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	58.16 382	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	90.61 278	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	61.94 307	95.65 25	89.70 303	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	61.41 324	92.97 141	88.36 358	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	90.36 285	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	61.60 317	94.87 51	89.06 331	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	61.78 310	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	90.22 279	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	91.08 226	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	62.63 290	95.02 45	90.28 274	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	60.04 358	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	88.45 356	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	88.14 366	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	90.36 285	90.66 254	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	60.74 338	93.21 133	87.94 373	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	61.48 321	94.69 60	91.16 212	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	62.55 291	94.26 76	89.78 294	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	90.83 242	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	90.02 287	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	63.29 270	94.04 87	89.99 289	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	59.36 370	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	91.79 211	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	58.92 374	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	89.32 314	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	62.33 296	93.84 102	88.81 343	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	89.46 308	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	62.44 292	92.87 149	91.31 202	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	60.10 356	93.35 128	90.35 267	83.41 31	86.54 63	96.27 45	60.50 166	90.02 397	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	91.54 230	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	91.85 209	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	60.53 345	94.41 69	87.31 381	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	90.24 290	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	87.84 348	92.69 134	62.18 392	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	85.95 371	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	91.29 207	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	91.13 218	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	62.36 295	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	62.27 299	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	88.73 346	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	91.23 209	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	87.91 346	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	61.37 325	94.42 68	91.09 222	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	91.13 218	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	91.13 218	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	60.41 348	94.13 81	85.69 406	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	90.88 263	89.24 317	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	84.65 378	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	89.99 299	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	91.87 207	89.01 334	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	91.14 216	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	90.85 241	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	58.19 410	93.64 131	73.64 27	92.35 352	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	90.99 235	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	90.76 268	89.05 332	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	81.75 410	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	91.35 243	86.12 400	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	91.37 239	83.99 424	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	91.76 216	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	89.07 324	90.69 251	65.80 359	87.13 56	94.34 110	64.99 91	92.67 338	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	75.26 441	61.72 314	92.17 187	87.24 383	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	90.06 294	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	91.69 220	90.96 236	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	91.69 220	90.95 238	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	91.69 220	90.95 238	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	91.69 220	90.96 236	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	91.78 213	89.63 304	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	60.49 346	90.74 270	92.04 164	64.35 369	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	60.49 346	90.74 270	92.04 164	64.35 369	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	84.83 382

test2506		83.29 144	82.92 139	84.37 191	88.39 221	63.18 276	92.01 197	91.35 201	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	90.44 280	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	62.88 285	90.91 260	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	59.74 362	90.37 284	92.08 162	63.70 376	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	63.19 275	91.38 238	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	40.09 386	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	62.02 303	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	87.66 352	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	62.92 283	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	91.56 229	90.39 266	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	61.16 328	92.23 185	91.28 208	64.48 368	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	59.58 365	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	87.70 351	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	90.50 279	89.01 334	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	91.90 206	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	82.39 407	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	91.04 232	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	90.44 263	73.12 227	84.20 88	94.36 105	38.04 399	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	90.44 263	73.12 227	84.20 88	94.36 105	38.04 399	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	90.44 263	73.12 227	84.20 88	94.36 105	38.04 399	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	62.88 285	89.77 302	88.93 339	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	56.17 403	91.13 256	90.69 251	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	91.08 257	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	60.21 354	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	80.64 316	82.24 365	55.09 412	94.76 55	86.87 387	81.67 51	84.40 87	94.63 98	38.17 396	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	58.74 376	91.39 236	91.01 234	63.35 380	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	83.08 398	89.13 325	78.55 125	67.50 329	87.02 287	51.79 286	90.07 396	87.48 75	90.49 102	95.10 94

ACMMP		81.49 179	80.67 181	83.93 207	91.71 125	62.90 284	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	90.65 255	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	86.88 363	90.61 256	74.41 197	70.31 288	84.67 317	63.79 111	92.32 354	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	61.96 305	92.56 171	86.79 389	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	61.85 308	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	88.84 329	90.80 245	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	91.40 234	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	89.15 323	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	57.48 391	89.40 315	91.16 212	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	58.56 379	90.90 261	90.45 259	62.76 387	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	90.18 291	90.01 288	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	91.20 210	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	61.80 309	93.44 124	88.26 365	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	60.76 336	92.62 163	86.86 388	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	80.20 326	89.33 179	57.05 397	91.91 205	94.71 42	75.67 178	75.01 217	89.37 241	63.13 130	91.44 379	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	88.40 337	90.80 245	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	87.91 346	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	61.64 316	92.19 186	90.58 257	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	81.70 411	89.72 301	77.63 143	75.96 202	79.54 391	64.94 93	92.71 335	75.43 208	77.28 273	93.55 190

1112_ss		80.56 203	79.83 198	82.77 248	88.65 201	60.78 334	92.29 181	88.36 358	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	91.31 202	60.42 408	79.34 159	90.91 205	38.48 394	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	89.42 311	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	79.97 335	78.25 419	57.01 399	94.04 87	88.33 360	79.06 115	82.81 106	93.70 129	38.65 391	91.63 370	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	86.79 364	90.80 245	76.73 164	73.95 238	90.18 220	51.55 291	92.45 347	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	91.23 250	90.76 250	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	62.42 293	93.13 134	91.31 202	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	85.35 374	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	59.95 359	91.32 245	89.16 322	65.23 365	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	83.00 401	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	60.08 357	91.97 201	88.27 363	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	61.17 326	92.70 156	91.54 193	71.51 282	75.62 206	86.94 288	53.83 265	92.38 349	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	91.05 231	79.18 109	72.43 261	90.51 211	37.05 411	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	57.72 386	86.33 369	89.00 338	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	61.17 326	92.70 156	91.54 193	73.85 211	75.62 206	86.94 288	49.84 312	92.38 349	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	65.34 364	67.39 333	91.75 181	47.83 335	98.96 19	57.71 370	89.81 113	92.54 226

UGNet		79.87 219	78.68 222	83.45 230	89.96 164	61.51 319	92.13 189	90.79 249	76.83 160	78.85 169	86.33 296	38.16 397	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	81.56 413	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	47.84 414	78.67 258	89.56 292

CPTT-MVS		79.59 222	79.16 216	80.89 314	91.54 131	59.80 361	92.10 191	88.54 355	60.42 408	72.96 246	93.28 137	48.27 327	92.80 332	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	60.39 350	92.09 192	87.99 370	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	61.43 323	89.75 303	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	80.64 316	92.91 83	61.47 322	91.17 255	93.28 105	83.09 33	64.04 363	82.38 345	66.19 76	94.57 255	81.19 159	57.71 420	85.88 365

FIs		79.47 226	79.41 209	79.67 343	85.95 299	59.40 367	91.68 224	93.94 75	78.06 131	68.96 306	88.28 260	66.61 73	91.77 366	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	89.59 304	87.03 384	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	88.09 368	72.93 233	73.37 243	91.12 203	46.20 357	96.12 161	56.28 376	85.61 166	92.91 213

viewdifsd2359ckpt11		79.42 229	77.95 235	83.81 211	83.87 346	63.85 245	89.54 309	87.38 377	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	89.54 309	87.38 377	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	87.03 359	91.10 221	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	78.24 364	88.46 216	52.29 423	90.41 282	89.12 326	74.24 202	69.13 299	91.91 178	65.77 83	90.09 395	59.00 366	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	63.49 378	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	81.35 416	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	89.21 320	87.03 384	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	91.64 226	90.30 272	76.36 173	71.97 267	89.93 235	46.30 356	95.17 230	75.10 211	77.70 265	86.19 353
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	62.73 288	89.53 312	93.21 107	72.39 248	72.14 264	90.13 229	60.99 157	94.72 246	67.73 290	72.49 307	86.29 350

AdaColmap		78.94 239	77.00 256	84.76 169	96.34 17	65.86 181	92.66 162	87.97 372	62.18 392	70.56 282	92.37 159	43.53 371	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	62.02 303	92.31 180	86.23 396	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	91.41 232	89.15 323	75.19 188	68.79 309	83.98 328	67.17 68	92.82 330	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	77.43 372	88.06 233	49.71 440	91.96 202	91.95 170	77.67 140	76.56 199	91.28 196	58.51 200	90.20 393	56.37 375	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	45.16 427	77.58 267	88.83 298

TR-MVS		78.77 245	77.37 250	82.95 245	90.49 154	60.88 332	93.67 110	90.07 283	70.08 307	74.51 226	91.37 193	45.69 360	95.70 197	60.12 360	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	45.16 427	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	88.59 352	71.57 279	66.83 340	88.65 254	51.75 287	95.39 217	59.03 365	84.77 177	91.32 263

OMC-MVS		78.67 248	77.91 237	80.95 311	85.76 306	57.40 393	88.49 335	88.67 349	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	83.21 397	91.14 216	78.31 128	73.67 241	79.68 389	64.01 107	92.09 360	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	43.88 370	98.10 44	54.61 381	90.65 99	89.44 295

EPMVS		78.49 251	75.98 274	86.02 110	91.21 140	69.68 54	80.23 425	91.20 210	75.25 187	72.48 259	78.11 400	54.65 253	93.69 302	57.66 371	83.04 201	94.69 122

AUN-MVS		78.37 252	77.43 245	81.17 300	86.60 283	57.45 392	89.46 314	91.16 212	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	63.21 274	91.19 254	96.33 1	72.28 251	70.45 285	87.89 271	60.31 168	95.32 222	45.16 427	77.58 267	88.83 298

GA-MVS		78.33 254	76.23 270	84.65 178	83.65 350	66.30 168	91.44 231	90.14 281	76.01 175	70.32 287	84.02 327	42.50 375	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	91.15 215	66.71 348	70.47 283	86.07 298	37.49 405	96.48 145	70.15 262	79.80 242	90.65 275

UniMVSNet_NR-MVSNet		78.15 256	77.55 243	79.98 333	84.46 336	60.26 352	92.25 182	93.20 109	77.50 146	68.88 307	86.61 291	66.10 78	92.13 358	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	89.33 316	90.45 259	68.25 332	74.73 224	85.51 308	41.15 381	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	84.63 379	90.80 245	76.73 164	61.16 386	90.18 220	40.17 385	91.58 372	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	91.30 246	96.33 1	72.43 246	70.46 284	87.89 271	60.31 168	94.92 239	42.64 439	76.64 278	87.48 319

FC-MVSNet-test		77.99 260	78.08 231	77.70 367	84.89 326	55.51 409	90.27 288	93.75 84	76.87 157	66.80 341	87.59 276	65.71 84	90.23 392	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	85.36 409	62.52 390	76.11 201	90.18 220	29.43 444	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	90.67 273	88.86 342	72.45 245	67.63 328	82.68 342	64.07 105	92.91 328	71.79 244	65.30 359	86.44 343

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	91.75 218	92.70 131	73.97 208	62.58 380	84.44 321	41.11 382	95.78 187	63.76 336	92.17 71	80.62 431

usedtu_dtu_shiyan1		77.89 265	76.39 266	82.40 262	81.92 370	67.01 145	91.94 203	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	91.94 203	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	91.87 207	91.99 168	73.35 223	67.04 336	83.19 337	56.62 229	92.14 357	59.80 362	69.34 326	87.28 325

VortexMVS		77.62 268	76.44 263	81.13 302	88.58 202	63.73 253	91.24 249	91.30 206	77.81 136	65.76 346	81.97 351	49.69 314	93.72 298	76.40 201	65.26 362	85.94 363

miper_ehance_all_eth		77.60 269	76.44 263	81.09 308	85.70 309	64.41 224	90.65 274	88.64 351	72.31 249	67.37 334	82.52 343	64.77 97	92.64 341	70.67 258	65.30 359	86.24 352

UniMVSNet (Re)		77.58 270	76.78 258	79.98 333	84.11 342	60.80 333	91.76 216	93.17 111	76.56 170	69.93 295	84.78 316	63.32 124	92.36 351	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	79.97 430	89.55 306	72.23 252	70.94 278	76.91 413	57.03 219	92.79 333	54.27 383	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	91.85 209	90.23 277	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	78.55 359	87.07 265	62.68 289	69.71 462	82.95 432	68.80 325	71.48 275	87.27 283	66.03 79	84.00 445	76.47 200	82.81 204	88.95 297

PS-MVSNAJss		77.26 274	76.31 268	80.13 328	80.64 384	59.16 372	90.63 277	91.06 228	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	71.78 456	94.72 41	49.61 453	77.12 191	45.92 482	77.41 8	93.98 289	67.62 291	93.16 59	95.05 97

WB-MVSnew		77.14 276	76.18 272	80.01 332	86.18 293	63.24 272	91.26 247	94.11 72	71.72 271	73.52 242	87.29 282	45.14 365	93.00 321	56.98 373	79.42 247	83.80 391

MVP-Stereo		77.12 277	76.23 270	79.79 340	81.72 372	66.34 167	89.29 317	90.88 240	70.56 300	62.01 383	82.88 339	49.34 317	94.13 277	65.55 320	93.80 47	78.88 447
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	62.11 302	82.06 408	89.09 328	76.77 162	70.84 280	87.12 284	41.43 380	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	63.24 272	86.47 368	86.37 392	79.08 113	66.31 344	79.30 393	49.80 313	91.72 367	79.37 175	65.70 357	93.23 200

dmvs_re		76.93 280	75.36 282	81.61 287	87.78 247	60.71 340	80.00 429	87.99 370	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	10.19 497	55.25 243	97.41 81	79.16 178	91.58 83	93.95 173

DU-MVS		76.86 281	75.84 276	79.91 336	82.96 358	60.26 352	91.26 247	91.54 193	76.46 172	68.88 307	86.35 294	56.16 234	92.13 358	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	91.09 222	53.57 441	73.00 245	87.42 279	35.91 415	97.32 87	69.14 273	72.41 309	92.36 231

UWE-MVS-28		76.83 284	77.60 242	74.51 402	84.58 332	50.34 436	88.22 340	94.60 49	74.46 195	66.66 342	88.98 252	62.53 138	85.50 437	57.55 372	80.80 235	87.69 316

c3_l		76.83 284	75.47 280	80.93 312	85.02 324	64.18 236	90.39 283	88.11 367	71.66 272	66.65 343	81.64 357	63.58 120	92.56 342	69.31 270	62.86 386	86.04 358

WR-MVS		76.76 286	75.74 278	79.82 339	84.60 330	62.27 299	92.60 166	92.51 144	76.06 174	67.87 325	85.34 310	56.76 225	90.24 391	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	91.68 224	90.21 280	73.69 217	69.06 302	81.89 352	52.73 279	94.40 265	69.21 271	65.23 363	85.80 366

IterMVS-LS		76.49 288	75.18 285	80.43 320	84.49 335	62.74 287	90.64 275	88.80 344	72.40 247	65.16 352	81.72 355	60.98 158	92.27 355	67.74 289	64.65 371	86.29 350
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	90.26 289	89.42 311	73.75 214	68.63 312	81.89 352	51.31 294	94.09 279	71.69 247	64.84 367	84.66 383

Elysia		76.45 290	74.17 300	83.30 233	80.43 386	64.12 237	89.58 305	90.83 242	61.78 400	72.53 255	85.92 301	34.30 422	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	89.58 305	90.83 242	61.78 400	72.53 255	85.92 301	34.30 422	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	58.80 482	86.18 398	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	91.75 218	90.23 277	73.68 218	67.13 335	80.84 372	55.92 239	93.86 297	68.95 275	61.73 400	85.76 369

Effi-MVS+-dtu		76.14 294	75.28 284	78.72 358	83.22 355	55.17 411	89.87 300	87.78 374	75.42 183	67.98 320	81.43 361	45.08 366	92.52 344	75.08 212	71.63 312	88.48 306

cl____		76.07 295	74.67 288	80.28 323	85.15 319	61.76 312	90.12 292	88.73 346	71.16 287	65.43 349	81.57 359	61.15 155	92.95 323	66.54 304	62.17 393	86.13 356

DIV-MVS_self_test		76.07 295	74.67 288	80.28 323	85.14 320	61.75 313	90.12 292	88.73 346	71.16 287	65.42 350	81.60 358	61.15 155	92.94 327	66.54 304	62.16 395	86.14 354

FMVSNet2		76.07 295	74.01 305	82.26 268	88.85 195	67.66 120	91.33 244	91.61 191	70.84 294	65.98 345	82.25 347	48.03 328	92.00 362	58.46 367	68.73 334	87.10 328

v144192		76.05 298	74.03 304	82.12 274	79.50 400	66.55 163	91.39 236	89.71 302	72.30 250	68.17 318	81.33 364	51.75 287	94.03 287	67.94 287	64.19 373	85.77 367

NR-MVSNet		76.05 298	74.59 291	80.44 319	82.96 358	62.18 301	90.83 265	91.73 183	77.12 153	60.96 388	86.35 294	59.28 188	91.80 365	60.74 355	61.34 404	87.35 323

v1192		75.98 300	73.92 306	82.15 272	79.73 396	66.24 170	91.22 251	89.75 296	72.67 239	68.49 314	81.42 362	49.86 311	94.27 271	67.08 299	65.02 365	85.95 361

FE-MVS		75.97 301	73.02 320	84.82 163	89.78 167	65.56 188	77.44 441	91.07 227	64.55 367	72.66 251	79.85 387	46.05 358	96.69 134	54.97 380	80.82 233	92.21 241

eth_miper_zixun_eth		75.96 302	74.40 296	80.66 315	84.66 329	63.02 278	89.28 318	88.27 363	71.88 263	65.73 347	81.65 356	59.45 183	92.81 331	68.13 282	60.53 409	86.14 354

TranMVSNet+NR-MVSNet		75.86 303	74.52 294	79.89 337	82.44 364	60.64 343	91.37 239	91.37 200	76.63 168	67.65 327	86.21 297	52.37 282	91.55 373	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	81.06 418	89.19 320	71.60 278	70.01 291	77.09 411	45.53 361	90.25 388	60.43 357	73.27 300	94.68 124

LPG-MVS_test		75.82 304	74.58 292	79.56 347	84.31 339	59.37 368	90.44 280	89.73 299	69.49 313	64.86 353	88.42 257	38.65 391	94.30 269	72.56 237	72.76 304	85.01 380

GBi-Net		75.65 306	73.83 307	81.10 305	88.85 195	65.11 200	90.01 296	90.32 268	70.84 294	67.04 336	80.25 382	48.03 328	91.54 374	59.80 362	69.34 326	86.64 337

test1		75.65 306	73.83 307	81.10 305	88.85 195	65.11 200	90.01 296	90.32 268	70.84 294	67.04 336	80.25 382	48.03 328	91.54 374	59.80 362	69.34 326	86.64 337

v1921920		75.63 308	73.49 312	82.06 278	79.38 401	66.35 166	91.07 259	89.48 307	71.98 258	67.99 319	81.22 367	49.16 322	93.90 293	66.56 303	64.56 372	85.92 364

ACMP	71.68 10	75.58 309	74.23 299	79.62 345	84.97 325	59.64 363	90.80 266	89.07 330	70.39 301	62.95 376	87.30 281	38.28 395	93.87 295	72.89 230	71.45 315	85.36 376
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	89.54 309	89.27 316	71.65 273	63.30 371	80.30 381	54.99 249	94.06 282	67.33 296	62.33 392	83.94 389

tpm cat1		75.30 311	72.21 333	84.58 183	88.52 208	67.77 117	78.16 439	88.02 369	61.88 398	68.45 315	76.37 422	60.65 163	94.03 287	53.77 387	74.11 294	91.93 250

PLC	68.80 14	75.23 312	73.68 310	79.86 338	92.93 82	58.68 377	90.64 275	88.30 361	60.90 405	64.43 361	90.53 210	42.38 376	94.57 255	56.52 374	76.54 279	86.33 349
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	90.75 269	89.11 327	71.63 277	67.41 332	81.22 367	47.36 340	93.87 295	65.46 321	64.72 370	85.77 367

blend_shiyan4		75.18 314	73.00 321	81.69 285	75.62 437	64.75 208	91.78 213	91.06 228	65.89 358	61.35 385	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	80.07 329	80.86 378	59.52 366	91.20 253	85.38 408	71.90 261	65.20 351	84.84 315	41.46 379	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	79.96 431	89.75 296	71.16 287	67.80 326	77.19 410	51.81 285	92.54 343	50.39 397	71.44 316	92.51 228

TAPA-MVS	70.22 12	74.94 317	73.53 311	79.17 353	90.40 156	52.07 424	89.19 322	89.61 305	62.69 389	70.07 290	92.67 151	48.89 325	94.32 267	38.26 454	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	79.74 342	86.53 285	60.31 351	89.03 327	92.70 131	78.61 124	68.98 305	83.34 335	41.93 378	92.23 356	52.77 391	65.97 355	86.69 336

SSM_04072		74.86 319	73.37 314	79.35 350	88.50 209	66.98 147	58.80 482	86.18 398	69.12 321	74.12 232	89.01 250	47.50 338	79.09 467	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	89.15 323	89.08 329	70.94 292	63.08 374	79.86 386	52.52 280	94.04 285	65.70 317	62.17 393	83.64 392

XVG-OURS-SEG-HR		74.70 321	73.08 319	79.57 346	78.25 419	57.33 394	80.49 421	87.32 379	63.22 382	68.76 310	90.12 231	44.89 367	91.59 371	70.55 260	74.09 295	89.79 287

ACMM	69.62 13	74.34 322	72.73 326	79.17 353	84.25 341	57.87 384	90.36 285	89.93 290	63.17 384	65.64 348	86.04 300	37.79 403	94.10 278	65.89 312	71.52 314	85.55 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 323	72.30 332	80.32 321	91.49 132	61.66 315	90.85 264	80.72 438	56.67 432	63.85 366	90.64 207	46.75 349	90.84 382	53.79 386	75.99 283	88.47 307

XVG-OURS		74.25 324	72.46 331	79.63 344	78.45 417	57.59 390	80.33 423	87.39 376	63.86 374	68.76 310	89.62 238	40.50 384	91.72 367	69.00 274	74.25 293	89.58 290

test_fmvs1		74.07 325	73.69 309	75.22 392	78.91 410	47.34 452	89.06 326	74.69 455	63.68 377	79.41 158	91.59 188	24.36 455	87.77 418	85.22 98	76.26 281	90.55 278

CVMVSNet		74.04 326	74.27 298	73.33 412	85.33 313	43.94 466	89.53 312	88.39 357	54.33 440	70.37 286	90.13 229	49.17 321	84.05 443	61.83 351	79.36 249	91.99 246

Baseline_NR-MVSNet		73.99 327	72.83 323	77.48 371	80.78 381	59.29 371	91.79 211	84.55 417	68.85 324	68.99 304	80.70 373	56.16 234	92.04 361	62.67 345	60.98 406	81.11 425

pmmvs4		73.92 328	71.81 338	80.25 325	79.17 404	65.24 196	87.43 355	87.26 382	67.64 341	63.46 369	83.91 329	48.96 324	91.53 377	62.94 342	65.49 358	83.96 388

D2MVS		73.80 329	72.02 335	79.15 355	79.15 405	62.97 279	88.58 334	90.07 283	72.94 232	59.22 403	78.30 397	42.31 377	92.70 337	65.59 319	72.00 310	81.79 420

SD_0403		73.79 330	73.48 313	74.69 399	85.33 313	45.56 462	83.80 386	85.57 407	76.55 171	62.96 375	88.45 256	50.62 303	87.59 422	48.80 407	79.28 253	90.92 272

CR-MVSNet		73.79 330	70.82 346	82.70 251	83.15 356	67.96 111	70.25 459	84.00 422	73.67 219	69.97 293	72.41 439	57.82 212	89.48 401	52.99 390	73.13 301	90.64 276

test_djsdf		73.76 332	72.56 329	77.39 373	77.00 430	53.93 417	89.07 324	90.69 251	65.80 359	63.92 364	82.03 350	43.14 374	92.67 338	72.83 231	68.53 335	85.57 371

pmmvs5		73.35 333	71.52 340	78.86 357	78.64 414	60.61 344	91.08 257	86.90 386	67.69 338	63.32 370	83.64 330	44.33 369	90.53 385	62.04 349	66.02 354	85.46 374

Anonymous20231211		73.08 334	70.39 350	81.13 302	90.62 151	63.33 269	91.40 234	90.06 285	51.84 446	64.46 360	80.67 375	36.49 413	94.07 281	63.83 335	64.17 374	85.98 360

tt0805		73.07 335	70.73 347	80.07 329	78.37 418	57.05 397	87.78 349	92.18 159	61.23 404	67.04 336	86.49 293	31.35 436	94.58 253	65.06 324	67.12 347	88.57 304

miper_lstm_enhance		73.05 336	71.73 339	77.03 378	83.80 347	58.32 381	81.76 409	88.88 340	69.80 311	61.01 387	78.23 399	57.19 217	87.51 424	65.34 322	59.53 414	85.27 379

jajsoiax		73.05 336	71.51 341	77.67 368	77.46 427	54.83 413	88.81 330	90.04 286	69.13 320	62.85 378	83.51 332	31.16 437	92.75 334	70.83 255	69.80 322	85.43 375

LCM-MVSNet-Re		72.93 338	71.84 337	76.18 387	88.49 213	48.02 447	80.07 428	70.17 469	73.96 209	52.25 436	80.09 385	49.98 309	88.24 412	67.35 294	84.23 186	92.28 236

pm-mvs1		72.89 339	71.09 343	78.26 363	79.10 407	57.62 388	90.80 266	89.30 315	67.66 339	62.91 377	81.78 354	49.11 323	92.95 323	60.29 359	58.89 417	84.22 387

tpmvs		72.88 340	69.76 356	82.22 269	90.98 144	67.05 141	78.22 438	88.30 361	63.10 385	64.35 362	74.98 429	55.09 248	94.27 271	43.25 433	69.57 325	85.34 377

test0.0.03 1		72.76 341	72.71 327	72.88 416	80.25 391	47.99 448	91.22 251	89.45 309	71.51 282	62.51 381	87.66 274	53.83 265	85.06 439	50.16 399	67.84 345	85.58 370

UniMVSNet_ETH3D		72.74 342	70.53 349	79.36 349	78.62 415	56.64 401	85.01 376	89.20 319	63.77 375	64.84 355	84.44 321	34.05 424	91.86 364	63.94 334	70.89 319	89.57 291

mvs_tets		72.71 343	71.11 342	77.52 369	77.41 428	54.52 415	88.45 336	89.76 295	68.76 327	62.70 379	83.26 336	29.49 443	92.71 335	70.51 261	69.62 324	85.34 377

FMVSNet1		72.71 343	69.91 354	81.10 305	83.60 351	65.11 200	90.01 296	90.32 268	63.92 373	63.56 368	80.25 382	36.35 414	91.54 374	54.46 382	66.75 350	86.64 337

test_fmvs1_n		72.69 345	71.92 336	74.99 397	71.15 456	47.08 454	87.34 357	75.67 450	63.48 379	78.08 177	91.17 202	20.16 469	87.87 415	84.65 107	75.57 285	90.01 284

IterMVS		72.65 346	70.83 344	78.09 365	82.17 366	62.96 280	87.64 353	86.28 394	71.56 280	60.44 394	78.85 395	45.42 363	86.66 428	63.30 340	61.83 397	84.65 384
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	72.10 424	87.87 242	49.45 442	88.07 342	89.01 334	72.91 234	63.11 372	88.10 266	63.63 115	85.54 434	32.73 470	69.23 329	81.32 423

wanda-best-256-512		72.42 348	69.43 358	81.37 292	75.39 438	64.24 233	91.58 227	91.09 222	66.36 351	60.64 390	76.86 414	47.20 342	93.47 307	64.80 326	50.98 441	86.40 344

FE-blended-shiyan7		72.42 348	69.43 358	81.37 292	75.39 438	64.24 233	91.58 227	91.09 222	66.36 351	60.64 390	76.86 414	47.20 342	93.47 307	64.80 326	50.98 441	86.40 344

blended_shiyan8		72.26 350	69.25 362	81.29 296	75.23 443	64.03 240	91.36 242	91.04 232	66.11 356	60.42 395	76.73 418	46.79 347	93.45 310	64.58 330	51.00 440	86.37 347

blended_shiyan6		72.26 350	69.26 361	81.27 297	75.24 442	64.00 243	91.37 239	91.06 228	66.12 355	60.34 396	76.75 417	46.82 345	93.45 310	64.61 328	50.98 441	86.37 347

PatchMatch-RL		72.06 352	69.98 351	78.28 362	89.51 175	55.70 408	83.49 390	83.39 430	61.24 403	63.72 367	82.76 340	34.77 419	93.03 320	53.37 389	77.59 266	86.12 357

PVSNet_0	68.08 15	71.81 353	68.32 369	82.27 266	84.68 327	62.31 298	88.68 332	90.31 271	75.84 176	57.93 415	80.65 376	37.85 402	94.19 274	69.94 263	29.05 485	90.31 280

MIMVSNet		71.64 354	68.44 367	81.23 299	81.97 369	64.44 221	73.05 453	88.80 344	69.67 312	64.59 356	74.79 431	32.79 428	87.82 416	53.99 384	76.35 280	91.42 258

test_vis1_n		71.63 355	70.73 347	74.31 406	69.63 463	47.29 453	86.91 361	72.11 463	63.21 383	75.18 215	90.17 226	20.40 467	85.76 433	84.59 108	74.42 292	89.87 285

IterMVS-SCA-FT		71.55 356	69.97 352	76.32 385	81.48 374	60.67 342	87.64 353	85.99 401	66.17 354	59.50 401	78.88 394	45.53 361	83.65 447	62.58 346	61.93 396	84.63 386

v7n		71.31 357	68.65 364	79.28 351	76.40 432	60.77 335	86.71 365	89.45 309	64.17 372	58.77 408	78.24 398	44.59 368	93.54 304	57.76 369	61.75 399	83.52 395

anonymousdsp		71.14 358	69.37 360	76.45 384	72.95 451	54.71 414	84.19 383	88.88 340	61.92 397	62.15 382	79.77 388	38.14 398	91.44 379	68.90 276	67.45 346	83.21 401

usedtu_blend_shiyan5		71.06 359	67.54 372	81.62 286	75.39 438	64.75 208	85.67 372	86.47 391	56.48 433	60.64 390	76.85 416	47.20 342	93.71 299	68.18 280	50.98 441	86.40 344

F-COLMAP		70.66 360	68.44 367	77.32 374	86.37 290	55.91 406	88.00 344	86.32 393	56.94 430	57.28 418	88.07 268	33.58 426	92.49 345	51.02 394	68.37 336	83.55 393

WR-MVS_H		70.59 361	69.94 353	72.53 418	81.03 377	51.43 428	87.35 356	92.03 167	67.38 342	60.23 398	80.70 373	55.84 240	83.45 450	46.33 422	58.58 419	82.72 408

CP-MVSNet		70.50 362	69.91 354	72.26 421	80.71 382	51.00 432	87.23 358	90.30 272	67.84 337	59.64 400	82.69 341	50.23 307	82.30 458	51.28 393	59.28 415	83.46 397

RPMNet		70.42 363	65.68 383	84.63 181	83.15 356	67.96 111	70.25 459	90.45 259	46.83 461	69.97 293	65.10 464	56.48 233	95.30 225	35.79 459	73.13 301	90.64 276

testing3		70.38 364	70.83 344	69.03 438	85.82 304	43.93 467	90.72 272	90.56 258	68.06 333	60.24 397	86.82 290	64.83 95	84.12 441	26.33 478	64.10 375	79.04 445

tfpnnormal		70.10 365	67.36 373	78.32 361	83.45 353	60.97 331	88.85 328	92.77 129	64.85 366	60.83 389	78.53 396	43.52 372	93.48 306	31.73 473	61.70 401	80.52 432

TransMVSNet (Re)		70.07 366	67.66 371	77.31 375	80.62 385	59.13 373	91.78 213	84.94 413	65.97 357	60.08 399	80.44 378	50.78 300	91.87 363	48.84 406	45.46 458	80.94 427

CL-MVSNet_self_test		69.92 367	68.09 370	75.41 390	73.25 450	55.90 407	90.05 295	89.90 291	69.96 308	61.96 384	76.54 419	51.05 299	87.64 419	49.51 403	50.59 446	82.70 410

DP-MVS		69.90 368	66.48 375	80.14 327	95.36 29	62.93 281	89.56 307	76.11 448	50.27 452	57.69 416	85.23 311	39.68 387	95.73 191	33.35 464	71.05 318	81.78 421

PS-CasMVS		69.86 369	69.13 363	72.07 425	80.35 389	50.57 435	87.02 360	89.75 296	67.27 343	59.19 404	82.28 346	46.58 351	82.24 459	50.69 396	59.02 416	83.39 399

Syy-MVS		69.65 370	69.52 357	70.03 433	87.87 242	43.21 468	88.07 342	89.01 334	72.91 234	63.11 372	88.10 266	45.28 364	85.54 434	22.07 483	69.23 329	81.32 423

MSDG		69.54 371	65.73 382	80.96 310	85.11 322	63.71 255	84.19 383	83.28 431	56.95 429	54.50 425	84.03 326	31.50 434	96.03 169	42.87 437	69.13 331	83.14 403

PEN-MVS		69.46 372	68.56 365	72.17 423	79.27 402	49.71 440	86.90 362	89.24 317	67.24 346	59.08 405	82.51 344	47.23 341	83.54 449	48.42 409	57.12 421	83.25 400

LS3D		69.17 373	66.40 377	77.50 370	91.92 117	56.12 404	85.12 375	80.37 440	46.96 459	56.50 420	87.51 278	37.25 406	93.71 299	32.52 472	79.40 248	82.68 411

PatchT		69.11 374	65.37 387	80.32 321	82.07 368	63.68 259	67.96 468	87.62 375	50.86 450	69.37 297	65.18 463	57.09 218	88.53 408	41.59 443	66.60 351	88.74 301

KD-MVS_2432*1600		69.03 375	66.37 378	77.01 379	85.56 310	61.06 329	81.44 414	90.25 275	67.27 343	58.00 413	76.53 420	54.49 255	87.63 420	48.04 411	35.77 476	82.34 414

miper_refine_blended		69.03 375	66.37 378	77.01 379	85.56 310	61.06 329	81.44 414	90.25 275	67.27 343	58.00 413	76.53 420	54.49 255	87.63 420	48.04 411	35.77 476	82.34 414

mvsany_test1		68.77 377	68.56 365	69.39 436	73.57 449	45.88 461	80.93 419	60.88 483	59.65 414	71.56 273	90.26 219	43.22 373	75.05 471	74.26 221	62.70 388	87.25 327

ACMH	63.93 17	68.62 378	64.81 389	80.03 331	85.22 318	63.25 271	87.72 350	84.66 415	60.83 406	51.57 440	79.43 392	27.29 450	94.96 236	41.76 441	64.84 367	81.88 419
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 379	65.41 386	77.96 366	78.69 413	62.93 281	89.86 301	89.17 321	60.55 407	50.27 446	77.73 404	22.60 463	94.06 282	47.18 418	72.65 306	76.88 459

ADS-MVSNet		68.54 380	64.38 396	81.03 309	88.06 233	66.90 152	68.01 466	84.02 421	57.57 423	64.48 358	69.87 451	38.68 389	89.21 403	40.87 445	67.89 343	86.97 329

DTE-MVSNet		68.46 381	67.33 374	71.87 427	77.94 423	49.00 445	86.16 370	88.58 353	66.36 351	58.19 410	82.21 348	46.36 352	83.87 446	44.97 430	55.17 428	82.73 407

mmtdpeth		68.33 382	66.37 378	74.21 407	82.81 361	51.73 425	84.34 381	80.42 439	67.01 347	71.56 273	68.58 455	30.52 441	92.35 352	75.89 205	36.21 474	78.56 452

our_test_3		68.29 383	64.69 391	79.11 356	78.92 408	64.85 207	88.40 337	85.06 411	60.32 410	52.68 434	76.12 424	40.81 383	89.80 400	44.25 432	55.65 426	82.67 412

Patchmatch-RL test		68.17 384	64.49 394	79.19 352	71.22 455	53.93 417	70.07 461	71.54 467	69.22 317	56.79 419	62.89 468	56.58 230	88.61 405	69.53 267	52.61 436	95.03 99

XVG-ACMP-BASELINE		68.04 385	65.53 385	75.56 389	74.06 448	52.37 422	78.43 435	85.88 402	62.03 395	58.91 407	81.21 369	20.38 468	91.15 381	60.69 356	68.18 337	83.16 402

FMVSNet5		68.04 385	65.66 384	75.18 394	84.43 337	57.89 383	83.54 388	86.26 395	61.83 399	53.64 431	73.30 434	37.15 409	85.08 438	48.99 405	61.77 398	82.56 413

ppachtmachnet_test		67.72 387	63.70 399	79.77 341	78.92 408	66.04 174	88.68 332	82.90 433	60.11 412	55.45 422	75.96 425	39.19 388	90.55 384	39.53 449	52.55 437	82.71 409

ACMH+	65.35 16	67.65 388	64.55 392	76.96 381	84.59 331	57.10 396	88.08 341	80.79 437	58.59 421	53.00 433	81.09 371	26.63 452	92.95 323	46.51 420	61.69 402	80.82 428

pmmvs6		67.57 389	64.76 390	76.00 388	72.82 453	53.37 419	88.71 331	86.78 390	53.19 442	57.58 417	78.03 401	35.33 418	92.41 348	55.56 378	54.88 430	82.21 416

Anonymous20231206		67.53 390	65.78 381	72.79 417	74.95 444	47.59 450	88.23 339	87.32 379	61.75 402	58.07 412	77.29 408	37.79 403	87.29 426	42.91 435	63.71 379	83.48 396

Patchmtry		67.53 390	63.93 398	78.34 360	82.12 367	64.38 225	68.72 463	84.00 422	48.23 458	59.24 402	72.41 439	57.82 212	89.27 402	46.10 423	56.68 425	81.36 422

USDC		67.43 392	64.51 393	76.19 386	77.94 423	55.29 410	78.38 436	85.00 412	73.17 225	48.36 454	80.37 379	21.23 465	92.48 346	52.15 392	64.02 377	80.81 429

ADS-MVSNet2		66.90 393	63.44 401	77.26 376	88.06 233	60.70 341	68.01 466	75.56 452	57.57 423	64.48 358	69.87 451	38.68 389	84.10 442	40.87 445	67.89 343	86.97 329

FE-MVSNET2		66.80 394	64.06 397	75.03 395	69.84 461	57.11 395	86.57 366	88.57 354	67.94 336	50.97 444	72.16 443	33.79 425	87.55 423	53.94 385	52.74 434	80.45 433

CMPMVS	48.56 21	66.77 395	64.41 395	73.84 409	70.65 459	50.31 437	77.79 440	85.73 405	45.54 464	44.76 465	82.14 349	35.40 417	90.14 394	63.18 341	74.54 290	81.07 426
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 396	62.92 404	76.80 383	76.51 431	57.77 385	89.22 319	83.41 429	55.48 437	53.86 429	77.84 402	26.28 453	93.95 291	34.90 461	68.76 333	78.68 450

LTVRE_ROB	59.60 19	66.27 397	63.54 400	74.45 403	84.00 344	51.55 427	67.08 470	83.53 427	58.78 419	54.94 424	80.31 380	34.54 420	93.23 316	40.64 447	68.03 339	78.58 451
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		66.06 398	62.45 407	76.88 382	81.42 376	54.45 416	57.49 484	88.67 349	49.36 454	63.86 365	46.86 481	56.06 237	90.25 388	49.53 402	68.83 332	85.95 361

Patchmatch-test		65.86 399	60.94 413	80.62 318	83.75 348	58.83 375	58.91 481	75.26 454	44.50 468	50.95 445	77.09 411	58.81 196	87.90 414	35.13 460	64.03 376	95.12 93

UnsupCasMVSNet_eth		65.79 400	63.10 402	73.88 408	70.71 458	50.29 438	81.09 417	89.88 292	72.58 241	49.25 451	74.77 432	32.57 430	87.43 425	55.96 377	41.04 466	83.90 390

test_fmvs2		65.78 401	64.84 388	68.60 440	66.54 470	41.71 471	83.27 394	69.81 470	54.38 439	67.91 322	84.54 320	15.35 475	81.22 463	75.65 207	66.16 353	82.88 404

dmvs_testset		65.55 402	66.45 376	62.86 452	79.87 395	22.35 497	76.55 443	71.74 465	77.42 149	55.85 421	87.77 273	51.39 293	80.69 464	31.51 476	65.92 356	85.55 372

pmmvs-eth3d		65.53 403	62.32 408	75.19 393	69.39 464	59.59 364	82.80 402	83.43 428	62.52 390	51.30 442	72.49 437	32.86 427	87.16 427	55.32 379	50.73 445	78.83 448

SixPastTwentyTwo		64.92 404	61.78 411	74.34 405	78.74 412	49.76 439	83.42 393	79.51 443	62.86 386	50.27 446	77.35 406	30.92 439	90.49 386	45.89 424	47.06 452	82.78 405

OurMVSNet-221017-0		64.68 405	62.17 409	72.21 422	76.08 435	47.35 451	80.67 420	81.02 436	56.19 434	51.60 439	79.66 390	27.05 451	88.56 407	53.60 388	53.63 433	80.71 430

test_0402		64.54 406	61.09 412	74.92 398	84.10 343	60.75 337	87.95 345	79.71 442	52.03 444	52.41 435	77.20 409	32.21 432	91.64 369	23.14 481	61.03 405	72.36 469

testgi		64.48 407	62.87 405	69.31 437	71.24 454	40.62 474	85.49 373	79.92 441	65.36 363	54.18 427	83.49 333	23.74 458	84.55 440	41.60 442	60.79 408	82.77 406

RPSCF		64.24 408	61.98 410	71.01 430	76.10 434	45.00 463	75.83 448	75.94 449	46.94 460	58.96 406	84.59 318	31.40 435	82.00 460	47.76 416	60.33 413	86.04 358

EU-MVSNet		64.01 409	63.01 403	67.02 446	74.40 447	38.86 480	83.27 394	86.19 397	45.11 466	54.27 426	81.15 370	36.91 412	80.01 466	48.79 408	57.02 422	82.19 417

test20.03		63.83 410	62.65 406	67.38 445	70.58 460	39.94 476	86.57 366	84.17 419	63.29 381	51.86 438	77.30 407	37.09 410	82.47 456	38.87 453	54.13 432	79.73 439

sc_t1		63.81 411	59.39 419	77.10 377	77.62 425	56.03 405	84.32 382	73.56 459	46.66 462	58.22 409	73.06 435	23.28 461	90.62 383	50.93 395	46.84 453	84.64 385

MDA-MVSNet_test_wron		63.78 412	60.16 415	74.64 400	78.15 421	60.41 348	83.49 390	84.03 420	56.17 436	39.17 476	71.59 446	37.22 407	83.24 453	42.87 437	48.73 448	80.26 436

YYNet1		63.76 413	60.14 416	74.62 401	78.06 422	60.19 355	83.46 392	83.99 424	56.18 435	39.25 475	71.56 447	37.18 408	83.34 451	42.90 436	48.70 449	80.32 435

K. test v3		63.09 414	59.61 418	73.53 411	76.26 433	49.38 444	83.27 394	77.15 446	64.35 369	47.77 456	72.32 441	28.73 445	87.79 417	49.93 401	36.69 473	83.41 398

COLMAP_ROB	57.96 20	62.98 415	59.65 417	72.98 415	81.44 375	53.00 421	83.75 387	75.53 453	48.34 457	48.81 453	81.40 363	24.14 456	90.30 387	32.95 467	60.52 410	75.65 462
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 416	59.08 420	71.10 429	67.19 468	48.72 446	83.91 385	85.23 410	50.38 451	47.84 455	71.22 449	20.74 466	85.51 436	46.47 421	58.75 418	79.06 444

tt0320		61.85 417	57.45 426	75.03 395	77.49 426	57.60 389	82.74 403	73.65 458	43.65 472	53.65 430	68.18 457	25.47 454	88.66 404	45.56 426	46.68 454	78.81 449

AllTest		61.66 418	58.06 422	72.46 419	79.57 397	51.42 429	80.17 426	68.61 472	51.25 448	45.88 459	81.23 365	19.86 470	86.58 429	38.98 451	57.01 423	79.39 441

UnsupCasMVSNet_bld		61.60 419	57.71 423	73.29 413	68.73 465	51.64 426	78.61 434	89.05 332	57.20 428	46.11 458	61.96 472	28.70 446	88.60 406	50.08 400	38.90 471	79.63 440

MDA-MVSNet-bldmvs		61.54 420	57.70 424	73.05 414	79.53 399	57.00 400	83.08 398	81.23 435	57.57 423	34.91 480	72.45 438	32.79 428	86.26 431	35.81 458	41.95 464	75.89 461

tt0320-xc		61.51 421	56.89 430	75.37 391	78.50 416	58.61 378	82.61 405	71.27 468	44.31 469	53.17 432	68.03 459	23.38 459	88.46 409	47.77 415	43.00 463	79.03 446

mvs5depth		61.03 422	57.65 425	71.18 428	67.16 469	47.04 456	72.74 454	77.49 444	57.47 426	60.52 393	72.53 436	22.84 462	88.38 410	49.15 404	38.94 470	78.11 455

KD-MVS_self_test		60.87 423	58.60 421	67.68 443	66.13 471	39.93 477	75.63 450	84.70 414	57.32 427	49.57 449	68.45 456	29.55 442	82.87 454	48.09 410	47.94 450	80.25 437

kuosan		60.86 424	60.24 414	62.71 453	81.57 373	46.43 458	75.70 449	85.88 402	57.98 422	48.95 452	69.53 453	58.42 201	76.53 469	28.25 477	35.87 475	65.15 476

FE-MVSNET		60.52 425	57.18 429	70.53 431	67.53 467	50.68 434	82.62 404	76.28 447	59.33 417	46.71 457	71.10 450	30.54 440	83.61 448	33.15 466	47.37 451	77.29 458

TinyColmap		60.32 426	56.42 433	72.00 426	78.78 411	53.18 420	78.36 437	75.64 451	52.30 443	41.59 474	75.82 427	14.76 478	88.35 411	35.84 457	54.71 431	74.46 463

MVS-HIRNet		60.25 427	55.55 434	74.35 404	84.37 338	56.57 402	71.64 457	74.11 456	34.44 479	45.54 463	42.24 487	31.11 438	89.81 398	40.36 448	76.10 282	76.67 460

MIMVSNet1		60.16 428	57.33 427	68.67 439	69.71 462	44.13 465	78.92 433	84.21 418	55.05 438	44.63 466	71.85 444	23.91 457	81.54 462	32.63 471	55.03 429	80.35 434

PM-MVS		59.40 429	56.59 431	67.84 441	63.63 474	41.86 469	76.76 442	63.22 480	59.01 418	51.07 443	72.27 442	11.72 482	83.25 452	61.34 352	50.28 447	78.39 453

new-patchmatchnet		59.30 430	56.48 432	67.79 442	65.86 472	44.19 464	82.47 406	81.77 434	59.94 413	43.65 470	66.20 462	27.67 449	81.68 461	39.34 450	41.40 465	77.50 457

test_vis1_rt		59.09 431	57.31 428	64.43 449	68.44 466	46.02 460	83.05 400	48.63 492	51.96 445	49.57 449	63.86 467	16.30 473	80.20 465	71.21 253	62.79 387	67.07 475

usedtu_dtu_shiyan2		57.76 432	53.69 438	69.95 434	57.60 484	41.80 470	83.50 389	83.67 426	45.26 465	43.79 469	62.82 469	17.63 472	85.93 432	42.56 440	46.40 456	82.12 418

test_fmvs3		56.82 433	54.86 436	62.69 454	53.59 486	35.47 483	75.87 447	65.64 477	43.91 470	55.10 423	71.43 448	6.91 490	74.40 474	68.64 278	52.63 435	78.20 454

DSMNet-mixed		56.78 434	54.44 437	63.79 450	63.21 475	29.44 492	64.43 473	64.10 479	42.12 476	51.32 441	71.60 445	31.76 433	75.04 472	36.23 456	65.20 364	86.87 334

pmmvs3		55.51 435	51.50 441	67.53 444	57.90 483	50.93 433	80.37 422	73.66 457	40.63 477	44.15 468	64.75 465	16.30 473	78.97 468	44.77 431	40.98 468	72.69 467

TDRefinement		55.28 436	51.58 440	66.39 447	59.53 482	46.15 459	76.23 445	72.80 460	44.60 467	42.49 472	76.28 423	15.29 476	82.39 457	33.20 465	43.75 460	70.62 471

dongtai		55.18 437	55.46 435	54.34 463	76.03 436	36.88 481	76.07 446	84.61 416	51.28 447	43.41 471	64.61 466	56.56 231	67.81 482	18.09 486	28.50 486	58.32 479

LF4IMVS		54.01 438	52.12 439	59.69 455	62.41 477	39.91 478	68.59 464	68.28 474	42.96 474	44.55 467	75.18 428	14.09 480	68.39 481	41.36 444	51.68 438	70.78 470

ttmdpeth		53.34 439	49.96 442	63.45 451	62.07 479	40.04 475	72.06 455	65.64 477	42.54 475	51.88 437	77.79 403	13.94 481	76.48 470	32.93 468	30.82 484	73.84 464

MVStest1		51.35 440	46.89 444	64.74 448	65.06 473	51.10 431	67.33 469	72.58 461	30.20 483	35.30 478	74.82 430	27.70 448	69.89 479	24.44 480	24.57 487	73.22 465

N_pmnet		50.55 441	49.11 443	54.88 461	77.17 429	4.02 505	84.36 380	2.00 503	48.59 455	45.86 461	68.82 454	32.22 431	82.80 455	31.58 474	51.38 439	77.81 456

new_pmnet		49.31 442	46.44 445	57.93 456	62.84 476	40.74 473	68.47 465	62.96 481	36.48 478	35.09 479	57.81 476	14.97 477	72.18 476	32.86 469	46.44 455	60.88 478

mvsany_test3		48.86 443	46.35 446	56.41 457	46.00 492	31.67 488	62.26 475	47.25 493	43.71 471	45.54 463	68.15 458	10.84 483	64.44 490	57.95 368	35.44 478	73.13 466

test_f		46.58 444	43.45 448	55.96 458	45.18 493	32.05 487	61.18 476	49.49 491	33.39 480	42.05 473	62.48 471	7.00 489	65.56 486	47.08 419	43.21 462	70.27 472

WB-MVS		46.23 445	44.94 447	50.11 466	62.13 478	21.23 499	76.48 444	55.49 485	45.89 463	35.78 477	61.44 474	35.54 416	72.83 475	9.96 493	21.75 488	56.27 481

FPMVS		45.64 446	43.10 450	53.23 464	51.42 489	36.46 482	64.97 472	71.91 464	29.13 484	27.53 484	61.55 473	9.83 485	65.01 488	16.00 490	55.58 427	58.22 480

SSC-MVS		44.51 447	43.35 449	47.99 470	61.01 481	18.90 501	74.12 452	54.36 486	43.42 473	34.10 481	60.02 475	34.42 421	70.39 478	9.14 495	19.57 489	54.68 482

EGC-MVSNET		42.35 448	38.09 451	55.11 460	74.57 445	46.62 457	71.63 458	55.77 484	0.04 498	0.24 499	62.70 470	14.24 479	74.91 473	17.59 487	46.06 457	43.80 484

LCM-MVSNet		40.54 449	35.79 454	54.76 462	36.92 499	30.81 489	51.41 487	69.02 471	22.07 486	24.63 486	45.37 483	4.56 494	65.81 485	33.67 463	34.50 479	67.67 473

APD_test1		40.50 450	37.31 453	50.09 467	51.88 487	35.27 484	59.45 480	52.59 488	21.64 487	26.12 485	57.80 477	4.56 494	66.56 484	22.64 482	39.09 469	48.43 483

test_vis3_rt		40.46 451	37.79 452	48.47 469	44.49 494	33.35 486	66.56 471	32.84 500	32.39 481	29.65 482	39.13 490	3.91 497	68.65 480	50.17 398	40.99 467	43.40 485

ANet_high		40.27 452	35.20 455	55.47 459	34.74 500	34.47 485	63.84 474	71.56 466	48.42 456	18.80 489	41.08 488	9.52 486	64.45 489	20.18 484	8.66 496	67.49 474

test_method		38.59 453	35.16 456	48.89 468	54.33 485	21.35 498	45.32 490	53.71 487	7.41 495	28.74 483	51.62 479	8.70 487	52.87 493	33.73 462	32.89 480	72.47 468

PMMVS2		37.93 454	33.61 457	50.92 465	46.31 491	24.76 495	60.55 479	50.05 489	28.94 485	20.93 487	47.59 480	4.41 496	65.13 487	25.14 479	18.55 491	62.87 477

Gipuma		34.91 455	31.44 458	45.30 471	70.99 457	39.64 479	19.85 494	72.56 462	20.10 489	16.16 493	21.47 494	5.08 493	71.16 477	13.07 491	43.70 461	25.08 491
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 456	29.47 459	42.67 473	41.89 496	30.81 489	52.07 485	43.45 494	15.45 490	18.52 490	44.82 484	2.12 498	58.38 491	16.05 488	30.87 482	38.83 486

APD_test2		32.77 456	29.47 459	42.67 473	41.89 496	30.81 489	52.07 485	43.45 494	15.45 490	18.52 490	44.82 484	2.12 498	58.38 491	16.05 488	30.87 482	38.83 486

PMVS	26.43 22	31.84 458	28.16 461	42.89 472	25.87 502	27.58 493	50.92 488	49.78 490	21.37 488	14.17 494	40.81 489	2.01 500	66.62 483	9.61 494	38.88 472	34.49 490
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 459	24.00 463	26.45 477	43.74 495	18.44 502	60.86 477	39.66 496	15.11 492	9.53 496	22.10 493	6.52 491	46.94 495	8.31 496	10.14 493	13.98 493

MVE	24.84 23	24.35 460	19.77 466	38.09 475	34.56 501	26.92 494	26.57 492	38.87 498	11.73 494	11.37 495	27.44 491	1.37 501	50.42 494	11.41 492	14.60 492	36.93 488
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 461	23.20 465	25.46 478	41.52 498	16.90 503	60.56 478	38.79 499	14.62 493	8.99 497	20.24 496	7.35 488	45.82 496	7.25 497	9.46 494	13.64 494

tmp_tt		22.26 462	23.75 464	17.80 479	5.23 503	12.06 504	35.26 491	39.48 497	2.82 497	18.94 488	44.20 486	22.23 464	24.64 498	36.30 455	9.31 495	16.69 492

cdsmvs_eth3d_5k		19.86 463	26.47 462	0.00 483	0.00 506	0.00 508	0.00 495	93.45 98	0.00 501	0.00 502	95.27 77	49.56 315	0.00 502	0.00 501	0.00 499	0.00 498

wuyk23d		11.30 464	10.95 467	12.33 480	48.05 490	19.89 500	25.89 493	1.92 504	3.58 496	3.12 498	1.37 498	0.64 502	15.77 499	6.23 498	7.77 497	1.35 495

ab-mvs-re		7.91 465	10.55 468	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	94.95 87	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

testmvs		7.23 466	9.62 469	0.06 482	0.04 504	0.02 507	84.98 377	0.02 505	0.03 499	0.18 500	1.21 499	0.01 504	0.02 500	0.14 499	0.01 498	0.13 497

test123		6.92 467	9.21 470	0.08 481	0.03 505	0.05 506	81.65 412	0.01 506	0.02 500	0.14 501	0.85 500	0.03 503	0.02 500	0.12 500	0.00 499	0.16 496

pcd_1.5k_mvsjas		4.46 468	5.95 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	53.55 269	0.00 502	0.00 501	0.00 499	0.00 498

mmdepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

monomultidepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

test_blank		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

uanet_test		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

DCPMVS		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

sosnet-low-res		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

sosnet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

uncertanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

Regformer		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

uanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 499	0.00 498

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						49.45 442								31.56 475

FOURS1					93.95 50	61.77 311	93.96 91	91.92 171	62.14 394	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					0.00 506

eth-test					0.00 506

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	58.56 379	90.90 261	90.45 259	62.76 387	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				69.61 435	61.38 480	41.35 472	49.07 489	85.86 404		50.18 448	66.40 461	10.16 484	88.14 413	45.73 425	44.20 459	79.32 443

MTGPA								92.23 152

test_post1							78.95 432				20.70 495	53.05 274	91.50 378	60.43 357

test_post											23.01 492	56.49 232	92.67 338

patchmatchnet-post											67.62 460	57.62 214	90.25 388

GG-mvs-BLEND				86.53 92	91.91 119	69.67 55	75.02 451	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	32.52 501

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				72.46 419	79.57 397	51.42 429		68.61 472	51.25 448	45.88 459	81.23 365	19.86 470	86.58 429	38.98 451	57.01 423	79.39 441

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		59.37 416	87.54 55			93.47 307	75.39 209

新几何2							91.41 232

新几何1				84.73 171	92.32 98	64.28 230		91.46 198	59.56 415	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	60.74 338		91.50 196			94.36 105	65.23 89			91.84 78	94.55 133

无先验							92.71 155	92.61 141	62.03 395				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	65.39 362	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	61.60 317	89.55 308	89.42 311	56.83 431	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	57.72 386		89.84 293	58.65 420	85.32 79	94.09 121	57.03 219	93.28 313	69.34 269	90.56 101	93.03 209

testdata1							89.21 320		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	61.51 319

plane_prior6					87.23 259	62.32 297						50.66 301

plane_prior5								91.31 202					95.55 211	76.74 195	78.53 260	88.39 308

plane_prior4											89.14 247

plane_prior3						61.95 306			79.09 112	72.53 255

plane_prior2							93.13 134		78.81 119

plane_prior1					87.15 262

plane_prior						62.42 293	93.85 99		79.38 104						78.80 257

n2								0.00 507

nn								0.00 507

door-mid								66.01 476

lessismore_v0				73.72 410	72.93 452	47.83 449		61.72 482		45.86 461	73.76 433	28.63 447	89.81 398	47.75 417	31.37 481	83.53 394

LGP-MVS_train				79.56 347	84.31 339	59.37 368		89.73 299	69.49 313	64.86 353	88.42 257	38.65 391	94.30 269	72.56 237	72.76 304	85.01 380

test11								93.01 118

door								66.57 475

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	63.04 277					90.30 217

MDTV_nov1_ep13_2view						59.90 360	80.13 427		67.65 340	72.79 249		54.33 260		59.83 361		92.58 225

MDTV_nov1_ep13			72.61 328		89.06 190	68.48 93	80.33 423	90.11 282	71.84 266	71.81 269	75.92 426	53.01 275	93.92 292	48.04 411	73.38 299

ACMMP++_ref															71.63 312

ACMMP++															69.72 323

Test By Simon												54.21 263

ITE_SJBPF				70.43 432	74.44 446	47.06 455		77.32 445	60.16 411	54.04 428	83.53 331	23.30 460	84.01 444	43.07 434	61.58 403	80.21 438

DeepMVS_CX				34.71 476	51.45 488	24.73 496		28.48 502	31.46 482	17.49 492	52.75 478	5.80 492	42.60 497	18.18 485	19.42 490	36.81 489

High-resolution multi-view benchmark