92.53 172	91.13 219	80.35 75	80.58 135	91.69 184	60.70 161	95.84 178	83.80 121	84.99 172	94.79 116

fmvsm_s_conf0.1_n_2		84.40 108	84.78 95	83.27 237	85.25 317	60.41 349	94.13 81	85.69 407	83.05 34	87.99 49	96.37 39	52.75 278	97.68 59	93.75 26	84.05 190	91.71 253

ACMMPR		84.37 109	84.06 104	85.28 143	93.56 62	64.37 226	93.50 120	93.15 112	72.19 253	78.85 169	94.86 92	56.69 228	97.45 77	81.55 152	92.20 70	94.02 171

region2R		84.36 110	84.03 105	85.36 139	93.54 64	64.31 229	93.43 125	92.95 123	72.16 256	78.86 168	94.84 93	56.97 223	97.53 73	81.38 156	92.11 72	94.24 157

LFMVS		84.34 111	82.73 143	89.18 13	94.76 34	73.25 12	94.99 47	91.89 174	71.90 261	82.16 111	93.49 135	47.98 331	97.05 106	82.55 138	84.82 176	97.25 8

test_yl		84.28 112	83.16 132	87.64 36	94.52 41	69.24 69	95.78 18	95.09 26	69.19 318	81.09 123	92.88 147	57.00 221	97.44 78	81.11 160	81.76 220	96.23 39

DCV-MVSNet		84.28 112	83.16 132	87.64 36	94.52 41	69.24 69	95.78 18	95.09 26	69.19 318	81.09 123	92.88 147	57.00 221	97.44 78	81.11 160	81.76 220	96.23 39

diffmvs		84.28 112	83.83 106	85.61 128	87.40 255	68.02 110	90.88 264	89.24 318	80.54 69	81.64 114	92.52 152	59.83 175	94.52 261	87.32 78	85.11 171	94.29 154
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 112	83.36 125	87.02 60	92.22 101	67.74 118	84.65 379	94.50 52	79.15 110	82.23 110	87.93 270	66.88 70	96.94 121	80.53 164	82.20 213	96.39 33

ETVMVS		84.22 116	83.71 110	85.76 121	92.58 95	68.25 103	92.45 176	95.53 15	79.54 100	79.46 157	91.64 187	70.29 48	94.18 275	69.16 272	82.76 206	94.84 109

MAR-MVS		84.18 117	83.43 120	86.44 96	96.25 22	65.93 180	94.28 75	94.27 68	74.41 197	79.16 162	95.61 62	53.99 264	98.88 25	69.62 266	93.26 58	94.50 143
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
MVS_Test		84.16 118	83.20 129	87.05 59	91.56 129	69.82 47	89.99 300	92.05 163	77.77 138	82.84 104	86.57 292	63.93 109	96.09 163	74.91 215	89.18 119	95.25 89

CANet_DTU		84.09 119	83.52 113	85.81 118	90.30 158	66.82 153	91.87 208	89.01 335	85.27 13	86.09 68	93.74 128	47.71 337	96.98 115	77.90 191	89.78 115	93.65 188

viewdifsd2359ckpt13		84.08 120	83.21 128	86.70 74	88.49 213	69.55 56	92.25 182	91.14 217	79.71 91	79.73 152	91.72 183	58.83 195	95.89 175	82.06 143	84.99 172	94.66 127

viewmacassd2359aftdt		84.03 121	83.18 131	86.59 82	86.76 280	69.44 57	92.44 177	90.85 242	80.38 74	80.78 131	91.33 195	58.54 199	95.62 203	82.15 141	85.41 168	94.72 121

ET-MVSNet_ETH3D		84.01 122	83.15 134	86.58 83	90.78 150	70.89 29	94.74 56	94.62 47	81.44 56	58.19 411	93.64 131	73.64 27	92.35 353	82.66 136	78.66 259	96.50 27

E4		84.00 123	83.19 130	86.46 94	86.99 267	68.85 81	92.39 179	90.99 236	79.94 84	80.17 142	91.36 194	59.73 178	95.79 186	82.87 134	84.22 187	94.74 118

diffmvs_AUTHOR		83.97 124	83.49 116	85.39 135	86.09 296	67.83 115	90.76 269	89.05 333	79.94 84	81.43 119	92.23 164	59.53 181	94.42 264	87.18 81	85.22 169	93.92 177

PVSNet_Blended_VisFu		83.97 124	83.50 115	85.39 135	90.02 163	66.59 162	93.77 106	91.73 183	77.43 148	77.08 194	89.81 236	63.77 112	96.97 118	79.67 171	88.21 130	92.60 223

MTAPA		83.91 126	83.38 124	85.50 131	91.89 120	65.16 199	81.75 411	92.23 152	75.32 186	80.53 137	95.21 82	56.06 237	97.16 102	84.86 105	92.55 67	94.18 160

XVS		83.87 127	83.47 118	85.05 152	93.22 71	63.78 249	92.92 145	92.66 136	73.99 206	78.18 175	94.31 112	55.25 243	97.41 81	79.16 178	91.58 83	93.95 173

Effi-MVS+		83.82 128	82.76 142	86.99 61	89.56 173	69.40 58	91.35 244	86.12 401	72.59 240	83.22 101	92.81 150	59.60 180	96.01 171	81.76 151	87.80 135	95.56 65

test_fmvsmvis_n_1920		83.80 129	83.48 117	84.77 167	82.51 363	63.72 254	91.37 240	83.99 425	81.42 57	77.68 180	95.74 59	58.37 202	97.58 69	93.38 27	86.87 145	93.00 211

EI-MVSNet-Vis-set		83.77 130	83.67 111	84.06 201	92.79 90	63.56 263	91.76 217	94.81 37	79.65 93	77.87 178	94.09 121	63.35 123	97.90 50	79.35 176	79.36 249	90.74 274

MVSFormer		83.75 131	82.88 140	86.37 99	89.24 186	71.18 25	89.07 325	90.69 252	65.80 359	87.13 56	94.34 110	64.99 91	92.67 339	72.83 231	91.80 79	95.27 85

CP-MVS		83.71 132	83.40 123	84.65 178	93.14 76	63.84 247	94.59 61	92.28 150	71.03 291	77.41 185	94.92 90	55.21 246	96.19 158	81.32 157	90.70 98	93.91 178

test_fmvsmconf0.01_n		83.70 133	83.52 113	84.25 197	75.26 442	61.72 315	92.17 187	87.24 384	82.36 43	84.91 82	95.41 68	55.60 241	96.83 130	92.85 31	85.87 161	94.21 158

baseline2		83.68 134	83.42 122	84.48 187	87.37 256	66.00 175	90.06 295	95.93 8	79.71 91	69.08 301	90.39 214	77.92 6	96.28 153	78.91 183	81.38 224	91.16 267

E5new		83.62 135	82.65 145	86.55 87	86.98 268	69.28 67	91.69 221	90.96 237	79.61 95	79.80 147	91.25 197	58.04 207	95.84 178	81.83 149	83.66 195	94.52 137

E6new		83.62 135	82.65 145	86.55 87	86.98 268	69.29 65	91.69 221	90.95 239	79.60 98	79.80 147	91.25 197	58.04 207	95.84 178	81.84 147	83.67 193	94.52 137

E6		83.62 135	82.65 145	86.55 87	86.98 268	69.29 65	91.69 221	90.95 239	79.60 98	79.80 147	91.25 197	58.04 207	95.84 178	81.84 147	83.67 193	94.52 137

E5		83.62 135	82.65 145	86.55 87	86.98 268	69.28 67	91.69 221	90.96 237	79.61 95	79.80 147	91.25 197	58.04 207	95.84 178	81.83 149	83.66 195	94.52 137

viewdifsd2359ckpt09		83.52 139	82.57 150	86.37 99	88.02 236	68.47 94	91.78 214	89.63 305	79.61 95	78.56 173	92.00 172	59.28 188	95.96 172	81.94 145	82.35 207	94.69 122

reproduce-ours		83.51 140	83.33 126	84.06 201	92.18 104	60.49 347	90.74 271	92.04 164	64.35 370	83.24 98	95.59 64	59.05 191	97.27 93	83.61 124	89.17 120	94.41 152

our_new_method		83.51 140	83.33 126	84.06 201	92.18 104	60.49 347	90.74 271	92.04 164	64.35 370	83.24 98	95.59 64	59.05 191	97.27 93	83.61 124	89.17 120	94.41 152

thisisatest0515		83.41 142	82.49 152	86.16 106	89.46 176	68.26 101	93.54 117	94.70 43	74.31 200	75.75 203	90.92 204	72.62 34	96.52 142	69.64 264	81.50 223	93.71 185

PVSNet_BlendedMVS		83.38 143	83.43 120	83.22 239	93.76 54	67.53 125	94.06 83	93.61 89	79.13 111	81.00 127	85.14 312	63.19 126	97.29 89	87.08 83	73.91 297	84.83 383

test2506		83.29 144	82.92 139	84.37 191	88.39 221	63.18 277	92.01 197	91.35 202	77.66 141	78.49 174	91.42 190	64.58 100	95.09 231	73.19 227	89.23 117	94.85 106

PGM-MVS		83.25 145	82.70 144	84.92 156	92.81 89	64.07 239	90.44 281	92.20 156	71.28 285	77.23 189	94.43 103	55.17 247	97.31 88	79.33 177	91.38 88	93.37 195

HPM-MVS		83.25 145	82.95 138	84.17 199	92.25 100	62.88 286	90.91 261	91.86 176	70.30 302	77.12 191	93.96 125	56.75 226	96.28 153	82.04 144	91.34 90	93.34 196
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 147	82.96 136	83.73 216	92.02 110	59.74 363	90.37 285	92.08 162	63.70 377	82.86 103	95.48 67	58.62 197	97.17 99	83.06 130	88.42 128	94.26 155

EI-MVSNet-UG-set		83.14 148	82.96 136	83.67 221	92.28 99	63.19 276	91.38 239	94.68 44	79.22 108	76.60 197	93.75 127	62.64 136	97.76 56	78.07 190	78.01 262	90.05 283

testing3-2		83.11 149	83.15 134	82.98 244	91.92 117	64.01 242	94.39 72	95.37 16	78.32 127	75.53 210	90.06 232	73.18 29	93.18 317	74.34 220	75.27 286	91.77 252

VDD-MVS		83.06 150	81.81 163	86.81 67	90.86 148	67.70 119	95.40 30	91.50 196	75.46 181	81.78 113	92.34 160	40.09 387	97.13 104	86.85 86	82.04 215	95.60 63

h-mvs33		83.01 151	82.56 151	84.35 192	89.34 177	62.02 304	92.72 154	93.76 81	81.45 54	82.73 107	92.25 163	60.11 171	97.13 104	87.69 72	62.96 385	93.91 178

PAPM_NR		82.97 152	81.84 162	86.37 99	94.10 48	66.76 156	87.66 353	92.84 126	69.96 308	74.07 235	93.57 133	63.10 131	97.50 75	70.66 259	90.58 100	94.85 106

mPP-MVS		82.96 153	82.44 153	84.52 185	92.83 85	62.92 284	92.76 152	91.85 178	71.52 281	75.61 208	94.24 115	53.48 272	96.99 114	78.97 181	90.73 97	93.64 189

viewdifsd2359ckpt07		82.95 154	82.04 157	85.66 126	87.19 261	66.73 157	91.56 230	90.39 267	77.58 144	77.58 184	91.19 201	58.57 198	95.65 200	82.32 139	82.01 216	94.60 131

SR-MVS		82.81 155	82.58 149	83.50 228	93.35 68	61.16 329	92.23 185	91.28 209	64.48 369	81.27 120	95.28 75	53.71 268	95.86 177	82.87 134	88.77 125	93.49 193

DP-MVS Recon		82.73 156	81.65 164	85.98 111	97.31 4	67.06 140	95.15 37	91.99 168	69.08 323	76.50 200	93.89 126	54.48 257	98.20 41	70.76 257	85.66 165	92.69 219

CLD-MVS		82.73 156	82.35 155	83.86 209	87.90 239	67.65 121	95.45 29	92.18 159	85.06 14	72.58 254	92.27 161	52.46 281	95.78 187	84.18 115	79.06 254	88.16 311
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 158	82.38 154	83.73 216	89.25 183	59.58 366	92.24 184	94.89 31	77.96 132	79.86 146	92.38 158	56.70 227	97.05 106	77.26 194	80.86 232	94.55 133

3Dnovator	73.91 6	82.69 159	80.82 177	88.31 27	89.57 172	71.26 23	92.60 166	94.39 63	78.84 118	67.89 324	92.48 156	48.42 326	98.52 32	68.80 277	94.40 36	95.15 91

RRT-MVS		82.61 160	81.16 168	86.96 62	91.10 142	68.75 85	87.70 352	92.20 156	76.97 156	72.68 250	87.10 286	51.30 295	96.41 148	83.56 126	87.84 134	95.74 58

viewmambaseed2359dif		82.60 161	81.91 161	84.67 177	85.83 303	66.09 172	90.50 280	89.01 335	75.46 181	79.64 154	92.01 171	59.51 182	94.38 266	82.99 132	82.26 209	93.54 191

MVSTER		82.47 162	82.05 156	83.74 214	92.68 92	69.01 77	91.90 207	93.21 107	79.83 87	72.14 264	85.71 305	74.72 19	94.72 246	75.72 206	72.49 307	87.50 318

TESTMET0.1,1		82.41 163	81.98 160	83.72 218	88.08 232	63.74 251	92.70 156	93.77 80	79.30 106	77.61 182	87.57 277	58.19 205	94.08 280	73.91 222	86.68 152	93.33 198

CostFormer		82.33 164	81.15 169	85.86 116	89.01 193	68.46 95	82.39 408	93.01 118	75.59 179	80.25 141	81.57 359	72.03 41	94.96 236	79.06 180	77.48 270	94.16 162

API-MVS		82.28 165	80.53 186	87.54 43	96.13 23	70.59 32	93.63 113	91.04 233	65.72 361	75.45 211	92.83 149	56.11 236	98.89 24	64.10 333	89.75 116	93.15 203

IB-MVS	77.80 4	82.18 166	80.46 188	87.35 49	89.14 188	70.28 37	95.59 27	95.17 24	78.85 117	70.19 289	85.82 303	70.66 46	97.67 61	72.19 243	66.52 352	94.09 166
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
xiu_mvs_v1_base_debu		82.16 167	81.12 170	85.26 145	86.42 287	68.72 88	92.59 168	90.44 264	73.12 227	84.20 88	94.36 105	38.04 400	95.73 191	84.12 116	86.81 146	91.33 260

xiu_mvs_v1_base		82.16 167	81.12 170	85.26 145	86.42 287	68.72 88	92.59 168	90.44 264	73.12 227	84.20 88	94.36 105	38.04 400	95.73 191	84.12 116	86.81 146	91.33 260

xiu_mvs_v1_base_debi		82.16 167	81.12 170	85.26 145	86.42 287	68.72 88	92.59 168	90.44 264	73.12 227	84.20 88	94.36 105	38.04 400	95.73 191	84.12 116	86.81 146	91.33 260

3Dnovator+	73.60 7	82.10 170	80.60 184	86.60 80	90.89 147	66.80 155	95.20 35	93.44 99	74.05 205	67.42 331	92.49 155	49.46 316	97.65 65	70.80 256	91.68 81	95.33 78

MVS_111021_LR		82.02 171	81.52 165	83.51 227	88.42 219	62.88 286	89.77 303	88.93 340	76.78 161	75.55 209	93.10 138	50.31 305	95.38 218	83.82 120	87.02 143	92.26 240

PMMVS		81.98 172	82.04 157	81.78 281	89.76 169	56.17 404	91.13 257	90.69 252	77.96 132	80.09 144	93.57 133	46.33 355	94.99 235	81.41 155	87.46 139	94.17 161

baseline1		81.84 173	81.03 174	84.28 195	91.60 127	66.62 160	91.08 258	91.66 190	81.87 48	74.86 221	91.67 186	69.98 51	94.92 239	71.76 246	64.75 369	91.29 265

EPP-MVSNet		81.79 174	81.52 165	82.61 254	88.77 199	60.21 355	93.02 140	93.66 88	68.52 329	72.90 248	90.39 214	72.19 40	94.96 236	74.93 214	79.29 252	92.67 220

WBMVS		81.67 175	80.98 176	83.72 218	93.07 79	69.40 58	94.33 73	93.05 116	76.84 159	72.05 266	84.14 325	74.49 21	93.88 294	72.76 234	68.09 338	87.88 313

test_vis1_n_1920		81.66 176	82.01 159	80.64 317	82.24 365	55.09 413	94.76 55	86.87 388	81.67 51	84.40 87	94.63 98	38.17 397	94.67 252	91.98 41	83.34 199	92.16 243

APD-MVS_3200maxsize		81.64 177	81.32 167	82.59 256	92.36 97	58.74 377	91.39 237	91.01 235	63.35 381	79.72 153	94.62 99	51.82 284	96.14 160	79.71 170	87.93 133	92.89 215

mvsmamba		81.55 178	80.72 179	84.03 205	91.42 133	66.93 151	83.08 399	89.13 326	78.55 125	67.50 329	87.02 287	51.79 286	90.07 397	87.48 75	90.49 102	95.10 94

ACMMP		81.49 179	80.67 181	83.93 207	91.71 125	62.90 285	92.13 189	92.22 155	71.79 268	71.68 272	93.49 135	50.32 304	96.96 119	78.47 187	84.22 187	91.93 250
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
KinetiMVS		81.43 180	80.11 190	85.38 138	86.60 283	65.47 193	92.90 148	93.54 93	75.33 185	77.31 187	90.39 214	46.81 346	96.75 132	71.65 249	86.46 157	93.93 175

CDS-MVSNet		81.43 180	80.74 178	83.52 225	86.26 291	64.45 220	92.09 192	90.65 256	75.83 177	73.95 238	89.81 236	63.97 108	92.91 328	71.27 250	82.82 203	93.20 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 182	79.99 194	85.46 132	90.39 157	68.40 96	86.88 364	90.61 257	74.41 197	70.31 288	84.67 317	63.79 111	92.32 355	73.13 228	85.70 164	95.67 60

0.3-1-1-0.015		81.31 183	79.49 206	86.77 72	85.74 307	68.70 92	95.01 46	94.42 58	74.29 201	77.09 193	85.61 306	63.31 125	95.69 199	76.63 198	63.30 382	95.91 52

ECVR-MVS		81.29 184	80.38 189	84.01 206	88.39 221	61.96 306	92.56 171	86.79 390	77.66 141	76.63 196	91.42 190	46.34 354	95.24 228	74.36 219	89.23 117	94.85 106

0.4-1-1-0.2		81.28 185	79.42 208	86.84 64	85.80 305	68.82 83	95.10 39	94.43 57	74.45 196	77.18 190	85.54 307	62.27 142	95.70 197	76.72 197	63.30 382	96.01 45

guyue		81.23 186	80.57 185	83.21 241	86.64 281	61.85 309	92.52 174	92.78 128	78.69 122	74.92 220	89.42 240	50.07 308	95.35 219	80.79 162	79.31 251	92.42 229

IMVS_0403		81.19 187	79.88 196	85.13 150	88.54 204	64.75 208	88.84 330	90.80 246	76.73 164	75.21 214	90.18 220	54.22 262	96.21 157	73.47 223	80.95 227	94.43 148

thisisatest0530		81.15 188	80.07 191	84.39 190	88.26 226	65.63 186	91.40 235	94.62 47	71.27 286	70.93 279	89.18 245	72.47 35	96.04 168	65.62 318	76.89 277	91.49 256

Fast-Effi-MVS+		81.14 189	80.01 193	84.51 186	90.24 159	65.86 181	94.12 82	89.15 324	73.81 213	75.37 213	88.26 262	57.26 216	94.53 260	66.97 301	84.92 175	93.15 203

HQP-MVS		81.14 189	80.64 182	82.64 253	87.54 251	63.66 260	94.06 83	91.70 188	79.80 88	74.18 228	90.30 217	51.63 289	95.61 205	77.63 192	78.90 255	88.63 302

hse-mvs2		81.12 191	81.11 173	81.16 301	86.52 286	57.48 392	89.40 316	91.16 213	81.45 54	82.73 107	90.49 212	60.11 171	94.58 253	87.69 72	60.41 412	91.41 259

SR-MVS-dyc-post		81.06 192	80.70 180	82.15 272	92.02 110	58.56 380	90.90 262	90.45 260	62.76 388	78.89 164	94.46 101	51.26 296	95.61 205	78.77 185	86.77 149	92.28 236

HyFIR lowres test		81.03 193	79.56 203	85.43 133	87.81 245	68.11 108	90.18 292	90.01 289	70.65 299	72.95 247	86.06 299	63.61 117	94.50 262	75.01 213	79.75 243	93.67 186

0.4-1-1-0.1		80.99 194	79.16 216	86.51 93	85.55 312	68.21 105	94.77 54	94.42 58	73.75 214	76.57 198	85.41 309	62.35 141	95.62 203	76.30 203	63.28 384	95.71 59

nrg030		80.93 195	79.86 197	84.13 200	83.69 349	68.83 82	93.23 131	91.20 211	75.55 180	75.06 216	88.22 265	63.04 132	94.74 245	81.88 146	66.88 349	88.82 300

Vis-MVSNet		80.92 196	79.98 195	83.74 214	88.48 215	61.80 310	93.44 124	88.26 366	73.96 209	77.73 179	91.76 180	49.94 310	94.76 243	65.84 313	90.37 105	94.65 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 197	80.02 192	83.33 232	87.87 242	60.76 337	92.62 163	86.86 389	77.86 135	75.73 204	91.39 192	46.35 353	94.70 251	72.79 233	88.68 126	94.52 137

UWE-MVS		80.81 198	81.01 175	80.20 327	89.33 179	57.05 398	91.91 206	94.71 42	75.67 178	75.01 217	89.37 241	63.13 130	91.44 380	67.19 298	82.80 205	92.12 244

IMVS_0407		80.80 199	79.39 211	85.00 155	88.54 204	64.75 208	88.40 338	90.80 246	76.73 164	73.95 238	90.18 220	51.55 291	95.81 184	73.47 223	80.95 227	94.43 148

1314		80.70 200	78.95 220	85.94 113	87.77 248	67.56 123	87.91 347	92.55 143	72.17 255	67.44 330	93.09 139	50.27 306	97.04 109	71.68 248	87.64 137	93.23 200

AstraMVS		80.66 201	79.79 199	83.28 236	85.07 323	61.64 317	92.19 186	90.58 258	79.40 103	74.77 223	90.18 220	45.93 359	95.61 205	83.04 131	76.96 276	92.60 223

tpmrst		80.57 202	79.14 218	84.84 162	90.10 162	68.28 100	81.70 412	89.72 302	77.63 143	75.96 202	79.54 391	64.94 93	92.71 336	75.43 208	77.28 273	93.55 190

1112_ss		80.56 203	79.83 198	82.77 248	88.65 201	60.78 335	92.29 181	88.36 359	72.58 241	72.46 260	94.95 87	65.09 90	93.42 312	66.38 307	77.71 264	94.10 165

VDDNet		80.50 204	78.26 228	87.21 52	86.19 292	69.79 49	94.48 63	91.31 203	60.42 409	79.34 159	90.91 205	38.48 395	96.56 139	82.16 140	81.05 226	95.27 85

BH-w/o		80.49 205	79.30 213	84.05 204	90.83 149	64.36 228	93.60 114	89.42 312	74.35 199	69.09 300	90.15 228	55.23 245	95.61 205	64.61 328	86.43 158	92.17 242

test_cas_vis1_n_1920		80.45 206	80.61 183	79.97 336	78.25 419	57.01 400	94.04 87	88.33 361	79.06 115	82.81 106	93.70 129	38.65 392	91.63 371	90.82 53	79.81 241	91.27 266

icg_test_0407_2		80.38 207	79.22 215	83.88 208	88.54 204	64.75 208	86.79 365	90.80 246	76.73 164	73.95 238	90.18 220	51.55 291	92.45 348	73.47 223	80.95 227	94.43 148

TAMVS		80.37 208	79.45 207	83.13 242	85.14 320	63.37 268	91.23 251	90.76 251	74.81 193	72.65 252	88.49 255	60.63 164	92.95 323	69.41 268	81.95 218	93.08 207

HQP_MVS		80.34 209	79.75 200	82.12 274	86.94 273	62.42 294	93.13 134	91.31 203	78.81 119	72.53 255	89.14 247	50.66 301	95.55 211	76.74 195	78.53 260	88.39 308

SDMVSNet		80.26 210	78.88 221	84.40 189	89.25 183	67.63 122	85.35 375	93.02 117	76.77 162	70.84 280	87.12 284	47.95 334	96.09 163	85.04 101	74.55 288	89.48 293

HPM-MVS_fast		80.25 211	79.55 205	82.33 264	91.55 130	59.95 360	91.32 246	89.16 323	65.23 366	74.71 225	93.07 141	47.81 336	95.74 190	74.87 217	88.23 129	91.31 264

ab-mvs		80.18 212	78.31 227	85.80 119	88.44 217	65.49 192	83.00 402	92.67 135	71.82 267	77.36 186	85.01 313	54.50 254	96.59 136	76.35 202	75.63 284	95.32 80

IS-MVSNet		80.14 213	79.41 209	82.33 264	87.91 238	60.08 358	91.97 201	88.27 364	72.90 236	71.44 276	91.73 182	61.44 153	93.66 303	62.47 347	86.53 155	93.24 199

test-LLR		80.10 214	79.56 203	81.72 283	86.93 275	61.17 327	92.70 156	91.54 193	71.51 282	75.62 206	86.94 288	53.83 265	92.38 350	72.21 241	84.76 178	91.60 254

PVSNet	73.49 8	80.05 215	78.63 223	84.31 193	90.92 146	64.97 204	92.47 175	91.05 232	79.18 109	72.43 261	90.51 211	37.05 412	94.06 282	68.06 285	86.00 159	93.90 180

UA-Net		80.02 216	79.65 201	81.11 304	89.33 179	57.72 387	86.33 370	89.00 339	77.44 147	81.01 126	89.15 246	59.33 186	95.90 174	61.01 354	84.28 185	89.73 289

test-mter		79.96 217	79.38 212	81.72 283	86.93 275	61.17 327	92.70 156	91.54 193	73.85 211	75.62 206	86.94 288	49.84 312	92.38 350	72.21 241	84.76 178	91.60 254

QAPM		79.95 218	77.39 249	87.64 36	89.63 171	71.41 21	93.30 129	93.70 86	65.34 365	67.39 333	91.75 181	47.83 335	98.96 19	57.71 371	89.81 113	92.54 226

UGNet		79.87 219	78.68 222	83.45 230	89.96 164	61.51 320	92.13 189	90.79 250	76.83 160	78.85 169	86.33 296	38.16 398	96.17 159	67.93 288	87.17 142	92.67 220
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
tpm2		79.80 220	77.95 235	85.34 140	88.28 225	68.26 101	81.56 414	91.42 199	70.11 304	77.59 183	80.50 377	67.40 67	94.26 273	67.34 295	77.35 271	93.51 192

thres200		79.66 221	78.33 226	83.66 222	92.54 96	65.82 183	93.06 136	96.31 3	74.90 192	73.30 244	88.66 253	59.67 179	95.61 205	47.84 415	78.67 258	89.56 292

CPTT-MVS		79.59 222	79.16 216	80.89 315	91.54 131	59.80 362	92.10 191	88.54 356	60.42 409	72.96 246	93.28 137	48.27 327	92.80 333	78.89 184	86.50 156	90.06 282

Test_1112_low_res		79.56 223	78.60 224	82.43 258	88.24 228	60.39 351	92.09 192	87.99 371	72.10 257	71.84 268	87.42 279	64.62 98	93.04 319	65.80 314	77.30 272	93.85 182

tttt0517		79.50 224	78.53 225	82.41 261	87.22 260	61.43 324	89.75 304	94.76 39	69.29 316	67.91 322	88.06 269	72.92 31	95.63 201	62.91 343	73.90 298	90.16 281

reproduce_monomvs		79.49 225	79.11 219	80.64 317	92.91 83	61.47 323	91.17 256	93.28 105	83.09 33	64.04 363	82.38 345	66.19 76	94.57 255	81.19 159	57.71 420	85.88 366

FIs		79.47 226	79.41 209	79.67 344	85.95 299	59.40 368	91.68 225	93.94 75	78.06 131	68.96 306	88.28 260	66.61 73	91.77 367	66.20 310	74.99 287	87.82 314

SSM_0404		79.46 227	77.65 239	84.91 158	88.37 223	67.04 142	89.59 305	87.03 385	67.99 334	75.45 211	89.32 242	47.98 331	95.34 221	71.23 251	81.90 219	92.34 232

BH-RMVSNet		79.46 227	77.65 239	84.89 159	91.68 126	65.66 184	93.55 116	88.09 369	72.93 233	73.37 243	91.12 203	46.20 357	96.12 161	56.28 377	85.61 166	92.91 213

viewdifsd2359ckpt11		79.42 229	77.95 235	83.81 211	83.87 346	63.85 245	89.54 310	87.38 378	77.39 150	74.94 218	89.95 233	51.11 297	94.72 246	79.52 173	67.90 341	92.88 216

viewmsd2359difaftdt		79.42 229	77.96 234	83.81 211	83.88 345	63.85 245	89.54 310	87.38 378	77.39 150	74.94 218	89.95 233	51.11 297	94.72 246	79.52 173	67.90 341	92.88 216

PCF-MVS	73.15 9	79.29 231	77.63 241	84.29 194	86.06 297	65.96 177	87.03 360	91.10 222	69.86 310	69.79 296	90.64 207	57.54 215	96.59 136	64.37 332	82.29 208	90.32 279
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 232	79.57 202	78.24 365	88.46 216	52.29 424	90.41 283	89.12 327	74.24 202	69.13 299	91.91 178	65.77 83	90.09 396	59.00 367	88.09 131	92.33 233

114514_t		79.17 233	77.67 238	83.68 220	95.32 30	65.53 190	92.85 150	91.60 192	63.49 379	67.92 321	90.63 209	46.65 350	95.72 196	67.01 300	83.54 197	89.79 287

FA-MVS(test-final)		79.12 234	77.23 251	84.81 166	90.54 152	63.98 244	81.35 417	91.71 185	71.09 290	74.85 222	82.94 338	52.85 276	97.05 106	67.97 286	81.73 222	93.41 194

SSM_0407		79.09 235	77.21 252	84.75 170	88.50 209	66.98 147	89.21 321	87.03 385	67.99 334	74.12 232	89.32 242	47.98 331	95.29 226	71.23 251	79.52 244	91.98 247

VPA-MVSNet		79.03 236	78.00 232	82.11 277	85.95 299	64.48 219	93.22 132	94.66 45	75.05 190	74.04 236	84.95 314	52.17 283	93.52 305	74.90 216	67.04 348	88.32 310

OPM-MVS		79.00 237	78.09 230	81.73 282	83.52 352	63.83 248	91.64 227	90.30 273	76.36 173	71.97 267	89.93 235	46.30 356	95.17 230	75.10 211	77.70 265	86.19 354
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 238	78.22 229	81.25 298	85.33 313	62.73 289	89.53 313	93.21 107	72.39 248	72.14 264	90.13 229	60.99 157	94.72 246	67.73 290	72.49 307	86.29 351

AdaColmap		78.94 239	77.00 256	84.76 169	96.34 17	65.86 181	92.66 162	87.97 373	62.18 393	70.56 282	92.37 159	43.53 372	97.35 85	64.50 331	82.86 202	91.05 269

GeoE		78.90 240	77.43 245	83.29 235	88.95 194	62.02 304	92.31 180	86.23 397	70.24 303	71.34 277	89.27 244	54.43 258	94.04 285	63.31 339	80.81 234	93.81 183

miper_enhance_ethall		78.86 241	77.97 233	81.54 289	88.00 237	65.17 198	91.41 233	89.15 324	75.19 188	68.79 309	83.98 328	67.17 68	92.82 331	72.73 235	65.30 359	86.62 341

VPNet		78.82 242	77.53 244	82.70 251	84.52 333	66.44 164	93.93 93	92.23 152	80.46 71	72.60 253	88.38 259	49.18 320	93.13 318	72.47 239	63.97 378	88.55 305

EPNet_dtu		78.80 243	79.26 214	77.43 373	88.06 233	49.71 441	91.96 202	91.95 170	77.67 140	76.56 199	91.28 196	58.51 200	90.20 394	56.37 376	80.95 227	92.39 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 244	77.43 245	82.88 246	92.21 102	64.49 217	92.05 195	96.28 4	73.48 221	71.75 270	88.26 262	60.07 173	95.32 222	45.16 428	77.58 267	88.83 298

TR-MVS		78.77 245	77.37 250	82.95 245	90.49 154	60.88 333	93.67 110	90.07 284	70.08 307	74.51 226	91.37 193	45.69 360	95.70 197	60.12 361	80.32 238	92.29 235

thres400		78.68 246	77.43 245	82.43 258	92.21 102	64.49 217	92.05 195	96.28 4	73.48 221	71.75 270	88.26 262	60.07 173	95.32 222	45.16 428	77.58 267	87.48 319

BH-untuned		78.68 246	77.08 253	83.48 229	89.84 166	63.74 251	92.70 156	88.59 353	71.57 279	66.83 340	88.65 254	51.75 287	95.39 217	59.03 366	84.77 177	91.32 263

OMC-MVS		78.67 248	77.91 237	80.95 311	85.76 306	57.40 394	88.49 336	88.67 350	73.85 211	72.43 261	92.10 168	49.29 319	94.55 259	72.73 235	77.89 263	90.91 273

tpm		78.58 249	77.03 254	83.22 239	85.94 301	64.56 215	83.21 398	91.14 217	78.31 128	73.67 241	79.68 389	64.01 107	92.09 361	66.07 311	71.26 317	93.03 209

OpenMVS	70.45 11	78.54 250	75.92 275	86.41 98	85.93 302	71.68 19	92.74 153	92.51 144	66.49 350	64.56 357	91.96 174	43.88 371	98.10 44	54.61 382	90.65 99	89.44 295

EPMVS		78.49 251	75.98 274	86.02 110	91.21 140	69.68 54	80.23 426	91.20 211	75.25 187	72.48 259	78.11 400	54.65 253	93.69 302	57.66 372	83.04 201	94.69 122

AUN-MVS		78.37 252	77.43 245	81.17 300	86.60 283	57.45 393	89.46 315	91.16 213	74.11 204	74.40 227	90.49 212	55.52 242	94.57 255	74.73 218	60.43 411	91.48 257

thres100view900		78.37 252	77.01 255	82.46 257	91.89 120	63.21 275	91.19 255	96.33 1	72.28 251	70.45 285	87.89 271	60.31 168	95.32 222	45.16 428	77.58 267	88.83 298

GA-MVS		78.33 254	76.23 270	84.65 178	83.65 350	66.30 168	91.44 232	90.14 282	76.01 175	70.32 287	84.02 327	42.50 376	94.72 246	70.98 254	77.00 275	92.94 212

cascas		78.18 255	75.77 277	85.41 134	87.14 263	69.11 72	92.96 143	91.15 216	66.71 348	70.47 283	86.07 298	37.49 406	96.48 145	70.15 262	79.80 242	90.65 275

UniMVSNet_NR-MVSNet		78.15 256	77.55 243	79.98 334	84.46 336	60.26 353	92.25 182	93.20 109	77.50 146	68.88 307	86.61 291	66.10 78	92.13 359	66.38 307	62.55 389	87.54 317

LuminaMVS		78.14 257	76.66 260	82.60 255	80.82 380	64.64 214	89.33 317	90.45 260	68.25 332	74.73 224	85.51 308	41.15 382	94.14 276	78.96 182	80.69 236	89.04 296

IMVS_0404		78.11 258	76.29 269	83.59 223	88.54 204	64.75 208	84.63 380	90.80 246	76.73 164	61.16 387	90.18 220	40.17 386	91.58 373	73.47 223	80.95 227	94.43 148

thres600view7		78.00 259	76.66 260	82.03 279	91.93 116	63.69 258	91.30 247	96.33 1	72.43 246	70.46 284	87.89 271	60.31 168	94.92 239	42.64 440	76.64 278	87.48 319

FC-MVSNet-test		77.99 260	78.08 231	77.70 368	84.89 326	55.51 410	90.27 289	93.75 84	76.87 157	66.80 341	87.59 276	65.71 84	90.23 393	62.89 344	73.94 296	87.37 322

Anonymous202405211		77.96 261	75.33 283	85.87 115	93.73 57	64.52 216	94.85 52	85.36 410	62.52 391	76.11 201	90.18 220	29.43 445	97.29 89	68.51 279	77.24 274	95.81 56

cl22		77.94 262	76.78 258	81.42 291	87.57 250	64.93 206	90.67 274	88.86 343	72.45 245	67.63 328	82.68 342	64.07 105	92.91 328	71.79 244	65.30 359	86.44 344

XXY-MVS		77.94 262	76.44 263	82.43 258	82.60 362	64.44 221	92.01 197	91.83 179	73.59 220	70.00 292	85.82 303	54.43 258	94.76 243	69.63 265	68.02 340	88.10 312

MS-PatchMatch		77.90 264	76.50 262	82.12 274	85.99 298	69.95 43	91.75 219	92.70 131	73.97 208	62.58 380	84.44 321	41.11 383	95.78 187	63.76 336	92.17 71	80.62 432

usedtu_dtu_shiyan1		77.89 265	76.39 266	82.40 262	81.92 370	67.01 145	91.94 204	93.00 120	77.01 154	68.44 316	84.15 323	54.78 251	93.25 314	65.76 315	70.53 320	86.94 331

FE-MVSNET3		77.89 265	76.39 266	82.40 262	81.92 370	67.01 145	91.94 204	93.00 120	77.01 154	68.44 316	84.15 323	54.78 251	93.25 314	65.76 315	70.53 320	86.94 331

FMVSNet3		77.73 267	76.04 273	82.80 247	91.20 141	68.99 78	91.87 208	91.99 168	73.35 223	67.04 336	83.19 337	56.62 229	92.14 358	59.80 363	69.34 326	87.28 325

VortexMVS		77.62 268	76.44 263	81.13 302	88.58 202	63.73 253	91.24 250	91.30 207	77.81 136	65.76 346	81.97 351	49.69 314	93.72 298	76.40 201	65.26 362	85.94 364

miper_ehance_all_eth		77.60 269	76.44 263	81.09 308	85.70 309	64.41 224	90.65 275	88.64 352	72.31 249	67.37 334	82.52 343	64.77 97	92.64 342	70.67 258	65.30 359	86.24 353

UniMVSNet (Re)		77.58 270	76.78 258	79.98 334	84.11 342	60.80 334	91.76 217	93.17 111	76.56 170	69.93 295	84.78 316	63.32 124	92.36 352	64.89 325	62.51 391	86.78 335

PatchmatchNet		77.46 271	74.63 290	85.96 112	89.55 174	70.35 36	79.97 431	89.55 307	72.23 252	70.94 278	76.91 414	57.03 219	92.79 334	54.27 384	81.17 225	94.74 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 272	75.65 279	82.73 249	80.38 388	67.13 139	91.85 210	90.23 278	75.09 189	69.37 297	83.39 334	53.79 267	94.44 263	71.77 245	65.00 366	86.63 340

CHOSEN 280x420		77.35 273	76.95 257	78.55 360	87.07 265	62.68 290	69.71 463	82.95 433	68.80 325	71.48 275	87.27 283	66.03 79	84.00 446	76.47 200	82.81 204	88.95 297

PS-MVSNAJss		77.26 274	76.31 268	80.13 329	80.64 384	59.16 373	90.63 278	91.06 229	72.80 237	68.58 313	84.57 319	53.55 269	93.96 290	72.97 229	71.96 311	87.27 326

gg-mvs-nofinetune		77.18 275	74.31 297	85.80 119	91.42 133	68.36 97	71.78 457	94.72 41	49.61 454	77.12 191	45.92 483	77.41 8	93.98 289	67.62 291	93.16 59	95.05 97

WB-MVSnew		77.14 276	76.18 272	80.01 333	86.18 293	63.24 273	91.26 248	94.11 72	71.72 271	73.52 242	87.29 282	45.14 365	93.00 321	56.98 374	79.42 247	83.80 392

MVP-Stereo		77.12 277	76.23 270	79.79 341	81.72 372	66.34 167	89.29 318	90.88 241	70.56 300	62.01 383	82.88 339	49.34 317	94.13 277	65.55 320	93.80 47	78.88 448
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 278	75.37 281	82.20 270	89.25 183	62.11 303	82.06 409	89.09 329	76.77 162	70.84 280	87.12 284	41.43 381	95.01 234	67.23 297	74.55 288	89.48 293

MonoMVSNet		76.99 279	75.08 286	82.73 249	83.32 354	63.24 273	86.47 369	86.37 393	79.08 113	66.31 344	79.30 393	49.80 313	91.72 368	79.37 175	65.70 357	93.23 200

dmvs_re		76.93 280	75.36 282	81.61 287	87.78 247	60.71 341	80.00 430	87.99 371	79.42 102	69.02 303	89.47 239	46.77 348	94.32 267	63.38 338	74.45 291	89.81 286

X-MVStestdata		76.86 281	74.13 303	85.05 152	93.22 71	63.78 249	92.92 145	92.66 136	73.99 206	78.18 175	10.19 498	55.25 243	97.41 81	79.16 178	91.58 83	93.95 173

DU-MVS		76.86 281	75.84 276	79.91 337	82.96 358	60.26 353	91.26 248	91.54 193	76.46 172	68.88 307	86.35 294	56.16 234	92.13 359	66.38 307	62.55 389	87.35 323

Anonymous20240529		76.84 283	74.15 302	84.88 160	91.02 143	64.95 205	93.84 102	91.09 223	53.57 442	73.00 245	87.42 279	35.91 416	97.32 87	69.14 273	72.41 309	92.36 231

UWE-MVS-28		76.83 284	77.60 242	74.51 403	84.58 332	50.34 437	88.22 341	94.60 49	74.46 195	66.66 342	88.98 252	62.53 138	85.50 438	57.55 373	80.80 235	87.69 316

c3_l		76.83 284	75.47 280	80.93 312	85.02 324	64.18 236	90.39 284	88.11 368	71.66 272	66.65 343	81.64 357	63.58 120	92.56 343	69.31 270	62.86 386	86.04 359

WR-MVS		76.76 286	75.74 278	79.82 340	84.60 330	62.27 300	92.60 166	92.51 144	76.06 174	67.87 325	85.34 310	56.76 225	90.24 392	62.20 348	63.69 380	86.94 331

v1144		76.73 287	74.88 287	82.27 266	80.23 392	66.60 161	91.68 225	90.21 281	73.69 217	69.06 302	81.89 352	52.73 279	94.40 265	69.21 271	65.23 363	85.80 367

IterMVS-LS		76.49 288	75.18 285	80.43 321	84.49 335	62.74 288	90.64 276	88.80 345	72.40 247	65.16 352	81.72 355	60.98 158	92.27 356	67.74 289	64.65 371	86.29 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 289	74.55 293	82.19 271	79.14 406	67.82 116	90.26 290	89.42 312	73.75 214	68.63 312	81.89 352	51.31 294	94.09 279	71.69 247	64.84 367	84.66 384

Elysia		76.45 290	74.17 300	83.30 233	80.43 386	64.12 237	89.58 306	90.83 243	61.78 401	72.53 255	85.92 301	34.30 423	94.81 241	68.10 283	84.01 191	90.97 270

StellarMVS		76.45 290	74.17 300	83.30 233	80.43 386	64.12 237	89.58 306	90.83 243	61.78 401	72.53 255	85.92 301	34.30 423	94.81 241	68.10 283	84.01 191	90.97 270

mamba_0408		76.22 292	73.37 314	84.77 167	88.50 209	66.98 147	58.80 483	86.18 399	69.12 321	74.12 232	89.01 250	47.50 338	95.35 219	67.57 292	79.52 244	91.98 247

v148		76.19 293	74.47 295	81.36 294	80.05 394	64.44 221	91.75 219	90.23 278	73.68 218	67.13 335	80.84 372	55.92 239	93.86 297	68.95 275	61.73 400	85.76 370

Effi-MVS+-dtu		76.14 294	75.28 284	78.72 359	83.22 355	55.17 412	89.87 301	87.78 375	75.42 183	67.98 320	81.43 361	45.08 366	92.52 345	75.08 212	71.63 312	88.48 306

cl____		76.07 295	74.67 288	80.28 324	85.15 319	61.76 313	90.12 293	88.73 347	71.16 287	65.43 349	81.57 359	61.15 155	92.95 323	66.54 304	62.17 393	86.13 357

DIV-MVS_self_test		76.07 295	74.67 288	80.28 324	85.14 320	61.75 314	90.12 293	88.73 347	71.16 287	65.42 350	81.60 358	61.15 155	92.94 327	66.54 304	62.16 395	86.14 355

FMVSNet2		76.07 295	74.01 305	82.26 268	88.85 195	67.66 120	91.33 245	91.61 191	70.84 294	65.98 345	82.25 347	48.03 328	92.00 363	58.46 368	68.73 334	87.10 328

v144192		76.05 298	74.03 304	82.12 274	79.50 400	66.55 163	91.39 237	89.71 303	72.30 250	68.17 318	81.33 364	51.75 287	94.03 287	67.94 287	64.19 373	85.77 368

NR-MVSNet		76.05 298	74.59 291	80.44 320	82.96 358	62.18 302	90.83 266	91.73 183	77.12 153	60.96 389	86.35 294	59.28 188	91.80 366	60.74 356	61.34 404	87.35 323

v1192		75.98 300	73.92 306	82.15 272	79.73 396	66.24 170	91.22 252	89.75 297	72.67 239	68.49 314	81.42 362	49.86 311	94.27 271	67.08 299	65.02 365	85.95 362

FE-MVS		75.97 301	73.02 320	84.82 163	89.78 167	65.56 188	77.44 442	91.07 228	64.55 368	72.66 251	79.85 387	46.05 358	96.69 134	54.97 381	80.82 233	92.21 241

eth_miper_zixun_eth		75.96 302	74.40 296	80.66 316	84.66 329	63.02 279	89.28 319	88.27 364	71.88 263	65.73 347	81.65 356	59.45 183	92.81 332	68.13 282	60.53 409	86.14 355

TranMVSNet+NR-MVSNet		75.86 303	74.52 294	79.89 338	82.44 364	60.64 344	91.37 240	91.37 201	76.63 168	67.65 327	86.21 297	52.37 282	91.55 374	61.84 350	60.81 407	87.48 319

SCA		75.82 304	72.76 324	85.01 154	86.63 282	70.08 39	81.06 419	89.19 321	71.60 278	70.01 291	77.09 412	45.53 361	90.25 389	60.43 358	73.27 300	94.68 124

LPG-MVS_test		75.82 304	74.58 292	79.56 348	84.31 339	59.37 369	90.44 281	89.73 300	69.49 313	64.86 353	88.42 257	38.65 392	94.30 269	72.56 237	72.76 304	85.01 381

GBi-Net		75.65 306	73.83 307	81.10 305	88.85 195	65.11 200	90.01 297	90.32 269	70.84 294	67.04 336	80.25 382	48.03 328	91.54 375	59.80 363	69.34 326	86.64 337

test1		75.65 306	73.83 307	81.10 305	88.85 195	65.11 200	90.01 297	90.32 269	70.84 294	67.04 336	80.25 382	48.03 328	91.54 375	59.80 363	69.34 326	86.64 337

v1921920		75.63 308	73.49 312	82.06 278	79.38 401	66.35 166	91.07 260	89.48 308	71.98 258	67.99 319	81.22 367	49.16 322	93.90 293	66.56 303	64.56 372	85.92 365

ACMP	71.68 10	75.58 309	74.23 299	79.62 346	84.97 325	59.64 364	90.80 267	89.07 331	70.39 301	62.95 376	87.30 281	38.28 396	93.87 295	72.89 230	71.45 315	85.36 377
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 310	73.26 318	81.61 287	80.67 383	66.82 153	89.54 310	89.27 317	71.65 273	63.30 371	80.30 381	54.99 249	94.06 282	67.33 296	62.33 392	83.94 390

tpm cat1		75.30 311	72.21 333	84.58 183	88.52 208	67.77 117	78.16 440	88.02 370	61.88 399	68.45 315	76.37 423	60.65 163	94.03 287	53.77 388	74.11 294	91.93 250

PLC	68.80 14	75.23 312	73.68 310	79.86 339	92.93 82	58.68 378	90.64 276	88.30 362	60.90 406	64.43 361	90.53 210	42.38 377	94.57 255	56.52 375	76.54 279	86.33 350
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 313	72.98 322	81.88 280	79.20 403	66.00 175	90.75 270	89.11 328	71.63 277	67.41 332	81.22 367	47.36 340	93.87 295	65.46 321	64.72 370	85.77 368

blend_shiyan4		75.18 314	73.00 321	81.69 285	75.62 438	64.75 208	91.78 214	91.06 229	65.89 358	61.35 386	77.39 405	62.16 145	93.71 299	68.18 280	63.60 381	86.61 342

Fast-Effi-MVS+-dtu		75.04 315	73.37 314	80.07 330	80.86 378	59.52 367	91.20 254	85.38 409	71.90 261	65.20 351	84.84 315	41.46 380	92.97 322	66.50 306	72.96 303	87.73 315

dp		75.01 316	72.09 334	83.76 213	89.28 182	66.22 171	79.96 432	89.75 297	71.16 287	67.80 326	77.19 411	51.81 285	92.54 344	50.39 398	71.44 316	92.51 228

TAPA-MVS	70.22 12	74.94 317	73.53 311	79.17 354	90.40 156	52.07 425	89.19 323	89.61 306	62.69 390	70.07 290	92.67 151	48.89 325	94.32 267	38.26 455	79.97 240	91.12 268
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 318	73.32 317	79.74 343	86.53 285	60.31 352	89.03 328	92.70 131	78.61 124	68.98 305	83.34 335	41.93 379	92.23 357	52.77 392	65.97 355	86.69 336

SSM_04072		74.86 319	73.37 314	79.35 351	88.50 209	66.98 147	58.80 483	86.18 399	69.12 321	74.12 232	89.01 250	47.50 338	79.09 468	67.57 292	79.52 244	91.98 247

v10		74.77 320	72.54 330	81.46 290	80.33 390	66.71 158	89.15 324	89.08 330	70.94 292	63.08 374	79.86 386	52.52 280	94.04 285	65.70 317	62.17 393	83.64 393

XVG-OURS-SEG-HR		74.70 321	73.08 319	79.57 347	78.25 419	57.33 395	80.49 422	87.32 380	63.22 383	68.76 310	90.12 231	44.89 367	91.59 372	70.55 260	74.09 295	89.79 287

ACMM	69.62 13	74.34 322	72.73 326	79.17 354	84.25 341	57.87 385	90.36 286	89.93 291	63.17 385	65.64 348	86.04 300	37.79 404	94.10 278	65.89 312	71.52 314	85.55 373
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 323	72.30 332	80.32 322	91.49 132	61.66 316	90.85 265	80.72 439	56.67 433	63.85 366	90.64 207	46.75 349	90.84 383	53.79 387	75.99 283	88.47 307

XVG-OURS		74.25 324	72.46 331	79.63 345	78.45 417	57.59 391	80.33 424	87.39 377	63.86 375	68.76 310	89.62 238	40.50 385	91.72 368	69.00 274	74.25 293	89.58 290

test_fmvs1		74.07 325	73.69 309	75.22 393	78.91 410	47.34 453	89.06 327	74.69 456	63.68 378	79.41 158	91.59 188	24.36 456	87.77 419	85.22 98	76.26 281	90.55 278

CVMVSNet		74.04 326	74.27 298	73.33 413	85.33 313	43.94 467	89.53 313	88.39 358	54.33 441	70.37 286	90.13 229	49.17 321	84.05 444	61.83 351	79.36 249	91.99 246

Baseline_NR-MVSNet		73.99 327	72.83 323	77.48 372	80.78 381	59.29 372	91.79 212	84.55 418	68.85 324	68.99 304	80.70 373	56.16 234	92.04 362	62.67 345	60.98 406	81.11 426

pmmvs4		73.92 328	71.81 338	80.25 326	79.17 404	65.24 196	87.43 356	87.26 383	67.64 341	63.46 369	83.91 329	48.96 324	91.53 378	62.94 342	65.49 358	83.96 389

D2MVS		73.80 329	72.02 335	79.15 356	79.15 405	62.97 280	88.58 335	90.07 284	72.94 232	59.22 404	78.30 397	42.31 378	92.70 338	65.59 319	72.00 310	81.79 421

SD_0403		73.79 330	73.48 313	74.69 400	85.33 313	45.56 463	83.80 387	85.57 408	76.55 171	62.96 375	88.45 256	50.62 303	87.59 423	48.80 408	79.28 253	90.92 272

CR-MVSNet		73.79 330	70.82 346	82.70 251	83.15 356	67.96 111	70.25 460	84.00 423	73.67 219	69.97 293	72.41 440	57.82 212	89.48 402	52.99 391	73.13 301	90.64 276

test_djsdf		73.76 332	72.56 329	77.39 374	77.00 430	53.93 418	89.07 325	90.69 252	65.80 359	63.92 364	82.03 350	43.14 375	92.67 339	72.83 231	68.53 335	85.57 372

pmmvs5		73.35 333	71.52 340	78.86 358	78.64 414	60.61 345	91.08 258	86.90 387	67.69 338	63.32 370	83.64 330	44.33 370	90.53 386	62.04 349	66.02 354	85.46 375

Anonymous20231211		73.08 334	70.39 350	81.13 302	90.62 151	63.33 269	91.40 235	90.06 286	51.84 447	64.46 360	80.67 375	36.49 414	94.07 281	63.83 335	64.17 374	85.98 361

tt0805		73.07 335	70.73 347	80.07 330	78.37 418	57.05 398	87.78 350	92.18 159	61.23 405	67.04 336	86.49 293	31.35 437	94.58 253	65.06 324	67.12 347	88.57 304

miper_lstm_enhance		73.05 336	71.73 339	77.03 379	83.80 347	58.32 382	81.76 410	88.88 341	69.80 311	61.01 388	78.23 399	57.19 217	87.51 425	65.34 322	59.53 414	85.27 380

jajsoiax		73.05 336	71.51 341	77.67 369	77.46 427	54.83 414	88.81 331	90.04 287	69.13 320	62.85 378	83.51 332	31.16 438	92.75 335	70.83 255	69.80 322	85.43 376

LCM-MVSNet-Re		72.93 338	71.84 337	76.18 388	88.49 213	48.02 448	80.07 429	70.17 470	73.96 209	52.25 437	80.09 385	49.98 309	88.24 413	67.35 294	84.23 186	92.28 236

pm-mvs1		72.89 339	71.09 343	78.26 364	79.10 407	57.62 389	90.80 267	89.30 316	67.66 339	62.91 377	81.78 354	49.11 323	92.95 323	60.29 360	58.89 417	84.22 388

tpmvs		72.88 340	69.76 356	82.22 269	90.98 144	67.05 141	78.22 439	88.30 362	63.10 386	64.35 362	74.98 430	55.09 248	94.27 271	43.25 434	69.57 325	85.34 378

test0.0.03 1		72.76 341	72.71 327	72.88 417	80.25 391	47.99 449	91.22 252	89.45 310	71.51 282	62.51 381	87.66 274	53.83 265	85.06 440	50.16 400	67.84 345	85.58 371

UniMVSNet_ETH3D		72.74 342	70.53 349	79.36 350	78.62 415	56.64 402	85.01 377	89.20 320	63.77 376	64.84 355	84.44 321	34.05 425	91.86 365	63.94 334	70.89 319	89.57 291

mvs_tets		72.71 343	71.11 342	77.52 370	77.41 428	54.52 416	88.45 337	89.76 296	68.76 327	62.70 379	83.26 336	29.49 444	92.71 336	70.51 261	69.62 324	85.34 378

FMVSNet1		72.71 343	69.91 354	81.10 305	83.60 351	65.11 200	90.01 297	90.32 269	63.92 374	63.56 368	80.25 382	36.35 415	91.54 375	54.46 383	66.75 350	86.64 337

test_fmvs1_n		72.69 345	71.92 336	74.99 398	71.15 457	47.08 455	87.34 358	75.67 451	63.48 380	78.08 177	91.17 202	20.16 470	87.87 416	84.65 107	75.57 285	90.01 284

IterMVS		72.65 346	70.83 344	78.09 366	82.17 366	62.96 281	87.64 354	86.28 395	71.56 280	60.44 395	78.85 395	45.42 363	86.66 429	63.30 340	61.83 397	84.65 385
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 347	72.74 325	72.10 425	87.87 242	49.45 443	88.07 343	89.01 335	72.91 234	63.11 372	88.10 266	63.63 115	85.54 435	32.73 471	69.23 329	81.32 424

wanda-best-256-512		72.42 348	69.43 358	81.37 292	75.39 439	64.24 233	91.58 228	91.09 223	66.36 351	60.64 391	76.86 415	47.20 342	93.47 307	64.80 326	50.98 441	86.40 345

FE-blended-shiyan7		72.42 348	69.43 358	81.37 292	75.39 439	64.24 233	91.58 228	91.09 223	66.36 351	60.64 391	76.86 415	47.20 342	93.47 307	64.80 326	50.98 441	86.40 345

blended_shiyan8		72.26 350	69.25 362	81.29 296	75.23 444	64.03 240	91.36 243	91.04 233	66.11 356	60.42 396	76.73 419	46.79 347	93.45 310	64.58 330	51.00 440	86.37 348

blended_shiyan6		72.26 350	69.26 361	81.27 297	75.24 443	64.00 243	91.37 240	91.06 229	66.12 355	60.34 397	76.75 418	46.82 345	93.45 310	64.61 328	50.98 441	86.37 348

PatchMatch-RL		72.06 352	69.98 351	78.28 363	89.51 175	55.70 409	83.49 391	83.39 431	61.24 404	63.72 367	82.76 340	34.77 420	93.03 320	53.37 390	77.59 266	86.12 358

gbinet_0.2-2-1-0.02		71.92 353	68.92 364	80.91 313	75.87 437	63.30 270	91.95 203	91.40 200	65.62 362	61.57 385	77.27 409	44.71 368	92.88 330	61.00 355	50.87 445	86.54 343

PVSNet_0	68.08 15	71.81 354	68.32 370	82.27 266	84.68 327	62.31 299	88.68 333	90.31 272	75.84 176	57.93 416	80.65 376	37.85 403	94.19 274	69.94 263	29.05 486	90.31 280

MIMVSNet		71.64 355	68.44 368	81.23 299	81.97 369	64.44 221	73.05 454	88.80 345	69.67 312	64.59 356	74.79 432	32.79 429	87.82 417	53.99 385	76.35 280	91.42 258

test_vis1_n		71.63 356	70.73 347	74.31 407	69.63 464	47.29 454	86.91 362	72.11 464	63.21 384	75.18 215	90.17 226	20.40 468	85.76 434	84.59 108	74.42 292	89.87 285

IterMVS-SCA-FT		71.55 357	69.97 352	76.32 386	81.48 374	60.67 343	87.64 354	85.99 402	66.17 354	59.50 402	78.88 394	45.53 361	83.65 448	62.58 346	61.93 396	84.63 387

v7n		71.31 358	68.65 365	79.28 352	76.40 432	60.77 336	86.71 366	89.45 310	64.17 373	58.77 409	78.24 398	44.59 369	93.54 304	57.76 370	61.75 399	83.52 396

anonymousdsp		71.14 359	69.37 360	76.45 385	72.95 452	54.71 415	84.19 384	88.88 341	61.92 398	62.15 382	79.77 388	38.14 399	91.44 380	68.90 276	67.45 346	83.21 402

usedtu_blend_shiyan5		71.06 360	67.54 373	81.62 286	75.39 439	64.75 208	85.67 373	86.47 392	56.48 434	60.64 391	76.85 417	47.20 342	93.71 299	68.18 280	50.98 441	86.40 345

F-COLMAP		70.66 361	68.44 368	77.32 375	86.37 290	55.91 407	88.00 345	86.32 394	56.94 431	57.28 419	88.07 268	33.58 427	92.49 346	51.02 395	68.37 336	83.55 394

WR-MVS_H		70.59 362	69.94 353	72.53 419	81.03 377	51.43 429	87.35 357	92.03 167	67.38 342	60.23 399	80.70 373	55.84 240	83.45 451	46.33 423	58.58 419	82.72 409

CP-MVSNet		70.50 363	69.91 354	72.26 422	80.71 382	51.00 433	87.23 359	90.30 273	67.84 337	59.64 401	82.69 341	50.23 307	82.30 459	51.28 394	59.28 415	83.46 398

RPMNet		70.42 364	65.68 384	84.63 181	83.15 356	67.96 111	70.25 460	90.45 260	46.83 462	69.97 293	65.10 465	56.48 233	95.30 225	35.79 460	73.13 301	90.64 276

testing3		70.38 365	70.83 344	69.03 439	85.82 304	43.93 468	90.72 273	90.56 259	68.06 333	60.24 398	86.82 290	64.83 95	84.12 442	26.33 479	64.10 375	79.04 446

tfpnnormal		70.10 366	67.36 374	78.32 362	83.45 353	60.97 332	88.85 329	92.77 129	64.85 367	60.83 390	78.53 396	43.52 373	93.48 306	31.73 474	61.70 401	80.52 433

TransMVSNet (Re)		70.07 367	67.66 372	77.31 376	80.62 385	59.13 374	91.78 214	84.94 414	65.97 357	60.08 400	80.44 378	50.78 300	91.87 364	48.84 407	45.46 459	80.94 428

CL-MVSNet_self_test		69.92 368	68.09 371	75.41 391	73.25 451	55.90 408	90.05 296	89.90 292	69.96 308	61.96 384	76.54 420	51.05 299	87.64 420	49.51 404	50.59 447	82.70 411

DP-MVS		69.90 369	66.48 376	80.14 328	95.36 29	62.93 282	89.56 308	76.11 449	50.27 453	57.69 417	85.23 311	39.68 388	95.73 191	33.35 465	71.05 318	81.78 422

PS-CasMVS		69.86 370	69.13 363	72.07 426	80.35 389	50.57 436	87.02 361	89.75 297	67.27 343	59.19 405	82.28 346	46.58 351	82.24 460	50.69 397	59.02 416	83.39 400

Syy-MVS		69.65 371	69.52 357	70.03 434	87.87 242	43.21 469	88.07 343	89.01 335	72.91 234	63.11 372	88.10 266	45.28 364	85.54 435	22.07 484	69.23 329	81.32 424

MSDG		69.54 372	65.73 383	80.96 310	85.11 322	63.71 255	84.19 384	83.28 432	56.95 430	54.50 426	84.03 326	31.50 435	96.03 169	42.87 438	69.13 331	83.14 404

PEN-MVS		69.46 373	68.56 366	72.17 424	79.27 402	49.71 441	86.90 363	89.24 318	67.24 346	59.08 406	82.51 344	47.23 341	83.54 450	48.42 410	57.12 421	83.25 401

LS3D		69.17 374	66.40 378	77.50 371	91.92 117	56.12 405	85.12 376	80.37 441	46.96 460	56.50 421	87.51 278	37.25 407	93.71 299	32.52 473	79.40 248	82.68 412

PatchT		69.11 375	65.37 388	80.32 322	82.07 368	63.68 259	67.96 469	87.62 376	50.86 451	69.37 297	65.18 464	57.09 218	88.53 409	41.59 444	66.60 351	88.74 301

KD-MVS_2432*1600		69.03 376	66.37 379	77.01 380	85.56 310	61.06 330	81.44 415	90.25 276	67.27 343	58.00 414	76.53 421	54.49 255	87.63 421	48.04 412	35.77 477	82.34 415

miper_refine_blended		69.03 376	66.37 379	77.01 380	85.56 310	61.06 330	81.44 415	90.25 276	67.27 343	58.00 414	76.53 421	54.49 255	87.63 421	48.04 412	35.77 477	82.34 415

mvsany_test1		68.77 378	68.56 366	69.39 437	73.57 450	45.88 462	80.93 420	60.88 484	59.65 415	71.56 273	90.26 219	43.22 374	75.05 472	74.26 221	62.70 388	87.25 327

ACMH	63.93 17	68.62 379	64.81 390	80.03 332	85.22 318	63.25 272	87.72 351	84.66 416	60.83 407	51.57 441	79.43 392	27.29 451	94.96 236	41.76 442	64.84 367	81.88 420
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 380	65.41 387	77.96 367	78.69 413	62.93 282	89.86 302	89.17 322	60.55 408	50.27 447	77.73 404	22.60 464	94.06 282	47.18 419	72.65 306	76.88 460

ADS-MVSNet		68.54 381	64.38 397	81.03 309	88.06 233	66.90 152	68.01 467	84.02 422	57.57 424	64.48 358	69.87 452	38.68 390	89.21 404	40.87 446	67.89 343	86.97 329

DTE-MVSNet		68.46 382	67.33 375	71.87 428	77.94 423	49.00 446	86.16 371	88.58 354	66.36 351	58.19 411	82.21 348	46.36 352	83.87 447	44.97 431	55.17 428	82.73 408

mmtdpeth		68.33 383	66.37 379	74.21 408	82.81 361	51.73 426	84.34 382	80.42 440	67.01 347	71.56 273	68.58 456	30.52 442	92.35 353	75.89 205	36.21 475	78.56 453

our_test_3		68.29 384	64.69 392	79.11 357	78.92 408	64.85 207	88.40 338	85.06 412	60.32 411	52.68 435	76.12 425	40.81 384	89.80 401	44.25 433	55.65 426	82.67 413

Patchmatch-RL test		68.17 385	64.49 395	79.19 353	71.22 456	53.93 418	70.07 462	71.54 468	69.22 317	56.79 420	62.89 469	56.58 230	88.61 406	69.53 267	52.61 436	95.03 99

XVG-ACMP-BASELINE		68.04 386	65.53 386	75.56 390	74.06 449	52.37 423	78.43 436	85.88 403	62.03 396	58.91 408	81.21 369	20.38 469	91.15 382	60.69 357	68.18 337	83.16 403

FMVSNet5		68.04 386	65.66 385	75.18 395	84.43 337	57.89 384	83.54 389	86.26 396	61.83 400	53.64 432	73.30 435	37.15 410	85.08 439	48.99 406	61.77 398	82.56 414

ppachtmachnet_test		67.72 388	63.70 400	79.77 342	78.92 408	66.04 174	88.68 333	82.90 434	60.11 413	55.45 423	75.96 426	39.19 389	90.55 385	39.53 450	52.55 437	82.71 410

ACMH+	65.35 16	67.65 389	64.55 393	76.96 382	84.59 331	57.10 397	88.08 342	80.79 438	58.59 422	53.00 434	81.09 371	26.63 453	92.95 323	46.51 421	61.69 402	80.82 429

pmmvs6		67.57 390	64.76 391	76.00 389	72.82 454	53.37 420	88.71 332	86.78 391	53.19 443	57.58 418	78.03 401	35.33 419	92.41 349	55.56 379	54.88 430	82.21 417

Anonymous20231206		67.53 391	65.78 382	72.79 418	74.95 445	47.59 451	88.23 340	87.32 380	61.75 403	58.07 413	77.29 408	37.79 404	87.29 427	42.91 436	63.71 379	83.48 397

Patchmtry		67.53 391	63.93 399	78.34 361	82.12 367	64.38 225	68.72 464	84.00 423	48.23 459	59.24 403	72.41 440	57.82 212	89.27 403	46.10 424	56.68 425	81.36 423

USDC		67.43 393	64.51 394	76.19 387	77.94 423	55.29 411	78.38 437	85.00 413	73.17 225	48.36 455	80.37 379	21.23 466	92.48 347	52.15 393	64.02 377	80.81 430

ADS-MVSNet2		66.90 394	63.44 402	77.26 377	88.06 233	60.70 342	68.01 467	75.56 453	57.57 424	64.48 358	69.87 452	38.68 390	84.10 443	40.87 446	67.89 343	86.97 329

FE-MVSNET2		66.80 395	64.06 398	75.03 396	69.84 462	57.11 396	86.57 367	88.57 355	67.94 336	50.97 445	72.16 444	33.79 426	87.55 424	53.94 386	52.74 434	80.45 434

CMPMVS	48.56 21	66.77 396	64.41 396	73.84 410	70.65 460	50.31 438	77.79 441	85.73 406	45.54 465	44.76 466	82.14 349	35.40 418	90.14 395	63.18 341	74.54 290	81.07 427
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 397	62.92 405	76.80 384	76.51 431	57.77 386	89.22 320	83.41 430	55.48 438	53.86 430	77.84 402	26.28 454	93.95 291	34.90 462	68.76 333	78.68 451

LTVRE_ROB	59.60 19	66.27 398	63.54 401	74.45 404	84.00 344	51.55 428	67.08 471	83.53 428	58.78 420	54.94 425	80.31 380	34.54 421	93.23 316	40.64 448	68.03 339	78.58 452
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
JIA-IIPM		66.06 399	62.45 408	76.88 383	81.42 376	54.45 417	57.49 485	88.67 350	49.36 455	63.86 365	46.86 482	56.06 237	90.25 389	49.53 403	68.83 332	85.95 362

Patchmatch-test		65.86 400	60.94 414	80.62 319	83.75 348	58.83 376	58.91 482	75.26 455	44.50 469	50.95 446	77.09 412	58.81 196	87.90 415	35.13 461	64.03 376	95.12 93

UnsupCasMVSNet_eth		65.79 401	63.10 403	73.88 409	70.71 459	50.29 439	81.09 418	89.88 293	72.58 241	49.25 452	74.77 433	32.57 431	87.43 426	55.96 378	41.04 467	83.90 391

test_fmvs2		65.78 402	64.84 389	68.60 441	66.54 471	41.71 472	83.27 395	69.81 471	54.38 440	67.91 322	84.54 320	15.35 476	81.22 464	75.65 207	66.16 353	82.88 405

dmvs_testset		65.55 403	66.45 377	62.86 453	79.87 395	22.35 498	76.55 444	71.74 466	77.42 149	55.85 422	87.77 273	51.39 293	80.69 465	31.51 477	65.92 356	85.55 373

pmmvs-eth3d		65.53 404	62.32 409	75.19 394	69.39 465	59.59 365	82.80 403	83.43 429	62.52 391	51.30 443	72.49 438	32.86 428	87.16 428	55.32 380	50.73 446	78.83 449

SixPastTwentyTwo		64.92 405	61.78 412	74.34 406	78.74 412	49.76 440	83.42 394	79.51 444	62.86 387	50.27 447	77.35 406	30.92 440	90.49 387	45.89 425	47.06 453	82.78 406

OurMVSNet-221017-0		64.68 406	62.17 410	72.21 423	76.08 435	47.35 452	80.67 421	81.02 437	56.19 435	51.60 440	79.66 390	27.05 452	88.56 408	53.60 389	53.63 433	80.71 431

test_0402		64.54 407	61.09 413	74.92 399	84.10 343	60.75 338	87.95 346	79.71 443	52.03 445	52.41 436	77.20 410	32.21 433	91.64 370	23.14 482	61.03 405	72.36 470

testgi		64.48 408	62.87 406	69.31 438	71.24 455	40.62 475	85.49 374	79.92 442	65.36 364	54.18 428	83.49 333	23.74 459	84.55 441	41.60 443	60.79 408	82.77 407

RPSCF		64.24 409	61.98 411	71.01 431	76.10 434	45.00 464	75.83 449	75.94 450	46.94 461	58.96 407	84.59 318	31.40 436	82.00 461	47.76 417	60.33 413	86.04 359

EU-MVSNet		64.01 410	63.01 404	67.02 447	74.40 448	38.86 481	83.27 395	86.19 398	45.11 467	54.27 427	81.15 370	36.91 413	80.01 467	48.79 409	57.02 422	82.19 418

test20.03		63.83 411	62.65 407	67.38 446	70.58 461	39.94 477	86.57 367	84.17 420	63.29 382	51.86 439	77.30 407	37.09 411	82.47 457	38.87 454	54.13 432	79.73 440

sc_t1		63.81 412	59.39 420	77.10 378	77.62 425	56.03 406	84.32 383	73.56 460	46.66 463	58.22 410	73.06 436	23.28 462	90.62 384	50.93 396	46.84 454	84.64 386

MDA-MVSNet_test_wron		63.78 413	60.16 416	74.64 401	78.15 421	60.41 349	83.49 391	84.03 421	56.17 437	39.17 477	71.59 447	37.22 408	83.24 454	42.87 438	48.73 449	80.26 437

YYNet1		63.76 414	60.14 417	74.62 402	78.06 422	60.19 356	83.46 393	83.99 425	56.18 436	39.25 476	71.56 448	37.18 409	83.34 452	42.90 437	48.70 450	80.32 436

K. test v3		63.09 415	59.61 419	73.53 412	76.26 433	49.38 445	83.27 395	77.15 447	64.35 370	47.77 457	72.32 442	28.73 446	87.79 418	49.93 402	36.69 474	83.41 399

COLMAP_ROB	57.96 20	62.98 416	59.65 418	72.98 416	81.44 375	53.00 422	83.75 388	75.53 454	48.34 458	48.81 454	81.40 363	24.14 457	90.30 388	32.95 468	60.52 410	75.65 463
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 417	59.08 421	71.10 430	67.19 469	48.72 447	83.91 386	85.23 411	50.38 452	47.84 456	71.22 450	20.74 467	85.51 437	46.47 422	58.75 418	79.06 445

tt0320		61.85 418	57.45 427	75.03 396	77.49 426	57.60 390	82.74 404	73.65 459	43.65 473	53.65 431	68.18 458	25.47 455	88.66 405	45.56 427	46.68 455	78.81 450

AllTest		61.66 419	58.06 423	72.46 420	79.57 397	51.42 430	80.17 427	68.61 473	51.25 449	45.88 460	81.23 365	19.86 471	86.58 430	38.98 452	57.01 423	79.39 442

UnsupCasMVSNet_bld		61.60 420	57.71 424	73.29 414	68.73 466	51.64 427	78.61 435	89.05 333	57.20 429	46.11 459	61.96 473	28.70 447	88.60 407	50.08 401	38.90 472	79.63 441

MDA-MVSNet-bldmvs		61.54 421	57.70 425	73.05 415	79.53 399	57.00 401	83.08 399	81.23 436	57.57 424	34.91 481	72.45 439	32.79 429	86.26 432	35.81 459	41.95 465	75.89 462

tt0320-xc		61.51 422	56.89 431	75.37 392	78.50 416	58.61 379	82.61 406	71.27 469	44.31 470	53.17 433	68.03 460	23.38 460	88.46 410	47.77 416	43.00 464	79.03 447

mvs5depth		61.03 423	57.65 426	71.18 429	67.16 470	47.04 457	72.74 455	77.49 445	57.47 427	60.52 394	72.53 437	22.84 463	88.38 411	49.15 405	38.94 471	78.11 456

KD-MVS_self_test		60.87 424	58.60 422	67.68 444	66.13 472	39.93 478	75.63 451	84.70 415	57.32 428	49.57 450	68.45 457	29.55 443	82.87 455	48.09 411	47.94 451	80.25 438

kuosan		60.86 425	60.24 415	62.71 454	81.57 373	46.43 459	75.70 450	85.88 403	57.98 423	48.95 453	69.53 454	58.42 201	76.53 470	28.25 478	35.87 476	65.15 477

FE-MVSNET		60.52 426	57.18 430	70.53 432	67.53 468	50.68 435	82.62 405	76.28 448	59.33 418	46.71 458	71.10 451	30.54 441	83.61 449	33.15 467	47.37 452	77.29 459

TinyColmap		60.32 427	56.42 434	72.00 427	78.78 411	53.18 421	78.36 438	75.64 452	52.30 444	41.59 475	75.82 428	14.76 479	88.35 412	35.84 458	54.71 431	74.46 464

MVS-HIRNet		60.25 428	55.55 435	74.35 405	84.37 338	56.57 403	71.64 458	74.11 457	34.44 480	45.54 464	42.24 488	31.11 439	89.81 399	40.36 449	76.10 282	76.67 461

MIMVSNet1		60.16 429	57.33 428	68.67 440	69.71 463	44.13 466	78.92 434	84.21 419	55.05 439	44.63 467	71.85 445	23.91 458	81.54 463	32.63 472	55.03 429	80.35 435

PM-MVS		59.40 430	56.59 432	67.84 442	63.63 475	41.86 470	76.76 443	63.22 481	59.01 419	51.07 444	72.27 443	11.72 483	83.25 453	61.34 352	50.28 448	78.39 454

new-patchmatchnet		59.30 431	56.48 433	67.79 443	65.86 473	44.19 465	82.47 407	81.77 435	59.94 414	43.65 471	66.20 463	27.67 450	81.68 462	39.34 451	41.40 466	77.50 458

test_vis1_rt		59.09 432	57.31 429	64.43 450	68.44 467	46.02 461	83.05 401	48.63 493	51.96 446	49.57 450	63.86 468	16.30 474	80.20 466	71.21 253	62.79 387	67.07 476

usedtu_dtu_shiyan2		57.76 433	53.69 439	69.95 435	57.60 485	41.80 471	83.50 390	83.67 427	45.26 466	43.79 470	62.82 470	17.63 473	85.93 433	42.56 441	46.40 457	82.12 419

test_fmvs3		56.82 434	54.86 437	62.69 455	53.59 487	35.47 484	75.87 448	65.64 478	43.91 471	55.10 424	71.43 449	6.91 491	74.40 475	68.64 278	52.63 435	78.20 455

DSMNet-mixed		56.78 435	54.44 438	63.79 451	63.21 476	29.44 493	64.43 474	64.10 480	42.12 477	51.32 442	71.60 446	31.76 434	75.04 473	36.23 457	65.20 364	86.87 334

pmmvs3		55.51 436	51.50 442	67.53 445	57.90 484	50.93 434	80.37 423	73.66 458	40.63 478	44.15 469	64.75 466	16.30 474	78.97 469	44.77 432	40.98 469	72.69 468

TDRefinement		55.28 437	51.58 441	66.39 448	59.53 483	46.15 460	76.23 446	72.80 461	44.60 468	42.49 473	76.28 424	15.29 477	82.39 458	33.20 466	43.75 461	70.62 472

dongtai		55.18 438	55.46 436	54.34 464	76.03 436	36.88 482	76.07 447	84.61 417	51.28 448	43.41 472	64.61 467	56.56 231	67.81 483	18.09 487	28.50 487	58.32 480

LF4IMVS		54.01 439	52.12 440	59.69 456	62.41 478	39.91 479	68.59 465	68.28 475	42.96 475	44.55 468	75.18 429	14.09 481	68.39 482	41.36 445	51.68 438	70.78 471

ttmdpeth		53.34 440	49.96 443	63.45 452	62.07 480	40.04 476	72.06 456	65.64 478	42.54 476	51.88 438	77.79 403	13.94 482	76.48 471	32.93 469	30.82 485	73.84 465

MVStest1		51.35 441	46.89 445	64.74 449	65.06 474	51.10 432	67.33 470	72.58 462	30.20 484	35.30 479	74.82 431	27.70 449	69.89 480	24.44 481	24.57 488	73.22 466

N_pmnet		50.55 442	49.11 444	54.88 462	77.17 429	4.02 506	84.36 381	2.00 504	48.59 456	45.86 462	68.82 455	32.22 432	82.80 456	31.58 475	51.38 439	77.81 457

new_pmnet		49.31 443	46.44 446	57.93 457	62.84 477	40.74 474	68.47 466	62.96 482	36.48 479	35.09 480	57.81 477	14.97 478	72.18 477	32.86 470	46.44 456	60.88 479

mvsany_test3		48.86 444	46.35 447	56.41 458	46.00 493	31.67 489	62.26 476	47.25 494	43.71 472	45.54 464	68.15 459	10.84 484	64.44 491	57.95 369	35.44 479	73.13 467

test_f		46.58 445	43.45 449	55.96 459	45.18 494	32.05 488	61.18 477	49.49 492	33.39 481	42.05 474	62.48 472	7.00 490	65.56 487	47.08 420	43.21 463	70.27 473

WB-MVS		46.23 446	44.94 448	50.11 467	62.13 479	21.23 500	76.48 445	55.49 486	45.89 464	35.78 478	61.44 475	35.54 417	72.83 476	9.96 494	21.75 489	56.27 482

FPMVS		45.64 447	43.10 451	53.23 465	51.42 490	36.46 483	64.97 473	71.91 465	29.13 485	27.53 485	61.55 474	9.83 486	65.01 489	16.00 491	55.58 427	58.22 481

SSC-MVS		44.51 448	43.35 450	47.99 471	61.01 482	18.90 502	74.12 453	54.36 487	43.42 474	34.10 482	60.02 476	34.42 422	70.39 479	9.14 496	19.57 490	54.68 483

EGC-MVSNET		42.35 449	38.09 452	55.11 461	74.57 446	46.62 458	71.63 459	55.77 485	0.04 499	0.24 500	62.70 471	14.24 480	74.91 474	17.59 488	46.06 458	43.80 485

LCM-MVSNet		40.54 450	35.79 455	54.76 463	36.92 500	30.81 490	51.41 488	69.02 472	22.07 487	24.63 487	45.37 484	4.56 495	65.81 486	33.67 464	34.50 480	67.67 474

APD_test1		40.50 451	37.31 454	50.09 468	51.88 488	35.27 485	59.45 481	52.59 489	21.64 488	26.12 486	57.80 478	4.56 495	66.56 485	22.64 483	39.09 470	48.43 484

test_vis3_rt		40.46 452	37.79 453	48.47 470	44.49 495	33.35 487	66.56 472	32.84 501	32.39 482	29.65 483	39.13 491	3.91 498	68.65 481	50.17 399	40.99 468	43.40 486

ANet_high		40.27 453	35.20 456	55.47 460	34.74 501	34.47 486	63.84 475	71.56 467	48.42 457	18.80 490	41.08 489	9.52 487	64.45 490	20.18 485	8.66 497	67.49 475

test_method		38.59 454	35.16 457	48.89 469	54.33 486	21.35 499	45.32 491	53.71 488	7.41 496	28.74 484	51.62 480	8.70 488	52.87 494	33.73 463	32.89 481	72.47 469

PMMVS2		37.93 455	33.61 458	50.92 466	46.31 492	24.76 496	60.55 480	50.05 490	28.94 486	20.93 488	47.59 481	4.41 497	65.13 488	25.14 480	18.55 492	62.87 478

Gipuma		34.91 456	31.44 459	45.30 472	70.99 458	39.64 480	19.85 495	72.56 463	20.10 490	16.16 494	21.47 495	5.08 494	71.16 478	13.07 492	43.70 462	25.08 492
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 457	29.47 460	42.67 474	41.89 497	30.81 490	52.07 486	43.45 495	15.45 491	18.52 491	44.82 485	2.12 499	58.38 492	16.05 489	30.87 483	38.83 487

APD_test2		32.77 457	29.47 460	42.67 474	41.89 497	30.81 490	52.07 486	43.45 495	15.45 491	18.52 491	44.82 485	2.12 499	58.38 492	16.05 489	30.87 483	38.83 487

PMVS	26.43 22	31.84 459	28.16 462	42.89 473	25.87 503	27.58 494	50.92 489	49.78 491	21.37 489	14.17 495	40.81 490	2.01 501	66.62 484	9.61 495	38.88 473	34.49 491
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 460	24.00 464	26.45 478	43.74 496	18.44 503	60.86 478	39.66 497	15.11 493	9.53 497	22.10 494	6.52 492	46.94 496	8.31 497	10.14 494	13.98 494

MVE	24.84 23	24.35 461	19.77 467	38.09 476	34.56 502	26.92 495	26.57 493	38.87 499	11.73 495	11.37 496	27.44 492	1.37 502	50.42 495	11.41 493	14.60 493	36.93 489
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 462	23.20 466	25.46 479	41.52 499	16.90 504	60.56 479	38.79 500	14.62 494	8.99 498	20.24 497	7.35 489	45.82 497	7.25 498	9.46 495	13.64 495

tmp_tt		22.26 463	23.75 465	17.80 480	5.23 504	12.06 505	35.26 492	39.48 498	2.82 498	18.94 489	44.20 487	22.23 465	24.64 499	36.30 456	9.31 496	16.69 493

cdsmvs_eth3d_5k		19.86 464	26.47 463	0.00 484	0.00 507	0.00 509	0.00 496	93.45 98	0.00 502	0.00 503	95.27 77	49.56 315	0.00 503	0.00 502	0.00 500	0.00 499

wuyk23d		11.30 465	10.95 468	12.33 481	48.05 491	19.89 501	25.89 494	1.92 505	3.58 497	3.12 499	1.37 499	0.64 503	15.77 500	6.23 499	7.77 498	1.35 496

ab-mvs-re		7.91 466	10.55 469	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	94.95 87	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

testmvs		7.23 467	9.62 470	0.06 483	0.04 505	0.02 508	84.98 378	0.02 506	0.03 500	0.18 501	1.21 500	0.01 505	0.02 501	0.14 500	0.01 499	0.13 498

test123		6.92 468	9.21 471	0.08 482	0.03 506	0.05 507	81.65 413	0.01 507	0.02 501	0.14 502	0.85 501	0.03 504	0.02 501	0.12 501	0.00 500	0.16 497

pcd_1.5k_mvsjas		4.46 469	5.95 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	53.55 269	0.00 503	0.00 502	0.00 500	0.00 499

mmdepth		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

monomultidepth		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

test_blank		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

uanet_test		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

DCPMVS		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

sosnet-low-res		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

sosnet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

uncertanet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

Regformer		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

uanet		0.00 470	0.00 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.00 502	0.00 506	0.00 503	0.00 502	0.00 500	0.00 499

MED-MVS test				87.42 46	94.76 34	67.28 130	94.47 64	94.87 32	73.09 230	91.27 23	96.95 17		98.98 16	91.55 44	94.28 37	95.99 47

TestfortrainingZip							94.47 64

WAC-MVS						49.45 443								31.56 476

FOURS1					93.95 50	61.77 312	93.96 91	91.92 171	62.14 395	86.57 62

MSC_two_6792asdad				89.60 9	97.31 4	73.22 13		95.05 29					99.07 13	92.01 39	94.77 26	96.51 24

PC_three_1452									80.91 65	94.07 2	96.83 29	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 13		95.05 29					99.07 13	92.01 39	94.77 26	96.51 24

test_one_0601					96.32 19	69.74 52		94.18 69	71.42 284	90.67 29	96.85 27	74.45 22

eth-test2					0.00 507

eth-test					0.00 507

ZD-MVS					96.63 9	65.50 191		93.50 96	70.74 298	85.26 80	95.19 83	64.92 94	97.29 89	87.51 74	93.01 60

RE-MVS-def			80.48 187		92.02 110	58.56 380	90.90 262	90.45 260	62.76 388	78.89 164	94.46 101	49.30 318		78.77 185	86.77 149	92.28 236

IU-MVS					96.46 11	69.91 44		95.18 23	80.75 66	95.28 1				92.34 36	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 15	96.89 6				97.00 15	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 60	71.65 273	92.07 11	97.21 9	74.58 20	99.11 6	92.34 36	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 60		94.44 55	71.65 273	92.11 9	97.05 12	76.79 9	99.11 6

9.14			87.63 39		93.86 52		94.41 69	94.18 69	72.76 238	86.21 65	96.51 36	66.64 72	97.88 52	90.08 56	94.04 43

save fliter					93.84 53	67.89 114	95.05 41	92.66 136	78.19 129

test_0728_THIRD									72.48 243	90.55 30	96.93 21	76.24 13	99.08 11	91.53 47	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 35	96.64 10	94.37 64					99.15 2	91.91 42	94.90 22	96.51 24

test0726					96.40 15	69.99 40	96.76 8	94.33 66	71.92 259	91.89 14	97.11 11	73.77 25

GSMVS																94.68 124

test_part2					96.29 20	68.16 107				90.78 27

sam_mvs1												57.85 211				94.68 124

sam_mvs												54.91 250

ambc				69.61 436	61.38 481	41.35 473	49.07 490	85.86 405		50.18 449	66.40 462	10.16 485	88.14 414	45.73 426	44.20 460	79.32 444

MTGPA								92.23 152

test_post1							78.95 433				20.70 496	53.05 274	91.50 379	60.43 358

test_post											23.01 493	56.49 232	92.67 339

patchmatchnet-post											67.62 461	57.62 214	90.25 389

GG-mvs-BLEND				86.53 92	91.91 119	69.67 55	75.02 452	94.75 40		78.67 172	90.85 206	77.91 7	94.56 258	72.25 240	93.74 49	95.36 76

MTMP							93.77 106	32.52 502

gm-plane-assit					88.42 219	67.04 142			78.62 123		91.83 179		97.37 83	76.57 199

test9_res														89.41 57	94.96 19	95.29 82

TEST9					94.18 45	67.28 130	94.16 78	93.51 94	71.75 270	85.52 75	95.33 71	68.01 61	97.27 93

test_8					94.19 44	67.19 135	94.15 80	93.42 101	71.87 264	85.38 78	95.35 70	68.19 59	96.95 120

agg_prior2														86.41 88	94.75 30	95.33 78

agg_prior					94.16 47	66.97 150		93.31 104		84.49 86			96.75 132

TestCases				72.46 420	79.57 397	51.42 430		68.61 473	51.25 449	45.88 460	81.23 365	19.86 471	86.58 430	38.98 452	57.01 423	79.39 442

test_prior4						67.18 137	93.92 95

test_prior2							95.10 39		75.40 184	85.25 81	95.61 62	67.94 62		87.47 76	94.77 26

test_prior				86.42 97	94.71 39	67.35 129		93.10 115					96.84 129			95.05 97

旧先验2							92.00 200		59.37 417	87.54 55			93.47 307	75.39 209

新几何2							91.41 233

新几何1				84.73 171	92.32 98	64.28 230		91.46 198	59.56 416	79.77 151	92.90 145	56.95 224	96.57 138	63.40 337	92.91 62	93.34 196

旧先验1					91.94 115	60.74 339		91.50 196			94.36 105	65.23 89			91.84 78	94.55 133

无先验							92.71 155	92.61 141	62.03 396				97.01 110	66.63 302		93.97 172

原ACMM2							92.01 197

原ACMM1				84.42 188	93.21 73	64.27 231		93.40 103	65.39 363	79.51 156	92.50 153	58.11 206	96.69 134	65.27 323	93.96 44	92.32 234

test222					89.77 168	61.60 318	89.55 309	89.42 312	56.83 432	77.28 188	92.43 157	52.76 277			91.14 95	93.09 206

testdata2													96.09 163	61.26 353

segment_acmp												65.94 80

testdata				81.34 295	89.02 192	57.72 387		89.84 294	58.65 421	85.32 79	94.09 121	57.03 219	93.28 313	69.34 269	90.56 101	93.03 209

testdata1							89.21 321		77.55 145

test12				87.09 57	94.60 40	68.86 80		92.91 124		82.67 109		65.44 86	97.55 72		93.69 52	94.84 109

plane_prior7					86.94 273	61.51 320

plane_prior6					87.23 259	62.32 298						50.66 301

plane_prior5								91.31 203					95.55 211	76.74 195	78.53 260	88.39 308

plane_prior4											89.14 247

plane_prior3						61.95 307			79.09 112	72.53 255

plane_prior2							93.13 134		78.81 119

plane_prior1					87.15 262

plane_prior						62.42 294	93.85 99		79.38 104						78.80 257

n2								0.00 508

nn								0.00 508

door-mid								66.01 477

lessismore_v0				73.72 411	72.93 453	47.83 450		61.72 483		45.86 462	73.76 434	28.63 448	89.81 399	47.75 418	31.37 482	83.53 395

LGP-MVS_train				79.56 348	84.31 339	59.37 369		89.73 300	69.49 313	64.86 353	88.42 257	38.65 392	94.30 269	72.56 237	72.76 304	85.01 381

test11								93.01 118

door								66.57 476

HQP5-MVS						63.66 260

HQP-NCC					87.54 251		94.06 83		79.80 88	74.18 228

ACMP_Plane					87.54 251		94.06 83		79.80 88	74.18 228

BP-MVS														77.63 192

HQP4-MVS										74.18 228			95.61 205			88.63 302

HQP3-MVS								91.70 188							78.90 255

HQP2-MVS												51.63 289

NP-MVS					87.41 254	63.04 278					90.30 217

MDTV_nov1_ep13_2view						59.90 361	80.13 428		67.65 340	72.79 249		54.33 260		59.83 362		92.58 225

MDTV_nov1_ep13			72.61 328		89.06 190	68.48 93	80.33 424	90.11 283	71.84 266	71.81 269	75.92 427	53.01 275	93.92 292	48.04 412	73.38 299

ACMMP++_ref															71.63 312

ACMMP++															69.72 323

Test By Simon												54.21 263

ITE_SJBPF				70.43 433	74.44 447	47.06 456		77.32 446	60.16 412	54.04 429	83.53 331	23.30 461	84.01 445	43.07 435	61.58 403	80.21 439

DeepMVS_CX				34.71 477	51.45 489	24.73 497		28.48 503	31.46 483	17.49 493	52.75 479	5.80 493	42.60 498	18.18 486	19.42 491	36.81 490

High-resolution multi-view benchmark