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 10	97.13 2	95.58 11	89.33 1	85.77 64	96.26 39	72.84 30	99.38 1	92.64 30	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 17	96.19 41	70.12 47	98.91 18	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 165	76.68 2	97.29 1	95.35 17	82.87 34	91.58 16	97.22 6	79.93 5	99.10 9	83.12 115	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 81	94.37 57	72.48 215	92.07 10	96.85 20	83.82 2	99.15 2	91.53 40	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 93	92.83 80	64.03 211	93.06 125	94.33 59	82.19 42	93.65 3	96.15 43	85.89 1	97.19 91	91.02 44	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
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 78	87.30 4	92.15 7	96.15 43	66.38 70	98.94 17	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 30	96.47 14	94.83 33	84.83 14	89.07 37	96.80 23	70.86 43	99.06 15	92.64 30	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 55	88.32 3	85.71 65	94.91 84	74.11 21	98.91 18	87.26 72	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 56	96.89 6	94.44 51	71.65 245	92.11 8	97.21 7	76.79 9	99.11 6	92.34 32	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 147	93.00 76	58.16 346	96.72 9	94.41 53	86.50 9	90.25 28	97.83 1	75.46 14	98.67 25	92.78 29	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 96	96.04 24	63.70 223	95.04 41	95.19 22	86.74 8	91.53 18	95.15 77	73.86 22	97.58 64	93.38 24	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 51	96.53 13	93.78 71	86.89 7	89.68 34	95.78 50	65.94 75	99.10 9	92.99 27	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 47	71.92 231	90.55 24	96.93 14	73.77 23	99.08 11	91.91 38	94.90 22	96.29 35
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 34	95.10 30	68.23 87	95.24 33	94.49 49	82.43 39	88.90 38	96.35 34	71.89 40	98.63 26	88.76 58	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 19	89.81 6	93.66 54	75.15 5	90.61 245	93.43 93	84.06 21	86.20 59	90.17 204	72.42 35	96.98 108	93.09 26	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 64	96.38 15	94.64 42	84.42 18	86.74 54	96.20 40	66.56 69	98.76 24	89.03 57	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 104	94.17 67	94.15 64	68.77 296	90.74 22	97.27 4	76.09 12	98.49 29	90.58 48	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.5_n_9		88.14 18	89.21 16	84.92 132	89.29 176	61.41 288	92.97 130	88.36 324	86.96 6	91.49 19	97.49 3	69.48 51	97.46 71	97.00 1	89.88 104	95.89 47

SMA-MVS		88.14 18	88.29 25	87.67 33	93.21 68	68.72 73	93.85 88	94.03 67	74.18 178	91.74 13	96.67 26	65.61 80	98.42 33	89.24 54	96.08 7	95.88 48
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 18	87.61 35	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 22	89.55 35	96.06 45	53.55 242	97.89 46	91.10 42	93.31 53	94.54 115

TSAR-MVS + MP.		88.11 21	88.64 21	86.54 73	91.73 119	68.04 91	90.36 252	93.55 85	82.89 32	91.29 20	92.89 139	72.27 37	96.03 162	87.99 62	94.77 26	95.54 59
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_8		87.96 22	88.93 18	85.07 127	88.43 207	61.78 275	94.73 54	91.74 172	85.87 10	91.66 15	97.50 2	64.03 101	98.33 34	96.28 4	90.08 100	95.10 84

TSAR-MVS + GP.		87.96 22	88.37 24	86.70 66	93.51 62	65.32 169	95.15 36	93.84 70	78.17 113	85.93 63	94.80 87	75.80 13	98.21 36	89.38 51	88.78 116	96.59 19

DeepC-MVS_fast	79.48 2	87.95 24	88.00 29	87.79 31	95.86 27	68.32 81	95.74 21	94.11 65	83.82 23	83.49 90	96.19 41	64.53 96	98.44 31	83.42 114	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
xiu_mvs_v2_base		87.92 25	87.38 39	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 26	89.55 35	95.95 48	53.45 246	97.68 54	91.07 43	92.62 60	94.54 115

EPNet		87.84 26	88.38 23	86.23 83	93.30 65	66.05 148	95.26 32	94.84 32	87.09 5	88.06 41	94.53 93	66.79 66	97.34 80	83.89 107	91.68 75	95.29 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 27	87.77 32	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 36	87.13 49	95.27 70	64.99 86	95.80 168	89.34 52	91.80 73	95.93 45

test_fmvsm_n_1920		87.69 28	88.50 22	85.27 120	87.05 250	63.55 230	93.69 98	91.08 208	84.18 20	90.17 30	97.04 11	67.58 61	97.99 41	95.72 7	90.03 101	94.26 131

fmvsm_l_conf0.5_n_3		87.54 29	88.29 25	85.30 117	86.92 256	62.63 256	95.02 43	90.28 246	84.95 13	90.27 27	96.86 18	65.36 82	97.52 69	94.93 12	90.03 101	95.76 51

APDe-MVS		87.54 29	87.84 31	86.65 67	96.07 23	66.30 143	94.84 49	93.78 71	69.35 285	88.39 40	96.34 35	67.74 60	97.66 59	90.62 47	93.44 51	96.01 44
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 31	88.72 20	83.84 184	86.89 258	60.04 322	95.05 39	92.17 151	84.80 15	92.27 6	96.37 32	64.62 93	96.54 134	94.43 16	91.86 71	94.94 93

fmvsm_l_conf0.5_n		87.49 32	88.19 27	85.39 112	86.95 251	64.37 199	94.30 64	88.45 322	80.51 64	92.70 4	96.86 18	69.98 48	97.15 96	95.83 6	88.08 124	94.65 109

SD-MVS		87.49 32	87.49 37	87.50 42	93.60 56	68.82 70	93.90 85	92.63 131	76.86 135	87.90 43	95.76 51	66.17 72	97.63 61	89.06 56	91.48 79	96.05 42
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 34	88.15 28	85.30 117	87.10 248	64.19 206	94.41 59	88.14 332	80.24 72	92.54 5	96.97 13	69.52 50	97.17 92	95.89 5	88.51 119	94.56 112

dcpmvs_2		87.37 35	87.55 36	86.85 58	95.04 32	68.20 88	90.36 252	90.66 227	79.37 88	81.20 112	93.67 123	74.73 16	96.55 133	90.88 45	92.00 69	95.82 49

alignmvs		87.28 36	86.97 43	88.24 27	91.30 133	71.14 26	95.61 25	93.56 84	79.30 89	87.07 51	95.25 72	68.43 53	96.93 116	87.87 63	84.33 167	96.65 17

train_agg		87.21 37	87.42 38	86.60 69	94.18 41	67.28 111	94.16 68	93.51 87	71.87 236	85.52 68	95.33 64	68.19 55	97.27 87	89.09 55	94.90 22	95.25 79

MG-MVS		87.11 38	86.27 56	89.62 8	97.79 1	76.27 4	94.96 45	94.49 49	78.74 104	83.87 86	92.94 137	64.34 97	96.94 114	75.19 182	94.09 38	95.66 54

SF-MVS		87.03 39	87.09 41	86.84 59	92.70 86	67.45 109	93.64 101	93.76 74	70.78 269	86.25 57	96.44 31	66.98 64	97.79 50	88.68 59	94.56 34	95.28 75

fmvsm_s_conf0.5_n_3		86.88 40	87.99 30	83.58 196	87.26 243	60.74 302	93.21 122	87.94 339	84.22 19	91.70 14	97.27 4	65.91 77	95.02 207	93.95 21	90.42 96	94.99 90

CSCG		86.87 41	86.26 57	88.72 17	95.05 31	70.79 29	93.83 93	95.33 18	68.48 300	77.63 160	94.35 102	73.04 28	98.45 30	84.92 96	93.71 47	96.92 14

sasdasda		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

canonicalmvs		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

UBG		86.83 44	86.70 49	87.20 48	93.07 74	69.81 47	93.43 114	95.56 13	81.52 49	81.50 108	92.12 158	73.58 26	96.28 146	84.37 102	85.20 157	95.51 60

PHI-MVS		86.83 44	86.85 48	86.78 63	93.47 63	65.55 164	95.39 30	95.10 25	71.77 241	85.69 66	96.52 28	62.07 136	98.77 23	86.06 84	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 46	86.90 46	86.58 71	90.42 150	66.38 140	96.09 17	93.87 69	77.73 122	84.01 85	95.66 53	63.39 115	97.94 42	87.40 70	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 47	87.63 33	84.27 171	86.15 273	61.48 285	94.69 55	91.16 200	83.79 25	90.51 26	96.28 37	64.24 98	98.22 35	95.00 11	86.88 136	93.11 179

PVSNet_Blended		86.73 48	86.86 47	86.31 82	93.76 50	67.53 106	96.33 16	93.61 82	82.34 41	81.00 117	93.08 133	63.19 119	97.29 83	87.08 75	91.38 81	94.13 140

testing11		86.71 49	86.44 54	87.55 40	93.54 60	71.35 21	93.65 100	95.58 11	81.36 56	80.69 120	92.21 157	72.30 36	96.46 139	85.18 92	83.43 177	94.82 101

test_fmvsmconf_n		86.58 50	87.17 40	84.82 138	85.28 290	62.55 257	94.26 66	89.78 265	83.81 24	87.78 45	96.33 36	65.33 83	96.98 108	94.40 17	87.55 130	94.95 92

BP-MVS1		86.54 51	86.68 51	86.13 86	87.80 231	67.18 115	92.97 130	95.62 10	79.92 75	82.84 97	94.14 111	74.95 15	96.46 139	82.91 119	88.96 115	94.74 103

jason		86.40 52	86.17 60	87.11 51	86.16 272	70.54 32	95.71 24	92.19 148	82.00 44	84.58 78	94.34 103	61.86 138	95.53 189	87.76 64	90.89 89	95.27 76
jason: jason.
NormalMVS		86.39 53	86.66 52	85.60 106	92.12 101	65.95 153	94.88 46	90.83 215	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	91.15 85	93.93 151

fmvsm_s_conf0.5_n		86.39 53	86.91 45	84.82 138	87.36 242	63.54 231	94.74 51	90.02 258	82.52 37	90.14 31	96.92 16	62.93 126	97.84 49	95.28 10	82.26 187	93.07 182

fmvsm_s_conf0.5_n_5		86.38 55	86.94 44	84.71 149	84.67 302	63.29 236	94.04 77	89.99 260	82.88 33	87.85 44	96.03 46	62.89 128	96.36 143	94.15 18	89.95 103	94.48 121

SymmetryMVS		86.32 56	86.39 55	86.12 87	90.52 148	65.95 153	94.88 46	94.58 46	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	86.59 145	95.51 60

WTY-MVS		86.32 56	85.81 68	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 35	81.91 105	94.73 88	67.93 59	97.63 61	79.55 149	82.25 189	96.54 22

myMVS_eth3d28		86.31 58	86.15 61	86.78 63	93.56 58	70.49 33	92.94 133	95.28 19	82.47 38	78.70 151	92.07 160	72.45 34	95.41 191	82.11 125	85.78 153	94.44 123

MSLP-MVS++		86.27 59	85.91 67	87.35 45	92.01 108	68.97 67	95.04 41	92.70 122	79.04 99	81.50 108	96.50 30	58.98 176	96.78 124	83.49 113	93.93 41	96.29 35

VNet		86.20 60	85.65 72	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 109	86.00 62	93.07 134	58.22 183	97.00 104	85.22 90	84.33 167	96.52 23

MVS_111021_HR		86.19 61	85.80 69	87.37 44	93.17 70	69.79 48	93.99 80	93.76 74	79.08 96	78.88 147	93.99 117	62.25 135	98.15 38	85.93 85	91.15 85	94.15 139

SPE-MVS-test		86.14 62	87.01 42	83.52 197	92.63 88	59.36 334	95.49 27	91.92 161	80.09 73	85.46 70	95.53 59	61.82 140	95.77 171	86.77 79	93.37 52	95.41 63

ACMMP_NAP		86.05 63	85.80 69	86.80 62	91.58 123	67.53 106	91.79 190	93.49 90	74.93 168	84.61 77	95.30 66	59.42 167	97.92 43	86.13 82	94.92 20	94.94 93

testing99		86.01 64	85.47 74	87.63 38	93.62 55	71.25 23	93.47 112	95.23 21	80.42 67	80.60 122	91.95 164	71.73 41	96.50 137	80.02 146	82.22 190	95.13 82

ETV-MVS		86.01 64	86.11 62	85.70 103	90.21 155	67.02 122	93.43 114	91.92 161	81.21 58	84.13 84	94.07 116	60.93 148	95.63 179	89.28 53	89.81 105	94.46 122

testing91		85.93 66	85.31 78	87.78 32	93.59 57	71.47 19	93.50 109	95.08 28	80.26 69	80.53 123	91.93 165	70.43 45	96.51 136	80.32 144	82.13 192	95.37 66

APD-MVS		85.93 66	85.99 65	85.76 100	95.98 26	65.21 172	93.59 104	92.58 133	66.54 318	86.17 60	95.88 49	63.83 105	97.00 104	86.39 81	92.94 57	95.06 86
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 68	85.46 75	87.18 49	88.20 219	72.42 15	92.41 161	92.77 120	82.11 43	80.34 126	93.07 134	68.27 54	95.02 207	78.39 163	93.59 49	94.09 142

CS-MVS		85.80 69	86.65 53	83.27 209	92.00 109	58.92 338	95.31 31	91.86 166	79.97 74	84.82 76	95.40 62	62.26 134	95.51 190	86.11 83	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 70	86.09 63	84.72 147	85.73 283	63.58 228	93.79 94	89.32 283	81.42 54	90.21 29	96.91 17	62.41 133	97.67 56	94.48 15	80.56 214	92.90 188

test_fmvsmconf0.1_n		85.71 71	86.08 64	84.62 157	80.83 349	62.33 262	93.84 91	88.81 310	83.50 27	87.00 52	96.01 47	63.36 116	96.93 116	94.04 20	87.29 133	94.61 111

CDPH-MVS		85.71 71	85.46 75	86.46 75	94.75 34	67.19 113	93.89 86	92.83 118	70.90 265	83.09 95	95.28 68	63.62 110	97.36 78	80.63 140	94.18 37	94.84 98

casdiffmvs_mvg		85.66 73	85.18 80	87.09 52	88.22 218	69.35 59	93.74 97	91.89 164	81.47 50	80.10 128	91.45 174	64.80 91	96.35 144	87.23 73	87.69 128	95.58 57
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 74	85.93 66	84.68 151	82.95 332	63.48 233	94.03 79	89.46 277	81.69 47	89.86 32	96.74 24	61.85 139	97.75 52	94.74 14	82.01 194	92.81 190

MGCFI-Net		85.59 75	85.73 71	85.17 124	91.41 131	62.44 258	92.87 138	91.31 192	79.65 81	86.99 53	95.14 78	62.90 127	96.12 154	87.13 74	84.13 172	96.96 13

GDP-MVS		85.54 76	85.32 77	86.18 84	87.64 234	67.95 95	92.91 136	92.36 138	77.81 119	83.69 87	94.31 105	72.84 30	96.41 141	80.39 143	85.95 151	94.19 135

DeepC-MVS	77.85 3	85.52 77	85.24 79	86.37 79	88.80 191	66.64 134	92.15 169	93.68 80	81.07 59	76.91 171	93.64 124	62.59 130	98.44 31	85.50 86	92.84 59	94.03 146
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 78	84.87 86	86.84 59	88.25 216	69.07 63	93.04 127	91.76 171	81.27 57	80.84 119	92.07 160	64.23 99	96.06 160	84.98 95	87.43 132	95.39 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
ZNCC-MVS		85.33 79	85.08 82	86.06 88	93.09 73	65.65 160	93.89 86	93.41 95	73.75 189	79.94 130	94.68 90	60.61 151	98.03 40	82.63 122	93.72 46	94.52 117

fmvsm_s_conf0.5_n_7		85.24 80	86.69 50	80.91 277	84.52 307	60.10 320	93.35 117	90.35 239	83.41 28	86.54 56	96.27 38	60.50 152	90.02 361	94.84 13	90.38 97	92.61 194

MP-MVS-pluss		85.24 80	85.13 81	85.56 107	91.42 128	65.59 162	91.54 200	92.51 135	74.56 171	80.62 121	95.64 54	59.15 171	97.00 104	86.94 77	93.80 43	94.07 144
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 82	84.69 90	86.63 68	92.91 78	69.91 43	92.61 151	95.80 9	80.31 68	80.38 125	92.27 154	68.73 52	95.19 204	75.94 176	83.27 179	94.81 102

PAPR		85.15 83	84.47 91	87.18 49	96.02 25	68.29 82	91.85 188	93.00 113	76.59 146	79.03 143	95.00 79	61.59 141	97.61 63	78.16 164	89.00 114	95.63 55

fmvsm_s_conf0.5_n_2		85.06 84	85.60 73	83.44 203	86.92 256	60.53 309	94.41 59	87.31 345	83.30 29	88.72 39	96.72 25	54.28 234	97.75 52	94.07 19	84.68 164	92.04 217

MP-MVS		85.02 85	84.97 84	85.17 124	92.60 89	64.27 204	93.24 119	92.27 141	73.13 200	79.63 135	94.43 96	61.90 137	97.17 92	85.00 94	92.56 61	94.06 145
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 86	84.44 92	86.71 65	88.33 213	68.73 72	90.24 257	91.82 170	81.05 60	81.18 113	92.50 146	63.69 108	96.08 159	84.45 101	86.71 143	95.32 71

CHOSEN 1792x2688		84.98 87	83.45 106	89.57 11	89.94 160	75.14 6	92.07 175	92.32 139	81.87 45	75.68 180	88.27 236	60.18 155	98.60 27	80.46 142	90.27 99	94.96 91

MVSMamba_PlusPlus		84.97 88	83.65 100	88.93 14	90.17 156	74.04 8	87.84 313	92.69 125	62.18 356	81.47 110	87.64 250	71.47 42	96.28 146	84.69 98	94.74 31	96.47 28

EIA-MVS		84.84 89	84.88 85	84.69 150	91.30 133	62.36 261	93.85 88	92.04 154	79.45 84	79.33 140	94.28 107	62.42 132	96.35 144	80.05 145	91.25 84	95.38 65

lecture		84.77 90	84.81 88	84.65 153	92.12 101	62.27 265	94.74 51	92.64 130	68.35 301	85.53 67	95.30 66	59.77 162	97.91 44	83.73 109	91.15 85	93.77 159

fmvsm_s_conf0.1_n_a		84.76 91	84.84 87	84.53 159	80.23 362	63.50 232	92.79 140	88.73 313	80.46 65	89.84 33	96.65 27	60.96 147	97.57 66	93.80 22	80.14 216	92.53 199

HFP-MVS		84.73 92	84.40 93	85.72 102	93.75 52	65.01 178	93.50 109	93.19 103	72.19 225	79.22 141	94.93 82	59.04 174	97.67 56	81.55 129	92.21 64	94.49 120

MVS		84.66 93	82.86 125	90.06 2	90.93 140	74.56 7	87.91 311	95.54 14	68.55 298	72.35 235	94.71 89	59.78 161	98.90 20	81.29 135	94.69 32	96.74 16

GST-MVS		84.63 94	84.29 94	85.66 104	92.82 82	65.27 170	93.04 127	93.13 106	73.20 198	78.89 144	94.18 110	59.41 168	97.85 48	81.45 131	92.48 63	93.86 156

EC-MVSNet		84.53 95	85.04 83	83.01 215	89.34 172	61.37 289	94.42 58	91.09 206	77.91 117	83.24 91	94.20 109	58.37 181	95.40 192	85.35 87	91.41 80	92.27 211

fmvsm_s_conf0.1_n_2		84.40 96	84.78 89	83.27 209	85.25 291	60.41 312	94.13 71	85.69 369	83.05 31	87.99 42	96.37 32	52.75 251	97.68 54	93.75 23	84.05 173	91.71 225

ACMMPR		84.37 97	84.06 95	85.28 119	93.56 58	64.37 199	93.50 109	93.15 105	72.19 225	78.85 149	94.86 85	56.69 203	97.45 72	81.55 129	92.20 65	94.02 147

region2R		84.36 98	84.03 96	85.36 115	93.54 60	64.31 202	93.43 114	92.95 114	72.16 228	78.86 148	94.84 86	56.97 198	97.53 68	81.38 133	92.11 67	94.24 133

LFMVS		84.34 99	82.73 127	89.18 13	94.76 33	73.25 11	94.99 44	91.89 164	71.90 233	82.16 104	93.49 128	47.98 302	97.05 99	82.55 123	84.82 160	97.25 8

test_yl		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 288	81.09 114	92.88 140	57.00 196	97.44 73	81.11 137	81.76 197	96.23 38

DCV-MVSNet		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 288	81.09 114	92.88 140	57.00 196	97.44 73	81.11 137	81.76 197	96.23 38

diffmvs		84.28 100	83.83 97	85.61 105	87.40 240	68.02 92	90.88 231	89.24 286	80.54 63	81.64 107	92.52 145	59.83 160	94.52 234	87.32 71	85.11 158	94.29 130
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	84.28 100	83.36 112	87.02 55	92.22 96	67.74 99	84.65 340	94.50 48	79.15 93	82.23 103	87.93 245	66.88 65	96.94 114	80.53 141	82.20 191	96.39 33

ETVMVS		84.22 104	83.71 98	85.76 100	92.58 90	68.25 86	92.45 160	95.53 15	79.54 83	79.46 137	91.64 172	70.29 46	94.18 247	69.16 245	82.76 185	94.84 98

MAR-MVS		84.18 105	83.43 107	86.44 76	96.25 21	65.93 155	94.28 65	94.27 61	74.41 173	79.16 142	95.61 55	53.99 237	98.88 22	69.62 239	93.26 54	94.50 119
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		84.16 106	83.20 115	87.05 54	91.56 124	69.82 46	89.99 266	92.05 153	77.77 121	82.84 97	86.57 267	63.93 104	96.09 156	74.91 187	89.18 111	95.25 79

CANet_DTU		84.09 107	83.52 101	85.81 97	90.30 153	66.82 129	91.87 186	89.01 301	85.27 11	86.09 61	93.74 121	47.71 308	96.98 108	77.90 166	89.78 107	93.65 162

ET-MVSNet_ETH3D		84.01 108	83.15 118	86.58 71	90.78 145	70.89 28	94.74 51	94.62 43	81.44 53	58.19 374	93.64 124	73.64 25	92.35 316	82.66 121	78.66 236	96.50 27

PVSNet_Blended_VisFu		83.97 109	83.50 103	85.39 112	90.02 158	66.59 137	93.77 95	91.73 173	77.43 130	77.08 170	89.81 212	63.77 107	96.97 111	79.67 148	88.21 122	92.60 195

MTAPA		83.91 110	83.38 111	85.50 108	91.89 115	65.16 174	81.75 370	92.23 142	75.32 163	80.53 123	95.21 75	56.06 212	97.16 95	84.86 97	92.55 62	94.18 136

XVS		83.87 111	83.47 105	85.05 128	93.22 66	63.78 216	92.92 134	92.66 127	73.99 181	78.18 154	94.31 105	55.25 218	97.41 75	79.16 153	91.58 77	93.95 149

Effi-MVS+		83.82 112	82.76 126	86.99 56	89.56 168	69.40 54	91.35 211	86.12 363	72.59 212	83.22 94	92.81 143	59.60 164	96.01 164	81.76 128	87.80 127	95.56 58

test_fmvsmvis_n_1920		83.80 113	83.48 104	84.77 142	82.51 335	63.72 221	91.37 209	83.99 387	81.42 54	77.68 159	95.74 52	58.37 181	97.58 64	93.38 24	86.87 137	93.00 185

EI-MVSNet-Vis-set		83.77 114	83.67 99	84.06 175	92.79 85	63.56 229	91.76 193	94.81 34	79.65 81	77.87 157	94.09 114	63.35 117	97.90 45	79.35 151	79.36 226	90.74 246

MVSFormer		83.75 115	82.88 124	86.37 79	89.24 181	71.18 24	89.07 289	90.69 224	65.80 323	87.13 49	94.34 103	64.99 86	92.67 302	72.83 204	91.80 73	95.27 76

CP-MVS		83.71 116	83.40 110	84.65 153	93.14 71	63.84 214	94.59 56	92.28 140	71.03 263	77.41 163	94.92 83	55.21 221	96.19 151	81.32 134	90.70 91	93.91 153

test_fmvsmconf0.01_n		83.70 117	83.52 101	84.25 172	75.26 408	61.72 279	92.17 168	87.24 347	82.36 40	84.91 75	95.41 61	55.60 216	96.83 123	92.85 28	85.87 152	94.21 134

baseline2		83.68 118	83.42 109	84.48 162	87.37 241	66.00 150	90.06 261	95.93 8	79.71 80	69.08 273	90.39 192	77.92 6	96.28 146	78.91 158	81.38 201	91.16 239

reproduce-ours		83.51 119	83.33 113	84.06 175	92.18 99	60.49 310	90.74 237	92.04 154	64.35 333	83.24 91	95.59 57	59.05 172	97.27 87	83.61 110	89.17 112	94.41 128

our_new_method		83.51 119	83.33 113	84.06 175	92.18 99	60.49 310	90.74 237	92.04 154	64.35 333	83.24 91	95.59 57	59.05 172	97.27 87	83.61 110	89.17 112	94.41 128

thisisatest0515		83.41 121	82.49 131	86.16 85	89.46 171	68.26 84	93.54 106	94.70 39	74.31 176	75.75 178	90.92 182	72.62 32	96.52 135	69.64 237	81.50 200	93.71 160

PVSNet_BlendedMVS		83.38 122	83.43 107	83.22 211	93.76 50	67.53 106	94.06 73	93.61 82	79.13 94	81.00 117	85.14 285	63.19 119	97.29 83	87.08 75	73.91 274	84.83 346

test2506		83.29 123	82.92 123	84.37 166	88.39 210	63.18 242	92.01 178	91.35 191	77.66 124	78.49 153	91.42 175	64.58 95	95.09 206	73.19 200	89.23 109	94.85 95

PGM-MVS		83.25 124	82.70 128	84.92 132	92.81 84	64.07 210	90.44 247	92.20 146	71.28 257	77.23 167	94.43 96	55.17 222	97.31 82	79.33 152	91.38 81	93.37 169

HPM-MVS		83.25 124	82.95 122	84.17 173	92.25 95	62.88 251	90.91 228	91.86 166	70.30 274	77.12 168	93.96 118	56.75 201	96.28 146	82.04 126	91.34 83	93.34 170
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 126	82.96 120	83.73 188	92.02 105	59.74 326	90.37 251	92.08 152	63.70 340	82.86 96	95.48 60	58.62 178	97.17 92	83.06 116	88.42 120	94.26 131

EI-MVSNet-UG-set		83.14 127	82.96 120	83.67 193	92.28 94	63.19 241	91.38 208	94.68 40	79.22 91	76.60 173	93.75 120	62.64 129	97.76 51	78.07 165	78.01 239	90.05 255

testing3-2		83.11 128	83.15 118	82.98 216	91.92 112	64.01 212	94.39 62	95.37 16	78.32 110	75.53 185	90.06 210	73.18 27	93.18 281	74.34 192	75.27 263	91.77 224

VDD-MVS		83.06 129	81.81 141	86.81 61	90.86 143	67.70 100	95.40 29	91.50 186	75.46 158	81.78 106	92.34 153	40.09 352	97.13 97	86.85 78	82.04 193	95.60 56

h-mvs33		83.01 130	82.56 130	84.35 167	89.34 172	62.02 269	92.72 143	93.76 74	81.45 51	82.73 100	92.25 156	60.11 156	97.13 97	87.69 65	62.96 355	93.91 153

PAPM_NR		82.97 131	81.84 140	86.37 79	94.10 44	66.76 132	87.66 317	92.84 117	69.96 278	74.07 208	93.57 126	63.10 124	97.50 70	70.66 232	90.58 93	94.85 95

mPP-MVS		82.96 132	82.44 132	84.52 160	92.83 80	62.92 249	92.76 141	91.85 168	71.52 253	75.61 183	94.24 108	53.48 245	96.99 107	78.97 156	90.73 90	93.64 163

SR-MVS		82.81 133	82.58 129	83.50 200	93.35 64	61.16 292	92.23 166	91.28 197	64.48 332	81.27 111	95.28 68	53.71 241	95.86 166	82.87 120	88.77 117	93.49 167

DP-MVS Recon		82.73 134	81.65 142	85.98 90	97.31 4	67.06 118	95.15 36	91.99 158	69.08 293	76.50 175	93.89 119	54.48 230	98.20 37	70.76 230	85.66 155	92.69 191

CLD-MVS		82.73 134	82.35 134	83.86 183	87.90 226	67.65 102	95.45 28	92.18 149	85.06 12	72.58 226	92.27 154	52.46 254	95.78 169	84.18 103	79.06 231	88.16 283
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 136	82.38 133	83.73 188	89.25 178	59.58 329	92.24 165	94.89 31	77.96 115	79.86 131	92.38 151	56.70 202	97.05 99	77.26 169	80.86 209	94.55 113

3Dnovator	73.91 6	82.69 137	80.82 155	88.31 26	89.57 167	71.26 22	92.60 152	94.39 56	78.84 101	67.89 294	92.48 149	48.42 297	98.52 28	68.80 250	94.40 36	95.15 81

RRT-MVS		82.61 138	81.16 146	86.96 57	91.10 137	68.75 71	87.70 316	92.20 146	76.97 133	72.68 222	87.10 261	51.30 268	96.41 141	83.56 112	87.84 126	95.74 52

viewmambaseed2359dif		82.60 139	81.91 139	84.67 152	85.83 280	66.09 147	90.50 246	89.01 301	75.46 158	79.64 134	92.01 162	59.51 165	94.38 238	82.99 118	82.26 187	93.54 165

MVSTER		82.47 140	82.05 135	83.74 186	92.68 87	69.01 65	91.90 185	93.21 100	79.83 76	72.14 236	85.71 280	74.72 17	94.72 221	75.72 178	72.49 284	87.50 290

TESTMET0.1,1		82.41 141	81.98 138	83.72 190	88.08 220	63.74 218	92.70 145	93.77 73	79.30 89	77.61 161	87.57 252	58.19 184	94.08 252	73.91 194	86.68 144	93.33 172

CostFormer		82.33 142	81.15 147	85.86 95	89.01 186	68.46 78	82.39 367	93.01 111	75.59 156	80.25 127	81.57 330	72.03 39	94.96 211	79.06 155	77.48 247	94.16 138

API-MVS		82.28 143	80.53 164	87.54 41	96.13 22	70.59 31	93.63 102	91.04 212	65.72 325	75.45 186	92.83 142	56.11 211	98.89 21	64.10 298	89.75 108	93.15 177

IB-MVS	77.80 4	82.18 144	80.46 166	87.35 45	89.14 183	70.28 36	95.59 26	95.17 24	78.85 100	70.19 261	85.82 278	70.66 44	97.67 56	72.19 216	66.52 325	94.09 142
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		82.16 145	81.12 148	85.26 121	86.42 265	68.72 73	92.59 154	90.44 236	73.12 201	84.20 81	94.36 98	38.04 365	95.73 173	84.12 104	86.81 138	91.33 232

xiu_mvs_v1_base		82.16 145	81.12 148	85.26 121	86.42 265	68.72 73	92.59 154	90.44 236	73.12 201	84.20 81	94.36 98	38.04 365	95.73 173	84.12 104	86.81 138	91.33 232

xiu_mvs_v1_base_debi		82.16 145	81.12 148	85.26 121	86.42 265	68.72 73	92.59 154	90.44 236	73.12 201	84.20 81	94.36 98	38.04 365	95.73 173	84.12 104	86.81 138	91.33 232

3Dnovator+	73.60 7	82.10 148	80.60 162	86.60 69	90.89 142	66.80 131	95.20 34	93.44 92	74.05 180	67.42 301	92.49 148	49.46 287	97.65 60	70.80 229	91.68 75	95.33 69

MVS_111021_LR		82.02 149	81.52 143	83.51 199	88.42 208	62.88 251	89.77 269	88.93 306	76.78 138	75.55 184	93.10 131	50.31 276	95.38 194	83.82 108	87.02 135	92.26 212

PMMVS		81.98 150	82.04 136	81.78 251	89.76 164	56.17 366	91.13 224	90.69 224	77.96 115	80.09 129	93.57 126	46.33 321	94.99 210	81.41 132	87.46 131	94.17 137

baseline1		81.84 151	81.03 152	84.28 170	91.60 122	66.62 135	91.08 225	91.66 180	81.87 45	74.86 194	91.67 171	69.98 48	94.92 214	71.76 219	64.75 342	91.29 237

EPP-MVSNet		81.79 152	81.52 143	82.61 226	88.77 192	60.21 318	93.02 129	93.66 81	68.52 299	72.90 220	90.39 192	72.19 38	94.96 211	74.93 186	79.29 229	92.67 192

WBMVS		81.67 153	80.98 154	83.72 190	93.07 74	69.40 54	94.33 63	93.05 109	76.84 136	72.05 238	84.14 296	74.49 19	93.88 266	72.76 207	68.09 313	87.88 285

test_vis1_n_1920		81.66 154	82.01 137	80.64 280	82.24 337	55.09 375	94.76 50	86.87 351	81.67 48	84.40 80	94.63 91	38.17 362	94.67 225	91.98 37	83.34 178	92.16 215

APD-MVS_3200maxsize		81.64 155	81.32 145	82.59 228	92.36 92	58.74 340	91.39 206	91.01 213	63.35 344	79.72 133	94.62 92	51.82 257	96.14 153	79.71 147	87.93 125	92.89 189

mvsmamba		81.55 156	80.72 157	84.03 179	91.42 128	66.93 127	83.08 359	89.13 294	78.55 108	67.50 299	87.02 262	51.79 259	90.07 360	87.48 68	90.49 95	95.10 84

ACMMP		81.49 157	80.67 159	83.93 181	91.71 120	62.90 250	92.13 170	92.22 145	71.79 240	71.68 244	93.49 128	50.32 275	96.96 112	78.47 162	84.22 171	91.93 222
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		81.43 158	80.11 168	85.38 114	86.60 261	65.47 168	92.90 137	93.54 86	75.33 162	77.31 165	90.39 192	46.81 313	96.75 125	71.65 222	86.46 148	93.93 151

CDS-MVSNet		81.43 158	80.74 156	83.52 197	86.26 269	64.45 193	92.09 173	90.65 228	75.83 154	73.95 210	89.81 212	63.97 103	92.91 292	71.27 223	82.82 182	93.20 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 160	79.99 172	85.46 109	90.39 152	68.40 79	86.88 328	90.61 229	74.41 173	70.31 260	84.67 290	63.79 106	92.32 318	73.13 201	85.70 154	95.67 53

ECVR-MVS		81.29 161	80.38 167	84.01 180	88.39 210	61.96 271	92.56 157	86.79 353	77.66 124	76.63 172	91.42 175	46.34 320	95.24 203	74.36 191	89.23 109	94.85 95

guyue		81.23 162	80.57 163	83.21 213	86.64 259	61.85 273	92.52 158	92.78 119	78.69 105	74.92 193	89.42 216	50.07 279	95.35 195	80.79 139	79.31 228	92.42 201

icg_test_0403		81.19 163	79.88 174	85.13 126	88.54 195	64.75 183	88.84 294	90.80 218	76.73 141	75.21 189	90.18 198	54.22 235	96.21 150	73.47 196	80.95 204	94.43 124

thisisatest0530		81.15 164	80.07 169	84.39 165	88.26 215	65.63 161	91.40 204	94.62 43	71.27 258	70.93 251	89.18 221	72.47 33	96.04 161	65.62 287	76.89 254	91.49 228

Fast-Effi-MVS+		81.14 165	80.01 171	84.51 161	90.24 154	65.86 156	94.12 72	89.15 292	73.81 188	75.37 188	88.26 237	57.26 191	94.53 233	66.97 272	84.92 159	93.15 177

HQP-MVS		81.14 165	80.64 160	82.64 225	87.54 236	63.66 226	94.06 73	91.70 178	79.80 77	74.18 201	90.30 195	51.63 262	95.61 181	77.63 167	78.90 232	88.63 274

hse-mvs2		81.12 167	81.11 151	81.16 265	86.52 264	57.48 355	89.40 280	91.16 200	81.45 51	82.73 100	90.49 190	60.11 156	94.58 226	87.69 65	60.41 382	91.41 231

SR-MVS-dyc-post		81.06 168	80.70 158	82.15 242	92.02 105	58.56 343	90.90 229	90.45 232	62.76 351	78.89 144	94.46 94	51.26 269	95.61 181	78.77 160	86.77 141	92.28 208

HyFIR lowres test		81.03 169	79.56 181	85.43 110	87.81 230	68.11 90	90.18 258	90.01 259	70.65 271	72.95 219	86.06 274	63.61 111	94.50 235	75.01 185	79.75 220	93.67 161

nrg030		80.93 170	79.86 175	84.13 174	83.69 321	68.83 69	93.23 120	91.20 198	75.55 157	75.06 191	88.22 240	63.04 125	94.74 220	81.88 127	66.88 322	88.82 272

Vis-MVSNet		80.92 171	79.98 173	83.74 186	88.48 204	61.80 274	93.44 113	88.26 331	73.96 184	77.73 158	91.76 168	49.94 281	94.76 218	65.84 284	90.37 98	94.65 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 172	80.02 170	83.33 204	87.87 227	60.76 300	92.62 150	86.86 352	77.86 118	75.73 179	91.39 177	46.35 319	94.70 224	72.79 206	88.68 118	94.52 117

UWE-MVS		80.81 173	81.01 153	80.20 290	89.33 174	57.05 360	91.91 184	94.71 38	75.67 155	75.01 192	89.37 217	63.13 123	91.44 343	67.19 269	82.80 184	92.12 216

icg_test_0407		80.80 174	79.39 187	85.00 131	88.54 195	64.75 183	88.40 302	90.80 218	76.73 141	73.95 210	90.18 198	51.55 264	95.81 167	73.47 196	80.95 204	94.43 124

1314		80.70 175	78.95 195	85.94 92	87.77 233	67.56 104	87.91 311	92.55 134	72.17 227	67.44 300	93.09 132	50.27 277	97.04 102	71.68 221	87.64 129	93.23 174

AstraMVS		80.66 176	79.79 177	83.28 208	85.07 297	61.64 281	92.19 167	90.58 230	79.40 86	74.77 196	90.18 198	45.93 325	95.61 181	83.04 117	76.96 253	92.60 195

tpmrst		80.57 177	79.14 193	84.84 137	90.10 157	68.28 83	81.70 371	89.72 272	77.63 126	75.96 177	79.54 362	64.94 88	92.71 299	75.43 180	77.28 250	93.55 164

1112_ss		80.56 178	79.83 176	82.77 220	88.65 193	60.78 298	92.29 163	88.36 324	72.58 213	72.46 232	94.95 80	65.09 85	93.42 278	66.38 278	77.71 241	94.10 141

VDDNet		80.50 179	78.26 203	87.21 47	86.19 270	69.79 48	94.48 57	91.31 192	60.42 372	79.34 139	90.91 183	38.48 360	96.56 132	82.16 124	81.05 203	95.27 76

BH-w/o		80.49 180	79.30 189	84.05 178	90.83 144	64.36 201	93.60 103	89.42 280	74.35 175	69.09 272	90.15 206	55.23 220	95.61 181	64.61 295	86.43 149	92.17 214

test_cas_vis1_n_1920		80.45 181	80.61 161	79.97 299	78.25 389	57.01 362	94.04 77	88.33 326	79.06 98	82.81 99	93.70 122	38.65 357	91.63 334	90.82 46	79.81 218	91.27 238

icg_test_0407_2		80.38 182	79.22 191	83.88 182	88.54 195	64.75 183	86.79 329	90.80 218	76.73 141	73.95 210	90.18 198	51.55 264	92.45 311	73.47 196	80.95 204	94.43 124

TAMVS		80.37 183	79.45 184	83.13 214	85.14 294	63.37 234	91.23 218	90.76 223	74.81 170	72.65 224	88.49 230	60.63 150	92.95 287	69.41 241	81.95 195	93.08 181

HQP_MVS		80.34 184	79.75 178	82.12 244	86.94 252	62.42 259	93.13 123	91.31 192	78.81 102	72.53 227	89.14 223	50.66 272	95.55 187	76.74 170	78.53 237	88.39 280

SDMVSNet		80.26 185	78.88 196	84.40 164	89.25 178	67.63 103	85.35 336	93.02 110	76.77 139	70.84 252	87.12 259	47.95 305	96.09 156	85.04 93	74.55 265	89.48 265

HPM-MVS_fast		80.25 186	79.55 183	82.33 234	91.55 125	59.95 323	91.32 213	89.16 291	65.23 329	74.71 198	93.07 134	47.81 307	95.74 172	74.87 189	88.23 121	91.31 236

ab-mvs		80.18 187	78.31 202	85.80 98	88.44 206	65.49 167	83.00 362	92.67 126	71.82 239	77.36 164	85.01 286	54.50 227	96.59 129	76.35 175	75.63 261	95.32 71

IS-MVSNet		80.14 188	79.41 185	82.33 234	87.91 225	60.08 321	91.97 182	88.27 329	72.90 208	71.44 248	91.73 170	61.44 142	93.66 273	62.47 312	86.53 146	93.24 173

test-LLR		80.10 189	79.56 181	81.72 253	86.93 254	61.17 290	92.70 145	91.54 183	71.51 254	75.62 181	86.94 263	53.83 238	92.38 313	72.21 214	84.76 162	91.60 226

PVSNet	73.49 8	80.05 190	78.63 198	84.31 168	90.92 141	64.97 179	92.47 159	91.05 211	79.18 92	72.43 233	90.51 189	37.05 377	94.06 254	68.06 256	86.00 150	93.90 155

UA-Net		80.02 191	79.65 179	81.11 268	89.33 174	57.72 350	86.33 333	89.00 305	77.44 129	81.01 116	89.15 222	59.33 169	95.90 165	61.01 319	84.28 169	89.73 261

test-mter		79.96 192	79.38 188	81.72 253	86.93 254	61.17 290	92.70 145	91.54 183	73.85 186	75.62 181	86.94 263	49.84 283	92.38 313	72.21 214	84.76 162	91.60 226

QAPM		79.95 193	77.39 222	87.64 34	89.63 166	71.41 20	93.30 118	93.70 79	65.34 328	67.39 303	91.75 169	47.83 306	98.96 16	57.71 335	89.81 105	92.54 198

UGNet		79.87 194	78.68 197	83.45 202	89.96 159	61.51 283	92.13 170	90.79 222	76.83 137	78.85 149	86.33 271	38.16 363	96.17 152	67.93 259	87.17 134	92.67 192
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		79.80 195	77.95 209	85.34 116	88.28 214	68.26 84	81.56 373	91.42 189	70.11 276	77.59 162	80.50 348	67.40 62	94.26 245	67.34 266	77.35 248	93.51 166

thres200		79.66 196	78.33 201	83.66 194	92.54 91	65.82 158	93.06 125	96.31 3	74.90 169	73.30 216	88.66 228	59.67 163	95.61 181	47.84 378	78.67 235	89.56 264

CPTT-MVS		79.59 197	79.16 192	80.89 278	91.54 126	59.80 325	92.10 172	88.54 321	60.42 372	72.96 218	93.28 130	48.27 298	92.80 296	78.89 159	86.50 147	90.06 254

Test_1112_low_res		79.56 198	78.60 199	82.43 230	88.24 217	60.39 314	92.09 173	87.99 336	72.10 229	71.84 240	87.42 254	64.62 93	93.04 283	65.80 285	77.30 249	93.85 157

tttt0517		79.50 199	78.53 200	82.41 233	87.22 245	61.43 287	89.75 270	94.76 35	69.29 286	67.91 292	88.06 244	72.92 29	95.63 179	62.91 308	73.90 275	90.16 253

reproduce_monomvs		79.49 200	79.11 194	80.64 280	92.91 78	61.47 286	91.17 223	93.28 98	83.09 30	64.04 333	82.38 316	66.19 71	94.57 228	81.19 136	57.71 390	85.88 329

FIs		79.47 201	79.41 185	79.67 307	85.95 276	59.40 331	91.68 197	93.94 68	78.06 114	68.96 278	88.28 235	66.61 68	91.77 330	66.20 281	74.99 264	87.82 286

mamba_0404		79.46 202	77.65 212	84.91 134	88.37 212	67.04 120	89.59 271	87.03 348	67.99 304	75.45 186	89.32 218	47.98 302	95.34 197	71.23 224	81.90 196	92.34 204

BH-RMVSNet		79.46 202	77.65 212	84.89 135	91.68 121	65.66 159	93.55 105	88.09 334	72.93 205	73.37 215	91.12 181	46.20 323	96.12 154	56.28 341	85.61 156	92.91 187

PCF-MVS	73.15 9	79.29 204	77.63 214	84.29 169	86.06 274	65.96 152	87.03 324	91.10 205	69.86 280	69.79 268	90.64 185	57.54 190	96.59 129	64.37 297	82.29 186	90.32 251
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 205	79.57 180	78.24 328	88.46 205	52.29 386	90.41 249	89.12 295	74.24 177	69.13 271	91.91 166	65.77 78	90.09 359	59.00 331	88.09 123	92.33 205

114514_t		79.17 206	77.67 211	83.68 192	95.32 29	65.53 165	92.85 139	91.60 182	63.49 342	67.92 291	90.63 187	46.65 316	95.72 177	67.01 271	83.54 176	89.79 259

FA-MVS(test-final)		79.12 207	77.23 224	84.81 141	90.54 147	63.98 213	81.35 376	91.71 175	71.09 262	74.85 195	82.94 309	52.85 249	97.05 99	67.97 257	81.73 199	93.41 168

mamba_test_0407		79.09 208	77.21 225	84.75 145	88.50 200	66.98 123	89.21 285	87.03 348	67.99 304	74.12 205	89.32 218	47.98 302	95.29 202	71.23 224	79.52 221	91.98 219

VPA-MVSNet		79.03 209	78.00 207	82.11 247	85.95 276	64.48 192	93.22 121	94.66 41	75.05 167	74.04 209	84.95 287	52.17 256	93.52 275	74.90 188	67.04 321	88.32 282

OPM-MVS		79.00 210	78.09 205	81.73 252	83.52 324	63.83 215	91.64 199	90.30 244	76.36 150	71.97 239	89.93 211	46.30 322	95.17 205	75.10 183	77.70 242	86.19 317
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 211	78.22 204	81.25 262	85.33 287	62.73 254	89.53 277	93.21 100	72.39 220	72.14 236	90.13 207	60.99 145	94.72 221	67.73 261	72.49 284	86.29 314

AdaColmap		78.94 212	77.00 229	84.76 144	96.34 17	65.86 156	92.66 149	87.97 338	62.18 356	70.56 254	92.37 152	43.53 337	97.35 79	64.50 296	82.86 181	91.05 241

GeoE		78.90 213	77.43 218	83.29 207	88.95 187	62.02 269	92.31 162	86.23 359	70.24 275	71.34 249	89.27 220	54.43 231	94.04 257	63.31 304	80.81 211	93.81 158

miper_enhance_ethall		78.86 214	77.97 208	81.54 257	88.00 224	65.17 173	91.41 202	89.15 292	75.19 165	68.79 281	83.98 299	67.17 63	92.82 294	72.73 208	65.30 332	86.62 311

VPNet		78.82 215	77.53 217	82.70 223	84.52 307	66.44 139	93.93 83	92.23 142	80.46 65	72.60 225	88.38 234	49.18 291	93.13 282	72.47 212	63.97 352	88.55 277

EPNet_dtu		78.80 216	79.26 190	77.43 336	88.06 221	49.71 402	91.96 183	91.95 160	77.67 123	76.56 174	91.28 179	58.51 179	90.20 357	56.37 340	80.95 204	92.39 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 217	77.43 218	82.88 218	92.21 97	64.49 190	92.05 176	96.28 4	73.48 195	71.75 242	88.26 237	60.07 158	95.32 198	45.16 391	77.58 244	88.83 270

TR-MVS		78.77 218	77.37 223	82.95 217	90.49 149	60.88 296	93.67 99	90.07 254	70.08 277	74.51 199	91.37 178	45.69 326	95.70 178	60.12 325	80.32 215	92.29 207

thres400		78.68 219	77.43 218	82.43 230	92.21 97	64.49 190	92.05 176	96.28 4	73.48 195	71.75 242	88.26 237	60.07 158	95.32 198	45.16 391	77.58 244	87.48 291

BH-untuned		78.68 219	77.08 226	83.48 201	89.84 161	63.74 218	92.70 145	88.59 319	71.57 251	66.83 310	88.65 229	51.75 260	95.39 193	59.03 330	84.77 161	91.32 235

OMC-MVS		78.67 221	77.91 210	80.95 275	85.76 282	57.40 357	88.49 300	88.67 316	73.85 186	72.43 233	92.10 159	49.29 290	94.55 232	72.73 208	77.89 240	90.91 245

tpm		78.58 222	77.03 227	83.22 211	85.94 278	64.56 188	83.21 358	91.14 204	78.31 111	73.67 213	79.68 360	64.01 102	92.09 324	66.07 282	71.26 294	93.03 183

OpenMVS	70.45 11	78.54 223	75.92 246	86.41 78	85.93 279	71.68 18	92.74 142	92.51 135	66.49 319	64.56 327	91.96 163	43.88 336	98.10 39	54.61 346	90.65 92	89.44 267

EPMVS		78.49 224	75.98 245	86.02 89	91.21 135	69.68 52	80.23 385	91.20 198	75.25 164	72.48 231	78.11 371	54.65 226	93.69 272	57.66 336	83.04 180	94.69 105

AUN-MVS		78.37 225	77.43 218	81.17 264	86.60 261	57.45 356	89.46 279	91.16 200	74.11 179	74.40 200	90.49 190	55.52 217	94.57 228	74.73 190	60.43 381	91.48 229

thres100view900		78.37 225	77.01 228	82.46 229	91.89 115	63.21 240	91.19 222	96.33 1	72.28 223	70.45 257	87.89 246	60.31 153	95.32 198	45.16 391	77.58 244	88.83 270

GA-MVS		78.33 227	76.23 241	84.65 153	83.65 322	66.30 143	91.44 201	90.14 252	76.01 152	70.32 259	84.02 298	42.50 341	94.72 221	70.98 227	77.00 252	92.94 186

cascas		78.18 228	75.77 248	85.41 111	87.14 247	69.11 62	92.96 132	91.15 203	66.71 317	70.47 255	86.07 273	37.49 371	96.48 138	70.15 235	79.80 219	90.65 247

UniMVSNet_NR-MVSNet		78.15 229	77.55 216	79.98 297	84.46 310	60.26 316	92.25 164	93.20 102	77.50 128	68.88 279	86.61 266	66.10 73	92.13 322	66.38 278	62.55 359	87.54 289

LuminaMVS		78.14 230	76.66 233	82.60 227	80.82 350	64.64 187	89.33 281	90.45 232	68.25 302	74.73 197	85.51 282	41.15 347	94.14 248	78.96 157	80.69 213	89.04 268

ICG_test_0404		78.11 231	76.29 240	83.59 195	88.54 195	64.75 183	84.63 341	90.80 218	76.73 141	61.16 355	90.18 198	40.17 351	91.58 336	73.47 196	80.95 204	94.43 124

thres600view7		78.00 232	76.66 233	82.03 249	91.93 111	63.69 224	91.30 214	96.33 1	72.43 218	70.46 256	87.89 246	60.31 153	94.92 214	42.64 403	76.64 255	87.48 291

FC-MVSNet-test		77.99 233	78.08 206	77.70 331	84.89 300	55.51 372	90.27 255	93.75 77	76.87 134	66.80 311	87.59 251	65.71 79	90.23 356	62.89 309	73.94 273	87.37 294

Anonymous202405211		77.96 234	75.33 254	85.87 94	93.73 53	64.52 189	94.85 48	85.36 372	62.52 354	76.11 176	90.18 198	29.43 408	97.29 83	68.51 252	77.24 251	95.81 50

cl22		77.94 235	76.78 231	81.42 259	87.57 235	64.93 181	90.67 240	88.86 309	72.45 217	67.63 298	82.68 313	64.07 100	92.91 292	71.79 217	65.30 332	86.44 312

XXY-MVS		77.94 235	76.44 236	82.43 230	82.60 334	64.44 194	92.01 178	91.83 169	73.59 194	70.00 264	85.82 278	54.43 231	94.76 218	69.63 238	68.02 315	88.10 284

MS-PatchMatch		77.90 237	76.50 235	82.12 244	85.99 275	69.95 42	91.75 195	92.70 122	73.97 183	62.58 350	84.44 294	41.11 348	95.78 169	63.76 301	92.17 66	80.62 394

FMVSNet3		77.73 238	76.04 244	82.80 219	91.20 136	68.99 66	91.87 186	91.99 158	73.35 197	67.04 306	83.19 308	56.62 204	92.14 321	59.80 327	69.34 301	87.28 297

VortexMVS		77.62 239	76.44 236	81.13 266	88.58 194	63.73 220	91.24 217	91.30 196	77.81 119	65.76 316	81.97 322	49.69 285	93.72 270	76.40 174	65.26 335	85.94 327

miper_ehance_all_eth		77.60 240	76.44 236	81.09 272	85.70 284	64.41 197	90.65 241	88.64 318	72.31 221	67.37 304	82.52 314	64.77 92	92.64 305	70.67 231	65.30 332	86.24 316

UniMVSNet (Re)		77.58 241	76.78 231	79.98 297	84.11 316	60.80 297	91.76 193	93.17 104	76.56 147	69.93 267	84.78 289	63.32 118	92.36 315	64.89 294	62.51 361	86.78 305

PatchmatchNet		77.46 242	74.63 261	85.96 91	89.55 169	70.35 35	79.97 390	89.55 275	72.23 224	70.94 250	76.91 383	57.03 194	92.79 297	54.27 348	81.17 202	94.74 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 243	75.65 250	82.73 221	80.38 358	67.13 117	91.85 188	90.23 249	75.09 166	69.37 269	83.39 305	53.79 240	94.44 236	71.77 218	65.00 339	86.63 310

CHOSEN 280x420		77.35 244	76.95 230	78.55 323	87.07 249	62.68 255	69.71 422	82.95 394	68.80 295	71.48 247	87.27 258	66.03 74	84.00 407	76.47 173	82.81 183	88.95 269

PS-MVSNAJss		77.26 245	76.31 239	80.13 292	80.64 354	59.16 336	90.63 244	91.06 210	72.80 209	68.58 285	84.57 292	53.55 242	93.96 262	72.97 202	71.96 288	87.27 298

gg-mvs-nofinetune		77.18 246	74.31 268	85.80 98	91.42 128	68.36 80	71.78 416	94.72 37	49.61 415	77.12 168	45.92 444	77.41 8	93.98 261	67.62 262	93.16 55	95.05 87

WB-MVSnew		77.14 247	76.18 243	80.01 296	86.18 271	63.24 238	91.26 215	94.11 65	71.72 243	73.52 214	87.29 257	45.14 331	93.00 285	56.98 338	79.42 224	83.80 355

MVP-Stereo		77.12 248	76.23 241	79.79 304	81.72 342	66.34 142	89.29 282	90.88 214	70.56 272	62.01 353	82.88 310	49.34 288	94.13 249	65.55 289	93.80 43	78.88 409
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 249	75.37 252	82.20 240	89.25 178	62.11 268	82.06 368	89.09 297	76.77 139	70.84 252	87.12 259	41.43 346	95.01 209	67.23 268	74.55 265	89.48 265

MonoMVSNet		76.99 250	75.08 257	82.73 221	83.32 326	63.24 238	86.47 332	86.37 355	79.08 96	66.31 314	79.30 364	49.80 284	91.72 331	79.37 150	65.70 330	93.23 174

dmvs_re		76.93 251	75.36 253	81.61 255	87.78 232	60.71 304	80.00 389	87.99 336	79.42 85	69.02 275	89.47 215	46.77 314	94.32 239	63.38 303	74.45 268	89.81 258

X-MVStestdata		76.86 252	74.13 274	85.05 128	93.22 66	63.78 216	92.92 134	92.66 127	73.99 181	78.18 154	10.19 459	55.25 218	97.41 75	79.16 153	91.58 77	93.95 149

DU-MVS		76.86 252	75.84 247	79.91 300	82.96 330	60.26 316	91.26 215	91.54 183	76.46 149	68.88 279	86.35 269	56.16 209	92.13 322	66.38 278	62.55 359	87.35 295

Anonymous20240529		76.84 254	74.15 273	84.88 136	91.02 138	64.95 180	93.84 91	91.09 206	53.57 403	73.00 217	87.42 254	35.91 381	97.32 81	69.14 246	72.41 286	92.36 203

UWE-MVS-28		76.83 255	77.60 215	74.51 365	84.58 306	50.34 398	88.22 305	94.60 45	74.46 172	66.66 312	88.98 227	62.53 131	85.50 399	57.55 337	80.80 212	87.69 288

c3_l		76.83 255	75.47 251	80.93 276	85.02 298	64.18 207	90.39 250	88.11 333	71.66 244	66.65 313	81.64 328	63.58 114	92.56 306	69.31 243	62.86 356	86.04 322

WR-MVS		76.76 257	75.74 249	79.82 303	84.60 304	62.27 265	92.60 152	92.51 135	76.06 151	67.87 295	85.34 283	56.76 200	90.24 355	62.20 313	63.69 354	86.94 303

v1144		76.73 258	74.88 258	82.27 236	80.23 362	66.60 136	91.68 197	90.21 251	73.69 191	69.06 274	81.89 323	52.73 252	94.40 237	69.21 244	65.23 336	85.80 330

IterMVS-LS		76.49 259	75.18 256	80.43 284	84.49 309	62.74 253	90.64 242	88.80 311	72.40 219	65.16 322	81.72 326	60.98 146	92.27 319	67.74 260	64.65 344	86.29 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 260	74.55 264	82.19 241	79.14 376	67.82 97	90.26 256	89.42 280	73.75 189	68.63 284	81.89 323	51.31 267	94.09 251	71.69 220	64.84 340	84.66 347

Elysia		76.45 261	74.17 271	83.30 205	80.43 356	64.12 208	89.58 272	90.83 215	61.78 364	72.53 227	85.92 276	34.30 388	94.81 216	68.10 254	84.01 174	90.97 242

StellarMVS		76.45 261	74.17 271	83.30 205	80.43 356	64.12 208	89.58 272	90.83 215	61.78 364	72.53 227	85.92 276	34.30 388	94.81 216	68.10 254	84.01 174	90.97 242

mamba_0408		76.22 263	73.37 285	84.77 142	88.50 200	66.98 123	58.80 443	86.18 361	69.12 291	74.12 205	89.01 225	47.50 309	95.35 195	67.57 263	79.52 221	91.98 219

v148		76.19 264	74.47 266	81.36 260	80.05 364	64.44 194	91.75 195	90.23 249	73.68 192	67.13 305	80.84 343	55.92 214	93.86 269	68.95 248	61.73 370	85.76 333

Effi-MVS+-dtu		76.14 265	75.28 255	78.72 322	83.22 327	55.17 374	89.87 267	87.78 340	75.42 160	67.98 290	81.43 332	45.08 332	92.52 308	75.08 184	71.63 289	88.48 278

cl____		76.07 266	74.67 259	80.28 287	85.15 293	61.76 277	90.12 259	88.73 313	71.16 259	65.43 319	81.57 330	61.15 143	92.95 287	66.54 275	62.17 363	86.13 320

DIV-MVS_self_test		76.07 266	74.67 259	80.28 287	85.14 294	61.75 278	90.12 259	88.73 313	71.16 259	65.42 320	81.60 329	61.15 143	92.94 291	66.54 275	62.16 365	86.14 318

FMVSNet2		76.07 266	74.01 276	82.26 238	88.85 188	67.66 101	91.33 212	91.61 181	70.84 266	65.98 315	82.25 318	48.03 299	92.00 326	58.46 332	68.73 309	87.10 300

v144192		76.05 269	74.03 275	82.12 244	79.50 370	66.55 138	91.39 206	89.71 273	72.30 222	68.17 288	81.33 335	51.75 260	94.03 259	67.94 258	64.19 347	85.77 331

NR-MVSNet		76.05 269	74.59 262	80.44 283	82.96 330	62.18 267	90.83 233	91.73 173	77.12 132	60.96 357	86.35 269	59.28 170	91.80 329	60.74 320	61.34 374	87.35 295

v1192		75.98 271	73.92 277	82.15 242	79.73 366	66.24 145	91.22 219	89.75 267	72.67 211	68.49 286	81.42 333	49.86 282	94.27 243	67.08 270	65.02 338	85.95 325

FE-MVS		75.97 272	73.02 291	84.82 138	89.78 162	65.56 163	77.44 401	91.07 209	64.55 331	72.66 223	79.85 358	46.05 324	96.69 127	54.97 345	80.82 210	92.21 213

eth_miper_zixun_eth		75.96 273	74.40 267	80.66 279	84.66 303	63.02 244	89.28 283	88.27 329	71.88 235	65.73 317	81.65 327	59.45 166	92.81 295	68.13 253	60.53 379	86.14 318

TranMVSNet+NR-MVSNet		75.86 274	74.52 265	79.89 301	82.44 336	60.64 307	91.37 209	91.37 190	76.63 145	67.65 297	86.21 272	52.37 255	91.55 337	61.84 315	60.81 377	87.48 291

SCA		75.82 275	72.76 294	85.01 130	86.63 260	70.08 38	81.06 378	89.19 289	71.60 250	70.01 263	77.09 381	45.53 327	90.25 352	60.43 322	73.27 277	94.68 106

LPG-MVS_test		75.82 275	74.58 263	79.56 311	84.31 313	59.37 332	90.44 247	89.73 270	69.49 283	64.86 323	88.42 232	38.65 357	94.30 241	72.56 210	72.76 281	85.01 344

GBi-Net		75.65 277	73.83 278	81.10 269	88.85 188	65.11 175	90.01 263	90.32 240	70.84 266	67.04 306	80.25 353	48.03 299	91.54 338	59.80 327	69.34 301	86.64 307

test1		75.65 277	73.83 278	81.10 269	88.85 188	65.11 175	90.01 263	90.32 240	70.84 266	67.04 306	80.25 353	48.03 299	91.54 338	59.80 327	69.34 301	86.64 307

v1921920		75.63 279	73.49 283	82.06 248	79.38 371	66.35 141	91.07 227	89.48 276	71.98 230	67.99 289	81.22 338	49.16 293	93.90 265	66.56 274	64.56 345	85.92 328

ACMP	71.68 10	75.58 280	74.23 270	79.62 309	84.97 299	59.64 327	90.80 234	89.07 299	70.39 273	62.95 346	87.30 256	38.28 361	93.87 267	72.89 203	71.45 292	85.36 340
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 281	73.26 289	81.61 255	80.67 353	66.82 129	89.54 276	89.27 285	71.65 245	63.30 341	80.30 352	54.99 224	94.06 254	67.33 267	62.33 362	83.94 353

tpm cat1		75.30 282	72.21 303	84.58 158	88.52 199	67.77 98	78.16 399	88.02 335	61.88 362	68.45 287	76.37 387	60.65 149	94.03 259	53.77 351	74.11 271	91.93 222

PLC	68.80 14	75.23 283	73.68 281	79.86 302	92.93 77	58.68 341	90.64 242	88.30 327	60.90 369	64.43 331	90.53 188	42.38 342	94.57 228	56.52 339	76.54 256	86.33 313
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 284	72.98 292	81.88 250	79.20 373	66.00 150	90.75 236	89.11 296	71.63 249	67.41 302	81.22 338	47.36 311	93.87 267	65.46 290	64.72 343	85.77 331

Fast-Effi-MVS+-dtu		75.04 285	73.37 285	80.07 293	80.86 348	59.52 330	91.20 221	85.38 371	71.90 233	65.20 321	84.84 288	41.46 345	92.97 286	66.50 277	72.96 280	87.73 287

dp		75.01 286	72.09 304	83.76 185	89.28 177	66.22 146	79.96 391	89.75 267	71.16 259	67.80 296	77.19 380	51.81 258	92.54 307	50.39 361	71.44 293	92.51 200

TAPA-MVS	70.22 12	74.94 287	73.53 282	79.17 317	90.40 151	52.07 387	89.19 287	89.61 274	62.69 353	70.07 262	92.67 144	48.89 296	94.32 239	38.26 417	79.97 217	91.12 240
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 288	73.32 288	79.74 306	86.53 263	60.31 315	89.03 292	92.70 122	78.61 107	68.98 277	83.34 306	41.93 344	92.23 320	52.77 355	65.97 328	86.69 306

mamba_test_0407_2		74.86 289	73.37 285	79.35 314	88.50 200	66.98 123	58.80 443	86.18 361	69.12 291	74.12 205	89.01 225	47.50 309	79.09 428	67.57 263	79.52 221	91.98 219

v10		74.77 290	72.54 300	81.46 258	80.33 360	66.71 133	89.15 288	89.08 298	70.94 264	63.08 344	79.86 357	52.52 253	94.04 257	65.70 286	62.17 363	83.64 356

XVG-OURS-SEG-HR		74.70 291	73.08 290	79.57 310	78.25 389	57.33 358	80.49 381	87.32 343	63.22 346	68.76 282	90.12 209	44.89 333	91.59 335	70.55 233	74.09 272	89.79 259

ACMM	69.62 13	74.34 292	72.73 296	79.17 317	84.25 315	57.87 348	90.36 252	89.93 261	63.17 348	65.64 318	86.04 275	37.79 369	94.10 250	65.89 283	71.52 291	85.55 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 293	72.30 302	80.32 285	91.49 127	61.66 280	90.85 232	80.72 400	56.67 395	63.85 336	90.64 185	46.75 315	90.84 346	53.79 350	75.99 260	88.47 279

XVG-OURS		74.25 294	72.46 301	79.63 308	78.45 387	57.59 354	80.33 383	87.39 342	63.86 338	68.76 282	89.62 214	40.50 350	91.72 331	69.00 247	74.25 270	89.58 262

test_fmvs1		74.07 295	73.69 280	75.22 356	78.91 380	47.34 415	89.06 291	74.69 416	63.68 341	79.41 138	91.59 173	24.36 419	87.77 382	85.22 90	76.26 258	90.55 250

CVMVSNet		74.04 296	74.27 269	73.33 375	85.33 287	43.94 429	89.53 277	88.39 323	54.33 402	70.37 258	90.13 207	49.17 292	84.05 405	61.83 316	79.36 226	91.99 218

Baseline_NR-MVSNet		73.99 297	72.83 293	77.48 335	80.78 351	59.29 335	91.79 190	84.55 380	68.85 294	68.99 276	80.70 344	56.16 209	92.04 325	62.67 310	60.98 376	81.11 388

pmmvs4		73.92 298	71.81 308	80.25 289	79.17 374	65.24 171	87.43 320	87.26 346	67.64 310	63.46 339	83.91 300	48.96 295	91.53 341	62.94 307	65.49 331	83.96 352

D2MVS		73.80 299	72.02 305	79.15 319	79.15 375	62.97 245	88.58 299	90.07 254	72.94 204	59.22 367	78.30 368	42.31 343	92.70 301	65.59 288	72.00 287	81.79 383

SD_0403		73.79 300	73.48 284	74.69 362	85.33 287	45.56 425	83.80 348	85.57 370	76.55 148	62.96 345	88.45 231	50.62 274	87.59 386	48.80 371	79.28 230	90.92 244

CR-MVSNet		73.79 300	70.82 316	82.70 223	83.15 328	67.96 93	70.25 419	84.00 385	73.67 193	69.97 265	72.41 404	57.82 187	89.48 365	52.99 354	73.13 278	90.64 248

test_djsdf		73.76 302	72.56 299	77.39 337	77.00 401	53.93 380	89.07 289	90.69 224	65.80 323	63.92 334	82.03 321	43.14 340	92.67 302	72.83 204	68.53 310	85.57 335

pmmvs5		73.35 303	71.52 310	78.86 321	78.64 384	60.61 308	91.08 225	86.90 350	67.69 307	63.32 340	83.64 301	44.33 335	90.53 349	62.04 314	66.02 327	85.46 338

Anonymous20231211		73.08 304	70.39 320	81.13 266	90.62 146	63.33 235	91.40 204	90.06 256	51.84 408	64.46 330	80.67 346	36.49 379	94.07 253	63.83 300	64.17 348	85.98 324

tt0805		73.07 305	70.73 317	80.07 293	78.37 388	57.05 360	87.78 314	92.18 149	61.23 368	67.04 306	86.49 268	31.35 401	94.58 226	65.06 293	67.12 320	88.57 276

miper_lstm_enhance		73.05 306	71.73 309	77.03 342	83.80 319	58.32 345	81.76 369	88.88 307	69.80 281	61.01 356	78.23 370	57.19 192	87.51 387	65.34 291	59.53 384	85.27 343

jajsoiax		73.05 306	71.51 311	77.67 332	77.46 398	54.83 376	88.81 295	90.04 257	69.13 290	62.85 348	83.51 303	31.16 402	92.75 298	70.83 228	69.80 297	85.43 339

LCM-MVSNet-Re		72.93 308	71.84 307	76.18 351	88.49 203	48.02 410	80.07 388	70.17 431	73.96 184	52.25 401	80.09 356	49.98 280	88.24 376	67.35 265	84.23 170	92.28 208

pm-mvs1		72.89 309	71.09 313	78.26 327	79.10 377	57.62 352	90.80 234	89.30 284	67.66 308	62.91 347	81.78 325	49.11 294	92.95 287	60.29 324	58.89 387	84.22 351

tpmvs		72.88 310	69.76 326	82.22 239	90.98 139	67.05 119	78.22 398	88.30 327	63.10 349	64.35 332	74.98 394	55.09 223	94.27 243	43.25 397	69.57 300	85.34 341

test0.0.03 1		72.76 311	72.71 297	72.88 379	80.25 361	47.99 411	91.22 219	89.45 278	71.51 254	62.51 351	87.66 249	53.83 238	85.06 401	50.16 363	67.84 318	85.58 334

UniMVSNet_ETH3D		72.74 312	70.53 319	79.36 313	78.62 385	56.64 364	85.01 338	89.20 288	63.77 339	64.84 325	84.44 294	34.05 390	91.86 328	63.94 299	70.89 296	89.57 263

mvs_tets		72.71 313	71.11 312	77.52 333	77.41 399	54.52 378	88.45 301	89.76 266	68.76 297	62.70 349	83.26 307	29.49 407	92.71 299	70.51 234	69.62 299	85.34 341

FMVSNet1		72.71 313	69.91 324	81.10 269	83.60 323	65.11 175	90.01 263	90.32 240	63.92 337	63.56 338	80.25 353	36.35 380	91.54 338	54.46 347	66.75 323	86.64 307

test_fmvs1_n		72.69 315	71.92 306	74.99 360	71.15 421	47.08 417	87.34 322	75.67 411	63.48 343	78.08 156	91.17 180	20.16 433	87.87 379	84.65 99	75.57 262	90.01 256

IterMVS		72.65 316	70.83 314	78.09 329	82.17 338	62.96 246	87.64 318	86.28 357	71.56 252	60.44 360	78.85 366	45.42 329	86.66 391	63.30 305	61.83 367	84.65 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 317	72.74 295	72.10 387	87.87 227	49.45 404	88.07 307	89.01 301	72.91 206	63.11 342	88.10 241	63.63 109	85.54 396	32.73 432	69.23 304	81.32 386

PatchMatch-RL		72.06 318	69.98 321	78.28 326	89.51 170	55.70 371	83.49 351	83.39 392	61.24 367	63.72 337	82.76 311	34.77 385	93.03 284	53.37 353	77.59 243	86.12 321

PVSNet_0	68.08 15	71.81 319	68.32 335	82.27 236	84.68 301	62.31 264	88.68 297	90.31 243	75.84 153	57.93 379	80.65 347	37.85 368	94.19 246	69.94 236	29.05 447	90.31 252

MIMVSNet		71.64 320	68.44 333	81.23 263	81.97 341	64.44 194	73.05 413	88.80 311	69.67 282	64.59 326	74.79 396	32.79 393	87.82 380	53.99 349	76.35 257	91.42 230

test_vis1_n		71.63 321	70.73 317	74.31 369	69.63 427	47.29 416	86.91 326	72.11 424	63.21 347	75.18 190	90.17 204	20.40 431	85.76 395	84.59 100	74.42 269	89.87 257

IterMVS-SCA-FT		71.55 322	69.97 322	76.32 349	81.48 344	60.67 306	87.64 318	85.99 364	66.17 321	59.50 365	78.88 365	45.53 327	83.65 409	62.58 311	61.93 366	84.63 350

v7n		71.31 323	68.65 330	79.28 315	76.40 403	60.77 299	86.71 330	89.45 278	64.17 336	58.77 372	78.24 369	44.59 334	93.54 274	57.76 334	61.75 369	83.52 359

anonymousdsp		71.14 324	69.37 328	76.45 348	72.95 416	54.71 377	84.19 345	88.88 307	61.92 361	62.15 352	79.77 359	38.14 364	91.44 343	68.90 249	67.45 319	83.21 365

F-COLMAP		70.66 325	68.44 333	77.32 338	86.37 268	55.91 369	88.00 309	86.32 356	56.94 393	57.28 383	88.07 243	33.58 391	92.49 309	51.02 358	68.37 311	83.55 357

WR-MVS_H		70.59 326	69.94 323	72.53 381	81.03 347	51.43 391	87.35 321	92.03 157	67.38 311	60.23 362	80.70 344	55.84 215	83.45 411	46.33 386	58.58 389	82.72 372

CP-MVSNet		70.50 327	69.91 324	72.26 384	80.71 352	51.00 395	87.23 323	90.30 244	67.84 306	59.64 364	82.69 312	50.23 278	82.30 419	51.28 357	59.28 385	83.46 361

RPMNet		70.42 328	65.68 349	84.63 156	83.15 328	67.96 93	70.25 419	90.45 232	46.83 424	69.97 265	65.10 427	56.48 208	95.30 201	35.79 422	73.13 278	90.64 248

testing3		70.38 329	70.83 314	69.03 399	85.82 281	43.93 430	90.72 239	90.56 231	68.06 303	60.24 361	86.82 265	64.83 90	84.12 403	26.33 440	64.10 349	79.04 407

tfpnnormal		70.10 330	67.36 339	78.32 325	83.45 325	60.97 295	88.85 293	92.77 120	64.85 330	60.83 358	78.53 367	43.52 338	93.48 276	31.73 435	61.70 371	80.52 395

TransMVSNet (Re)		70.07 331	67.66 337	77.31 339	80.62 355	59.13 337	91.78 192	84.94 376	65.97 322	60.08 363	80.44 349	50.78 271	91.87 327	48.84 370	45.46 420	80.94 390

CL-MVSNet_self_test		69.92 332	68.09 336	75.41 354	73.25 415	55.90 370	90.05 262	89.90 262	69.96 278	61.96 354	76.54 384	51.05 270	87.64 383	49.51 367	50.59 410	82.70 374

DP-MVS		69.90 333	66.48 341	80.14 291	95.36 28	62.93 247	89.56 274	76.11 409	50.27 414	57.69 381	85.23 284	39.68 353	95.73 173	33.35 427	71.05 295	81.78 384

PS-CasMVS		69.86 334	69.13 329	72.07 388	80.35 359	50.57 397	87.02 325	89.75 267	67.27 312	59.19 368	82.28 317	46.58 317	82.24 420	50.69 360	59.02 386	83.39 363

Syy-MVS		69.65 335	69.52 327	70.03 395	87.87 227	43.21 431	88.07 307	89.01 301	72.91 206	63.11 342	88.10 241	45.28 330	85.54 396	22.07 445	69.23 304	81.32 386

MSDG		69.54 336	65.73 348	80.96 274	85.11 296	63.71 222	84.19 345	83.28 393	56.95 392	54.50 390	84.03 297	31.50 399	96.03 162	42.87 401	69.13 306	83.14 367

PEN-MVS		69.46 337	68.56 331	72.17 386	79.27 372	49.71 402	86.90 327	89.24 286	67.24 315	59.08 369	82.51 315	47.23 312	83.54 410	48.42 373	57.12 391	83.25 364

LS3D		69.17 338	66.40 343	77.50 334	91.92 112	56.12 367	85.12 337	80.37 402	46.96 422	56.50 385	87.51 253	37.25 372	93.71 271	32.52 434	79.40 225	82.68 375

PatchT		69.11 339	65.37 353	80.32 285	82.07 340	63.68 225	67.96 429	87.62 341	50.86 412	69.37 269	65.18 426	57.09 193	88.53 372	41.59 406	66.60 324	88.74 273

KD-MVS_2432*1600		69.03 340	66.37 344	77.01 343	85.56 285	61.06 293	81.44 374	90.25 247	67.27 312	58.00 377	76.53 385	54.49 228	87.63 384	48.04 375	35.77 438	82.34 378

miper_refine_blended		69.03 340	66.37 344	77.01 343	85.56 285	61.06 293	81.44 374	90.25 247	67.27 312	58.00 377	76.53 385	54.49 228	87.63 384	48.04 375	35.77 438	82.34 378

mvsany_test1		68.77 342	68.56 331	69.39 397	73.57 414	45.88 424	80.93 379	60.88 445	59.65 378	71.56 245	90.26 197	43.22 339	75.05 432	74.26 193	62.70 358	87.25 299

ACMH	63.93 17	68.62 343	64.81 355	80.03 295	85.22 292	63.25 237	87.72 315	84.66 378	60.83 370	51.57 405	79.43 363	27.29 414	94.96 211	41.76 404	64.84 340	81.88 382
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 344	65.41 352	77.96 330	78.69 383	62.93 247	89.86 268	89.17 290	60.55 371	50.27 410	77.73 375	22.60 427	94.06 254	47.18 382	72.65 283	76.88 420

ADS-MVSNet		68.54 345	64.38 362	81.03 273	88.06 221	66.90 128	68.01 427	84.02 384	57.57 386	64.48 328	69.87 414	38.68 355	89.21 367	40.87 408	67.89 316	86.97 301

DTE-MVSNet		68.46 346	67.33 340	71.87 390	77.94 393	49.00 408	86.16 334	88.58 320	66.36 320	58.19 374	82.21 319	46.36 318	83.87 408	44.97 394	55.17 398	82.73 371

mmtdpeth		68.33 347	66.37 344	74.21 370	82.81 333	51.73 388	84.34 343	80.42 401	67.01 316	71.56 245	68.58 418	30.52 405	92.35 316	75.89 177	36.21 436	78.56 414

our_test_3		68.29 348	64.69 357	79.11 320	78.92 378	64.85 182	88.40 302	85.06 374	60.32 374	52.68 399	76.12 389	40.81 349	89.80 364	44.25 396	55.65 396	82.67 376

Patchmatch-RL test		68.17 349	64.49 360	79.19 316	71.22 420	53.93 380	70.07 421	71.54 428	69.22 287	56.79 384	62.89 431	56.58 205	88.61 369	69.53 240	52.61 405	95.03 89

XVG-ACMP-BASELINE		68.04 350	65.53 351	75.56 353	74.06 413	52.37 385	78.43 395	85.88 365	62.03 359	58.91 371	81.21 340	20.38 432	91.15 345	60.69 321	68.18 312	83.16 366

FMVSNet5		68.04 350	65.66 350	75.18 358	84.43 311	57.89 347	83.54 350	86.26 358	61.83 363	53.64 396	73.30 399	37.15 375	85.08 400	48.99 369	61.77 368	82.56 377

ppachtmachnet_test		67.72 352	63.70 364	79.77 305	78.92 378	66.04 149	88.68 297	82.90 395	60.11 376	55.45 387	75.96 390	39.19 354	90.55 348	39.53 412	52.55 406	82.71 373

ACMH+	65.35 16	67.65 353	64.55 358	76.96 345	84.59 305	57.10 359	88.08 306	80.79 399	58.59 384	53.00 398	81.09 342	26.63 416	92.95 287	46.51 384	61.69 372	80.82 391

pmmvs6		67.57 354	64.76 356	76.00 352	72.82 418	53.37 382	88.71 296	86.78 354	53.19 404	57.58 382	78.03 372	35.33 384	92.41 312	55.56 343	54.88 400	82.21 380

Anonymous20231206		67.53 355	65.78 347	72.79 380	74.95 409	47.59 413	88.23 304	87.32 343	61.75 366	58.07 376	77.29 378	37.79 369	87.29 389	42.91 399	63.71 353	83.48 360

Patchmtry		67.53 355	63.93 363	78.34 324	82.12 339	64.38 198	68.72 424	84.00 385	48.23 421	59.24 366	72.41 404	57.82 187	89.27 366	46.10 387	56.68 395	81.36 385

USDC		67.43 357	64.51 359	76.19 350	77.94 393	55.29 373	78.38 396	85.00 375	73.17 199	48.36 418	80.37 350	21.23 429	92.48 310	52.15 356	64.02 351	80.81 392

ADS-MVSNet2		66.90 358	63.44 366	77.26 340	88.06 221	60.70 305	68.01 427	75.56 413	57.57 386	64.48 328	69.87 414	38.68 355	84.10 404	40.87 408	67.89 316	86.97 301

CMPMVS	48.56 21	66.77 359	64.41 361	73.84 372	70.65 424	50.31 399	77.79 400	85.73 368	45.54 427	44.76 428	82.14 320	35.40 383	90.14 358	63.18 306	74.54 267	81.07 389
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 360	62.92 369	76.80 347	76.51 402	57.77 349	89.22 284	83.41 391	55.48 399	53.86 394	77.84 373	26.28 417	93.95 263	34.90 424	68.76 308	78.68 412

LTVRE_ROB	59.60 19	66.27 361	63.54 365	74.45 366	84.00 318	51.55 390	67.08 431	83.53 389	58.78 382	54.94 389	80.31 351	34.54 386	93.23 280	40.64 410	68.03 314	78.58 413
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		66.06 362	62.45 372	76.88 346	81.42 346	54.45 379	57.49 445	88.67 316	49.36 416	63.86 335	46.86 443	56.06 212	90.25 352	49.53 366	68.83 307	85.95 325

Patchmatch-test		65.86 363	60.94 378	80.62 282	83.75 320	58.83 339	58.91 442	75.26 415	44.50 430	50.95 409	77.09 381	58.81 177	87.90 378	35.13 423	64.03 350	95.12 83

UnsupCasMVSNet_eth		65.79 364	63.10 367	73.88 371	70.71 423	50.29 400	81.09 377	89.88 263	72.58 213	49.25 415	74.77 397	32.57 395	87.43 388	55.96 342	41.04 428	83.90 354

test_fmvs2		65.78 365	64.84 354	68.60 401	66.54 433	41.71 433	83.27 355	69.81 432	54.38 401	67.91 292	84.54 293	15.35 438	81.22 424	75.65 179	66.16 326	82.88 368

dmvs_testset		65.55 366	66.45 342	62.86 413	79.87 365	22.35 459	76.55 403	71.74 426	77.42 131	55.85 386	87.77 248	51.39 266	80.69 425	31.51 438	65.92 329	85.55 336

pmmvs-eth3d		65.53 367	62.32 373	75.19 357	69.39 428	59.59 328	82.80 363	83.43 390	62.52 354	51.30 407	72.49 402	32.86 392	87.16 390	55.32 344	50.73 409	78.83 410

mamv4		65.18 368	67.43 338	58.44 417	77.88 395	49.36 407	69.40 423	70.99 430	48.31 420	57.78 380	85.53 281	59.01 175	51.88 455	73.67 195	64.32 346	74.07 425

SixPastTwentyTwo		64.92 369	61.78 376	74.34 368	78.74 382	49.76 401	83.42 354	79.51 405	62.86 350	50.27 410	77.35 376	30.92 404	90.49 350	45.89 388	47.06 415	82.78 369

OurMVSNet-221017-0		64.68 370	62.17 374	72.21 385	76.08 406	47.35 414	80.67 380	81.02 398	56.19 396	51.60 404	79.66 361	27.05 415	88.56 371	53.60 352	53.63 403	80.71 393

test_0402		64.54 371	61.09 377	74.92 361	84.10 317	60.75 301	87.95 310	79.71 404	52.03 406	52.41 400	77.20 379	32.21 397	91.64 333	23.14 443	61.03 375	72.36 431

testgi		64.48 372	62.87 370	69.31 398	71.24 419	40.62 436	85.49 335	79.92 403	65.36 327	54.18 392	83.49 304	23.74 422	84.55 402	41.60 405	60.79 378	82.77 370

RPSCF		64.24 373	61.98 375	71.01 393	76.10 405	45.00 426	75.83 408	75.94 410	46.94 423	58.96 370	84.59 291	31.40 400	82.00 421	47.76 380	60.33 383	86.04 322

EU-MVSNet		64.01 374	63.01 368	67.02 407	74.40 412	38.86 442	83.27 355	86.19 360	45.11 428	54.27 391	81.15 341	36.91 378	80.01 427	48.79 372	57.02 392	82.19 381

test20.03		63.83 375	62.65 371	67.38 406	70.58 425	39.94 438	86.57 331	84.17 382	63.29 345	51.86 403	77.30 377	37.09 376	82.47 417	38.87 416	54.13 402	79.73 401

sc_t1		63.81 376	59.39 384	77.10 341	77.62 396	56.03 368	84.32 344	73.56 420	46.66 425	58.22 373	73.06 400	23.28 425	90.62 347	50.93 359	46.84 416	84.64 349

MDA-MVSNet_test_wron		63.78 377	60.16 380	74.64 363	78.15 391	60.41 312	83.49 351	84.03 383	56.17 398	39.17 438	71.59 410	37.22 373	83.24 414	42.87 401	48.73 412	80.26 398

YYNet1		63.76 378	60.14 381	74.62 364	78.06 392	60.19 319	83.46 353	83.99 387	56.18 397	39.25 437	71.56 411	37.18 374	83.34 412	42.90 400	48.70 413	80.32 397

K. test v3		63.09 379	59.61 383	73.53 374	76.26 404	49.38 406	83.27 355	77.15 408	64.35 333	47.77 420	72.32 406	28.73 409	87.79 381	49.93 365	36.69 435	83.41 362

COLMAP_ROB	57.96 20	62.98 380	59.65 382	72.98 378	81.44 345	53.00 384	83.75 349	75.53 414	48.34 419	48.81 417	81.40 334	24.14 420	90.30 351	32.95 429	60.52 380	75.65 423
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 381	59.08 385	71.10 392	67.19 431	48.72 409	83.91 347	85.23 373	50.38 413	47.84 419	71.22 413	20.74 430	85.51 398	46.47 385	58.75 388	79.06 406

tt0320		61.85 382	57.45 391	75.03 359	77.49 397	57.60 353	82.74 364	73.65 419	43.65 434	53.65 395	68.18 420	25.47 418	88.66 368	45.56 390	46.68 417	78.81 411

AllTest		61.66 383	58.06 387	72.46 382	79.57 367	51.42 392	80.17 386	68.61 434	51.25 410	45.88 422	81.23 336	19.86 434	86.58 392	38.98 414	57.01 393	79.39 403

UnsupCasMVSNet_bld		61.60 384	57.71 388	73.29 376	68.73 429	51.64 389	78.61 394	89.05 300	57.20 391	46.11 421	61.96 434	28.70 410	88.60 370	50.08 364	38.90 433	79.63 402

MDA-MVSNet-bldmvs		61.54 385	57.70 389	73.05 377	79.53 369	57.00 363	83.08 359	81.23 397	57.57 386	34.91 442	72.45 403	32.79 393	86.26 394	35.81 421	41.95 426	75.89 422

tt0320-xc		61.51 386	56.89 394	75.37 355	78.50 386	58.61 342	82.61 365	71.27 429	44.31 431	53.17 397	68.03 422	23.38 423	88.46 373	47.77 379	43.00 425	79.03 408

mvs5depth		61.03 387	57.65 390	71.18 391	67.16 432	47.04 419	72.74 414	77.49 406	57.47 389	60.52 359	72.53 401	22.84 426	88.38 374	49.15 368	38.94 432	78.11 417

KD-MVS_self_test		60.87 388	58.60 386	67.68 404	66.13 434	39.93 439	75.63 410	84.70 377	57.32 390	49.57 413	68.45 419	29.55 406	82.87 415	48.09 374	47.94 414	80.25 399

kuosan		60.86 389	60.24 379	62.71 414	81.57 343	46.43 421	75.70 409	85.88 365	57.98 385	48.95 416	69.53 416	58.42 180	76.53 430	28.25 439	35.87 437	65.15 438

TinyColmap		60.32 390	56.42 397	72.00 389	78.78 381	53.18 383	78.36 397	75.64 412	52.30 405	41.59 436	75.82 392	14.76 441	88.35 375	35.84 420	54.71 401	74.46 424

MVS-HIRNet		60.25 391	55.55 398	74.35 367	84.37 312	56.57 365	71.64 417	74.11 417	34.44 441	45.54 426	42.24 449	31.11 403	89.81 362	40.36 411	76.10 259	76.67 421

MIMVSNet1		60.16 392	57.33 392	68.67 400	69.71 426	44.13 428	78.92 393	84.21 381	55.05 400	44.63 429	71.85 408	23.91 421	81.54 423	32.63 433	55.03 399	80.35 396

PM-MVS		59.40 393	56.59 395	67.84 402	63.63 437	41.86 432	76.76 402	63.22 442	59.01 381	51.07 408	72.27 407	11.72 445	83.25 413	61.34 317	50.28 411	78.39 415

new-patchmatchnet		59.30 394	56.48 396	67.79 403	65.86 435	44.19 427	82.47 366	81.77 396	59.94 377	43.65 432	66.20 425	27.67 413	81.68 422	39.34 413	41.40 427	77.50 419

test_vis1_rt		59.09 395	57.31 393	64.43 410	68.44 430	46.02 423	83.05 361	48.63 454	51.96 407	49.57 413	63.86 430	16.30 436	80.20 426	71.21 226	62.79 357	67.07 437

test_fmvs3		56.82 396	54.86 400	62.69 415	53.59 448	35.47 445	75.87 407	65.64 439	43.91 432	55.10 388	71.43 412	6.91 453	74.40 435	68.64 251	52.63 404	78.20 416

DSMNet-mixed		56.78 397	54.44 401	63.79 411	63.21 438	29.44 454	64.43 434	64.10 441	42.12 438	51.32 406	71.60 409	31.76 398	75.04 433	36.23 419	65.20 337	86.87 304

pmmvs3		55.51 398	51.50 404	67.53 405	57.90 446	50.93 396	80.37 382	73.66 418	40.63 439	44.15 431	64.75 428	16.30 436	78.97 429	44.77 395	40.98 430	72.69 429

TDRefinement		55.28 399	51.58 403	66.39 408	59.53 445	46.15 422	76.23 405	72.80 421	44.60 429	42.49 434	76.28 388	15.29 439	82.39 418	33.20 428	43.75 422	70.62 433

dongtai		55.18 400	55.46 399	54.34 425	76.03 407	36.88 443	76.07 406	84.61 379	51.28 409	43.41 433	64.61 429	56.56 206	67.81 443	18.09 448	28.50 448	58.32 441

LF4IMVS		54.01 401	52.12 402	59.69 416	62.41 440	39.91 440	68.59 425	68.28 436	42.96 436	44.55 430	75.18 393	14.09 443	68.39 442	41.36 407	51.68 407	70.78 432

ttmdpeth		53.34 402	49.96 405	63.45 412	62.07 442	40.04 437	72.06 415	65.64 439	42.54 437	51.88 402	77.79 374	13.94 444	76.48 431	32.93 430	30.82 446	73.84 426

MVStest1		51.35 403	46.89 407	64.74 409	65.06 436	51.10 394	67.33 430	72.58 422	30.20 445	35.30 440	74.82 395	27.70 412	69.89 440	24.44 442	24.57 449	73.22 427

N_pmnet		50.55 404	49.11 406	54.88 423	77.17 400	4.02 467	84.36 342	2.00 465	48.59 417	45.86 424	68.82 417	32.22 396	82.80 416	31.58 436	51.38 408	77.81 418

new_pmnet		49.31 405	46.44 408	57.93 418	62.84 439	40.74 435	68.47 426	62.96 443	36.48 440	35.09 441	57.81 438	14.97 440	72.18 437	32.86 431	46.44 418	60.88 440

mvsany_test3		48.86 406	46.35 409	56.41 419	46.00 454	31.67 450	62.26 436	47.25 455	43.71 433	45.54 426	68.15 421	10.84 446	64.44 451	57.95 333	35.44 440	73.13 428

test_f		46.58 407	43.45 411	55.96 420	45.18 455	32.05 449	61.18 437	49.49 453	33.39 442	42.05 435	62.48 433	7.00 452	65.56 447	47.08 383	43.21 424	70.27 434

WB-MVS		46.23 408	44.94 410	50.11 428	62.13 441	21.23 461	76.48 404	55.49 447	45.89 426	35.78 439	61.44 436	35.54 382	72.83 436	9.96 455	21.75 450	56.27 443

FPMVS		45.64 409	43.10 413	53.23 426	51.42 451	36.46 444	64.97 433	71.91 425	29.13 446	27.53 446	61.55 435	9.83 448	65.01 449	16.00 452	55.58 397	58.22 442

SSC-MVS		44.51 410	43.35 412	47.99 432	61.01 444	18.90 463	74.12 412	54.36 448	43.42 435	34.10 443	60.02 437	34.42 387	70.39 439	9.14 457	19.57 451	54.68 444

EGC-MVSNET		42.35 411	38.09 414	55.11 422	74.57 410	46.62 420	71.63 418	55.77 446	0.04 460	0.24 461	62.70 432	14.24 442	74.91 434	17.59 449	46.06 419	43.80 446

LCM-MVSNet		40.54 412	35.79 417	54.76 424	36.92 461	30.81 451	51.41 448	69.02 433	22.07 448	24.63 448	45.37 445	4.56 457	65.81 446	33.67 426	34.50 441	67.67 435

APD_test1		40.50 413	37.31 416	50.09 429	51.88 449	35.27 446	59.45 441	52.59 450	21.64 449	26.12 447	57.80 439	4.56 457	66.56 445	22.64 444	39.09 431	48.43 445

test_vis3_rt		40.46 414	37.79 415	48.47 431	44.49 456	33.35 448	66.56 432	32.84 462	32.39 443	29.65 444	39.13 452	3.91 460	68.65 441	50.17 362	40.99 429	43.40 447

ANet_high		40.27 415	35.20 418	55.47 421	34.74 462	34.47 447	63.84 435	71.56 427	48.42 418	18.80 451	41.08 450	9.52 449	64.45 450	20.18 446	8.66 458	67.49 436

test_method		38.59 416	35.16 419	48.89 430	54.33 447	21.35 460	45.32 451	53.71 449	7.41 457	28.74 445	51.62 441	8.70 450	52.87 454	33.73 425	32.89 442	72.47 430

PMMVS2		37.93 417	33.61 420	50.92 427	46.31 453	24.76 457	60.55 440	50.05 451	28.94 447	20.93 449	47.59 442	4.41 459	65.13 448	25.14 441	18.55 453	62.87 439

Gipuma		34.91 418	31.44 421	45.30 433	70.99 422	39.64 441	19.85 455	72.56 423	20.10 451	16.16 455	21.47 456	5.08 456	71.16 438	13.07 453	43.70 423	25.08 453
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 419	29.47 422	42.67 435	41.89 458	30.81 451	52.07 446	43.45 456	15.45 452	18.52 452	44.82 446	2.12 461	58.38 452	16.05 450	30.87 444	38.83 448

APD_test2		32.77 419	29.47 422	42.67 435	41.89 458	30.81 451	52.07 446	43.45 456	15.45 452	18.52 452	44.82 446	2.12 461	58.38 452	16.05 450	30.87 444	38.83 448

PMVS	26.43 22	31.84 421	28.16 424	42.89 434	25.87 464	27.58 455	50.92 449	49.78 452	21.37 450	14.17 456	40.81 451	2.01 463	66.62 444	9.61 456	38.88 434	34.49 452
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 422	24.00 426	26.45 439	43.74 457	18.44 464	60.86 438	39.66 458	15.11 454	9.53 458	22.10 455	6.52 454	46.94 457	8.31 458	10.14 455	13.98 455

MVE	24.84 23	24.35 423	19.77 429	38.09 437	34.56 463	26.92 456	26.57 453	38.87 460	11.73 456	11.37 457	27.44 453	1.37 464	50.42 456	11.41 454	14.60 454	36.93 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 424	23.20 428	25.46 440	41.52 460	16.90 465	60.56 439	38.79 461	14.62 455	8.99 459	20.24 458	7.35 451	45.82 458	7.25 459	9.46 456	13.64 456

tmp_tt		22.26 425	23.75 427	17.80 441	5.23 465	12.06 466	35.26 452	39.48 459	2.82 459	18.94 450	44.20 448	22.23 428	24.64 460	36.30 418	9.31 457	16.69 454

cdsmvs_eth3d_5k		19.86 426	26.47 425	0.00 445	0.00 468	0.00 470	0.00 456	93.45 91	0.00 463	0.00 464	95.27 70	49.56 286	0.00 464	0.00 463	0.00 461	0.00 460

wuyk23d		11.30 427	10.95 430	12.33 442	48.05 452	19.89 462	25.89 454	1.92 466	3.58 458	3.12 460	1.37 460	0.64 465	15.77 461	6.23 460	7.77 459	1.35 457

ab-mvs-re		7.91 428	10.55 431	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	94.95 80	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

testmvs		7.23 429	9.62 432	0.06 444	0.04 466	0.02 469	84.98 339	0.02 467	0.03 461	0.18 462	1.21 461	0.01 467	0.02 462	0.14 461	0.01 460	0.13 459

test123		6.92 430	9.21 433	0.08 443	0.03 467	0.05 468	81.65 372	0.01 468	0.02 462	0.14 463	0.85 462	0.03 466	0.02 462	0.12 462	0.00 461	0.16 458

pcd_1.5k_mvsjas		4.46 431	5.95 434	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	53.55 242	0.00 464	0.00 463	0.00 461	0.00 460

mmdepth		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

monomultidepth		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

test_blank		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

uanet_test		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

DCPMVS		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

sosnet-low-res		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

sosnet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

uncertanet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

Regformer		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

uanet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 461	0.00 460

WAC-MVS						49.45 404								31.56 437

FOURS1					93.95 46	61.77 276	93.96 81	91.92 161	62.14 358	86.57 55

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

PC_three_1452									80.91 61	94.07 2	96.83 22	83.57 4	99.12 5	95.70 9	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 62	71.42 256	90.67 23	96.85 20	74.45 20

eth-test2					0.00 468

eth-test					0.00 468

ZD-MVS					96.63 9	65.50 166		93.50 89	70.74 270	85.26 73	95.19 76	64.92 89	97.29 83	87.51 67	93.01 56

RE-MVS-def			80.48 165		92.02 105	58.56 343	90.90 229	90.45 232	62.76 351	78.89 144	94.46 94	49.30 289		78.77 160	86.77 141	92.28 208

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 62	95.28 1				92.34 32	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 12	83.82 2	99.15 2	95.72 7	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 245	92.07 10	97.21 7	74.58 18	99.11 6	92.34 32	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 51	71.65 245	92.11 8	97.05 10	76.79 9	99.11 6

9.14			87.63 33		93.86 48		94.41 59	94.18 62	72.76 210	86.21 58	96.51 29	66.64 67	97.88 47	90.08 49	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 127	78.19 112

test_0728_THIRD									72.48 215	90.55 24	96.93 14	76.24 11	99.08 11	91.53 40	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 57					99.15 2	91.91 38	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 231	91.89 12	97.11 9	73.77 23

GSMVS																94.68 106

test_part2					96.29 19	68.16 89				90.78 21

sam_mvs1												57.85 186				94.68 106

sam_mvs												54.91 225

ambc				69.61 396	61.38 443	41.35 434	49.07 450	85.86 367		50.18 412	66.40 424	10.16 447	88.14 377	45.73 389	44.20 421	79.32 405

MTGPA								92.23 142

test_post1							78.95 392				20.70 457	53.05 247	91.50 342	60.43 322

test_post											23.01 454	56.49 207	92.67 302

patchmatchnet-post											67.62 423	57.62 189	90.25 352

GG-mvs-BLEND				86.53 74	91.91 114	69.67 53	75.02 411	94.75 36		78.67 152	90.85 184	77.91 7	94.56 231	72.25 213	93.74 45	95.36 68

MTMP							93.77 95	32.52 463

gm-plane-assit					88.42 208	67.04 120			78.62 106		91.83 167		97.37 77	76.57 172

test9_res														89.41 50	94.96 19	95.29 73

TEST9					94.18 41	67.28 111	94.16 68	93.51 87	71.75 242	85.52 68	95.33 64	68.01 57	97.27 87

test_8					94.19 40	67.19 113	94.15 70	93.42 94	71.87 236	85.38 71	95.35 63	68.19 55	96.95 113

agg_prior2														86.41 80	94.75 30	95.33 69

agg_prior					94.16 43	66.97 126		93.31 97		84.49 79			96.75 125

TestCases				72.46 382	79.57 367	51.42 392		68.61 434	51.25 410	45.88 422	81.23 336	19.86 434	86.58 392	38.98 414	57.01 393	79.39 403

test_prior4						67.18 115	93.92 84

test_prior2							95.10 38		75.40 161	85.25 74	95.61 55	67.94 58		87.47 69	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 108					96.84 122			95.05 87

旧先验2							92.00 181		59.37 380	87.54 48			93.47 277	75.39 181

新几何2							91.41 202

新几何1				84.73 146	92.32 93	64.28 203		91.46 188	59.56 379	79.77 132	92.90 138	56.95 199	96.57 131	63.40 302	92.91 58	93.34 170

旧先验1					91.94 110	60.74 302		91.50 186			94.36 98	65.23 84			91.84 72	94.55 113

无先验							92.71 144	92.61 132	62.03 359				97.01 103	66.63 273		93.97 148

原ACMM2							92.01 178

原ACMM1				84.42 163	93.21 68	64.27 204		93.40 96	65.39 326	79.51 136	92.50 146	58.11 185	96.69 127	65.27 292	93.96 40	92.32 206

test222					89.77 163	61.60 282	89.55 275	89.42 280	56.83 394	77.28 166	92.43 150	52.76 250			91.14 88	93.09 180

testdata2													96.09 156	61.26 318

segment_acmp												65.94 75

testdata				81.34 261	89.02 185	57.72 350		89.84 264	58.65 383	85.32 72	94.09 114	57.03 194	93.28 279	69.34 242	90.56 94	93.03 183

testdata1							89.21 285		77.55 127

test12				87.09 52	94.60 36	68.86 68		92.91 115		82.67 102		65.44 81	97.55 67		93.69 48	94.84 98

plane_prior7					86.94 252	61.51 283

plane_prior6					87.23 244	62.32 263						50.66 272

plane_prior5								91.31 192					95.55 187	76.74 170	78.53 237	88.39 280

plane_prior4											89.14 223

plane_prior3						61.95 272			79.09 95	72.53 227

plane_prior2							93.13 123		78.81 102

plane_prior1					87.15 246

plane_prior						62.42 259	93.85 88		79.38 87						78.80 234

n2								0.00 469

nn								0.00 469

door-mid								66.01 438

lessismore_v0				73.72 373	72.93 417	47.83 412		61.72 444		45.86 424	73.76 398	28.63 411	89.81 362	47.75 381	31.37 443	83.53 358

LGP-MVS_train				79.56 311	84.31 313	59.37 332		89.73 270	69.49 283	64.86 323	88.42 232	38.65 357	94.30 241	72.56 210	72.76 281	85.01 344

test11								93.01 111

door								66.57 437

HQP5-MVS						63.66 226

HQP-NCC					87.54 236		94.06 73		79.80 77	74.18 201

ACMP_Plane					87.54 236		94.06 73		79.80 77	74.18 201

BP-MVS														77.63 167

HQP4-MVS										74.18 201			95.61 181			88.63 274

HQP3-MVS								91.70 178							78.90 232

HQP2-MVS												51.63 262

NP-MVS					87.41 239	63.04 243					90.30 195

MDTV_nov1_ep13_2view						59.90 324	80.13 387		67.65 309	72.79 221		54.33 233		59.83 326		92.58 197

MDTV_nov1_ep13			72.61 298		89.06 184	68.48 77	80.33 383	90.11 253	71.84 238	71.81 241	75.92 391	53.01 248	93.92 264	48.04 375	73.38 276

ACMMP++_ref															71.63 289

ACMMP++															69.72 298

Test By Simon												54.21 236

ITE_SJBPF				70.43 394	74.44 411	47.06 418		77.32 407	60.16 375	54.04 393	83.53 302	23.30 424	84.01 406	43.07 398	61.58 373	80.21 400

DeepMVS_CX				34.71 438	51.45 450	24.73 458		28.48 464	31.46 444	17.49 454	52.75 440	5.80 455	42.60 459	18.18 447	19.42 452	36.81 451

High-resolution multi-view benchmark